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flow_core.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 16 15:15:37 2019
This is the core function for Flow-Py, it handles:
- Sorting release pixels by altitude(get_start_idx)
- Splitting function of the release layer for multiprocessing(split_release)
- Back calculation if infrastructure is hit
- Calculation of run out, etc. (Creating the cell_list and iterating through
the release pixels, erasing release pixels that were hit, stop at the border
of DEM, return arrays)
Copyright (C) <2020> <Michael Neuhauser>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import sys
import numpy as np
from datetime import datetime
import logging
from flow_class import Cell
def get_start_idx(dem, release):
"""Sort Release Pixels by altitude and return the result as lists for the
Rows and Columns, starting with the highest altitude
Input parameters:
dem Digital Elevation Model to gain information about altitude
release The release layer, release pixels need int value > 0
Output parameters:
row_list Row index of release pixels sorted by altitude
col_list Column index of release pixels sorted by altitude
"""
row_list, col_list = np.where(release > 0) # Gives back the indices of the release areas
if len(row_list) > 0:
altitude_list = []
for i in range(len(row_list)):
altitude_list.append(dem[row_list[i], col_list[i]])
altitude_list, row_list, col_list = list(zip(*sorted(zip(altitude_list, row_list, col_list), reverse=True)))
# Sort this lists by altitude
return row_list, col_list
def back_calculation(back_cell):
"""Here the back calculation from a run out pixel that hits a infrastructure
to the release pixel is performed.
Input parameters:
hit_cell_list All cells that hit a Infrastructure
Output parameters:
Back_list List of pixels that are on the way to the start cell
Maybe change it to array like DEM?
"""
#start = time.time()
#if len(hit_cell_list) > 1:
#hit_cell_list.sort(key=lambda cell: cell.altitude, reverse=False)
#print("{} Elements sorted!".format(len(hit_cell_list)))
back_list = []
for parent in back_cell.parent:
if parent not in back_list:
back_list.append(parent)
for cell in back_list:
for parent in cell.parent:
# Check if parent already in list
if parent not in back_list:
back_list.append(parent)
#end = time.time()
#print('\n Backcalculation needed: ' + str(end - start) + ' seconds')
return back_list
def calculation(optTuple):
"""This is the core function where all the data handling and calculation is
done.
Input parameters:
dem The digital elevation model
header The header of the elevation model
forest The forest layer
process Which process to calculate (Avalanche, Rockfall, SoilSlides)
release The list of release arrays
alpha
exp
flux_threshold
max_z_delta
Output parameters:
z_delta Array like DEM with the max. kinetic Energy Height for every
pixel
flux_array Array with max. concentration factor saved
count_array Array with the number of hits for every pixel
elh_sum Array with the sum of Energy Line Height
back_calc Array with back calculation, still to do!!!
"""
temp_dir = optTuple[8]
dem = np.load(temp_dir + "dem_{}_{}.npy".format(optTuple[0], optTuple[1]))
release = np.load(temp_dir + "init_{}_{}.npy".format(optTuple[0], optTuple[1]))
infra = np.load(temp_dir + "infra_{}_{}.npy".format(optTuple[0], optTuple[1]))
alpha = float(optTuple[2])
exp = float(optTuple[3])
cellsize = float(optTuple[4])
nodata = float(optTuple[5])
flux_threshold = float(optTuple[6])
max_z_delta = float(optTuple[7])
z_delta_array = np.zeros_like(dem, dtype=np.float32)
z_delta_sum = np.zeros_like(dem, dtype=np.float32)
flux_array = np.zeros_like(dem, dtype=np.float32)
count_array = np.zeros_like(dem, dtype=np.int32)
backcalc = np.zeros_like(dem, dtype=np.int32)
fp_travelangle_array = np.zeros_like(dem, dtype=np.float32) # fp = Flow Path
sl_travelangle_array = np.zeros_like(dem, dtype=np.float32) * 90 # sl = Straight Line
back_list = []
# Core
start = datetime.now().replace(microsecond=0)
row_list, col_list = get_start_idx(dem, release)
startcell_idx = 0
while startcell_idx < len(row_list):
sys.stdout.write('\r' "Calculating Startcell: " + str(startcell_idx + 1) + " of " + str(len(row_list)) + " = " + str(
round((startcell_idx + 1) / len(row_list) * 100, 2)) + "%" '\r')
sys.stdout.flush()
cell_list = []
row_idx = row_list[startcell_idx]
col_idx = col_list[startcell_idx]
dem_ng = dem[row_idx - 1:row_idx + 2, col_idx - 1:col_idx + 2] # neighbourhood DEM
if (nodata in dem_ng) or np.size(dem_ng) < 9:
startcell_idx += 1
continue
startcell = Cell(row_idx, col_idx, dem_ng, cellsize, 1, 0, None,
alpha, exp, flux_threshold, max_z_delta, startcell=True)
# If this is a startcell just give a Bool to startcell otherwise the object startcell
cell_list.append(startcell)
for idx, cell in enumerate(cell_list):
row, col, flux, z_delta = cell.calc_distribution()
if len(flux) > 0:
# mass, row, col = list(zip(*sorted(zip( mass, row, col), reverse=False)))
z_delta, flux, row, col = list(zip(*sorted(zip(z_delta, flux, row, col), reverse=False)))
# Sort this lists by elh, to start with the highest cell
for i in range(idx, len(cell_list)): # Check if Cell already exists
k = 0
while k < len(row):
if row[k] == cell_list[i].rowindex and col[k] == cell_list[i].colindex:
cell_list[i].add_os(flux[k])
cell_list[i].add_parent(cell)
if z_delta[k] > cell_list[i].z_delta:
cell_list[i].z_delta = z_delta[k]
row = np.delete(row, k)
col = np.delete(col, k)
flux = np.delete(flux, k)
z_delta = np.delete(z_delta, k)
else:
k += 1
for k in range(len(row)):
dem_ng = dem[row[k] - 1:row[k] + 2, col[k] - 1:col[k] + 2] # neighbourhood DEM
if (nodata in dem_ng) or np.size(dem_ng) < 9:
continue
cell_list.append(
Cell(row[k], col[k], dem_ng, cellsize, flux[k], z_delta[k], cell, alpha, exp, flux_threshold, max_z_delta, startcell))
z_delta_array[cell.rowindex, cell.colindex] = max(z_delta_array[cell.rowindex, cell.colindex], cell.z_delta)
flux_array[cell.rowindex, cell.colindex] = max(flux_array[cell.rowindex, cell.colindex], cell.flux)
count_array[cell.rowindex, cell.colindex] += int(1)
z_delta_sum[cell.rowindex, cell.colindex] += cell.z_delta
fp_travelangle_array[cell.rowindex, cell.colindex] = max(fp_travelangle_array[cell.rowindex, cell.colindex], cell.max_gamma)
sl_travelangle_array[cell.rowindex, cell.colindex] = max(sl_travelangle_array[cell.rowindex, cell.colindex], cell.sl_gamma)
#Backcalculation
if infra[cell.rowindex, cell.colindex] > 0:
#backlist = []
back_list = back_calculation(cell)
for back_cell in back_list:
backcalc[back_cell.rowindex, back_cell.colindex] = max(backcalc[back_cell.rowindex, back_cell.colindex],
infra[cell.rowindex, cell.colindex])
release[z_delta_array > 0] = 0
# Check if i hit a release Cell, if so set it to zero and get again the indexes of release cells
row_list, col_list = get_start_idx(dem, release)
startcell_idx += 1
end = datetime.now().replace(microsecond=0)
# Save Calculated tiles
np.save(temp_dir + "./res_z_delta_{}_{}".format(optTuple[0], optTuple[1]), z_delta_array)
np.save(temp_dir + "./res_z_delta_sum_{}_{}".format(optTuple[0], optTuple[1]), z_delta_sum)
np.save(temp_dir + "./res_flux_{}_{}".format(optTuple[0], optTuple[1]), flux_array)
np.save(temp_dir + "./res_count_{}_{}".format(optTuple[0], optTuple[1]), count_array)
np.save(temp_dir + "./res_fp_{}_{}".format(optTuple[0], optTuple[1]), fp_travelangle_array)
np.save(temp_dir + "./res_sl_{}_{}".format(optTuple[0], optTuple[1]), sl_travelangle_array)
np.save(temp_dir + "./res_backcalc_{}_{}".format(optTuple[0], optTuple[1]), backcalc)
print('\n Time needed: ' + str(end - start))
print("Finished calculation {}_{}".format(optTuple[0], optTuple[1]))
def calculation_effect(optTuple):
"""This is the core function where all the data handling and calculation is
done.
Input parameters:
dem The digital elevation model
release The list of release arrays
Output parameters:
z_delta Array like DEM with the max. Energy Line Height for every
pixel
flux_array Array with max. concentration factor saved
count_array Array with the number of hits for every pixel
z_delta_sum Array with the sum of Energy Line Height
back_calc Array with back calculation, still to do!!!
"""
temp_dir = optTuple[8]
dem = np.load(temp_dir + "dem_{}_{}.npy".format(optTuple[0], optTuple[1]))
release = np.load(temp_dir + "init_{}_{}.npy".format(optTuple[0], optTuple[1]))
alpha = float(optTuple[2])
exp = float(optTuple[3])
cellsize = float(optTuple[4])
nodata = float(optTuple[5])
flux_threshold = float(optTuple[6])
max_z_delta = float(optTuple[7])
z_delta_array = np.zeros_like(dem, dtype=np.float32)
z_delta_sum = np.zeros_like(dem, dtype=np.float32)
flux_array = np.zeros_like(dem, dtype=np.float32)
count_array = np.zeros_like(dem, dtype=np.int32)
#backcalc = np.zeros_like(dem, dtype=np.int32)
fp_travelangle_array = np.zeros_like(dem, dtype=np.float32) # fp = Flow Path
sl_travelangle_array = np.ones_like(dem, dtype=np.float32) * 90 # sl = Straight Line
# Core
start = datetime.now().replace(microsecond=0)
row_list, col_list = get_start_idx(dem, release)
startcell_idx = 0
while startcell_idx < len(row_list):
sys.stdout.write('\r' "Calculating Startcell: " + str(startcell_idx + 1) + " of " + str(len(row_list)) + " = " + str(
round((startcell_idx + 1) / len(row_list) * 100, 2)) + "%" '\r')
sys.stdout.flush()
cell_list = []
row_idx = row_list[startcell_idx]
col_idx = col_list[startcell_idx]
dem_ng = dem[row_idx - 1:row_idx + 2, col_idx - 1:col_idx + 2] # neighbourhood DEM
if (nodata in dem_ng) or np.size(dem_ng) < 9:
startcell_idx += 1
continue
startcell = Cell(row_idx, col_idx, dem_ng, cellsize, 1, 0, None,
alpha, exp, flux_threshold, max_z_delta, True)
# If this is a startcell just give a Bool to startcell otherwise the object startcell
cell_list.append(startcell)
for idx, cell in enumerate(cell_list):
row, col, flux, z_delta = cell.calc_distribution()
if len(flux) > 0:
z_delta, flux, row, col = list(zip(*sorted(zip(z_delta, flux, row, col), reverse=False))) # reverse = True == descending
for i in range(idx, len(cell_list)): # Check if Cell already exists
k = 0
while k < len(row):
if row[k] == cell_list[i].rowindex and col[k] == cell_list[i].colindex:
cell_list[i].add_os(flux[k])
cell_list[i].add_parent(cell)
if z_delta[k] > cell_list[i].z_delta:
cell_list[i].z_delta = z_delta[k]
row = np.delete(row, k)
col = np.delete(col, k)
flux = np.delete(flux, k)
z_delta = np.delete(z_delta, k)
else:
k += 1
for k in range(len(row)):
dem_ng = dem[row[k] - 1:row[k] + 2, col[k] - 1:col[k] + 2] # neighbourhood DEM
if (nodata in dem_ng) or np.size(dem_ng) < 9:
continue
cell_list.append(
Cell(row[k], col[k], dem_ng, cellsize, flux[k], z_delta[k], cell, alpha, exp, flux_threshold, max_z_delta, startcell))
for cell in cell_list:
z_delta_array[cell.rowindex, cell.colindex] = max(z_delta_array[cell.rowindex, cell.colindex], cell.z_delta)
flux_array[cell.rowindex, cell.colindex] = max(flux_array[cell.rowindex, cell.colindex],
cell.flux)
count_array[cell.rowindex, cell.colindex] += 1
z_delta_sum[cell.rowindex, cell.colindex] += cell.z_delta
fp_travelangle_array[cell.rowindex, cell.colindex] = max(fp_travelangle_array[cell.rowindex, cell.colindex],
cell.max_gamma)
sl_travelangle_array[cell.rowindex, cell.colindex] = max(sl_travelangle_array[cell.rowindex, cell.colindex],
cell.sl_gamma)
startcell_idx += 1
# Save Calculated tiles
np.save(temp_dir + "./res_z_delta_{}_{}".format(optTuple[0], optTuple[1]), z_delta_array)
np.save(temp_dir + "./res_z_delta_sum_{}_{}".format(optTuple[0], optTuple[1]), z_delta_sum)
np.save(temp_dir + "./res_flux_{}_{}".format(optTuple[0], optTuple[1]), flux_array)
np.save(temp_dir + "./res_count_{}_{}".format(optTuple[0], optTuple[1]), count_array)
np.save(temp_dir + "./res_fp_{}_{}".format(optTuple[0], optTuple[1]), fp_travelangle_array)
np.save(temp_dir + "./res_sl_{}_{}".format(optTuple[0], optTuple[1]), sl_travelangle_array)
logging.info("finished calculation {}_{}".format(optTuple[0], optTuple[1])) #ToDo!
print("Finished calculation {}_{}".format(optTuple[0], optTuple[1]))
end = datetime.now().replace(microsecond=0)
print('\n Time needed: ' + str(end - start))
#return z_delta_array, flux_array, count_array, z_delta_sum, backcalc, fp_travelangle_array, sl_travelangle_array