Bring data to life: real-time visualizations | Deephaven

twitter_table_col_definitions = {"tweet": dht.string, "clean_tweet": dht.string, "length": dht.int32, "category": dht.string, "point_x": dht.double, "point_y": dht.double}
twitter_table_writer = DynamicTableWriter(twitter_table_col_definitions)
tweet_table = twitter_table_writer.table

TERM_1 = 'good'
TERM_2 = 'news'

RADIUS_1 = 10
SHIFT_1 = -6
CENTER_1 = (SHIFT_1, 0)
X_1 = [(-RADIUS_1 + SHIFT_1) + i * 0.001 for i in range(2 * 1000 * RADIUS_1 + 1)]
Y1_UPPER = [np.sqrt(RADIUS_1 ** 2 - (i - SHIFT_1) ** 2) for i in X_1]
Y1_LOWER = [-y for y in Y1_UPPER]

RADIUS_2 = 10
SHIFT_2 = 6
CENTER_2 = (SHIFT_2, 0)
X_2 = [(-RADIUS_2 + SHIFT_2) + i * 0.001 for i in range(2 * 1000 * RADIUS_2 + 1)]
Y2_UPPER = [np.sqrt(RADIUS_2 ** 2 - (i - SHIFT_2) ** 2) for i in X_2]
Y2_LOWER = [-y for y in Y2_UPPER]


def distance(point1, point2):
    return np.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)

def in_circle(point, center, radius):
    if distance(point, center) < radius - 0.7:
        return True
    return False

def out_circle(point, center, radius):
    if distance(point, center) > radius + 0.7:
        return True
    return False

def collide(rand, points):
    r = 0.4
    for i in points:
        if distance([rand[0], rand[1]], [i[0], i[1]]) < 2 * r:
            return True
    return False

def write_live_data():
    """
    The function to write twitter data to a table
    """
    response = get_tweets()
    diag_points = []
    for response_line in response.iter_lines():

        if response_line:
            json_response = json.loads(response_line)
            lang = json_response["data"]["lang"]

            
            if lang == "en":
                tweet = json_response["data"]["text"]
                clean_tweet = preprocess(tweet)

                words = clean_tweet.split()
                for word in words:
                    word_table_writer.write_row([word, 1])
                length = len(words)

                
                count = 0
                category = ''
                point_x = None
                point_y = None
                if TERM_1 in words:
                    category = 'left'
                    count += 1

                if TERM_2 in words:
                    category = 'right'
                    count += 1

                if count == 2:
                    category = 'middle'

                if category == 'middle':

                    rand = [random.uniform(CENTER_1[0], CENTER_2[0]),
                            random.uniform(CENTER_1[1] - RADIUS_1, CENTER_2[1] + RADIUS_2)]

                    while (not (in_circle(rand, CENTER_1, RADIUS_1) and in_circle(rand, CENTER_2, RADIUS_2))) or collide(rand, diag_points):
                        rand = [random.uniform(CENTER_1[0], CENTER_2[0]), random.uniform(CENTER_1[1] - RADIUS_1, CENTER_2[1] + RADIUS_2)]

                    point_x = rand[0]
                    point_y = rand[1]
                    diag_points.append(rand)
                if category == 'left':
                    rand = [random.uniform(CENTER_1[0] - RADIUS_1, CENTER_1[0] + RADIUS_1),
                            random.uniform(CENTER_1[1] - RADIUS_1, CENTER_1[1] + RADIUS_1)]
                    while (not (in_circle(rand, CENTER_1, RADIUS_1) and out_circle(rand, CENTER_2, RADIUS_2))) or collide(rand, diag_points):
                        rand = [random.uniform(CENTER_1[0] - RADIUS_1, CENTER_1[0] + RADIUS_1),
                                random.uniform(CENTER_1[1] - RADIUS_1, CENTER_1[1] + RADIUS_1)]
                    diag_points.append(rand)
                    point_x = rand[0]
                    point_y = rand[1]
                if category == 'right':
                    rand = [random.uniform(CENTER_2[0] - RADIUS_2, CENTER_2[0] + RADIUS_2),
                            random.uniform(CENTER_2[1] - RADIUS_2, CENTER_2[1] + RADIUS_2)]
                    while (not (in_circle(rand, CENTER_2, RADIUS_2) and out_circle(rand, CENTER_1, RADIUS_1))) or collide(rand, diag_points):
                        rand = [random.uniform(CENTER_2[0] - RADIUS_2, CENTER_2[0] + RADIUS_2),
                                random.uniform(CENTER_2[1] - RADIUS_2, CENTER_2[1] + RADIUS_2)]
                    diag_points.append(rand)
                    point_x = rand[0]
                    point_y = rand[1]

                twitter_table_writer.write_row([tweet, clean_tweet, length, category, point_x, point_y])


thread = threading.Thread(target=write_live_data, args=[set])
thread.start()


venn_plot_fig = plt.figure()
venn_ax = venn_plot_fig.subplots()
venn_ax.plot(X_1, Y1_UPPER, color='k')
venn_ax.plot(X_1, Y1_LOWER, color='k')
venn_ax.plot(X_2, Y2_UPPER, color='k')
venn_ax.plot(X_2, Y2_LOWER, color='k')
venn_ax.text(SHIFT_1 - RADIUS_1 + 2, 10, TERM_1.upper(), ha='center', color='white')
venn_ax.text(SHIFT_2 + RADIUS_2 - 2, 10, TERM_2.upper(), ha='center', color='white')
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.axis('equal')
venn_ax.set_title('Twitter Venn Diagram')

def animate_venn_fig(data, update):
    venn_ax.plot(data["point_x"], data["point_y"], marker='o', ms=10, color='green', lw=0)

tweet_table_with_category = tweet_table.where(filters=["category in `middle`, `left`, `right`"])
venn_ani = TableAnimation(venn_plot_fig, tweet_table_with_category, animate_venn_fig)