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flow_class.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 16 15:14:39 2019
This is the flow class
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 numpy as np
import math
class Cell:
def __init__(self, rowindex, colindex, dem_ng, cellsize, flux, z_delta, parent, alpha, exp, flux_threshold, max_z_delta, startcell):
'''This class handles the spreading over the DEM!
Depending on the process different alpha angles are used for energy dissipation.'''
self.rowindex = rowindex
self.colindex = colindex
self.altitude = dem_ng[1, 1]
self.dem_ng = dem_ng
self.cellsize = cellsize
self.tan_beta = np.zeros_like(self.dem_ng)
self.dist = np.zeros_like(self.dem_ng)
self.persistence = np.zeros_like(self.dem_ng)
self.r_t = np.zeros_like(self.dem_ng)
self.no_flow = np.ones_like(self.dem_ng)
self.flux = flux
self.z_delta = z_delta
self.alpha = float(alpha)
self.exp = int(exp)
self.max_z_delta = float(max_z_delta)
self.flux_threshold = float(flux_threshold)
self.min_distance = 0
self.max_distance = 0
self.min_gamma = 0
self.max_gamma = 0
self.sl_gamma = 0
if type(startcell) == bool: # check, if start cell exist (start cell is release point)
self.is_start = True # set is_start to True
else:
self.startcell = startcell # give startcell to cell
self.is_start = False # set is_start to False
self.parent = []
if type(parent) == Cell:
self.parent.append(parent)
def add_os(self, flux):
self.flux += flux
def add_parent(self, parent):
self.parent.append(parent)
def calc_fp_travelangle(self):
dist_min = []
dh = self.startcell.altitude - self.altitude
for parent in self.parent:
dx = abs(parent.colindex - self.colindex)
dy = abs(parent.rowindex - self.rowindex)
dist_min.append(math.sqrt(dx ** 2 + dy ** 2) * self.cellsize + parent.min_distance)
self.min_distance = np.amin(dist_min)
self.max_gamma = np.rad2deg(np.arctan(dh / self.min_distance))
def calc_sl_travelangle(self):
dx = abs(self.startcell.colindex - self.colindex)
dy = abs(self.startcell.rowindex - self.rowindex)
dh = self.startcell.altitude - self.altitude
ds = math.sqrt(dx ** 2 + dy ** 2) * self.cellsize
self.sl_gamma = np.rad2deg(np.arctan(dh / ds))
def calc_z_delta(self):
self.z_delta_neighbour = np.zeros((3, 3))
self.z_gamma = self.altitude - self.dem_ng
ds = np.array([[np.sqrt(2), 1, np.sqrt(2)], [1, 0, 1], [np.sqrt(2), 1, np.sqrt(2)]])
tan_alpha = np.tan(np.deg2rad(self.alpha))
self.z_alpha = ds * self.cellsize * tan_alpha
self.z_delta_neighbour = self.z_delta + self.z_gamma - self.z_alpha
self.z_delta_neighbour[self.z_delta_neighbour < 0] = 0
self.z_delta_neighbour[self.z_delta_neighbour > self.max_z_delta] = self.max_z_delta
def calc_tanbeta(self):
ds = np.array([[np.sqrt(2), 1, np.sqrt(2)], [1, 1, 1], [np.sqrt(2), 1, np.sqrt(2)]])
distance = ds * self.cellsize
beta = np.arctan((self.altitude - self.dem_ng) / distance) + np.deg2rad(90)
self.tan_beta = np.tan(beta/2)
self.tan_beta[self.z_delta_neighbour <= 0] = 0
self.tan_beta[self.persistence <= 0] = 0
self.tan_beta[1, 1] = 0
if abs(np.sum(self.tan_beta)) > 0:
self.r_t = self.tan_beta ** self.exp / np.sum(self.tan_beta ** self.exp)
def calc_persistence(self):
self.persistence = np.zeros_like(self.dem_ng)
if self.is_start:
self.persistence += 1
elif self.parent[0].is_start:
self.persistence += 1
else:
for parent in self.parent:
dx = (parent.colindex - self.colindex)
dy = (parent.rowindex - self.rowindex)
self.no_flow[dy + 1,dx + 1] = 0 # 3x3 Matrix of ones, every parent gets a 0, so no flow to a parent field.
maxweight = parent.z_delta
# Old Calculation
if dx == -1:
if dy == -1:
self.persistence[2, 2] += maxweight
self.persistence[2, 1] += 0.707 * maxweight
self.persistence[1, 2] += 0.707 * maxweight
if dy == 0:
self.persistence[1, 2] += maxweight
self.persistence[2, 2] += 0.707 * maxweight
self.persistence[0, 2] += 0.707 * maxweight
if dy == 1:
self.persistence[0, 2] += maxweight
self.persistence[0, 1] += 0.707 * maxweight
self.persistence[1, 2] += 0.707 * maxweight
if dx == 0:
if dy == -1:
self.persistence[2, 1] += maxweight
self.persistence[2, 0] += 0.707 * maxweight
self.persistence[2, 2] += 0.707 * maxweight
if dy == 1:
self.persistence[0, 1] += maxweight
self.persistence[0, 0] += 0.707 * maxweight
self.persistence[0, 2] += 0.707 * maxweight
if dx == 1:
if dy == -1:
self.persistence[2, 0] += maxweight
self.persistence[1, 0] += 0.707 * maxweight
self.persistence[2, 1] += 0.707 * maxweight
if dy == 0:
self.persistence[1, 0] += maxweight
self.persistence[0, 0] += 0.707 * maxweight
self.persistence[2, 0] += 0.707 * maxweight
if dy == 1:
self.persistence[0, 0] += maxweight
self.persistence[0, 1] += 0.707 * maxweight
self.persistence[1, 0] += 0.707 * maxweight
# =============================================================================
# # New Calculation:
# theta_child = np.array([[np.pi*5/4, np.pi*3/2 , np.pi*7/4], [np.pi, 0, 0], [np.pi*3/4, np.pi/2 , np.pi/4]])
# theta_parent = (np.arctan2(dy, dx))
#
# pers1 = theta_parent - theta_child - np.pi
# pers = np.zeros((3,3))
#
# for idx, element in np.ndenumerate(pers1):
# pers[idx] = max(0, np.cos(element))
# if pers[idx] < 2*np.finfo(np.float64).eps:
# pers[idx] = 0
# pers[1, 1] = 0
# self.persistence += pers * maxweight
# =============================================================================
def calc_distribution(self):
self.calc_z_delta()
self.calc_persistence()
self.persistence *= self.no_flow
self.calc_tanbeta()
#print(self.persistence)
if not self.is_start:
self.calc_fp_travelangle()
self.calc_sl_travelangle()
threshold = self.flux_threshold
if np.sum(self.r_t) > 0:
self.dist = (self.persistence * self.r_t) / np.sum(self.persistence * self.r_t) * self.flux
# This lines handle if a distribution to a neighbour cell is lower then the threshold, so we don´t lose
# flux.
# The flux of this cells will then spread equally to all neighbour cells
count = ((0 < self.dist) & (self.dist < threshold)).sum()
mass_to_distribute = np.sum(self.dist[self.dist < threshold])
'''Checking if flux is distributed to a field that isn't taking in account, when then distribute it equally to
the other fields'''
if mass_to_distribute > 0 and count > 0:
self.dist[self.dist > threshold] += mass_to_distribute / count
self.dist[self.dist < threshold] = 0
if np.sum(self.dist) < self.flux and count > 0:
self.dist[self.dist > threshold] += (self.flux - np.sum(self.dist))/count
row_local, col_local = np.where(self.dist > threshold)
return self.rowindex - 1 + row_local, self.colindex - 1 + col_local, self.dist[row_local, col_local], self.z_delta_neighbour[row_local, col_local]