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//Opens the image to be processed
Circle = imread('C:\Users\csrc-lab03\Desktop\yakal5.bmp');
Circle = rgb2gray(Circle)

//Use edge() function sobel
edge_sobel = edge(Circle,'canny');
imshow(edge_sobel);
imwrite(edge_sobel,'Circle_sobel.bmp');

//Edge pixel coordinates

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select SQLDATE, MonthYear, EventRootCode, GoldsteinScale, NumMentions, AvgTone, ActionGeo_CountryCode, ActionGeo_Lat, ActionGeo_Long 
from `gdelt-bq.full.events`
where 
SQLDATE > 20000101 and
ActionGeo_CountryCode = 'RP';

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import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
from mpl_toolkits.basemap import Basemap
from matplotlib.animation import FuncAnimation


### Create a figure and draw a basemap
fig, ax = plt.subplots(figsize=(10,8), dpi=500)

m = Basemap(resolution='h',

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import pandas as pd
import numpy as np

### Read in county data and drought info data
dr = pd.read_csv('rallies.csv')
dr1 = dr[['event_date', 'event_type', 'latitude', 'longitude', 'location']]

### Data cleansing
dr1.event_date = pd.to_datetime(dr1.event_date)
num_m = len(dr.event_date.unique())

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### Set county location values, drough level values, marker sizes (according to county size), colormap and title 
x, y = m(dr_m.nth(0).longitude.tolist(), dr_m.nth(0).latitude.tolist())
colors = (dr_m.nth(0).code).tolist()
sizes = (dr_m.nth(0).count1*80).tolist()
cmap = plt.cm.plasma#autumn_r
#cmap = plt.cm.hot_r
sm = ScalarMappable(cmap=cmap)
plt.title('Social Movement (Year-Month): '+dr_m.nth(0).event_date.iloc[0].strftime('%Y-%m'))

### Display the scatter plot and its colorbar (0-5)

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#assuming that x is a list of values and is not empty
#because we are not allowed to divide by 0
def mean(x):
  return sum(x) / len(x)

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def median(x):
  sorted_ = sorted(x)
  N = len(x)
  middle_value = N // 2
  if n % 2 != 0:        #CASE 1: if N is odd
    M = sorted_[middle_value]
  else:                 #CASE 2: if N is odd
    M = (sorted_[(middle_value) - 1] + sorted_[middle_value]) / 2
  return M

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import random
import pandas as pd
import matplotlib.pyplot as plt

random.seed( 30 ) #for replicability

n = 100
y = []
for i in range(n):
    y += [random.randint(25, 50)]