victormurcia

#Rotating an image
rotated0 = imutils.rotate_bound(rgb_img,0)
rotated45 = imutils.rotate_bound(rgb_img,45)
rotated90 = imutils.rotate_bound(rgb_img,90)
fig,axs = plt.subplots(1,3, figsize=(30,15))
axs[0].imshow(rotated0)
axs[1].imshow(rotated45)
axs[2].imshow(rotated90)
plt.show()

victormurcia

#Convert image to RGB from BGR
rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=figsize)
plt.imshow(rgb_img)
plt.show()

victormurcia

#Render image from URL
req = urllib.request.urlopen('https://www.sportbible.com/cdn-cgi/image/width=648,quality=70,format=webp,fit=pad,dpr=1/https%3A%2F%2Fs3-images.sportbible.com%2Fs3%2Fcontent%2Fcf2701795dd2a49b4d404d9fa38f99fd.jpg')
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
bgr_img = cv2.imdecode(arr, -1) # 'Load it as it is'

# Determine the figures size in inches to fit image
dpi = plt.rcParams['figure.dpi']
height, width, depth = bgr_img.shape
figsize = width / float(dpi), height / float(dpi)

victormurcia

def get_thermo_info(material):
    '''Carries out query from NIST Chemistry WebBook for thermodynamic properties of a chemical compound
    Args:
        material (str): Name of compound to get thermodynamic info for
        
    Returns:
        Pandas dataframe containing query results
    
    Raises:
        If table is not found, then dataframe is filled with NaNs.

victormurcia

#Replace commas with %2C for proper querying (this is the HEX code to represent a comma)
mat_corr = material.replace(",", "%2C").replace("(", "%28").replace(")", "%29").replace("+", "%2B")

victormurcia

contigency= pd.crosstab(vid_df_copy['sentiment'],vid_df_copy['Live or Studio'])

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emotions = ['joy', 'positive', 'anticipation', 'sadness','surprise', 'negative', 'anger', 'disgust', 'trust','fear']
for emotion in emotions:
    vid_df_copy[emotion] = [0 if emotion not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores[emotion] for x in vid_df_copy['denoised_str']]

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vid_df_copy['joy']          = [0 if 'joy'          not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['joy']          for x in vid_df_copy['denoised_str']]
vid_df_copy['positive']     = [0 if 'positive'     not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['positive']     for x in vid_df_copy['denoised_str']]
vid_df_copy['anticipation'] = [0 if 'anticipation' not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['anticipation'] for x in vid_df_copy['denoised_str']]
vid_df_copy['sadness']      = [0 if 'sadness'      not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['sadness']      for x in vid_df_copy['denoised_str']]
vid_df_copy['surprise']     = [0 if 'surprise'     not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['surprise']     for x in vid_df_copy['denoised_str']]
vid_df_copy['negative']     = [0 if 'negative'     not in NRCLex(x).raw_emotion_scores else NRCLex(x).raw_emotion_scores['negative']     for x in vid

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sid = SentimentIntensityAnalyzer()
vid_df_copy['compound'] = [sid.polarity_scores(x)['compound'] for x in vid_df_copy['denoised_str']]
vid_df_copy['neg']      = [sid.polarity_scores(x)['neg'] for x in      vid_df_copy['denoised_str']]
vid_df_copy['neu']      = [sid.polarity_scores(x)['neu'] for x in      vid_df_copy['denoised_str']]
vid_df_copy['pos']      = [sid.polarity_scores(x)['pos'] for x in      vid_df_copy['denoised_str']]
vid_df_copy

victormurcia

classifier = NaiveBayesClassifier.train(train_data)

print("Gaussian Naive Bayes accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))