From a7bb5b4d41771e798870e63000348eceadd5899a Mon Sep 17 00:00:00 2001 From: philippun Date: Wed, 9 Aug 2023 15:48:05 +0200 Subject: [PATCH] working BTCS prototype --- proto/BTCS.ipynb | 320422 +++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 320329 insertions(+), 93 deletions(-) diff --git a/proto/BTCS.ipynb b/proto/BTCS.ipynb index 85e3bb2..1843d9c 100644 --- a/proto/BTCS.ipynb +++ b/proto/BTCS.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -94,21 +94,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ - "# CONSTANT\n", + "################\n", + "### CONSTANT ###\n", + "################\n", "\n", "# creates the coeffiecient matrix for a given row\n", - "def create_coeff_matrix(alpha_x, row_index, s_x, transpose=False):\n", - " \n", - "\n", + "def create_coeff_matrix(alpha_x, row_index, s_x):\n", + " # alias\n", " r = row_index\n", - " if (transpose):\n", - " alpha_x = alpha_x.transpose()\n", "\n", - " # this is always a square matrix -> column by column, so \n", + " # a square matrix of column^2 dimension for the coefficients\n", " cm = np.zeros((alpha_x.shape[1],alpha_x.shape[1]))\n", "\n", " # top left\n", @@ -117,45 +116,76 @@ "\n", " # inner cells\n", " for i_cm in range(1, cm.shape[0]-1):\n", - " j_alpha = i_cm - 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", + " + 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", + " cm[n,n-1] = -s_x * alpha_interblock(alpha_x[r, n-1], alpha_x[r, n]) ## changed to - !!!\n", + " cm[n,n] = 1 + s_x * (alpha_interblock(alpha_x[r, n], alpha_x[r, n-1]) + 2 * alpha_x[r,n]) # alpha_x[n,n]? -> changed to [r,n]!!! and changed to +alpha_interblock\n", "\n", + " print(cm)\n", " return cm\n", "\n", "# creates the solution vector for a given row\n", "# s_x, alpha_x, row_index?\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", + "def create_solution_vector(concentrations, alpha_x, alpha_y, row_index, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom):\n", "\n", - " # \n", - " sv = np.zeros(alpha_y.shape[1])\n", + " sv = np.zeros(concentrations.shape[1])\n", "\n", " # inner\n", - " # TODO: iterate over columns of alpha_y alpha_y[i_sv, row_index+ -1 .. 0 .. 1]\n", - " # TODO: error with current indices: i_sv starts from 0 --> i_alpha = -1 and i_alpha-1 = -2\n", - " for i_sv in range(0, sv.shape[0]):\n", - " print(i_sv)\n", - " i_alpha = i_sv-1\n", - " print(i_alpha)\n", - " print(concentrations[i_alpha-1,col_index])\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", + " # TODO: iterate over columns of alpha_y alpha_y[i, row_index+ -1 .. 0 .. 1]\n", + " # TODO: error with current indices: i starts from 0 --> i = -1 and i-1 = -2\n", "\n", - " # first\n", - " sv[0] += 2 * s_x * alpha_x[row_index,0] * bc_left # alpha_x[row_index,0]?\n", + " # inner rows\n", + " if row_index > 0 and row_index < sv.shape[0]-1:\n", + " for i in range(0, sv.shape[0]):\n", + " sv[i] = s_y * alpha_interblock(alpha_y[row_index+1,i],alpha_y[row_index,i]) * concentrations[row_index+1,i] \\\n", + " + (\n", + " 1 - s_y * (\n", + " alpha_interblock(alpha_y[row_index+1,i], alpha_y[row_index,i]) \\\n", + " + alpha_interblock(alpha_y[row_index,i], alpha_y[row_index-1,i])\n", + " )\n", + " ) * concentrations[row_index,i] \\\n", + " + s_y * alpha_interblock(alpha_y[row_index,i], alpha_y[row_index-1,i]) * concentrations[row_index-1,i]\n", "\n", - " # last\n", + " # first row\n", + " if row_index == 0:\n", + " for i in range(0, sv.shape[0]):\n", + " sv[i] = s_y * alpha_interblock(alpha_y[row_index+1,i],alpha_y[row_index,i]) * concentrations[row_index+1,i] \\\n", + " + (\n", + " 1 - s_y * (\n", + " alpha_interblock(alpha_y[row_index+1,i], alpha_y[row_index,i]) \\\n", + " + alpha_y[row_index,i]\n", + " )\n", + " ) * concentrations[row_index,i] \\\n", + " + s_y * alpha_y[row_index,i] * bc_top\n", + "\n", + "\n", + " # last row\n", + " if row_index == sv.shape[0]-1:\n", + " for i in range(0, sv.shape[0]):\n", + " i = i\n", + " sv[i] = s_y * alpha_y[row_index,i]*bc_bottom \\\n", + " + (1 - s_y *(alpha_y[row_index,i] \\\n", + " + alpha_interblock(alpha_y[row_index,i], alpha_y[row_index-1,i])))*concentrations[row_index,i] \\\n", + " + s_y * alpha_interblock(alpha_y[row_index,i], alpha_y[row_index-1,i])*concentrations[row_index-1,i]\n", + "\n", + "\n", + " # first column (additional fixed concentration change from perpendicular dimension)\n", + " i = 0\n", + " sv[0] += s_x * alpha_x[row_index,0] * bc_left # alpha_x[row_index,0]?\n", + " # sv[0] += 2 * s_x * alpha_x[row_index,0] * bc_left # alpha_x[row_index,0]?\n", + "\n", + " # last column (additional fixed concentration change from perpendicular direction)\n", " n = sv.shape[0]-1\n", - " sv[n] += 2 * s_x * alpha_x[row_index,n] * bc_right\n", + " sv[n] += s_x * alpha_x[row_index,n] * bc_right\n", + " # sv[n] += 2 * s_x * alpha_x[row_index,n] * bc_right\n", + "\n", "\n", " return sv\n", "\n", @@ -163,74 +193,320254 @@ "def solve_row(A, b):\n", " return np.linalg.solve(A,b)\n", "\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", + "def calc_sweep(concentrations, alpha_x, alpha_y, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom):\n", "\n", - "def calc_round():\n", - " calc_sweep()\n", - " calc_sweep()\n" + " concentrations_t1 = np.zeros((concentrations.shape[0], concentrations.shape[1]))\n", + "\n", + " for i in range(concentrations.shape[0]):\n", + " A = create_coeff_matrix(alpha_x, i, s_x)\n", + " # print(i)\n", + " b = create_solution_vector(concentrations, alpha_x, alpha_y, i, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom)\n", + " # print(\"C t1\")\n", + " # print(concentrations_t1[i,:])\n", + " # print(\"C result row\")\n", + " # print(solve_row(A,b))\n", + " concentrations_t1[i,:] = solve_row(A,b)\n", + "\n", + " return concentrations_t1\n", + "\n", + "def calc_round(concentrations, alpha_x, alpha_y, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom):\n", + " concentrations = calc_sweep(concentrations, alpha_x, alpha_y, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom)\n", + " # concentrations = calc_sweep(concentrations, alpha_x, alpha_y, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom, True)\n", + " concentrations = calc_sweep(concentrations.transpose(), alpha_y.transpose(), alpha_x.transpose(), s_y, s_x, bc_top, bc_bottom, bc_left, bc_right).transpose()\n", + " \n", + " return concentrations\n", + "\n", + "\n", + "def sim(concentrations, alpha_x, alpha_y, iterations, delta_t, delta_x, delta_y, bc_left, bc_right, bc_top, bc_bottom):\n", + " s_x = sub(delta_t, delta_x)\n", + " s_y = sub(delta_t, delta_y)\n", + " for i in range(iterations):\n", + " concentrations = calc_round(concentrations, alpha_x, alpha_y, s_x, s_y, bc_left, bc_right, bc_top, bc_bottom)\n", + "\n", + " return concentrations\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 170, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.56726545 0.41188268 0.11335637 0.29221913 0.5365537 ]\n", - " [0.8120275 0.96249867 0.18743648 0.30176834 0.45374814]\n", - " [0.17014599 0.26796332 0.4616269 0.23477139 0.71348435]\n", - " [0.00654629 0.61879048 0.22742861 0.25794781 0.91561469]\n", - " [0.04155862 0.46501565 0.8463777 0.81575064 0.1985415 ]]\n", - "Test A:\n", - "[[ 7.77257437 -1.49738434 0. 0. 0. ]\n", - " [-2.78866756 5.2860519 -1.49738434 0. 0. ]\n", - " [ 0. -1.49738434 2.94508723 -0.44770289 0. ]\n", - " [ 0. 0. -0.44770289 1.90753379 -0.4598309 ]\n", - " [ 0. 0. 0. 1.69958064 4.50889084]]\n", - "0\n", - "-1\n", - "0.618790482597486\n", - "1\n", - "0\n", - "0.4650156531117613\n", - "2\n", - "1\n", - "0.41188268496900426\n", - "3\n", - "2\n", - "0.9624986706480608\n", - "4\n", - "3\n", - "0.26796331918576755\n", + "Test Concentrations t=0:\n", + "[[2000. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "\n", - "TestB:\n", - "[6.33240747 1.20531698 0.62203484 1.97054078 6.92562211]\n", + "Test Alpha:\n", + "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 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0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 -0.05 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 -0.05 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 -0.05 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 -0.05 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", + "[[ 1.15 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [-0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1 -0.05\n", + " 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05 1.1\n", + " -0.05 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0.05\n", + " 1.1 -0.05 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " -0.05 1.1 -0.05 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. -0.05 1.1 -0.05 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. -0.05 1.1 -0.05 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. -0.05 1.1 -0.05 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. -0.05 1.1 -0.05 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. -0.05 1.1 -0.05]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. -0.05 1.15]]\n", "\n", - "Result\n", - "[1.01442248 1.03665212 0.96541206 1.49411867 0.97279954]\n" + "Test Concentrations t=t+200:\n", + "[[4.21083073e-03 9.95242560e-03 1.50613714e-02 1.91002607e-02\n", + " 2.18607952e-02 2.32761252e-02 2.34141813e-02 2.24526730e-02\n", + " 2.06421925e-02 1.82651682e-02 1.55979056e-02 1.28810514e-02\n", + " 1.03012003e-02 7.98376821e-03 5.99524143e-03 4.35177115e-03\n", + " 3.03083742e-03 1.98315903e-03 1.14289671e-03 4.46079141e-04]\n", + " [1.02012362e-02 2.41109299e-02 3.64879563e-02 4.62726441e-02\n", + " 5.29603660e-02 5.63891706e-02 5.67236278e-02 5.43942599e-02\n", + " 5.00081565e-02 4.42495335e-02 3.77877739e-02 3.12058728e-02\n", + " 2.49558778e-02 1.93416242e-02 1.45241825e-02 1.05426811e-02\n", + " 7.34256267e-03 4.80443766e-03 2.76880267e-03 1.08067956e-03]\n", + " [1.54379057e-02 3.64879563e-02 5.52185651e-02 7.00260927e-02\n", + " 8.01468681e-02 8.53358040e-02 8.58419503e-02 8.23168323e-02\n", + " 7.56791808e-02 6.69644451e-02 5.71856269e-02 4.72249940e-02\n", + " 3.77666470e-02 2.92703907e-02 2.19799791e-02 1.59546267e-02\n", + " 1.11117699e-02 7.27073206e-03 4.19013082e-03 1.63543209e-03]\n", + " [1.95777672e-02 4.62726441e-02 7.00260927e-02 8.88044383e-02\n", + " 1.01639222e-01 1.08219634e-01 1.08861510e-01 1.04391089e-01\n", + " 9.59734706e-02 8.49217729e-02 7.25206460e-02 5.98889487e-02\n", + " 4.78942312e-02 3.71196007e-02 2.78741770e-02 2.02330533e-02\n", + " 1.40915257e-02 9.22046700e-03 5.31376519e-03 2.07399303e-03]\n", + " [2.24073151e-02 5.29603660e-02 8.01468681e-02 1.01639222e-01\n", + " 1.16329000e-01 1.23860470e-01 1.24595115e-01 1.19478590e-01\n", + " 1.09844385e-01 9.71954005e-02 8.30019557e-02 6.85446165e-02\n", + " 5.48163189e-02 4.24844458e-02 3.19027937e-02 2.31573088e-02\n", + " 1.61281546e-02 1.05530885e-02 6.08175641e-03 2.37374440e-03]\n", + " [2.38580284e-02 5.63891706e-02 8.53358040e-02 1.08219634e-01\n", + " 1.23860470e-01 1.31879549e-01 1.32661757e-01 1.27213973e-01\n", + " 1.16956023e-01 1.03488107e-01 8.83757381e-02 7.29823897e-02\n", + " 5.83652832e-02 4.52350096e-02 3.39682712e-02 2.46565788e-02\n", + " 1.71723372e-02 1.12363254e-02 6.47550660e-03 2.52742736e-03]\n", + " [2.39995358e-02 5.67236278e-02 8.58419503e-02 1.08861510e-01\n", + " 1.24595115e-01 1.32661757e-01 1.33448604e-01 1.27968509e-01\n", + " 1.17649716e-01 1.04101919e-01 8.88999150e-02 7.34152651e-02\n", + " 5.87114611e-02 4.55033088e-02 3.41697448e-02 2.48028226e-02\n", + " 1.72741903e-02 1.13029707e-02 6.51391432e-03 2.54241812e-03]\n", + " [2.30139898e-02 5.43942599e-02 8.23168323e-02 1.04391089e-01\n", + " 1.19478590e-01 1.27213973e-01 1.27968509e-01 1.22713455e-01\n", + " 1.12818405e-01 9.98269512e-02 8.52492210e-02 7.04004515e-02\n", + " 5.63004624e-02 4.36347058e-02 3.27665569e-02 2.37842894e-02\n", + " 1.65648220e-02 1.08388117e-02 6.24641903e-03 2.43801317e-03]\n", + " [2.11582473e-02 5.00081565e-02 7.56791808e-02 9.59734706e-02\n", + " 1.09844385e-01 1.16956023e-01 1.17649716e-01 1.12818405e-01\n", + " 1.03721247e-01 9.17773641e-02 7.83751152e-02 6.47236823e-02\n", + " 5.17606516e-02 4.01162034e-02 3.01244122e-02 2.18664335e-02\n", + " 1.52291108e-02 9.96481973e-03 5.74273648e-03 2.24142298e-03]\n", + " [1.87217974e-02 4.42495335e-02 6.69644451e-02 8.49217729e-02\n", + " 9.71954005e-02 1.03488107e-01 1.04101919e-01 9.98269512e-02\n", + " 9.17773641e-02 8.12088634e-02 6.93499327e-02 5.72705125e-02\n", + " 4.58002224e-02 3.54966752e-02 2.66554755e-02 1.93484333e-02\n", + " 1.34754227e-02 8.81733412e-03 5.08143927e-03 1.98331489e-03]\n", + " [1.59878532e-02 3.77877739e-02 5.71856269e-02 7.25206460e-02\n", + " 8.30019557e-02 8.83757381e-02 8.88999150e-02 8.52492210e-02\n", + " 7.83751152e-02 6.93499327e-02 5.92227617e-02 4.89072993e-02\n", + " 3.91120157e-02 3.03130955e-02 2.27629763e-02 1.65229815e-02\n", + " 1.15076067e-02 7.52973878e-03 4.33939667e-03 1.69369141e-03]\n", + " [1.32030777e-02 3.12058728e-02 4.72249940e-02 5.98889487e-02\n", + " 6.85446165e-02 7.29823897e-02 7.34152651e-02 7.04004515e-02\n", + " 6.47236823e-02 5.72705125e-02 4.89072993e-02 4.03885914e-02\n", + " 3.22994572e-02 2.50331392e-02 1.87981050e-02 1.36449969e-02\n", + " 9.50320362e-03 6.21820357e-03 3.58355750e-03 1.39868304e-03]\n", + " [1.05587303e-02 2.49558778e-02 3.77666470e-02 4.78942312e-02\n", + " 5.48163189e-02 5.83652832e-02 5.87114611e-02 5.63004624e-02\n", + " 5.17606516e-02 4.58002224e-02 3.91120157e-02 3.22994572e-02\n", + " 2.58304363e-02 2.00194357e-02 1.50331707e-02 1.09121408e-02\n", + " 7.59987680e-03 4.97280527e-03 2.86583310e-03 1.11855110e-03]\n", + " [8.18336241e-03 1.93416242e-02 2.92703907e-02 3.71196007e-02\n", + " 4.24844458e-02 4.52350096e-02 4.55033088e-02 4.36347058e-02\n", + " 4.01162034e-02 3.54966752e-02 3.03130955e-02 2.50331392e-02\n", + " 2.00194357e-02 1.55157195e-02 1.16512006e-02 8.45726716e-03\n", + " 5.89015389e-03 3.85408725e-03 2.22111469e-03 8.66913801e-04]\n", + " [6.14512246e-03 1.45241825e-02 2.19799791e-02 2.78741770e-02\n", + " 3.19027937e-02 3.39682712e-02 3.41697448e-02 3.27665569e-02\n", + " 3.01244122e-02 2.66554755e-02 2.27629763e-02 1.87981050e-02\n", + " 1.50331707e-02 1.16512006e-02 8.74922198e-03 6.35080542e-03\n", + " 4.42308616e-03 2.89414509e-03 1.66789897e-03 6.50990535e-04]\n", + " [4.46056543e-03 1.05426811e-02 1.59546267e-02 2.02330533e-02\n", + " 2.31573088e-02 2.46565788e-02 2.48028226e-02 2.37842894e-02\n", + " 2.18664335e-02 1.93484333e-02 1.65229815e-02 1.36449969e-02\n", + " 1.09121408e-02 8.45726716e-03 6.35080542e-03 4.60986470e-03\n", + " 3.21058942e-03 2.10077563e-03 1.21067929e-03 4.72535070e-04]\n", + " [3.10660835e-03 7.34256267e-03 1.11117699e-02 1.40915257e-02\n", + " 1.61281546e-02 1.71723372e-02 1.72741903e-02 1.65648220e-02\n", + " 1.52291108e-02 1.34754227e-02 1.15076067e-02 9.50320362e-03\n", + " 7.59987680e-03 5.89015389e-03 4.42308616e-03 3.21058942e-03\n", + " 2.23604924e-03 1.46310759e-03 8.43190500e-04 3.29102088e-04]\n", + " [2.03273800e-03 4.80443766e-03 7.27073206e-03 9.22046700e-03\n", + " 1.05530885e-02 1.12363254e-02 1.13029707e-02 1.08388117e-02\n", + " 9.96481973e-03 8.81733412e-03 7.52973878e-03 6.21820357e-03\n", + " 4.97280527e-03 3.85408725e-03 2.89414509e-03 2.10077563e-03\n", + " 1.46310759e-03 9.57350932e-04 5.51722386e-04 2.15340412e-04]\n", + " [1.17146913e-03 2.76880267e-03 4.19013082e-03 5.31376519e-03\n", + " 6.08175641e-03 6.47550660e-03 6.51391432e-03 6.24641903e-03\n", + " 5.74273648e-03 5.08143927e-03 4.33939667e-03 3.58355750e-03\n", + " 2.86583310e-03 2.22111469e-03 1.66789897e-03 1.21067929e-03\n", + " 8.43190500e-04 5.51722386e-04 3.17958213e-04 1.24100914e-04]\n", + " [4.46079141e-04 1.05432152e-03 1.59554350e-03 2.02340783e-03\n", + " 2.31584820e-03 2.46578279e-03 2.48040792e-03 2.37854944e-03\n", + " 2.18675413e-03 1.93494136e-03 1.65238186e-03 1.36456882e-03\n", + " 1.09126936e-03 8.45769562e-04 6.35112717e-04 4.61009825e-04\n", + " 3.21075208e-04 2.10088206e-04 1.21074063e-04 4.72559010e-05]]\n" ] } ], "source": [ - "row = 5\n", - "col = 5\n", + "row = 20\n", + "col = 20\n", + "domain_row = row\n", + "domain_col = col\n", + "iterations = 200\n", + "delta_t = 0.1\n", + "delta_x = domain_col / col\n", + "delta_y = domain_row / row\n", + "bc_left = 0\n", + "bc_right = 0\n", + "bc_top = 0\n", + "bc_bottom = 0\n", "\n", - "testConcentrations = np.random.random((row,col))\n", + "\n", + "testConcentrations = np.zeros((row,col))\n", + "testConcentrations[0,0] = 2000\n", + "print(\"Test Concentrations t=0:\")\n", "print(testConcentrations)\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", + "# seed = np.random.seed(42)\n", + "# testAlpha = np.random.random((row,col)) * 10\n", + "testAlpha= np.ones((row,col))\n", + "print(\"\\nTest Alpha:\")\n", + "print(testAlpha)\n", + "\n", + "testConcentrations = sim(testConcentrations, testAlpha, testAlpha, iterations, delta_t, delta_x, delta_y, bc_left, bc_right, bc_top, bc_bottom)\n", + "print(\"\\nTest Concentrations t=t+\" + str(iterations) + \":\")\n", + "print(testConcentrations)\n", + "\n", + "\n", + "# testA = create_coeff_matrix(testAlpha, 1, 3)\n", + "# print(\"\\nTest A:\")\n", + "# print(testA)\n", + "\n", + "# testb = create_solution_vector(testConcentrations,testAlpha,testAlpha,1,1,3,3,1,1)\n", + "# print(\"\\nTest b:\")\n", + "# print(testb)\n", "\n", "# needed to solve: TODO calc boundary in functions\n", "# testA[0,0] = 1\n", @@ -238,27 +320448,53 @@ "# testb[0] = 1\n", "# test[4] = 1\n", "\n", - "result = solve_row(testA, testb)\n", - "print(\"\\nResult\")\n", - "print(result)" + "# result = solve_row(testA, testb)\n", + "# print(\"\\nResult x:\")\n", + "# print(result)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 171, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n" - ] + "data": { + "image/png": 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I27a1tbWYOXMmhgwZAp1Oh6ysLJftioqKMHz4cBgMBgwfPhy7d+/2OC8WAkREpCktKwt6u3li586dyMrKwtKlS1FRUYGUlBSkpaWhurraZfumpiZERERg6dKlGDVqlMs25eXlSE9PR0ZGBj755BNkZGRgxowZ+PDDDz3KjYUAERGRj61ZswZz5szB3LlzMWzYMOTl5SE2NhYbNmxw2X7gwIF49dVXMWvWLISHh7tsk5eXh/vvvx85OTkYOnQocnJycO+99yIvL8+j3AJusqDQ6yD0Xt5nOkRSMgC6dW+SEie+2wUpcQBgcMh5KXGGBOulxAGAIMg56aG6eilxAOCS4x9S4tR06y0lDgBUd+slJc63hm5S4gCAI0TO+8ARLCXMd7HkXOPt7d8SFZ2kWIrE69cVbX4XlDlZ0GKxqPYbDAYYDAbVPqvViuPHj2PJkiWq/ampqTh69OgN51BeXo5Fixap9k2aNMnjQkCb7wIiItIsBxTnMsM3vH03RyA2Nhbh4eHOLTc3t9W/V1dXB7vdjsjISNX+yMhImM3mG34dZrNZSsyA6xEgIiLqLDU1NQgLC3M+vr434FrKdb05QohW+zwlIyYLASIi0hRxg7P+r48BAGFhYapCwJW+fftCr9e3+qZ+/vz5Vt/oPREVFSUlJocGiIhIU7weFvDw7oUhISEwmUwoKSlR7S8pKcHYsWNv+HUkJye3inngwAGPY7JHgIiINKUrVhbMzs5GRkYGkpKSkJycjE2bNqG6uhqZmZkAgJycHJw9exZbt251HlNZWQkAaGhowD//+U9UVlYiJCQEw4cPBwAsXLgQ48ePxwsvvICpU6di7969OHjwIN577z2PcpPeI7By5UooiqLaoqKi2jymrKwMJpMJRqMRgwYNwsaNG2WnRURE1GXS09ORl5eHVatW4fbbb8fhw4dRXFyM+Ph4AFcXELp+TYHExEQkJibi+PHjeOutt5CYmIjJkyc7nx87dix27NiBLVu24LbbbkNhYSF27tyJ0aNHe5SbT3oEbr31Vhw8eND5WK93f3lRVVUVJk+ejHnz5mHbtm14//33MX/+fERERGDatGm+SI+IiDTM0659dzE8NX/+fMyfP9/lc4WFha32CdH+nY2mT5+O6dOne5zLtXxSCAQFBbXbC9Bi48aNiIuLc173OGzYMBw7dgyrV69mIUBERNLd6BLB18cIFD6ZLHj69GnExMQgISEBjzzyCL766iu3bcvLy5GamqraN2nSJBw7dgw2m83tcU1NTbBYLKqNiIiIPCO9EBg9ejS2bt2Kd955B6+//jrMZjPGjh2LCxdcr4znbkGE5uZm1NXVuf13cnNzVYs4xMbGSn0dREQUmDr7qgF/J70QSEtLw7Rp0zBy5Ejcd9992L9/PwDgjTfecHuMqwURXO2/Vk5ODurr651bTU2NhOyJiCjQsRBQ8/nlgz169MDIkSNx+vRpl8+7WxAhKCgIffr0cRvX1XrORERE5BmfLyjU1NSEkydPIjo62uXz7hZESEpKQnCwxLuQEBERgT0C15NeCCxevBhlZWWoqqrChx9+iOnTp8NisWD27NkArnbpz5o1y9k+MzMTZ86cQXZ2Nk6ePInNmzejoKAAixcvlp0aERERC4HrSB8a+L//+z88+uijqKurQ0REBMaMGYMPPvjA7aIJCQkJKC4uxqJFi7B+/XrExMRg7dq1vHSQiIioE0gvBHbs2NHm864WTZgwYQJOnDghOxUiIqJWBLxfB6D9pX6+P3ivASIi0pSuWlnQX7EQICIiTWEhoMZCwMdkvVV0EjuiguGQEicIIVLiAIBekTNv1f1dLTynk3SedIq8n520WDL/hkmKFUB/V10SbayL4okAP03UBVgIEBGRprBHQI2FABERaQoLATWfLyhERERE/os9AkREpClCKBBefqP39nh/wkKAiIg0xQHF63UEvD3en3BogIiISMPYI0BERJrCyYJqLASIiEhTOEdAjUMDREREGsYeASIi0hQODaixECAiIk3h0IAaCwEiItIUIaFHIJAKAc4RICIi0jD2CBARkaYIAMLLG3nKu6do12MhQEREmuKAAoUrCzpxaICIiEjD2CNARESawqsG1AKvEBDC68EfxS4pFwBNVjmn+IKth5Q4AHDOHiolTqiuXkocANBLinPOHiIpEmBu7iklzjfW7lLiAMAVa7CcQM3y/ogpNjlxdM1y4gCAYpczgqs45I0EKw6HnEDeDm6rYknK6XvGIRQoXEfAiUMDREREGhZ4PQJERERtkNBxLLVjpquxECAiIk3hHAE1Dg0QERFpGHsEiIhIU9gjoMZCgIiINIVXDaixECAiIk3hZEE1zhEgIiLSMPYIEBGRplztEfB2joCkZPwACwEiItIUThZU49AAERGRhrFHgIiINEV8t3kbI1CwECAiIk3h0IAahwaIiIg0THohMHDgQCiK0mpbsGCBy/alpaUu2//tb3+TnRoREdG/xwa83QKE9ELg448/Rm1trXMrKSkBAPzkJz9p87hTp06pjhs8eLDs1IiIiIDvhga82XADQwP5+flISEiA0WiEyWTCkSNH2mxfVlYGk8kEo9GIQYMGYePGja3a5OXlYciQIejWrRtiY2OxaNEiNDY2epSX9DkCERERqsfPP/88br75ZkyYMKHN4/r164eePXvKToeIiEilK1YW3LlzJ7KyspCfn49x48bh97//PdLS0vD5558jLi6uVfuqqipMnjwZ8+bNw7Zt2/D+++9j/vz5iIiIwLRp0wAAb775JpYsWYLNmzdj7Nix+OKLL/DYY48BAF555ZUO5+bTOQJWqxXbtm3D448/DkVpu3pKTExEdHQ07r33Xhw6dMiXaREREXWqNWvWYM6cOZg7dy6GDRuGvLw8xMbGYsOGDS7bb9y4EXFxccjLy8OwYcMwd+5cPP7441i9erWzTXl5OcaNG4eZM2di4MCBSE1NxaOPPopjx455lJtPrxrYs2cPLl686KxQXImOjsamTZtgMpnQ1NSE//zP/8S9996L0tJSjB8/3u1xTU1NaGpqcj62WCwAAF2zAzrh8CrvoG+9OlzlyuUQKXH+3hDRfqMOOmEYKCXOJcc/pMQBAB28+5m1MDf3lBIHAP73cn8pcWoaekmJAwCXG4xS4ugv66XEAQB9U/ttOjMOAOiscgZwFZtdShwAgF3Oe1zqknaOABro9oDMqwZaPntaGAwGGAwG1T6r1Yrjx49jyZIlqv2pqak4evSoy/jl5eVITU1V7Zs0aRIKCgpgs9kQHByMu+++G9u2bcNHH32Eu+66C1999RWKi4sxe/Zsj16LTwuBgoICpKWlISYmxm2bIUOGYMiQIc7HycnJqKmpwerVq9ssBHJzc/Hss89KzZeIiDTgBsf4W8UAEBsbq9q9YsUKrFy5UrWvrq4OdrsdkZGRqv2RkZEwm80uw5vNZpftm5ubUVdXh+joaDzyyCP45z//ibvvvhtCCDQ3N+OJJ55oVXC0x2eFwJkzZ3Dw4EHs2rXL42PHjBmDbdu2tdkmJycH2dnZzscWi6XVD4SIiMiXampqEBYW5nx8fW/Ata4fIhdCtDls7qr9tftLS0vxu9/9Dvn5+Rg9ejS+/PJLLFy4ENHR0Vi+fHmHX4PPCoEtW7agX79++OEPf+jxsRUVFYiOjm6zjavuFyIiovbInCwYFhamKgRc6du3L/R6fatv/+fPn2/1rb9FVFSUy/ZBQUHo06cPAGD58uXIyMjA3LlzAQAjR47E5cuX8Ytf/AJLly6FTtexaYA+KQQcDge2bNmC2bNnIyhI/U/k5OTg7Nmz2Lp1K4Crlz4MHDgQt956q3NyYVFREYqKinyRGhERaV0nrzEcEhICk8mEkpIS/PjHP3buLykpwdSpU10ek5ycjL/85S+qfQcOHEBSUhKCg4MBAFeuXGn1Ya/X6yGEcPYedIRPCoGDBw+iuroajz/+eKvnamtrUV1d7XxstVqxePFinD17Ft26dcOtt96K/fv3Y/Lkyb5IjYiIqNNlZ2cjIyMDSUlJSE5OxqZNm1BdXY3MzEwArb8kZ2ZmYt26dcjOzsa8efNQXl6OgoICbN++3RlzypQpWLNmDRITE51DA8uXL8eDDz4Ivb7jE4J9Ugikpqa6rUYKCwtVj5966ik89dRTvkiDiIiola6410B6ejouXLiAVatWoba2FiNGjEBxcTHi4+MBtP6SnJCQgOLiYixatAjr169HTEwM1q5d61xDAACWLVsGRVGwbNkynD17FhEREZgyZQp+97vfeZQbbzpERETa0wVXTs6fPx/z5893+dz1X5IBYMKECThx4oTbeEFBQVixYgVWrFjhVV686RAREZGGsUeAiIg0hbchVmMhQERE2tLJVw34OxYCRESkMcp3m7cxAgPnCBAREWkYewSIiEhbODSgwkKAiIi0hYWACocGiIiINIw9AkREpC0Sb0McCFgIEBGRpsi8+2AgCLhCQLHaoeibvYoRfFneT1h3MVhKnL9/00dKHAAI0tmlxKnp1ltKHADQKXLO+TfW7lLiAEBNQy8pcc5eCJcSBwDEv0KkxDFY5H2bCW6Q87MLanRIiQMA+iY5sRSbvJwUm3d/l1qIZjlxAHh0h7q2A0k4TzJi0A0JuEKAiIioTZwsqMJCgIiItIVzBFR41QAREZGGsUeAiIg0RRFXN29jBAoWAkREpC2cI6DCQoCIiLSFcwRUOEeAiIhIw9gjQERE2sKhARUWAkREpC0sBFQ4NEBERKRh7BEgIiJtYY+ACgsBIiLSFl41oMKhASIiIg1jjwAREWkKVxZUYyFARETawjkCKhwaICIi0jAWAkRERBrGoQEiItIUBRLmCEjJxD8EXCGgWJuh6PVexQhukDf4Y/hGTqdLQ/ceUuIAwOf2KClxqrv1khIHAHSSZt5csQZLiQMAlxuMUuKIf4VIiQMAId949952xqmXEuZqrEtyfnbBlx1S4gCA/ttmKXF0TTYpcQAANjk5wW6XEwcAHPLO+fcKLx9U4dAAERGRhgVcjwAREVGbeNWACgsBIiLSFhYCKhwaICIi0jD2CBARkaZwZUE1j3sEDh8+jClTpiAmJgaKomDPnj2q54UQWLlyJWJiYtCtWzdMnDgRn332Wbtxi4qKMHz4cBgMBgwfPhy7d+/2NDUiIqL2CUlbgPC4ELh8+TJGjRqFdevWuXz+xRdfxJo1a7Bu3Tp8/PHHiIqKwv33349Lly65jVleXo709HRkZGTgk08+QUZGBmbMmIEPP/zQ0/SIiIjIAx4PDaSlpSEtLc3lc0II5OXlYenSpXj44YcBAG+88QYiIyPx1ltv4Ze//KXL4/Ly8nD//fcjJycHAJCTk4OysjLk5eVh+/btnqZIRETkHicLqkidLFhVVQWz2YzU1FTnPoPBgAkTJuDo0aNujysvL1cdAwCTJk1q85impiZYLBbVRkRE1J6WOQLeboFCaiFgNpsBAJGRkar9kZGRzufcHefpMbm5uQgPD3dusbGxXmRORESkTT65fFBR1EsvCiFa7fP2mJycHNTX1zu3mpqaG0+YiIi0o2WJYW+3ACH18sGoqKtr2JvNZkRHRzv3nz9/vtU3/uuPu/7bf3vHGAwGGAwGLzMmIiLN4RwBFak9AgkJCYiKikJJSYlzn9VqRVlZGcaOHev2uOTkZNUxAHDgwIE2jyEiIroRnCOg5nGPQENDA7788kvn46qqKlRWVqJ3796Ii4tDVlYWnnvuOQwePBiDBw/Gc889h+7du2PmzJnOY2bNmoX+/fsjNzcXALBw4UKMHz8eL7zwAqZOnYq9e/fi4MGDeO+99yS8RCIiInLH4x6BY8eOITExEYmJiQCA7OxsJCYm4j/+4z8AAE899RSysrIwf/58JCUl4ezZszhw4ABCQ0OdMaqrq1FbW+t8PHbsWOzYsQNbtmzBbbfdhsLCQuzcuROjR4/29vURERGpddGCQvn5+UhISIDRaITJZMKRI0fabF9WVgaTyQSj0YhBgwZh48aNrdpcvHgRCxYsQHR0NIxGI4YNG4bi4mKP8vK4R2DixIkQwv0ZUBQFK1euxMqVK922KS0tbbVv+vTpmD59uqfpEBEReUZG176Hx+/cuRNZWVnIz8/HuHHj8Pvf/x5paWn4/PPPERcX16p9VVUVJk+ejHnz5mHbtm14//33MX/+fERERGDatGkArg6933///ejXrx/+/Oc/Y8CAAaipqVF98e4I3muAiIjIx9asWYM5c+Zg7ty5AK4upPfOO+9gw4YNzmHya23cuBFxcXHIy8sDAAwbNgzHjh3D6tWrnYXA5s2b8c033+Do0aMIDg4GAMTHx3ucW8AVAorVBkXn3RxIg8UuKRvA8I2c+ZiOYHk/qsamHlLifGvoJiUOAEDWlTjN8i7p0V/WS4ljsMjLKaReThzDRXkznQwWh5Q4wZeapcQBAP0Vq5xAVpucOADQLOn1yYoDAHY5f+uEw/v3U1s9zdJJvGrg+sXsXF3RZrVacfz4cSxZskS1PzU11e3Cee4W2isoKIDNZkNwcDD27duH5ORkLFiwAHv37kVERARmzpyJp59+Gnp9x/9+8TbERESkLRLnCMTGxqoWt3P17b6urg52u92jhfPcLbTX3NyMuro6AMBXX32FP//5z7Db7SguLsayZcvw8ssv43e/+51HpyPgegSIiIg6S01NDcLCwpyP21rfxtOF81y1v3a/w+FAv379sGnTJuj1ephMJpw7dw4vvfSScwJ/R7AQICIiTZGxDkDL8WFhYapCwJW+fftCr9d7tHCeu4X2goKC0KdPHwBAdHQ0goODVcMAw4YNg9lshtVqRUhISIdeC4cGiIiIfCgkJAQmk6nVwnklJSVuF85zt9BeUlKSc2LguHHj8OWXX8Lh+Pc8nS+++ALR0dEdLgIAFgJEREQ+l52djT/84Q/YvHkzTp48iUWLFqG6uhqZmZkArt4/Z9asWc72mZmZOHPmDLKzs3Hy5Els3rwZBQUFWLx4sbPNE088gQsXLmDhwoX44osvsH//fjz33HNYsGCBR7lxaICIiLSlC+41kJ6ejgsXLmDVqlWora3FiBEjUFxc7Lzcr7a2FtXV1c72CQkJKC4uxqJFi7B+/XrExMRg7dq1zksHgasTFQ8cOIBFixbhtttuQ//+/bFw4UI8/fTTHuXGQoCIiDRF5hwBT8yfPx/z5893+VxhYWGrfRMmTMCJEyfajJmcnIwPPvjA82SuwUKAiIi0J4BuGuQtzhEgIiLSMPYIEBGRtnTBHAF/xkKAiIg0pavmCPgrDg0QERFpGHsEiIhIWzg0oMJCgIiINIVDA2ocGiAiItIw9ggQEZG2cGhAhYUAERFpCwsBFQ4NEBERaVjg9QhYbYDOu/om2NIsKRnA+I2+/UYdocir2fTfysnJESLptQGAIimMTU4cANA3yYkT3CDvq0PIJTmxDBZH+406KPiSnN8X/RWrlDgAoHwrJ5bSKC8nYZPz5hR2eT87IQLoa60HOFlQLfAKASIiorZwaECFhQAREWkLCwEVzhEgIiLSMPYIEBGRpnCOgBoLASIi0hYODahwaICIiEjD2CNARESawqEBNRYCRESkLRwaUOHQABERkYaxR4CIiLSFPQIqLASIiEhTFHi/qrmkVdH9AocGiIiINIw9AkREpC0cGlBhIUBERJrCywfVPB4aOHz4MKZMmYKYmBgoioI9e/Y4n7PZbHj66acxcuRI9OjRAzExMZg1axbOnTvXZszCwkIoitJqa2xs9PgFERERtUlI2gKEx4XA5cuXMWrUKKxbt67Vc1euXMGJEyewfPlynDhxArt27cIXX3yBBx98sN24YWFhqK2tVW1Go9HT9IiIiMgDHg8NpKWlIS0tzeVz4eHhKCkpUe177bXXcNddd6G6uhpxcXFu4yqKgqioKE/TISIi8lwAfaP3ls/nCNTX10NRFPTs2bPNdg0NDYiPj4fdbsftt9+O3/72t0hMTHTbvqmpCU1NTc7HFosFACCabBA67y7s0DdYvTr+WsZ/yTnFMsejgq7IufDFESwlDABASLoWR9csJw4A6Jvab9MRQY0OOYEABF+WEyv4krwTpb8i5/dF1yDphANQmuTkJGw2KXEAAM2SzrndLicOADgk/WEREt6XMmJ0EOcIqPn08sHGxkYsWbIEM2fORFhYmNt2Q4cORWFhIfbt24ft27fDaDRi3LhxOH36tNtjcnNzER4e7txiY2N98RKIiIgCms8KAZvNhkceeQQOhwP5+fltth0zZgx+9rOfYdSoUUhJScEf//hH3HLLLXjttdfcHpOTk4P6+nrnVlNTI/slEBFRIOJkQRWfDA3YbDbMmDEDVVVVePfdd9vsDXBFp9PhzjvvbLNHwGAwwGAweJsqERFpDIcG1KT3CLQUAadPn8bBgwfRp08fj2MIIVBZWYno6GjZ6REREdE1PO4RaGhowJdfful8XFVVhcrKSvTu3RsxMTGYPn06Tpw4gf/6r/+C3W6H2WwGAPTu3RshISEAgFmzZqF///7Izc0FADz77LMYM2YMBg8eDIvFgrVr16KyshLr16+X8RqJiIj+jSsLqnhcCBw7dgz33HOP83F2djYAYPbs2Vi5ciX27dsHALj99ttVxx06dAgTJ04EAFRXV0On+3dnxMWLF/GLX/wCZrMZ4eHhSExMxOHDh3HXXXd5mh4REVGbODSg5nEhMHHiRAjh/gy09VyL0tJS1eNXXnkFr7zyiqepEBERkZd4rwEiItIWDg2osBAgIiJtYSGgwkKAiIg0hXME1Hy6siARERH5N/YIEBGRtnBoQIWFABERaYoiBJQOXOHWXoxAwaEBIiIiDWMhQERE2tJFNx3Kz89HQkICjEYjTCYTjhw50mb7srIymEwmGI1GDBo0CBs3bnTbdseOHVAUBQ899JDHebEQICIiTWm5asDbzRM7d+5EVlYWli5dioqKCqSkpCAtLQ3V1dUu21dVVWHy5MlISUlBRUUFnnnmGTz55JMoKipq1fbMmTNYvHgxUlJSbuR0sBAgIiLytTVr1mDOnDmYO3cuhg0bhry8PMTGxmLDhg0u22/cuBFxcXHIy8vDsGHDMHfuXDz++ONYvXq1qp3dbsdPf/pTPPvssxg0aNAN5cZCgIiItKWThwasViuOHz+O1NRU1f7U1FQcPXrU5THl5eWt2k+aNAnHjh2DzWZz7lu1ahUiIiIwZ86cjid0ncC7aqDZCijehdA1fCsnFwAhBr2UOEpzsJQ4ABDcTU5OjmAvT7QPKHZ5M3l1Vjmx9E0OKXEAQP9ts5w4V6xS4gCA8q2cWEqTvJxEY5OcQFZb+206SNjk/OyE3S4lztVg8t6b3ycyFxSyWCyq/QaDAQaDQbWvrq4OdrsdkZGRqv2RkZHOO/Rez2w2u2zf3NyMuro6REdH4/3330dBQQEqKyu9ei3sESAiIrpBsbGxCA8Pd265ublu2yqK+suTEKLVvvbat+y/dOkSfvazn+H1119H3759vXgFgdgjQERE1BaJCwrV1NQgLCzMufv63gAA6Nu3L/R6fatv/+fPn2/1rb9FVFSUy/ZBQUHo06cPPvvsM3z99deYMmWK83mH42oPT1BQEE6dOoWbb765Qy+FhQAREWmKzKGBsLAwVSHgSkhICEwmE0pKSvDjH//Yub+kpARTp051eUxycjL+8pe/qPYdOHAASUlJCA4OxtChQ/Hpp5+qnl+2bBkuXbqEV199FbGxsR1+LSwEiIhIW7pgieHs7GxkZGQgKSkJycnJ2LRpE6qrq5GZmQkAyMnJwdmzZ7F161YAQGZmJtatW4fs7GzMmzcP5eXlKCgowPbt2wEARqMRI0aMUP0bPXv2BIBW+9vDQoCIiMjH0tPTceHCBaxatQq1tbUYMWIEiouLER8fDwCora1VrSmQkJCA4uJiLFq0COvXr0dMTAzWrl2LadOmSc9NESIwFky2WCwIDw/HvT0zEKSEeBcsoo+cpADY+9wkJY7tJnlXDdh51UCH8KqBjuFVAx0jrHJen2iW8x4AAEi6AkHGlQzNwoZSsQf19fXtdrXfqJbPCdOM3yEo2OhVrGZbI47/calP8+0s7BEgIiJtEeLq5m2MAMHLB4mIiDSMPQJERKQpMq8aCAQsBIiISFu64KoBf8ahASIiIg1jjwAREWmK4ri6eRsjULAQICIibeHQgAqHBoiIiDSMPQJERKQpvGpAjYUAERFpCxcUUmEhQEREmsIeAbWAKwSErRlC8W7qg+5bSeuUA9DXy1nXX7HJWRMcABxX5PzYhd7/ppgoDnm/nbLOuWKTN71Y1yRp7XuJa+grjZLW0LfJy0nW65N1fwAA0tb1lxYHgJD1+yLj23EAfcP+vgm4QoCIiKhNvGpAhYUAERFpCocG1Pyvb5eIiIg6DXsEiIhIW3jVgAoLASIi0hQODah5PDRw+PBhTJkyBTExMVAUBXv27FE9/9hjj0FRFNU2ZsyYduMWFRVh+PDhMBgMGD58OHbv3u1pakREROQhjwuBy5cvY9SoUVi3bp3bNg888ABqa2udW3FxcZsxy8vLkZ6ejoyMDHzyySfIyMjAjBkz8OGHH3qaHhERUduEpC1AeDw0kJaWhrS0tDbbGAwGREVFdThmXl4e7r//fuTk5AAAcnJyUFZWhry8PGzfvt3TFImIiNzi0ICaT64aKC0tRb9+/XDLLbdg3rx5OH/+fJvty8vLkZqaqto3adIkHD161O0xTU1NsFgsqo2IiIg8I70QSEtLw5tvvol3330XL7/8Mj7++GP84Ac/QFOT+9X6zGYzIiMjVfsiIyNhNpvdHpObm4vw8HDnFhsbK+01EBFRAHMIOVuAkH7VQHp6uvP/R4wYgaSkJMTHx2P//v14+OGH3R6nKIrqsRCi1b5r5eTkIDs72/nYYrGwGCAiovZxZUEVn18+GB0djfj4eJw+fdptm6ioqFbf/s+fP9+ql+BaBoMBBoNBWp5ERKQNCiTMEZCSiX/w+cqCFy5cQE1NDaKjo922SU5ORklJiWrfgQMHMHbsWF+nR0REpGke9wg0NDTgyy+/dD6uqqpCZWUlevfujd69e2PlypWYNm0aoqOj8fXXX+OZZ55B37598eMf/9h5zKxZs9C/f3/k5uYCABYuXIjx48fjhRdewNSpU7F3714cPHgQ7733noSXSEREdA2uLKjicSFw7Ngx3HPPPc7HLeP0s2fPxoYNG/Dpp59i69atuHjxIqKjo3HPPfdg586dCA0NdR5TXV0Nne7fnRFjx47Fjh07sGzZMixfvhw333wzdu7cidGjR3vz2oiIiFrh5YNqHhcCEydOhGijEnrnnXfajVFaWtpq3/Tp0zF9+nRP0yEiIiIv8F4DRESkLbxqQIWFABERaYoiBBQvx/i9Pd6fBFwhIJrtEEqzdzG+/VZSNq3XR7hROpt3r0kVK1jSj10n76ITIek8KQ6HlDgAALucWIrEnx1kxWqWl5Ow2eQEkpqTpFh2u5w4AISk95OQuZCNkPj7Qt9bAVcIEBERtcnx3eZtjADBQoCIiDSFQwNqPl9QiIiIiPwXewSIiEhbeNWACgsBIiLSFq4sqMJCgIiINIUrC6pxjgAREZGGsUeAiIi0hUMDKiwEiIhIUxTH1c3bGIGCQwNEREQaxh4BIiLSFg4NqLAQICIibeE6AiocGiAiItIw9ggQEZGm8F4DauwRICIibWmZI+Dt5qH8/HwkJCTAaDTCZDLhyJEjbbYvKyuDyWSC0WjEoEGDsHHjRtXzr7/+OlJSUtCrVy/06tUL9913Hz766COP82IhQERE5GM7d+5EVlYWli5dioqKCqSkpCAtLQ3V1dUu21dVVWHy5MlISUlBRUUFnnnmGTz55JMoKipytiktLcWjjz6KQ4cOoby8HHFxcUhNTcXZs2c9yk0RIjD6NywWC8LDw3GPYQaClGCvYulu6iEpK0Dp3l1KHGHw7jWpBEsaEdLJqyOFokiJozgkXtxrlxNLsTVLiQMAkBWrWV5OwmaTE0hqTpJi2e1y4gAQkt5PQmJOEJJ+XyR8jDQLG0qxF/X19QgLC5OQVGvOz4k7chCkN3oVq9neiEMncjuc7+jRo3HHHXdgw4YNzn3Dhg3DQw89hNzc3Fbtn376aezbtw8nT5507svMzMQnn3yC8vJyl/+G3W5Hr169sG7dOsyaNavDryXw5gjY7YDi3QeUsEr6wwYAuCIlimKTWAgESfqxS/rwBgBpkWTWtZJiCYkfcNI+mGR+6Er6gJP7oSsplsycHJLem7I+vIGAugTOEzLnCFgsFtV+g8EAg8Gg2me1WnH8+HEsWbJEtT81NRVHjx51Gb+8vBypqamqfZMmTUJBQQFsNhuCg1t/Jly5cgU2mw29e/f26LVwaICIiLRFQMIcgauhYmNjER4e7txcfbuvq6uD3W5HZGSkan9kZCTMZrPLFM1ms8v2zc3NqKurc3nMkiVL0L9/f9x3330enY7A6xEgIiLqJDU1Naqhget7A66lXNeLKoRota+99q72A8CLL76I7du3o7S0FEajZ8MeLASIiEhbJK4sGBYW1u4cgb59+0Kv17f69n/+/PlW3/pbREVFuWwfFBSEPn36qPavXr0azz33HA4ePIjbbrvN01fCoQEiItIYh6Stg0JCQmAymVBSUqLaX1JSgrFjx7o8Jjk5uVX7AwcOICkpSTU/4KWXXsJvf/tbvP3220hKSup4UtdgIUBERORj2dnZ+MMf/oDNmzfj5MmTWLRoEaqrq5GZmQkAyMnJUc30z8zMxJkzZ5CdnY2TJ09i8+bNKCgowOLFi51tXnzxRSxbtgybN2/GwIEDYTabYTab0dDQ4FFuHBogIiJN6YqVBdPT03HhwgWsWrUKtbW1GDFiBIqLixEfHw8AqK2tVa0pkJCQgOLiYixatAjr169HTEwM1q5di2nTpjnb5Ofnw2q1Yvr06ap/a8WKFVi5cqUnryUwrh9xXh8aNM3rdQSUbt0kZQUoIXIu+1NcXCpyw/zw8kFpePlgx/DywY7h5YOdpjPXEbj31v+HIL37SX0d0Wxvwv989pJP8+0sHBogIiLSMA4NEBGRtki8aiAQsBAgIiJtYSGgwqEBIiIiDWOPABERaYsD3t/gROKcza7GQoCIiDSlKy4f9GcsBIiISFs4R0DF4zkChw8fxpQpUxATEwNFUbBnzx7V84qiuNxeeukltzELCwtdHtPY2OjxCyIiIqKO87hH4PLlyxg1ahR+/vOfq1Y4alFbW6t6/N///d+YM2eOy7bXCgsLw6lTp1T7PL2DEhERUbscAlC8/EYva4EoP+BxIZCWloa0tDS3z0dFRake7927F/fccw8GDRrUZlxFUVodS0REJB2HBlR8evngP/7xD+zfvx9z5sxpt21DQwPi4+MxYMAA/OhHP0JFRUWb7ZuammCxWFQbERERecankwXfeOMNhIaG4uGHH26z3dChQ1FYWIiRI0fCYrHg1Vdfxbhx4/DJJ59g8ODBLo/Jzc3Fs88+22q/cAgIb7t8rFbvjr+WrLXKbRLXq9dJukeA4ofLUMhch11S15/U23k4/HBdf1mvT2ZXq6T3gbT7A1wNJilO4HwT7ToSegQQOD8Hn/4l37x5M37605+2O9Y/ZswY/OxnP8OoUaOQkpKCP/7xj7jlllvw2muvuT0mJycH9fX1zq2mpkZ2+kREFIhahga83QKEz3oEjhw5glOnTmHnzp0eH6vT6XDnnXfi9OnTbtsYDAYYDN7dPYqIiEjrfNYjUFBQAJPJhFGjRnl8rBAClZWViI6O9kFmRESkaQ4hZwsQHvcINDQ04Msvv3Q+rqqqQmVlJXr37o24uDgAV+/5/Kc//Qkvv/yyyxizZs1C//79kZubCwB49tlnMWbMGAwePBgWiwVr165FZWUl1q9ffyOviYiIyD3h8H7Ohsz5SF3M40Lg2LFjuOeee5yPs7OzAQCzZ89GYWEhAGDHjh0QQuDRRx91GaO6uho63b87Iy5evIhf/OIXMJvNCA8PR2JiIg4fPoy77rrL0/SIiIjIA4qQOqW561gsFoSHh2Oi7mEEKcFexVKC5U2dUPR6OXGCJE7n4FUDHcOrBjqEVw10OJikOAHxJ7uVZmFDKfaivr4eYWFhPvk3Wj4n7ot9AkE67+aYNTuacLBmg0/z7Sy81wAREWmLQ8Dry/+0PEeAiIjoe40rC6r4Yd8uERERdRb2CBARkbYISOgRkJKJX2AhQERE2sKhARUODRAREWkYewSIiEhbHA4AXl7OKetSXj/AQoCIiLSFQwMqHBogIiLSMPYIEBGRtrBHQIWFABERaQtXFlQJvELAYfd6DXxhlTcJREhaj1/RWaXE+S6YvFjUPj+8S5nUNfRl8cPzFEjf+ojcCbxCgIiIqA1COCC8LDy9Pd6fsBAgIiJtEcL7rv0A6i1iIUBERNoiJMwRCKBCgIPFREREGsYeASIi0haHA1C8HOPnHAEiIqLvKQ4NqHBogIiISMPYI0BERJoiHA4IL4cGePkgERHR9xWHBlQ4NEBERKRh7BEgIiJtcQhAYY9ACxYCRESkLUIA8PbywcApBDg0QEREpGHsESAiIk0RDgHh5dCACKAeARYCRESkLcIB74cGePkgERHR9xJ7BNQ4R4CIiEjDAqZHoKU6a4bN63UiAMXrfP5NTq2lCP/LiTrID7sQ/fLbjB+ep0CaGe7vmmED0DnvzWbR5PX7rSXfQBAwhcClS5cAAO+h2PtgMt+HsmL54d9IIiLZLl26hPDwcJ/EDgkJQVRUFN4zS/icABAVFYWQkBApsbqSIvzyq4HnHA4Hzp07h9DQUCiK62/PFosFsbGxqKmpQVhYWCdneOOYd+f7vubOvDsX85ZHCIFLly4hJiYGOp3vei0bGxthtVqlxAoJCYHRaJQSqysFTI+ATqfDgAEDOtQ2LCzMb978nmDene/7mjvz7lzMWw5f9QRcy2g0BsSHt0wcLCYiItIwFgJEREQapqlCwGAwYMWKFTAYDF2dikeYd+f7vubOvDsX86ZAEDCTBYmIiMhzmuoRICIiIjUWAkRERBrGQoCIiEjDWAgQERFpWMAVAvn5+UhISIDRaITJZMKRI0fabF9WVgaTyQSj0YhBgwZh48aNnZTpVbm5ubjzzjsRGhqKfv364aGHHsKpU6faPKa0tBSKorTa/va3v3VS1sDKlStb/ftRUVFtHtPV57rFwIEDXZ6/BQsWuGzfVef78OHDmDJlCmJiYqAoCvbs2aN6XgiBlStXIiYmBt26dcPEiRPx2WeftRu3qKgIw4cPh8FgwPDhw7F79+5Oy9tms+Hpp5/GyJEj0aNHD8TExGDWrFk4d+5cmzELCwtd/gwaGxs7JW8AeOyxx1r9+2PGjGk3bleebwAuz5uiKHjppZfcxuyM803+I6AKgZ07dyIrKwtLly5FRUUFUlJSkJaWhurqapftq6qqMHnyZKSkpKCiogLPPPMMnnzySRQVFXVazmVlZViwYAE++OADlJSUoLm5Gampqbh8+XK7x546dQq1tbXObfDgwZ2Q8b/deuutqn//008/ddvWH851i48//liVd0lJCQDgJz/5SZvHdfb5vnz5MkaNGoV169a5fP7FF1/EmjVrsG7dOnz88ceIiorC/fff77zvhivl5eVIT09HRkYGPvnkE2RkZGDGjBn48MMPOyXvK1eu4MSJE1i+fDlOnDiBXbt24YsvvsCDDz7YbtywsDDV+a+trZW6Qlx75xsAHnjgAdW/X1zc9pr1XX2+AbQ6Z5s3b4aiKJg2bVqbcX19vsmPiABy1113iczMTNW+oUOHiiVLlrhs/9RTT4mhQ4eq9v3yl78UY8aM8VmO7Tl//rwAIMrKyty2OXTokAAg/vWvf3VeYtdZsWKFGDVqVIfb++O5brFw4UJx8803C4fD4fJ5fzjfAMTu3budjx0Oh4iKihLPP/+8c19jY6MIDw8XGzdudBtnxowZ4oEHHlDtmzRpknjkkUek5yxE67xd+eijjwQAcebMGbdttmzZIsLDw+Um1wZXec+ePVtMnTrVozj+eL6nTp0qfvCDH7TZprPPN3WtgOkRsFqtOH78OFJTU1X7U1NTcfToUZfHlJeXt2o/adIkHDt2DDZb19xisr6+HgDQu3fvdtsmJiYiOjoa9957Lw4dOuTr1Fo5ffo0YmJikJCQgEceeQRfffWV27b+eK6Bq++bbdu24fHHH3d7s6oWXX2+r1VVVQWz2aw6pwaDARMmTHD7fgfc/xzaOsbX6uvroSgKevbs2Wa7hoYGxMfHY8CAAfjRj36EioqKzknwGqWlpejXrx9uueUWzJs3D+fPn2+zvb+d73/84x/Yv38/5syZ025bfzjf1DkCphCoq6uD3W5HZGSkan9kZCTMZrPLY8xms8v2zc3NqKur81mu7gghkJ2djbvvvhsjRoxw2y46OhqbNm1CUVERdu3ahSFDhuDee+/F4cOHOy3X0aNHY+vWrXjnnXfw+uuvw2w2Y+zYsbhw4YLL9v52rlvs2bMHFy9exGOPPea2jT+c7+u1vKc9eb+3HOfpMb7U2NiIJUuWYObMmW3e/Gbo0KEoLCzEvn37sH37dhiNRowbNw6nT5/utFzT0tLw5ptv4t1338XLL7+Mjz/+GD/4wQ/Q1NTk9hh/O99vvPEGQkND8fDDD7fZzh/ON3WegLn7YIvrv9UJIdr8pueqvav9neFXv/oV/vrXv+K9995rs92QIUMwZMgQ5+Pk5GTU1NRg9erVGD9+vK/TBHD1j2KLkSNHIjk5GTfffDPeeOMNZGdnuzzGn851i4KCAqSlpSEmJsZtG3843+54+n6/0WN8wWaz4ZFHHoHD4UB+fn6bbceMGaOamDdu3DjccccdeO2117B27VpfpwoASE9Pd/7/iBEjkJSUhPj4eOzfv7/ND1Z/Od8AsHnzZvz0pz9td6zfH843dZ6A6RHo27cv9Hp9q0r7/PnzrSryFlFRUS7bBwUFoU+fPj7L1ZVf//rX2LdvHw4dOtTh2ylfa8yYMV1arffo0QMjR450m4M/nesWZ86cwcGDBzF37lyPj+3q891yhYYn7/eW4zw9xhdsNhtmzJiBqqoqlJSUeHwrXJ1OhzvvvLNLfwbR0dGIj49vMwd/Od8AcOTIEZw6deqG3u/+cL7JdwKmEAgJCYHJZHLOAG9RUlKCsWPHujwmOTm5VfsDBw4gKSkJwcHBPsv1WkII/OpXv8KuXbvw7rvvIiEh4YbiVFRUIDo6WnJ2HdfU1ISTJ0+6zcEfzvX1tmzZgn79+uGHP/yhx8d29flOSEhAVFSU6pxarVaUlZW5fb8D7n8ObR0jW0sRcPr0aRw8ePCGCkEhBCorK7v0Z3DhwgXU1NS0mYM/nO8WBQUFMJlMGDVqlMfH+sP5Jh/qqlmKvrBjxw4RHBwsCgoKxOeffy6ysrJEjx49xNdffy2EEGLJkiUiIyPD2f6rr74S3bt3F4sWLRKff/65KCgoEMHBweLPf/5zp+X8xBNPiPDwcFFaWipqa2ud25UrV5xtrs/7lVdeEbt37xZffPGF+N///V+xZMkSAUAUFRV1Wt6/+c1vRGlpqfjqq6/EBx98IH70ox+J0NBQvz7X17Lb7SIuLk48/fTTrZ7zl/N96dIlUVFRISoqKgQAsWbNGlFRUeGcXf/888+L8PBwsWvXLvHpp5+KRx99VERHRwuLxeKMkZGRobpq5v333xd6vV48//zz4uTJk+L5558XQUFB4oMPPuiUvG02m3jwwQfFgAEDRGVlpeo939TU5DbvlStXirffflv8/e9/FxUVFeLnP/+5CAoKEh9++GGn5H3p0iXxm9/8Rhw9elRUVVWJQ4cOieTkZNG/f3+/Pt8t6uvrRffu3cWGDRtcxuiK803+I6AKASGEWL9+vYiPjxchISHijjvuUF2GN3v2bDFhwgRV+9LSUpGYmChCQkLEwIED3f6i+AoAl9uWLVvc5v3CCy+Im2++WRiNRtGrVy9x9913i/3793dq3unp6SI6OloEBweLmJgY8fDDD4vPPvvMbc5CdP25vtY777wjAIhTp061es5fznfLZYvXb7NnzxZCXL2EcMWKFSIqKkoYDAYxfvx48emnn6piTJgwwdm+xZ/+9CcxZMgQERwcLIYOHSq9oGkr76qqKrfv+UOHDrnNOysrS8TFxYmQkBAREREhUlNTxdGjRzst7ytXrojU1FQREREhgoODRVxcnJg9e7aorq5WxfC3893i97//vejWrZu4ePGiyxhdcb7Jf/A2xERERBoWMHMEiIiIyHMsBIiIiDSMhQAREZGGsRAgIiLSMBYCREREGsZCgIiISMNYCBAREWkYCwEiIiINYyFARESkYSwEiIiINIyFABERkYaxECAiItKw/w+vGbW9ZsLWTgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "test2 = np.random.random_sample(3)\n", - "print(test2.shape[0])" + "plt.imshow(testConcentrations)\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(testConcentrations)\n", + "plt.colorbar()\n", + "plt.show()" ] }, {