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Copy pathTOPO_CORR_@I.py
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TOPO_CORR_@I.py
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import datetime
import math
from datetime import datetime, date
import numpy as np
import glob
import os
from osgeo import gdal
from scipy.stats import linregress
import pandas as pd
from dict import dict
#Load Metadata
#metadata digunakan sebagai inputan function (year_date, hour, second, leap)
f = open('D:/FORESTS2020/TRAINING/PyQgis/DATA/Landsat8/NO SEA/FINAL/GEO/LANDSAT/mtl.txt', 'r') #open file for reading
def build_data(f):
output = {}
for line in f.readlines():
if "=" in line:
l = line.split("=")
output[l[0].strip()] = l[1].strip()
return output
data = build_data(f)
#Load data raster yang akan dikoreksi
raster_list=glob.glob('D:/FORESTS2020/TRAINING/PyQgis/DATA/Landsat8/NO SEA/FINAL/GEO/LANDSAT/TRIAL/*.TIF')
read=[]
for i in raster_list:
band=gdal.Open(i)
read.append(band.GetRasterBand(1).ReadAsArray())
data_name=['band2', 'band3', 'band4', 'band5', 'band6']
my_dict= dict(zip(data_name, read))
#Load data raster aspect, slope & sample area
raster_list_dem=glob.glob('D:/FORESTS2020/TRAINING/PyQgis/DATA/Landsat8/NO SEA/FINAL/GEO/DEM/*.tif')
read2=[]
for d in raster_list_dem:
band=gdal.Open(d)
read2.append(band.GetRasterBand(1).ReadAsArray())
dem_name=['aspect', 'sample', 'slope']
dem_dict= dict (zip(dem_name, read2))
def year_date():
year_file=data['DATE_ACQUIRED']
date_file=data['SCENE_CENTER_TIME']
date_file2= date_file [1:16]
all= year_file+" "+date_file2
parsing = datetime.strptime(all, '%Y-%m-%d %H:%M:%S.%f')
return parsing
dt=year_date()
def hour():
h=dt.hour+7
return h
def second():
s= float(dt.microsecond)/1000000+dt.second
return s
def leap():
if (dt.year % 4) == 0:
if (dt.year % 100) == 0:
if (dt.year % 400) == 0:
a = int(366)
else:
a = int(365)
else:
a= int(366)
else:
a= int(365)
return a
def cos(x):
cos= np.cos(np.deg2rad(x))
return cos
def sin(x):
sin=np.sin(np.deg2rad(x))
return sin
def day():
day_date= date(dt.year, dt.month, dt.day)
sum_of_day=int(day_date.strftime('%j'))
return sum_of_day
gamma=((2 * math.pi) / leap()) * ((day() - 1) + (((hour()+dt.minute/60+second()/3600) - 12) / 24) )# degree
#sun declination angle
decl=0.006918 - 0.399912 * cos(gamma) + 0.070257 * sin(gamma) - 0.006758 * cos (2 * gamma)\
+ 0.000907 * sin (2 * gamma) - 0.002697 * cos (3 * gamma) + 0.00148 * sin (3 * gamma) #radians
decl_deg= (360 / (2 * math.pi)) * decl
#lat long value
# get columns and rows of your image from gdalinfo
xoff, a, b, yoff, d, e = band.GetGeoTransform()
def pixel2coord(x, y):
xp = a * x + b * y + xoff
yp = d * x + e * y + yoff
return(xp, yp)
rows=read[0].shape[0]
colms=read[0].shape[1]
coordinate=[]
for row in range(0,rows):
for col in range(0,colms):
coordinate.append(pixel2coord(col,row))
coor_2=np.array(coordinate, dtype=float)
long=coor_2[:,0]
lat=coor_2[:,1]
long_n=long.reshape(rows,colms)
lat_n=lat.reshape(rows,colms)
#eqtime
eqtime = 229.18 * (0.000075 + 0.001868 * cos(gamma) - 0.032077 * sin(gamma) - 0.014615 * cos(2 * gamma) - 0.040849 * sin(2 * gamma)) # minutes
timeoff= eqtime - 4 * long_n + 60 * 7 #minutes
tst=hour() * 60 + dt.minute + second() / 60 + timeoff #minutes
ha=(tst /4)-180 #degree
#sun zenith angle
zenit1 =sin(lat_n)* sin(decl_deg) + cos (lat_n)* cos(decl_deg) * cos(ha)
zenit2=np.arccos(zenit1) #radians
zenit_angle= np.rad2deg(zenit2)
#sun azimuth angle
theta1= -1 * ((sin(lat_n)) * cos(zenit_angle)- sin(decl_deg)/(cos (lat_n) * sin (zenit_angle)))
theta2=np.arccos(theta1) #radians
theta3=np.rad2deg(theta2)#degree
azimuth_angle=180 - theta3 #degrees
# IC calculation
delta=azimuth_angle - dem_dict['aspect']
IC=(cos(zenit_angle)* cos (dem_dict['slope'])) + (sin(zenit_angle) * sin (dem_dict['slope']) * cos(delta))#radians
# sample
area_true= dem_dict['sample'].nonzero() #outputnya index row n col
a_true=area_true[0]
b_true=area_true[1]
#correction
cos_zenith= cos(zenit_angle)
#auto
temp={}
IC_final={}
slope={}
for y in my_dict:
val2=my_dict[y]
temp[y]=val2[a_true,b_true].ravel()
IC_true=IC[a_true,b_true].ravel()
slope[y]=linregress(IC_true, temp[y])
IC_final[y]=my_dict[y]-(slope[y][0]*(IC-cos_zenith))
print(IC_final)
#export data to raster (GeoTiff)
for item in IC_final:
print(item)
geo = band.GetGeoTransform()
proj = band.GetProjection()
shape = my_dict['band2'].shape
driver = gdal.GetDriverByName("GTiff")
dst_ds = driver.Create("D:/FORESTS2020/TRAINING/PyQgis/DATA/Landsat8/NO SEA/FINAL/GEO/RESULT/"+item+"_X.tif", shape[1], shape[0], 1, gdal.GDT_Float32)
dst_ds.SetGeoTransform(geo)
dst_ds.SetProjection(proj)
dst_ds.GetRasterBand(1).WriteArray(IC_final[item])
dst_ds.FlushCache()
dst_ds = None # save, close"""
#function untuk export ke CSV
def csv():
csv2 = {}
for c in IC_final:
val4=IC_final[c]
csv2[c]= val4[a_true, b_true].ravel()
#print "hasil", csv2
df=pd.DataFrame(csv2)
df2=pd.DataFrame(temp)
df3=pd.DataFrame({'IC':IC_true})
dfn=pd.concat([df3, df, df2], axis=1)
dfn.to_csv("D:/FORESTS2020/TRAINING/PyQgis/DATA/Landsat8/NO SEA/FINAL/GEO/RESULT/sample.csv", index= False)