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map_area.py
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map_area.py
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from io import BytesIO
from PIL import Image
from urllib import request
import pandas as pd
import numpy as np
import cv2
import os
# Parse a csv file of data.
def parse_csv_file(csv_file):
data = pd.read_csv(csv_file)
return data
# Get the map region using Google Static Maps.
def get_area_map(latitude, longitude):
url = "https://maps.googleapis.com/maps/api/staticmap?center=%f,%f&zoom=14&size=224x224&maptype=satellite&key=" % (latitude, longitude)
img_array = np.asarray(bytearray(request.urlopen(url).read()), dtype=np.uint8)
image = cv2.imdecode(img_array, 1)
cv2.imwrite(os.path.join(path ,str(latitude)+','+ str(longitude)+'.png'), image)
data = parse_csv_file('500_Cities__Local_Data_for_Better_Health__2017_release.csv')
data_value = data[['Data_Value','GeoLocation']]
data_value = data_value[np.isfinite(data_value['Data_Value'])]
min_val = data_value['Data_Value'].min()
max_val = data_value['Data_Value'].max()
increment = (max_val - min_val)/6
conditions = [
(data_value['Data_Value'] >= min_val) & (data_value['Data_Value'] < (min_val+increment)),
(data_value['Data_Value'] >= (min_val+increment)) & (data_value['Data_Value'] < (min_val+(2*increment))),
(data_value['Data_Value'] >= (min_val+increment)) & (data_value['Data_Value'] < (min_val+(3*increment))),
(data_value['Data_Value'] >= (min_val+increment)) & (data_value['Data_Value'] < (min_val+(4*increment))),
(data_value['Data_Value'] >= (min_val+increment)) & (data_value['Data_Value'] < (min_val+(5*increment))),
(data_value['Data_Value'] >= (min_val+increment))
]
choices = [1,2,3,4,5,6]
data_value['Data_Category'] = np.select(conditions, choices, default='')
print(data_value)
# for i in range(6):
# max_temp_val = min_val + increment
# data_value['Data_Category'] = np.where((data_value['Data_Value']>=min_val & data_value['Data_Value']<(max_temp_val)), 1, 0)
# # print(min_val)
# # print(min_val+increment)
# #data_value['Data_Category'] = np.where(data_value['Data_Value']>=min_val & data_value['Data_Value']<(min_val+increment), i, '')
# conditions = [
# (data_value['Data_Value'] >= min_val) & (data_value['Data_Value'] < (min_val+increment))]
# choices = [i]
# data_value['Data_Category'] = np.select(conditions, choices, default='')
# min_val = min_val + increment
# path = os.path.join(os.getcwd(), 'images/')
# i = 1
# for index, row in data_value.iterrows():
# print(i)
# coords = row['GeoLocation']
# coords = coords.translate(str.maketrans('','','() ')).split(',')
# get_area_map(float(coords[0]), float(coords[1]))
# i = i + 1