#!/usr/bin/env python
# coding: utf-8

# Juypter notebooks have magic functions! one of them is the timeit. %timeit is great for single lines and %%timeit for multi lines

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get_ipython().run_line_magic('timeit', 'data = [x * x for x in range(1000)]')


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get_ipython().run_cell_magic('timeit', '', 'data = []\nfor x in range(1000):\n    data.append(x * x)\n')


# That makes a pretty convincing case for using list comprehension! What is the fastest way to read a satellite image into a numpy array? Lets test using a Landsat 8 image (Band 4) downloaded from AWS
# https://landsat-pds.s3.amazonaws.com/c1/L8/139/045/LC08_L1TP_139045_20170304_20170316_01_T1/index.html

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image_path = "D:/Fastest_image/LC81390452014295LGN00_B4.TIF" ### set the path to your image choice


# Import all the Python libraries. I recommend using a virtual python environment for running the tests, but it is not essential

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## method 1 - opencv
import cv2
import numpy as np
# Import GDAL method 2
from osgeo import gdal
## import method 3 - skimage
from skimage import io
## import method 4 - rasterio
##import rasterio




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get_ipython().run_cell_magic('timeit', '', '### Open CV approach\n\nimg = cv2.imread(image_path, -1) ### add your image here\nprint img.shape\n')


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get_ipython().run_cell_magic('timeit', '', '## GDAL approach\n\nraster_ds = gdal.Open(image_path, gdal.GA_ReadOnly)\nimage_gdal = raster_ds.GetRasterBand(1).ReadAsArray()\nprint image_gdal.shape\n')


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get_ipython().run_cell_magic('timeit', '', '## skimage\n\nim = io.imread(image_path)\nprint im.shape\n')


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get_ipython().run_cell_magic('timeit', '', 'with rasterio.open(image_path) as r:\n    arr = r.read()  # read raster\n    print(arr.shape)\n')


# In my test (albeit just 1 single band Landsat 8 image) GDAL is the winner!

# 4 different ways to read satellite images into numpy arrays
# The case for using list comprehension to speed up code
# GDAL is the fastest!
# BUT use the most suited approach to your needs!
# use the timeit%% for multi line testing and timeit% for single line testing

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