From e017926f10ee6ed5a6f057a2a7785d40b6e9b45d Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 21 Apr 2026 17:02:33 +0200 Subject: [PATCH 01/13] Added option to turn of cloud screening by setting cloudScreenMode = 0. --- ppcpy/cloudmask/cloudscreen.py | 35 +++++++++++++++++++++++----------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/ppcpy/cloudmask/cloudscreen.py b/ppcpy/cloudmask/cloudscreen.py index e91005e..3ebb3e5 100644 --- a/ppcpy/cloudmask/cloudscreen.py +++ b/ppcpy/cloudmask/cloudscreen.py @@ -2,6 +2,7 @@ from scipy.ndimage import label, uniform_filter1d from ppcpy.misc.helper import uniform_filter, savgol_filter import matplotlib.pyplot as plt +import logging def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: @@ -30,7 +31,7 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: """ # return uniform_filter(signal, window_len) - return uniform_filter1d(signal, window_len, mode='nearest') + return uniform_filter1d(signal, window_len, mode='nearest') # <- Should switch to the incoming moving average filter here! def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: @@ -52,14 +53,22 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: Notes ----- + - The config variable 'cloudScreenMode' decides the cloud screening method used. + cloudScreenMode = 0 + No cloud screening is performed, falgCloudFree = True for all timestamps. + cloudScreenMode = 1 + Cloud screen with maximum signal gradient algorithm. + cloudScreenMode = 2 + Cloud screen based on Zhao's algorithm. - The cloud screening is done for both the far range and near range total channels of - wavelength wv if the channels exist. - + wavelength wv, if the channels exist. + **History** - + - xxxx-xx-xx: First edition by ... - 2026-03-18: Updated to include necessary configurations for cloudScreen_Zhao. - 2026-04-09: Added 'maxCloudSearchHeight' config parameters. + - 2026-04-20: Added option for no cloud screening ie. 'cloudScreenMode' = 0 """ print('Starting cloud screen') @@ -69,15 +78,19 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: hFullOL = np.array(config_dict['heightFullOverlap'])[data_cube.gf(wv, 'total', 'FR')][0] # Cloud screen mode dependent configuration - if config_dict['cloudScreenMode'] == 1: - print(f'cloud screen mode {config_dict['cloudScreenMode']}: MSG method') + if config_dict['cloudScreenMode'] == 0: # No cloud screening + logging.warning(f'cloud screen mode {config_dict['cloudScreenMode']}: No cloud screening.') + return np.ones(bg.shape, dtype=bool) + + elif config_dict['cloudScreenMode'] == 1: # MSG method + logging.info(f'cloud screen mode {config_dict['cloudScreenMode']}: MSG method.') screenfunc = cloudScreen_MSG sig = 'RCS' kwargs = { 'slope_thres': config_dict['maxSigSlope4FilterCloud'], } - elif config_dict['cloudScreenMode'] == 2: - print(f"cloud screen mode {config_dict['cloudScreenMode']}: Zhao's method") + elif config_dict['cloudScreenMode'] == 2: # Zhao's method + logging.info(f"cloud screen mode {config_dict['cloudScreenMode']}: Zhao's method.") screenfunc = cloudScreen_Zhao sig = 'PCR_slice' kwargs = { @@ -155,7 +168,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea raise ValueError("Not a valid search_region.") if search_region[0] < height[0]: - print(f"Warning: Base of search_region is lower than {height[0]}, setting it to {height[0]}") + logging.warning(f"Base of search_region is lower than {height[0]}, setting it to {height[0]}.") search_region[0] = height[0] flagCloudFree = np.zeros(signal.shape[0], dtype=bool) @@ -166,7 +179,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea for indx in range(signal.shape[0]): if np.isnan(signal[indx]).all(): - print(f'Skipping timestamp {indx}') + logging.info(f'Skipping cloud screening for timestamp {indx}.') continue slope = np.concatenate(([0], np.diff(smooth_signal(signal[indx, :], smooth_win)))) / (height[1] - height[0]) @@ -235,7 +248,7 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search print(f"--------- iTime: {iTime} ---------") if np.isnan(signal[iTime, flagDetectBins]).all(): - print(f'Skipping timestamp {iTime}') + logging.info(f'Skipping cloud screening for timestamp {iTime}.') continue ## layer detection From 6937cd6cbb73913877e669a122d22f6f23daec60 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Wed, 22 Apr 2026 14:28:40 +0200 Subject: [PATCH 02/13] Updated docstrings in PollyPreprocessing.py --- ppcpy/interface/picassoProc.py | 123 ++-- ppcpy/preprocess/pollyPreprocess.py | 909 ++++++++++++++++------------ 2 files changed, 600 insertions(+), 432 deletions(-) diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index fbdc199..e566e00 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -81,6 +81,7 @@ def mdate_filename(self): DD = mdate[2] return f"{YYYY}{MM}{DD}" + def gf(self, wavelength, meth, telescope): """get flag shorthand @@ -131,6 +132,7 @@ def mdate_infile(self): mdate_infilename = self.rawdata_dict['measurement_time']['var_data'][0][0] return f"{mdate_infilename}" + def check_for_correct_mshots(self): """check if mshots are more than 1.1 * laser_prf * deltatime or smaller 0""" laser_rep_rate = self.rawdata_dict['laser_rep_rate']['var_data'] @@ -142,6 +144,7 @@ def check_for_correct_mshots(self): return condition_check_matrix + def filter_or_correct_false_mshots(self): """ filter or correct the mshots (currently only logging) @@ -161,6 +164,7 @@ def filter_or_correct_false_mshots(self): ##TODO return self + def mdate_consistency(self) -> bool: """check mdate consistency""" if self.mdate_filename() == self.mdate_infile(): @@ -170,6 +174,7 @@ def mdate_consistency(self) -> bool: logging.warning('... date in nc-file differs from date of filename') return False + def reset_date_infile(self): """correct the date in the file """ logging.info('date consistency-check... ') @@ -181,6 +186,7 @@ def reset_date_infile(self): self.rawdata_dict['measurement_time']['var_data'] = np_array return self + def setChannelTags(self): """set the channel tags @@ -274,63 +280,70 @@ def setChannelTags(self): return self + def preprocessing(self, collect_debug:bool=False): - """ - Preprocessing of Lidar data. Including in the followin processes in order: - Dead time correction - Background correction - Range correction - etc. + """Preprocessing of Lidar data. Including in the followin processes in order: + 1. Deadtime correction + 2. Background correction + 3. First-bin shift + 4. Mask for low-SNR + 5. Mask for depolarization-calibration process + 6. Range correction. + + Parameters + ---------- + collect_debug : bool + If true, collects debug information. Default is False. - Returns: - - Background corrected signal including the background per channel. - - Range corrected signal. - - etc. + Yelds + ----- + Background corrected signal including the background per channel. + Range corrected signal. + etc. .. TODO:: This is just a first draft for a docstring. Improve it. There is more processes and outputs of the function. """ preproc_dict = pollyPreprocess.pollyPreprocess( self.rawdata_dict, deltaT=self.polly_config_dict['deltaT'], - flagForceMeasTime = self.polly_config_dict['flagForceMeasTime'], - maxHeightBin = self.polly_config_dict['max_height_bin'], - firstBinIndex = self.polly_config_dict['first_range_gate_indx'], - firstBinHeight = self.polly_config_dict['first_range_gate_height'], - pollyType = self.polly_config_dict['name'], - flagDeadTimeCorrection = self.polly_config_dict['flagDTCor'], - deadtimeCorrectionMode = self.polly_config_dict['dtCorMode'], - deadtimeParams = self.polly_config_dict['dt'], - flagSigTempCor = self.polly_config_dict['flagSigTempCor'], - tempCorFunc = self.polly_config_dict['tempCorFunc'], - meteorDataSource = self.polly_config_dict['meteorDataSource'], - gdas1Site = self.polly_config_dict['gdas1Site'], - gdas1_folder = self.picasso_config_dict['gdas1_folder'], - radiosondeSitenum = self.polly_config_dict['radiosondeSitenum'], - radiosondeFolder = self.polly_config_dict['radiosondeFolder'], - radiosondeType = self.polly_config_dict['radiosondeType'], - bgCorrectionIndexLow = self.polly_config_dict['bgCorRangeIndxLow'], - bgCorrectionIndexHigh = self.polly_config_dict['bgCorRangeIndxHigh'], - asl = self.polly_config_dict['asl'], - initialPolAngle = self.polly_config_dict['init_depAng'], - maskPolCalAngle = self.polly_config_dict['maskDepCalAng'], - minSNRThresh = self.polly_config_dict['mask_SNRmin'], - minPC_fog = self.polly_config_dict['minPC_fog'], - flagFarRangeChannel = self.polly_config_dict['isFR'], - flag532nmChannel = self.polly_config_dict['is532nm'], - flagTotalChannel = self.polly_config_dict['isTot'], - flag355nmChannel = self.polly_config_dict['is355nm'], - flag607nmChannel = self.polly_config_dict['is607nm'], - flag387nmChannel = self.polly_config_dict['is387nm'], - flag407nmChannel = self.polly_config_dict['is407nm'], - flag355nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag532nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag1064nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - isUseLatestGDAS = self.polly_config_dict['flagUseLatestGDAS'], + flagForceMeasTime=self.polly_config_dict['flagForceMeasTime'], + maxHeightBin=self.polly_config_dict['max_height_bin'], + firstBinIndex=self.polly_config_dict['first_range_gate_indx'], + firstBinHeight=self.polly_config_dict['first_range_gate_height'], + pollyType=self.polly_config_dict['name'], + flagDeadTimeCorrection=self.polly_config_dict['flagDTCor'], + deadtimeCorrectionMode=self.polly_config_dict['dtCorMode'], + deadtimeParams=self.polly_config_dict['dt'], + flagSigTempCor=self.polly_config_dict['flagSigTempCor'], + tempCorFunc=self.polly_config_dict['tempCorFunc'], + meteorDataSource=self.polly_config_dict['meteorDataSource'], + gdas1Site=self.polly_config_dict['gdas1Site'], + gdas1_folder=self.picasso_config_dict['gdas1_folder'], + radiosondeSitenum=self.polly_config_dict['radiosondeSitenum'], + radiosondeFolder=self.polly_config_dict['radiosondeFolder'], + radiosondeType=self.polly_config_dict['radiosondeType'], + bgCorrectionIndexLow=self.polly_config_dict['bgCorRangeIndxLow'], + bgCorrectionIndexHigh=self.polly_config_dict['bgCorRangeIndxHigh'], + asl=self.polly_config_dict['asl'], + initialPolAngle=self.polly_config_dict['init_depAng'], + maskPolCalAngle=self.polly_config_dict['maskDepCalAng'], + minSNRThresh=self.polly_config_dict['mask_SNRmin'], + minPC_fog=self.polly_config_dict['minPC_fog'], + flagFarRangeChannel=self.polly_config_dict['isFR'], + flag532nmChannel=self.polly_config_dict['is532nm'], + flagTotalChannel=self.polly_config_dict['isTot'], + flag355nmChannel=self.polly_config_dict['is355nm'], + flag607nmChannel=self.polly_config_dict['is607nm'], + flag387nmChannel=self.polly_config_dict['is387nm'], + flag407nmChannel=self.polly_config_dict['is407nm'], + flag355nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), + flag532nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), + flag1064nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), + isUseLatestGDAS=self.polly_config_dict['flagUseLatestGDAS'], collect_debug=collect_debug, ) self.retrievals_highres.update(preproc_dict) - return self def SaturationDetect(self): """Saturation Detection @@ -339,10 +352,9 @@ def SaturationDetect(self): """ self.flagSaturation = pollySaturationDetect.pollySaturationDetect( - data_cube = self, - sigSaturateThresh = self.polly_config_dict['saturate_thresh']) - - return self + data_cube=self, + sigSaturateThresh=self.polly_config_dict['saturate_thresh'] + ) def polarizationCaliD90(self): @@ -380,6 +392,7 @@ def cloudFreeSeg(self): """ self.clFreeGrps = profilesegment.segment(self) + def aggregate_profiles(self, var=None): """Aggregate highres profiles over cloud free segments @@ -407,6 +420,7 @@ def aggregate_profiles(self, var=None): if self.retrievals_profile[var] is None: del self.retrievals_profile[var] + def loadMeteo(self): """Load meteorological data.""" self.met = readMeteo.Meteo( @@ -555,7 +569,8 @@ def overlapCalc(self, collect_debug=False): self.retrievals_profile['overlap'] = {} self.retrievals_profile['overlap']['frnr'] = overlapEst.run_frnr_cldFreeGrps(self, collect_debug=collect_debug) self.retrievals_profile['overlap']['raman'] = overlapEst.run_raman_cldFreeGrps(self, collect_debug=collect_debug) - + + def overlapFixLowestBins(self): """the lowest bins are affected by stange near range effects""" if not self.polly_config_dict["flagOLCor"]: @@ -602,6 +617,7 @@ def calcDepol(self): self.retrievals_profile[ret_prof_name] = depolarization.pardepol_cldFreeGrps( self, ret_prof_name) + def estQualityMask(self): """estimate the quality mask @@ -618,6 +634,7 @@ def Angstroem(self): self.retrievals_profile[ret_prof_name] = angstroem.ae_cldFreeGrps( self, ret_prof_name) + def LidarCalibration(self, collect_debug:bool=False): """calculate the lidar constant @@ -671,6 +688,7 @@ def quasiV1(self): quasi.quasi_angstrom(self, version='V1') quasi.target_cat(self, version='V1') + def quasiV2(self): """quasiV2 retrivals and target categorisation. """ @@ -680,6 +698,7 @@ def quasiV2(self): quasi.quasi_angstrom(self, version='V2') quasi.target_cat(self, version='V2') + def write_2_sql_db(self, parameter:str, db_path:str|None=None, method:str|None=None): """ write LC or eta to sqlite db table @@ -727,6 +746,7 @@ def write_2_sql_db(self, parameter:str, db_path:str|None=None, method:str|None=N sql_db.write_rows_to_sql_db(db_path, table_name, column_names, rows_to_insert) + def read_calibration_db(self, db_path:str|None=None): """read the calibration constants from database @@ -745,7 +765,6 @@ def read_calibration_db(self, db_path:str|None=None): self.pol_cali.update(sql_db.get_from_sql_db(db_path, table_name, ts_interval)) - def adding_retrieving_infos_2_polly_config_dict(self): """ some infos from the polly_config_dict should have there own keys, e.g. reference_search_range @@ -768,9 +787,11 @@ def adding_retrieving_infos_2_polly_config_dict(self): return self + # def __str__(self): # return f"{self.rawdata_dict}" + def __del__(self): type(self).counter -= 1 diff --git a/ppcpy/preprocess/pollyPreprocess.py b/ppcpy/preprocess/pollyPreprocess.py index 437bba4..072ad63 100644 --- a/ppcpy/preprocess/pollyPreprocess.py +++ b/ppcpy/preprocess/pollyPreprocess.py @@ -9,34 +9,59 @@ #from ppcpy.preprocess.compute_pcr import compute_pcr #import numpy as np #from multiprocessing import Pool, cpu_count - from ppcpy.retrievals.collection import calc_snr -def compute_channel_pcr(args): - """ - Computes PCR for a single channel. - Parameters: - args: Tuple containing (rawSignal, mShots, scale_factor, channel_index). +def compute_channel_pcr(args:tuple) -> np.ndarray: + """Computes PCR for a single channel. - Returns: - np.ndarray: Computed PCR for the given channel. + PCR = (signal * c)/(mshots * 2 * hRes) + + Parameters + ---------- + args : tuple + rawSignal : ndarray + 3D Raw signal [Photon count] (shape: [M, N, P]). + mShots : ndarray[time, channels] + Mesurments shots [counts] (shape: [M, P]). + scale_factor : flaot + Scaling factor for the computation: c/(2*hRes). + channel_index : int + Index of the channel to compute the PCR for. + + Returns + ------- + ndarray + Computed PCR for the given channel (shape: [M, N, P]). + + Notes + ----- + .. TODO:: change scale factor with hRes and include c/2 as a part + of the function. Could even add an flag option for MCPS or CPS. """ + rawSignal, mShots, scale_factor, ch = args return (rawSignal[:, :, ch] / mShots[:, np.newaxis, ch]) * scale_factor -def compute_pcr_parallel(rawSignal, mShots, scale_factor): - """ - Computes PCR using multiprocessing for channel-wise parallelism. - Parameters: - rawSignal: 3D input array (shape: [M, N, P]). - mShots: 2D multiplicative factors array (shape: [M, P]). - scale_factor: Scaling factor for the computation. +def compute_pcr_parallel(rawSignal:np.ndarray, mShots:np.ndarray, scale_factor:float) -> np.ndarray: + """Computes PCR using multiprocessing for channel-wise parallelism. - Returns: - np.ndarray: 3D output array (PCR) with the same shape as rawSignal. + Parameters + ---------- + rawSignal : ndarray + 3D input array (shape: [M, N, P]). + mShots : ndarray + 2D multiplicative factors array (shape: [M, P]). + scale_factor : float + Scaling factor for the computation. + + Returns + ------- + PCR : ndarray + 3D output array (shape: [M, N, P]). """ + M, N, P = rawSignal.shape PCR = np.zeros((M, N, P), dtype=np.float64) @@ -53,50 +78,97 @@ def compute_pcr_parallel(rawSignal, mShots, scale_factor): return PCR -def faster_polyval(a, x): - """faster version than np.polyval(), using numba would provide 10% increase, but with different function""" - y = a[-1] - for ai in a[-2::-1]: + +def faster_polyval(p:np.ndarray, x:float|np.ndarray) -> float|np.ndarray: + """Faster version of np.polyval(). + + If `p` is of length N, this function returns:: + + y = p[N]*x**(N-1) + p[N-1]*x**(N-2) + ... + p[1]*x + p[0] + + Parameters + ---------- + p : ArrayLike + Polynomial coefficient including coefficients equal to 0, from constant term to highest order term. + x : float or ArrayLike + Value(s) at which to evaluate the polynomial `p`. + + Returns + ------- + y : float or ArrayLike + Polynomial `p` evaluated at values `x`. + + Notes + ----- + - Using numba would provide 10% increase, but with different function. + + Example + ------- + >>> faster_polyval([-1, 0, 3], 5) # 3*5**2 + 0*5**1 + (-1) + 76 + >>> faster_polycal([-1, 0, 3], [5, 2, -1]) + [76, 11, 2] + """ + + y = p[-1] + for pi in p[-2::-1]: y *= x - y += ai + y += pi return y -#@jit(nopython=True) -#def faster_polyval(p, x): -# y = np.zeros(x.shape, dtype=float) -# for i, v in enumerate(p): -# y *= x -# y += v -# return y + + +# @jit(nopython=True) +# def faster_polyval(p, x): +# """Numba verison of faster_polyval().""" +# y = np.zeros(x.shape, dtype=float) +# for i, v in enumerate(p): +# y *= x +# y += v +# return y + #@profile def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:dict) -> np.ndarray: - """ Dead Time Correction + """Dead Time Correction. Parameters ---------- - rawSignal (ndarray): Raw signal [Photon counts]. - mShots (ndarray): Measurment shots per ... []. - hRes (ndarray): Height resolution [m]. + rawSignal : ndarray + Raw signal [Photon counts]. + mShots : ndarray + Number of measurment shots for each profile. + hRes : ndarray + Height resolution [m]. - keyword arguments: - device (str or bool): Name of PollyXT device, default: False - flagDeadTimeCorrection (bool): ....., default: Flase - DeadTimeCorrectionMode (int): deadtime correction mode: - 1: use the parameters saved in the netcdf files, - 2: nonparalyzable correction with user define deadtime (default), - 3: paralyzable correction with user defined parameters, - 4: no deadtime correction, - deadtimeParams (list): ...., default: [] - deadtime (list): ...., default: [] + Keyword arguments + ----------------- + device : str or bool + Name of PollyXT device. Default is False. + flagDeadTimeCorrection : bool + If true, perform dead time correction. Otherwise, no dead time + correction is performed. Default is False. + DeadTimeCorrectionMode : int + Deadtime correction mode. Default is 2. + 1: use the parameters saved in the netcdf files, + 2: nonparalyzable correction with user define deadtime, + 3: paralyzable correction with user defined parameters, + 4: no deadtime correction, + deadtimeParams : list + Deadtime parameters from config-file. Default is []. + deadtime : list + Deadtime parameters from level0 nc-file. Default is []. Returns ------- - signalDTCor (ndarray): Dead time corrected signal [Photon counts]. + signalDTCor : ndarray + Dead time corrected signal [Photon counts]. - .. TODO:: - - Finish docstring and remove all unnecessary comments - - Could think of moving the scale convertion to after the loops ie. form PCR to PC + Notes + ----- + .. TODO:: Finish docstring and remove all unnecessary comments. + .. TODO:: Could think of moving the scale convertion to after the loops ie. form PCR to PC. """ + ## Defining default values for param keys (key initialization), if not explictly defined when calling the function polly_device = varargin.get('device', False) flagDeadTimeCorrection = varargin.get('flagDeadTimeCorrection', False) @@ -104,7 +176,7 @@ def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:d deadtimeParams = varargin.get('deadtimeParams', []) deadtime = varargin.get('deadtime', []) - # print('mShots', np.all(mShots[:,0] == mShots[0,0]), mShots[0,0], np.min(mShots[:,0]), np.max(mShots[:,0])) + # print('mShots', np.all(mShots[:, 0] == mShots[0, 0]), mShots[0, 0], np.min(mShots[:, 0]), np.max(mShots[:, 0])) if not np.all(mShots[:, 0] == mShots[0, 0]): logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) @@ -123,7 +195,7 @@ def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:d # end_time_command1 = time.time() # elapsed_time_command1 = end_time_command1 - start_time_command1 # print(f"Time taken: {elapsed_time_command1:.4f} seconds") - PCR = rawSignal * (150.0 / hRes) / mShots[:, np.newaxis, :] + PCR = rawSignal * scale_factor / mShots[:, np.newaxis, :] #PCR_Cor = np.zeros_like(PCR) signalDTCor = np.zeros_like(PCR) @@ -186,22 +258,32 @@ def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:d # return PCR_Cor, signalDTCor return signalDTCor -def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: + +def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, + maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: """Background correction. Remove mean background noise from signal. Parameters ---------- - rawSignal (np.ndarray): Lidar Signal to be processed - bgCorrectionIndexLow (list of int): lower index of background noise per channel - bgCorrectionIndexHigh (list of int): upper index of background noise per channel - maxHeightBin (int): maximum height bin index (default: 3000) - firstBinIndex (list of int): first height bin index per channel (default: 0 per chanel) + rawSignal : ndarray + Lidar Signal to be processed + bgCorrectionIndexLow : list of int + lower index of background noise per channel + bgCorrectionIndexHigh : list of int + upper index of background noise per channel + maxHeightBin : int + maximum height bin index (default: 3000) + firstBinIndex : list of int + first height bin index per channel (default: 0 per chanel) Returns ------- - - signal_out (np.ndarray): Background corrected signal - - bg (np.ndarray): Removed background noise + signal_out : ndarray + Background corrected signal + bg : ndarray + Removed background noise """ + logging.info(f'... removing background from signal') if firstBinIndex is None: @@ -218,18 +300,23 @@ def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionI signal_out = slicerange(rawSignal, maxHeightBin, firstBinIndex) - bg return signal_out, bg + def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.ndarray: """Slice a given array across the height/range dimension from firstBinIndex to maxHeightBin + firstBinIndex. Parameters ---------- - array (np.ndarray): array to be sliced - maxHeightBin (int): length of slice - firstBinIndex (list of int): start hight/range index of slice per channel + array : ndarray + array to be sliced + maxHeightBin : int + length of slice + firstBinIndex : list of int + start hight/range index of slice per channel Returns ------- - out (np.ndarray): sliced array + out : ndarray + sliced array """ assert len(firstBinIndex) == array.shape[2], f"first bin index and array do not match {len(firstBinIndex)}, {array.shape}" @@ -238,8 +325,54 @@ def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.nda out = array[:, heightBins, np.arange(array.shape[2])] return out -def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): - """ """ + +def pollyPolCaliTime(depCalAng:np.ndarray, mTime:list, init_depAng:float, maskDepCalAng:list) -> tuple: + """Retrieve the time for the polly depolarization calibration + period. depolarization calibration: 5 min (+45°) + 5 min (-45°) + 0.5 min. + + Parameters + ---------- + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + mTime : list + Datetime ndarray for the measurement time of each profile. + init_depAng : float + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskDepCalAng : list + Mask for positive and negative calibration angle of the polarizer, in + which 'p' stands for positive angle, while 'n' for negative angle. + Default is {}. + + Returns + ------- + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + + Notes + ----- + .. TODO:: Clean comments of the function. + + **History** + + - 2021-04-21: First edition by Zhenping + - xxxx-xx-xx: Translated to Python by ... + """ + depCal_P_Ang_time_start = [] depCal_P_Ang_time_end = [] depCal_N_Ang_time_start = [] @@ -257,40 +390,37 @@ def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): ## invalid profiles with different ## depol_cal_angle - flagPDepCal = np.zeros(len(maskDepCalAng),dtype=bool) - flagNDepCal = np.zeros(len(maskDepCalAng),dtype=bool) - for iProf in range(0,len(maskDepCalAng)): + flagPDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + flagNDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + for iProf in range(0, len(maskDepCalAng)): if maskDepCalAng[iProf] == 'p': flagPDepCal[iProf] = True elif maskDepCalAng[iProf] == 'n': flagNDepCal[iProf] = True + flagDepCal = (np.abs(depCalAng - init_depAng) > 0.0) ## the profile will be treated as depol cali profile if it has different ## depol_cal_ang than the init_depAng - #print(init_depAng) - #print(depCalAng) - #print(flagDepCal) + maskDepCal = flagDepCal ## search the calibration periods valuesFlagDepCal = flagDepCal.astype(int) - print('flagNDepCal', flagNDepCal) - print('flagPDepCal', flagPDepCal) + # print('flagNDepCal', flagNDepCal) + # print('flagPDepCal', flagPDepCal) ## label connected components in the matrix; 0 will stay 0 ## connected 1s will be numbered consecutively depCalPeriods, nDepCalPeriods = label(valuesFlagDepCal) - print('depCalPeriods', depCalPeriods) + # print('depCalPeriods', depCalPeriods) - if nDepCalPeriods >= 1: - pass - else: + if nDepCalPeriods < 1: logging.info(f'No Depolarization Calibration phase found.') return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal for iDepCalPeriod in range(1,nDepCalPeriods+1): - #flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. + # flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. flagIDepCal = depCalPeriods[depCalPeriods == iDepCalPeriod] # flag for the ith calibration period. indices = np.where(depCalPeriods == flagIDepCal[0])[0] print('flagIDepCal', flagIDepCal) @@ -298,14 +428,14 @@ def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): if len(flagIDepCal) != len(maskDepCalAng): logging.warning(f"Depolarization Calibration from Timestamp " - f"{mTime[indices[0]]} - {mTime[indices[-1]]} " - f"does not match the maskDepCalAng pattern in the polly-config file.\n" - f"This calibration phase will be skipped.") + f"{mTime[indices[0]]} - {mTime[indices[-1]]} " + f"does not match the maskDepCalAng pattern in the polly-config file.\n" + f"This calibration phase will be skipped." + ) continue - else: - pass + tIDepCal = mTime[indices[0]:indices[-1]+1] - + t_all_p_depCal = list(itertools.compress(tIDepCal, flagPDepCal)) t_all_n_depCal = list(itertools.compress(tIDepCal, flagNDepCal)) depCal_P_Ang_time_start.append(t_all_p_depCal[0]) @@ -313,198 +443,217 @@ def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): depCal_N_Ang_time_start.append(t_all_n_depCal[0]) depCal_N_Ang_time_end.append(t_all_n_depCal[-1]) - return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal -def calculate_rcs(datasignal, ranges) -> np.ndarray: - """ - Function for calculating RCS. + +def calculate_rcs(signal:np.ndarray, ranges:np.ndarray) -> np.ndarray: + """Function for calculating RCS. Parameters ---------- - datasignal: - signal to range correct - ranges: - ranges that are squared + signal : ndarray + Signal to range correct [PCR]. + ranges : ndarray + Ranges dimension [m]. Returns ------- - np.ndarray: Computed RCS array. + RCS : ndarray + Range corrected signal [PCR]. """ - print(datasignal.shape) ranges_squared = ranges**2 - ranges2d = np.repeat(ranges_squared[np.newaxis, :], datasignal.shape[0], axis=0) - print('ranges2d', ranges2d.shape) - - # Perform the computation - #RCS = ( - # datasignal / mShots_broadcasted * 150 / float(hRes) * height_squared_broadcasted - #) - RCS = ( - datasignal * ranges2d[:, :, np.newaxis] - ) - + ranges2d = np.repeat(ranges_squared[np.newaxis, :], signal.shape[0], axis=0) + RCS = signal * ranges2d[:, :, np.newaxis] return RCS -def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): - """Deadtime correction, background correction, first-bin shift, mask for low-SNR and mask for depolarization-calibration process. +def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) -> dict: + """Preprocessing of Lidar-data. + + Includes the following processes in order: + 1. Deadtime correction + 2. Background correction + 3. First-bin shift + 4. Mask for low-SNR + 5. Mask for depolarization-calibration process + 6. Range correction. Parameters ---------- - data: struct - rawSignal: array - signal. [Photon Count] - mShots: array - number of the laser shots for each profile. - mTime: array - datetime array for the measurement time of each profile. - depCalAng: array - angle of the polarizer in the receiving channel. (>0 means - calibration process starts) - zenithAng: array - zenith angle of the laer beam. - repRate: float - laser pulse repetition rate. [s^-1] - hRes: float - spatial resolution [m] - mSite: string - measurement site. + rawdata_dict : struct + rawSignal : ndarray + Signal [Photon Count]. + mShots : ndarray + Number of the laser shots for each profile. + mTime : ndarray + Datetime array for the measurement time of each profile. + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + zenithAng : ndarray + Zenith angle of the laer beam. + repRate : float + Laser pulse repetition rate [s^-1]. + hRes : float + Spatial resolution [m]. + mSite : str + Measurement site. + collect_debug : bool + If true, collects debug information. Default is False. - deltaT: numeric - integration time (in seconds) for single profile. (default: 30) - flagForceMeasTime: logical + Keyword arguments + ----------------- + deltaT : numeric + integration time (in seconds) for single profile. Default is 30. + flagForceMeasTime : logical flag to control whether to align measurement time with file creation time, instead of taking the measurement time in the data file. - (default: false) - maxHeightBin: numeric - number of range bins to read out from data file. (default: 3000) - firstBinIndex: numeric - index of first bin to read out. (default: 1) - pollyType: char - polly version. (default: 'arielle') - flagDeadTimeCorrection: logical - flag to control whether to apply deadtime correction. (default: false) - deadtimeCorrectionMode: numeric - deadtime correction mode. (default: 2) - 1: polynomial correction with parameters saved in data file. - 2: non-paralyzable correction - 3: polynomail correction with user defined parameters - 4: disable deadtime correction - deadtimeParams: numeric - deadtime parameters. (default: []) - flagSigTempCor: logical - flag to implement signal temperature correction. - tempCorFunc: cell - symbolic function for signal temperature correction. + Default is False. + maxHeightBin : numeric + Number of range bins to read out from data file. Default is 3000. + firstBinIndex : numeric + Index of first bin to read out. Default is 1. + pollyType : char + Polly version. Default is 'arielle'. + flagDeadTimeCorrection : logical + Flag to control whether to apply deadtime correction. Default is False. + deadtimeCorrectionMode : numeric + Deadtime correction mode. Default is 2. + 1: polynomial correction with parameters saved in data file. + 2: non-paralyzable correction + 3: polynomail correction with user defined parameters + 4: disable deadtime correction + deadtimeParams : numeric + Deadtime parameters. Default is []. + flagSigTempCor : logical + Flag to implement signal temperature correction. + tempCorFunc : cell + Symbolic function for signal temperature correction. "1": no correction "exp(-0.001*T)": exponential correction function. (Unit: Kelvin) - meteorDataSource: str - meteorological data type. + meteorDataSource : str + Meteorological data type. e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' - gdas1Site: str - the GDAS1 site for the current campaign. - meteo_folder: str - the main folder of the GDAS1 profiles. - radiosondeSitenum: integer - site number, which can be found in + gdas1Site : str + The GDAS1 site for the current campaign. + meteo_folder : str + The main folder of the GDAS1 profiles. + radiosondeSitenum : integer + Site number, which can be found in doc/radiosonde-station-list.txt. - radiosondeFolder: str - the folder of the sonding files. - radiosondeType: integer - file type of the radiosonde file. - - 1: radiosonde file for MOSAiC (default) - - 2: radiosonde file for MUA - bgCorrectionIndexLow: 1-dim. array - base indecis of bins for background estimation. - (defults: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]) - bgCorrectionIndexHigh: 1-dim. array - top index of bins for background estimation. - (defults: [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]) - asl: numeric - above sea level in meters. (default: 0) - initialPolAngle: numeric - initial polarization angle of the polarizer for polarization - calibration. (default: 0) - maskPolCalAngle: cell - mask for positive and negative calibration angle of the polarizer, in + radiosondeFolder : str + The folder of the sonding files. + radiosondeType : integer + File type of the radiosonde file. + - 1: radiosonde file for MOSAiC (default). + - 2: radiosonde file for MUA. + bgCorrectionIndexLow : 1-dim. array + Base indecis of bins for background estimation. + Defults is [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. + bgCorrectionIndexHigh : 1-dim. array + Top index of bins for background estimation. + Defults is [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]. + asl : numeric + Above sea level in meters. Default is 0. + initialPolAngle : numeric + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskPolCalAngle : cell + Mask for positive and negative calibration angle of the polarizer, in which 'p' stands for positive angle, while 'n' for negative angle. - (default: {}) - minSNRThresh: numeric - lower bound of signal-noise ratio. - minPC_fog: numeric - minimun number of photon count after strong attenuation by fog. - flagFarRangeChannel: logical - flags of far-range channel. - flag532nmChannel: logical - flags of channels with central wavelength (CW) at 532 nm. - flagTotalChannel: logical - flags of channels receiving total elastic signal. - flag355nmChannel: logical - flags of channels with CW at 355 nm. - flag607nmChannel: logical - flags of channels with CW at 607 nm. - flag387nmChannel: logical - flags of channels with CW at 387 nm. - flag407nmChannel: logical - flags of channels with CW at 407 nm. - flag532nmRotRaman: logical - flags of rotational Raman channels with CW at 532 nm. - flag1064nmRotRaman: logical - flags of rotational Raman channels with CW at 1064 nm. + Default is {}. + minSNRThresh : numeric + Lower bound of signal-noise ratio. + minPC_fog : numeric + Minimun number of photon count after strong attenuation by fog. + flagFarRangeChannel : logical + Flags of far-range channel. + flag532nmChannel : logical + Flags of channels with central wavelength (CW) at 532 nm. + flagTotalChannel : logical + Flags of channels receiving total elastic signal. + flag355nmChannel : logical + Flags of channels with CW at 355 nm. + flag607nmChannel : logical + Flags of channels with CW at 607 nm. + flag387nmChannel : logical + Flags of channels with CW at 387 nm. + flag407nmChannel : logical + Flags of channels with CW at 407 nm. + flag532nmRotRaman : logical + Flags of rotational Raman channels with CW at 532 nm. + flag1064nmRotRaman : logical + Flags of rotational Raman channels with CW at 1064 nm. Returns ------- - data: struct - rawSignal: array - signal. [Photon Count] - mShots: array - number of the laser shots for each profile. - mTime: array - datetime array for the measurement time of each profile. - depCalAng: array - angle of the polarizer in the receiving channel. (>0 means - calibration process starts) - zenithAng: array - zenith angle of the laer beam. - repRate: float - laser pulse repetition rate. [s^-1] - hRes: float - spatial resolution [m] - mSite: string - measurement site. - deadtime: matrix (channel x polynomial_orders) - deadtime correction parameters. - signal: array - Background removed signal - bg: array - background - height: array - height. [m] - lowSNRMask: logical - If SNR less SNRmin, mask is set true. Otherwise, false - depCalMask: logical + data_dict : dict + rawSignal : ndarray + Signal [Photon Count]. + mShots : ndarray + Number of the laser shots for each profile. + mTime : ndarray + Datetime ndarray for the measurement time of each profile. + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + zenithAng : ndarray + Zenith angle of the laer beam. + repRate : float + Laser pulse repetition rate [s^-1]. + hRes : float + Spatial resolution [m]. + mSite : string + Measurement site. + deadtime : matrix (channel x polynomial_orders) + Deadtime correction parameters. + signal : ndarray + Background removed signal. + bg : ndarray + Background. + height : ndarray + Height [m]. + lowSNRMask : logical + If SNR less SNRmin, mask is set True. Otherwise, False. + depCalMask : logical If polly was doing polarization calibration, depCalMask is set - true. Otherwise, false. - fogMask: logical + True. Otherwise, False. + fogMask : logical If it is foggy which means the signal will be very weak, - fogMask will be set true. Otherwise, false - mask607Off: logical - mask of PMT on/off status at 607 nm channel. - mask387Off: logical - mask of PMT on/off status at 387 nm channel. - mask407Off: logical - mask of PMT on/off status at 407 nm channel. - mask355RROff: logical - mask of PMT on/off status at 355 nm rotational Raman channel. - mask532RROff: logical - mask of PMT on/off status at 532 nm rotational Raman channel. - mask1064RROff: logical - mask of PMT on/off status at 1064 nm rotational Raman channel. + fogMask will be set True. Otherwise, False. + mask607Off : logical + Mask of PMT on/off status at 607 nm channel. + mask387Off : logical + Mask of PMT on/off status at 387 nm channel. + mask407Off : logical + Mask of PMT on/off status at 407 nm channel. + mask355RROff : logical + Mask of PMT on/off status at 355 nm rotational Raman channel. + mask532RROff : logical + Mask of PMT on/off status at 532 nm rotational Raman channel. + mask1064RROff : logical + Mask of PMT on/off status at 1064 nm rotational Raman channel. - """ + Notes + ----- + .. TODO:: Revamp docstring. + .. TODO:: Rewrite the function, and get rid of all unecessary comments. + .. TODO:: Change to PCR in pre-range corrected space. + + **History** + + - 2018-12-16: First edition by Zhenping. + - 2019-07-10: Add mask for laser shutter due to approaching airplanes. + - 2019-08-27: Add mask for turnoff of PMT at 607 and 387nm. + - 2021-01-19: Add keyword of 'flagForceMeasTime' to align measurement time. + - 2021-01-20: Re-sample the profiles into temporal resolution of 30-s.. + - xxxx-xx-xx: Translated to Python by ... + """ + logging.info('starting data preprocessing...') + + ## Extracting data from rawdata_dict rawSignal = rawdata_dict['raw_signal']['var_data'] mShots = rawdata_dict['measurement_shots']['var_data'] mTime = rawdata_dict['measurement_time']['var_data'] @@ -530,6 +679,7 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): mTime_obj = [ datetime_obj.replace(tzinfo=datetime.timezone.utc) + datetime.timedelta(seconds=int(s)) for s in seconds_of_day] mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] + # Convert to Unix timestamp mTime_unixtimestamp = [int(datetime.datetime.timestamp(dt)) for dt in mTime_obj] @@ -570,36 +720,25 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) isUseLatestGDAS = param.get('isUseLatestGDAS', False) - - # print(flagFarRangeChannel) - # print(flag1064nmRotRaman) - - -#%% Determine whether number of range bins is out of range -#if (max(config.maxHeightBin + config.firstBinIndex - 1) > size(data.rawSignal, 2)) -# warning('maxHeightBin or firstBinIndex is out of range.\nTotal number of range bin is %d.\nmaxHeightBin is -# %d\nfirstBinIndex is %d\n', size(data.rawSignal, 2), config.maxHeightBin, config.firstBinIndex); -# fprintf('Set maxHeightBin and firstBinIndex to default values.\n'); -# config.maxHeightBin = ones(1, size(data.rawSignal, 1)); -# config.firstBinIndex = 251; -#end -# logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - #if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): - # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') - # maxHeightBin = np.ones(rawSignal.shape[2]) - # logging.info(f'maxHeightBin: {maxHeightBin}') - # firstBinIndex = 251 + ##### Picasso code: + # % Determine whether number of range bins is out of range + # if (max(config.maxHeightBin + config.firstBinIndex - 1) > size(data.rawSignal, 2)) + # warning('maxHeightBin or firstBinIndex is out of range.\nTotal number of range bin is %d.\nmaxHeightBin is + # %d\nfirstBinIndex is %d\n', size(data.rawSignal, 2), config.maxHeightBin, config.firstBinIndex); + # fprintf('Set maxHeightBin and firstBinIndex to default values.\n'); + # config.maxHeightBin = ones(1, size(data.rawSignal, 1)); + # config.firstBinIndex = 251; + # end + ##### Unsure what this is? + # logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): + # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') + # maxHeightBin = np.ones(rawSignal.shape[2]) + # logging.info(f'maxHeightBin: {maxHeightBin}') + # firstBinIndex = 251 mShotsPerPrf = deltaT * repRate -# print(mShotsPerPrf) -# print(mShots) -# print(mTime) -# print(deltaT) -# print(np.nanmean(np.diff(np.array(mTime[:,1])))) -# print(np.array(mTime[:,1])) -# print(len(mTime)) -# print(np.diff(mTime)) if len(mTime) > 1: # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s @@ -607,17 +746,19 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) - ## Deadtime correction + # ---------------------------------------------------- + # Deadtime correction + # ---------------------------------------------------- # PCR_Cor, preproSignal = pollyDTCor(rawSignal = rawSignal, preproSignal = pollyDTCor( - rawSignal = rawSignal, - mShots = mShots, - hRes = hRes, - polly_device = pollyType, - flagDeadTimeCorrection = flagDeadTimeCorrection, - DeadTimeCorrectionMode = deadtimeCorrectionMode, - deadtimeParams = deadtimeParams, - deadtime = rawdata_dict['deadtime_polynomial']['var_data'] + rawSignal=rawSignal, + mShots=mShots, + hRes=hRes, + polly_device=pollyType, + flagDeadTimeCorrection=flagDeadTimeCorrection, + DeadTimeCorrectionMode=deadtimeCorrectionMode, + deadtimeParams=deadtimeParams, + deadtime=rawdata_dict['deadtime_polynomial']['var_data'] ) # most likely the preprocesssed deadtime corrected signal can be omitted if collect_debug: @@ -625,19 +766,23 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): data_dict['preproSignal'] = preproSignal # data_dict['PCR_slice'] = slicerange(PCR_Cor, maxHeightBin, firstBinIndex) - ## Background Substraction + # ---------------------------------------------------- + # Background Correction + # ---------------------------------------------------- sigBGCor, bg = pollyRemoveBG( - rawSignal = preproSignal, - bgCorrectionIndexLow = bgCorrectionIndexLow, - bgCorrectionIndexHigh = bgCorrectionIndexHigh, - maxHeightBin = maxHeightBin, - firstBinIndex = firstBinIndex + rawSignal=preproSignal, + bgCorrectionIndexLow=bgCorrectionIndexLow, + bgCorrectionIndexHigh=bgCorrectionIndexHigh, + maxHeightBin=maxHeightBin, + firstBinIndex=firstBinIndex ) data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix # Store the background corrected signal data_dict['sigBGCor'] = sigBGCor - ## Height and first bin height correction + # ---------------------------------------------------- + # Height and first bin height correction + # ---------------------------------------------------- logging.info('... height bin calculations') # TODO first bin hight might change for different telescopes... data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] @@ -653,16 +798,18 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) - ## Mask for bins with low SNR + # ---------------------------------------------------- + # Mask for bins with low SNR + # ---------------------------------------------------- logging.info('... mask bins with low SNR') SNR = calc_snr(sigBGCor, bg) data_dict['SNR'] = SNR - #print(SNR) + ## create mask and mask every entry, where SNR < minSNRThresh - #data_dict['lowSNRMask'] = np.ma.array(np.zeros(sigBGCor.shape, dtype=bool), mask=np.ones(sigBGCor.shape, dtype=bool)) + # data_dict['lowSNRMask'] = np.ma.array(np.zeros(sigBGCor.shape, dtype=bool), mask=np.ones(sigBGCor.shape, dtype=bool)) # a plain bool mask should be faster. Let's give it a try data_dict['lowSNRMask'] = np.zeros_like(sigBGCor).astype(bool) - #print(data_dict['lowSNRMask']) + for iCh in range(0, sigBGCor.shape[2]): #data_dict['lowSNRMask'][:,:,iCh].mask = SNR[:,:,iCh].data < minSNRThresh[iCh] #data_dict['lowSNRMask'][:,:,iCh] = np.ma.masked_where(SNR[:,:,iCh].data < minSNRThresh[iCh], SNR[:,:,iCh]) @@ -674,6 +821,7 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) print('flag 532 FR', flag532FR) print('flag 355 FR', flag355FR) + if any(flag532FR): data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532FR])) elif any(flag355FR): @@ -689,26 +837,32 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): # TODO mask for single channels on 607, 387, 407, 355RR 532RR 1064RR flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - print('flag 607 FR', flag607FR) if any(flag607FR): data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag607FR])) + flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) if any(flag387FR): data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag387FR])) + flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) if any(flag407FR): data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag407FR])) + flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag355RRFR): data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag355RRFR])) + flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag532RRFR): data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532RRFR])) + flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag1064RRFR): data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag1064RRFR])) - ## Mask for polarization calibration + # ---------------------------------------------------- + # Mask for polarization calibration + # ---------------------------------------------------- logging.info('... mask for polarization calibration') (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], @@ -717,22 +871,18 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): mTime=mTime_unixtimestamp, init_depAng=initialPolAngle, maskDepCalAng=maskPolCalAngle - ) - -# print(data_dict['depol_cal_ang_p_time_start']) -# print(data_dict['depol_cal_ang_p_time_end']) -# print(data_dict['depol_cal_ang_n_time_start']) -# print(data_dict['depol_cal_ang_n_time_end']) -# print(data_dict['depCalMask']) - -#%% Mask for polarization calibration -#[data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... -# data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... -# depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... -# config.initialPolAngle, config.maskPolCalAngle); -#data.depCalMask = transpose(depCalMask); - - ## Range-corrected Signal calculation + ) + ##### Picasso code: + # % Mask for polarization calibration + # [data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... + # data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... + # depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... + # config.initialPolAngle, config.maskPolCalAngle); + # data.depCalMask = transpose(depCalMask); + + # ---------------------------------------------------- + # Range-corrected Signal calculation + # ---------------------------------------------------- logging.info('... calculate range-corrected Signal') # mask = data_dict['lowSNRMask'].mask # mask = data_dict['lowSNRMask'] @@ -743,9 +893,7 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): data_dict['PCR_slice'] = data_dict['sigBGCor']*(150/hRes)/mShots_norm[:, np.newaxis, :] data_dict['RCS'] = calculate_rcs(data_dict['PCR_slice'], data_dict['range']) - logging.info('finished data preprocessing.') - return data_dict @@ -755,12 +903,12 @@ def any_signal(sig: np.ndarray) -> np.ndarray: Parameters ---------- - sig: np.ndarray + sig: ndarray BGCor signal with shape [height, time]. Returns ------- - flag: np.ndarray + flag: ndarray Boolean array of shape [time,] where True indicates the laser shutter is turned on. Notes @@ -783,86 +931,85 @@ def any_signal(sig: np.ndarray) -> np.ndarray: return flag - -# -#%% Temperature effect correction (for Raman signal) -#if config.flagSigTempCor -# temperature = loadMeteor(mean(data.mTime), data.alt, ... -# 'meteorDataSource', config.meteorDataSource, ... -# 'gdas1Site', config.gdas1Site, ... -# 'meteo_folder', config.meteo_folder, ... -# 'radiosondeSitenum', config.radiosondeSitenum, ... -# 'radiosondeFolder', config.radiosondeFolder, ... -# 'radiosondeType', config.radiosondeType, ... -# 'method', 'linear', ... -# 'isUseLatestGDAS', config.flagUseLatestGDAS); -# absTemp = temperature + 273.17; -# -# for iCh = 1:size(data.signal, 1) -# leadingChar = config.tempCorFunc{iCh}(1); -# if (leadingChar == '@') -# % valid matlab anonymous function -# tempCorFunc = config.tempCorFunc{iCh}; -# else -# tempCorFunc = vectorize(['@(T) ', '(', config.tempCorFunc{iCh}, ') .* ones(size(T))']); -# % fprintf('%s is not a valid matlab anonymous function. Redefine it as %s\n', config.tempCorFunc{iCh}, tempCorFunc); -# end -# -# corFunc = str2func(tempCorFunc); -# corFac = corFunc(absTemp); -# data.signal(iCh, :, :) = data.signal(iCh, :, :) ./ repmat(reshape(corFac, 1, [], 1), 1, 1, size(data.signal, 3)); -# end -#end +##### Picasso Code???: +#% Temperature effect correction (for Raman signal) +# if config.flagSigTempCor +# temperature = loadMeteor(mean(data.mTime), data.alt, ... +# 'meteorDataSource', config.meteorDataSource, ... +# 'gdas1Site', config.gdas1Site, ... +# 'meteo_folder', config.meteo_folder, ... +# 'radiosondeSitenum', config.radiosondeSitenum, ... +# 'radiosondeFolder', config.radiosondeFolder, ... +# 'radiosondeType', config.radiosondeType, ... +# 'method', 'linear', ... +# 'isUseLatestGDAS', config.flagUseLatestGDAS); +# absTemp = temperature + 273.17; + +# for iCh = 1:size(data.signal, 1) +# leadingChar = config.tempCorFunc{iCh}(1); +# if (leadingChar == '@') +# % valid matlab anonymous function +# tempCorFunc = config.tempCorFunc{iCh}; +# else +# tempCorFunc = vectorize(['@(T) ', '(', config.tempCorFunc{iCh}, ') .* ones(size(T))']); +# % fprintf('%s is not a valid matlab anonymous function. Redefine it as %s\n', config.tempCorFunc{iCh}, tempCorFunc); +# end + +# corFunc = str2func(tempCorFunc); +# corFac = corFunc(absTemp); +# data.signal(iCh, :, :) = data.signal(iCh, :, :) ./ repmat(reshape(corFac, 1, [], 1), 1, 1, size(data.signal, 3)); +# end +# end # # -#%% Mask for polarization calibration -#[data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... +#% Mask for polarization calibration +# [data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... # data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... # depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... # config.initialPolAngle, config.maskPolCalAngle); -#data.depCalMask = transpose(depCalMask); +# data.depCalMask = transpose(depCalMask); # -#%% Mask for laser shutter -#flagChannel532FR = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -#flagChannel355FR = config.flagFarRangeChannel & config.flag355nmChannel & config.flagTotalChannel; -#if any(flagChannel532FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel532FR, :, :))); -#elseif any(flagChannel355FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel355FR, :, :))); -#else -# warning('No suitable channel to determine the shutter status'); -# data.shutterOnMask = false(size(data.mTime)); -#end +#% Mask for laser shutter +# flagChannel532FR = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; +# flagChannel355FR = config.flagFarRangeChannel & config.flag355nmChannel & config.flagTotalChannel; +# if any(flagChannel532FR) +# data.shutterOnMask = pollyIsLaserShutterOn(... +# squeeze(data.signal(flagChannel532FR, :, :))); +# elseif any(flagChannel355FR) +# data.shutterOnMask = pollyIsLaserShutterOn(... +# squeeze(data.signal(flagChannel355FR, :, :))); +# else + # warning('No suitable channel to determine the shutter status'); + # data.shutterOnMask = false(size(data.mTime)); +# end # -#%% Mask for fog -#data.fogMask = false(1, size(data.signal, 3)); -#is_channel_532_FR_Tot = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -#data.fogMask(transpose(squeeze(sum(data.signal(is_channel_532_FR_Tot, 40:120, :), 2)) <= config.minPC_fog) & (~ data.shutterOnMask)) = true; +#% Mask for fog +# data.fogMask = false(1, size(data.signal, 3)); +# is_channel_532_FR_Tot = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; +# data.fogMask(transpose(squeeze(sum(data.signal(is_channel_532_FR_Tot, 40:120, :), 2)) <= config.minPC_fog) & (~ data.shutterOnMask)) = true; # -#%% Mask for PMT on/off status of 607 nm channel -#flagChannel607 = config.flagFarRangeChannel & config.flag607nmChannel; -#data.mask607Off = pollyIs607Off(squeeze(data.signal(flagChannel607, :, :))); +#% Mask for PMT on/off status of 607 nm channel +# flagChannel607 = config.flagFarRangeChannel & config.flag607nmChannel; +# data.mask607Off = pollyIs607Off(squeeze(data.signal(flagChannel607, :, :))); # -#%% Mask for PMT of 387 nm channel -#flagChannel387 = config.flagFarRangeChannel & config.flag387nmChannel; -#data.mask387Off = pollyIs387Off(squeeze(data.signal(flagChannel387, :, :))); +#% Mask for PMT of 387 nm channel +# flagChannel387 = config.flagFarRangeChannel & config.flag387nmChannel; +# data.mask387Off = pollyIs387Off(squeeze(data.signal(flagChannel387, :, :))); # -#%% Mask for PMT of 407 nm channel -#flagChannel407 = config.flagFarRangeChannel & config.flag407nmChannel; -#data.mask407Off = pollyIs407Off(squeeze(data.signal(flagChannel407, :, :))); +#% Mask for PMT of 407 nm channel +# flagChannel407 = config.flagFarRangeChannel & config.flag407nmChannel; +# data.mask407Off = pollyIs407Off(squeeze(data.signal(flagChannel407, :, :))); # -#%% Mask for PMT of 355 nm rotation Raman channel -#flagChannel355RR = config.flagFarRangeChannel & config.flag355nmRotRaman; -#data.mask355RROff = pollyIs607Off(squeeze(data.signal(flagChannel355RR, :, :))); +#% Mask for PMT of 355 nm rotation Raman channel +# flagChannel355RR = config.flagFarRangeChannel & config.flag355nmRotRaman; +# data.mask355RROff = pollyIs607Off(squeeze(data.signal(flagChannel355RR, :, :))); # -#%% Mask for PMT of 532 nm rotation Raman channel -#flagChannel532RR = config.flagFarRangeChannel & config.flag532nmRotRaman; -#data.mask532RROff = pollyIs607Off(squeeze(data.signal(flagChannel532RR, :, :))); +#% Mask for PMT of 532 nm rotation Raman channel +# flagChannel532RR = config.flagFarRangeChannel & config.flag532nmRotRaman; +# data.mask532RROff = pollyIs607Off(squeeze(data.signal(flagChannel532RR, :, :))); # -#%% Mask for 1064 nm rotation Raman channel -#flagChannel1064RR = config.flag1064nmRotRaman; -#data.mask1064RROff = pollyIs607Off(squeeze(data.signal(flagChannel1064RR, :, :))); +#% Mask for 1064 nm rotation Raman channel +# flagChannel1064RR = config.flag1064nmRotRaman; +# data.mask1064RROff = pollyIs607Off(squeeze(data.signal(flagChannel1064RR, :, :))); # -#end +# end From ac489df766203dbcc5c212c470349dbfb400342b Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Wed, 22 Apr 2026 18:04:17 +0200 Subject: [PATCH 03/13] Converted to PCR (MCPS) in pre range-corrected space. --- ppcpy/interface/picassoProc.py | 1 + ppcpy/preprocess/pollyPreprocess.py | 567 +++++++++++++--------------- ppcpy/retrievals/collection.py | 8 +- 3 files changed, 274 insertions(+), 302 deletions(-) diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index e566e00..88b15e5 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -341,6 +341,7 @@ def preprocessing(self, collect_debug:bool=False): flag1064nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), isUseLatestGDAS=self.polly_config_dict['flagUseLatestGDAS'], collect_debug=collect_debug, + flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'], ) self.retrievals_highres.update(preproc_dict) diff --git a/ppcpy/preprocess/pollyPreprocess.py b/ppcpy/preprocess/pollyPreprocess.py index 072ad63..6ba87e4 100644 --- a/ppcpy/preprocess/pollyPreprocess.py +++ b/ppcpy/preprocess/pollyPreprocess.py @@ -12,6 +12,94 @@ from ppcpy.retrievals.collection import calc_snr +def photonCount2PCR(signal:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute PCR from photon counts. + + PCR = (c * signal)/(2 * hRes * mShots) + + Parameters + ---------- + signal : ndarray + Signal [Photon Count] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. + + Returns + ------- + PCR : ndarray + Photon count rate signal [MCPS]. + + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly get MCPS. + """ + c = 3e2 # meter / microsecond + PCR = (c * signal)/(2 * hRes * mShots[:, np.newaxis, :]) + return PCR + + +def PCR2PhotonCount(PCR:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute photon counts from PCR. + + photonCount = 2 * PCR * mShot * hRes / c + + Parameters + ---------- + PCR : ndarray + Photon count rate signal [MCPS] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. + + Returns + ------- + photonCount : ndarray + Signal [Photon Count] (shape: [M, N, P]). + + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly translate form MCPS. + """ + c = 3e2 # meter / microsecond + photonCount = 2 * hRes * PCR * mShots[:, np.newaxis, :] / c + return photonCount + + +def compute_channel_photon_count(args:tuple) -> np.ndarray: + """Computes Photon count from PCR for a single channel. + + Photons = 2 * PCR * mShot * hRes / c + + Parameters + ---------- + args : tuple + PCR : ndarray + Photon Count Rate signal [MCPS] (shape: [M, N, P]). + mShots : ndarray[time, channels] + Mesurments shots [counts] (shape: [M, P]). + scale_factor : flaot + Scaling factor for the computation: c / (2 * hRes). + channel_index : int + Index of the channel to compute the PCR for. + + Returns + ------- + ndarray + Computed PCR for the given channel (shape: [M, N, P]). + + Notes + ----- + .. TODO:: change scale factor with hRes and include c/2 as a part + of the function. Could even add an flag option for MCPS or CPS. + """ + + PCR, mShots, scale_factor, ch = args + return PCR[:, :, ch] * mShots[:, np.newaxis, ch] / scale_factor + + def compute_channel_pcr(args:tuple) -> np.ndarray: """Computes PCR for a single channel. @@ -25,7 +113,7 @@ def compute_channel_pcr(args:tuple) -> np.ndarray: mShots : ndarray[time, channels] Mesurments shots [counts] (shape: [M, P]). scale_factor : flaot - Scaling factor for the computation: c/(2*hRes). + Scaling factor for the computation: c / (2 * hRes). channel_index : int Index of the channel to compute the PCR for. @@ -128,17 +216,13 @@ def faster_polyval(p:np.ndarray, x:float|np.ndarray) -> float|np.ndarray: #@profile -def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:dict) -> np.ndarray: +def pollyDTCor(PCR:np.ndarray, **varargin:dict) -> np.ndarray: """Dead Time Correction. Parameters ---------- - rawSignal : ndarray - Raw signal [Photon counts]. - mShots : ndarray - Number of measurment shots for each profile. - hRes : ndarray - Height resolution [m]. + PCR : ndarray + Photon count rate signal [MCPS]. Keyword arguments ----------------- @@ -154,14 +238,14 @@ def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:d 3: paralyzable correction with user defined parameters, 4: no deadtime correction, deadtimeParams : list - Deadtime parameters from config-file. Default is []. + Deadtime parameters from config-file at MCPS scale. Default is []. deadtime : list - Deadtime parameters from level0 nc-file. Default is []. + Deadtime parameters from level0 nc-file at MCPS scale. Default is []. Returns ------- - signalDTCor : ndarray - Dead time corrected signal [Photon counts]. + PCR_DTCor : ndarray + Dead time corrected photon count rate signal [MCPS]. Notes ----- @@ -170,93 +254,55 @@ def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:d """ ## Defining default values for param keys (key initialization), if not explictly defined when calling the function - polly_device = varargin.get('device', False) + polly_device = varargin.get('device', False) # <-- is this needed?? flagDeadTimeCorrection = varargin.get('flagDeadTimeCorrection', False) DeadTimeCorrectionMode = varargin.get('DeadTimeCorrectionMode', 2) deadtimeParams = varargin.get('deadtimeParams', []) deadtime = varargin.get('deadtime', []) - # print('mShots', np.all(mShots[:, 0] == mShots[0, 0]), mShots[0, 0], np.min(mShots[:, 0]), np.max(mShots[:, 0])) - if not np.all(mShots[:, 0] == mShots[0, 0]): - logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") - mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - print('mShots_norm', mShots_norm.shape, 'mShots_norm', mShots_norm[0, :]) - logging.info(f'... Deadtime-correction (Mode: {DeadTimeCorrectionMode})') - Nchannels = mShots.shape[1] - - scale_factor = 150.0 / hRes - - ## convert photon counts to Photon-Count-Rate PCR [MHz] - # start_time_command1 = time.time() - # compute_pcr(rawSignal.astype(np.float64), mShots.astype(np.float64), scale_factor, PCR) - # Compute PCR in parallel - # end_time_command1 = time.time() - # elapsed_time_command1 = end_time_command1 - start_time_command1 - # print(f"Time taken: {elapsed_time_command1:.4f} seconds") - PCR = rawSignal * scale_factor / mShots[:, np.newaxis, :] - #PCR_Cor = np.zeros_like(PCR) - signalDTCor = np.zeros_like(PCR) + Nchannels = PCR.shape[-1] + PCR_DTCor = np.zeros_like(PCR) ## Deadtime correction if flagDeadTimeCorrection: - ## polynomial correction with parameters saved in the level0 netcdf-file under variable 'deadtime_polynomial' if DeadTimeCorrectionMode == 1: for iCh in range(Nchannels): - # Extract polynomial coefficients for the channel and reverse their order - # coeffs = deadtime[:, iCh][::-1] # <-- Not used - # PCR_Cor[:, :, iCh] = np.polyval(deadtime[:, iCh][::-1], PCR[:, :, iCh]) - # PCR_Cor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / (150./hRes) - signalDTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor - + PCR_DTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) ## nonparalyzable correction: PCR_cor = PCR / (1 - tau*PCR), with tau beeing the dead-time ## reading from polly-config file under key 'dT' (only the first value from each channel) elif DeadTimeCorrectionMode == 2: for iCh in range(Nchannels): - # PCR_Cor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / scale_factor - signalDTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor - + PCR_DTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) ## user defined deadtime, reading from polly-config file under key 'dT' (the whole matrix, polynome) elif DeadTimeCorrectionMode == 3: if np.array(deadtimeParams).size != 0: - # deadtimeParams=np.array(deadtimeParams) - # signal_out = np.zeros_like(PCR) - # process_signal(PCR, mShots.astype(np.float64), deadtimeParams, Nchannels, scale_factor, signal_out) - # PCR_Cor = PCR - # Pre-extract polynomial coefficients for all channels and reverse their order coeffs_matrix = np.array([np.array(deadtimeParams[ch][::-1]) for ch in range(Nchannels)]) for iCh in range(Nchannels): - # Evaluate the polynomial for the current channel - # PCR_Cor[:, :, iCh] = np.polyval(coeffs_matrix[iCh], PCR[:, :, iCh]) - # PCR_Cor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / scale_factor - signalDTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor + PCR_DTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) else: logging.warning(f'User defined deadtime parameters were not found in polly-config file.') logging.warning(f'In order to continue the current processing, deadtime correction will not be implemented.') ## No deadtime correction elif DeadTimeCorrectionMode == 4: - signalDTCor = rawSignal.astype(np.float64) + PCR_DTCor = PCR.astype(np.float64) logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') else: logging.error(f'Unknow deadtime correction setting! Please go back to check the configuration.') logging.error(f'For deadtimeCorrectionMode, only 1-4 is allowed.') - ## flagDeadTimeCorrection = False + ## flagDeadTimeCorrection equals False else: - signalDTCor = rawSignal.astype(np.float64) + PCR_DTCor = PCR.astype(np.float64) logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') - # return PCR_Cor, signalDTCor - return signalDTCor + return PCR_DTCor def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, @@ -266,22 +312,22 @@ def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionI Parameters ---------- rawSignal : ndarray - Lidar Signal to be processed + Signal to be processed [MCPS or Photon counts]. bgCorrectionIndexLow : list of int - lower index of background noise per channel + Lower index of background noise per channel. bgCorrectionIndexHigh : list of int - upper index of background noise per channel + Upper index of background noise per channel. maxHeightBin : int - maximum height bin index (default: 3000) + Maximum height bin index. Default is 3000). firstBinIndex : list of int - first height bin index per channel (default: 0 per chanel) + First height bin index per channel. Default is 0 per channel. Returns ------- signal_out : ndarray - Background corrected signal + Background corrected signal [MCPs or Photon counts]. bg : ndarray - Removed background noise + Removed background noise [MCPS or Photon counts]. """ logging.info(f'... removing background from signal') @@ -307,16 +353,16 @@ def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.nda Parameters ---------- array : ndarray - array to be sliced + Array to be sliced. maxHeightBin : int - length of slice + Length of slice. firstBinIndex : list of int - start hight/range index of slice per channel + Start hight/range index of slice per channel. Returns ------- out : ndarray - sliced array + Sliced array. """ assert len(firstBinIndex) == array.shape[2], f"first bin index and array do not match {len(firstBinIndex)}, {array.shape}" @@ -468,7 +514,7 @@ def calculate_rcs(signal:np.ndarray, ranges:np.ndarray) -> np.ndarray: return RCS -def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) -> dict: +def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, flagPicassoComparison:bool=False, **param:dict) -> dict: """Preprocessing of Lidar-data. Includes the following processes in order: @@ -481,7 +527,7 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - Parameters ---------- - rawdata_dict : struct + rawdata_dict : dict rawSignal : ndarray Signal [Photon Count]. mShots : ndarray @@ -501,37 +547,39 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - Measurement site. collect_debug : bool If true, collects debug information. Default is False. + flagPicassoComparison : bool + If true, use Picasso values and logic. Keyword arguments ----------------- - deltaT : numeric - integration time (in seconds) for single profile. Default is 30. - flagForceMeasTime : logical - flag to control whether to align measurement time with file creation + deltaT : float + Integration time (in seconds) for single profile. Default is 30. + flagForceMeasTime : bool + Flag to control whether to align measurement time with file creation time, instead of taking the measurement time in the data file. Default is False. - maxHeightBin : numeric + maxHeightBin : int Number of range bins to read out from data file. Default is 3000. - firstBinIndex : numeric + firstBinIndex : int Index of first bin to read out. Default is 1. - pollyType : char + pollyType : str Polly version. Default is 'arielle'. - flagDeadTimeCorrection : logical + flagDeadTimeCorrection : bool Flag to control whether to apply deadtime correction. Default is False. - deadtimeCorrectionMode : numeric + deadtimeCorrectionMode : int Deadtime correction mode. Default is 2. 1: polynomial correction with parameters saved in data file. - 2: non-paralyzable correction - 3: polynomail correction with user defined parameters - 4: disable deadtime correction - deadtimeParams : numeric + 2: non-paralyzable correction. + 3: polynomail correction with user defined parameters. + 4: disable deadtime correction. + deadtimeParams : list Deadtime parameters. Default is []. - flagSigTempCor : logical + flagSigTempCor : bool Flag to implement signal temperature correction. - tempCorFunc : cell + tempCorFunc : list Symbolic function for signal temperature correction. "1": no correction - "exp(-0.001*T)": exponential correction function. (Unit: Kelvin) + "exp(-0.001*T)": exponential correction function [K]. meteorDataSource : str Meteorological data type. e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' @@ -539,51 +587,51 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - The GDAS1 site for the current campaign. meteo_folder : str The main folder of the GDAS1 profiles. - radiosondeSitenum : integer + radiosondeSitenum : int Site number, which can be found in doc/radiosonde-station-list.txt. radiosondeFolder : str The folder of the sonding files. - radiosondeType : integer - File type of the radiosonde file. - - 1: radiosonde file for MOSAiC (default). + radiosondeType : int + File type of the radiosonde file. Default is 1. + - 1: radiosonde file for MOSAiC. - 2: radiosonde file for MUA. - bgCorrectionIndexLow : 1-dim. array + bgCorrectionIndexLow : list Base indecis of bins for background estimation. Defults is [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. - bgCorrectionIndexHigh : 1-dim. array + bgCorrectionIndexHigh : list Top index of bins for background estimation. Defults is [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]. - asl : numeric + asl : float Above sea level in meters. Default is 0. - initialPolAngle : numeric + initialPolAngle : float Initial polarization angle of the polarizer for polarization calibration. Default is 0. - maskPolCalAngle : cell + maskPolCalAngle : list Mask for positive and negative calibration angle of the polarizer, in which 'p' stands for positive angle, while 'n' for negative angle. - Default is {}. - minSNRThresh : numeric + Default is []. + minSNRThresh : list Lower bound of signal-noise ratio. - minPC_fog : numeric + minPC_fog : float Minimun number of photon count after strong attenuation by fog. - flagFarRangeChannel : logical + flagFarRangeChannel : list Flags of far-range channel. - flag532nmChannel : logical + flag532nmChannel : list Flags of channels with central wavelength (CW) at 532 nm. - flagTotalChannel : logical + flagTotalChannel : list Flags of channels receiving total elastic signal. - flag355nmChannel : logical + flag355nmChannel : list Flags of channels with CW at 355 nm. - flag607nmChannel : logical + flag607nmChannel : list Flags of channels with CW at 607 nm. - flag387nmChannel : logical + flag387nmChannel : list Flags of channels with CW at 387 nm. - flag407nmChannel : logical + flag407nmChannel : list Flags of channels with CW at 407 nm. - flag532nmRotRaman : logical + flag532nmRotRaman : list Flags of rotational Raman channels with CW at 532 nm. - flag1064nmRotRaman : logical + flag1064nmRotRaman : list Flags of rotational Raman channels with CW at 1064 nm. Returns @@ -604,9 +652,9 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - Laser pulse repetition rate [s^-1]. hRes : float Spatial resolution [m]. - mSite : string + mSite : str Measurement site. - deadtime : matrix (channel x polynomial_orders) + deadtime : ndarray (channel x polynomial_orders) Deadtime correction parameters. signal : ndarray Background removed signal. @@ -614,32 +662,43 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - Background. height : ndarray Height [m]. - lowSNRMask : logical + lowSNRMask : ndarray If SNR less SNRmin, mask is set True. Otherwise, False. - depCalMask : logical + depCalMask : ndarray If polly was doing polarization calibration, depCalMask is set True. Otherwise, False. - fogMask : logical + fogMask : ndarray If it is foggy which means the signal will be very weak, fogMask will be set True. Otherwise, False. - mask607Off : logical + mask607Off : ndarray Mask of PMT on/off status at 607 nm channel. - mask387Off : logical + mask387Off : ndarray Mask of PMT on/off status at 387 nm channel. - mask407Off : logical + mask407Off : ndarray Mask of PMT on/off status at 407 nm channel. - mask355RROff : logical + mask355RROff : ndarray Mask of PMT on/off status at 355 nm rotational Raman channel. - mask532RROff : logical + mask532RROff : ndarray Mask of PMT on/off status at 532 nm rotational Raman channel. - mask1064RROff : logical + mask1064RROff : ndarray Mask of PMT on/off status at 1064 nm rotational Raman channel. Notes ----- .. TODO:: Revamp docstring. .. TODO:: Rewrite the function, and get rid of all unecessary comments. + Could use the same logic as for the rest of the processing ie. Send + the PicassoProc object in as an input and then extract the info needed at + the start as it is already done. Example: + config_dict = data_cube.polly_config_dict + raw_dict = data_cube.rawdata_dict + etc. + Would need to define the default values somwhere than. .. TODO:: Change to PCR in pre-range corrected space. + .. TODO:: Move this function up. This should be the first function in the file to match + the structure of the rest of the processing chain. + .. TODO:: change to PCR before DT correctiong and then use PCR for the rest of the processing + I could also save both PCR and Photon Count version of signals. **History** @@ -653,6 +712,9 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - logging.info('starting data preprocessing...') + # print all of the large arrays to screen, not only starts and ends of an array + np.set_printoptions(threshold=np.inf) + ## Extracting data from rawdata_dict rawSignal = rawdata_dict['raw_signal']['var_data'] mShots = rawdata_dict['measurement_shots']['var_data'] @@ -665,9 +727,6 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - data_dict = {} - ## print all of the large arrays to screen, not only starts and ends of an array - np.set_printoptions(threshold=np.inf) - ## converting raw-mTime format from [YYYYMMDD seconds-of-day] to unixtimestamp-format logging.info(f'... time conversion') date_string = str(mTime[0][0]) @@ -677,7 +736,9 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - DD = int(date_string[6:8]) datetime_obj = datetime.datetime(YYYY, MM, DD) mTime_obj = [ - datetime_obj.replace(tzinfo=datetime.timezone.utc) + datetime.timedelta(seconds=int(s)) for s in seconds_of_day] + datetime_obj.replace(tzinfo=datetime.timezone.utc) +\ + datetime.timedelta(seconds=int(s)) for s in seconds_of_day + ] mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] # Convert to Unix timestamp @@ -720,16 +781,7 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) isUseLatestGDAS = param.get('isUseLatestGDAS', False) - ##### Picasso code: - # % Determine whether number of range bins is out of range - # if (max(config.maxHeightBin + config.firstBinIndex - 1) > size(data.rawSignal, 2)) - # warning('maxHeightBin or firstBinIndex is out of range.\nTotal number of range bin is %d.\nmaxHeightBin is - # %d\nfirstBinIndex is %d\n', size(data.rawSignal, 2), config.maxHeightBin, config.firstBinIndex); - # fprintf('Set maxHeightBin and firstBinIndex to default values.\n'); - # config.maxHeightBin = ones(1, size(data.rawSignal, 1)); - # config.firstBinIndex = 251; - # end - ##### Unsure what this is? + # ## .. TODO:: What does this part do and why is it omited? # logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') # if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') @@ -738,59 +790,75 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - # logging.info(f'maxHeightBin: {maxHeightBin}') # firstBinIndex = 251 - mShotsPerPrf = deltaT * repRate - if len(mTime) > 1: - # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s - nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s - else: - nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) + # ## .. TODO:: This part is currently not used! Should we use it? + # mShotsPerPrf = deltaT * repRate + # if len(mTime) > 1: + # # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # else: + # nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) - # ---------------------------------------------------- + # ------------------------------------------------------------------------ + # Transform to Photon Count Rate (MCPS) + # ------------------------------------------------------------------------ + if not np.all(mShots[:, 0] == mShots[0, 0]): + logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") + mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) + PCR = photonCount2PCR(rawSignal, mShots, hRes) # Use mShots directly + # PCR = photonCount2PCR(rawSignal, mShots_norm, hRes) # Use mean mShots per channel + + + # ------------------------------------------------------------------------ # Deadtime correction - # ---------------------------------------------------- - # PCR_Cor, preproSignal = pollyDTCor(rawSignal = rawSignal, + # ------------------------------------------------------------------------ preproSignal = pollyDTCor( - rawSignal=rawSignal, - mShots=mShots, - hRes=hRes, + PCR=PCR, polly_device=pollyType, - flagDeadTimeCorrection=flagDeadTimeCorrection, + flagDeadTimeCorrection=flagDeadTimeCorrection, DeadTimeCorrectionMode=deadtimeCorrectionMode, deadtimeParams=deadtimeParams, deadtime=rawdata_dict['deadtime_polynomial']['var_data'] ) - # most likely the preprocesssed deadtime corrected signal can be omitted + + if flagPicassoComparison: + # preproSignal = PCR2PhotonCount(preproSignal, mShots, hRes) # Use mShots directly + preproSignal = PCR2PhotonCount(preproSignal, mShots_norm, hRes) # Use mean mShots per channel + if collect_debug: - # data_dict['PCR_cor'] = PCR_Cor - data_dict['preproSignal'] = preproSignal - # data_dict['PCR_slice'] = slicerange(PCR_Cor, maxHeightBin, firstBinIndex) + # Store dead time corrected signal + data_dict['preproSignal'] = preproSignal + - # ---------------------------------------------------- + # ------------------------------------------------------------------------ # Background Correction - # ---------------------------------------------------- + # ------------------------------------------------------------------------ sigBGCor, bg = pollyRemoveBG( rawSignal=preproSignal, bgCorrectionIndexLow=bgCorrectionIndexLow, - bgCorrectionIndexHigh=bgCorrectionIndexHigh, + bgCorrectionIndexHigh=bgCorrectionIndexHigh, maxHeightBin=maxHeightBin, firstBinIndex=firstBinIndex ) + # Store the background and background corrected signal data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix - # Store the background corrected signal - data_dict['sigBGCor'] = sigBGCor + data_dict['sigBGCor'] = sigBGCor + - # ---------------------------------------------------- + # ------------------------------------------------------------------------ # Height and first bin height correction - # ---------------------------------------------------- + # ------------------------------------------------------------------------ logging.info('... height bin calculations') - # TODO first bin hight might change for different telescopes... + # .. TODO:: first bin hight might change for different telescopes... --> should expand range to be channel spesific. data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] data_dict['height'] = data_dict['range'].copy() * np.cos(zenithAng*np.pi/180) - correction_firstBinHight = (( - (np.arange(0, sigBGCor.shape[1]) * hRes)[:,np.newaxis] + firstBinHeight)**2 - / data_dict['range'][:,np.newaxis]**2) + # correction firstBinHight = range per channel ^ 2 / range first channel ^ 2 + correction_firstBinHight = ( + ((np.arange(0, sigBGCor.shape[1]) * hRes)[:, np.newaxis] + firstBinHeight)**2 + / data_dict['range'][:, np.newaxis]**2 + ) + data_dict['sigBGCor'] = data_dict['sigBGCor'] * correction_firstBinHight[np.newaxis, :, :] data_dict['alt'] = data_dict['height'] + float(asl) ## geopotential height @@ -798,71 +866,67 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) - # ---------------------------------------------------- + # ------------------------------------------------------------------------ # Mask for bins with low SNR - # ---------------------------------------------------- + # ------------------------------------------------------------------------ logging.info('... mask bins with low SNR') - SNR = calc_snr(sigBGCor, bg) + # SNR = calc_snr(sigBGCor, bg) # This do not consider the correction for different first bin heights. + SNR = calc_snr(data_dict['sigBGCor'], bg) data_dict['SNR'] = SNR - ## create mask and mask every entry, where SNR < minSNRThresh - # data_dict['lowSNRMask'] = np.ma.array(np.zeros(sigBGCor.shape, dtype=bool), mask=np.ones(sigBGCor.shape, dtype=bool)) - # a plain bool mask should be faster. Let's give it a try + ## Create mask and mask every entry, where SNR < minSNRThresh + # .. TODO:: check the low SNR mask data_dict['lowSNRMask'] = np.zeros_like(sigBGCor).astype(bool) - for iCh in range(0, sigBGCor.shape[2]): - #data_dict['lowSNRMask'][:,:,iCh].mask = SNR[:,:,iCh].data < minSNRThresh[iCh] - #data_dict['lowSNRMask'][:,:,iCh] = np.ma.masked_where(SNR[:,:,iCh].data < minSNRThresh[iCh], SNR[:,:,iCh]) - data_dict['lowSNRMask'][:,:,iCh][SNR[:,:,iCh] < minSNRThresh[iCh]] = True - # TODO check the low SNR mask + data_dict['lowSNRMask'][:, :, iCh][SNR[:, :, iCh] < minSNRThresh[iCh]] = True - # TODO mask for laser shutter? + # .. TODO:: mask for laser shutter? flag532FR = (np.array(flag532nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) - print('flag 532 FR', flag532FR) - print('flag 355 FR', flag355FR) if any(flag532FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532FR])) + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532FR])) elif any(flag355FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag355FR])) + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355FR])) else: - raise ValueError('No suitable channel to determine the shutter status') + raise ValueError('No suitable channel to determine the shutter status.') - # TODO mask for fog? - # the original matlab code raises questions. Why 40:120 and why hard coded? + ## Create Fog mask + # .. TODO:: mask for fog? the original matlab code raises questions. Why 40:120 and why hard coded? # When sum is used (as in matlab), minPC_fog is range resolution dependent - fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:,39:120,flag532FR]), axis=1) + fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:, 39:120, flag532FR]), axis=1) data_dict['fogMask'] = fogsum < minPC_fog - # TODO mask for single channels on 607, 387, 407, 355RR 532RR 1064RR + ## Create single channel masks + # .. TODO:: mask for single channels on 607, 387, 407, 355RR 532RR 1064RR flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) if any(flag607FR): - data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag607FR])) + data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag607FR])) flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) if any(flag387FR): - data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag387FR])) + data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag387FR])) flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) if any(flag407FR): - data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag407FR])) + data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag407FR])) flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag355RRFR): - data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag355RRFR])) + data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355RRFR])) flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag532RRFR): - data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532RRFR])) + data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532RRFR])) flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) if any(flag1064RRFR): - data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag1064RRFR])) + data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag1064RRFR])) - # ---------------------------------------------------- + + # ------------------------------------------------------------------------ # Mask for polarization calibration - # ---------------------------------------------------- + # ------------------------------------------------------------------------ logging.info('... mask for polarization calibration') (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], @@ -872,33 +936,24 @@ def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict) - init_depAng=initialPolAngle, maskDepCalAng=maskPolCalAngle ) - ##### Picasso code: - # % Mask for polarization calibration - # [data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... - # data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... - # depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... - # config.initialPolAngle, config.maskPolCalAngle); - # data.depCalMask = transpose(depCalMask); - - # ---------------------------------------------------- + + + # ------------------------------------------------------------------------ # Range-corrected Signal calculation - # ---------------------------------------------------- + # ------------------------------------------------------------------------ logging.info('... calculate range-corrected Signal') - # mask = data_dict['lowSNRMask'].mask - # mask = data_dict['lowSNRMask'] - # masked arry might be slow - # RCS_masked = np.ma.masked_array(sigBGCor+bg, mask=mask) - # data_dict['RCS'] = calculate_rcs(datasignal=preproSignal, data_dict=data_dict, mShots=mShots, hRes=hRes) - mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - data_dict['PCR_slice'] = data_dict['sigBGCor']*(150/hRes)/mShots_norm[:, np.newaxis, :] - data_dict['RCS'] = calculate_rcs(data_dict['PCR_slice'], data_dict['range']) + data_dict['RCS'] = calculate_rcs(data_dict['sigBGCor'], data_dict['range']) + + if flagPicassoComparison: + # data_dict['RCS'] = calculate_rcs(photonCount2PCR(data_dict['sigBGCor'].copy(), mShots, hRes), data_dict['range']) # Use mShots directly + data_dict['RCS'] = calculate_rcs(photonCount2PCR(data_dict['sigBGCor'].copy(), mShots_norm, hRes), data_dict['range']) # Use mean mShots per channel logging.info('finished data preprocessing.') return data_dict -def any_signal(sig: np.ndarray) -> np.ndarray: - """check if there is any signal +def any_signal(sig:np.ndarray) -> np.ndarray: + """Check if there is any signal POLLYISLASERSHUTTERON determine whether the laser shutter is on due to the flying object. Parameters @@ -925,91 +980,7 @@ def any_signal(sig: np.ndarray) -> np.ndarray: std_sig = np.std(sig, axis=1, ddof=0) # Detect when both mean and std dev are below threshold - # for some reason had to set the thresholds higher than in matlab version + # for some reason had to set the thresholds higher than in matlab version .. TODO:: <-- Check this!! flag = (mean_sig <= 0.02) & (std_sig <= 0.9) - return flag - - -##### Picasso Code???: -#% Temperature effect correction (for Raman signal) -# if config.flagSigTempCor -# temperature = loadMeteor(mean(data.mTime), data.alt, ... -# 'meteorDataSource', config.meteorDataSource, ... -# 'gdas1Site', config.gdas1Site, ... -# 'meteo_folder', config.meteo_folder, ... -# 'radiosondeSitenum', config.radiosondeSitenum, ... -# 'radiosondeFolder', config.radiosondeFolder, ... -# 'radiosondeType', config.radiosondeType, ... -# 'method', 'linear', ... -# 'isUseLatestGDAS', config.flagUseLatestGDAS); -# absTemp = temperature + 273.17; - -# for iCh = 1:size(data.signal, 1) -# leadingChar = config.tempCorFunc{iCh}(1); -# if (leadingChar == '@') -# % valid matlab anonymous function -# tempCorFunc = config.tempCorFunc{iCh}; -# else -# tempCorFunc = vectorize(['@(T) ', '(', config.tempCorFunc{iCh}, ') .* ones(size(T))']); -# % fprintf('%s is not a valid matlab anonymous function. Redefine it as %s\n', config.tempCorFunc{iCh}, tempCorFunc); -# end - -# corFunc = str2func(tempCorFunc); -# corFac = corFunc(absTemp); -# data.signal(iCh, :, :) = data.signal(iCh, :, :) ./ repmat(reshape(corFac, 1, [], 1), 1, 1, size(data.signal, 3)); -# end -# end -# -# -#% Mask for polarization calibration -# [data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... -# data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... -# depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... -# config.initialPolAngle, config.maskPolCalAngle); -# data.depCalMask = transpose(depCalMask); -# -#% Mask for laser shutter -# flagChannel532FR = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -# flagChannel355FR = config.flagFarRangeChannel & config.flag355nmChannel & config.flagTotalChannel; -# if any(flagChannel532FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel532FR, :, :))); -# elseif any(flagChannel355FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel355FR, :, :))); -# else - # warning('No suitable channel to determine the shutter status'); - # data.shutterOnMask = false(size(data.mTime)); -# end -# -#% Mask for fog -# data.fogMask = false(1, size(data.signal, 3)); -# is_channel_532_FR_Tot = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -# data.fogMask(transpose(squeeze(sum(data.signal(is_channel_532_FR_Tot, 40:120, :), 2)) <= config.minPC_fog) & (~ data.shutterOnMask)) = true; -# -#% Mask for PMT on/off status of 607 nm channel -# flagChannel607 = config.flagFarRangeChannel & config.flag607nmChannel; -# data.mask607Off = pollyIs607Off(squeeze(data.signal(flagChannel607, :, :))); -# -#% Mask for PMT of 387 nm channel -# flagChannel387 = config.flagFarRangeChannel & config.flag387nmChannel; -# data.mask387Off = pollyIs387Off(squeeze(data.signal(flagChannel387, :, :))); -# -#% Mask for PMT of 407 nm channel -# flagChannel407 = config.flagFarRangeChannel & config.flag407nmChannel; -# data.mask407Off = pollyIs407Off(squeeze(data.signal(flagChannel407, :, :))); -# -#% Mask for PMT of 355 nm rotation Raman channel -# flagChannel355RR = config.flagFarRangeChannel & config.flag355nmRotRaman; -# data.mask355RROff = pollyIs607Off(squeeze(data.signal(flagChannel355RR, :, :))); -# -#% Mask for PMT of 532 nm rotation Raman channel -# flagChannel532RR = config.flagFarRangeChannel & config.flag532nmRotRaman; -# data.mask532RROff = pollyIs607Off(squeeze(data.signal(flagChannel532RR, :, :))); -# -#% Mask for 1064 nm rotation Raman channel -# flagChannel1064RR = config.flag1064nmRotRaman; -# data.mask1064RROff = pollyIs607Off(squeeze(data.signal(flagChannel1064RR, :, :))); -# -# end + return flag \ No newline at end of file diff --git a/ppcpy/retrievals/collection.py b/ppcpy/retrievals/collection.py index a537dfe..6b16f72 100644 --- a/ppcpy/retrievals/collection.py +++ b/ppcpy/retrievals/collection.py @@ -2,7 +2,7 @@ import numpy as np -def calc_snr(signal, bg): +def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: """Calculate signal-to-noise ratio (SNR). .. TODO:: @@ -11,14 +11,14 @@ def calc_snr(signal, bg): Parameters ---------- - signal : numpy.ndarray + signal : ndarray Signal strength. - bg : numpy.ndarray + bg : ndarray Background noise. Returns ------- - SNR : numpy.ndarray + SNR : ndarray Signal-to-noise ratio. For negative signal values, the SNR is set to 0. References From 96aba4c9d5ae961180e3633f22996964ac8211c8 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Mon, 27 Apr 2026 10:16:21 +0200 Subject: [PATCH 04/13] Fixed mean_stable() issue in overlapCalcRaman(). --- ppcpy/qc/overlapEst.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ppcpy/qc/overlapEst.py b/ppcpy/qc/overlapEst.py index 4e70402..228fd38 100644 --- a/ppcpy/qc/overlapEst.py +++ b/ppcpy/qc/overlapEst.py @@ -347,12 +347,12 @@ def overlapCalcRaman( ovl_norm, normRange, _ = mean_stable(olFunc, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) ovl_norm0, normRange0, _ = mean_stable(olFunc0, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) - if ovl_norm.size == 1: + if ovl_norm is not None and ovl_norm.size == 1: olFunc /= ovl_norm else: olFunc /= np.nanmean(olFunc[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) - if ovl_norm0.size == 1: + if ovl_norm0 is not None and ovl_norm0.size == 1: olFunc0 /= ovl_norm0 else: olFunc0 /= np.nanmean(olFunc0[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) From 02d8759bc895e751a3c85b20b24fb1ed73194460 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Fri, 19 Jun 2026 16:38:42 +0200 Subject: [PATCH 05/13] Fixing merge issues --- ...8-21_PicassoPy_workshop_plotExamples.ipynb | 3301 +++++++++++++++++ 1 file changed, 3301 insertions(+) create mode 100644 tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb diff --git a/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb b/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb new file mode 100644 index 0000000..0f8455d --- /dev/null +++ b/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb @@ -0,0 +1,3301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "07a5e20f-3b1c-4d25-90ec-58e659beb604", + "metadata": {}, + "source": [ + "# **PicassoPy Workshop --> Test case: cpv 2024-08-21**\n", + "---\n", + "\n", + "\n", + "- Folder for data `test_case_data` -> download separately\n", + "- Folder for configs `test_case_config`\n" + ] + }, + { + "cell_type": "markdown", + "id": "61e316d2", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d9357e59-6b1c-48ef-887f-92bc1bfdad3f", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "import argparse\n", + "import datetime\n", + "import logging\n", + "from pathlib import Path\n", + "import numpy as np\n", + "\n", + "sys.path.append('../')\n", + "import ppcpy\n", + "import ppcpy.io.loadConfigs as loadConfigs\n", + "import ppcpy.io.readPollyRawData as readPollyRawData\n", + "import ppcpy.interface.picassoProc as picassoProc\n", + "import ppcpy.misc.helper as helper\n", + "import ppcpy.misc.startscreen as startscreen\n", + "from ppcpy.io.write2nc import write_channelwise_2_nc_file, write2nc_file, write_profile2nc_file\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.tab20.colors)" + ] + }, + { + "cell_type": "markdown", + "id": "a1dcfcd1", + "metadata": {}, + "source": [ + "## Defining Script Inputs\n", + "\n", + "- The parameters `args.device`, `args.timestamp`, `args.picasso_config_file`, `args.level0_file_to_process`, need to be manually specified per case.\n", + "- The parameter `DATABASE_PATH` is the path to the database used for storing the retrieved calibration constants" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "757db1a3-8f88-4d02-bb42-235dee8f1ff6", + "metadata": {}, + "outputs": [], + "source": [ + "## For purpose of the notebook mimic the argparse interface\n", + "from types import SimpleNamespace\n", + "args = SimpleNamespace()\n", + "\n", + "## The used device and time of mesurment\n", + "args.device = 'pollyxt_cpv'\n", + "args.timestamp = '20240821'\n", + "dt = datetime.datetime.strptime(args.timestamp, \"%Y%m%d\")\n", + "\n", + "## The used config file\n", + "args.picasso_config_file = \"test_case_config/pollynet_processing_chain_config_test.json\"\n", + "\n", + "## The data file to use\n", + "args.level0_file_to_process = f\"test_case_data/{dt:%Y_%m_%d_%a}_CPV_00_00_01.nc\"\n", + "\n", + "\n", + "## Database path\n", + "DATABASE_PATH = \"PicassoPyDatabase.db\" # I should test if it works to read this directly from the config files." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "4fcd069d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ____ _ ____ ___ ____ \n", + " / __ \\(_)________ _______________ / __ \\__ __ < // __ \\\n", + " / /_/ / / ___/ __ `/ ___/ ___/ __ \\/ /_/ / / / / / // / / /\n", + " / ____/ / /__/ /_/ (__ |__ ) /_/ / ____/ /_/ / / // /_/ / \n", + " /_/ /_/\\___/\\__,_/____/____/\\____/_/ \\__, / /_(_)____/ \n", + " /____/ \n" + ] + } + ], + "source": [ + "startscreen.startscreen()" + ] + }, + { + "cell_type": "markdown", + "id": "e2144e6f", + "metadata": {}, + "source": [ + "## Load Data and Config-files\n", + "\n", + "Loads data and information from the config files into the following three dictionaries:\n", + "- `picasso_config_dict`: Paths and other information stored in the picasso config file\n", + "- `polly_config_dict`: Configuration variables from polly config and polly default files\n", + "- `rawdata_dict`: Measurement data and information extracted from the level0 file" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "648de474-fe46-4f39-82ee-d1731db90ec8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:50,062 - INFO - picasso_default_config_file: c:\\Users\\buholdt\\Documents\\PicassoPy\\ppcpy\\config\\pollynet_processing_chain_config.json\n", + "2026-05-11 19:23:50,063 - INFO - picasso_config_file: test_case_config/pollynet_processing_chain_config_test.json\n", + "2026-05-11 19:23:50,065 - INFO - pollynet_config_link_file: test_case_config/pollynet_processing_chain_config_links.xlsx\n", + "2026-05-11 19:23:50,109 - INFO - polly_default_config_file: c:\\Users\\buholdt\\Documents\\PicassoPy\\ppcpy\\config\\polly_global_config.json\n", + "2026-05-11 19:23:50,111 - INFO - polly_config_file: test_case_config\\pollyxt_cpv_config_20230927.json\n", + "2026-05-11 19:23:50,112 - INFO - keys default/template file, but not in specific file {'molDepol1064', 'minSNR_4_sigNorm', 'refH_FR_532', 'clear_thres_par_beta_1064', 'zLim_VolDepol_355', 'depol_cal_time_fixed_m_start', 'overlapFile_532_total_FR', 'flagUseManualRefH', 'yLim_Profi_WV_RH', 'depol_cal_time_fixed_p_end', 'indexing_convention', 'volDepolerror355', 'large_thres_ang', 'zLim_quasi_ANG', 'ice_thres_par_depol', 'zLim_FR_RCS_607', 'xLim_Profi_LR', 'yLim_all_profiles_low_range', 'flagSigTempCor', 'polCaliEtaStd355', 'turbid_thres_par_beta_532', 'depol_cali_mode', 'tTwilight', 'zLim_NR_RCS_607', 'overlapFile_355_total_FR', 'xLim_Profi_RH', 'polCaliEta355', 'bgCorRangeIndxHigh', 'bgCorRangeIndxLow', 'droplet_thres_par_depol', 'isParallel', 'yLim_beta_532_Poliphon', 'volDepolerror1064', 'cloud_thres_par_beta_1064', 'zLim_VolDepol_532', 'yLim_all_profiles_high_range', 'zLim_VolDepol_1064', 'depol_cal_time_fixed_p_start', 'polCaliEtaStd532', 'flagMolDepolCali', 'depol_cal_time_fixed_m_end', 'polCaliEta1064', 'spheroid_thres_par_depol', 'prodSaveList', 'xLim_beta_532_Poliphon', 'flagPicassoComparison', 'ice_thres_vol_depol', 'turbid_thres_par_beta_1064', 'zLim_NR_RCS_407', 'min_atten_par_beta_1064', 'yLim_cloudinfo', 'search_cloud_below', 'xLim_Profi_AE', 'polCaliEta532', 'maxCloudSearchHeight', 'deltaT', 'tempCorFunc', 'zLim_FR_RCS_407', 'logbookFileName', 'molDepolStd1064', 'radiosondeFolder', 'flagUseTheoreticalMDR', 'radiosondeType', 'refH_FR_355', 'smoothWin_klett_NR_532', 'logbookPath', 'zLim_FR_RCS_387', 'maxCloudSearchHeight_NR', 'volDepolerror532', 'small_thres_ang', 'refH_FR_1064', 'colormap_basic', 'imgFormat', 'zLim_NR_RCS_387', 'polCaliEtaStd1064', 'search_cloud_above', 'smoothWin_klett_NR_355', 'unspheroid_thres_par_depol', 'flagUseRetrievedExt4LCCalc'}\n", + "2026-05-11 19:23:50,113 - INFO - keys specific file, but not in default/template file {'meteo_file', 'bgCorRangeIndx', 'overlapFile355', 'overlapFile532'}\n", + "2026-05-11 19:23:50,114 - INFO - reading nc-file: test_case_data\\2024_08_21_Wed_CPV_00_00_01.nc\n" + ] + } + ], + "source": [ + "## Path to dafault Picasso config file\n", + "picasso_default_config_file = Path(\n", + " helper.detect_path_type(Path.cwd().parent), 'ppcpy', 'config', 'pollynet_processing_chain_config.json')\n", + "\n", + "## Load Picasso config file\n", + "picasso_config_dict = loadConfigs.loadPicassoConfig(args.picasso_config_file, picasso_default_config_file)\n", + "\n", + "## load polly config file\n", + "polly_config_array = loadConfigs.readPollyNetConfigLinkTable(picasso_config_dict['pollynet_config_link_file'], timestamp=args.timestamp, device=args.device)\n", + "polly_config_dict = loadConfigs.getPollyConfigfromArray(\n", + " polly_config_array, picasso_config_dict\n", + ")\n", + "\n", + "## Load level0-data file\n", + "rawfile_fullname = args.level0_file_to_process\n", + "rawfile = helper.detect_path_type(rawfile_fullname)\n", + "rawdata_dict = readPollyRawData.readPollyRawData(rawfile)" + ] + }, + { + "cell_type": "markdown", + "id": "1ca5548d", + "metadata": {}, + "source": [ + "## Initialize PicassoProc object\n", + "\n", + "PicassoProc is the main object in the PicassoPy, and is responsible for running all processes included and storing the data." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "1141175f-1f22-47ae-96dd-d9226e854e18", + "metadata": {}, + "outputs": [], + "source": [ + "## Initialize PicassoProc\n", + "data_cube = picassoProc.PicassoProc(rawdata_dict, polly_config_dict, picasso_config_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "70b3264d-9b4d-4994-8d88-557cba209d84", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:50,392 - INFO - date consistency-check... \n", + "2026-05-11 19:23:50,394 - INFO - ... date in nc-file equals date of filename\n", + "2026-05-11 19:23:50,395 - INFO - ChannelLabels: ['FR-total-355 nm', 'FR-cross-355 nm', 'FR-387 nm', 'FR-407 nm', 'FR-total-532 nm', 'FR-cross-532 nm', 'FR-607 nm', 'FR-total-1064 nm', 'NR-total-532 nm', 'NR-607 nm', 'NR-total-355 nm', 'NR-387 nm', 'DFOV', '1058', '1064s', 'none']\n", + "2026-05-11 19:23:50,395 - WARNING - removed none tag from channel list [15]\n", + "2026-05-11 19:23:50,396 - INFO - date consistency-check... \n", + "2026-05-11 19:23:50,397 - INFO - ... date in nc-file equals date of filename\n" + ] + } + ], + "source": [ + "## reset date if date in filename differs date within nc-file \n", + "data_cube.reset_date_infile()\n", + "\n", + "## checking for correct mshots\n", + "data_cube.check_for_correct_mshots()\n", + "\n", + "## setting channelTags\n", + "data_cube.setChannelTags()\n", + "\n", + "## check for correct date in nc-file\n", + "data_cube.reset_date_infile()" + ] + }, + { + "cell_type": "markdown", + "id": "7215c4d2", + "metadata": {}, + "source": [ + "## Preprocessing & Saturation Detection\n", + "\n", + "The preprocessing includes the following processes:\n", + "- Deadtime correction\n", + "- Background correction\n", + "- SNR claculations\n", + "- Flagging of data\n", + "- Range correction" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "75e45529-0a19-4580-9bc5-269eadba869b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:50,403 - INFO - starting data preprocessing...\n", + "2026-05-11 19:23:50,404 - INFO - ... time conversion\n", + "2026-05-11 19:23:50,409 - WARNING - ... mShots not constant min 2993 max 2999\n", + "2026-05-11 19:23:50,410 - INFO - ... Deadtime-correction (Mode: 1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mShots_norm (720, 15) mShots_norm [2995.73333333 2995.73333333 2995.73333333 2995.73333333 2995.73333333\n", + " 2995.73333333 2995.73333333 2995.73333333 2995.72361111 2995.72361111\n", + " 2995.72361111 2995.72361111 2995.72361111 2995.72361111 2995.72361111]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:53,477 - INFO - ... removing background from signal\n", + "2026-05-11 19:23:53,864 - INFO - ... height bin calculations\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\preprocess\\pollyPreprocess.py:653: UserWarning: no explicit representation of timezones available for np.datetime64\n", + " data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj])\n", + "2026-05-11 19:23:53,926 - INFO - ... mask bins with low SNR\n", + "2026-05-11 19:23:55,099 - INFO - ... mask for polarization calibration\n", + "2026-05-11 19:23:55,102 - INFO - ... calculate range-corrected Signal\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flag 532 FR [False False False False True False False False False False False False\n", + " False False False]\n", + "flag 355 FR [ True False False False False False False False False False False False\n", + " False False False]\n", + "flag 607 FR [False False False False False False True False False False False False\n", + " False False False]\n", + "flagNDepCal [False False False False False False False False False False False False\n", + " True True True True True True True True False]\n", + "flagPDepCal [False False True True True True True True True True False False\n", + " False False False False False False False False False]\n", + "depCalPeriods [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + "flagIDepCal [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n", + "21 21\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:55,339 - INFO - finished data preprocessing.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(720, 4000, 15)\n", + "ranges2d (720, 4000)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Perform preprocessing, this includes Dead-time correction, Background correction, and Range correction\n", + "data_cube.preprocessing(collect_debug=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "774aa365", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:23:55,363 - INFO - saving product: SNR\n", + "2026-05-11 19:23:55,480 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_SNR.nc\n", + "2026-05-11 19:23:57,541 - INFO - saving product: BG\n", + "2026-05-11 19:23:57,636 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_BG.nc\n", + "2026-05-11 19:23:57,675 - INFO - saving product: RCS\n", + "2026-05-11 19:23:57,767 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_RCS.nc\n" + ] + } + ], + "source": [ + "## Save high resolution signal-to-noise ratio, background, and range corrected signal\n", + "write_channelwise_2_nc_file(data_cube=data_cube, prod_ls=['SNR', 'BG', 'RCS'])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "0df6f7b6-f5a2-4cc2-9257-64e0d5fe8a88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'FR-total-355 nm',\n", + " 1: 'FR-cross-355 nm',\n", + " 2: 'FR-387 nm',\n", + " 3: 'FR-407 nm',\n", + " 4: 'FR-total-532 nm',\n", + " 5: 'FR-cross-532 nm',\n", + " 6: 'FR-607 nm',\n", + " 7: 'FR-total-1064 nm',\n", + " 8: 'NR-total-532 nm',\n", + " 9: 'NR-607 nm',\n", + " 10: 'NR-total-355 nm',\n", + " 11: 'NR-387 nm',\n", + " 12: 'DFOV',\n", + " 13: '1058',\n", + " 14: '1064s'}" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Display available channels\n", + "data_cube.channel_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "3dfe7541-d789-4a4a-be43-b7a6d08d4dd5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:00,651 - INFO - Saturation detection\n" + ] + } + ], + "source": [ + "## Detect and flag saturated signal\n", + "data_cube.SaturationDetect()" + ] + }, + { + "cell_type": "markdown", + "id": "ec6c121c", + "metadata": {}, + "source": [ + "### Plot Example: Set correct first range bin\n", + "\n", + "The config variable `first_range_gate_index` should indicate the bin at which the signal is reset to 0 after the signal spike." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "72ef8fc9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 1, figsize=(10, 9))\n", + "for i in range(len(data_cube.channel_dict)):\n", + " ax[0].plot(\n", + " np.nanmean(data_cube.retrievals_highres['preproSignal'][:, :, i], axis=0), \n", + " label=data_cube.channel_dict[i]+f\": {data_cube.polly_config_dict['first_range_gate_indx'][i]}\"\n", + " )\n", + " ax[1].plot(\n", + " np.nanmean(data_cube.retrievals_highres['sigBGCor'][:, :, i], axis=0), \n", + " label=data_cube.channel_dict[i]+f\": {data_cube.polly_config_dict['first_range_gate_indx'][i]}\"\n", + " )\n", + "ax[0].set_xlabel('Range index')\n", + "ax[0].set_ylabel('Photon count')\n", + "ax[0].set_xlim(249, 255)\n", + "ax[0].set_ylim(-100, 1300)\n", + "ax[0].grid()\n", + "ax[0].legend()\n", + "ax[0].set_title(\"Pre-BG corrected signal\")\n", + "ax[1].set_xlabel('Range index')\n", + "ax[1].set_ylabel('Photon count')\n", + "ax[1].set_xlim(0, 10)\n", + "ax[1].set_ylim(-100, 1300)\n", + "ax[1].grid()\n", + "ax[1].legend(loc=\"upper right\")\n", + "ax[1].set_title(\"BG corrected signal\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "f24de77a", + "metadata": {}, + "source": [ + "### Plot example: Range corrected signal" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "8fa26092", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmesh = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " np.squeeze(data_cube.retrievals_highres['RCS'][:, :, data_cube.gf(1064, 'total', 'FR')]).T,\n", + " shading='nearest',\n", + " vmin=5, vmax=1e7\n", + ")\n", + "cbar = fig.colorbar(pcmesh)\n", + "cbar.set_label('RCS 1064nm total FR [MCPS]')\n", + "ax.set_xlabel('Time [UTC]')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_ylim(0, 5)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "8a555194", + "metadata": {}, + "source": [ + "## Depol Calibration\n", + "\n", + "\n", + "- Depol. calibration constants (DC) are retrieved at each depol. calibration period included in the data\n", + "- All retrieved DCs are stored in a dedicated database\n", + "- The optimal retrieved DC, ie. the one with the lowest standard deviation (std) is used for the processing\n", + "- If no DCs can be retirieved, the DC with the lowest std in the time range [24h before the measurement, 24h after the measurement] included in the database will be used" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "2bf29575-306c-4e4b-84e9-16e80cc0d559", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:06,509 - INFO - and even a 355 channel\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:314: RuntimeWarning: divide by zero encountered in divide\n", + " dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win)\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:314: RuntimeWarning: invalid value encountered in divide\n", + " dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win)\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:315: RuntimeWarning: divide by zero encountered in divide\n", + " dminus = smooth_signal(sig_x_m, smooth_win) / smooth_signal(sig_t_m, smooth_win)\n", + "2026-05-11 19:24:06,559 - INFO - pol_cali_355 [{'eta': 47.742418057789656, 'eta_std': 1.0396617649540707, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,560 - INFO - and even a 532 channel\n", + "2026-05-11 19:24:06,613 - INFO - pol_cali_532 [{'eta': 12.380587648929659, 'eta_std': 0.13602867159593543, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,614 - INFO - and even a 1064 channel\n", + "2026-05-11 19:24:06,667 - INFO - pol_cali_1064 [{'eta': 0.14153155793311498, 'eta_std': 0.008380733632373237, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,669 - INFO - Using retieved polarization calibration constants.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting loadGHK\n", + "data_cube keys dict_keys(['rawfile', 'rawdata_dict', 'polly_config_dict', 'picasso_config_dict', 'device', 'location', 'date', 'num_of_channels', 'num_of_profiles', 'retrievals_highres', 'retrievals_profile', 'pol_cali', 'LC', 'channel_dict', 'flags', 'flag_355_total_FR', 'flag_355_cross_FR', 'flag_355_parallel_FR', 'flag_355_total_NR', 'flag_387_total_FR', 'flag_387_total_NR', 'flag_407_total_FR', 'flag_407_total_NR', 'flag_532_total_FR', 'flag_532_cross_FR', 'flag_532_parallel_FR', 'flag_532_total_NR', 'flag_532_cross_DFOV', 'flag_532_rr_FR', 'flag_607_total_FR', 'flag_607_total_NR', 'flag_1058_total_FR', 'flag_1064_total_FR', 'flag_1064_cross_FR', 'flag_1064_total_NR', 'flagSaturation'])\n", + "Using GHK from config file\n", + "G [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + "H [ 0.02041 -0.998 1. 1. -0.01477 -0.9987 1. -0.02439\n", + " 1. 1. 1. 1. 1. 1. -0.996 ]\n", + "K [0.97859 1. 1. 1. 0.99343 1. 1. 0.9947 1.\n", + " 1. 1. 1. 1. 1. 1. ]\n", + "pol_cali_nang_start_time [1724207790]\n", + "[{'eta': 47.742418057789656, 'eta_std': 1.0396617649540707, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "pol_cali_nang_start_time [1724207790]\n", + "[{'eta': 12.380587648929659, 'eta_std': 0.13602867159593543, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "pol_cali_nang_start_time [1724207790]\n", + "[{'eta': 0.14153155793311498, 'eta_std': 0.008380733632373237, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n" + ] + } + ], + "source": [ + "## Delta 90 polarization calibration\n", + "data_cube.polarizationCaliD90()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "e7bae91c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'355_FR': np.float64(47.742418057789656),\n", + " '532_FR': np.float64(12.380587648929659),\n", + " '1064_FR': np.float64(0.14153155793311498)}" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Display depolarization calibration constants\n", + "data_cube.etaused" + ] + }, + { + "cell_type": "markdown", + "id": "9031d7c5", + "metadata": {}, + "source": [ + "## Cloud Screening\n", + "\n", + "Three modes of cloud Screening are currently implemented:\n", + "\n", + "0. No cloud screening. Return cloud free for all timestamps\n", + "1. Cloud screen with Maximum Gradiant Signal (MSG) algorithm\n", + "2. Cloud screen with Zhao's algorithm\n", + "\n", + "Clouds are screened per timestamp (30s). After the screening the data is splitt up into cloud free segments and aggregated." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "9279e9d1-b4d9-4f81-a50d-e970ccbf4c48", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:06,684 - INFO - cloud screen mode 1: MSG method.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting cloud screen\n" + ] + } + ], + "source": [ + "## Apply cloud screening\n", + "data_cube.cloudScreen()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "cb1ecc63-0bb1-40da-9a2d-d03a7e3a2202", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intNProfiles 120 minIntNProfiles 30\n" + ] + } + ], + "source": [ + "## Segmentate cloud free groups\n", + "data_cube.cloudFreeSeg()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "2a502a15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 113],\n", + " [144, 244]])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Display cloud free groups\n", + "data_cube.clFreeGrps" + ] + }, + { + "cell_type": "markdown", + "id": "2aeda343", + "metadata": {}, + "source": [ + "### Plot example: Cloud free groups" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "685a5a0f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wv, t, tel = 532, 'cross', 'FR'\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmesh = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " np.squeeze(data_cube.retrievals_highres['RCS'][:, :, data_cube.gf(wv, t, tel)]).T,\n", + " shading='nearest',\n", + " vmin=5, vmax=1e7\n", + ")\n", + "for cldFreGrp in data_cube.clFreeGrps:\n", + " ax.axvspan(\n", + " data_cube.retrievals_highres[\"time64\"][cldFreGrp[0]],\n", + " data_cube.retrievals_highres[\"time64\"][cldFreGrp[1]],\n", + " color=\"red\", alpha=0.2\n", + " )\n", + "\n", + "cbar = fig.colorbar(pcmesh)\n", + "cbar.set_label(f'RCS {wv}nm {t} {tel} [MCPS]')\n", + "ax.set_xlabel('Time [UTC]')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_title('Cloud free groups marked in red')\n", + "ax.set_ylim(0, 5)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "623aeeb1", + "metadata": {}, + "outputs": [], + "source": [ + "## Aggregate background, background corrected signal, and range corrected signal\n", + "data_cube.aggregate_profiles()" + ] + }, + { + "cell_type": "markdown", + "id": "8c222841", + "metadata": {}, + "source": [ + "### Plot example: Aggregated profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "ee64aec2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(5, 8), sharey=True)\n", + "ax[0].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax[0].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax[0].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax[0].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax[0].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 18)\n", + "ax[0].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('RCS [MCPS]')\n", + "ax[0].set_title('cldFreeGrp 0')\n", + "\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][1, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][1, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][1, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][1, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][1, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_xlabel('RCS [MCPS]')\n", + "ax[1].set_title('cldFreeGrp 1')\n", + "fig.suptitle(\"Aggregated Profiles\", fontweight='bold')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9152280a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 5)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('RCS [MCPS]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "80e83b5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 5)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('sigBGCor [Photon Count]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "7c86b4b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(355, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(532, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(1064, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(355, 'total', 'NR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(532, 'total', 'NR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 650)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Range Index')\n", + "ax.set_xlabel('DTCorSig [Photon Count]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4e8eb3be-3dcc-4518-9117-4c490044d3af", + "metadata": {}, + "source": [ + "## Molecular Profiles\n", + "\n", + "The molecular profiles are calculated from cloudNet ECMWF model data. Gdas1 data is not supported in PicassoPy!" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "48fc41a3-bab6-40eb-bd05-38f23fa98582", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\io\\readMeteo.py:263: FutureWarning: In a future version, xarray will not decode the variable 'forecast_time' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n", + "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n", + "To opt-in to future behavior, set `decode_timedelta=False`.\n", + " ds = xr.load_dataset(filename)\n", + "2026-05-11 19:24:19,646 - INFO - Performing model height correction.\n", + "2026-05-11 19:24:19,647 - INFO - Uncorrected model height[:,0]:\n", + "[10.466079 10.4652 10.460253 10.455192 10.449221 10.447984\n", + " 10.453651 10.460172 10.464973 10.472917 10.479133 10.485509\n", + " 10.4861145 10.477216 10.487477 10.491782 10.485256 10.485559\n", + " 10.488882 10.489608 10.490549 10.499391 10.501605 10.50699\n", + " 10.502246 ]\n", + "2026-05-11 19:24:19,649 - INFO - Geopotenital surface height:\n", + "[-44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", + " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", + " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", + " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", + " -44.945835]\n", + "2026-05-11 19:24:19,650 - INFO - Model surface altitude:\n", + "[-44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", + " -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", + " -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", + " -44.94552 -44.94552 -44.94552 -44.94552]\n", + "2026-05-11 19:24:19,651 - INFO - Station altitude: 10.0\n", + "2026-05-11 19:24:19,652 - INFO - Heigth shift:\n", + "[-54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", + " -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", + " -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", + " -54.94552 -54.94552 -54.94552 -54.94552]\n", + "2026-05-11 19:24:19,653 - INFO - Corrected model height[:,0]:\n", + "[-44.47944 -44.480316 -44.485268 -44.490326 -44.4963 -44.497536\n", + " -44.491867 -44.485348 -44.480545 -44.472603 -44.466385 -44.46001\n", + " -44.459404 -44.468304 -44.458042 -44.453735 -44.460262 -44.45996\n", + " -44.456635 -44.45591 -44.45497 -44.44613 -44.443913 -44.43853\n", + " -44.44327 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time slices of cloud free [array(['2024-08-21T00:00:00.000000', '2024-08-21T00:56:30.000000'],\n", + " dtype='datetime64[us]'), array(['2024-08-21T01:12:00.000000', '2024-08-21T02:02:00.000000'],\n", + " dtype='datetime64[us]')]\n", + "get_mean_profiles(time_slice: ) -> \n", + "len mean_profiles 2\n", + "shape of the molecular scattering (2, 4000)\n", + "for the wavelengths [355, 387, 407, 532, 607, 1058, 1064]\n" + ] + } + ], + "source": [ + "## QuickFix for loadMeteo bug:\n", + "METEO_DATA_DIR_PATH = \"E:\\\\data\\\\level1a\\\\cloudnet\\\\mindelo\\\\calibrated\\\\ecmwf\"\n", + "data_cube.polly_config_dict['meteorDataSource'] = 'nc_cloudnet'\n", + "data_cube.polly_config_dict['meteo_folder'] = METEO_DATA_DIR_PATH\n", + "data_cube.polly_config_dict['meteo_file'] = r\"[\\\\/]{0:%Y}[\\\\/]{0:%Y%m%d}_.*\\.nc\"\n", + "\n", + "## Load meteorological data\n", + "data_cube.loadMeteo()\n", + "\n", + "## Calculate molecular profiles\n", + "data_cube.calcMolecular()" + ] + }, + { + "cell_type": "markdown", + "id": "bd4bc602", + "metadata": {}, + "source": [ + "### Plot example: Molecular Backscatter and Extinction" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "69098193", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grpIdx = 1\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(5, 8), sharey=True)\n", + "## Molecular Backscatter\n", + "ax[0].plot(data_cube.mol_profiles['mBsc_355'][grpIdx], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='355nm')\n", + "ax[0].plot(data_cube.mol_profiles['mBsc_532'][grpIdx], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "ax[0].plot(data_cube.mol_profiles['mBsc_1064'][grpIdx], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='1064nm')\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 18)\n", + "ax[0].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [m]')\n", + "ax[0].set_xlabel('Bsc. Coef. $[m^{-1}Sr^{-1}]$')\n", + "ax[0].set_title('mBsc')\n", + "\n", + "## Molecular Extinction\n", + "ax[1].plot(data_cube.mol_profiles['mExt_355'][grpIdx], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='355nm')\n", + "ax[1].plot(data_cube.mol_profiles['mExt_532'][grpIdx], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "ax[1].plot(data_cube.mol_profiles['mExt_1064'][grpIdx], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='1064nm')\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_xlabel('Ext. Coef. $[m^{-1}]$')\n", + "ax[1].set_title('mExt')\n", + "fig.suptitle(\"Molecular Profiles\", fontweight='bold')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "133e9d29", + "metadata": {}, + "source": [ + "## Rayleigh-Fit\n", + "\n", + "- Douglas-Peucker algorithm is used to segment the signal into potential reference heights\n", + "- The reference height with the best fit to the molecular backscatter per channel is chosen\n", + "- Currently only done for FR-channels. NR reference heights are read from the config variables `refH_NR_{wavelength}`" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "5535caf2-eaaf-45d4-823c-f2b6cfc2b7c0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:20,796 - WARNING - Potential for differences to matlab code due to numerical issues (subtraction of two small values)\n", + "2026-05-11 19:24:20,797 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 19:24:20,797 - WARNING - at 10km height this is a difference of about 4 indices\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\rayleighfit.py:571: RuntimeWarning: divide by zero encountered in divide\n", + " std_aer_norm = sig_aer_norm / np.sqrt(pc + bg)\n", + "2026-05-11 19:24:20,997 - INFO - Using Config values for NR refH.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start Rayleigh Fit\n", + "0 [ 0 113]\n", + "refH for 532\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 107 508 509 910 911 1312 1313 1714 1715 2116 2117 2408]\n", + "refInd: (2117, 2408)\n", + "refH for 355\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 120 922 923 1725 1726 2408]\n", + "refInd: (923, 1725)\n", + "refH for 1064\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 107 374 375 642 643 687 954 955 1222 1223 1490 1491 1758 1759\n", + " 1814 1837 1846 1847 1870 1943 1954 1958 2003 2056 2064 2068 2069 2076\n", + " 2084 2097 2125 2126 2137 2140 2145 2158 2159 2170 2186 2244 2319 2361\n", + " 2395 2396 2409 2436 2507 2510 2535 2543 2546 2552 2584 2603 2604 2607\n", + " 2638 2689 2795 2846 2858 2902 2906 2913 2926 2927 2935 2952 2954 2957\n", + " 2983 2999 3001 3002 3030 3038 3041 3045 3046 3047 3060 3063 3072 3084\n", + " 3086 3094 3109 3158 3183 3191 3209 3214 3296 3309 3313 3323 3341]\n", + "one tests failed?\n", + "refInd: (1759, 1814)\n", + "1 [144 244]\n", + "refH for 532\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 107 508 509 910 911 1312 1313 1714 1715 2116 2117 2408]\n", + "refInd: (1715, 2116)\n", + "refH for 355\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 120 922 923 1725 1726 2407]\n", + "refInd: (1726, 2407)\n", + "refH for 1064\n", + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", + "DPInd: [ 107 374 375 642 643 693 960 961 1228 1229 1496 1497 1764 1765\n", + " 1938 2069 2099 2121 2122 2164 2179 2201 2338 2377 2475 2531 2602 2637\n", + " 2644 2650 2675 2719 2730 2784 2816 2840 2878 2918 2932 2959 2966 2973\n", + " 2997 3010 3048 3061 3124 3132 3133 3134 3178 3202 3227 3271 3274 3339\n", + " 3344]\n", + "one tests failed?\n", + "one tests failed?\n", + "one tests failed?\n", + "refInd: (1938, 2069)\n" + ] + } + ], + "source": [ + "## Rayleigh-fit procedure --> produces the reference heights\n", + "data_cube.rayleighFit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f5fd81a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DPind: [ 107 508 509 910 911 1312 1313 1714 1715 2116 2117 2408]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wv, t, tel = 532, 'total', 'FR'\n", + "grpIdx = 0\n", + "\n", + "mSig = (\n", + " data_cube.mol_profiles[f'mBsc_{wv}'][grpIdx, :] * \\\n", + " np.exp(-2 * np.cumsum(data_cube.mol_profiles[f'mExt_{wv}'][grpIdx, :] * np.concatenate(([data_cube.retrievals_highres['range'][0]], np.diff(data_cube.retrievals_highres['range'])))))\n", + ")\n", + "print('DPind:', data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])\n", + "colorList = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n", + "\n", + "fig, ax = plt.subplots(figsize=(3, 8), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(wv, t, tel)]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label=f'{wv}nm {tel}')\n", + "# ax.plot(data_cube.mol_profiles['mBsc_532'][grpIdx]*7e11, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "# ax.plot(mSig, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "ax.axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "\n", + "# for i in range(len(data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])-1):\n", + "# ax.axhspan(\n", + "# data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + "# data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + "# color=colorList[i], alpha=0.3\n", + "# )\n", + "\n", + "ax.grid()\n", + "ax.set_ylim(0, 20)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('RCS [MCPS]')\n", + "ax.set_title('cldFreeGrp 0')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "7d37bfc5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(5, 8), sharey=True)\n", + "ax[0].plot(\n", + " mSig,\n", + " data_cube.retrievals_highres['height']/1000,\n", + " color='green',\n", + " alpha=0.9,\n", + " linewidth=1,\n", + " label=f'MolSig {wv}nm')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(wv, t, tel)]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label=f'{wv}nm {tel}')\n", + "# ax.plot(data_cube.mol_profiles['mBsc_532'][grpIdx]*7e11, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "\n", + "for i in range(len(data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])-1):\n", + " ax[0].axhspan(\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + " color=colorList[i], alpha=0.3\n", + " )\n", + " ax[1].axhspan(\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + " color=colorList[i], alpha=0.3\n", + " )\n", + "\n", + "ax[0].grid()\n", + "ax[1].grid()\n", + "ax[0].set_ylim(0, 20)\n", + "ax[1].set_ylim(0, 20)\n", + "ax[0].legend(loc='upper right')\n", + "ax[1].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('RCS [MCPS]')\n", + "ax[1].set_xlabel('molSig [MCPS]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cd3a4d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference heights in meters for cloud free period 0 ['2024-08-21T00:00:00.000000' '2024-08-21T00:56:30.000000']:\n", + "355 total FR: [ 6877. 12849.]\n", + "532 total FR: [15768. 17935.]\n", + "1064 total FR: [13102. 13512.]\n", + "355 total NR: [3005. 3995.]\n", + "532 total NR: [3005. 3995.]\n" + ] + } + ], + "source": [ + "## Display reference heights for a given cloud free group\n", + "grpIdx = 0\n", + "print(f\"\"\"Reference heights in meters for cloud free period {grpIdx} {data_cube.retrievals_highres['time64'][data_cube.clFreeGrps[grpIdx]]}:\n", + "355 total FR: {np.round(data_cube.retrievals_profile['refH'][grpIdx][\"355_total_FR\"]['refHeight'], 0)}\n", + "532 total FR: {np.round(data_cube.retrievals_profile['refH'][grpIdx][\"532_total_FR\"]['refHeight'], 0)}\n", + "1064 total FR: {np.round(data_cube.retrievals_profile['refH'][grpIdx][\"1064_total_FR\"]['refHeight'], 0)}\n", + "355 total NR: {np.round(data_cube.retrievals_profile['refH'][grpIdx][\"355_total_NR\"]['refHeight'], 0)}\n", + "532 total NR: {np.round(data_cube.retrievals_profile['refH'][grpIdx][\"532_total_NR\"]['refHeight'], 0)}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "79bf9f7f", + "metadata": {}, + "source": [ + "## GHK-Transmission Correction" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "e6c7b77c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:21,018 - WARNING - not checked against the matlab code\n", + "2026-05-11 19:24:21,019 - WARNING - 'flagMolDepolCali' set to False\n" + ] + } + ], + "source": [ + "## Molecular polarization calibration \n", + "data_cube.polarizationCaliMol()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "640e277b-a513-4b55-978d-596f56d51725", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:21,028 - WARNING - transmission correction\n", + "2026-05-11 19:24:21,082 - INFO - and even a 355 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.]\n", + "H [0.02041] [-0.998]\n", + "polCaliEta 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "calculated R_t [0.95999647]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:168: RuntimeWarning: divide by zero encountered in divide\n", + " sig_ratio = sigc / sigt\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:168: RuntimeWarning: invalid value encountered in divide\n", + " sig_ratio = sigc / sigt\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:178: RuntimeWarning: invalid value encountered in divide\n", + " vol_depol = (sig_ratio / eta * (Gt + Ht) - (Gr + Hr)) / ((Gr - Hr) - sig_ratio / eta * (Gt - Ht))\n", + "2026-05-11 19:24:21,222 - INFO - and even a 532 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.]\n", + "H [-0.01477] [-0.9987]\n", + "polCaliEta 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "calculated R_t [1.02998285]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:21,369 - INFO - and even a 1064 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.]\n", + "H [-0.02439] [-0.996]\n", + "polCaliEta 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "calculated R_t [1.04999949]\n" + ] + } + ], + "source": [ + "## Apply GHK-transmission correction\n", + "data_cube.transCor()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "a0fd215d-efa5-47d8-aa7d-4ef6e44953a6", + "metadata": {}, + "outputs": [], + "source": [ + "## Aggregate GHK-transmission corrected profiles\n", + "data_cube.aggregate_profiles(var='sigTCor')\n", + "data_cube.aggregate_profiles(var='BGTCor')" + ] + }, + { + "cell_type": "markdown", + "id": "fbef7a1b", + "metadata": {}, + "source": [ + "## Klett and Raman retrieval\n", + "\n", + "Produces the following profiles per channel:\n", + "- Klett: Aerosol Backscatter and Extinction\n", + "- Raman: Aerosol Backscatter, Aerosol Extinction, and Lidar Ratio\n", + "\n", + "If `nr=True`, perform the retrievals for FR and NR channels. Otherwise only FR." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "18872d17-27d1-49e8-b58d-d7df1d220f62", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:21,585 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 19:24:21,586 - WARNING - at 10km height this is a difference of about 4 indices\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "retrievalname klett\n", + "Starting Klett retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "== 532, total, FR klett =================================\n", + "== 355, total, FR klett =================================\n", + "== 1064, total, FR klett =================================\n", + "== 532, total, NR klett =================================\n", + "== 355, total, NR klett =================================\n", + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "== 532, total, FR klett =================================\n", + "== 355, total, FR klett =================================\n", + "== 1064, total, FR klett =================================\n", + "== 532, total, NR klett =================================\n", + "== 355, total, NR klett =================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\klettfernald.py:311: RuntimeWarning: invalid value encountered in divide\n", + " aerRelBRStd = np.abs((1 + noise / signal) / (1 + aerBsc + molBsc / 1e3) - 1)\n" + ] + } + ], + "source": [ + "## Klett retrieval for GHK-transmisson corrected profiles\n", + "data_cube.retrievalKlett(nr=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d6c83105", + "metadata": {}, + "source": [ + "### Plot example: Klett retrieved optical profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "32a8ce62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nT/785z8DIITgxhtvBMBsNvPll1/y9NNPM3r0aJxOJyaTiYyMDMaMGcP999/P9OnTw9cZNmwY//znP+nZsycmk+mI7arjwQcf5OOPP+a0004jKSkJg8FAUlISxxxzDDNmzGhQqq+jfqm6qX2A008/nRUrVnDppZeSlZWF0WjE5XLRt29fLr30Ut56661w3JycHBYvXsypp56K1WolLi6OSy65hI8++uiQ03PDDTewYsUKfvvb35Kbm4vFYsFisZCbm8sVV1zB8uXLW5yV7J577uG+++4jKysLk8nEgAED+O9//8uFF14YjmOxWHj77bcZOXIkDoej1bbde++9zJs3j9NPP53k5GQMBgPx8fGceOKJ4Wr7Cy64gOeff55evXphNpvp3bs3L774IhdddNEh58XRRlEU/vvf//L8888zatQoYmJiMBgMZGZmMn36dFauXBnut9DW922t5uPi4rjxxhsZPXo0KSkpGI1GLBYL/fr1Y+bMmbz22mttbp/k8FGEEKKjjZBIJJHH3XffHa4Kf+WVVxrNPS+RSNoHWaKWSCQSiSSCkY5aIpFIJJIIRlZ9SyQSiUQSwcgStUQikUgkEYx01BKJRCKRRDDSUUskEolEEsFIRy2RSCQSSQQjHbVEIpFIJBGMdNQSiUQikUQw0lFLJBKJRBLBSEctkUgkEkkEIx21RCKRSCQRjHTUEolEIpFEMNJRSyQSiUQSwUhHLZFIJBJJBCMdtUQikUgkEYx01JJ2QVGUBpvdbqdv377cc889VFdXd7R5EklUc6A+62+XXXbZIV/vzTff5Mknn2xzO6MVucylpF1QFIVzzz2XG2+8EYCqqioWLlzIgw8+yLRp03j33Xc72EKJJHo5UJ/1SUpKIjc395CuN3XqVH755Re2bdvWRhZGN4aONkDSdRFC4PF4sFqtAKSkpDBq1Kjw8VNOOYXt27fzxhtv4PF4sFgsHWWqRBJ1HEyfkshBVn1HKZs2beLyyy+nZ8+e2Gw2unXrxumnn87q1asbxa2oqOCmm26iR48emEwmunXrxvXXX9+oylpRFK6++mqef/55+vbti9ls5tVXX23RDpfLhaIoaJoWDlu5ciVTp04lOTkZs9lMeno6U6ZMYdeuXeE4uq7z9NNPM2TIEKxWK7GxsYwaNYoPP/zwCHNGIul4IkWf9SkqKiIzM5PjjjsOv98fDl+7di12u51LLrkEgHHjxvHJJ5+wffv2BlXoksNHlqijlD179pCQkMDDDz9MUlISJSUlvPrqqxx77LGsXLmS3r17A+B2uxk7diy7du3i1ltvZdCgQaxZs4Y777yT1atX89VXXzUQ4dy5c1m8eDF33nknqampJCcnh48JIQgEAsD+qu9XX32VCy+8EKPRCEB1dTWnnnoqPXr04NlnnyUlJYW9e/cyf/58Kisrw9e67LLLmD17Nr/97W+59957MZlM/Pjjj7KqTdIl6Gh91kfTNBRFITExkTlz5jBu3DhuueUW/v73v+N2uznvvPPIysri+eefB+Cf//wnv//979m8eTPvv//+Uc6pKEFIJEKIQCAgfD6f6Nmzp5gxY0Y4/KGHHhKqqoply5Y1iP/OO+8IQHz66afhMEC4XC5RUlLS6PpAk9vkyZNFVVVVON7y5csFIObOndusrYsWLRKAuO22244kyRJJp6Gj9AmI119/vUHcRx55RADi/fffF9OnTxdWq1X8/PPPDeJMmTJFdO/evQ1SLhFCCFmijlICgQCPPvoos2fPZtOmTQ2qsn799dfw748//pgBAwYwZMiQBl/bEydORFEUFixYwOTJk8PhJ510EnFxcU3e8/zzz+fmm28GoKamhlWrVnHfffcxadIkvvrqK8xmM3l5ecTFxXHLLbeQn5/PmDFj6NevX4PrfPbZZwBcddVVR54REkkE0tH6rE9OTk6D/ZtvvplFixZx0UUX4fF4eOmllxg4cOBhp1VycKSjjlJuuOEGnn32WW655RbGjh1LXFwcqqpy5ZVXUlNTE45XUFDApk2bwlXTB1JUVNRgPy0trdl7JiUlMXz48PD+iSeeSFJSEhdddBGzZs3iD3/4Ay6Xi4ULF/LAAw9w6623UlpaSlpaGr/73e+4/fbbMRqNFBYWomkaqampR5gLEklkEgn6bI66IVuffPIJqamp4bZpydFDOuooZfbs2Vx66aU8+OCDDcKLioqIjY0N7ycmJmK1Wnn55ZebvE5iYmKD/UPtNDJo0CAAfvrpp3DYwIEDmTNnDkIIfv75Z2bNmsW9996L1Wrlr3/9K0lJSQSDQfbu3dvii0ci6axEij6bIj8/n6uuuoohQ4awZs0abrrpJv7xj38c8XUlzSN7fUcpiqJgNpsbhH3yySfs3r27QdjUqVPZvHkzCQkJDB8+vNGWnZ19RHasWrUKoEGnlvo2Dh48mCeeeILY2Fh+/PFHgHBV3nPPPXdE95ZIIpVI0eeBBINBLrroIhRF4bPPPuOhhx7i6aef5r333msQz2w2Nyj5S44MWaKOUqZOncqsWbPo06cPgwYNYsWKFTz22GNkZGQ0iHf99dfz7rvvMmbMGGbMmMGgQYPQdZ0dO3bwxRdfcOONN3Lssce26p4FBQV8//33AHg8HlatWsX9999PbGwsl19+ORBqc/vnP//JtGnTyMnJQQjBe++9R1lZGaeeeioQqjK/5JJLuP/++ykoKGDq1KmYzWZWrlyJzWbjmmuuacOckkjan47WZ32cTme4n8hdd93F4sWL+eKLL0hNTeXGG29k4cKF/Pa3v2Xo0KH06NEDCNWKvffeezz33HMMGzYMVVVbVa0uaYaO7s0m6RhKS0vFb3/7W5GcnCxsNps44YQTxOLFi8XYsWPF2LFjG8StqqoSt99+u+jdu7cwmUzC5XKJgQMHihkzZoi9e/eG4wHiqquuavJ+HNCT1Gg0ipycHHH55ZeLTZs2heOtW7dOXHTRRSI3N1dYrVbhcrnEyJEjxaxZsxpcLxgMiieeeEIMGDAgbNPo0aPFRx991HaZJJF0EB2tz/rb8ccfL4QQ4osvvhCqqoq77rqrwbnFxcUiKytLjBgxQni9XiGEECUlJeLcc88VsbGxQlEUIV3NkSGnEJVIJBKJJIKRbdQSiUQikUQw0lFLJBKJRBLBSEctkUgkEkkE06GOetGiRZx++umkp6ejKApz585tcLyqqoqrr76ajIwMrFYrffv2lUNyJBKJRBJVdKijrq6uZvDgwTzzzDNNHp8xYwbz5s1j9uzZ/Prrr8yYMYNrrrmGDz74oJ0tlUgkEomkY4iYXt+KovD+++8zbdq0cNiAAQO44IILuOOOO8Jhw4YN47TTTuO+++7rACslEolEImlfInrCkxNOOIEPP/yQK664gvT0dBYsWMCGDRt46qmnmj3H6/Xi9XrD+7quU1JSQkJCglwTVRJ1CCGorKwkPT0dVe3YLilSmxLJfg5Jmx05iLs+1C6bVh+v1ysuvfRSAQiDwSBMJpN47bXXWrzOXXfd1eLg/UPddhsQc3MQmNvumnKTW3tvO3fuPIrqbR1NaXO3DTH02I7PH7nJraO21mgzoqu+H3/8cV588UUef/xxunfvzqJFi5g5cybvv/8+p5xySpPXOfCrvby8nKysLGYtWIbN4Wj2/v99wcFX79r5zVWVTDjbi6YopCyeT7+7ZvL7CTUM+/1TjMo+nj1iC7vEFiYZz8GgKsSYjGiqLA00h9/vZ/78+YwfP77ZFX4kzXOk+VdZWUmPHj0oKyvD5XIdBQtbT1Pa3DAki9/9pTtvXPApv4gfiFPiGGIcicNowKh1rkEpXe1Zl+k5uhyKNiO26rumpoZbb72V999/nylTpgChlZZWrVrF448/3qyjNpvNjSazB9DsJjR74/A6TrssyFfvx/DmczHk763ijMvLMDnLCaoBVqcbSIxZh2LxAGAXiRQGQAR10jUVVVXQFFAVBU1V0BQFg6pgMWhtkBOdG6PfiM1mw+a0YTBG7OMWsRxp/vkJrWMcCVXLTWlzlQOI0Vhq+QYFhbhADruDOqlmBZOmYqjVlEFVMKoqZkPkOu+u9qzL9BxdDkWbHW9tM/j9fvx+f6O6e03T0HX9kK8ngusIBm0IBKEqBIEQoNf+NdrhL//azCN/OI5v3rdg67aLk0xf4w96MegmPGV+glgxCwc+3cAi32ICwYPbYTdpaKqCSsiBKyqoCjjNBuItzX84dBVEMJTb3+78FkXreGfR2TjS/KuurG5rk9oUowkC/gDB3VbMuoOdwb2s9e5uNr5BU7EaVDQlpCdVBYOikh5j6fCara72rMv0HF0ORZsd6qirqqrYtGlTeH/r1q2sWrWK+Ph4srKyGDt2LDfffDNWq5Xu3buzcOFCXnvtNf7+978f8r1iLVbiY+JajGNLN6EpoQ+D5e8O5qrf90dhIQYdTA7BqKQ+DeILIQgi0AXoukAXAh0I6oJitzfcCBHUCR0TgqAQVHoDVHphb5U/VApXFBRCJXJFAU1RyImz1b6IQk5ejYAS0eGgB3VKKMFhcqB2sqrMSOBI88+v+o+CVW2HpoDNYqVfQjpO1QnU05Ue0ktQQEAXFLm9KEBQhDSmC0GVP0BQF5R5/Ri1kANX62q3anVkUOvCj66GutqzLtNzdDkUbXaoo16+fDnjx48P799www0ATJ8+nVmzZjFnzhxmzpzJxRdfTElJCd27d+eBBx7gj3/84yHfy2wwYzFYWoxjtxio03JRgRnHkBNQlOfILQqCCtVKNRbVghUrqtL8P1oIQbzVTlCEXjY6tY5ah6AQ1ASClHh8BHVBUIeA0AnU/q5jTaG32esPTXVhj4Cqm9agK6FEWQyWiBBHZ+NI889siOxam9zK0F+f4gMNLErTGq3TlK4LAmL/x3FA6Oyr9uELBgnoENBDWqqjKR3FWowMSHK2eVq62rMu03N0ORRtdujbfty4cbTUly01NZVXXnmlXWz5YE4Czz2aDoDVqnP749vxJ8WjopJaqeMNBlnsXhyyy5DKKOuoZq+1rdzN7kpPq++t1JYAVAVQIBBsPk80VWm6ik+v3SKNIBgMBggAEdFtsZNxsPxT6dQTAaf5oNJTyXLPcgCGWYaRacxsFG9zWTV7q5r/eK1DqW3TVlrQUbu3c0eqNg9GV9Nue6enDbXZOYpl7UBKmi/8+09/2cOw0VVYl29GQeHXZI3LY8ayU9tJWbAMP348gWCoxCwaVmvrAopr/DjMBro7rbUOOOSENaX2RaIoKLXV3QpH2NFHAFWg+lQUlIjoNFQfIQSpqaloVVrE2dYZaCn/hAj1uNBNOjgIPUydjPUx0DumNzFaDJXBSjzCi9sfCFVv19ZIBRGUegI4zAaynbZw581w05ACoac/MjrNhYlwbR6Mrqbd9kxPW2tTOupatHodtI8bX05NIEBZRhpJQT85NU50HBQFS4kXKRiqurHcU9b8xRToGxtDnMV01O2mKtSbMTE5EbPVXPu6iiwCngAGi3zUDpfm8k8g8NZ4KSoswl/lh5gOMO4ISRCQEZNLabCCRJFO/j4HBaK8cUQFesQ6iLV0/LCaVtMJtHkwupp22ys9ba3NrvMfOEIK8kNZIQRUVOvsrC6hVNtNH4MgvVrlJ/E/dF1grEnHKFzYzAo9Ym2h0jL7h2YpioJGO33Z66Gv9cTkRFxxHTtGtiVUXcXYmV6wEUZL+WexhNp0C/YVhEZDdLJq8LQqKDEWkKYnY6hJx6JZSHNYcFkMaBAqPasKWmcrkXYSbR6Mrqbd9kxPW2ozahz1+uJKYoP7S7i+YBB3QEcBNv/s5OX704lPc3PxnavZEqih2lRAtWkLmi5I2VdFmqUHedY87C57xyXiQHRQUDBbI7vDkOToEi6t6XQ6R+3ygatc57SYCThczU9I1OmQ2pTQdtqMGkdd5vWj1/gahFWXG/jk+Z78sjgJVYUrHvqZpFQfRlUDk4LXYGR3dgIjdpbiMw9ssad3RxFq6+5EJQ1Jm9MZ2z/rUAScnB+LQ+1CTroWqU1JW2kzahz1oGQnsbGu8LCOan+Qpx7vwdolCagKnP3nLaSnBwAFny6o9gqCBp2vesMVm4L87C8lVkvo6GRIJF2O1PzSjjZBIoloIq+IeJSwGgw4jAacJgOxFiPdYiz8/o+ldMsIYFAVPnwul61f5TIkJZYspxW7P40TrSewMUlDQWHfr4s6OgkSSZdjhxMsu3Z2tBkSSUQTNY76QCp8AVbmV7FrpwFfUJA7shAldwdLdhazqaQaRVGJVWNZmFCFQTFgX7aqo03uUrz4rxc5dtixpCWmkZaYxkljTuKLeV80iPOHK/+Aw+xosI0/cXyDOJNOndQozvT/m96eSQkz+7XZjWxxmB3MenlWk8d7ZPbgvLPOY+3atR1ibyTwowt6bCoGf6CjTZHUUl+bWVlZXUqbcXFxnVKbUVP1fSCr8stZ9nlPBIJjp+xhyp82NTguhGBbeSUFZp2Vg7ox7IO1rLrYg6qAzWjAZtA67bSekUC3bt249/57ycnNAeCN2W9wwbkX8O0P39KvX79wvFMnnMrzLz4f3jeaGvfYvOyKy7jjrjvC+xZryzPQHU2cTicrV69sGOZyNjouhGDPnj3cfuvtnDvtXFb9sgqTqR2G80UYr/WBixeXs/fb5ZSNHEKcxYRB6bxt7l2B+toMeAK89c5bXUabP/zwA0brfjs7izajtkRt9zpZPi8NgAFj9jU67jEWsUpZRKzDRam/FCEMbCqpZkNxNav2lvPdrhK+3VXC93tK+CG/lOX5ZawsKGNXZU17J6VTctrU05g4eSI9e/WkZ6+e3H3v3TgcDpYtXdYgntlsJiU1JbzFx8c3upbNZmsQp/6Scdu3bcdhdvDB3A+YPGEySbFJjBo+iqXfLw3Hmf3abLold+OzTz5j6IChJMUmcfGFF1NdXc0br79Bv179yEjJ4MbrbyQYDLaYLkVRGtiSkpqC1WptdDw1LZVjhh3D1ddczY7tO9iwYcNh5mQnxxqat3ubHzYUV7N0dynf7d6vqxV7y/ilsAI9MlbjjQrqazMvL69raTOlc2ozah314B5Gpp1fhklT+c+dQxlgT+aEzITwlhrnx6AJUlwpdCuoJCm/hONTnQxPc9E70UHPeDs5sTYyYqyk2s0k2owEdCjz+PHrOkEhWpweVbKfYDDIf9/+L9XV1YwcNbLBscWLFpOdkc2Q/kO4+k9Xs29f44+qt+a8RVZ6FsOHDOfWW26lsrKyUZx77ryH666/ju9++I68nnlcfunlBAL7q1vdbjfPPfscs2bP4v2P3mfxosX85vzf8Pm8z3n3g3d58eUXeeXfr/D+e++3WbrLysp4+623ATAaus5Y1UMhqIZeQqOKCxiY7KRXgp2cWHtYVxZNpczjxxfUpbPuAKQ2I0ObUVv1bTDCtbftJiHJz+vPp3DhKf14+5u1OGNDX2U9TD1w626EEKxPUMhOS0cxGrEAFkPT2barohhvIMjS3U33Yk11mEmymWsncACldu3qupV+2oqaGti8uf2rDnNzBfU+UA/KL7/8wsljTsbj8eBwOPjP2/+hb9++4eOnTjyVs84+i8zumWzfup377rmPKROnsOT7JeF1jS+48AKys7NJTk1m7Zq13H373az+eTUfffZRg3tdN+M6Jp02CYDb7ryNEUNGsHnTZnr36Q2EllV98uknw1Xx086axpw357Bl5xYcDgd9+/ZlzNgxLFqwiHPPO7fZNJWXl5MSnxLetzvsbNmxpdFxIQRutxuAKVOnhO2INgw6BEQAT9+euMxGoOFLcUtZNaUeP8vzyxqdm+Iwk2K3hKcT1erpKVKR2uxYbWZkZIT3O5M2o9ZR13HOJYW8/nzoxXr+Sf34bMVqFAXsip1cUy7dEroR5/4ZW2lZi9cJtGKN7L1V3mYXFhiY7Kx9UR05mzcrTJva/m1Bcz/2MGBA60s9vXr14rsfvqO8vJwP3v+A31/5e+Z9NS/8Qqgvuv79+3PMsGPo27Mv8z6bx5nTzgTg8t9e3iBOXl4eJ44+kVUrVzFk6JDwsQEDB4R/p6amAlBYWBgWoc1mC78IAJJTksnqnoXD4WgQVlhY2GKaYmJiWPL9kvD+geup1x0PBAIsWbyEp/7+FE8981TLGdWFMXgBAVp5RZPH3f7mqzMLqrwUNKGn4zLiI9ZZdzZtFhcU88lnn3QZbS5YsCA8hWhn0mbUO2qfV0VTCS8xuWxJDCNOqERRQvO1WjUzvQuD7D2lX4vXKfeGqmoSbCaMtatbKbXTi+rUTdIemqJUJ/RDF6F5+82agsPUdv+K3FzB3I9bv3pXW973UDCZTOTm5QJwzLBjWLF8Bf98+p88/c+nm4yfmpZKVlYWmzdtbvaaQ4YOwWg0smnTpgYvA6Nx/0dQXUclvd7HVf3jdXGaChN6y2lUVTWcpoMd792nNwUFBVz6f5fyxddfNHtOV2bkDvBrUNO/6VKLL6hjNWo4zYbwmu0QWghHQEhDtQvjCCGIs5oieoqRzqbNrPQsRh4/sstoMycnB6Ot6QJRJGsz6h21MzbIh0tXc+lpfSkuNHDnddlceMU+Lru6AAWFPpUmYjyCeUNiGNTCddyB0Jd/sdvX5PEMp5Vsl+0opKAxViuH9PUcKQgh8Pmazj+A4uJidu3aFf7qboq1a9fi9/tbjBNJXH3t1Tzz1DN8+MGHnHHmGR1tTrtz7E5YkQ5pCXFNHq8rUdccULKOt5rol9j5ViGR2pTaPByi3lFDaOWs2fN+5ZqL89j4q5U3Xkrm5OnbKNYrGfL9bgB+zOmGsbgSXRfoCIK1M5zptcvx+YMtV33LjjANufuOuzl14qlkZGRQWVXJO2+/w+JFi5n70VwAqqqqePC+BznzrDNJTU1l+/bt3HPnPSQkJnD6macDsGXzFt6a8xYTJ00kISGBdb+uY+YtMxk8ZDCjjxvdgalrPU6nk+mXT+eBex/g9DNOj7phSSPL4cVjVHL3FaMpxtqlYgVBHXwtNCcFRGdc4LlzUF+bpYWlzP1ortRmB2szKh11qcfHmsL9vQ/L9pn5+xXH1u7pDJu4lz2VHvyairOqGgCfZR/56nrS6I1BUdEUwmtNa6qCioLNqGI3GUJhRNjauBHGvn37+N0Vv2Nv/l6cLicDBgxg7kdzOemUkwDQNI01v6zhzTfepLysnNS0VMaMHcOrs18lJiZUkjKZTCyYv4DnnnmOqqoqMjIymDh5IjNvn4lWf93SCOfPV/+Z5559jvfefY9zzj2no81pV0yATTVSaNxIht4fs6qiEtKUoVZX8VYjBq0TrqDVSWmgTaeTAQOlNjtam4ro4mOIKioqcLlcfP7L5yTEhebq3lbuZlfF/vHOuzc6+NeMY8L7KRleXnt/M0aToPzvD9H3hff420d/ICOjNydaT2z3NDRLAAwVBjKzMjFbIneVHr/b32y7kOTgHCz/vB4vO3fsJOAMNPr0Li4tZuKAiZSXl+N0Opu+QAdRUVFBVYqLd05OwPvkLZxkO6mjTToi9KDOnh/3kH5MOqpQO4U2D0ZX0257p6ettBmV46j3uRv2FO3Ws4p7P17E7x4PzShVvMfKH87thd+nkPDjr7hNCimWdAaZW2qllkgkh4wKJUk2hpqHdrQlEknEEpWOurkxy5l9Kpnyp00owO5dRk4f2ZPEn7fw7vEp5Cb2x6V23gXgJZJIpMQKu6edRJzWdGcyiUQShY46KARGrfl2rp++CY2p9gs/mqsSj6rTo9pMojGxvUyUSKKGGB+Msg44eESJJIqJOketC0GFp/mVenatj6GuwH3MBT+xKFdl9PpKrMohTOsjkUhahTEIQ9c2PdmJRCIJEXWOWgGyY21kOC2kOMzEW03EmA1YDBqqqnD3+98xelwFBgws/EThqyyV2Co/xr0tz3ojkUgOnUIVnOu3HDyiRBLFRJ2jLq7xsa3MTZHbT5UvgF/X0RQFm1Ej3mIiL8nMXX/fzl1/2wnmcnruUimpdrCUdWz2byYg5Lq5EklbEe+FL36NYaN/I0HR8upHEkm0EjXjqFfvKyfWp+HXBQZNYXhabIvxf/3JBq5tjF6v4zHGsttWzi5PKatZzSjrKFINnWN2HYkkktGCsLuPnXzPGtYqa5lgnyCbmSSSA4iaErU7EKTKF8Qb0AkEBeuKq9hYWsXmsmq2lbvZWVETmuRE11n+rYN3Xk8CYzWfZTvoZtrDmZ5jsaqhF8j3Nd/jFU0vriGRSFqPT4Xf1mwFQtNUfl71eQdbJJFEHlHjqHvGO+id4CDZbibOasKv61T7g5R7/BS5feyu8rClrJoVe8t47skkgrrAuPYS1vfx4BUePD+tp5c+ElAJIiiormGf20NhjZeaQCC8KIBEImk9m9NB7C6gP8cRRGAnNqwrTyAoNSWREEVV3/EWEwm20HrQTSGEYF1JFcXFCts22EOBlVnkZ+UixFqqtvzEstFFCEAVKtsrvSgc0KamgIKCqoBS+1tRQK39G1r9JxTVqKr0iLXtn4K0dqUtOUWiJJpYFwc9d+9ipb4EgcDrM7LBU90gjlI7Xa+ihLSl1q6PpakhTQkgy2nDbtIw1K7tLnUk6UpETYn6YCiKQt+EGE7o5eCEcVUYNQWjpuCwpeHXAN8eTKqKWVU5wzWZ4zOSGZ0Rz5BUF70S7PSMt5MXa6dHrI0sp42MGCvpDgspdjOJNhPxFiMuixGHyUCVL0ipx8+Pe8tZnl/G0t2l/G9XCd/uKmHJzuJmt3XFVR2dTW3GA/c9gMPsaLDlZOU0ijN04FCS45LJSMlg6qSpLPthWfh4SUkJN15/I0MHDCUpNok+eX24acZNlJeXt3dyAJj92uxGaXKYHcx6eVaTx3tk9uC8s85j7dq1HWJvJFBkBYO7HJOqYtNMTE4cxeiMeAanhHSVF28nJ9ZeqykLqbV6SrAacZqMWAwGqn1Bfi2qZPmeMr7fXdqijraVuzs6yRFPfW3GxcV1KW3WpaezaTNqStStZdUPdpYuDk0s74wNMnzLBtRgkB1DM4nVYhlsHoxZ2V8qdxgNOIyHlo0BHYrch97G7de7Vq/Yvv368vFnH4f3Va3hd2PPnj35+5N/J7tHNjWeGp79x7OcOeVMflr7E0lJSeTn55Ofn88DDz9An7592LFjB9dffT35+fm8MeeN9k4OEFpxZ+XqlQ3DXM5Gx4UQ7Nmzh9tvvZ1zp53Lql9WYTKZ2tvcDmfKVtjRMwazauYE6wloSmjBhhiTgZhWrNFe5vFT6vERPMhaxHUEWliRS7KfOm36a/wYrcYuo80ffvgBo3X/XN+dRZvSUdfDXa3y1z+Gvhyzcz3c+dJKHnqtFINiYHhRBuWGExBBQVnADyLUQW1LaXWz11NrVwASAgSi3gL34DBpxFtNoarv2iq90IpBKrHmrjMJfksYDAZSUlOaPX7+hec32H/o0Yd49ZVX+WX1L4w/aTz9+/fnzbfeDB/Pyc3hznvv5MrLriQQCGAwGNi+bTv9e/fnjbfe4Pl/Ps/yH5aTm5fLU888xbGjQiumzX5tNrfcdAsvvfISt95yK7t27WLCpAm88O8XmPveXB647wEqyiu44KILePRvj7a4+o+iKC2mqf7x1LRUrr7mas4/53w2bNjAgAHRN0PXTgcM9lo4wXgqIiAo0/14gkE2lTStK6Omoij7dVTnoDNdVjRFwaAoId0pCrEWI6qsAj8s6rTZ3CIWnVabKSnNLsoRydqUjrqWGn+Ax+/LQhcgBKT3qmSDsoayeDNCVFG4YSGrC4/BoLd+6IhZU0m0meq1UYf+agok2szNzjkeLWzetJm87DzMJjPDRw7n7nvvpkdOjybj+nw+XnnpFVwuFwMHDWz2mhXlFcQ4YzAYGj7a99x5Dw8+/CC5ebncc9c9XH7p5fy89udwPLfbzXPPPses2bOorKzkNxf8ht+c/xtcsS7e/eBdtm3ZxsUXXszo40dz7nnntkn6y8rKePuttwEwGqLj4+xACq3g2LmXH4q3Yw7EHjS+02TAbtJCbda1/UEcrSx9S1pPnTZNRhMjjh0htdnByKe7lvWl1egxlQT00OIAVX4/fq+J0VUu/GIv3u6ZDElKxKgYwp2+lHqdx6jrDEbHdwir8dewuXRzu983Ny4Xq7F1HzIjRozghZdfIK9nHoUFhTzy8COcPO5klq1cRkJCQjjeZ598xmWXXIbb7SY1LZUPP/2QxMSm510vLi7mkYce4Yorr2h07LoZ1zHptEkA3HbnbYwYMoLNmzbTu09vAPx+P08+/SQ5uaEalWlnTWPOm3PYsnMLDoeDvn37MmbsGBYtWNTiy6C8vJyU+P0larvDzpYdWxodF0LgdofaS6dMnRK2I9roUQF7YlUGJ6RgVSy1HTFD61Dv/93xmmorOps283fk87cn/tZltJmRkRHe70zalI66lkBQcPIl21j0dhYAyz9P5ZhLzTiL3WiKRvGofiQaVUxK5GfZ5tLNTHtrWrvfd+4FcxmQ3LoqogmTJuzfGQAjR41kYN+BvPn6m1xz/TXhQ2PGjeG7H76juLiYWS/P4tLfXMr8JfNJTk5ucL2KigrOnXYuffr04dbbb210vwED99uVmhqarKawsDAsQpvNFn4RACSnJJPVPQuHw9EgrLCw5alkY2JiWPL9kvC+qqpNHg8EAixZvISn/v4UTz3zVIvX7Mok1cDm4zNJNIBdjXxtHSmdTZu9c3pz3Njjuow2FyxYgMESes46kza7vjJaQVCIUOlYgXNuXMemH+P4aX4KL1x8Hk/3uZvtSWa2xtWwuepTRltHk2Jovg0yEsiNy2XuBXM75L6Hi91up3///mzatKlReG5eLrl5uYw8diSD+w3mtVmvcdNfbgrHqays5KzTz8Jut/Of//4Ho7FxVVX9sLrSmV6vY9GB5yiK0mSYOEinJVVVyc1rPh/qH+/dpzcFBQVc+n+X8sXXX7R43a5Kshs+HJiIv/orTrSdSIKWcPCTOjFSmx2rzZycnGbbqCNZm1HlqCt9Acq9foK6ICgEuhAEBfiDOjX+ICkOM2ecVYV6VjVXjE1FCIWkDQKPzUMueWxSNpEfyCdGi8GCBVWJzNFtVqO11V/PkYLX62X9+vUcd8JxLcYTQuD17u8xX1FRwbSp0zCZTbz93ttYLJajbWqbcvW1V/PMU8/w4QcfcsaZZ3S0OR3CiprdDAL2BfcRo8ZgUrpu73epzc5DJGkzqhz1mqIKAsHmv7pyYu3hDl7vLljLueP6UyCyGFK0nf9+t5Ks8Rls829jm38bAKqiEqPGMMwyDKfqbPa6ksbcesutTJ4ymczMTAoLC3n0oUeprKjk4v+7GIDq6moee/gxTpt6GqmpqZSUlPDiv15k9+7dnHXOWUDoa/3MKWfidrt56ZWXqKyopLKiEoDEpMQWe4BGCk6nk+mXT+eBex/g9DNO7zJtsYfCgP/txn1iOuuz1rPeux5FUXCpLoZbhuNQHQe/gKRNqa/N/J35/P2Jv0ttdrA2o8pRt+Sk7SatQS9sh1Pn9se28/OMC5ggFvP1t1ZmnTaWalFNlV5FUbCITb5NlAfL2RfcJx31IbJ7924uv/RyiouKSUxKZMTIEXyz+Buyuof6CGiaxvr163lj9hsUFxUTnxDPsGHD+OKbL+jXrx8AK39cGZ5kYVC/QQ2uv2b9Grpnd2/fRB0mf776zzz37HO89+57nHPuOR1tTrvi94PPr7Lo6WN5+J+lVAWrWO5ZTlmwjH3BfdJRdwANtJmYyIhjpTY7WpuK6OKT6VZUVOByuZi3eh7r3SqqEpq+U1UIj7dUFYVEm4nkJqYXXX3+W4xb/XcGD3gR0+aLOf/Gn4k7/QPqf1x1WNtaAAwVBjKzMjFbmp4aNRJobiympHUcLP+8Hi87d+wk4Aw0+vQuLi1m4oCJlJeX43RG1sdkRUUFVYkubj8plg9/2YrfbyCl3y5iM0oZd/1XTEgeTZwW19Fmtho9qLPnxz2kH5OOKtROoc2D0dW0297paSttRmYj61FAURR6J9hJc1hwWQxYDKGqF09Ap9Tjo8jta3SOEIKd45di0aHflh4ERZDXHutF0B86d6BlIGc4zujyHWAkkqPFtkSYUJmK7jejC538Nems/2Ig3bee1amctERyNImaqm9PIMiOkupmpxq0GBp/s3xT/Q0v+9bxW7+V88o/4N/jErjgsU+JtySSacgkw5jRxJUkEklrWZUIpy71YIstpEKL46LblnH+JDuxjqh5NUkkByVq1OAN6s06aZNBJd3RuHqqvLic7ULlDcc0zi79gAEPTSch7pSjbapEEjU8mgRn6W5O9X/LwMdHMHaCq6NNkkgijihy1EGg6bYJX0Cnyh/EcsDUdvsKC0Go2H0qvkQTCUlda1EMiaSjce4J/T3lt1X0mdAxKytJJJFO1DjqGJMBPwbc/obOtq4n+M6KGso8/toFMhR2b7bznxWfYinoz8Tqb/lxzCBK3D9jV2xkGDKwG8xywn+J5Ag5cwPUKBb2XqhjqNlOlikDUycYuiORtCdR46itBgOD4hpXqwV0nU2l1fh0nSpfgKCAf90ygE2rYnFfqtHfsYIkG9j6B1nm3wjArrIA1kBSuENaHYoCGU4LKbbONbBfIukohtjAEB+gIGY7Bb7t7Ck2Y1RMGOtN7xhrMZATa5cfxpKoJWp6fTeHQVXpkxDDoCQXuZYEVr3Vhx2r4zBpCt22/gV3CigGP+mOdEyqiktzYNTtQKiDWv2txh8kv9LTwSmSSDoPpUlgdZhD2lLiUIWBoC4a6GpvlRefXEdaEsVEvaOuz8tPp/LfV5PoP8TNs//ZyJwnhtK/1EBQBKjsm42CQqwWiyqan+Iw1tJ1pz+USNqaDD+U9MxAQcGsOwmtP9cYi6wOl0QxUVP1DaFx0UEEeu1c30EBFd4A1TU6iz9JYv0mjYAu+OlHK+9+ZGLCJTVcvM5BkamEjxO3gx7Ldt8uktRkulmScB6wBq6i0OSkKRKJpGn6l8GsuB149SD7lO10I5ucOEcDd+0yR9VrSiJpRNQoYOmeEpxVTR/74pUefPtuFiih7/luOW4GHVtBjAqTfyzh1V4a62v20F+NJ08ZQGZcOnEWU1TOyyyRtCVaEP7Ro5DL9CBDjKPpnhqL1Rg1ryWJpFVETdV3c051x1onyz7KxKgp9B/o4YMla3n1vS1MOhFyk2LZNbgHGeU62Ynd0VDYKtZgt3Sdhew7iiWLl3DeWeeRl52Hw+zgow8+ahRHCMED9z1AXnYeia5EJp06ibVr1zaKt/T7pZw28TSS45LpltyNSadOoqamplE8r9fL6BGjcZgd/PzTz0clXQfjgfsewGF2NNrmfz2/0fEYSwx52Xn87ne/Y9fOXR1ib3swMpiMxWBiu7JWOukIoL424+LipDZb0OYV069oF21GjaMWQuAwacRajCRYTSTZzfj2xPPKLUPRAypX/3U3Dz+/BbujYacVX88c0ip0Nu/dHA5TmmlHk7Qed7WbAYMG8Lcn/9ZsnCf+9gTPPPUMf3vybyz8biEpKSmccdoZVFZWhuMs/X4pZ51+FiefcjILvl3Awm8X8oc//aHRovAAt8+8nbS0tKOSnkOhb7++bN6+ucF2/InHNzq+YcsGXp39KmvXruXSiy/tQIuPLuquvQT1IGr0vI4iGqnN1mtzzS9r2kWbUaUMVQ052KAQ+IM68ZlVjBxfiqLAMw934+wT+/P9wpgG55gK9pBVqrO9aDsApzlO69Lr5bYXEyZN4K577uLMaWc2eVwIwbNPP8vNf72ZM6edSf/+/Xnh3y9Q467h7Tlvh+P99ea/8ser/siNN99Iv379yOuZx1lnn4XZ3LCvwBfzvuDrr77mgUceaHSvRQsXhb6cv5nPiaNPJCk2iZPHnsyG9RvCcR647wFGjxjNa7Neo09eH1LiU7ju6usIBoM88fgT5GTlkJ2RzaMPP3rQtBsMBlJSUxpsJpOp0fG09DSOP+F4pk+fzg9Lf6CiouKg1+50BCGxWrBpzybG28Z3tDUSpDYPRZuXXXFZu2gzqhx1hSdAmccf3qqCPibN+JmgLtB1yOlXRVz3ckpqfFT5fazzraM0JjQhikU3MNExUTrpdmLb1m0U7C3g5FNODoeZzWZOOPEEln6/FIB9+/ax7IdlJCWFxNsjswcTT5nId99+1+BaBQUFXP3nq3nplZewWW3N3vPeu+7lwUceZNH/FqEZNP78hz83OL51y1a++PwL3v/ofV557RVef/V1zjnzHHbv3s28L+dx3wP3ce9d9/LD0h/aLB8K9hbw0UcfoWlap1jD95DRIbZGMNw+HIMiq707A51Zm8uWLWuzfCjYW8CHH3zYLtqMGmXYjQZMByy8IQQEAwqhyckEMZllFGqlFBaBX6uk0L4WZ2YqeYqG4jOzuciPpgRQVQVNAU1R0NTQTGah36AqtUtn1oabVBWT1s7fQzU1KJs3HzxeGyNyc8FqbZNrFRQUAJCcnNwgPCk5iZ07dgKhFwbAQ/c/xAMPP8CgwYN4c/abTJ00lR9+/IG8nnkIIfjjlX/kt7/7LccMO4bt27Y3e88777mTE8ecCMCNN93IOdPOwePxYLGEJrDRdZ3nXniOmJgY+vbty5ixY9i4YSPvffgeqqrSq3cv/v7431m8aDEjjx3Z7H3W/LKGlPiU8H6fvn1Y+O3CRsd1XQ+35/3pqj9ht9tbm32dhyDY/Aob9hWRbq/EoCphXWmKgkFV6oWpaApYDVrnnfxEarNDtblkyRKOG3tcs/eJVG1GjaMekOQkIa7pZfPGn1rJt984+enzDOz+GM7/y4/8Yl2JioLqM+JXdNKc2RjUkFMP1C7wERriJdCFIKgLmlvZe1S3OAxNtMscLZTNm7FMndZu96vD8/FcxIABbXrNpjrt1YXptZNgXHHlFVwy/RIABg8ZzIL5C3j91de55/57eO7Z56isrOSmv9x00HsNGLjf9tS0VAAK9xWSmZUJQFb3LGJi9jeNJKcko2lagza35JRkCvcVtnifnr168va7+6sID6wKrDvu9Xn55KNPeO+/73HXvXcd1P5OiREqLSpZrhyCusBXq62ACA2jDDSxkE63GAs9YjvnR4vUZsdqs6ioqMX7HKo233/3/XbRZtQ46pb48y27yc7z8MYLyXz3jYvcY7phPz30wO3U9nCMLujhUFhn+pqe5p4MNPVvdA0hBIJQ+3edE99Z6aGo2suGkuraEkLDUjiEOqYptcPCGv4NhdfFUWuP6QJ0Qh8FSgBSROMqF5Gbi+fjuUclr1pC5Oa22bVSUkJftQUFBWFhQkicySmhL/nU1FB4n759Gpzbu09vdu4MfdkvWrCIH5b+QHxMfIM4J44+kQsuuoAX/v1COMxo3L9oy4EvnAOP18VpKkw/yCxaJpOJ3Lzm86r+8X79+rFx3Uauv+Z6XnrlpRav2xlxmyCjWqEiZRcOaxLZxuwGx0Xtx3CgVlMr95azu9KDVxcYFEKlbVUN6aZWMyq1GlJC/4/wMUBVIFirIV0XJNhMGJT2+4iW2uxa2ty8aXO7aFM6aiAhKcAlfyzgjRdCD5lNxDDFfBZBYw3pReuotBr4vmoLp5KHWWl6QpO6F4KqKBhrdd/NYQEButhfUgiXwGur24UIOV/RXHG8BayoJFiaqM6yWtv867m9ye6RTUpqCt989Q2DhwwGwOfzsWTxEu594F4Aumd3Jy09jQ0bNjQ4d9PGTUyYOAGAx/7+GHfcc0f42N49ezlz6pm8+sarjBgxop1Sc2TcfPPNDB8+nKuvvZohQ4d0tDltiq0GViUFUQq2MrJH4+YCpbYpqa7VKstlpcoXxB/U8eg6AT30cSzCWmq+Zqspyr0BesU72ig1rUBqs0tp85aZtzBkwJCjrk3pqOvx77nruWdGNv/6Wxr/+lsaf3t5M30KKljpAlVouDQXZsxU6pXEqDEHvV6MyUCfhIYvgaAQ/G9XyRHbqigKMSYjBrVzttVVVVWxZfOW8P72bdv5+aefiYuLIzMrE0VRuOqaq3j80cfJ7ZlLbl4ujz/yOFablfMvPB8I5cH1M67ngfseYOCggQwaNIg3Zr/BhvUbmP2f2QDhqrE6HPbQ/yMnJ4duGd3aKbVHRnZ2NlNOn8L999zPO3Pf6Whz2hYFNidq9NZDNUM+4Wuxw2aWs2GHo8IaL+uLmpnJ6CD3tRs1cuKa78AUrUhttp4eOT3aRZvSUdejW5aPmQ/v4E8X9ATg/XdsjCvaxT6HQlpiGuXBclYEV2BQDEx1TD2seyiAy2Kg3BNofEwJlcgVJVTVrdaO1/YG9UYlbiEEQV1HMXROR/3jih85bcJp4f2//uWvAFx8ycX866V/ATDjxhnU1NQw49oZlJWWMXzkcD745IMGbVFXXXsVHq+Hv978V0pLShk4aCAffvohObk57Zugo8y111/LKeNOYdkPyxgxsnOUNlqFClUmQVJCEstqlpFjymGQeVCrTzc309u2rjNnnY4aaUhATUBHk3MiNEJq89BoD20q4nDqXDsRFRUVuFwuPv/lcxLiEg4a3+dVOGP0AHSh08+3lLnBM7hlUhDfn89mWOYwTJjIMGaQbkhv8Tr73B4qvAEEod7l+/+KevuhrBeIUPU3oUj1z6k5YP3sMArkxdjJCDrIzMrEbIncOcb9bj9Gm/HgESVNcrD883q87Nyxk4Az0OjTu7i0mIkDJlJeXo7T6TzKlh4aFRUVrOzu4oLbEpl51u3YFBu9TL1wqI2rooUQ7Kry4A0E0anVh9jfN0Sv1VJd/436WqsJBJusDk+0m+nThtXeelBnz497SD8mHVWoGCoMEa/Ng9HVtNve6WkrbcoS9QHoOlitOjU1Kn2T92Io0vk5XWOAaqBSr6SPsS/Jaip+XQ+/JBo5YGBLWQ0GRcGo1XVwqes8RrjErCgq1O/8UmtDuFOMAorNhNNkwKSpDTqiqYASVKALzoEhiR5+SgDVYKQyWEkPcw5mbPiCIWdMrSPWAX9QsL3Mjc2oYdCUcOewur4hhpCgGmip7ni8YsSkaTjNhtr27tBwLzkNsKSzIB11PTassfLcs/GUVylk9Kpg/J9L4VoVu09gVJ1UBNys8P3Crio7SivmiumTFCOXvZRIWsDuBZvmpCLoZlnFOhLdJppb6hKgX2IMFkMXnPhFImmBqHXUnkAQtz9IkNBwqlce6843c5MICjjjtzuYfE4xqUVO6vpqpZiSCKg1oPoJxm5ggGF4vWFTSnjlrbov/LqJGSQSSfOoRjCpRgyqStBUTqylnDQ1MzyCgrqaJUKjKaSTlkQjHTqF6KJFizj99NNJT09HURTmzp3bKM6vv/7KGWecgcvlIiYmhlGjRrFjx45DvteqgnKW55c12NYWVbK+qIovvjHy5fuJBIVAAWyqkax4I4kOC0LoaAKOjx1OoiEeDQWPWkmi1UyC1UScxUSsxUis2YjLbMRpMhBjMmAzGmTVmkRyEFQzZNm7YVetaChYTZBgNRFv3a8rp8lIjMmAxRC15QpJlNOhjrq6uprBgwfzzDPPNHl88+bNnHDCCfTp04cFCxbw008/cccdd4SnjTsUvMEgnsD+rT7pPSsZNG4fEGprnvNiKnOX+FheVIVPD5CsxbO+GIKlWXj1IOWBar7Yu5YlO4vZW+059IRLJBIgVPOUaB6AoSIHrx5kZdU6Fu3ay44Kd0ebJpFEDB36iTp58mQmT57c7PHbbruN0047jUcf3b/qSU7O4XXtH54WR6wrDl3oBAShKT8h1GElSRB7eSm3LgrNpvPXJzbQf4SOZbcdIYIkGUK9xYOKh1AFt8Bt2oPNn0KZN0Bq55zNUCLpcAw6ZDhyCSpeAIKqB79WxZ4qc6Mx0xJJtBKxq2fpus4nn3xCr169mDhxIsnJyRx77LFNVo+3Bk1RMGkqFoMBh9GAyxyqVou1GImzmHjrmSxUBRITAxx3rI8Uu4UkswFd6MS4AwxJdVLj2IRRVbCqZoZYhgCQYOk6QxckkvbGqEO/JBdu+yaMqkoPQy52EUeqXXbClEjqiNhGn3379lFVVcXDDz/M/fffzyOPPMK8efM4++yzmT9/PmPHjm3yPK/Xi9frDe/XrROqB3X0YGie14AQVHv94bHLAsHv7tjAA9f0IX+HhUum9mLaZbs5OcdNMlDpLeWrqs/RVR0FlRSRTY3HhKL7MNZeu0MIHt7Uo+1JeKy4ELLN/jBobf4JIULP4QFROuzZbIKmtOnywmdVC0mNSUEB7CKFMr/AKJSIsr011NmrB/Xw0LLOTFfTbkelpy20GbGOum7y9DPPPJMZM2YAMGTIEL777juef/75Zh31Qw89xD333NMovHRNKV6bt4kzQtiAB+7Yy6pVSTz11DHMeqQ7ywNlvKcY2OdQOGnL0APOqCAGqNrn4TAmMGwTDAYDqampBDwBVD1iK0cACNQ0nolN0npayr+AL0DQF2Tf2n0EAg3jud2R09bblDZn9wHzZgMjqofVhgRxEUTs87Cn/U1sE/b+tLdTafNgdDXttmd62kqbEeuoExMTMRgM9OvXr0F43759WbJkSbPnzZw5kxtuuCG8X1FRQWZmJnH948Izk+0or2F3ZU2T5w86N8BHF6/mz7/pRelmB8EaM/F7i/DkeshxRdjUdwHQqjQMFgPGCK2CF0IQqAlgsMpe8IdDa/JPV3U0k0Zyv+QmZz+KFJrS5u7hmbxkWsv0odM7/fOhB3X2/rSX1MGpqEKNeG0ejK6m3Y5IT1tpM2I/9UwmEyNGjGD9+vUNwjds2ED37t2bPc9sNuN0OhtsAKqmhrfseDvHd09kdFYCIzPjGdYtjgGpLmpqDPz7mTSu+kMmBeV+Agh0vwFdgfVFNfy4r4JVhRX8XFTB6uJK1pZUNdoKanwN7nW0t0gXUJ19B9r54r9e5Nhhx5KWmEZaYhonjTmJL+Z90SDOH678Aw6zo8E2/sTxDeJMOnVSozjT/2/60U1UM8x+bTYOs4NpB6w3XFZWhsPsYNHCReGw+vamxKcwavgoZr82u9E1m8u/puI194xECk1q0wfFbg8rCyv5qaiCn4srWXOAprZW1KArtJumjmSD/XZGujabo06b6UnpZGVlcfLYk7uENmMsMZx77rkN/i9Hos3W0hba7NASdVVVFZs2bQrvb926lVWrVhEfH09WVhY333wzF1xwAWPGjGH8+PHMmzePjz76iAULFrTJ/UOT9u+fSGHrV7n8773QHN5jLl5LinsR5re9bIvXMFRvJSM2i4AuqAmElqxsijKPn1S7udOKtL3o1q0b995/b3iC/jdmv8EF517Atz9826AW5dQJp/L8i8+H942mxqWTy664jDvu2r9cnsV66MP32gqDwcCC+QtYuGAhY8c13TxTx/MvPs+pE06lurqad995lz/+7o+kpqZyyoRT2snajmdLAuyoWI+mCoK6gifQuFqyDD+JNhMuc+R8dHRl6msz4Anw1jtvdRltLly4sFNqs0Of/OXLlzN06FCGDg21/95www0MHTqUO++8E4CzzjqL559/nkcffZSBAwfy0ksv8e6773LCCSccFXtGjfagKnD6eSXcflOQ68aCpioUOBQKTVsoDO6jxh8IO+n0GAu9Euz0TYxhQJKTwSkuRqTHSifdCk6behoTJ0+kZ6+e9OzVk7vvvRuHw8GypcsaxDObzaSkpoS3+Pj4Rtey2WwN4rhcrvCx7du24zA7+GDuB0yeMJmk2CRGDR/F0u+XhuPMfm023ZK78dknnzF0wFCSYpO4+MKLqa6u5o3X36Bfr35kpGRw4/U3Egw2s0hKLXa7nf+79P+46/a7DpoHLpeLlNQUcnJzuPmWm4mPj+frr74+6Hldib1+8ONjT2BbeH4Du0kL62pgspNjUl04TRHbStflqK/NvLy8LqXNiy++uFNqs0Md9bhx40ILWxywzZo1KxzniiuuYOPGjdTU1LBq1SrOPPPMo2LLsv/ZuenKHISAU87Lp8zjZ2//fqgYOXZHgN1lOyi2/UxADU1w0i3GQk6snWSbhYTaWZRiTIZml92TNE8wGOS/b/+X6upqRo4a2eDY4kWLyc7IZkj/IVz9p6vZt29fo/PfmvMWWelZDB8ynFtvuZXKyspGce658x6uu/46vvvhO/J65nH5pZc36Nzhdrt57tnnmDV7Fu9/9D6LFy3mN+f/hs/nfc67H7zLiy+/yCv/foX333v/oOm57Y7bWPPLmlbFrUv/u++8S0lJCUZj52zPPFxGl4b+7mIFAdWDQVPpE+8I68plNspZ/jqQrqbNW265pVNqU36mAquW2fngAzv+2p7mP++sodhShcHj4RijgaRqD4Fg6MGpMG/GFHSxuzKDWIuxdg0fwnN9Q712xdqtXpRG4Uq9k+sfp3bO8Lo5j9VDeFEFdYE70P5DW2wGFU1tvZ2//PILJ485GY/Hg8Ph4D9v/4e+ffuGj5868VTOOvssMrtnsn3rdu675z6mTJzCku+XYDaHlg684MILyM7OJjk1mbVr1nL37Xez+ufVfPTZRw3udd2M65h02iQAbrvzNkYMGcHmTZvp3ac3AH6/nyeffjJcFT/trGnMeXMOW3ZuweFw0LdvX8aMHcOiBYs497xzW0xXWnoaf776z9x7572cfsbpzca7/NLL0TQNj8dDMBgkPj6e6Zd3TBteR5HgB1UXVHuqKY/dCN4eFNaYcQZrm5bCc+iHdvb/PkBPTWipbu59CGlIiwBnL7XZwdpM65zajBpH7Q148QQaTvcZCMA91/bm52VOgkKQllfGCedvISmnCF8QfEZY3S+d0dvWssM5koBfp0LJB0M+wm9i5d7mh3u1NS5zw685m1Ejy27DKRy1S2zubzOv9gf5saDpXu1Hk2NSrMSYGtYo1M7/hi70/R81tfTs2ZNvf/iW8rJyPnj/A35/5e+Z9+U8+vTtA8A5554TjtuvXz+GHjOUfr368dmnn3HmtFDNymVXXNYgTm5uLmOOG8PKH1cyZOiQcL70H9A//DslJQUIjdXv1bsXQghsNhs9cnqE4yQnJ5PVPQu73R4OS0pOorCwsNnxsfXHaV5/4/W8/NLLvPrKq5x97tl1mdHg3Icfe5hxJ41j987dzLxlJlddcxU5uTkN4rSUf/XjCCHwBrzh+HV4A+33jB4OdmDIHoUkVzKV7KXSvBe95MSjci+TQcVaW+NlUFUynRZsxrZ7BdaNi/UEPGhCQxNaRGuzJXr27MmSH5ZQWlDKx59+3GW0CXDdjde1iTZbQ1tpM2octU/3UenbX+0S8Cv89dIR5O+wMXhUETc8vBqDsS4jQ7Mi6ULgrKlC9wkmdhuNxWxhpXcF5XoZhabljLCPJkaLAQivS73/d72Xbb1w6v276r+OATyB0EpeQV0QRCeoQ7HbB0CNv3GaHEoQh7ChC52g2N8+Y9YEQ5Lbf7F6syYa2FEfXTQuRWhGjeycbAAGHzOYFStW8Owzz/LkM082eY2k1CQyszLZtHFTs/cZOGQgRqORjRs3MnDIQIKE4qkGNXyOTu3EN3qAoAiio2MwGhpcUyiiURhKqCqs2TSiIwjlQYwrhhk3zeDhBx5mwuQJAARpeG5iciLZOdlk52Qz641ZjBk1hsHHDA6/DA+Wf/WP6UKn2l9NUG9om0/3NXteRKDAyMRjGGk8lu+8oWGXqnMLeYae+6f45QBN1Q8Lq6oOQSAo8AYFQV0nIAQlHj/BoKDGD+X1bi2ElW7OtuvcJGprAap8VRgwYBGWTqHNptCMGj1yepCdns0xo4/pMtoEcLqcbarNlmgrbUaNoz4+6/jwUC0Anw8cBjMmTeHXZanMeSiZ517wcWAT877CCvBpDOk5CYPRwOjgSN6tfBeAMTHHkaglHrIt+6q9FNc0/CcV1/io8jXu8ZoWA0l2M6k2M5pau+i9qmJSFQx6kD0792Az2rCYGr5wXI2u1EEIqPRU4jA6WlpmGABVUdH9Og6To8njxcXF7N61m+4Z3ZuN88svv+D3+8nOzMZhcmA3hiZitxlt4XMCplA+Ww1WHCYHFs2CgtLgmibNhKZoDcKMqhGDamj23gde54brb+CF517g38//u8H96qi/P6jvIM4++2weuOsB3p9br/2sFfnn0T2YDWaGpw/HbGnoBOpm5otYFBiRegyn500hqSaBX72/0sPUg3G2ca06PaDrbCl1ExR1NQ+CnRU1WOvFSXGAUVPokxCDQVUxqApGVcFhMhxSk9JBbfEHmL9uPsdnHk8w2Am0eTDqPXtdRZsADqOjbbTZCtpKm1HjqI2qBUUxU+T24autopr9qYeKMpg82sqXX6h8thAGD9f3l3wF1PTuyZ4ty/hy1+skJibgw4Oqhaqhd1YEKCMQ6gRXe5/9H/2iQSk7dEygs7+U3BATFoOJ0RnxGFQFg1LnlJtvn/Z4PKiKiqZqaGpkdmKrm2FOURRUdX/fxVtvvZXJkyeTmZlJZWUlc+bMYeGChcybNw9N1aiqquLuu+/mnHPOIS0tjW3btnHrrbeSmJjIOeecg6ZqbN68mTfeeIPTTjuNxMRE1q5dy4033sjQoUMZc+KYBvly4O/6YXV21c9DVWkcpigKiqI0m9cHXsdus3PPPfdw1VVXNbKhLn79/ZtuuonBgwez8seVDB8+vMX8q4+maqiKitVoxWJs6BQCxgifVUrAe3s+w10xAq9SjaoZ8fjNbCrdr6sGmhLU05qgsJGWFCwGGwOTncRajBhVBYOqoikHH4t+pPgJVXtZjVaCajDitdkcddrs1q0b+fn5fPLJJ11Gm3Vx20KbraGttBk1jnr+tiJsMfvbBIyaigJUlCoEhYWEFB2PycPuSj3cMcWnVpKS6iJ9lc6/t3zDRc6ricGMQVhw6AkoIoYqAg06stTv6FK/MwuAqipoQJzViD8o0NT9rY6aqjA42YnF0LlEfbgUFBRwySWXkJ+fj8vlYtCgQcybN49TTz0VAE3TWL16Na+99hplZWWkpaUxfvx43nrrLWJiQs0NJpOJr7/+mqeeeoqqqioyMzOZMmUKd911F1qE9L6fPn06f/vb31i7du1B4w4cOJBTTjmFO++8k08//bQdrIsA/CB27aSiJEB3x0AswoVDT26gK6W5DmKqQqrDTKUviKYo1PWVSnWYyYixyJ7ih0l9bTqdTgYPHiy12cHaVERnnzn+IFRUVOByuXhv1Sbi42JxGDWGpLjCPSB1HW68Ed56a/85N98Mv7uuijkVcxj+pxep+v5H5v7rOh6f8HgHpaJpPB4PW7dupUePHoe1Rnd7oOs6FRUVOJ3OZkuEkuZpTf619BzUPf/l5eUNmn4igYqKCqpSXFz521ief+AnslxZHW3SEeH3+/n000857bTTCAaDEa/Ng9HVtNsR6WkrbXb+3G8lQV1Q7QtQUO3FHdjfqK+q8MQT8P33++PW1MCcijkAxP+8HXNQcM3wa9rbZImk66PAsITBZDozO9oSiSRiiRpHXR9bE9XLmZnw8MOQmwvPPCN4fvrJFGx2YlAtLM80NBraJZFI2gAVfsm2y2pqiaQFosZRd4+14jQbwh20DkRR4NJL4euv4djLV7Lph2ReOG0MzvxSVqcbyXBmdIDVEknXZkcMpMTJ0rRE0hJR46i3l9VQ4Q0QaGYxjToKCuC7VwYB0PuEXQREAL9BJaBHeO9ZiaQT4o2MfkUSSUQTNY66Dqe55fla7XbQMJCRYOPS25bgCXiwxGdgM9rayUKJJHootMHlAy7vaDMkkogmahy1qiqYDSrdYlrugRkfD8cfDyUlCjMuGYQQgtGiJ8s9y9nlD5WwJRJJ25BYBsWWYnb6dx7SzFkSSTQRNeOoTZqK02wk1dHy9H0VFVBcHPodtAVwm1VclTVs9W9lTXANCgq9Tb053nq87AAjkRwhKX5Y6tvL59V7MCgGxtvG093YvaPNkkgiiqgpUXv8QQqrvVR4Wy4Rr1wJ69aFfr/35AlsSTLg2LmLcx3nck7MOQgE63zr+KDqg3awWiLp2ri8MNU9krNjziYgAnxZ/SUVwQif9lQiaWeixlEfk+ZibPcEUuzNl6jvuAMuuij0e8gQGD3cRqbbyC/2chRFIU6LC89BXBQs4qWyl9js23z0jZdIuigBA2y31BCvxROrxQLwduXbrPGu6VjDJJIIImoc9Y/55SzcXtxir+9du/b/fuQRMJvBohop9xazvGo5C90LWedb1+CcalF9tEyWSLo8ioCbFt7AQvfCBuHlenkzZ0gk0UfUOOo6ArqO3sysqc89B71Da5UzcSI8+KCO0x2gwqKwuHQxZXoZNsXGCMsIznScyRWuKxhkHtSO1ncd7r777vBE+nVbampqozh9+vTBbrcTFxfHKaecwtKlS8PHS0pKuOaaa+jduzc2m42srCyuvfZayss75iU/a9YsFEVh0qRJDcLLyspQFIUFCxaEw+qn2+FwMHjwYGbNmtW+BkcA1RpsqdlBmV5GkpbESbaTuNR1KcdZj+to06KWOm1qmkZcXByapnUJbWqaxrnnntsgvLNoM2o6k9XxzbYigP1LRiqh1XWMtROhPDFHoWgv/N9kB4897WW8MZMROzaSpIxijGNMB1vftejfvz9fffVVeP/Ayfp79erFM888Q05ODjU1NTzxxBNMmDCBTZs2kZSUxJ49e9izZw+PP/44/fr1Y/v27fzxj39kz549vPPOO+2dHAAMBgNff/018+fPZ/z48S3GfeWVV5g0aRLV1dW89dZbXH755aSlpTFx4sR2srbjcfqgb77CmY4zO9oUST369+/PF198QWVlJTExMRiNDYe1dlZtLly4kPnz53PyySe3GDfitCm6OOXl5QIQJaVlorDaI/ZU1oid5W6xpbRabCiuFGsLK8TqfeXix71lYtmeUvHmqq1i0GlbhCu1SrhSq8QHg0eJL3saREF5QUcnpRE1NTVi7dq1oqampqNNaZZgMChKS0tFMBhsEH7XXXeJwYMHH9K16v6XX331VbNx3n77bWEymYTf7xdCCLF161YBiHfffVeMGzdOWK1WMWjQIPHdd9+Fz3nllVeEy+USH330kejVq5ewWq3inHPOEVVVVWLWrFmie/fuIjY2Vlx99dUiEAg0e++66/zud78TI0eODIeXlpYKQMyfPz8cBoj333+/wfnx8fHihhtuaBDWXP7Vp6XnoC7PysvLmz2/oygvLxe7bYiHHjqzo01pE3w+n5g7d67w+XydQpvNUafN1jx7dXQWbU6fPr3NtNka2kqbUVP1rakKiTYzaQ4LGU4rPWJt9Ix30DcxhgFJToamuBieFoulJIHtK1IAmP6PxfSuWkthYgxx1rgOTkHXY+PGjaSnp9OjRw8uvPBCtmzZ0mxcn8/HCy+8gMvlYvDgwc3Gq1uJxmBoWFl02223cdNNN7Fq1Sp69erFRRddRCCwfwSA2+3mH//4B3PmzGHevHksWLCAs88+m08//ZRPP/2U119/nRdeeKFVpYG7776b1atXt7rkEAwGefvttykpKWlUcunyKNBn9LSOtkJyABs3biQjI4PBgwdz0UUXdRlt3nLLLZ1Sm1FV9a0LQUCv2/R6vwUBIfjmS4Wbr7JjEAJV1fFtzyKhxMOenjG4dTcuXB2dhFYREAHK9LJ2v2+sGotBad0jdeyxx/Laa6/Rq1cvCgoKuP/++znuuONYs2YNCQkJ4Xgff/wxF154IW63m7S0NL788ksSExObvGZxcTH33Xcff/jDHxodu+mmm5gyZQoA99xzD/3792fTpk306dMHCC1R+Nxzz5GbmwvAueeey+uvv05BQQEOh4N+/foxfvx45s+fzwUXXNBi2tLT07nuuuu47bbbmDZtWrPxLrroIjRNw+PxEAwGiY+P58orr2zx2l0NjwHWiZ0dbUa70Zm0mZeXx9atW3nyySe7jDbT0tK49tprO502o8ZRf7OtCJOtpvkICuRXmxDCiKoonH2GkWdv6MvuJ014TBqfVX9GX9GXdEM6SYak9jP8MCjTy5hbObfd7zstZhqJWtNCPZDJkyeHfw8cOJDRo0eTm5vLq6++yg033BA+Nn78eFatWkVRUREvvvgi559/PkuXLiU5ObnB9SoqKpgyZQr9+vXjrrvuanS/QYP2d/pLS0sDYN++feGXgc1mC78IAFJSUsjOzsbhcDQI27dvX6vSd8stt/Cvf/2Ll19+mfPPP7/JOE888QSnnHIKO3fu5IYbbmDGjBnk5eW16vpdBZ8K29RdrPKsIseYg1OLrDWz25rOpE1d1+nevTsnn3wyPXv27DLa/Mtf/sILL7zQqbQZNY46ENQxNRGeYDWRF2fHbFDJPU7haU0FFPr0CU07Wtgzg/7btoFiYZlnGQDnxpwbHvMZicSqsUyLmdYh9z1c7HY7AwcOZOPGjY3C8/LyyMvLY9SoUfTs2ZN///vfzJw5MxynsrKSSZMm4XA4eP/995usoqofVjejnK7rTR6vi9NUWP1zWiI2NpaZM2dyzz33MHXq1CbjpKamhtP23//+l6FDhzJ8+HD69evXqnt0BZw+SFxfxPJBy1nuWc6VsV27RkFqU2rzcIgaR51oN+FuInxvmY+H7zCxa4uB3Vs1vEGda+92M/ZsnV8KFbxJqWRt3Ai+wRSYQj2U51ct5mTLBJzmlqcj7SgMiqHVX8+Rgtfr5ddff+XEE09sMZ4QAq/XG96vqKhg4sSJmM1mPvzwQyyWludyb0+uueYa/vGPf/DUU08dNG5eXh7nnHMOM2fO5IMPomjWOwHWjcUEa4dM/lq9jV7W7k0uRdsVkNqMDDqbNqPGUR+TGovT2bBaza/r7Nil8/08jbqh1XO+riAuQeALCKp1nYwdZcxP19i7fhndcrOpsGxht76HuUWLODtpHA5TlHX+aSNuuukmTj/9dLKysti3bx/3338/FRUVTJ8+HYDq6moeeOABzjjjDNLS0iguLuaf//wnu3bt4rzzzgNCX+sTJkzA7XYze/ZsKioqqKgITT+ZlJTUaLhXe2OxWLjnnnu46qqrWhX/xhtvZPDgwSxfvpzhw4cfZesiBAFv27ZylTcGn7Gcrz2f4605iyGJyQc/V3JUqNNmRkZGuI1aarNjtRk1vb6bwqiq5GYZeP45BVUJbdMnuHBVxXJstzhOyEwgwWDE4od4azmTE0diUlU0RaHCsgWh+Do6CZ2WXbt2cdFFF9G7d2/OPvtsTCYT33//Pd27hxZk0DSNdevWcc4559CrVy+mTp1KYWEhixcvpn///gCsWLGCpUuXsnr1avLy8khLSwtvO3dGRgel6dOnk5OT06q4AwcO5JRTTuHOO+88ylZFEDqIoJuTnKMxqSoKCgWqnJa3I6nTZt++fbn00kulNul4bSq148a6LBUVFbhcrvDQgOb4+mv4v0sE/qDOcx+X4krU0QXEPHEbWa+/zJvvzaZf9xPJj1mMT6tAQcGsqVzhugJV6ZjvHY/Hw9atW+nRo0dEVSvVR9d1KioqcDqdqGpUfxceFq3Jv5aeg9Y+/x1BRUUFZSku/nlmGqMfXMxOV6hpyaiqnOc8h3gtvoMtPDT8fj+ffvopp512GsFgMOK1eTC6mnY7Ij1tpc2oqfo+kJ8Kytld6Qnv65mQkh3Lzs0GflhkZPyZoWM7zTXk6IKp6f2pTi7FrRspF6Ev/1HWUSh0zbY0iaQ98JpgoCGDlKQqioMmggRIMaTgVCPro0Ii6Uii1lGXH7DcpR6EhBSdnZth5bemsKM2lpZQalOoSq5hdXA1AH1MvTnBekKHlaQlkq6CPQilmQ5+CYZGVEywT5DrUUskBxC1nuaEzHhOyk5kXPdExmQlULY6nlXfhQZwTTx//3jrbXGQ4Fbo4Y3HrIR6eW/wbUCndUMBJBJJ8xRZYXj1/irurf6tHWiNRBKZRGWJOqDrLN1TRrnHHw7TslQgHqOqYimOZVJuqHPZUkcNBkXDta2IkcNGsti9GIBZ5bM4xX4K2cbsjkmERNIFKLbCkEIFxTyQ1d7VbPJtQkdnvHV8eEytRBLtRI2j/qWwAluNQBdQ6PYSrF2Xev4HFl5+dP8MN6oCf/y9St0wzu+y4BpVQ/nmG3qPvo1sYzZb/Vv51v0tX1V/RQ9jD1yai+6G7h02Y1kX7w8oOQid+f+/pBuc8MMKRppH0N/cn8+rP2eLbwte3UuqIZU+pj5YVWtHm3nYdOb/jeTIaav/f9Q46j0VHmxi/5jnor0qM84JVbkpwKXX1jD1DMGQPDM1ukLAr2JQFdRAkIAIIGqnxTMrZvqY+pCupbPWt5Yt/i1s9W9lFas4wXoCfcx92i1NdbPzuN1urNbO+zKTHBlud2gqn864oIcxCDVOG05VxYGDMx1n8rP3Z9Z417A7sJsVnhVc4rok3OzUWZDalEDbaTNqHHX3WCs2hxVdhBbnWPKuCbW2au2/S8px48MDfL+74fxl/Y3HgP4tG/aVU7SjGEPtOtZB1U2lZqJCqwotsq4oJBrad8YhTdOIjY0Nz3Frs9kirrpQ13V8Ph8ej6dLDPFob1rKPyEEbrebffv2ERsb2+GTSBwOx+yFveYiVu0oxqgpqGqQKoORaq0GRVGI1ZwYOuFrqjNo82B0Ne22Z3raWpudTwGHSe+EmAZj1cREmFU7e9yYnDhQBL5gaEUtf73VtTbEhlaLiXVY8ZgMbPRtpNC6AgBFKBgCdlRhYEr8uA6ZGjA1NRWg1RPStzdCCGpqarBarZ3uRRUJtCb/YmNjw89BZ2O3HXJEqPZqnbqEalM+AMaAHbOI4fSkU9GUzvcBApGvzYPR1bTbEelpK21GjaOujxCCTz7Zv//DCp2BQ0S4tA0Q1AXFNT6KbaEqt3x1H6v4mhp7IaqiYBUOhjERq8GM3WQgzdgxkxooikJaWhrJycn4/f6Dn9DO+P1+Fi1axJgxYzpl1WxHc7D8MxqNnbIkXUe+E7xu+NUwnxpjEZqicExwEnGGeOIsRmxq56ryrk+ka/NgdDXttnd62lKbUeOo1xdXYvGAN6izr9qLL9GENxgqYZ9zvuDFL4ubPM9TWYYC7HMV4TMV0VcdQrYtkQxjRkS1m2maFpEvbE3TCAQCWCyWLiH29qar55/dD167is9UzGBtOD3t3Ug1dM7ageaIVG0ejK727HXm9HT+hodWsr2shvwqD25/kHirkWkTTRhVFaOqEvRoPP7HJD56KhH/hgROyExgbPcETu2RRK8tWxBAdW4qRlXFbyzFIzxodD7hSSSRxrAC2JtmD2nLUI5A9pKWSA4kakrUAAFdUOULzUimKH5e+sbN5tVGdm0xULjDxK9rDHzwrkq/fvDccwo9e8IJOxQEgh49JrNd28Eu/y52+XdhUSzkmnIPckeJRNISOSXwpgvSFCtb/FvYE9zD/zn/r6PNkkgiiqhx1KMy4rA5Yhp0FAvogrxknS3HVAPVACz92sQzdzq5coaff70aIEmJZ7tT5aN188g+JgO76mCQaaCc5lAiaQt0+FHdR5/izfRJ7MOJtpbXPJZIopGocdROsxGn1dTksdw4OyWVQS67WGPlCgWDKlBVwZbSauIXLWFzokapIUhi0EtV0Mv6Yo3twVCbtqJAqt2CqoCqKDjNBrKcXaOXpERy1BGwLB2OjUlkn7+UH7eBQgEAaQ4LmhpapS4vzo6mSk1JopOocdQtYVRVTLrKTz+GZib701WCi/6k4yv24Nq7l8UnmthVs5r+wV7EeLMxB2PD5woB+VWeBteLsxhxmjtXZwWJpCPYaYB9Tp3CohKGWM9Aqddtpr6uLAaV7i5bR5gokXQ4Ue+odSFYX1zFvtIgA0baWPm9gR/WexhWUoVVs5AVl8BQXyzztv/MaSOmEmOqIs1VTje1O5qqoqmgolBXgNZUBbsx6rNVImkVqTVgUDR+3PI9J55wIrlaKrFqLKoS0lJdGTrGJDUliV6i/ukv9fjZWubmwWtc/LpSAwS5fUNjHr1CIWC1kRTwU+0uAdXPXrazV99Ob0cGdrVzLggvkUQKRiAvphclVUXsFBsRSg2TbJM62iyJJKKIekcdbzFRvDSJgs0KJi1Ule0I2BmcYsKvCywBH241iNliREEl05BBrBaLVZHz90okbUFRoIBsSy7djd3pZerV0eZIJBFH1Dvqf/8b7rwz1C527TVw0UWQnW2gLmtEZQUb7OCwOIkrG4UeTKRCVZmvFDM42UWirekOahKJ5ODsBYrcJRyjnkKwYDAbFAV3TCV9E2M62jSJJGKIekdtqudndR2ys0O/hRAEhUDx+yjQq8hOGkSGNRYRMFHtD+AN6Pywp7TBtbq7rPRPciKRSFqHr7Z/2AlZJ+I0GSirbYraWrZ/cZyx3RNkvw9JVBP1T//5vwmyZy889YTK08/A1Csr2Of2EdB1hIATjSZMviAmo4Vlhk8wKnaceg6uQE6jaxm1qJnoTSJpE2x66G+ZcydrjF+R6B+EORjXII6KHJYliW6i2lGXevz8b1cJph5GfLoLgDlvC06crNM/KQaDqmJ2xRLj3UdNkZuxthNZ6VtOtWkNx6Wmy0lPJJIjJEYPrfEeVxULcRV4rauYGnMORkUOb5RI6ojqImBJjQ+APkP99B4c6un9wv0xvHhbAiu+ttHNaoDCvZRZFc7pfRYDrX0ZbR0FwJfVX/KT56cOs10i6QqUWkBF5fd9f0uilkCVXsV7le91tFkSSUQR1SVql9lIjNlAQBc88GIV+4p03njKwaLPzSxZqPOHPylsCwZJL9ep1Cv5ovoLdvh3AGBSTMRqsR2bAImkk5PqBV0E+aTiE0rVUJ+PbGN2xxolkUQYUe2oE20mTrQlhPf1LMGeMV6+/0IJreGjKPxoGEXvws95pXgRRo+dRE8/rJ5uGISVNcAaClAUGJEWJ3uASySHig7dC2pYXforfU2n4vBmUozGpxRgN2mMTIvDapQr1Umim6h21AeiKgozrrSwcy3MmQOnnFFFBVVkfqyT5Ewhu2wyimjcdiYEeIPBDrBYIunkGCCnTKF3cAox3vQGh6p9QQJCLnspkUhHfQBer2DOHOjZz88xM9+h7HUbQyt0hqmnh520qiqMyYzHoKoYVAVVLsAhkRwWbhWGuFNwqSEn3SPWRk6sHaMmdSWR1BG1jloIQZnXD4TmE9aF4PvdpaxcYsIbdBK0ejA4vLiEEb+mUOAupK6P97HpcdjkuE6J5IixBKBICY2ZNhtUcuPsmOQwR4mkAVHrbbaV1/BrUWWDsDf+YWfeW6GpQfuNqgLAE2vHFBQo+i+clje93e2USLoy5WYwV5ZyQqYVp1lOFiSRNEXUOuosp5WAruPXBV6v4K6bTXz7dahq+7Znyuk9VEP1jMWrLgagyuxp6XISieRwCIIiBDEmOWWoRNIcUeuoNVXBv9fBG2/Arl2C778RJKcHePD1Usy1i2KJmjj09aHhWAN9sR1nrETSRYkT0NtrR5Ht0RJJs0SNo95aVo0tEGr70kVoUfpbr3WyfnVo6MfwsT6unFkZdtIAOkF+CuzmBEVD6Z5EebAcl+bqCPMlki5JlRlcvfpSGCgkyZDU0eZIJBFJ1PTa2FhczcaS0La5tBq3P8hV95eHj2/51UCME2xGjViLkRSHGXNsPpa9pZQ5LbhdNjZVFSDkcBGJpM1w+KC6b3c2VO+W2pJImiFqStTD0mKxx8QgINw27Y8TxMZCZblCWaGGRTdiMYR6gBdUeRGk4/BqKEaVgK7zg+9byqv3kGxMJN2QTrwW39HJkkg6ParVzs++lQTd1XQzppJtzEZT5CQnEkkdUVOiTrCZSLabSbGb6RZjJdtlo2eCnTOnaBhUFaOq8svncYzqFsdxGfHYjBoVnlL2aj4SKnykl48mLtCDzf7NfOv+H+9UvEuF14/bH8Qf1GVpQCI5HHTI3pFKbKA7v/rW8nX1N6yu3oDbH6xdwU7qSiKJmhJ1czzwAHz5JRQXw2OPwZAhcPzxkOWyUlhdSV6hn5qEFKxKAubqZKw13dnp+gKAD4sWE+PNxqjbw9eTa+dKJIeADv60Hriq+6MEbBTZfuL7mh/Yvs8c1lXPeDs94x0dbKhE0nF0aIl60aJFnH766aSnp6MoCnPnzm027h/+8AcUReHJJ59sUxusVvjqKzjvPPD74fzz4b6HgmwsqSagOzD5BR58BPXQl71Rt5FRfjIG3UqZZSM7XV9SYygKXcuoYVSjppJCIjly1FDnToAYbzYx3iwCqqehrgyyGlwS3XSoV6murmbw4ME888wzLcabO3cuS5cuJT09vcV4h0tKCjz1FLz4Ymh/x25BUBe4zAmM2RqkoLbAbFAVNHsJVa6fMWg6RlUlUUvkuMQcju0Wx7isBDmrkkRyKKiwp1cOAD7nRoSlDKOq4tRiOC4xh3HdE8lwWjvYSImkY+nQOtrJkyczefLkFuPs3r2bq6++ms8//5wpU6YcNVv27YPf/S70e+ZfBQGbnZpAkPLkeLYYy4gDArqgSOyggkJU3UBy1bFYA8mspAKAoaku0hyW5m8ikUga8cPWuZza/a/sVTYRFD7i3H2J9eSFdXVaXkoHWyiRdCwR3Ziq6zqXXHIJN998M/3792/VOV6vF6/XG96vqAiJ3e/34/f7mzzn228VfvOb/dVrX60qJ6NX6BrG3jlkrF5Gmd+DEQt+vQoloAA6sSaFJJMJVQGzQSPOqOLz+eTkDfWoy/Pm8l7SMkeaf5GU701pUwX8O39FBAPouh9FV1C1CrrZLGiKQpLd1Gk01dWedZmeo8uh2BHRjvqRRx7BYDBw7bXXtvqchx56iHvuuadR+BdffIHNZmvyHJ9P5dhj+/DTT0mUlVmYOd3Bww//SEpKDX41QIJPsH3dEmKNsSRiAEJf+NVsoZot4ev8emjJiyq+/PLLjjahU3O4+ed2u9vYksOnKW3utkNV4SYC65aTQV3JOch2QlP3bqHz0dWedZmeo8OhaDNiHfWKFSt46qmn+PHHHw/pa3rmzJnccMMN4f2KigoyMzOZMGECTmfzk/5Pmwaffabwxz+GStZjxownNxe2fjaHSvEjWl4PkhP6oikK+TVlVBvzKbWGXLPNn0Jy9XAAFCW0rnV3l428OHtzt4sa/H4/X375JaeeeipGY+O1vCUtc6T5V1ejFAk0pU16ZpJocJJ8zHFU+XyUKfsotP2IUII4fJkkugeBApqi0CveQWYEt1d3tWddpufocijajFhHvXjxYvbt20dWVlY4LBgMcuONN/Lkk0+ybdu2Js8zm82YzeZG4Uaj8aD/nEGD9v+eMcPIZ5+BWu2hxqhQ6vFQ6tURCMrtm6k0bUepzb7untEMz47Dae74f36k0pr8lzTP4eZfJOV5U9qsAsq0IKVeHa9WRYl9LagKQhHkBUYwIi++03XQ7GrPukzP0bOjtUSso77kkks45ZRTGoRNnDiRSy65hMsvv/yo3HPq1NDf7Gy4887Qb19ZPmVWhWNyY/GZN5PvK6ZC2QNAjDcDuy8NT0AQY4rYrJRIIhcV4rpnYknezBb/6nBwStUogorS6Zy0RHI06FDvUlVVxaZNm8L7W7duZdWqVcTHx5OVlUVCQkKD+EajkdTUVHr37t3mttTUQEmJQABbtsLpZ/u5/fs3GejZQ0qNygrxE0a/DSVgxxXMxRJIwu5PBSDNYekUnV0kkohDgVi/ly3+rdh96ZiDsTg9PVAxkhLXuGZMIolGOtRRL1++nPHjx4f369qvpk+fzqxZs9rVFqsV/vlikM9/cPP283YE8OikM/hMv5Zfspx0K52Ipu1/ccRZjSgGhZIaH/lVHvI37V+vWs5OJpG0DqGA2mMq6aWh2jObUcNiVSmp8bO5NLSADsDI9DgSbaaONFUi6TA61JuMGzfukObyba5duq3o11fhuutCHcAUNHJG7sU5z0P+4BTKYueR6B1KjC/UZl5a03zXel1OTyyRtIoiK/j7VlNu3oTLm4fbH8TtDzaKF5RzfkuiGFnsq8e3a9xUVe4fwpWX3A1TUKFK9WNHId4UQ5zRjFrbC1WtbUPrGW9HlVXfEskh41NhY/4GRiZMJdFgRlMVtDpdxdnRVKkriSSqe2oEdB1fUMcb0PEEgvQ5xs/kC2vCxzN76FiCKrpBQ0dQENzDvmove6u87K70UOb1k+owSyctkRwmfhWMBhP5Ygf7qr3kV3oodHtJsBqlk5ZIaonaErU3oPP1tsJG4dvW7y9R75xXgBCCshgTKUCZZSNerZSUqtGoaFR6AxS7fThNBtmZTCI5DPbZwGFxUG3aTZEwkugegjegk1/lJdFqkrqSSIhiR23SFPokOih0h1bGCuiCgK4z88kK1q828P4rNr7/n5NAAGK0GOy+blQZdlFjLMJrKMUSiEdBZV1xFeuKqwAwG1TGZCagKgqqgnzJSCQHQQEyTQMBHxXmbcTW9MYgrOyqqGFXRah2q39SDJlOKwpSU5LoJGodtaIo5MTayYndP3vYihVw/eVQWgrBIPQblIJ/ZZDs4gDTEsaxMDCfgmA+xa7vMCtWjtVOIoZ4dCFYva8Cb0Dny62NS+l1DEx2RvTMShJJe9OjHAYmj2eVYRVFeiF747+knzaMHko/ArrOmsLK8HYgA5JiyHI1PS2wRNKViOo26gP597+hqCjkpJ95Bj7/QmVNpoljVmzFbjRxRswUpsSchqoo+PGwJPgpsfYgmU4rBu3gX/oOk1xXVyKpT2IN1Pzva85wTmW4dRiqorBO/xFhKTroEEeHnGRIEiXIJ70eN98Mc+eGfl99NVRUKGT2G4S6eBmzdv0HPTa08o8AzFjppvdie0kAXVRgVFUCwYbDSo7PjMclpxWVSFrkzaXPknWKTt0ArHSRS2m5FT1Y0yjuKT2S5Gxlkqgjahx1udePz+0jKAS6EKG/uiAoQBehNurf/d6MXw+VeoWAlTvcuCxOuvkF5YZSLLqF+Jq+xPt6YlAMqKpCoeJHUxTMmkqMyYBRVTBqKjajhlN+8UskB0X3VuHXdZLcQ4gLZGNQVEpUgaYESLCaMGkqJoOKy2yQTloSlUSNJ1m6qxRbTKBhYO14aE1RqCxXSUw3wGqNsRP83PW4F6Om8OujcbhqBC5PECVGIz1eZ5wtrWMSIZF0MTwaZJUGMWoaeUkGhllSDn6SRBJlRI2jBhiblRCeUEFTFWrcMG+ewn//C4sXh0rRPXPgiQfMZCaHpgvdOfwY4G0m/mjj+8lONvk2kWXIIseU07GJkUi6AGtcMHK9j9WKgZWelfQy9SJGjelosySSiCJq6pFSY8zYTQYsBg2jphIMKJx4osI110BxMTz2GPzvf/Dtt5CZuf88PS8PgMC2HZzuOB2ApZ6lBESgqdtIJJJDYKsdzCVlnGQ7CYBNvk0HOUMiiT6ipkRdUFjK4iBoCqiKgoJCbt9Ydu628NPP8PRTMPUkL8oB/Vfi95QCUJFoZ+Hel9EAD16K2UmKQVbTHZRA7QdNTQ34m58fXdIMR5p/bnfb2tPGdKuEghQTywo/QgNWub9jaGzbr47XLnS1Z12m5+hyCNqMGkfdd91qHDYrQQSFVT4Abj4JMgK9mf15H7ZtVNn78XKcGQ0zz/nVIgDWlK0i9btu4fB4+y+grG+/BHRW6hZT+PZbkJNVHDpHmn/V1W1rTxvTvxyW5Hjo9t1GALKN3cG8qIOtOky62rMu03N0OQRtRo2jzkh04oyPp8oXIJ/ycHhO7xrElyqKqvDcl3k8ce3WBucpHg9eg0J1djoxdkvoWsYMVrONPqY+2FQ54UKL6DqUlIDDAWrUtLS0HUeaf5FQcmgBZwA2pZpJdoS0VWmAQqOHJENSB1t2GHS1Z12m5+hyCNqMGke9sdqP2ejDG9TRzRaGpbowGzScWHmodshHfqmFbSVOuqf5wh9cpRbIDAr6Owai2j1oaGwP7AHAZUgh1xTfUUnqHOh66K/FEhni6Gwcaf6ZzQeP04F4TJCmxpLiyMIjPOwOllCCm0mWSR1t2qHT1Z51mZ6jyyFoMwKsbR88AR1NUXCaDPSItWM1GlAVhf65Hp69ZScuR5BFKx0cd0VvTp+RG27OsAZAVyDLls5o62hGWkdiVELfNyZFLmQvkRwJFj9YDTaOtR7LMMswAMyKpYOtkkgii6hx1AOTnfRNjKFnvINuMftfBIoCZ40v49d31rLunTUA/LjORlFZyBnH7i1jj1Nln68MgPJgOf7aHt9VejV7A3vRhd6+iZFIugoCtsSEqgC3+kLNTjpB8qWuJJIwUVP1vaG4EpdixlA7jlqtHUutAqoa2vcF93+3rNpgY1JiBYqiUGVW0AKhTmZ+sb9dYZ1vHQC5xhwGWQa1a3okkq5CoSE0Na9BCU23W6FX8n3N9/Qx9aGvuU9HmiaRRARR46h1AdX+IAG9dgpRXaADoq4nIDBvQRq+YKiK/Jl3Yhk0cB9lVSWoAjKtqQAkGhKZ5jiTTf5N/OJd00GpkUi6CCqYRGja3r7mPqRqKSyoWdjBRkkkkUXUOOo+iTE4E1yNwnUhKK1UGXNlH4rLDSiK4I4Zq+mVW86WsiB2rw+fBpqyv7TtEZ6wk7YpVvqb+7dbOiSSrkaMxRn+/av/1/DvXqaeHWGORBJxRI2jbopCt5f1xVXk77NQUKoCOpPG5TNkQEk4zt60GHr/JLAZjKz3rsct3Lj1/WOta0QNmiKXr5RIDgsFRlbEsM67jkpRRUFgX/iQ1JVEEiKqHbVWOwZr6w5HOGzegjQuPW8LNmtoycrE7UWsS1LZWfop8RYbsVosNsVGD0MOKDp5xrwOsV0i6RII+DRmJ6JmLQmGeLppGZhVI92N3TvaMokkYohqR20SFj54rwdvfBaPSYNrLijklJHlDMl1ss/tY2tZNVmb8pk3QCNLM6ELgVKRhQjGUQXkxNpxBxX8mg+DomLQFIy1ndWUSJj5RiKJcPwarEnTmIiA8mx8egxGo4YvxkKp5sekKZhUFYMqNSWJXqLaUf+61cIbn4UmLPnpP7+SFFe30IZKtxgL3WIslFgsqMJLjJ6AnzJKbL+QWjUKVRjZVu5Gr9cZrT7HpsdhlGvnSiQtouqQagh11CyzriOpejjuQJANJY2nVxyRFovZIKvDJdFH1HoSIQRD+lQhCE0B++5CG4VuLwVVHvKrPOyqrGFHuZuy5ARyinW8bhtZYgCKolMQ8z889q34LXsxG5rOQoMqv/4lkoOh6VCyexMmfzxBzU1ZzE/4LLsRSuMx1PLDVxKtRE2JuqTGx7Z95fh1QSAo8AsdBIw7fg9fLkrl9mczyei5i1inH0UBldA462xrCpp3E/dtfZY3+j+DwbSRgOKjVNsBGvQ1xJOIE63WLwsgwWqS1XQSSWvwgjdQw5tb53N+7yF4lHI8xnK6Gywkk4GqhDQVazaiSk1JopSocdSbC0qwunRS7GbMRg1N1TCoCo/+dicjl6QBULY5jdNOLWrwQtAryiA2CbMKXuNqNH8NGqFJTroZuxGrxTa+mR4Aj1yvGtg/v67HExnz63Y2jjT/vN62taet0aHUoZKqFGL2BUk2JNHd2J1ULfWAj90geIIdZmar6GrPukzP0eUQtBk1jlrx+TFUVVNc1bjt64wTXMxd2JM9uwRUVjZcAs3no1iroYcxB1t1EKPfA8AO1pJssYFsM2uZujb8qqrIWFqus3Gk+efzta09bY0KHgP01BIxVnkoZSc2Y5A0s+Pg50YaXe1Zl+k5uhyCNqPGUWdPOQWrPQYhBKUePzsrasLH3pmZCUbI757IFxmhecBHdYvHZTZQ3TeXuE0bOXHwmaztlUiVHjqebkjHYRsDqrVD0tNpCARg/nz4f/buO17Oqk78+Oc8ZfrMndtvbknvIaGGQIjSm0gREQuurGWtq2tX3NUVV8Wyq9j9uRZsWFYRsYAEgSAQWiDUQHrP7W36POX8/phUcgO5yU1mcuf7fr3yuneead/nyZz7nec853zPGWeAVTUft7FzuMdveHjsYxpLFrRlDOZccDGdtoGpTE6MXoA2m4+9y0fj7bMu+3NkjaJtVkC0R0dDfS2JRKkCUisw1fVwPB9Pwx//ornqMotffa+Bdc8kaZ+XY/jSPLFEnra6ZiYN+KzRMdrNiTjB9RhKs4U+fun+AYBLY5fSbDWXce8q2K41V8NhsO3yxnIsOtzj51b4JRgNrf0+keAU0sEdeErxZ/9u8OGy2GU0WU3ljvDgjbfPuuzPkTWKtlkBHfXlEbZMEkGb2pDNkoU2Dy1XfOo6xcP/MPnNd6Ms+1PpzDkUSBAvaLYN95ApehT9/Q9uXuePdvhCjA8aogWfbYO9OP6+Ux0LusKvrwtxlFTNGfWBFD2fzlSB679k8dc/mri+5uRXFrno9aWu8cTzz3Bnh017wiIf3UCQPdekz4ueR71ZT9yIlyt8IY5tGlZODjDZWEfSLFUjWxRexCRrEgkz8TJPFqI6VGWi7szkGdo5KntrKsfyeyxu+0OC0kQQeP/nhzF35uOsO4SPz+LJp9IeaWdVYRU9Xg8AE62JGKpqOyWEOHwBmJacwhs63sDywnIAgiooSVqIvVRdoi56Po/vGNpn21C/sbvu9/dvcrh01p7rzRtnT2HeXx7lL9lldGQ7CBkh5gbncnLoZEnSQhwuDfVbO1leWE7ciLMwtJCpganljkqIilJ1mSbv7j8X8/Tz88w/vYCnNf9yrUU6vedaWY0RwfKhP11aUWuKPYXF4cUEVfCoxSzEuOVDZngArTWzA7MlSQsxgqo5o85mSyPyB7OafO5Fdypom+LyxAMBAL7xsyxnXeRiGYq6xqkk8hpnKEg2Y0CumU2ZAvGARUjmUL+sXQMbc7k9gy7FwTvc45fNvvxjykpDLKtJpTTFQi3bAg7JoHXsTc1i/H3WZX+OrNG0zapJ1A88ANEoQIAIpa5trTWur3F9nz/fZOKj0RoM0+ehBxQFz6epM8UlRc3g+lq2dJ7IYKEeyBK0DI5vrinnLh0TdtUYeOCByqgxcKw53OOX2b++T0UZDEBjOsyO+xZyeyEOpJleH6EudOz1WI23z7rsz5E1mrZZNYk6EID4foOz1c5/Bv/1ec11/1G6ecN1URaelWfeG+4jFV+FAs4YmI2qn0IkbqIU1EeCxKXWycvyfejvh1isMqr2HWsO9/hVwpnDSxmIwwn9SbpCk4nFLUKWQVvcPiYL/o23z7rsz5E1mrZZNYk6GIRQ6MD3n3oqfOvmNE894/PDLyZ47N4gD993Jhdf8d8oDGojDfSZHintgYZpsTChCpgzX+l2ldcNhSqjcRxrDvf4BSv8xDQbgEigCW35pHQRRxvESl1fx5zx9lmX/TmyRtM2KyDcymAoxXGNCd50dpLbb9c0tfkY2qLvqdLczm11q8hZ3bsfHz4Wv/ILUWFMDcPhLBl7e+l2JfRJClFhJFGPwNeaN7w3jdImdf4AGo1nWfSHn2M4sJ5YPM2uOddCiENXlwdPuwyGVpO2N9OWlHYlxItVTdf33rTWeL7G8X0cX5f+eT7uzm292SJ/+nmp+y3T1Im5xqR9xwScmjpSwc2sVVsJFXPMDs4u854IcWxLBWBSro0NTgtDofXcX9zIa4JXHJOjvoU4UqomUfta80z3MFnXw/H93SMA92YZCttUGNpg0wsWlqE4pasXtEHL8a8gZbxAURsYwPPFVZKohThM0wbh+RknUBe2cLSBAra5W2m3O8odmhAVo2oS9aM7Bkgk4zSEA9SELGzDwDIMbENhGwrLNDD2+hb/59vgiitKXXNrk3HiUZvO7FYMIG4kOD64oGz7IsR4kpo2g269HQVMtacxwWotd0hCVJSqSdRNkQAT66I0RoKj6lab35ujq1axdnAFlOqhMNmeTIPVeIQiFaK63NbZRUOukUDYY6o9BVPJQE0h9lY1ibo7UyTfn0Fr8NAkgzYR+8C7HwrBbbfB9nc1ER1aw/VvOhnD0lz/ne1MnSRlDoUYK8/fleKpX5yPrQL8PGry05+W5roKIUqqbtT3moEM6weyPN459LKPDQTA7lA0+6X64L6r2Gy+QJfXeaTDFKJqnHbq8wA4ukgmo7Gq5vRBiINTdYkaSnM1J8RCDBccMkWXvOtRdH1c30e/aJRZ1Ihi+A7aKtV7e+wPU9nsbGGjs4GiLpYjfCHGFRXopnX2AACOdvjxz4r7tUMhqlnVfHdNhmwSIRvP16SKLjvSeXak8/s/UIGtDMydX2GWFGrZFtO88z/u5KYPvZ6n/zqdV7z1z2x3t7GSlRjK5KLIRQSMwNHdISHGAw+eVC/wmc8X+McTa/jlF2bw299D9Oz7eOP0VxJQ0q6EqJpEPas+Tn19aTF6z9cUfQ/P1/gafA2e1vi+xtM+jgd9uQJpxyOTS5A2DX75x20UKTL5hG4cz8dUCstQhAiVyoULIUbPhT5P89VvD7L23tJ0x/r2NI2NPoY0LCGAKkrUezMNRdh46V2vDVus7BpmyzOKXHMYL7GeBRdvZOHrVhPwEtTm5hA2wgQMk+ezOUyVx1QKQ4FhKEyliAcsGiIVXmxZiHLyIe0r0sXnmH56DbXRCK9+jUlN/lRWFXOYRh5TldqspRQNkQCxgBTZF9WlKhP1wQgZNs//rZ724hC+WeR9l56KV/s8ngZXp8iGN2Dl5uJ43gFfI2KbkqiFeCkmxKIhXv1+l6C5Eq1BZReScQxgpLalJFGLqiOJeqec67JuIIvr+ZiGoq/b4Ne/jXKePYgO+9ju8YQzYfpDzwGQNnsIBLcS8xoJEcEwFIYqFU+xTQPbMKgPyx8UIV6SCU3xOSTdOaSNLQCkghupdycT1gkso3SJybYMgoZBS0y++IrqI4l6p6G8w2C+tECo78OadTa+hunFzdxeo1AECDtN1GnwggOkzS4GA+sYZB2n6IuwjFK3XEhW1RLioGkTeuNRagpTMbSFHxwkZ/WwzephIRcRMg2aYy+xPq0QVUAS9U7N0RDJUICi5/OtGw2W3W1gGdBgpsjZiumJemwjSMFvYpW5CjT4O1fQ2pJKozAxDGiNhcu8J0IcO4omtIQ7aI6GUITZrjbi613tKgMYNEaD+5T3FaLaVOU86pEopQhZJomgzeOPmBhK8bZ/hnDMwDUV05M1zKiLMac+ydTgJGz2dMF1xh9kMLgGzx7A1375dkKIY0zBgEQ4wIy6GMfVtBI34rvv64w9RD60lYIeYRqlEFVEzqhfZN06yGQANFe9XpP9raLOsSm4kPccfA1TmU8uNRnwcY0sWaubbGAHK4o76NLtnBg8SeoVC3EQ4i7EVD3DBQetI0wpnkZPtoBjZEgHNzMYWM/d2a2cHz1f5lSLqiWJGujNFujPF8nmNB95T6nI8ClnFnhoW5bTwprmQpCnelLo/Q6XgeXHSBRjzA/PZQV3stXZylZnK6eGF9EqqwAJ8ZKUBmsoyVPdw3tvxfZj1Obmkgz3stZ/jr+m/8Jp4dNosSaULVYhyqXqE7XWmuf70gDc9fsQWoOp4H3vg3AgRrhxKn7mcVL6Pl7R8hoMAxSlEd6GAkMpFJDyUwRyQYq6AMBmZ7MkaiFejg+P936dC06+nMZwM4pSDQLDUKA1zxS3wc6rSVvdbZKoRVWq+kStlMLvC/ODbwbYsbV0yf5Vb8xjGKUE3B6YwMqCotfZRDSw7+HK+zm2Ozt4vvj87gQ9OzCHRrORpJk8ynsixDHIgYw/iGk6hHauxlHURdYXN/KC8wKedkkYNcwPzqfOrCtzsEKUR9Un6nxB81+fCKOAK99UpGVWluZ2n407F9ea2zKJ6ZsVD3m53c/RWvNo/lG2u9sAiBlxjgseR51RS8yMj/AuQogRaZjU75H3sgDscHfwcO4hAJJGkjmhuTSYDTLmQ1S1qkvUvtYUPR/H83F8zdNdKRL1CQZ6TP72Z5MPnL7vqj19uU3klcu85KkApL00d2WX7r7/zPBZ1Fq1R3UfhBg3AhAN1tMSmsiT+SfZ4KwHYH5wAdMC08ocnBCVoWoS9WPb+4lmnf22myY4xdIczfSQwdyGOK31eyqKFUMhGjIa19TclVlK2i9dz06atZwaOpWIETk6OyDEONWej/JAYTkZXWpbi8Nn0Gg2ljkqISpHWedR33fffVx66aW0trailOLWW2/dfZ/jOHziE59g/vz5RKNRWltbectb3sL27dsP6b0aowcuPfjKS/bM03T2yuVbna3sqLepzWqWDy0DFKeETuHi6Ks4K3KWJGkhDpcP+cENACwKn8ZlsctpsppQUuBEiN3KmqgzmQzHH3883/72t/e7L5vN8vjjj/PpT3+axx9/nFtuuYXVq1dz2WWXHdJ7TaqJsqSjnhNbava7b+FZBc69Mocd0LzjWotvfM/h+fQmHsk9SqCvh86E4uTIIs6JnEO73UHQkHrDQoyVXG0j50TPYYI1AUNJDSYhXqysXd8XX3wxF1988Yj31dTUsHTp0n22fetb3+LUU09l8+bNTJw48ZDeM2pbLOmoB2C44PBU9zCmBWe+Ok8orPnLLyP88RaT2llhgif50LOVnpiBk5vJg1sH9nmtWXWxlzxTF0K8DBMK0aks3zq4e1MiaLGgaf8v1EJUq2Pq6+vQ0BBKKZLJ5Ji8Xm+uSMw2iQcsoraJbardS9XfdEMDRucMatJ5+iIK10ize0LnTkHrmDp8QlSkwYTB3m0rEaiaoTNCHJRjpkXk83k++clP8qY3vYlEInHAxxUKBQqFwu7bw8Olike+7+B5RZ7vzzCYL4743Asv0uT7LJbebqOAh2+aw1X105m8agP3qrtpq5nF8YEFTAzsOZv3/f0HqIk9dh0fOU6H5nCPXyUd95HapuFBpHsVvYl/cGroVFrsZqCy4j5Y4+2zLvtzZI0mjmMiUTuOwxve8AZ83+e73/3uSz72hhtu4Prrr99v+8DAnRQKEWqAmgONUylAc+1E8OeggSdXQP6qmcz4xx2Etr/AycYc4Bm288zh7lLV6exc+vIPEgd0qMcvm82OcSSHbqS2uc6GaVt6mNw3gB9YwaENFa0s4+2zLvtzZIymbVZ8onYch6uvvpoNGzZw9913v+TZNMB1113Hhz/84d23h4eH6ejoYH1+IfHwAYqRKDDY2e3dYuEre3dp0OV31dFqK3bUJVmViHCSuRil2PVoFKAUGDuTv9r5vNKg1dLvplEqi1iNI1l936GzcyktLedjGPbLP0Hs43CPX1/f8Ms/6CgZqW0aUzvoiSjCdY102OfubEeldmModrc1pfa0KVMpLKPy2tN4+6zL/hxZo2mbFZ2odyXpNWvWcM8991BfX/+yzwkGgwSD+w/wmlGfpLYugaZUWUzvXE9aa9DondsheHyB//5Zihs+FqO3U9HVZ+LZikJWEx46iVUc2pJ7plJ4ek8xlaBpsKApQdCqjopLhmFXROM4Vh3q8aukYz5S2xxW4KOgbzKrAgfftgyldq9bDXBcY4JkqDL2dbx91mV/jlwcB6usiTqdTrN27drdtzds2MDKlSupq6ujtbWVq666iscff5w///nPeJ5HZ2cnAHV1dQQCo1vyLhkKUBd++ee0xcMAvPl18M3vODzjLSTpGNRte4ZTjkugMNj150HrUoLf9QejlPT3+onG1+B4PlnXY3tqzx+igudT9PyqSdRCjGStCydtdxkKPc/C5nm7243WO9uVLg0z27tNuZ5msODQldlzvXuo4FRMohZirJU1UT/22GOcffbZu2/v6ha79tpr+exnP8ttt90GwAknnLDP8+655x7OOuusIxbXxo3w3e+CpSxe8b4pqO8o6rZu50nncSZ5CxguuhgoUKWubq01nenCPq9hKIXPzr8uI1BKEQ/KHxZR3db6MKPX4ye5x5ibfjVF39/d3T1UcMg63u7HqtKCWiOKyUhxMY6V9dN91llnoQ/U8uAl7zuSlt3n42kFaPxkIwqFW0jRVRjASR3cAIDJyTAGu66vqZ3X23b+RBG0Kuv6mhDl0AdM8022pNezJZh7ycdOromUlsDctbzszt9NpYhLohbjmHy6R/CnP4Pnl74kJCcNAxozlyVHhu3xZbTkTiHgx/a5zr2345sScrYsxEGwgWDRY7jYS2f8ftoyp6O0hfeidjW7PkZDRIoLieokiXoEX/iC5l//VYGG558Okw4atLst9FIaidoXW8FpodOYYMsi9kIcjt4AJAqatsRsMHyGa57gwtiF5Q5LiIoipbX2smED3HMP/Nv7TUxlcNKJBpOmp+gOOUxR04m4TXg7l8ncMnRoo7+FEHv0dEDEgXkNZ+P5PoNumnShMgpSCFEp5Ix6p95eeM97Sr+bJtx4IzR0FHlo2wAaCKoAWat79+Ob7IayxCnEeOIHwNIKyyr9KUrmZ2LH5PxBiL1Ji9jp8cf3/H7zzTBjBvRmiwypTjxD0eM+D4DCoDFzEiHTpiud32cupxBidHwF+D6D+S4CXoyE18JAvojr+y/7XCGqRdWfUff3w223wa9/vWdbNFr6Ob02ynNWEQyTQj5Fc/pULB0GFGsHMkBpPvTEGlmXWohD4W0HQ2v6OgeZV38KHrB2IMtA3mVOwwEqCQpRZao+UX/0o7B9OyxaBJ2dmlMWe/TkHBy/dC3aMzRBTDw3i01kv2nRdWEZ3S3EoTIcMA1IOT37bK+T4iVC7Fa1idpx4LnnSkm6oQGuvx4603nWDmRZN7jncSGdo6PfpWdRAif6ELYXozkcI2wEiRoh0oZDSNdjKfnDIsRonZSDzW0GRLKkY6toDMSImmG05eDqBixVtX+ihNitKluB68Kll+68oTQXvDHNQ9scXH//681zn7YwUdzaMcyrcyGCEUWeYYa8Ijl3T/GTsyJnkzSTR2cHhBgnTvJh+bQkL/Q8xLkdb2KAXrY4Kdg58Pvi6KsIGjJ/WlS3qhxMtm57geLOWtsTpjpkC5qiu3+SPqE5wSld69DxWnbUmsyJ1HBR8pWcFT2bMyOvJKz2XJt+ofjC0dwFIcaFqUDjgqvJOj2cGlrAOZFzaLXadt+/wx0PC18KcXiqMlHHan0+fuMgV70rzabVFr/9XoyVD+6/YMfKrmHyTz9K14R6HK3J7jW9M+1nyOnSGXXSqOWU0ClHK3whxpWHIj7GzktHHh7b3W0AtFgtTLInlzEyISpD1XR9bxrKsGpnplUKYglYsMihe2uO+/4S5o83RVjztE19k8ep5xRI1JbOsE3HYUMoQ9CIU3QnsCK/ggGvn4ze0+09OzgbU8kqWEIciof0fbSFF/Cs9zwDxYHd26fZ0ytuzWkhyqFqEnVnukCitnTWrDVMTUYImAbz3q14x5s9Hlim+PH/lq6FLb8jwi1/9LFMaNBF7jczTE2cSHfsEXBgsj2ZyUacqBGlyWySJC3EIfKBDdFeTqhdSJfbyezAbGJGnCariYAa3VK2QoxXVZOoX6wlFsLY+W29NgSvfy2csQje8Y7S/W/9J5O2NvhFT46B9hTN0cm7n9vvDXBC6MQyRC3EOGOB5WvaErMAsFWAdru9zEEJUVmq8ho1wPqBDJuGsmxN5ehM5+nJFojUF/n1Hxxu+G8Hw9SsWqW5d3AuCze7JI0azggvwVYBsvrglroUQrw0Q8Gpmz1OiZwKQNpPlzkiISpP1STqRW11LOmoZ2pthLpwgLTj0ZUpsHUox9qBDC/0pXmuN8UzvcNkaoY5++oURU/z18LFTO3xWXX/4zyQux9HF5kdmF3u3RFiXOhScOpzRf73jhUATLInljkiISpP1XV9t8bCtMb23aa1xtMad2c1spzj4Z2QoWO6yz/WnAUFmxO79ixqPz0w/egGLcQ4tQw4odvhD7EsSSNJ0qwtd0hCVJyqS9S7bB3OsSOdx9NQE7RQQMZ1yTk+ngd3/i7MlrUWqDgqYhEyn8XxCpwVO7fcoQsxbmx14NwcpBr+ygmhD5U7HCEqUtUk6oe39ZPIjrzObV+uSMA0cNIWf7wpzNrnTbJZCJjw2is97D+FMZwMSWI0mLK8pRBjJaDBDsTpzW4hoRLlDkeIilQ1ifrlFD2fZ5/VrHjUADTv+0ya9qkekaFO7N/m2FRnElI5Mn6GmBl72dcTQry8icCmWpOgFaNAgTDhcockRMWpmkS9qK2O+vp9v7FrrfE1OL7PI49qfv99E8sADbTX24Rtjd3fhdaa7qjJZM/nrtQ/aM2ehmUoEkGLqcno7mleQojRaQb6gpqcm+bvg4/QUlhAxLaYURvFNqtmrKsQL6mqW4JSCtNQhCyToGFhKIWhFF/6omLR3DALmmpY3P0CWdNjU3sSpRQFsmwJLSft5ulMF8g5Xrl3Q4hj1kkKHmrJ0p6YTcbopVdtpD9XpCtTKHdoQlSMqjmj3pHKMWyaFFwfV2v8nWfTaI0PrBkwcfwoAI+sy5KvL6IsWLjqCYyYx5KWD9LuzafTWoVr5BgOrmeGMZ+ILVXJhDhUtgVbawK8ruNzZOllKLSeOlppjsqKWULsUjVn1FtTOfpyRRxfYylFyDKI2SaxoEUyZHPyAoMlZ3lMnKS55UdRvvrhJBNCYZrTfXTFFCFrkFAsjW0qgqbBjEQ9s+rjUotYiMOhoTuh6NIrCexsW8c3JaXbW4i9VM0ZdcgyCVkm1s7ubtNQ2IZBwFSELZN4jcX1n1IMD8PVV0M+q+jfFCaBIh+yGXK72L7Xknsyl1qIMeBDOmTQW9hBfbyDGfZMWX9aiBepmkQds0tJ2tWaguPjaY3jaRzfB8BQikTA4pafBvG0jUIxlHdxzAABxyNnOmhdeux0e4YsxCHEWFBgFx2SoSYAJkplMiH2UzWJekptjPr6+H7bXd+nK1Og6PkUXJ+LX5/H0T4PLA3y+c+aXHyCh42J4+6pTCar+ggxRjSYWpEq9NEcm4xCLiUJ8WJVfyHI8zWmKnV/R2yT2pjJUI/FrkvPdi6DE69hONez+zlFXSxTtEKMMz5komE2Dz0HIGM+hBhBVSdqX2se3THI2oEMawcybB7O0ZkpMGVBHq1BKejTLUzPhFk3/CypwhA+0Ol2knc9tNbl3gUhjnkXcxbPdt+P4ztsK3bi+dKuhNhbVSfqkfJswDQ47iSXCRNdtIZ1TzhYuVo0Fit23Inj+RhD03hsxyDbUvmjH7QQ40yzfxKudnls29/o7kmwfFt/uUMSoqJUzTXqkZiGYklH/X7bv/AT6NumCZjQ0BIgFLaZFXslvemNpefp0qjUbak8GcfF8Usrb02qCVMbkuvXQoxGXWAKLdFpbE+thnjp3OH5vhSurwkYiqm1USyjqs8pRJWTT/+LuL5P20QPX4OvYfKpMSJ+hoZwDcorjfTujC0nb/Xg+D492SKDeYd00WXjYLbM0Qtx7JkQgLCqoejl6Y4+Avj07mxX3dkiWan+J6pc1Z1R9+WKrOlLUxpcumeMqVKlgWWe1pgtFq5fGiHeG4szIZ1mU3YtVtDe/Yxau4a2eJSgZRAwDYKmId/6hTgEff0bKCRThK04EV1DeyJCTdAmZBkETVNq6YuqV3WJuidTwNW6tPIGIw9a6dy8Z470nfdO4KR0mr7iehJmE4ujp9JmtcnoVCHGgAuYzlo602uZX3saF9UvLndIQlScqjsFnN0QZ0lHPcmQfcDHnH5BgfOvKnVjr+rtYLjPJDyYpSkykXa7XZK0EGOkF9h2Zwhfw/FJSdJCjKRqEnW66DBccBjMO/Tnijie/5KPv+b1Jl/9ksGQWQdAaLDArPCcoxGqEFWjBzC2ZVGuTUNwQrnDEaIiVU3X97M9KRLuCHcoiFomdeEAQdMgaJWuN//uVyY33wyzVRjMIlHPY3Js9lGPW4jxLAOE659AAbNqTihzNEJUpqpJ1LtYhkEsYDK5JkzEtg44UOW1r4WczrL1p91oK0+3N4Ezmi45ytEKMb7VAc+2rsHfeD6zLzmp3OEIUZGqput7F9f3Gcw7vNCfpjtToC9XJFN08V9U/SQahcdWFqn1e8AsQPpyQkTLFLUQ41O9Db1Rn47O9zA4WO5ohKhMVXdGvUve1awdyOyzLREsnWGbSmEoeMUlOQaf8MC36NfPs2Z7itkd+y/sIYQ4NLYHhm+wufgkDz10DhddVO6IhKg8VZOoF7XVUV+f2Geb1pqi59ObK5JzPXxf42tKS2D6mqamAA+Yx6Ecm/mF1TwdvZfeLYuZURulORYq054IMX4848PxW8MUFzyP1TFIxokRtavmz5IQB6Xqur73ppQiaJm0xcNMr40xsz7O7IY4QdNguODy82/EiflF0Aa5ib1kvD62xe/lWe8JWZBDiDEQATKREHWnLqOv7e9sc7aVOyQhKk5VJ+qRuL5PZ6YAwI7NFkHyBAOagmUyxZyBZRgMqE7SRVnqUojDFQTiDXUYpotlGPR7/fIlWIgXkUT9Ip6vsQzF0t+XurYt7WKZUDQ1q4tP4/o+4Vw7T3anyxypEMc+C7CDMTLOIAW3QHY4SW9OvgQLsTdJ1C8StExOa6tjwexSGdGUSuA6NrU5n4wziGUY5MJbWdAcKXOkQhz7hoFYroCPj6eL1NUUaAjLCnRC7E0S9QFcfn6Ad7zLY705nULeYnKfxk414vsarcE3ZS1qIQ7XeiD0JBjaJJXvZ6NeLSV6hXgRGV65l8F8kWd6UgCEbQPXDpImDqZDtKjpCJ4Mboqc1cOy3H3UqARxM8HcwFzCRrjM0Qtx7EkBYasXA5uoasTVHndllpI0azkuMI+QtCshqidRpwsOZt7B9X1cX+Nqjev55F2fglf6V9yr/nfO8Vn7nEUDfWA4FEJJjqufgUKRDmxhrfccg3qQQX8Qjc8poYVl3Dshjk0NQF94kHbzNF4RvYQn1T2k/TRpP02AAAtCC8odohBlVzWJ+tneFIm91p+3lMIyFUHTJGwZJEM2QdMgZJkEzdK0rdrLFD/4ew+g2BI1mRA3WeOsYZ2zFoCwijDJnkS71V6enRLiGNcEbI+E0bko68xHUTsHfM8JzGWiPbGssQlRKaomUQPMa4wTty1MQ73sdbBf/xpuukkzG4dgwMcNpfjj8O8J2TEm2ZOpM+uYaE2U62lCHAYTqG0MMJB4HFc7zLBnMi0wVbq8hdhL1STqxkiAvmyRrW4ex/eZEAsyITbyH4M774Qf3+RR1EW0zuF7JgXlUauSLAwtptaqPcrRCzE+GYC2FPg+Z4TPIGHVlDskISpO1Yz6zjoenZkCQwWHrOOxbiDLqt4UwwVnn8dpDd/8Zun3M960mtdfcy/BgCYTDZJwApKkhRhDbUC0dhq+V6Q7u6Xc4QhRkaomUWccb79tfbkiq/v3LVzi++C6YOw8NFPNDRBNUEjW8fcdvzsqsQpRLVqAxLRz8bTH/236TrnDEaIiVU2iPpB5DfuuhtXbu+s3xQdePYf2njy5hgYKOMSkW06IMfdkTRpTWSTsunKHIkRFqppr1AfiaY3Wmi1bFD/6ETz8cGn7G94AzfUB6s0GlO8zITGNtamnyxusEOOQ5SuOqztd2pcQB1D1iXpl1zAA9/5fnAeXW6AgGIKpxxdYnR5gR6vmnDs7CRUnUBNqLHO0QowvRWBaV5Z8Q5oau77c4QhRkaqm63tRWx1LOuo5va2OkyfUMDGx74jvi16f458/lOXcy/PkcvCfn7B5sG8FA5EAhutS6O+hxpZELcRYGgKs7jQbU6uZGJ1R7nCEqEhVd0ZtGoqwYTGxxmJizb4La5yys27J/X+FogPP/e4UXjF5OQBdkQx+/nlu6/0Hk0PNtIcaqLPkmpoQhyMI9MfzDHn9PDH0NBvyW2gPtmAru9yhCVExquaMehetNa6vcTyfobzDtlSOzUNZ1g1mWN2X4rneFJdfm8H14bE7mhl4tB3XUOQKWWqMdjyVY63/LPflltHp7ij37ghxTAsBGUehtEEsnGCl8xhP5J8od1hCVJSqOaN+eFs/NTl3xEXpA6aBZaid/wzOPMdn+yaH++60qdm6hR21Nsr0+fRxXyIRqCXv57gjcwcP5R5igtXKyaGTsVTVHEohxkwAeDrxPFNjs/j8nO/ylPM0W5zNPJRbzsLQqZjKLHeIQpRd1WSX2pDNpGQYA4WpFEqVusGjlkXA2r9j4e1vgOV3QXpjit6JYWJ2mJAdZbOzmcfzK3Y/boe7HU+fIIlaiEM0FLZojUzi/vwDDPmDAHS6nXjak0QtBFWUqAfyDv5gFlOVErXra7ydZ9dt8RCtsRCWYWAapdrd4Z1jzer9XjY5cfqGCvw1/ZfdrzcjMJMOq4OIEZEkLcRheKB/gKHnWpnfliIcsVgSWkLcjGHJdWohgCpK1FOTEWqSETwfXF+zNZXbfd+2VJ5tqTwAhip1gQO89m1B9I2aouuQ7o1TyFjMTU7muOBxshiHEGOk4Kfo3RrCfP4ULn1la7nDEaLiVE2iboyGqN9rEY5JNWEcT+NpH8cvDTDbvVa1r9mSyjHl+DxdqplZKQ+7YQt+IM1WZzsteirJQBjbrLqxeEKMuYmbp5A+cyVNJ24mXWwkFpAzaSH2VtZMc99993HppZfS2tqKUopbb711n/u11nz2s5+ltbWVcDjMWWedxbPPPjsm762UImAZhG2LRNCmLhygMRIs/YsGWNCY4ISOKJHFk2jPZSjmTLqHtzHspbkvdx8rOgfGJA4hql17yqcv1cXTPc/zYP+a/RbKEaLalTVRZzIZjj/+eL797W+PeP9XvvIVvva1r/Htb3+bRx99lJaWFs4//3xSqdSYvH93Js/GwSxrB9I835vike2DPLx9gBU7hniye5ib/1jknkfaiLtFYpZPc/EkFApt5pndKOvlCnG4BoHGvkZsyyYeiRCIDhEPVE1HnxAH5aBaxJVXXjnqF/7+979PU1PTSz7m4osv5uKLLx7xPq01N954I//+7/+++/1/+tOf0tzczM0338y73vWuUccE4Ho+D23f/2w4GbKpCVrUhCyChsWD9yn+8lOTJWYTwYBmEprmRIohs3Rt+t7C3+jwJ3Jy6ORDikMIAX3AVSdE+FlNgaLqZhCbv2Xu4JzouQRUoNzhCVERDipR33rrrVx99dWEwwd3FnnzzTeTTqdfNlG/lA0bNtDZ2ckFF1ywe1swGOTMM8/kwQcfPGCiLhQKFAqF3beHh0u1vJ/t7idWLJBzfUYaBja3PgFAdze86Z9LhyURh9delSD8Q5+mrMVQoRsj0rD7OXkvg+9LN91L2XV85DgdmsM9fpV03Edqm73AnHwRA4OB7A4aw20UdR7Pc/CNY2vA5nj7rMv+HFmjieOg+5i++c1vHnTi/d3vDn/d5s7OTgCam5v32d7c3MymTZsO+LwbbriB66+/fr/tLeZyIl6EEbM0sH176efvfz8Dz5sKgOsWaZn+OACBfI6BwSc53zx/r2cNsH3grwe5R9Wts3NpuUM4ph3q8ctms2McyaEbqW3+HCC1Gd8rMDULJ9mlq3F9w3cd/QDHyHj7rMv+HBmjaZsHlajvuece6uoOvq717bffTltb20E//qW8eBqU1volp0Zdd911fPjDH959e3h4mI6ODmprL6B+51nzSNJpePvbLVIpME143/s8Tj/dpDFzBhqNb5i4iTYeT/gAnB0+i6gZO8y9G/9836GzcyktLedjGDKad7QO9/j19Q0fgagOzUht896ODgpmCN8w2Z6I8mSN4uLoyJfDKt14+6zL/hxZo2mbB5WozzzzzFEFsGTJklE9fiQtLS1A6cx6woQJu7d3d3fvd5a9t2AwSDAY3G/7UEETKEI0YGIZe8bQaQ3f/S786U97Hvud78C0aaVDYw55+Nonb2jSZpFFgWnMCMwgbMhgstEwDLsiGsex6lCPXyUd85HaZhDY5nfi4XFSYjGLYq/AOMarkY23z7rsz5GL42Ad8vDK7u5uuru78X1/n+0LFiw41Jfcx5QpU2hpaWHp0qWceOKJABSLRZYtW8aXv/zlUb/e6v40nbp0Jh6zTSzTwFIK0zf56x0htC6VFZ06VTFp0p7neZuexNUuGxtCLIxNpcPqkCQtxBiZDtyVyJAMNTE3cryUDBViBKNO1CtWrODaa69l1apVuxe4UErt7pL2PO+gXyudTrN27drdtzds2MDKlSupq6tj4sSJfPCDH+SLX/wiM2bMYMaMGXzxi18kEonwpje9abRhc0JzDWYsTM7xcbWP62kcX5NyC3zk6zk+/55aAF5Yo/nq/ytgmwpDwcyNKd7j+gSDjbwieCnKjZLHI2gaUp1MiMNkAD2FIY6Lno/hx3B9f58eLyHEISTqt771rcycOZMf/ehHNDc3H1ayeuyxxzj77LN33951/eraa6/lpptu4uMf/zi5XI73vve9DAwMsGjRIu68807i8fio3ytomdRHQyPe19Wjsc1SN7hG8/c/lrrnNJByJuG6Jm5fjg19LkqVritEbZMTW5KjjkMIsUcGCKZMNuzI8FRTqW3Na4xTG5KpWULsMupEvWHDBm655RamT59+2G9+1llnjbjs5C5KKT772c/y2c9+9rDf66U0Nyo++Qn48pc1oHjL23xa2zRZ1yOx2ST4E029TuHjYlK6ruD4B45bCHFwBoB2w8auGdy9LSCleYXYx6gT9bnnnsuTTz45Jom63NJFh5VdpW/xX/tWDUWv9Aeibt4QVo0mASRiYQzDp8WJYEYH0Nk6QmaABU0HHkEuhDg4g0CNUyAZqUXjMykRJWpLZTIh9jbqFvHDH/6Qa6+9lmeeeYbjjjsO29535Npll102ZsEdaeZe3fbXfjTNI3cHeWxZkKcfCfCeNwexDIOa4RCg6DJS9NirUMnSc6ZzFkFqyxS5EONDAugKwPr0o/TNvB/TamYii8sdlhAVZdSJ+sEHH+T+++/n9ttv3+++0Q4mO5q2DefI2jae7+NqjeuV1qOOByzyrk9Lh8fpF+R5bFmQO38TYVJMcfXVCrOQK82jjsQ4P3o+d2VLhRg2uZuImTFsWTNXiEMWBXJBk6ZAaXnLbreLtJcmJjUKhNht1BeDPvCBD/BP//RP7NixA9/39/lXqUkaYGsqx6ahLFtTeTrTBXpzRQbyDqmii+P7KAUzp1qceV5pH377e82W4RyDw8MYGKSVw2CuwHzrFDSw0dnAX9J/Zoe7o7w7JsQxLATYwXryTo52YwoAd2WXsra4pryBCVFBRn1G3dfXx4c+9KGXLDpSiabXRonVRsg5HgVPo7VGa/DR+LpU8ez+exTL7irN47z6/cNsGvIwBoaZC2RMzROpNfh2Ac8MYFlFADJ+pox7JcSxLQgUjTApN83WXB+urbEMRd7Plzs0ISrGqBP1lVdeyT333MO0adOORDxHTH0kSH1s5EIlW1M5Ng5m+cedEQBOXFKgdZKHUmBpH1Ao5TIU2EDUrKHNaKU91ECtUStddEIcBgPwjSyGHcAyYXpgHq12I7WmjP8QYpdRJ+qZM2dy3XXXcf/99zN//vz9BpN94AMfGLPgjiStNd7OM+nhggvAWz6S5qv/Vsszy0OYmRBnngnHDW4lk4gwGHKxDYjbQU6Pnljm6IUYH5qBrc1xDIq0hes4LjSr3CEJUXEOadR3LBZj2bJlLFu2bJ/7lFIVm6if6BwglvfQWuNT6vbeW8A0qGuw+NlP4a6lil/+Ep56Ck6ZZ3CyFUQrl+7MJoJWmF63h6gRI6RCUp1MiMMUDNbRl1vBuvQqptpTiRkxKSUqxF4OqeDJsagxEqQ+HkIpMBQYKAxVqu8dMA0SwT09A7sKn5kmTJ8fxFjnYGGRtBsIqCD35+4HwFIWZ4XPlu5vIQ5Ds06WErNtcU/2bgBOC59Oi9VS5siEqAyjHvX91FNPHfC+W2+99XBiOaLaExHaE2Ha4mEmxMI0x0I0RoM0RIL7JOm+PrjtttLv110HgWgAw3WxlMWSyBIuiF5Aq1VawtPVLlldOev9CnEsingGAQKcH71g97Y+r6+MEQlRWUadqC+88ELWr1+/3/bf//73XHPNNWMS1JHw2PZ+tg7nSBcdso5L3vUouB5Fz8f1fDxfk8tp3vY22L4d3vc+WLIEvFgCu1DEdjVBM8yy7DK2u9t2v27CGH3dcSFEyTAQSqeI2Anu3nk2DdBqtpYvKCEqzKi7vt/znvdw7rnn8uCDD+5eJ/o3v/kNb3vb27jpppvGOr4x42nYOJSFoZd4jAsT50V59tEAX/8mfONbcP3CCG/HwHIc7uj7C8maVkIqzInWIsIqgusaZCgNRtNaE7JNWf1HiIOUBYJOgL5iN8OFfk6KvIJWcyK2b5FxXLTWWIZByJJr1qJ6jTpRf+Yzn6Gvr4/zzjuPf/zjH9xxxx284x3v4Oc//zmvfe1rj0SMY+KE5hoStbGdc6ZLK2PpveZQ7/r575/SrH6hyPXXBfA13P1AlHfYCtNx6SsM0JI/lXhhMuvwgfR+72MZitPa6o76/glxLMoChUIPaFDpeoby9Qyx/+WkhROSBCVZiyp1SNXvv/GNb/BP//RPnHbaaWzbto1f/epXXH755WMd25gKWiaxwMGV+2w4HubNhmdfKOIYGt+HoDZZHF9IwJ1OAf+Az00GbXakc9iGgW0YBCxFyDRldLgQI9BAVIUIGkESXgdY+7eTsG3Qly8SMAyCpkFYeq1ElTmoRH3brtFVe7niiitYtmwZb3zjG1FK7X7MsbQox4tpDV/9Kty981KZoQzO/+Bz8E0f24NkqJ5Z8SRDBYdVvandz4vZFmmn1P3dmyvSmyuO+PrJkM28hrgkbSF2GgDsVD8azSvbp7F92GSw4AClBJ1zfHKOz/qB/c+yG8IBZjfIGBEx/h1Uor7iiisOeN+Pf/xjfvzjHwOVvSjHwdqVpKdNg698xcLqXIJG0z7gcl/mXrqtFIvDi+lIhNmSyqFQ5NyD2+ec60mSFmIvW4EF/YMUdYH1/hpm1ZxAqsfFB3LOgXuuAAreS98vxHhxUP1HL15840D/jvUkrRR873uQTMK6dfC618FQ7EQIhJjVp9g6/DxD/iAZP8PWVA70rgpn+mVfG6Dg+hQOMqkLUQ22AJOGSl9e7+r8PcOOg6dLtfhfTt71cX1J1mL8kws9LzJlCvz61/D2t4Pvw1uutUnHmjjZncQLvY8CsKmwjZl1MabXRpmcjNAWD9EQDhCy5HAKMRqdQEOqQGt8Oo9tv52CGmbGznY1IRaiJnjgcSWOJGlRJQ6q6/ub3/wm73znOwmFQgf1ot///ve55ppriMeP3etHr3sd/OhHpd8LfgDHHcIyarF1mMHBGBl//xHfSkHIMrCUQcg2aAgHqAna2KYkcCFGUgSKXo64ijKsLLb1myj2XZEuYBoEDEUsaFEfDpAM2nIJSVSVg8ogH/rQh0ilUi//wJ0+/vGP09PTc8hBlYPj+eRdj6zjki46/OMhF19rps9ysTpiNA7mGcr34BejKD3y9xutS91xacelN1ssjfyWJC3EAXUBaE14OEN/bhuwfwIuej5px6MzXSBiyQwKUX0O6oxaa825556LZR3cbK5cLndYQR1tedfjsR2D+2xbuTGA60d57jmDP2WPZ1L740yNX0LW6iYb6ybkNlCfOw7LMGiMBDAVgCrVEVeKoGWQCB7S7DchqkYXkB0M4q5ex6TTT2N7/D6aM6cQJkFjNLCzJn+pTcUCpsylFlXpoDLJf/7nf47qRS+//HLq6o6doh9B02BKMkJq53KXAK84y2PGrBTf/2IEP2uAY5LvaiI4NUHBHCZv9VI0hsFPsCOdZ3Z9jIZIsIx7IcSxZ1ehwEDWYtPT7WQbAuRD/VjFGNtTeU5qqSFiyxdeUd2OSKI+lhRcj5VdwyMOTIk1wIe+3M+Cr/WR2RzimR2PccLSL9B2wYM4Roae6OM0Zk4i4Cf2WzZTCPHybCAU0ZhunC79EDe980bO/JenOWPJdqJOq7QrITjEymTjievrlxw9atkQ1ykGdQSl4PQZMbbaOYI7L++fPqGFsBE+WuEKMa7UAQFL0W3WoYwsoKmpdVjUOIOkWVvu8ISoCFU90sn1fVZ2v8QqHTu1OFsZmAKh4Xl896Y0hUzp+80Z4SWSpIU4DE2U1n2vnR+nNbiAUMTj7m+dzDPLJUkLsUtVJ2p0qfzny7FzOZxYgKY5PaxfbXPzJxYzKX86/UMhNgxmcKRCkhCHpAVwtKIvPkT99D7+5WvPUMgGuP6/fH7xSx/XfdmXEGLcq+qub8s0OL65Zvdt19e4vr/zX+mM208PEunqYuvps9H2IBoY6ozwkTcFec3bM5x4Rh5Pa6bXxsq3I0Ico2YDwx0zUJbFUKGbQBga2zy6tpr89Gfwi5+XHveNb8CsWWUNVYiyGfUZ9ec+9zmy2f0L5OdyOT73uc+NSVDlYhmK0M5VtpIhm4ZIkNZMH9mMxbKVJ7KxdzXaKO6eR613nki3xQ+uEIwQYl8TgIc3tLD21ovYuKWTr/3LNLq2lqZgGXvNl5ZBZaKajfqM+vrrr+fd7343kUhkn+3ZbJbrr7+ez3zmM2MW3JGyqz634/u4nsbT4O/c5vkaH43nl0oURrt6aCoa9LY9B8UaTrhsNWefG6Ux3ExLLEgyGCN8kPPLhRD7qgE2Fetxp98CXhDQXPi6HGctspk71eYYLm4oxJgZdYbRWo9YGejJJ5+s6LnTD2/rJ5FzUCgUpcR8IIZSmEphm4rMxjwA2ZrtmDtO4/jLBziuvYWOQJKQFF8Q4rCEgLwy0fFNWOteQ6DYyLWXKVpapPqYELscdKKura1FKYVSipkzZ+6TrD3PI51O8+53v/uIBDlmNGg0e6fomG0SDVjYhoFlKCbEQpiGYtUquOUWKNwd5ULAN31OO9/jmpmvJGRIV7cQY8EAtOkCin8+5wze9e/VPb5ViJEcdKK+8cYb0Vrztre9jeuvv56amj2DsAKBAJMnT+b0008/IkGOhXmNcZK1CRzt43o+fXmXgVyRtOORdvYsPWkbis51IT7xidLtfz/PI3pnnlBjlkUtZ0iSFmIMKaDttKeJ1zm89sRzyh2OEBXpoBP1tddeC8CUKVNYvHgxtn3g5ecqUSxgUxPaE3OquP/qVxHb5I9/MPjdLzQ1Sc01b/FZMjGA+rtHMg95C3ztYyj51i/EWEgDEa8fZRjUB1vKHY4QFWnU16jPPPNMfN9n9erVdHd347+oqtcrX/nKMQvuSJpRF2NqMkrR99iRLpBzPHKux69/ZgGaM16doWZ2kSeycWaiqcuCaUS5K30PF8TPLXf4QowLncCU4TRBM8rDmRWcFjul3CEJUXFGnagfeugh3vSmN7Fp0yb0iwZkKaXwPO8Az6w8pqEIGxZTk6XD8HxvipkLHFY/ZXPbz6Lc9rModdrjKtfA9hW+LpJh+IAD6oQQo+MCthsm7w4ypPvKHY4QFWnUifrd7343p5xyCn/5y1+YMGHCuEpYGdflzR8s4rmwdb3Fn34ewdvik88Fcf0iXem1JEOnssXdwkR7YrnDFeKYZwB9W2rwvD7Wp54mHVlMzJQ5WULsbdSJes2aNfzud79j+vTpRyKeo67gevTmivg+1IcCuEFNwdNE57oMXJTn9h9GAUikQBulw/V4fgXtVrtcqxbiMGWBGqdIIWthEOT+3P1cFLu43GEJUVFGnagXLVrE2rVrj7lEnXc9ejIFXO3j7SwP6viavmwRV2sCpoGhwFQKc2eFsvPOgxMmZ+BDLllfsf6Xl9D4hgEmNockSQsxBhRQCGfwMjX89bo3cvYrLBa8Clpbyx2ZEJXjoBL1U089tfv397///XzkIx+hs7OT+fPn7zf6e8GCBWMb4Rh5smuIRNFHKTCVgWWAZRjURWxaoiESwZFHsRt6iGi0SDFms2NlA7/+5Bwuv9LljNfCMTbwXYiKEwEiHUXi9YqWacPc/7dJ3HUrnHEGzJ8P550HIZkRKarcQSXqE044AaXUPoPH3va2t+3+fdd9lTyYrCZoMSUZIWJbmIqdCbtUwMVSCl/rfWoL72JmhgGFU2Nyw5c03/3fPn71kwn84SZ485tL/4QQhyYBOJEYrYkoF3/oSSb6OV746zweeQS+/e3SP4APfxguuKCsoQpRNgeVqDds2HCk4zjihgou6wf3X0xkb7u6vU0Dck5p2llt5wCTgaLWBAMmwUhp3T0Pj74+KSEqxOEwgJzrUfQLAHR73bRPnM0jj+zbtoaHyxCcEBXioBL1pEmTjnQcR9yJLUlq6+K7F+TwNfg7S4p6vsb19yzKMVhwyFFK1HY+A1rRWzR417+AmWtn4evWcNarikyyZ+J4MWxTrlcLcShigBtoZHP3Br5+1YUYBhgezJzu88lPGpxyCsRkBVlR5UY9mOy2224bcbtSilAoxPTp05kyZcphBzbWAqZx0ItoTASG8g5px6XBz1EoWPQEDHTNeqbMh4VXraMm9QoGvdJjas3AkQ1eiHGqFniwN4Hn+vjJNRgDc9Fa8bHPFZjaEC53eEJUhFEn6iuuuGK/69Ww73XqJUuWcOutt1JbWztmgR4pGcelN1vE0xoFpbPrnWfcnq8JDw0TDSmytsJrXMm6h84gmc/REA4yoy6GaYyfeeRCHG1RoHVWI3bExWt+DLt/HrapaA5LkhZil1H32S5dupSFCxeydOlShoaGGBoaYunSpZx66qn8+c9/5r777qOvr4+PfvSjRyLeMfdE5xBbhnNsT+XZlsrTmSnQmy3SnyvSPeRQ7CzguYpCsQ0d7qG+I00g3kusZhCU/7KvL4Q4sAjgZtuw/CjEtqGMUpuKRssblxCVZNRn1P/2b//GD37wAxYvXrx727nnnksoFOKd73wnzz77LDfeeOM+o8Ir2SkTkmQcD60129N5hgulwWKuA194by1nu+2clLFp23ISXZOX86pXpul3B3nMfwyAy2KXy5xqIQ5RD7B5mcKLvQpj5h1Et72HUy7dRMGfT9AIljs8ISrCqDPMunXrSCQS+21PJBKsX78egBkzZtDb23v40R0FIcukPhygIRJkXkOCyckI5mCU//lQqdt+5gKHmpoil5wzE3vCaqZNDxDJTdm9pvUThSfoc6VGsRCHatG5OV7/qpMJtKznyv++jQUXbuH+3P0MezLUWwg4hER98skn87GPfYyenp7d23p6evj4xz/OwoULgVKZ0fb29rGL8igxDUV7PMy6J0MU84o3/muak64IYChNnVs6VJOSLuc2zGOGPQOALc5m/pG7T/6oCHEIBoG4O0zMrsM2DaarNgBS/jB3Z/+OpyuzLoMQR9Oou75/9KMfcfnll9Pe3k5HRwdKKTZv3szUqVP54x//CEA6nebTn/70mAc71jJFl6Gig4FCAT4az4f2ueD4QX717RjT/qk0qCWqwxhK4TCAYbazNrcGgMn2ZGrNOuKGLCQgxGg5gOG5tEcno4CN+XXEgq20Wm1MsidhKqlVIMSoE/WsWbNYtWoVf/vb31i9ejVaa2bPns3555+PYZTOOq+44oqxjvOIeKJraMTtOVsBpetjD/49xvuBdcP3EKoJ0hGbwdLMnQDEjQQnhE48StEKMf54gKl9Hh78Hh4egVjpktOp4VPLG5gQFWTUiRpKU7EuuugiLrroorGO56g6eUINqYKLryHjenSlCmxaZ/C/Xyhdg582z+GSy7JwA3RnX6C9fSZLc3ehtc9kewrHBeeVeQ+EOLZ5AH6RB3vvpT05h4iV4KzIWWWOSojKclCJ+pvf/CbvfOc7CYVCfPOb33zJx37gAx8Yk8COhrBlEbZKh0BrzW9/HOCuO0pdbWdclOe81+YIdJZ6CRyVIhyeyxx7Dm12G1FD5o8Icbh8IGf0oAyD6ZG5XBS9CEsd0vmDEOPWQbWIr3/961xzzTWEQiG+/vWvH/BxSqljKlHvLed6zDgtw3PPR9i2weKBO0IkG3wunFMqaBLwDcIEmRmcWeZIhRg/PMDSPkU3x9TwLEnSQoxg1ItyHKsLdLi+Ju96OF5pHepdP/OuR9rxSBddWjrgXZ9O8cV/TZLPKlonuaVa30Aw3szm7NYy74UQ40sMcGybttg0nh58iDfyb+UOSYiKc8iVOorFIi+88AKu645lPEfMih0DPLZjkCe7h3muN8WagQwbh7L05hwilkF7PExTJMBJLTXc8DmTgGnw0y8lSQyVVgQIhJN05rfsVzpVCHHoLMC3LepiHWzJrCl3OEJUpFEn6mw2y9vf/nYikQjz5s1j8+bNQOna9Je+9KUxD/BIc32f7myRurDNzPo4Edvi+OPhn/+5dP+XvhGj6JgECg4AGT9TvmCFGGdyQMILkC4OUPSL5Q5HiIo06kR93XXX8eSTT3LvvfcSCoV2bz/vvPP4zW9+M6bBjaVFbXWcPKHmgPc/1T2Mv9fZ8hveAN/4BgwadeRyFs46i2wuJ4PIhBhD/UDn40UymyfSk+llxw7psRLixUadqG+99Va+/e1vs2TJEpTas3LU3LlzWbdu3ZgGN9bClsUpE5Kc2FzDgqb9y6A+tmOQp7qHeKZnmLUDaabN0HzkhlpMQ+OvaGZo0OWRLU+VIXIhxqc+IJEdZPPSc0in4JrP3FXukISoOKNO1D09PTQ1Ne23PZPJ7JO4K1XIMokGLBJBmyUd9Zw6IUnYLk3JKno+wwWXwbxDZ7pAX7bA/ON8IlEHPVwa7f3w0OMUtXTRCTEWDKBtuofRuRCtAQ1FaV5C7GPUiXrhwoX85S9/2X17V3L+3//9X04//fSxi+wo8bQm5+xfTzhkGTREgiiniFJgmDkAEk0OeT9/tMMUYlyyAVMrdLgf0Bj5BgKBckclRGUZ9aTFG264gYsuuojnnnsO13X5xje+wbPPPsvy5ctZtmzZkYjxiArbFqe11fLQtoF9th/fVIOhwA9HGVI1tJurAUU8lJS5nkKMka1AZHsfespaAK44c0p5AxKiAo36jHrx4sU88MADZLNZpk2bxp133klzczPLly/n5JNPPhIxHnGWYdAQ3vdr/MPbB3hgaz9r+jOk/ShWsAur0IJp2GT9bJkiFWJ8yQJu3sVregKVa+Kyi2LlDkmIinNIp4bz58/npz/96VjHUlazG+K4vk/R83E8zUDeIVV0eHKlZnK6BmtCitpGk4Lno7zgIR45IcTeckAh5eLXrCVi1tPeLqtlCfFiB31GPTw8fFD/xpLruvzHf/wHU6ZMIRwOM3XqVD73uc/h+/6Yvs8ulmHQn3N4umeYrakcm7b5PP2khak9jLBLbZ0NwJPOYxT8whGJQYhqYgC+MrAD0DHLYX2xsmeOCFEOB31emEwmX3JUt9YapRSeN3YLvX/5y1/m+9//Pj/96U+ZN28ejz32GG9961upqanh3/7tyJQa3J7eM1Ds1p9EWL/KIqCKqKYumsxXATCoB3ko/xBnRs48IjEIUS0CgBk2iEx/gmRoIc8Un6bGrKHRaix3aEJUjINO1Pfcc8/u37XWvOpVr+KHP/whbW1tRyQwgOXLl3P55ZdzySWXADB58mR+9atf8dhjj43p+2hd6uouej4R26Tolc7YL3hdjv/3uThZIoQLRULEMJVCAdPsqWMagxDVKAeoXAHfdVE7O/iSZrKsMQlRaQ46UZ955r5nj6ZpctpppzF16pFLWEuWLOH73/8+q1evZubMmTz55JPcf//93HjjjWP6Pl2ZPGsH9h8gtmhBgB+bBp1qAvXbXqDIZiyj1KvQ4/XSZrUfE3PHhahU2wFTayJbg+xIrAagqIvYyi5vYEJUkIoeEvWJT3yCoaEhZs+ejWmaeJ7HF77wBd74xjce8DmFQoFCYc/1413XzX3fwfedEZ+TCCgU+y8u0hIxec1rHMI35dhhhskNhzB2VjjcUtzIfHsuhjrkdU2qwq5jfqBjL17a4R6/SjruI7XN8M7f0yGHwvPzMBbAgNNL2D72JlOPt8+67M+RNZo4KjpR/+Y3v+EXv/gFN998M/PmzWPlypV88IMfpLW1lWuvvXbE59xwww1cf/31+20fGLiTQiFywPeaNMKJ8aMPx/nd7xZzjU7jRQKEzAFOGt6TmDuH7xj9TlWpzs6l5Q7hmHaoxy+brZyphCO1zdcDoMGbwrxWdravlWxn5dEPcIyMt8+67M+RMZq2eViJ+kh3+37sYx/jk5/8JG94wxuA0rSwTZs2ccMNNxwwUV933XV8+MMf3n17eHiYjo4O1ucXEg/HUUoRs00ChoFtKgKGiWUqIpZBPFjqbvN9+OEPDW691cA0NUFdIDbBJhOHxxM+S0JLSFrJI7rv44XvO3R2LqWl5XwMQ7ozR+twj19f39jOxDgcI7XN/+jowAxahCZtx2qaTsuEi47ZXqrx9lmX/TmyRtM2DzpRX3nllfvczufzvPvd7yYa3Xc1qVtuueWg3/zlZLNZDGPfRmua5ktOzwoGgwSDwf22+1horFI9YWWR9yBkW7TXRPb7wjE8DLfeWvr9Ax/wmPnzXp62bDTgKwhbsYr4jz6WGIYtx+wwHOrxq6RjPlLbDAPhugAhO0JRF7DM/dvusWa8fdZlf45cHAfroBN1Tc2+S0S++c1vPviIDtGll17KF77wBSZOnMi8efN44okn+NrXvsbb3va2Ub9WwNyTjIcLpevRacdluOhSFw6QCFhELBPLNEgm4Ze/hGuugb/9VfOhVIpMZAI5N02r1UbYCB/gXYQQoxEFCqEg8WAdXqFyuumFqCQHnah/8pOfHMk4RvStb32LT3/607z3ve+lu7ub1tZW3vWud/GZz3xm1K91YkstiWSM5/vSZBwXhUKp0opZW4ZyeDvXorYMhWUohnpNil6MZ54HN2iB55ApFvDx6XQ7aTabZcS3EIdpGDCzKfJuhLTbR9pPEzOkjKgQe6vowWTxeJwbb7xxzKZj2abB/L3WodZa42twfJ9UwSXjuGRdn3TeZfl9pVKGWhkUG2qJ9gzTEjqOlJfiIXc5URVjkj2JBrOBOqtuTOITotoMAVa+iM6maKqbwd+zf6fD6mCiNZEGq6Hc4QlRESo6UY+lHakcPVpR9H0c38fxwNU+6P0fu+5Zi7tvDaMUvPe6FPm/NtCRGUa7ec6PnU+P28MLxRdY5axCF33qzQZeEXnF0d8pIY5xXYCBYk6xmR35DNOsaWxwN7DZ2cREexInhU4qd4hClF3VJOqtqRytkRAh0yAesLANA8sAUxkYhsJUClOBaSgKawwCZmkQ20WnRnHvCZPs9wiZpWvTjVYjjVYjnvb4U/o2+rxe7sncQ62ZJGnW0mI2E5Lr2EK8rGFKba7VTzBowXGh45ir53Jb+o9sdjZR0AUazAYmmBOImdIlLqrTsTkP4hDYhiJd9AhaJg3hAK3xEC2xMI3RIPXhAMmQTTxoE7EtzlhsMHly6Xkf+5iJE42SKCoy7r7D6U1lcmnsMk4JnULCTLDR2cjK/BPckbkDV1fGpHohKtnQzp8T/AQZdxitNYYyeHXs1RwXnE/Gz/Bs4Rnuyi6l6BfLGqsQ5VI1Z9Q1QZu81mwZzrFlOIepFLapsAwDy1D4WtMcCRINmNiWwdy5Bhs3KgIB8G2bGh1mc2YNnbkttIQ78LRHt9fNdnc7GT9D1s8AEFYRpgWmYUkJRCFe1q4lcI6PnsR3M8t4YfgJWmJTWeesI+UNk9VZTGUx3Z4uZUVF1aqaRD2lNkZ9fQLX98k6HsMFF9f3cX1NzvUZLri7p21lM4pb/5QENO/5cB73zyHieY1l2Nyz4xYWtV/KU4UnAYgbCWrMGuqNeuqtehkNLsQo7JqQNVN1ELNr+Mba/+DCmf9CSIVpsppoM9rpsDoIGaGyxilEOVVNot7FMgwSQYNEcN9v577WFD2fHek8v7tzzwizXpVC+T45wyAebGXZwJ2EGycCcFbkbFnpR4jDYAFF32NFcROtE+axYWAlk+3JzA8uwFRmucMToiJUXaIeSX+uyHO9qd23m9r3HJbOLSbh3l76G2Pk/BQtqoPjgycwwWqRAWNCHKY2QCuXgaYoytUkzCTHB0+QXikh9lI1g8leSt7z9rk9ba7L7BMclILvfrYOO5slFTYZzG/n5Ppz6LAmSZIWYgzEd/5MR32e67ufCye8XpK0EC8iiRpojYVZ0lHP6W11zG2IM602wpvfojEUKEA5LkGzDUOZvDC0hoe2DbCic7DcYQtxzAsChrbw7Ag1gQ4e7HqUobzMmBBib5Ko92IairpwgAmxMIvnh3jNFaXR4E+vqGXN6jiB4XlsyT+Fa6Rpix37iwcIUW4BIJsyWfqDhTgbzmBV7yMEjtHVs4Q4UuQa9Ut473sUE+akqP9qio3dkxjoXUcwO4uumY/hDR/P2oHa3Y89ZUKSkCWDX4QYjQBgWrBmxWT6EwX8Yjv/+dUil12bxTIMFk5IYhrSFS6qm3x1HUF3Js/9W/q4f0sfTdMKTGnqoW6yBcqn94UWtIa+yNMUjcpZ61eIY1EUCIbhNe/2wMqjco08ep9J5xYTywC5XC2EJOr95F2P1f2Z3bfrQgEifX00nzAR7CxWdIgrEq8mYMJwYiULWm2WdNTL2bQQh6AdcOqaeO2FcVrachDpQSuPRaekOWVCLYZkaiEkUb/Yi/8w9OeLKM9j0/Yw9razKSaf5to3BTEzSQDuzv6dopbShkIcCgvw7SC6YDO0/Er8xAb8cBf/dGk9b7nWJ5crd4RClJ8k6hcJmAZ14cA+25TvM29hkUVzp+DWrqJvOE/K23PWrfUIS3AJIV6WorQmfD6Q4yufmk8wVsRveRSAri7Fi2ZOClGVJFGPYGZdjJl1UTriYepDAdCaTL7I+txjGIMz8DyTVQ804/qaac5pbB506M7kJWELMUomgPbZPJRjWdff0RpUzwmY2sb3NT/4oWRqISRRj8AyFE3REJOSEYY3xOkaTPC3XwTZkV1LYNvZTOyAjuN78bRmvf8CXZnCPte1hRAHpwD0btb897/V8oc7V+JuWISRbifWnCKa9DluSf5lX0OI8U6mZ73IrprffbkiOdfjU/8R4dW+w5BvEmzZxHkLTWZOvg2FQaTYSqzYAcCsuphUVBJilGwg44cYGPZJzl5LNJjkyssfodmahO37nDIhWe4QhSi7qk7UWmtSRZenukeeZhW1SyO5B1QdV5z/DL8O+tQ2BAmYBvMDC5ienHY0wxVi3BkAGnQfH/vv9Xy562lOmHgVV3e8AsOQzj4hdqnqRL12IENXprDf9vpwgCnJCBYmtqnZbEwk1Ps8LkWU0ihgUmDi0Q9YiHFmExDTaTbkbgUgrEKSpIV4kapuEbXhkRein1QT5pc/M3j1q0FrGFD11KddNJpUsZ8zw2fJIvZCjIF+wDA0LQUTULSbreUOSYiKU9WJuiEcZFFr7X7TsR7vHKLXz1D0fHytaZ6Qo6m2tM6PQtHpdZYjXCHGHR9I1GjqW8IEzTBFLQtyCPFiVZ2oAWzTYHZ9bL/tZ1xY4BM3DuL6mvR2j41DBRSKoUI/TWZzGSIVYvyJAYMDis1r6wnZMfry28sdkhAVp+oT9S7xwP6X66MJzZv+dZgaPcSjzx5HNFCLTkUo5GUtaiHGQnLnz5u+cREt5ny6czvKGY4QFUkSNaWyocc313BaW22pVNJechlFiBzETdoSs9mSe4i6kE3B9XA9H8/XUuhEiEMUBjxMjLDBK5vP57mhR9ia3iTtSoi9VPWo7xezDIMl7fUUXI9HdwwC0NTmEdJ5BtNxhlMZtKl4oHsDQa9un+cu6agvQ8RCHNtCQF4FKeZN7n5uHU7A54VexUazn5BlcMqE2pd9DSHGO0nUQMH1eKxzkJG+wLdPdYkHcxR1mB3d/cyc2kFv5CnqcnOp0S3YhmJCLHj0gxZiHAgA2rYxtMUzazZRO7eRoBkhYptMromUOzwhKoIkamCo4IyYpHdpqhmmkDLIeD3U7HgH9gyDWM0gp0bmHL0ghRiHgkC0wWLqJJenG55m+L43kglFWXK2jAMRYpeqS9S+1nRnCri+xtMa1/fZkd5T9CRmm1imgaUUpqFQ2sTyHYqqdNacqHMxFHR5ndya+gMXRC8kYsg3fyEORRAwwwFyw6ADw6hcI+sTy+l1F9BgNZQ7PCEqQtUl6sG8w9qBkRfQmFobYUI0tE/Nbt938E2TGlIoBdMi7cwMzGJ18QUABvwBSdRCHKIiEHELTJsYYp0TQ4cGSDTl6PV6JFELsVPVjfquDdnMaYjTHt+/a239QJYnuob2266VSUNzLwA/+tok4r2zmBuYB8CjuUfodXuObNBCjFM+oFyH5IR+dHCIwNAs/vcd5/CL3xbod/vLHZ4QFaHqErVSivpwgEk1+yfqZMhmWjK633YDj9bjtwJgDszGc0ymBaZxXHA+APfn7pepJEIcAgPQpsWki24hFAK7eyG2CrBtVR335ZaVOzwhKkLVdX37WuN4PjnXxzIUrr8nwc5riI+4VKVv2zz+tANT4fSFIdrbNfdk7yHtpwCYbE+WJS6FOARFAGXwwMN58goCTphw/TBnv+PZ3b1WQlS7qknUD2/rJ5lz8dGwMzeHbYM59XGClsI2zAMmW130cGwTfIsLXzfE/YVndifpsyJnkzSTR2kvhBhfbEAbJk88HEYvAscxueYzD3NOx3za7fZyhydERaiaRA2ls2nLMAjbBiHLIB60KPo+2jXQlkdQmRgjJGvbLUA0AE6U//kfWHhlmDefP4M2q03OpIU4DGHAC4aYvngdT+bjKD/E0s+dz9bZBtdcA1OnljtCIcqvahK144DvKHwFwwWfAe2xTRf3e9zpbXUvGvUNSmsWLtTU1Gj6Nse548YTeP1xJoUmSdIvx/dLP/N5kGWGR+9wj19h/+XWK4724ZUnTGDDhmEueksnd/1sMvffD/ffD7/4BcTj5Y7w4Iy3z7rsz5E1mrZZNYn6+Po6otHEPtt8rUkXXZ7vTe/e9uy2HGHLIGiZhC0D2wDPU2zZYjGYHCaZ6GfRhSkMazKp1NHei2PPrjF26TRI58PoHe7xK+7/XbSiKEoxPva4Ihf0+MvvNAGtOeMMxYwZpf0/VtrZePusy/4cWaNpm1WTqM84AxKJF29VgM15boLtqTxDeYeUkyXnemgNmRz89IYaZgwm6HliJvaJd/GaL/2Mk17RzA4e5MLohbTastD9S3FduOee0vG3qubTNnYO9/gND499TGOpCOzY4rL291fjve6LxM75ER9+88W8b/EZ5Q5t1MbbZ13258gaTdusgHCPjkik9O/FHN/HdzUtlk3SMyi4NnnPZyjv8EKfx6MP2LjYXHutxYrZc6hJdhOMlAoxbLFfYHpEEvVLcZzSz3AYbLu8sRyLDvf4ue7YxjPWHCBkO2x8polrb38Fj3XfTO0pkwmHFx9z4z/G22dd9ufIGk3brJpEvXEwS8BR5F2fguuT9zzybmmZyr1ZhiJgGgQtg5ATwjIUPgaTkjantZ3GvZvu5WzrbLrcLkIqVKa9EWJ88IGamCYcVpw95WzuXH8nw5lhVPLYStJCHElVk6hX96WJFEuNf0IsRCJolZKxaRKyjN2/m4bCceDrX4fvfAdMQ9M4IU/Y8pnVOIsfrPwBq4dWUxOtYZo9rcx7JcSxzQOa6j2yfpbEhNK1qc3rNkNbeeMSopJUTaLeW2+uyGmttcSD++5+oQA3/xZuvhmefRbe8ha44goX+/Wax7KPsj15Eo52MAYNrplwDWFDVvgR4nB4QMoZ5PfDNxMIBzhlwils79pe7rCEqChVmagdz+cfW/qYVBMmYBqYhsI2DL70nza//7XJokXwxz8qGo/byj3D9zBda1AwOz6bsArT7DdLkhZiDBiAb0DCSHBJ7BLujd3LttS2coclREWpmkR9WnstfdrC98FH4/ma/rxD0fMZHISvfjTOmqcVrZMc3vnVAXYoeLzvEYpGHiuXY3rdScwOn41CYSqz3LsjxLiQAALxOq6KX4WhDNLFtLQvIV6kahJ1ImjTvnN+luuXan0P5h36c0UyPZo1T5eGAc47pYjvgWlBc/o0tsb/jOF5YNtsHtoMQH2kvmz7IcR4EgZyIYMYpfEjm4c2s6B5QXmDEqLCVE2i3pHOc393br/tEdukvcPixl+l+Og1Ce67NcrPbowRCJTuX9u5EMN1eTyygWxhPQD1YUnUQoyFCJC1fZ4oPMGJwRNpjjVjqAooGyVEBamaFvF0176zy2c3xDhrUgNnTqznpJYk/3ROElMZgOITnyhVrwGYlIoB4NfVsj2yHUc5/GnLn8j4maO8B0KMP42AX1/H4/nHuS19G9Map3H/5vvLHZYQFaVqEvWLPd+bZqjg7FNU4b//u/TzN7+BSy7ZWXIunwfg7NqLuCx5Ga01rTzc+zC/Hv41t6dvp9vtLkP0QowPGmi1WrkgegEazXB0mO3Z7azLrSt3aEJUjKpJ1AuaE8xvSjCnYU+F/yc6h3iqe5juTIGBvMOVV3ts3w6vfjWsWQM/+AGlMjaAUSjSarZSyBU4p+4cZgRmsM3dxoO5B8u0R0Ic+/KAKhaZaE/k8tjlNBQbiIajLC8uL3doQlSMqrlG3RILkUiUkm5zNMi9m3oB2DqcY+vwnmvXF01r4lvfUjQ2wvXXw9Lf1XOTa2ANDbEjvYPhwjAza2cyKzCL1cXV9Hq9PF98ntmB2WXZLyGOZXnATecJAkopnt32LC21LeR1ng3OBqbYU8odohBlVzVn1L7WdKbzdGcK3L+lb7/76yMB5jTEMZQiGITPfx5uugl6UiFSaZvPfLxI90Apoef9PH1eHyGjVEL0qfxTR3NXhBg3CsDD97m0tsKyZdCZ6UT5pctRqwqryhucEBWias6o71rfQ+QAC9vOaYgzJbnvih1KwQUXwOLFFrlmg1x3mi/e2ANt8IJ6gaHcEAEVYF5wHicGTzwauyDEuDMMzGgZBgde/+Ys2fduZOGcBcwNzuXk0MnlDk+IilA1iXokk2oizGt86VXpg0Hw602mhDLc9OgG3Bqora8laSZ5Tew1UpxBiMOQBSbU5Lj7t5rTL+kiPWAxeMfrWXDeYoKyLocQQBV1fSdCFvWRAC2xPStebRrK0pnJv+TzurpgIB2lv7OIFxgkl7YJ2kEui10mSVqIw+QCvduLvP9TQ7hWCoBNj8whlSpvXEJUkqpJ1MN5l75skc50nmTIpi4coCESoCb40guTfuxjJl3DNSyYkeOMi7cxeVop0f9s6Gf8PfN3PO0djfCFGJcKQG7QoT4SZd5r7icUd7j+D88Tb06XOzQhKkbVJOq9LZyQ5LS2Wk5trSVsHfis+Fe/gmXLFMGY5vJXFVi8MEiMMK+JvYZGq5ENzgZW5FccxciFGF800Nam+fUvbD7/zpnEIgY9zjb+nv17uUMTomJUZaLemsrTlyuSKrjkXQ9f6/0ec/vt8JGPwIknatrjvZBIcHrH6XRnukmlU1wWvYyACrC6uLoMeyDE+BADjESp+t9Zk8+iJlDDtq3b6PP2n5khRLWqmsFk9REb3zIouD6reve/AGYaCs8vJew5DXHe/vYwoHjPuz3CbxtG19XRnmgHYEXvCtbZ6yjqIpPsSUdzN4QYV6KAjpTqGyilCAVDPLnjSS45+ZLyBiZEBamaRH3yhFoSiQRaaxxfU/R8HN+n6Gkcz6fo+zzfW7outqo3RcELolA0TR/A8DyGrCBF16KoiyxL38cCZz7zgvNkCokQhyEGFEJherPD3FG8hUg8gumZnBM5p9yhCVExqiZR76KUImAqAub+vf5tsRA516fo+fz7Zz2+dL3Fpz9s8Udg43COLcM2jnbpGe7DzrYxN3wKAfXSg9GEEAdmAinXZ3n3VjLxAsFgmKGeNAkzUe7QhKgYVXmN+kCClkkyZNMUDXLVJQEsw+CZlTUMZSPUDPoMZnswdYCmZDOp4GaCpkz0FOJw9AH5tWmKPQ2E3HoG04N0NEwsd1hCVBRJ1AcwcSJs2ACf/6Kmy23gZ18e4vP/Poz2IWAFMJVi83COwbxT7lCFOGblgExfkfe9up6PHv9qVj+XRfkWA9KuhNhNEvVLCAbhzW/WTKrvIWPE6H1+Mr7yyOQzeFqzpj/DmgFZl1qIQ6WBCe3uzt89vK5pDGTyPNsz/NJPFKKKSKJ+CRnH5R9b+jACBob2wXBQhiZkh6j3O5iajLKgSa6lCXGoioBNkUkzS2fQdROHaI03cEJzTXkDE6KCVHyi3rZtG29+85upr68nEolwwgknsGLF0Sky0pstknM8nGCIuErjR3fgFBRNNXXMjbcxpyFOcIRBaUKIg5MHzGKBSbPzKAW9Q334m+YSC1TdOFchDqiiW8PAwABnnHEGZ599NrfffjtNTU2sW7eOZDJ5WK+rtebZ3hRbU3kMBfWhAJ7WuH7pn+P7eDt/B/BDQS68sIf/fsGikLHJFrJMD0wfgz0UoroVAMNxeN3H1+I1wF8zAf527xB8uNyRCVE5KjpRf/nLX6ajo4Of/OQnu7dNnjz5sF93zUCGzUOltaV9oCtToCUWImQpLENhGwrTUNiGAb6HFwzSaoMdSqM1hMwYVmUfOiGOCQUgYBosaU3yW3MAHRhCOQlWr4aZM8sdnRCVoaKzzW233caFF17I6173OpYtW0ZbWxvvfe97+Zd/+ZcDPqdQKFAoFHbfHh4uDUpxHAfHKV0H2zGUQXvuPs/bMZRmYk2EGbWxfbY7jibjOETiLkXHQvsK01UYnoHjycjUl7PrmO/6KUbncI9fJR33kdpmEdBo7u2+g9mnTebPd9bgBwbI5RwqKPSDMt4+67I/R9Zo4qjoRL1+/Xq+973v8eEPf5hPfepTPPLII3zgAx8gGAzylre8ZcTn3HDDDVx//fX7bb/zzjuJRCIv/X47/73YYuCuu13M/BSCAY+B5X38ddVfR79DVWzp0qXlDuGYdqjHL5vNjnEkh26ktnkeUCwUafxHgh988DzUlf9DcupKNm78Kxs3liXMwzbePuuyP0fGaNqm0nqEFSkqRCAQ4JRTTuHBBx/cve0DH/gAjz76KMuXLx/xOSN9a+/o6KC3t5dEYt8R2tmiy6r+NH3Z4n6v0xAJcFJLEsdxGDz3XLoaWzjzqRtJX30a377ka/zL4qvGaC/HN8dxWLp0Keeffz62LVXcRutwj9/w8DANDQ0MDQ3t9/k/2kZqm2/q6OBvTbXcuvQ67r3ldfx08/tQboRvn/kr3vKWiv3TNKLx9lmX/TmyRtM2K/qMesKECcydO3efbXPmzOH3v//9AZ8TDAYJBoP7bbdte7//nBrbpr7g0V/w93t8R2189+OdaJQ5YcW8xXke1ibPrkthn1n+/+hjyUjHXxy8Qz1+lXTMR2qbRcBWNobncMFHVvDM1+byaOcDfOtbFm9/e3niPFzj7bMu+3Pk4jhYFT236IwzzuCFF17YZ9vq1auZNGnsVqyaVhvl5AnJ/e/Y68u8Ngy075HpDaPy9XTM7B+z9xeimpWK8Gq0oXAdxbZnpuCHu7jjb/t/eRaiWlX0GfWHPvQhFi9ezBe/+EWuvvpqHnnkEX7wgx/wgx/8YMzew1CK5uj+Z+Aru4ZY2TWE9lzmmibDfZrNq+oxJ0xleefDuL6PZVT09xwhKp4L+FrhDnjc93gNqfW16Ck5unmGFhaUOzwhKkJFJ+qFCxfyhz/8geuuu47Pfe5zTJkyhRtvvJFrrrlmzN/r4mlNFDyfrONR9H1cT7M1laMv7eKGQjx3/zA6Cg2ThykW2rhzfQ8AQcugJmhzQnNCErcQo9QPFMmz4sft/PbOxfgtD2EHNE91O1hmijn1MZSSxW9EdavoRA3w6le/mle/+tVH/H2UUoQsk5Bl7t7WnghTLBZZGwrTENgIQH5w3+lbBden2y2Qc33iAUnUQoyGB6ANOuZ0o/5mgLZwHUXR89g4mGVWXQxZpE5Uu4pP1OWWycL/3X0ibxx6HBqgviON1hEWtdYStAxCliFn0kIcIhdI9we586snoAMeQRVCRWFRa5x5Tc3lDk+IiiCJ+kW01uzIFBjMO7i+5hPvN5mXCpBI+nxuxf/xjT9vxIrFqAvb0iUnxGFyAe0rLvrXFbzi6i7sTB833g+N0fpyhyZExZBEvZe7N/WSd7x9tgVCUWr1ACmjnszzEzENg5Q3iEajkEQtxOHwAKVg4/0t9CSmklywCoCCW3jpJwpRRaTPdi8d8dB+297xySGOP7GHoUH4xj+9Eq01tmVT1PsXSRFCjI4LRCIatzvOX/7nBP7441J3t2mYL/1EIaqIJOq9zKiLcdG0pn22GQbYYbC0Q6TGI5VL0RBpIKACZYpSiPHDA5Sp6dtaSsytMzoxlEFduK68gQlRQSRRv4ihFOdPaWR+056SbqteaMDA44r3dlETTRLREQwlh06Iw+UBuazCxAEN73ndTHztS/sSYi9yjXqnjYNZnutNjXifkS9gREJ89toO/nizwyCD8sdEiDHQCPieohhu4kO33M6a3Bps08ZU0vUtxC6SaQBfawregUsWLpy2nm2FJj51HeSdPKZpSpIWYgzUAskaTafbwNevvJgtnWksw5L2JcReqr41aK25Y1036wYyI95/0oQkDXU5tFL84Q+KYGYq2/q28XDu4aMcqRDjjwICAbjwgtIMilu+dDr9qSyreleVNzAhKkjVJ2qlFAuaEiRD+65kYhmKhkiA7nSBTD5AR7uPNhw6h7bQkmjh6cLT/GToJxR8mUYixKHyAceFGdNh5hQbr2kFQ4MmhvnSa8cLUU3kGjWlUqHtiTAA6wYyvNCXxvU1vdkivuvSs6KRCcVO5i6x2NyYZKY9EwBPe2xyNzEzMLOc4QtxzFLAwIDiO99VbLUDTJrbzLbaHBvN55nN5HKHJ0RFqPoz6l02DmX569ouXuhL7942sSbMvMY402cMYeKx6jnF8RPmsXlwM2+teSshFeLR/KNljFqIY5sG4jFNTaxUaCj/7NkEAvCPrf/A17LUpRAgiRqAbakcz/XsGfG9oCnBWZMaOK4xQXsizMTaHuYtLBVDeere6Wwa3EpBF8jrPDk/V66whTjmxYBwGH51a5grroDilgUM9wZ4emW+3KEJUTGqOlFrrfnHlj6e7BreZ7tSELL2HBonGqUlkSUUgs6tIdb3bOeW9C0AvCLyiqMasxDjyeDOn1EyfOs7Hsef14Xvw99/1iQjv4XYqepbQsDc/xA82TXMHeu66c+VyoT6gQDkcoTDYGRbyBkDpJ00V8avZFZg1tEOWYhxY/d5cz7Pemc9zzwSxcg1cfJCB897qWcKUT2qejCZUopFrbVA6eza9TX3bO7F9fQ+jyvGYqi1a5k1C7Z3JSjmTFzPZYe7gzpTSh0KcagGd/8ySIM5k2mLOukq1rDi+R56eqClpYzBCVEhqv6MehelFLZpYOxcEWtabZS6cKmetxOLwcAAQ4Ma3yhiBFxs12ayPbmMEQtx7Bvc9cvAAA/lHmLbc3UobVDb2kcyWb64hKgkVX1GvUvR87l/a/8+S1xOq43uuT8WQxeKbFyVQ8/K4xZNTkycSNSIjvRyQoiD5AE6FoP+frp31OMUTDCLDOyoZft2mDq13BEKUX6SqAHX1/utQ703JxYjm4VkqJ+aRZvZljRoCDQcxQiFGL/8ZBLd18MnLnozxaxNvKmfd1/aIUlaiJ0kUQNDBWe/bXeu7wbAxCNgBSgUIBzMYjXCeqdAk2ra7zlCiEMQCvH8kxnyWRMLg/pIA3mzq9xRCVEx5Bo10BINclJLDXMa4sysi6GU2n2f62keX9GM44CPwcJ5pVKjf8j8AUfvn+CFEKOTTmmW/i2EiYUKFvBC/QStYLnDEqJiSKKmNJCsJRZiSjLC9LooF05tpD4S2H3/tOmDpceh6U5vYWbjTAoUWJFfUaaIhRg/XNMnFPAJKJt85Hl6i9s5rf20coclRMWQRL2XoueTcVxWdg3Tly3u3t4YGcayNUN+mF/d8yRTQycB8EzhGVztlitcIcYFs5DhpEtMFAqv6QmGe8I8/ufF5Q5LiIohiXqnoYLDXRt6WLapj870vuULfdMkGNZYFDByTcSCUGuW5l93e93lCFeIccO3bWprc5x/AQQzUwAIz5Ua+kLsIol6p3jAYnpdlObo/tfGzGIRpcBXNiFnAiu2r2bAG2BucC4tplRkEOJwGMUC0XCAidPzeAOTUQoeXWawbVu5IxOiMkii3slQipl1MU6ekOTiaU2c2FKzu7xouKcHI2zz9h9EcFN1PPOkS8+mGDEjJvWIhTgMJhDu6aa7uZ23fqyT9/34YQxT8/d/ZFi4EB57rNwRClF+kmVGcPu6bp7oHKLolZbZM4tFvHCYGfM9dKgPo1DLw/83nacKT+FpKUgsxKEK7/zpRSJ0WJOon9SFHXYx8qXSvJ/9bNlCE6JiSKIewfymBKaxZ4qWFwhg5vPMqo9hdy/CbVjJzIseI+/n2eZK/5wQh2rXaJB2S2ObBrPrZhMLJshM/zkAX/96+WITolJIoh5BRyLMmRPrd98u1tRgFIuseqKb0LrXA4rewtMAaPQBXkUI8XJcwIknSG/agtaayOAkkuv+FWfS7Vh122hvL3eEQpSfJOoDMJXaXfgk1dEBwI+v2oh2gzQ02gz5ncwIzKDFksFkQhyO7NRptG1ayzfuWsGVp06l6x9nYZjwk99vIRx++ecLMd5Joj4Ay1C8cmIdLdEQmZYWsm3tvCPwQ1zfY2jF2Ty39nkW2YsIKqmgJMTh2H7yqei//53GyAqmnNxDeGg+aqiDD//62wwOyRgQISRRv8iq3hR/XdvF7eu6S3OqM3lQim0f/Bjn+H/nW003EFj5fjb19PCvy/4VraXrW4jD0f2a1+I3NHHZdT/iQ9+5jUs+9AzJJ/6LZ9L3cvYXPiltTFQ9SdR76c8V2TCYHfG+yVdfTugL/8Uben7Av3XdTeChf+d3K3/HR5/4KM8VniPjZ45ytEKMD01N9cT+79fE+zK87r1/4hVvWs5//nyI6ENf4Sl+yWfv+bwka1HVJFHvxfEP/MfAVAr9jnfAxz7Ov+W/zFXL25iYuopfLPslf9j+B/6Q+gN5P3/A5wshRtYcDaKnT4df/pLwCxu46tP/oLHOoNk9h/Aj/8kX7/wer/rK5+jqkmQtqpMk6r00R4O8anozC1uTTElG9rnvnk29pe7wK6/BuPY1fMX4BIn/9x7Sa+fw69v+Qmeqk0fyj5QpciGOXQ9u7ef2dd083DiJrhu+Qvivy7j8D0PctHQDH/tiA2d4n+XOvv/HlA++g39+1zCbN5c7YiGOLknUI2iMBJnTEOfiaU2c1l6q6T2nIQ6Aq+GR938Uf3Ybv4q8m7o7vsaq1QW+8qtv8/ft95B3PemmE2IUZtfHMQzFcNFlxYlnsOE1r8f/zGcYuu8p6mf18sPPXch3L/wxxqQHuNm8gJNf9RT/9V/ljlqIo0cS9UtQSpEIlNaf7kiEuXhaE2dOrGfuxGae+tKN1Bc7+cV5dxL/0+2kNk7mZ3f9lF9tX8qdG3rKHLkQx47WeIgLpzSypKNUjWzN+z9CZvI05n//TwDckb2Djkkn8tx1d3Lq/FoGz3sD376pi9Wryxm1EEePJOqX4e28br18Wz9/29DDXRt6eKYnRX5CGz1nnsucJ/6EUahn4rqvMpge4LEN93Bic02Zoxbi2PHwtgH+tqGHezb2AqBtm21XXEXjw48SGMpioFjQlGBizURuu/Zm8IKkTr2OUKjMgQtxlFjlDqASFT2f9YNZhgoOvanSKPBUwaU5EaUtHiJkmdiGIrjkNPSyZQQNxazWDmItU3l0wz00nSlzq4U4WBnHpcE0mNscJ2wZ2KaBfd4SjK8bNGwbpL8uTnuiVPnEz9YSfeZfSS38NJ3qCSZyYpmjF+LIkzPqETzbk2L9QIa+bJFkuNT1PbM+xkktNUyIhagN2cQCFnZLM5ZXIKxzPHB7kva6KXQNd+F4Tpn3QIhjx5RkhFd21NMcDZII2oQtE6upCYUiOLTvdEnLguTQWQB89jvPHP1ghSgDSdQjsM09C3IM5kpJd0c6z+OdQzzXm2LdQIatwzkGo3EGBqBW93Hp27czZ/J00sU0f+78Mz2uXKcW4mB0ZQqs7Bri+b4UG4eydGUKDEdLgzeDA6X6BI/lH6O70M/cuVDsnEY0O4fknCfKGbYQR410fY/guMYEs+pi5FyP4VyBFc9DMmhT0JrebJGC5+F4mtr+LLMtjes5TG9ppj0wGw+PldmVpDIp3lLzlnLvihAVry4cwNOaznSBnOujtcYoFDjL85mp59FvJFiZX8l6Yz2LF1/Ngw9CJF4kUT9ycSIhxhtJ1Adgm6VrZeGdfQ5zGuLYtr37fs/XrL4vhefCoKpl6gkFHlm3hZAdpqVuAmdGzixT5EIcW2bWxaivL02D1FrzXG+KHetKPVK5QCuFoo+2NGeEz+Bjj4KbWIufWMerZnysnGELcdRIoj5ERd8n0tyI1pDUA6xfU8djPMyUCVMxDJNao7bcIQpxzNFAyDJxEqWZE5nuHoaUxvINwkaY2loYqr0PvwgnJy8ob7BCHCVyjfoQ3be5j60Fn1hCE9B5/vu6IDty66iP19Jg1RFQgXKHKMQxZ3V/hhf60mjLwlc+g4FHCJgGtVaCkApx333Q1gaZlMWbrzHLHa4QR4Uk6kM0vTZKsLcHZcCcN+fRaHp6s8yNnMRr468lZMgkTyFGqykSwDYNAgN9aFxSDVFChSZen3g9ESNCIgGveO0zWEOzeOE5m5tuKnfEQhx5kqgPQdHz6c8VCfV04QeC1LZMQ8c3k04XmVzXUu7whDhm5Vwfx/MJdnVhaptscw0qlNp9f97N8/etf8buWQjApz4FWRlTJsY5SdSHION49GSLWIODFOvqiNeCn9gIQJd28LVf3gCFOEZt3Dlv2h4eRKNxG+MUdJ6UX0rWPZkehnNpJuTP3v2cP/yhLKEKcdRIoj4EvdnC7t+NQoHv/WcMo+E5lB+gLpHgx0M/Zk1xTRkjFOLYk3M9hvI7iwUphYHBKcwD4DfDv2Gjs5Flm5YxNASpF07a/bwpU8oRrRBHjyTqQxC1S4PlsxMnERjo550f7sO2A+AF2PrgfPKexyOZlXJmLcQoWEoRtEp/krLtE9HA4ONFYsOlNrUi8wy2YZNIwDlnhmhvLz1PzqjFeCeJ+hC0xkO8anozx09uIWAavPGKPHNn16LtDMdNqyNabGNYD/CL4V9Q0IWXf0EhBLZpcO7kRi6a1sTCKaWxHmaxSLw4iXhhIj3+dtKRNJYFH/nCWl71qtLzrrqqjEELcRRIoj4MplIYwI4na3jqYYUCOlpMok4LntaknAJPdg/I+tRCjIKhFPFkgqBp0BFUTIiFCPl1uFqzdrAbX8Nb317kBz8oPX7RovLGK8SRJon6EPlak7GCDKXgI/+awQt3EQtFSKs03dEVALSkF9ObUniSqIU4aFnHpTeVxdOaojLod4foCa8k1Rvitm+30tMN3S9MA+Dii8scrBBHgVQmO0h6Z53vnOuRcz3W9md49JcB3pWFs87cyjUfnsN/r0jT3b+acDyIh8NQ8mGuqLkWU8n3ISEOZFfbSjsuGwezZB0PI5/nTAzy23ZQa0dZs3Q6P/3QaahoJ3VvDXLN//wvX77sE1jyF0xUAfmY7yXruGQdn7zrkXd98p5HtlAE4O5NvfjKBAVB06A2bPP/7jyNt6og75j2NPPPfhs/fCbGU+l7WKAWEFA2p4VPw1RSPUmIl3Lflj5CcQ+lFBHbYEIsxMxJ9YROPYVZa57hyZpt3Pm92aVlL7OTmZiYySb3UUnSomrIR51SAZO7Nuy7LGXANAhZBoGdK15OSUapi4aoDwcwVGnjF78MPe9s5u4fb2fq5w0WtC7ggY0PsOC4Bbwl8RYMOZMW4mV1JMJMnpCkIRxAqT1LzNLWht62lUfzj3LlfzbwkzdfDihW33I1W171GbrSXTTHmssWtxBHi2QSYEXn4D6368IBzpvSyJKOek5sLi0OMDUZoTES3J2koTTa9Gn7ZE52lvOD5Y8Qb4mzsXsj9X69JGkhDtLUZJTGSHDfJA0MHz+dzOMPYOYdppzUy1e+WhrrEdp4BbmsxW+e/n05whXiqJNsQqkre2/9uSJ/XdvFX9d2cef6bgCyRRfX33dedOz/s3fecXZU5f9/T7m9773bW3Y3vRISCISS0DuCBRQUAVFU0K8VVCxgA/zZRf2KIuoXUFQQESK9JUBIgfS6Sbb3u7fXab8/ZrObkAQSINlNMu99ndfcO3dm7jmz98znlOc8jxdO/NoCpihrePAGJzPGzQAd2L1zbmFh8RakisoedQugd/5EBFWjcvEmLvNdxpVXiDz0EIiFMMr6C/jNC5ZQWxwdWEPfwOzyAJkSc166qOms6kuY8fZ2YUnHIKIs47PbUHWdvKZzQlWIlukXUid+jy8r9+AInIbb5ebuzXdz/sTzcQiO0SmQhcVhxPKuOBuzIgGHTDyvMDXiY1zQzbjp55A8bhbT7n6Oh886hg/5L0MQPADYe04mZX+UlV0rmVM1Z5RLYGFxcLF61IAgCHjtMhG3nSqfk/MayzinsYwzxkU4uaYEgLlVQcaHPCQLCllFQ9cNYnmFhmlu7vF8njN6H+LCjqlcNvcyXt3yKs93PD/KpbKwODw4tiJArd9JfMh96IaBFIZhYBNshG/6AeXreqh4ZjX9Wj+TJ5vnOHe8n2qO46pHriJTzIxi7i0sDj6WUO8FQRCQRAGHLOG2m4MOJU47Qadtt+M2DqRoFnsZf/tH6VXD/PuSP/G9ud/DZXfx05U/HY2sW1gcdgSd9mG3vDv577Y+EgUFTj4Zed58Jt6/hFX5VQSDsGQJ1FY6abn7p2zvivHrZb8ZnYxbWBwiLKHeB7phkCmqvNIxCMDrvQk2DqR5k70LNklk4TkyD7k+xvGtf8eWznH2sWezrHkZsVxsFHJuYXF4sbovSXsyt9s+uyQOG24aV11F+dJm5OYdgBmE4/OfBznVgGvLVfx6+V3k1fwhz7eFxaHCmqN+E8mCwpL2weH3hqYCIAAlLhv1ARchpw2fXR62Ur36athQchVfyf6KjX/6Fk0XNmETbKzsXsmZjWeOQiksLA4fotkCUyI2GkNuytwO7G8y7lx5ZgVNYR9z/rYWZsMLL8BNN8HMmXD+hy7jjra/sLh1MWc1nTU6BbCwOMgcVj3q22+/HUEQ+MIXvnDQvqOo7W5FVut3AabB2fRSP/UBN36HbVikm5vhqafg7I+W4b78Yhrve4lKfxlV4Sr+vPrPBy2fFhZHCgvrI0wv9VPjc+0h0gBBZynbPjQP/0NPsvaVFNdcA6IIDz0EN3xgFifWnsjtS24fhZxbWBwaDhuhXr58OXfffTczZ848qN8Tcds5pS48/H7nkNyS9ijLumKs6k2wfiDF1sE0HckcH/u4Dhh86lPABz6Ap3OQQOsAc+rmsLxzOeli+qDm18LicGdJ+yDLumKs6UuyKZqmJZEllleGg9mMt42n5YJjkDMFvnPR6xSL8Otfg8cDkijxkekfYdPAJtb3rR/lklhYHBwOC6FOp9NceeWV/P73vycUCh307/PZZc4fX87548s5rT4CQKnbgSQK5FWNaLZIWzLHmr4kZePzFDSdT31OIT11LoYkUrZiB5PGTyKjZHhw3YMHPb8WFocz1T4nkiCQLqp0p/Ns6E/xascgz7QM0J3OkzfyJBvLyAQdXN74GgCf+Qzcf795/vsmvY9xwXHc9uJto1gKC4uDx2Eh1DfccAMXXHABZ5556Od7bUNDcZPCXuZUBDmhuoRT68KcMa6UBXVhrvt6iss+nWH5UpFPfUeCWcdQtXgTYX+YmeUzea3ztUOeZwuLw4mmkIc5lUHm15RwWn2E08aZjWNF03mjJ0GhaANRpP+4Ri4re5Ff/QrmzIGvfhW++U3QVRtXzbqKVzteJVlIjnJpLCzee8a8Mdnf/vY3Xn/9dZYvX75fxxcKBQqFwvD7ZNKsuKl8BsF+4AEyVNU0JksXssja7rfLMGB8WOaSjyV48LdunnjIxtIPn8msf/0A+0Ceqkg1S7ctJZU/etd5vtX9s3h73u39G0u/vX3VzXQhiz3vHN5vGNAYkNkSNaeNFrd04YsEaD3zGBpvfpBzfrqRE+6pY+4sF/fcA2vW6nz/t6ei6rfxyMZ/c+nk9x/agg1xpP3WrfIcXA6kbgqGMXaDJbe3tzN37lyeeuopZs2aBcDChQs55phj+PnPf77Xc2699VZuu23PIbAHHngAt9t90PK6enUpP//5sVTKPSxVjmfLx6/g8bkl/Lz153yp/kvM8M04aN9tYbEvstksV1xxBYlEAr/fP6p5ebd1UyoUOO3zn2f7RRex/cILWby4mj/+cToAgWAO+ZMn47JLfKfpO8jC6D+ILSzeigOpm2NaqB955BEuvfRSJGmkJ6xpZjg8URQpFAq7fQZ7b7XX1tayeMfrBAKBA86Drmq0vLKBcfOnIsoj3xUvFOhOjXyPgMDq54P877erWFl1Lo6pfv5z5wf46aLbmeCfxA/O/vEBf/eRwL7un8X+8W7vXyKR4JSGY8eEUO+rbr7YvJxQiekBsKiqbItnh4+xiSIhp42Qy0aH1sbkT96CV3fS9+d7ho+56twqOttkvnzPE/xkwye4ZeFtnN509qEr2BBH2m/dKs/B5UDq5phudp5xxhmsXbt2t33XXHMNkydP5uabb95DpAEcDgcOx54+tu02G6697H87NEkDwOlwIO3yz3U5HJS4NeJ5hZxqoOoaTzxYgm7A1uMXcsrj/4usXkRjxXi2tG/CZpOQxTF9uw8K+7p/FvvHu71/OZvt7Q86ROyrbtrkkbrpcjiY7nQRyxXJqjoFVSda0IgWilSGKmg/+xjmff8fZDJZ9JIQry2209VmQwCmlhyH3xlgdc/rXDD1okNcuiPvt26V5+ByIHVzTBuT+Xw+pk+fvlvyeDyEw2GmT58+2tnDIUuUe53UB1y4bDLnfcx0lPK1f30IJacw/e4nOGfS+cSygzyz9clRzq2FxeGBXRIp9zqp9bkoaCNRtdqyUTpPnYYsyFt76EQAAIfrSURBVATv+gMAE6aoXPxhswd+3aURxqeuY9HmR9nSv2lU8m5hcTAY00J9uJBTNAYyBY45NcM5V8bYThPLTrmSSf/3AuGiwtTKafz59XvIK7m3vZaFhYVJX65gWpYNoWkimbIQWz95Id4H/oHU2UVJROfmHyT5wa9Nd735JZ+i0lfNj176oeXDwOKI4bAT6hdeeGGfhmSjQUHVyKra8HtRMh8s3V+8CtkT4KTv/4uF0xfQmmzhpme+REYbO1a4FhZjlbyiUVB3j1EdFCI02Mex/Jr5pL0S9m9/i0LBFOOds2DHnSDwjdO/Q1ushRsf+RQ5q3FscQRw2An1WKMlkaNzl4ACs2aaBjCpnjpiP/ou1a9u4X/uXMV1J13PK9sX87sNv2F9YT0JLT56mbawGONsT+RIDoW9lESRmeUBJoW9NNjHcULkTDZ9/zP4l6zAdsP1tOZ3UNdkHvuX33gZ753Lry/5A23xVm595haKWnE0i2Jh8a6xhPpd0hh0E9gl/OW44+Ious43Pudn7bTj6fvdT/G++CpffNbgrKZz+c/Sh2lPtvFGfhUdSgdj1+bewmL0aAq5cdrMbrKOwZreBKt7EiQKCpIgUXbuVfTf9WPqnl+H449/JF29himzTLH+5o1BGoITufWs77O07WV+uvhOdEN/q6+zsBjTWEL9LrFJIuOCbqaV+qkPuvF7BGYvMIfjHrrPzdKJ02i//lpCv/o937efS9gV4f7n7qWg5GkuNtOv9Y1yCSwsxh4OSWRiiZcJJV5CjpGGcEssy6aBNJpuoJ91FpnrruaYX/wH/Y0VfOTrWwFY8bKDe3/l5dSG0/jqqd/gsY2PcMfz30PTtX19nYXFmMYS6vcIWRIIOm00lXj46o+iTDo2x3//UkIhJ7D54x8ld8qJNH7+O/y26nq6kp089KrpAzwshd/myhYWRyeCAG67RG3ARYnLPry/oGqoQz3k2FduJDa1joWf+Q1nBPp5bFkfx59S4E93eXn8ny4unPI+vnn6d/nvlsf4fy/9kDHsNsLCYp9YQv0eYRjmAyRRUMjoRS7/Qh/ZlMimlW7CXid9v/l/FGZMYcbNP+byCWfx+vbl2AYlJGH01/NZWIxVVM0gWVAZzI3MM8uSiEOS0AyNdfpmnvvNp9HLyqi+8RuUeLL89N4YM+cWue9uDwO9IudOuoCvLfwW/9nwCM9te3oUS2Nh8c6whPodousQyyvsiGdZ3ZNgTW+CTQNpWuNZJFFk3AQNm93gf79eRTEtY7hc9P3yTsimufyFKA6bk+b+raNdDAuLMYVhQKqg0jyYZnVPgvX9SXbEMoiigF2WkEWRMrfpHEU1VAbUARS/m46ffRvbtha8//wPggAf/2yatm0y75tfRjolcMHkixkfmciTWxaNcgktLA4cS6jfIT3ZPG3x7LBlKjA8Vz2t1Mf0Ci/fvCOFoIsIqumRTC8Nk/30J5n+11eox8eWpOWUwcJiV7KKyvZYhkzRnE+WRJHJES/TS/1MiXiZVuaj1GMOgztEB/Oc8/CKXtY26KQvPZ/gXb8H4IQFRa767O7rqCdEJlrRtSwOSyyhfodUuJ1U+Vy77XPZJGRJQBBA0+Bnt5n+W7VdDU6dLkTZQUY2yBrWGk8Li11x22TqgiMBOgQB7JKEIOz9eJfkIigFyet5DLsd3esBYMNqG3/5jReAXMY8uTPRgVN27v1CFhZjGEuo3yGiCG7b7vPLG/tTrO9L0RLP0dqtkk4JeP06FVUjSu156nnS846lKx+lIlRJQS+8+dIWFkctggBOaeSxpGo6a/uT7IhnieUUitqey6zEoceY49nnyS08CYDvftkMwHPRZVki5TqqrrKhbx0njTv1EJTCwuK95eiLEvEe4rFLTC71omoGqgGarlPQdPrSBVa9akPVDS68vo+iruEYcp0kb2/hxfPqMQyDSeHJVjg+C4s34bJJTC71oWo6im5QUHV6MvnhaaYKr5NyrzlP3aV00aa04c0Y2AbiJKdMYqBXpH2HWa++/N0kggDre9aiaioNocZRK5eFxTvFUol3iUOScOzSsR7ImtapT9xnhu2bNi9DTrFjFyUEDPTEIJ1iBJtk4+LqSxH3NaZnYXEU45BEHEM962RBhV1WVaWKCqW6Ax2VLcUtAByTrUNAQAsG+NE3zd70tZ9PszNA0ZruVciSzOyqOYe0HBYW7wXW0Pd7TNBhI+Jx4JAFwuUa4UqV1niWNb0J2hM5dAH6Yz3URuppU1stz2QWFm+DxyZT7nXgd9oQBIFMUWNtX4LOVH74mA69CzDAMHC4zEp11sUjNiDPNj/N7Ko5SKK1HNLi8MMS6vcYWRKo9jkpKxGRRQFN2/2zYjhASVqnoORpUVooGtYctYXFWyGJ5nB3Q9CNSx55ZAm7PL06vBl0UUCKDlLXoAJQyI+MVtkkG+lC6pDl2cLivcQS6oPEaeflGOyTeeSXFcO95p5shp5ZdZy0pUAsEWWe/XgcomN0M2phcRhR7h2x2u4q9KEZ4BW9HBc5HXXSRFwvvsxp5+Xxh3Q+9YEw/7rfhabB8bUnsG2wmUzRil5ncfhhCfVB4rz357npewme/WeAh26vo9rnwibKNH/ofMZ1Zrn2+Tjfe+XbDGgDo51VC4vDBr9DZnpZgLqAG1l3IaouElqa1wsr6fzw2Xieep6Z2//Lvxb3cc4lOX787QBXXRBhCh8EBL7xxFcsN6IWhx2WUB9E3veRHJ+5KcVzj7t48K4SMimR4vQL2Hb9Z/jSywK1f/gPL/Q9T6fShWqoo51dC4vDAkkEr13CprspyU8mmJ1CTivw0sX1DFywgNL/+TrBN5bxtR8mufuhKK3NMjd/ZAYLhe/yWtsy/rb6/tEugoXFAWEJ9UHmsmsynHhRjD//r5tbPtRAsSCw7Zqr6PzIFdz8bAH91p+wPruOJdkldCpdlnGZhcXb0JbIsaF/ZL5Z1p3U69Nx2jw8ftv5dM9tpOxTX8C5+FWmHaPw+4cHOP6UAk/d8VFyi6/jJ8//nIfX/XMUS2BhcWBYQn2QsdngmlsGOPuKGMlBiVcX+anwOnH96DsMfvZaPvFoN6Ev/wRD09ha3EJKt1wcWli8FQ5p9yWNkyNemvwRjnfOo849nmfvuo7u45oo/+QXcD/5HFNmqvzsTzHu+N8YnhXfQVl2FT9+/v/x4vbnRqkEFhYHhiXUB5lUUUXVdC68ZhCA+35URst6FwgC8je/zcYvXcVpj2+k/jN3IBYUPKJnlHNsYTF2MQzoSY+slJBlCWnI/FsQIKkn0Jx2Wn79PbJnn0bp527G+/dHADjlrAJf/HYaz4rbyK89ny89/DX+s/a/o1EMC4sDwhLqg4zPbvqUcXl1rru1BwGBL15RyrbN5v6ar36P+754KpNe3MwZH/s5tkT6rS5nYXFUIwgQdI7Epg7YJSTR7GEbhkFMiwFQ7R5H/89+QPryS4l8/XsEf3wXGAaXXJHlc1/LsVD9Ccq6i/nKP77LwyufHJWyWFjsL5ZQH2QEAWZVBKgLupl3TopzPjqIpoM/MOKzePznvsYtX51OfvMGwpdfjdTbP4o5trAY29QHXVT7zYA40WyRjGIaYuq8yQ+4JBH97tcZ/Nr/EPztvZT88KfYZIOPfCLLT+5J4n3lxzha3sePl36Dp7c+caiLYWGx31hCfYgIOmxE3A6OOzOFgcF9/9RZ3ZOgoOk02ps47qM3cN11Efo7NlPxkeuQO7pGO8sWFmOWsMuOONST3jaYMeuSCrW2GgB2FFtMw0xBIPnJq4h+5yb8f3yAklvvBF1HEOAjV2t4X/4Z050X8/3nvsOKjmWjWCILi31jCfUhIK9qbBhIMZAtUFZr+gL/zx/Cptcyw8AtuTg+eDzHX3o1l3wEOuKtVFz+CaSevtHNuIXFGETVdLYOptH13ZdIGAY02BrxS35alRa2K83Dn6WuupyBH9yC//5/UPL9nwBwzvtyCAisu+OXTA0dz5cf/xx/X/PAIS2LhcX+YAn1IaCg6qhD4fmcbgN/iUY+JRPMhHHIpu/hUqmUq8dfy3HnXs75lxXJFlOEfvSL0cy2hcWYRNENcsqIb95Kr5NZFQE8dglREJntmI0syLQrHcNz1gDpD7+f6Ldvwv/nv2Ffs4GJ01RKKzQE3UbD+t9z0ZRL+MWSn7CuZ81oFMvCYp9YQn0I8DtsyOLIrT79g3EMAzav2z14mUtycfXM60iHPTx+cgm2xxdhFIqHOrsWFmMapywhyyPBNbrT+d38DwiCwDTHdABW51fTrXYPf5a68oPoQT+ex8w56R/dbQr5v+8L8j8nfQWH7GR19xuHoBQWFvuPFebyENCezKHqZo+6eY2TN54NIAoCLvee3k2q7dVUyCGa1naTLw9hyCJWIEwLixGiuSKqOtKjnlzq483RYkNSkFK5lH61fzevf47XVyOkM6RLqvnLbz2seMW0IJ82u4gsygRdQaJZy62vxdjCEupDQNhlJ5Yze8Z3Xl+LKAh860cJFpyze+Qsw4Al25/ha3/cysQeN7G//AJRsv5FFha7EnTY6GQkhKWi6cOxq3eS0TL0q/3YBBvVcjUA9vWbKP/kFygcN5sbX7iWZct9+EM6n7slyQUfyPHI+ofoTfUwv/6UQ1oeC4u3w1KBQ4DHLhF229nWZvYC5p2ZYeHFKXbefs3QGNAG2DC4Gs/1n+PUVoG+v/wU+4nzRzHXFhZjE1kSKHHZGRxq/GYVDbcsI4qQ1/O0Kq10q93Igsx0x3REQUTe0Ur51TegNNTT9eufsuxYHwDf+2WcufOLLNr0H36y+HY+MOMy5tYcP5rFs7DYA0uoDxE1fhc/+Zk55H3uNf00DxYRRIGJYSdL86+gKkVCn/wap2wqEP/dr7AvPGe0s2xhMWapDbjwO20M5op0p/J0p/KUlyhsUtYBUG+rp85WhyRISN29VFz1WfSSIL33/JKv32T2sG/6QYK584s8vulRbn/+Ni6ccglfOPmro1ksC4u9Ygn1IWKgV2TZCy4kAaoazJ5AQUjycu51JAGc3/wFpy4foPVH3yRywUWjnFsLi7FPwCFTVHWSeYWilGJ9sRlJgDnOufgkr3mQolD26S+BAD1/+g1/f6yaJc84EUS4+PIc/17/ED968YdcNPUSblpwC6Jg2ddajD2OGqFWDY2iduChJDXNHK4uaiqS8M5DW7kCYGAwcZpCfchBd6rAoNyBZhj4H13HqQ+vZdPHL6L8I9e+o3yOVd6r+3e08m7vn2pob3/QKKMZ+jv+zfsdAmlFpJVuNMOgzpiKA+fw9Upv/xn2TVtp+/s9FMrCjJuUx8DHzLkFOpOd/PilO7ho6qV88eSbhww+9bf+wrcqxxH2W7fKc3A5kLp51Ai1JmgUKLz9gW9CZ+ifSwER6W2OfovrSGAAJ5+XZDCfI6sVcRsVJNnBjJ//kdWNbkq//d13lMexzHt1/45W3u3904TDQKgF9R3/7lXNYLCQxSWWoggZovRSgdmbtrV3EfzzX+m56bMkpzcCBXKqgAG8/9oY/1j/V+yyg2vmfYqi8O6XQR5pv3WrPAeXA6mbR41QT3TX4Xf7D/g8VVHoYAsTXXXINts7/n7DALdDon9LCYW0gntov63vJUrbozzxjXO4ITj5HV9/rPJe3b+jlXd7/5Lq2A+bOt5VQ4k7/I7O3TiQwq2aEecy/k3k6KLUPouwHEFa/CyS003kui8QcbuHjhGQBYnNS/08U72Iq2ZexbHBae9JOY6037pVnoPLgdTNo0aoXZIdl2R/+wPfhKKbCzSdkh2b9O7+uVOmGixdKvJ+bMPrPp3tA+gYKLMa31H+xjrv5f07Gnm39085DH5TzndYN+N5hbwiIAnmfdEMDZsoUhBy5vX6o1BZhcsXHD7npOOhrBTu/3cn8rUp3jfpoves3h1pv3WrPAeXA6mbluXEIaThmBw9PbD8aTdlHgclbhtFvw0BmJCtGO3sWVgcNhiGwfLuOPG8QqnbTsgtD4e7bLI3mQe5XJDL7XaeIMAXvgCi4iMeh/6MFanOYuxjCfUh5PP/YyDbYNliiTkVAWZVuFDLgoiCxMbNL4529iwsDhsEQRh2cuKyScyocLDTh9+gNmgeVFICg4Og7m6ods01MGdiNVqshn+vffaQ5tvC4p1gCfUhZEq5B1WBaK9EX7aIS3Qxt+x0BEEgk7Za9hYWB8L8mhAAbYkcHsHDRPtEAOJa3DzA7wdF2UOoARx2ASldR088tsdnFhZjDUuoDyHJJIiCQONUhZXdcfoyBYKqF03X8HgDGMboLxmwsDhcEHdx8L1+IEWNbMaizhpZc+fQchykPS1827tzKOE3OLZhwkHPp4XFu8US6kPIwADYRJG6EgcAK7rjbF+9FjAIjW8gplutewuL/UUUBM5pLAPMXjWZcgA2FzebB3R1gc8H4u6PuWwWdmivYMg5PjzjA4c0zxYW7wRLqA8hDwzFpP/MZ0b2tbZ3YBgChfIS1maaUbR37nDBwuJoQ9wlatamaBqKXvqUKDlVg1QKwuE9etTf+Q4oJWsoj9iZEB5/iHNsYXHgHDXLs0YbXYe//AXmzYNxlTZq9TL6sgVer/IgAKGEwobCRjJ99ZzZUIpdstpQFhZvhyAInNdUxkCuSEHV6c3byRsGT7W1ckYghLd/d9uPvj64/36oPkfCcDsR3hwf08JiDGKpwSHixhshnYapU833kihQ6XUy0L0GURDxB4JoYp6imMImWg8PC4v9RRAESt0OavwuTg/OBSDh2IYtkwFZNr0NAc88A8ccAwY64oT/Mqdqzijm2sJi/7F61IeAYhEeecRg2kyDG75WpC2ho+gGOUWh+flHwG6nZ0oVNs1Lnc9vtfItLN4GVddJFlRyqkZRMyhoGrF8ng3SSgRZJEAE+7o1cOyx7PQu9M1vmud+4Wcv8ouO1Vx7zP+NYgksLPYfS6gPAXlDwV9m8PobEs+sThGp0LFJIhklSj9JJENn5gaN4+Zfjstm/UssLN6KoqbzzI7dh7TtNp0uzyso0iDnOM6lKVyD4HbDpk0QjUI4zNlnwx/+AI+8vBG9DgJOc6WF1TC2GOtYQ9+HAK9d5vu/zBOp0Pnm1SHu/6WHRr2UYHUvXZcezxulOlMv+TJtZx5D2w+/hrZ8mdkNt7Cw2AObKFDrdw2/n10RwFPWQlGOcbbnDMa7a03x/dznTIcnc+fCjTfy7UkPctu1rfQ+/34S2yZxym8uZtIdp3LNAzfx19UP0ZHssJZIWoxJrO7bIUAUBOrGa9z6+xiP3+/mxUddzP+HjqtsBk0n/pIHPr+UldvvY9rzrzP9pz9m4M4fIzgcxCbVw3FzCR97CuFjT0IYPx7sY993s4XFwUQQBAq7rI5IF1U6xT50DNqVDtyim4gUQZ45E158Ef76V3jkEeR//YtPGgbXlFayft08nhGO5Um6eaJhCX9ZdR+SBC7JT7VjEpPCk5hTP4mTJk9iUlkjFd4KK1a1xahhCfUhYlzAjU3K03BTkSs+nWf1Sok1KwTWrqxi5SNXYvARSurihC99gcmup5gcfYPxO1qZ+vctqPfcTx8ComwjVR1BGd+INGUa/qnHEJp4DLaGRnMZijWEZ3GUUB9wIQkCGUVjRzyL3ZiFy7mNjfYW1uU3IQjgwotPChC++ljKP3kW4bRAaGUz9mWvM2vpUmatX88XC0XUjZByhWj2RVjtcrHSlWVz8Dl+X3U/tz2vI9gEbKKDsDiOKs84GkMNTCqvZ2ptFZNrKqnyVVLiKhntW2JxBGMJ9SGizOOgzGM6OqEazptsUPiwTk7V6ezReO6VIm+scrBp7fk8s/H9PJ4XMDDwNWynvHExE+wvU5dfR+1AJ3XNS5nwysvoOZ0oAqIgojrtpMuD5KvKEWprcVaPw1vbRKB+Eu7aBqioMJ0/WGJucQRQ6nZQ6jbrk2EYFDWDjFJFulikWx0grsZI6AlSJIgqO9hQXI8ggnAc2I+rx3fjTIK6l0hHknBzD/4t7RyzpZXjNm/jk9vbMXQDVYW8IdLrDtHi8rDNWWCHcw0d/pd5Phznb17o8YpknAI2yY6XCuxFH01bFlETrKK6JMK4sggN5WEq/BHCrjARdwSXzfU2pbOw2B1LqEcJQRBwyhJOWSI0zsb0cU64wvxM02DbNoNVa3RWrJ7Ixs3jWbv5Gp7uFNENA11QcNdup2LcG9T7VlDJJsoyHZTFBygb2ET5ljWUpQ3IGySA5E4xt8ukwx4K4SBGOIQYjmCPVOAuryFQ0YCnvBYxHDGDGZSUgNdrCbvFmEcQBByygEO2U+KyU4cXGAeYIp4oqHSmM8TVBAk9TookWZIkxAG2VeXQakRYOA6BcQgsQFZ0SrryhNuThDoSBDrjzO6IclJ7H7b+DEJbCl2TUDUDVdPIiTL9ThtdjjRdUoIBVzsDngJRb4GtvgJRl0jcJRDziiQ8IqLNg1+OEHJEiLjDlPsiVAUjVIdLqAgGCTmDBJ1BAs4AIWeIgDOAU3aO6j22GF0soR6DSBJMnCgwcaLEZR8c8aqUy8G2bbB1q50tW6azecs0tmz9KCtaBIqKgYGBYRj4yzIEprfhL99KxLWBEqGZoNJKINuLPxHDO9iLt72d4GadUNZAyhmkiwYZQBBEhCFh120SOb+LYtCLFgpASQg5UoajrApPWQ2u0irkcBlySRlCOGyKu8tlibvFmEEQBIJOG0FnEAgC9YAp4AVNJ6/qZJUiCS1LUkuT1rJkyJCrzNJWmWP7vByamEcV8+iCgoiArGh4+hO4+9IEe7P4+7P4BjJM7UkxdVMUv01FGkghtmlQMNB0A00z0A0NTVdISzpRW5EBex9Rp0DUrRP1qGzz5lnmVYm5RGJukbhHIO4xe+x22YlPDhJwBAm5g4Q9AcoDIUp9AUKuIAFHgJArZAr80OuAI4Df4UcS9/R1bnF4YQn1YYTLBdOnmwmEoWR6PevtFWhthdZWaG8P0No6g9bWGWxrfz+v9JqWrAZmI6CswqC0VsFd2Y+9vAs50onD246XVhyFTsTBAaTYILZYAmcijSs5iDfVQ6jfIPSGQShnoGd0ipqAIJhJFEQEQUC328gH3CghH0YoiBAO05h1El3/MrayMqRwGVJJKbZwOfZwBWJJGByO0bqlFkcpu45oBZ02qvAApbsdYxgGRd0gr2rkVZ2UkqczlyCpZsj78xSCBbomFWgXCmhiAfQiJStk+k6IodqKCIAtW8QZS+OKZ/HE8nhjCt54Hn8sT1k0i9SXRerLIA2kkLsTyKkUum6YI2e6jm6oKAgM2jQG5DSDjgJR5wAxFwy4dbo9KusDBQZ9OQa9Ogm3SNwjotsERNF0c+61+Qm5gpR4gkS8AUqGRPzNvfagM0jIaYp90BlEMiyBHytYQn0EIIpQWWmmE07Y8/NCQaCjA9rboa0N2tsF2tsdtLfX0PxyDdHoiJDbZKisNqisMSiv0imboOGq0BDL8mRK+hiw9xDN9xAr9JFNdmMMdiEO9iPFosixGPZ4Emcigy+bJBRrJ9hpUJI2MJY+TkkO7Lop7AVBQBQEBEFEddnNXnuJH8IlSJFS0ziuxByeF0siyOFy5Eg5tnA5jpIKJNmyfrc4uJgxr8241wEHlONgfDAAgKYbKLpOUdMpagZ5TSOdK7CZF5lRvIB8ATJaliIFdF8BPVgkM14hKxXpFYvoYgFNLKIJeQrk0Qwz0pegqDgSORyDaXxxBV+8iCdWxB3L0RDN0tSTQ+pLI0VTyNEEth0pBEVF1yV0XUQzDHRdIym5iIoeBm1OBh12Yk6ZqCvDoCtDwt/GloDKYCBP1J+lz5sh7xAQpRFxF0VwyHZkVeRngz8j5B4ScEdwuOe+M+0q7kFnEK/da61Nf4+xhPoowOGApiYz7Y1sVhgW8Y4OaGsz3+/YKPHSUzYSCQAPEMblmkJtrUF1DZRX6YTKNUqrNCKzNUoqNWxujaKmEi+kSOSjtGT7eX3bUrSqUnJqlEKyGz3agz7YB9EociyBM5UjmIkRykYJdm4nuFUnnBcoyQsEszqSIYAgoAsCRUFEEUUUvws1HMQIB9HCQfRQECMcRigpQQhHkEpKkcLl2CJl2CIVOD0l2ATbIbzrFkcykiggiWaPfCeKU2YzMKciiM1mwzAMVN2cI08UFPKqRk7VyRd08qpGcWiJmSwKeBwCLoeKw6FCpYJeVUAVC+SNHDEjT5eeI2fkyBt58noeg6H13oaBLV3AEy/gjyn44wqeWAFPLE9NLEddbwahJ4M4kEKMxpG740jNSXQdDN0cjdN1kZxgZ0DwMyh7idrdDNodDDpl+uwCqaBIKqjTE+xjU6iNaCBH0pWkKCVA1IaFXRDNICmyJO1TyEtcJXtNIWcIm2TVz31hCbUFbjdMmmSmvZFMmr3xXXvkbW2weqVEW5tEJjNyrM8HNTVQW1dKRVUD4fIi0mCI2fVTKGnUyRsaur67UwnVKJJX4mSVGEU9TqsSY01hkHguSjzbRz7eQ6GvG3WgF1s8STCrE8rkCGUylKQ6KB+QTWHPGgSyOs6CNjwULwoioiCQdTsoRvyokRBaaRihtBSxtBy5tBJbeQ2uslrspVUIZWXmDbGweJcIgoBNEoi47UTce44AFTWdZEEZFvJERiAXFwE7ZsMY7JKIUxYpd9nx22XskojNDg6bRp48OT1H1pMlV5YjNyTmg3qWnGG+zxv53fOEgE9zEkmIlMQNgoMqvniRyGCeqsE0as8gxe4oau8Aev8ASk8Xnp4ChrFT1M2Ux0k/1fQJIQZsAfodXgYcTnpddmI+mXQEkmGdlkiRbb4UODpQ5DUUpRhZYxAEDUnaPQKp3+Hfp5Dvmso8ZZR7yo8q63lLqC3eFr8fpk0z05sxDIjFdhXxnUnglcXmEHsicSzOux04nQLTphlMnW4weZrOpKk6NY06GjpFLURWqWMgV0TRddhFyx2yiNcm47FLSIJKThkkU4ySKAzQn+ljQ6aX3nQvfdk+etI9xOM9aNEBghnNFPWsRnUB6osKFcVBSjsGKdm8CV+ygDORR9QNVEBDQEBA97jQIiUYpRFT0CtrsNWMw17TgFBba7ZErHXrFu8SuyQScTuIuEdsNBRNJ6dqw4ZueVUjrWh0pvK07OLkxSGL+B02fPYAfnuYCoeM1yntMeSsG7op2kPCndEzJPUkcWecraUJklpuqKo5kQUvfnEiATFAUAri0TxsfmYzZ565AG+maLpi3Zn6+6nu60Pt7qfY0Yve3QL9/UiDA+j9BnrziKinpCADYhm9Rhk92nS6pSo67aV0On30BZ1kG+x4q1J4SqPYA4NI3kHi+UH609tJKSsYzA0Sz8f3uH9+h58KbwUV3goqvZXU+Guo8ddQG6ilxl9DpbfyiOmlW0Jt8a4QhJHVXLNm7fl5sajy178+T2PjWWzYILN2rcCrLwvc9xcRw4BgEM44A84+G849F2w2hocMFV0nXdSI5RUyirnNKhqa7gN8yFIDEyIyc6pl/A6ZsMuOz27+pBVNoT/bT3eqm/ZkO63xVjYl2ngy0cq22DZ6072AALqDRiHEsfYaZjmqmSJFCGaKGAMDiH0D2PtbcG9ejacrhpZXEDGt4vH7MZoasTdNQZw6FY47DmbMsAzjLN4VNknEto8Qt7phUNR0MopGf7ZIuqjSlcqzXTXnt2XRtHAPOW2Uuh0EnTZEQcQjePDggb3YhumGTkpPEdfjJPUkCS1BQk+wubiZXDFHhAh/yz1EwBWgtL6U0sZSKuVphKSQ+Z28SURU1XTb2t9vxhTt6yPU309dby/096N3daC1LoXeHrScjpYGtU2k31nHdmkC65SJrNDPZo19LoNihJoaOHEGTJmm0jg1waRjosSKZoO8N91Ld7qbnnQPWwa38FzLc/RnRnzAi4JIhbeCppImZpXPYlpkGhktw+GIJdQWBxVBgGCwyAknGJxyysj+dBrWrTM9PD71FDz0EFx1Fdxxx8iQoU0ScdvkEUcxjCyryRQ1UkWVZFElWVDoSOYACDht+OwyUyNeqnxVVPmq9hrOMFVIsS22jW2D29g6uJXXu1/n0c4XKGpFvnzil/ny/F8CUDSKpPU0MS1JMtpGrqMZpXUb4o4WfNt7CWxdTPixf+AsgGh3wvvfD3feabY4LCzeQ8RdLNXDrpGhdEXTSRZV4nmFwbzC1sEMWwczOGSRSSVeavz7HiIWBZGAFCAgBfb4LFPM8AzPMN81n0FxkD61j+ZiMwYGftHPXOdcGu2Nu58ky1BWZqa9DMGJQwlFwdbTYw6/7dhBoLmZ8Vu3ctbGhzG6fo2mwUDtHB466ee81NnEH/8gE4+HOeGEMA8/PHGf5cmreTqTnXQkO+hIdtCebGdzdDP/3PBP7krfRT6fx2gyuHLWlW93u8cUllBbjAper9kDz2ZhYAA2boTa2rc/b9dlNWG3HVXX6csWaR7MkC6qJPIKibxCnd9F0Llv38w+h49jKo7hmIpjKGpFXut4jYc3PsyD6x9kXf+64ePsgp0SqYQSqQQqx0HlqXAcaIZGl9rFk5kncQ6kmPTXVznut0vgb3+Dq6+GmTPf/U2ysNgPbJJI2GUn7LJTXlQRgL5MgYKqs6Yv+ZZC/VbYBbMxMN4+HttQw1MxFJ7PPk+b0sZz2edosDW8Mwtvm82s8LW1MH/+8G5BURCefhrhllsobVvJWamHUE68CU2DV1+FcePe+rJO2UlTSRNNJablrKIpbOjfwLLOZdy76l429WxiRdcKS6gtLPZGLgfNzdDSYjpteflls+IVi1BdbcYK/sxn3v46umEQHxoKzygq22NZAPwOmUqfE4ckUh9w4dlLuNB0MU17op22RButiVY2DWxiff96Ng1sQtEUKrwVfGn+l7hy9pX0qX3DVrY5PUe+kMToaEdobUNs60Bu68LT3s9FW3rwtg0gI8PUWfCJT5hD4BYWh4idBmnbYhmyijkMXul14rKJNIU8B3StolEko2fI6lmSxSQAr+ZeJV/Mk9JTJPQEujEyV25gmFNBB4JhoA/GSa1tIbWuleKWFvQdrcht2/G3roV8nkEjyCP26/n9Y9cjvQKzZ8Pdd8MFF+x+qZ1TXD3pnuHh8I5kB82xZrbHttMab0XVVRyygzkVc1hQs4Bvn/7tA8vvGMASaov3jGzWdLiyfbspyNu3w/btEmvWLCCXG/mpeb1m5MFbboHTTjOXje2tUa4bBjlFI1lU6csUyCgaWWVoWYsArqGlMdV+JzMiPuKFOL3pLl7r6KY71U1boo2WRAutiVZaE63EcjGG/Ldhk2xUBauoC9dyefm5TBSDVCoS9u07aH7tZlwDSVz9KTxdMSLtMdx9CQTDNDZDltFqKjDq6hBPPx557gnY5s6DujrLwMzioGIYBnlVZzCv0JPOk1Y0MkUVBHAMzW2fXFuC32Hb47ycYRqTZY0sGT1DxjAFeefrjJ5BNdThcwRVIEKEAW0Ar+SlQq5gsjiZoBg0jc1Ez5u+AxJxg4GtMZJbe8ns6KPQ3ofa1YfQ34st2os/1kI43YpbSWJgClBGDNEujaPXUU9P+fkUZ83BcVIjVROT/LK2GcE1SDQ3wNZ0D197poeezIgo92f7dwtNapNsVPuqaQw1ctq402gKNTG9bDozy2eCDosWLUIWDz/ZO/xybDGqZDKmCO/YMZJ2vu/tHTnO54OGBqivNzj55E7OOcfLhAkyDQ0QCJmOIgqaTl7TaUnoFDTT+1NB0ymo5ntFMytgUcuTVwdRtCjJQj/pYj+xQjfd6S660l30Z/rpS/ehaMqwEDtUgwbdS4PuYbbi5KKin4pciLI0hFMqgbiCuz+Oc6ANURmy9xaGhNjlxCgrR4iUItbMQzx5HNTXm0JcV2d6lpEsr00W7z26YZBTNXKKPrTu2lx7be4zX+8UpoDTRsRloyHgxO/RyJMlq2dpNfrI5DK7iXHWyO7WExYQcItuPKIHj+AhJIWGX3tED27RjV2184TxJKdqFxFvg3jzAP3bemlvW4/S0Yve24c00Icj3osr3Ucg10tIH8BrKHh3fo8AWVuAlCdM2h+gr6mUrRXjGax0Eq220V8JCW+anBAlUWxmILuUwdwgRa0IGzHTEBF3ZNjKe1b5LMoby6n0VQ7vq/BWEHKG9jkUr+jKQfqvHXwsobbYg3R6TzHeKch9fSPH+f3Q2GjOG514oinMDQ0wbpyBy28+aFK5AqsWN1M3t4KcIfBGSkOJmw8MTddIFgZJFvtJFwaIF7oZLHQRzXcTzfUymO0nmh0gU0hjYCDqBqGsTmVepk5xUlt0MKdgoyIjUJYJEEkbhBJFfPECjnQBgRyCkDdFWLZBJIxQVo5QVoEwvXTE6KX0Ta89BzZcaGFxoOiGQaaokVZU0kWVdNEcOcoo6h5LE+2yAbYsgj2NXc6iCXk0MUsfOXYYGfJ6HtIj58iCjFtw4xW9+EU/lVIlbtF87xbcuAUPatpFdEBgoDVDsrmPgdY+utpb0Lr7EPt7kWP9OJM9lKR7GDBuIagP7ia+SAJJV4Ck10865Cbe6KCtpIpEqJrBkEZfsEiXN0urI02vGkfREruV3/TBHiQshQkLYcKuME3hesKuMGF3eI/t0e4QxRLqoxRdNw0ut2yBrVvNtHPIun9khQPB4E7xhVNOMbc1dQY19Roun0FRM9d8FofWfcYLCsviGmpUIZbvZyDdQX98Kc+vW8tgsZtYvodoto9otp9YdhBNV/EWDErSGmUZqFWdTC3YqcrLVORkylM2Iik/gUQRdyKPjGw6M0FDIAvB4JD4lsOkXQT3zSIcDO7uXcHC4iCjGwaFod5wfnirMZhXSBdVdo7Y2iSwOwrIrgJBfx5VzFIQMuTJ0GekyegjS4ocgsPs+YoeSoVS6sX64Z6wKcBuop02BrbGSW7tpXdHP8WOFujtQ4724kj04c70EVZ6Cet91BnmtQUBBElDlWUSXj9Jv5t4o41mW471VX6iQS89vgId3iwt9hQDLh1djANxwLQeD7lCuwhsNRPcEU5whSlxlewmuhF3hKAzeFgOQY8W1p06wjEMsxe8aZNpWb1xo/l661bIDzkt8nphwgSzd3zqqaYwV9fplFapOP3mUHR3Oj88XB3VDfoTOoM9PfSkWunO7GAg10Y030Us18NAtodYbhDd0PHmNWr7DGoUkcaCk3k5maq0QGUKSuM2SgY1HAUDSbAjiZLZ+3XaEMorEEpLYdw+er1lZRCJgN3y+W0xuhiGQVrRSOaKACztilE0BAra7o57BFFFsGeQnFlkX4aCmCJDkrSeGnEJCrhx4xW8eEUvFUIFASmAXwggZYMMdDno2ZYhta6VeHMnSmszQk83joEuPMluSpUuyvUuQoaZF1EEUdLIOl0kfG7iZXbiQZkNQRjweej2ybS7M7S78gx4RdIOHYQEgpAk6Agi5kUm1lRS6ikl7ArT6A5z6V56vUFn0IrSdRAZ00J9++238/DDD7Np0yZcLhfz58/nzjvvZNK+fF1aAJBKwUsvwTPPwPPPjwxXu1ymm9Bp08zlvpMmwcSJ5pQrGMQLKv3ZAv3ZItGCQjQLSqpIZ2obHclNDBa205XeTkeyhc5kB4qmgK5RnYKpBQ+zMw4akyL1caiOeinry+JOGaAY2Bx2BJuEUFGOUFGFMLlqJJJIRQWUl+8+9GwZZVmMcTKK6XCkK50nU9QwNHVof4FAsIhdTpEXkmSFJCkjQd7IDZ/rET0ExSB1Ui0BMUBADOAVvXhED7ois/W1Qdpe2kHP6y3kN7Vg62qhItdCtdbKFH0AARAlMCSZlD9EPOQl0WhjR8TF0nA9Xf4cO1wJmuUE/V6RolwEioTdYco8YUrdpWbylLLAHRl+XeouJeKOEHaHMTSDRYsWcf755w8vz7IYHca0UL/44ovccMMNHHfccaiqyi233MLZZ5/Nhg0b8FjziLthGPDf/8L998OSJaAophB/8IMwZw5MmWLaQe1t9Leg6izpiFJQdXSKdCVXsbr/BVZ0vUxLbAeKbhppBT0Bjin4uaJTYnZ7BQ3dBcra4jjyGqKgIoggVFUh1I+DE+ugvh61upolra2cdPnlyJWV1vCzxWGPYRjsiGfZFE0jCAKlXgiWJEloPcQ2QWfwKTpkc9lSUAwSFIPUSJUExaDpXEQM7BYgRounWHX3MvofW4pj63rKohsJa72EMf2H5H2lKLX1aHX1pGonsrwiz/pgP68YrazRuijSD/QPWzzX+Guo9lUz11/D+/zVVPuqqfZXU+Wrwik797ucinb4Gl8daYxpoX7iiSd2e3/vvfdSVlbGypUrOfXUU0cpV2MPRTGXOt13Hxx/PHzrW6ZLzrq6tz83r2o81zJALNfPouZ7WdL+TxLFBH63n8k1k/nI1I9wUrqUU55tJ/y3pchdvSBKCFOmwDFT4cOTYfLkofHy6j08chmKQmLRIrO3bIm0xRFAWzLHpmgaHRVv+Q5WqZtABZ/uw4mT+a75lDvLCYgBJGEfw8GKAo88Qt9vH4JXXqZa0fC5yonXzSJ3ykfInDiZ6oUTcEyoZVl8PX9f/3ce2/oYqUIKm2RjRukMjqk4iw+UTKCppInGUCMV3gpEwapjRyJjWqjfTMKMt0hJSck+jykUChQKheH3yaS5aF9RFBTlwFuIO895J+ceKn79a5H77hP50Y80Lr98ZK5rf7KsaTobepfyi9e+jCJmOXHyPBZMWMAHKj9AaQxst30P8bFfQySCfv75aGecgTFnjrn+am+86UsPh/s3lnm3928s3ff3um6OFoqiYmgqg671dOXaKJdLOcV9Ck7NydM8Tb1Qj023oes6Ovoe5wvPPIP47W9T2NbJ8tyJrG/6Lmd9/xSmX7D7OvxoNspXn/oMz+54llp/LdfOupYF9QuYXjodh7ynT3lN1dDQ3sNyHll1d6yV50DyIRi7rhYfwxiGwfve9z5isRiLFy/e53G33nort9122x77H3jgAdxHaPjCW289Ebtd4xvfWPaOzr95y83YRTtfa/gaHmlkSmHOj39McNs2Nn34w3SddBKGfFi16yyAbDbLFVdcQSKRwO/3j2pejsa6+WY83d2cdMstDE6ezA3RH7FWmcYddyzGbt9T0P/a/Veeij7FZ2s/y1z/3HfmqtNizHIgdfOwefLeeOONrFmzhiVLlrzlcV//+tf50pe+NPw+mUxSW1vL2Wef/Y4eVIqi8PTTT3PWWWeNWYOKjRtFfvlLkcWLL+Q739Fx7v80FADP2Z7j31v+zSLjDU5rOh9vaZqsPIBybx6xVSNcb6P6pGlEwk0HnLfD4f6NZd7t/dvZax0L7KtunnbaaYTD4VHM2YGj6QbLe+L0qT0o/u1khCg+3YfjVQdzTp9DubN87yc2NyM7HFSGQly3QOP6P/n5/vcv5LrrdK68Ut9toMq+w87i/yxmtbya+VPms7B+4SFdS3yk1d2xVp4DqZuHhVB/7nOf49FHH+Wll16ipqbmLY91OBw49hJq0Gazvat/zrs9/2By881miORvfUti7VqJb30LTjpp/w2n7zz7Tip8Fdy76k88tf2vzK+7gBtPuJbcb39G/+0/p/Qn96D/7I+0X3wK+uUfIjzvHILSvj0A7Y2xfP8OB97p/RtL9/xg1c3RwAbMqQrzQquBM1HG3OoiXUoLMWI8WXiSRqmRJlsT1XL17vPUU6bAgw8ifOtbXPinD7O1YSqPBK7mhz+6iHvuCfChD5n2JcceC+dNPI8HP/Qgty+5neseu46AM8AFEy7g4kkXM792/iFbh3w4/n/eirFSngPJw5i2PDAMgxtvvJGHH36Y5557joaGhtHO0phEEOCjH4V//MO0/r7sMjOA08DA/p3vlJ18/ZSv8/r1K/ncvK+yru9VLn/wfXzx2V+x9Povkn3pdbKfvY7w4jVUffAzKCfMZdUPP8arOx6hQ+nYzTWhhcXRgtsmMTXiQ0Bga5cTKT4ZgFn2Y4lqUZ7KPMX9yft5KfsSA9oulfGUU8x1k3/7G+6mKq5Y8zVWGTP5jf5p1t3zGu+72AwJe++9MK/yZB77yGM8e9WzXD3ral5uf5kP//PDzP7dbL753DfZEt0ySqW3OJSMaaG+4YYbuO+++3jggQfw+Xz09PTQ09NDLpd7+5OPQubOhSefhN/8BlasgDPPhH//G/bXCsFr9/KtU/+HdZ9ewQ/O+DHRbCc3PP5BPrvsThZfeC0vP7iYwl//heuUM5lyzwvMXPBJBr98Hf9e/0tezL5It9p9cAtoYTHGGBd0c/q4CJPDXjJF05Crt7uMSGwBl3gvZapjKt1qN4+kHuHR9KNsLm42fXULguld6C9/gZUrkb/5deYH1vNA/lLWT7iECwOL+dY3DWbPhh/9SMCRmsLNJ9/MK9e+whMffYLLpl7Go5sfZeGfFvKRhz7Cmt41o3wnLA4mY1qof/vb35JIJFi4cCGVlZXD6cEHHxztrI1ZBAEuuQSeftr0G/KZz5huQg8Ep83Op+d8lKXXvcCPzvoBr3c/zW0vXEVWy5CfeyK+n92Nc9UG3Dd9hynPt3H+eXfAn//M4+nH6VV7OUzsEy0s3hOcskRD0M3CupF59mxRJyCEmOucy4d8H+J09+nEtBiLs4vZorypQpaXw6c/DS++iPCXvxDyqXxj9eWsW3gjF51d4Be/gMsvNz0JCoLAzPKZfGvBt1j5qZX85oLf0JXq4tz7zuWuZXcd4pJbHCrGtFAbhrHXdPXVV4921sY8VVXw+OPm68cee2fXkEWZa2dfy58v+Tvd6R281vEUa/uTLGmP0iXaEW68EduylTg+eg0nfe9xwqta+U/6P2wsbnz7i1tYHGG82Wbj5Y5BtgymEQWRRnsjl/ouRRREFmcXs7W4dc8LiKI5DPbYY/DrXxNc8hi31/yar30NOjvN0bFdsUk2Lpl8Cc9e9SwXT7qY25fcTl7NH8QSWowWY1qoLd45PT1w/fVm3X83vmHW9K7hR698C6/dwRXTF1LqcpAsqGyPDQUKUFUoKUFGIjgUc7pE2vc6dwuLI52zGkqZVR4gq2g0D2bQh0aY/KKferkeAJ+4Dz8EAIaBYXdQxMbLT2W44w7TQ9npp+956LbBbXzn+e+waOsiFo5beECexywOHw4Lq2+L/UNRTPehDzwATzwBTqc5X33ccQdwDU1hQ/8GXmh5gSe2PcHqntWMLxnPT8+5j8FCKQU1jy02yIQdG1F/9QrGvx9Bz2VovmYB206oAsAuWIEyLI5eNgyk6cmbfr8rvU6KRoF2pZ3tynbalXbAjIK1K1peofvJNfT/4wW8L/yHYO8WXpFP5Tv6Z7niCvj2t8HtVdkRa2dVzypWdK1gcdtimgebCblC3HTSTVw/5/pDXlaLQ4Ml1Ic5ySS88IJpRPbss+b7yZPhttvgQx/atwMxgEQ+wfr+9azvW29u+9ezJboFRVNw2TwcW3EC35v2fWYkfNj/8SSBLT+hZONmHF196OgM1Efo+NB02j5+NpHq6Zwu11Btq97jIWRhcSRT1MzocvGMOey8LdeB3ZdBdqXYZERZmTRHn8rlck5wnoivv5bmx7qJLXke3ngD/45V1MTWIutFfKKfDVXHsfqaj5E/w86F4btoT2/n/H9uoy3RhqqbDYDGUCPHVx/PLafcwqn1p+KyuUat/BYHH0uoDzN6e+G112DZMjNt2GDGlp42Da67Ds45B6ZP330NtWEYtCfbhwV5Xd86NvRvoCPZgYE51zXJO47jMkE+FptDY0+Bqq44weaXcMQeAwxUt53YtBo6zp2FOmsawrFz8NRNokkqYY4YtLwmWRwVGIZBRi0QVRMMFJP0FxNE1QRFIYMhZgjjJupfhsNuw5OqhNV1+F4eRH6jA2HLcyS7f0Uwv5laitSKKr2BErbVhnlxwTjWjcuxtGKADM8CzyJ1SdSma2kqaeKMhjNoDDXSGGpkWtk0SlzW9NLRhCXUY5xcDl591Vx2+fzzsH27uX/cODMAx8c/DgsXmvEwAApqgXV9W4YFeV3fOtb3rydZTGIYBl67l2P0Mi5KOpk80EhjT47Kljj+9tUIBhiCQLa2lOzkcUSvOg8mT8YxdTa+hmnU2kL7DjJgYXEEYRgGaSNNTI3RU4zRkR8gqSfICSk0ccRgSxQk1ESY4o4yWOak/6ltVCYGKOnaQn16LaVGFwgaih1aS31smSXySKXK+nKF5kqZlCtFhddDU0k1jcFGvhxqoClkBtmoC9QdUk9kFmMXS6jHGIYBzc0jwvzqq1Asmp7HTjvN9EJ2/PHmio6d88mP97zGijUrWNu7lh2DO1B1FQODck+YOQU/nx0sZUpPmMbWNGXNA9gzWxAQ0EJBCpOa0E8/icKUaXimz8IxeQZeK4SoxVGCZmgk9AQxLTa87VdiJPUEKprpg8AQcRo+bNkSCpsmMrAhgLa8F+/mrZS0rmNi7l9M1NbgJg2iwoDfxuY6kX+VKTRXyDRXySQqQzSUThjuFZ8caqQp1MS44Dg8dqu+Wbw1llCPAVIpePlleO45U5w7O8FuhxNPNMNXnnYaNDVBohDn6fanuW3987z+1OtsH9hOUSsiCiLloQomuir4gDiXWW0a4zcNUrqhDakYQ0RArR8H00/AecEspJkzYOpUKC3Faw1ZWxxFKIZCq9JKp9pJv9pPXE+gGTqGYSAZTiTVi6z5sMXrSG0uo29zCX3rBeyvr6Ky/TVmGz/nEvV1XGIGQVRoKZFYP97guSqZLdV2ustKmTJjPpMik5hc0sQFoSYaQg2EnAfmctfCYlcsoR4ldN1cF3nffbB8ubnKqbERzj3XFOYTTwTBlmdJ2xLubX2ep19+muaBZnRDJ+AOMKF0CleXX8S8DjtTtqYpX7sV744NCIKAHokgHH88tkuvRZw9G6ZOxeH1jnaRLSxGjbyeZ2l+KTuUHWiGhssIYOSDeJUa7Lofrx5k1Qt+Xn/JTt+6NOU7ljOLv3Om8ALTC1uRKJJ0aqysk/ldvY2eCVU4Z8+loXYmE8MTuTo8kTpvHc8+9Sznn3/+mPAlbXHkYAn1KLBsGdx6K6xaZa5x/u53TXGurzeHs//b/F9ueOoRXmx9kbSSxu12M75yAlfWfJoLt/lpWtFB5LVXcPesQhJFhIkTEE45BeHLXzbXYtXX739EDguLI5yMnuGJzBOktAzuzAS8SjWC6sYmiiitAZ593MaWR7dwTP9f+Kznn0xObsKgSK/P4I16O0snV6DOnUPpsSczo2IWXymbTti9Z7SvsRLn2OLIwxLqQ4iiwBe/CA8/DDNnwkMPmT1nMI1X/rH+n/xwyQ/pTfcyp2oOnz/h8+TKcky3j6Punu3U/+1vuKL9GPX1GOeejf3MM0xhDgZHtVwWFmOZlfmVpPQUwcGTses+GoJunvynk/v+IFPasoIbpR/w9dwzJBwFltbYeHnOeJynnsHUY8/h4pp5hFyh0S6CxVGOJdSHkDvvhEcfhZ/9zFzjLA75hUsVUnz16a/y6OZHuXjSxXzxhC8yrmQcT7U9QPm9LzL1/ruRskV6zj6f+pu+gDh16ugWxMLiMKFT7WRLcQs+pRa77sMj2rj3Ti/Nf3yJP4R+wjjtBTYGi9xxaRVNV32B9834IFW+qtHOtoXFblhCfYh48EHTS9jNN5sO9sHsRT+z/RluffFW+jP9/O+F/8v5E89n+9bnab7jayz8xxIQXXRdfDn9V36cE4+bhmgNaVtYvC2aobGusI43Cm/gUiO4kpNJdoq88IUVnLP5J3zd9hobnAb/c7mLWR/7Nj+Y9zkcsuWox2JsYgn1IWDFCvjKV+CMM+Dznzf3vdL+CrcvuZ2VXSs5sfZEHph7J8FnX2Hgf75L9epm1ECA9qs+Rf+HruHEaY1MlCy37BYW+0NGz/BK7hW2F1soKTRQ/koB/wO/Q378XyygndVNBp9a4KH0vA/wvVO+Tl2gbrSzbGHxllhCfZB58UW45hpz7fNdd8Ga3tXcvuR2Xmp5kfcpTfw0eSFVj61B3HQxqiyQPmUGO269DW3h+4lEQhwfcGG3RNrC4m0p6AVWFlayObOOstdaOPWxLiqf+yVKeyddLo3/zNVZeWItJ7//en494wrKPGWjnWULi/3CEuqDSDRquvU8+WS4+26DHy/7Afe++hs+v8bNr9Z58PdtIufbRuuCKRQ++wWKx1/MgFKBgMA5jWVIojXMbWHxdiS0BBuKG2jreYOJf3iGyx9ehTiYYSAU5K5QmkXzRJxzz+Cr513LV5vOQhatx57F4YX1iz2IrFgBmYzpvOSnf9rGzwZ/w5dfTHDN6ykEUWDLR0/DcelljKs/Hnd5Pc91JxHQANPRv0u03HVaWOyLhJZgWX4ZrUorTsHJObc+Qfjp5egIbP7U57l+8I80o+JTrmZB8iKWPVVGtDxFfXmQsjKBsjIIBEaMOi0sxiqWUB9ETj3VjAn9/PPw9LP15GZ8nIdDf6cp3MWsfoXw3Y8h3L2IFBJpRJqcDmJeHxmfn2VuH6I/gDMUxlNagrc0jK80jKekFHsoZIbF8nrB7QaXa2TrcFhrqC2OCp7OPk1ci3Os81hmOmYif7QcBn8Ja9cx4e5fsUjNky3m0ISfM+D6BX1ukZhDZLNdYqXoJSP4SQk+cjYfRVsAxRFAc5SApwTJHcHlK8HlL8UdDOHyhvB7vQTcbgJuF26XiMvFbkmSIJeT0DSw/J1YvJdYQn0QcbngO98xk67beLH1ap7afBL/actzTzQO3R3YBrtwprvxpaOEcglKCklKlE686QK+QR3fFgO1aKArBkbRIKcZgICAsPtWEM13gohit6E6HGhOJ4bTheB2IXk92Hwe7H4fNq8fu9eP3RdAfLPQ79zua5/LZUaxtxoDFqNMtVxNXIvzev51VhdW457nxvnAdTgNG/6+LK6+DFpXArUjitrVibe3F280BrEUUiqNLZPClevHWVBwFRUMDAx0wMB485cZkJcFsjaBrCyQlyTisky3ZCMn2siJDnKSnQwOfvT9W8lLbhTJjSK7UW0eNJsP3ebDcPgxHH4EVxDZFUD2hLB7Qjg9IXweL36XG5/bhstlxpN/c2Pgzftk6wl+VGD9mw8RoginNUzilHFNRLUoGT1D2kiT1/MUjSIFo0DRKJLXC6SzGrGEypZolngiRyJZJJNWSacVlFQWIZFBTKewZdKI+SxyMYetmMWu5rBrOZxqHidZ3HoMl9aPSwV3wsAVNXApBm7VwKUZuBUDlyriVsCtgkMzhsReGNkOJXHnVhQQRBHdZTYCcDkRXB5TzB3mU0RwucDpQnC5MRxOJnZ1oWzYiO71ILjcCG7zGNHtRnA6EXY2BHZ9CjmdZrLGJS32wYmuE5npmElUi5LUk2T1LHkjT97I019hJ1+mk58mUDTcGNRgGAwLsAFgmIJsAIKqI2cK2NJ5bMkCRiqLkMyip7IImRxiJo+YLkC6gJguIGWKiFkFOVcklCtSli9iK+SwZxI4dA17UcWpqDgVDVnTGfmmXb7/TRQkgZxsprwok5MlsqKNqGgnL9rJiXbyopO84CQvuMjjoih6KMoeVNmLJnsx7H4Muw/B7kdwBpBdQWR3ELunBKe3BJfbh8cj7VP4d90ny5DNyiiKNUIw2lhCfYiRBZlyufytD/IDFeY6a23oTzd085WhDW919N1fD32m6BrplEE8aRCNF+mPZ+mLZxlM5oilcyQzWVLZPOl8lkwhS17Jk1eyFNUMUjGFrKWw62lcpHEJKVxGFhcZnJqOUwWXquPW0ji1NC5VN0U/K+BOyrhVEY8m4tIE3Bq4NQNvXqBzySKcmo5D1bGr+lCAgl0aA7s0EGCkt67bbehOB7rTgTa0NRsJZtKdTgyX+WQxHE4ElwvD5UZ0usDlHmoMuBCdHkSPB8npRnJ7kVxuRJ8P0eNBdLtMV6xgBU44zPCIHjziW0ef0g0dFRXN0Ia3Ghqqoe621YLm56quUjRUirqKYqiohja03ZnM44rGLvURFUPViSz1033CALqsDYuxqGhI+SJitoiYyWJksgipLKTNRgDZPEImj5grImWLiNkCUkZBzCpIWRVHTsGbV5ALWeyFJPaCgr2oYVdVHEUNp6pjU/U9yr23xoAuQE4SyUsSOUkmJ8kURBsZ0c6AaCcvOkYaAoKTjO5kzXf+TF7woklec2RA9oPdj2APgMNsDEjuEmyeMLLfj+xx4HILezQA3O7dB+msWbv9xxLqMYwgCMhDfxzoD9gJlL7z79Y00xAukTCjeyUSBv3xLD3xBAOpFP2pJLFMmh3ZNMlcimQhTaaYIaOkyGkZCnoaRUxhyGl0OQWONIYtjWFLIQoFXLqOSwe3quPWDZyagUs1cGvg1W34NBs+XcZnSHgQ8RkCXqOIlyIePY1LBWcGnHEDp2LgUAwcRQ25qGErqEh5s7cj7O1pNYQ+lAxBQHPaUd0OVJeZNJcDzeVEdTvR3E40twvdtXPrQne7MYYaBYbHAy4PgseL4HYjeHyILg+S14dsc2AXZGyCjE2UsQs27KKIXbAjSyLyUEPF4uAhCiJ27Adehw4QRVFYxCI+UXIVsiwPN6J3bSSohkpRHxL+oQaBYiiohopi6EPboeMN1TwXjYyhoe1sUKDtbKKjo6GjYmgqRjaLnk5jZLIY6SxkcpA1t2Imj5DNI+YKZkMgVxhqGJiNATmnYssXcOcz2Auq2RAoqjgKZkPAoeq4FQNxz/bAmxoEZkM7I0tkZZmsZDYCcpKdtOigT3SREV3kBA95vGTxksVP1giQF4LoUgjdFsJwhBGdEURnBMnrQ/J7sHtse52R25vo761RcDgPzllCbbFXJAn8fjOZCIBnKO0figLRqMK///0Cs2cvJJu1kUhANK7QH0/Tn0wxmMoQy6aI5VMk82l6CynSSoqcmkaTUxi2FLotjWEfem1PIrgS4EggSAqiaFZAYWgrigYOyYnPESbo8hJxeSgVvIRFNyHRTRAnQcOOV5fxaRJeVcKtgKOoImSzCNkc4s5tLo89m0fqjSHlupGyeaRsASmXNxsBijpU0n23BhS3HcXrpOhxoHidZD0Oil4niteJ6nGhetyoHg+a14Pm8WJ4vBheH4YviBEqQfKba323xZOE3F5CTpu1rv4wYLiRLcg4OHQez3YdcRsZHdCGGghmo0AxVBRDQ9WHtkMNAnXotYJG3lBRVRX51SLZE0CVVPJqjmIuhZJOomYS6Jk0eiaDkMoi5ApIqSxiNo+UySNnCsjZArZcETlbxJ5VsWdT+HIxHHkVR8FsBLiKGu6iOSW3G7u9NcW/KEmkbTYysoOU7CQtukkJXlJ46SNAmiBZI0jaiJAxImS0MjJaBQmjgoQYJiP70YXTKS+Xhm1x9yd5POZzMBiEUMjcd6jb1pZQWxw0bDYIh6GiIsvs2bvOc9mA0FDaO4Yx0qNPJiEeN7exGAwOwsCAQd9gnt5Egr5knGg6SSybIGvEyduSZB1xeuwJcMZxBGPIvkEE9zZ0xyDY00hvWvlml+xUlFZQF6ij1l9LXaCO8SXjmV87f99BGRTF7LFks5DJYGQyaJnk0AMshZZOISYT2FJJ5HQKI5mEdAZSKYS2FEK6BzGdQUxnkNJZ0PVhMybTqGnoXkge0hUODH8N/b5SQrVVuBvHQV0dHHMMTJjwzv5BFkcckiAhIY2MHryLNt3OEYIPluw7bOfOaQXVUCkaCgVNMbe6QlFXKegKBaNAwSiQNwrk9TwZo0iRAjnSFMmhair5Qg7boJ1Q/zhqxSweNUMxGUNJximm4mjJOEYqBckkRjqNmE5jT2coy8SpyRSw5wp48jrugo63YOwxkiYggiCRsNvIDgTIpIKk7WFS9iai4lxabNNYaZtLf9ZDOm0+e1R1r0XGZhsR7Z3bigqorYXx42HePHPZ33uJJdQWYxJBGGnRVlfv9QjANZQqhvcWCqaQ9/dDdzd0dUFnp7l97nFT7A2xwL+ejOEtjTKYGySaixLNRulOd9OWaGN513L+uu6vw9d89qpnmVI6Zc8s2GxmjRyqlQJ7VijDMFBQyOumkVPOyFEwCuT0oe3O91qWQiaJlopDKoUtmcMxmMYZzVL2hoTqTxBuF/HuaMGx/JXdm/S//z1ccME7us8WFu+GndMKdsGOGzBEg4JRIGtkyepZsoaKoOvohoKi50HPUNTNzzR0BANssowk+SjV5+L1VNMQ8dIY3P+Ru50U1AKJQoJkLk4q1ktmsIf8YB+FgV6K/T3k+7oY2LoFn1rA17GNib2bKU0tAf5MmacMcfoMePEpwOwoFAqQTptpZ2chHjefL7HYSOrpgT//eSQfLhds2GDOub9XWEJtcUQhSWYF6+uDlhbYuhWam81tMmkes3CBTDCkM1hI0pXqojXRSmu8lbZkG63xVgayA8PXqwvU7RZ72DCMYcvinJEbFuDhNCzIeXJ6jryRRzPM3vGulsaSYUNWbThjRVx9WZyDeYIDWZzRNK5oGlcshSeWxjmYQGhvx1ssIinK7gZvDgdMnw6TJx+KW2txFGMYIwKc0TPkjNxu26yeJaNnyRpZNEMf+q2bC90kw45sOBE0B6LmRDJ8eHUXsuHEJ3oJiAF8NidlEQfBdzq1YxjImRxCewtG+1aMzu3Q1YrU242rtxfvQBRnNMG0wTQlWR1ZH+lyy3YngtcHH/jA8D5BMNvhmmba6PT1mamnB3p7R7a9vWanYFc++lH2GLF7t1hCbXFYEovBtm1mam4eed3SYo5IG3IWKdxK6fhW/E0tVM5royLQSs7eyivpdk77qzJ8rQpvBdWBaqqD1cyunU2Zv4wSXwmRQAS7085KfSW5VG5YmHX04aU+BiAYApJhRzIcSEUJ52ABR1+G8mgW10AGVzSFazCFO5rEORjHGUtgH4wix+MIuo4gMLwqHpcTobQUIRKBSAStdhyba2qYeNJJiBUV5lxCJAJVVbsaEFhYvGN2inDGyJDW02T1LGnDNA4F+FfqX2SkDKqhjvzuDZCxI+lORN0BmhNJ8+MxnMi6EzsunIILt+jGIco4ZQmXTcTtlnDJEm6bhEMS39qQUtfNua/+fordncQ7m0l17SDf1Y7S24XR3488GMMRS+FJ5JAVFQPwDSVNFEj57GRCXgrhAIXJE+gwoGrGsRBsouCZgCrPoFOrozcq07sF+j5mim9fHwwMmFnYiSDsnMoz08yZUF5uVsWJE80ZqINVJS2hthjTZDKwaROsX28OJ23caApzLAaGoKH72gg0bcRTvxl5QQslF7SQllpJ6X3oIvQCSZuLmkAN5f5yIv6ZLPCegt/rx+fz4fF40CRtt+80MMgbRXoZRMo7EHUHguLF3ScS6VDx9MTwdMdwDcRxDiZwDcZxxqIj4osxNEUomCPUfj9CJIJQWgqVlWYNj0SgtNRMQ6JMJGKap+7y8NIVha2LFjHh/POtxawW7wrd0BnQBhjUBonpMeJanJSeImNk0IyROmAAdsONTXHiQULPRnBSg6Q5kYd6wm7RjVu24ZIlnLKIy2EKsEsWcckStrfrFReL0NFuzkl1dZFt3UaqZRPFthb0nm5s0RiORBpUFc3Q0YdGpeySQMojkvDbyQW9qJUBmFmPVFqOo6IGV8U4NE8TWXEysUwDPT02Ojt3Tn/pbNuWRn3Fx65LACTJrIbl5VBWZpp97HxdUWFuy8tNkR6tKmgJtcWYIh6HV16Bl182t1u2mK13SYK6aZ34pr5K2QlLcTjX0a9tQRXypAHZGaSmpIn6QD3jgqdQE6jB4/UgekRSthRpIw2Y69h9og+/6McjenAJLkTdQa4goSl2iv0Z5M1teNva8bRtwtfeiqe9FWdnB6KqjPR83W6orDB7v1VlMGvaiPCWle0uxO/lZJWFxQES1aI0F5vZpmwjq2cREPCJPkJSiHpbPS7BTTZvQys6SGUlNNWGIIi4BIMkrzFdno3f5cBtk3HZRJyStH8BgwzD7J5u2WK2rpubKW7eQHHTBoS+PhRdQdM1NF0j7oTegEi3XyJV4kZrLEEoHY+tvApnRS2+mkZKaidSWt7AeF8lRsHL1q3mlNaWLUPpBejoML92JyUlpo1LdTXMm2cwfnwrCxdOoapKHhbjkpKxv3TLEmqLUUVR4KWXYMkSM23YYFa0+no4Yb7GGR9fQbvrMd5IPMWOVDsAkyOTWVA+iyml72dyZDKTI5MpdZfuNoz2fPZ5thW34Rbd1NvqqZFrKJVKcQmu3Y4ztm1jy6NP4l+9ipL1a3C3tyIKIIoi1NQgNDXBWWdAYyPU1IzUer/f8s5gMebpUXt4LP0YAFMdU2m0NRKRIsjCyKN/RzxLx0AKhyxS73VS4XXit8vomsqitTAp7N2n1fduGIY5/PXyy2ZEomXL0Lq7KGpFcqhsL4FNQZWWBpm+49x4xk3HWz+RksZpVFdMoCHYwKxgPX7HyPixrsO6dWZa/qopyJs3m4aiYFbBujpz6Pmii6ChYaSaVlWZ7emdKIrOokXbOf/8yYfd4JQl1BajQiwG998P995rVrrKSjMc6HXXwXEnFFkS/xu/Xv5r2qPtVHgrOG/8eZxcdzLzauZR4ip52+vXy/VsK27DL/qZ65iLQ9ylV9vbC/fdB48/jrphI3WCSHrCJPxnnY4wZw5MnWrWeKfzIN4BC4uDT1AM4hAcyILMLMesPby45VWNjQMpAE6sDuG2jUiCvvuM0L7p7jbr07//Ddu3o9tstDeGeb4xydPHFmmusFEyYQ4n1J/MrIpZfKRsBg2hBkRh793Yvj544QUzvfii+awQRbPxPnEifPCD5nbSJGhqMq2sj3QsobY4pDQ3w+9+Bw89ZLaW3/9++MQnTG0UBHi57WWueuZmdsR3cNHEi7jrvLuYUzVnn5V6XzTaG5EFmacyT7GpuIlZzllmU/x//xceftgcjj7nHIpf/BKvT5xJSnLgsUucWhu2PIVZHDE4RSfv876Pv6f+zvrCeo53Hb/b5w5JpD7gojWRY2lnnAV14f0b1gbTevOuu8z65HSSP/t0Hr5sCj/QXiBJP2c2nsmHJ7+fk+tO3rcvAsznwIoV8PTTpjivX2/unzkTrroKFi40542P5hkkS6gtDglr1phrDR9+2Jy2/Z//MZcxhIdWPrXEW7h98e38Z8t/OL76eP5w8R+YHHlny46yepYWpYVVhVXYFahdugn+fIfZPK+ogK9/Ha68Enw+PMDsospLbVEyRQ3NMJAtobY4AjAMgz6tj+X55QgIe40xIAgCE8NeWhM58qr2Fj72hlBVWLoU/vAHU1nLyui98Rp+OyXN/23/FwICV8+5mk8e+0nKvfuOaZBKmSPkL7xgXqa72zTrWLAAPvMZM0RwJPKuin9EYQm1xUFBVWHlSnj6aZG//30+AwMy1dVw661mK9luh3g+zkMbnuXB9Q+ypG0JFd4Kfn7uz/ng1A/udw/aMAzSRppetZderZfBvmbE1WuIrGnn9NUDlK3cgZgvmE3yX/4S3vc+sNlQdZ2+VJ7ebIHudB6ACq8D0RJpi8MY1VDpUrvoUDvoUDpI6kl8oo8LvBdQIVfsdmxB0+lK5dmRyAJQ4XXy5s60WCggLF9utrRXrIAlS9CTCZL1lTx/7Xx+WdPJ5vTdhNvDfHbuZ7lm9jV7TE3FYiOrNtavN9OWLeYzYtw4OO88uPhimDt37Bt1jRaWUFu8Z/T3w/PPw3PPmS3lZBJCIZGGhhQ//GGIc86RaI5v4ndvPMMzO55hZddKdENnXs08fn7uz7lo4kW4bG894aQZGlEtSq/Wy0CileLalXhWbyW8tp3J67rxdgwiCSJiMIw4+1j46odQTj2VRMME0kWVdDxHupginlfRDQOfQ2Zq2EddwGWJtMVhh2EYJPQE7Wo7HUoH3Vo3uqHjFb3UyDXMt82nSq5CFERUXWcwpzCYL5IoqAzmTF8CZR47J1SFcEuCOTf1+uvwxhtIK1dy5htvINgkMjaR1roAS441eKRCZ1VVNxGPylnjzuIb42/j1PpTsYkO2trglXW7i3JXl5lXhwOmTIHZs+HjHzd7zfX1o3jzDiMsobZ4x2garFplCvOzz5qNbkEwO6+f/CScfjo0TEjxi0f+jxftaW6993m6Ul24bW5OqT+FO8+8kzMaz6DCW7HP7yjoBXq1XvpynWQ2rEBYtZrQmlYi6zoYv60fSRcQ3G7E6TORLjwdY9YsctNmMFhWSaygEi8opIoqdMUQBQGPXcJrk5kUdlLhceCyvccuhCwsDjJFo0i32k270k6H2kFaTyMKIpVSJcc7j6dGriEgBjCAeF6hOZVlIFskUVAwDHDIIgGHjSnkqN66CdvqVaY4r16NkUyiGhrR6hI2VNv575kyy2s1tpWK1IZLOL76eK6sOo47w8dT6Gli00aR516AX603fRxkTB8plJXBtGlw6aWm87yd9pmypTjvCOu2WRwQg4Nmb/m558zecyxmurpeuNC02F64EFRnD09te4qfbHuKl198mUQmwcT8RM4bfx5nNp7JibUnYpfse1zbMAxSeooepYtE8yrUN5bjXL3J7C1v6sZW1BFkO0yehHzChYifORZ15iwSdeOIqwaxvEIsr6BoOvSn8NplQk4bDQE3QacNj02yDMUsDjsMwyCux2lT2uhQO+jVetENHb/op85WR41cQ5VchSzI5FSN3nSB5myCwVwRVTewSSIR0aChfSuhDWtxrF6FsGoVtLdjANmgh9bGElac7OO/viwrIjo5d5rppdPxp6dwzcyP4E2eSHdzGesegd9tgO3bTSMwSTIDUUydCueea4rz1KmmHYrFe4cl1BZviWGYTgWefBKeespseBuG2Uq+6io44wyYNctga3wjTzY/yUefeJI1vWuQRIl51fP4yolfQWqVuPaSa7Hb9xTnrJ6lM7aJzMol6CuW4X9jK2Wr26hNFRAR0RvrkY45AduHjkeYPRumTUO1O+jNFuhJF+jNFNB7U8iiQNBpY1zARdBpI+iwvb13JAuLMYphGPRr/bQoLbQoLST1JJIgUSVXMc85j1q5Fr9krjfWDYPeTIHWRIrBXBFBgNJMgumb1hNatwrnG68jrFkDioLusBMbX8Om6QEWL7TxqKuNHd40HgfMrZzLePcVHJM8nuy22Wz8j5OlS9MsVkxPXj6fKcKnnGIafE2dai6RslYxHnwsobbYA8Mwh7QffdQU6JYW03HAggXwk5/AaaeZLvXW963nHxv+wWf/tIiOZAc+h4/Txp3G9XOu5/SG0wk4A2aovJ5Fu/VktVSC/pceIfvCf3G+tpqq5h5EQ4CAH+3Y2Tg//UHkufPMMXT/yMOoL1ukN1mgN5NC0XQ8domJYQ8Rlx2fXbZ6yxaHPYZh0Ka2sbqwmj61D6fgpM5WxzzbPKrl6t0cleRVjc5UnrZkDmUwRu0by5i+cinu11citraY16uqIjqtgRUfPYFF/l4el7eTYzsBZ4DZkRM4Jvsxjuk4gd5l03hjjcyLQ4FrKipg+nSd009v4/3vn8ysWTK1tZaPn9HCEmqLYRIJc33z/feb803l5XD22fC975nOSBwOKGpF/rXxX/zhqT+wvm89EXeEiyZexDnjz+HEmhOxSftw+dPRQWHRI2Sffgz7ytX4VBV7XQXG/BOxfeZ07MedaHr/epPZZ0HV6Urn2RHPkFdNca71u6j1O/HYrJ+vxZGBYRh0qp2syK9gQBugXC7nLM9Z1Mq1e6yAUDSd1mSO9k3bKHv6vxz76kv41q5B1DWYNAnj9NPYOqGEf7pa+FvsJfozL+N3+Dmh8lQuznyc3JYT2PzKJF7YZF63utpcs/zpT5vbGTPMoWtF0Vi0aBvnnjvpsPPkdaRhPeksiMfh17+Ge+4xl0ycfTZ885umVebOcG26ofP39f/kjiV30JPu4czGM7lp/k0sHLdw3+KczyM8/DDH/+IX6G3NFO0i0ZMmoX/zU5Se+SFKmmbvM0+abrApmqItObR0yuNgfMiDz2H9ZC2OLLrVblbmV9Kj9lAml3Ge9zyq5b0GYacnnqL7n49Q+c+/c/LqlchOB8KCBXDl7eROPoG/x1/mD2/8gW3RbVR4Kzi98v3YWs9h49NzeXqFjGGYLjfnz4cbbzC3lZWHuMAWB4z11DuK0TTTS9gvfmG+vv56uOYa02JzV7bHtvO5/36ON7rf4MKJF/LV+V9lQnjCvi+sKPB//4f2kx+hDvahjZ/GituvJnTR5TSVzMIh7NvFkGEY9GQKbBhIUdR0JpZ4qfW73lmMWguLMYxiKLyUfYkdyg7CUpizPWdTK9fudQpH03Q6H/gH/v93O9P6epFOPAHpFz+H885DcTv506o/8bPHLyZZSHLu+PP4WOUdvPy3E3nwKRGHw5y2+vGPzZGx2tpDX1aLd4cl1Ecp3d3mUNeKFaY4f+ELe/cEtKxzGVc+fCVlnjIe+fAjHF99/J4H7cr27XDddaibN7Dx/bNou/ZG1LZazjvvvL0ak72ZpV0xYjmFUredSWEvfoc15mZx5KEaKo+lHyOhJ1joXkiTrWmfNhZGPE7049dStvQVimeejfyNmxGnTQOgPdHOJx74BBv6N/Dh6VcwNX0jD/2ujlvfMP1h//SnpjORXYNTWBx+WEJ9FLJ2rWmxLUmmS8958/Z+XE+6h48/8nFmlc/iz5f8GY/ds/cDd9LaCpdeihLw8J9/fpbgzJM53XYiT7U9tV+GXgPZArGcQoXXwbEVwQMvmIXFYcLqwmpieoyLvRcTkd7CV2Yuh/Lhj+Datp3+391D7SUXDH/Um+7l0gcvRRZlbp/2X37//Rk80AwnnQT/93+m0afl6evIwBLqo4yWFjP6TFMT/OlPew5z78rPXv0ZNtHGHy7+w9uLNMAtt4DLxZoHbiXp6+J97gXoqr7feevJFACYWeZ/myMtLA5vWpQW6uX6txZpMOemNmxg1W//zEnnL9jto1ueuwVV1zhu6+Pc/O1yjjsO/vtfmDXrIGbcYlSw2ltHEYYBN91kBsJ48MG3FmnDMPjXpn/xsZkfI+gMvv3FMxnTA8oNN+AqrUEzNHJGbr/zli6qtCVy2CQB2eoGWBzh+EU/WSP7tsfpi/5L98IzSU2aspuL2950L4u2LqJ8680s+ns5d94J//qXJdJHKtYT8SjixRdhyRK47Tbw+d762KJWJF1MMy44bv8ubrOZng9SKZpsTdgFO6vyq/Y7b+4hV54C1kJNiyOfKrmKXrWXjJ55y+NEvw9HLoNN2r1edKW6yGTgjSdncNdd8LGPWcPcRzLWv/Yo4t57TRd/Z5759sc6ZAcO2UGikNi/i9vtpknpM8/gEB3Mcs5ic3EzCW3/zhcFgZDLtkf0HguLI5HxtvHIgszG4sa3PvC00wiuWIZDU3fbnewLksnAB66Mc9FFBzGjFmMCS6iPEp591oz7eu21++9dyGPzkFXefnhumDPOgOXLIZlkqn0qbtHN8sLy/T497LKTV3Vyqrb/32lhcRjiEB1Msk9iY2EjqqHu+8DTTkPO5bCtXEFhyN6juxu+9oUQoghnXRQ7RDm2GE0soT4KiMfhK18xrUA//OH9O8cwDIpaEVk8AHvDE04wF2SvX48syBzrPJYupWu/Ty/3mOure9KF/f9OC4vDlGn2aRSMAs3F5n0fNGUKgs+Lb9MG4gWFLVvgkksgrxQIhcDttOyBjwYsoT7CyWRMRya5nOmne3970xv6N5Auppkcmbz/X7YzZE7MbOVPtE0kIAX2+3SXbM5TZ5S36GFYWBwh+CU/9bZ6NhU37fsgQUAIhxFjSf5wt8C555proq/6/n+RZYGZ5TMPXYYtRg2rOXYEs3EjfO5z0NYGf/yj6Wh/fzAMgztfvpNKXyWn1p+6/1+4Y4e5LS8HQBAEvKKXAvvXQ9YNA8Cy+rY4anAKTmLGvoevE7152NrLAx113GPYuPZq+PBnt3LZI3fywSkfpMpXdegyazFqWEJ9hJHJwOLF8MADZszoCRPgkUfMkHT7g6qr3PbCbTyz/Rn+fMmf93/oW1XhV78ye9VDa0S61C46lU4ivM1a0SHakuZyrojr7T2YWVgc7hSMAi1KC032puF9/f2wZg2sXm2u0Jj2/B/4bLJI76kn89h3Ffqcz3LFozdR6a3ke6d/bxRzb3EosYT6MEXXoa8Ptm2DLVvMmNFr1phJVU2tvOMOuOwy0yD77ehN9/LYlse4d9W9tMRb+OEZP+SsprPe/sRczlw//atfwbp18PvfgyyjGRqLs4spk8vQeWunJ7phsL4/RXsqx7igm4jbEmqLIwvDMKPTDQyYqb8fVnZ3s7p1GsG+GXyvA9rbYXAQBENnnnM1Nzp+z/HGv1h51fkEr/gPX339aTYNbOKsprP4ydk/we+wHAMdLVhCPQZRVYhGzdTfb1p5dnSYqbPT3HZ1mbEvwFzC3NQEkyebwjx/Powfv+/rK5rC9th21vSu4bXO11jasZTtse3IosyZjWfyuwt/x7Syafs4WTEF+fXX4eWX4YUXIJ+HOXNMf6THHQfAG4U3SOtpTneeziu8ss+8aLrB6r4EvZkCE0NeGoKWU2KLsYthmG3TZNI00kwmTQFOJGBwUGDp0iaWLxdJJs26u1OYo1GzXg9fBwNFilBZXcKUWpkTaju4uvJZ6qPPMW7ji7iiA/T5RL58oZ2HpzxOaFOA08Yt5LunfZeTak+yYq8fZVhCfQgwDEilzIodi+1egfv7R97vfB2LmefsSnk51NSYsWNnzTK3NTVmCOf6epD38p/MKTla4i20xFvYEd/BpoFNbOjfwNbBrSiaqfKTI5M5tf5Uvjr/q5xafyohV2jkAgMDsGmT2WXftMmc9F67FopFs3Uwe7ZpTn7OOWZLYYiElmBVfhVznHMISsG3uC8Ga/uT9GWLHFsRHLb6trB4r1FVc1oonR5Ju75/q89SqRExTiZHGshvxuGQEIQaamsFQiFzFmjcODPYTThs4AzFMJxtOAZXkWp5GravoWFHjsrlvfhjOQwMOkISf5/oYuuHp9A9dTaTy2fw+MQTmF1xzL7DyVoc8VhCfQBo2u6CG4+PpFhs7/vicbOCa3tZGhwMmpW5tNSszBMnjrwvLTVdfZaWmiK9t+FrwzBIFVNsjLayI75jWJR3CnNvunf4WI/dw+TIZGZXzObKGVcypXQKU0un4hecZjCNlhZ48e/mdutWU5gHB82TbTZzsnvyZHNtyLHHmp5T9jGmvrqwGrfoZoZjBoZq7PUYgL5ska5UnlnlAUukLQBTBLNZs9eaze497euzXG7fYlt4G3tGmw283pHk8Zhbv99sFAeD5utAYM/k9enojhi9mTYeff5RaqbUEBtop7htC2JLC9LyboSuKFU9BRoGNGyaARjE/XZitWXsOG026pzZuOedQnXjLD4drKc5VqAjlWNBXQSHFeL1qOeoEWpdN1vD6fTet6nU3lMyKdHScgo33yyTSu3Z0wWzkpeUmJV5Z5owAUKh3fftfG+2sM3z9sVOEe5KdfFyZzddqS660910JjvpTo+8zxRHXBAGnAEagg2MC47jxJoTGRccR0OogQYxTMlgDqG9HbbvgOc2w/b/mqLc2TlSKJfL7AKMH296Rpk0yRTnfXXZ90JGz9CsNDPXORdZkFHYR/cDaE/mcMgiVV5LpI9mFi6UhwVa3Y+VeaJoLlFyu82f7K6vvV6oq9tdbN8svnv77M11sagVGcgOMJAdoD/TP/y6NTtAf7af/sF+Mlt7kLt68HVFqYkq1Ec1jh0o0hiHsgxIgogoShT9XvK1DainNKFPnUFqagOvTBjkvHEfZbK8d4f7vZkkZW6HJdIWwFEk1JMn79sXriCYvq93tqC9XvN9SQnU1hqEw73MmxcgHJaGBXdXEXY693998k6ShSTbB0YEtyvVNZx2vt9VhAVBoMxTRpWviipvFQvHLRx6XUmDHqA+LeHvS5gT2Bs7oGM9dDxpvk8mR77Y7TbFuKHB7B03NJjvGxvNKB3vcu5rh2Iu0Zpsf+v114ZhkCwoRFx2a77tKOdDH9KJREYEd9e0qxDvTHb7gf9MDcMgo2SGhbclO0D/YD8DHbuIcW5ElJOFJO6CTlVcoyphbhtSMnNSMjUJnfKYgienIgoioiCie32odbX01Lhp+tiZ2CZMNOtUY6PZ7d6Fpbml2JTtlEqle82rqhvkFI1xActew8LkqBHq73/f1CG/3xThXZPbvW8RVxSdRYu2cP7547ENBY54O1KF1G6C253qpjPVudv7dDE9fPyuIlzprWRB/QIqvZVU+6updJVRk7dTOlhA7u4xhXdVO3Q0Q8cLZo84t0uUKrfbnLyuqYG5c00x3vm+tvY9EeO3IqpFKRFLsAtvbbmdKqrkVZ1yqzd91HPDDTrh8IGfpxs6sVzMFNps/5494F2Etz/bT0HdffzbpwhMyfuYmHVxbMZGXUKgMqYQiYoE+kWc6eKQENsQHT6EnXVofp25ra0161VdHUQiKKrK5kWLaDr//LccLovrcSJSZJ8N1Fi+CEDYZc1JW5gcNUJ92WWmSL9bFE2hM9VJR7KDzqS57Up10ZU2Bbgr1bVfIlzlqzL3OcKUJzRs3b2mCG8ZMu/u2DBi3r3reGAgMCK6CxeOiPDOFAodVCF+O1J6Crf41j0BTTfYGE1jk0QiLkuoLUYwDINYPkZfpo++TB+96V5zqDnTPyzGO7fRbBTd2H3pn0N2UOoupdRTSrUQYEGunPp0JZUJnbJogdBACl9vHEdPP1I8iUASSJrCWlMDtQ0wdUiE6+pGxLi09D0LT5XRM5RJex/yNgyDlngWpyzisx81j2eLt8H6JbwJTdfoSnWxPbadlngLrfFWXm1/lbv/fjdd6S76Mn0Yu0xUDw9H+6o4pe6U4ddVvioqfZWUe8qx6ZiLJLdtgy3bYft22P6cue3p2X3iu6xsRHSPOWZPIfZ6D/k92V8SWoIetYdT3KcM79N0s2zRXJFiViGaUxjIFdF0g7mVQWQrXNbRzZlw4zM3klAT9GX66M/2D69I2Inf4afUU0rEHaHUXUpTqImwO2wKsi1I9aBCWU+KYGcU+442hO3bzboW28Xj17AQ18KcySM94p1CXFZ2SOJEJrUkMS3GMY5jgJ0+9Q2SReX/t3fvUVGc5x/Av7N3WO6gsItyEwUJaBTjDS9YLOJJNDVJvSQ1JIemUdNEY6zapE3EGCU2UdsqGjkejTlpjYjk9IiKJD8VrxDpgsqlWi5qKgRBETDCXub5/bHsysqC3BYGeD+ekd3Zd+Z932EenrnszOBegw7l9Y14oNNjnJcLOyXEmA3YRK3n9Si+W4yCOwUorCpE8d1ilNSUoPReKbQG46EnsUgMlYMKYr0Y41zGIdIvEkOchpgHtaMackmzPUIi415wQQFwNdP4s6jIeA9P09e+FYpH565eesl4ftiUhNVqQG7bPUwiAgHgqflrAhHAo+nnY2X0ZEAjNaKBb0RD089GaoSWtGikRjSiAT9TLepEVRCRDFV3XHGHr0ajgUdDo3FZ5pTXgJNI4CyXwsfJDt6OCjiwPQZGBRjIgJEeIxHpF4lB9oPg6eAJT6UnBikHYbByMBQShTG2KiuNlwdeuWq8lr+gwLgBbIotR0fjZYLDhhmfQOPn9ygZd9MesSlmDDzBQE0DT+AJaNQZ1/Ub9fegE+vxkBrQwDegEQ1opAbU033UoBI8JLj1kxJlhjvQGnjzdrpULIK7nQyjBjvBVcEOezOPDJi/lKU1pSi6WYRLty8h76c8XKu+Zk7IKkcVhrsNx+Qhk/GbsN/A39UfAa4BGOI0BLyex7FjxxA9MwYSidSYvGAMTL1WC8OlLHDZ2RD/8APEOZfA3asBASBXF+iDQ6CfFgm9fwB0fn7Q+fnDMNgTPMdZJEbz63od+DqtRfK0lkiJCDyM5+gMZIABBuihB08G6KED3zSG54yf8aSHgTOAhx4EA3jOAIIBxFm+Nr7XW/ms5Z3FOHDgIIKYZJCSHAo4wEsXgsHkB3uJHGIRB7lYBBkUyC0Cpgxxg4OdAmK2B8009xWQuCURbu7u5g1Dngg8z4OuXQOyvoM2KwuiH7IhqjBebsg7OUIfEgr99BnQ+Q+DLsAfOv8A6N09wINrtqFJj+ZZXd8Uu4/GG4iHgXTQm/6RrimS9DDg0Wseehg4XVNMGd/znN4YI9CDOON7GAxQwwPp2m9BEmP2fRQncsh5JVzIF2oKhJPC3hgfYhHkYuNhbnupmO1FM1b1iUSdmJiIv/zlLygvL8dTTz2Fbdu2YerUqU+e0GQZEPXPaHAiEXycAxHoFoYFYc/C1zkIPk7DoZQ7N0uMBB0BRdVAQXU1qOn88P+VVYETS6C4/SM8zp+Bx9nTcMnLgUirhUGuQE3oKNTM/TVqQ8JQN3wEGjzcQCK+KZhNibABVFMCEpmSnx5oKmNKjjAlyKZx5tdNfxT4pvmZkujjuGb/N38pNv+TQAwJJJwEEogh5iSQQNZsXNPPptdSTgKpSAoFJ4dCJIe9SAEFJ4dcJIeEe/Lqo9PpkAvAXiZhSZqx6uSNKihrDBA/eAD3rHPwOHsK7hfPQXq/BiSW4F5wCGoio1ETFob7IwLxUOUBXtQ8JvQgrhJUdxskMgCcKXkaQM1fi/QwmBJr03StMcWRCGJIuKaYgRQySCDlpJDAHlJOChknhYSTGH8aJLiNG/ilQxQcZQ6wFylgJ7KDFFKWgJkuEXyi/uabb7BixQokJiYiIiICX3zxBWbPno2CggL4+Pi0byZ5wIeL/o5w30lwkDsZkyF0xqTH6cCjtimY+aa9Tt685WzQ68HrdPDO/AKqlAw4510DLxHj7vhgFL73LKrCA3B3pBp6CWDcf60Fj6xmlXNWXj0i5prSp5UkKeHkjz5r/nmLcsafYk5stZwYbEudES7XnzIQsOcUPI+eh6hRi7oR3ihZFI6fJg7HndFD0Wgvgh56cHgI4EqzKS03S8WcGFI8SpxSTtr03phUH72XmF9LuZbvzeMghYhr/+FynU6H27gBf5kPpG3dJIFhOkjwiXrLli2Ii4vDb3/7WwDAtm3bkJ6ejp07d2LTpk3tm8k5wMGvHsWOJ6EjHYwHxtpmSnTepwswde1ByB/cxd2IUORvfQs1kWPBOTpBwkngyEng2iwhtieBNk/MLIEyA9kuAJNf/xCNQwfhxlvPo3rONPA+3pByUrhAgkGPJU5jEpY1He3pfFJlmL5E0Ilaq9UiJycHa9eutRgfHR2N8+dbf9CDNQGyALjKXSHjZOYAN79u+gNgSqrmPdB//Qv09j78OGIElF+kwTsoCN7d2UGGGeCmAjAkbIHrq6/CVdy++xQwzEAj6ERdVVUFg8EAT09Pi/Genp6oqKiwOk1jYyMam93Y9/79+wAAv5/94Ch2tDqN6cskFngeTh9/DN20aTg7dy5mODtDWl3dhd4MTDqdDj///DOqq6vZ4cBO6Oryq6urAwCLSwp7i7XYfBHAv6ZMgbamptfa1V3627rO+mNbHYlNQSdqk8cPDxNRq4eMN23ahPj4+Bbj/f39O1f5pUvAN990blqGEYi6ujo4P3Yry57WWmyOGDGiF1rDMMLQntjkSAib2q3QarWwt7dHcnIy5s2bZx6/fPly5Obm4vTp0y2meXyrned53L17F+7u7p06H1xbW4uhQ4fi1q1bcOqOW5sNMGz5dU1Xlx8Roa6uDmq1GqIeuKFHWx6PzZqaGvj6+uLmzZu9vhHRHfrbus76Y1sdiU1B71HLZDKEh4cjIyPDIlFnZGTg+eeftzqNXC6H/LGbhri4uHS5LU5OToL45fZVbPl1TVeWn1CSoLXYBIzt60/rRn9b11l/bKe9sSnoRA0AK1euxOLFizFu3DhMmjQJu3fvxs2bN7FkyZLebhrDMAzD2JzgE/WCBQtQXV2N9evXo7y8HKGhoTh69Ch8fX17u2kMwzAMY3OCT9QAsGzZMixbtqxX6pbL5fjoo4+sHrJjnowtv67pz8uvv/WN9UfY+nJ/BP1lMoZhGIYZ6NitfBiGYRhGwFiiZhiGYRgBY4maYRiGYQSMJWqGYRiGETCWqGF83rW/vz8UCgXCw8Nx5syZNsufPn0a4eHhUCgUCAgIwK5du3qopcLUkeV36tQpcBzXYigqKurBFgtHZmYm5syZA7VaDY7j8O233z5xmt5a/2wRJykpKQgJCYFcLkdISAhSU1M7XC8RYd26dVCr1bCzs0NkZCTy8/P7bH8OHz6MWbNmwcPDAxzHITc394l9EWp/dDod1qxZg7CwMCiVSqjVarz66qu4fft2n+sLAKxbtw7BwcFQKpVwdXXFzJkzkZWV1WI+3Y4GuAMHDpBUKqWkpCQqKCig5cuXk1KppBs3blgtX1JSQvb29rR8+XIqKCigpKQkkkqldOjQoR5uuTB0dPmdPHmSANB//vMfKi8vNw96vb6HWy4MR48epQ8++IBSUlIIAKWmprZZvrfWP1vEyfnz50ksFtPGjRupsLCQNm7cSBKJhC5evNihehMSEsjR0ZFSUlLoypUrtGDBAlKpVFRbW9sn+7N//36Kj4+npKQkAkAajab1X4zA+1NTU0MzZ86kb775hoqKiujChQs0YcIECg8P73N9ISL6+uuvKSMjg4qLi+nq1asUFxdHTk5OVFlZ+YTfUNcM+EQ9fvx4WrJkicW44OBgWrt2rdXyq1evpuDgYItxb775Jk2cONFmbRSyji4/U6K+d+9eD7Sub2lPou6t9c8WcTJ//nyKiYmxKDNr1ixauHBhu+vleZ68vLwoISHB/HlDQwM5OzvTrl27+lx/mistLW13ou4L/THJzs4mAK0m3r7Ul/v37xMA+u6771ot0x0G9KFv0/Ouo6OjLca39bzrCxcutCg/a9YsXLp0CTqdzmZtFaLOLD+TMWPGQKVSISoqCidPnrRlM/uV3lj/bBUnrZUxzbM99ZaWlqKiosKijFwux/Tp01ttm5D70xl9rT/3798Hx3FWn8HQl/qi1Wqxe/duODs7Y/To0a32tzsM6ETdmeddV1RUWC2v1+tRVVVls7YKUWeWn0qlwu7du5GSkoLDhw8jKCgIUVFRyMzM7Ikm93m9sf7ZKk5aK2OaZ3vqNf3sSNuE3J/O6Ev9aWhowNq1a/Hyyy9bfTBGX+jLkSNH4ODgAIVCga1btyIjIwMeHh5W29Zd+sQtRG2tI8+7bq28tfEDRUeWX1BQEIKCgszvJ02ahFu3buGzzz7DtGnTbNrO/qK31j9bxEl75tldZdrTPqH0pzOE3h+dToeFCxeC53kkJia20RNh92XGjBnIzc1FVVUVkpKSMH/+fGRlZWHw4MFt9qkrBvQetYeHB8RicYstpsrKyhZbViZeXl5Wy0skEri7u9usrULUmeVnzcSJE3H9+vXubl6/1Bvrn63ipLUypnm2p14vLy8A6FDbhNyfzugL/dHpdJg/fz5KS0uRkZHR6mMm+0JflEolAgMDMXHiROzZswcSiQR79uyx2rbuMqATdfPnXTeXkZGByZMnW51m0qRJLcqfOHEC48aNg1QqtVlbhagzy88ajUYDlUrV3c3rl3pj/bNVnLRWxjTP9tTr7+8PLy8vizJarRanT59utW1C7k9nCL0/piR9/fp1fPfdd21uUAq9L9YQERobG9ss02U2/apaH2D6Sv6ePXuooKCAVqxYQUqlksrKyoiIaO3atbR48WJzedOlAO+++y4VFBTQnj172OVZHVh+W7dupdTUVLp27RpdvXqV1q5dSwAoJSWlt7rQq+rq6kij0ZBGoyEAtGXLFtJoNOZvxApl/bNFnJw7d47EYjElJCRQYWEhJSQktHrJTGv1Ehkvz3J2dqbDhw/TlStXaNGiRe2+PEuI/amuriaNRkNpaWkEgA4cOEAajYbKy8v7XH90Oh3NnTuXhgwZQrm5uRaXZDY2NvapvtTX19Mf//hHunDhApWVlVFOTg7FxcWRXC6nq1evtvq76Q4DPlETEe3YsYN8fX1JJpPR2LFj6fTp0+bPYmNjafr06RblT506RWPGjCGZTEZ+fn60c+fOHm6xsHRk+X366ac0bNgwUigU5OrqSlOmTKG0tLReaLUwmC5Xe3yIjY0lImGtf7aIk+TkZAoKCiKpVErBwcFWN9jaqpfIeInWRx99RF5eXiSXy2natGl05cqVPtufvXv3Wl0nPvrooz7XH9MlZtaGkydP9qm+PHz4kObNm0dqtZpkMhmpVCqaO3cuZWdnt9qP7sIec8kwDMMwAjagz1EzDMMwjNCxRM0wDMMwAsYSNcMwDMMIGEvUDMMwDCNgLFEzDMMwjICxRM0wDMMwAsYSNcMwDMMIGEvUDNPNMjMzMWfOHKjVanAch2+//dam9a1btw4cx1kMpntgMwzT97FEzTDd7MGDBxg9ejS2b9/eY3U+9dRTKC8vNw9XrlzpsboZhrEtlqiZfmnevHlwdXXFSy+91OP1vvzyyygqKsILL7xgtYxWq8Xq1avh7e0NpVKJCRMm4NSpU12qVyKRwMvLyzwMGjSoS/NjGKHprZgWApaomX7pnXfewf79+wVZ7+uvv45z587hwIEDuHz5Mn79618jJiamS4/6vH79OtRqNfz9/bFw4UKUlJR0el4MI0S9FdNCwBI10yVEhN/97ndwc3MDx3HIzc21Wi4yMtJ8/rS1Mt1pxowZcHR0tHk97a33tddeM/f/n//8J5KTkzF16lQMGzYMq1atwpQpU7B3795O1TlhwgTs378f6enpSEpKQkVFBSZPnozq6uqudocZoNoT10KJ6eaxZevvg/QWlqhtoPmKw3Ec3N3dERMTg8uXL/doOyoqKvD2228jICAAcrkcQ4cOxZw5c/D99993Wx3Hjx/Hvn37cOTIEZSXlyM0NLTVsm+88cYTy/SEH3/8EUuXLkVgYCAUCgU8PT0RHR1t0/O6f/3rX1FeXg7A+EdwxIgRcHBwMA+nT59GcXExAKCsrKzFl8MeH37/+9+b5z179my8+OKLCAsLw8yZM5GWlgYA+PLLL23Wn4Hq8dg2DTExMe2aPjIyEitWrOh0/T0R00D741oIMd08tvorSW83oL+KiYkx7yFVVFTgT3/6E5577jncvHmzR+ovKytDREQEXFxcsHnzZowaNQo6nQ7p6el46623UFRU1C31FBcXQ6VStevB9/b29t3ybeTw8HCrD2o/ceIE1Gp1m9OWlZXhmWeeQWRkJL766iuoVCrcunULKSkpkMvlrU6n1+sxYcKETtfr7OwMZ2dnAIBIJEJOTg7EYrFFGQcHBwCAt7c3CgsL25yfq6trq58plUqEhYV16VA607rmsW3S1rrTXXoqpoH2x7UQYrp5bPVbNn+Q5gAUGxtLzz//vMW4zMxMAkCVlZVEZHw2amhoKCkUCnJzc6OoqCiqr68nIiKDwUAJCQk0bNgwkslkNHToUNqwYUOH2jB79mzy9vY2z7O5e/fuERFRQ0MDvf322zRo0CCSy+UUERHR4tmqPM/Tp59+Sv7+/qRQKGjUqFGUnJxs7ieaPV/W19e31fZMnz6dli9fbjHO9KzalJQUmjp1KikUCho7diyVlpbSyZMn6ZlnniE7OzuKjIyk6urqDvWfyPis5xdffNFi3MqVK8nX15cMBkOr05nalZycTFOnTiWZTEYHDx7sVL0AKDU11fyZaVllZmZ2rDMd0NDQQN7e3hQfH2+zOgYqa7FtUllZSZ6envTJJ5+Yx128eJGkUimlp6e3iBcAVFpa2u66eyKmTX1sT1wLJaZNHo+1/oQlaht4PJjr6urozTffpMDAQDIYDHT79m2SSCS0ZcsWKi0tpcuXL9OOHTuorq6OiIhWr15Nrq6utG/fPvrvf/9LZ86coaSkpHbXX11dTRzH0caNG9ss984775BaraajR49Sfn4+xcbGkqurq0UAvf/++xQcHEzHjx+n4uJi2rt3L8nlcjp16hTV1NTQ+vXraciQIVReXm7eCLHGWlCnpqYSAIqKiqIzZ86QRqMhX19fmjJlCsXExFB2djZlZWWRu7s7bd68ud39N7EW1K+//jp5enq2+QfS1K5x48bRiRMn6Pr161RTU9Pueo8ePUpRUVGk0WgIAG3ZsoU0Gg3duHGDANC0adPIz8+PUlJSqKSkhLKzsykhIYHS0tI63Eciovfee49OnTpFJSUldPHiRXruuefI0dGRysrKOjU/pnVtJWoiorS0NJJKpfTDDz9QXV0dBQYGmtf7mpoamjRpEr3xxhtUXl5O5eXlpNfr21VvT8W0qZ3tiWuhxLQJS9RMh8TGxpJYLCalUklKpZIAkEqlopycHCIiysnJIQBW/5DW1taSXC7vUGJ+XFZWFgGgw4cPt1qmvr6epFIpff311+ZxWq2W1Gq1OYDq6+tJoVDQ+fPnLaaNi4ujRYsWERHR1q1b29yTNrEW1OvWrSNXV1e6c+eOedxrr71GPj4+FnsNMTExtHLlyifW0Vx0dDR5eHiQnZ0deXt7m/cqcnJyyMfHhziOo/DwcFqzZg3l5+e3aJdSqezQ3k7zep2cnFrsOQEw76kkJyfThx9+SH5+fiSVSsnLy4vmzZtHly9f7nB9REQLFiwglUpFUqmU1Go1vfDCCy36xHSPx2PbNKxfv95cZtmyZTRixAh65ZVXKDQ0lB4+fGj+zFoctEdPxjRR++JaKDFt0p8TNTtHbSMzZszAzp07AQB3795FYmIiZs+ejezsbIwePRpRUVEICwvDrFmzEB0djZdeegmurq4oLCxEY2MjoqKiOl03EQEAOI5rtUxxcTF0Oh0iIiLM46RSKcaPH28+P1pQUICGhgb88pe/tJhWq9VizJgxnW6fSW5uLubOnQsPDw/zuJs3b2LRokVQKpUW45599tkOzTs9Pd3q+LFjx6KkpARnz55FRkYGDh48iM8//xwHDx7EvHnzLNrl5+fX4T61Vq/Jl19+CYlEgvj4eMTHx3d4/tYcOHCgW+bDtE/z2DZxc3Mzv/7ss88QGhqKgwcP4tKlS1AoFF2uk8X0k2OrP2Pf+rYRpVKJwMBABAYGYvz48dizZw8ePHiApKQkiMViZGRk4NixYwgJCcHf//53BAUFobS0FHZ2dl2ue/jw4eA4rs0vJLUW+ERkHsfzPAAgLS0Nubm55qGgoACHDh3qcjvz8vIwceJEi3G5ubmYMGGC+X1DQwOuXbuGp59+usv1mYjFYkyfPh0bNmxAfn4+Bg8ejH/84x8W7YqMjOy2+pj+pXlsm4bmibqkpAS3b98Gz/O4ceNGt9TJYnpgY4m6h3AcB5FIhIcPH5rfR0REID4+HhqNBjKZDKmpqRg+fDjs7Oy6dLmFm5sbZs2ahR07duDBgwctPq+pqUFgYCBkMhnOnj1rHq/T6XDp0iWMHDkSABASEgK5XI6bN2+2+MM0dOjQTrcPAGpra1FWVmaxFX/jxg3cvXvXYlx+fj4MBgNGjx7dpfpaw/M8GhsbzXfystYuhmkvrVaLV155BQsWLMCGDRsQFxeHn376yfy5TCaDwWDo8HxZTA9s7NC3jTQ2NqKiogIAcO/ePWzfvh319fWYM2cOsrKy8P333yM6OhqDBw9GVlYW7ty5g5EjR0KhUGDNmjVYvXo1ZDIZIiIicOfOHeTn5yMuLg4AsH37dqSmpraZzBMTEzF58mSMHz8e69evx6hRo6DX65GRkYGdO3eisLAQS5cuxR/+8Ae4ubnBx8cHmzdvxs8//2yux9HREatWrcK7774LnucxZcoU1NbW4vz583BwcEBsbGynl09eXh5EIhFGjRplHpebmwsXFxeLQ855eXkICAjolpuXLF68GCEhIfjFL34BT09PlJSUYOPGjSAirFy50qJdYWFhXa6P6Z+ax7aJRCKBh4cHPvjgA9y/fx9/+9vf4ODggGPHjiEuLg5HjhwBAPj5+SErKwtlZWVwcHCAm5sbRCIRi2mmTSxR28jx48ehUqkAGIMjODgYycnJiIyMRGFhITIzM7Ft2zbU1tbC19cXn3/+OWbPng0A+POf/wyJRIIPP/wQt2/fhkqlwpIlS8zzrqqqMt8cozX+/v7497//jU8++QTvvfceysvLMWjQIISHh5vPryUkJIDneSxevBh1dXUYN24c0tPTLa7R/fjjjzF48GBs2rQJJSUlcHFxwdixY/H+++93afnk5eUhODjY4lC/RqNpsZWdl5fXbYfIxo4di0OHDmHLli2or6/H0KFDER0djX379mHIkCEW7eqO84pM/9Q8tk2CgoKwa9cubNu2DSdPnoSTkxMA4KuvvsKoUaOwc+dOLF26FKtWrUJsbCxCQkLw8OFDlJaWws/Pj8U00yaOTCc2GMaGIiMj8fTTT2Pbtm293ZRexXEcUlNT8atf/aq3m8IwXSK0mO7PscXOUTM9JjExEQ4ODgPyEYxLliwx33mMYfoLIcT0QIgttkfN9Ij//e9/5i/S+fj4QCaT9XKLelZlZSVqa2sBACqVyuJSFYbpi4QS0wMhtliiZhiGYRgBY4e+GYZhGEbAWKJmGIZhGAFjiZphGIZhBIwlaoZhGIYRMJaoGYZhGEbAWKJmGIZhGAFjiZphGIZhBIwlaoZhGIYRMJaoGYZhGEbAWKJmGIZhGAFjiZphGIZhBOz/ARzMrEcaxDW8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(5, 8), sharey=True)\n", + "## Aerosol backscatter\n", + "ax[0].plot(data_cube.retrievals_profile['klett'][grpIdx]['355_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR', zorder=2)\n", + "ax[0].plot(data_cube.retrievals_profile['klett'][grpIdx]['532_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR', zorder=3)\n", + "ax[0].plot(data_cube.retrievals_profile['klett'][grpIdx]['1064_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR', zorder=4)\n", + "ax[0].plot(data_cube.retrievals_profile['klett'][grpIdx]['355_total_NR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR', zorder=0)\n", + "ax[0].plot(data_cube.retrievals_profile['klett'][grpIdx]['532_total_NR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR', zorder=1)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 18)\n", + "ax[0].set_xlim(-1e-6, 11e-6)\n", + "ax[0].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('Bsc. Coef $[m^{-1}Sr^{-1}]$')\n", + "ax[0].set_title('aerBsc')\n", + "\n", + "## Aerosol Extinction\n", + "ax[1].plot(data_cube.retrievals_profile['klett'][grpIdx]['355_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR', zorder=2)\n", + "ax[1].plot(data_cube.retrievals_profile['klett'][grpIdx]['532_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR', zorder=3)\n", + "ax[1].plot(data_cube.retrievals_profile['klett'][grpIdx]['1064_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR', zorder=4)\n", + "ax[1].plot(data_cube.retrievals_profile['klett'][grpIdx]['355_total_NR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR', zorder=0)\n", + "ax[1].plot(data_cube.retrievals_profile['klett'][grpIdx]['532_total_NR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR', zorder=1)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].set_xlim(-1e-6, 3.7e-4)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_xlabel('Ext. Coef $[m^{-1}]$')\n", + "ax[1].set_title('aerExt')\n", + "fig.suptitle(\"Klett Retrieved Optical Profiles\", fontweight='bold')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5b657215-f454-49ff-b843-7c7cdb36bbb0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:57:55,616 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 10:57:55,617 - WARNING - at 10km height this is a difference of about 4 indices\n", + "c:\\Users\\buholdt\\AppData\\Local\\miniconda3\\envs\\PicassoPy\\Lib\\site-packages\\numpy\\lib\\_nanfunctions_impl.py:2015: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", + " var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([900, 900, 900, 900, 800, 800, 800, 800, 150, 150, 150, 150, 150,\n", + " 800, 800]), array([900, 900, 900, 900, 800, 800, 800, 800, 150, 150, 150, 150, 150,\n", + " 800, 800])]\n", + "Starting Raman retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "== 355, total, FR | 387, total, FR raman ========\n", + "900 370.01248\n", + "refHInd (923, 1725) refH [ 6903.17491198 12898.12483549] hBaseInd 170 hBase 1274.4999837875366\n", + "filling aerExt below overlap with 0.00010661377423382957 for calculating the backscatter\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\raman.py:525: RuntimeWarning: invalid value encountered in sqrt\n", + " sigElasticSample = sigGenWithNoise(sigElastic, np.sqrt(sigElastic + bgElastic), MC_count[2], 'norm').T\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== 532, total, FR | 607, total, FR raman ========\n", + "800 370.01248\n", + "refHInd (2117, 2408) refH [15828.32479811 18003.54977036] hBaseInd 157 hBase 1177.3249850273132\n", + "filling aerExt below overlap with 0.00010067973285248971 for calculating the backscatter\n", + "== 1064, total, FR | 607, total, FR raman ========\n", + "800 370.01248\n", + "refHInd (1759, 1814) refH [13152.27483225 13563.399827 ] hBaseInd 157 hBase 1177.3249850273132\n", + "filling aerExt below overlap with 8.177735427394038e-05 for calculating the backscatter\n", + "== 532, total, NR | 607, total, NR raman ========\n", + "150 190.6125\n", + "refHInd [403, 536] refH [3016.17496157 4010.34994888] hBaseInd 46 hBase 347.59999561309814\n", + "filling aerExt below overlap with 0.00010918399070423215 for calculating the backscatter\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\raman.py:362: RuntimeWarning: invalid value encountered in sqrt\n", + " noise = np.sqrt(sig + bg)\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\raman.py:526: RuntimeWarning: invalid value encountered in sqrt\n", + " sigVRN2Sample = sigGenWithNoise(sigVRN2, np.sqrt(sigVRN2 + bgVRN2), MC_count[2], 'norm').T\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== 355, total, NR | 387, total, NR raman ========\n", + "150 190.6125\n", + "refHInd [403, 536] refH [3016.17496157 4010.34994888] hBaseInd 46 hBase 347.59999561309814\n", + "filling aerExt below overlap with 0.00010872964355742773 for calculating the backscatter\n", + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "== 355, total, FR | 387, total, FR raman ========\n", + "900 370.01248\n", + "refHInd (1726, 2407) refH [12905.5998354 17996.07477045] hBaseInd 170 hBase 1274.4999837875366\n", + "filling aerExt below overlap with 8.681196683899163e-05 for calculating the backscatter\n", + "== 532, total, FR | 607, total, FR raman ========\n", + "800 370.01248\n", + "refHInd (1715, 2116) refH [12823.37483644 15820.8497982 ] hBaseInd 157 hBase 1177.3249850273132\n", + "filling aerExt below overlap with 8.292641830103832e-05 for calculating the backscatter\n", + "== 1064, total, FR | 607, total, FR raman ========\n", + "800 370.01248\n", + "refHInd (1938, 2069) refH [14490.29981518 15469.52480268] hBaseInd 157 hBase 1177.3249850273132\n", + "filling aerExt below overlap with 6.735718198625796e-05 for calculating the backscatter\n", + "== 532, total, NR | 607, total, NR raman ========\n", + "150 190.6125\n", + "refHInd [403, 536] refH [3016.17496157 4010.34994888] hBaseInd 46 hBase 347.59999561309814\n", + "filling aerExt below overlap with 0.00010590176397057119 for calculating the backscatter\n", + "== 355, total, NR | 387, total, NR raman ========\n", + "150 190.6125\n", + "refHInd [403, 536] refH [3016.17496157 4010.34994888] hBaseInd 46 hBase 347.59999561309814\n", + "filling aerExt below overlap with 0.00010776923476616501 for calculating the backscatter\n" + ] + } + ], + "source": [ + "## Raman retrieval for GHK-transmisson corrected profiles\n", + "data_cube.retrievalRaman(nr=True)" + ] + }, + { + "cell_type": "markdown", + "id": "08779469", + "metadata": {}, + "source": [ + "### Plot example: Raman retrieved optical profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4d49a4ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(7, 8), sharey=True)\n", + "## Aerosol backscatter\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355 total FR', zorder=2)\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532 total FR', zorder=3)\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['1064_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064 total FR', zorder=4)\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_NR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355 total NR', zorder=0)\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_NR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532 total NR', zorder=1)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[0].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 18)\n", + "ax[0].set_xlim(-1e-6, 6.5e-6)\n", + "ax[0].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('Bsc. Coef. $[m^{-1}Sr^{-1}]$')\n", + "ax[0].set_title('aerBsc')\n", + "\n", + "## Aerosol Extinction\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355 total FR', zorder=2)\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532 total FR', zorder=3)\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['1064_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064 total FR', zorder=4)\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_NR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355 total NR', zorder=0)\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_NR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532 total NR', zorder=1)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].set_xlim(-1e-6, 3e-4)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_xlabel('Ext. Coef. $[m^{-1}]$')\n", + "ax[1].set_title('aerExt')\n", + "\n", + "## Lidar Ratio\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355 total FR', zorder=2)\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532 total FR', zorder=3)\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['1064_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064 total FR', zorder=4)\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_NR']['LR'], data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355 total NR', zorder=0)\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_NR']['LR'], data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532 total NR', zorder=1)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[2].grid()\n", + "ax[2].set_ylim(0, 18)\n", + "ax[2].set_xlim(0, 100)\n", + "ax[2].legend(loc='upper right')\n", + "ax[2].set_xlabel('Lidar Ratio $[Sr]$')\n", + "ax[2].set_title('LR')\n", + "fig.suptitle(\"Raman Retrieved Optical Profiles\", fontweight='bold')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "1a38e951", + "metadata": {}, + "source": [ + "## Overlap Correction\n", + "\n", + "Two methods are available for calculating the Overlap Function:\n", + "\n", + "1. FRNR method\n", + "2. Raman method\n", + "\n", + "And four methods (currently only 3 implemented) are available for applying the Overlap Correction:\n", + "\n", + "0. no overlap correction\n", + "1. overlap correction with using the default overlap function (read function from file)\n", + "2. overlap correction with using the calculated overlap function\n", + "3. overlap correction with gluing near-range and far-range signal -> Not implemented yet!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "46e87f57", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:01,439 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 10:58:01,440 - WARNING - at 10km height this is a difference of about 4 indices\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting Overlap retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "355 both telescopes available\n", + "387 both telescopes available\n", + "532 both telescopes available\n", + "607 both telescopes available\n", + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "355 both telescopes available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\misc\\helper.py:910: RuntimeWarning: invalid value encountered in scalar divide\n", + " relStd.append(thisStd / abs(thisMean))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "387 both telescopes available\n", + "532 both telescopes available\n", + "607 both telescopes available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:03,562 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 10:58:03,563 - WARNING - at 10km height this is a difference of about 4 indices\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting Raman Overlap retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "532 607 both wavelengths available\n", + "355 387 both wavelengths available\n", + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "532 607 both wavelengths available\n", + "355 387 both wavelengths available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:08,558 - INFO - overlap Correction\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixing lower bins for frnr overlap functions\n", + "Fixing lower bins for raman overlap functions\n", + "overlapCorMode 2 overlapCalMode 2\n", + "dict_keys(['frnr', 'raman'])\n", + "overlap correction source raman\n", + "[array(['2024-08-21T00:00:00.000000', '2024-08-21T00:56:30.000000'],\n", + " dtype='datetime64[us]'), array(['2024-08-21T01:12:00.000000', '2024-08-21T02:02:00.000000'],\n", + " dtype='datetime64[us]')] ['2024-08-21T00:00:00.000000' '2024-08-21T00:56:30.000000'\n", + " '2024-08-21T01:12:00.000000' '2024-08-21T02:02:00.000000']\n", + "[[ 0 113]\n", + " [144 244]]\n", + "532_total_FR len(olFuncs) 2 2 2\n", + "(4, 4000)\n", + "(720, 4000)\n", + "355_total_FR len(olFuncs) 2 2 2\n", + "(4, 4000)\n", + "(720, 4000)\n", + "correct overlap 355\n", + "correct overlap 387\n", + "using 355 instead of 387\n", + "correct overlap 532\n", + "correct overlap 607\n", + "using 532 instead of 607\n", + "correct overlap 1064\n", + "using 532 instead of 1064\n" + ] + } + ], + "source": [ + "## Calculate overlap function\n", + "data_cube.overlapCalc()\n", + "\n", + "## Fix spike in lower bins\n", + "data_cube.overlapFixLowestBins()\n", + "\n", + "## Apply overlap correction\n", + "data_cube.overlapCor()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e7db198e", + "metadata": {}, + "outputs": [], + "source": [ + "## Aggregate overlap corrected profiles\n", + "data_cube.aggregate_profiles('sigOLCor')\n", + "data_cube.aggregate_profiles('BGOLCor')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "32032715", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:08,962 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 10:58:08,963 - WARNING - at 10km height this is a difference of about 4 indices\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "retrievalname klett_OC\n", + "Starting Klett retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "== 532, total, FR klett =================================\n", + "== 355, total, FR klett =================================\n", + "== 1064, total, FR klett =================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\klettfernald.py:277: RuntimeWarning: invalid value encountered in scalar divide\n", + " denominator1 = RCS[iAlt + 1] / (aerBsc[iAlt + 1] + molBsc[iAlt + 1])\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\klettfernald.py:311: RuntimeWarning: divide by zero encountered in divide\n", + " aerRelBRStd = np.abs((1 + noise / signal) / (1 + aerBsc + molBsc / 1e3) - 1)\n", + "2026-05-11 10:58:09,106 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 10:58:09,107 - WARNING - at 10km height this is a difference of about 4 indices\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "== 532, total, FR klett =================================\n", + "== 355, total, FR klett =================================\n", + "== 1064, total, FR klett =================================\n", + "[array([138., 900., 138., 900., 130., 800., 130., 130., 150., 150., 150.,\n", + " 150., 150., 800., 800.]), array([138., 900., 138., 900., 130., 800., 130., 130., 150., 150., 150.,\n", + " 150., 150., 800., 800.])]\n", + "Starting Raman retrieval\n", + "cldFree 0 [ 0 113]\n", + "cldFree mod (np.int64(0), np.int64(114))\n", + "== 355, total, FR | 387, total, FR raman ========\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\raman.py:339: RuntimeWarning: divide by zero encountered in divide\n", + " temp = number_density / (sig * height**2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "138.0 370.01248\n", + "refHInd (923, 1725) refH [ 6903.17491198 12898.12483549] hBaseInd 68 hBase 512.0499935150146\n", + "filling aerExt below overlap with 0.00022117783215994086 for calculating the backscatter\n", + "== 532, total, FR | 607, total, FR raman ========\n", + "130.0 370.01248\n", + "refHInd (2117, 2408) refH [15828.32479811 18003.54977036] hBaseInd 67 hBase 504.5749936103821\n", + "filling aerExt below overlap with 0.00015649698999919176 for calculating the backscatter\n", + "== 1064, total, FR | 607, total, FR raman ========\n", + "130.0 370.01248\n", + "refHInd (1759, 1814) refH [13152.27483225 13563.399827 ] hBaseInd 67 hBase 504.5749936103821\n", + "filling aerExt below overlap with 0.00012711505514938134 for calculating the backscatter\n", + "cldFree 1 [144 244]\n", + "cldFree mod (np.int64(144), np.int64(245))\n", + "== 355, total, FR | 387, total, FR raman ========\n", + "138.0 370.01248\n", + "refHInd (1726, 2407) refH [12905.5998354 17996.07477045] hBaseInd 68 hBase 512.0499935150146\n", + "filling aerExt below overlap with 0.00022633148478896803 for calculating the backscatter\n", + "== 532, total, FR | 607, total, FR raman ========\n", + "130.0 370.01248\n", + "refHInd (1715, 2116) refH [12823.37483644 15820.8497982 ] hBaseInd 67 hBase 504.5749936103821\n", + "filling aerExt below overlap with 0.00016702286848580293 for calculating the backscatter\n", + "== 1064, total, FR | 607, total, FR raman ========\n", + "130.0 370.01248\n", + "refHInd (1938, 2069) refH [14490.29981518 15469.52480268] hBaseInd 67 hBase 504.5749936103821\n", + "filling aerExt below overlap with 0.0001356647251738858 for calculating the backscatter\n" + ] + } + ], + "source": [ + "## Klett retrieval for overlap corrected profiles\n", + "data_cube.retrievalKlett(oc=True)\n", + "\n", + "## Raman retrieval for overlap corrected profiles\n", + "data_cube.retrievalRaman(oc=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b7929a3e", + "metadata": {}, + "source": [ + "### Plot example: Overlap Corrected Raman retrieved optical profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "7f29ccee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 4, figsize=(10, 8))\n", + "## Overlap Function\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['raman'][grpIdx]['355_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, linestyle='solid', label='Raman: 355nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['raman'][grpIdx]['532_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, linestyle='solid', label='Raman: 532nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['frnr'][grpIdx]['355_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, linestyle='dashed', label='FRNR: 355nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['frnr'][grpIdx]['532_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, linestyle='dashed', label='FRNR: 532nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['frnr'][grpIdx]['387_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='cyan', alpha=0.9, linewidth=1, linestyle='dashed', label='FRNR: 387nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['overlap']['frnr'][grpIdx]['607_total_FR']['olFunc'], data_cube.retrievals_highres['height']/1000, color='yellow', alpha=0.9, linewidth=1, linestyle='dashed', label='FRNR: 607nm FR')\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 2)\n", + "ax[0].set_xlim(0, 1)\n", + "ax[0].legend(loc='upper left')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('Overlap')\n", + "ax[0].set_title('olFunc')\n", + "\n", + "## Aerosol backscatter\n", + "ax[1].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['355_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR', zorder=2)\n", + "ax[1].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['532_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR', zorder=3)\n", + "ax[1].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['1064_total_FR']['aerBsc'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR', zorder=4)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[1].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].set_xlim(-1e-6, 6.5e-6)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_xlabel('Bsc. Coef. $[m^{-1}Sr^{-1}]$')\n", + "ax[1].set_title('aerBsc')\n", + "\n", + "## Aerosol Extinction\n", + "ax[2].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['355_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR', zorder=2)\n", + "ax[2].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['532_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR', zorder=3)\n", + "ax[2].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['1064_total_FR']['aerExt'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR', zorder=4)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[2].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[2].grid()\n", + "ax[2].set_ylim(0, 18)\n", + "ax[2].set_xlim(-1e-6, 3e-4)\n", + "ax[2].legend(loc='upper right')\n", + "ax[2].set_xlabel('Ext. Coef. $[m^{-1}]$')\n", + "ax[2].set_title('aerExt')\n", + "\n", + "## Lidar Ratio\n", + "ax[3].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['355_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR', zorder=2)\n", + "ax[3].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['532_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR', zorder=3)\n", + "ax[3].plot(data_cube.retrievals_profile['raman_OC'][grpIdx]['1064_total_FR']['LR'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR', zorder=4)\n", + "ax[3].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_FR']['refHeight'])/1000, color='blue', alpha=0.2)\n", + "ax[3].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "ax[3].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['1064_total_FR']['refHeight'])/1000, color='red', alpha=0.2)\n", + "ax[3].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['355_total_NR']['refHeight'])/1000, color='lightblue', alpha=0.2)\n", + "ax[3].axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_NR']['refHeight'])/1000, color='lightgreen', alpha=0.2)\n", + "ax[3].grid()\n", + "ax[3].set_ylim(0, 18)\n", + "ax[3].set_xlim(0, 100)\n", + "ax[3].legend(loc='upper right')\n", + "ax[3].set_xlabel('Lidar Ratio $[Sr]$')\n", + "ax[3].set_title('LR')\n", + "fig.suptitle(\"Overlap Corrected Raman Retrieved Optical Profiles\", fontweight='bold')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "a4763b2e", + "metadata": {}, + "source": [ + "## Depol and Ångström Profiles\n", + "\n", + "Retrieval of Volume and Particle depolarization as well as Ångström 355/532 backscatter 532/1064 backscatter, adn 355/532 Extinction" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "1394daab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:13,258 - INFO - voldepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,261 - INFO - voldepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,263 - INFO - voldepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,266 - INFO - voldepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,269 - INFO - voldepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,271 - INFO - voldepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,275 - INFO - pardepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,277 - INFO - pardepol at channel 355 cldFree 0 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- INFO - voldepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,302 - INFO - pardepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,304 - INFO - pardepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,305 - INFO - pardepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,306 - INFO - pardepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,308 - INFO - pardepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,309 - INFO - pardepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,310 - INFO - voldepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,313 - INFO - voldepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,316 - INFO - voldepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,319 - INFO - voldepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,322 - INFO - voldepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,324 - INFO - voldepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,326 - INFO - pardepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,328 - INFO - pardepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,329 - INFO - pardepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,330 - INFO - pardepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,332 - INFO - pardepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,333 - INFO - pardepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,334 - INFO - voldepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,336 - INFO - voldepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,338 - INFO - voldepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,342 - INFO - voldepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,344 - INFO - voldepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,347 - INFO - voldepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,349 - INFO - pardepol at channel 355 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,350 - INFO - pardepol at channel 532 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,351 - INFO - pardepol at channel 1064 cldFree 0 (np.int64(0), np.int64(114))\n", + "2026-05-11 10:58:13,352 - INFO - pardepol at channel 355 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,354 - INFO - pardepol at channel 532 cldFree 1 (np.int64(144), np.int64(245))\n", + "2026-05-11 10:58:13,355 - INFO - pardepol at channel 1064 cldFree 1 (np.int64(144), np.int64(245))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "klett\n", + "no_profiles 2\n", + "dict_keys(['532_total_FR', '355_total_FR', '1064_total_FR', '532_total_NR', '355_total_NR'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 25 \n", + "est. mdr 532_total_FR 0.006980075890695062 0.0004123753149513847\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR -0.002564597803008701 0.000360376986442669 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 25 \n", + "est. mdr 355_total_FR 0.007487748125272333 0.0002782226384317622\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR 0.008694584490275539 0.0002999015546750807 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 25 \n", + "est. mdr 1064_total_FR -0.2659112526502042 0.5017229522163786\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -1.2501748538810151 0.24590681956045324 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 25 \n", + "est. mdr 532_total_FR 0.005401728779509893 0.0003800923624930344\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR 0.008188281897148581 0.00044224925827077946 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 25 \n", + "est. mdr 355_total_FR 0.008691635805471698 0.0002974785481557542\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR -0.054335033966697456 -0.00022485174959624579 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 25 \n", + "est. mdr 1064_total_FR -0.22961605548565184 1.6789754315659422\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -0.9502287188330376 0.08046282974496088 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "no_profiles 2\n", + "dict_keys(['532_total_FR', '355_total_FR', '1064_total_FR', '532_total_NR', '355_total_NR'])\n", + "=== 532_total_FR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 532_total_NR ==============================================================\n", + "=== 355_total_NR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 532_total_NR ==============================================================\n", + "=== 355_total_NR ==============================================================\n", + "raman\n", + "no_profiles 2\n", + "dict_keys(['355_total_FR', '532_total_FR', '1064_total_FR', '532_total_NR', '355_total_NR'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 99 \n", + "est. mdr 355_total_FR 0.007484025790092697 0.0002781558224428612\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR 0.008694584490275539 0.0002999015546750807 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 99 \n", + "est. mdr 532_total_FR 0.006769965230897037 0.0004079623510771633\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR -0.002564597803008701 0.000360376986442669 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 99 \n", + "est. mdr 1064_total_FR -0.143998600393021 -0.0010701223963859659\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -1.2501748538810151 0.24590681956045324 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 99 \n", + "est. mdr 355_total_FR 0.008594207432880613 0.0002958472699227912\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR -0.054335033966697456 -0.00022485174959624579 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 99 \n", + "est. mdr 532_total_FR 0.005451172068800982 0.0003809709100917168\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR 0.008188281897148581 0.00044224925827077946 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 99 \n", + "est. mdr 1064_total_FR -4.349475542133461 15.364763974457475\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -0.9502287188330376 0.08046282974496088 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "no_profiles 2\n", + "dict_keys(['355_total_FR', '532_total_FR', '1064_total_FR', '532_total_NR', '355_total_NR'])\n", + "=== 355_total_FR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 532_total_NR ==============================================================\n", + "=== 355_total_NR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 532_total_NR ==============================================================\n", + "=== 355_total_NR ==============================================================\n", + "klett_OC\n", + "no_profiles 2\n", + "dict_keys(['532_total_FR', '355_total_FR', '1064_total_FR'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 25 \n", + "est. mdr 532_total_FR 0.006980075890695062 0.0004123753149513847\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR -0.002564597803008701 0.000360376986442669 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 25 \n", + "est. mdr 355_total_FR 0.007487748125272333 0.0002782226384317622\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR 0.008694584490275539 0.0002999015546750807 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 25 \n", + "est. mdr 1064_total_FR -0.2659112526502042 0.5017229522163786\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -1.2501748538810151 0.24590681956045324 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 25 \n", + "est. mdr 532_total_FR 0.005401728779509893 0.0003800923624930344\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR 0.008188281897148581 0.00044224925827077946 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 25 \n", + "est. mdr 355_total_FR 0.008691635805471698 0.0002974785481557542\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR -0.054335033966697456 -0.00022485174959624579 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 25 \n", + "est. mdr 1064_total_FR -0.22961605548565184 1.6789754315659422\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -0.9502287188330376 0.08046282974496088 (smooth1)\n", + "dict_keys(['aerBsc', 'aerBscStd', 'aerBR', 'aerBRStd', 'aerExt', 'aerExtStd', 'retrieval', 'signal', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "no_profiles 2\n", + "dict_keys(['532_total_FR', '355_total_FR', '1064_total_FR'])\n", + "=== 532_total_FR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "raman_OC\n", + "no_profiles 2\n", + "dict_keys(['355_total_FR', '532_total_FR', '1064_total_FR'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 99 \n", + "est. mdr 355_total_FR 0.007484025790092697 0.0002781558224428612\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR 0.008694584490275539 0.0002999015546750807 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 99 \n", + "est. mdr 532_total_FR 0.006769965230897037 0.0004079623510771633\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR -0.002564597803008701 0.000360376986442669 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 99 \n", + "est. mdr 1064_total_FR -0.143998600393021 -0.0010701223963859659\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -1.2501748538810151 0.24590681956045324 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "355_total_FR 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 99 \n", + "est. mdr 355_total_FR 0.008594207432880613 0.0002958472699227912\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "est. mdr 355_total_FR -0.054335033966697456 -0.00022485174959624579 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "532_total_FR 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 99 \n", + "est. mdr 532_total_FR 0.005451172068800982 0.0003809709100917168\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "est. mdr 532_total_FR 0.008188281897148581 0.00044224925827077946 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "1064_total_FR 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 99 \n", + "est. mdr 1064_total_FR -4.349475542133461 15.364763974457475\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "est. mdr 1064_total_FR -0.9502287188330376 0.08046282974496088 (smooth1)\n", + "dict_keys(['aerExt', 'aerExtStd', 'retrieval', 'signal', 'aerBsc', 'aerBscStd', 'LR', 'effRes', 'LRStd', 'refBeta', 'vdr', 'vdrStd', 'mdr', 'mdrStd'])\n", + "no_profiles 2\n", + "dict_keys(['355_total_FR', '532_total_FR', '1064_total_FR'])\n", + "=== 355_total_FR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n", + "=== 355_total_FR ==============================================================\n", + "=== 532_total_FR ==============================================================\n", + "=== 1064_total_FR ==============================================================\n" + ] + } + ], + "source": [ + "## Volume and particle depolarization\n", + "data_cube.calcDepol()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b130da9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "klett\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 355_total_NR 532_total_NR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_NR 532_total_NR Ext\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 355_total_NR 532_total_NR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_NR 532_total_NR Ext\n", + "raman\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 355_total_NR 532_total_NR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_NR 532_total_NR Ext\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 355_total_NR 532_total_NR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_NR 532_total_NR Ext\n", + "klett_OC\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "raman_OC\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n", + "channels available 355_total_FR 532_total_FR Bsc\n", + "channels available 532_total_FR 1064_total_FR Bsc\n", + "channels available 355_total_FR 532_total_FR Ext\n" + ] + } + ], + "source": [ + "## Ångström ratios\n", + "data_cube.Angstroem()" + ] + }, + { + "cell_type": "markdown", + "id": "2672fd74", + "metadata": {}, + "source": [ + "### Plot example: Depol and Ångström ratio profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "86fab6bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(7, 8), sharey=True)\n", + "## Volume Depolarization Ratio\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['vdr'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['vdr'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax[0].plot(data_cube.retrievals_profile['raman'][grpIdx]['1064_total_FR']['vdr'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax[0].grid()\n", + "ax[0].set_ylim(0, 18)\n", + "ax[0].set_xlim(-1e-3, 6e-1)\n", + "ax[0].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('Depol. Ratio')\n", + "ax[0].set_title('vdr')\n", + "\n", + "## Particle Depolarization Ratio\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['pdr'], data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['pdr'], data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax[1].plot(data_cube.retrievals_profile['raman'][grpIdx]['1064_total_FR']['pdr'], data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax[1].grid()\n", + "ax[1].set_ylim(0, 18)\n", + "ax[1].set_xlim(-1e-3, 6e-1)\n", + "ax[1].legend(loc='upper right')\n", + "ax[1].set_ylabel('Height [km]')\n", + "ax[1].set_xlabel('Depol. Ratio')\n", + "ax[1].set_title('pdr')\n", + "\n", + "## Ångström ratios at cloud free period 0\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['AE_Bsc_355_532'], data_cube.retrievals_highres['height']/1000, color='orange', alpha=0.9, linewidth=1, label='AE_Bsc_355_532')\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['532_total_FR']['AE_Bsc_532_1064'], data_cube.retrievals_highres['height']/1000, color='violet', alpha=0.9, linewidth=1, label='AE Bsc 532 1064')\n", + "ax[2].plot(data_cube.retrievals_profile['raman'][grpIdx]['355_total_FR']['AE_Ext_355_532'], data_cube.retrievals_highres['height']/1000, color='black', alpha=0.8, linewidth=1, label='AE_Ext_355_532')\n", + "ax[2].grid()\n", + "ax[2].set_ylim(0, 18)\n", + "ax[2].set_xlim(-1, 3)\n", + "ax[2].legend(loc='upper right')\n", + "ax[2].set_xlabel('Ångström Exp.')\n", + "ax[2].set_title('AE')\n", + "fig.suptitle(\"Depol. and Ångström profiles\", fontweight='bold')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "aea7dd7f", + "metadata": {}, + "source": [ + "## Lidar Calibration\n", + "\n", + "- Lidar calibration constants (LC) are retieved for each channel at each cloud free period for both Klett and Raman retieved profiles\n", + "- All retrieved LCs are stored in the database\n", + "- If no LCs can be retrieved for a given channel, the LCs in the time range [24h before the measurment, 24h after the measurement] for the given channel included in the database will be used\n", + "- The optimal LC, ie. the one with the lowest standard deviation per channel is used for the processing.\n", + "- The following priority order is used when choosing the optimal LC\n", + " 1. Raman retrieved LC from data\n", + " 2. Klett retrieved LC from data\n", + " 3. Raman retrieved LC from database\n", + " 4. Klett retrieved LC from database" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "053776c7-9b0e-42fd-972c-0446cb821683", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:13,865 - INFO - LC retrieval: klett method\n", + "2026-05-11 10:58:13,873 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:13,929 - INFO - cldFreGrp 0, Channel 532 total FR, LC_stable 110519246161610.45, LCStd 0.0022345184524830176\n", + "2026-05-11 10:58:13,936 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:13,994 - INFO - cldFreGrp 0, Channel 355 total FR, LC_stable 25673613689464.64, LCStd 0.001697933679133036\n", + "2026-05-11 10:58:14,001 - INFO - Using Retrieved Exticntion\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\misc\\helper.py:907: RuntimeWarning: Mean of empty slice\n", + " thisMean = np.nanmean(window)\n", + "2026-05-11 10:58:14,084 - INFO - cldFreGrp 0, Channel 1064 total FR, LC_stable 70707728983268.77, LCStd 0.0030263297684652185\n", + "2026-05-11 10:58:14,093 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,195 - INFO - cldFreGrp 0, Channel 532 total NR, LC_stable 7339709186305.094, LCStd 0.0014185825176983608\n", + "2026-05-11 10:58:14,201 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,254 - INFO - cldFreGrp 0, Channel 355 total NR, LC_stable 2573459848291.3633, LCStd 0.0017814524429655663\n", + "2026-05-11 10:58:14,261 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,315 - INFO - cldFreGrp 1, Channel 532 total FR, LC_stable 100796807564409.77, LCStd 0.0018459930458763164\n", + "2026-05-11 10:58:14,321 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,372 - INFO - cldFreGrp 1, Channel 355 total FR, LC_stable 22959968454498.54, LCStd 0.0026408855624344184\n", + "2026-05-11 10:58:14,379 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,429 - INFO - cldFreGrp 1, Channel 1064 total FR, LC_stable 56601395575695.63, LCStd 0.0032925870134280343\n", + "2026-05-11 10:58:14,435 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,489 - INFO - cldFreGrp 1, Channel 532 total NR, LC_stable 7421831434571.952, LCStd 0.0021248390768817274\n", + "2026-05-11 10:58:14,496 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,553 - INFO - cldFreGrp 1, Channel 355 total NR, LC_stable 2565967965315.777, LCStd 0.0015532591453381584\n", + "2026-05-11 10:58:14,554 - INFO - LC retrieval: raman method\n", + "2026-05-11 10:58:14,561 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,614 - INFO - cldFreGrp 0, Channel 355 total FR, LC_stable 18537857712115.51, LCStd 0.005688576848401589\n", + "2026-05-11 10:58:14,679 - INFO - cldFreGrp 0, Channel 387 total FR, LC_stable 51907858419124.91, LCStd 0.0014067273968144164\n", + "2026-05-11 10:58:14,688 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,743 - INFO - cldFreGrp 0, Channel 532 total FR, LC_stable 113827580950926.9, LCStd 0.0037916867356577537\n", + "2026-05-11 10:58:14,805 - INFO - cldFreGrp 0, Channel 607 total FR, LC_stable 307527913685577.5, LCStd 0.001288213702299064\n", + "2026-05-11 10:58:14,812 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,869 - INFO - cldFreGrp 0, Channel 1064 total FR, LC_stable 51000118564518.48, LCStd 0.008641182064026387\n", + "2026-05-11 10:58:14,876 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:14,936 - INFO - cldFreGrp 0, Channel 532 total NR, LC_stable 8987539753134.658, LCStd 0.0065916436418356076\n", + "2026-05-11 10:58:15,000 - INFO - cldFreGrp 0, Channel 607 total NR, LC_stable 13657683512637.668, LCStd 0.0023244082205122374\n", + "2026-05-11 10:58:15,006 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:15,060 - INFO - cldFreGrp 0, Channel 355 total NR, LC_stable 2753655573745.7373, LCStd 0.004160411756104806\n", + "2026-05-11 10:58:15,120 - INFO - cldFreGrp 0, Channel 387 total NR, LC_stable 4007540841170.811, LCStd 0.0014548561143754276\n", + "2026-05-11 10:58:15,127 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:15,182 - INFO - cldFreGrp 1, Channel 355 total FR, LC_stable 16667136285998.08, LCStd 0.003656781342833464\n", + "2026-05-11 10:58:15,242 - INFO - cldFreGrp 1, Channel 387 total FR, LC_stable 48944093882776.27, LCStd 0.0007600049206648669\n", + "2026-05-11 10:58:15,248 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:15,310 - INFO - cldFreGrp 1, Channel 532 total FR, LC_stable 89355572720096.28, LCStd 0.00544130058765483\n", + "2026-05-11 10:58:15,366 - INFO - cldFreGrp 1, Channel 607 total FR, LC_stable 290981081297381.9, LCStd 0.0008085985557301123\n", + "2026-05-11 10:58:15,372 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:15,428 - INFO - cldFreGrp 1, Channel 1064 total FR, LC_stable 34113693885558.87, LCStd 0.009932385524826831\n", + "2026-05-11 10:58:15,433 - INFO - Using Retrieved Exticntion\n", + "2026-05-11 10:58:15,487 - INFO - cldFreGrp 1, Channel 532 total NR, LC_stable 8686033355261.775, LCStd 0.004616845578046064\n", + "2026-05-11 10:58:15,543 - INFO - cldFreGrp 1, Channel 607 total NR, LC_stable 13499399145944.1, LCStd 0.002263696297716918\n", + "2026-05-11 10:58:15,548 - INFO - Using Retrieved Exticntion\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\lidarconstant.py:110: RuntimeWarning: invalid value encountered in divide\n", + " LC = (signal * height**2) / (bsc * trans)\n", + "2026-05-11 10:58:15,618 - INFO - cldFreGrp 1, Channel 355 total NR, LC_stable 2698389817432.6167, LCStd 0.003970478741517248\n", + "2026-05-11 10:58:15,677 - INFO - cldFreGrp 1, Channel 387 total NR, LC_stable 3921760499749.09, LCStd 0.0018460497966003243\n", + "2026-05-11 10:58:15,678 - INFO - Choosing best LC per channel...\n", + "2026-05-11 10:58:15,678 - INFO - Database LC values will be used when no retrieved ones are available.\n" + ] + } + ], + "source": [ + "## Lidar calcibration for both Klett and Raman retrival\n", + "data_cube.LidarCalibration(db_path=DATABASE_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "991253e0-5eba-45d5-af85-905819ef6e00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'1064_total_FR': np.float64(51000118564518.48),\n", + " '355_total_FR': np.float64(16667136285998.08),\n", + " '355_total_NR': np.float64(2698389817432.6167),\n", + " '532_total_FR': np.float64(113827580950926.9),\n", + " '532_total_NR': np.float64(8686033355261.775),\n", + " '387_total_FR': np.float64(48944093882776.27),\n", + " '387_total_NR': np.float64(4007540841170.811),\n", + " '607_total_FR': np.float64(290981081297381.9),\n", + " '607_total_NR': np.float64(13499399145944.1)}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Display Lidar calibration constants per channel\n", + "data_cube.LCused" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "22a66601-5127-4a38-b43e-be9d6a52632a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:15,719 - INFO - writing to sqlite-db: PicassoPyDatabase.db\n", + "2026-05-11 10:58:15,720 - INFO - writing LC to table: lidar_calibration_constant\n", + "2026-05-11 10:58:15,729 - INFO - 18 rows inserted into 'lidar_calibration_constant'.\n", + "2026-05-11 10:58:15,730 - INFO - writing to sqlite-db: PicassoPyDatabase.db\n", + "2026-05-11 10:58:15,731 - INFO - writing LC to table: lidar_calibration_constant\n", + "2026-05-11 10:58:15,738 - INFO - 10 rows inserted into 'lidar_calibration_constant'.\n", + "2026-05-11 10:58:15,741 - INFO - writing to sqlite-db: PicassoPyDatabase.db\n", + "2026-05-11 10:58:15,742 - INFO - writing DC to table: depol_calibration_constant\n", + "2026-05-11 10:58:15,750 - INFO - 3 rows inserted into 'depol_calibration_constant'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['klett', 'raman', 'klett_db', 'raman_db'])\n", + "dict_keys(['klett', 'raman', 'klett_db', 'raman_db'])\n", + "dict_keys(['klett', 'raman', 'klett_db', 'raman_db'])\n" + ] + } + ], + "source": [ + "## Store calibration constants in database\n", + "data_cube.write_2_sql_db(db_path=str(DATABASE_PATH), parameter='LC', method='raman')\n", + "data_cube.write_2_sql_db(db_path=str(DATABASE_PATH), parameter='LC', method='klett')\n", + "data_cube.write_2_sql_db(db_path=str(DATABASE_PATH), parameter='DC')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "11f2b56c-109f-4986-863e-e15c4b064ba3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:15,763 - INFO - saving product: profiles\n", + "2026-05-11 10:58:15,877 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0000_0056_profiles.nc\n", + "2026-05-11 10:58:16,294 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0112_0202_profiles.nc\n", + "2026-05-11 10:58:16,622 - INFO - saving product: NR_profiles\n", + "2026-05-11 10:58:16,732 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0000_0056_NR_profiles.nc\n", + "2026-05-11 10:58:16,980 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0112_0202_NR_profiles.nc\n", + "2026-05-11 10:58:17,132 - INFO - saving product: OC_profiles\n", + "2026-05-11 10:58:17,245 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0000_0056_OC_profiles.nc\n", + "2026-05-11 10:58:17,648 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_0112_0202_OC_profiles.nc\n" + ] + } + ], + "source": [ + "## Save retrived optical profiles\n", + "write_profile2nc_file(data_cube=data_cube, prod_ls=[\"profiles\", \"NR_profiles\", \"OC_profiles\"], collect_debug=True)" + ] + }, + { + "cell_type": "markdown", + "id": "c4e1c3a5", + "metadata": {}, + "source": [ + "## High Resoulution Retrievals\n", + "\n", + "The following high resolution (30s) time-height data are retrieved: \n", + "\n", + "- Attenuated backscatter\n", + "- Volume depolarization\n", + "- Molecular backscatter and extinction\n", + "- Quality mask\n", + "- QuasiV1 and QuasiV2 retrievals\n", + "- Target categorization V1 and V2" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "c948f973-c451-4e1e-815d-ff4ac2a8ad17", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:17,931 - INFO - attBsc 2d retrieval\n", + "2026-05-11 10:58:18,335 - INFO - and even a 355 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exprimental, attenuated backscatter solution for 387_total_NR\n", + "G [1.] [1.]\n", + "H [0.02041] [-0.998]\n", + "polCaliEta 47.742418057789656\n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:18,525 - INFO - and even a 532 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calculated R_t [0.95999647]\n", + "G [1.] [1.]\n", + "H [-0.01477] [-0.9987]\n", + "polCaliEta 12.380587648929659\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "calculated R_t [1.02998285]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:18,720 - INFO - and even a 1064 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.]\n", + "H [-0.02439] [-0.996]\n", + "polCaliEta 0.14153155793311498\n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n", + "calculated R_t [1.04999949]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:19,037 - INFO - voldepol 2d retrieval\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", + "G [1.] [1.] H [0.02041] [-0.998] Eta 47.742418057789656 error [ 0.00016 0.01567 -0.00958] Window 1 \n", + "G [1.] [1.] H [-0.02439] [-0.996] Eta 0.14153155793311498 error [ 0.00133 0.02379 -0.01205] Window 1 \n" + ] + } + ], + "source": [ + "## Highres attenuated backscatter and volume depolarization\n", + "data_cube.attBsc_volDepol()\n", + "\n", + "## Highres molecular signal\n", + "data_cube.molecularHighres()" + ] + }, + { + "cell_type": "markdown", + "id": "976d6a2a", + "metadata": {}, + "source": [ + "### Plot Example: Highres Attenuated Backscatterm & Volume Depolarization" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "34383cc9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wv, t, tel = 532, 'total', 'FR'\n", + "\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 7), sharex=True)\n", + "pcmeshATB = ax[0].pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'attBsc_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=1.5e-5\n", + ")\n", + "cbar = fig.colorbar(pcmeshATB)\n", + "cbar.set_label(f'Attenuated Backscatter {wv}nm {t} {tel}')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_ylim(0, 5)\n", + "\n", + "pcmeshVD = ax[1].pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'voldepol_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=0.3\n", + ")\n", + "cbar = fig.colorbar(pcmeshVD)\n", + "cbar.set_label(f'Volume Depolarization Ratio {wv}nm {t} {tel}')\n", + "ax[1].set_xlabel('Time [UTC]')\n", + "ax[1].set_ylabel('Height [km]')\n", + "ax[1].set_ylim(0, 5)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "118e3241", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5ENLhSNco27w4fVdmHtLhCPFWS3/n/p/XXtl8CNHauuytSEC7VyzovLjriXuSbVzZfEjHwK9V4deqmfdzbY2PM68NwK73kWVrVzYfkkDAVBMLtZMKIPlvv1bFieItZn7lHlz7bCP7wXF159O9P/c3+8G5YD/sOLiKg67MiZkPwD4HvhzmhdKvpNvLMMurnVOIt7eRDkdIOl0sXf+mzDodv4bXrGtwQQGlea+7jrjPk05Xn4Ocr2ByMjOeS9e/SfcDx4TPHa6PE8Vb9Nnj3oP7l3N2KRt/XnCd8Rq6R6sVnXf21SvsjcdyjY4HMC5m44qG/d5/oniL9tH9N9vOtRscmdU1Z58fIyTbLR0Ld2yuxA7yfre/LvA+qKXDEZJeb997XWl7c8stt+fGXB/sq9XiNDn066vRXlCgcjAY4G/+5m9w7NixK/pcMNlAOhxh6SU/bb5ki+oYAhYI8AvPdfgI8E4PH0NwZFadPDou7hda0u3BPzavIHRp/l51lBZqJ+FXKvpZ1znkNVY2H7IAzPnypxEcisNvk4O9Zj0DLhZqJ+FXK5mfLSAt6BgEU00Ek8LqicNsAY8FUaPM5wgU6LzQ8SMw5AsQB4tOHp1QglCC6nHnm+DbvTcBsztmfq2KYHIy014XYHhhAV5YUEeT5vYxmBRGWAIJQ3H8zbitbD6kQIMg2SsW9O/xmXPwqxV1ZgEgPH5MwQD75QYYuOZcAMy1544h2U67JiyjzHvRoRVmu4lgqmkdSQMIGDBgOxZqJzPzTBDNvyfbrcz1XTB/onjL2GezDjudewZN3L4v1E7ucfDHgYgL1hREmvFNh0N4oQBdu0cM+Or1MvdKh0MdX+45d4+6bQBkfysIKRZtkMOAAnd9Jp1uBviTkXTBgr1W9j4yNkN9fnBNc8659rlHXEd/nE3dD8A918ax51y7Y0Gwc3r4GJafvl8DXAzCuGAxPiPHPzFQMh7YW5y+S4M2S/P3ytyaYBn7znHnZ+IL65m1y2ADx4vzzWfM+HOJfbkUoHPNBmdsMMAFpQxaZII0Giwq7gFQ7nq8FCDaz2Eb7wPbtV+wIfP9MbLPHXf8Oc9c/9zrV8vYX5eBP+jnvLBwVduWW2655XalliA99Our0Z5XUPkzP/Mz+PSnP40nnngCf/RHf4Qf+qEfws7ODk6ePHn5DzvGHK9kbQNeQRhAr1BQsIg0BdJ0j/Nk89bMF/JoBKRpJgo8DjbS9U297/L5D2Bx6k5xaAryHtdJI9ijM7o4dac6CKeHj8ErhJkv/8WpO7E4daeV7I4M8D1zzjqAhQL8yaY6OjSvINFqsk7pcGgcasl55Jf84tSdCKaaCog5Hn61on3wqxUdk3Q4hD/ZzIAKFwC54NevVpStWO2cwuLUnRkAnxl7x/FBmuq92cZ0OATSVB1YtpFzk0bi+HN+TxRvwdL8vVjtnNK+xdvbDhixbaejReM4cFwJkFxncqF2EvGFdR0fAAiOz2fWCcGI6xiubD6EpNPNMILjEfl0OFSmwa/KXPnVCoKpJvxqJcM+czwWp+7UzwIC+mQOQ3iFAhan7lQ20mXn02ikfSBTzWAG1wjBH9cpIOyRO1ZcD1wrCjTDgsOUD3Vd8DO8TzocWse8sBegEVz6lUpmzXGduEGRpfl7EUw1EW9v6z3GnVOOK9vBNiqI3CcItDh1ZyZ4M95G/l9BTsGCDskb7Snb5raJ/XbHBHCYaCdocbUs6do1yOcgWV13fKmISB2G1t2DMv9DBfduu/kcSodDLE7dqQA/7fZ0/AgmM4oJA96oDGEgh2veDaop812w+9xtg/v8uJgR3LHP40CQf3fXKde/16gBabpnnYyrLlxzAe94u1z2lM8DGgNvbjBMVTZbLZ23zDO3YAE3n0PP1g4C2MeDLge55qV+Pshncsstt9yea0uexX9fjfa8gsqnn34aP/IjP4Kv+7qvw2tf+1oUi0X84R/+IV70ohdd0XX8aln/nY5G8GenkY6sk5h0e0i6vQxzNs4YARKRTro9dVoIEFc7p7Cy9bBef1ze5xVEBpZ0e5nrZwBksSBtMw794tSd2ib+O+n2MoB0+fwHFNAqcBmN1MHzCtKH1c4peNUK4gvrmS98AsU97FKhkAGlwVRT5IumDa7TC0CvO84GjDtS6WiEpevfJKzE9W9COrKS3qTX0/d7YSEDRtLRCF61gpWthzUYkA5H0lcDXDiPLtvnV4zTadpLQMv/h3Oz9p5O2znOJ4q3wJ+0zKowJNn+uQ4aZXB0yBMTYHAdNc25jLLgdaF2UoMGfqWSYQpXO6ew/PT92jaO5fL5D+i64zW8QgFpNFLZoguIls9/AEm3h3Q0wsrWwxlGjePNe7AP+zmtbn8UCG23bGDFcdy1vWOgjIGW08PHELdEDbA4dece9p99HbcTxVsEpPXsngimmljZeljXFQMpSbenwQDew92f43M+7vBzTxIUnSjeovPIPUgGnn3j3HIv6zp29r37fGFb3ECOu1ddqfhBpJPP1giUlubvFeBu1tQ4I6bBpFEWeHGM3LlbqJ0UtYcT/OHYrmw9rEG+pNvT8eQ8ErAFzWZmnfmTTQRHbJ44f590e/Bnp/XZmHR7mb3O+44rMy5l+4ElKUBknz9uwMErFrD8xXfpfhsfN15z3FwW9FLtOlG8JfMsHp8bXocvvtdd38l2S+fQved+0lv3vpf6+TDS4oPaxcblYuOYW2655XY1LU7TQ7++Gu15BZWPP/44nnnmGQyHQ5w5cwYf+9jH8PVf//VXfJ2PP/ledVL8agXJ+mYmnzEjm9zHyR6X86VD65TzPXR+AAGfBJx00ONnzmecIQAI5mYQzM3odTSaPrKA6UTxFnVIgqmmOnYKZCabGXBF0EVg6x8Ttiy+sJ5xMNiHcack6fb0vbTl8x9A0GxKPtyRWR2vcUDlRupPDx/Tdrm2/PT9Aoifvj8DzL2woP2kA356+JgybPGFdWEoR6NMm4OpZsZx9QqFDGuqv3ekhvy/MmaRHT86typ/Nvd1rz++BjgOcauF4Pg8guPzWJq/Vz/jSo+V9Wk294BzrhfaieItWLr+TVioncTSi39qT0DC/ZnBDY6FC4TYBmVLHYkmxzfp9hAcnxeJtsMKMxBCBorAipJbAk8XWHCNuMb3aN5xQeZ4af5eWVtmPN2/c234TVvgwwVfSbdngyZOsIV9Wtl6GNEzZzMSSK4rzuO4JNf9P5k57kcCOmUv3aCIE/BZ7ZxSgME9RhUBAyW8BsEnx9Fl+tx1MN62gzjuz8YYkCF7yHtzDDNBEOdZuTR/ryoQ2F/aaucUku0so758/gNYPv8BDdRxLOKtljK57ue9YgHL5z+g8592e1h++v49ARAAmaAO97umGmw9vAfoXcr2y4N0gwuAfcawHe58uqkVrj2bOWQAww1osF3c45wj7nfWCGBb3EDFxdjR/e57pTYOyK80IDIesHwu2pTbxS1neXPL7WCWy1+vzF5QOZWHtdfe+JPqrCfdHrxCQZ0iAPCbDXVcgaxzT0YFQMYR1fwfR9roAlJKLRkVD6aaFwVgrmQSwB4wtHTdfdp2AgUFuE4EnoBr+fwHlFWNv/S0On6LU3diaf5eYR+M8z7eBjq6S/P3qoNEpjRoNoWtM87iQu2kSjB5f0CccTJJLivgNxvq9DPKPv7lRckoACy9+KeUaaSDTvaBTB8BPABloFVyXCzAn50GYBmVcefeLUSiclYHmAPigNIRI/ChxMx1ZvyKBCyS9U1hxgqWldJ10GxmQEOGjTTOsq67ouQ5eoUC4mfOa6BApayG3XPlqulwJCDNACFXfu06oa7UO+nKMRFpq42kt5eNHgemCiIdcMrfLdROAqMR/GYjwypwbcStlgJKwDJ9aTTKsPPcV6udU0h7/cw64tgDwnBTNk6J39J19+lchnOzmdwzV43AoEYwN7Onr5Rqj8u6OQYua3sx9pYOPX/HtePK511gwzEZ3xPuc2Y/6e7VMPcZ5FcrGfDEsUi6PXiVsrbPZfrddcX+8pmYjuw8MAWA13PnNmhalQDHhnJmBoZc9o1rR5+RhUIGsLqg5EpyKvczdw0A2WATAwzMxwdspeMrueel2EIaAxgqwTUBC8AGITlGbrDDZcD5/M7s1ysowHEQQDcOqK8UBI4XqRoHqDkIem4tB+m55Zbb1bBrAlQCyILGSlm//IPj81g+8x4sn3lPxkkAsiyGVyyoTM8FC/vJeChN1Lw2A/biVjZSjigCoghJt6eyPZfxIICI1zZsfpMj8SKwpTO9Xz6gX7USNFe+6oITAl6V542s1I3/JpvljoMWtTFsjMsS0uFTmepohLQnR4u4QIQAm4xtsr6p/Vj+4rsyABYQ9oHMKQAEN16n48z3sy0EY2QQ3PnnGBJA0lyGkYDHZTjdOWB+pgtwaMHxeR03rqvl8x9A3BKwwzEcZzQXaiczubxcp/yZ73NlsG5OpcsQEyC6a5/gleBaxySUAikEDy6rQfDsAuuMzNesX5e5Xj7znozMlKDWlUTy2vFWS3+veXJmrY33NR3ZyriuTI+sKQAkrR1tP/eGWwBKx4j7orWjv3fByn7OquYLmjVO0E4A7OYcuqy62y9edz8pKcd+PzUAwcCXS/7qqi3Y3/HnXdLa0f1GYK/5rNFoD6hfmr9Xg3quFNgN3nEMxnNLGXTQfdHLglAX1C7UTiJutTSIxc8tTt2pVX/dytWXs/1kl+5a4J4Ljs/vYb8535djl10QeSlWb5xRTIcjlXaPS/HddcQ1x+cl58KtWEx287m2Z8tU0jjXzwag5pZbbrk9V5YgRXyIV85UfgVb0u0j7fX3SFpPDx9D/Mz5jJOnElKyWQWbj+Pm87gOjxsd5vUZqafz7ubQrGw9rA4mr52RMjrvd5mOcaYlHY6A0BbzoSPrtsdlXN2+8dp0htLRKNNnl3lV2W0kDBTliihPKDBQ5uyV/99M7hWlpZQ3si10QF3H3mVx6HyvbD0szFrB5sC54C45dwFes65zwf67c0PjOC+feY+ynczTJIvHvEOOzzhTdaJ4i7JqLuMVzM1IHqcBysn6pnXsCtmqtPvl5O3nZLnBBZfVch378SCIC1bUSY0ieM26glYFr87aCKaaSFo7ugcAZAACGdHxdnKM+G9+jiw2xyfp9rD04p/aswddhp7Ov9s3VzJOyTqQPcqGTDKdalfSy6DKeEEWVx5NgOq2g+ZKxgG79tgPl1nlZzVftZBl78YZoTTKtov/Hgeb421y+/HlMu5jABl1AtvmPnf0WRYWEK2t77mWyyiOyxpdJtxdh4Blct09znF2Ay40FnHiunAZXzcQc5ixdINMbnsZFKPq4PTwMR0vfs9crEgPgx4axBkrEDTeVnes+SxjlV3uCfe5RYUCx45qFleBM86eX3YcQjsPl2IMn61cm8/di+ViXm1gmTOhueWW236Wy1+vzK4JUOkV5XgA/eI3zMSJ4i0I5mb0C48sHaPkrhPIL2AWOiEzwvwvOipuPo2bk7U0fy+80EoqM8DAKeABOAVnCoU94JXOejDVFDBVntD3qVyQskRHfjouaVw+/4EMoKRlpLVDWzkwmGrCr1QQr21oW5a/+C7pa7Ohstvk757IyBSDpnVk1Akz0kiXtaWDTqeSjjqrQrJ4B4ExWS0ACuA47n6zgbjVUlaQOYLsF+fEr1QyFSM5j5TNEvyzv/w/8yFdNiVe29AcMJdhdAtkiCRPggBkI+nwjuegsc3sM4MJmWJSTuVGt33u/znXOleO9FrHzMwfGU03F1arZxZtPuK4EWC7zAjvm6nG22ojWrMVcjlGfrWy52gQt+38/NJ196kUnGBBGVVTDIWSXwKG8eIy4wGcxak7ERyfzwQJFqfulCBB1VbAJbPGz8UtW03TrbjrBhrc/zMwMz5+LsB2gY8LoF3wsJ9U92qYXy5nAAv7wOccn1mrnVNYevFPSYVTs370+eFIKMeDVLqvnCCX++zleNFc9ozP8fDY0UzgwlUecA+OX8dl9C9W9OVyxlxENwDGfc6+uKyfy+Tzs5cCYMpY7yN/HW8rxzScm5X9ENpnrRsQGf9+4WeZe8xni/t8OYhdrjIuzQWdfMZeiVHlsB8g/3IAvpwNzS233PazvFDPldk1ASr9iTLcIwr86SkgTSUabCSZQLbIi2Uepdy9HMUQ6vEW8VYLC9XbxZGplDP3cxkQHlciks2GAr7FqTvhl8vwy+WMFNErFBA0G0CaQo4+kfsGzYa8J03FGe90sfzFdyE5ex7pcIig2dD8R68QKpB0QY5XCOGF8mLb3XYm3R6W5u7Rcv10mv1qBSiV1Jnj+XEL1dsBCKAK5mawdN196qR4YWiYuVCdFQVb2y11LvxyWVkwgq6k25NrO2MQNBtjZ0qO1Il0ASmSVPIQzTmVrkTNHudg+muATNwyhZUqZZ3j+JnzWKjeriymzFFo7st5kflgMIFgU5xLmTfK3lzZ2WrnlFOZVc7GXKjero6nXy7DC0MB/sMRkKQmSBDq/wGbH+yOL6u/pkN7pMri1J1YmrtHr8Ux8MJQnOHtFvzpKaS9vhx5UrBMN9JUx56ge+ml/7f2zy14EjA32Ry7Q2ePeatWxjvUswe57ihnTocj+OWyrk0t+hKaczSdKrPsP49COFG8RQFyMDeTKTjkl8vOZ2yQKR0OpUKnmVO2Z/nMewBA28jXaueU7ltldMye5Bhw3a9sPaygmACbe8YrhHIdMn5pqmvKBcG6T5jbbPbO1bak34dXCHWtBs0mkKQ6dlz7ABA/cx5Jv494bUPaZp6t4cxM5sxUlEqy/uo1fYbySBGuY5/PuVJJ1xifCXyGBXMzQJLKs7tUglcpa76qC+y4D/g85PPrMPmNNAYOFBzxewE8fmao/wZEaj8O1A4ihSUzt5+NF+Txq3IcDte2m39o22kYyDSV54tZa27A69nKXi8HvFzgeZjqr0kvW6Gd/88BX2655fZ8WfIsXl+Ndk2AyjSOAEBBFfq7AMRBWNl6WAtqpMOhAVtD/axfqYyxhcUMi+lXK6aYSpj5crNHjETwikUsXXefHH8QWickjSKkUaSAQoFgJO31J5vyqlSQRpHkgo4irGw9jNXuo+Jsmr/DRH5Xth6Gf2xewQodMq8QIh1FiFs7iA1Ty/aLRFDuyb+tbD0Mr14DYCLb/V0BHKFcR51L059ku4V4bUOj3nFrB8HRI9q/jDNdsQAybu0ImJuZMhHsomHzijr+bBc/6xWLcu3hUIERncXltQf0CIQ0GunYpsOhXjNoNrS/Xugccj8cYbX7qKmAKm3gmtD5N/22R40UHYmmXTfpKNJ7ZKrHmja4DqMwbJHDoBWxvPYAlubuycwJ77/afTQz/uMOMhkOe2j7EGkUCYA0eZbB3AyW1x4QhqL7KNJ2R9a1AV8L1dsFJE42NXeN/Vv+/P9P5qlYzDh2cWtH9oLz+4Xq7QZwNxQcesWi9lfHYzjE0nX3yVrqWUeX4y9Mc1H3nwtQeQSLVwhV5p5stwwQMW1ee0D7RuO6d+eXY8zngPs3zqccYyLt5vgx2MKxils7mfy0xak7JUhRLOr/01Hk5IvKemF7x/cMg2LpKHrWUsKDmCtr5DMk6cn9NbA0ijQgRPDIdXmieAuijQ29nl8uI213sLz2gA2EOayrVymrNB2APG9GEVa7jyrApSXbLV3Hy0++G8l2ywRNhtrW4CU3GlAb6jjz+XoYMMnPjEtDuSYWqrdjae6ePcdQpb3+nvV1qWJLHHc3X/hixucenx8sqOTeL2g2EDQbme+jNJJx5Xjx+aHpCc5YH9QuJ39128z3H9bGc4vH25BbbrnlltsL064JUOkF4lj4lYqCqtXuo+pguE4qHdeF6u2IWy2kUaQOLeBIhwwYcVlKXotFZwAom8NS+gDgz0xrhJ5gZxzMpqNImRmRVQ4Rr22ow0v2ItluqTOfDocCRAxo9sJQQQ+BEo8xYX+8SlnlW3rw/aSRuZ27YPPkWjsIZ2Z0PAiWOZYEOi5TuvzkuzPzcHr4mIIUkS4WEczNIF7bQLqxZZlUA+bYxtXuozZH1DiT0dNnkI4iLK89kJkXvif8mhcj/JoX27E1jmUwN6MgzQsL0p9yWQEX+0qna7X7qDrPbIs/2UTa62fvyXVhmAsCerfvLNZEmRrn2V0rAshbhqkQkEHnkUCEoIRt5T1d4Md7L07didXuoyLlrd4O/9h85m9kSdlGd8yTXg9wGEMCGoJd3mdp7h5dj+7v3bGJWzsyD1G2eM3K1sMI5maQ9Pt6Lxeou0CS5t4nHUU6b/71x828hhnQTxYm6fe1jxyr08PHdI4JEPzJpgW7BHum7yeKt2SAhF67K0CTwR637/sFm5j/5krsJSfa7i/ex72/u/+vpllGUp6dQbORAc9ss4IVk2csBZciBUVpNMLS3D3wXnwDAGT2Uby2ofNLMLlQvV0BLH/2woINAhWLeo80GmHpxrfoc51/A4D4iScRbWzoXnfHmXvzSsCT5tSaAIfKSs01VruPYnntAcStnT2gh4EyF/RcLE+SOZFXMr/cvytbDyPa2NCxAszzsiffc6K8kO+jpevugz8znbmOLSwX4TB2JYGOwwBXv1zOPKtoHK8vR7Alt9xy298OUq36WrTDFOnh66vRrglQ+bEn3g2/WskwPvwSyjjCDmviV0TO6IWhgj99H9mqmWl1hlz2jmyJey8CAmUhKxUFY+69g7kZdXYJ3JS1M87mavdRAYaVsjrgCnp7PQGYowgoTyhbQ6cr7UnRIkpm47WNTJ7ZytbDUgnXkZ25zCqv50q+yKq4TAtgnR23n/GadXrS4VBZ3qTbU8aV7AIAoGgPrOeYA3LcQDA3Y4+FMeBooXo7/Mkm4ifPIH7yjBQFMc6wX62oVJfFdQhel+buUfBIx46MmzLIBtCRBfMKIbx6zcg3GwJ++n0k/b5eNzByTGWdwkJm/OjgumyxXy7L/JpxYB9t4Zkow0S7AMRdn3K/UB3KdBQhflLAOEGxNzWZdUK7ckwEgatb0dYFoDQ60y5jwjXgVyuZsee8ufLdpbl7kPb6CI8d1b1BBprs03if+DtKvum0M5iyvPaAymTd6wRNc9RNGCrY5thI/m9DwQ4ABUdsP51yN/hBJpxjTQm4yG9DBd1cDy47GzSbKnmm2dw8Gyjg77X/+zDgz7Wxja4t3fgWWyyq+yiWbnyLrgdKvzmHmgNqZM145rx9dhSLGnzis5W5soBRM0zKubgKJlWyPFTgFjSbQH83u/6iyEkZaNr5MsEzKlK4Zw4C3sadJaox3HHiMypoNnRMRJ6a/e5wx4bmzu0443upz7lrT9dnWLC5vnwmOyymfk9stxCfu6DBRo6tSnoP6SBeDNSNPzv4fXBQc/fNuFEFsd9nLmdfrY5wbrk913YlxxBdSxanh399Ndo1ASp/8GX/OpM74lcrCI4eUadMHY+JkoKQuLUDLwwzEXACR79iCouYoiD8guTfVarmONcZcNbuAHDyXQxwXe0+qozm4tSdCkgAAaFeGFqnZTjC8pn3WLYzDOE36vre08PHsPzku9UxC44eGTsTzzI8vEfQbCoLQmY3aDbgN+omH9TIy/p9dfYo/2M/XcAoclubP7l041sQzExl2kBmIZiZEmau11f2DRAQ6k2ULJhqdzJ/o6PoygiXz7xHZV9sozsfHGP9v2FGlB0LC5n+unmdHF9afO6C/GN6Uv5m8u3kOqE62QSYwcxU5vMrWw8rc00QoeCfDnLoSJhdNtuMsZu/qQy0ymyFZaLzSPDkhSbv9vwFvS5BE8dFr18s6t7wG3XErR1dd7qGJ0qyZg1bSHOVACpDrFQUEDPQk2y34E1NZoIorswUALx6DV69pmCPQQ5eM9nY1Ha47Kbew9mD3kQJS8feqKCUku2luXv27Ffew2U0OQZesYilY2/UNi6feU9GmcA9udp9FN6EyROcmtyjTCC4VafeGQcCU67vYGYqUwDqahiZVx3/MEB87oIGXxaqtyuI5/icHj6me9W9DmDmrhDCe+mL5NoTJVkH80fgzx9RFYbfqKtKI5w/ImvTcVYYRHMZTTd1QZUO5tnD8deKz+Y5SzuIHFOrslZvh9+o6xyxXzpeZj3r9wRTJBz2lt8/4zmW4/dyJe7jbeVnGHBy94jkkQ4z6gfu7+W1B3TNEWhSlTF+j+faQRwf9ysNiHC978eiUq3i2n7y2H3b9VXqCOeWW27PjeU5lVdm1wSoTAeDDCvpOnOu8xRvbAEAlo69UUBFJA6cG4EHxNn1KxWkuwPNyRzPTQQsaKLDkfSzkkn9wo8izQtzP0Nwxeh83NrRvKbltQewdOyNmfvFG1vi+Jo8xsWpOxFef52AoZHI0DKFZIpFzT10x4VjFbd2RNK1sSUAxzhrPFpFjuMIMyyljrFxfsP5I1aW1N9FujsAAM0hVYcgisXpDi1rpyB2p23H0MwXf3aBt8vgMF9VgRoddScq78rlmKuqY0mJrOkzAwl+ow7Ua8pMqoxupy3BBMNUMg+W86ySvijWPlMunGxsqvO7OHUnvImSBgoAKBOz2n00I0/lXBHoELDLUQpDZeri1g4QBtm82TDY46T5lUomYLDafVTBdbo7EOBv/r+HQTfzGj/9jM2FI9BwWFlAgC77RhlqOoqQnL+g+a9pFOmYUDmQbGwi2dgEYMGqe03uAbcolxug0AI/Zh6SnbbMw0QJwfXHhblv7eja5H1oLlPJYI0XhoiNdNtVQjA3d/ns+xT4IIpl7Z6/IP3t23NbXVku9wefLZQik1GNN7b031fLFFib/YgoVok412LS68GfmdY5Xpy6E8tn35dhkoOZKaBeQ9ruwG/UsfIX/0HGamNLANjWNtKtbWXc+Qz2KxWgIPm3frmcyYF2cwg1UGHAKCXjGngyz0SX1RsHdCwGdTHjMUCr3UcRb2xlgChl+NpmOOtsbA0yADMuib7YvZN+f192kp/hc4Xj4s9M61hlpLfmOUf1AQMnBMTKGhvm9krlr9wXl7JxZvIwAREysOO2X0Agl8HmlltuXw5L4CE+xCuBd0X3+cxnPoPXvOY1OH78ODzPwyc/+ckDf/YP/uAPEIYhvvmbv/nKOncV7JoAlV6tiqWv+RmVIfqVCtJ2RyRxM1PqwLmOSNKTaDqdZddRcR2chertyuK4YI3Gwh2U07pFEygLJWiljEePVxi/n/niZyVIRLH+3T2igg57Ohwi2dhUZ3d57QHLypq8xaDZRDh/JCOhpQNGR4jAQtswHMKbKMnPBnTpvx3Avnz2fUAUqyw1Ngel8x6srAuI45XuDpQBYq6byxC792exHc15NX/n+LLKLSWOXrGoclJl3xwm0W3TfuPO8U13B0C7k+kLAxLpKEI4fwThvBQoWtl6WIGbylRZeKZR17xN/7pjSHba+nO8sQW/UdfghcvEUGp7oniLdRQpVTbgm4GHuNUCwkDbrZVJjVPPAjo0AXsFeIXQypXN2kmHQwVh8cZWZr3z7wAU2Lqgh8ELAMoWsW8nircIkDV/X7rxLTo3yU5bGS0XsFH2KoGPlu5FVzHgmmdyHbkv2C9lM6MYy3//n4HyhMoG+TkGRPaTnxJIMsDgMlBc71oIyQSGXMVEOGNzr91CKekowtLcPSqbXTr2xkw+LQHoVc2rTBIJ2Jh8bpdxZ3sBUV0oK2ak1i4LhnIZyfkLiFstJDttDUAx2OPms7vqAoQB0N/VIJI7BywihRuPZdY+AHim+I+oK5rK8rrj5UqaL8b4urJIVlHm2LvATlUJJjjIIAmVCW4g62J5nBebx6DZ3AOOXHDmfg9FGxtINjaz3w+wwQllUM0zzwtDLD/5bg088VoXk95eytz0hoPYQUDofsbv23HLAWRuueX2fFmSHv51JdbtdvFN3/RNeO9733tFn2u1Wrj99tvxXd/1XVd2w6tk1wSojM+vIzUMBwEY2Y+011cHjk6nX54QliSKgSRRCSIZK5c9pFFWCEBlboD9IoxbO0CSIOlJHhkLlygomCjZYiZjbCfbxEqrlMvy/irLNO9RJtW9jnN0CgAFO3GrJUyLYcfoICw275Dod3lC8jQNy6rVFI0TRxDGPrhtXqjerm2hlNYdF0oCAcvmsX+LzTtUVkiACUCvFbd24JcnMkda0NxcJjohSbeH5bUH1HHleLP9fnlCnS4ACK4/Ln03fab0EUmCaGPDOJQTKk31yxNyn502EsNaLjbvyMwPAGXgovMX9O/p+TUA0DXolycEUJm5JDMDCGhzCwq5plVrzToJZ2aEHSuEQJJkKuhy7siIemGoa92vVLA0d4+AX647Op5Jou0IZqZ0zMiOpr2+MJlm3S/d8GbZa2XJaUOSZBzQYGYK4XXHJResUUdqZOducEKBm2lH3Npx2NyCrtGluXt0HvzyhLTRCeLYAk2htJ3BCbNOUgJo00/+frF5h50rpwiN36hbhjJJdPy4d7jWKXFn28icJb1exrnmmmcQg9Wil8++T/cG5+5qSl8BaD6tK2cHTKGhJNF14QZhpMKu9JFjweeuHPFjKxf7lQoWm3fIs9Z5bsWtHQTzc4CZ52BmCkiSPWAibu0g/fyXdO35lYqsgzNnpc3m2U2pMkGyO34uwAf2l6Gy7QAU1Ls5zOMBI871ytbDOn98Frvgi+vJvb97L7dg17i5hXw43uHMTEZZwPuqPL0QIrj+uDw7zNpeuuHN+m8+zwAr2T6ouTmk4+0bfw9w5fmU/Ly7Hq/E8oqwueWW21e6LS0t4e1vfzte+9rXXtHnfuInfgK33HILXvWqV12lll2ZXROg0isEpsqnODD+9FSGtaADt9L6kLy/UtZiNoAtww5YKZ5bcIcReGUEz74PgHUWCKjitmWjCGLobMRO9VM6xHRMCUook2XUWfpmQVrS37WFVaaaWSe0v6v5UCJhlWuGMzMWEPUFYC8275B8tf6uyMlGkb5PZKUi/Vts3qEFh/xGXcCUk+NHc6W7CoxZlbXX3wOiE5OrRcZYWZFRJNUNmRvZ3xX5WvMO+PNHMuAtbrd1vN1xggGT7njTNK9zFCHd2ETS31UGQtmrShleWMiwWX55AiutD4kzbExzRY0jR2c6A8gjm1NK51dYvazjdKJ4i66XZKetffXLE/Dnj1jQZ5xJMhEEjekogjczrf11JaGUCev6phNu5LKUPa+0PmQk3LtWvrixpWwW5zbp78o6iCL480eQbG5lAhxJf1eDBXFrR4IduwPpX6+PpL8ra6W/q3uLIMurlOFVpFIvTG4lAKy0PiTzPFFSyTPXECZK9rgHEyzyKmUF7elwqBWcOc+nh49hpfWhzPX5Myt7Evj7lYpdNw6bl5gjMQA3T01+Xvqan8nk1Wpl4/4uls++T/cTnfuluXtU7eAet3IYtuegplVTnWAL8z4BwGvUs5VYDbvK52zS39X8Run7SMfXn57SceKccs0GzQaSzS0sP/VfLPve39VAF9vlsuBJrydybvM+BhnYfsq1CVr5ewXAxvYDd+N/d59VeoyNAzD98gS8SlmPF9G94chfub4uZWRBud7G/+Yyt7zHQvV2wATpOCYM/vjTU0g3NrPfe5tbuk+W5u7RNlGCfFAbX+cXe0/maKbw8mvXnQNX7j9uBz3GJLfccsvtubbDSF/5AoCdnZ3MazAYPGdte+SRR/CFL3wBP//zP/+cXfPjH/84vvEbv/HQn782QCUj5I26OHs7bXiVsjqvrlMEAIhiey6kUywFgAK3TL6ZA/BckOAeCyGFfwSMqMNrbGnuHv0snVN1zI0MMo0i64wVLNj0KxWVOGbMcapVItbrKVOX9IRd4xiIk13QnwGHsYWVC3qhYcDMWFDi5zKhdEIFZNjfy5lzuxk2NenvGuYnVMaQsjEAykAszd2D4LqjCOp1BTYqoevvIn76GXUaxWGUgjmLzTuy87LThm9kjrw/+7J89n0ZwE2JrRtZjze2VOLpMefPgAVx0mz11zSSPEGyduxLOooQ1Ov22A5YJ1elz45D7kp7mXtIMJScv5DJb3TzFeN2W2V46camrh+/PKGFn1jgyXXyXDkh20j2jeauI/0cc0zNfkvOX3DWrzm4fn5OZdvMW+aaY1vda5NxASwLvDR3D+IvPqXrZbF5hwQoTJt1zKIYMGfyYaopcx3FOs8rrQ/J/Y0TzX1M559z5RrZNV3rRq6s57eSYZufE4BhwISCrEKI5PwFHTfKswkSGSSh8XlDQOU654dhfA5qukacwk8aFOnvYvmp/4Kk19P8YL88IYVgHICR9Hf1rMqgXrdKACNzx0RJgxPaX0cWTcDK4mQErHxWJ/0+MFGCPz2FZKeNYH5OAxb+9JQGRABo4TXmgF/OXKaS1aBdY1CHf88Al4mS3dfOPiMQHGf2xu/rFnDjWNJcFnCxeYd9Vps9nDJYMpKiZ5goARMlLD/1X/S7ZaX1IQnaTE/pHiTT7xYhupSNt939+WJHe3CtnyjecllQzbFwLenvPfOT98stt9xyez7s2YLKG264Ac1mU1/vfOc7n5N2fe5zn8O//bf/Fh/5yEcQHiCI59pDDz2E173udbjlllvwR3/0RwCAT33qU7jpppvwoz/6o8+K9bwmQCUj5Mnmls15NI4tYEEaAQlBY7yxpU4BAAU8ehbdVBMrrQ9lnE9GjBX0Ner6BaqFVEwk33WelSGCdRa1CAglp5tbGWeFIIoAgOym36gLeOL/WUCCEsUxIOwypDQW/CFYTKOROEv9XWVa9wOB4w4J5Yl+eUIdFjIKBLYE2mTZlNV0GKrltQeATk/HjebKwMgUjDN9rOYLiJPOg9YxUVJZJWAZIYSBADbTBjqqKpM1YxKfX5M1c2QWSze8WdglA2YV5I2sY5sOhxkgymqV/FmZclawdI4+UEbGWNLflaNrnDlVdqxgc0WVJTbBFGUmh0MFVTpXhnV19wUluXReCdDJjriAdHntAX0vc+vSXt/2cRQhPr+m/SAgIeDkNQFkgg7jMl/K9dhm10F1QaBbFTI5c1b6ZphLlaoboMG1A5h9VAiVnaX8lUCJr9Xuo3pvl9UOZqYQn1/LsG8qUZ+2kmEAet3xtvPoH/7eDfQAuOpnVXKfu8En17guyABzzr1CNk/ZL5flORRZpppHgiDaq2oAIPvPeR7HrR0NbnGNxq0dCTAZVtOfngJ2ByIV7u/K+I8ieWbttBG89MUCpJz9l0YHO1KE5r6XyhZlk294s/1u6fQsq7xPwI/y5/3AEJ+X4+OofzeBNAIzPr+0WnSlbAN1xSLSzW2km9tS6fi6YwrYvUKY+T5x8/LlO+/SR224bWeQ7lI2/ly50rWrx6WY8b4UqM0tt+fS8mNnspbvtawlqXfoFwA89dRTaLVa+vrZn/3ZZ92mOI5xyy234K1vfSte/vKXX9Fn//N//s944xvfiCeeeAK/9Vu/hX/xL/4F3vGOd+CHf/iH8f3f//148skn8cEPfvDQbbsmQCUAeCZvx5+eyhSDCWam5G/mixwFW9zFdaboSOoxIY0a0vNryip4hRBeeSJTEGR57QEsn32fZTeYu0Zw4gA896gNd9MG1x2FVylrO+l4MvIOWCkRq7Qy8p/stAHjrLPfNB7JQNBCuSsB4GLzDmUW/fIEgnod0ZlnJKevv6vO9ErrQ/aICTKUjkPEMUXBnrlHIKnyyvKEMFhmDNNopDmMLNiy2LwDqFWQRiN1Mjlu42Bd59sBmKeHj4lj1aippDLt9zNjQtbVZU/8Rl2BvVeekPE04I3SuuTCOpLNLOPiAnfXKU4p5+wTrFswSZBJYM0+jTv16SiC36jptV3ppV+eQLxpigZdd9yOuXk/14uOkxlfshVgfpjDuimzbpz/pNez42uAj2eCBu4aoiwUBTn3jnlbBFY0v1y2aoLQVlG1rPME/COzDvu9i4Xq7RosWDr2RstMmTVEdpjSdAKb1FS3TdNEqicbNQCDEeqgjyIszt4t/TV7P3OUhAM2g5kp+I2a3fdn35cBSmmaCOCulBGfX8uASAIbNziisnNHss81zLnY78iJ59LcPewyhOPrcfwMRs8BvX6jBv9F12vhKYKlYH4OC9XbNU2An1MVw04bHvtdsMEuv1EHJkoqNWf7+AxmRWwGBfzyBOLza3LNLz1t72XWGqWylx0L41RyD7kyWHk2FDSIx3x97hHuLY7JOMs87qC5Qbdxyaj7Oa4h7n8A8I8flSCOYWPTNMkoX9DumEBQTeZjeipzX9p4AGE/s8WZCnuUN/uZKy13238lDmowM7UntQTARQF6brk9F5YfO5O1fK9l7dkylY1GI/MqlUrPuk3tdht/8id/gp/8yZ9EGIYIwxBve9vb8Bd/8RcIwxCf+tSnLvrZX/3VX8UDDzyAP/mTP8Fv//Zvo9/v41Of+hQ+//nP4+d//ucxOzv7rNp2TYDKdBTL+XZRpPJHFEJ1GlfWH8TK+oPqKGhRDjc/iECQDNPUJLxKWdkev1FHapxddXZm79YvzaS/i2TTyOyM0+iCrWSnLUDVldsVQsRnzglYNM47HRuVYBZCxG0Bj17BVsCEFmwpiPNKIGWcjMXZuxVI++UJ+MePyv0deRSlo/xMMD2l7XAlWAQPdEwIDrxK2eQBdpDsdMxcOLlnpo1pfxfRmWekxP1IKqhqlUteo7+L5MI6gukp+OUJROflfEgyOnT8WfQDowgYybzQmUtHI6T9XQX/bFPcblvGllJTM77sb9Lf1XVCpxWw+Uf75Qi5gGA8TymYnsoU7XDl1YB5cDt5j8zXtAWBOpn+umA+qJtCG+YsxmBmCml/1wJGs66Xjr1R14muO5MT6zdq4rzPTCkYAMRxlbkrwCsUtM3e1KR+zj3eY7X7qFnvI30v96BbNZTgSvvk3BOFENGXntLcRq5z2WsjkcWaveUfmUWy05FcPcoTRxHS0UgKqKw/KIDEML1sZ2pksgR0gNmnzn4crwBLeXy8sSXrzYzv0g1v1gBQ0t8FTP5xOhppv8dl9ZQMsn8E7TRXtsl1czXlrwA0kKLFwho1fWYByORdA3Zt2jzmArDV0mNQKOGnRJNgXwoeFbIAj88J7n0jfY5NUau0388EJ9J+X+SgO23LjlP2b0Aac4EBKyM9GCASeevK+oN7WDa/UdPvCAafOD6AWSfme4PBGregzbi56QJuviZt/LMEiGkUIXnmXCadA7DBNa4lAe0dWft8Dpv9v9p9VGXYl3Ok3fMsD+Jkjj8f3Qq6F/9MliHiM3/cDlO8J7fccsvtWrVGo4G/+qu/wmc/+1l93XPPPfi6r/s6fPazn8W3fuu3XvSzX/rSl/Dd3/3dAIDv+I7vQKFQwC/+4i9icnLyOWnbNQEqP7nxq0gurANAhr0CgHQ0wuLs3VicvVucGuPEEODRyQWgjoEXhoi/9LSyIMD+4CLZ6SgjGExPwq/XlI1z33eieIv+LsNwmHYABpTudMTZGtmcS1cqC5icL+czyloaZ0MdDOeLOOnvIr2wrk40q36SudFCMAaYEODwekjTTH9Y6IdR5WB6EsH0pABZ4yjRyWEBlKBeR3JhXZ10rzyBxdm7M7mafr0mLFwUGSmU7Sdg2RVKTGUO2tpWjDkfKt+t1/c4Pa6DOB4VT0cjZVZ8Zy69gjDhygA4EkpluKYn4Rk2UefZlaAaFpMBjzSK4Ndr4kTXLdvofjbDVB6ZVcASfeEJIE2V+Ul2OuKEHpk1AGgkINIAOHXEyxNI+7tYOvoGldUBgFcuI/7S03IPB8z75QnEX3pa1wjZRJcdDep1lYUrK27YPd1XzjgR8AMS9OHYk+F2mRINYoSh5hInO22zl/vadpXlmoJS8vlQ51AkzDWdP6047Mi3M/Nk1hYLNfHf8fk1rKw/KEqI645qoCY1gQ+CRuZ10lzGkgEMgjYXQO5XXfO5Nv/4Ubm3GU9huyQ4lBp5JceBe5nv5xwR4J0o3oLgRderhJ+MPwBVBSjjfWQ2Mze6fwy7SJYTo0gK0RiFA/c2545gHpDnD1UAnCsBY7XLAqLxceY69Rs1+RuDNI2arm0GKFN3jY+yexzYP/dQA0IQgDn+93Gmzw2OuIEY/c7iHI4iUcqYisWy/kb2O6FgCzEdpIiOaweRYnOPA/sHQy6WX0qT/V3Yw9yyr7kkL7fccns+LIZ/6NeVWKfTUYAIAE888QQ++9nP4sknnwQA/OzP/ixuv12ej77v4xWveEXmdeTIEUxMTOAVr3gFqtXqRe+zu7uLiQkb4C0Wi5ibm7vo+6/UrglQ+QNH77Zypc1tBRjJTifDagE2N8oLQwTTkyo5o0Ov+ZDTk5lcMQAZZgSQKPLS0Tdk8ubolAHQ+2oVv3ZHHKcjsyL3M+CPToy2pWwrxy5Ub0d4/BiStnXCvYkJdWj4OWWRjJNBJ5dSyizQjlSKRtYybrelAI1hXOm8pFGk92Y+H2CPUgmmJ5G0O0jaHXWu/IYwknp0iWETtNhJQRglMrrqsPV34YUFkcaxL5HNhVMZsMlny+Z2iTPJ9/B8RPYBsAwrc/zGwd5C9XYFXKkTbKDj74UmT2lzSxlAyoiVUdncNs5t2VbSDLPMBgBh1s2YcK24YIxt8sIQ/tQk/KlJmbcL63qNoF5H0u7oeue4Lz/xKxnwx7XrlScy93XnlYDAL08g3dzOMsNO0IVrBhCHlv1X5tzJT06dcWOwQdvu7K/F5h0Ij8xZWS5ZZefvLrgOpid1DYdH5jR/TJljrlETOFlpfUhkzZtbur6BvefpUfaohaU4N0b14LLHPIpk+Ylf0fWm8tG6nT8GD1is52IggoVbgulJ/ezVdKRTE4RLRxECIz0Opif12bNQvR1+vaaBhOC6o5nnEo2AJXnmnIL35XPv3wNcqNxIt7b1eYBCKM9Ps74Wm3fo3FC5kJLZ4/PI/OyXJ0SB4AQtKF1XUDq6fE7leJEcLVbkBAwJIL0whF+vabqDjsN4sPASzBpVE+49XdsP5ALZZ4ircshc+/yassTx5rY+E/h9xPV9qSI6+4I/5zuM79kj6x2TGY+PwcXyS2kMBOxnzCG9lOWgM7fccrsalh4ynzI1OZUHtT/5kz/BTTfdhJtuugkA8NM//dO46aab8HM/93MAgLNnzyrAfLb28MMP4/7778f999+PKIrwa7/2a/ozX4e1awJUAo4DXs4WFXCd0TSK7JmI5QnEm9vKYC6fe3+mYIZG8Akg+aVpwBvfk7Q78Oo1AVY7nUwFRbdYRLLTgX9kVhzk3V3EZ86Jo1KvKZBZnL1bHRnfACu/XkOytZ2VWbU76vAlOx11ADGyzKN7CDlZM79eQ3BkVnLfGrWMNEzYvEIGSPuG/XLHYGX9Qe2XF4aILqzpPeNNcRgJ4LWiY70m4NOAdb9eUyeaDpx7NMjK+oMZOS3zrxIjbeXv3RflvGQ1giOzCKanrLPDPKQoEiDCfppx1/VCIO6whl4htEDBgCyya5RLu+sjJrCsOyz02FinDH4UwkzAIFPwgg7z7i7SXcuaBobt4diqk27aszh7t+Qjmvng2kz7u0g3t4UZHQPkKsXbh2UhwCbTmekvoOtb98fUpAYRXMDp9o+5tvx/Zgw49mQ2nc8DEjhSAMlAhlEJkPEPjswCToABAPypyX0Pcuf8sKjO4uzd2SCMw5Qz8OMCdl0jlEJvbutaIThxx8orT2i+JvcBCwfFm9sZcPvlsLTd0b64gYSk3bGM45lz8Oq1TP7nHnBp5pNBAMAWCgMsqyZHqtT0OaFjafYZx8Ov15Q9dZltACYXs6zzrXvDCVawnfsBjnHppVboDW3VafbLbWO8ua0Amc/H5afv18+xr5eqAEsLj8xdNMhAY5BEx9MEsjTwZQKU/M7gPJwePpYJBrlt2q//4/cfr/Z6ueqv7jp3i1NdyvZIcEfRviwngziXsjwPLLfccrsa9mxzKg9q3/Ed34E0Tfe8fu3Xfg0A8Gu/9mv4/d///Yt+/hd+4ReU5byU3XjjjXjooYfwrne9C+9617tw9OhRfPjDH9af3/Wud+Hd7373FbXdtWsCVPo1CwBcp4Zf+n5dwBmZMzIMwfSkONvMo3QcQ4JCAjApArKrDo7KWcMQqWGLAiN9BPZWz/PLE0iNk0YnKB1FWH76fvl5alIBIhkl1zkik0fHX50bY4njGALimFOaFR6Z0y/r+MK6RvJPDx/D0tE3CEiJLKPpyt3cvEFKVtl3YQYLmaIjrvRNHU2H+UuNYxYcmdXKul4hxNL1b1JWYHH2bgXUgDjpK60PITg+rw5+cGRWGBYzZxr9Z2DAsMbx5rawfM7YJO2OyhNV2moAEIG4W8SEn+EaSPu7WHrxTzmyUQccMbBgGF4CebcADBnJYHpSHWtluSPLOrogmu9ZaX0I6a5l28mgBMfnM+ty6aX/d2YOFPC0s7LqccaDYJlsBNvKPir77wRPXECNUYT4wrqwiSagosETAsSppsqcAeyZ03hzO5MTy2szAKFBHQO4gyOzmTXmN2qIL6wjvrBuJecjee9C9XYsHX0D/PKEzjedZHf/JP2+PR8T0L1O55bsyUL1dvhzM/o5suzjUmy+V/KrO5nf8z7jzOlBjsY4rLmgT551Iyyfe7/80YyXrmcGHcweoByRLJi7x1ViHYbw52YyOX/+1CQAyUXnnMeb23JsiCPnBKBMOnMayeBSos9gF/f+SutDAANbYwGP/fufBTQEThwXzcl09rNXkICO9tf8brF5h6ok+Dxxj5JxzQsLKu92n9+0i825pic4P3thiOUnfkWUCU7KAb8DyVaOS3Pl3gfLqeRY7Mecj8vF2faV1oeumDm81PuZM5tbbrnl9uW2OPUP/Xoh2he/+EU88cQTl3z9/d///aGv/8Ls9bOwDBCbaopzbuSZC9Xb1YleqN6eYSPdL2TXiZEKk478E7AMXmTPBQOgjJDfqGn1ytXuo8JkmrwXZVHMdZaOvkEc8LZ1lpOdjrCTBoC5eY+nh49ZKe9ORyPzXmiLqBD8rKw/KM64yTWDYXIJ1k4UbxGwtLsrUtGRdfJdgA1AGTVXXjvuBLt5oerYGUYNDpjyCiGSrW2bVziKtKgEPwMA0YW1DKhNt1oAoOOVGjmxStTM9U8PHxPgSEmww4Klowj+1KSyXcwTIwDynXYD2UIY7itZ21CHemX9QWUGGSxQYN7uKNPM9UlnGKGtNAvsLdzhMuJkeBZn77aAxZERxs+ct7LiKEL85BnNIaYjLmM3hWSnI7Jk83fPBFzICHhm/QfTIrv1GzWRNJYnENx4nQ0auCxUJPmbcbutwQOyql4o881xjZ85D79c1nWSRjInydY2kq3tbCXb8oSVe5Ox3ukoI0qwSNaW6/308DEJQhA8GdmvPzWpAQI6/czldB1jwBb08udmhBGNbLGT4MgsThRvkfXX6SqTNS4/JwAbd8BpJ4q3YOnoGzL3Jfi8mgVKNM9WmbiCqja06JPZq27gg8WO5DOhPmu0/+bn5XPvR7K2ofdL+7uZNUDwGUxPInpSKrd6M9N72sm55jOEz3dWGgagcl2Ege59ss8MGBwE5HDvco75O//YvJXem9x0AuSs3D6rSLgce7aHfcVe2ag3NyPjO9WUXG0TFGJBJdYL0KCjK6kHlFFd7T5qq5fvI529mLGqM4Mv44ylvq9s5cB8jnph4YrApUqXx+xi4+heO5e/5pZbblfDEnhI4B/idWVM5bVi1wSo/NjnJa9Jj4MgA9LpKrOixRRGUYYBYiScEV2/XsvkndHB1twj82WueW9lez4jgZ0r5wSAxOQR0ZGLL6xLG4xsdvnc+7UyqptXpWyrcVRWWh/C0tE3aN6dX55QFg/AnuI8zAOjRc+cFcBhovm8j4DTKctIOU4kGbiV9Qf3OA4A9kTB/YYdPwXezPkz8jl/bkZ/59UtcPKvP67j6/aDzoYrlXSlxcpYGFZq6egbZG6vP57JuVLJa9uyD+pwhbb6r+uUkbkjWCZTTZaLfye76ubsuiwoWWOys4A4qMyJZc6kmiNlpumB6QXnumULuMjypv1dBMfnLWDj2jJyQrKIzBGML6xbiejcDOLNbX2R8eUcxU+eyTiuZCu5foJ6PcNswsl9JfBgIIQgbLX7qABDh1VR59Kw+ZrL6cgOk63tTFEqXXMGZMTPnLf3NYAmNbm/fr2WOaieRsklj1JYbN6BdKuF5afvV9n2ieItuoZYzIUFn8hY6TmekZWxKviBDVaw3WSY3f11Jc7/FRvXmnmuuawjZfWr3Ufh12uZ81UZCDo9fExlvv7UpEhj+UwMQyx93b/VXMS0v6uyVn9uRvc0nw9+uSyBkSee3KN04Fy7cvOMosORp6adrgL9cTC5HzAZByIsGjQeBIifPKMFx4BsEIxrPbjxOr3n5cAk3+OyheMASZnSs+dlL2214E1I0bHV7qMaLGLBJ5pnglUA9PnuPi+4Lg96Nh/b6BYQ2i+n0v2e0WDdASvHup/bL5ByscJV7rVz+WtuueWW2+XtxhtvxMaGDfi+973vxc7O5Y+ZOqhdE6Dy+2d+PFOsxHXOAOhRBQC00IceEWAcHDoUiWHA0ihSoLd87v3yRUnHlIwcbL4ZjUwlK+2x2h7lXG7eJ6/HPEs6VJS50vHm8Q8qaTL3ISii3NeVYGnhm0KouVB+uawy3eVz77f5TSrnGtn/k0UyzovL7PIzKi00Nh4h5++YJ6pgcKtliwc5wDnd2FSZV2oACkFX6jjBgM2pVLbMSOD8qUl1qtJzF7QNBIyArZTL9aDAwmFCyAC6TDVz/Ch5840ceLF5hzh2LqhvdzLyydS9nvk3gxknireok0rAx/FiIR7NcW13MusJcCSuRnbplSeQdroKkPV6ZIqcMWGxp2B60hRSETZYWUEDnpijS4mzBl0KFtjquMEW9WF7aa6EmYBeC6sYy6xLR86+2LzDMshmfXEM/KnJjKTTNTI07pwvn3u/9knva9Y6WTAqCsjKU+6oMl4zRpSJci3s1w5ek4EqlYybfcA15rLVV5OpVEatEAqgN3ti+dz7FQiTqQWgwQwNopgCXIAER0DGks+wjU1ZW6bIlO7JTteRL48sy2mCHVRDrLQ+pM84V9quY27W9fK591tJuUkrOFG8RRm8S4GNcYknAJvv6YxTMD2JpevfpPslOD5v152Zy+Ts+QNV7eVeku8FW+3UBW17gCn39K5VVDCvl+kcBOvL596vygzdk0aZkLQ7+wLa/exSLODlgPN4IaCDsogqkR/77B7VTG655Zbbl8m+XDmVXy57+umnEcex/vzv/t2/w/r6+nN2/WsCVPr1qgUNDjtAUDV+KLUCsAkLjFhAA4BWg41NhUQCAUbxaS4AIStBudgeWWjfVltVR3nkOP39Xet40Xlle0xk3s1fyxzX4OQBEUC7TrSCYfNzvLmtUmA3tyc8Mgf/2DzC48cyDBg/y/66zibHgQA42TGg3HFaF6q3KxPn5uaNs5ycOzcowEI6bhSbhX8yFXqNxHf56ftFQmnkaRibI4IlFgJx2Q5X0uw6MXq2G9cA+2D6u9L6EJauf5PNQZ2aRDocaoVQt1oq8+9SB3xRok0Q509N7slD43sBZMAknW8AGijxJmx/mH/G9/NelBSq40lpswO8MxV7yxMIjs/rdXlvXne8EA/b4uYkMqAwbso2EbSHIZZe/FMynsZZdvfOnly0Qojlp+/P3J/gYJz5JJjSqrWOMa+STjuDS1zvmaDJKMrMocc8v8hKnzl2eoyIoyhg4GRcXeDmZF9V9iVNtC3KqJtgiT43+lKIbHH2bg0SsQgLmTvfnF9KRpyMPkaRyJZNkC7Z2rbHgBh2NKjXbcEpN2BigjVUiHjlCVUfJDsdeEeP6N5bnL1bPwNAVSTjObCXM5eBY9EZBgLSKNK1gFEERPKFrIW4zP5y2VEGFcct6fdVYgtc+ixSN5jG/an7jPnvU014U00giu0xKMYY/AmmJ3XtHQT4jueC7le851Lv36+q8kEs2drOyIn5WX63XOzeueWWW25Xy661nMpxS9P0Ob3eV0avL2NerZKR3FGSOh7p1ypyo0jO6Nvdtc5Ro4aktWMLtRiGkA4tv9wA57ywgnWk/fIE0uFQK1+6Z+spKIlsZU5XBgkIaIudsyTZ9uDIrEoZtR9OJJ1OF3NlGKE/UbxFnLLh0EoR00TBmJvHJ3lSfY2404FyD4sng6bOApmaei0DxFQCWJ7IANL4/IVMrh2BnVbKDJ0Kh5TEmjZQskzGM97cVhYvcRwtLUJhJJNk51SSWrAA0wvtvQmmWDHRn5vJACY62r7Jb3KZNi3sZHIBk17PVGotq8O4OGuPvEnWNqzT6wIwWMATX1jXaq3J1rYWsNFxGg61D3oMQt/mcxHUq/wNgPfSF5kc3S0rS2RQwwmYEHjynmkUSR7lKMLyF9+VAbYuAPFMQaKk11PWk8GQdDjKzBPfr/M9YQG0gujdgfRrq6V7x2UWWcnVC0Nle/263a+Ls3fDm2zKuu3bQkCcU17PPerEZY2493lNgh8AGUmoAiX2ayzwxD2hzjxzUJ09zmcOpfNkCa+mw+xXKipdzshezXNv6cU/ZfsUhkhaVh7Dcwt9w55xHceb23sklzT2ETBzvyvHGPmNWubZRiAE2OAJACvpPDKL5OlnMlJbFzyR9eTzATg48FA2eSy3NsOiR+Y5PZLnkKoXytncWbLw4+YFwYGPx9CzldnP0BYMUoZ3bQPJ2oZKxLnG+Zz06/ZIEbbrIOPgtvdSEmL+nu9R2fcVsuz8fuW+cu+1H6C8XJtyyy233J4Lk5zKw72+Gu2aAJVkP7wgAJJUJWxeECAdDoEkBZJUwE4QIOn1kLTaQJJisfF6+WIajuDX6/AnSsLyDUdIWjvwa1WkQ5EKJa22gIZuH0hSeCX7Xhi0n/b68CdKOFG4Gd5ECd5ECUvX3adtwHCExcbrsdh4PZJeD95ECX6timCqiWCqKY5yEMCfKAFpKgzMKJJ7Avb+wxEQBPBrVe3HavdRvc/p4WOINzYlN6y1AyQp/HpdDrUfjhAcPYKV9QfhX3cMJwo3I5iclD4FARAE8EolLJRv0/FYPvd+eEGgQNMrlRBvbCK6sGaP9ej1tG9pV+6zsv4gvCCQHJ40RTocyniZa7GfnBuvVBKGOE2QDodyreEIAStsJimQJvBKJXglGTt+Vv8WBDL+XA/RyP57OJJ7BoHcz4yhFwRYuu4+affmNhYbrwfMGkBqxvPMWfi1qry/VNJ5X5y+S65v1pBXqSDudLBQvg1Jr6ft4tzCgKx0OLLXSVJpV5IimGrCnyjpeCQbW0g27Pl9wfQU/FoVSasta9yMPT+PVNZA2uvLGp4oIfl/PydnpLJScpLKmJr3JjsdbWvS2rHVU4MA8ZeeQmrWLceK6/708DFp/2iEZGPLrLe2giMvCAQw1apyL77MweyL03eZdra1n+lwJHm/Zq/afTaEb/YLRiOcKNyMdDjSOY03NmUvB4EULLqwLvucfdzYgmf+pky+2SvSjyGQJjL3Zi8sn3u/GacEabdvnikjWSvDofRvomSDBq0dxBubuq4RBOYhlQBpgqS1I44zf+7v6tqUtZZgoXwbkJr1cNUemjL/wdEjQOrse7M+052OrMPJpj7/9BlaKuncpN2+Pl+8INDnS9rtm30s8809402UAN+XvVKaAIYjOfIoCOBPNnWu+AqmmobRHcn9dtrwSiXdU2mvr9WC0+EIaa9vnwVJKs9EsrKXsBOFm531NEQ6HOJE4WbZE0EgY2SeN3xmBFNNeQ6b57orXd3vLEh9Duoc7G3XicLNCpRW1h+0+yUIBFCWSvBnpuHPTOu6c/cRRiOdRz4jkKbmOTqU/cQ1eUAbB24cq0zfCqGuaaTJFa/dE4WbEbfb+/8xTS579EpuueWW29WwBD7iQ7ySFzC8uprnVF7FShBfPkv6u/jd6GMAIF+eowgL5duEwdv8sDhpkC8u90ud8tgThZulWERkv6Dds9W8QiiOpjF/ehLxhTUkm9tYnL4r25Z2G365DJaOBwAPtuBBGgnwcPNdvPKE5NSFobabBVIWyrdZZjJNNC8y6ffhhQXLtIzlyCyUb4NfryNpt7XPyr4aFkz7DbJ1I3mvuR4/xzH1pyexNH+vOCub2wiOHc1UePQK1rlmPzn2vK8cQTIC+tC/eeUJcYpGEVLTr2QwUsAkxY3WdP7c8Vvtf1jnJo1GCKanEG8acNPvS6AAgDfZxMqT75Y+m/njWHLsmYuU9PvmDLy+Xm+x8Xr5nVOQYrUva0sYHjMfAOKz5xDUakZuK+sqmJ6Se9frcr800TEHpCorf14+/wEB+tNTmbll5VbN541GCI7MIdncht9o2uM+zHjzOok5MH6x8XodE0QR4gtrct1jR7H85LuxNH+vKapSx8rmQ7pnuOYBU+imPKFrh+s1WlvPOM1+rSp7IwwRb2/DLzlnYQJAKMzwQvm2zLwBwOnR49rWNI6BKNqzd915SKORYdoK2p7F6bsMA+ZU1y1PIGm37TqaKGFl5xFdh7z3icLNCF72NVj5f9+h7VNzij3p/PR3BbibNeA3aog3t2S9tNtmjRQye0rHybw381yKRlLkZPQ4rqbxKCQAiDvrsmZNX4XJ39J2c89xr6k8n1LkKNL9wKI/3GcAZB+EIVASsMF1mBpQzedstCbt4HOUbeD/V3YeEUDv7NVgegpL8/dmjgJa7X8YADLP7UvZ6dHjuna8KMTKziO6TvSZPtlEur6h/cVkEyvnPyDBo35/D9DidXW84xhpHOvavlg7XPPqNaBaQfLUM/BvOI7kqWeQtNsIpqeAMNRgW3xhTfaN+T7i9d3r8b5eIQQGBxoWpHGse+BS63E/VvbV/usOdhPYfbefHbSoUG655Zbbc22HlbLGz7Gs9LkynlNJ4zmVrnmehze96U2Huv4LF0pfgXmhgD460JSSrWw+JM6vKUZCAOXX6wBgC9KEBXF4mH/XbsvxGw1xspgHA8iXn+QalsVJLIT6Hi8sWAAXjZC020jabWFOR4/DL5fV0ZTiA2XtQzqK5L4AVjYfUkmu66ApUDJ98coTmZ8XyrfZvDQjcwqOzNlcuoHkbdrctYL2yTOFfJjLF0xPmTPs+vqZjIyqUUN89lzG4RYJ7cjkKwpw4b2yEqyyjps9eqOdOcsxmJxEcGROr+uFBevUGImzV55QQMD36Dl3BmRI9dERkrUNdbjiC2sCuPt9lYGubD4kcz49qeuDgQHOk451owZ/bgZLN75F51k/Y8aOYJDVGOPNLX0PwlDBR3DsqADDnQ4QhuoYnh49ru1y1xWl15zzhBJeN4/X5PIRCLt94VizX15YQLrdwmLj9Yg3t2S+IwnKLJRvg18u67grCIkinB49jpWdR7Da/7BID2s1nB49jmRgjqFw8jW9INBxYE5p0m5jcfou+HMzmfEmMJTjb8oIr79OiksdOyp7Y+cRlWv65bLuC86zSinH8nbdozOC6SkdM0AAyGr/wxbolctInz6LxcbrdR0RXKX9XRuEMP+2ub51DZ5k9rbZExzHE4WbdV8sn/+Avuf06HFd51cbULL//rTkRPolJ2e5IaAuODKn6yze3s7mH5u9w+dk0u/rXqQkH1Fk165TqCfdbskYcx7rdfjHpIBTOMfjlsoKjPzpSX2OLZRvQ2KqV/v1ug0MOdWI02iExem7sDh9F1Z2HsmM+6Vstf9hfQ4szd8r88FnGYB0fQMrO4/Im8MQydnzsoa5Bo1x/vabQy8IkPT78n3A54Fj421M2x3ET3wJ/vQk0nMXNACDMMTymfdozqpfLmsghW3263X9Xlwo36Ztv1JpKoMhbNt+fVso36Z/5/2u1PYbD2Bv4a1xu9y85pZbbrnlJpafU3kA01wnA9rSkTiUZHvI7kgulQWC6nAXQgWANAJLMmMAlKFZ2XxI2SwCTAIywDJdwfSUOgBLN75FLmycZVuUZ6RRerZncfouZTDSvjl6xD3U2zBjMOX41Xk3eWzJTkeZJubXJTsdYQQMMEj6fXFA6jVxng2LlLTbiLe3FWyRTaATuXz+Axb8OWCH4y9HS/RlPPt9e5/yhDqwSb9vnT2tejqFlZ1HMuCHxzawbwqay/awdQYGtP9kGdtt+Mfm9WfOqa4Rw8hxTSw2Xi85hxfWtL+6vnh9w5jEm1uIz55DfPZcJlDAwjBaddcBucEN19vCLg5jjDg2OawjZcS9Qqis4UL5NgU92o7JZgYQuWBSATzBjpNvqoGDsKABidX+hzVw4DrpGgwwrH+yuW3HJAyxdN19KnXm+5auu8+CE+YVOkyWVwiBOFZAAECZbu5PBkB0rLalUnCytiEMJFlpsz6TzW1lpZS5dwIL/P/K5kM6L1zDaTRS8Ew2Zmn+Xg0iMUDllSew2v+wzLVRDfAZwD3MNanPGfN5rg+XEdX9b5QJgGXK0mik8sKr6SwTfLnz6gaIlDnv78KbnZF55Z4bSVAhjUYyv6WSgj6vEMKrVTOFxwDI+/hyij8l7bYEAMzRGWwb/+3X6wKszL5kG5OdDrxaVa+daqGpka4DqlBolwPqfC/XCn92nwcnCjdnckBXNh/SgMY42Npv/tI41rmOt7f3gK/92sjnVTqKNACV9neFsTXyV459srmt+99t52r/wwI6zd6/Ulvtf/jSTKX5+7ii4GI2PjYnCjerImH87+NqgnE7SAAmB5655ZbbYexwZ1S+sOWvV9OuiV775QmNwvLL3yuEKqnLMH71OuLNLZwePY6401HnmgCJDjpZIBZnAaCRd3U2jOOYjiJx9F0JqXM0BqII6XZLnXUCNL2vaQMAjfBLIYaCMkP+9KQ66PGFNWUtrZwwe4zKYuP18GanTUEUStoK6igHx46qM4bQVggNjh1FeOyoADzXQXeksGTVgKwjSjDvAurTo8eVqYsvrOnnFhuvFyfaKRpDJpEOetzpZPtH1thhPYPpKfg3HFd2UJkLAPFTT+tnx5lhdcKMPJhONoGDzgsZVFMQhOskmJ5ScKBA1jh7BFXuXMdPPY00Glmwb1gid3wVjFWryk77jZqyLgrQAHi1KuKOBE4YDFAQYO5rC3oYpsg56oL9Xpy+y8qJI+ucuqwUQRPZ+aTdloqezpr1CoY5MdJNBmg4ZhqUOPMeARFmXDnXyp47VXYJvCiLdefdZeSXbnyLXR87nWygwVx/sfF6Bf0uMFjtf1j7enr0uABOh7FMdjpAFKkEnPtksfF6YWaNjDPZ6WibOMfuM4L3ojGIxeDD0nX3ZdjSq20KvrimqWwgy+7IwpOz5rzPgshC/elJGQ9Tljw+e86CmTDE8pPvlptEEfxj88JCOsVbdE9FJmjlFCziXGckwWGI4Mic7s9xUOTONd/H+x0EcLg5hq7ihQoO9z4axDN7hbJ9Aqr9QKz7O+Z7cy24a2Kc4TtRuFmf6yoPdtQhyeZ2Jvijsvix+WM7VjYfkiBSu33FeZUHAWV8D4Ngl7L95iXudPZd/+54Hda+HMx/brnldu1ZnHqHfn012jUBKoF9vvRCy6a5FTvpHC82Xq9FS/x63UpGDTtJR5kSzzQaKVA4PXpcIvMOixafPZeR2LqOucukUe5GI4tCRtMFMGk0gj89mQUrgMr3+Hl1IGCd7aTfR3ruguQ+Tk/ZvLwbrgcgDBFBDAFAGo0E/LY7WLruPnGY52YEWJONMcB4+cx7MtIw3pvMl0bT5+8VcF4uW0msAVBki5TxiBzZXHkCQc1ITZ0cSldKBxhH9IkvZQAHAMvImZwomjKnBQesULJbLsOrm3y46UkrR955RB1httcFjm5+nI6/kZhyLVH+7OZ4Md9MGTHjaKPblWBAu2PnnhVI+7syR9stlZxyHtycRa4n9k+ZdzOHfr2u406WlfPGcXXzOdmfYHpKgYCrCtB8YDMWwbGjmfXCeWP+GQEb91Amx8ypSKv5bIbpHt87Sbst7KcJlPhzM9JGw4p7k01l5OmYU8IOmJw7s/YIKACIVJx7YhRpkMVv1LIMHCDgyIxfOoqwNH+v/sllcMi0j/fBK08gvrCWYS1d2fHVMM5T0m7rOJIFI3voVmLNtNuw4Hx+us+jtL+rMsx0JAwkWcgM2J+dyQRtCHa4jpUVjqxqgSoIWrK2oWw7ZdHJ5raAfUd5cjlARHCcyYPn+Jg1qrnnZFG5Rs2+pdTcL5cvCi55L64Hv5QFx6dHj2fAkz4vzRpZ2XkEweSk7AeytO6cFkS+z+cb+8IA3kL5Nsv0O+eUHcQutxYZZHHX+pVaMDmpz5uMVNwJ5OaWW265fTntMEV6+PpqtGui1+okGyeejJBK6uo1cSpHtjiJMpRkbeq1DIOS7HTg1WsKTgCog7N041ukUA/z+Bhpd4CfOuVj+SuUIGnuXhRptJtsk7Y9LEi+jAEr/vRkhm2j5IyOiD89iZWdR8T5cECFW9QkOXteHSOCCjf6v7LziEprvbCgJeuD6Sl1QJlzpCDHAC03x5Hj60rW6OgQnNFYgMMFxzq3/V0ZFyMvy4yFARkuA8tc1JWdRzJgUXOhzDXJSNGBVxaFINw4scH1x4X1NcEJ5ipxPcSbW8p26TiHFuSReVNG0YBaVwrL/Mlkp6POMSIrv8yMRzSC12zodehsse8MfrhnUyqrimzuZSbXzQGarryYn3XXHaJIcyO5/lzwwTxNSl7jTscCbRasMvOkrKRZpwTRHBuuDR0zFkVyGSTnXNR0u6VtT3Y6iJ96WvbT5nbGyXal6nodBgFMjiHl2PwMIAwa9wF/x7WrSocx0OlKAsefB+yvykmdtowXAXsuTXOnj8yphD/R430KurcBe4QRWV5XngpA55D7IO3L0TUs3rPa/7AUqzp2VNdHcva8Xp+MO4tacTwJ5pN2WyXfwfSUBU2GbV267j5tH59B49L8S46FYe0oJScjz4JtVEys9j8Mf25GJb7uOagA9Nmbyf/e517jgQvXXPknx2dl8yEFrrT47LlskCWyzy8GgDQQZtrONrp9PohdrIiO29bDSGrH7xFvb+8Zt0uB2Rxo5pZbblfbktQ/9Our0a6pXivrBVsMQ/NtTH6eC2CYbwlApZkEGMw3Wtl5JONUIRRWMpNLaOSSrrNFGZxK4VwZE/OvyKDp+XcCjJmnBlgnINnpZKSqdMiTzW0L8Node1yJke25jouCLMMA+PW6tstl81h10f2cghSOKXOiBrsqdSWAZI4hpXReWEDc6Vhm0Iw92S7mSwJQ8M+cTDe3ChCnfvn8BzLSYkr4OBfMRaQDyrxA9i3p99VhXJq/10phHcBH1ik1R3m4zLNKlEe2qqsL3HVOnXxGZdscZpcgjm3xGzUsXXefMFdG/uc1G5bF5Hy0djK5tMyJBKAggaZgk2DSldBSeuoETSjp1TxOMxYuwE1HIufmZ9yKoK5MWe8TBCJhNCwn38sxcxkqvYcJeBC8aX6tKRKkgMddwybvMh1F8KoVGQ9W3WUAwvyd61L7Y/Y8WS8gW1BHGW6y3ww61etYnL7LMn79vsoS0zi2Unlz7T1jY66vQMjkAWpRoKtk8fa2VgBmu90x4rgwWJIBTybn0M2xVYmwyRcOrj8OAPo8Qhgi3W7JmB2dy4BvLwhkfzQbwvQakIhI/u2FBWWjNe/ZBEEAAVKLjddrioCrKLhoRVEHVJG1O1G4WeeebKdXnpDjliDFaJK1DVm3prKfG7ykEcDtd29Ns7gM4HWDDAvl22yAtBBKkDQsaIBUzRQaYxEwBsJcqbeuxwMylV4QZKqnj7eR/092OvocOCjD7s7BicLNCE0xrv3utZ/lktavTMuDAV+ZdqWS+dy+Ou2aAZWuPFKL65DVqFbgVSvqGC0de6PNWyyECGZn9lZrNLJFAoYMQ2jkXnQCk50O/Nlp6wgbx9gtAIIogj/ZyOTVKWhw8sr0+BAA/uy0dSjohPH6vi8OzWBXzn0DMgwJ3x/UpOqrMq6hSHC1OIeTB6ryTAOuyTzwesxHJZOJKEI4f8QyTMbR88oTCI7OZ+SNPG6CoEwL/jhVd73yhDBKzJ0KC1YubIo4sKKhgkpXvmvYPldql+x04E82LiqzZOEdjeCXihY8lIq2KBLN9NuVhrJfysL1d+FVK0jabcuyOXlR3vystieYnbGBBvMel2XEcGjXgxO8YH+D6SkBOQbIxBfW7Boya9x1LN0Kx2l/V8eGc0FQ5Er4uJ/GWTavWrG5ppQ7MpeSOak7HZUvu+wXwhD+ZCOzj1xmX/dbZHMS+XfKK8kaUn7qX39Mxzht7Whxo2B2RgtdcX3sV4EZsHltOreOJF6lkI2a9LctgRuvWlHVAfdN0u/LMSrmebTnqAiCUxMQcPPQWAV2/Jig59LC+SOZoA5VEW7giSAOsOCX8k6CZnfvejPyXEx2Ooiffmbv8wgCAOOnn9E51fXb7yPt9gCY6syz0/pvyoNpfr0O/+icyrapTGAQkP3i3O4HPlwgSRkqZZbu8SDR2roCZbcfabeHZKeDpZf8dCZAwmueKNy8730vxU6yreMFncig6t5J5HzL4Oi8Pj8Z9IkvrGmahfs8ciu3XkneLsfpSj530BxIF9iygrMrAb/cPS71c24vXMuDAV+ZdqWS+WvFriX5687OzoFfh7UXXq8PYXR4eAwGS+KvbD6EYHYG8foG4vUN+yXb7dkCGmGIdDDMFDJxqzzy+i6IoJPuRqfjc+flfL+dR/YUzdHcs22ZqKX5ewWwAEjWNy0QMrIxrT65vmmr1VLORBZhIIdze0Eg7QcyLJTrwLOMvV+r6rl5dIJ5Bh0/436e+WZuDiTln3xvvL4Bf3ZanUDmKibrm5oH5BbYoDNP501BgMNM0XnxCqEWkgGgEly3nZlzOOmEF4uZayTbO1bubACSFkoxYHpp/l5hMbd3LANo5isjB/R9wPft2X1GxukCW//oHKLzF+DX61g69kZllvn3+PNPCCCLZE0Q/LjMFNleLYrk5BkqG+dIbil7dcG812wo6NMgill3Oh/bO/Dm5yzoZeVMF/SYcQagbLMy6GY+lDl0KiFr2/q7sj6Y92sYlHh9Q4HHODOuOasGsKUjc7yKK6+E5GFpDu/GllYWRhhq8Che38iytJEF5W4+K3PCCBwYRHHnRfNOzVpN+n3E587Da9Z1bsiSp9EIfqOWcbDHK4RyLSqjh+emMMnljHtCAzEFGWuu9XhzS9e2my8KWLl5OH9EwU7a30W6sWUB6TgL7EhpWejKPXZGx4N56cOhgkOe8wrAHuXSk73MdnLs9LxKc18etXQx4zE4QLboGAMBDMql/V0JWripDtEIaaut8tJxMLlfJVMvCDIBhP3AJ//GcdOCZrx3tyfPtfVNm4tuxoKM7+L0XZn8fo4DWfArMe6HS61Jl+E/jKXRSJ+r+9nlZLE5UMktt9yuhiU4XLGe5Plu+D42OTmJqampS774nsPapQ+A+goyr1oRgMMjEUzU2a/XM6DDlcgGk5NYXnvAHoZ+dA7pxpZxNuULOpicRLy9jeDoPNLWTuaMOmXhjFPqlYpaJZIHgQPZg77d/D2/VpUDsY0Ulu3kF+Ti9F3wgyrSwcC2zbBziCJxqDtdddqT9U07HgXLsOn73EhTlGWENPctCiywKJWQdLo6dkhS/RydKL9cRnzuvF6LfQTE8Vp6yU9nquFqgRaCPhd8jCL4pRJAsB4JkNDDws04uuyVFxbg16oi5zOWrG/q2Cb9vvR7x743HQzsmNCJCSMBYYYt8cgKG/BHRjQwssrTo8dtnl27revDK5WQnFsTqUgUAeaMRgXCUSTt2t6BX6sCoc2VSqMR/KAq/Y9sRcnVzQ/revCCAEmnK58FgEIIb2YKybk1YW4Mk72y84i+HwCi7W1hzqIYXrMhQIjjcX4tk2uJMJR5AJAOBsr2pYMh/LnrkDx5xrIpZj44l7wf50Mr5JoggwcbvOA68WtVRBsbCGZnzBjFSDpd0x97/M7S3D3Sfx4HFIa6ptNoBC8SMEy2PdneUfBPiW1qgis+xlhrY2QpCXxput7ZX8Nycz/HTz9j22QcZDr6ADJM0WLj9Tp/SaerkmyuyUsdBP9cmhcEgCNpTtY3rGQyLMCfbCj4BGSeltceyIyVX6tKXyZKOsduMIt72QsCWfdOoAGQOQhmZ5Bs79ggz2QD6WBoAyO7AwEc3V722BxYUE5mN1M1Fvb8WmB/AJcZD4cd1mNqSiX4JrdTq1M7UnSqYNy82YXybZn7uP/2woJdY0Fw0fa413ODmgy26PcPc6JZhbjdhm/axaO3M4E7mPSGTmefu+5v4/vEXcs88mix8foMqDxIoR6uCY5LvL6x7/u498bH6nLzmVtuueX2bO2wx4O8EI8U+b3f+72rfo8XXq8PY8aBVMkrpW3mS5ZAkrkxeh6fyXli/goBpetQxNtSuTTt9lRC6x4L4ZVKNv+OTo/JA8uwmY4sVhmTMFC2ELAR5cXpu+TzpaK8BxDQ4LAF6ShC0ulaWRmddt4ztNI+BYZO/hxZGuafpSOp5ujKAePt7UxUPd7ezshVg9kZWy2XbGapJK9qRYDq08/syeWjQ012UhngaKQgG5ACHnRWWA1T2LaSXssrT6gjTskl28rCOmQH/FpVxiIM5TB3J9fRCwIFxwSESb+PtLWj524CwhCTJSbT7YUFAZTNhqyXyYZKA9PBIFOJ0iuVENSMfHIwyBSfCSYnkQ4GIsFtNrB0w5sBWDld0m4jHQy0f0mnq063X6sqqNHPRpGuH78k8tx4e1vaGhZEsm3a6deqlokKAmnbYCDXB7B89n0yV2fP2yIpZty8Usnmona6AtpnZ2T/9fvKjjK30j86p2sw7e8ijWMBVNs7AgTHqnFq8Sxzbe7zlc2HZLxN4EhZx2pl32rABB/x9ja8UknXDAus0NlnoRa2gUVSlN13WF063G4gRYH0YFeDW67zu7LzCOLtbZmLaKTrm0GOw1TOvFLzJiRg5LL+bKOr6GCfJKgwwInCzdkquWZO0taOHbNqxQZrKBePY2WaCYZ0H0d2vv3ZaaTdnry3WkGyvilANoqtPLzf1zVHYMZ8ZQ2IOezzeEXR/cxlh/16XeeAz4mF8m3wSkWRi5t2U2at4zQWtNx33M3z6fTo8Ux+tdsOGnNwuQf5LNMjWczcuIE8DeCZMXS/d8i+7hdMOYjtF+gYLwDkfp9eztwgJ4/T2W+OFqfv2vf3OaDM7fm2XHZ97Vuc+od+vdDs27/92w/8Oqy98Hp9GIusJMmVtq1sPpQpjJAOBvLla4osUDa0NHePfBkb8KHVPlkUZBSJo21kR0tz91gw1+kimJ2BN1ECCEbabXE8jEPl10yVwCDQYhReqZiRV0oOn3Gy0lSli8m2gBpvoiTSQiMj9J2S8nTyVzYfkr6lqTjwY4nVXiFUR4UgxJ9sKJhy38+2LDZej6W5e8QJqtWANBUGcu4eHXOyFWk0sk6mkRiThUn6/YyjIZ+pAaZIh/zOVAOdkLFjrhMLaKh0KwwQXHcMwXXHFDxonpf5mTLGtL+L4EU3CGAYDAToOqxmOoqANNXgAaJIQTggzpJfq9lS/kEALwjk51pNAGDBOcpjchLLZ9+HoNlUaahfLst7C6EyfyubD1nnzqwzOvnp7gDYHSA+d17AWa2mbUhHEfz5WXP/qji1uwOkBuwgTRGfO4/43Hm9Fvt9evS4MKqGlYzPnVdJLJ1nAApcASkKRFClUth+X+YuiuS+Bjh7EyV7jd2BWRs1ZSAIouOnn9H3ptFI15S9aWpz+Mw6ZcEi9pGFYcKZGSvjNmstXt+AP9mAPz8Lv2alyV5YkPuaZ8Hi1J0KGMi4+OVydh84c0twTODg16qZfFAEge1/p6usrev8nijcjMWpO61M2DBX3OMuW3YluW9XanGrJfebkACQX6sqsHKVCgAU6Hmlkj4nAeiezzxn0lRBooyZCTKVijYwMjJA0ygG0iiS59v8LKIzz0ixnrl79Pl4onAzUDU53jpGtlL0Qvk2edYzeFEqqVz0oGwW2WEvLGRAm4K1iZIw6Ns78nwwz1evVALS1Aa1nCI84w4nAaEbpLtU8RvAHLPR2tGx1SAnA3hmDrkv+OzkuHgl+V7i0SsybzUc1JgLP9628THlsSXj/TioLZRvgzdR2lcynLTbF3Xec6c+t+fT8sDGtW8JvEO/vhKs1+vhb//2b/GXf/mXmddh7doAlbDl4NORyEK9iZI4jcZ5dR2/dHdgHQJAGYPEMGOMDCOOsXTsjfKlzYqnhr0kABUWLkbS6QBxbCP/BJmOA4Y4BnYHNlLP9zjmTU8qqEgNcxFcdwyAAUC7A3GcTZvVmTWyPy8M4YWhOF1xrMVQAGEQFECH0rZ4fUPAXDSS+8I4kQZwe9UKltceEBAZx9LHaITU5AMmg11ltYLZmUybeE/X2Ur6fX0/AL0nx5vzQOBD6aVIBgVcpd2e5DK1BCz4k40MU0W2h3ORnDknbTHzHc7MZGTI7plv6cgCdi8sYOmGN2sb405HZJmdrowvTMEOFpJpicN5onCzjL3jvPEa6UgcaJEOZo9qUIYlDLG89oA4lN2eOo5kstLNbQFyxrnl2mO7g8lJKyPcHYjz2WxIfuf6ZnZNTJTkHiaooWvfrF096sVcV/PcuB5jkdOyL970pIxzHJs2dzR4kQFduwNbKGaipG3Rvvb7yvwm/T6Wn/ovtr1mD0i+bgRvWgA+wS5zYxEnSHcHGfZ9ZevhTBv2ywNLOh0D5qsS9JidzhSSUbAXBtpWf3YaSacjayEI4DnFrgBkCpCw7+ko0lxkYSptYavxMwufa/NLE7LPdgdGJizBr8WpO5EMdnWfeoUQqVlnbrCBQRAvLNjnaShVWjFRUtY9NYGH5bPvs2fHmjEDJNBAgJtubiNoNnWtA9BgmsrEzboCoG1i0INBv+W1BzTQdVAp8YnCzfBrNQuqSxbgpKNIihBVyxroAKDP2XR3oICc3wkumOXcp3GcKRAUXALcsWCUjkMhlHU5Oalgks8g/j3dHYhsn+oBE8TywjAToFjZeviyY0FLzXOFaomLsuhBoMc+XexalzJVB+0TSNkPxLp/yy233HLL7cpsbW0N3/u934t6vY5v+IZvwE033ZR5HdauCVBJp9wFaHS06cCQCSBwoeOy2v+wZZ3CArxGXaSkJieR0fKk01EwBYizs3TsjZJPZaLHPMbAL5ex/NR/Eedxd4CVrYcFjPT7QNU45C1bXYmsSry9jXTTMkauM0BHIOl0s+11HPJ0FGUA28rOI+JosLonHClst6cOd9LpGMBmZKmdrji4EyUk2zviaLL9xnlMTE5OUKvBOzIL78gsls++T8DMDce1XcKijWw7Wb3UgCF1yh220gVbvAYddY5D3GohbrUEhJo58stleM7ZbQRPZH6STleqRBon2Z9swJ+ZQtrpCutpAF+0saHrJVnfhD8/a/vKAMXuQMeALKTrWHnVCrxmXecBsLlJcaul4JHj4gK1NBKmxnXiOcfqdJl7IY41FzHpdJW1dMc+HUiAApE5lsGAIwI6r2FkeKWi5OUGgQYQKAnl2iLQckFysr4pgKrTQXzmrLCe1YoAXadIFT9DZl9BUxQbaXLHrqvJSRl795xYdyxMECXpdARk7w4sqAEUDOm6K5Xg16oyriO7h8mQeBMllVp7YUGCBZ0uEMfK5DOoxJzLeH1D7+vuZ8SxMtIKKJyjjpRxmygh7XThX3dU++TKUa8mC6MVX6keiGNd0zziA0FgZPadzOdWth6WIMiEjIU/M5V91q5vauCNRjaW85yppt1sOsGPuq4NBhAAIL2wDoA5kjYoANhgkTc9CZhzIBn8AS4PPCihdQNcrlohjUaIv/QU0s1tC7ZDB7AVQgSTkwq8VnYeydzTDQ6451TGnc6eto1XP9UCSiUL1AHoGHDPeNWKyUUd2L+Z5zXTGJbm7jmQtHq8TctrDyiwGw906BqN40yBIjfv8iDreKF8m34379eG3HLLLbfnw64l+atrb3nLW7C1tYU//MM/RLlcxsrKCk6dOoWXvexl+O///b8f+rov7F4f0CR6LEBEju0wbGWjjrTbs+Xl41i+fMtlcbCNU5l0ugpWGO1W55JFfdRxN0VmolhZpGB2RnMqeQ6dsqQTJWEHJySHKzWHsHvVijoDCAJgMBxz5MSpEYljW4t7eBMlIAwsYNgdAHGsjp/XqMvLSInI+hEg08l2GRrXaZciR6E66AQuFpRap1+LjZw5h+TMOWELohHScxd0bGDy5ejo+JMNqbZoAIe2oVoRh81E25PBroJR/i4dDLQQUjgzg3BmRhk5BgHiM2d13lgMx82zsn02hVx22tLPlpW8Bc2mlUpOlJCcX9exUYbpJTfAL5fNOjHsgAF9i1N3yriYzy2Ub9M16teqItk0zl66O5A1S8ZZAx82X5e5hnRgCfooq1YZNfvnMNTB7IwC5NQUDkpHzgHz3R7SHXNI+k5b59itNsm9lfT7DnAdZcYzmJ3RYIdfq9lAQMOcP0l2CZKfGbdaKs+joxxMTio7mxhASBCn1YLJpk82NBeR8k2t4sl8YRPU8UolAcydrrJY7KfrJK/sPKJ7dTzvTGSaI1uAaLKBYHLS5ur2+wjmjyCYP2I+IICKrLnrJLuKgqTTUcDkyl6vdl5lbIJkulec/pL1BQxT6DBqfLbRgslJwFSf1gAJ83vhgB7DOMbb23uCBApawwDp5jYQxfrsogw3A6yMCoOBtKTTlXXSkr3shYVMLvjlzM3t0zNCGRwqlRC86AbLxJu2x9vyHMdUU/eyew1WWt3PKJWlvN81t808msYNFKlKhXvmRTcgeNENIjk2MleaG6jj99xq/8OZ+TuIXQoUah6uE0AZn9+LAUT33Dv29WL3v9wRIrkMNrfccrsadi0dKeLapz71KbzrXe/Ct3zLt8D3fbzoRS/Cj/7oj+KXfumX8M53vvPQ131h9/qA5hUNKzYaWUYk8G0ekHnxC5rOMWAldwu1k/CbdaxsPYw0SfQLMpgx56Wtb2hxDy1kkSRIRyMBmNEIaZIguO6Yrdo3NQlvalLvlZFkbu/ItQxrlGzvyPV2B8oAsBgLAHiVMlAqai6my1AqIxDY6SSDQiATTE7q3zV3JfCFPTCMmoAOHwh8aUOSKHAhyPbCAvz5WaSRMLRexTnnLwgQzEwL86Vtkmql8fY2/GZdgF6losU2aEmbrJ84/DzjjwAGMMxrkihjnEZSVClpmzMyx+RkKqMLfP3s0tE3AJDoOwJfwFOSqHRRgwvRSJkozRU1INebKCF54il4JVk3LoBB4KukMDMnhpnlWKS7A5XbJe2OjHkYIh2NxEHeHWB57QFh2I7Nwz82rxLspNNR5haBbx0ygtIgMLlfHQmeOKwa15cyTSMLNEXy6itYWu1/2IzRyLSvYNcQ9xUZR/cweXOfdDRCsrHlSM593VssHMRrUU2g6yLwhfk0IETyV0t6/6TdwULtJNIkMaxtBL9izqI1fUDgK+BLuz34zTqW5u6xgMDsSZchlb+NdO5YSZqggcB5+ez7kEaRtCGKEMxMIz4vwRTODYMMrATtPof48ms18xyJdN19OXIq/dKEjOlESfKTTcqAFxaESaesFQ4wNHude4S5uEmnizRJEK9vIJiZViWAZ4rxcD0jkFxoPne496SvodkHAtBUjVERIBqvb5g1LvsrTRJd/6xK65WKdl5rJ7FQO3lFYGO1/+GM1FIZzzPnZG8miXxH7Dyi3wvpmuwrfo6f5bXGjcXhmNu+nxGULtRO6thI8NG3wJrfPa020GrLs8HI2FNTZIyqDY8ViQNfnleGjT6ouWdnXtICX3PtD2Lj5965+ajjv8+PFMktt9yeD0tS79CvF7J1u10cOSJB8OnpaaytyVnQr3zlK/Fnf/Znh77uNQEqk10LBuhkIxZnhVUW6RTSYfSbdaSjkYBIdZ66pnhHwcj8fM3L4pfd0tE32Cqy5bLcJ/ARvORFcr8zZwEI84ZeD+j1DCMQwp+ZyhTYWaidNEC4ZRiBqmFnilbSZfLS0sFQASgdK32PYQFXO6eEeTLSVr4v2tgw0X1xRNLRKBN5V/ZpZIqdtNrmnoNMW3W8z69bWRrPyDTtSAdDJNstHWu2QxipoYy7KdBBB57jCUDBIsGtC5QXyrcJM1UqqaO7dP2bRPI1UZJ8OcO8SF5UUcaA/YgTrXQqxZZMJdwxmSnHxQsL8Jv1DJhR8AgjL4sTvb9ey2k37+FKmaVAkYBIkX0l8AoFLXIk76li6cU/Jdfc2paXAYTBzLQtzjMYZJxPwDr9fq0GsHiJAYRpNIJXKpr8yJGp+jqy4NH0a+noG7B09A0iXx3YvERh0319XzoamaIqLR0fAba+ymt53XQwkL6ate7XapkjKmhkE1kYxs339QoF3ZdeoWDyJ1v6N84Dj11Jo0j2xe7ABmjMNck+0ghAVjunBMyXyxpwCWam9YgdtmVl62Fph2FBuS5p6UgA4tLRNwhI75zCaueUOMBxos8o/t+v1ZTFutrnVMoYSVtFvtzVuQtnrDw53R0guOF6bSdZVI6FrYRaEJZWKwlL4McrFPR56pmjZQDYvMDBAMH8EWduQgu8Ox0kLVtkyS+XsXzu/XosEEpFXZcLtZP6LGbRJlaMPoiRWeSLe5WKCZSKukcXp+5E0mqrioJBAwJZAJlr0caZyf0OE2cupluBPOl0tbI44sTIsmXN6PMSTt42n09mrDlPq51Tprp06YoPMr/UejxRuFn3yfLaA/vmVu5nLrClDH0/oL3f73JmMrfccvtyWHJIlvKFeKSIa1/3dV+Hv/u7vwMAfPM3fzM++MEP4syZM3jggQdw7NixQ1/3hd3rA1o6GOqZaQDkS9/k3rhMJR1Fr1RCvLGpDrkU0UlMHpRTodTItQBI7t1ggOVz788Wawl8YZ+e+JI4UMZ5j89fEHDWaltGbjA0BTGkiIRXKABxIk7SrlwbAJJWG7457Jvt8ULJ45Nz2grqmKejkbI7S0ffAK9eg1c3OYnMvwkCARAzU+rk8T0IfHH6ZqbEYen39SgEAAoKpYJpVSPeftOU7zdsAV8ixxXmz59sZudpNJK+1WpWBmfuyXnRvK4CAViWZZViPAUtehGfvyAgazTKyrqC7NJenLpTWCuCH7fIhWHCmBeVjkYKjOKNTV0bZCfc66fRCMGxo5m50sqeBlDpUSbmcxwjFlvi+wiGl9ceEMC6O5C+mkCAjqFhXxl8EKe9lmHE9b0bW+qUuWt/ee0Bc3SGyS80AI3O6vK592P53PuVNeK1tfrkYKDzyzyzpNOVoAFz00pFO5cmsODPzSDdHWhAYXHqTrmmE2xZnLpTx53ySwIFzwAJOvSUAjI4pNVuJ6SfSacjeZRmDDjm+xlB3dL1b8qMDQJfwY0W3qmdVIl7vLGZkXJzTJKeKCW4rwHLQrn7RZUPuwMFoO69roZ5ppgOACQ9G6wiO8ziMwh8oN2xkliueyd3WxjOUPbWYCjPTAO2uXYV+DgBnnTXAMqNLf0dwZvm0gY+gmNHdT8u3fgWlYZjMNTn8GrnlA3kGGbZlaRezjJ5jwbUnx49Dr9SMcfdtHTfMljlVSvwbziu6geOycWqpI6vu3G2kIDS5ikmmaBU2u3pd4fmv3N8d20wy5+R7w59dpi8amURGcw4oPF5ckkg5wRJDppHmQG2Zu1djJ0fZ0pzZjK33HL7cliS+od+vZDtLW95C86eFRLs53/+57GysoIbb7wR999/P97xjncc+rov7F4f0D65+YgtomPYOMACEJpK4QaDbJ6Qyd8Kjh1VZ4fOSjB/RCtkAsDi7N0WVFUr8KensNo5Bb/ZMLJEKw9UMxF0lCdUZimFLLJM3OLs3QpokrUNzc/ywgJQZ4XWXf0cHV3eI2m1EZ89h/jsOSDwbVl6cx2RA4tDFJ89pwAz3R0gPn9B+nVkTs911JxSA/yk32OVQ9NEnRwFO9Uy/GYDy0/fL+NEx2iiBK9aVrkY5ZX6WYex0ai7icSr/NQ4SkGziaDZ1Plz592v1TSiT6CfjkZI00TBnFcqKrBe7X9Y2CYDzpNeT94/sGywX6vJgexG/krA45VKWH7y3QJ2TP4m5zY1oFDn2syTVy0b1rqWWYd+swEEvoDj/Rw/cw0NOJj2KBtsWG4WOfEnm5niPmkUwZ9sIpiZxkL5NmEVHIljmibKAmZYFjrR5lo6Ti4YgDjN0YU1eNWyzFO3p/uCYCLZ3JLghsmlJGuu82/2ihvUSHcF0KepzGmaJpa5DAvwmw0FeLKeajbHuCRr1N2PPB6HgQ2tTNzpyLg8fb8yt8vn3i9ryym04yoYqGJYXntAmTzOu1+Rwl0LtZNYKN8mzw5Ax8dlVjlny+fef9mcvOfKWLk0mJ5CMtjNsMMcR+4jgnW2VXPpBgNlzb2JUiZnMmm1dU4p/ZajfiSIJYXAWvCPzVuGeWNLn6NeVYIXy0++29x/aM63LGYDNRAFCQN2sp9lb3LMaRcbUzfvlXmwlIgTnGpxpcEQ/mRT1v/uQAIgyMo3vbCw7/EYgHN8iVNhdr+/A9BgYNLpmvM/Tb6wUzCO657BlGS7ZQF/oaC5oazonUajK5K/jvdhnIHV45BM7jP/diX34Hfofve92LmXOVuZW2655XY4u/XWW/FjP/ZjAICbbroJX/ziF/HHf/zHeOqpp/Cv/tW/OvR1rwlQ+dobf1KBiN+sS9QaRg43NWkdm1bbsokwTkChoJLAZHPLgqKZKQGXG1uZgh9pl6Xwh0h3d5FsbkmkOk5EImckXfyipzOedLpITcSfzicj0elgKE7W1KTNlazKmYmUBKVb2+IcTEwAcYKV9QeVZSRQoTPOfjGCrs6/YVUBWGef+XIlASHJ2fNSbGb2bgVN3oSwYdBiO00krbZhHndkLAZDK3OrVIBY8heTVlvGsddDYipkehNy3Mvi7N3af5t7aGWYlEuS7dLcLHVSO8rUcSy8qqlmyzzR3YEpDJTI/R0pLsH3Qvk2YZsMY+0FAfzpqYwEmePk5t9JXmTVlv7n+DQb4lCbvvjNhkjPmC8XJ6YYTccUIClmgLdlQSNhng0AASCfbdv7exMTyg4SBC0dfQPS0Uhy/IxTyqNahLmMNL+K48S1TaCj8t9aLSNtZRv9yaYGDNJopPJQVvJUixMEL74RfrMBr1DAaucUko2tTLBHKsUmTrDEVlLleCStthZrIqDXcTJ7kuCVQJX5xF6losqAYGbastH7FBShlJHrT6vDGhBMUKTsYjSC36wr2NTfj0YSnHAYvaQledQr6w8qw0S22WViXKbsapmyutEIabevARoA8K830hcnB1vXRMEWopKKujVNJUi7fX1e8Lmnz0AjIV86+gYpyrXd0j2bnD1vmTY4Abk40bxlQNZJurUtf9rYhN+sKysXb2xqMJCqFb9WleckLAC52JEXbn4vc50Bu7ZE+tqxQZiNLQ3OaXEm55ou86lg1TBzBLBJr5dpz/i/5XiSguyXahkoFm3AlEoQ892m5/4yMNdsKINJiT0rmB82V5dg91JHfAAWXF+JxPZE4WZdj+OA9WJnjeZsZW655Xa1LYZ36NcL2d72trehR5USgEqlgn/0j/4RqtUq3va2tx36utcEqIQvUkBAnAsyPelIwJhGmCMrIwSQkUu5uTrp7kAPkCa7pjlzTsTUa/DIisgwW0NxrJnz5jiU+qVOVqlqi154dTlIPjl73oKoSgXL596v10s6HYm6p6nIBmfvtnJdk5/nVtgkgKRcFbAOklbQNJFh/8icnBd3/oL2N2ntKCNA54S5mix6wuiyZwoICQsaKHOHKcdRrVRsxHx3AK9eQ9qVAjcrWw8LEOdxK4yyl0rq/FNGyjlxTUHeaISV9QeV+Up6PWFcKhVbmdKJhmvFYAOsyDgAwqhR9qpghU6zcZC9QkE+Q7bVgC9l2wrCoqXdvoCTsKBSXQAIjsxZkGfmx83ZTHcHSNY2rMRtMND5ZO4mC8MQNC1O3QkUi+qQy3V2rQTcMMV+swF/ekqDAQB0/Lk+GajQNWxAk2W2eQZrTa/jMlo6lk+f1f6Q6dd5HQyQbLdEcu2AF79Zz7C9LECk82fa5Tfr8KcnrUxc+2wZtGRtQ4MgKp1EFsBnzOwdv1bTMfHNWZzjlTO9sKCSbv/IXCb/Npie0gJDfG4slG/be42CgMik09Hqo3zvVbXAR3BkTn9kwInPIS0aZiTpDKoQqOgadIJfKuU2kmTto2H3441NLD99v11zlbLMPZ+xznyko5GV+jfr+vzh3CSdruwfzj1z4QENdF0MTNJcqap8bqDzQMbWCyVdwF17GsQKC9Iffr843w8Xu+c4cHJtP6mpVxVZenJhzQYRwwJgmGbNF3b2i0rH52b2Pvv2W/OXMvP+i4E7t79kFS8mMb+UUca/XxGenJXMLbfcng+7VuWvb33rW9FxakDQer0e3vrWtx76ui/sXh/UkkSlVlosw0TKyeCkoxGCI3MS0W3WLQNUKsKrlvUsRr/ZMGBC2DPmanmlojkDrGDZk7PnlcWgE4Y41ra4xyt41bL8PCdHkzA3Z6F8mwAHU9TCLaIjBSGECdRz1MwZeLw+wCh610oyjQPnO4fSU1qpLI9KTauIn3pa2ulElsnmZc5rc/L60iiy0rIgkFdRQCWlaMlTz5i/yzJbqJ1E2utb+a1xJPU6LL5iqlIqk8uzFp2iQBxbOnZ0UqXCrji3fmlCx4qM4fKZ9yizqbmNG5s253Z60q4BE9WnXJLgkS9WaiW4JCDX/M9OB8mmDWpQOqfOeM9Ws3THScBoXX/nNxsC3o3UM90dIDh2VB3gpLUDFItanTM+e86uAzOuBHrK6rV2ZC05QRYCfgkc9BVEu/Oh499sIO31bb4n+2Gc8EweqAGi9kzERIGUMuFBoPnAzFNc7ZySQikmqJG0djLtk1xLKVbkMrnMr6Mz7jrW0u9Ex9E1Vgtd7ZzSgATXu8ueau7XGLseP/W0jo1XKNj2kuF0wKWayXdm8RvAVn09jGN+YDNtXz7zHmX3AZOHZ8A62TmvWtZ1TtC82v+wBkzSbl+BDseJEmsyaWm3r/Mtz9qOzqlnKsVybQICClXm3e0r8+8fndPghTvnruTWn57EytbDWNl6+JIgaByoLNROZuTQvDYAUQfEVh7uMu0LtZP7ArX9gBDPxOS/x221/+GMbNar1zJBKsAJhhSL9tligCQA3Y8r6w8i7fVs0K3bV4XMQVlELwi0uNR+51Dq+8YCfYcxVwVCGz/vMgeXueWW25fTYhyWrXxhW5qm8Ly9bOpf/MVfYHp6ep9PHMyuDVAJR75kQAlgi8yofDCOkQ6GUmxnc0vzdFbWH4RXKY/lygnbw4i8yO8KFqh2zVEccYKYeWSUuUYjzdMjg5p2+xKVX9vQL2CyXGSPXCeWZ0+yiA8j2PHGprJdGsk38knmS6pM0zi13kRJAAxggUulbKP+RioVTE8Z9qtvr8eI91FhNFzQSmYt2dxGsrmNtNdD2utpHpqaATXsC2Dllmzn6dHjMhaVchaoGJCtlVON1E7nnTmoZqzILFHqqjmLpr0LtZMmDy5UAKjrZTDA8pn3qJyYDrTkPxXhNxuILqwhurBmq1WyWm9YUNkvHd7g2FHLalTK+lLQauZPcjeLtp1a0KiqbLeylICw2msbhiU2YGlzS0Gi32wIwHTAZHBkzjrFZp+kvX5WLtfr6zom2+BKN4UxamSAugtc3aCFFr8ii2Sq1vJ+ScccCWIOaw+uP47k3BqSc1LW2q/VcKJwM0LDonN+GBDw52YcBr2fkVhqJUnDsC0/+e6MKgGBj/jCmkqROUeUzQNSqZOgx50Xzfc012MupALVgewH3osySIJVPSbHzGls2CdtCy7NZD1XxnW3dPQNEjgwa9KrcN0HGqSQwJYtYubXqsKoduX8U396MpPDylQDjivHinJ87jl/ekoLMbHf2rbdAZLNLb2uPz2JlfUHlbnPgDhzj3Q0gj83g6S1I9VYL1NgZpwBc3NG+QyX3PmyrjVVrDjPawBAnOwBXhc7UgSAykP3ayOv45VKSNsd83wJNXBj2cjYYSoFkPlzM1YdUTupc+v26UqYSh6BwjZeTP6a7g4yFcIPY/uxlIBlby8nu80tt+fL8mDHtWvXGlM5NTWF6elpeJ6Hl7/85ZientZXs9nEq1/9avzwD//woa//wuz1FZrnHPugDodxqL0gAJIESBJT8EWcWW+iBKTiKJ4o3Iz4wpo4kOb8r7S/C69o5E9BAK8o+XpIEv057e8i7fcRTE/BK5cNGEw0R4pVYlU2aBgqgr3AROIBaFsZvU42NvW4j0w7jNxOwW+xoNI+UH5o5I2IInjFAuLNLaT9vs3RNEdX6PiM5PcYRUh5P0COOika9mE4skVhzFmAfJ+bN5XuDrDYvEPaE0Xwpydl7Mx5leO5opRknSjcjLTTReJIUClBTjoduUalnCnKw3sgSeTsy1ZbovMcy4mS9JvtM4U/vKocQ0HAYI+ZaUjbzVEhLjuXdLrSn0pFC7AgkvFiICHt9eDVqvL7fh9pawd+tWJyczf1JRdMtH1eEIjU2awrWryxKdLdySa8yaaVr4WhjAeLCDUbUuG3v2uLCznjko5GGviAYfZ0/gJf2joYyP9N7rC+eHbli26QfTQy50GadmMUwQsCxK2Wrje55i6QJiqRTjtdKSBi5sMLAp1fSlS9YkE+nyS6npLtljjYRcPSmvuSjeX6U9YniuA3akjNfJ0ePS5n87V2pH1mDQTTU1qQClEERFG2+mbgwz96ROY6inT/rqw/mAHbS9fdZzawjM3p0eOW+Qt8LEzcCsCc9Tdxq7wvtQEXr1TS/UH2D0DmWXA1LDXFltL+rsxNawdIE90Ty2feo//2KxXdTwC04A7SxKhE5OxZzkMajYTxNzniWqCrv5uVbG5u6bMk7e9awOCAHlVYRBEWm3fAv+6YgHMTSPKqZcmBrlYE0F9Yk7kx47wfuBsHcF4QZHIrCcZOFG6WXM1OV/rvBJq8YkHWfrEg9x6NsDBxKxYmbtVr7nc/v1jEwsStCqDd3MtxJm61c0q/t9L+rvRzdlraUatKoMfsGb/ZQHB0HsmFNVFH8Bne2gFGUaYwkBs8OYhx7RLU7csYmr1+WNPv6X3ujTR5VoD1au6j3HID8vze3L5y7N3vfjd+5Vd+BWma4q1vfSve9a536euBBx7A//7f/xvve9/7Dn39FwyofOc73wnP8/CWt7zlij+brG+qY7HaOaUOkt9sII3jjBRQQVtrRwCA8zu/UoHXqKuDoqDLFGABIAdKF8Js3l8cA8OhOKdpIv8fiwYH01Pi6PI+1TKWz39AncdgWgoDebWqnGe3+xH4Dcm1TOMYflWAjFcti8M2Oy0O3K7NRUy2tvV6SWtH2x3OzYozFIbaH8rONO9zFCFu22qyab8PhKGMX6mkbUcUwZueRNrpYqX1IXXI9QXo+Hi1qoBjOv8GhMEcj6LtL5UEYJv2KiCMYz0zEIVQnDvDmrpOdzoYSDurZTkzjaBvZI8NYR9ddo/Agm0DoP0l+HcBgMsweUEAr1ZVEBlMTxkmZV0c4TCU/vNsUfd4m8mmHImyK+1GGAqYHQyAQmiB3JE5cZ7PXUBy7oL2xwsCnQOCxKTXs0dDGMedP+vZdGYOgyNzSFo7SLo9WVdhqPPiVysZYC+5tdIGHeuBBC2SXk/WAMRZRmj3BQohEIaWGS6V4NWq8E0xK4ShBcO7A13feg8T+PBqVQVubCMKoc4Hx4Nz45VKFgyZIIHufTMGDH6s7n5E54Zrj4ACANDftWNqbKF2Uo6ZMFWSk9aOKB1M1eDF5h26zmgELZxr17wgQNLtKSPEyshpHNv2XQUbr+LMZyV/XmzeIf92zjRkIMALAlmvRorPvaDXM3OQ9vvYz6QQ1qT8u1bVdb1QOwm/WrF/5/MCEGk9gOTMWSzN32sZ5FGk64EycQB65JALGGl7WLY4VsB0evS4rGETkNBnTBxng16FUJ9RaRwDUYTV3Y/onLkM3/i93HkdB7ju7xYmbpXnnvOcSEyhomRtXZ5ZhVCfjcnWNvyZaQHppu/cpzp/5vq+Gc+D2HgV23FGlv3iz67E90oYnHQw0HU3fu9nA1iv9EzO3HLLLTdanPqHfl2JfeYzn8FrXvMaHD9+HJ7n4ZOf/OQl3//xj38cr371qzE3N4dGo4FXvepVWF1dvex9Tp48iR/7sR/D7/3e7+Hee+/FyZMn9fUjP/IjeNWrXnVF7R63FwSo/OM//mM8+OCD+MZv/MZDX4PAgk6f+0XCL36CtnQwQHBkTh0Wr1RSIJL2+9b5C0MkvZ46XGmnK3mU5bI6VHSqtPjJ3Kw4yxMl+NWKOMtRhHhzS4DFhDjXaaeLhYlbxeEtFoF6Tdu2OHu3siv+3KxcP47FgXCBkgHMZBpZeVbBsHE6+FkCCDobdOzZXv29MQXbBOfGeUw3t+HVquIAhCH8mWn4pjgRAHimEiiBBMGpOlxsH6BgVIHA3Kz2069UsDR/r5Ef7+gcouc4qwQEBkixL+pUGtDnOscESjqmHE8WyKETbdqvEtNeT5nKNI6VcSZIXqidlPExAIfA3Z+bsRLOOEby9DO2QrEJhqy0PmSvacaaDBIBtIKtOEba7lhHs7UDv9lQyRwAmW+yXoa55Jxz3TMA44I8jhvXLtckwateI47hNxvK+nu1qrSThZjKZQ0O+HMziNttC8p3B/CnJpF0LbOrEsvBwFbRrVSAUaSBCa53ZY6Yi2n+xvb7M9NY3f2IjKepOsz97YKfE4WbdQ9qbnQU6Z6FYYO1b85cJuacWwIwrumkJwBxZf1BXXdc96dHj2vwIjgyp0EDF/S66+JqWry5JQELBh4M2AZs8ASAqgr8udnMvtG9YCS/+hw0a8UF3CvrD9pnba1qg04TJQ2arbQ+pAA76fZM8TWT51ytID53Xtcl93lwZM4+lw0jp0EqM76rux+5LIswPtZcxycKN+vzzX02agXYbk/Xs1erytExhlFzgZd7fxfkusCODGAwPZWpFutKbuH7eg2ud4wieYWh7mcCcz5PeH03OJAMbX76QYxrmCzlfgWQFADufiQzDgcxtm28yNdhwGluueWW23NlKTwkh3ilV1j9tdvt4pu+6Zvw3ve+90Dv/8xnPoNXv/rV+J//83/iT//0T/Gd3/mdeM1rXoM///M/P9Dnv/3bvx2+7+NjH/sY3v72t+MXf/EX8YlPfALxswzCPe+gstPp4NZbb8VDDz2EqampS753MBhgZ2cn8wKyToE3IawWmT86rHqchcNweaUS/NlpBYgABKARiJj3LZ95jzgVR4+Iw5sk6vAo0DHgLdnatrJKp5JncN0xdQLonPnNBlCvSfS5bc/y88xB6l6phGRrO8OKevOmWqORndGhoXOh7BkZkZEFbEm3h+Uz71HWjs44wlCZAI28k6U04IZOihY3MQxB0usJ0Ol04U01Zby/9JQ4rKZ98QXJk1ts3qGSynQwUMdfGeOJkkTgR5E6rvHmlmUXHTZRX2RKnL8TZBGEuWuEa0JzY1np1wB+Aidtp2FHXWDK+2p+ISWl5voMRPjVigLAYHoqA6ZdMJbuDlSeFlx3TK/n3ousnT8zbVlrAh0DvhZn77YspXHoOa4ua5r0egiOzNkCSYZZS1o7SE2Ore6ZUZQJvhD8KVNLtnmMuY3PndfxT86eR3jsqJU+DgbC6FYr6hC780qQq2sRkKIkpZLsDWdtp3EsRy048vGktSMSw92BBgHcsVbwCstAkRnlPkmGQwkgGYkvQXeytqGBDF4PQHbPQZzgZChFmSjBXJi4NRNY0WCJGV++N8NUXUVbWX9QgZwLnL2JkjBegJUuG4aMrFyGweMYGkAPQJi8kgBul9WkfJ57S4/ZMODen5qUaw+H9vlULGZYbWXmqIIwwI/PDI4r18GljAwypav8OcP6m7laPv8Buz5hz1ZkX1y1xsVAUDIcZoM/jp0ePS7BR/N5fY8JJCZr6zpf3KNkKv3ZaSzO3g2/2UC8uSXtjyJpY6Wi+csXu/dBzGVz9wN77vfwlTKL/D5d3f3InjMxvVJpX3CaA83ccsvtatuzZSrHMctgLHBGW1pawtvf/na89rWvPVC73v3ud+Pf/Jt/g2/5lm/By172MrzjHe/Ay172MvyP//E/DvT5z3/+8/iH//Af4vbbb8fHP/5x/OZv/iZ+9Ed/FN/wDd+AL3zhCwcbnH3seQeVb3zjG/E93/M9+O7v/u7Lvved73wnms2mvm644YbM39NOV53gzLEgjrOUdHvqHBMQAVBGJh0MgP6uXNA4RicKNwvAOXM2E41Pej0b3XfYnzSO4U1PZdiyZH1TwZVeo1hEurYBf2pS7se29/vicDsyUo32G/CZDgYIZqWSbHxhzTrgjFwDKslj7qYLTlVmyc/3+1q1EoCNdtPRdiVUZD9h2KQbjwE3HkO6ZY7UoLzVKWUfzM5IER6HiVps3pEB+AQJlHMqgzyyYNKv1+BNNtWB8yuVjBxZ5XLGCFiTXs+ysXOz9liVqUnLbjqAKr6wpoVKVtYfzIDOdCBgJe10dQ4IankPQMAJf87IrV12btf2FVEE9HczkjoF8w7LqkELB3iy736zkXHo6chzP/h1W5RoZf1BOz4bm8pMrqw/KOMyNamMJwE1mdt0d4B0q2Wd+G7PjlvJjsHq7kcybGoGkLhrZG7WssC7Fqgrg0hmhWNs9qtfrSDdadt1xPE37DUDHwrejFRZ95xjdLj9es2ySFzrZr64Lwg2yU7qenMca79YVGnlwsStCl44HipRN+oBdy9cTekrIPuWUkN/dlqCTzxXs9MFyhMaBNCqtLWqjn0mJYDS5npN+zReWZcMN2AZOIQhvHJZpPxch+YIqHR3oPsn3WnbgmNkBh1WFbCFpHQMnfHcz8gYUuLKANf4WZFc6whDLN3wZl3bXhAIUOt04TXqmYDTuFzUvScAZfH2YwtddlPZX6Zw1KoZtp79T3cHme8+Btm8UglL8/dm5ulE4WaVrB/ULnWkR6aSrZnfw1Rp5ffmfjmw4xL08XvnlltuuV0tS1Lv0C8AuOGGGzK45Z3vfOfVaWeSoN1uH7hy65ve9CZ87dd+LZ566in82Z/9Gf78z/8cTz75JF7ykpfgTW9606Hb8byCyscffxx/9md/duBB/tmf/Vm0Wi19PfXUUwBM+fmpSXiGkVCWisDHSDyVVTDgxq/XM8xbGsfwKhXrAI9ECkdwExydtwxcHMOv2/xLr1QCklSuXyohfuJL1jEfiTPvlUoKVACTFwMg3WmLU0TnpNNFahjG4Ibr1YHxJkpI2h35Ak9SZQ+C2RmV7bmABA5z4xUKCKanLGtgJGOUvenxB70+vGJRAG9s8ikJRulAjyJ4xaLNhTtzQV6AVEA1plLDUYSk3dEKtJ7Jv4NxRLxyWeeEuZ/B5KQFshMleMWiAMgk0fw+6Xxgc9hM3iIgzpZfr6vzdXr0uLJR8bnz8rko0rFXNtc5OiLtSV7p4vRdktsZBDLn9bplWnt9+EeP2HFPUnWq/WpFfvZ9m88K6NEMLvjy6zVdewJs6nYspqcyObkqMeXnOdeVClIttlTM7gGzNlAuw6/XgVEkDucogteoa98py07aHSTtjqwzk/MHQJ1WmICDsh4G5LssiD83i4WJW3WPLJ//QCa3NI1jYU+jSBn5dDCQuTZA1pU9S5+Glj0lSBlYCXJK9pLMslNtWKWeuwO7j2BZEgaP0sEQyXBox9mMrVepKJOUDgYZ59w+Wyr2us5e4F5a3f2Itk33lsnH07kdiJT6agJLlbA2G0jWN1WVgDCUfn5Jnq08izTp9WQtm6CVMlFJagtWmYCXVyxm1iX4DDLPIznyxVx3a1uCEybHdzwfmIEtZeiOH5N1YJ7Vvjnj16/XNPjkV2xO/KVyG12ZKQvRMH1CAx+DgYBuQJ4bgDybuz1Zp3OzclYs7DoaZ/NoroydP1+uiBCCQJ6XSSqVzauVTL68X69J35PU7ncH8Lt71ysWcXr0eAZoHtQuBdDJ7rpHj1wp4DtRuHnP88O997NhJa+2lDy33HK7di2Gf+gXADz11FMZ3PKzP/uzV6Wdv/zLv4xut3vgyq2f/vSn8Uu/9EsZEDozM4P/+B//Iz796U8fuh3PG6h86qmn8OY3vxn/9b/+V0xMTBzoM6VSCY1GI/MCAIQB4vUNwGFzAFOIpVLRPB1lJ+jgM3esXpMv71IJ6XBojtiIzPEWVtKUbG1nI/emiILmovV6WJq/V++T7rTlZZwIv1oxZxNaJ5Xmz80Cw6HNlTFOKPp9aUtgC5qko0jbmfR6Cn7oeHvFIlZ3PwKvEArYLgkYhe8DJoePDIl//Bi8QAoF0VlLh0OkxmmC43jTwQaAdDgU57BQgFetwDOSzHQ4FGdrdhrpjjlzzNxTZbVGmpUOh/L7bk+cfOa0GTaNDpiA4lAZDDcSnxqZHHxf2WMtONNui+M5NSnyukrFAjbD5imwNXPCcdC8XNPOhYlbZdzNsSlepYLF6bukIq0BuX69LkED046ktYOk3UY6GFqZ6ISRNMexjC/ECY7XN+S9dO7p9DsFbMi+eZWyjmdi2uIVQqTDIVY2H8rkUnqlkg0A1OtAEsNr1PRIGV2nZr15lYoW3FH218mTAwTkp8NhBgjpvqIUtVIR1skwj8HsDJaOvTEj7VXZ8FjxmqQtQF8DMVxzvR5Sw+5w/GyAh2dp2nGleaUSVlofEmmxWY9sIwCstD4kOX28VxybaqZBRsaXEmiaday5cSa45B+Z0/GSPdfWsSHzNO4cB0fnAQCL03fpuHtj970axj0kFURlzlR6agJmEjSRAlBBvS5HWOyTb+FVKsI0s3jYcCj7IAxt5eJ6XQMvCxO36jxyPVNNkVm7Zh49sxdXO6ekumuvZ/fI1rbMiQlYuQWHAGQA4sWACZlK2krrQ5IXaJ4ZVK7w2eAVQn0uEFByTbk5geMyTu5tBg7YzksCpjiW+wES6OlaVYM/NQk593ao3306Zvy/WUucFwC2eNhFbFwy7Mq29zOyvfzcYWXbngkGjF97P7uiAkDPMkcot9xyy+2wNo5ZSodMP7iUffSjH8Uv/MIv4Nd//ddx5MiRA32mVCqh7RTnpHU6HRSvoJDbuD1voPJP//RPceHCBfzjf/yPEYYhwjDEpz/9adx///0Iw/CKk0W1mEq3l5VmFayDTGaQDB8gBSvo0LuyxXQoX9Zpr2dY0LIyLnR25awyE0GfleI06UhYF8oiaenAfKEbxzgdDsVZCOQ8uHSnLcUpOJmM9Jsy6wIehOVMjUwNEIcWgBa84NmYS9e/CfB943QYuW67I5/dddiErs0lA3NLo0icFL5cls2wwaudUyL/aneQdnsCDCmf6/WQrG+qg++VihlJIwGMldcVM8UnkrY9847gW9lEsqWGAVntnBIGLEksS2SkisH0FNLh0HE8e0ZqWlRGiyA4ZV8BM/cDBbcA4Jv5JPvslYripJn8WmG0zdyFIYJ6XZztivzeLRrjVSpyT45XHCOYP4LTo8d1PjWfddfmh3kmrxBJoo5hMD0Fr1QUBs0cu7B8/gPSl0COICDw9gqhzAsdUScvjiCKbVJ2dDi0RT8Me0RGiSDRmyiplC9pt23gwFlTwkQOBfATvLBoFCWlZq5983+CcR0PrhkjP3bXJgM+cJxn9s+vSgAA5v4EhwCUnWLOmZrZAycKNxuFQFuBtV+vWQn0YCBrrNtD8sxZJO22spF+va4BKT1qomTz8VY7p2Tfu2vOAN2rnU/prm+vUbMMP8FOHGsl3MCR8vPvC7WTGkhL2m15Bux0RBUy2UTS7khg55mzOi5esajKD2XozfmoKQuQMfA12dR96pWK8Ceb0p5iMROMcysOM72Bz8I0jhUgAhcHKC64Swe2AimfD9x7APRsSP/oEcOsmVz04VBVDPuZWyiHjN7ljGtAnhlW4cKiW+lgqGffZoKik00B3hMlePWarTxsrjkO3MZtHECS7b0UkCMLf1hg6Zlnz/i4ENCO/z6Xvl57lufI5vZCtGcrf73a9uu//uv48R//cfy3//bfDpRGSPve7/1e3H333fijP/ojpGmKNE3xh3/4h7jnnnvwfd/3fYduz/MGKr/ru74Lf/VXf4XPfvaz+von/+Sf4NZbb8VnP/tZBFciWYnijFyJDirzcpirRQbRr1bM+WfymWD+iJXHGcfR+5ob4M3PigOTJOL47FoGBoDNB6vXlK1iVDle39BjHsgkAVaq6hXFYUIcC8saBFg++z7rxFTK8JsNOXbEgC6CLX9qEsH0lIAsgm86V5QmskIq8zqNI8ez41Y2H7LSSFOJE4BKLtPBAJ5hfxQITpSUvV2cvstKf+nYGNMiN8Ypc6WSGqknSACAUlHGqVhEZIA/YmdODfuIMFSGkcCcgICsIGCYk2ZDovpmLYC5SQaAAI4czTiECAI505NMk2Ha/GoFyfqGONBb2/pyq9bK/UXiy5zQpN1WyTIABUtkFTX4EUVIe3JYedrrZSLrLAjkNxsWLJnP6BoyslyRLpZFvtrrwSuEmgfMNnIdKWth1jwM80O2dvn8B7B8/gPioBrQman2aVgihCHizS2Ec7M2x9WsKeYxkw1m4CZx8oL3y78juFXlgcPYemNtUCk1C/uYfjEQlA4GyjAn7Y6830h10+FQgYRb8CkdDLTITnDD9bo3FFT1+hmZnlZsNWO0NH+vtMll5vh/w5AqkOVckwkObH7fVXWyzD7wKhWkWy3j1Pcts82iUkEgwM4oIPx6XQM3HHuyhgxYJesbehs3UADsrbBMeSbnLV7fkL3d7gClorluH8n6hu5VBj7cnO00jlXRwfv5Lvi8jGUKzlTKmXxLBiJSk3cKAMm5CwIsHeMz6VLz5s7rxWSZ+nk+hysVYDTSQKB/9Ih8FxVCCeq1OxkWOe314c/OyFxst+xz1wSlMkcpHdDGi/OM91EBs1m7B5Fuu21wgX3mrM6rnFuc2wvH8kBBbi9ES+Af+nW17aMf/Sh+7Md+DI899hi+53u+54o+e//99+Nrv/Zr8apXvQoTExOYmJjAP/tn/wwvfelL8e53v/vQbXreQGW9XscrXvGKzKtarWJmZgaveMUrDnXNlI72YGCkj2X7ZVosIl7fgF8WUKQ5UMWiOCzFIoL5I+KMFovwzq4jPb8ujq3JlQQg8sJ6XSWFCurogO0O4Jsvbd4Xx47INUolZYjoyK60PiSAII6xdOyNyoJiIE7M0rE3Ss6oc890MFTGK6WEME0RHDsqoMYAm3QwlDHhl3cQINlpA0kqoNBUyE122jYSn6YWzLU7Kl30J5vWYTRMBtk+giyCNPabjml8/kJGRkvnKB0MpP1bLQSTk0AcI5yZybSXLJ8WrdmVvmoOn7mWV6nINcwcIUnlfUEgYJOsqQGYe9aMATFWcldWBzpp7VjptJlnymgBaODAL5eFITBrySsWkTAHkW2l7JTAxqyflc2H4E825fppmmETWIzDr9eQRpH0zYw1nW4YCWja61smlHmIZITpZEeRtHUw0DHzJpsaGEg6XSxO34XF6bt0bwDQIil+va5BGt/kaCrzy+MkWC3UBE4SI1lUx9isZ88w9RhzcrWSpzPvqWEPE6eib7y9LXuG1yoW4ZfLQKEADIYIJictAA0CWRu8V5rqdVQO2uvJGbFG+h0/9TT8clnXNEHnaueUtK1UElYmipQJp2TeLbSiYNvsdX+yKWwc15th5dNeDwvV2/XZdLWMwR6YOVPFBNtdKsnvAWUjuT68SlnHwQsC+EfmdCxSruk0FRDWbstn6zVnD5b1maVr3Sm85Zvq18nahmWbj8zJeKSpSPZLJakMXS6b82RTCZQNBljZfEgDejw25nIAnQCSa5d5fMwRTQcD+3wwwTn4zpqNIu2PVyxioXr7RZ1kLwh0jnlvAApk9XNRBH92RthQ8x2UDgZIdyQowyqvDMh5QaDPIAkYlHV8EyPb5x6+EmN7yD6yjeOVXxUsF4sHYir3lVKbvM+DWM5s5ZZbblfb4tQ79OtKrNPpKMEGAE888QQ++9nP4sknnwQgNWVuv91+b3z0ox/F7bffjl/+5V/Gt33bt+HcuXM4d+4cWq3Wge43OTmJ3/qt38L/+T//B7/5m7+J3/iN38Df/d3f4ROf+AQmjV94GHveq78+F+aVsrl/UsChnpGcJr0egtkZZZAI7PyKI8caDCUq7h4kD2B57QFxmuo1cQbKE3pPr17T+wjTU5YiIoHN+/M2t+Xv/V25pymM4plD79WxAxS0gOzkTlslVmy3MIPmkHAChTiWfCOyLoYF0/8Xi5b5abcFRCWJ9Itsg+PYZ8CXGRs6UyrFNdFxPbbB9Ddpt5EOhwjmj2ihCb9RF4fTYWT8el3uXRQpaWpyEQn0NE/IjKUWrTA5X3TQFWyYKqqLzTvk35RXGvDnN+qZ4jJ0hL3Jpvbbb9QRzB9BOhhiee0BmYdSCYhilVZqW00OpG/AgDpJBuh5pRL8Rt2yG72egLRmQ9coZacLE7dacDh/JCs3rrCQ0RBeaCvvIuSaN6DPsNMqByR7TAlqu22dfwKdUlFyszY2kTj5v5RRj68Jgo40irB89n12LdHh5pyR8TJ9D+eF1UmNLDdpt2VtMtDhyK21WE+9bg5zD7JrEbagjc/iPabfzLtMLqzJ/7kOisKGLzbvsMdeOGw4ILl0XrEozmpgcyvJorG9fr2Ohert+nvNLWX1WhNkIOvoBUEGoAGQ/eTIe3WsAke+exVzwbjnWGyIQGS1+6isMUcSz7XKQEGytS3nqjK/tdOV9zEfMgj0GafGYNpwKMoSHhEThvrs0DkOLavGfY3+rh2XdgdpFMk+peIhjkXibMCNBvRw+Zw697xIlZE6ADIdDBAcO5otsrbTBvp9eT6b3692H828LmZpHOvfLwa+dA0OrDohNc83ri8NdBSLwurC5P2a74R4fSPTjtXdj8ha5XP0IjbeJrKH46yhC/7GJbXuuZYHsdOjx+X5sk9F3MXmHfteJ2e2csstt6ttXy7565/8yZ/gpptuwk033QQA+Omf/mncdNNN+Lmf+zkAwNmzZxVgAsAHP/hBRFGEN77xjTh27Ji+3vzmNx/ofm9729vQ6/Xw0pe+FK95zWvwfd/3fXjpS1+Kfr+Pt73tbVfUdtdeUKDy93//9w9Fu9Jh84zkzFZ1LO75svNKpUy+I4EaQns0gD81qQAEQYCluXvghSGS9Q1xlkYjAxqKGZkpq1N6RrbFyDedKR4YPl5YQY+UKBSQtjvw5mYkR2w4FFDCfxuHNVnfEJBBp7lUgj87A//YvDjQxvnVHMJKxeYhAvBvuE6ceSNTI5OT9nrwG1JsRvM2TeRe5ZJb2xawBwHCmRkFWnRW6CCmRvYJQJzxSgVeGCLe3raFI1zQYqrsAsj8jdVWk3Zb+0sGRIvJ0Gl3QLULkJjX51cqyrooGNxuaZGIeH1DGNpeT/PwmDfHthCkeqWSVqnVCosE43EMb27GSlONo82cyJXNh2QczDwyj82rVJButywjWqsKoDUS79QwYiqbhji4BOMaJCArDpuPqCB8YI+JIYucmGumQwNcDePNoITOkxNEWKjenik4pQWszJpO2m2bI2oKVHEeOaeIY2lvvaYvtTgG+rs6lwQPXPfcq4hjLYpDiaq2ie03gQj2w3WwaYvTd9l9y4CQ2x6zvvTe5nqLzTv2rGOu78Xpu2zxK8oTowjx9jbi7e3/P3t/HitJlp2H4d+9kRG5L2+prbtn4UgYbzQXiDBAywYpyd3VTUsiLEOe0Qxm2jMgBVI/mIIpyTYNWAZl0yK8aKNkjWmaQw7NwTS0AoLUm2CSAkHKskyNJFsiIImj4Ux3Vb2q917uS0TGvb8/vnNORL6q6lqma9gu5gUeuvq9zMiIe29Enu983/mOSa/L8dj2QN2V9EkObStiiQph0ExGbwmCDC5Ld2Tt+n5LviUJwsnt2rNnZUkv3+kwAaLscy2BYCwgYCoRbMsdcJFcu2rSdgBAj8dU1YQyraZI0HOXuXwnN1LtJ1oHUvZMyfPKMK0oLKnjOm34y8cElgCfEbW10mPdq6VIXZJbn0vgbpD0+uJzZOynM9vf7pAu55q4s2TMJt8Bi77TYb1w95OWWHtx+GkmRcsSYbW65zkCuKtX5EWp7L3kr5o80XlTJvZRWEeXJDusrf73fi1F9mM/3u2xZ7/34zdqfOd3fqfVNtZ/fuqnfgoA8FM/9VP4+Z//eXv9z//8z7/j6x80fviHfxjz+fyu3y+XS/zwD//wY1/HewpUPu5QRqqefXXDPgNxASp1MMeAeVmZkwiYsLrKtCHgsLnD8PjRkF/w52MGzedjcwPVwNxlKaB1ZIkHEm/slO/3qoBMAtIwm5n0E6s1JXe/9i95vlJr5/t9Y/FUImlmQyXNKOJyBcx3++WZaUSNTXONBsp/+esSHHUquS1oRhOXKwOOXiVrjUYls1XJnACCOpiJyyXQbhlzWA/0FJiqZC7MZnDt1g5gM9myMEsuSynhrQFwk90pmEkSnp+CPpWQAlZHGaYzqw0MyyXnU+oKFUzWs/8apMbNhmxlLVhyWco1Bqo1EdZYawbVTTjePEF568R6+MVNzmtervDSpe8TIxdem8pQLZCvO6LKPkLijdXxoyGS554xsJ4cHfIz8lwCz7YFnMlotLN+daOTZDQyiaveK/Xa3J31ljlVJl+DWgX7CvzK8RjJaGSunUGYXdubaowka+6yzGpUsd3Cy7XY+ing3Wzg2i0eOxHZpYBVPZbOT1TGW+ovrQayLBEnU2t2//ricwYwlQkCYNdU3jrZNc+6wKgqqNF5q7PSds/J+WmLiNcXn0MyGtma7wAmVAH2Ex1y/nG7hWu3Knmu7i+dR233slpz3yceyWhUOd9qYqZWl6x7V815FChqgkVdmfX+qY8wm5F10/tpOGBiQV7vGg3EyYyy55rCwNQmqmqozWVdamqvr68JqlpABdh1CT7KEpAemHG75bPjbFy5AYuZ1fXuJwnKZW9fBEgAUMpaX+9+EiHP79tSxBILuo/kmOVX32ZSRu75HRdxvRdl7754+L22jvq7i+N+oK/+e22Tcq95rA8FowbqH2GYxPY+YHTPSu7H12Ps99l+XBwxeoTH+InxvQ2vYoxw7m429R/+w3/40L0u7zXe21f9sCPGSrIqYC6c3CETVAeaEhS4dsvYO3WRTC5fgut1GehJALXjkJgkgEq2sgyJSOisRYWwpGE8QbhxSxgx9vpz8hmhxtxpgOz7/crMRgK95OjQatDqLJJdR61VBJIEL139QwzIh5XENAjA8+023HBQMSFSe4kkEbOSYrfGrBb4WfuTdsuYF5ckBMgqv6qzEcYs9io2R+vR5DqMtTs6NLmnfq4lBpIE29NTxLyQGsXUwFO9xsnMf7QOpyx3jqkAFABiXti5xs2GwGs42GGTXZbuBNe+06kYjCQhOFMGT8CjazZ3avMItgq4dotzPRqhvHETKIMA6MKksnoNvt22AFznIYqRTDg9s33En5mA9kBJoDE/AeXpGed3ta5qvAQc+6NDO2cDvwK0NXAOyyXPG6hq4bKUrxODlHoQ7dvtSsacZWRWpF5T58MlCZJeb2f/ALBEjO2Z0RDodYFel/1EJfGj86PzonOAJJH7NCPQPDrktUtNmzHmNUBSZ7VUmnq9+0kDmHpfQq5Nf+pskpnV1ICmJkv07zu1ioD1RHwh/ehOS5R6WxkzjVKwXfvbkxgKRrQdh36WJomqfVZWNdJ5Uc2/vFb/lly+BMgz0/W6fM52OkCWAllqMmljrzXxBFTzGaP9zWUpn4OnZ0Di+TzOqxpxlyQI40m1rpvK0fouKebicw9kIPTvdXk+IGxljIin55a00Ges7/f5bKyteZ1p1mFrX1evlKXdJ/d6rf54+S7Q+zQZjSgVbbdQnp5ZUrNulJMcHfJ+qiWDDCSXVd3zw47XJj9J+atIae/F/L44+JT9rS6TfVjm58XBp+76ntNRr9e819izS/uxH/vxpEYJ99g/78VxcHCAw8NDOOfw4Q9/GIeHh/YzHA7x/PPPP3Svy3uNpwNUyrDMdRmqzPnBsGJWNGATIxunbCKA8uQ24ngisr05gUGzCZRB7O2bKG/cJMBpNBDzgnLTXpfHkIDI5JGS/ddMvppz7Az90k9qssBOh0APqOpnhF3y7baBI9duVVLQ8YTM1GRWGYA0mwinZ3bevt8nKMpzlDduVhJICRD1HJVtUcChNWBhtaIRkAROxkwJ+6q/13rS5Lln4IYEzGG1kl6CfTvnWHMgRaNBAxV18dxsKKuVoNXcShNvdZwWhMvavjj4VBUIamCi9YN5bokEtFsEVjdusWG7sCyvLz4HlGH32suqwXqYkR0xF1qddwECJokVsF6vX3VZxveWZeXaKgxtvaaz3rJBwXK9VtUCLjm3OkunCQRst0xszOZ2nNemn+W5ddoIY5oihdkMUNCe1MysTs/M7Ma320C7xfYQAuyiJgZkfxoIXa3IjCjQFoChLUYAIDk6tGuut16IG7pUxrMxotQfu04b/rlnRDpZtbTxnQ5rImUOwnKJMK4K02NZ8rOFVVYwY4CkxmJpXWTd2dRAou5p7Q1aC8LDckn5oNwfel/V6y91nV5f/6yBh3o/P00o1Gv3kCQ7cs171Ze9W0NbpGjLEzPdkWeH1TzKNWkSzJJZgO1j27+rtT13bD1Wa2E5M2Ggq3W3f2cp70PpB6lzEcYTS85ZIkfuTZszwPqDqnM1UAPtFxhJO69y1123btSjz7J6UsFcsUX1QXOodEf2bHumNurMox7bmPoL56Csqq6/SxKE0zP4y8dMdmp7mu4nEWZzMvaaQBHVhyXBNFnUaBjwrV/Lo0ir1UBIz7WehLFrv+B6e5GtfdDxw2q108JJR70Vy/3Gnl3aj/3Yjyc1Qnzcusrf6DO/9/gzf+bP4E/9qT+FGCN++Id/GH/6T/9p+/nMZz6DX/zFX8Rf+At/4bGP33jwS977g7KlzIJLM+pZLoGbVZuDugsjNpsKHB4dAnlRAQppeeA7HaDVZIPr7ZbsZBkYWD/3DDBfkDESYEpZZhthNsfrtz/DGi2ArNxqDZcEAzpot6xe59Xbn8FLl74PuHyMeHq+Exi5TseCCdfvMfgGgKWAaO8tKK8bowAwOW+dhXCdDqAZdwV6ypKhMkCJeWH1h6/e/J8ZRAgw1qDRdTqAAGBAgsQsZXC4WsN1OzsMowF5uR4yYGT1rnc/uSNh03OLZQnfbAKdDkr5XJ9lVhNkjNR2S/BXA03qpumybAfEGjOZJIgyzy8OPoXXpp/FS+/7w4gLYezywlhAl7FXXn2PWVBdk+QCsJ6gmgzAdgvX7wvbUphs0rfbXBOVm+reEAdNY13UbEmOtcOc6XqpxFNNjnT/iFQXq7XNiQ7bD9st+0c2m4gilTMHzhM6ILt+D64MlRNvp13JyPs9c5MFqkSFb7dNjhw1UaOfJzV6ej163+l5A8Br//x/sHuozvBhCfhLx1wnk6jPK/CdF3IvrCyItxpeVEy/7kczbqo7fOr8tls8L5UjNxqAAlEJ2uv3f90BuH7OO20kavLAWLs/LzpoPslgObl8CeXJ7codV+dG7seo+yXL8Pr6Z/HSs/+JPMMSlPq8qLHcUZIYNm9WXyv7L0v5rNA93mWrJixRSYplXyAvKDeVZEzy3DOIkxmiAHm7b+UZgDwXqX6w55+CybphzP0klXWpa1itKja9rOqckXhLTuh7/GgoJQseXp/d2AWSFz/TGHtZ8zdEMnrxtXqM5OiQNabC7Jenp2gc0ROg/mzgXBOslqdnPD9R05Qnt+FqzLw+fx9l7NScPgDkXW99/K560XcabxRfkO+X8Nh1xPda3/3Yj/3Yj691qJz1cd73Xhwvv/wyAOAbvuEb8Nt/+29Ho/HuwsD35lU/4vDtjtV3WU2YyJM0y272/rW6p7pUSqU3YTwRQCNZ06IweaNKGH2/zwBCZIoW0HbaBCBJghcHn7L6O82y25DfAQBaTbx09Q+R/Ti5Y/UvLkko5RvUJJYC4LSFQlyuEKVlhQYU9VYF9ZqmqIBZa9jE5CZuqrYSAMi4XrsC162kpi9d+j7+SeW7Ink0mZm2x5Cg3IKWEAjMY6ya1ScJ3OGoCh5itOMq+2rSXKl11Hogk9LW2FFf67mmgM+AdB2wlKVIbpd31bA5CSavdz+JcHZOOfXpmc3Rq7c/w/0lYGUHtCkwEmknazPblXGRBlfbLWttVb7Xbu+CHAE3rt0yww3XaZtk2dqoqES4Jm0FKPHbYdNWImksA/tWiuzQnEj1tQpSVfIo81yXpupeU3YagNUcoyyBNK1AW4y2Tsqm6j0Vl0vuqzzn77TGU46vLrdsjUL22e7ddhuu3ba9b/dljPY7Zd/UAAcAkmevWW1wXZZr7GVZ1aHFPGdtnRhPvb74nPX5ixu2wND7SNdfnw2+399JdOj+rMsGbV7lHrL5FffQi5LXR20g/yjDklCbTXXekuSgHDbl+ZYl5fXdTuUeLAy6nnsdvOu11ZNHAOzZaUmA23cqhrkM3JvqpJ14hNt34A5Zd1p+9W0m9Z69Zj1OVX79RvEF7rHas8n+K6wwcP+ekADnXZNmlJZWbq4qx1WJsEp9fb9v5x+XK8pjZd/UW27UTW3qLGVdCn6/ukqXSaJ0zf3vr11BQ1jZsFxyvrSm33uyfaNhJQWXddZ9r8y8rvf9xkWTnots4f2Mj/SatT71kUbCkod7Sb7rBj77sR/7sR9fzxHgHvvnvTy+4zu+410HlMBTAipjviGQareNNdDaSgOWec4seD0L3aLhCVJOrAbdVh+YeBrF5DkZyOUSr00/u9NSY8fBsAxAv8daPQmQzIFTnTk7bcpSpUWE1o9ZsGzXRKdBrDfM2g93LfqDuDapc6YxW1rz18yqHoRNSs/qbqyu3YZ/9prMlReALe0yTs8Rbt+x89D51ACS0jNPUNGq5IQqCQVqctqavE7dTOPZuDrXPCcLliQG2upzYBb4tSA8jCfwWUbG8uzceuMpK1EPeH2b8+07nSqQVqApYLA+XJYhrlbWysIlCV48+B4BBL7mruvhLx0xqSBBGut128ZS6nX7fo8S4LOxAdEwn3MNRkOT4IbZjMyzuToyQRHmc76+00ZcCfhq1iSIF6Rnet0q+WTtV7tKpjQa8JeOhIVp89/ALliW69TreG362ao9gQBIk05Ppvb+qu1GrzqWynjbbYJ2AdUAKiOoPLf7RRlpPY4dFyAL02gQpKxWcMO+AYrk6JBrWwPFcVLVBus5luNxtaf0vOXalWUBqjoxY9hNfhyq9V2tDOj4Xq9yLVaJdpbZ2oTVSmTWtdo6oEo6lbWavHtIKd/NUWca1dQrzOfcq/O5sUa6n+NtcYpWc6a6AmI+32Hg6jWmSFMgTTnnYHsmAPBXL1dsrxgA6fMAoLoD0ptUEyzlWzcqZYI8f1569j/h6y8dSe1ltvsMUUb4AXOpz48X0o9yX7XblGXOZkzQyL3nOx1+X2SpPCO8vb8uf71ouANI65J7MOf1UWfcjNFMPJ2k5wto72GAST673qJA8r7n+IxX9YyUStRBpO/1TFp7v7HTKqQmY31HYCcsuwLLxzGaSvT+ucexL46LdZR70Lkf+7Ef+/EbP54KUOl6VYa7PD1jkCIyIGSpfdmH8QRu2K9qG0Ng8HY2NpYsrFYESt4bOLNRlnjpfdIDxnt+uWdpxTRsNrSeX60IHHtd1lwC/Hcz2+nB6JoZwnyO16afrZxp1ehEGKW43SJOpqw1S7zUzuTGIiDxgPdW2xZXK/4I84TE2/UoC2Tne/PEsuFWR3TpyIIqNxwQjG428M89UwFGDaRjYA2lmnEsl7t9BVNK3wwAJYkBEIDgx7VbCKdnVd2W9oLLUoQ8x4sH3wO1wI9Sd+MPD+wzFAS6RAyLkgQxBqn/q8CEAaA8hx8O8OpbP1bJ58Bgy1pq9LoEojJXrllJGa0+d5MzeEMFxoyhlGBcjx+XK3P+RMmaP2NXpYaRtbm1+dH1z3MkR4cETMtVJROtgRzf6xnoBbBTY6iN5g2oZimPezaWZEiOOJlZ3atejzKEOs8vDj5l7QlwIVB3/R7c4aian/m8MkwCCBi2WyY3dP10ZCnXC4A7GPFHkg/6Pt3PcbXiPMQAjAZ8zXpDtrq2x+r3q36GyzKRZu/KGw0EKSASoFWZEPWq85S/mZlPv1f1vKyBhLhascWCHPsuWWNS9TtVE6R6skvZnicZKNu55jn3qkh3FdDXTZt0DnyPa6fPT9fv8XnVq54prtOGHw5Mwq7Po+TyJbjDEV48/oO8t2+eVHWx44k9v62GWiX9w74x2VqTG2NA3EhbGe+5LoUk7aSOW3tFuhrYud9QJszcTZPE9ol/37OW0NJnYTwbE1h22ta/uF6XXK+LvNdn70i5ZVxvfXzHXfWN4gs2r+XpGVB3UC5LUzTUR7h1QhWIyMH1eeB7veo5OJ/vONQ+aLiErrYXWde7Ro2pvmhu9VCj3K2Bvfi3PYjcj/3Yj9+IUUb32D+/GcdTASrDRMDOfGG/i3lO5kCCJd9uE9ScjRFn893gXOoAVVIV5wsznEGMxuIZu4KqnscdjChpbLeADz7L32kAMpkSJCj4UzZOzE/CbI7k6hW89MH/1ALL185/ogpSDkaUvCYJ++WlaZWlHw4I5tKUEtg6eO11CQo1Iz4huAzjiUj4BExJvdNOZv9sbIxvODuv5JHyHgLBNvwzV+W9qX2m/8Bzdg6QgBIQJkZddNstM1NR2bAX8Go1oyIN08y1a0ugCjDgrAFTBXBhteIaJYk1Y1fDFn2vggYNlIDKnRYij1XJqB8OOF+tJtmxdptSS5MXp4iTKYPoXtfktqgBFdduGzv+4sH3WNAMYTT1/xl4r+2SnNTbGuuu8teabHqnFYvMc5jNawxKxYIZqEoSY0Jdu1Ux+MqciaGH/t6AKMCgWsC8uQCrtFoC7dcXn7M95/o9rtvhAc1a8pxrJ+DQTJf0XgOD4nDrRO7pKcJkasYwcbUi2B9PeLxbt6WGbsX9EKMleVReiCw11k0/y9UCXkvM6HNE72lj3VLuAdlTXu4x3XdhPGF9sdYE6v2XZXjx4HuMNVUJ5hvFFypmKanMaBR8uV7XnkEvpB81pvSJjaRqw+SkHY3v9QhmNLgvxbVa96OAm5jnfH7mhbHMOicoA58Hhwd8fuUF0Gkj3D6lGdZkas9S3+vyfrp2WZQKVcJF573u/FqZsUnvT5V5pw3eQ2Vg7WDNTOZBAESBfL1e0vW6lHGeS/JlNre94Po9MuC3bu98J9goS3MVBipWLagkXupU639T2XV9aHLG93qs3RR2XpNS7mBUMaRqADaZ8Rkg9d/uyqXqHs/ukXi5z9gx3alJevVvd/XgbLdNyfDa9LM850cYsSx376/6uI/8de/6uh/7sR9PejxOO5HHrcN8GsZTcdW+38dr5z/Bf7elrrHd5pdyLViPZQl3fLQrz9pu4RRM6NCgNPEoz86NxTP3TsnguzQFigJhvgAGfbhVDuQ5IJl1+9ycbJC+N8wX8IM+fK9rLKQf0MjlxYPvAYbiQnk+riSjyxWlnhfs8jUgL9+6YVI2lySUryYJzw2w5thmENTrigGNAMt2i7LdwxFcq2nzZoDg7JzgzNPeH9r4WwKZOJkhntxhkJ0SaMbVGk56dpoJixzPSUAfxhOE+QJxNkd5ctvmK0ymZpqhoIt1dS0DDfUgrC499AejKiAV0xEDEj1lXQSEdztczyzdda2sSR2T4yM7d60fDfNFVTOo/84LzpGAuDBn0G2AP0lYUygBepwvyPApQGu37HwBVIYz4qAZV2uep8yHgeV2m9eRJHCDARDE/bjXrYC9AMlwRjORMF/ADwd27bqfL7a8gNYFr7j/bO/U5lD3oNYRa01sBeRatsfiai2gPxi40vPQvet6XfheD8nhQVVvK4BLQZvuU00cwFPuFySJY/WTkjCIeQ5/wPfovL02/ayxjzv1o8JavnrrL/K1+hrvdwAP10zanehcqnGVgOb6XqozUXatmvzJMktkKYP6oNq3r2VoEgGJ59qOJ7xv9DmnzxFlduV+innBvdeu6oYBwB0f2r7QUVcCQO/FPEdyfFQ9F1ZrKhVunPBY3S7cc9eq/VEGe4ZpqYBJc7PMgGiczATE5njx+A9aDS7wzrWpOwZKqLFsecH3aVLvcFTtpdmc4Lfd4j5XtlR+9PmkbPOOnFQA3vXWx98ReNl5HR9VUujEw3/gffzeAYCFfI8M+nBpWnuWs0zBDwcEvrUEwaO6Ckd5z0UW9S72Vdba6kcfYu9eBLdR+jQ/7LhX/eV+7Md+7Me7OQIex/n1vV9T+aTGU+H+iiTBSx/8TxmcXblEMCZsE/IC7opYzk9nzDxLsGFfrjUHU8sCA0C7haTVQjinPBbeI67FIfLaFUqupHF8vHW7qqXyxOomSS1LuONDxDvimqpB2npDkFEUBAKrNYOI03NKPYcDBuGTqZ2XMaiAMZQa5KEsUZ4xCNdr9wcjxPWan9VusbdhWQKrFWuhagGtS1OE22NKvtYbqFtpcniAMF+QYcgyuEEfKAq4FkGKf+4Zmz6XJMBqtQMabA6EFXZlSfBde48ayxizmBdcAw1aFZAIm6IjlqW9rv4aDfhdkiAmgbWV8zl8r8frEHASi2KHwfLqXivH5Hx7e6+B06NDOgg3m4AEmVa/pf0TBSBAmDkLPPOC89BuW30pgCqA1vcJC2ujLOHSFMnhAfeiuNTGsuQezTLE6RTaUieWLZHhrg3ExLKEv3yJ7XPmi4opLUvukfp+krWJZQn/zDXEO6cVS3nlEve8gU5JQggLpf/vuh26J5dlJRfudbm+164AJ3cQ6/efrqsGpSq3LUNtPRKEt24gefYaTUx6XcT1ZqeeMuY5fNLiXg2BYHw643zJMa63Pl45T4KAwoCcgJO4WvMeOh8jTGfwgz7C+XjHVZbgJodrtVBOpgTD5hy8KwPU+dPP8cOB1c2Zs6n+965ZefcGjacqFUCYL/gcgjy30hThfIzkymVbP398xD1Xv0/lWYfpbLfu+eyciYKuOG/Xjh0XS7grl6qvXEkc+eMjPk8H0rJGgKcb9hEn7JkahDG2ZAlAp+njI2Ah4PNCIvGdxhvFF/C8//32ulA/LsBzLMuq1c1oyP0EMKE2nSLKvgqioKi33wAIak0qLkmv8s4pcEEaXTf3qQ+9tnj5EPHGbe7JK5e5ZxRwL5Y212aQlKZw3nPuBegy8ZPtPH8vjouS3eutjxv7ez/WV2uH9X56GPnrjltw7Tn/sMNqvPdjP/ZjP57QiI9puhPf46ByvV7jx37sx/BzP/dzODk5QdDvNRm/8iu/8ljHfSqYyjibERh0O9bmQwNd1+2QVZvO+KWfeMoOD0YMbiUo1syzq7F7cTJDlEAS4Bd3zHMGyjduWQDkup3qOFcuVWBR2CXf60qjdtZ6uiuXKvnWekOGSOpkGLgXu2AiSRhUCxCxWicBnX7QN5ll8qEPIvnQBxnsHh3ALPF1NJt8fV5QFtzrkuXqdRELYdWmDODgfVWzmWVkMuT3GkC5JAHGE2A8QfjyVwh2JIhB4hHunEKNgPT1ThgKlTwpUHRJguTKZa7fQNuTrIyRiHlVS2pGJzVmQI9PplLqCydTkVsmxlBRbriuAhgFLrqfRFYHwOR4rEvyJsvUFhlxucRr5z/BQFZYHJdlcN0OmbGMbrUoA8GksCwVY7sbRNXXV4PturwynI/5WSEYm2hS1EHfpJ7KcAKwGixNClSf1TJmkcdId/dTllbXez62uUPimSCRv9cdiHeSCNeuEJCo7FfWRKWWUaSjrtU01pH10KE6TwXjAMqzc2P2fK9HOSVQA+Ul78UauOc9Swmm73WrxIjM8fXWx6vEioBS3xNzoJSMs4J3126hlCRS/RgoS4KyxZL7N6XxjLLnFojXGEg7hrSrUFOVWJZ0nX2CLCVQMycShr1eZ4paUqy8dQJ3fMh7cb0WYFczolos4bIU5dk5oNJUk3Vzn2rCrL7/MV/Y35y6956P4UZD2eMr9hg+HyPcPqXqpHaeVlMttYXxfMzzFhbP97omS3+Q/NXLa2xN9H6XsWPCEwIw6LF2/nxs+ypKgqYOrgCy08pY6j0SzqtE0sU+lXUW0LfbwGqFeOeUZQ6/9hWuS5JQhixKFqgCYrGEGw0AKT9AUfDfgLHPdTOh+40H9fSsn6/+t37Mx6kH1vuvXsJix1t87r5S170Edj/2Yz+e5Hi8HpX8eS+PT3/60/jv//v/Hh/4wAfwu3/378Z3f/d37/w87ngqmMooTEZYzeAvHyPcObVauVL+DWBHWhPevslAsSWBoWTStfVEOLkNf+0KXv3Sn2JrA83qt1vGeKAMDJp1pCnC2zcJaoAqc5ymdEMVhiqej+FaLQlChEFJEqCdwF0+RrhxyxhGYygt6Kw5T2pPubW0echz4OSOXKtHvHHLDCYAQPu4GTNnLqOlgaG4WiEqQKgHIIm3bLj1whR5pAWhvW71mjIYW2wActBnUF6XGioIy1LEHGRVa6CuDlKS555BHE9FsidMmNQmxvmC8sfVCr4GEv3xEcKdUzMfMmCqbCBQAS5JKNTlqiqFC6uVgQ1d2zhl7zytfVOQoRJMlZVq/7pYlvC6J8oSTlgv3+taIkPXXRkxL9JbAHAF5Z2a2PDCKoac8ttkIA6ewwEwz6u6zXa7uq68QBxP4I4O2INS11rOz8n+rtcIosY6elRMbJ3hJyAjMxXXa75/PCFYDAERPPfk8ABxTDbTQK4yrvo5NZBIcNa1/e9azdr9V1YMsQb+hbDA+QWWVF6nzwKV2mntJABgVVY1c7N59ewQhlSTBMZSpinCiufi0w7CdGaJArKRZIiNEVWjIwV0EKMmkSBHkVE/yVYiO+t1dMi1ESm7sbIyvy5NyWJ+5S3ex3nB548AY1+7P1yWIXz5K/DPPYN4em7gE54JOd2rLktZOy0GVdU6Ux0SxTk5liXiV29Y/0fX61ndKZpNOGVzjw+Bt2/BdTu8X44ODSgDFSB5J5ATaqZFvtfDa+c/gRcPvmdnrS0xVhRUkkiSizL3RXUdqBjHN4ov3MWm6XOEtb7lXeelbT8AkJGXZ0A4H8P91g8AX/oqz3VRewbL/7url4HZHAir6n5SZQT4fHrtzo/vSFkfZrgkMWfce4062193gH2Y41bfL0xq4R6qhfvVFu97U+7HfuzHfjze+Jt/82/ib/2tv4Xf/tt/+7t63KeCqVRg6LIMcTyVAChn/VS7TTZx0JegtAIQLk3JZCyWDAyaTQZ2YtWOMuCl534AbjRkcJ94tgwRQGl1U2orXzBrDe/pDFgU/JEm8yaHnS+Y9ZfaRZdlcKMhyrNzhK++zcD+fFyZmPiqPgtAVSP4gWeMlXVmxuKN+dB5QFEAH3jGgmcvdTp6LGbmK6MfP+hXgV691q3d4vVoQL5YwrVaNofKQoTprGKMvCcTKhn65MrlHUmrZtltSNBqwRyUUcvELr+saoqAqjZRa9sEKJgMT2ShBAR8X1itqrpRBb/1WssyEDDMFwZ0/AW7+zid7TDH5jA8mVpSgTVoYnqjLJWywIAxrrYfVT7d4jwrSxbunJLxFdMaPxwgTGfcs7qmwwEDUGWvhZXVObpY3xdncxoTSZ1bHajf+x5rklU/OmANV79icqw9jfcEJK1WlUwoKAN1gz5/p0znasV7U5mmsoQ/GMEfjLg+wv4DTJ7YPSbyS5emlYxdFAmWJEhTuKMD+2wAxo5rTaquEVCZAmkyI44nVpep86Pza0y4yoXlWRCms50a0yBmUSqrvt79JNTFWNuKmItnjaVU59QnXi9Wlggnt03Ka79TRjrns8t1OsasA9jZS/VWIoAkcG7c4l5RVlGl/vLMinnB56uw5K7fk7pFX/1uNKQ8vdthEilLmdA4PuLrQ6jm+O1bvJ+KgvOqMtusqjF8J+DxQvpRwFVfg5okUnMgWwdRIfBCPZMlZQk3Gkg9/G5v0nuNnXv8Hmus9Y4Xme24pqrCTau6VNdq8vqE7UWWIrx1w9bOPlPrpGUNHgbs3fWaJNkBoirRrc+ruh3r7x7GDKjObsY836npvt/rHniu+/Gujv387sdv9vG0GvU8++yz6Es/43dzvLev+iFHXK8ZlNQC3XrQiKJgDeBoyKC0FuxQqpUyky6911yryQBhNmfQNZsTOImEy7VaZDNUXgtUYKfZRBxP2K6jVWNCAYICCbj1HNygz8+THor+uWesFtLMJtLUggIAzGBPpnBnU8qdwMDYtZrmtqiZav/h38Kg48tvAxBzIe8NMPpnr/FLW4weCHg31TW1Woi5mBGllCaqHFhfYy0Zup1KKrxe74DFi4G5SnqVYVCwwYC0YhOt7YCYLqkEzQI0MdKwc5A6SiTezGq0rq4u/4zrjYGA185/AupiC4jrogCGSlrsK3CtDJfIU4PIP3V9ghosCdtbnysLJEV2GgV46DWE6YxzVAPO6q4bxd015kV1XJWKqgxPkgQ653SMXFd1qkliElNsNjsgWOfTZLs1t0hlRuJszv0k7rNqZBNvnpCRL8sqSZOl3P/C6LtWy+Rt/vioVoO6Iht8Phbmlk7HJplMvNVUArA9A8DAelws0bhyuQKy42lVSypSdC9MrW+3mRRQxlHW1FhMSQKYvFjYKD1nSnoTA7oAa2yV/avPna6TMlYXgYSr3dsuywzcAk+2bYIlYSRhQ3mvOCHXAFk4uV17XnpzbvXDgRlYGahS864QBPRklnBSqbvLUj6nQPATTm4z6ST7PZYln5/6HJIEWTgfc1+d3AHSBh2jpfSAc1aQ0aofR+b+nQJjlaXWeysGuacB8L4/OuA+GA25L6UG3mUZe2nWkhcA75d71UeGelKqvHf7jHqZANep+g6Jsznv1+MjPifkus086WAEdDv23HWjIROdsk6aIHyYsTNnZbkD9urtUnRcb33c9vhFZvFhgEnMc1u7i8NlWaU2qo0H9s7cj6957Od3P36zj6dV/vo//U//E/7z//w/x5e//OV39bhPBaj03S6Dkg2DENfp2Je/MhjGkCiLJXJO5AVBlvfCflBOagG1AAPXahoICedjxOUS/toVfkaSkGm8c8os/LNXyUC0mkCryS/38YTS0+mMoKDZRFyveSwBMPZZamYhTelNmltr8ZAcHzHAT1Og065qIoWVDWruc+eskmMClDYW0gZgviAYAAy8KagOUoeoNXFkZtZwVy/ZvLjLx4iLpTFMcbG0uXOtFhmFxRJoNclAXajps2sTBhkQsHf5eMfkhICgV4FZAcUKMnyvy3NQhhWomCzvCUD0PRrMlayDc0cH1rctrtZiBNKzYFYNRuqA3ljQyRT+2hVoLaEf9I09CwLCzDBI2czVqmJxxE2UrCmNSvzRoUlfUQswFcjG1bqaYwG1VY9SAU4il/aDPrTfnrmcaq2xyJYVFBrYzFKCg4vAv1bjF1drIE3JTtQkbMZAST0p6uA3ERaztv5ai+YvX7IEh3/2GpMbszlBiBrrKKsHTSJV5kZOz1trH1stA0Vu0Od8DfpVUkL/po6ytZpKXS8Icxrni2oOFTTJ9UCk6HG1IsherSkNv0ernotMT1it6OJbk65rSxb9/yc53NEBr08kkqW4AqtckrXI6yqxVbLEwID3fEG5r4JsmVdNbES9rwWg+kHfnnvsDxzsHOw5ozL60dA+k0mt3OqkTbGg4E72cMxzPo9Emq/3zIMkkgomL9ZUAhXghOxrfm6Dv9NkwHpj8t+Lx7X310bI88pRF/d3pn19/bO857yX3sdru1f0XvDXruy+qSwRbtyy/41LJnL0+0Pr4C+aR10c9wJrOkd6znXTHu29qtfy2vSz8iZvx3uooc7RF0aYz++qMd4zaPuxH/vx9RhBjHoe5+e9PL7t274N6/UaH/rQh9Dv93F4eLjz87jj6aip3GwQVuzTVU6maDiPsFzCdzp49cZfwIvDT/OFKjPLcyBENkwvA2V7tboxFFuCxn6fQWijwYC12aThTqMB5zwdAcsScJRgJlevAOsNwq+/Bd/pILx1EwDgD0dwytJlGdyAxi2u0aC8LM8B5xhsrdZ0FZwv4BoJf78t2VzccZO6JGHvxyyjCZH0rwzaYxIAGg3WrZ2PpcF6ATgHf+kIKLbwnQ5iDLyOzQaxoGOg67QRNzmvXeRfcb2B7/fFbXDJfpCNBtsEdNpV70S198+Ligl0jg6nva7Np7tyifWezqE8O+e5zOZVf8uzMfylI4Rbd6o+hSX75flOh/0tlY0LAc65qmWJOFi5rdTXOce5kfci8YjFFrHYwg/7wGTGeZXfuSQhOA8JwnoDF7Q2VExwdHTIfoWbt6v6JecM3EPm24KkYgvXaCA6J2sdUd64yXPalnDNJnsursi66/zrWvKiuP7++ABxOkNYLtkQfrVm39IQsL15gkSdbZer6jgh2ue+vvic3RNxs+EeFOMilyRAs4kwEwDlPFwzk8bzBe+BRgOIES9d/UMozyeV7DPP4VzG/nhJwvlsZsAm5xqsN5zLZpPsv9xb4exc2FC5X3pd7ndNEDSbCKs1kn6Pn52mvH9lrwVRH7jjIzJfjQbQ7cKHyFYZ0nPUZRk/W++3YrsT4McQuUZFATQa2N64hcb7nkG4ecL1kffRMXiDZDTiZzebvNZGAwiRiYl2q7pfm5SDGpPcqDEyzslxPV7IPsb3hbjDaj2JEd66CX98yGdQuwWn+zMErrOYe2miIjoHlyQoF0vWABdb+Eaj2j9lCSxWcMM+1w4AXLDnijH7avDVbiHepukRYrTno2+32WZJGOo4nnD9x7LPXMCrtz+Dl67+ISDE6niNBo/X6wLFFuU5excroHk++QjeLF+5ax5iiIh5jueTjwAAfNpADBG+yXtOe6Vaj8y5JA2yDC5GeeayTZSua8hz+6wg16/D67zKM/KN/PN3nY+er88y9v+U63OjIZDnsr/a/D6TuXaNBtDMePxNLuvSRnnrBMn7nkM8n/A5mxeI27vrFu87nOf3Ww2IXrymN4ov4KVL34dS1kIB9fP+9z/0x7xRfIHPpHsBXufvmqc9g7Yf+7EfX4/xuKzje52p/AN/4A/grbfewn/33/13uHLlCpx7d8736QCVZQkEB9duIZGMuSspYX3pQ3/UXhcmU8TNRr5ca43dy8odU4MfPxqafT3KANRaEVhNpQ4xBsFaJIGXjoDFqmrmLC6qfjhAEGt6q5ep1buYzO79z8JNZwhf+gqPkaVArwuXNlC+fYvnN+wL4+WNWfK9rskCfbsNFAUz29ryIi8IWuXz/PEh39tomElNOV+gcXhAlrWsuaS2mgTW0q4iCIC0zwJqLqLZXfJBdNoob5/yPL56w+YguXzJJI5hMjXHRixWxkyUk6lJlFVqaixlliFMqjpFNf/RdSwnU4KsJBGTDFkTlS5mKXzid+RuKrVMrlxCuHMm8tuFtCWpyeKUOctzzuViiXDnDH7YR3k2rhjN+QKJtrUJweYhGY12jCqCNHqn6dTK6nadvEZbXgRpTRNDZHJCanBRBjSuXkY4G1d1VcbolbYu13svV/K6Jtc55Dl84gFhy71o7SkrLmw+oa0ywARN8swVYLES5m2NxvEh3IJJhiA1qy5JEM5oSIRuG/F8QpawLLG9fYpEa3/1Pr0w1+rAq9JZY2xmc+vPGVYrOGE3Y14gzm+LYdFit9+n1mbK/PhOB69NfhIAcL33Ms11pD7Z5QXXP0RsBfgqM+uaTTMIs3VerZEMB/LMqKTOYTJFLEsLugHwHkMlUTY1gNYXJgleyD52VzD9bo5w56yqvz4cWW0vgB2TLnMprsuqAaAMSEYj/jvPEUVq/drkJ/FC9jEkckyAa6hGSbaubUrr+VUmbJr2NU2EGe11qzYfkhR86eof2nkO2PonCUp1BM5qwP0dxpvlKwQ/jnOuoC6WJV7IPlY94/o9c3itXJ5T29Ne1+4ex6+vY8wLvJF/Hi9kHwNwN9jV13mpj7e6ztr+ByAS9gsATL6T7DmXJPyuk/67QQ3T2q3KbO1BIwY+G7LMgPdFcP588hGadOkzpZHa9T3s0P1yr/rJ+9VU3i9RsB/7sR/7sR/vPH7pl34Jv/zLv4xv/uZvfleP+1TIX1UCFyZTuINhVUuVpYDWSUpdBtmqxIJxky15XwEakQ8CzNSXk2klnwQDH60htN6IZQl0KXMs374FjAYmccN6g3CrcmV1CZu4l9reQEFSt83A4PYZZawaGHmPePsU8LwmP+xXNZZpakG/yfcU1OWF1b7p9VoPvyvHlCM+e9XkjX7YR+PSUVW3KEYuqAWXGhAml46sJYkBSDF8MamSGVKkgDCXan7irxxDWz3w2IUBW5MrC4A1U4syMOi/0GSdwVdhEkdjS+WYpQSDYbU2NpXHLqyhu8syJLWegeV8QVB+OCJ7JOeirQo0OH7t7H/l+Yvzp+4PbR+DsmRwfecM27dvIq7WSKSRus5TWJGhTJ6hnM0fjgxo+Nq1Wo1olsJfOUZD+odCWl/4YV/qJ/uwxvayL2ye1LkVqGo6s4zXKJ9Rl/KZQ7EkF3Tvq0FVnEpf1F4XyXBgILyUBEE4H8McVhOPeD4xx9CYF2hcommUv3alWjN1Fz0+rACeurzqvZk2TMK+FelmUGnxoGfATlvWmJlVQqMdDWDDao3rvZdxvfeyGU1pskfX2fe6wv6KlLOeTBhW9cWJGhHJ/lUQqfVzLkkIFqBgXe/Z1D779dXPGDh4UOuHr2lIqxa95+w+tHu9Mg9yva7VuOq1231tx5Ma0yThXILPzmQ4INC+fKla18PRrszxcGRr74esk/TiXKyfpYmrUmS5amhk+1CSHReBycOCmzfLVyrgJ/vijfzz9mwOkxnckbRWkf0fV2vew8rGyrrXARiAuxID13sv22vfLF+x19b/+/rqZzgf7TasPVavC3d0yLnwXmqrqaRQ2br18cwLA9j6Xeay1PbfDlv+EHPz+upn8Gb5yj1B3JvlK/bM0Gt42NpNHU7mOObFztzp8S/+Tn+/H/uxH/vxJMfTWlP5r/6r/ypWT6B12dMBKgH7Ao7nE8Q8Z0BXBusZZz9ghtwP+wZgICAKabpTv+aHAwSVf01n/GIf9CpnzaQCmjHPCR4nMyTPXEE8PYc/PoQ/PmRN4bBvZipac5VcOrLzcklCBidLGTgJ+1hOpmQUyhJYLiuQIQEzEi/AR2RQ9b6bAmxMSqZgOEkMDESR6OqXujEFAOLpGcJ8gbBao7x1u1bvVVj7A4CBXiksR3k2tqDcZRnloHlhPT7VGEk/H1kKjKr+kQAuGPxU4N7Ysk7bPlPnzl+7gu3bN3cCTGUSFCh7OS/9W8xza7Qe85wsmpw3wU4w0GnXLcGgMlfXey9XtX81+e8OcJOkQ+PwoFqP2vDiEAqplVQJrJrslGdjlGdjcZflvojnk8odVRxly7OxuI4Gma/CTKEUEBpDW2PcrZ2JymYlULcEgTCgVr8oCYpXb/7P5mhagU2C70RrNbOsAvVy7uZSLIE5kgTx9NwAiGu3dlh3JB6u18Xr8582kG5N31dr+FodnNb+WcCta2B7QsxVJPFkjqTydz2Wy7JdR1kFU1lmgMvJcZ0A17qU1othVJgvkEjixWWpsT51I5k6GLnee9nA75MMmoPI/evJHD1n/9y1KjFTlghnNFAyttIYsfUu8JX6WwWcdVfkeHpm8xWkbEBbIoVff4vLdDjiHhGDHpdllqhTttS3W3Dab9T6hIZq72pCsfbzQEZLpMf3Ai5Bak5du4Xyq2/TbE16ljpJMLHlUPW5F5m9+lDAWt+zF4GlnquZJSlQXK0Ru2JyVEtiAVW9pDKUrtuxJJXNW5bx3A5GD5TA1s/9hexjuN7+xM45Xry2MF9UrVn0+fEII8hz2bdbNh/6GSoLv9fn7sd+7Md+PMnxtILKH/3RH8Uf+SN/BD//8z+P09NTTKfTnZ/HHU8NqKQcNDdb/uTSkZjNkJ2pG62UtabpVje4WjOYkZYD5oSpsp4kgX/fM4jnE6ujidN51fhdg8leF+HOGQFPsWXd1vkYEOlmXK0pT9S+imqNL+6jLsuQPPeMgdXkuWcsiAFQOeSVATgYAosVjyVBnBqslGdjIE1FhumZaRcZaCnGJ4D0IVRjIpV+imFGLEuT8ylD4a8cGzvpxFhBwUBYrQmUVea2WplJkMn89NxBRi7OF/L5qTFDFwFoxbwxWA1fvQEvgVKUgC6OJ5aFB8C/lepMWxjzxL0y2HENZfuTwPXMCzgNbpOqlkivuQ6g/fHhTt9DfY3vdVGOxzXwWlTsVkkXyZ32JHlubKi59grQrOSY7Bmo/45lCWgLBmFxXZYScHX53uSZK5R+DvoiEc4q5q7W59ECQGXIhJG2YLYsq/Yo+p40xYuH37vLjkvywkBy/dqA6jOVkRbnXvbs7NUY6mrOFaxHYRSNsRbGKpaVgY+txYWAmy1t0qrFTrdtzLS3OeR99frqZ+z+N1Ar4NeApyRKAFStQ5IEr89/2vZbOZlakB1Wa7I98582WaNvt62XbiVV5v1XCmB+kgF0Ive0v3LMxFdtr4ev3uDzbsg2TF7l42r4Ja9NnnvmLnae925R1anKs1efLyaRFMfZuhlXzAsqGGoqhTidU4oPmNNzmEy5xyRZUd8zQO3evwe4qc+p/TsGYw31/JLhoJK/yvM9uULJte7besLJEhRyHsrs3W/ugyTI6pLS+ucDwOvzn65KMFR18KWvWjLMntl1RUPirdxCGVTKg9f23NoxnHqI4dstO9/7zaXLUruGurriHYfbXZvkyiX73quzonr+92NK90BzP/ZjP57UeFpB5Ysvvohf/uVfxu/6Xb8Lly9fxsHBAQ4ODjAajXBwcPDYx30qaiqNPcky1udovZTUQ1o2XYIdBSOAsE8aAJV05/S9rgXharFf3jxHQxkMBQBZirgqLQjVVg8KjuqNnMPNE7KVvS7cJq/qL9ttYNhnrWNZsnZxPKnArjZHN9YlGLvjxJnVHDJrX/rKriZXLpn7nwZljfc/y+udzCpHVg2WhZVUkBPmC5H4UYIV7tSalqeUtVbsTMsYTJelzFiILFmNSrSGTiVdACr3SGFmG4cHFQMsTMkb+efx0qXvo0GGXi+E1ayzq8LyqYut9rQz9lmdPQUYuGEf5Vs3LTGQXDpC+etvwbdb2N4+JeiQYydXLlVAQ857B7zUZNONZ64hnJ5Da10BcG0nU4QT1vtFCcTDnEAkEQZGGcLkcCQSu3V1bbbnM0RhL1GWcMM+vDLTU9bQot0i+11j6C3AzwtKEdttCdJbBgz84YEZoCjz6bIUpbBukMDUmKb5whILUfYwxP3WAGbi4RKP8myBZNAHavWtEdjZR+XZGMmlo50aMmWBt2fnlYRUGaok4TFEtlwH8AAQz5U9F5B8PjEZeJ01rteb6l6hLJztWBKZvyAJDyQJQbyc30tX/xDnaDjgc0hZUDkPAylaMyt7Jrl0JICN93gi8/Lm6mfwJIfLMs4FanXRmqQA+AxMKndfleqHyZS1zOo0WqtTDTmvv5xMkdT3vsyXyoVZ313u1BG7LBW1RraThNIaYgeRVRqbLo7Fq6p+NQIVAL7HqIMS+7fzVvtYZwd1xDy35JvVKyqY7HbuAml6LAWqF4fdzxf+Xmcpd9hVbYUzTIyJRyArrGZQEJmy1qWjgCUY9bMahwfYnp3z893D55PrUuiLc1i/puvtTzApo0D4QXWbMez+v7iS18fzyUd4P72DFHwvg92P/diPJzUi8FhOrvHdP5V3dfzcz/3cEznu08FUqkzUDB1qzdylibwCJ6AmZVNTDfnydVkG94HnyBQdjgCpS4PUfoXVCmhmVkujbMtd9SNiLoNGAjQSk5WhDIi3T63OzMDufME+israiVTJAECtxipMpnT6BCygULmlMkChxirFxZLutcKeAASTCmJNVpUXZDUhtWFS36QAW0G77/eMHdP+klofpvOox9yenZv5h5eAvHHtigFKq5+Uz1fGh7LWyoTDt1vmVqoBo7K37GvYNebKHx2Ig2mG8tZtAWmJsQtkTyuZb5zMjAkIqxVZaHl949IRvPQ+JWsyq1jpC/WeLkngmllVl1uWFRPbZMBrYB2gWY8Ad99um1zU6lSFnVPgouAlyLxZGxCZEwgDDmHWXZYhvn2L59JoMFgWEG1GKTLXJjm9dMQ2NnmOUkybzCxIJMGvTX7SGHUFE77dAhqJMSEIgbWmNUmqglPf65IdlvezDjKv7qnVimy39AEsZzO5H7jnGocH8COCV3/1kqgIvO2nZDiQPZrZ+ihgYkIh2F6ry1hVbhrzAqXc1wq6y9unZtZk841dMxjWdM/QODywc1HG8aIU0lQHoFIgnI0NaOhQs5snNcqZ3AO9Lsr5gs+2XmcHBOs8A9iVjCu7rQktAfwqNQdoRKTg3NQC8wWfJajMl5QJVRMuBftIEt7LzQx+NLQ5VxDqmpntm4usf52drNcR7ow6qIq1GtYyUJqq9bftttVi1+sVjSF3Dk7uTb0OgHWUCnbut44uS3f2xUVw9OLh9/If8ryO4gxOSXpJMLta80ecpvX7Qp8f9fkwmXtt/91v3A+oqUz4Yh2ozzLWA8vxy/uA+t0JqNZAEyn6XXfRFOhiXerFz9+P/diP/XgS42llKr/jO77jHX8edzwVoNKpgc1oCDQS+0INtTYPTsBjWK0YZDczk1cCEow0Ejh1znSOjItZ/juyLsuVgRTf78EPemyrUbLth2u3gDxHcvmYTF6aGqjQ3o9BwEIlNxRmtd0GDobi3inBgBfgKODLj4bsx1mWQKddZfhrNSl1Jz6UJdt1NLMKsNVq88x5Umsd61LRGKvMvHc8hyVr7NywvwN8DVjVAFciTLHrVI3W43JlYNLXwIk/OjB5ZnJ0AJc2gLK0wLxez+gvHWF7+5RMojS21zquMJ5YgG4mL81KIvf6/KcrMLDdVuyfSnJ7XXiVYuYF0GrumAe5wxF/dmrscpubuFrz3LelscDBmOcUrt+r6vEUIHqH7dm5zX2YL5AcDG1fhsmMrJGsoe/3gCTB9pTXj8Rzf44GCDdvV9cuMkAyAAXX8GyM5PKxnXPy/md5z6zWrCuWWkRlHq22U87jxcPvRbh9yvYF7XbVPmI0MBfX8vYpXrr2/+MxvdtJvLhhv5KTijusb7erxEiWAc4hjKeAd2hcOmbLiX6PoEfm2SUJr9W7yqEYIqPc5LYvy8m0kp2vVsCwb5LGWJY7rTteyD4GlyRoXD6GH/Sq54skDFySwPV7VTJITVLEAdYPegZwkuHAzH1eX/0Mkl4Xb+SfJ0CR61bGyQ96Bo5fX/2M7flHNTt5pCFtIpSl9v0e297IfYdmhu3JHVtTlenuSFtztrSIqzW8mH3Z/m8kIs/O7B7UXr+6HiYl1nppeZ2O8uQOsJX1ljpqU20EMe1Kq4SZHuPVm/+zPQcvgvV3Gr6WKNHaSABMhIkxFMpgc6RGaPoM1YTeRaBjgEgcZn2vS6b3wrk9n3wE19ufAEAwVYprMJoZ4NnSBUXBuZ7MeO+1W/zZ5NaaB40q2aSGYo3jQ7tHwmp9N0v4DiPosytJDCwr2KvXf+p1q5vyA0ftHOp1zRcZ2zBf3AXML37+1wVcPgK7ux/7sR/78V4f4/EYb7zxBv73//1/x+c+97mdn8cdT4X8Fe0mXNxaLaAf9NhYXiRSsZY1jXlBOagAsjBfwHc7BJ0hAJsc/kjs/m/dJlDstPk374C8hGs1GYxPZhbI+G4HYUwpmcsyuDIAPelJphni0ZDHVzZp0GOfv3aLgXCeA9JSJN48YQArNTzKzFk/xrwAxMBH64p8llWUe51hSNh7EQAax4fs71ZWJjvJwZBNvFP22dOA3GqbyhJxvobrthFXIhc8y4G0AT8aoDyl+6Zvt6zPpWs1GaRmqfWxRJIwIPOOnyf/1WAJjQSukbA3W8l5dqs1mYCzsTm7Yr4k4AEqI6a0wRqiMiB59iqBdMZgLOqcZxlePP6DPO8sY2/BLJUgMYVrNREWSwI1wIBLcjAE+j2Et26yNynA184XiEnCtZ/O4Q4PgMkUcb2xWsiY5wS+640xZGQUeAwk7AuZDAcMqANrO4PIR9XsSPeRa7fI4nXaSDz7TqKZGtDyowGTIXN5f6fNuTDH0sLqwlyWUpZ96QhuW1ovR9fMADXdyItqf8hwWUr3ybKkIdWgxx6BnQ6cd/AAAXZZsh/opSPegw0BgprAEFlsWK3gum04CdricsW9rn0uQxBgXiC5epkgWfdmseX+WLMvH1pNMjvdNhAikoMh17Tf416RYDyMp7YnFAg6gOcj87PD6Gyl3nBcyTNjDIi11jfaL7PeBsN3O3g++chO/Vx9xE0OlzYMAFxvf4KGPQIuntRoXD7G9uQOfCLXGII9g/y1Kwg3bnHutFYyz9lWQ5J1TkA0ioKM5nJl9w28N0Cqw0FkpIcjYLUC0gZcmvIc9Pklz2B9ZiVXL7P1RUkjteToAGE25/3TaVf3sMpsG+zfe7338k6t4z17fl4AVdoXEuD3R3nOGm1tZwTw+R3GE97PWhu8WMKHWJUEAHBlYlLQ+nBJgrgtKoM47CYOVDar52PzV7CnpWs1pUcrExFxk5soa3t2jsblY947vRGwXNl3gZ6n7ul7tT6573DeALBL7t3mRve3/h2QOsyHbVtS/7gsxRvzzxuwfLN8xZyZ32l8XSSwjwDE92M/9uPpGE9rn8q/8Tf+Bj7+8Y9jsVig3+/v9Kl0zuGTn/zkYx33qUi9xeWKGevlksDOe0Alp2mD4KTVZCDS6yIWW9YTnp3zi7rDhtvh9JzAsSylZUH1hR/Xm6qRd1kijieAMEbKRLpWk8GrJ7sZz8aIZ+OKJT09Z/A0GlrATdaooMyr2yFwu3kCd/nYjulETqRZfNfvEWQO+3BqOtOSvnneGauodYUxL4AQ4Y8OeO0x7NaSKeABKMG8dIQYg4HPOhPkuu2qHnWxYmBTMzqJG9YfuU6H7FK9JqvTtiDQtZoEYmLCUQctxgzKa4PID5F4Xk8Mlcx3LYx0saWr4qAnTEYkWAyRWX5ZF8gxd0x8iu09Ay1lRuJ6g1f/+f/AY9fqlHy7BaQNBpajASANx5EkSC4fc/6PDnmwVPI3IRAwi2QOAPdPWSLMCCZdlsJ3O8bCxuUScbkk+JD9i6IAii12+q0K4Aw3bxvjGU7PDcyGxbJKmCjzqvMbA/dar8v7Se8ZAZBIKaF1wz7XYrMh+9RuAU1ZQ03eSNCvgF174hEQeMpT1ZVWWH83GJhcXFuxxDyvQIayXLdPCapbTbhu22rMwmzO94wnXOcQec1TJhfCbG7XGSQxo0mlOksSJKGSXL1cyRyztJIOyj5yrSbcYEBALskSnTNVS4TVukqagEyYsr/+0hH72QJ49dZftH0PVD0dL4KSd3OEsSST8oLn7D3c0QH33Ok5gfNitetSu1zRPXRQY2u9Z9IobQBN3tNhNmfyCuC8pA1bQxRbzmGIrO8+PoQf9KhOaGbmwK1r7zodO7aVKPR7TAoMenRgvXRERnosPTFrkub71TXed15Wa+4ZAaP6PHFJgjibw1+Ttj/dDlzaYP2x7tXa0LWrJxLitoBriKw2yyrJpwytw7Rz0brOThuQOYzyzItSkx+XK8TlCo3Lx3ZfIMt4v3ba1dyr47Akkx6adRMTI02waI9NBY8vZB+7q8/mG/nnHwgC7/oYmUPXat7TrXfHZfgeYy+DvffYz8t+7MfXNp5W+esf+SN/BJ/+9Kcxm80wHo9xfn5uP2dnZ4993KcCVAIinxFgEosCyHO4o0PE8ZQBSbOJMJ7CDXpAjJTUpSmDlmLLoHXEWsK4XBEMCkCI6w17NjYZ0LhWi0FwoORVHQ/jYsUv9l6XgZAXJ75GA4gR/sqxgD7PAEpqHpXN0/MPqzUdGF3F6DUuHxvbiKJgb8nbpwyUxdTCCfuKMsAfjADvGCB12zSUmM0ZqBRbBmQCZGNZ8v8HAyBtoLx5QsCi7ppZCsQo2fKWMYP++JAgdDQkC+s951bPU/9fRyOx45TnE5N47qyjBOFmljLoUWYsstFYlnCDAZLLxwRugwEal4/JHgujFkUKF7V1RKvFhIGcU5jOGcCok653cN02AV2aYnvnDMnlYzIvMSKs1njpyvejlHY1Mc+NqXO9LrwEuQCBk0sbnAPvEKfS47QMFcPRasq6Sd3TyR3uKzkfdzC0ANAfHVRSSYAATZMGR4fcf8sV3VNnc8T1hkBHwIvLhIFdreFSunMCGhQTvLg05Z5OUzm+t3vGdzsEYQWVAHE8rdZa5K/h7Jx7X+5DeMd5EObX+j/K9YXxFOHs3IC273aMGa0POz+RVLu0IRJTTYAE22MEKmLsMp0b0+2PDqxml3PB6zRn4Zr7q0sSmiXNFwi3T2G9RgFbDyQJ7wEB9nG53Al443rD54wcq5wv8Eb+eZPaWiuTyZQgPEa8ePA9cN22ARG9vifJVmoSSPc9mhkllIulyZY1qWAgrdXkc0gTQGXg3pNnYrh9alLeWJa8dxZL2QMlkFKeDpETs7SgsHnjf9dM/nU7fK7O5vyc2Zx/SxsIp+f2rC5vnvBz2y0+52Sv11s1PTCwFqMeA08igX2zfMXUFirzjudjeSZ79u7U+13rGGsSznuNuC1oZiN1yzt7R/b6znu9I0teBt6nkojQBKYlMtIU6HZ5305ncB0+7yF18Crz1+fBg1i3HTAse18ZSgWOzycfsf8q6NR6yweBwItDy1fiYnUXoLyrdQ3uBkt7s557j/287Md+fG3jaQWVb731Fn7gB34AnU7nwS9+hPFUgErXahkg0GAZALCQvnxSd+KPDjAVfZ0AAQAASURBVIDV2tgx1+2QCRFQqP29XKct2fAJAVyvi3g+5muFEcFqhTiZMdCRht2u22bAsckZjIjxSTwfSy1ZqAL3oG6u3tjUmBe8hkGPQdN8YbLOuN4wcJem1+HOWcUmKdu3WPJ16w0goCp55gpB1GplNTzlfIG43mB7cqeqmVtv6DSbCusoGW6fZQQutaDPzGtWwmSs15yHVpNz2swIJjc8F5eS7Qq37jDA9F56RhaV7X2/ZwySMif8vI3IH6c8fpIgnp7xWhdL/rsoGODlhQF5l6YGWuJiaQDHgmnZI14ZFe3z2e0Q7Cr7KyA3TOdIrlzi/IgEjeBgVsnR1hs4+XxlO8rzCX9fbxTvPQOoomAQ3moS7AmLF8V107WaQLdTrbH3tg4uTRGnUyYjxAQFiWf7kLNzxCUln1bzqCPx3AchGGDQmkxo7TFQgZ6UwR6luiJbFaYETf7bdztm6qJOnv74EHFBSWSQ+UfibX/rnIYV75+4lprOEIRVFkDcbpv0L2hv06B1jMJcr8jWIMvgWi2yxsIgRwG3ycGwYosBA/h1sx5tOP9G/vmK+VfgK88PBTqu0+Y9OBjY/Vc9j4RFlfdprSYg/fjqrtCLJWJRoBQX1nvV9D2JQQVEG67dZquk+YJ75mBEcJam1moHgCXJdP/5dsuSXOH0HOH0vLpfdM/J+hD0MNHiWk0+QxsN66+qyZm43lSss/copzPbN2a+05b1lWvwcn+UkixTCbIOZSofhrEhs57as+F6+xPVM6Qo+JzWYw/6sPYq0krn4mcC9w/qfZbtGAgpO1c3+GE99BbYbuUe2RAwJok9H3WE26dMzDQanL8QmJyazsjSS2ufsFrze+QBTGXdGRfADti9aNTzZvmKfbe8Wb5iRkePPOR5eHGtrFb/wu/2Yz/2Yz+e9IjRPfbPe3lcv34df//v//13/bhPR01lyZ5pWK0IIK5cYs9J7/n/UtMX15Kt9Z6BjxqYSLCLonLctCCh0TAAV55PCB6KAmE6Z3CcpgQWQMUkLpYMjAU4uuNDxDtnKKczNI4PRZKXWmALVK0wAADPXEb5q78mzoUd2uQ3awGb98LGFoghwKHmQinBrBoJYbWGbzVZa3jlEoOg+QJxsUIitX5+NBBQw9pL3+2gPJ9ULNd418kvzOZw3lOGKe64AIBmk3Ms5kEATBbI+d9UcyZzpWAh1mqVwnRuyQGtZzUZl/zOpG4q2ZwvLKi1uUgSuNEAYTY319r6OcXFknNRFJXBkAT5Gri5bptzP5sTaNWTAd5XbUvygkA6S42li2WJ5GBopjxuOKgYubK0GrSwWLJthfcob91G8swV21MuhJ0sve92CFpnc+7ZGOlU7D2w3VofUzcYAItFrZ0HjYzQaCA5Eqmy9OiMiyXfN80pnQPgBtL6QdueLJbwz1xFFDOQsN4At09tnmxP6H6W/qjGNMqwde+0meDJUt5faYqobKyCUMDm1vW6FZgY9DmP3hu4h+wBbZtjgFOAvkq4lalFlrHtiYJsAC8OPoWQ52QO63tWEh5R5euDnh0n3LhlYFoBlLJCWodme1HPqzZPPBbli9fbn4BLGyhXawbmTzBwjnnBejzvEW+f2j0c88KeXWE8qdahDIiQtUtTkx27bsck3nFa1R8qGNUkhfXAnS/4+s3GgJE7GNEBW/eOqE70+YTE8x7cbOh+eovJMH8wAsolYrFF433PUF2Spojlpjrv1fqeckodzycfAWKoXEzl967bJvjKC7jhAOFcji37K964hRgkeSLu2PX7VD9T6w3fyD9vRj0ArO9jHXxePM9EADNCQPk2a1z1XrP5lev0V47prntyRxQbTPz440PW6dfUDq7bvqcy4OK5czECgWXdUVeuoV5b6bIUYbU2Ce89HXffYcS8QMASzlftXfT4vtVEqfetjIt1nfuxH/uxH/vx8OPf//f/ffyxP/bH8E/+yT/Bv/lv/ptI091E4O/9vb/3sY77dIDKOivW7wHLFcK1I/iv3NqV1qlL34ygRfsUWtuH1Rru+IjSxTSFa26M6XBZCp8wADRZmAQ6NKfZwCEl4wew5mxLFi5M52zPIc6W6thJlm/Dc0kCvABGfOUmQVWWmnw1TKYMEITpcAIUw2KJpNU0gOmHUsO3WlfAQ0FyCIjaH69LlgLihmtN6IVlNNMNVEDVNZuUu3npOSeW+gpkoGBMG5MfHcA1yHCE83EVqCtjsVhZtt/AZVnCt5oGbgFYAB/XG7hmk3Px7FV+prRh0X0Qi6JiWx1lmL7XJdMpdbJaJ+naLbJf0pLCwJm6Lio4WG8YwIbAYFjmorx9ClcU0g+1ZI+/JOHe0YblZ2Og3wSyFHG1onxtvqyYZqmTjeoiCbZ82b1+MX0Z9Mi6F1sDoWzTICxuo4F459RYTORFdU7dNtmhzcbWsVws0VAjKpVpK/sk/Tj9wcgcJ7Faw7XbXEsvAbJzQKPBWuVNLtLHAGQp/GCAcCJutGIWYvt2PGWyA6iSGcqSStImORwhbjbwH3wO4V9+lcmAfq+au067SuRMZmyXcudsp6OUJiHULMplKfscSouTelP3IKx0LCiVjUVRObSKG3JcrXmvSUKlnhhAXiBmBMzscyus13pjn+MAhLyo3GXzwhQOBmSAezabf1dHWSJMN+wbqcmpNCXAWy5pbnX1MjBfIG63TFIsl0CviXDrTpXAyTIglAi37vCaRLKvDKQ9bxoNHidLgcWCz7W0ASAFpjO+VhhoX2cntZ5xs+F+nkxZRgAQaGnbntp1ubRRMfq1mm4A9waYIn+93ntZ/rdKlMUQ4FQqn0gycrtFWBTwly9Vahjcn5W0z48BsazqEPXzdFwElmG9gRe23reavH9VzZE2WJetdf6ShOI9I3XtoyFBfJIgSBLLlSVZ8Vi17niQRFLNfe5l1HPx/7Xm8lHlrzZXNd8CPb+LrO5+7Md+7MfXawS4x+pT+Tjv+XqO7/1etqz6E3/iT9z1N+ccysd8hj8V8lcAlAhp8/fNBn5MAw40GiZ/1ay5Pz603n3hzhmDt/WGr1PHugWlp65PZjJKQE3mKiPbMJszWBJA4Dpt+CvHDLj7DLDgnAVBrivulIDVySmj5ZoZ4nZrwa9+pjI9XqW6ygqKnX/j+FBqPsVkYzKjCdF0brJW18wIdqX+yaSscwZ3Kp2MISBut+KMWxpwdklCZ1PvCcKLLdlBqZuiaQzbb2hvUE60J9iqB2X9HgM2Acax2DKIKwP8sC9B/baqUdIgajiAv3qZLpO9LsLNE4SbJ3SKBUGe1clqQKuOlFLXqddudVxlWUn9LvRuc4k0Vgfgh5Qil8roSN2tgoa4ya13qK1P4i3QK2+fCohtA+cTaVzetp6RVkvZzJC8/9mq7kmCOX/1EvzVSwzgswxxw7WENGV3KkvcAcxtMXQimHRtBsOAAFgAjUtHXG/AelziwjzE+YJz7BzXVdxsIewrYiQAbbcIkA9HPKd2G+HkNu8FAbRM8CjbVeWzFGyq6YjzHn7Qs96W8eZtSkGl9tgPenava5sPSBLAd8UgSubTak6lhk9ddesyUzMXAVkR5ymZd97DD/tWZw2AeyJJeJ8LcxVXa17ncMDPjrEytFJGMsv4GbU2RgCq9dI+oiKxvAiG3u3h2i3O+San3F/rvBeVWRDmC7t34p1Tro8oAsrpjMxZgxJwlZfHLR17w2JpzyBjyMSUCeD6x2LLfdVuEThdvcxkgT4jtN2Rc9wHQZ2oF2QlEzpF++FAEh6tu+TeF5nyd2IsX5//NBMN6419ljo7QxKAUc20Wk1sv/wVbO+c2fP0evsTuN7+BJ5PPmL9HO859wLO1BlYP78+3ixfMfUEGgncEZ+/kLZPbjgQhlRMrQrpvbpcsdQiz4HZnImQvCDYlPNMRIVwv/nYkQs7v5NgvDj0vtFn2PPJR6pkUW1u7z0Ru+xnXUau51Vv83LxmBePuzel2Y/92I93ezytNZUhhPv+PC6gBJ4SUBm132CIgADH8NbNKsDQwEaYTErzEga6l45M0oRCQN16Y/UscbOBPzyomKZ+T+q3JDBMU7Jx+uU+mTHjroY/YvrDwLYp5yJOd2L8Y+cnLrAAjLVRp8UwX5iZD3tsMnuvAW04ubNTq2U1XcO+9dWM6w1bOijjkviKFQAqtktqxpSVQbOJePOE8uD5gscW++EdF1kBURj0eV3SrFsllXGxQhxPheVJ7Dpdv1cxGd02waWwDUHYSYRAZ8/EI2638Acj+IORmRTFzYaOr40GwYiuaYjWtNwAjdSIuibZUtcngImzOdnWkbhidjoitZ2IRLqBIOcU15vKvEjb0cjaImH9KAA6wR6O2C/u7Bxxk1vrGTTEPTLPhfXOzVXYNZso375VyYvra9towGljepHj+uEA2v9R6wwhATh6XdZHZhnBZqcDNxoQhDYarJvsdYHpzPagb7fZh3I04PVJL0aVJ9elin44qFrd3D6lTHi1EuaYoJX3D5lfMlwKpJtIDkdyXvIzHBBkWH9Zj0Tr++QaESLv+9qaIkT+frkyN139nLhcVRLd1UpMfhz8oIcXB5/Ci4NPwbeaXNtia0Zdcct65lLrrgFeu7YS0R6oSULzofnS9qGuf8hzhDxn0FuGXfMq5+BSusvGYrvjtnoxOH83hzpOxzznfTccUHHQaFi/1TCZcU51/cqS95fWLYbI586afXN9u233sT88qHo7yvMlihsunBPDo6Y9+8LZOeJ4wvXOUgPortlk4qOUvrZa43ftCp9tZcl7w7FfqbYcUTM2BfP3AxwK3t4sXzH5sW818fr8p0WyShMvNxrC6zmJbLdx9TIal45ENeBsnfUz68fmScvXrfb8rfWpvFiD+UL2MXsGxvGU95fc68nhyO5v7YVM1QMBebh9ymSqPgvLEtvbp3ZteIiAoQ421Y27nnypj+eTj6Cczux9oVaCoL+75/zXzIJCnjPx5XcDsZDnTFxdkOsa6L7POe/HfuzHfrwb42mtqXxS46kAla7fhRv0CSwmM7hmk1JGYS01wAAgjng1l8s601EzmICYvoTpnO/RgHK+3A0IioJBWadN8KQul+ICGMuyapieF9ZPLyqALbYMQAUoogwM3GZzuEGfAKDRsCw8WzQIayIyPNfvsU9a4vnaRgPOeZqCbLe0whdw6tIGfKfD4zWb1XkpA7fJjSVwoyFKcahUZ9m4WDGwqzfu1sCmmbHebcEWGOHs3JgG1+8JwCX7E6TuNG427EEYA5zzlcS2yfq0RNqb2O80MBMW2Gk/tLzgWifeehxar8ckYYsUCEMym8MfHWB784SB7WoN3+lYu5A4nlSgQJu3yxokwwF7SnbbfI/sAWUySpFBmqumZtnVRVN7YDbZTzEul/Cdjp07AMTxBHGzQXL1Ms8jS/kTyFpA2hu4dtskrnG5AoYDtqKR4F/BPEQeGEUqHJdLYLXmZ7ZbBJzCFOoeNDAwX/L6hmQ3XKdDw6sslZYx3urryFJR6qv3iwKBOJ7yXjs6MLfMndrl9YagepNXrUV6Xf5+wfMKi6Ww+s1qPTucgzCbk2VNPIFSyTYKQcyo3NHBDguuLGc9sEemRl9k0cJkRrfYbrsyb3LemFCTJXc6Vreq7W7idouk1jamXkun740LSs9jnhug1HYTAO4Kzt/VkXgaM7VbiIsV71WpCXbSdsZk73LP+dGQayR1pUg8/NEhQdegb30JXaNRmU7JdcYNr9EfHexK7cF96TvCCAobqKx4GE+g/UgBJhCd84intDx3o2GlFNE1zvOqr29Z7rS9qIMb/bea0ARZBwiTeL37SQO9cTa3ZAWAKpEFVP2J78Ew74CwGIAY+DyRuuQ6EKqf2xv55/ks8Y51q84Dw74lJrVdkz4HAQDNDPFInLjldy5JmLhRdUFZCkP8cF/9Wttb70FZH3rOjcOD6jIFgNbHgwCf1R5n2T0B6L0SLPu6yv3Yj/140uPrxVT+nb/zd/B7fs/vwTPPPAPnHP76X//rD3zPL/zCL+C3/bbfhlarhQ996EP4zGc+89Cf99WvfhXz+fyu3xdFgb/zd/7Oo5z6zngqQKVKXN3xEQNLZamyjC1DNjnt8pdLuFaLbE5HDEqEoVIjDnd8VGW705TN1gH+fy4AMsvoiDno8zOcrwKPsqT5yXYLf3QILyYWLhN5q7AaXmVem5zBiSf4ivXavE0uwX++w1bFYmssLI10JhXbpUxH4tF49lrFNJSBLThGQ6shDGdjvqcQMCCAFBIUQuobzdAIwnwCBipco0HpsDg3xsWSALDRqNxRxW1UATNKqVHLc86F9K2L2+0uWAEq9q3RMDkwDTRy+SmqgESCXtdqwT93jZ+RSK1giGR2BZTAezQuHTH4amb8XGkh46SPKNqtioEWoB03Gwti43Yrn0Gg5y8doXF4ADcawjUaZOBkb5ABbAKbnCBEeyA2xRVVX6eyaOfJOMh7IVLKeOOEtZxNcYzt9+gyPBpwLyvDJtJv18xkv5NdJusTKKPV+sDf8n4y0MdHsiYe6HT4o/On5xCCuazqvkKWAc9cqVjuFu9F12wCvY5JIt3BiEkUld85x72t69fMeL6n52TUtZ5PGPTkt3yAAFzBXUf7eK5sr2gNnpP6ZdeUemNJNmlyJpYlXpt+lsBN1q88n5BxERmxqgU0maL3Vjgby7OkKefvTf6urU/igsxovUWO/WRZBXqmM/jREMlwQPl6sSW4eQyzk0caIdJ4aEE5uT86lOfhkkC80eCz0xIakfOY50iGA5v3OJtzblSJkWX8vRhp+cORJfi89EANqhTR50gzA3od9uzV2km9D5pN64caZvMKiC6EbZbkT9xuEW7eJrhdb4y1vgjy6uCm7rKqzNeO5NienaWpSqKUHTi5J2Je0DG32TQm737MqGtIS53FknNRljsyzjfLV/DiwffgevsTBuD8aMjnUDMDpnNKuOcLlG/dlHMMVT9PAG4s7HwkYx/LEnG7NaObWGyZ1HnIliIGtB8w4nZr4PzN8hUC8kcYLm0w6eXuHYg90QTLfuzHfuzHfcbXi6lcLBb45m/+Zvz5P//nH+r1X/rSl/Bd3/Vd+Hf/3X8X/+Af/AP8l//lf4kf+IEfwF/5K3/lHd9348YN/Fv/1r+FD3zgAxiNRnj55Zd3wOXZ2Rl+x+/4HY907vXxVIDKeHJHgIsEBP2eOEHmldRIgwUJ6lkrJmYODbJ32G6B2Rx+0Gdd0K0Ts8HXYEZfjyQRprEADjRbXlZMW79X1RqWZcWsyfto11+yXub4COGUtZ3WfH7QN9mRycBUFjfss+VHsWUWO8t4TbN5lS3f5Px/kefF9QbhfEwWLC8Y2KYN1qKmlKwpwxJXa0qnNPhTh9XtlgBCJcDizBi3W6vNs8y5cwy+VB6YZcDxAYPOslonAPxbjHzfas1AdZNzDhsN+KNDlJOpmSL5Yb/6HGX/asAkTmeId84om5rNyWBIfai/IkmCwIDU9bqsCRQGM5yesT6r0+a+kky/67SlbYz86LkLq63yNMpwqzoyNWRS5i9ut8bEOZHkluKU6lpNuONDugWXJd1OhZm061MGVeWnd04Rb90hw7Nasw+rgt/zMc9ju+UaKmCX+kMFXO7Lb5OxbSRWPxlunXD/ixzS6mS3W3NZNrZptYYTB0yTT56z5k0Z7ZgXfO8mZwJIjHZcM6vmTRMaYvyjLWYUDEeRFGtyQI/tGg34QZ+JkqWymZk1j9f1w+HIwJBrNil5zTLrPZrUa8e2NOJRwKr1voiRIKxWt7m9fSrnlVtiSKXSCh6AWn2fSDUV7FsdtRj6KNjRvpVPYsQNSwG8sJK2vo3KrRYh7D639Py1RrnJOkpz5c1ScVOm+sLk/8sV3PGR3b9aTxtmc6pDGo3K7bjZJCu9lISJJEMgknd169Y6da0Tds1MerqW1tJCa6uBd663q/eV3JFUqhQzSRAmMz5PB3wmofZ9EM7HwHaLFw+/Fy8efu9OC5mLIxkOBdRRmvtm+cqOPDQslmxtI+cdxhNgPOV1eu5HlzaslYomvZiAKhHHE0qSV2v7boBzTA7W1vFBo+7+qs7b9bmsA+E3y1csAanX8jBAtD6c3IeIcWc+3ixfweuLz93FdO7rJ/djP/bj/wtjOp3u/Gw2906QvfTSS/hv/9v/Fr/v9/2+hzruZz7zGbz//e/Hn/kzfwb/2r/2r+F7vud78OlPfxr/4//4P77j+/6L/+K/QJIk+D//z/8Tr732Gv7JP/kn+M7v/E6cn5/ba2K9ROcRx1MBKl2nw+BlOmcgIOCKwQh7oLleF/7KZQM/rtelsyUYVITl0uR4Zv6SJFULAmE9LFOun9HMEG+ekJ2UL12abuQI0xnCdFYF9FkKjMTMo9dlcBIjsGKQW06mVWuM6YzmMTHyWGogBLFfv33Kv8u5ot0iA9TM+OMcg43xlGBq2Icf9IFGg5JWCX7DekOGyjlKFQ9GBG2b3JiacPvU+nvG8YSAQYOqsgo8Y1kyaBxUZkA2VitgtrB5db0uj6ngG4ATAyWr9VutKUE+PSOTc3RY9WWUukbXzKq61qIgi3Y4MtbLZZm0KxCQpwBYAY4wvAiU+PqDEQNrlZAGkYLq6+161sZU7SQtAEsW2Lmu1sZ0QphqbGlghGZGI4xOh70275wBYp7CxY4M1FXCWpZIjg6shYnJ/wAGvCECjaSS5mpSYTqv2EvpJWr7V5yTMZ3BHYxQnp7DXzqCv3TEpMOgh/L0nIxJHcg3GvL/NcON40OeV7/HpEGjaimDQiTKkjQgMA0oT88ZhG6rn1iWlGmLoZaNhoD8LDVpu15/XK3JGkkgnah50naLcHoOLJYGPHQufa9ba/eRmENzLEs+ByLrI7dSp2ZJmk1u5lcWsMu5+oOROckqsK2PeoKqPBsDZcl70jv4LGN/ROCecsN3dch9F7dbS+rEep/HsqTyQOrlFBSiLE1ZQFaW/V+VSXStJplcfW3Jvr5uNORxkwTu+JDy2NNzuglnKde+kfAYSULTNDk3dV2FJLbidF4BYEnkxemM+9g73ndifGTA535DWDsD/+sNgZ8ylc5xD8v3SVwssb1xi6BN50qeq+Vkase6V1/HsFiyLlu+Vy4a+tTBaNLtUOnSblWOxdttdd/FyOfItuR3gdYli2lZXCzZJ1ldrOeLh2Yed6an2JoM9qJ82MD4oG/HfRyGPa438O9/lsAS2JErq/lRfTxs79H92I/92I+vZcTHlL4qU/m+970Pw+HQfv7kn/yT78p5/fIv/zJeeOGFnd9p78lCk8T3GH/7b/9t/Nk/+2fxbd/2bfj3/r1/D7/4i7+I5557Dr/zd/5OnJ1JWcl9FCMPM54KUIlANsbc8gDEZy9XrMlM6iIXCzpuLpc0RAENPLDdGoCoDCwoT9RAwuRazgHdttXGYbGy7Lof9Bn8SIDmOx0yiP0Os/KNBuLJHX72eIpwPqZEbsHgyfe6YkISzHWVbUDIKrlUaivTRsWmzmYMBidTgh4N4gBzuHWpNMTelsB2y2Bb6t98q4l4h0yLBn9a5wVfsRRWLyUMK81r7tGQepMjTucmV+W1ZLYOKAqCXu849yGQlWw0ADWTUUmbZODZrmLDNi1pY4c1jMsVr10CTKzWnIc0Zc2YNKw3cKUSq0aD16Rgc8X+muH03ECfrn9cypwoIJT5MJmvrIe+TveLrdXxYRUwd0WOfXxQSdTUqXEjpj9FwXlUKfFoyIBcPgvNDG7Qk2NVRkZ2L/R7FROqa9NkvS+KAmE6I1DwDmg1aarS6yJeJfuTXLmEV//ln8ar//JPC+s4IXAKwWTlKAoCZzWwkfmPt27zHjs9I4iczQGpU0SjweBztTbwgWbGGtUspTNyq8laYtlbUSWtqhZokF3XNXRd1gdjNGDPwKW0bUkS1qN12raX42xOANNuEbgWW4TZDOViSSOelTgLSxsG1+uStV0ukfS6/FyT1zJJpM8Bk0U7h3Dn1BIyLm2QAfVOWD7udzV1SS4fV7J3gADHO3hhh57UqANHhFCBc0meheVSnoc5nLbvKcXtudEgi6rybYhh06DPtdb7Ny9sbqHMpz6jptLbUeqZ1RzIgPzhqOr96tyuA6+C4U1u+9C1W1XCBEBcSl33enNPp1AdJjMV190g9ZgA9x6NwMrKjE3Y6GQ4sOSjPp8sOSGjbr7zZvmKJZ982sAb+efhsnSnLvD55CMGRg0EaxsjTRwdH/E+UJMq74xRjadnsn5ZlYxZrckklyVbQAHSCurhv/pjWe4Y5ej17ID1oqgY6DzfcXe+76idQyxL1m/X5qI+h/difvfGPPuxH/vxpEdEZcvySD/y/q985SuYTCb280M/9EPvynndvHkTV65c2fndlStXsN1ucefOnfu+bzKZ4OCgqoFvNpv4y3/5L+ODH/wgfsfv+B04OTn5ms7r6QCV3lcgb7tlT74vfbUyj5Gm5EhTMnAHIwMMyrioHEy/DF2XgNBJgK7ulmE6o3tsqwkM+ww6pO4wTGfiDJtVIKYs4c5nZI6UyWwymPadDuWYknl2nTYZGK0d1ECi1bTziupOqQHNM9f4mixjIKhMpYBs12kD7RYDPjN6KCvQKnJDCwK2pTETCJEgTjPyIqt1zSb88RHnRFg9lCU/t5FUtYuasd7KvzWwzEQyeTgS0EapW1xvKhmeMI3+2atkDg5HVR2g1MhCzsVMmBbLitHNc6v1dJePGXjKfLgOey36QV+cVgXkakC54ZoqO+KGA2N+9XO9OtxeYksXZXhc2gC6bZHDsq4L6w33WVvYR++AO+cEV1cvEYB49vczw6ZWE+7yMT97seBPlpG1kFpbFAWCMCPIMgLR5aqqw01T9pi8dMQ9osyj1M8CIh3vd8mM3zplD8Gzc7z0vj+Ml973hyvm+HAEKCBeLHhdIpNWhjluZJ1kfnRfYjYnYycg2rWaxvigLDkf779GNnUqkteGGK6I8QpaTNyYM7PsD6jxyNmY1yCSW71f1XzLd9haxnc6XC+RqCJJkHQ7lZFJnktfvlyYoK31SQzrTSW9DbFqyaEGQIC0mslQTqZw3plbcNDzBgGPbzGho/1bfavJ542CtBCfaNAc87xSZAz6QIgVywv2J1XnUMwWZL+F+cV2SzAm94Q+SzQ5Bu/sOhrHh2gcH9bcgnOr45YL5Tk0KyOWuN7w3hYjpbjJybKJc2xckAV1kvwJ01k1//JvpwZbKhu/wLLpoMOrR9BaQ3B9tNwg1IycXJZVTGrasOcz0vS+TqT3BLNyLqqaqQM0dY1VgGnsq5YrLOgCrsm+8nzCvpOS8LKevnlONnk6q5JBeDggdvGc1SRHa0/vNeJ6g9emn+U8iRHTowznmRjVZMzO53gH5x8/c74f+7Ef+/G4Q/tUPs4PAAwGg52fZu277msdFxlFla2+E9P4oQ99CP/oH/2jnd81Gg38pb/0l/ChD30Iv/t3/+6v6ZyeDlDZrfUm25ZWK4fVikH6wZA/yjpp0LvdkklrtygzEkCzPblDUNJI+LsQGJB7B3/5mAHQal3V3skXv798TPCQM+i3zHWMlk12WcYAWdmJxaIyFmkkVZ+27ZYBWJYhTKYMLNotuCuXxNGP7B8WCzKR4iJq5yXsSNzk7Oe4XCF89e2KbWsIcJEACe2WBZSu35OAmQZCcbHk+daMMqylyHJl8uK4pMuj73R2+xAu6WIINb9ZrQxYxsmUc79YwF29TNCgAX2TgMScZyfTir2RukT7f+krp+dkrQvSBpMAzvF61P2106nalOieORiyx5vIj439FNdV12pyr3XbfO9szmtQ5ixLzXHV9bp0TD0YktW8dEA2MgTrBem6HeD2GSBuwE7cgzVwxoxumPCeP4tFtYdHQ5HsdkyCqMmVuFjyWoV1RsGaSpUTq/GJJWNmUku7yeXvTYIxBdVJwhYayxWvRcBVmC/k/XOu03PXCBZVJiy1nHG1hr98LLXCPanZTQiYPU2j8OW3q3u4LKtEhLKNAhohtbFYrLhuwtCyoXu0QJz3nEf49a/SWVRqLcNyaUkIHZr8icVW5mFjpkpB9qPv98lOSSugWJacKwnWk+GAktcsY0JKWjz4XhdOWEomNTqVm7AA+zCZGmuKhK6gzrsnKn/1/b6BsqiJCZUnt5q8R3pdgisFJkHmVGtFt1sErWPNpQa62Foyyu73doufs1zBSf25Sxt8nsqcopHw2dZuCcvs+TtxiE7UNVgTYZt8py2N1laqjN8Set4ZS/hODrBvlq8gFlu8Wb4C36eT+JvlK/xclaJn7EmrvYbVtVkN2uotN14cfGrHdRao2FCXJPDyfKyvsfa31KHOrUgShNt3OCejIZ83knhKLh0h0aSRJlYbDTOjcq2mmUXFzQYvDj8tapp3NuqpD5Vyv6PkNEZc734SzycfeTiWErjrHFy3UyXJ6i8rtggXJLv3Oo+9HHY/9mM/3u3xXm0pcvXqVdy8eXPndycnJ2g0Gjg6Orrv+1566SX8+I//+F2/V2D5Ld/yLV/TeT3k0/89PmYLxNKbxA5iABFncziVXAH8m8jKnLYfkAbSrtNhEL1cofH+ZxkErVYMkC4dsW5S2hegkVRBjKfBCrKMQa4YntT70cXprGJK1eVV5bfe8TN6XdZTpmQq3XBAQxSRRLokQTxnH7cozCAZBpqcuHaLbRY0QVxsLXvtMmn1sRIWNknYLHu5MjlbnJxIvzUvtVOdnWyzE3BgbSdWq2r+QxUcmOGKmg5JkKCSwng2ZquSmyds0yDuknG1Bm7cksbndARV1kqBrBsN4ZTBLeQzy5IS18lMei422NKl0SDY32x4XVu66pqUWQ2cZH7jao04lp6kgIFR16XDKFZrrrnWiioLLAEXhLkw9lf2XVyQ9XDjuclFw/kEyZVLvM5Wk+vgHfeNHN8N+wIowX0DcH8kCSWkp2dwacp1V9AOGFPhe11g0Gc9aZIg3LhFhr4o2Ipku2UN53wBB8A/9wzi7VPu517XWjYoGFewqnXFsSjgk1Z1j6QNtttZLO21kBpblQS7YZ/3ZLdjyQrXbnGv53nFniqrWZZM/ngPpOK2uVqbcQj3OWWbycEQaDaBzYYJloMRwnQGf/kS4mRqkkZuxggHVO67uscF0CUHQ2MzvdSp1qWh8H6ntQ8b0ZNlCcslAWOzyT2fNhBDNLDxfPIRggqVx0a598WFVmsAX8g+9kT7VMLTLMi1W3DPXkX5L76MRFrFhNt3WL88X8D1uihvn8LLPaquuHG5NBdbnTvd/zZX8kwDWPfuEm/3FB2Fpb4vbXCNJ1MzCUMIxmqrtNwfHki/1ZTzHFs8R60fRvWcMsZ3ubwL3L1ZvoLn/e/f+be6lZocVtYnFoVdNzas18RW9l6WSgKEUuo6IFTWDthtXRLL0pxYXdhNbtTP0TwBshRYlbx3F0tguaIjLKp7HQDnUFo3xemMfT7l/IEaQ7zZVKZq9xkKHuvnU68RvdcoF0sz6gGA662Pv+Nn3HPIfQNg5/N9u4XyQm3yvRjTvRx2P/ZjP97tEaKDewyA+KgtRR51fPu3fzv+xt/4Gzu/e+ONN/Bt3/ZtSNP0Pu8CfuRHfgTLmkdLfTQaDfzVv/pX8dWvfvWxz+vpYCp1ZJkFNNZqQpvKb3Kg0zZmR9sAwDsCMIDBfVkiTmYMRIWFCG/dgDqJqlFCLEuCUAWUIdAFcL1hrYj0Z4RzDJqByqQlFyMWsepXYx/XaDAIsICZn6UBBmvQpLby6IAGPspiBjFHUcllWZqRBrLUpIFxta5aiEiNpknTxHgkai/DIO6pmqkX5k8luu5wxMBR5teljaqtidZASmDs2i2enwRiftCrJGtn46rVgzJ/JUGI1doVRVVXlCQI0zl7iCpTIG0YAJhrqOt14S8d8/cNskTlrdvVa4ste+8VW86ncwjHIwa7ynQKgMC1ywaWFZSizfpZ125JTW4ksClLk546DTzLkoBuNCQAKgr2XTwcWFN5c1oNtb547fZOTVU8oVbeicmTSvVco4EwmbL20jvO7WTKPaptQdrtyn10QxDnRkOe65gSOnUxRrHlj+wfOtqK06RKS7WHXdqg8Yi+FzBzHd0XcaqtZaIlV1zawPbmSTUXm0peqi0E4nJF4CD9OmOe87NCJAOTpQQAYlwSiy2TCFKXHCdTXkOxZR9EQAA83ZNjnu/U3wEwxhLe0SQpz4Wl2hhQCoulJIoa7PEogNg1KsBbZ2xeHH4a11sfh2+3eP/UPtMY9jzH9s4ZXsg+xnsoPDyb9MjDOURl9+dL7skYjf1WxiiMJzv9NslWusoRudmEa7d3nG2RMdkRJlNJQjQRzs5p+AVwfy+W0jNU3idsPaQGHCEQrK+ZpHLtlqlKrLdviPDDAX/nHJMWxdYk9XG53AH0F0cdJFmfUO/gUpruXO+9zM8QxhaQ+64hPVPnC+6lLLX31ddcQabVVG6lLZPInbWmsn4eL2Qfs/VX6S17iS7hrrF+xpyDs9RUIq7fq8oepFdtLAqa2NXdBpUJb9w/6Lg4fLtlTKUCzotsr35XPp98BNe7n9wB+vcdF+s6a8/w+tA61PrYs5L7sR/78TSN+XyOL37xi/jiF78IgC1DvvjFL+LXf/3XAQA/9EM/hE9+smrV9H3f93348pe/jB/8wR/EP/2n/xQ/+ZM/if/tf/vf8Ef/6B99x89pNBoYDAb3/XuSJPjABz7w2NfxdIDKbofBsfTriqs1A/9ul0GL9oQ7PWOQOeizF5uClOGAwEX6rME7BippWtU1HosbZkPcDrVP2WQG9LtsnD4cWNNu1OU69eAwz03+CDD4jnnBJvR5YbVaKAoGC2q5r8zSeAKkKcJX3rK6RddqiqFEMFOXWJZse+Bc1QNS+/SlNNFxrZbJcP03vN/YRWNR2i3LigMw45cwnjBzLs6VGkyZ06U4ZpbnkyrAVjAmrI/rdJg973QY9Ksrr8obRW4LgABL6kyByrXTa0B1+biqnWo2uQ7iUhpXK37GcsVatkGPcyZzUd4+ZWC4pEmL+7WvkGFqNXcB97/8Cq9fWeJmk/ItbYWitX8hANqyZrUm8Gq3eM0t9puLxbaq17slgbacqzU0B+CuXNpxVoV3tV6Scr0tms6E+YIAeTxhn0h1TN0SHLosQzi5zTVqZrwWddfcbCrGqLbWCqDias17I62cXONqTWCw3th8xWJbuWRekJi6GjCJ84XVviXDAYJKLTsduE6HxkFZWrFdYkrikoR/k7XxvS7vVzH/0Vo73SMuFeZaGKLkYFhJ0vXvKiVMEgbPajayWllSR+dQz13ZfzjWwfqDkT1ndK9HYeHCesNG9vMFYogI4gQcRNKXXL3MVijChKtjrfZNfFJje/vUpJEq34/ShzJKMsgdjujQKj1qrZ2NPE/CeCK9TqkEidrWRpUKqm5Yre055VpsQwLvqhY3zSbi6XmVzGg0+KyryXIB7jl0u3BHh9yrpfbklc+a0f07breV43ftWXtfyaSAG5cklFk6Z8ZMUe8Nve7VmqoI2VMotrzfOuwxquAUwN39Kp2nBFYSTzqv9XNRCe2b5Ss8d+kL7EZDqmXE1MpUOdqv13kmnNKUz7rVmn1hVcoN2Jz5VtMA7v1GnfULq/XOtdzPkEj/ZnXSDxoX5K+WmMBuTareLxfP74m7I+/HfuzHb/rxWCY98a4Q6IHj7//9v49v/dZvxbd+67cCAH7wB38Q3/qt34o//sf/OAD2l1SACQDf8A3fgL/1t/4Wfv7nfx7f8i3fgv/mv/lv8Of+3J/Df/gf/ocP/Kx/+k//KT772c/iV3/1VwEAv/qrv4rv//7vx6c//Wn8H//H//FoJ35hPB3y11AC29wy1K7fI2AU0xMNTtxwwN9PZ9IuobQveGxLBtkCiKIatJyP4UdDhJPb8NeukAHNKK9ElsE5h3jjxOogGWiynhJdcd+UFgmWlS8DQex8C3Q6cMmGgLDdohnKdEbporQjYQ3RRtwnKbfyoyFZILHtj9gYawUAfjgg+Ov3CKaODsk4LVE1s5d6NtdqAtN5VQcVYxUgCjOAtFEV/yYJwp1T9niczQ14OnGNJRgLZDhiJGBeb+Acs/kEugwowtk5wUGjYfPmUv4bZUCEsBKtlvxO5rAloClG4GwMeM9rlDnavnUDjQ+8j4Ayz7kW0ymDwHbLpH1eTYiU2RXWMa6ZmIjzBdzBkAGeypJBoIsaA+D6PXiZOyzIYLjB0OR/rGsEMJ1Z4O6HA+BgCDdbVG1VBICi2PK1223V2DzPude1rjIEY+F8t8P1Hg1N0sx2NWtjUxEjA820YSY7APeDazQIHAXcK6D3wwHZ8MmUzNFsLv3xAu+D2ADKBve0sPPh7NwAhMpIdd1cr8s1Pzxgb1at5RUGHwCiOCGH2bySb7dbCKdnvD4N3nW9QrDWNLpvXJfGPBCmixPoqr2tbHi7XUkEawxjMpQkVasJlCl7IwogqvdZdFlGcF2WJoGN2y1fiyVcbOwEvwSuacXkCqAOkynvK5HMXm99/K5A+t0cjauXq4SZ1jE6J/WjPc7TeEqZs+wBlwrr7j0TAgfDnfmAF4VGlnFta6yt7kGt8wOExY5RjpFa0opseeDx5L1IUxhMWSz4nGu1pO59y+dLv4dw5xQxRABisPMOhjE7wEX2ZzLoIxYFwX9ZojEaikEZa2tdS0yJvCNgGw4Q12sCcZWgy0fWGTygAq1vnv2veD75yF3sG0CmUgGcbzURf/0tcY/mvCPP+Vw+OuTzRxnBBZ9T8LU2K+djuMGgem6XwaT+jzJcltr5ALjLsfbN8hX4QQ+v3anqdK73Xn6kz9Dz22FVwTV6cfjpHcMkHU/SHXk/9mM/9gOoaiof532PMr7zO7/zHftD/tRP/dRdv/uO7/gO/Mqv/Mojfc5rr72G7/7u70av18NyucRf+2t/DZ/85Cfxzd/8zYgx4vr163j99dfxO3/n73yk4+p4KpjKOKdJSTwckLkBgOFA5HY02XGXjgiqul2g2yXDqG0SypJf0MI+YNDncaYzBlgtGm7suG0C1jfNXTqymrC4yeGcJ5uoDeQBY2EswPUJs8+nZ2zPoY3GldWczqqWHfMFZWAadCmrp8cqg7F2JrWcL4RBCfB9Zru1jtOa1oNZaBRbIKcEEVlm0j7fIQPs2i24wwMyRusNfL8H127DtVrw164gjCcIY9Z7kq2lARJazYpdlFrLuN4gzOZ8fZLAt9tmHBRXa8rKtKejsnRJQrBQbI01i+s14nrN9ciyCtTLtSeDPrBcEhyVgQGWBGBWU2QmP7EytBEwpi0EXJYhnk+q3nAhcE9dPuYaiIxAZalqtMPPnDC4W2+MjY05g3QvPRxxPqFs+2J93wGDWec8pYNn0upkIXV8wja6phg7Oce5lL6W2G6NVYvrDcFxk2ylBagxMuHiXNUHczYngBR3VUqHGybzdJ121b9TEx4iI9W18r0unPOVmY3uCdmvvtclmJL9CYAtSJTNllYTrt3aMYdy6p4LGl75gxFcq8X7BzAZsbZcCOMJAWuWwbfbCPPFbiCq7LCkFZVljWLO4/o9hKl8fhn4/rzg/Q3wtasV61/1/XLucc0emhADF2uRktP4KyyWstcp5w3CajqVLgM7LNu7PcJ4wgSLzrf0/PS9Lv+/Nk9a44qEa1pOZ9JuoyDIb7dMVRHniyqJ1+tW7OR6w3mT9XbOI65WVB2029xr8wXBmxpAJZ41jXlRtTDabMhiSiuocOeMib5el47O/X6VHCvLhwLmKu1UUOhHTCIlvS62t09Z2+l8tb7KUMrz1kk/VXXS9mnDwFa9FtF5B99u4cXjP0jAKDWVVjuYNuC8q9hN5wjkfVK1IyoDnw0AnylikhUlMRqnU97752PO93LJfSumdHG+EInwo331+3YLry8+BwB3yV8BAGWwnpsvZB/jeT/KkISkzkm9x2d58b6V8SSZ/P3Yj/3YD+C9a9TzuONP/Ik/gT/2x/4YTk9P8dnPfhYf+9jH8L3f+71488038bf/9t/Gf/af/Wf40R/90cc+/lMBKl2PbJRbi6RHHSnFURWLJbBYItw8qRpm18xCtJ+i9irE+YQgRqV8ar7SkJ6DzhHAiEQWqzUzxiEAz1yhO2jd4VEZozwXiW1KxmmTwx0dMrjW/m2LJY+VZQzkb5+aPFDrJpVJVcYLEGnYusacSZ1RjAE4HBEI6Re9gYECXuYBAHy7TUCRpnxt4hFPzxmQTGdwqdSvHdLwJS6WwHxh/TjDeEJJ8HYLd3RImVi7zQAbYPuEfo+BmADOGANldOeTSj6ofdbSlNc6X1gw57KMwGe9IcC9dGR99tS9UudIs/gxEqAGNZ8BKmOLbkcMjspK/ilM686YLyhrFTCLKY2B4lTmq99jsLsl8HW9rkkkmZDYSjsWOryGOVsDxO3W5NJBAB6KLbCgSRMOhvD9HhlnZT17XV5bV6TDIjNE4hlgi2TaSY2xMn1xK/LUEDhX0v7CwFeeV5Jr3UeNhgENM2ERFincPLG6UwuANfhLfAXwYqRRVLvFOmCRy9o+WK14D+QF909RiBNuEPOllExZgxJrdSmOa0pefZstYnSO45YsJqWbeSWfzAupw8yqH5WEC5uuDBXKEuF8jOSZK+wlWxRcS9kvCizVidYJsIrS8zZuKTn2/T6DbGEm38g/z0SOAFnf6VQtRdLKqIS9Up8cUwlIcqXOloWA8mxsSZdYFHwmqGFSsSWQF6lwzKWX6nxh7WxMKTHocy+223DtNms2Ey9JvEAzpX6/es5st9LSKWUdtJQYuDSFOxjBX75kEmzWpovKIklMikvGmyyqyV9bzbt6Ht49DwXZyhCra5I91HjfMwRjMVSAWA3XtCfycmmy5otSzXodYshzvk5atVwESqHYWiuZN8tXpMaVztNxwn7EaGb892pFQC1Dn+1Onuuu1eRzX5JNloAE+Mx/B/fXi5Jd3asX5aZ1Mx11Ebd+nHUjt/uNOrDdbunIfWHcTwauUuH92I/92I8nOUJ0j/3zXhz/7//7/+I//o//YwDAf/Qf/UeYzWY7ktk/8Af+wF0tRx5lPBWgElth+RJPwHQ2pvQ1zxHVabDRoGT01m06l7aaiCrTU5ODwxGDEjXNmS+Y/VUwssn5hQ4ws3p6ZkAuTqc8xldvMHheLq0nImIEum0Gp6dnCDdu0bQiRsQ+5asY9gkStiVdXydTYFvCX5JaTucIlNUUZzTk8VMa2WgQbqPV5LmVgefZalrGXa8hrjdiblH1hoSaGKk5kLA0SFOe/2pNua/KG7dba++hQY/vdPiZCVk23ybI8QLK/GhIBkhAo5r4aBCJRgNhtRLTomBBrh9Je46isECnfKuyVFZn0zibMwjU4KokCGCtngDWXpcBmsgltyd3CEoWS85bryMtE1YVyFqt4A5GlJA2M6DXJVM2nXFdVmIq0++xNUoUQxLtByhMNhoNmz9KtfNK6if9StFtk8k+O6+YtEGfJiE3T7iHZ3Pu0cWS66aBY4w85vGB1LeROXfO8xrHkx1XSH/piPt+vUacziojHh0aTLdbBqZiURgDDjCoDbM50OtKf8NgAA/bLUHfZMZ7Q65HEwOu2WSvQQWD7XbFJicMap2w2eqwGmMgYGk0WCOqPVETzxq3GisW1+IIOyCbHWZMCASRraIkYHDabzBGuHabbPJiBT/ok706GDEJsd6wD6VIc6NIEsOdMwPErtHgflDzEZHAa2BuQFZMoYLMQ9xuGSyXpbE2T2oYU5xUc5VIHW8UkIRuWwzCOMdxtaoYvcvHcKMBGeQkAZ67wvtYn3mAtaZhwiFUElmAe3k6q/bLei3uzN6eO67VlBZNK8QuHbTteQVIKxjZyzXgr2uq9cn3qr+7yLipK7gCoijJHaRUWLgWDbG0lY1rNMjSpumOmdG92oUY+FJX7BB33H1VDqvS1+vdT3JPpw0zcePzvDRTOD8aAr0Of1TtsWW9dpjN6aZbMMmnCbnX1z+7M3/3GjutQ2KwWlEFcVpTuQPSxZTufrLee44asH19/bNMLMje2qnTlLYwF89xP/ZjP/ZjPx5/eO/RarUwGo3sd/1+H5PJ5PGP+S6c12/8kC9SlNI+RGVj7RZw6/ZO5awZhiirJ4EBAAKhRkN6o4mcr9s2KaTJItW8QwOy9YaB1SaX1h1r1kSmKX+EOfKHB3DDAZk9kWu6s6k4fOb86XXo0qlAcr3hj/Qlw8GQf1ssK3msc9bKRANzaEAPyV7Plzw3/f/ttjJDSRJpt0GWxRqZS7Douh0GMdeuVE6lZZDgKjIwnBKohvHEWBp1JQVA1mi7NUdZc+xMEsTFCuXJHWOmVM7oHNubqHEQNmzo7byvDHPE2VbZTKQp3LNXxel2BYwGDKyiBJi9DsHOfEEAKHLpxrPXeM6dNq9hPCUwiZFBmLDV8c4Zf84nBGcCktHvwQ0GBFobMTsR8BiLgrVXyi5Law4mNsZ0j+y0zbUUIRCUikTY3ItjZB1wmtp5uWaz6stZD+akTlbZU6w3XGPdMzLXrtfl39KU9VedNmuH+z1A1wywvRK3W/4N0n90w4DXZSkZw6++zbY42ruzxg4iS4H1hi0iturgSaBhpkvK9h3wPtXrdlnGpIZKLkNk774sJQPe65pcWOcfnvvLNRrwVy7zb40GXLtNYCnSzzCfI8zneO38J1jPK/LtuFqzv62C9DyX+4asjBv0EWbStmbQl1rJzOqxXbu9UyPm2y1KILudyqU5SSiH1lZHWYbrrY/Lvx9RQvgIwx8esIZZ1BhuNDCXZdduw/e6bDFSSE3vYmXgM4a4Y16EGKnMuH3O/08SPvc0wXc25l6PAe740IyjIOviL18iOD06rHrD6hBmH97D3RnThGYt99agb8ZN/v3PsueltCHR+yis1nizfOWerJYxgvV/57zXfadDwLrdcj4ORnze9TrAaMBk0Ghgz/i4WtMB1jtLBtz1mSIJj/MFwZMA3jrjZ7LZPDdwG+6cCXAUdrzX5T3qnPUlxqCmZBDVQNxUjuKvz38acbvFC+lH+fx8wLjYTkQTIs8nHyEYFED4QvpRsrBSZvBm+YqUCrzzZ1yUz1oLknu97xGluvuxH/uxH+/W+HoZ9Xy9xgc/+EH883/+z+3/f/mXfxnvf//77f+/8pWv4Nq1a499/KfjaS3sgmXAy9KkrC5JKsAGsG2IZpidJ4OyEefUTqeScUldGLQ3I1BJDY8ERBwfEjweH1S1kM4hDrps56CtINabqpF9UTAoVxDcSGjsst4gjnrAfIlwcptuiBqYdzqV5Ha94bHTFPGAwDWcndtUqNxM+2e6g5EEfan1eEOWVhJbkUDSvbQAOh02kncO8c6p9ab0hwfsdShAw3UYmAOwtgHOe1r8ZylZiMmUzIRkn00Oqe6gW0o+/WhAY5PVimxUvwfnvR0fCiIhDE+3Y/JF/XzNlMfzMXB6TqfbJEG8eRv+/c9ybdW5sdflPCUJAWRR8G/bLWv9rl0xya62TDCJp0ia3TNXpE+k/G21IpuioF7AIELgeYok1kv/z7hc8VxEzgv5bITAdVGA5L0xeibvbTURJjO6US5XbO2SpmK+5BjoigwXwoBZ64N2qwL77ZbUppXcV6sVZa03bvE+Wa44R/MFW7jM5lzb2ZySOnFKhncoZQ96bZ8jLHepMkaVc2pAG9iH0g0HlJpO51WNcq/L+0XWPJyNuS+6HesbGvO8AhFNJlRcM6uSK8W26p/pHRkX3UMi/9bWQq6RwjVSXG99XPZVyntn0GfNa7tF8Ci1fU4dZbV2UGqlybpmVX/GsoS/dAznHSWRZQCcR5jPK7ZWpfcyYlGYDPIuCfa7OOKChmbxeMTPzwtxu85r8xfMsAm9DutJO20CIpFZYivmVAL40W7xR8HhpUP+KAs8X8AN+iYXDye3K3nnfFEl2DodPqec4/NS56jTrlQZcn6u3QbunHN/P3uV16by5odxIRXQcr31cWu3ZMY7zlHxMl8gXqX7tyUvNzninTN+19SAmn7mC+lHd/578VwU3Jk8ttjav9XUxyUJ/DNXqZhZrphgms0Rx9MqmRoj4o0T7r80tbmyRIv3NM5Rua3ej484YlnexRC+UXyBvyuDyYhfn//0A4/1ZvnK3WCxLBG0Nrs27vW7/diP/y+Mfeub/+8PPmIfp6byN/rM7z2+//u/H2Wt9OIbv/Eb0aiVR7z66quPbdIDPKT76+/7fb/vkQ/8mc98BpcvX37k9z3W6HaARW7N1/G+q3DnMwY2ITLLC0gzeV8xgN5Z30ls2KPR9XsMTjW4WizZrqQsLYvtVnll297t8Bgis0SMbBPRafP1AGWgiyXch94Hd+MOEAPCdA7/7FWpoWwzgD+bMng+PCBAFplV1FpEgAFEXhAUfPUWohq0bKVmT183mfK6FlXTaGW1XKctDqWen70tq5YIqxWbv2vg3m4RaKiMdVsa02luq1oHpp8XImIhLpLbsrLkh5hV9HsmsWTd4EiATrLbfkWOGcZTBv9A1f9O60MLyhl9v891LqVecDZnUJUXiDdvc00biQCKhExklhIkL5bV3/KcBk2HoyqZANaD+m6HwBeAHwsbA5rbKCNrtUsqP/aUU8N7Cypdv8f3CABz/R7CyW2Ru0ldYrslbQGG8DonzSYbmm9yeKmTBUCWqNMWdiYa+6eSUdvjW8owMRwA2ymBrvTdQwhAt4N4Nua62YXHSvYn52Bgwjnu42tX4KWVQlzlnCdpzN44PLC+ma4ldcdbaecRYtXyoNGwPeEvHTHpo+fQgjWcj/NF1VJBGCHXzBhkA3AZ58QPevb3mBecN3E2DWMmMgjUa3WLWifpPd/jKaePp+dsobPJ4ft93sve815brcluzhc875IA3XXZVgbrDaWOyiInHg6psVT++JCf2aYMvlwsec+VFRv0JIa14FnIM1HYcZo+eQEknnPR78Jt1dQqwB+M8OqNv4CXnv1P2E5pLckTlXJvNpWTsJYLaC/K8ZRrJIkiPxwAYgwFbdOjSTdPGX5MG3zObnIm23Ro/XAIvPfzwkodlCH2/f4OE1gfd/1eFSgqt2+3jbl2jQaw2PBZfO0ysHAwV+XtlmoITVo0m3A1+fIbxRdszl2Dyb4wnwN5jhfSj1od5QvpR/FC+lH7t89kThZLSu21rdR8QZnuspLmO0kiotVkskqltWoQJWviGulOL82HGQp0da70eh44HsQw1vZ3KLbwjQZ8lt3F3Drv8EL60Z3Pvd+a7sd+vJfGm+UreN7//t/o09iPr2F8vdxfv17j+77v+97x7z/yIz/yNR3/oZjKv/7X/zqyLMNwOHyon7/5N/8m5vP5gw/8bo0ZZX7mQnhnDACI2oPQeUqPpL4tnJ0ba+L7fb5fTFvQ6yB89QYDDO/hDkcMDqUhN4qCcq4QGAgFSi6RFybvQ5BWBlpPdvsU/viQgDJLxcBCJLpNsoVhseQxmpSFYbki2NHaHenDZ4GfuM5qbaA2Bo+3T/lTbHcBH1DJfJcr6UuYGssbV2uTT4bpnNKy557h+7YS/LVbZDzVERSsOwq3T1kjuhIWuJGY8YkBd82qQ1oxpCmc90gODzi/UnMK5wh6xGgGYi7jUvlccU9Vd1Ikvmq0vd5UoCWjLI19QCXTLYGz1Vp5b+0R1BmxzjgAlH3GxZIGLTmNjXy3Q2nofCHgsA902+J2Kg8SaaXCPdbjZ3jP+VuuuNfWGzLliwWvacC2GFo/69othLdvVlLJoiDQHBD0Rb1eMf2B96wLHvYr1hIwmWWcL1hHePOEr21mfG0z42ukV2Jcb8w0R+WHcbNhsL/Z2Lqyj+mAMl3pVeq81G+qRFckz04+32q/8kIkqmSynTAqzrPuE571ufDOGBY10GFdndSJ9XtM9Gj9smcCB03WAULdcUMUZtUbE21stNQ3+kHPfoey5L/VqEtGFCBU70kJkf8qmIqbDe9VfX0tiKfLag0sKtMkUubG8SHnIIaqTvRJDDV82W6ljjHhfi0KrvF8wXU4GAI3ThB+/S3p+0mTGvZ3dIhZA3HQqeZI9kkc9Zjs0x+A63T5eCcZA+cqhtTTNRnLFVlLLS2YSd3wemMSaTSkT6RziMcjPoPOxnIvLBFD4M9yaaCo7igK1CSeuh7CJMcQeL9IwsF15N5Wp1+tSZc11mdVMhohGY0oNa0BxYujnM4QQ6xaBYnj6xvFF+CalbGQ09Y/IVTti1Zr+95xRwfmmBw3OddRHX2bmZhXcV/5nqg/8hzbO2cPbMdRP+/nk48Y2LvvkDl8s3ylkrI+QlKE0mHW2+7UdAI0UHqH+uI9G7Qf+7EfT2rEr+HnN+N46JTln/tzf+6hmce//Jf/8mOf0GONEKQHY02mKsAiLJZVH0V1Hs0YGAXpK2dOi0kCzJfwz10j0Oz3EE/P+foQzSAn3jmFa1fsZdxKsDFfEDSs1nDdtrErvt1GPBuznlNkTFbbGSIw6MM3GnxfmtJMSIxyECPCasVrUNAijdXdtjQgy1YHrmIqIayOsEAqhzRmNUkIiKRuy3XazHD3e1VT+LnIb7MGewQOB5S4OUd5WMK2KLYMszlrtWQdtJ+nG/SNSY1lKX35ZtbA20ltkBeTFx8j5/l8bMeJOQEV2i24rbPWLk5ccWMMcD6tnD6LLeLJbfjDA7J62xIYTw20KwDBag1/dIA47MNNZgRuxZbB20BaRQB8Tz2w2ZYIsxmSZ69xnpOEta7zBQHNclm9tkWH0VgUllCImxxu0CP70GhwLbSWN0brtep73croR48nPducAFuVFcfVmozcbE558nxB1jsKI6h1kas1XAhVn8CzMQHVai3nsLGm69huEQNb1sTzcdVfcbWGe/Yq5aEquQWkhnJt/VN3xqas6uOkTQikByFaTWAcqt/JNcbVGu7yccVQZVxjl4nT7njCddJ2E0HaYuS5NZd3WUq5+pKqgHg+ZjKi3YJLG5TeAkBsiiSW9bj+mMyjznPMc2uH4gZ9XqeyMSESdC8WvJdE9RCl3YNLEgRh/mnSJAmexVJki+LoLOZUaDR2GP53fWy3BvTtWaTy01DCJZxDnHHN9R40d1eRuLrb51zTRoOO1gDvnRCpWqgntWZzAtduB/HkDj+33WKPXHGrRp5X/XfTlMB2teZ9OxpUclvvyO7O5sA2MNHTpNLAtdu2T/U5UWe17sUeXG9/AiHPkXQ7BJUAQZH0HeZFe2FgS2HstztS/npNs6oS6uyausL6tEGJ83J5X9bvjeILuN7+BIF44hGn8qz2bHnjjg4qFhgwxpTJUnmedqkMiJsNGXA5v2TQf6A7ax3EOe8Qt7yX7sUQqjmPtc0pSwCP3kMyyrnWP+PN8hVbm/q4uJ77sR/7sR9PYjxtTGWe58hqCet/8S/+BX7sx34M/+yf/TNcu3YN3//934/f9tt+22Mf/6GYyp/7uZ/D4eHhQx/01VdfxbPPPvvYJ/XIo9OyPoC0YY8iSdrS8fXGLcQbt/g76RUI5yg9EzYMAuqstYiwg27QY8CbS6Cz2dh/42zO4LTbqd6TNhgsaSsRqfV0o6G8Rli88cSc/CgnlFqyTb4DKAG6NMY1ZXb6eycOs/FszPdq1jrUa40qZlJZO0SC43B6ZnWIGjgp26Lvs/5/qzUBpTADrt0ikMsLxNMz1vQdjsjmKetbMxmJ05ldi2ux5sop67lcIUxmCJOpMYGozY+CJW0/oeybPzqk/DZEYzhMrtptW10czVVK6fsp0ufFgnOe5zR6OT0H3r5lDrZu0DPwEs7G0jqmYXsn5gXidMpkwXReye6kPU0cT4TVzXhO0nPRtVpVwLrdGoi3Od1u4YZ9OvLq3Nd+V47HVbANEETFSGZskzOxAXBeNyJDlVrHmBdk0fIc/uiAtWnS9N4P+5QUi5GPSxLrf6dgH+1W1ark6IDzM51XiZbpjMG3c2RgtluEyYzXr1LL4cDqR/XauJh+R9Zo69ho8FrGE6vRpCJA2PzA+yAulojLJe/hsqxaAEGSDjECoZT9ei4ML+/NV2/9RWNZY1HY+nhtai/r6VJxj1ZFgrb/UCY+8dxXaUo3U2VmGw0L0JNuh2yMGt1IGw0/7BNAliU/Y1uw9cT2nV06v9YRN3R/RlnaHOs1oMu1NgZT2GA1KwJQtUeSdj4GttIGj1eXUUurDwCsqxz2EfsdYLaojNXkOezEMRYxAnovek+FyGrNPaAuxtstYtawuksF5upY6rLsnmyhjhfSj1LJEgN8llUKD+dMkeGSBG7Q476cTM0YzPW6O3LZMJkiTKbvyJw5z+M6UdDUX/tC+tEdY6cg3zkxL/j5+t1VlnSAPj6o3F/LknOmig5lVxsNJoWkxtSL4uZB/TsNqDnK6Os9NS+65r5ZvsLacJX/NtLH6yFZayuia2bGQPuxH7/BY8+I78fTMNrtNk5OTgAAX/ziF/FN3/RN+IVf+AU8++yz+Ef/6B/h3/63/238vb/39x77+A8FKr/jO75jp5DzQePf+Xf+HTTrGer7jL/4F/8ivumbvgmDwQCDwQDf/u3fjldfffWhP8dGURqoC2djZnO11mk2h7tyCe7KJQKrXpfBaVEQkIUAdOku6gY964MWFPRJ43g1P9GazLBYMrjW3zvHwGq1NpdSSvCa7G+4lPYFIou1L119D8CATRglA5B63ECJqtrDIwQGGJ7Or/HkTiVhlfdqI/FYFAyEtlsDnU4dVcU51losiExTWQkN8Am4aXwS9RyHwhDNF5SdKfADmUIMBKyIuYqZDSmTKSDYjwZkheR94SY3vLEXTmryhDEOiyUZIGVatRG5GDXF6dz63sUVe0qqk2N57chkqera6K9e4lpvcqt1Y5/LNgGYMoUiuVUzFtcR8CqMgDJoKr1FUVA+LfJapA0Bxh7u6mW5HumRKZ9rc6OGJ4A5APvhoNoPXvr0jQZcF3WnFMdVJ/1Uvfa0lGSEFxBr7Qmk5YAfDhjEi+FMXBFQQRyD4aTuTtnBVsvAVDwbEzQ3Ggx+J1P2EL1yzLXfbCzJogyx1gUHAV7Wm9Qcc7fGhtOBVECGXIeyR7HY0rhpMOB8qLlLSklqnC8I7s8ntjZa++iaGV48/N7KvAqw+rjy7Fz6tQrT7n3FAoVIULNaE+DqOkFY4lazSqqoBNl5lIsae60Jp4IGOX7Y53PlVGqXgXv27Xu3hpptWU2psm5dcQMWcySkkhzTZIj2Q0wSJmqc1GUfjhAPB3zOzReS/GiYkVO99hTNJuATlilkGaWtWSryzULqvCmbxmxRJd8A/n7D/QmAc/2VG8KytlgSoMkn7+iiGuJOQFiXwSpTGPT5IrJjSmgjgpiVAajUAMMB98lyySTCdI4YaPSmklY97sVANIYI3+8TpF2QekaRiddZujChIU959YD3eOJZmtAlII+n50zINBr892Jpa+paLT7rW00gkf2niZcHjJ2WIgqCUTGV9ZpH9vgMFVO5LfD6+mcf+Bn14cVhN4hcuc7gPsz57sd+POmxZ8R/k46nTP8aaw5C/9V/9V/hu77ru/Arv/Ir+PEf/3H8vb/39/Dxj38c//V//V8/9vEfrWK/Nk5OTnBycoJwwVjlm77pmx76GM899xx+9Ed/FL/1t/5WAMBP//RP47u/+7vxD/7BP8C/8W/8Gw9/MgI2UJaUnapr4nAAl0p9DkDp6XRTtdY4PiKQWy7NOh4Are4FdLE5fVGxQ85Jj74eP0f7VGpD9HYLcbmEK4odUOA6HZPkuaYYsjhhLaSPoI4oQEwlgtrrzw36BvKCGJP4YZ/nIIAtno15mu0WQfbpOeWdKtVrU/7nZGNFQExMCtrUa92btDfQ64re8f3NJrP1ADAlqLW+hiECXVhNTzyvmb5kKeKC5kbwjuzrcoWwXCIZ9Ksa1Cw12WGYzixQ8c9dA8ZTBnLOMxiF1Gc2mwymtlvERo0RzTKu9XrDayhLJHemZDXFzMekycLCoSgISldr+DQ1gI7FqmpvAPD/tbWGmOw4kdbGxRLufEKGW10vlflutXiM5YpzqS6aRUGp9uGBsK9sjeHS1Nq1+Gev0W1Sg/Tjo6o1TJ7zuK1mxYpLwiCOJ3CjYWUGpOAmSyl33laN2N1qJeC3BpRWa0qUtbdkXsAN6Nrq1Gl2uyUTO1/wM1pNYwNdo8H9p86ZiZeAv6gMRcpQsTRZKvuvxcRJ2rD1dsLyuTSlVFNkceHGLdvnqBuYdDsG2NW91BxNxXnX6iO9R1lskfS6NKjZ5NJ2pOSzI895T4AJCHd8CJxP7PdoZgzoQwT6bamDXlkwcr37SWMkd+S/6w3rfqMkbfKcrFn+5JhKdf/FeoM4nXGeAM5n2gCQVcZOOtealJLWMq7XrZxP50vKs1sttre4eVtYWXkWt1t8Nn3gGWBdVLLn7ZbHBvhtFNPKqRtUHzhhhmme5itX79GArKfsq3g+5jNclCjA3aY5Coqe97//rtq9sFxa31EA8K2mrK2ck9aRT6a8x2Qu/PEhEykCPutmM1pX+UbxBQJWUQL4LANixPXWx3dApJ4jQFZT3ZSTf/G2yOjbXC+VL2mytyyrMoOtozz/fCy9cTd89nU7kpCMD2T/dvpEJsmO86uyiOr8SjMd3jsAwfH19ife8fgXR5QElz7vdc50/u7l4ntXbex+PNLYM2/7sR8PMR5T/or3qPy1Pr74xS/iC1/4AuM7GX/4D/9hXL9+/bGP+cgtRf7v//v/xjd+4zfi2rVr+KZv+iZ8y7d8C771W7/V/vso4/f8nt+D7/qu78KHP/xhfPjDH8aP/MiPoNfr4e/+3b/7aCe1XDEwF6bDMuwCNNQREImvHAoVrUtQ69ptM40wkCY97dBgKxFkZDidSltXa2M5nNSFxeWyqmvUHpNgIIIyVCzWao1w68QYNHutBCphPBUXQ6kRajYZyGYpWZ12i7WCes5AxQ6kDWMGXbvF9zlPc4vTM/g+Lf1dv8fgfkNAEk/u8N9aG9RIGMREZuFjnlsQr8M5z76bnQ4/S5kPDfzFvdW12/DDPvylI3MuVIOWcPuOfB7XL87mCNNZxao1GgSU0qIBgDER6kQbxUWUfR3JwsUbt6qgttepZJ1FAee8tFJhL0pLRCSJGdDUXU7RkLY1ajySkLmKp2dcV21noYFR4k3WDMAYQbI3AoqjBHcC1PzVy2KQIp8rP77f41yIjBYhWl9R1voVBsLjRKTGyjDNl8CzlynX7fcqabY+RLYlcOkQbjRkPa/sb2WC48kdBqta85tx7yEGqQeUljvCdLl2m/ukKCiBVAOtZrPqtVoGsoBJwj1SFFU7IL0XRW5MZUGTLSuWq0rC3JK61bIkCFb5sJN7XB1UAd6TvS6Bk8qw2y1K2ptNY2VjsaVEVV1g660xpKWG1mKiJa6cia96cKbCuEq9MsCkz/X2Jxhka51ajXmJyxXCfMHAX1jX19c/yxqyJyj7i3lhzyZ7XqUCnvQ5FGnwZC2RtBWOgt3ZnPOgSgu9R7Z0ko7zRcXI5gXwwWeBt0+kDnJrrTrMhGZOBYImWeKwWxmardZkxTc5P1PVGQpAs5TrVqjjNg1sgjCOF4EbsMvG+bQB10irtZE94IcDUYCk5pCtbXCwybnHex0mC6R/4+urn9mZ6zeKL1ibEJck2J6dU9oc4n1lqM47nk+x5c+wT7XJYmnzH6Xlj0sS3vvTGe8NSbwo0+2yrEpsleUju78q63gvEKLgWWs29f8fuQ2IsP+hBkxtLpLkrtrTi4zpfjz62M/dfuzHg8fT1qfSOWcgMkkSDAaDnb8PBgNMJpPHPv4jg8pPfepT+PCHP4xf+qVfwq/92q/hS1/60s5/H3eUZYkvfOELWCwW+PZv//Z7vmaz2WA6ne78AGAALaARAIMaYVLQalqje2vX0GA7iHg+JjMn7InVe40GzJ43M2aFtTXFclntlF6XwY3Y3pubZi6yWmHr0JSMv/S4VBYwxgA/Gpq8D5IVj5sNg9MumQF1CUWWIppxRFVLZ5KxC4GCUwnZdssm9Yk3Z9OwlOBNapPUyZNgIVb1bd4zUBFTCZdlUi8prFG3Y83fo9T3We2huIiSBWnT6XQyY3BYbAlimk340VBq1Wq1WiJBRhlMQqfH1+M5cRjV2kzWB9I90CXSCqbbYW2R9oCUBuzxSFq2SP2de/YqXVPV5Gez4V7pdKo9I/OhUl/06dTqel0yFcuVtZxBq2nNwFGGKnHhPPdps0lTE5GhRq1j3ZLFMrdM6fdpMmgzEIlkXidT/ruZIV45gh/0a4Yd3PthuaTM8JnLBIYCenQN42pFsx1AZKhNY1xsHwFke9stAj1t4SGBvLt2hQzPfME9K4DOHY54fOnvqBLhqHWu0rJHa+E0QcOWCe0KJHhf9aHsdZlwkaSJzbsmBhJf9VcU2Z/rdau2GWo2I2sUZjPraeh7XSZ5cqlrbjbJaK83gPNUBWhvUwGCAPj6EDgnUQx7tN/hhdpIlzbgel2Ud05R3jmVe0Bay2QE9c8nH4FXBvcJDZrkcG3jcsU9t9nw/lhvTP4fl6ud2lcFMa7RQLx0wLrGPOe8t5qVwzIgpmJF1Yrp5Mx6r6oDszGkgLl305G5gLt5ans+CmNqPUAL6ZnZzPhcAfgM13OVb3WVEl+sAwR26wYNyENYsxqg96Mh5bDSRiquVoibDZ+rIr/3Ws8M3FUnqZ8VpTerzzJKazdra5dRf6+e1+urnxEFTYuGcb1uVRqhtazyzPOHB6JWKXmO6w2fexthwZ3It0Wx8aBh1+D8TtsTgCBZQZ7+rl4rer31cSSyrg/LhoU8h2u3kGhZxoVxsS52D4gePPZM5H7sx9c+Hq9H5WOym1+HEWPEhz/8YRweHuLtt9/GP/7H/3jn7//sn/0zXL169bGP/8jy1y996Uv4q3/1r5pk9Wsd//gf/2N8+7d/O9brNXq9Hv7aX/tr+Nf/9X/9nq/9k3/yT+KHf/iH7/5DDTAhyyhPkvqzqNJXYAcgWruJlThVhsgawcXKnDgtWy/Bo4ECBXFquKHBvoA/12xWQADg77TeZLmq6lwABlJezHf6PasVC5Np1W4jBDj5ncsyZqCbWcUAxFj1oFQ5VAgWICvbGtdrfu5KatO8q1jNLKPj5XCAuJa6Im35AFTAtyiMjYxY3eW06I+P7Lxi2JqcLHq/kyGPp2eV1Kye0hFw4A9GBBzagqXb4f8nCaXECnw0cE0SoJkw8CtLzlEIwHRm/QTjdAZ39TLCr/26ST/JPlPibEzcaEjTneVS5J0yjwdDIJdz1XrD6YznJ4Cb1y6yySzlMTodk29C25ZoIqQoCGrHUwLYtEFXx2GfgWSnbQ6adB1usZZXei9qg3h3Nq3A6GxOYNbt0Myo22bvVO/h7pwRqCYJJdrS+sPaieQF2z4IyxdunfAY3llCg+6lTQOGyNkOwnoG1pzFlPXzByOyYIcjZsm09Uu7VXO+lfus1UQc9eDOCAJjUXAO1dCq1SSDn6VmluK6HZHYZvzvYFCBa+kh6AYDfob+BIIOqwNei6Nxq0lTn/Mx5+JszNdLvaof9K1mFildp+N6Q6C1XBlgD/NFrcUM21SE1QpJs4nk8MBe45KEgMGxp2nS65pk8YmNifTx3ZZct9WaeyJNAcdetHElzwu91vWGrXOEHfdnU4KV4aByV/aexjHzJddAr7/Wl9Z1RRpclnD1WslBv3J8VtMygD0aZ+zrGNW9W83MvIe/dMT7rNWCc45zqveMAMWLUlegAnAvpB+1thkubTAhIP0pkRcIixWdqbdbK4sAQHl9KkZSebGzXgp67tUK4/XVz/Dzmi2EPN8BSHXp7AvpR+GyjBJ/vU+azerfRweVlHuTE3DL/6vs1an5UAx8DpclmdMH9M67CNouSnMvvsZ3WDv9+vpnCULv4y57P6Djs8zaW1089r3Y3H2fygeP/fzsx37sx8Xx2c9+duf/f8tv+S07//93/+7fxX/wH/wHj338RwaVv+t3/S78w3/4D981UPmv/Cv/Cr74xS9iPB7jr/yVv4KXX34Zv/ALv3BPYPlDP/RD+MEf/EH7/+l0ive9733AagM0pG5KMusuBKCZ7db1iFmKso6u02HwoQ6sgLzHVUHKkbRkaLcYCJ1PWJd18zZZEFfrPajBqRhVaB8zp39XCa4yljFWLFgjYWDQaiFOZ0iOj6R/nJjP5DmSEY0MYgyId07ZY1Nr0wBm6iXD7tpdxNunJt8KEqwhScxxFJucAVS7VdVLhmgOo64ogKY3cx+2gmiS0RAm0Q37BvD85UvVPHrH4Hy1lrYmCRkQCbRdo8FzVSv6ZpOOtCrtUqYRkNqr0gAmZXDCEhVbtsPIC55j4glgCrq0WqJAjUkWS/hveB/n/s45JWyRjKJribHSas36JQV/Yc12IetNVfuaNgg2shRoCCDxjixVWbL2L2W/UWsGL/OPQQ+4fUZZcatFMxKRRLtetzLb6feYPJDEiBsNeSx1wNQaz1bTWi/EzQbu8jHcfMnfz+ZA2bTzstY2NQdUMu1k/dylI2AytWSCH/QNHKMsaXKjPStz9jSE9sgUoKjtNFBIC5zAGlY3GiLePLF5jsslkAcB9ymcrH0YT8nUhWCgIE6mkhAR05Ruh5+pTCZgEnQXI+J0arVgrtnk9eZ5ZeokCSUA1psyzOdMKgQa07iDITCeiJuusG9lkLpDSqhjUVTPCDV72Wz43rMxDYdChG9nVXJJjMQAsGfmeELpnxp4OTqDuuwxHDQfchjjLOZSmC8BFJRyno05D5rkWsv1aKudlkiLt1s+t4Tx17rteOME7uol3h86hH1UQ54oPSe5cM7a++g6xsnMWhLF8YQmNUUBpwAjL+hQu8n5rHLeDH18v2INQ621z/2AkrZ8iduiMhZybKOhibAYpV+w9zz3bgcuRNZ9a62lAEit/3sh/aiB2TfLVwjsgsf19ickkRh2wFpdNso6RWfrAABoBcrN52uCOFWbyByG8QSu0YCXPrau2+H6ArX2NGXlePtQGyXgjeIv7chN71UvGWug2jVoeFSvw6zP+b0AbdwWTFbsTXnes2MP5PfjN+WI7vHqI9+jTOXLL7/8jn//43/8j39Nx39kUPkTP/ETePnll/H//D//D77xG78Rac1gBgB+7+/9vY90vCzLDKB+27d9G/6v/+v/wp/9s38W/8v/8r/c9dpms3lvV1nnyBqdjcmipL2KfWm3rF9kPB7BjaUNSHRirFEAq1x6oG3NaMM5qd1arsgo+aIKSEFZYBhPGIRLLZQ2mtbWHiYdVHOeUkBEI6ncFs2UAlXdp7qTNhO4RsLr6HaB2ZwBTIiVi1+JKvhSkxSARjIAXWqTxOoN0WKAHbWhvXPw7RYlU2VJls03qyb3AA2C5gsGWlor1m4jrFdVYAhYDRWDLzHKGA4Y0LcZAPorxxV7pI26tUdms2mtQ+KaSQEAfH+vA7daE8wIqwKgaj6vLJrU1Frz+cmMAH/Fc42LZdXvUWWp2meulM+UGsG4WgMr0BXYOb635oIcyxIOnHt3uS9OroFrmxCku6aAEa0xGvTZiiPPyVCenlcMZ4MsCYHtkozPal0xuTHy/cpOSAudcPM2HWzTlHtuSVOheOs26yGXK94HyxX/33nWrHbYiiBK6xzX7Yijp7O5wXxpskdr3xIiJd7q9iuyO2Qp96yvzVWLCQWn7qmyz825tyWsy8HQ1tsnnues9ZtqKiPmVHbfNDMyltImIeYFk0laQ9ZpE6hqexlpxROXKzjPWuByPK5qzxqpMVzKblsSoaicn5EkTP7oeS9X/H1JGa/tEWlH4QG2Klms2L5Faw0BxJsnZMEWUmPcbFYOz9snV1OpRkGuLffe8WHFul+5VLU/ipIAmHBfaLIsSt25G1Dqy7lt8/lVbJkoqRX/o5lxbYTNtT7BUoPr2i2g02HfVi8AsdE2F2+Mp1bKoJJ37ecJUWTEpcx9klSqCOdR75lYD4zt387TedVXLtNRzH3CaiWJxyjqjZxgXJ4vvtOxxJ7XVkyrdWXOU/tcng7n5I3iCzuSzot9F5WltBZXmjxsNMjaewcUcWeOTd0hexEuVm13SklMqaKi1rrknTeKNxZX5+tizSgAIPF4Y/WFHfOcB7GhmvQBUPU/vYf81beaeH3xuXc81h7wPPmxn9/9+M04Hrc+8r1aU/mkxyODyl/6pV/CL/7iL96z9YdzDuXXmGmMMWLzsF94OjKRoKkLo/cWtMbpnEEcgESZDTVJmc4MnLmSLAs2OQNYlWXqa7V/W0ZXU9fMyIj5BCg3Jp00K/x+rwISYgYT85yNrJcrBj2e9TAY9BHeulFJBBU4b9QRNaMpiXdmNOT6PQbs8yUB52JRGdsAzNgfjGQesqrWSIFikrCp/NmYYKQMcInUOnXarA/z0oNTHUb//+y9eaylyV0e/FTVu571Lr3MjDfmC7YDYnHAkOCwiJiZgVhxLBJrnEE2sYGEJMIxEFAAgWMnAUVExBAUFmE+zBc7tgwRQcjxzCAMBkVAQpywJMQQbMb2bN13O9u7VtX3x/Ores/t7pnp7pk2Y3NLuuru2+ecdz3n1FPPlufwBw2j3w+OCGR6GwNIlPhHvbVcTd+RmgqtKHedbbGGEjiEJKFULoTb+CSype7SAfSMk3DtWSUS2KyYjFsxKMZ3Hf1TmkmtAeQHBlPPJXjnuReBw8WQKgnw/E7GwMkS/vIhnzsewR+fQO/MeY2FaQ7nUQX/bqjnqJuhly/LKCu+dBDTR/1qTabx6IQT7L0dXrtMkoA9PXlqPOK1NkzU9G1LYA4w3OnwiAsEIZSpqpl6erKEuniOiwlyHdV8tuUz7odeQcjkbW+HxfPjFH46YpH9fEZ2LnRFzglKfVhQUYqS7apmNHVkYHVcgHHrjZw3HVlfTMbA8UmsSlHbnxNGFk72dvjv1YYLLwIs/XpD4C8pyUoWRZSAnHAdFMBrUDc8jsUydiwGNiz6RcXbqSeTqGQI90N4n2JUcl8mo6HzNSwQBe+oY4qpl4ohFbyeRyciPyeDo4scejKGPTikrD3UPkzGkYV1qxWc+O4eaN+Fe8ave8KPvKc7AkBhWFHGcxW+BUPqq6TSqumEbOxqzUTR0M/pPfxiFc8HhBH2rYRVJUNfKCyVDvG9OhkT6Dvx3h4v4A6PY5euynMJ+0oE1C6gEgNX1dBArJ2JC2UAP/8CWA+fE2lyyh95JXg7JccMAU2B4TaG1R1Sz6R2d4T5N+J/pV897MeVvaJ3Z/fhgfZdp1JTvRvUKVdWxpwCRiKFDp/dvqPNIZy78H1zitnruriw5SpKdn3bUg4sLLnbbLhwcb3f094R+IdzjSdIXfUed2f3xd+Hvz/pS28HVrUd9N548MduP+5GQ3/Oxtk4G5/08Wm7sHOz9SB/TkHlDQf1vPGNb8RrX/taPPLII3DOnfq5UUD53d/93fj1X/91fPSjH8Xv/d7v4Xu+53vwq7/6q/i6r/u6G3oddnN1nESXBSftUpitJmNo+QEAf353qPDQrADZlln6VqLjw4puy7RBHwJUQsR8SNBsW66wh8cEr6KzQzH2aARMJxEcRL9OSORbLJkEKH1j6CjP9SK3ZCiIyADrGv7CfgzEcKs1sF5HVjGmHi5WZFJHJSWBkljqJagoSj5nTBWMIRd5Nvh0nOM+pSknaV3HQIi+h96ZEZAKWPZty30QRkif24vniDLUGX02uci5tJZE0Wo4v4mcF62G0JhNxVqNAFQsJZVutWZqpiSEaump9G03pI+23QAUgtTx/z40nJfRaEgGXq5iEBKsBU4WPIYsGwrfEzP0UGbZ6S5CrbjwEFJPvQeecyEuLKjJmJO+EO4UmMk0BYqMcsf93Vjh4Pue92boiQSAyYhhPCGECQBEoqiCHDcEplT0BseFEeti3yl7ATNKuEdMPVbrZmBKs4wLFeMxwfHulDLiIG89WfBcTMb0uS1XA6vX95zcpwQsEdS2XKzxIbhlMo4gPMit0XT8kQRRiDdZzaVKJwAUzYAXv5IOw76XeyuPdRL+8GiozCilS1Ik0Go6kRCmJPrMwgQ3TmC3qz4CwBa5upd6Cd/wPotM2eHx0McqTNm2j9geE5CdqgoJ8t6uI8gwGno8wt3ZfVuSxVswwn0R2DCAoF56SHm+JUVb7mkf/KReGLA8I3MprKVfrnhOxC7AYJtKwprk8yHI/8UX7lZryra7DmaXoV2hnsfJgqA/WcRFND0ZiyycwDbULcEYnlel4BYrylPHI9y//tmreiqvPhcSBiT3gavqgUULEmw5vtj5eHQSwa+S9GGVpFBJCp1leKB7N7y1p7a7HQB0d/oaWFns3AZpAYxFwBUXPBgmpaRbN0jWYy+xLJzE5GNZXldpCi1dsfG+8/5J5a/b+6yMgZPvlgfadz3hY13dRAB9d/oa3GhysTKG4Wbm9H6xA/Pa1++J/J1n42ycjU/++HR9D366BfXc6nHDoPLg4ADf+q3fiosXLz7tjT/22GN47Wtfixe/+MV4+ctfjt/6rd/C+9//ftx111039DpqPhvSWLuOq8nif0FD7w5TBRVU1XICXFXCQG6A6ThWQKiyiP/nV2tO7IJEdbHkpCuUxkvoij8+GSYBMrlxlw85aZ5OJBikHcChsJ9+UzFJsqH8S8/IbgYAqc7vDxPhc3txtVhdPiSokiRUH6omjIkTRVXk0NMJXyuAIqnL8NJ/SE9bOzy3Zbdm6OZTo1JqQphy6rsOvqq4mpwkEdyoUcnJZdeRsZHr4NcbAv7QQZam9GhJt6W7fEi2dLWWgAzxMJUFPaCSJsvUQ2GsrpBHqQD64r8pAXULqUCI8iqe88g+BFYmsMBbwzdyDcJ1L3IBvB0ns+IrRWBjwuQ8ePvKguXuhwsJyfDi5wv3jHzYBCa6bllNst7wfilyqPP78HszCb5JBz+pyP1i1Yp14hEka+F2pjFkR+UZGbODQ+7DZAzkaQxTUbs7wGLJBYwJ+zPdfHxatth0wCce5yRUkmjV7nwroTaLbDvKgn8Ccu3SoYuwbobqlXDumzYmuyLIY3uRhadpVAv44wXvr1AdpJQsIk1jYFWUXI/FD7wzH4D7I5eGrtnEDHUkkc1ikrAXOTFSqhcIgkxcAPKhviRNmI4qkmiMR6fTkyVRWo/p2Q6ySgCx/uXUPVwWXJQKQTBK4YH2XbfWXyYSSL/eiBS+hfvYw3DBB37hnHx2CBCRUDLfMTgJQEy9VqOS4FBCdNRkDHd4JD5bAlDf0cMJx/dgkF/qKf3Q4U8CHhXPi55Ohs+1TRXvnxjAFt7f8p72LX2R7ugY7uj4FHt2LWAS5K8P2vcwqEcrXgP5rKCqQu6xC+fiQlbwLfqm4WLX1vB998QgVomUXOkolz39ZLf1Vx+lwCEZONwnKs+iX1jl+fB+k88YJR24MRBO6a2/X/9kZ/sefKKgI4CgMMh5vfPXDCi6ru1doVJ60L4HJig1rvj9p+sk9mycjbPxLBv+Jn7+nI4bBpVf+7Vfiw984APPyMbf/va346Mf/SiapsHjjz+OX/7lX75hQAlgmIyGIBWRKwUmMg6l+Lj5bJi8JMLw9D2BTJDBBkbN+6GbUjoLVWAyAWFvpD6gbSM7CgD+6JhphhJiouZTqCSBOzziBGE84sp7zpX50JUXIvsh3iu/qWLNh69qIM+h93ZlIlvSr2UZpuMeexzusccp1VxLBYowfrCWLJ1MBt1iBbfeUF4lbIw/OuZ5ySjpCxPJKJtNklgRwiAhE/sUlSSJusUyeh4BSPJsEz1z/mQ5pG4eHg/scCpepjBhFJ9k8C+FuggAQ3djeP1WeiCdh19voPd2IjMcpGNkV4QpDFLoUP2hNcHFZBwTQCE9kv7wGO7wmBPsquaPk+TdtTAxwuK5y4cEgE1DlnUuheQhUCRJ4E+WPMZWWM3NBnju7YNnr2YYj7okE/MtgOtnInuWShx/fELJa9cxifejnxiqUfoeajYdgmo2lSR4joDFigBrJPUsj1yGP7cDfbwkmEsImEPiLLQa2M51NbyXJiOovV2G+9QNfCFpslIZAakBifU6AWRmKf9d1fDT0cCwhlqK5SrW8qhRyQWR/V2Cu/1dvqZSZFaThGC2bui9axqmm2YZMB3TgyaLI1hXUuMyyIrDoogL/bSOIFUV+cBMh8AfYcNC958aj4YeWJHGhsm7W2+GRNs04cQ++J8Duy/vPV83g59yK7H0Vg0VFpFAUB39seI7dY88xmsuMs+w30rJ+6TgZ4pfrQFjyOrL54pfbyg3Vyou6ClRUFDCycTs8NzQJazmU8pM5TNFZSmvkzHAZiM+U/GCS+pzUJm4o+Ot5GsfmT1g8DGeOp/bTJ13uDt9De6v/j8yjG3LYxYGMspQH7sUAVlkXUNKtvcD4x2A41UnXUeZ7APtu+C2qnvC2PZhKq1ivU0EtzJc8BdbG4F16LBUocO2qgdfOgYW/hRTjqvB4pXn6UH7nquSbcNPeK4ej6CyjF5QrZjm+lRj6xr4vovXbHt/7jL3wjfNGYD8czLOalDOxtm49aPrOnzlV34lPvzhDz/jr33DnsoXvehF+K7v+i78xm/8Bj73cz/3qqCeN77xjc/Yzl3v8OsNsDsGumGVUxUiAVpX0Ufmp2Oog6Oh0L4sKVP1IlXLsuFLeC0BJbs7g5wROF3GHbY1mwKJ4UTKaAnkmA2rrmFlWAJc9O4OJyEVUzJDih/9i1aSBrk67k4WnMSNSkrKDo446VeKFQlHx4O0VxhPAGRshEX0xydRFhsmQSrPYPZ2IquFghM4OB87JvV0QkDWkqELVR4qz7nPSQIsmyGdMDAVo1LqRfbouZLaC3f5kPuaCxOajwi88jweJ5TihCx0UMqqfAD6frGMICNe/9CRCfH7SWVGlJ4GMBCqCLqOgOlkOVR/hMk/ZHU+TKJkchZqC+IiRaiTCLUIzgG7cwLdtuXvd3fIeveW13mz4YRX6i9g9FAPcnDEfQtBOYmckyJnNQMAVBWUeGD9cjVUePSWMlgJ/fES6KSmE3oCywLIRdYr5zjIGuPiiPfwScKJuXWUjwJDUvB6M9SP7Mx4/0/G8EpB1U0MxFIrkakGViTU1RweQ53b5zYnYwlZMcC5Xai64wSz32LmcvFGlxmUdELGEJaqGRgd8c7i6IT71bAzctuTibIg0NQayDQBidFkdIyBO+EEXZcFVAglso5y6JBGHDyQgelPkqHWR6TKDE/po2QyhqIIa6ODzHurmsItV9xe8FynKVxV457x654ynOTpjCB/h7XwwADORDavR6NY3xFrT8oivk992/Lzoe9Pe8d7K92RC3r6jk/4f0kCNKFHVOTNbcdtz2ThZS3MtGUHZewPFtmoUtJDCx0/N0I4ly7ZR6umE3aPKnmfendKPhnlmTLC772jJzCAvlB3pPKcYEeOHdYNtTpKw8Px82tLrXJlENCp9FeL2E15JfAM+xWAJVODi+G7wVp+HisFVXL7IS9AT8b0nofvHemQ9at1rMqB88PCRnNtn+nVN4p7Un9keK5brbae4iOAfVKf1RYr+0D3bnz17PXwUrFy5fM+bf1aZ+PUOLvGnxrjz9P78WalrM9m+Wuapvj93/991rs9w+OGmcqf+qmfwmQywa/92q/hR3/0R/Fv/s2/iT9ve9vbnvEdvJ7h2xb+MouyfdPwzyCV02QtfdNArTdwyxVX2MUP5S4dcGK4qWIyJFJJKAxSWIisMkjsmoaesrAy3rTwRyfD6nuWDrLNyVg68VxkHQFIjUkIfSHj4ZuWk2xj4Bdks/TOPEpz/cERJ1/ifYTIoQhuhDGdTghgjk6kS1DYKkm3DUmLocgbWg01JFuJqip0UgZ2L2PlhRcPXWCvVFlw4rJak5ULrJ8xDMwxhpKxrosyspASS09SxtcJk7gwOTc61pH4rovAUQU5XHh+knCSK/saJMnRU1kUg/9Qa/oB9QBgXLi+apjsMi1X9qsooPd2eHxFETsO/aYiSFSK101r+Ecf5yKFSHgDoAy1GlFK2IUgk4S+1CD5lGvrJX1WTycE2qFXMTCsVR0ZUHe8kMCdJVkyYyj9TFNJkpVzXtdD2vFqzcWWEFZlmEasH708LJ4oqcsJoE08sAhpvt4DqzXUejPU7Wwq+Allqv7oGP7xy+LjtOx7DLLf4FsNCchVzQn3pODPnoBooylX350PdSpG8xwDBCWl9NuNS/69qukTBWLnK0SOivEovkdVUUQZZ+jdDAyOqyT0a2cOdftFmcQX0oerhs+Z0BObJNKDKl2MXQd94Vz0ezpZZAgMmKub6OVVZSFKA/FgWwstXs8rC9+f0SHnPqZCSxWMnozJpod9lqokV1Wn5MlBGholwsErK+wiU127wbOsFe+poOgohtAoNO3wHhdvNT9HCNLVqBzAfZDo5/lWzYeKCb/u8AhmZyd+5gc2bFsu6Z2PgOZB+x4oYyIbp7KUSbDGENzJAlMIPIuBTas1Q28KWfxJEpjJGEYWC0KdSNgGsPX5mmVxv7avcQCToU5Ehw5hkXvTfjCkQbvtLsitgDb2bKY8P4r9wL7vrpmsej1DaSaEP9kkUpdlPAYdFpRuYNxTfN01ez4BXJP1PWO0zsbZ+LMbf14AJYCbk75+CkhgX/e61+Htb3/7M/66N8xUfuQjH3nGd+LpDkoa0zhJ9GEiIsEaEfx1PfRoFJPxAuNHBiODOzqm367roXbn8JcOhN1L4xe5PzrhynotQBX8ctdlGR8T/ItxBX86kYmtZTBOXUv8Pr01kK5MVRbsTixyKEmB9DV74XxdM/Z/I2AghHj0EibhHbxzUIHtEbbPHRxSJqtHQJoyDKQsoHI5HmEhkKZDcbsimApdkX65ilKs6MtxnhP97RqTTUWAWklnZ5CftWRAAysSJK6RYV2t2S0Xwo9EfuhDIuilA05Wlyt45xgWFMJFJHRJ78y5SCCS3ditKBUYKqUcOIBgFYJ2ZCKPLIVKS8pZrYWCsN1NK/UFdggwAoZjlv5Fsp8MugksLYwhqwWyo2pvB+7RxxnsJIypmk7ImgogUbKI4B+/zG2UxcCaZin8wRErH6zjcV88x7RWpYDdOVC3vNdGo6EzNNyHXS9yZoYb+SOmsfrjk8i++EcfjxNEAGQonZdtH9LrOZtAbSqyj8YA/SamZeJwAS89pJCKGPQ2sk4Ir71c8X0hYU/IMtb9APHfaBpA9XyshPYQNA6pmeqxA56jUQl1uBgqYXrL91jfM8344IggP9SiiKzSN81wfryDq5s4KfaPX5aaH4aiRBZT6kRiKqjRMfBHpYHpPR7qKOSeV3kOrTVBZPDrWgeldfz+CVUWoWrjlg7vIxur8lzSWL2AqyyCEgBAkTMsa38PfrmCqxuYsiA4lDRn3/fDebnSN5rmAoikD7Ydjh+JjrJiAMNCV8X3lF8seZ/KYkbssGwtfC+snMj99XjExSgBItcbdnR3dp90R3rolJ9Dd2f3RXY59IsC4H0wnXLxYb0ZFrX6YVtXsqOxUsSY2EXKYCGNB+1WAI5UoDzQvRt3Z/fBTEc8rxLOo6cTLtJtNkNwFSjH1bs7lAEbw0U8qW/xjmytPVkM0tjr9TwqDdf1UNbGmpQrWV/vPIww3YHBuGf8Oriquu7JJ4E84vsljBBeFCTM2+NG2ZI/T+zK2TgbZ+OZGkp+buZ5z97Rti1+6qd+Cg8++CBe+tKXYjw+vej4Qz/0Qzf1ujfMVP7u7/7uE/7fL/zCL9zUTjwjwzp+YSsGfviaBfF+27cikxY9GZPJCaERwhLpvV1+0Rf5IMXzlIOyDFvkpItl7Hh0VcVC+DHlWhEMuUHag/VmSEUEKKmV3kQsV3xskCQaSry8d6woKEvxOxV8bOhwC8EmoWZBaUovA5sgTJ4ejSiDWq6GiWzwBZrg8WKlQ2AJ3XI1gPFwzGGbYXJv9ODz8yIV1JqAXOkIHLcBnCpyqL0d+iF3d/hca6HPn+PkvCz42mMBHmkSw1y2pbH+ZMFrsFgOE1lh05QEh6DtmCRpDINoQgiIhBhFj6B0RIbrCaNjUm7oWGQia0LGV1gtaMWieOn2RN3EvsQY3NGQ7YmSavHiwVoCIaUIKFdr+vomYzLuqzUrR8YiXQ4Jj5b1CyH4xy+WQ0pqkgDLNdxjjw/hOQAfM53wXgv3fWB2ZtMhPXdnThnhdML/F3+jbwZpK9KEx9nI8QbAG2o9JNlWlaX4PhPuqzyeDGeQrY75b6koQdfxPIafqhru9QDM9mZcZFiv6UN1bggGOjzmn5J2jDHfpyEgSeUZPafGcP+AWA+jp1Po6TSG6sTrJDUMsPYUUxZSdWNYT6iZSVOCc+k5DEBNhwRYUQq4qh7UE1k6SGUxgCBXN3C3MP011nmILN57yjp920YpuF+tYU8WBE9VTWnuySKCZ1/V8QeWi1oAYvCQKot473pROcTPaPEXRxYUQPS5y2dIkHwCBE1qdx4l8X4jn58BPIYe3ZDYG45TwMiTMVtegDTABUKVZUxm9W6wDASlgQBw3zT8rMlzfuZfI2AmsKCn5a823gPwDg+07zq1b6cSVr0bzo347IPHVM9n8bMoJNK6w6PBDx56dsN5MIbbb7sbC9HxTt4T1/aJBnbVrjex6/Pu7L6rwqiecjOykLENzOO+a3XTwT/b4wxQno2zcTZueHyaMpW///u/jy/4gi/AbDbDhz/8YXzoQx+KP//jf/yPm37dGwaV99xzD/7kT/7kqt///M///A1XgTxTg34RAQhZyjj5rRXPkObpNxW/pK1joEpiyEgGf2BVS3eb+FZGJcGQ0vxSn3MCqy6co3RtNiVrVtUsBw8T0p05Jzhh0tW0sVcNWcYuxMDiAAQurYSxWMfk0NFoq+TeDlUGoZpBwmt80/LvIeBkSxaKvufxis/RW0uwkXMS4g+OxAvG28CJdFTvzGOZPdMUNSeAImVjgqWLEzk1m9HfKUxgkAar6YTnCiBACUm6AWiE0CMg9jnaEIYRUk178ajJOQzdbCH1MIDNEP4RpHhBpuYWK/FFpSLPZa+mC+cMQKghgFSiRHmu+JEwKrmfzsWkV9/1IpkuKX0NEtbJmNezFon0/t7AoLb0iqlC0mPTlGxhnkeAruYzAr+aicFqPmNPppZ7pKoju6qmk0EuKABVb6UNY1Ty/IcAo4NDnnPxgpIdLzkBDT2FRR7Dkfya/aF+ueIiiBxrlPGGICvxcKLItzyncj5CxY5WkZn3lw8ZCCUBLaEblR4/N3TDhhoKgOz6YwdkIXfmVBbcdo5gtCy4L6FKIdSxhN5L64ZzFKTkQdbohkocAiO+f0I/Yww7OrfP6xSlsGScmEpcDf5W7wVYZvH+u79+Z5Q7eueRnN+P54YhOLLokCSSPMqJ/DXDXp6hweqdLqYkQ1KKVZHHwKAIwrdDb0TtEP2GsigXwY+8d8Prh/TXUBEU/Kvoe37e9AImQjiZvH+9SG/Dex2GHmi33gy9rU0TZe8AtpKe+VnjOyZ1b7Nc22xVBHNyvnVZQiXS4ypBTLCWtShNM7DWRgvTKJ/FfQ+VpDH19O7sPtydvgZ3mXtP9VSGe+D++p1xe0EmG/Yl+Bfvzu6TRQ4dfZSuquTPmu9R+XzydTOELW17pEV+HBKvVcLFjmumzm6NKwF4kAuHRQ6l1SmwHsCzTpNYpfJklSXXGjpNhs8ynJYFQ+mrmMqz9NdPrXEmVz4bn7Lj0xRUfuADH3jCn1/5lV+56de9YVD5D/7BP8DLX/5yPPLII/F373nPe/C6170OP/MzP3PTO/J0Boup55yMVPXAMmYpJ0SS7ghrKe3LM+jA+Kwr+g3D6rYwR+g6ygnD5GHLA4kgrwqsimJvXkh5jexI6BbL2QWn8myYCLUdV/G1GhgbSRBEVYnPzEvdAiW6KqNEFasNjyf0kgExMTVM5rxUXujbLkRJmgp9ZtJZpvZ3peqBqYX6eXdwm86x9N5aYffYy0hGUyaPeTYECx2fwIfuRyCCVL9aU5o5Ljl5nE95XoOUzLqh608CZ8xz7yDDbG1MKlXbKa99P/hMQxpskB0CQ7WAlLDr8/uRzXCXDwfwJd5ANSqBTEKLeiuTYEfQKEDaHx4TsIRwm65jB+V0Eq+3yiWURcKOKON1UXKpA6sSqjdGZaxqQdud7nPcqqdBYughdAO76Rer08nGG5H8WkdgF4BcAKAyAVWKyZxRdh0kh2XBEJ0xA3SUeBjVzjzWEjDdVK5V8Hc29Dv6vZ3YkwfpjMV4NATlbPu92hZqNmFdjoA8d26H/x8qePJskLTu7vD+O1nwvNaDXFU/eplBRAdHPHZJyfVHJ/CPXeYxitcYzkX5YqjsAXBqsqpnE+gip+dRfKYBfMMz/Met1rEWRMm+6v3dLW9hGz2/ypj4+uFPXRaDL05+IsOXZeKhG8JpbtVwqxXUmJVBAbSpsG+gmgJKQc8mUXnha3rD3XI5yEHD/RFAtrD4apvlDT/S6wij6buUBZgQNhN905K6G1KEvVTLQCmqSU4WQ6J0CBrT7PcM8vzI4CmNB7p3nwp/uUqWKo+x6w1c2/L6C6soF4/y/LAo571UqYyoJAhyX++GH1ybGVNa4Z7J1xPgte1VDJy3dvB+Nk0EljyHcu718LkfOph93TAJXHy74bXIjnd4/9FPUYVhTGRJr3corU6l0j7QvTv+O+yrmc9OSZ5vtA4nsvLCVG77S13b3jBIPRvPrnG2AHA2zsazc/zxH/8x7r//flQyt/f+6aHhG/6k/r7v+z688pWvxFd91Vfh8PAQ73rXu/D6178eP/uzP4tXv/rVT2tnntZo2i1GREdgQ/BiTwOkwCpNJ/wSPjjiqn1VcVIrnkj30CcAYCigt5aesMB4hD69wBCGiXUYoSJBegTDhF+H4vewmr6pYkWCyuT1RNbqT5YxZTQmoiYmMpd+s+FzjCZQFACt5jMyMofHwzED0T+qRmVMsA2yXkiyKIyJFSLRxxaOERjAo8hvQyVKeK3AGAJhYmOHoKFQIZAGiaCwUqNSJLWSwpoSQPrD45gqG66jOz7ZCgARuet0EidRIYE2MnqzSaxq8SI39lVNya6A7ABYvUhP4/NDwThwKp1U1VKzEs5LAJFJMlR6dFtgJjCdJwuCnpPl1rmSezX0KobakzThvXl0wiCouqEfcybl730fey0hMul43WSxINS+qLGwSzNhN9sWmIy4wHB8MgTgBL/fes2fkJI8LnmNgrc4+LoWK6iTJe+xhfypFPzhEa9HkBUDXEwJ+7iporpAXzqK1x3AIM9WmvtgJa15Z6uvzkiIkOaE3k/l+LUa7s/1Oi7InLp+ssCiyvL0RLiq6dkNPaZB9r6peL02VUxqpcevJqMrYTboey5WAExNlaCpu7P7cH/9Triuh28aMk2BHQ0y5S25ojLmGZH7PdnQZTlIdUUy79aDT889fonntm6g93dF1ixdnEmC++t38vMmFXCndazUcItVlHnGmg3Dvk81m1J1EO6TuuHn4bZH2lq4kyWVDiExF+BnXmA6gdgZGmTYvqpZ49J2kcGDd6dA5DVHCCrbYofD+ffO8fiDRF4806fe9yGdNkkjY7ntPQwjLCy4qobvry1DVVrxed7Rz7gc6pn0c26LfuchNXvJczWfxu89VRbwXU9vsFTi0JdIxi8C/icYV54nlfD5QcZ9l7n3qmPbDg26GYb9QfueYbHrit8rY07Lgs/G2TgbZ+OTNby6+Z9n8Tg4OMDLX/5yvOhFL8Jf/+t/PRKF3/iN34hv//Zvv+nXvanlvx/+4R/GF3zBF+Cv/JW/gm/6pm/Cf/gP/wF/62/9rZveiac7IpPlfCwx9+uNJC52Q5l9klDKJ74of1lCbMYjyjW1Jns1ljAbKb2HY3Kk7/uhsD4Vaais4HuRHPoQ0jNi0iScI5gKXZKaYBGSHBpSQFkQLvsaJmF1PTCgobpFVuhDEXaMuE/TAeiGrkMBoWoyjtsI+x79ldI9R+lwxs5FaymHlO44ZJn0XDrZfj2cDwzy4uCTwng0dODJJDX08AEQD5Yd0naTBFhtBqZZmMLojw0grWljWIWva+6H+IxwsjwlhfUdvU8YlfGaRmlYWXBSCwyppuE+Csxx30u/Yj6wwcsV762+ZxhOWFRQIu0MwRkCzNV8OrCWYWgNNZ8NacKh6zMk/lY1/GLBfy9XnFiHFGFgkEm3LRlLuS7+4JBAM6RThkUEYYH9ah09qj54Pdsu9lRG6alSZJ+DVy5NKMNOEthPPCqMbToANXlvYCWVI/Knmk7IdoX6CJEqe5E9o+/JjALixTQDC+wc99+LLLYXP691ElzkYm8rJP0XH/l4ZLt91w0LDV3P4wxySjkvgR2D0sM1DfsFRK9kCIhRWQq32URWyK3WsSJIhToVea+GSb3aWli5Z/w6ggrxtqmyjN5OFZj9viMrI+DmlspfRXbrliuCW0U5JsOtZIEqSciehvqbLBVvZId7ytcK65/wfhbGEuCCUJRIBzZWFr/c5UOCxi3pZkiRhlS5qPkMej6N3boq9PxmUjMj4Dyc2yibDFLdrfTRkOy6DYK2QVP4+93payJDvM0ww3vYxx7nfoxGcfEwpIwDVIew0qRjHYlUhgAEYOHv4XrGIKgr0k7vMvdGVjUAzmjj8F66V1MJphN7hgBrf7KEl/5Y3whQDxYM6f4MIVDo+xtmEh9o30UZ9xUjAMywEHKXuZf7c8XrP5X88Z7ytayiulY41VPUmpyNs3E2zsatGluiohv+eTaPb/3Wb0WapnjooYcw2go/vPfee/H+97//pl/3utJff/EXf/Gq373qVa/Cr/3ar+Hv/J2/A6VUfMwrX/nKm96Zmx4hwTVIzQoBK0GOFSZnAtYwLumrC2mWh5TPsci8IcB7/DI9VbUU3W+qwcPmHP1mkzHvnFQmtgBQ9+x5298bmIfwuKYZgm7SNHoQVQtgLTUHC67Qo6G0y1cVwZWExvgT2W4rATbBSymhG36x5DZF7svjdgQoEqQSvJq+72M1gGoVMJsyr8ptgccaQ0+mUpQvBnbPOqBMB8CzXBGAHp8MIUgBHIcO0FT6E0O4yVi6KqcTbtv76BeFyIXDuVXzaawfODXZkrRIZaQyQ6pVoBTTUkclz6MxZP+Cj0zr4Z0ffHadLBj0PRm4XGoqipyMdezkk1oTo4dj3Jnz2K1cj+DzaxqpBJDzvamGrk3LYBQ1GfMcJXKvyn1INmdgsNBbWbjQfJ7UZMR7Zis4hnUX0vkJxKTg2FcpkjpMxgTJgWnNt6oTliuZxG6xfUG2G+4VCUzCqr+KAYeRapKui0yhD7Lj5UrkvQIWk63QFefgjxdRzhzvLy0+xPlskJB3/VBPIbLT//zxH8HXfOZ3RCYzJpQKq6Z35nCHx6fYKbfZRFkiqyqSKAulwoCLC9sLEkqxMxFKMcV4NhFZ5harLbLY6NebTq/6/FIihdch9MU7uO4WfiuJgkMlCeWQ4PXS8xlBUpHHxNHwGeFWJ1CjESW8bccArKAsMGaoCwmLNPJ+A8BrFa5jlsIvGllYKuEWK5j5LCZPu8OjKJN16w3M7pwM6HLJc1eA2xHvdAC6AUz67dTdtjtV73GtEQCR0o6AL4AvyKLY1sKQms8GC0DXw1U1Adx2wIzSgHdXgdko8RQ5p87MKRB1VUqs1GHp6RQ+vK8Ds72u4FENAFq8virP4BZLfg73fTwPbrUGLIGsHg+fY9eTiOqtJaiTzs9rgvJt0Cef/b6/ugvzicClt0zXDimy1zPO0lzPxqfqOLt3P4XGzfojn+Wg8oEHHsD999+P5z73uad+/8IXvhB/+qd/etOve12g8lWvetUT/t9P//RP46d/+qcBAEop2BtcAX1GRtUAqeJKcpJwYq9VTNWM8rsQErPg5FRPJ/CXDjhJ3N0BjheUREpBPaylPOu2C6wXgUx4jYnAzvc9vWcnSzJg4xFU3w9dggCBwGo9ROmHFf4sJQDd2x0YqCQZZFWByZxNOemXhFEfuhUFbPmO++AXi4GNAwaGLwBKYJCZjUvWj/R9BN7ukcd4Thp6BMmGFoPsD+DE3/vIrCqA+wZEv2IIzoCRCbfRTONcraH6JPYBqvmU8t7ApAWGRqRx6IbeO5WlAxgUYAZw0qfKcjiv4Op+ON9KAJ9fV/TypSnlnMsV96tuCEinE6jVhvUTEscPgGAxYeopNCXTAOL9AMNOS183UCfLoYplOpH9zwY/qmxTzShVCz5HNRoNYLBto88TXT8wdADvjTKHctJJOhpFAM4U34TS5qaV1FoBcZsNz1GecfHE+dh7pxR7L2N9RACiIThIQI7q+4H9sJb3eAi/ke0oSYtVaUog6xzc8QnU3u7Q7SoyUWQp3OExPa/S8RjBmtHDQkzwOId7ytO/HIKD/GItbA2PP4QLfc35b449hlw8kfsuJTPvThbClG8iIFWdMJPhGkzGXFwasXuTDJ4s5uRZlFA7qbLQ8ymTk9UAVFWWQTUNoFLcX/1/uKd8LUvqZwJQldQZbULwVhMDX27lpENteV31eMR7IYTkVNWgCGha7puw8b5pYlqtKsfx3gyVGkpqRvzRyZAYDURZscpZY6KFmfTC/rrVWtjQjLUZxyfch/EoXns1YtpyfH9K2jQAqLEETm37kQF4Wz+plDhITbeZ4cBW+qCWluRq5DncwSG/HyTgbJDLulPSzcDanbqGImlVSRplx9v/f3f6GuiU0mJWdWjeJ2J3UFlK5l389fG8y/X0geUuC97fAeR7z/MIRNl12O9r3WOn5MISkuO6PoLeK+XEd5l7YWZT+KrC/fU7cU/xxIF9D9r34C79BDaZULOC05PuwLKejbPx6TL+rADlGZi9iXGzUtZnufx1vV6fYijDuHz5MvIrqp1uZFyX/NU5d10/fyaAEhg8adZGNkMZw8lgmGALCxK8f7G7MrCGB0enH2MMMBkT8IUuxral3y7n38mEFhFQouvgH79MH6BSlEsuVwQP41GUSLqjY5Hnutiv6NtOJH/C1IUexTwfpH6TIMstIuNFOeUU7pHHyJDkZAQDm6eM4YRbkhh91/ExIvEMEl4APFYZYbIS5WvecXLZi9SsaejvkWTCIJ+FdMkFT2SsBFisYgWA2t+NEjmVJjGYQ6WU1yHIQp2Pj4mAUSSq0cdpNH1WaRr3xffsGWUFSsaqjL0desa8p9cOGLoTnefkVKSdIeQnSC0hkf2YTRlEs95wUj2dxNRPvbXCrsLCwHg8hDl5T8DZdry+QR4pDGMMUxrR6+aOjqNXTIkvKiSiIjExuVZlUhESEnWXK7lPSoIlx0USv97EGhl/x3mRiI54L0wnBF8hnMS5+J7xTcv7R+uYphpSOsO++BBa00v1jfe870PvqWYYEMNsdAy1CR2ZlPRlUYYae/zWm3jfqDzjn0XBnkyRe6tROSzEOEfPaGJ47Zzn/otPVSXJwLCGAB+lhwRNAZkqyyjpPjrm/nvP972kIasshVuu+LvJmBN/xSoevTOPnkxv7QAWLXv+VMrFJL+uKFc0khwcmP2tcUsTEyUYLPgmY/hMlrKuIkjTQ6Ko96eAMqzlMQRWNiROS2BSlKGKPFVpLenSaggq0mqod9l+jPdkREOPbdNwW0HKGRQa4teMKbZa8zNM6UG+jeuYwCmN0McYGOUoFZXr6Lse7pCfw3oyjky+nk8H0BmGpMleyVRyU/T9O7EAnKoTCcmp8nd4x3ukHwJ3AAz5AFoPybyyOBg+783ebjx/XFhrBmuDeF2faFwJhLcTdMNxXXlOfd0MtSxdf81eyatOuzD3YTtRDbE17jL3wnX9qaCg8PuzyfHZOBs3Ns7eM8/u8e/+3b/DnXfeiaIo8IVf+IX49V//9Sd9/Dvf+U58/ud/PkajEW6//Xa8/vWvx8HBwXVt68u//Mvxsz/7s/HfSik45/CDP/iD+Mqv/MqbPoZPn0i17SqBrRVjldKvFxNTgSjLDHHr2543NZJE08VS0iSbAXx4T4DX80vdr9bc5s4s+nrUfMYJtny5qzQdwmgkHVQ//7nA7Rei9M+HcBmZXKvxKEoIIavPkXUM2wzH1vfAagM9Y6iFOzomIBFfn7f0eqnpRCSiwkIFGWaeSaBKO2wTInlNEgLinZn0+xXDuQ6spGHRtg+poMI0+q4XFpHXI5bCL1eUSwqLQX9oL/43YSY31bDqnucDUyEMZmCko4cxS3m9hGlRCSWubrmidFTqT3QpQTMCSLyEe6iSsmO/WsMdHHJSHdJkE5FXJgn845eH7SYm9mOGqhgkZEWDtJBhJwSj/vCYE7+d+dCjl9CvGGoT2AnJ+hK9uxO7DONw0pnai2w14fGik8oQSSjFpuJ+NQ0XJ4KcNbDfD1+SGhMBE8HfOxoJQ90PkurdnWGyVzcxvTfIwv2KTGFgmgGIbJIsjtrboV8yhD1VNSfg680QsqJDwAh9hgyv4vtVBflxeC90HdTh8ZDOGgJUAmN0eAx4HwEAhC0NyZ1+tZb3RHmqzxAQRjfUZMRgK/FBSr2FnozjPYPVRs5JH1m6U92Acv96x0WWmPgZFlPSRO5R8Qe2Le+9W+ynBASodQRj2wxxCLuJTL3cBwAGX7ZSsWZCz6dcFCiLGMrjO4ae+boZ3i8Ze4GR0hMYvKpQXCDSUmWiZxNuf28nesfj53OobJKFF+8cAea4pFy3bdlha230N4agngDergnUA3AKQTFS9wFg6CoFWNfjPNxmM0i0qxputeZ53GY7w2fWFrjavqYheEanyZPuWzivajbl52mawDvZ31EZt+ll8Sh4mAPYjqB4G/Bdx711avIp7GrwfF7rsds1IjrLIlB/srENbINCxffdVdvXWXbVubmSNT0bZ+NsnI1bMZS/+Z8bGe95z3vwpje9Cd/zPd+DD33oQ/iyL/syfM3XfA0eeuihaz7+N37jN/C6170O3/AN34A/+IM/wHvf+1781//6X/GN3/iN17W9H/zBH8RP/MRP4Gu+5mvQti2+8zu/E5/zOZ+DD37wg/hX/+pf3djOb43rApU/8iM/grqun/qBMn78x38cy+XypnfqRocPExhhE2MlgazmxtFJEIyAIQTWru85mW74XN+2BHYiX/Rb8euhRoBJrSJtlPTEEDTiQ+VCGEYkfHXD/dpUUIsVwdimIrsTJkohMCSEBMmqvTKGwFgxjt+tN1sMISdsSmno0Qh6xMAQf3xCcCll4QBi3YhfrQksAntjtqR9wcdmHYH4puJkMPhGJRETwuiqguxp8P/5tou9k8jzOMH0W+mgqhSfY0gUPV4QfCbJkNgZZGuaLBCShK9R1TGoxy1X3L74NCObMhkTGMskUYV02XDP9D0n9kHaKImxejaVwvaUQGKraiaCaoAT7XQr/Cgxg9dTM/hIz2e89kfHPB8A/MEhmSxreZ/kWVzY0Of3gfWaCaFHx1LJ4OjDzfMoX41pqHk2AII8g7pwju+D8Wk2ztcNa1qalkAIXJwIzIdbrngNui3fm2zTHx1zP3tZeJCUWZWmg2R2Z07wJ8m3saOxqshEbjZcZIiBWvQkI02HztSQBuscr8tkPIShaKlCSRIC3jwjoA+LQ+F6KAU1m1JeWpZbwTlllBGrNI3nlQzc0O0K8Q567+JiCITldyeLyJQFeasP1yEMqb+I/YCjEVwA3V0fr1UAUKH30vddTAj2VR2VFiFt81aMUFWhi5xsagg5c46AyjqCtnRgk/yGCwF6POKiS+iVXFfCCjcStOWHBZwQpCO1On5TxUWpUDniJBSM70l+zriPPxyVG/B+WFBK09inGxcHu/6qkJ7tJFZgAClPtFKvtEJIgfVdD1c3UhvVxeNwyxXvC/nMDF5UPRmzqqNtY/1FAJPb1RgRuMZkWR9lpWHfgmw2ht9UNc91RyWLr8nUBh9nYPd1KQswiXjkRTGgpFM3hEQpLYty11vREeSvbRuTae9OX3MqiAg4DZgjWNxK3r3m2PpeDSA4gNcwnoxZ2WZMz8Dl2TgbZ+OWDP80fm5g/NAP/RC+4Ru+Ad/4jd+Iz/qsz8Lb3vY2PO95z8OP/diPXfPxv/mbv4nP+IzPwBvf+Ebceeed+NIv/VL8/b//9/Hf/tt/u67tffZnfzZ+93d/F1/8xV+Mu+66C+v1Gl/7tV+LD33oQ/gLf+Ev3NjOb43r+mb51m/91hsCid/5nd+JS5cu3fRO3ehQhfhz1psh2TKET6w3lPV5N9RlCMvorSUwOV4AIrFD0w6l6eI7Ujvz6CXzDeVevpcEQvHlRU+c95J66aKszq/WnFAn9BCFKgJlDP11XUcGR6StgADlTUUQuFrHIBNvLVSR0wcVElwFYECrIckSAoBLCYQRQKXyjEXuYdLe90xkDGE8gZlIEkoWw+Q/1F0khhN0I+zT1kq8bzuo2YzbEs+dX6446ZJ6ACWTzRhI5By9ldae6qyDtTymRABXCMMIXZ4tuz4puROJbfBUBRZRALevhrRapqsuyWYG2V6WSoouQSrCwgNAoLZax9RUf7Kg3HYqEkzZP2wqer4ECEUQv6mivDDKctOU1SChfiTP+fi6YRrrqBy654qc98xmE+/HcI/7E6bFxlAmmaz7ywdkCiXESeVcGFHj0XAPp0m8Tvr8Ps95CC4KXaXORTDMugIFf3Qc/XVK7hG0cm5C36B4Gcn4pkNvKjD4MAPb2XXwJ0s5Trnnu57+Wee5qCPS1AB6/WI1LApJzQOKPL6enox573Rc3Ihy9bCYJIwXwIl9lBA6P9xLbcfPjK6DPVlAS29jBFIhUTbIxOW4VJGfkhnqgky7Esmjdz7ep4H5NBfOc/FC0nd91zMJNgS73IJBny1Tas3+XuztVWnKegijhzRlSRPVuzuSrNwPXYhNE7sp9WQMvTOL1U4+VAuJ7zjKlQHWYGxV1MQRQo1GoyHwJdR3hAAsuSfifRoW8MQDvw1wngp4BN/gA927eW0sO291kVPGKvsbQoxCwBeVD2TZnVgudJZBZ1kEj1f5D6/oH70WYxjkpdtJwV6OK5yf6D1UKiZDh4XP8HnjF8soy9XSRRoWyEIQ1PXdKFeH81x1/oBTx3Vlqu31bcZHqf/2c06B8ScZZ7K+s3E2zsYtGU+zUmSxWJz6abbVZzLatsXv/M7v4O677z71+7vvvhv/5b/8l2vu1ste9jJ8/OMfx/ve9z547/HYY4/h537u5/CKV7ziug7roYcewsWLF/GWt7wFv/RLv4T3ve99+Bf/4l/g9ttvf0J29HrGdS2Fe+/x8pe/HElyfSvn1VZn1SdrhDLuwBL4xZKTpOlkCIsIwT1bqYFqVBLILZac7Fc1J8qhmkCSK8MEH0CcUHuAK75evFthAnJwFEEVgBjLr/Kc2+o6eLBmAfE1Ndxjl9mJVtWc0AVJEwCsq9g5FgJ01BUsLMLkOLzeZUo5QzolxBsUovkDU6nHI5G+8Xh9L8mM4xHUfEYAnGcEcnnGCWlIndUGUFsTlPWa+17kQ5VDZHJ76R9UTGE9WQ6skbCAqmD1BHKCBAaZUBIbJHmBFQAYMkK5l4RPiA9VhU7CtoO6eJ6+2Dzn9dp6PS/3ggrnuch5rQHeB4sl2a/DI+gL5yLTFyWZScJjsRaqaTgZlom2C2BEQEwMnNHCclvHCV70Srl4TsLiAULVDQBUFUM4jk+4z6OSCcYhlfh4MSTfNu0w4RY5Z0wslvsDnfRyZhnvfanK0Hs7MXHW1w3PsRS9+7rhIsCheD5FEq7KAl4rAoFwXjPEQKqwkICM7LGajGN9CKTjM7K5iyXBvEhZVSadplJ9E9/r4tFEYJSsA4qCCwDViqE83vMzQfoPERh+SStV3eD7DdsJ4S9ko1OYvR34roPe3RkYZCAuwgRYFIJuwsKVCvUXPdlbnZko2QyDNTkaejKJwCEscihznWzSzQ6pAXEnyyhF9nXD0BthEdV0Mly/kMocALEphuRUuWeDV5yfVVuhYRKSFs9dkD2LBUFlKZyEdoUFFT0ZcztpwvfSfMbz2bZk78JimXymeUUwi2aoLwqppA+073picOndUPsRuyTpB/ZVzWNByvevsJbbzHPwp25LN69MnI3dk6DENlSYbI9tn6B3HkoWPPiZsVXpIj5X37SDFSJUKwF8/zZiKxC20gWvcOhHfYoR90WsCwwO8lf5RMPjdJHDrjeRYb3R8aB9D7763N+DX62v6Zc866n81B9nPtiz8Sk5nmb66/Oe97xTv37zm9+Mf/bP/tmp312+fBnWWly8ePHU7y9evIhHH330mi//spe9DO985ztx7733oq5r9H2PV77ylfi3//bfXtfu3XnnnXjkkUdw4cKFU78/ODjAnXfeedMZOdeFEt/85jff0Iv+zb/5N7G3t3dTO3Qzw9c1MBGPTmBYyoJdkd7FQvjoEQzJnMAw2VcKKvgEi3wAdoERce4UcKS/q5eCdc3XDBOQPIOXIAoAcfU7StumE4Ie74GThST6KbIFkowJ8c1wF3WcFPuWUjknISIqz6I3El03JIWG5NMAKIHYBec3m0FGKd4cSFE7AAKTwKAmDDyKLJYWQNCINyynHBEAE0GFgVJBPjubxolPnHyHdNYw6RTZbWR9AIJB8b0OUkcCQiRmKAGvG54HYzgR7Hv4kwW9ViGs5eg4ehZ9VUMn04GVbduYFOsDwBBZqz8U2WrwJG6qU7UXIaE0JOR6WEBSK1WeEVCmKdNxDUOZfAR9TZRZ+rrheXIOaFy8Vn615v5sMVaqyOMCAbznNfM+LjQg4QQ8gnNZHEHXn6qfQVoCbRW3G0C2FgZbzWbynhG57PEJAEr9UNUDkA3XTCpCImNhLWANfMt7UxU5Q4lOFnzPLZYDaFYqMsXh/mA4lvSpasVzvFwNDKvIHlWQjFrL90IAPmHhJ7xnwzFKGnS818I9BRC4lGVksLUoFOA9lPhyAyBU0t2qAhMZFqmSJL4nvfRYwvK9G/xsXrYFgKmZEpoVAnF82936tEulmQo9KmPViltvYvctwuJcqLaRbsMAMANLFyTUMXzJ8PNTKYbuqLF8XlrHc75yccHEHR7z/x2Zy5iea1lzovo+KgqUeFmBgeHj4kXLx8r50jtzynGDdHjjTqW/holtSCANAOmB7t24O7tvK4wpiym34fjCQl+syglgTQK97l8z9OCe8etOvXZgS+8y95INFSlpYPbCPgV2Mxyja1too3ls4j9XuTDhSg2gFuC9Jyxp/LyX5OwQNBQvfZ6f8v5eOU4B4vBeVhrKXA0Mtv+utMID3bvZYXq9TOjWdXkyL/Hd6WtO+TnPAMqn3ji7XmfjU3I8TVD5sY99DDOZTwF40mRVtW2nAQm9K38Xxv/6X/8Lb3zjG/F93/d9uOeee/DII4/gO77jO/DN3/zNePvb3/7Uu/cEr71arVAUxTWecX3jloDKT/YI4ToAhkASY9gnlmdDtULbEjzUtfSqMa2UPYb1wDhqDVdVZGjC42dTxJoL58gKhhXqph2YreDjNF384ia4SKCqepA5es+JZ0gOnc8GFkXkgtvhF6rIOREPxycr5YHp8BtOpNyCHZS6H/E5AkoCq0TwrIH5mJNkY4aaEE0pb5ABw2gCq9l0SOkMoUUiRcNkxPRbIPYgsmPOEDiFeo2mIQtq7QAyBQhEwC6+KR/2ASCQAIZtWgffbraufRmBc5j06fkMUIp1LdMJsG4iCNGTMVM6pxNh0bIhACQAg9kM/uCQG3COx971ZMFigIs+3dEX/JWzaQTc6Hv4o2MCsbBQIaEvbr0ZqjdCj6YEoXhYdpcKixsARqj0UEU+TOatJdi1ZL5V8GgaAenWRb+jbzsoCfvxyxV0WcJdPoyhRChyuMWSMkcBkTH9MwC7uoZfN9B33DYsiIj8lzvkodIssoMqMXDHrO9QhmE3ShgySDl7mJRvj+AL8xVTPRXok3XHJwzjCgnDTRsXPNx6E9OUIxsZuk1lcq3kONF1cMslXNcj2e7GCx+yYbFJ6wheAJmQhwmz1hGAQmpePACVBMmmg0olgMlkUJD3stFIJGmZrFvK758ssGFXdxw+40OrIRTLDZUaocs29JOGSortc+PFI+najdSEZPQbBlbceSA3fK+F+iOt4aqafZTbkte2g9qZD59l8tmmZxNRlozgNhuGv+T0fypjoKYTAnPvYc7vx2sF8UrHqpEswwPrIeHuKvYQ9F8GJk75REKVEriQbCufv4GJDp2cTvowldKwqwXBFBBVFNvXMF7LrfoSMn/viv9/5TXXZTGkmIegNlF9qN0dLogFcBhY9pSLcLEyxrmhwskYuNbyM/xJxpVhPCHNNTCVgdU99TgBgxEcXkf66/ZQxsRrHs7Dg/Y90GlyzUqY7cecjbNxNs7Gs3XMZrNToPJa49y5czDGXMVKPv7441exl2H8wA/8AP7qX/2r+I7v+A4AwOd93udhPB7jy77sy6KM9Vrj277t2wAQwH7v937vqVoRay1+67d+Cy95yUuu9/CuGp8e6a8hnVASWCOTc8VQF88jFJYjBKoERkjkqmH1N9Rb+OWKE/uqGiYu43H0bzKZbzIwmm0bUz71+f3Bryb+SLUzH1gSpaAvnOdkYEFg5rsuJrb6mkBMhTChwGRIpQUgUjnnoXbnUMbAnN+HOb+PUBzOx0hwUZJwEhnCRtKUIFQ8oZCQkugPDSE+EoAC0C+HAMS8Z4WKpJR6LxMYKUaHBJFAGELvXawHUbOpHJdIyUJQkmdHqA9JviLzVGUJt1xGsDmksCYEsaF2RGvxs0rqq3R/Rl+eUkOISNvyeGTl3i+Z4Iil1J8IyFNFwX+HECWlhJGiR9S3oY8yFUmfLFhIMAwrWFqyOfJvfW5vqLVxApZlYUONhC3Tsp2wgu+Z7BgXHQRA+pDIuqkio0v2Oud+B0ZYXhPOEZgGb1eec1IaUj8DUAr7IgsCnFyL7Dr0EApA8K1IxAWgKek49BtKdkNoTkiwdfL+ieX1UtQe2PIQXqQm45gm7FfryD4j1IQUrAoJwVl+vYmBV35TxeReNR5FZjSw4KosOWmV+gmCqI5hMVk2eFU15dqxp9K6Le+vhPOsNxJsk/D8St0DALKfbRsTU5Whdzi8RrjGfl3J55fGPeVrb3kCbAjj4TFIaNNiGTsivXzmhLTR0PMZ/OVxEajtuEgTzhUk/EmuozIGajxisb33PE8hETpJ6BkOCdhFHu+p2L3ohnoWHd6Xwb9eFmQ85VoFljf83L/+2atCZcIIDKLvuwiQfGCT2+EzOix0Bbl1+HxSkjgbgG7wkobXvtafIRXVi8/2yv3a7soMNSLRTynfcyrLYibA9vPiopgwlk5StlWaxP0LXtEbYRJDYNR2+NCVrKGvhi7jm7pvvYtsN4DTMuAneL0zQPmpN84Clc7Gp9z4JAT1ZFmGL/zCL8SDDz546vcPPvggXvayl13zOZvNBvqKGiYTvj+uzCrYGh/60IfwoQ99CN57/N7v/V7894c+9CH84R/+IT7/8z8fP/MzP3P9O3/FuHXxgp/MUeZAp6Dy6cAWKkXvnzFk0wDgiCwHgsRLJuCceDPtE7Np7PNTIZhmSn9gkMmGABB6DfMhSbW3A+u3Wg81B5tKfFJDgBDSlCv2YR9D0t+oJEvoPZnHqoI+t0dQ0vecZIsvL0zoArAMgAoA0HbQe7twh0csnxdJmxqV9LaFbr40iUCQ0rut8J/Q+xgYOekrhLVkhYQRjOwdABjZj007AHvxb7rVmsxv23JyKExPrA8pxGMqgMgfHMVUV9QNWagkGfx2QAxE4Yn2Qzdj28X+S7daD6yzRP9H9sV53ifzWWSbYqpoCBwJssw0HRYEJFlVSbAOwj60HTAu2dkYysqF9VWjMk4UVWuGCXgIbcqyKOGN/ql06FaM4C5JIrvLREcdg5j8Upg9YXDVdEKAD2H/XBYZ0FCzAa3g100EQk7kwgBipYP3DkqnQwCSnDs1Hp06viA/VUU+SMWVipPOwKKqseG5CcnL9Za3NMh0BRiq+Yz3vDFSk5JS4qs1r1sI/dEK2J4rBwZffJihTsdVlSwaEcT58J6JALmNITEhCdmvq1jnoLwfZIEhsMgYaGNiX982q+WqCjER1GSiZJAvA2Ni+moM0wppsbcw/TUwzLos4/mAsMhIk8HfmWexlxLOxw5Xby2UJ4OrZWHFW2HYhU2HVgPwWW9iaFb4LKCvMkifG/lc5sJgYCl928Zkaj0aRU9lAJQQRjwsrIRk5CBTvju776oE2O2xzSQqrcjIte3Qtbj15Ry6NL1rozQ2yIV5XsTTeEUFxql6DPFfAwBMdoptC/sQ2EDvPPREzlFgXrfv1/B+kevpjk5YyVJVA7PqXNxXL6zrdSe/QthNSagN4Pye4usAAPfX74xS2XvGr4Nv2/h3V19HyJTSEdyqhKnMIWQuyF2DnP6WL7CcjU/KOFsIOBufcmMrdOeGn3cD49u+7dvw2te+Fi996UvxJV/yJfjJn/xJPPTQQ/jmb/5mAMB3fdd34ROf+ETslvwbf+Nv4Ju+6ZvwYz/2Y1H++qY3vQlf/MVfjDvuuOMJt/OBD3wAAPD6178eP/zDP/yULOqNjk8PphJgymmRw4+k1zBjzyKyDP54AX+84KTFe/jZODIxoVga1lGC2kgXniR5+rohELQWan8vMlyhh49eLmHkjIC6IOcLrNbuDtmisOJrDHsRJcTGt5TUqsmYk+PphL6koiAY0oYgNGOYSux8C5P7jOE5255EsrLCyhk9JLdu+UKVlHhH+abR0M+5nfsegHmQTgV2b2u1PHrq5FyGoIjoXSuLyHp576D3d8lGhGoOkX4GX1oI9/HWRqYNSpEZbljH4vdmvB7PvcifPKNHdVTyz8B0jcrIKum9XU6IF0xpRFkQ4DqGwKg0HVJ5NxXBzPEJ96Xrh+7CbZmgeGcREmWDZ8lo7p/3Q0LmViJvPEdbXZW6LIdJHwC3Wov/lqx6SEUNk0jftnEfEErnuz4GUG0z9qyBSGOVgZNaCN91w+RU6iRULnJpqXfwPfsv1WTM554sYsCKb4ZJdNhfH3yQAuYig71kkqtbb1gzsqlifyYZGZlkh0WFIDvsRXobWPCt1LQg7/PrDRd3ggdA/GdK6QjUfNNI5QsTm0NNhhJfbqixCEBOCzvqqop1F9aJ164dQpTKMt7HYWEkStVl0cL33SAHByfh7BKk5D1Uy4T+wVhN5DwZpVvoq1SS1uqbJn6OhYAhWDKtuiwHv3bwskJkmaH2ZEx5Kqw9ZT1wixUZzLLkZ1AA9qK2iCFP8tmq93d5fsNnQ6hTKnIuPIRAs8DehbTcLOUCVVgkAeJnF9UYyVPWUoTzERb3VMbezABGQ7VKrCuZjAXgSvq0ITiKKcLeQT/Bdl3XQyUpHujeTXntlfuiNAGpLELw/LhhwSMoVpQaPh9DQu94JPdSGv3ouiwpIRYZshPbxJOBtLvMvdFzSiCtYkflXfrVcF0fk4kDYxlUF3enr+H1lO+68DrXHNtsqahchn7UrYWAYJs4G2fjbJyNT/L4ZPVU3nvvvXjb296Gt771rXjJS16CD37wg3jf+96HF7zgBQCARx555FQq69/9u38XP/RDP4Qf/dEfxed8zufg1a9+NV784hfjP/7H/3h9xxVyZK4Y6/Uab3jDG25s57fGpweotA7IU/oW1/XANknCpDq/DyWSUABQByd8zGTEgBGZwDCIJYlyQTWd8Es7hP6EqoBQNr/N0IUV7VHJqoftL8HNBjHQZVtGBpG+7cyGsJsL5xBK2pEYYXDa0yXifQ/szoHdeQzoUdKnFx8XpJrjkoml6zWll6FOwQ/MSGAUlDHwh0f8vVKc+Id6Fe95PrUe9iFNh2AIpYauQgmOCamgkF5KeM8qjumE51gkXlE6liQx9EhNxnLeS/jZCOq5t3PC11lgPAKs50+oPgn9oEpR9huCWlouEgT/ZpB3BsYsSGYpjy1jzUKU1ga2ue3Y/xg6I2fTodJFU6bpOzknoTcvHNeWBzUyCAJAAkgPlR+qyKHvuI2P26oKABBlsSrLhpCgJOFjTABNLcFhXUdm0y1XZIhEKhqSjFWWxb5JWMt9EGChRHoMa+lds1bYLPHSbqV7hsWR4GtE2zE8RTGZM6QvR4AhTNSpY08SSsyrKi4q+LaNQTFhkhx9puE9Je8jv1gO2wphKuG8SahODDYCuB2RLsZETO/gtgJrVJFT1mkkkEZpTuplgQHOD2wsMIBeSX1VWUaprzDF95SvFWZYwx6dwB6dsGak7wm2nSd7o1krcisrRQAuBqg8HwKzBBwHObFbrQdg3fUEwSGsK2Gnpa+byNRGtUQvnsiWrLCTa2qPTiTgJo3hQNGnKT7nEFqkxwRtvm6k/5QLAt5amP3dwf/pvMi+dVwMCSDLNw1cVeHu9DVPDm5AcOSEBd1OSCW752JdCZRiT66AO7dc8b5TOh7r/fU7uYAgI25b3suuqU9XjWw9JgZdSZWHShO4FQOVIF5PAFwEC17PIpfPrS5+JrkQ8gVIgFQavY52uXxSkPagfQ8edO/lP6RuJf6fe2/cv7v0q4f9lgWveB7l9ePrPMXwzkONy+E9FEKM9KuHz8qzcTbOxtn4ZI9Pgvw1jH/4D/8hPvrRj6JpGvzO7/wOvvzLvzz+38/8zM/gV3/1V089/lu+5VvwB3/wB9hsNnj44Yfx7//9v8dznvOc69rWO97xjms2dVRVFdnQmxk3DCrf+ta3YrPZXPX7qqrw1re+9aZ35OmM/nnnAevQnZ/Anp+hf+45tM+Zw+5NYz0IeksgMx3D706BnRnQdANYCsPZIYxnNiEYDUxKSfZN7e9yUj0iO+pOFtwOQCAliYqRDY0MgI3BC7HaIWNYEKTUHSfCjEjdB719ZpCYBnCzWAKLFfdtOiHrN58NbE9gYa3j/+cMisFsGqtOGKhBkKTKkn/uzIE7LhBQjMvoH0RgEyVBVt12gf93fh/+/C78+V0BoinUbRdif1rwM5KBkYoUSYi0zzlPsD6fkQW2DpiO+f+RsTBQiw0ncqMcWFfwRQokGkg0fJ7Bj3JeJ/HM6gvngfkUan9PWDY3nG/v2QW5vwt/cZ/XPrAdALAzpwdpZy5puZrnXCm4xy/x3ug6BhqNRyL1K2L6aPQhyqJAAPsqSciECxDX4xH9mJtqYNWFCYuhNzLUeMRtJQkBrDGD9M1o8SJm0PNp9HkBgBYDdpCiqgAEt7xPwZepxiMCzACagiR6WypsCKoGeSc9k2Z3HifiIYBHzyZDeb0ZQo1804hc28EtGAYTAEMIYgqVFWG/GUCSDmxNVUHv7w29pKE/Ehj6Cw3De+KxZVn0K8NaSqmdHybb4Ufk4PboZAiGCnL4wJYBEQhuSxBjWJS10LNJlIzqnItJLJDXlAKnZCfN7hx6NDoVbuLbliznDSZo3uhQWUaWuSFw810fvcsRsAcvbQh+CYs2wsxFD2OQpEoXaui4DIysSlN6vWO/JYO5dLivpcc1VLI4SYfWuzs8Z1v76JuWgLZpyXZ3fZQnx+so/lyV8P32ZOBm2wuosmz4fAy/k8+86BsFhs/ysCAZHmtMBFt3Z/cB3p0CaEormCn96TrL+Fhzb3xMvAeEqVOjkqzoRiTbstAS+35DZ2ioipKFqnAsXHAapNbeeZjJJB7fU4HtWLciIDIcB5TGg+69w7FdEcyzzYQ+5TbkuN1iFRc4Tl2vJ3gfxATfrde/nm2djbNxNs7Gn9exWCxwcnIC7z2Wy+WpDs2joyO8733vu6pm5EbGDYPKt7zlLVitVlf9frPZ4C1vectN78jTGaq36G6fw5YJYD36KeWh7V6O7rYZ3P4Ubl/SW62Dz6VqYTYiQNFqAGMAgUaghQWEuF0Bd5kkowbGrqo52RC5oLp4ns/brm8QSR7Go6EuZLUmkKpqgknn2G/pZBKQJvQhhS/nTtjNnRlZWevIsu7MCEJkEo2mHX4M01y53ymwtzN4IJWCOz8nW6sV/8wzuN0J1KUj+HM7cnLl3AhIAsD96K2kaAo7pDWfI4DIj0d8fFlA3X6Bjw3MHQBYB3O4hB/nIsttgCKDNypKkDEZwafSsTifANbD70ygNs0A2FMDdUJwrZyD25/z9azjtsYjgvCQYhlqIrIMqu7gM3ZquufKm0iSfbHZEEyIvE3NJtFT6UMgk0j9AiuIfpADhxAijEcDSJZrrVKyq2gl8XY84j2X59zXNCH4T1MmG2cZf7znZHI6AeZTXv/pBD4w3btzTrQD0yvpvzGNN4QIjUd83S2QBKm4UYG5D++tUFsQJKVGk0VqO8pExf+nS1ko2WLh4dwQyhISZGdTgk2tB8+gvJcC6xIkmREwaiXhMQJwSgHtRscAIUgnLWXJwmBVUgS/XMXeQbUdWCW9gl72EyKZJQjidXfSTUgwriUYqY+TZnt8HINqotRcvJXBuxYkhDrLouQxgB6ylRXZnSDXDYDlFvrIVEbptt7bie9LPSPYUGUJu95wX/3pBRky2e5UijPBOGXP7uAIIXU0hp1J1Yo7PomqAABDOjUwVMTIeY9dpGsycnpnLmy5KA0C0OzpC9azSbxffNvBbTaUeocwnC1WLYxT7KD8yVohAubgfwyhWIPMv+E9IwssMNKbK2ArXPcH2ndFcHSXfnWU2NrlktLWviMw22IrY9KpnOOQ6q1Cd3JgYts2yu9jiFtQpxim4waJsNLiXXSO4G1Ldnu9TOJVIO8KoBcWsu5OX3NVYuuD7r3XBfbC/bf92Afde6HL8pr7GX63/X/Xezxn42z8WY6zxY+z8Wc1dnZ2sLe3B6UUXvSiF2F3dzf+nDt3Dm94wxvwj/7RP7rp17/hJIgn6jb5n//zf35Suym3RzfLkaQGNtdQ0wy6dQSY3sPlBt2EE+Oy7YHeUUKpFMEJMICBxAzpsZMRmUwjE+lVA7s/gap7qMRAOQc/msQVeSyWnPD3PQGA2/KKBdYygMu242NPFrFqApKQ6Uc51PEqFq/76RiqacnUbCoCj1DrIVIspMng4dz2JPU9cG5XGFk3eNUEQOvD5cBAritgXEJ1Fn5vB6rt4/H78YhBKZuKk8JJyXqUsoSd5EiOhLlergmKug4YCZBJDFDL/lvH55cE0aqV87N1XKoniA+vpyaU3KkVt+3EB+dGoRvTRbbLTfh/dmcM5RxU00E1PfdnOoHbGUMvKvg0gS9T6EUFpAbOJNDLmn5NQCZhBdRiyToZYXnV+f2BRew6CRMpBcS44XqHJFph/AKzjeWa4Hw8BpYrMsEyGfejHLDileodHzsdA8sOfiaM44Ew6yEMqsjhUwPVO9gLuzDHa6YLT0uoo6V4hUViKwEsDGxqoy+QL6y4UBE6GfNsUG5I7cp2rUYES007SFidY12HyBqjjNaYKG8FKHNUZQG3XjJU5GQhDIsDzFYabUiKbdrBP2evCB2RlOJYudCIXHbL6xyYVz0ZE0guV9zXIOG0Fvev3gGA/YJkfbbYRzOsu8UeSqlviB47reEWS+63lVqHTt5zxgDB6td3QyhXDL9KYqgMPMOJArN5ZbXDMzkiYG+aGLwVQazR9E2mg480pt1Crk9Ih46hTfSdqvkU9uAQXpjhcGwRPDoXJe7u8AgAuyXdAWX3Sg/KABVqcPKc90HTRE9mkKbHFFYJEArbUkkazzmUfkJf5bUmd06k0FAaWuTI4fh1WdIfLu8BNR7BHRxBl8WQGOsdVJJS4ipM5Taw0nnBe+GKvrLtxzzo3stAHFmgUJPxsAgizLEPdofwu+26mwB4xaN7SnFwo0NpspXXCPiJHZtJGlN07zL3QmdZ3N9wPE+1DV/VDPuR8xCfY+3pf4dtX+N3Z+NsfCqMT8Z9e/b+eGaGwo37I8Pzno3jAx/4ALz3+Gt/7a/h53/+50/htizL8IIXvOBJg36ealw3qNzd3Y3Gzhe96EWngKW1FqvVKqYUfbKHMwou07CZgm4V+lEC0zgop6A6Cy372tw2QbLqoJsecAlU78iE2YTdcnkCtW7gxwGw0AvnixSqtdBVB3QWdl4CCgOYspZMUVkAixX8zgyqagZmb3vyMC342PWGcsgAUtJUWK4sso4+FGsLcxckpupwQbYKIChUCkhkFT0Xr+ZCAGPTwY9zqHUzBLgE4Ow9gSPAfT9esJR8Z8pJuTAnSnoQ/f6cgEdAijpZITlZwZ2bc5uJTLRWElQzLjjZ7AmaVG/hQ+WC9fCJgbp0BPv822AuL2AvzmEeOZS6F0WpcmoILHsrwMtDVS1UusXitC365+zDHG3gpjnMSQWfGtj5CMnjC7gLO9CrhoC5pERMtT36c1OCT+th98awRYL08RXcJCdY7UdQgZUclcBiOfgolYI6twccLwgYJ2P4o2PAgufN9kBfAfvsI3SjDBogE+sAVWQEjpniMQr76nbGsLMESVXD5SlUOoMK1yhNAWfhpgXBcSZhPMZDVw2TYr2H2jRw5+fQJxsoiNTQe8An8LtTqNby/lCKjGjdROYUSsOXOdQyVGZYBk1tNrGA3lXVwMoBXAwINSZlwYm+VIpAqkJ80wCtIxPqHP1TgXEKXY1hUq7pu3PrTWRDnQB4WMtu18mYjJdzsJcPONkHIrAL9QpwHl4NycewDn65jLU4Sicx1ZLXdZDNxkRka6GUgh6NuB9pMqSmCkDUuztkZkPQTZHDLTooxcCeAHJCX6gOLLEs/mjZH9/1gKek9Jb28DnPe1B8hGY+GypOQt9kYPDESxhAdKy42AqPgVK8j7SGnk4RQnaiJ1kAeej9dIdHsTLEL1e8H1ZryuHD68qCQpBaa5GNhioblbESSYv/Oi4uSPhPHFv+vCeaZMXOSq2gMgGIIh2l9zgRr7QGmh7IJL24roe03gDKA5MnwHT79enL7cSDWQ/9lAImAziLxxmqkLa7JbfAYfQUS40OkiR6oYf98ZRqS0DQ3dl9T3l7nBri79RPIk0N9/hd5l6G9NyodNs7KJMB4X0i4y796ii1vXKcTZjPxtl44nH2/niGxicp/fWTNb7iK74CAPCRj3wEz3ve866qJXm647pB5dve9jZ47/GGN7wBb3nLWzCfz+P/ZVmGz/iMz8CXfMmXPKM7d73D9A7OAC5TUB7Ijzu4VKMbGyRrj81FflFNPt6gm6ZIPdDvl0gXLdp5huLRNdqLE2SPLCg1WlVAWaC+cx/JuoOyDiqxUNYKMNJIH13AjXJo6ziJkYTMU/5M6XaMqanOA6sNJZxhAt11w8Q+TQn+vIe9fR9mVcONMjKjbUcw0nXw8ylBq7CfZA5b/nnMvkt32zno4yW/rI9XBJhlSUaqqgl485xAIkvhEwOc3+WksG7j/vjUQC0tj8t7MnZZJmxjBmhAnwi4FvDu9qdQbQ+fGujjmhPStocvc0ADyopfyHu4510k61vm0Isa9sIulHPQl0/gpyOojTDEjYWuW7idEWwxJdsM8HVnY+iqJftYS2Jp3cEoRbln28OXGfdnURGULWsk3Rr9/gS6bsjMhn3KEujNZpgYjcdRUhsnam1HtrEseA2Xq6FyBQBmEwYkWUc2aJzDjXOgd9Criq83IjDzqeG+HS6ZDFq1sLftoptm0J2DqXivuHyK5PIauupgdycwJxuyKOOcwNMo2HM8NzYsjExKeKWg6nZgLNqKUlmloDYV3Pk5VN3DzXf4mkYNNTxtJ+xbSrluzv5GX9dQFmRGnI/gEWkKfW6f1zyALE0WIkhPEUCJ+OsC+3PKn4hUpLCUCCvpl/SO0kQEeZ3W9Jy1nUg4p+znO1lK2FAIWmqlZmU5hDABw+sBMS02JrwGCar45tyGAT7BIxolusaQqRTWyFU19FYyLIBYHh/ASkjeVVp8yKEeyEhB/RWT62d8eEcmsciZrtsTMAVPaHgvs7uToD9I3VWWwbX0FkcfIjBI/QOrrRS8gCE1Lnl+RT6qJM06MNkxZXgLPIVzHV47JOwqkXK79YassXdQyki4E9UVkYWvqphaeiUDdkqWKQDQtS0Xha48XdFLKSCqqskirutTz99+7ClZpgDHyFaD135734AB+N5l7oWR74/hPtPDYqBjWqpv1PCYoBZoOy50pRJwZIcakRsGlFvH40SpsM2mhn1l/Ypj9chNbMN1PcxkHLuVt4cyhtUwArafLYzlGRN0Ns7Gn4Nxk6E7N/WcT+IIqbKbzQYPPfQQ2qBEk/F5n/d5N/W61w0qv/7rvx4AcOedd+JlL3sZ0vQWT3puYNQ7KUyhkR/2cJmGqS26HX5xVxdymIZXt5sl0K2HHSfoSw3dkenp5wV06+CKDMo5dLfPkB6sCTBSDTdOkWwYOmJ0i+RoAzei9NAXKfzsHLxSMJcXnKxNplC9jZNme24Gc+kYyDL0t+0gefiAE9PMDF6Y6TjKJ30pMtREE7CF4vUyhR9l0EcrIDHoz01hTirAAW5nTOZ1R0IgHr0Mf3Efal0TbPbSwbhYESA24n0rpF9TI0qC/SiPXZiqCtJV6aAL6alWDxUXpUwqjQE6C32whJ8UPIZxAbWs4AsFn6dQVQdXptCrhq+/aaE6gr5+XiB9+Bh+XMBPxYvYbaDXDbr9MVJreYyJHtg7Y+ATDTtKkT62hC8z2PkIZsl+Qlem8FkCc3kJe3EOVWYEjc6hPzeDWdbczywhUPUe5mAFP8phd8cwG0o8VUXwjfC+m+acSLYdPZGhO7TtGASlFPxkj8cHgkloEBhPSjLkAM+7459ub4p+XiBZtlBtj/zxDq5I4ENqcefQ74+gWwvVWPTnJjCrBq5IBXh66E2LfqdEclxBLdasm5mV9BF3loxolnG/8wxubwq9aXmvVQ18Zsiqhp651AwTWaWgTlbwJ8vY4algACX9piLzDr2s0asmfa7KGPiTBT2dXsdQFp43JsryDeOGKpsiJ7MfwnsCaxT6IHXwa+rByyfMjhqVrDPxBL3u+GRgzpot5j4AqNTA1U0EotgK7gEQg1kiI5QKQE6TWIWjtJYOVgXVC5Pcd3BtC51L56DzAxCTpOAAipUxUD1fK9Qy3IqhypKMow0g3Z3qO421HKEKJ3iFg99WpLpIQ3VTSlAnTKGei3c2gEQn90jfQ0+nBIgCzKItwHKRzNUNw59C2moIfIIsQASfo1wLst2sq9GTCX3Poe6m668CkmGE6o7QgxiBo8hXvZXtNWQL3WoBNZ0O9SuSZOv7Hno6hz04lJOrY6/jlaAxLFSEe3n7/4LENLBzsZuzZUexnowZpLRaR9lvvJ7CeAcPtN9UPD/b0mpDObPeqi+61rgSLKkkHXy0W+fwlFR38vVwVR2PQWkFf43XesIh10CnyangIiAw0+70tXuCa/rJHGeA8mycjT8H49MUVF66dAmvf/3r8Z//83++5v/bm0zcvmHe8yu+4itgjMGHP/xh/MZv/AY++MEPnvr5sxjeAF4D/cignWp0kwTaeujewxaafxd2rN5P0JcGzdygLwy6iYE3Cu1uBjvNYKcEmHZa8nF7GZJVh/p8juq2Au3+CP3uCN1eCV238FpDtT2UtXC7Y3ZgyuTM7U3g9sgouXNz+DyBWVawt+2yh7KSYu/ZGKhbuGkBNysJQBINN2KyKbyHnY+4rbqFm444cWs6qE1FAKtERrmqhGnlSr0vUqjlhsCxLIA8JytZZJTKVjW7K3vHOpaWgCauincdGUZj+LzEwO1SeutHOQGl+MTUpiHLZTQBemfh8hT23Ayqd9CPHUF5TxmxVnCTAtCgbLOzMEthW5cEynACkrWG8iAj11n+VC3Z2YTnPzmpYecj9PMC5ngdQZTetGThJpKWmGiYVY32OTvQrUVzcQI3ymAnOVyewM5L+DSBKxLotqfceVJIT58Z5MNmi5G0Fl4e43cmvH6dhVcKPkuGtFot58V72EkKO0qg1w2gATtK0e2RVYRz6Kc52Ual0E1TdNMUZlUjOea58amBOa7Q7ZFRtDsluosTHv+qQbc/gp9P4DPWX9gJWWJfpLDzEdzuBP25MbwxBEYO8Ln8X8b3hDcKTs4jegLr5jMvwN95B1RRwN1xgQB1XELNpoN/MwQhSQJwSDv21jKhWAJHlNR1AIjBLqFDMgZWaUrQVajDCZUWoRIlzxh+lGdQswnBXiGpvdu9rXkeAYkuS8QSeO/jdq2EwgxJtKyR8JIGrWZTSl5zSlXDMYa05ggW8myoQfGeIUYCZl3dEKiGABtRNijx4ca+U+8iM3RLRgDpUmUUJKwx+EjAuZdOzehLDQAwsHJSzeKXK6nrcfH4EOSiRpM1TtMoeVVFHmXOMLJwIdJmJRVH3AD9k66mnzIsBvgt1jSGSSl9qqsXQDzv1wzG2Aqc8XK9QnIsk3oVvNTMAMHrq4aFCOuANIHe3YE7WcaXDZLlKwFl2I63lkE2V6TSnvLQhvqRilU4emfOeyakIuf5APDlcSrPpB9WvldC8qtSBMnChKunkKdeGayz7Y0M5zIc1+B7dJF59f3g9byecZd+NRn69eYq/2sE41cw92eA7mycjScfZ2FAZ+PJxpve9CYcHR3hN3/zN1GWJd7//vfjHe94B174whfiF3/xF2/6dW94Kfw3f/M3cd999+FP//RPKW3bGkqpm0a3T2dkKwevAd17OKugHGCNguk8issduikPsy8Uko2DNwqm9Wh2DXQP9KWBV2C4D+jR1NbD5hpJ7bD4zBGyhYWpPdZ3ZBg91hGInie4yi6v0c8Lvm5lYZY17KyAHfGLMK06SjCzBN1ugeyxFfq9MZJLy5iUaS+KHNF7+NSg3clhGpEd7ZQwG9nmbVOo3sPk+1DWw+1NAQd67MossoZuUkCvG4IrN4KbFWRTV5o9neMRpVLWUerY9XC7EzKjbQdojf7cFAkA1bTw44Iev87ydbOEbJv3UYrqy4zAUmvohoykcg563cDOS+jUwI1zmMM12jvmSB9fEWwpD1Qd/LyEPt4AeUoAaS30sgacQ9I72HnJ85hkcHORZwbglhroRU2gLyyybnvYqRTcOwezbqOc1jQWtkyQHVawY0mddZ7E4Yznzk4LsleF4bH0Dn5KWaNqOrjxBLru4YsMel2jvzCDOdnwGhr6eevbxzCNQ7JuYYsEpu7hAejGoh+n8EUGWyTwRsFmGsXDa/S7BXRt0c1S6M4jv0RJmBtl6KcZbKqRbHr4RAMKcKmGTxTSozqCUS8MfHpphX6coR+ncOkEyaKGTzXBK8DrtDPlgkRn4TINeKCfyXFah25nhuyoRjdOkR438MbA3b4P9A5+dwpvDHyRQLU9vasi7YwVIcH3GxKEEwM1ngLrDdRsBlQV/Y5BFjidcLFjXNJPLL2oSmng3A6Zq6qOHkumMctkv2npN95U8K6P3spYC5Gl9EDuzAmkuj7673SaxEqJAHZ8VZPZkb5TPZ0Q8HY9sJNxP42O8lolPZnBH6nSBDZUcBgTfWl6h6FQsSpoqztWmZx+6lspgU0S6AJD2IuAxcA4+6pmd2SQFns/eF4h3sM8jx2CwVuqyoLnQmtKfIP8NwTbTCdkdYPKoe8HeS0APRnT29kN7CSMgZlNo6Q2PA5a8XEi6za7c7j1hp5VkR+7urkuABJSYrlTBJj31+/EPZOvjyE8ZCvX8dhUYFnTNIJQQEDSE1SZhOsPa69iUcMIktLtKhd3fDJ0rIrs2EsfMIAhuCeTQLSuH7qQtabfGOzI9C69ZujOk5ycU/t+Jdv7oH3PAJTD4+S+vp5z/6B772lf85Wbl8ThbWb0THp6Ns7Gk4+z98czM5S/yaCeZzlT+Su/8iv4T//pP+GLvuiLoLXGC17wAtx1112YzWb4gR/4AbziFa+4qde9Yabym7/5m/HSl74Uv//7v4/Dw0McHR3Fn8PDw5vaiac7TGWhLSfy2UkPl5JlsamCTzWSjUWysSgfb8l4GaA47JEtHeAZntJONapzCWyhKf30DOP04QwphXovwfyP1+jHBjZXqM+l6McG1fNngAeSVQezamAnORky66GsR78/ksAah2TZojs35sT9/BQ+0ej2RgRrbQ/VO3SzDNlRjb40UNajmySwo5SvVRphUAt0sxyuIMPVPH8HzcUx+l0yqQBgpwW8yGZhPcy6gU8N+s+4SPDZdvD7c4JDI6BizMm3GxfQbQ+Xp+gvzoHewTx+RBAIwI4ySjvboaDdjQTUaiVyyg7dvIArMphFDVgPvajRXZwi+8QxfJEwbEYqSVTbc381pZh6UaG/MEP7/D0ynJ1l6qv1wmSCYClLoBc1ZbSpYeprZ+HKFK4w0GtKRH2iUd+5R7mr0QR1WtPjGeSrniDNTgvopoc3mtLoMiNrF286A9WQjex3C/gyg6479Htj2HkJV+aw0wzln54gPakBB5jGkg0tyVJCAfX5Ei4nmMoPanR7JWxuYMsEPtXoxwbdToluh+C4Lw2yRUevr/dxf/vSoDk/QjdL0Oxl0bvaPEcSba1HPyHD0u7lsKWBLejl7Oc5/ZizHLq2lNdaxx8HZIcVXGoiU+gzA5cZtBdGsLMSdpbzPlAqsu1+XLCGZzoWb1fHrs1QZt9T9on1WqpOkqE6BQDyjDLbsVSqjEdc/Fiuot8XAP8cl/SnZmkMwVKzSZQmqjsu8rFZKtUlBYGDVJ2o6YQ/o1EEgoGFUmXB52z7Bkcl1IVzQ33KHReYDDydcD+LHGp3B3o2gRqPYGZTdoYaQ7CqVLznIQxn7PecTujJzLLTQOcZHqykyMULm/HvSg1e0pD42/dMH5WKCl2WEchEfyPA84QhOAZZSmlyYJ8TM3S+aum+FSZYFfkQCtQQkAZA5J3jfSMVHmpURi95SPcNNUUh7TQylyBYfKIqEWCYeEXfochOQ1BSYFcBwJ0shR0fWO8gudZpws7ThIDtgfZdpwJ44nmX43ygfRd0mlw18Qv7G9i6ANr1aASVZxIURZm5mox5jsNigLWx9giavtLtsB6ylvp06M81xpX7rLSKftMr/y+yilthSDcEWLe3I0nYV+6LkoqSbWb0emtKzsbZOBtn42kN/zR+nsVjvV7HPsq9vT1cunQJAPC5n/u5+O///b/f9Ove8Kf/H/3RH+H7v//78Vmf9VnY2dnBfD4/9fNnMZrdFN1IwyVMgdWtg+49urGG6hzWtyVY35agOp9BOQ/lgW5s0OwYmJbpn6bx0B1Q7xh00wTtzCBbOdhMI6k8XKKQn1gs7xwhWVsCDwkGMrXlpP5cQX9m08POyrh/XitOvscZ7CiFWREQ9JMU3f4YybJBP8tR3z5Gs1/AZRrtTo5k3aM5V8KlCrq1WD9vTJCryMrW+ykDiaYGqnMwNdk3WyZYPX8El+rIGnW7Odm2TgDDuoY9NwUEqAWPqHJMu9Una/Gb5jCLimCyLCi/zVN6OTUIMgIQ3LC/s98dE/RZh/xjR4AGgXSigdRAt4NMUl865qp9TFy18CWZSngPXbXIPnECl6eUZWoNN0pjuE16KMxgmcLN2Wvps4TS2NQgOdrQh1hRqpwdVOinOZR1SNYdup0c1UUyAP04gR0nUL1HO89gxxlUS+aZ+0Jw6oqU0tbUwI0zAaX0enoFdDNKf03Vo37+XIBbBq9ZF9JNE5jKkhHvPVnywmD9vDH6sUG6YFhIsuxgagdT9zB1D1skGD20pNxZWBxbBEDaIL+0QXbUwjQeqnNo9jLoxkJ3Drp3BOPew2wsVOeQrDrejw2TjZPLa/iUTKVZt/w52VAyvGlhNh3PiQDO9KTh+63pYSc5FxI2bUxJVi09df3+BG53xgn4dBL7S1EW8Bf2eC+URWQx0XWUlu7MCD4PjyUwKDxOej69LARYx78fHsfqFLc742sUBQOTygJ+VMLtzyS0SoJ0vGNgVQBY4l9GSF2WHkDs7TCwKc+jRxOTET2Fj1yWN/rWxL1uKNEcjwicthLWgvQTWsfn+LqJASxapNa3VP7qPXzoQuy6KCsNEt4ATAACRr9cxRTWsCAQGEx4T2bTDf2VvqpZkdQywdUtlvCrNdxmM/grBUzHHlWA4DJLY9iMnk54HdKE8tuuEza0i5Jbby2ft1WxEXtsr0gOfSIvXvRUeumoDNVMVrohrY3BQq6qeL48GVLf9afqQZQxA/OmNO4y9w5gS8DXPcXXReAa9008ngEweeclpVfAs/cw89ngBd7qmwxdqr6htFglCXxFX3nobYX315XMekrWGl5+S4F0rc7P0OsJUC4bWMvrBX7eWkrkT4V1cV8oAb56nz9VmJgz8Hs2zsan8Pg0BZUvfvGL8X/+z/8BALzkJS/BT/zET+ATn/gEfvzHfxy33377Tb/uDYPKv/yX/zL++I//+KY3eCtGSOvbZhV166CcRztLkNQeSe1hOg/dejij4DWQrh26MaWD3ijYXCHdeFTnNPpSQ3UeyZoTeJsrKOuRHVvYgpPBbGnhNbB6Tgbdcx+a3RT1hRFsvuVLE4+ayw2a3RTtfoF2zmRP3Tr66aYJnAFsodGVml7Q3sPUFtmCgEI5D5tp2FyLhJfPdykPvB8lBNaJQlI59NMUdpSin+XIH1sLs5nB5Qn6C/OY0KmXMjlxDnaco58X6J63B5caMorC0NXP3yFgE0DjxrkwjSAz1vZQTYfk0WP6BnfZuwmlKN9NNFnFdSOJtR2Q59Er6RMNu8eUWdU7+EkJb1gNErap257nskzgygTd/phSTqUoHw41MUYz4OfchBLQaS4+xhTpwZpS4GXLxYKjDv00RbLseD4zjaSy8EZB1y3qO6ZwmYGdldBVC121QKLhUw3VWiQnNVyZM9xGKyTrHq5M0ZwrWVeSGzijUJ/Psbm9QLroeI02BIvKA32uMPpEBdMQbHutoDuHdNHA5QYuJ7PYnh/BJ4bAuCcwzi5tuH/jDJvbCgJIpWAaB905ymsVAawdZ0g2XWR3+nEKVffo9kp0FyZQvYPL+fr9NGe4UsdzAUCAvyPDK8BZVx3MpqMcu0zR74y4gGAM+nMTJAcrBhHtz1kpo7gIwcoZALtzgjEndTXi8wMAPx4RiJasp0EAX5uK7GToZk1T+PO78HkGPx1Br2vKOPdm8LMJvHSfKknSpTfWEOAcn/Cn6wkEe1n0GI8GRnS9Yfds13F7xjD0Sin453C1DyFYxlESqWaUesJoMneh7iFJeIzhZytwJfaHKjVIJW/BUHkeg7aCNBXGkP1SGno84n4Hj2l4TgDHzlOCKn2RKssIzGpKiwHALVcEM86TaRuNoqcydHkqw9RWGD3IO+vQNcqEWldV8XXgPHzXQ+/MGMRkLX+fJAMwxcDwKa0GUCcj/P2UbzDIdJOU94B1PP9JQnZUXj8EA0XwKvUmvmng2jZ2XHrnh0qY7ZAZAZau66Vzc7sqZAjDif7cNOH1EQ+nD35Sa+mjFJZdj0ZcINEKfl2dClwiIHW8v6OXeItZvIFxreTVcD8ENnH7fF4P8AtyYbjh+E/tlxo8vKeetwXIn83jUwX8no2zcTauHkH+ejM/z+bxpje9CY888ggA4M1vfjPe//734/nPfz5+5Ed+BN///d9/0697XZ7K3/3d341//5Zv+RZ8+7d/Ox599FF87ud+7lUpsDcbQ/t0hu48mkIhX3Dyu9kzyI8tspVDX+h4cSklNRg/XJOVKjRMywl8UjnAUwo5/dMOLueXWDcxSNcWLmXaZZ8ruFShLxUmH+9Qn0uQn7joyUOi0Y8Uksqi2eW5STYW3TRFOzfID3sC3cbB5RqqMKjPJUgqAliX0QsKAPX5HNkJvZT9yEC3HumyQzdL4ip8dXsB5Ty8IRB1oRrzqEezlyJZadT7GfqSrKydpLA52bBmJ0cpaaHwHt08Q7Lu0RcGUEB2wuoKLfUgqvfwIZ0yY7Kg7umdBACfJXB5Gntv+nGGtLNo90tkBxW9lm0Pn6fodguYdQ+zbpg+miVMhs3TmOiqPWsyIoPTUtKqa4tuhxNQ3Tv4REGtOtjdEbpxgvywRntxSkZU0V9KQG6glMLmM+YEdlmO/LBGN8uhG4d2N4NuHPKjBvX5giYkjJFf2qDdK9GPDOA50c8Pa3ok0cPpBOZwDbtPj63NDRc0pgbZokc/SgDDeyxZ92hnKbLjVupLhGk86eEKA1P1ZFGXljLTPImArvjYAt2FCVymyVDPmNLbXhghWRIYZ4uesuCRQXZMH2d6eYXqeXPkl2uyxrOc3s5Eo3hkhW6X4Dc52sAVGbLDdVxIsKOMycEybJGw51WBgEomr3pVw41zuDJDcrCCKzIg0VCWoVe66SjHnmZIL3umyoY+QqUoXR4JuPIZVN0B0GSuJwVDrYQZggBE9FJfkSZQJ0uo0Au71Q+lOst7tyKQQd2yVufoBC6EywTvWZC5CvDrL8yRPHpMCW8AOmXB7abJ4F8DKPfdm0JXEiiTJux3hY9SWwTp5mTMACWAx+k8mTgJPfKLFV/3JmWE1zWEEdQ5ga/e2yUDGSSwGYN1vPMxWRRGw1cDk+QrdjRC2MLQqetWa8oujQG2vx6kW9LXTG5FwdcI5953/VC1UuRw0qUawmVCsJBv2ljRpKTf81Q9TdvCxs7N0xLiK6s7To0AylphLMXTGpOCJfE1nj/xcvqmAZQ+1VOpjIHvu6vlr30HlReR9b1WlUh4js4y6caU1GHv4ntGFTn8yYLnAZQzbzPL8RrkOfxqzXOXpHCtPdXh+YS9ndvnR2ko7eCdvuo5MUgnS+E3Q0oyYOGvM14heCrdySL2t8bzYu4dzuuVz7tVHa5n42x8Go8z5vwGx6dZT2UYX/d1Xxf//pf+0l/CRz/6UfzhH/4hnv/85+PcuXM3/brXBSpf8pKXsEdtK5jnDW94Q/x7+L8/q6CebqwxumzR5xr1nkZx5GAFFHoNFJc54ajOJ/BaoZ2l6Edc/W0nDEjZ/4MNuucX8A5o9hLkhz36sYE3QLNjKJktNUzn4TWgHMjGZQrZsRUJo0Yz5/bbnRSmlgS/XKPeNSgPLFzONFqbkl3TyqE4suhGBL+682jnGunaoxsptJMc+YkloEsUrDGRFdWNg7YezU6CdidhCFEpnsdCw7QOzV4aJyL1bgqXAroDdEvms7ltAt2S2coOGoKweUpAWRq4aYpk3UHXFsmmg64atLdNkaw6+ESxd1EmmvQnWqiuhx0P7IupyGCq3tF3KZMfn+qY3mpHKYwwp8rSX2onOVyqoVsLOysJrrxHt5ciXXKbydEG/e4IbpzD5gZJxdqR9IgBPLq2qG8bITto0I80ko2DacjGUS5tht9XFqa2aOcZ0kWHZo/S1vr2MVyiRIrK+7ufElz2k5RMuQAsm2kuFvQek4fWsEUClxloed7mthzlpRbtTgbTOXRjA+UAUzskqxbtHs+bqS0XC7bzQ0pWh/TTjBLvhqA53OvZ5Q26nRLJpkOyAbyhP9iNc5QfX5AR7h3S4wa6atDvjVlhsqgp6U0NoIF+bwxd8/yaZcWJs9SzZI8u6YU1Ct0u60+6vRFltk0Hs6wpA04NdNUAWYJ+VsSO12RRRwYf2kBBfLTZ1kdRoiOohedCBsY5VC9JqZ2lvDYxDIBa1XC370M/dkSZa27YWXq4hD03hXnsmCxPkUIZTVA4GhHEaQWsK25rVEoITwdMRkgeOSSw6yTQJ2U9DfIU6ngJd25O73RDaa7uHdysgOk3UMs1+0lnU+jphLK+piHr1bZANqQzoxT/ZJA0ekoe3Wp1g5+E1z/UbErf6uExE2ATA78InkEB1m1HSeJyFUOLVJryeyDIMusmehjpOUxjj2RIc+WNpAmMrqhQgVIDqDeGzLBzrKEpC7JuWuTBWsPbJvorQ62LtzZ2MrrNBrA2sryuqU/57051Qpp7BwDoPJ/jPHSRM202pA1LcjgXBww8WHmjxmXcb+8Gbzmln6eZuruz+wBP9tM1dfRfXhk+sy3XvcvcS29tnnEBREJ7VJFLcNDgb1VlIZUu9JQqOY+hokWnCXtcs5D2e1pmuj2ulramIvc9zW6eSrWVZNnYWal0PAfXMx7o3o2vnr8hyl/jdfIOgMED7buu+7XOxrN7nIUs/dmOMz/y2bjWGI1G+IIv+IKn/TrXBSo/8pGPPO0N3crhjUKfKfFFeiQ1Za02pQ+yPsfDNA2rRbqpQX7Uo50ZlAf0XJ78hRLFsYRFaM/gkgxINx7OAKb1qPcMrGUyLDxwcmeO0SUrHk2CwtGjPXyq0Bc6giebMwCmzxVcphgQZD3qcwns3MA09Egq57G+PYVpwYTXxgMZ4FIFO6HXLd3IJE0rtDtkPuG5KKLcUJ3Sj7i6my57dBNDX2ZPGXA30ehmCfqSabiq0EiXPTZ30EeUrh3aWYJmblBe6tFNUiQJWdz+til0Y9Gco4yzPzcmIwhKI7udHLkkgOrewRVkFp30Vob0RlNZ6LqDneSwxQjJqkM/TqFbh/ZCAa9xSt6rPOAyTVZ53aObc3LUBVAHAvJ+ksClGsm6AzxgxwlspmDLhDJX6SDsxwbZcY9k00HZFFr2TVmPtqC8OD/qsHpegfyw50JAoSPIU54sadj25o4SuvfID1o0WYb8Us2QoN6j3dEYfZzAZXJSs/ajc/CK7KXX4pl9wRjF5RbKEvTqTQ+XG2SPk3XxiYYVVjhZ1AQ4HkhXPVnPIkWybuEyA5caSms7zxRiB8B7tHsFfaW70nfZ9nBFChVK6iXEKIA8b5iiC1B63J8bw0lYk24sbEk5cfAQh5RcXbGmxOUpzLqLgU52XkKvG+ijNWttEi5EdHsjZI8s4r0dejxdkUE3HdpzYySbDl4rmEYY7N7DXFowUGdRESTlCUGoA1AWMI8vKH1NuIjU3TGG2XTQiwrYbAb2EWCSayKAqOtjv6ubFIAbUd5dtQyjGlG+HaXbRomP2DOleJRBHy4JhAIbl+cETR1fB5B+2cD+VTV9naF/8QkYmmdkKAU/KYFRDizWlOvu78GPc6h1QxauLOCmBbRW9JJuKqmfEVA1KglApfZDKdZYKKXhlkvo0OkIUB6JLaZRABq2JL7eu/j5oEYlgaNIh31VcxFOKlxitUhY6HSOYGstn0UCdNUVbOH2hCowXeHfrm2h0wSu7qG0wv3rn8VX737jcMqShFJcy7oVpTTB+FYNzPafp+pC2nfhLnNvZAx9310dxCTMdACXZjxCSAVWUmOklIbbbCil3uqb9I0k4xrNUKX1JvpKzWzKapy2He6pJ2HBr/JTtp0wsaf//1T/ZlnALZc8dpVG0Hq9AOLu7D6ylCGo6IpAntD5GcYZMPnUHWfX7Wx8So2b9Uc+y+Wvf/tv/2289KUvxT/9p//01O9/8Ad/EL/927+N97735t6n1wUqX/CCF9zUi3+yRn7Uw+3nsBmlqd1IwxmF8nKPZsfAZvzyzloyP+VjDdqdFM1cI194JJVHfmKhO0lrLTS6kcbk4w1O/p8C3UQh2XjkJ+JVax0yB7iEMth6N8Hk4x2qCynqXY3xwz1U6tHM+MWd1B7pmhONZOPQTjTyY4v8mAxkvZ8gqRxsoVEcWmzOJzA1AZQX/2eycXCJwuq2BKNLFt3MRBBaHlh0Ex6zEhbT54C2fO1sxW33OX2j+ZFFP9LIliIPtvSehvRc5QHTANnSwacK7dTAVhJM5IB0pQHn0Y2ZYKqsANhFB9U5uEmOfpKhHyUoHlmj35FJe25gNpyAddMUJlHoRwmyk5ZpqA7o5glsrlA+VlNa6T3aczlcolA+UqPdydBcyFFe5qTcyzH3kwToLLLDhuzjWCoKeodsQUlpNx62VVxqYvBMdnmNbpcprLp1yA9bOfca5eMd2nkSE4SdSFGz4wbdNEM7M0jXBIamsrClQbog6DEbBuGUj5EJtLlBsmTgDQCsnj9GeamFLQya/QyjT1RQvYMdJUhOWgnIIWPLgwVlq1rBFSngyZar1sKOWFei1w30RsEVGZngVMOsuD29bJBJz6cWTyQ0kFxawk0LAj+tkRxt4nuL/tWcck3P94fpe5h1G2tIfJ7ChOc4QIVUTDCxVy/YteeTBHrTkq2eFDAnFdRiDb8zRXpUxW3aKYGncg5a94BzyA429Lj2bvD1WdbgqE0LZDr2lyJjL6jPEiA1BM5ZBtUwndkHGWOSwNdDV2QElKOS6ZlNP9TErJv4GJ8lUXaoNrwPVQf4EVN3zUkVPXA4PObkXpJnw3BTJjSr3lEKnKb86XuCN6NPeQSf8RE8rEoNvlEAqhW5cd2xIqiih9RrLbUsLLZXWQa/M4NarOLreGFifdtB7+7QMxnCW0Jaa9OIXNUSgBrxtVb1kAqcpazQCOmlXR/TZQPDqcY8f369iUDUrdbR93nKI+k8QiVFCKHZ7lrkA3Vk2ELgTAQ6AO+VpiEDmGLo4vQpFzVCaBToCQ1hPVd1T6qhUxM4DdC2fx88kGpcRpktWl6LU4sNUt2hQiBUmsAvlvTwTsbwJwuyxYGRDdUfTxHWc9WQ518F0IXtNZMJdFni/Yv/N/ZOXqtS5YmG0iqy2lfVsOTFqaCgaz3mbJyNs3E2bsX4dK0U+bVf+zW8+c1vvur3X/3VX41//a//9U2/7g33VD5RKaZSCkVR4DM/8zNx55133vQO3cxwKcNpdO8xepyACSDTVV72UR5oGod2atBPU2gLTB5mop5LEtS7Bi4lM2kags/V8wqkG4984WAzhWZHwzRMjk1XrFtQFpSqTg2SDdNiuwlls0nDu2r0WCu1D2TclAPaGR8TgJpLpH+t9yiOHWtRNJBUjmDRU9KaNB79iCE+2cqhG2n0RQjq0dDy3ZufkKFkCIxG0jgGEa3pKzWtQ7VvoDuP4tChLzWSinLazcUM2ZI1LS5R0K1HXyjonmygzbn9pLKod1OYlpMBbSkvrmYTZCcEMm6cMVhIM5im28mZfFtqKGfICBcJrIQNMbyGzKKynt2IJx280Uwa7T2Koy6yZaax2FzMYQuFNFPQHb2f6ZpeRgdhWKcp0pMW/SRFekTAoqqO/Zdjhgklyw52nEA3HkoD7Yjy0eKgheod+nHKIB0Ai78wRra0yI966M5yv9ctXJ6QacwMtPdwqbCGVQ9oso0uJ4uYNPRImZrhT825HOlKmOfMwMrjwoqXljAj3fRkxGY5VNPDrGrompJTOyvhUi0spTCpGmTRALJtvQMKgj+XZvQyOgBKwY0y6KqLTGE/YYKs12D4Vc99pg/SwhUJgWXBRQHV9gRXzkHXNbxPYXdHMdzHZVkEoP3+CKZIoJc1fJFSCgrAFQbKJnCpgRY2sB9nyD9+zJTgugdSAzcroeqW25Z9V20PfbKBG02gGh6HOqlj+qofGQYq1S3TYZWKHk2/WEGlCdysJPgdF9BVC9X08BMBNSkragAAbQe7N4U5XMKPyFjCEUy6aQG9cEyNPTgkIFBk3XhuJNnVeQL6DWXGGJd8bNfHwJtbMfyopJ/TaLhJyYUAm0B1PdTBCfzeDKrp0O9PYJY1Fxx6+XDZmcM/8hjU8UIClJhgq8T7rMYjSojl+OI2g88xEwm880Ci4C2rQtxyRSbScQECzkHNpvDrDaW3xgwBOQHIipdTJQR2vu2kz1Kut7Wx2uNKAHdVnYd4GHUpIEYCiqAUe0MlQAnew603MZzInSygd+Zwh8d8uFaAdxFQxsAe2R+CVhP38e70NRHoKq3wQPfegdVsOwaRGcNzJOcQ3g++XhBcq9mU+5ckZC5rAfnhOYFtVP6G/D6RDbXtACSx5QPVr4Z3jtLjeDJvTP7quh4mz+GE1d/2mJrJGG5ZP+Fzt/fjDGyejbNxNp7R8WnKVK5WK2TXCANM0xSLxeKmX/eGQeWrXvWqq/yVwGlf5Zd+6ZfiF37hF7C7u3vTO3Yjw2sF03J/uomOzFEzTyhNFZCZn/DxNlPwigffzOhp05Y/XgHtlEyiBdCXCrpX8nwP3Tn0I41urIWlQwSh/YheyLQiYAujOpfBdB7rCwbjx3rkxz36khUo2YmVepAE6dphfTGB7jyyFb2b7VRj9GiH+lwKm1JKO3m4RzdmKE9SO6wvJCgP+AUegnpOPiPD6LKlhFaAZX5smX4rxz96jPvhE4V0ReBQ76WUCbc+7qOyHronC+xSBd15JolmOla0AJTcqs4jXfXoZim6kv5R3XnYgkDRa6nB8PQCulRBN0wq9QkTckPVhhtr6MahH5NFTVeektOe9R0A4BICZtMppAsCNyOyUQBg/YqD6+nfTNbClM4LYLeAqQQwdQ7tfo7i46sIqLJjMm31+RLJhiydFVlxftgj2fSxlkRZDyeS0X6cwVQ9AWZIspXz2O7nMaU4O2rR7mZINhbF4zXggW6WIl31BJRVT9+qDH1Qwe6NxfvI2hC9bGAnBeWgErbTnRszRMla6JMa9XPnSI8bSkC9R3d+wtdODXTDcKQQaNPs5SgepgcWANIg7azAsJ15SYBhFHwotO8dZa6rDfs6D5bsqBSmWVkvrKiHrltKpqueVSxKwe5PKFk+4KTULBlipGwfZa8E1AbeGLidlMDaOXT7Y+jWwaybKCO152fQCwYH6aZD/5x9KOdhjgdWC2nCRNeuAw5DgTz9x/pwSTln08EXGcFr1dGnuWkZTKU1kLMH1d+2A930PEYAbsZAHj+f8jgCCykeNwDRM6qOl9CtSBetBZZrMmB9D3dy8x/sTzWUSDbjfiw3lKImBn5nFh9njjZw0xwqS6DyFOryMaDaIW3VeUpmVxXrU0oCeyzX/E5ohMkdlfCo+XiRuaqU4EdJ4FFIf3VVFWWuEPYNYBiNAghGlYe3PfRsCvQ2yj991xG4CRMcARWuTzKpsvS0ZFmY19DbycRgAbRtOLYRE2xlBIlrAJPbgTIRGLddlIhus5kPdO+OTJ9K6UFVZcFzkKZM5QUIapWmRBiQ0Kitrs8ANkVG7YHoV9RFDvsUft1TEtStvs0n7KHs+xhsBAjz+ASvu/WgU4zpdu/p8EtKnbdf+8pxBiTPxtk4G7ds3GyS67McVH7O53wO3vOe9+D7vu/7Tv3+3e9+Nz77sz/7pl/3huMFH3zwQXzRF30RHnzwQZycnODk5AQPPvggvviLvxi/9Eu/hA9+8IM4ODjAP/kn/+Smd+pGh3IeRti04oDMTnGZEkZlPcYPtxg/3KLaNwSdK4uk8QLKPFyqCI6ch+6BbOHIXBqgOGJgyuRjNZKNJZCyQLZyMI0weDI5Vh5wCWWmkcl0gE/I8mQrh2TjsHpOir4kq+hShc2FFLpleE+2cmQXSwWXELT2IzKK5YHF9GMdQe8RkxpN5VAcO7QzjX6kI/CbfpwVD14xfdZlCs2OIbjxQLq0aKesOulzhX6kRV7IgCCfKKRrC294DPlRj/HHa/oLew9TO3QTg6RyEmBDlrbZS9BNEpiNxfjhGukRV/vzS7UASo1s0aN8rAYUkB92wjI7qJ7djNmigy3ZNwogypJV79CXBu2O9C820rnZC+DL+Zx+kgrboRh4Yz1ZNitS0UkKU/fIHl2S/awtq0Ek/KefZujnTD1tztEr6TIT90m3DtlxyzTgaUZw1zr6MgUsk/HskSxb8UImMKsOyapHdsQeSq8VikcqeKVQXyigeof8EoFVsuaENTkQIKQU+nMTwHpJyzUEc+McuiaTqryHnRRIjiuCqTHrPZJVR1YyT+FGEvZRtzxn4xyu5KRdL2tkR62AVvYo9vMC+mQDZS3spICqe3iphUHvoBoLVXcMVtobw2UJ+tt3oOoe/e6YIFCSXlXbo37OjPUj0pcaKmbMmgnAPkvYrWopZVXeQ1cd0oeP4aY5U3QP1wQujgsIyjmozsJKYBOshx9lPOYyRXK4hl6IJ2/dDD6/RDx92z97O/DTEdykhCtzbsd7hhNpDZenaC6w9qa9MIKuuSjhUhNZW5dJ16VRZKYXS8o0AfG9GahVBbWq2KcJxORVaE0AcSulrwB8zmvOdN8eKChxZh2Pgp2PmOALyDVueS7GIwb45BJyJKDUT0ogMdEriiIHilwSddMoM1Z5BpXnsQ9UjUcEcEXOCpaygD63RwCUSHdkmrJaQ2S6SkJnQrCPq6qYUKvHI+jJhL2ZV7Be1+qnDONB+x4Bih1810GPRwQyklircnZFuiVrZEIYTtgXKEVZsFZ40L0Xvu+eOJ00JLhKsM32Pt6d3cfnO8/AodkkVrpAQKyXfkpvbewBVZMxfZOhu1KqVwb/KSWwEcQ9RbLw9jm6O30Nz7WECAWgfCqoRypcQtfmlXLVa73uNqBUxkCXZZQQb8uStzsvnyxg5Axcno2z8Wc/nup9ejaeHeN7v/d78c//+T/H13/91+Md73gH3vGOd+B1r3sd/uW//Jf43u/93pt+3RtmKv/xP/7H+Mmf/Em87GUvi797+ctfjqIo8Pf+3t/DH/zBH+Btb3vbqXTYWz28UWgn9Ai2c4NupNCXGQGdVtjcxslItnRopxqbiynyE4tq32B02aI46FHvJ7AZMHqcICSpyci1E41047G+I6fUdWXRj0301qUVH5NUPgIFr4FuQqkpdxCAJmC0pYbuCBqT2qPeNRg/0qHZTZAfW7TaoDzuWS1SE4BmKwYFqd6j3THoS8VeywTwykB3DqbRMO2QFtrsJpGVVB4oLnfox4ZS0JGB2yFo7EuCyVakuZOP1bB5DtWJx/Ogh0sUmp0ESc2OzGRDuaY3TC3tJ8LsnbBD0iug3k9RHADICWTb/QK69/ACEF1mkC5Ye6I7+jO1MHnZYYN00cMWBtlxg/pCgey4i/UV6aqPATn9KGEvZM7zAIAgSmpWkuMKdsIKDbNqCJJaMp2uzOlvlC5R5UHQIoCun5Jx7MfcBhSQHXKi6hOet2TVsULEMqTIbHqYxsE0lsmvuRG/JStDIJLYbpZg9NAS3Q7rPIrHa3S7ObwC8oOaabhrsnr9iG/T9KSNfZ6qZ9qqK1PWvNQtQVDwM2odU4LNSYXu/JhhUL2FWTWwsxJmUUE5B3O5IpDYn8CsG9hJDm8lUGndMkyms9A1/ZB6VaPfGyP5xEGsybDjjAC/s0OwT9NRlpom0eOZHdQRpOlFDeUkETg1UaILpwAYqNYifYyMip+UMIdr+DRhOE7VUcq6aSOQMUsCRzvOoFcNfYGZiZJeVoEYuCKDaTfw4xFU20VfGqwDLh/B3XkHlPfodnLoPoVZEQTDUP7K97lG9viGvs+GHax61ZCpbXuoqoM6PAZmU4bcIASeKKi9nVifEmWwmQTedB2lnqPyVPXDMz4c4OYjKOfgiwzeSYqzHe4vHquDneVIH2+BpoPbm0B3PVBV9FpmUm9kPbDaUL5b1WQP59MBCAbPZDjOview6TrKZfMsSmX9pIDqLTCbQNUt0PfiLxzB1zXvuaom817V0NOJ9FpaOAmoUeKF9OvNVdLIJ+qs1GUZmcDI+lkLVRTwzUbAJIOGlMhwfUfprl1cjkDq7vQ1w+uL5zACHs8qlOiv3Ar1uTt9zcDISdclnGeya93Q+2st2cc8H5J2ASDcM+FeThKoXsUgJF9VUd4bk22fYFzlNU1SHpsfwnfCPkcwnL6GIUddzyoUa3Ej8ld4F5nf7fGgfU9Mzt0GjduS4rNxNs7Gs2d82i3ufJrKX1/5ylfiF37hF/D93//9+Lmf+zmUZYnP+7zPwy//8i/jK77iK276dW8YVP7f//t/MZvNrvr9bDbDn/zJnwAAXvjCF+Ly5cs3vVM3OpT1yE4smjnTNvNjsn02J/OmO36B2lyhOKbPrBtrZEuCumzpkK4d8mMmvObHliDOAa2AsnTlpEpEWDMF9KlCfkSJYjNjt2P5eAdbaOgTAl2ArGa9Y8SnCElhBWWQCthcTGFaj+o8L0c3STB6tMfmYoqkQZRL2kJDtx6jlUNSWfQlmVfTANW+xuRhG+WS+QkZFNSIjB88z5Xu2Ktpaodkw+d0Y4KfbpKgOOjRzAnyEudhc/Yt6s4DwnyaqkNSZbCFjuFA/UhDOfA6WILsdp6IBJfpqemSwNOmlM4ma4vsuIEtuOKfXd6gOT8SaSllzOmiR72fobjcINmAoEQmRcp5mMbC1JSb6qaHTwxU20HlBvUdU5ia/88eTYP0ckXmKaGEGYpA2DQW/SxHsmQ6pR0Z6FYhO+lYh7Hu4ITh0U1PH+iIiaveaGHNKJntxwkB6KqFHaXio02RH9b0k4nP0TRkSfmaMpGVkB9YD28Uik9QBtnvlHAC3rR0euplQ+mrMcJU5kiONlBNx01kBr5IWLEyLYSl7GHaHj5JYuWJWbYEBkpBdQ79lJNy5UFgPs4JFh0ArWEWFfxsTLZJroNeN+yqNBp6Qz+j1xpm0UQwxmoRqdqZFXCZQXK4JkMpE2xVsQfV7o2hVzXgJJDJ0x8anm8WNX2N52fQq5rbaHv2bY5zhsv09JB25ydod1IkG4v0sKJMcyU+xhDGsmaCrLKWgUUr1uBAAzAKylr4jNdVbVoCH63kemrY3RGvR6KhmjYCHTUqWcvhPSf+aRKlsj5LGHbTs7IEBVNWfdsNdRy3YKiuh81HUK2NqcuuzOghtWSHXZmiuW2C7PENZc8dWW7kKfzBIbC3g263RHqwJjjdmxH07M5i2m/wySog+hGRGMqOUwM3H0M/fgis1gT1OzM+bjyCVwp+d0wf8LgEVhuo6YTgdD6jvLqTYKOyEG+lgXvoE1HmrCRNNICvbUAZGDhvLXQuYWJGU4o7KuFPltyeMQRkxyfC0OrILKs0gV9vYOYzWJErn2LpPPtGg2xUBQ+L0pGVC/sUuy2FvQyyX1UUgCHgh1KsqKlqACnPJcDFqpMFezq9MP2hokbY8bA/sa/yScapSo+tkJ+rbyQd//TODyDwBkdgI69cSImhP85f+YQb3sbZOBtn42zc8Pg0BZUA8IpXvAKveMUrntHXvGH56xd+4RfiO77jO3Dp0qX4u0uXLuE7v/M78UVf9EUAgD/6oz/Cc5/73GduL59qKIVq38BmBFfdWFMy2bNjMmkckoZAs52waiRdOfSlQnmph80VnKSslgf84jY1ewsnD3dQvTByI6Zpjh7ewLQM8HGZRp8TIE0eIotlGo90aTH7aI3ZR2tkix7Th2o+X0M8n6whSdcM3DGtR3G5R35skdQM4xk93mPysRq65+tB8bm686h3U/SFRnZMZrU4JlBONw7pxiFZdjCVQ/mxFYpHVtDWIz/ukB02BAobh25qUF3ICNzWFqNHKriU9SejR2vkRx2BT8eqEp8oJBuLbpqgvljCrHtJg+0ZMKMUikcrzP7vBroDmj0mnxI8S0rrOKF0tfcEjpWFHaXRA9lcGCNddtCdg67J9unGUi4LENSu2igJTSrWYkApghdDwGMnOZKTBtlRzdeqOgKYkxZunMGlAig9AaJprEgyGUITezsBqfigr9Ml/GnOlUglVdWOUjK0iWZITtVH76ZPTQTx6bpHN8t5DMsW7YURgUZgxTqHZNmwNsQ5INFIFvRM2klB7++agLfbKwVgWaaZhvqARROZSpcakQc7AViWMt4dCRxJNJJlQ3lqoqHXDRlH53jOOsfX60XaayX0x/u4DS8pq15z4uqNZkCQMCbJI4d8jgbMwYo+Sk+mzyuF9BLlhOYTB7CjlPfCrIBuOuh1A5+n3J/DJXyWor0wRbs/gh+xjqS/MIfNDWs/vEe/O0J725T+3SJBd26C9sIYqnf0rTqgnxVkeEc5cHwy9FCGSfvBgnJP56DbnmxoZ6EPlzDHG+hLlOK6UU6JqFGUIFuP7jxZXXtuKr2XnuxamhAMJQkliSmlpt3eCH5/DozHgyxyNqVH7RZKYK10kfpU8zqfVFyQyXif23GObpazNmaWM41XgCJ6CzWd0GspPahuUsCOMrgyZyDVKGNgkyWL6csMbpTznCt2lAKAXtfw+3P487vwt5+DzyjR7s9P433qiwztxSnPk9JwsxJuWsDlKfy4oCS3aQlKFyvovV2Ryybwzp8CJdsM3APdu08DJedZEyIyWISeylo6GjXBLm/shNenLMSHSTnu9k+QgoahtILvhHUVP6pK0kGaq/Qpn6FKEuD8HtnGnTlZ7yShx7PIxdPqYsWLnk6ih1IVfD/A6FglEthVlSZPCsq2pcIAq1Z838X7cTtBNx6bMU8L6Ckt/ZtXgMcH3Xuj1/bK32+Pu8y9Z5K7s3E2/ozGp/N7L6S/3szPs3l87GMfw8c//vH479/+7d/Gm970JvzkT/7k03rdGwaVb3/72/GRj3wEz33uc/GZn/mZeOELX4jnPve5+OhHP4qf+qmfAsBUoaejyb3RUe0blAcWSQOMH7WwmYpBOcVBH72NoadS9x7riwnDfQILuTV/y044obBFSP/s4DVfqxtprJ4/htmwDiRZW5SXW+RHFtXFHLY0ETzanAmeqmOdxejhhs+pLPJj7ldx2FFK68FU1oa+y+Kgg6kZkpJfbgh4H6nJCLUOSc3HKOdRPN5g/PEak080yC/XyC/XZJ8aCzdK0e+UrLJYUaZXPF7DVD2yRY/Row19lyct1s8pMfrTFUzNHsj0YINk02H0x4fRw9iNDff/oIVPFLKDWiS/CuM/PkJ9ewlbJgRIG0cJ6sYStDkgO2xQnyOAYnquH+SlHmQDU82JaKpj0E0Aa2bVSfdlB113SE5qdikuaoIsmZSYTQefs/Ki2cvQ7RQwxxXsKIFqrdwT3IbLE6aGblp2bgpw0p1F9viGoMISCAVPpWmsAC8yX+1uFlngAOb6SQbVWbTzTPZT2JOOQR/pooWuCXazwwp6Qx9ssiD4gYQSmXVDWWpOv1uzV8AlGu1ugX5/AjvNUN8+oZcv0QQEOwVDZBLDLtBZDp8Y6IoA3I1zPn9WwI4M2t0C7W1Typ7HWVyd08dLSm3XDfSqglnUTA3VlP8GMKubHq4gUwwN+ILno/l/LqCf5rBlSjBQJGhum7ICZd2gvTAhAN6fI334GOnDx/BGo98dkZktUvR7Y/R37DEttiKwcRmTbvtJCp/Ss9pPc7iMqaK6c9G3CQf04zS+J6AoCVabhhN2YRpjxUaSSNdmB7UhsEXv4PZn8HmK/o596GUD1ffwOStGgv9Q955yXq3pgTMafl0RtAIErnUTwVZ6QJYWiSEITch8IUsHVusWDCMeU3iQdR5lXDgRJls3HUxjkZw08FrBzkpWwqzC8zx8In5ZgM/rHaXkJe97rxTsvISdUyLtxpS+eqXgpyX681PeP1pHD2fznBkBfNNDbzp0uwUDk4ymX/e2HTLXKZn50Btqn7NP0JcmPO9yTVWanAZ5wMDAXWN4S/Y5SE2RJASSaQo1m1GK2zRM6N3b4bnYmQ/bSpMBqHoXk2Cjz9A7hhAB8bFhQhaCauJiQko1hR+LlDeVrsq9HfgLe0O/qrwWerLi0Dou+gCAnozFe9kNj3+KBYtTgHG7lkXAW2BeT4UQJWlMsL0ugLkNuJOUYPkKWe5d+tVMhL3i9a7ybV0hjz0bZ+NsfPLG2XvvU2/cd999+MAHPgAAePTRR/FVX/VV+O3f/m1893d/N9761rfe9OvesPz1xS9+Mf73//7fuP/++/HhD38Y3nv8xb/4F3HXXXdBy5fYq171qpveoZsZ2dKhmUt9xkhj/Bj7KfsR6yqyJb+Q6l12OdpUIa0YytPsUu6abhySlUV6Ip45paAThX5kkKwsXM4k0qRmxYcdsbrC5aysSCpWcKjew2UapnUEAYDIZ1ncni47Jn6uLPITi26SML20cmh2E3glUj/roFtLtqC2MLVFcz5H8UjFcBiRRqaLDv0kZX9g3UffodeArnrYcQrdsqQenpJU1Uliq/XoSwJZVyb0i+4U0I3F/8/enwfbut1lwegzmrebzVprN+ccQgiNoIU0Fw0Jn0KAUJAYtUREQ3KVG0UsioIECbdSyoUqROQLioLySQiigAVImltogViYIwmJIpeC4IWCIH6X5ktCcs7ZzVprdm83mvvH8xvjnWt3Z+99zj7NZo+qVWefueZ85zvfZq7xjKfE4l8fAAEAAElEQVQrTgf0jyygXUBUnASZnYMtxfsWItyigLEavpQU0PNzzP6vNcZzDcpTh1Bo6dlkEFJxSvZn/qEtfGPhGwJuux7hGwNfG5jO57oQvqckFrrICZZS8I3J/ivtR5hLJxg//iITRQemmo5JxhqA+rEWoTYIBzXshqE248yivNoiXJxDt5yEdi86hF2PORQolBZxXsKsWtgnVnAPH+T3La628LOSSaW7HrPf8xNzoDkBLq7uEAvDbswFz6NtRabZOYR5yY7Hk5aSyXWLcH7B/V+WZHKvbBg+A3ZUhjJ1g8r5tgq68yjHgOGoQrEaEGQxozgdMByVDGk6NDj4/Rbae4wzqXWZadgWMCKBDiWZ+CiJuQDgHzqSwBuyu2Y7svIjQOTggG4HpukCKE47SoQl9dXuCJrNpkcoLQOMXBDvY4VoNPyi4n2wFP+XVojGZBBuN2TU7GmH7gUL3iO2YE3NVpJb5b6LWsGMDGvqDxuUaw+7ddxXHzEuC4Yh+cD012slpt4jVnOycJ4A0VzZIBw0PG8uwB5vs0/T/OEVxCNKJINRsJfWDLwpbWbTcyiNkQCkcWQNCkDv60ZYePEZphTaFDRzL0aYlfxuGT38QcPFlMBjqA5quFmB4qSFO6woqe8BWM3PfcqKD7PuEE2DaCUFVq4daAV/YUbPsfTSxtIySXZeQSsHPyu5EHE0h58xzddsOi76uAA/KxEbi+K4gztqUF7aSL+owvDQjPduaVkJo0HQecjqGqVNBmLaefjT1RlGct9HmcBVdGNmxFTTkP0TTyOUXCPBA9oQcHbs24wL1tGQDRWGUSu+VtjQ/L7mNdN7SMpsrhBJ+7IfpBO48BDmFeJSw6xbLjzIQgnKArjAhPX42CXecwAl1EVBibFSDO9RmkzgZmSAz5OMawN18r5d623c81+GFCQkn/1OPJXRjcB44zVuvvetK1AeTGofjAfjwbgn4z6Vv/7mb/4mPudzPgcA8I53vAOf+ZmfiV/8xV/Eu971Lnzt137tdamwtzvuGFQCgFIKr3rVq/CqV73qrt706R62DfBzoFx5tBcN5ZYRWHxowOZFJbojWbEdwaqQWqM/ULAdsPzQgGA1dBtQXt4y4bEp8mRYDwG+llCZcwUTUWcEQ3qM8IrAqbtYoHm8Z8pn52G2I4YDCVhZx1wRodsRs9MO7rBGX7FGJHkeiw3BY7Ea4RuL3cfUKLZ+YsGGmOsr9CihFlbBdA6+Zj9i8hKVp0wdJZNEVq54Yo3hYw6g1gP8skSx6qEkSAZKAIJ4bZSPKNYDolJkvJTK3itoIJQ6A9rU3dg+VGLm6XfULqBYD3AiDQ2lhlvw2ChHdiuF3JjOYSwr1B/dwi1KVCc9xoMKxdUWYVYg1BpWvIkAUH5knYGW8h7uY89DjQGhNige3yEsKpSPrTmxB+gFlMqNUBdk3lyEP6hhTztWaoSA8gpL6/2sQGgsgZA28Msa4fxMQB0noZSJRuhNmwFH8ibqboBpBzJzhcm1Ib7SKCOvheLEZVCW9tN/7Dn69CKPvXIB7sJiutBjhO5HlMfAcI4hQr6xcu7JYEMxxAkA3KJgUm8fUB2PCIWBvzhDcUqJbHGZxzcahaBZ8zIe8FpLcuTy8g5+LlJTCRqiD9Rlxhha5/qTqHnN6YHSZe6fRhUjzI7Xi90M6D6mgdUKxUmL8ahBeTrkY8sQpx7DuRrFiox4VEBoCpRXCDy6hypUVyfmxTeW3a4F5bf2lAsK7AdVOV22POmhdwODXoDJxwgAu0C/43YH1RSS/GpzpYjqx8wCqcEhVAXcx3wM7Irb1KNHLE3+PcqS7yOLDTEG+jWXC+hT6ep85JB1Hcdr7oMBey7bNofN3JMRI4+N4vFWskhUrHoMRxXK0wHjUQNfaVRXuVDQHZWwrUe56YGPewRRawmpIqtvtgPcgoE7thsxHlZT/y6A8eEFlA+U3A4eblHmRR63LHn+lYI7rOEleMvPudDgJNBK70YYeR7BOxdQSqmj8fMKth2yTznGPaC3xwrmw5AWp5qaLGFVEoyVJYGfNQjzml5gz+sjzOZMHN52CIdLmMtrXvMNAWbwQQJ1Rryy/OtMgg3v5D4kttAHqLrKDGpKV43eI4YIXTMhN1o9eb+1hm8KmCeOoUDlQVqc0EeHXJSYC+iUz0BJdc/PkhZQjEbsbw74blS9Er3Pybb7z8vDaDnG+cDe+vq75jns7ywQnTtbZyIhQPvgHMB1LOmD8WA8GA/Gg3H7YxxHVLLI+V/+y3/Bl37plwIAPvVTPxUf/ehH73q7twUqv+/7vg9f8zVfg7qu8X3f9323fO43fMM33PXO3O3QLuLgN47h5xWU56T44IkeblFg9rijTxBAd86gOnUYgkWxhaRhRtieLOJ4rpGglZD9XtEoGKlSUJ5yznrLRFQ9Brh5RevKEAk+T1k1MVyoUZ5yRVj5ABVYet9frKBH+glnj/Vkec43ZFg6yipDSaDVPCE+HgGFrpkmcDFo+JoeNrOlBzFag+GAwCuxptEoMoBDgD+asYbDOZjOcdJ+OkAPZMDsOLBiZFlKTYfUdbgAHTB1LmqVgbDykR5HAJVIl0zvs8zN9JSyIcbJl1iYKfRGK7h5OXkSSwNsR07grIa9ugVGRxbCRya4Xphl8BurQio9KL+MtfTM1TaH2aiBwTRhXjFB1GoCxcpSxqg9YkUGNtTCkIUId1DDbEdAA1rkfcPDnDg2HyEA9i88gj3tuW+7Ee6wgtWAO6REVY+BTJlJPt+AOCvgZyX6CxXK4wFmGxiE0xMk6s7DLQv6GJXK6a/lSZ+3G01aCCC75GtuP5Qa5fGAcUGwaLcjti+sMXt8yKDYLUsUJz30toM7qlmr0ntYCTsiUyTA9KDmooXRqI579OeYcNs/PEexGoSVpdxX7wKB5hgkvIaVLvVH1hgvzOhDXY/w8wLlyQA3L+DnZU7bHY547RZrx/TbjvdC1Ky4USEiNqyAmX20y3UwsdDQIIukdwPcsoQXn7PdRagtQ2egJLSqHykdNJqMUwo7cY7yxhc8xM5LCf5R3Yg4q5gy6weE5Qx620Gtd7BG0UN5ULOuxBhgFAm19/TctR19bUoTwPaUxQKAffwUsSlzQAxAIKS0zqz4vRixMExaPh6k2iX1uiqoALhZgVBq2Jb3mZtZuJlG86FTuIsL2CdW8I8colgPsvjmEbVmz+pqzNtLcvTukRkKqezh9yhZ3P5iA9MHFCc9Fy6SnFZIqxTIhch9UKK8CKWhwkCJz7kpyFKGQPlrkob6kAGRMib3QALIXkudElK9Z1rtOAIh0KNYF5SUzwpowzRc5SUZt7Awpy1iVRIAXrrC7YpnMSqduyhfWbwWUBo6pdJKGFCQqpl3jW9jgmrTILRc3EJTcz9aJlSP52ewJ6KkEWCtV9KPaTTC+SMGQCkFtQm81pxnCFKMTNlNiylPUilyQ2C5V+2RRn6OZwDQu8a34c/VfwMxTEzlk/WDvkK/mucgTEm4+XfmNdCFvR5YPgjqeTAejHs67mef5J2Mu/VHPtc9lZ/+6Z+Ot771rfiLf/Ev4tFHH8V3fMd3AAA+8pGP4MKFC3e93dsCld/7vd+Lv/E3/gbqusb3fu/33vR5SqlnBVTWH1kDRQPdj2j+cMg+Gz1MckgAqC9JUIGjdFANLoeMJCbDPHaM4RMfAmLE8MgSevBMwzyidDIxd9Aqszr0eVmoCPQXK9itp8z1UJIPw5RWWmzoHYxaicyzpNQ2gbTBITYWrimYitoU0rcZyB4clIiRMlC7HTEuCygfmJ6qkCeBpnPYvaBBdTxC957yz4JS3fHiAnbVcXJWWQRl8+QvakqEi5YJpsVxj/YFM5QnDr7WACh91KPUOPQewzmRPgozyQAZgnM3swgHBBG6J2ix44hoDYN1akolw0hfX7EeM3MSrcb40AJ21Wc5pDtkD6M7IrgzrUN/sUZ5OsCsmOapBkeAOivhDioUV6RCpDAI5whIo1HQvcPwMUuY3ucwId06uKWBFa8jjIKbEeCNhzXslsd3PKwpD1xW07UhAGs83yAYhepKx57LIcDuRrhZgeF8DUSC12LFtNj2hUtKkEsuPLg5AZUeA/SqQ3xE2EqRlIZaob7UYTwgeCpPpdYksb/CXtjtCLMbUV+hV053BOuqJ4iOs4qgWRWwa7KXygSEysBI0JCSxQE34/VTiBS1WA1Z3pHYFH8giwWlyfLsYDXUITs4rTzmGoP6EgHBuChgegEgwr6b1qE/X8EMIdeMBKOgtZLFB0Wvrvhp7aU1wrKGXbHbLzQVihMmuKZEWj04wDN9Ns4r+kJ3LZnKNELMJfKxtkAAF3Bqsjz8r+E2ZhUwq6CvbuAfPiBbVRaUBgNQW5m87zjpV2UJlAXiakPW6PwR33JekQFVEgfsA5Rl6X1KB70Xg4sgnsAMQKgKjBdLmN7AbkYuariQvyNM71nFU5esbgFBnsmJxRphZlCeUH4dA1lud8DvBj1GuLkwzQv5XnQB9Ue22d87HFrUl3oylJXOigc9eC4QPTLPqgzTM6DKzwsuqg0Ow4UZGdMQ6YMFoOoKum2zNDN5AYEJJIVhgK4r6IMl4npD8AUg9gMlty4AlYU7qgmItUJx0vH75bCGXfVQV1aUzQIIbQdVMkU4hil4JwfwzGdMA5bHHvVv30s53QNL2rDuxSj4RmwMBxX0Zd4XcVZkZQprYKhUiFak1v3IdN1dw97OfoDSisB1t3uSC0QsDcnjqeK0/yIZftf4tlztocoCoe3IumpFj6QbnxRQphEDpbtBpLn76bMxxBsG+DwYD8aDce/GtWFctzNu935/3o3nOEC8m/GP//E/xl/5K38F3/3d342/+Tf/Jj7rsz4LAPDTP/3TWRZ7N+O2QOXv//7v3/Dfz5mhNYHkQYMYAuKSrEE0pTBZMvEBQVt5eY1w0HDSuO0RZiUZCRcQZw2Kq+wki0pJh6ElaJB0zwRqyt3IxEqR3hVrT09e4Mp/SgAlY+XJQB2WKC/tMB7VKE7or7Mbyhq7j5kDqIEQUWxYjVFcoX/LiQQxgT+7I5CoLnXw8wKhUChPhjzJ8JVBsWW4znCuRP2RLdk4RYbLHdYZ3BabHm5ZwY4En/VjW7iDCuVVhsvUlweY3Qi7VQSlBxV9nI6saWINfWOhYsQwtyhPRujeoexG+HlJUFsZjEuLYuPgGoNCwCcMJ5DF6UCwdbXNp1aNGn5WSnUDpb7DIwsY8Wq1L5hl1lDLBMxc3QKzStJeKW+NVkP3o/RCkoH1AoB1N/L1Q+CEVwP9hZoS4I2DHinZjbXUWgAwW8ofi5MO41HNIBiROEers5wzna/+fCWMpSGzUrD+xluLYjNyf+YE96zeCAyfmS3JWALYvmiO5vEO5YrsR3mVfZb2pIXRGrGiBFf3HhaAXfXA6FFeYsKsO2DyZmwKTs4lBVc5ygzNlsFBPPD8TygMzG6EGYRNSkz2SBkjdvQA685DJymlMFF2PcDXXLQJFcEKIvL+696hGpi6G0oLJWlZafEjBRrZVQdbGOjOsXN00yPUNleLwDCUJarIFNuOYECNwlCmr4l+5L3ejojzmh8xRmC9nbajFNTJhtJTy0qUWBhuL0YyQ0Yjio84NpXUupjJbzirGBizY0VJFE+finUGLCnplcdakR0DyC6JL/deMpUIAcWqp1e21CgubzH7UMBwvoKNEdXjW4wConxt83U/HlUoTgC16VBI0vBwVHExwWooI98RS4tipfN3w3hgyUDPC36vaiBYCxxRpaHHgPpSz7qnZcHFpcqIt9mQwe8lnXekX903Fnbn4BcFPAqMBxblcWB/piwkKidy/uSd3A/RSXUiZZl/r8qCslHnoAo7qTacR7HpmXA758IRF7sUhgsN6t1AX6OczyxjlTCe6D3rPkYHVRRkovf2Kw85zrHvgfUG8ZFDHq8YJdzKsbolRi50aD29p4SO2cdPMb7gEMX/+RFo8fOm7s6wXt/4fffG/qQwek+ADI/or2cvHvVvxyuL1+aQIR7DMleD3PYEMwZE5zJrnIe6Rlb7YDwYD8Zzdty3gPI+9FS+/OUvx+XLl7FarXDu3Ln8+Nd8zddgNpvd9XbvOP01jWEY8Du/8ztw7slN//d6MKHRQu0GxMpArzuCSR/ZX2fIcJhLK2ECpRw9TewCV82ZUFhRumlYuYAYYdYt1E6YOekPtKcdfVSdR/XYFvUTLfQYMB4UAiaQk0KL047MjWX3XWgoEQxNCd0xoAJao35sC9OOKI5b7oMG+hcs4ZblxAhoyjB9w85FP7MYjtiDGIrkoZSJiQLGgwLl6cgU2KVUaUCAwOBRPrEmoNg5uEWZE2KNVFek0Bo3Jzj3jUVx0hI8xwizHTIQisnPecIJ8nhQwc8KuJnFuLCUZp6OMJsxA24oSt1YNTKSDSkMQmlZ+yB+omBV9l8lwKFG9nWGyooUuSBbeNDAz8tckeFnMilWZHzHgwq6HeUYULIJAMOFihP5QMa3urTLslwVU6Iof1KwjTusc6qoCjGnYfqZzUBqOLTQLgKawSW28zBDyIm6oTBkKeX6AJhWihDRXShyum6x8UzAXXdMNh097OUtF0RihDltYdYDzIYstDusEWseR79g4q4epLNTmHY98nhCMRwnvXeouAjgGoPxiJK98YhAvTgmU2XXQw54gVbwDdnyFBTUXyBLpSLZx/GAAMY3khoawJTQwrCu5riDlQoYu5OaF5GEKhcwXJhRDh0o9U79kKyzKCampiqk05KAWY0eejdgfGjB39eWya/9wMlvXfHHyKLLkv2b/mhGGfXjx9zGpgOMZpqp9+KxJNuI7Y73bC31MoWhnHUvcRNBOhoDGUn4kM9jCvXJ+2R07im8F8MvKrhFiai5+DA8tICfWRTrEW5eYDzfyD1F7y6kf7U4ZkcnrGGVjqbflUnYIxcCQkRUXJgZD0qMB/QU64Epzlqqe6CAcSk+bQWm+Nb0XY8HXORx8+JM4Fk0yXZgUT22pSy34WJO/QSvnVgTuHJRcPoTF72/afJrHAZKRYuCMui6IgCUKqWoNYaHZgwtEq+uWxAAFSt6dNXBgj81v0dUqtlJQDZEqKaeuhgLCyh9Xa+jKksCM5HwMgWb7DIAMqda029qpoXPaDUXXI7mDK76mItku51D7HrErud27U06J2WcSVZVwnpeUyeyP3l81/g2AnNbEByO7o6Six8N75z2R9j5vH1Jzr02FfaVxWtve/sPxoPxYDwYdzvu10qRH//xH4cx5gygBIBP/MRPxHd/93ff9XbvGFTudjt89Vd/NWazGT790z8dH/zgBwHQS/ld3/Vdd70jT2WEJTu5EkgMi4rysxgR6hKhKiRU44gTN89JcIrBV97DLxnFPp6rphL4biDgCEBYVgSR2x6q7Vla3rvJxwNADZ6T4dZRaij71D80gxPJF3yEGj3MuoM53qF/ZA5/0IjXjSE8qSjenrQoL+9QffgUuh1gV71s28OuBygXURy3rAjpfAZNKQzDtvSzaQF+dj2QFeg9zI7SS068I1QI2RsJKT9Xo2dH4SAMQUs5JZkljeIqQ1ZSp2FxQoZRjzwedjdiXBQojzs0H9nCbnm8/IJAM1QGuncMOhkJ5qIGEDjZ9sKshoKy0gT+spy3JsAJJSWjlHCCXk0A4yMEEWbn4CuCELuhtxBGCagJuaeyvNrD9EwANi2ZyAyaYpSwDJVZU3oHVa6PSSFGvqIMtD8iKK6OWf1iWnpxXWPgE7gvNIYjHgvlI5n0SzvUH1lzX9qQ3zNVzISmyJUNyiVQJsyH92TMdiPs1S3cYQ23oD8u+RMBMBX5pJc0YFlQaSzG802WrkajcggTvbNMxvWzQib4OoerhFIz3ddHguXBs0M0xHz92J2Dijx/4yEnnaG09CIqBXeuhjsnIF18likdNpQWdj0gzEoMjyzoM9WgHL00TIQVRsctyrwwMZ5rCKoPauiWntJYGMTKAn0/VX0ABIAC+GJdQG86AtUDkR+Pjsd89IgV+xShFP28H3M+s2MJIMbzBzx31gpQYKorkret7RBqy31oataJnJOajRARVpun4dvxxiP5nKMRv+DoMR4U6C9WDJBygYBv5LlyczLOblEChQGcl3tE0qMrg+GwhG8s+gsltKfUPy2sBavRPlIhFGTpo3ghbetzDYxyEUGkzdXlFm5m4RsN7QLsMRfmELjYZoYAvyyhXWAFFEDv+Rigdj3MpoPZdAibbWYNofSZ7sgEMmOqDhHpMyxrSVRVZhl+CiuLRtQiPsLsHEzreU8lr2w/sEIGlHTGUdg38VdmhtCyAoQMnciflaaPcxgofx4GqM7BbKT2adVnNYg7rKkAqAsJH9sDytIrqwIX6GAtwazRUNYirDdPylTugzq/3dETLKEOrzCvyfUsgLCX1iJIH2Y6tvl3eHJ/Fo+RAiRM6drnXxvUc+3/P/B/PRgPxoPxYNz+eP3rX4//+B//43WPv/GNb8SP//iP3/V27xhUfvM3fzN+/dd/Hb/wC7+Aup56sr7kS74Eb3/7s5PGpo+Z9OkPG+gdpW9xVk0MUz9KmbrI4pzPMlE4LyCPEfXV41v4o4ay2IbhKWp0uTcwlhZxIZ9bK7KMMVJyCcBe2SEUJq9iA0DzB8ew65FJrRsynMoFSqqOe5hVC7MdYHYO+nTHsJTRI9TS91Za7vfgYE9axELTnyRptX7G+gqA8sMsgTzt4JfcV3PasmdxpJxMjWQNQlXwMwDSD0f5biylFiIEqH6EPREZacHux+K4pZ9oPZApk2J0I0xbSpMsTwZEozGeqyWsh3LK6nLH9FHxqxXrgWDJR4xHDcaLDdlZWZ1nGqoEZEgXZCgMJ3dSK+EOa3pgAxcFTOsQjcJ4jiwdNNB+7DxPxoz08UWryfJtepjLa3YybnsYYaeL45bdiXObpRDRMCzHLSjB6y+Uck4jQqkwnKsx+/AuMynKRwE0rJtJ10/UlBfbzYjqia1ILcl06W2P6lJL+eluZMLkumfHYsuU2bCsM8OYmLsk2fTLhgDcKuldpB8NEvYECV6CVHfo1sGeDrDbAcVJT7mrVrAb8eUWGohk610jQSE9q2O4MODJWknHaHF5y0TQGcOUiksb+uPGwH7Tkt5kf9DwM7ayIDPwmve1hdmOMCt+ZshxsZsRxVoCrkqm1lIdQDmg7sUzOzjY7chzedpChcmfpzYtJ/ZJHqg1QYUxiA17Jv1Bw4TPWcltz2v6M3dDlsv7RQ2168k4ClOqZPtq3eaJMhRZK/QDgauwo8oFhEVDptM5vsZzIUGVe37Pp3mY05arqSHCzUSibBXsjn5w1gDxOq8u7XKIGL9kAlNRRToNQIKe+PvqmEqEccl7zHQEsMWGDKWvxA4gHtrxgBYC1xhUxz2KVZ894/XjLe+3ymBcWBTHLRfTLvOeDNKVGqSrFEYhlsXEBAug4w0Spp+9kfx6cb0RqahIaPuB1/Zxx8U0H+FmZO/9ooDuBknmDgh1SQ9mPxDAxQBdWCit6BMUxi3sdtPfHq3pJbRF3r+w2U7sIADoKSzLHVSs4xE1itm5vPijRiYy+1mJWFv0F2r21V6cw7+AoQvK8lrXy8WT9lTmofQEiofxDJgEJg9VSrKNiaFVZxnH25HFUXJur3t+9P6G73vtYw/Gg/F8Gw8WQ54HIz6Fn+fweNvb3oav/MqvxPve97782Bve8Aa84x3vyP2VdzPuGFT+h//wH/Av/+W/xMte9jJ2hMn4tE/7NPzu7/7uXe/IUxlxMZtkZDLU6KE6YSGC/FhNuVoCnN5zUl5RamhO2VunO/FeeU4UY2VZBWA1J5Nplb8wZBtmJcLhjACltDDrFsVJl8GJXzbQAyfLSdIZlSIb0jJIJDQFZUwHDSWlLkC3PVTvZKIshfMFWRx7Si9omBU5VKV8bEVvW+dhj3fwi4oTjhgxPrSAHhzMdkBxeZNj/fW25/FygQzhrqe8Kv07RrKyg4PuKImFD1AdpW66G85I+PS2hz3ewZ60qP5wRVA6BjKjY2B3JBgA45esH9h90qEcT43hsKQ/9NIOvrFkXLejADYG1yjvYbcEP/p0Cz04VE9sGZgxOIKIVZtlwKZ1TDNV7PVUkV4r3TvoVQtzdcvjnyZs6Xh4TzCjGOTU/MEJysdW8rOG2Y1oPrgClML8g9ucyFoeD9AuMESk86gvcWLsK8OFA5mA696jWMm5lvCgmCorROKm2xGq91C9R/lRHk9KPC1iVbAeQ+7iMK/IpO359fTgUa5GVu1p8eLOLMHbuoeSBQ49BgF5yFLtaDVs6gkdxDurQCbQMPgnFgbl1XaSAl7aCvvr0b9gyePfOQapPLwg++wj3GGZg4P0dsgsI68vYfO3I2sjCkN2zE8SazV6lMLC635kl6XWCA29lua0BUafZethRtAfE4s4axCPDgDnEQvLYvkQhKn0vPdDxPDx57ndSiTEA9nKMOd3iNkNcA8t83XjFxVQGITDGRA84qwheC8sQWXB75rYlJThr1re2wB9lUZPAT33UP7K8KI9P3RtyKiPIbNx9YdOuFgl/baU/fcEXruWCwkCJFXgIpXd0jeLEFGe9BiXRe4G1UMgU+0jxkWBYBXrQE4GKgVOB7gFZfrjkSzopXTpABTrkQndjiyl6YT5Fh9weWlDJcnoyEL3/eRd3QeSwgom5lIZkSqnHlHnhUVkojM0EK2B3Y0wXUB1tYPZjPCLGmbnszdbzWf0zEpPZRhdBrTKFmTv0vUn+7bf/6hskdNqoRVwsKSawrODNgW/hXnFxUWrueCy6nmPaPbM9ucrKgw0F9bM8QZq1lDSO/A7fD8QJ40z/06ALYazUtkYGMSzJ1Pd7/tM4Bl3aouRQCBVFGcCQh4N78wJuvsM5qPhnWcqRZ4uL9eDSf6D8UyO+9KDeJ+N+1X++qpXvQpvfetb8WVf9mX41V/9VXzd130dfuqnfgrvec978Kmf+ql3vd07BpWXLl3Cww8/fN3j2+32DMi8nfHmN78ZL33pS7FcLvHwww/jy77sy/A7v/M7d7pLuWhc9WkCzPqI2BQMAHEOyrnJx+WZHJgCOKJSrK2YVdmDRQ9VTxmtEWniiqE5w8ceZrYxLhpO+neDJPCR1VS7niybTJDzNttRKgoo94lWAkd8zAwI94fpleGgIUiQCW+WPQ4us2vRSh3Hos6sbCzZW2lPWiAEmN0IfbLNKYFJfgsNTkiCTKqcg+qHDGpSgiasZiXH4yeUDmoF+xFG6OeS98EhNkVmVkNVQAsbqHtKTunD0nw/xSTP5sOU+SkfUZ4O7NhUDPwwazLBCfygMNBX1zBXNjBXNkBFD5va9TzXA8NwVEuwa1rKa6v/6xh6N6C4IqCnc7xeOkog9aolcBEPXLqmQlNwWydrynNTbxzABYZ2QPHYCnrTobi0QfnhE9hVj/KxNcrH1yg/cgrVDSiu7lBe2cEe76A7j+LKjrJTAPZ4l9kGs255HfQjQVE3Qu966F3PcKmun6onCsPz3LnpOou8jsymo/y6H+lj62WS5+l9AwB3YcZzJdJmSqfjJAt3DEQhE2Sy99I3FvVjWwznG8rxRvrlEALCsqIHLADFaZ8ly6G0MDsnvjsPX2i4JUFwLEyuq0nM6XChAQzl3Kmew2w6+ptHT990UzClVSmYbc90TqmXiVWBcFCzDuKgyeeSfaUeynmo9VYWT1qonTCXTS3VMiUBO3jO876I7zrdH1nyqhmSwnoO1tmwnoPVEXF0iLsdQaPjwpTqHUN9RifBPLKooRSvQ38PaxPkHI/LEtXlFtWlHYqrOwK1QM94SlAFgOKJDczJlp9n0wKOFT66H9knW5pJHl0b+p9Hj2LjUGzYaUq/eI9ixXu8PBlQXukoXT/tBOQGmM2I6rH1BAgDzykAFFd3XKDryMxVj295D7gIv6h5HMeR59Ly2shAJ41rGMvoRgIX73OdSAL4iYlNkmD6S+WU9yPrcCLyYwC46CZgMQGiJH1VEgKkF3Pey5iYUgBQZUGG01BiPDw0498txcqpUFKu7Jc1oMjgxsogNBbDOSo1yhMCx+GgmL7H/V5gUVHkQJz9SW369xlgtednzHJhSWM9U80yjGe7Nq+Rv1479lN4eY4jUJaIckz2n6erOndk5n28RyzlDY/Bg3HD8eAYPRh/JMYzyFS+5S1vwSd90iehrmt89md/Nv7rf/2vt3x+3/f4lm/5FnzCJ3wCqqrCJ3/yJ+OHf/iHb/v9Xvva1+I7v/M78bKXvQw/8zM/g/e+9734E3/iT9z5ju+NO14Kf+lLX4qf/dmfxRve8AYAyEDyh37oh/Bn/+yfvaNtvfe978XXf/3X46UvfSmcc/iWb/kWvPKVr8QHPvABzOfz296OcgH+oMpJmyrVhaQkyLrMzwtzRuITWNrMvoXDOeVDhw0nhN4jWsNYd6XICMY5lAt59d6dn0PvRgLWg5oSU8OJZJxVGeTCOcS6hN6IlK5TEnahyfAFmbxo5EoMJOAyeqhtR2bDGCjHihS96xEWFUNlruwQDtilmbq+lFfQl1aU9Lk9JnFHX5petQxssARiYV4xMfdwwUn34NkNKf6xKLK+eLhgX59WCBcPCQQun/BzNuJt9ZG1DYlFMwaqHcnKFBaxKTL7h8g+yOLqDgg9orX5s5sdAbhyAdjs+Fm0vm4yoq+sgKaGfeKUIHjN56qBTIc63XAfTjfArOF5Ki20j5S5zSqo1Y5AXnrk1E4qNroRsS4QDmcwxxvWSgBQw0iZXeo4lJX/OK+5nTS8h9oMCOeXmSlTAhbRFKwMMQrmeMdt+0ggO/CagchUATB0atZwYj94xBr0WM4aSpiVYgpyZen3A+CX7NpkpYjL/YCJ8VaDy6BNrzxU28NfPJiAcwQXTSxTf7ULKI87hjulPtIyBfRIV6CK0O0Ad1Dl4CI9eqioEMFgkebDKy78aDKr0ers99S7EVYr6Yb1DMLRDEUyxzsGlCiVr3U/K2BPPSXaM3ZfqkBwDUcmFIbbCw2vIX9YwewIjiBeMXRUKmBWcWGmMCiu7OSe5cJUrEuY0x38+Tm7R3v6p91hLdfrgOFhYWg3Ox4b58hgJbBoDO9dIMtulXNkMbc7gpKqIpt1j0Y0iuz3cQd9uoN76ACqkE7EIIsqcp3p1vO7bjtQsSFsum5HXj+lpTR0UaA47WG3ZI2V9zz2AKt5lhV7ckOA2QzwiwrKM+jML0vWxGx4zrKkWymgMIiGbLlf1CLJZy1OqKjiUJJwGgsDVVfcfwDxo70AowCA8sxU4QGlc3pp/k4pSwJKpRA3BKy0BICLc1vHPmQXJivAboA62UwgUf6bZK1KB0QPppjudlAHC8TVhvvrPffHvIZAzBOUKQDY7VA9tsbwyALa0ZeqB6oKUvp21PInXAPa8XE90ONaXe6YmrtRE0MeAhTOeh6vZUvO/H8UCXFiNqVC5My1lBhYIANQVZaIbXtTJua6uoIYeC8mlj6dj2tA6jM1HjBITz4eHKMH44/EuFsp6x2+5u1vfzu+8Ru/EW95y1vweZ/3efjBH/xB/Pk//+fxgQ98AB//8R9/w9d8xVd8BR5//HH8m3/zb/Apn/IpeOKJJ24ZnvpN3/RNN3z84Ycfxp/+038ab3nLW/Jj3/M933NnH0DGHYPKN7/5zXjVq16FD3zgA3DO4V/8i3+B3/qt38Iv/dIv4b3vfe8dbevnfu7nzvz/j/zIj+Dhhx/G+9//fnzBF3zBdc/v+x793kRrtVoBoJ9KG8WQhhjhjupcC5L9KwD9bMe73EGXV3EjA1LCsprqR2pWeOhtD71pOfFOfziFCbHH4mOUCXyY0QPplxWBpZl8larnREvv2IeZahRQYurLrMVH1o4Z+Ph5CW0UWRGjoDvK/RKg9MsSZqdgTnZw5+bQUxsHMK850U9l7CAbqDxTKtXgCbqFjYyllYmSePA2HQNDFpUweRZBKaBhZLwWdjYFmcTSQl05BayBFglZnFeA+ELVXhKmEjmvX1RMu1USnBMC1EePgVlDRtQaVjNI3QNSomMq8daajISnbDFXOSjFiop24ASlsIhNCXW8Zo/grOF2rIHatIjLhkDP6OybY/Is39NsOqCwBNQAgcG8gjndIVoybSisJHcq+FkFc9oiLGcEt51jwNOiFnBSwDx+QiDeOTLSEq6BQdhu8aqm60uteuBwAV9yAUD145SG2zt+HqMz22gurab3HzhRRkHfmd4N8Ac1j9PoyVIbIMyWMJsuy0zjYcOFmNMdysFhPNdMPZin7BlV3QDTK7J7A4Eb2c4IE3yWJyafqDtqEHVN9knuP18zHRgACvH7qsLwnqgMfD2jd1YDxUdPEQ4ahKqAPd5CFyaztrobee4KwxRYRR9uqFkvY7b9JCdP11OayKb/poWZNsAfNrDHW/jDRlQIAbEpxZc6TpUuW5GB9w7FCdlm1PJ9UteIbZuDTmBtBk0qBPhzM9hdK9eyQdydchId7+Yv2e0N3Y7w8wrQisyYKDSi1jwvwpjGuoQvDcymz99h6fqHUpNKZBQWD8hhXuO5Jod/hbqAXXVyrY7i9xVVgQrsCRbViEqJy55BZ6rzCLWFa0rYk44WhoYLGPaUPZdqcASjtQU2O6iU+loxxTWxgdcColcWr2X1RymLQ94DTQOMI9RsltOfdc/FPL+oYLYD2ehh7493XU3dk4nZG/zEkirxTxZ28swaC9WqDHCj91CebF9oW5jlArG0KC/t0L1gAV9rmI4Bc8Vxh/aF89ztqiU4KJT0npYnA/qLNeyO3x/6+BSYzaDnM6AqM0t7I2CQWMB0rHgthix93ZegZmAYI79/vD7jqQTOAtebslsiPVZleR3Y1IsDek33RloYeABsHowH48G4l+NupazpNQmnpFFVFao0F9gb3/M934Ov/uqvxt/5O38HAPDP//k/x3/+z/8ZP/ADP4A3v/nN1z3/537u5/De974Xv/d7v4fz588DYHLrrcb/+B//44aPf/InfzJWq1X+/Z2qTvfHHYPKz/3cz8Uv/uIv4p/+03+KT/7kT8a73vUuvPjFL8Yv/dIv4TM/8zPvekcA4PT0FADyAbp2vPnNb8a3f/u3X/d4qC3K044r36kyoSA7FpbVBAbHaZIY5hVckrGBvpwkQ0WMEspjpsAakaMiRoQZ0yWVMI0p6MUf1MIiDmS6ZAIWDhpEAyjvMuvHFIzIXrHHriK84AJU52BFJpuAhD0m26E2Lfy5GRmdgpLEWFvKwEYPd25OaWDySB3OgKAy46JPBWRb9unFhumW0TJNULUD2cllLexpRDhoCCyVIgMH0Du0GdjVl2SzqfahHRDPLSnrk+AUtWnJFET6yFTHICCECJgIvZNqj90gdR4MMYmVJcjTGtCGYGh0EzCCxO03JY/1MIh0MWbGNGoJvFAB6HomHz50Dnq9QywsVD9MQBCQ/kHeEmEmlSTCXKe+w3A09fckcE+m/IBAY9sDBhPbI8csVgX8uRl0O4isMiAezHnMGkoto9aUHI7078aCibXJGxzOLxGNoY+1HxCO5gCcSKXJPvp5xdCpwkA1JfS2o/9yxi+x0BSsUyktQe+izkwc9N4CR2KL160AToNQW4auBHAfXEAMAf5wRuCoyYBBwEAs9OT3ggCZRc16kSPKZO0pE1DNdoSReWMsNOy6l9RmvtZuhsx6hoOGctp1OwGKtEikVJZ4x5L3MoGu5QJLYtp2woqkCglAAKbO/uvxQsP9mJXT9oPL3yHRatiezKPqRwZ7JTBeFcKQU46pJIAEhQWqIidGh7qYvKRGk4kehRXt7h1TieRR7gb4RY3QWBSnO4RZlRm/aOnp1TFC7Xr4o7l4TUNmioeHFkyVtjr3ovqjBtozmTpUKbGZ965flIgHFVQAVEvvavJsJ9+z3QwTqw5KpxGQv9eRpKjrFn7Z8JzGSDl0YbgQlbzRVQms12QJ92WmxuBd49v2QJHoV0MA2hZYzIG2zYs2MQTEsoBuGdCk4aj6qAr4gwZ2L31VWYvoHL2HpsR/7n4iAzVlhblWmtfevuoCEGDm+biXvwVVASXdxXrwMLtBupP32DsN7D62YRKvZXhPeTJgPChgOsPP0w8Imy10VeX3vRHziBgmsKb2FhFCnJJq5bVQDDZSVYkoXavqmgnTtRLbGwFLVZbAwYLXlwT+AJIKq9V19TrXyWcfjAfjwXgwnoPjRS960Zn//7Zv+zb8g3/wD848NgwD3v/+9+Pv//2/f+bxV77ylfjv//2/33C7P/3TP42XvOQl+Cf/5J/gx37sxzCfz/GlX/ql+I7v+A40TXPD1zyVAJ7bHXeVBPGZn/mZ+Lf/9t8+rTsSY8Q3fdM34WUvexk+4zM+44bP+eZv/uYz9O1qtcKLXvQiRAX4eZVlaLr3UEqReZSJJgApsmZCpHKBfkRDFgNKsYoiQkAPgAg+FwQIejeIb4eyrFjZDCyDSDoRo3gmqwxEVE+2RPcjfC1+Kxlm3SE8fEQfmHgO9cDJnDtqEOoCejcCtXgRjclMi+o6rszHCLPt6XmTFXpzdc2wEPGqxXnNwvjRU4q6S4wbpYdK01fqlhXKxwf6S/sRoabU0EvQiRo94D0B57YnkF5Of9xVjCRFlUI4RwZTdQMnXn94BeHCAfS6y1JAPbCIPnsFJfEyA8qekty0SKCcB7pher9NR+my42QPowPqEti1UNsdsJiTlfWM8Nfbjj7Y1Q6xqaCGEeH8kvvjAlnV2vK8D47ey47Hfz8IKvlxAQID3ZH5StUzADCen7GwHQWguHARDVNZlfjn4uEMCAHjhTn0GOhzBQNfoiXoSumsKQjJn5tBy3UQZwSkoSG40oPjwspGPHFmmmCHWckAm25AODeHe2hBCecY4A+FMQalv/7hAwAgoJDPrUS6GCpKSN25GmbTEyi3Y17wUIG+QrPp4Q8qmFVP9nZewqxahKaiD68pyfjIfZWYn6h4bLV4kKF5jJUPwnCNZBwtz1OQNOZolQSraPh5Md13qxa6FhA6KxEKA9vLNeQckBQF4sFTMQLD5P+MFZM+0Y/Aooa9suGiDQDd9jllOlqNAErqQ13CKDLqLLwXf5+X7590TK3UsjQ1sJKqB6VYKaHv3aSZQVWjfA92UKPcd85xoakd4S+KBSEA/mguv2fSqd62/Iyt2AxkcQ1WQ3cjxosNysc3MMdk78YXHDI0ZtVmhUIsLfSGUmodDYrjFn5eCZgka6m3PTBjsqtpJ1uDW1YABFAKYxqt5iKY88BM/qj2A5QtztRdJECSQFF6XDW1JNsS+MEz+TovKnjWTwHIgJILMZ7nruJ1FNsuL1RF7ymvjZTf8sFI0Gn0xGSmoJ4Yz/xbjR46BFTdACeLighc8NQuZHYfAOorXOhxM4v+vNS6rEbeQ9sdsFzQy2ntxFReCyhlJLBJYGwom93v3EzXkVaIHvk7JnpPf+WdhkwpBWx2iMM49YkCCKODCZEBQ2ffeFoIwI1lvA/Gg/FgPBhPeTxF+euHPvQhHBwc5IdvxFJevnwZ3ns88sgjZx5/5JFH8Nhjj91w87/3e7+H//bf/hvqusa///f/HpcvX8bXfd3X4erVq7flqzw9PYX3/joS7+rVq7DWntnnOxm3PWtZrVa39XO34/Wvfz1+4zd+Az/5kz950+dUVYWDg4MzPwBQXN5BhcCJhqxwugVBkJ8VOeQmragnxi/UBUNFnPzx3o1kZiTMR7nA5y8b8WOSXckSMJFsMgREJuxgT2auqTht6S2SyWdmpFIQjNSGhNISnAxeqgqqHKjDjXJlPlpWiMS9CWfqzHNHs9zJGZYzyslEUuZnJcyqm14jsq6oFPSqhT9sEJYzlI+t4M7LZDJGMhmzAvZkl2W0cV7nyZYafe6EAwBzssvJmGpgAE6s2ZuIps7SW8TIfZwliWBFWVYhE7rkmStLqHWbK0vCokJsKNuDVgSGjt5JjA7hcA4MI+J8xj5AICc6xrpgyifA52nQN5pKxeXa4EIEpsmvAPowr8jcCCgPDbsSESESS3aajhfnrLNY90zc3XS5F9DPC7JmNQvmQ5JtjqyY8UdNPn66ZxhKrAr+aI3x4QXMpud5KA2iNawz0Bp+WcMtSvhzM/az1oVUi7BeAJ4pwP6RQ4SmZErwZsi+N3fUsNfyoSWDbKR6J8zKHGzll7WwWxWUDxjPNSILrDFemCM0JcOYrEZoSoYRiZ/ObCn9ZsqsZuprQXY2hfQo8UDGQotflP5SvWM1CNlC3nOxMmTEXcgdqmlxQK/7LLf1R3Myx3vvg2EkAx6F2W5qMlwJ7FfC5Iu3EADiwYyeV5Eq50CsfpSwIqmoMQa6FfZ81kzVIN5Tbr3enpHm68ED2zYvqMBz4Sazm/dgxIogLFSFeFYBd3EhCxEK/oigLBoCYOU9/JwLZ/pkDYwjk6srg2jZPRlKfi/5eQmzdRgvzOHPLeDPLZhunCQ1qYez0BgfWkz7ZDUXQ+Q8hMaKn1LJwlYBd9RQ0XHa816S+yKlJiewHy3vDbjJj5f++2h4J/aDehAFSKZqmSzx1VMCc4wYD8Xbe1DBHc3gDyr4eclu47rKjH4GWNKNGUPM7xmHETHZAJSi5FWrCSTlYyTXXElp8nh+Nl27cu+EUrMrc+dgtqzjSQnXeoy51xYAJb3WkP0209+OG8pR98J5gIl5VE2dk19zeI98Rhwsb1pTclPJ6zVMYzy3JLBX+sxrbgpQ915/Mwb0wXhq4/l8TJ/P+/5gPIfGUwzquRaz3AhUpnGt7DTGeFMpaggBSin8xE/8BD7ncz4Hf+Ev/AV8z/d8D370R38Ubdve8DX747WvfS3e9ra3Xff4O97xDrz2ta990tffbNw2qDw6OsK5c+du+pN+fzfjDW94A376p38a73nPe/BxH/dxd/z6/mOXcPOSADHK5KRz2ZfjlpX8lDAbJpFGrckSFjqDNXdYUU4Xo4RKjJktiSKr0x1ZEhUC7NUta0hipHR0Tp9jqDj58suGgPRkkwEkJXZAmJdSX8IVaPqYMDEYnuXj48U5J891mUN17GbI++rnJdy8QDR6SvyUnygVKoAk4s4INPxRA3844wq8BtxDS3oqawa85CCjikmuZjfSf2dUrh5JLJ3ecPIeC07u/aFMuFetMKsaqhsyKEzSOn80F9+pIaObmBmlEOezyU8JAYAAWZANk2j9+SX8+eUkb5YgCTU4srm1xfjCcwSwIdArOSvJQqVJZgIZ7SBMtYRZaFYqRKVg1l32mgYBT35Rw53j5NXNSwESmr6zGKVuoEIsDMZDyktDZYWtplRPbweYVliPbc8OTmumcybyXT9jh59vLMZzFUw7cgGiInPnZpasp1FcFOmcpJCyMsU3FspJwbwGogKGw5LXrKS6jucb9v+NASoAoTJ8XwGHPFYmV5VEw45C3TkUV3fwtVS/rDrodoA9aSWhkwCc/jOffXrKsYZB9yPMalow2E/W1buRnZzbgedJZLkq0DeXgmb0dpBaHh7j/aTi0NjMCutuyJJOFSPi4Zygz9KDh82ObHmSwnoPf9hMk3zF1Ngk1VTe5+sehsBHDQ5+WdMHu6gJNGJkf+GQUkUdvZbil9W7gftcFpOH0lqo5QLxTmsZ7mD4BaX6KW06LXLBx1xxw4UQnnu3rFBc3hA4Hi4Y4iMdsemaAUSpINUzuvfwM0tZq/xuvDCnqmT09KF61nbojvf/eGFO5k9r6JYeXt05qalx3CcNhFnBe0ntscWD4/cxQHbfKPEumgyCcr/hPqCRDsa4ayWoqeF5GqU+RiwLepiYsVDq7CMtr7SIFVNd4RxiStIGmPq6B9D0wYJ+7uTbtQWBWQxMVU2A01oukMo1x9Rbz65bpagA2DkM5ysM5ytAUT0zHlBaXZ4MsOsBrhagF4RhlEqoW0pH92pEUkiOKgqGDF0DHDPA7Ad6IqVfNQcBmdc8KYOYU2RF7fGof3veP6XJ2ucuz5tt4zbe58G48/F8PqbP532/0XiqIPkByL678UxUily8eBHGmOtYySeeeOI69jKNF7zgBXjhC1+Iw8PD/Nif/JN/EjFGfPjDH37S9/zlX/5lfNEXfdF1j7/85S/HL//yL9/+zl8zbhtUvuc978G73/1uvPvd78bP//zPo6oq/NiP/Vh+LP3+TkaMEa9//evxUz/1U3j3u9+NT/qkT7rjDwAAdsPidOWiFLwbFq77KGms7FmzazIgyQfpFzW0i3niqCKQPFl2Q0YxSHcfEzIjGQ+pV4hGMZVVJH9+VjDYpx+h+lG6KR3CQ4eZ1UvsZgoU8YsaUQEYfZYypclxeTpA9+ySDCXBV6wKqH5EcWVLz9p6QHHSEfxUrFAh62lhr2ygfESYkbEKUjOiPLs4ESBVJyGXzScpbzRG2CZO6EPJnkAAiHUpHqgiBwalkB3d0sMZm3ICIZaMQ/aiih8q9WwCQDi3QGxKAl1ZDOAvQvbBucOayaYSPpT7MTMYrQmgDhqm4p52E+vQ8/NO/jguOkSj4C4uCLK8RyxkMjM4KVK39J+FMKWUWnZ8qjGgvLwFtIZZMfkXIYhELeYKgmg0pZtNQebaRYKgUgMKGJIMdTtAjYHBU2MQaeJIJt3FvC0/L3IYiB48ukdmBG+zAqHktRmaEm5JOV5acPFNQYltH3JpuluwoN7LYgpCROoZRIh5e74p6NMcmLppN6x9CTVTYFXgYoE7rBnEI8dJxUjQLWARntv0tQB0kWjHQmdvIa9zI0BbHpekTdWPU/+sER/p4UySSUMG+6zvcdyuVKGkNGbVsfcQBaXjqEqgKjmhL0vKHqXiJ1QSqpNrViQQRhaOtCRNZ9WB85nRTteeSv2ApSwqOS+MrcV4XlJhjWGlwqwCuh5xu5sA7T0YKeU1zCsMF2qM0hnrDyq4g0rSeBnao3zkYkVTsUu3ZPWKF9mx8kzEdovp+tO7kUxa75kSHEDZ8arjd4RRcAd1XmxoX7jI171fsCaIzO/Ug6k8F1q6Fx6I0kLBzQtKotP3QABQFPRudyMXibzPwC1PNNO/93oXVVlSdpzOlSg1poU5Mn+mddBSBxTKdG1MHZQEbVOVSQwRryxeS/lo2yGu1tyg81x0k7oMXVhoSZ+OfT95vNOCZAKiMWaA3XxojeZDayACoTaoH9thOKrghFW2LcOXoKe6JMjnvZVkNEuCjSGwHUeoqqKMuKoye3kmmVUkssDEbp7pB90b+xPctA9JwXJtXYhq6jPsatqvvFBgXnPT93kwHoz7ZTxVkHy/gexnbDwDlSJlWeKzP/uz8eijj555/NFHH8Xnfu7n3vA1n/d5n4ePfOQj2Gw2+bH/9b/+F7TWt0XO9X1/w6TYcRxvi+m82bhtUPmFX/iF+eflL385jDH4M3/mz5x5/Au/8Avv6M2//uu/Hj/+4z+Of/fv/h2WyyUee+wxPPbYY3f8gWLB0mc9BsRCo7zaoTjtKC3UCnqUGPZ5wbCeBWWsoSSoiFVBZlKkp1k2axRCocV3Wcj7GLhFyZS9i4uJoZsViFpK0Q2ZRb+o+F6G/ZiQiXwsDEIlLEpkal+UEJRQF9C9w/jQAiqxrY5AMPnwwryiPG/w0yQv1U50AydCSiHMKrjDGsqzykKPXgIwhA1qpTZDGMPiKqWrSVoITMypHiiBQxCf0bxiyqMLBDMzTgiSXCsaAz8jSExBGinRFDHKBDxkJlCNPktyIb69WBj4i6xngPfZL6e8n9jYggEyMFJQfzgTyXJJj+qckxt/8QB6cHCLMneEZlAtPrfQlGRP9j7HeCCTJ7mWVAgZPPOzc7Lnzs1yWm+oDD2yiRUz7NzUg7BfAHzD4AvK1Xo5XwWi5fVKIHrWv5Qmtbr3iBrwFSfh5SmDhFSI0KP4rELkgsQYoF1AcSx9n+M0+WKFw5jTXJUE/thVB7sd6SlMvZU+sBbEc5LqG1a/6H7keTda2NqRwEC2GRpWfIQstSyhB4/iuCeb2vH1undTl+vAfycpY2hKkauXBHOaagD2QfYEv3MGK+luZIefZsiQHnxm4GNh8nlTA98XZZHlpgAILiWhM8zKHHwVrZZO0JE+ZkAWVmQhRE8sKzSfj5QGGlgRkkBBBrNx6gzFMEyJxiAQvZfpr8p7uGUJ3Q0wnUexHnjt9Y7M4abLC2UqfW8IwDdPHNOf3FLeDc/7xW5H+WPK82HXPc/Jtsd4VCGU0zWQF41EFVKeDFw4K6cFpVBbsuaLiteXJcitLu32elNZVzMe0IupnCcrJ8c3y0uvOwCUWGam0BjEvoc6WJAlCx6wFu78XOwKhouTsigCgAqC2jJAyntJoA5QheUCxR4bGENEkKAxdeEcr4F5Q4Y07MtlZYEzMYJKYTyq87+V+Dz1ts+/G49qMsWBKeYqAnY7onuIXZZhVjB8CALQRGZ9O5PMtG+5Y1NpxL7PdTepmxLWSiKwLKSI9eBWlSL7QFDNKdnHfEagv8+ICgN8zY7lrsyb+UKfzXGnrNADFunBeDCem+OZYCoB1n3863/9r/HDP/zD+O3f/m288Y1vxAc/+EF87dd+LQBmyrzuda/Lz//rf/2v48KFC/iqr/oqfOADH8D73vc+vOlNb8Lf/tt/+6ZBPfvjpS99Kf7Vv/pX1z3+1re+FZ/92Z99Zzu/N+6daec2xg/8wA8AIN26P37kR34Ef+tv/a3b3k4UWeZwVMLuKLkKRkGPZCiDsE+mJxPFEnPFMI8YoVvFSYsOcAcVystbqFICccYAP7PQQ8hABqA8L4X7hJLeRCNyLz0GKv1Sebmkm2aAODjEUGQAmaLwc41DwfoDvyyzVFd3lIKFUqN6bIPxXAOzEw9XYgD7kUXvkDTbSGkZ5W0KGJmkSK9egShA2HSc7KSkWzcvUV7ZURZ5lZK+YOUYX2hQHPfwC8rPlLB9AMSLGMmIWo1oFdTGZT8eNICewNcfTJUCuk3AxFIGl+YaPkKve7Kcklqq+hGoiikEKfUHVjyXCZyaUVEqC5BREvBj19IllyoBFGWUABAa8ZnJpDEU7D5VziHoQsAhN+kOqnzs/Jz+RD+zGdhFO4WLeNluVGpKyjwdGLbRDvDzEr620u/HaxExYjwSBkaG6gDdeYxHFdn5gZUWKlD6lthL0zqRyck1bxTL5yXhNVqF4vIO0VrK6HqfAbpuh5w6CwBm0yM0Beyqh19UZEjakV2iQYCXC9BbLkikwCnjY2Z9zW6EwZh9ogwasQgl2TElzLRfcvKse4fQkGU1l3a8b1r2evJ+J2gPlSVLmWpeNLdjVu2Z6zmFU4V5BX/IoCzlA9mbts3eW2UM0Hb0MFiTAYQeKOcO8xrxcJZTb6PWMNspoVX3I0JFWa8+3dHH1o+U7xnxaxoDjC5XXoSmYM+jkwWbFSNww3pafbwXQ40e5RNruHMz2BUZfQ0QPIrPF4Cw3sXkMwYIIEC5YpwzgCxqDSX1NGFewmx6uIMKuuP1W17Z5XMUCgM3LyidjZES6oMmd1gmUBmVgtnSu+kPmnwu/bLmAtluRDEGDOdqlFd2UiNDiXSQ70H1+CX+VwJlrk0NzaDTe/pqgQnAOCeqDpdDqPysgK8MTO9h10MORIuzCkpqL2Ig+4lIr2Ri8rSwn9i1BKDbFrqu4Le760+QtcByQdWKi3CHJcxOvqcrfheF0mA4x/ukPB3hGgtfLxAqDV9REuvmFmXvgYMlmU/vKc0WsHgztnI/KTclb4c1GdYE+GJgVcs+aA/DAFMW7PrcGzdKmT0T/KMUQmNhToD9EJ4EtFVZnnltroh5jrIvd7pfz9XP8XSPB4FKz93x4Nw8u+M1r3kNrly5gn/4D/8hPvrRj+IzPuMz8J/+03/CJ3zCJwAAPvrRj+KDH/xgfv5iscCjjz6KN7zhDXjJS16CCxcu4Cu+4ivwj/7RP7qt9/vO7/xOfMmXfAl+/dd/HV/8xV8MAPj5n/95/Mqv/Are9a533fXneFYzuWOMN/y5E0AJIEv27M6LjDHCtj4DysSiuFmB8aAky6IUijU9PbE08DUllNqFnOQaS4NQMpUwWk6YkrcmzPlHTncj++9amYA4loDvg4Eofr40yU3SuagpxVMh5MTQ1HcXCwOzltX7gxp+Jj44ketqJ1UJ84rPkWj7JG8NJasyQsEKCrMecg8iA2UidOcJlpOUVOSBxaoXH1yY6iAGAm67dbkShHJPAh3dsQbFz8vscTK7UUKECmFQHf1uszIH18AmyS+lw9Fqdh8myaMkiepVRzAilRvpnAJAaCoeHwB+VsJJtYs/nEF1AopnieXSOVnVHdYTe8QLkhUvq55y0y3Zm/HCHKE0WVJGuRsZZr0b8rWi3TQRG87V9FY2FsVqoF/Wh8mjWJo8sdOd5zGvDEzn6BPsRgJQo5ma2bocDGS3Y651UC5JvIMslkTAR9iTFr42UB1fq7uRoT4V6xnc+fkkoxO2ED5Okk1hZOlDFM+sMKUQwNw/NENUCsP5Gn7ZiAw65G24eSGgXGR7KT04AdbTXf4ciQ1VPkCvO6aTbocse4sJ2PgoPj16SrPsMID1LIXBeGEOKAUnYTPKBUqqU3VHjNlnm5M+ncse3igBR1EpmFVLf3BTUvoqlUN6vcvJo7wIw5S+6wLiopl6Q8uC/kjnOdmOVA5EkdTmfZo1QFUBywWU1mQr79HgeWUlUSy5oJWrc1yQ1GlRPJRGArn6nFwdT9cIiwZ+TtY/1pZ+Rg2eE1EWmJMtzMkW41HD+zJdqxFZuZF9lApyX9OfCk2/sV82XCiQgCblA+tLNIF8eWkHt6x4nZaW1R/rjinT1p4N6AEmhiwF0kiPpFJavIFyTo3Ji4HjuZrse4ywOwfdckEvHM0zg52YysQ+JsCqjAFiQBgGMqIiQ40xUBYdA16hX8391NyXBGz9zGJcFghGYTygZD7UFm5ZYTgs4RoF11BR42sNLYBc+QjTsl5EhQgMlKsrpWFOps7HG00i91lCJYsgrLwxuZolM5iaYUOwZvKvliX9xJgYuBuxiWeSZL3HOLfAdodH/dvz75RWrJi6QersPpi908nwA2bw2RnPNGh5cJ5vf9zs3PyRP4bPgPw1ja/7uq/DH/zBH6Dve7z//e/HF3zBF+Tf/eiP/ih+4Rd+4czzP/VTPxWPPvoodrsdPvShD+Gf/bN/dlssJUD57C/90i/hRS96Ed7xjnfgZ37mZ/Apn/Ip+I3f+A18/ud//p3vvIynBCqfSkHm0zl8bQgUwZVZMjWUtxYnffZUms7BdJS4DgcFC9Yd/YXVVWEcIovYESJ8w2RDv6goc6oYluLnU1F7LAzGIzKITiSgdsNJupsVfAycLDHVtc7vozsPd24Gd8h0TrPpEEpLiaahhFOL/M5se5jeozhpMZ5v4EWemgKEzLY/I5dTQeoXWqkFATKAMusO0YrUVLGuwR0w2VDvBqZndgPMqiWjsGoRSvbQqeQX0gQ0obFZfkzZqxIWijJhQGSbVZHDNPyiok+xZWomxOMa6wQWyHa6w5r1BSK3habHMxzUOZ00AdAkDdbtmKsx9EBZsT2W5FphicmQEvQmn1isTJb3hUakzkb8YUOAaUeUJz1CoTPzrbyXdF3AzSx8YxhaUmoUp0yHTayLHjyKk46JtVbBV5T6jeI/9LXN/rHxYoPxqMms93C+xnCe/jM/mwIrxgN2UureYTys4CvDZODSYDw/g209/EElvaiTtLE47XKyLAJyKE/qmQyVZX1IL+8XIq+J5B0sueBRf2QN5QKK017CXRwlqnJNFul4NVNCZ5D7CECWPafakPSerICZvMBp8SCFH9FDPJDFFqABo7Iny24GIEYUlzZkOEWyq0ZPb2hTIDYl5aZGZzDAk6qm7tlNx4CsykrKq4Pe8NqKDRNx3UHFgKdrA0xO1vIZi7zddN9jGHOQjGopMcaSPYLoe2C3E7nsvZO/QsJelCwmBPn+1O20YAGlcvBSStDGXtCV7oYzPbNhISmho891L/78HP78HMVJCyPS42g0XCNy8nEK20k9lamaCQFwF2b5POhtDyWhWKm7MjSiNhkC1OChT3dQp2ues0oW8QQEvbJ4bZ44Kc1AmMy07XvyqopBTpBAI/Fqpu/i/fMSKkuP717lRfJF5r5F8VbqskRsW6b6KpV/r2wx+QqHYQK6bQdfaEQNjEuLqIHuvPR+ugjtIsp1QLkOCEYBCgw0GrioOi4MTOtlUYW+dmjFa14+77X+RQB41/i2CbDFQE/w3gKKrqv8GQEwRMdOi2TQU3Ba+lw3ep8zFS/DiGIznlUN8ETlfuJX6FdPoT57XZY5fOkOxgNG5o/GeLDY8NTHH/l75RkElc/0+FN/6k/hJ37iJ/Bbv/Vb+NVf/VX88A//MP74H//jT2mbty1//fIv//Iz/991Hb72a78W8/n8zOM/9VM/9ZR26G6G6TyKlkXbRibspvX0rUmSJgBhBylhrI57Tk4KSdgUcFKc9HBLBpvQu8ZJtN05jMsCxWrMgFL5wF60RQFEJd6eEsVqOJO8GkRymwBTFP+V7kcoL9uKEe6o4Qo8hMlSSuL1Yw76GY8aFKfsdvOLKgfCJO9gktCyw4ysEr2hBdRopnqHlvJdLTUPoTKsAtj0cEc1iquUZNnTHrEqKKPUWiZ0UoRdaIwLi+oKvWCJ5Qu1+CoLzZJ7pVBe2XHyncrejXQ6tiMZyRjh5wTT41ENux3Zb2g1sHNQlrUrqnMEG6mPcz0AVk+gPQHxSGAaCwnviCJjbejPChUTO0NBdtBsJAxJpMdqJCjWHX2GwRIwZqbRUEaqBzLiJklorXSiLgrYnXSPVgRGvraIVsGuR2j4/N7KBcTGwlUGdiMMpTUIVhHMb4W1kOMaSgPdAypEuHMzqAA+J0T4QhNwG3Y2qsFhuDjnxNIoWTQpcpJlLDmJtyum+KrRIzYqM/EAFzTGcw3DeLSkYGoAHmSLpFOV17zKHYihFmZXQAt7YW1mRUOSLLcin9TJv2gF4BM06J6hUGY9IMwK+ILeV3dhNvl6aysnRXo1Q8idsrqb0lpN6mjVmkButUE8z2oitWnZKylBUEFCsRgAI35m8XlCpX7YIdf2QCo1oONUcp9kgM7xb0wMrGbopSJnWcuChyfIbWqymqO7jp15WocLMFthImsDe7yjNxQE97obKcuflYAWprkbZBHIkX21muytsP+6d5nZC/OK0vSU7CseZXgu8JUnXIgIVQG7HujRbn1OQfZLLqLoUZhvqXSCUXtyZ94TKQgLRsFfWMBcjlAiS05S1MQcAlP9RJpAZk+j91CLGbDdQe0oQ1eBiyWmmzyzKtIKEaoCqU4IVYW4mRhA+iqHvN0subWWwVDrDePijUEYBvo7jYEyBr7rCdqWc9ido4R15aZOysBFLNM5jEt+788+0qE3NXxtsuS92Hr4SudFJbXaTamyMm7EIO4DtNw7aXm9B+8Ruv7M86OjxQIx7qW1cmqRJHU3ZCr3JKx//uO+ged6HPEK85rrfbAxnpnchvF6j+WD8fwZz1Wp5XNxnx6MZ3coTM6PO33d82W0bYvxGsvCPe+pPDw8PPPzlV/5lfjYj/3Y6x5/NoZpHVMnl5yg6IFhJWbryACJPJOeuJADeZIXMAWWmB0nr1GpKTBCiXwIQHW5lRCWIAmWFsPHLFnkXmi4ecGwCpGS6d6LTygSxGgFNyPbSX/iDL6WyH0fcxgFw1Agk3CVpbfjYZ29b1FTPpuu3FAZprgKaxgNA19CYnqUEr8bgXTUTMX08wqhslm+S1ZJof34A/hFlRNFQ8lOwGgIzJmwayeGF5iAnGHcvh5D/m9oSrJ0GpQm7snV0mvNbpSKggg/KzBeZD2JipMsk+eLgMnsRgbyKCUeI34OBCBaIwwmAayfl5R8CgtTXN6yp3FNtoW+MZ5nLz2RdjNIJYZ0mfqpSzFq6c6rrfzX5AqPIJU2lKRxkmdaR2lr7+U48FoMBUNA3MKQYS+0hH/oHAyS5JFmm1gaDbeUYCStxKcYJdk4ZAnhcJ5JrH5mEBKbL3Mv3Y4M91CJNbX5+gMANXiRJKrsdUxVPTnhtySjGRoGYKlIdj95UhM7GmoD3Urliwtkt6yW48prLqWMJh+jcgH2ZMfrNFLOC015uFm1Uu/jM6sVDRcPQlVwscVQOg3gbJiOYeqz6kZWiHg/fdZhBAphFvUEmEK6RgUgI0ao3SALJ0YChwhUtcjZw3JG9tMytEVVFVRhyUAqnvMoicpqlGCZxYygXEJPbtb793SMWDNUip+DVR/s6p3STmF1ZgdDYSaJ53KOnIYr35Fm3RGIz8r8XQYNsmOFIWOudV6E0HL8zFq6fUuTvbAMbZKwtBipohDZLHzM4WhqcCIFpw8cICDGegPUJVCXUHUFVZYZsCUwmQNmRAJLtlJY5MJSvpnYawm4isIE+trm70PlAlyq6GkaqKZhpYgwkJl1BHI1B+S6Uos5cvps3h9+oYeuB66eQEWgWDMAKS1Y6cFBu4DhXAnTBZguoHu4hqs13JwLfuPcQLkIX8nJdJ7HxHtZsLz1n/799NcEEGOqbdqXnirN50hlzrULIbecpO8DwZF+8ulXZ5f5lbVnFgKu32H9vGaZnkv7/kzsywPw9mA8b8Z9ylTudju8/vWvx8MPP4zFYnFdTeTdjttmKn/kR37krt/kXo9oNMqrrcg5CcISw6SisH4A7DHDHEJTImqZKJQ6r9aTRWQap1uUDF0ZPIajEuXxIAXbTP3TI8FpiFqYP/7BZUInV+7TBCEagg7TeQSrUK4ktGSU8AtP70xKQgRA0FFLGE0A3LLKHYgJLO2HuyTw5Bp+1nKgTzJJwJQPcIc1dO9QrLywmyVrF0ojIEnDzSwBdu9lYkdv5biwKE8GmM2A/qEZivWI4ajMACbtsxZAbneUNBoJrXHLAnbrGG6zoz+Q/YkGKAnIQmmhRXpoTztow+RHNcikxkodjJkm+mY3wi1K2C39ixjoNfWlyVUgfkEQnyalavAYL8xh2pE1H1tIwIgh0KsM9CgscaRfFwoIVkMllWQguBgPSxRrJ5+NFQq+IuNpes9wHB8R5ha+0tBjRFBR+iYnUKd7HqfiasvPppFDoJQcX+MDolUwHTv+3EEF3UuK5xAQrIYeGRhUPrElu+gCitNBJuuecmWjMV5syBCVZEf9XOS3EhIUpN9O+Sj+WTPt07KElW2GecGO1sbAnnQohEkNiZFWXAAYD2vYLe8h7UK+htVIxjZWFnHGz1lc3mJ8aEGfXy8A1hj4eQG76hBr+ohVAH3YDXsw2QNKSXWoS+mndFO9yeihk38xgYi6ouQ0f5lEqNMN4qxBTN5qURXEykLveobAiOcy+UVDTYAWm4KM/bGwQl0PlToopfoGxSSLjiUEJHmCXKkdUcbc055K3ZLFjjPKy1UI7JwtDfS6Q1jWDLM63iAsZ7wvZ/Kd5j2UUhK+RSaMPbpMZ0VhYNZUL6T0aLNiD64aovi0eY+kYU87HkO5/5ULZKGlhilUluFj3gNBsYoFgJEO4eK0y+xyfPj8dHxl4eBaX+UZuWQCfeMIVSz5/4WFsqx2cudmWZbrG0nplvqTUBvY9TD5ZQF2Orr2DEP5qH87/lzz/6C8tSonia8i5U8ZLtnwXJehNewJq5fMdgc9yN8GCaYrVi4DMV9r1OsBwfI73PQRvtYoTx0XPGLgPvoA1bsz0tNrJ/j7cuAYIqL8W9UVFzz3Ad9ep2Xe91mTj8WT1Zbk1xaSZu4nZjmPkl7467azB0qfiwmwdzKeSyDrubQvD8aD8WDcm/GmN70J73nPe/CWt7wFr3vd6/D93//9+MM//EP84A/+IL7ru77rrrf7rAb1PF3DCHMCEHjodpDADE8J1XagzFNi6FMpuxL/lZHwCTUGmE7SK7ecrKgxMPQnRpk0MUwkvcaue1aCCPtEf5p4ZhLzYjWKFb1T5cmA/gInvEYi+F1thEkMDFcRD6ibM2nQNwZuQcbVHTJoSPmIYj1gPCjprxGfYHVph+rSjsDhXE2/3sySuXOB7KDRTC9VyMXk0ZBFLVZ9ZjqVJ8uqonh0lgXG8w1M5+FrC9OFzJZFmaSHgrJFv2TtCmXEqWvRk8k1KoPvlAiapJOhEpmc1uJJDHs9cQ7usEL5+JqgQJM5Ki5tM0hPKY2UDNOTl+TPUYms0yheM1rT57XXdalHkTlaHo9QaF4LsmCR+huh2LtXX+pyxUfynRanA7QLuefRHZawJx0DPlzguV2WIt2TBQFZgOgfmWM4X8HPCrhGPFFyHfma5133TJy1a/Za6s7nepw0UR8epizdLYrp9TOCVNM52M0ojCCDpUzLybubF7mDL10XKkYUpx0BryUbCvFe6u2A8YjgdnhoJtdmyECO14ZGseonljfVszjp/RSfaurjHC/OM/MZGi5c6H6EPenyeTLbgcAlcmGB9xvPY6gpXw8Vw6p4D4gXbceOypgAnrCJKdEUAEN2AGFvVd5nfr8YgjGlxAfK3kx3WJG5HRwlkVbTZ9bUDG4ZyU7FrgN27dR3mq6/YWBQjzWI40hAaW97ze/Oh3RlqoGSVd2OCIsK/qChNxW8vt3Dh9BbAYipJqcdELs+d9rqXr5LL68pZRYAFktLz/i5GfR6Jx2rJfSqhZL3NLsBZrdnF/CRbGOMUN2QlREAgXAC+cWVLYqrO/pw5bpiQnQPtaWUO5+zPd9kZrtimH5kKE0fI8OUIs+fUjDrgV3CMcLu5L7xwkYmpnmfZet6BvIkya28L2LILDScQ9y1U4fm/hAPJjRDzbwE86RwMjV6+ttDZAru6GG3Dq42CAW7KY0sqrKP1sh7+vyZrgsv2n/7fbYxhqnrs23z50kM63TwFHssR36uxPgnqfGNxv77xF3L6yYD1DA9J0Ye02uOkZZE2EfDO/EK85q7YtieSwzh/T4eHOsH4/k4nqlKkWd6/MzP/Aze8pa34K/9tb8Gay0+//M/H9/6rd+K//1//9/xEz/xE3e93fsCVJ4NkeAEXa9amVRG+GUJv0zyqDFLKrmyTQBoWnrb0sTdzwv4xsItS5g0AY8CSEeCM93LRLHQvIByimWkzFMASNT0wCBSploeDxl8qhBRbEb42iBUJvfWKR/IFnoGVhSnA316G8oWCTaZLDouLHxDAOoOa7hDSVl0ZGMJbCXQR2o2XE2ZonYxe2zshsxdSqQMNb2myctnW0m/dZETai8S4j3Rue4ZvBEqg3HJiYWfl7nmIoW0+MoI80Cpq+4cogYrLQxrB+zV7RTSMmPAi+kcmYPUQ1cY+EMJLoqRXsSafY+h0PBNgeLSRtgOk7evei9hRQbuXM196BkIYTqWm6f/xkITgFdm6kTtOJlLgTuJxUjg2M0s3JLR//Z0YKn8vOB5Fmm2FvlgKMkOIsTJ62gUitV4xv9EdjzAN2S++gs1QkM/LCLoz/SUHNvNKAnBsj0NmNaLxNsS7ElQk91JAiwwgddAhnycMxnULSsB3Zz0+YYAdTzfiOQbKK+2cDPx04pUkP2OBoMc41hSiouYKjisdIMiX0f00knFhnweyleB/kINve15jrXOgM+LnBMhCPup8/2p1z2VCyHAH9RMgh3GKVwnTbrThDbJNAdHP6Yw3On5UesJsNQlYm1RPrEhi1MVZ0OzxjEXyCPJH5OMVvodQ1MwjKTtpgJ5c4+/mm2SbjK5l12MljJWWdRREnwVK14DYVHTY1pYqLJgorKSbl6lEOY8trAMZNKrNsuA4qLhYl/H7UEjh2ul6y5UNnvAoenLZLr0yGug4nWf9i9K1ZDZdNJvCx5b56B2PdSup+RUztuj4Z1nQVRiKB0TeZGYM2vIGoMMdEoDT0nZSQmTWeqqoGw2BT6JhDmBnhTEk9Ndq4oLBnsy0kf926UzM0z7pDVDgJLSVEBkrAomV+8NLYufxcZhXPLeLI/ZQRoaCduSBYszzPxNxj7QjX0v16QRYFxwX/cBcQhTyFBSA+BJvHNy/JMcWfcjYt/jXePb8u+UhP6osjgb1GOL7Kvc76q8U+DygJV76uN2j/n9eqz/KILlW33mW8rUn4/jPpW/Xr16FZ/0SZ8EgP7Jq1evAgBe9rKX4X3ve99db/e+AJVhtlemDXCy1lDaqbuBrMZ2YIhEVWQAqgY3ybQGl8N5WDnCib3pZBKukOWKygcGu4i0VA8+S1B9rbMUNgEQMxAM6DEIENOZxeJkyghoIZhKHrHhMMlfY2aQotEMYxF/H8GE5/+nwvveYzwoCO5KDV8ymEa5QH9ZZVCeDEznLDX2HcVmO4r8k4CgvNryMdkvPVDCq4cgQRbTa1UQWWEgmLVbhhsxbIMghtJQykQJDOSUyTkMTSnv4xAWNfR2EKljhJZQluRl9LXIREudwZbuBVgFhgzZVScJsg568HBLpqTGisfeV0bqHSjtZIork4GznFrkp3YzZlZWSUowOx4l8XU1YDyU5EfPcxEKLZUtQWL+HaWjIAD3lYHdOgZAie+22LgMzn1jeR23ZNAzGwxkdjOd+zSioSQxWD5fd2SKgyWwTqwkZdlk49yyymAueT1DoVEd98LKku1LvlMtCx6hUCIP5jaKjcNwocnXApnUkSnMjsfbbAeEykhthQT6pM+Tw6kIeFLgU/p9dWknHaOeAERD+kDH7K2G1bCnfZbgRvFs5vOy7emb857+tjQJPjzI3wUJoHipyXFHNVLAFDRgNqys8LNi2v+9EUtLkKgUQUp6j6qknE/AV7RMGcas4b4oNTGU8d79VYqKDLK9sqMPNAXqpE7ZICFkVk/Jq5L+C2FdeR1wMUK3PZRz2R+qRp8ZT26XNSEEqQUBowT1BAHWZifbkgTqVAUTUnK0hOPEwqB/ZA63YMWGOyeJoSHQ/2utLAywliVIYM6NJjsEcpShxnGEWi74+YTZU/JdpHvPa7ayvI7nsk/pvmtqnucUrLbbTWA1BsptlSbjtt5wgcFaJE/iK4vXSgUJuyRVWdJnWFFeq8cwKUJCgL26hWtMZveVp8olaoXylD5wN2cwmNmJ/NVKPUg/cN9w48nhdXLYSpJe03ESOfGj4Z2TH9Q5ROeu8wEnFvFJr0cvi0jLxdl9ysDfnVkUSPuf3yMFAj0Hgct9NcG+wbjdY36/HodbsfH367jVOX+u3odPadxngBIA/tgf+2P4gz/4AwDAp33ap+Ed73gHADKYR0dHd73d+wJUApwI6uOtSJHAiU3q8pMexknCZrM8KhYm97ExyZMoSfce5aUd9G6kxFVSOpNPLNSWICd5AE86mO1AIHVQ5lRKJTJI+nDEvymgKgWb6N6j2PL3bpGCdZCltr4iaIoiydQShGFaR3ljiPSlSXgM/XMBrjHwtUF5tc+dnQmAQdNHqgcCCCgFtyCThMhURUDqRg5LyqyGBEYkZCVG8QMSyEbLiUxKQjW9hx5ZrRIKnSeNvrZZJmmkKzSxm75JQUj0M/kDYcg6lqqTqfCw2wF2S8ZXD/RL6nHq/wMAd1TnSahvyBTbzSDSNZAtc4GeKICVG4ntC5Fg3PJ4mp3DeEDfjx7oBdWDz8eJia2U4I4Li+KEMmK7GbH55CUXLISZ9DOm0roFg518RZZayzWRQqJUWoSQUBAuKiimO1pNP62CBIfoHCBiesphtYQkJVYj+V9TYJXZ8bNGYQj7i7UwkGaSc0tokN25fJ5SCJFuHUw/Mat2TYYupSYjRIIOT5mrb3ht+HlJJnjLNFflPIN8BHipgdLMHK4VIqJi2FKqm4npvvYx11T4hunDEDACgEmu0o+Z0k1DVeS0SmhNueM4EgSKLDbOSsAFbvO0o/RWiZQwBUPNCTTNpmdfrFT3+HnF7xRLYBK3exUhPhA87qkr9OCAXcv33rXTl9p+h+o9GLofEZuC3Y417zeYPa+ktTyWMdIPOXqm1hoBnesuL2z4wxn8soE7mmUGdjw/y32noS4Qq4KJvC7AnLa5UiZVDcGFCWg2BUGjdNWqwAWxWFBSbbeOTPzI4KdQWfHUOiD4vSCkAH1Nz+F+X2UGJzFAzycFBPoBYbcjA5dkytKHnL4DKOsPUsvi8zlVdQVVVZTdpsAbYSRVWXBhQQKoEkBLUtkYyARG6YY0p22uyzKdgHpZ8CjW4q8XhQYDwUbowTNhuvcwrXTLjg5Yb3kcCjKNZ47F3sjy4P0R4w3Z84mpjFBlSfYwBDK36XjvsYhnJt/yHmkfwqKerv89yTBuAlZzLco1bOidTPDvBRi4dpv3aoL9fAMyT+U4PNc/67MNop7rx+f5PO5X+etXfdVX4dd//dcBAN/8zd+Mt7zlLaiqCm984xvxpje96a63e1+ASnN1C2x2TPsTj1AKiAAwTRR8hNr1eaLLvkVOaEJJeWU0EhKTIv/nJRMr25HSUU+GU29Zvh0Lk1f01ehRPrFhvYOPMKsWZtVKiicBRVQEMkACjeJJNOKFaSdJmPYRZudQrMnCRC3STAGbw3lWb6QkTQDwDYFZ1ECxcShPB3oYey+hLPSiuTmB3biQbrgYM3OnJPVQuSB1KHEPrDJ8RgmAM63LKzPKBXp5EvAuDYpcUu6zhLhYD7LybylNFl8nQoRdD2Tn+hH+oCHD3Dv4BcGHW1asfLEG0bL+I1ph3DTZYWjF8JhCI9QmhzBxYiqS3Jnl74TtMjuXPYYpFdUMlCBTZqth+j2voEJO0g2llsCfAQgR9WPbDCh9bVFfHXMVh5/Z7HVSLmZQCgVoJ/UwJRmQ/mKdAXwoOZkO4gs2m3HyWHmyFaZzsDsCPiVSazcvMhhMn5MpvSJ/HggumajrUZxSGpe8WmY7Zkl2kG5U3xRwy4JeysHnJFq3oP9Lu+nbNLF8aWKcWNOokf2+EH9iOrahNlPqb0BenFHi9XTLQhhqKaMXf9l+qE4abkmmkdtmejGZuZGhOMZMnso9723uak2ptTESkAu4SkMPnnU+MrlXu4FgrbTSR6kJMIaBLP7I980LTun95LsIB4tpQl7cQ0+lRi61T+x/8h+nRTho5PNC0CfASSt6RQtL33ZixQGY3ZAX5uyqg5uXTGYVya/uBi5GLelbVYPLoWGQrlomTmvpI233GGw1nSNJx0YE/AGvQzWKJNX5zFRCScXFHih5hX419tNPc+VI2+VORBQFU2NHDy2eWaoPxnzeodTe3wI9+a1TDYckzu57K/d7PtN7J7aPAT06s5tR9iUt9CjxcrOuh9+bSVGgBs/vQvlOMp3jotEgPbB1xc9UJKXOrRcscp9nYiFvUG+TGMh9CWwO7hEQvQ8kbyY/Tr/Xu4H3pPwus5Le53qWM+dwb1+v/f/bHfcCDOQwqKd5on+nYPV+Ahr3GrQ934/VnRyf5/tnfcbHfSp/feMb34hv+IZvAAB80Rd9Ef7n//yf+Mmf/En82q/9Gv7u3/27d73d+wJUApGSsl3LyWLXA20HffkUarXlym4A2YtxBJyHXu/or+xHhFkplQaU6iVGAxowa/p1/LxioqscsVhPEyUE0GsU2SeZ/GmxZGm6Pe2gOwez6lEct2fBmoTyJIBZnPSZCdG9Ryw48VYucPV5DLkXsVgNImd0k7RyPcCuB7KTksQZDetOTO8xnG/IILiIYjXA9CFLXe1uzIASKZhIZI6pqoL+P+SuwhR4pFK9xeDh5pSl6d6he6gRj6oEAgnYDKXJ4DNXvzj6K1WITKptB4zn6vy6BKJ0P01w/KKSFF1JoLUSWFObHLqUAG7q0UxS5CCfOZ3rNHE1W+mOFFZZOYIw002BTFCKXaCWEs+U3MuApIbyWdlesDrLVs3OkYETIGVaeisZsqOlL5Wf1+7IXpvW8RzLuY8aCA3TWgsBVKlDEmGSwiUvrZvZnOJLb6qZrkE5z3Y9sEtUFgJCwXTO0JAV1NsBZjdSXhwiitWYK2NMy8+khwAnCbrcpzAx862DHrkokKoohvMNTOrgFFButmSuk1QawMQW9SPcQSVy7wi/KODn9LyFMrFUwHhxzl7LTQ972vEeLpk2nO5Ldh/KdZSYyhjpgdzuCFrnFcxuoDRbwI8aHFm0VE+DNMkvoLoBYVlRDhsCvXOzBigJUGCtMJU+A2honQOGeCACn5+kkvdoqG6kr1IAshrc1CUZxCsdQE+qSFaZ0CuMq2bnbTp/KdDLHdY5mdUvKmgXuIgWI/xBBb+o4cUfm7pQgwSPpe8q5QO0ixjPNxgvzMnmtmNm3gFwYcKwJsme0ouaE1WtgWoHqHYQeXO4vp5FwnCULfi7GKTuRRGAjSPBYcH+XrvhgpGflxIQxX+PF2bwBw2PWeRP+i5CDFOforxX/l0IQFme6cgEMPl8lYaaNfz7sXUw7YhxWcrCJOX3Zucy4FZBgLlsP5RGjqt0oKb3BSbghpvLXzNg9AKeTZJw7wUbXXtMtZ6AtHzuBCRvOJHd31Zh89/d25kg5wqWm3yGWz3+TI2nGwjd6fae7vd/to/nvRy3c6yezc//VN/7bhddno73fjCeH+PjP/7j8eVf/uX4rM/6rKe0nfsDVIoXKS5nk38JyP4kdbLiTztwwtB2DJvoRv6IhE6JvEgNLoPFNPHT7ZiDZdTgMrupvM/SN9UO02TRTOmvUTO4IkqYTPZ4diPsaY/i8javNIfGTiBTUl2Lq5QEmXUr/X1MytMrJnJGpVCsmaKYE2dTciro9yuvdIgKmXkDgOGwFF8O+xpzN6LIyuzxTno5ff78ZEodzLqF2TqEWZFDR+wmhewwRdXXlOoqPwXGAMjdm3FfTqXZw5Yksva0w3jUkM2bERAnP2soDMYFWdbEribZlxZGN0r9h10PmRFSLiDUNjNuevA5jEm5AN07Jr5WJvs1o6TM+kpjOCzFu5ckc0zFtZuB3tvaCHgK2WfJcA3xlKb4f6klMFv2WurBZylqsEpCjcg2mt5n/2iqnVGB24akDfNYS4iQJZuZthEtwWk+zLJ4EQpNL3ACviUnz5ys68xUJvAWZMIarc7Ar7i6Q2hsfly5gPJkEDBupPOVAT6xYDKu2Y6U8g4+96zy/SiRDHUB3Y15cQGgBDuxacqzzzSUhvU6jiE6ZsPFmNAw+Mde3QrrKZJLWcCIRgJeFuITGwb64Zo6sznxcMFrpbI5ndYdzfL21MjrzB3WMMc7erNXLReagMlnl65vYxBaYdzKgo/n4BOp2BCvHGsfPKJz0E3zVL4VbzliXcAfNll5EY2BXnWAC5S9QsByYTITm8FTZen3MwZm2++dT3ZMMtU6numTRQTDm0I4qyyYlfJ4lAUcz2TexPoB/B42XMBL91AoeS5SWA+vaZMrSDLYkl7QMLrMfmUwtJcwqsqSAMp7vm9Tsyt09OxVTfUnSupMlKL8eyMLceKRVKmXdA/0pKTU6CTVNwU+bXeTLFbSaaMEOyktXtzEigeIL5nSbtWPcqz5tyRqnY+/7jzrdoyi9F0p+jj32b5b9FReJ3/VOndvQu0xqXueUQTuO5SGauoz27ppWM/+PoRIxU+g//TaipBrmVL6T9WZCW96r31m9LrP9SyMeyXHfabB9LMt8Xy2x7P5+W+Xlb7ZuX8q+3630vL7ZdyP8tf1eo33v//92Gw2AIBf+7Vfw+te9zq8+tWvfkrJr8D9AiotC6XVesdJWVFM/qV+AA6X/AlSDVAUfF7bAcMAdbKG2nY5EVa37FiMAj6yFA8iuRM/jNnQU6QHB932CItGGEl2puXER+ekRNwjGvZkJl9MmBXCBJGVsKcd7PGOrJB0ySFIIEYAP0NpYa9sgIKl9SpQnrsf7GE2fZbWpooDPYYMVpQPKE96FJuRskBFIGhaB3Pa0u9Wl7kHTqUVa2Ed2CM38H32PKtqYAJhVIqSUQF5rDOh1DRKkiI9e0aOO9mtUE2dg8Wqz7JRBLJupnNcpe8DTB+y1DManZNlEUVGKzJWt2BokZ9Jkq10UfK5BI6xIJNZbCT5tfcwW0dQ1xhKYTuf2VIAOZxpOM/0zPKkZwKshAfFQpMN3QxyDMkA6dELcyqTS6lWUT6i/uh2AnaDl27MPTZbJtpqlM+epNTSK6qliiPto219ZgZTQm5isOldlZTFALh5wWOFyTeGGDPQj4o1IrlK5FyTqxZ8YzGcr+SYUJ6tJaDKLQja3QG7VpP8tpC+1iThS0DCi+RcBcDPC2E+QwYKbkmAOx5VQARCzT5TeAnNKi0ZLqWyZFb1XmTx9FfqTU9wp9SU2tn1Wf6Z/YyKwCi9f1pgSSA5LCqCSa0lrTZOSbGR30FxsyVATEE8zmXvb2JAedNSaghrAaNzP+A9GSkp1bK2Qg2O92ZtEeYl/KJiP+Km4/VaF5Lma6FONsCsEalqKeFYIg/WZ4FC/pwuwG7HLIW1m4EhXyHw351jarKoHFSMOYEZkIWFlBQbIuwppa96NzANuGQoTWgkHCh5VvdqLx71b8crzGvO1GlERyAURycSZT35a5XiomNOCjfTwlyQVOgy2R8IJuOelzAzpGoK31FNneWvqipz4ux+12P+d2LSw9Qb6xYFPaeygEEwqScwjUmtYVqH4tKGwVfGkKEMgX/zrmUZrx0J8ClNCbewubnuJMlT8/MUQ4bcyEqRdMxvYzL7aHgnYt/zmnQunycewgglYUZnPJl7gDS9R2JFb/aezxYouFdy3Dv9nM80IHg6UkCfrn1+Ktt5PgCp/Xvgbsbtnqs/kgsL95n89X3vex9e+MIX4qUvfSk+4RM+Ae9617vw8pe/HL/yK7+C3/7t38brXvc6/NAP/dBdb//+AJVdNwGqfuCkQCLbESPN/7sW2O6gNl0uZkZdIc5nnMw6B3W6gerGqc9yzT90nLAw3l3FSOlYKtdO9QIiY0tBKNFqhKZCaKpJRrZpoXc9Y/sDQym0TNhVmpACAs4cpVcHjXjLgtR4ROjTHWJloaSP06w6YPT0i2466E2XGVV70pHFa0fo3UjJFCCeOwI43Y30LgGsAQAoOdsSmNrjLfyi5nZb9vKhHwnEr64yc6sGspgpmCgBR+WDMBWc5PtKY5xbevZal4NqACBYSkDHgwpuwS5GvaUfzdf0UYZCw6572LUEEEl3YjQKSIExwJTQ2nne4IFhF25mCXYlEKk45vOjoicVEkDkG7vnWRRwIgCP50nYASdBQbshs5tmS7lqedwJcCIAHZcF6zp8QHHcMuDHpPRZAy+saXlpIwXnPcyGP6HUMOsB5niH4uqOn38MMOsOdjvCbqT7b0M21ey4D27BZE27HSm9tfR7QiGHD4WSQUIp7TiBxywdtkok4jGD/mi0nAOyItXjO6goIU9KfK1pESNEFrkXRljGnt2GJ20OekoyaoDyRlaseCaslpYezk4WOZzPnyFJIcejarp2h8DEZPHAcdGAwMTPK8Sm5HeF1lNqp4SRpHoKNRKYhNLwuyCSTWGI0DgtMIUgnZayCOW9qB08gWuMZKqHMb+P7gaW0ssCVU58bWqgH6CUpr/vHo2YvLxBOh7nFcaLC4RGkpJbdqFG6epMNgBoIJ5bApstvwNjFC86w7QAYDxXsQO23/NllgbdQw2Z/N4xRdVzgczP2H8LUFmgXcx9nynIR/dOFqP81I85ip81eWlH8anuH7f9jkphwBKgSlUWyhiosiDwF9CPqkTse4YxyYKC6VxmVFNgk/KB/lCloMqCP3U1sdSp0zHVygwjt2fM5MMEzoCzPPqBrGSQ5NckI++5yBHKvW7VwsCu+6wYSPJ2f9BkmTbGMYPLlIh7UxYiLWjEQCl28lXugbnMaArwBJIsVY7hLcYr9KvP9Fyq5QKQBd99plTlmpfr/cUxxOfVJPdaVvWZHs/Gsbodhu1Wx+Xp2ueni6m7dtzueXyyz/lUxtMB3J9P99EzPe43pvJbv/Vb8epXvxof/OAH8Y3f+I14zWteg9e//vX47d/+bfzmb/4mvv3bvx3f//3ff9fbvz9AZRpOAgK0yMlkxR9FwZ+qZPhFjMBqTQls22epE0TGhBgJMJ2HvnIK88Qx9FX5/1XLAnQBqPrKKfTJFkoKwvWqJbO46WFOdzCnuwwg47zmqm83IpW1qzAlVSYfI1dsA3Q/Ul53dQW1pbxPbVr2jRlDYJm8UDEizmvxPhX8HOLb0+0oclwPc9LCrHrK0jY99Lqnr3RRU/6mOWFXux5qGMmgKgX70atAPwHgNDmOBwvWLBgFvemh2gFm2zNddDvQi6QUoESKKjdbseExSAAk+eQAAq4kJ/UzK0DLk5m09Jr6eZnTQFWMGXCH0mC4MMvsGCs9OCkzrWO4z87lKo0gYTG+tpJIKkE3sg9q8GdCktxiYvNMO9VvKB/hl3UO/EkMia8thqMKRmSn5YlI0BTrKkIjftnNQHloS2kxfIRetTCrDvp4C328RfnBq5xQjg6qZyCVOd2RKd6xMketdpQmP7GCvbxB+cQG1WObnOIaSyPBNpy0NX+4EZmqyuxG6lpVAhoSWIh7k2C3LOm9rAtKt7fjBFRA5pRdj9ME152bIUjtwXh+RoZyVpKV6pmC66VWhdJhSpLT/paXdvRhSuepEu+fCgG6I8h0iyLLd5PUm2DYQw9O5OmKssJU7+HEdybfAUlimCplzEnL+0cATjQKqmNdERmsArqVhQm5f6P4aDE6SiJHBzVryIABOXkaAAHKfiJsWfDnHgb1KPEHh3klUvHAxafeMUla9p+A3mA8P+PxKwzUbvqsygVh5unh843lIo2m+iIFagFA/fiWPYtWQmcik6Gj4YKG7njNKMdjyuusoiJEvhunHy52AGAvb6EBH8lUzhrkHlvpZMxMG85KKXPITIyUpmrN68J76Bm9oaEuc6I0wEWjZC9IKgwolSXNcRi4gLDH9qXAHgAMatoPwEmSUqWnxVFrAcuFkEmBAaYbgwudvrG5tirUVFokpUBKqeUB8vnvQRwGoG2h5dq6Ha9VOl7KFkhBPPsA81H/dn4vJFnvHghMoUjXyvNyb2d6j7ab/vYK0AdAJtnd2p+Z6lqejnGnE/Q7ef7+8X06JvHPdQbtRoFFNwKN++f0qR6XZ/qY3O7+7jPoTzeIe6qVLrfDcj7Xr7V7Ou4zpvI3fuM38KY3vQkf93Efh7/39/4eVqsVXvOaqfbpta99LX73d3/3rrd/f4BK6QYj6zAliaYCa7Qdf6zlKrY1wHwObFv+MU9ek0h2RJ1QZwwvE0/5A49h4Mr+6ZoTl/SakQAp+bbUajNNECVkRK02eTIGYTKSlDRJmABkxi9NYpXzwLxh+EQ3cqV7dNCbluXeihNkeo789HoJD1Kdo4RLSuJVCDmchcdOwkL6ESgM9yNGxLJArAtut+0YKjEX+WKKjTeGACelRRqCFtWNBKstQak97chWasBe3aK6tIM93kGNAXbnMlOWUguhFeyqJ/u2HSmTE3mudjzGwVJaGjWyvBMio02SuTxZHcgwx0LDzWyWlkYtMs/kSbSazO/OZU+pCpRL2lPKfIuTnmFKABMoJck2V2FYbpu1J6whKE6HLCEla0uvrO5HFE+suQ+rlj6pbU85W5LRdcPEpFnDc7FfNSE+PBUjsN5yArbeApV495wHXGCSZucYIqUUdEcmKBYG9riDXQ8wrUcoDBlAkaLqbszSOxgGhITCwG7TPQPp8SMYNbuRstd+zKxL8uVGxfsySWBz916SocqP6RwDrXzMLKVyAaEpYMVHydcKO5WSfRuL4mqbk5vV4LjIItc4pEPRnrS8r0YJ9Ur3at/zGHthpk4J7mPNwK0MHAKBD2s3NMxuQKzL6Z4y02eJs4rl8YW8ftbw2OZEasrjUeyFuDiHuNlOLNY9GDlkqHP0spY2H2+z7XOFkNkOkog9SYNjWVB5MZJFTkBPD56hU5KI7I6avFCgpCMWgKg5eKwKAUlkvzXGgwrRGvbUSv1RqGUBKcbcr5nYfj3SP5gqpJST7/PC8qcscmUHcOPk1zP+wWHIacD0P6oM2pL/WUswFxOzA++PVKMh9+0+iFXGZGYQAP2oCcCK3zLvS9pX8cPrwWULgB4chgszYcb53Rb3Fj+SJ57/D/qte8e+0NmMAVCywHqjNNe9gzJVoRjD67eqWIcij+3//pXFa/nv5MVtGqjFHMD1E9Ybpb/m4wUAw3jduVJVNYHt/W2kShL/dqQKkqc6+X22OxfvFqQ+le3cy7EPoG4Epm7pub2L8WwzbrfyuT5dwPl23vd2+mfvZDxX/MnPyrjPQOVqtcL58+cBAGVZYjabYblc5t8vl0vsdru73v49zKx/BocPgJUuLlMDmFZmAUzJik5i8QsLBGE1U8T6MIrX0jGwA+CE02iR96gzIT/YtZz0LWY50VG1g6zYapEu8fCqdiA4TfvjPTCAoMwHKJBBU7teJq7CVvYjYlNB9QPiouHvk5+vLKB2rbCgHvAB4dx8CnaQgCAVI4HerocyZJpUP0Jf04GnusmTGUvxLznxhC0aqJ6sHbRCbEr+O0bEQibcAPSOckyIPDEmsO899NpDidwwTdDNumUoiFHiqWRYi1318ItqAmKOsf6hKaC3PbRI5gAwlCL59AoDrQKlezNFMDMGjOdqmC1ZxpQgGUojHsgC47IQ8OkRSgv7xKlcI1GK2D3Z4ZJ+SQCUmkkdjV2zOkMPwrYKcwOAk9/IHjnlPMyVDSBJh3rTA8MALQsgCiCTPcjqfCUyunSutJ6u177ndbec85rsel6LbQfUzbSgAuSFALNpESuyvjAaJgSed6OBjkxkrO3UxyrXUbHuCI4sFz9M1yOWFu6ggj3t6JV0keFLgay03g0IkpALlNnDaU92DLYqDdm+EBBmJc/7RibeqV92cDDSA5hYPSWLNL6xwnwn+aD0d6bJbmRXol51iAc1Aa30IrI300ANUuORpHqSihlLpofGpoTedpRAGkznwVrKZwHeA7KvdtXSY5neZ6SkPipFT116nz3WSw0ikT1Z8fsl+S6BM37Ap3vEwkB3TNPVIpHWJ54hYKnapdBQAtxD6prtPUF1YgItv1OKS1u4C7N83ZhNunfl/EjfqJsXuSeXXmI+zuCelB5tYWXRQcVIlnmcUoSTr1z5CLcsc/Jw1HoC9EkCGyIrXYQh5ocPGdDEQOmEwuRpRC+BbooLe1oWDFMtDdnvVDVSs0poz0uqrEVI7ycs5BS+IwnAsuCjjMkJsTFEqEISW+W5SdYLzZ5WAIh1yWtdnb0+lKZ/PHUGA7ynlTHAdgscHhD4SRfmrSbzMcQ8cVRyXMJud5YRjAExYVOtmH4byPgqYYj3Q3duNRFNjDKsyRUvj4Z34pXFaym9vYEUPPksgWe+cmIfJN9OYM7N+kCvffzpSuZ8JsDV7YDBJ2Pn7oS9e6rg81bX39MFkm72ee41M/1Ur6M7fd9nG7w/GHc/lFJQ+3OQa/7/qY77g6mMUSSvGtjuCBBTYE/qoLPCFCznnHCPbgouSDIzWSnO9QJGy+rzQBC52QmLoafXdgQGGAZORoa9rq30OEBw1XZknpL0qS5Y0A0Cp1gytj+tUuf9MsljaSfACyAeMIwk1gViKlxPDIiwklEpqPWOk2DFJFNsdjlyX/WOjGdh+f5lQVAzsAsNRkNte75HNa1BxMoizGsBfOIPA7gKX8jERSYCqhsz8IxNCS2STRbTcxKtBzKeOU1z3RH8rjuYq1uoTQtzaUXZ57ojk7nqpXOQUtDiaoviiTWrOnp2LqYexlDSR1gctwwTOqZ3tDhuUX9kjer3r4ChNAQTftnAn5thvDhHOGgQGmFt0+cXfxmMogQ0pQVrnGVWuxHmZAd7ZUNA2fWstDnZTt6qEIB5Q0ZB+hZzb2LXT6yL87yeErhMXwTWctHCeV57zk+vr6vpegoRarvL15gS5i4K4wZQtq2GiX1W/cBrQLpdzcmWSaHrDuVHV9CrVmo7GO6kHGXPYVbyNaOHSWy1AEi97SkplXtXtyPslQ3UboDaDQiNyA2TNNyLvHfF8BR7vEV5eQuz4TUCAHrVUaq8bvPnSr5nNfA61N1AQDSMULtWWFw3BfU46W3ckr2MSlFi7j30uiPj30ugjVFTvyR4vsNMugx3A+AE8AszjdGxIkSA5b68OFrxdQKZJVVN/eRhKk9hREMJfWKLESBJwAJGxL8YC00/pVH0y1aGLLQAfdPSZ+iPGnaRyjb8vMge2extTp5u6ZHlfjBFFZIAq0KkFH5kmJaSkKBodPbOJs85z6mfekm1nGtrp3sGOJPGeobl2mMJkbyAaWExRgbpSN2PW5Tw8h0QCjKDaSEpiq+Wqb70H+6fuyx7VZpsX2LDy4JAcu+5+d8hZpVLrgmqDCXl2x5OujyzD1nJ4pvULdF76SnT3VJpgmFgCrEcl31AdN3EMYazXZX7Mt28s3pKudVmAvLeT3U4e8+/LkRHfpfeOyykPupaebJS1/eMyvP2J/FPNYzlTibtT7at9JpbVarcLYB8KvUQt9runY5nug/0qQLKW73+6egWfbrPy7XbfTaDpu7mvZ8O5cBzYdxvnsoYI774i78YL37xi/HiF78Yu90Of+kv/aX8/694xSue0vbvD6YSIFhK4QhapEejBGMkSWwkoALAScA40k85jBPoLAtOxLctUsk3+oGTDa1yoTk6kco1NbdZV3zerCEAbeppwplSCBczYLXhc62FOt3kP/YoCqiogdMdXytD7doJKCRgARCwtSzUVkpnVkNJSA0OFpyMpCs7pa/6yATCGM/IhlXbkxVNjzvP4CGrERdLJhz6kBkaStACQXGaN1hAoZLHC07yesq89FbYrsIAVQk1OuirG65w1yW9oirF6E+ezSQpRlnkyTlCRIZ3uxbxYMHJqTC5ZiW+tdICkcFLZjVSnloXZEhLC/PYcWYDY1Nmhi40VS6wT6xp1MieL4AhR8VHTzG+4BC6ZUDRKH4/vRvk3PWy4KFz6T2c47XT1FI+L4sUzkF1mBhC5/mcfYY7Xa/Ok2kvizOSOyxmlL5aA4jM9ToJZVVxvzSvhdiUnEBvWl7j44hw8ZDnG6BHrDDCVMsCQJrEK4U4rzlpFR8mAO7DwYKMaIy8TquCVaCDm6Tee4xk/n8A9okVt90QgGIY8+JJTrkMBILKk22NDbsPEyhS7chrRICG7tM++mlhKNLTltnOGHlsawbwaFmMyemsOsm7KaeGD3mfolJQVkPJda7avY5Jkc3SRyeAP3VmSrURtHynJIaq76fzei+GmfoMAcBuB4xHFaXPCWhFCdZaVEjJtwCPsdJpEaKDEumvaV1mJlUKSZOR5MqhFvBmNZnSw1rSihWUE6CtmPqb3otydwXTB7hlCXvKhYdgpTYkxtxvCUCUKwmkn5V5pnCeBFyi99ClyX6+2HVQBweiVAiItYU5baG7gt5NzcUieFmAkgTuHLgEMpXROQGMcihDBNyI2Hb01qZkcK2yP3G/EzJ6JszqdoQ7aoAxwG5G8U4WKK62CLMCThQbxWmH/mID0wdhf+W4946fRUCekr8lGbglX+S1YG+fkSwL7rdWiIoJr+m18ZrvFyUdvqqcOidvylYKSH00vBOvuvg1lJsXRQarr9CvZo9oYRE2/sxredymTe13a+6f69sd99rjdiP54O08dqNt3IodvZtxt9tL+3s3n+Xa59xrwHQn3sd7/R5PJgm+3e0+2Tl7tgN40nt/kfqyZ20fnpZxt1LW5yio/LZv+7Yz//+X//Jfvu45f/Wv/tW73v79ASr3+w6VeGQSU5PqQwCgbSdWJ3ku226aqJuK/z0+FSmaTGATYEzyJYCPxZhBZqxKTrTC3oQmpcwmudMooGmP3cigycpKbynyxpGviYsmS1NVCDltktsb+Yc5rciXk9cFoyOwsxqqEDnrKMmI2bvl8/biwYyMpXQCxrIgIASgOgEqRotfk4mKGRwk2aw2iJUicHUesSkRa/n/5KNbd4ilQTicQXUD1OChr6yBmkmTqifTmQBHArEpDRdFShcUv9JyzveqC0TL1fowq8i2tWSKlQD3OGsI5BsmOoZzBwSiAKJl2qmSiW8CnoiRrOngEOZVZr90APzRHGbTZ1maPWWVjGolgXjWkJnuOyYNW0NPk3jpzqz6lyWvxaqcJNhptT/JJpP8Oi2UpMWQxEhudhNoTYx2ivdPCyxaAbshb0MdrwW4apHOVtDHmwmMagW16hkuIgscUUkYSGKjtZ6uK2Fr1HbHz6QUUFDGBycycQjY7EGGaHQSsCUsWWGFTezJFBZW2CBhopxHrGouZhiVmfBUy8PrZczXMhLjuGklSZnAMnZ7kkiAE2eRPSIEYNZAnW4J1kF2nvLZkCWWStQMqRw+NiWrjSCfLX0XpEUvpc70BaqR5yHuWr53UQB9T6bHB9yroU+2iIuG4NJTYmp3jt5UYcyVMdA9Q61UhAQuibdb2Dx3bpardRKQCbXJ/sz8fiMXoJTjEm4sNIIuoXsPtyjpGWzdFBok/mRWEZks9zRb+hx174BoYNYd/JKKCSoxZIEtfceJ7DTsLeDsM4f5HhTFgFrMzzKcjp7OWDBAih5ghTBL6hZI8JP4YgHef95fzzSnapH0fjJiiFwctMUERgXYR03vuBnpVU19v/THR8qBQfBt+kCmciAD7BsLu6EMXCkJsEsKHPEg3swrlRlcNYVzpXtVC2AMUk0SQ+Tf1hinY1uVeZs3lb/uMZX2wgWRkQ/CIqszYEeVBd41/Lv8/Cidmbfq57sbsHSvJ+K3Yp2eS5LCa4HPjcatAOHNwOaNXn/t+z3fx42O3c3O+1MFsbeSXt/r6/h2Jd3P97GfSn+nr3sujmtB5dM97g/5K8BJTllOKa7SM4ZdK15KB+TIeGEL6ooBBuIxwzhmFhHWcnsxchtVKSmxegr3aLssn8vBKlqkQABSnxf9OZr7pc0ELJua/y4KMpijMBYyEc8MUvp8KSGvH6bgIT35h9R6J5NzBoSo0ZFFSuAXkLRLAQBqb6IbKbdSqy0nIcMonsiAOCPAiVYjVgXCokKoS078U3hG2oYkscbSZIlcrG2eiIdZxaCVLdkyd3GBcDjnJKkdGKwinstQFYAL2auafXUSZASj6HOsi5yCS78nmaQwqxAuHCAsa8TDBYvWD+ZMvKwIuP3hTCavLLBXMcIe7zh5WrVQqy3UumUozMmWvXi7AeZkC3PCVFazZleePt4y5Mkanud0zGfsKFSrzbSo0XYTG+Acr7Ekd02PFcXkz03nPIHGfpIbA5gWPYzh9WZkcp2Y5yS527bT5DddB4m1SP7gxMabPb/hKJLyfW+Tl0WOvs/XXZ5cJ6Y+sTftQACaPgsg13I/BeaMIzCKNFUeU1tK1dO1pta7nHqsRpdZSrVjyrDqRi4UyL/Rj8K677GR+2BtYK9e3LW5MxAx8j5Jz5OgL9VKknHqMYxyrJKPGgRe+X5vu+kYDWSpyDLvnROlgV1Lr5sxGVCqBI7u1Sgs/KwkoAyBkmMNqN7DnnZM7ZVOTwCUqKb6kcNZVnaY9ZDDYaIGoBWKq21OW9adh+48QqGhRR6NCJFKe+jdyHqg4ykYQDkykimtF5B7PiWtgp2qKhAwmXVH8Jm+h/ohJ/qqqswgTRmDR/3bM8jJVR9AZiqhFK9JCZoKMzLg3B8mQSOyMikFWEWtGIwl7OOZRNdrg3jkezZ/7+73QcbA77mUnhojv5cScNRKpLAeuu1z/6dyAar3MFvxsYpvvDjteZxXu+wHVmUBaHPTOo702BkG0jHBGHvsbhTQnJ9XV4ijgy4s91/u8VtK4PbY0BhF6p56PcXTqWyR/bD72zoDevfGU61tuFmQzNM1biQlvNtaimtfezthLbf7nk+HH/CZlsfei/Fk5//JZM37wPtuWdmn4zre39bNrpPbCRl6su0/V8/jUxpPFsZzq58/guP+AJVaJsw7kVBW5RScUFdTRH8MOYAhe6liEGmrTC76QZisyOdoQ4DnA2V9SdqavG4JmElYT2YqrSEjEnwOesmPCRsHHwhuQgDmsymYJU08kndO60kS6z0/Q1UJeNFZqoWmZujJrORk0BqE5Syv2qdkV9QVfXUCEsiutNxWKVJUH/LknrJHeivVVfrokhfyjERPJ2bBklU6XkO3sto/EwldNxDUSaCGWXdTXYNI8bSk2mZwuKwzS5SBZZpMtUMOkFHOTUFFQGZH1eizxJJgWDxT/cgqCK0p7+wd9PGG4OEalkFtd2f9d2nEAH28glpveR604oR0r+cNwzC9xkl/YZLEJs9djFOthEsLB+1Z+WpKjEyMVpoIJw9wn+pb1LSA4T2vWbMnJ47yGUY3XbsxkOkcRf64D/4S4KkrMr/DmAEg02jjdLwS+7+RiWzyFRd26o9NXmRgYum1nqS8iQksCzl2k/cxzhqowROgJiC3ZmCVaodJNg1wO+n+Sz7r9B0xjpSNS0ehKnk8VWJ+U9CKyGxRlZOvOn2GNKE1BIboBgH8o7wvr7OYgH9kiEns+1w/gRjyvR3TvgEMJnkazfPXjbZjwms/Sg2SLKZYPQVvDUyGVZFMWH78ZCvnJcIfVLBXdgzMEoVCqG1ORA6NRWhsrqVRqZ7ChdzxGyoLL346KLJspp/uY91S7qq7kRUahoFBrHOxXIgqDPToudiUJONKIfbDGfBzZoIk4CWGSNa+qiQJ+OwiQagLmN6THVWQDlX6Fpk8HLNqA8BZhjIxo/u9i5LgHeX+UlrhUf92Ps9oAqa9UCezG3iuhK1ktY2lDF2Di3i1RaqPUt5n+XosjCwwnV2k2O/qTLLRfGyu9U5K6uuZ6pNrpa9iD7nWI3qzyfQrzGtYayLbUkoS0IcxA8ZHwzu5GFDRF76fOpvvvbSf+/7Ovfe+3fFUwehTGbf0tl7zvFsxXWk7t+PBu9dBLvuP3wyYPV3H/G7Y6Nt9zZMtAtwuG/5M+gvv9hq6U8/pnYDQB+OPzrg/QOUwnvHv5IlxYnTSZDUxQUm6CkjNyL6PKE6sj96Tq6baEGMmaaNMSjNgSExjKlBPjCfAyUoKFEqSuL7PYOcMa7gPFEVmF60AzH1w0DFgB1ohHDScWK1aThK7AegGegMbMrgpETMK+EsyQLURSWViowDkZFwlMtc00bGWLKiEmdATN/JntWMIzeUTTnSWc4SmIHvUDTm1Ua8ZqoIQEI05kzYamkJkcn7qIUwA0gXodTf1CorUlXUcAyd4w0gAOIxS1xAImAEem7aXzw4m4q7ZA4q2k8oY8UDu18wA08JFeu+uJ5CwdgqEynJrJ4DNTMxdYsYTm5jOYQKEiUE3ZupWTexAuo7S80Z3dv8SMHN7IHMcgd2O/9/UDLBKMvG034m1u5a9S3LtVEmSWNL9ip7kW6tKvlffT37OdBw22+l4rbdyrw577KmdAGtZAIs5f5pagrBk3xOQ3/E8Z1Z32/LfWk0sbvJMb3cTA5pGWqhJ+6fV5H9LP/0g4V2FqAjs5OdNoUq7Vj6nMJXOTYtQRiTGPmSPKmWBIYNEdXQ47dO+lzu9Lt1/93L0DEVCCAgHDVlBqV9RHWWwUWvo3SjMl4feslYHe32lAChJB6S3kZ5ms+35fdSzbiZ5OGNp+Tvx5kbpkfUNQZEKUSqCCOYAwB3U7M+spGtVJLtpgYlhUwpuXlCerjVVCjO531I/5Y38fxDmLS2OpfutHximpiSZ2gdWoiimSkMjBxtpSa4mcNNTjyPfNL1Zfq/83xhz3ckr9KsnJjMBI2G5kwpEeUqEKRcHva7ps3QM14riB07puHByvcoCUEzBcvugMVVz7E8qk7Q0hhy6owQEngF3aaw307Hc+/yvMK/JwDX3Se6BU3Ow5OdO301GwOqf/b/hFeY1BP2pkkeGrmooraALC7NYwF64gPD5nzUl7N7FuFVq55145dJ/73RS/XSBvLsJVXkywHerx2/XE/hknsu7DYK5ndfeSIL6dHonrwWWN2Mvb3fxYH88VTnp/rV4t8z1jc7THyXQeL8F9dzrcX+ASi8hNnN6nzLDsh/yUhbTyr9z4qsM0+QvVYAkz0mSHyaglSbQadV2FDZCwAv6fvJvjk6CT/ZSCLuef3jrMgOx7KPLEizZH2GX4rzOwFYN8hll4gJrppRZH/KkIv8eoE8xyQAHgkw1OPZsbgVYGM19yuBJmK5+yBMctS/x04ogLbG0e/LKOJek0VlDueN2B31lJcdcJIhKISzJSigXmN667ab/b8epZqEfp5CUYSRQTiyzMGB6t8cYDyOBTKDcV3XjxF6ttwSrp2SbdT/yXFoBDRnI+ImFCsL2pUlPCmtKE5dZMwGuNCFMvsj5jJM5kTETaI6T3DQ9lq45YZqzrBoQFtFP8u000sJHSi4uCn72eYNc3p6el66RRvbVaLLciT1MaccJ9KRFlAQG0/mdNRPbnuS1zkvFTjFJOhMDl1Jq02JJAnM5vCpO0l0tnzP1RSYZ4UIktGlh5kbHI4VNpe5Ha3jc5zMeh+VCmFk/ydeluiPVFKiC/X16NpveL/XQNjWP05Ck8RLOpeX+02kxIAEBnwFX7OhR5WSXC0XqcHl2ASxNlpMSIS063MOOSgDAwTL7EGNhoHcDw406xxCddszKgH1viO7kGmpqJjb7CL+ooFte2ypG+EUNd1izo7Ip4JsCqhNPtJeFrYKBM9Eo8QPy+yEUUmdSpvMKmM5NyobOZe9lSs51ixK+5nFUIxd89OkW+nSb9/sM2LiGadOFZbWH+ALTQoCqKi5CidKBVURaKjsILCEhQehHxL7P1Rj7PY5pRO+hypLM+H568zVez31gpvpR7AOR50YD0Dw/evD0FQdIWBEXdJSkHCN5gVJmQPpbtC+7leOxz95eCzKV3NNxdFOvp8h6dWH5WFXys2kFlWpM0mdLbyOgTxcW5tP/BP7XP/lTaD/3T+DDb/rf8LP/3/8invkC9uNfiPUnNvT0NjXUwRLRe5iHH4J5+CHogwU+9P98Cf7Xd78Yqz//GXjJux/H7//lGvqPfQJ009zxrXCjcTshKNeOW4Gk2wGaTzbpvxN5651s+05Cdm4FcG4kkbwRoHm6fHe3KyO+2bm4XcB1o3Grz3q7YUu3et8bbetO2NX9f98q4Ona97mdxYR0/m7G0t7qd8/L8UD+ekfj/gCVCRQltkImufHCuSkAJElJDSWeZCAkFCXIpDDVOag9uanSkx8tMRLJM5WktmXBSWz6A+r9xFIlD9qs4QR3dBNwSCxKeiylxIp0S+36KSxodJPXbRQPnhcZ4DhSfglMDFgQ/5tRZxms9XYPFMi+7XlDkaSNCWgLm5llwgk8JMZJqclztt7RW5Qki0lSHAK3JR5PfbojCE8MWQp68WE6DskL23Y8DmnhYBiQI+xTjH1i00TGluWJibEbR2A5J7hOQHK9nepdUm1M+txS+ZIZ7CQpjRPgOTPS4kDHQBskNk2SDPNxTkA9yTsTS55GCvAp0jEME0gzZqoRyYE48vmT3M+HiWU5PBBAFc+mYaaRJN7G8HlanwW4CUAmsJ0WKtKiR1nyfa1BPJzLwk05bbupES8cTvu2mPH3s4b3irUTcErMoUx641z2zUcmBi8bAXdyPOtqkuNaO52XFESSJtHpvpf9gRFwPI4EVVU5SeWFjVdp8SkB/2EkgJk3VAWk60OUBOHCAcLFIz4ugT6JRVaJhTSGTCWAeHw6KSZSGI81iFeOc3k8tIKaN2cXEp7u4T1l5SX9qdEohnoNLldZkGkcs1w8yV95rxJw6nYgE6kBX03JuLrzlIwOXqpJDPzMsoKnEEAmUlK3KBAsmUYG+TBJWKdU4L3EYC3bV4Fds/Z4h+LqDsXVFnY7Th2PCUBdI1XP/01y1L0QH5U8+Ubn+0V1I4+BSHvtqkeo2fEZjYavLXQnXkZh8FIyLoDcQZmBY1Kz7PcEX+MLTDLOxM4lGwCPmUI4nE02AKn+iWIhUL1HWNRyfPZy+OS6jinoC9NE8epX/xnoqs5sqb1wYeqATGqcyA7NfbZX/8lPyYE5WXoPyHewmVhJpTG+4sW49Hdeist/+3+DftEL8Z8efSd+99U/iPf8m3+N3/yGHwAArD9ljs1LXoSf/aX/iP/+T9+K7c++CP/pd/4bNi/9ePy/fuv/g5f83Ifxkp/7MP7P/+OF+M03/AB+99U/iP/2L34Q3/7Qb+H/939/K3723f9v7F7+aXn//lz9N25+/d9k3EnC6q1AzA09aHvn+cleeyc+ttvZpyf73c324U62cyMAdCNp5Z0A02sfvxWrejNglD7Ltf7Gp5PVvRuAfyfg/U6B+N0A0Nt5jxuxvjd73/vFX/mAqbyzcX+kv6Yv6yQfS8XS290k7wPIFKYqB6OnWoYU56/2PJejI+s1m02eqrSdVnq/1i2gR6klkT+qvTBnSQ4HALWefp8m/QnQJnbTGnqxAMpL1wI8hgFxPstpqPm5UUBtkuoprtBHYViUTF5UYvK8QjysoVbT6n1mgHyY2LckZwwyOUsSzMTa5g5N2W/ssU9lKZ4zmdx7NwWezBt+vv0ez66fAJTULeRajRD52vQcYALr+yxXjEAUIJl8nomN0nrywCZgndjVFErT99NrtZzbLJEsZTFhnLyH6ZhZLSyzyERLATyJAUwdiCm8ZtacBXipAzW9t/TWZRZ6Pw04SfPSZxvdlO6apLjWkIEMgRLS9LxZM0l702vGPZazqacE5ATEZ8006e0HxKMFGeOyBGJ/Nql21pCBTtJQrRmc1JFljouGybkz8bJ6To7jvGbNTGEnT+01oSwAKMccyQypoBAWDfSmnXpXJTwGRcHUWCfe3DQRHv20MJOuvbT/ztNzlzyN+57KtIBjLaXeEvSSFwRilF5RkBFLqgAjQDxGLlwkyeG+tHUcea7S9dsN4umU+2S1meS292qUBWXwUmMR5gzQipUkRQeqCKIhUMzsYurDlWMfmlKkqQXMzsEfVBLSMyIUOofMuHmR/8iqSA+iGgOTljvxJo4CZlPCamlzfY092cEdzaY+VWFSoXUGVNmrnK5pgOA3xLPS170QnRg0vX0AoqSZUo5tEXY7qIcvEKDVFtFo6M7BnnZ70l4Q4MWQme8Ypu9TJT7D62SjSSaddin5a7mB/LxcqdLYbAHYfOICUQHzD7dAxeeFyuZqI9VKGvLw/2fvveMsOcpz/29VdThpZnZmk9Iqo4SEhARISCYnyRhjjDGYbDAg2eAAvj8bX3MxYJvrBNfgiwjGYGyMMcEyNhiQiUKAiMpZu9JqV5snn9Cp6vfHW9V9ZrUrrRYJC13q89nPzpw5p7tPd3X1+7zP8z6vyHdJE5GNl5Ww8c67dwMqivnuWy/mZz/+M5CNMBuOwN61bQXoBlCdNp/9/hd45qMv4LPf/U8ATvj6ySRXP5oNf/WDxiFdiZqgOmo9Jsvh0HUsPGKGR/2PH/Cb677CcVGPh5144T6n5K4zNDe/9OL698tO+1cAvnbx+wF4XOs6AN6y9rr9TusL3/FJnqa/I6e416sTPCFgHg+c9/dzGPcELPcFvO4toK4NomCf+70nx9T78tr+vsvex3hP497Ytr2B2v7krPd2jsfHgYCsezKg2fv4DuQz9zWJcF9B8n05znvb532Rse79fQ7kGPd3Dp6mmzZM48eyr2t7X0HqT8Q4WNbxQQgq3/Wudx3we3/zN3/zoPbx0ACVWkm7DBjLlpoGYIYRwEww0ei2qRvKB1lrCPyDNNH6YCVsd9x1s9tp5K+BlYi81FWPBQ0BUAZ2M7i2RpEA3W6nAaqjzAPCpjZNBRYv1Fwa3bw/HIsHJXV/vChqJLmh/9ncUlNrGo8ByraX2S77GrxobFrknt0rvaNhGjXnOZgK+d509AfyXcL5KsbOTX/oGaaWtCvpDzyL1zBCdNvyvlDDFkBikCCHfpxlY9JC6VkzawVoD0crpZyBrQjXwpun1GyBDgDPgonqmtia2Q3XLzDXdeAXZL9RA3zDtj2TU4O4AG7DKGmuZQDsLX8uqqqpaw2S3hqAeHn01ETDhPr3uU677g+qRsKuqVCX2xubX4HJTn292SjHTfWa/orB4CS0ckkEOAmIqGpZqJue8PeSDz6dkhY2oVG6CbVvlQS4CwMBYRqRRsdyjaQ1CSJ1rs2MGjmpXhg016os0f49oQ7XxcbXG3tpZTup2eTacCkA5BDIF1XjwDkmpVXGSOP6ACyjMaYrL8DlTashrcS4yvnrMBzJ59tJ3QJoRZIDcEUhssdwf4ZrHHoUgkhS8SznOJt1Pw/bjlGVSE6dQoC+b5thZvtYr/5QZYmdbBFcYFXp65zjCJvGYswzJpEVhrHyfSObtiJmUNaO0DYxjemMle9sE4POLDglplnh+iZRzcTpYVH3tHReAmq7KZS2ZlwBWUPGJcbjdY2BpRz/2Q8Vx5JY8HJwZYwHlInIcPvDGlzrpYy6HUshZRDKP0OcdwZ21nnAWtVmPS4vpE9leL748cX8nyR4C8dZXyhAKcqOB/sOLnv3+wB4wkWvJl6U72wyMSmybe+E7eXBuiw9oBRFg8sy1NQEqt2GPMdMTQJQnXYcfOtqaCVkTzqd9rVbqXbswlUVn7v+a/XhzP1tp/755sd/BB4PJ0xexMP+5g5UHGGmJnGHrObzl/wjp/7NRVz72gYkQg+AW1743n3OyXFAebDjBb05PsSRAKipSXkmjbUuGf9/X2Nv8HlvYGxv2eN4sL0vBu3egv0DYYr29Z696wX39b79AeD9AYn97edAQPC+zt+B1F7u/fkD/b77O7Z9bfueRuipek/AfF/Hsq9rfDAS0HuTKR/I3N3X+/cnp12xTaX3vQ+fEAu133vLWw/2u/6kjINlHR+MTOU73/nOFb/v2rWLwWDAqlWrAJifn6fT6bBu3bqDBpUPDflrqPMJAAoaCWSQGAbnyXFnyVHWmE743mI1YAkSnlEmgXQwOwg1l0GqGHr8BVZyvKbNsyIuyP3SuOkXON7PK8txE761SZBvtry0MPStDExZfyBBU2DcAggJMr5WIv+8k2v9vcJxx7F38SyagDfLYHa+CcSqqjE3CvLPfAxMl6W33feS0zhujE2C9DUM5xpAFRrWLy77z0UN2LN2zI20aljZ4Mwb2L3A+qixf15+C4iUM4DtYAITRmg1EI4r94xs5tnFYMIUDHmgqdULEuVQaxiOL9QpLi03tZlZLjWMhW//UZZN0mLc9TXzICVsNxxDAJtZ3kgtg1OiVt4Qx7/mkw+qPxBgNip8W45MrpFSXtro2Zh20oBLwE53BVC2YqmVzYXFVKOwrVKATqi/89tTC8vynryS+uFC+oKq5aF8bjjyZk6IgVMsPTHV0M+Pvt/XKJOfQ8uc8TY9NRjUK51jw/UoxLFX9TPZdyb7xyI9VoNyABqGO9w74/dymKqht+B43XIayxwJSYRwvWFlXadPRMm5KEQ1kBfN8TqHaqUeLLpmTVpcAluhvANmMIixg0Fj9vIADL00whmDbXug70RurCpLNdUWw6w0pppoC9ullLQBCi67lcUMvMzXG+XUZjxGo7NSTHeKpjWJMyJrdVpRTASm2Qnbm1dQOfTiSFjLod+2r9UUSayvbRzk4gqdxihvRgb41hrFyvpcVrKAtQwxAEtEolo7rwZ5fFBF+DrRcSMeVTlpT+TbH7lY5kwwe5KEkbi4BiMeQP7XwvC6okSFekQ/VLj3wSdAdG0IFC3lPmPeRCrbzjFULU3V0sLQayVgvrS1+6tt+XUiHVsTtebmt5yMOe0kPnvNlwG47TcU0cw0z73kMmZPjrn5t46SmsawlvrxzdM/dbe5dPNLLxbn8cPWccvvnsi2t8t5XQkof7xDGQMTXTl+pVGRV5L4fzqVJOG+wNXer90bANvX3/bFMIVxf8ou97XNAwnu741pu6d6uX0dx30BFfuS994XFi4cx72BvgM5J3d7fT/9W/d1/Pva74Ec0/h+fxRmb1/z6kCkuPv7u06SfQJKnJX6cp8k31fiJLx3b+frn44H19i0aVP970/+5E8444wzuOGGG5idnWV2dpYbbriBM888k7e97W0HvY+HzlWvbGM4UpQNSzDKGvAxbowy7mSpVVNzlaYNcIoimJzwZj4+qzzRlZsluH6GWrsA8kKQEIJW56Q1RzjG0NRee6CmfeCyPJTtBEDoA2c11gKhNkIJUstQRxcAZ1E0dZzjYHOcIQ3sYa/rXTu9QVGSNGA3LAZJ3ICqVtqwfsY0TGxgPkfZyhq5wOLFsTBrgR10Y0AhADtoWF9owJPWvq1G1ciJtW5qP8P7w7YCKAxGTAPfksP3f6t7J0IjHR53be0PVprclN7EJkkaoBuC1QDKQc53u9WwXqF9DTT1fwH0BtbaaAHAYW7VRkm6uc6hZjDUcWol87Nush5cik0DMoNEN9SXOnHTDK1MVFE156vvnYIj08y/MI9CS5FQHzqS+l6V5RLwd9rNXAvJjRCIj0nO1fyiHOvyQD4TbP/D9w3zOUiWgzOzswIA63vUP6zKsqnfCuAhvG6duJqG5EGoE7VOznstRy6av0Fj9FMJk0SWNdc5JE3CdQ6JqfH2LyH5Eq5DUC1oJcyUMQImi7IBiiERYwSounB9vVGPGLqsDOjvz2GnOjXTBqCywrsta6xXI9g0wkXivmxjg+sk0g6oLcoA24pwkbCbkrzQkmTIK4qplm8vpHCRMGg6rygmJTCJFzNUXqGLCuUcZmEopluhrtMz+SooGfw/FzdJF708rBMdekFKHerWHmOmNAHUXVp9XBxIx2sroTGbyYsGFBqDK4qaAdX9rDkOkDrHrBB2MB0DbeAVC9bvYmW6OiQVVBI3ZjxaNc6oYe77ue2SCNsyuFhj+gWjtY3jqy6opVkuMthUXLOrXorttcAi5yOsU2nqAbbj1l9+HzvOm6m3deuTPkz/3ON4xeQOrvz/LuaWF78XfeThtTT83sbTL/kBN79ykptfdjE/fNTH7/0DD/BQxpCv66FaqZj9JHFtyKTbLVS3vd8A+EDB2YHWn43/PN6+Ze+g/B4BwbiZ0n72P/75+3Js9/a+/QG+ezqefYGnewKF+2LVxl/fG3Dd07na1zHv93z4674v5hOo3Yv39559nZN7urbjrOC+wOn+zum+zu3eUtS9/773du5VbuusJF/GvnuQ4qvDD0Gvnrlb257x76e0qoFl3bLIz/ef6OF+hH8P4vGmN72Jd7/73Zx44on1ayeeeCLvfOc7+cM//MOD3u5DR/4aQGBgmEK94rgMysvy6oAzSNxcEwTXfexCMDocNOYudU9BC9oDyqJsQId1TS1dYC/CCJLWMHxfPJxt5LChp6CX00lwqleC4bpfZS4yyjRp5J25BeeBVpB1rjCC8ccTjjdIR4OBjHVgy8aoJQTtQUoaAmylAEfdBzEE3UH+GY5fG8gzqDxgDUC7fr8PGFHyvVse0LuqqT3UPlgLIDEAam9hL4yzPz/L/QaI5UXTT1Rr2WaWNbLU/qCppQ3nvaxAl40xjrWNcUfhPFDwC2pguYJkVXlZZXAyLVSTvBg32QkjyKjj2LcdiZp62dyDnnGJMTROwZ5twI7N9SCndVbmQdeDvsDEl/6cBhffUTY2lxrWhk7S1JmGeWv0ymO3rmkREhIOQVJdB8V+3gTmNoD24D7Zact+wtxOkobR9kkSN9EVBjaOG7Cr/TwMxjeJSDDl3NDM26BaCPuOIlD+PgmveTMkF27LUBMZgL11UHope6jTC8x2GuZ02Rg71XLaysvWC4gVri+so4pjX7OnGwZVKZjoodxSk3jx884FdcADMLRv+eEiYd6CkypKYfp5XePqjJGfYw2llyz794dekyiZE1U7IipF+hr15didT7IZnwAymcYsZ8KeW6StiFLSr9fXwFY9kduqssSlka+rdbX0tj5veBm0v29cGntAauq11gVpcxTz9PgF9RqoorgGdK4Sab3CM1ze0ViFZ4RP8FXTXUw/E3Y30nWNo8lHjWojDKWBvZjmIH8NiaZQa6kVyggAVVHke5SGhAhECyNsO8a2DOlswSvvPI+vfvtU0gqKnjfsscIA66zCxpqoX0hdaiouu3pxGTdhUKsm+exllwDwL3/wFwRJKsAX3vseoAkqtz91PdFgHe+e38TrVm2+x/n0W9O381u/9L7695M+eBE3vvK/h6kM7OrmZ7Q5fuc61LK0U3ILS9LGq5WiJnqwsFiff5BHBOwFFPy6GBISQRpZJyfuhY25G1DwTNj4fsaDd1dV+wZLY0z73rLDvY/5nljGAwGT+wIo+5Oj3lMd3vjfx4HYPYH2/YHLvbd9jzJOVgKv/W0nvOcZrRdh96q9rceYPH5fkua9v+uBfocVLX3Gt+OZwXuSlu4PeN7TPvd13HvLdPWa1bjRiGpugdrhOZIE+dyZa5i8eRm9sIgdjnDV3e8TZQyq06kVPK4ocWUhEv6f8PFglLL+qGPbtm0U4/jAj6qq2LFjx0FvVznnfmJP1+LiIlNTUzz10NcQOdME4kEqNx4IQ8M0hT6BATCFYDiAxvHP1cFnJZLGEMgHw53AEo5/JtRdBcfNAHTHQd7erF4r9UxL1jivBuAZTHGC++mYGRGttJEMhhYHYSRJU48YgFwAFMHttih8uwkPvsYBIsjfA1ALxx7YzXCu64bUqjHFCU6qwRyl7vOpG7ADDXANxzDOKqdJAyDCNA0/1yZDHsgFFnO8Vi2Y5IzX1IXPBIOcAAjC58L1LwrodmuGr661HK93hMaUKADksK8gn66BnW3+3uvKNRifpwFMtlsNAxvA/fj1DEBDG2Fx2+0xUOnB0HDUmO2M3w9hhO9Ylis/E0yqnBOADsKmjrKmvjiAUuVB7b4SNyHQSuPm3gjAOfOMds8z0wFkLg8ECEPTpiTMszDv262GnR3/XgGQBwOccA3CfNv7b+F7Gy3yUz/n3WgkNY9ay/kL/VmDg2dgPJWS48/2WpDDWhCSXD5ZYxeWpL41iUXiqlUj0fZtStyuPbJ97R06/XX4Qv8jPBDjgmNe39ThRloe/EY1yTZrUVkpDrGx8dJXRCY7yqHfh9XTjZHMmMFPqG1UlWsSa1pal6iigqLETnTQg0x6ZPazuh8tStVgVmdFDSJV3jCWdrItLUx8Isx2UpHGBoC5uFTf63Z+QYKbkMDbx1CeGQZQaSo1j85h5xdQxx0pP7dTcaN1DtuOMYsjX4OqRX67uFyvj9YbxLmyINTL1ln8OEJP9KR21xjccMT2lz2C9X/7AzAa3etSzc6jux3U9CrsdFfOmwfwVS9GVY49p7bFV25WvlN7V+5rTRXKOky/pJyI2fbYhMO/NuLSj33oAZlH99c47hOvobtZc/UbfnQg+sxznolbWOJ//vCrvOxbr6DzAzlXh162RD6TUrUNsycZNnzghtozQMUxdm4eG8ya/Ag/B4fgvaXTAWQ2H9D7nWe1PNDX2678k6rf48pmXQnM0Iq+p+NAdq997QvsrDim8Fln9w+47uk7cGCg9UBlnft8/7iawB/P3ud5XyBy723t/f33BYqfZp6PfuTJ6Nllqi13Aaw416GWMACs8Wszvu29X1txvHu/Z+wa1PJSn1AAVvxcf7fA+O1dHz429q6P3BsQrzgn3P0amZNPQOUFdtsOSX7FkayHccRnr/oSp3/nVzj8Dyu4c7uscdAYkQFm3RpGpxxOa+Nu2ah12Lu2UZQjvlJ9moWFBSYnJ/lJGQFfnPW8PyaKD0yxMT7KYsT3P/GHD9rv/axnPYvNmzfzwQ9+kLPOOgulFN/73vd41atexYYNG/jMZz5zUNt9aMhfg2vmOPsYguNxW/4AbIIJS80wlSK1C4xMCDxHGbW0dRxUBSlmnjctHYC6V+EKZ1LXyKFCHV05JnULUsdQYxZAS7fd1CYOvAQ0y5oFKTCiAUyF4D70ulSqaRIf3FyDrDEE3c55V1s7BprKpl1IqC2tLMwvNvK+drsBU+Fch/PqTSrqbQWDl9DnMxxb6I/YaTcMZXiYB7anP2zqXMNnoqjpFxiNJRLCtQ+tQIIM2rsfrrgW45b+4fWwnySm7tUYelYG1jJc93A8QHCtXSHnDMdUm7zkjRttFMl1CYkEaIB2MOSBlSAwXNNQc1vL2fy1G3cmDomGqmr2H87veD2vcwJsjJwLN9lrzktkBPj2us0+oWG/A9sWN6DRpb5NT8u3ADEap7W4u4bEiVa1DNkpheukdXLFreo186jTllribtvXNFqpTU4EQLqOgF830fHy0UJMgNotXBpqTXVzDO0EN9HBtRPsZBs31ZNjDOfA/1Oh3rjbwfbalGsnpKbQ70/WGn/O+sNmXkPzAG8lY/XKvt41tIZQ2jOcUTN//VxTaVobvdRtKR7AmkpA+s+Gdhm51OGqTHq7qkqumV7OpIaxn8n78wKGQ5EJ++RHAFYqK9ALg9r5l7ISpjGNmh6Vvv5QzGSM/K+EtXSJmP/IfNF1b0qXxk1yqLLoZZGiujQGraTXbVY298DYmqSiqGn1YQyX2k80Eq7wD8T5NUmausbQU3J5JIxk6J+qlABKL9PVo7yWsAc5a1g7w/aCu2zdtsRJD8w7XnUCn7v5chbPHfKfm66Qa26MSGOLgmrdJFioWhE2iah6sfTI1IrOjopkyREvVcRLFWXLYCONzi020pQTMZt/taJs86AFlMde+or6597tch3eMXcMAGd89wX1305750V8dGmGY/7jVRzzH6+qXz/+Ky9fsb2Hfe1lAMw+fgNq7WrOa2lufdKHufoNF3P5G97JzS/r0v2DrZz3lm9jzp2jOuFI1OGHoFZNUR21HqKorr1USYJut1G9LqrXxXQ7K8BcAJtPj1/QgMXw9/20iNFxhJ7syb3u+3XW7/H70+3WirkZXqvf7/elk6T+fXweBxb1afp5K45JGYNOW3Wv0LplTH0AK7ez4h7x4251jHv9vWbaxn9npYR1nE0bZzDH/x7Oyzi4XwGWxqTA+zMCGv/+AbDvS6qqjGHP6ZPMn30oZvU05qgjMJMTK0B+fTz+fOskQft7e1zyWZ/nOKr3Vfdm9Z/RvpdrPTywrI81yE/HXnuafl6jsKjbFqmV74NGQg8r9zF2vutzpnQ9T8LroyOn6J+yFr1urcjF16+jOuko3JoZAK56zMe46ymr4bD16HZL5nK7jfa9mMuj13PnUxP6p6yjf8o6Bg9fj56YqJ9rP6njx9lS5D3veQ/HHHMMrVaLs846i8suu+yAPnf55ZcTRRFnnHHGAe/r7/7u7zj88MN5zGMeQ6vVIk1Tzj77bA499FD+9m//9r4fvB8PDflrXoCKG8lrYJb2ZmgCYwYNUAw/h4A3sFfQsEfQsGEBowYZZwAjex9P+Ax4NrDVtM4IgCPISAOLA/J/CPzDe50TMDjuNpvEEqCGfYVjqs12fIBufeAc9hH6DAZgAL5WzDSy4HDc4+xbK/Ugiwb4ar2yVcV4E3htoAi1aYGp83WFcSwuhC1fhxgkx+GajNe6BBAYJIdB9jgUqaQKctBxpnG8l6RzTU/SMO5mhiHXVrJzcXMetRGZcc1kV82xBVAa9rNCauuaViABYIb+nvXnHSSRyG1DjZ+zkHmg4qx3tR0DLuHchu32Os21q81iqoapTOQ1cYKN5XuMclyvhVoa4NIEVchxKR+0u3Yi/UbD+UxjAVABuCaeLYwEMKosFwAYaaz2nwnsVRKhh1ntJBvAv0siMSAxBhUb30JE42K5LlUvBQdmkEuvPe/8aVsRZpjLe31LkWpaWmEAuBTfO7HAJpEYrAykLUY1mWKWM6peiiosKNBJhJ7vS30hoGcRRq0Vi1GNUVRTCZFRcs5Ki1n2ZkKaBrwYLUrHyoK2XvLra/CmJsXRc0WtX4ydaNX3kFMKs7gs5kTtts+Muwe0prKWvvt549qJsIPLQ38/eElpK4bSYqe7wg5WtmaRnU+m6f4Il0aoymEnOnWbErkosh6pkQf+Y9dOWpLEqKwStq+oUK6sGU1VOVykpIVJMOoKTHlR+oSDaxJgWdGwxN7VlKVl/5qwQ08zz19pjhOCQq2kh6TRqNxLWZWSfQAqK0ReO5T1VhXyHV2kBdCO1RGRpmLadPgh2M1bQfk6Si9pLs44jmTHEoddNoTXSj0jwL/e9FVSJfv72VOegAOKNW2yVTHpbEGVarSXK8fLFWVnDLwoqBJFPpFgcsvShphqWHDcY+6836bM7eUSR0cTK167Jh9yWtK+x8+d+q6L6l6Ux3/sQm79FXF/VfMxx3/sQjhkxNQInIK/+frTOPsZ76V/8yqO33ghh56yg6nbLW/64vNo75bv++ot5/Llm0+gdX2bP3j4I/jTdVcDkF7TgSfA6Jfn6C+sWXEMPd1i43Mbee6frruaE3/npegb1zFzvWXuRM3Ro6PQ23bJHC2kFprV0/KBskJv3gKITFn3urg89zXTSMIgyGjDtQ7GX14KrdIUNTONWlrGOnmPDky2Mag1q+W8+Pp7lcSoQ9bhWjH69q0A6MBstVuodksUDoH5HAMUzjZzowajrbRRWjgnrY7q9zcRcAPmPMOOqe+d8bYo9WcrqJmxcafdMaC7P0no3RhPpevzJ4AJf07Hv5tb8fkalJrnc6n9BE+PX1AzjOPAL7CMNeunNGZ6iuzZC8xunCJZPprRtGH6ygR9107ccl9A5KpJmF9ET/Tkuodnvq/5VyAMdxTX+9JpqwGCaVqv//W8ynOJWfFzp91GM2zig5oxr3BVAybD/JINu+b3cTDpt6uilWxlkPmHNbD+nAe2cyfGDB+3zKF/t45OUTB4+Hr2nBwxsaW5v6/8vYt51PAi1n/ZJ/5b0p5LFSXbz+rwxRf+Oc868TVyehZanHzDBKocwpj5/U/HvsfHP/5xfvu3f5v3vOc9nHfeebzvfe/jggsu4Prrr+fII4/c7+cWFhZ46UtfylOe8pT7JFtdu3Ytn/vc57j55pu58cYbcc5x8sknc8IJJ/xI3+OhASpjIyDK+Hqq1C+eobdiYBLLqmY97iYNDIYoweUvTXDLfdGIh0UkgJAAtIJsUinp/TfOgMVxI0W1TZ3O3eSiY6Y8tcR2OKqlcDUYjKLGSMbEDeALLNA4uATqnoPB6TMYiwQ2ZpRJD87hUHr19brN9wrAOxjbBFDrvFR0vO9jAFPQ1O8ZJdLMvdsijLG9qt2umYeaZY29pNhn7OvkQKgnrc9V0TSKD/vN8waQBslw6KW4N0s5btYzdv1VK22kxYFZC8Yx4VqPt6gJIC5JGjlz6P8I3mU31PQZyKsxs6MQdNumR6R1YNxKme44e5lJfZ9bO43qyxypW1h4YOlaMWqgGrCQRr6Po+zXdeX7uMkuapDhOmnNIqmyxHZTTOWEPQJcK5I6y3CeoQGCRtjGaqIlbST8Z2zLoP33qyba6LwUMxOHgLlMgEPVisBF6CKu+w8C0j4it1S9lKIXES8WFNNtTL/ATrYpu3JvmWFJMZEQL+VU7QidyX2ar+4QDQqs0rg1XXHFjKRPa5Vo6ETES4WYFGndmLv4a+zSGJt4maqFYjJBlY5oUJCvn8D0WwKCgglSCCbbYyAwMR4UaVQ/quekm+jgjKGckOtqEyP1bzOTNbunyrIGOA/YCPe6RZx5RwXEYwAN5FhGObQS1IJPQgT5dVkKcxnYaIuA4pDUChJrbwQkCQiffPPrqWsnmD3LAjYHQ0mElBV4cxyXRFKnXFnZRzDx8Z9Vy2NBWGVrAFiv79CAeR+g1hKzMTAZpNAqjiXRVVWyraVl6l6x/ru4xMvmUbWCwSUGNbLN/TE1geu1+eyln+A980fw2Wc9Crtth99+WjOHZ//Byn6NAVCCsCKqqNjz8JSiB6tuidGFw5VOmGXfCiYayvcZHOLluyHXFcPEtTGXXvAf93lq7G/sDSgBfvGbF/Hhc/6O81r7n6tTT9zBsZe+grVrFulsU5zyzRczWkxp7daYEZRLErTqEnq3Gn5t52/QGoDOYHb3IaxfyFn7vZjSdzP50rdPo3e7JhrCpz53Hn/6cgGV/aNlbvzwUR/n+KWX3+v3uelxH4HHCTv6+ulNnMJFbPivlGwmob11GRsbdpwt3znuO9Z+MYPhCDscSluWJMFu3SYMcxTVrWTqYD7ML2Pkeq6eYffPrCddWMvE5Rtx61ej7tyONgZmVpEduYoq0bQXF+UA84LlU9aQrTKsnl+WOb24hGq3qI46FNsyRIOhgEMvtQYagKs0OlbybAfc0Yehd85Dr4Pbuh0dVyLrhRUOyWZqsjYOc9693Fl53zgg00ERM+Z0LLt3NcC8p9rAu8l4A5s7MYEdDEAplNY4a1GVbfoJZ5lcAy9H3VsaGpJxOjzTlcINhtihXSErVcaQn7yBqx7zYf7s+OP5r9NP4i+P+Qy/8de/wWFf0ahNW1CrZyg2zBBvjCg3rK1dsnU/Q++Zq2XsKphDgrSyKcT0SyntFTey3qvlfn08LstQaYrLMmH/du7xhogxbm5eFAujDDscSYLBy/RDnXhIrI8PZx2622GFMSVgl/uoSIB17YisFXY4RCcJzjoWHzXitvP+gWN3v5rjl9ez7ZyIV//i57lk6+kr9vHc132JS+94PIN1EcmyFRn+jhE/98rLODqa4Jqz/6l+7+P+/TW0bjEwf7dD/ckZzv87mM/dh/GOd7yDV77ylfzar/0aAP/n//wfvvCFL3DxxRfz9re/fb+fe81rXsMLX/hCjDFccskl9/kwjz76aJxzHHfccUTRjw4JHxqgEqhljv1BAx6Cq2QIdMblhkDtGDreMzBJ6vpE1aOpZQxgBxrgED4DDWAMACmAHGhATpCLhvpHkPd1OsJIhfrI4B5ajgGUMcOIGvgGSVaeNxLYcXOPYA4T6tGCU6eOGrfSWOpJ6npPY5r3BrOg4BYb6u4CkC3KlWDWmKY2M8huwzkPAC8cdwBz4XsFJ9bhqDnPAcQH2SvUEmYXwGk476H+MUh6oQFrSsm5COcrbNtoAfzh+o+yle1WArCvbPP9wwiMSZAyB3Om2m20wE11RTYXAt9wzf38s60ElZga2OhBJsmR0Kcz9AcNfQ6nejXD47oiIQ11cGqYYydaEmx2W41st3LS2H6Uo9AeRBgoKmGntBZWr7TYToxNDVU7xozkwWUTI8weYBONLh0mBPZJ5BvUC2hUobZiyTOZkUYXFWWvkYebYQModWGxka+Zq1z9Mw6KiRgzqjCZpWpHqMpi2xFl20hvQ+eoUoPOK6p2hFOKqiVupTZSuCgBBzZScsxZRdGNUA6chnwqRg9TkXaOty5x0v5CF4ayF6Oso0o1cV7K75WjnEhgIsGMSjk3RqHyciULmpXCksZmRasIVVphU31v17KryFantIogsXRoJ47RLpj5PBAjGHQlTYJKhfrfMP+DxN3/rnJ/P/eHY2ZRFmWVtNbQSLIjrFlBfg4iSwaRvPrf1fII1xWm2U10UHklvVLzMXl5pLEtYUHrxIuthDFXSnpfgge3PjkSaWxb1h1TlrCwJACgP5CAyoV7wwPMIDsOZRQTXZEFJzGuJ2UILja42GAWhvJzElQuYnaktRZjJsDOTDB7+hQAv75qC79+2RaO/+cLOfH/bmfLsw/lrLcdz/ffdDFX/Om++zUCfPaqLwEi/Zy+sWK42tDe49lbozAjS7JsKSblONJ5YS6tUegK4gEUHXjP/BH8+qotP+JkWTlO/sBFANzwqovZsHaW81qaR//weXz3kfuum7v8EZ8G4JT3XYROIN/co7NboUrQBbR3QXu3ZTStSRbB5LIsp4uOZFmJ823uyCdlTsTzmiqFdN5RTDSR26af+0D9c2B/D2S8fnoTANdfeDFnnPUCvn7Wezn3b15PtsbxtCf8QL5zdxsfTp/JzHVDouWcvJeQTyf0vFLJTfXQc0u4hUXU2tWy/s8voLodSRT3++RHzvD03/4Gl951Etn8kWw7L+Xwr7Uxg4K5h0+wcJwiW1Nxwq4jADALA+56QcEJh21huONw4sWcaJu0DJs/ucfsKYrjlg5H3bkNhqpObKpWR54RngGtTjgSm2gWj22RLvRo7cqIq3Wwczfa5HdTFbhDVqNKKwnBUQ53bsck1YraZGUMekJMnuxyXwCPc95crBJmcR8mNzWw9IAymCrZPBdZbqdDcfIG4h1LvuZ9GRVijcmeJLrmF+WeDAxv1DgpP808HzM1iVo1BWVJdciMgO/FDHXrHbjBoJGzttvc9GK5939v5lZ+b+ZWQHPl713MaelFHPnPSwxOWsfOR8Wsbx/GrkcmOA1mBK3ZHr27Jon6JTrz7tBLfWi3KNdN+r66JWZhwOC4GYZrZZ/tXSXJfE60c0FcyuMYFRl2nreGNd8VdU2+ukPSTbGtBL1nCT2/IGvTtKwpqqzEoG7LNgHa4bpVFXrVlKy/1okRlb9GGlY8T/TURG0Gp9IUBdz2VEl2bXz2+zl5x0V88WV/zpHRRH1/hPHG1TfzgRc8jvNP/SHXzB7Glo1raW3v8oV1197t3tr6rIJVX5qCqw7oVnxQDmWbZN19/RxIbeb4SNOUdJwsAPI85/vf/z6///u/v+L1pz/96Xzzm9/c7z4+9KEPcdttt/GP//iP/PEf//F9Or7BYMDrXvc6/v7v/x6Am2++mWOPPZbf/M3f5LDDDrvbsRzoeEiASjszCSM889eTrHh/0IAJLx8MTbslG+5BRGAuB0Nv5FGJfCC0+9A+4167QkaNVDa4RBZlY3hjK8nOBwMRaIBRABeBEQuZJFt5sOgfkKOsaTkQGItxA6GygnbcyFLDZ4xeCVbH68BCwBhAZvhbcLWsxralNG5Vp2mForxbYQCcIy/tC/LMULtnbcM2hN/Dz2m6ch9x1BighG1bJ4tgkMLW2/CMbWjDUZZ19rWWKysFrU5jBhKcV9stb06UN6xf5GskQ/CsTcNoGwO6agLqcBxLyysltKG/pPYscTBnUgrSSGr2SovrtaX1ADQ9R/2ccLGYkQgrpnGtGKfFdESYw0pkkuMmUJXD9loi7SytAMtEWjYI8yptQ2zXA6aigEzmr20Jw2iTCF1UTZ/CEFMYmde6bGTUqhJgXaWGaFBQdmL0yNStHZQVAxFnVA0+y6kUk1WUnRgzlH6FVSsiWi5qN1Dl23jYRO4nVYl7KEAyn9XHpCqLLgQcWs9m1myYUlSpxuQWm4gs0CbynqolgbeLFZSOsm3AKKySbRnPLLkkWnGfuMSQr+2gSmGCqk7QXzlUKf+7RKNzcdmsJiQoiYbeTCXWFJMxyaxcKxfk2U7mfjChKbuRP07N1//v+wE4/+dfjEs1rhVhtpeovY3G7s9hNGgvHx15ljAkeIKCIoxwr47VK4c60VrK75xIXJMYNKh+VkuGQeZRAJY4LeemnfqWHeKQbDsRLjW4npwnVVpGa1N04YH9ckXUL2qnU7qSOFDWUnYT0FC1DNFSwfKRwmpMRhoz0eFzX/wXTv7ARagS2rvh+2+6mFfeeR7XvPc01n7mZjkfkxPYqQ6Dwzvo3NG5fhuDo4SpSuZkzVQt2Y+LDdmaFlWiKNsanXdQr5KEXufN8d0A45uf+Ule8oLdXHDBr3DXk1bVr5/wDxdy80v2Dy7bexy6gu6OkrKtYYiw+KkmGlgqzxC6CIYzhnTRs02Zo0oUf/n1C9jw1I/yrM5gn9u/pVjmYXHvACZMM1reh+OJ1z2bHQsTnPAPF6JKxdn8Eqeu2cYHN1y+z8+ZoQDG7p0KkwkzaSMBllWqaM1aAY65Ihk5dAm6tOjckixD4eW+7Z1Q+ENO5n2CDstfzJ7ggcHBjysf/c9Au5brjo/XvelifnXz4/ja907m8IftYnHYov+fh6Fzx9wpcNTnusSL02w/Z5LWnGP6OzsYHT3D7tMSVl+Xs+XJCZeuu5a3rbuW417xq9z2lIs5/uSX07pmkmf88hWc2b2dF03MctKsgPbJTT1ueaLMjTNf+3wWNq7iyP9MyGYidp5t+bef+2teuu31TN4xQe/63U0ZSbeN7SToXQsAbHpOh6rt+I2nfIH/+4Mn0rm2w+QdLVZ9VxjAoCoKzNeuc9YQjRz5hKI1Z5lyDrXQF5VCZQVExhHVkYfIdd26q0nGbNvp208VOKtr19y6lUrdOsdKjadnH3UAoIev4/ZntVn33RY2UZhsNfFSia4c88entPZYulsn0T+4CT3REbYv9OP2MuDq5KOYO7lDsuTY8RhFvKSIlzocsXsel2Xo6VWyr153RSJifFzz2xfz1O+/gjueadj43Is5fvWFXPzzH+DsdIl/7x/Of809nK9eeyLRXJt4STG10dGamybvabafCzpXJPOK3tYeSz+3zBOPugWAz199GvH2LmuuadO7Y8jyUW2yCc2vv+HT/PX7fxGnYPlhJeu+uZrlwxUzN3Tp3ZRSTaTsfNSE3DeVQ1Uwo5HShE5COSHP3NHaFgvHRsTLjtVXthgdKnFS6642estO6Y87NYGb6FD1WuiQBBlf74GvvPIvOMTcXZkQxsZnfNBfL+C0/b6NjU//Ox7ZeTZ8ZP/vedCPH5Gp3LBhw4qX3/zmN/NHf/RHK17bvXs3VVWxfv36Fa+vX7+e7du373Pzt9xyC7//+7/PZZdddlAM4xvf+EauuuoqvvrVr3L++efXrz/1qU/lzW9+8//boLLsxphUgkOzMMS1Y+zkDHp5RDXZ6MGrViRBrg9KbTxJNO+btdd1ga6RLdatJHRj0BGYvtCOYhxwBoObUFM0HhT6OpvaNAUExIyDrsFAQFAARGMytFCLRZZD24y12Egahm28FrHT8WypWelUG9wnxyWioS4suItG0u9MJGVibqLyEpULKHWT3Qa4FUqANzQMgHMeRFa1o6lLG/mbnWyjF4fiPtkfeHbY13C1E1xssKkwYDXDk5cSkId6q5ota9cLYtmNyVfFmJEl3TEQGVK4DkYYEdtKUGUpAKDbEuCXROj+qHH5BTlPZdG03ajrGv3/o8wzoq6R2AajJpBMcUskbEGmKowsvlk84m7pGRCVjZn9+BYotpeKcYkfyjmUEuBSeRYQpWqm0GmFca4OyNEa222hQs1vabGptCZQNvb71iKjy8UARBe+bcOY9FJVAp7Ljmfuug241nlF1THyesffgyORpZpRSdWOBPhllqobYQYlZTcm6hee+RRjEW0FMADCCBYWXTqqtjClZTfC5BYzqrCJkWOMwMYKm2p0ZrGxgOJoKMYlqnTECwU2kSbx0dCSrYrYfh6s+X5EMm8wo7yR+vqa11pGO5mgS7lW+eEp8cBhhhXKweDQlGShwsUCUPMpmXeSya4oJhPSUUE5lYCeFJlnJOuHbUc1K1uzs8COx07QmnV86y/eyzMf9wt3l7TfnyOoJaq8WduyQmSfoUygcsIcjnzyJchKK8+CQKNa8MymSyL0IMNNtGV+GZ9FT2WOC8tnRAIdaQHm3QRnNMVkRDSoKNtGrmukKLpKgKODoqeJlw1Og9OKqqVIlizZpGY0o3AaohE4HfGD/ymg4Nm3PIN/e9gXAGHWxscHN1wOf3I5/In8vmxH3FAYXvuW15H3FPHyGuaOi0iWYOnwiHTBEo0SzMgyXBORTSsWTi/49FPexRnJmDvgp+9+ui/Z+UheMnEpthNz1f/XHMc9AUqA7/zxe3nUWy6ivdvS3lmQzcRUqWI0rZi8o0TnIUmlSBctqnLo3KKcoTXv0FdFvGHuZbzzUVvZtdzjsYffzvd2bOBPT/lX/n77z3DVtsP4n6d9jmd376Knm+9wW7nMldmhPLe7dLdj+uEb5fhP/fYLyRZatPoKVcGuXZN85Y4Z2HA5x//zhdz6gpXfLV6GKpUqimTJUaUK5cAUjiqBKlZ0dlVkUwZdOKKRQ2dW1pZE2EoZCufvfTOCh3/rRRwxPc/GHWv4vSf8aKDy3saHjrwMjhwzz3hM8+OzHn0+191+GBufLufnhI9cxDGP2cxVJ32WC276WW4+8XP1e297irBCtz7pw/CklfsIrVg+1W+C+h+c9XE4C8489vn00oyNp14CtPmD1/4Tf3Xr0xj9yzp0Ab0tGYNDEsq2orelg031ijn2+qdsonhyyeOuegEL5XqqliJeFnXFxA2zABz/ipu4ctth/PLDfsg3dh3Ljk8ewdTGSdLdQzHj2m2g12XP6RNULVhzlahHBoekTOaF1IwuLuPyfEWfwlAXKAxjIoDy8HXyty07UJ0286fOcPNL3suzzj6fURWxZW4V2c4OejLndY/8Tz6/8xRu/e6RnLBtvTCmVZfqsBnMjnnszt2oquL287t87GXv5E+2/BzfOPa/6v0/ftOr6X1lQHncYQDMnXTPtcC3PzNh43Pl3N36/HAO27xoYpYXTaycB3+8+yQ+evOjOH7Nbi4/4fP16++eP3JlO57DrwDgubc9jeu+cjzHPf52zlx1J6+Y3MErfvdibiz6nBR3+ecnT/Pk9p387FWvYPlza3AaTn3R9Zw/cw0Dm7KjmOJf3/MkhuuEOcXJfeUevcj/OePj/M2Wp7Bx7bH0T5RE18Q1q1j/vZRobsDgqCnmj4uoEtiwZZrsqNXsbdl5iLlviaZ7Gl87/dNM329b+/GPgzXdCZ+58847V7i/7s1SrvjMXolk59w+k8tVVfHCF76Qt7zlLQddA3nJJZfw8Y9/nHPOOWfFPk455RRuu+22g9om8NBoKfLIF/0Jq7Zpyd4OqzrbXkxE8oANz93SUrYlIFWVQ1lJmochtvaOYsIQDSW4tgYJLI1CF1LLogsnErfCYpYLMSNJYzGUCIFgHNUSKZdE0jw8FoZDlZLZ10teTlmUUvAcmMEgrUySpgVDkGYF6Wtweg1ALNLiijgORAPzYIxII0uRf6qspFrVJZ9JmT8+IZ+CbNrhDhthFxI6d5paJpjNOMxQkSyIpGpyc0m8WIj0sLCYobhHhqHyEqc15XSLYiLCGkV7+4iyFxMNgqRSk09GREOLLqy3y1dULUPVNuQTmuEaRZWAjSHug6qg7EB/Q4VLHGYg37N79CKPOnQzRjkiVdEvU540fSNfmTuJq3cdSvGtGQaHV8Ke9AqSTS3MEKo2lB1HtaYAB92bEtJZ6G0rSfdk6JHU3LkkFuDbaTftLIC6HjPUeSQRBJdIpVb201NKAF3XtyUoKnm/d7fEWmENW579C8Yzfj7aVsO0m36JTQ0o6rqqwNxE/QKbesCiFfGegRjOaO2b2PsaQKOwsQBus5jVzKKNNWU3Jl7Mpd4RQFM7SgJinJJJL0LlnGdNzApw54wSADisKHqyHV05qlaof3PoyvkHobRAUKWwUXKfOt+eAorJiHi5pOjKPWkTjSosRc9gckfR06gSyrYiXRDAV3Q0JnNeBgtmJD8vHaGJh/DUC7/J/15/Fcf+26v5i6d+jHe+8YUAnPg/ruWui46i7CVULcPgkJjFoxTOQHT6Atec/U884aJXoyrHaMZQthRx3zGaUSSLrllnKjAjS5Q5vvKBJhN+/nNeglOK0bqUwbogW7TMH6/3yYwA6ENu3ufrP+p4+mPfhl4ciQQLRArlpaR1a47AUEamdm11kUbvXqhVAG6iI6y6c1RTbWxiKNsGMxJZuI39vNGKaFCK7Lhw2Fh5tq2iSrWYzowpA5wGpyBbpSjbIiUyGeST8v/wEEdni8LkULZh+bQck1Q86qjNfOyYL99v5+lh/3QhnbuUMGNOmLZ0VgKG5SMgn7bc9rz33et2wrjg517Ibb88xZue8wleMrH7gD5z+l9cRDSE1ryt53WVKqKhqyXnTivipZLR6oh4IOt+2daULUU+ocinoOyCM46q5UhnNdGyzNXBoY5ywkKrQi1HqNUZbk+KKhVubcZXnvAu/mP5ZA6L5/iFbn/FsT3iHRdJMKuFiaw6MDxphOvHzGyY53tn/kv93nN+/0Kswbc+gWgkjIsuw/NUfq5iDzYzS9SvUNZR9iKWDwkSasgm5bkwXNfIzGzMf1tvzAfT2FwucWQ0weZyiU8uPeJu8sUwrisGPDzu8Ft3PZq/Puy7HPcJMVjZ33x+2g0/xx3f3cDqaxyzD1fc9HI51+de9Vx27Jrktqd+iNP//CKmbyvpXr8L5hep5ucBxMSmLe6zzlmU0oweeQybflHWnIf9w4i5kzp87C1/yXHRvQOaU//mIqyBzk4YPG2J7n9OsObKRfTyiM9+5VP7/dwFz/wVOu+S++5Tx116r/t5KI0LtzyWL1z1cN70uH/nFZNi6PLoN13E43/jCj7zX4/hlhffc4LrYMfiUsX0CRsftK019jcCvnjMz7/toFuKfOczbzqg753nOZ1Oh0984hM85znPqV//rd/6La688kq+9rWvrXj//Pw809PTmDEiyVqLcw5jDF/84hd58pOffI/77HQ6XHvttRx77LFMTExw1VVXceyxx3LVVVfx+Mc/noWFhfv8neEhwlSazDE4NCEaWqq2ZDSdkoeSzqpaVkcp2c1oYLGpxhoJDqyBfMLgjIAXAF1KcKMr6sAon5Rg1SkoO0oe8EekxP2O113L35LFEhtLfQvI60VPAu9wbOlCCVMtoqVcgvMFL08KNXmhP2KaNkAGsFMdqo44UzqlyKYjhmsNVQLRECZvlyAx2SPy36qbkE/FNVsEAhIWj0mYP1lqlKrJErMUoe9s09kDxQToXB7eykLZdVRtYLti2zkRkxsNc6dANFDgWnSlvRPZKkjnGnlGuihsUz4tjoRlNyJbFVF0FUVHka+SAM0ZkUE5JYGBTWB4QiZB3K4EmyjKjqN34hx6qc2R62aZHYhrQ1Eart59GLG2DPKYOKo4rruL3EakUcXSw4ccsW6evIzIK8O81YDDJJZqIUEZizKOwakjsq0tRqsj0vkIm0A6P0V7V0lrhzRVV9AkDbptMcXpZxBHlFMt4m0LdTKhNhpxTmpjI40eSY0dcbIygE6kJlCPKjG/MdIgvljVwo0bYFioulHd5FwPS2zLYGODqixlN5b57AF/OdVCjyqUclRpRDTKKacSSbRUDqcd5ZRkzapUjIlMVtW1iSCJk/E6RmWdMKWeCSwmIi9PAj2oqFoGayAayHXHOoquMJm6kmssjd5VzdLpylGmmsjPz6KlBYT64DKfjHAKRjMR0UgCTF2IvK9MFa4tgerShghVQWvOSlIkgrKlRD6bKNIFR7ZK8Ymvn8P/ft5VbHy2yE6f+66xIOo/LufJL38lVUtTJcqfG8g3T/Cs6fO562c0Vc8yfbVi8XFDet9ukz9hkcFN8tCYvA2W18G1r3s/754/csU69fl//QcAfua3XkPpk+SD9Zp81Y8/r2djTbW2S7xLflfO4aII1/JzMQvtl2KwYNsx2kurScXoy3XaVF7iqlwjX3ZGUUzGwsYX8t104chXxWKa5EDcSoW9widGzEgAetGTerto6HAGsmkBQ2ioVheg4OjDd3H7zFrWrl9g6YY1qIWIx59z436llwc7bnnhezn2869k6ocJ2YzILqsETnvGzXz82C9x/D9fyNv3nMAbVx8Y+P/P//ine3/TXsNpGK2GaBjqEEWiPp7M04Wj7EqAoXNL0ZWkiw5KG+Ulp4WqJejRSJ597e0Kt8uQTfsAZblN2bUkc4oya/GUy1/LqskB/WHKL5z7j/zWXY8G4Nj2LmwkCbpsbcX01QZdQX5Xi2J1wdxsl08sT/G8XhOc2FgRD4LEHp8ck+ewLiw21nWSTDkYrY5J50tG000dct5WtHcL05ksChgt2xDtW+HLX84ex+/OHHzm/SdtHOkNlfZVDzc+Hh7LM/SvD/susH8wGcalJ/8HnAy/vvVs3uMZN4Bvnt6AuL983Qd4zRdfwVHlGjqbIgxIQhxwqyYZHD1F55Y9uFbM5vNjNv6CAJlT77yIT154YIAS4OrX/l80mg8sHMqrprbxqdMneNM/vpjpm+7589uesIorj/vYAe3joTbee8S34IhvrXjtZ3/z67xl7XW85YXfBO47cPrpuH9GkiScddZZXHrppStA5aWXXsqzn/3su71/cnKSa665ZsVr73nPe/jyl7/MJz/5SY455ph73eejH/1oPvvZz/K6170OaFjSD3zgAzz2sY896O/ykACVeU/TLiRgBShTRdy3wvKkpn74li0j4GokYMq2NEVHEw8sunIUqSIqfYZ8UoOSn6ORq5k7pwQEhQd2lSiKjjwIlVNSh5UmlC1FNtU8vGX/CGNWwXCNIVl2cGhCOlsStyNhMbMg7UxBQznTJZtJyCcE0GZTmrIF+ZSAL6fwQRlEfVDW13itncTGymeqYbjeoWyEjR06j3DGES3Kd2ztjtFZA7CTOTnOpWMsulDoXBEvyX6iAVL3ogQ4xotQBYXo0EuZYkWcO8qWor2rYrQ6QlnjwaNiaYNidFgFiWVwgoWlmGjtkJnJAVOtIUVlWNvu8/FjxazishE8bmy9+1R/gm8sCuX/7zeexnA5RRvL+Sddz/NnviPvXXMDZ37/+djCsHNhQoBVqYnbBcVsi8pVdNYvMxoknH3M7Vy57XAGawwuigSAzMFwtaJsxwzWT5IsWrpbBlKQjwBBVTmpnVRitgJTmKVcZK3eYMRFBt0X45qqHWEGviatqKSmISsI2pOqF9dytqqbCMgbFGSrmy+fzOcUXpZZdRo2EZ/xt56RtJHGVCK1BKRG0DuS4hxlN8ImGjMS848QzOmsEmOUMkiape4vsPpmZDGZyDyJFGWqUTaqA1ll5b4oJiKpi9RKAGKiACfzuBCW1WSOoquJRsJUlB5Aq8phlSIayjadZzdwjmxSEw0dw9VSP1Z2PGiMpUarSmH5cC1JCiOgpegobALJgpP35eoeDUx2n5YwudmiC0drj2KkZf/XbTqct/38v/C/vvNsvv8mAaTH73wNtz72o/BYeNw1z2F0ekQb+JVNT+Zjx3yZ06544Qo3PIDs5bNE/yotD4oulKtKTrzspeJG+WMaLtIUvQhnJogXM8gKqsnUn2dqllHnPsvm8PWgSoyaWr4e0gmTXnkznaqlyacM0UAY68Kzz85ANLAUHQVK12AzmzaoUtaFfLXGxj65lAorvnBKiW6XHHXIHm6/aw1POukmvnrTCZROc8Mz3kuqYv79xA4jG/OE9lbg/pNthbHx/A9yyu0X0d4hoHK0hnptuvUF7+XYf3s1b3z2A8MogyQ2nVew20TVc73s+XuHBryni7Zm/FXlKLty78Z9RzRUvj5VmEKTO1xbwGbVgt4Wf+4TSaAqK/WT5ajDsu6QrS855ZsvJrtL6rQmj55ntN4nEHcZ4r6j6ImqRecxZTfiz25+BpzwBU5Jt1ElimjkKDqKaCSJp5Bswrk6eWsTVc+JZKlCOUc0sCwfIaA3XnZULSXfa1nuoXSBOlGz9/jdmds4/isv59YnfZjHXfMcLjvtXx+gK/X/xhgHlHuPp7VLNj77/RzbeiXTV6xl5voe+aqY4RrD7sdU/MVT/pm3/+WLae2x/PMvvQuQUoprX3cx0D3gY9D+mfmqqW0APLe7xHG/+k5+7dqX3uPnZp659YD38f/CeMva6wBWSN9/OlaOH1X+eqDj9a9/PS95yUt41KMexWMf+1je//73s3nzZi68UFzC3/jGN7J161Y+8pGPoLXm1FNPXfH5devW0Wq17vb6/sbb3/52zj//fK6//nrKsuSv//qvue666/jWt751N2b0voyHBKgcHALJDqgSCTijkQSrqiPmHZU371BWJGq5d8zLpuT1heN8nU4koMnGYAaQLoo1uzNSsK4LJ7U2fXmo5V1hRasETK4oU5EhOS1BVLrgwWxHpJxVIgAzBL+ZkYdn3ouJhmLPrEtHNKiwkcYmisFaQ9ETCViVKmFN1pYkuwUY6lw1NvIpLB/ugexApGHDdSIXSxYU2RqRsgo4UHVm1+QCdHUB+JqXsgedbZpoIH9zRs5F+D2ZU6TzTZYYpE7GRopkWWRauoKdZyYUkxAty3cfHAK2ZVl95Bwax9xSh0efsJEf3nUEeWXIqoiTpnbyojVNRi0AykuHEU9rlxQu4hWrvwFA++EFv7zqOwDM2zZby2lgjtvLJS45/W954pd/i7WTyzz78KtZKNvEuuLD3z2PXz7je2jleMrEdTy+lREfHfGJ5SluzdbzDzc9hsWtXZJ5jdNQHqKY3KRZOrZLNJTsro0U6XyBjTTt2/YwWGtI5jXVRILyoBFgtDYlNQqdV+i8opxM0blFI4yyi30NqtE14CsmE+LFXGpAE0PUl4iyakfSTgOo2kbqE3vC/lS9iNauTKSxTqTeqhC5qIt187NRYCX5YUbCLOrM1kx2MRHX8jQA7ZMn1rOG9fasgEBlJYlQpsbPdUOZQndnRRUpb5gjoLHoakksWMhXaR9Mwmha5rAObu8OsilIFwzJkiXvaUzhMBkM1il0JXO3f4jG5MLitHZLYFx05T6o2o6yI08Dl1qOOHIP84M2fHsKc3SfR7c3Aftuynz1Gy7mjD+7qL5/O9vgO3/0Hh72iYt40cQs75pZ4lc3P47r96yntVNz7Gdexcaf/wCLwxZXPWZlFnxvQAnw3Ud+gsXTRer+B9ufQG4jvvmvp/Oe0+5/p879jvCg1Ei7l8RQdCPKrpG54aX+UV9R9owkE0ICIo3Roxzbiak64sgbLxY4IyUIOKhSTT6h0AEMRVB0DCbzP7flmmarVG2EYELXorZXSZSgfE3mHdcfyhEn72BUxfzMw27jSdM38o7Zh9Mzo7GapfsfUIZx/YUXc8r7LmJ4RMGmZ/5t/fojv/d8zJK+h0/e83j+xqfUADWMzBV1e5HHX/sLFD1ZP8EndXJH2dK0ZyvKdGW9jY0kieM0oIQVrGIxrtKlJZvQxAMBdqqSNbvoKnQOOEmg2kJqFcsWoGT+lx0wWUS1fYLYE5qLt6+ivUueYcP1ci07uyzWaNJ56B+q6I8S3nrdM+kkOTaG0Yyiu13WDNeVa07u9xtLYkyStXJOi55BVRrlJCkE8hxUVkBplSiqtpclzztO/4uLeMdr389T2k3riI8uzWAzOehtN6xbYSwyfq5/Ou6/sfEZH+TdZx/J3288h78/7cM1Kwrw3DdfzMnffDFn3c99eM9IWivk1vsaX374Z+7Xff50/D8wfkSjngMdz3/+89mzZw9vfetb2bZtG6eeeiqf+9znOOqoowDYtm0bmzdvvpetHPg499xzufzyy/nLv/xLjjvuOL74xS9y5pln8q1vfYvTTrsH96V7GQ+JmsqTfvNPiU0LnQsg7OxylC0oeoqoL5lXEDavvatiNGMYeSmTriCfkqDXDBRV19LarmtWLhqIPM9ksj1rJOtbpaoGThLECzMX6rgYsyEuer42cNkXUxsfMDmp94p8zZc8UD2AS+WhX/QUg/XUWdkqlQd85GVjma+ANrkUbJvQuigwmJXfr26Cbjzj2t4t/5ctkTDpXLadLMnrw0McvTtUfZw2ke9v4zHA3FJNRsZK3U5viyWfUNhYsfoFm9m+OMkoi8kXE5LJnHwhZc3h88zO9jj60N0c2Ztn09IMOxYmsJVmVW/Auu4yk8mI0hrWtpaYjgZ8fuvJnLp6O3csTTPdki86n7U4ortApCuOae/hitmjefnhl/ORu87l5MntXLdwKP0iobCG2eUO1ilaScFUe8Rj1mxmsWgxl3c4ojPHfNGmbQr2ZD1unV/N7GwPOzLogaGzVRMvUzMD4AF1SzFxZ8HyETHRSAK47s6KeEHcIoueMILpnqyeg/lkTNwv6/YATjVZLacVxWRUz51k1gNFoGyLgYVyrq4xrFIBilWsSOfFhKryPRajflnXQsq2pa+dU9Tuo2E/ZVuTLJSgIVsVkyz6+lejKDtaarb6FfmUwRqZ+8oK2MymBHwP10M2Y5m+XrN0FCTz0N3mJPGSKkYz3pzGwGCDZWKj1EPqUtif2Dtv2whG6wQo6rK5f5yGfJVFrc5Q21voTNXsencbjGYkgVJ2wcZSO3bCI+5kOU9528Mu4Yktx/Fffjm3PvnDB7S+POYPL0RZCY6vfd3FnPn95/ODsz5eO2ae9I2XoK/tMVpree2Tv1hLzfZm1u9tfHbQ4q5imr/65C9QTFqYyen0Mq45+58esJrKJz39z7CpFjMjX0M7OCRmNKOpYuhtt+hcJNq6EDmxqCFKzHKBWRhIX72Woeho74IqdX7DNYqJLZbB2jFzFQdFVxENPehPpEa7bHv3zwRQcu1RIsG3sXyunC4xS4ZqbY5JK2ypedQxm0lMSWkNx3d3MhUN2V30+O01l3OI6bG9EhS2ZOGIKKat9m+OcKDj2E+/mmjNCLu1gzliQOmBCvMJxz58q8gD7+N47V2P4ZbFtXzhpM8CcPnIclqScf41L2bXtWtRGwZUO9ukezQTdzjSBevNoxRFV9PaI/epJGxULY0NiSFVeX+BriZZqihbmqKjfDJUQH9QwAR5aZUoUekYkSBXiWx3NCO1mYm/T4ue3OPKyT0HMHGH1EsP1mnKttTA60xjuxXtOyPSOblH46Fj+VBFd4cjna8ouqLECfWVNlK+ZtlSdjTxsiWbMv67Sp1ze7dlNKMZTctcConR/uHwhAuuZG2yxNvG2hx8aWh41WUvY+PT/67+/de/96Ifq0Lgp+PBO0KN6U/H/Tt+0msqz3nmwddUfvuzB1ZT+VAaDwmmMgCj2IOaQWjCHkmvrpAtB8gmxeQEIFnwAK3v2T4H9LWvwZJAx2mI+8I0JsuObLIJnmTnAiyiEcKqTGrp+6s9uEQedOm880GV77elfTDVkgc4TupNaDdW66VnKM2QBrwWUrdoI3k4610S7FUpxEvNdw1MqfUAVVcOVyqSJQnkbCQP4aoDg8Mc8ZI8qG0KakG2Z0YitYpGwTQGiLz0quXNH8ZqWVwkx7h8hBYQ3oGt81OUlaHY0/LdO6SGcW6uy9rVS9y+bQ2boxmqLCJuF2Ioaw03bltPFFcUWUSrnZPnEVVu+ObyMRRzKXd0PegZRtzeXkOnl3FVfDiT6Yj/8fXnQ2S5qbOOfLaF0w5VavREjq00ZR6xMNtl89bV6MgSpRXfzzZgSyN9HBUw8oxfoXDGUXblPFSeGVD+GhVdWNoQU7UkGFMOYS3TFJM7Yf+6il2P7LD62oxiwrB4VERrTuoCy7YinxQJ2sz1wzqgGqwzREOHGUiPRplICjMqyKcTnJYERpVIjVQdjBkJDrNVRpIkLZEaKiuBZtk28tlEjFEkYWCJlyuKCTEKikYiO5UvKjK5bEqjnMGMHCoE/l3ta7vkXjEjUKsz5k5JcYnjyLPvYudnN9DZKfMFB/kqGB5R0JoesbRGJLLm9jbqYcsMdvom6JmmmixxOiYayr1dTla42HHEUbuxTjFzxHYibVnfWuKI1hx/98NzOe+E2/jul06h7DjsqoIjjtjDpl2rmeoO+R83/BI/c8hGfvnUH7Boh0zqe3b+A3HeDONLQyMOjFC3YLjxZ/6B44uX02nnxL4Ye84OeFzrvgUmX1s6me/sPpJi0pLMa8y2FqOZFM6+T5u5T6PsGoquzAGnE6khTQVkOAPZhCZdFJZYl4rWvMUaKNYkdCqHWVL0D00o2rK2FF1FNg3pvNwDsydr4iVRaYAAkdack3XHIaUCBl+r2/RVDOUBrT0efLZAzYps3+xKcBG4yZLvbToSO4yIJnKuSQ/BWs10b8C6ZJFTWlv509teDMCwiFnVGnLBIdcRq4rnT1zPGtOp5XPjY4/ts1qvlOAt2xE93eK8q3+RaNlQ2TbtPQo324Uu5GtKlHFsvmIDf7z2JACOSXeyKVvHXdkUJ3e3sVB2WKzanNu7+W5GN5+7+jTQjtMWRCZ9UjLkzH95A3aqxBw+olhMidZkqB1tSVgq0Jml7BiSpUqeGYhkVOch0ef7yY6aVjvas5vOgCkQQzCfDK1SUZeEbVE04DKUV9hE0ZpzxANVK1OML+tQFrCeZW6J+Zg1kqTs3mmwEdhlqVNffJhjYqOiPWuJB4bBGoXJRMZrhhU2kZKUZNmK5FrLMzOof8L+TC71tjoXiXpIyoXn5Q93Hc6uHVO87fxr+ejSDG/59PMpVpcoC8de+gpcoWnfGZOM4HeOP4t3Hvr9+pqMg4vX3iX2rn9z2Hfqv+9dQ/sPS2sO2HTpp+PBOS7pd7n4JS9l23ldXvSrl/7IrWkeiuOrI8UTWz+xHNTBj2B4eTCfexAPYwzbtm1j3bp1K17fs2cP69ato6qq/XzynsdDg6l83Z+S6JawcYl3kSvGGMqxc6Oqpv5KVf4h5CVZygmrV3bkn86pmUOTUddYhmaoJpeHnbLULnYh4xrqVoDaITLuO9/2QNhPa5S3U5dak9GMIl72gDEX9tLGIjvVNkhPfRba+CBsbBgvYQojHkjA3z9EeeMZCQSyVQKWbQLDDaWAqFSiu+iuFJNDtlocU13kiBYNybxi1a2W/nrtgYiwpKpsZItOjwWC3q3VJg7WjXB7BGhgQVUKN1GiZ2Ns20Kl0IXCtsSBkIGB2BHNG8oJi84VVIp4WWRa+bRsB0TW6zQUU454Qb6nLoW9CgEqyHG6qLnOqvRNtr3yyUbN53RFfe2qVMyEQiY+sGkukvlhg5mRT15UrUZC3d3q6G4vGayPGKwX5llZmUMheAf5TLwsx9TdIV9stEpTteQ4Zm4UqexgfeyTH8JU2FhYhuFaMZ1qzTk6uypxeQ2ukEoSAsFtM16SE5LNSA1cPqFpzVae8VNeJif1jnJeFCaz2EgxmjaYwtVMfXBQzifkuwWnznzakcwpRusdrB3B7lRcH9sVKnIwNKhc4WKHmiiwwwjdKqVhNuAKhU59A+2hQfUK0nZBnkXYUtOdHNFNcxYHLTqtnKI0TLZH7F7qYrSlrAy9dsZSv0WZGXTkMFHFRHdEWWmm20OMlvPcz1NGpWR/WlHJdHvA8b3dnN7bzInJNq4YHsdc0eWLW09iTbfPsIgprOHIiTmO7ezm8RM38rR2yTNufCZb56ewVlOWmm4noygNw35Kq5MTGcvo+lV0tssaFRjiwWGOqlfhjENVinhPJAqJocyL6//4d+5hBTz4cd7z/krclSNFe7YSGWEqSYpsStxs80lZs4qOMIzpotTTJgsl8e5lFk+eoWqJWVLZFrBStrwaw6+Tod7aRuLUqHPnnZ4hWRRW03n1g6yh/p60kuACz1Ibea3qSAKsbIFNm8XORQ7XKyHXci5zfwO3K/ASWpNUOKtQ2lHNp+iBxq4qoVSoTokdGaJuSTWbgEHW0tRK26TEQqGJ5vz16SNgK5a1oWw7WcsAs0ru12o+gW4JI4PqlMStEqMtRWF47FG3892tR1Le1kMXil/7hS/WZjIvuv2JnNjbwUdveDRRVJFtniBaVkxukudHa9Y2Jj0+gVq2pb1KFcszBeeBpK9zDmoIGwlLWXSl7jgauDqpWXRVDfJtJC0+opHIWqsEWrOSBAvJ0so/R6OBnIvRakl4BuVN3pNNB/BpIzlf0RDaeyxlS+ZcPJBj1X49CWUqZUsT9Sup6XZyPCDrTewZ1HAf5T1ZD5WVfWTTULUc5aoKs2SIl8J5EjOtKpVnepXCwskVj33kzdw0u475W6eJljTZIQV//oRP8D8/Jc7QVcdywiPuZGHUZtudM6i0gpFBdwuipCIfJKxeu8jZ6+9YAUB/Ov77xzGf+zUmr13ZymfvccGzXoTuZ1STLRaP6/LNvzpwR+f/V8Zxn3gNj3jkJq7auIGNT//gPt/zsH+6kFteuNJF9iedqXzsBW89aKbyW//5vx6031trzfbt2+8GKu+66y6OO+44hsPhfj55z+MhwVSaAqn39kye8S754sS6MrBRVVNDKOBNAEK6IIFy1ZJtxEseOGaOfEoefDaSzKmyAtZCX7QqppaHCbCSB7f1oKFsKald6chD0MUCXIL5T5CpRgN5oEYjn/FFTAl0qmojBpv4gMoHWuk8tebbRQ0YsjHkWqErGK3zD2uniLy7agB/8Z6IquNg2QgA9MlqM9JUMwVmIRbzoUTMi0zuA8eOBAdWe8kawvxaD8Cll5zUfFZ7UpRVJDs1qoLRoRXRzhhVKZTVteuu6WvSO019DWwMujAC7HWz3WReZM0QWFOExcvlfIRkQQ34/fkpJqA9J8cYgpF4qbkGwYE2MMU2bYJjnfvkg09UmEzMkqKBlzN7oJrMe5ngQJjnuRNin1GXY6jact7A17AivxddUFrqBMfnpo1h/njZac2mK0XRDrIzSUSYXObd/DHSUsBkkC5U6MJ5UCnN450PzFQl810XjtG0IV2QxuNOe9Z9XsBnOROhrKZsK4oJqAqRpSXLwk7Fg6aVhjgyQrzsZamxoih9c+sKzFJcg2rlpKWL6qdEhcIZg6qC26pDFzFV2+ISh1uKyXem2LYlWjAMZxP6icUllmKhRzlVslROoHKFsoqqV5LvbBMtGdxkhRtq8rZldlsHZxzzySRmoKVlggadyX77DmZLuGH1Bv6jPBOXONKdhmLC0dqt2LN6DS6WuuStvbV8K3oYH83PY9XR8wxGCcVQmNd4U4t+2gGnaC+CGbaIRjBRNgmEZEnu52RRYePIu1XLPVx4gKbzA14C7/OwkQAM5WA4Y2TOjHxrmqEoKFSFT3yBcgIoYn/cqrS1S6euHKYQ4BH3vVw5kWvcktZ35BPULUJCwi9bJft3zq8lvmwg3FMon6wpIPKqEpML450sAFpqzAWAKMxsUtdihnXQGd9+x/p5Vcn3ikLicTn291Tkjd5i4kqOo2qBTTVmqGpZro0F/Fde7VG1BDQlC4rKy2HtwD9wOhY9F6NyRakgX4jRHux+Y8fJ2LZFtxzVYRl/e8O5DE6U+1x72i2KKkZbe7jE1m7EuhQvgGTJEg9snTzKpqTFTTovLTiqlsb6li3ZKtMwfwpwwmKWqzQugnjZerDp78tU5PTheRbWUetlsQEkli1Q3mfMRsIu5xN+HhuIh+EZ6fy5VR7Mubp+M4zhWs2qW3OyVREmc5QdUVi42CdnPdgEaM9KTabWsq3RtPbGeUHyC61dMoezyjtC7/aeBsuSsFVOAHNr3mFywxVTR2M2t+ntlOkQDWP+554X0vIEpI01N6sNuMjR2mXART6ZqTG7NGbSsWcwzed2THJZMIvba5xz5S/x7TM+eTC360/HjzCmromZuLPi2E+/mo2/+P4Vf3vKS1/JwrEJa1ms+zsP1muO/fwr2Xi+AKd3zB3D66c3UbiSWN09ZP5uVnDRdS/ie2f+C8de8mo2/sL77/aeh8JQazJu/Y/jaCew56l3V3UAdbL/p+PBO971rncBoJTib//2b+n1Gi+Cqqr4+te/zkknnXTQ239IgEpdgvKZ5cBSArSGzrcQ8W9UjeGMdh6MOi8D9X2/iq6qWRqTS/a2vcv57HsjxbGxZNpDpjXIItNFWwfNyoTMqj9G/yCtEuWNKZrMbDg2q0RyZCNAQ+Gt1fUoZI+91LAA58FTOi/bjPty/OADKwNFC1q7RarqtAQCuvAgbuSDolnJ8tpCwFo0FElwtZTUgM7GAkSt8aBINSxutkpOr40FiJmhvEeVimzGke7W6LIBZiiDGQQQ7IPX1AcvXr4bakYrL7Wqs9x9vMNuc/2jLEh0qZna9h5XM2i6lPMUL8v+VTzGYFq/bf+9QnsT45MTqZ9LIbCKvWEGToIoM/LHMvQMiz8vISAOYEGkzmAWGma3ivE95SR4Txe96U23ARTKNudEWS9Xaynas5KkMJkH397kxBRhPns5btU4rFbeXTW08pDEhyLKrJhW+YTCcI1CVb43ppKAPZtUcmp98qToeIa4Td2ioEpkzupSQHKyBMp6aZpnnurz5SAdqhVAOSQnRHIMZs67zvqkgx0Yn+gREOqMQeegqoh4ybtiFmAXPZAvIJ0z/tiMBNCLsr32Lm+2NTkG3pxct3jZiLOyn0/xkiJeBDMUaZ9Ivk1tUrV87Ywcl4H2joYR6ux0tclVmJfxUH4u2/4+tU5AXKLqpvBOScAbWhI9ECPUfwO1sUs22biGOq1qkAuexU7AHhoxebvFRb5esi/3WegF6lTDaIUkDVADlrA2q7HgIxp58JF7MKhXJv8Cc5Ys+A84yGa83D8XpjCd80oBn/QJiURdNAmfdFZkmQF41r0RfQJLWa9cGcm6k85JYsRGTVIrXpaelbrwzxB/jPlkUwaQJ4CFZI/BjGC0zpJuj+p+jroKYNSgc0VpU/JpxSV3PIJhFlOVmqtbh9Fp5VSHDEBBvr3j1QZBBaKxRtf3TNyXOZcsVL6XrLjv4uWjgzWGKAOcIxpa8knj71tvalfJdw3u5tAkElpzco2DCiisnbFfi4uO+BhIraac18RLp4Pjt6pcbeQGvr42E/m9qhytOVmfBPDJ2mSGVW0KpiuH8s9lZwRQqoza20Bed1Ra7p2yrSCTOYKfP3KOwtpuiUaOvKdpzVni29pEy/L+KpW5ZAY+RgD0MsQDzWi1vKfsQrwA8ZI82yQZY3DK8Pobnsd3H/kJoOkZCbDjrlVwxn28UQ9ibK2WONxMPPA7Oojxx7tP4g/X3Phj2dcj/uoi8im48fcu5pwrf4mN+wD00aBkYrMSx33n0IOcQ765xNSmDpwv7/nP33gid/zlGv792kfcjaE77+pfZPiZ9RQ9OHbTa+jeKWvopcOI/73pAr50yr8f0LG+5I4n8A9HfY1L+l3+x3efxy1P/DAA/z7o8KzOfnrl/LjHzhbtXY500TG5jxr1R73lItbPWs497bnAylYzP9HDkxIH9bkH4XjnO98JgHOO9773vSt6XSZJwtFHH81733vwPUsfEqDSZA6lIVm2PpjxoC0LTJNc3RDMq9IziT77rEsf7AH0xXxHl45sojEviAYOY5yX16i6h5qYInjgFTW1bdAEOAFI6tz3zfMgobZ3VyLpCVLKojvmyOqDH6fwtXMCokJwEvVF+hMNQxZatp0sSdCceQY3ZFzrIM3LPAOw1CUwlN91IQ9NnQnQ0qWYoFSpBGOlZynDwzoE5SEgC/Ut0RDMtibLryp5SEfesChY2ZuRgLUACpxpjskMAA2RBx9BahqHOk4f7IVjkWtCk2AoYTQNrTkPSr3cKwB7/OeN8izrCKxP3EQDajlta7Y5PvAA2geoAYxGQwmQSs9Ix0vy3mJCjjudl2AnNHPHMmbv7+ef8rLaEMDZJrB3nv2rmdu8CbKqtGE2XVs1dZDWMwVhfhbOz0EHyjsVO0XSF2fIEGgP1un6HDg/VyrkWEof+Jthwzzh/1Z0hS1OlqVBeTSQYK5KQS94pnmxAfKKBkCUY0Cg8DFRkBhrz4jkk2OGVz55YzI538m8zK/A9AV5W5XKfWJ9l5PIM4VVIccS5msVh3nsz9+kIh7Iea5iAf2qkoA4yOYTDxiDs7McjyNedjXbVhlRDOjc1dcy7o8lrrxxigAcR7Is3y1dfuDSvtFIzMwCky8qBFcn1sL6pvy1DW6xykIxGZHskrk0XK2Il30riSVZH6vYm5nFTcIhyFdN3pzPoNQwuQASXUEZN4qEuO97kbZ8KULZrEHJ4lhwvyxrj/IgTxfN+lBnzrXc22UH1KhRH1Te4TQaNYoLk4f6ab+2AK27vMt1KH3I/Lrnaw513iS60llVM50A6W65l0rvni1lCpLAk4SJohqlLCzEKKuwkWOQdRj4eeliR9zX9ZwPYM52BcyBl4ZqqZWNlyvKriHqV1ijKHqiMMm7ingI2SpTXxNVNoA6mPI4J0DRVQLCy7aslcWE3KtBaREN5J6NRjBYo4gyOcdOqRo0Vp4RLNqy5qSLQbYqSZNQVy4lJLpeR3VuGa2O6GwvoGOEmQyKXw9ErfF1pN6hXZJvnkmvgNivNWFOeZl1FUN71vpEniWf0EzdKs8FkzvsSHkX4kYhVLZFEg6yn5BctImodYKyyWnYvXUVbzr8VN627lp+785n87JDvsH57Rw9MJz0wYu48ZV3l2EOXXbQZlLjtcDbq2Wuz6f5ZtXleb2F+2wa9kCOF93+RK7ecdiPBVSe+7sX0sNRzMv6sz+G2CwOiXyiA62hkl68nTuXOfXdF5Gvchw3t8B//eujWLvFcfzchax62CzfO/NfOO2KFzLYPMFMDtFux6rbYPEoeMr1z2Lrt48gXgBOObDjvWbnoZwz90ucufZOzMY2PFFef/MNz+KZZ31snzXgP44x7o7c2iHS+mSxxKi7H4+NoDVbsOMrXk55+o/zSB+48eNqKfLjGps2bQLgSU96Ep/+9KeZnp6+X7f/3zNT7+ehS7xNumRcTeZI+pKRTRetsGRj1vbRyPn2HdR1GUGqKtl0CXqSZVvb3AtTJyY94qgnwaWN5MGmSx+8G1UDwsg7u9YBVO2m5+uP2krcNdu+3q7ygeqiPFiTJW8N78bYQh9gRUMPhsK+M+eZLM/MeSZJe0e8aODZrFDnOaA2P2jtEYAQakhDABZkTsI+eMBZSVBjMnEhDUwEyPEL+PXAMPXns/RBmA/wapmqFdCoPGscjQSYxAPo7HS1rDMaSPATmIBkoZFl6RwJ9KqmvifU9ASJcmenqyXLoaG900jtEWE7rganJoPODlsHnrpwtXQ6uPcGo6TgghqMI5yRgFznkul3pjmPVaJqeV/U99cmF6CrS1ezfyZzTdA6NqKh83WZzkt0VR0MxgOx4o/7rpYtKgvD1WLIYo30fUR5gOilr6EuOO+KFK6Wp/r5GKRtVdwkIUzeMOFBWijz1tGaHVMKzDZOkUFKrgswIy/PXXQiHfTzPLBGyglLFC/JnIiyZh60vTFVsuj/NvQmMHt8sG5l/oTWQrr0zPtI/iWLrnYyDj1o5Xy7mqFp77HEA5HGteYs6YKlPWtrd8pk2dHeI8xLa04+F/ehNW+J+/J6YFtw/voWjnhoazOV8F2DvDoaynoV5rUpXF3v+0CNYCylS1fXqGWrfP2ad5/Oe4p8orlnQoIOwEW+Hq5yMic8oDQ+6cHY2hDOiS5cU2/p1QmF7zUaDV19/zrta/ycnNtkuVE6RKNGiq49AxoNJWkT5uf4+lUnvlxIYLl6bisra194BoSEjfR2lM/tDUziZa888etZYD3DiPyalszLNurfx1QKJmuAbWtW/mYGmmRe0dqtSWelP3A0UJiBrtn/4IKMDkC4YZzB/90zg1VL+5pouWbxoHGG1aUjWZT1INRcK4tvfaVqdYUuqYF0sjD23MkaRs9Gco8GtUSdBNNyzE5JwrduU1Q0budFxzum63B95HlZpRqUIpuOfLmCtLhRlRgO2cirGXxfX124OjmnLKQLVu51z1aKMkfu1SpRRIOqcd6u/FpRuFoVIoks55Mg4VnrnwdVU0qj8yaBJaAUkl0RH73yMbx7/kiuuOUYPrpTGomrQhQPf7e4/m734lnfeiXPufXpB3rrrhgvvfWXuK6Qh+Plo0N5220/x+9f8YsA/P7Nv8hXR/tWPJzw9Zfy8PdcxBu2n3lQ+z3QkbmCocvYPerSn+1gfwSN5O3l0gG9L+8qJjYOmLhz/zUENxZSQ6McuNjgtMYlkfwcaQ75TsaaKx06KzAjmLlmkZlrIPvaagDcFVNMbNK091R07ypp7c5p73Js+9oRrL7Wsuq2infPH8mvbHoyF9z0s1xw08/y0aWZux2HxdK/ZRWDL63l8q3H0tkmLtAA6rMzfHDh8Pq9754/cp/f5d8HjcHGp/qSkd18D+cquGPva5x71XPrn78XHK6RdWrqlj5mVLG7WsmeLtohozVghhX5KjHke8gM6w7+34N4fOUrX7nfASU8RJhKZUE7R7rgmpqaiMZifayOLGTBbSRZl3in1KsECZiY9vj3S0xIvCxSIuWaLGiwu9fesr1MJShySpH3VC3ZAgF81stng0w173nGKZJ/lX/gli15SIUHr8kadtSMpJ4nMJ9VGmQ94T1j7JUS0xobeRmi+O4IwPMmQKGeUNisJtALQZaL/O/egMNkYyynlw6bTFxxAd9WoGHYkqIJOsL/0tYCzwCrMcDePKRBWqmE+i0bKYxrwIuyqd8wZAAA8+VJREFUYBb3+p4KWnOSdbbWf8eY2mQkgLUQvAhTo2pJXmBUBLD47Llz4MRMIrAaoRYusOChD2PZUjXLGuadU8080vmYnFHJuU2WvETXqppBFtmjvE2XDlU0BhVlW669qnzw7uVjQcZVA5SRZ0sdK0w6RPLmr59nc/MJ+Rz+9QAWw/e0RrYTDVzNVEkGX75HsuCP0zYgalzmKPMClFU41dQmR8NQi+kIzdlruYhnkuO+8y1FBIyHgDiAa7kGXirqk0V4Rj8kN+K+XPMqViuADTTMTLjf03lbO+nq0coesLoUKb3Ug0kiIfTSi4aK9u4KUzjpG6ia5vK6lOvutNSqOd+OJSQ3BOQ74oGtEyGtuYoy1UT5A8dUJsveKMWz90VXvndIyICfB8tyDapY1rH6uirFyDNeZUvW0rKl6u8aEh/BUMbGcr8YXwbgtKsTTtbINasSVa9f2oOAcD3lNXGwlgSA8iZhfo60lZdC0vRpROZd2fagonA4n4yJB3K80dDVZQ7pgleZOGr3bmNlvhRG1fLNKpHjcz5BU7QbECr79EmvyCfNIs/cTwE+ybeCgQfShWaNDKZFrT2eAe97ozCvqgnzumw15wsnqoEqVnUSsz5nRsn8Mqo2KwtlB4O1/lpHYIaqTgQqC3nHJ2r8sy4wsuE+VRV1Usn6524wrTN5MHqSBGc0xCfelE8u+lKTHFwp90k+qUjnXX3MJnPCdnpVifX3jC4sKFXX9CoL8cCiC0U+oWvAH5Q7unJYwr0n8zqbimpHd+nVqYn70qJJkju6TjIBfg2hXn8ZO/e1l0LhY4plReEUF1//eNI7Er53+ynw6q9JojmDS/c8nFdM7lhxPxabely9tcubpx5eN6QfHy+6/Yk8Zup2/vqyp+Eix7mn3MpHj/4qb971cLbMr+L9u57AjYvr2Dw7TbGli14nFPuOPZP82jdfxuOOv40PHXlZvb3n3vY03OYO6Sx87jNn81ev/sE+14n7Y8zZjMtHh7Lx+xvozCke9omLuO15B2eE85H5x3BoPM+rprateP2Vd57H6w+5lItueiFfP/USvveWi3naC15OspBz3u+8htmTNTe8eiVD/LOf/R1OquZkPc8KXCuBrECVlmCGlyxZKEoO+c4QKkf3rhKTRTz6h8+jNSfJCzOyJIsFup/TmogAQ3tHgckq3nXlk6nmEoJZ2B/v+VledO4/rjiO9y9soHeHYubGnK1Tq+gAn5x7NN9IF1AOPnT7ubzKS0n/z/efwuue8qG7nZc/v+0ZfLizzKeOu5T/dfXP849rd6GV41PHXbrP83hlPsP57RyLvRsL+ktH/oD/b8cj+fP1P+S8VvO3aORQoxLbiTn3a6/ldY/8Kr81fTsA8RhzWW4Y7f8C/iQORxOb3NfPPcjHli1b+MxnPsPmzZvJ85UJmHe84x0Htc2HBKjUuUNrR9mWLLtt+cylAWcbq3WQh6f2LGHctx6sSS8s42sx4oHzGWEBB6F1QniQVal8NoBC6bnWyAqV89bvfp/R0JFNagGXY1LQEMBEY0kfpyQDL1l252WBknm2iQQ2pXcFTeddzZpJrZ2mvVsCUQnsvKzI/9OVBO6h9qgOeE3IvEvkkC6KjFj0jhJ8tPeInM1GinjU9Oc0uUjfZKchoKCRu/lANACNYFsf2LDg9BhYu5Dxd6UH0nkDOrJJ1YBB2+wTJyyRADtXf84pMZQRgwsfALgGFMV9VxuWCNvUtDyIRoBztSTZRhKA2rE7JhoFsOE/GwOVN3JyoHA41wQ48VBkYE2w3ATOQa5bepMn7WuLyrQBNumibxGTiPuh1FTKNlUlphbGz3tduNqQqEoF6Ma+/q2KFRUh2+6wLQHNcSHzoTXnaqMoaUeiqGtotdRLBSYrgAnl8O0nnGc9PPjOHa5SOCsBYuHl6VHVfO+wnxrM6Iat0pWr2QEBN408XRgV61UBHrTklrKt0QHkIomFkBiKhrZmd0JgnQ6EPrKR1I06DWUkUuAosxRt+V7pbEnRM0Q+KRONZP3o7CxxWtWtHJS1VImu2a9oaCUgHgNT4w8cYSdV3Q8w1LUF0PpADFVYkVTWkk85oJC4iAfI/I/D+ql8PZ0jmS/A2rqHoLBE8vd0wUoiJ9S1Ea4v3rla5IJyf3mVQAk28kkGP8/MyEkNIP6alwIcgmupyVxjqmUg6cu6WJu0BUVG35JVvv1OqmqX0SoRF9V8cqxmsmgUK04L2A4tUaK+JFhCiw1V+npvL0fVpatBT6jfCxLYoICIl2l6GLum73BIUJqRyPADg1snaHL/oPYS/bIta6MwcR6El/Jdh6sNVWLo7K4oOrL+mdzWCS2dO7Svj1U6rMnUxmOq9M8fr7YJxjc1K2ubcxsUKcpKHW3R83PcS0fjZSnBSJYcZQq6lL8rK9e2qemlLllxRqFCr02fiIn7lmzakM7JfRpqw8N9YzJHPmHQuagIwnFJ8lXOYdWhXm9CAk/uT79eW2oG10YixZbn31gSMSSjnavfZ3JZ/8J5cEqua2tjwuhoRW+pkcoqv+8rrjkOjvky389zzkoStlZLmEzR2qX4yHceywmPF8D0oolZnnL9s9h4+3p03/C6n/8vzOQTSa5r8+3kGE6+6wiKO3rY1TlXJEexc/sUFJpkSZPHUnuT3NQGB1fcfCpPe8IUl578H/zBzkdw3VePp1orAKlYZTn7h7/Erx17OcDdANuPMgpX8q495/Kpm86QhGcfJm/RPPxbL+K6x370Hj97ZT7ijES0u0EePB31ed9tj+NVZ/4LAG/aeSovnf42X776ZL695WiyOyZ42O6XE93c5siijx7kxIMWa652nPFnFzE4FDY8agsA09cYMNKH16Ux+UyL1uaRmHtFGjMsiRdl4pvlDN0f0doBmB53bJxhTe5I5ypaWxZqV/50NkeVMbqoMEsjqj2r6G5uEhTDvMvvbDuL1XGfP1xzIy+74/Fs6U95ZUXF5O3yDP63q84g6hRMaJj7zrpaSuoWYm4vlzg6WlkzW1SGH15/NBwHSjlu/PJxFA8bsvNomXzrTG/F+/9j7gw+sTtm96jHvz3sCyv+9uj2Jl727V/jzy/44Ypzv3yEYvUVOdCmc2Wbm05cDx5ULtlCYiPn0Dt+9J7APx0P/PjSl77Ez//8z3PMMcdw0003ceqpp3L77bfjnOPMMw9evfCQAJU2AkfTCkGkov4BVrngui6SILzjXb8BX9ZLYKuQfXQio3JaAoXwAKpt3D0QKdsNWMGzQeMsZ5DSlL6Xm7COIdBqWM/gFhsy9cHcRHk2SCzifSDt61/iwViWNJGnZbzcGAbVbEEuTF5UhNYADUsD1GYtgb2Jc1eDgxC8hyyt9DdzdTuM0J8wqFkCaxsetKFmKki+CBInLy82uaulmKpcGQSEIKZseVbBS63CqGtdR8FOXtWtQORcgnICyqtgUqaCFFeuqw3BnT+XVYpnF/0cMgKmjDe4UGNAJZ/Q6EKCtWgkwVqJ8q1iGjZYV66WqRbtse/s621bc9LbNMwFVLMfG6uaBQT5fHBMrGIJ4HMP6OOhw+bUYM4Z5WvaGnZDmCmpdZIMvmcnB66u3Y0qS5Womn0OoMhF1I6QQa4ZAszAHIKAQ70kAC4EYeNgIdLAyDaftYpo4EFqIOaUqAO0l7rFA1cnFxIPCCSotvU5CvNUV5DOSxsCp2QdCPd6FYBf5Yiy5hjq4LZwhGbxzsnPEjhaL5GSxFAVC5gF0KoJiHVha1ZaeqM6bCqgC782xP1KwKwODJ4wmzq3JGUw6RJmSYW15QEYgf0L60AwEwoJA124upUMeIVAzTrKfNWVa84ZDq09S+moWd1xMyA9auY9apwtFOap9AkXFwG5WsFo60L6okYDYZPLtkIpGiltLOy69W6h47J8ub99cskDBV0IYEwWBfikC5IwCvdmPJDrG3vDMKdcvZ653EsovWGZnJNmDQ2uwkF+HdYBMdUaqycsQ4sQf0+3QimFampKU1WvveEeC4mtbFqRl/78lg1DOFyrQBkPlrR8F9Ww7bKGCLAMz5tQ64wCG5I4fg0SyW9I5jFmjIPvR6xqQKlLkeJbE4yzZC2OMllLwlorAagH4Fm4z3zPZ/D3lyR+xo2EoFnjIu8SK/PYA2fn17Jw3n0fzlAbK+t5OK9S4oJ13oRO7nVTiZNs2W6kw1UqNaOMZH0MiovwHAogUzmI9vikxe2xJIeV9LJUpT9/kwVfGhquz47nYdFNHG4mKHuW7lZN97aYt009E4AvbriD27etYeKGGJvAq//mdaze6ctOTAsbt2gvQ3xbys5HTZHclVBMWt+v2nDsv72aXt9LsIeO2zav5/JjLB//2mPp7YGpW8VpvXOnprp+Le+vfgGAj/zydi477V/vYfU48PHFYY8fzh2BvrFLZzsEX4TWpZNcuOGxvPeIb+3zc3+w8xHcOZzm51dfyaNbW3j37icwm3dZLlMG31nNP58wzQt6c3xp24msSxZRmYYfTNJbhsVuzNRdUHUizHJGujujakXEyxW9rRHFFYcCMFGWUEm8Z5NI5mM3BaXAWnQuN5zrttBLwr6poiKZy1n/7ba4B29ZACtxg8orzMKAKOoSzQ9xScT0dbo2dwRIFjXXn3oIt2xezz/1HgXXTDA6tGT9wKFKy8TmgmTPABuvYu4MITrWXGXZWi1xSzHJxEbDU/71d/mZx9zA3x/19fp87dwxhVk0/PPyNDO9AYvzU7SuaPOc1S8F4PJHfHrF+d20vJrtHz+a0VMXyY4v0ChiFfG0G36Onz/0aiauieECee+pX7qQ2576Icxj5qm+2CaeH3LIFZb/etSJnHrncfzNGR/jvLRFfmTGaG3KId+UtfP55zyF56z7AT/LT3YPVwUHV1N5vx/J/Tve+MY38oY3vIG3vvWtTExM8KlPfYp169bxohe9iPPPP/+gt/uQqKlEC0MjjJM8iEwe6txUU3eifIDoH1DWP1REjiSBRmC6iq6WB5ZfBIuuqgFmqM2QGhT/oCslkEmWKgGPvuF0qLcEGlmkD3pC3VHoJykMqaqZzNCuIdRrBrlYNBTgEmR/gbXRlYCvMm2CxiDZlbqWJjAyXqKqSxp2q2oYzRo4+EDS+PqvaERd6xX67SXLtrasN6MGAAc5ovJAK7C0sj0PBBRe5ugDTJ/VDxly6Y3npOZyGFqjNJc+746DaGpWM5h2jPf81GUThIq0KoB6amfDeCDfo0oaiWj4/kH+pwsBg+G710HOqAHszmfA68SGFYljMEMJMrHRdDPHJEiXc2QjMcmoExpe1iqAyDV1b5baoVCuN3WtnilkDlXe1RHkeMM5wY0F2sqzos5fDz93JTB2dc1baEUSrqvUGeIls9avwMKcBvlnNLDyNx/Mp/NV/bloIEAszKFkWeoSm1pN6yVncrzKhbkDZmTrOtEq1bUkMdRJSXueQLHIvR/mtjXKs77a/6xWFOQHMK8zK+1YlnwAUjm5XzxYDo6nYQ6oQhIWZiSMaTS0oFV9PGXH1L1DnWePkvkSFzUg3IwEzLnogXssRaMKk9v6fEXD5twkS7YG1GEdCecePHj2DrGBLQrzXZeOuG/9GkgD+Me/ivJA38l7UdKmRn4O22sYMuVVFNEgXEvZrsl9bbx/X96T86wLV9ezly0xjQlgyviERgDOVWslUDEjV7t6j9echqRGUGfEQ7mfx+tywzCFq+u1xQTO1QkqSXz6r+GaNdlGyps7CeNrMs+iVQKWo6GrZZ3RENDUPYJ1qH/Gu6oOg/Q6MIvC0joj7LrIJ5pjUM75Nk6uTgqE7QlwdHXiM+mHe1zORbZKvlc0aI6xSpra8LgffAW8tDeiPmfR0PsaeNWDrlYely79eayzwhBM68pUQheTyzWUdbCR6AeVEci61pq3Dftr5TVdSgIV3XwemiQASMLZJqqWOQqbiV+fJFEr9wT1/FCV1FxP3CnGRAAf3HweupRrmVzX5g3XPo8v7z6Ji+dPk/q2NZmYcy2AubqHubrHN75zMm4hro2UkmXq2MF4E710TuZQvC3Bxo5oSVixdBZadxniQVMC0L414ZpsAy61xH15NiSL1tfPet8B67jrxqZvXajtu6/j88OE7+c5/z77SG687TBae+T1fAqWj5Tr+41Lztjv5zMbce2uQ/j0rrN4z57H88M9R/D1K05BI3PsQ1vOZegyCqv55tzxtHYZWnt8TJVYyrZf29MYnUubHTOqaO/OSRZLksVS1vaJVNojOSeMpdZSUpPGqKyimEwl0TwcyXOtlDkwuXFANLKovEINhgL0WzEuimpVm6xzcg/EQ/m3+voRt125gekrUuyNE0xucrS3RN7hXySzKi+Z2lQQzxlvEljw9h1PIaYCC6tu1Fx2/QmAmDR9OyuJdsZ0tiquHmwgUvL9u3dZdtywjh03rOO1dz2mPrfXFQNu2HgY+SRkw5jCVbz8jqdyTT5kcdTiI5vOXiH9bt3U4h+W1nDEqnlZd0Yl0cKI3mVd+ru7vHvrU7E4ZlYvM1xjaO3Oae3OuXl2DR+887yDmj8PquHcwf97EI8bbriBl73sZQBEUcRwOKTX6/HWt76VP/uzPzvo7f63gsqvf/3rPOtZz+Kwww5DKcUll1xycBty+AxlE7xYIw/SsqUIEsmyrSh6RswBfEa7SrRftF3NYBVdaQBujTykh2vkteAOWZv5JM3DO4wq0fUDKdRL4sTwA9fUiphcFvsAQG0cHCQd6YI8uEufubbeHCY88J3vHRcyqbVJRiKgLPIGPoGZS5ZWso4rJExKwFcAqkVHHpraS1MjDxLrNilBKuSBqdRRyj8U5JOqZiaD6UgICoKhTDD0UU4knUHuF40EFAfjhsr/XNvGjzGNYUTZSpBu8sZNcO+2LRK0y+eCfC4AayXJ+/r6pgs+KKw8q+2aYFdMiIRJqZMKoSjbeeA3tPUxh0SEvE/eliwKwAzmGbX8UzW1eLr09VdV+Oea+rKxNiOBiavrwSIJkIKUrzVX1X1RxcTC1cFRPLD1uYiHfvuqmbvC6Mg9EsBlstgYzoQaM6nzbJxga8AbgnFk3iYLZQP4hp7t86yfGVbyL7eYoUWN1U4p54Pn0mGGVkAe1CDCZMLshd52QGOKkzfut+DboRjlAYZcL0nIKPKelv35/p4YRdSvKHoR0agS4FXYRjKf+7ouvybg5XtFV3sZXgPoa0OVMQArAb+W7aWiEKjaWs5R8cA9lOq2IeNmVWF/SiSGAYSEpIaw0sIU2jQmGlQ1oxcY1/o7ugZYgczRUPca6lLr4TwAbGu5jtb5tcLWiSgBsKpWYoR1QNgz2Vdr3icMI1WvPSaTWvtoKOtYAJfhXhDg4yXUQ5FwOl+nXsvQS7mOyoPuuh4W6Oyu6p+DuVVg7mvJvU8+BRM1p+V3CPXprlFpWFezxitqQ6vmffV9uNQkuRKfiKkBpn/mFR1FNhlYezk3RU8L+zlu3LbsEwreyAq8gqcK30teq+LmeZouCgAP9fxB7m0jeTY5rSg6qq4JN3mz1gYli8lkf7qSNSjyDKA1Sph6rxgwWZMACeoBm2pviFTV1zv04Qyf0aWjvaeSpIVn5YPBUL32l/79w8on9/BqBLn2qoSyrWV++mQF9VpqPfD1SUF/zQOYBmjNWvp5QpXAaEYxtcmh/muaqzZu4HvzR/NnO5+AzaI6OTKx2TGx2bHmh4qJ2wxlj7r3atWSZEdvq/PO2nI/VR1HvKho7RZDlfYeR3u3/3shSdnpmy3/suUsVh2+SPBpUA562yzJcpOIWXWD5uRvvhiA6zMxiSncWJBzAOOW7BD+afYcvnr78bS2CmNbpjBc50hn5XnY2+L49a1n8+tbz77b50/rbGF++yRX3Ho0/3n7yQyKhO5mzXeuP5Z8Am7ZfAgAM+0BP7zr8Pr8xANwpZe0Rwrbjvxc1jUo1Ln8iwYlVSvCaY0qKkmy9mKRxOYlLjbo0lJNptBugdFQWZHCDgqifglVBUpexzlUXqL7OWqQgZWEaLJY0t2a0d2aEe/uM7FJkc5b0j3Q2VHS2eaTesMCPZTznM5mrP+OozVXYZYL/vObj+SP7/g56Xd8Z0nn1pgbiz6/fefP8uFdj0Nnita845bldWzeMUM6D50dGeu+C+u+C5//+plcuEUMox4ed4h2xeJWv73FPy4dx3fuOIr37X4ChTXM7unhImlNE9avP/r8c1nMWlTtGGKD6o+Yuq2gd1vE1VsO4+ocNkzOM1zdKIfyK2bYtH0N/7C07m7X9ydphGf1wfx7MI9ut0uWSYbzsMMO47bbbqv/tnv3wbPL/62gst/vc/rpp/M3f/M3P9J2Qt3jYJ0EvzZR9QNUWcdgnfytv14zWqUYrhUznWyVJptSdf1a0dPkPS1M5QQM1gm4zFZJhk0YT3nwW8/wBOOSKpUgvWw3QWQ0sJJdV415QjXG8gWZbu0Y6hmm0KrE5LKfeOCD/QDuqpBVpDYECgxD5S3ThZ2SY6l8zU6oewqZ5OC6aiMYrtG11XvIWoI02passvNAugnYirYEpcENUOp6PCvXd0QD+T8YWoR+ZdHI1XKulQwGHtAGsNUEKmVL1aAwgMFQwyK1jqoGZiGwgwZsBtZRAunmhtelq9lINQaGbdy8x8aKdLGq31MzMOF8eHYOmmOSX1TN3FYtCbobAKxqJl3kfb52dNnWx2HysQDcNVb84xLm8N3D/Aj7N5mcZ2XlfjB5wzpXSQPMtb/+dcsLn80PTKUzini5EnY+BLjhfHmwni7aWlKqfSAoTp5VzTaYkRVA6T8bnE6jQVUzcuGaWiPyUqdVHeg5JcGf09I2wWkl4NGD4HCdnKYGY0F6LLVojcNlcHENwDIEjAF4WyPshM4tZlDJvMxtzWo2wL6prQQoetL6ACOSXl01gVvRM97wqPL3pK9tbcn7rWm2YzzYfiAfSmXbCLNTNuydqjwDHsvxrwBQqmGyzLBElVJHGhjeeLny89Rfi0S+X1gryrYmWbZ1C6Uq0TVTCz4g7NuGqfMJgQBag6ojGrkV5jQgAaoq5brpypEsVfW9EdbSwM6FGszAktU9GQNo9uymJKTGzGHq9j2eZfPJmFA6EBzIV8g7VUhuOVkfXJPUqr9nQS0VD2qMAGjDmi61vQ34D2qMKlVjDuMeyAwlKbnCZMavnzhWtDAKbDCE50iT7IhGcv8HE7jAEoZnTXjuiaLE1QY4YV0PSU0I0lFX14+KRwDi1m5dXY4Sau8DGJMeof7+jXXN7tf1nV4BU3aNKAuqseST88Zknh0OfgIyp3yyz4Nhm3ilUxoyrV6NYJokYjSwtPaUKxIFdVKsCMmtsLZQr19BZTR342phB5fkmCfurGBg+P51x3Dz0jpQjrIN7d22VkyF8z4+N8TZXuqZJ++o6trl3h3SjztZEsOxcO3C8yHc30v/fDjuizN0dlf+Wd8kDEKCIl2wlJt6PO6a5/DO657Cd7OCN+/af41VcJ4N4/KRxTrNf20+kfKuDr07fdKxDZ27pH4/SJm/8rkz+crn7r7tZ/duRw810faE6sopds/2RA2zKyKdhWRzwk2FY37Yxt3cI1kUxra92zJ5XUzZhWzKUKXG96Ckvub1faG9R0Yv9gl/DVb+d5EGDXpUiXGPUrDUh6IU4DjIiBZGYB04K+BSa68gCBbC0Lujj8kEiJrlDJWVzNyQ09pTsvq6nKhfMLGlEFl/5MtgshJVVHTv7JPukaB/6ibNjbceRrLkSGdzWnvgkwtncfkNx/PVO44nWZDrNp+1SG5p091eYRYzccDdOGD6OsWlNzUN7Y13BG7tVPzDHWejteNrdx7PYJTgFgVwfmzhDACGh1h6mzStqGC0JsG2IogjWjv6pPOgN3Z41dUv4Ymrb2Z4qKPsRpTdiNXXVdjFhA9tOne/c+cnYrgf4d+DeJxzzjlcfrnUUT/zmc/kDW94A3/yJ3/CK17xCs4555yD3u5/a03lBRdcwAUXXHDA78+yrEbWAIuLiwC1MYCy8qCS15Q3UGkARh7La8FxVIJVRT6ppdi/J83Qg4FMKLCPBtKjK5sGkzdBLDSmL1UCdT1hRxiH0bQ82ZUFvCTOKOr6P1RTkxkYn/HaGTEikSAnWbZ1jZjUi8pD3yaqlhFWLdUEH17yWfo6yuD6GRgpsWeXB34tCeqCyaS+yGTipBovB9mto/TsVthOU8MqJyTIYyvPBoQAJrA4AZSI3EkemsHcSAJUL9lctAxndC1XDfUrJheJWwDC4dyawtWBjnKuBhCBmQXq7xCkKZXPwtdyZKfqOkaTebOZytUP42zKNEwODfNoCs9KFN60xDNXeClRlUp9XNkSFsaaxikTf92cUeJA7Hu2heCkCsYvIRjyAD3UKgVHzRDghOsgDIQiuG+6Wsomx1J0dM3qAp5Z9nVNoyahAA14tnWrCHE9DYG68eZC0UCYg3i59My7pkq1MH6+3qzsmEZ+qkW+GmrboqGtDTiioaXqmDoJobzkNxhpqEqaogepaKhJtKn2SgBL6V1dgyMtSrZbtjXBqMoavUImV/j+onLtRVJbdE0tvdSV80GHqpkUm4pMVYycrL+Pm16iqpJWR+E+L7qmBgZAw6zF8vmiq1FRYwj2QA0zqigSTdXW9TUJsnVpnRPmsJ87ULNFVTvCLPs1qHJQCssYmMyyo2sFhinkO9hIyXnwNcXj92WVqDppsVJV0NyDYS6GdUxq8rzZWqTq9Vzq0Js1OpxfVYW2NWNJPp/8kuMT4BINLCokAFP5jtY7LQeZeX1+/JwRYyO7gsFMlhrJqMw/2U+y7FnsAHY8w1J0/HpUQuVLK+ra9LEkU32+fA3+uBqh6Opa+pksyVpRtmU+hnZXwblbOcBL/2sn55GtHVyrpEm+qdKJEZZp2tCEc1b3qDVN8k47CO6/OEmWFF2pawwtvAKzpyzePVs394R/LjQO0cg22k0tr5z/4H5tazZUzOdCstH6e1jVAMJ491pJktp6f2GS19JX/8yqn/WKWgGBc74eWDfKl1wST6U39Srbof5X/p7OKXQWrk2FGVk6m2OqFtw8sZ5oZ+KTL2oM5ImTbLwkTLQZead6/4wJ+06WfIlDFEpiFMpQJ4Jrs7lEZMDMuZqJNX7dDVJugDzWdLcotsXriJY0L7/yN3nEBTfx/Tzn+Ze/ml885Sr+19pv0dNiovO3ux/HOw/9PgDvmDuG109v4iVffBrJ7ojJbb7Od40iXqaOJ5IlYea7W/a9xn1jtIZkXtPbDNkqcLe1/VyBdFGeWf8y/xjmljqk82JaKAknS2teMVyvGM0o4mWDLjvE8xnlhBR060xAn4sMqqywSlF1E1QlpRjSDgJsS24upxW2l6LzvC5lkHXAQWRwcSplRs4JsCwtaI2yFjWSWkk1CjVQjmTPgOHhPdp39ak6MWbk6+xHBdo5MMKcqqzAtsX0Zur2EmVj4kGFKiu6Oyr+/rqzMbtjcqfojCSW2LR9DZO7pKRCWVuXP03cmTO6psWbTjuVh3e2UrYdqlR0dji23boGgKrQxBuWUZUim4Iv7TqR3525jUeddSvXbzmB23esoXWszA3nWd/OzgowDAfTvL88DztVkk3JxOzsyEh3ttk9mN7nNf5JGcq5+jze1889mMc73vEOlpcF5PzRH/0Ry8vLfPzjH+f444/nne9850Fv9yfKqOftb387b3nLW+72epUonJcjKm8EEdgNaSzu3xgYxD4iEfQPSouiXBXMFfCZcnngBnbQjOSBmU8qutv9A3uyMaURK3Z5c3hwh4xrCFStNxgJoEtZV9fORZlDZap27AwOhwEMhYemM3iznjFw61ztThpqPGo20kuhQl1hzer4B0/YZshs20jq/NIF/7tnVp2XtoWaTPmbojArrdWLtqpBa8jaZ5NCz8XekCU0gS/bygcmclDB2CKf0OLamoYstf9OKb49S2OgIMfm993xbSM8o6k8M9i0PMHPBVfXawUZqB7a+oFeMzJZU6fb2VGQT0Y14MonxEBG5wJ48p4vyFdhHsix6cJRpiJnLNuaZEkeIOHaBjMJnPN1XuI4WqaaeFkCxdpIyAPlKoCrrMlCB3lezdZVTXAOXtKlm4A0SAQDiLAe1ASDq2BEU7W0SIcArARptTmFz+YnS8K+maE0WyfStfxs3NXRZFaYRd8z1KYapyVwE2nZGCtVOaLc1nV7UjcpdEkISuPlysvcdR3MBTkqQNkxxEvi2FrLvo3CeRfS4P4agoSobCSqzozV+SHBtfNsXdHSnnlqAL8pnLQj8AFu2fa1sr5FSZB710mWIJH3gWTknU2rlpwHMYx54IQkzmgB4rFnfZUbc+F0dTsGaMBc2fagsKwa9g0JpvXY2hISE+PS31pZ4QN442thAWJvXlb6xErYZ2CeQo/CmtlU3nDMzxtpkySAMeyzluAnDQOvbHPMwhpbqrauEz3xsm3a9lhxjw6uw5KIolaXhGRQSHgFWSbIe5Ili/Hsdu2wO/Kqjbzpl4g3BauNdLyBjdN4IBbMfuQYtE9MiUtzU88t9yV1jX1oSxIM1+K+9UmTxvzLGdAD6vszSOdD+6Mgtw8AO8z/8YRIqMkOyc+QaJL+kw0DXCe4nKrX6+C6W7O4HrwHQ7DKr5um8OZxwQipWqnwKDsCSHXua4ETXQNpqcVWtQNuWNfkesr9WqZedeQTD9Ynh6NBRT4pF68+F9ah/KTWhZUkaqLqGuS6F7BXwIT7qWxDOhI2SfYPU5ssy4dpoivbjNbIOc8mxX0bZE63d1dkU7ImiMlbg4BrZY5nd4uuqs2Q6gQqoMsxIGT9/WyQZ07LO+abOu+BLqGzy6EqzWitXJcrrjmOF297BXpLi89sOodNT1zND75/HLc9731csfNoztt1FDtuWMevP/0LPOvm8yWW6jdlGmIe5GrmVhI8kC6NUYdj41mdAb9XQntPRTzUDAtF0RNpryrFVOq6hUMp+wltL6mOBxadiat1NGgSe2XLYJYl+1H0otptX5UCHsO107ldAQSUTy4oPFhspbA8qNd+Cr/IRNqrwnwbskgSbNiQtLAoG7Jzsv10T4YaFriJFDMqPUtqUcNcth8bVFbiJtroYUG6a0jPtGU9GJW0doxQt3cxOXSu84ZQzmEzI+uqBkqLXhSDobSomLgz4qOXn8vpp93u1wxozUO6x1C1HN2tioWkA5Ec400bD4MT4d1H/Rvnrf9d7K6U5RMKiisT0sUROEc6V5BPaNo7FdkVk3BMVfc1bu+U2t5465hs4qfjQTOOPfbY+udOp8N73vOe+2W7P1Gg8o1vfCOvf/3r698XFxfZsGGDyFO8GUI0aGpSVCVgL4yo37CUOpcHaOjRFqQRQa4VssZm5MFlTg1EsinfQ69wDFcrH9AI4JSm9o58QtegMkhaQ4/FyBv9FP6BbGOFC2ZB+EXfBz+BjYpGKwFj2ZXvKI21pZ9SOdboWpeS7S1SAcohW2vy0BuxYdtspJrelD7jLO6GksUMRg2h7kU5VbMageUEvwgrCd6KrtSbSRY/sDSSoY/GMrHSJiIEi9T2+qoKfe5C4CLXt04chzXaMxoiAwvZc6GmtJdghUBQVa6uW9UFlB3ts+FuRQ1gYIgauaSl6JoV+61NaVxgQnxQMsZwSisaiPuVbGto62AKzzjU7EtwKVVqRUCnC+r2HiKJ8/JXJcBFF873YfNyT88KBga3kV03fd2CQUvtVGypAzaTy2cCkNdeIhnkjyZyxAuFgJLSUrbNCqZazGh0zbKHrL9NtAQxiSYeCguuSoiLqmaq6+EEcJb18TlKD8pq9spBOSUGB+LMq+prWPlaR1U5kZ1aauBcG1IUjcFO7eCMgMDQoyweuLoVSGBRw303HmQGJ2MXq0YO7QNxCaTEtj70xtW5G2uL0MjvwUvqnACsB3K4SDFaE/v1QgLvbMZ4SZyX4vr57LQHQuF6VE4y6KGPa0v7e0ijanDk509g2YZVHezrUrZVg84ALpcqqrbGDEVGXfRMPZcD6JHz1ICLeOBqObkuHFapumUIUKsd6kDQNeDUJo2UOZvU9XGUbUnaVbHMKRuLKUo0EmazMvLZMpU5Etbc8H+UiTIhHLPICiUhIyY/1p+HhlGL+/Idg8Q9GNeMJ4Hq+9fXaofaQdl58wyTNcEDNqdQVsCyGJeNgZ9xyY0KDDWN43kJ2lpfWwzajrGcNGZmwdwsnG+Q9TFIzJ1CIg0VEnVjYFKNJfsq6hYfoVRA1kvvmh2Md0LtrTfvGmcUXSzlK+EaRJlQ8ONrnSQqZCEPa3xIXGjfEgrrcPFYAm2shUhIwNa9Sj1Ityokc30tshM1BkYxfaOoUEIpgIsUyWLF6nlJHLpN0k91vC7dZPKM6W4vKdu6TkAGl+twDs2gIgFMoWq1BWNrZQCYVaKJhuKMrcbm1vhchEbB0t3u0JUApHgpYjDosepWxeAwuPLOw9HrRrx198ls37QaOiWmgPddcj7KwvT2kIiX+Cqdl/UvXag8y+rPcyU7Pf0vL+Kq35U+kqGVSD7t6oSbKg06E0YyXINrbjiS9pZIanhb3jk7lbkxtVGRTypRTRSOfCatk1o2lQtobIWLNHqY47qejRi7n3Quf3d+7bC9VpNQS2KZQ6ZJMBIbcQ3PS9AioVWlReVlLW0l0gJkC4vy21J5KddM+/eMMpTWftuSpFaDnM4dFbYdQ1ERzQ9Yc3WbxaO0GDYtWtI9IyaunhTZswfRxD5uyUsmNg2pkg5XV8fQnhNpdzpXovOYeFHx/7P379GWpVV5MP6871p7n3Oqqrugaa6CdKOoCBJCk2AT/SkmgEo0fCMXxjcIl4h+6XgXTbwknyRkoIkjEiIJRhOQmBsMhxowIdFWURkCXrA7+RKQxNjcmgYCdFd3V9U5e6/1zt8fcz5zzrXPPlWnqqu6u07vOcapOmdf1nrXe53PnM+c89inVQG86+lLtHmHh/3BDLf+2V08Y34CaMATfrXh1HUzLI+PmF21he7uXcw+dS8efvcezj7uOE7e1nD35+YYt60rtzs8/ENL3Hvywe2xO680+7mY7z0E5QGNqbxQ2drawtVXXz35AeCF6Puz6cOF9Ex4gH/nHihmn4usp515KMctA48SIGt+dyTDYVze8rgqRfN7baOyWl8ArKB63Jcg15Vasww6oBJVBDRDn1GWtotnIJROky2MlkikjoLZvQoo65joihL0WLW+Gdi9KpQHHlrFvDJMGd8tp9bt1od1mklpPCZxQVpnKA0EKbSe0pNAgKIUzTigCUaZFZEHNQ/p5XGLL6C3wsaE4MhjKgloasRd6hjo//S8cU5Q8Rt2qtLoUoxPGSP2Totpa98uTkSsDX/ogfOxHPMYsO1wgFmNajvO40ClolQHTVpTCKbHsEJ3i+ZxU4xfYx8y8yVBTlmKxj8umU1XDyXO/ToqVUyf29pwVpPONCuT0bpQksKTG8mZ5ncu0LY6yxjZuYdA+oJu2TAc79Sqbp5YgiNmPGXykc6oSm2rWpZLjWlqWxXLE53XOPR5RS8adMwzDc/LRCysdqdlti0Cp6TS0EMvB2nwqshX9ShV1pmF0coticjSxtzWsWfn7YvVeS1eI5eegH63pQyXdk2LERt2qpZhOBY012Gr2v4UyrRcxt25dcwyq38rILfEL4NM1hmg8WnLHfWkLk/0+C/v+DfYu6b3vqDxRfcRm/8tki+NO53SKK18iHTqGeDnMsV1cbLDuBPzhhl2q4GI7mzz+DrGZpJCTjoos38zrpfzj3NIwWL1/l8eL15yxymdvRkWLUu0FJbliUQz3JcjiZmt04ZI1GUgqYxar/jsNZ2Dpy6BZmZ71U6xdWuJ2phoSBO1maHMzgbpIkstE+F4nLU9d04cxKRInGO6hsXBLAA3JNK7rnuA+Bh43PTZZueDlSqRWHujGRFoHCN1v1sIZveMlnE9Svu4wUcUhM9PDW5MjEzDNF4WzaRsINLnUBP0py3+necEwyMk+macVzPKsvxR8T2xNDW6DG4USom+BB4DCzOiMtFY3Wvo96xc0SA6v4t+bvvOESfu0IyjEE32xfO6Wwi27hwwu7dh61TD7Kz+ALrXiRmjmPTNjWu21zRjj2g8rfh+o+dNAOMyqsGP4JFZv7lvZZmdaej3Gmb3MvM3sPOp4p7F4Z456h8fw7/94J/CI5/4OdS+YevOgvndwM6nVGeiUZ21sxXARjIunl2r8Wd/uHisNVBw+tEVZx/R2b4UukO3Kzj5gR7jtoY4MYcGjajbnx3Qn9H9m+f3uG3J08yrqHNuhMx7zRArAlQ4GGxbPcpy9KQ92jEaT4h7z6jhoVb3SKI1/RxBpmWA1b2kh/S97gOLAfX0HtBpWTKtWyt6nVKAvgeWg4fQ+JoaBtSzS5TFEhgbdj4zKONuz2Kwzyxw1e0N87ub0meHMfaHQRMBbd854uo/qhqDeo/O5RMfF3fMnPhEQ3+n7uv9GcGPfvyFeMNdn49H/H/A7NQSV390tOfpNEOuKL136zN7mN2z8IRsrQcWV/fozwyRSPAKFdJfL+bnwSy1VnRdd+DPxcoV5ak8SMY5UBnjcrx4cgEmxnGvVjHgR4v0Niky+ncZBdufi8Oj1PAu+vXM0+WFsJsuvjoandOSoLSuaLp1wDPk0WtWF4KulrBymnKyfVdz5S57tHjwj10UvXcPXKIO5XpIqsADDVpzc3ZGsDxGpTcy4OmH6VWJPq2jWo/3rkZ48CweZtjWGKgzj6qY3y2umM7OWn0uxvcMcOqXZrfV1OwEDIsTmq1WzJpP5dEzoC5NEe/Us0ygxYLdAPxwIt2J2SBnp63si4E0x93W7/1uxBzNTkc8onqvlcLYzNtGOpwn3oCBMwOHFeHZrEuBtFAm6yheLqIsBZ0EEO5oNTZFpVg5m27BzLTihxKAUFYanO5Z9xTQ0bvXzECgnsXmXrG619zrVJpRR2eW7MaUrjZXz2ebV3RndVGQ4trtjWh9xXCs15jFY50rAs0SZSyu6tVIsFXd211S+n3pSBevlsgCTv0eZ8VrznV7gu6MlvLQ62rs0uy00glJQWN5E5aLYY06jUsttsa7aS1WWy9SVHHU2rH6IKPFcTEZFgGKrl3GPHbo9zQuVTN2Rsxam3EOFH8+qRGjNTut3iomUuJ8ZOIgp7jX8DpdNumKl0zSrInaz8M2sPPZFvMS8M8odbfg19/yJgDAb7/up/BVf+P/ce9qKUpZhIjH/w3HtBP7M+qh4LzNyjKBI5PihGcOPsaekMpAUn96dC8yvc0sM8FkaoAaeGjYK6PS1rfvauYtK87oIFDDjIYinbvcV9uc88zWts3rzGxgmMVwzPaBol5olszgPqrnB9zLyDXZcZNaAkqZVJaI9EqrY3xmP4SRJlOU6WHPIIiglEYZUvppeOl3mVyuuNJOiqrWieUaga81j4c1I2tnpaP0WbQv3Xto1/E6tEX7dnxYFyVRrJ+xTF7uSg84DacIqrnRmfszzQHlsF3Rnxl1rs4CMKIrGje8FEjyAnv25eSsZX93LZgeOZlUt9DrSQUq942l3s9DTSyLvH/PjFAMBeh3R7SZhhSQTuwZbZeqoDvAmhU3+BUxyrjFcssMmrXW9IBxrs8/7HSuz7StitnpEcvjnRkVtW/n9wwYdjoFuNwXEfuNltbQtauJoxrOPqLDsU9q323dCUjtsTzZcPWxPZw6vQP53BZ2HyW46jYNQdFnsZJWzA1xrKIbxce1dfB5c/xToYD/X8c/jTfc9QV4xJM/i70PPxJM0LV9l+0FRnPv9oC6p+uTTCpA+4lZnmkQHXc6nx9ee7eU8ETWokAfQCminkURyKzTzLA0RhJIAQr8en1ftmbqlVzuqdFsrgBSZh0wjgEwarWkPoA0es8VBIKKfN+pUtRVdHedgcw6yPYc9e6zuskakJ1/9izabA6IGnvLcsD255ZYnui19Mn2LIHaClRgfmrA8TuqhfrE3r91SveN/syI7f8zx7gD7Hyu4dbbPw+/96Hrcf0dWu5k+1O72Lt2C8OxHlt3nlHK75k9lHmP8ViP7c8OWJyY23Opt//4R+/GFS0rRo8L+t6DWH7xF39x8vdyucQtt9yCf/Wv/tXaMMPDypEAlXWkIgLUZZkcGKUhUonPQ0kiUGSMH4CIOTRrcLdrVNari1urWdhZKSgRjybCpDXmOd2GJ4EA9Hqz03pIM01+MUshY+U8VtEsz92eREKZUVA7KkzaRlR4IfvlDi1voay2PoBjBo3M8Kkp/jXVN783bhfUXWvHLCg+VCZUMdLXdj4jGLZjHJz2N1PQSWWKh8f87uaW68VxTSpURj3sGXMzbmuCiLoQV0CLwGuR9Wc07T+toExWRAV0ZvESuSYmYxU1gUKJGCUYRdQovgBQlnBaY3/W6DcswD6m8WSMFsKS7/F/QFLuinv/CFKzJ5C0QipNdRC1gBsd1pUxn+zqKfEEPublY6wiFU56KIZjUYeSisckGRGprQvLvNkUANCL1GZGC2yMYxIMJ3qLDdPvzE8tsTg5U+VpGXRe7VBL3HS2YayhmLdZxL4tLVkMYyrZfwTljMEZDazq4oFv2roemBjFPDkF3t/0hjOJBQEF10em3g47QZtm3GwRQXdmdI8AASrLjzCGjfuKJoNh2/RzHHf1IIcFm/U826wYXVn7kMluLpcsTqjiTYri4qri7V9c1aEuBHsne4xbSlmjN+nuJ07dp+OsoD8ddN5uKUDT+SglYnM1pb8ZEGy9Mpaz220Y550r41oaqRkjpEN/xpL/2PxoXQG2LcHQKFhc1WHvavNQfM6onnscT2uXUea4BwNIwFIgQwEMSAzbJZXCifXXLWWyp6rXA/4s45yWK/G+JVOiDuLZTnl/AqjwKkWiKMaeAfA4b6XPNsuqW9ywxWup54wAwYyloqjGqZMFftYMOxVbp0Z0u+H9Yr/0ZxuGXsMD2Pf0dkZtT+tDMjEsSQ6zWtODx/AAxqIyodnsrCbfqYOgmmHTEyfNguYrtWDYDiYSE/U0cP8EeqNUky1D2ieZQOOW1oytS4FsF6egMtEP8ylQaBCitL5gds+o69b3tmKGOvNMzawEGNkxBQrazMBUmmZdLk0BIVkbRfRadVeNdVFiqTlYbZNSZdw/aLwE+tNW7sjqfvbJm6/xwfB5tri61/4RQJoo5d/WrD97ij8tAmzf1Xz+zO4RnHrGEjjTYRwrtuYDtp94Cqc/9DAc+3TzkJWOYS5Wkmd+9wiWiBo8Nt8+I8BLPvzV+LfX/QY+03bx5j+6EV/z+P+F/3TNIzG7J5gHeycrZqd1TRy/Y4CUHvN7BYMZbZy+ygRsdg709zTdg8zIrM9ZPLNraQ1te4YyNgVKrWkin8VoILHTPtoCylmjMtSKIuatHNXjSQprPb2H8aTGQ0rfe0ZY6XtUEUipQBV0pxf6PRGlw3bV6JbN51hZjgFOhwrGZdYzC/T3Wv/OKmTWoz+1sH21BFg10bI5S8zu6dDtaS4CAJjfPSpTbBR0Zwcc/+TMDf/dB46jdEAddlF2B9RxhDxqC3sP77H1yYJy766C33vOou71qFdt46qPcX8Q1L3RQ7muWLnYmpMPck/lX/gLf2Hfa3/pL/0lPPWpT8Xb3vY2vPKVr7yo6z6goPLee+/FH/3RH/nft912G2699VZcc801+PzP//xDX8cTk1T9YVzT7CyVOP1cf1ZQvO4ZnIpal/BkNVQoYcpp6wq2PxdxdwrmgrpBi3zr9H4DEwaR3oFQPpbHQ3FjhlnAALHR8bxcSbJwSweNLytBAaGFldRajSGDW0hVud3fV55J1GqpKbgy+tKeAHsR30VvrKRshOyDzqzpdQili1kuM7ga58UVgzaDx4UyrfvewxhbJuj2okwL43pIwdWYAwOXY4oztMPS4yLTOmZJjNYD6AARJrVBxC2aF41lNrzwuhRIFadu5KQR7F/pCwRGlzILfM+xEcY/NfdgerILoyO1reoGhbowbw/M4jpGqRCnCXLsdtVjxlqKtUFjOUZYLEiDzEwJt75hzUEC2NYXjfXsVEGpg9GtjBZECqOOTcFwrNfvupGgKNV1Idi9ZuYxO4zNYaIgQBVOeqwE4aFx2tsYiiSl39VYTiraDtB7GHVSnIZGgEwPtiph9pkSBiYpoSx65smUvZheGWZv7vciaROTFXFeK23RFOSlUpeHnWoeseJepHGreqmMcV7dWESFbTjWKdPCqNc+14ZUzuMyyOJExeIkMDtdwCL10gGnr62aKETgwGbcUorhqesL/se3/SRe8uGvxl9+5O/hh372Zdi5FuiWHbqz6okUy5xbFwF4dLDN2FF1HyGYKFAao3vKF0296OZ1X1ylNFWN0+lUaR10rnAvuPuJBcMx4MTHgTu/qKIuNAkK59GwY2vaSog45dPoxs2o8vSAEcx7DOwINNJCl1MPC0tYaEZmU8qZ+MwSwTHUYH7P6HWR6xAUVWcDMAaaIHLO7ODFE2SVQVBrAYY2Sfg0bFf3LtaFxqM6uDPDEcehDgJpWiuTHk3uSVICBHSWMZaJo6iwRzbahjqUtN8FOHWaZvLYMUlZb2ViBvOGM7wBEM+MPpJ9JWqkYeZYwEDsENRsUuG73aZAyfYv6azOaK94INPa26y4UYzJ8Qi++JOTQ83vHnR+7jXdN+cVWFq23KogUfvOEgJZkiaMapSSUt2Yy4RADO2QBBDzWHHfqUs9H+ogaEWfrY5hdG2zglqA5bYaHUin7tK5wbrFnPek4+f905O6wbyhKcEcPd4scXby1jl2/k/Dp1+4hXLHNsZHLPG43xWPcfe6rnbGMnyI9SNnBpgHizWsg+B/vukpwN//DZ0bY4f3ffo6X69aS1awdVfxdTpsaUK/uhTMeJYw0ddeQ1csgdsSXvOYcwrQvh13ej2ndwc/s6UrwFAt6VOHbmgOEKTvIHPROpTVAN68B5rSS6VTT2LbUuVOZh3KmYXHNqIrQaUdBeiAsrsAug6ypR5G1AJUo8AC6g3dHRywAlAK7tiwdcr6hrG/e0v0pzswZljmpIppbGfdhdGPw5Dd7Y6oyw79mQF12ayEnY7j1ueSEwNAOzbH/NSAs4+a67PTq+oU/4beWE5tVgO4b+SKkWc/+9n4lm/5lov+/gMKKn//938fz33uc/1vJuF5+ctfjre85S2Hvk4dALFMrcyoumVesezlEfNYMNawsxgUBnmTEllNcc4KL6+lMRrq3ZvthkLaLeAekdXEAgRWrAnINrDtjAMbzCulGUOLU0cim2dSisz7xPqLnlkyW+FaAC5acBkD2bqiFrPKpAziCgWlCBUt0sOmJUVg9yDAdQqggXrWKqMniVZ+9QQ0Txu/PFY8AYTl/oB6uMwC3vR3Afx6pJqJee4IdJuNWV3yYNUYL2aGdA+h0Y5kFu3XAu6I+B+jNpIqJl1xGm0ZlXo67pBeV52em2kPVJKYVKGlMhGlGbgQqDLeAFhMFjql43RLUeQMe7aFYJxVB1rNxh0G5Gf3DEqvqoj6icejHAqBpsaf0AhgAG4rHVpcW8uGses0MUnynnW7zUGdZuTlIpNQtE0JIZ2PSmZL3ldXqC07I/tFKjCal9TjO4uYt0ImCtDyeLUkR0BDxIAygzKBJJNvSeUgqqfMgeIe44rtfSqocwPey+nhSKMEvSqsL8f6gYB68jTuL8q+SI3EP4DOU7GMq0Hp3DcUl1QWJ9Xzo6VVtG6vWNZqzt+zj6ix/ivwvS/9BQDAi669Be++54tx7VfegTt/7bFY3m3lXYyeLTONEa2LFpmDYfPU9gJSIqngcv8TSyLSOuCez+9w5rFK3997tForzl7bYefTwNUfG7E4UTHba254u/fzgMWjB2zf3qsBDxGT2O0aoDBPpMfo2vyih4keytbD66Jyf5kkM7G9fHZa9waWoQEA96JzL7F9mTHS0lWPtSaAcC/iGDH5XcruKYi4UGZ5buQcQ9dpHRWoLY93uvYFaJaJmW3gvsjYM60FbHGDpNtLZHn22qXzoEnOrWbvuJ3i8yxRFWueenI0Ky3C84P9UxrQSpyvjAdlNnTP4uwAK56VhqV+V+eXs1ny/pVAZDFg7sbJUYCU5Zl7UFkqrVHo7WzA4JnHq+95dWgo+TwxZkJBnLvjVufPChCsM25VMG51GE4oSNWzUMst0UsJwIHvuF09aV4ZxbN092c0Zq6z87v0AHj97RqMp4XYutRzcNzW9To70zTMgEYAG1/QOJT1hsTGAYATt2tc5FXv28GxzzTURaf9Igr2tu4avS94FpRiRl+7bn9m9D1fqho5/u/bvgY/9Hn/Cce3FrjjEw/HfATGHQSjZlCDlMasNxSpBiZt7+mKh71oDLvpFbOqbJNUH1c/06HbGzVGcFbVs1aLZnxtAlSgzTsDTCNa36Ft9ZqMZByBbqaJeUb1bErfA9TDlprop/Q1gKR5NgsT+5QCGHUWXQcx47myj2oAtpla96UrKLujljLpOxz79KjP1xfNGLsYUMYGDA2yM3NPZZv3KJ1S8svYULrOdQ8tmTWizTsFlfcMaLMepQEP/9ACZx6jLCSZdRi3Nf6UewBKgbqJbR86s9D+gH5easW4fWUTImn4upjvXWly9uxZvOENb8DjH//4i77GAzraX/3VXw25FC7iZO2kFZPeSqbbBgKMcbNzpcKADKmlzEanG6QlS9nT3+mV1Ex6xT14pMQWswAzCQ6gm2oxCzdBWxVYFlbLKDhXL9z8HssmWIrXcOwWxRWXrNywxpkniRiRFBsq01N67Tiz8iUDUgyTbmLMHMdkD1KAgjhwlOJFKzuAAvdwAPDaV17nz1Kt6ybNwxleWqQzhYuxaUKrJgHhUtvbrN5bt9BEF5pJdzpvWLesW1pZExFPatTvsu1GKd2Oou2lGWXIYjGLxeaJjV++D72O1sGhHM2Lx8x0S/XyZNpR6xA0VZiy2mt7MK9qBbdLAnCLdBGoNZxWQrtmAQAbT8boSBGUUtzD2J/RGMg6NFeG0Aw8Huvcckslddyq7imSWQXM8jn0nbmDzYu3owCOtMI66MGrdClxr6gbYcboNwJu1srMmWuHpBA2Gyd6PzNNcdyObKnjFmPEzOvj53YYYsZtM1i0UCC7PSpsASQAKs6qAJPNUEQVZip16hmovr/M721u+Gm93pMGC2ZAZkwg1xQZDNyTpAPKno4rgbvGre4H+ZdKuud+Dnd+/CT6Ux26L7wHJ3b2sPtb16oifUzpsMNxoDtTgKpJwe4aj+E/ndnGXz5xCu/83HH80Be8E9/7m6/EsK2U/+Xx6t4/evSdUVGj/4fjURdvOFYxv0dpkJ15jlAKdq/psPdw7Zu9xy+BoWD22Q7DVYLTW+Yd3RWceVSHxcOANhfMdgtmn+n1Ow+n8qv72nAMXv5JqnrCmEW59YoBMy2dRjz3fM/C692fYT3ZAKiZeVIt3pJeNi9/MUTGTXoVpVlNw104+KMnBgiKbL8nnhiGVH/pY0/xZHQLVa61hFV1sFn2mtcSzTR1nllAAFY1oBjwt71cKaK2p5Fyu2drYhSP9fNkdKOg2PVyO+nJXKXiepzmKOjGMJJKqgvoRp1S1AO41JhCFGWMMGYQglCYUdzgxjhIev+Y4AjQ/WB+1mISB32eYatgdq8BaPPacf8PA6W4h0yNkhYrvGvfm8U+STDjr+01O+c7N7jlMJje2tOf0VIX7Fc3NMzUEzrOisfjMuEZkx61uRon25Ya6CbGgqUaRYt5wHlfjMogQNNs9GUpblhzL60ZCU9+eHBqMedFv2ex/FtwIMh9kfHHmiW8C+bPoOvgd9//ZPzhIx+DO+85hnp3jzpobUvpFWDPTo+aoMn6fHbPoGDZaoRqmIFSWOd3Kz12cVVFt1uAYx3604OPgxRjACwVFNWF1j8GY9rHBozVEt0YGBwjvwK9dDLr3PALGs5ag9Te9r6qtSuhQAsVDrxKa5CO1NamnsVmFNxagKIxn2U5om3P9Xvbc2DUOXHsE2cw7sy0HVaKJNNNmXW2LEe0ea/ZbgcBanPjBqAxmcOJGeregL4rWFzdY3bvYB7ymQJuGqHtmrI9Rxn3IP2WPr9IUHVtndbFgOZlcK5QOaL014c//OEoea6I4J577sGxY8fwb/7Nv7no617ZJgSTuhB0rPPH+WvZt5i9FFBlQHotvMsaiYBtes7Bp+USgGiNw9YXKwBNOlB4MtzzOSqliMorM7pSGJvHul7jDFH+BAqkmD2QRbdJPxu2w+rhFt2ZLeIWirVnqEOAQir6BL+MXXQqSIHHlBJIO03H7qlxXlqexeMLrYA849cAeGyRJyWiAm1Ag2CcyrtSZfSnQsFYTzoiv2903zaDl2lhXCb7I2fVHWfhDS1Cr0NBBWOZFJTO72nuQWZiDS93YF5HL2dAQLOV4hlaAFXSzOi1FcsK6vQS85Z4TKLRK+kRGbeqW3BVAdK+cEWGh2BXFOyNkTgEpyOVen96BKnGEDhttsIUUtKids1CbN5XTVZgiupMKbkg9dLmOJXtzuowekKjefVkK7UR3BUHS4yTG3ZU8WhdZOCk5zeyVNo8GpRKzTlFgAcYxZhJdDjHFzHXGavpiuguXMHXrK+mMHMdjEGr84Qh0HlOBkMGpPxstxfUOfGxhYIU835v3T1icVWH2b30GNdICmXzSL34xQ1UXA8K0HDZ5K7PnMDVj7sb9yxO4sVfeCt+8a1fia27geUxYHkCGCyp17gNLB7R0B6+xE/+t/8fnnPdbfjPs1385h88Bb9zx9PQdvR6Zx9hdVtNmYVoFtfYWyzutCgAWx6rDp7c6JW8Rm6o2m7AbsX8s50asU6pQn3mMUDdKxi3jXWyRwMfogwUgHs/T6nE/Z5+btgOavSwY+tk1PfdG1OA0nQOs0h7HTVOzZNG3duckUGwSXDWemtHiqUjdbVwf0ZQQ0lvhO0zOq+1bQCcmgozDKmB0TxnzLRLcEQjo2gyLu7lwzHuD1ECxo2SzCxKECfhQSTbpC6SF7/GGTOJM+/CqMgi8dKbB5BHJrPvWgwxRgWGrM8Ksd+X2l+wElFMNqaNMmPpTvXYSZ4xnD+suwvbP7uzkkpiJTDNDJtnm9PT6yjmRY0x6nabGlfOjsZ8EafHSl/UwwTomTzAFXudT3oWIRmTCB7r0NAsKYwacCNxoJTwSpdGnaBh3O4tc7N6FN2QWWGJhPQ6Xupnp1MP55nm9XNrU1Ba91okLDJ6ZRkEYv3aRMdM2TY2ngSwNi97SyaU8xvQq0rmVtRhDqNEt9ucsVGMYfR5vw58/9V/Cf0n5+gXuifvPaxgfk/FfClKrzyj+2okO4Nfz0uPcaosBcc/yXhGBfCdGScc5G/3ZswwIzC9+32NvdrqxNI42/WdjyuBlGwZuJupZ7LuLSHzHrKlXkUAqFaHUrripUcA6Od6oIyM260QmGdyaJDSoTBukyBn0JjJfm80o7UC0+7eXae9ekylmOHnrLa1s9jRTmhIa+jPDJBa0Z1eYOczFYuTM+zccQZbnxsMfHeoe5pvoWx3kF5pvqU1Bc59xXh8C9ViTmVuroS0R1yJQt3nYr73YJbXv/71k79rrXjkIx+JZz/72Xj4wx9+0dc9EqCSGUbdm7DQ2LdhR8Eag45LEwiK0+IgZQJK6hJAEd8g0alXZdgmxUK89Awtyu6VGgGhcjSGRU7vqxlgh2MlDnLSQhAKSUUAGE8yMy/uufTizL16v7ZORTZJUjoZHyRFCwuzrIR6YPXay+PVKal6WAX48bgHU06GrUhsMzvNel+2SZiFOMdxMimNvh3gDwiqVdcC0KhXrgBGYWOWzXHbDtoWmz/Bfr8XGWfpZWK/5iI53V5Stmjtt+sM2yUAiN1DE7nAgZTUgrEGLYvACVDPGrN6UhlljFRpQDdq9lJIih8TPYh7o4fRO9J6y/K3pbSkMi/ozjb3aPrzWEIJQOeOdPA4UreESzr4KzypDDPueSbapmPrKcC7Akk0Tx9TA7ssQaDXUODHNcUkTo0GlgEO8qkUsaC6e+vMsu8e41Hci+8ZXW28peiYe2yPUS2Zsr00cUDLeU3DjitADU6LqqNojHJHJkAcenURtENm6czWdVU0xdcds4pmI0wZlVrPOnG5rA29NGOK0ayDJYixOoa6/gNYXQ7ptgfce68Gm//Gp56sxhWWU7L6vAT69WzFievP4NT/OYHnX/Pf8ff/2wvR321ZXe+FG9BaB5x9pGaM7BbKuCC4X1zdaZz76YbdR1QHbK3XuOrtO5t7uRl6AAH6ezqrxRtzSzqg7gFtTsAGX8f9GfMucn5aFmtN9ESvtTiQJVuCJaBcobf9XZP+qDLLsIYyhEHMa/iebQ4sPNZvK+iKmmk6DDBqACu+LxB8SR/JnzrzBBL0weK9uCdIinvOoMpfmus16yhoBiI8aVgHZRjUAszUa1YsfKM/M1poQHXg4EYnaycQFG56veitYPiAMi30swwtoDfO46i37HmXUS92pBGJjKJ5xJUDBmZ6zQDsFFmGoexZDd650j+7ZdNtwvYCLRcxVXIzxRO2p2sWb0w87WgFMGNWXYwQ1hG02MEy6jg2grtek/TomaltkVI85gwIkO0U5xpnppadahHfLsBwvA/Dr7WNYRjFDBKadbOE986MN16apNHzJ2YEKx5vDhizyjKY11Ewso1JSdZwiRK1Oo3+zARCpE030q6tfNS4VT1mmvkM/JpNDaDzj86x/Tk1bO09TDC7NxwADNnozzRjFgk7T/uKdPsClEVDGarSW0vBMO/0jPF1auuq6FpgDGC3N/r5IX0BWkFpo1KeS9U+25rpfF+OYCkQsWyvZWgKtJhZtkG9noB68ZqCvbazhWLfLXtLyPYcWAyQ7R717BLt2FznK0EZAWhfUfaWZqiw9SlQjyfjLhkys6NW0rI3KvCr0BhfAXJmeR3TEXVP74sm6M4qSJ2dHrA83uvz0chydtD5P+uUjttZBunFACH1u9r5/CD32J1Xjqin8uUvf/llue6RAJVlBNoONJW/0XSaJd3xRAUwK2ymJnFjHEhnNaXKFM+ymMYUuDcqA8oK91zq9yyD5CgTBYVUU0CVovndYaVzIIMARzm2kW3MAdNODSQAtr2V3g1NPkMwDLduZ6pOf0Y1I81KZwp/F/eAwEuuDE6NTR69ArWu2iZdl9EOjXMKwNEtTJEb9aCZn1bQP2wVB72sBbc8ocrP/N5IyEM6msaAhfIHmPJihyJjmNjvbsFlX86KZ/nThC4pfgcAi25LAidDr4mFPN299W8GCiwgrTEQ8JTkpQGlpHgq82KuxqhQOYGBKEmZWanYDMeqU99Q4DS6RmPG3Cza1iZUPbjHHVX2MqCDKS+kxBEwMgbFLuOUrn43kohgpoCHsYKecGep2WfHWcG4owq85xYo6XNDADU+N0u9AKqo1zQmhX3AZCBjzCtfm31xLyNQXLHNMc48nMcU56cUc5svVIQlaIooNKoE1R1MLjGKJX9iRtjmQMLpfUtLTHMivJTjPGLRykjrvs6PxVWdt3VIcZeXWobTMzzpuk/hI5/6PNx+27XAdQNO/n+9e/5I41pepcaZez58ErMzFb97zxdgcWaGrT2NyZx5nV4t3l5HYO9kwfadsLmpzzBsFcxPK315aca+5Y56GnX/q05fVKMPjFUCqPdK/4cA3ZkwEvRWo65KzOH53eFx3jplDAWxvcOAfrdkEqbisduMMfQSMeaNZykhAkzOFc6b+d2jJQSxc8AAY7/XvG+63QbsRaIsBdhMlKX7qIMnK2/gRpJCb11xanU1Cp5TUQfB2JEiXu3+quiPxmqpCwVYjcwIib1kONZ5BmZmyQSzhnqsJZwayrmrXkwFDPxuGcIz6WevWJjArhkPEgWUoNoz2ZoXkECRGbK7M4wPq549dSQraNEMGOl6zJmkPdTFqLEEmDwbSovawfoF+PNVUww5ToxHl9oZzVvHi4ZpJoADABm1r+te872nG0aMW50bAOtodE1LHpZZDDrPjHJs4NnBtZ15xQwPUoBS9cwozLq7xZh5REmZ3TiDSg8FSmS20AAw142zNB1vrV+sRjhmT+W1PE+AGc4IIN2oV4KqWwal0w5WgorZuQGCN619/LD/pefmmUcV7CyUIbU4oTR51iCF0WHLGAbQYAgZ42tbayYPx3sdgyHoz3n+6jwcFSgf770/aATpbHxgCRjpEYYI2s5ME/TMe/Uk9lVBnydJ0mQ/BJVt3qE7O6jXkHUpRTQWszUPNRFSvNGCwrq31BqWFm8pRTTrbG8lRPoKDOY57Wamy9ATbN5MowaX5YhSGlpRSixajUy4QwO2e8sm3BKotf/OLNF2Zjqm1YCyiBqpUFF3dWMcTvYa29lfvnPsfhHbKy/qew9yueuuu/C7v/u7+PSnP43W2uS9l73sZRd1zaMBKluUCwAiXsRpEJI/a4exKbkavyTubWTyG1IKmRGWSWSYoY9JE+hVZLIHBZkay5U9DbrRMc5ONw5mEaxuVZdQ4BNIKbZBM+aTSRBQImFKmxXIGNcitbMMBMvF069zI48yI+KxY3WYeqVI8fG4z7kqK22mgf6MYwAQh7odZGUAYLFLShGGKylaazO8oKyZVAdBPavXZ6wak2Kg5PjY4s/JGposzaC1GqlkinsZCcSUniZO9aISKR3cIprr5JVStJ7lvemwT5RNxoW2LujXTmO2uQPzci13qnvmnCZtHklSR3vLjAcqlQTJBIX2/0glrQbwHC3+FFRWm4J4AiOIKtx89sg4WNxzkr26ixPm6fQainqvflc93lRavLagkBKnFFnYmEsXBonSwkrOeeOlcnQ6OrWQnkgCxaxgeXvs725PHCSqUmqf6eAZaxXAWm1TZlemYmFK3bil858GDVLW6zKok4urKkpjfUrS7Dtd58nLy6zHdREgMcfMAbY+Oh1Xxhpq/NLl48/MPtvjw1vXAr2gu9eoWYMZ3W0MAGA4IZifKujvrVg+YsB/+tBTMf/ols8jMiCGY9C5dEb7+/RjCrq94gm1FieAxcmCuiyReMvmRZsreK2LAtLRURQwMlaRntPOSgbRmMGY+GreVdLy+9PxrPRES43ETTlunEla9JmKxQxH2xmDxvhZCsGpMhHgsbKlaagElfFq12WZFRpShmPdJOGVlxdKdLFiyncTOFCSvriSTq9b68KbV5ZCPOZZxHW+m9eD2Engxteu6d403xvUE2uKeTMKYDHgpoCOCrJ6UbsEIjLo0bp+1ev/Tbz5Sy2f0QGoZ0Y0W6wax27xm0MkXykt4hnZx5nm6F5Ij920sAFb3L5vmBD8FaMK5XqUhXHitUTCHYLsvmoZEQNdMhoAqQXjserlQ+ooHl5HT02bFU3mUjXZj74INwy0XmMDS402EsDnLOBtZh6jrgBjjIsQ986qGwQ43yFAvzBmk+ktnDtliEy6ADz0g/3OBEici9KKjwtqiZh9O/vbvHqOAbJtis0HfS41xJcSGWcxqjFkdu+I0qp56it2H15x+vFKZ9d7BNAGlLrKEk5SCvozA5qNGc/Obnd0OrnEMEe/GrAuFZbF2vbtuWalHrc77fsiBmIrxuNzlKV5JElJrZpNtg4N2BP1HAIOKAH18LVZh7pUb2jZUzpqQXOwWIYG9BVlGCD93D2h6DoHop7sB/qMvBesPTLrJqBZ5gpG26yz/WdE2+q1H2nA6Qqa0WbrYgT2oMmImvjZ2J2JmIxioJN1M6WrqMPgMZXdmaV6LfewkQeh/NIv/RJe8pKX4PTp07jqqqsm8ZWllIc4qDQFknWJ3HMwTBNxeJbPnkou/PCj0HtZhgRIi1qz6W1jOm4CPG5IsNgBfs+9jebx6hOFh9lR6bUZZ1rUmlRDBrUTrDYWkocqt+pthFvVnH7jVlZ7FrPSc1MopCa1+NEDSzd1WSYPa6cNZXxcf1asxIAqPswIOHpKfiojaWxaKNCRiROTeLScECN7dZX+w41fQQgTPfj1bXy0FmPxfs81CJXuVdwLxT5sc/PKlGiPK7E0NJj3ri7V2+SxeilZEmuPUsGWarGng1Le1AOQSwSkBDJjHN7ah+IUSbWydsmC39yjR0+eKxMtZV2lR7EE1dXniZf5EAjphqRFIZJLudfQKNFjDYMKlfns/QOs3xZhOMAYxdJL6i+tDWtjLnYzgltEjGNOhEFvT06QASaRMss5DRzjVhh0msV5uRHAFUt6PeGfJd3N/+7Mq5UyoEotaObVZ1vrGEYN1soEBLsPr+gWwNLmHmM58zwYtiNT7GAeJD5zVoIvtdRlwXDvDN0iEpq1mSp8/aA00r1rAFRB3TOF7K4e5XO9Zo01quziKlv3tm7GLTPsdrHPAvr+cBwO1PRFfb3bsznaA8vj8BhI7jt1AOoCnkXZjXGm3xBM+j61G1TqXOqpW8CBDeMeAYuvznttXxJzpWhWVDILEigsotlWNaZtCgQBuNeMXjyp0Fi2LvZs1ootUKWOXj8HfyIo9pwepyZQL0aL+qDVgBipjf3ZKOnCrMPqNQmAgKLtoeI5oxfI2u5eN5uHjOOfKOYiZkxR8CQo+6iZTIjniWZsD2EynfFY589TrU4wKYuT2pO8pcXfFwBMx00PX7GEJY3eERE/70gJJUjWOMLi/VpotGK/nR4wDjqBxUIbsqfZPbmAxjqW6h5Ia6hSXi2baCTPqVOGSlP6sMyq6StmHKixf69mUq4WWw+x2FJSpCVYHp590oyHoyXwGTtjwXhMfUFZxLhSr/EEY9yXd1ivVDxJUPZ0co7QY+u5Dgaj4+6ZR9t0G/eKwww+5intzzRL1jaiDoK9h3eaXM1AKmoYL/ozlu28rwqEbM05A2QMI0HHs5Y89QZ0NlbLq2eY3TO4LsBs+AV2PInY+YvkHa16xm0VdHfvQbYVLAJA25lr3Ul6IknZKRKAchTIdq9xj16yxBhOI5QeuxwVYC4MrJWi3j/L9AoAbXtmdOwCjJp9tu4OChC9jIrFX5u+S+AnXayTwtIpFk/Kfa7uLTFeu4X5qaUaVeaa3KjRQ1kKULX/21bvRiTpqz/rlSzF9pWL+d6DWb73e78X3/RN34Qf+ZEfwbFjxy7ZdY8EqAQIBi2DoFuvCcxo0Sle2FoIplIsWoEepP1Zsyz2QUmh91DfEPNS6IY6zODZUTtTbkiPBbQtwwyAUeWkK04vlFE8I2tdakbY+T2m+FtyBILjSAgi7i0inctfT4eVHsbw7JiRdCUA8TgHNLMnwNgW1qwiuAyQWfzgZ7H3frd55k6NBdL7EJihVEgVV+T0c9oppRZUA2/0ZtK7A8Cpjt1CsDw+zcTqySv4TMw2KwGaoV2exk2tmd3CqGRmReW1uqYKLZVPjhHjLhycQZXnbs/qWzLhSKLzLI9Xj30ZDQh2e3o/lm7Ic8Hrj9r1WcON8V7ab8UPhdHAOZ+xjkmxGvn84T3kPCQNatiylPz23XFeIT3nXFDRyAKgR5eUU4876orX+Oz2jNK0q8+vWXeT55+ZJM+GEs85VYbwDDJuLpecGK0OYMzP6AsxT7cmorCYXHr6Ed559o+W7VDgMKE0mkLitHRLgw9oLODyRNSay6BmcVw/T2C53FGKa+sKlg/Tz3QLoHYxV6l1dwvNUtyZMUcTTRVPInO5pNsFyqJAesH8TqWbD8eglPQTwPxOHYPudMXuIwX9mQKWZAGA7myssW5hybwWHGeYASPN3dHuaeEHpdJYY/2pjgDMT9GTHsptf1Y/I310iSZZwSQmMgNWeta5V+aMu0yGQ0OG7qGxjpmgq5TYG5TG29x4E1RuS4SWYhlRFISUPVXkyjIUdqfClqJJt7hvI/YDp36nuK4yWoxlAqgQoDvb/J6qSFeQpknPTEEYk/yzBFWAe5gcuJAqT2PWQhOxMLGLZ2p1DxfpqnAvnXo4tQ2VAHhWvXSSx4qP4lljYd5jrW2YkmohACsASwikScnarIK5EvLZwQygpKSrN1qfrz89RFKexQgmyyGQLRYqMu50cT3rm2aJ/GjELkIKdTBQikgke6EnLIG3umxOwddzsaIbYw8iZVznLeffdD4XEbRaHPCP21Xp1H1Rp5mBZBr4Rp4nxQBZAWiE1fsifrf10p+xEir0Utlcc+ryIBh3UgKlJubtWjGGC5wSTTZXsXg7rytpY0CDKONDu6Vg65RNlbl5DQdNDOMGirEpAGfsadM+5R6Bol717uyg5S2kePt9rJi/wNhJEHFwD6meoMepqKZzsX/bMePbGxuG3knSUsNbXdEtR0jtwmDCOba7DHBZiif6IaAsy1ET43QWu8gwjD39XboKOd6h7i5jfdHo0gfluhgzyfUPK6Uy7mjCItXxlMrbjm9ZmRI1IJShoS417tJrYXZFQey2Akrvo8HA92VMOHe/yBGNqbz99tvxnd/5nZcUUAJHBFSWJpolzg45WhO9ppNZSrS8hFkGB7XAjrMoVMxNZNUz5N4txtQVeuqqexx1Q7Ysn7SWUZkfmQTILKBnmmWAVYAxvzeUDq+VOWhWPPXi1QTwCDTh3pM2q+5ZQ7qnH4gGeD3+zZQKCovEM1g/Cr4XjwsDAIgYKA+gubQ4PyAO9DaL5+gsM1x4RWGARPvLLuvgnrRTxqq2ucVqLcMDCUHKiBrK4GrJBmaebDNTRFhHc1a8f7TfQsFqKdaPcSq5b2LO0ROjNQYZy6EHqvjzdGa11ayLcb/WqzeTHjQaBDwxRKfAcpyH8k6gxMLy9Jy4Fb3QOtj8wPOEQCPS3Iy5LFVBrY6VWrv7sxGnQi8vk3sQIKo3gtSh6BjOM/Y75w7LdLQZlWQEVbnBSrroZ4dteJ1Xjmm/xyRSAXazB3zcKhhmqmjkJFm6FszgYMm06K3iOiPwEUs2QFqgtoWUXouDlHxN+PhpnT+4t2xxvETyFwM/i6sLtj9n9GvrF1KIsbR7NWD3YfSq47JJ3QO6sxVtS+fQ7F5YqRU4JbW/FxZjWWxeKOhkMh+W0hh2FDDquAGz05hQ/wFgPAZs3Wne0Krf7Uh9FxpV4AaXcZuGGzhIKoPpwpalmowQ6fQ9p8QKNOYQ8PhlIK1ZoZck5gK94NxHpdq+YGC3PzPqvLVzwxPjLJqzAlzx5/UKwgNlMWjKkEh1Kq1kUOuVRZJj1Tn8/dnRy1D0Zw34hL6r42nGlrJs7tGL0AwaVEK598Q7Fr/p9ESL89Os050bbrpFQ2twkEaRriilbx5lBAABwzyk6nMUixmkh1ZaiRIaXfFspnpuxsR3z10TjMdooeA+Y6A7gUePC5wZg2DbaIAEvuPoGUC13m8AidZXbwOzlBKMZ2MtPZxSlU7YmOXawiw680hyjxytNiLjGScMBJ47BkLp2QQQSXpsjoJ9Y57YPK9Zc5rGktxeZ/Ik47re1Awii/Awsu+azw9YFlQ48Op2lZbMpIicv54oigYWMjTMi+4ZwAn+arRlLNXZHKzPSVr4zmca9k7qGarX0DOwDM2orgraxr4zL2oYNlRf0tJabd75GOg6FaPFphjg5Yjh+EwprOZ9107Rc6ttdRHeYwBMOjWc+74wq0p9thJdZRg9+6vGoIbanUsOtR1NkFP3liCltewtNassMAGnuWyIZlDW/cqBb4lnBKAlTVpFaQ3jzgzVjDLaxwXMsUBDr3TV2q0H2vzUUo1PlumWmWul08RDbVu9okWa0nXZvyg+x69YMaPnRX3vQSwveMEL8Pu///t40pOedEmveyRAZV0K2naJ4Py0OQVVDk5/lAqv20QKRn9G6x8xGyRjsBjkrnFfCkbqCCyPRYyXe8tonbcYxaxk1FGtccMWlQ8e+HDrvsaCIWKVtiIDJZUvegCyclsTlYUUWadXWKxosYVBkEDrvsdzSvxPTyUT/HBzL+bdYU1MJjnwg7fwegEOp7UE4d6h2enmGyOtr1IUaOUkLvq+WVuNatgSuCOgZ5IhjjHpfB6vtAxAqWBOJwrHiKVIqJhqkoYASRnssmmadEX7IH/P6ayM6wHBhWaTJPAdjhkdpQIQo9GVqSKs4M6MGssAauqBDYuxxzqeGd3TBgnPJKQ5kFFDSHhz3IghaZ7T+NwFyOL8kxSbCVSPlSQQhAhmZ3QcSZV2WljRg7dZ0gPQ2jsGE2B2hoqctZGHUk63b16iYbsYdZlUWBhYEDeIeNxc8uSPNVmaqTdYLC7plRArcVPiPYiVorDvMY6TXmqP6zb6Jiq0eHfT1zTrMLC4OuY2pHj9RPUIJwPKZZLStG/mdxUsHgbM7rH9y2ITWf6jbQHzuwwM2r5UF8kraICwNKWscp0ThFJIrQWA2mz+GWj0siweNiCQoaA/DfWemzeSe1KbA/UsfF7kfqcBjQo0Qx84B+vAeM7q4zfWoC0zlpB9RGs+M5F6XGSidHmmZFLBBe6N8gyqFX4dJtvhfGwWUuGerNFo5qZwjtudJ+7pFkHxRJq7jB/OxkKngppRS1+E75esERsUvTgnwpMmzvDozwyafZTO0bH5feuyOejO5TRowNOENelctPaHgS9AsPa9eMIordkXXrtqWVS9bZ0ZdhgHKOLeRDXoBpAA4CUlvLQEE/iMAtmu6O8ZvcZnDDIwbIWlZJwX1EKGDtwD2Loy8ciiWejDzGIZlw3NSn1wr6XHzNtB77gZCAnCyigYjndWeoUlYQhkQtdwj7WBeM4vJnEiDRWWAZYZWidSFWzD9v5qGa6DqWNsjpkCECZc8j0e+uxI4N+TTC0CSAUwZnwrJgZ4eke7RedrUVkA4tlVNRGeeXfN0DMe6yIel6wmy3hM3UkT+Yx6ZnTF5lJ1au24VdEtmwJMe/biRpABnqm3AFKrJ1xCAcYdnSvDsR79mZU9IseuNbHxFbt3UGZ1HKqCtAbIVoeyp3RY2YrEOpqRWHw/EYvL9H0CMA+yHkhaZqZ3HYVtKJaxvi5GtHmHfm9AWaoH0r2hM6Wqo2N5Fe3Tshy9jIlLayirQaxXoBxV+usLX/hC/M2/+TfxgQ98AF/2ZV+G2Ww2ef8bv/EbL+q6RwJUSl/QG22PXirWVvJ6WEAokLZx50BzpdqFp9LpRVZegKUZWl/QIO6J80MsZQal1c/LXSw0KYF7sKql4jZrpnqOEFbQmQJepQ+WoCA2BbNOMzPlrVjNR3qa9GGjHRob0dwz5QkaLKaU9bvqENaq4JmZ50lWvFszq1O4FQCEmQ5JeVSvovWBJR1qs4LZvU09U5JAy8zAjMWEkPrJwyYnZ8meJHqg+MweOzoI0MFjIzwr4CLAS6aAkX48bpn3zNrPJDwUfpcgjkoxKaerVDxPMCFwpbbYXIT/FLSZuHLk9N8iE8ovx8HTxCcwxoyj7gWxZ6NBROMt9ECjkSInFiHVO+aw9YtZljnX2I80bvg6McW+9TZ/Ee8ROHYLBQQEcwTyDgisPY01wWh06eL6xQ5QjxeVWOPj3DyNVNbnnOdqmCHoH+dFAWJTjxbBNeuNMumP7wMI0O01VhvnLDyemfNXUMKj3uv6JKWzDgmg25iKPfu4Fddlf10u6fYUHJZRASWK/W31HetCwXDd03b1p9V72O2F8QWAJwKrBugxS3OuIoDfbvxOZbXbNRA60xhO9jvXoIKCYCxoHyMZzvQ6NGgw0VpeM/RkVgtbYukotq0Ye8U9/TXG3AFpEwSNLNgEZF8waRppgNIVDDt01wdAZTmf1T2adNbsweI+xrnLuHKP06TBNIGBCTU8G1RmOVmOJokZLQ6Nz8esrq0v6Jf0qJYJFbXNejtnLJaLhdbt+q2rAQws07WGX2g8HT1vEYPcJs/MPgEAxosXi2cVyYlqzPNUKko1g5qzDMTHoA5Nk+rkmFDQoNL8Otnr058dY4zN6wTA6+tW21s9qyrLEXXFnRk566om8NFyOsOxqvPcnrM2QbP9Kuoap8Q49Cza2YEe7iHsz2rmYAJ2xlP6mbpMGXkNvBNEOjXV+kZmZULRpleU16tj8cypZbf52aWhKTqXaKCYjGctzoLi2eLJDW3uZoN021IgnM9lqdBkUHtTfcrnbkqCVAc1IAyWWEdrn8aeDgAYoSAM5kG1Mhm+jnhfaycZaaojhaFBeovjZJ3RAiyZ+KZoLWiGYy2vmqE/o2tm3OoMpGa9xiw9okC5oY8SPRZDib4qcKNho8EyzZLuqrR19ZoWtfVkUJnGtoyCUgV1MWqiI8ZSltRP9owOHEU0vpOAsqo3tC6WaNu9eizz/QD1ZPb1CNBfESD/Qr/3IJZv+ZZvAQC85jWv2fdeKQXjOO57/TByNEBljRiw1okrpK0PTyQQhz5d/B4vYzEAjD8jNak085QI0O81V14dEAAp4YYeqOPcYsbmFWITcXF158HrTMQBS5rgFL8aCvTs3tHq4IWCXKDKqINZlhIBgvaa1nOxtpFeNG5ZvEIT+1548IbtiCsjwAgvp24UdUyAyihqShdrbnljX7EeZWnFE2hwA2U/00Kbi2eXZkqqKTOqNEH323n0dWlKmQQwOeB0/LWPNRGAuLdynMGVRwLVYhlAeeAx2Y7Xqyzh0QXg3lBA+40ZVhk76AkprKxGzmjKWFv+6IYTnlc3htCwgGibH9R2EE2USzeAJK9grW71b2ZIEZsnOV519AyaGpfYDAzVdAiwbAhgwHkU/zuyAxb/To7l9BhREa+r6R4JUrXElJj03N2gVEoHvSwDZH2gc0/c+uv05xbKC71MFabIkG2QPJ/8DsVjrAvcK7Q8VhxMuvHE2luX8CRVOs/F6djNxp+0X3fSWnKo0edRUGGVogaUTr15rDl7WcQMIv48ovfr9+B7SZsDIBA24FdoiKL30Awqme6ajXdOGS+RkAdQoMqsz0yWpa/r+NCwRZq0ZguGKmzmUcQQ65kZousSE+MIY1ip2DLpE+Pm3RMpomvU79HcaDFRlNk3TIYzC4X0IG8E9yin/tuezP3T4xDtO83LEZkSbR4nfm5MRhufk4N5PEpxaieFYCwMigaOhqnBq4yCzhLvsAQJgRoNLuOsQpyGauu/rxqP3qCA1eI6PWOpefSkhKdHDZQVpNvyfakAWkG1ZCr9vUsMx2fISYZoJMseyJwJFoB7l0oTdHujlpUgLbVXb5fuOfp8xUqDSCs+juNOr7GbANBXBxms1VlKGApKg5bVAPRsZ6hJVyAl9uvheIf+zGj7o+1JHY29GifJ/lBmS6dzUcKr2ayvOL89ptHmRNuqXnpDS0sVqz0Z7dUsu1Mg6fOYHi+jbTsTqpZJnVKuDacSm/JdBDpWBMU2F6iL5fIsXFuMu8w1hpVuKaAXGiKuO6jBXz1445bRXot52I250p1tuv7FjKBGhc3xxWUQDLPOvcW6D+q6HHY6FEnjkcZFeo3rdeMRvfNWW5k1L/m9HFNdeMgXtmuaPKpt9+gw6BwHNL5RRLO68ppmlSQ9GaWo99G8lzKrwNACtNfqgHc4pvO6zZmF1mKlbS9o88700w71rIJhMVDbjs1Uf1oMwAinvQKa6EeTA7F326TfNvLgktUSIpdK6vk/8uAXJg/IGS8ZX0nPmVQFYkx+AMC9TdmqPrGcdQRHsSm4Uizq3XJahFFLZ2eaWsMTMO32qFRHrKUm/2neBm56/dnmsQUEhVQqsmLMGpylWRZOUa8qlZVGb9DcPLCiYJdWfyDThQDWYiNd0xMmZJqX04n0b1UAi2/0pbGt4v3hcRlJqeO9c7ZaxqXR4kpqFMdF0k8+FDwDqZUnITgmWK9LfW12NowNMOvkuK2HG6mHVELGeXFQQO+003EW4hlRq1lQM/25Dpp4CUXBQr83jWFi2+ilFAOlVH7pGVZPIieqOJhiv0uxeFZTNug1I/V13LbPHK9GuYyalICCgHFL6bvMIixG+V4Xk+he2BkNNsWt3XVQSvNwrDjdmJ5UN3wQdJtS657yEs/ZLbVEA+laS6uvyfuxj9s8ssrmEjD+Oes20oilAoOlpFeDiGYDjTHnT2R3Zv+Q0s5xZCxl62LP0PvDPWee8VaMBmtjPR7jGClga3OgbQG71wC7jwD2Hq7xieN22pMuk3R7WgOSiXLGHSigZdZUA2g5fhKAATp4v9ADqTHA0Z/Nvcj6ox5F2J6k1+p3A1AzdpiGJWZ9pVGJe1N/VkK5E507ncUidhZrCdAjyUQydo9ZZBKN/Tw8lNy/wmtRHARqnxgjYCleVsn3rXRP32c886i+XhfiXklmqMzt8ARZEnuGl2WwuTAc6/x8oKFOafg1zqpKjxrPQ8Z5N2+/09ihACxTtbrdEd3uqNpBVQW5zapTbnnGeEymeROLAYLKODZmPiX9sAZ4BXQ/7HZHMwqr10fPveaeRtLxmEioLpqPEZ+1M+plHZolE4k9WUrBuNNZLCmphpzr2u4AUNX3L7Zv3OrsJwCxGzLplR1jrXI8MwuEYJZ/55hUIPSC1lk77PzWpDRisZ+YGKbpnW2dlm0hmJRZxezeAYzhdG8Jz94WnkqPJbS5Nu5UjDtRQoZUVX9un4sKVKVGO51ebHRrjoGYd5JZb7u9MUq52BomGM0J2NT4bQmizIBDYMzXeF+CJe5LGNN3SwDXYaez/VnHepxVDX06G3Nw3DJP+paWRQn3c9xXS+lEQshgBel84tmuDCyNg5SZjk2lTlXgWYDbVhfeWRoXDJS2eYc27xWsMZ7W3hsN0I07MwzHZvB4StFY4ralTAIaa5ydZ31RRgOgfUHb7tzI02bVPZ4yS6VLSoT1eMym6Xsex1kUJCtQrsZyWAmwv9KEiXou5ucC5Y1vfCOuv/56bG9v44YbbsC73/3uAz/7C7/wC3je856HRz7ykbj66qtx44034pd/+Zcv6hF3d3cv6nvr5EiASlekc8mGWRwOzn2HKoPNazgGqCII0s/HZjGxfo4BxGit9pgIW1zNNl8gAGn2aroS01ldwJGgQH/G7epUG71IWGBJkWI2TlpCs+Jd0oFLLxQ3AKb5hiiQ6SzFO2lSddDXNKaU2k54JSKw2+K+FskjkJ6vzZPCLgGi1BMEjz+c1IS0zJIKpM3jkOr51VEBHL2AXkurWh+ZB41USalKFWYCmHE2Bb0wqyQQijVpWfoscAu0gkhxoE6F33+Y6MXAc2dJhQicSc9lX7EfOK7LYzre2lZ7/vQdP3gLAQsz7IknktH5ZsrBVswxP6zNQNLSunDaNWKMWCKGiqMn2PEsnuLjRJqmXyuVVhCj7Il5fPVDSemnd0uo0OsYcZwAYH46LLhZuecB3u0KnP5oRgQ14Biwt/aWUT1wfF72P8DkOEy4oNcZrH4rmQEA54+2vz+blKIGV3bcuNCszI4B8f4s0J0Btj4X88rB7k5cp3WAWGmdNtPyGpdLZqfFwL8m1unPpHXZKbAFFDRKtb44G/OF8dh1FNQF4LHnnV6zN1p4TsBDmrt7aHoaT2ClcGgMFPVqjgCqlgSh0czjyITtUuMPawfn7Mc0hCjgQMQsO925+XW5vplwa0yxfgRF3Z4qquptgVMJMw2xbVU9j7qYL57IxNkMZvRb8a6xxAZB6aQEQI3PZ+MOvW66n06P85GZUfly434V3jhXcs2LRW+OjnFNfa3ghbGlpGpSoZcaZw8AMIGOe2+N8gjAgUARccZCpjS2TDnd6lzBJbjtzo7qFen5fLFn+JlrVDwCUraJ9SHVcCWmdBvA3RtRl0rT1YQq1c/loKuTChr97MBFYi57fKoBKxqSaSCQWVEQtx1jrA0Tf4YyaHkxsTbQUy19wXCsU2+ghTW0LfMMtjiDx+0asZbM/WCAjV5/nutMWpUN7tl7WUZBf3rARAhKm9jcTUYSe2bYc9DLrMAFyYOGOI+G5ElOOlvUvQ69h+CxMVbQzrk2t/7KlFabS55F2Ywm/e7o8600i/td2rxZWptN5/KaxzaGbavzuZsdD15rtOl9GGeLBp//hXGyTde4FKXLs7wH65i78fvETI0N887nSdvqXB/LDLXR1kt/evDziAYQgkyWhBl3el3jKamPWLK74bgZCooCW+k71bOWiRJpgFGz9ZohaG9QMFk1KRBsj7qipd2HnwuQt73tbfju7/5u/O2//bdxyy234Cu/8ivxdV/3dfjoRz+69vO/9Vu/hec973l45zvfife///147nOfi2/4hm/ALbfccqj7jeOIv//3/z4+7/M+DydOnMAf//EfAwD+3//3/8Wb3vSmC2t8kiMBKqO4fcHiqupKv3qc4hHphXCQ5XQVK5dggMEBG3VhbmTpgFYlUtDvNY/vIzCl53HyA7jCXZomVOHh0zlAKr4RTLx2EptpZ5k/aQHnAegp89OGRKs3YPeglRoIBcfaRcWH8WUOgNOz0WvAPiGY83GwzZnKPmMfnVZooIsHMsFjl2IWA3ipkumAaYA9k3kDLQlMZ9Z/ejj4fKGEUnkOpZGKabcnfm8vM+DjLQ78xL8n3rdUrj3Lqo3ZOGe9y/3W2I6lOIZkuTaAT4UsxyxmRTTouTz1tS29lxQoDtrYZxoTGh4FGjRoEfZkVlY7sRmIPCiWjzGB6uEsbqkXi1ukZ9it1QKnzHLNaaygeP+4t9EospFYCB7DFGnWkRIHadkNgkkHpvMw1mSaq3sWJeaVj29SYDjXdV2G516z+Np1kveeVn4qZnsni8c9VyujwayqCkRszluym8isTHp2auPlYafYQ8INOVrmRV8bt/X/2b02djAjCQ0vi/gOE1iNW5jUjvSYxhrgjpliuZ8QoM7ujT2xLmJ8uf9yfarRwvrQ4q76XThrYHa6ueGJ+znnKA1O3V581pNa2FDSS17JehhjnEnH96yZ3I9j2/KEPIXsEqs3SQXZKffCvUFc2WL8oHuhCCgBpfIxZq5qrFaO1SnLZrUCtV0Rj630wDKYp2yp3shur4XXjzkEDEhTKRYDc9rvUWLHvYAmqpDa+jZgxAQ/AMxTCvdQ+nkLVZzVyDn9fF5buX8BoNsblbbXm3enKVAtIhNAq+cWa+RV3z/obS1k/7QwFtCDpcyVzr16rpQvleHj5TjYprMtGEbWr+x/T/hl87db6hyqlhiGNGT2L8E+jROMiXdPoXkx3ftNA/pO9XnuSXo4rst4Tl3MGgrjXnJSTPm+fQYWW8x5KLVgON7HuLQYK9aizONVF8EeK02s1ATPKAmKPOeDgaNs7AAC1JKC6wb8URxge61McD3YmdDFmSdGh5Ven3/cUm9lSed+xwzPktamsRVyngIKs8uiBXOAscTFvJ2MIabxxw0lNYwanB+8phhQbrPqmYNH+w73BuHaETiFe9zulK5dNUEQCjAcn2E4PlODzFz3I2aJbmkucAxapwCaSS/Hrc6z/7Yt9ZhKp15Jqba/leKJy9pqsp4jIEzUczE/AHD33XdPfvb29tbe53Wvex1e+cpX4pu/+ZvxlKc8Ba9//evxhCc8AT/5kz+59vOvf/3r8bf+1t/Cn/pTfwpPfvKT8SM/8iN48pOfjF/6pV861HO99rWvxVve8hb82I/9GObziLX5si/7MvzLf/kvL7CXQo4EqKRCJyWUWmDqOaGExzJoUFQ+3GvoCRXMCkQ9noBIZKIoV8uuSu9OHY2KalY4JsLJiXw8CYFtWBrfGFokD3lek+CSAE0blIBHFwDCKQnmeaAV3zdX8zCyLiBpm/pMLHWSPGCSNvcxDjPWv+Rzrm1zMTqUWQ7d+yuqYErag6igeGKgpQLFAP+k1cLrIg47Va2y8ziUShMr2gwH9e6RG5jQyZ7BknzkREDs5xxDoZSu7NXS+cF2EEjNzlhCgaL9M85JbQqQlKlSNICEt1HcO8dndVpdGqu6hINCpcam2EvAY3ZY9iQnRQAiIQr7wA0mNi7jPCiJHi9XlGZbl5aoKq0hPpNSU5N3l5Q+63t6kaWGkh9UPs4j7QeCYb13XsMBYpmkiFJHMQAT9MDePF0Z+Lo3g4qfzT16JOlRZF9RwWO2YVKWJ7GAMEBeY453xgLYOmVbSadzOsfO0iDCGEL3Zl5m1pAqfQrmyhC/V0tiRGBXzNvY7UZ/5HGjJZz747BTJvPHDT52D/2+OH06W3X7vVgf/W5k4lYPvI5Bv6vzf9iKeURxg9CY4rWtn1sXNSDpkWXMcmTE1vVEwxATjRBQVpa6sZIT9CwBCA+T1UN0r00BSDMjzZA1H50Gb94rsmjQxCmLrsiOiQkj8P2OgGOaVdKemXGIpoD6310JZXrRlEpoCeK0/EJ1kOnhB1TckzfQDUv06KQ20GvD5EfBuEleVz4/lVMDTJP+NCWfVFQYMCTVjoq5KuKde5bG7d4Vu+ydbWYAkFlkV53Ezdrn1PPSNLzGaNOkYutcmMbUegy5gUwfe8C9iBzT/szoYLaIzS+eBTa/OA+cGdVF2yoz1CMMqRPD2DJAF3zc4oylR4/9SGotjWiMCQXXkHlEpcI8cfodAsXRPPSkpEdDQpegsd3ptauGPJuTZZAAvpkdNKsToKp7U7SPYEz7wubarHjiJOoRbmDtihs+6FUX+53xnQAmxg5lW8T84NyieDzuVhdnWilpLmsfsCYqEPqfh2qtGFbYVhgd1p+hxHuMpx63OgW5QySiqoPVai3wTNCkyGtYRodhOw4bltaZ9AkNXzRYzLsoX8bMu1X3NQfQ9pnLahy9P+Q+0l+f8IQn4OTJk/7zoz/6o/tusVgs8P73vx/Pf/7zJ68///nPx3ve855DNbO1hnvuuQfXXHPNoT7/sz/7s/jpn/5pvOQlL0HXxfg//elPxx/+4R8e6hrr5MiYFVg2w2vj8XDLlIxZQXHLd1gImVhj2K4OrLhoC+BUI8YsMqNkLh9B6ikQ4C3HZ9JSLZ0VnSeN0T7HRAiT79UCgSUJSkWp1YJaw5uRXs9xWNWKSVPBanRl0fJGhUSA8VhxZZFxhQRvDPYvzeLMBlNCBR53CATAJHCgd63fBUDQy/PGlFVJtErdrMsku6NbYEXHlBu1l0IZIymMfkn/Gw04jl1xyl2/K5at1oAQMyhL/N+fEYhl2dOA+6Dy6pjrRwkwXUksYUhgPzFon+9xTD1FuymbKNPYKh9Pa1MeU2a6lJ6HI/vAgOLc+snAoQJcpReP28WNIcN2AmP2+f6sOGjMyiHANRWHXx0C6FLYFnTFwawnNGI/l6CrMibND2oOYQEKij+XeuHTwW6KbK4bS8v3FFibssQEUxn8mpeAoIfiv4vNKwCL7Yqtu5vSgqlEscYhPWp7Eq/Z2ugW6vkrYoBoGfefeOAl5jo99DXqYF82qUtVmCAWa5rKmJQGLZ5uoJuUUk+CYwqKl5Mxjysp0Uzgw/VCIF8kKZgI44bGnlnXl9gDtR8DCIq1pW3Rkxxj5mC9Cw8kae7dQg1QGtvYJsY5ehdogJIanm4qpPpwYpkPdX9gDUqB/u0hA6bsTmplJo8K49kaqaujpLjD4l4iggvGERdbl+4Nm9VQ+mlMJPgTgaC4cYZ9wbp4VDqlQmtILhqakFJbPU+BZrI0Cl9XNImL0+UAsdI8bgCit9TWunoH4WEjvkcYmCRDRmPgRTPIGgDoLFEQlfwc98kYPTV4Nj2fxhjPZsmC0MS9mqQLMy5QStGMqmPsLa2rZuAVVeJXanISbBBQeQgG9wvzaLeu+DkI6D3GbZt/LOnBIV+QOqgZS6OOorafXnKeg22ruI3NWVK2z7qREmVC+6XRlFl+4/UAMW4s5l5k+s9k7g6mN5FRQ3aNAGWPtSWL6zpZl3EaqwNM8XOce4EnFmJMpPW5zKLUBz+D1WszCRv7wfrUMwDbuvLnnBWn42p/C9Di/KAxXLPdIVhsaV9mDKb2X1C4dR3AnQ1OfbU4Xo2BtjGc14nRBYh7MX5Tn7u6vsbxlAbXf/IcB0MMEhMgsxi830ZxT7WXLrLwIjfG2frhPFSwKlq2JXn8tRNK6EgWMzrWKzym8j7Kxz72MVx99dX+99bW1r7PfOYzn8E4jnj0ox89ef3Rj340PvnJTx7qPj/+4z+O06dP46/8lb9yqM/ffvvt+MIv/MJ9r7fWsFxefMreIwEqyyiQbSr9tEbZ5tqKK0rSFRR/4uKUqFzPMi9uZunz+JFBILOgxWXPXDEPCWMUgESdY6HtFB/pRa6LbgqdeVW5IMd59ZTreihHDSQe3izHUFqBQFCTxT/oUGwrfGPkNfozoVgosDbwUIuX3qigF8ue0QAA61TW5ElkrJ0eGhG7N86Kvz7ONM7OPQaJRprjGNsMnrCDiqeCmBhfisZaJqtmLU5XJPBh9loCrzpENlsUpcQx86QmTFCQhS7GRMfFnpXKNb2qpFHy4Gl8JnELYZuFcq5Wfia7gVM66TkR1zjikGPSB33+EnPfDvXWi4O/NsuU3VAu/EAnoBUqPkUz7C5zXyNKxnRxyPia4UGWLOEA6dxAm4UBh5ZPJjqKYtuh4GQLt3uObX0QdEVG2aQMJZCtsX9iANq8fh090Un5LaHMxphGxl7OR3om+RxA6k4b8zaHU32rKygBrPwRC3x9MvOqz2OJOc9SGnI5d2eBl0uilKbtGrc5R+NZPImVGZUIkgczPtETqYBbn73fTYDP+sev6UYVuDcSBRitdI3WwtQ9btixfXRPPeTDDhNURTudTWFTKiurGrtap5T0lgw3VLYSY0D/tnnNRDmmyFKlF/PeRKIQ8delLxA+o8AVY4LANje6bAI0jBNDUVBAyqsATr8sAi8hxfT+bKPHXxvgcvBJBs0izqqI/+X4CDxLq+0HQOwVRQAMsk9xLgLUXYIJ7QdBGFsjcQ8zXVvij9TP7u1eime11DMganwizUeN1Y617IqtAUXS5wvEabzhgSsoNNw1PafatgLY5oyg4pnZvf4n6Y+2j9BY6MDY+psUZtBGserN4jW4LZOtZPOj220+B91I0ZWJ4l5Hfc3DF/gW96dBjOZYMTs9RsgA+8yAZx11fhWJc1fHnJPWpsEY89vB2gozhqwHT5ZIQ0LJ74dhXcc47fc8is3AMm7VFEMZTC8af6QCFbYeXSfRvhy3q8V6NzcKiG/aCeg3MxotNOmOJDpM661G5VICOJZiSRKb74ekjJYa/QMJ47czUvh85rl09pao15IgUzrNKusGHya7osceYQDIc6sVZRxIX4EmXmNztaSIZ5Km4Y3jah7oaWKw5CjJnvxS0HiQNQFjQXmmixtxS3hHr2S5yKQ7/M7VV189AZXnkrJizBeRfa+tk3//7/89/u7f/bt4+9vfjkc96lGHutdTn/pUvPvd78YTn/jEyes/93M/hz/5J//koa6xTo4EqGyzgiqkwhnlzxQEVa71c50pzKoUG7DYLomSZ4eKbQaFWbOaHvKegKaRNgXfkKUrGA1QdmaJppI8WrFjpznkhAgiURKBihEQBxiAKG1RXBnECmjROA39LKCbzkDFmm21wxswxWOWYmKgm7Q/T2UyGdtUEJtGLkDvweBA8haFx4OHDzf9OgrE0PTkMBM40IpxTdZL2wD9IM2bXUnKebF7J/CrG3h6zqV+R2mr1nYDEW1mHs25KvXu8TAAxlpZ9NZlmiwAtYqmuVkQ48j6oqRgtlkAeU/2U7QfnLpao90End6vRqV0BQBwcMUYxXFuXpcuwBznR10YgBzEjSRU1D2ZSa59CutbM1ioB65E++2wIlBmP3m8KOlNjeMaigb66CfPMOfzB2awKeENIaBcio9TaYLOysRwXjHuLzzacCCs9NKIRy6jfl/MqDLslEjak8rKeF95uSHNVFtHUaDZmdJZdWyZ9VQ6m8Y2z3yOtABcBZotVQ95XDZhv9DTmA0n/Rlrs4FcVzZdiYErplLs89sAWNbDvpOZCJ3V2WW9Ocb2Ms7S6d4AwPjrmhus7R12bB+hcrWY0rrHbS0lNKmXZmvKr2lr2ROldSmJTx+UWM4p3+cMABA8OFAYYIp/ai+ph4VGj/D26NrR84ShEwDcwwTovRiPWQ0keIKSTpVITX6jXx6OVczuGeMc456REsmxIDzjvdwTA9L17CyxEi8szZOpqpz72t6i9FOLFXOQakoz4zqZlKi5AhvX4L6v80o9qDSoNosdUw+JfTZLE8XB9IyKgWN6Ie07ORSDhjmyJEjJdFoglW9L4rR6T/dq+9wqlmFdIhbcvco29/cMbNrvBEx5nrGmJBBnDpPdlGXDxGjB8JFZGIvdC7fXPA6yDinTLHWLFQMgRvXwca7bQ3nfFHoB+wKMmHi+Ve/g2FUHn1kPiMRGpteM9EgXY0RF+2iAoUcbfYBXJpcpS20v120xAOyeSrEYWZsTHC/2lRqc9eFpTPS5a+0HYk1qX4mfy5q9OfrHwWETN+JOKKxFnQrjMTNulBTWUXT9tN7WIjO3Ml4YpodxndqaKmbJoCdZyxKViHHm/OG6gAJOfWbdOUuLucDM/T4OZsQgXb+zGrS+NtSFoR/vqhs2IkQIk+tO6NBXotxHUHkYufbaa9F13T6v5Kc//el93stVedvb3oZXvvKV+Lmf+zn8uT/35w59z1e/+tV46Utfittvvx2tNfzCL/wCPvShD+Fnf/Zn8R//43889HVWpZ7/I1eAlLRJFoT73eJj+l2x2oO2QK2AOWWcKx2w9VRIS1B13GpoH5YENICw+gr88GY7IjOf7AOGgHofJxx9cv0LPN4HgFu7qh9EBHQZgMI3dFr1AHhGU17Trc4OluB1OssoHgeVD1O3vkrabK3PCZByuQsq79WS64iVW9CNL7cRrnxPgJmNocZIlKDUGfWSihOBL8A2i48PE9FERl87iGZlotDzu15aw+7DPuBzOPhhPzSOu1kr++JKGL0g2bOV44RaZ/ciHbiE8kew7dk1U3Igj/1EgBxak8d5zC+dK/oc9IBLlzz2RRX+cbsqYEoeUvYjy9nEmIbyD8ShSco5x0uNJTFnPYaGSinLtSQLuydUmcSnJU//guvFnsU9DQEu+Vw8yOi99jIQA+e9zht6qz1ex76rBpBYk5luzRjOMEBp3w/bcKUit6Xbk/CwMilOs7G1NVOaLTVRbzegHr661Kysl0sYC+wxiUmB5hj4mp4nRbco6PVYSVHg2O3p7/0uE1YglHKJLLoeWkCjFdcTQS3B5TAd95wZ1tkeS02Ulg1wfCbOXyYOY1/TU0OvN4BI2lL4vHW6/2Zvk611OOUTHpvmseSkGnKtlFCwAXi8Yl1GvChLPXgIQ1qL9Mb43mvezDa3e1VNQjKJ1S+w7JVBiew82yVjoRPASEacCcvB8gfkDKZeKxAAE+4w1r4OzfdfrmF6RyYeXZmuOc6v1iugHOdTg2cZFICMlpFTX7T+tDguZ/Ek8YQ0KamcZ7W0eyMDysL9Os7nSd9XOK2Ue0PeE9XzZQCHgIz4ooVnkd5NNzwMYkbreL/mJDs2b4ad6sCVhtPJWW1zF0AYqe3s9zhf+5ECnceDGSC3LS7SxwvRX6R+zxI9l8a5Ajdi+DyhfjAGI4BxeeJ7cKifRXTOq4HCjEV2vlYDlIC1l9Rc2389ARDDIQAHRfQC53Hg/kOvKMebOhp/PFu+586wGrES9yegdPDEuWZj2O2qEYHlcBjm4xmjTc8cZ9Xv5fGiqd9Ye7P6/rX//PPX0hLgntSsZI4/fzr3uN+7AYrJi5LhfnJNG0c6Nor1MQ0zLkaFHleyUl9x0u7DzyFlPp/jhhtuwM033zx5/eabb8ZznvOcA7/37//9v8crXvEK/Lt/9+/wwhe+8PA3BPAN3/ANeNvb3oZ3vvOdKKXgh3/4h/HBD34Qv/RLv4TnPe95F3StLEfCUwkkKiMCFNA7EcXbzctmwdkscK+Z1EJRZ1FupzcBvpHRmhblQFIjbEORGgchAKMohceFFrp+t6FhujG0DiiSQWYopf5+CwV0amWSyQFHT5wr/A1KTSKwozdlCDCo4BuT5CltFgqeZ3kcxAGMK6ENZo0OJdQ9o+aRoCKv1J84mMdt7vihYNJL1S3Cs6N1BtP1E2XYPV1UAkqAIVqSa/YSmdJCIwPB4LBtf1tfujexImJA7TpFtE/zAaBetUTTLEAB45WKZT81INsVjTclaO6MQmlJcbKVTL2O9ryupK7QkIqNA2MsrZYolcXswctUIPceUDHj88zpFTWPPw9L8/Z7Qe4ebsUtIyJxVjo4+91IdkTPkwPuol5BwOq/loh1Hi3rape8DXxWP0SZXTZ55KmUZsMNvR51pKIQBiMqiD7WBb72c6ZoUludhsUyNwNBpKAuI9tlEaV1eq3HRM1sxbyw5iHu9nRedAt4PcfLIXWpnlj3dBD4VKCMxb3qMqOCDes/oON3GlCqeiEzyICBTSZsAsLrRY9JHYHlcV0L/a7+rkDUjChpbgK2/9jrbhypqd8dxEzHNNgQiWZKylwNBZieDCqC7m2hIcW87F7+KFEPpSi9lp4mj1mjIcq+S4DnzIN0T8/IPMABKsMisscSgJeJWK3DGcDMABQ9R7OKbnc0oKhr1MEDOO4KLtTQtGIgMeDB/oxsohHi4ECyBDVPr28xkfTkGF24JWU0DA2ImE/uV6Q7Zu9gI4CL5DnaCKDwPGW/WPuUzWRzy7xtxRLzZZqw02CLgtV9MYF9QR0bBIlmO8RcnXgyCcRszFUZL76fsu+dEWXP4dlgC9k0ZEiZhz6drX4tAkGOebWzwem46X4S564zeghi7bo+n+gdF3gywG4vsie7McLATqYAE1hKX1BdFxHXWXK8aqtxLuh+qNdQKmu0h/OvGC3cmQeWB8FzFyTqsOtQnlTO+sLGntRlKQBqDfpwVwII9kUBpXlaudjcQ8d+5tCQmUM2G/u0C08e5z33o87qZWYjhLNXc+zpsrlXk9RZn6MElpyX89jzWGKoDpqYi4YsH6s0NprhPhh8vmcmKrbrkbWoV5mhPjauZEUwtvxKlZzJ9UK/dyHyqle9Ci996UvxrGc9CzfeeCN++qd/Gh/96Edx0003AQB+8Ad/ELfffjt+9md/FoACype97GX4J//kn+DLv/zL3cu5s7ODkydPHuqeL3jBC/CCF7zggtp5PrnCTQgqtO7xwCKliId2WGTiO63nZqkLYZzTO1kcPFCZpEV13C5JkSRg5QFk5TgsYD/Xnoz4TvhmrEWN1YI0zXJJZTx9J29WfoCKe/ho9WZfTBQQo/nR6hxcerhHgPfRrLBw75+Y5WliMXQLfVh+3fJqimMG2gRQ+kHz0HWxyWob9X/33HTweLRKSp1RNR3s2Ge8ltNW8ZgjIEBaprdGn6VDFYlCV0xJMKXZrdycL0ZTzCDWN/tkzWS/shwGx989HzbHCPonFs6WDkT7bHhBJeZnVub4jOaZ5N+Lq6o/N7PEMqMl5y6z5ub7kfLnMSE2z6blaNgXBtaXqlir5TgU5zxvJ/GDBq69P0p4hbM3tlgf0tPlGQFtbk48Hfa5DIpVcYV7rdmGbqG1LN2LVOE09BgvbSvSPsH+ZWIwxhaTPknPjT83gWTyTKKkOU6v3hDX6nf1M/0ZXDZx7zrngPVXfzaUP4LJQkPYlraLyoP07Fftu9mZ5hZ7lvroz1omZlNKokxPeC9bb/eV8PB7TKSkPcoUZ2ZE5vhyzno2aypLLc4G0tv9+Ss/Z+ueWSodjNGApgp7MbAziclKyqQn8wGUpkZ6rO/rNq+6NDfpCTJlubPSDKRmetw24DVc/SwzsMkzhu3pLFkKAKuBB5Rl89IELEnBvnO6X4s459gDDJy4cTPG0cE4vSfm2eReRuNNLiNCj+Vk3IRjamCXHpA+vDL+fcuCTUonk/v4mPbh/ZJe6YBci1pCRZVwZ1QkoNrm1Y0TrFGo5371ZC5kHTHeLc8l7knjVnXwRQ+f9CXql0KTu3D98dwE1EM+blcfa/7NhDVtXjFsFb/XhPnjHqky2a80ztz2fpvrNPLl8jDOzrL5Mc7S/lrgyc6YiMi9XUbj5P8+14uBDgPDwcbiWrCz1jxjLOlF7yzP8bqINjo4o/HYziQCwlyDmWCQel/rjIXWxw8TNrG2J9fiYIn3ch/DACa9b3x2qdAak7M6eY17xbgdVNY8TyfxpB1ZRnENjokzQpr4+vWYZsv2SgeKj7npMVm7d6DP74/iOiETXjH7czYwR+cHrdnbk8FvYhYxY7KHqVzZmBL3NfvrYeXFL34xXv/61+M1r3kNnvGMZ+C3fuu38M53vtNjHu+4445Jzcqf+qmfwjAM+LZv+zY89rGP9Z/v+q7vOtT9nvSkJ+Gzn/3svtfvuusuPOlJT7qgtmc5Ep5KxmCFtTMmc5E0p5MCqTFt/D7BHjd4pp43hYbXMo+flCghMPGGSvHMltl74larIq4E16Xy/v0gY2Y0KhpmRUQBGr1LNT+HAT7bEKOu3XSzAuAUx24pWG6Z1V9COWQylgyg6sDNUT+bk/E4TaeZx4pKjHuE4V5JWnOlD6oILaxFgMWJGtfxTLLan4zdYjKgble9Y+M8wDXpuvSyZg8TKbeMP+QDesY+KpKkxaVsrxFLJMBYrA+iez1W1RTPnCwGiLlYRvExVspLeDmkire9DgBEky2xoLt7bAliOQ/T4eWF5W1ujsyYK8VBkXRaViOS0BAQWbsYO2pZYsW8MTretKqLrzU/3IWgzeiJo7ixxvsO8DmaKTvqrbf1wbYkT9KwFRk4dT2k7Ip2X48JHSM+TrxMRAJ9ZCB0SVkuCvRGXzcxDqPFW6vyPPUEq7cFHrdL5oLG+iGMV+nApedk3DIAWcOTxnXDseL4VEvYc7mEa7XN4YYRnxuNawrOKCB9l3trzsjqrAOxPrU1yczDAGmpOt81szT3AVtXVP5mJVFYAcbCOUXXpFta+ZdRJh4lGuso1QAV97dq67ruthRnFfOPRkIA5s1ElLsA4nlFIF2deOW8nmOTdF2bE0MYdpBAgLfTsl3CqGpM0tY6pGRpsa6oFLuRrQIQAyrWproXHjjST0mlo3eOmUezh9/DKhh3ZbGgTC6UgR6ZGPTwlTGU4jJYDBqBp4FISUCWSntpQftzL2oCKa0r/ieBq3QFgjIBlro3JaBMry2ZHza4ThNO/R9rI/qn5TON50qNcQQwSR7jBjPGWE68dvFMNOzmvaWMpP9zXxQHdNyjeF0yb/iMXH+eWGaIfRZp7kqRyGzMZE40FHfxORpJOmNh0NhDsFhtX2MWXMZ9jlsKUGAGDpag8HlDg1/xx1RpevbR+0VvYenyh+w7BLZ98RhLX5dG1dZBDe9y9g4GrTPGm8mg3EBUwuucc2E4cCz0ziZa9ay694/1n6VgYmRUhgd1vhaJthpi3Um6hyemi/5mkqm8Lvz8brJvjcXEFtsfq99DU/9L9LmNixs/CTAR+yG97zpBguJNI1i+dx0ahiPhurp/5Fu/9Vvxrd/6rWvfe8tb3jL5+zd+4zfu070+/OEPYxzHfa/v7e3h9ttvv+jrHglQCdgitEyhuiAsa1JKW926mOxBQ9HNhUkjOrPUOr2NlL8WC5xANXtDw2MVMWJCagjiIGF2zfCKqaWoUW+nQp4VYwKZTOtrVHhjE3SlG1T6Q+GpZq3rFnE4jLSIpxIS7qVMGwEpq7wPZLrROhC29nv5khXxzGPVgKxM+9RrwtnvTvux72SwF4pPKLBMbJMTx3h21YZIStMXz0AbtNXpM7s3ohWfBzkOlwaJOgCtSAA7mxxOD+2mmzJAQGKvEdh10OyjpEF5gqEyaRfH2fuFoNgPqumzhFEADiA91m8A0GvGZCrK/kxJMXdFmFb8fnptT87DmpE29zIV2706ZgACgt5KxSnqn8ZzVIJAW7u5P91rZvOCCTP0Pbuu0bU5L7m2ST+eAsDwSHMOEFxPDDZGJ14ei3ITWkZElZ2s6LEOo85vwJOA2T2UUmRjTqDuVK39a+iSicTj0HPKP9QraaV4FnAPKgw0Nnrr0tphCnoagQCsgA9NwqUGK6MezqyU0YJrAegMPNHo5EaPrjhAkQrImFgBVmsw0wrz3HPWQRcsEHqixA2S9vQrMUnZYh9USEC26hQoInkrC0Lhp7fTQBBgzMwS39O486B8MqlGpkRyj9QSTbZOZsU9Oa58WoyYAO4Ji+ewv4cwrBSbizTMOKiuBZLi4eJ7MXch4iVV+Eyti37SrJiIDK5Qb0xmMZRBPBur01yrrhmGkQCYeC3dQ0oluNn+koCiU1PdOAhkbwtBuO/NQyQZcUqlTNkZEXcuHruYAQBBZ03GJXrqQI+XgRAq6HmJZM+xjhn3cpvD3Fe473PvHeM9elyDIlv83M8UTM4Jfn+yd6b5xqypOcwgjORwb2ybx7iWtI+TahzXw6SMzr6YwEGADMYrHDhC4B56B/7Um7z/6YW1fidIWgWXfE7S9gEH18X6SGm7kfgoZ8ql8YBnm2Ygjj72MjHWXqeMi95HJPqEtGV6maslHcplPaSGocPPCoSOoOtfwogmSimeiHmUJ6yhWqNcjAHyyfzh93I/86ujQDJzIRnotc22B1zGhHP3i7SkhF/o9x6E8o53vMN//+Vf/uUJVXYcR/zar/0arrvuuou+/pEAlQQ8LWVrbQz4HtKmJaFc0DPlVlWjs3kmN7OuesKbQbDcqlF7qmASB0AQM6E0+cpUxcUPFd+gefiIZ9EkhaEgHYiMwWEMmivuCZQkOqE2OA5WxpF46vySDtq0GWg/WNmAcQpI6jJqL2o5DgNvXdBdGYPEuEomQ6FSyA2HoIXPXhdicWQ6fp6x1LrOY1Xt89mq6sA5xb40i0VTi34ouTER4OCClmi3aOd5lYF1At5ZMjDx2LMCP9g6zrN0TXqO2RbvO5sT7qHup/fjwUMarpjHyzOjsrzJGN62yfNSMbFYTdYHdIt+Cw9i9t45CE3UXkiAdPYzr1PH8GbmQ4bAP7zMcZBl2i7AAyvGpTS4RXZCIeXcMACoSrlgmFvSGH5nBQAFGJ+OM9cEx4EA3mm59rrQM57iYdFoAAhgI6QvGeBs1pbRjBLdMhySrG8piUp02cTmGuOTM/ho5tkKmqQ4E4CZV0fzTAMB7ON37ScquxQHnGlN69xScBRJKKaKZh0Kxhoe0nFefN/WPSaBM1tpmeLpCTF6oO5FW1xp5bMmymAY0OzzZGMUGh7hCV/02afxjU4JS1kn9QJ6P2YVDk9Aibq53mFwgyeNQOM8Hk49TnY2zNVLwu/Q8+HeE8sl4MqjK5f6kIJ4XQCv5enzme0fY6whxWvoTaYWx9z2nGaZZvN80vlVwEzSnuFSEJ4Ttk+m8wiIc88zx1K5d2OdNZFhGkk30O+k5y1wTSiXbfD1Du5t1fc4p1USlHRJ8U7ad+sK0CfDH2hUhhsEdIyrxcjGOT2a8bOMUO+WrRk3ZDU1PJGlwT5xz519p1tG+zJVW2n8vFaZnKk+jjzLlrYcbB2Srsrzh/Uvw4tffPx9/EpxYOYUVhtWJqxyWmryMDM5D5X0uhTNX5WSqXk8qSQjfvYC5vlrY6ZZaWNOuSfVwC1jmf0a9hlhkjne073t8BJDThunJ5B65FLU8z+rTkWnsWw1/p96jc8JJqaqsb+QJh+G1RW3YNZZkicUCI8wjVCZAty2qtfIhUiA9eRRBcJbK3kd58Q8nAtXslwEldW/9yCUF73oRf77y1/+8sl7s9kM1113HX78x3/8oq9/JEAlY9GofJLO1LXiBylAABCHvG/igFvrHIQhDoySDjznywO+ecg8DohxO1FV9uLQgNDDyYQxcaDpwWfgrBQDa4mGaZbTiMkL5S0oruKWIbYtDp+cSCQ2J/f+pZhHMQqTUyDscw4QkwdLLIulxzKl/odMk43Q6pbr9sXY8NAXtwiPs1Da6XGUPgAA+3a0RDJMAOPUs7SRUTEj7ZUboL6X+y99ht5Qa3Ozsgs8hOsyLP0Ovj3Bk/498ai60p6U+KQAUdljxmJu+tNxoIIV/eBWcRCsch4hlOISY+Jz2+Jsiyk+pFVl7zHnEcvuTOaMee5zYD/pNe71kfCea7xUAEt6b3KfUHLMLqm6NJpIog0DcGpdFThQHK124uQ7iGdy2iZBepr/2WI89WCWdPhyfDk3ErAhVdEMOl1el4XgVBU9bQzAGF8aqJAAzOWSSc1IxF7W7QHtGCbzjtZzAkFAn6Mz4xvpePQ+sixPznRNai1BlZeTYZkCp0qr4kqgpzRXoCR2BJVNTfgTwFaNIToP3bAscOv7SMXa+yDaSAWcFMJ9nlhS4YDwSrWk3CGdFwkAuRJmZ1Tr1bDgVnz3YIQXjMwFIMIsAEwSphVjnoBhCQsJKlpXgLGF98j63IFaQfQlCjzrpc35fYl6/LngiU8AeEyaAwm7/jhTEFkGTcbi8ZOkT6a9JTyw1b/vSnkOH6E33Nrj5U5SuIHWwYw17lnNaWxNfbDqsXJDXsf4wPS6XU8K0IzizHhkMn3Uqx/z1o2iiP2yjIJxJzzqUgsajSBmBGYNbMDOCDM0eXmsIfdz0FLdKExvZkln4OS5k6JrIM4NEa5fZF2jOJvC8z0MsT6YVCdfE6yxOEgkFDTvmZcnyRRuKM12YtwbePaKg9NiYy/sG/uftFffNyvg1NeueNkUts+NxwX+HkGx60M05vPRuKfznoO1qwuWVZsVlK6Y157rA77+OD5Si4NP3pMGAy9blAzcOas8m529s/vGi+dIoq4yrMsptJybnCdFn5GGLlLnI152/5pRo1OiAlcaOGTyPvYzLK8wuUhQiYv5zuWX1nQgr7/+evze7/0err322kt6/SPBdnZFEHBl2sFlth6nJCsAgiNPYJC+B4QC7RajvB/Tmkov4ry4tzIs3vzRxcwDywFhhYMh93JKKJMeY0GqVHqPB4d7YFYU0DbX+M7W6UL32C4e/rlfeNjw+iX1nR04zIjJQ5ReOdIpuz1RygkPqPQ9KlS+ETJOJXmxAuSZMknwYYCCKdR5UA7bWgZGwQqBEhO3xHhzQ2S9RR5CpDp6AgNrQ1jhY2Nkv7Qek+RLue/dMm602gysIo3+lELkdKcS7V/1Tk/mURf9ujqG+v0Ys1XvbCPN2zyKVFS8zI4dXD5HTWFnWRP9bLQlZzZ1L0Aa+6xINjvIgqKclB1bB1wj/OGB7utMSCsXB5kcT1dsoW1lLUqlIJui4VZ0+DwLT779SIy3H6Kp/53al+Zw7A8xH2Dzd1WJpuHBx9WBCVz5d6+74LLuztxb6HEoo7VZYFRpuELF9UJlpNuVFAMqzj7g8zNZ0oTmKGk/EwmPfsd7hUKYvzumRDRcN90ilQoqU+MfFWz+ZG9jtxeW98xCyYZFjw/nnMh7rClN+p3YI6k8dXvNMmPy86FEukfIPUMIAxgVQ1OCc0yoP4u1gevH9ynbP9pWNVp/rKf+7OgKbcxBSbGjxfc/77sxQMnE6GP3HE0Bp1LtSnGX1iD3LlIYS+w1BM8OWm1NTqitBcjeAT4P916WY/A9uMS9ZVYdwLgXkXsMDUPJmOvJd/o0xw4wTDIREAGlh87Yd1vyoOc9m+M2zitQ4ElvyJSa0FstgRevzXJObpTxBsX8JyNA+6ju24e5Z7quUzHRF8b5dK3mEBpPgtPHc4VVJrFp7Lo+lpyvszK5F0uGxM1KeBENCKJAAWIymnv8MPd8PrfnR5juqZxzbFtp2Ee3ZSkSB2CDBL3WXldaqyBK/MA9mpnhwTEl5ZbGJD/nRwEmRuPYE2VWI2N0KdO5y3YQjCJot9QZM8uJJcjomfb5ZeuMmXv9/nZtGph9n7C2cD6s6mlZ9/C9uA8PtuogTWuCPkg9doeW+ylRz/0tf+/v/T1cddVV+15fLBaeYfZi5GiAyhWLsR+6BCHpgAygB3hhdFeAp5+jsiVFN14WJW9JEco0Tgq9N+M8AE/ejIftYjQmAyNl+hlS6rIiDUQcYo69oELNjTTXF9TOgfPdSUOV3mrPMQ7QQQo3jvQ6k4YYcPJSKwilKsbBlM5Feq+w1AR8A6fHb5+1GJgop/7aaAu0BA2VoCcUY6uT5jFwpIRQwYFvdh4nJ9N78TCOUheAZkcLRdKVVSTl28ZGrZOIWmTJs8x4R9JFmbBJP0BleL/iWce4J1OiO20zl03IZwEVOcbqSbSZ5S/4u88hX0P6ExkIk+JdkjLLRBVCUGGK+ZJzJ2irKEHhy3MlA0H+z1qv2XrrShKZAaTwlXhOMglaUqojEdfKXEtAcaqM2BwzwKRGC+1DjkPEvcT3CcYYk80x59ojCKpGt9IamokKuiJCL+6a9XGpZALEDcBnWnsZtbzJJAOngRBmfeU+0J+ViVIKIMX6hQed93XrtmeW5XOzrwXOPrF5Ty8NAFfM+Dk3fCU6m6yMq1OUEV4mX3O2RtAksvumNUWAwfCFST+mfdNBTx9t989xz+wy8AhPh7JBrG/MeKVUR2sDSzHlM64rybim83W5o7Uc27xgcVVvn4V7JGjAqIMqew6yqMCbQjrsVAzHOsDP0qA1t3n1BCyuYPc0tnE87BkzQLVzK4y2Zmy1BEX0nLGP8hyd3LcAq4BvMh6jYDhWPYP6cEyzqPq53xeMOxXLEx2GLaudOCtYXtVj2NF7LI8reBy39Ifjz6Rmee8vTXwuE/TTwLm67/AMzXt4lKwq7pHktXTviPq4QHiFeH61LuVHyHkfrC+yYXLcqv5ZGhH3icTY7F7Ta0baLT0LlscrFld1k7CgDHBWx8TPMuE8DgBCwwzHxGMg6RW2vbx1Ni9S9mOf1wkAIj2Ke4rzXHGvOry9YWiIuZrb3LZqlM6weZypuP5MEnqWNs7AYjK4OvW4hLGV1yIN1Y3ck/3Hrk8DlZ3VOakYnRb+95ph1T4rfjbHvCg+Z92gU0LvdMdGGsdMC+f/rIPrRthea5Ku6nMbeXDIX/trfw2nTp3a9/o999yDv/bX/tpFX/dI0F/brKD3GIpQIoD9yjagnHTWKyRNLnsPGV/pVnT7rirIxWlfeROjokRqRAaoLA5Pihav1+aRZdatTtlTaiCKcZ5ZYYr7whduVpwqszDyvrPiz1wG08Wo+Jq1a5xHjBM36m4R3haCUN3kbGOcB32QsRYTb2EL6k7ur6k133QOuz6NAfqc5tWwsaASxns2xgXy0KKS6EpU+psgznmM+nquq5Ulx1GFt2I65p6kgwCyz++bUmyJSYZtpfQyNpWGCafgjqLZ1fJGnA7LbPkPgB7KPefcaDQ5HqA5rlQqPTJwzzozfvKgdzCKqSK/ejh4PBIkqM4dPEau0bOYAUeOKbbDa98BuEZxZx/mzL8Rh1Im46LXgIMg0sT8YKYSZMoE60Yy/s2z4WI6T+mR5nrN4+P1W5erIAo+98Mblb4Ptit9nqyE2WrHXDrxeVQTNZf7WoWVb4Bns3ZaYok9ZRLbbONZGsEBHMDwfk6FLvDsiL7/TIyCdi0qfp3SBMdZcdrwBPTZcnZFKNFHfV/MCpzEenVqms2LrPy6EsvYe0lnRJo/TnFNfQvE89JwAQRl3BNlmTFssIynClBMmc57SR+GjAz+9yVFsvXf+gpm6q7pbGy1oLDOHXSe1qUAplxqgqZqcfWx95Mury+I35PnDyxmWK8Z1MPWFyvZBKdagrFjK4CGSjJDFQD9fdjq/Nm0X9WQoedwGHGlGtC18mCkGxPMdaMl2OnVqOt0704zCLcZUFEwWCyjs3M4j0iLNe8w9yUHgk71zEkAw6saYRrTseXfWqM03tM1Qx0F6JbFs2xnoy3jLLmnZqNpM6qw9iXPaHj/e1sTvXLYrp7YDcX0pbFg2Na1MPQ6+N0eJmuU+znnh4ec5DCi2dR47/ugG39X9xOAQI/G2sw+YP8xkQ/43yie6XkiLaiw2fDD+ZqBbjOvPJNoSYEaPwikJpTZ2OdyQiBvMw1PbkQqzhbKmY4jxIUbUfF1xv03KP/N96VcmWACRlMG5UmZuaSfUdyAmAClnp91Ug7IdSky8rqCIpbNNl1Tqkx0vitWGpXFi/neg1dERJOZrsjHP/7xQ9e5XCdHAlTWhWA8kWgrvvFjYlUiMNKDK5Qjj5fo4uCmVZiUQv6MKa4lx6owmJlCxQiwwzeXqqjwzJuZSsLvZfoG67rFYVT8c6pURPzLROkXTXBBxccVG4LlzgAkFYM6vT4QFtGwxnNTT4frynrzTdEVaThodUWvRTvo1VwFBfQaEXyTYseNigdvzr5JzxQPkm4hEfc2i43aCxVLAAz2n3szHKiJKxG5g0tKkpEt1Iw9K6N2UcSgiisgLJ+SsxHWhcYuuqJOZTfFBPsYFlgsC+K9NHfoWQZCKaE3p1sE1XViFKFHWhSkZS8rlfDswekWQOtlmjKf51BJ/W0HZ8ci5fSOeLeI93P23Lq3hwewxVfyfZ/L9Fpm2o4967ilihhf42HI+GCO+aS8R421hSK+J0CgSkp6zkyvVGNSjEFuy7AdnmsqYa2LPYmJjTI1mUroZRMbZwjiFCjaZ/1ZCQ95pjsJ3PsDTJV//ZAlnykxnuGx0HuSaubGqo7Jnrhnxn7BNhE0sJi2kLZtijv3CkqmmJEmBweR4lZ99nVOpS92z3V06YjZ1QybncXkM3Zx1YtP5c/PpNS2uiyuNE8SqkmUVtJ5b2vTQg24B3CNe+/TqymaSCl7OobtGmdjBdB3uk+l9QlgSom1pFeLqypmZ0LBnXqurZ1pH6IRhZ6nksC6Jv1qMaZUynlOSOzNanBtWJyoGHbseZKRr/WC+T12ra54GaQ6mNHOgJjWnCzmEaxq+LKMqDR+LGfVlfxhWx+N5ZGY6E8Qew6NTiwp1Vk2XrJVdI6FXuBGtqQDAPC1QnYSDQUuEu/RcKUZoc0gMLfcAimelZ5mlnhxoE7atM0f35OMURWlo2A03tB3fN+nsbtTgDmmutCze5uVwtGJx2v79LT4Wgc2nCv8O4c8tHh2frglw6DrBcko7gZWB9AHgFcJFoVfkzGXaS1Unlf2Halwiix/Z/KkiVqe+2rURHETAMz37CzUC9h+VXIoCo0RFpfKuZBu1brpd0lT94zo7CPA9xkarrjOsgMmvK0xV/zsrzE+QNpXC8AyKnz+ybNOOucKFWn6czHfexDKn/yTfxKlFJRS8Gf/7J9F3wcMHMcRt912G772a7/2oq9/JECldGqFLFazDwjlbrJoYK9RMSpZYS1++HMz8np+EopyxxhBbgoACoxSkGIXssXOA6lLKAgAFa2kjNtm4wdP8lo6QKOSwWyUiTaJUpIFlAqQZlXNGfD8h1hljQfU6YVmhZt47ehR5e8y/b7/DUT2utT/OQPoxOvacUeO6wQNl9Zoa1/yvGnbQmliOQ9PmMPaiab4UMHPHuopqKGiGgdTzrjLOefWulmZHLwO4vo4WMsAz+4YWes0z2CAMM4bxEFhbaWwxIjHTyKUfyoQPu/rNCsgDwHPoJnmutc9M6AxGZ80J+ldHecBBn28OJ9rSpglQQnlgUtgoYYPOMB2qnKhQSAOTHrTslWXQKxxrjN7prWZAIjzTxO+KK3T62mGjjYRxjFx/ec4FDcGpIRWjcphDQ8VqfMZXLS+oFTBtFYfwtORlLfLLfSock8atstkPXOekLIFxFxyGrvE2gxaN9RyvUKJ1QvEniy1OO0PCMXYvS6IvtekPGl/RuxNdll9rUznJMck5nBkz/TnsbErlkysDgoUYaDH70WPpHlw8r6Uti2fH9lzw/0nGzwnGimXfW/gsJTJvpn3fgARupAMdbonJG97VyBddS9GqYl50AEyqwryqKhTqSfFzujrLRlzmBVd21SAbOipquQ2PjuzQIpAoPFcgBkjLBlK0O+AMojXTAaAcd5h2Cnh3eGzmpFwcXWNdV40KRZpp0D0OQ0I2ZDHM7B0JdFPU3+XBPpgZzyZHakfI5O0+N4vs+Ie8fBEcRzjs3628lxK+7k+vxnALIkgafQO7mp4tDnms9MtwMUoWByrDuzjjNL7jSlWc3m8Trz7pQHDVvp92zIvp2Q1buwRYHFVDXaLlUOSWtDvakwmeiuJkYx/Dh4Rew5p6DnEJo9J4bwzgwyNgtyHeC3hNWXl+2VqOJeic6AsY63nZEIEqzpxp4ASBFoCp9EC0BIdySjlid/sPdSq308MMxosJzG5E5qsJHZEVpqClk7DgId0lehXKTHXBJjoqt5usQzHKV7Zz/FkuMvx3zkp2mptzOyVvaLlYuMjH6Qxlcz+euutt+IFL3gBTpw44e/N53Ncd911+It/8S9e9PWPBKikTOiqrnhMqVBZafOCxkkBcLDFBVWQ0mane5mCohuOgYOuQHiYIepjlmbWU2YwtWQuDgLHUBKApOBnBZqbTgJpES8Ym1ZuH5V4LwieFTVa+TOdr4RCF8A4bSLJ85D700FttaQCI6Lm0TL6loeIerl4kVDYpCbNzNqj46QbWpuAw/zsaRM05YNxKLl4cHROSuBSUvuNvucA0z1miapqbVJPS1IaEQcdgIkCotQvgFRYliFxJYTJW0zJmHq8p31OytUkOU6BH9Y+T0yJyDGmpNpKSQmgqEQ7aNPvM+6SNfGcatrUS+DGGVr8+X+NWpZFgj65quTnODR6IbPH3WvUlRgPKkslKR3st5a83WEIoPIMVz49EUYFwM/RoJgUV58b9no1g1WsDetrn0MxPz25ENIcNgUoPL6hmGcqZgbel7OsSAb90/2koD8bZWUcVLNmWorzo/ie4Z7yqedRny2osfm5s5W8Gmhjsg5Pw2/zU9xDLf4Mei1rf1Jg2KcstzAxFlK5tuY4I4NeDs6Bgmn9QiqvdjYUdZS59zE81DJdm4h5q/OohFI5JiBO40cq10TmgZT426mzae7q2oykLnWA1pBLhklBYt74XplYCYxlzHtyVyAe3oB98zIr065kJq+X9rt2dEtj5vPLFNhxKydei/4ltdSNODZPhS6gtB7H7eK/e+xzycq4lVqisSmfMQQgtuejamkq92ql84rKts85RKIf1i2djH9XgEkNyOhPLeUB0BM7MZyOAhnDgMUziIwGZ2zYfVsX+750sFIciZmSjJFtBoh5xkbbg5lpuLTIrh51MWEAP/qXxnE+e5c8gHxdasXsNF2zoV+59z6dw2S95HqjOVwiQLc4w2FyNrJ/rI0sf5OZAqt0WDeY5wy2ySPrCalW6Lm+bdHjJ5h+hnom4zgTWy6uY+diZ6WmklFhYlQbJSjlyaDBWEgahPwMp/ErOTZq9mJOzk5ta9Z5uCeoJzfu5TRu/m/021UQyf2BZYKyx/qKlCNGf331q18NALjuuuvw4he/GNvb2/s+c+utt+IZz3jGRV3/frKJX2aZHDiJxjfGIsy1nVzpTF4+t/akIHqntdFqa4cOrT4EOxHTZtei0mKKrsY2Bn2v0MsoUVw5lzXIZRhCKUFklwV8s2A8BS36GgsV7dBDWq2sTEyUvYOetays2cxaXDc2ZR4Kcajl4G8eLAECtI0+VFTmfJyiLaT65vecGpsPcvL7JfphYhEriZpTpuMRgeXwg9zHrMW1Od7upcmZEmn15jgk4EPL8ap1jkCPcbze/ymLap5jfm2bx/yJ+p3RcW5p5PglIJPfp7LD53HFLcXUxVgYXbePNjp45OUYY0uP3aBKGJVfNSCIf5dAdOK1Sc+aEwX5s7XUvxzHdGgzaZbPG16/aC3AVdDIvuc6X6Um8XnqitKe5yCz4WXqWgZQsb+keZVZBytCJT5e0Ne6y0h/pWJABYz7otIsaQBSb2tx4xgBY6w5gGvcPpMTQHAupj2FSoxTlkt40AHSzoKWSm9BZgnoxcrK3ovYr9P+EEAv2rvf42nXGMRLE4gpzm2ek++IK2BAjBut+L5XWfu4H3jilBoALMc6ab9RsdMx8HOHbSSgTOBzsg+W2FtyYqJ91Lak1Pq8szHRNwRuOMjrM+2j/AljSJyz6p2rk7PLARfj+0qEmzBhWd4fI3YR3t85q6QrwQVuwNLrxxj481OBtj7Skh1hbGPf6xxKcbw2riyR4yV4JK1X/734PKLhjSA1J4zhOtOkZUFFzfNmnMezMzkPX9tXsqYUL7PFvtaY2JR0yeNc2Qf6PDl7LYqVBePzVWadLRi3CEBX5pKB3Om6oHc1zr7WF+yd7CzRYcyJ0eJe8/ydeFFnxeeLgyfOITNeNLKFCo0RNZIoSrTZQeb0SNa/bU16opz8NtcM9x56JgEHmv53fj1dm33vGZe7orRYrl22iXtFyqbLNUd9K58RZCxxPDQpUIxfNmj587QAjKGHUU+OscmgPX9Xf8nrulo/lxiHAtcvjpyn8mJ+HsTy8pe/fAIoT506hTe+8Y145jOfiRtuuOGir3s0QCUwVQ4bD9CITXGQQ8sSs66m7IHZQktqnV4w3SZ57fzQS9RIBsFn6lJNSpl7I+ht8fhExPVcOU0xQ0nhpaLiVJ4SbZsABgllkdbObiH+PoD9sUCAKzykUboCJEoxdnoaKYu2gDzdu+hnXXEjOHIle7rYsrc0qD1wsLLaf4XxhE6b5MafKIem/IrFbwEBmggetWRG3jhjrPN8IODZl0CmRj86jYQB96YEcB5ON+PY+DN9xK8p07H09xId1NtrkuuXTZKjrBy8uawD+8xThk+AM/YZaDhWdRkKb6akMRU+vSj6BhyUtN7W1Mpzcx3kQ1wBTorlQMybHHtTh+R5aIw70tdyNtDJGCOBqRXlHYhYkW4R7+VkVAEeZWIAce+tzV16RXjfSbwUlUwCqFWFZgXwXi5hVugpkA7g6Jlt0zoPL2SiqbIPS7R9HzAqQLfgH4hkP7b2614Lr1nyOmdjVVwzGfeSx2t1nTJDNNtfGgAzzhRTFPU+yWvqIJHAK1Ll76ef2X/ptWy8Yuwl28J7sS9XgWYkQpNJv03mcVKQ89xxz5HtwZ5JO58pYrF/6Z7ZEEXhOnWAy/AJ2zOyMW9fP9h7ng22TOPvXPGEgYRZrCv3JtPggWh/txDPhprnVazpCO9gv4wzeNb2fCatM/RQT/C9OQuVfCrsBsY8eVqHFOJSJmcDKZA0VBDoSI0M1Xp/vfawVdQg5vuMrcmRWduLfz7ovirjLMBa61OYRAl9ZrJOOXZJB6FhtM2nz+9MIQOoMVYpdMH6pTHDrCgoHXYqFldVDMesJMusYHlMs8ky0+7e1Z1mMD6m5wE9p/q3GSDo+Uo6SWaP+PnZ6bXHrTqZW250yOc7MEkmRAouPYNFECU/BNOSOkDsFyyXQtbDmM7kvMfzWpwr1g4xwOljAkz2shw3ubr+veQKx5d7dpLcT6tCgwr14RwOlM/pyfPwHKY3NHku3XljY7SRB6/8+q//Ov7qX/2reOxjH4s3vOEN+Pqv/3r8/u///kVf72jQX21TdMU2Lbh8wANAaTl5hC7ECX/cDk2patXMQc1Kc9TPa5bUuKdbMvMekDbtiZJoigkP6rBAx2bpgdJpg8i0BbaHtAZSMPPB4UXmkWLcPMaOF0KAOljijZLijHwjLm6dnsQfJqlpY3Pw3qKPCfac5mlKnRhwJB0UQIA0UiypHFFJp8eptUlQvsfCGfDMSmumFxH4c+xqUlAyhYwAcHWTZlwP5xcVXJ+SRhMxhhYEOj75Wfm8TptsHFh4Uh/19tlFLSlG0Cr84vqdFTohwdU4A/o9+2yixOxT8hJgzVpVpollTwnbn6l5kzVQpn+vGlv4kdVDJ3vAPJYjKxFZYavxnAA8Oyjf84M2PUcZgUqAwIMWaQzYhQawXLEeoHF2COs9+4wJkJR2FXOI4z1RgEs8Y0ugHNnCXDAxeFxq4fwet/T3cV7QnRb0u5rJV+NdpzHiBOk53jSuFwq1v54VC5sL7m22vuNnu922z5NWF02pcOaxLAJLyCKhBBpVFrW4caEuWuxD/Bxgc1G02DnHY9l8bjk7hKBA4GEIup6tzTaupH4GaI3+4P7auoJK1kxSTGvLoCni/yaKIOOk03LPcX6ra9m/zz4stl8Dk/hxNSzq9VoHdC0AYC6nlJPYuQGxhMeMHt+8NrW/CmgMZGxnXSq9UopR6rnX2HpEj4grt70eQLAgVuLy+b978Urst6tKs9Sg0U5et891KTnf7IxAtlNMWvIUQ3R/8RhMy5zrzIlMKywrlNkUi0xG1GrfETzWMcaPHmAal/26neonnJelCTAqIB3tLNf5o109YQ7AwjcSoHbdJ/UZ99UyGsD09thY8bodMLs3njcbmvzaSS/jGsuxpgTXnFvDtt6P8ZLL48nDVqsZUXmjrHSx7mY8K5kXggSyTaplEHb9CAWM6Ry3EoDlOjPKuhQ4NZ56Ehk/Deax36qh160wUTKwLA1o5mikd5Kfm6w9TK8xMUbzTHG66v73cwxn7rc8t9gmHbdpPCdfWxX1WOq+M9ENGVLW9uuKV5Skfr3g7z1I5eMf/zje8pa34M1vfjNOnz6Nv/JX/gqWyyV+/ud/Hl/6pV96n659ZDyVtKAxnk9j+fQ91krLXhe3SAGuMDntap3COcbv+2OKkrJoCTrcc5G8ORn4hpdEXIHTa4VVnX+7R9Oes1rWQT/4Ep2BItU8LbTgj+neiLbldkYbV63k4dWjB46eIMC8QpnmgxVFq/Az4vfOnjvfsJKXwj870jtq12a/rWyqHCMCl9IkaHukNFq9O/caSTw/kEAiKZ0pNoYet3gP081W0sFEDy+vzT62eZnpTpmq4p/1uqBTKqjH3ky8OHHPPJalmXdHJIBfimkhTc770mnB+p1MzcrPDMBjQD2jJ/Kzx9oKY0W6BuI6+37nOKb1FAesTPrb+26MBC68Hvt6Anw41mPMWf+s90F8vi5ZB9XGwvrFjTbWRu4pk3IUaRyCZisxn1a9olnSvL6ctKE8ttro/JopA3XKePA9Kc1Lp0+58hPP6p5/30PFFeBMoWW5pMxi8ORpppy5gsrXB9F6nxyjZFBxmpl9bgJ+B/E4Ob6XQyAme2gCFTnr7SqgnlADeR9TRNwoYn01MUKtjPPEO5AU8mAOxBmlX5TJ3uuKoaQzZWVvDZp+8b20ZQXX2j0pUdHSeUO64IqSqSEBxWOP2S/xXnw3U171+QECZm/vyr6UPSQTo5X1fbeM8fLkX+mZ8p7rc03ieYudbxrPaWEkORzD4wfTGI9YAf3ipaJkBUR4rK+NSR5/vVjxz2a9IYf1+HwY4xloGHBmTIu5mvcP9gu/m8+LKCWR+hbxmTZPe4Uk8MizZJm8xCWu697m9LcCSlszjOFN9FaOPfdJjcvL51dxAMfv7z+D0nNwz097b/ZWjlYPdlKOw+aog9Iu2urZ9+m1s/0n0259jpeYz60r/pM9wlLMu8w+oJdzNXzGWRPWxpWcAhkUThwkyK/ZHp7YVB4mVqb3onj+Bn7WztHs5Z22L9/0CgeTlCNGf/36r/96fOmXfik+8IEP4A1veAM+8YlP4A1veMMlu/6R8FRWK89Rh/0TO3sIaV0DMFV4k4IjMDDSiRqoSgAcWif9O4zr4b2cQgjdOFr6XqIS1IVMlMcASlovq4zqgWxIikABIEE/cHoZEBsFyx8gbRgtGqiKAZNCRLIafs69bbbJ5vTfVEY8xtA2mIppX69aKfPG6vSxAtSlpiDXJBzx+oQ2weuUUBgm/ZHG3z2rAyaH9cQAsKpgCQ9gs8YNMUYU1u2kh2ZyvRVF08c6WUNX35fkSebclC4rhvocdEJO2tPi2u5lXVEQeL+s3Hnsk83ZUNARVKYRntCmIJQZ7y8eEGynKWPZ+kljQb52t7ADiHXfONeH+N29egTayfPvIC+B5wrx51WFTcxqH/OyrcSO+BSoYXmVruyrv+XPbO3JJYZ0fG1+dkU9krTYO+LOsW3xHSpSq3HEeW7o79an4xQMXWoJ4CsoY6qfCZ0vo3vzQ4vPxrYATXbBph/YZyhKzwZ6JhLlswwRA06vD/cMeiU9UQv3vbH550GFqhaIQGPECzSxkIk0cSp1t7TabssAvdz/x63q1DbxEgZxHY8HRsyH7CXPiuxEqGgmg4Lvp9aeIoz1m7JEeEOpaS4bmKQnJ/dzpUeUY8V7JoU6M1Xynk4FOSvH+hnouk6GBb6uQCVovLLSB1w/qPosbjCT+Bg9lWUMJlD2dPq+QaNjV9xr3LXinqZumfqBZYDMiEcw6slDbByrhcHEeMi+PVtvamdkjXkwPYcChJRRUIomsmkzZajkz+YEJnnPyp5EIJ0lY+pDvmdzjmCG5cc850Huf85V9oOdIzzLWhdMHz73KlU2C8uAgfMq1b1kGS9JOkr2ehLc5RJV3sW2H7dkoOOzZsaZJ3JKZyCACdDh+85yoS7XrN1I905zWrNzs7+oy6UEjBUYt6vuPQbsGFdKg5knv5GgOU/mkrd16gWUqp7mzmpilkQ39dhyNjWxopzRYm0lHTdn35YS85MhL81DTnTyOftiGTRWjl0xzzcBN1kdjeeERDv9eQogswpZx7e9kqSlSXPB33vwya/8yq/gO7/zO/E3/sbfwJOf/ORLfv0jASq7RUPpELQEKgMy3SwzgMgLnyK27uogKMtET0qJQiYB/kz+k+PRJA6K+GAJS3pBxL9xc/PPKdhy4JUATKXSxpjO1Q25yMS4Fb8m6mszBdwUfWZ4IwCuDrLUy5WB0dQbEHSuumhTWq79s24TrWmzAfRZ61JQaL3TS+t/jJ1w61iKP5LpNiXd9NxzGioPEveARlIBlAAhVGZ7FhOG3sMterZJ97vp0CJVjiAuewyZnj6BlQDbUyVC+xWgB5LegJzhLX6HZ2IEgCKMXwiFJFNbPPZ38jxTxbAkpcpB4cQbwjYZkFsBXlo1O6WrL377KQB1urL4B9jmbm+lj/zg1P+iplYa30RJYiZcVwhFywt4H3uD7TnsufNneF8HvU3i0G+YAGvhGjaaWVbQhJlxE6AEqFwXtH4VcUyVI65R79/LJQRaHPsxKHllEFTG31rcI6oqGwBUmbI1RI+9xyauzI/srQIMsEr172pCqBbXG6FKXSmoe8372j3gQFBJBapc1aJnfg9UGoFoCBxifyqM52yma/IcMA9KyXHYe9z84M9MYOPUaa53Pv84HbdVz1g36D5NYO0xV9Y3Hem7DnYT0BhjD81nx0RsDfmzLxJNmIancfXz4dFXoCChjNtrBAO5HYCtEytuH/uQGXe5/6W2asZXXUc0muTaeeEFjTO0JyhM/VqMSaEgStBlr5vV8+zG6I8sdQnLTgvfGBwU2dqtzHQMTPZhkTg7vI+bnlUiouXsOiuTY+tcQxesvfbsOhfEvXBpOGKvtbmXWU4c95aMLSW9D8DnZg4FKmlPdzBrlEQvMdOHbjPOIg8BAJSFeQZ7AO7ZjH3P+8L7JvYy9YTB7lG8f6UW1BzSI2YsROyt1FPQB8MoG2zqKBBhokJm4OV8nxqnydhxVpr1detonEpnvV17ZP+MKbOq8OyHG65oZOA4l1F8v3DKfD5PB3VYtHlxfcsNGtyrzLDFz/v5nejuEypqA6o0SNqAtXQPPy++tzbzutZFQ87uyqzU43a1+qc2Hzvt9tr0Ot1eWvNpXLXfp/sehti3r1i5WK/jg9RT+e53vxtvfvOb8axnPQtf8iVfgpe+9KV48YtffMmufyRAZV00dGieWctfZymDdLCIFV0W8uwnaMSsbXuqiDjIKiu0O1O8YIsQPAipIOxz+4t/D4jNgJZ3/1TORIqVDcwOhNpUia9t/4QtUifPj1VLVy3xP7D/d9uoQglPYDpteOwrxpdJuqc/U0nPtu53niW2CYe1zjZjKoAJCBFwrfal1LBwena1HAux7v80LgSsfg17DnF3yvTj8RxJQSRY5ZzIn+dnALVeSby2zzhQocp7t+6m0WdsJOtr5etwftMAkF/LVsR4tkQ/5CGTAR6fDfBrSAXqXh6jOEwAOEVx3xz0uZHWj/WfH6jjdK5NLbE8uDJgbpN5lwHuqrcvv7YKYiPOGa4gl1HS+Iay6c/YAJgBRroCSZ7yVU+pg88DDpvwVlAhuXyHUrdo6He1hmEzZb8/09RbNwrqXguaKz0S1p42Syn/+d6imaJZdA7Z61lqAnW8Xhlaotty36sx7VMYwGS/khTCUKzvGYs2qw6A67JB+ooymPGjKyipKLXT0buKwoQnfM4uKUv0cAq0PEQCLGp0aJPnlRLnSHEl3JS8Gp9ZNQRlihkNX2s9WvzKyrm073XOIZ/jK/M97VWuDOa1k/fqGufB5D65tEMpkCJ+zmaaXrfbrBRF8/2hDrEHOJhCBaQlz9bUC5pZEvl/9rV60tJZymezy9dMM07XqkujQ6cEX35PiIKAhTit2mUZ4F0THAmwCAU7Awqp6fwRQc845QQufTxrQVlYW9MZSmOwzwu28QAvtRsyBOisMdN4yigBpdfhG/C12UkYXJk52g2qq1mqDVhCzCtrZUn63eZAE7RQ0uBbgML63ZxjHN9lnC/x/TR2CAZJ6dL6QbQv1urUGtMtU1+NUUIMADqLDZYuvIdSjb1UwttK4211CniUmFmnC/n39preqysoZN+I7jWl2bSkXjaKonY3rLZ9Z5vTfLuCNusgs4pmH1Kga0BzKbYvFmCwa5WiKQhqiflle5V0NRwodHq01DcpnICvAbFfXM7cAPeLHDFQeeONN+LGG2/EP/kn/wRvfetb8eY3vxmvetWr0FrDzTffjCc84Qm46qqrLvr6RwJUdouGTtpE8ZFS/G+33lRAliXF64TyQHd+tUOo7Db/jJT4LK9dRIA9vSbMgqOHRt5BVLLXQw9ImShNE0WBsUz5M4BuRhYH6O9n2m6nh/VERBVAABNr8ESpgUzen8T1jOGFApJC7PFWK17ZBEInAduY/p2V5yn1J7yT0qdTr6V7YhpzKl1FMQtdv2zuecn95tfg53mwJNzGPi1DGvNG5TFe88va5u3Kc4vX/HDPw7GicOo1puMVrwvErP2Fc4vvpzZPlPd0KaUu6wtZMYZbaNNcNkWZHhImQYlYpvD6AIAMUwWnDs0A4QqC4DNzjSTxNbnSJ/Qk8Xd/XrZvXOmHpAjrc00Va/dsr8rKfI02UMGI15mswBUn62vpiiqTWZkeUywaVkBhgydDmc7P4vdhG7JH/nJJd3ZUi3Xp/ODvzxKICYo0eOz5ipShxZzK3mLvk9R/Y7yvfzfUxeg0f1WgDPiR0dFLrLcSfQMR93iXtNcDsXbQFcggqHv6QV2PTddHZwk88ropBWgFQEvKtO5LVLL20eTWxINnoSeK9yezQsZEFc97Xt5n7W/fO/lcK4qpXjzaVLgGRcIIwo/znvtYAmmx8BoEKS0Z8fjcBNX586kfJrFxS6zdm+qS70t4ke1zzgxYyto1Eh08fY7Yz/K+NvVUuTHZ+z4YMKsgO8eFOzhlOEgB6u4Yt7PzwZkYK2A2DGHiBtM8LrxOqXmc4PfUa/F54WObwZVn0p2ZIaMgPRP2Sy1+TYbBKFvErtNFn7gBr2hD81ogYM606kniIXuPYJ3tniQK5OVmNXn67bx3Y/7KIlvxutIQ0KQESCyx//o+kY1SvCyba+3uTYeiHsczffXsR8M+fcl1NJixyvSGNgtQNtFRaXAZk25EoAndL1DNGCeCcbtHHUfP/wEBxuO9syz6xRLSVdWdaPS1W+QMwUymVfdGMBymDg0NFQWhG5cmwCjobB+uyzbZ1/cZGdlH/Izto/3uShKSjTwo5NixY/imb/omfNM3fRM+9KEP4U1vehP+wT/4B/iBH/gBPO95z8M73vGOi7ru0QCVZwf0y+VUCUubWz6UJmAIAATos9Lu38fkEJ8qn0mx5oFRytQKZkoMgFTDCAmACqTWKWAA9nlbvTmSFPHsBaC1b/XwaID0VTeCkaeU3nP1mhlkuMK12o6KsPavHPJC4GqbkP7evN+9b1a+W1qb9MGkjV2LtvD+KwYCf/7VdnPMjYayrz+71Gfp+hMlqE6vLbVOxzfPqQwGV/sTcbiowhYK3ypQPei16QfggDIr3fm+k8Qfea6m769VUtfdzj10ZQpq970XVJoJdTi366BnTc8waSN/X13LbtgIhSYuGiA4J8XJn50AosnewOfS99zzwWddfYYSisE+qnhW+EipWhJYyeR7k+ew9q9VBi+h1N0lesY2mVLS7Q6oe2nPaIg5LxLrLzMW2Id5jPJrK4ovzNjRn13ZM9OaLeNodK71Bj0g9lD+0Pgis25yn5KNUyueLQC6rvsK7LXp+7lNuf15LazuSbzN6n66KnkOu1FB90Lf17IxKRuo1uzz/L4D+9X38nmTP5fen/Qr0p61ahzLbVjdOwGfG+vWvV6vrgBe2bd/dpmyD0yfyfpvSkFnH+wHUXldTeZiGpdVunI2zu5rezL2hnHSnpmgeyXkYO2eAEQ/pM9PQkny89i+4LUqBVjdDw+au6sU4Mz4AWC5DVoCsdN9OO/T0TErcyWfp1nPAYK1ImEskK66MQUAZC/tM/nZ+CwI3Y3P2tn/ZApIKcpSGNqkjRH2Emte+0H2j0lal6U116Oi8zC5lvd70hkmc6aWMEJ4/6/oB84AWHP+AUDVBEF12dC2Okgnk76rCzVCjds9ILIvJAkAOug8kM4Ap/VR3RtAw1q3bFhNEAQaWZLhoIzGLmkNMuvQuf6hY1BtXeh6AWRc3UCuMGkCrBo2Dv29K0O++Iu/GD/2Yz+GH/3RH8Uv/dIv4c1vfvNFX+tIgMqyHFGGsIasArDJ5kfLMUEHpdsPBiYbBhDXq9i3mayLcSmLNZMqbUYFLZSirJitfN6BFxWZROVCKShD4tLnQ2jRAjxxA8YY9xnFg9JdWvRfBoJ5Ay6rG2ej65MW3ummW1Z/z8BShn2KW5Fo4+TeE+CUDobcXatK58phq16wKVDap0itGYfCAD8CjwwM2PbV/kyH3mR+5ee3fpt8Pv0++fzKs5b0ell9PY2Vf/cgRd87Lyna/AwVp2YKOl9fAeVlnbe2lGhXTTpJMooU/t2V/diWbRUBap0+fwaDadzy2s9eXv8cv74G6NYMivP9+byr1M5a17MYsvEhXUuGg+esg4ouzaPLeBaXxYBa6wTU1sWonsNR5zoVB38OGr5puMmK1KzTfSmvu8kNbe4ZYCycs6MpMmUGSNP7ZdsNMAFvk3nuc1NcGSqLQa9pnnOlI69Z3zVdg0dHXh95vdBAltdVmo++hmmwWzf+uR/S7yV9bnIGpL2rpO/t29/yfWoFhhWAy7Zz728NqHWyR0Bk8jx6uzV70eo+lP4mYCUw9uvzvATi92y041zP98vryhTXyZzIQDS9xv7ZB2Y5f6hot5VrrI4R52Des9hWkcn4ugGr15R1+4ykub9yn62+vvqetb/K9DzqktNnwspaMZ66QWDNWcZ1xD5Qj1V639gtntSJoCqfp7xP9lLyGYzhM/k7tUuv08Loi5V5nV6f9FHWTdaMXwEgNCa1FjpDjitEgETXm7qybyx8/o+m36ye0SssiUl7DpjbfAYX7kG8Zl5fXI+ihrJuNF2FgE70gWUs+8dAYECw7Z9TXZzPbtzeHVQvxnT+0qDk3mX2lbVRPdPN+16fKb5fxhEQQXuQJqw5rIg0iFz4M1zMdx5o6boOL3rRi/CiF73ooq9xNEDl7oCSDh4Cl32URWCqEAFxuC2SkrxqiQf2W4pXLHITRTptfAD2KZ77lNa06ay1otlBXcfpdcFFv06JW+fpWFUK2vRzuf1UlKRWtzxNrjM5BMrkGhMFhErjOlnz/C6mKJbVz/NXgrxVkMpfDjiwc1+tHtpUeLKHZJ8impWb1XIQpTjFdJ8MmD7n6rMnJQyrB1G+/upr55I8l9cB1BUF139FerCsYA8rNJZS9nnhAURszKonOLdr1TPcrVxrtV0MMsntsnmY1/tEUV+5xuSQzZ/N/ZCU8El/i0zBxwgAYygCfCv3Wb42lYZa4x75O62hdBFYtQ+cXmIpewNqKZDMIBiarvWhBU1LTPnIfT+U9fOpWR+v0syQlBVarcc0x8YEQhKrwhWrDDj43urvzfakDIIBlJJAVQaEWVboomtl3x427l/Po00QerTyNc8Vz75uTR7w+wE7qSvq+f3JWTHZN9IcZzs8iOs8e8y6PYztKgUF4wS87fu6TMHdvn0ACFDK72Tjht9n9dlX5gg/k/eJhcQcWbdPUGz++zP6r2W6H6b9qozjdD85aO9O91/7fron2z/ZY8rUuz1pV/4f8OdVT2EYov1qNBLxvMn3yefkSljNPr1mFTRzrPJet2LcBkLfAKZnxbrzNs9jtjKPtT5Pi7W9YgwDnxNAWcbeU9Lra89gMieyrMzrmtbAaljHgfok38uMqPyZfG5kg1pf49n8bJyOu58dEz3M2tiVqeFjaO6R1TM0OR2GNM/SGnL2QmuazTnN2zLJJGhrcrX/rjRZ3csv5HsPQTkaoHI5oLRu/yEn+y2wWK5+uWCSiIOf4etATKhssQb0/VEXb5SjSAoub8H7rE6yNWCwYNx3QGTv4sQLlcEcr7evc9ZsWKQgrd5/jeI2ARjpmvnALlnZzocM/18HvvLvq4esH77Y/9rq7/nvdWBg3fezAiLJQ5DaPzm41lmf1z3LuYDbmrGefG+FFjb5/Oo8pEwSyJT97wG2+a8o+TlpU/4s3+vSmsmfWff7uZ5zdSxWf69FQbjIPg/LvnusM0wcVgE+V78f9B7fp6w+O/+nR4wH+aqyvgIeNE5ojSImYtlAQ6E8cM5dChlHYOj0HrXaOh5QFuN60DakYMY1Y1MW0L4Y7Psra36yD48NmiqT67YCS2MsDKO+V+r+sfXYyilwRGUB1ww8any2VKAlZWkfPWNFVoFfHvt182V1nlOhy2O/XDOXWRScyne+zjrP/Lq9bfUz6f8Dabire9aYXjvXHrbuWmuuWYay/7OT99e0IT8j4OsqQG/uX1t39EifC8AdtMbXKeGugK9nGgDYv/5X97PVOcPvZFk1KBwWxNtzrz7NPo+rBLtJSkFZYvJ7tAvnNjxmnSO/b+/tM1iIAF03nctZBon7GXg7aCmWlc9Nnm/y8Ku6wLh/DM63j+Z5su57fI9z7qA1UspUf3JwtqYNuc3r9pv8/9DcsYAx9Um/pvOybrYajmXfL8kLugq6s2NBn7kEIKUTpcVrGMcDw37i3leex24iIsDF0F83oPIKlnGE11qQlZ1qckCsvMe/1x0GtehhO1GeD/g8lZnVHXLV/X2uA25VqGzJqBv1MMS9xzHudS4r+7rDrYkqbrntq32SnyUrY5P2HXAauELY1j/XugP1oIN1naKzeu2DrHzr/j/o83Ul9d5qn7Dv+d6kjXX/57McBN6ytNUDQKafXa58/yCgt/r9/NxLWT8fVt/j/Fq9VgYJq9n+Vtuyrq0HtX/d8/L9rDCey9h50DxaVcwP+m6WdQouX8+Am+2ht2DdNfiZlABm7QzIBqRVA8nlkmFQ5cKKJxaYcrpkbZS0141j2kfphciKnLW3NWDJKvHWV1wXY9JmReJ6pUCzGto+k++zOoeB/Qp/OUh5ZD2bAqxaz/kMB+0Jq8aarHBSVtdY7aZg140LmH4n9906ZZ2vjyvg/lzGj9V7HtaIsu76B+3HB7WV763bo/n/vs+f43VK9ni3lecSiT2qrTkL1v29bi+wv1c9gTjA267jIuf3WqyO73IN+F1JCjW5B7+3LoZ/jYJe1lyHxumJxzdfO0s7x9wqZR9gWEvBdqA/TF9bFZFgOpzPQEPjPl9fnQMHfRfQdSdprM51Rmc5SJ+SBgxp3q7T9dYZ3IAwkKy2+1zneD47ijkWMsOiFmCxqovod9d6kLlvdhXTDFYy3eta8zlTSgl2Th4X6VSHTGPhQDa1w99rq56cjRxlORqgMiv9ACYa6OqBJ6vaaaIxrTuU9ymZ49SrtGrNyt9bPahXPXIHgR7/LgtHtbi3fzdtDPSYZjmfsuwbxEFK7Lj+u+uedZ+sUFNXD4DVc3HdwbquvVQIV5X9dcrEmjiofePL38e2/1oTpdT6fu1zryIHHKykXsjvgxx8wJ1LsocSONz3awmP5mpbzvUMB835g8Zk9XPnkyHda51Cn39fpwRmOciYcT5ZVZDayn0Puhcw7TNauc8FKA5as5dDhhEoS1WUDBwVQMGdj61NotbWrOEVBZXPNiQwdBBtvrWp4cI8pZN1S+B+UJ9YbCDGpGzxtXxf/p3/92dY+duf5YA+ayvzR/I+c4ARjW3Oc47NW322dbTIVTmfseG+zKF1Z9TFyOoeehjDXj7v8nXWKdzrPDp8/wCGwD7qPl8/KDzjXPvfOjkfqG/n2MfO5wnn+XSu/Y1yvrMU2A/614Gt1Ndl5b6TvybzusX/+Rkp+V4ZhAFhKF9t10GGeYKhdYan/PfEm3wOj9m5QK7LePBc5fvnGvvVa/KzB523bH+ex113bqNsKVOAt9r/ab/3tvjZNq6fI/wufx/y3k1d4Bx9267w7K+tnXvuHCRXYEzlpZCjASqXAybxN8DBgOagDehcSvCqtBVF5Vz3WyfnO4DOd6ivKk75mhciBwHNVQCw+to60L2u3at9csHg9AA5qJ/Xjf9hwfVquw667/nud5CsKrf5dUCt2fn3/N5h6COr3833PFd7Vr+/2hZX3g9ox7kUeuDc/bKq4B8kvH+WbOl3Kg72z40MHNZZ5M+1F0zuN+5vx4HgY+VZMr0xt/tC7n+pZTCPAgEdRSTaz9fHccpuoFKyz3hiz3lAluSJTIxg435F56DvZWWpJtBbjXq7OijVvE6eaKul75d4rguV87V13Zmy7vvrvnfQ9dcZyNbd76DrORhecybma5zrDDxovz/fmbZODgOQV/cX/r4qeW2usfOtvTbX9LKpsr66b6y7x+o+d742UdiWvLevu9a6Pl239/Ce64wifL7znWMXMl7nOicPa8hYpyNNnneI1841LybjdA59Lu9lB43Zuc7Ic917nd540H64es/Vz/BsXXcGr86NZeqjLOfTvfz1Ybp3r+4h+XoH7ROrr52r/0SufFApAmzor4eWIwEqZW8PUcvpkMDufHKuA/xC5XwHTv4cXz8IuN0fcq7D6r5cEzi3J+Fi23BQ/x523PL3V5WLLOc7PM91YK5+51yH0YUCiwvxuq1TWi5Eztefh1FmDnvtg/roQijfq68fIlazZGo5jVUPPwnceQroe6V42ryV5ZoD8zCGqQdYZG/htKyy4qmQYdDYLXtdhmG9MqRfXt/n55oDWdlbvd5BStj5lL+D3j8M6LsS5KC98FxGo9XPXYiR6kJkXd8fNJ4XKhcCbC+lXMwaPowSfr49+CCv6aWerweFHhxCVvcLikhDKRWrWS/XvXZRcqGsnSyHNZTwM268ughdaN25tU6XPOz8oKzGGdv/B43H/kvHGPjefgHjcsHfsfESWRz6Hg9GkdYgF+GpvBKzv14KORqgcrGY1F2avnk/KQ+Huc9hDsMHS6as+9qOdRvnubzH6+43jhc/fuOIUqtmt1zXtoMO9kM+94HXvkC5VNdZvdbq7wAm91l97VztuK9tXHf//DpltS35e/sydl4OsTngtVy7itL3ami4+15gZxtYDgokh2HfXD5fH90vz3BIkeUSZbT+Bfavh3H02ClZF9/HsiPJeMNx8n4439o9F/DkOjxoH+Fr5/MCXk6D3Lnufznue6EK8erreW87V78dtu3rPF3n8zyufu4C9va8fg67H63uIev2ldV9ct31D9rDznXffevhgM9M7rGmj871/fz+Qe1fff2i9qE0N2T1tSST0be5IYeZT+fz6h/iGgc92306Xx9Exifu16uyb/8+l3BOXYTTRC50f/AvbjyVDyU5EqASY7vyXewbueRyOZf0pbr2pWyjnOv3lTiV1ftO/l5J3CTrXrf39smaOBhZlxwLOLAoMl/P719QAeXDJmUA9lvpS0653yC0Bm9tKfVoGIBaDvBQHtDG8zzvBbf5UsgwTMdlneR4pwNk9RqymhhtnRyUqOV+EFkTI3Yh72/kfpY0v/bNrYOSpyWRNNdkdR2u7G2yLiHburW7LtZsJfOwWIbjc+3vsronH5ToZfUZ8+fT+7KuTakth9qHrnDZ92zn2mcOsQ89YPvBmrkt+fUHQi4mxwMAudJBZROgbEDlYeVIgEoZx/MrSBvZyENaDhNotPrZw75+vs8cFBB5OWVcrzSs7hM5v5IpD5KSekyUiuXgHjn13h1WOTvs895/LAUZ18QfHqBQn/s5L6bNDywbY1+utgt8fyP3p5xrMC50PzrMtQ67V53ruhe63tP/hzK4HKat6z5zmSf2+bL+HvQdyv1uaDpcfzww+8FFnMHn0oEvZDzuIxDfd+srHVRu5IJkAyo3spGN3H9yENA7CPyt80gceq0fBuCGHKQ8yAiUu+9FqUU9l2IeTP/AfVSGLgrM3Tfx9ud7ynKlb8c1nzlg7A7wRp+zdMRDNOZkIxtZK6VCM4yurLfz7Y8Xe6+Dyomd697r9mX//jKe4dCS9phzZRzNbTxXm8/1LEdZztfv52LCrJ4Bq9edjMnygvtZrnQrnQiwr2TBYb/30JMjASo3spGNXCGy7nA/32v70srf/wqCDOemst23iz8Az3fQPc9173ON04U8w1FX8DaykYuRC1mT93UNnW+9H2ZdX+p2rbvnhdzjQl8/SnKYZzzfZw67vz/E+lmaRCLQC/neQxRUPuDuvTe+8Y24/vrrsb29jRtuuAHvfve7H+gmbWQjG9nIRjaykY1sZCMbeSiLtIv/uUC5UDz0m7/5m7jhhhuwvb2NJz3pSfjn//yfX+xTXjJ5QEHl2972Nnz3d383/vbf/tu45ZZb8JVf+ZX4uq/7Onz0ox99IJu1kY1sZCMb2chGNrKRjWzkISzS5KJ/LkQuFA/ddttt+Pqv/3p85Vd+JW655Rb80A/9EL7zO78TP//zP38pHvuipcgD6KN99rOfjWc+85n4yZ/8SX/tKU95Cl70ohfhR3/0R8/7/bvvvhsnT57EV+MvoC+zy9nUjWxkIxu53+Xm9nOX5brPq3/5slx3IxvZyEY2shHKIEv8Bt6OU6dO4eqrr36gm3NocXxR/q+LwheDLPEb8ouHfu4LxUPf//3fj3e84x344Ac/6K/ddNNN+K//9b/ive997wW391LJAxZTuVgs8P73vx8/8AM/MHn9+c9/Pt7znves/c7e3h729vb871OnTgEA/vXH/ulFTda/cPJlePupnz3w7wu9FoAL+v5h73euz53vGrld57vOYT53ofe/mHbwM/z8+a53vrZd6Dgf9D7veb52HfYZL1bW3eOw8+Bcbb8vcq7nXjdWhx2TCxmr+9qvF3Lfy3mN/L277777PrXhIBlkOek3AOfsx3ONK+VCn/V88/hi1+mF3Pt817mYff2g9h2mn893rUs1xy/F2XOx3z1oj79UZ9lB3zlsOw/T7oPG9XL210GvHXadrDsDzvXeudpwvjPycpwxl0LO1ZeX68w+zHfXjedh5tmlOGPO9f6lOF9fePL/BnDlxhgOsndRVNYBmvRo9Qzf2trC1tbW5LWLwUPvfe978fznP3/y2gte8AK86U1vwnK5xGz2ADna5AGS22+/XQDIb//2b09ef+1rXytf9EVftPY7r371q1mFdPOz+dn8bH42P5ufzc/mZ/Oz+dn8PMh/Pvaxj90f0OKSydmzZ+Uxj3nMfXrmEydO7Hvt1a9+9b57XQweevKTnyyvfe1rJ6/99m//tgCQT3ziE5esHy5UHvDsr6VMC8uKyL7XKD/4gz+IV73qVf53aw0f+chH8IxnPAMf+9jHrijX+kYuTu6++2484QlP2Iz3Q0Q24/3Qks14P/RkM+YPLdmM90NLRAT33HMPHve4xz3QTbkg2d7exm233YbFYnHR11iHZ1a9lFkuBA8d9Pl1r9+f8oCBymuvvRZd1+GTn/zk5PVPf/rTePSjH732O+vcxrVqrqGrr756s0E9hGQz3g8t2Yz3Q0s24/3Qk82YP7RkM94PHTl58uQD3YSLku3tbWxvb1/2+1wMHnrMYx6z9vN93+MRj3jEZWvr+eQBy/46n89xww034Oabb568fvPNN+M5z3nOA9SqjWxkIxvZyEY2spGNbGQjG7n8cjF46MYbb9z3+V/5lV/Bs571rAcunhIPcEmRV73qVfiX//Jf4s1vfjM++MEP4nu+53vw0Y9+FDfddNMD2ayNbGQjG9nIRjaykY1sZCMbuexyPjz0gz/4g3jZyyKh1k033YSPfOQjeNWrXoUPfvCDePOb34w3velN+L7v+74H6hEAPID0VwB48YtfjM9+9rN4zWtegzvuuANPe9rT8M53vhNPfOITD32Nra0tvPrVrz4nT3kjR0c24/3Qks14P7RkM94PPdmM+UNLNuO9kY3sl/PhoTvuuGNSs/L666/HO9/5TnzP93wP/tk/+2d43OMeh5/4iZ/AX/yLf/GBegQAD3Cdyo1sZCMb2chGNrKRjWxkIxvZyJUtDyj9dSMb2chGNrKRjWxkIxvZyEY2cmXLBlRuZCMb2chGNrKRjWxkIxvZyEYuWjagciMb2chGNrKRjWxkIxvZyEY2ctGyAZUb2chGNrKRjWxkIxvZyEY2spGLlksKKt/4xjfi+uuvx/b2Nm644Qa8+93vnrx/77334tu//dvx+Mc/Hjs7O3jKU56Cn/zJnzznNT/84Q/jla98Ja6//nrs7OzgC77gC/DqV78ai8Vi8rnv+q7vwg033ICtrS084xnPOFR79/b28B3f8R249tprcfz4cXzjN34jPv7xj08+c+edd+KlL30pTp48iZMnT+KlL30p7rrrrkNd/6EgR3HMX/va1+I5z3kOjh07hoc97GGHuu5DRY7aeB/23g9VOWrjDQDf+I3fiM///M/H9vY2HvvYx+KlL30pPvGJTxzq+kddjuJ4588+4xnPQCkFt95666Gu/1CQozjm1113HUopk58f+IEfONT1N7KRjdwHkUskb33rW2U2m8m/+Bf/Qj7wgQ/Id33Xd8nx48flIx/5iH/mm7/5m+ULvuAL5F3vepfcdttt8lM/9VPSdZ38h//wHw687n/+z/9ZXvGKV8gv//Ivy//+3/9b3v72t8ujHvUo+d7v/d7J577jO75D/uk//afy0pe+VP7En/gTh2rzTTfdJJ/3eZ8nN998s/zBH/yBPPe5z5U/8Sf+hAzD4J/52q/9Wnna054m73nPe+Q973mPPO1pT5M//+f//IV1zhGVozrmP/zDPyyve93r5FWvepWcPHnygvrkKMtRHO/D3vuhKEdxvEVEXve618l73/te+fCHPyy//du/LTfeeKPceOONF9Y5R1CO6nhTvvM7v1O+7uu+TgDILbfccqjrH3U5qmP+xCc+UV7zmtfIHXfc4T/33HPPhXXORjaykQuWSwYq//Sf/tNy0003TV77ki/5EvmBH/gB//upT32qvOY1r5l85pnPfKb8nb/zdy7oXj/2Yz8m119//dr3Xv3qVx9qc7rrrrtkNpvJW9/6Vn/t9ttvl1qr/Jf/8l9EROQDH/iAAJD3ve99/pn3vve9AkD+8A//8ILafBTlKI55lp/5mZ/ZgMokR328D3Pvh5I8VMb77W9/u5RSZLFYXFCbj5oc5fF+5zvfKV/yJV8i/+N//I8NqExyVMf8iU98ovzjf/yPL6h9G9nIRu67XBL662KxwPvf/348//nPn7z+/Oc/H+95z3v876/4iq/AO97xDtx+++0QEbzrXe/C//yf/xMveMELLuh+p06dwjXXXHOf2vz+978fy+Vy0ubHPe5xeNrTnuZtfu9734uTJ0/i2c9+tn/my7/8y3Hy5MnJcz0U5aiO+UbWy0NpvC/Fva90eaiM9+c+9zn823/7b/Gc5zwHs9nsPt3/SpajPN6f+tSn8C3f8i341//6X+PYsWP36Z5HSY7ymAPAP/yH/xCPeMQj8IxnPAOvfe1rNyENG9nI/SCXBFR+5jOfwTiOePSjHz15/dGPfjQ++clP+t8/8RM/gS/90i/F4x//eMznc3zt134t3vjGN+IrvuIrDn2v//2//zfe8IY34KabbrpPbf7kJz+J+XyOhz/84Qe2+ZOf/CQe9ahH7fvuox71qMlzPRTlqI75RtbLQ2W8L9W9r3Q56uP9/d///Th+/Dge8YhH4KMf/Sje/va336d7X+lyVMdbRPCKV7wCN910E571rGfdp/sdNTmqYw5orOZb3/pWvOtd78K3f/u34/Wvfz2+9Vu/9T7deyMb2cj55ZIm6imlTP4WkclrP/ETP4H3ve99eMc73oH3v//9+PEf/3F867d+K371V38VAHDTTTfhxIkT/rMqn/jEJ/C1X/u1+Mt/+S/jm7/5my9l0w9s8+ozrfvMQ1mO4phv5GA5yuN9f9z7SpOjOt5/82/+Tdxyyy34lV/5FXRdh5e97GUQkcty/ytJjtp4v+ENb8Ddd9+NH/zBH7ws9zoKctTGHAC+53u+B1/1VV+Fpz/96fjmb/5m/PN//s/xpje9CZ/97Gcvy/03spGNqPSX4iLXXnstuq7bZw3+9Kc/7Vaws2fP4od+6Ifwi7/4i3jhC18IAHj605+OW2+9Ff/oH/0j/Lk/9+fwmte8Bt/3fd+39h6f+MQn8NznPhc33ngjfvqnf/o+t/kxj3kMFosF7rzzzonV69Of/jSe85zn+Gc+9alP7fvu//k//2efde+hJkd1zDeyXo76eF/qe1/pctTH+9prr8W1116LL/qiL8JTnvIUPOEJT8D73vc+3Hjjjfe5HVeiHNXx/vVf/3W8733vw9bW1uS7z3rWs/CSl7wE/+pf/av73I4rVY7qmK+TL//yLwcA/NEf/REe8YhH3Od2bGQjG1kvl8RTOZ/PccMNN+Dmm2+evH7zzTf7Ql8ul1gul6h1esuu69BaA6C00i/8wi/0H8rtt9+Or/7qr8Yzn/lM/MzP/My+a1yM3HDDDZjNZpM233HHHfjv//2/e5tvvPFGnDp1Cr/7u7/rn/md3/kdnDp16iEPQo7qmG9kvRzl8b4c977S5SiP96rQQ7m3t3ef23ClylEd75/4iZ/Af/2v/xW33norbr31Vrzzne8EALztbW/Da1/72vvchitZjuqYr5NbbrkFAPDYxz72PrdhIxvZyDnkUmX8YWrqN73pTfKBD3xAvvu7v1uOHz8uH/7wh/0zX/VVXyVPfepT5V3vepf88R//sfzMz/yMbG9vyxvf+MYDr3v77bfLF37hF8rXfM3XyMc//vFJiugs/+t//S+55ZZb5K//9b8uX/RFXyS33HKL3HLLLbK3t3fgtW+66SZ5/OMfL7/6q78qf/AHfyBf8zVfs7akyNOf/nR573vfK+9973vly77syzYlRUyO6ph/5CMfkVtuuUX+3t/7e3LixAm/7kM9JflRHO/D3vuhKEdxvH/nd35H3vCGN8gtt9wiH/7wh+XXf/3X5Su+4ivkC77gC2R3d/c+9tiVLUdxvFfltttu22R/TXIUx/w973mPvO51r5NbbrlF/viP/1je9ra3yeMe9zj5xm/8xvvYWxvZyEbOJ5cMVIqI/LN/9s/kiU98osznc3nmM58pv/mbvzl5/4477pBXvOIV8rjHPU62t7fli7/4i+XHf/zHpbV24DV/5md+RgCs/cnyVV/1VWs/c9tttx147bNnz8q3f/u3yzXXXCM7Ozvy5//8n5ePfvSjk8989rOflZe85CVy1VVXyVVXXSUveclL5M4777zgvjmqchTH/OUvf/na677rXe+64P45anLUxvuw936oylEb7//23/6bPPe5z5VrrrlGtra25LrrrpObbrpJPv7xj19cBx0xOWrjvSobULlfjtqYv//975dnP/vZcvLkSW/vq1/9ajl9+vTFddBGNrKRQ0sR2WQn2MhGNrKRjWxkIxvZyEY2spGNXJxsgoc2spGNbGQjG9nIRjaykY1sZCMXLRtQuZGNbGQjG9nIRjaykY1sZCMbuWjZgMqNbGQjG9nIRjaykY1sZCMb2chFywZUbmQjG9nIRjaykY1sZCMb2chGLlo2oHIjG9nIRjaykY1sZCMb2chGNnLRsgGVG9nIRjaykY1sZCMb2chGNrKRi5YNqNzIRjaykY1sZCMb2chGNrKRjVy0bEDlRjaykY1sZCMb2chGNrKRjWzkomUDKjeykY1sZCP75O/+3b+LZzzjGff7fX/jN34DpRSUUvCiF73ofr8/5brrrvN23HXXXQ9YOzaykY1sZCMbuRJkAyo3spGNbOQhJgRLB/284hWvwPd93/fh137t1x6wNn7oQx/CW97yFv/7q7/6q/Hd3/3d+z73H/7Df0ApxT9zrue67rrrAACf/OQn8R3f8R140pOehK2tLTzhCU/AN3zDN0ye9/d+7/fw8z//85fzETeykY1sZCMbOTLSP9AN2MhGNrKRjdy/cscdd/jvb3vb2/DDP/zD+NCHPuSv7ezs4MSJEzhx4sQD0TwAwKMe9Sg87GEPu6Dv/MIv/AIWiwUA4GMf+xj+9J/+0/jVX/1VPPWpTwUAdF2HD3/4w/gzf+bP4GEPexh+7Md+DE9/+tOxXC7xy7/8y/i2b/s2/OEf/iEA4JGPfCSuueaaS/pMG/n/t3fnsPBucRjHn5dQ/I1txjKFCIIhQ0NIlFPYEsmIEBGNqIRCQhRUNEqFBI1lGolGryHMkNgTlQQTu0JhKSwJZm7xD/fOdYsxuYzrfj/JW7znTX7nnPJ5z3nfAwD4qVipBID/GavV+nbFx8fLMIx3bX/f/trS0qLa2loNDg4qNTVVCQkJGhgY0PPzs3p6emQ2m5WWlqbJycmAvs7Pz9XY2KjExERZLBY5nU4dHR19yrzMZvPbHJKTkyVJFosloK29vV2GYWh9fV319fXKzc2V3W5XV1eXVldXP2VcAAD8dIRKAEBQFhYWdHFxIbfbraGhIfX396umpkaJiYlaW1tTW1ub2tradHp6Kkm6v7+Xw+GQyWSS2+3W8vKyTCaTqqqq3lYUv9LV1ZXm5ubU0dGhmJiYd88/ujIKAAB+I1QCAIJiNps1PDwsm82m1tZW2Ww23d/fq6+vTzk5Oert7VV0dLRWVlYkSTMzM4qIiND4+LgKCwuVn5+vqakpnZycaHFx8cvHf3BwIL/fr7y8vC/vGwCAn4xvKgEAQbHb7YqI+PNdZGpqqgoKCt7uIyMjZbFYdHl5KUna2trSwcGBYmNjA+o8Pj7K6/V+zaD/wu/3S9Lbj30AAMC/g1AJAAhKVFRUwL1hGP/Y5vP5JEk+n0/FxcWanp5+V+v1m8d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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmeshATB = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'attBsc_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=1.5e-5\n", + ")\n", + "cbar = fig.colorbar(pcmeshATB)\n", + "cbar.set_label(f'Attenuated Backscatter {wv}nm {t} {tel}')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_ylim(0, 5)\n", + "ax.set_xlabel('Time [UTC]')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "0ed6880d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmeshVD = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'voldepol_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=0.3\n", + ")\n", + "cbar = fig.colorbar(pcmeshVD)\n", + "cbar.set_label(f'Volume Depolarization Ratio {wv}nm {t} {tel}')\n", + "ax.set_xlabel('Time [UTC]')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_ylim(0, 5)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "a5c11967-c1a2-45b0-a785-5a2b274741ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['FR-total-355 nm', 'FR-cross-355 nm', 'FR-387 nm', 'FR-407 nm', 'FR-total-532 nm', 'FR-cross-532 nm', 'FR-607 nm', 'FR-total-1064 nm', 'NR-total-532 nm', 'NR-607 nm', 'NR-total-355 nm', 'NR-387 nm', 'DFOV', '1058', '1064s']\n", + "shape of quality mask (720, 4000, 15)\n", + "0 FR-total-355 nm\n", + "1 FR-cross-355 nm\n", + "2 FR-387 nm\n", + "3 FR-407 nm\n", + "4 FR-total-532 nm\n", + "5 FR-cross-532 nm\n", + "6 FR-607 nm\n", + "7 FR-total-1064 nm\n", + "8 NR-total-532 nm\n", + "9 NR-607 nm\n", + "10 NR-total-355 nm\n", + "11 NR-387 nm\n", + "12 DFOV\n", + "13 1058\n", + "14 1064s\n" + ] + } + ], + "source": [ + "## Quality mask of signal\n", + "data_cube.estQualityMask()" + ] + }, + { + "cell_type": "markdown", + "id": "5a04d669", + "metadata": {}, + "source": [ + "### Plot example: Quality Mask" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "10c7efb9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\buholdt\\AppData\\Local\\Temp\\ipykernel_13624\\4235260950.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + " cmap=plt.cm.get_cmap('Set3', 6),\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmesh = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'], \n", + " np.array(data_cube.retrievals_highres['height'])/1000, \n", + " np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, data_cube.gf(355, 'total', 'FR')]).T, \n", + " shading='nearest',\n", + " cmap=plt.cm.get_cmap('Set3', 6),\n", + " vmin=-0.5, vmax=5.5\n", + ")\n", + "\n", + "cats = {\n", + " 0: \"good data\",\n", + " 1: \"low-SNR data\",\n", + " 2: \"depolarization\\ncalibration periods\",\n", + " 3: \"shutter on\",\n", + " 4: \"fog\",\n", + " 5: \"saturated\"\n", + "}\n", + "formatter = plt.FuncFormatter(lambda val, loc:cats[val])\n", + "cbar = fig.colorbar(pcmesh, ticks=np.arange(0, 6), format=formatter)\n", + "cbar.set_label('Quality Mask 355nm total FR')\n", + "ax.set_ylim(0, 15)\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel(\"Time [UTC]\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "3d3a31d9-87bc-4d0f-9464-a9fa3885123d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(720, 4000) 1 5\n", + "(720, 4000) (720, 4000)\n", + "hFullOverlap 900 120\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV1.py:112: RuntimeWarning: divide by zero encountered in divide\n", + " quasi_par_bsc = att_beta / (mol_att * quasi_par_att) ** 2 - molBsc\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV1.py:112: RuntimeWarning: overflow encountered in divide\n", + " quasi_par_bsc = att_beta / (mol_att * quasi_par_att) ** 2 - molBsc\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV1.py:114: RuntimeWarning: overflow encountered in multiply\n", + " quasi_par_ext = quasi_par_bsc * LRaer\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV1.py:111: RuntimeWarning: overflow encountered in multiply\n", + " quasi_par_att = np.exp(-np.nancumsum(quasi_par_ext * diff_height, axis=1))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(720, 4000) 1 5\n", + "(720, 4000) (720, 4000)\n", + "hFullOverlap 800 107\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\AppData\\Local\\miniconda3\\envs\\PicassoPy\\Lib\\site-packages\\numpy\\_core\\fromnumeric.py:57: RuntimeWarning: overflow encountered in accumulate\n", + " return bound(*args, **kwds)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(720, 4000) 1 5\n", + "(720, 4000) (720, 4000)\n", + "hFullOverlap 800 107\n", + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasi.py:58: RuntimeWarning: divide by zero encountered in divide\n", + " quasi_pdr = (vdr + 1) / (mBsc * (molDepol - vdr) / quasi_bsc / (1 + molDepol) + 1) - 1\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasi.py:73: RuntimeWarning: divide by zero encountered in divide\n", + " ratio_par_bsc = data_cube.retrievals_highres[f'quasiBsc{version}_532_{t}_{tel}'] / \\\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasi.py:73: RuntimeWarning: invalid value encountered in divide\n", + " ratio_par_bsc = data_cube.retrievals_highres[f'quasiBsc{version}_532_{t}_{tel}'] / \\\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasi.py:320: RuntimeWarning: overflow encountered in divide\n", + " if np.min(bsc1064[hIndLargeBsc:(hIndLargeBsc + jump_hBins), iTime] / bsc1064[hIndLargeBsc, iTime]) < (1 / minAttnRatioBsc1064):\n" + ] + } + ], + "source": [ + "## QuasiV1 retrievals and Target categorization\n", + "data_cube.quasiV1()" + ] + }, + { + "cell_type": "markdown", + "id": "23ace408", + "metadata": {}, + "source": [ + "### Plot example: Target Categorization Version 1" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "619815ee", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\buholdt\\AppData\\Local\\Temp\\ipykernel_13624\\316367719.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + " cmap=plt.cm.get_cmap('Set3', 12),\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmesh = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " np.array(data_cube.retrievals_highres['height'])/1000, \n", + " data_cube.retrievals_highres['tcMaskV1'].T,\n", + " shading='nearest',\n", + " cmap=plt.cm.get_cmap('Set3', 12),\n", + " vmin=-0.5, vmax=11.5\n", + ")\n", + "\n", + "cats = {\n", + " 0: 'No signal',\n", + " 1: 'Clean atmosphere',\n", + " 2: 'Non-typed particles/low conc.',\n", + " 3: 'Aerosol: small',\n", + " 4: 'Aerosol: large, spherical',\n", + " 5: 'Aerosol: mixture, partly non-spherical',\n", + " 6: 'Aerosol: large, non-spherical',\n", + " 7: 'Cloud: non-typed',\n", + " 8: 'Cloud: water droplets',\n", + " 9: 'Cloud: likely water droplets',\n", + " 10: 'Cloud: ice crystals',\n", + " 11: 'Cloud: likely ice crystal',\n", + "}\n", + "formatter = plt.FuncFormatter(lambda val, loc:cats[val])\n", + "cbar = fig.colorbar(pcmesh, ticks=np.arange(0, 12), format=formatter)\n", + "cbar.set_label('Target Categorization V1')\n", + "ax.set_ylim(0, 15)\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('Time [UTC]')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "d83cdc74", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV2.py:115: RuntimeWarning: divide by zero encountered in divide\n", + " quasi_par_bsc = (att_beta_el / att_beta_ra) * quasi_par_att - molBscEl\n", + "c:\\Users\\buholdt\\AppData\\Local\\miniconda3\\envs\\PicassoPy\\Lib\\site-packages\\numpy\\_core\\fromnumeric.py:57: RuntimeWarning: invalid value encountered in accumulate\n", + " return bound(*args, **kwds)\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV2.py:114: RuntimeWarning: overflow encountered in exp\n", + " quasi_par_att = np.exp((1 - (wv / wv_r) ** AE) * OD_par + (OD_mol - OD_mol_r)) * molBscEl\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\quasiV2.py:112: RuntimeWarning: overflow encountered in exp\n", + " quasi_par_att = np.exp((2 - (1064 / 607) ** AE - (1064 / 532) ** AE) * OD_par + (2 * OD_mol - OD_mol_532 - OD_mol)) * molBsc532\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n" + ] + } + ], + "source": [ + "## QuasiV2 retrievals and Target categorization\n", + "data_cube.quasiV2()" + ] + }, + { + "cell_type": "markdown", + "id": "d4f2d1f4", + "metadata": {}, + "source": [ + "### Plot example: Target Categorization Version 2" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4089a68b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\buholdt\\AppData\\Local\\Temp\\ipykernel_13624\\906464641.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + " cmap=plt.cm.get_cmap('Set3', 12),\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmesh = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " np.array(data_cube.retrievals_highres['height'])/1000, \n", + " data_cube.retrievals_highres['tcMaskV2'].T,\n", + " shading='nearest',\n", + " cmap=plt.cm.get_cmap('Set3', 12),\n", + " vmin=-0.5, vmax=11.5\n", + ")\n", + "\n", + "cats = {\n", + " 0: 'No signal',\n", + " 1: 'Clean atmosphere',\n", + " 2: 'Non-typed particles/low conc.',\n", + " 3: 'Aerosol: small',\n", + " 4: 'Aerosol: large, spherical',\n", + " 5: 'Aerosol: mixture, partly non-spherical',\n", + " 6: 'Aerosol: large, non-spherical',\n", + " 7: 'Cloud: non-typed',\n", + " 8: 'Cloud: water droplets',\n", + " 9: 'Cloud: likely water droplets',\n", + " 10: 'Cloud: ice crystals',\n", + " 11: 'Cloud: likely ice crystal',\n", + "}\n", + "formatter = plt.FuncFormatter(lambda val, loc:cats[val])\n", + "cbar = fig.colorbar(pcmesh, ticks=np.arange(0, 12), format=formatter)\n", + "cbar.set_label('Target Categorization V2')\n", + "ax.set_ylim(0, 15)\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('Time [UTC]')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "4ec087b6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:46,435 - INFO - saving product: att_bsc\n", + "2026-05-11 10:58:46,584 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_att_bsc.nc\n", + "2026-05-11 10:58:48,256 - INFO - saving product: NR_att_bsc\n", + "2026-05-11 10:58:48,411 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_NR_att_bsc.nc\n", + "2026-05-11 10:58:49,147 - INFO - saving product: OC_att_bsc\n", + "2026-05-11 10:58:49,242 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_OC_att_bsc.nc\n", + "2026-05-11 10:58:50,066 - INFO - saving product: vol_depol\n", + "2026-05-11 10:58:50,163 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_vol_depol.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "removing variable: volume_depolarization_ratio_532nm_DFOV\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:50,621 - INFO - saving product: quasi_results\n", + "2026-05-11 10:58:50,709 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_quasi_results.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "removing variable: quality_mask_355nm\n", + "removing variable: quality_mask_532nm\n", + "removing variable: quality_mask_1064nm\n", + "removing variable: quality_mask_voldepol_532\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:52,647 - INFO - saving product: quasi_results_V2\n", + "2026-05-11 10:58:52,782 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_quasi_results_V2.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "removing variable: quality_mask_355nm\n", + "removing variable: quality_mask_532nm\n", + "removing variable: quality_mask_1064nm\n", + "removing variable: quality_mask_voldepol_532\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 10:58:56,683 - INFO - saving product: target_classification\n", + "2026-05-11 10:58:56,819 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_target_classification.nc\n", + "2026-05-11 10:58:56,962 - INFO - saving product: target_classification_V2\n", + "2026-05-11 10:58:57,080 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_target_classification_V2.nc\n" + ] + } + ], + "source": [ + "## Save highres retrivals\n", + "write2nc_file(data_cube=data_cube, prod_ls=[\"att_bsc\", \"NR_att_bsc\", \"OC_att_bsc\", \"vol_depol\", \"quasi_results\", \"quasi_results_V2\", \"target_classification\", \"target_classification_V2\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PicassoPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 13b5d5ada14c224d6f90ae631dfd467e5eeed316 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Thu, 23 Jul 2026 16:48:00 +0200 Subject: [PATCH 06/13] DFOV voldepol not calculated Fixes #85 --- ppcpy/retrievals/highres.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/ppcpy/retrievals/highres.py b/ppcpy/retrievals/highres.py index 42d83be..31b9821 100644 --- a/ppcpy/retrievals/highres.py +++ b/ppcpy/retrievals/highres.py @@ -8,7 +8,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): - """Attenuated Backscatter + """Attenuated Backscatter. Parameters ---------- @@ -39,11 +39,10 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): sig = np.squeeze( data_cube.retrievals_highres[f'sigTCor'][:, :, data_cube.gf(wv, t, tel)]) - if channel in data_cube.LCused.keys(): - pass - else: + if channel not in data_cube.LCused.keys(): logging.info(f'{channel} skipped at attbsc_2d') continue + attBsc = sig * ranges2d / data_cube.LCused[channel] attBsc[data_cube.retrievals_highres['depCalMask'], :] = np.nan @@ -53,7 +52,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): # experimental, the calibration constant requires the OL corrected signal if 'sigOLCor' in data_cube.retrievals_highres: print(f"Exprimental, attenuated backscatter solution for {channel}") - sigOLTCor, _ = transCor.transCorGHK_cube(data_cube, signal='OLCor') + sigOLTCor, _ = transCor.transCorGHK_cube(data_cube, signal='OLCor') channels = [(355, 'total', 'FR'), (532, 'total', 'FR'), (1064, 'total', 'FR')] for wv, t, tel in channels: channel = f"{wv}_{t}_{tel}" @@ -62,9 +61,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): # data_cube.retrievals_highres[f'sigOLCor'][:, :, data_cube.gf(wv, t, tel)]) sig = np.squeeze(sigOLTCor[:, :, data_cube.gf(wv, t, tel)]) - if channel in data_cube.LCused.keys(): - pass - else: + if channel not in data_cube.LCused.keys(): logging.info(f'{channel} skipped at attbsc_2d OL') continue @@ -75,7 +72,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): def voldepol_2d(data_cube): - """Calculate the volume depolarisation ratio + """Calculate the volume depolarisation ratio. Parameters ---------- @@ -88,7 +85,7 @@ def voldepol_2d(data_cube): channels = [ (532, 'FR'), (355, 'FR'), (1064, 'FR')] - if '532_DFOV' in data_cube.pol_cali: + if '532_DFOV' in data_cube.etaused: channels += [(532, 'DFOV')] print('voldepol also for DFOV') @@ -107,9 +104,12 @@ def voldepol_2d(data_cube): vdr, vdrStd = depolarization.calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], - window=1) + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], + window=1 + ) vdr[data_cube.retrievals_highres['depCalMask'], :] = np.nan data_cube.retrievals_highres[f"voldepol_{wv}_total_{tel}"] = vdr From a0e679abaeafc5ae22ae92e4b8b4ca0c1f5c2270 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 13:34:08 +0200 Subject: [PATCH 07/13] Convert back to Photon Counts in pre range-corrected space --- ppcpy/preprocess/pollyPreprocess.py | 1689 ++++++++++++++------------- 1 file changed, 884 insertions(+), 805 deletions(-) diff --git a/ppcpy/preprocess/pollyPreprocess.py b/ppcpy/preprocess/pollyPreprocess.py index 6ba87e4..641184b 100644 --- a/ppcpy/preprocess/pollyPreprocess.py +++ b/ppcpy/preprocess/pollyPreprocess.py @@ -1,955 +1,916 @@ import numpy as np import logging import datetime -import time +# import time import itertools from scipy.ndimage import label -from multiprocessing import Pool, cpu_count -#from ppcpy.preprocess.optimized_polyval import process_signal -#from ppcpy.preprocess.compute_pcr import compute_pcr -#import numpy as np -#from multiprocessing import Pool, cpu_count -from ppcpy.retrievals.collection import calc_snr +# from multiprocessing import Pool, cpu_count +from ppcpy.retrievals.collection import calc_snr -def photonCount2PCR(signal:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: - """Compute PCR from photon counts. - PCR = (c * signal)/(2 * hRes * mShots) +def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, flagPicassoComparison:bool=False, **param:dict) -> dict: + """Preprocessing of Lidar-data. - Parameters - ---------- - signal : ndarray - Signal [Photon Count] (shape: [M, N, P]). - mShots : ndarray - Mesurments shots [counts] (shape: [M, P]). - hRes : float - Reight or range resolution [m]. + Includes the following processes in order: + 1. Deadtime correction + 2. Background correction + 3. First-bin shift + 4. Mask for low-SNR + 5. Mask for depolarization-calibration process + 6. Range correction. - Returns - ------- - PCR : ndarray - Photon count rate signal [MCPS]. - - Note - ---- - - The speed of light `c` is given in meter per microsecond to directly get MCPS. - """ - c = 3e2 # meter / microsecond - PCR = (c * signal)/(2 * hRes * mShots[:, np.newaxis, :]) - return PCR - - -def PCR2PhotonCount(PCR:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: - """Compute photon counts from PCR. - - photonCount = 2 * PCR * mShot * hRes / c - Parameters ---------- - PCR : ndarray - Photon count rate signal [MCPS] (shape: [M, N, P]). - mShots : ndarray - Mesurments shots [counts] (shape: [M, P]). - hRes : float - Reight or range resolution [m]. + rawdata_dict : dict + rawSignal : ndarray + Signal [Photon Count]. + mShots : ndarray + Number of the laser shots for each profile. + mTime : ndarray + Datetime array for the measurement time of each profile. + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + zenithAng : ndarray + Zenith angle of the laer beam. + repRate : float + Laser pulse repetition rate [s^-1]. + hRes : float + Spatial resolution [m]. + mSite : str + Measurement site. + collect_debug : bool + If true, collects debug information. Default is False. + flagPicassoComparison : bool + If true, use Picasso values and logic. - Returns - ------- - photonCount : ndarray - Signal [Photon Count] (shape: [M, N, P]). - - Note - ---- - - The speed of light `c` is given in meter per microsecond to directly translate form MCPS. - """ - c = 3e2 # meter / microsecond - photonCount = 2 * hRes * PCR * mShots[:, np.newaxis, :] / c - return photonCount - - -def compute_channel_photon_count(args:tuple) -> np.ndarray: - """Computes Photon count from PCR for a single channel. - - Photons = 2 * PCR * mShot * hRes / c - - Parameters - ---------- - args : tuple - PCR : ndarray - Photon Count Rate signal [MCPS] (shape: [M, N, P]). - mShots : ndarray[time, channels] - Mesurments shots [counts] (shape: [M, P]). - scale_factor : flaot - Scaling factor for the computation: c / (2 * hRes). - channel_index : int - Index of the channel to compute the PCR for. + Keyword arguments + ----------------- + deltaT : float + Integration time for single profile [s]. Default is 30. + flagForceMeasTime : bool + Flag to control whether to align measurement time with file creation + time, instead of taking the measurement time in the data file. + Default is False. + maxHeightBin : int + Number of range bins to read out from data file. Default is 3000. + firstBinIndex : int + Index of first bin to read out. Default is 1. + pollyType : str + Polly version. Default is 'arielle'. + flagDeadTimeCorrection : bool + Flag to control whether to apply deadtime correction. Default is False. + deadtimeCorrectionMode : int + Deadtime correction mode. Default is 2. + 1: polynomial correction with parameters saved in data file. + 2: non-paralyzable correction. + 3: polynomail correction with user defined parameters. + 4: disable deadtime correction. + deadtimeParams : list + Deadtime parameters. Default is []. + flagSigTempCor : bool + Flag to implement signal temperature correction. + tempCorFunc : list + Symbolic function for signal temperature correction. + "1": no correction + "exp(-0.001*T)": exponential correction function [K]. + meteorDataSource : str + Meteorological data type. + e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' + gdas1Site : str + The GDAS1 site for the current campaign. + meteo_folder : str + The main folder of the GDAS1 profiles. + radiosondeSitenum : int + Site number, which can be found in + doc/radiosonde-station-list.txt. + radiosondeFolder : str + The folder of the sonding files. + radiosondeType : int + File type of the radiosonde file. Default is 1. + - 1: radiosonde file for MOSAiC. + - 2: radiosonde file for MUA. + bgCorrectionIndexLow : list + Base indecis of bins for background estimation. + Defults is [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. + bgCorrectionIndexHigh : list + Top index of bins for background estimation. + Defults is [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]. + asl : float + Above sea level in meters. Default is 0. + initialPolAngle : float + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskPolCalAngle : list + Mask for positive and negative calibration angle of the polarizer, in + which 'p' stands for positive angle, while 'n' for negative angle. + Default is []. + minSNRThresh : list + Lower bound of signal-noise ratio. + minPC_fog : float + Minimun number of photon count after strong attenuation by fog. + flagFarRangeChannel : list + Flags of far-range channel. + flag532nmChannel : list + Flags of channels with central wavelength (CW) at 532 nm. + flagTotalChannel : list + Flags of channels receiving total elastic signal. + flag355nmChannel : list + Flags of channels with CW at 355 nm. + flag607nmChannel : list + Flags of channels with CW at 607 nm. + flag387nmChannel : list + Flags of channels with CW at 387 nm. + flag407nmChannel : list + Flags of channels with CW at 407 nm. + flag532nmRotRaman : list + Flags of rotational Raman channels with CW at 532 nm. + flag1064nmRotRaman : list + Flags of rotational Raman channels with CW at 1064 nm. Returns ------- - ndarray - Computed PCR for the given channel (shape: [M, N, P]). + data_dict : dict + mShots : ndarray + Number of the laser shots for each profile. + sigDTCor : ndarray + Dead time corrected signal [photon count]. + BG : ndarray + Background. + sigBGCor : ndarray + Backgound corrected signal [photon count]. + range : ndarray + Height above ground at zenith angle [m]. + height : ndarray + Height above ground [m]. + alt : ndarray + Altitude [m]. + time : list + Measurement time in unix time. + time64 : ndarray + Measurement time in numpy.datetime. + SNR : ndarray + Signal to noise ratio. + lowSNRMask : ndarray + True if SNR is less SNRmin. Otherwise False. + shutterOnMask : ndarray + True if at timesteps where the shutters was on. Otherwise False. + fogMask : ndarray + If it is foggy which means the signal will be very weak, + fogMask will be set True. Otherwise, False. + mask607Off : ndarray + Mask of PMT on/off status at 607 nm channel. + mask387Off : ndarray + Mask of PMT on/off status at 387 nm channel. + mask407Off : ndarray + Mask of PMT on/off status at 407 nm channel. + mask355RROff : ndarray + Mask of PMT on/off status at 355 nm rotational Raman channel. + mask532RROff : ndarray + Mask of PMT on/off status at 532 nm rotational Raman channel. + mask1064RROff : ndarray + Mask of PMT on/off status at 1064 nm rotational Raman channel. + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + Time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + Time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + Time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + RCS : ndarray + Range Corrected signal [MCPS]. + PCR_slice : ndarray + Background corrected signal in photon count rate [MCPS]. Notes ----- - .. TODO:: change scale factor with hRes and include c/2 as a part - of the function. Could even add an flag option for MCPS or CPS. - """ - - PCR, mShots, scale_factor, ch = args - return PCR[:, :, ch] * mShots[:, np.newaxis, ch] / scale_factor - - -def compute_channel_pcr(args:tuple) -> np.ndarray: - """Computes PCR for a single channel. - - PCR = (signal * c)/(mshots * 2 * hRes) - - Parameters - ---------- - args : tuple - rawSignal : ndarray - 3D Raw signal [Photon count] (shape: [M, N, P]). - mShots : ndarray[time, channels] - Mesurments shots [counts] (shape: [M, P]). - scale_factor : flaot - Scaling factor for the computation: c / (2 * hRes). - channel_index : int - Index of the channel to compute the PCR for. + .. TODO:: + - Remove all unecesary comments. + - Why does this function not take data_cube as an input like the rest of the functions? + - Implement Temperature correction. + - Is using mShots_norm to convert back needed? - Returns - ------- - ndarray - Computed PCR for the given channel (shape: [M, N, P]). - Notes - ----- - .. TODO:: change scale factor with hRes and include c/2 as a part - of the function. Could even add an flag option for MCPS or CPS. - """ - - rawSignal, mShots, scale_factor, ch = args - return (rawSignal[:, :, ch] / mShots[:, np.newaxis, ch]) * scale_factor - - -def compute_pcr_parallel(rawSignal:np.ndarray, mShots:np.ndarray, scale_factor:float) -> np.ndarray: - """Computes PCR using multiprocessing for channel-wise parallelism. + **History** - Parameters - ---------- - rawSignal : ndarray - 3D input array (shape: [M, N, P]). - mShots : ndarray - 2D multiplicative factors array (shape: [M, P]). - scale_factor : float - Scaling factor for the computation. + - 2018-12-16: First edition by Zhenping. + - 2019-07-10: Add mask for laser shutter due to approaching airplanes. + - 2019-08-27: Add mask for turnoff of PMT at 607 and 387nm. + - 2021-01-19: Add keyword of 'flagForceMeasTime' to align measurement time. + - 2021-01-20: Re-sample the profiles into temporal resolution of 30-s. + - xxxx-xx-xx: Translated to Python by ... + - 2026-06-24: Cleaned and revamped by Buholdt. - Returns - ------- - PCR : ndarray - 3D output array (shape: [M, N, P]). """ - M, N, P = rawSignal.shape - PCR = np.zeros((M, N, P), dtype=np.float64) - - # Prepare arguments for each channel - args = [(rawSignal, mShots, scale_factor, ch) for ch in range(P)] - - # Use a pool of workers to compute each channel in parallel - with Pool(processes=min(cpu_count(), P)) as pool: - results = pool.map(compute_channel_pcr, args) - - # Collect the results - for ch, result in enumerate(results): - PCR[:, :, ch] = result - - return PCR + # ------------------------------------------------------------------------ + # Extract input data + # ------------------------------------------------------------------------ + ## print all of the large arrays to screen, not only starts and ends of an array + np.set_printoptions(threshold=np.inf) -def faster_polyval(p:np.ndarray, x:float|np.ndarray) -> float|np.ndarray: - """Faster version of np.polyval(). + ## Extracting data from rawdata_dict + rawSignal = rawdata_dict['raw_signal']['var_data'] + mShots = rawdata_dict['measurement_shots']['var_data'] + mTime = rawdata_dict['measurement_time']['var_data'] + depCalAng = rawdata_dict['depol_cal_angle']['var_data'] + zenithAng = rawdata_dict['zenithangle']['var_data'] + hRes = rawdata_dict['measurement_height_resolution']['var_data'] + # repRate = rawdata_dict['laser_rep_rate']['var_data'] # <-- Not used + # mSite = rawdata_dict['global_attributes']['location'] # <-- Not used - If `p` is of length N, this function returns:: + data_dict = {} + + ## converting raw-mTime format from [YYYYMMDD seconds-of-day] to unixtimestamp-format + logging.info(f'... time conversion') + date_string = str(mTime[0][0]) + seconds_of_day = mTime[:, 1] + YYYY = int(date_string[:4]) + MM = int(date_string[4:6]) + DD = int(date_string[6:8]) + datetime_obj = datetime.datetime(YYYY, MM, DD) + mTime_obj = [ + datetime_obj.replace(tzinfo=datetime.timezone.utc) +\ + datetime.timedelta(seconds=int(s)) for s in seconds_of_day + ] + # mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] # <-- Not used - y = p[N]*x**(N-1) + p[N-1]*x**(N-2) + ... + p[1]*x + p[0] + # Convert to Unix timestamp + mTime_unixtimestamp = [int(datetime.datetime.timestamp(dt)) for dt in mTime_obj] - Parameters - ---------- - p : ArrayLike - Polynomial coefficient including coefficients equal to 0, from constant term to highest order term. - x : float or ArrayLike - Value(s) at which to evaluate the polynomial `p`. - - Returns - ------- - y : float or ArrayLike - Polynomial `p` evaluated at values `x`. - - Notes - ----- - - Using numba would provide 10% increase, but with different function. - - Example - ------- - >>> faster_polyval([-1, 0, 3], 5) # 3*5**2 + 0*5**1 + (-1) - 76 - >>> faster_polycal([-1, 0, 3], [5, 2, -1]) - [76, 11, 2] - """ - - y = p[-1] - for pi in p[-2::-1]: - y *= x - y += pi - return y + ## Defining default values for param keys (key initialization), if not explictly defined when calling the function + # deltaT = param.get('deltaT', 30) # <-- Not used + # flagForceMeasTime = param.get('flagForceMeasTime', False) # <-- Not used + maxHeightBin = param.get('maxHeightBin', 3000) + firstBinIndex = param.get('firstBinIndex', False) + firstBinHeight = param.get('firstBinHeight', False) + pollyType = param.get('pollyType', False) + flagDeadTimeCorrection = param.get('flagDeadTimeCorrection', False) + deadtimeCorrectionMode = param.get('deadtimeCorrectionMode', 2) + deadtimeParams = param.get('deadtimeParams', False) + # flagSigTempCor = param.get('flagSigTempCor', False) # <-- Not used + # tempCorFunc = param.get('tempCorFunc', False) # <-- Not used + # meteorDataSource = param.get('meteorDataSource', False) # <-- Not used + # gdas1Site = param.get('gdas1Site', False) # <-- Not used + # gdas1_folder = param.get('gdas1_folder', False) # <-- Not used + # radiosondeSitenum = param.get('radiosondeSitenum', False) # <-- Not used + # radiosondeFolder = param.get('radiosondeFolder', False) # <-- Not used + # radiosondeType = param.get('radiosondeType', False) # <-- Not used + bgCorrectionIndexLow = param.get('bgCorrectionIndexLow', False) + bgCorrectionIndexHigh = param.get('bgCorrectionIndexHigh', False) + asl = param.get('asl', 10) + initialPolAngle = param.get('initialPolAngle', False) + maskPolCalAngle = param.get('maskPolCalAngle', False) + minSNRThresh = param.get('minSNRThresh', False) + minPC_fog = param.get('minPC_fog', False) + flagFarRangeChannel = param.get('flagFarRangeChannel', False) + flag532nmChannel = param.get('flag532nmChannel', False) + flagTotalChannel = param.get('flagTotalChannel', False) + flag355nmChannel = param.get('flag355nmChannel', False) + flag607nmChannel = param.get('flag607nmChannel', False) + flag387nmChannel = param.get('flag387nmChannel', False) + flag407nmChannel = param.get('flag407nmChannel', False) + flag355nmRotRaman = param.get('flag355nmRotRaman', False) + flag532nmRotRaman = param.get('flag532nmRotRaman', False) + flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) + # isUseLatestGDAS = param.get('isUseLatestGDAS', False) # <-- Not used + # ## .. TODO:: What does this part do and why is it omited? --> I think it is shown in the mail form Martin!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + # logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): + # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') + # maxHeightBin = np.ones(rawSignal.shape[2]) + # logging.info(f'maxHeightBin: {maxHeightBin}') + # firstBinIndex = 251 -# @jit(nopython=True) -# def faster_polyval(p, x): -# """Numba verison of faster_polyval().""" -# y = np.zeros(x.shape, dtype=float) -# for i, v in enumerate(p): -# y *= x -# y += v -# return y + # ## .. TODO:: This part is currently not used! Should we use it? + # mShotsPerPrf = deltaT * repRate + # if len(mTime) > 1: + # # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # else: + # nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) -#@profile -def pollyDTCor(PCR:np.ndarray, **varargin:dict) -> np.ndarray: - """Dead Time Correction. + # ------------------------------------------------------------------------ + # Check and store mesurment shots + # ------------------------------------------------------------------------ + if not np.all(mShots[:, 0] == mShots[0, 0]): + logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") - Parameters - ---------- - PCR : ndarray - Photon count rate signal [MCPS]. - - Keyword arguments - ----------------- - device : str or bool - Name of PollyXT device. Default is False. - flagDeadTimeCorrection : bool - If true, perform dead time correction. Otherwise, no dead time - correction is performed. Default is False. - DeadTimeCorrectionMode : int - Deadtime correction mode. Default is 2. - 1: use the parameters saved in the netcdf files, - 2: nonparalyzable correction with user define deadtime, - 3: paralyzable correction with user defined parameters, - 4: no deadtime correction, - deadtimeParams : list - Deadtime parameters from config-file at MCPS scale. Default is []. - deadtime : list - Deadtime parameters from level0 nc-file at MCPS scale. Default is []. - - Returns - ------- - PCR_DTCor : ndarray - Dead time corrected photon count rate signal [MCPS]. + # take mean/median value over time to recalculate photon count signal normalized to a common integration time + mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) + if flagPicassoComparison: + mShots_norm = np.repeat(np.median(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - Notes - ----- - .. TODO:: Finish docstring and remove all unnecessary comments. - .. TODO:: Could think of moving the scale convertion to after the loops ie. form PCR to PC. - """ + # For now store mShots in data_cube.retrievals_highres + data_dict['mShots'] = mShots - ## Defining default values for param keys (key initialization), if not explictly defined when calling the function - polly_device = varargin.get('device', False) # <-- is this needed?? - flagDeadTimeCorrection = varargin.get('flagDeadTimeCorrection', False) - DeadTimeCorrectionMode = varargin.get('DeadTimeCorrectionMode', 2) - deadtimeParams = varargin.get('deadtimeParams', []) - deadtime = varargin.get('deadtime', []) - logging.info(f'... Deadtime-correction (Mode: {DeadTimeCorrectionMode})') + # ------------------------------------------------------------------------ + # Deadtime correction + # ------------------------------------------------------------------------ + PCR = photonCount2PCR(rawSignal, mShots, hRes) - Nchannels = PCR.shape[-1] - PCR_DTCor = np.zeros_like(PCR) + logging.info('... calculate dead-time corrected signal') + PCRDTCor = pollyDTCor( + PCR=PCR, + polly_device=pollyType, + flagDeadTimeCorrection=flagDeadTimeCorrection, + DeadTimeCorrectionMode=deadtimeCorrectionMode, + deadtimeParams=deadtimeParams, + deadtime=rawdata_dict['deadtime_polynomial']['var_data'] + ) - ## Deadtime correction - if flagDeadTimeCorrection: - ## polynomial correction with parameters saved in the level0 netcdf-file under variable 'deadtime_polynomial' - if DeadTimeCorrectionMode == 1: - for iCh in range(Nchannels): - PCR_DTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) + # .. TODO:: Is it correct to use mShots_norm here to convert back from PCR?? + # In the matlab version they use mShots_norm here but with median value + # insted of the mean... + # data_dict['preproSignal'] = PCR2PhotonCount(PCRDTCor, mShots hRes) + data_dict['preproSignal'] = PCR2PhotonCount(PCRDTCor, mShots_norm, hRes) - ## nonparalyzable correction: PCR_cor = PCR / (1 - tau*PCR), with tau beeing the dead-time - ## reading from polly-config file under key 'dT' (only the first value from each channel) - elif DeadTimeCorrectionMode == 2: - for iCh in range(Nchannels): - PCR_DTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) - ## user defined deadtime, reading from polly-config file under key 'dT' (the whole matrix, polynome) - elif DeadTimeCorrectionMode == 3: - if np.array(deadtimeParams).size != 0: - coeffs_matrix = np.array([np.array(deadtimeParams[ch][::-1]) for ch in range(Nchannels)]) - for iCh in range(Nchannels): - PCR_DTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) - else: - logging.warning(f'User defined deadtime parameters were not found in polly-config file.') - logging.warning(f'In order to continue the current processing, deadtime correction will not be implemented.') + # ------------------------------------------------------------------------ + # Background Correction + # ------------------------------------------------------------------------ + logging.info('... calculate background corrected signal') + sigBGCor, bg = pollyRemoveBG( + rawSignal=data_dict['preproSignal'], + bgCorrectionIndexLow=bgCorrectionIndexLow, + bgCorrectionIndexHigh=bgCorrectionIndexHigh, + maxHeightBin=maxHeightBin, + firstBinIndex=firstBinIndex + ) + # Store the background and background corrected signal + data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix + data_dict['sigBGCor'] = sigBGCor - ## No deadtime correction - elif DeadTimeCorrectionMode == 4: - PCR_DTCor = PCR.astype(np.float64) - logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') - else: - logging.error(f'Unknow deadtime correction setting! Please go back to check the configuration.') - logging.error(f'For deadtimeCorrectionMode, only 1-4 is allowed.') - - ## flagDeadTimeCorrection equals False - else: - PCR_DTCor = PCR.astype(np.float64) - logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') + # ------------------------------------------------------------------------ + # Height and first bin height correction + # ------------------------------------------------------------------------ + logging.info('... height bin calculations') + # .. TODO:: first bin hight might change for different telescopes... --> should expand range to be channel spesific. + data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] + data_dict['height'] = data_dict['range'].copy() * np.cos(zenithAng*np.pi/180) - return PCR_DTCor + # correction firstBinHight = range per channel ^ 2 / range first channel ^ 2 + # .. TODO:: write a better explenation for this correction. + correction_firstBinHight = ( + ((np.arange(0, sigBGCor.shape[1]) * hRes)[:, np.newaxis] + firstBinHeight)**2 + / data_dict['range'][:, np.newaxis]**2 + ) + data_dict['sigBGCor'] = data_dict['sigBGCor'] * correction_firstBinHight[np.newaxis, :, :] -def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, - maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: - """Background correction. Remove mean background noise from signal. + data_dict['alt'] = data_dict['height'] + float(asl) ## geopotential height + data_dict['time'] = mTime_unixtimestamp + data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) - Parameters - ---------- - rawSignal : ndarray - Signal to be processed [MCPS or Photon counts]. - bgCorrectionIndexLow : list of int - Lower index of background noise per channel. - bgCorrectionIndexHigh : list of int - Upper index of background noise per channel. - maxHeightBin : int - Maximum height bin index. Default is 3000). - firstBinIndex : list of int - First height bin index per channel. Default is 0 per channel. - Returns - ------- - signal_out : ndarray - Background corrected signal [MCPs or Photon counts]. - bg : ndarray - Removed background noise [MCPS or Photon counts]. - """ + # ------------------------------------------------------------------------ + # Mask for bins with low SNR + # ------------------------------------------------------------------------ + logging.info('... mask bins with low SNR') - logging.info(f'... removing background from signal') + SNR = calc_snr(data_dict['sigBGCor'], bg) + data_dict['SNR'] = SNR - if firstBinIndex is None: - logging.warning('No firstBinIndex value were given, default value 0 is used', exc_info=True) - firstBinIndex = [0]*rawSignal.shape[2] + data_dict['lowSNRMask'] = np.zeros_like(sigBGCor, dtype=bool) + data_dict['lowSNRMask'][SNR < minSNRThresh] = True - # Calculate the mean across the channel specific column range for each row and page - mean_matrix = np.empty((rawSignal.shape[0], 1, rawSignal.shape[2]), dtype=rawSignal.dtype) - for iCh in range(rawSignal.shape[2]): - mean_matrix[:, :, iCh] = np.mean(rawSignal[:, bgCorrectionIndexLow[iCh]:bgCorrectionIndexHigh[iCh] + 1, iCh], axis=1, keepdims=True) - # Replicate the mean matrix along the second dimension - bg = np.tile(mean_matrix, (1, maxHeightBin, 1)) - signal_out = slicerange(rawSignal, maxHeightBin, firstBinIndex) - bg - return signal_out, bg + # ------------------------------------------------------------------------ + # Mask for bins whit laser shutter on + # ------------------------------------------------------------------------ + logging.info('... mask bins with laser shutter on') + flag532FR = (np.array(flag532nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) + flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) + if any(flag532FR): + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532FR])) + elif any(flag355FR): + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355FR])) + else: + raise ValueError('No suitable channel to determine the shutter status.') -def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.ndarray: - """Slice a given array across the height/range dimension from firstBinIndex to maxHeightBin + firstBinIndex. - Parameters - ---------- - array : ndarray - Array to be sliced. - maxHeightBin : int - Length of slice. - firstBinIndex : list of int - Start hight/range index of slice per channel. + # ------------------------------------------------------------------------ + # Mask for bins with fog + # ------------------------------------------------------------------------ + logging.info('... mask bins with fog') + # .. TODO:: The original matlab code raises questions. Why 40:120 and why hard coded? + # When sum is used (as in matlab), minPC_fog is range resolution dependent + fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:, 39:120, flag532FR]), axis=1) + data_dict['fogMask'] = fogsum < minPC_fog - Returns - ------- - out : ndarray - Sliced array. - """ - assert len(firstBinIndex) == array.shape[2], f"first bin index and array do not match {len(firstBinIndex)}, {array.shape}" - firstBinIndex = np.asarray(firstBinIndex) - heightBins = np.arange(maxHeightBin)[:, None] + firstBinIndex[None, :] - out = array[:, heightBins, np.arange(array.shape[2])] - return out + # ------------------------------------------------------------------------ + # Mask for bins where a single channel was off + # ------------------------------------------------------------------------ + logging.info('... mask bins where a single channel was off') + flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag607FR): + data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag607FR])) + flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag387FR): + data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag387FR])) -def pollyPolCaliTime(depCalAng:np.ndarray, mTime:list, init_depAng:float, maskDepCalAng:list) -> tuple: - """Retrieve the time for the polly depolarization calibration - period. depolarization calibration: 5 min (+45°) + 5 min (-45°) + 0.5 min. + flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag407FR): + data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag407FR])) + + flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag355RRFR): + data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355RRFR])) + + flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag532RRFR): + data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532RRFR])) + + flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag1064RRFR): + data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag1064RRFR])) + + + # ------------------------------------------------------------------------ + # Mask for polarization calibration + # ------------------------------------------------------------------------ + logging.info('... mask bins during polarization calibration periods') + (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], + data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], + data_dict['depCalMask']) = pollyPolCaliTime( + depCalAng=depCalAng, + mTime=mTime_unixtimestamp, + init_depAng=initialPolAngle, + maskDepCalAng=maskPolCalAngle + ) + + + # ------------------------------------------------------------------------ + # Range-corrected Signal calculation + # ------------------------------------------------------------------------ + logging.info('... calculate range-corrected Signal') + + # .. TODO:: Is it correct to use mShots_norm here for converting to PCR??? + # This is not done in the matlab version. + data_dict['PCR_slice'] = photonCount2PCR(data_dict['sigBGCor'].copy(), mShots_norm, hRes) + if flagPicassoComparison: + data_dict['PCR_slice'] = photonCount2PCR(data_dict['sigBGCor'].copy(), mShots, hRes) + + data_dict['RCS'] = calculate_rcs(data_dict['PCR_slice'].copy(), data_dict['range']) + + return data_dict + + +def photonCount2PCR(signal:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute PCR from photon counts. + + PCR = (c * signal)/(2 * hRes * mShots) Parameters ---------- - depCalAng : ndarray - Angle of the polarizer in the receiving channel - (>0 means calibration process starts). - mTime : list - Datetime ndarray for the measurement time of each profile. - init_depAng : float - Initial polarization angle of the polarizer for polarization - calibration. Default is 0. - maskDepCalAng : list - Mask for positive and negative calibration angle of the polarizer, in - which 'p' stands for positive angle, while 'n' for negative angle. - Default is {}. + signal : ndarray + Signal [Photon Count] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. Returns ------- - depCal_P_Ang_time_start : list - Time for the first profile with valid positive angle depolarization - calibration. - depCal_P_Ang_time_end : list - time for the last profile with valid positive angle depolarization - calibration. - depCal_N_Ang_time_start : list - time for the first profile with valid negative angle depolarization - calibration. - depCal_N_Ang_time_end : list - time for the last profile with valid negative angle depolarization - calibration. - maskDepCal : ndarray - If polly was doing polarization calibration, depCalMask is set - True. Otherwise, False. - - Notes - ----- - .. TODO:: Clean comments of the function. - - **History** - - - 2021-04-21: First edition by Zhenping - - xxxx-xx-xx: Translated to Python by ... - """ + PCR : ndarray + Photon count rate signal [MCPS]. - depCal_P_Ang_time_start = [] - depCal_P_Ang_time_end = [] - depCal_N_Ang_time_start = [] - depCal_N_Ang_time_end = [] - maskDepCal = np.zeros(len(mTime), dtype=bool) + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly get MCPS. - if len(depCalAng) == 0: - ## if depCalAng is empty, which means the polly does not support auto depol calibration - return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal + ** History ** - if len(maskDepCalAng) == 0: - maskDepCalAng = ['none', 'none', 'p', 'p', 'p', 'p', 'p', 'p', 'p', 'p', 'none', 'none', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'none'] - ## the mask for postive and negative - ## calibration angle. 'none' means - ## invalid profiles with different - ## depol_cal_angle + - 2026-04-22: First edition by Buholdt - flagPDepCal = np.zeros(len(maskDepCalAng), dtype=bool) - flagNDepCal = np.zeros(len(maskDepCalAng), dtype=bool) - for iProf in range(0, len(maskDepCalAng)): - if maskDepCalAng[iProf] == 'p': - flagPDepCal[iProf] = True - elif maskDepCalAng[iProf] == 'n': - flagNDepCal[iProf] = True + """ - flagDepCal = (np.abs(depCalAng - init_depAng) > 0.0) - ## the profile will be treated as depol cali profile if it has different - ## depol_cal_ang than the init_depAng + c = 3e2 # meter / microsecond + PCR = (c * signal)/(2 * hRes * mShots[:, np.newaxis, :]) + return PCR - maskDepCal = flagDepCal - ## search the calibration periods - valuesFlagDepCal = flagDepCal.astype(int) +def PCR2PhotonCount(PCR:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute photon counts from PCR. - # print('flagNDepCal', flagNDepCal) - # print('flagPDepCal', flagPDepCal) + photonCount = 2 * PCR * mShot * hRes / c - ## label connected components in the matrix; 0 will stay 0 - ## connected 1s will be numbered consecutively - depCalPeriods, nDepCalPeriods = label(valuesFlagDepCal) - # print('depCalPeriods', depCalPeriods) + Parameters + ---------- + PCR : ndarray + Photon count rate signal [MCPS] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. - if nDepCalPeriods < 1: - logging.info(f'No Depolarization Calibration phase found.') - return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal + Returns + ------- + photonCount : ndarray + Signal [Photon Count] (shape: [M, N, P]). - for iDepCalPeriod in range(1,nDepCalPeriods+1): - # flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. - flagIDepCal = depCalPeriods[depCalPeriods == iDepCalPeriod] # flag for the ith calibration period. - indices = np.where(depCalPeriods == flagIDepCal[0])[0] - print('flagIDepCal', flagIDepCal) - print(len(flagIDepCal), len(maskDepCalAng)) + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly translate form MCPS. - if len(flagIDepCal) != len(maskDepCalAng): - logging.warning(f"Depolarization Calibration from Timestamp " - f"{mTime[indices[0]]} - {mTime[indices[-1]]} " - f"does not match the maskDepCalAng pattern in the polly-config file.\n" - f"This calibration phase will be skipped." - ) - continue + ** History ** - tIDepCal = mTime[indices[0]:indices[-1]+1] + - 2026-04-22: First edition by Buholdt - t_all_p_depCal = list(itertools.compress(tIDepCal, flagPDepCal)) - t_all_n_depCal = list(itertools.compress(tIDepCal, flagNDepCal)) - depCal_P_Ang_time_start.append(t_all_p_depCal[0]) - depCal_P_Ang_time_end.append(t_all_p_depCal[-1]) - depCal_N_Ang_time_start.append(t_all_n_depCal[0]) - depCal_N_Ang_time_end.append(t_all_n_depCal[-1]) + """ - return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal + c = 3e2 # meter / microsecond + photonCount = 2 * hRes * PCR * mShots[:, np.newaxis, :] / c + return photonCount -def calculate_rcs(signal:np.ndarray, ranges:np.ndarray) -> np.ndarray: - """Function for calculating RCS. +def faster_polyval(p:np.ndarray, x:float|np.ndarray) -> float|np.ndarray: + """Faster version of np.polyval(). + + If `p` is of length N, this function returns:: + + y = p[N]*x**(N-1) + p[N-1]*x**(N-2) + ... + p[1]*x + p[0] Parameters ---------- - signal : ndarray - Signal to range correct [PCR]. - ranges : ndarray - Ranges dimension [m]. - + p : ArrayLike + Polynomial coefficient including coefficients equal to 0, from constant term to highest order term. + x : float or ArrayLike + Value(s) at which to evaluate the polynomial `p`. + Returns ------- - RCS : ndarray - Range corrected signal [PCR]. + y : float or ArrayLike + Polynomial `p` evaluated at values `x`. + + Notes + ----- + - Using numba would provide 10% increase, but with different function. + + ** History ** + + - xxxx-xx-xx: first edition by + + + Example + ------- + >>> faster_polyval([-1, 0, 3], 5) # 3*5**2 + 0*5**1 + (-1) + 76 + >>> faster_polycal([-1, 0, 3], [5, 2, -1]) + [76, 11, 2] """ - ranges_squared = ranges**2 - ranges2d = np.repeat(ranges_squared[np.newaxis, :], signal.shape[0], axis=0) - RCS = signal * ranges2d[:, :, np.newaxis] - return RCS + y = p[-1] + for pi in p[-2::-1]: + y *= x + y += pi + return y -def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, flagPicassoComparison:bool=False, **param:dict) -> dict: - """Preprocessing of Lidar-data. +#@profile +def pollyDTCor(PCR:np.ndarray, **varargin:dict) -> np.ndarray: + """Dead Time Correction. - Includes the following processes in order: - 1. Deadtime correction - 2. Background correction - 3. First-bin shift - 4. Mask for low-SNR - 5. Mask for depolarization-calibration process - 6. Range correction. - Parameters ---------- - rawdata_dict : dict - rawSignal : ndarray - Signal [Photon Count]. - mShots : ndarray - Number of the laser shots for each profile. - mTime : ndarray - Datetime array for the measurement time of each profile. - depCalAng : ndarray - Angle of the polarizer in the receiving channel - (>0 means calibration process starts). - zenithAng : ndarray - Zenith angle of the laer beam. - repRate : float - Laser pulse repetition rate [s^-1]. - hRes : float - Spatial resolution [m]. - mSite : str - Measurement site. - collect_debug : bool - If true, collects debug information. Default is False. - flagPicassoComparison : bool - If true, use Picasso values and logic. + PCR : ndarray + Photon count rate signal [MCPS]. Keyword arguments ----------------- - deltaT : float - Integration time (in seconds) for single profile. Default is 30. - flagForceMeasTime : bool - Flag to control whether to align measurement time with file creation - time, instead of taking the measurement time in the data file. - Default is False. - maxHeightBin : int - Number of range bins to read out from data file. Default is 3000. - firstBinIndex : int - Index of first bin to read out. Default is 1. - pollyType : str - Polly version. Default is 'arielle'. + device : str or bool + Name of PollyXT device. Default is False. flagDeadTimeCorrection : bool - Flag to control whether to apply deadtime correction. Default is False. - deadtimeCorrectionMode : int + If true, perform dead time correction. Otherwise, no dead time + correction is performed. Default is False. + DeadTimeCorrectionMode : int Deadtime correction mode. Default is 2. - 1: polynomial correction with parameters saved in data file. - 2: non-paralyzable correction. - 3: polynomail correction with user defined parameters. - 4: disable deadtime correction. + 1: use the parameters saved in the netcdf files, + 2: nonparalyzable correction with user define deadtime, + 3: paralyzable correction with user defined parameters, + 4: no deadtime correction, deadtimeParams : list - Deadtime parameters. Default is []. - flagSigTempCor : bool - Flag to implement signal temperature correction. - tempCorFunc : list - Symbolic function for signal temperature correction. - "1": no correction - "exp(-0.001*T)": exponential correction function [K]. - meteorDataSource : str - Meteorological data type. - e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' - gdas1Site : str - The GDAS1 site for the current campaign. - meteo_folder : str - The main folder of the GDAS1 profiles. - radiosondeSitenum : int - Site number, which can be found in - doc/radiosonde-station-list.txt. - radiosondeFolder : str - The folder of the sonding files. - radiosondeType : int - File type of the radiosonde file. Default is 1. - - 1: radiosonde file for MOSAiC. - - 2: radiosonde file for MUA. - bgCorrectionIndexLow : list - Base indecis of bins for background estimation. - Defults is [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. - bgCorrectionIndexHigh : list - Top index of bins for background estimation. - Defults is [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]. - asl : float - Above sea level in meters. Default is 0. - initialPolAngle : float - Initial polarization angle of the polarizer for polarization - calibration. Default is 0. - maskPolCalAngle : list - Mask for positive and negative calibration angle of the polarizer, in - which 'p' stands for positive angle, while 'n' for negative angle. - Default is []. - minSNRThresh : list - Lower bound of signal-noise ratio. - minPC_fog : float - Minimun number of photon count after strong attenuation by fog. - flagFarRangeChannel : list - Flags of far-range channel. - flag532nmChannel : list - Flags of channels with central wavelength (CW) at 532 nm. - flagTotalChannel : list - Flags of channels receiving total elastic signal. - flag355nmChannel : list - Flags of channels with CW at 355 nm. - flag607nmChannel : list - Flags of channels with CW at 607 nm. - flag387nmChannel : list - Flags of channels with CW at 387 nm. - flag407nmChannel : list - Flags of channels with CW at 407 nm. - flag532nmRotRaman : list - Flags of rotational Raman channels with CW at 532 nm. - flag1064nmRotRaman : list - Flags of rotational Raman channels with CW at 1064 nm. + Deadtime parameters from config-file at MCPS scale. Default is []. + deadtime : list + Deadtime parameters from level0 nc-file at MCPS scale. Default is []. Returns ------- - data_dict : dict - rawSignal : ndarray - Signal [Photon Count]. - mShots : ndarray - Number of the laser shots for each profile. - mTime : ndarray - Datetime ndarray for the measurement time of each profile. - depCalAng : ndarray - Angle of the polarizer in the receiving channel - (>0 means calibration process starts). - zenithAng : ndarray - Zenith angle of the laer beam. - repRate : float - Laser pulse repetition rate [s^-1]. - hRes : float - Spatial resolution [m]. - mSite : str - Measurement site. - deadtime : ndarray (channel x polynomial_orders) - Deadtime correction parameters. - signal : ndarray - Background removed signal. - bg : ndarray - Background. - height : ndarray - Height [m]. - lowSNRMask : ndarray - If SNR less SNRmin, mask is set True. Otherwise, False. - depCalMask : ndarray - If polly was doing polarization calibration, depCalMask is set - True. Otherwise, False. - fogMask : ndarray - If it is foggy which means the signal will be very weak, - fogMask will be set True. Otherwise, False. - mask607Off : ndarray - Mask of PMT on/off status at 607 nm channel. - mask387Off : ndarray - Mask of PMT on/off status at 387 nm channel. - mask407Off : ndarray - Mask of PMT on/off status at 407 nm channel. - mask355RROff : ndarray - Mask of PMT on/off status at 355 nm rotational Raman channel. - mask532RROff : ndarray - Mask of PMT on/off status at 532 nm rotational Raman channel. - mask1064RROff : ndarray - Mask of PMT on/off status at 1064 nm rotational Raman channel. - + PCR_DTCor : ndarray + Dead time corrected photon count rate signal [MCPS]. + Notes ----- - .. TODO:: Revamp docstring. - .. TODO:: Rewrite the function, and get rid of all unecessary comments. - Could use the same logic as for the rest of the processing ie. Send - the PicassoProc object in as an input and then extract the info needed at - the start as it is already done. Example: - config_dict = data_cube.polly_config_dict - raw_dict = data_cube.rawdata_dict - etc. - Would need to define the default values somwhere than. - .. TODO:: Change to PCR in pre-range corrected space. - .. TODO:: Move this function up. This should be the first function in the file to match - the structure of the rest of the processing chain. - .. TODO:: change to PCR before DT correctiong and then use PCR for the rest of the processing - I could also save both PCR and Photon Count version of signals. - **History** + ** History ** + + - 2021-05-16: first edition by Zhenping + - 2025-02-19: edition by Cristofer Jimenez + - xxxx-xx-xx: translated to python + - 2026-06-24: Removed signal conversion (photon count to PCR) from function - - 2018-12-16: First edition by Zhenping. - - 2019-07-10: Add mask for laser shutter due to approaching airplanes. - - 2019-08-27: Add mask for turnoff of PMT at 607 and 387nm. - - 2021-01-19: Add keyword of 'flagForceMeasTime' to align measurement time. - - 2021-01-20: Re-sample the profiles into temporal resolution of 30-s.. - - xxxx-xx-xx: Translated to Python by ... """ - logging.info('starting data preprocessing...') + ## Defining default values for param keys (key initialization), if not explictly defined when calling the function + # polly_device = varargin.get('device', False) # <-- TODO: is this needed?? + flagDeadTimeCorrection = varargin.get('flagDeadTimeCorrection', False) + DeadTimeCorrectionMode = varargin.get('DeadTimeCorrectionMode', 2) + deadtimeParams = varargin.get('deadtimeParams', []) + deadtime = varargin.get('deadtime', []) + + logging.info(f"... Deadtime-correction (Mode: {DeadTimeCorrectionMode})") - # print all of the large arrays to screen, not only starts and ends of an array - np.set_printoptions(threshold=np.inf) + Nchannels = PCR.shape[-1] + PCR_DTCor = PCR.copy().astype(np.float64) - ## Extracting data from rawdata_dict - rawSignal = rawdata_dict['raw_signal']['var_data'] - mShots = rawdata_dict['measurement_shots']['var_data'] - mTime = rawdata_dict['measurement_time']['var_data'] - depCalAng = rawdata_dict['depol_cal_angle']['var_data'] - zenithAng = rawdata_dict['zenithangle']['var_data'] - repRate = rawdata_dict['laser_rep_rate']['var_data'] - hRes = rawdata_dict['measurement_height_resolution']['var_data'] - mSite = rawdata_dict['global_attributes']['location'] + ## Deadtime correction + if flagDeadTimeCorrection: + ## polynomial correction with parameters saved in the level0 netcdf-file under variable 'deadtime_polynomial' + if DeadTimeCorrectionMode == 1: + for iCh in range(Nchannels): + PCR_DTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) - data_dict = {} + ## nonparalyzable correction: PCR_cor = PCR / (1 - tau*PCR), with tau beeing the dead-time + ## reading from polly-config file under key 'dT' (only the first value from each channel) + elif DeadTimeCorrectionMode == 2: + for iCh in range(Nchannels): + PCR_DTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) + + ## user defined deadtime, reading from polly-config file under key 'dT' (the whole matrix, polynome) + elif DeadTimeCorrectionMode == 3: + if np.array(deadtimeParams).size != 0: + coeffs_matrix = np.array([np.array(deadtimeParams[ch][::-1]) for ch in range(Nchannels)]) + for iCh in range(Nchannels): + PCR_DTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) + else: + logging.warning(f"User defined deadtime parameters were not found in polly-config file.") + logging.warning(f"In order to continue the current processing, deadtime correction will not be implemented.") + + ## No deadtime correction + elif DeadTimeCorrectionMode == 4: + logging.warning(f"Deadtime correction was turned off. Be careful to check the signal strength.") + + else: + logging.error(f"Unknow deadtime correction setting! Please go back to check the configuration.") + logging.error(f"For deadtimeCorrectionMode, only 1-4 is allowed.") - ## converting raw-mTime format from [YYYYMMDD seconds-of-day] to unixtimestamp-format - logging.info(f'... time conversion') - date_string = str(mTime[0][0]) - seconds_of_day = mTime[:,1] - YYYY = int(date_string[:4]) - MM = int(date_string[4:6]) - DD = int(date_string[6:8]) - datetime_obj = datetime.datetime(YYYY, MM, DD) - mTime_obj = [ - datetime_obj.replace(tzinfo=datetime.timezone.utc) +\ - datetime.timedelta(seconds=int(s)) for s in seconds_of_day - ] - mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] + ## flagDeadTimeCorrection equals False + else: + logging.warning(f"Deadtime correction was turned off. Be careful to check the signal strength.") - # Convert to Unix timestamp - mTime_unixtimestamp = [int(datetime.datetime.timestamp(dt)) for dt in mTime_obj] + return PCR_DTCor - ## Defining default values for param keys (key initialization), if not explictly defined when calling the function - deltaT = param.get('deltaT', 30) - flagForceMeasTime = param.get('flagForceMeasTime', False) - maxHeightBin = param.get('maxHeightBin', 3000) - firstBinIndex = param.get('firstBinIndex', False) - firstBinHeight = param.get('firstBinHeight', False) - pollyType = param.get('pollyType', False) - flagDeadTimeCorrection = param.get('flagDeadTimeCorrection', False) - deadtimeCorrectionMode = param.get('deadtimeCorrectionMode', 2) - deadtimeParams = param.get('deadtimeParams', False) - flagSigTempCor = param.get('flagSigTempCor', False) - tempCorFunc = param.get('tempCorFunc', False) - meteorDataSource = param.get('meteorDataSource', False) - gdas1Site = param.get('gdas1Site', False) - gdas1_folder = param.get('gdas1_folder', False) - radiosondeSitenum = param.get('radiosondeSitenum', False) - radiosondeFolder = param.get('radiosondeFolder', False) - radiosondeType = param.get('radiosondeType', False) - bgCorrectionIndexLow = param.get('bgCorrectionIndexLow', False) - bgCorrectionIndexHigh = param.get('bgCorrectionIndexHigh', False) - asl = param.get('asl', 10) - initialPolAngle = param.get('initialPolAngle', False) - maskPolCalAngle = param.get('maskPolCalAngle', False) - minSNRThresh = param.get('minSNRThresh', False) - minPC_fog = param.get('minPC_fog', False) - flagFarRangeChannel = param.get('flagFarRangeChannel', False) - flag532nmChannel = param.get('flag532nmChannel', False) - flagTotalChannel = param.get('flagTotalChannel', False) - flag355nmChannel = param.get('flag355nmChannel', False) - flag607nmChannel = param.get('flag607nmChannel', False) - flag387nmChannel = param.get('flag387nmChannel', False) - flag407nmChannel = param.get('flag407nmChannel', False) - flag355nmRotRaman = param.get('flag355nmRotRaman', False) - flag532nmRotRaman = param.get('flag532nmRotRaman', False) - flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) - isUseLatestGDAS = param.get('isUseLatestGDAS', False) - # ## .. TODO:: What does this part do and why is it omited? - # logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - # if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): - # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') - # maxHeightBin = np.ones(rawSignal.shape[2]) - # logging.info(f'maxHeightBin: {maxHeightBin}') - # firstBinIndex = 251 +def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, + maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: + """Background correction. Remove mean background noise from signal. - # ## .. TODO:: This part is currently not used! Should we use it? - # mShotsPerPrf = deltaT * repRate - # if len(mTime) > 1: - # # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s - # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s - # else: - # nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) + Parameters + ---------- + rawSignal : ndarray + Signal to be processed [MCPS or Photon counts]. + bgCorrectionIndexLow : list of int + Lower index of background noise per channel. + bgCorrectionIndexHigh : list of int + Upper index of background noise per channel. + maxHeightBin : int + Maximum height bin index. Default is 3000). + firstBinIndex : list of int + First height bin index per channel. Default is 0 per channel. + Returns + ------- + signal_out : ndarray + Background corrected signal [MCPs or Photon counts]. + bg : ndarray + Removed background noise [MCPS or Photon counts]. + + Notes + ----- - # ------------------------------------------------------------------------ - # Transform to Photon Count Rate (MCPS) - # ------------------------------------------------------------------------ - if not np.all(mShots[:, 0] == mShots[0, 0]): - logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") - mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - PCR = photonCount2PCR(rawSignal, mShots, hRes) # Use mShots directly - # PCR = photonCount2PCR(rawSignal, mShots_norm, hRes) # Use mean mShots per channel + ** History ** + + - 2021-05-16: first edition by Zhenping + - xxxx-xx-xx: translated to pyhton + - 2025-08-13: allow channel dependent background range + + """ + + logging.info(f'... removing background from signal') + + if firstBinIndex is None: + logging.warning('No firstBinIndex value were given, default value 0 is used', exc_info=True) + firstBinIndex = [0]*rawSignal.shape[2] + + # Calculate the mean across the channel specific column range for each row and page + mean_matrix = np.empty((rawSignal.shape[0], 1, rawSignal.shape[2]), dtype=rawSignal.dtype) + for iCh in range(rawSignal.shape[2]): + mean_matrix[:, :, iCh] = np.mean(rawSignal[:, bgCorrectionIndexLow[iCh]:bgCorrectionIndexHigh[iCh] + 1, iCh], axis=1, keepdims=True) + + # Replicate the mean matrix along the second dimension + bg = np.tile(mean_matrix, (1, maxHeightBin, 1)) + signal_out = slicerange(rawSignal, maxHeightBin, firstBinIndex) - bg + return signal_out, bg + + +def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.ndarray: + """Slice a given array across the height/range dimension from firstBinIndex to maxHeightBin + firstBinIndex. + + Parameters + ---------- + array : ndarray + Array to be sliced. + maxHeightBin : int + Length of slice. + firstBinIndex : list of int + Start hight/range index of slice per channel. + + Returns + ------- + out : ndarray + Sliced array. + + ** History ** + - xxxx-xx-xx: first edition by ... + - 2025-08-13: vectorized by Buholdt - # ------------------------------------------------------------------------ - # Deadtime correction - # ------------------------------------------------------------------------ - preproSignal = pollyDTCor( - PCR=PCR, - polly_device=pollyType, - flagDeadTimeCorrection=flagDeadTimeCorrection, - DeadTimeCorrectionMode=deadtimeCorrectionMode, - deadtimeParams=deadtimeParams, - deadtime=rawdata_dict['deadtime_polynomial']['var_data'] - ) + """ - if flagPicassoComparison: - # preproSignal = PCR2PhotonCount(preproSignal, mShots, hRes) # Use mShots directly - preproSignal = PCR2PhotonCount(preproSignal, mShots_norm, hRes) # Use mean mShots per channel - - if collect_debug: - # Store dead time corrected signal - data_dict['preproSignal'] = preproSignal + assert len(firstBinIndex) == array.shape[2], f"first bin index and array do not match {len(firstBinIndex)}, {array.shape}" + firstBinIndex = np.asarray(firstBinIndex) + heightBins = np.arange(maxHeightBin)[:, None] + firstBinIndex[None, :] + out = array[:, heightBins, np.arange(array.shape[2])] + return out - # ------------------------------------------------------------------------ - # Background Correction - # ------------------------------------------------------------------------ - sigBGCor, bg = pollyRemoveBG( - rawSignal=preproSignal, - bgCorrectionIndexLow=bgCorrectionIndexLow, - bgCorrectionIndexHigh=bgCorrectionIndexHigh, - maxHeightBin=maxHeightBin, - firstBinIndex=firstBinIndex - ) - # Store the background and background corrected signal - data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix - data_dict['sigBGCor'] = sigBGCor +def pollyPolCaliTime(depCalAng:np.ndarray, mTime:list, init_depAng:float, maskDepCalAng:list) -> tuple: + """Retrieve the time for the polly depolarization calibration + period. depolarization calibration: 5 min (+45°) + 5 min (-45°) + 0.5 min. + Parameters + ---------- + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + mTime : list + Datetime ndarray for the measurement time of each profile. + init_depAng : float + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskDepCalAng : list + Mask for positive and negative calibration angle of the polarizer, in + which 'p' stands for positive angle, while 'n' for negative angle. + Default is {}. + + Returns + ------- + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + + Notes + ----- + .. TODO:: Clean comments of the function. + + **History** - # ------------------------------------------------------------------------ - # Height and first bin height correction - # ------------------------------------------------------------------------ - logging.info('... height bin calculations') - # .. TODO:: first bin hight might change for different telescopes... --> should expand range to be channel spesific. - data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] - data_dict['height'] = data_dict['range'].copy() * np.cos(zenithAng*np.pi/180) + - 2021-04-21: First edition by Zhenping + - xxxx-xx-xx: Translated to Python by ... - # correction firstBinHight = range per channel ^ 2 / range first channel ^ 2 - correction_firstBinHight = ( - ((np.arange(0, sigBGCor.shape[1]) * hRes)[:, np.newaxis] + firstBinHeight)**2 - / data_dict['range'][:, np.newaxis]**2 - ) + """ - data_dict['sigBGCor'] = data_dict['sigBGCor'] * correction_firstBinHight[np.newaxis, :, :] + depCal_P_Ang_time_start = [] + depCal_P_Ang_time_end = [] + depCal_N_Ang_time_start = [] + depCal_N_Ang_time_end = [] + maskDepCal = np.zeros(len(mTime), dtype=bool) - data_dict['alt'] = data_dict['height'] + float(asl) ## geopotential height - data_dict['time'] = mTime_unixtimestamp - data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) + if len(depCalAng) == 0: + ## if depCalAng is empty, which means the polly does not support auto depol calibration + return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal + if len(maskDepCalAng) == 0: + maskDepCalAng = ['none', 'none', 'p', 'p', 'p', 'p', 'p', 'p', 'p', 'p', 'none', 'none', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'none'] + ## the mask for postive and negative + ## calibration angle. 'none' means + ## invalid profiles with different + ## depol_cal_angle - # ------------------------------------------------------------------------ - # Mask for bins with low SNR - # ------------------------------------------------------------------------ - logging.info('... mask bins with low SNR') - # SNR = calc_snr(sigBGCor, bg) # This do not consider the correction for different first bin heights. - SNR = calc_snr(data_dict['sigBGCor'], bg) - data_dict['SNR'] = SNR + flagPDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + flagNDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + for iProf in range(0, len(maskDepCalAng)): + if maskDepCalAng[iProf] == 'p': + flagPDepCal[iProf] = True + elif maskDepCalAng[iProf] == 'n': + flagNDepCal[iProf] = True + + flagDepCal = (np.abs(depCalAng - init_depAng) > 0.0) + ## the profile will be treated as depol cali profile if it has different + ## depol_cal_ang than the init_depAng - ## Create mask and mask every entry, where SNR < minSNRThresh - # .. TODO:: check the low SNR mask - data_dict['lowSNRMask'] = np.zeros_like(sigBGCor).astype(bool) - for iCh in range(0, sigBGCor.shape[2]): - data_dict['lowSNRMask'][:, :, iCh][SNR[:, :, iCh] < minSNRThresh[iCh]] = True + maskDepCal = flagDepCal - # .. TODO:: mask for laser shutter? - flag532FR = (np.array(flag532nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) - flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) + ## search the calibration periods + valuesFlagDepCal = flagDepCal.astype(int) - if any(flag532FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532FR])) - elif any(flag355FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355FR])) - else: - raise ValueError('No suitable channel to determine the shutter status.') + # print('flagNDepCal', flagNDepCal) + # print('flagPDepCal', flagPDepCal) - ## Create Fog mask - # .. TODO:: mask for fog? the original matlab code raises questions. Why 40:120 and why hard coded? - # When sum is used (as in matlab), minPC_fog is range resolution dependent - fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:, 39:120, flag532FR]), axis=1) - data_dict['fogMask'] = fogsum < minPC_fog + ## label connected components in the matrix; 0 will stay 0 + ## connected 1s will be numbered consecutively + depCalPeriods, nDepCalPeriods = label(valuesFlagDepCal) + # print('depCalPeriods', depCalPeriods) + + if nDepCalPeriods < 1: + logging.info(f'No Depolarization Calibration phase found.') + return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal - ## Create single channel masks - # .. TODO:: mask for single channels on 607, 387, 407, 355RR 532RR 1064RR - flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag607FR): - data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag607FR])) + for iDepCalPeriod in range(1, nDepCalPeriods+1): + # flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. + flagIDepCal = depCalPeriods[depCalPeriods == iDepCalPeriod] # flag for the ith calibration period. + indices = np.where(depCalPeriods == flagIDepCal[0])[0] + # print('flagIDepCal', flagIDepCal) + # print(len(flagIDepCal), len(maskDepCalAng)) - flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag387FR): - data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag387FR])) + if len(flagIDepCal) != len(maskDepCalAng): + logging.warning(f"Depolarization Calibration from Timestamp " + f"{mTime[indices[0]]} - {mTime[indices[-1]]} " + f"does not match the maskDepCalAng pattern in the polly-config file.\n" + f"This calibration phase will be skipped." + ) + continue - flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag407FR): - data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag407FR])) + tIDepCal = mTime[indices[0]:indices[-1]+1] - flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag355RRFR): - data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355RRFR])) + t_all_p_depCal = list(itertools.compress(tIDepCal, flagPDepCal)) + t_all_n_depCal = list(itertools.compress(tIDepCal, flagNDepCal)) + depCal_P_Ang_time_start.append(t_all_p_depCal[0]) + depCal_P_Ang_time_end.append(t_all_p_depCal[-1]) + depCal_N_Ang_time_start.append(t_all_n_depCal[0]) + depCal_N_Ang_time_end.append(t_all_n_depCal[-1]) - flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag532RRFR): - data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532RRFR])) + return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal - flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag1064RRFR): - data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag1064RRFR])) +def calculate_rcs(signal:np.ndarray, ranges:np.ndarray) -> np.ndarray: + """Function for calculating RCS. - # ------------------------------------------------------------------------ - # Mask for polarization calibration - # ------------------------------------------------------------------------ - logging.info('... mask for polarization calibration') - (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], - data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], - data_dict['depCalMask']) = pollyPolCaliTime( - depCalAng=depCalAng, - mTime=mTime_unixtimestamp, - init_depAng=initialPolAngle, - maskDepCalAng=maskPolCalAngle - ) + Parameters + ---------- + signal : ndarray + Signal to range correct [PCR]. + ranges : ndarray + Ranges dimension [m]. + + Returns + ------- + RCS : ndarray + Range corrected signal [PCR]. + Notes + ----- + + ** History ** - # ------------------------------------------------------------------------ - # Range-corrected Signal calculation - # ------------------------------------------------------------------------ - logging.info('... calculate range-corrected Signal') - data_dict['RCS'] = calculate_rcs(data_dict['sigBGCor'], data_dict['range']) + - xxxx-xx-xx: First edition by ... - if flagPicassoComparison: - # data_dict['RCS'] = calculate_rcs(photonCount2PCR(data_dict['sigBGCor'].copy(), mShots, hRes), data_dict['range']) # Use mShots directly - data_dict['RCS'] = calculate_rcs(photonCount2PCR(data_dict['sigBGCor'].copy(), mShots_norm, hRes), data_dict['range']) # Use mean mShots per channel + """ - logging.info('finished data preprocessing.') - return data_dict + ranges_squared = ranges**2 + ranges2d = np.repeat(ranges_squared[np.newaxis, :], signal.shape[0], axis=0) + RCS = signal * ranges2d[:, :, np.newaxis] + return RCS def any_signal(sig:np.ndarray) -> np.ndarray: @@ -983,4 +944,122 @@ def any_signal(sig:np.ndarray) -> np.ndarray: # for some reason had to set the thresholds higher than in matlab version .. TODO:: <-- Check this!! flag = (mean_sig <= 0.02) & (std_sig <= 0.9) - return flag \ No newline at end of file + return flag + + + + + + + +# # The functions below are old functions currently not in use. +# # They are only saved for ... resons. + + +# def compute_channel_photon_count(args:tuple) -> np.ndarray: +# """Computes Photon count from PCR for a single channel. + +# Photons = 2 * PCR * mShot * hRes / c + +# Parameters +# ---------- +# args : tuple +# PCR : ndarray +# Photon Count Rate signal [MCPS] (shape: [M, N, P]). +# mShots : ndarray[time, channels] +# Mesurments shots [counts] (shape: [M, P]). +# scale_factor : flaot +# Scaling factor for the computation: c / (2 * hRes). +# channel_index : int +# Index of the channel to compute the PCR for. + +# Returns +# ------- +# ndarray +# Computed PCR for the given channel (shape: [M, N, P]). + +# Notes +# ----- +# .. TODO:: change scale factor with hRes and include c/2 as a part +# of the function. Could even add an flag option for MCPS or CPS. +# """ + +# PCR, mShots, scale_factor, ch = args +# return PCR[:, :, ch] * mShots[:, np.newaxis, ch] / scale_factor + + +# def compute_channel_pcr(args:tuple) -> np.ndarray: +# """Computes PCR for a single channel. + +# PCR = (signal * c)/(mshots * 2 * hRes) + +# Parameters +# ---------- +# args : tuple +# rawSignal : ndarray +# 3D Raw signal [Photon count] (shape: [M, N, P]). +# mShots : ndarray[time, channels] +# Mesurments shots [counts] (shape: [M, P]). +# scale_factor : flaot +# Scaling factor for the computation: c / (2 * hRes). +# channel_index : int +# Index of the channel to compute the PCR for. + +# Returns +# ------- +# ndarray +# Computed PCR for the given channel (shape: [M, N, P]). + +# Notes +# ----- +# .. TODO:: change scale factor with hRes and include c/2 as a part +# of the function. Could even add an flag option for MCPS or CPS. +# """ + +# rawSignal, mShots, scale_factor, ch = args +# return (rawSignal[:, :, ch] / mShots[:, np.newaxis, ch]) * scale_factor + + +# def compute_pcr_parallel(rawSignal:np.ndarray, mShots:np.ndarray, scale_factor:float) -> np.ndarray: +# """Computes PCR using multiprocessing for channel-wise parallelism. + +# Parameters +# ---------- +# rawSignal : ndarray +# 3D input array (shape: [M, N, P]). +# mShots : ndarray +# 2D multiplicative factors array (shape: [M, P]). +# scale_factor : float +# Scaling factor for the computation. + +# Returns +# ------- +# PCR : ndarray +# 3D output array (shape: [M, N, P]). +# """ + +# M, N, P = rawSignal.shape +# PCR = np.zeros((M, N, P), dtype=np.float64) + +# # Prepare arguments for each channel +# args = [(rawSignal, mShots, scale_factor, ch) for ch in range(P)] + +# # Use a pool of workers to compute each channel in parallel +# with Pool(processes=min(cpu_count(), P)) as pool: +# results = pool.map(compute_channel_pcr, args) + +# # Collect the results +# for ch, result in enumerate(results): +# PCR[:, :, ch] = result + +# return PCR + + +# @jit(nopython=True) +# def faster_polyval(p, x): +# """Numba verison of faster_polyval().""" +# y = np.zeros(x.shape, dtype=float) +# for i, v in enumerate(p): +# y *= x +# y += v +# return y From 6c722abac8ff3ecf3623a84ad5afac022cf81795 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 14:21:45 +0200 Subject: [PATCH 08/13] Fix to several small inconsistencies in: - output type of functions. - SNR and noise is calculation. - uncertainty calculation. - etc. --- ppcpy/calibration/polarization.py | 437 ++++++++++++++++-------------- ppcpy/calibration/select.py | 40 +-- ppcpy/cloudmask/cloudscreen.py | 36 ++- ppcpy/qc/overlapEst.py | 307 +++++++++++---------- ppcpy/retrievals/angstroem.py | 32 ++- 5 files changed, 471 insertions(+), 381 deletions(-) diff --git a/ppcpy/calibration/polarization.py b/ppcpy/calibration/polarization.py index daf7729..164619d 100644 --- a/ppcpy/calibration/polarization.py +++ b/ppcpy/calibration/polarization.py @@ -1,7 +1,6 @@ import logging from collections import defaultdict -import pprint import numpy as np from ppcpy.misc.helper import uniform_filter @@ -14,6 +13,7 @@ def onemx_onepx(x:float|np.ndarray) -> float|np.ndarray: """Calculate the fraction of (1-x)/(1+x)""" return (1-x)/(1+x) + def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: """Uniformly smooth the input signal @@ -47,22 +47,43 @@ def loadGHK(data_cube): ---------- data_cube : object Main PicassoProc object. + + Yields + ------ + data_cube.polly_config_dict : dict + Updated to parameters: + TR --> Removed + G --> filled and converted to array + H --> filled and converted to array + K --> filled and converted to array + voldepol_error_355 --> converted to array + voldepol_error_532 --> converted to array + voldepol_error_1064 --> converted to array + + Notes + ----- + .. TODO:: + - Write a proper docstring. + + ** History ** + + - xx-xx-xxxx: First edition by ... + """ - print('starting loadGHK') - #print('flag_532_total', flag_532_total_FR) - #print('flag_532_cross', flag_532_cross_FR) - - print('data_cube keys ', data_cube.__dict__.keys()) - G = np.array(data_cube.polly_config_dict['G']).astype(float) - H = np.array(data_cube.polly_config_dict['H']).astype(float) - K = np.array(data_cube.polly_config_dict['K']).astype(float) - #print(TR[flag_532_total_FR]) - #print(TR[flag_532_cross_FR]) - #if data_cube.polly_config_dict['H'][0] == -999: + logging.info("Starting loadGHK") + # print('flag_532_total', flag_532_total_FR) + # print('flag_532_cross', flag_532_cross_FR) + + G = np.asarray(data_cube.polly_config_dict['G'], dtype=float) + H = np.asarray(data_cube.polly_config_dict['H'], dtype=float) + K = np.asarray(data_cube.polly_config_dict['K'], dtype=float) + # print(TR[flag_532_total_FR]) + # print(TR[flag_532_cross_FR]) + # if data_cube.polly_config_dict['H'][0] == -999: if (np.all(np.isclose(H, -999))): - TR = np.array(data_cube.polly_config_dict['TR']).astype(float) - print('H is empty -> calculate parameters') + TR = np.asarray(data_cube.polly_config_dict['TR'], dtype=float) + logging.info('H is empty -> calculate parameters') K[data_cube.flag_355_total_FR] = 1.0 K[data_cube.flag_532_total_FR] = 1.0 @@ -83,92 +104,82 @@ def loadGHK(data_cube): H[data_cube.flag_1064_cross_FR] = onemx_onepx(TR[data_cube.flag_1064_cross_FR]) if np.any(data_cube.flag_532_total_NR): - print("GHK for 532NR") + logging.info("GHK for 532 NR") K[data_cube.flag_532_total_NR] = 1.0 G[data_cube.flag_532_total_NR] = 1.0 H[data_cube.flag_532_total_NR] = onemx_onepx(TR[data_cube.flag_532_total_NR]) - print("H", H[data_cube.flag_532_total_NR]) + logging.info(f"G: {G[data_cube.flag_532_total_NR]}, H {H[data_cube.flag_532_total_NR]}, K: {K[data_cube.flag_532_total_NR]}.") if np.any(data_cube.flag_532_cross_DFOV): - print("GHK for 532DFOV") + logging.info("GHK for 532 DFOV") K[data_cube.flag_532_cross_DFOV] = 1.0 G[data_cube.flag_532_cross_DFOV] = 1.0 H[data_cube.flag_532_cross_DFOV] = onemx_onepx(TR[data_cube.flag_532_cross_DFOV]) - print("H", H[data_cube.flag_532_cross_DFOV]) - print('TR', TR) + logging.info(f"G: {G[data_cube.flag_532_cross_DFOV]}, H: {H[data_cube.flag_532_cross_DFOV]}, K: {K[data_cube.flag_532_cross_DFOV]}.") + logging.info(f"TR: {TR}") else: - print("Using GHK from config file") - #print('TR', TR) - #print('TR from H', (1-H)/(1+H)) - print('G', G) - print('H', H) - print('K', K) + logging.info("Using GHK from config file") + # print('TR', TR) + # print('TR from H', (1-H)/(1+H)) + logging.info(f"G: {G}, H: {H}, K: {K}") data_cube.polly_config_dict.pop('TR', None) # remove the TR from the config to avoid inconsistencies - data_cube.polly_config_dict['G'] = np.array(G) - data_cube.polly_config_dict['H'] = np.array(H) - data_cube.polly_config_dict['K'] = np.array(K) - data_cube.polly_config_dict['voldepol_error_355'] = np.array(data_cube.polly_config_dict['voldepol_error_355']) - data_cube.polly_config_dict['voldepol_error_532'] = np.array(data_cube.polly_config_dict['voldepol_error_532']) - data_cube.polly_config_dict['voldepol_error_1064'] = np.array(data_cube.polly_config_dict['voldepol_error_1064']) - - -""" -.. TODO:: can this be removed? -"TR": [0.898, 1086, 1, 1, 1.45, 778.8, 1, 1, 1, 1, 1, 1, 1], -"G": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"H": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"K": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"voldepol_error_355": [0.003, 0, 0], -"voldepol_error_532": [0.004, 0, 0], -"voldepol_error_1064": [0.005, 0, 0], -""" - + data_cube.polly_config_dict['G'] = np.asarray(G) + data_cube.polly_config_dict['H'] = np.asarray(H) + data_cube.polly_config_dict['K'] = np.asarray(K) + data_cube.polly_config_dict['voldepol_error_355'] = np.asarray(data_cube.polly_config_dict['voldepol_error_355']) + data_cube.polly_config_dict['voldepol_error_532'] = np.asarray(data_cube.polly_config_dict['voldepol_error_532']) + data_cube.polly_config_dict['voldepol_error_1064'] = np.asarray(data_cube.polly_config_dict['voldepol_error_1064']) def calibrateGHK(data_cube) -> dict: - """Estimate the polarization calibration from the delta 90 Method [1]_ + """Estimate the polarization calibration from the delta 90 Method [1] Parameters ---------- - data_cube - the input data cube + data_cube : object + Main PicassoProc object Returns ------- pol_cali : dict polarization factors from delta 90 for each wavelength containing sub-dicts with 'eta', 'eta_std', 'time_start', 'time_end', 'status' - - Notes - ----- - **History** - - Function is called here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5f5e4d0fd3dcebe7f87220cf802fcd6f414fe235/lib/interface/picassoProcV3.m#L548 - The two most relevant functions here are https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/pollyPolCaliGHK.m - which also calls https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/depolCaliGHK.m - References ---------- .. [1] Freudenthaler 2016 + + Notes + ----- + - Compleate the reference. + - Currently only implemented in the far-range receiver. + - Matlab relevent functions: + Function is called here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5f5e4d0fd3dcebe7f87220cf802fcd6f414fe235/lib/interface/picassoProcV3.m#L548 + The two most relevant functions here are https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/pollyPolCaliGHK.m + which also calls https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/depolCaliGHK.m + + **History** + + - xxxx-xx-xx: First edition by ... """ pol_cali = {} - tel = 'FR' # currently only implemented in the far range receiver + tel = 'FR' # currently only implemented in the far-range receiver for wv in [355, 532, 1064]: if np.any(data_cube.gf(wv, 'total', tel)) and np.any(data_cube.gf(wv, 'cross', tel)): logging.info(f'and even a {wv} channel') + # Extracting necessary data sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, data_cube.gf(wv, 'total', tel)]) bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'total', tel)]) sigBGCor_cross = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, data_cube.gf(wv, 'cross', tel)]) bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'cross', tel)]) pol_cali[f"{wv}_{tel}"] = depol_cali_ghk( - signal_t=sigBGCor_total, bg_t=bg_total, - signal_x=sigBGCor_cross, bg_x=bg_cross, + signal_t=sigBGCor_total, bg_t=bg_total, + signal_x=sigBGCor_cross, bg_x=bg_cross, time=data_cube.retrievals_highres['time'], pol_cali_pang_start_time=data_cube.retrievals_highres['depol_cal_ang_p_time_start'], pol_cali_pang_stop_time=data_cube.retrievals_highres['depol_cal_ang_p_time_end'], @@ -184,9 +195,8 @@ def calibrateGHK(data_cube) -> dict: rel_std_dminus=data_cube.polly_config_dict[f'rel_std_dminus_{wv}'], segment_len=data_cube.polly_config_dict[f'depol_cal_segmentLen_{wv}'], smooth_win=data_cube.polly_config_dict[f'depol_cal_smoothWin_{wv}'], - collect_debug=False + collect_debug=False, flagPicassoComparison=data_cube.polly_config_dict['flagPicassoComparison'] ) - print(pol_cali[f'{wv}_{tel}']) logging.info(f"pol_cali_{wv} {pol_cali[f'{wv}_{tel}']}") else: logging.warning(f'calibrateGHK no {wv} channel') @@ -196,12 +206,13 @@ def calibrateGHK(data_cube) -> dict: return pol_cali -def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg_x:np.ndarray, +def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg_x:np.ndarray, time:np.ndarray, pol_cali_pang_start_time:np.ndarray, pol_cali_pang_stop_time:np.ndarray, pol_cali_nang_start_time:np.ndarray, pol_cali_nang_stop_time:np.ndarray, K:float, cali_h_indx_range:list|tuple, SNRmin:list, sig_max:list, rel_std_dplus:float, rel_std_dminus:float, - segment_len:int, smooth_win:int, collect_debug:bool=False) -> dict: + segment_len:int, smooth_win:int, collect_debug:bool=False, + flagPicassoComparison:bool=False) -> dict: """Polarization calibration for PollyXT lidar system. Parameters @@ -251,8 +262,31 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg 1 if calibration is successful, 0 otherwise. global_attri : dict, optional Information about the depolarization calibration. + + Notes + ----- + .. TODO:: + - Decide on the output format. Current procedure is to return a + list containing one dict with the element 'status':0 when no eta + was found. When eta was succesfully retieved status is 1 and only + the periods with a succesfull retrival is returned. The other possible + option would be to always return a list with as many elements as the + number of polarization calibration periods, both when eta is succesfully + retrived and not (NaN-filling the non-existing values such as eta, or + returning {'status':0} N times). The same system should also be used for + the Lidar constant calculations. Test which system works best with the + reading / writing to db. procedures. + - Check the input shapes defined in the docstring. I do not believe they are correct. + + ** History ** + + - xx-xx-xxxx: First edition by ... + - 30-06-2026: Made output datatype consistant and changed from `nanmean` to + `nansum` in signal aggregation to be consistant with SNR requierment. + """ - # Initialize outputs and intermediate storage + + ## Initialize outputs and intermediate storage pol_cali_eta, pol_cali_eta_std = [], [] mean_dplus, mean_dminus, std_dplus, std_dminus = [], [], [], [] pol_cali_start_time, pol_cali_stop_time = [], [] @@ -261,12 +295,10 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if signal_t.size == 0 or signal_x.size == 0: logging.warning("Warning: No data for polarization calibration.") - #return pol_cali_eta, pol_cali_eta_std, pol_cali_start_time, pol_cali_stop_time, 0, global_attri - return {'status': 0} + # return [{'status': 0} for i in range(len(pol_cali_nang_start_time))] + return [{'status': 0}] # the iteration of days can be omitted if unixtimestamps are used - print('pol_cali_nang_start_time', pol_cali_nang_start_time) - time = np.array(time) for i_depol_cal in range(len(pol_cali_nang_start_time)): indx_45p = np.where( @@ -276,41 +308,46 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg indx_45m = np.where( (time >= pol_cali_nang_start_time[i_depol_cal]) & (time <= pol_cali_nang_stop_time[i_depol_cal]))[0] + if len(indx_45p) < 4 or len(indx_45m) < 4: logging.warning(f'calibrateGHK array to short {len(indx_45p)}{len(indx_45m)} in period {i_depol_cal}') break + this_cali_start_time = min(pol_cali_pang_start_time[i_depol_cal], pol_cali_nang_start_time[i_depol_cal]) this_cali_stop_time = max(pol_cali_pang_stop_time[i_depol_cal], pol_cali_nang_stop_time[i_depol_cal]) - # Exclude the first and last profiles + + ## Exclude the first and last profiles indx_45m = indx_45m[1:-1] indx_45p = indx_45p[1:-1] - # matlab -> python swap from signal_t[:, indx_45p] to signal_t[indx_45p,:] - # to be a profile - sig_t_p = np.nanmean(signal_t[indx_45p, :], axis=0) - bg_t_p = np.nanmean(bg_t[indx_45p], axis=0) + ## Calculating SNR (only sum should be used to aggregate the signal for SNR calculations!) + func = np.nansum + if flagPicassoComparison: + func = np.nanmean + + sig_t_p = func(signal_t[indx_45p, :], axis=0) + bg_t_p = func(bg_t[indx_45p], axis=0) snr_t_p = calc_snr(sig_t_p, bg_t_p) indx_bad_t_p = (snr_t_p <= SNRmin[0]) | (sig_t_p >= sig_max[0]) - sig_t_m = np.nanmean(signal_t[indx_45m, :], axis=0) - bg_t_m = np.nanmean(bg_t[indx_45m], axis=0) + sig_t_m = func(signal_t[indx_45m, :], axis=0) + bg_t_m = func(bg_t[indx_45m], axis=0) snr_t_m = calc_snr(sig_t_m, bg_t_m) indx_bad_t_m = (snr_t_m <= SNRmin[1]) | (sig_t_m >= sig_max[1]) - sig_x_p = np.nanmean(signal_x[indx_45p, :], axis=0) - bg_x_p = np.nanmean(bg_x[indx_45p], axis=0) + sig_x_p = func(signal_x[indx_45p, :], axis=0) + bg_x_p = func(bg_x[indx_45p], axis=0) snr_x_p = calc_snr(sig_x_p, bg_x_p) indx_bad_x_p = (snr_x_p <= SNRmin[2]) | (sig_x_p >= sig_max[2]) - sig_x_m = np.nanmean(signal_x[indx_45m, :], axis=0) - bg_x_m = np.nanmean(bg_x[indx_45m], axis=0) + sig_x_m = func(signal_x[indx_45m, :], axis=0) + bg_x_m = func(bg_x[indx_45m], axis=0) snr_x_m = calc_snr(sig_x_m, bg_x_m) indx_bad_x_m = (snr_x_m <= SNRmin[3]) | (sig_x_m >= sig_max[3]) - # Calculate dplus and dminus - #print('smooth_win', smooth_win) + ## Calculate dplus and dminus dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win) dminus = smooth_signal(sig_x_m, smooth_win) / smooth_signal(sig_t_m, smooth_win) dplus = np.where(np.isfinite(dplus), dplus, np.nan) @@ -318,36 +355,35 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg dplus[indx_bad_t_p | indx_bad_x_p] = np.nan dminus[indx_bad_t_m | indx_bad_x_m] = np.nan - # Subset the calibration range - dplus = dplus[cali_h_indx_range[0]:cali_h_indx_range[1]] - dminus = dminus[cali_h_indx_range[0]:cali_h_indx_range[1]] + ## Subset the calibration range + dplus = dplus[cali_h_indx_range[0]:cali_h_indx_range[1]+1] + dminus = dminus[cali_h_indx_range[0]:cali_h_indx_range[1]+1] if np.all(np.isnan(dplus)) or np.all(np.isnan(dminus)): - logging.warning(f'calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}') - print(f'calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}, len(dplus) {len(dplus)}') - print(' snr_t_p ', np.sum((snr_t_p <= SNRmin[0])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_t_p ', np.sum((sig_t_p >= sig_max[0])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_t_p in height interval', np.sum(indx_bad_t_p[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_t_m ', np.sum((snr_t_m <= SNRmin[1])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_t_m ', np.sum((sig_t_m >= sig_max[1])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_t_m in height interval', np.sum(indx_bad_t_m[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_x_p ', np.sum((snr_x_p <= SNRmin[2])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_x_p ', np.sum((sig_x_p >= sig_max[2])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_x_p in height interval', np.sum(indx_bad_x_p[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_x_m ', np.sum((snr_x_m <= SNRmin[3])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_x_m ', np.sum((sig_x_m >= sig_max[3])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_x_m in height interval', np.sum(indx_bad_x_m[cali_h_indx_range[0]:cali_h_indx_range[1]])) + logging.warning(f"CalibrateGHK all values in dplus or dminus masked in period {i_depol_cal}") + logging.debug(f"calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}, len(dplus) {len(dplus)}") + logging.debug(f" snr_t_p {np.sum((snr_t_p <= SNRmin[0])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_t_p {np.sum((sig_t_p >= sig_max[0])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_t_p in height interval {np.sum(indx_bad_t_p[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_t_m {np.sum((snr_t_m <= SNRmin[1])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_t_m {np.sum((sig_t_m >= sig_max[1])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_t_m in height interval {np.sum(indx_bad_t_m[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_x_p {np.sum((snr_x_p <= SNRmin[2])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_x_p {np.sum((sig_x_p >= sig_max[2])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_x_p in height interval {np.sum(indx_bad_x_p[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_x_m {np.sum((snr_x_m <= SNRmin[3])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_x_m {np.sum((sig_x_m >= sig_max[3])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_x_m in height interval {np.sum(indx_bad_x_m[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") continue - # Analyze segments for stability - #print('before analyze segments', len(dplus), segment_len) + ## Analyze segments for stability seg = analyze_segments(dplus, dminus, segment_len, rel_std_dplus, rel_std_dminus) if seg.shape[0] == 0: logging.warning(f'calibrateGHK no stable segment found in period {i_depol_cal}') continue - # translate manually - # min(sqrt((std_dplus_tmp./mean_dplus_tmp).^2 + (std_dminus_tmp./mean_dminus_tmp).^2)); + ## Translate manually + # Matlab code: min(sqrt((std_dplus_tmp./mean_dplus_tmp).^2 + (std_dminus_tmp./mean_dminus_tmp).^2)); indx_best_seg = np.argmin(np.sqrt((seg[:, 1]/seg[:, 0])**2 + (seg[:, 3]/seg[:, 2])**2)) # the best segment searching was flawed by the AI translate best_segment = seg[indx_best_seg] @@ -376,8 +412,8 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if not mean_dplus or not mean_dminus: logging.warning("Plus or minus 45° calibration is missing.") - #return pol_cali_eta, pol_cali_eta_std, pol_cali_start_time, pol_cali_stop_time, 0, global_attri - return {'status': 0} + # return [{'status': 0} for i in range(len(pol_cali_nang_start_time))] + return [{'status': 0}] pol_cali_eta = [float(1 / K * np.sqrt(dp * dm)) for dp, dm in zip(mean_dplus, mean_dminus)] pol_cali_eta_std = [float(0.5 * (dp * std_dm + dm * std_dp) / np.sqrt(dp * dm)) for @@ -389,6 +425,7 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if collect_debug: results['global_attri'] = dict(global_attri) + return results @@ -419,45 +456,25 @@ def analyze_segments(dplus:np.ndarray, dminus:np.ndarray, segment_len:int, results = [] for i in range(len(dplus) - segment_len): - #print(i, i+segment_len) + # print(i, i+segment_len) seg_dplus = dplus[i:i + segment_len] seg_dminus = dminus[i:i + segment_len] + if np.sum(~np.isnan(seg_dplus)) <= segment_len / 4 or np.sum(~np.isnan(seg_dminus)) <= segment_len / 4: continue + mean_dp = np.nanmean(seg_dplus) std_dp = np.nanstd(seg_dplus) mean_dm = np.nanmean(seg_dminus) std_dm = np.nanstd(seg_dminus) - #print('mean_dp', mean_dp, 'std_dp', std_dp, '-> ', std_dp / mean_dp, rel_std_dplus) - #print('mean_dm', mean_dm, 'std_dm', std_dm, '-> ', std_dm / mean_dm, rel_std_dminus) + # print('mean_dp', mean_dp, 'std_dp', std_dp, '-> ', std_dp / mean_dp, rel_std_dplus) + # print('mean_dm', mean_dm, 'std_dm', std_dm, '-> ', std_dm / mean_dm, rel_std_dminus) if std_dp / mean_dp <= rel_std_dplus and std_dm / mean_dm <= rel_std_dminus: results.append([mean_dp, std_dp, mean_dm, std_dm]) - return np.array(results) - -""" - [data.polCaliEta532, data.polCaliEtaStd532, data.polCaliTime, data.polCali532Attri] = - pollyPolCaliGHK(data, PollyConfig.K(flag532t), flag532t, flag532c, wavelength, ... - 'depolCaliMinBin', PollyConfig.depol_cal_minbin_532, ... - 'depolCaliMaxBin', PollyConfig.depol_cal_maxbin_532, ... - 'depolCaliMinSNR', PollyConfig.depol_cal_SNRmin_532, ... - 'depolCaliMaxSig', PollyConfig.depol_cal_sigMax_532, ... - 'relStdDPlus', PollyConfig.rel_std_dplus_532, ... - 'relStdDMinus', PollyConfig.rel_std_dminus_532, ... - 'depolCaliSegLen', PollyConfig.depol_cal_segmentLen_532, ... - 'depolCaliSmWin', PollyConfig.depol_cal_smoothWin_532, ... - 'dbFile', dbFile, ... - 'pollyType', CampaignConfig.name, ... - 'flagUsePrevDepolConst', PollyConfig.flagUsePreviousDepolCali, ... - 'flagDepolCali', PollyConfig.flagDepolCali, ... - 'default_polCaliEta', PollyDefaults.polCaliEta532, ... - 'default_polCaliEtaStd', PollyDefaults.polCaliEtaStd532); - %print_msg('eta532.\n', 'flagTimestamp', true); - %data.polCaliEta532 - %Taking the eta with lowest standard deviation - [~, index_min] = min(data.polCali532Attri.polCaliEtaStd); - data.polCaliEta532=data.polCali532Attri.polCaliEta(index_min); -""" + + return np.asarray(results) + def calibrateMol(data_cube) -> dict: """Calibrate the polarization with the molecular signal. @@ -469,57 +486,70 @@ def calibrateMol(data_cube) -> dict: Returns ------- - dict - ... - + eta : ndarray + Polarization calibration eta. + etaStd : ndarray + Uncertainty of polarization calibration eta. + fac : array + Polarization calibration factor. + facStd : ndarray + Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good + time_start : int + Start time of the cloud free segment for the retrieval in unixtime. + end_time : int + End time of the cloud free segment for the retrieval in unixtime. + Notes ----- - - Converted from the matlab code to the best knowledge, but not cross-validated yet + - Converted from the matlab code to the best knowledge, but not cross-validated yet. - .. TODO:: had to calculate TR_t, TR_c again when calling depol_cali_mol() - .. TODO:: Finish docstring. - """ + .. TODO:: + - had to calculate TR_t, TR_c again when calling depol_cali_mol(). + - Finish docstring. + + ** History ** - #temp = {'eta': [], 'eta_std': [], 'fac': [], 'fac_std': [], - # 'time_start': [], 'time_end': [], 'status': 0} - pol_cali = defaultdict(lambda: defaultdict(list)) + - xx-xx-xxxx: First edition by ... + - xx-xx-xxxx: Translated to python + + """ + # temp = {'eta': [], 'eta_std': [], 'fac': [], 'fac_std': [], + # 'time_start': [], 'time_end': [], 'status': 0} + pol_cali = defaultdict(list) config_dict = data_cube.polly_config_dict for i, cldFree in enumerate(data_cube.clFreeGrps): - print(i, cldFree) cldFreeTime = np.array(data_cube.retrievals_highres['time'])[cldFree] - print(cldFreeTime) - #for wv in [355, 532, 1064]: + # for wv in [355, 532, 1064]: for wv, t, tel in [(532, 'total', 'FR'), (355, 'total', 'FR')]: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv, 'cross', tel)): logging.info(f'and even a {wv} channel') - sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][slice(*cldFree), :, data_cube.gf(wv, 'total', 'FR')]) - bg_total = np.squeeze(data_cube.retrievals_highres['BG'][slice(*cldFree), data_cube.gf(wv, 'total', 'FR')]) - sigBGCor_cross = np.squeeze(data_cube.retrievals_highres['sigBGCor'][slice(*cldFree), :, data_cube.gf(wv, 'cross', 'FR')]) - bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][slice(*cldFree), data_cube.gf(wv, 'cross', 'FR')]) - + # Extracting necessary data + sigBGCor_total = np.squeeze(data_cube.retrievals_profile['sigBGCor'][i, :, data_cube.gf(wv, 'total', 'FR')]).copy() + bg_total = np.squeeze(data_cube.retrievals_profile['BG'][i, data_cube.gf(wv, 'total', 'FR')]).copy() + sigBGCor_cross = np.squeeze(data_cube.retrievals_profile['sigBGCor'][i, :, data_cube.gf(wv, 'cross', 'FR')]).copy() + bg_cross = np.squeeze(data_cube.retrievals_profile['BG'][i, data_cube.gf(wv, 'cross', 'FR')]).copy() refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] - print(f'referenceH {wv} {t} {tel}', refHInd) + # Check for valid reference height if np.any(np.isnan(refHInd)): logging.info(f"skiping {wv} channel") continue - + ret = depol_cali_mol( - signal_t=sigBGCor_total[:, slice(*refHInd)], - background_t=bg_total, - signal_c=sigBGCor_cross[:, slice(*refHInd)], - background_c=bg_cross, - TR_t=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, t, tel)])), - TR_t_std=0, - TR_c=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, 'cross', tel)])), - TR_c_std=0, - minSNR=10, - mdr=config_dict[f'molDepol{wv}'], - mdrStd=config_dict[f'molDepolStd{wv}'], + signal_t=sigBGCor_total[refHInd[0]:refHInd[1]+1], background_t=bg_total, + signal_c=sigBGCor_cross[refHInd[0]:refHInd[1]+1], background_c=bg_cross, + TR_t=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, t, tel)])), TR_t_std=0, + TR_c=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, 'cross', tel)])), TR_c_std=0, + minSNR=10, mdr=config_dict[f'molDepol{wv}'], mdrStd=config_dict[f'molDepolStd{wv}'], + flagPicassoComparison=config_dict['flagPicassoComparison'] ) ret['time_start'] = int(cldFreeTime[0]) ret['time_end'] = int(cldFreeTime[1]) @@ -530,44 +560,49 @@ def calibrateMol(data_cube) -> dict: def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.ndarray, background_c:np.ndarray, - TR_t:float, TR_t_std:float, TR_c:float, TR_c_std:float, minSNR:float, mdr:float, mdrStd:float) -> dict: + TR_t:float, TR_t_std:float, TR_c:float, TR_c_std:float, minSNR:float, mdr:float, mdrStd:float, + flagPicassoComparison:bool=False) -> dict: """Molecular polarization calibration. Parameters ---------- - signal_t: numeric - Total signal (photon count). - background_t: numeric - Background at total channel (photon count). - signal_c: numeric - Cross signal (photon count). - background_c: numeric - Background at cross channel (photon count). - TR_t: scalar + signal_t : ndarray + Total signal [photon count]. + background_t : ndarray + Background at total channel [photon count]. + signal_c : ndarray + Cross signal [photon count]. + background_c : ndarray + Background at cross channel [photon count]. + TR_t : float Transmission ratio at total channel. - TR_t_std: scalar + TR_t_std : float Uncertainty of the transmission ratio at total channel. - TR_c: scalar + TR_c : float Transmission ratio at cross channel. - TR_c_std: scalar + TR_c_std : float Uncertainty of the transmission ratio at cross channel. - minSNR: float + minSNR : float The SNR constraint for the signal strength at reference height. - mdr: float + mdr : float Default molecular depolarization ratio. - mdrStd: float + mdrStd : float Default standard deviation of molecular depolarization ratio. Returns ------- - polCaliEta: array + eta : ndarray Polarization calibration eta. - polCaliEtaStd: array + etaStd : ndarray Uncertainty of polarization calibration eta. - polCaliFac: array + fac : array Polarization calibration factor. - polCaliFacStd: array + facStd : ndarray Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good References ---------- @@ -576,6 +611,8 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda Notes ----- + .. TODO:: + - The inputs TR_t_std & TR_c_std are currnetly not used in the function! **History** @@ -583,17 +620,15 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda - 2024-12-23: converted to python """ - polCaliEta = [] - polCaliEtaStd = [] - polCaliFac = [] - polCaliFacStd = [] - - #print(signal_t, signal_t) - sig_t = np.nansum(signal_t[:, :], axis=0) - bg_t = np.nansum(background_t[:], axis=0) * signal_t.shape[1] + + # Summing signal and background along height dim. + sig_t = np.nansum(signal_t, keepdims=True) + bg_t = background_t * signal_t.shape[0] + sig_c = np.nansum(signal_c, keepdims=True) + bg_c = background_c * signal_c.shape[0] + + # Calculate SNR SNR_TSig = calc_snr(sig_t, bg_t) - sig_c = np.nansum(signal_c[:, :], axis=0) - bg_c = np.nansum(background_c[:], axis=0) * signal_c.shape[1] SNR_CSig = calc_snr(sig_c, bg_c) # Check validity of signals @@ -601,16 +636,14 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda flagValidCSig = (SNR_CSig >= minSNR) if not np.all(flagValidTSig) or not np.all(flagValidCSig): - print("Too noisy at the reference height to enable molecular polarization calibration.") + logging.warning("Too noisy at the reference height to enable molecular polarization calibration.") return {'status': 0} - sig_t = np.nansum(sig_t) - bg_t = np.nansum(bg_t) - sig_c = np.nansum(sig_c) - bg_c = np.nansum(bg_c) - - std_sig_t = np.sqrt(sig_t + bg_t) - std_sig_c = np.sqrt(sig_c + bg_c) + std_sig_t = np.sqrt(sig_t + 2*bg_t) + std_sig_c = np.sqrt(sig_c + 2*bg_c) + if flagPicassoComparison: + std_sig_t = np.sqrt(sig_t + bg_t) + std_sig_c = np.sqrt(sig_c + bg_c) # Calculate derivatives for uncertainty propagation polCaliFacFunc = lambda x: (x / sig_c) * (1 + mdr * TR_t) / (1 + mdr * TR_c) @@ -632,7 +665,7 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda polCaliEta = polCaliFac * (1 + TR_c) / (1 + TR_t) polCaliEtaStd = polCaliFacStd * (1 + TR_c) / (1 + TR_t) - print(polCaliEta, polCaliEtaStd, polCaliFac, polCaliFacStd) results = {'eta': float(polCaliEta), 'eta_std': float(polCaliEtaStd), 'fac': float(polCaliFac), 'fac_std': float(polCaliFacStd), 'status': 1} + return results \ No newline at end of file diff --git a/ppcpy/calibration/select.py b/ppcpy/calibration/select.py index c513d1d..0e5c9e1 100644 --- a/ppcpy/calibration/select.py +++ b/ppcpy/calibration/select.py @@ -6,10 +6,9 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict: - """Select the best calibration constant + """Select the best calibration constant. - - generalization of lidarconstant.get_best_LC + Generalization of lidarconstant.get_best_LC Parameters ---------- @@ -22,27 +21,26 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict relative : bool If true, choose calibration constant based on the relative error. - Returns ------- best : dict Lidar constants/Etas with lowest standard deviation per channel. - - Notes - ----- - Since ``LC = LC_stable`` and ``LCStd = LC_stable * LC_Std`` so will any negative LC also have - a negative LCStd, and thus be chosen as the best LC. **History** - 2026-02-16: Added additional checks to hinder negative LCs to be chosen. - 2026-03-27: generalized to also hold for depolarization calibration + """ best = {} for k, l in d.items(): - val = np.array([e[name_val] for e in l if e[name_val] >= 0]) - min = np.array([e[name_min] for e in l if e[name_val] >= 0]) + val = np.array([e[name_val] for e in l if name_val in e and e[name_val] >= 0]) + min = np.array([e[name_min] for e in l if {name_min, name_val}.issubset(e.keys()) and e[name_val] >= 0]) + + if len(val) == 0 or len(min) == 0: + best[k] = np.nan + continue if relative: best[k] = val[np.argmin(min / val)] @@ -52,18 +50,24 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict return best -def plot_cals(d, param, used=None): - """plot the calibration constants +def plot_cals(d:dict, param:str, used:dict=None) -> tuple: + """Plot the calibration constants. Parameters ---------- d : dict - the dict as in data_cube + The dict as in data_cube. param : str - the parameter to extract - used : dict - the LCused or etaused (will produce a dashed line in the plot) - + The parameter to extract. + used : dict, optional + The LCused or etaused (will produce a dashed line in the plot). + + Returns + ------- + fig : figure + Matplotlib figure object. + ax : axis + Matplotlib axis object. Examples -------- diff --git a/ppcpy/cloudmask/cloudscreen.py b/ppcpy/cloudmask/cloudscreen.py index aa0cb53..40f2b81 100644 --- a/ppcpy/cloudmask/cloudscreen.py +++ b/ppcpy/cloudmask/cloudscreen.py @@ -1,6 +1,7 @@ import numpy as np -from scipy.ndimage import label, uniform_filter1d -from ppcpy.misc.helper import uniform_filter, savgol_filter +from scipy.ndimage import label +from ppcpy.misc.helper import savgol_filter, moving_average +from ppcpy.retrievals.collection import calc_snr import matplotlib.pyplot as plt import logging @@ -28,10 +29,11 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter - 2026-03-17: Changed back to scipy.ndimage.uniform_filter1d due to NaN-related issues in Zhao's cloud screening algorithm. + - 2026-07-24: Changed to ppcpy.misc.helper.moving_average. + """ - # return uniform_filter(signal, window_len) - return uniform_filter1d(signal, window_len, mode='nearest') # <- Should switch to the incoming moving average filter here! + return moving_average(signal, window_len) def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: @@ -68,10 +70,10 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: - xxxx-xx-xx: First edition by ... - 2026-03-18: Updated to include necessary configurations for cloudScreen_Zhao. - 2026-04-09: Added 'maxCloudSearchHeight' config parameters. - - 2026-04-20: Added option for no cloud screening ie. 'cloudScreenMode' = 0 + - 2026-04-20: Added option for no cloud screening ie. 'cloudScreenMode' = 0. + """ - print('Starting cloud screen') config_dict = data_cube.polly_config_dict height = data_cube.retrievals_highres['range'] bg = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'total', 'FR')]) @@ -97,7 +99,8 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: 'bg': bg, 'minSNR': 2, 'heightFullOverlap': hFullOL, - 'showDetails': collect_debug + 'showDetails': collect_debug, + 'flagPicassoComparison': config_dict['flagPicassoComparison'] } else: raise ValueError(f"cloudScreenMode {config_dict['cloudScreenMode']} not properly defined.") @@ -162,6 +165,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea - 2021-05-18: First edition by Zhenping. - 2025-03-20: Translated into python. + """ if len(search_region) != 2 or search_region[1] <= height[0]: @@ -192,7 +196,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search_region:list=[0, 10000], minDepth:float=100, heightFullOverlap:float=600, smoothWin:int=7, minSNR:float=1, - showDetails:bool=False) -> tuple: + showDetails:bool=False, flagPicassoComparison:bool=False) -> tuple: """Cloud layer detection based on Zhao's algorithm. Parameters @@ -231,6 +235,7 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search - 2021-05-18: First edition by Zhengping. - 2026-03-11: Translated into python. + """ if (signal.shape[1] != height.shape[0] or @@ -260,7 +265,8 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search minHeight=heightFullOverlap / 1e3, smoothWin=smoothWin, minSNR=minSNR, - showDetails=showDetails + showDetails=showDetails, + flagPicassoComparison=flagPicassoComparison ) for iLayer in range(len(layerInfo)): @@ -282,7 +288,7 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float=0.2, minHeight:float=0.4, smoothWin:int=3, savgolSmoothWin:int=9, minSNR:int=5, - showDetails:bool=False) -> tuple: + showDetails:bool=False, flagPicassoComparison:bool=False) -> tuple: """Cloud layer detection with VDE method. This method only required elstic signal. Parameters @@ -342,6 +348,8 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= - 2021-06-13: First edition by Zhenping. - 2026-03-11: Translated into pyhton. - 2026-04-09: Enhanced robustness against NaN values. + - 2026-07-28: Fixed SNR / noise calculations. + """ if np.floor(minLayerDepth / (height[1] - height[0])) < 3: @@ -357,7 +365,9 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= # ---------------------------------------------------- # 1. Semi-Discretization Process (SDP) # ---------------------------------------------------- - noise = np.sqrt(P + BG)*3 + noise = np.sqrt(P + 2*BG)*3 + if flagPicassoComparison: + noise = np.sqrt(P + BG)*3 min_noise = np.ones_like(P)*np.sqrt(3)*3 noise_tresh = np.nanmax(np.vstack([noise, min_noise]), axis=0) Ps = smooth_signal(P, smoothWin) @@ -425,7 +435,9 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= layerDepth = height[topIndex] - height[baseIndex] layerIndex = (L == iLayer) - layerSNR = np.nanmean(P[layerIndex]) / np.sqrt(np.nanmean(P[layerIndex] + BG)) + layerSNR = calc_snr(np.nansum(P[layerIndex], keepdims=True), BG*np.sum(layerIndex)) + if flagPicassoComparison: + layerSNR = np.nanmean(P[layerIndex]) / np.sqrt(np.nanmean(P[layerIndex] + BG)) if showDetails: print(f"layer: {iLayer} at height [{height[baseIndex]:.3f}, {height[topIndex]:.3f}] km, Depth: {layerDepth:.3f} >= {minLayerDepth}, SNR: {layerSNR:.3f} >= {minSNR}") diff --git a/ppcpy/qc/overlapEst.py b/ppcpy/qc/overlapEst.py index 0ab3d2a..9b425eb 100644 --- a/ppcpy/qc/overlapEst.py +++ b/ppcpy/qc/overlapEst.py @@ -20,8 +20,6 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: Returns ------- - overlap : list of dicts - Per channel per cloud free period: overlap : ndarray Overlap function. overlapStd : ndarray @@ -31,14 +29,13 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: normRange : list Height index of the signal normalization range. - Notes ----- - The matlab version preforms some convertio from PC to PCR after calculating the overlap function (this might just be relevent at all) - The matlab version also uses calculates 1 overlap function for each channel while we calculate 1 per clFreeGrp per channel... - The frnr overlap calculations are very unstable and frequently results in values outside the range [0,1]. - """ + height = data_cube.retrievals_highres['range'] logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') logging.warning(f'at 10km height this is a difference of about 4 indices') @@ -46,26 +43,32 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: overlap = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Overlap retrieval') + logging.info('Starting Overlap retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree', i, cldFree) + logging.info(f"Cloud free segment: {i}. Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) for wv in [355, 387, 532, 607]: if np.any(data_cube.gf(wv, 'total', 'FR')) and np.any(data_cube.gf(wv, 'total', 'NR')): - print(wv, 'both telescopes available') + logging.info(f"Channels: {wv} total FR | {wv} total NR.") sigFR = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, 'total', 'FR')]) bgFR = np.squeeze(data_cube.retrievals_profile['BGTCor'][i, data_cube.gf(wv, 'total', 'FR')]) sigNR = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, 'total', 'NR')]) bgNR = np.squeeze(data_cube.retrievals_profile['BGTCor'][i, data_cube.gf(wv, 'total', 'NR')]) hFullOverlap = np.array(config_dict['heightFullOverlap'])[data_cube.gf(wv, 'total', 'FR')][0] - ol = overlapCalc(height=height, sigFR=sigFR, bgFR=bgFR, sigNR=sigNR, bgNR=bgNR, hFullOverlap=hFullOverlap, collect_debug=collect_debug) + ol = overlapCalc( + height=height, + sigFR=sigFR, bgFR=bgFR, + sigNR=sigNR, bgNR=bgNR, + hFullOverlap=hFullOverlap, + collect_debug=collect_debug + ) overlap[i][f"{wv}_total_FR"] = ol return overlap -def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.ndarray, bgNR:np.ndarray, hFullOverlap:float=600, collect_debug=False) -> dict: +def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.ndarray, + bgNR:np.ndarray, hFullOverlap:float=600, collect_debug=False) -> dict: """Calculate overlap function. Parameters @@ -88,9 +91,9 @@ def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.n Returns ------- - overlap : ndarray + olFunc : ndarray Overlap function. - overlapStd : ndarray + olFuncStd : ndarray Standard deviation of overlap function. sigRatio : float Signal ratio between near-range and far-range signals. @@ -99,42 +102,59 @@ def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.n Notes ----- + .. TODO:: Define the Default values of the output ei. olFunc = 1 & olFuncStd = 0. + + .. TODO:: Is the first if-statment needed? + + .. TODO:: are bgFR and bgNR actually ndarrays of floats?? **History** - 2021-05-18: First edition by Zhenping """ - if sigNR.shape[0] > 0 and sigFR.shape[0] > 0: - # Find the height index with full overlap - full_overlap_index = np.where(height >= hFullOverlap)[0] - if full_overlap_index.size == 0: - raise ValueError("The index with full overlap cannot be found.") - full_overlap_index = full_overlap_index[0] - - # Calculate the channel ratio of near and far range total signals - sigRatio, normRange, _ = mean_stable(sigNR / sigFR , 40, full_overlap_index, len(sigNR), 0.1) - - # print('is normRange index or slice?', normRange) # -> is list of indices - # Calculate the overlap of the far-range channel - if normRange is not None and normRange.shape[0] > 0: - if collect_debug: - print("bgFR.shape", bgFR.shape) - print("bgNR.shape", bgNR.shape) - print("normRange.shape", normRange.shape) - print("normRange", normRange) - print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) - print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) - SNRnormRangeFR = calc_snr(np.sum(sigFR[normRange], keepdims=True), bgFR*normRange.shape[0]) - SNRnormRangeNR = calc_snr(np.sum(sigNR[normRange], keepdims=True), bgNR*normRange.shape[0]) - sigRatioStd = sigRatio * np.sqrt(1 / (SNRnormRangeFR**2) + 1 / (SNRnormRangeNR**2)) - overlap = (sigFR / (sigNR + 1e-6)) * sigRatio - overlapStd = overlap * np.sqrt((sigRatioStd / sigRatio)**2 + 1 / (sigFR + 1e-6)**2 + 1 / (sigNR + 1e-6)**2) - + + if not (sigNR.shape[0] > 0 and sigFR.shape[0] > 0): + logging.critical("sigNR and/or sigFR can not be empty.") + raise ValueError("sigNR and/or sigFR can not be empty.") # TODO: This check seams unecescery. Might be something leftover from the matlab version. + + # Find the height index with full overlap + full_overlap_index = np.where(height >= hFullOverlap)[0] + if full_overlap_index.size == 0: + raise ValueError("The index with full overlap cannot be found.") + full_overlap_index = full_overlap_index[0] + + # Calculate the channel ratio of near and far range total signals + sigRatio, normRange, _ = mean_stable(sigNR / sigFR , 40, full_overlap_index, len(sigNR), 0.1) + + # print('is normRange index or slice?', normRange) # -> is list of indices + # Calculate the overlap of the far-range channel + if normRange is not None and normRange.shape[0] > 0: + if collect_debug: + print("bgFR.shape", bgFR.shape) + print("bgNR.shape", bgNR.shape) + print("normRange.shape", normRange.shape) + print("normRange", normRange) + print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) + print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) + SNRnormRangeFR = calc_snr(np.sum(sigFR[normRange], keepdims=True), bgFR*normRange.shape[0]) + SNRnormRangeNR = calc_snr(np.sum(sigNR[normRange], keepdims=True), bgNR*normRange.shape[0]) + sigRatioStd = sigRatio * np.sqrt(1 / (SNRnormRangeFR**2) + 1 / (SNRnormRangeNR**2)) + overlap = (sigFR / (sigNR + 1e-6)) * sigRatio + overlapStd = overlap * np.sqrt((sigRatioStd / sigRatio)**2 + 1 / (sigFR + 1e-6)**2 + 1 / (sigNR + 1e-6)**2) + + else: + # .. TODO:: Implement default values for the overlap function and its Std for when mean_stable fails. + # What shape do the ovelap function have. Does it go only up to height full overlap or does + # it spann the whole height grid? + # overlap = np.ones(...) + # overlapStd = np.zeros(...) + raise NotImplementedError('Default overlap values are not yet implemented') + ret = {'olFunc': overlap, 'olFuncStd': overlapStd, 'sigRatio': sigRatio, 'normRange': normRange} - return ret + return ret def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: @@ -157,8 +177,8 @@ def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: Standard deviation of overlap function. olFunc0 : ndarray Overlap function with no smoothing. - """ + height = data_cube.retrievals_highres['range'] hres = data_cube.rawdata_dict['measurement_height_resolution']['var_data'] logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') @@ -167,29 +187,24 @@ def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: overlap = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Raman Overlap retrieval') + logging.info('Starting Raman Overlap retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment: {i}. Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) channels = [((532, 'total', 'FR'), (607, 'total', 'FR')),((355, 'total', 'FR'), (387, 'total', 'FR'))] for (wv, t, tel), (wv_r, t_r, tel_r) in channels: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv_r, t_r, tel_r)): - print(wv, wv_r, 'both wavelengths available') + logging.info(f"Channels: {wv} {t} {tel} | {wv_r} {t_r} {tel_r}.") sig = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, t, tel)]) # bg = np.nansum(np.squeeze(data_cube.retrievals_highres['BGTCor'][slice(*cldFree), data_cube.gf(wv, t, tel)]), axis=0) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] + # molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :] - if f"{wv}_{t}_{tel}" in data_cube.retrievals_profile['raman'][i].keys(): - pass - else: + if f"{wv}_{t}_{tel}" not in data_cube.retrievals_profile['raman'][i] or \ + 'aerBsc' not in data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"]: + logging.info(f"Skipping channel pair {wv} {t} {tel} and {wv_r} {t_r} {tel_r}.") continue - if 'aerBsc' in data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"].keys(): - pass - else: - continue aerBsc = data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"]['aerBsc'] sig_r = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv_r, t, tel)]) @@ -271,14 +286,23 @@ def overlapCalcRaman( ------- olFunc : ndarray Overlap function. - olStd : float - Standard deviation of overlap function. - olFunc0 : ndarray + olFunc_raw : ndarray Overlap function with no smoothing. + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. + + References + ---------- + Wandinger, U. and Ansmann, A. (2002) ‘Experimental determination of the lidar overlap profile with + Raman lidar’, Applied Optics, 41(3), pp. 511–514. Available at: https://doi.org/10.1364/ao.41.000511. + Notes ----- - .. TODO:: What is returned by the function and what is described in the docstring does not corresponed. + .. TODO:: + Finish docsting! (LR output...) .. TODO:: This function uses a mix of ppcpy.misc.helper.unifrom_filter and scipy.ndimage.uniform_filter1d @@ -293,112 +317,118 @@ def overlapCalcRaman( be considered. Optimally, should we designe our own filter for this purpuse that do not have the issue with propagating NaN values, Like what is used in the rest of the modules. However, without reducing dimension or filling in NaN values. + + .. TODO:: + Is the if statment that privusly checked if len(aerBsc) > 0, now len(aerBsc) > 6 to be consistant + with the action of the statment actually needed. I believe it might be a leftover from the Matlab + version where they often used staments like this to check if the aerBsc existed or not... ** History ** - 2023-06-06: First edition by Cristofer + - xxxx-xx-xx: Translated to python """ - if len(aerBsc) > 0: + aerBsc = aerBsc.copy() + aerBsc = np.array([1, 2, 3]) - sigFRRa0 = sigFRRa.copy() - for _ in range(5): - sigFRRa = uniform_filter1d(sigFRRa, smoothbins) - sigFRel = uniform_filter1d(sigFRel, smoothbins) + sigFRRa0 = sigFRRa.copy() + for _ in range(5): + sigFRRa = uniform_filter1d(sigFRRa, smoothbins) + sigFRel = uniform_filter1d(sigFRel, smoothbins) - aerBsc = aerBsc.copy() - if len(aerBsc) > 0: - aerBsc[:5] = aerBsc[5] - else: - aerBsc = 0 + aerBsc = aerBsc.copy() + if len(aerBsc) > 6: # TODO: is this even necessary?.... + aerBsc[:5] = aerBsc[5] + else: + aerBsc = np.zeros_like(height) - aerBsc0 = aerBsc.copy() - # Use ppcpy.misc.helper.unifrom_filter here to avoid propagating the NaN-values in aerBse througout the smoothed array. - aerBsc = uniform_filter(aerBsc, smoothbins) + aerBsc0 = aerBsc.copy() + # Use ppcpy.misc.helper.unifrom_filter here to avoid propagating the NaN-values in aerBse througout the smoothed array. + aerBsc = uniform_filter(aerBsc, smoothbins) - LR0 = np.arange(30, 82, 2) # LR array to search best LR. + LR0 = np.arange(30, 82, 2) # LR array to search best LR. - diff_norm = [] + diff_norm = [] - for ii in range(len(LR0) + 1): - if ii == len(LR0): - indx_min = np.argmin(diff_norm) - LR = LR0[indx_min] - else: - LR = LR0[ii] + for ii in range(len(LR0) + 1): + if ii == len(LR0): + indx_min = np.argmin(diff_norm) + LR = LR0[indx_min] + else: + LR = LR0[ii] - # Overlap calculation (direct version) - transRa = np.exp(-np.nancumsum((molExt_r + LR * aerBsc * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) - transel = np.exp(-np.nancumsum((molExt + LR * aerBsc) * np.concatenate(([height[0]], np.diff(height))))) - transRa0 = np.exp(-np.nancumsum((molExt_r + LR * aerBsc0 * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) - transel0 = np.exp(-np.nancumsum((molExt + LR * aerBsc0) * np.concatenate(([height[0]], np.diff(height))))) + # Overlap calculation (direct version) + transRa = np.exp(-np.nancumsum((molExt_r + LR * aerBsc * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) + transel = np.exp(-np.nancumsum((molExt + LR * aerBsc) * np.concatenate(([height[0]], np.diff(height))))) + transRa0 = np.exp(-np.nancumsum((molExt_r + LR * aerBsc0 * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) + transel0 = np.exp(-np.nancumsum((molExt + LR * aerBsc0) * np.concatenate(([height[0]], np.diff(height))))) - if sigFRRa.shape[0] > 0 and sigFRel.shape[0] > 0: - fullOverlapIndx = np.searchsorted(height, hFullOverlap) - if fullOverlapIndx == len(height): - raise ValueError('The index with full overlap cannot be found.') + if sigFRRa.shape[0] > 0 and sigFRel.shape[0] > 0: + fullOverlapIndx = np.searchsorted(height, hFullOverlap) + if fullOverlapIndx == len(height): + raise ValueError('The index with full overlap cannot be found.') - olFunc = sigFRRa * height ** 2 / molBsc_r / transel / transRa - olFunc0 = sigFRRa0 * height ** 2 / molBsc_r / transel0 / transRa0 + olFunc = sigFRRa * height ** 2 / molBsc_r / transel / transRa + olFunc0 = sigFRRa0 * height ** 2 / molBsc_r / transel0 / transRa0 - for _ in range(5): - olFunc = uniform_filter1d(olFunc, 3) + for _ in range(5): + olFunc = uniform_filter1d(olFunc, 3) - ovl_norm, normRange, _ = mean_stable(olFunc, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) - ovl_norm0, normRange0, _ = mean_stable(olFunc0, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) + ovl_norm, normRange, _ = mean_stable(olFunc, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) + ovl_norm0, normRange0, _ = mean_stable(olFunc0, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) - if ovl_norm is not None and ovl_norm.size == 1: - olFunc /= ovl_norm - else: - olFunc /= np.nanmean(olFunc[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) + if ovl_norm is not None and ovl_norm.size == 1: + olFunc /= ovl_norm + else: + olFunc /= np.nanmean(olFunc[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) - if ovl_norm0 is not None and ovl_norm0.size == 1: - olFunc0 /= ovl_norm0 - else: - olFunc0 /= np.nanmean(olFunc0[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) + if ovl_norm0 is not None and ovl_norm0.size == 1: + olFunc0 /= ovl_norm0 + else: + olFunc0 /= np.nanmean(olFunc0[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) - # first bin to start searching full overlap height. % Please replace the 180 by a paramter in future. - bin_ini = int(np.ceil(180 / hres)) + # first bin to start searching full overlap height. % Please replace the 180 by a paramter in future. + bin_ini = int(np.ceil(180 / hres)) - full_ovl_indx = np.argmax(np.diff(olFunc[bin_ini:]) <= 0) + bin_ini + full_ovl_indx = np.argmax(np.diff(olFunc[bin_ini:]) <= 0) + bin_ini - if full_ovl_indx == 0: - full_ovl_indx = fullOverlapIndx + if full_ovl_indx == 0: + full_ovl_indx = fullOverlapIndx - diff_norm.append(np.nansum(np.abs(1 - olFunc[full_ovl_indx:full_ovl_indx + round(1500 / hres)]))) + diff_norm.append(np.nansum(np.abs(1 - olFunc[full_ovl_indx:full_ovl_indx + round(1500 / hres)]))) - olFunc[full_ovl_indx:] = olFunc[full_ovl_indx] - olFunc /= olFunc[full_ovl_indx] # renormalization + olFunc[full_ovl_indx:] = olFunc[full_ovl_indx] + olFunc /= olFunc[full_ovl_indx] # renormalization - if (full_ovl_indx - bin_ini) < 1: - full_ovl_indx = bin_ini + 1 + if (full_ovl_indx - bin_ini) < 1: + full_ovl_indx = bin_ini + 1 - norm_index0 = np.argmax(olFunc[full_ovl_indx - bin_ini:full_ovl_indx + bin_ini * 3]) - norm_index = norm_index0 + full_ovl_indx - bin_ini - 1 - olFunc /= np.mean(olFunc[norm_index - 1:norm_index + 2]) + norm_index0 = np.argmax(olFunc[full_ovl_indx - bin_ini:full_ovl_indx + bin_ini * 3]) + norm_index = norm_index0 + full_ovl_indx - bin_ini - 1 + olFunc /= np.mean(olFunc[norm_index - 1:norm_index + 2]) - half_ovl_indx = np.argmax(olFunc >= 0.95) + half_ovl_indx = np.argmax(olFunc >= 0.95) - if half_ovl_indx == 0: - half_ovl_indx = full_ovl_indx - int(np.floor(180 / hres)) - # smoothing before full overlap to avoid oscilations on that part. - for _ in range(6): - # smoothing before full overlap to avoid S-shape near to the - # full overlap. - olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)] = uniform_filter1d(olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)], 5) + if half_ovl_indx == 0: + half_ovl_indx = full_ovl_indx - int(np.floor(180 / hres)) + # smoothing before full overlap to avoid oscilations on that part. + for _ in range(6): + # smoothing before full overlap to avoid S-shape near to the + # full overlap. + olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)] = uniform_filter1d(olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)], 5) - olFunc[olFunc < 1e-5] = 1e-5 + olFunc[olFunc < 1e-5] = 1e-5 - #olFunc = olFunc.T - #olFunc0 = olFunc0.T + # olFunc = olFunc.T + # olFunc0 = olFunc0.T ret = {'olFunc': olFunc, 'olFunc_raw': olFunc0, 'LR': LR, 'normRange': normRange} return ret - def load(data_cube) -> list: - """read the overlap function from files into a structure similar to the others. + """Read the overlap function from files into a structure similar to the others. Parameters ---------- @@ -408,34 +438,35 @@ def load(data_cube) -> list: Returns ------- overlap : list of dicts - + ... """ - print(data_cube.picasso_config_dict['defaultFile_folder']) - print(data_cube.polly_config_dict) - print(data_cube.polly_default_dict) + + # print(data_cube.picasso_config_dict['defaultFile_folder']) + # print(data_cube.polly_config_dict) + # print(data_cube.polly_default_dict) ovl_files_for = [k for k in data_cube.polly_default_dict.keys() if 'overlapFile_' in k] - print(ovl_files_for) + # print(ovl_files_for) height = data_cube.retrievals_highres['range'] polly_default = data_cube.polly_default_dict folder = Path(data_cube.picasso_config_dict['defaultFile_folder']) - #data_cube.retrievals_profile['overlap']['raman'][i][f'{wv}_total_NR']['olFunc'] + # data_cube.retrievals_profile['overlap']['raman'][i][f'{wv}_total_NR']['olFunc'] overlap = [{}] for k in ovl_files_for: - print(k) + # print(k) full_f = folder.joinpath(polly_default[k]) - print(full_f) + # print(full_f) if polly_default[k] == "": - print('empty string') + # print('empty string') continue dat = np.loadtxt(full_f, skiprows=1, delimiter=',') - print(dat.shape) + # print(dat.shape) h_ovl = dat[:, 0] ovl = dat[:, 1] - print(h_ovl.shape, h_ovl[:10], h_ovl[-10:]) - print(ovl.shape, ovl[:20]) + # print(h_ovl.shape, h_ovl[:10], h_ovl[-10:]) + # print(ovl.shape, ovl[:20]) if height.shape != h_ovl.shape or not np.all(np.isclose(height, h_ovl)): logging.warning('Heights not equal, need interpolating') ovl = np.interp(height, h_ovl, ovl, left=0, right=1) diff --git a/ppcpy/retrievals/angstroem.py b/ppcpy/retrievals/angstroem.py index 18d270b..ce53992 100644 --- a/ppcpy/retrievals/angstroem.py +++ b/ppcpy/retrievals/angstroem.py @@ -27,6 +27,7 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter. """ + return uniform_filter(signal, window_len) @@ -61,8 +62,9 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di the higher of the two wavelengths, and discriminates between FR and NR. """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] + + config_dict = data_cube.polly_config_dict.copy() + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() for i, cldFree in enumerate(data_cube.clFreeGrps): cldFree = cldFree[0], cldFree[1] + 1 @@ -77,10 +79,10 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di flag = (f'aer{prod}' in opt_profiles[i][ch1]) and (f'aer{prod}' in opt_profiles[i][ch2]) else: flag = False - #print(prod, wv1, wv2, tel, opt_profiles[i].keys(), ' -> ', flag) + # print(prod, wv1, wv2, tel, opt_profiles[i].keys(), ' -> ', flag) if flag: - print('channels available', ch1, ch2, prod) + logging.info(f"Channels: {ch1}, {ch2}. Product: {prod}.") retrieval = opt_profiles[i][ch1]['retrieval'] ae, aeStd = calc_ae( @@ -89,7 +91,8 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di param2=opt_profiles[i][ch2][f'aer{prod}'], param2_std=opt_profiles[i][ch2][f'aer{prod}Std'], wavelength1=wv1, wavelength2=wv2, - smooth_window=config_dict[f"smoothWin_{retrieval}{'_'+tel if tel=='NR' else ''}_{wv2}"] + # smooth_window=config_dict[f"smoothWin_{retrieval}{'_'+tel if tel=='NR' else ''}_{wv2}"] + smooth_window=1 # Hard Coded! ) opt_profiles[i][ch1][f'AE_{prod}_{wv1}_{wv2}'] = ae @@ -102,7 +105,6 @@ def calc_ae(param1:np.ndarray, param1_std:np.ndarray, param2:np.ndarray, param2_ wavelength1:float, wavelength2:float, smooth_window:int=17) -> tuple: """Calculates the Ångström exponent and its uncertainty. - Parameters ---------- param1 : array @@ -127,28 +129,36 @@ def calc_ae(param1:np.ndarray, param1_std:np.ndarray, param2:np.ndarray, param2_ angexpStd : array Uncertainty of Ångström exponent. - Notes ----- - Should the ratio be smoothed or not?? If we do not smooth the ratio then why are we + .. TODO:: Should the ratio be smoothed or not?? If we do not smooth the ratio then why are we considering the smoothing window in the error calculations?? **History** - 2021-05-31: first edition by Zhenping + Examples -------- - angexp, angexpStd = pollyAE(param1, param1_std, param2, param2_std, wavelength1, wavelength2) + angexp, angexpStd = calc_ae(param1, param1_std, param2, param2_std, wavelength1, wavelength2) """ + + param1 = param1.copy() + param2 = param2.copy() + # Replace non-positive and 0 values with NaN param1 = np.where(param1 > 0, param1, np.nan) param2 = np.where(param2 > 0, param2, np.nan) # Compute smoothed ratio - #ratio = smooth_signal(param1, smooth_window) / smooth_signal(param2, smooth_window) - ratio = param1 / param2 + if smooth_window > 1: + ratio = smooth_signal(param1, smooth_window) / smooth_signal(param2, smooth_window) + elif smooth_window == 1: + ratio = param1 / param2 + else: + raise ValueError("Unsupported smoothing window size") # Compute Ångström exponent angexp = np.log(ratio) / np.log(wavelength2 / wavelength1) From d97a6427d576964e775a58a048b1037b9043e0e7 Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 16:03:24 +0200 Subject: [PATCH 09/13] Improvement to aggregation procedure. - Now allows multiple variables to be aggregated in the same call. - Added aggregation of variables `mShots`, `mask387Off`, `mask607Off`, `mask407Off`. And fixed aggregation of `RCS`. - Added dependencies on `mask387Off` and `mask607Off`to Raman retrieval. --- ppcpy/interface/picassoProc.py | 77 ++++++++++++++++-------- ppcpy/preprocess/profiles.py | 38 +++++++----- ppcpy/retrievals/raman.py | 106 ++++++++++++++++++--------------- 3 files changed, 132 insertions(+), 89 deletions(-) diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index 5d19398..7c24029 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -409,40 +409,65 @@ def cloudFreeSeg(self): self.clFreeGrps = profilesegment.segment(self) - def aggregate_profiles(self, var=None): - """Aggregate highres profiles over cloud free segments + def aggregate_profiles(self, var:str|list=None, func=np.nansum): + """Aggregate highres profiles over cloud free segments. Parameters ---------- - var : str - Name of variables to aggregate. - Default is `RCS`, `sigBGCor`, and `BG`. - + var : str or array_like + Name of variable to aggregate. Default is None. + func : function, optional + Function to do the aggregation (mean, sum, median, etc.). Default is np.nansum. + + Yields + ------ + self.retrievals_profiles[`var`] : ndarray + Aggregated profile. + Notes ----- - .. TODO:: Decide on a consistent way for doing the aggregation, do not mix mean and sum + - The variable(s) `var` need to be stored in the dictionary data_cube.retrievals_highres. + All aggregated profiles will be stored in the dictionary data_cube.retrievals_profiles under the + the same key. + - If var is None. The following variables will be aggregated: + `sigBGCor`, `BG`, `RCS`, `mShots`, `mask387Off`, `mask607Off`, `mask407Off` + - `func=np.nansum` is needed for correct SNR calculations. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - 2026-07-28: Added option to aggregate multiple variables in the same call. + And corrected the aggregation for PCR-signals like `RCS`. + """ - if var == None: - self.retrievals_profile['RCS'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'RCS', func=np.nanmean) - self.retrievals_profile['sigBGCor'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor') - self.retrievals_profile['BG'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'BG') - - # Remove empty dict keys (temporarly solution) - for v in ['RCS', 'sigBGCor', 'BG']: - if self.retrievals_profile[v] is None: - del self.retrievals_profile[v] + if var is None: + # Take care of the default scenario + self.aggregate_profiles(['sigBGCor', 'BG', 'RCS', 'mShots']) + self.aggregate_profiles(['mask387Off', 'mask607Off', 'mask407Off'], np.nanmean) + return - else: - self.retrievals_profile[var] = \ - preprocprofiles.aggregate_clFreeGrps(self, var) - - # Remove empty dict keys (temporarly solution) - if self.retrievals_profile[var] is None: - del self.retrievals_profile[var] + if isinstance(var, str): + var = [var] + + for variable in var: + logging.info(f"Aggregating variable: {variable} ...") + if variable in self.retrievals_highres: + if variable == "RCS": + self.retrievals_profile[variable] = pollyPreprocess.calculate_rcs( + signal=pollyPreprocess.photonCount2PCR( + signal=preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor', func), + mShots=preprocprofiles.aggregate_clFreeGrps(self, 'mShots', func), + hRes=self.rawdata_dict['measurement_height_resolution']['var_data'] + ), + ranges=self.retrievals_highres['range'] + ) + else: + self.retrievals_profile[variable] = \ + preprocprofiles.aggregate_clFreeGrps(self, variable, func) + else: + logging.critical(f"{variable} is NOT in data_cube.retrievals_highres") + raise ValueError(f"Could not locate variable '{variable}'.") def loadMeteo(self): diff --git a/ppcpy/preprocess/profiles.py b/ppcpy/preprocess/profiles.py index da0a44d..a6dd661 100644 --- a/ppcpy/preprocess/profiles.py +++ b/ppcpy/preprocess/profiles.py @@ -1,23 +1,29 @@ - import numpy as np -def aggregate_clFreeGrps(data_cube, var:str, func=np.nansum): - """ - Aggregate the highres signal over the periods of the cloud free signal. - Input: - - data_cube (object): Main PicassoProc object. - - var (string): name of variable to be aggregated. - - func (function): function to do the aggregateion (mean, sum, median, etc), defult: np.nansum. - - Output: - - out (np.ndarray): Aggregated highres signal for each cloud free segment. - """ - # Check if variable exists, if not return. - if var not in data_cube.retrievals_highres: - print(f"Retrieval {var} do not exist.") - return +def aggregate_clFreeGrps(data_cube, var:str, func=np.nanmean) -> np.ndarray: + """Aggregate the highres signal over the periods of the cloud free signal. + Paremeters + ---------- + data_cube : object + Main PicassoProc object. + var : str + Name of variable to be aggregated. + func : function + Function to do the aggregation (mean, sum, median, etc.). Default is np.nanmean. + + Returns + ------- + out : ndarray + Aggregated highres signal for each cloud free segment. + + Notes + ----- + .. TODO:: This function could easily be separated from the data_cube object + + """ + shp = list(data_cube.retrievals_highres[var].shape) shp[0] = len(data_cube.clFreeGrps) out = np.empty(shp) diff --git a/ppcpy/retrievals/raman.py b/ppcpy/retrievals/raman.py index 5f92c80..5660d26 100644 --- a/ppcpy/retrievals/raman.py +++ b/ppcpy/retrievals/raman.py @@ -45,26 +45,30 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n retrieval : str Name of retrieval type eg. 'raman'. signal : str - Name of the signal used for the retrievals, eg. 'TCor'. + Name of the signal used for the retrieval eg. 'TCor'. + refBeta : float + Reference value used for the retrieval. Notes ----- + .. TODO:: + - sigma_angstroem and MC_count are hardcoded. Can this be automated? + - in raman_ext calulations we use a different hard coded MC_count than the global parameter. + - Should sigBGCor, sigTCor or RCS be used for the Raman retrievals? RCS dampens the effect form + the wrong first bin and makes the profile more straight, insted of s-shaped, in the lower bins. + - use flag367Off and flag607Off to give warnings or error if the channels are on less then X%of the + time during a cloud free segment. + - Add flag for nighttime measurements. **History** - xxxx-xx-xx: TODO: First edition by ... - 2026-02-04: Modified and cleaned by Buholdt - 2026-02-27: Added ext_aer_mod and ext_mol_mod to backscatter calculations. - - - .. TODO:: - - sigma_angstroem and MC_count are hardcoded. Can this be automated? - - in raman_ext calulations we use a different hard coded MC_count than the global parameter. - - Should sigBGCor, sigTCor or RCS be used for the Raman retrievals? RCS dampens the effect form - the wrong first bin and makes the profile more straight (insted of the s-shape) in the lower bins. """ - height = data_cube.retrievals_highres['range'] + + height = data_cube.retrievals_highres['range'].copy() hres = data_cube.rawdata_dict['measurement_height_resolution']['var_data'] config_dict = data_cube.polly_config_dict @@ -73,15 +77,12 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n opt_profiles = [{} for i in range(len(data_cube.clFreeGrps))] - if not heightFullOverlap: + if not heightFullOverlap: heightFullOverlap = [np.array(config_dict['heightFullOverlap']) for i in data_cube.clFreeGrps] - print(heightFullOverlap) - print('Starting Raman retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) # Define channels to run the retrieval for channels = [((355, 'total', 'FR'), (387, 'total', 'FR')), @@ -93,8 +94,17 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n for (wv, t, tel), (wv_r, t_r, tel_r) in channels: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv_r, t_r, tel_r)): - print(f'== {wv}, {t}, {tel} | {wv_r}, {t_r}, {tel_r} raman ========') - # TODO add flag for nighttime measurements? + logging.info(f"Channels: {wv}, {t}, {tel} | {wv_r}, {t_r}, {tel_r} raman.") + + # Check availability of Raman channel during retireval priod + if data_cube.retrievals_profile[f'mask{wv_r}Off'][i] > 0: + logging.warning(f'Channel {wv_r} {t_r} {tel_r} was not opertional {data_cube.retrievals_profile[f'mask{wv_r}Off'][i]*100:.2f}% of the profile retrieval period.') + # .. TODO:: add this information to a quality flag for the profile based on how long wv_r was unoperational. + if data_cube.retrievals_profile[f'mask{wv_r}Off'][i] > 0.5: # <-- .. TODO:: decide on a treshold for skipping the channel processing. + logging.warning('Skipping Raman retrival for this channel.') + continue + + # .. TODO:: add flag for nighttime measurements? # Telescope type dependent configurations if tel == 'NR': @@ -102,8 +112,6 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n keyminSNR = 'minRamanRefSNR_NR_' angstrexp = config_dict['angstrexp_NR'] refBeta = config_dict[f'refBeta_NR_{wv}'] if f'refBeta_NR_{wv}' in config_dict else None - # TODO seperate klett and raman refBeta in config file? - # refBeta = config_dict[f'refBeta_NR_raman_{wv}'] if f'refBeta_NR_raman_{wv}' in config_dict else None else: key_smooth = 'smoothWin_raman_' keyminSNR = 'minRamanRefSNR' @@ -117,22 +125,22 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n print('angstrexp', angstrexp) # Elastic signals - sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]) - bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] - molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :] + sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]).copy() + bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]).copy() + molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() + molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :].copy() # Inelastic signals - sig_r = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv_r, t, tel)]) - bg_r = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv_r, t, tel)]) - molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :] - molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :] + sig_r = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv_r, t, tel)]).copy() + bg_r = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv_r, t, tel)]).copy() + molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :].copy() + molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :].copy() - number_density = data_cube.mol_profiles[f'number_density'][i, :] + number_density = data_cube.mol_profiles[f'number_density'][i, :].copy() if wv == 1064 and wv_r == 607: # calculate the extinction based on the 532nm molecular profiles and a correction afterwards - molExt_mod = data_cube.mol_profiles[f'mExt_532'][i, :] + molExt_mod = data_cube.mol_profiles[f'mExt_532'][i, :].copy() wv_mod = 532 else: # calculate normally @@ -165,14 +173,12 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] if np.isnan(refHInd).any(): - print('No valid refHInd found, skipping Raman retrieval for this channel.') + logging.warning("No valid refHInd found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue hFullOverlap = heightFullOverlap[i][data_cube.gf(wv, t, tel)][0] hBaseInd = np.argmax(height >= (hFullOverlap + config_dict[f'{key_smooth}{wv}'] / 2 * hres)) - print(hFullOverlap, config_dict[f'{key_smooth}{wv}'] / 2 * hres) - print('refHInd', refHInd, 'refH', height[np.array(refHInd)], 'hBaseInd', hBaseInd, 'hBase', height[hBaseInd]) # Calculate SNR in the reference height SNRRef = calc_snr( @@ -186,7 +192,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n # Checking SNR treshold if SNRRef < config_dict[f'{keyminSNR}{wv}'] and SNRRef_r < config_dict[f'{keyminSNR}{wv_r}']: - print('Signal is too noisy at the reference height, skipping Raman retrival for this channel.', SNRRef, config_dict[f'{keyminSNR}{wv}'], SNRRef_r, config_dict[f'{keyminSNR}{wv_r}']) + logging.warning(f"Signal is too noisy at the reference height, skipping Raman retrival for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue @@ -196,18 +202,18 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n if 'aerBsc' in opt_profiles[i][f'{wv}_{t}_FR']: refBeta = np.nanmean(opt_profiles[i][f'{wv}_{t}_FR']['aerBsc'][refHInd[0]:refHInd[1] + 1]) else: - print('No valid refBeta found, skipping Raman retrieval for this channel.') + logging.warning("No valid refBeta found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue else: - print('No valid refBeta found, skipping Raman retrieval for this channel.') + logging.warning("No valid refBeta found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue aerExt_tmp = prof['aerExt'].copy() aerExt_tmp[:hBaseInd + 1] = aerExt_tmp[hBaseInd] aerExt_mod[:hBaseInd + 1] = aerExt_mod[hBaseInd] # should the hBaseInd for 1064 or 532 be used here for the 1064nm channel?? - print(f'filling aerExt below overlap with {aerExt_tmp[hBaseInd]} for calculating the backscatter') + logging.info(f"Filling aerExt below overlap with {aerExt_tmp[hBaseInd]} for calculating the backscatter") # Change equation back for comparison with Picasso. This is only a temporary addition that will be removed # once picasso is updated or the comparison to picasso is completed. @@ -320,21 +326,20 @@ def raman_ext( in cirrus clouds by using a combined Raman elastic-backscatter lidar. Applied Optics Vol. 31, Issue 33, pp. 7113-7131 (1992). + Notes ----- - + .. TODO:: `moving_smooth_varied_win` and `moving_linfit_varied_win` functions are not yet implemented. + Investigate what smothing function is used, Savitzky-Golay or something else? + **History** - 2021-05-31: First edition by Zhenping - 2025-01-05: AI supported translation - 2026-02-04: Cleaned by Buholdt - .. TODO:: - - moving_smooth_varied_win function is not yet implemented. - - moving_linfit_varied_win function is not yet implemented. - - Investigate what smothing function is used, Savitzky-Golay or something else? - """ + # Prepare variables temp = number_density / (sig * height**2) temp[temp <= 0] = np.nan @@ -487,12 +492,13 @@ def raman_bsc( - 2026-02-27: Added ext_aer_mod and ext_mol_mod for better consistancy with the 1064nm channel. """ + if isinstance(MC_count, int): MC_count = np.ones(4, dtype=int) * MC_count if np.prod(MC_count) > 1e5: - print('Warning: Too large sampling for Monte-Carlo simulation.') - return np.nan * np.ones_like(sigElastic), None, None + logging.warning("Too large sampling for Monte-Carlo simulation.") + return {'aerBsc': np.nan * np.ones_like(sigElastic), 'aerBscStd': None, 'LR': None} # Calculate beta_aer: beta_aer, LR, ODs, signalratio = calc_raman_bsc( @@ -633,7 +639,8 @@ def calc_raman_bsc( References ---------- - Ansmann, A., et al. (1992). "Independent measurement of extinction and backscatter profiles in cirrus clouds by using a combined Raman elastic-backscatter lidar." Applied optics 31(33): 7113-7131. + Ansmann, A., et al. (1992). "Independent measurement of extinction and backscatter profiles in cirrus clouds by + using a combined Raman elastic-backscatter lidar." Applied optics 31(33): 7113-7131. Notes @@ -648,8 +655,11 @@ def calc_raman_bsc( - 2024-11-12: Modified by HB for consistency in 2024. - 2026-02-04: Cleaned by Buholdt - 2026-02-27: Added ext_aer_el_raman and ext_mol_el_raman for better consistancy with the 1064nm channel. + - 2026-07-28: Added dependencies on `mask387Off` and `mask607Off`. """ + + ext_aer = ext_aer.copy() ext_aer[~np.isfinite(ext_aer)] = 0 if height[HRefInd[0]] >= height[-1] or height[HRefInd[1]] <= height[0]: @@ -753,15 +763,17 @@ def lidarratio( **History** - 2021-07-20: First edition by Zhenping (translated to Python) - 2026-02-04: Changed from scipy.signal.savgol_filter to ppcpy.retrievals.ramanhelpers.savgol_filter + - 2021-07-20: First edition by Zhenping (translated to Python) + - 2026-02-04: Changed from scipy.signal.savgol_filter to ppcpy.retrievals.ramanhelpers.savgol_filter + """ + # Adjust smoothing window for backscatter to match extinction resolution if smoothWinExt >= smoothWinBsc: smoothWinBsc2 = round(0.625 * smoothWinExt + 0.23) # Eq (6) in reference smoothWinBsc2 = max(smoothWinBsc2, 3) # Ensure minimum value of 3 else: - print("Warning: Smoothing for backscatter is larger than smoothing for extinction.") + logging.warning("Smoothing for backscatter is larger than smoothing for extinction.") smoothWinBsc2 = 3 # Smooth the backscatter using Savitzky-Golay filter From 22f73a1ec204fe9b61bd1eed9dce196897cfaa1e Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 16:20:52 +0200 Subject: [PATCH 10/13] Added option to use improved SNR for quality mask by smoothing the signal and BG before SNR calculations. Added features: - New config variable `flagUseImprovedSNR`. - New retrievals_highres variables for improved SNR: `SNR_quasi` & `lowSNRMask_quasi`. - New 2d smoothing functions: `uniform_filter_2d` & `moving_average_2d`. --- ppcpy/config/polly_global_config.json | 1 + ppcpy/interface/picassoProc.py | 31 ++- ppcpy/misc/helper.py | 266 +++++++++++++++++++++----- ppcpy/qc/qualityMask.py | 126 ++++++++++-- 4 files changed, 362 insertions(+), 62 deletions(-) diff --git a/ppcpy/config/polly_global_config.json b/ppcpy/config/polly_global_config.json index 94ad0cc..bddcafb 100644 --- a/ppcpy/config/polly_global_config.json +++ b/ppcpy/config/polly_global_config.json @@ -220,6 +220,7 @@ "LCMeanMaxIndx": 1000, "LCCalibrationStatus": ["none", "Klett", "Raman", "Defaults", "History"], "flagUseRetrievedExt4LCCalc": true, + "flagUseImprovedSNR": true, "quasi_smooth_h": [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], "quasi_smooth_t": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10], "flagOnlyUseValidQuasiData": true, diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index 7c24029..26995c9 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -681,8 +681,35 @@ def calcDepol(self): def estQualityMask(self): - """Estimate the quality mask.""" - logging.info("Estimate quality mask") + """Estimate the quality mask. + + Yields + ------ + self.retrievals_highres['quality_mask'] : ndarray + High resolution (time, height) quality masks per channel. + 0 : good data + 1 : low-SNR data + 2 : depolarization calibration periods + 3 : shutter on + 4 : fog + 5 : saturated (NEW) + + Notes + ----- + - If the config variables `flagUseImprovedSNR=True`, SNR and the lowSNRMask are + recalculated with quasi-smoothed signals to improve data quality. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - 2026-07-17: Added quasi-smoothed SNR and lowSNRMask. + + """ + + logging.info("Estimate quality masks ...") + if self.polly_config_dict['flagUseImprovedSNR']: + self.retrievals_highres['SNR_quasi'], self.retrievals_highres['lowSNRMask_quasi'] = qualityMask.improvedSNR(self) + self.retrievals_highres['quality_mask'] = qualityMask.qualityMask(self) diff --git a/ppcpy/misc/helper.py b/ppcpy/misc/helper.py index db69f56..09aef48 100644 --- a/ppcpy/misc/helper.py +++ b/ppcpy/misc/helper.py @@ -13,7 +13,8 @@ from pathlib import Path import numpy as np from scipy.sparse import diags -from scipy.signal import savgol_coeffs +from scipy.signal import convolve, savgol_coeffs +from scipy.ndimage import uniform_filter as _uniform_filter def detect_path_type(fullpath:str): @@ -296,9 +297,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): laserlog_filename = polly_laserlog_files_list[0] laserlog_filename = Path(laserlog_filename).name - laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc',laserlog_filename)[0] + laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc', laserlog_filename)[0] laserlog_filename = f'{laserlog_filename_left}_00_00_01.nc.laserlogbook.txt' - destination_file = Path(output_path,laserlog_filename) + destination_file = Path(output_path, laserlog_filename) # Open the source file in binary mode and read its content with open(result_file, 'rb') as source: @@ -365,10 +366,10 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): 'measurement_height_resolution', 'laser_rep_rate', 'laser_power', -# 'laser_flashlamp', + # 'laser_flashlamp', 'location_height', 'neutral_density_filter', -# 'location_coordinates', + # 'location_coordinates', 'pm_voltage', 'pinhole', 'polstate', @@ -393,23 +394,23 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): diff_var=0 print('\n') print('checking differences in selected variables ...') - for ds in range(0,len(polly_file_ds_ls)-1): -# print('\n') -# print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) + for ds in range(0, len(polly_file_ds_ls)-1): + # print('\n') + # print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) for var in vars_of_interest: if var in polly_file_ds_ls[ds].variables.keys(): ## check if var is available within the polly-datastructure (depending on polly-system) var_value_1=str(polly_file_ds_ls[ds].variables[var][:]) var_value_2=str(polly_file_ds_ls[ds+1].variables[var][:]) - # print(var + ": " + var_value_1) - # print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) if var_value_1 == var_value_2 and diff_var==0: # print('no difference found ...') add_to_list(ds, polly_files_list, selected_var_nc_ls) elif var_value_1 != var_value_2 and diff_var==0: print('difference found in var:') print(var) - #print(var + ": " + var_value_1) - #print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) diff_var=1 add_to_list(ds, polly_files_list, selected_var_nc_ls) if force == True else None elif var_value_1 == var_value_2 and diff_var != 0: @@ -424,7 +425,7 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): add_to_list(-1, polly_files_list, selected_var_nc_ls) print('\nno differences found in selected variables!\n') elif diff_var != 0: -# add_to_list(-1,polly_files_list,selected_var_nc_ls) if force == True else None + # add_to_list(-1, polly_files_list, selected_var_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: add_to_list(-1, polly_files_list, selected_var_nc_ls) @@ -480,15 +481,15 @@ def checking_attr(timestamp, device, raw_folder, output_path): print('checking differences in attributes ...') for ds in range(0, len(polly_file_ds_ls) - 1): ## get global attributes as a list of strings -# print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) -# print('\nglobal attributes:') + # print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) + # print('\nglobal attributes:') for nc_attr in polly_file_ds_ls[0].ncattrs(): # att_value=repr(input_nc_file.getncattr(nc_attr)) att_value_1 = polly_file_ds_ls[ds].getncattr(nc_attr) att_value_2 = polly_file_ds_ls[ds+1].getncattr(nc_attr) -# print(nc_attr) -# print(" " + att_value_1) -# print(" " + att_value_2) + # print(nc_attr) + # print(" " + att_value_1) + # print(" " + att_value_2) if att_value_1 == att_value_2 and diff_att==0: add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) elif att_value_1 != att_value_2 and diff_att==0: @@ -504,15 +505,15 @@ def checking_attr(timestamp, device, raw_folder, output_path): print('difference found!') add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) if force == True else None -# print("\nvariable attributes:") + # print("\nvariable attributes:") for var in polly_file_ds_ls[0].variables.keys(): -# print(var) + # print(var) for var_att in polly_file_ds_ls[0].variables[var].ncattrs(): var_att_value_1 = polly_file_ds_ls[ds].variables[var].getncattr(var_att) var_att_value_2 = polly_file_ds_ls[ds+1].variables[var].getncattr(var_att) -# print(" " + var_att) -# print(" " + var_att_value_1) -# print(" " + var_att_value_2) + # print(" " + var_att) + # print(" " + var_att_value_1) + # print(" " + var_att_value_2) if var_att_value_1 == var_att_value_2 and diff_var_att == 0: pass elif var_att_value_1 != var_att_value_2 and diff_var_att == 0: @@ -534,7 +535,7 @@ def checking_attr(timestamp, device, raw_folder, output_path): add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) print('\nno differences found in global attributes!\n') elif diff_att != 0: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) if force == True else None + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: @@ -548,14 +549,14 @@ def checking_attr(timestamp, device, raw_folder, output_path): if diff_var_att == 0: print('\nno differences found in variable attributes!\n') -# elif diff_var_att != 0: -# ## if force==true, merge, but if force==false: the whole day will not be in list anymore -# if force == True: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) -# print('\ndifferences found in variable attributes! But will be force-merged.\n') -# else: -# print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') -# sys.exit() + # elif diff_var_att != 0: + # ## if force==true, merge, but if force==false: the whole day will not be in list anymore + # if force == True: + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) + # print('\ndifferences found in variable attributes! But will be force-merged.\n') + # else: + # print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') + # sys.exit() print(selected_att_nc_ls) @@ -595,8 +596,8 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): polly_file_ds = Dataset(files, "r") polly_file_ds_ls.append(polly_file_ds) - for elementNR,ds in enumerate(polly_file_ds_ls): - # print(selected_timestamp_nc_ls[elementNR]) + for elementNR, ds in enumerate(polly_file_ds_ls): + # print(selected_timestamp_nc_ls[elementNR]) timestamp_ds = ds.variables['measurement_time'][:] if 19700101 in timestamp_ds.T[0]: print(f'The file: {selected_timestamp_nc_ls[elementNR]} contains incorrect timestamps!') @@ -604,9 +605,9 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): ## get correct timestamp_ds from filename timestamp_filename = selected_timestamp_nc_ls[elementNR] timestamp_filename = timestamp_filename.stem - timestamp_filename = re.split(r'_',str(timestamp_filename))[-3:] + timestamp_filename = re.split(r'_', str(timestamp_filename))[-3:] ## del. nc-file - #os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps + # os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps ## calc. the deltaT between measurementdatapoints laser_rep_rate = float(ds.variables['laser_rep_rate'][0]) measurement_shots = ds.variables['measurement_shots'][:] @@ -633,7 +634,7 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): ## check if seconds_ls does not contain seonds of day larger than 86400 ## TODO ## if so, remove file from list and del. file ## TODO t_check = any(value > 86400 for value in seconds_ls) - #t_check = False ## do not skip files which are longer than 24h, seconds_ls > 86400 + # t_check = False ## do not skip files which are longer than 24h, seconds_ls > 86400 if t_check == True: print('seconds of day exceeds 86400. file will be removed from merging list.') ds.close() @@ -703,7 +704,7 @@ def concat_files(timestamp, device, raw_folder, output_path:str): ## merge selected files -# concat='concat' + # concat='concat' sel_polly_files_list = checking_timestamp(timestamp, device, raw_folder, output_path) @@ -720,12 +721,12 @@ def concat_files(timestamp, device, raw_folder, output_path:str): os.rename(sel_polly_files_list[0], Path(output_path, filestring)) return () else: -# sel_polly_files_list = [ str(el) for el in sel_polly_files_list] + # sel_polly_files_list = [ str(el) for el in sel_polly_files_list] ## parameters for controlling the merging process compat='override' ## Values of variable "laser_flashlamp" often changes, but those files will be merged anyway. This option picks the value from first dataset. coords='minimal' - ds = xarray.open_mfdataset(sel_polly_files_list,combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) + ds = xarray.open_mfdataset(sel_polly_files_list, combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) ## save to a single nc-file print(f"\nmerged nc-file '{filestring}' will be stored to '{output_path}'") print("\nwriting merged file ...") @@ -741,8 +742,8 @@ def write_netcdf(ds: xarray.Dataset, out_file: Path) -> None: enc[k] = { "zlib": True, "complevel": 1, -# "fletcher32": True, -# "chunksizes": tuple(map(lambda x: x//2, ds[k].shape)) + # "fletcher32": True, + # "chunksizes": tuple(map(lambda x: x//2, ds[k].shape)) } ds.to_netcdf(out_file, format="NETCDF4", engine="netcdf4", encoding=enc) @@ -866,11 +867,16 @@ def uniform_filter(x:np.ndarray, win:int, fill_val:float=np.nan) -> np.ndarray: **History** - 2026-02-02: First edition by Buholdt + """ if isinstance(win, int): if len(x.shape) > 1: raise NotImplementedError('Support for smoothing of mulitdimensional arrays is not yet implemented') + + if len(x) < win: + logging.warning("`win` is bigger than `x`. No smoothig applied.") + return x f = np.ones(win)/win x_smoothed = np.convolve(x, f, mode='valid') @@ -931,12 +937,17 @@ def moving_average(x:np.ndarray, win:int) -> np.ndarray: - 2026-03-24: First edition by Buholdt """ + if isinstance(win, int): if len(x.shape) > 1: raise NotImplementedError('Support for smoothing of mulitdimensional arrays is not yet implemented') + if len(x) < win: + logging.warning("`win` is bigger than `x`. No smoothig applied.") + return x + if win % 2 == 0: - print("Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.") + logging.warning("Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.") win -= 1 f = np.ones(win)/win @@ -1003,8 +1014,12 @@ def savgol_filter(x:np.ndarray, window_length:int, polyorder:int=2, deriv:int=0, [3] Jianwen Luo, Kui Ying, and Jing Bai. 2005. Savitzky-Golay smoothing and differentiation filter for even number data. Signal Process. 85, 7 (July 2005), 1429-1434. - """ + + if len(x) < window_length: + logging.warning("`window_length` is bigger than `x`. No smoothig applied.") + return x + f = savgol_coeffs(window_length, polyorder, deriv=deriv, delta=delta) x_smooth = np.convolve(x, f, mode='valid') fill = np.full(int((window_length - 1)/2), fill_val) @@ -1018,6 +1033,169 @@ def savgol_filter(x:np.ndarray, window_length:int, polyorder:int=2, deriv:int=0, return out +def uniform_filter_2d(x:np.ndarray, Nr:int, Nc:int, fill_val=np.nan) -> np.ndarray: + """2 dimensional uniform smoothing filter. + + The regions affected by edge/padding effects are replaced with ´fill_val´. + + Parameters + ---------- + x : ndarray (time, height, channel, ) + 2 dimensional time-height signal(s) to be smooth. + Nr : int + Smoothing window size along x-axis (rows). + Nc : int + Smoothing window size along y-axis (columns). + fill_val : float, optional + Value to be used for filling edges, in order to recreate the input + dimension. Default is np.nan. + + Returns + ------- + out : ndarray (time, height, channel, ) + Smoothed 2 dimensional time-height signal(s). + + Notes + ----- + - Invalid pixels are masked out and replaced with 0 during + the convolution process to avoid propagation during smoothing + process. + - scipy.ndimage.uniform_filter is used instead of alternative convolution + based filters due to its increased efficiency. + + **History** + + - 2026-07-17: first edition by Buholdt + + """ + + # Copy and check input dimensions + x0 = x.copy() + if len(x0.shape) == 2: + filterShape = (Nr, Nc) + elif len(x0.shape) == 3: + filterShape = (Nr, Nc, 1) + else: + raise ValueError(f"´x´ must either be a 2D or 3D array (multiple 2D arrays). Not {len(x0.shape)}D.") + + # Smoothing process + invalid = np.isnan(x0) + x0[invalid] = 0 + out = _uniform_filter( + x0, + size=filterShape, + mode="constant", + cval=0.0, + ) + out[invalid] = np.nan + + # Mask edge affected pixels + top = Nr // 2 + bottom = (Nr - 1) // 2 + + if top: + out[:top] = fill_val + if bottom: + out[-bottom:] = fill_val + + left = Nc // 2 + right = (Nc - 1) // 2 + + if left: + out[:, :left] = fill_val + if right: + out[:, -right:] = fill_val + + return out + + +def moving_average_2d(x:np.ndarray, Nr:int, Nc:int, sum:bool=False) -> np.ndarray: + """2 dimensional uniform smoothing filter ala. 2d version of smooth(y, span, method='moving') + in Matlab. + + This function uses a centered moving filter with dynamic size reduction at edges. + + Examples + -------- + The output of the function for win = 5 in one dimension follows the following logic: + out[0] = x[0]/1 + out[1] = (x[0] + x[1] + x[2])/3 + out[2] = (x[0] + x[1] + x[2] + x[3] + x[4])/5 + out[3] = (x[1] + x[2] + x[3] + x[4] + x[5])/5 + ... + out[-4] = (x[-6] + x[-5] + x[-4] + x[-3] + x[-2])/5 + out[-3] = (x[-5] + x[-4] + x[-3] + x[-2] + x[-1])/5 + out[-2] = (x[-3] + x[-2] + x[-1])/3 + out[-1] = x[-1]/1 + + Parameters + ---------- + x : ndarray (time, height, channel, ) + 2 dimensional time-height signal(s) to be smooth. + Nr : int + Smoothing window size along x-axis (rows). + Nc : int + Smoothing window size along y-axis (columns). + sum : bool, optional + If True, return moving sum results. Otherwise, return + moving average results. Default is False. + + Returns + ------- + out : ndarray (time, height, channel, ) + Smoothed 2 dimensional time-height signal(s). + + Notes + ----- + - Invalid pixels are masked out and replaced with 0 during + the convolution process to avoid propagation during smoothing + process. However, unlike in ´uniform_filter_2d´ these pixels + are not counted in the averaging procedure if ´sum=False´. + - By using `sum=True` the function will return the moving sum result + instead of the moving average. + - scipy.ndimage.uniform_filter is used instead of alternative convolution + based filters due to its increased efficiency. + + ** History ** + + - 2026-07-17: first edition by Buholdt + + """ + + # Copy and check input dimensions + x0 = x.copy() + if len(x0.shape) == 2: + filterShape = (Nr, Nc) + elif len(x0.shape) == 3: + filterShape = (Nr, Nc, 1) + else: + raise ValueError(f"´x´ must either be a 2D or 3D array (multiple 2D arrays). Not {len(x0.shape)}D.") + + # Smoothing process + invalid = np.isnan(x0) + x0[invalid] = 0 + num = _uniform_filter( + x0, + size=filterShape, + mode="constant", + cval=0.0, + ) + if sum: + num *= Nr * Nc + den = 1 + else: + den = _uniform_filter( + (~invalid).astype(float), + size=filterShape, + mode="constant", + cval=0.0, + ) + out = num/den + out[invalid] = np.nan + + return out + + def mean_stable(x:np.ndarray, win:int, minBin:int=None, maxBin:int=None, minRelStd:float=None) -> tuple: """Calculate the mean value of x based on the least fluctuated segment of x. The searching is based on the std inside each window of x. diff --git a/ppcpy/qc/qualityMask.py b/ppcpy/qc/qualityMask.py index 46aee88..c0e974c 100644 --- a/ppcpy/qc/qualityMask.py +++ b/ppcpy/qc/qualityMask.py @@ -1,11 +1,15 @@ import numpy as np import logging +from ppcpy.retrievals.collection import calc_snr +from ppcpy.misc.helper import uniform_filter_2d, moving_average_2d -def qualityMask(data_cube): + +def qualityMask(data_cube) -> np.ndarray: """Estimate quality mask. - Categories: + Categories + ---------- 0 : good data 1 : low-SNR data 2 : depolarization calibration periods @@ -13,31 +17,121 @@ def qualityMask(data_cube): 4 : fog 5 : saturated (NEW) + Parameters + ---------- + data_cube : object + Main picassoProc object. + useQuasiSNR : bool + If True, use `lowSNRMask_quasi`. Otherwise use `lowSNRMask`. + + Returns + ------- + quality_mask : ndarray + Quality mask with categories listed above. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: translated to python + - 2026-07-03: vectorized and added option to use `lowSNRMask_quasi` + - 2026-07-03: added resistance toward missing mask-variables + + """ + + quality_mask = np.zeros_like(data_cube.retrievals_highres['sigBGCor']).astype(int) + + # Flagg pixels with low SNR + if data_cube.polly_config_dict['flagUseImprovedSNR'] and 'lowSNRMask_quasi' in data_cube.retrievals_highres: + logging.info("Using improved SNR in quality mask estimation.") + quality_mask[data_cube.retrievals_highres['lowSNRMask_quasi']] = 1 + elif 'lowSNRMask' in data_cube.retrievals_highres: + logging.info("Using raw SNR in quality mask estimation.") + quality_mask[data_cube.retrievals_highres['lowSNRMask']] = 1 + else: + logging.warning("Missing SNR information!") + + # Flagg pixels during depol. calibration periods + if 'depCalMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['depCalMask'], :, :] = 2 + else: + logging.warning("Missing depol. calibration period information!") + + # Flagg pixels where the shutter is on + if 'shutterOnMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['shutterOnMask'], :, :] = 3 + else: + logging.warning("Missing shutter position information!") + + # Flagg pixels affected by fog + if 'fogMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['fogMask'], :, :] = 4 + else: + logging.warning("Missing fog information!") + + # Flagg saturated pixels + if hasattr(data_cube, "flagSaturation"): + quality_mask[data_cube.flagSaturation] = 5 + else: + logging.warning("Missing saturation information!") + + return quality_mask + + +def improvedSNR(data_cube) -> tuple: + """Artificially improve SNR by smoothing the signal and background. + Parameters ---------- data_cube : object Main picassoProc object. + Returns + ------- + SNR : ndarray + Improved signal to noise ratio + lowSNRMask : ndarray + True if SNR is lower than the config variable 'mask_SNRmin'. Otherwise False. + Notes ----- - - The lowSNRMask is actually calculated twice, once in pollyPreprocess.m - and then again when the quality mask is evaluated in picassoProcV3.m. - Also the original processing chain has a quality_mask_vdr, which should be a composite - of cross and total, maybe this can be handled more logically here. - + - Could also be in the collection retrieval together with ´calc_snr´. + ** History ** - xxxx-xx-xx: First edition by ... + - 2026-07-03: translated to python + - 2026-07-12: vectorized for better efficiency + """ - quality_mask = np.zeros_like(data_cube.retrievals_highres['sigBGCor']).astype(int) + logging.info("Calculating SNR from smoothed signal to improve data quality...") + signal = data_cube.retrievals_highres['sigBGCor'].copy() + BG = np.repeat(data_cube.retrievals_highres['BG'].copy()[:, np.newaxis, :], data_cube.retrievals_highres['range'].shape[0], axis=1) + + quasi_smooth_t = np.asarray(data_cube.polly_config_dict['quasi_smooth_t']) + quasi_smooth_h = np.asarray(data_cube.polly_config_dict['quasi_smooth_h']) + + # Group based vectorization + windows = np.stack((quasi_smooth_t, quasi_smooth_h), axis=1) + unique_windows, order = np.unique(windows, axis=0, return_inverse=True) - for ich, ch in enumerate(data_cube.retrievals_highres['channel']): - logging.info(f"channel {ich}, {ch}") - quality_mask[:, :, ich][data_cube.retrievals_highres['lowSNRMask'][:, :, ich]] = 1 - quality_mask[data_cube.retrievals_highres['depCalMask'], :, ich] = 2 - quality_mask[data_cube.retrievals_highres['shutterOnMask'], :, ich] = 3 - quality_mask[data_cube.retrievals_highres['fogMask'], :, ich] = 4 - quality_mask[:, :, ich][data_cube.flagSaturation[:, :, ich]] = 5 + smoothFunc = uniform_filter_2d + if data_cube.polly_config_dict['flagPicassoComparison']: + smoothFunc = moving_average_2d + + for grp_idx, (Nr, Nc) in enumerate(unique_windows): + logging.info(f"Vectorized group {grp_idx}, Nr = {Nr}, Nc = {Nc} ...") + grp = np.where(order == grp_idx)[0] + signal[:, :, grp] = smoothFunc(signal[:, :, grp], Nr, Nc) + BG[:, :, grp] = smoothFunc(BG[:, :, grp], Nr, Nc) + + # Multiply with smoothing window size to get back to photon counts + signal *= quasi_smooth_t * quasi_smooth_h + BG *= quasi_smooth_t * quasi_smooth_h + + SNR = calc_snr(signal, BG) + + lowSNRMask = np.zeros_like(signal, dtype=bool) + lowSNRMask[SNR < data_cube.polly_config_dict['mask_SNRmin']] = True - return quality_mask \ No newline at end of file + return SNR, lowSNRMask \ No newline at end of file From 4ebfd52c22d829d8df2b3a34138ad6918669e8cc Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 17:03:18 +0200 Subject: [PATCH 11/13] Update to documentation and logging. --- ppcpy/calibration/lidarconstant.py | 7 +- ppcpy/calibration/rayleighfit.py | 65 +- ppcpy/cloudmask/profilesegment.py | 29 +- ppcpy/interface/picassoProc.py | 1019 ++++++++++++++++++++++++---- ppcpy/io/loadConfigs.py | 144 ++-- ppcpy/io/readMeteo.py | 14 +- ppcpy/io/readPollyRawData.py | 121 ++-- ppcpy/io/sql_interaction.py | 52 +- ppcpy/io/write2nc.py | 193 +++++- ppcpy/misc/concat.py | 436 ++++++++---- ppcpy/misc/molecular.py | 7 +- ppcpy/qc/overlapCor.py | 40 +- ppcpy/qc/overlapEst.py | 10 +- ppcpy/qc/pollySaturationDetect.py | 53 +- ppcpy/qc/transCor.py | 178 +++-- ppcpy/retrievals/collection.py | 22 +- ppcpy/retrievals/depolarization.py | 205 ++++-- ppcpy/retrievals/highres.py | 24 +- ppcpy/retrievals/klettfernald.py | 37 +- ppcpy/retrievals/quasi.py | 7 +- ppcpy/retrievals/quasiV1.py | 2 + ppcpy/retrievals/quasiV2.py | 4 +- ppcpy/retrievals/ramanhelpers.py | 25 +- 23 files changed, 2007 insertions(+), 687 deletions(-) diff --git a/ppcpy/calibration/lidarconstant.py b/ppcpy/calibration/lidarconstant.py index 8ccb2d2..e3a0d52 100644 --- a/ppcpy/calibration/lidarconstant.py +++ b/ppcpy/calibration/lidarconstant.py @@ -39,11 +39,14 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li .. TODO:: Check if LC's are normalized with respect to the mean of the profiles. .. TODO:: Add option for Aeronet and rotational Raman retrieved LC. + .. TODO:: insted of performing an additional check for if the raman channels was off here. we could just pass on the information form the quality flag and use it when choosing the best LC. >-- What did I mean here??? + .. TODO:: Add a check at the statr if to see if the channels where on more than x% of the time during each cloud free preiod. Give a warning if it was less than ...% and an error/skipp the period for the channel if it was less than ...%. **History** xxxx-xx-xx: First edition by ... 2026-03-18: Changed beta_mol for inelastic wavelengths and added the 'flagUseRetrievedExt4LCCalc' variable. + """ logging.info(f'LC retrieval: {retrieval} method') @@ -75,7 +78,7 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li # Elastic signal: sig = profiles[channel]['signal'] - signal = np.nanmean(np.squeeze( + signal = np.nanmean(np.squeeze( # TODO: try to use PCR --> normalized data_cube.retrievals_highres[f'sig{sig}'][slice(*cldFree), :, data_cube.gf(wv, t, tel)]), axis=0) molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :].copy() @@ -139,7 +142,7 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li wv_r = elastic2raman[int(wv)] ## Inelastic signal, backscatter and extinction: - signal_r = np.nanmean(np.squeeze( + signal_r = np.nanmean(np.squeeze( # TODO: try to use PCR --> normalized data_cube.retrievals_highres[f'sig{sig}'][slice(*cldFree), :, data_cube.gf(wv_r, t, tel)]), axis=0) molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :].copy() molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :].copy() diff --git a/ppcpy/calibration/rayleighfit.py b/ppcpy/calibration/rayleighfit.py index b6f604e..dece333 100644 --- a/ppcpy/calibration/rayleighfit.py +++ b/ppcpy/calibration/rayleighfit.py @@ -26,8 +26,8 @@ def getVal(array:np.ndarray, indices:list) -> list: out : list Values of the array at given indices. If indx is NaN value is also NaN. - """ + if indices[0] >= indices[1]: raise ValueError('Invalid index pair.') @@ -63,8 +63,8 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: with the best fit to the molecular signal. - Currently, only the FR reference heights are calculated, while the NR reference heights are taken from the config files. - """ + # ..TODO:: is data.distance0 and height the same? https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/preprocess/pollyPreprocess.m#L299 # --> data.distance0 equals range, not height. However, Picasso uses a constant 7.5 m height resolution while PicassoPy uses the height resolution form the raw data dict ~ 7.475 m height = data_cube.retrievals_highres['range'] @@ -76,12 +76,12 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: if not data_cube.polly_config_dict['flagUseManualRefH']: for i, cldFree in enumerate(data_cube.clFreeGrps): - print(i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") refH_cldFree = {} for wv, t, tel in [(532, 'total', 'FR'), (355, 'total', 'FR'), (1064, 'total', 'FR')]: if np.any(data_cube.gf(wv, t, tel)): - print(f'refH for {wv}') + logging.info(f"Channel: {wv} {t} {tel}.") # Extract signal rcs = np.squeeze(data_cube.retrievals_profile['RCS'][i, :, data_cube.gf(wv, t, tel)]) @@ -113,7 +113,7 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: window_size=config_dict[f'decomSmoothWin{wv}'], showDetails=collect_debug ) - print('DPInd:', DPInd) + logging.debug(f"DPInd: {DPInd}.") # Rayleigh fitting refInd = fit_profile( @@ -129,7 +129,7 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: flagShowDetail=collect_debug, flagPicassoComparison=config_dict['flagPicassoComparison'] ) - print('refInd:', refInd) + logging.debug(f"refInd: {refInd}") refHeight = getVal(data_cube.retrievals_highres['height'], refInd) refRange = getVal(data_cube.retrievals_highres['range'], refInd) @@ -215,6 +215,7 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-04-17: Changed to ppcpy.misc.helper.moving_average to get values at edges. """ + # return uniform_filter1d(signal, window_len, mode='nearest') return moving_average(signal, window_len) @@ -240,7 +241,7 @@ def DouglasPeucker(signal:np.ndarray, height:np.ndarray, epsilon:float, heightBa window_size : int Size of the average smooth window. Default is 1. showDetails : bool - If true, print debug information. Default is False. + If true, collect debug information. Default is False. Returns ------- @@ -262,7 +263,8 @@ def DouglasPeucker(signal:np.ndarray, height:np.ndarray, epsilon:float, heightBa - 2024-12-20: Direct translated from matlab with ai """ - #print('height', height[:30], 'epsilon', epsilon, 'height', heightBase, heightTop, 'maxHThick', maxHThick, 'window_size', window_size) + + logging.debug(f"height {height[:30]}, epsilon {epsilon}, height range [{heightBase} - {heightTop}], maxHThick {maxHThick}, window_size {window_size}") # Input check if len(signal) != len(height): @@ -319,8 +321,8 @@ def DP_algorithm(pointList:list, epsilon:float, maxHThick:float, recursive_depth ------- sigIndx : list Indices of simplified points. - """ + if len(pointList) == 1: if showDetails: print(f'Recursion: {recursive_depth}, len(pointList) == 1, returning [0]') return [0] @@ -386,8 +388,8 @@ def my_dist(pointM:list, pointS:list, pointE:list, full:bool=True) -> float: ----- - If pointS and pointE are constant only the numerator is neede to calculate the extreme points with respect to pointM. - """ + num = abs(pointM[1] - pointS[1] + (pointS[1] - pointE[1]) / \ (pointS[0] - pointE[0]) * (pointS[0] - pointM[0])) if full: @@ -437,6 +439,7 @@ def chi2fit(x:np.ndarray, y:np.ndarray, measure_error:np.ndarray) -> tuple: -------- ``a, b, sigmaA, sigmaB, chi2, Q = chi2fit(x, y, measure_error)`` """ + if len(x) != len(y): raise ValueError("Array lengths of x and y must agree.") @@ -521,8 +524,8 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar residual calculations due to the subtraction and mean of very small numbers [-1e-24, 1e-24]. these numerical discrepancies may cause a different reference height to be chosen compared to Picasso. - """ + # if len([height, sig_aer, pc, bg, sig_mol, dpIndx]) < 6: # #if len([height, sig_aer, pc, bg, sig_mol, dpIndx]) < 6: # raise ValueError('Not enough inputs.') @@ -532,7 +535,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar raise ValueError('sig_aer and sig_mol must be 1-dimensional array') if dpIndx is None or len(dpIndx) == 0: - print('Warning: dpIndx is empty') + logging.warning('dpIndx is empty!') return np.nan, np.nan # parameter initialize @@ -551,7 +554,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar test1 = test2 = test3 = test4 = test5 = True iDpBIndx = dpIndx[iIndx] iDpTIndx = dpIndx[iIndx + 1] # + 1 # matlab slicing issue?? - #print(iIndx, iDpBIndx, iDpTIndx) + # print(iIndx, iDpBIndx, iDpTIndx) # check layer thickness if not ((height[iDpTIndx] - height[iDpBIndx]) > layerThickConstrain): @@ -566,11 +569,11 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar sig_factor = np.nanmean(sig_mol[iDpBIndx:iDpTIndx+1]) / \ np.nanmean(sig_aer[iDpBIndx:iDpTIndx+1]) - #print('sig factor ', sig_factor) + # print('sig factor ', sig_factor) sig_aer_norm = sig_aer * sig_factor std_aer_norm = sig_aer_norm / np.sqrt(pc + bg) - #print('sig_aer_norm ', sig_aer_norm.shape, sig_aer_norm[iDpBIndx:iDpTIndx+1]) - #print('std_aer_norm ', std_aer_norm.shape, std_aer_norm[iDpBIndx:iDpTIndx+1]) + # print('sig_aer_norm ', sig_aer_norm.shape, sig_aer_norm[iDpBIndx:iDpTIndx+1]) + # print('std_aer_norm ', std_aer_norm.shape, std_aer_norm[iDpBIndx:iDpTIndx+1]) # Quality test 2: near and far - range cross criteria winLen = int(layerThickConstrain / (height[1] - height[0])) @@ -596,15 +599,15 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar continue # Quality test 3: white-noise criterion - #print('white noise criterion ', iDpBIndx, iDpTIndx) + # print('white noise criterion ', iDpBIndx, iDpTIndx) residual = (sig_aer_norm[iDpBIndx:iDpTIndx+1] - sig_mol[iDpBIndx:iDpTIndx+1]) - #print(sig_aer_norm[iDpBIndx], sig_mol[iDpBIndx], sig_aer_norm[iDpBIndx]-sig_mol[iDpBIndx]) - #print(sig_aer_norm[iDpBIndx+1], sig_mol[iDpBIndx+1], sig_aer_norm[iDpBIndx+1]-sig_mol[iDpBIndx+1]) - #print(sig_aer_norm[iDpBIndx+2], sig_mol[iDpBIndx+2], sig_aer_norm[iDpBIndx+2]-sig_mol[iDpBIndx+2]) - #print(sig_aer_norm[iDpBIndx+3], sig_mol[iDpBIndx+3], sig_aer_norm[iDpBIndx+3]-sig_mol[iDpBIndx+3]) - #print('residual ', residual.shape, residual) + # print(sig_aer_norm[iDpBIndx], sig_mol[iDpBIndx], sig_aer_norm[iDpBIndx]-sig_mol[iDpBIndx]) + # print(sig_aer_norm[iDpBIndx+1], sig_mol[iDpBIndx+1], sig_aer_norm[iDpBIndx+1]-sig_mol[iDpBIndx+1]) + # print(sig_aer_norm[iDpBIndx+2], sig_mol[iDpBIndx+2], sig_aer_norm[iDpBIndx+2]-sig_mol[iDpBIndx+2]) + # print(sig_aer_norm[iDpBIndx+3], sig_mol[iDpBIndx+3], sig_aer_norm[iDpBIndx+3]-sig_mol[iDpBIndx+3]) + # print('residual ', residual.shape, residual) x = height[iDpBIndx:iDpTIndx+1] / 1e3 if len(residual) <= 10: @@ -614,10 +617,10 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue # Note: chi2fit implementation needed here - #print('white noise chi2fit input', np.nanmean(x), np.nanmean(residual), np.nanmean(std_aer_norm[iDpBIndx:iDpTIndx+1])) + # print('white noise chi2fit input', np.nanmean(x), np.nanmean(residual), np.nanmean(std_aer_norm[iDpBIndx:iDpTIndx+1])) thisIntersect, thisSlope, _, _, _, _ = chi2fit( x, residual, std_aer_norm[iDpBIndx:iDpTIndx+1]) - #print('white noise chi2fit ', thisIntersect, thisSlope) + # print('white noise chi2fit ', thisIntersect, thisSlope) residual_fit = thisIntersect + thisSlope * x et = residual - residual_fit @@ -628,21 +631,21 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar print(f'Region {iIndx}: {height[iDpBIndx]} - ' f'{height[iDpTIndx]} fails in white-noise criterion.') test3 = False - #continue + # continue # Quality test 4: SNR check sigsum = np.nansum(pc[dpIndx[iIndx]:dpIndx[iIndx+1]+1]) # adaption needed for the background not given as a profile bgsum = bg * (dpIndx[iIndx + 1] - dpIndx[iIndx]) SNR = sigsum / np.sqrt(sigsum + 2*bgsum) - #print('SNR', sigsum, '/', bgsum, '=', SNR) + # print('SNR', sigsum, '/', bgsum, '=', SNR) if SNR < SNRConstrain: if flagShowDetail: print(f'Region {iIndx}: {height[iDpBIndx]} - ' f'{height[iDpTIndx]} fails in SNR criterion.') test4 = False - #continue + # continue # Quality test 5: slope check x = height[iDpBIndx:iDpTIndx+1] @@ -667,9 +670,9 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue _, aerSlope, _, deltaAerSlope, _, _ = chi2fit(x, y_aer, std_y_aer) - #print('aer chi2fit', aerSlope, deltaAerSlope) + # print('aer chi2fit', aerSlope, deltaAerSlope) _, molSlope, _, deltaMolSlope, _, _ = chi2fit(x, y_mol, np.zeros_like(x)) - #print('mol chi2fit', molSlope, deltaMolSlope) + # print('mol chi2fit', molSlope, deltaMolSlope) slope_condition = (molSlope <= (aerSlope + (deltaAerSlope + deltaMolSlope) * slopeConstrain) and @@ -686,7 +689,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue if not (test1 and test2 and test3 and test4 and test5): - print("one tests failed?") + logging.info("One tests failed") continue # save statistics @@ -730,7 +733,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar else: # QuicFix for all NaN arrays: if np.isnan(X_val).all(): - print("None valid clean region found.") + logging.warning("None valid clean region found.") return np.nan, np.nan indxBest_Int = np.nanargmin(X_val) diff --git a/ppcpy/cloudmask/profilesegment.py b/ppcpy/cloudmask/profilesegment.py index 34b7c72..1a06046 100644 --- a/ppcpy/cloudmask/profilesegment.py +++ b/ppcpy/cloudmask/profilesegment.py @@ -1,14 +1,15 @@ import numpy as np +import logging from scipy.ndimage import label def segment(data_cube) -> np.ndarray: - """Perform cloud free profile segmentation + """Perform cloud free profile segmentation. Parameters ---------- data_cube : object - Main PicassoProc object + Main PicassoProc object. Returns ------- @@ -19,34 +20,34 @@ def segment(data_cube) -> np.ndarray: - xxxx-xx-xx: First edition by ... - 2026-03-18: Updated flagValPrf to include 'shutterOnMask' and 'fogMask'. + """ config_dict = data_cube.polly_config_dict flagValPrf = data_cube.flagCloudFree & (~data_cube.retrievals_highres['depCalMask']) \ & (~data_cube.retrievals_highres['shutterOnMask']) & (~data_cube.retrievals_highres['fogMask']) - print('intNProfiles', config_dict['intNProfiles'], 'minIntNProfiles', config_dict['minIntNProfiles']) + logging.info(f"intNProfiles: {config_dict['intNProfiles']}, minIntNProfiles: {config_dict['minIntNProfiles']}") clFreeGrps = clFreeSeg(flagValPrf, config_dict['intNProfiles'], config_dict['minIntNProfiles']) return clFreeGrps - -def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): - """splits continuous cloud-free profiles into small sections. +def clFreeSeg(prfFlag:np.ndarray, nIntPrf:int, minNIntPrf:int) -> np.ndarray: + """Splits continuous cloud-free profiles into small sections. Parameters ---------- - prfFlag: array-like (boolean) + prfFlag : array-like (boolean) Cloud-free flags for each profile. - nIntPrf: int + nIntPrf : int Number of integral profiles. - minNIntPrf: int + minNIntPrf : int Minimum number of integral profiles. Returns ------- - clFreSegs: 2D numpy array + clFreSegs : 2D ndarray Start and stop indexes for each cloud-free section. [[start1, stop1], [start2, stop2], ...] @@ -57,15 +58,15 @@ def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): - 2021-05-22: First edition by Zhenping - 2025-03-20: Translated to python + """ # Label contiguous cloud-free segments clFreGrpTag, nClFreGrps = label(prfFlag.astype(int)) - clFreSegs = [] if nClFreGrps == 0: - print("No cloud-free segments were found.") + logging.warning("No cloud-free segments were found.") else: for iClFreGrp in range(1, nClFreGrps + 1): iClFreGrpInd = np.where(clFreGrpTag == iClFreGrp)[0] @@ -91,7 +92,5 @@ def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): ]).T clFreSegs.extend(subClFreGrp.tolist()) + return np.array(clFreSegs, dtype=int) if clFreSegs else np.empty((0, 2), dtype=int) - - - diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index 26995c9..2009881 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -11,7 +11,6 @@ import ppcpy.qc.overlapCor as overlapCor import ppcpy.qc.qualityMask as qualityMask - import ppcpy.calibration.select as select import ppcpy.calibration.polarization as polarization import ppcpy.cloudmask.cloudscreen as cloudscreen @@ -33,26 +32,62 @@ import ppcpy.io.sql_interaction as sql_db + class PicassoProc: + """Picasso Processor. + + This class is responsible for perfoming the processing of PollyXT data. + """ counter = 0 def __init__(self, rawdata_dict:dict, polly_config_dict:dict, picasso_config_dict:dict): - """Initialize the data_cube. + """Initialize the PicassoProc object. Parameters ---------- rawdata_dict : dict - The dict returned by readPollyRawData.readPollyRawData(filename=rawfile) + The dict returned by readPollyRawData.readPollyRawData(filename=rawfile). polly_config_dict : dict - The configuration specific to the specific polly loadConfigs.loadPollyConfig(polly_config_file_fullname, polly_default_config_file) + The configuration specific to the specific polly loadConfigs.loadPollyConfig(polly_config_file_fullname, polly_default_config_file). picasso_config_dict : dict - The general picasso config loadConfigs.loadPicassoConfig(args.picasso_config_file,picasso_default_config_file) + The general picasso config loadConfigs.loadPicassoConfig(args.picasso_config_file,picasso_default_config_file). + + Yields + ------ + self.rawfile : str + Path to level0-file. + self.rawdata_dict : dict + The input parameter `rawdata_dict`. + self.polly_config_dict : dict + The input parameter `polly_config_dict`. + self.picasso_config_dict : dict + The input parameter `picasso_config_dict`. + self.device : str + Name of pollyXT device. + self.location : ... + Location of PollyXT device during the measurement. + self.date : str + Measurement date. + self.num_of_channels : int + Number of measurment channels. + self.num_of_profiles : int + Number of profiles in the measurment (time-dimension). + self.retrievals_highres : dict + Dictionary to store high resolution time, height data. + self.retrievals_profile : dict + Dictionary to store profile date. + self.retrievals_profile['avail_optical_profiles'] : list + Availabele optical profiles. + self.pol_cali : dict + Dictionary to store polarization calibration constants. + self.LC : dict + Dictionary to store lidar calibration constants. Notes ----- - - The `polly_default_dict` is not longer available as a separate variable, but is included into the `polly_config_dict` - + - The `polly_default_dict` is not longer available as a separate variable, but is included into the `polly_config_dict`. """ + type(self).counter += 1 self.rawfile = rawdata_dict['filename_path'] self.rawdata_dict = rawdata_dict @@ -66,22 +101,28 @@ def __init__(self, rawdata_dict:dict, polly_config_dict:dict, picasso_config_dic self.retrievals_highres = {} self.retrievals_profile = {} self.retrievals_profile['avail_optical_profiles'] = [] - self.pol_cali = {} self.LC = {} - def mdate_filename(self): - """Get the date from filename in YYYYMMDD.""" + def mdate_filename(self) -> str: + """Get the date from filename in YYYYMMDD. + + Returns + ------- + str + Measurement date. + """ + filename = self.rawdata_dict['filename'] - mdate = re.split(r'_',filename)[0:3] + mdate = re.split(r'_', filename)[0:3] YYYY = mdate[0] MM = mdate[1] DD = mdate[2] return f"{YYYY}{MM}{DD}" - def gf(self, wavelength, meth, telescope): + def gf(self, wavelength:float|int|str, meth:str, telescope:str) -> np.ndarray: """Get flag shorthand. i.e., the following two calls are equivalent @@ -95,45 +136,63 @@ def gf(self, wavelength, meth, telescope): Parameters ---------- - wavelength - wavelength tag - meth - method - telescope - telescope + wavelength : float or int or str + Wavelength tag. + meth : str + Method type. + telescope : str + Telescope type. Returns ------- - array - with bool flag - + ndarray + With bool flag """ + return getattr(self, f'flag_{wavelength}_{meth}_{telescope}', False) + # def msite(self): # #msite = f"measurement site: {self.rawdata_dict['global_attributes']['location']}" # msite = self.polly_config_dict['site'] # logging.info(f'measurement site: {msite}') # return msite # +# # def device(self): # device = self.polly_config_dict['name'] # logging.info(f'measurement device: {device}') # return device # +# # def mdate(self): # mdate = self.mdate_filename() # logging.info(f'measuremnt date: {mdate}') # return mdate - def mdate_infile(self): - """First date in file as string.""" + + def mdate_infile(self) -> str: + """First date in file as string. + + Returns + ------- + str + Measurement date. + """ + mdate_infilename = self.rawdata_dict['measurement_time']['var_data'][0][0] return f"{mdate_infilename}" - def check_for_correct_mshots(self): - """Check if mshots are more than 1.1 * laser_prf * deltatime or smaller 0.""" + def check_for_correct_mshots(self) -> np.ndarray: + """Check if mshots are more than 1.1 * laser_prf * deltatime or smaller 0. + + Returns + ------- + condition_check_matrix : ndarray + Boolean array. True for elements where the condition above holds, otherwise False. + """ + laser_rep_rate = self.rawdata_dict['laser_rep_rate']['var_data'] mShotsPerPrf = laser_rep_rate * self.polly_config_dict['deltaT'] mShots = self.rawdata_dict['measurement_shots']['var_data'] @@ -149,23 +208,74 @@ def filter_or_correct_false_mshots(self): .. TODO:: that might be covered via the mcps conversion + --> How exactly??? + + Yields + ------ + self.rawdata_dict : dict + With filtered or corrected enteries for timestamps where `data_cube.check_for_correct_mshots()=True`. + + Notes + ----- + - Now includes a first try for the function. I Do not know if it actually is needed yet. + + .. TODO:: Not yet crosschecked with the matlab version. + + ** History ** + + - 2026-06-24: Translated to python """ - logging.info(f"flagFilterFalseMShots: {self.polly_config_dict['flagFilterFalseMShots']}") - logging.info(f"flagCorrectFalseMShots: {self.polly_config_dict['flagCorrectFalseMShots']}") + + ## Extract the necesary information + laser_rep_rate = self.rawdata_dict['laser_rep_rate']['var_data'] + mShotsPerPrf = laser_rep_rate * self.polly_config_dict['deltaT'] + mShots = self.rawdata_dict['measurement_shots']['var_data'] + mTime = self.rawdata_dict['measurement_time']['var_data'] + rawSig = self.rawdata_dict['raw_signal']['var_data'] + + condition_check_matrix = self.check_for_correct_mshots() + condition_check_matrix = np.any(condition_check_matrix, axis=0) + ## Filter out timesteps with faulty measurement shots if self.polly_config_dict['flagFilterFalseMShots']: logging.info('filtering false mshots') + + if np.sum(~condition_check_matrix) == 0: + logging.critical('No profile with mshots < 1e6 and mshots > 0 was found Please take a look inside level0 file.') + # raise ValueError('No profile with mshots < 1e6 and mshots > 0 was found Please take a look inside level0 file.') + return + + rawSig = rawSig[~condition_check_matrix] + mShots = mShots[~condition_check_matrix] + mTime = mTime[~condition_check_matrix] + if 'depol_cal_angle' in self.rawdata_dict: + self.rawdata_dict['depol_cal_angle']['var_data'] \ + = self.rawdata_dict['depol_cal_angle']['var_data'][~condition_check_matrix] + + ## Correct timesteps with faulty measurement shots elif self.polly_config_dict['flagCorrectFalseMShots']: logging.info('correcting false mshots') - condition_check_matrix = self.check_for_correct_mshots() - ##TODO - return self + mShots[condition_check_matrix] = mShotsPerPrf + # .. TODO:: In the matlab vesion this also does a fix to mTime. I do not know if this is necesary yet. + + ## Overwrite the filtered / corrected information + self.rawdata_dict['measurement_shots']['var_data'] = mShots + self.rawdata_dict['measurement_time']['var_data'] = mTime + self.rawdata_dict['raw_signal']['var_data'] = rawSig def mdate_consistency(self) -> bool: - """Check mdate consistency.""" + """Check mdate consistency. + + Returns + ------- + bool + True, if measurement date of file name equals + that of in the file, otherwise False. + """ + if self.mdate_filename() == self.mdate_infile(): logging.info('... date in nc-file equals date of filename') return True @@ -175,7 +285,18 @@ def mdate_consistency(self) -> bool: def reset_date_infile(self): - """Correct the date in the file.""" + """Correct the date in the file. + + Yields + ------ + self.rawdata_dict['measurement_time']['var_data'] : ndarray + Updated .... + + Notes + ----- + .. TODO :: Finish docstring. + """ + logging.info('date consistency-check... ') if self.mdate_consistency() == False: logging.info('date in nc-file will be replaced with date of filename.') @@ -183,7 +304,6 @@ def reset_date_infile(self): mdate = self.mdate_filename() np_array[:, 0] = mdate ## assign new date value to the whole first column of the 2d-numpy-array self.rawdata_dict['measurement_time']['var_data'] = np_array - return self def setChannelTags(self): @@ -210,11 +330,61 @@ def setChannelTags(self): data_cube.flag_355_total_FR ``` - Returns - ------- - self - + Yields + ------ + self.retrievals_highres['channel'] : list + Updated channel tags. + self.polly_config_dict['channelTags'] : list + updated channel tags. + self.flag_355_total_FR : ndarray + True if the channel is `355nm total FR` else False. + self.flag_355_cross_FR : ndarray + True if the channel is `355nm cross FR` else False. + self.flag_355_parallel_FR : ndarray + True if the channel is `355nm parallel FR` else False. + self.flag_355_total_NR : ndarray + True if the channel is `355nm total NR` else False. + self.flag_387_total_FR : ndarray + True if the channel is `387nm total FR` else False. + self.flag_387_total_NR : ndarray + True if the channel is `387nm total NR` else False. + self.flag_407_total_FR : ndarray + True if the channel is `407nm total FR` else False. + self.flag_407_total_NR : ndarray + True if the channel is `407nm total NR` else False. + self.flag_532_total_FR : ndarray + True if the channel is `532nm total FR` else False. + self.flag_532_cross_FR : ndarray + True if the channel is `532nm cross FR` else False. + self.flag_532_parallel_FR : ndarray + True if the channel is `532nm parallel FR` else False. + self.flag_532_total_NR : ndarray + True if the channel is `532nm total NR` else False. + self.flag_532_cross_DFOV : ndarray + True if the channel is `532nm cross DFOV` else False. + self.flag_532_rr_FR : ndarray + True if the channel is `532nm rr FR` else False. + self.flag_607_total_FR : ndarray + True if the channel is `607nm total FR` else False. + self.flag_607_total_NR : ndarray + True if the channel is `607nm total NR` else False. + self.flag_1058_total_FR : ndarray + True if the channel is `1058nm total FR` else False. + self.flag_1064_total_FR : ndarray + True if the channel is `1064nm total FR` else False. + self.flag_1064_cross_FR : ndarray + True if the channel is `1064nm cross FR` else False. + self.flag_1064_total_NR : ndarray + True if the channel is `1064nm total NR` else False. + + Notes + ----- + .. TODO:: + - Several channels are still missing channelFlags. + - We are not consistant in the naming scheam of the RR-channels. + - Need to implement new config flags for fluorescence and HSRL. """ + ChannelTags = pollyChannelTags.pollyChannelTags( self.polly_config_dict['channelTag'], # TODO key: channelTags vs channelTag??? flagFarRangeChannel=self.polly_config_dict['isFR'], @@ -277,31 +447,97 @@ def setChannelTags(self): self.flag_1064_cross_FR = ChannelFlags[18] self.flag_1064_total_NR = ChannelFlags[19] - return self - def preprocessing(self, collect_debug:bool=False): - """Preprocessing of Lidar data. Including in the followin processes in order: + """Preprocessing of Lidar data. Includes the followin processes in order: 1. Deadtime correction 2. Background correction 3. First-bin shift 4. Mask for low-SNR - 5. Mask for depolarization-calibration process - 6. Range correction. + 5. Mask bins with laser shutter on + 6. Mask bins with fog + 7. Mask for depolarization-calibration process + 8. Range correction. Parameters ---------- - collect_debug : bool + collect_debug : bool, optional If true, collects debug information. Default is False. Yelds ----- - Background corrected signal including the background per channel. - Range corrected signal. - etc. - - .. TODO:: This is just a first draft for a docstring. Improve it. There is more processes and outputs of the function. + self.retrievals_highres : dict + With preprocessed lidar data. + + Keys + ---- + mShots : ndarray + Number of the laser shots for each profile. + sigDTCor : ndarray + Dead time corrected signal [photon count]. + BG : ndarray + Background. + sigBGCor : ndarray + Backgound corrected signal [photon count]. + range : ndarray + Height above ground at zenith angle [m]. + height : ndarray + Height above ground [m]. + alt : ndarray + Altitude [m]. + time : list + Measurement time in unix time. + time64 : ndarray + Measurement time in numpy.datetime. + SNR : ndarray + Signal to noise ratio. + lowSNRMask : ndarray + True if SNR is less SNRmin. Otherwise False. + shutterOnMask : ndarray + True if at timesteps where the shutters was on. Otherwise False. + fogMask : ndarray + If it is foggy which means the signal will be very weak, + fogMask will be set True. Otherwise, False. + mask607Off : ndarray + Mask of PMT on/off status at 607 nm channel. + mask387Off : ndarray + Mask of PMT on/off status at 387 nm channel. + mask407Off : ndarray + Mask of PMT on/off status at 407 nm channel. + mask355RROff : ndarray + Mask of PMT on/off status at 355 nm rotational Raman channel. + mask532RROff : ndarray + Mask of PMT on/off status at 532 nm rotational Raman channel. + mask1064RROff : ndarray + Mask of PMT on/off status at 1064 nm rotational Raman channel. + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + Time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + Time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + Time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + RCS : ndarray + Range Corrected signal [MCPS]. + PCR_slice : ndarray + Background corrected signal in photon count rate [MCPS]. + + Notes + ----- + .. TODO:: + - This is just a first draft for a docstring. Improve it. There is more processes and outputs of the function. + - Also go over the inputs and see if all of them are actually needed. """ + + logging.info("Preprocessing ...") preproc_dict = pollyPreprocess.pollyPreprocess( self.rawdata_dict, deltaT=self.polly_config_dict['deltaT'], @@ -335,9 +571,9 @@ def preprocessing(self, collect_debug:bool=False): flag607nmChannel=self.polly_config_dict['is607nm'], flag387nmChannel=self.polly_config_dict['is387nm'], flag407nmChannel=self.polly_config_dict['is407nm'], - flag355nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag532nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag1064nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), + flag355nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask + flag532nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask + flag1064nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask. Also this will not work for the 1058nm channel. isUseLatestGDAS=self.polly_config_dict['flagUseLatestGDAS'], collect_debug=collect_debug, flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'], @@ -346,8 +582,16 @@ def preprocessing(self, collect_debug:bool=False): def SaturationDetect(self): - """Saturation Detection.""" + """Saturation Detection. + + Yields + ------ + self.flagSaturation : ndarray + 1 dimensional temporal boolean array per channel. + True if the channel is saturated, else False. + """ + logging.info("Saturation detection ...") self.flagSaturation = pollySaturationDetect.pollySaturationDetect( data_cube = self, sigSaturateThresh = self.polly_config_dict['saturate_thresh'] @@ -363,10 +607,20 @@ def polarizationCaliD90(self, db_path:str=None): Path to database to read DC values from in case none are successfully retrieved. Default is None. - The stuff that starts here in the matlab version - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5efd7d35596c67ef8672f5948e47d1f9d46ab867/lib/interface/picassoProcV3.m#L442 + Yields + ------ + self.pol_cali['D90' & 'D90_db'] : dict + All retrieved and read delta-90 depol calibration constants and retrieval information. + self.etaused : dict + The used depol calibration constants per channel. + + Notes + ----- + - The stuff that starts here in the matlab version + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5efd7d35596c67ef8672f5948e47d1f9d46ab867/lib/interface/picassoProcV3.m#L442 """ + logging.info("Delta 90 polarization calibration ...") polarization.loadGHK(self) self.pol_cali['D90'] = polarization.calibrateGHK(self) isUsable = [element['status'] for key, val in self.pol_cali['D90'].items() for element in val] @@ -386,26 +640,53 @@ def polarizationCaliD90(self, db_path:str=None): def cloudScreen(self, collect_debug:bool=False): - """Basic cloud screenting. + """Basic cloud screening. + + Parameters + ---------- + collect_debug : bool, optional + If true, collects debug information. Default is False. + + Yields + ------ + self.flagCloudFree : ndarray + 1 dimensional temporal boolean array. 0 = cloudy, 1 = cloud free. - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L663 + Notes + ----- + - Matlab equivalent code at: + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L663 """ + + logging.info("Cloud screening ...") self.flagCloudFree = cloudscreen.cloudscreen(self, collect_debug=collect_debug) def cloudFreeSeg(self): """Cloud free profile segmentation. + + Yields + ------ + self.clFreeGrps : nested list + List with start, stop index of each cloud free segement. - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L707 - + Notes + ----- + - The matlab eqvivalent code starts here: + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L707 + .. TODO:: Write a more extensive example for the docstring. + + Example + ------- .. code-block:: python data_cube.clFreGrps = [ - [35,300], - [2500,2800] + [35, 300], + [2500, 2800] ] - """ + + logging.info("Segment cloud free groups ...") self.clFreeGrps = profilesegment.segment(self) @@ -471,36 +752,110 @@ def aggregate_profiles(self, var:str|list=None, func=np.nansum): def loadMeteo(self): - """Load meteorological data.""" + """Load meteorological data. + + Meteorological data like; + - Temperatue + - Pressure + - Relative Humidity + - Spesific Humidity + + is read form the cloudnet ECMWF file specified by the config variable; + - `meteorDataSource` + - `meteo_folder` + - `meteo_file` + + and saved in the dataFrame `self.met`. + + Yields + ------ + self.met : object + Object to handle meterological data. + + Notes + ----- + - Only `meteorDataSource = 'nc_cloudnet'` is currently suported. + """ + + logging.info("Loading meteorological data ...") self.met = readMeteo.Meteo( self.polly_config_dict['meteorDataSource'], self.polly_config_dict['meteo_folder'], self.polly_config_dict['meteo_file'] ) self.met.load( - datetime.datetime.timestamp(datetime.datetime.strptime(self.date, '%Y%m%d')), - self.retrievals_highres['height'], float(self.polly_config_dict['asl']), - self.polly_config_dict['flagPicassoComparison'] + times=datetime.datetime.timestamp(datetime.datetime.strptime(self.date, '%Y%m%d')), + heights=self.retrievals_highres['height'], asl=float(self.polly_config_dict['asl']), + flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'] ) def loadAOD(self): - """Load the AOD from a co-located fotometer - - .. TODO:: Not yet implemented! + """Load the AOD from a co-located fotometer. + + Notes + ----- + .. TODO:: Implement the option to load AOD from co-located fotometer. """ - # raise NotImplementedError - pass + + logging.critical('Function loadAOD is not yet implemented!') + raise NotImplementedError('Function loadAOD is not yet implemented!') def calcMolecular(self): """Calculate the molecular scattering for the cloud free periods with the strategy of first averaging the met data and then calculating the rayleigh scattering. + + Yields + ------ + self.mol_profiles : dict + Dictionary containing calculated molecular profiles for each channel. + + Keys + ---- + mBsc_355 : ndarray (channels, height) + Molecular backscatter at 355 nm [m^{-1}Sr^{-1}]. + mExt_355 : ndarray (channels, height) + Molecular extinction at 355 nm [m^{-1}]. + mBsc_387 : ndarray (channels, height) + Molecular backscatter at 387 nm [m^{-1}Sr^{-1}]. + mExt_387 : ndarray (channels, height) + Molecular extinction at 387 nm [m^{-1}]. + mBsc_407 : ndarray (channels, height) + Molecular backscatter at 407 nm [m^{-1}Sr^{-1}]. + mExt_407 : ndarray (channels, height) + Molecular extinction at 407 nm [m^{-1}]. + mBsc_532 : ndarray (channels, height) + Molecular backscatter at 532 nm [m^{-1}Sr^{-1}]. + mExt_532 : ndarray (channels, height) + Molecular extinction at 532 nm [m^{-1}]. + mBsc_607 : ndarray (channels, height) + Molecular backscatter at 607 nm [m^{-1}Sr^{-1}]. + mExt_607 : ndarray (channels, height) + Molecular extinction at 607 nm [m^{-1}]. + mBsc_1058 : ndarray (channels, height) + Molecular backscatter at 1058 nm [m^{-1}Sr^{-1}]. + mExt_1058 : ndarray (channels, height) + Molecular extinction at 1058 nm [m^{-1}]. + mBsc_1064 : ndarray (channels, height) + Molecular backscatter at 1064 nm [m^{-1}Sr^{-1}]. + mExt_1064 : ndarray (channels, height) + Molecular extinction at 1064 nm [m^{-1}]. + number_density : ndarray (channels, height) + Number density. + + Notes + ----- + .. TODO:: How are these molecular profiles aggregated (mean or sum??). + .. TODO:: Idea: As we already need the highres molecular signal for the WV-product. Would it not make more sens to + retrieve this first and then use the aggregate_profile() function to get the cloud free profiles? + --> This only makes sens if we can directly retrieve the highres molecular signal which I dout we can. """ + logging.info("Calculating molecular profiles ...") time_slices = [self.retrievals_highres['time64'][grp] for grp in self.clFreeGrps] - print('time slices of cloud free ', time_slices) + logging.info(f"time slices of cloud free {time_slices}") mean_profiles = self.met.get_mean_profiles(time_slices) self.mol_profiles = molecular.calc_profiles(mean_profiles, flagPicassoComparison=self.polly_config_dict['flagPicassoComparison']) @@ -510,14 +865,30 @@ def rayleighFit(self, collect_debug:bool=False): Parameters ---------- - collect_debug : bool + collect_debug : bool, optional If true, collects debug information. Default is False. + Yields + ------ + self.retrievals_profiles['refH'][idx][ch] : dict + Reference height information for cloud free segment `idx` and channel `ch`. + + Keys + ---- + DPInd : list + List of Douglas-Peckur indecies used as potensial reference index candidates. + refInd : list + Indices (start, stop) of the retrieved reference height. + refHeigt : list + Height above ground (start, stop) of the retrieved reference height. + refRange : list + Height above ground at zenith angle (start, stop) of the retrieved reference height. + Notes ----- - Direct translation from the matlab code. There might be noticeable numerical discrepancies (especially in the residual) seemed to work ok for 532, 1064, but with issues for 355. - - The Douglas Peucker algorithm works fine (gives the same result as the Matlab version). However, due the numerical discrepancies + - The Douglas Peucker algorithm works fine (gives the same result as the Matlab version). However, due to the numerical discrepancies In the residuals of the fit algorithm sometimes different refH segments are chosen. """ @@ -528,69 +899,199 @@ def rayleighFit(self, collect_debug:bool=False): def polarizationCaliMol(self): - """Calibration with molecular signal in reference height.""" + """Calibration with molecular signal in reference height. + + Yields + ------ + self.pol_cali['mol'][ch][idx] : dict + Molecular depol calibration constants and retrieval information for + channel `ch` and cloud free index `idx`. + + keys + ---- + eta : ndarray + Polarization calibration eta. + etaStd : ndarray + Uncertainty of polarization calibration eta. + fac : array + Polarization calibration factor. + facStd : ndarray + Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good + time_start : int + Start time of the cloud free segment for the retrieval in unixtime. + end_time : int + End time of the cloud free segment for the retrieval in unixtime. + + Notes + ----- + - Calibration is only performed if the config variable `flagMolDepolCali = True`. + """ - logging.warning(f'not checked against the matlab code') if self.polly_config_dict['flagMolDepolCali']: + logging.info("Calibrating molecular signal ...") + logging.warning(f'not checked against the matlab code') self.pol_cali['mol'] = polarization.calibrateMol(self) else: logging.warning("'flagMolDepolCali' set to False") def transCor(self): - """GHK-Transmission correction + """Perform GHK-transmission correction on the signal. - .. TODO:: + Yields + ------ + self.retrievals_highres : dict + With corrected signal. - flagTransCor = True: - Fix the GHK - Transmission correction - flagTransCor = False: - Check if it is correct to use the BG corrected signal and find a better solution. - It is a bit confusing to overwrite the signal as it is called sigTCor but actually is sigBGCor - Like storing a dedicated signal dict to be used throuhot the processing, the dictionary could - have elements like signal (sig), background (bg), and name. which we could overwrite each time - a new correction is made. And by checking the name of the signal (TCor, BGCor) you can find out - which signal it is. + Keys + ---- + sigTCor : ndarray + GHK-Transmission corrected signal [MCPS]. + BGTCor : ndarray + GHK-Transmission corrected background. + + Notes + ----- + - if the config entry `flagTransCor = False`, no GHK transmission correction + will be done and the background corrected signal will replace the transmission + corrected signal for the rest of the processing steps. + + .. TODO:: Overwriting `sigTCor` with `sigBGCor` when `flagTransCor = False` can + cause confiusion among useres. It would be more intutive to not have + a `sigTCor` variable in this case. However, this requiers re... the + rest of the processing chain. """ if self.polly_config_dict['flagTransCor']: - logging.warning('transmission correction') + logging.info('GHK Transmission correction ...') self.retrievals_highres['sigTCor'], self.retrievals_highres['BGTCor'] = \ transCor.transCorGHK_cube(self) else: - logging.warning('NO transmission correction') + logging.warning('No GHK transmission correction done.') self.retrievals_highres['sigTCor'], self.retrievals_highres['BGTCor'] = \ self.retrievals_highres['sigBGCor'], self.retrievals_highres['BG'] - def retrievalKlett(self, oc=False, nr=False): - """Apply Klett retrieval.""" + def retrievalKlett(self, oc:bool=False, nr:bool=False): + """Apply Klett retrieval. + + Parameters + ---------- + oc : bool, optional + If true, apply the retrieval on the overlap corrected profiles. The outupt will then also be saved under + the key 'klett_OC' insted of 'klett'. Default is False. + nr : bool, optional + If true, apply the retrieval on the near range profiles as well as the far range profiles. Default is False. + + Yields + ------ + self.retrievals_profile['klett' or 'klett_OC'][idx][ch] : dict + Klett retrieved optical profiles and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + aerExt : ndarray + Aerosol extinction coefficient [m^{-1}]. + aerExtStd : ndarray + Uncertainty of aerosol extinction coefficient [m^{-1}]. + aerBsc : ndarray + Aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBscStd : ndarray + Uncertainty of aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBR : ndarray + Aerosol backscatter ratio. + aerBRStd : ndarray + Statistical uncertainty of aerosol backscatter ratio. + retrieval : str + Name of retrieval type, eg. 'klett'. + signal : str + Name of the signal used for the retrieval, eg. 'TCor'. + refBeta : float + Reference value used for the retrieval [m^{-1}Sr^{-1}]. + + Notes + ----- + - The retrieval is by default applied on the FR profiles. if `nr=True` the retrieval will be applied both on + the NR and FR profiles. + """ retrievalname = 'klett' kwargs = {} if oc: # Check if overlap corrected signal exist if "sigOLCor" not in self.retrievals_highres: # This should be done more general not only for OLCor but for other signals as well. + logging.warning("No overlap corrected signal found.") return retrievalname +='_OC' kwargs['signal'] = 'OLCor' if nr: kwargs['nr'] = True - print('retrievalname', retrievalname) + logging.info(f"Klett retrieval for FR {'& NR' if nr else None} {'overlap' if oc else 'GHK-transmission'} corrected signal ...") self.retrievals_profile[retrievalname] = klettfernald.run_cldFreeGrps(self, **kwargs) if retrievalname not in self.retrievals_profile['avail_optical_profiles']: self.retrievals_profile['avail_optical_profiles'].append(retrievalname) - def retrievalRaman(self, oc=False, nr=False, collect_debug=False): - """Apply Raman retrieval (nighttime only).""" + def retrievalRaman(self, oc:bool=False, nr:bool=False, collect_debug:bool=False): + """Apply raman retrieval (nighttime only). + + Parameters + ---------- + oc : bool, optional + If true, apply the retrieval on the overlap corrected profiles. The outupt will then also be saved under + the key 'raman_OC' insted of 'raman'. Default is False. + nr : bool, optional + If true, apply the retrieval on the near range profiles as well as the far range profiles. Default is False. + collect_debug : bool, optional + If true, collects debug information. Default is False. + + Yields + ------ + self.retrievals_profile['raman' or 'raman_OC'][idx][ch] : dict + Raman retrieved optical profiles and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + aerExt : ndarray + Aerosol extinction coefficient [m^{-1}]. + aerExtStd : ndarray + Uncertainty of aerosol extinction coefficient [m^{-1}]. + aerBsc : ndarray + Aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBscStd : ndarray + Uncertainty of aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + LR : ndarray + Aerosol Lidar ratio [sr]. + effRes : ndarray + Effective resolution of aerosol lidar ratio [m]. + LRStd : ndarray + Uncertainty of aerosol lidar ratio [sr]. + retrieval : str + Name of retrieval type eg. 'raman'. + signal : str + Name of the signal used for the retrieval eg. 'TCor'. + refBeta : float + Reference value used for the retrieval. + + Notes + ----- + - The retrieval is by default applied on the FR profiles. if `nr=True` the retrieval will be applied both on + the NR and FR profiles. + """ retrievalname = 'raman' kwargs = {} if oc: # Check if overlap corrected signal exist if "sigOLCor" not in self.retrievals_highres: # This should be done more general not only for OLCor but for other signals as well. + logging.warning("No overlap corrected signal found.") return retrievalname +='_OC' kwargs['signal'] = 'OLCor' @@ -604,6 +1105,7 @@ def retrievalRaman(self, oc=False, nr=False, collect_debug=False): kwargs['nr'] = True kwargs['collect_debug'] = collect_debug + logging.info(f"Raman retrieval for FR {'& NR' if nr else None} {'overlap' if oc else 'GHK-transmission'} corrected signal ...") self.retrievals_profile[retrievalname] = raman.run_cldFreeGrps(self, **kwargs) if retrievalname not in self.retrievals_profile['avail_optical_profiles']: self.retrievals_profile['avail_optical_profiles'].append(retrievalname) @@ -614,48 +1116,104 @@ def overlapCalc(self, collect_debug:bool=False): Parameters ---------- - collect_debug : bool + collect_debug : str, optional If true, collects debug information. Default is False. + Yields + ------ + self.retrievals_profile['overlap']['frnr'][idx][ch] : list of dicts + Far-range-Near-range retrieved overlap function and retrieval + information for cloud free segment `idx` and channel `ch`. + + Keys + ---- + olFunc : ndarray + Overlap function. + olFuncStd : ndarray + Standard deviation of overlap function. + sigRatio : float + Signal ratio between near-range and far-range signals. + normRange : list + Height index of the signal normalization range. + + self.retrievals_profile['overlap']['raman'][idx][ch] : list of dicts + Raman retrieved overlap function and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + olFunc : ndarray + Overlap function. + olFunc_raw : ndarray + Overlap function with no smoothing. + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. + Notes ----- - Different to the matlab version, where an average over all cloud - free periods is taken, it is done here per cloud free segment - - .. TODO:: - The data structure of retrievals_profile['overlap'] differs from - retrievals_profile['raman']! - + free periods is taken. Here the overlap is applied per cloud free period. """ + if not self.polly_config_dict["flagOLCor"]: return + logging.info("calculating overlap functions ...") self.retrievals_profile['overlap'] = {} self.retrievals_profile['overlap']['frnr'] = overlapEst.run_frnr_cldFreeGrps(self, collect_debug=collect_debug) self.retrievals_profile['overlap']['raman'] = overlapEst.run_raman_cldFreeGrps(self, collect_debug=collect_debug) def overlapFixLowestBins(self): - """The lowest bins are affected by stange near range effects.""" + """The lowest bins are affected by stange near range effects. + + Yields + ------ + self.retrievals_profile['overlap']['frnr'][idx][ch]['olFunc'] : ndarray + FRNR retrieved overlap func with subdued near-range effect. + self.retrievals_profile['overlap']['raman'][idx][ch]['olFunc'] : ndarray + Raman retrieved overlap func with subdued near-range effect. + """ + if not self.polly_config_dict["flagOLCor"]: return height = self.retrievals_highres['range'] for k in self.retrievals_profile['overlap']: - print(f"Fixing lower bins for {k} overlap functions") + logging.info(f"Fixing lower bins for {k} overlap functions") overlapCor.fixLowest(self.retrievals_profile['overlap'][k], np.where(height > 800)[0][0]) def overlapCor(self): - """Overlap correction + """Overlap correction. - the overlap correction is implemented differently to the matlab version - first a 2d (time, height) correction array is constructed then it is applied. - In future this will allow for time varing overlap functions + Yields + ------ + self.retrieval_highres : dict + With overlap corrected signal and retrieval information. + + Keys + ---- + overlap2d : ndarray + 2 dimensional (time, height) overlap function. + sigOLCor : ndarray + Overlap corrected signal [photon count]. + BGOLCor : ndarray + Overlap corrected background. + heightFullOverCor : ndarray + Retrieved height of full overlap [m]. + Notes + ----- + - the overlap correction is implemented differently to the matlab version + first a 2d (time, height) correction array is constructed then it is applied. + In future this will allow for time varing overlap functions """ + if self.polly_config_dict['overlapCorMode'] == 0 or not self.polly_config_dict["flagOLCor"]: - logging.info('no overlap Correction') - return self - logging.info('overlap Correction') + logging.info('no overlap correction applied') + return + logging.info('Apply overlap correction ...') if self.polly_config_dict['overlapCorMode'] == 1: logging.info('overlapCorMode 1 -> need file for overlapfunction') if not 'overlap' in self.retrievals_profile: @@ -669,10 +1227,37 @@ def overlapCor(self): def calcDepol(self): - """Calculate the volume depol and the particle depol.""" + """Calculate the volume depol and the particle depol. + + Yields + ------ + self.retrievals_profiles[sig][idx][ch] : dict + Volume and particle depolarization profiles and retrieval info + for signal type `sig` for cloud free segment `idx` and channel `ch`. + + Keys + ---- + vdr : ndarray + Volume depolarization ratio []. + vdrStd : ndarray + Uncertainty of the volume depolarization ratio. + mdr : ndarray + Molecular depolarization ratio []. + mdrStd : ndarray + Uncertainty of the molecular depolarization ratio. + pdr : ndarray + Particle depolarization ratio []. + pdrStd : ndarray + Uncertainty of the particle depolarization ratio. + + + Notes + ----- + - This retrieval is done for all avaiable optical profiles. + """ for ret_prof_name in self.retrievals_profile['avail_optical_profiles']: - print(ret_prof_name) + logging.info(f"Calculate volume and particle depolarization ratios for product {ret_prof_name} ...") self.retrievals_profile[ret_prof_name] = depolarization.voldepol_cldFreeGrps( self, ret_prof_name) @@ -714,18 +1299,52 @@ def estQualityMask(self): def Angstroem(self): - """Calculate the angstrom exponent.""" + """Calculate the angstrom exponent. + + Yields + ------ + self.retrievals_profiles[sig][idx][ch] : dict + Volume and particle depolarization profiles and retrieval info + for signal type `sig` for cloud free segment `idx` and channel `ch`. + + Keys + ---- + AE_{prod}_{wv1}_{wv2} : ndarray + Angstroem Exponent for product `prod`, and wavelengths `wv1` and `wv2`. + AEStd_{prod}_{wv1}_{wv2} : ndarray + Uncertainty of Angstroem Exponent for product `prod`, and wavelengths `wv1` and `wv2`. + + Notes + ----- + - AE_{prod}_{wv1}_{wv2} = log(prod_wv1 / prod_wv2) / log(wv2 / wv1) + - This retrieval is done for all avaiable optical profiles. + """ + for ret_prof_name in self.retrievals_profile['avail_optical_profiles']: - print(ret_prof_name) + logging.info(f"Calculate Angstrom exponents for product {ret_prof_name} ...") self.retrievals_profile[ret_prof_name] = angstroem.ae_cldFreeGrps( self, ret_prof_name) + def LidarCalibration(self, db_path:str=None, collect_debug:bool=False): - """calculate the lidar constant + """Calculate the lidar constant. + + Yields + ------ + self.LC['klett' & 'klett_db'] : dict + All retrieved and read Klett lidar calibration constants and retrieval information. + self.LC['raman' & 'raman_db'] : dict + All retrieved and read Raman lidar calibration constants and retrieval information. + self.LCused : dict + The lidar claibration constant used per channel. + Notes + ----- .. TODO:: Find out how we prioritise raman, klett, and database retrieved LC... """ + + logging.info("Calculating lidar calibration constants ...") self.LC['klett'] = lidarconstant.lc_for_cldFreeGrps( self, retrieval='klett', @@ -758,19 +1377,44 @@ def LidarCalibration(self, db_path:str=None, collect_debug:bool=False): def attBsc_volDepol(self): - """highres attBsc and voldepol in 2D.""" + """Highres attBsc and voldepol in 2d. + + Yields + ------ + self.retrievals_highres : dict + With attenuated backscatter. + + Keys + ---- + attBsc_{wv}\_{t}\_{tel} : ndarray + High resolution (time, height) attenuated backscatter at chennel {wv}\_{t}\_{tel}. + attBsc_{wv}\_{t}\_OC : ndarray + High resolution (time, height) overlap corrected attenuated backscatter at channel {wv}\_{t}\_FR. + """ # for now try with mutable state in data_cube - logging.info('attBsc 2d retrieval') + logging.info("2D attenuated backscatter retrieval ...") highres.attbsc_2d(self) - logging.info('voldepol 2d retrieval') + logging.info("2D volume depolarization ratio retrieval ...") highres.voldepol_2d(self) def molecularHighres(self): - """calculate the molecular signal for the 2d high resolution.""" + """Calculate the molecular signal for the 2d high resolution. + + Yields + ------ + self.mol_2d : xr.Dataset + xarray dataset with dimensions time and height and variables molecular + backscatter (mBsc) and molecular extinction (mExt) per wavelength. + + Notes + ----- + - mol_2d's time dimension differe from the time dimension of the measurement. + """ + logging.info("2D molecular retrieval ...") self.mol_2d = molecular.calc_2d( self.met.ds, flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'] @@ -778,7 +1422,44 @@ def molecularHighres(self): def quasiV1(self): - """QuasiV1 retrivals and target categorisation.""" + """QuasiV1 retrivals and target categorisation. + + Yields + ------ + self.retrieval_highres : dict + With high resolution Quasi version 1 products as well as Target categorization. + + Keys + ---- + quasiBscV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle backscatter at channel `{wv}_{t}_{tel}`. + quasiExtV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle extinction at channel `{wv}_{t}_{tel}`. + quasiVdrV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 volume depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiPdrV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiAEV1_{wv1}\_{wv2} : ndarray + Quasi version 1 Angstroem exponent between two wavelengets `wv1` and `wv2`. + tcMaskV1 : ndarray + Classification mask version 1 (time x height). + 0: No signal + 1: Clean atmosphere + 2: Non-typed particles/low conc. + 3: Aerosol: small + 4: Aerosol: large, spherical + 5: Aerosol: mixture, partly non-spherical + 6: Aerosol: large, non-spherical + 7: Cloud: non-typed + 8: Cloud: water droplets + 9: Cloud: likely water droplets + 10: Cloud: ice crystals + 11: Cloud: likely ice crystal + + Notes + ----- + - QuasiV1 products uses Klett-retrieved products. + """ logging.info('Calculating Quasi V1 particle backscatter coefficient') quasiV1.quasi_bsc(self) @@ -794,7 +1475,44 @@ def quasiV1(self): def quasiV2(self): - """QuasiV2 retrivals and target categorisation.""" + """QuasiV2 retrivals and target categorisation. + + Yields + ------ + self.retrieval_highres : dict + With high resolution Quasi version 2 products as well as Target categorization. + + Keys + ---- + quasiBscV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle backscatter at channel `{wv}_{t}_{tel}`. + quasiExtV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle extinction at channel `{wv}_{t}_{tel}`. + quasiVdrV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 volume depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiPdrV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiAEV2_{wv1}\_{wv2} : ndarray + Quasi version 2 Angstroem exponent between two wavelengets `wv1` and `wv2`. + tcMaskV2 : ndarray + Classification mask version 2 (time x height). + 0: No signal + 1: Clean atmosphere + 2: Non-typed particles/low conc. + 3: Aerosol: small + 4: Aerosol: large, spherical + 5: Aerosol: mixture, partly non-spherical + 6: Aerosol: large, non-spherical + 7: Cloud: non-typed + 8: Cloud: water droplets + 9: Cloud: likely water droplets + 10: Cloud: ice crystals + 11: Cloud: likely ice crystal + + Notes + ----- + - QuasiV2 products uses Raman-retrieved products. + """ logging.info('Calculating Quasi V2 particle backscatter coefficient') quasiV2.quasi_bsc(self) @@ -816,12 +1534,11 @@ def write_2_sql_db(self, parameter:str, db_path:str|None=None, method:str|None=N ---------- parameter : str can be LC (Lidar-calibration-constant) or DC (Depol-calibration-constant) - method : str + method : str or NoneType 'raman' or 'klett' - db_path : str + db_path : str or NoneType location of the sqlite db-file - Notes ----- - The unique columns are needed that new entries overwrite old ones, @@ -862,10 +1579,21 @@ def read_calibration_db(self, db_path:str|None=None): Parameters ---------- - db_path : str - path to database of calibration values. + db_path : str or NoneType + path to database file... Default is None. + + Yields + ------ + self.LC : dict + Original `LC` with all additinal lidar calibration constant availabel + in the datafram `db_path` in the time period of the measurement +-24h. + self.pol_cali : dict + Original `pol_cali` with all additinal polarization calibration constant + availabel in the datafram `db_path` in the time period of the measurement +-24h. - Time interval includes 24h before and after the actual date + Notes + ----- + - Time interval includes 24h before start of the measurement and after the end of the measurement. """ if db_path == None: @@ -881,20 +1609,37 @@ def read_calibration_db(self, db_path:str|None=None): def adding_retrieving_infos_2_polly_config_dict(self): - """Some infos from the polly_config_dict should have there own keys, - e.g. reference_search_range. + """Some infos from the polly_config_dict should have there own keys, e.g. reference_search_range. + + Yields + ------ + self.polly_config_dict : dict + With additional info. + + Keys + ---- + reference_search_range_355_total_FR : list + ... + reference_search_range_532_total_FR : list + ... + reference_search_range_1064_total_FR : list + ... + + Notes + ----- + .. TODO :: + - Finish docstring. + - Is this function necessary? """ - + lower_overlap = np.array(self.polly_config_dict['heightFullOverlap']) - reference_search_range_355_total_FR = [int(lower_overlap[self.flag_355_total_FR][0]),self.polly_config_dict['maxDecomHeight355']] - reference_search_range_532_total_FR = [int(lower_overlap[self.flag_532_total_FR][0]),self.polly_config_dict['maxDecomHeight532']] - reference_search_range_1064_total_FR = [int(lower_overlap[self.flag_1064_total_FR][0]),self.polly_config_dict['maxDecomHeight1064']] + reference_search_range_355_total_FR = [int(lower_overlap[self.flag_355_total_FR][0]), self.polly_config_dict['maxDecomHeight355']] + reference_search_range_532_total_FR = [int(lower_overlap[self.flag_532_total_FR][0]), self.polly_config_dict['maxDecomHeight532']] + reference_search_range_1064_total_FR = [int(lower_overlap[self.flag_1064_total_FR][0]), self.polly_config_dict['maxDecomHeight1064']] self.polly_config_dict['reference_search_range_355_total_FR'] = reference_search_range_355_total_FR self.polly_config_dict['reference_search_range_532_total_FR'] = reference_search_range_532_total_FR self.polly_config_dict['reference_search_range_1064_total_FR'] = reference_search_range_1064_total_FR - - return self # def __str__(self): @@ -903,5 +1648,11 @@ def adding_retrieving_infos_2_polly_config_dict(self): def __del__(self): type(self).counter -= 1 + + + def runProcessing(self): + """Idea: Include a run function in the object as an alternative to the default run script... + """ + raise NotImplementedError() diff --git a/ppcpy/io/loadConfigs.py b/ppcpy/io/loadConfigs.py index 79a1131..d44c80e 100644 --- a/ppcpy/io/loadConfigs.py +++ b/ppcpy/io/loadConfigs.py @@ -4,20 +4,30 @@ import numpy as np import traceback -def loadPicassoConfig(picasso_config_file, picasso_default_config_file): - """load the general Picasso config file + +configIdxKeys:list = [ + 'first_range_gate_indx', 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', + 'LCMeanMinIndx', 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', + 'depol_cal_minbin_1064' +] + + +def loadPicassoConfig(picasso_config_file:str, picasso_default_config_file:str) -> dict: + """Load the general Picasso config file. Parameters ---------- picasso_config_file : str or path - the specific config file + The specific config file. picasso_default_config_fil : str or path - the default (template) file + The default (template) file. Returns ------- - picasso_config_dict + picasso_config_dict : dict + Mereged config file containing all config variables. """ + picasso_default_config_file_path = Path(picasso_default_config_file) picasso_config_file_path = Path(picasso_config_file) picasso_config_dict = {} @@ -48,7 +58,7 @@ def loadPicassoConfig(picasso_config_file, picasso_default_config_file): picasso_config_dict[key] = picasso_config_file_dict[key] return picasso_config_dict except Exception: - logging.critical('picasso_default_config_file: {picasso_default_config_file} can not be read.', exc_info=True) + logging.critical(f'picasso_default_config_file: {picasso_default_config_file} can not be read.', exc_info=True) else: logging.warning(f'picasso config file: {picasso_config_file} does not exist') @@ -59,9 +69,24 @@ def loadPicassoConfig(picasso_config_file, picasso_default_config_file): return None -def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): - """ +def readPollyNetConfigLinkTable(polly_config_table_file:str, timestamp:str, device:str) -> pd.DataFrame: + """Read PollyNet processing chain config link table. + + Parameters + ---------- + polly_config_table_file : str + Path to the link tabele file. + timestamp : str + Date of measurement. + device : str + Name of polly device. + + Returns + ------- + config_array : pd.DataFrame + Row of intrest in the PollyNet processing chain config link table. """ + polly_config_table_file_path = Path(polly_config_table_file) if polly_config_table_file_path.is_file(): @@ -73,12 +98,12 @@ def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): before_end_date = excel_file_ds['Stoptime of config'] >= timestamp between_two_dates = after_start_date & before_end_date filtered_result = excel_file_ds.loc[between_two_dates] - # print(filtered_result) + # print(filtered_result) ## get config-file for timeperiod and instrument config_array = filtered_result.loc[(filtered_result['Instrument'] == device)] if len(config_array) > 0: - #polly_config_file = str(config_array['Config file'].to_string(index=False)).strip() ## get rid of whtiespaces - #return polly_config_file + # polly_config_file = str(config_array['Config file'].to_string(index=False)).strip() ## get rid of whtiespaces + # return polly_config_file return config_array else: logging.warning(f'no polly-config file could be found for {device}@{timestamp}.') @@ -88,9 +113,20 @@ def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): return pd.DataFrame() -# def fix_indexing(config_dict, keys=['first_range_gate_indx', ]): -def fix_indexing(config_dict, keys=['first_range_gate_indx', 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx', ]): - """ +def fix_indexing(config_dict:dict, keys:list=configIdxKeys) -> dict: + """Fix indexing convention of config variables to 0based. + + Parameters + ---------- + config_dict : dict + A dictionary containing all config variables. + keys : list + Key(s) of `config_dict` variables to fix indexing convention. + + Returns + ------- + config_dict : dict + `config_dict` with 0based indexing. """ if not 'indexing_convention' in config_dict: @@ -108,38 +144,39 @@ def fix_indexing(config_dict, keys=['first_range_gate_indx', 'bgCorRangeIndx', ' return config_dict -def getPollyConfigfromArray(polly_config_array, picasso_config_dict): - """function to load the config for the time identified +def getPollyConfigfromArray(polly_config_array:pd.DataFrame, picasso_config_dict:dict) -> dict: + """Function to load the config for the time identified. - aim is to declutter the runscript + Aim is to declutter the runscript - Parameters ---------- polly_config_array : pandas dataframe - selected line form the links.xlsx + Selected line form the links.xlsx. picasso_config_dict : dict - general picasso config + General picasso config. Returns ------- polly_config_dict : dict - + A dictionary with the polly config / default parameters. """ + assert len(polly_config_array) == 1, 'given config array has more than one value' polly_config_file = Path( picasso_config_dict['polly_config_folder'], - polly_config_array['Config file'].item()) - #print(polly_config_file) + polly_config_array['Config file'].item() + ) + # print(polly_config_file) polly_default_config_file = Path( picasso_config_dict['path_config'], 'polly_global_config.json' ) - #print(polly_default_config_file) + # print(polly_default_config_file) polly_config_dict = loadPollyConfig( - polly_config_file, polly_default_config_file) - + polly_config_file, polly_default_config_file + ) polly_config_dict['name'] = polly_config_array['Instrument'].item() polly_config_dict['site'] = polly_config_array['Location'].item() polly_config_dict['asl'] = polly_config_array['asl.'].item() @@ -149,9 +186,28 @@ def getPollyConfigfromArray(polly_config_array, picasso_config_dict): return polly_config_dict -def loadPollyConfig(polly_config_file, polly_default_config_file): - """ +def loadPollyConfig(polly_config_file:str, polly_default_config_file:str) -> dict: + """Load the spesific and default config files and combine them into one dictionary. + + Parameters + ---------- + polly_config_file : str + Path to polly config file. + polly_default_config_file : str + path to polly default file. + + Returns + ------- + dict + A dictionary with the polly config / default parameters. + + Notes + ----- + .. TODO:: + - Maybe change `polly_default_config_file` to `polly_global_config_file` + as the defualt on is no longer used. """ + polly_default_config_file_path = Path(polly_default_config_file) polly_config_file_path = Path(polly_config_file) polly_config_dict = {} @@ -212,8 +268,10 @@ def loadPollyConfig(polly_config_file, polly_default_config_file): if 'first_range_gate_indx' in polly_default_config_file_dict.keys(): fix_indexing_keys = ['first_range_gate_indx'] elif 'LC' in polly_default_config_file_dict.keys(): - fix_indexing_keys = ['LC'] - return fix_indexing(polly_config_dict, keys=fix_indexing_keys + ['bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx']) + fix_indexing_keys = ['LC'] # TODO: Why are we subtracting one from LC??? + return fix_indexing(polly_config_dict, keys=fix_indexing_keys + [ + 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', + 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', 'depol_cal_minbin_1064']) except Exception: logging.warning(f'polly_config_file: {polly_config_file} can not be processed.', exc_info=True) @@ -224,21 +282,29 @@ def loadPollyConfig(polly_config_file, polly_default_config_file): if 'first_range_gate_indx' in polly_default_config_file_dict.keys(): fix_indexing_keys = ['first_range_gate_indx'] elif 'LC' in polly_default_config_file_dict.keys(): - fix_indexing_keys = ['LC'] - return fix_indexing(polly_default_config_file_dict, keys=fix_indexing_keys + ['bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx']) - else: + fix_indexing_keys = ['LC'] # TODO: Why are we subtracting one from LC??? + return fix_indexing(polly_default_config_file_dict, keys=fix_indexing_keys + [ + 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', + 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', 'depol_cal_minbin_1064']) + logging.critical(f'polly_default_config_file: {polly_default_config_file} can not be found. Aborting') return None + def checkPollyConfigDict(polly_config_dict:dict) -> dict: - """ - Check and potentially modify polly config dict + """Check and potentially modify polly config dict. - Parameters: - - polly_config_dict (dict): polly config dict to be checked - Output: - - new_polly_config_dict (dict): checked (and modified) polly config dict + Parameters + ---------- + polly_config_dict : dict + Polly config dict to be checked. + + Returns + ------- + new_polly_config_dict : dict + Checked (and modified) polly config dict. """ + logging.info(".. checking polly config dict") new_polly_config_dict = polly_config_dict.copy() diff --git a/ppcpy/io/readMeteo.py b/ppcpy/io/readMeteo.py index f09bc06..3d22312 100644 --- a/ppcpy/io/readMeteo.py +++ b/ppcpy/io/readMeteo.py @@ -102,8 +102,9 @@ def __init__(self, meteorDataSource:str, meteo_folder:str, meteo_file:str): Notes ----- - **History** + ** History ** - 2021-05-22: First edition by Zhenping. + """ assert meteorDataSource == 'nc_cloudnet', "Other meteo sources are not implemented yet" @@ -114,7 +115,6 @@ def __init__(self, meteorDataSource:str, meteo_folder:str, meteo_file:str): def load(self, times:float, heights:np.ndarray, asl:float, flagPicassoComparison:bool=False): """Load the data and resample to 15 minute intervals. - Parameters ---------- times : float @@ -149,7 +149,6 @@ def get_mean_profiles(self, time_slice:list) -> list: for t in time_slice: mean_profiles.append(self.ds.sel(time=slice(*t)).mean(dim='time')) - print(f"get_mean_profiles(time_slice: {type(time_slice)}) -> {type(mean_profiles)}") return mean_profiles @@ -178,8 +177,9 @@ def __init__(self, basepath, filepattern): Notes ----- - **History** + ** History ** - xxxx-xx-xx: First edition by ... + """ if not '/' == basepath[-1]: @@ -203,14 +203,14 @@ def find_path_for_time(self, time:float) -> str: """ candidates = glob.glob(self.basepath + "**", recursive=True) - #print('candidates ', candidates) + # print('candidates ', candidates) dt = datetime.datetime.fromtimestamp(time) regex = re.compile(self.filepattern.format(dt)) - #print('regex ', regex) + # print('regex ', regex) filename = [s for s in candidates if regex.search(s) ] - #print('filename ', filename) + # print('filename ', filename) assert len(filename) == 1, f"{os.getcwd()}, {self.basepath} found {candidates} reduced to filenames {filename}" diff --git a/ppcpy/io/readPollyRawData.py b/ppcpy/io/readPollyRawData.py index c2b07f1..5c443e2 100644 --- a/ppcpy/io/readPollyRawData.py +++ b/ppcpy/io/readPollyRawData.py @@ -4,77 +4,70 @@ #from pathlib import Path #import logging #import sys + def readPollyRawData(filename: str) -> dict: - """read the Polly raw file + """Read Polly raw data. Parameters ---------- filename : str - + Absolute path of the polly data. Returns ------- data_dict : dict - + rawSignal : ndarray (channel x height x time) + backscatter signal [Photon Count]. + mShots : ndarray + number of the laser shots for each profile. + flagValidProfile : ndarray + flag to represent the validity of each signal profile. + mTime : ndarray + datetime array for the measurement time of each profile. + depCalAng : ndarray + angle of the polarizer in the receiving channel. (>0 means + calibration process starts) [degree]. + zenithAng : numeric??? + zenith angle of the laser beam [degree]. + repRate : float + laser pulse repetition rate [s^-1]. + hRes : float + spatial resolution [m]. + mSite : str + measurement site. + deadtime : ndarray (channel x polynomial_orders) + deadtime correction parameters. + lat : float + latitude of measurement site [degree]. + lon : float + longitude of measurement site [degree]. + alt : float + altitude of measurement site [degree]. + filenameStartTime : datenum?????? + start time extracted from filename. + Notes + ----- + .. TODO:: + - Go over the Returns section in the docsting. Are these variables actuly returned by the function? + - Deleat the matlab code underneath.S + + ** HISTORY ** + + - 2024-03-21: First edition by Andi Klamt. + - xxxx-xx-xx: Translated to python. + + .. Authors: - klamt@tropos.de """ -#% READPOLLYRAWDATA Read polly raw data. -#% -#% USAGE: -#% data = readPollyRawData(file) -#% -#% INPUTS: -#% file: char -#% absolute path of the polly data. -#% -#% -#% OUTPUTS: -#% data: struct -#% rawSignal: matrix (channel x height x time) -#% backscatter signal. [Photon Count] -#% mShots: array -#% number of the laser shots for each profile. -#% flagValidProfile: array -#% flag to represent the validity of each signal profile. -#% mTime: array -#% datetime array for the measurement time of each profile. -#% depCalAng: array -#% angle of the polarizer in the receiving channel. (>0 means -#% calibration process starts). [degree] -#% zenithAng: numeric -#% zenith angle of the laser beam. [degree] -#% repRate: float -#% laser pulse repetition rate. [s^-1] -#% hRes: float -#% spatial resolution [m] -#% mSite: char -#% measurement site. -#% deadtime: matrix (channel x polynomial_orders) -#% deadtime correction parameters. -#% lat: float -#% latitude of measurement site. [degree] -#% lon: float -#% longitude of measurement site. [degree] -#% alt: float -#% altitude of measurement site. [degree] -#% filenameStartTime: datenum -#% start time extracted from filename. -#% -#% HISTORY: -#% - 2024-03-21: First edition by Andi Klamt. -#% -#% .. Authors: - klamt@tropos.de - - data_dict={} + data_dict = {} filename_path = Path(filename) if filename_path.is_file(): logging.info(f'reading nc-file: {filename}') else: -# print(f'{filename} does not exist. Aborting') logging.critical(f'{filename} does not exist.') sys.exit(1) - return None + return ## open nc-file as dataset nc_file_ds = netCDF4.Dataset(filename, "r") @@ -86,8 +79,8 @@ def readPollyRawData(filename: str) -> dict: data_dict['global_attributes'] = {} for nc_attr in nc_file_ds.ncattrs(): att_value = nc_file_ds.getncattr(nc_attr) - #global_attr[nc_attr] = att_value - #data_dict[f'global_attr__{nc_attr}'] = att_value + # global_attr[nc_attr] = att_value + # data_dict[f'global_attr__{nc_attr}'] = att_value data_dict['global_attributes'][nc_attr] = att_value var_ls = [] @@ -95,8 +88,8 @@ def readPollyRawData(filename: str) -> dict: var_ls.append(var) ## fill data_dict with variable-values - #for var_name in var_ls: - # data_dict[var_name] = nc_file_ds[var_name][:] + # for var_name in var_ls: + # data_dict[var_name] = nc_file_ds[var_name][:] ## fill data_dict with variable-value and get variable attributes from nc-file for var_name in var_ls: @@ -106,13 +99,13 @@ def readPollyRawData(filename: str) -> dict: for var_att in nc_file_ds.variables[var_name].ncattrs(): var_att_value = nc_file_ds.variables[var_name].getncattr(var_att) - #data_dict[f'{var_name}___{var_att}'] = var_att_value + # data_dict[f'{var_name}___{var_att}'] = var_att_value data_dict[var_name]['var_att'][var_att] = var_att_value - #print(var_name, type(data_dict[var_name]['var_data'])) + # print(var_name, type(data_dict[var_name]['var_data'])) if isinstance(data_dict[var_name]['var_data'], np.ma.MaskedArray): data_dict[var_name]['var_data'] = data_dict[var_name]['var_data'].data - #print(var_name, type(data_dict[var_name]['var_data'])) + # print(var_name, type(data_dict[var_name]['var_data'])) assert data_dict['measurement_height_resolution']['var_att']['units'] == 'ns' # measurement height resolution should be given in ns @@ -122,8 +115,10 @@ def readPollyRawData(filename: str) -> dict: nc_file_ds.close() return data_dict -# -#%% variables initialization + + +# ------ Matlab eqvialent code: +#% variables initialization #data = struct(); #data.rawSignal = []; #data.mShots = []; @@ -140,7 +135,7 @@ def readPollyRawData(filename: str) -> dict: #data.angle = []; # # -#%% read data +#% read data #try # rawSignal = double(ncread(file, 'raw_signal')); # if is_nc_variable(file, 'deadtime_polynomial') diff --git a/ppcpy/io/sql_interaction.py b/ppcpy/io/sql_interaction.py index 91cf7ee..6f47525 100644 --- a/ppcpy/io/sql_interaction.py +++ b/ppcpy/io/sql_interaction.py @@ -7,31 +7,34 @@ import ppcpy.misc.helper as helper from ppcpy.misc.helper import default_to_regular + mapping:dict = {'far_range': 'FR', 'near_range': 'NR', 'dfov': 'DFOV'} mapping_inverse:dict = {y: x for x, y in mapping.items()} + def string_to_ts(s): - """string of format %Y-%m-%d %H:%M:%S to timestamp (timezone-aware)""" + """String of format %Y-%m-%d %H:%M:%S to timestamp (timezone-aware)""" return datetime.strptime(s, "%Y-%m-%d %H:%M:%S").replace(tzinfo=timezone.utc).timestamp() + def get_from_sql_db(db_path:str, table_name:str, ts_interval:list[str]) -> dict: - """read lidar calibration constant or depol calibration from database + """Read lidar calibration constant or depol calibration from database. Parameters ---------- db_path : str - name of the specific sqlite db file. + Name of the specific sqlite db file. table_name : str - default 'lidar_calibration_constant' + Default 'lidar_calibration_constant'. ts_interval : str - the date or timestamp to look for - + The date or timestamp to look for. Returns ------- dict - in calibration storage format + In calibration storage format. """ + delta = timedelta(hours=24) start = ( datetime.fromtimestamp(ts_interval[0], timezone.utc) - delta @@ -82,21 +85,31 @@ def get_from_sql_db(db_path:str, table_name:str, ts_interval:list[str]) -> dict: return ret + def prepare_for_sql_db_writing(data_cube, parameter:str, method:str) -> list[tuple]: - """ - Collect all necessary variable and save it to a list of tuples for inserting into a SQLite table. + """Collect all necessary variable and save it to a list of + tuples for inserting into a SQLite table. Parameters ---------- data_cube : object + Main PicassoProc object parameter :str - LC or DC + Name of parameter to stoere eg. 'LC' or 'DC'. method : str - klett or raman + Name of retrieval method used to retrieve the parameter + eg. 'klett' or 'raman'. Returns ------- rows_to_insert : list of tuples + Rows to insert in the database. + + ** History ** + + - xxxx-xx-xx: First edition by + - xxxx-xx-xx: Translated to python. + """ rows_to_insert = [] @@ -139,7 +152,9 @@ def prepare_for_sql_db_writing(data_cube, parameter:str, method:str) -> list[tup wv, tel_db, str(data_cube.rawfile), data_cube.device)) return rows_to_insert -def setup_empty(db_path:str, table_name:str, column_names:list[str], data_types:list[str], unique:str=''): + +def setup_empty(db_path:str, table_name:str, column_names:list[str], + data_types:list[str], unique:str=''): """Create/Initialise an empty database. Parameters @@ -165,7 +180,8 @@ def setup_empty(db_path:str, table_name:str, column_names:list[str], data_types: conn.close() -def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], rows_to_insert:list[str]): +def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], + rows_to_insert:list[str]): """Insert multiple rows into a SQLite table. Parameters @@ -182,15 +198,15 @@ def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], ro Notes ----- - The IGNORE syntax somehow did not work. - With the UNIQUE colums defined and INSERT OR REPLACE at least the new values are updated. - Though they are given a new ID. + - The IGNORE syntax somehow did not work. + With the UNIQUE colums defined and INSERT OR REPLACE at least the new values are updated. + Though they are given a new ID. """ placeholders = ', '.join(['?'] * len(column_names)) columns = ', '.join(column_names) - #sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders}) ON CONFLICT(cali_start_time, cali_stop_time, wavelength, polly_type, telescope) DO UPDATE SET data = excluded.data" + # sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders}) ON CONFLICT(cali_start_time, cali_stop_time, wavelength, polly_type, telescope) DO UPDATE SET data = excluded.data" sql = f"INSERT OR REPLACE INTO {table_name} ({columns}) VALUES ({placeholders})" try: @@ -207,5 +223,3 @@ def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], ro logging.info(f"{inserted} rows inserted into '{table_name}'.") except sqlite3.Error as e: logging.warning(f"SQLite error: {e}") - - diff --git a/ppcpy/io/write2nc.py b/ppcpy/io/write2nc.py index 824d641..c7ffde5 100644 --- a/ppcpy/io/write2nc.py +++ b/ppcpy/io/write2nc.py @@ -10,12 +10,32 @@ import ppcpy.misc.json2nc_mapping as json2nc_mapping from ppcpy._version import __version__ + ## getting root dir of PicassoPy root_dir0 = Path(__file__).resolve().parent.parent.parent root_dir = helper.detect_path_type(root_dir0) -def get_git_info(path="."): - """ """ + +def get_git_info(path:str=".") -> tuple: + """... + + Parameters + ---------- + path : str + Path to ... + + Returns + ------- + branch : ... or None + ... + commit : ... or None + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + try: repo = Repo(Path(path).resolve(), search_parent_directories=True) branch = repo.active_branch.name @@ -24,24 +44,76 @@ def get_git_info(path="."): except Exception: return None, None -def date_splitting(timestamp): - """ """ + +def date_splitting(timestamp:str) -> tuple: + """Split date into YYYY, MM, DD. + + Parameters + ---------- + timestamp : str + Date in format 'YYYYMMDD'. + + Returns + ------- + YYYY : str + Year. + MM : str + Month. + DD : str + Day. + """ + YYYY = timestamp[0:4] MM = timestamp[4:6] DD = timestamp[6:8] - return YYYY,MM,DD + return YYYY, MM, DD + +def adding_fixed_vars(data_cube, json_nc_mapping_dict:dict): + """Add fixed variables to mapping dict. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ -def adding_fixed_vars(data_cube, json_nc_mapping_dict): - """ """ ## adding fixed variables json_nc_mapping_dict['variables']['altitude']['data'] = data_cube.polly_config_dict['asl'] json_nc_mapping_dict['variables']['latitude']['data'] = data_cube.polly_config_dict['lat'] json_nc_mapping_dict['variables']['longitude']['data'] = data_cube.polly_config_dict['lon'] json_nc_mapping_dict['variables']['tilt_angle']['data'] = data_cube.rawdata_dict['zenithangle']['var_data'] -def adding_global_attr(data_cube, json_nc_mapping_dict): - """ """ + +def adding_global_attr(data_cube, json_nc_mapping_dict:dict): + """Add global attributes to mapping dict. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## adding global attributes json_nc_mapping_dict['global_attributes']['location'] = data_cube.polly_config_dict['site'] json_nc_mapping_dict['global_attributes']['source'] = data_cube.polly_config_dict['name'] @@ -56,15 +128,34 @@ def adding_global_attr(data_cube, json_nc_mapping_dict): json_nc_mapping_dict['global_attributes']['history'] = f'Last processing time at {now_utc} UTC, git branch: {gitbranch}, git commit: {gitcommit}' -def write_channelwise_2_nc_file(data_cube, root_dir=root_dir, prod_ls=[]): - """ """ +def write_channelwise_2_nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[]): + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + root_dir : str + Path to root directory. + prod_ls : list of str + List of product names. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## writes data from products, listed in prod_ls, to nc-file # available products: prod_ls = ["SNR", "BG", "RCS", "att_bsc", "vol_depol"] for prod in prod_ls: logging.info(f"saving product: {prod}") # json_nc_mapping_dict = {} # if prod in polly_config_dict["prodSaveList"]: - json_nc_mapping_dict = json2nc_mapping.read_json_to_dict(Path(root_dir,'ppcpy','config',f'json2nc-mapper_{prod}.json')) + json_nc_mapping_dict = json2nc_mapping.read_json_to_dict(Path(root_dir, 'ppcpy', 'config', f'json2nc-mapper_{prod}.json')) if prod == "SNR" or prod == "BG" or prod == "RCS": """ map channels to variables """ @@ -98,14 +189,34 @@ def write_channelwise_2_nc_file(data_cube, root_dir=root_dir, prod_ls=[]): """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod) -def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): - """ """ + +def write2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[]): + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + root_dir : str + Path to root directory. + prod_ls : list of str + List of product names. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## writes data from products, listed in prod_ls, to nc-file for prod in prod_ls: logging.info(f"saving product: {prod}") @@ -122,7 +233,7 @@ def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): adding_global_attr(data_cube, json_nc_mapping_dict) ## adding dynamical variables - json_nc_translator = json2nc_mapping.read_json_to_dict(Path(root_dir,'ppcpy','config', f'json2nc_translator.json')) + json_nc_translator = json2nc_mapping.read_json_to_dict(Path(root_dir, 'ppcpy', 'config', f'json2nc_translator.json')) for var in json_nc_translator[prod]['variables'].keys(): #print(f'var: {var}') if var in json_nc_mapping_dict['variables'].keys(): @@ -133,7 +244,7 @@ def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): parameter = json_nc_translator[prod]['variables'][var]['parameter'] if "quality_mask" in var: ch = getattr(data_cube, parameter) - qm = np.squeeze(data_cube.retrievals_highres['quality_mask'][:,:,ch]) + qm = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, ch]) json_nc_mapping_dict['variables'][var]['data'] = qm else: pass @@ -152,30 +263,33 @@ def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod) def write_profile2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[], collect_debug:bool=False): - """ - Saving profile data to NetCDF4 files + """Saving profile data to NetCDF4 files. Parameters ---------- data_cube : object - Main PicassoProc object + Main PicassoProc object. root_dir : str + Path to root directory. prod_ls : list - List of product names + List of product names. + Notes + ----- .. TODO:: - Missing comment in variable attributes. - Not all retrievals / information needed for the profiles are in data_cube.retrivals_highres... - write docstring + - Missing comment in variable attributes. + - Not all retrievals / information needed for the profiles are in data_cube.retrivals_highres... + - write docstring """ + ## writes data from products, listed in prod_ls, to nc-file ## available products: prod_ls = ["profiles", "NR_profeils", "OC_profiles"] @@ -246,15 +360,34 @@ def write_profile2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[], col #print(data_dict_copy['variables'].keys()) """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{start}_{stop}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod, cldFreeIndx=n) def adding_mol_profiles(data_cube, json_nc_mapping_dict:dict, cldFreeGrp:int) -> dict: - """Temporarily quick fix for adding molecular profiles as variables to the NetCDF profile outputs""" + """Temporarily quick fix for adding molecular profiles as variables to the NetCDF profile outputs. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + cldFreeGrp : int + Index of floud free segment. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring. + """ + # molExt and molBsc: for wv in [355, 387, 407, 532, 607, 1058, 1064]: for var_name in [f'mExt_{wv}', f'mBsc_{wv}']: diff --git a/ppcpy/misc/concat.py b/ppcpy/misc/concat.py index 17a3a87..6e02db0 100644 --- a/ppcpy/misc/concat.py +++ b/ppcpy/misc/concat.py @@ -15,23 +15,43 @@ from scipy.sparse import diags import tracemalloc # memory usage tracking + def os_name(): - """""" + """ """ return platform.system() def date_splitting(timestamp): + """ """ YYYY = timestamp[0:4] MM = timestamp[4:6] DD = timestamp[6:8] - return YYYY,MM,DD + return YYYY, MM, DD -def get_input_path(timestamp, device, base_dir): - """ - Checking for the correct subpath - TODO: using glob or similiar to be more flexible in terms of level0b or martha... + +def get_input_path(timestamp, device:str, base_dir:Path): + """Checking for the correct subpath. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of polly device. + base_dir : Path + Path to base directory. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: + - Using glob or similiar to be more flexible in terms of level0b or martha... """ - YYYY,MM,DD = date_splitting(timestamp) + + YYYY, MM, DD = date_splitting(timestamp) search_root_normal = Path(base_dir) / device search_root_special = Path(base_dir) search_pattern = f"**/*{YYYY}_{MM}_{DD}*.nc*" @@ -40,20 +60,36 @@ def get_input_path(timestamp, device, base_dir): return file_path.parent.resolve() for file_path in search_root_special.rglob(search_pattern): return file_path.parent.resolve() + return - return None +def get_pollyxt_zipfiles(timestamp, device:str, raw_folder:Path): + """This function locates multiple pollyxt level0 nc-zip files + from one day measurements, and returns a list of zip-files. -def get_pollyxt_zipfiles(timestamp, device, raw_folder): - """ - This function locates multiple pollyxt level0 nc-zip files from one day measurements, - and returns a list of zip-files + Parameters + ---------- + timestamp : ... + ... + device : str + Name of Polly device. + raw_floder : Path + Path to raw data folder. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + input_path = get_input_path(timestamp, device, raw_folder) if input_path: path_exist = Path(input_path) else: - return None + return if path_exist.exists() == True: @@ -71,21 +107,40 @@ def get_pollyxt_zipfiles(timestamp, device, raw_folder): #for file in polly_zip_files_list0: # polly_zip_files_list.append(str(file)) -def get_pollyxt_nc_files(timestamp, device, raw_folder): - """ - This function locates pollyxt level0 nc-files (i.e. already 24h-merged level0b files) from one day measurements, - and returns a list of nc-files + +def get_pollyxt_nc_files(timestamp, device:str, raw_folder:Path): + """This function locates pollyxt level0 nc-files (i.e. already 24h-merged level0b files) + from one day measurements, and returns a list of nc-files. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of polly device. + raw_folder : str + Path to raw data folder. + + Returns + ------- + polly_files_list : ... + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + input_path = get_input_path(timestamp, device, raw_folder) if input_path: path_exist = Path(input_path) else: - return None + return if path_exist.exists() == True: ## set the searchpattern for the zipped-nc-files: - YYYY,MM,DD = date_splitting(timestamp) + YYYY, MM, DD = date_splitting(timestamp) nc_searchpattern = str(YYYY)+'_'+str(MM)+'_'+str(DD)+'*_*[0-9].nc' @@ -93,16 +148,34 @@ def get_pollyxt_nc_files(timestamp, device, raw_folder): polly_files_list = [x for x in polly_files if x.is_file()] return polly_files_list else: - return None + return -def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): - """ - This function checks the size of the zip-files. - If smaller than threshold (e.g. 500000 Byte), the file will be skipped. - The files passing the filesize check will be unzipped - returns a list of unzipped files +def unzipping_pollyxt_files(polly_zip_files_list:list, timestamp, output_path:Path): + """This function checks the size of the zip-files. + If smaller than threshold (e.g. 500000 Byte), the file will be skipped. + The files passing the filesize check will be unzipped + returns a list of unzipped files. + + Parameters + ---------- + polly_zip_files_list : list?? + ... + timestamp : ... + ... + output_path : Path + Path to output folder. + + Returns + ------- + polly_files_list : list?? + ... + + Notes + ----- + .. TODO:: Finish docstring. """ + # Ensure the destination directory exists Path(output_path).mkdir(parents=True, exist_ok=True) @@ -123,14 +196,14 @@ def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): continue ## go to next file ## unzipping - YYYY,MM,DD = date_splitting(timestamp) + YYYY, MM, DD = date_splitting(timestamp) date_pattern = str(YYYY) + '_' + str(MM) + '_' + str(DD) if len(to_unzip_list) > 0: ## if working remotly on windows, copy zipped files first, than unzip if os_name().lower() == 'windows': logging.info("Copy zipped files to local drive...") for zip_file in to_unzip_list: - logging-info(zip_file) + logging.info(zip_file) shutil.copy2(Path(zip_file), Path(output_path) / Path(zip_file).name) logging.info(f"Unzipping to: {output_path}") for zip_file in Path(output_path).iterdir(): @@ -154,15 +227,34 @@ def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): return polly_files_list else: logging.warning('no files to unzip') - return None + return -def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): - """ - This function locates multiple pollyxt level0 nc-zip files from one day measurements, - unzipps the files to output_path - and returns a list of files to be merged +def get_pollyxt_files(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """This function locates multiple pollyxt level0 nc-zip files from one day measurements, + unzipps the files to output_path and returns a list of files to be merged. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + polly_files_list : list?? + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + unzip = kwargs.get('unzipping', True) if str(unzip).lower() == 'true': @@ -175,7 +267,7 @@ def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): sys.exit() ## unzip files - polly_files_list = unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path) + polly_files_list = unzipping_pollyxt_files(polly_zip_files_list, timestamp, output_path) if polly_files_list: pass else: @@ -190,19 +282,39 @@ def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): logging.error('No files found. Aborting') sys.exit() - ## sort lists polly_files_list.sort() return polly_files_list -def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): - """ - This function locates multiple pollyxt logbook-zip files from one day measurements, - unzipps the files to output_path - and merge them to one file +def get_pollyxt_logbook_files(timestamp, device:str, raw_folder:Path, output_path:Path) -> tuple: + """This function locates multiple pollyxt logbook-zip files from one day measurements, + unzipps the files to output_path and merge them to one file. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + tuple + Empty tuple. + + Notes + ----- + .. TODO:: + - Finish docstring! + - Why are we returning an empty tuple? """ + input_path = get_input_path(timestamp, device, raw_folder) path_exist = Path(input_path) @@ -215,10 +327,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): zip_searchpattern = str(YYYY)+'_'+str(MM)+'_'+str(DD)+'*_*laserlogbook*.zip' - polly_laserlog_files = Path(r'{}'.format(input_path)).glob('{}'.format(zip_searchpattern)) + polly_laserlog_files = Path(r'{}'.format(input_path)).glob('{}'.format(zip_searchpattern)) polly_laserlog_zip_files_list0 = [x for x in polly_laserlog_files if x.is_file()] - ## convert type path to type string polly_laserlog_zip_files_list = [] for file in polly_laserlog_zip_files_list0: @@ -239,7 +350,6 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): to_unzip_list.append(zip_file) - ## unzipping if len(to_unzip_list) > 0: for zip_file in to_unzip_list: @@ -265,9 +375,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): laserlog_filename = polly_laserlog_files_list[0] laserlog_filename = Path(laserlog_filename).name - laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc',laserlog_filename)[0] + laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc', laserlog_filename)[0] laserlog_filename = f'{laserlog_filename_left}_00_00_01.nc.laserlogbook.txt' - destination_file = Path(output_path,laserlog_filename) + destination_file = Path(output_path, laserlog_filename) # Open the source file in binary mode and read its content with open(result_file, 'rb') as source: @@ -283,7 +393,7 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): def add_to_list(element, from_list, to_list): - """""" + """ """ if from_list[element] in to_list: pass else: @@ -291,17 +401,41 @@ def add_to_list(element, from_list, to_list): -def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): - """""" +def checking_vars(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """... + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + selected_var_nc_ls : ... + ... + + Notes + ----- + .. TODO:: + - Finish docstring! + - Variable `force` is not defined. + """ + ## select only those nc-files where the values of some specific variables haven't changed vars_of_interest = [ 'measurement_height_resolution', 'laser_rep_rate', 'laser_power', -# 'laser_flashlamp', + #'laser_flashlamp', 'location_height', 'neutral_density_filter', -# 'location_coordinates', + #'location_coordinates', 'pm_voltage', 'pinhole', 'polstate', @@ -327,22 +461,22 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): print('\n') logging.info('checking differences in selected variables ...') for ds in range(0,len(polly_file_ds_ls)-1): -# print('\n') -# print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) + # print('\n') + # print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) for var in vars_of_interest: if var in polly_file_ds_ls[ds].variables.keys(): ## check if var is available within the polly-datastructure (depending on polly-system) var_value_1=str(polly_file_ds_ls[ds].variables[var][:]) var_value_2=str(polly_file_ds_ls[ds+1].variables[var][:]) - # print(var + ": " + var_value_1) - # print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) if var_value_1 == var_value_2 and diff_var==0: # print('no difference found ...') add_to_list(ds, polly_files_list, selected_var_nc_ls) elif var_value_1 != var_value_2 and diff_var==0: logging.info('difference found in var:') logging.info(var) - #print(var + ": " + var_value_1) - #print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) diff_var=1 add_to_list(ds, polly_files_list, selected_var_nc_ls) if force == True else None elif var_value_1 == var_value_2 and diff_var != 0: @@ -357,7 +491,7 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): add_to_list(-1, polly_files_list, selected_var_nc_ls) logging.info('no differences found in selected variables!') elif diff_var != 0: -# add_to_list(-1,polly_files_list,selected_var_nc_ls) if force == True else None + # add_to_list(-1, polly_files_list, selected_var_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: add_to_list(-1, polly_files_list, selected_var_nc_ls) @@ -371,26 +505,31 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): return selected_var_nc_ls -def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): +def checking_attr(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): """... Parameters ---------- timestamp : ... ... - device : ... - ... - raw_folder : ... - ... - output_path : ... - ... + device : str + Name of PollyXT device. + raw_folder : Path + Path to raw data folder. + output_path : Path + Path to output folder. Returns ------- ... - TODO: Variables 'force' and 'polly_files_list' are not defined anywhere + Notes + ----- + .. TODO:: + - Finish docstring! + - Variables 'force' and 'polly_files_list' are not defined anywhere. """ + ## select only those nc-files where the global attributes and the var-attributes haven't changed selected_var_nc_ls = checking_vars(timestamp, device, raw_folder, output_path, **kwargs) if len(selected_var_nc_ls) == 1: @@ -409,15 +548,15 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): logging.info('checking differences in attributes ...') for ds in range(0, len(polly_file_ds_ls) - 1): ## get global attributes as a list of strings -# print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) -# print('\nglobal attributes:') + # print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) + # print('\nglobal attributes:') for nc_attr in polly_file_ds_ls[0].ncattrs(): # att_value=repr(input_nc_file.getncattr(nc_attr)) att_value_1 = polly_file_ds_ls[ds].getncattr(nc_attr) att_value_2 = polly_file_ds_ls[ds+1].getncattr(nc_attr) -# print(nc_attr) -# print(" " + att_value_1) -# print(" " + att_value_2) + # print(nc_attr) + # print(" " + att_value_1) + # print(" " + att_value_2) if att_value_1 == att_value_2 and diff_att==0: add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) elif att_value_1 != att_value_2 and diff_att==0: @@ -433,15 +572,15 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): logging.info('difference found!') add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) if force == True else None -# print("\nvariable attributes:") + # print("\nvariable attributes:") for var in polly_file_ds_ls[0].variables.keys(): -# print(var) + # print(var) for var_att in polly_file_ds_ls[0].variables[var].ncattrs(): var_att_value_1 = polly_file_ds_ls[ds].variables[var].getncattr(var_att) var_att_value_2 = polly_file_ds_ls[ds+1].variables[var].getncattr(var_att) -# print(" " + var_att) -# print(" " + var_att_value_1) -# print(" " + var_att_value_2) + # print(" " + var_att) + # print(" " + var_att_value_1) + # print(" " + var_att_value_2) if var_att_value_1 == var_att_value_2 and diff_var_att == 0: pass elif var_att_value_1 != var_att_value_2 and diff_var_att == 0: @@ -458,12 +597,11 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): for ds in range(0, len(polly_file_ds_ls) - 1): polly_file_ds_ls[ds].close() - if diff_att == 0: add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) logging.info('no differences found in global attributes!') elif diff_att != 0: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) if force == True else None + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: @@ -477,22 +615,41 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): if diff_var_att == 0: logging.info('no differences found in variable attributes!') -# elif diff_var_att != 0: -# ## if force==true, merge, but if force==false: the whole day will not be in list anymore -# if force == True: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) -# print('\ndifferences found in variable attributes! But will be force-merged.\n') -# else: -# print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') -# sys.exit() - + # elif diff_var_att != 0: + # ## if force==true, merge, but if force==false: the whole day will not be in list anymore + # if force == True: + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) + # print('\ndifferences found in variable attributes! But will be force-merged.\n') + # else: + # print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') + # sys.exit() logging.info(selected_att_nc_ls) return selected_att_nc_ls -def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): - """""" +def checking_timestamp(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """... + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_folder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ selected_timestamp_nc_ls = checking_attr(timestamp, device, raw_folder, output_path, **kwargs) if len(selected_timestamp_nc_ls) == 1: return selected_timestamp_nc_ls @@ -503,8 +660,8 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): polly_file_ds = Dataset(files, "r") polly_file_ds_ls.append(polly_file_ds) - for elementNR,ds in enumerate(polly_file_ds_ls): - # print(selected_timestamp_nc_ls[elementNR]) + for elementNR, ds in enumerate(polly_file_ds_ls): + # print(selected_timestamp_nc_ls[elementNR]) timestamp_ds = ds.variables['measurement_time'][:] if 19700101 in timestamp_ds.T[0]: logging.info(f'The file: {selected_timestamp_nc_ls[elementNR]} contains incorrect timestamps!') @@ -512,9 +669,9 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): ## get correct timestamp_ds from filename timestamp_filename = selected_timestamp_nc_ls[elementNR] timestamp_filename = timestamp_filename.stem - timestamp_filename = re.split(r'_',str(timestamp_filename))[-3:] + timestamp_filename = re.split(r'_', str(timestamp_filename))[-3:] ## del. nc-file - #os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps + # os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps ## calc. the deltaT between measurementdatapoints laser_rep_rate = float(ds.variables['laser_rep_rate'][0]) measurement_shots = ds.variables['measurement_shots'][:] @@ -566,7 +723,6 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): else: new_var[:] = var[:] - ds.close() new_dataset.close() os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps @@ -580,7 +736,6 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): if ds.isopen(): ds.close() - logging.info('the following ' + str(len(selected_cor_timestamp_nc_ls)) + ' files can be merged:') logging.info(selected_cor_timestamp_nc_ls) return selected_cor_timestamp_nc_ls @@ -591,11 +746,27 @@ def get_memory_usage(): current, peak = tracemalloc.get_traced_memory() return current / 1024 / 1024, peak / 1024 / 1024 # MB + def write_netcdf_robust(ds: xr.Dataset, out_file: Path, comp_level: int = 1, max_retries: int = 3) -> bool: - """ - Robust NetCDF writing with retries and error handling + """Robust NetCDF writing with retries and error handling. + + Parameters + ---------- + ds : xr.Dataset + ... + out_file : Path + ... + comp_level : int, optional + ... Default is 1. + max_retries : int, optional + ... Default is 3. + + Returns + ------- + bool + True if ..., otherwise False. """ # Start tracing memory @@ -701,17 +872,27 @@ def write_netcdf_robust_old(ds: xr.Dataset, out_file: Path, comp_level: int = 1, max_retries: int = 3, timeout_seconds: int = 600) -> bool: - """ - Robust NetCDF writing with retries and error handling + """Robust NetCDF writing with retries and error handling. - Args: - ds: xr Dataset to write - out_file: Output file path - max_retries: Maximum number of retry attempts - timeout_seconds: Timeout in seconds + Parameters + ---------- + ds : xr.Dataset + Dataset to write. + out_file : Path + Output file path. + max_retries : int + Maximum number of retry attempts. + timeout_seconds : int + Timeout in seconds. - Returns: - bool: True if successful, False otherwise + Returns + ------- + bool + True if successful, False otherwise. + + Notes + ----- + - What is the difference between this function and the one above... """ for attempt in range(max_retries): @@ -784,11 +965,33 @@ def write_netcdf_robust_old(ds: xr.Dataset, out_file: Path, return False -def concat_files(timestamp, device, raw_folder, output_path, **kwargs): - """""" - ## merge selected files - sel_polly_files_list = checking_timestamp(timestamp,device,raw_folder,output_path, **kwargs) +def concat_files(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs) -> Path: + """... + + Parametrs + --------- + timestamp : ... + ... + device : str + Name of Polly device. + raw_folder : Path + Path to the raw folder. + output_path : Path + Path to the output folder. + + Returns + ------- + destination_file : Path + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + + ## merge selected files + sel_polly_files_list = checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs) if len(sel_polly_files_list) == 0: logging.info('no files found for this day. no merging.') @@ -800,19 +1003,19 @@ def concat_files(timestamp, device, raw_folder, output_path, **kwargs): if len(sel_polly_files_list) == 1: logging.info("Only one file found. Nothing to merge!") - os.rename(sel_polly_files_list[0],Path(output_path,filestring)) + os.rename(sel_polly_files_list[0], Path(output_path, filestring)) return () else: ## parameters for controlling the merging process compat='override' ## Values of variable "laser_flashlamp" often changes, but those files will be merged anyway. This option picks the value from first dataset. coords='minimal' - ds = xr.open_mfdataset(sel_polly_files_list,combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) + ds = xr.open_mfdataset(sel_polly_files_list, combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) ## save to a single nc-file logging.info(f"merged nc-file '{filestring}' will be stored to '{output_path}'") logging.info("writing merged file ...") - merge_proc = write_netcdf_robust(ds=ds,out_file=Path(output_path,filestring_dummy)) + merge_proc = write_netcdf_robust(ds=ds, out_file=Path(output_path, filestring_dummy)) ds.close() @@ -820,16 +1023,15 @@ def concat_files(timestamp, device, raw_folder, output_path, **kwargs): pass else: logging.error('merging failed. Aborting') - return None + return logging.info("deleting individual .nc files ...") for el in sel_polly_files_list: logging.info(el) os.remove(el) - destination_file = Path(output_path,filestring) + destination_file = Path(output_path, filestring) if os.path.exists(destination_file): os.remove(destination_file) # Remove the existing destination file - os.rename(Path(output_path,filestring_dummy),destination_file) + os.rename(Path(output_path, filestring_dummy), destination_file) logging.info('done!') return destination_file - diff --git a/ppcpy/misc/molecular.py b/ppcpy/misc/molecular.py index 5a83eaf..3b167a2 100644 --- a/ppcpy/misc/molecular.py +++ b/ppcpy/misc/molecular.py @@ -2,6 +2,7 @@ from collections import defaultdict import numpy as np import xarray as xr +import logging # Global variable ASSUME_AIR_IDEAL:bool = True @@ -816,10 +817,10 @@ def calc_profiles(met_profiles:list, wavelengths:list=[355, 387, 407, 532, 607, input wavelength. """ - print('len mean_profiles', len(met_profiles)) + logging.debug(f"len mean_profiles: {len(met_profiles)}") shp = (len(met_profiles), met_profiles[0].height.shape[0]) - print('shape of the molecular scattering', shp) - print('for the wavelengths ', wavelengths) + logging.debug(f"shape of the molecular scattering {shp} for the wavelengths {wavelengths}.") + m_p = defaultdict(lambda: np.zeros(shp)) for i, p in enumerate(met_profiles): diff --git a/ppcpy/qc/overlapCor.py b/ppcpy/qc/overlapCor.py index f3b4359..5ba4200 100644 --- a/ppcpy/qc/overlapCor.py +++ b/ppcpy/qc/overlapCor.py @@ -24,22 +24,20 @@ def spread(data_cube): config_dict = data_cube.polly_config_dict height = data_cube.retrievals_highres['range'] time = data_cube.retrievals_highres['time64'] - print('overlapCorMode ', config_dict['overlapCorMode'], - ' overlapCalMode ', config_dict['overlapCalMode']) + logging.debug(f"overlapCorMode: {config_dict['overlapCorMode']}, overlapCalMode: {config_dict['overlapCalMode']}.") overlap = data_cube.retrievals_profile['overlap'] - print(overlap.keys()) if config_dict['overlapCorMode'] == 1: k = 'file' elif config_dict['overlapCorMode'] == 2: if config_dict['overlapCalMode'] == 1: k = 'frnr' - print("Warning: The frnr overlap calulations are very unstable.") + logging.warning("The frnr overlap calulations are very unstable.") elif config_dict['overlapCalMode'] == 2: k = 'raman' elif config_dict['overlapCorMode'] == 3: raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') - print('overlap correction source', k) + logging.info(f"overlap correction source {k}") ol_profiles = overlap[k] # TODO: add code to select only one profile #ol_profiles = [overlap[k][0]] @@ -50,36 +48,36 @@ def spread(data_cube): clFreeGrps = data_cube.clFreeGrps time_slices = [time[grp] for grp in clFreeGrps] - print(time_slices, np.ravel(time_slices)) - print(clFreeGrps) + # print(time_slices, np.ravel(time_slices)) + # print(clFreeGrps) ret = {} for channel in set(channel_per_profile): olFuncs = [o[channel] for o in ol_profiles if channel in o.keys()] time_slices_this_channel = [t for i,t in enumerate(time_slices) if channel in ol_profiles[i].keys()] - print(channel, 'len(olFuncs)', len(olFuncs), len(time_slices), len(time_slices_this_channel)) + logging.debug(f"channel {channel}, len(olFuncs) {len(olFuncs)}, len(time_slices) {len(time_slices_this_channel)}.") if len(olFuncs) > 1: logging.debug('overlap function set to time varying') olFunc_2d = np.zeros((2*len(olFuncs), height.shape[0])) - print(olFunc_2d.shape) + # print(olFunc_2d.shape) # set the estimated overlap profiles to the beginning and # end of the profile for i, f in enumerate(olFuncs): - olFunc_2d[[2*i, 2*i+1],:] = f['olFunc'] - #print(f.keys(), f['normRange']) + olFunc_2d[[2*i, 2*i+1], :] = f['olFunc'] + # print(f.keys(), f['normRange']) finterp = interp1d( np.ravel(time_slices_this_channel).astype(float), olFunc_2d, axis=0, fill_value='extrapolate', kind='nearest') olFunc_2d = finterp(time.astype(float)) - print(olFunc_2d.shape) + # print(olFunc_2d.shape) else: - #print('only one overlap function') + # print('only one overlap function') # then just use that function for the whole time period ol = olFuncs[0]['olFunc'] olFunc_2d = np.repeat(ol[np.newaxis, :], time.shape[0], axis=0) - print(olFunc_2d.shape) + # print(olFunc_2d.shape) ret[channel] = olFunc_2d return ret @@ -105,16 +103,16 @@ def apply_cube(data_cube): indxt = np.where(flag)[0] # TODO fix that error, that is for now required for debugging - #sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigTCor'][:, :, flag]) + # sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigTCor'][:, :, flag]) sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, flag]) bg_total = np.squeeze(data_cube.retrievals_highres['BGTCor'][:, flag]) if config_dict['overlapCorMode'] in [1, 2]: - print('correct overlap', wv) + logging.info(f"correct overlap {wv}") if f"{wv}_total_FR" in overlap2d.keys(): olFunc = overlap2d[f"{wv}_total_FR"] - elif f"{alt_wv[wv]}_total_FR" in overlap2d.keys(): - print(f'using {alt_wv[wv]} instead of {wv}') + elif wv in alt_wv and f"{alt_wv[wv]}_total_FR" in overlap2d.keys(): + logging.info(f'using {alt_wv[wv]} instead of {wv}') olFunc = overlap2d[f"{alt_wv[wv]}_total_FR"] else: logging.warning(f"no overlap correction function for {wv}") @@ -125,9 +123,9 @@ def apply_cube(data_cube): sigOLCor[:, :, indxt] = np.expand_dims( sigBGCor_total / olFunc, -1) BGOLCor[:, indxt] = np.expand_dims(bg_total, -1) - #print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) + # print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) heightFullOverlapCor[:, indxt] = np.expand_dims(np.take(height, idxOL), -1) - #print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) + # print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) elif config_dict['overlapCorMode'] == 3: raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') @@ -151,7 +149,7 @@ def fixLowest(overlap:np.ndarray, indexsearchmax:int, thres:float=0.05): np.split(lt, np.where(np.diff(lt) != 1)[0] + 1), key=len, reverse=True)[0] if len(longestrun) == 0: - print(f"Warning: Fix not applied for channel {channel} in cloud free group {i}.") + logging.warning(f"Fix not applied for channel {channel} in cloud free group {i}.") continue idx = np.argmin(var[longestrun]) + longestrun[0] vals['olFunc'][:idx] = vals['olFunc'][idx] diff --git a/ppcpy/qc/overlapEst.py b/ppcpy/qc/overlapEst.py index 9b425eb..55e1ae4 100644 --- a/ppcpy/qc/overlapEst.py +++ b/ppcpy/qc/overlapEst.py @@ -169,14 +169,14 @@ def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: Returns ------- - overlap : list of dicts - Per channel per cloud free period: olFunc : ndarray Overlap function. - olStd : float - Standard deviation of overlap function. - olFunc0 : ndarray + olFunc_raw : ndarray Overlap function with no smoothing. + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. """ height = data_cube.retrievals_highres['range'] diff --git a/ppcpy/qc/pollySaturationDetect.py b/ppcpy/qc/pollySaturationDetect.py index f12edde..8a23e4d 100644 --- a/ppcpy/qc/pollySaturationDetect.py +++ b/ppcpy/qc/pollySaturationDetect.py @@ -3,27 +3,30 @@ from scipy import ndimage import numpy as np -def pollySaturationDetect(data_cube, rfill=250, sigSaturateThresh=500): - """detect the bins which are fully saturated by the clouds. - - INPUTS: - data: dict - Data dictionary. See documentation for format. - - KEYWORDS: - hfill: float - Minimum range gap to fill (m). Default: 250 - sigSaturateThresh: float - Threshold of saturated signal (photon count). Default: 500 - - OUTPUTS: - flag: boolean ndarray - True indicates current range bin is saturated by clouds. - - HISTORY: - - 2018-12-21: First Edition by Zhenping - - 2019-07-08: Fix the bug of converting signal to PCR. - - 2025-05-14: translated and changed the algorithm to use scipy.ndimage + +def pollySaturationDetect(data_cube, rfill:float=250, sigSaturateThresh:float=500) -> np.ndarray: + """Detect the bins which are fully saturated by the clouds. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + rfill : float, optional + Minimum range gap to fill [m]. Default is 250. + sigSaturateThresh : float, optional + Threshold of saturated signal [photon count]. Default is 500. + + Returns + ------- + flagSaturation : boolean ndarray + True indicates current range bin is saturated by clouds. + + ** History ** + + - 2018-12-21: First Edition by Zhenping + - 2019-07-08: Fix the bug of converting signal to PCR. + - 2025-05-14: translated and changed the algorithm to use scipy.ndimage + """ logging.info('Saturation detection') @@ -37,10 +40,10 @@ def pollySaturationDetect(data_cube, rfill=250, sigSaturateThresh=500): flagSaturation = np.full(PCR.shape, False, dtype=bool) for iChannel in range(nChannels): - flag = PCR[:,:,iChannel] > sigSaturateThresh + flag = PCR[:, :, iChannel] > sigSaturateThresh # manually unmask the first 3 range gates as they might only be affected by straylight - flag[:,:3] = False - flag = ndimage.binary_closing(flag, structure=np.ones((2,hfill_bins))) - flagSaturation[:,:,iChannel] = flag + flag[:, :3] = False + flag = ndimage.binary_closing(flag, structure=np.ones((2, hfill_bins))) + flagSaturation[:, :, iChannel] = flag return flagSaturation diff --git a/ppcpy/qc/transCor.py b/ppcpy/qc/transCor.py index 46ccf1d..f1c2207 100644 --- a/ppcpy/qc/transCor.py +++ b/ppcpy/qc/transCor.py @@ -5,69 +5,91 @@ import ppcpy.retrievals.depolarization as depolarization -def transCorGHK_cube(data_cube, signal='BGCor'): - """ """ +def transCorGHK_cube(data_cube, signal:str='BGCor', collect_debug:bool=False) -> tuple: + """Perform GHK transmission correction. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + signal : str + Signal type. Default is 'BGCor'. + collect_debug : bool + If True, collects debug information. Default is False. + + Returns + ------- + sigTCor : ndarray + Transmission corrected signal [Photon counts]. + BGTCor : ndarray + Transmission corrected background. + """ config_dict = data_cube.polly_config_dict + # Use the background corrected signal as a default BGTCor = data_cube.retrievals_highres['BG'].copy() - # Store the background corrected signal sigTCor = data_cube.retrievals_highres[f'sig{signal}'].copy() - tel = 'FR' + tel = 'FR' # only done for the far-range signals for wv in [355, 532, 1064]: flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) indxt = np.where(flagt)[0] - #print(flagt, indxt) + if np.any(flagt) and np.any(flagc): - logging.info(f'and even a {wv} channel') + logging.info(f"Channel: {wv} total FR | {wv} cross FR") - sigBGCor_total = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:,:,flagt]) - bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:,flagt]) - sigBGCor_cross = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:,:,flagc]) - bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:,flagc]) + sigBGCor_total = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:, :, flagt]).copy() + bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:, flagt]).copy() + sigBGCor_cross = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:, :, flagc]).copy() + # bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:, flagc]) # TODO: Not used! - print('G', config_dict['G'][flagt], config_dict['G'][flagc]) - print('H', config_dict['H'][flagt], config_dict['H'][flagc]) - print('polCaliEta', data_cube.etaused[f'{wv}_{tel}']) + if collect_debug: + print('G', config_dict['G'][flagt], config_dict['G'][flagc]) + print('H', config_dict['H'][flagt], config_dict['H'][flagc]) + print('polCaliEta', data_cube.etaused[f'{wv}_{tel}']) # similar to voldepol_2d vdr, vdrStd = depolarization.calc_profile_vdr( - sigBGCor_total, sigBGCor_cross, - config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigBGCor_total, sigc=sigBGCor_cross, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], ) sigTCor_total, bgTCor_total = transCor_E16_channel( - sigBGCor_total, bg_total, vdr, - config_dict['H'][flagt], + sigT=sigBGCor_total, bgT=bg_total, + voldepol=vdr, + HT=config_dict['H'][flagt], ) - sigTCor[:,:,indxt] = np.expand_dims(sigTCor_total, -1) - BGTCor[:,indxt] = np.expand_dims(bgTCor_total, -1) + + sigTCor[:, :, indxt] = np.expand_dims(sigTCor_total, -1) + BGTCor[:, indxt] = np.expand_dims(bgTCor_total, -1) return sigTCor, BGTCor -def transCor_E16_channel(sigT, bgT, voldepol, HT): - """ transmission correction for the total channel using the Mattis 2009/Engelmann 2016 method + +def transCor_E16_channel(sigT:np.ndarray, bgT:np.ndarray, voldepol:np.ndarray, HT:float) -> tuple: + """Transmission correction for the total channel using the Mattis 2009/Engelmann 2016 method Parameters ---------- - sigT : array + sigT : ndarray Signal in total channel (background-corrected) - bgT : array + bgT : ndarray Background in total channel - voldepol : array + voldepol : ndarray Volume depolarization ratio HT : float Transmission ratio of total channel in GHK notation Returns ------- - sigTCor : array + sigTCor : ndarray Signal in total channel corrected for polarization induced transmission effects - bgTCor : array + bgTCor : ndarray Background of total signal @@ -92,60 +114,74 @@ def transCor_E16_channel(sigT, bgT, voldepol, HT): """ R_t = (1 - HT) / (1 + HT) - print('calculated R_t', R_t) + # print('calculated R_t', R_t) sigTCor = sigT * (1 + R_t*voldepol) / (1+voldepol) bgTCor = bgT return sigTCor, bgTCor -def transCorGHK_channel(sigT, bgT, sigC, bgC, transGT=1, transGR=1, transHT=0, transHR=-1, polCaliEta=1, polCaliEtaStd=0): +def transCorGHK_channel(sigT:np.ndarray, bgT:np.ndarray, sigC:np.ndarray, bgC:np.ndarray, transGT:float=1, transGR:float=1, + transHT:float=0, transHR:float=-1, polCaliEta:float=1, polCaliEtaStd:float=0) -> tuple: """Corrects the effect of different polarization-dependent transmission inside the total and depol channel. + Follows the matlab code at: https://github.com/PollyNET/Pollynet_Processing_Chain/blob/master/lib/qc/transCorGHK.m - INPUTS: - sigT: array - Signal in total channel. - bgT: array - Background in total channel. - sigC: array - Signal in cross channel. - bgC: array - Background in cross channel. - transGT: float - G parameter in total channel. - transGR: float - G parameter in cross channel. - transHT: float - H parameter in total channel. - transHR: float - H parameter in cross channel. - polCaliEta: float - Depolarization calibration constant (eta). - polCaliEtaStd: float - Uncertainty of the depolarization calibration constant. - - OUTPUTS: - sigTCor: array - Transmission corrected elastic signal. - bgTCor: array - Background of transmission corrected elastic signal. - - REFERENCES: - Mattis, I., Tesche, M., Grein, M., Freudenthaler, V., and Müller, D.: - Systematic error of lidar profiles caused by a polarization-dependent receiver transmission: - Quantification and error correction scheme, Appl. Opt., 48, 2742-2751, 2009. - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. - Atmos. Meas. Tech., 9, 4181–4255 (2016). - - HISTORY: - - 2021-05-27: First edition by Zhenping. - - 2024-08-14: Change to GHK parameterization by Moritz. - - 2024-12-28: AI translation - - Authors: - zhenping@tropos.de, haarig@tropos.de + Parameters + ---------- + sigT : ndarray + Signal in total channel. + bgT : ndarray + Background in total channel. + sigC : ndarray + Signal in cross channel. + bgC : ndarray + Background in cross channel. + transGT : float + G parameter in total channel. + transGR : float + G parameter in cross channel. + transHT : float + H parameter in total channel. + transHR : float + H parameter in cross channel. + polCaliEta : float + Depolarization calibration constant (eta). + polCaliEtaStd : float + Uncertainty of the depolarization calibration constant. + + Returns + ------- + sigTCor : ndarray + Transmission corrected elastic signal. + bgTCor : ndarray + Background of transmission corrected elastic signal. + + Notes + ----- + .. TODO:: Input parameter ´polCaliEtaStd´ is not used. + + References + ---------- + - Mattis, I., Tesche, M., Grein, M., Freudenthaler, V., and Müller, D.: + Systematic error of lidar profiles caused by a polarization-dependent receiver transmission: + Quantification and error correction scheme, Appl. Opt., 48, 2742-2751, 2009. + - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. + Atmos. Meas. Tech., 9, 4181–4255 (2016). + + ** History ** + + - 2021-05-27: First edition by Zhenping. + - 2024-08-14: Change to GHK parameterization by Moritz. + - 2024-12-28: AI translation + + + Authors + ------- + - zhenping@tropos.de, haarig@tropos.de """ + if sigT.shape != sigC.shape: raise ValueError("Input signals have different sizes.") diff --git a/ppcpy/retrievals/collection.py b/ppcpy/retrievals/collection.py index 6b16f72..ae848c3 100644 --- a/ppcpy/retrievals/collection.py +++ b/ppcpy/retrievals/collection.py @@ -1,5 +1,3 @@ - - import numpy as np def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: @@ -14,12 +12,12 @@ def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: signal : ndarray Signal strength. bg : ndarray - Background noise. - + Background noise. + Returns ------- SNR : ndarray - Signal-to-noise ratio. For negative signal values, the SNR is set to 0. + Signal-to-noise ratio. For negative signal values the SNR is set to 0. References ---------- @@ -30,12 +28,22 @@ def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: Notes ----- + - `signal` and `background` must be in Photon counts! **History** - 2021-04-21: First edition by Zhenping - 2024-12-10: Translated with AI, moved to own function + + Example + ------- + >>> # SNR for time-height array + >>> SNR_array = calc_snr(signal_array, bg_array) + + >>> # SNR for aggregated signal + >>> SNR = calc_snr(np.sum(signal_array, keepdims=True), np.sum(bg_array, keepdims=True)) """ + tot = signal + 2 * bg tot[tot <= 0] = np.nan @@ -43,6 +51,4 @@ def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: SNR[SNR <= 0] = 0 SNR[np.isnan(SNR)] = 0 - return SNR - - + return SNR \ No newline at end of file diff --git a/ppcpy/retrievals/depolarization.py b/ppcpy/retrievals/depolarization.py index fbff880..903c1e0 100644 --- a/ppcpy/retrievals/depolarization.py +++ b/ppcpy/retrievals/depolarization.py @@ -21,30 +21,46 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: Notes ----- + .. TODO:: Check which smoothing shoule be used here! + **History** - + - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter """ + return uniform_filter(signal, window_len) -def voldepol_cldFreeGrps(data_cube, ret_prof_name): - """ +def voldepol_cldFreeGrps(data_cube, ret_prof_name:str) -> dict: + """... + + Parameters + ---------- + ret_prof_name : str + ... + + Returns + ------- + opt_profiles : dict + ... + + Notes + ----- + .. TODO:: Finish the docstring. .. TODO:: Should the GHK-Transmission corrected (TCor) or the Background corrected (BGCor) signal be used for calculating the volume depolarisation ratio? - - - With the GHK formula the BGCor signal has to be used (in the current implementation, the voldepol is even - needed to perform the polarization-transmission-correction + - With the GHK formula the BGCor signal has to be used (in the current implementation, + the voldepol is even needed to perform the polarization-transmission-correction. + .. TODO:: Is the return in this function actually needed?? """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] - print('no_profiles ', len(opt_profiles)) - print(opt_profiles[0].keys()) + config_dict = data_cube.polly_config_dict.copy() + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() + logging.debug(f"no_profiles: {len(opt_profiles)}") + logging.debug(f"{opt_profiles[0].keys()}") - signal = 'TCor' + # signal = 'TCor' signal = 'BGCor' for i, cldFree in enumerate(data_cube.clFreeGrps): @@ -53,61 +69,67 @@ def voldepol_cldFreeGrps(data_cube, ret_prof_name): wv, t, tel = channel.split('_') flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) - #indxt = np.where(flagt)[0] + # indxt = np.where(flagt)[0] retrieval = opt_profiles[i][channel]['retrieval'] if np.any(flagt) and np.any(flagc): logging.info(f'voldepol at channel {wv} cldFree {i} {cldFree}') - sigt = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i,:,flagt]) - #bgt = np.nansum(np.squeeze( - # data_cube.retrievals_highres[f'BG{signal}'][slice(*cldFree),data_cube.gf(wv, 'total', tel)]), axis=0) - sigc = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i,:,flagc]) + sigt = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, flagt]) + # bgt = np.nansum(np.squeeze( + # data_cube.retrievals_highres[f'BG{signal}'][slice(*cldFree),data_cube.gf(wv, 'total', tel)]), axis=0) + sigc = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, flagc]) - print(channel, data_cube.etaused[f'{wv}_{tel}']) + logging.debug(f"{channel} {data_cube.etaused[f'{wv}_{tel}']}") vdr, vdrStd = calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], window=config_dict[f'smoothWin_{retrieval}_{wv}'] - ) + ) opt_profiles[i][channel]['vdr'] = vdr opt_profiles[i][channel]['vdrStd'] = vdrStd if np.isnan(data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd']).any(): + logging.warning("No valid refHInd found, skipping retrieval for this channel.") continue + mdr, mdrStd, flgaDeftMdr = get_MDR( - vdr, vdrStd, data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'], + vdr=vdr, vdrStd=vdrStd, + refHInd=data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'] ) if config_dict["flagUseTheoreticalMDR"]: - logging.info("use the theoretical MDR value") + logging.info("Use the theoretical MDR value.") mdr = data_cube.polly_config_dict[f"molDepol{wv}"] - print(f"est. mdr {channel} {mdr} {mdrStd}") + + logging.debug(f"est. mdr {channel} {mdr} {mdrStd}") opt_profiles[i][channel]['mdr'] = mdr opt_profiles[i][channel]['mdrStd'] = mdrStd # experimental code to calculate the mdr without smoothing(?) vdr, vdrStd = calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], window=1 - ) + ) mdr, mdrStd, flgaDeftMdr = get_MDR( - vdr, vdrStd, data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'], + vdr=vdr, vdrStd=vdrStd, + refHInd=data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'] ) - print(f"est. mdr {channel} {mdr} {mdrStd} (smooth1)") + logging.debug(f"est. mdr {channel} {mdr} {mdrStd} (smooth1)") - print(opt_profiles[i][channel].keys()) + logging.debug(f"{opt_profiles[i][channel].keys()}") return opt_profiles -def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, - voldepol_error, window=1, flag_smooth_before=True): -#def polly_vdr_ghk(sig_tot, sig_cross, GT, GR, HT, HR, eta, -# voldepol_error_a0, voldepol_error_a1, voldepol_error_a2, -# smooth_window=1, flag_smooth_before=True): +def calc_profile_vdr(sigt:np.ndarray, sigc:np.ndarray, Gt:float, Gr:float, Ht:float, Hr:float, eta:float, + voldepol_error:float, window:int=1, flag_smooth_before:bool=True) -> tuple: """Calculate volume depolarization ratio using GHK parameters. Parameters @@ -118,11 +140,11 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, Signal strength of the cross channel [photon count]. Gt : float G parameter in the total channel. - Gc : float + Gr : float G parameter in the cross channel. Ht : float H parameter in the total channel. - Hc : float + Hr : float H parameter in the cross channel. eta : float Depolarization calibration constant. @@ -151,9 +173,14 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. Atmos. Meas. Tech., 9, 4181–4255 (2016). + Notes ----- - + - Be awere: `sigT = 0` will cause `vol_depol` and `vol_depol_std` to be NaN! + .. TODO:: Should consider implementing something to avoid NaN values in this function, + as these will also harm the qulity of the retrievals that depend on the + transmission corrected signal (ei. introduce more NaN's). + **History** - 2018-09-02: First edition by Zhenping @@ -163,7 +190,7 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, """ - print(f"G {Gt} {Gr} H {Ht} {Hr} Eta {eta} error {voldepol_error} Window {window} ") + logging.info(f"G {Gt} {Gr} H {Ht} {Hr} Eta {eta} error {voldepol_error} Window {window} ") # Smooth signals before or after ratio calculation sig_ratio = sigc / sigt if window > 1: # smoothing with a window size of 1 equals no smoothing @@ -184,60 +211,105 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, return vol_depol, vol_depol_std -def get_MDR(vdr, vdrStd, refHInd): - """get the vdr at reference height - in the matlab pollynet processing chain this is done by recalculating vdr for the - reference height chunk - (Pollynet_Processing_Chain/lib/calibration/pollyMDRGHK.m) +def get_MDR(vdr:np.ndarray, vdrStd:np.ndarray, refHInd:list) -> float: + """Get the vdr at reference height. - Assuming that it is more efficient to use the precalculated vdr + Parameters + ---------- + vdr : ndarray + Volume depolarization ratio. + vdrStd : ndarray + Uncertainty of the volume depolarization ratio. + refHInd : list + Reference height indcies [bottum, top]. + + Returns + ------- + mdr : ndarray + Mean volume depolarization ratio at reference height. + mdrStd : ndarray + Mean uncertainty of the volume depolarization ratio at reference height. + bool + False. + + Notes + ----- + - in the matlab pollynet processing chain this is done by recalculating vdr for the + reference height chunk (Pollynet_Processing_Chain/lib/calibration/pollyMDRGHK.m) + - Assuming that it is more efficient to use the precalculated vdr + - The snr criterion is missing for this very first version. + + ** History ** - The snr criterion is missing for this very first version """ + mdr = np.mean(vdr[slice(*refHInd)]) mdrStd = np.mean(vdrStd[slice(*refHInd)]) return mdr, mdrStd, False -def pardepol_cldFreeGrps(data_cube, ret_prof_name): - """ +def pardepol_cldFreeGrps(data_cube, ret_prof_name:str) -> dict: + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + ret_prof_name : str + ... + + Returns + ------- + opt_porfiles : dict + ... + + Notes + ----- + .. TODO:: + - Finish docsting. + - Is the return here necessary? + - Cleare unused variables. """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] - print('no_profiles ', len(opt_profiles)) - print(opt_profiles[0].keys()) - signal = 'BGCor' + # config_dict = data_cube.polly_config_dict # <-- Not used! + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() + logging.debug(f"no_profiles {len(opt_profiles)}") + logging.debug(f"{opt_profiles[0].keys()}") + # signal = 'BGCor' # <-- Not used! for i, cldFree in enumerate(data_cube.clFreeGrps): cldFree = cldFree[0], cldFree[1] + 1 for channel in opt_profiles[i]: wv, t, tel = channel.split('_') - print(f"=== {channel } ==============================================================") + logging.info(f"Channel: {channel}.") flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) - #indxt = np.where(flagt)[0] - retrieval = opt_profiles[i][channel]['retrieval'] + # indxt = np.where(flagt)[0] # <-- Not used! + # retrieval = opt_profiles[i][channel]['retrieval'] # <-- Not used! if np.any(flagt) and np.any(flagc) and 'aerBsc' in opt_profiles[i][channel]: - logging.info(f'pardepol at channel {wv} cldFree {i} {cldFree}') + logging.info(f"pardepol at channel {wv} cldFree {i} {cldFree}") pdr, pdrStd = calc_pdr( - opt_profiles[i][channel]['vdr'], opt_profiles[i][channel]['vdrStd'], - opt_profiles[i][channel]['aerBsc'], np.ones_like(opt_profiles[i][channel]['aerBscStd'])*1e-7, - data_cube.mol_profiles[f'mBsc_{wv}'][i,:], opt_profiles[i][channel]['mdr'], - opt_profiles[i][channel]['mdrStd'], + vol_depol=opt_profiles[i][channel]['vdr'], + vol_depol_std=opt_profiles[i][channel]['vdrStd'], + aer_bsc=opt_profiles[i][channel]['aerBsc'], + aer_bsc_std=np.ones_like(opt_profiles[i][channel]['aerBscStd'])*1e-7, # TODO: Hard coded! + mol_bsc=data_cube.mol_profiles[f'mBsc_{wv}'][i, :], + mol_depol=opt_profiles[i][channel]['mdr'], + mol_depol_std=opt_profiles[i][channel]['mdrStd'] ) opt_profiles[i][channel]['pdr'] = pdr opt_profiles[i][channel]['pdrStd'] = pdrStd - #print('after ', opt_profiles[i][channel].keys()) + # print('after ', opt_profiles[i][channel].keys()) return opt_profiles -def calc_pdr(vol_depol, vol_depol_std, aer_bsc, aer_bsc_std, mol_bsc, mol_depol, mol_depol_std): +def calc_pdr(vol_depol:np.ndarray, vol_depol_std:np.ndarray, aer_bsc:np.ndarray, aer_bsc_std:np.ndarray, + mol_bsc:np.ndarray, mol_depol:float, mol_depol_std:float) -> tuple: """Calculate the particle depolarization ratio and estimate its standard deviation. Parameters @@ -266,7 +338,8 @@ def calc_pdr(vol_depol, vol_depol_std, aer_bsc, aer_bsc_std, mol_bsc, mol_depol, References ---------- - - Freudenthaler, V., et al., Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006, Tellus B, 61, 165-179, 2009. + - Freudenthaler, V., et al., Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006, + Tellus B, 61, 165-179, 2009. Notes ----- diff --git a/ppcpy/retrievals/highres.py b/ppcpy/retrievals/highres.py index 31b9821..78930d0 100644 --- a/ppcpy/retrievals/highres.py +++ b/ppcpy/retrievals/highres.py @@ -18,7 +18,22 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): If Ture, calculate the attbsc for FR and NR channels. Default is True. collect_debug : bool, optional If True, collects debug information. Default is False. + + Yeilds + ------ + data_cube.retrievals_highres[f"attBsc_{channel}"] : np.ndarray + Attenuated backscatter per channel. + Notes + ----- + - If ´nr=True´ also yeild the attenuated backscatter of the near-range + channels. + - In addition to the standard far-range channels the function also yeilds + the attenuated backscatter of the overlap corrected far-range channels. + + ..TODO:: is it correct to transmission correct the overlap corrected signals + before calculating their attenuated backscatter? Are not the OL + signals already transmission corrected? """ rgs = data_cube.retrievals_highres['range'] @@ -51,7 +66,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): # experimental, the calibration constant requires the OL corrected signal if 'sigOLCor' in data_cube.retrievals_highres: - print(f"Exprimental, attenuated backscatter solution for {channel}") + logging.warning(f"Exprimental, attenuated backscatter solution for {channel}") sigOLTCor, _ = transCor.transCorGHK_cube(data_cube, signal='OLCor') channels = [(355, 'total', 'FR'), (532, 'total', 'FR'), (1064, 'total', 'FR')] for wv, t, tel in channels: @@ -79,6 +94,11 @@ def voldepol_2d(data_cube): data_cube : object Main PicassoProc object + Yeilds + ------ + data_cube.retrievals_highres[f"voldepol_{wv}_total_{tel}"] : np.ndarray + Time-hight volume depolarization ratio at wavelengths: + 353 nm, 532 nm, 1064 nm, and 532 nm DFOV. """ config_dict = data_cube.polly_config_dict @@ -87,7 +107,7 @@ def voldepol_2d(data_cube): (532, 'FR'), (355, 'FR'), (1064, 'FR')] if '532_DFOV' in data_cube.etaused: channels += [(532, 'DFOV')] - print('voldepol also for DFOV') + logging.info("voldepol also for DFOV") for wv, tel in channels: if tel == 'DFOV': diff --git a/ppcpy/retrievals/klettfernald.py b/ppcpy/retrievals/klettfernald.py index 82722ff..bcdda36 100644 --- a/ppcpy/retrievals/klettfernald.py +++ b/ppcpy/retrievals/klettfernald.py @@ -38,33 +38,33 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b retrieval : str Name of retrieval type, eg. 'klett'. signal : str - Name of the signal used for the retrievals, eg. 'TCor'. + Name of the signal used for the retrieval, eg. 'TCor'. + refBeta : float + Reference value used for the retrieval [m^{-1}Sr^{-1}]. Notes ----- + .. TODO:: Should sigBGCor, sigTCor or RCS be used for the Klett retrievals? **History** - xxxx-xx-xx: TODO: First edition by ... - 2026-02-09: Modified and cleaned by Buholdt - - .. TODO:: Should sigBGCor, sigTCor or RCS be used for the Klett retrievals? """ - height = data_cube.retrievals_highres['range'] - config_dict = data_cube.polly_config_dict + height = data_cube.retrievals_highres['range'].copy() + config_dict = data_cube.polly_config_dict.copy() logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') logging.warning(f'at 10km height this is a difference of about 4 indices') opt_profiles = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Klett retrieval') + # print('Starting Klett retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) # Define channels to run the retrieval for channels = [(532, 'total', 'FR'), (355, 'total', 'FR'), (1064, 'total', 'FR')] @@ -73,7 +73,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b for wv, t, tel in channels: if np.any(data_cube.gf(wv, t, tel)): - print(f'== {wv}, {t}, {tel} klett =================================') + logging.info(f"Channel: {wv}, {t}, {tel} klett.") # Telescope type dependent configurations if tel == 'NR': @@ -81,8 +81,6 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b keyminSNR = 'minRefSNR_NR_' key_LR = 'LR_NR_' refBeta = config_dict[f"refBeta_NR_{wv}"] if f"refBeta_NR_{wv}" in config_dict else None - # TODO seperate klett and raman refBeta in config file? - # refBeta = config_dict[f"refBeta_NR_klett_{wv}"] if f"refBeta_NR_klett_{wv}" in config_dict else None else: key_smooth = 'smoothWin_klett_' keyminSNR = 'minRefSNR' @@ -90,18 +88,18 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b refBeta = config_dict[f'refBeta{wv}'] # Elastic signals - sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]) - bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] + sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]).copy() + bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]).copy() + molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() if np.isnan(sig).any(): # Current temporary version of fernald() does not support NaN values in the signal. - print(f'NaN-values detected in signal {signal}, skipping Klett retrieval for this channel.') + logging.warning(f"NaN-values detected in signal {signal}, skipping Klett retrieval for this channel.") continue # Reference height refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] if np.isnan(refHInd).any(): - print('No valid refHInd found, skipping Klett retrieval for this channel.') + logging.warning("No valid refHInd found, skipping Klett retrieval for this channel.") continue # Calculate SNR in the reference height @@ -112,7 +110,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b # Checking SNR treshold if SNRRef < config_dict[f'{keyminSNR}{wv}']: - print('Signal is too noisy at the reference height, skipping Klett retrival for this channel.', SNRRef, config_dict[f'{keyminSNR}{wv}']) + logging.warning("Signal is too noisy at the reference height, skipping Klett retrival for this channel.") continue if refBeta is None and tel == 'NR': @@ -121,10 +119,10 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b if 'aerBsc' in opt_profiles[i][f'{wv}_{t}_FR']: refBeta = np.nanmean(opt_profiles[i][f'{wv}_{t}_FR']['aerBsc'][refHInd[0]:refHInd[1] + 1]) else: - print('No valid refBeta found, skipping Klett retrieval for this channel') + logging.warning("No valid refBeta found, skipping Klett retrieval for this channel.") continue else: - print('No valid refBeta found, skipping Klett retrieval for this channel') + logging.warning("No valid refBeta found, skipping Klett retrieval for this channel.") continue # Print debug info @@ -227,6 +225,7 @@ def fernald( propagating NaN-values in the backward and farward retrievals. """ + # Convert units height = height / 1e3 # Convert to km molBsc = np.array(molBsc) * 1e3 # Convert to km^-1 sr^-1 diff --git a/ppcpy/retrievals/quasi.py b/ppcpy/retrievals/quasi.py index 0c0de04..4ff7154 100644 --- a/ppcpy/retrievals/quasi.py +++ b/ppcpy/retrievals/quasi.py @@ -27,6 +27,7 @@ def quasi_pdr(data_cube, wvs:list=[532], version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-27: Added flag to use only valid data + """ rgs = data_cube.retrievals_highres['range'] @@ -108,6 +109,7 @@ def quasi_angstrom(data_cube, version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-21: Fixed calculation + """ t = 'total' @@ -140,6 +142,7 @@ def target_cat(data_cube, version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-21: Added dependency on Quality mask + """ config_dict = data_cube.polly_config_dict @@ -295,6 +298,7 @@ def target_classify(height:np.ndarray, attBeta532:np.ndarray, quasiBsc1064:np.nd - 2021-06-05: First edition by Zhenping - 2025-03-25: AI based translation to python - 2026-05-21: Fixed dimension issue + """ # Default parameter values @@ -413,10 +417,11 @@ def detect_liquid_bits(height:np.ndarray, bsc1064:np.ndarray, cloudThresBsc1064: - 2021-06-05: First edition by Zhenping - 2025-03-25: AI based translation to python - 2026-05-21: Fixed dimension issue + """ logging.warning("Still in testing phase, may show strange classifications.") - # bsc1064 = np.nan_to_num(bsc1064) # Replace NaN 0 and inf with large positive or negative numbers + # bsc1064 = np.nan_to_num(bsc1064) # Replace NaN with 0 and inf with large positive or negative numbers bsc1064[~np.isfinite(bsc1064)] = 0 # Replace NaN and inf with 0 flagLiquid = np.zeros_like(bsc1064, dtype=bool) diff --git a/ppcpy/retrievals/quasiV1.py b/ppcpy/retrievals/quasiV1.py index 06aaab4..41f16b6 100644 --- a/ppcpy/retrievals/quasiV1.py +++ b/ppcpy/retrievals/quasiV1.py @@ -20,6 +20,7 @@ def quasi_bsc(data_cube): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python + """ rgs = data_cube.retrievals_highres['range'] @@ -115,6 +116,7 @@ def quasi_retrieval(height:np.ndarray, att_beta:np.ndarray, molExt:np.ndarray, - 2018-12-25: First edition by Zhenping - 2019-03-31: Added the keyword 'nIters' to control iteration times. - 2025-03-21: AI based translation to python and debugging + """ # Compute differential heights diff --git a/ppcpy/retrievals/quasiV2.py b/ppcpy/retrievals/quasiV2.py index 53f0f51..a19aef2 100644 --- a/ppcpy/retrievals/quasiV2.py +++ b/ppcpy/retrievals/quasiV2.py @@ -44,7 +44,7 @@ def quasi_bsc(data_cube): if config_dict['flagOnlyUseValidQuasiData']: quality_mask = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, data_cube.gf(wv, t, tel)]) att_beta_qsi[quality_mask != 0] = np.nan - # TODO check if halving the window is needed: Yes at the moment they need to be halved. + # TODO check if halving the window is needed: Yes, at the moment they need to be halved. smooth_t = int(np.array(config_dict['quasi_smooth_t'])[data_cube.gf(wv, t, tel)][0] / 2) smooth_h = int(np.array(config_dict['quasi_smooth_h'])[data_cube.gf(wv, t, tel)][0] / 2) att_beta_qsi = helper.smooth2a(att_beta_qsi, smooth_t, smooth_h) @@ -54,7 +54,7 @@ def quasi_bsc(data_cube): if config_dict['flagOnlyUseValidQuasiData']: quality_mask_r = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, data_cube.gf(wv_r, t_r, tel_r)]) att_beta_r_qsi[quality_mask_r != 0] = np.nan - # TODO check if halving the window is needed: Yes at the moment they need to be halved. + # TODO check if halving the window is needed: Yes, at the moment they need to be halved. smooth_t_r = int(np.array(config_dict['quasi_smooth_t'])[data_cube.gf(wv_r, t_r, tel_r)][0] / 2) smooth_h_r = int(np.array(config_dict['quasi_smooth_h'])[data_cube.gf(wv_r, t_r, tel_r)][0] / 2) att_beta_r_qsi = helper.smooth2a(att_beta_r_qsi, smooth_t_r, smooth_h_r) diff --git a/ppcpy/retrievals/ramanhelpers.py b/ppcpy/retrievals/ramanhelpers.py index 2158c67..d8c9a9b 100644 --- a/ppcpy/retrievals/ramanhelpers.py +++ b/ppcpy/retrievals/ramanhelpers.py @@ -3,7 +3,8 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: - """calculates the slope of the signal with a moving slope. + """Calculates the slope of the signal with a moving slope. + This is a wrapper for the `movingslope` function to make it compatible with height-independent smoothing windows. @@ -32,6 +33,7 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndar - 2018-08-03: First edition by Zhenping. """ + if winWidth is None: raise ValueError("Not enough inputs. `winWidth` must be specified.") @@ -52,16 +54,19 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndar return slope -def moving_smooth_varied_win(signal, winWidth): - """ """ + +def moving_smooth_varied_win(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: + """.. TODO:: Implement function!""" raise NotImplementedError -def moving_linfit_varied_win(height, signal, winWidth): - """ """ + +def moving_linfit_varied_win(height:np.ndarray, signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: + """.. TODO:: Implement function!""" raise NotImplementedError + def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float=1) -> np.ndarray: - """estimates the local slope of a sequence of points using a sliding window. + """Estimates the local slope of a sequence of points using a sliding window. Parameters ---------- @@ -86,7 +91,9 @@ def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float= **History** - Original MATLAB implementation by John D'Errico (woodchips@rochester.rr.com) + """ + vec = np.asarray(vec) n = len(vec) @@ -130,6 +137,7 @@ def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float= # Scale by spacing return Dvec / dt + def _getcoef(t:np.ndarray, supportlength:int, modelorder:int) -> np.ndarray: """Helper function to compute the filter coefficients. @@ -147,13 +155,14 @@ def _getcoef(t:np.ndarray, supportlength:int, modelorder:int) -> np.ndarray: coef : ndarray Filter coefficients for slope estimation. """ + A = np.vander(t.flatten(), modelorder + 1, increasing=True) pinvA = np.linalg.pinv(A) return pinvA[1] # Only the linear term def sigGenWithNoise(signal:np.ndarray, noise:np.ndarray=None, nProfile:int=1, method:str='norm') -> np.ndarray: - """SIGGENWITHNOISE generate noise-containing signal with a certain noise-adding algorithm. + """Generate noise-containing signal with a certain noise-adding algorithm. Parameters ---------- @@ -180,7 +189,9 @@ def sigGenWithNoise(signal:np.ndarray, noise:np.ndarray=None, nProfile:int=1, me - 2021-06-13: First edition by Zhenping. - 2026-02-04: Modifications to reduce computational time, Buholdt + """ + if noise is None: noise = np.sqrt(signal) From a106a9bc5813189e7de826b8093ee68a87db06ef Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Tue, 28 Jul 2026 18:55:50 +0200 Subject: [PATCH 12/13] update to the documentation in overlapCor --- ppcpy/interface/picassoProc.py | 22 ++-- ppcpy/io/loadConfigs.py | 2 +- ppcpy/qc/overlapCor.py | 181 +++++++++++++++++++++++---------- 3 files changed, 144 insertions(+), 61 deletions(-) diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index 2009881..67169dd 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -382,7 +382,7 @@ def setChannelTags(self): .. TODO:: - Several channels are still missing channelFlags. - We are not consistant in the naming scheam of the RR-channels. - - Need to implement new config flags for fluorescence and HSRL. + - Need to implement new config flags for fluorescence and high intensity channels. """ ChannelTags = pollyChannelTags.pollyChannelTags( @@ -735,14 +735,18 @@ def aggregate_profiles(self, var:str|list=None, func=np.nansum): logging.info(f"Aggregating variable: {variable} ...") if variable in self.retrievals_highres: if variable == "RCS": - self.retrievals_profile[variable] = pollyPreprocess.calculate_rcs( - signal=pollyPreprocess.photonCount2PCR( - signal=preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor', func), - mShots=preprocprofiles.aggregate_clFreeGrps(self, 'mShots', func), - hRes=self.rawdata_dict['measurement_height_resolution']['var_data'] - ), - ranges=self.retrievals_highres['range'] - ) + if self.polly_config_dict['flagPicassoComparison']: + self.retrievals_profile[variable] = \ + preprocprofiles.aggregate_clFreeGrps(self, variable, np.nanmean) + else: + self.retrievals_profile[variable] = pollyPreprocess.calculate_rcs( + signal=pollyPreprocess.photonCount2PCR( + signal=preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor', func), + mShots=preprocprofiles.aggregate_clFreeGrps(self, 'mShots', func), + hRes=self.rawdata_dict['measurement_height_resolution']['var_data'] + ), + ranges=self.retrievals_highres['range'] + ) else: self.retrievals_profile[variable] = \ preprocprofiles.aggregate_clFreeGrps(self, variable, func) diff --git a/ppcpy/io/loadConfigs.py b/ppcpy/io/loadConfigs.py index d44c80e..7293895 100644 --- a/ppcpy/io/loadConfigs.py +++ b/ppcpy/io/loadConfigs.py @@ -205,7 +205,7 @@ def loadPollyConfig(polly_config_file:str, polly_default_config_file:str) -> dic ----- .. TODO:: - Maybe change `polly_default_config_file` to `polly_global_config_file` - as the defualt on is no longer used. + as the defualt one is no longer used. """ polly_default_config_file_path = Path(polly_default_config_file) diff --git a/ppcpy/qc/overlapCor.py b/ppcpy/qc/overlapCor.py index 5ba4200..eb1946a 100644 --- a/ppcpy/qc/overlapCor.py +++ b/ppcpy/qc/overlapCor.py @@ -7,140 +7,219 @@ def spread(data_cube): - """select the correct overlap method, spread the profiles to 2d for each wavelength + """Select the correct overlap method, spread the profiles to 2d for each wavelength. design decision for now: drop the signal glue option (overlapCorMode == 3) - in the matlab version any olFunc is additionally smoothed with - `olSm = smooth(olFuncDeft, p.Results.overlapSmWin, 'sgolay', 2);` - (e.g. here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/pollyOLCor.m#L106) - This should probably be done more explicitly + Parameters + ---------- + data_cube : object + Main PicassoProc object. + + Returns + ------- + dict + 2d overlap function per channel. + + Notes + ----- + - in the matlab version any olFunc is additionally smoothed with + `olSm = smooth(olFuncDeft, p.Results.overlapSmWin, 'sgolay', 2);` + (e.g. here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/pollyOLCor.m#L106) + This should probably be done more explicitly. + - Also the 'glueing' in function [sigCor] = olCor(sigFR, overlap, height, normRange) + seems wired (https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/olCor.m#L34). + + .. TODO:: Using different overlap function at different periods of the signal introduces harsh transitions as long as the overlap functions + are not stable. Look into options for smoothing the overlap function in the time dimension to ease the transition from + one period to another. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: Translated to python. - Also the 'glueing' in function [sigCor] = olCor(sigFR, overlap, height, normRange) - seems wired - (https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/olCor.m#L34) """ + + logging.info("Computing 2d overlap function.") config_dict = data_cube.polly_config_dict height = data_cube.retrievals_highres['range'] time = data_cube.retrievals_highres['time64'] - logging.debug(f"overlapCorMode: {config_dict['overlapCorMode']}, overlapCalMode: {config_dict['overlapCalMode']}.") - overlap = data_cube.retrievals_profile['overlap'] + + ## Get overlap mode if config_dict['overlapCorMode'] == 1: - k = 'file' + logging.info("overlapCorMode = 1 --> using overlap function form file.") + olMode = 'file' elif config_dict['overlapCorMode'] == 2: if config_dict['overlapCalMode'] == 1: - k = 'frnr' + logging.info("overlapCorMode = 2, overlapCalMode = 1 --> using overlap function calculated thorugh the FRNR-method.") + logging.warning("FRNR calculated overlap functions may be unstable.") + olMode = 'frnr' logging.warning("The frnr overlap calulations are very unstable.") elif config_dict['overlapCalMode'] == 2: - k = 'raman' + logging.info("overlapCorMode = 2, overlapCalMode = 2 --> using overlap function calculated thorugh the Raman-method.") + olMode = 'raman' elif config_dict['overlapCorMode'] == 3: + logging.critical('overlapCorMode 3 not implemented, see docstring for further information') raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') - logging.info(f"overlap correction source {k}") - ol_profiles = overlap[k] - # TODO: add code to select only one profile - #ol_profiles = [overlap[k][0]] + + ol_profiles = overlap[olMode] + # .. TODO:: Add code to select only one profile. + # ol_profiles = [overlap[olMode][0]] - # get the channel information for all the cloud free profiles - # and convert into a plain list + ## get the channel information for all the cloud free profiles and convert into a plain list channel_per_profile = list(itertools.chain(*[list(e.keys()) for e in ol_profiles])) clFreeGrps = data_cube.clFreeGrps time_slices = [time[grp] for grp in clFreeGrps] - # print(time_slices, np.ravel(time_slices)) - # print(clFreeGrps) ret = {} for channel in set(channel_per_profile): + logging.info(f"channel: {channel.replace('_', ' ')}") olFuncs = [o[channel] for o in ol_profiles if channel in o.keys()] - time_slices_this_channel = [t for i,t in enumerate(time_slices) if channel in ol_profiles[i].keys()] - logging.debug(f"channel {channel}, len(olFuncs) {len(olFuncs)}, len(time_slices) {len(time_slices_this_channel)}.") + time_slices_this_channel = [t for i, t in enumerate(time_slices) if channel in ol_profiles[i].keys()] + logging.debug(f"# of olFuncs: {len(olFuncs)}, # of time slices: {len(time_slices)}, # of time slices for this channel: {len(time_slices_this_channel)}") + logging.debug(f"time slices for this channel: {time_slices_this_channel}") if len(olFuncs) > 1: - logging.debug('overlap function set to time varying') + ## Use different overlap function for different times, centered around theire cloud free period. + logging.info(f'Using time-varying ovrlap function for channel for channel: {channel.replace('_', ' ')}.') olFunc_2d = np.zeros((2*len(olFuncs), height.shape[0])) - # print(olFunc_2d.shape) - # set the estimated overlap profiles to the beginning and - # end of the profile + + ## Set the estimated overlap profiles to the beginning and end of the profile for i, f in enumerate(olFuncs): olFunc_2d[[2*i, 2*i+1], :] = f['olFunc'] - # print(f.keys(), f['normRange']) + finterp = interp1d( - np.ravel(time_slices_this_channel).astype(float), - olFunc_2d, axis=0, - fill_value='extrapolate', kind='nearest') + x=np.ravel(time_slices_this_channel).astype(float), + y=olFunc_2d, + axis=0, + fill_value='extrapolate', + kind='nearest' + ) olFunc_2d = finterp(time.astype(float)) - # print(olFunc_2d.shape) + else: - # print('only one overlap function') - # then just use that function for the whole time period + ## Use same olFunc function for all timestamps. + logging.info(f'Using time-constant overlap function for channel: {channel.replace('_', ' ')}.') ol = olFuncs[0]['olFunc'] olFunc_2d = np.repeat(ol[np.newaxis, :], time.shape[0], axis=0) - # print(olFunc_2d.shape) + ret[channel] = olFunc_2d return ret def apply_cube(data_cube): - """ + """Apply overlap function to 2d (time, height) signal. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + + Returns + ------- + sigOLCor : ndarray + Overlap corrected signal (time, height). + BGOLCor : ndarray + Overlap corrected background (time, ). + heightFullOverlapCor : ndarray + Height of full overlap [m?]. + + Notes + ----- + - Shoud we apply the overlap correction on the GHK-Transmission corrected signal or the Background Corrrected signal?? + Currentlly we are using a wired mix here. sigOLCor is inintialized with the sigTCor but the correction is applied + based on the sigBGCor. The correction to the BG is alwaysed based on the BGTCor. + - Also, in highres the GHK-Transmission correction is again applied on sigOLCor before the OC_attBsc is calculated. + + .. TODO:: At times the retrieved overlap function is outside the range [0, 1], the values of the function should + be checked and corrected before applying. Currnetly only values less then 0.07 are corrected (set to NaN). + + .. TODO:: This function could be easaly vectorized by cunstructing all the 2d overlap function into a 3d Matrix / Tensor + Channels that do not have overlap function can be replaced by ones. This alongside the use of alternative overlap + functions (using 355 insted of 386 etc.) can be handled in the construction of the 3d Matrix / Tensor. + Then the correction itself can be applied like this: sigOLCor = sigTCor / olFunc_3d + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: Translated to python. """ + height = data_cube.retrievals_highres['range'] config_dict = data_cube.polly_config_dict - BGOLCor = data_cube.retrievals_highres['BGTCor'].copy() - heightFullOverlapCor = np.repeat( - np.array(config_dict['heightFullOverlap'])[np.newaxis, :], - BGOLCor.shape[0], axis=0) + # .. TODO:: why are we using sigTCor as the basis when we update it with the sigBGCor is used to apply the correction?? sigOLCor = data_cube.retrievals_highres['sigTCor'].copy() + BGOLCor = data_cube.retrievals_highres['BGTCor'].copy() overlap2d = data_cube.retrievals_highres['overlap2d'] + heightFullOverlapCor = np.repeat( + np.array(config_dict['heightFullOverlap'])[np.newaxis, :], + BGOLCor.shape[0], axis=0) + alt_wv = {607: 532, 387: 355, 1064: 532} for wv in [355, 387, 532, 607, 1064]: flag = data_cube.gf(wv, 'total', 'FR') indxt = np.where(flag)[0] - # TODO fix that error, that is for now required for debugging + # .. TODO:: fix that error, that is for now required for debugging + # .. TODO:: should the we use sigBGCor or sigTCor here??? # sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigTCor'][:, :, flag]) sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, flag]) - bg_total = np.squeeze(data_cube.retrievals_highres['BGTCor'][:, flag]) + bg_total = np.squeeze(data_cube.retrievals_highres['BGTCor'][:, flag]) # TODO: This is still the GHK-transmission corrected background... if config_dict['overlapCorMode'] in [1, 2]: - logging.info(f"correct overlap {wv}") + logging.info(f'Applying overlap correction for channel: {wv} total FR.') + + ## Extract overlap function if f"{wv}_total_FR" in overlap2d.keys(): olFunc = overlap2d[f"{wv}_total_FR"] elif wv in alt_wv and f"{alt_wv[wv]}_total_FR" in overlap2d.keys(): - logging.info(f'using {alt_wv[wv]} instead of {wv}') + logging.info(f'Using overlap function for channel {alt_wv[wv]} total FR.') olFunc = overlap2d[f"{alt_wv[wv]}_total_FR"] else: - logging.warning(f"no overlap correction function for {wv}") + logging.warning(f"No overlap function found for channel {wv} total FR. Using O(R) = 1.") olFunc = 1 + ## Check and correct low / negative values idxOL = np.argmax(olFunc > 0.07, axis=1) olFunc[olFunc < 0.07] = np.nan - sigOLCor[:, :, indxt] = np.expand_dims( - sigBGCor_total / olFunc, -1) + + ## Aplly overlap correction + sigOLCor[:, :, indxt] = np.expand_dims(sigBGCor_total / olFunc, -1) BGOLCor[:, indxt] = np.expand_dims(bg_total, -1) - # print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) heightFullOverlapCor[:, indxt] = np.expand_dims(np.take(height, idxOL), -1) - # print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) elif config_dict['overlapCorMode'] == 3: + logging.critical('overlapCorMode 3 not implemented, see docstring for further information.') raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') return sigOLCor, BGOLCor, heightFullOverlapCor def fixLowest(overlap:np.ndarray, indexsearchmax:int, thres:float=0.05): - """very rough fix for exploding values in the very near range of the overlap function + """Very rough fix for exploding values in the very near range of the overlap function. in the lowest heights (below indexsearchmax, e.g. 800m) - search for chunks, where the overlap function is smaller than 0.05 + search for chunks, where the overlap function is smaller than a treshold (thres e.g. 0.05) in that chunk take the miniumum and fill heights below + Parameters + ---------- + overlap : list + List of dicts including ovelap functions per cloud free period. + indexsearchmax :int + Maximum height index for the applying the fix. + thres : float + Threshold for searching for a stable low overlap value. Default is 0.05. """ + for i, grp in enumerate(overlap): for channel, vals in grp.items(): var = vals['olFunc'][:indexsearchmax] From 79dd4143a2865e85fe010ddddfbc4962b35651fb Mon Sep 17 00:00:00 2001 From: HavardStridBuholdt Date: Wed, 29 Jul 2026 14:37:26 +0200 Subject: [PATCH 13/13] Added saving routine for improved SNR. The SNR used in quality mask estimation for channels `355_total_FR`, `532_total_FR`, `1064_total_FR`, `355_total_NR`, `532_total_NR` are now stored in the attenuated backscatter netCDFs. --- ppcpy/config/json2nc-mapper_NR_att_bsc.json | 8 ++++---- ppcpy/config/json2nc-mapper_att_bsc.json | 12 ++++++------ ppcpy/config/json2nc_translator.json | 10 +++++----- ppcpy/io/write2nc.py | 17 ++++++++++------- 4 files changed, 25 insertions(+), 22 deletions(-) diff --git a/ppcpy/config/json2nc-mapper_NR_att_bsc.json b/ppcpy/config/json2nc-mapper_NR_att_bsc.json index e24db75..1f14ff8 100644 --- a/ppcpy/config/json2nc-mapper_NR_att_bsc.json +++ b/ppcpy/config/json2nc-mapper_NR_att_bsc.json @@ -143,7 +143,7 @@ "unit": "", "long_name": "near-field quality mask for attenuated backscatter at 355 nm", "standard_name": "quality_mask_355", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_532nm": { @@ -158,7 +158,7 @@ "unit": "", "long_name": "near-field quality mask for attenuated backscatter at 532 nm", "standard_name": "quality_mask_532", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "SNR_355nm": { @@ -173,7 +173,7 @@ "unit": "", "long_name": "SNR at 355 nm", "standard_name": "near-field signal-noise-ratio 355 nm", - "comment": "" + "comment": "Near-field signal-noise-ratio at 355 nm used for quality mask" } }, "SNR_532nm": { @@ -188,7 +188,7 @@ "unit": "", "long_name": "SNR at 532 nm", "standard_name": "near-field signal-noise-ratio 532 nm", - "comment": "" + "comment": "Near-field signal-noise-ratio at 532 nm used for quality mask" } } } diff --git a/ppcpy/config/json2nc-mapper_att_bsc.json b/ppcpy/config/json2nc-mapper_att_bsc.json index d156f95..b0c5d72 100644 --- a/ppcpy/config/json2nc-mapper_att_bsc.json +++ b/ppcpy/config/json2nc-mapper_att_bsc.json @@ -164,7 +164,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 355 nm", "standard_name": "quality_mask_355", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_532nm": { @@ -179,7 +179,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 532 nm", "standard_name": "quality_mask_532", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_1064nm": { @@ -194,7 +194,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 1064 nm", "standard_name": "quality_mask_1064", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 1064 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 1064 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "SNR_355nm": { @@ -209,7 +209,7 @@ "unit": "", "long_name": "SNR at 355 nm", "standard_name": "signal-noise-ratio 355 nm", - "comment": "" + "comment": "Signal-noise-ratio at 355 nm used for quality mask." } }, "SNR_532nm": { @@ -224,7 +224,7 @@ "unit": "", "long_name": "SNR at 532 nm", "standard_name": "signal-noise-ratio 532 nm", - "comment": "" + "comment": "Signal-noise-ratio at 532 nm used for quality mask." } }, "SNR_1064nm": { @@ -239,7 +239,7 @@ "unit": "", "long_name": "SNR at 1064 nm", "standard_name": "signal-noise-ratio 1064 nm", - "comment": "" + "comment": "Signal-noise-ratio at 1064 nm used for the quality mask." } } } diff --git a/ppcpy/config/json2nc_translator.json b/ppcpy/config/json2nc_translator.json index 62eb65a..fb9fe09 100644 --- a/ppcpy/config/json2nc_translator.json +++ b/ppcpy/config/json2nc_translator.json @@ -710,13 +710,13 @@ "parameter":"attBsc_1064_total_FR" }, "SNR_355nm": { - "parameter":"SNR_FR_total_355nm" + "parameter":"flag_355_total_FR" }, "SNR_532nm": { - "parameter":"SNR_FR_total_532nm" + "parameter":"flag_532_total_FR" }, "SNR_1064nm": { - "parameter":"SNR_FR_total_1064nm" + "parameter":"flag_1064_total_FR" }, "quality_mask_355nm": { "parameter":"flag_355_total_FR" @@ -744,10 +744,10 @@ "parameter":"attBsc_532_total_NR" }, "SNR_355nm": { - "parameter":"SNR_NR_total_355nm" + "parameter":"flag_355_total_NR" }, "SNR_532nm": { - "parameter":"SNR_NR_total_532nm" + "parameter":"flag_532_total_NR" }, "quality_mask_355nm": { "parameter":"flag_355_total_NR" diff --git a/ppcpy/io/write2nc.py b/ppcpy/io/write2nc.py index c7ffde5..23a24d9 100644 --- a/ppcpy/io/write2nc.py +++ b/ppcpy/io/write2nc.py @@ -173,7 +173,7 @@ def write_channelwise_2_nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[ ## adding dynamical variables for v in list(json_nc_mapping_dict['variables'].keys()): ## use list here to suppress RuntimeError: dictionary changed size during iteration - #for v in json_nc_mapping_dict['variables'].keys(): + # for v in json_nc_mapping_dict['variables'].keys(): if v in data_cube.retrievals_highres.keys(): json_nc_mapping_dict['variables'][v]['data'] = data_cube.retrievals_highres[v] ## update variable attribute @@ -235,30 +235,33 @@ def write2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[]): ## adding dynamical variables json_nc_translator = json2nc_mapping.read_json_to_dict(Path(root_dir, 'ppcpy', 'config', f'json2nc_translator.json')) for var in json_nc_translator[prod]['variables'].keys(): - #print(f'var: {var}') if var in json_nc_mapping_dict['variables'].keys(): pass else: logging.warning(f'variable {var} not in json2nc_mapper_file') continue parameter = json_nc_translator[prod]['variables'][var]['parameter'] - if "quality_mask" in var: + if "quality_mask" in var and 'quality_mask' in data_cube.retrievals_highres: ch = getattr(data_cube, parameter) qm = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, ch]) json_nc_mapping_dict['variables'][var]['data'] = qm + elif "SNR" in var: + ch = getattr(data_cube, parameter) + if data_cube.polly_config_dict['flagUseImprovedSNR'] and 'SNR_quasi' in data_cube.retrievals_highres: + snr = np.squeeze(data_cube.retrievals_highres['SNR_quasi'][:, :, ch]) + json_nc_mapping_dict['variables'][var]['data'] = snr + elif 'SNR' in data_cube.retrievals_highres: + snr = np.squeeze(data_cube.retrievals_highres['SNR'][:, :, ch]) + json_nc_mapping_dict['variables'][var]['data'] = snr else: pass - #print(f'para: {parameter}') - if parameter in data_cube.retrievals_highres.keys(): - #print(f'para in retrievals_highres: {parameter}') json_nc_mapping_dict['variables'][var]['data'] = data_cube.retrievals_highres[parameter] ### remove empty key-value-pairs for var in list(json_nc_mapping_dict['variables'].keys()): ## use list here to suppress RuntimeError: dictionary changed size during iteration if json_nc_mapping_dict['variables'][var]['data'] is None: - print(f'removing variable: {var}') json2nc_mapping.remove_variable_from_json_dict_mapper(data_dict=json_nc_mapping_dict, key_to_remove=var)