victormurcia

#Compound list
y_predict  = ['PT08.S1(CO)','PT08.S2(NMHC)','PT08.S3(NOx)','PT08.S4(NO2)','PT08.S5(O3)']

#Select compound to optimize model for
compound = y_predict[0]

#Designate independent and dependent variables
x  = aq_final.drop([compound], axis = 1)
y  = aq_final[compound]
#Split data into test and training sets

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#Need function that reads pixel hue value 
hsv = cv2.cvtColor(ori_img, cv2.COLOR_BGR2HSV)
#Plot the image
fig, axs = plt.subplots(1, 3, figsize = (15,15))
names = ['BGR','RGB','HSV']
imgs  = [ori_img, img, hsv]
i = 0
for elem in imgs:
    axs[i].title.set_text(names[i])
    axs[i].imshow(elem)

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i=0 ; j=0
#Initialize array the will contain Hues for every pixel in image
hues = [] 
for i in range(height):
    for j in range(width):
        hue = hsv[i][j][0] #This is the hue value at pixel coordinate (i,j)
        hues.append(hue)

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#Define frequencies that make up A-Harmonic Minor Scale
scale_freqs = [220.00, 246.94 ,261.63, 293.66, 329.63, 349.23, 415.30] 
def hue2freq(h,scale_freqs):
    thresholds = [26 , 52 , 78 , 104,  128 , 154 , 180]
    note = scale_freqs[0]
    if (h <= thresholds[0]):
         note = scale_freqs[0]
    elif (h > thresholds[0]) & (h <= thresholds[1]):
        note = scale_freqs[1]
    elif (h > thresholds[1]) & (h <= thresholds[2]):

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pixels_df['notes'] = pixels_df.apply(lambda row : hue2freq(row['hues'],scale_freqs), axis = 1)     

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frequencies = pixels_df['notes'].to_numpy()

song = np.array([]) 
sr = 22050 # sample rate
T = 0.1    # 0.1 second duration
t = np.linspace(0, T, int(T*sr), endpoint=False) # time variable
#Make a song with numpy array :]
#nPixels = int(len(frequencies))#All pixels in image
nPixels = 60
for i in range(nPixels):  

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song = np.array([]) 
octaves = np.array([0.5,1,2])
sr = 22050 # sample rate
T = 0.1    # 0.1 second duration
t = np.linspace(0, T, int(T*sr), endpoint=False) # time variable
#Make a song with numpy array :]
#nPixels = int(len(frequencies))#All pixels in image
nPixels = 60
for i in range(nPixels):
    octave = random.choice(octaves)

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song = np.array([]) 
octaves = np.array([1/2,1,2])
sr = 22050 # sample rate
T = 0.1    # 0.1 second duration
t = np.linspace(0, T, int(T*sr), endpoint=False) # time variable
#Make a song with numpy array :]
#nPixels = int(len(frequencies))#All pixels in image
nPixels = 60
for i in range(nPixels):
    octave = random.choice(octaves)

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def get_piano_notes():   
    # White keys are in Uppercase and black keys (sharps) are in lowercase
    octave = ['C', 'c', 'D', 'd', 'E', 'F', 'f', 'G', 'g', 'A', 'a', 'B'] 
    base_freq = 440 #Frequency of Note A4
    keys = np.array([x+str(y) for y in range(0,9) for x in octave])
    # Trim to standard 88 keys
    start = np.where(keys == 'A0')[0][0]
    end = np.where(keys == 'C8')[0][0]
    keys = keys[start:end+1]
    

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#Load note dictionary
note_freqs = get_piano_notes()