From 7013c987aaf857b7e280bca8fe4fe42b112053db Mon Sep 17 00:00:00 2001 From: Tim Sainburg Date: Sun, 18 Jul 2021 13:40:15 -0700 Subject: [PATCH] Update to version 2, which adds several features (#46) * update to v2 * updated readme * updated noisereduce logo * updated noisereduce logo * added tests * added tests * added tests * added tests * updated tests Co-authored-by: Timothy J Sainburg --- README.md | 147 ++- assets/noisereduce.png | Bin 618671 -> 369369 bytes noisereduce/noisereduce.py | 800 +++++++++++----- noisereduce/noisereducev1.py | 295 ++++++ notebooks/1.0-test-noise-reduction.ipynb | 1076 +++++++++++++--------- setup.py | 2 +- test_reduction.py | 89 +- 7 files changed, 1695 insertions(+), 714 deletions(-) create mode 100644 noisereduce/noisereducev1.py diff --git a/README.md b/README.md index 84acdc8..1296379 100644 --- a/README.md +++ b/README.md @@ -4,58 +4,138 @@ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/noisereduce/blob/master/notebooks/1.0-test-noise-reduction.ipynb) [![PyPI version](https://badge.fury.io/py/noisereduce.svg)](https://badge.fury.io/py/noisereduce) +
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# Noise reduction in python using spectral gating +Noisereduce is a noise reduction algorithm in python that reduces noise in time-domain signals like speech, bioacoustics, and physiological signals. It relies on a method called "spectral gating" which is a form of [Noise Gate](https://en.wikipedia.org/wiki/Noise_gate). It works by computing a spectrogram of a signal (and optionally a noise signal) and estimating a noise threshold (or gate) for each frequency band of that signal/noise. That threshold is used to compute a mask, which gates noise below the frequency-varying threshold. + +The most recent version of noisereduce comprises two algorithms: +1. **Stationary Noise Reduction**: Keeps the estimated noise threshold at the same level across the whole signal +2. **Non-stationary Noise Reduction**: Continuously updates the estimated noise threshold over time + +### Version 2 Updates: +- Added two forms of spectral gating noise reduction: stationary noise reduction, and non-stationary noise reduction. +- Added multiprocessing so you can perform noise reduction on bigger data. +- The new version breaks the API of the old version. +- The previous version is still available at `from noisereduce.noisereducev1 import reduce_noise` +- You can now create a noisereduce object which allows you to reduce noise on subsets of longer recordings + +# Stationary Noise Reduction +- The basic intuition is that statistics are calculated on each frequency channel to determine a noise gate. Then the gate is applied to the signal. - This algorithm is based (but not completely reproducing) on the one [outlined by Audacity](https://wiki.audacityteam.org/wiki/How_Audacity_Noise_Reduction_Works) for the **noise reduction effect** ([Link to C++ code](https://github.com/audacity/audacity/blob/master/src/effects/NoiseReduction.cpp)) - The algorithm takes two inputs: - 1. A *noise* audio clip containing prototypical noise of the audio clip (optional) - 2. A *signal* audio clip containing the signal and the noise intended to be removed + 1. A *noise* clip containing prototypical noise of clip (optional) + 2. A *signal* clip containing the signal and the noise intended to be removed -## Steps of algorithm -1. An FFT is calculated over the noise audio clip -2. Statistics are calculated over FFT of the the noise (in frequency) +### Steps of the Stationary Noise Reduction algorithm +1. A spectrogram is calculated over the noise audio clip +2. Statistics are calculated over spectrogram of the the noise (in frequency) 3. A threshold is calculated based upon the statistics of the noise (and the desired sensitivity of the algorithm) -4. An FFT is calculated over the signal -5. A mask is determined by comparing the signal FFT to the threshold +4. A spectrogram is calculated over the signal +5. A mask is determined by comparing the signal spectrogram to the threshold 6. The mask is smoothed with a filter over frequency and time -7. The mask is appled to the FFT of the signal, and is inverted +7. The mask is appled to the spectrogram of the signal, and is inverted +*If the noise signal is not provided, the algorithm will treat the signal as the noise clip, which tends to work pretty well* -## Installation -`pip install noisereduce` +# Non-stationary Noise Reduction +- The non-stationary noise reduction algorithm is an extension of the stationary noise reduction algorithm, but allowing the noise gate to change over time. +- When you know the timescale that your signal occurs on (e.g. a bird call can be a few hundred milliseconds), you can set your noise threshold based on the assumption that events occuring on longer timescales are noise. +- This algorithm was motivated by a recent method in bioacoustics called Per-Channel Energy Normalization. -*noisereduce optionally uses Tensorflow as a backend to speed up FFT and gaussian convolution. It is not listed in the requirements.txt so because (1) it is optional and (2) tensorflow-gpu and tensorflow (cpu) are both compatible with this package. The package requires Tensorflow 2+ for all tensorflow operations.* +### Steps of the Non-stationary Noise Reduction algorithm +1. A spectrogram is calculated over the signal +2. A time-smoothed version of the spectrogram is computed using an IIR filter aplied forward and backward on each frequency channel. +3. A mask is computed based on that time-smoothed spectrogram +4. The mask is smoothed with a filter over frequency and time +5. The mask is appled to the spectrogram of the signal, and is inverted -## Usage +# Installation +`pip install noisereduce` + +# Usage See example notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/noisereduce/blob/master/notebooks/1.0-test-noise-reduction.ipynb) + +### Simplest usage ``` import noisereduce as nr # load data rate, data = wavfile.read("mywav.wav") -# select section of data that is noise -noisy_part = data[10000:15000] # perform noise reduction -reduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noisy_part, verbose=True) +reduced_noise = nr.reduce_noise(y=data, sr=rate) ``` -### Arguments to `noise_reduce` +### Arguments to `reduce_noise` ``` -n_grad_freq (int): how many frequency channels to smooth over with the mask. -n_grad_time (int): how many time channels to smooth over with the mask. -n_fft (int): number audio of frames between STFT columns. -win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`.. -hop_length (int):number audio of frames between STFT columns. -n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal -prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none) -pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data - use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation -verbose (bool): Whether to plot the steps of the algorithm +y : np.ndarray [shape=(# frames,) or (# channels, # frames)], real-valued + input signal + sr : int + sample rate of input signal / noise signal + y_noise : np.ndarray [shape=(# frames,) or (# channels, # frames)], real-valued + noise signal to compute statistics over (only for non-stationary noise reduction). + stationary : bool, optional + Whether to perform stationary, or non-stationary noise reduction, by default False + prop_decrease : float, optional + The proportion to reduce the noise by (1.0 = 100%), by default 1.0 + time_constant_s : float, optional + The time constant, in seconds, to compute the noise floor in the non-stationary + algorithm, by default 2.0 + freq_mask_smooth_hz : int, optional + The frequency range to smooth the mask over in Hz, by default 500 + time_mask_smooth_ms : int, optional + The time range to smooth the mask over in milliseconds, by default 50 + thresh_n_mult_nonstationary : int, optional + Only used in nonstationary noise reduction., by default 1 + sigmoid_slope_nonstationary : int, optional + Only used in nonstationary noise reduction., by default 10 + n_std_thresh_stationary : int, optional + Number of standard deviations above mean to place the threshold between + signal and noise., by default 1.5 + tmp_folder : [type], optional + Temp folder to write waveform to during parallel processing. Defaults to + default temp folder for python., by default None + chunk_size : int, optional + Size of signal chunks to reduce noise over. Larger sizes + will take more space in memory, smaller sizes can take longer to compute. + , by default 60000 + padding : int, optional + How much to pad each chunk of signal by. Larger pads are + needed for larger time constants., by default 30000 + n_fft : int, optional + length of the windowed signal after padding with zeros. + The number of rows in the STFT matrix ``D`` is ``(1 + n_fft/2)``. + The default value, ``n_fft=2048`` samples, corresponds to a physical + duration of 93 milliseconds at a sample rate of 22050 Hz, i.e. the + default sample rate in librosa. This value is well adapted for music + signals. However, in speech processing, the recommended value is 512, + corresponding to 23 milliseconds at a sample rate of 22050 Hz. + In any case, we recommend setting ``n_fft`` to a power of two for + optimizing the speed of the fast Fourier transform (FFT) algorithm., by default 1024 + win_length : [type], optional + Each frame of audio is windowed by ``window`` of length ``win_length`` + and then padded with zeros to match ``n_fft``. + Smaller values improve the temporal resolution of the STFT (i.e. the + ability to discriminate impulses that are closely spaced in time) + at the expense of frequency resolution (i.e. the ability to discriminate + pure tones that are closely spaced in frequency). This effect is known + as the time-frequency localization trade-off and needs to be adjusted + according to the properties of the input signal ``y``. + If unspecified, defaults to ``win_length = n_fft``., by default None + hop_length : [type], optional + number of audio samples between adjacent STFT columns. + Smaller values increase the number of columns in ``D`` without + affecting the frequency resolution of the STFT. + If unspecified, defaults to ``win_length // 4`` (see below)., by default None + n_jobs : int, optional + Number of parallel jobs to run. Set at -1 to use all CPU cores, by default 1 ``` -
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+ + + ### Citation If you use this code in your research, please cite it: @@ -70,9 +150,8 @@ If you use this code in your research, please cite it: doi = {10.5281/zenodo.3243139}, url = {https://doi.org/10.5281/zenodo.3243139} } -``` -or -``` + + @article{sainburg2020finding, title={Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires}, author={Sainburg, Tim and Thielk, Marvin and Gentner, Timothy Q}, diff --git a/assets/noisereduce.png b/assets/noisereduce.png index cc4d396e1be65f4b4b98b5c0683af052b628e84c..fcad3d5b09db198810d358faf7a4b7c3cba3d76d 100644 GIT binary patch literal 369369 zcmb@u2RPU7-!}fK6ln_;iI8Mwmzhd-_R7lMo2*1qLM1Yjkv+4sDUmYD9vPJ)D^gYp z&-wBF-M{<3|M&Ae|Koog|IcxJ`{KQ>_jSF-d7iJ=^$AkGEWL-EhMYhk?2(nZs7fG^ zco7I&S;@BIFR>!DUkQZmx>oAiuG)$U!X^%OTt=o2#%5d|c8)ksAc#tMI2xJQn7J|- zn^{=di!shs)iE+ynTj!L2`HXcbi80@X(i+3Y^LUQS>43T#^kIiql7rQsE06aU}xrP z#Nc6PYwsfLA;!3QUt#=A{4+Nr!{#ZjHe!t0ipmTZ9GuM<1h@paPBV&=Gl)8ynhUF5 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zht_1aSmReY+6OG1_)T+8iiYntPKL&!xsWg7`YDAMi`U6yBW{^+sWFtoj?hPQD-uDN zu>?Si2H&w&Q3(s9eu7J?edpm;tPOrCZyi1egRfB&oc*uK1^h>b;8$R|hJ;@huG(c& zS(a<2;BZPI>|gptez{BQ736nn-X&}+@c)IU-&U3-{+i8xd-(J8haX9M&)nSHxV=+2 zeE{yr*2BZY$;nCo_wc!fZbvyB4#(s1%gf94^|djkEuYWlA3y&5<;%Udqva2V3m{4) z2&y2P^S?z8@&TyB9$FnR`IkaFt+W7$KuMJvp7te!su} z<e$TN;0-^Ic> z*=9$E$P98#mm$D-vi0QCh4(YOnO*{8mOpFRCW=H}+v+1dH|`FK1I;{(9<_V)h%zMO!v zwY9aoyZdH3DK3x>4i044D0_Q*M@L6*Rw<*=$fcDmSBAsk&d!cZD8=1g;Z_@2u2fa! zvT2=emsH*v^h#Gz6h%=KMR~uNwa!r#MNt$*QFOznD2k#eilXR-Pf-*_Q4~ed4WFVY milQirq8mO%Q4~c{6y;wt%~!?0DMYdW0000 2: + raise ValueError("Waveform must be in shape (# frames, # channels)") + else: + self.y = y + + self._dtype = y.dtype + # get the number of channels and frames in data + self.n_channels, self.n_frames = self.y.shape + self._chunk_size = chunk_size + self.padding = padding + self.n_jobs = n_jobs + + self.use_tqdm = use_tqdm + # where to create a temp file for parallel + # writing + self._tmp_folder = tmp_folder + + ### Parameters for spectral gating + self._n_fft = n_fft + # set window and hop length for stft + if win_length is None: + self._win_length = self._n_fft + else: + self._win_length = win_length + if hop_length is None: + self._hop_length = self._win_length // 4 + self._time_constant_s = time_constant_s + + self._prop_decrease = prop_decrease + + if (freq_mask_smooth_hz is None) & (time_mask_smooth_ms is None): + self.smooth_mask = False + else: + self._generate_mask_smoothing_filter(freq_mask_smooth_hz, time_mask_smooth_ms) + + def _generate_mask_smoothing_filter(self, freq_mask_smooth_hz, time_mask_smooth_ms): + + if freq_mask_smooth_hz is None: + n_grad_freq = 1 + else: + # filter to smooth the mask + n_grad_freq = int(freq_mask_smooth_hz / (self.sr / (self._n_fft / 2))) + if n_grad_freq < 1: + raise ValueError( + "freq_mask_smooth_hz needs to be at least {}Hz".format( + int((self.sr / (self._n_fft / 2))) + ) + ) + + if time_mask_smooth_ms is None: + n_grad_time = 1 + else: + n_grad_time = int(time_mask_smooth_ms / ((self._hop_length / self.sr) * 1000)) + if n_grad_time < 1: + raise ValueError( + "time_mask_smooth_ms needs to be at least {}ms".format( + int((self._hop_length / self.sr) * 1000) + ) + ) + if (n_grad_time == 1) & (n_grad_freq == 1): + self.smooth_mask = False + else: + self.smooth_mask = True + self._smoothing_filter = _smoothing_filter(n_grad_freq, n_grad_time) + + def _read_chunk(self, i1, i2): + """read chunk and pad with zerros""" + if i1 < 0: + i1b = 0 + else: + i1b = i1 + if i2 > self.n_frames: + i2b = self.n_frames + else: + i2b = i2 + chunk = np.zeros((self.n_channels, i2 - i1)) + chunk[:, i1b - i1 : i2b - i1] = self.y[:, i1b:i2b] + return chunk + + def filter_chunk(self, start_frame, end_frame): + """Pad and perform filtering""" + i1 = start_frame - self.padding + i2 = end_frame + self.padding + padded_chunk = self._read_chunk(i1, i2) + filtered_padded_chunk = self._do_filter(padded_chunk) + return filtered_padded_chunk[:, start_frame - i1 : end_frame - i1] + + def _get_filtered_chunk(self, ind): + """Grabs a single chunk""" + start0 = ind * self._chunk_size + end0 = (ind + 1) * self._chunk_size + return self.filter_chunk(start_frame=start0, end_frame=end0) + + def _do_filter(self, chunk): + """Do the actual filtering""" + raise NotImplementedError + + return chunk_filtered + + def _iterate_chunk(self, filtered_chunk, pos, end0, start0, ich): + filtered_chunk0 = self._get_filtered_chunk(ich) + filtered_chunk[:, pos : pos + end0 - start0] = filtered_chunk0[:, start0:end0] + pos += end0 - start0 + + def get_traces(self, start_frame=None, end_frame=None): + """Grab filtered data iterating over chunks""" + if start_frame is None: + start_frame = 0 + if end_frame is None: + end_frame = self.n_frames + + if self._chunk_size is not None: + if end_frame - start_frame > self._chunk_size: + ich1 = int(start_frame / self._chunk_size) + ich2 = int((end_frame - 1) / self._chunk_size) + + # write output to temp memmap for parallelization + with tempfile.NamedTemporaryFile(prefix=self._tmp_folder) as fp: + # create temp file + filtered_chunk = np.memmap( + fp, + dtype=self._dtype, + shape=(self.n_channels, int(end_frame - start_frame)), + mode="w+", + ) + pos_list = [] + start_list = [] + end_list = [] + pos = 0 + for ich in range(ich1, ich2 + 1): + if ich == ich1: + start0 = start_frame - ich * self._chunk_size + else: + start0 = 0 + if ich == ich2: + end0 = end_frame - ich * self._chunk_size + else: + end0 = self._chunk_size + pos_list.append(pos) + start_list.append(start0) + end_list.append(end0) + pos += end0 - start0 + + Parallel(n_jobs=self.n_jobs)(delayed(self._iterate_chunk)(filtered_chunk, pos, end0, start0, ich) + for pos, start0, end0, ich in zip( + tqdm(pos_list, disable=self.use_tqdm), start_list, end_list, range(ich1, ich2 + 1) + ) + ) + if self.flat: + return filtered_chunk.astype(self._dtype).flatten() + else: + return filtered_chunk.astype(self._dtype) + + filtered_chunk = self.filter_chunk(start_frame=0, end_frame=end_frame) + if self.flat: + return filtered_chunk.astype(self._dtype).flatten() + else: + return filtered_chunk.astype(self._dtype) + + +class SpectralGateNonStationary(SpectralGate): + def __init__( + self, + y, + sr, + chunk_size, + padding, + n_fft, + win_length, + hop_length, + time_constant_s, + freq_mask_smooth_hz, + time_mask_smooth_ms, + thresh_n_mult_nonstationary, + sigmoid_slope_nonstationary, + tmp_folder, + prop_decrease, + use_tqdm, + n_jobs + ): - Arguments: - sig_stft {[type]} -- spectrogram of signal - sig_mask {[type]} -- mask to apply to signal - - Returns: - sig_stft_amp [type] -- masked signal - """ - sig_stft_amp = sig_stft * (1 - sig_mask) - return sig_stft_amp + self._thresh_n_mult_nonstationary = thresh_n_mult_nonstationary + self._sigmoid_slope_nonstationary = sigmoid_slope_nonstationary + + super().__init__( + y=y, + sr=sr, + chunk_size=chunk_size, + padding=padding, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + time_constant_s=time_constant_s, + freq_mask_smooth_hz=freq_mask_smooth_hz, + time_mask_smooth_ms=time_mask_smooth_ms, + tmp_folder=tmp_folder, + prop_decrease=prop_decrease, + use_tqdm=use_tqdm, + n_jobs = n_jobs + ) -def convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow=False): - """ Convolves a gaussian filter with a mask (or any image) - - Arguments: - sig_mask {[type]} -- The signal mask - smoothing_filter {[type]} -- the filter to convolve - - Keyword Arguments: - use_tensorflow {bool} -- use tensorflow.signal or scipy.signal (default: {False}) - """ - if use_tensorflow: - smoothing_filter = smoothing_filter * ( - (np.shape(smoothing_filter)[1] - 1) / 2 + 1 - ) - smoothing_filter = smoothing_filter[:, :, tf.newaxis, tf.newaxis].astype( - "float32" - ) - img = sig_mask[:, :, tf.newaxis, tf.newaxis].astype("float32") - return ( - tf.nn.conv2d(img, smoothing_filter, strides=[1, 1, 1, 1], padding="SAME") - .numpy() - .squeeze() - ) - else: - return scipy.signal.fftconvolve(sig_mask, smoothing_filter, mode="same") + def spectral_gating_nonstationary(self, chunk): + """non-stationary version of spectral gating""" + denoised_channels = np.zeros(chunk.shape, chunk.dtype) + for ci, channel in enumerate(chunk): + sig_stft = librosa.stft( + (channel), + n_fft=self._n_fft, + hop_length=self._hop_length, + win_length=self._win_length, + ) + # get abs of signal stft + abs_sig_stft = np.abs(sig_stft) + + # get the smoothed mean of the signal + sig_stft_smooth = get_time_smoothed_representation( + abs_sig_stft, + self.sr, + self._hop_length, + time_constant_s=self._time_constant_s, + ) + # get the number of X above the mean the signal is + sig_mult_above_thresh = (abs_sig_stft - sig_stft_smooth) / sig_stft_smooth + # mask based on sigmoid + sig_mask = sigmoid( + sig_mult_above_thresh, -self._thresh_n_mult_nonstationary, self._sigmoid_slope_nonstationary + ) -def load_tensorflow(verbose=False): - """loads tensorflow if it is available - Used as a backend for fft and convolution - - Returns: - bool -- whether to use tensorflow - """ - try: - # import tensorflow as tf - globals()["tf"] = __import__("tensorflow") - - if verbose: - available_gpus = tf.config.experimental.list_physical_devices("GPU") - print("GPUs available: {}".format(available_gpus)) - if int(tf.__version__[0]) < 2: - warnings.warn( - "Tensorflow version is below 2.0, reverting to non-tensorflow backend" + if self.smooth_mask: + # convolve the mask with a smoothing filter + sig_mask = scipy.signal.fftconvolve( + sig_mask, self._smoothing_filter, mode="same" + ) + + sig_mask = sig_mask * self._prop_decrease + np.ones(np.shape(sig_mask)) * (1.0 - self._prop_decrease) + + + # multiply signal with mask + sig_stft_denoised = sig_stft * sig_mask + + + # invert/recover the signal + denoised_signal = librosa.istft( + sig_stft_denoised, + hop_length=self._hop_length, + win_length=self._win_length, ) - return False - except: - warnings.warn( - "Tensorflow is not installed, reverting to non-tensorflow backend" + denoised_channels[ci, : len(denoised_signal)] = denoised_signal + return denoised_channels + + def _do_filter(self, chunk): + """Do the actual filtering""" + chunk_filtered = self.spectral_gating_nonstationary(chunk) + + return chunk_filtered + + +class SpectralGateStationary(SpectralGate): + def __init__( + self, + y, + sr, + y_noise, + n_std_thresh_stationary, + chunk_size, + clip_noise_stationary, + padding, + n_fft, + win_length, + hop_length, + time_constant_s, + freq_mask_smooth_hz, + time_mask_smooth_ms, + tmp_folder, + prop_decrease, + use_tqdm, + n_jobs + ): + + super().__init__( + y=y, + sr=sr, + chunk_size=chunk_size, + padding=padding, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + time_constant_s=time_constant_s, + freq_mask_smooth_hz=freq_mask_smooth_hz, + time_mask_smooth_ms=time_mask_smooth_ms, + tmp_folder=tmp_folder, + prop_decrease=prop_decrease, + use_tqdm=use_tqdm, + n_jobs = n_jobs ) - return False - return True + + self.n_std_thresh_stationary = n_std_thresh_stationary + + if y_noise is None: + self.y_noise = self.y + + else: + y_noise = np.array(y_noise) + # reshape data to (#channels, #frames) + if len(y_noise.shape) == 1: + self.y_noise = np.expand_dims(y_noise, 0) + elif len(y.shape) > 2: + raise ValueError("Waveform must be in shape (# frames, # channels)") + else: + self.y_noise = y_noise + + # collapse y_noise to one channel + self.y_noise = np.mean(self.y_noise, axis=0) + + if clip_noise_stationary: + self.y_noise = self.y_noise[:chunk_size] + + # calculate statistics over y_noise + abs_noise_stft = np.abs(librosa.stft( + (self.y_noise), + n_fft=self._n_fft, + hop_length=self._hop_length, + win_length=self._win_length, + )) + noise_stft_db = _amp_to_db(abs_noise_stft) + self.mean_freq_noise = np.mean(noise_stft_db, axis=1) + self.std_freq_noise = np.std(noise_stft_db, axis=1) + + self.noise_thresh = self.mean_freq_noise + self.std_freq_noise * self.n_std_thresh_stationary + + def spectral_gating_stationary(self, chunk): + """non-stationary version of spectral gating""" + denoised_channels = np.zeros(chunk.shape, chunk.dtype) + for ci, channel in enumerate(chunk): + sig_stft = librosa.stft( + (channel), + n_fft=self._n_fft, + hop_length=self._hop_length, + win_length=self._win_length, + ) + + # spectrogram of signal in dB + sig_stft_db = _amp_to_db(np.abs(sig_stft)) + + # calculate the threshold for each frequency/time bin + db_thresh = np.repeat( + np.reshape(self.noise_thresh, [1, len(self.mean_freq_noise)]), + np.shape(sig_stft_db)[1], + axis=0, + ).T + + # mask if the signal is above the threshold + sig_mask = sig_stft_db > db_thresh + + sig_mask = sig_mask * self._prop_decrease + np.ones(np.shape(sig_mask)) * (1.0 - self._prop_decrease) + + if self.smooth_mask: + # convolve the mask with a smoothing filter + sig_mask = scipy.signal.fftconvolve( + sig_mask, self._smoothing_filter, mode="same" + ) + + # multiply signal with mask + sig_stft_denoised = sig_stft * sig_mask + + # invert/recover the signal + denoised_signal = librosa.istft( + sig_stft_denoised, + hop_length=self._hop_length, + win_length=self._win_length, + ) + denoised_channels[ci, : len(denoised_signal)] = denoised_signal + return denoised_channels + + def _do_filter(self, chunk): + """Do the actual filtering""" + chunk_filtered = self.spectral_gating_stationary(chunk) + + return chunk_filtered + def reduce_noise( - audio_clip, - noise_clip, - n_grad_freq=2, - n_grad_time=4, - n_fft=2048, - win_length=2048, - hop_length=512, - n_std_thresh=1.5, - prop_decrease=1.0, - pad_clipping=True, - use_tensorflow=False, - verbose=False, + y, + sr, + stationary=False, + y_noise = None, + prop_decrease = 1.0, + time_constant_s=2.0, + freq_mask_smooth_hz=500, + time_mask_smooth_ms=50, + thresh_n_mult_nonstationary=2, + sigmoid_slope_nonstationary=10, + n_std_thresh_stationary = 1.5, + tmp_folder=None, + chunk_size=600000, + padding=30000, + n_fft=1024, + win_length=None, + hop_length=None, + clip_noise_stationary = True, + use_tqdm=False, + n_jobs = 1 ): - """Remove noise from audio based upon a clip containing only noise - - Args: - audio_clip (array): The first parameter. - noise_clip (array): The second parameter. - n_grad_freq (int): how many frequency channels to smooth over with the mask. - n_grad_time (int): how many time channels to smooth over with the mask. - n_fft (int): number audio of frames between STFT columns. - win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`.. - hop_length (int):number audio of frames between STFT columns. - n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal - prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none) - pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data - use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation - verbose (bool): Whether to plot the steps of the algorithm - - Returns: - array: The recovered signal with noise subtracted - """ - # load tensorflow if you are using it as a backend - if use_tensorflow: - use_tensorflow = load_tensorflow(verbose) + Reduce noise via spectral gating. + + Parameters + ---------- + y : np.ndarray [shape=(# frames,) or (# channels, # frames)], real-valued + input signal + sr : int + sample rate of input signal / noise signal + y_noise : np.ndarray [shape=(# frames,) or (# channels, # frames)], real-valued + noise signal to compute statistics over (only for non-stationary noise reduction). + stationary : bool, optional + Whether to perform stationary, or non-stationary noise reduction, by default False + prop_decrease : float, optional + The proportion to reduce the noise by (1.0 = 100%), by default 1.0 + time_constant_s : float, optional + The time constant, in seconds, to compute the noise floor in the non-stationary + algorithm, by default 2.0 + freq_mask_smooth_hz : int, optional + The frequency range to smooth the mask over in Hz, by default 500 + time_mask_smooth_ms : int, optional + The time range to smooth the mask over in milliseconds, by default 50 + thresh_n_mult_nonstationary : int, optional + Only used in nonstationary noise reduction., by default 1 + sigmoid_slope_nonstationary : int, optional + Only used in nonstationary noise reduction., by default 10 + n_std_thresh_stationary : int, optional + Number of standard deviations above mean to place the threshold between + signal and noise., by default 1.5 + tmp_folder : [type], optional + Temp folder to write waveform to during parallel processing. Defaults to + default temp folder for python., by default None + chunk_size : int, optional + Size of signal chunks to reduce noise over. Larger sizes + will take more space in memory, smaller sizes can take longer to compute. + , by default 60000 + padding : int, optional + How much to pad each chunk of signal by. Larger pads are + needed for larger time constants., by default 30000 + n_fft : int, optional + length of the windowed signal after padding with zeros. + The number of rows in the STFT matrix ``D`` is ``(1 + n_fft/2)``. + The default value, ``n_fft=2048`` samples, corresponds to a physical + duration of 93 milliseconds at a sample rate of 22050 Hz, i.e. the + default sample rate in librosa. This value is well adapted for music + signals. However, in speech processing, the recommended value is 512, + corresponding to 23 milliseconds at a sample rate of 22050 Hz. + In any case, we recommend setting ``n_fft`` to a power of two for + optimizing the speed of the fast Fourier transform (FFT) algorithm., by default 1024 + win_length : [type], optional + Each frame of audio is windowed by ``window`` of length ``win_length`` + and then padded with zeros to match ``n_fft``. + Smaller values improve the temporal resolution of the STFT (i.e. the + ability to discriminate impulses that are closely spaced in time) + at the expense of frequency resolution (i.e. the ability to discriminate + pure tones that are closely spaced in frequency). This effect is known + as the time-frequency localization trade-off and needs to be adjusted + according to the properties of the input signal ``y``. + If unspecified, defaults to ``win_length = n_fft``., by default None + hop_length : [type], optional + number of audio samples between adjacent STFT columns. + Smaller values increase the number of columns in ``D`` without + affecting the frequency resolution of the STFT. + If unspecified, defaults to ``win_length // 4`` (see below)., by default None + use_tqdm : bool, optional + Whether to show tqdm progress bar, by default False + n_jobs : int, optional + Number of parallel jobs to run. Set at -1 to use all CPU cores, by default 1 + """ + if stationary: + sg = SpectralGateStationary( + y=y, + sr=sr, + y_noise = y_noise, + prop_decrease =prop_decrease, + n_std_thresh_stationary = n_std_thresh_stationary, + chunk_size=chunk_size, + clip_noise_stationary = clip_noise_stationary, + padding=padding, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + time_constant_s=time_constant_s, + freq_mask_smooth_hz=freq_mask_smooth_hz, + time_mask_smooth_ms=time_mask_smooth_ms, + tmp_folder=tmp_folder, + use_tqdm=use_tqdm, + n_jobs = n_jobs + ) - if verbose: - pbar = tqdm(total=7) else: - pbar = None - - update_pbar(pbar, "STFT on noise") - # STFT over noise - noise_stft = _stft( - noise_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow - ) - noise_stft_db = _amp_to_db(np.abs(noise_stft)) # convert to dB - # Calculate statistics over noise - update_pbar(pbar, "STFT on signal") - mean_freq_noise = np.mean(noise_stft_db, axis=1) - std_freq_noise = np.std(noise_stft_db, axis=1) - noise_thresh = mean_freq_noise + std_freq_noise * n_std_thresh - # STFT over signal - update_pbar(pbar, "STFT on signal") - - # pad signal with zeros to avoid extra frames being clipped if desired - if pad_clipping: - nsamp = len(audio_clip) - audio_clip = np.pad(audio_clip, [0, hop_length], mode="constant") - - sig_stft = _stft( - audio_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow - ) - # spectrogram of signal in dB - sig_stft_db = _amp_to_db(np.abs(sig_stft)) - update_pbar(pbar, "Generate mask") - - # calculate the threshold for each frequency/time bin - db_thresh = np.repeat( - np.reshape(noise_thresh, [1, len(mean_freq_noise)]), - np.shape(sig_stft_db)[1], - axis=0, - ).T - # mask if the signal is above the threshold - sig_mask = sig_stft_db < db_thresh - update_pbar(pbar, "Smooth mask") - # Create a smoothing filter for the mask in time and frequency - smoothing_filter = _smoothing_filter(n_grad_freq, n_grad_time) - - # convolve the mask with a smoothing filter - sig_mask = convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow) - - sig_mask = sig_mask * prop_decrease - update_pbar(pbar, "Apply mask") - # mask the signal - - sig_stft_amp = mask_signal(sig_stft, sig_mask) - - update_pbar(pbar, "Recover signal") - # recover the signal - recovered_signal = _istft( - sig_stft_amp, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow - ) - # fix the recovered signal length if padding signal - if pad_clipping: - recovered_signal = librosa.util.fix_length(recovered_signal, nsamp) - - recovered_spec = _amp_to_db( - np.abs( - _stft( - recovered_signal, - n_fft, - hop_length, - win_length, - use_tensorflow=use_tensorflow, - ) - ) - ) - if verbose: - plot_reduction_steps( - noise_stft_db, - mean_freq_noise, - std_freq_noise, - noise_thresh, - smoothing_filter, - sig_stft_db, - sig_mask, - recovered_spec, + sg = SpectralGateNonStationary( + y=y, + sr=sr, + chunk_size=chunk_size, + padding=padding, + prop_decrease =prop_decrease, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + time_constant_s=time_constant_s, + freq_mask_smooth_hz=freq_mask_smooth_hz, + time_mask_smooth_ms=time_mask_smooth_ms, + thresh_n_mult_nonstationary=thresh_n_mult_nonstationary, + sigmoid_slope_nonstationary=sigmoid_slope_nonstationary, + tmp_folder=tmp_folder, + use_tqdm=use_tqdm, + n_jobs = n_jobs ) - return recovered_signal + return sg.get_traces() + +def _amp_to_db(x): + return librosa.core.amplitude_to_db(x, ref=1.0, amin=1e-20, top_db=80.0) + + +def _db_to_amp(x,): + return librosa.core.db_to_amplitude(x, ref=1.0) \ No newline at end of file diff --git a/noisereduce/noisereducev1.py b/noisereduce/noisereducev1.py new file mode 100644 index 0000000..b469743 --- /dev/null +++ b/noisereduce/noisereducev1.py @@ -0,0 +1,295 @@ +import scipy.signal +import numpy as np +import librosa +from noisereduce.plotting import plot_reduction_steps +from tqdm.autonotebook import tqdm +import warnings +import copy + + +def _stft(y, n_fft, hop_length, win_length, use_tensorflow=False): + if use_tensorflow: + # return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=True) + return _stft_tensorflow(y, n_fft, hop_length, win_length) + else: + return librosa.stft( + y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=True + ) + + +def _istft(y, n_fft, hop_length, win_length, use_tensorflow=False): + if use_tensorflow: + # return librosa.istft(y, hop_length, win_length) + return _istft_tensorflow(y.T, n_fft, hop_length, win_length) + else: + return librosa.istft(y, hop_length, win_length) + + +def _stft_librosa(y, n_fft, hop_length, win_length): + return librosa.stft( + y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=True + ) + + +def _istft_librosa(y, hop_length, win_length): + return librosa.istft(y, hop_length, win_length) + + +def _stft_tensorflow(y, n_fft, hop_length, win_length): + return ( + tf.signal.stft( + y, + win_length, + hop_length, + n_fft, + pad_end=True, + window_fn=tf.signal.hann_window, + ) + .numpy() + .T + ) + + +def _istft_tensorflow(y, n_fft, hop_length, win_length): + return tf.signal.inverse_stft( + y.astype(np.complex64), win_length, hop_length, n_fft + ).numpy() + + +def _amp_to_db(x): + return librosa.core.amplitude_to_db(x, ref=1.0, amin=1e-20, top_db=80.0) + + +def _db_to_amp(x,): + return librosa.core.db_to_amplitude(x, ref=1.0) + + +def update_pbar(pbar, message): + """ writes to progress bar + """ + if pbar is not None: + pbar.set_description(message) + pbar.update(1) + + +def _smoothing_filter(n_grad_freq, n_grad_time): + """Generates a filter to smooth the mask for the spectrogram + + Arguments: + n_grad_freq {[type]} -- [how many frequency channels to smooth over with the mask.] + n_grad_time {[type]} -- [how many time channels to smooth over with the mask.] + """ + + smoothing_filter = np.outer( + np.concatenate( + [ + np.linspace(0, 1, n_grad_freq + 1, endpoint=False), + np.linspace(1, 0, n_grad_freq + 2), + ] + )[1:-1], + np.concatenate( + [ + np.linspace(0, 1, n_grad_time + 1, endpoint=False), + np.linspace(1, 0, n_grad_time + 2), + ] + )[1:-1], + ) + smoothing_filter = smoothing_filter / np.sum(smoothing_filter) + return smoothing_filter + + +def mask_signal(sig_stft, sig_mask): + """ Reduces amplitude of time/frequency regions of a spectrogram based upon a mask + + Arguments: + sig_stft {[type]} -- spectrogram of signal + sig_mask {[type]} -- mask to apply to signal + + Returns: + sig_stft_amp [type] -- masked signal + """ + sig_stft_amp = sig_stft * (1 - sig_mask) + return sig_stft_amp + + +def convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow=False): + """ Convolves a gaussian filter with a mask (or any image) + + Arguments: + sig_mask {[type]} -- The signal mask + smoothing_filter {[type]} -- the filter to convolve + + Keyword Arguments: + use_tensorflow {bool} -- use tensorflow.signal or scipy.signal (default: {False}) + """ + if use_tensorflow: + smoothing_filter = smoothing_filter * ( + (np.shape(smoothing_filter)[1] - 1) / 2 + 1 + ) + smoothing_filter = smoothing_filter[:, :, tf.newaxis, tf.newaxis].astype( + "float32" + ) + img = sig_mask[:, :, tf.newaxis, tf.newaxis].astype("float32") + return ( + tf.nn.conv2d(img, smoothing_filter, strides=[1, 1, 1, 1], padding="SAME") + .numpy() + .squeeze() + ) + else: + return scipy.signal.fftconvolve(sig_mask, smoothing_filter, mode="same") + + +def load_tensorflow(verbose=False): + """loads tensorflow if it is available + Used as a backend for fft and convolution + + Returns: + bool -- whether to use tensorflow + """ + try: + # import tensorflow as tf + globals()["tf"] = __import__("tensorflow") + + if verbose: + available_gpus = tf.config.experimental.list_physical_devices("GPU") + print("GPUs available: {}".format(available_gpus)) + if int(tf.__version__[0]) < 2: + warnings.warn( + "Tensorflow version is below 2.0, reverting to non-tensorflow backend" + ) + return False + except: + warnings.warn( + "Tensorflow is not installed, reverting to non-tensorflow backend" + ) + return False + return True + + +def reduce_noise( + audio_clip, + noise_clip=None, + n_grad_freq=2, + n_grad_time=4, + n_fft=2048, + win_length=2048, + hop_length=512, + n_std_thresh=1.5, + prop_decrease=1.0, + pad_clipping=True, + use_tensorflow=False, + verbose=False, +): + """Remove noise from audio based upon a clip containing only noise + + Args: + audio_clip (array): Waveform of audio + noise_clip (array): The second parameter. + n_grad_freq (int): how many frequency channels to smooth over with the mask. + n_grad_time (int): how many time channels to smooth over with the mask. + n_fft (int): number audio of frames between STFT columns. + win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`.. + hop_length (int):number audio of frames between STFT columns. + n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal + prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none) + pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data + use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation + verbose (bool): Whether to plot the steps of the algorithm + + Returns: + array: The recovered signal with noise subtracted + + """ + # load tensorflow if you are using it as a backend + if use_tensorflow: + use_tensorflow = load_tensorflow(verbose) + + if verbose: + pbar = tqdm(total=7) + else: + pbar = None + + # STFT over signal + update_pbar(pbar, "STFT on signal") + + # pad signal with zeros to avoid extra frames being clipped if desired + if pad_clipping: + nsamp = len(audio_clip) + audio_clip = np.pad(audio_clip, [0, hop_length], mode="constant") + + sig_stft = _stft( + audio_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow + ) + # spectrogram of signal in dB + sig_stft_db = _amp_to_db(np.abs(sig_stft)) + + update_pbar(pbar, "STFT on noise") + # STFT over noise + if noise_clip is None: + noise_stft = copy.deepcopy(sig_stft) + noise_stft_db = copy.deepcopy(sig_stft_db) + else: + noise_stft = _stft( + noise_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow + ) + noise_stft_db = _amp_to_db(np.abs(noise_stft)) # convert to dB + # Calculate statistics over noise + mean_freq_noise = np.mean(noise_stft_db, axis=1) + std_freq_noise = np.std(noise_stft_db, axis=1) + noise_thresh = mean_freq_noise + std_freq_noise * n_std_thresh + + update_pbar(pbar, "Generate mask") + + # calculate the threshold for each frequency/time bin + db_thresh = np.repeat( + np.reshape(noise_thresh, [1, len(mean_freq_noise)]), + np.shape(sig_stft_db)[1], + axis=0, + ).T + # mask if the signal is above the threshold + sig_mask = sig_stft_db < db_thresh + update_pbar(pbar, "Smooth mask") + # Create a smoothing filter for the mask in time and frequency + smoothing_filter = _smoothing_filter(n_grad_freq, n_grad_time) + + # convolve the mask with a smoothing filter + sig_mask = convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow) + + sig_mask = sig_mask * prop_decrease + update_pbar(pbar, "Apply mask") + # mask the signal + + sig_stft_amp = mask_signal(sig_stft, sig_mask) + + update_pbar(pbar, "Recover signal") + # recover the signal + recovered_signal = _istft( + sig_stft_amp, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow + ) + # fix the recovered signal length if padding signal + if pad_clipping: + recovered_signal = librosa.util.fix_length(recovered_signal, nsamp) + + recovered_spec = _amp_to_db( + np.abs( + _stft( + recovered_signal, + n_fft, + hop_length, + win_length, + use_tensorflow=use_tensorflow, + ) + ) + ) + if verbose: + plot_reduction_steps( + noise_stft_db, + mean_freq_noise, + std_freq_noise, + noise_thresh, + smoothing_filter, + sig_stft_db, + sig_mask, + recovered_spec, + ) + return recovered_signal diff --git a/notebooks/1.0-test-noise-reduction.ipynb b/notebooks/1.0-test-noise-reduction.ipynb index 5d1e23d..b6da9bf 100644 --- a/notebooks/1.0-test-noise-reduction.ipynb +++ b/notebooks/1.0-test-noise-reduction.ipynb @@ -35,7 +35,6 @@ "outputs": [], "source": [ "colab_requirements = [\n", - " \"pip install tensorflow-gpu==2.0.0-beta0\",\n", " \"pip install librosa\",\n", " \"pip install noisereduce\",\n", " \"pip install soundfile\",\n", @@ -73,16 +72,7 @@ "start_time": "2020-01-24T06:23:43.361344Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/cube/tsainbur/conda_envs/tpy3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", - " \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n" - ] - } - ], + "outputs": [], "source": [ "import IPython\n", "from scipy.io import wavfile\n", @@ -134,7 +124,7 @@ "data": { "text/html": [ "\n", - "

" ] @@ -228,7 +218,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 8, @@ -237,7 +227,7 @@ }, { "data": { - "image/png": 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Rw5Gi0qgvgARRFBNFUSwF8CuASRLsl0gSSZmFePXPEwCAAW8bH9VbeSjZasffzBXa7NJtX/wnyX7saRokm2LTagsbsBszc5XzTIEZbKSZdUNMX364ztef/uUoJn3ufP3GijXaOleZsxdyVOo9/ctRPP3LURSVajHlS2nOY/aCA6qO77eYFIt7NhKRdKRIGrUCUL2r66WK52q6XRCE44IgrBIEobWxHQmCMF0QhBhBEGLS09ONbUIOzJorxaReL8Ls1Sestn+imnbEp+FCRoHcYTTIqSu5yC1mg3iqn99iHKM6xNbYcLy2Xw4kS5a4t1fvbTyDa7nFmLH8cIMaon+67Zy+h6M902h1OHChvLdaanYRLmba53lVSqI9jUZJbOaq4xb3bCQi6UiRNDJ2uVPz0+wfAGGiKHYHsAXAD8Z2JIriN6IoRouiGB0UFCRBaGRPrN1bZcXBuqqJeNVO0nrwu0MYvmSH/mLXnsz5Mw53frVP7jDIDkk1bcuZZBcyQWvMidQcuUOQ1efbz+O9jfHYcPIq/j1ePsX4ak4xzhlZaAEoTyS89lfV4NgXO87bJE5r+3DLWYNG3kPf2yFfMDJJqpEoWx93VaZI5LEh7qrDTFklsldSJI0uAaheORQKwGAytSiKmaIoVjbp+BZAbwmOSzZi7YaU8VfzrF6Gbm4kVyVxzkgURYdsxkjO48zVupcDJzKm1AGmFNnaZ9utV2VL8pK6IqT/21sx+kPjffGu5Zbgp/3Wm2ovl3PX6l4tLC232KotBpTgcI2Ksc+d7DNjxk+HuQIgkcykSBodAtBBEIR2giC4AZgKYE31DQRBaFHt4S0A2GWzAXKKyrAhzrKGtlKa8MluHL5onWqJS9cLMfajXXjzX9s2iqxJ6ukBKw+lsBkjERERNdrHW87hak7VQNTMVbH6xTyA8uvDZXsvyBGa1Zm7Pnvkhxi88scJfLj5rG0CkkFhqeFKricdaHVBIrIPjU4aiaKoAfA0gI0oTwb9JoriSUEQ3hAE4ZaKzZ4VBOGkIAixAJ4F8GBjj+uMnl95DDN+OiLLsWuuxiSV6wXlZfk/7Ltolf1XOpRk23n9723kMuZE5HycuNUGkdWkZhdhSLXm67/FXML59KoKnHl/x+GbXYlyhCa7jPzyiQwfbz2nfy4xPd8gqUZERI0jRaURRFFcJ4piR1EUw0VRXFTx3DxRFNdUfD1bFMUuoij2EEVxuCiKZ6Q4rrNhrwjrESTuaZRZYPsVU4iIiOxVfonGIBFCMOjjUtfUT0v7YsUkZaG4TGt+QwUxdn324m+xdU79G/H+ToeuPCIisjVJkkZkGxwzsR6pexqR/Soo0SBs1lq5wyAicioPLDuIFQdTzG9oR5R03ZaSVYgpX+3Dq3/a/0qyfxy5BE1FJZGpy7cYK7VVsLWzJhqfExHZEpNGJIsNcVcxYskO6BQyl0HgmsdUIYtVYuSkEtJY5UHyqdns1xHY4hKnTKtDicZ89VBOUXk10ukr9pWEMHV5Zu6qTeoKcrkkpheY34iIyMqYNCJZzFp9HIkZBZj0+V65Q2kwURQtXhmlx+ubrBxN3S5nF2Hp7kRotDq8te403vjnFN7dcAbHL2XLGpfS6HQiNp26JncYRDaXkV+CUR/slDsMMiG/RCN3CKRQU77ah/2J5qtq7HFs7Hx6PvYnZhp97dSVXOh0oslBP5WD3OE0pDfTmau5SEgrTw4+9N1BLF5vn11BdDoRL1dbNa16FThbdhDZllruAKhuoz7YibziMrw3pQeuZFunGbUlWIlTW7vZ6zCkYxB+fLiv2W0rR/jk8sgPMTh9pXy1jerNMr/YcR5ju4QgLjUXe2eNkCs8xVi+/yIWyrySH5Ec8oqZlFCy+/93AKufHCh3GKRAsSn1G/yxdLBLCUa+bzqRfctndQ86xl91jMrJhqyKN+6j3QCAhEXjsT0+Hdvj0zFrfKTUoVldanYRfq/W16u6we9ux/EFY+Dn4WrjqIick4Pk4R1XQlo+ruWW4P5lB1FQal/NC+3J9/8lNej7dp1NlzYQK8mtSFoZa6S58eQ1pGYX4VBSFv49ftnWoSnK5Rz5ErNEjkHEsyuOsmpJYkeSWRVKhpIyCuqVAKqcrnXmah7+PpZqrbAUo3JVtd9jUrAh7orM0TTc9UZMmY+Ys17CSJRn9Ac7seJgstxhEDkFJo2IJFakoOTeC78dw5Qv/9M/rmuO/x1f7cPTvxxFUgbnz1O5HfFpcodAduZYSg7WxF5mfyQFSM4sxOzVJ6CpY9UtsjbrVfVMX37YoHLYnOoF48/9eswKESnTy6uOY8ZPR+QOQ3LPrjiKZ1YcxeGLWVgTexnXcoslP0ZyZiGGL9mBtDzp9y2Fa7klmL26vLH70t2J2JuQIXNERI6LSSOyqT8OX8L+xEybNIe0FlEU8fOBi/rH284Y9sDpPG+DrUMyafWRVMRcvK4fcVt5yPyIzLAlO3DFSStuHKVxplQe/O6Q3CGQjUj1zn+pWv8Jktezvx7FioPJiL2Uo3/u72Op7I/kQN6uR6+aE9XeB87CkVdCXRN7Gf/EXsbtX+7DsyuO4u5v9wOApImTZXsv4EJGAf6NVX6l1ptrT+PepQfkDoPIYTFpRBZZujsRhaWNv9B88fdYTP1mvwQRyee/85mY82ec/vHD38fIGI1lSjTlI81JmZY1Dtx00jmbQXNEnogcReXYTGWFSVxqDp779Zh+ZJ6sq6BEg9f/UU6PvJl/HJc7BLKiS1lFSMookDRxcjGzvPJcKSsdE5F8mDQiixy/lIN3N8RLtj977qvtDKO09vzv0xhL99S/4aSjKyjRYOG/p1Bcppxpl0RyuizjohT1UnGjV/lxXnnuupajvKkmjenbolRf7TyPKwr8XduDMq3OoauE6sXC67FSrQ7DluyQ9NDb48v7dp6+kifpfqV28rLzVdER2RqTRmQxKZMl2YXyriZmTJmFVSamGk8eSMys88Y6bNZai6aHKYGT5ozIiC7zN+J/ey4gcu4G/LgvSe5wiGR34+JtcodgkapKo/JPdP2pS4Ef8L0WbpY7BMlpGrBUui0dSMxU7EpqhSUcpFASpQ8kTvxkj9whEDk8Jo3IYgq9tpDMK6ssK90+kVp7RCMhLQ93fbPfbCn6T/vtJGlkoyuElKxCZBc63gizo3p7neX9M4hIGSo/zUUYVh6RdSn9mumub/ZjTaxzr5hqFxTwPuJnBhExaUQWq2ym7KjWx121aLurObV/D5U301tP190LyF7mhb/2VxwS06Vf/SgpowDHL1UtHT343e0YLnE5NRHVz59HL0k+rYHkV+t0U/FY6VUDjkJUwt2+GSlZlvU5tDV7+N05E35mEBGTRmSxnWfT5Q7BqlQWnhSNXcxsPVO+NHlaXt2JNTvJGQEARry/E0t3W76cryWGLdmBWz7ba/DcdYVMVTxvhSQZkT34fPt5uUMgK9BXFtU4t9VcJfJ6QSnWxF7GxpNXFbu0NjmP/BINZlpY+U0kpZd/j2UvLSITmDQiqqCycChF14g+BfZSaVTp4y3nrLJfURQV1x/H3lf1s4WiMi2i39yMv4+lyh0KSUipfU2ocSr/WSuTRP8cNz4VacZPh/HsiqN4fPlh3PMtl6wmef3wXxI2nXLOFVyVqmai2VH9fvgSACAtrxjpeSU4fSWXiXSiCmq5AyBSijwLG303prdlqg1X3flur3JXAoucuwElmqrG42tiL+OWHi1ljAgo4epgFsnIL8Vzvx7DpJ6t5A6FiCpczi7C+fR8DO4QVOs1QSifGrziYIr+cXXVV/hKVuh0JbIOJeaLmcS2rVWHL2HmqlgsmtwNLZt4YmjH2p8hzqbvoq36rz1dXXB64TgZoyFSBlYaEVVjSR+fxlQL5RVLtwKdOeaaclsir0SD/YmZEkRjqHrCCACeXXFU8mPUl62afxMRNURd04XHfbQL0/53EAlpVeew6n0Iq/esupJTDI2p1UJtfL9eXKZVbF8dZ6DE097eBOmvOYDyFWztZWl2nU5E2Ky1+Hx7AhIzCiTZ5/b4NKNVM2+tOw2dCMxefQIPLDtoEEMlOd4ncak5eOj7Qw363pSsQpy9lidJHEUcUCQCwKQRkYFSUxfS1TjbGJizTNvKKVJGbyV7kZbLkm0iW3pz7WnsiE8z+lpuxYDElK/+A1B+w3UttzxpVPOG70JGAd5eb3wlREvOgVJ66ucjGPzudpsek6qkZivvc3yfFQaqKm09bfzvR2kq/w6lbBHw0HeHDCpozFmyKV7/tRxJo5mrjhskwetj8LvbMebDXRJHROTcmDQim/l6p2M0W21s6TT7wZAjyCoslTsEkginJNmP/87XvqGufk7KLizDpeuFuOnTPfrnkjNr//vuTcio+v4aQyGzV9uuCXHlIhKm/Hn0ko0icU4rDibLHQIZUTll1BZJ3JrXtK/+eQLFZVqDFYWdvRJ79urjDr8YEJE5TBpRvRy+eB2Rc9cjq6B+N4zpeSUmRzbtzboTV81vVIfnfj0mUST25YJEJdZEJK0yrbPVT9ovYyPvG08aNg0e9I5h5c7y/Rdrfc+Zq3lIyyvGmau5SMky7LVX2fvI2vIt6CP4/MpYi7ZTgsz8EpRqbFupJQVnGsiyl3ZJSZnWu16q2Vuz5q/klwPJ6DJ/o0EyyZ5SRq9YYeW9FQdTDKbuETkjJo2oXr7ckYDiMh1ikrLq9X0HL9Rve6Vy1t4LZRKMdg2v1lPDmGLOG7crn21LQImG/2b27l8TK2qRccv3Jcl6/N3n0nHuWh6WbIzX39SZG8Qx1duj76KtGPfRbsljtFTX+Rst2k7J54Zley5g+o8xCJu1Fr3f3IInfz4CoDzmPecyzHy3MjjTQJapgplPt54zmlyVS2NW6TVn4OJtBo+zC2tPzdfqRCQZqVAEyq+DrdHrsqaG/gZWxlQlvX+PScFFCxJwpRodzknUA4nIUTFppGA5Rj7I5aapOJGpXSwfd7iWW4ynfjlirZBsSqreC0Wl1rsILtXoMOqDnZLus8Oc9ZLuz5jIuRuseqFE0vr3+BUsWHNS7jCoEVKyCvH0L9ZrQh82ay3Wn7hitf3bklYnYt7fcZj7t7zv+TKtiLu/3Y/Ptifob+oKS+uuxMnIt++ppLa4QW2oN/49ZbA8/JbT5V8v/PcUTl7OlSusejPZGN3BGKs00upEvL/5LOb+FWf7gEzQWvlaqL73Fz8fSMZPFUm1we9ut5tely+vOo6h7+1A9Jub9X0YP9t2rta017l/xWE0eyAR1YlJIwXLK1Fe0qhyxM9FZflbx15KywHgeEoOLmYW4PSV8ou9ghINZv1xHHnF0v5bjP1oF77ccd4qS8ueSM1ucPNAuR2qZwUbyWvFwRSsPMSeGPbKFpViX+8yveKXPTmRmoMf9ymjEqEyCTR8yQ4Ul2nx5trTMkfknOoa/Im/al9VCzviDfu1XMgo0CcJHEnMRcNrjMvZRQh/dZ1M0ZjWmFV6LdHjjU24mlO/Juiv/RWH/xIMq+f+S8iweoJLChn5pfoWGUs2ncXzK2MNXt9/QbmJaSKlYNJIwZQ493p/YvkJ94FlBzH1m30Im7XW6PSGpIr+NVqdiE01+i0o2fL9FzH0vR0Y//FuXMkpwnd7L+DXQyn4eqe0Nz7JWYV4Z8MZnE+XNrlz8nIObv9yn6T7tCVfD1dZjns0+bosx3UEr/xxQu4QqMGs36lCgacxh/LYjzFW2e/1evYttCZBoR1V3vjXeNVZmVaHmIv2dU4prpFAvvXzvXjtrzirDGzVpWa/HantPpdh8DMt3X3BqserL1EUcdfX+2xy3dz/7a3YdLJ+PTrvWXpA//WCNSdxz9IDGPfRLmw7I228aXnFyMgvkXSffx417N314eazOHk5B4Ay77eIlIZJI2qwygTS078cxdrjV/TzgZ/6+QiGLdmBbWeu4ZtdiXhng/00wD6RmqP/esDb21BZsa2y0jVrYrq0zQ6t2TvKVDnz8n1J2HbmGu5fdhDXGrkM+4RPdkueSLPE5C/+s/kxieRmkxtCB7kaV2baovwm2Bp6Ldxslf1Gv7kZYbPWKrpPkaUuXTee4LDFdG6p7au2Kt+hpCzkFJWf763RJL9Eo8Wl68b75dTst2MNt31Zdb7yHQ03AAAgAElEQVTfdKpxC5tIrUSjw4ELWVh91DbNydc1Yvrw9/8lAQDOpeXj4e+lTV73XbQV6XnSJo0A4EC1qa4fbz2HyZ/X79rv9JVc/d8GkbNh0kjB7Ola+6lfjmD0h7sQNmst1lachM5czcPVHOuOGllbZYnwJ9sSEDZrreT7v2ii0WBDFVqxV1KPNzYhNbsIkXPXGzRWnfv3STz8fQx2nU3H59sT8FtMCsZ9VP5emPzFXmw7cw2rDlu+bPLKQ1VNDONSc/D2+tMQRREarc4qPY84JY6c1TYzy51LITW7cYlkpeDnROPd+fU+/dS6+qzAKiqwXu1ydpHVEnZy+PlA1TTjO76qqlZe8M9J5BSV1ZqW1BjPrTiGQe9sly1xeDQ5G4WlGsSl5qC4zLCX09My9t8s0+oQOXeDTY/51zHpFkKIUOA0v5ruqtGLSVvPG63xH+/GXV/bbzU/UWMwaaRgSrxQqo93N8Qjr9h++hkZ8/HWc1bd/0EJbkRyCsuQkJaHMR/uxHsb4yWIyrQNcVdRXKbDLxUXmNvjDW86f9x3ETNXHceZin4OR5Oz8fD3MXjp99ha+zLlQkaBfhTypk/34OudiUjPL0HEnPW4t1pptFR2nU03vxHVad2JKziafF2SVfbIdup7wdwQGfklCJu1FmGz1mL0Bzvx1c7zVj+m1M6n5ztl36BtZ67h8eUxkjRJ/vnARYNK2PpU2SqtZ8qitadwow0qYmwtLjWn1r/1ppNX8fjyGNyz9AByzfR2LCzVYHt8GtrNXmuyMnn9iSvYUDEl6sXfY7H51DXEJGXZPIEUNW8jbvp0T60pUP8ev4KwWWvxcj2uWaTSmKofJdDoRAxfsgNbT1/DqUY0gT9iw3YBWp2IBWtOIrkeKyOfsbN+ZURSEWw9X9lS0dHRYkyMdebq24ukjAIMM7NMOdm/xwa3w1PDI9DEy03/3Cdbz2FEZDC6tvI3+j05RWXwcFUhu7AM/d7aaqtQDQxo3wz7rLiqzZmF44yOugX6uOlHq29o0wRf3Nsbzf09GnSM7MJS9HzDOtMwnNXJ18eiy/yNmD0+Eh1CfNDM2x0FJRrcGBFodPvU7CJM/nwvVs24EW2aedk4Wuf27IqjWBMr3UizpRbcHIUHB7bDjvg0BPq4o2srf2h1Ii5mFqB9kI9F+9DqROQWlaGpt5v5jRvpoy1n8dEW6w4gKNn2l4ahXaB3g79fqxMb3Ww4afFExKXmoFSrQ1QLP2w6dQ0BXm74YV8S3NUqpFwvQmxKNu7u2wZRLf0wrX/bRh3PlAsZBRjuZNdlHq4qFJfpMKlnSyy8tSvSckuw/UwaHhvSHgBw4lIOkjIL8MyKqpUYP7unF27q3hIA8NXO8yjT6NA+yMfkSrqTe7XCqM4hiltp96v7bsC4ri1scixrVLPL6f07eqB/eDO0auJp8fckpOVh1AfKX8VszyvDIYrArNXHMblXKKb0DpU7JKIGEQThsCiK0RZty6SRMklxkUX25fiCMfCraARdefHw7f3RGB0VAgDILS5DcakWKw6m4MMtZ9E+yBt+Hq44lpItW8xK9NMj/dCrTRO4q1VQu6iQV1yG4jIdDlzIxNO/HMXdfdtgWKcgDO4QiKh5G+UO1+H0aN0EsXW8Jzc9PwQHEjMxbUAYisu0+HDLWXy9MxFPDAvHK+Mi9VMQSzQ6eLq5AChfgdHHXQ2NVgeNTsT1wlK4q11wISMfvdsG1DqGKIrYePIqRkc1h4vEDcnS8ooR4OUGtUvtQt0yrQ6uRp5XKqXcpCQtnoglG+Px2fYEbH9pGFo28YC7uvzf/s6v9uFgUhaSFk/Ub//trkT8dSwVJy/nInbeGPh7VTXQ12h1Rv9tGqKoVItF607hp/1cIXDXy8PRppmXPmmy4rH+GBDezKLv/ePwJbzYyMqNUZ1D9MvZW6L6+8VSxWVaZBaU6m9y0/NKEOTrjm93JcLfyxV3RrdWzN+MEnx7fzR+3JdU5zS9QB93yRsa21JTL1ccnTfG7HYarQ4uKgEHLmRhe3waZo/vXK/j/H0sFc/9eqyhYSra5ueHQKUSEG7BgMC/xy/j6V+Omt1OaZ4cFo4Zw8IxYslO/fs9afFE/HzgIub8GYcDr46Eh9oFXu4udV4j6HQiijVaeLmpbRW6IpVotCgq1RoMpjfE4YtZ+L+Vx7D+uSHwcZfud3rmai7CmnnDw9VFsn3KhUkjBSnV6PDOhjP4354LePf27nBVC5jcKxT5JRr8dTQVapWAu/q0hiAY3tgUlWrReZ5t5zaT/AK83erV64HqNnt8pH6ZVbI/W14Ygls+21tnr66xXUJwR+/W+O98Jjq38MXtN4Tin+OXDS7AZ47rhCeHRRh8n04nQqUSkF1YijWxl9GrdVOoVEBTLze0rLhpTEjLR1puMbadScOIyGC4qATc9c1+3BXdGotv74YyrYjkrEKcT8/HnnMZWL7/Ij65uxfyisvw9c5EjIgMxqzxkfBwdUGpRgdXF0H/WS+KItLzS3AgMQuBPu7Ydz4DTwyLwMWsAri6qODmooKPuxqZBeUXoDvi03Ff/7b6i5RSjQ5/Hr2E4jIdPtpyFgMjAiGKwOLbu0GtUuFabjHCKqpDtDoRV3OL0czbDW4uKrR/dR0evDFM38hUCfw9XetsMLrtxaEoKtPin9grBlPcAn3cUVymxd5ZI/DboRQsWncan93TS3/jERHsgy0vDMXZa3mICPLBqiOXMHPVceyeORwrD6Xg6RHl74v0vBKoXQS4uqjw5r+nMKFbC0xffti6P7SdWXhrV8z9K07/+MzCcXBXq7Dp1DXsOpuOnw8kY3CHQOw+l4GurfzQv10zLN0jz+pU/p6uKC7TokSjg5+HGrnFGv374t5+bTC8UzB0oojm/h44cyUPbmoVvtp5Hmeu5iHxrQnYnZCBB5YdxPBOQdhesRx9zZ+fnMc7t3dDQYkWb/x7CnGvj0Vmfgm+25uEmItZiEutPRVrzdMD0T20CQDgWm4xHv0hBh/e1RMRwVWJk9f+OuFUCenI5r54dHB7fbuC/bNHQu0iYEPcVbzmgH9X30zrrT+HRLXww6kr5e+Tpl6uODJ3NIDy807ft7YirJkXlj/SD9//l4T/7bmAEwvG6FcTvl5QCkEAPFxdMOqDnXh3Snckphfgtb/i0D3UH38+ORAuKgGxKdnw9VDj7LV8dAzxQQt/T7ipVUjKLEBxmRYuFYm7eX/H4aGB7dAxxBdA+cwFf0/DlYsPJWUhv0SDQRGB+iSXKIoo0eggiuVTCAdWqx5fujsR/p6uyMgvRfdQf4PXTl7OwcXMQnRp6Ye2zbyxIz4ND353CLtnDkfrAC/9YKCHqwuOJl/H+fQCzPnzBEo0OhydOxpe7i5IyihEmwAveLq5oLhMi33nMzE8MhgAkFdcBg/X8mRcZn4JjqfmYHin8tfu+nofDlzIwvt39MCwTkFoVnG9AJRXjZ5Ly0f7QG8E+rjjls/24H8P9EFGfgke+v4QBkUEYny35mjd1AtFZVq0D/RGiUaH9zfFY3t8Om7p0RKf3N1L2jeNDJg0UpDnfj2KvyVsNEdERM5rQrfmWHdCWSv+EBERkTw6BPvgXJo0Kw+HB3njfD16zjXxckW2iR5mjqxNgBd2zRwudxiNVp+kkSQ13IIgjBMEIV4QhARBEGYZed1dEISVFa8fEAQhTIrj2gMmjIiISCpMGBEREVElqRJGAOqVMALglAkjAPVqnu4oGp00EgTBBcDnAMYDiAJwtyAIUTU2ewTAdVEUIwB8COCdxh6XiIiIiIiIiIisR4pKo74AEkRRTBRFsRTArwAm1dhmEoAfKr5eBWCkULOJDxERERERERERKYYUSaNWAFKqPb5U8ZzRbURR1ADIAVBr2Q1BEKYLghAjCEJMenq6BKEREREREREREVFDSJE0MlYxVLO7tiXbQBTFb0RRjBZFMTooKEiC0IiIiIiIiIiIqCGkSBpdAtC62uNQADW7P+u3EQRBDcAfQJYEx1a8Kb1D5Q6BiIiIiIiIiBop2Ndd7hBsToqk0SEAHQRBaCcIghuAqQDW1NhmDYAHKr6eAmCbKIq1Ko0c0ZI7emB4J/NVU0fmjkbXVn42iIiIyPk083bDbb1a4av7bsBTw8Px7pTu+OupgZgxNByPD23f4P0+PLAdAGB4pyCMiQoxus2SO3rgh4f76h9HtSj/rF/z9EAkLZ6I/xvVQf/asyM74PEhteP56ZF+mH9zFIZ2rDqftAnw0n89qWdLAMDYLsZjqPTdg33M/UgOx89Drf86PMjbou9xc5FkcVlSOB93NdzUKrQO8AQAvDGpCzb+3xB4uPLfn0yb0jsU9/VvU+c2jw1uh0Afd4PP5IERtTpzEBlQqwQsnNRF/3juTVH4/J4bDLYJ8HYzeLz8kb5YOKkL2gWaPr+1alL+GffHEwPwxxMDMKRjEOZM6IwVj/U32O7Tu3sZPO7S0vDetIW/R619+7irDR5v+L/BWDVjAL64tzzuBwa0xRuTumDPK8P1yZZ7+rXB7pnD8eqESKPx3te/jcF98dybytfYurdfG/w+Y4DBtpufH4IXRnc0/oNXGNwhEF9P6w0A6BPWFBfenlDn9sb0aN0Ea54eiF0vD8fBOaPq/f32TpAidyMIwgQAHwFwAbBMFMVFgiC8ASBGFMU1giB4AFgOoBfKK4ymiqKYWNc+o6OjxZiYmEbHphQarQ46EXBTq5CRXwJ/T1e4uqhQqtEhu6gUwb4e0OlEpOWV4PTVXAzrGISnfjnC5ZWdTNLiifgtJgX7EzNxObsI+xOz8Nk9vXBT95Yo1eigE0VEzt0gd5h24Z3bu+HO6NbYejoNj/4Yg8EdAjHvpihcySnG/csOAig/+V3JKZY5UgKAYZ2CsCM+HR9P7YnPtydAqxPh465G7KUcg+1GRgZj+pD2uOub/QCAHx7ui6Edg5CcWQh/T1cs2RSP+/q3RafmvkjOLMSQ97bj3n5tsGhyN7MxaLQ6uKgEFJfpAADZRaVwc1HhXFo++rdvhsT0fGw+dQ2PDw2vcz9P/nxY/9n96/T+6N++7hsFrU7EsZTr6N02QP/c4YvX0bKJB5r7eaDmuhEFJRp4urpApTK+nkROYRnS8orRIcQXOp2Ivecz4OnqgmdWHMWm54cgJuk6uoX6Y+3xK5i/5qTZ34u1TekdivemdMfHW8/hoy3nAAAD2jfDN/f3hquLSv+Zt+SOHnjp91hEt22KHx7uiy7zN+KTu3uhY4gPxn20GwAwsVsLzBofidYBXigs1SC3SIPm/h745UAyerdtirbNvJBVUAq1SoDaRYVDSVnIzC/FPf3a4PDFLLRq4gU/TzW83MovgsNmrZXnl6IgwzsFYXt8Oj68qwc8XdXoFuqPtNxiNPN2R6umnigu00Irijh1ORc3tGkKN3V5wqXnG5tsvhzz19N64/HlhwEAXm4u2PvKCOSXaNDEyxWp2UUY99FutA/yxjfTohER7GPwvanZRfobK1EU8em2BFzLLcbPB5IBAH8/NRCTPt9r05+HlKPyeqF9kDe2vTgMQPlnd/ir6zCtf1u8fksXxF/LQ7tAb7i5qAw+n9PzSnAlpwjdQ5sgI78ECWn5aBPghRsXb0OrJp5IzS6S6aeyjr5hAfhtxgAcSb6O2774D0D5gEXK9UIM7hCEuNQctGzioT/nbYi7gs+2JyAuNVfOsCUV6OOOjPwSAMD0Ie3xza5EDOsUhO8f6muw3R+HL6FPWADaNKsaBCou0yJy7gb0CPXH308P0j+/Iz4NXm5q9G0XgMJSDYrLdLUSSHGpOQj2dYe3uxpqFwHrT1xFaFNPRIcFwJSjydfRtZU/XCsGS85czYUoAp1bVCVtzl7Lg7e7Wv8ZmVNYhrySMoQ29YIoili2NwnL9lzA3lkjzP5ucorK4O/pWuv5/BINXF0ECBD055HiMi08XF1qbZuaXQRfDzX8PKr2U6rRIS2vGIE+7lAJAnbEp2H68sPYO2uEPm5TMvJLEOjjjuzCUqhdVHB1EeCqKo9BpRJMxmHvBEE4LIpitEXbKrXgx9GSRg2xfF8S5v4t/wU92UbS4okWbZddWAp3tQs83VxwNacYGfkluOnTPVaOTrm+mdYbYYHeuJhZiGGdgqBWCbicU4yW/rVvtisVl2mhEgSsj7uC5349ZuOInVunEF/EX8sDALw4uiOW7rmA50d1wIMVFTvVabQ6xFy8jr5hAcgr1sDfq/ZFhjm5xWXwdlPDxUSCxVqu5hTD39MVnm7KvchQwjmm+ueeKIrYn5iF/u0DDP5280s0KNXUvjiuThRF/HwgGbfd0Eqf8JFCel4JtsenYeaq45Lt055Yel4y5t6l+7E3IRMvj+2E9zbGSxiVoV8e64d7vj0AoDzeT7aeQ/sgb9zUvWWtbTPyS9DUy83izwOtTsRfR1Nxa69WcFEJGPH+DiSmF0gav734eGpP3NKjJdrNXid3KDaz7cWh8HZXI9jX3eT1RGOUaXUQAETMWS/5vuW046VhCKuoevn1YDJmrT6BmNdGIdDH/JQee0vUB/q4YftLw5BTVAY/T1e8uvoEpvVvi44hvui1cDP8PV0RO38MNFodVIJgcrCnpr0JGejS0g9NvEyf94gai0kjB7HyUDJe+eOE3GGQFf3xxACsOXYZoU298JiRKSmWSsoowLAlO6QLzE58cGcP3HZDw/uG/X0slUkjib1zeze8/s8pFJZqjb7+1uRu6Bjig9NXcjFtQJhtgyMDcieNDr46EsF+tUvdlWjKl/8h5uJ1ucOwqWdHdjBb8l+XvOIynLmahz5hAbiYWYCh7+3Qv/blvTfgiZ+PWLyvLS8MxYI1J/H0iAj0b98M59PzMfL9nQDKE0WzV5/Ak8PC0bralE1r+H7vBSz455RVjyGXTiG+WDS5K44kX8db684AAN69vTtu7dUKalXVzW7lTX0TL1fMmdAZkc39kJ5fjLbNvOHv6YpJn+1F//bN8MeRS1CrBLioBJRodFAJgK7GLcexeaPR843NeGNSF0zpHYqoeRtt+jPXZVTnYCx9wDbTedPzStBn0RabHMvaGpNoBoC3153G17vqnIyiGEvvj8YoE9PSiexBfZJG0g3HkeQGRgTKHQJZWe+2AQZTUhoq0EYN2e6Kbo2VMSkYHRWCKb1D8fOBZOw6my75cRbe2hVz/4oz+fqv0/tj6jf70c/MtB9zbmjTtFHfT4ZmDA3HXX3a4K4+Vb0eXvvrBK7mFOP/RnVE+yBveLq6QBCEOkulyTak+OxpqM3PD7GbhBEArHriRrsbAW+sOxq5kIevhyv6VPydt21W1Wvj2LzR9S7zjwj2wU+P9tM/Dg/yweHXRiGvWAMAePs289NPpTCycwgW/HMKfcKa4lCSYyURpw1oi+iwAESHBWBk5xD4uKsRYuRv9Nv7oxHo44ZeBudPf/1XldNT3r+zh8H3peUVo++irbinXxv8fTQVO14ejiZebo1OMjTElheG4ty1vDoTl7ZKGAFAkK872gR4ITmr0GbH9HR1QVGZ8cGdhoh7fSykKESYPaGzTZJG218ahge/O4iLmZb/zivfq5XnAiaMyJmw05+ChTa17oiZNdnqAo7K+birsXfWCMwab7yhnFTKdDokLZ6Ib++PxtguzTFnQmc09/PAI4NqTy3a9uLQeu9/dFQITr0xFq4VI5p3Rofi76cGGsyrvrdfG/Rv3wxJiyeanaNsTusAL1kuWB3RK+Mi8dKY2lUJb97aDUsf6IOurfzh5aa2Sok/NUxUS3kWX3hkUDt0CPGV5dhkmYRF461WtdPEy02S3hDNfNz1U2BspfKc8fuMG01u06yOaZT2IjzIx2jCCCg/T/dqwIBLsK8Hzr45Hotu7YqTb4xDkIyrD0UE+6B3mPGfYfb4SMTOH2PjiAxXY/rfAxYN/DfKydfH4s1bu0q2Px93NXw96j+F3JaqN3VuF+iNnS8Px7F5o+u9n6X3R+sbMxM5CyaNSDKeri548MYwLLmjB6b2aY2HBobJHVKjjepsP6MIrZp4YsbQcByyYkd/bY3a8k7NfbH/1ZGYe1OUwYpPH0/tifZBPji+oH4XXn4ervByUyO64mJuTFRz9GjdBOueHYS/nhqI+DfHYeEk6S5ySBpL74/GE8PCoeaKU2TEvf3a4MzCcfjsnl5oH+SNZ0ZEyB1Sg3w8tafcIdi17x/qg03PD5E7DMm8fksXo8/zZtI0N7VKMQMHwb4e+PZ+w+RM6wBPPDq4vdEmvdZWPaER7Gv9KkyVSsB9/ds2+GetPnV1scQDxTGvSX8dGxHsg+8eql091sTLDU8ND8eCm6Ms6rkElFcYGRssJXJknJ5Gkrijdyhmjos0GDl6YXRHaLQilu+/KGNkjfP86A7YcvoaOrfww+kr0q3q0CHYB69Z6cLSmqOcreuofps9oTOmDWiL/BINIpuXX/z4ebhi5rhOyMovxdI9Fyw+TkSwr0EFkCAI6Nm6ScMDJ6tiiTbVpXLlupu6tzTanNheDAhX1nLZC26OwqZT1/Df+UzJ922NG/thnYINHq97djAmfLJb8uPYygM3hqFDiI++CTdg31VGSsjlzBzXCe9usF7T9JpG1zh37Xp5uGxJrTkTo3BLz5YQBAHdQv3Nf0MjDKrW/kLXwCllHSpWHxzXpTmm9m1jZuv6CfRxR9tmXvWaOlYpafFEdJu/EXklGoPnBZhOxr08trxK/8CFLKyP46rVRMZwWJjq5XYTTYfn3RxVq9TY18MVC2/tis4t/PBiI5ppyslafeIHRgRiaMcgq+zb0pUZGuK2G1rV+XpoUy99wqjSk8MiLE6QiVBmY34y7bsHbdf3gaS34f8Gyx2C/VDYx9ODA9vh52p9fqRki9tmS6dHnlk4zsqRNJxQ4zelUgl2dx6Lblte2VvzZ5HDk8NsX4X4w8NVS6DLWQXlplahd9sAm/RarN4frD5v13v7SZscqktjKv1HdC5PUK99dpC+mqxyxcSxXULgZWJV05rV9ERUhUkjqpexXWp/iHdu4QfvOpY4Xv/cYDwzsoM1w7IatUv5ScbXQ42418dKtt8SjXTNB23l4JyRaB/kY92DyHS+dlPzo7ChhkcGm9+IFKtmkpdMU8r9xJ3Rodjx0jAAlt3k3tOAGz1rDj7UlxT9j2zlwzt7Wm2wyVpmT+gMABjcQRmLr1i711G7Gn2wrDWAZy9MvV0fH1p7Rd83bNgeYHKvugcp6/LulO74b9YIdGnpj3aB5RXylT3avp4WjVNvGE9EPz+6o76CiogM8U6J6qX6yeX7h/ogYdF4rH9usKIuMBtrxWP99V93CvHF7PGR+OzuXvBxVxtt9NsQUS3s60ZtzdMDbTLHXi4O9PYlUhRrN+e3JSVM3wHKe3PU1QC65hSp0KaNWzBATvZWyTioQ6DdJY16t22KpMUTrdb4vL7UVjwh//HEAPzxhOkm5s7I1Ipns8d3NnictHgiXFQCTr4+FrHzx6BDSHlyZXikdZJukc0bvliCu9oFLSsWSokI9sXHU3vWWs3PmM4t/LD5hfov4kLkDJg0IotN7N4Cvh7lFUV3922DYZ2C69X49s8nlX+iHhkZjAHhzTCqczBGdQ6GIAh4fGi4fmnop0d0QMKi8Y0+zmQT0/yksvPlYZLur3uobfoJ3dJTnn4nAV7224eCSKnev6OHQYN8exfi54HZCkiCmUtKrHtuMH57fID+sbFpRwdfHYmEReNxToLzmTVZu7dLY1VPJEZUVCjYU85IiVW2KitmZ6Na+CPASN+pX6f3x2o7uEaVwsE5Iw0eV75fXxrTEeffmmD2+73d1fD3dEVEsC+OLxiDO6NbWyFKQO2iwqLJ0lQ2TerZCn6NWNktum1TdGul7M8iImtT3tmCFKtdM28MaN8M797eHfMa0MS5IUu02lrltcrSB/pg6QPGRzgbu0LUgVdHwsfduj3o2zaTbhliD1dpPia+uu+GOl/f/PyQWo1SbWVltRssskygjzueGBYudxikYAHebopZKUkq0xWQBKuZlDj4quFNYIifB/q2C9A/dq+RGOjXLgDBfh5Qu6jgyhUPG6V326aIbO6LhZO66BN1N3VvgbbNlFG1Y44S/zrv7W+9vjmmPo76t29mk15CSlCzavyuPuVJn8eHhuv7/li6ipifh6tVP+MtrdqTahaAKaueuBH/PDPIqscgUjpeLZDFRIgQBAF39mkNTxNN5OxdzZU0THlgQNsGHyPEzzbTvBp6Hq85dU6qaWnjurao83U57y2VUpZvL96a3A0xr43CK+Pkr7qgxnt2hO2bz9orJSTBat5IBZs5p/RtF2DQ18jcj/Dp3b0aGprk5P9t183VRYUN/zcE0waE6StYPFxdrNagXGoKeDvX8sTQcPzzNG/QK/laeZBx7sQonFk4Tp9A3j1zOLba0RSttyZ3w9MjpO2bqsS/CyK5MWlEFrO3efoNYWmZ7es2bAbYUAmLzJcZV/f8qPKRGi83F/0o04D2zbDMZj0leJZWOje1CtteHNqgxrqkXC+M6YRVM1htZy8sbRTcudoAwFuTu+HDu4z39BhSrRFwgLcbbu4hzzRhY5SQpGuI0KZe+OupgVY9hqMm7QVB0LdCcGYLb+2Khbd2RaCVG4OrVIJBs/nWAV7w92r4VC4pmbvtWHxbN6tcj/z8aD9MkqldApFSMWlEFnOCnJHdXqAa41LPZpKVP7pWFBHz2igkLZ6IFdP76/s0WJvcv3pbLiVrr4Z3CrL+Cnoki26h/hjeyblXEbIHX913A26/wfSqQgsndan1XOVna2XVaM0eR5OqJYnGd20uQZSWc1erEOzrjo+n9rTpcW0h0Me6vfJ6SNDvyVi/K1KGaf3bYlr/hle1O4LuZvoIWeu68cbwQHw8VTkVl0RKwFQ+kQ0pZUnbSm9M6oI3/z2NUq1Ov4KYUpaVtjVHXh1OKrzBcFzuahd891BfhM1aK3coVAdz06djQ/kAABylSURBVHynDQgzu4+aN1q39w7FqKgQaHUi/Gxc4RH3+lgIKO8V+Nyvx2x6bHt3Y4SyrifIOkytbmZLcq3426N1E4yJCsGmU9eMvq6AXw2R02ClEVmMH87mPT08AnMmdDb5eq/WtlmFzBIB3m64f0CYfsWynq2bwt/TFS+Otm5DwZoql1V1kbvUiMxS4ko7RM6ivp/N/SqaYVf22qnrHO7v6YoAb7dGL/RQX64uqjqPWd+KWSWxxTXTjw/3bdT32+tp97mRDetho7bD95OcFfCPDy1v/P9/o6TtGVQf39wfXeu5ts28cEfvUNzay7Dq0k3iz687eocaTPMlcmasNCKLiQ4+Qe2GNvVL6LirVSjR6Aye83ZXI9jPuvPPpXJk7mgA5dVGgzsEYlCHQMTOH2PzOL69Pxqrj6TKvtrMiMhgfLjlrKwxKNEfTwzA3oRMlGi0eGyw/CtHkR2xv/szi3x0V0/830rbV8U8U88b5TkTO+P+AW3Rwt8TQNU53J4SBf6eyuitolTV+1E5EnPv0edHd8TXu86juExX94bVJC2e2Mio5GHJn2t026Z4fnRHHEjMxCfbEiQ79ktjOqF7qyYWLxJjC+0DvfH7jAFoVmOFtzMLx0l+rPfuMN4HjsgZcdiYLHZjuGOXQq9+sn5NKw/OGYWY10Zh8/NDMLlitCM6rClu6m66eZ6t025n3xyPb6uN0hx8dSRWzRiA9c8N1j/n5abGpJ6me2RIqXpzy4W3dsWKx/qjdYAXnhvVQfZ+Ut1C/fWJNKrSu20Anh3ZAS+PjUQTL+v26CD5bfi/wVj+SOOqFxxdzdFtJZg5rlOt/nOuLiqjPcg4zdSxnHx9bIO/V6nvBEveo2cWjrdBJPYhPMgHAyMC8cKYTvoKw+oqm6a/Nblbvfbr6qLCxO4tZL8+q3T+rQnY+uLQWgkjoHzVwuoNvYlIWkwakUVev6ULhkowovXy2E4SRKMM/p6uCPRxR4cQX3x4V0+cWTgOfcICFFVO76ZWYXRUCH6fMQDzb45CsJ8HosMCZCu3PfxaVVLmvn5tMCC8mSxxmKLRWj5qSeSIIpv7YXAHx6xecGRPDovAFjPLZNvbFPNRnZVT3aAUKx7rX+s5b3c1woO8G7Q/pSQDamod4IkZQ8PRzLv2QEXsvPpXRL9ze/2SJfbsRyNJ/yeGhSN2/hi7X/nURSUo9j1L5OiYNLIT7Rt4QSCVu/pYthS9OU8Nj7DbEmFzLBnhGBNl25VpKvUJC8BDA9vJcuzq3NTlo1aKxWsRIskEW3mpaKqfVk3Lp6ndGKGsZL0p30zrLXcIivLU8HCTAy0rHx+g7HNrPQmCgFnjI9Gvfe2qmerLwVvaw8ae+9J0NbOCGGDZlFNO9SSixmDSyE5se3EYbpOxJN7eRijrq7+RC5PGMFaVlbR4IrpJsESuvfvwzp6IeW2UIkeLgn098Nk9XGaVqDG+vT8a/z4zCF1a8vNOScKDfLB31gjMGBIudyhGVU8STR/SHioFVe3KrU2Al37g55FBtQeAAn3ccXef+leReLsrezqPudUCnUF9/41qTu2bOc5xKvyJSD5MGpHTax/kjV8erV3y3Ri81jXNTa1CoJH56EpRvSfVQwPD5AuESEbH5jWsv9d9/dtgdFSIRaPjZLnq/eAao1UTT8UmY8Z0aY7pQ8qb7RublmRvWjXxxJTeoZLs67fHB+jPm3NvijJasT2oQ/36Tj43sgNWTh8gSXzWckuPljg4Z6TJ14d2smwqraMPfFbnplbh46k9AQC39WqFJ4dFyByRNLzclJ3gJHJ0XD1N4R4aGIbDF6/LHYZDr5ymVgmKvYgmeTwzIgKtA7wwuVcrfLc3Se5wZFOzsS45jyZebnhmRAQ+redKPF5uvKywhsH1TAjYO0e44lCpBCy5owdWHb4kdyhGTR/SHt7uyv979XUvn1YV1swL799puJrVp3f3QuTcDXKEpWiTerbCxG4toFJgRXdD7J89Ep5sck0kK+WfLZzc/Ju7VD2Q8bPfkUdpuJIM1fTimKpy7qNzR6PXws0AgKZerrheWGawbXTbpohRQGJXah1DfPDXU/VbUZAcy4tjOuG/85n1Grh4flRHK0ZEjq7ybOzI1xz1EeLnjmu5JQ363rBmXkjKLDT5upIW7aiLp5sLDr46Ek293eBao4eRc6yWZfzfKTzIG+fTCwAY71ektrDfkz1o7u8hdwhETs9xPlGIGshBBmLISppWmyaxqMZytZufH4LvH+6LVk08bR2W1TXzdmfVCNV7SoAnpxCYJXUPPYdScT52pOrmu/ua7jU0vI7pVQ1ZNOTnR/th9ZM3ImHReOx4eXid29ZMwChZsJ9Ho+Jt6YDn6AW3dEHiWxOw4OYoPD+ayXoisi77OWMQEcms+sjsAwPaokOIL3zc1QjnNC5yUB/c2ZPVQxL7/qHaS2IPinCu6Wem9AxtAgDo3Nx+V7uqqWOI8fPDmYXjMK96NbkRbur6XaYPjAjEDW2aGq0yeXZkB/3XSYsn2k2lkTnvTumOid1MN8yOf3McghxsJcfRUSEY3CEIKpWABwe2c5KKKyKSE5NGdiS0qZdsx3aEMT9T1SBPDpe+SaAflzZ1aKM6B2PBLXVf7Ns7VuARAAT5uuO5UR3Mb0gW83B1wajOIQbPzRofWef3OMs06vHdWmD3zOEYHhksdyhW465W4cCrI+Hh6oJ2gd5IfGsC5t0UZTRx+OPD/fDsyA4I8Wt80uMFB61GuTO6NT6/9waTr7uqHO9Wh9M3icjWOPfAjjwzIgJdW/ph+vLDcodil9Y+OwgZ+aU4lJSFnKIyqFUCpg1oC3e19CM0C2/tirBm3vh46znJ903ycanIpLirXSBUy6qYup0b37U51sddtUFkRGRPpvZpjS2nrwEAnhwWbna1ufm3RNkiLEVoHSDfAJk11LzGCPJ1R4hfVY8WlUrAw4Pa4eFB7bB8XxIgCOjdpikAoF2gt8Mme2xFJ4pQ2XHS9anh4VhxMLnGs8waEZFtOV763YG5uqgwpktzmx93Su9Q+NjBChumdG3lh0/u7oUmXm6ICPbB3X3bYMbQcDw6uL1VEkYA4Ofhiscqlg4mxzE8MhjTh7THG5PMVxk9OqgdPp7ay+r9jjY9P8TsNsEWlubfGN5M/7WjlfMTKcmoqBAkLBqPl8d2Mpg2ZMyfT96IYF82grVXd0SHIjzIW/+4XaC3yW2nDQjDtP5tEdVSmul5Tbycp+r5kUHtjD5v79PwQpt6IdDHzfyGRERWxKQRmfXEsHC5QzBpQPtmZrf53wN9cEuPljaIhhxVn7DyUV+VALw6oTOa+RgmVGpO5Wrm7YbXboqCm1qFQAuTL5N61v89OiYqBB1DfM1u9/Zt3cxuA1RdXN/So2Wtpt9EJC21iwpPDY8w24/EnhoWU22uLiq8MLp8RU4vN5c6p1JJ7di8MTY7ltzm3hSFG9o0qfW84IBzrTk9jYhszX7LR4gAjOkSgvT8EiSk5Rt9fefLwwzKwIkaYtmDfZCcVdiwi08zV3eDOwQiKbMAz4yIwN/HLhvdpqW/By7nFNfetQQhfDy1J9xcVGjV1BPvbYwHANxu59WFRPaoiZcrRnQKxuqjqXKHQhKrPHUM7RgEPw/5qn9C/NzRq3VT2Y5PDWV47cGcERHZWqOGrwRBCBAEYbMgCOcq/m/0TCQIglYQhGMV/61pzDHJ9pR88+iiErDlhaH49O5e+PeZQXKHQw7K18MVXVqa7jlSM5VU/YKuf3jd1XDLH+mH3TNHICK4qmLIvcaKOfVJVn1+T92j2DPHddJ//cSwcEzs1gLju7VA99AmeGZEB3i7uehXMCKqdGKB81QsWMus8ZGYf7Pp3kTH5o3BB3f1tGFE5GwOvDoKX03rLXcYVlN57l3xWH9Z45Canc+wIyIH0Nia51kAtoqi2AHA1orHxhSJotiz4r9bGnlMp3drA6ax1BTa1PI+K9ao1JnYvWp51GUPRuPefm3qXDLVnJt7tDTbSJTIWm7vHQqgvNk6AHi7V003eXlMJ9zbr02j9v/Dw1VLdFdfRcdYBdHQTkGIf3McEt+aUOu1kZHBeHJY1WqBr4yLNFiauW+7AJx8Yxz8nagPBlnGV8bqCEcxY2g4HhpovO8KEUnHTe1YUzprjhuN62r7/qZE5Nwa+6k6CcAPFV//AODWRu6PLPDR1F64uZE9ekaaWM529ZM3GjyeUnEzLLVPpvZCv3YB+seLJnfD5/feYHQ++ojIYAR4G28CWFcFRrtAbzT3l29qWvWRoTkTOssWB1nfTd1bImnxRHRp6Y/XJnbGz49UjXKqXVRoU7Ea0PSK5ujjujTHe1O6o1eN93t4kDcm9WyJu/q01j/35LBwRAT76B8P7RhU7TuMF6m7q12gUgnwcitPXlX+mThibweSl7taha0vDsWJBWNw8vWxcofjEIZ1CjK/ERHVMrlXKwBA64qBUcPzpf0SqtUzj4gMxp3RrevYmohIeo2ddxQiiuIVABBF8YogCMYzEYCHIAgxADQAFoui+JexjQRBmA5gOoD/b+/Og+So7gOOf3+7K2kl0OpEB2J1IYwQp2QhhIRkGTAWp7hEBMScQmVsqiCAbTBl44rtAseJU0lBheCEckjsADa4kGMbzBXjVMxlLnEaIRMDUiBAEI6jxBC9/DG969HOzF6j2d6d+X6qurbnTc/0m/31m575zev3mD69ul/m1b0vHDePC1fMYcqYVmZe8cOK2/3p6gNrsv/mpuDaUw7gy//0HEv2nNhZfsenlpbU56ZzDmbL1m0ces39O5QfNW8yq7tJaj1w+YqdWue+GjW8hb9YcxCHzJqQa/JKA2vtsu5nzXvl2mM711d3+eB332UrANi+PbF12/vc+eTmkumnOwbY/oeHfs2Zh8woef5U1P3ogctXsGXr/7Df7m2cdegMLvronJLtpWrcfuES9txt1543rDNjRg5j67b3a/Lc3zp3UbfnZQ1deQxgfPqi6WWmbK9Pn1g8gzMWTaeluWmHc+1QV/x7z3AHxpeUgx6TRhFxL1CuH+RVfdjP9JTS5oiYDdwfERtSSi933SildCNwI8DChQsd562GWpqbShIZJ8+fxgEDeInXrIm7cNM5B5eUTxs7ktff3bZD2dQxI/nzPziQP7r1qc6yq0/Yt2TWmevPWMD9L7zZOdtV3lYdNC3vKmgQOKi90KNo0czxPWxZ0NQUrFs+mx9t2NLZ6+CJL3yMCGhrHcZXTtyfr5xYfnaz4jfOyW2tnZeX/vGq/TrLf3zxMh595Z1+vBJJAHddsqzkhwypkjz7eF5z8v69nkFzqIsIWprrr0dt/b0iSUNNj0mjlNKRle6LiDciYmrWy2gq8GaF59ic/d0UEf8MzAdKkkbqvTUHt/ODp8rPtNRfg2UAztsvXMKG17dywc2P7VB+0vw9dkgalXPsAVN3GC9JGgwOmT2Bp754VJ/GCtp39zG89NXfj0s0rsIlml315pfsfaa2sc/Utl7XRQL4+ZWHs+k/fsuZf/Nw3lXJ3dQxvR8X8IY//DCTisYi641HrjqCRV+9r6/VklSHvLRcUt6q7eO4Hjg7Wz8buLPrBhExLiJGZOsTgaXAc1Xut+EtnTOxrrreFpsyppWPzZsMFJJjUj2o5eDSd12yrGbPLXWYOmYkS+dM7HlD7WDlflNYML1vvV8njW7lwc98lHOXzmSeCd4hr2OijpMW2PtYfdd1RlVJGmjVjml0LXBbRJwP/BpYDRARC4FPppTWAvsAfx0R2ykkqa5NKZk0Uo9e+PJKr92WemHulDYuWDaLb/7sV7QOs81I9WD6hFFcffy+eVdDO0H7+FF1+0Ofau8je+/Gprd+m3c1JDWwqpJGKaW3gSPKlD8GrM3W/xVojIuph4iOXjyDXdfxirpKeYwoKQ1SVx69D5cdtTcjWrpvN5Jq45qT9+fKOzbkXQ1JdabJy9Mk5cyfpBvMuuWzue6M+d1u8/lj5g5QbSTtLE1N0WOiVVJtXHn0XNpaC5egLtvr95fwndrNDJ+S1BvFKaMlcybkVg9Jjcuk0RC3/EO7da7fe+lHKm7308+soH38SNYeNqvHngjrlu+50+pXS/7yIkkD785PL2VYHc5QVI3tZTq+jhrezNdPPWDgKyOprnR83F172Cw+sXhGvpWR1JBMGg1xN5+3iLbWwlWGE3etPLvSjAm78LPPHs6kbPrtoe7q4+ex+9jez14jSdo5Dmwfy16TRuddjUFrl+EtzJ0ymm+cdqCzHkmqWsf7yMTRI3xPkZSLagfC1iAS9O9E8vDnj2Drtvd3cm1q69yls/KugiQ1rHOWzOSztz9N+7hReVdlUJg65vc/yDQ1wV2XLM+xNpLqScePwmNH1m4WVknqjkmjOpLo38DQk9tamVwnPZAkSbV32sHtnHZwe97VyNUPLjqM46/7F6Bw+Uj7+ELv14Pax+ZZLUl15rylsxg3ajinLHCMNEn58PI0SZKkPtp/jzGcu3QmALuOaOGAPcZy76XLuWDZ7HwrJqmutDQ3sXphO01NXpomKR/2NKoj/b08bSj5ztpDeOLVd/OuhiRJfG7lXPaePJrD504CYI5jPUmSpDpj0khl7bt7G89ufi/vapRYMmciS+ZM7HlDSZJqrHVYM2sWTc+7GpIkSTVj0khlrb/oMLan/o2RJEmSJEmShj7HNKoD5x9WGD+hdXgTMybsnJlsmpuCYc0eHpIkSZIkNSqzAnXg4iP34pVrj2VESzMPXLaCg2eOy7tKkiRJkiRpiPPytDrT1BSdA2Lfsm4xY0cN43cfbM+5VpIkSZIkaagxaVTHApg7pS3vakiSJEmSpCHIy9MkSZIkSZJUwqSRJEmSJEmSSpg0qkOJlHcVJEmSJEnSEGfSSJIkSZIkSSVMGtWhjtnTJEmSJEmS+sukkSRJkiRJkkqYNJIkSZIkSVIJk0Z1yIGwJUmSJElStUwa1bEIxzaSJEmSJEn9Y9JIkiRJkiRJJUwaSZIkSZIkqYRJozp03AG7AzB9/KicayJJkiRJkoaqlrwroJ3vrENnsGZROyNamvOuiiRJkiRJGqLsaVSHIsKEkSRJkiRJqopJI0mSJEmSJJUwaSRJkiRJkqQSVSWNImJ1RDwbEdsjYmE3262MiBcjYmNEXFHNPiVJkiRJklR71fY0egY4GXiw0gYR0QxcDxwNzANOj4h5Ve5XkiRJkiRJNVTV7GkppeehMPByNxYBG1NKm7JtbwFWAc9Vs29JkiRJkiTVTlVJo16aBrxadPs14JByG0bEOmBddvO/IuLFGtdtoEwE3sq7EsqFsW9Mxr1xGfvGZNwbl7FvTMa9cRn7xlSPcZ/R2w17TBpFxL3AlDJ3XZVSurMX+yjXDSmV2zCldCNwYy+ec0iJiMdSShXHfFL9MvaNybg3LmPfmIx74zL2jcm4Ny5j35gaPe49Jo1SSkdWuY/XgPai23sAm6t8TkmSJEmSJNVQtQNh98ajwF4RMSsihgNrgPUDsF9JkiRJkiT1U1VJo4g4KSJeAw4FfhgRd2flu0fEjwBSSh8AFwF3A88Dt6WUnq2u2kNO3V1yp14z9o3JuDcuY9+YjHvjMvaNybg3LmPfmBo67pFS2eGFJEmSJEmS1MAG4vI0SZIkSZIkDTEmjSRJkiRJklTCpFGNRcTKiHgxIjZGxBV510d9FxHtEfFARDwfEc9GxMVZ+Zci4vWIeDJbjil6zJVZzF+MiI8XlZc9HrKB4h+OiJci4tZs0HjlLCJeiYgNWXwfy8rGR8Q9WazuiYhxWXlExF9msX06IhYUPc/Z2fYvRcTZReUfzp5/Y/bYGPhXqa4iYu+idv1kRLwXEZfY5utPRNwUEW9GxDNFZTVv45X2oYFTIfZfj4gXsvh+PyLGZuUzI2JbUdu/oegxfYpxd8eRaq9C3Gv+3h4RI7LbG7P7Zw7MK1aHCrG/tSjur0TEk1m5bb5OROXvcZ7r+yKl5FKjBWgGXgZmA8OBp4B5edfLpc9xnAosyNZHA78E5gFfAi4vs/28LNYjgFnZMdDc3fEA3AasydZvAC7M+3W7JIBXgIldyv4EuCJbvwL4WrZ+DPBjIIDFwMNZ+XhgU/Z3XLY+LrvvEQoTCUT22KPzfs0uJcdAM/DvwAzbfP0twHJgAfBMUVnN23ilfbjkHvujgJZs/WtFsZ9ZvF2X5+lTjCsdRy65xr3m7+3Ap4AbsvU1wK15/y8abSkX+y73/xnwxWzdNl8nC5W/x3mu78NiT6PaWgRsTCltSin9DrgFWJVzndRHKaUtKaXHs/XfUJgFcFo3D1kF3JJS+t+U0q+AjRSOhbLHQ5aNPhz4Xvb4vwNOrM2r0U6wikKMYMdYrQJuTgUPAWMjYirwceCelNI7KaX/BO4BVmb3taWUfp4KZ5ObMe6D0RHAyymlf+tmG9v8EJVSehB4p0vxQLTxSvvQACkX+5TST1Jh1l+Ah4A9unuOfsa40nGkAVChzVeyM9/bi4+H7wFHdPRG0MDoLvZZLE4D/rG757DNDz3dfI/zXN8HJo1qaxrwatHt1+g+2aBBLutOPB94OCu6KOu6eFNRl8NKca9UPgF4t+iDqsfJ4JGAn0TELyJiXVY2OaW0BQonImBSVt7XuE/L1ruWa3BZw44fIm3z9W8g2nilfWjwOI/CL8YdZkXEExHx04hYlpX1J8Z+Nhycav3e3vmY7P6t2fYaHJYBb6SUXioqs83XmS7f4zzX94FJo9oq9wtCGvBaaKeIiF2B24FLUkrvAX8F7AkcBGyh0K0VKse9r+XK39KU0gLgaODTEbG8m22Ne53JxqI4AfhuVmSbb2zGuUFExFXAB8C3s6ItwPSU0nzgUuA7EdFG/2LscTH4DMR7u3Ef3E5nxx+IbPN1psz3uIqblilr+HO9SaPaeg1oL7q9B7A5p7qoChExjMIbzbdTSncApJTeSCn9X0ppO/BNCt2VoXLcK5W/RaHrY0uXcuUspbQ5+/sm8H0KMX6jo1tx9vfNbPO+xv01drz0wbgPPkcDj6eU3gDbfAMZiDZeaR/KWTa46XHAmdmlBmSXJ72drf+Cwng2H6J/Mfaz4SAzQO/tnY/J7h9D7y+TUw1l8TgZuLWjzDZfX8p9j8NzfZ+YNKqtR4G9ojCTwnAKlzmsz7lO6qPsOue/BZ5PKX2jqLz4euSTgI7ZGNYDa6IwU8YsYC8KA6SVPR6yD6UPAKdmjz8buLOWr0k9i4hdImJ0xzqFAVKfoRDfjhkTimO1Hjgrm3VhMbA164p6N3BURIzLurwfBdyd3febiFicHWNnYdwHmx1+ebTNN4yBaOOV9qEcRcRK4HPACSml/y4q3y0imrP12RTa+KZ+xrjScaScDNB7e/HxcCpwf0dSUrk7EnghpdR5iZFtvn5U+h6H5/q+SYNgNO56XiiMwP5LChnqq/Kuj0u/YngYhW6GTwNPZssxwN8DG7Ly9cDUosdclcX8RYpmxKp0PFCYgeMRCoMsfhcYkffrbvQli8lT2fJsR7wojEFwH/BS9nd8Vh7A9VlsNwALi57rvCy2G4Fzi8oXUvhw+jJwHRB5v26XztiMAt4GxhSV2ebrbKGQFNwCvE/h18LzB6KNV9qHS+6x30hhzIqOc33HbFenZOeBp4DHgeP7G+PujiOX3OJe8/d2oDW7vTG7f3be/4tGW8rFPiv/FvDJLtva5utkofL3OM/1fVg6XpAkSZIkSZLUycvTJEmSJEmSVMKkkSRJkiRJkkqYNJIkSZIkSVIJk0aSJEmSJEkqYdJIkiRJkiRJJUwaSZIkSZIkqYRJI0mSJEmSJJX4f3dr70Z3r1MyAAAAAElFTkSuQmCC\n", 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\n", 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