max/tug
1
0
Fork 0
mirror of https://git.gfz-potsdam.de/naaice/tug.git synced 2025-12-13 09:28:23 +01:00
Code Issues Packages Projects Releases Wiki Activity

added more BTCS proto stub functions

Browse Source
This commit is contained in:
philippun 2023-08-08 17:34:38 +02:00
parent ab08d5af1a
commit 04a2280311
1 changed files with 96 additions and 39 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

135
proto/BTCS.ipynb
View File

@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -68,7 +68,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -82,7 +82,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -92,88 +92,145 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# CONSTANT\n",
"\n",
"# creates the coeffiecient matrix for a given row\n", "# creates the coeffiecient matrix for a given row\n",
"def create_coeff_matrix(alpha_x, row_index, s_x, transpose=False):\n", "def create_coeff_matrix(alpha_x, row_index, s_x, transpose=False):\n",
" \n",
"\n",
" r = row_index\n",
" if (transpose):\n", " if (transpose):\n",
" alpha_x = alpha_x.transpose()\n", " alpha_x = alpha_x.transpose()\n",
"\n", "\n",
" cm = np.zeros((alpha_x.shape[0]+2,alpha_x.shape[1]+2))\n", " # this is always a square matrix -> column by column, so \n",
" cm = np.zeros((alpha_x.shape[1],alpha_x.shape[1]))\n",
"\n", "\n",
" offset = 2\n", " # top left\n",
" r = row_index\n", " cm[0,0] = 1 + s_x * (alpha_interblock(alpha_x[r,0], alpha_x[r,1]) + 2 * alpha_x[r,0]) # alpha_x[0,0]\n",
" for i in range(2, cm.shape[0]-2):\n", " cm[0,1] = -s_x * alpha_interblock(alpha_x[r, 0], alpha_x[r, 1])\n",
" j = offset\n",
" cm[i,j-1] = -s_x * alpha_interblock(alpha_x[r, j-1-1], alpha_x[r, j-1])\n",
" cm[i,j] = 1 + s_x * (alpha_interblock(alpha_x[r,j-1], alpha_x[r, j+1-1]) + alpha_interblock(alpha_x[r, j-1], alpha_x[r, j-1-1]))\n",
" cm[i,j+1] = -s_x * alpha_interblock(alpha_x[r, j-1], alpha_x[r, j+1-1])\n",
"\n", "\n",
" offset += 1\n", " # inner cells\n",
" for i_cm in range(1, cm.shape[0]-1):\n",
" j_alpha = i_cm - 1\n",
" cm[i_cm,i_cm-1] = -s_x * alpha_interblock(alpha_x[r, j_alpha-1], alpha_x[r, j_alpha])\n",
" cm[i_cm,i_cm] = 1 + s_x * (alpha_interblock(alpha_x[r,j_alpha], alpha_x[r, j_alpha+1]) + alpha_interblock(alpha_x[r, j_alpha], alpha_x[r, j_alpha-1]))\n",
" cm[i_cm,i_cm+1] = -s_x * alpha_interblock(alpha_x[r, j_alpha], alpha_x[r, j_alpha+1])\n",
"\n",
"\n",
" # bottom right\n",
" n = cm.shape[0]-1\n",
" cm[n,n-1] = s_x * alpha_interblock(alpha_x[r, n-1], alpha_x[r, n])\n",
" cm[n,n] = 1 + s_x * (-alpha_interblock(alpha_x[r, n], alpha_x[r, n-1]) + 2 * alpha_x[n,n]) # alpha_x[n,n]?\n",
"\n", "\n",
" return cm\n", " return cm\n",
"\n", "\n",
"# creates the solution vector for a given row\n", "# creates the solution vector for a given row\n",
"def create_solution_vector(concentrations, alpha_y, row_index, s_x, transpose=False):\n", "# s_x, alpha_x, row_index?\n",
" pass\n", "def create_solution_vector(concentrations, alpha_y, alpha_x, col_index, row_index, s_y, s_x, bc_left, bc_right, transpose=False):\n",
"\n",
" # \n",
" sv = np.zeros(alpha_y.shape[1])\n",
"\n",
" # inner\n",
" for i_sv in range(0, sv.shape[0]):\n",
" i_alpha = i_sv-1\n",
" sv[i_sv] = s_y * alpha_interblock(alpha_y[i_alpha+1,col_index],alpha_y[i_alpha,col_index])*concentrations[i_alpha+1,col_index] \\\n",
" + (1 - s_y *(alpha_interblock(alpha_y[i_alpha+1,col_index], alpha_y[i_alpha,col_index])) \\\n",
" + alpha_interblock(alpha_y[i_alpha,col_index], alpha_y[i_alpha-1,col_index]))*concentrations[i_alpha,col_index] \\\n",
" + s_y * alpha_interblock(alpha_y[i_alpha,col_index], alpha_y[i_alpha-1,col_index])*concentrations[i_alpha-1,col_index]\n",
"\n",
" # first\n",
" sv[0] += 2 * s_x * alpha_x[row_index,0] * bc_left # alpha_x[row_index,0]?\n",
"\n",
" # last\n",
" n = sv.shape[0]-1\n",
" sv[n] += 2 * s_x * alpha_x[row_index,n] * bc_right\n",
"\n",
" return sv\n",
"\n", "\n",
"\n", "\n",
"def solve_row(A, b):\n", "def solve_row(A, b):\n",
" # use existing implementation\n", " return np.linalg.solve(A,b)\n",
" pass" "\n",
"def calc_sweep(concentrations, alpha_x, alpha_y, s_y, s_x, bc_left, bc_right, transpose=False):\n",
" for row in range(concentrations.shape[0]):\n",
" A = create_coeff_matrix(alpha_x, row, s_x, transpose)\n",
" b = create_solution_vector(concentrations, alpha_y, alpha_x, )\n",
"\n",
"def calc_round():\n",
" calc_sweep()\n",
" calc_sweep()\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[[ 0. 0. 0. 0. 0. 0.\n", "Test A:\n",
" 0. ]\n", "[[ 2.70902018 -0.77802779 0. 0. 0. ]\n",
" [ 0. 0. 0. 0. 0. 0.\n", " [-0.34610519 2.12413297 -0.77802779 0. 0. ]\n",
" 0. ]\n", " [ 0. -0.77802779 3.74324914 -1.96522135 0. ]\n",
" [ 0. -1.28906303 3.7948142 -1.50575117 0. 0.\n", " [ 0. 0. -1.96522135 4.21016215 -1.2449408 ]\n",
" 0. ]\n", " [ 0. 0. 0. 0.43089455 5.13559523]]\n",
" [ 0. 0. -1.50575117 4.6735985 -2.16784733 0.\n", "\n",
" 0. ]\n", "TestB:\n",
" [ 0. 0. 0. -2.16784733 5.48692172 -2.31907439\n", "[2.45811954 1.70379428 2.70189936 0.92506681 4.0009449 ]\n",
" 0. ]\n", "\n",
" [ 0. 0. 0. 0. 0. 0.\n", "Result\n",
" 0. ]\n", "[1.38151174 1.65087116 1.70267339 1.21472714 0.67714168]\n"
" [ 0. 0. 0. 0. 0. 0.\n",
" 0. ]]\n"
] ]
} }
], ],
"source": [ "source": [
"result = create_coeff_matrix(np.random.random_sample((5,5)), 0, 3)\n", "row = 5\n",
"col = 5\n",
"\n",
"testConcentrations = np.random.random((row,col))\n",
"testAlpha = np.random.random((row,col))\n",
"testA = create_coeff_matrix(testAlpha, 1, 3)\n",
"print(\"Test A:\")\n",
"print(testA)\n",
"# print(test.shape[0])\n",
"testb = create_solution_vector(testConcentrations,testAlpha,testAlpha,1,1,3,3,1,1)\n",
"print(\"\\nTestB:\")\n",
"print(testb)\n",
"\n",
"# needed to solve: TODO calc boundary in functions\n",
"# testA[0,0] = 1\n",
"# testA[4,4] = 1\n",
"# testb[0] = 1\n",
"# test[4] = 1\n",
"\n",
"result = solve_row(testA, testb)\n",
"print(\"\\nResult\")\n",
"print(result)" "print(result)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2\n", "3\n"
"3\n",
"4\n"
] ]
} }
], ],
"source": [ "source": [
"for i in range(2,5):\n", "test2 = np.random.random_sample(3)\n",
" print(i)" "print(test2.shape[0])"
] ]
}, },
{ {