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main.py
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#-*-coding:utf-8-*-
"""
Author: ls-huang
Edition: 1.0
Data: 2018/10/18
File Name: main.py
Function： main part
Function： Use .tif image file and calculate multifractal spectrum and visualize the spatial spectrum.
"""
import numpy as np
import winsound
import time
import os
import gc
# import relevant class defined in the project
from fileList import FileList
from clipRaster import Clipper
from resizeRaster import Resampler
from calculation import Calculator
from saveDataAndPlot import SaveDataAndPic
class Main():
def __init__(self):
"""
setting of the main parameters
"""
# 1. the .tif file folder
# use "\\" when in Windows platform and "/" in Linux
self.filefolder = u'C:\\Tiff_File_Path'
self.filetype = '.tif'
filelist = FileList(self.filefolder, self.filetype)
filelist.get_file_name()
self.file_list = filelist.file_list
# 2. the extent of the outermost box
# upper-left coordinates and lower-right coordinates
# [x: horizontal coordinate; y: vertical coordinate]
self.box_ulx = 12950524
self.box_uly = 4847567
self.box_lrx = 12952284
self.box_lry = 4846532
# whether to be clipped
# 0: not to be clipped
# 1: would be clipped as coordinates set above
self.is_to_be_clipped = 0
# save path of clipped file
self.clip_filefolder = u"C:\\Tiff_File_Path\\Clip"
# 3. resample parameters
# scale of resample size
# CAUTION: must be the power of 2, e.g. 128, 256, 512, 1024, ... 4096)
self.resample_size = 4096
self.resample_filefolder = u"C:\\Tiff_File_Path\\resample"
# 4. OLS regression parameters
# from left to right: q_min, q_max, q_interval
self.q_list = np.arange(-20, 21, 1)
# 0 - Fix the intercept to zero during OLS
# 1 - Do NOT Fix the intercept to zero during OLS
self.intercept = 1
# 5. save data tp .hdf5 and save pic to .png.
# 1 - Export the data to .hdf5 file
# 0 - Do NOT Export the data to .hdf5 file
# self.is_export_data = 1
# 1 - Export the result to .png file
# 0 - Do NOT Export the result to .png file
# self.is_export_png = 1
# whether to save data and pic
self.is_save_data = True
self.export_file_folder = u'C:\\Tiff_File_Path\\Result'
# 6. set the scaling range for different parameter
## scaling range for D_q and tau(q) during OLS estimation
# minimum: 1
self.dq_scale_min = 1
# maximum
self.dq_scale_max = 512
dq_scale_ul = np.log2(self.dq_scale_max)
dq_scale_dl = np.log2(self.dq_scale_min)
## scaling range for alpha(q) during OLS estimation
# minimus: 1
self.alpha_scale_min = 1
# maximum
self.alpha_scale_max = 512
alpha_scale_ul = np.log2(self.alpha_scale_max)
alpha_scale_dl = np.log2(self.alpha_scale_min)
## scaling range for alpha(q) during OLS estimation
# minimus: 1
self.f_scale_min = 1
# maximum
self.f_scale_max = 512
f_scale_ul = np.log2(self.f_scale_max)
f_scale_dl = np.log2(self.f_scale_min)
# combined scaling range (would be distinguished by "label")
self.scale_limit = [(dq_scale_dl, dq_scale_ul), (alpha_scale_dl, alpha_scale_ul), (f_scale_dl, f_scale_ul)]
def start_to_calculate(self):
print('Begin to calculate...')
time.sleep(1)
for i in range(len(self.file_list)):
file_name = self.file_list[i]
if self.is_to_be_clipped == 1:
self.clip_box = [(self.box_ulx, self.box_uly), (self.box_lrx, self.box_lry)]
print("Cliping..." + file_name)
clipper = Clipper(file_name, self.clip_box, out_folder=os.getcwd())
clipper.extract_subset_from_file()
file_name = clipper.out_file_name
print(file_name.split('\\')[-1] + "has been clipped.")
print("Resampling..." + file_name.split('\\')[-1])
resampler = Resampler(self.export_file_folder,file_name,self.resample_size,self.resample_size)
resampler.resample_file()
resize_data = resampler.resize_data
print("Calculating..." + file_name.split('\\')[-1])
calculator = Calculator(resize_data, self.q_list, self.scale_limit, self.intercept,
self.resample_size, self.resample_size)
calculator.calculation_and_save()
prob_data_list = calculator.prob_data_list
parameters = calculator.parameters
parameters_calculation = calculator.parameters_calculation
miu_q_list_list = calculator.miu_q_list_list
data = calculator.data
if self.is_save_data == True:
print("Saving data and pic...")
file_name_without_dir = file_name.split('\\')[-1].split('.')[0]
fig_name_prefix = os.path.join(self.export_file_folder, file_name_without_dir)
save_data_and_pic = SaveDataAndPic(prob_data_list, parameters, parameters_calculation, miu_q_list_list, data, self.q_list,
os.path.join(self.export_file_folder, file_name_without_dir + '.h5'),
self.scale_limit, self.intercept, fig_name_prefix)
save_data_and_pic.save_to_hdf5()
save_data_and_pic.plot_spectrum()
print("Data and pic have been saved.")
print("The calculation is done!\n")
# Play music when finished.
winsound.PlaySound("Bongos.wav", winsound.SND_ASYNC)
if __name__ == "__main__":
gc.collect()
main = Main()
main.start_to_calculate()