{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# What is Music?","metadata":{}},{"cell_type":"markdown","source":"In the first lesson of Popular Music History lecture, a professor came and asked us \"What is Music?\". We all had different answers. Music is a song made by instruments. Music is the expression of feelings. Music is singing. Music is what takes us to other worlds. The answers were all correct. However, the professor said there was a more general definition. Music is a set of sounds that harmonize with each other. It is harmony that distinguishes music from noise.\n\nPopular Music History was one of the elective courses I took during my engineering degree. After many years, I never forgot this answer from the professor. It was very reasonable. Above all, musical notes we hear from different instruments are just sound waves. They have mathematical expressions in both the time and frequency domain. Some waves go well with each other. Some just don't get along and make noise.\n\nRecently, I was searching for datasets about music in Kaggle and came across [Deep Contractor's](https://www.kaggle.com/deepcontractor) dataset \"[Musical Instrument Chord Classification (Audio)](https://www.kaggle.com/deepcontractor/musical-instrument-chord-classification)\". The dataset contains audio files which are chord recordings from either a piano or guitar. Chords are labeled as Major or Minor, so the data is suitable for a classification problem. I thought why not give it a try and started.\n\nIn the first section, I explained the mathematics behind the music as much as I can. There will be a bit of music theory, a bit of math, and a bit of digital signal processing. Using the knowledge from section 1, I created a DataFrame from all the audio files in section 2. In section 3, I explored data and applied some feature engineering to make it ready for machine learning. Finally, I build a model and make predictions in section 4. \n\n**Index**\n\n1. [Understanding Math Behind Music](#1.-Understanding-Math-Behind-Music)\n\n 1.1. [Notes and Chords](#1.1.-Notes-and-Chords)\n \n 1.2. [Time and Frequency Domain Representations](#1.2.-Time-and-Frequency-Domain-Representations)\n \n 1.3. [Spectrogram](#1.3.-Spectrogram)\n \n 1.4. [Detection of Harmonic Frequencies](#1.4.-Detection-of-Harmonic-Frequencies)\n\n2. [Importing Dataset](#2.-Importing-Dataset)\n\n3. [Data Exploration](#3.-Data-Exploration)\n\n 3.1. [Min and Max Harmonics](#3.1.-Min-and-Max-Harmonics)\n \n 3.2. [Number of Harmonics](#3.2.-Number-of-Harmonics)\n \n 3.3. [Feature Engineering on Harmonics](#3.3.-Feature-Engineering-on-Harmonics)\n\n4. [Model Building](#4.-Model-Building)\n\n 4.1. [Preprocessing Data](#4.1.-Preprocessing-Data)\n \n 4.2. [Model Selection](#4.2.-Model-Selection)\n \n 4.3. [Model Training and Prediction](#4.3.-Model-Training-and-Prediction)","metadata":{}},{"cell_type":"markdown","source":"# 1. Understanding Math Behind Music","metadata":{}},{"cell_type":"code","source":"# importing necessary packages for the section\nimport os\nimport IPython\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.io import wavfile\nfrom scipy.fft import fft, fftfreq\nfrom scipy.signal import spectrogram, find_peaks","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-04-24T19:47:50.438637Z","iopub.execute_input":"2023-04-24T19:47:50.439036Z","iopub.status.idle":"2023-04-24T19:47:51.679073Z","shell.execute_reply.started":"2023-04-24T19:47:50.438964Z","shell.execute_reply":"2023-04-24T19:47:51.678228Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## 1.1. Notes and Chords","metadata":{}},{"cell_type":"markdown","source":"![Notes.png](attachment:b6f37314-e253-479a-b37c-9296e4ca19b8.png)\n\nNotes are the smallest building blocks of music. In western music, there are 7 natural notes which are represented with letters as A, B, C, D, E, F, G. These natural notes are the white keys of a piano. Except for 2 cases, the interval between two natural notes is a whole step. Since the smallest interval is half step, there are also other notes between natural notes which are black keys of a piano. For example, we can call the note between A and B as A Sharp (A#) or B flat (Bb). The two exception cases are, there are no notes between B&C and E&F. After placing all the notes in between, all the consecutive intervals are half steps and there are 12 notes in total as:\n\nA A# B C C# D D# E F F# G G#\n\nOne of the most important properties of a note is frequency. By knowing the rules, all the note frequencies in western music can be calculated.\n1. You can use reference note as **A** with frequency **440** Hz.\n2. If you **double frequency** of a note, you again obtain the **same note** in one octave higher.\n3. All the intervals between consecutive notes are **equal** in **logarithmic** scale.\n\nLet's make some calculations. Using the first two rules, I know that if 440 Hz is \"A\", 880 Hz is also \"A\". The reverse is also true, 440 is the double of 220, so 220 Hz is \"A\" too. Be careful, 660 Hz is **not** \"A\". What about other notes? The third rule says that intervals are equal on a logarithmic scale. Since there are 12 notes, all I have to do is multiply by 2^(1/12) to go to the next note. For example starting from \"A\", ( 440 \\* 2^(1/12) ) is \"A#\", (440 \\* 2^(1/12) \\* 2^(1/12) ) is \"B\", and goes on. After 12 steps, resulting frequency is ( 440 \\* 2^(12/12) ) = 880 Hz which is again \"A\". Cool, right? Enough calculation for us, let's leave the remaining to 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"}}},{"cell_type":"code","source":"# Our hearing range is commonly 20 Hz to 20 kHz\n# Starting with 55 Hz which is \"A\" (I divided 440 by 2 three times)\ncurr_freq = 55\nfreq_list = []\n\n# I want to calculate 8 octaves of notes. Each octave has 12 notes. Looping for 96 steps:\nfor i in range(96): \n freq_list.append(curr_freq)\n curr_freq *= np.power(2, 1/12) # Multiplying by 2^(1/12)\n\n#reshaping and creating dataframe\nfreq_array = np.reshape(np.round(freq_list,1), (8, 12))\ncols = [\"A\", \"A#\", \"B\", \"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\"]\ndf_note_freqs = pd.DataFrame(freq_array, columns=cols)\nprint(\"NOTE FREQUENCIES IN WESTERN MUSIC\")\ndf_note_freqs.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:51.680796Z","iopub.execute_input":"2023-04-24T19:47:51.681028Z","iopub.status.idle":"2023-04-24T19:47:51.725237Z","shell.execute_reply.started":"2023-04-24T19:47:51.681000Z","shell.execute_reply":"2023-04-24T19:47:51.724264Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"NOTE FREQUENCIES IN WESTERN MUSIC\n","output_type":"stream"},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":" A A# B C C# D D# E F \\\n0 55.0 58.3 61.7 65.4 69.3 73.4 77.8 82.4 87.3 \n1 110.0 116.5 123.5 130.8 138.6 146.8 155.6 164.8 174.6 \n2 220.0 233.1 246.9 261.6 277.2 293.7 311.1 329.6 349.2 \n3 440.0 466.2 493.9 523.3 554.4 587.3 622.3 659.3 698.5 \n4 880.0 932.3 987.8 1046.5 1108.7 1174.7 1244.5 1318.5 1396.9 \n5 1760.0 1864.7 1975.5 2093.0 2217.5 2349.3 2489.0 2637.0 2793.8 \n6 3520.0 3729.3 3951.1 4186.0 4434.9 4698.6 4978.0 5274.0 5587.7 \n7 7040.0 7458.6 7902.1 8372.0 8869.8 9397.3 9956.1 10548.1 11175.3 \n\n F# G G# \n0 92.5 98.0 103.8 \n1 185.0 196.0 207.7 \n2 370.0 392.0 415.3 \n3 740.0 784.0 830.6 \n4 1480.0 1568.0 1661.2 \n5 2960.0 3136.0 3322.4 \n6 5919.9 6271.9 6644.9 \n7 11839.8 12543.9 13289.8 ","text/html":"
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AA#BCC#DD#EFF#GG#
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1110.0116.5123.5130.8138.6146.8155.6164.8174.6185.0196.0207.7
2220.0233.1246.9261.6277.2293.7311.1329.6349.2370.0392.0415.3
3440.0466.2493.9523.3554.4587.3622.3659.3698.5740.0784.0830.6
4880.0932.3987.81046.51108.71174.71244.51318.51396.91480.01568.01661.2
51760.01864.71975.52093.02217.52349.32489.02637.02793.82960.03136.03322.4
63520.03729.33951.14186.04434.94698.64978.05274.05587.75919.96271.96644.9
77040.07458.67902.18372.08869.89397.39956.110548.111175.311839.812543.913289.8
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We can either play the notes in sequence to create a melody or play several notes at the same time to form a chord. In music, a chord is three or more different notes that sounded simultaneously. The most common types of chords are Major chords and Minor chords which both have three notes. To form a Major chord, we first choose a root note, then move 2 whole steps to find the second note, and finally move 1.5 steps to find the third note. For C Major chord, root note is \"C\", second note is \"E\" and third note is \"G\".\n\nForming a Minor chord is also similar, the difference is steps. In Minor chord, we first move 1.5 steps and later move 2 whole steps. Example from the same note \"C\", C Minor chord is formed of \"C\", \"Eb\" and \"G\". (Reminder: Eb and D# have same frequencies)\n\nThe dataset contains different Major and Minor chord recordings in wav format. Since the project is about distinguishing Major and Minor, the steps between the notes are important for us. We will not be interested in the notes of the chord. Now let's listen to some chord examples. Using the IPython package, audio can be displayed and listened to in the notebook.","metadata":{}},{"cell_type":"code","source":"path_1 = \"../input/musical-instrument-chord-classification/Audio_Files/Major/Major_0.wav\"\npath_2 = \"../input/musical-instrument-chord-classification/Audio_Files/Minor/Minor_169.wav\"\npath_3 = \"../input/musical-instrument-chord-classification/Audio_Files/Major/Major_111.wav\"\nIPython.display.Audio(path_1, rate = 44100)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:51.726556Z","iopub.execute_input":"2023-04-24T19:47:51.726809Z","iopub.status.idle":"2023-04-24T19:47:51.765691Z","shell.execute_reply.started":"2023-04-24T19:47:51.726777Z","shell.execute_reply":"2023-04-24T19:47:51.764972Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"","text/html":"\n \n "},"metadata":{}}]},{"cell_type":"code","source":"IPython.display.Audio(path_2, rate = 44100)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:51.767246Z","iopub.execute_input":"2023-04-24T19:47:51.767471Z","iopub.status.idle":"2023-04-24T19:47:51.790127Z","shell.execute_reply.started":"2023-04-24T19:47:51.767443Z","shell.execute_reply":"2023-04-24T19:47:51.789295Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"","text/html":"\n \n "},"metadata":{}}]},{"cell_type":"code","source":"IPython.display.Audio(path_3, rate = 44100)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:51.791643Z","iopub.execute_input":"2023-04-24T19:47:51.792128Z","iopub.status.idle":"2023-04-24T19:47:51.815896Z","shell.execute_reply.started":"2023-04-24T19:47:51.792083Z","shell.execute_reply":"2023-04-24T19:47:51.815239Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"","text/html":"\n \n "},"metadata":{}}]},{"cell_type":"markdown","source":"## 1.2. Time and Frequency Domain Representations","metadata":{}},{"cell_type":"markdown","source":"In the previous section, we have seen that notes have frequencies that describe themselves. A sound wave with 220 Hz frequency is an \"A\" note. Here comes the fun part. In nature, musical sound waves never vibrate at a single frequency. When we play a note with an instrument, harmonics of the note occur at the integer multiples of the base note. Playing \"A\" note with 220 Hz also creates waves at 440 Hz, 660 Hz, 880 Hz, 1100 Hz and goes on. We know that 220 Hz, 440 Hz and 880 Hz are all \"A\" notes. But if you look at the table we created, 660 Hz is the \"E\" note and 1000 Hz is somewhere between \"C\" and \"C#\". That's the reason why we love music. That's the reason for harmony. That's the reason why playing some notes together forms a chord and sounds beautiful. All notes contain other notes within themselves.\n\nI would like to create a computer-made note to show the concept of harmonics better. To represent a wave at a single frequency, I can define a sine wave as A\\*sin(2\\*pi\\*f\\*t). A is amplitude, f is frequency and t is time. For a sound wave with harmonics, I will first create a sine wave with fundamental frequency and then add its harmonics using a loop. Another important variable in this cell will be the sampling rate. Sound waves are analog signals and to save on a computer, it has to be converted to a digital signal. The sampling rate determines how many numbers we will save in each second. Using 44100 as a sampling rate is very common in practice.\n\nThe signal I defined will be in time domain. After creating the signal, I will also apply Fourier Transform to convert it into frequency domain. Scipy fft package can be used for this transform. Fft method gives us both positive and negative frequency terms, I only need positive terms. For detailed information about FFT, [Scipy fft documentation](https://docs.scipy.org/doc/scipy/tutorial/fft.html) can be viewed.","metadata":{}},{"cell_type":"code","source":"freq = 220 # note frequency\nfs = 44100 # sampling rate\nduration = 1 # duration of a signal [seconds]\ntime = np.linspace(0, duration, fs*duration, endpoint=False) # array for time stamps\n\n# Creating signal in time domain\nnp.random.seed(42)\nsignal = np.zeros(len(time))\nfor i in range(1,12):\n amp = np.random.randint(0,10) # using random numbers for amplitudes\n current_freq = i*freq # current harmonic\n signal += amp*np.sin(2 * np.pi * current_freq * time)\n\n# Fourier Transform\nN = len(signal)\ny_freq = fftfreq(N, 1/fs)[:N//2] # array for frequency stamps\nsignal_f = fft(signal) # Signal in frequency domain\nsignal_f_onesided = 2.0/N * np.abs(signal_f[0:N//2]) # taking positive terms\n\n# Displaying audio\nIPython.display.display(IPython.display.Audio(data=signal, rate=44100))\n\n# Plotting signal in time and frequency domains\nfig, axes = plt.subplots(1, 2, figsize=(12, 3))\naxes[0].plot(time[:480], signal[:480])\naxes[0].set_title(\"Sound Wave in Time Domain (Zoomed)\")\naxes[0].set(xlabel='Time [sec]')\naxes[1].plot(y_freq[:3000], signal_f_onesided[:3000])\naxes[1].set_title(\"Sound Wave in Frequency Domain (Zoomed)\")\naxes[1].set(xlabel='Frequency [Hz]')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:51.817316Z","iopub.execute_input":"2023-04-24T19:47:51.817739Z","iopub.status.idle":"2023-04-24T19:47:52.154151Z","shell.execute_reply.started":"2023-04-24T19:47:51.817696Z","shell.execute_reply":"2023-04-24T19:47:52.153315Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":"\n \n "},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"Listening to the audio that I created, it's pretty clear it's a computer-generated sound, not a natural one. Still, it is aa \"A\" note, and the frequency plot gives us really valuable information. We can see at what frequencies our signal has values. We can see the harmonic structure of a musical wave. We can find frequencies and determine which note it is.\n\nIt's time to analyze our real recordings. Wav files are Major and Minor chords that contain at least three notes. So, I expect to see a more complicated frequency plot after the Fourier transform. It will be like the three plots are overlapped. Intervals between harmonics will not be equal. This is great because I am planning to build my classification model on the relationship of harmonics.","metadata":{}},{"cell_type":"code","source":"path = \"../input/musical-instrument-chord-classification/Audio_Files/Major/Major_0.wav\"\nfs, signal = wavfile.read(path)\nN = len(signal)\ntime = np.linspace(0., N/fs, N)\n\n# Fourier Transform\ny_freq = fftfreq(N, 1/fs)[:N//2] # array for frequency stamps\nsignal_f = fft(signal) # Signal in frequency domain\nsignal_f_onesided = 2.0/N * np.abs(signal_f[0:N//2]) # taking positive terms\n\n# Plotting signal in time and frequency domains\nfig, axes = plt.subplots(2, 2, figsize=(12, 7))\naxes[0,0].plot(time, signal)\naxes[0,0].set_title(\"Sound Wave in Time Domain (No Zoom)\")\naxes[0,0].set(xlabel='Time [sec]')\naxes[0,1].plot(y_freq, signal_f_onesided)\naxes[0,1].set_title(\"Sound Wave in Frequency Domain (No Zoom)\")\naxes[0,1].set(xlabel='Frequency [Hz]')\naxes[1,0].plot(time[(N//2):(N//2+480)], signal[(N//2):(N//2+480)])\naxes[1,0].set_title(\"Sound Wave in Time Domain (Zoomed)\")\naxes[1,0].set(xlabel='Time [sec]')\naxes[1,1].plot(y_freq[:5000], signal_f_onesided[:5000])\naxes[1,1].set_title(\"Sound Wave in Frequency Domain (Zoomed)\")\naxes[1,1].set(xlabel='Frequency [Hz]')\nfig.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:52.155468Z","iopub.execute_input":"2023-04-24T19:47:52.155717Z","iopub.status.idle":"2023-04-24T19:47:52.756749Z","shell.execute_reply.started":"2023-04-24T19:47:52.155687Z","shell.execute_reply":"2023-04-24T19:47:52.755868Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 1.3. Spectrogram","metadata":{}},{"cell_type":"markdown","source":"In the previous section, we have seen the time and frequency plots of a sound wave. Now, I also want to plot the spectrogram of the signal. A spectrogram is a powerful way to visualize a signal over time at various frequencies. It is calculated by splitting the signal into small pieces in time and later applying Fourier transform. As a result, a 2D matrix is obtained and can be plotted. Good news: Scipy has a method for spectrogram, so we don't have to do calculations from the scratch. Documentation of the spectrogram method can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html).","metadata":{}},{"cell_type":"code","source":"# applying spectrogram\nf, t, Sxx = spectrogram(signal, fs, nperseg=10000, nfft = 50000)\n\n# Plots\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\naxes[0].pcolormesh(t, f, np.log(Sxx), cmap=\"jet\")\naxes[0].set_title(\"Spectogram (No Zoom)\")\naxes[0].set(xlabel='Time [sec]', ylabel='Frequency [Hz]')\naxes[1].pcolormesh(t, f[:1500], np.log(Sxx)[:1500,:], cmap=\"jet\")\naxes[1].set_title(\"Spectogram (Zoomed)\")\naxes[1].set(xlabel='Time [sec]', ylabel='Frequency [Hz]')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:52.757936Z","iopub.execute_input":"2023-04-24T19:47:52.758210Z","iopub.status.idle":"2023-04-24T19:47:53.333378Z","shell.execute_reply.started":"2023-04-24T19:47:52.758177Z","shell.execute_reply":"2023-04-24T19:47:53.332551Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 1.4. Detection of Harmonic Frequencies","metadata":{}},{"cell_type":"markdown","source":"To summarize what we have done so far, we can read a wav file and save it in an array that is in the time domain. By applying the Fourier transform, we can obtain an array in the frequency domain. Also, Spectrogram is applied to obtain a 2D matrix that has both time and frequency information. The time-domain array is not suitable to use in this project. I will not use the Spectrogram matrix too, because our recordings contain only one chord, so frequency information doesn't change with time. For example, if we had a recording that changes chords every second, we would see in the visualization that harmonics are changing every second.\n\nIn this project, I will continue with the frequency array. There are peak values in the frequency plot which are harmonics. I believe that if I can find at which frequencies peaks occur, I can use that data to build a model. To do this, I will use find_peaks method from Scipy which returns the indices of peaks. When I plug these indices into the frequency stamp array, I will obtain harmonic frequencies. \n\nUpdate: In some files, I have seen a peak at a really small value like 2 Hz. If there is a peak less than 50 Hz, I will ignore that value, because it is most likely noise.","metadata":{}},{"cell_type":"code","source":"# h: height threshold. I defined as %5 of max value\nh = signal_f_onesided.max()*5/100\npeaks, _ = find_peaks(signal_f_onesided, distance=10, height = h)\n\nfreq_50_index = np.abs(y_freq - 50).argmin() # finding index for 50 Hz\npeaks = peaks[peaks>freq_50_index] # filtering peaks less than 50 Hz\nharmonics = y_freq[peaks]\nprint(\"Harmonics: {}\".format(np.round(harmonics)))\n\n# Plot\ni = peaks.max() + 100\nplt.plot(y_freq[:i], signal_f_onesided[:i])\nplt.plot(y_freq[peaks], signal_f_onesided[peaks], \"x\")\nplt.xlabel('Frequency [Hz]')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:53.334823Z","iopub.execute_input":"2023-04-24T19:47:53.335316Z","iopub.status.idle":"2023-04-24T19:47:53.553388Z","shell.execute_reply.started":"2023-04-24T19:47:53.335271Z","shell.execute_reply":"2023-04-24T19:47:53.552523Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Harmonics: [ 131. 165. 196. 262. 330. 392. 496. 525. 588. 659. 787. 826.\n 989. 1050. 1158. 1179. 1312. 1319. 1376. 1576. 1838.]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# I would like to create a method so that I can use in the next section\n# The method will read sound file, apply Fourier, find peak frequencies and return\n# Input: path of the sound file\n# Output: Frequency peaks\n# print_peaks = true to plot peaks\n\ndef find_harmonics(path, print_peaks=False):\n fs, X = wavfile.read(path)\n N = len(X)\n X_F = fft(X)\n X_F_onesided = 2.0/N * np.abs(X_F[0:N//2])\n freqs = fftfreq(N, 1/fs)[:N//2]\n freqs_50_index = np.abs(freqs - 50).argmin()\n \n h = X_F_onesided.max()*5/100\n peaks, _ = find_peaks(X_F_onesided, distance=10, height = h)\n peaks = peaks[peaks>freqs_50_index]\n harmonics = np.round(freqs[peaks],2)\n \n if print_peaks:\n i = peaks.max() + 100\n plt.plot(freqs[:i], X_F_onesided[:i])\n plt.plot(freqs[peaks], X_F_onesided[peaks], \"x\")\n plt.xlabel('Frequency [Hz]')\n plt.show()\n return harmonics","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:53.556254Z","iopub.execute_input":"2023-04-24T19:47:53.556508Z","iopub.status.idle":"2023-04-24T19:47:53.565359Z","shell.execute_reply.started":"2023-04-24T19:47:53.556471Z","shell.execute_reply":"2023-04-24T19:47:53.564273Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"# Another example to check if method is working correctly\npath = \"../input/musical-instrument-chord-classification/Audio_Files/Minor/Minor_169.wav\"\n\nharmonics_2 = find_harmonics(path, print_peaks=True)\nprint(\"Harmonics: {}\".format(np.round(harmonics_2)))","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:53.566571Z","iopub.execute_input":"2023-04-24T19:47:53.567293Z","iopub.status.idle":"2023-04-24T19:47:53.781998Z","shell.execute_reply.started":"2023-04-24T19:47:53.567231Z","shell.execute_reply":"2023-04-24T19:47:53.781085Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"Harmonics: [208. 294. 370. 416. 494. 589. 625. 742.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 2. Importing Dataset","metadata":{}},{"cell_type":"markdown","source":"In this section, I will create a DataFrame so that I can analyze all the sound data together. There are more than 800 wav files. First, I will loop through all the files and find harmonics. I will save chord type, file name and all harmonics for each file. I will also save minimum & maximum harmonics and the number of harmonics for easier analysis. After the loop, I will convert it to a DataFrame.","metadata":{}},{"cell_type":"code","source":"import librosa\nimport statistics","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:53.783316Z","iopub.execute_input":"2023-04-24T19:47:53.783599Z","iopub.status.idle":"2023-04-24T19:47:55.420646Z","shell.execute_reply.started":"2023-04-24T19:47:53.783562Z","shell.execute_reply":"2023-04-24T19:47:55.419466Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"path = \"/kaggle/input/musical-instrument-chord-classification/Audio_Files\"\ndata = []\nmax_harm_length = 0 # i will keep track of max harmonic length for naming columns\n\nfor dirname, _, filenames in os.walk(path):\n for filename in filenames:\n foldername = os.path.basename(dirname)\n full_path = os.path.join(dirname, filename)\n y, sr = librosa.load(full_path)\n centroids = librosa.feature.spectral_centroid(y = y, sr=sr)\n \n cenmean = [np.mean(np.nan_to_num(centroids))]\n cenmin = [np.min(centroids)]\n cenmax = [np.max(centroids)]\n \n \n max_harm_length = max(max_harm_length, len(centroids))\n \n cur_data = [foldername, filename]\n #cur_data.extend([freq_peaks.min(), freq_peaks.max(), len(freq_peaks)])\n cur_data.extend(centroids[0,:])\n cur_data.extend(cenmean)\n cur_data.extend(cenmin)\n cur_data.extend(cenmax)\n \n data.append(cur_data)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:47:55.422905Z","iopub.execute_input":"2023-04-24T19:47:55.423308Z","iopub.status.idle":"2023-04-24T19:49:35.307759Z","shell.execute_reply.started":"2023-04-24T19:47:55.423265Z","shell.execute_reply":"2023-04-24T19:49:35.306802Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"path = \"/kaggle/input/musical-instrument-chord-classification/Audio_Files\"\ndata = []\nmax_harm_length = 0 # i will keep track of max harmonic length for naming columns\n\nfor dirname, _, filenames in os.walk(path):\n for filename in filenames:\n foldername = os.path.basename(dirname)\n full_path = os.path.join(dirname, filename)\n freq_peaks = find_harmonics(full_path)\n \n max_harm_length = max(max_harm_length, len(freq_peaks))\n \n cur_data = [foldername, filename]\n cur_data.extend([freq_peaks.min(), freq_peaks.max(), len(freq_peaks)])\n cur_data.extend(freq_peaks)\n \n data.append(cur_data)","metadata":{"execution":{"iopub.status.busy":"2023-03-20T19:51:51.161544Z","iopub.execute_input":"2023-03-20T19:51:51.162210Z","iopub.status.idle":"2023-03-20T19:51:57.987658Z","shell.execute_reply.started":"2023-03-20T19:51:51.162163Z","shell.execute_reply":"2023-03-20T19:51:57.986892Z"}}},{"cell_type":"code","source":"# Column Names for DataFrame:\ncols = [\"Chord Type\", \"File Name\"]\nfor i in range(100):\n cols.append(\"Centroids {}\".format(i+1))\n \ncols.append(\"CenMean\")\ncols.append(\"CenMin\")\ncols.append(\"CenMax\")\n\n# Creating DataFrame\ndf = pd.DataFrame(data, columns=cols)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.309383Z","iopub.execute_input":"2023-04-24T19:49:35.309798Z","iopub.status.idle":"2023-04-24T19:49:35.368852Z","shell.execute_reply.started":"2023-04-24T19:49:35.309765Z","shell.execute_reply":"2023-04-24T19:49:35.368236Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":" Chord Type File Name Centroids 1 Centroids 2 Centroids 3 \\\n0 Major Major_337.wav 875.837719 488.782825 425.884426 \n1 Major Major_19.wav 917.859325 582.559719 519.121262 \n2 Major Major_444.wav 347.526074 373.435467 529.542709 \n3 Major Major_380.wav 483.024051 490.128035 613.712264 \n4 Major Major_368.wav 483.024184 503.563786 625.609012 \n\n Centroids 4 Centroids 5 Centroids 6 Centroids 7 Centroids 8 ... \\\n0 418.530858 408.669695 402.815725 417.205433 408.936471 ... \n1 500.955215 508.893261 903.582401 2293.249574 2320.641050 ... \n2 547.327275 582.125882 595.242820 697.998047 753.016471 ... \n3 616.285086 602.002164 621.463473 724.975678 764.249056 ... \n4 657.493171 795.018113 820.846958 854.845368 884.775564 ... \n\n Centroids 94 Centroids 95 Centroids 96 Centroids 97 Centroids 98 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 651.775598 0.000000 5005.768406 \n2 1028.491379 741.474271 681.311208 751.427639 751.631801 \n3 1213.835513 932.319499 715.829355 752.889532 710.608423 \n4 748.193654 784.195716 750.894414 732.466760 712.221692 \n\n Centroids 99 Centroids 100 CenMean CenMin CenMax \n0 0.000000 0.000000 395.834008 0.000000 1752.311138 \n1 NaN NaN NaN NaN NaN \n2 730.874442 4957.255822 721.524115 347.526074 4957.255822 \n3 483.024051 2797.532041 NaN NaN NaN \n4 483.024184 963.726855 NaN NaN NaN \n\n[5 rows x 105 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Chord TypeFile NameCentroids 1Centroids 2Centroids 3Centroids 4Centroids 5Centroids 6Centroids 7Centroids 8...Centroids 94Centroids 95Centroids 96Centroids 97Centroids 98Centroids 99Centroids 100CenMeanCenMinCenMax
0MajorMajor_337.wav875.837719488.782825425.884426418.530858408.669695402.815725417.205433408.936471...0.0000000.0000000.0000000.0000000.0000000.0000000.000000395.8340080.0000001752.311138
1MajorMajor_19.wav917.859325582.559719519.121262500.955215508.893261903.5824012293.2495742320.641050...0.0000000.000000651.7755980.0000005005.768406NaNNaNNaNNaNNaN
2MajorMajor_444.wav347.526074373.435467529.542709547.327275582.125882595.242820697.998047753.016471...1028.491379741.474271681.311208751.427639751.631801730.8744424957.255822721.524115347.5260744957.255822
3MajorMajor_380.wav483.024051490.128035613.712264616.285086602.002164621.463473724.975678764.249056...1213.835513932.319499715.829355752.889532710.608423483.0240512797.532041NaNNaNNaN
4MajorMajor_368.wav483.024184503.563786625.609012657.493171795.018113820.846958854.845368884.775564...748.193654784.195716750.894414732.466760712.221692483.024184963.726855NaNNaNNaN
\n

5 rows × 105 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"#Se guarda en nandata las columnas donde hay hay algún dato de tipo NaN\nnandata = df.columns[df.isna().any()].tolist()\n\nprint(\"Las columnas con datos faltantes son:\",nandata)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.370212Z","iopub.execute_input":"2023-04-24T19:49:35.370585Z","iopub.status.idle":"2023-04-24T19:49:35.378619Z","shell.execute_reply.started":"2023-04-24T19:49:35.370553Z","shell.execute_reply":"2023-04-24T19:49:35.377618Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Las columnas con datos faltantes son: ['Centroids 97', 'Centroids 98', 'Centroids 99', 'Centroids 100', 'CenMean', 'CenMin', 'CenMax']\n","output_type":"stream"}]},{"cell_type":"code","source":"#Preprocesamiento\nfrom sklearn.impute import SimpleImputer #Herramienta para lidiar con datos faltantes\n#Se corrigen datos faltantes con SimpleImputer\nimputer = SimpleImputer(missing_values=np.nan, strategy = \"mean\")\nif nandata != []:\n df[nandata] = imputer.fit_transform(df[nandata])","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.380362Z","iopub.execute_input":"2023-04-24T19:49:35.380600Z","iopub.status.idle":"2023-04-24T19:49:35.409888Z","shell.execute_reply.started":"2023-04-24T19:49:35.380572Z","shell.execute_reply":"2023-04-24T19:49:35.409133Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"#Se guarda en nandata las columnas donde hay hay algún dato de tipo NaN\nnandata = df.columns[df.isna().any()].tolist()\n\nprint(\"Las columnas con datos faltantes son:\",nandata)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.411297Z","iopub.execute_input":"2023-04-24T19:49:35.412207Z","iopub.status.idle":"2023-04-24T19:49:35.422802Z","shell.execute_reply.started":"2023-04-24T19:49:35.412135Z","shell.execute_reply":"2023-04-24T19:49:35.421819Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Las columnas con datos faltantes son: []\n","output_type":"stream"}]},{"cell_type":"code","source":"# Se obtienen las columnas con variables categóticas\ncols = df.columns\nnum_cols = df._get_numeric_data().columns\ncatvar = list(set(cols) - set(num_cols))\nprint(\"Las columnas categóricas son:\",catvar)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.424095Z","iopub.execute_input":"2023-04-24T19:49:35.424399Z","iopub.status.idle":"2023-04-24T19:49:35.434862Z","shell.execute_reply.started":"2023-04-24T19:49:35.424365Z","shell.execute_reply":"2023-04-24T19:49:35.433920Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Las columnas categóricas son: ['File Name', 'Chord Type']\n","output_type":"stream"}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.436041Z","iopub.execute_input":"2023-04-24T19:49:35.436310Z","iopub.status.idle":"2023-04-24T19:49:35.471835Z","shell.execute_reply.started":"2023-04-24T19:49:35.436278Z","shell.execute_reply":"2023-04-24T19:49:35.470456Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":" Chord Type File Name Centroids 1 Centroids 2 Centroids 3 \\\n0 Major Major_337.wav 875.837719 488.782825 425.884426 \n1 Major Major_19.wav 917.859325 582.559719 519.121262 \n2 Major Major_444.wav 347.526074 373.435467 529.542709 \n3 Major Major_380.wav 483.024051 490.128035 613.712264 \n4 Major Major_368.wav 483.024184 503.563786 625.609012 \n\n Centroids 4 Centroids 5 Centroids 6 Centroids 7 Centroids 8 ... \\\n0 418.530858 408.669695 402.815725 417.205433 408.936471 ... \n1 500.955215 508.893261 903.582401 2293.249574 2320.641050 ... \n2 547.327275 582.125882 595.242820 697.998047 753.016471 ... \n3 616.285086 602.002164 621.463473 724.975678 764.249056 ... \n4 657.493171 795.018113 820.846958 854.845368 884.775564 ... \n\n Centroids 94 Centroids 95 Centroids 96 Centroids 97 Centroids 98 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 651.775598 0.000000 5005.768406 \n2 1028.491379 741.474271 681.311208 751.427639 751.631801 \n3 1213.835513 932.319499 715.829355 752.889532 710.608423 \n4 748.193654 784.195716 750.894414 732.466760 712.221692 \n\n Centroids 99 Centroids 100 CenMean CenMin CenMax \n0 0.000000 0.000000 395.834008 0.000000 1752.311138 \n1 413.471139 2623.355413 601.461816 198.722770 4606.284564 \n2 730.874442 4957.255822 721.524115 347.526074 4957.255822 \n3 483.024051 2797.532041 601.461816 198.722770 4606.284564 \n4 483.024184 963.726855 601.461816 198.722770 4606.284564 \n\n[5 rows x 105 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Chord TypeFile NameCentroids 1Centroids 2Centroids 3Centroids 4Centroids 5Centroids 6Centroids 7Centroids 8...Centroids 94Centroids 95Centroids 96Centroids 97Centroids 98Centroids 99Centroids 100CenMeanCenMinCenMax
0MajorMajor_337.wav875.837719488.782825425.884426418.530858408.669695402.815725417.205433408.936471...0.0000000.0000000.0000000.0000000.0000000.0000000.000000395.8340080.0000001752.311138
1MajorMajor_19.wav917.859325582.559719519.121262500.955215508.893261903.5824012293.2495742320.641050...0.0000000.000000651.7755980.0000005005.768406413.4711392623.355413601.461816198.7227704606.284564
2MajorMajor_444.wav347.526074373.435467529.542709547.327275582.125882595.242820697.998047753.016471...1028.491379741.474271681.311208751.427639751.631801730.8744424957.255822721.524115347.5260744957.255822
3MajorMajor_380.wav483.024051490.128035613.712264616.285086602.002164621.463473724.975678764.249056...1213.835513932.319499715.829355752.889532710.608423483.0240512797.532041601.461816198.7227704606.284564
4MajorMajor_368.wav483.024184503.563786625.609012657.493171795.018113820.846958854.845368884.775564...748.193654784.195716750.894414732.466760712.221692483.024184963.726855601.461816198.7227704606.284564
\n

5 rows × 105 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"y, sr = librosa.load(\"/kaggle/input/musical-instrument-chord-classification/Audio_Files/Major/Major_0.wav\")\ncent = librosa.feature.spectral_centroid(y=y, sr=sr)\nnp.mean(cent)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.473605Z","iopub.execute_input":"2023-04-24T19:49:35.474056Z","iopub.status.idle":"2023-04-24T19:49:35.588019Z","shell.execute_reply.started":"2023-04-24T19:49:35.473999Z","shell.execute_reply":"2023-04-24T19:49:35.586920Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"660.0759649089915"},"metadata":{}}]},{"cell_type":"code","source":"cent.shape","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.589224Z","iopub.execute_input":"2023-04-24T19:49:35.589458Z","iopub.status.idle":"2023-04-24T19:49:35.595258Z","shell.execute_reply.started":"2023-04-24T19:49:35.589429Z","shell.execute_reply":"2023-04-24T19:49:35.594655Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"(1, 97)"},"metadata":{}}]},{"cell_type":"code","source":"S, phase = librosa.magphase(librosa.stft(y=y))\nlibrosa.feature.spectral_centroid(S=S)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.596598Z","iopub.execute_input":"2023-04-24T19:49:35.596906Z","iopub.status.idle":"2023-04-24T19:49:35.623867Z","shell.execute_reply.started":"2023-04-24T19:49:35.596873Z","shell.execute_reply":"2023-04-24T19:49:35.622704Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([[ 492.03462209, 496.70090375, 584.01602192, 576.73535671,\n 593.38692937, 595.38339597, 687.50277703, 730.93866027,\n 747.96950054, 783.58652791, 746.97782119, 727.93501882,\n 721.39418997, 705.76871813, 704.84135685, 697.64091039,\n 687.97233368, 670.10327401, 663.83109012, 668.99010324,\n 680.42734429, 688.19829356, 680.23420722, 670.96448812,\n 665.9917673 , 676.73115313, 680.90087286, 684.93454695,\n 678.14945496, 676.9048285 , 674.65023427, 673.31377107,\n 669.25823776, 667.62678739, 665.10756521, 662.87690825,\n 658.57038631, 654.31000645, 653.43253458, 656.20451314,\n 653.84355785, 643.29926501, 635.30055316, 620.30480168,\n 620.9283676 , 618.45122128, 619.67367862, 611.65376111,\n 605.82533582, 600.24303699, 603.82224485, 604.906121 ,\n 605.76042007, 603.68644688, 601.56982019, 592.45125448,\n 583.02560962, 578.9021901 , 581.66678472, 580.50168084,\n 577.88766995, 569.79235659, 559.76349775, 559.70709456,\n 564.87826623, 567.89055644, 565.42096022, 559.02436783,\n 545.89201323, 543.81775636, 549.8190324 , 551.86655012,\n 544.94589879, 537.51156946, 534.32410972, 532.15551679,\n 536.78262767, 543.61522493, 546.92963427, 547.47759452,\n 546.88892444, 547.51162723, 545.87966103, 546.29885645,\n 544.7640293 , 573.83123517, 658.87200826, 645.50236987,\n 834.61625847, 862.66679509, 771.1641822 , 808.94666176,\n 1047.30613353, 1190.67112689, 1113.70133642, 1311.12020435,\n 1569.81132269]])"},"metadata":{}}]},{"cell_type":"code","source":"freqs, times, D = librosa.reassigned_spectrogram(y, fill_nan=True)\nlibrosa.feature.spectral_centroid(S=np.abs(D), freq=freqs)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.625388Z","iopub.execute_input":"2023-04-24T19:49:35.625724Z","iopub.status.idle":"2023-04-24T19:49:35.651682Z","shell.execute_reply.started":"2023-04-24T19:49:35.625681Z","shell.execute_reply":"2023-04-24T19:49:35.650652Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"array([[ 491.99575163, 496.67796949, 583.83060915, 576.78259916,\n 593.45127803, 595.40264233, 687.50974097, 731.02104211,\n 748.00361417, 783.65540575, 746.96378922, 727.97071107,\n 721.41821883, 705.7955447 , 704.77377736, 697.58645617,\n 688.04003373, 670.08271292, 663.7170533 , 669.02442591,\n 680.42320107, 688.14020869, 680.27612562, 671.03367103,\n 665.93963753, 676.72034082, 681.01422177, 684.91593067,\n 678.13361438, 676.93728872, 674.65666057, 673.27782173,\n 669.29011309, 667.58634344, 665.04440184, 662.884964 ,\n 658.59730334, 654.27963077, 653.43060525, 656.14026545,\n 653.77649263, 643.30341673, 635.2310519 , 620.17714947,\n 620.88551601, 618.35589444, 619.48763476, 611.55945413,\n 605.75116258, 600.09307145, 603.77275662, 604.89108859,\n 605.68662688, 603.56552731, 601.54734169, 592.30911566,\n 582.89962849, 578.90414442, 581.56579395, 580.34031715,\n 577.88026335, 569.71181577, 559.60324934, 559.69547653,\n 564.8260101 , 567.69839197, 565.33793303, 559.05834773,\n 545.8021879 , 543.71891368, 549.77298166, 551.76505623,\n 544.89991514, 537.48526451, 534.25889979, 532.05003448,\n 536.75175606, 543.57223703, 546.86517158, 547.44169759,\n 546.76635101, 547.44161908, 545.92343113, 546.23750547,\n 544.63018538, 573.74473203, 658.60265059, 645.5014869 ,\n 834.58863649, 862.60579155, 771.25440344, 808.83557484,\n 1047.33924583, 1190.63192606, 1113.85109962, 1311.10622053,\n 1569.80128797]])"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom librosa import display\ntimes = librosa.times_like(cent)\nfig, ax = plt.subplots()\nlibrosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),\n y_axis='log', x_axis='time', ax=ax)\nax.plot(times, cent.T, label='Spectral centroid', color='w')\nax.legend(loc='upper right')\nax.set(title='log Power spectrogram')","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:35.653285Z","iopub.execute_input":"2023-04-24T19:49:35.653647Z","iopub.status.idle":"2023-04-24T19:49:36.069415Z","shell.execute_reply.started":"2023-04-24T19:49:35.653600Z","shell.execute_reply":"2023-04-24T19:49:36.068711Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"[Text(0.5, 1.0, 'log Power spectrogram')]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# 3. Data Exploration","metadata":{}},{"cell_type":"markdown","source":"Finally, we have a nice DataFrame to make some exploration. The first column, Chord Type, is the value that we will predict. It is a categorical column consisting of 2 categories: Major and Minor. By printing value counts of Chord Type, it seems that most of the chords are Major. We have 502 Major chords and 357 Minor chords. \n\nThe second column is the file name that the row is created from. We will not need this column for model building, I just keep it in case I want to analyze a specific row deeper.","metadata":{}},{"cell_type":"code","source":"df[\"Chord Type\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:36.070728Z","iopub.execute_input":"2023-04-24T19:49:36.071369Z","iopub.status.idle":"2023-04-24T19:49:36.084567Z","shell.execute_reply.started":"2023-04-24T19:49:36.071329Z","shell.execute_reply":"2023-04-24T19:49:36.083616Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Major 502\nMinor 357\nName: Chord Type, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## 3.1. Min and Max Harmonics","metadata":{}},{"cell_type":"markdown","source":"In this part, I have plotted distributions of Min Harmonics and Max Harmonics. By setting hue as Chord Type, I can see if chord type affects distribution. As I expected, the distribution of Min Harmonics for Major and Minor chords is extremely close. This is because the min harmonic determines the note of the chord. For example for 110 Hz, we will have either \"A Major\" or \"A Minor\" chord. We cannot determine what chord it is just by the first note. The difference between Major and Minor chords is in the intervals between harmonics.\n\nBy looking at the distribution of Max harmonics, Major and Minor chords have again a similar behavior. There is a slight difference that I will ignore for now. I decided not to use \"Min Harmonic\" and \"Max Harmonic\" columns in my model building.","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(15, 5))\nsns.kdeplot(ax=axes[0], data=df, x=\"CenMin\", hue=\"Chord Type\", shade=True)\nsns.kdeplot(ax=axes[1], data=df, x=\"CenMax\", hue=\"Chord Type\", shade=True)\nsns.scatterplot(ax=axes[2], data=df, x=\"CenMin\", y=\"CenMax\",hue=\"Chord Type\")\naxes[0].set_title(\"Distribution of Min Centroids\")\naxes[1].set_title(\"Distribution of Max Centroids\")\naxes[2].set_title(\"Scatter Plot Min vs. Max Centroids\")\nfig.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:36.086050Z","iopub.execute_input":"2023-04-24T19:49:36.086371Z","iopub.status.idle":"2023-04-24T19:49:36.964775Z","shell.execute_reply.started":"2023-04-24T19:49:36.086336Z","shell.execute_reply":"2023-04-24T19:49:36.963833Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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0/hLt8mmKJxogUODf9Oagn+j+8Mj3sfe4IeEPR7iy1YkaKwrPDv8QGg3cz+0czS4d/4UTbBJVyHcAvwZjM7Leyn/4HgBtYfh2jbkEp1zRq6neCL96cJPgf6oDjXgOGosbcCZ4ePCyUIPrd2AjkzOxPYvYS0BcuffptgStFFBP3yUAGH6wmCFD8O/39FzGyOmf1TeMxE3/fRXnuP+LkaTodaA3zKzBJm9mqC31H/636dmR1twY2JNoKA95To1xXAmLp+YWbtBH+E/0yQ4OV9Q+y7jCDi2EGQDOZb7t4fNf0P4F8sGCb1sTGc/3qCKPg2gijshwHcvZUg8vttgshxJ0FioX4/Cv9tNLM/D1Lvd8K67yXI/JshSGg2Hn8bnn8dQTT3h2H9Y+buv3f3LeNsx3DOI/hAuJngi8wTBFm37wq3v4eg436K4KLzVoYf+lboq8B5ZtZsZl8bbIcwiPKvBJHtrQR3MlYNti/BnYw6gvf8e8B3C7Y1ENztaCYY3tbI8BFwkalGffLIytEn3wH8iiBw8yJBezfC7iUZv0+QSX+zu/+OYJWn74bBpIHneIHgjt4S4EkzayXoK9cA7e6+ETiHYHjxzvA8H2cU117hndGrgT+E7/XLh9h1VL8zd99FkLTwswT97zKCFU36vQy438w6COaI/104t1ukmrUDJxP83+0kCFw8QfAFH3f/EcHf0Q/DfX9GsALTUwT5Yu4j+EJ5NHv/PdxNsHrENjPbFZZdCywP/x5/FgYQ30KQE2I9wYiobxNMSSsqd3+WIKfO18PzvJVgae6ecJcxfS6U6prVg3wXPyFYOeqHBZuGvAYMAwP/O8r6n3T3Jwcpbyf4TLslPMc7CPqxftcAPw/zUjQSrPjxbdt7SdnC1/AGghWb7mTPynpzCW7gTfR9H817NZbP1XcQ/A00EQTVC6fXLCD4XtBGMKrwt2G9k15/VnMRERERERERkaqlERgiIiIiIiIiUvUUwBARERERERGRqqcAhoiIiIiIiIhUPQUwRERERERERKTqxSrdgHKYO3euL1mypNLNEBHhoYce2uXu8yrdjkpTvywi1UL98h7qm0WkWgzVN0+LAMaSJUtYs2ZNpZshIoKZvVjpNlQD9csiUi3UL++hvllEqsVQfbOmkIiIiIiIiIhI1VMAQ0RERERERESqngIYIiIiIiIiIlL1pkUODBGpjN7eXjZt2kQmk6l0U8oulUqxePFi4vF4pZsiIrKb+mX1yyJSfdQ3j75vVgBDREpm06ZN1NfXs2TJEsys0s0pG3ensbGRTZs2sXTp0ko3R0RkN/XL6pdFpPqobx5936wpJCJSMplMhjlz5kyrjhjAzJgzZ860jKKLSHVTv6x+WUSqj/rm0ffNCmCISElNt46433R93SJS/aZr/1RNr9vMvmNmO8zsiYKy2WZ2p5k9H/47Kyw3M/uama01s8fM7ISCYy4K93/ezC4qKD/RzB4Pj/maVdOLF5FBTdc/07G+bgUwRERERETK63vAGQPKLgd+7e7LgF+HzwHOBJaFP5cC/wVBwAP4JHAycBLwyf6gR7jPBwqOG3guEZFJSQEMESmrbdu2sWrVKg455BBOPPFEzjrrLJ577jnuuece3vKWtxTtPIPVd8cdd3Dcccdx3HHHUVdXx+GHH85xxx3He97znqKdV0RkslG/XH7ufi/QNKD4HOC68PF1wLkF5d/3wJ+AmWa2P3A6cKe7N7l7M3AncEa4rcHd/+TuDny/oK6iaW3cRmb9n+h+8WF6stliVy8y7alvHlxJk3ia2RnAV4Eo8G13/+yA7UmCTvVEoBG4wN03hNuuAC4B8sCH3f2OsPwjwPsBBx4H3ufumtAoMgm4O29729u46KKLuOmmmwB49NFH2b59+4TrzufzRKPRYfc5/fTTOf300wE49dRT+eIXv8jKlSsnfG4RkclK/XJVme/uW8PH24D54eNFwMaC/TaFZcOVbxqkfFBmdinByA4OPPDAUTU089JDNNzzb9i630C8hr5T/oHGw97OnAWjO15Ehqe+eWglG4FhZlHgmwTD3pYDF5rZ8gG7XQI0u/uhwJeBz4XHLgdWASsIhrx9y8yiZrYI+DCw0t2PIgiMrCrVaxCR4vrNb35DPB7nr/7qr3aXHXvssZxyyikAdHR0cN5553HEEUfwzne+k+DGEfz617/m+OOP5+ijj+biiy8mG97pWbJkCf/4j//ICSecwI9+9CN+9atfccQRR3DCCSfwk5/8ZFRtuvvuuzn33HN3P7/zzjt529veBkBdXR0f+chHWLFiBaeddho7d+4E4IUXXuCMM87gxBNP5JRTTuGZZ56Z8O9GRKQS1C9Xp3DkhJfpXNe4+0p3Xzlv3rwR9+/s6iDx0LeD4AVAbxeRu/+NGY2PlLahItOI+uahlXIKyUnAWndf5+49wE0EQ+AKFQ6VuxU4LUwydA5wk7tn3X09sDasD4JRI2kziwE1wJYSvgYRKaInnniCE088ccjtDz/8MF/5yld46qmnWLduHX/4wx/IZDK8973v5eabb+bxxx8nl8vxX//1X7uPmTNnDn/+858599xz+cAHPsAvfvELHnroIbZt2zaqNr3uda/jmWee2d3Rfve73+Xiiy8GoLOzk5UrV/Lkk0/y2te+lk996lMAXHrppXz961/noYce4otf/CJ/8zd/M95fiYhIRalfrirbw+kfhP/uCMs3AwcU7Lc4LBuufPEg5UXhzZuIPP+rfcojO54q1ilEpj31zUMrZQBjqGFtg+7j7jmgFZgz1LHuvhn4IvASsBVodff/G+zkZnapma0xszX9v2SZ/J7Y3Mr1922odDOkRE466SQWL15MJBLhuOOOY8OGDTz77LMsXbqUww47DICLLrqIe++9d/cxF1xwAQDPPPMMS5cuZdmyZZgZ73rXu0Z1TjPj3e9+NzfccAMtLS3cd999nHnmmQBEIpHd9b/rXe/i97//PR0dHfzxj3/k/PPP57jjjuODH/wgW7duHe4UItPe3c9s5xt3P09vvq/STZExUr9cVquB/pVELgJ+XlD+nnA1kpcTXP9uBe4A3mRms8LknW8C7gi3tZnZy8Mbg+8pqGvC8ulZ+NzD9yn3hiFnqYhIkU3nvrmkOTCKLeyczwGWAi3Aj8zsXe5+w8B93f0a4BqAlStXlmUInpSWu/OvP3+CdTs7ufCkA4lFlYN2slmxYgW33nrrkNuTyeTux9FolFwuN2KdtbW1E27X+973Pt761reSSqU4//zzicUG7xrNjL6+PmbOnMkjjzwy4fOKTBc/e3gLf3xhFw+/1MK1731ZpZsjBdQvV4aZ3QicCsw1s00Eq4l8FrjFzC4BXgTeHu5+O3AWwYjkLuB9AO7eZGb/BjwY7vdpd+9PDPo3BCudpIH/DX+KYsbs+eRe8wlit7wbejoA8MUn0T3veOqKdRKRaU5989BK+Q1wqGFtg+4TTgmZQZDMc6hj3wCsd/ed7t4L/AR4ZUlaL1Xnt8/tZFdHljm1Cda82Fzp5sg4vP71ryebzXLNNdfsLnvsscf43e9+N+Qxhx9+OBs2bGDt2rUAXH/99bz2ta/dZ78jjjiCDRs28MILLwBw4403jrpdCxcuZOHChXzmM5/hfe973+7yvr6+3R8eP/zhD3n1q19NQ0MDS5cu5Uc/+hEQBNYeffTRUZ9LZDp6dls7F79qKQ9uGLjoglSa+uXKcPcL3X1/d4+7+2J3v9bdG939NHdf5u5v6A9GhKuPfMjdD3H3o919TUE933H3Q8Of7xaUr3H3o8JjLvP+CfJFEjv09WTe8TN6z/4WvX95He1nfIO6A48q5ilEpjX1zUMrZQDjQWCZmS01swRBss3VA/YpHCp3HnB32MGuBlaZWdLMlhKsX/0AwdSRl5tZTTgk7jTg6RK+Bqkit6zZyOkrFrByySx+9cSkHBo67ZkZP/3pT7nrrrs45JBDWLFiBVdccQULFiwY8phUKsV3v/tdzj//fI4++mgikcheCY0K97vmmmt485vfzAknnMB+++03pra9853v5IADDuDII4/cXVZbW8sDDzzAUUcdxd13382VV14JwA9+8AOuvfZajj32WFasWMHPf160kbkiU04u38eGxk6O3L+BfJ/T0tVT6SZJAfXLMl6pJS8jfsI7iR99Lg2L951SIiLjp755aFbkgOzelZudBXyFYLWQ77j71Wb2aWCNu682sxRwPXA8wVrYq9x9XXjsPwMXAzng7939f8PyTwEXhOUPA+9392EXn165cqWvWbNmuF1kEjjzK/fyjpMPJBGL8pW7nuO+K06rdJNkBE8//fRenVs1u+yyyzj++OO55JJLdpfV1dXR0dEx7joHe/1m9pC7V8c6VBWkfnn6eGFnB+/69v186e3HceXPn+Dz5x3D8QfOqnSzpi31y+qXh6O+WaQy1DePvm8uaQ4Md7+dYN5eYdmVBY8zwPlDHHs1cPUg5Z8kmCco04i782JTFwsa0tQmozR39dCe6aU+Fa9002QKOPHEE6mtreU///M/K90UkSnnuW3tHDC7BoD5DSlebOxSAENGpH65+mVadxFv2wDRBNGFx1S6OSJSBtXQN0+qJJ4yfe3q6CEWNepSwX/Z+fUpNrd0c8QCBTBk4h566KFByycSSRaRwHPb21k0IwXAvPokG3Z1VrhFMhmoX65u+ZceJPnAf2NP/RRSM/FTr6B76enUzDuw0k0TkRKqhr5ZyzjIpLChsZOFM9K7n8+tT7KpqbuCLRIRkdF4ems7i2btGYHxwk59ARWZzHqy3UQe+yH2xK3Ql4euRuz2j5He9VilmyYi04ACGDIprN/VyfyG1O7nc2sTbG5RAENEpNq9sLODhTODAPSChhQbGrsq3CIRmYhI01rs6YF5+YEdT5W/MSIy7SiAIZPC+p2d7NewZ73j2XVJXmrSRbCISLVr7OxhZk0w3W9+Q5IXGzWFRGQyyyca8BmDTBWpnVf+xojItKMAhkwK63Z1sKBgBMa8uiQbFcAQEalqfX1Oa3cv9WH+ohnpOLk+p7Wrt8ItE5HxSs45CF77CYgmdpf5fsvp2++oCrZKRKYLJfGUSWHdzk5ee9ieNYrn1Se582kFMCabV/7Hr9nSmilafQtnpPjjCMvpmhnvfOc7ueGGGwDI5XLsv//+nHzyyfzyl78c8rg1a9bw/e9/n6997WtFa6/IdNPa3UtNIkosEtwvMTPm1CbY2ZFhRo2SMFcD9csyHvmDXkf0HT+CxrUQT9M370iii0+odLNEZAImS9+sAIZMCltbM8yr2zOFZF59kq0txbvgkvLY0prhxg+8vGj1Xfg/fxpxn9raWp544gm6u7tJp9PceeedLFq0aMTjVq5cycqV+yw9PaRcLkcspi5VpFBjZ5aZ6b0DFfWpOI0dPRy63xAHSVmpX5bxiKVScMipwQ8QrWhrRKQYJkvfrCkkUvWyuTyZ3jy1yT0fjw2pGN29eTqzuQq2TCaLs846i9tuuw2AG2+8kQsvvHD3tgceeIBXvOIVHH/88bzyla/k2WefBeCee+7hLW95CwBNTU2ce+65HHPMMbz85S/nsceCTOtXXXUV7373u3nVq17Fu9/97jK/KpHq19jRQ8M+AYwYzV09FWqRVAv1yyIi4/ezhzfzqs/ezdLLb+NVn72bnz28uSj1Toa+WQEMqXqNHUECODPbXWZm7NeQ1EokMiqrVq3ipptuIpPJ8Nhjj3HyySfv3nbEEUfwu9/9jocffphPf/rT/NM//dM+x3/yk5/k+OOP57HHHuPf//3fec973rN721NPPcVdd93FjTfeWJbXIjKZNHb20JDaO4BRl4zR1KkcGNOd+mURkfH52cObueInj7O5pRsHNrd0c8VPHi9KEGMy9M0aVydVr7GjhxnpfedKz61NsqWlm8Pm11egVTKZHHPMMWzYsIEbb7yRs846a69tra2tXHTRRTz//POYGb29+36x+v3vf8+Pf/xjAF7/+tfT2NhIW1sbAGeffTbpdLr0L0JkEmrs7NmdwLNfnUZgCOqXRUTG6wt3PEt3b36vsu7ePF+441nOPX7kKR/DmQx9s0ZgSNXb1ZFlZjqxT7mGIctYnH322XzsYx/baygcwL/+67/yute9jieeeIJf/OIXZDJjy61SW1tbzGaKTCmN7VnqBgYwkjEaO7IVapFUE/XLIiJjt2WIEehDlY9VtffNCmBI1dvVkaU+ve9gobpUjMYOBTBkdC6++GI++clPcvTRR+9V3traujtB0fe+971Bjz3llFP4wQ9+AATz/ObOnUtDQ0NJ2ysyFezsyO4zhaQ/iaeI+mURkbFbOHPwUQxDlY9VtffNmkIiVW9XRw/1yUECGEldBE82C2ekRpWhfiz1jdbixYv58Ic/vE/5Jz7xCS666CI+85nP8OY3v3mvbf15V6666iouvvhijjnmGGpqarjuuusm1nCRaWJXR5bDB0zzq0/FeFKj56qG+mURkcnl46cfzhU/eXyvaSTpeJSPn354Ueqv9r5ZAQypers6svtksYdgJZLGTg1Dnkz+eMVpZT9nR0fHPmWnnnoqp556KgCveMUreO6553Zv+8xnPgNAY2Mjs2fPBmD27Nn87Gc/26eeq666qujtLRYzOwP4KsHqdt92988O2J4Evg+cCDQCF7j7hnDbFcAlQB74sLvfMVydFnxqfQY4Pzzmv9y9PIuBS1Vr7OihYcne/XeDRs9VFfXLIiKTS3+eiy/c8SxbWrpZODPNx08/fML5LyZL36wAhlS9HW0ZDphds095fSrO+l2dFWiRTHWrV6/mn//5n/nOd75T6aaMi5lFgW8CbwQ2AQ+a2Wp3f6pgt0uAZnc/1MxWAZ8DLjCz5cAqYAWwELjLzA4LjxmqzvcCBwBHuHufme1X+lcpk0HTIKuQ1Kfiyl8kYzbZ+2URkWI69/hFEw5YFEMl+mYFMKTq7eroYcXCGfuUByMwdBEsxXf22Wdz9tlnV7oZE3ESsNbd1wGY2U3AOUBhAOMc4Krw8a3AN8KRFOcAN7l7FlhvZmvD+himzr8G3uHufQDuvqOEr00mkabOnn1G0NWnYrR2axlVGZsp0C+LiEw5leiblcRTqt6ujiwzavadQlKfjtOkAIbIYBYBGwuebwrLBt3H3XNAKzBnmGOHq/MQgtEba8zsf81s2WCNMrNLw33W7Ny5c1wvTCaPfJ/Tns3tk8MoHY/Sk+sjM2AJOBEREZGRKIAhVW+wIcgQ3MVr0TBkkWqQBDLuvhL4H2DQcYTufo27r3T3lfPmzStrA6X8mrt6qEvGiERsr3IzY0Y6TkuXRmGIiIjI2CiAIVWtr89p6e6lYbBlVJMxOnvy5PJ9FWiZSFXbTJCTot/isGzQfcwsBswgSOY51LHD1bkJ+En4+KfAMRN+BTLptXb3Up8afKZqg0bQiYiIyDgogCFVraW7l9pElFhk3/+qETPqkzGadRdPZKAHgWVmttTMEgRJOVcP2Gc1cFH4+Dzgbnf3sHyVmSXNbCmwDHhghDp/BrwufPxaYE+Kapm22rp7qUlEB91Wn4opgCEiIiJjpiSeUtWGyn/RryEdZLOfV58sY6tk3L60Ato2Fa++hsXw0SeH3cXMeOc738kNN9wAQC6XY//99+fkk0/ml7/8JatXr+app57i8ssvL167Kszdc2Z2GXAHwZKn33H3J83s08Aad18NXAtcHybpbCIISBDudwtBcs4c8CF3zwMMVmd4ys8CPzCzjwAdwPvL9VqlerVnctQkBr/MqEvGaNIUwOqgfllERJg8fbMCGFLVmjt7qE8OE8BIxWjs6IH5ZWyUjF/bJrjol8Wr77q3jLhLbW0tTzzxBN3d3aTTae68804WLdqTz7JY2ZNzuRyxWPV0qe5+O3D7gLIrCx5ngPOHOPZq4OrR1BmWtwBvnliLZappz+RIDzECoy6pHEZVQ/2yiIgwefpmTSGRqtba3UttcvALYNBKJDI6Z511FrfddhsAN954IxdeeOHubd/73ve47LLLAHjve9/Lhz/8YV75yldy8MEHc+uttwLg7nz84x/nqKOO4uijj+bmm28G4J577uGUU07h7LPPZvny5WV+VSLVrT3TSzo+eP+djkdpz+TK3CKpJuqXRUQm4LFb4MtHwVUzg38fu6Uo1U6GvlkBDKlqrd29Qw5BBqjXMGQZhVWrVnHTTTeRyWR47LHHOPnkk4fcd+vWrfz+97/nl7/85e4hcj/5yU945JFHePTRR7nrrrv4+Mc/ztatWwH485//zFe/+lWee05pH0QKBVNIBg9gpBJRWruVv2g6U78sIjJOj90Cv/gwtG4EPPj3Fx8uShBjMvTNCmBIVWsdJgkcQG0yRlOHAhgyvGOOOYYNGzZw4403ctZZZw2777nnnkskEmH58uVs374dgN///vdceOGFRKNR5s+fz2tf+1oefPBBAE466SSWLl1a8tcgMtm0dveSGmIERk0iSpsCGNOa+mURkXH69aeht3vvst7uoHyCJkPfrImBUtVGCmDUp2I0dmbL2CKZrM4++2w+9rGPcc8999DY2DjkfsnknoSwwaIcw6utrS1K+0SmmrbM0P13TSLG9rZMmVsk1Ub9sojIOLQOkXh5qPIxqva+uaQjMMzsDDN71szWmtk+6UrDZfpuDrffb2ZLCrZdEZY/a2anh2WHm9kjBT9tZvb3pXwNUlktXb3UJoeOs9UkYhqGLKNy8cUX88lPfpKjjz56zMeecsop3HzzzeTzeXbu3Mm9997LSSedVIJWikwdwwWga5QDQ1C/LCIyLjMWj618jKq9by7ZCAwziwLfBN4IbAIeNLPV7v5UwW6XAM3ufqiZrQI+B1xgZssJlvRbASwE7jKzw9z9WeC4gvo3Az8t1WuQymvp6mHxrJoht9dqHvXk0rB4VBnqx1TfKC1evJgPf/jD4zrN2972Nu677z6OPfZYzIzPf/7zLFiwgGeeeWZc9YlMB+2ZHIfMHfwyoyYRpb1bAYyqoH5ZRGRyOe3KIOdF4TSSeDooL4Jq75ttNMM9xlWx2SuAq9y9f/TEFQDu/h8F+9wR7nOfmcWAbcA84PLCfQv3Kzj2TcAn3f1VI7Vl5cqVvmbNmqK9Nimfd197PycvncOJB80adPtTW9u47bEt/ORvRvxvIBXw9NNPc+SRR1a6GRUz2Os3s4fcfWWFmlQ11C9Pfef99x85Y8UCViycsc+2Fxs7+Z/frePX/3Bq+Rs2zalfVr88HPXNIpUx5r75sVuCnBetm4KRF6ddCce8vXQNLLGx9M2lzIGxCNhY8HwTMDCN6e593D1nZq3AnLD8TwOOXTTg2FXAjUOd3MwuBS4FOPDAA8fRfKkGbSMso1qbiNKmYcgiIlWnvTs35CpSNYmYppCIiIiM1zFvn9QBi4mYlKuQmFkCOBv40VD7uPs17r7S3VfOmzevfI2Tomrt7qV2mGVUa5Mx2jOaQiIiUm3as0Mvo1qTiNKRVQBDRERExqaUAYzNwAEFzxeHZYPuE04hmQE0juLYM4E/u/v2IrdZqkxbJjdCEk8lgqt2pZqmVu2m6+sW6deR6SU9RAAjHY+S6c3T16e/k0qYrv3TdH3dIjI5TNc+aqyvu5QBjAeBZWa2NBwxsQpYPWCf1cBF4ePzgLs9eAWrgVXhKiVLgWXAAwXHXcgw00dkanB32jO91A0TwEjFo2R7+8jl+8rYMhmtVCpFY2PjtOuQ3Z3GxkZSqVSlmyJSEe5OZ09+yBEYkYiRjEXp6FEAutzUL6tfFpHqo7559H1zyXJghDktLgPuAKLAd9z9STP7NLDG3VcD1wLXm9laoIkgyEG43y3AU0AO+JC75wHMrJZgZZMPlqrtUh0yvX0YRiI2dJwtYkZNMhiFMas2UcbWyWgsXryYTZs2sXPnzko3pexSqRSLFxdnOSuRyaa7N088asQiQ/fftWHf3ZCKl7Flon5Z/bKIVB/1zaPvm0uZxBN3vx24fUDZlQWPM8D5Qxx7NXD1IOWdBIk+ZYpr7e6lLjXyf9HaZIy2TK8CGFUoHo+zdOnSSjdDRMqsrTs3bP4igNpEfw6jdHkaJYD6ZRGRaqS+efQmZRJPmR5au4efPtKvNhGlrVvDkEVEqkV7ppeaYVaQAuUwEhERkbFTAEOqVmt377AJPPv1j8AQEZHq0JYZegnVfumEVpESERGRsVEAQ6pWW3cvtUMkgCtUk4jS1q2LYBGRatGeGbn/1ggMERERGSsFMKRqjXYERk1CIzBERKpJeyY35BKq/dKJCG0KYIiIiMgYKIAhVau1u3fIJfgK1SgHhohIVWnL9JKODx+ATsWimkIiIiIiY6IAhlSt1u7eEe/gAaTjUVq6e8rQIhERGY3ObI5UfPhLjHQiRrum/4mIiMgYKIAhVas900s6PpoRGDFau3QRLCJSLTqzeZKx4S8xahJRWjV6TkRERMZAAQypWm2jmEMNUJuM0qq7eCIiVaMjmyM1QgA6SOKpvltERERGTwEMqVptmV5qRphDDeEIDCWCExGpGh2ZkQMY6URUSTxFRERkTBTAkKrVMdoRGFpGVUSkqnRkcyNOAUzHo3RmFcAQERGR0VMAQ6pWRyY3ulVIkjEFMEREqkhnT47kCEk8UwpgiIiIyBgpgCFVq30Ud/AgmEfdoYtgEZGqMZoRGKl4lM6efJlaJCIiIlOBAhhStTqzoxuBkY5H6exRAENEpFp0ZfMj58DQCAyRQZnZR8zsSTN7wsxuNLOUmS01s/vNbK2Z3WxmiXDfZPh8bbh9SUE9V4Tlz5rZ6RV7QSIiRaQAhlStjuzocmCk41G6e/L09XkZWiUiIiPp7Bk5iWcqHqG7VyMwRAqZ2SLgw8BKdz8KiAKrgM8BX3b3Q4Fm4JLwkEuA5rD8y+F+mNny8LgVwBnAt8xs5IsqEZEqpwCGVKV8n5PpHfkOHkAkYiRjEbp0ISwiUhW6snlSsRFyYMQUfBYZQgxIm1kMqAG2Aq8Hbg23XwecGz4+J3xOuP00M7Ow/CZ3z7r7emAtcFJ5mi8iUjoKYEhV6r97FzEb1f41iZiGIouIVImu3pFH0EUiRjKu4LNIIXffDHwReIkgcNEKPAS0uHv/hc4mYFH4eBGwMTw2F+4/p7B8kGNERCYtBTCkKnVkctQmYqPevyYRpT2jAIaISKW5O909eZKx0U0B7FLwWWQ3M5tFMHpiKbAQqCWYAlLKc15qZmvMbM3OnTtLeSoRkQlTAEOqUvsol1Dtl9ZKJCIiVSHT20c8GiEaGXkEnVaREtnHG4D17r7T3XuBnwCvAmaGU0oAFgObw8ebgQMAwu0zgMbC8kGO2Yu7X+PuK9195bx584r9ekREikoBDKlKHdneUSXw7JeOR+nQCAwRkYobzRKq/VLxKJ1ZTSERKfAS8HIzqwlzWZwGPAX8Bjgv3Oci4Ofh49Xhc8Ltd7u7h+WrwlVKlgLLgAfK9BpEREpm9GP0RcpofCMwekvYIhERGY2untGtIAVBAEMjMET2cPf7zexW4M9ADngYuAa4DbjJzD4Tll0bHnItcL2ZrQWaCFYewd2fNLNbCIIfOeBD7q5ooYhMegpgSFVqz4y8BF+hdFw5MEREqsFYRmCk41G6etR3ixRy908CnxxQvI5BVhFx9wxw/hD1XA1cXfQGiohUkKaQSFUaywUw6C6eyEBmdoaZPWtma83s8kG2J83s5nD7/Wa2pGDbFWH5s2Z2+kh1mtn3zGy9mT0S/hxX6tcn1aurZ3RLYIP6bhERERkbjcCQqtSRGf0QZAgvgjUCQwQAM4sC3wTeSLB03oNmttrdnyrY7RKg2d0PNbNVwOeAC8xsOcEQ5BUEGfDvMrPDwmOGq/Pj7n5ryV+cVL2xBKCTsYhyYIiIiMioaQSGVKW2TO+oluDrl4pHaVMAQ6TfScBad1/n7j3ATQTL8hU6B7gufHwrcFqYMO4c4CZ3z7r7emBtWN9o6hShK5snGR/d5UVKU0hERERkDBTAkKrUnukdWxLPeJT2jJJ4ioQWARsLnm8Kywbdx91zQCswZ5hjR6rzajN7zMy+bGbJwRplZpea2RozW7Nz586xvyqZFDqzo89hlIxHNIVERERERq2kAYwSzcGeaWa3mtkzZva0mb2ilK9BKqNtjFNIahJK4ilSQVcARwAvA2YD/zjYTu5+jbuvdPeV8+bNK2f7pIw6e3IkY6O7vEjG1HeLiIjI6JUsB0Yp5mCHyz99FfiVu59nZgmgplSvQSqnPdPLgbNG/9am41HadRdPpN9m4ICC54vDssH22WRmMWAG0DjCsYOWu/vWsCxrZt8FPlaE1yCTVGd29AGMdDxCp/pukUnpkY3NPL+9g5pElBULZ7Bkbm2lmyQi00Apk3juni8NYGb986ULAxjnAFeFj28FvjFwDjawPlzb+iQzewp4DfBegHAedk8JX4NUSPsYR2CkE1E6NIVEpN+DwDIzW0oQZFgFvGPAPquBi4D7gPOAu93dzWw18EMz+xJBAHkZ8ABgQ9VpZvu7+9aw/z4XeKLEr0+qWEc2R3IMq5DsaM+WuEUiUmy/e34nH7z+Ibp6giS8xx84k8/95TEcNr++wi0TkamulFNISjEHeymwE/iumT1sZt82s0HDvZprPbl1ZMa2jGo6oaX4RPqF/ellwB3A08At7v6kmX3azM4Od7sWmBMGiD8KXB4e+yRwC0Gw+VfAh9w9P1SdYV0/MLPHgceBucBnyvE6pTqNpf9OaxlVkUmnqSPLV3/9/O7gBcDDL7Xw6MaWyjVKRKaNybaMagw4Afhbd7/fzL5KcNH9rwN3dPdrgGsAVq5c6WVtpUxYZ09+bCMwdBEsshd3vx24fUDZlQWPM8D5Qxx7NXD1aOoMy18/0fbK1NGezbH/jNSo9k2p7xaZdFq6e3lhR8c+5VtauivQGhGZbko5AmMsc7AZ5RzsTcAmd78/LL+VIKAhU0xndowjMOJRurL5kXcUEZGS6uoZ/SokKfXdIpPOopkpTjty/j7lR+zfUIHWiMh0U8oAxu452GGyzVUEc64L9c/BhoI52GH5qnCVkqWEc7DdfRuw0cwOD485jb1zasgU0TmGC2AIppB09ugunohIpXVm86Rio59Cor5bZHJJxmNc9IolvOawYDWpmkSUT5x+OCccOKvCLROR6aBkU0jcPWdm/fOlo8B3+udgA2vcfTXBHOzrwznYTQRBDsL9+udg5wjnYIdV/y3BfOsEsA54X6leg1SGu9Pdkx9TACMeDWJx2Vye5CgvnEVEpPi6sqPvv1PxiEZgiExCRy+ewVcvOI71jZ2kYhGOWFBPJFLK+6IiIoGS5sAo0RzsR4CVRW2oVJXu3jzxaIRoxMZ0XE0iRkcmR7JOAQwRkUrp6s2RjI/ui0wqHqWrVyMwRCajWbUJZtUmKt0MEZlmFCqVqtORzVEzhgSe/WoSUdozuhAWEamkrp7RTyFJxaN09+QJZo+KiIiIDE8BDKk6ndn8mBJ49tNcahGRyuvuyY96BEY0YsSiEbp7NY1ERERERqYAhlSdzmxuTEuo9ksnonRqLrWISEV19+ZJxkZ/eZGOR+nqUd8tIiIiI1MAQ6pOe2ZsS6j2S8WjdGY1AkNEpFLcnUzv6KeQgJbBFhERkdFTAEOqTmd2bEuo9kvFI3QogCEiUjHZXB+xaITIGJIwp+IRTf8TERGRUVEAQ6pOZ8/4ppCkYhqBISJSSUEAemyXFql4lC4FMERERGQUFMCQqtORzY1p/nS/pEZgiIhUVFfP2JMwp5QDQ0REREZJAQypOuOdQpKMKYmniEgljWUJ1X6peER9t4iIiIyKAhhSdTqyY8tg3y8Vj9Ke7S1Bi0REZDS6esYegE7GNIVERERERkcBDKk67ZnecY3ASMcjdGR0ESwiUildPXmSY8yBkYxF6NQUEhERERkFBTCk6kxkGVXlwBARqZyunrGPoEvEInSp7xYREZFRUABDqk7HOHNgpBXAEBGpqPFNIdEIDBERERkdBTCk6nRmx7mMqgIYIiIV1dWTJxEd+zKqWgJbRERERkMBDKk6HeOcQpJO6CJYRKSSxpcDI6r8RSIiIjIqCmBI1ekcxxBk6L+Lp2HIIiKV0pXNjWMERkSrkIiIiMioKIAhVaczmx/fCIy4luITEamkzp4cydjY+u9UPKocGCIiIjIqCmBI1QmSwI39v2ZwF08XwSIildLZkx9z/60cGCIiIjJaCmBI1enM5sedxDPTm6evz0vQKhERGUlnduwjMJIxBZ9FRERkdBTAkKrSm+8j3+djnkMNEDELLoR7dSEsIlIJ40nimdL0PxERERklBTCkqnRmc6QSEcxsXMenEzENRRYRqZDObI7UWHNgaASGiIiIjJICGFJVOrI5auKxcR+fjkfpUABDRKQiusc9AkMBDBERERmZAhhSVbp6xpf/ol86oWRwIiKVMp5lsFPxKN0KYIiIiMgoKIAhVaUjmxvXEqr90vGIRmCIiFRId0+eZGxslxbxqJHr6yOX7ytRq0RERGSqUABDqkpnNjfm4ceFguX4dCdPRKQSunvzY16FxMxIx6NKwCwiIiIjUgBDqkrnBEdgJGOaQiIiUindPXlS4whCpxJRuhR8FhERkREogCFVpTObH/P86UIpTSEREamY8YzAAEjFonRqKVUREREZQUkDGGZ2hpk9a2ZrzezyQbYnzezmcPv9ZrakYNsVYfmzZnZ6QfkGM3vczB4xszWlbL+UX2dPjtQY508XCqaQ6CJYRKTcevN95PuceHTsy2Cn4hGNwBAREZERjX+9yhGYWRT4JvBGYBPwoJmtdvenCna7BGh290PNbBXwOeACM1sOrAJWAAuBu8zsMHfvv7p5nbvvKlXbpXI6sjkSEwhgJGMROpXNXkSk7Lp68qTjUczGE8DQCAwREZFq0NWT44lNrbywq4O5dUlW7h9j1vb74ZnboWYWLDsDlr56VHW1dPbQkull8Yw0sQl8xytUsgAGcBKw1t3XAZjZTcA5QGEA4xzgqvDxrcA3LLjyOQe4yd2zwHozWxvWd18J2ytVoCMz9iX4CqXiUToyvUVskYiIjEaQ/2J8/XcqHqVLAQwREZGKW/3IFi7/yeMA1Kdi/PncVvjZB/bssOa78I5bYMmrhq3nd8/v5Bt3P8+6XV28afl83nHSgaxYNGPC7SvlFJJFwMaC55vCskH3cfcc0ArMGeFYB/7PzB4ys0uHOrmZXWpma8xszc6dOyf0QqR8OrMTC2Ck41HaM7oIFinRFL6R6vyamXWU7EVJVevqyY0rgSeEo+c0hURERKSiNjZ1cfVtT+9+/g+vmkvs/m/uvVNPB7z4h2HrefilZi79/kPcv76Zne1ZfnD/S3zt7udp7574jebJmMTz1e5+AnAm8CEze81gO7n7Ne6+0t1Xzps3r7wtlHFrn+AqJKl4VEk8ZdormMJ3JrAcuDCcmldo9xQ+4MsEU/gYMIXvDOBbZhYdqU4zWwnMKukLk6rW1TO+BJ4Q9N3dmv4nIiJSUdlcnvaC71I1cbBcZt8dBysr8Nz2droHLI/+f09tZ92uzgm3sZQBjM3AAQXPF4dlg+5jZjFgBtA43LHu3v/vDuCnBFNLZIroyI7/Dh70z6PWRbBMe7un8Ll7D9A/ha/QOcB14eNbgdMGTuFz9/VA/xS+IesMgxtfAD5R4tclVaxrAlNIgvxFCj6LiIhU0v4z07z+8D03///zj830nvj+vXeKRGHJ8DkwBrshXZeIkSxCHoxSBjAeBJaZ2VIzSxDc0Vs9YJ/VwEXh4/OAu93dw/JV4RDnpcAy4AEzqzWzegAzqwXeBDxRwtcgZdY5wRwY6XhEq5CIlGYK33B1XgasdvetwzVKU/umtq6eHMlxBqATsQhdCj6LiIhUVG0ixr+8ZTnnn7iYdDzKghkpdi16Hbz1a7D4ZbDsTbDqRjho+ADGioUNLN+/Ya+yy15/KEcMKBuPkiXxdPecmV0G3AFEge+4+5Nm9mlgjbuvBq4Frg+TdDYRBDkI97uFIOFnDviQu+fNbD7w0zDDeQz4obv/qlSvQcqvM8xiP15aRlWkvMxsIXA+cOpI+7r7NcA1ACtXrvTStkzKrbsnP+47K6lYlA7lLxIREam4g+fV8e9vO5qPvPEw6pIxGtJxOOBQOOovIRKHeHLEOg7Zr57/PP8Y/ryxha0tGVYsbGDlktlFaV8pVyHB3W8Hbh9QdmXB4wzBhe9gx14NXD2gbB1wbPFbKtWiM5sjlZhYAEN38UTGNIVv02in8A1RfjxwKLA2DC7XmNnaMLeGTCMTyYGRjEfo0BQSEZFp77nt7azd1sHpDeuJbHkQMq2w+GXk6g8ivv+RMGCp7vbuXh7e2MKjG1tYPCvNyiWzOWB2TYVaP4RcD2z5M7z0J0jNhINeAfMOr3SrhhWPRVg4M713YbJuTHUcuXAGRy6c+KojA5U0gCEyVp09eVLjvAAGjcAQCe2ewkcQZFgFvGPAPv1T+O6jYAqfma0GfmhmXwIWEk7hA2ywOt39SWBBf6Vm1qHgxfTU1ZOb0AiMXZ3ZIrdIREQmk2e3tXHBNX9izbvriN76HugMp5uaEf/LayHfBQes3L2/u/Pjhzdx1eqndpcds3gG//OelcxvSJW7+UNbfy/88DzwcPBp7Tx4721VH8SoVpNxFRKZwrommMQzrREYIv05Lfqn8D0N3NI/hc/Mzg53uxaYE07h+yhweXjsk0D/FL5fEU7hG6rOcr4uqW5dPXkS4w1gKH+RiMi098cXGjlwVproljV7ghcQfPH/w9foy7buCQIAm5q7+cKvnt2rjsc2tfL01rZyNXlk2Xa4+zN7tZvOncFoDBkXjcCQqtLVmyc9gSkkyXiETG+evj4nErGRDxCZooo9hW+oOgfZZ2zjC2XKmEgAIxmP0plV8FlEZDpr6erlgFnp4Ev/QN3NGID3gQXfFXrzfXT17vvZkammm5m5Huhu2rc8W0VBlklGIzCkauTyfeTyfSSi4/9vGTEjFY8M2pmJiEjpdPbkxp0DI8hfpBEYIgBmNtPMbjWzZ8zsaTN7hZnNNrM7zez58N9Z4b5mZl8zs7Vm9piZnVBQz0Xh/s+b2UVDn1GkOrzq0Lnc/uR2fNHKfXJdcPy7sEg8WMIztGhmmnOOXbjXbrWJKIfOr6J7KbVz4OV/vXeZReCAkyvTnilAIzCkanRmgxVIbGCHNUbpRIzObI66pP57i4iUS1c2T2rcOTC0jKpIga8Cv3L388wsAdQA/wT82t0/a2aXE0z7+0fgTIJcRcuAk4H/Ak42s9nAJ4GVgAMPmdlqd28u/8sRGZ1jF8/gOxe9jPtznbz8vO/BH76CdTfDce+k76BXE5m/fK/9k/EoH33jYew/I83PHtnM4Qvq+bvTlnHofvWVeQFDWfEXwb/3/zfUzIXX/RMsPH7QXXvbdxFteh6PxrD9jiWSSJSxoZODvuFJ1ejoyU1o+ki/dDxKRzbH/CK0SURERqczm6MhPb6kaVpBSiYzM3uDu981oOwid79uHHXNAF4DvBfA3XuAHjM7hz3LVV8H3EMQwDgH+L67O/CncPTG/uG+d7p7U1jvncAZwI1jbZNIuSTjUV53xH50ZntpzS8isWAlsVwXiYb9iaQHD0ocOKeWT5xxOO8/ZSm1yRip+MS/SxRd/fxgFMYxF0A0MeRqHvmNDxK7/7+wJ38KsRT+yr+l58i/ILHgiDI3uLqN6laJmf3EzN5sZppyIiXTlS1SACMRpUtzqWWKUP8rk0VXb274VaReug+aXhh0k6aQyCR3pZn9l5nVmtl8M/sF8NZx1rUU2Al818weNrNvm1ktMN/dt4b7bIPd92kWARsLjt8Ulg1Vvg8zu9TM1pjZmp07dw62i0hZ1SbjzKxJUDNnMYn5h8EQwYt+ZsacumR1Bi8K1cwedinSyNOrsSd+HOT56O3Cfvs54tsfLWMDJ4fRXhB/i2AJvufN7LNmpjVfpOg6sjnSReh4UrEIHcpmL1OH+l+ZFLqyeZJDrSK17TG494vw2y9A3779cyoeoVu5i2Tyei3wAvAI8Hvgh+5+3jjrigEnAP/l7scDnYSrRPULR1v4IMeOi7tf4+4r3X3lvHnzilWtiIxB784XsGdv23fDxvvL35gqN6oAhrvf5e7vJOhQNwB3mdkfzex9ZhYvZQNl+ujM5osSOU3Fo1qOT6YM9b8yWXT15AdP4ul98NvPwXHvhEQtPPnzfXZJxqJ09+RxL9p3MpFymgWcRBDEyAIH2fgTem0CNrl7/7eWWwn6/+3h1BDCf3eE2zcDBxQcvzgsG6pcRKpRahY+Z9me5we8Al72AZi9tHJtqlKjHpJsZnMI5uO9H3iYIMHQCcCdJWmZTDsd2VxRAhjpRJRODUWWKUT9r0wG3b15koMl8WxeD9EkzD0UDj0Nnv/VPrtEI0YsGiGb6ytDS0WK7k8ESTfPAF4GLAT+MJ6K3H0bsLFgtN1pwFPAaqB/JZGLgP5I4GrgPeFqJC8HWsOpJncAbzKzWeGKJW8Ky0SkCsXrZ8PJfwX7rYD3/AIOfi2ey0L9/tC4odLNqyqjSuJpZj8FDgeuB95aMAfvZjNbU6rGyfTS1ZMbdwb7QslYhE7lwJApQv2vTBZdPXmSgwWhtz2+5w5S/ULoboZMK6Rm7LVbOhw9V/VzmEX29QZ3fwnA3buBD5vZayZQ398CPwhXIFkHvI/gpuMtZnYJ8CLw9nDf24GzgLVAV7gv7t5kZv8GPBju9+n+hJ4iUn26e3Jc++Ii/uasLxK59b3QsR0DePj7cNYXYM6lFW5h9RjtKiT/4+63FxaYWdLds+6+sgTtkmmoWBeuyZimkMiUov5XJoXuniGWUd36KMw6OHgcicDMA2HnM3DAyXvtlooHS6nOKUNbRYrJ3V8KRzksA8a3FM/e9T1CsPzpQKcNsq8DHxqinu8A35loe0Sk9DY0dvKfdz7Ph/5iG3Rs33vj778MB70aBiwjO12N9nb3ZwYpu6+YDRHpyA4x/HiMknEl8ZQpRf2vTArdvYOMwPA+2P7k3nN4ZxwQlA2Qimv6n0xOZvZ+4F6CKRqfCv+9qpJtEpHJJZd33IF8774be7rANbq837AjMMxsAcGSS2kzOx7oT0jUANSUuG0yzXRke4uTAyMeVQBDJj31vzKZuDvdvYOMwGh+ERI1kGrYUzbzIHhx3/QAQQJmXaDJpPR3BLkv/uTurzOzI4B/r3CbRGQSOXBOLa84eDZ982qJxlKQy+zZuPJiWHB05RpXZUaaQnI6QeK4xcCXCsrbgX8qUZtkmurIFGcKSSoepSOjAIZMeup/ZdLoyQfJN2PRgQGMDdCweO+ymYvhkfXBXabonoV0UrEIXRqBIZNTxt0zZtY/xe8ZLXktImMxIx3n3//iGH6xsZlzLrgBHrgGa9sMR50Hh+wze2xaGzaA4e7XAdeZ2V+6+4/L1CaZptqzOebWJidcTyoeZVtrZuQdRaqY+l+ZTLp78qTig0wBbN0INQOyWsRSkJoJ7VuDfBghjcCQSWyTmc0EfgbcaWbNBIk2RURGbencWpbMqaGrZz7pA07CMp0wc2Glm1V1RppC8i53vwFYYmYfHbjd3b80yGEi49KRzbF4ZnrC9aSUA0OmAPW/Mpl09eRJDzaCrmXjXkGK3WrnQdumvbYlNQJDJil3f1v48Coz+w0wA9h3vWARGZPmzh6e295Orq+Pw/arZ17Dnhy5u9qzbGzuIp2IsCi/hUTbi5Ceje1/LDSvx1pexGvmEFt4LJFYfJizVBczozYZB2bss1qXBEaaQlIb/ltX6oaIdBZpCkn/Unwik5z6X5k0unryg/ffbZtg0Qn7ltfMhtbNexUFSTw1AkMmDzObPUjx4+G/dYCWLRUZp+e2t/OVu57j9se3AfDyg2fzr29ezopFM3h6axt/84OH2NHWw/3viFN3+2XQthliSfx1/4w1rYeHvgvxNH2nXUXvMRcSr1EwYKoYaQrJ/wv//VR5miPTWWd2iDt4Y5RSAEOmAPW/Mpl09wyyipT3Qft2qJ277wE1c6B1015FiViEbo3AkMllF7AJ6P+PawXbHDi47C0SmSL+uHbX7uAFwJ/WNXHb41tZMq+Wf7/tadbv6uKbb9mP2l+HwQuAXBa780o477tBAKO3m8gdl5OftxwOeU2FXokU26jWrDSzz5tZg5nFzezXZrbTzN5V6sbJ9NLRkyOVKNIIDN3FkylC/a9MBp09g4yg69wZrEASGyS3Ue3cID9GgWQsohwYMtl8DWgmmC5yEXCwuy8NfxS8EJmA+9fvO4Dp92t3saMtwx9e2AXA0Q1d2M5n9j24Y/uex+5Yi1LSTCWjCmAAb3L3NuAtwAbgUODjpWqUTE+d2ZxGYIjsS/2vVL1BR2C0bgpyXQymZh60bd2rKKUlsGWScfe/B44DfgS8G3g4DDovrWS7RKaCYxbvO+Xj+ANmMrc2ybGLZwKwoTuNzzxo34MHJo+u378ELSyfnlwfOzevp/Glp+nuaK90cyputAGM/qkmbwZ+5O6tJWqPTGNDzqEeo3Q8SpdGYMjUof5Xql5XT55kbED/3bZl34vIfsk6yPdAz54LsWRMwWeZfDzwG+ATwH8D7wPeUNlWiUx+rzlsHscdsCeIcfDcWt52/GLq03GufOtyZtbE+eidrWTf8B+Q2JMuzF/+N7Dl0eCJGX2v/ij5BceWu/lFs3lHE5mHb2HeD05jzndfgf3sUlo2PlXpZlXUSEk8+/3SzJ4BuoG/NrN5gNaplKLq6inOCIxkPEI2l6evz4lEbOQDRKqb+l+pel09uX2XUR1sCdV+ZsHojNYtMO9wIFhBake7AhgyeZhZLXAOcAEwD/gJcKK7v1TRholMASsWzuDrFx7Ps9s6yPf1cdj8epbOCwIVxx84i9WXvZoXGzt5Jhnj8Hf8nGjrBqx2Lrm5y4m2bcT2Pw7q9sMXHEOydlZlX8wEJBufouG2D+5+nlp7Ox6vpefcr5NITnz1xsloVAEMd7/czD4PtLp73sw6CTpskaLoyfXR1wfx6MQDDhGzMJt9jvrU5Fk2SWQw6n9lMujqyZMYOIWkfTvsd+TQB9XODRKv7Q5gaASGTDo7gOeBm8J/HVhpZisB3P0nFWybyKR3wOxaDphdO+i2A2fXcODsmvDZyvAH4gAz94MDTyxHE0uqr6+PWPPz+5Snn/sZHS3/TGL+9JytNtoRGABHAEvMrPCY7xe5PTJNdWZzpBNRzIozYiKdCOZSK4AhU4T6X6lqnT25fXNgdO6E1MuHPig9Mwhy9D9VDgyZfH5EELQ4PPwp5AQjMkRExiUSiZBP7TuSMT/rYCKpukGOmB5GFcAws+uBQ4BHgP7kAo4uoKVIOsIARrHU6E6eTBHqf2Uy6MoOkgOja1cQpBhKcsZemeJTca1CIpOLu7+30m0QmXb6+sg1voDlurHOXRCvobFhBTu7+1g0q4YZ6al18zIz7ygyB5xCauPvgoJIjK7XX039jCGSZE8Dox2BsRJY7u4+lsrN7Azgq0AU+La7f3bA9iTBRfiJQCNwgbtvCLddAVxCcMH+YXe/o+C4KLAG2OzubxlLm6Q6dfbkqClC/ot+qUSUDl0Iy9Qwrv5XpJw6srm9Axi5bsj3Qrxm6IPSM6F5/e6nqXiUrh4FnmXyCa9n/xJYQsG1tbt/ulJtEpmSsp3kHvkhsZmL4Tf/AdsehXiaua/5BI/HX8u//izLZ//yaJbNr690S4tm0QEHs+X0b5BqfIpIto2+OcuYueS4Sjeroka7CskTwIKxVBwGGb4JnAksBy40s+UDdrsEaHb3Q4EvA58Lj10OrAJWAGcA3wrr6/d3wNNjaY9Ut85sjlQRR2CkNQJDpo4x978i5daZHZDEszMcfTHctMDUzGCaSUgrSMkk9nOC3EQ5oLPgR0SKadtjxJrXwf3XBMELgN5u7Nef4tQZ23jopWa+/pu1ZHNT67Nk4eIlzD72LGaetIrZh5xIJFq870yT0WhHYMwFnjKzB4Bsf6G7nz3MMScBa919HYCZ3UTQuReu+3IOcFX4+FbgGxYkQTgHuMnds8B6M1sb1nefmS0mWE7wauCjo2y/VLnObL4oK5D0S2kutUwd4+l/RcqqsyfH/IZUQcHO4aePAKRmQGcjwYwoIxkmXxaZhBa7+xmVboTIlNe8Dha/DO7/7302WcuLwEHc+eR2Gs/oYeHM6blCx3Qw2gDGVeOoexGwseD5JuDkofZx95yZtQJzwvI/DTh2Ufj4KwRrbQ87NsjMLgUuBTjwwAPH0Xwpp33u3k1QKhbRCAyZKq6qdANERtLVk987iWfnziDHxXDiKYhGIdsOyQbS8SjdPXncvWgJnUXK5I9mdrS7P17phohMabXzYfsTwQpX258csC3ICbFiYQMNqbGsUyGTzai+Mbr7b4ENQDx8/CDw5xK2a1Bm9hZgh7s/NNK+7n6Nu69095Xz5k3fJCeTRUc2R6rIIzAUwJCpoFr6X5HhdA7swzt3Qqph5APTs6BzBwDRiBGLRsj09pWolSIl82rgITN71sweM7PHzeyxSjdKZDLK5vI8u62dbTt3kVl/H9nnf0PnrvCe+P7Hkm9cD6+9HBJ7llf1I95M94yDOXFxHf981pHUaRXCKW20q5B8gGA0w2yCbPiLgP8GThvmsM3AAQXPF4dlg+2zKVwecAZBMs+hjj0bONvMzgJSQIOZ3eDu7xrN65Dq1ZnNkRq4BN8EJGMR2hXAkClgnP2vSFl19eT3HkXXsWPkERgQ5MHo2AWzDwX2LKVazFWpRMrgzEo3QGQq2NWe5Zp7X+Ci5VH2e/rbRNZcC315EotOpOf0z5M4cCXRN16F73oezv2vYBWSRB2WaqDmke9x/RGL2RE7rNIvQ0pstONrPkSQg+J+AHd/3sz2G+GYB4FlZraUIPiwCnjHgH1WAxcB9wHnAXe7u5vZauCHZvYlYCGwDHjA3e8DrgAws1OBjyl4MTV09uRJFjsHRkYBDJkSxtP/ipRVd8+AZVQ7dsCMg0Y+MNmwdyLPRP9KJMniN1KkRNz9RTN7NbDM3b9rZvOAukq3S2Sy+fNLzbywo4P5izYQeeCa3eW2+SFiD11L7/zlRDqbiPzfP2GbHtz74L/4NjU/eT/5+qNon/0m6qfYcqqyx2gDGFl37+mfkxqOlhh2Sb8wp8VlwB0Ey6h+x92fNLNPA2vcfTVwLXB9mKSziSDIQbjfLQQJP3PAh9x9aqWTlb20Z3pJxYq4CklCSTxlyhhz/ytSbvuMwOjcBenRjMBogI7tu5+mlYBZJiEz+yTBkteHA98F4sANwKsq2S6Ryebhl5p54/L9iOz45T7bIut+Q6ZlC6mepn2DFxAEzoFU+wa2tWUUwJjCRhvA+K2Z/ROQNrM3An8D/GKkg9z9duD2AWVXFjzOAOcPcezVBCuNDFX3PcA9o2i7TAIdmeLnwNjZnh15R5HqN67+V6ScunpyBaPoHLqaRpcDIzUTWjfteRqP0pnV/QqZdN4GHE+Yn8jdt5jZsMnmRWRfhy9o4A9rd3De0kOIzD4YVrwtWI67fTueaSfV2wbJGfjcw7Bdz+19cM1sALI1+zO7NlGB1ku5jDbpwOXATuBx4IMEQYl/KVWjZPppH80qJH152Hg/vPRH6Bv+Dl1aSTxl6lD/K1Uv09u3ZxRdTxcYEEsNewwQLqVaMIUkHtFSqjIZ9bi7E46OM7PaEfYXkUGctHQ2jR29ZOefgB9zAdz3Tbj3i/DSH7HjVhH53pnYEz+GN34GEgWztI57BzS9QMfhfwkLj2NO3SSYhvjCb+AXfw8/eDs8/ANoXFvpFk0aoxqB4e59ZvYz4GfuvnOk/UXGqjObIz3SCIxHfgDr7w0isdufhpddMuSuKQ1DlilC/a9Uu958H7m+PuLRcOnT7ubRjb6AIIDR3bTnqYLPMjndYmb/D5gZJl6+GPifCrdJZNJZODPNF99+HMlN92D3/MeeDY0vwL2fh0NPw+79HP6OW+h714+x5g2Qmokn6mjJOl1HXsLB++9fsfaP2rp74eZ3QU9H8Pz5O+CMz8GcQyvbrkli2FveFrjKzHYBzwLPmtlOM7tyuONExqo9M0LW+R1PwXP/CyvfBysvhnW/gS0PD7m7RmDIZDfR/tfMzgiX9FtrZpcPsj1pZjeH2+83syUF264Iy581s9NHqtPMrjWzR8PlA281MyWvm0a6evKk41H687TQ3QSJUQYwkvWQaaE/rUsyFlHfLZOGmR1qZq9y9y8CtwI/JsiD8b8MmEItIqMzuzZBrPWlfTds/jPsd2TweNvjRA58OXbsKuzwM4gsfTWzjziFxZMheAGw7bE9wYt+f/oW7Hi2Mu2ZZEaaQvIRggREL3P32e4+GzgZeJWZfaTkrZNpoyObo2a4AMZjt8ChbwwudhO1sOxN8NiPhtw9FY9oBIZMduPuf80sCnyTYGm/5cCFZrZ8wG6XAM3ufijwZeBz4bHLCRIqrwDOAL5lZtER6vyIux/r7scALwGXTfC1yyTS3ZMnVdh/dzcHffVoROMQTUKmDVAODJl0vgK0Abj7ne7+cXf/GPDTcJuIjEf9gn3L5hy6J2dSw6LytqccvA/laB+dkQIY7wYudPf1/QXuvg54F/CeUjZMppeObI50fIgZTZlW2P4ELDhqT9n8FdC0dq+504XSugiWyW8i/e9JwFp3X+fuPcBNwDkD9jkHuC58fCtwmgW30M8BbnL3bHjutWF9Q9bp7m0QjBoB0ugTeFrp7MntvYpUVxMkxzAIJzUjOAaNwJBJZ767Pz6wMCxbUv7mlNmu5+GZ22DdPbtXgBApiv2Pg+Pfved5og5e9n548mf44WfRO+fIijWtKBYcA/GavctO/ivY74jKtGeSGSkHRtzddw0sdPedZqa1aaRourL5oZN4rvttMGSsMCFcNA7zjwqmkhz99n0OSSWidCkRnExuE+l/FwEbC55vIhi9Meg+4bLXrcCcsPxPA47tv9UxZJ1m9l3gLILlr/9hsEaZ2aXApQAHHnjgCC9BJot9+u+uxr2Tq40kWR/mwVhKMh6lXQEMmTxmDrMtXa5GVMSmNXD9uZBtD54vPRXO/RbMmIJ3xqX8Zi6G064MViHJtNJXvxBr3wp/eS3ZWYeSWjDJv+gf/Bq44AZ4/EfQvhVW/AUc+IpKt2rSGCmA0TPObSJj0tkzTA6MdXfDQa/et3z/Y4O8GIMEMNLxKJ09edx9z7xskcllUvW/7v6+cJrJ14ELgO8Oss81wDUAK1eu1CiNKaKrZ8Ay2F2NUDd/9BUkG4JjUP4imXTWmNkH3H2vhJ1m9n7goQq1qfR6OuHXn94TvABYfw9sfkgBDCmeuv3g0NOAvacMjGJ9q8nh0NN2vz4Zm5ECGMeaWdsg5cYU+v8jlZXvczK9+b0vgPv1dEDLRjj+4H23zTwIOndBdwukZ+61KR6NEDHI5voGr1ek+k2k/90MHFDwfHFYNtg+m8wsBswAGkc4dtg63T1vZjcBn2CQAIZMTV09eZKFU0i6m8aWST1Zt3sKSSoepbmz6uJzIkP5e+CnZvZO9gQsVgIJ4G2ValTJZdph+z4zZ4LrNRGREhs2B4a7R929YZCfenfXFBIpis7w7l1ksJES25+EWQdCdJBYWyQCs5cGmXwHUZOIKZGnTFoT7H8fBJaZ2VIzSxAk5Vw9YJ/VwEXh4/OAu93dw/JV4SolS4FlwAND1RmulnIo7M6BcTbwzMR/AzJZBH14weXEWJJ4QrBvVzBbSgmYZTJx9+3u/krgU8CG8OdT7v4Kd99WybaVVO0cOPLcfcsLc5WJiJTISCMwREquIzPMCiRbH4WZS4Y+eOZS2PoILH3NPptqElE6Mjnm1iWL0k6RySLMaXEZcAcQBb7j7k+a2aeBNe6+GrgWuN7M1gJNBAEJwv1uIchlkQM+5O55gCHqjADXmVkDweiQR4G/LufrlcrqyuZJ7hXAaAmmhYxWogF2BUvHBdP/FMCQycXdfwP8ptLtKJtoHF7xIWjdCGvvhHgaXv+vsPD4SrdMRKYBBTCk4jqzw+S/2PooHHbG0AfPOSRIgDOImkRUd/Jk2nL324HbB5RdWfA4A5w/xLFXA1ePss4+guVeZZrq6smRjIYBjHwWctngC81opRr2mkKiFaREJoG5h9Jx+peIn7QWj9fg848inRpD8l4ZUd9L92Mb74dtj8Pil+ELjydywMsq3SyRilMAQyquPZujZrAlVLNt0L4NZiwe+uD6+UESqa5dUDN3r001iSjtGQUwRERKqbMwB0Z3czD6YizJk3evQtIfwFC/LVLtWl98nLpfXkp051NgETpWfojGkz7EnHn7V7ppU0Lf1sex//sXbNMDQcHjt8DR55GrmUdszpKKtk2k0obNgSFSDh2ZIUZg7HoeZh4AkWGScFok2GfX8/tsSsc1AkNEpNQ6szkSsfByoqspGFExFsn6IGDtedLxCF09GoEhUs26uzpJ3PeVIHgB4H3UPfh1olsfrmzDphBrXr8neNFf9sSPibSsr1CLRKqHAhhScR3ZHOnBVgppXAv1o4jk1+8/aAAjFY/Ske0tQgtFRGQoey2j2t0crCoyFpEoxGsh0xqMwFAODJGqlmlvJP3S3fuURxufq0Brpqj8INev7pBX/yiiAIZUXEc2RyoxyH/FXc9B/cKRK2hYtDsBXKF0PEjiKSIipdNRmMQz0wKJccyDTzVAdzPpeJSubJ5gQRwRqUaJ2llk9z9pn/K+WYMseS/j4rMPxmct3bvsoFfhMw+qUItEqocCGFJxHZkcqdgQIzBmLBq5goZF0LgO2PuCNxWP0q4pJCIiJdWZLejDu5shUTP2ShL10N1MLBohGjUyvX3FbaSIFE1tXT3ZV38C6ubvLus64jxyC7QKSbFEFh0P53wTP+EimH8U/orL8Nd/kuh+h1W6aSIVpySeUnEd2YLhx/0yrdDTCTWzR64g1RAs3tixE+r221Mcj9LerQCGiEgp7TUNsLt5fCMwknXBsUBtIkp7tnfo1alEpOIalp5I0zt+hTeuxeK1ROcfzpxZc0c+UEbNlryKnoUvI9q5g+isxYwhNbLIlKYAhlRcW6Z33wBG49pg9REbxSAhs2AURtPavQIY6USU9oxyYIiIlFJnNkeqP9jQ1Rwsbz1WidrdS6mmE8H0v/3qi9hIESm62QsPhoWaNlJKiUQCEsOsxicyDWkKiVRce/cgSTybXhhdAs9+9fvDrrV7FaXjUdqUA0NEpKQ6s3lS/auQZMY5AiNRtzuAUZuIaQlsERERGZQCGFJxHdkcNQOHCjet32tu5Yjq50Pzhr2KNAJDRKT0OnsKp5C0jjOJZz10NQLhCAzlLxIREZFBKIAhFdc+WA6M5g17TQcZUd0CaHlxr6K0kniKiJTcXlNIsq1jX0YVdifxhLDv1ggMERERGYQCGFJxHZkBydr68tC+FWrHEMComRPcvctndxelE1E6dREsIlJSXT35YARGPhv037HU2CtJ7glgpOIagSEiIiKDUwBDKm6vDPYA7VsgNRNiidFXEolC7Txo3bi7qEYXwSIiJZXvc3rzfSRjkT0rkNg4cuUn6yDTAkA6HqFD0/9ERERkEApgSMV1ZPN758BofjHIaTFW9Qug+aXdTzWPWkSktDp7gimAZhbkv0iOc+mQaBLcobeLpILPIiIiMgQFMKTiugbmwGjZEIymGKvaeXsl8qxJxOjsyU+4fSIiMrjObG7PFMDu5vHlv4Bg1EZqBnQ3UxOP0tatAIaIiIjsq6QBDDM7w8yeNbO1Znb5INuTZnZzuP1+M1tSsO2KsPxZMzs9LEuZ2QNm9qiZPWlmnypl+6X03H3fKSRN66F2HCMwaveDpnW7n8ajRr7P6cn1FaGlIiIyUGdh/51pGd8KJP2S9dDVRDoRpU1TSERERGQQJQtgmFkU+CZwJrAcuNDMlg/Y7RKg2d0PBb4MfC48djmwClgBnAF8K6wvC7ze3Y8FjgPOMLOXl+o1SOllevuImJGIFfxXbN04thVI+tXNh7ZNu5+aGbWJKJ0aiiwiUhId2XzBEqotEK8df2VhIs90IqZVSERERGRQpRyBcRKw1t3XuXsPcBNwzoB9zgGuCx/fCpxmZhaW3+TuWXdfD6wFTvJAR7h/PPzxEr4GKbH2TC+1yQErkHTsgNq5Y68sPSu4gC5YiaQmEdNcahGREuksnALY3QSJCQQwEnVBAENLYIuIiMgQShnAWARsLHi+KSwbdB93zwGtwJzhjjWzqJk9AuwA7nT3+0vReCmPtkyOmkRsT0HHNkg2QDQ+9soikWA51bYtu4vSiaju5ImIlEgwBTC8lOhuGX8ODAiCH93BFBKtQiLTXXi9+7CZ/TJ8vjScbr02nH6dCMvHNB27qHI9sPPZIPm6iEiZTLoknu6ed/fjgMXASWZ21GD7mdmlZrbGzNbs3LmzrG2U0WvP9O69Aknr5iCXxXjVzoPWPdNIarQSiYhIyXT1FI7AaC5KDowaBZ5FAP4OeLrg+eeAL4fTrpsJpmHD2KdjF8f2J+FX/wjXnArfezM8ciNk2otWvYjIUEoZwNgMHFDwfHFYNug+ZhYDZgCNoznW3VuA3xB0yvtw92vcfaW7r5w3bxwrWkhZtGdy1CYLRmC0bYKa2eOvsGbOXgGMdDxKu+7kiYiUREc2vyeAkW2dYACjLhiBoWVUZZozs8XAm4Fvh88NeD3BdGsIpl+fGz4e03TsojSwrw/+fD2s+Q70dgW5y372V7DxvqJULyIynFIGMB4EloVD3hIEUeDVA/ZZDVwUPj4PuNvdPSxfFQ6LWwosAx4ws3lmNhPAzNLAG4FnSvgapMTaMwVL8AE0vzS+JVT71c6Dlpd2P9WdPBGR0unM5kj2J2Hubp3gFJJ66GomreTLIl8BPgH0L6M2B2gJp1vD3tOyxzwde6Axj1puWg9P3Lpv+dbHRj5WRGSCShbACDvRy4A7CIbA3eLuT5rZp83s7HC3a4E5ZrYW+ChweXjsk8AtwFPAr4APuXse2B/4jZk9RhAgudPdf1mq1yCl15bp3XsJ1dZN40vg2a92bjANJZSOazk+EZFS6cjkSMSiQQLmXDfE0uOvLFkPmRbS8SidPXmC+xki04uZvQXY4e4PleucYx61nKyH+v33La+ZU/zGiYgMEBt5l/Fz99uB2weUXVnwOAOcP8SxVwNXDyh7DDi++C2VSmkfGMBo3zyxERg184I6cMBIaQSGiEjJdGTDPjzbGiyhGpnAfZFEHfR0EI84UTOyub4901NEpo9XAWeb2VlACmgAvgrMNLNYeIOwcGp1/7TrTWOdjj1u9fvBqVfALe+GvvAaa/bBsGhlUaoXERnOpEviKVNLW3dBArie9iCjdbJ+/BUm0hBJQFcjADXxKC1dGoEhIlIK7ZkcqURk4iuQQBD8iNdCplXT/2Tacvcr3H2xuy8hmH59t7u/kyDv23nhbhcBPw8fj2k6dtEaeugb4N0/hTO/AGd/A95+Pex/dNGqFxEZSklHYIiMpK1wFZLWLcHoC7OJVVo7N1hKtWYuNckYrd09E2+oiIjsozObZ+HMKGRaJh7AAEjVQ3cTtckgAfO8+uTE6xSZGv4RuMnMPgM8TDANm/Df68Pp2E0EQQ/Cadv907Fz7JmOXRyxBCx9TfAjIlJGCmBIRbV297L/jFTwpH3LxPJf9KuZEwQwFhxDbSLK5pbuidcpIiL76OxfRrV7giuQ9Es0QHcL6URKK5HItOfu9wD3hI/XMcgqImOdji0iMtlpColUVFt3LzXxMI7WthnSsyZeaXr27kSe6USMtm5NIRERKYWOTC7IgZFpCaZ/TFSyDrqbqUlEaetWAENERET2pgCGVNRey6i2bi5OBuvaOdC2KXiYiCqAISJSIntGYDRDombiFcZroaspCGBoBSkREREZQAEMqaj2TK4gB0aRAhg1c6Fta/AwGaNdw5BFREqiM5snFY+EAYwiTCFJ1oUBDI2eExERkX0pgCEV1Z7NUZOIAQ4d24qUA2N2UJf3KZO9iEgJdfUUTCFJFGMKSf3uERitCmCIiIjIAApgSEV1ZHqpSUYh0wbeB/EiDEGOJYN6OndRm4jRoQCGiEhJdGbzwTTAYiXxTNZBdxPpuAIYIiIisi8FMKRi3J3ObD6YQtJepCVU+9XOhfYtxKOG42R6i7dymIiIQDaXJ+9OIhqBTGtxllFNNEB3M7XJGC1dCmCIiIjI3hTAkIrp6skTixqxSCTIWVGM6SP9wqVUzYzaREzTSEREiqwjk6M2GQ3iztnW4kwhSdVBdwu1SY3AEBERkX0pgCEV057JUZvsX0J1C6RnFq/y9OygTqA2GVM2e5l2zOwMM3vWzNaa2eWDbE+a2c3h9vvNbEnBtivC8mfN7PSR6jSzH4TlT5jZd8wsXvIXKBUXJGGOQU8nWBSiiYlXGkuBQW0kR0t3z8TrExERkSlFAQypmPZML7X9K5C0bYZ0sUdgbAagNqmlVGV6MbMo8E3gTGA5cKGZLR+w2yVAs7sfCnwZ+Fx47HJgFbACOAP4lplFR6jzB8ARwNFAGnh/CV+eVIn2TC7ow7tbg+SbxZJqoNY7aevWyDkRERHZmwIYUjFt/XfvIAg21MwuXuU1c/YspaopJDL9nASsdfd17t4D3AScM2Cfc4Drwse3AqeZmYXlN7l71t3XA2vD+oas091v9xDwALC4xK9PqkB7pjfIYZRpLm4AI9FAbV+7Rs6JiIjIPhTAkIpp6+6lNhmOwGgv0hKq/WpmQ8f23Uup6kJYpplFwMaC55vCskH3cfcc0ArMGebYEesMp468G/jVhF+BVL22TI50IgbdLcXJf9EvWU9NrlUj50RERGQfCmBIxbR29wY5MLJFXEK1XywJ8dTu5fg0FFmkLL4F3Ovuvxtso5ldamZrzGzNzp07y9w0KbaObI50PAqZluIsodovWU9tromObI5gUI+IiIhIQAEMqZiWrp5wCdWtUDO3eEuo9quZB21bSSeitGsEhkwvm4EDCp4vDssG3cfMYsAMoHGYY4et08w+CcwDPjpUo9z9Gndf6e4r582bN8aXJNVm9xSSoo/AqCXR3YiZkentK169IiIiMukpgCEV09od5sBo3xbkrCi2mtnQvoV0XMvxybTzILDMzJaaWYIgKefqAfusBi4KH58H3B3msFgNrApXKVkKLCPIazFknWb2fuB04EJ31zfOaaI9kyMZi0B3U3EDGIkG6G6iTitIiYiIyAAKYEjFNHdlqU3EoHUL1Mwq/gnSs6BtC7XJGC1dugiW6SPMaXEZcAfwNHCLuz9pZp82s7PD3a4F5pjZWoJRE5eHxz4J3AI8RZDL4kPunh+qzrCu/wbmA/eZ2SNmdmVZXqhUVGt3b5gDo7noU0joCgIYCj6LiIhIoVilGyDTV3NXL/vPSMG2Iq9A0q9mDjRvoHZGjJ3tncWvX6SKufvtwO0Dyq4seJwBzh/i2KuBq0dTZ1iuz5JpqK27l4Z0PJxCUuQARncTtQktgS0iIiJ70wgMqZjW7t5gBEb7lhJNIZkD7VuoS0Zp6eopfv0iItNYeyYXLqPaAsliBzCaqdUIDBERERlAAQypmJaucBWSkubA2E5dMkaLLoJFRIpqdxLPTGtxR2DE09CXoyaOAhgiIiKyFw37lYpp6+6lLpqD3u7gjluxxdMQiVHrXcqBISJSZO2ZHOlIPlgGO5YsXsVmkJpBjfVqColItepug5f+CBv/FOQcO+hVsHhlpVslItOAAhhSMW2ZXmp7d0FtCZZQ7Vc3l7rencpkLyJSZO3ZHDV9nZBsKH4fnpxB2rK0dueKW6+IFMfaO+DH79/zvG4+XHgTLDqhcm0SkWlBU0ikYtq6c9RldpRm+ki/9Gxqu7fTlc2T7/PSnUdEZJrpyOSo6esozQi6VAO13kVzV7b4dYvIxLRtgXu/sHdZx3bYtKYy7RGRaUUBDKmITG8egETXlmDoYamkZxPp2EJNUtnsRUSKqSObI51vK27+i37Jehr62mnqVL8tUnVy2SD3zUC9WvFNREpPAQypiJauXupSMWjdXPIRGLRupl6JPEVEiiaX7yOby5PqaS7uCiT9EvXU55tp7NAIDJGqM3spfcdftHdZJErv/GMr0x4RmVZKGsAwszPM7FkzW2tmlw+yPWlmN4fb7zezJQXbrgjLnzWz08OyA8zsN2b2lJk9aWZ/V8r2S+m0dvdSn4wFwxBLGcConQNtW6hLxbSUqohIkXRm86QTUSLdTZCoLf4J0g3U9+6iSf22SNXZ1NzFfbWnkTv1n2H2wfjik8i87Tru7Dy40k0TkWmgZEk8zSwKfBN4I7AJeNDMVrv7UwW7XQI0u/uhZrYK+BxwgZktB1YBK4CFwF1mdhiQA/7B3f9sZvXAQ2Z254A6ZRJo7e5fQnVraQMYNXOgYxt1czQCQ0SkWNoyvdQkYtDVBIkS5MBIzqC+52laetRvi1Qbd+cf7+kkFTuJvzvp9WzqhC/fvJOPvSlf6aaJyDRQyhEYJwFr3X2du/cANwHnDNjnHOC68PGtwGlmZmH5Te6edff1wFrgJHff6u5/BnD3duBpYFEJX4OUSEtXD3UJg56OIIN9qcRrwPuojTmtWkpVRKQo2jM5ahNR6GoszRSSVAMNma1aAlukCh0wu5aLX7WUtTs7+dvbtvO5e7YTNeO4xTMr3TQRmQZKuYzqImBjwfNNwMlD7ePuOTNrBeaE5X8acOxegYpwusnxwP2DndzMLgUuBTjwwAPH+xqkRFq7e6mxbDBCIlLCOJoZ1M6llm5NIRERKZL2/hEY3c2lWYUkUU8yu4t8Xx+Z3jypeLT45xCRcTvz6AWcdugM9s9vpjeS4sW+OSxfNLvSzRKRaWBSJvE0szrgx8Dfu3vbYPu4+zXuvtLdV86bN6+8DZQRtXb3UtPXBbVzS3+ymrnU9HVqComISJG0dvdSl4yGAYwSjKKLxrBELQ2pKE2dCj6LVJvZbc9w4J8/S+L6N1Pzo7dz5K47aW3cWelmicg0UMoAxmbggILni8OyQfcxsxgwA2gc7lgzixMEL37g7j8pScul5Fq6ekn3tQerhJRazSxq8y006yJYRKQoWrp7gykk2dbSJPEESM2gIe4KYIhUmWw2S+LJH2EP/D/ItGJN67CffZD6XQ9VumkiMg2UMoDxILDMzJaaWYIgKefqAfusBvrXYToPuNvdPSxfFa5SshRYBjwQ5se4Fnja3b9UwrZLiTV2ZmnobSxPACM9l7reXTRrLrWISFG0dvVSE3OIpSAaL81JUg00xPIKYIhUGW9ciz3xowGFjm17vDINEpFppWQBDHfPAZcBdxAk27zF3Z80s0+b2dnhbtcCc8xsLfBR4PLw2CeBW4CngF8BH3L3PPAq4N3A683skfDnrFK9Bimdxs4e6nt2BMucllrtHOoy22lWDgwRkaJo7uoJ8hiVIv9Fv2Q9dZGM+m6RKtMXr8Xr5u+7IT2z7G0RkemnlEk8cffbgdsHlF1Z8DgDnD/EsVcDVw8o+z1gxW+plFtTRw8N2S1Qc0LpT1Yzl7rMFq1CIiJSJM1dPdSRKW0AIzWDurZOjcAQqTI185bgr/k43Po+6AuWTvVZS8jPP7a0XyxERChxAENkKE0dGepzzZAq4RKq/RI11FmWls5M6c8lIjINNHf1cqh3ljiAMZPafKsCGCJVqPuA15K68BZs5zOQqCW/31HEDnxZpZslItOAAhhSEc2dWRpq0mDlWQinrraWljZdBIuIFENLVw91yXZI1JXuJKmZNOQ2sqsjW7pziMi41NTPhPo3wLI3APpCISLlMymXUZXJra/PacvmqaurKds562pr6eztozffV7ZziohMVa1dvdTlWkobwEjPpL53J40agSEiIiIhBTCk7NoyvaSjfcRqyrACSShSM5uGWE5LqYqIFEFrppe6np2QLGEAI9lAfa6JJo3AEBERkZACGFJ2jZ09zIhkoXZu+U5aO48Z1q07eSIiRdDWnaM2ux1SM0p3kkiEhmSE5vau0p1DREREJhVNWZOya+rsocE7oHZe+U5aO48Gb6exQwEMEZGJyOX76O7JU5PZBsnSJmKemY6zS/mLRKpT4wuw8zlI1ML8FVA7p9ItEpFpQAEMKbumjiz1fS1Qu6x8J62ZQ0N+I40d3eU7p4jIFNSWyVGbjBLJtJR8Jam6mjSdu5xsLk8yFi3puURkDF66H37xYaibD93NMP8oeO0nYPbSSrdMRKY4TSGRsmtqaqLOMhAvXxJPYgnqYzkad+0o3zlFRKaglq4e6hMRiCaCnxKKpBuYlcixo015MESqRrYdXvg1rDgX8lmYeQAsWAHbnqh0y0RkGtAIDCm7psZtwcWvWVnPW5eMKoAhIjJBLd291MYd4iXMf9EvNZPZkW52tGc5YHYZg94iMrTOneB5+O0X95StvQt/2/9Q3is7EZmONAJDyq6xqZm6VPmHAjck4+xsaSv7eUVEppLWrl7qIr2QLE8AYybt7GjLlP5cIjIq+T7g0Zv3LsxlofWlirRHRKYXBTCk7BpbO6hPJ8t+3obaJLvalANDRGQiWrp7qLUspOpLf7L0bGb2NbFdAQyRqtFLFKLxfco9sm+ZiEixKYAhZbers5eGmnTZz9tQW0tTd77s5xURmUpaunqppRuS5QhgzGRGvoltLVpKVaRaxGcfSN+rPrJ3YbKB3gXHVaQ9IjK9KAeGlF1TFuobyjD0eICGGTNp7NEUEhGRiWjp6qWmr6PkS6gCEI0xM55nW2NT6c8lIqMSjRg+52B483/C+nuhZg4sPZV4vPw3p0Rk+tEIDCmv3gyNuRpmzphZ9lM31DfQ3FcbLPclIiLj0tTZQ22+peRLqPablY6yvbm9LOcSkVFo3YI9ciPc/nHY9Tw8+7/wo/cQaXq+0i0TkWlAIzCkrPp2PU8T9cxMlz+JZ23C6CZBdtuzJJe+vOznFxGZCnZ2ZDk01wjJA8pyvlk1CXa09JTlXCIyCt3NsP1JeP2/QE8XRGPBv83rK90yEZkGFMCQsmra9Cw1kQixSPkX2jIzZkZ7aN78PAsUwBARGZdd7VlO7N1RvhEYdWl2btXijCJVo24BnHgR3PZR8L6gbNYSOPMLFW2WiEwPmkIiZbVjy4vMjvdW7PwzE3l2bnmxYucXEZnsGjsyzOhrg3hNWc5XXzeD7j4j06skzCJVIduCr/nOnuAFQPMGaN9SsSaJyPShAIaU1Y5tW5iVqtydtFmpCNt3bK/Y+UVEJrvGjiwzahJg5enLrXY2s6yTHW3ZspxPREbQ0411bNun2JVjTETKQAEMKasdza3MSFdunfCZNQm2tygZnEx9ZnaGmT1rZmvN7PJBtifN7OZw+/1mtqRg2xVh+bNmdvpIdZrZZWGZm9nckr84qZjefB+dPX3UlXMp7JrZzKOJTU2d5TuniAypN1EPR56zd6EZzFpamQaJyLSiAIaUT76XnZ15ZtSmKtaEGTUptmcTkNFyqjJ1mVkU+CZwJrAcuNDMlg/Y7RKg2d0PBb4MfC48djmwClgBnAF8y8yiI9T5B+ANgOZnTXFNnT00xPNE0mVcCjuWZF6kg41bNTxdpj4zO8DMfmNmT5nZk2b2d2H5bDO708yeD/+dFZabmX0tDCI/ZmYnFNR1Ubj/82Z2UbHamO/NQrIOTnwvpGbCnEPgDZ+C3kyxTiEiMiQFMKR8dj3PttgiZqTKvwJJv1npCFviB8GOpyvWBpEyOAlY6+7r3L0HuAkYcLuMc4Drwse3AqeZmYXlN7l71t3XA2vD+oas090fdvcNpX5RUnm7OrLMjPZAqowBDGBeynlx89aynlOkQnLAP7j7cuDlwIfCYPHlwK/dfRnw6/A5BEHlZeHPpcB/QRDwAD4JnEzQf3+yP+gxUakFh+H1C2H9vXDM22HRifDHr+MNC4tRvYjIsBTAkPLZ/iTbowuYWdEcGMb2yH6w/YmKtUGkDBYBGwuebwrLBt3H3XNAKzBnmGNHU+ewzOxSM1tjZmt27tw5lkOlSuzq6GGGdUK6KN+DRm1ebYQNOzVyTqY+d9/q7n8OH7cDTxP0tYVB5+uAc8PH5wDf98CfgJlmtj9wOnCnuze5ezNwJ8GouqLoPPD1+Gv/EW/djCca6PuLbxM5+DXFql5EZEgKYEj5bHucHcxiVrKyAYxtfTNg2+MVa4PIdOXu17j7SndfOW/evEo3R8ahsSNLQ19rMGy8jObXp9jYmivrOUUqLcxNdDxwPzDf3fuHIW0D5oePJxx0Hk9wuS7Sg21+GMu0YLueIdKxDTLKMSYipacAhpTPtsfYkaup6Coks1PGzt4UbHusYm0QKYPNwAEFzxeHZYPuY2YxYAbQOMyxo6lTprhdHVnq862Qnl3W886bVc+m7kRZzylSSWZWB/wY+Ht332v4kbs74MU615iDy319+NOroWYWLHkVLDkFXroPNv6pWE0SERmSAhhSNr79KXb1xJlZwREY9QnozEfJ7lgLfX0jHyAyOT0ILDOzpWaWIEjKuXrAPquB/qRu5wF3hxfFq4FV4SolSwnmVT8wyjplitvV0kmDt0KytqznnTlrHl19UTozvWU9r0glmFmcIHjxA3f/SVi8PZwaQvjvjrC8/EHnpvVYzRz4/Zfht5+He/4DNv8Z7+kqSvUiIsNRAEPKo6uJtp4+4lEjGatcACNixqyUsSO6P7S+VLF2iJRSmNPiMuAOgvnTt7j7k2b2aTM7O9ztWmCOma0FPkqYEM7dnwRuAZ4CfgV8yN3zQ9UJYGYfNrNNBBfIj5nZt8v1WqW8djS30JAwsPJePliylgWRVjZu3lTW84qUW5hM+VrgaXf/UsGmwqDzRcDPC8rfE65G8nKgNZxqcgfwJjObFSbvfFNYNmG9GP7YzZArWHVk22PQ3VSM6kVEhhUrZeVmdgbwVSAKfNvdPztgexL4PnAiwdDlC/oz2ZvZFQTL/OWBD7v7HWH5d4C3ADvc/ahStl+KaPuTbK0/mrlWueBFv9kpY3vNERyw7QmYtaTSzREpCXe/Hbh9QNmVBY8zwPlDHHs1cPVo6gzLvwZ8bYJNlklgZ2sHh6Uqc+9jv3iGl158gSMOWVqR84uUyauAdwOPm9kjYdk/AZ8FbjGzSwiWrH57uO124CyCFaO6gPcBuHuTmf0bweg5gE+7e1EiDJHeLqxp3b4buhTAEJHSK1kAw8yiwDeBNxIkDnrQzFa7+1MFu10CNLv7oWa2CvgccEG4XNQqYAWwELjLzA5z9zzwPeAbBIEPmSy2Pcam5DLm5Ss/6GdWytieWgpbH4Uj31Lp5oiITBpNHVlmNCQrcu65KefFzdsqcm6RcnH33wND3e05bZD9HfjQEHV9B/hO8VoXiM5agh/xFuzP1+29Ye6yYp9KRGQfpfw2eRKw1t3XuXsPcBPBUk+FCpeEuhU4LRw6dw5wk7tn3X09QVT5JAB3vxdQiHey2fwQm+MHMidd+REYM5PGtthi2PxQpZsiIjKp7OiGmXXlzX/Rb2F9jKe3aZUDkUrr6mzBjrkAjngrmAWrEr3pavJlTu4rItNTKQMYo1m+afc+4fzqVmDOKI8d1niWhJIS2vIIL/kCZldBAGN22tjo84IRGF60JN4iIlNaNpenrTfKzBkNFTn/gXPreaYtXpFzi8gezfkU3tsFy98GF/0SLrwRognlRheRsihpDoxKcvdrgGsAVq5cqW+plZRth7bNbIzXcUR95QMY82uMh7cnwfPQtgVmjCk2JiIyLW1vzTI70kGkdm5Fzn/AfrNZ90iSfHc70XR9RdogIpCMAA/+Dzy3d07Q6NuuqUyDRGRaKeUIjNEs37R7HzOLATMIknmWbuknKb+tj8GcQ9jU4cyrqXwAY15NhI3tDnMPgy0PV7o5IiKTwubGVuZ6M6RnVeT86WScmdEMG559tCLnF5FAbe8ubOMD+5Rb0/oKtEZEpptSBjAeBJaZ2VIzSxAk5Vw9YJ/CJaHOA+4OkxGtBlaZWdLMlgLLgH17SpkctjwMsw5ma4czN135JJ771RibO/rwWQcrgCEiMkpbt2xiTjwLkWjF2nBgqptnX1hbsfOLCFj9AvyAl+9T7rMPrkBrRGS6Kdm3yTCnxWUEa04/Ddzi7k+a2afN7Oxwt2uBOWa2FvgocHl47JPALcBTwK+AD4UrkGBmNwL3AYeb2aZwOSmpZpvX0DXjUDpzzozKJK/fS03ciEegse5w2PTgyAeIiAhbtu9gVrKyMzIX10V4etOuirZBZLrLRmvIvvzD+Kw9Sxr3HfcudtQfVcFWich0UdIcGO5+O8H61IVlVxY8zgDnD3Hs1cDVg5RfWORmSqltfJAtJ57BfjVGsMhM5c2vjbAxtYy5W/4T+vIVvaMoIjIZbGpsZXaqsn344jl1PLFWiblFKqkn18fbfpzlmrdez8LcS3i8lp9unkF+Ww0XaRCGiJTYlE3iOZk1d/bwlbue48d/3kw0Yrzt+IV85A2HM6NmEmZfb9sK2XY2+jzmpXsr3Zrd5tUYL2VrOD49C3Y8DQt010BEZDibW3t4+ezKDqM7ZMFMbnj6ALz5JWzWgRVti8h0Na8+xfkvO4A3fO/ZsKSTiHXy479eUslmicg0UfmEBLKXne1Zzv9/97G5pZv/+Iuj+cy5R7GxqZu/+K8/0NzZU+nmjd3GP8H85WzqgLlVsIRqv3lpY1NbH8w7El66r9LNERGpelu6jDkVWkK137yaCNFIhHWPq98WqaS/OGExV5x5BItmpjn+wJl8/+KTOHrRjEo3S0SmAQUwqkhvvo/3fe8Bjl08g4tftZS5dUnm1iW55NVLWbFwBu/97gP05ifZItsv3gdzD+P55jwLaqsngDE3baxv7YN5h8OLf6x0c0REqltfnm29tcyZM6eizTAzljdkuf+pFyraDpHpbn5Dig++9hB+8bev4oZLTubVy+YRi+prhYiUnnqaKvKl/3uOZDTCX56weK9cEWbGqpcdQDRifOPuSZZ9/aX7YL8jea65j4X11fPfbb/aCC+29cF+K+ClP1W6OSIiVa1t2wv0EaU2nap0Uzhsvxr+uLWyyURFJDC7NkltUjPSRaR8qucb5TT3zLY2bnzgJd5/ysGDJro0My559cFc98cNPLutvQItHIdsB+x6DuYsY11LH4vqqmcExqI644WWPmhYCPksNL9Y6SaJiFStzeufZl68uyoSMR+5/wwe6FmK73y+0k0RERGRMlMAowq4O5/8+ZOce/wiZtYkwsI8tG+Fjm3BKhnA7NoEZx+3kM/88qkKtnYMXvwD7Hck7X0J2rLOnCrKgTE7ZWTzTmPGYf/jYN1vKt0kEZGq9cKLG1mYqo5EzAvqIng0wXMP3lHppoiIiEiZKYBRBX7z7A62tHbzhiPnQ8uLcO8X4Idvh9s/Drd9DG66MChr3cgbj5zPul2d3PvcJFhG7oW7YcHRvNDSx6L6CJEquHPXz8w4qCHCc819sP8x8PydFWmHu7OlpZsnt7TSkc1VpA0iIiN5YVsLC+qqY7lpM+Pk/fL84tEtlW6KiIiIlJkmrVWYu/OFO57lvOMXEn30BnjmNjjolfDqf4BkXbBTth02PwS3fYzY8nP5y+PfwOd/9QynLJtbFcN5h/TC3XDSB1nblK+q6SP9FtVFeL6pj1ccfBw8+J1gpEukfBfov3piK5//1bM0dfUwIx1nV3uW01cs4PIzj2C/hsrPMxcR6fdca4QDD66pdDN2e/nS2fy/+w7nH5o2YLOXVLo5IiIiUib/v737jo+juho+/jtb1bsty1XuNcYNN5rpAUINeYGQQOB5kpBASONJIO1NnvRKSYMkpL0hmISeEJoxpuMi9yb3JsuSLFldWu3u3PePGWHZyLZkbZmVz/fz2Y9mZ3dnzhTdvXvm3juawEiylzZWEeqIMGvbA9DRBPPugLSjblMXzIZRC6BkOqx/nDlp5TzTfjWvlldz3oTipMR9Qk0H7EfhGLZuDzMo032NfQZnC5tqozClEDILYf8qGDor7usNRy2+/ewGlpTX8PG5I5g6NBcRobk9wnPrKrn0gTf4zY0zmT2yIO6xKKXUCTXXsD1cyLzC7GRH8p5R+V6i3nTWvfkcU6+4PdnhKHXqCbXAnrftv8aCglIYMiPZUSmlTgHu+1V5CjHGcO9Lm7nKehmPxwszbn5/8qKr9FyY+Qk8WFxtFvOLFzdjjEtHYt++2B5bwuNlc12UwS5sgTEs28PmOue2tINnQPnzcV+nZRm+/I81rK9o4H+vnMxpw/Lea0WTlebjutOH8cmzRvHJv67g3R21cY9HKaVOxNqzlF2mmBKXdCEBuxvJ2UM8PFxWC1HtfqdUwlWssMdp2/CEncho2g8H1ic7KqXUKUATGEm0ZGMFbXUVzMxvg8lX9az7gscLU67h9NwGGg5W8GZ5ZdzjPCkbn4GhszDGsK7GYlSe+061YdkettdH7STQ8Hmw8em4r/P7/9nEtupmvnjhODIC3TeAmjo0jzvOHcNn/lbGtuoUueOMUqrf2r91JZk+iwy/uxLRF0wo4NXwJPas+E+yQ1Hq1NJYBTXl8MwdsOlZWP57eOo2aK5OdmRKqVOA+35VniJMNMr9T7zC5Tnb8Uy6HKQXh0I8eCZfxeXZ27j/ny+DZcUv0JPR0QK73oShs9nfbDBAkYvuQNIpJyj4PcK+JgNFYyHUCNWb47a+Z1ZX8J91lXzxgnEEfcdPVk0Zkst1pw/jlj8vp6HNHSP/K6VOTdt37mRIZrKjeL9Mv3B+cRv3v7wJ3NoaUan+qP0QvPubI+eFmqA6Re6Sp5RKaZrASJJ3Hr+XmpCfebPn9i550cnjYf7sOVS0B1j+5L2xD7Avtr0CA8ZDMIvV1VHG5nlcO9jouAIPZVVR+xgMm2e3HImDHTXN/N9nNvD588eSldazoWfOGTeQySU5fOXxNe7tKqSU6t/CbWyrC1OS686BhT/0gRJebx3BW4ueTnYoSp06/OlgddN1y0QTH4tS6pSjCYxkWLOQB9b7uWxCDh6f/6QX4/UHuGx8FvevEVj3eAwD7KMNT8HQ2QCsqo4wMte9p9mYPA/LKp0v4dIzYe3CmF/J64hY3PH3VVwzYwgjCnt3GfOG2SPYWtXM35ftiWlMSinVIxUrWemdSmneyX9XxVNG0MutE8Lc9WoLNbU6bpBSCZE/AjPn00fO86VhlUxPTjxKqVOKe39Z9lcVKyn710Ps8I7irNK+35Lu7NJMyr1jWfvsA1C5JgYB9lF7A2x92U4GAKuqLEblu/c0G1fgYfkB54rBgAl28mL32zFdx32LtpAe8HLBxKPuGGNF7OaWG5+FFX+0HxuegsrVEO0AIODz8NkFY/jpC+XsPNgS07iUUuqEtrzIquhIxrq4HJ82eihn5NZy06+ep7GtI9nhKNXvNTbUE/Wmw6U/hxFnwJQPw4cfpqUtlOzQlFKnAPfWSPqjpipY+FF+EbiNK8al4fP0vVuF3ytcNibIz4OfgUdvgOaaGATaB+ufhMHTIC2XjqhhU22UUS5ugVGa62Ffk0VjyIAIjL0QVjwcs+Wv2nOIR5ft4b/PHHm4G01zFSx9EB77GLx1PxxYCx3N9qN6Eyz/Ayz8KLx5LxzayZD8dK6aPoTPL1xFJOqy8U6UUv1a9cbXaLLSKHHhnaS6umbuBEZSydU/fZY9tZrsVSqe2pvr8b32fVj8XSieAtEwPHajjoGhlEqInnXGV30XCcFjN1I28Bq27szmtmGxux3ducN9PLc9l1UjL2f6Yx+DT/wbvElq7lv2F5h0BQDLK6MMzfaQFXBvxdfnEUbneVhZFWXBcB+MOhee+hQ0HYDsQX1adltHlC88tpqb5pWSlxGwBwld/Xf7FrNDZsGc2yCjoPsPh5qgogxe/BoMOo0LZ97Cqj3w2yXb+dz5Y/sUl1IqCZqq7NH6d74ONZugrR68AcgdCkNmwvhLYMSZ4HFRwrd2Oyub8xlX6MPj0nGMOokvwMfOmsjLby/l8l94+dz54/nYWeNJ87vn1q9K9RuZBbSMvITMDY/Asofemx0unJDEoJRSpwoX1ZT6MWPg31/EeAL84OBZXDXWH5PWF50CXuHKMT5+dGiBPXzDc19Kzojs+1ZAUyUMngHA4j1hThvo/srjlCIvi3Y7d/oIZsPoC+zWD330w+c3MSw/g7mjCuwfLU9/BloOwplfhPEfPHbyojOOUQvgzC+DL4jnX3fyyeJyHn5zJ2v31fc5NqVUguxfBQs/Br+aCeXPQ+EYmHcnXPozuOA7MPFyu+vdv78I90+Fd34NHa3JjtpW/jxlafMZlef+chxAgplcdNZ8vlWylOcXL+bs7z/HvS9s1O53SsXYwLwcWk//LKFhdndhfEHq53+dxoIPJDcwpdQpQVtgJMJbD8Ced1k06QccXGZxTgxbX3Q6Z7iPF3dFWDL5C5y78Wvwzm9g/u0xX89xvfFzmHwleOztW7Q7widPCyQ2hpMwa5CXHy8N8b9nGvsq4+Sr4dk74IwvQE7JSS1zSXk1z68/wA8uLYUlP4Ta7XDaDZA3vHcL8gVgzPlQMo3CjU9zU3AEn/sbPP+l88gI6L+vUq51cBu89A27JdXkq+DDD4O/m3GPsgdByTSYej0c3GKPg/PmfXDu12D6x8GbpP9zY2D1IyyL3MVlLh7/4n18aQyefjF31e1i55ZXef2tNVz75mTSgkFmjBzAaSOKmFiSw6SSHPIz3f/9pJRbDSidwu5L/oC/cR/40ujIGU7pwNxkh6WUOgXoL6B42/AUvPMrQhf9mO8+Z3H9hEBcmuL6PMJ1E/x8d3mEMy75BoGX74a8Ye9154i7mnLY8y7MuhWA3Q0WjR24+g4knYZke0j3wZrqKNOLfXbLiHEX2903PvKnXi+vuqmd//nnWj49xSLrhTth4GSY+9m+devJLIRZtzBv77us27CKr/+hkXs/82F73A6llHu01cOSH8GaR2HKNXD6b8EXPPHnROzbTy+4Gw5uhbI/2WPlXPozGHlW3MN+n30rqGqBHa1pTChwfzn+PgWljJxbysi2em46sJ6KA5VsLfeyfNc4nvIOZVdbGvmZQeaNKuTiyYM4a1wRQV9qtDRRyi1GDC6BwSd3oUcppU5WCtZKUsj2xXaz4PO+wYNbsxmU4WF6cfwqSDOKveSnwUPbc+yrd/+6E3a8Frf1HeGFe+yrjL40AJ7Y0sHsQV7X95vuNLPYy3M7wodnTL0O9i2Dzf/p1XKiluFzj5RxduYeJm+8FyZeBRMujc2YJOKB4fO5aX4pKyraWPjA3dBY2fflKqX6LhqB5Q/DL2fCoV20X/Yr9g6/iq1NPvY0WrRHetGtr2gsXPg9mHQVPPnf8NjHoT7Bt1Je9jtezL2WGQO9+L2pUY53Kz0PGXkmQ+d9hHPPv5RPTBS+UbCY3/l+xp2+p0lr2Ma9L29m7g9e4ScvbKauRe9iopRSSrmZtsCIlx2vweO3wjl3U84I/riuhe+dmRbXVYoIN08J8M032rlk5EjGnP0V+OfNcN3f3rutaVxsedFu+jzvDgDCUcOjm8LcNbsHVx1d4qxhPr77dogvzzKk+8VOxMy7w+5KUvAfGNizgam+/9hi2iq2cU1xGUy7HQKZMY81LXcgnz8jzPfezmLsL29k5qW3wrQbtTWGUslgWbDpGepe+jlLrKm8lvkTVu5Mp2q9IS+thaAXOqJQ124YkCFMH+jlnGE+Fgz3MTDjONcQROxye+gs2PA0PHgmzLwFzvoSpMW5mXbdTtj6Iv8KXMnZI/pRqwR/GgyaDIMm45l4BcMObmFYxXNc2rSXytLLeWF/Jgt+upv/OnMknzp7NOmBfrTtSimlVD8hJhmDPSbYrFmzzIoVKxK3wvIX4Onb4Oyv0F40hcufbOH8ET7OGZaYfNEruyO8sTfCM9dkklazFt74GVz9Oxh3UexX1lILD86HubfD4OkAvLAjzAMrQ3xzfnwTNrF27/IQHxrt46YpXRIv2xfD2oXw0X/AoOMMTtV0gEce+TO/2TOEb0+tJ2vo5LgnFFZXRfnDmjYey/8NY7IicMmPYciM2Cw8GrZv77qvDCrXwMFyu7VHqAEiHXa//GAu5Ay2rxaXnAZDZ9t/k9VnP0WISJkxZlay40i2hJfLsRZqpmLpEyx66x2ea5vKhugwpgz0M7nIw4QCL4OzBG+XwZotY6hsNmw5ZLHhYJQ11VHG5Hu4bJSfi0f6GZ5zggaRLQftbin7ltld0mZ/CtLz4rNtj36Ufd7BfHDdufz6wnQCqdwCoydaamH323BgDdXDPsjCttnsaYjyvauncO74gcmOLqUZYwhFLAJeD54YDl4ea1ouH5byZbNSqt84VtmsCYxYMgaWPgSv/wQWfA1TNI7PvdLGoXbD7dMDSIKukBtj+OXKDgZlCr84Nx2p2Qyv/QjO+apd6Y1VHNEI/P0jkF4AMz8BQMQyXPp4C5eO8jFncGr9kC2vi/L7NR28cl0Wab4u+2jHEljxMMy+DWZ/0h6PAsCK2ndeWf0IT63ax/fCN/L1uWmU5HczUF+cvLYnwtPbwiycUkbp1j/DsDn2FdohM3t3nI2B6k2w41XYtgj2LoWsQVA0DvJH2rd6zBwAadng8YEVgVAztNRAYwXU7YCazfbzEWfChMvscUSytPJ/NK0o21KlkmyMoS0cpba5g4qaQ+zYvJo1W3eytC6DQ+Qyvchi5vA8PjDAS9DX8/+5iGVYX2Ox4kCEsqooeWnCvBIf04u9jMrzUJguBDxC2IKGkKG61aKy2VBR20BV5V6aWloxOYPJHDCCkuJiJpZkM3tkIUPy0vu2weUvwL8/z1fz7yNkPFw34RQa6LK9CXa9AftXsqb4Gv5ycBwzSwv5zhWTKcxKnRaFyXSwOcSijVW8vrWG9RWN7K9vQ8TuXlmYGWT8oGzOGGOPOzJqQFayw32PlsuHpUrZrJTq/zSBEe/CuLXOHu+iagOc81VMVjHff7edN/ZG+dq8YMKvYLVHDD94N8R5w33cPSeINFfZd8MYNAU+dN/xb+HZE5YFT30a6rbDed+0f9QCf1oX4sktYe6ZG0xYwiaWflkWYmKhh2/OP+pHQGMFrH8Cdr1p7ztvEBr3Q04Jfwp8lF8dmMjdc9MZmp34YWVe2R3hma1h/nihlw/UL4LNz0EwCyZdDaPOsVuOBLOP/FDbIajZYt/icfdb9tVHX8C+G8KgqfYjLeeY6zTG0Nhhn2cegeyA2EmftkP2MivK7MfAiTDpSpjwISgYGd8dkSK0omxzUyU5FImydl8Dq/fUs/lAI3sPtVHTFKK+NURzewQPFrnSSqGpY1AwzPD8IOOGl1BalBWTcX4sY9jdYNhUF2VPo0VVi6GxwxC2wCuQ5Rfy0oTCNCE3aE9nWK146rbSXruXWl8xe9PGs7E5k6LsdK44bTAfnjW098mM6k3wp0vZe/o3uGzJIH52bjrZgdQrx/usrQF2vkaociNPZN/Im03F3HPJJK6dOdTVrQiSJRK1WLSpmr8v3c3KPfWcNjSXyUNyGTMgi+KcNAI+D5Yx1LV0sPNgCxv2N7B81yGG5KXz8bkjuGLaYNL8ye2uo+XyYW4qm5VSpzZNYMSrMLYsWPsYLPoWDJsHM24iJAG+9UY7ZVVR/md2kJxgcio8jSHDj5eGmDvYy3fOSMNvOmDlX2HP23DBt+3benpOotLQVg9PfhKaq+G8b7w3cOf6mig3PtfC1+elMSwJP+RjoTFk+Nrr7Xz/rDQ+OKqbgTetKDQfACtKk6+QbywVVlVFuWt2kAHH688eZ8srIzy8roO7Tg9y40QfnupNdlPzmk12f3Zv0E5qWFEINQJi39K1YKTdyqJ4in07x26Eo4a1NVGWVUZYVW1RXhelstng90LQK1jG0BKGdB+MyPEwucjLjGIvMwcYRrWuRfYts1t0ZBTarTJGnwfD50EgcS1V3EQryrZkV5LrWjp4acMB/rOukuW7DzE0L53ROYYhcpCB7TvIbywnO3SAjLwiAnmDIa8U8kvtRJ+bWBbUboOqdVhVm9gamMQ7/jm805DP9BEF3DR/FAvGDzyiO0u3ti2CJz9NaMatXLd2BhMKvFw5NgaDD6eytnrY+Ro7Kw7wZ8/VpOUU8a2rpjOrtI8XAPqJ6qZ2Fi7dy9+W7qYoM8A5w/3MKWwlGG6EaAiMBd4ABLIgI99u1RewW11ELcOavfUsLq9mR00zN8wezs3zSynOSU7XUy2XD0t22ayUUp2SksAQkQ8C9wNe4A/GmB8d9XoQ+CswE6gFrjPG7HJeuwf4LyAK3GmMebEny+xOXArjjlbY+DS8eZ+dBJh5CwycyLqaKF95rY1sv/DpaQEy/Mm9WtMaNvxmVYi2CPx0QTqTi7z2LU9X/hk6Wuy+1B+4FtLzT7ywSAes+ye88h27q8LMW967u8a6mii3Pt/Kxyf7Ob0ktbqOHG1ng8XPloW4fXqAmyYH3jcCf0vY8ER5Bw+s7GB6sZePTvQf2eUkSSqaLH6/poMMv3D3nODhLjzG2EmLcJt9rvozj5s8MMawrd7ijX0RXtsbYXlllJIsD+PyPYzO8zA810NxhhzRqqizRcb+ZovdDRY7GizK6ywiFswp8TJvsIeZwQrGtyzHV7XWHvR1wHgYero9bsaAiVA42j4PU7DlTm8kqqKcyPJXREYCC4FCoAz4uDHmuLdzSEYlub61g5c2VvHM6gpW76nntGIfM9OrmNqxiqyDa+3yLL8UcofZCb6sYvCkUDLWitp3Kzm4lVDtbt5pyOdVmU0D2fyfsXD1tCGMKh1hDwJqDDRX2S2lVv0NqjbQMvdL3LVxFLXths/PjM8tv1NSWwPW7nd4c3crj1tnM7YwyG0XT+fMCadei4yOiMUbmytZ+NYm3tnbxtzMA1wgyxnRvgmCOZCWZyfLvU6iz0Tt7572Brulqi9gJwMLx9jjJw0YR2Ukh5c2VvHmtlrOGlvEjXNGMG904YmTbjGkCYzDNIGhlHKLhCcwRMQLbAEuBPYBy4EbjDEbu7zns8BUY8xtInI9cLUx5joRmQQ8CswGBgOLgHHOx467zO7EpDA2Bg7tgj3vwtYXYdsrMGACTLiM9oHTeWt/lL9vDLOqOsq14/0sGOZ1TRcKYwyv7onyxJYwM4u93DDRz/zBXtJq18OWF6Bipf0jsvQsKJ5sj3cQzAGM3SWgdrvdzaD8ebtiP+2jdtcA4GCbxcNrO3h0U5hPTPGn3LgXx1LZbPGX9R1UtxrOHe5jSLaHtrBhQ63dZ31ioZcrxvgYneeuUeotY3h9b5Rnt4XJDghXjfVxxhA/4ws83SZZjDHUtNoJiw0Ho6w4EGX5gSgBL0wu9DB5gJcpRd6TbkZe3WqxqdZiS12UrYcsDrYZxuR5GJtnGOU7xKBoBYUd+8lq20tay368JoLJLCKaVkRHsIB2fx4t3hxaPVk0k06TCdJsBWmK+mmO+miJemm3vHRYHiJ4MHgQjxDwekj3e8kK+sjN8FOYGaQwK8CA7CCDctIozkmjKCuAz5v4H6eJqCgnuvwVkX8ATxpjForIg8AaY8xvjxdjIirJoUiUDfsbeXdbDa9u3MeGA61MzWridO8WZrS9RVow3W6FlDfcHuslXoNiJkukHRr2setAHa/XZLC0pZh8mjjHs4Zp3h2UprWRWTCQ+oJprPBO468bo4wv8PDxyQFXJGVdJ9pBpHIDb+xsZFHjUFo8OVwyLMpZk4YzdcoUBuQf/+4wbR1RqpvaqWkKcbA5RF1LmKb2MK0dUSKWhSAEfR4ygz7yMvwUZgUZ6JRZeRn+pNQpTKiFfTs3s2zjdpbsbOa12lyGUs38jL3MK+ogI2+gPZhz1sAT3y7cGGhvhOZKe1Dopv3QsM8eT6twNC1543krNJrXq9NpDAsXTR7EgvEDmV1aQG5GfFsDaQLjME1gKKXcIhkJjHnAt40xFzvP7wEwxvywy3tedN7zjoj4gAPAAODuru/tfJ/zseMuszu9LowjHfDyt+y7LzRV2le0TNR+LbsEBn2A5ZkL+PbmIZQ3+IgYIegxnFncwRkDOgh63dktpz0qvFUd4M3qAB2W4PcYxmZHGJreQaF1kJzQAdI7agmGG/ATRbBABBPMI5xRTHvWMJq9udSGPOxu9rK+3kfYEkZnR7hiWDtFQSvZmxhTBtjT4mVDvY+GDg9Br6Ek3WJSXphcvzuPcScDbKj3sbI2QHnjkUklDwaLIyvCQY9hRFaU0dkRxudEGJAWn2PZGhH2tHjZ3+qlJuShLuShMSy0RoSIOTImn1gEJUKaREiTMOl0kCYdpBEi3YRIM22kmXYChPERxYuFYDAIEbx04KNN0mnxZNFMBo0mk3qTQV30/eMCFPjDFAQi5Puj5AYMOX6LDJ/dLSbNawh4BZ8HfB7wen14Bp9GdmYmHzqthKCvd0msBCUwElb+Aj8CaoBBxpjI0es+lpOqJDdWwqZ/gbHY2uDhlQNBQhGL1jA0hw31IUNNu4+KUJCK8OFxX8bJXj4gO5gSrCaQmWe3rMgqBn8fB7xMMQbY3uRjQ72PXc1eqtvtc9eDoTQryhkDO5iQG0lukCnCRNrZV11H2aF0NnYMpIXD55KfCD4xWHgImfeXDz4x5AUssv0WGV5DwGuPdWKAsCW0R6El4qG+Q2iJHJlkTfcaBqRFKQwaCoIWuX6LLL8h3WsIeg1Bj8HvAa8YvGKPUSTv1fMMxrIwGCzLIhq1iEQjhMMR2sMRWjqiNIYMB0NeKsMZ7LWKaMcevLTY08Dk9ENML4xQkJ8Hnhh2pQq32F1SW2qgtRZCjew3RZSZcay3RtHs7NsiXxvD0topTouQH7DICQjpfjvpc05xO5PyLBAPlJ5hX4zpBU1gHKYJDKWUWxyrbI7n5fIhwN4uz/cBc471Hqfi24DdBHkI8O5Rnx3iTJ9omQCIyKeATzlPm0WkvKeBe4SB0wd5hnWdF7EIRywisBXYSoO87K32DAgA+E3YCIZ/AP/o6Up6INzWjD89PqN0WwgR8cueHr4/2tqAN+P9V5f8JmxqMSyLbXjHFc/90ltuiaUncUTwiiWHK9MeY+ElagS7crsfeCdBscSLHJ4QATpamwlkdB+LASJ4ieKl8iTXF67dt8mE21t7+PYi4CAw4iRX1xuJLH8LgXpjTKSb9x+hL+UyQG6QUWMKPPkA+00htRz/ineAMB4M7xpMLM7tvnJLedGdGuwmNSfi5m3oqXhtgxGPRPBijkoQe7DwEcXD4cR3TS+XbTmJWQsPOzj2d3KseU3UeIlSC2wE/hmj5Z7oGAiAvCyCXVZ34KcSYd0x3p9PE0PF3qsNIVO3rc7s7GVIiSiXU0JZWdlBEdndy491fr+lilSLFzTmREi1eCH1Yu5tvN2Wzf2jvX83jDG/A353Mp8VkRUr9keHxzikk4qjvaHWFVcERGRFpKHaNbG4ab+4IRa3xAHui6XNRbGc6lf4+lIuw3tlc8ruQzf9b5ws3QZ3cNN38snoD8egvzLGDOjtZ1Lt+y3V4gWNORFSLV5IvZhjFW88O4BXAF1bMQx15nX7HqcJcy72YHLH+mxPlqmUUqe6RJa/tUCes4xjrUsppZRSSqk+i2cCYzkwVkRGikgAuB549qj3PAvc7ExfCyw29qAczwLXi0jQGd1+LLCsh8tUSqlTXcLKX+czrzrLwFnmM3HcNqWUUkopdYqKWxcSp0/1HcCL2Lfc+6MxZoOI/C+wwhjzLPAw8P9EZBtQh10hxnnfP7C7XEaA242xR9HsbplxCP+kmzjHmFviAI3lWNwSi1viAI3lWBIWSxLK368CC0Xke8AqZ9nx4KbjeTJSPX7QbXCLVN+GVI9fHSnVjmeqxQsacyKkWryQejHHJN643YVEKaWUUkoppZRSKlbi2YVEKaWUUkoppZRSKiY0gaGUUkoppZRSSinXO6UTGCLyERHZICKWiMw66rV7RGSbiJSLyMVd5n/QmbdNRO6OY2wJWU+X9f1RRKpFZH2XeQUi8rKIbHX+5jvzRUQecGJbKyIzYhjHMBF5VUQ2Osfm80mMJU1ElonIGieW7zjzR4rIUmedjzkDGuIMeviYM3+piJTGKhZn+V4RWSUi/05mHM46donIOhFZLSIrnHnJOEZ5IvK4iGwWkU0iMi9JcYx39kXno1FEvpCMWPqjRJeHvRHLMktEbnbev1VEbj7WOuO0HX0uX+QY35sJij8mZUGSj8EXnXNovYg8KvZ3kKuPg8So7nCs/S4iM8X+rtnmfFbiuT2qd9xaNsfqvExgvK6p+/YiZlfVkXsRt2vq0j2M1xX17V7GHP+6uTHmlH0AE4HxwBJgVpf5k4A1QBAYCWzHHrTO60yPAgLOeybFIa6ErOeodZ4NzADWd5n3E+BuZ/pu4MfO9KXA84AAc4GlMYyjBJjhTGcDW5zjkYxYBMhypv3AUmcd/wCud+Y/CHzGmf4s8KAzfT3wWIyP0ZeAvwP/dp4nJQ5nubuAoqPmJeMY/QX4b2c6AOQlI46jYvICB4ARyY6lPzySUR72Mr6YlFlAAbDD+ZvvTOcncDv6VL5wjO/NBMbf57IgmccAGALsBNK77P9PuP04EIO6w/H2O/YdkOY6n3keuCRR55Q+TnjsXVs2x+K8THC8rqn79iJmV9WRexG3a+rSPYx3Fy6ob/cy5rjXzRO+UW588P4Exj3APV2evwjMcx4vHut9MYwnIevpZr2lRxX25UCJM10ClDvTDwE3dPe+OMT0DHBhsmMBMoCVwBzgIOA7+lh1nifOtM95n8Ro/UOBV4DzgH87/+gJj6NLPN0VqAk9RkAudoVfkhlHN3FdBLzlhlj6wyNZ5WEf4j2pMgu4AXioy/wj3hfnmPtcvhx9XLq+LwHxx6QsSPIxGALsxf4R73OOw8WpcBzoY93hWPvdeW1zl/lHvE8fyX24vWzu63mZ5NhdUfftRbxJrSP3Ik5X1aV7GPMuklzf7mW8Cambn9JdSI6jsyLRaZ8z71jzE7X+RCs2xlQ60weAYmc6IfE5zbWmY2d1kxKL09RsNVANvIx9taHeGBPpZn3vxeK83gAUxiiU+4CvAJbzvDBJcXQywEsiUiYin3LmJfoYjQRqgD85zQH/ICKZSYjjaNcDjzrTyY6lP0iZfdXHMiuZ23kffS9fkhl/rMqCpG2DMaYC+BmwB6jE3q9lpNZx6BSr/T7EmT56vnIHN5xrvZES38duqPv2lIvqyD11H+6qS/eEG+rbvZGQunm/T2CIyCKx+5Me/bgy2bGlEmOnxUyi1iciWcATwBeMMY3JisUYEzXGTMPO2s4GJiRivV2JyIeAamNMWaLXfRxnGmNmAJcAt4vI2V1fTNAx8mE3Ef2tMWY60ILdLC3RcbzH6Tt5BfDPo19LdCwqsdxSZvWWS8uX3nJdWdBbTn/gK7Erf4OBTOCDSQ0qBty+39Wpya3nZap9j7ihjtxTKfxd54b6dm8k5Pu43ycwjDEXGGOmdPN45jgfqwCGdXk+1Jl3rPmxlqj1nEiViJQAOH+rnflxjU9E/NgF+CPGmCeTGUsnY0w98Cp287I8EfF1s773YnFezwVqY7D6M4ArRGQXsBC76dv9SYjjPc7VQowx1cBT2F9ciT5G+4B9xpilzvPHsQvNZJ4rlwArjTFVzvOknrf9hOv3VYzKrGRtZ6zKl2Qep1iVBcnchguAncaYGmNMGHgS+9ik0nHoFKv9XuFMHz1fuYMbzrXecPX3sRvrvj2V5DpyT7muLt0TLqlv90ZC6ub9PoFxkp4FrndGoB0JjMUeSGo5MNYZsTaA3VT82TisP1HrOZFngZud6Zux++R1zr/JGTl2LtDQpVlQn4iIAA8Dm4wxv0hyLANEJM+ZTsfuj7gJu5C+9hixdMZ4LbDYyTL2iTHmHmPMUGNMKfa5sNgYc2Oi4+gkIpkikt05jT3mw3oSfIyMMQeAvSIy3pl1PrAx0XEc5QYOdx/pXGeyYukv3FIediuGZdaLwEUiku9cjb/ImRdXMSxfjvW9GXcxLAuScgwce4C5IpLhnFOd25Ayx6GLmOx357VGEZnr7JObuixLJZ+ry+ZuuPb72E11355ySx25p9xWl+4Jt9S3eyNhdXOT4MFI3PQArsbOFIWAKo4cjOjr2H25yuky6jX2aKlbnNe+HsfYErKeLut7FLvfbdjZJ/+F3dfrFWArsAgocN4rwK+d2NbRZQDUGMRxJnazorXAaudxaZJimQqscmJZD3zLmT8Ku0K4DburQNCZn+Y83+a8PioOx2kBh0dOTkocznrXOI8Nnednko7RNGCFc4yexh7BPuFxOMvPxM7O53aZl5RY+tuDBJeHvYwtZmUWcKvzf7sNuCUJ29Kn8oVjfG8mKPaYlAXJPAbAd4DN2N83/w/7TiKuPg7EqO5wrP0OzHL2x3bgVyRhED19HPf4u7JsjtV5mcB4XVP37UXMrqsj9yL2BSS5Lt3DOF1T3+5l3NOIc91cnA8rpZRSSimllFJKuZZ2IVFKKaWUUkoppZTraQJDKaWUUkoppZRSrqcJDKWUUkoppZRSSrmeJjCUUkoppZRSSinleprAUEoppZRSSimllOtpAkP1eyIySEQWish2ESkTkf+IyLiTWM6fRaS1857Mzrz7RMSISJHz/O1Yxq6UUv1RIstlpZRSPaN1ZpUKNIGh+jUREeApYIkxZrQxZiZwD1B8kovcBlzpLNsDnAdUdL5ojJnft4iVUqp/S3S5rJRS6sS0zqxShSYwVH93LhA2xjzYOcMYs8YY84aI/I+ILBeRtSLyHQARKRWRTSLyexHZICIviUh6l+UtBK5zphcAbwGRzhdFpNn5u0BElojI4yKyWUQecb4YlFLqVJfocvlp50riBhH5lDNvhIhsFZEiEfGIyBsiclGct1sppdxM68wqJWgCQ/V3U4Cyo2c6FdWxwGxgGjBTRM52Xh4L/NoYMxmoBz7c5aNbgAEikg/cgF04H8t04AvAJGAUcEYftkMppfqLRJfLtzpXEmcBd4pIoTFmN/Bj4LfAl4GNxpiXYrN5SimVkrTOrFKCJjDUqeoi57EKWAlMwC6EAXYaY1Y702VA6VGffRK4HpgDvHGcdSwzxuwzxljA6m6Wo5RS6rB4lct3isga4F1gWOcyjTF/AHKA24C7YrgdSinVn2idWbmKL9kBKBVnG4Bru5kvwA+NMQ8dMVOkFAh1mRUFujaHA3gMu5D+izHGOk4rt6OXo/9vSimVwHJZRBYAFwDzjDGtIrIESHNeywCGOp/PAppOdoOUUqof0DqzSgnaAkP1d4uBYGe/ZwARmQo0AreKSJYzb4iIDOzJAp2mx18HfhOHeJVSqr9LZLmcCxxykhcTgLldXvsx8AjwLeD3J7sxSinVT2idWaUEzW6pfs0YY0TkauA+Efkq0A7swu5nVw+842SDm4GPYWd9e7Lch078LqWUUkdLcLn8AnCbiGwCyrG7kSAi5wCnA2cYY6Ii8mERucUY86c+bJpSSqUsrTOrVCHGmGTHoJRSSimllFJKKXVc2oVEKaWUUkoppZRSrqcJDKWUUkoppZRSSrmeJjCUUkoppZRSSinleprAUEoppZRSSimllOtpAkMppZRSSimllFKupwkMpZRSSimllFJKuZ4mMJRSSimllFJKKeV6/x8Zrd6EHv/cigAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 3.2. Number of Harmonics","metadata":{}},{"cell_type":"markdown","source":"In the column \"# of Harmonics\", I have information that how many harmonic values are not null in that row. For example if \"# of Harmonics\" is 12, that row will have values from \"Harmonic 1\" to \"Harmonic 12\", but after \"Harmonic 13\" we will see NaN values. This \"# of Harmonics\" column will not be directly related to my classification model but will make it easier to analyze other columns.\n\nUsing describe method on \"# of harmonics\", I see that\n* min is 8 --> every row at least 8 harmonics value\n* max is 38 --> last column will be \"Harmonic 38\"\n* starting with column \"Harmonic 9\", there will be NaN values\n* since harmonics are ordered, missing values will increase with each column\n* the mean value for the number of harmonics is 20\n\nLooking at the number of missing values, I know that I will drop most of the columns. Harmonics bigger than 20 are gone. The first 8 harmonics are absolutely important. But I am not sure about the harmonics in between. I have to make more exploration to decide for them.","metadata":{}},{"cell_type":"code","source":"df[\"CenMean\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:36.966141Z","iopub.execute_input":"2023-04-24T19:49:36.966442Z","iopub.status.idle":"2023-04-24T19:49:36.977673Z","shell.execute_reply.started":"2023-04-24T19:49:36.966380Z","shell.execute_reply":"2023-04-24T19:49:36.976727Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"count 859.000000\nmean 601.461816\nstd 55.216494\nmin 376.968345\n25% 601.461816\n50% 601.461816\n75% 601.461816\nmax 854.570143\nName: CenMean, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4. Model Building","metadata":{}},{"cell_type":"code","source":"# importing packages\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nimport librosa\nimport numpy as np\n\nimport sklearn\nimport sklearn.cluster\nimport sklearn.pipeline\n\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:36.982143Z","iopub.execute_input":"2023-04-24T19:49:36.982438Z","iopub.status.idle":"2023-04-24T19:49:37.025135Z","shell.execute_reply.started":"2023-04-24T19:49:36.982405Z","shell.execute_reply":"2023-04-24T19:49:37.024304Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"markdown","source":"## 4.1. Preprocessing Data","metadata":{}},{"cell_type":"markdown","source":"There is just one step left before training the classification model. Since the Chord Type column is categorical and consists of strings, I will replace \"Major\" with 1 and \"Minor\" with 0. Finally, select columns that I will use in training and split the data into training and validation sets. I used test size as %40.","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:37.026356Z","iopub.execute_input":"2023-04-24T19:49:37.026790Z","iopub.status.idle":"2023-04-24T19:49:37.053920Z","shell.execute_reply.started":"2023-04-24T19:49:37.026748Z","shell.execute_reply":"2023-04-24T19:49:37.052910Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":" Chord Type File Name Centroids 1 Centroids 2 Centroids 3 \\\n0 Major Major_337.wav 875.837719 488.782825 425.884426 \n1 Major Major_19.wav 917.859325 582.559719 519.121262 \n2 Major Major_444.wav 347.526074 373.435467 529.542709 \n3 Major Major_380.wav 483.024051 490.128035 613.712264 \n4 Major Major_368.wav 483.024184 503.563786 625.609012 \n\n Centroids 4 Centroids 5 Centroids 6 Centroids 7 Centroids 8 ... \\\n0 418.530858 408.669695 402.815725 417.205433 408.936471 ... \n1 500.955215 508.893261 903.582401 2293.249574 2320.641050 ... \n2 547.327275 582.125882 595.242820 697.998047 753.016471 ... \n3 616.285086 602.002164 621.463473 724.975678 764.249056 ... \n4 657.493171 795.018113 820.846958 854.845368 884.775564 ... \n\n Centroids 94 Centroids 95 Centroids 96 Centroids 97 Centroids 98 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 651.775598 0.000000 5005.768406 \n2 1028.491379 741.474271 681.311208 751.427639 751.631801 \n3 1213.835513 932.319499 715.829355 752.889532 710.608423 \n4 748.193654 784.195716 750.894414 732.466760 712.221692 \n\n Centroids 99 Centroids 100 CenMean CenMin CenMax \n0 0.000000 0.000000 395.834008 0.000000 1752.311138 \n1 413.471139 2623.355413 601.461816 198.722770 4606.284564 \n2 730.874442 4957.255822 721.524115 347.526074 4957.255822 \n3 483.024051 2797.532041 601.461816 198.722770 4606.284564 \n4 483.024184 963.726855 601.461816 198.722770 4606.284564 \n\n[5 rows x 105 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Chord TypeFile NameCentroids 1Centroids 2Centroids 3Centroids 4Centroids 5Centroids 6Centroids 7Centroids 8...Centroids 94Centroids 95Centroids 96Centroids 97Centroids 98Centroids 99Centroids 100CenMeanCenMinCenMax
0MajorMajor_337.wav875.837719488.782825425.884426418.530858408.669695402.815725417.205433408.936471...0.0000000.0000000.0000000.0000000.0000000.0000000.000000395.8340080.0000001752.311138
1MajorMajor_19.wav917.859325582.559719519.121262500.955215508.893261903.5824012293.2495742320.641050...0.0000000.000000651.7755980.0000005005.768406413.4711392623.355413601.461816198.7227704606.284564
2MajorMajor_444.wav347.526074373.435467529.542709547.327275582.125882595.242820697.998047753.016471...1028.491379741.474271681.311208751.427639751.631801730.8744424957.255822721.524115347.5260744957.255822
3MajorMajor_380.wav483.024051490.128035613.712264616.285086602.002164621.463473724.975678764.249056...1213.835513932.319499715.829355752.889532710.608423483.0240512797.532041601.461816198.7227704606.284564
4MajorMajor_368.wav483.024184503.563786625.609012657.493171795.018113820.846958854.845368884.775564...748.193654784.195716750.894414732.466760712.221692483.024184963.726855601.461816198.7227704606.284564
\n

5 rows × 105 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"dfarray = np.array(df)\ndfarray = dfarray[:,2:].astype('float64')","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:37.055362Z","iopub.execute_input":"2023-04-24T19:49:37.055624Z","iopub.status.idle":"2023-04-24T19:49:37.074865Z","shell.execute_reply.started":"2023-04-24T19:49:37.055591Z","shell.execute_reply":"2023-04-24T19:49:37.073879Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"df[\"Chord Type\"] = df[\"Chord Type\"].replace(\"Major\", 1)\ndf[\"Chord Type\"] = df[\"Chord Type\"].replace(\"Minor\", 0)\n\n#columns = [3::]\n#columns.extend([\"Interval 4_1\", \"Interval 5_1\", \"Interval 6_1\"])\ntrain_X, val_X, train_y, val_y = train_test_split(dfarray, df[\"Chord Type\"], test_size=0.2, random_state=0)\n\ntrain_X","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:49:37.076148Z","iopub.execute_input":"2023-04-24T19:49:37.076407Z","iopub.status.idle":"2023-04-24T19:49:37.090662Z","shell.execute_reply.started":"2023-04-24T19:49:37.076378Z","shell.execute_reply":"2023-04-24T19:49:37.089723Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([[ 483.02418446, 503.56378605, 625.60982367, ..., 852.74681035,\n 483.02418446, 5809.2213111 ],\n [ 347.52597369, 343.60861231, 396.0557607 , ..., 601.46181591,\n 198.72276985, 4606.28456398],\n [ 374.06019178, 385.31080172, 530.1504496 , ..., 655.59471125,\n 374.06019178, 5559.72483976],\n ...,\n [ 485.21205404, 482.40631354, 571.08653705, ..., 601.46181591,\n 198.72276985, 4606.28456398],\n [ 821.8074044 , 442.71029468, 374.0451914 , ..., 601.46181591,\n 198.72276985, 4606.28456398],\n [1106.02202623, 568.94048439, 440.68263452, ..., 601.46181591,\n 198.72276985, 4606.28456398]])"},"metadata":{}}]},{"cell_type":"markdown","source":"## 4.2. Model Selection","metadata":{}},{"cell_type":"markdown","source":"In order to select a classification model, I will try 6 different models in this section and compare their cross validation score.","metadata":{}},{"cell_type":"code","source":"path = \"/kaggle/input/musical-instrument-chord-classification/Audio_Files/\"\ndef FeatureExtractor(path, n_mels, fmax=20000, fmin=20):\n\n data = []\n max_harm_length = 0 # i will keep track of max harmonic length for naming columns\n \n for dirname, _, filenames in os.walk(path):\n for filename in filenames:\n foldername = os.path.basename(dirname)\n full_path = os.path.join(dirname, filename)\n \n y, sr = librosa.load(full_path)\n mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, fmax=fmax, fmin=fmin)\n logam = librosa.power_to_db(mel) \n data.append(logam)\n \n \n data = np.array(data) \n return data","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:58:21.049336Z","iopub.execute_input":"2023-04-24T20:58:21.049696Z","iopub.status.idle":"2023-04-24T20:58:21.059399Z","shell.execute_reply.started":"2023-04-24T20:58:21.049661Z","shell.execute_reply":"2023-04-24T20:58:21.058245Z"},"trusted":true},"execution_count":97,"outputs":[]},{"cell_type":"code","source":"NX = FeatureExtractor(path, n_mels = 10)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:58:21.745139Z","iopub.execute_input":"2023-04-24T20:58:21.745520Z","iopub.status.idle":"2023-04-24T21:00:46.199809Z","shell.execute_reply.started":"2023-04-24T20:58:21.745487Z","shell.execute_reply":"2023-04-24T21:00:46.197963Z"},"trusted":true},"execution_count":98,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:18: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_17/4286230538.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mNX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFeatureExtractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_mels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/tmp/ipykernel_17/2160383838.py\u001b[0m in \u001b[0;36mFeatureExtractor\u001b[0;34m(path, n_mels, fmax, fmin)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (10,100) into shape (10,)"],"ename":"ValueError","evalue":"could not broadcast input array from shape (10,100) into shape (10,)","output_type":"error"}]},{"cell_type":"code","source":"plt.figure(figsize=(25, 10))\nlibrosa.display.specshow(NX[1], \n x_axis=\"time\",\n y_axis=\"mel\", \n sr=sr)\nplt.colorbar(format=\"%+2.f\")\n\nplt.show() ","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.411615Z","iopub.status.idle":"2023-04-24T20:38:49.412493Z","shell.execute_reply.started":"2023-04-24T20:38:49.412053Z","shell.execute_reply":"2023-04-24T20:38:49.412093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(NX)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.427002Z","iopub.status.idle":"2023-04-24T20:38:49.432746Z","shell.execute_reply.started":"2023-04-24T20:38:49.432137Z","shell.execute_reply":"2023-04-24T20:38:49.432226Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NX[0]","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.435465Z","iopub.status.idle":"2023-04-24T20:38:49.436620Z","shell.execute_reply.started":"2023-04-24T20:38:49.436224Z","shell.execute_reply":"2023-04-24T20:38:49.436267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Itera a través de cada archivo de audio en la carpeta\nfor filename in os.listdir(path):\n i=0 \n data_min=np.zeros((357,10,80))\n\n if filename.endswith('.wav'): #Solo lea archivos .wav\n\n\n # Cargar archivo de audio\n audio_file = os.path.join(path_audio, filename)\n y, sr = librosa.load(audio_file)\n \n # Calcular espectrograma\n spec = librosa.feature.melspectrogram(y=y, sr=sr, n_mels =10)\n spec_flat = spec.reshape(-1)\n mel = librosa.power_to_db(spec, ref=np.max)\n \n data_min[i]=mel[:,0:80]\n i=i+1","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Now, build a learning object. We'll use mini-batch k-means with default parameters.\nC = sklearn.cluster.MiniBatchKMeans()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.448700Z","iopub.status.idle":"2023-04-24T20:38:49.450385Z","shell.execute_reply.started":"2023-04-24T20:38:49.449892Z","shell.execute_reply":"2023-04-24T20:38:49.449941Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Now, chain them all together into a pipeline\nClusterPipe = sklearn.pipeline.Pipeline([('Cluster', C)])","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.452940Z","iopub.status.idle":"2023-04-24T20:38:49.454051Z","shell.execute_reply.started":"2023-04-24T20:38:49.453659Z","shell.execute_reply":"2023-04-24T20:38:49.453701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Let's build a model using just the first 20 seconds of the example track\n\ny_train, sr = librosa.load(train_X, duration=20, offset=0.0)","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.456367Z","iopub.status.idle":"2023-04-24T20:38:49.457469Z","shell.execute_reply.started":"2023-04-24T20:38:49.457059Z","shell.execute_reply":"2023-04-24T20:38:49.457100Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lr = LogisticRegression(random_state=0,solver='liblinear')\nknn = KNeighborsClassifier()\nsvc = SVC(random_state=0)\ngnb = GaussianNB()\ndtc = DecisionTreeClassifier(random_state=0)\nrfc = RandomForestClassifier(random_state=0)\n\nscore_lr = cross_val_score(lr, train_X, train_y, cv=10).mean()\nscore_knn = cross_val_score(knn, train_X, train_y, cv=10).mean()\nscore_svc = cross_val_score(svc, train_X, train_y, cv=10).mean()\nscore_gnb = cross_val_score(gnb, train_X, train_y, cv=10).mean()\nscore_dtc = cross_val_score(dtc, train_X, train_y, cv=10).mean()\nscore_rfc = cross_val_score(rfc, train_X, train_y, cv=10).mean()","metadata":{"execution":{"iopub.status.busy":"2023-04-24T20:38:49.459994Z","iopub.status.idle":"2023-04-24T20:38:49.460508Z","shell.execute_reply.started":"2023-04-24T20:38:49.460311Z","shell.execute_reply":"2023-04-24T20:38:49.460333Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Cross Val Score for Logistic Regression: {:.2f}\".format(score_lr))\nprint(\"Cross Val Score for KNeighbors Classifier: {:.2f}\".format(score_knn))\nprint(\"Cross Val Score for SVC: {:.2f}\".format(score_svc))\nprint(\"Cross Val Score for Gaussian NB: {:.2f}\".format(score_gnb))\nprint(\"Cross Val Score for Decision Tree Classifier: {:.2f}\".format(score_dtc))\nprint(\"Cross Val Score for Random Forest Classifier: {:.2f}\".format(score_rfc))","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:52:01.993036Z","iopub.status.idle":"2023-04-24T19:52:01.993385Z","shell.execute_reply.started":"2023-04-24T19:52:01.993208Z","shell.execute_reply":"2023-04-24T19:52:01.993229Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.3. Model Training and Prediction","metadata":{}},{"cell_type":"markdown","source":"In the previous section, I tried 6 different models and Random Forest Classifier works really well with my dataset. I obtained 0.92 success rate with this model. After Random Forest Classifier, Decision Tree Classifier obtained %90 and KNeighbors Classifier obtained %83 success rate. \n\nNow, I will continue with Random Forest Classifier. First, I will train my model with the training dataset and then make a prediction on the validation dataset to see the accuracy.","metadata":{}},{"cell_type":"code","source":"# defining my classifier\nclassifier = RandomForestClassifier(random_state=0)\n\nclassifier.fit(train_X, train_y) # training classifier\npred_y = classifier.predict(val_X) # making prediction on validation","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:52:01.995535Z","iopub.status.idle":"2023-04-24T19:52:01.996087Z","shell.execute_reply.started":"2023-04-24T19:52:01.995878Z","shell.execute_reply":"2023-04-24T19:52:01.995900Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix(val_y, pred_y)\nacc = accuracy_score(val_y, pred_y)\n\nprint(\"Confusion Matrix:\")\nprint(cm)\n\nprint(\"Accuracy Score: {:.2f}\".format(acc))","metadata":{"execution":{"iopub.status.busy":"2023-04-24T19:52:01.997139Z","iopub.status.idle":"2023-04-24T19:52:01.997695Z","shell.execute_reply.started":"2023-04-24T19:52:01.997510Z","shell.execute_reply":"2023-04-24T19:52:01.997531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For the validation set, I obtained an even better score, %94 success rate.\n\nSo, I guess this is the final cell in this notebook. Thank you very much for your interest in this project :)","metadata":{"execution":{"iopub.status.busy":"2023-03-20T20:43:13.065493Z","iopub.execute_input":"2023-03-20T20:43:13.066373Z","iopub.status.idle":"2023-03-20T20:43:13.072982Z","shell.execute_reply.started":"2023-03-20T20:43:13.066294Z","shell.execute_reply":"2023-03-20T20:43:13.071743Z"}}}]}