From 17aa9e17f08cae530ecf8f8b2bde44901976bc99 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 30 Oct 2020 15:04:06 -0300 Subject: [PATCH 01/63] Scratch of the README file --- README.md | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 81 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 33b376d..00d5071 100644 --- a/README.md +++ b/README.md @@ -4,4 +4,84 @@ [Designed to Madagascar package](https://ahay.org) -[![Github release](https://img.shields.io/github/v/release/Dirack/creVelocityInversion)](https://github.com/Dirack/creVelocityInversion/releases/latest) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Madagascar](https://img.shields.io/badge/Madagascar-v3.0-blue)](https://github.com/ahay/src/tree/master) +[![Github release](https://img.shields.io/github/v/release/Dirack/creVelocityInversion)](https://github.com/Dirack/creVelocityInversion/releases/latest) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Madagascar](https://img.shields.io/badge/Madagascar-v3.0-blue)](https://github.com/ahay/src/tree/master) [![Build Status](https://travis-ci.org/Dirack/creVelocityInversion.svg?branch=master)](https://travis-ci.org/Dirack/creVelocityInversion) + +#### TODO: ABSTRACT + +## Development setup + +- Madagascar package (3.0) + +You need to have the actual Madagascar package stable release installed on your computer. Please follow the +[Installing Madagascar page](http://www.ahay.org/wiki/Installation) in the official documentation. You can install +Madagascar automatically from Shell Script using program _madagainstall_ from [Shellinclude library](https://github.com/Dirack/Shellinclude/tree/v1.2.2-beta.1). + +## Installation + +After Madagascar installing process, you need to install the programs of this repository in your local Madagascar user's +directory. You can compile and install it as any other Madagascar program. +Usually, Madagascar keeps the path of your local copy source files in the $RSFSRC environment variable. You can +show that on a bash terminal using 'echo' command: + +```sh +~$ echo "$RSFSRC" +``` + +And Madagascar will install executable files on your $RSFROOT directory. You can show that environment variable +with 'echo' too: + +```sh +~$ echo "$RSFROOT" +``` + +Madagascar stores user programs in $RSFSRC/user directory. So, you can create a new directory or put this +repository inside that directory. In this repository, such as every user's repository in Madagascar, we have a compilation +[SConstruct](https://github.com/Dirack/vfsa/blob/master/SConstruct) that compile the C programs. +Run 'scons' on your $RSFSRC/user/creGatherInterpolation repository to compile it: + +```shell +~$ scons +``` + +And run 'scons install' in the top directory of your local Madagascar installation +(the directory path in your $RSFSRC variable): + +```shell +~$ sudo scons install +``` + +If you have any doubt about this process, please reffer to the Madagascar oficial documentation in +[Adding_new_programs_to_Madagascar](http://www.ahay.org/wiki/Adding_new_programs_to_Madagascar) + +## Usage example + +A few motivating and useful examples of how that product can be used. +_For more examples and details, please refer to the [Wiki](https://github.com/Dirack/creVelocityInversion/wiki)._ + +We also have many SConstruct examples in this repository in the +[experiments directory](https://github.com/Dirack/creVelocityInversion/tree/master/experiments) + +## Release History and Versions + +This package version is referenced in VERSION.md file, and you can see the [complete release history in our wiki](https://github.com/Dirack/creVelocityInversion/wiki/Release-history) or in the CHANGELOG.md file. + +## Meta + +[main page](https://github.com/Dirack/creVelocityInversion) + +Rodolfo Dirack – [@dirack](https://github.com/Dirack) – rodolfo_profissional@hotmail.com + +Distributed under the GPL3 license. See ``LICENSE`` for more information. + +## Contributing + +In order to contribute with this project you should follow the list of steps bellow, please check out ["How to contribute with this project?"](https://github.com/Dirack/creVelocityInversion/wiki/Contribute) in our Wiki for more details. + +1. Create an issue to your request or choose an issue already defined +2. Fork this project in https://github.com/Dirack/creVelocityInversion/fork +3. Create a branch for your contribution (name it using gitflow) +4. Do clear _commit_ messages (a title with 50 characters and two paragraphs of text) +5. _Push_ your contribution to this repository +6. Create a new Pull Request with a clear description of your contribution + +###### Important: The commit history should be clear, with commit mesages around one or two paraghraps describing your modifications. Pull Requests with unsatisfactory commit history will be rejected. From 6dadf429967bf89add889ff3a0eb01982769d29b Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 30 Oct 2020 15:18:35 -0300 Subject: [PATCH 02/63] README.md file updated #4 Also add CHANGELOG and VERSION files --- CHANGELOG.md | 0 README.md | 5 ++++- VERSION.md | 1 + 3 files changed, 5 insertions(+), 1 deletion(-) create mode 100644 CHANGELOG.md create mode 100644 VERSION.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..e69de29 diff --git a/README.md b/README.md index 00d5071..355578e 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,10 @@ [![Github release](https://img.shields.io/github/v/release/Dirack/creVelocityInversion)](https://github.com/Dirack/creVelocityInversion/releases/latest) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Madagascar](https://img.shields.io/badge/Madagascar-v3.0-blue)](https://github.com/ahay/src/tree/master) [![Build Status](https://travis-ci.org/Dirack/creVelocityInversion.svg?branch=master)](https://travis-ci.org/Dirack/creVelocityInversion) -#### TODO: ABSTRACT +Common Reflection Element (CRE) velocity inversion algorithm is based on [Cameron (2008)](http://www.reproducibility.org/RSF/book/tccs/time2depth/paper_html/) velocity inversion algorithm and +in diffraction focusing migration and simulation. The depth velocity model is obtained from stacked section in two steps: +First, simulation and migration of diffraction hyperbolas in the stacked section to obtain the time migration velocity model. +Second, time to depth conversion of the time velocity model. ## Development setup diff --git a/VERSION.md b/VERSION.md new file mode 100644 index 0000000..745d4dc --- /dev/null +++ b/VERSION.md @@ -0,0 +1 @@ +v0.1.2-alpha.1 From b1f55915dedefd14c4ed4be65dc416f43d1a67e8 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 30 Oct 2020 15:27:40 -0300 Subject: [PATCH 03/63] Scratch of the CHANGELOG.md #4 --- CHANGELOG.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e69de29..a42e6fc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -0,0 +1,22 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## UNRELEASED - Contribute with the next version + +## [v0.1.2-alpha.1](https://github.com/Dirack/creGatherStack/compare/v0.1.1-alpha.1...develop/0.1.2) - 2020-10-30 + +## RELEASED + +## [v0.1.1-alpha.1](https://github.com/Dirack/creGatherStack/releases/tag/v0.1.1-alpha.1) (Development) - 2020-10-07 + +## Added + + +## Changed + + + From 86940d2e3cf2246ad7f47420d7aad46da4e579cc Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 30 Oct 2020 15:43:26 -0300 Subject: [PATCH 04/63] Update CHANGELOG #4 --- CHANGELOG.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a42e6fc..ea0f65f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,8 +15,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Added +- Scratch of the velocity inversion algorithm with iterative picking #2 (42c68d5) -## Changed - +- Use the Cameron (2008) velocity inversion algorithm Resolve #9 (3d7331e) (2800abb) +- Add parameters dictionary to allow test and command line input parameters (0e28fc0) +- Casting parameters function (5090942) From 9155f32d1da6969db232a8adbb490ac75b192ef3 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 30 Oct 2020 15:51:59 -0300 Subject: [PATCH 05/63] Update CHANGELOG.md Add links to CHANGELOG latest release closed commits and issues --- CHANGELOG.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ea0f65f..3466499 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,18 +7,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## UNRELEASED - Contribute with the next version -## [v0.1.2-alpha.1](https://github.com/Dirack/creGatherStack/compare/v0.1.1-alpha.1...develop/0.1.2) - 2020-10-30 +## [v0.1.2-alpha.1](https://github.com/Dirack/creVelocityInversion/compare/v0.1.1-alpha.1...develop/0.1.2) - 2020-10-30 ## RELEASED -## [v0.1.1-alpha.1](https://github.com/Dirack/creGatherStack/releases/tag/v0.1.1-alpha.1) (Development) - 2020-10-07 +## [v0.1.1-alpha.1](https://github.com/Dirack/creVelocityInversion/releases/tag/v0.1.1-alpha.1) (Development) - 2020-10-07 ## Added -- Scratch of the velocity inversion algorithm with iterative picking #2 (42c68d5) +- Scratch of the velocity inversion algorithm with iterative picking [#2](https://github.com/Dirack/creVelocityInversion/issues/2) [42c68d5](https://github.com/Dirack/creVelocityInversion/commit/42c68d5) -- Use the Cameron (2008) velocity inversion algorithm Resolve #9 (3d7331e) (2800abb) +- Use the Cameron (2008) velocity inversion algorithm Resolve [#9](https://github.com/Dirack/creVelocityInversion/issues/9) [3d7331e](https://github.com/Dirack/creVelocityInversion/commit/3d7331e) [2800abb](https://github.com/Dirack/creVelocityInversion/commit/2800abb) -- Add parameters dictionary to allow test and command line input parameters (0e28fc0) +- Add parameters dictionary to allow test and command line input parameters [0e28fc0](https://github.com/Dirack/creVelocityInversion/commit/0e28fc0) -- Casting parameters function (5090942) +- Casting parameters function [5090942](https://github.com/Dirack/creVelocityInversion/commit/5090942) From ca9464fa975261a6f984012de3fccfb5670ec37c Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 5 Nov 2020 22:03:23 -0300 Subject: [PATCH 06/63] Correct program name in SConstruct and parameters The correct name is sftransp. Correct parameters for the new stacked section in SConstruct experiment, tmin is the time axis origin of the stacked section and tmax is the maximum time in the stacked section --- experiments/algorithmVelocityInversion/SConstruct | 2 +- experiments/velocityInversionCreStackedSection/SConstruct | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/experiments/algorithmVelocityInversion/SConstruct b/experiments/algorithmVelocityInversion/SConstruct index 3f999e9..1a4aaf2 100644 --- a/experiments/algorithmVelocityInversion/SConstruct +++ b/experiments/algorithmVelocityInversion/SConstruct @@ -91,7 +91,7 @@ Flow('vxz x0 t0','vdix2', Flow('vz','vxz', ''' put label1=Lateral unit1=km label2=Depth unit2=km n3=1 | - trasnp + transp ''') End() diff --git a/experiments/velocityInversionCreStackedSection/SConstruct b/experiments/velocityInversionCreStackedSection/SConstruct index 7c59dee..94316db 100644 --- a/experiments/velocityInversionCreStackedSection/SConstruct +++ b/experiments/velocityInversionCreStackedSection/SConstruct @@ -26,7 +26,7 @@ from diffSimulationAndMigration import diffsimul from inputScons import castingParametersFromCmd # Parameters dictionaries to the functions -diffSimulParameters = {'numberOfReflectors':2, +diffSimulParameters = {'numberOfReflectors':1, 'section':'stackedSection.rsf', 'velocities':(1.5,1.6), 'diffSimulatedSection':'diffSimulatedSection'} @@ -39,8 +39,8 @@ diffMigParameters = {'diffSimulatedSection': 'diffSimulatedSection', 'nx':201, 'padx':1000, 'nt':1001, -'tmin':0, -'tmax':4, +'tmin':0.3, +'tmax':2.296, 'rect1':10, 'rect2':10, 'srect1':1, @@ -91,7 +91,7 @@ Flow('vxz x0 t0','vdix2', Flow('vz','vxz', ''' put label1=Lateral unit1=km label2=Depth unit2=km n3=1 | - trasnp + transp ''') End() From d22f027ca3c74ed9a837140a40fc5795cdb3a2e7 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 5 Nov 2020 22:12:10 -0300 Subject: [PATCH 07/63] Remove unused python module, version --- .../algorithmVelocityInversion/diffSimulationAndMigration.py | 1 - .../diffSimulationAndMigration.py | 1 - 2 files changed, 2 deletions(-) diff --git a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py b/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py index 81ea996..582190d 100644 --- a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py +++ b/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py @@ -19,7 +19,6 @@ from rsf.proj import * import string, sys, os -import version frect=0 diff --git a/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py b/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py index 81ea996..582190d 100644 --- a/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py +++ b/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py @@ -19,7 +19,6 @@ from rsf.proj import * import string, sys, os -import version frect=0 From 6d6be505475dd7547846aca5a1d9138daebca632 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sun, 22 Nov 2020 21:04:16 -0300 Subject: [PATCH 08/63] Velocity Inversion Algorithm experiment Correct diffSimulationAndMigration.py library to generate a simulated section with diffractions only, use add to sum up diffraction sections instead of rcat to concatenate, and run the experiment --- .../algorithmVelocityInversion/diffSimulationAndMigration.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py b/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py index 582190d..0426fa4 100644 --- a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py +++ b/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py @@ -237,11 +237,11 @@ def diffsimul( ''' % (velocities[i])) section = returnedSection - reflectorsList.append(returnedSection) + reflectorsList.append(diffSection) Flow(diffSimulatedSection,reflectorsList, ''' - rcat ${SOURCES[1:%d]} + add ${SOURCES[1:%d]} scale=%s ''' % ( len(reflectorsList), From 939f07035a16c5a9bfdd4fd1f9c3f1fd00544ce2 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 24 Nov 2020 12:40:27 -0300 Subject: [PATCH 09/63] Experiment result images --- .../diffractionSimulation.png | Bin 0 -> 129912 bytes .../focusingAnalisysResult.png | Bin 0 -> 82764 bytes .../Experiment_velocity_inversion/model.png | Bin 0 -> 73416 bytes .../velocityInversionResult.png | Bin 0 -> 79533 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/diffractionSimulation.png create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/focusingAnalisysResult.png create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/model.png create mode 100644 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experiments/algorithmVelocityInversion/Experiment_velocity_inversion/model.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/model.png create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/stackedSection.jpg create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/vdix.jpg create mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/velocityInversionResult.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/velocityInversionResult.png diff --git a/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb b/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb new file mode 100644 index 0000000..2f85620 --- /dev/null +++ b/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inversão do modelo de velocidades\n", + "\n", + "Utilizando o modelo de 3 camadas, v1=1.508, v2=1.690, v3=2.000, abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='model.jpg', width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtemos a seção empilhada a partir do processamento sísmico convencional (Análise de velocidades, correção NMO, etc). A seção empilhada é exibida abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='stackedSection.jpg', width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fazendo o picking iterativo na seção empilhada, simulamos hipérboles de difração em pontos escolhidos da seção associados a eventos de reflexão" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='diffractionSimulation.jpg', width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Refazemos a análise de velocidades utilizando a continuação de velocidades (velocity continuation) e a focalização de difrações das difrações simuladas como critério de otimização das velocidades." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='focusingAnalisysResult.jpg', width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Este modelo é suavizado antes da inversão de velocidades para estimativa do modelo de velocidades em profundidade " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='vdix.jpg', width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Por fim, o modelo de velocidades em profundidade é obtido a partir do algoritmo descrito no paper _Time-to-depth conversion and seismic velocity estimation using time-migration velocity_ (Cameron, 2008)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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nwd4X/wChb0f/AMAYv/ia3aaa6YV6v8z+855NmH/wh3hf/oW9H/8AAGL/AOJpP+EO8Mf9C3pH/gDF/wDE1uUhrojWqfzP7zCUn3MP/hDvDH/QuaR/4Axf/E0f8Id4Y/6FzSP/AABi/wDia2qK2Vap/M/vMnKXcxP+EP8ADH/QuaR/4Axf/E0f8If4Y/6FzSP/AABi/wDia26SrVap/M/vIcpdzF/4Q7wx/wBC5pH/AIAxf/E0n/CH+GP+hc0j/wAAYv8A4mtuiqVaf8zFzS7mJ/wh/hj/AKFzSP8AwBi/+Jo/4Q/wx/0Lmkf+AMX/AMTW3SYp+2n/ADMXNLuYv/CH+GP+hc0j/wAAYv8A4mj/AIQ/wx/0Lmkf+AMX/wATW1iij2s/5mHNLuYv/CH+GP8AoXNI/wDAGL/4mj/hD/DH/QuaR/4Axf8AxNbVFP2s/wCZhzy7mL/wh/hj/oXNI/8AAGP/AOJo/wCEP8Mf9C5pH/gDF/8AE1tUUe1n/Mw55dzF/wCEP8Mf9C5pH/gDF/8AE0f8If4Y/wChc0j/AMAYv/ia2qMUvaz/AJmHNLuYv/CH+GP+hc0j/wAAY/8A4ml/4Q/wx/0Lmkf+AMX/AMTW1RSdaf8AM/vGpS7mL/wh/hj/AKFzSP8AwBi/+Jpf+EO8Mf8AQuaR/wCAMX/xNbVFQ61T+Z/eUpS7mL/wh3hj/oXNI/8AAGL/AOJo/wCEO8Mf9C5pH/gDF/8AE1tUVPtqn8z+8rml3MX/AIQ7wx/0Lmkf+AMX/wATR/wh3hj/AKFzSP8AwBi/+JrboqXWqfzP7yuaXc47xX4U8O2/g/W54NA0qKaOwndJEs41ZWEbEEEDgg969drznxj/AMiRr/8A2Dbj/wBFtXo1fRZJOUqc+Z31O3DNtO4UUUV7Z0hXx14sghfxt4iZokZjqt3yVB/5bPX2LXx/4pUnxn4h/wCwrd/+j3rDEO0T1cnipV2mr6fqjD+zQf8APCP/AL4FH2aD/nhH/wB8Cptpo2muPmfc+k9jD+VfcekfAaKOP4h3WxFXOlTZ2jGf3sNfR1fOnwKGPiHc/wDYKm/9Gw19F13UXeCPlMyio4qSXl+SCiiitThCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuW8Y/wDH94X/AOwq3/pHc11Nct4x/wCP7wv/ANhVv/SO5rkzD/dKv+GX5MCSjFLRivzDmJ5QxRS4oxS5h8olFLilqHMpRExRiloxUORaiFFLilxUORaiJilxS0Vm5GiiGKKKWs3ItREpcUtITUORaiFHApCaaW5pFJD80m4UzNJmixVh26m5pM0maqw0hSaTNJmmk00ikhcimk0ZptUUkBNMJpSeaaapIpCHrSGlppploKKKKYwoop2KAEApRSgUoFIlsUDNOAoApwHpUNkNigZpwFAFOAqWyGwApQKMU8D1qbktiLTxQBRSIbA0hopDWsUYyYlJRRXRFGEmFGaSitEiLhmlpKKLCuFBopKpITCmmnU01tAykJSGlpprogjnkxKQ0tIa6YmEhKSlpK2RkwoooqiQpMUtFO4rCUUtJVEhRRRQAUUUUAFFFFMQUUUUAFKKSikxoWikzS1LRSYUCiipaKuLRSUZqbDuY3jH/kSNf/7Btx/6LavRq848Y/8AIka//wBg24/9FtXo9fS5H/Dn6ndhdmFFFFe4dQV8heJwf+Ey8Qf9hW7/APR719e18jeJf+Rw8Qf9hW8/9HvXNivgXqe3kKviZf4X+aMfBowakorgufWch6J8DBj4h3H/AGCpv/RsFfRNfPPwP/5KHcf9gqb/ANGwV9DV6WH/AIaPis3VsZNen5IKKKK2PNCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuW8Yf8f8A4X/7Crf+kdzXU1y3jD/j/wDC/wD2FW/9I7muPMP9zq/4ZfkwJqKKK/LChaKM0UhhRRRUspC0UlLUMtC0tJRUNFJi0UUVDRaYtKKbS1m0WmLTe9OzSYqdi0xp6U2nkcUhHpTGmMNJTyMU3FMpMaaSnYpMU0xjSPem08ikI9KopMYaSnEYpMUykxh600ipCKbtNVcYykIp5HNJimVcbijFOxS4FFwuNxS4p22lApXFcQCngUYp4FS2S2IBTgKcBTgtS2Q2IBTgKUCnYqGyGxoFOAo6UZo1ZDkFJRRVxiQ5BTSaCaSt4xMJSCkzRmkzXRGJg5BRSZozWnKRzC0UmaM0+QOcWikzSZpqInIXNNozSE1tCJlKQE000pppNdMInPKQE0maM0ldEYmEpBRSZozWqiZOQtJmkzRmq5SeYWikzRmnyiuLRTaKfKHMOzSZpKKfKLmFopKKfKLmFozSZozT5RcwuaM0maM0cocwuaKTNFHIHMLmlzTaKnlK5h2aM02lzUuA1IXNLTc0ZpchXMY/jH/kSNf/AOwdcf8Aotq9Hrzbxgf+KJ1//sHXH/otq9Jr6DJVanL1PQwTvFhRRRXtHaFfJPiQf8Vdr/8A2Fbz/wBHvX1tXyZ4iGfFuv8A/YVvP/R71yYz+GvU+g4cV8VL/C/zRlYHpRgelSbR6UbR6V5tz7T2Z6B8Ev8Akoc//YKm/wDRsFfQlfPvwVGPiHN/2Cp//RsFfQVethv4SPgM7VsfUXp+SM+51mztNcsNImfZdX8U0tvkgB/K2blGTkth8gAHhWPGKx/EmvaxHqMeg+GLKC51l4hcSTXodbS0hyQGkZeWZyrKqrzwWOAvOf4v07UT478H6/b6fPd2GlfbTetBtZ41kiVFIQkM/c4QM3HAJIBk8P8AiHTP+Ez1bThPJFPqrpf20V3BJbSuViSKRBHIoYhRCrBsYbe4GfKcjc8oj07xJ4p0jWLHTvGmnaVHDqEvk22p6bckQiYqWSFo5cPuIR8MMglkXGSa7iuP1PWLfxJfyeHNPSc39jqFpPc+ZEVSKKKdJSzHtu8tlVT8zcMB5Z8yuwoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArlvGP/H94X/7Crf8ApHc11Nct4y/4/vC//YVb/wBI7muTHq+Eq/4ZfkwehLRTc0Zr8y9mZ+0HUU3NGaPZj5x+aM03NGan2Y+cfmimZpc1DplKY6lzTM0ZqHTLVQfmlzTM0ZqHTLUx+aWmZozUOmUqg+jNNzRmodMtVB+aOtNzRmodMpVB3FBGaTNGankZamG2kK+lLmlzS5WNTGbKaVqXNHFGpSmQ7abtqxgU3bRcpTIdoFN21Y2U3YafMUpEOymkVY20mynzD5iDFLtzU2yl20cwcxCF5p22pQnrS7aXMS5kYXin7afgUZpaslzEC0vFJmkzTUGzNzHZpKTNJmtFTM3MdSZpM0ma0VMh1BxNNJpM0hNaRpmcpi5pCaQmkJrohTMJVBc0maaTSZrqjSOeVQdmjNMzRmtFSM/aD80ZpmaM0eyD2g/NJmm5pM01SE6g7NITSE00mtoUjKVQUmkJpCaaTXTCkc8qg7NJmm5pM10RpmDqDs0ZpmaM1oqZDqDs0ZptFV7MnnHZozTaKPZhzjs0ZptFHsw5x2aTNJRTUBOYuaM03NGarkFzjs0lJmjNHILnFopM0Zo5A5haXNNzS0cg+YXNGaSipcB8w7NGabRmpcSuYdS5puaKXKPmMfxh/wAiTr3/AGDrj/0W1elV5n4v/wCRK17/ALB1x/6LavTK9vKlaEvU9XLneMgooor1T0Qr5P8AEAz4r17/ALCt5/6PevrCvlLXhnxVr3/YVvP/AEe9ceO/hr1PpOF1fGS/wv8ANGZto21Jto215Vz7z2Z2Hwp1XTdG8dSXGqahaWMDaZMiyXUyxKW82E4BYgZwCcexr3Sx8WeG9TvI7Ow8QaVd3UmdkMF7HI7YBJwoOTgAn8K8Z+DIx8Q5f+wVP/6Ngr36vZwv8Ff11PzfP1bMai9P/SUFFRzzw2tvLcXEscMESF5JJGCqigZJJPAAHOar6bq2m6zbtcaXqFpfQK5RpLWZZVDYBwSpIzgg49xXQeOF5qtjp9xa293cxxS3T7IVb+I5A/AbmRcnA3Oi9WUG5Wf9v0e81j+zvtdjPqdn+/8As3mI00GV279v3l+WTGfR/etCgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuV8Z8Xnhf8A7Crf+kdzXVVynjXi78Mf9hVv/SS5rnxivh6i/uv8iKjtBsduo3VFupd1fB/Vzzfbkm6jdUe6jdR9XD25LmjdUW73o3e9S8OUq5LmlzUW6jdUvDlKuS5pc1Fuo3VDw5arku6lzUW6jdWbw5SrkuaM1Huo3VDw5arkuaM1Huo3VDoFqsS5ozUe6jdUPDlKsS5oz71Huo3VDoFqsSZpc1Huo3VLoFKsSZpc1Fupd1Q8OUq5JmjNR5pc1DoFqsSZ96M1HuozUugUqxJmlyKjzRn3qfYFe2JMj0pM0zPvRmj2Ae2H5ozUeaN1UqBLrD80maZmk3VpGgZusSZpM0zdSbq1VAzdYfmjNR596M+9aqgZusPzSZpm6jdVqgQ6w/NITTN1ITWsaBnKsOJpCaYWpC1dMKBzzrDiaTNMLU3dXVGgc0q5JmjdUeaTNa+wM/bEuaM+9R7qM0vYB7Ykz70ZqPNGaFRG6o/NITTM0ma1jRMpVRxNITTc0ma6I0jGVQdmkzTc0ZrZUzF1B2aTNNzRmtPZkc47NGabmijkDnHZozTc0uaXIPnHZozTc0UuUOYdmjNNoo5Q5h2aM02ijlDmHZozTaWlYdxc0UlFFh3FoooqbDCiiipKFoooqSgoooqRmN4v/wCRK17/ALB1x/6LavTK8z8X/wDIla9/2Drj/wBFtXplezl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+ "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='velocityInversionResult.jpg', width=800)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/diffractionSimulation.jpg b/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/diffractionSimulation.jpg new file mode 100644 index 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zNsLZTCbYd4KWI?<+YJ{ib`3arIF-Wjq@>vOT(>*%Ub8pjAkK88C1Vb`ud>4KXnjI8 zpG*$ZU=uZ#sk*Jg#`8GxG1<05p*fBaM@D!xRZxNtq$g-3mgRHg_kA?~m*^o4yw3g@ z_xgZA`Onu+C;!8Y{4hH=X90wk4%s&bS@*i#Ndl-%li{l{^hTfY(RaVYAHX?w>j7WM zUt{?&y2iQyIMEofbN|7{AHeIeJ}@fOV8(DA4u|_P4+Kkpx3otc{{kFVz^G7zn~IB| zDp~)dL$j!5NJz*jt-+sDKt6iN_Ot)-+iytZc&NxHx$yAt@v$+9M*ct7)q5HDpX-YJ zi-fVH>in|{F5u-|I*Itx)$!GTNYYlG^vj#Qic#Dy~bM*Vk{{J4||1~@Q z?U0n#PF`85%)89bRE-g#u0)#r_sRD^pwWWS7mN?5yZQPWtE#GMf&L8Y&Cb`R|983F k{ZG}V|EGxm|8xb|t$8 Date: Sat, 5 Dec 2020 12:44:04 -0300 Subject: [PATCH 11/63] Scratch of NIP tomography program --- Mniptomo.c | 223 +++++++++++++++++++++++++++++++++++++++++++++++++++ SConstruct | 88 ++++++++++++++++++++ atela.c | 132 ++++++++++++++++++++++++++++++ eno3.c | 164 +++++++++++++++++++++++++++++++++++++ grid2.c | 173 +++++++++++++++++++++++++++++++++++++++ grid3.c | 122 ++++++++++++++++++++++++++++ raytrace.c | 231 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 7 files changed, 1133 insertions(+) create mode 100644 Mniptomo.c create mode 100644 SConstruct create mode 100644 atela.c create mode 100644 eno3.c create mode 100644 grid2.c create mode 100644 grid3.c create mode 100644 raytrace.c diff --git a/Mniptomo.c b/Mniptomo.c new file mode 100644 index 0000000..4c01c9f --- /dev/null +++ b/Mniptomo.c @@ -0,0 +1,223 @@ +/* NIP tomography */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +*/ + +#include + +#include + +#include "raytrace.h" + +int main(int argc, char* argv[]) +{ + bool velocity, sym, verb, escvar; + int is, n[2], im, nm, order, nshot, ndim; + int nt, nt1, nr, ir, it, i; + float t, dt, da=0., a0, amax, v0, deg2rad; + float x[2], p[2], d[2], o[2], **traj, *slow, **s, *a; + raytrace rt; + sf_file shots, vel, rays, angles; + + sf_init (argc,argv); + vel = sf_input("in"); + rays = sf_output("out"); + + /* get 2-D grid parameters */ + if (!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); + if (!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); + if (!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); + if (!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); + if (!sf_histfloat(vel,"o1",o)) o[0]=0.; + if (!sf_histfloat(vel,"o2",o+1)) o[1]=0.; + + /* additional parameters */ + if(!sf_getbool("vel",&velocity)) velocity=true; + /* If y, input is velocity; if n, slowness */ + if(!sf_getint("order",&order)) order=4; + /* Interpolation order */ + + if (!sf_getint("nt",&nt)) sf_error("Need nt="); + /* Number of time steps */ + if (!sf_getfloat("dt",&dt)) sf_error("Need dt="); + /* Sampling in time */ + + if (!sf_getbool("sym",&sym)) sym=true; + /* if y, use symplectic integrator */ + + if(!sf_getbool("verb",&verb)) verb=true; + /* Verbosity flag */ + if(!sf_getbool("escvar",&escvar)) escvar=false; + /* If y - output escape values, n - trajectories */ + + /* get shot locations */ + if (NULL != sf_getstring("shotfile")) { + /* file with shot locations */ + shots = sf_input("shotfile"); + if (!sf_histint(shots,"n1",&ndim) || 2 != ndim) + sf_error("Must have n1=2 in shotfile"); + if (!sf_histint(shots,"n2",&nshot)) + sf_error("No n2= in shotfile"); + + if (sf_histfloat(shots,"o2",&t)) sf_putfloat(rays,"o3",t); + if (sf_histfloat(shots,"d2",&t)) sf_putfloat(rays,"d3",t); + + s = sf_floatalloc2 (ndim,nshot); + sf_floatread(s[0],ndim*nshot,shots); + sf_fileclose (shots); + } else { + nshot = 1; + ndim = 2; + + s = sf_floatalloc2 (ndim,nshot); + + if (!sf_getfloat("zshot",&s[0][0])) s[0][0]=0.; + /* shot coordinates (if no shotfile) */ + if (!sf_getfloat("yshot",&s[0][1])) s[0][1]=o[1] + 0.5*(n[1]-1)*d[1]; + } + + deg2rad = SF_PI/180.; + + if (NULL != sf_getstring("anglefile")) { + /* file with initial angles */ + angles = sf_input("anglefile"); + + if (!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); + } else { + angles = NULL; + + if (!sf_getint("nr",&nr)) sf_error("Need nr="); + /* number of angles (if no anglefile) */ + if (!sf_getfloat("a0",&a0)) a0 = 0.; + /* minimum angle (if no anglefile) */ + if (!sf_getfloat("amax",&amax)) amax=360.; + /* maximum angle (if no anglefile) */ + + /* convert degrees to radians */ + a0 *= deg2rad; + amax *= deg2rad; + + /* figure out angle spacing */ + da = (nr > 1)? (amax - a0)/(nr-1) : 0.; + } + + a = sf_floatalloc(nr); + + /* specify output dimensions */ + nt1 = nt+1; + sf_putint (rays,"n1",escvar ? 4 : nt1); + sf_putint (rays,"n2",nr); + if( nshot > 1 ) sf_putint (rays,"n3",nshot); + sf_putfloat(rays,"o1",0.); + sf_putfloat(rays,"d1",dt); + if (NULL == angles) { + sf_putfloat(rays,"o2",a0/deg2rad); + sf_putfloat(rays,"d2",da/deg2rad); + } + if (!escvar) { + sf_settype (rays,SF_COMPLEX); + sf_putstring(rays,"label1","Time"); + sf_putstring(rays,"unit1","s"); + } else { + sf_putstring(rays,"label1","Escvar"); + sf_putstring(rays,"unit1",""); + } + sf_putstring(rays,"label2","Angle"); + sf_putstring(rays,"unit2","Degrees"); + sf_fileflush (rays,NULL); + sf_settype (rays,SF_FLOAT); + + /* get slowness squared */ + nm = n[0]*n[1]; + slow = sf_floatalloc(nm); + + sf_floatread(slow,nm,vel); + + for(im = 0; im < nm; im++){ + v0 = slow[im]; + slow[im] = velocity? 1./(v0*v0): v0*v0; + } + + /* initialize ray tracing object */ + rt = raytrace_init (2, sym, nt, dt, n, o, d, slow, order); + + free (slow); + + traj = sf_floatalloc2 (sym? ndim: 2*ndim,nt1); + + for( is = 0; is < nshot; is++) { /* loop over shots */ + /* initialize angles */ + if (NULL != angles) { + sf_floatread(a,nr,angles); + } else { + for (ir = 0; ir < nr; ir++) { + a[ir] = a0+da*ir; + } + } + if (verb) + sf_warning ("Shooting from z=%g, x=%g;", s[is][0], s[is][1]); + + for (ir = 0; ir < nr; ir++) { /* loop over rays */ + /* initialize position */ + x[0] = s[is][0]; + x[1] = s[is][1]; + + /* initialize direction */ + p[0] = -cosf(a[ir]); + p[1] = sinf(a[ir]); + + it = trace_ray (rt, x, p, traj); + if (it < 0) it = -it; /* keep side-exiting rays */ + + if (escvar) { + /* Write escape variables only */ + sf_floatwrite (traj[it],ndim,rays); /* z, x */ + t = it*dt; /* t */ + sf_floatwrite (&t,1,rays); + /* a */ + if (it > 0) { + i = it >= 2 ? it - 2 : it - 1; + /* Escape vector */ + x[0] = traj[it][0]; + x[1] = traj[it][1]; + x[0] -= traj[i][0]; + x[1] -= traj[i][1]; + /* Dot product with unit vector pointing upward */ + t = sqrt(x[0]*x[0] + x[1]*x[1]); /* Length */ + t = acos(-x[0]/t); + if (x[1] < 0) t = -t; + } else + t = a[ir]; + t /= deg2rad; + sf_floatwrite (&t,1,rays); + } else { + /* Write full trajectory */ + for (i=0; i < nt1; i++) { + if (0==it || it > i) { + sf_floatwrite (traj[i],ndim,rays); + } else { + sf_floatwrite (traj[it],ndim,rays); + } + } + } + } + } + if (verb) + sf_warning ("."); + + exit (0); +} diff --git a/SConstruct b/SConstruct new file mode 100644 index 0000000..acae1ad --- /dev/null +++ b/SConstruct @@ -0,0 +1,88 @@ +import os, sys, re +sys.path.append('../../framework') +import bldutil + +# Put your name programs in progs variable +# without 'M' preffix and '.c' extension +progs = ''' +niptomo +''' + +try: # distributed version + Import('env root pkgdir bindir') + env = env.Clone() +except: # local version + env = bldutil.Debug() + cflags = env.get('CFLAGS','') + if isinstance (cflags, str): + env['CFLAGS'] = cflags.replace('-O3','-g').replace('-xHOST','') + env.Prepend(CPPDEFINES=['DEBUG']) + root = None + SConscript('../../api/c/SConstruct') + +# Check if the TDD option is set +if ARGUMENTS.get('tdd',0): + print("TDD option is set...") + ## Set a c flag to compilation process + ## the flag -Wp,-DTDD=1 informs to the preprocessor that TDD=1 + env['CFLAGS'] = '-Wp,-DTDD=1' + +if sys.platform.startswith('linux'): + env.Prepend(CPPDEFINES=['LINUX']) + +src = Glob('[a-z]*.c') + +sse = None +#sse = env.get('SSE') +CC = env.get('CC') +if CC.rfind('icc') >= 0: + env.Append(CCFLAGS=['-restrict','-wd188']) +threads = env.get('PTHREADS') +if threads: + env.Prepend(CPPDEFINES=['PTHREADS']) + +if sse: + env.Prepend(CPPPATH=['../../include'], + LIBPATH=['../../lib'], + LIBS=[env.get('DYNLIB','')+'rsf'], + CCFLAGS=[sse],CPPDEFINES=['HAVE_SSE']) +else: + env.Prepend(CPPPATH=['../../include'], + LIBPATH=['../../lib'], + LIBS=[env.get('DYNLIB','')+'rsf']) + +if env['PLATFORM'] == 'darwin': + # bug in Mac's gcc handling of OpenMP + env.Append(CPPDEFINES=['_FORTIFY_SOURCE=0'],CCFLAGS='-O1') + +for source in src: + if source.rstr() == 'einspline.c': + einspl_o = env.StaticObject('einspline.c') + env.Depends(einspl_o,'einspline.h') + else: + inc = env.RSF_Include(source,prefix='') + obj = env.StaticObject(source) + env.Ignore(inc,inc) + env.Depends(obj,inc) + +# Regular progams +mains = Split(progs) +for prog in mains: + sources = ['M' + prog] + bldutil.depends(env,sources,'M'+prog) + prog = env.Program(prog,[x + '.c' for x in sources]) + if root: + env.Install(bindir,prog) + +###################################################################### +# SELF-DOCUMENTATION +###################################################################### +if root: + user = os.path.basename(os.getcwd()) + main = 'sf%s.py' % user + + docs = [env.Doc(prog,'M' + prog) for prog in mains] + env.Depends(docs,'#/framework/rsf/doc.py') + + doc = env.RSF_Docmerge(main,docs) + env.Install(pkgdir,doc) diff --git a/atela.c b/atela.c new file mode 100644 index 0000000..42512e5 --- /dev/null +++ b/atela.c @@ -0,0 +1,132 @@ +/* Symplectic Runge-Kutta ray tracing. */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + */ + +#include +#include + +#include +/*^*/ + +#include "atela.h" + +static const double a1 = 0.5153528374311229364; +static const double a2 = -0.085782019412973646; +static const double a3 = 0.4415830236164665242; +static const double a4 = 0.1288461583653841854; +static const double b1 = 0.1344961992774310892; +static const double b2 = -0.2248198030794208058; +static const double b3 = 0.7563200005156682911; +static const double b4 = 0.3340036032863214255; +/* magic coefficients from + R.I.McLachlan and P.Atela "The accuracy of symplectic integrators". + Nonlinearity, 5 (1992), 541-562. + */ + +int atela_step (int dim /* dimensionality */, + int nt /* number of ray tracing steps */, + float dt /* step in time */, + bool intime /* if step in time (not sigma) */, + float* x /* [dim] point location */, + float* p /* [dim] ray parameter vector */, + void* par /* parameters for slowness functions */, + void (*vgrad)(void*,float*,float*) + /* function returning 1/2*(gradient of slowness squared) */, + float (*slow2)(void*,float*) + /* function returning slowness squared */, + int (*term)(void*,float*) + /* function returning 1 if the ray needs to terminate */, + float** traj /* [nt+1][dim] - ray trajectory (output) */) +/*< ray tracing + Note: + 1. Values of x and p are changed inside the function. + 2. The trajectory traj is stored as follows: + {z0,y0,z1,y1,z2,y2,...} in 2-D + {z0,y0,x0,z1,y1,x1,...} in 3-D + 3. Vector p points in the direction of the ray. + The length of the vector is not important. + Example initialization: + p[0] = cos(a); p[1] = sin(a) in 2-D, a is between 0 and 2*pi radians + p[0] = cos(b); p[1] = sin(b)*cos(a); p[2] = sin(b)*sin(a) in 3-D + b is inclination between 0 and pi radians + a is azimuth between 0 and 2*pi radians + 4. The output code for it = atela_step(...) + it=0 - ray traced to the end without termination + it>0 - ray terminated + The total traveltime along the ray is + nt*dt if (it = 0); abs(it)*dt otherwise + >*/ +{ + int it, i; + float f[3]; + float v, h=0.; + double sum, one; + + if (traj != NULL) { + for (i=0; i < dim; i++) { + traj[0][i] = x[i]; + } + } + + if (!intime) h = dt; + + for (it = 0; it < nt; it++) { + v = slow2(par, x); + sum = 0.; + for (i=0; i < dim; i++) { + sum += p[i]*p[i]; + } + one = sqrt(v/sum); + for (i=0; i < dim; i++) { + p[i] *= one; /* enforce the eikonal equation */ + } + + if (intime) h = dt/v; + + vgrad (par, x, f); + for (i=0; i < dim; i++) { + p[i] += b1*h*f[i]; + x[i] += a1*h*p[i]; + } + + vgrad (par, x, f); + for (i=0; i < dim; i++) { + p[i] += b2*h*f[i]; + x[i] += a2*h*p[i]; + } + + vgrad (par, x, f); + for (i=0; i < dim; i++) { + p[i] += b3*h*f[i]; + x[i] += a3*h*p[i]; + } + + vgrad (par, x, f); + for (i=0; i < dim; i++) { + p[i] += b4*h*f[i]; + x[i] += a4*h*p[i]; + if (traj != NULL) traj[it+1][i] = x[i]; + } + + if (term != NULL && term (par, x)) return (it+1); + } + + return 0; +} + +/* $Id$ */ diff --git a/eno3.c b/eno3.c new file mode 100644 index 0000000..be42e55 --- /dev/null +++ b/eno3.c @@ -0,0 +1,164 @@ +/* 3-D ENO interpolation */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +*/ + +#include +/*^*/ + +#include "eno3.h" + +#ifndef _eno3_h + +typedef struct Eno3 *eno3; +/* abstract data type */ +/*^*/ + +#endif + +struct Eno3 { + int order, ng, n1, n2, n3; + sf_eno **ent; + sf_eno2 jnt; + float **f, **f1; +}; +/* concrete data type */ + +eno3 eno3_init (int order /* interpolation order */, + int n1, int n2, int n3 /* data dimensions */) +/*< Initialize interpolation object >*/ +{ + eno3 pnt; + int i2, i3; + + pnt = (eno3) sf_alloc(1,sizeof(*pnt)); + pnt->order = order; + pnt->n1 = n1; + pnt->n2 = n2; + pnt->n3 = n3; + pnt->ng = 2*order-2; + if (pnt->ng > n2 || pnt->ng > n3) + sf_error("%s: ng=%d is too big",__FILE__,pnt->ng); + pnt->jnt = sf_eno2_init (order, pnt->ng, pnt->ng); + pnt->f = sf_floatalloc2(pnt->ng,pnt->ng); + pnt->f1 = sf_floatalloc2(pnt->ng,pnt->ng); + pnt->ent = (sf_eno**) sf_alloc(n3,sizeof(sf_eno*)); + for (i3 = 0; i3 < n3; i3++) { + pnt->ent[i3] = (sf_eno*) sf_alloc(n2,sizeof(sf_eno)); + for (i2 = 0; i2 < n2; i2++) { + pnt->ent[i3][i2] = sf_eno_init (order, n1); + } + } + + return pnt; +} + +void eno3_set (eno3 pnt, float*** c /* data [n3][n2][n1] */) +/*< Set the interpolation table. c can be changed or freed afterwords. >*/ +{ + int i2, i3; + + for (i3 = 0; i3 < pnt->n3; i3++) { + for (i2 = 0; i2 < pnt->n2; i2++) { + sf_eno_set (pnt->ent[i3][i2], c[i3][i2]); + } + } +} + +void eno3_set1 (eno3 pnt, float* c /* data [n3*n2*n1] */) +/*< Set the interpolation table. c can be changed or freed afterwords. >*/ +{ + int i2, i3; + + for (i3 = 0; i3 < pnt->n3; i3++) { + for (i2 = 0; i2 < pnt->n2; i2++) { + sf_eno_set (pnt->ent[i3][i2], c+(pnt->n1)*(i2+(pnt->n2)*i3)); + } + } +} + +void eno3_close (eno3 pnt) +/*< Free internal storage. >*/ +{ + int i2, i3; + + sf_eno2_close (pnt->jnt); + for (i3 = 0; i3 < pnt->n3; i3++) { + for (i2 = 0; i2 < pnt->n2; i2++) { + sf_eno_close (pnt->ent[i3][i2]); + } + free (pnt->ent[i3]); + } + free (pnt->ent); + free (pnt->f[0]); + free (pnt->f); + free (pnt->f1[0]); + free (pnt->f1); + free (pnt); +} + +void eno3_apply (eno3 pnt, + int i, int j, int k /* grid location */, + float x, float y, float z /* offsets from grid */, + float* f /* output data */, + float* f1 /* output derivative [3] */, + der what /* to compute [FUNC|DER|BOTH] */) +/*< Apply interpolation. >*/ +{ + int i2, i3, b2, b3; + float g; + + if (j-pnt->order+2 < 0) { + b2 = 0; + } else if (j+pnt->order-1 > pnt->n2-1) { + b2 = pnt->n2 - pnt->ng; + } else { + b2 = j-pnt->order+2; + } + + j -= b2; + + + if (k-pnt->order+2 < 0) { + b3 = 0; + } else if (k+pnt->order-1 > pnt->n3-1) { + b3 = pnt->n3 - pnt->ng; + } else { + b3 = k-pnt->order+2; + } + + k -= b3; + + for (i3 = 0; i3 < pnt->ng; i3++) { + for (i2 = 0; i2 < pnt->ng; i2++) { + sf_eno_apply (pnt->ent[b3+i3][b2+i2],i,x, + &(pnt->f[i3][i2]), + &(pnt->f1[i3][i2]), + (what==FUNC? FUNC: BOTH)); + } + } + + sf_eno2_set (pnt->jnt,pnt->f); + sf_eno2_apply (pnt->jnt,j,k,y,z,f,f1+1,what); + + if (what != FUNC) { + sf_eno2_set (pnt->jnt,pnt->f1); + sf_eno2_apply(pnt->jnt,j,k,y,z,f1,&g,FUNC); + } +} + +/* $Id$ */ diff --git a/grid2.c b/grid2.c new file mode 100644 index 0000000..af3fc84 --- /dev/null +++ b/grid2.c @@ -0,0 +1,173 @@ +/* 2-D velocity grid for ray tracing. */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +*/ + +#include + +#include + +#include "grid2.h" + +#ifndef _grid2_h + +typedef struct Grid2* grid2; +/* abstract data type */ +/*^*/ + +#endif + +struct Grid2 { + sf_eno2 pnt; + float ***vel; + int n1, n2; + float o1, d1, o2, d2; +}; +/* concrete data type */ + +grid2 grid2_init (int n1, float o1, float d1 /* first axis */, + int n2, float o2, float d2 /* second axis */, + float *slow2 /* data values [n1*n2] */, + int order /* interpolation order */) +/*< Initialize grid object >*/ +{ + int i, j; + float v, vgrad[2]; + grid2 grd; + + grd = (grid2) sf_alloc(1,sizeof(*grd)); + + grd->n1 = n1; grd->o1 = o1; grd->d1 = d1; + grd->n2 = n2; grd->o2 = o2; grd->d2 = d2; + + grd->pnt = sf_eno2_init (order != 0 ? order : 3, n1, n2); + sf_eno2_set1 (grd->pnt, slow2); + grd->vel = NULL; + if (order <= 0) { + grd->vel = sf_floatalloc3 (3, n1, n2); + for (j = 0; j < n2; j++) { + for (i = 0; i < n1; i++) { + sf_eno2_apply (grd->pnt, i, j, 0., 0., &v, vgrad, BOTH); + grd->vel[j][i][0] = slow2[j*n1 + i]; + grd->vel[j][i][1] = 0.5*vgrad[0]/d1; + grd->vel[j][i][2] = 0.5*vgrad[1]/d2; + } + } + sf_eno2_close (grd->pnt); + } + + return grd; +} + +float grid2_vel(void* par /* grid */, + float* xy /* coordinate [2] */) +/*< Extract a value from the grid >*/ +{ + grid2 grd; + float x, y, f, f1[2]; + int i, j; + + grd = (grid2) par; + x = (xy[0]-grd->o1)/grd->d1; i = x; x -= i; + y = (xy[1]-grd->o2)/grd->d2; j = y; y -= j; + + if (grd->vel) { + if (i < 0) { + i = 0; x = 0.0; + } + if (i >= (grd->n1 - 1)) { + i = grd->n1 - 2; x = 1.0; + } + if (j < 0) { + j = 0; y = 0.0; + } + if (j >= (grd->n2 - 1)) { + j = grd->n2 - 2; y = 1.0; + } + f = grd->vel[j][i][0]*(1.0 - y)*(1.0 - x) + + grd->vel[j][i + 1][0]*(1.0 - y)*x + + grd->vel[j + 1][i][0]*y*(1.0 - x) + + grd->vel[j + 1][i + 1][0]*y*x; + } else + sf_eno2_apply(grd->pnt, i, j, x, y, &f, f1, FUNC); + return f; +} + +void grid2_vgrad(void* par /* grid */, + float* xy /* coordinate [2] */, + float* grad /* output gradient [2] */) +/*< Extract (1/2 of) gradient values from the grid >*/ +{ + grid2 grd; + float x, y, f, f1[2]; + int i, j; + + grd = (grid2) par; + x = (xy[0]-grd->o1)/grd->d1; i = floor(x); x -= i; + y = (xy[1]-grd->o2)/grd->d2; j = floor(y); y -= j; + + if (grd->vel) { + if (i < 0) { + i = 0; x = 0.0; + } + if (i >= (grd->n1 - 1)) { + i = grd->n1 - 2; x = 1.0; + } + if (j < 0) { + j = 0; y = 0.0; + } + if (j >= (grd->n2 - 1)) { + j = grd->n2 - 2; y = 1.0; + } + grad[0] = grd->vel[j][i][1]*(1.0 - y)*(1.0 - x) + + grd->vel[j][i + 1][1]*(1.0 - y)*x + + grd->vel[j + 1][i][1]*y*(1.0 - x) + + grd->vel[j + 1][i + 1][1]*y*x; + grad[1] = grd->vel[j][i][2]*(1.0 - y)*(1.0 - x) + + grd->vel[j][i + 1][2]*(1.0 - y)*x + + grd->vel[j + 1][i][2]*y*(1.0 - x) + + grd->vel[j + 1][i + 1][2]*y*x; + } else { + sf_eno2_apply(grd->pnt, i, j, x, y, &f, f1, DER); + grad[0] = 0.5*f1[0]/grd->d1; + grad[1] = 0.5*f1[1]/grd->d2; + } +} + +int grid2_term (void* par /* grid */, + float* xy /* location [2] */) +/*< Termination criterion. returns 0 if xy (data coordinates) + are inside the grid >*/ +{ + grid2 grd; + + grd = (grid2) par; + return (xy[0] < grd->o1 || xy[0] > grd->o1 + (grd->n1-1)*grd->d1 || + xy[1] < grd->o2 || xy[1] > grd->o2 + (grd->n2-1)*grd->d2); +} + +void grid2_close(grid2 grd) +/*< Free internal storage >*/ +{ + if (grd->vel) { + free (grd->vel[0][0]); free (grd->vel[0]); free (grd->vel); + } else + sf_eno2_close (grd->pnt); + free (grd); +} + +/* $Id$ */ diff --git a/grid3.c b/grid3.c new file mode 100644 index 0000000..3af2c3f --- /dev/null +++ b/grid3.c @@ -0,0 +1,122 @@ +/* 3-D velocity grid for ray tracing. */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +*/ + +#include + +#include + +#include "grid3.h" +#include "eno3.h" + +#ifndef _grid3_h + +typedef struct Grid3* grid3; +/* abstract data type */ +/*^*/ + +#endif + +struct Grid3 { + eno3 pnt; + int n1, n2, n3; + float o1, d1, o2, d2, o3, d3; +}; +/* concrete data type */ + +grid3 grid3_init (int n1, float o1, float d1 /* first axis */, + int n2, float o2, float d2 /* second axis */, + int n3, float o3, float d3 /* third axis */, + float *slow2 /* data [n1*n2*n3] */, + int order /* interpolation order */) +/*< Initialize 3-D grid. >*/ +{ + grid3 grd; + + grd = (grid3) sf_alloc(1,sizeof(*grd)); + + grd->n1 = n1; grd->o1 = o1; grd->d1 = d1; + grd->n2 = n2; grd->o2 = o2; grd->d2 = d2; + grd->n3 = n3; grd->o3 = o3; grd->d3 = d3; + + grd->pnt = eno3_init (order, n1, n2, n3); + eno3_set1 (grd->pnt, slow2); + + return grd; +} + +float grid3_vel(void* par /* grid */, + float* xy /* location [3] */) +/*< Extract a value from the grid. >*/ +{ + grid3 grd; + float x, y, z, f, f1[3]; + int i, j, k; + + grd = (grid3) par; + x = (xy[0]-grd->o1)/grd->d1; i = floor(x); x -= i; + y = (xy[1]-grd->o2)/grd->d2; j = floor(y); y -= j; + z = (xy[2]-grd->o3)/grd->d3; k = floor(z); z -= k; + + eno3_apply(grd->pnt, i, j, k, x, y, z, &f, f1, FUNC); + return f; +} + +void grid3_vgrad(void* par /* grid */, + float* xy /* location [3] */, + float* grad /* output gradient [3] */) +/*< Extract (1/2 of) gradient values from the grid >*/ +{ + grid3 grd; + float x, y, z, f, f1[3]; + int i, j, k; + + grd = (grid3) par; + x = (xy[0]-grd->o1)/grd->d1; i = floor(x); x -= i; + y = (xy[1]-grd->o2)/grd->d2; j = floor(y); y -= j; + z = (xy[2]-grd->o3)/grd->d3; k = floor(z); z -= k; + + eno3_apply(grd->pnt, i, j, k, x, y, z, &f, f1, DER); + + grad[0] = 0.5*f1[0]/grd->d1; + grad[1] = 0.5*f1[1]/grd->d2; + grad[2] = 0.5*f1[2]/grd->d3; +} + +int grid3_term (void *par /* grid */, + float* xy /* location [3] */) +/*< Termination criterion. Returns 0 if xy (data coordinates) + are inside the grid >*/ +{ + grid3 grd; + + grd = (grid3) par; + return (xy[0] < grd->o1 || xy[0] > grd->o1 + (grd->n1-1)*grd->d1 || + xy[1] < grd->o2 || xy[1] > grd->o2 + (grd->n2-1)*grd->d2 || + xy[2] < grd->o3 || xy[2] > grd->o3 + (grd->n3-1)*grd->d3); +} + +void grid3_close(grid3 grd) +/*< Free internal storage >*/ +{ + eno3_close (grd->pnt); + free (grd); +} + +/* $Id$ */ + diff --git a/raytrace.c b/raytrace.c new file mode 100644 index 0000000..5a1b433 --- /dev/null +++ b/raytrace.c @@ -0,0 +1,231 @@ +/* Ray tracing interface. */ +/* + Copyright (C) 2004 University of Texas at Austin + + 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 2 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, write to the Free Software + Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + */ + +#include + +#include "raytrace.h" +#include "grid2.h" +#include "grid3.h" +#include "atela.h" + +#ifndef _raytrace_h + +typedef struct RayTrace* raytrace; +/* abstract data type */ +/*^*/ + +#endif + +struct RayTrace { + bool sym; + int dim, nt; + float dt, z0; + grid3 grd3; + grid2 grd2; +}; +/* concrete data type */ + +static void iso_rhs(void* par, float* y, float* f) +/* right-hand side for isotropic raytracing */ +{ + raytrace rt; + int i, dim; + float s2, sds[3]; + + rt = (raytrace) par; + dim = rt->dim; + + switch (dim) { + case 2: + s2 = grid2_vel(rt->grd2,y); + grid2_vgrad(rt->grd2,y,sds); + break; + case 3: + s2 = grid3_vel(rt->grd3,y); + grid3_vgrad(rt->grd3,y,sds); + break; + default: + s2 = 0.; + sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); + } + + for (i=0; i < dim; i++) { + f[i] = y[i+dim]/s2; /* p/s^2 */ + f[i+dim] = sds[i]/s2; /* 1/2 grad(s^2)/s^2 */ + } +} + +static int term(void* par, float* y) +/* grid termination */ +{ + raytrace rt; + + rt = (raytrace) par; + + switch (rt->dim) { + case 2: + return grid2_term(rt->grd2,y); + case 3: + return grid3_term(rt->grd3,y); + default: + sf_error("%s: Cannot raytrace with dim=%d",__FILE__,rt->dim); + return 0; + } +} + +raytrace raytrace_init(int dim /* dimensionality (2 or 3) */, + bool sym, /* if symplectic */ + int nt /* number of ray tracing steps */, + float dt /* ray tracing step (in time) */, + int* n /* slowness dimensions [dim] */, + float* o, float* d /* slowness grid [dim] */, + float* slow2 /* slowness squared [n3*n2*n1] */, + int order /* interpolation order */) +/*< Initialize ray tracing object. + * Increasing order increases accuracy but + decreases efficiency. Recommended values: 3 or 4. + * slow2 can be changed or deallocated after + raytrace_init. + >*/ +{ + raytrace rt; + + rt = (raytrace) sf_alloc (1,sizeof(*rt)); + + rt->dim = dim; + rt->sym = sym; + rt->nt = nt; + rt->dt = dt; + rt->z0 = o[0]; + + switch (dim) { + case 2: + rt->grd2 = grid2_init (n[0], o[0], d[0], + n[1], o[1], d[1], + slow2, order); + break; + case 3: + rt->grd3 = grid3_init (n[0], o[0], d[0], + n[1], o[1], d[1], + n[2], o[2], d[2], + slow2, order); + break; + default: + sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); + } + + return rt; +} + +void raytrace_close (raytrace rt) +/*< Free internal storage >*/ +{ + switch (rt->dim) { + case 2: + grid2_close (rt->grd2); + break; + case 3: + grid3_close (rt->grd3); + break; + } + free (rt); +} + +int trace_ray (raytrace rt /* ray tracing object */, + float* x /* point location {z,y,x} [dim] */, + float* p /* ray parameter vector [dim] */, + float** traj /* output ray trajectory [nt+1,dim] */) +/*< Trace a ray. + * Values of x and p are changed inside the function. + * The trajectory traj is stored as follows: + {z0,y0,z1,y1,z2,y2,...} in 2-D + {z0,y0,x0,z1,y1,x1,...} in 3-D + * Vector p points in the direction of the ray. + The length of the vector is not important. + Example initialization: + p[0] = cos(a); p[1] = sin(a) in 2-D, a is between 0 and 2*pi radians + p[0] = cos(b); p[1] = sin(b)*cos(a); p[2] = sin(b)*sin(a) in 3-D + b is inclination between 0 and pi radians + a is azimuth between 0 and 2*pi radians + * The output code for it = trace_ray(...) + it=0 - ray traced to the end without leaving the grid + it>0 - ray exited at the top of the grid + it<0 - ray exited at the side or bottom of the grid + * The total traveltime along the ray is + nt*dt if (it = 0); abs(it)*dt otherwise + >*/ +{ + int i, dim, it=0, nt; + float y[6], s2; + + dim = rt->dim; + nt = rt->nt; + + if (!rt->sym) { + switch (dim) { + case 2: + s2 = grid2_vel(rt->grd2,x); + break; + case 3: + s2 = grid3_vel(rt->grd3,x); + break; + default: + s2 = 0.; + sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); + } + + for (i=0; i < dim; i++) { + y[i] = x[i]; + y[i+dim] = p[i]*sqrtf(s2); + } + + sf_runge_init(2*dim, nt, rt->dt); + it = sf_ode23_step (y, rt,iso_rhs,term,traj); + sf_runge_close(); + + for (i=0; i < dim; i++) { + x[i] = y[i]; + p[i] = y[i+dim]; + } + } else { + switch (dim) { + case 2: + it = atela_step (dim, nt, rt->dt, true, x, p, + rt->grd2, + grid2_vgrad, grid2_vel, grid2_term, traj); + break; + case 3: + it = atela_step (rt->dim, nt, rt->dt, true, x, p, + rt->grd3, + grid3_vgrad, grid3_vel, grid3_term, traj); + break; + default: + sf_error("%s: cannot handle %d dimensions",__FILE__,rt->dim); + break; + } + } + + if (it > 0 && x[0] > rt->z0) { + return (-it); /* exit through the side or bottom */ + } else { + return it; + } +} + +/* $Id$ */ From 0e52cc92f49fd858a940917bb669f49907da9ec9 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 5 Dec 2020 16:29:08 -0300 Subject: [PATCH 12/63] Scratch of sfniptomo program and test #14 The program sfniptomo setups the NIP model used in NIP tomography. The purpose of this program is to trace normal rays from the surface, emergence angle and t0 given, until the time t0 is used up. The ray end point can be used as a NIP source during tomography. --- Mniptomo.c | 341 ++++++++++----------------- TDD/Makefile | 9 + TDD/gdb_script | 9 + TDD/niptomoDebug/README.md | 24 ++ TDD/niptomoDebug/SConstruct | 64 +++++ experiments/setUpNIPModel/SConstruct | 64 +++++ 6 files changed, 298 insertions(+), 213 deletions(-) create mode 100644 TDD/gdb_script create mode 100644 TDD/niptomoDebug/README.md create mode 100644 TDD/niptomoDebug/SConstruct create mode 100644 experiments/setUpNIPModel/SConstruct diff --git a/Mniptomo.c b/Mniptomo.c index 4c01c9f..cad8a06 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -1,21 +1,4 @@ -/* NIP tomography */ -/* - Copyright (C) 2004 University of Texas at Austin - - 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 2 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, write to the Free Software - Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA -*/ +/* NIP tomography ray tracer */ #include @@ -25,199 +8,131 @@ int main(int argc, char* argv[]) { - bool velocity, sym, verb, escvar; - int is, n[2], im, nm, order, nshot, ndim; - int nt, nt1, nr, ir, it, i; - float t, dt, da=0., a0, amax, v0, deg2rad; - float x[2], p[2], d[2], o[2], **traj, *slow, **s, *a; - raytrace rt; - sf_file shots, vel, rays, angles; - - sf_init (argc,argv); - vel = sf_input("in"); - rays = sf_output("out"); - - /* get 2-D grid parameters */ - if (!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); - if (!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); - if (!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); - if (!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); - if (!sf_histfloat(vel,"o1",o)) o[0]=0.; - if (!sf_histfloat(vel,"o2",o+1)) o[1]=0.; - - /* additional parameters */ - if(!sf_getbool("vel",&velocity)) velocity=true; - /* If y, input is velocity; if n, slowness */ - if(!sf_getint("order",&order)) order=4; - /* Interpolation order */ - - if (!sf_getint("nt",&nt)) sf_error("Need nt="); - /* Number of time steps */ - if (!sf_getfloat("dt",&dt)) sf_error("Need dt="); - /* Sampling in time */ - - if (!sf_getbool("sym",&sym)) sym=true; - /* if y, use symplectic integrator */ - - if(!sf_getbool("verb",&verb)) verb=true; - /* Verbosity flag */ - if(!sf_getbool("escvar",&escvar)) escvar=false; - /* If y - output escape values, n - trajectories */ - - /* get shot locations */ - if (NULL != sf_getstring("shotfile")) { - /* file with shot locations */ - shots = sf_input("shotfile"); - if (!sf_histint(shots,"n1",&ndim) || 2 != ndim) - sf_error("Must have n1=2 in shotfile"); - if (!sf_histint(shots,"n2",&nshot)) - sf_error("No n2= in shotfile"); - - if (sf_histfloat(shots,"o2",&t)) sf_putfloat(rays,"o3",t); - if (sf_histfloat(shots,"d2",&t)) sf_putfloat(rays,"d3",t); - - s = sf_floatalloc2 (ndim,nshot); - sf_floatread(s[0],ndim*nshot,shots); - sf_fileclose (shots); - } else { - nshot = 1; - ndim = 2; - - s = sf_floatalloc2 (ndim,nshot); - - if (!sf_getfloat("zshot",&s[0][0])) s[0][0]=0.; - /* shot coordinates (if no shotfile) */ - if (!sf_getfloat("yshot",&s[0][1])) s[0][1]=o[1] + 0.5*(n[1]-1)*d[1]; - } - - deg2rad = SF_PI/180.; - - if (NULL != sf_getstring("anglefile")) { - /* file with initial angles */ - angles = sf_input("anglefile"); - - if (!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); - } else { - angles = NULL; - - if (!sf_getint("nr",&nr)) sf_error("Need nr="); - /* number of angles (if no anglefile) */ - if (!sf_getfloat("a0",&a0)) a0 = 0.; - /* minimum angle (if no anglefile) */ - if (!sf_getfloat("amax",&amax)) amax=360.; - /* maximum angle (if no anglefile) */ - - /* convert degrees to radians */ - a0 *= deg2rad; - amax *= deg2rad; - - /* figure out angle spacing */ - da = (nr > 1)? (amax - a0)/(nr-1) : 0.; - } - - a = sf_floatalloc(nr); - - /* specify output dimensions */ - nt1 = nt+1; - sf_putint (rays,"n1",escvar ? 4 : nt1); - sf_putint (rays,"n2",nr); - if( nshot > 1 ) sf_putint (rays,"n3",nshot); - sf_putfloat(rays,"o1",0.); - sf_putfloat(rays,"d1",dt); - if (NULL == angles) { - sf_putfloat(rays,"o2",a0/deg2rad); - sf_putfloat(rays,"d2",da/deg2rad); - } - if (!escvar) { - sf_settype (rays,SF_COMPLEX); - sf_putstring(rays,"label1","Time"); - sf_putstring(rays,"unit1","s"); - } else { - sf_putstring(rays,"label1","Escvar"); - sf_putstring(rays,"unit1",""); - } - sf_putstring(rays,"label2","Angle"); - sf_putstring(rays,"unit2","Degrees"); - sf_fileflush (rays,NULL); - sf_settype (rays,SF_FLOAT); - - /* get slowness squared */ - nm = n[0]*n[1]; - slow = sf_floatalloc(nm); - - sf_floatread(slow,nm,vel); - - for(im = 0; im < nm; im++){ - v0 = slow[im]; - slow[im] = velocity? 1./(v0*v0): v0*v0; - } - - /* initialize ray tracing object */ - rt = raytrace_init (2, sym, nt, dt, n, o, d, slow, order); - - free (slow); - - traj = sf_floatalloc2 (sym? ndim: 2*ndim,nt1); - - for( is = 0; is < nshot; is++) { /* loop over shots */ - /* initialize angles */ - if (NULL != angles) { - sf_floatread(a,nr,angles); - } else { - for (ir = 0; ir < nr; ir++) { - a[ir] = a0+da*ir; - } - } - if (verb) - sf_warning ("Shooting from z=%g, x=%g;", s[is][0], s[is][1]); - - for (ir = 0; ir < nr; ir++) { /* loop over rays */ - /* initialize position */ - x[0] = s[is][0]; - x[1] = s[is][1]; - - /* initialize direction */ - p[0] = -cosf(a[ir]); - p[1] = sinf(a[ir]); - - it = trace_ray (rt, x, p, traj); - if (it < 0) it = -it; /* keep side-exiting rays */ - - if (escvar) { - /* Write escape variables only */ - sf_floatwrite (traj[it],ndim,rays); /* z, x */ - t = it*dt; /* t */ - sf_floatwrite (&t,1,rays); - /* a */ - if (it > 0) { - i = it >= 2 ? it - 2 : it - 1; - /* Escape vector */ - x[0] = traj[it][0]; - x[1] = traj[it][1]; - x[0] -= traj[i][0]; - x[1] -= traj[i][1]; - /* Dot product with unit vector pointing upward */ - t = sqrt(x[0]*x[0] + x[1]*x[1]); /* Length */ - t = acos(-x[0]/t); - if (x[1] < 0) t = -t; - } else - t = a[ir]; - t /= deg2rad; - sf_floatwrite (&t,1,rays); - } else { - /* Write full trajectory */ - for (i=0; i < nt1; i++) { - if (0==it || it > i) { - sf_floatwrite (traj[i],ndim,rays); - } else { - sf_floatwrite (traj[it],ndim,rays); - } - } - } - } - } - if (verb) - sf_warning ("."); - - exit (0); + bool verb; + int order=4; /* Interpolation order */ + int n[2]; + float d[2]; + float o[2]; + float t; + float** s; + int ndim; + int nshot; + int nm; + float deg2rad; + float* a; + int nr; + float* slow; + int im; + float v0; + int nt0; + float dt0; + float* t0; + int nt; + int it; + float** traj; + float x[2]; + float p[2]; + int i; + sf_file shots, vel, rays, angles, t0s; + raytrace rt; + + sf_init(argc,argv); + + shots = sf_input("shotsfile"); + vel = sf_input("in"); + rays = sf_output("out"); + t0s = sf_input("t0s"); + angles = sf_input("anglefile"); + + /* get 2D grid parameters */ + if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); + if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); + if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); + if(!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); + if(!sf_histfloat(vel,"o1",o)) o[0]=0.; + if(!sf_histfloat(vel,"o2",o+1)) o[1]=0.; + + if(!sf_getbool("verb",&verb)) verb=true; + /* verbose */ + + if(verb){ + sf_warning("Verbose on"); + } + + if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) sf_error("Must have n1=2 in shotsfile"); + + if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); + + if(sf_histfloat(shots,"o2",&t)) sf_putfloat(rays,"o3",t); + if(sf_histfloat(shots,"d2",&t)) sf_putfloat(rays,"d3",t); + + s = sf_floatalloc2(ndim,nshot); + sf_floatread(s[0],ndim*nshot,shots); + sf_fileclose(shots); + + deg2rad = SF_PI/180.; + + if(!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); + + a = sf_floatalloc(nr); + a[0]=180.*deg2rad; + + /* specify output dimensions */ + if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); + t0 = sf_floatalloc(1); + sf_floatread(t0,1,t0s); + dt0=0.001; + sf_putfloat(rays,"d1",dt0); + sf_putfloat(rays,"o1",0.); + sf_putfloat(rays,"o2",180.); + sf_putfloat(rays,"d2",1.); + + /* get slowness squared */ + nm = n[0]*n[1]; + slow = sf_floatalloc(nm); + sf_floatread(slow,nm,vel); + + for(im=0;im i) { + sf_floatwrite (traj[i],ndim,rays); + } else { + sf_floatwrite (traj[it],ndim,rays); + } + } + } diff --git a/TDD/Makefile b/TDD/Makefile index 10b6972..ae6cca8 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -15,6 +15,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) +SRCNIPTOMO= ../Mniptomo.c ../raytrace.c ../grid2.c ../grid3.c ../atela.c ../eno3.c all: $(DEP) @@ -25,3 +26,11 @@ test_%: integ: cd ../experiments/modelingAndStack/ && scons cd ../experiments/algorithmVelocityInversion/ && scons + +# Version of the sfniptomo program for GDB (GNU debugger) +niptomo.x: $(SRCNIPTOMO) + gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm + @echo "Program niptomo.x for GDB built" + +clean: + rm *.x diff --git a/TDD/gdb_script b/TDD/gdb_script new file mode 100644 index 0000000..5f2e013 --- /dev/null +++ b/TDD/gdb_script @@ -0,0 +1,9 @@ +# Command scripts for GNU debugger (GDB) +# +# You can call this script to set up the program niptomo.x +# for debugging. Inside GDB session call it: +# source gdb_script + +cd niptomoDebug +set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf +show args diff --git a/TDD/niptomoDebug/README.md b/TDD/niptomoDebug/README.md new file mode 100644 index 0000000..14557e6 --- /dev/null +++ b/TDD/niptomoDebug/README.md @@ -0,0 +1,24 @@ +This is a experiment for sfniptomo test with GDB. +Run this experiment before test to generate the input. + +After that, go to the TDD directory, build the program version for +GDB with: + +```sg +~$ make niptomo.x +``` + +Call this program in GDB with: + +```sh +~$ gdb niptomo.x +``` + +Use the gdb\_script inside gdb session to set up the arguments +of the program: + +``` +(gdb) source gdb_script +``` + +You are ready to debug sfniptomo with GDB. diff --git a/TDD/niptomoDebug/SConstruct b/TDD/niptomoDebug/SConstruct new file mode 100644 index 0000000..53e3132 --- /dev/null +++ b/TDD/niptomoDebug/SConstruct @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConstruct (Python) +# +# Objetivo: Exemplo de uso do programa sfrays2. +# +# Site: https://dirack.github.io +# +# Versão 1.0 +# +# Programador: Rodolfo A C Neves (Dirack) 02/12/2020 +# +# Email: rodolfo_profissional@hotmail.com +# +# Licença: GPL-3.0 . + +from rsf.proj import * + +# create a model +amp=1 +rad=1 +Flow('model',None, + ''' + math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | + math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | + put label1=Depth unit1=km label2=Position unit2=km + ''' % (amp,rad,rad)) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title=Model allpos=y') + +Flow('s1',None, + ''' + math n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1" + ''') + +Flow('s2',None,'spike n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 mag=1') + +Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',None,'spike n1=1 mag=180') + +Flow('t0s',None,'spike n1=1 mag=1') + +# do a ray-tracing +Flow('ray',['model','shotsfile','anglefile','t0s'], + ''' + niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} + ''') + +# do a ray-tracing +#Flow('ray',['model'], +# ''' +# niptomo +# ''') + +# plot the ray +Plot('ray','graph transp=y yreverse=y min1=0 max1=3 min2=0 max2=10 wantaxis=n wanttitle=n scalebar=y plotcol=7 plotfat=3') + +# overlay model and ray +Result('overlay','model ray','Overlay') + +End() diff --git a/experiments/setUpNIPModel/SConstruct b/experiments/setUpNIPModel/SConstruct new file mode 100644 index 0000000..53e3132 --- /dev/null +++ b/experiments/setUpNIPModel/SConstruct @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConstruct (Python) +# +# Objetivo: Exemplo de uso do programa sfrays2. +# +# Site: https://dirack.github.io +# +# Versão 1.0 +# +# Programador: Rodolfo A C Neves (Dirack) 02/12/2020 +# +# Email: rodolfo_profissional@hotmail.com +# +# Licença: GPL-3.0 . + +from rsf.proj import * + +# create a model +amp=1 +rad=1 +Flow('model',None, + ''' + math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | + math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | + put label1=Depth unit1=km label2=Position unit2=km + ''' % (amp,rad,rad)) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title=Model allpos=y') + +Flow('s1',None, + ''' + math n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1" + ''') + +Flow('s2',None,'spike n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 mag=1') + +Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',None,'spike n1=1 mag=180') + +Flow('t0s',None,'spike n1=1 mag=1') + +# do a ray-tracing +Flow('ray',['model','shotsfile','anglefile','t0s'], + ''' + niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} + ''') + +# do a ray-tracing +#Flow('ray',['model'], +# ''' +# niptomo +# ''') + +# plot the ray +Plot('ray','graph transp=y yreverse=y min1=0 max1=3 min2=0 max2=10 wantaxis=n wanttitle=n scalebar=y plotcol=7 plotfat=3') + +# overlay model and ray +Result('overlay','model ray','Overlay') + +End() From 620482bee3630a1e94844fcc75e817c82aef0297 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 9 Dec 2020 12:45:15 -0300 Subject: [PATCH 13/63] Escape angles #14 Calculate escape angles in the final sample of the ray --- Mniptomo.c | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/Mniptomo.c b/Mniptomo.c index cad8a06..a0bf526 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -75,7 +75,8 @@ int main(int argc, char* argv[]) if(!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); a = sf_floatalloc(nr); - a[0]=180.*deg2rad; + sf_floatread(a,1,angles); + a[0]=a[0]*deg2rad; /* specify output dimensions */ if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); @@ -135,4 +136,19 @@ int main(int argc, char* argv[]) } } + if(it>0){ + i = it >= 2 ? it - 2 : it - 1; + /* Escape vector */ + x[0]=traj[it][0]; + x[1]=traj[it][1]; + x[0]-=traj[i][0]; + x[1]-=traj[i][1]; + /* Dot product with unit vector pointing upward */ + t = sqrt(x[0]*x[0]+x[1]*x[1]); /* Length */ + t = acos(x[0]/t); + if(x[1]<0) t = -t; + }else{ + t = a[0]; + t /= deg2rad; + } } From 45a9770d67175a9d0a0a32783facbcf0e6f875a0 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 10 Dec 2020 12:43:41 -0300 Subject: [PATCH 14/63] Constant velocity model for tests #14 --- TDD/niptomoDebug/SConstruct | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/TDD/niptomoDebug/SConstruct b/TDD/niptomoDebug/SConstruct index 53e3132..f01cf82 100644 --- a/TDD/niptomoDebug/SConstruct +++ b/TDD/niptomoDebug/SConstruct @@ -20,15 +20,22 @@ from rsf.proj import * # create a model amp=1 rad=1 +#Flow('model',None, +# ''' +# math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | +# math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | +# put label1=Depth unit1=km label2=Position unit2=km +# ''' % (amp,rad,rad)) + +# Constant velocity model for test Flow('model',None, ''' - math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | - math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.5 | put label1=Depth unit1=km label2=Position unit2=km - ''' % (amp,rad,rad)) + ''' ) # plot the model -Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title=Model allpos=y') +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') Flow('s1',None, ''' From 014eb947cb980482534ce75e927fbb71e7bd9617 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 10 Dec 2020 15:15:24 -0300 Subject: [PATCH 15/63] Use macro definitions #14 Define macros for interpolation order and degrees to radians conversion --- Mniptomo.c | 23 +++++++++-------------- raytrace.c | 4 ++++ 2 files changed, 13 insertions(+), 14 deletions(-) diff --git a/Mniptomo.c b/Mniptomo.c index a0bf526..4420696 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -9,7 +9,6 @@ int main(int argc, char* argv[]) { bool verb; - int order=4; /* Interpolation order */ int n[2]; float d[2]; float o[2]; @@ -18,7 +17,6 @@ int main(int argc, char* argv[]) int ndim; int nshot; int nm; - float deg2rad; float* a; int nr; float* slow; @@ -59,10 +57,9 @@ int main(int argc, char* argv[]) sf_warning("Verbose on"); } - if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) sf_error("Must have n1=2 in shotsfile"); - + if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) + sf_error("Must have n1=2 in shotsfile"); if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); - if(sf_histfloat(shots,"o2",&t)) sf_putfloat(rays,"o3",t); if(sf_histfloat(shots,"d2",&t)) sf_putfloat(rays,"d3",t); @@ -70,21 +67,19 @@ int main(int argc, char* argv[]) sf_floatread(s[0],ndim*nshot,shots); sf_fileclose(shots); - deg2rad = SF_PI/180.; - if(!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); a = sf_floatalloc(nr); - sf_floatread(a,1,angles); - a[0]=a[0]*deg2rad; + sf_floatread(a,nr,angles); + a[0]=a[0]*DEG2RAD; /* specify output dimensions */ if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); - t0 = sf_floatalloc(1); - sf_floatread(t0,1,t0s); + t0 = sf_floatalloc(nt0); + sf_floatread(t0,nt0,t0s); dt0=0.001; sf_putfloat(rays,"d1",dt0); - sf_putfloat(rays,"o1",0.); + sf_putfloat(rays,"o1",o[0]); sf_putfloat(rays,"o2",180.); sf_putfloat(rays,"d2",1.); @@ -100,7 +95,7 @@ int main(int argc, char* argv[]) /* initialize ray tracing object */ nt = (int) t0[0]/dt0; - rt = raytrace_init(2,true,nt,dt0,n,o,d,slow,order); + rt = raytrace_init(2,true,nt,dt0,n,o,d,slow,ORDER); free(slow); /* Ray tracing */ @@ -149,6 +144,6 @@ int main(int argc, char* argv[]) if(x[1]<0) t = -t; }else{ t = a[0]; - t /= deg2rad; + t /= DEG2RAD; } } diff --git a/raytrace.c b/raytrace.c index 5a1b433..da82dd3 100644 --- a/raytrace.c +++ b/raytrace.c @@ -26,6 +26,10 @@ #ifndef _raytrace_h +#define DEG2RAD SF_PI/180. /* Degrees to radians conversion */ +#define ORDER 4 /* Ray tracing interpolation order */ +/*^*/ + typedef struct RayTrace* raytrace; /* abstract data type */ /*^*/ From 585d40612e5756c2535b75f0afe077152ad07db6 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 10 Dec 2020 16:55:13 -0300 Subject: [PATCH 16/63] Remove 3D grid libraries #14 The program is built for 2D models, so there is no need to use 3D grid --- TDD/Makefile | 2 +- eno3.c | 164 --------------------------------------------------- grid3.c | 122 -------------------------------------- raytrace.c | 25 -------- 4 files changed, 1 insertion(+), 312 deletions(-) delete mode 100644 eno3.c delete mode 100644 grid3.c diff --git a/TDD/Makefile b/TDD/Makefile index ae6cca8..99e6328 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -15,7 +15,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) -SRCNIPTOMO= ../Mniptomo.c ../raytrace.c ../grid2.c ../grid3.c ../atela.c ../eno3.c +SRCNIPTOMO= ../Mniptomo.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) diff --git a/eno3.c b/eno3.c deleted file mode 100644 index be42e55..0000000 --- a/eno3.c +++ /dev/null @@ -1,164 +0,0 @@ -/* 3-D ENO interpolation */ -/* - Copyright (C) 2004 University of Texas at Austin - - 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 2 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, write to the Free Software - Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA -*/ - -#include -/*^*/ - -#include "eno3.h" - -#ifndef _eno3_h - -typedef struct Eno3 *eno3; -/* abstract data type */ -/*^*/ - -#endif - -struct Eno3 { - int order, ng, n1, n2, n3; - sf_eno **ent; - sf_eno2 jnt; - float **f, **f1; -}; -/* concrete data type */ - -eno3 eno3_init (int order /* interpolation order */, - int n1, int n2, int n3 /* data dimensions */) -/*< Initialize interpolation object >*/ -{ - eno3 pnt; - int i2, i3; - - pnt = (eno3) sf_alloc(1,sizeof(*pnt)); - pnt->order = order; - pnt->n1 = n1; - pnt->n2 = n2; - pnt->n3 = n3; - pnt->ng = 2*order-2; - if (pnt->ng > n2 || pnt->ng > n3) - sf_error("%s: ng=%d is too big",__FILE__,pnt->ng); - pnt->jnt = sf_eno2_init (order, pnt->ng, pnt->ng); - pnt->f = sf_floatalloc2(pnt->ng,pnt->ng); - pnt->f1 = sf_floatalloc2(pnt->ng,pnt->ng); - pnt->ent = (sf_eno**) sf_alloc(n3,sizeof(sf_eno*)); - for (i3 = 0; i3 < n3; i3++) { - pnt->ent[i3] = (sf_eno*) sf_alloc(n2,sizeof(sf_eno)); - for (i2 = 0; i2 < n2; i2++) { - pnt->ent[i3][i2] = sf_eno_init (order, n1); - } - } - - return pnt; -} - -void eno3_set (eno3 pnt, float*** c /* data [n3][n2][n1] */) -/*< Set the interpolation table. c can be changed or freed afterwords. >*/ -{ - int i2, i3; - - for (i3 = 0; i3 < pnt->n3; i3++) { - for (i2 = 0; i2 < pnt->n2; i2++) { - sf_eno_set (pnt->ent[i3][i2], c[i3][i2]); - } - } -} - -void eno3_set1 (eno3 pnt, float* c /* data [n3*n2*n1] */) -/*< Set the interpolation table. c can be changed or freed afterwords. >*/ -{ - int i2, i3; - - for (i3 = 0; i3 < pnt->n3; i3++) { - for (i2 = 0; i2 < pnt->n2; i2++) { - sf_eno_set (pnt->ent[i3][i2], c+(pnt->n1)*(i2+(pnt->n2)*i3)); - } - } -} - -void eno3_close (eno3 pnt) -/*< Free internal storage. >*/ -{ - int i2, i3; - - sf_eno2_close (pnt->jnt); - for (i3 = 0; i3 < pnt->n3; i3++) { - for (i2 = 0; i2 < pnt->n2; i2++) { - sf_eno_close (pnt->ent[i3][i2]); - } - free (pnt->ent[i3]); - } - free (pnt->ent); - free (pnt->f[0]); - free (pnt->f); - free (pnt->f1[0]); - free (pnt->f1); - free (pnt); -} - -void eno3_apply (eno3 pnt, - int i, int j, int k /* grid location */, - float x, float y, float z /* offsets from grid */, - float* f /* output data */, - float* f1 /* output derivative [3] */, - der what /* to compute [FUNC|DER|BOTH] */) -/*< Apply interpolation. >*/ -{ - int i2, i3, b2, b3; - float g; - - if (j-pnt->order+2 < 0) { - b2 = 0; - } else if (j+pnt->order-1 > pnt->n2-1) { - b2 = pnt->n2 - pnt->ng; - } else { - b2 = j-pnt->order+2; - } - - j -= b2; - - - if (k-pnt->order+2 < 0) { - b3 = 0; - } else if (k+pnt->order-1 > pnt->n3-1) { - b3 = pnt->n3 - pnt->ng; - } else { - b3 = k-pnt->order+2; - } - - k -= b3; - - for (i3 = 0; i3 < pnt->ng; i3++) { - for (i2 = 0; i2 < pnt->ng; i2++) { - sf_eno_apply (pnt->ent[b3+i3][b2+i2],i,x, - &(pnt->f[i3][i2]), - &(pnt->f1[i3][i2]), - (what==FUNC? FUNC: BOTH)); - } - } - - sf_eno2_set (pnt->jnt,pnt->f); - sf_eno2_apply (pnt->jnt,j,k,y,z,f,f1+1,what); - - if (what != FUNC) { - sf_eno2_set (pnt->jnt,pnt->f1); - sf_eno2_apply(pnt->jnt,j,k,y,z,f1,&g,FUNC); - } -} - -/* $Id$ */ diff --git a/grid3.c b/grid3.c deleted file mode 100644 index 3af2c3f..0000000 --- a/grid3.c +++ /dev/null @@ -1,122 +0,0 @@ -/* 3-D velocity grid for ray tracing. */ -/* - Copyright (C) 2004 University of Texas at Austin - - 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 2 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, write to the Free Software - Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA -*/ - -#include - -#include - -#include "grid3.h" -#include "eno3.h" - -#ifndef _grid3_h - -typedef struct Grid3* grid3; -/* abstract data type */ -/*^*/ - -#endif - -struct Grid3 { - eno3 pnt; - int n1, n2, n3; - float o1, d1, o2, d2, o3, d3; -}; -/* concrete data type */ - -grid3 grid3_init (int n1, float o1, float d1 /* first axis */, - int n2, float o2, float d2 /* second axis */, - int n3, float o3, float d3 /* third axis */, - float *slow2 /* data [n1*n2*n3] */, - int order /* interpolation order */) -/*< Initialize 3-D grid. >*/ -{ - grid3 grd; - - grd = (grid3) sf_alloc(1,sizeof(*grd)); - - grd->n1 = n1; grd->o1 = o1; grd->d1 = d1; - grd->n2 = n2; grd->o2 = o2; grd->d2 = d2; - grd->n3 = n3; grd->o3 = o3; grd->d3 = d3; - - grd->pnt = eno3_init (order, n1, n2, n3); - eno3_set1 (grd->pnt, slow2); - - return grd; -} - -float grid3_vel(void* par /* grid */, - float* xy /* location [3] */) -/*< Extract a value from the grid. >*/ -{ - grid3 grd; - float x, y, z, f, f1[3]; - int i, j, k; - - grd = (grid3) par; - x = (xy[0]-grd->o1)/grd->d1; i = floor(x); x -= i; - y = (xy[1]-grd->o2)/grd->d2; j = floor(y); y -= j; - z = (xy[2]-grd->o3)/grd->d3; k = floor(z); z -= k; - - eno3_apply(grd->pnt, i, j, k, x, y, z, &f, f1, FUNC); - return f; -} - -void grid3_vgrad(void* par /* grid */, - float* xy /* location [3] */, - float* grad /* output gradient [3] */) -/*< Extract (1/2 of) gradient values from the grid >*/ -{ - grid3 grd; - float x, y, z, f, f1[3]; - int i, j, k; - - grd = (grid3) par; - x = (xy[0]-grd->o1)/grd->d1; i = floor(x); x -= i; - y = (xy[1]-grd->o2)/grd->d2; j = floor(y); y -= j; - z = (xy[2]-grd->o3)/grd->d3; k = floor(z); z -= k; - - eno3_apply(grd->pnt, i, j, k, x, y, z, &f, f1, DER); - - grad[0] = 0.5*f1[0]/grd->d1; - grad[1] = 0.5*f1[1]/grd->d2; - grad[2] = 0.5*f1[2]/grd->d3; -} - -int grid3_term (void *par /* grid */, - float* xy /* location [3] */) -/*< Termination criterion. Returns 0 if xy (data coordinates) - are inside the grid >*/ -{ - grid3 grd; - - grd = (grid3) par; - return (xy[0] < grd->o1 || xy[0] > grd->o1 + (grd->n1-1)*grd->d1 || - xy[1] < grd->o2 || xy[1] > grd->o2 + (grd->n2-1)*grd->d2 || - xy[2] < grd->o3 || xy[2] > grd->o3 + (grd->n3-1)*grd->d3); -} - -void grid3_close(grid3 grd) -/*< Free internal storage >*/ -{ - eno3_close (grd->pnt); - free (grd); -} - -/* $Id$ */ - diff --git a/raytrace.c b/raytrace.c index da82dd3..f29458b 100644 --- a/raytrace.c +++ b/raytrace.c @@ -21,7 +21,6 @@ #include "raytrace.h" #include "grid2.h" -#include "grid3.h" #include "atela.h" #ifndef _raytrace_h @@ -40,7 +39,6 @@ struct RayTrace { bool sym; int dim, nt; float dt, z0; - grid3 grd3; grid2 grd2; }; /* concrete data type */ @@ -60,10 +58,6 @@ static void iso_rhs(void* par, float* y, float* f) s2 = grid2_vel(rt->grd2,y); grid2_vgrad(rt->grd2,y,sds); break; - case 3: - s2 = grid3_vel(rt->grd3,y); - grid3_vgrad(rt->grd3,y,sds); - break; default: s2 = 0.; sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); @@ -85,8 +79,6 @@ static int term(void* par, float* y) switch (rt->dim) { case 2: return grid2_term(rt->grd2,y); - case 3: - return grid3_term(rt->grd3,y); default: sf_error("%s: Cannot raytrace with dim=%d",__FILE__,rt->dim); return 0; @@ -124,12 +116,6 @@ raytrace raytrace_init(int dim /* dimensionality (2 or 3) */, n[1], o[1], d[1], slow2, order); break; - case 3: - rt->grd3 = grid3_init (n[0], o[0], d[0], - n[1], o[1], d[1], - n[2], o[2], d[2], - slow2, order); - break; default: sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); } @@ -144,9 +130,6 @@ void raytrace_close (raytrace rt) case 2: grid2_close (rt->grd2); break; - case 3: - grid3_close (rt->grd3); - break; } free (rt); } @@ -186,9 +169,6 @@ int trace_ray (raytrace rt /* ray tracing object */, case 2: s2 = grid2_vel(rt->grd2,x); break; - case 3: - s2 = grid3_vel(rt->grd3,x); - break; default: s2 = 0.; sf_error("%s: Cannot raytrace with dim=%d",__FILE__,dim); @@ -214,11 +194,6 @@ int trace_ray (raytrace rt /* ray tracing object */, rt->grd2, grid2_vgrad, grid2_vel, grid2_term, traj); break; - case 3: - it = atela_step (rt->dim, nt, rt->dt, true, x, p, - rt->grd3, - grid3_vgrad, grid3_vel, grid3_term, traj); - break; default: sf_error("%s: cannot handle %d dimensions",__FILE__,rt->dim); break; From 9890b0e5887257c2a7c57be64b52ce7cf8618eab Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 10 Dec 2020 17:50:39 -0300 Subject: [PATCH 17/63] Modify sfniptomo to return rays end points #14 Organize the program and include a for loop to several rays. The purpose is to store rays end points and angles (It will be the NIP sources in NIP tomography inversion). --- Mniptomo.c | 101 +++++++++++++++++++++++++++-------------------------- 1 file changed, 51 insertions(+), 50 deletions(-) diff --git a/Mniptomo.c b/Mniptomo.c index 4420696..c5e358d 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -42,7 +42,7 @@ int main(int argc, char* argv[]) t0s = sf_input("t0s"); angles = sf_input("anglefile"); - /* get 2D grid parameters */ + /* Velocity model: get 2D grid parameters */ if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); @@ -53,27 +53,23 @@ int main(int argc, char* argv[]) if(!sf_getbool("verb",&verb)) verb=true; /* verbose */ - if(verb){ - sf_warning("Verbose on"); - } - + /* Shotsfile: get m0 shot points */ if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) sf_error("Must have n1=2 in shotsfile"); if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); if(sf_histfloat(shots,"o2",&t)) sf_putfloat(rays,"o3",t); if(sf_histfloat(shots,"d2",&t)) sf_putfloat(rays,"d3",t); - s = sf_floatalloc2(ndim,nshot); sf_floatread(s[0],ndim*nshot,shots); sf_fileclose(shots); + /* Anglefile: get initial emergence angle */ if(!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); - a = sf_floatalloc(nr); sf_floatread(a,nr,angles); a[0]=a[0]*DEG2RAD; - /* specify output dimensions */ + /* T0s file: File with ray's traveltime */ if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); t0 = sf_floatalloc(nt0); sf_floatread(t0,nt0,t0s); @@ -93,26 +89,7 @@ int main(int argc, char* argv[]) slow[im] = 1./(v0*v0); } - /* initialize ray tracing object */ - nt = (int) t0[0]/dt0; - rt = raytrace_init(2,true,nt,dt0,n,o,d,slow,ORDER); - free(slow); - - /* Ray tracing */ - traj = sf_floatalloc2(ndim,nt+1); - - /* initialize position */ - x[0] = s[0][0]; - x[1] = s[0][1]; - - /* initialize direction */ - p[0] = -cosf(a[0]); - p[1] = sinf(a[0]); - - it = trace_ray (rt, x, p, traj); - if (it < 0) it = -it; /* keep side-exiting rays */ - - sf_putint(rays,"n1",nt); + sf_putint(rays,"n1",nr); sf_putint(rays,"n2",1); sf_settype(rays,SF_COMPLEX); sf_putstring(rays,"label1","time"); @@ -122,28 +99,52 @@ int main(int argc, char* argv[]) sf_fileflush(rays,NULL); sf_settype(rays,SF_FLOAT); - /* Write full trajectory */ - for (i=0; i < nt; i++) { - if (0==it || it > i) { - sf_floatwrite (traj[i],ndim,rays); - } else { - sf_floatwrite (traj[it],ndim,rays); + if(verb){ + sf_warning("Input file (Velocity model)"); + sf_warning("n1=%d d1=%f o1=%f",*n,*d,*o); + sf_warning("n2=%d d2=%f o2=%f",*(n+1),*(d+1),*(o+1)); + sf_warning("Command line parameters"); + sf_warning(""); + } + + for(i=0; i0){ + i = it >= 2 ? it - 2 : it - 1; + /* Escape vector */ + x[0]=traj[it][0]; + x[1]=traj[it][1]; + x[0]-=traj[i][0]; + x[1]-=traj[i][1]; + /* Dot product with unit vector pointing upward */ + t = sqrt(x[0]*x[0]+x[1]*x[1]); /* Length */ + t = acos(x[0]/t); + if(x[1]<0) t = -t; + }else{ + t = a[0]; + t /= DEG2RAD; } - } - - if(it>0){ - i = it >= 2 ? it - 2 : it - 1; - /* Escape vector */ - x[0]=traj[it][0]; - x[1]=traj[it][1]; - x[0]-=traj[i][0]; - x[1]-=traj[i][1]; - /* Dot product with unit vector pointing upward */ - t = sqrt(x[0]*x[0]+x[1]*x[1]); /* Length */ - t = acos(x[0]/t); - if(x[1]<0) t = -t; - }else{ - t = a[0]; - t /= DEG2RAD; + + /* Raytrace close */ + raytrace_close(rt); } } From 7136cf042fd711a20fba31c54b6a22a698fa91b5 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 10 Dec 2020 18:38:41 -0300 Subject: [PATCH 18/63] Add a for loop and store two rays endpoints #14 Now the program is ready to receive more than one shotpoint, trace rays and store it's end points --- Mniptomo.c | 19 ++++++++++--------- TDD/niptomoDebug/SConstruct | 8 ++++---- 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/Mniptomo.c b/Mniptomo.c index c5e358d..0aba7bf 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -26,7 +26,7 @@ int main(int argc, char* argv[]) float dt0; float* t0; int nt; - int it; + int it,ir; float** traj; float x[2]; float p[2]; @@ -67,7 +67,6 @@ int main(int argc, char* argv[]) if(!sf_histint(angles,"n1",&nr)) sf_error("No n1= in anglefile"); a = sf_floatalloc(nr); sf_floatread(a,nr,angles); - a[0]=a[0]*DEG2RAD; /* T0s file: File with ray's traveltime */ if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); @@ -107,22 +106,23 @@ int main(int argc, char* argv[]) sf_warning(""); } - for(i=0; i Date: Fri, 11 Dec 2020 11:50:14 -0300 Subject: [PATCH 19/63] Show NIP points in experiment image --- TDD/niptomoDebug/SConstruct | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/TDD/niptomoDebug/SConstruct b/TDD/niptomoDebug/SConstruct index 63de56f..d893334 100644 --- a/TDD/niptomoDebug/SConstruct +++ b/TDD/niptomoDebug/SConstruct @@ -56,14 +56,8 @@ Flow('ray',['model','shotsfile','anglefile','t0s'], niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} ''') -# do a ray-tracing -#Flow('ray',['model'], -# ''' -# niptomo -# ''') - # plot the ray -Plot('ray','graph transp=y yreverse=y min1=0 max1=3 min2=0 max2=10 wantaxis=n wanttitle=n scalebar=y plotcol=7 plotfat=3') +Plot('ray','graph symbol=* symbolsz=5 transp=y yreverse=y min1=0 max1=3 min2=0 max2=10 wantaxis=n wanttitle=n scalebar=y plotcol=7 plotfat=3') # overlay model and ray Result('overlay','model ray','Overlay') From 5d42504fa5c7d8ccacc01c3a5a1ff4bc3e245d2f Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Fri, 11 Dec 2020 12:22:40 -0300 Subject: [PATCH 20/63] Generate nip angles output #14 Generate a file with slowness vector in the NIP point (ray endpoint) coordinate --- Mniptomo.c | 6 +++++- TDD/niptomoDebug/SConstruct | 4 ++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/Mniptomo.c b/Mniptomo.c index 0aba7bf..fe3db0f 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -31,7 +31,7 @@ int main(int argc, char* argv[]) float x[2]; float p[2]; int i; - sf_file shots, vel, rays, angles, t0s; + sf_file shots, vel, rays, angles, t0s, nipangles; raytrace rt; sf_init(argc,argv); @@ -41,6 +41,7 @@ int main(int argc, char* argv[]) rays = sf_output("out"); t0s = sf_input("t0s"); angles = sf_input("anglefile"); + nipangles = sf_output("nipangles"); /* Velocity model: get 2D grid parameters */ if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); @@ -144,6 +145,9 @@ int main(int argc, char* argv[]) t /= DEG2RAD; } + /* Write angles in the ray endpoints */ + sf_floatwrite(&t,1,nipangles); + /* Raytrace close */ raytrace_close(rt); free(traj); diff --git a/TDD/niptomoDebug/SConstruct b/TDD/niptomoDebug/SConstruct index d893334..af963a5 100644 --- a/TDD/niptomoDebug/SConstruct +++ b/TDD/niptomoDebug/SConstruct @@ -51,9 +51,9 @@ Flow('anglefile',None,'spike n1=2 mag=180') Flow('t0s',None,'spike n1=2 mag=1') # do a ray-tracing -Flow('ray',['model','shotsfile','anglefile','t0s'], +Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], ''' - niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} + niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} ''') # plot the ray From 130f69ad0486c87804e86c461584cbf42f7e244f Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Mon, 14 Dec 2020 15:09:35 -0300 Subject: [PATCH 21/63] Scratch of the program sfnipmodel #14 Scratch of the program sfnipmodel to receive NIP sources and trace rays from there to acquisition surface in order to get traveltime curves (x,t). In the directory TDD/nipmodelDebug is an usage example of the program for a constant velocity model, and in TDD directory is a gdb script for set up the program debug with gdb. The program version for gdb can be built with the following command: ~$ make nipmodel.x --- Mnipmodel.c | 142 +++++++++++++++++++++++++++++++++++ SConstruct | 2 +- TDD/Makefile | 8 ++ TDD/gdb_script2 | 9 +++ TDD/nipmodelDebug/README.md | 24 ++++++ TDD/nipmodelDebug/SConstruct | 54 +++++++++++++ raytrace.c | 2 +- 7 files changed, 239 insertions(+), 2 deletions(-) create mode 100644 Mnipmodel.c create mode 100644 TDD/gdb_script2 create mode 100644 TDD/nipmodelDebug/README.md create mode 100644 TDD/nipmodelDebug/SConstruct diff --git a/Mnipmodel.c b/Mnipmodel.c new file mode 100644 index 0000000..16fb7f7 --- /dev/null +++ b/Mnipmodel.c @@ -0,0 +1,142 @@ +/* NIP Point source modeling ray tracer + +Trace rays from NIP sources to acquisition surface in order to get +traveltime curves used in NIP tomography. + +*/ + +#include + +#include + +#include "raytrace.h" + +#define DANGLE 15.0 +#define INITIAL_ANGLE 45.0 + +int main(int argc, char* argv[]) +{ + bool verb; // Verbose parameter + int n[2]; // Velocity grid dimensions n[0]=n1, n[1]=n2 + float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 + float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 + float t; // Ray traveltime + float** s; // NIP source position (z,x) + int ndim; // n1 dimension in shotsfile, should be equal 2 + int nshot; // n2 dimensions in shotsfile + int nm; // Number of samples in velocity grid + float* a; // Normal Ray initial angle + int nr=7; // Number of rays to trace + float* slow; // slowness + int im; // loop counter + float v0; // Velocity + float dt=0.001; // ray time sampling + int nt=1000; // Numer of time samples for each ray + int ns; // Number of NIP sources + int it,ir,is; // loop counters + float** traj; // Ray trajectory (z,x) + float x[2]; // Ray initial position + float p[2]; // Ray initial slowness vector + float currentRayAngle; // Ray initial angle + sf_file shots, vel, angles, timeCurve,xCurve; + raytrace rt; + + sf_init(argc,argv); + + shots = sf_input("shotsfile"); + vel = sf_input("in"); + timeCurve = sf_output("out"); + xCurve = sf_output("x"); + angles = sf_input("anglefile"); + + /* Velocity model: get 2D grid parameters */ + if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); + if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); + if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); + if(!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); + if(!sf_histfloat(vel,"o1",o)) o[0]=0.; + if(!sf_histfloat(vel,"o2",o+1)) o[1]=0.; + + if(!sf_getbool("verb",&verb)) verb=true; + /* verbose */ + + /* Shotsfile: get m0 shot points */ + if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) + sf_error("Must have n1=2 in shotsfile"); + if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); + s = sf_floatalloc2(ndim,nshot); + sf_floatread(s[0],ndim*nshot,shots); + sf_fileclose(shots); + + /* Anglefile: get initial emergence angle */ + if(!sf_histint(angles,"n1",&ns)) sf_error("No n1= in anglefile"); + a = sf_floatalloc(ns); + sf_floatread(a,ns,angles); + + + /* get slowness squared */ + nm = n[0]*n[1]; + slow = sf_floatalloc(nm); + sf_floatread(slow,nm,vel); + + for(im=0;im0){ + t = it*dt; + sf_floatwrite(&traj[it][1],1,xCurve); + }else if(it == 0){ + t = abs(nt)*dt; + sf_floatwrite(&traj[nt][1],1,xCurve); + }else{ + t = -it * dt; + sf_floatwrite(&traj[-it][1],1,xCurve); + } + + sf_floatwrite(&t,1,timeCurve); + + /* Raytrace close */ + raytrace_close(rt); + free(traj); + } + } + +} diff --git a/SConstruct b/SConstruct index acae1ad..9dc64a4 100644 --- a/SConstruct +++ b/SConstruct @@ -5,7 +5,7 @@ import bldutil # Put your name programs in progs variable # without 'M' preffix and '.c' extension progs = ''' -niptomo +niptomo nipmodel ''' try: # distributed version diff --git a/TDD/Makefile b/TDD/Makefile index 99e6328..3ceee89 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -16,6 +16,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mniptomo.c ../raytrace.c ../grid2.c ../atela.c +SRCNIPMODEL= ../Mnipmodel.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) @@ -32,5 +33,12 @@ niptomo.x: $(SRCNIPTOMO) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program niptomo.x for GDB built" +# Version of the sfnipmodel program for GDB (GNU debugger) +nipmodel.x: $(SRCNIPMODEL) + gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm + @echo "Program nipmodel.x for GDB built" + clean: rm *.x + +.PHONY: clean niptomo.x nipmodel.x diff --git a/TDD/gdb_script2 b/TDD/gdb_script2 new file mode 100644 index 0000000..c6e65fa --- /dev/null +++ b/TDD/gdb_script2 @@ -0,0 +1,9 @@ +# Command scripts for GNU debugger (GDB) +# +# You can call this script to set up the program nipmodel.x +# for debugging. Inside GDB session call it: +# source gdb_script + +cd nipmodelDebug +set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=x.rsf >time.rsf +show args diff --git a/TDD/nipmodelDebug/README.md b/TDD/nipmodelDebug/README.md new file mode 100644 index 0000000..946d450 --- /dev/null +++ b/TDD/nipmodelDebug/README.md @@ -0,0 +1,24 @@ +This is a experiment for sfnipmodel test with GDB. +Run this experiment before test to generate the input. + +After that, go to the TDD directory, build the program version for +GDB with: + +```sg +~$ make nipmodel.x +``` + +Call this program in GDB with: + +```sh +~$ gdb nipmodel.x +``` + +Use the gdb\_script inside gdb session to set up the arguments +of the program: + +``` +(gdb) source gdb_script2 +``` + +You are ready to debug sfniptomo with GDB. diff --git a/TDD/nipmodelDebug/SConstruct b/TDD/nipmodelDebug/SConstruct new file mode 100644 index 0000000..656f67f --- /dev/null +++ b/TDD/nipmodelDebug/SConstruct @@ -0,0 +1,54 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConstruct (Python) +# +# Purpose: Program sfnipmodel test in a constant velocity model. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 02/12/2020 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +from rsf.proj import * + +# create a model +amp=1 +rad=1 + +# Constant velocity model for test +Flow('model',None, + ''' + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.5 | + put label1=Depth unit1=km label2=Position unit2=km + ''' ) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') + +Flow('s1',None, + ''' + math n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1+4" + ''') + +Flow('s2',None,'spike n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') + +Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',None,'spike n1=2 mag=0') + +Flow('t0s',None,'spike n1=2 mag=1') + +# do a ray-tracing from NIP sources +Flow(['tCoordinates','xCoordinates'], + ['model','shotsfile','anglefile','t0s'], + ''' + nipmodel shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} + ''') + +End() diff --git a/raytrace.c b/raytrace.c index f29458b..b23d33a 100644 --- a/raytrace.c +++ b/raytrace.c @@ -1,4 +1,4 @@ -/* Ray tracing interface. */ +/* Ray tracing interface modified to NIP tomography. */ /* Copyright (C) 2004 University of Texas at Austin From d68161e816b86a85611a9e788d50a77422b0a64f Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sun, 27 Dec 2020 17:02:49 -0300 Subject: [PATCH 22/63] Scratch of the program sfniptomo #14 This program is used to set up NIP model tracing normal rays from acquisition surface into the model. The rays end points will be NIP point sources in NIP tomography --- Mniptomo.c | 80 ++++++++++++++++++++++++++++++++---------------------- 1 file changed, 48 insertions(+), 32 deletions(-) diff --git a/Mniptomo.c b/Mniptomo.c index fe3db0f..3779b86 100644 --- a/Mniptomo.c +++ b/Mniptomo.c @@ -1,4 +1,8 @@ -/* NIP tomography ray tracer */ +/* Set up NIP model to NIP tomography + +Trace Normal rays from acquisition surface into the model to estimate NIP point sources position. + + */ #include @@ -6,31 +10,32 @@ #include "raytrace.h" +#define DT 0.001 + int main(int argc, char* argv[]) { - bool verb; - int n[2]; - float d[2]; - float o[2]; - float t; - float** s; - int ndim; - int nshot; - int nm; - float* a; - int nr; - float* slow; - int im; - float v0; - int nt0; - float dt0; - float* t0; - int nt; - int it,ir; - float** traj; - float x[2]; - float p[2]; - int i; + bool verb; // Verbose parameter + int n[2]; // Velocity grid dimensions n[0]=n1, n[1]=n2 + float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 + float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 + float t; // Ray escape angle at NIP point + float** s; // NIP source position (z,x) + int ndim; // n1 dimension in shotsfile, shoul be equal 2 + int nshot; // n2 dimension in shotsfile + int nm; // Number of samples in velocity grid + float* a; // Normal Ray initial angle + int nr; // Number of rays to trace + float* slow; // slowness + int im; // loop counter + float v0; // Velocity + int nt0; // Number of normal rays to trace + float* t0; // Normal ray traveltime + int nt; // Number of time samples + int it,ir; // loop counters + float** traj; // Ray trajectory (z,x) + float x[2]; // Ray initial position + float p[2]; // Ray initial slowness vector + int i; // loop counter sf_file shots, vel, rays, angles, t0s, nipangles; raytrace rt; @@ -73,8 +78,7 @@ int main(int argc, char* argv[]) if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); t0 = sf_floatalloc(nt0); sf_floatread(t0,nt0,t0s); - dt0=0.001; - sf_putfloat(rays,"d1",dt0); + sf_putfloat(rays,"d1",DT); sf_putfloat(rays,"o1",o[0]); sf_putfloat(rays,"o2",180.); sf_putfloat(rays,"d2",1.); @@ -92,26 +96,36 @@ int main(int argc, char* argv[]) sf_putint(rays,"n1",nr); sf_putint(rays,"n2",1); sf_settype(rays,SF_COMPLEX); - sf_putstring(rays,"label1","time"); - sf_putstring(rays,"unit1","s"); + sf_putstring(rays,"label1","Position"); + sf_putstring(rays,"unit1","z,x"); sf_putstring(rays,"label2","Degrees"); sf_putstring(rays,"unit2","Angle"); sf_fileflush(rays,NULL); sf_settype(rays,SF_FLOAT); + sf_putint(nipangles,"n1",nr); + sf_putint(nipangles,"n2",1); + sf_putstring(nipangles,"label1","Angle"); + sf_putstring(nipangles,"unit1","Degrees"); + if(verb){ sf_warning("Input file (Velocity model)"); sf_warning("n1=%d d1=%f o1=%f",*n,*d,*o); sf_warning("n2=%d d2=%f o2=%f",*(n+1),*(d+1),*(o+1)); - sf_warning("Command line parameters"); - sf_warning(""); + sf_warning("Input file (shotsfile)"); + sf_warning("n1=%d",ndim); + sf_warning("n2=%d",nshot); + sf_warning("Input file (anglefile)"); + sf_warning("n1=%d",nr); + sf_warning("Input file (t0s file)"); + sf_warning("n1=%d",nt0); } for(ir=0; ir0){ i = it >= 2 ? it - 2 : it - 1; /* Escape vector */ From e7855c7117c82431967a7285af1a40e94a7b19a0 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 29 Dec 2020 11:35:10 -0300 Subject: [PATCH 23/63] Change sfniptomo name to sfnipmodsetup #14 Change the name of the program to sfnipmodsetup to align the name with the program purpose --- Mniptomo.c => Mnipmodsetup.c | 0 SConstruct | 2 +- TDD/Makefile | 10 +++++----- TDD/gdb_script | 4 ++-- TDD/{niptomoDebug => nipmodsetup}/README.md | 0 TDD/{niptomoDebug => nipmodsetup}/SConstruct | 16 +++++----------- 6 files changed, 13 insertions(+), 19 deletions(-) rename Mniptomo.c => Mnipmodsetup.c (100%) rename TDD/{niptomoDebug => nipmodsetup}/README.md (100%) rename TDD/{niptomoDebug => nipmodsetup}/SConstruct (69%) diff --git a/Mniptomo.c b/Mnipmodsetup.c similarity index 100% rename from Mniptomo.c rename to Mnipmodsetup.c diff --git a/SConstruct b/SConstruct index 9dc64a4..e65b68a 100644 --- a/SConstruct +++ b/SConstruct @@ -5,7 +5,7 @@ import bldutil # Put your name programs in progs variable # without 'M' preffix and '.c' extension progs = ''' -niptomo nipmodel +nipmodsetup nipmodel ''' try: # distributed version diff --git a/TDD/Makefile b/TDD/Makefile index 3ceee89..7c1b9d9 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -15,7 +15,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) -SRCNIPTOMO= ../Mniptomo.c ../raytrace.c ../grid2.c ../atela.c +SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mnipmodel.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) @@ -28,10 +28,10 @@ integ: cd ../experiments/modelingAndStack/ && scons cd ../experiments/algorithmVelocityInversion/ && scons -# Version of the sfniptomo program for GDB (GNU debugger) -niptomo.x: $(SRCNIPTOMO) +# Version of the sfnipmodsetup program for GDB (GNU debugger) +nipmodsetup.x: $(SRCNIPTOMO) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm - @echo "Program niptomo.x for GDB built" + @echo "Program nipmodsetup.x for GDB built" # Version of the sfnipmodel program for GDB (GNU debugger) nipmodel.x: $(SRCNIPMODEL) @@ -41,4 +41,4 @@ nipmodel.x: $(SRCNIPMODEL) clean: rm *.x -.PHONY: clean niptomo.x nipmodel.x +.PHONY: clean nipmodsetup.x nipmodel.x diff --git a/TDD/gdb_script b/TDD/gdb_script index 5f2e013..bf2439c 100644 --- a/TDD/gdb_script +++ b/TDD/gdb_script @@ -1,9 +1,9 @@ # Command scripts for GNU debugger (GDB) # -# You can call this script to set up the program niptomo.x +# You can call this script to set up the program nipmodsetup.x # for debugging. Inside GDB session call it: # source gdb_script -cd niptomoDebug +cd nipmodsetup set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf show args diff --git a/TDD/niptomoDebug/README.md b/TDD/nipmodsetup/README.md similarity index 100% rename from TDD/niptomoDebug/README.md rename to TDD/nipmodsetup/README.md diff --git a/TDD/niptomoDebug/SConstruct b/TDD/nipmodsetup/SConstruct similarity index 69% rename from TDD/niptomoDebug/SConstruct rename to TDD/nipmodsetup/SConstruct index af963a5..fe29bc2 100644 --- a/TDD/niptomoDebug/SConstruct +++ b/TDD/nipmodsetup/SConstruct @@ -3,29 +3,23 @@ # # SConstruct (Python) # -# Objetivo: Exemplo de uso do programa sfrays2. +# Purpose: Test sfnipmodsetup in a constan velocity model. # # Site: https://dirack.github.io # -# Versão 1.0 +# Version 1.0 # -# Programador: Rodolfo A C Neves (Dirack) 02/12/2020 +# Programer: Rodolfo A C Neves (Dirack) 02/12/2020 # # Email: rodolfo_profissional@hotmail.com # -# Licença: GPL-3.0 . +# License: GPL-3.0 . from rsf.proj import * # create a model amp=1 rad=1 -#Flow('model',None, -# ''' -# math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | -# math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | -# put label1=Depth unit1=km label2=Position unit2=km -# ''' % (amp,rad,rad)) # Constant velocity model for test Flow('model',None, @@ -53,7 +47,7 @@ Flow('t0s',None,'spike n1=2 mag=1') # do a ray-tracing Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], ''' - niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} + nipmodsetup shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} ''') # plot the ray From f8641be02692cdbf07a06d87feb5d70ad85ab6dc Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 29 Dec 2020 12:42:43 -0300 Subject: [PATCH 24/63] Update python unit tests for programs #14 Update scons module directory for unit tests in examples of diffraction simulation and diffraction migration --- TDD/test_diffmig.py | 2 +- TDD/test_diffsimul.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/TDD/test_diffmig.py b/TDD/test_diffmig.py index 4758f4b..b6b1b2a 100644 --- a/TDD/test_diffmig.py +++ b/TDD/test_diffmig.py @@ -1,7 +1,7 @@ import unittest import sys,os -sys.path.append('/usr/local/lib/python2.7/dist-packages/scons/') +sys.path.append('/usr/lib/scons/') from rsf.proj import * testdir = os.path.dirname(os.environ['PWD'])+'/experiments/algorithmVelocityInversion/' diff --git a/TDD/test_diffsimul.py b/TDD/test_diffsimul.py index b3124d7..7f51e1e 100644 --- a/TDD/test_diffsimul.py +++ b/TDD/test_diffsimul.py @@ -1,7 +1,7 @@ import unittest import sys,os -sys.path.append('/usr/local/lib/python2.7/dist-packages/scons/') +sys.path.append('/usr/lib/scons/') from rsf.proj import * testdir = os.path.dirname(os.environ['PWD'])+'/experiments/algorithmVelocityInversion/' From b648f4641e907c6baed0116d068487f1dd56537e Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 30 Dec 2020 18:19:59 -0300 Subject: [PATCH 25/63] Change sfnipmodel name to sfniptimecurve #14 Change the name of the program to sfniptimecurve to align the name with the program purpose --- Mnipmodel.c => Mniptimecurve.c | 3 +-- SConstruct | 2 +- TDD/Makefile | 10 +++++----- TDD/gdb_script2 | 6 +++--- TDD/{nipmodelDebug => niptimecurve}/README.md | 0 TDD/{nipmodelDebug => niptimecurve}/SConstruct | 4 ++-- 6 files changed, 12 insertions(+), 13 deletions(-) rename Mnipmodel.c => Mniptimecurve.c (98%) rename TDD/{nipmodelDebug => niptimecurve}/README.md (100%) rename TDD/{nipmodelDebug => niptimecurve}/SConstruct (87%) diff --git a/Mnipmodel.c b/Mniptimecurve.c similarity index 98% rename from Mnipmodel.c rename to Mniptimecurve.c index 16fb7f7..bcb831c 100644 --- a/Mnipmodel.c +++ b/Mniptimecurve.c @@ -1,7 +1,6 @@ /* NIP Point source modeling ray tracer -Trace rays from NIP sources to acquisition surface in order to get -traveltime curves used in NIP tomography. +Trace rays from NIP sources to acquisition surface in order to get traveltime curves used in NIP tomography. */ diff --git a/SConstruct b/SConstruct index e65b68a..37a33a4 100644 --- a/SConstruct +++ b/SConstruct @@ -5,7 +5,7 @@ import bldutil # Put your name programs in progs variable # without 'M' preffix and '.c' extension progs = ''' -nipmodsetup nipmodel +nipmodsetup niptimecurve ''' try: # distributed version diff --git a/TDD/Makefile b/TDD/Makefile index 7c1b9d9..1177aed 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -16,7 +16,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c -SRCNIPMODEL= ../Mnipmodel.c ../raytrace.c ../grid2.c ../atela.c +SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) @@ -33,12 +33,12 @@ nipmodsetup.x: $(SRCNIPTOMO) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program nipmodsetup.x for GDB built" -# Version of the sfnipmodel program for GDB (GNU debugger) -nipmodel.x: $(SRCNIPMODEL) +# Version of the sfniptimecurve program for GDB (GNU debugger) +niptimecurve.x: $(SRCNIPMODEL) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm - @echo "Program nipmodel.x for GDB built" + @echo "Program niptimecurve.x for GDB built" clean: rm *.x -.PHONY: clean nipmodsetup.x nipmodel.x +.PHONY: clean nipmodsetup.x niptimecurve.x diff --git a/TDD/gdb_script2 b/TDD/gdb_script2 index c6e65fa..e429bf5 100644 --- a/TDD/gdb_script2 +++ b/TDD/gdb_script2 @@ -1,9 +1,9 @@ # Command scripts for GNU debugger (GDB) # -# You can call this script to set up the program nipmodel.x +# You can call this script to set up the program niptimecurve.x # for debugging. Inside GDB session call it: -# source gdb_script +# source gdb_script2 -cd nipmodelDebug +cd niptimecurve set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=x.rsf >time.rsf show args diff --git a/TDD/nipmodelDebug/README.md b/TDD/niptimecurve/README.md similarity index 100% rename from TDD/nipmodelDebug/README.md rename to TDD/niptimecurve/README.md diff --git a/TDD/nipmodelDebug/SConstruct b/TDD/niptimecurve/SConstruct similarity index 87% rename from TDD/nipmodelDebug/SConstruct rename to TDD/niptimecurve/SConstruct index 656f67f..0ca9473 100644 --- a/TDD/nipmodelDebug/SConstruct +++ b/TDD/niptimecurve/SConstruct @@ -3,7 +3,7 @@ # # SConstruct (Python) # -# Purpose: Program sfnipmodel test in a constant velocity model. +# Purpose: Program sfniptimecurve test in a constant velocity model. # # Site: https://dirack.github.io # @@ -48,7 +48,7 @@ Flow('t0s',None,'spike n1=2 mag=1') Flow(['tCoordinates','xCoordinates'], ['model','shotsfile','anglefile','t0s'], ''' - nipmodel shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} + niptimecurve shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} ''') End() From fc3145d351c5114095695242ab717953f005f965 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 13 Jan 2021 21:23:26 -0300 Subject: [PATCH 26/63] Use --command flags in GDB debugging #18 Call GDB after compilation in Makefile. The --command flag is used to load the gdb scripts when calling gdb shell --- TDD/Makefile | 2 ++ TDD/{gdb_script => gdb_script1} | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) rename TDD/{gdb_script => gdb_script1} (92%) diff --git a/TDD/Makefile b/TDD/Makefile index 1177aed..399fa6d 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -32,11 +32,13 @@ integ: nipmodsetup.x: $(SRCNIPTOMO) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program nipmodsetup.x for GDB built" + gdb -q --command=gdb_script1 --args $@ # Version of the sfniptimecurve program for GDB (GNU debugger) niptimecurve.x: $(SRCNIPMODEL) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program niptimecurve.x for GDB built" + gdb -q --command=gdb_script2 --args $@ clean: rm *.x diff --git a/TDD/gdb_script b/TDD/gdb_script1 similarity index 92% rename from TDD/gdb_script rename to TDD/gdb_script1 index bf2439c..8ff1b68 100644 --- a/TDD/gdb_script +++ b/TDD/gdb_script1 @@ -2,7 +2,7 @@ # # You can call this script to set up the program nipmodsetup.x # for debugging. Inside GDB session call it: -# source gdb_script +# source gdb_script1 cd nipmodsetup set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf From ab8dcc92abed23c7cd597dc478ad353c1cc8ea78 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 20 Jan 2021 14:55:23 -0300 Subject: [PATCH 27/63] Run all the tests with 'make' #14 Run all unit tests and integration tests with 'make' command --- TDD/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TDD/Makefile b/TDD/Makefile index 399fa6d..b52ad4c 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -18,7 +18,7 @@ DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c -all: $(DEP) +all: $(DEP) integ test_%: @echo $@ From 136608d39df9eab73f7d63a2d6ba533bcfeed56a Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 20 Jan 2021 15:02:55 -0300 Subject: [PATCH 28/63] Correct GDB script for nipmodsetup test #14 --- TDD/gdb_script1 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TDD/gdb_script1 b/TDD/gdb_script1 index 8ff1b68..bc57600 100644 --- a/TDD/gdb_script1 +++ b/TDD/gdb_script1 @@ -5,5 +5,5 @@ # source gdb_script1 cd nipmodsetup -set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf +set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf nipangles=nipangles.rsf > ray.rsf show args From d71dc04321658501a695c3a93841f6ebe1750d16 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 20 Jan 2021 20:10:50 -0300 Subject: [PATCH 29/63] Scratch of the tests and new version of the program #16 Scratch of the tests in datamisfit directory: Calculate the datamisfit between a pre calculated traveltime curve and modeled traveltime with the new version of the program sfniptimecurve Copy the program sfniptimecurve source file to build the new version and keep the old version --- Mniptimecurve2.c | 141 ++++++++++++++++++++++++++++++++++++++ SConstruct | 2 +- TDD/datamisfit/SConstruct | 63 +++++++++++++++++ 3 files changed, 205 insertions(+), 1 deletion(-) create mode 100644 Mniptimecurve2.c create mode 100644 TDD/datamisfit/SConstruct diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c new file mode 100644 index 0000000..bc2dac6 --- /dev/null +++ b/Mniptimecurve2.c @@ -0,0 +1,141 @@ +/* New version of NIP Point source modeling ray tracer + +Trace rays from NIP sources to acquisition surface in order to get traveltime curves used in NIP tomography. + +*/ + +#include + +#include + +#include "raytrace.h" + +#define DANGLE 15.0 +#define INITIAL_ANGLE 45.0 + +int main(int argc, char* argv[]) +{ + bool verb; // Verbose parameter + int n[2]; // Velocity grid dimensions n[0]=n1, n[1]=n2 + float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 + float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 + float t; // Ray traveltime + float** s; // NIP source position (z,x) + int ndim; // n1 dimension in shotsfile, should be equal 2 + int nshot; // n2 dimensions in shotsfile + int nm; // Number of samples in velocity grid + float* a; // Normal Ray initial angle + int nr=7; // Number of rays to trace + float* slow; // slowness + int im; // loop counter + float v0; // Velocity + float dt=0.001; // ray time sampling + int nt=1000; // Numer of time samples for each ray + int ns; // Number of NIP sources + int it,ir,is; // loop counters + float** traj; // Ray trajectory (z,x) + float x[2]; // Ray initial position + float p[2]; // Ray initial slowness vector + float currentRayAngle; // Ray initial angle + sf_file shots, vel, angles, timeCurve,xCurve; + raytrace rt; + + sf_init(argc,argv); + + shots = sf_input("shotsfile"); + vel = sf_input("in"); + timeCurve = sf_output("out"); + xCurve = sf_output("x"); + angles = sf_input("anglefile"); + + /* Velocity model: get 2D grid parameters */ + if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); + if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); + if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); + if(!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); + if(!sf_histfloat(vel,"o1",o)) o[0]=0.; + if(!sf_histfloat(vel,"o2",o+1)) o[1]=0.; + + if(!sf_getbool("verb",&verb)) verb=true; + /* verbose */ + + /* Shotsfile: get m0 shot points */ + if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) + sf_error("Must have n1=2 in shotsfile"); + if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); + s = sf_floatalloc2(ndim,nshot); + sf_floatread(s[0],ndim*nshot,shots); + sf_fileclose(shots); + + /* Anglefile: get initial emergence angle */ + if(!sf_histint(angles,"n1",&ns)) sf_error("No n1= in anglefile"); + a = sf_floatalloc(ns); + sf_floatread(a,ns,angles); + + + /* get slowness squared */ + nm = n[0]*n[1]; + slow = sf_floatalloc(nm); + sf_floatread(slow,nm,vel); + + for(im=0;im0){ + t = it*dt; + sf_floatwrite(&traj[it][1],1,xCurve); + }else if(it == 0){ + t = abs(nt)*dt; + sf_floatwrite(&traj[nt][1],1,xCurve); + }else{ + t = -it * dt; + sf_floatwrite(&traj[-it][1],1,xCurve); + } + + sf_floatwrite(&t,1,timeCurve); + + /* Raytrace close */ + raytrace_close(rt); + free(traj); + } + } + +} diff --git a/SConstruct b/SConstruct index 37a33a4..3565921 100644 --- a/SConstruct +++ b/SConstruct @@ -5,7 +5,7 @@ import bldutil # Put your name programs in progs variable # without 'M' preffix and '.c' extension progs = ''' -nipmodsetup niptimecurve +nipmodsetup niptimecurve niptimecurve2 ''' try: # distributed version diff --git a/TDD/datamisfit/SConstruct b/TDD/datamisfit/SConstruct new file mode 100644 index 0000000..1e70d7f --- /dev/null +++ b/TDD/datamisfit/SConstruct @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConstruct (Madagascar script) +# +# Purpose: Calculate data misfit for pre calculated traveltime curve. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 20/01/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +from rsf.proj import * + +def arr2str(array,sep=' '): + ''' + Convert a tuple array into a string with a given separator + Example: (1,2,3) is converted to '1,2,3' for sep=',' + :param array: tuple, array to convert + :param sep: string, separator + ''' + return sep.join(map(str,array)) + +def buildVectorFile(filename,array): + ''' + Build a 1D vector file using sfspike + :param filename: string, RSF filename + :param array: tuple, array 1D vector + ''' + l=len(array) + s=arr2str(array,',') + + Flow(filename,None, + ''' + spike mag=%s nsp=%d n1=%d k1=%s + '''%(s,l,l,arr2str(range(1,l+1,1),','))) + +# time coordinates +t=(0.472, 0.385, 0.346, 0.334, 0.346, 0.385, 0.472) + +# x coordinates +x=(4.499, 4.711, 4.866, 5, 5.134, 5.289, 5.501) + +# Constant velocity model for test +Flow('model',None, + ''' + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.5 | + put label1=Depth unit1=km label2=Position unit2=km + ''' ) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') + +# Generate traveltime curve +buildVectorFile('t',t) +buildVectorFile('x',x) + +End() From ccec1216286daa64284c282ad2e49b0372f790f1 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 20 Jan 2021 21:56:15 -0300 Subject: [PATCH 30/63] Read true model (t,x) coordinates #16 Store true model (t,x) coordinates in td and xd vectors to be used to calculate data misfit after modeling --- Mniptimecurve2.c | 16 +++++++++++++++- TDD/Makefile | 8 +++++++- TDD/datamisfit/SConstruct | 21 ++++++++++++++++++++- TDD/gdb_script3 | 9 +++++++++ 4 files changed, 51 insertions(+), 3 deletions(-) create mode 100644 TDD/gdb_script3 diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c index bc2dac6..37abf3b 100644 --- a/Mniptimecurve2.c +++ b/Mniptimecurve2.c @@ -37,7 +37,11 @@ int main(int argc, char* argv[]) float x[2]; // Ray initial position float p[2]; // Ray initial slowness vector float currentRayAngle; // Ray initial angle - sf_file shots, vel, angles, timeCurve,xCurve; + int ntd; // number of time samples in the data + int nxd; // number of x samples in the data + float* td; // t coordinate of the data + float* xd; // x coordinate of the data + sf_file shots, vel, angles, timeCurve, xCurve, xdata, tdata; raytrace rt; sf_init(argc,argv); @@ -47,6 +51,8 @@ int main(int argc, char* argv[]) timeCurve = sf_output("out"); xCurve = sf_output("x"); angles = sf_input("anglefile"); + tdata = sf_input("tdata"); + xdata = sf_input("xdata"); /* Velocity model: get 2D grid parameters */ if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); @@ -72,6 +78,14 @@ int main(int argc, char* argv[]) a = sf_floatalloc(ns); sf_floatread(a,ns,angles); + /* Read (t,x) true data */ + if(!sf_histint(tdata,"n1",&ntd)) sf_error("No n1= in tdata file"); + if(!sf_histint(xdata,"n1",&nxd)) sf_error("No n1= in xdata file"); + if(ntd!=nxd) sf_error("n1 dimension in tdata should be equal to n1 in xdata!"); + td = sf_floatalloc(ntd); + sf_floatread(td,ntd,tdata); + xd = sf_floatalloc(nxd); + sf_floatread(xd,nxd,xdata); /* get slowness squared */ nm = n[0]*n[1]; diff --git a/TDD/Makefile b/TDD/Makefile index b52ad4c..e9a55da 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -17,6 +17,7 @@ SRC = diffmig diffsimul DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c +SRCNIPMODEL2= ../Mniptimecurve2.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) integ @@ -40,7 +41,12 @@ niptimecurve.x: $(SRCNIPMODEL) @echo "Program niptimecurve.x for GDB built" gdb -q --command=gdb_script2 --args $@ +# Version of the sfniptimecurve2 program for GDB (GNU debugger) +niptimecurve2.x: $(SRCNIPMODEL2) + gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm + @echo "Program niptimecurve2.x for GDB built" + gdb -q --command=gdb_script3 --args $@ clean: rm *.x -.PHONY: clean nipmodsetup.x niptimecurve.x +.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x diff --git a/TDD/datamisfit/SConstruct b/TDD/datamisfit/SConstruct index 1e70d7f..023579c 100644 --- a/TDD/datamisfit/SConstruct +++ b/TDD/datamisfit/SConstruct @@ -49,7 +49,7 @@ x=(4.499, 4.711, 4.866, 5, 5.134, 5.289, 5.501) # Constant velocity model for test Flow('model',None, ''' - spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.5 | + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.4 | put label1=Depth unit1=km label2=Position unit2=km ''' ) @@ -60,4 +60,23 @@ Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Posit buildVectorFile('t',t) buildVectorFile('x',x) +Flow('s1',None, + ''' + math n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1+4" + ''') + +Flow('s2',None,'spike n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') + +Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',None,'spike n1=2 mag=0') + +Flow('t0s',None,'spike n1=2 mag=1') + +Flow(['tCoordinates','xCoordinates'], + ['model','shotsfile','anglefile','t0s','t','x'], + ''' + niptimecurve2 shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} tdata=${SOURCES[4]} xdata=${SOURCES[5]} + ''') + End() diff --git a/TDD/gdb_script3 b/TDD/gdb_script3 new file mode 100644 index 0000000..9bd1f54 --- /dev/null +++ b/TDD/gdb_script3 @@ -0,0 +1,9 @@ +# Command scripts for GNU debugger (GDB) +# +# You can call this script to set up the program niptimecurve2.x +# for debugging. Inside GDB session call it: +# source gdb_script3 + +cd datamisfit +set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=x.rsf tdata=t.rsf xdata=x.rsf >time.rsf +show args From 5ab45aa620399fde07149151baf2377b667265ba Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 20 Jan 2021 22:45:58 -0300 Subject: [PATCH 31/63] Store data misfit in tmis vector #16 Data misfit will be used as optimization criteria for velocity inversion, so store it in tmis vector --- Mniptimecurve2.c | 12 +++++++++--- TDD/gdb_script3 | 2 +- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c index 37abf3b..4a730b0 100644 --- a/Mniptimecurve2.c +++ b/Mniptimecurve2.c @@ -41,6 +41,7 @@ int main(int argc, char* argv[]) int nxd; // number of x samples in the data float* td; // t coordinate of the data float* xd; // x coordinate of the data + float* tmis; // data misfit vector sf_file shots, vel, angles, timeCurve, xCurve, xdata, tdata; raytrace rt; @@ -86,7 +87,8 @@ int main(int argc, char* argv[]) sf_floatread(td,ntd,tdata); xd = sf_floatalloc(nxd); sf_floatread(xd,nxd,xdata); - + tmis = sf_floatalloc(ntd); + /* get slowness squared */ nm = n[0]*n[1]; slow = sf_floatalloc(nm); @@ -149,7 +151,11 @@ int main(int argc, char* argv[]) /* Raytrace close */ raytrace_close(rt); free(traj); - } - } + + /* Calculate data misfit */ + tmis[ir] = td[ir] - t; + } /* Loop over rays */ + + } /* Loop over NIP sources */ } diff --git a/TDD/gdb_script3 b/TDD/gdb_script3 index 9bd1f54..09788b6 100644 --- a/TDD/gdb_script3 +++ b/TDD/gdb_script3 @@ -5,5 +5,5 @@ # source gdb_script3 cd datamisfit -set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=x.rsf tdata=t.rsf xdata=x.rsf >time.rsf +set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=xCoordinate.rsf tdata=t.rsf xdata=x.rsf >tCoordinate.rsf show args From 39290ba5750dbe0dab2a9b7043b733d0dcdb2436 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 21 Jan 2021 10:43:42 -0300 Subject: [PATCH 32/63] Test file dimensions to avoid segfault errors #16 --- Mniptimecurve2.c | 8 ++++++-- TDD/datamisfit/SConstruct | 8 ++++---- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c index 4a730b0..ffd51f2 100644 --- a/Mniptimecurve2.c +++ b/Mniptimecurve2.c @@ -41,7 +41,8 @@ int main(int argc, char* argv[]) int nxd; // number of x samples in the data float* td; // t coordinate of the data float* xd; // x coordinate of the data - float* tmis; // data misfit vector + float* tmis; // data misfit t vector + float* xmis; // data misfit x vector sf_file shots, vel, angles, timeCurve, xCurve, xdata, tdata; raytrace rt; @@ -78,6 +79,7 @@ int main(int argc, char* argv[]) if(!sf_histint(angles,"n1",&ns)) sf_error("No n1= in anglefile"); a = sf_floatalloc(ns); sf_floatread(a,ns,angles); + if(ns!=nshot) sf_error("n1 in anglefile should be equal to n2 in shotsfile!"); /* Read (t,x) true data */ if(!sf_histint(tdata,"n1",&ntd)) sf_error("No n1= in tdata file"); @@ -88,6 +90,7 @@ int main(int argc, char* argv[]) xd = sf_floatalloc(nxd); sf_floatread(xd,nxd,xdata); tmis = sf_floatalloc(ntd); + xmis = sf_floatalloc(nxd); /* get slowness squared */ nm = n[0]*n[1]; @@ -152,8 +155,9 @@ int main(int argc, char* argv[]) raytrace_close(rt); free(traj); - /* Calculate data misfit */ + /* Calculate data misfit (t,x) */ tmis[ir] = td[ir] - t; + xmis[ir] = xd[ir] - x[1]; } /* Loop over rays */ } /* Loop over NIP sources */ diff --git a/TDD/datamisfit/SConstruct b/TDD/datamisfit/SConstruct index 023579c..a52b7c2 100644 --- a/TDD/datamisfit/SConstruct +++ b/TDD/datamisfit/SConstruct @@ -62,16 +62,16 @@ buildVectorFile('x',x) Flow('s1',None, ''' - math n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1+4" + math n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1+4" ''') -Flow('s2',None,'spike n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') +Flow('s2',None,'spike n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') -Flow('anglefile',None,'spike n1=2 mag=0') +Flow('anglefile',None,'spike n1=1 mag=0') -Flow('t0s',None,'spike n1=2 mag=1') +Flow('t0s',None,'spike n1=1 mag=1') Flow(['tCoordinates','xCoordinates'], ['model','shotsfile','anglefile','t0s','t','x'], From 24c5f3712132d18cd21b203601be26d9bc401e04 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 21 Jan 2021 13:26:34 -0300 Subject: [PATCH 33/63] Simulation of velocity model update #16 Use a function to update de velocity in each iteration of the ray tracing process and calculate the data misfit in time and x coordinates --- Mniptimecurve2.c | 10 ++++++++++ TDD/datamisfit/SConstruct | 8 ++++---- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c index ffd51f2..ef91fe6 100644 --- a/Mniptimecurve2.c +++ b/Mniptimecurve2.c @@ -12,7 +12,16 @@ Trace rays from NIP sources to acquisition surface in order to get traveltime cu #define DANGLE 15.0 #define INITIAL_ANGLE 45.0 +#define DSLOW 0.04 +void updatevelmodel(int nm, float* slow){ + + int im; + + for(im=0;im Date: Sat, 20 Feb 2021 16:08:15 -0300 Subject: [PATCH 34/63] Scratch of stereotomography algorithm #17 The program sfstereoniptomo uses the NIP sources to trace rays from them to acquisition surface in order to obtain the traveltime curve and to compare it with the CRE traveltime calculated values. The data misfit will be used to update the velocity model. We also add the program sfgetparameter to extract the parameters from parameters cube for (t0,m0) location given --- Mgetparameter.c | 70 +++++++++++++ Mniptimecurve.c | 2 +- Mstereoniptomo.c | 148 ++++++++++++++++++++++++++++ SConstruct | 2 +- experiments/stereotomo/SConstruct | 66 +++++++++++++ experiments/stereotomo/ascFormat.sh | 7 ++ experiments/stereotomo/gdb_script | 2 + tomography.c | 123 +++++++++++++++++++++++ 8 files changed, 418 insertions(+), 2 deletions(-) create mode 100644 Mgetparameter.c create mode 100644 Mstereoniptomo.c create mode 100644 experiments/stereotomo/SConstruct create mode 100755 experiments/stereotomo/ascFormat.sh create mode 100644 experiments/stereotomo/gdb_script create mode 100644 tomography.c diff --git a/Mgetparameter.c b/Mgetparameter.c new file mode 100644 index 0000000..440ae65 --- /dev/null +++ b/Mgetparameter.c @@ -0,0 +1,70 @@ +/* Get a parameter from parameters cube for (t0,m0) pairs given + +Use beta=y if you want to get BETA parameter with automatic conversion +to degrees + */ + +#include + +#define RAD2DEG 180./SF_PI /* Degrees to radians conversion */ + +int main(int argc, char* argv[]) +{ + bool verb; // Verbose parameter + bool beta; + int nm0, nt0, np1, np2; + float dp1, dp2, op1, op2; + float* a, *t0, *m0; + float** par; + int i, it0, im0; + sf_file in, t0s, m0s, parfile; + + sf_init(argc,argv); + + in = sf_input("in"); + m0s = sf_input("m0s"); + parfile = sf_output("out"); + t0s = sf_input("t0s"); + + /* Velocity model: get 2D grid parameters */ + if(!sf_histint(m0s,"n1",&nm0)) sf_error("No n1= in m0s file"); + if(!sf_histint(t0s,"n1",&nt0)) sf_error("No n1= in t0s file"); + + if(!sf_histint(in,"n1",&np1)) sf_error("No n1= in input"); + if(!sf_histint(in,"n2",&np2)) sf_error("No n2= in input"); + if(!sf_histfloat(in,"d1",&dp1)) sf_error("No d1= in input"); + if(!sf_histfloat(in,"d2",&dp2)) sf_error("No d2= in input"); + if(!sf_histfloat(in,"o1",&op1)) sf_error("No o1= in input"); + if(!sf_histfloat(in,"o2",&op2)) sf_error("No o2= in input"); + + if(!sf_getbool("verb",&verb)) verb=true; + /* verbose */ + + if(!sf_getbool("beta",&beta)) beta=false; + /* Get beta parameter */ + + t0 = sf_floatalloc(nt0); + sf_floatread(t0,nt0,t0s); + m0 = sf_floatalloc(nm0); + sf_floatread(m0,nm0,m0s); + a = sf_floatalloc(nt0); + par = sf_floatalloc2(np1,np2); + sf_floatread(par[0],np1*np2,in); + + sf_putint(parfile,"n1",nt0); + sf_putint(parfile,"n2",1); + + for(i=0;i +#include +#include "tomography.h" + +int main(int argc, char* argv[]) +{ + bool verb; // Verbose parameter + int n[2]; // Velocity grid dimensions n[0]=n1, n[1]=n2 + float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 + float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 + float** s; // NIP source position (z,x) + int ndim; // n1 dimension in shotsfile, should be equal 2 + int nshot; // n2 dimensions in shotsfile + int nm; // Number of samples in velocity grid + float* a; // Normal Ray initial angle + int nr=2; // Number of rays to trace + float* slow; // slowness + int im; // loop counter + float v; // Velocity + float v0; // Near surface velocity + int ns; // Number of NIP sources + int is; // Loop counter for NIP sources + int ir; // Loop counter of reflection rays + int i; // Loop over iterations + float x[2]; // Ray initial position + float tmis[1]; // data misfit t vector + float dmis; + float *ts, *tr, *xs, *xr, offset, cmp; + float *m0, *t0, *RNIP, *BETA; + sf_file shots, vel, angles, timeCurve, xCurve, m0s, t0s, rnips, betas; + + sf_init(argc,argv); + + shots = sf_input("shotsfile"); + vel = sf_input("in"); + timeCurve = sf_output("out"); + xCurve = sf_output("x"); + angles = sf_input("anglefile"); + m0s = sf_input("m0s"); + t0s = sf_input("t0s"); + rnips = sf_input("rnips"); + betas = sf_input("betas"); + + /* Velocity model: get 2D grid parameters */ + if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); + if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); + if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); + if(!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); + if(!sf_histfloat(vel,"o1",o)) o[0]=0.; + if(!sf_histfloat(vel,"o2",o+1)) o[1]=0.; + + if(!sf_getbool("verb",&verb)) verb=true; + /* verbose */ + + if(!sf_getfloat("v0",&v0)) v0=1.5; + /* Near surface velocity (Km/s) */ + + /* Shotsfile: get m0 shot points */ + if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) + sf_error("Must have n1=2 in shotsfile"); + if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); + s = sf_floatalloc2(ndim,nshot); + sf_floatread(s[0],ndim*nshot,shots); + sf_fileclose(shots); + + /* Anglefile: get initial emergence angle */ + if(!sf_histint(angles,"n1",&ns)) sf_error("No n1= in anglefile"); + a = sf_floatalloc(ns); + sf_floatread(a,ns,angles); + if(ns!=nshot) sf_error("n1 in anglefile should be equal to n2 in shotsfile!"); + + /* allocate parameters vectors */ + m0 = sf_floatalloc(ns); + sf_floatread(m0,ns,m0s); + t0 = sf_floatalloc(ns); + sf_floatread(t0,ns,t0s); + RNIP = sf_floatalloc(ns); + sf_floatread(RNIP,ns,rnips); + BETA = sf_floatalloc(ns); + sf_floatread(BETA,ns,betas); + + /* get slowness squared */ + nm = n[0]*n[1]; + slow = sf_floatalloc(nm); + sf_floatread(slow,nm,vel); + + for(im=0;im. + +from rsf.proj import * + +# create a model +amp=1 +rad=1 + +# Constant velocity model for test +Flow('model',None, + ''' + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | + put label1=Depth unit1=km label2=Position unit2=km + ''' ) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') + +Flow('s2',None,'spike n1=3 d1=1 o1=0 n2=1 o2=1 d2=0 mag=0') + +Flow('shotsfile',['s2','m0s'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',['beta','t0s','m0s'], + ''' + getbetaangle t0s=${SOURCES[1]} m0s=${SOURCES[2]} beta=y + ''') + +# do a ray-tracing +Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], + ''' + nipmodsetup shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} + ''') + +# Rotate nipangles +Flow('angles','nipangles','math output="input-180"') +Flow('nipsources','ray','dd type=float | put n1=2 n2=3') + +# do a ray-tracing from NIP sources +Flow(['tCoordinates','xCoordinates'], + ['model','nipsources','angles','t0s','m0s','rnips','betas'], + ''' + stereoniptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} m0s=${SOURCES[4]} rnips=${SOURCES[5]} betas=${SOURCES[6]} x=${TARGETS[1]} v0=1.508 + ''') + +Flow('raysteste',['model','nipsources','angles'], + ''' + rays2 nt=3000 dt=0.001 shotfile=${SOURCES[1]} a0=13.49 nr=1 escvar=y + ''') + + +End() diff --git a/experiments/stereotomo/ascFormat.sh b/experiments/stereotomo/ascFormat.sh new file mode 100755 index 0000000..c3babb9 --- /dev/null +++ b/experiments/stereotomo/ascFormat.sh @@ -0,0 +1,7 @@ +INPUT=$(cat "-" | tr '\t' ' ' | cut -d' ' -f"$2") +NUMBER_OF_POINTS=$(echo "$INPUT" | wc -l) +FORMATED_INPUT=$(echo "$INPUT" | tr '\n' ' ') +FILE="$1" + +echo "${FORMATED_INPUT}n1=$NUMBER_OF_POINTS d1=1 o1=0 data_format=ascii_float in=$1" + diff --git a/experiments/stereotomo/gdb_script b/experiments/stereotomo/gdb_script new file mode 100644 index 0000000..369cf3f --- /dev/null +++ b/experiments/stereotomo/gdb_script @@ -0,0 +1,2 @@ +set args testes.rsf +show args diff --git a/tomography.c b/tomography.c new file mode 100644 index 0000000..1959b70 --- /dev/null +++ b/tomography.c @@ -0,0 +1,123 @@ +#include +#include +#include +#include +#include "raytrace.h" +#include "tomography.h" + +#define DSLOW 0.04 +#define DANGLE 5.0 +#define MAX_ITERATIONS 1 +/*^*/ + +void updatevelmodel(float* x, float* slow, float dmis,int i){ +/*< TODO >*/ +} + +float creTimeApproximation(float h, + float m, + float v0, + float t0, + float m0, + float RNIP, + float BETA, + bool cds){ +/*< CRE traveltime approximation >*/ + float alpha; + float d = m-m0; + float c1; + float c2; + float a1, a2, b2, b1, Fd, Fd1, Fd2; + float t; + + if(!cds){ + c1 = (d+h)/RNIP; + c2 = (d-h)/RNIP; + alpha = sin(BETA)/RNIP; + t = (t0-2*RNIP/v0)+(RNIP/v0)*sqrt(1-2*alpha*(d+h)+c1*c1)+(RNIP/v0)*sqrt(1-2*alpha*(d-h)+c2*c2); + }else{ + + a1=(2*sin(BETA))/(v0); + a2=(2*cos(BETA)*cos(BETA)*t0)/(v0*RNIP); + b2=(2*cos(BETA)*cos(BETA)*t0)/(v0*RNIP); + b1=2*b2+a1*a1-a2; + Fd=(t0+a1*d)*(t0+a1*d)+a2*d*d; + Fd2=(t0+a1*(d-h))*(t0+a1*(d-h))+a2*(d-h)*(d-h); + Fd1=(t0+a1*(d+h))*(t0+a1*(d+h))+a2*(d+h)*(d+h); + t=sqrt((Fd+b1*h*h+sqrt(Fd2*Fd1))*0.5); + } + return t; +} + +void raystraveltimes( + float *ts, /* sources-NIP rays traveltime */ + float *tr, /* NIP-receivers rays traveltimes */ + float *xs, /* sources-NIP rays position */ + float *xr, /* NIP-receivers rays position */ + float x[2], /* NIP position */ + float nrdeg, /* Normal ray angle in degrees */ + int n[2], + float o[2], + float d[2], + float* slow, + int nr /* number of reflection rays */ + ) +/*< Return traveltimes of source-NIP-receiver rays and endpoints >*/ +{ + + float currentRayAngle; + int i, ir, it; + float p[2], s[2], t; + float nt=5000; + float dt=0.001; + raytrace rt; + float** traj; // Ray trajectory (z,x) + + s[0] = x[0]; + s[1] = x[1]; + + for(ir=0;ir0){ + t = it*dt; + if(i==0){ + ts[ir]=t; + xs[ir]=x[1]; + sf_warning("ts=%f",ts[ir]); + }else{ + tr[ir]=t; + xr[ir]=x[1]; + sf_warning("tr=%f",tr[ir]); + } + }else if(it == 0){ + t = abs(nt)*dt; + nt += 1000; + }else{ + sf_error("Bad angle, ray get to the model side/bottom"); + } + + /* Raytrace close */ + raytrace_close(rt); + free(traj); + + x[0] = s[0]; + x[1] = s[1]; + } /* Loop over source-NIP-receiver rays */ + } /* Loop over reflection rays */ +} From 88760bc6038056d2502aa3fcb8f7493ea6dac3c7 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Thu, 25 Feb 2021 15:40:25 -0300 Subject: [PATCH 35/63] Do the stereoniptomo experiment for more NIP sources #17 Picking of 26 nip sources and experiment repetition --- Mstereoniptomo.c | 6 +++--- experiments/stereotomo/SConstruct | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index e31fe2f..e1a4d74 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -126,16 +126,16 @@ int main(int argc, char* argv[]) raystraveltimes(ts,tr,xs,xr,x,a[is],n,o,d,slow,nr); - /*for(ir=0;ir Date: Sat, 6 Mar 2021 12:52:23 -0300 Subject: [PATCH 36/63] Program to visualize nip sources from txt file #18 --- experiments/stereotomo/viewFoldGraph.py | 40 +++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100755 experiments/stereotomo/viewFoldGraph.py diff --git a/experiments/stereotomo/viewFoldGraph.py b/experiments/stereotomo/viewFoldGraph.py new file mode 100755 index 0000000..5391246 --- /dev/null +++ b/experiments/stereotomo/viewFoldGraph.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# viewFoldGraph.py (Python) +# +# Objetivo: Visualizar gráfico de cobertura dos CMP's. +# +# Site: https://dirack.github.io +# +# Versão 1.0 +# +# Programador: Rodolfo A C Neves (Dirack) 27/09/2020 +# +# Email: rodolfo_profissional@hotmail.com +# +# Licença: GPL-3.0 . + +import matplotlib.pyplot as plt + +f = open("x.txt","r") +xstr = filter(None,f.read().split('\n')) +x = map(lambda i: float(i),xstr) + +f = open("y.txt","r") +ystr = filter(None,f.read().split('\n')) +y = map(lambda i: float(i),ystr) + +f = open("z.txt","r") +zstr = filter(None,f.read().split('\n')) +z = map(lambda i: float(i),zstr) +z0 = [0]*26 + +#print(len(list(z))) +#print(len(list(z0))) + +plt.xlim(0, 6) +plt.ylim(3,0) +plt.plot(list(x),list(y),'*') +plt.plot(list(z),list(z0),'v') +plt.show() From fa616336d74b322e5657aafc15a66d2e71d7fd45 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 6 Mar 2021 12:56:51 -0300 Subject: [PATCH 37/63] Add more NIP sources for stereoniptomo #18 Add 26 nip sources for the stereoniptomo experiment and try to recover the reflector shape --- Mnipmodsetup.c | 2 +- Mstereoniptomo.c | 2 +- experiments/stereotomo/SConstruct | 4 ++-- tomography.c | 11 ++++++++--- 4 files changed, 12 insertions(+), 7 deletions(-) diff --git a/Mnipmodsetup.c b/Mnipmodsetup.c index 3779b86..620181e 100644 --- a/Mnipmodsetup.c +++ b/Mnipmodsetup.c @@ -124,7 +124,7 @@ int main(int argc, char* argv[]) for(ir=0; ir*/ +void updatevelmodel(float* x, float* slow, int nm, float dmis,int i){ +/*< TODO update velocity model >*/ + float v; + int im; + for(im=0;im Date: Sat, 6 Mar 2021 13:47:13 -0300 Subject: [PATCH 38/63] Utility software to prepare files for sfdd #17 This script prepares the txt files generated by sfipick in a format readable by sfdd that convets them to RSF --- TDD/stereoniptomo/ascFormat.sh | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100755 TDD/stereoniptomo/ascFormat.sh diff --git a/TDD/stereoniptomo/ascFormat.sh b/TDD/stereoniptomo/ascFormat.sh new file mode 100755 index 0000000..7e369b1 --- /dev/null +++ b/TDD/stereoniptomo/ascFormat.sh @@ -0,0 +1,32 @@ +#!/bin/bash +# +# ascFormat.sh (Shell Script) +# +# Purpose: Convert txt files generate by sfipick (iterative picking) +# to asc format accepted by sfdd to be converted to RSF. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 06/03/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +[ -z "$1" -o "$1" == "-h" -o "$1" == "--help" ] && { + echo "Usage: outfilename.asc" + echo "'pick.txt' is a file generated by sfipick" + echo "'outfilename' is the name of the output file" + echo "'column' is the number of the column selected from 'pick.txt'" + exit 0 +} + +INPUT=$(cat "-" | tr '\t' ' ' | cut -d' ' -f"$2") +NUMBER_OF_POINTS=$(echo "$INPUT" | wc -l) +FORMATED_INPUT=$(echo "$INPUT" | tr '\n' ' ') +FILE="$1" + +echo "${FORMATED_INPUT}n1=$NUMBER_OF_POINTS d1=1 o1=0 data_format=ascii_float in=$1" + From 9c35973b40c711a4694e61e6c68e9d05ca3b1f0c Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 6 Mar 2021 15:09:30 -0300 Subject: [PATCH 39/63] Backup of input files and setup #17 Backup of the input files needed to run this test and a setup SConscript. Run 'scons -f SConscript1' to setup input files and allow the test --- TDD/stereoniptomo/SConscript1 | 76 ++++++++++++++++++++++++++++ TDD/stereoniptomo/SConstruct | 63 +++++++++++++++++++++++ TDD/stereoniptomo/parametersCube.asc | 1 + TDD/stereoniptomo/pick.txt | 26 ++++++++++ TDD/stereoniptomo/stackedSection.asc | 1 + 5 files changed, 167 insertions(+) create mode 100644 TDD/stereoniptomo/SConscript1 create mode 100644 TDD/stereoniptomo/SConstruct create mode 100644 TDD/stereoniptomo/parametersCube.asc create mode 100644 TDD/stereoniptomo/pick.txt create mode 100644 TDD/stereoniptomo/stackedSection.asc diff --git a/TDD/stereoniptomo/SConscript1 b/TDD/stereoniptomo/SConscript1 new file mode 100644 index 0000000..dc62882 --- /dev/null +++ b/TDD/stereoniptomo/SConscript1 @@ -0,0 +1,76 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConscript1 (Python) +# +# Purpose: Setup input files for this test. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 06/03/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +from rsf.proj import * + +Flow('t0s.asc','pick.txt', + ''' + ./ascFormat.sh ${TARGETS} 1 + ''') + +Flow('t0s','t0s.asc','dd type=float') + +Flow('m0s.asc','pick.txt', + ''' + ./ascFormat.sh ${TARGETS} 2 + ''') + +Flow('m0s','m0s.asc','dd type=float') + +Flow('stackedSection','stackedSection.asc', + ''' + dd type=float form=native | + put n1=251 d1=0.008 o1=1 label1=t0 unit1=s + n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km + ''') + +Flow('parametersCube','parametersCube.asc', + ''' + dd type=float form=native | + put n1=8 d1=1 o1=0 label1=parameters + n2=20331 d2=1 o2=0 label2="(t0,m0) index" n3=1 d3=1 o3=0 + ''') + +Flow('rnip','parametersCube', + ''' + window n1=1 f1=1 | + put n1=251 d1=0.008 o1=1 label1=t0 unit1=s + n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km + ''') + +Flow('rnips',['rnip','t0s','m0s'], + ''' + getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} | + put n1=26 d1=0.008 o1=1 label1=t0 unit1=s + n2=1 d2=0.025 o2=1.5 label2=m0 unit2=Km + ''') + +Flow('beta','parametersCube', + ''' + window n1=1 f1=2 | + put n1=251 d1=0.008 o1=1 label1=t0 unit1=s + n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km + ''') + +Flow('betas',['beta','t0s','m0s'], + ''' + getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} | + put n1=26 d1=0.008 o1=1 label1=t0 unit1=s + n2=1 d2=0.025 o2=1.5 label2=m0 unit2=Km + ''') + + diff --git a/TDD/stereoniptomo/SConstruct b/TDD/stereoniptomo/SConstruct new file mode 100644 index 0000000..ce3cc39 --- /dev/null +++ b/TDD/stereoniptomo/SConstruct @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConstruct (Python) +# +# Purpose: Test sfnipmodsetup in a constan velocity model +# with picked (t0,m0) pairs. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 07/02/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +from rsf.proj import * + +# create a model +amp=1 +rad=1 + +# Constant velocity model for test +Flow('model',None, + ''' + spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | + put label1=Depth unit1=km label2=Position unit2=km + ''' ) + +# plot the model +Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') + +# Generate shotsfile and anglefile for NIP sources +Flow('s2',None,'spike n1=26 d1=1 o1=0 n2=1 o2=1 d2=0 mag=0') + +Flow('shotsfile',['s2','m0s'],'rcat ${SOURCES[1]} axis=2 | transp') + +Flow('anglefile',['beta','t0s','m0s'], + ''' + getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} beta=y + ''') + +# NIP model setup, ray-tracing from acquisition surface to NIP sources +Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], + ''' + nipmodsetup shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} + ''') + +# Rotate ray angles in NIP sources positions in 180 degrees +Flow('angles','nipangles','math output="input-180" | put n1=26 d1=1 o1=1') +Flow('nipsources','ray','dd type=float | put n1=2 n2=26') + +# forward modeling +# do a ray-tracing from NIP sources to acquisition surface and measure traveltime +Flow(['tCoordinates','xCoordinates'], + ['model','nipsources','angles','t0s','m0s','rnips','betas'], + ''' + stereoniptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} m0s=${SOURCES[4]} rnips=${SOURCES[5]} betas=${SOURCES[6]} x=${TARGETS[1]} v0=1.508 + ''') + +End() diff --git a/TDD/stereoniptomo/parametersCube.asc b/TDD/stereoniptomo/parametersCube.asc new file mode 100644 index 0000000..b1676a3 --- /dev/null +++ b/TDD/stereoniptomo/parametersCube.asc @@ -0,0 +1 @@ +4.88 3.59034 -0.0483435 0.36259 0.5 10 1 1.5 4.54628 3.34159 -0.117649 0.363259 0.5 10 1.008 1.5 3.34985 3.67188 -0.125639 0.359595 0.5 10 1.016 1.5 4.602 3.9056 0.0990689 0.35935 0.5 10 1.024 1.5 4.155 3.936 0.0831929 0.358657 0.5 10 1.032 1.5 4.38 3.97054 0.0720092 0.349728 0.5 10 1.04 1.5 4.36707 3.92965 -0.0874567 0.34754 0.5 10 1.048 1.5 4.45 3.98401 -0.225927 0.338977 0.5 10 1.056 1.5 4.92 3.952 -0.0916874 0.337013 0.5 10 1.064 1.5 4.91 3.76 -0.22365 0.335694 0.5 10 1.072 1.5 4.9023 3.69784 -0.237532 0.319675 0.5 10 1.08 1.5 4.9636 2.89088 -0.153085 0.310905 0.5 10 1.088 1.5 4.885 2.868 -0.123404 0.312288 0.5 10 1.096 1.5 4.88 2.98 -0.178168 0.318138 0.5 10 1.104 1.5 4.085 3.304 -0.165935 0.314399 0.5 10 1.112 1.5 4.295 3.42 -0.186883 0.316502 0.5 10 1.12 1.5 4.875 3.28 -0.18852 0.313494 0.5 10 1.128 1.5 4.83996 3.64397 -0.180467 0.304189 0.5 10 1.136 1.5 4.98 3.424 0.076295 0.29127 0.5 10 1.144 1.5 4.4403 3.704 0.087081 0.291821 0.5 10 1.152 1.5 4.43939 3.932 0.0756228 0.294335 0.5 10 1.16 1.5 3.895 3.744 0.0598001 0.308067 0.5 10 1.168 1.5 4.43757 3.87806 0.0552702 0.296081 0.5 10 1.176 1.5 4.79501 3.39763 0.196998 0.29269 0.5 10 1.184 1.5 4.79585 3.86 0.180137 0.299278 0.5 10 1.192 1.5 4.7028 3.95024 0.171019 0.281696 0.5 10 1.2 1.5 4.32 3.73449 -0.0937022 0.283059 0.5 10 1.208 1.5 4.875 3.40011 -0.0620888 0.278106 0.5 10 1.216 1.5 3.355 3.808 -0.0252417 0.278852 0.5 10 1.224 1.5 3.695 3.93717 -0.0189471 0.274584 0.5 10 1.232 1.5 4.39545 3.67636 -0.0454795 0.269676 0.5 10 1.24 1.5 4.49236 3.98008 -0.0427408 0.269947 0.5 10 1.248 1.5 4.6 3.79634 -0.0169502 0.263316 0.5 10 1.256 1.5 4.895 3.94 -0.0988786 0.265842 0.5 10 1.264 1.5 4.335 3.97586 0.0265802 0.250229 0.5 10 1.272 1.5 4.91835 3.91094 -0.0254672 0.244559 0.5 10 1.28 1.5 4.80451 3.98761 -0.0809406 0.229732 0.5 10 1.288 1.5 4.5901 3.95608 -0.0707659 0.220802 0.5 10 1.296 1.5 4.50416 2.44733 0.0516515 0.224841 0.5 10 1.304 1.5 4.39579 3.20063 0.0552706 0.22059 0.5 10 1.312 1.5 4.54 2.488 0.0394775 0.218068 0.5 10 1.32 1.5 4.61 2.692 0.0551905 0.20809 0.5 10 1.328 1.5 4.63 2.744 0.0633274 0.208115 0.5 10 1.336 1.5 4.955 2.888 0.0613689 0.207551 0.5 10 1.344 1.5 4.955 2.94932 0.0668959 0.198904 0.5 10 1.352 1.5 4.87668 3.516 0.0705532 0.195447 0.5 10 1.36 1.5 4.78587 3.7567 0.0423668 0.19456 0.5 10 1.368 1.5 4.57152 3.024 0.0249909 0.188519 0.5 10 1.376 1.5 4.94633 3.12506 0.0280828 0.201439 0.5 10 1.384 1.5 4.8121 3.94568 0.0156789 0.207871 0.5 10 1.392 1.5 4.79589 3.916 0.0196206 0.210013 0.5 10 1.4 1.5 4.91 3.99123 0.0510173 0.211833 0.5 10 1.408 1.5 4.8 3.93717 0.00685906 0.221285 0.5 10 1.416 1.5 4.78612 3.89922 0.0447969 0.212654 0.5 10 1.424 1.5 4.905 3.80223 0.0387443 0.217118 0.5 10 1.432 1.5 4.645 3.976 0.0324116 0.198118 0.5 10 1.44 1.5 4.81016 3.84189 0.0321224 0.132745 0.5 10 1.448 1.5 2.99202 3.01761 0.0380369 0.0480191 0.5 10 1.456 1.5 3.13611 3.236 0.0405232 0.0453333 0.5 10 1.464 1.5 3.17 3.176 0.0339058 0.050596 0.5 10 1.472 1.5 3.34072 3.71257 0.0211722 0.0439239 0.5 10 1.48 1.5 3.365 3.636 0.028 0.0477915 0.5 10 1.488 1.5 3.565 3.55896 0.0337622 0.0450931 0.5 10 1.496 1.5 3.45 3.724 0.0308534 0.0537095 0.5 10 1.504 1.5 3.5 3.73706 0.0339019 0.0483509 0.5 10 1.512 1.5 3.8852 3.52016 0.0347754 0.0563547 0.5 10 1.52 1.5 3.81818 3.98944 0.03324 0.0582474 0.5 10 1.528 1.5 4.0996 3.97011 0.0325353 0.0564525 0.5 10 1.536 1.5 4.43142 3.92387 0.0259733 0.054592 0.5 10 1.544 1.5 4.635 3.664 0.034 0.0561326 0.5 10 1.552 1.5 4.755 3.9336 0.0328604 0.0585752 0.5 10 1.56 1.5 4.84 3.832 0.0288082 0.0587091 0.5 10 1.568 1.5 4.835 3.972 0.028 0.0533329 0.5 10 1.576 1.5 4.80549 3.94839 0.0261957 0.0153732 0.5 10 1.584 1.5 4.30744 3.80595 0.0292171 0.00668693 0.5 10 1.592 1.5 4.53901 3.98321 0.0307776 0.00628232 0.5 10 1.6 1.5 4.59569 3.912 -0.00484843 0.005681 0.5 10 1.608 1.5 4.98654 3.95723 0.029336 0.00657453 0.5 10 1.616 1.5 4.87 3.94 0.0332964 0.0051622 0.5 10 1.624 1.5 2.8275 3.90107 -0.109348 0.00431525 0.5 10 1.632 1.5 2.98 0.02 0.349462 0.00453175 0.5 10 1.64 1.5 2.2278 3.75824 -0.17754 0.00406139 0.5 10 1.648 1.5 2.991 3.956 -0.107845 0.0040778 0.5 10 1.656 1.5 3.675 3.98 -0.0797133 0.00429306 0.5 10 1.664 1.5 2.97841 3.95873 -0.132955 0.00375704 0.5 10 1.672 1.5 2.615 3.97596 -0.163202 0.00420342 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2.016 1.8 4.93 3.99484 -0.0263427 0.17944 0.5 10 2.024 1.8 4.73853 3.992 -0.0902452 0.184238 0.5 10 2.032 1.8 4.675 3.96 0.0843963 0.186774 0.5 10 2.04 1.8 4.35611 3.98089 0.103648 0.185914 0.5 10 2.048 1.8 4.91021 3.912 -0.173592 0.183448 0.5 10 2.056 1.8 4.65971 3.92 -0.195328 0.183284 0.5 10 2.064 1.8 4.98942 3.98353 0.155996 0.191224 0.5 10 2.072 1.8 4.84905 3.89487 -0.226452 0.186118 0.5 10 2.08 1.8 4.93 3.916 -0.237161 0.185574 0.5 10 2.088 1.8 4.995 3.9 -0.247655 0.182587 0.5 10 2.096 1.8 4.935 3.96 -0.265469 0.190482 0.5 10 2.104 1.8 4.97 3.884 -0.277505 0.187803 0.5 10 2.112 1.8 4.55179 3.96161 0.241043 0.177776 0.5 10 2.12 1.8 4.516 3.9721 -0.307512 0.179279 0.5 10 2.128 1.8 4.99298 3.70638 -0.305496 0.174438 0.5 10 2.136 1.8 4.94132 3.95586 -0.317823 0.185992 0.5 10 2.144 1.8 4.615 3.86 -0.334 0.17467 0.5 10 2.152 1.8 4.955 3.932 -0.335275 0.181592 0.5 10 2.16 1.8 4.99164 3.8686 -0.338813 0.167375 0.5 10 2.168 1.8 4.92 3.992 -0.352158 0.177479 0.5 10 2.176 1.8 4.79 3.852 0.309587 0.164424 0.5 10 2.184 1.8 4.95189 3.99291 -0.364854 0.16658 0.5 10 2.192 1.8 4.59 3.64 0.331667 0.154735 0.5 10 2.2 1.8 4.53 3.91977 -0.389983 0.157763 0.5 10 2.208 1.8 4.97087 3.84976 0.339792 0.15986 0.5 10 2.216 1.8 4.34117 3.91541 0.358626 0.154557 0.5 10 2.224 1.8 4.41 3.784 0.366772 0.148536 0.5 10 2.232 1.8 4.735 3.80983 -0.364203 0.151162 0.5 10 2.24 1.8 4.995 3.908 -0.37036 0.148612 0.5 10 2.248 1.8 4.5 3.996 -0.38701 0.145749 0.5 10 2.256 1.8 4.395 3.96 -0.390253 0.146088 0.5 10 2.264 1.8 3.87995 3.9646 0.416063 0.137637 0.5 10 2.272 1.8 4.015 3.916 -0.421097 0.134969 0.5 10 2.28 1.8 4.12754 3.96203 -0.422745 0.136899 0.5 10 2.288 1.8 4.42115 3.97292 -0.423288 0.133299 0.5 10 2.296 1.8 4.545 3.984 0.422 0.123474 0.5 10 2.304 1.8 4.235 3.95416 -0.440039 0.12644 0.5 10 2.312 1.8 2.775 3.712 0.504165 0.125203 0.5 10 2.32 1.8 2.9 3.636 -0.499212 0.116105 0.5 10 2.328 1.8 3.10674 3.93739 0.499649 0.125315 0.5 10 2.336 1.8 3.195 3.544 0.50104 0.119251 0.5 10 2.344 1.8 3.15 3.948 0.510414 0.115915 0.5 10 2.352 1.8 3.41 3.87943 0.502839 0.119512 0.5 10 2.36 1.8 3.59244 3.86 -0.503092 0.109753 0.5 10 2.368 1.8 3.8495 3.7196 0.501831 0.108664 0.5 10 2.376 1.8 3.895 3.97806 0.500797 0.110867 0.5 10 2.384 1.8 4.00046 3.98437 0.503863 0.102106 0.5 10 2.392 1.8 4.40698 3.84958 0.498213 0.100652 0.5 10 2.4 1.8 4.85792 3.68 0.488101 0.0903472 0.5 10 2.408 1.8 4.78851 3.63081 0.49192 0.0805278 0.5 10 2.416 1.8 4.8787 3.78329 0.489178 0.0723496 0.5 10 2.424 1.8 2.14159 3.4689 0.642202 0.0574879 0.5 10 2.432 1.8 2.72649 3.74 0.602659 0.0558463 0.5 10 2.44 1.8 1.885 3.62678 0.678584 0.0549408 0.5 10 2.448 1.8 1.47685 3.94568 0.732816 0.0526553 0.5 10 2.456 1.8 1.985 3.772 0.678592 0.056555 0.5 10 2.464 1.8 2.675 3.816 0.625792 0.050464 0.5 10 2.472 1.8 1.47 3.91105 0.755453 0.0494818 0.5 10 2.48 1.8 2.01211 3.812 0.691009 0.0514301 0.5 10 2.488 1.8 1.545 3.97896 0.752 0.04681 0.5 10 2.496 1.8 1.47676 3.32541 0.765069 0.0456462 0.5 10 2.504 1.8 1.76819 3.72256 0.737684 0.0504552 0.5 10 2.512 1.8 1.56 3.992 0.765787 0.0445154 0.5 10 2.52 1.8 2.28059 3.53317 0.696051 0.0429947 0.5 10 2.528 1.8 1.35011 2.968 0.809752 0.0408763 0.5 10 2.536 1.8 2.205 3.612 0.714052 0.0399598 0.5 10 2.544 1.8 1.91 3.904 0.744779 0.0407377 0.5 10 2.552 1.8 1.795 2.58 0.766901 0.0413768 0.5 10 2.56 1.8 1.86798 3.56 0.759721 0.0385325 0.5 10 2.568 1.8 1.7 3.716 0.787233 0.0413506 0.5 10 2.576 1.8 0.99 3.628 0.914363 0.0368334 0.5 10 2.584 1.8 1.345 3.74 0.849983 0.0431975 0.5 10 2.592 1.8 1.12334 3.08268 0.894226 0.0398357 0.5 10 2.6 1.8 1.535 3.62 0.829437 0.0361452 0.5 10 2.608 1.8 1.54746 3.336 0.832965 0.0339675 0.5 10 2.616 1.8 0.85 3.528 0.971618 0.0355223 0.5 10 2.624 1.8 0.88 3.148 0.964143 0.0366042 0.5 10 2.632 1.8 1.255 3.68 0.894628 0.037441 0.5 10 2.64 1.8 1.0908 3.25659 0.927013 0.0334285 0.5 10 2.648 1.8 0.827501 3.078 0.993266 0.0353036 0.5 10 2.656 1.8 1.44 3.26 0.876965 0.0302194 0.5 10 2.664 1.8 0.875 3.892 0.988991 0.0348035 0.5 10 2.672 1.8 1.06153 2.94122 0.955661 0.0276571 0.5 10 2.68 1.8 1.41333 2.87466 0.893806 0.0247028 0.5 10 2.688 1.8 1.11 3.952 0.946 0.0254627 0.5 10 2.696 1.8 1.43829 3.21063 -0.016028 0.0305614 0.5 10 2.704 1.8 1.44755 3.67203 -0.0134926 0.0295051 0.5 10 2.712 1.8 1.015 2.616 0.981543 0.026477 0.5 10 2.72 1.8 1.135 3.612 0.958156 0.020218 0.5 10 2.728 1.8 1.525 3.572 -0.0166741 0.0315785 0.5 10 2.736 1.8 1.52 3.52105 -0.0149309 0.0228465 0.5 10 2.744 1.8 1.655 3.56 -0.0227684 0.0214153 0.5 10 2.752 1.8 1.655 3.62 -0.0146613 0.0249322 0.5 10 2.76 1.8 1.145 3.456 0.983342 0.0190951 0.5 10 2.768 1.8 1.69771 3.88617 -0.00947686 0.0237303 0.5 10 2.776 1.8 1.64665 3.98132 -0.0220473 0.0188684 0.5 10 2.784 1.8 1.92 3.732 -0.055548 0.0137289 0.5 10 2.792 1.8 1.775 3.796 -0.0167176 0.0299528 0.5 10 2.8 1.8 2.03412 3.91025 -0.0502702 0.0155028 0.5 10 2.808 1.8 1.88 3.968 -0.0332352 0.0244747 0.5 10 2.816 1.8 1.86 3.88 -0.00635827 0.0155327 0.5 10 2.824 1.8 1.965 3.704 -0.0145002 0.018882 0.5 10 2.832 1.8 1.995 3.768 -0.02 0.0223573 0.5 10 2.84 1.8 1.98564 3.82453 -0.0267168 0.0164204 0.5 10 2.848 1.8 4.76089 3.616 0.775132 0.015268 0.5 10 2.856 1.8 4.97 3.456 0.769384 0.0136265 0.5 10 2.864 1.8 4.25102 3.3564 0.797033 0.014958 0.5 10 2.872 1.8 4.9537 3.30696 0.793791 0.0185099 0.5 10 2.88 1.8 4.99761 3.76136 0.798171 0.0203673 0.5 10 2.888 1.8 4.105 3.744 0.824227 0.0232199 0.5 10 2.896 1.8 4.855 3.73368 0.814761 0.0287616 0.5 10 2.904 1.8 4.84753 3.97775 0.818072 0.0241372 0.5 10 2.912 1.8 4.26 3.816 0.842 0.0314065 0.5 10 2.92 1.8 4.775 3.772 0.836832 0.0306134 0.5 10 2.928 1.8 3.55114 3.62579 0.876379 0.036046 0.5 10 2.936 1.8 2.255 3.312 0.946057 0.0481986 0.5 10 2.944 1.8 2.655 3.276 0.925464 0.0569203 0.5 10 2.952 1.8 2.71 3.83878 0.929335 0.0513411 0.5 10 2.96 1.8 2.19 3.836 0.968107 0.0394257 0.5 10 2.968 1.8 2.60217 3.48807 0.944801 0.0320411 0.5 10 2.976 1.8 3.02 3.472 0.927759 0.0202339 0.5 10 2.984 1.8 2.075 3.832 0.994342 0.0169114 0.5 10 2.992 1.8 2.14 3.504 0.987769 0.0106109 0.5 10 3 1.8 3.985 3.48 0.0700178 0.379272 0.5 10 1 1.825 3.22038 3.736 -0.206819 0.376308 0.5 10 1.008 1.825 3.42 3.92 -0.204157 0.372193 0.5 10 1.016 1.825 4.9603 3.92189 -0.0634428 0.368513 0.5 10 1.024 1.825 4.89357 3.93583 0.0757977 0.368264 0.5 10 1.032 1.825 4.945 3.72 0.189842 0.362893 0.5 10 1.04 1.825 4.975 3.876 -0.157176 0.355691 0.5 10 1.048 1.825 4.65424 3.984 -0.116047 0.353025 0.5 10 1.056 1.825 4.47347 3.94829 0.123521 0.352744 0.5 10 1.064 1.825 4.485 2.896 0.134226 0.344751 0.5 10 1.072 1.825 4.555 3.172 0.118383 0.342092 0.5 10 1.08 1.825 4.515 3.18 0.105383 0.340568 0.5 10 1.088 1.825 4.29959 3.19251 0.106311 0.329708 0.5 10 1.096 1.825 3.8194 3.09552 0.176423 0.331373 0.5 10 1.104 1.825 4.175 3.27222 0.176046 0.319959 0.5 10 1.112 1.825 4.555 3.536 0.166422 0.311368 0.5 10 1.12 1.825 4.94469 3.43975 -0.247 0.305442 0.5 10 1.128 1.825 4.73 3.824 -0.196384 0.29976 0.5 10 1.136 1.825 2.645 3.804 0.00139759 0.300258 0.5 10 1.144 1.825 3.49704 3.99313 -0.0219734 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1.608 1.825 4.825 3.98 -0.028248 0.00854323 0.5 10 1.616 1.825 2.385 3.768 0.154049 0.00487768 0.5 10 1.624 1.825 3.415 3.95538 0.0772126 0.00561386 0.5 10 1.632 1.825 3.07755 3.8987 0.106039 0.00521853 0.5 10 1.64 1.825 2.91 3.98768 0.125955 0.00500444 0.5 10 1.648 1.825 2.48784 3.964 0.166712 0.00535442 0.5 10 1.656 1.825 2.145 3.904 0.217953 0.00463123 0.5 10 1.664 1.825 2.745 3.928 0.16073 0.00441177 0.5 10 1.672 1.825 3.0529 3.968 0.137301 0.00460515 0.5 10 1.68 1.825 2.355 3.99957 0.20869 0.00476711 0.5 10 1.688 1.825 2.30764 3.99247 0.22179 0.00414359 0.5 10 1.696 1.825 3.87 0.024 -0.599913 0.00406101 0.5 10 1.704 1.825 3.815 0.036 -0.557313 0.00415705 0.5 10 1.712 1.825 3.41 0.0196002 -0.442849 0.00396244 0.5 10 1.72 1.825 3.2 0.032 0.338479 0.00337442 0.5 10 1.728 1.825 2.365 0.000883937 0.318332 0.00351563 0.5 10 1.736 1.825 4.775 0.016 0.453847 0.00323817 0.5 10 1.744 1.825 3.88 0.052 0.564402 0.00326761 0.5 10 1.752 1.825 4.625 0.02 -0.443093 0.00379861 0.5 10 1.76 1.825 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2.784 1.975 1.7763 3.85154 -0.0346797 0.0209305 0.5 10 2.792 1.975 1.78 3.852 -0.0511646 0.0305262 0.5 10 2.8 1.975 1.815 3.98 -0.0361301 0.0236931 0.5 10 2.808 1.975 1.905 3.836 -0.0509478 0.0230298 0.5 10 2.816 1.975 1.535 3.796 0.932038 0.014371 0.5 10 2.824 1.975 2.02 3.824 -0.044 0.0178358 0.5 10 2.832 1.975 1.975 3.872 -0.054 0.0257394 0.5 10 2.84 1.975 2.155 3.948 -0.0680002 0.0168661 0.5 10 2.848 1.975 2.14926 3.74741 -0.0608282 0.0147787 0.5 10 2.856 1.975 2.265 3.996 -0.054 0.013605 0.5 10 2.864 1.975 2.312 3.732 -0.0576865 0.0141144 0.5 10 2.872 1.975 2.415 3.804 -0.050019 0.0130116 0.5 10 2.88 1.975 1.51 2.748 0.967046 0.00886215 0.5 10 2.888 1.975 2.5 3.872 -0.0494476 0.0141586 0.5 10 2.896 1.975 2.35099 3.96879 -0.0487439 0.0125625 0.5 10 2.904 1.975 2.695 3.992 -0.0591533 0.0120664 0.5 10 2.912 1.975 2.61 3.98 -0.0486492 0.0134243 0.5 10 2.92 1.975 3.165 3.628 -0.0475517 0.0090592 0.5 10 2.928 1.975 3.11653 3.89722 -0.054101 0.00948935 0.5 10 2.936 1.975 3.18 3.86 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2.608 2.25 1.46923 3.45938 0.788046 0.0379326 0.5 10 2.616 2.25 1.32 3.676 0.814703 0.0371121 0.5 10 2.624 2.25 1.195 3.308 0.847232 0.0395888 0.5 10 2.632 2.25 0.785 3.85898 0.944508 0.0368197 0.5 10 2.64 2.25 1.45 3.328 0.81212 0.0370243 0.5 10 2.648 2.25 1.18 3.824 0.86 0.037508 0.5 10 2.656 2.25 0.711922 3.35354 0.980604 0.0348422 0.5 10 2.664 2.25 1.315 3.836 0.848 0.036858 0.5 10 2.672 2.25 1.39963 3.408 0.837385 0.0342579 0.5 10 2.68 2.25 0.95861 3.52 0.927831 0.0373501 0.5 10 2.688 2.25 0.95 3.316 0.93512 0.0338066 0.5 10 2.696 2.25 1.07984 3.27987 0.911425 0.0347821 0.5 10 2.704 2.25 1.06 2.91939 0.91877 0.0306043 0.5 10 2.712 2.25 0.865501 3.4884 0.966427 0.0318533 0.5 10 2.72 2.25 0.945 3.31274 0.950185 0.0299083 0.5 10 2.728 2.25 1.3 3.8 0.887556 0.0271486 0.5 10 2.736 2.25 0.86 3 0.983842 0.0300672 0.5 10 2.744 2.25 0.91 2.848 0.976 0.0298697 0.5 10 2.752 2.25 1.085 3.856 0.937916 0.0270598 0.5 10 2.76 2.25 1.48 3.892 0.87792 0.0230796 0.5 10 2.768 2.25 1.18 2.96 0.931619 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1.392 2.275 4.955 3.884 -0.120945 0.257961 0.5 10 1.4 2.275 4.91802 3.81042 -0.118882 0.236231 0.5 10 1.408 2.275 4.80385 3.88596 -0.084543 0.230218 0.5 10 1.416 2.275 4.99494 3.75483 -0.0959322 0.229407 0.5 10 1.424 2.275 4.92 3.724 -0.100914 0.173685 0.5 10 1.432 2.275 4.895 3.85246 -0.10186 0.0685015 0.5 10 1.44 2.275 2.88858 3.43773 -0.101023 0.0486357 0.5 10 1.448 2.275 2.94 3.676 -0.092 0.052249 0.5 10 1.456 2.275 3.065 3.7 -0.0910612 0.0525129 0.5 10 1.464 2.275 3.12549 3.67239 -0.0975085 0.053716 0.5 10 1.472 2.275 3.19 3.72495 -0.0961027 0.0558282 0.5 10 1.48 2.275 3.415 3.57712 -0.100544 0.0543441 0.5 10 1.488 2.275 3.545 3.75427 -0.100149 0.0533799 0.5 10 1.496 2.275 3.625 3.74 -0.0983358 0.0582438 0.5 10 1.504 2.275 3.955 3.508 -0.094627 0.0482045 0.5 10 1.512 2.275 4.13 3.512 -0.106135 0.0508595 0.5 10 1.52 2.275 4.17253 3.792 -0.103132 0.0532661 0.5 10 1.528 2.275 4.305 3.98 -0.1 0.0574353 0.5 10 1.536 2.275 4.66959 3.74367 -0.101488 0.05887 0.5 10 1.544 2.275 4.615 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1.704 2.7 4.285 3.92 -0.0129451 0.00639323 0.5 10 1.712 2.7 3.635 3.992 0.015301 0.00672388 0.5 10 1.72 2.7 4.965 3.84939 -0.244858 0.00731486 0.5 10 1.728 2.7 4.74761 3.98707 -0.259093 0.00774959 0.5 10 1.736 2.7 4.77273 3.95019 -0.262891 0.00800034 0.5 10 1.744 2.7 4.965 3.404 -0.231659 0.00773414 0.5 10 1.752 2.7 4.98 3.44231 -0.23559 0.00901785 0.5 10 1.76 2.7 4.64102 3.54082 -0.245845 0.00995259 0.5 10 1.768 2.7 4.58 3.656 -0.249407 0.010509 0.5 10 1.776 2.7 4.95216 3.88173 -0.249324 0.0126752 0.5 10 1.784 2.7 4.995 3.9 -0.197838 0.0154447 0.5 10 1.792 2.7 4.92694 3.95756 -0.188748 0.0289815 0.5 10 1.8 2.7 4.885 3.996 -0.184989 0.0510963 0.5 10 1.808 2.7 4.635 3.94905 -0.170266 0.0680435 0.5 10 1.816 2.7 4.88 3.992 -0.156919 0.0817998 0.5 10 1.824 2.7 4.50339 3.97072 -0.124906 0.0774962 0.5 10 1.832 2.7 4.48 3.99439 -0.108717 0.0751313 0.5 10 1.84 2.7 4.39117 3.98894 -0.0766668 0.0739515 0.5 10 1.848 2.7 4.13 3.9972 -0.0631399 0.0706684 0.5 10 1.856 2.7 4.32703 3.98962 -0.191374 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4.67404 3.82723 -0.0818574 0.322825 0.5 10 1.304 2.95 4.73 3.952 -0.078 0.325887 0.5 10 1.312 2.95 4.98218 3.91375 -0.0672169 0.305256 0.5 10 1.32 2.95 4.97 3.988 -0.0694175 0.312621 0.5 10 1.328 2.95 4.24521 3.71217 -0.138551 0.264243 0.5 10 1.336 2.95 4.58045 3.53517 -0.124033 0.267754 0.5 10 1.344 2.95 4.79 3.94 -0.167635 0.265362 0.5 10 1.352 2.95 4.35914 3.988 -0.139035 0.272577 0.5 10 1.36 2.95 4.91367 3.90294 -0.128305 0.284949 0.5 10 1.368 2.95 4.98 3.884 -0.115593 0.294816 0.5 10 1.376 2.95 4.92 3.952 -0.119333 0.281912 0.5 10 1.384 2.95 4.75412 3.38698 -0.176546 0.262377 0.5 10 1.392 2.95 4.80047 3.848 -0.165835 0.253106 0.5 10 1.4 2.95 4.94565 3.94 -0.153597 0.245675 0.5 10 1.408 2.95 4.86949 3.964 -0.159582 0.245468 0.5 10 1.416 2.95 4.79718 3.78174 -0.0486086 0.0604598 0.5 10 1.424 2.95 4.9043 3.884 -0.052931 0.061707 0.5 10 1.432 2.95 4.7205 3.9 -0.0618228 0.0621444 0.5 10 1.44 2.95 3.02 3.584 -0.163682 0.0598673 0.5 10 1.448 2.95 4.69 3.972 -0.0723109 0.0630013 0.5 10 1.456 2.95 3.155 3.864 -0.147113 0.0634653 0.5 10 1.464 2.95 3.21954 3.73457 -0.1536 0.0667226 0.5 10 1.472 2.95 4.66302 3.81429 -0.100798 0.0582436 0.5 10 1.48 2.95 3.42 3.93176 -0.158285 0.0718035 0.5 10 1.488 2.95 3.65 3.704 -0.158 0.0700985 0.5 10 1.496 2.95 3.92732 3.868 -0.162907 0.0626496 0.5 10 1.504 2.95 3.855 3.98 -0.154723 0.0830232 0.5 10 1.512 2.95 4.385 3.84229 -0.146591 0.0656775 0.5 10 1.52 2.95 4.65152 3.86922 -0.147873 0.0684652 0.5 10 1.528 2.95 4.565 3.988 -0.148 0.0805846 0.5 10 1.536 2.95 4.63 3.92 -0.154678 0.0862091 0.5 10 1.544 2.95 4.96009 3.992 -0.14177 0.0624137 0.5 10 1.552 2.95 4.915 3.916 -0.149008 0.0639728 0.5 10 1.56 2.95 4.885 3.98352 -0.15969 0.0320471 0.5 10 1.568 2.95 4.05 3.796 -0.152399 0.00782475 0.5 10 1.576 2.95 4.035 3.99542 -0.209755 0.00661753 0.5 10 1.584 2.95 4.41147 3.65246 -0.145741 0.00716647 0.5 10 1.592 2.95 4.81587 3.928 -0.148407 0.00726849 0.5 10 1.6 2.95 4.74 3.988 -0.196 0.00778908 0.5 10 1.608 2.95 4.94278 3.83023 -0.150147 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1.11 3.68 0.820793 0.0312828 0.5 10 2.608 2.975 1.07179 2.11744 0.836135 0.0323061 0.5 10 2.616 2.975 1.135 3.684 0.824738 0.0315036 0.5 10 2.624 2.975 0.87 2.264 0.896168 0.0273789 0.5 10 2.632 2.975 1.215 3.652 0.813972 0.0279434 0.5 10 2.64 2.975 0.61 3.984 0.992 0.0342728 0.5 10 2.648 2.975 0.870702 1.95256 0.910617 0.0290197 0.5 10 2.656 2.975 1.01 3.08128 0.87775 0.0273539 0.5 10 2.664 2.975 0.855 3.136 0.924327 0.0268833 0.5 10 2.672 2.975 0.98 3.8 0.890297 0.0275794 0.5 10 2.68 2.975 0.68 1.38 0.985273 0.0276629 0.5 10 2.688 2.975 2.9 3.892 -0.376868 0.0242448 0.5 10 2.696 2.975 1.39814 3.71051 -0.138153 0.0287979 0.5 10 2.704 2.975 1.38815 3.8 -0.136115 0.031792 0.5 10 2.712 2.975 1.915 3.332 -0.252613 0.022845 0.5 10 2.72 2.975 0.715 3.788 0.986 0.0257752 0.5 10 2.728 2.975 0.78 2.74 0.96999 0.0241247 0.5 10 2.736 2.975 1.52174 3.89739 -0.13533 0.0263458 0.5 10 2.744 2.975 2.58639 3.952 -0.307789 0.0246624 0.5 10 2.752 2.975 1.71364 3.07524 -0.174934 0.0242371 0.5 10 2.76 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2.912 2.975 2.5 3.94788 -0.162788 0.025485 0.5 10 2.92 2.975 2.73 3.812 -0.166917 0.0190965 0.5 10 2.928 2.975 2.67 3.78 -0.157921 0.0163449 0.5 10 2.936 2.975 2.94 3.84 -0.155496 0.0193857 0.5 10 2.944 2.975 3.15 3.876 -0.160628 0.0161231 0.5 10 2.952 2.975 3.20918 3.856 -0.154508 0.0177703 0.5 10 2.96 2.975 3.305 3.80043 -0.158659 0.0177061 0.5 10 2.968 2.975 3.325 3.804 -0.15668 0.0165527 0.5 10 2.976 2.975 3.485 3.98212 -0.154809 0.0197095 0.5 10 2.984 2.975 3.67668 3.90135 -0.154406 0.0188079 0.5 10 2.992 2.975 4.91 3.328 -0.14999 0.0162793 0.5 10 3 2.975 4.02 3.964 -0.0645455 0.3948 0.5 10 1 3 3.285 3.976 -0.0347438 0.394232 0.5 10 1.008 3 3.7 3.996 -0.040524 0.386115 0.5 10 1.016 3 4.275 3.8 0.104 0.371657 0.5 10 1.024 3 3.8 3.94015 -0.110057 0.377161 0.5 10 1.032 3 4.91804 3.96243 -0.133038 0.379298 0.5 10 1.04 3 4.12266 3.82213 0.00444657 0.378199 0.5 10 1.048 3 3.115 3.964 0.04 0.373739 0.5 10 1.056 3 4.79923 3.82 -0.0921478 0.373157 0.5 10 1.064 3 4.71705 3.844 -0.0911033 0.363999 0.5 10 1.072 3 4.765 3.624 -0.06779 0.361806 0.5 10 1.08 3 4.91972 3.87178 -0.0582639 0.354827 0.5 10 1.088 3 3.99702 3.884 -0.0291541 0.353891 0.5 10 1.096 3 4.48533 3.91627 -0.0329344 0.355852 0.5 10 1.104 3 4.6 3.66208 -0.0238271 0.355499 0.5 10 1.112 3 4.75266 3.90213 -0.0272485 0.355586 0.5 10 1.12 3 3.66 3.79599 0.0177669 0.351817 0.5 10 1.128 3 4.86 3.5 0.000949442 0.349929 0.5 10 1.136 3 4.87 3.54321 0.000505973 0.344617 0.5 10 1.144 3 4.88651 3.79474 0.0299165 0.340697 0.5 10 1.152 3 4.85 3.836 0.0206492 0.33739 0.5 10 1.16 3 4.87722 3.99487 0.0290677 0.330456 0.5 10 1.168 3 4.82917 3.57534 0.0382554 0.317906 0.5 10 1.176 3 3.93608 3.97868 -0.185676 0.338034 0.5 10 1.184 3 3.8266 3.89328 -0.174728 0.355729 0.5 10 1.192 3 4.905 3.996 -0.146233 0.373958 0.5 10 1.2 3 3.69368 3.99651 -0.132851 0.362086 0.5 10 1.208 3 4.50535 3.96428 -0.113704 0.360669 0.5 10 1.216 3 4.45984 3.99995 -0.0796616 0.358546 0.5 10 1.224 3 4.8 3.91125 -0.0841919 0.361719 0.5 10 1.232 3 4.98 3.868 -0.074076 0.356124 0.5 10 1.24 3 4.21118 3.98894 -0.0631697 0.353336 0.5 10 1.248 3 4.69343 3.98675 -0.0606771 0.343279 0.5 10 1.256 3 4.9574 3.88592 -0.0486289 0.326129 0.5 10 1.264 3 4.6497 3.89092 -0.0345253 0.320445 0.5 10 1.272 3 4.98 3.932 -0.034 0.315088 0.5 10 1.28 3 4.46 3.612 -0.0830048 0.309271 0.5 10 1.288 3 4.65533 3.92148 -0.0817533 0.326166 0.5 10 1.296 3 4.96 3.912 -0.0812111 0.340771 0.5 10 1.304 3 4.82731 3.92184 -0.0691597 0.315511 0.5 10 1.312 3 4.955 3.94226 -0.0826782 0.287048 0.5 10 1.32 3 4.88469 3.45256 -0.102168 0.265792 0.5 10 1.328 3 4.64519 3.97615 -0.153995 0.266492 0.5 10 1.336 3 4.955 3.948 -0.159179 0.287021 0.5 10 1.344 3 4.23 3.936 -0.129167 0.285331 0.5 10 1.352 3 4.59106 3.944 -0.132519 0.295659 0.5 10 1.36 3 4.96 3.932 -0.122639 0.300475 0.5 10 1.368 3 4.55455 3.99719 -0.132046 0.272472 0.5 10 1.376 3 4.52 3.556 -0.172678 0.257456 0.5 10 1.384 3 4.77 3.728 -0.149953 0.259077 0.5 10 1.392 3 4.88916 3.75933 -0.16677 0.269429 0.5 10 1.4 3 4.835 3.928 -0.169357 0.271066 0.5 10 1.408 3 4.98 3.916 -0.161284 0.293694 0.5 10 1.416 3 2.65774 3.70219 -0.156269 0.0609235 0.5 10 1.424 3 4.605 3.844 -0.0629261 0.0629621 0.5 10 1.432 3 2.835 3.804 -0.16079 0.0638038 0.5 10 1.44 3 2.95 3.72726 -0.154372 0.0689429 0.5 10 1.448 3 4.67393 3.94715 -0.0789679 0.0658556 0.5 10 1.456 3 3.205 3.56 -0.16275 0.0671185 0.5 10 1.464 3 3.475 3.76 -0.150686 0.0604972 0.5 10 1.472 3 3.285 3.99477 -0.155366 0.0771285 0.5 10 1.48 3 3.57 3.796 -0.156 0.0747317 0.5 10 1.488 3 4.785 3.988 -0.105195 0.0668042 0.5 10 1.496 3 3.81642 3.86 -0.153685 0.0780359 0.5 10 1.504 3 4.125 3.948 -0.147394 0.0741028 0.5 10 1.512 3 4.00874 3.89934 -0.157164 0.0812749 0.5 10 1.52 3 4.345 3.976 -0.15314 0.0879784 0.5 10 1.528 3 4.63215 3.90699 -0.147989 0.0742269 0.5 10 1.536 3 4.615 3.968 -0.155859 0.0951041 0.5 10 1.544 3 4.885 3.952 -0.153549 0.0933858 0.5 10 1.552 3 4.95449 3.96359 -0.15662 0.0845481 0.5 10 1.56 3 4.975 3.68024 -0.155238 0.0236288 0.5 10 1.568 3 4.12 3.828 -0.152117 0.00813856 0.5 10 1.576 3 4.11424 3.93539 -0.157729 0.00761606 0.5 10 1.584 3 4.46915 3.77791 -0.149365 0.00809913 0.5 10 1.592 3 4.65439 3.972 -0.149336 0.00890641 0.5 10 1.6 3 4.81157 3.928 -0.149708 0.00890826 0.5 10 1.608 3 4.87182 3.98546 -0.198229 0.00895168 0.5 10 1.616 3 4.3869 0.04 -0.364736 0.0083129 0.5 10 1.624 3 4.94 3.372 -0.264 0.009086 0.5 10 1.632 3 4.905 3.496 -0.271078 0.0106272 0.5 10 1.64 3 4.9 3.984 -0.230175 0.0121484 0.5 10 1.648 3 4.88427 3.92 -0.251566 0.0142387 0.5 10 1.656 3 4.89431 3.96276 -0.223573 0.0140226 0.5 10 1.664 3 4.865 3.93244 -0.259676 0.0159 0.5 10 1.672 3 4.76698 3.98 -0.259887 0.0134975 0.5 10 1.68 3 4.6617 3.98071 -0.292764 0.0143742 0.5 10 1.688 3 4.76264 3.72593 -0.0468377 0.0127825 0.5 10 1.696 3 4.38966 3.5972 -0.23096 0.0151587 0.5 10 1.704 3 4.825 3.816 -0.218832 0.0212545 0.5 10 1.712 3 4.965 3.8261 -0.217848 0.0296715 0.5 10 1.72 3 4.905 3.96901 -0.186982 0.0432391 0.5 10 1.728 3 4.75 3.976 -0.180208 0.0762718 0.5 10 1.736 3 4.86 3.93434 -0.170212 0.101696 0.5 10 1.744 3 4.69246 3.964 -0.144301 0.117687 0.5 10 1.752 3 4.96557 3.94846 -0.128547 0.118232 0.5 10 1.76 3 4.16241 3.98962 -0.108817 0.116086 0.5 10 1.768 3 4.36842 3.97874 -0.0850355 0.114143 0.5 10 1.776 3 4.435 3.96159 -0.0756487 0.109255 0.5 10 1.784 3 4.62 3.964 -0.189679 0.299877 0.5 10 1.792 3 4.93505 3.94303 -0.197258 0.384023 0.5 10 1.8 3 4.375 3.988 -0.126368 0.348985 0.5 10 1.808 3 4.7911 3.96 -0.1104 0.345746 0.5 10 1.816 3 4.99853 3.88855 -0.0961713 0.328539 0.5 10 1.824 3 4.975 3.964 -0.0815155 0.326156 0.5 10 1.832 3 4.98737 3.808 -0.277157 0.297907 0.5 10 1.84 3 4.91356 3.89137 -0.178172 0.329542 0.5 10 1.848 3 4.85167 3.98406 -0.182281 0.401682 0.5 10 1.856 3 4.61645 3.86079 -0.173129 0.432302 0.5 10 1.864 3 4.865 3.948 -0.173754 0.494868 0.5 10 1.872 3 4.93307 3.97846 -0.207954 0.532378 0.5 10 1.88 3 4.96 3.952 -0.212354 0.555256 0.5 10 1.888 3 4.91169 3.75335 -0.188858 0.564182 0.5 10 1.896 3 4.905 3.88116 -0.179125 0.589495 0.5 10 1.904 3 4.765 3.97596 -0.202118 0.61545 0.5 10 1.912 3 4.99856 3.868 -0.203012 0.634623 0.5 10 1.92 3 4.84475 3.8918 -0.189276 0.637051 0.5 10 1.928 3 4.74376 3.96701 -0.21125 0.656819 0.5 10 1.936 3 4.835 3.992 -0.21409 0.67675 0.5 10 1.944 3 4.98 3.91321 -0.206249 0.689185 0.5 10 1.952 3 4.76029 3.78958 -0.209231 0.673802 0.5 10 1.96 3 4.90782 3.94625 -0.216 0.702292 0.5 10 1.968 3 4.985 3.992 -0.198357 0.708681 0.5 10 1.976 3 4.97825 3.84508 -0.241452 0.69615 0.5 10 1.984 3 4.67614 3.988 -0.213504 0.701332 0.5 10 1.992 3 4.875 3.988 -0.216351 0.714274 0.5 10 2 3 4.97015 3.96018 -0.241894 0.7148 0.5 10 2.008 3 4.995 3.968 -0.250986 0.701267 0.5 10 2.016 3 4.935 3.90038 -0.253319 0.681264 0.5 10 2.024 3 4.85499 3.79999 -0.259908 0.650427 0.5 10 2.032 3 4.953 3.83884 -0.246222 0.647656 0.5 10 2.04 3 4.93994 3.99928 -0.274272 0.631047 0.5 10 2.048 3 4.92 3.828 -0.246537 0.612139 0.5 10 2.056 3 4.97 3.904 -0.275043 0.590778 0.5 10 2.064 3 4.92365 3.94692 -0.261903 0.577553 0.5 10 2.072 3 4.68083 3.84867 -0.283957 0.539187 0.5 10 2.08 3 4.9587 3.95781 -0.281495 0.532065 0.5 10 2.088 3 4.59922 3.84891 -0.277589 0.477818 0.5 10 2.096 3 4.98463 3.916 -0.290675 0.455632 0.5 10 2.104 3 4.9 3.9584 -0.28093 0.412877 0.5 10 2.112 3 3.98 3.984 -0.00279756 0.370069 0.5 10 2.12 3 4.98524 3.9962 -0.0118712 0.363972 0.5 10 2.128 3 4.84 3.97945 -0.00965321 0.357069 0.5 10 2.136 3 4.795 3.936 -0.0181998 0.341812 0.5 10 2.144 3 4.365 3.968 0.0453701 0.336548 0.5 10 2.152 3 4.01953 3.992 0.0522133 0.33393 0.5 10 2.16 3 4.91 3.968 0.044 0.329101 0.5 10 2.168 3 4.72873 3.98819 0.0416172 0.324747 0.5 10 2.176 3 3.635 3.992 0.0900392 0.319394 0.5 10 2.184 3 4.03 3.96 0.076 0.312902 0.5 10 2.192 3 3.9662 3.93296 0.09055 0.304677 0.5 10 2.2 3 4.72369 3.99895 0.0734253 0.30164 0.5 10 2.208 3 3.14221 3.93777 0.130966 0.29463 0.5 10 2.216 3 3.41584 3.95509 0.127688 0.293504 0.5 10 2.224 3 4.535 3.988 0.103645 0.290166 0.5 10 2.232 3 4.21673 3.98402 0.107787 0.287444 0.5 10 2.24 3 3.145 3.9998 0.146 0.284207 0.5 10 2.248 3 3.505 3.992 0.15841 0.282753 0.5 10 2.256 3 4.21435 3.96748 0.123164 0.275424 0.5 10 2.264 3 4.1232 3.972 0.139986 0.271243 0.5 10 2.272 3 4.74646 3.96253 0.123975 0.266297 0.5 10 2.28 3 4.54 3.952 0.148282 0.262893 0.5 10 2.288 3 4.19091 3.96873 0.14564 0.260546 0.5 10 2.296 3 4.625 3.9413 0.144572 0.255812 0.5 10 2.304 3 4.375 3.96 0.141292 0.247954 0.5 10 2.312 3 4.86849 3.98413 0.141322 0.250806 0.5 10 2.32 3 4.97702 3.864 0.123672 0.231539 0.5 10 2.328 3 4.885 3.98 0.130461 0.228494 0.5 10 2.336 3 4.96 3.89556 0.117738 0.205371 0.5 10 2.344 3 4.945 3.848 0.108671 0.177078 0.5 10 2.352 3 4.76659 3.93727 0.0985246 0.149056 0.5 10 2.36 3 4.975 3.996 0.10039 0.151457 0.5 10 2.368 3 4.965 3.80622 0.0877877 0.104058 0.5 10 2.376 3 4.69733 3.87487 0.314337 0.0975888 0.5 10 2.384 3 4.48772 3.98369 0.323632 0.0943499 0.5 10 2.392 3 4.595 3.968 0.311078 0.072798 0.5 10 2.4 3 4.75 3.984 0.316 0.0690872 0.5 10 2.408 3 4.86727 3.96148 0.298544 0.0470325 0.5 10 2.416 3 1.585 3.85762 0.621427 0.0453374 0.5 10 2.424 3 1.455 3.696 0.645299 0.0412785 0.5 10 2.432 3 1.29 3.896 0.685692 0.0439066 0.5 10 2.44 3 1.56 3.80081 0.641027 0.0435109 0.5 10 2.448 3 1.42809 3.928 0.670637 0.0433248 0.5 10 2.456 3 1.1 3.78786 0.743374 0.0405215 0.5 10 2.464 3 1.19 3.436 0.726033 0.0392555 0.5 10 2.472 3 0.965 3.948 0.786548 0.0384476 0.5 10 2.48 3 1.24242 3.94337 0.726573 0.0399673 0.5 10 2.488 3 0.905 3.9 0.818916 0.0366234 0.5 10 2.496 3 1.46 3.85488 0.691407 0.0383637 0.5 10 2.504 3 1.145 3.516 0.764 0.0375588 0.5 10 2.512 3 1.19 3.696 0.754694 0.0376362 0.5 10 2.52 3 1.42334 3.98267 0.713214 0.0361853 0.5 10 2.528 3 1.31197 3.45186 0.739324 0.0345184 0.5 10 2.536 3 1.315 3.852 0.741799 0.0330973 0.5 10 2.544 3 0.810271 3.62068 0.874461 0.0342382 0.5 10 2.552 3 0.92 3.46735 0.847766 0.0374466 0.5 10 2.56 3 0.72 3.62 0.913807 0.0337737 0.5 10 2.568 3 0.515 3.028 0.999414 0.0288634 0.5 10 2.576 3 1.15232 3.304 0.79865 0.0285742 0.5 10 2.584 3 1.155 3.55396 0.806382 0.0331026 0.5 10 2.592 3 1.05 3.436 0.834516 0.0321708 0.5 10 2.6 3 0.56 3.276 0.996912 0.032208 0.5 10 2.608 3 1.12 3.164 0.825273 0.0309043 0.5 10 2.616 3 0.91911 3.632 0.88338 0.0298171 0.5 10 2.624 3 0.8 3.568 0.91483 0.0256328 0.5 10 2.632 3 1.105 3.89804 0.843421 0.0288671 0.5 10 2.64 3 1.09 3.012 0.847333 0.026462 0.5 10 2.648 3 0.685 2.408 0.966187 0.0280375 0.5 10 2.656 3 0.959463 3.89157 0.888401 0.0275298 0.5 10 2.664 3 0.78 3.072 0.944622 0.028768 0.5 10 2.672 3 1.74994 3.98489 -0.0456406 0.0238929 0.5 10 2.68 3 0.665 3.328 0.986034 0.0244794 0.5 10 2.688 3 2.87 3.872 -0.387826 0.0238145 0.5 10 2.696 3 1.37109 3.82 -0.153454 0.0330939 0.5 10 2.704 3 2.91951 3.96361 -0.381666 0.0253379 0.5 10 2.712 3 2.905 3.768 -0.369445 0.0242046 0.5 10 2.72 3 1.47 3.544 -0.165581 0.0268364 0.5 10 2.728 3 1.49517 3.81614 -0.147813 0.0250429 0.5 10 2.736 3 1.535 3.364 -0.166933 0.0231428 0.5 10 2.744 3 2.54 3.92 -0.320305 0.0251771 0.5 10 2.752 3 2.21 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0.5 10 2.176 3.1 2.65 3.932 0.125104 0.298405 0.5 10 2.184 3.1 3.39 3.9815 0.123551 0.29776 0.5 10 2.192 3.1 3.36109 3.976 0.130271 0.293728 0.5 10 2.2 3.1 3.97666 3.952 0.106359 0.284187 0.5 10 2.208 3.1 3.81901 3.95921 0.140975 0.275504 0.5 10 2.216 3.1 3.72 3.984 0.1358 0.273389 0.5 10 2.224 3.1 3.85229 3.95732 0.125415 0.267935 0.5 10 2.232 3.1 4.07 3.9 0.132375 0.262678 0.5 10 2.24 3.1 4.09782 3.99826 0.121579 0.261643 0.5 10 2.248 3.1 4.50426 3.99803 0.136658 0.256948 0.5 10 2.256 3.1 4.975 3.988 0.132 0.252317 0.5 10 2.264 3.1 4.975 3.984 0.122889 0.247505 0.5 10 2.272 3.1 4.83 3.876 0.123233 0.227787 0.5 10 2.28 3.1 4.675 3.9 0.107514 0.219706 0.5 10 2.288 3.1 4.95586 3.9096 0.0992509 0.211094 0.5 10 2.296 3.1 4.995 3.77744 0.0985062 0.18643 0.5 10 2.304 3.1 4.86368 3.984 0.105871 0.143461 0.5 10 2.312 3.1 4.96009 3.98807 0.0758202 0.139522 0.5 10 2.32 3.1 4.89006 3.96 0.0827152 0.126489 0.5 10 2.328 3.1 3.46719 3.83775 0.339436 0.110666 0.5 10 2.336 3.1 3.58343 3.792 0.336681 0.10715 0.5 10 2.344 3.1 3.52 3.86951 0.346876 0.110602 0.5 10 2.352 3.1 3.98 3.95132 0.327419 0.10222 0.5 10 2.36 3.1 4.01683 3.728 0.334197 0.0947513 0.5 10 2.368 3.1 4.4 3.876 0.321708 0.0919935 0.5 10 2.376 3.1 4.29 3.976 0.332615 0.0920367 0.5 10 2.384 3.1 4.355 3.936 0.328257 0.081236 0.5 10 2.392 3.1 4.71858 3.99887 0.331562 0.0843501 0.5 10 2.4 3.1 4.98 3.948 0.320652 0.0789708 0.5 10 2.408 3.1 4.91 3.988 0.324986 0.0563972 0.5 10 2.416 3.1 1.37222 3.67903 0.665992 0.0423096 0.5 10 2.424 3.1 1.215 3.76984 0.706889 0.0437662 0.5 10 2.432 3.1 1.405 3.86342 0.668962 0.0430112 0.5 10 2.44 3.1 1.50494 3.752 0.657424 0.0424441 0.5 10 2.448 3.1 0.965 3.82 0.784007 0.0386169 0.5 10 2.456 3.1 1.27749 3.75144 0.712407 0.0418936 0.5 10 2.464 3.1 1.315 3.81659 0.709619 0.0404142 0.5 10 2.472 3.1 1.135 3.816 0.752343 0.0391853 0.5 10 2.48 3.1 1.405 3.712 0.698121 0.0390178 0.5 10 2.488 3.1 0.99 3.688 0.79644 0.0360464 0.5 10 2.496 3.1 1.035 3.2 0.791548 0.0382359 0.5 10 2.504 3.1 1.00808 3.784 0.803064 0.0371675 0.5 10 2.512 3.1 1.09628 2.888 0.784051 0.0354424 0.5 10 2.52 3.1 1.19 3.936 0.768699 0.0360202 0.5 10 2.528 3.1 0.545 3.14 0.974761 0.0305376 0.5 10 2.536 3.1 1.31953 3.02362 0.746278 0.0311912 0.5 10 2.544 3.1 0.96 2.916 0.839299 0.0329489 0.5 10 2.552 3.1 0.78931 3.8009 0.894286 0.0356693 0.5 10 2.56 3.1 1.095 3.688 0.813488 0.0323585 0.5 10 2.568 3.1 0.887639 2.69811 0.872769 0.0321905 0.5 10 2.576 3.1 0.57 3.90928 0.986798 0.034219 0.5 10 2.584 3.1 0.795 3.52835 0.907907 0.0341367 0.5 10 2.592 3.1 0.885 3.62 0.884198 0.0343113 0.5 10 2.6 3.1 1.045 2.668 0.842823 0.0264512 0.5 10 2.608 3.1 0.95635 2.28468 0.87173 0.0278043 0.5 10 2.616 3.1 1.06556 2.0054 0.848001 0.0266198 0.5 10 2.624 3.1 0.78 1.888 0.932 0.0247856 0.5 10 2.632 3.1 3.15158 3.81814 -0.433937 0.0260275 0.5 10 2.64 3.1 1.105 3.984 0.848291 0.0264257 0.5 10 2.648 3.1 2.575 3.54873 -0.381492 0.0255838 0.5 10 2.656 3.1 0.64388 2.3071 0.993584 0.027071 0.5 10 2.664 3.1 1.335 3.472 -0.142024 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1.16 3.15 4.88 3.94 -0.149115 0.391513 0.5 10 1.168 3.15 4.78513 3.92011 -0.119496 0.383275 0.5 10 1.176 3.15 3.685 3.776 -0.0882823 0.389438 0.5 10 1.184 3.15 3.99 3.876 -0.0806183 0.39375 0.5 10 1.192 3.15 4.23 3.96994 -0.0803639 0.384496 0.5 10 1.2 3.15 4.815 3.93521 -0.07821 0.375878 0.5 10 1.208 3.15 3.58 3.984 -0.0356508 0.360758 0.5 10 1.216 3.15 4.09585 3.79668 -0.0475779 0.348074 0.5 10 1.224 3.15 4.68429 3.988 -0.0343085 0.343285 0.5 10 1.232 3.15 4.84 3.792 -0.0361077 0.340529 0.5 10 1.24 3.15 4.89351 3.444 -0.218288 0.345556 0.5 10 1.248 3.15 4.68409 3.97763 -0.218355 0.34079 0.5 10 1.256 3.15 4.87039 3.84 -0.226131 0.338158 0.5 10 1.264 3.15 4.96707 3.97027 -0.237305 0.345651 0.5 10 1.272 3.15 4.61622 3.89698 -0.248532 0.323218 0.5 10 1.28 3.15 4.845 3.96 -0.0443529 0.313377 0.5 10 1.288 3.15 4.4865 3.95677 -0.062492 0.286144 0.5 10 1.296 3.15 4.10708 3.99766 -0.184643 0.289659 0.5 10 1.304 3.15 4.82682 3.9297 -0.151716 0.29454 0.5 10 1.312 3.15 4.97723 3.912 -0.155593 0.311845 0.5 10 1.32 3.15 4.47872 3.976 -0.128294 0.322234 0.5 10 1.328 3.15 4.49 3.976 -0.117623 0.313835 0.5 10 1.336 3.15 4.41987 3.956 -0.102234 0.306868 0.5 10 1.344 3.15 4.9323 3.80334 -0.117613 0.269939 0.5 10 1.352 3.15 4.93856 3.432 -0.145058 0.274643 0.5 10 1.36 3.15 4.935 3.76 -0.129926 0.275714 0.5 10 1.368 3.15 4.76282 3.83391 -0.156268 0.278233 0.5 10 1.376 3.15 4.865 3.89636 -0.142441 0.300898 0.5 10 1.384 3.15 4.725 3.988 -0.14127 0.309757 0.5 10 1.392 3.15 4.88531 3.69625 -0.15704 0.277684 0.5 10 1.4 3.15 4.70114 3.88708 -0.163287 0.245907 0.5 10 1.408 3.15 4.97192 3.8 -0.16687 0.164413 0.5 10 1.416 3.15 4.495 3.916 -0.0619082 0.0692145 0.5 10 1.424 3.15 2.82536 3.70076 -0.157974 0.0755135 0.5 10 1.432 3.15 2.94 3.688 -0.154886 0.0750311 0.5 10 1.44 3.15 4.96457 3.92766 -0.067108 0.0765134 0.5 10 1.448 3.15 3.00955 3.9867 -0.155094 0.0774355 0.5 10 1.456 3.15 3.14904 3.73984 -0.156872 0.077045 0.5 10 1.464 3.15 3.26357 3.74686 -0.156555 0.0775088 0.5 10 1.472 3.15 3.37 3.99189 -0.15321 0.0915806 0.5 10 1.48 3.15 3.695 3.748 -0.159723 0.0856912 0.5 10 1.488 3.15 3.795 3.93354 -0.156245 0.0898521 0.5 10 1.496 3.15 3.86 3.76 -0.15649 0.085597 0.5 10 1.504 3.15 4.06723 3.8482 -0.153316 0.0935891 0.5 10 1.512 3.15 4.05425 3.9954 -0.151989 0.0972226 0.5 10 1.52 3.15 4.33377 3.99432 -0.150116 0.100347 0.5 10 1.528 3.15 4.85036 3.79053 -0.162205 0.103789 0.5 10 1.536 3.15 4.905 3.988 -0.164514 0.0832892 0.5 10 1.544 3.15 4.99602 3.93585 -0.159704 0.110619 0.5 10 1.552 3.15 4.97337 3.9787 -0.150186 0.08529 0.5 10 1.56 3.15 4.895 3.96991 -0.157047 0.0309593 0.5 10 1.568 3.15 4.47985 3.99855 -0.18584 0.0100754 0.5 10 1.576 3.15 4.99 3.16486 -0.231105 0.010465 0.5 10 1.584 3.15 4.93839 3.90354 -0.201892 0.0110335 0.5 10 1.592 3.15 4.89351 3.88665 -0.206311 0.0138199 0.5 10 1.6 3.15 4.87025 3.7282 -0.216373 0.0150456 0.5 10 1.608 3.15 4.71 3.96877 -0.210762 0.0178389 0.5 10 1.616 3.15 4.91293 3.94788 -0.230122 0.0211776 0.5 10 1.624 3.15 4.68574 3.98059 -0.231712 0.0233171 0.5 10 1.632 3.15 4.77 3.972 -0.23841 0.0248951 0.5 10 1.64 3.15 4.74885 3.95998 -0.251349 0.0224488 0.5 10 1.648 3.15 4.63205 3.76738 -0.0675221 0.0171284 0.5 10 1.656 3.15 4.69771 3.73816 -0.0665147 0.0201538 0.5 10 1.664 3.15 4.93833 3.588 -0.189998 0.0223761 0.5 10 1.672 3.15 4.89216 3.72 -0.199448 0.0302726 0.5 10 1.68 3.15 4.98726 3.932 -0.192878 0.0476684 0.5 10 1.688 3.15 4.88853 3.872 -0.205684 0.0639136 0.5 10 1.696 3.15 4.64935 3.95548 -0.169675 0.104506 0.5 10 1.704 3.15 4.96034 3.924 -0.147591 0.12749 0.5 10 1.712 3.15 4.625 3.964 -0.138257 0.141205 0.5 10 1.72 3.15 3.785 3.916 -0.105596 0.124918 0.5 10 1.728 3.15 4.44157 3.932 -0.0999455 0.131469 0.5 10 1.736 3.15 3.855 3.928 -0.0718734 0.123296 0.5 10 1.744 3.15 4.67583 3.87266 -0.171367 0.186632 0.5 10 1.752 3.15 4.98 3.968 -0.186498 0.417506 0.5 10 1.76 3.15 4.96372 3.95498 -0.136 0.405839 0.5 10 1.768 3.15 4.64049 3.996 -0.228083 0.400804 0.5 10 1.776 3.15 4.755 3.9999 -0.242825 0.376078 0.5 10 1.784 3.15 4.515 3.956 -0.0834948 0.355709 0.5 10 1.792 3.15 4.63094 3.91675 -0.0731544 0.344306 0.5 10 1.8 3.15 4.875 3.968 -0.0624926 0.335436 0.5 10 1.808 3.15 4.965 3.8 -0.188146 0.374286 0.5 10 1.816 3.15 4.40721 3.94457 -0.1769 0.436262 0.5 10 1.824 3.15 4.79115 3.924 -0.177172 0.509445 0.5 10 1.832 3.15 4.91291 3.87636 -0.18434 0.553227 0.5 10 1.84 3.15 4.90107 3.94086 -0.169126 0.583585 0.5 10 1.848 3.15 4.95824 3.90303 -0.182221 0.615678 0.5 10 1.856 3.15 4.96733 3.96059 -0.189114 0.64498 0.5 10 1.864 3.15 4.78787 3.9743 -0.179921 0.650978 0.5 10 1.872 3.15 4.975 3.964 -0.191185 0.683454 0.5 10 1.88 3.15 4.98077 3.92462 -0.175019 0.685287 0.5 10 1.888 3.15 4.99446 3.94357 -0.187823 0.71219 0.5 10 1.896 3.15 4.86422 3.84351 -0.203194 0.705427 0.5 10 1.904 3.15 4.98 3.948 -0.212 0.726075 0.5 10 1.912 3.15 4.95185 3.988 -0.219726 0.728223 0.5 10 1.92 3.15 4.99 3.94 -0.187258 0.747644 0.5 10 1.928 3.15 4.94565 3.82052 -0.201973 0.74896 0.5 10 1.936 3.15 4.61955 3.9717 -0.203189 0.745664 0.5 10 1.944 3.15 4.905 3.892 -0.198336 0.759017 0.5 10 1.952 3.15 4.745 3.952 -0.215534 0.756558 0.5 10 1.96 3.15 4.62019 3.93615 -0.215827 0.741183 0.5 10 1.968 3.15 4.915 3.99431 -0.230762 0.744377 0.5 10 1.976 3.15 4.905 3.99098 -0.220687 0.73445 0.5 10 1.984 3.15 4.94067 3.9897 -0.212003 0.716005 0.5 10 1.992 3.15 4.84074 3.992 -0.229501 0.697755 0.5 10 2 3.15 4.97864 3.94691 -0.216096 0.677871 0.5 10 2.008 3.15 4.935 3.992 -0.227269 0.666382 0.5 10 2.016 3.15 4.96 3.924 -0.236411 0.636612 0.5 10 2.024 3.15 4.825 3.98 -0.22951 0.593525 0.5 10 2.032 3.15 4.92693 3.808 -0.252167 0.56694 0.5 10 2.04 3.15 4.81 3.952 -0.257267 0.54065 0.5 10 2.048 3.15 4.785 3.984 -0.229079 0.501137 0.5 10 2.056 3.15 4.83167 3.972 -0.235454 0.481121 0.5 10 2.064 3.15 4.99 3.99447 -0.266017 0.417389 0.5 10 2.072 3.15 3.6 3.94 -0.00999489 0.375464 0.5 10 2.08 3.15 2.26 3.896 0.0768054 0.37005 0.5 10 2.088 3.15 2.27533 3.95507 0.0821287 0.366052 0.5 10 2.096 3.15 2.72 3.968 0.0740347 0.356217 0.5 10 2.104 3.15 2.68 3.996 0.0718648 0.34826 0.5 10 2.112 3.15 3.20749 3.97799 0.0576997 0.335405 0.5 10 2.12 3.15 2.7517 3.996 0.108703 0.325711 0.5 10 2.128 3.15 3.02 3.896 0.104817 0.315637 0.5 10 2.136 3.15 2.97 3.984 0.0935784 0.311746 0.5 10 2.144 3.15 3.225 3.96372 0.105786 0.303274 0.5 10 2.152 3.15 3.40521 3.912 0.0950082 0.295861 0.5 10 2.16 3.15 3.365 3.988 0.0871699 0.290355 0.5 10 2.168 3.15 3.81888 3.876 0.0823179 0.27939 0.5 10 2.176 3.15 3.99467 3.97574 0.0867385 0.274436 0.5 10 2.184 3.15 3.925 3.984 0.0978326 0.271271 0.5 10 2.192 3.15 4.22 3.976 0.094 0.262834 0.5 10 2.2 3.15 4.61 3.996 0.102791 0.257236 0.5 10 2.208 3.15 4.67 3.85998 0.0884383 0.243267 0.5 10 2.216 3.15 4.71933 3.99115 0.106439 0.234869 0.5 10 2.224 3.15 4.825 3.928 0.0795543 0.227296 0.5 10 2.232 3.15 4.59 3.976 0.0774267 0.214201 0.5 10 2.24 3.15 4.815 3.86942 0.0626262 0.187155 0.5 10 2.248 3.15 4.975 3.944 0.0783203 0.193746 0.5 10 2.256 3.15 4.415 3.988 0.0613624 0.137877 0.5 10 2.264 3.15 4.585 3.996 0.0582923 0.126105 0.5 10 2.272 3.15 2.82 3.592 0.342912 0.119933 0.5 10 2.28 3.15 2.885 3.36 0.35 0.108696 0.5 10 2.288 3.15 2.965 3.716 0.351223 0.113764 0.5 10 2.296 3.15 3.095 3.96571 0.345668 0.113576 0.5 10 2.304 3.15 3.1 3.90602 0.341305 0.110645 0.5 10 2.312 3.15 3.335 3.816 0.333338 0.102402 0.5 10 2.32 3.15 3.48 3.87083 0.330944 0.0997516 0.5 10 2.328 3.15 3.38778 3.85123 0.332489 0.0909738 0.5 10 2.336 3.15 3.695 3.88218 0.34237 0.0933011 0.5 10 2.344 3.15 4.06824 3.85459 0.325141 0.0875049 0.5 10 2.352 3.15 4.145 3.836 0.330054 0.0856924 0.5 10 2.36 3.15 4.31786 3.896 0.325051 0.084894 0.5 10 2.368 3.15 4.55441 3.872 0.326083 0.0793265 0.5 10 2.376 3.15 4.98115 3.96092 0.312809 0.0718736 0.5 10 2.384 3.15 4.78152 3.97284 0.322309 0.0735081 0.5 10 2.392 3.15 4.64 3.856 0.306 0.0434488 0.5 10 2.4 3.15 4.955 3.93311 0.367697 0.0424533 0.5 10 2.408 3.15 1.32 3.852 0.679071 0.0417142 0.5 10 2.416 3.15 1.21 3.864 0.705068 0.0425948 0.5 10 2.424 3.15 1.065 3.684 0.742525 0.0357282 0.5 10 2.432 3.15 0.745 3.58094 0.851452 0.0375549 0.5 10 2.44 3.15 1.255 3.676 0.712087 0.0378146 0.5 10 2.448 3.15 0.97 3.19976 0.786046 0.0375232 0.5 10 2.456 3.15 0.77 3.97985 0.853634 0.0370672 0.5 10 2.464 3.15 0.89 3.82 0.821012 0.0383971 0.5 10 2.472 3.15 1.25 3.292 0.7308 0.0363857 0.5 10 2.48 3.15 0.655 2.404 0.91 0.0318466 0.5 10 2.488 3.15 1.015 3.89063 0.797217 0.0382587 0.5 10 2.496 3.15 1 3.39428 0.804567 0.0358429 0.5 10 2.504 3.15 1.1 3.496 0.780997 0.0319515 0.5 10 2.512 3.15 0.82 3.568 0.86491 0.0334222 0.5 10 2.52 3.15 0.83 1.484 0.866083 0.026137 0.5 10 2.528 3.15 0.595 2.988 0.959033 0.0309539 0.5 10 2.536 3.15 0.56 2.852 0.975137 0.0307199 0.5 10 2.544 3.15 0.625 2.988 0.952 0.0316949 0.5 10 2.552 3.15 0.675 2.792 0.94 0.0304112 0.5 10 2.56 3.15 0.925 2.98 0.8626 0.027155 0.5 10 2.568 3.15 0.78 2.384 0.906609 0.0281398 0.5 10 2.576 3.15 0.80085 3.26068 0.902034 0.0289185 0.5 10 2.584 3.15 0.855 3.956 0.892 0.0315024 0.5 10 2.592 3.15 4.31 3.94 -0.517581 0.0269869 0.5 10 2.6 3.15 3.82 3.90209 -0.495165 0.0260662 0.5 10 2.608 3.15 3.89918 3.81202 -0.491041 0.0266412 0.5 10 2.616 3.15 0.605 3.32 0.993028 0.031517 0.5 10 2.624 3.15 3.45 3.864 -0.464438 0.0269973 0.5 10 2.632 3.15 0.645 3.7832 0.984797 0.0278503 0.5 10 2.64 3.15 3.975 3.996 -0.469076 0.0271853 0.5 10 2.648 3.15 3.475 3.94 -0.445619 0.0277063 0.5 10 2.656 3.15 3.42843 3.85475 -0.434571 0.0271181 0.5 10 2.664 3.15 1.32298 3.732 -0.155303 0.0288146 0.5 10 2.672 3.15 2.225 3.38578 -0.338937 0.0261108 0.5 10 2.68 3.15 2.46733 3.412 -0.355366 0.0254111 0.5 10 2.688 3.15 3.64909 3.94994 -0.41452 0.0264261 0.5 10 2.696 3.15 3.335 3.976 -0.399289 0.0280365 0.5 10 2.704 3.15 2.6628 3.792 -0.354836 0.0277234 0.5 10 2.712 3.15 1.585 2.944 -0.190047 0.0260964 0.5 10 2.72 3.15 2.245 3.57641 -0.30336 0.0258999 0.5 10 2.728 3.15 2.03696 3.32556 -0.267122 0.0257442 0.5 10 2.736 3.15 1.53 3.692 -0.16156 0.0309455 0.5 10 2.744 3.15 1.54 3.804 -0.159791 0.0307469 0.5 10 2.752 3.15 2.57 3.864 -0.307527 0.028412 0.5 10 2.76 3.15 1.655 3.35115 -0.175508 0.0279401 0.5 10 2.768 3.15 2.11 3.54 -0.242439 0.0270496 0.5 10 2.776 3.15 1.83 3.468 -0.196795 0.0265005 0.5 10 2.784 3.15 1.7703 3.388 -0.17497 0.0315266 0.5 10 2.792 3.15 1.775 3.652 -0.175985 0.028709 0.5 10 2.8 3.15 1.88308 3.5 -0.185277 0.0298508 0.5 10 2.808 3.15 1.75 3.9445 -0.158963 0.0277298 0.5 10 2.816 3.15 2.03052 3.48858 -0.184533 0.0261018 0.5 10 2.824 3.15 2.095 3.67871 -0.188959 0.0246067 0.5 10 2.832 3.15 1.92 3.66872 -0.16404 0.0285158 0.5 10 2.84 3.15 2.055 3.84294 -0.180204 0.033257 0.5 10 2.848 3.15 2.17842 3.884 -0.174802 0.02265 0.5 10 2.856 3.15 2.16455 3.88 -0.17116 0.0287576 0.5 10 2.864 3.15 2.23389 3.98311 -0.170257 0.0259891 0.5 10 2.872 3.15 2.28 3.84 -0.171917 0.031002 0.5 10 2.88 3.15 2.395 3.852 -0.165426 0.024056 0.5 10 2.888 3.15 2.69 3.66 -0.181065 0.017684 0.5 10 2.896 3.15 2.485 3.9629 -0.175827 0.0299101 0.5 10 2.904 3.15 2.49507 3.876 -0.174675 0.025736 0.5 10 2.912 3.15 2.805 3.976 -0.175153 0.019696 0.5 10 2.92 3.15 2.615 3.936 -0.164346 0.0292968 0.5 10 2.928 3.15 3.00426 3.944 -0.172053 0.0184477 0.5 10 2.936 3.15 2.82 3.928 -0.165219 0.0275417 0.5 10 2.944 3.15 3.09292 3.97834 -0.169177 0.024057 0.5 10 2.952 3.15 3.16244 3.912 -0.157915 0.021918 0.5 10 2.96 3.15 3.98 3.348 -0.161251 0.0190027 0.5 10 2.968 3.15 4.88937 3.0795 -0.156408 0.0209946 0.5 10 2.976 3.15 3.95288 3.62631 -0.159759 0.0208378 0.5 10 2.984 3.15 3.665 3.86 -0.159975 0.0224036 0.5 10 2.992 3.15 4.64951 3.47639 -0.155856 0.0233898 0.5 10 3 3.15 3.65 3.888 -0.00037982 0.401417 0.5 10 1 3.175 4.65575 3.92578 -0.00913792 0.400187 0.5 10 1.008 3.175 4.8276 3.936 -0.00739824 0.397543 0.5 10 1.016 3.175 4.33947 3.716 -0.104917 0.398105 0.5 10 1.024 3.175 4.79 3.97731 -0.103161 0.394523 0.5 10 1.032 3.175 3.76938 3.98 -0.0807379 0.390946 0.5 10 1.04 3.175 3.77 3.612 -0.0616395 0.388952 0.5 10 1.048 3.175 4.18811 3.87848 -0.0640101 0.390082 0.5 10 1.056 3.175 4.46261 3.956 -0.0570566 0.383556 0.5 10 1.064 3.175 4.38 3.936 -0.0553682 0.37986 0.5 10 1.072 3.175 3.37 3.956 -0.015355 0.373224 0.5 10 1.08 3.175 3.34346 3.86701 -0.00269072 0.363485 0.5 10 1.088 3.175 3.71263 3.93317 -0.00680538 0.364085 0.5 10 1.096 3.175 4.58693 3.50955 0.0141044 0.364078 0.5 10 1.104 3.175 4.78585 3.55252 0.0114984 0.35814 0.5 10 1.112 3.175 4.385 3.628 -0.00909753 0.353887 0.5 10 1.12 3.175 4.365 3.676 -0.00128863 0.344393 0.5 10 1.128 3.175 4.88 3.15945 0.0410305 0.348793 0.5 10 1.136 3.175 4.9807 3.83727 -0.16399 0.361681 0.5 10 1.144 3.175 4.89197 3.78558 -0.145879 0.380089 0.5 10 1.152 3.175 4.72 3.95311 -0.133458 0.388274 0.5 10 1.16 3.175 4.895 3.99696 -0.126748 0.41747 0.5 10 1.168 3.175 4.845 3.976 -0.120428 0.387333 0.5 10 1.176 3.175 3.915 3.8726 -0.0791353 0.387414 0.5 10 1.184 3.175 3.73 3.94 -0.0772599 0.387808 0.5 10 1.192 3.175 4.37668 3.98888 -0.0806099 0.371975 0.5 10 1.2 3.175 4.7 3.97815 -0.0691426 0.374256 0.5 10 1.208 3.175 3.95026 3.9998 -0.0451065 0.357561 0.5 10 1.216 3.175 4.21 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10 1.256 3.2 4.945 3.936 -0.230997 0.339018 0.5 10 1.264 3.2 4.93456 3.73175 -0.0586771 0.315864 0.5 10 1.272 3.2 4.885 3.8482 -0.0396989 0.313773 0.5 10 1.28 3.2 4.40379 3.84061 -0.160728 0.289169 0.5 10 1.288 3.2 4.99628 3.89426 -0.0459945 0.309851 0.5 10 1.296 3.2 4.44854 3.99483 -0.155206 0.308293 0.5 10 1.304 3.2 4.87054 3.99889 -0.176516 0.310133 0.5 10 1.312 3.2 4.70343 3.996 -0.112058 0.318056 0.5 10 1.32 3.2 4.48371 3.99625 -0.113549 0.316608 0.5 10 1.328 3.2 4.88 3.968 -0.0969611 0.31136 0.5 10 1.336 3.2 4.63 3.392 -0.135465 0.279498 0.5 10 1.344 3.2 4.97248 3.52352 -0.144267 0.278608 0.5 10 1.352 3.2 4.80665 3.62569 -0.134197 0.284259 0.5 10 1.36 3.2 4.835 3.76 -0.141037 0.2993 0.5 10 1.368 3.2 4.87632 3.96506 -0.128505 0.318793 0.5 10 1.376 3.2 4.97872 3.78298 -0.14012 0.323308 0.5 10 1.384 3.2 4.92146 3.98725 -0.144987 0.307185 0.5 10 1.392 3.2 4.73456 3.91965 -0.158282 0.257783 0.5 10 1.4 3.2 4.71974 3.892 -0.15639 0.143977 0.5 10 1.408 3.2 4.7588 3.992 -0.0508646 0.0676309 0.5 10 1.416 3.2 4.905 3.824 -0.0536373 0.0736426 0.5 10 1.424 3.2 4.675 3.796 -0.0649924 0.0734198 0.5 10 1.432 3.2 4.18 3.876 -0.087696 0.0712799 0.5 10 1.44 3.2 4.88882 3.95229 -0.0726968 0.0764025 0.5 10 1.448 3.2 3.04591 3.77673 -0.157486 0.074331 0.5 10 1.456 3.2 3.245 3.864 -0.158551 0.0830379 0.5 10 1.464 3.2 3.2213 3.98104 -0.15516 0.0805827 0.5 10 1.472 3.2 3.38609 3.88195 -0.156582 0.0884428 0.5 10 1.48 3.2 3.57 3.77451 -0.158571 0.0871762 0.5 10 1.488 3.2 3.915 3.91718 -0.158941 0.0851497 0.5 10 1.496 3.2 3.78 3.96 -0.153604 0.100242 0.5 10 1.504 3.2 4.15 3.82 -0.159484 0.102713 0.5 10 1.512 3.2 4.57123 3.68412 -0.159956 0.0997316 0.5 10 1.52 3.2 4.645 3.6968 -0.16362 0.100256 0.5 10 1.528 3.2 4.69629 3.76503 -0.16 0.10421 0.5 10 1.536 3.2 4.91 3.83459 -0.155991 0.118932 0.5 10 1.544 3.2 4.98102 3.74408 -0.153179 0.0817579 0.5 10 1.552 3.2 4.88392 3.79114 -0.144754 0.032076 0.5 10 1.56 3.2 4.89862 3.96904 -0.14749 0.0252203 0.5 10 1.568 3.2 4.95239 3.23391 -0.228589 0.012692 0.5 10 1.576 3.2 4.64 3.964 -0.189209 0.0143829 0.5 10 1.584 3.2 4.95332 3.824 -0.195301 0.0159158 0.5 10 1.592 3.2 4.875 3.996 -0.189864 0.0196374 0.5 10 1.6 3.2 4.83 3.848 -0.216939 0.021903 0.5 10 1.608 3.2 4.95762 3.91454 -0.204564 0.0262707 0.5 10 1.616 3.2 4.63 3.968 -0.224014 0.0266897 0.5 10 1.624 3.2 4.885 3.956 -0.22 0.0281607 0.5 10 1.632 3.2 4.81938 3.992 -0.247157 0.0260032 0.5 10 1.64 3.2 4.935 3.98347 -0.249801 0.0205251 0.5 10 1.648 3.2 4.79876 3.684 -0.0673523 0.0237563 0.5 10 1.656 3.2 4.75 3.61476 -0.190115 0.0259082 0.5 10 1.664 3.2 4.84998 3.92799 -0.191515 0.0366043 0.5 10 1.672 3.2 4.965 3.988 -0.192612 0.065479 0.5 10 1.68 3.2 4.90133 3.99229 -0.189197 0.0998281 0.5 10 1.688 3.2 4.59878 3.94703 -0.152296 0.119893 0.5 10 1.696 3.2 4.67472 3.97177 -0.145516 0.14993 0.5 10 1.704 3.2 4.57738 3.98572 -0.13384 0.146596 0.5 10 1.712 3.2 4.61 3.98329 -0.110044 0.153515 0.5 10 1.72 3.2 4.48016 3.97613 -0.0898901 0.147493 0.5 10 1.728 3.2 4.97958 3.99966 -0.0819766 0.152372 0.5 10 1.736 3.2 4.93139 3.77312 -0.170803 0.31073 0.5 10 1.744 3.2 4.9 3.956 -0.190689 0.448283 0.5 10 1.752 3.2 4.94 3.83438 -0.215017 0.41177 0.5 10 1.76 3.2 4.815 3.89877 -0.220398 0.393609 0.5 10 1.768 3.2 4.9 3.996 -0.096 0.393634 0.5 10 1.776 3.2 4.975 3.992 -0.0848496 0.381419 0.5 10 1.784 3.2 4.9912 3.96 -0.0701862 0.357052 0.5 10 1.792 3.2 4.575 3.94 -0.177471 0.35134 0.5 10 1.8 3.2 4.82649 3.92 -0.164439 0.426487 0.5 10 1.808 3.2 4.8109 3.98472 -0.174787 0.501745 0.5 10 1.816 3.2 4.9657 3.93256 -0.157016 0.539434 0.5 10 1.824 3.2 4.84579 3.92463 -0.162121 0.572555 0.5 10 1.832 3.2 4.705 3.9 -0.173914 0.598374 0.5 10 1.84 3.2 4.715 3.992 -0.180691 0.634748 0.5 10 1.848 3.2 4.975 3.98 -0.179946 0.671937 0.5 10 1.856 3.2 4.91 3.79287 -0.185352 0.666178 0.5 10 1.864 3.2 4.71987 3.948 -0.194334 0.685264 0.5 10 1.872 3.2 4.78 3.94399 -0.174249 0.699681 0.5 10 1.88 3.2 4.75008 3.788 -0.200007 0.698752 0.5 10 1.888 3.2 4.785 3.9953 -0.200279 0.735582 0.5 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-0.318142 0.389129 0.5 10 2.064 3.2 2.77467 3.984 0.0425095 0.38071 0.5 10 2.072 3.2 2.62 3.99359 0.0616794 0.369404 0.5 10 2.08 3.2 2.47 3.932 0.0745332 0.357241 0.5 10 2.088 3.2 2.57821 3.936 0.0833285 0.352163 0.5 10 2.096 3.2 2.61747 3.988 0.0757612 0.345829 0.5 10 2.104 3.2 2.57 3.96255 0.0766163 0.330682 0.5 10 2.112 3.2 2.735 3.97201 0.0705088 0.323017 0.5 10 2.12 3.2 3.08 3.956 0.06658 0.313619 0.5 10 2.128 3.2 3.3775 3.976 0.0614299 0.301662 0.5 10 2.136 3.2 3.32 3.996 0.0885693 0.287784 0.5 10 2.144 3.2 3.90054 3.86443 0.0896678 0.275209 0.5 10 2.152 3.2 3.66 3.876 0.075749 0.270095 0.5 10 2.16 3.2 3.85639 3.9656 0.0779999 0.267991 0.5 10 2.168 3.2 4.48654 3.936 0.0714484 0.2625 0.5 10 2.176 3.2 4.43 3.98226 0.0841128 0.257595 0.5 10 2.184 3.2 4.82942 3.95553 0.0704967 0.248604 0.5 10 2.192 3.2 4.63713 3.9737 0.0793083 0.233688 0.5 10 2.2 3.2 4.70423 3.97986 0.0693983 0.223485 0.5 10 2.208 3.2 4.92 3.864 0.0698667 0.200772 0.5 10 2.216 3.2 4.825 3.96202 0.0686772 0.176168 0.5 10 2.224 3.2 4.97415 3.91532 0.044935 0.164983 0.5 10 2.232 3.2 4.96 3.828 0.0335636 0.1355 0.5 10 2.24 3.2 2.44156 3.796 0.341678 0.128601 0.5 10 2.248 3.2 2.85681 3.584 0.328564 0.109316 0.5 10 2.256 3.2 2.58 3.824 0.346565 0.119691 0.5 10 2.264 3.2 2.81864 3.67891 0.336263 0.109124 0.5 10 2.272 3.2 2.825 3.65331 0.353319 0.110372 0.5 10 2.28 3.2 2.92 3.85811 0.338602 0.107829 0.5 10 2.288 3.2 3.085 3.992 0.335175 0.108067 0.5 10 2.296 3.2 3.04974 3.86732 0.349674 0.104716 0.5 10 2.304 3.2 3.26 3.94459 0.346768 0.0968162 0.5 10 2.312 3.2 3.305 3.97995 0.344994 0.100193 0.5 10 2.32 3.2 3.305 3.9011 0.341706 0.0927989 0.5 10 2.328 3.2 3.615 3.72871 0.337705 0.0850516 0.5 10 2.336 3.2 3.7 3.90934 0.339271 0.0890711 0.5 10 2.344 3.2 4.055 3.864 0.329526 0.0787975 0.5 10 2.352 3.2 4.1093 3.98744 0.33766 0.0796659 0.5 10 2.36 3.2 4.69915 3.99932 0.323653 0.0690774 0.5 10 2.368 3.2 4.61355 3.86284 0.322318 0.0689208 0.5 10 2.376 3.2 4.855 3.7 0.31967 0.0636749 0.5 10 2.384 3.2 4.98233 3.804 0.309881 0.0546314 0.5 10 2.392 3.2 4.1 3.964 -0.191351 0.0528805 0.5 10 2.4 3.2 4.60289 3.95032 -0.19753 0.052373 0.5 10 2.408 3.2 4.9381 3.77448 -0.183849 0.0506391 0.5 10 2.416 3.2 4.97 3.884 -0.190203 0.0486483 0.5 10 2.424 3.2 4.69 3.83271 -0.16901 0.0480384 0.5 10 2.432 3.2 4.925 3.90027 -0.170571 0.0483823 0.5 10 2.44 3.2 4.91 3.824 -0.155787 0.045373 0.5 10 2.448 3.2 4.835 3.988 -0.16503 0.0440894 0.5 10 2.456 3.2 4.89 3.99268 -0.152245 0.045777 0.5 10 2.464 3.2 0.985 3.532 0.796 0.0352838 0.5 10 2.472 3.2 0.815 3.3 0.852 0.0349079 0.5 10 2.48 3.2 0.87662 3.8493 0.836418 0.0353666 0.5 10 2.488 3.2 0.855739 3.18459 0.846002 0.0339525 0.5 10 2.496 3.2 0.635 3.952 0.93198 0.0338942 0.5 10 2.504 3.2 0.68 3.72525 0.916671 0.0329659 0.5 10 2.512 3.2 0.765 3.568 0.888903 0.032264 0.5 10 2.52 3.2 0.755738 3.416 0.896 0.0323381 0.5 10 2.528 3.2 0.74 3.308 0.909105 0.0321932 0.5 10 2.536 3.2 0.823113 3.4828 0.882191 0.0320926 0.5 10 2.544 3.2 0.99 2.92079 0.837851 0.0289877 0.5 10 2.552 3.2 0.755 3.984 0.914623 0.03175 0.5 10 2.56 3.2 3.86 3.644 -0.523996 0.0265411 0.5 10 2.568 3.2 1.005 3.668 0.844 0.0288723 0.5 10 2.576 3.2 0.664197 3.1903 0.956515 0.0301058 0.5 10 2.584 3.2 3.50563 3.7645 -0.493381 0.0271981 0.5 10 2.592 3.2 4.115 3.92 -0.502782 0.0256907 0.5 10 2.6 3.2 3.16 3.756 -0.466 0.0273252 0.5 10 2.608 3.2 0.63 3.804 0.981325 0.0288079 0.5 10 2.616 3.2 3.57956 3.90239 -0.471608 0.0282867 0.5 10 2.624 3.2 3.99 3.992 -0.477446 0.028343 0.5 10 2.632 3.2 3.545 3.744 -0.452164 0.0268751 0.5 10 2.64 3.2 3.235 3.932 -0.437512 0.0281813 0.5 10 2.648 3.2 3.65769 3.92215 -0.444 0.0278179 0.5 10 2.656 3.2 2.66 3.64 -0.386087 0.0271163 0.5 10 2.664 3.2 3.085 3.808 -0.407432 0.0284029 0.5 10 2.672 3.2 3.175 3.94105 -0.406661 0.0290495 0.5 10 2.68 3.2 3.13 3.94 -0.3944 0.0273436 0.5 10 2.688 3.2 2.3144 3.72352 -0.332489 0.0264903 0.5 10 2.696 3.2 1.41635 3.46508 -0.156598 0.0305655 0.5 10 2.704 3.2 1.44 3.932 -0.143742 0.0317327 0.5 10 2.712 3.2 2.66243 3.76008 -0.340892 0.028316 0.5 10 2.72 3.2 3.745 3.94748 -0.387815 0.0266776 0.5 10 2.728 3.2 2.955 3.89179 -0.344763 0.0280473 0.5 10 2.736 3.2 1.565 3.97597 -0.14654 0.027361 0.5 10 2.744 3.2 1.58554 3.47243 -0.163685 0.0287102 0.5 10 2.752 3.2 1.56053 3.84842 -0.154249 0.0328153 0.5 10 2.76 3.2 2.39202 3.76562 -0.276319 0.0288085 0.5 10 2.768 3.2 1.99334 3.73067 -0.223964 0.0267078 0.5 10 2.776 3.2 2.495 3.876 -0.276756 0.0294593 0.5 10 2.784 3.2 1.775 3.632 -0.176797 0.0290612 0.5 10 2.792 3.2 1.875 3.388 -0.183325 0.0302011 0.5 10 2.8 3.2 2.14 3.812 -0.219902 0.0289193 0.5 10 2.808 3.2 1.92 3.512 -0.167521 0.0281217 0.5 10 2.816 3.2 2.64787 3.98295 -0.254155 0.027971 0.5 10 2.824 3.2 1.98 3.852 -0.179271 0.0310282 0.5 10 2.832 3.2 2.075 3.636 -0.180932 0.0315305 0.5 10 2.84 3.2 2.16341 3.764 -0.185726 0.0287021 0.5 10 2.848 3.2 2.18858 3.97487 -0.189147 0.0290496 0.5 10 2.856 3.2 2.205 3.724 -0.176339 0.0308125 0.5 10 2.864 3.2 2.90644 3.80569 -0.220227 0.021116 0.5 10 2.872 3.2 2.34268 3.6755 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3.924 -0.0527202 0.380229 0.5 10 1.2 3.25 4.85557 3.88927 -0.0453685 0.363534 0.5 10 1.208 3.25 4.37381 3.748 -0.0274286 0.344367 0.5 10 1.216 3.25 4.98972 3.15362 -0.196046 0.35905 0.5 10 1.224 3.25 4.82 3.328 -0.212673 0.358842 0.5 10 1.232 3.25 4.585 3.652 -0.225287 0.352369 0.5 10 1.24 3.25 4.82 3.74144 -0.223228 0.351378 0.5 10 1.248 3.25 4.77292 3.83033 -0.229319 0.337461 0.5 10 1.256 3.25 4.795 3.848 -0.229124 0.310079 0.5 10 1.264 3.25 4.875 3.97119 -0.0469467 0.316304 0.5 10 1.272 3.25 4.94405 3.89924 -0.0431112 0.317927 0.5 10 1.28 3.25 4.235 3.976 -0.168204 0.318144 0.5 10 1.288 3.25 4.32 3.98319 -0.166412 0.318479 0.5 10 1.296 3.25 4.86 3.872 -0.123518 0.322106 0.5 10 1.304 3.25 4.655 3.996 -0.132475 0.326277 0.5 10 1.312 3.25 4.265 3.97257 -0.10849 0.312741 0.5 10 1.32 3.25 4.87518 3.93615 -0.106878 0.301062 0.5 10 1.328 3.25 4.93 3.268 -0.134834 0.290875 0.5 10 1.336 3.25 4.94 3.48 -0.121914 0.282318 0.5 10 1.344 3.25 4.86 3.54 -0.12689 0.289578 0.5 10 1.352 3.25 4.78265 3.976 -0.187712 0.31799 0.5 10 1.36 3.25 4.91 3.804 -0.134801 0.329582 0.5 10 1.368 3.25 4.92234 3.87484 -0.132442 0.340451 0.5 10 1.376 3.25 4.92 3.956 -0.142931 0.295024 0.5 10 1.384 3.25 4.81 3.96654 -0.164027 0.264646 0.5 10 1.392 3.25 4.89257 3.956 -0.153348 0.234853 0.5 10 1.4 3.25 4.80407 3.90482 -0.161087 0.16416 0.5 10 1.408 3.25 4.915 3.96 -0.0441306 0.076161 0.5 10 1.416 3.25 4.445 3.768 -0.0660691 0.0746281 0.5 10 1.424 3.25 4.805 3.788 -0.0646831 0.0750229 0.5 10 1.432 3.25 2.9818 3.82544 -0.15566 0.0795997 0.5 10 1.44 3.25 4.625 3.94595 -0.0799821 0.0778955 0.5 10 1.448 3.25 4.975 3.936 -0.0716364 0.0824073 0.5 10 1.456 3.25 3.395 3.88197 -0.143142 0.0836558 0.5 10 1.464 3.25 4.345 3.992 -0.105731 0.0815545 0.5 10 1.472 3.25 3.58568 3.60055 -0.157391 0.0890762 0.5 10 1.48 3.25 3.66 3.88415 -0.146301 0.0926314 0.5 10 1.488 3.25 3.845 3.988 -0.156071 0.0995861 0.5 10 1.496 3.25 4.115 3.948 -0.157131 0.095874 0.5 10 1.504 3.25 4.34124 3.96899 -0.152203 0.100865 0.5 10 1.512 3.25 4.26 3.836 -0.153756 0.111847 0.5 10 1.52 3.25 4.8089 3.804 -0.151025 0.113009 0.5 10 1.528 3.25 4.855 3.82 -0.15 0.118239 0.5 10 1.536 3.25 4.96217 3.688 -0.159362 0.0906739 0.5 10 1.544 3.25 4.89213 3.93586 -0.146982 0.0932985 0.5 10 1.552 3.25 4.99615 3.56492 -0.155208 0.0154554 0.5 10 1.56 3.25 4.90929 3.98343 -0.153689 0.0233006 0.5 10 1.568 3.25 4.9178 3.41424 -0.214531 0.016194 0.5 10 1.576 3.25 4.45243 3.9819 -0.195959 0.0185704 0.5 10 1.584 3.25 4.62901 3.99521 -0.204234 0.0214523 0.5 10 1.592 3.25 4.88368 3.988 -0.191234 0.0281633 0.5 10 1.6 3.25 4.89761 3.81409 -0.210495 0.0282162 0.5 10 1.608 3.25 4.915 3.868 -0.199263 0.0263278 0.5 10 1.616 3.25 4.75 3.992 -0.222218 0.0331232 0.5 10 1.624 3.25 4.585 3.992 -0.231914 0.02439 0.5 10 1.632 3.25 4.615 3.836 -0.07747 0.0240082 0.5 10 1.64 3.25 4.955 3.892 -0.0816087 0.0278537 0.5 10 1.648 3.25 4.745 3.87836 -0.0690986 0.0307345 0.5 10 1.656 3.25 4.905 3.99427 -0.185145 0.0473049 0.5 10 1.664 3.25 4.865 3.948 -0.19867 0.0724902 0.5 10 1.672 3.25 4.79297 3.96638 -0.190379 0.118251 0.5 10 1.68 3.25 4.9242 3.96 -0.173454 0.150151 0.5 10 1.688 3.25 4.62 3.948 -0.14341 0.149322 0.5 10 1.696 3.25 4.225 3.956 -0.111278 0.154005 0.5 10 1.704 3.25 4.995 3.97171 -0.0947522 0.152547 0.5 10 1.712 3.25 4.08 3.99306 -0.0841179 0.15252 0.5 10 1.72 3.25 4.9052 3.852 -0.174164 0.153115 0.5 10 1.728 3.25 4.885 3.96 -0.157618 0.416799 0.5 10 1.736 3.25 4.91 3.988 -0.181402 0.463375 0.5 10 1.744 3.25 4.94244 3.99795 -0.115975 0.428447 0.5 10 1.752 3.25 4.87786 3.86982 -0.0970325 0.377912 0.5 10 1.76 3.25 4.99023 3.99527 -0.0947022 0.397645 0.5 10 1.768 3.25 4.62714 3.9277 -0.0767459 0.369425 0.5 10 1.776 3.25 4.35663 3.97252 -0.0604651 0.361201 0.5 10 1.784 3.25 4.965 3.9 -0.158 0.397885 0.5 10 1.792 3.25 4.99507 3.88806 -0.181729 0.470791 0.5 10 1.8 3.25 4.915 3.98075 -0.156736 0.533647 0.5 10 1.808 3.25 4.65611 3.928 -0.169938 0.565244 0.5 10 1.816 3.25 4.9 3.972 -0.167902 0.618799 0.5 10 1.824 3.25 4.95 3.94 -0.166516 0.642878 0.5 10 1.832 3.25 4.79 3.9534 -0.191347 0.654641 0.5 10 1.84 3.25 4.845 3.976 -0.195959 0.675065 0.5 10 1.848 3.25 4.85823 3.968 -0.17862 0.704697 0.5 10 1.856 3.25 4.88709 3.94 -0.16467 0.708055 0.5 10 1.864 3.25 4.85946 3.79957 -0.192545 0.714962 0.5 10 1.872 3.25 4.67012 3.96999 -0.17803 0.73368 0.5 10 1.88 3.25 4.72061 3.976 -0.177446 0.747538 0.5 10 1.888 3.25 4.7 3.88418 -0.189219 0.75174 0.5 10 1.896 3.25 4.84574 3.9126 -0.182686 0.771237 0.5 10 1.904 3.25 4.83 3.936 -0.197464 0.780953 0.5 10 1.912 3.25 4.89 3.98 -0.20424 0.790353 0.5 10 1.92 3.25 4.8316 3.98528 -0.179079 0.784108 0.5 10 1.928 3.25 4.995 3.904 -0.194791 0.796583 0.5 10 1.936 3.25 4.92517 3.94013 -0.207537 0.784726 0.5 10 1.944 3.25 4.77864 3.97891 -0.198214 0.764146 0.5 10 1.952 3.25 4.95 3.988 -0.223153 0.750656 0.5 10 1.96 3.25 4.99946 3.96191 -0.19397 0.736592 0.5 10 1.968 3.25 4.85346 3.95305 -0.201338 0.714459 0.5 10 1.976 3.25 4.8438 3.93553 -0.211184 0.695523 0.5 10 1.984 3.25 4.90798 3.97039 -0.201907 0.671638 0.5 10 1.992 3.25 4.8446 3.81866 -0.216375 0.638694 0.5 10 2 3.25 4.97 3.956 -0.223899 0.622828 0.5 10 2.008 3.25 4.96 3.96 -0.235561 0.591469 0.5 10 2.016 3.25 4.855 3.94825 -0.209792 0.539741 0.5 10 2.024 3.25 4.77 3.9 -0.231363 0.491623 0.5 10 2.032 3.25 4.97799 3.98639 -0.216784 0.490041 0.5 10 2.04 3.25 2.40571 3.98211 0.0397952 0.395058 0.5 10 2.048 3.25 2.54541 3.99299 0.0408739 0.387915 0.5 10 2.056 3.25 2.54 3.896 0.0724304 0.376848 0.5 10 2.064 3.25 2.905 3.92 0.0651716 0.364533 0.5 10 2.072 3.25 3.09 3.936 0.0561276 0.353386 0.5 10 2.08 3.25 3.18 3.984 0.057565 0.344468 0.5 10 2.088 3.25 3.38879 3.95838 0.065269 0.32803 0.5 10 2.096 3.25 3.08 3.94244 0.0591223 0.319158 0.5 10 2.104 3.25 3.44 3.912 0.0655139 0.309087 0.5 10 2.112 3.25 3.51368 3.924 0.0573039 0.301448 0.5 10 2.12 3.25 3.98058 3.94847 0.0648206 0.291325 0.5 10 2.128 3.25 3.87019 3.896 0.0564831 0.282897 0.5 10 2.136 3.25 4.11 3.96 0.0599999 0.272663 0.5 10 2.144 3.25 4.06181 3.97745 0.0503449 0.253295 0.5 10 2.152 3.25 4.96553 3.89643 0.068194 0.242574 0.5 10 2.16 3.25 4.825 3.89146 0.0551932 0.232862 0.5 10 2.168 3.25 4.75279 3.848 0.05 0.223044 0.5 10 2.176 3.25 4.885 3.916 0.0522968 0.220272 0.5 10 2.184 3.25 4.96 3.81694 0.0319999 0.191059 0.5 10 2.192 3.25 4.825 3.90796 0.0273854 0.168921 0.5 10 2.2 3.25 2.145 3.87724 0.346478 0.153025 0.5 10 2.208 3.25 2.16455 3.99564 0.337932 0.127212 0.5 10 2.216 3.25 2.715 3.92478 0.327773 0.124382 0.5 10 2.224 3.25 2.435 3.748 0.346467 0.127967 0.5 10 2.232 3.25 2.39754 3.804 0.351777 0.121044 0.5 10 2.24 3.25 2.515 3.88 0.359885 0.118614 0.5 10 2.248 3.25 2.69 3.94343 0.341361 0.118446 0.5 10 2.256 3.25 2.695 3.58 0.354951 0.107324 0.5 10 2.264 3.25 2.755 3.88965 0.354283 0.107588 0.5 10 2.272 3.25 2.78 3.884 0.355036 0.100004 0.5 10 2.28 3.25 2.925 3.884 0.345953 0.0942302 0.5 10 2.288 3.25 3 3.796 0.348 0.0969765 0.5 10 2.296 3.25 3.17204 3.556 0.346982 0.0899041 0.5 10 2.304 3.25 3.5266 3.92928 0.32815 0.0858969 0.5 10 2.312 3.25 3.295 3.756 0.339029 0.0805297 0.5 10 2.32 3.25 3.31625 3.777 0.338834 0.0766838 0.5 10 2.328 3.25 3.625 3.77747 0.341554 0.0817153 0.5 10 2.336 3.25 4.465 2.992 -0.235912 0.0738044 0.5 10 2.344 3.25 4.5 3.508 -0.263799 0.0794995 0.5 10 2.352 3.25 4.25 3.748 -0.262272 0.0751814 0.5 10 2.36 3.25 4.97498 3.79199 -0.266624 0.0749389 0.5 10 2.368 3.25 4.99997 3.95198 -0.269193 0.0724831 0.5 10 2.376 3.25 4.94831 3.804 -0.255889 0.0726778 0.5 10 2.384 3.25 4.86 3.884 -0.249425 0.0717187 0.5 10 2.392 3.25 4.8 3.976 -0.249451 0.0668359 0.5 10 2.4 3.25 4.965 3.81301 -0.237154 0.0581627 0.5 10 2.408 3.25 4.96541 3.22033 -0.197559 0.0591424 0.5 10 2.416 3.25 4.725 3.264 -0.196 0.0585788 0.5 10 2.424 3.25 4.50913 3.596 -0.196235 0.0592703 0.5 10 2.432 3.25 4.765 3.836 -0.208703 0.059982 0.5 10 2.44 3.25 4.995 3.7 -0.199231 0.0576966 0.5 10 2.448 3.25 4.79256 3.78014 -0.192851 0.0520416 0.5 10 2.456 3.25 4.93157 3.94549 -0.197207 0.0522345 0.5 10 2.464 3.25 4.89899 3.896 -0.193262 0.0475705 0.5 10 2.472 3.25 0.86 3.63049 0.842363 0.0350804 0.5 10 2.48 3.25 0.965 2.764 0.817702 0.030363 0.5 10 2.488 3.25 0.91 3.856 0.838135 0.0346164 0.5 10 2.496 3.25 0.775 3.716 0.886746 0.0318439 0.5 10 2.504 3.25 0.625 3.08513 0.943688 0.0332085 0.5 10 2.512 3.25 0.535 3.892 0.986089 0.0349885 0.5 10 2.52 3.25 0.71 2.764 0.915406 0.0296449 0.5 10 2.528 3.25 0.795 3.628 0.891448 0.0329969 0.5 10 2.536 3.25 4.23 3.78734 -0.551301 0.0275598 0.5 10 2.544 3.25 0.66 3.496 0.946 0.0298804 0.5 10 2.552 3.25 0.725 3.044 0.924932 0.0282282 0.5 10 2.56 3.25 0.625 3.916 0.97089 0.0309984 0.5 10 2.568 3.25 4.90659 3.99327 -0.537228 0.0283246 0.5 10 2.576 3.25 3.60651 3.77721 -0.498915 0.0281724 0.5 10 2.584 3.25 4.10209 3.92167 -0.504914 0.0277775 0.5 10 2.592 3.25 4.20618 3.89694 -0.502787 0.0280821 0.5 10 2.6 3.25 2.84 3.58207 -0.44143 0.0276497 0.5 10 2.608 3.25 2.95 3.676 -0.440611 0.0279287 0.5 10 2.616 3.25 2.745 3.668 -0.423379 0.027653 0.5 10 2.624 3.25 3.335 3.716 -0.445498 0.0279717 0.5 10 2.632 3.25 2.9 3.54552 -0.416341 0.027622 0.5 10 2.64 3.25 3.77246 3.85797 -0.448444 0.0281579 0.5 10 2.648 3.25 3.61861 3.8917 -0.434728 0.0277763 0.5 10 2.656 3.25 3.4802 3.932 -0.426558 0.0291916 0.5 10 2.664 3.25 3.23 3.916 -0.410961 0.0287831 0.5 10 2.672 3.25 3.49 3.964 -0.413668 0.0294583 0.5 10 2.68 3.25 2.505 3.752 -0.351403 0.0282634 0.5 10 2.688 3.25 2.575 3.776 -0.34887 0.0288109 0.5 10 2.696 3.25 1.4 3.89538 -0.146162 0.0357326 0.5 10 2.704 3.25 3.01268 3.94 -0.362381 0.0288096 0.5 10 2.712 3.25 2.7114 3.70912 -0.336817 0.028874 0.5 10 2.72 3.25 3.665 3.99277 -0.376441 0.0278649 0.5 10 2.728 3.25 2.095 3.468 -0.2626 0.0277054 0.5 10 2.736 3.25 2.83264 3.98611 -0.326693 0.030274 0.5 10 2.744 3.25 2.63 3.66692 -0.300901 0.0261072 0.5 10 2.752 3.25 1.61 3.712 -0.15903 0.0320479 0.5 10 2.76 3.25 1.895 3.584 -0.208584 0.0272999 0.5 10 2.768 3.25 1.665 3.828 -0.156715 0.0349531 0.5 10 2.776 3.25 1.8163 3.39317 -0.177455 0.0325867 0.5 10 2.784 3.25 1.82644 3.43378 -0.172598 0.0336912 0.5 10 2.792 3.25 1.895 3.308 -0.174389 0.0300512 0.5 10 2.8 3.25 2.58 3.856 -0.252 0.0286722 0.5 10 2.808 3.25 1.9 3.8366 -0.168054 0.0325678 0.5 10 2.816 3.25 1.89176 3.76285 -0.16415 0.0362882 0.5 10 2.824 3.25 2.135 3.912 -0.195007 0.029771 0.5 10 2.832 3.25 2.02 3.812 -0.16608 0.0363458 0.5 10 2.84 3.25 2.065 3.98 -0.166728 0.0349054 0.5 10 2.848 3.25 2.185 3.844 -0.178 0.0330349 0.5 10 2.856 3.25 2.255 3.804 -0.176434 0.0316287 0.5 10 2.864 3.25 2.1623 3.88487 -0.15586 0.030456 0.5 10 2.872 3.25 2.53 3.728 -0.184 0.0223547 0.5 10 2.88 3.25 2.38 3.956 -0.160475 0.0320737 0.5 10 2.888 3.25 2.40048 3.96499 -0.157091 0.032407 0.5 10 2.896 3.25 2.51 3.796 -0.158676 0.0288077 0.5 10 2.904 3.25 2.60438 3.876 -0.161944 0.0319638 0.5 10 2.912 3.25 2.495 3.884 -0.154338 0.0211733 0.5 10 2.92 3.25 2.645 3.944 -0.161692 0.0275464 0.5 10 2.928 3.25 2.86 3.8576 -0.164159 0.0257266 0.5 10 2.936 3.25 3.02 3.944 -0.154229 0.0256228 0.5 10 2.944 3.25 3.29 3.86 -0.168921 0.0203179 0.5 10 2.952 3.25 3.27 3.916 -0.155699 0.0266009 0.5 10 2.96 3.25 4.95448 3.05441 -0.15086 0.0243677 0.5 10 2.968 3.25 4.54638 3.25271 -0.149818 0.0232356 0.5 10 2.976 3.25 4.35518 3.51478 -0.152692 0.0244403 0.5 10 2.984 3.25 3.99042 3.81634 -0.151383 0.0269858 0.5 10 2.992 3.25 4.945 3.46 -0.145907 0.0269045 0.5 10 3 3.25 3.675 3.868 -0.0963679 0.408262 0.5 10 1 3.275 3.72012 3.9961 -0.0878249 0.410623 0.5 10 1.008 3.275 4.16282 3.91026 -0.0839718 0.408384 0.5 10 1.016 3.275 4.465 3.976 -0.089716 0.405803 0.5 10 1.024 3.275 4.665 3.96799 -0.0863041 0.385916 0.5 10 1.032 3.275 3.87744 3.84789 -0.0609488 0.385659 0.5 10 1.04 3.275 3.61865 3.97092 -0.046 0.392135 0.5 10 1.048 3.275 4.61844 3.884 -0.0409345 0.382339 0.5 10 1.056 3.275 3.06 3.772 0.0131951 0.374487 0.5 10 1.064 3.275 4.56411 3.904 -0.0655024 0.369115 0.5 10 1.072 3.275 3.24821 3.98 0.0198809 0.364365 0.5 10 1.08 3.275 4.59311 3.9 -0.00403255 0.353944 0.5 10 1.088 3.275 4.83211 3.56169 0.0201723 0.351303 0.5 10 1.096 3.275 4.81697 3.756 0.0236396 0.35511 0.5 10 1.104 3.275 4.04033 3.84736 -0.00799176 0.35082 0.5 10 1.112 3.275 4.94 3.944 -0.160325 0.361396 0.5 10 1.12 3.275 4.84782 3.57425 -0.151615 0.393991 0.5 10 1.128 3.275 4.455 3.884 -0.130848 0.40936 0.5 10 1.136 3.275 4.4959 3.93272 -0.120977 0.419843 0.5 10 1.144 3.275 3.85684 3.89747 -0.10117 0.404979 0.5 10 1.152 3.275 3.97 3.832 -0.0989187 0.397555 0.5 10 1.16 3.275 3.64 3.824 -0.078 0.395422 0.5 10 1.168 3.275 3.405 3.972 -0.0638758 0.389032 0.5 10 1.176 3.275 4.55094 3.80075 -0.0580186 0.3833 0.5 10 1.184 3.275 4.71956 3.73165 -0.0472811 0.370552 0.5 10 1.192 3.275 4.825 3.984 -0.0533251 0.372012 0.5 10 1.2 3.275 4.28 3.94505 -0.030964 0.364799 0.5 10 1.208 3.275 4.76257 3.91412 -0.0362294 0.357531 0.5 10 1.216 3.275 4.7141 3.30328 -0.190444 0.359949 0.5 10 1.224 3.275 4.92381 3.59174 -0.215513 0.360335 0.5 10 1.232 3.275 4.92 3.78426 -0.218232 0.35446 0.5 10 1.24 3.275 4.73 3.64051 -0.217639 0.336847 0.5 10 1.248 3.275 4.94387 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2.04 3.275 2.465 3.99956 0.0549107 0.391888 0.5 10 2.048 3.275 3.14 3.99235 0.022836 0.376536 0.5 10 2.056 3.275 2.925 3.852 0.0529428 0.364236 0.5 10 2.064 3.275 3.05849 3.98092 0.0394835 0.357222 0.5 10 2.072 3.275 3.6 3.95923 0.0346776 0.339652 0.5 10 2.08 3.275 3.125 3.988 0.0657463 0.336555 0.5 10 2.088 3.275 3.72083 3.96066 0.0427591 0.317007 0.5 10 2.096 3.275 3.395 3.936 0.0635207 0.306942 0.5 10 2.104 3.275 3.59 3.98682 0.0648085 0.298 0.5 10 2.112 3.275 4.05 3.948 0.0509689 0.284913 0.5 10 2.12 3.275 4.08107 3.89285 0.0525475 0.278801 0.5 10 2.128 3.275 4.27442 3.95554 0.0553555 0.271877 0.5 10 2.136 3.275 4.205 3.964 0.0511696 0.259425 0.5 10 2.144 3.275 4.7 3.916 0.0490174 0.243302 0.5 10 2.152 3.275 4.95 3.936 0.0474413 0.229529 0.5 10 2.16 3.275 4.94 3.968 0.0474106 0.216041 0.5 10 2.168 3.275 4.965 3.896 0.0323294 0.191258 0.5 10 2.176 3.275 4.94622 3.90472 0.0385582 0.181365 0.5 10 2.184 3.275 2.19 3.968 0.342712 0.161668 0.5 10 2.192 3.275 2.3064 3.88512 0.348056 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3.275 3.98535 3.736 -0.155407 0.0242579 0.5 10 2.984 3.275 4.87 3.424 -0.1501 0.0283283 0.5 10 2.992 3.275 4.98 3.504 -0.14581 0.0292611 0.5 10 3 3.275 3.54735 3.876 -0.0857774 0.410279 0.5 10 1 3.3 4.075 3.768 -0.0843012 0.407654 0.5 10 1.008 3.3 4.34 3.764 -0.0914223 0.399668 0.5 10 1.016 3.3 2.925 3.776 -0.0273055 0.398134 0.5 10 1.024 3.3 3.38 3.99845 -0.043287 0.388561 0.5 10 1.032 3.3 3.5 3.86951 -0.041566 0.396038 0.5 10 1.04 3.3 3.655 3.912 -0.0391684 0.382144 0.5 10 1.048 3.3 4.83949 3.77559 -0.0379032 0.377845 0.5 10 1.056 3.3 4.97576 3.96061 -0.036269 0.372592 0.5 10 1.064 3.3 4.705 3.96543 -0.0606978 0.3751 0.5 10 1.072 3.3 4.89 3.992 -0.052 0.361551 0.5 10 1.08 3.3 4.50333 3.98666 0.002236 0.351585 0.5 10 1.088 3.3 4.095 3.948 0.0362471 0.349065 0.5 10 1.096 3.3 4.32399 3.96197 0.0203042 0.347756 0.5 10 1.104 3.3 4.58258 3.94359 0.0399641 0.348738 0.5 10 1.112 3.3 4.875 3.944 -0.161231 0.389021 0.5 10 1.12 3.3 4.11479 3.77583 -0.126608 0.405061 0.5 10 1.128 3.3 4.28328 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4.89151 3.76474 -0.12931 0.324879 0.5 10 1.296 3.3 4.305 3.888 -0.105465 0.322818 0.5 10 1.304 3.3 4.63622 3.97698 -0.100247 0.323127 0.5 10 1.312 3.3 4.625 3.99639 -0.10517 0.302727 0.5 10 1.32 3.3 4.99 3.916 -0.0839781 0.288244 0.5 10 1.328 3.3 4.81555 3.46444 -0.127385 0.286137 0.5 10 1.336 3.3 4.99083 3.728 -0.147381 0.307086 0.5 10 1.344 3.3 4.19328 3.97062 -0.153098 0.31715 0.5 10 1.352 3.3 4.58316 3.91053 -0.12681 0.342161 0.5 10 1.36 3.3 4.92649 3.92 -0.126 0.353914 0.5 10 1.368 3.3 4.94715 3.92172 -0.142043 0.280575 0.5 10 1.376 3.3 4.555 3.996 -0.160607 0.251626 0.5 10 1.384 3.3 4.88512 3.77609 -0.15803 0.232807 0.5 10 1.392 3.3 4.95 3.88473 -0.164502 0.178227 0.5 10 1.4 3.3 4.605 3.784 -0.0485787 0.0761308 0.5 10 1.408 3.3 4.935 3.827 -0.0472622 0.0782015 0.5 10 1.416 3.3 4.51027 3.912 -0.0614047 0.0781626 0.5 10 1.424 3.3 4.99815 3.88652 -0.0553474 0.0837213 0.5 10 1.432 3.3 3.07 3.492 -0.156544 0.0832194 0.5 10 1.44 3.3 3.12 3.564 -0.151794 0.0848417 0.5 10 1.448 3.3 3.24 3.868 -0.143986 0.0905202 0.5 10 1.456 3.3 3.25 3.90999 -0.149235 0.0992752 0.5 10 1.464 3.3 3.45568 3.8986 -0.154553 0.0936355 0.5 10 1.472 3.3 3.635 3.852 -0.15458 0.0985756 0.5 10 1.48 3.3 3.785 3.632 -0.152502 0.0984159 0.5 10 1.488 3.3 3.98 3.948 -0.139948 0.0971397 0.5 10 1.496 3.3 4.345 3.604 -0.156862 0.105967 0.5 10 1.504 3.3 4.415 3.796 -0.153137 0.116084 0.5 10 1.512 3.3 4.85 3.428 -0.164187 0.112006 0.5 10 1.52 3.3 4.795 3.684 -0.154483 0.130069 0.5 10 1.528 3.3 4.98901 3.992 -0.14722 0.142072 0.5 10 1.536 3.3 4.81946 3.96757 -0.152369 0.130995 0.5 10 1.544 3.3 4.90359 3.61583 -0.148161 0.0427106 0.5 10 1.552 3.3 4.98772 3.92 -0.154875 0.0446534 0.5 10 1.56 3.3 4.65 3.424 -0.213244 0.0167077 0.5 10 1.568 3.3 4.65316 3.86273 -0.187758 0.0205269 0.5 10 1.576 3.3 4.89992 3.79994 -0.199029 0.0257203 0.5 10 1.584 3.3 4.82282 3.81826 -0.20474 0.0299845 0.5 10 1.592 3.3 4.755 3.956 -0.196054 0.0366603 0.5 10 1.6 3.3 4.67 3.972 -0.196507 0.0338183 0.5 10 1.608 3.3 4.84395 3.97235 -0.206624 0.0361952 0.5 10 1.616 3.3 4.71 3.524 -0.0829625 0.0258056 0.5 10 1.624 3.3 4.99994 3.9628 -0.0878401 0.0281111 0.5 10 1.632 3.3 4.74244 3.76595 -0.0748286 0.0322157 0.5 10 1.64 3.3 4.63 3.768 -0.192353 0.0332048 0.5 10 1.648 3.3 4.97557 3.75645 -0.166798 0.0579784 0.5 10 1.656 3.3 4.575 3.928 -0.17041 0.0860571 0.5 10 1.664 3.3 4.90455 3.98764 -0.184512 0.160066 0.5 10 1.672 3.3 4.73 3.96333 -0.153626 0.163275 0.5 10 1.68 3.3 4.4 3.996 -0.134358 0.161588 0.5 10 1.688 3.3 4.815 3.97928 -0.113073 0.171515 0.5 10 1.696 3.3 4.82 3.956 -0.091753 0.168215 0.5 10 1.704 3.3 4.50879 3.96143 -0.0784272 0.163974 0.5 10 1.712 3.3 4.95 3.912 -0.164641 0.286271 0.5 10 1.72 3.3 4.96 3.988 -0.177085 0.491096 0.5 10 1.728 3.3 4.815 3.964 -0.199172 0.466179 0.5 10 1.736 3.3 4.845 3.99607 -0.203702 0.437366 0.5 10 1.744 3.3 4.81572 3.93658 -0.0993735 0.405487 0.5 10 1.752 3.3 4.63439 3.968 -0.0833083 0.394513 0.5 10 1.76 3.3 4.54517 3.852 -0.0708363 0.359923 0.5 10 1.768 3.3 4.95146 3.936 -0.155203 0.375261 0.5 10 1.776 3.3 4.83791 3.93935 -0.18 0.432103 0.5 10 1.784 3.3 4.90891 3.90406 -0.17154 0.513876 0.5 10 1.792 3.3 4.9513 3.90504 -0.163004 0.570909 0.5 10 1.8 3.3 4.97 3.968 -0.163464 0.620506 0.5 10 1.808 3.3 4.92535 3.90438 -0.156381 0.631135 0.5 10 1.816 3.3 4.895 3.908 -0.177121 0.665517 0.5 10 1.824 3.3 4.88253 3.896 -0.171928 0.684887 0.5 10 1.832 3.3 4.86279 3.99023 -0.166088 0.708596 0.5 10 1.84 3.3 4.75852 3.92282 -0.17203 0.71423 0.5 10 1.848 3.3 4.94424 3.96339 -0.174805 0.748884 0.5 10 1.856 3.3 4.92142 3.88913 -0.175295 0.754529 0.5 10 1.864 3.3 4.97 3.876 -0.171054 0.766094 0.5 10 1.872 3.3 4.82353 3.688 -0.185452 0.749024 0.5 10 1.88 3.3 4.81 3.78109 -0.190945 0.765891 0.5 10 1.888 3.3 4.67 3.968 -0.188836 0.783678 0.5 10 1.896 3.3 4.89819 3.93455 -0.194362 0.801792 0.5 10 1.904 3.3 4.9869 3.86507 -0.19796 0.801516 0.5 10 1.912 3.3 4.92 3.88311 -0.189559 0.804393 0.5 10 1.92 3.3 4.83 3.96 -0.2 0.799028 0.5 10 1.928 3.3 4.975 3.87071 -0.182727 0.785073 0.5 10 1.936 3.3 4.99 3.884 -0.192373 0.772282 0.5 10 1.944 3.3 4.56161 3.976 -0.208068 0.733561 0.5 10 1.952 3.3 4.945 3.976 -0.200673 0.735588 0.5 10 1.96 3.3 4.97226 3.90359 -0.195488 0.716183 0.5 10 1.968 3.3 4.87343 3.856 -0.203727 0.692305 0.5 10 1.976 3.3 4.8343 3.9 -0.207319 0.673874 0.5 10 1.984 3.3 4.93088 3.85727 -0.201019 0.631446 0.5 10 1.992 3.3 4.835 3.916 -0.217901 0.605816 0.5 10 2 3.3 4.98 3.944 -0.201157 0.56957 0.5 10 2.008 3.3 4.99315 3.97565 -0.222341 0.547716 0.5 10 2.016 3.3 4.825 3.98327 -0.215859 0.495546 0.5 10 2.024 3.3 4.93294 3.9521 -0.199244 0.410416 0.5 10 2.032 3.3 2.525 3.936 0.0452867 0.391556 0.5 10 2.04 3.3 2.93676 3.92 0.0537977 0.378402 0.5 10 2.048 3.3 2.89988 3.988 0.0635296 0.368716 0.5 10 2.056 3.3 2.875 3.928 0.0506986 0.359404 0.5 10 2.064 3.3 3.38184 3.96947 0.0296976 0.344222 0.5 10 2.072 3.3 3.45 3.856 0.0541553 0.329032 0.5 10 2.08 3.3 3.47562 3.98049 0.0482171 0.323546 0.5 10 2.088 3.3 3.5 3.9 0.0463828 0.305433 0.5 10 2.096 3.3 4.01 3.94418 0.040465 0.294115 0.5 10 2.104 3.3 3.8 3.948 0.04 0.280789 0.5 10 2.112 3.3 4.25125 3.941 0.048 0.27282 0.5 10 2.12 3.3 4.6886 3.904 0.0409133 0.262646 0.5 10 2.128 3.3 4.53 3.89107 0.0442973 0.252843 0.5 10 2.136 3.3 4.975 3.88 0.0439315 0.241842 0.5 10 2.144 3.3 4.61587 3.9447 0.0358001 0.215304 0.5 10 2.152 3.3 4.69829 3.91943 0.0326324 0.1919 0.5 10 2.16 3.3 4.99882 3.96916 0.0178476 0.17548 0.5 10 2.168 3.3 3.375 3.828 -0.452025 0.151483 0.5 10 2.176 3.3 2.08 3.776 0.363464 0.154965 0.5 10 2.184 3.3 2.40843 3.97474 0.326572 0.13523 0.5 10 2.192 3.3 2.2 3.94 0.364924 0.144524 0.5 10 2.2 3.3 2.27907 3.70725 0.360771 0.133171 0.5 10 2.208 3.3 2.32944 3.74994 0.358152 0.131426 0.5 10 2.216 3.3 2.45756 3.972 0.3571 0.125738 0.5 10 2.224 3.3 2.465 3.904 0.365269 0.115136 0.5 10 2.232 3.3 2.50732 3.7433 0.353537 0.110544 0.5 10 2.24 3.3 2.68 3.84766 0.346931 0.11227 0.5 10 2.248 3.3 2.695 3.972 0.358325 0.105121 0.5 10 2.256 3.3 2.695 3.25473 0.354622 0.0916676 0.5 10 2.264 3.3 3.01813 3.976 0.336925 0.099098 0.5 10 2.272 3.3 2.46 3.356 -0.303957 0.0838628 0.5 10 2.28 3.3 2.90731 3.924 0.352563 0.0985453 0.5 10 2.288 3.3 3.05 3.988 0.358171 0.0949864 0.5 10 2.296 3.3 2.455 3.78754 -0.291586 0.0785117 0.5 10 2.304 3.3 3.255 3.788 0.354002 0.0873659 0.5 10 2.312 3.3 3.3425 3.81832 0.344626 0.0854644 0.5 10 2.32 3.3 4.5742 3.19536 -0.293743 0.0890715 0.5 10 2.328 3.3 2.715 3.664 -0.279814 0.0892644 0.5 10 2.336 3.3 2.97017 3.71213 -0.28905 0.0873735 0.5 10 2.344 3.3 3.63 3.704 -0.294711 0.0845618 0.5 10 2.352 3.3 3.01205 3.872 -0.282166 0.0882856 0.5 10 2.36 3.3 3.815 3.856 -0.298228 0.0903279 0.5 10 2.368 3.3 4.92921 3.83137 -0.314351 0.0870784 0.5 10 2.376 3.3 4.36631 3.90294 -0.288252 0.0824333 0.5 10 2.384 3.3 4.4 3.872 -0.289732 0.0817114 0.5 10 2.392 3.3 4.72777 3.852 -0.283645 0.081247 0.5 10 2.4 3.3 4.77473 3.97978 -0.276421 0.0787153 0.5 10 2.408 3.3 4.8885 3.968 -0.280727 0.0745677 0.5 10 2.416 3.3 4.78 3.94627 -0.272547 0.0707868 0.5 10 2.424 3.3 4.82 3.82285 -0.260927 0.0686969 0.5 10 2.432 3.3 4.49667 3.796 -0.229874 0.0652399 0.5 10 2.44 3.3 4.29664 3.952 -0.229006 0.0660316 0.5 10 2.448 3.3 4.67737 3.82989 -0.234995 0.0561495 0.5 10 2.456 3.3 4.59894 3.99322 -0.23852 0.0521175 0.5 10 2.464 3.3 4.93166 3.91969 -0.232646 0.047902 0.5 10 2.472 3.3 0.545 3.204 0.967169 0.0327877 0.5 10 2.48 3.3 0.58 2.124 0.956 0.0317359 0.5 10 2.488 3.3 0.66 3.468 0.927614 0.0322426 0.5 10 2.496 3.3 0.645 3.868 0.938 0.0349876 0.5 10 2.504 3.3 0.56 2.884 0.976 0.0307597 0.5 10 2.512 3.3 4.305 3.72443 -0.566428 0.0278438 0.5 10 2.52 3.3 0.74 3.156 0.912595 0.0315739 0.5 10 2.528 3.3 4.775 3.9 -0.560767 0.0282601 0.5 10 2.536 3.3 4.73 3.892 -0.554722 0.0283252 0.5 10 2.544 3.3 0.715 3.62 0.934578 0.0292128 0.5 10 2.552 3.3 4.48995 3.81926 -0.5347 0.0279847 0.5 10 2.56 3.3 4.13 3.84236 -0.520973 0.0286607 0.5 10 2.568 3.3 3.935 3.908 -0.508715 0.0289813 0.5 10 2.576 3.3 4.20317 3.93853 -0.508152 0.028975 0.5 10 2.584 3.3 3.89689 3.84151 -0.494763 0.0283564 0.5 10 2.592 3.3 4.905 3.928 -0.507338 0.0275959 0.5 10 2.6 3.3 3.6208 3.90861 -0.470505 0.0290744 0.5 10 2.608 3.3 3.51 3.744 -0.46 0.0284388 0.5 10 2.616 3.3 3.64912 3.9233 -0.461085 0.0289523 0.5 10 2.624 3.3 3.17 3.71076 -0.430453 0.0290838 0.5 10 2.632 3.3 2.90882 3.7144 -0.413185 0.0284199 0.5 10 2.64 3.3 3.37 3.87898 -0.426993 0.0297189 0.5 10 2.648 3.3 3.56919 3.956 -0.431645 0.0293581 0.5 10 2.656 3.3 1.34 3.596 -0.138236 0.0309198 0.5 10 2.664 3.3 2.595 3.724 -0.36249 0.0292972 0.5 10 2.672 3.3 3.24 3.876 -0.396 0.0298651 0.5 10 2.68 3.3 2.56 3.704 -0.347284 0.0294907 0.5 10 2.688 3.3 2.98 3.97289 -0.372986 0.0286244 0.5 10 2.696 3.3 1.44 3.74 -0.149201 0.03347 0.5 10 2.704 3.3 2.51398 3.60718 -0.317454 0.0284358 0.5 10 2.712 3.3 2.83 3.76 -0.337667 0.0292648 0.5 10 2.72 3.3 2.605 3.78775 -0.317812 0.0301933 0.5 10 2.728 3.3 2.315 3.948 -0.28626 0.0287812 0.5 10 2.736 3.3 3.37225 3.9778 -0.344563 0.0286035 0.5 10 2.744 3.3 2.515 3.864 -0.289477 0.0312058 0.5 10 2.752 3.3 1.7 3.228 -0.168482 0.0323788 0.5 10 2.76 3.3 1.71071 3.56404 -0.162176 0.0306968 0.5 10 2.768 3.3 2.45145 3.916 -0.266412 0.0308917 0.5 10 2.776 3.3 1.70598 3.956 -0.153915 0.036014 0.5 10 2.784 3.3 1.695 3.992 -0.145189 0.030581 0.5 10 2.792 3.3 1.89 3.504 -0.160468 0.0285309 0.5 10 2.8 3.3 1.91997 3.63998 -0.170001 0.0345267 0.5 10 2.808 3.3 1.865 3.66002 -0.156223 0.0324037 0.5 10 2.816 3.3 2.1154 3.75502 -0.187421 0.0311364 0.5 10 2.824 3.3 2.07 3.656 -0.167873 0.0330344 0.5 10 2.832 3.3 2.28799 3.864 -0.195385 0.0298028 0.5 10 2.84 3.3 2.19167 3.93969 -0.179677 0.0329966 0.5 10 2.848 3.3 2.1 3.788 -0.162201 0.0321441 0.5 10 2.856 3.3 2.26348 3.80123 -0.162258 0.0333455 0.5 10 2.864 3.3 2.225 3.73268 -0.154257 0.0277408 0.5 10 2.872 3.3 2.44039 3.896 -0.169425 0.0305049 0.5 10 2.88 3.3 2.415 3.968 -0.157443 0.0332439 0.5 10 2.888 3.3 2.55656 3.73554 -0.165412 0.0279894 0.5 10 2.896 3.3 2.69173 3.78938 -0.171331 0.0234732 0.5 10 2.904 3.3 2.6766 3.78928 -0.157647 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1.136 3.45 3.465 3.972 -0.0432058 0.400193 0.5 10 1.144 3.45 3.915 3.848 -0.0403794 0.393719 0.5 10 1.152 3.45 4.38183 3.73406 -0.0304044 0.38422 0.5 10 1.16 3.45 4.37133 3.82506 -0.0295584 0.37879 0.5 10 1.168 3.45 4.665 3.904 -0.02752 0.380622 0.5 10 1.176 3.45 4.625 3.952 -0.023786 0.358657 0.5 10 1.184 3.45 4.935 3.436 -0.20073 0.352497 0.5 10 1.192 3.45 4.815 3.972 -0.00147414 0.359949 0.5 10 1.2 3.45 4.985 3.72 -0.0728955 0.356589 0.5 10 1.208 3.45 4.89976 3.71908 -0.205209 0.355748 0.5 10 1.216 3.45 4.635 3.99414 -0.0528838 0.34558 0.5 10 1.224 3.45 4.39909 3.92327 -0.0409098 0.338313 0.5 10 1.232 3.45 4.84435 3.92988 -0.043074 0.347496 0.5 10 1.24 3.45 4.77 3.856 -0.031275 0.342259 0.5 10 1.248 3.45 4.96 3.924 -0.139207 0.353166 0.5 10 1.256 3.45 4.745 3.972 -0.128 0.349496 0.5 10 1.264 3.45 4.89253 3.97403 -0.108018 0.33835 0.5 10 1.272 3.45 4.445 3.956 -0.0720342 0.323972 0.5 10 1.28 3.45 4.565 3.98 -0.0728393 0.329289 0.5 10 1.288 3.45 4.97773 3.892 -0.0657942 0.336188 0.5 10 1.296 3.45 4.885 3.84 -0.06 0.29818 0.5 10 1.304 3.45 4.93 3.864 -0.135495 0.323919 0.5 10 1.312 3.45 4.93181 3.98291 -0.13481 0.380266 0.5 10 1.32 3.45 4.82564 3.92851 -0.139175 0.373649 0.5 10 1.328 3.45 4.44 3.88 -0.10642 0.342317 0.5 10 1.336 3.45 4.69091 3.99583 -0.100609 0.356453 0.5 10 1.344 3.45 4.76615 3.76492 -0.113833 0.303266 0.5 10 1.352 3.45 4.81929 3.75045 -0.117057 0.247498 0.5 10 1.36 3.45 4.96264 3.97011 -0.164874 0.222886 0.5 10 1.368 3.45 4.64672 3.99738 -0.149526 0.205622 0.5 10 1.376 3.45 4.87 3.952 -0.144405 0.206146 0.5 10 1.384 3.45 4.99048 3.92838 -0.138151 0.196933 0.5 10 1.392 3.45 4.95 3.772 -0.147568 0.118912 0.5 10 1.4 3.45 2.80165 3.59736 -0.141027 0.0850328 0.5 10 1.408 3.45 4.77 3.81407 -0.05 0.0853645 0.5 10 1.416 3.45 4.91472 3.87214 -0.0474179 0.0916403 0.5 10 1.424 3.45 3.06156 3.63325 -0.137492 0.0953915 0.5 10 1.432 3.45 4.55 3.988 -0.070812 0.0920396 0.5 10 1.44 3.45 3.365 3.608 -0.143524 0.0992228 0.5 10 1.448 3.45 3.35211 3.72968 -0.14084 0.113574 0.5 10 1.456 3.45 3.50169 3.82935 -0.136683 0.114439 0.5 10 1.464 3.45 3.59 3.828 -0.137894 0.121341 0.5 10 1.472 3.45 3.81 3.684 -0.14789 0.119451 0.5 10 1.48 3.45 4.11539 3.62431 -0.148473 0.127845 0.5 10 1.488 3.45 4.095 3.596 -0.143844 0.126945 0.5 10 1.496 3.45 4.4186 3.74288 -0.140656 0.145483 0.5 10 1.504 3.45 4.57319 3.66434 -0.144394 0.159292 0.5 10 1.512 3.45 4.74679 3.79478 -0.142798 0.177523 0.5 10 1.52 3.45 4.94 3.848 -0.141659 0.193493 0.5 10 1.528 3.45 4.76159 3.84 -0.139914 0.12884 0.5 10 1.536 3.45 4.815 3.964 -0.132 0.0806672 0.5 10 1.544 3.45 4.98963 3.90022 -0.143845 0.0675569 0.5 10 1.552 3.45 4.81339 3.79471 -0.175956 0.0345506 0.5 10 1.56 3.45 4.90471 3.82377 -0.174711 0.0459673 0.5 10 1.568 3.45 4.895 3.92 -0.168868 0.0536075 0.5 10 1.576 3.45 4.555 3.976 -0.195975 0.0512708 0.5 10 1.584 3.45 4.875 3.956 -0.188872 0.0505693 0.5 10 1.592 3.45 4.64662 3.7653 -0.0869579 0.0335629 0.5 10 1.6 3.45 4.74649 3.7878 -0.0784202 0.0384895 0.5 10 1.608 3.45 4.94494 3.66 -0.157788 0.0437964 0.5 10 1.616 3.45 4.96 3.952 -0.159222 0.0703723 0.5 10 1.624 3.45 4.83 3.728 -0.168114 0.0955228 0.5 10 1.632 3.45 4.84883 3.944 -0.159021 0.190431 0.5 10 1.64 3.45 4.705 3.968 -0.182756 0.189489 0.5 10 1.648 3.45 4.46 3.97442 -0.11611 0.196234 0.5 10 1.656 3.45 4.98778 3.98623 -0.104446 0.213198 0.5 10 1.664 3.45 4.51548 3.98294 -0.0859232 0.20436 0.5 10 1.672 3.45 4.76793 3.95835 -0.0706204 0.217731 0.5 10 1.68 3.45 4.77 3.9 -0.143708 0.263281 0.5 10 1.688 3.45 4.72095 3.90073 -0.161815 0.498085 0.5 10 1.696 3.45 4.7252 3.92416 -0.11739 0.490846 0.5 10 1.704 3.45 4.94 3.976 -0.189834 0.487155 0.5 10 1.712 3.45 4.945 3.984 -0.0880934 0.465043 0.5 10 1.72 3.45 4.93883 3.9696 -0.0771831 0.443681 0.5 10 1.728 3.45 4.985 3.9 -0.0627609 0.386865 0.5 10 1.736 3.45 4.96504 3.868 -0.161989 0.397035 0.5 10 1.744 3.45 4.98044 3.71235 -0.154514 0.456762 0.5 10 1.752 3.45 4.98849 3.99079 -0.135337 0.547376 0.5 10 1.76 3.45 4.95656 3.75309 -0.148956 0.592943 0.5 10 1.768 3.45 4.79463 3.9837 -0.134952 0.632989 0.5 10 1.776 3.45 4.855 3.996 -0.165556 0.68801 0.5 10 1.784 3.45 4.965 3.872 -0.154425 0.715121 0.5 10 1.792 3.45 4.94 3.956 -0.148887 0.739769 0.5 10 1.8 3.45 4.925 3.952 -0.158978 0.761261 0.5 10 1.808 3.45 4.95 3.948 -0.166207 0.774491 0.5 10 1.816 3.45 4.715 3.952 -0.155957 0.775454 0.5 10 1.824 3.45 4.98276 3.868 -0.16019 0.801265 0.5 10 1.832 3.45 4.8758 3.688 -0.162452 0.785359 0.5 10 1.84 3.45 4.965 3.932 -0.173894 0.815522 0.5 10 1.848 3.45 4.79046 3.88437 -0.161715 0.822538 0.5 10 1.856 3.45 4.91366 3.95666 -0.165578 0.846254 0.5 10 1.864 3.45 4.93393 3.91114 -0.151793 0.832826 0.5 10 1.872 3.45 4.875 3.76576 -0.17625 0.828967 0.5 10 1.88 3.45 4.9 3.78468 -0.172224 0.834793 0.5 10 1.888 3.45 4.92949 3.944 -0.166018 0.842613 0.5 10 1.896 3.45 4.93179 3.91343 -0.176598 0.823357 0.5 10 1.904 3.45 4.93 3.952 -0.157686 0.790976 0.5 10 1.912 3.45 4.94813 3.944 -0.191248 0.780298 0.5 10 1.92 3.45 4.95296 3.916 -0.172301 0.748537 0.5 10 1.928 3.45 4.955 3.868 -0.166475 0.723075 0.5 10 1.936 3.45 4.865 3.904 -0.188 0.697362 0.5 10 1.944 3.45 4.85227 3.86582 -0.190366 0.671271 0.5 10 1.952 3.45 4.69 3.872 -0.19145 0.63587 0.5 10 1.96 3.45 4.95055 3.84103 -0.19859 0.608333 0.5 10 1.968 3.45 4.84899 3.996 -0.181847 0.559959 0.5 10 1.976 3.45 4.94923 3.94178 -0.172954 0.536418 0.5 10 1.984 3.45 4.96 3.8 -0.171766 0.464378 0.5 10 1.992 3.45 4.97 3.95252 -0.184 0.401122 0.5 10 2 3.45 3.605 3.972 0.0094713 0.382933 0.5 10 2.008 3.45 3.97263 3.9621 0.00542315 0.36425 0.5 10 2.016 3.45 3.77865 3.983 0.00868339 0.351449 0.5 10 2.024 3.45 4.15 3.992 0.0142499 0.332797 0.5 10 2.032 3.45 4.28252 3.99327 0.0115824 0.313143 0.5 10 2.04 3.45 4.835 3.9294 0.0137236 0.290673 0.5 10 2.048 3.45 4.805 3.976 -0.00600423 0.270407 0.5 10 2.056 3.45 4.945 3.964 0.0112451 0.259424 0.5 10 2.064 3.45 2.81 3.96 -0.411956 0.240182 0.5 10 2.072 3.45 2.27 3.90471 -0.474208 0.228604 0.5 10 2.08 3.45 2.305 3.94822 -0.457714 0.222201 0.5 10 2.088 3.45 1.785 3.932 -0.530008 0.215006 0.5 10 2.096 3.45 2.06 3.94 -0.502353 0.209164 0.5 10 2.104 3.45 1.69687 3.84549 -0.559228 0.201905 0.5 10 2.112 3.45 1.685 3.988 -0.566 0.199145 0.5 10 2.12 3.45 1.72243 3.932 -0.563479 0.190348 0.5 10 2.128 3.45 1.88 3.964 -0.560535 0.18778 0.5 10 2.136 3.45 2.015 3.856 -0.553493 0.175511 0.5 10 2.144 3.45 1.98 3.776 -0.542313 0.16329 0.5 10 2.152 3.45 1.995 3.724 -0.565547 0.154372 0.5 10 2.16 3.45 2.16778 3.78623 -0.554248 0.148827 0.5 10 2.168 3.45 2.185 3.996 -0.547709 0.147298 0.5 10 2.176 3.45 2.5 3.972 -0.535089 0.139422 0.5 10 2.184 3.45 2.375 3.888 -0.561858 0.130717 0.5 10 2.192 3.45 2.42216 3.78762 -0.561534 0.124195 0.5 10 2.2 3.45 2.335 3.764 -0.550789 0.112564 0.5 10 2.208 3.45 2.375 3.984 -0.552326 0.108134 0.5 10 2.216 3.45 2.61308 3.82646 -0.550072 0.104314 0.5 10 2.224 3.45 2.57398 3.76557 0.369885 0.0968704 0.5 10 2.232 3.45 2.715 3.216 -0.313267 0.0996912 0.5 10 2.24 3.45 2.53 3.57736 -0.322905 0.108955 0.5 10 2.248 3.45 2.79374 3.676 -0.318523 0.106736 0.5 10 2.256 3.45 3.755 2.972 -0.282477 0.113915 0.5 10 2.264 3.45 4.32192 3.11754 -0.262801 0.11201 0.5 10 2.272 3.45 4.12386 3.11109 -0.277053 0.115347 0.5 10 2.28 3.45 4.02689 3.26951 -0.282064 0.109793 0.5 10 2.288 3.45 3.09951 3.4 -0.33101 0.109619 0.5 10 2.296 3.45 3.235 3.528 -0.325758 0.111416 0.5 10 2.304 3.45 3.91 3.00286 -0.306836 0.110981 0.5 10 2.312 3.45 4.285 2.972 -0.308274 0.110329 0.5 10 2.32 3.45 2.25 3.86 -0.419447 0.115741 0.5 10 2.328 3.45 2.15 3.936 -0.414814 0.115372 0.5 10 2.336 3.45 3.22 3.86996 -0.363041 0.101316 0.5 10 2.344 3.45 2.54184 3.652 -0.401168 0.110704 0.5 10 2.352 3.45 2.865 3.42 -0.401318 0.114157 0.5 10 2.36 3.45 2.835 3.804 -0.402234 0.113292 0.5 10 2.368 3.45 3.135 3.764 -0.399323 0.111668 0.5 10 2.376 3.45 3.45 3.36254 -0.396587 0.115746 0.5 10 2.384 3.45 3.7333 3.44647 -0.38506 0.112919 0.5 10 2.392 3.45 4 3.51734 -0.384401 0.114558 0.5 10 2.4 3.45 4.35 3.504 -0.38664 0.116039 0.5 10 2.408 3.45 3.77 3.816 -0.387783 0.114132 0.5 10 2.416 3.45 3.87 3.96 -0.393426 0.12236 0.5 10 2.424 3.45 4.565 3.80647 -0.396798 0.120037 0.5 10 2.432 3.45 4.82779 3.64623 -0.389935 0.11919 0.5 10 2.44 3.45 4.70224 3.332 -0.366864 0.115043 0.5 10 2.448 3.45 4.325 3.74 -0.365826 0.122444 0.5 10 2.456 3.45 4.64229 3.908 -0.366 0.125646 0.5 10 2.464 3.45 4.58124 3.872 -0.364939 0.129735 0.5 10 2.472 3.45 4.86462 3.86318 -0.359461 0.132025 0.5 10 2.48 3.45 4.92 3.912 -0.353999 0.128533 0.5 10 2.488 3.45 4.87 3.916 -0.361596 0.053413 0.5 10 2.496 3.45 0.545 3.692 0.996 0.0321851 0.5 10 2.504 3.45 0.545 3.268 0.996 0.0292453 0.5 10 2.512 3.45 4.99 3.99117 -0.554761 0.0296062 0.5 10 2.52 3.45 4.21103 3.98439 -0.532655 0.029673 0.5 10 2.528 3.45 4.53 3.836 -0.530412 0.0294379 0.5 10 2.536 3.45 3.94302 3.96241 -0.511296 0.0299435 0.5 10 2.544 3.45 4.355 3.992 -0.514892 0.0306718 0.5 10 2.552 3.45 3.76 3.96 -0.494523 0.0296816 0.5 10 2.56 3.45 4.065 3.80941 -0.491747 0.0299429 0.5 10 2.568 3.45 3.645 3.968 -0.472573 0.0293749 0.5 10 2.576 3.45 3.735 3.968 -0.472178 0.0305078 0.5 10 2.584 3.45 3.69485 3.892 -0.463899 0.0304221 0.5 10 2.592 3.45 3.8 3.96944 -0.460443 0.0311143 0.5 10 2.6 3.45 3.29318 3.90914 -0.435683 0.0305475 0.5 10 2.608 3.45 3.18 3.65162 -0.418075 0.0296765 0.5 10 2.616 3.45 3.7 3.856 -0.432152 0.0298408 0.5 10 2.624 3.45 3.15238 3.944 -0.406159 0.0309816 0.5 10 2.632 3.45 3.3 3.88044 -0.407434 0.0313169 0.5 10 2.64 3.45 2.805 3.792 -0.375825 0.0299445 0.5 10 2.648 3.45 2.87 3.68747 -0.370549 0.0293912 0.5 10 2.656 3.45 1.45498 2.83085 -0.161463 0.0305208 0.5 10 2.664 3.45 1.38802 3.396 -0.139821 0.0316502 0.5 10 2.672 3.45 1.50857 2.90403 -0.163698 0.0316025 0.5 10 2.68 3.45 3.065 3.75898 -0.348889 0.0294902 0.5 10 2.688 3.45 2.19754 3.8103 -0.278226 0.0278151 0.5 10 2.696 3.45 3.21 3.98 -0.349944 0.0318286 0.5 10 2.704 3.45 1.49103 3.87282 -0.124333 0.0308155 0.5 10 2.712 3.45 1.535 3.71245 -0.130822 0.033536 0.5 10 2.72 3.45 2.175 3.59485 -0.247025 0.0303572 0.5 10 2.728 3.45 1.96542 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in=stackedSection.asc From 931c258b101358fec81bdffad0a78f49d824f77d Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 6 Mar 2021 16:02:23 -0300 Subject: [PATCH 40/63] Test of sfgetparameter for GDB #17 Generate the GDB version of sfgetparameter with 'make getparameter.x' command --- TDD/Makefile | 14 +++++++++++++- TDD/gdb_script4 | 9 +++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) create mode 100644 TDD/gdb_script4 diff --git a/TDD/Makefile b/TDD/Makefile index e9a55da..4396d54 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -18,6 +18,7 @@ DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL2= ../Mniptimecurve2.c ../raytrace.c ../grid2.c ../atela.c +SRCGETPARAM= ../Mgetparameter.c all: $(DEP) integ @@ -46,7 +47,18 @@ niptimecurve2.x: $(SRCNIPMODEL2) gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program niptimecurve2.x for GDB built" gdb -q --command=gdb_script3 --args $@ + +# Version of the sfgetparameter program for GDB (GNU debugger) +getparameter.x: setup $(SRCGETPARAM) + gcc -g $(SRCGETPARAM) -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm + @echo "Program getparameter.x for GDB built" + gdb -q --command=gdb_script4 --args $@ + +setup: + @echo "Setup input files..." + cd stereoniptomo && scons -f SConscript1 + clean: rm *.x -.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x +.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x getparameter.x diff --git a/TDD/gdb_script4 b/TDD/gdb_script4 new file mode 100644 index 0000000..709b528 --- /dev/null +++ b/TDD/gdb_script4 @@ -0,0 +1,9 @@ +# Command scripts for GNU debugger (GDB) +# +# You can call this script to set up the program getparameter.x +# for debugging. Inside GDB session call it: +# source gdb_script4 + +cd stereoniptomo +set args < rnip.rsf m0s=m0s.rsf t0s=t0s.rsf > rnips.rsf +show args From bdace3ca1a036740953e4cb2fae542a0552703f4 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 6 Mar 2021 16:27:56 -0300 Subject: [PATCH 41/63] Plot NIP sources and m0s #17 Utility python script to plot the NIP sources using depth coordinates and to plot m0s in the acquisition surface. To setup input files run 'scons -f SConscript2' and after run the viewNipSources.py script. It will load generated files and plot sources using matplotlib python library --- TDD/Makefile | 14 ++++++++-- TDD/stereoniptomo/SConscript1 | 4 +-- TDD/stereoniptomo/SConscript2 | 22 +++++++++++++++ TDD/stereoniptomo/viewNipSources.py | 43 +++++++++++++++++++++++++++++ 4 files changed, 78 insertions(+), 5 deletions(-) create mode 100644 TDD/stereoniptomo/SConscript2 create mode 100755 TDD/stereoniptomo/viewNipSources.py diff --git a/TDD/Makefile b/TDD/Makefile index 4396d54..e7ebff8 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -49,15 +49,23 @@ niptimecurve2.x: $(SRCNIPMODEL2) gdb -q --command=gdb_script3 --args $@ # Version of the sfgetparameter program for GDB (GNU debugger) -getparameter.x: setup $(SRCGETPARAM) +getparameter.x: setup1 $(SRCGETPARAM) gcc -g $(SRCGETPARAM) -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @echo "Program getparameter.x for GDB built" gdb -q --command=gdb_script4 --args $@ -setup: - @echo "Setup input files..." +setup1: + @echo "Setup input files for getparameter.x test..." cd stereoniptomo && scons -f SConscript1 +setup2: setup1 + @echo "Setup input files for NIP sources plot..." + cd stereoniptomo && scons nipsources.rsf && scons -f SConscript2 + +# Plot NIP sources using matplotlib python library +plotnip: setup2 + cd stereoniptomo && ./viewNipSources.py + clean: rm *.x diff --git a/TDD/stereoniptomo/SConscript1 b/TDD/stereoniptomo/SConscript1 index dc62882..53f0a05 100644 --- a/TDD/stereoniptomo/SConscript1 +++ b/TDD/stereoniptomo/SConscript1 @@ -22,14 +22,14 @@ Flow('t0s.asc','pick.txt', ./ascFormat.sh ${TARGETS} 1 ''') -Flow('t0s','t0s.asc','dd type=float') +Flow('t0s','t0s.asc','dd type=float form=native | put n2=1 d2=1 o2=0') Flow('m0s.asc','pick.txt', ''' ./ascFormat.sh ${TARGETS} 2 ''') -Flow('m0s','m0s.asc','dd type=float') +Flow('m0s','m0s.asc','dd type=float form=native | put n2=1 d2=1 o2=0') Flow('stackedSection','stackedSection.asc', ''' diff --git a/TDD/stereoniptomo/SConscript2 b/TDD/stereoniptomo/SConscript2 new file mode 100644 index 0000000..1a42d9a --- /dev/null +++ b/TDD/stereoniptomo/SConscript2 @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# SConscript2 (Python) +# +# Purpose: Setup input files for viewNipSources.py. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 06/03/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +from rsf.proj import * + +Flow('nipsources.txt','nipsources','disfil col=2 format="%g " number=n') + +Flow('surfsources.txt','m0s','disfil col=1 format="%g " number=n') diff --git a/TDD/stereoniptomo/viewNipSources.py b/TDD/stereoniptomo/viewNipSources.py new file mode 100755 index 0000000..6f47886 --- /dev/null +++ b/TDD/stereoniptomo/viewNipSources.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# viewFoldGraph.py (Python) +# +# Purpose: Plot NIP sources in depth model. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 27/09/2020 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +import matplotlib.pyplot as plt + +x=[] +y=[] +f = open("nipsources.txt","r") +for line in f: + ys,xs = line.split() + x.append(float(xs)) + y.append(float(ys)) + +f.close() + +z=[] +f = open("surfsources.txt","r") +for line in f: + z.append(float(line)) + +f.close() + +z0 = [0]*len(z) + +plt.xlim(0, 6) +plt.ylim(3,0) +plt.plot(list(x),list(y),'*') +plt.plot(list(z),list(z0),'v') +plt.show() From 890c85d617fbe8284c8123b246936cbf57e04787 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 6 Mar 2021 17:30:09 -0300 Subject: [PATCH 42/63] Scratch of stereoniptomo program #17 sfstereoniptomo is a program that does the forward modeling for a set if NIP sources given and normal ray angles --- TDD/Makefile | 9 ++++++++- TDD/gdb_script5 | 9 +++++++++ TDD/stereoniptomo/README.md | 24 ++++++++++++++++++++++++ tomography.c | 15 ++++++++++++--- 4 files changed, 53 insertions(+), 4 deletions(-) create mode 100644 TDD/gdb_script5 create mode 100644 TDD/stereoniptomo/README.md diff --git a/TDD/Makefile b/TDD/Makefile index e7ebff8..b350c4a 100644 --- a/TDD/Makefile +++ b/TDD/Makefile @@ -19,6 +19,7 @@ SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL2= ../Mniptimecurve2.c ../raytrace.c ../grid2.c ../atela.c SRCGETPARAM= ../Mgetparameter.c +SRCSTEREO = ../Mstereoniptomo.c ../tomography.c ../raytrace.c ../grid2.c ../atela.c all: $(DEP) integ @@ -66,7 +67,13 @@ setup2: setup1 plotnip: setup2 cd stereoniptomo && ./viewNipSources.py +# Version of the sfstereoniptomo program for GDB (GNU debugger) +stereoniptomo.x: setup1 $(SRCSTEREO) + gcc -DGDB_DEBUG -g $(SRCSTEREO) -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm + @echo "Program getparameter.x for GDB built" + gdb -q --command=gdb_script5 --args $@ + clean: rm *.x -.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x getparameter.x +.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x getparameter.x stereoniptomo.x diff --git a/TDD/gdb_script5 b/TDD/gdb_script5 new file mode 100644 index 0000000..8a61f9c --- /dev/null +++ b/TDD/gdb_script5 @@ -0,0 +1,9 @@ +# Command scripts for GNU debugger (GDB) +# +# You can call this script to set up the program stereoniptomo.x +# for debugging. Inside GDB session call it: +# source gdb_script5 + +cd stereoniptomo +set args testes.rsf +show args diff --git a/TDD/stereoniptomo/README.md b/TDD/stereoniptomo/README.md new file mode 100644 index 0000000..22278b3 --- /dev/null +++ b/TDD/stereoniptomo/README.md @@ -0,0 +1,24 @@ +This is a experiment for sfstereoniptomo test with GDB. +Run this experiment before test to generate the input. + +After that, go to the TDD directory, build the program version for +GDB with: + +```sg +~$ make stereoniptomo.x +``` + +Call this program in GDB with: + +```sh +~$ gdb stereoniptomo.x +``` + +Use the gdb\_script inside gdb session to set up the arguments +of the program: + +``` +(gdb) source gdb_script5 +``` + +You are ready to debug sfstereoniptomo with GDB. diff --git a/tomography.c b/tomography.c index 391bfb5..003db6b 100644 --- a/tomography.c +++ b/tomography.c @@ -5,9 +5,15 @@ #include "raytrace.h" #include "tomography.h" -#define DSLOW 0.04 -#define DANGLE 5.0 -#define MAX_ITERATIONS 2 +#ifndef GDB_DEBUG + #define DSLOW 0.04 + #define DANGLE 5.0 + #define MAX_ITERATIONS 2 +#else + #define DSSLOW 0.04 + #define DANGLE 5.0 + #define MAX_ITERATIONS 1 +#endif /*^*/ void updatevelmodel(float* x, float* slow, int nm, float dmis,int i){ @@ -81,6 +87,9 @@ void raystraveltimes( s[0] = x[0]; s[1] = x[1]; + #ifdef GDB_DEBUG + nr=2; + #endif for(ir=0;ir Date: Sat, 6 Mar 2021 21:44:26 -0300 Subject: [PATCH 43/63] WIP --- Mstereoniptomo.c | 115 ++++++++++++++++++++-------------- TDD/Makefile | 2 +- tomography.c | 146 +++++++++++++++++++++++-------------------- vfsacrsnh_lib.c | 158 +++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 308 insertions(+), 113 deletions(-) create mode 100644 vfsacrsnh_lib.c diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index eccfa8d..63d832a 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -7,6 +7,10 @@ Trace rays from NIP sources to acquisition surface in order to get traveltime cu #include #include #include "tomography.h" +#include "vfsacrsnh_lib.h" +#define MAX_ITERATIONS 2 +#define temp0 1 +#define c0 0.01 int main(int argc, char* argv[]) { @@ -15,32 +19,29 @@ int main(int argc, char* argv[]) float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 float** s; // NIP source position (z,x) + float** cnew; + float** ots; + float tmis0, otmis=0, deltaE, Em0, PM, temp=1, u=0; int ndim; // n1 dimension in shotsfile, should be equal 2 int nshot; // n2 dimensions in shotsfile int nm; // Number of samples in velocity grid float* a; // Normal Ray initial angle - int nr=2; // Number of rays to trace float* slow; // slowness int im; // loop counter float v; // Velocity float v0; // Near surface velocity int ns; // Number of NIP sources int is; // Loop counter for NIP sources - int ir; // Loop counter of reflection rays - int i; // Loop over iterations - float x[2]; // Ray initial position - float tmis[1]; // data misfit t vector - float dmis; - float *ts, *tr, *xs, *xr, offset, cmp; + int q; + float tmis; // data time misfit value float *m0, *t0, *RNIP, *BETA; - sf_file shots, vel, angles, timeCurve, xCurve, m0s, t0s, rnips, betas; + sf_file out, shots, vel, angles, pots, m0s, t0s, rnips, betas; sf_init(argc,argv); shots = sf_input("shotsfile"); vel = sf_input("in"); - timeCurve = sf_output("out"); - xCurve = sf_output("x"); + out = sf_output("out"); angles = sf_input("anglefile"); m0s = sf_input("m0s"); t0s = sf_input("t0s"); @@ -67,6 +68,8 @@ int main(int argc, char* argv[]) if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); s = sf_floatalloc2(ndim,nshot); sf_floatread(s[0],ndim*nshot,shots); + cnew = sf_floatalloc2(ndim,nshot); + ots = sf_floatalloc2(ndim,nshot); sf_fileclose(shots); /* Anglefile: get initial emergence angle */ @@ -106,43 +109,63 @@ int main(int argc, char* argv[]) sf_warning("n1=%d",ns); } - sf_putint(xCurve,"n1",2); + /*sf_putint(xCurve,"n1",2); sf_putint(xCurve,"n2",ns); sf_putint(timeCurve,"n1",2); - sf_putint(timeCurve,"n2",ns); - - ts = sf_floatalloc(nr); - xs = sf_floatalloc(nr); - tr = sf_floatalloc(nr); - xr = sf_floatalloc(nr); - - for(i=0; i fabs(tmis0) ){ + otmis = tmis; + /* optimized parameters matrix */ + for(is=0;is u){ + for(is=0;is*/ - float v; + //float v; int im; for(im=0;im*/ +float calculateTimeMissfit(float** s, /* NIP sources matrix */ + float v0, + float* t0, + float* m0, + float* RNIP, + float* BETA, + int *n, + float *o, + float *d, + float *slow, + float *a, + int ns) +/*< Return time missfit sum of source-NIP-receiver rays >*/ { float currentRayAngle; - int i, ir, it; - float p[2], s[2], t; - float nt=5000; + int i, is, ir, it; + float p[2], t, nrdeg; + int nt=5000, nr=2; //TODO to correct nr float dt=0.001; raytrace rt; float** traj; // Ray trajectory (z,x) + float m, h, tmis; + float xs, xr, tr, ts, *x; + + x = sf_floatalloc(2); + + for(is=0;is sx=%f sy=%f sa=%f",x[1],x[0],nrdeg); + + for(ir=0;ir0){ + t = it*dt; + if(i==0){ + ts=t; + xs=x[1]; + sf_warning("xs=%f ts=%f",xs,ts); + }else{ + tr=t; + xr=x[1]; + sf_warning("xr=%f tr=%f",xr,tr); + } + }else if(it == 0){ + t = abs(nt)*dt; + nt += 1000; + }else{ + sf_warning("=> x=%f y=%f t=%f",s[is][1],s[is][0],t); + sf_error("Bad angle, ray get to the model side/bottom"); + } - s[0] = x[0]; - s[1] = x[1]; - - #ifdef GDB_DEBUG - nr=2; - #endif - for(ir=0;ir tc=%f t=%f tmis=%f dtmis=%f\n",t,ts+tr,ts+tr-t,tmis); - it = trace_ray (rt, x, p, traj); + } /* Loop over reflection rays */ + } /* Loop over NIP sources */ - if(it>0){ - t = it*dt; - if(i==0){ - ts[ir]=t; - xs[ir]=x[1]; - sf_warning("ts=%f",ts[ir]); - }else{ - tr[ir]=t; - xr[ir]=x[1]; - sf_warning("tr=%f",tr[ir]); - } - }else if(it == 0){ - t = abs(nt)*dt; - nt += 1000; - }else{ - sf_error("Bad angle, ray get to the model side/bottom"); - } - - /* Raytrace close */ - raytrace_close(rt); - free(traj); - - x[0] = s[0]; - x[1] = s[1]; - } /* Loop over source-NIP-receiver rays */ - } /* Loop over reflection rays */ + tmis = (tmis)/(2*ns); + return tmis; } diff --git a/vfsacrsnh_lib.c b/vfsacrsnh_lib.c new file mode 100644 index 0000000..938c470 --- /dev/null +++ b/vfsacrsnh_lib.c @@ -0,0 +1,158 @@ +/* + vfsacrsnh_lib.c (c) + + Purpose: 'Mvfsacrsnh.c' library. + + Version 1.0 + + Site: http://www.dirackslounge.online + + Programer: Rodolfo A. C. Neves (Dirack) 19/09/2019 + + Email: rodolfo_profissional@hotmail.com + + License: GPL-3.0 . + +*/ + +#define MAX 2 +#define MIN 0 +#define APERTURE 2 +#define Rnip_MAX 4 +#define Rnip_MIN 0 +#define RNIP_APERTURE Rnip_MAX-Rnip_MIN +#define Rn_MAX 5 +#define Rn_MIN 0 +#define RN_APERTURE Rn_MAX-Rn_MIN +#define hMAX 50 +#define mMAX 50 +#define ITMAX 3000 +#include +#include +#include +#include +#include +/*^*/ + +#define signal(s) ((s<0)?(-1.):(1.)) +/*< Signal function >*/ +/*^*/ + +float getRandomNumberBetween0and1(){ +/*< Function to get a random number between 0 and 1 >*/ + + return (float)(rand()%1000)/1000; +} + +float getVfsaIterationTemperature(int iteration,float dampingFactor,float inicialTemperature){ +/*< Temperature function for VFSA algorithm >*/ + + return inicialTemperature*expf(-dampingFactor*pow(iteration,0.25)); + +} + +void disturbParameters(float temperature, float** disturbedParameter, float** parameter, int ns /*Number of parameters */){ +/*< Perturbar os parâmetros da iteração anterior >*/ + + float u; + float disturbance; + int is, i; + + for(is=0;is= MAX || disturbedParameter[is][i] <= MIN) { + + disturbedParameter[is][i] = (APERTURE) * getRandomNumberBetween0and1() + MIN; + + } + + sf_warning("d=%f p=%f i=%d d=%f",disturbedParameter[is][i],parameter[is][i],i,disturbance); + } + } +} + +void nonHyperbolicCRSapp(float t[2*mMAX+1][hMAX], float m0, float dm, float om, float dh, float oh, float t0, float v0, float RN, float RNIP, float BETA){ +/*< Non hyperbolic CRS approximation (FOMEL; KAZINNIK, 2013) >*/ + float m0_index=(int)(m0/dm); + float a1, a2, b2, c1, Fd, Fd1, Fd2; + int im, ih; + float m, h, mmh, mph; + float sinB=sin(BETA),cosB=cos(BETA); + + om = om+(m0_index-mMAX)*dm; + + a1=(2*sinB)/(v0); + a2=(2*cosB*cosB*t0)/(v0*RN); + b2=(2*cosB*cosB*t0)/(v0*RNIP); + c1=2*b2+a1*a1-a2; + + for (im=0; im < 2*mMAX+1; im++){ + + m=(im*dm+om)-m0; + + for(ih=0;ih*/ + + int im, ih, numSamples=0; + float amplitude=0.; + float amplitudeSampleSum=0.; + float amplitudeSquaredSampleSum=0.; + float semblance=0; + int tetai; + float teta[2*mMAX+1][hMAX]; + int m0_index_init, m0_index_end; + + m0_index_init = (int)(m0/dm)-mMAX; + m0_index_end = (int)(m0/dm)+mMAX; + + nonHyperbolicCRSapp(teta,m0,dm,om,dh,oh,t0,v0,RN,RNIP,BETA); + + for (im=m0_index_init; im < m0_index_end; im++){ + + for(ih=0;ih=0 && tetai < nt){ + amplitude = t[im][ih][tetai]; + + amplitudeSampleSum += amplitude; + + amplitudeSquaredSampleSum += (amplitude*amplitude); + } + + numSamples++; + } + + } + + if(amplitudeSampleSum==0) + return semblance=0; + else + return semblance=(amplitudeSampleSum*amplitudeSampleSum)/(numSamples*amplitudeSquaredSampleSum); + +} + From c6cf73e849a5429951cebc9d8b051c34e3960dc9 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 10 Mar 2021 16:47:57 -0300 Subject: [PATCH 44/63] Experiment of optmization of the nipsources #17 Coordinates of the nipsources are optimized using VFSA and summation of time misfit root mean squared criteria --- Mstereoniptomo.c | 27 +++++++++++++++++---------- TDD/stereoniptomo/SConscript3 | 28 ++++++++++++++++++++++++++++ tomography.c | 10 +++++----- vfsacrsnh_lib.c | 10 +++++----- 4 files changed, 55 insertions(+), 20 deletions(-) create mode 100644 TDD/stereoniptomo/SConscript3 diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index 63d832a..0729b0b 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -8,7 +8,7 @@ Trace rays from NIP sources to acquisition surface in order to get traveltime cu #include #include "tomography.h" #include "vfsacrsnh_lib.h" -#define MAX_ITERATIONS 2 +#define MAX_ITERATIONS 20 #define temp0 1 #define c0 0.01 @@ -21,7 +21,7 @@ int main(int argc, char* argv[]) float** s; // NIP source position (z,x) float** cnew; float** ots; - float tmis0, otmis=0, deltaE, Em0, PM, temp=1, u=0; + float tmis0=100, otmis=0, deltaE, Em0=0, PM, temp=1, u=0; int ndim; // n1 dimension in shotsfile, should be equal 2 int nshot; // n2 dimensions in shotsfile int nm; // Number of samples in velocity grid @@ -127,19 +127,19 @@ int main(int argc, char* argv[]) /* Calculate time missfit through forward modeling */ tmis=calculateTimeMissfit(cnew,v0,t0,m0,RNIP,BETA,n,o,d,slow,a,ns); - if(fabs(tmis) > fabs(tmis0) ){ - otmis = tmis; + if(fabs(tmis) < fabs(tmis0) ){ + otmis = fabs(tmis); /* optimized parameters matrix */ for(is=0;is u){ for(is=0;is. + +from rsf.proj import * + +txtout='testes.txt' +ascout='testes.asc' +rsffile='testes' +Flow(txtout,rsffile,'disfil col=2 format="%g " number=n') + +#Flow('x.txt',txtout,'') + +#Flow('y.txt',txtout,'') + diff --git a/tomography.c b/tomography.c index 28b6404..da3b1de 100644 --- a/tomography.c +++ b/tomography.c @@ -90,7 +90,7 @@ float calculateTimeMissfit(float** s, /* NIP sources matrix */ x[0]=s[is][0]; x[1]=s[is][1]; nrdeg = a[is]; // TODO is in degree? - sf_warning("=> sx=%f sy=%f sa=%f",x[1],x[0],nrdeg); + //sf_warning("=> sx=%f sy=%f sa=%f",x[1],x[0],nrdeg); for(ir=0;ir tc=%f t=%f tmis=%f dtmis=%f\n",t,ts+tr,ts+tr-t,tmis); + //sf_warning("=> tc=%f t=%f tmis=%f dtmis=%f\n",t,ts+tr,ts+tr-t,tmis); } /* Loop over reflection rays */ } /* Loop over NIP sources */ - tmis = (tmis)/(2*ns); + tmis = (tmis*tmis)/(2*ns); return tmis; } diff --git a/vfsacrsnh_lib.c b/vfsacrsnh_lib.c index 938c470..c6d1361 100644 --- a/vfsacrsnh_lib.c +++ b/vfsacrsnh_lib.c @@ -15,9 +15,9 @@ */ -#define MAX 2 +#define MAX 3 #define MIN 0 -#define APERTURE 2 +#define APERTURE 0.5 #define Rnip_MAX 4 #define Rnip_MIN 0 #define RNIP_APERTURE Rnip_MAX-Rnip_MIN @@ -66,15 +66,15 @@ void disturbParameters(float temperature, float** disturbedParameter, float** pa disturbance = signal(u - 0.5) * temperature * (pow( (1+temperature),fabs(2*u-1) )-1); - disturbedParameter[is][i] = parameter[is][i] + (disturbance/10) * (APERTURE); + disturbedParameter[is][i] = parameter[is][i] + (disturbance/100) * (APERTURE); if (disturbedParameter[is][i] >= MAX || disturbedParameter[is][i] <= MIN) { - disturbedParameter[is][i] = (APERTURE) * getRandomNumberBetween0and1() + MIN; + disturbedParameter[is][i] = (APERTURE) * getRandomNumberBetween0and1() + parameter[is][i]; } - sf_warning("d=%f p=%f i=%d d=%f",disturbedParameter[is][i],parameter[is][i],i,disturbance); + //sf_warning("d=%f p=%f i=%d d=%f",disturbedParameter[is][i],parameter[is][i],i,disturbance/100); } } } From 784ce6369ed92b6ca6448249e28afb868e588aad Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 10 Mar 2021 17:09:25 -0300 Subject: [PATCH 45/63] Plot the true interface with the nip sources #17 Plot the true interface using the program viewNipSources.py --- TDD/stereoniptomo/modelin.txt | 595 ++++++++++++++++++++++++++++ TDD/stereoniptomo/viewNipSources.py | 12 + 2 files changed, 607 insertions(+) create mode 100644 TDD/stereoniptomo/modelin.txt diff --git a/TDD/stereoniptomo/modelin.txt b/TDD/stereoniptomo/modelin.txt new file mode 100644 index 0000000..2c39793 --- /dev/null +++ b/TDD/stereoniptomo/modelin.txt @@ -0,0 +1,595 @@ +1.3 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+1.26174 +1.2629 +1.26406 +1.26522 +1.26639 +1.26756 +1.26874 +1.26991 +1.27109 +1.27227 +1.27346 +1.27464 +1.27583 +1.27702 +1.27822 +1.27941 +1.28061 +1.28181 +1.28301 +1.28421 +1.28542 +1.28662 +1.28783 +1.28903 +1.29024 +1.29145 +1.29266 +1.29387 +1.29508 +1.29629 +1.2975 +1.29872 +1.29993 diff --git a/TDD/stereoniptomo/viewNipSources.py b/TDD/stereoniptomo/viewNipSources.py index 6f47886..3d66a56 100755 --- a/TDD/stereoniptomo/viewNipSources.py +++ b/TDD/stereoniptomo/viewNipSources.py @@ -36,8 +36,20 @@ z0 = [0]*len(z) +m=[] +m0=[] +i=0 +f = open("modelin.txt","r") +for line in f: + m.append(float(line)) + m0.append(0.01*i) + i=i+1 + +f.close() + plt.xlim(0, 6) plt.ylim(3,0) plt.plot(list(x),list(y),'*') plt.plot(list(z),list(z0),'v') +plt.plot(list(m0),list(m),'-') plt.show() From 429275c14d425a799914a77b607cbef86619b0aa Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 10 Mar 2021 20:59:53 -0300 Subject: [PATCH 46/63] Correct ray tracing time in sfnipmodsetup #17 The program sfnipmodsetup uses t0/2 instead of t0 and the casting is corrected --- Mnipmodsetup.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Mnipmodsetup.c b/Mnipmodsetup.c index 620181e..0a12a29 100644 --- a/Mnipmodsetup.c +++ b/Mnipmodsetup.c @@ -124,7 +124,8 @@ int main(int argc, char* argv[]) for(ir=0; ir Date: Wed, 10 Mar 2021 21:02:22 -0300 Subject: [PATCH 47/63] Build nipsources coordinates optimization #17 Use VFSA optimization to locate nipsources. Do the process for each nipsource at time --- Mstereoniptomo.c | 103 ++++++++++++++++++++++++++--------------------- tomography.c | 30 +++++++------- vfsacrsnh_lib.c | 14 +++---- 3 files changed, 78 insertions(+), 69 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index 0729b0b..d30b2c8 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -8,8 +8,8 @@ Trace rays from NIP sources to acquisition surface in order to get traveltime cu #include #include "tomography.h" #include "vfsacrsnh_lib.h" -#define MAX_ITERATIONS 20 -#define temp0 1 +#define MAX_ITERATIONS 50 +#define temp0 10 #define c0 0.01 int main(int argc, char* argv[]) @@ -19,7 +19,7 @@ int main(int argc, char* argv[]) float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 float** s; // NIP source position (z,x) - float** cnew; + float* cnew; float** ots; float tmis0=100, otmis=0, deltaE, Em0=0, PM, temp=1, u=0; int ndim; // n1 dimension in shotsfile, should be equal 2 @@ -35,6 +35,7 @@ int main(int argc, char* argv[]) int q; float tmis; // data time misfit value float *m0, *t0, *RNIP, *BETA; + float x[2]; sf_file out, shots, vel, angles, pots, m0s, t0s, rnips, betas; sf_init(argc,argv); @@ -68,7 +69,7 @@ int main(int argc, char* argv[]) if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); s = sf_floatalloc2(ndim,nshot); sf_floatread(s[0],ndim*nshot,shots); - cnew = sf_floatalloc2(ndim,nshot); + cnew = sf_floatalloc(ndim); ots = sf_floatalloc2(ndim,nshot); sf_fileclose(shots); @@ -114,56 +115,64 @@ int main(int argc, char* argv[]) sf_putint(timeCurve,"n1",2); sf_putint(timeCurve,"n2",ns);*/ - for (q=0; q u){ - for(is=0;is u){ + //for(is=0;is*/ { float currentRayAngle; - int i, is, ir, it; + int i, ir, it; float p[2], t, nrdeg; - int nt=5000, nr=2; //TODO to correct nr + int nt=5000, nr=4; //TODO to correct nr float dt=0.001; raytrace rt; float** traj; // Ray trajectory (z,x) - float m, h, tmis; + float m, h, tmis=0; float xs, xr, tr, ts, *x; x = sf_floatalloc(2); - for(is=0;is sx=%f sy=%f sa=%f",x[1],x[0],nrdeg); @@ -125,7 +125,7 @@ float calculateTimeMissfit(float** s, /* NIP sources matrix */ t = abs(nt)*dt; nt += 1000; }else{ - sf_warning("=> x=%f y=%f t=%f",s[is][1],s[is][0],t); + sf_warning("=> x=%f y=%f t=%f",s[1],s[0],t); sf_error("Bad angle, ray get to the model side/bottom"); } @@ -133,8 +133,8 @@ float calculateTimeMissfit(float** s, /* NIP sources matrix */ raytrace_close(rt); free(traj); - x[0] = s[is][0]; - x[1] = s[is][1]; + x[0] = s[0]; + x[1] = s[1]; } /* Loop over source-NIP-receiver rays */ m = (xr+xs)/2.; @@ -144,8 +144,8 @@ float calculateTimeMissfit(float** s, /* NIP sources matrix */ //sf_warning("=> tc=%f t=%f tmis=%f dtmis=%f\n",t,ts+tr,ts+tr-t,tmis); } /* Loop over reflection rays */ - } /* Loop over NIP sources */ + //} /* Loop over NIP sources */ - tmis = (tmis*tmis)/(2*ns); + tmis = (tmis*tmis)/(2*nr); return tmis; } diff --git a/vfsacrsnh_lib.c b/vfsacrsnh_lib.c index c6d1361..06ea73b 100644 --- a/vfsacrsnh_lib.c +++ b/vfsacrsnh_lib.c @@ -51,14 +51,14 @@ float getVfsaIterationTemperature(int iteration,float dampingFactor,float inicia } -void disturbParameters(float temperature, float** disturbedParameter, float** parameter, int ns /*Number of parameters */){ +void disturbParameters(float temperature, float* disturbedParameter, float* parameter, int ns /*Number of parameters */){ /*< Perturbar os parâmetros da iteração anterior >*/ float u; float disturbance; - int is, i; + int i; - for(is=0;is= MAX || disturbedParameter[is][i] <= MIN) { + if (disturbedParameter[i] >= MAX || disturbedParameter[i] <= MIN) { - disturbedParameter[is][i] = (APERTURE) * getRandomNumberBetween0and1() + parameter[is][i]; + disturbedParameter[i] = (APERTURE) * getRandomNumberBetween0and1() + parameter[i]; } //sf_warning("d=%f p=%f i=%d d=%f",disturbedParameter[is][i],parameter[is][i],i,disturbance/100); } - } + //} } void nonHyperbolicCRSapp(float t[2*mMAX+1][hMAX], float m0, float dm, float om, float dh, float oh, float t0, float v0, float RN, float RNIP, float BETA){ From c26af5c160c43a2ab6c2d54c6a5f86b489026cbe Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 23 Mar 2021 16:34:25 -0300 Subject: [PATCH 48/63] Stereoniptomo experiment for a two interface reflector #17 Use Makefile to run the experiment: 'make help' to get help. This experiment uses a two interfaces model with sfstereoniptomo --- TDD/stereoniptomo/Makefile | 47 + TDD/stereoniptomo/SConscript1 | 44 +- TDD/stereoniptomo/SConscript2 | 4 +- TDD/stereoniptomo/SConscript3 | 28 - TDD/stereoniptomo/SConstruct | 14 +- TDD/stereoniptomo/{ => model1}/modelin.txt | 0 TDD/stereoniptomo/model2/parametersCube.asc | 48595 +++++++++++++++++ TDD/stereoniptomo/model2/stackedSection.asc | 6076 +++ TDD/stereoniptomo/modelin1.txt | 595 + TDD/stereoniptomo/modelin2.txt | 594 + TDD/stereoniptomo/parametersCube.asc | 48596 +++++++++++++++++- TDD/stereoniptomo/pick.txt | 34 +- TDD/stereoniptomo/stackedSection.asc | 6077 ++- TDD/stereoniptomo/viewNipSources.py | 35 +- 14 files changed, 110651 insertions(+), 88 deletions(-) create mode 100644 TDD/stereoniptomo/Makefile delete mode 100644 TDD/stereoniptomo/SConscript3 rename TDD/stereoniptomo/{ => model1}/modelin.txt (100%) create mode 100644 TDD/stereoniptomo/model2/parametersCube.asc create mode 100644 TDD/stereoniptomo/model2/stackedSection.asc create mode 100644 TDD/stereoniptomo/modelin1.txt create mode 100644 TDD/stereoniptomo/modelin2.txt diff --git a/TDD/stereoniptomo/Makefile b/TDD/stereoniptomo/Makefile new file mode 100644 index 0000000..1380dee --- /dev/null +++ b/TDD/stereoniptomo/Makefile @@ -0,0 +1,47 @@ +# Makefile +# +# Purpose: Run and control SConscripts and do model backup. +# +# Site: https://dirack.github.io +# +# Version 1.0 +# +# Programer: Rodolfo A C Neves (Dirack) 23/03/2021 +# +# Email: rodolfo_profissional@hotmail.com +# +# License: GPL-3.0 . + +# model files: Stacked section and parameters cube from CRE stack +PARCUBE=model2/parametersCube.rsf +STKSEC=model2/stackedSection.rsf + +# Extract parameters from stacked section +NP=$(shell ( $(PARCUBE:.rsf=.asc) + echo "n1=8 d1=1 o1=0 n2=$(NP) d2=1 o2=0 in=$(notdir $(PARCUBE:.rsf=.asc)) data_format=ascii_float" >> $(PARCUBE:.rsf=.asc) + <$(STKSEC) sfdisfil format="%f " number=n > $(STKSEC:.rsf=.asc) + echo "n1=$(N1) d1=$(D1) o1=$(O1) n2=$(N2) d2=$(D2) o2=$(O2) in=$(notdir $(STKSEC:.rsf=.asc)) data_format=ascii_float" >> $(STKSEC:.rsf=.asc) diff --git a/TDD/stereoniptomo/SConscript1 b/TDD/stereoniptomo/SConscript1 index 53f0a05..f24e450 100644 --- a/TDD/stereoniptomo/SConscript1 +++ b/TDD/stereoniptomo/SConscript1 @@ -17,6 +17,14 @@ from rsf.proj import * +np = int(ARGUMENTS.get('np',0)) +n1 = int(ARGUMENTS.get('n1',0)) +d1 = float(ARGUMENTS.get('d1',0.0)) +o1 = float(ARGUMENTS.get('o1',0.0)) +n2 = float(ARGUMENTS.get('n2',0)) +d2 = float(ARGUMENTS.get('d2',0.0)) +o2 = float(ARGUMENTS.get('o2',0.0)) + Flow('t0s.asc','pick.txt', ''' ./ascFormat.sh ${TARGETS} 1 @@ -34,43 +42,43 @@ Flow('m0s','m0s.asc','dd type=float form=native | put n2=1 d2=1 o2=0') Flow('stackedSection','stackedSection.asc', ''' dd type=float form=native | - put n1=251 d1=0.008 o1=1 label1=t0 unit1=s - n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km - ''') + put n1=%d d1=%g o1=%g label1=t0 unit1=s + n2=%d d2=%g o2=%g label2=m0 unit2=Km + '''%(n1,d1,o1,n2,d2,o2)) Flow('parametersCube','parametersCube.asc', ''' dd type=float form=native | put n1=8 d1=1 o1=0 label1=parameters - n2=20331 d2=1 o2=0 label2="(t0,m0) index" n3=1 d3=1 o3=0 - ''') + n2=%d d2=1 o2=0 label2="(t0,m0) index" n3=1 d3=1 o3=0 + '''%(np)) Flow('rnip','parametersCube', ''' window n1=1 f1=1 | - put n1=251 d1=0.008 o1=1 label1=t0 unit1=s - n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km - ''') + put n1=%d d1=%g o1=%g label1=t0 unit1=s + n2=%d d2=%g o2=%g label2=m0 unit2=Km + '''%(n1,d1,o1,n2,d2,o2)) Flow('rnips',['rnip','t0s','m0s'], ''' getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} | - put n1=26 d1=0.008 o1=1 label1=t0 unit1=s - n2=1 d2=0.025 o2=1.5 label2=m0 unit2=Km - ''') + put n1=%d d1=%g o1=%g label1=t0 unit1=s + n2=%d d2=%g o2=%g label2=m0 unit2=Km + '''%(n1,d1,o1,n2,d2,o2)) Flow('beta','parametersCube', ''' window n1=1 f1=2 | - put n1=251 d1=0.008 o1=1 label1=t0 unit1=s - n2=81 d2=0.025 o2=1.5 label2=m0 unit2=Km - ''') + put n1=%d d1=%g o1=%g label1=t0 unit1=s + n2=%d d2=%g o2=%g label2=m0 unit2=Km + '''%(n1,d1,o1,n2,d2,o2)) Flow('betas',['beta','t0s','m0s'], ''' getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} | - put n1=26 d1=0.008 o1=1 label1=t0 unit1=s - n2=1 d2=0.025 o2=1.5 label2=m0 unit2=Km - ''') - + put n1=%d d1=%g o1=%g label1=t0 unit1=s + n2=%d d2=%g o2=%g label2=m0 unit2=Km + '''%(n1,d1,o1,n2,d2,o2)) +End() diff --git a/TDD/stereoniptomo/SConscript2 b/TDD/stereoniptomo/SConscript2 index 1a42d9a..3c7b1fe 100644 --- a/TDD/stereoniptomo/SConscript2 +++ b/TDD/stereoniptomo/SConscript2 @@ -9,7 +9,7 @@ # # Version 1.0 # -# Programer: Rodolfo A C Neves (Dirack) 06/03/2021 +# Programer: Rodolfo A C Neves (Dirack) 10/03/2021 # # Email: rodolfo_profissional@hotmail.com # @@ -19,4 +19,6 @@ from rsf.proj import * Flow('nipsources.txt','nipsources','disfil col=2 format="%g " number=n') +Flow('result.txt','tCoordinates','disfil col=2 format="%g " number=n') + Flow('surfsources.txt','m0s','disfil col=1 format="%g " number=n') diff --git a/TDD/stereoniptomo/SConscript3 b/TDD/stereoniptomo/SConscript3 deleted file mode 100644 index 115380e..0000000 --- a/TDD/stereoniptomo/SConscript3 +++ /dev/null @@ -1,28 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConscript3 (Python) -# -# Purpose: Setup input files for viewNipSources.py. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 10/03/2021 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * - -txtout='testes.txt' -ascout='testes.asc' -rsffile='testes' -Flow(txtout,rsffile,'disfil col=2 format="%g " number=n') - -#Flow('x.txt',txtout,'') - -#Flow('y.txt',txtout,'') - diff --git a/TDD/stereoniptomo/SConstruct b/TDD/stereoniptomo/SConstruct index ce3cc39..3726a55 100644 --- a/TDD/stereoniptomo/SConstruct +++ b/TDD/stereoniptomo/SConstruct @@ -18,14 +18,14 @@ from rsf.proj import * -# create a model -amp=1 -rad=1 +ns = int(ARGUMENTS.get('ns',1)) # Constant velocity model for test Flow('model',None, ''' - spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | + spike n1=301 d1=0.02 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | + math output="x1*0.05+input" | + smooth | put label1=Depth unit1=km label2=Position unit2=km ''' ) @@ -33,7 +33,7 @@ Flow('model',None, Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') # Generate shotsfile and anglefile for NIP sources -Flow('s2',None,'spike n1=26 d1=1 o1=0 n2=1 o2=1 d2=0 mag=0') +Flow('s2',None,'spike n1=%d d1=1 o1=0 n2=1 o2=1 d2=0 mag=0'%(ns)) Flow('shotsfile',['s2','m0s'],'rcat ${SOURCES[1]} axis=2 | transp') @@ -49,8 +49,8 @@ Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], ''') # Rotate ray angles in NIP sources positions in 180 degrees -Flow('angles','nipangles','math output="input-180" | put n1=26 d1=1 o1=1') -Flow('nipsources','ray','dd type=float | put n1=2 n2=26') +Flow('angles','nipangles','math output="input-180" | put n1=%d d1=1 o1=1'%(ns)) +Flow('nipsources','ray','dd type=float | put n1=2 n2=%d'%(ns)) # forward modeling # do a ray-tracing from NIP sources to acquisition surface and measure traveltime diff --git a/TDD/stereoniptomo/modelin.txt b/TDD/stereoniptomo/model1/modelin.txt similarity index 100% rename from TDD/stereoniptomo/modelin.txt rename to TDD/stereoniptomo/model1/modelin.txt diff --git a/TDD/stereoniptomo/model2/parametersCube.asc b/TDD/stereoniptomo/model2/parametersCube.asc new file mode 100644 index 0000000..3d3115f --- /dev/null +++ b/TDD/stereoniptomo/model2/parametersCube.asc @@ -0,0 +1,48595 @@ +4.974738 3.739790 0.060842 0.115575 0.500000 +10.000000 1.000000 1.500000 4.530000 3.988000 +0.056358 0.135594 0.500000 10.000000 1.008000 +1.500000 4.560609 3.964000 0.047669 0.148110 +0.500000 10.000000 1.016000 1.500000 4.931919 +3.940875 0.052117 0.142628 0.500000 10.000000 +1.024000 1.500000 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2.125 1.1925 3.39882 0.848272 0.0405073 0.5 10 2.616 2.125 1.11 3.19911 0.872714 0.0334529 0.5 10 2.624 2.125 1.56 3.564 0.796184 0.0357061 0.5 10 2.632 2.125 1.67 3.516 0.789556 0.0362487 0.5 10 2.64 2.125 0.99 3.412 0.912071 0.0374027 0.5 10 2.648 2.125 0.967624 3.3741 0.919028 0.0359153 0.5 10 2.656 2.125 0.874089 3.08548 0.949015 0.0350863 0.5 10 2.664 2.125 1.375 3.212 0.850133 0.0316149 0.5 10 2.672 2.125 0.78 3.724 0.980754 0.032211 0.5 10 2.68 2.125 1.25 3.428 0.873812 0.0258699 0.5 10 2.688 2.125 1.085 1.96677 0.920008 0.0347187 0.5 10 2.696 2.125 0.9 3.53861 0.965366 0.0352249 0.5 10 2.704 2.125 0.99 3.628 0.94475 0.032489 0.5 10 2.712 2.125 1.01733 2.67766 0.944883 0.0320641 0.5 10 2.72 2.125 0.95 3.6765 0.964825 0.0285998 0.5 10 2.728 2.125 1.09 3.64383 0.938 0.0304061 0.5 10 2.736 2.125 0.875 2.46 0.99 0.0288182 0.5 10 2.744 2.125 0.986005 3.5688 0.961419 0.0213529 0.5 10 2.752 2.125 1.09 3.868 0.95122 0.0286324 0.5 10 2.76 2.125 1.665 3.656 -0.0683825 0.0258388 0.5 10 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1.392 2.15 4.96 3.73629 -0.0545535 0.254615 0.5 10 1.4 2.15 4.34 3.844 -0.0778755 0.244929 0.5 10 1.408 2.15 4.89653 3.86122 -0.0860045 0.239761 0.5 10 1.416 2.15 4.69503 3.73202 -0.0840191 0.226441 0.5 10 1.424 2.15 4.98216 3.85373 -0.0875154 0.209574 0.5 10 1.432 2.15 4.96 3.996 -0.0746035 0.175809 0.5 10 1.44 2.15 3.15166 3 -0.091021 0.0421674 0.5 10 1.448 2.15 4.82175 3.85671 0.00929228 0.037436 0.5 10 1.456 2.15 3.17083 3.34349 -0.0811509 0.0496429 0.5 10 1.464 2.15 3.17396 3.38 -0.0769121 0.0516501 0.5 10 1.472 2.15 3.27797 3.76637 -0.0740172 0.0521428 0.5 10 1.48 2.15 3.43128 3.756 -0.0788539 0.0501957 0.5 10 1.488 2.15 3.48 3.804 -0.0723433 0.0566711 0.5 10 1.496 2.15 3.725 3.388 -0.0844978 0.0513406 0.5 10 1.504 2.15 3.78788 3.5623 -0.0819765 0.0574828 0.5 10 1.512 2.15 4.115 3.92 -0.0689278 0.0473802 0.5 10 1.52 2.15 4.29 3.588 -0.0859243 0.0551352 0.5 10 1.528 2.15 4.23554 3.936 -0.0759048 0.0617616 0.5 10 1.536 2.15 4.41 3.99533 -0.0771639 0.0633678 0.5 10 1.544 2.15 4.52052 3.80367 -0.0758931 0.0597894 0.5 10 1.552 2.15 4.605 3.852 -0.0811143 0.0386195 0.5 10 1.56 2.15 4.92 3.828 -0.0819992 0.0402491 0.5 10 1.568 2.15 4.85389 3.91286 -0.0668999 0.0115347 0.5 10 1.576 2.15 4.17276 3.73719 -0.0766615 0.00798167 0.5 10 1.584 2.15 4.25 3.984 -0.0743073 0.00875525 0.5 10 1.592 2.15 4.65 3.428 -0.0759008 0.00600766 0.5 10 1.6 2.15 4.58936 3.90749 -0.072 0.00789436 0.5 10 1.608 2.15 4.965 3.94513 -0.0790561 0.00705913 0.5 10 1.616 2.15 4.89651 3.98521 -0.0693877 0.0066722 0.5 10 1.624 2.15 3.485 3.704 0.02595 0.00522332 0.5 10 1.632 2.15 2.89858 3.844 0.0664068 0.00510932 0.5 10 1.64 2.15 3.48 3.94 0.0347638 0.00533386 0.5 10 1.648 2.15 3.555 3.936 0.0381024 0.00547897 0.5 10 1.656 2.15 2.93 3.996 0.0779999 0.00546101 0.5 10 1.664 2.15 2.61155 3.75324 0.114259 0.00496202 0.5 10 1.672 2.15 2.745 3.856 0.107494 0.00533598 0.5 10 1.68 2.15 2.55051 3.716 0.134908 0.00500382 0.5 10 1.688 2.15 3.30979 3.96783 0.0828818 0.00535802 0.5 10 1.696 2.15 4.255 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4.9417 3.32936 -0.234235 0.00440102 0.5 10 1.864 2.15 4.81126 3.344 -0.252329 0.00510845 0.5 10 1.872 2.15 4.83 3.48 -0.262158 0.00482106 0.5 10 1.88 2.15 0.0326 3.728 -0.0301554 0.00509217 0.5 10 1.888 2.15 0.005 3.792 -0.00584871 0.00816361 0.5 10 1.896 2.15 0.04 3.94 -0.00137476 0.00902796 0.5 10 1.904 2.15 0.13 3.98639 -0.0785695 0.0105947 0.5 10 1.912 2.15 4.845 3.992 -0.143499 0.0155371 0.5 10 1.92 2.15 4.285 3.988 -0.126245 0.0253849 0.5 10 1.928 2.15 4.85026 3.99621 -0.111186 0.0395777 0.5 10 1.936 2.15 4.755 3.968 -0.157436 0.0424321 0.5 10 1.944 2.15 4.93883 3.89907 -0.191007 0.0404489 0.5 10 1.952 2.15 4.755 3.98 -0.228 0.0409111 0.5 10 1.96 2.15 4.87 3.924 -0.236348 0.0383137 0.5 10 1.968 2.15 4.83686 3.97349 -0.264092 0.0396086 0.5 10 1.976 2.15 4.855 3.92862 -0.131321 0.167745 0.5 10 1.984 2.15 4.665 3.956 -0.156365 0.21619 0.5 10 1.992 2.15 4.805 3.996 -0.188328 0.219648 0.5 10 2 2.15 4.39183 3.98919 -0.226571 0.219974 0.5 10 2.008 2.15 4.60367 3.90694 -0.248752 0.219014 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4.52668 3.69735 -0.0620737 0.334036 0.5 10 1.12 2.25 4.56879 3.75372 -0.074281 0.338889 0.5 10 1.128 2.25 4.82445 3.97556 -0.07556 0.335422 0.5 10 1.136 2.25 4.99 3.744 -0.215804 0.338847 0.5 10 1.144 2.25 4.93939 3.5275 -0.240401 0.346134 0.5 10 1.152 2.25 4.725 3.744 -0.252401 0.334278 0.5 10 1.16 2.25 4.625 3.843 0.0320517 0.335477 0.5 10 1.168 2.25 4.6268 3.7 0.0552761 0.330512 0.5 10 1.176 2.25 4.69596 3.996 0.0779228 0.323982 0.5 10 1.184 2.25 4.87134 3.95793 0.0723158 0.321087 0.5 10 1.192 2.25 4.95273 3.904 -0.146265 0.32161 0.5 10 1.2 2.25 4.78558 3.764 -0.0343995 0.319744 0.5 10 1.208 2.25 4.965 3.848 -0.03 0.316498 0.5 10 1.216 2.25 4.83871 3.99329 -0.0349908 0.321413 0.5 10 1.224 2.25 4 3.99332 -0.0813825 0.307379 0.5 10 1.232 2.25 4.515 3.888 -0.061689 0.294355 0.5 10 1.24 2.25 4.72507 3.93511 0.000356314 0.289067 0.5 10 1.248 2.25 4.995 3.7 0.0277188 0.275438 0.5 10 1.256 2.25 4.62932 3.73656 0.0179978 0.271597 0.5 10 1.264 2.25 4.935 2.52 -0.109399 0.269124 0.5 10 1.272 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2.86 3.332 -0.0953832 0.0486242 0.5 10 1.44 2.25 3.08836 2.95069 -0.106184 0.041691 0.5 10 1.448 2.25 3.05556 3.38445 -0.0966896 0.0498241 0.5 10 1.456 2.25 3.205 3.236 -0.0995608 0.0494299 0.5 10 1.464 2.25 3.21 3.612 -0.0942364 0.0518497 0.5 10 1.472 2.25 3.51656 3.264 -0.103197 0.0481811 0.5 10 1.48 2.25 3.53 3.592 -0.0913517 0.049791 0.5 10 1.488 2.25 3.47 3.99519 -0.0921047 0.0534391 0.5 10 1.496 2.25 3.79748 3.448 -0.0965149 0.0539726 0.5 10 1.504 2.25 3.8 3.568 -0.093592 0.0560568 0.5 10 1.512 2.25 4.15 3.488 -0.0985647 0.0553457 0.5 10 1.52 2.25 4.25 3.868 -0.0971924 0.0527869 0.5 10 1.528 2.25 4.535 3.704 -0.0974357 0.0543383 0.5 10 1.536 2.25 4.46 3.89205 -0.0906233 0.0626577 0.5 10 1.544 2.25 4.66 3.924 -0.0946627 0.0640095 0.5 10 1.552 2.25 4.91522 3.78417 -0.0863135 0.0480841 0.5 10 1.56 2.25 4.76994 3.92762 -0.0932868 0.0421206 0.5 10 1.568 2.25 4.79186 3.843 -0.0886572 0.0118343 0.5 10 1.576 2.25 4.265 3.74 -0.0864586 0.00788557 0.5 10 1.584 2.25 4.2956 3.836 -0.0914743 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1.744 2.25 2.72129 3.992 0.154619 0.00491555 0.5 10 1.752 2.25 4.03 0.008 -0.836 0.00472445 0.5 10 1.76 2.25 4.25 0.036 -0.443822 0.00396912 0.5 10 1.768 2.25 1.55709 3.984 0.344724 0.00404547 0.5 10 1.776 2.25 3.92044 3.96723 0.105679 0.00362235 0.5 10 1.784 2.25 2.815 0.032 -0.658226 0.00414966 0.5 10 1.792 2.25 4.665 0.012 0.548405 0.00397804 0.5 10 1.8 2.25 3.815 0.016 0.32 0.00396603 0.5 10 1.808 2.25 4.93674 2.95586 -0.213032 0.00363758 0.5 10 1.816 2.25 4.85 3.648 -0.265814 0.00429596 0.5 10 1.824 2.25 4.94 3.824 -0.270842 0.00478196 0.5 10 1.832 2.25 4.83 0.024 -0.417786 0.00548364 0.5 10 1.84 2.25 4.92 3.208 -0.237196 0.00448495 0.5 10 1.848 2.25 4.97 3.468 -0.245333 0.00542661 0.5 10 1.856 2.25 0.005 3.75012 -0.216904 0.00549825 0.5 10 1.864 2.25 4.845 3.588 -0.268 0.00605489 0.5 10 1.872 2.25 0.02 3.78 0.0995197 0.00763068 0.5 10 1.88 2.25 0.005 3.956 -0.115701 0.0111276 0.5 10 1.888 2.25 0.035 3.968 0.033728 0.0125867 0.5 10 1.896 2.25 4.96065 3.964 -0.139274 0.019933 0.5 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2.216 2.25 4.71 3.94 0.287198 0.149731 0.5 10 2.224 2.25 4.90201 3.92161 0.294041 0.140202 0.5 10 2.232 2.25 4.7 3.95308 0.298585 0.140945 0.5 10 2.24 2.25 4.075 3.88 0.324886 0.138203 0.5 10 2.248 2.25 3.72908 3.96726 0.34 0.133945 0.5 10 2.256 2.25 4.71764 3.89003 0.321517 0.137288 0.5 10 2.264 2.25 4.97112 3.87828 0.322862 0.134025 0.5 10 2.272 2.25 4.50358 3.78936 0.338943 0.128405 0.5 10 2.28 2.25 4.36149 3.98519 0.349887 0.131235 0.5 10 2.288 2.25 4.215 3.96 0.36 0.127563 0.5 10 2.296 2.25 4.87 3.956 0.34922 0.124167 0.5 10 2.304 2.25 3.06 3.956 0.418766 0.119673 0.5 10 2.312 2.25 4.02844 3.968 0.384806 0.12004 0.5 10 2.32 2.25 2.925 3.87134 0.440945 0.118129 0.5 10 2.328 2.25 3.16514 3.26567 0.429683 0.116077 0.5 10 2.336 2.25 3.34 3.34 0.432536 0.118007 0.5 10 2.344 2.25 3.415 3.384 0.434628 0.117034 0.5 10 2.352 2.25 3.48 3.92374 0.431527 0.117592 0.5 10 2.36 2.25 3.725 3.90046 0.430113 0.118983 0.5 10 2.368 2.25 3.94617 3.86094 0.433202 0.119586 0.5 10 2.376 2.25 4.275 3.552 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1.864 2.45 4.655 3.96908 -0.143531 0.0439768 0.5 10 1.872 2.45 4.93737 3.992 -0.184764 0.0574259 0.5 10 1.88 2.45 4.775 3.996 -0.123447 0.0579292 0.5 10 1.888 2.45 3.87196 3.99357 -0.0891342 0.0531978 0.5 10 1.896 2.45 4.91 3.976 -0.0667838 0.0513231 0.5 10 1.904 2.45 4.68933 3.98347 -0.0526765 0.0476746 0.5 10 1.912 2.45 4.205 3.948 -0.176072 0.120315 0.5 10 1.92 2.45 4.72165 3.96264 -0.18949 0.253439 0.5 10 1.928 2.45 4.55 3.904 -0.224744 0.256087 0.5 10 1.936 2.45 4.915 3.78225 -0.250427 0.250225 0.5 10 1.944 2.45 4.73 3.98 -0.263405 0.263619 0.5 10 1.952 2.45 4.95406 3.924 -0.276956 0.261828 0.5 10 1.96 2.45 4.79 3.976 -0.293426 0.257906 0.5 10 1.968 2.45 4.875 3.764 -0.305383 0.240021 0.5 10 1.976 2.45 4.735 3.9 -0.319424 0.237039 0.5 10 1.984 2.45 4.7841 3.95222 -0.212887 0.245533 0.5 10 1.992 2.45 4.74304 3.89843 -0.238 0.24783 0.5 10 2 2.45 4.83202 3.80561 -0.262552 0.240405 0.5 10 2.008 2.45 4.9692 3.92965 -0.268633 0.242844 0.5 10 2.016 2.45 4.485 3.956 -0.277047 0.233168 0.5 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0.5 10 2.184 2.45 4.82146 3.872 0.172558 0.240981 0.5 10 2.192 2.45 4.93 3.928 0.171502 0.250508 0.5 10 2.2 2.45 4.88107 3.96085 0.181467 0.251592 0.5 10 2.208 2.45 4.41 3.99407 0.203924 0.240751 0.5 10 2.216 2.45 4.97 3.94 0.191779 0.248543 0.5 10 2.224 2.45 4.87988 3.89256 0.206931 0.240774 0.5 10 2.232 2.45 4.52 3.948 0.212641 0.232287 0.5 10 2.24 2.45 4.825 3.932 0.219962 0.242379 0.5 10 2.248 2.45 4.94708 3.83056 0.219647 0.227711 0.5 10 2.256 2.45 4.64793 3.90267 0.239746 0.229821 0.5 10 2.264 2.45 4.82738 3.95791 0.241893 0.236816 0.5 10 2.272 2.45 4.6 3.91555 0.249069 0.226517 0.5 10 2.28 2.45 4.31457 3.892 0.266121 0.215848 0.5 10 2.288 2.45 4.825 3.812 0.269183 0.20675 0.5 10 2.296 2.45 4.64891 3.76313 0.278415 0.201672 0.5 10 2.304 2.45 4.695 3.98473 0.272066 0.225621 0.5 10 2.312 2.45 4.80971 3.90777 0.280892 0.21621 0.5 10 2.32 2.45 4.895 3.948 0.281192 0.218224 0.5 10 2.328 2.45 4.71792 3.93833 0.292523 0.211968 0.5 10 2.336 2.45 4.45 3.988 0.313038 0.204201 0.5 10 2.344 2.45 4.66023 3.96019 0.312326 0.201422 0.5 10 2.352 2.45 4.945 3.96445 0.305984 0.206192 0.5 10 2.36 2.45 4.375 3.94794 0.32763 0.192333 0.5 10 2.368 2.45 4.43846 3.95908 0.335753 0.190027 0.5 10 2.376 2.45 4.69716 3.99588 0.335032 0.190033 0.5 10 2.384 2.45 4.95334 3.744 0.332625 0.166082 0.5 10 2.392 2.45 4.945 3.816 0.339112 0.171283 0.5 10 2.4 2.45 4.89986 3.82788 0.345693 0.168782 0.5 10 2.408 2.45 4.83584 3.95162 0.343762 0.158719 0.5 10 2.416 2.45 4.935 3.764 0.329085 0.061788 0.5 10 2.424 2.45 4.945 3.936 0.339484 0.0700492 0.5 10 2.432 2.45 2.57 3.708 0.526173 0.0521265 0.5 10 2.44 2.45 2.32 3.5 0.555799 0.0540434 0.5 10 2.448 2.45 2.41 3.676 0.553796 0.0534586 0.5 10 2.456 2.45 2.30642 3.872 0.568153 0.0550031 0.5 10 2.464 2.45 2.41 3.884 0.565656 0.0537082 0.5 10 2.472 2.45 1.68329 3.41863 0.649003 0.0482945 0.5 10 2.48 2.45 2.215 3.74 0.594605 0.0509563 0.5 10 2.488 2.45 1.83 3.96 0.641531 0.0531845 0.5 10 2.496 2.45 2.025 3.7994 0.621279 0.046292 0.5 10 2.504 2.45 2.415 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10 2.72 2.75 0.972562 2.88205 0.916917 0.0287425 0.5 10 2.728 2.75 1.37 3.464 0.840178 0.0224696 0.5 10 2.736 2.75 1.135 3.86051 0.884306 0.0231047 0.5 10 2.744 2.75 0.76 3.336 0.986714 0.0269827 0.5 10 2.752 2.75 0.75 3.4 0.992 0.0243937 0.5 10 2.76 2.75 1.625 3.608 -0.161071 0.0233962 0.5 10 2.768 2.75 1.70919 3.33535 -0.172449 0.0241696 0.5 10 2.776 2.75 1.64 3.768 -0.148438 0.0261157 0.5 10 2.784 2.75 1.635 3.888 -0.152 0.0248843 0.5 10 2.792 2.75 1.75126 3.63701 -0.16335 0.0253932 0.5 10 2.8 2.75 1.85 3.536 -0.179028 0.0222075 0.5 10 2.808 2.75 1.805 3.86065 -0.161109 0.0250731 0.5 10 2.816 2.75 1.815 3.748 -0.153719 0.0241592 0.5 10 2.824 2.75 2.16557 3.84103 -0.205654 0.022213 0.5 10 2.832 2.75 2.015 3.48831 -0.169011 0.0219675 0.5 10 2.84 2.75 2.07 3.916 -0.183017 0.0202625 0.5 10 2.848 2.75 2.165 3.432 -0.166131 0.0200609 0.5 10 2.856 2.75 2.22136 3.45862 -0.163164 0.0189898 0.5 10 2.864 2.75 2.01 3.97597 -0.152082 0.0257197 0.5 10 2.872 2.75 2.20594 3.944 -0.169309 0.0257815 0.5 10 2.88 2.75 2.21 3.928 -0.147519 0.0176541 0.5 10 2.888 2.75 2.28 3.931 -0.16205 0.0261347 0.5 10 2.896 2.75 2.29511 3.72808 -0.155224 0.0157832 0.5 10 2.904 2.75 2.50853 3.78449 -0.163285 0.020718 0.5 10 2.912 2.75 2.5 3.824 -0.162111 0.0194442 0.5 10 2.92 2.75 2.65 3.99623 -0.156588 0.020566 0.5 10 2.928 2.75 2.90845 3.76674 -0.163308 0.0146633 0.5 10 2.936 2.75 2.8 3.928 -0.150708 0.0176913 0.5 10 2.944 2.75 2.97 3.988 -0.162542 0.017722 0.5 10 2.952 2.75 3.085 3.7 -0.156287 0.0127441 0.5 10 2.96 2.75 3.28732 3.78986 -0.151976 0.0155203 0.5 10 2.968 2.75 3.15583 3.94 -0.152113 0.017149 0.5 10 2.976 2.75 3.375 3.984 -0.145524 0.0147284 0.5 10 2.984 2.75 4.61 3.30892 -0.148694 0.0127557 0.5 10 2.992 2.75 4.39 3.58 -0.147009 0.012785 0.5 10 3 2.75 4.85283 3.504 0.123298 0.403413 0.5 10 1 2.775 4.555 3.47316 0.136044 0.393345 0.5 10 1.008 2.775 4.62 3.992 0.0822841 0.390857 0.5 10 1.016 2.775 4.98762 3.98866 0.0880401 0.385696 0.5 10 1.024 2.775 4.99499 3.97199 0.115872 0.382982 0.5 10 1.032 2.775 4.84881 3.95105 0.111838 0.385559 0.5 10 1.04 2.775 4.44056 3.97645 0.106393 0.383404 0.5 10 1.048 2.775 4.55454 3.85398 -0.0285306 0.373463 0.5 10 1.056 2.775 4.95887 3.92 0.10624 0.367994 0.5 10 1.064 2.775 4.48329 3.86663 -0.0401147 0.367579 0.5 10 1.072 2.775 3.49911 3.98446 -0.184165 0.363913 0.5 10 1.08 2.775 2.99347 3.86678 -0.208645 0.361243 0.5 10 1.088 2.775 4.065 3.184 -0.00882959 0.357599 0.5 10 1.096 2.775 4.76438 2.74 0.020788 0.360343 0.5 10 1.104 2.775 4.285 2.88 0.00399776 0.356315 0.5 10 1.112 2.775 4.33367 2.96294 0.0340956 0.358349 0.5 10 1.12 2.775 4.35 3.156 0.00570301 0.351284 0.5 10 1.128 2.775 4.99355 3.34284 -0.000945543 0.349791 0.5 10 1.136 2.775 4.89 3.74659 -0.00813554 0.348435 0.5 10 1.144 2.775 4.86485 3.82384 0.000470376 0.341887 0.5 10 1.152 2.775 4.74852 3.868 -0.0170584 0.334488 0.5 10 1.16 2.775 4.71728 3.78982 0.0210704 0.332305 0.5 10 1.168 2.775 4.97 3.796 -0.007159 0.327813 0.5 10 1.176 2.775 4.545 3.708 0.0395706 0.325084 0.5 10 1.184 2.775 4.93687 3.9095 0.0368864 0.320931 0.5 10 1.192 2.775 4.96741 3.984 0.0406965 0.313057 0.5 10 1.2 2.775 4.83753 3.92 0.030252 0.309442 0.5 10 1.208 2.775 4.97136 3.62511 0.021155 0.280916 0.5 10 1.216 2.775 4.82664 3.628 -0.198699 0.288746 0.5 10 1.224 2.775 4.99 3.73475 -0.195749 0.311452 0.5 10 1.232 2.775 4.67976 3.95707 -0.183034 0.33268 0.5 10 1.24 2.775 3.245 3.992 -0.154865 0.341071 0.5 10 1.248 2.775 3.82 3.976 -0.157382 0.341797 0.5 10 1.256 2.775 4.74297 3.75038 -0.112679 0.346431 0.5 10 1.264 2.775 4.70985 3.92388 -0.114909 0.338583 0.5 10 1.272 2.775 3.98046 3.98739 -0.064042 0.331531 0.5 10 1.28 2.775 4.3042 3.69862 -0.0773107 0.337043 0.5 10 1.288 2.775 4.98719 3.812 -0.0708459 0.325118 0.5 10 1.296 2.775 4.615 3.972 -0.0568872 0.316921 0.5 10 1.304 2.775 4.97341 3.99473 -0.0468257 0.307873 0.5 10 1.312 2.775 4.2307 3.64856 -0.109417 0.318604 0.5 10 1.32 2.775 4.62955 3.79897 -0.113553 0.32462 0.5 10 1.328 2.775 4.88826 3.85061 -0.114133 0.318634 0.5 10 1.336 2.775 4.9973 3.80584 -0.105391 0.315219 0.5 10 1.344 2.775 4.66 3.61006 -0.103267 0.284059 0.5 10 1.352 2.775 4.75062 3.956 -0.102565 0.268717 0.5 10 1.36 2.775 4.93534 3.6565 -0.178799 0.247491 0.5 10 1.368 2.775 4.945 3.928 -0.178374 0.24451 0.5 10 1.376 2.775 4.85 3.992 -0.154862 0.237333 0.5 10 1.384 2.775 4.35 3.904 -0.159744 0.240148 0.5 10 1.392 2.775 4.3975 3.94146 -0.155264 0.246255 0.5 10 1.4 2.775 4.86 3.90299 -0.14302 0.262641 0.5 10 1.408 2.775 4.955 3.896 -0.146554 0.253043 0.5 10 1.416 2.775 4.84 3.82229 -0.155796 0.170615 0.5 10 1.424 2.775 4.99353 3.93399 -0.153156 0.170077 0.5 10 1.432 2.775 2.83 3.876 -0.145697 0.0565386 0.5 10 1.44 2.775 4.33241 3.89875 -0.0721985 0.0525492 0.5 10 1.448 2.775 3.00305 3.796 -0.152373 0.0550139 0.5 10 1.456 2.775 4.25 3.956 -0.0886841 0.0514651 0.5 10 1.464 2.775 3.865 3.96 -0.108 0.0522719 0.5 10 1.472 2.775 4.13 3.972 -0.103201 0.0536568 0.5 10 1.48 2.775 4.373 3.9904 -0.102233 0.0537976 0.5 10 1.488 2.775 3.575 3.78 -0.152681 0.0627654 0.5 10 1.496 2.775 3.565 3.908 -0.147915 0.0609252 0.5 10 1.504 2.775 3.995 3.724 -0.152476 0.0651068 0.5 10 1.512 2.775 4.14 3.892 -0.144096 0.0667675 0.5 10 1.52 2.775 4.275 3.892 -0.148145 0.0716581 0.5 10 1.528 2.775 4.41414 3.97531 -0.152035 0.068288 0.5 10 1.536 2.775 4.92 3.6878 -0.154658 0.0671209 0.5 10 1.544 2.775 4.83483 3.90965 -0.152928 0.0689407 0.5 10 1.552 2.775 4.9337 3.86696 -0.141725 0.0578454 0.5 10 1.56 2.775 4.91285 3.6878 -0.143335 0.0262694 0.5 10 1.568 2.775 4.975 3.596 -0.136 0.00856676 0.5 10 1.576 2.775 4.12 3.76 -0.140639 0.00699033 0.5 10 1.584 2.775 4.32 3.93392 -0.145829 0.00815197 0.5 10 1.592 2.775 4.555 3.832 -0.144522 0.00805789 0.5 10 1.6 2.775 4.785 3.608 -0.139592 0.00769583 0.5 10 1.608 2.775 4.975 3.604 -0.142262 0.00651151 0.5 10 1.616 2.775 4.975 3.936 -0.140162 0.00742654 0.5 10 1.624 2.775 3.85 3.688 -0.0590797 0.00580405 0.5 10 1.632 2.775 3.505 3.624 -0.036 0.00573978 0.5 10 1.64 2.775 3.89 3.736 -0.0531001 0.00562938 0.5 10 1.648 2.775 3.71 3.688 -0.0376752 0.00597511 0.5 10 1.656 2.775 4.05963 3.7957 -0.0443099 0.00630517 0.5 10 1.664 2.775 4.61119 3.868 -0.0559511 0.00681922 0.5 10 1.672 2.775 4.905 3.90625 -0.0582683 0.00660311 0.5 10 1.68 2.775 3.96574 3.99659 -0.0270009 0.00646597 0.5 10 1.688 2.775 3.785 3.896 -0.00994295 0.00681742 0.5 10 1.696 2.775 3.60447 3.93158 0.000954765 0.00717824 0.5 10 1.704 2.775 4.88134 3.98765 -0.282769 0.00805181 0.5 10 1.712 2.775 4.71 3.99028 -0.254731 0.0088872 0.5 10 1.72 2.775 4.87 3.99704 -0.303599 0.00958868 0.5 10 1.728 2.775 4.86846 3.93077 -0.32262 0.0101576 0.5 10 1.736 2.775 4.44282 3.408 -0.244041 0.00923979 0.5 10 1.744 2.775 4.34 3.536 -0.254 0.0102531 0.5 10 1.752 2.775 4.76 3.72477 -0.233869 0.0116333 0.5 10 1.76 2.775 4.34 3.8 -0.261888 0.0132818 0.5 10 1.768 2.775 4.63 3.996 -0.190846 0.0201821 0.5 10 1.776 2.775 4.85 3.996 -0.18996 0.0387731 0.5 10 1.784 2.775 4.96 3.99648 -0.172684 0.0637233 0.5 10 1.792 2.775 4.845 3.984 -0.187678 0.080918 0.5 10 1.8 2.775 4.31349 3.99479 -0.146201 0.0887163 0.5 10 1.808 2.775 4.78932 3.99945 -0.112468 0.0906771 0.5 10 1.816 2.775 3.83332 3.95598 -0.094535 0.0788779 0.5 10 1.824 2.775 3.95655 3.99724 -0.0789917 0.0826173 0.5 10 1.832 2.775 4.75 3.916 -0.184496 0.088728 0.5 10 1.84 2.775 4.82887 3.988 -0.180395 0.29094 0.5 10 1.848 2.775 4.94758 3.87289 -0.201832 0.31765 0.5 10 1.856 2.775 4.745 3.976 -0.239669 0.32048 0.5 10 1.864 2.775 4.82014 3.97103 -0.24488 0.291157 0.5 10 1.872 2.775 4.365 3.976 -0.279344 0.291291 0.5 10 1.88 2.775 4.93321 3.96656 -0.0683914 0.27017 0.5 10 1.888 2.775 4.55 3.98763 -0.303846 0.272454 0.5 10 1.896 2.775 4.71 3.988 -0.182584 0.303186 0.5 10 1.904 2.775 4.82836 3.86269 -0.174312 0.350333 0.5 10 1.912 2.775 4.915 3.964 -0.18532 0.418027 0.5 10 1.92 2.775 4.98422 3.944 -0.209325 0.452514 0.5 10 1.928 2.775 4.71008 3.99182 -0.197726 0.478837 0.5 10 1.936 2.775 4.69 3.996 -0.171854 0.490584 0.5 10 1.944 2.775 4.92744 3.92196 -0.214496 0.521758 0.5 10 1.952 2.775 4.99858 3.93086 -0.188059 0.539612 0.5 10 1.96 2.775 4.91 3.984 -0.215641 0.560833 0.5 10 1.968 2.775 4.92 3.932 -0.225428 0.567027 0.5 10 1.976 2.775 4.64465 3.96 -0.214599 0.571623 0.5 10 1.984 2.775 4.865 3.988 -0.201822 0.593517 0.5 10 1.992 2.775 4.975 3.96 -0.214937 0.592887 0.5 10 2 2.775 4.91 3.89213 -0.190159 0.595476 0.5 10 2.008 2.775 4.955 3.872 -0.193558 0.594002 0.5 10 2.016 2.775 4.89901 3.91399 -0.187555 0.590518 0.5 10 2.024 2.775 4.765 3.876 -0.167755 0.57238 0.5 10 2.032 2.775 4.80134 3.90413 -0.14799 0.552558 0.5 10 2.04 2.775 4.84062 3.95544 -0.147737 0.552178 0.5 10 2.048 2.775 4.84111 3.95862 -0.15201 0.548122 0.5 10 2.056 2.775 4.89774 3.81019 -0.145381 0.522412 0.5 10 2.064 2.775 4.84985 3.99988 -0.133838 0.522281 0.5 10 2.072 2.775 4.97692 3.976 -0.133086 0.513337 0.5 10 2.08 2.775 4.99566 3.81253 -0.126153 0.478155 0.5 10 2.088 2.775 4.89 3.956 -0.102374 0.470576 0.5 10 2.096 2.775 4.46024 3.996 -0.0917905 0.452392 0.5 10 2.104 2.775 4.63 3.93343 -0.0786958 0.437415 0.5 10 2.112 2.775 4.82 3.89088 -0.0793413 0.428646 0.5 10 2.12 2.775 4.92814 3.96251 -0.0631793 0.422674 0.5 10 2.128 2.775 4.76028 3.956 -0.054506 0.415872 0.5 10 2.136 2.775 4.6811 3.99919 -0.0738297 0.402839 0.5 10 2.144 2.775 4.89213 3.9777 -0.054108 0.400292 0.5 10 2.152 2.775 4.77599 3.95868 -0.0184659 0.391564 0.5 10 2.16 2.775 4.95394 3.956 -0.0165188 0.391305 0.5 10 2.168 2.775 4.65 3.988 0.0152712 0.382805 0.5 10 2.176 2.775 4.73 3.964 0.0334144 0.37664 0.5 10 2.184 2.775 4.93632 3.97705 0.033253 0.374173 0.5 10 2.192 2.775 4.9929 3.99767 0.0204853 0.37035 0.5 10 2.2 2.775 4.995 3.916 0.0496461 0.364896 0.5 10 2.208 2.775 4.91 3.964 0.0500845 0.363971 0.5 10 2.216 2.775 4.73073 3.90292 0.0552467 0.358849 0.5 10 2.224 2.775 4.87819 3.916 0.0670541 0.356329 0.5 10 2.232 2.775 4.885 3.996 0.083835 0.354347 0.5 10 2.24 2.775 4.82297 3.98638 0.0853035 0.351703 0.5 10 2.248 2.775 4.95014 3.98411 0.0907887 0.346937 0.5 10 2.256 2.775 4.61201 3.9856 0.104116 0.343504 0.5 10 2.264 2.775 4.76746 3.96596 0.0918763 0.338829 0.5 10 2.272 2.775 4.415 3.98639 0.122259 0.336551 0.5 10 2.28 2.775 4.78866 3.98455 0.103679 0.335378 0.5 10 2.288 2.775 4.005 3.984 0.134653 0.332172 0.5 10 2.296 2.775 4.59 3.984 0.123846 0.331871 0.5 10 2.304 2.775 3.93 3.972 0.142976 0.328488 0.5 10 2.312 2.775 3.895 3.98 0.168592 0.32516 0.5 10 2.32 2.775 3.26 3.976 0.19488 0.322394 0.5 10 2.328 2.775 2.62 3.976 0.23 0.317386 0.5 10 2.336 2.775 3.1393 3.98947 0.221944 0.316752 0.5 10 2.344 2.775 3.07785 3.99187 0.202963 0.311207 0.5 10 2.352 2.775 3.94543 3.988 0.175802 0.30749 0.5 10 2.36 2.775 2.97623 3.95698 0.224997 0.305927 0.5 10 2.368 2.775 3.385 3.93413 0.211107 0.302602 0.5 10 2.376 2.775 3.54465 3.98 0.230681 0.297726 0.5 10 2.384 2.775 3.645 3.996 0.230381 0.29492 0.5 10 2.392 2.775 3.36 3.968 0.21766 0.29349 0.5 10 2.4 2.775 3.52 3.932 0.221004 0.289554 0.5 10 2.408 2.775 3.62 3.956 0.217258 0.285458 0.5 10 2.416 2.775 4.29557 3.86846 0.207024 0.27783 0.5 10 2.424 2.775 4.10216 3.928 0.210086 0.274344 0.5 10 2.432 2.775 4.42275 3.9862 0.222966 0.271977 0.5 10 2.44 2.775 4.92465 3.90414 0.213186 0.260404 0.5 10 2.448 2.775 4.67061 3.95249 0.206134 0.250867 0.5 10 2.456 2.775 4.77217 3.90917 0.20475 0.241278 0.5 10 2.464 2.775 4.95 3.84594 0.210465 0.230451 0.5 10 2.472 2.775 4.62497 3.98798 0.206719 0.197143 0.5 10 2.48 2.775 4.9 3.916 0.212829 0.0850533 0.5 10 2.488 2.775 4.93616 3.956 0.207254 0.0819351 0.5 10 2.496 2.775 1.41246 3.01397 0.694178 0.039838 0.5 10 2.504 2.775 1.275 3.56148 0.724174 0.0414822 0.5 10 2.512 2.775 1.68373 3.29899 0.663099 0.0386491 0.5 10 2.52 2.775 1.65703 2.41494 0.672148 0.0351683 0.5 10 2.528 2.775 1.63613 3.9 0.678498 0.0404502 0.5 10 2.536 2.775 1.00312 3.86184 0.805426 0.0384022 0.5 10 2.544 2.775 1.29346 3.7553 0.745988 0.0375522 0.5 10 2.552 2.775 0.875 3.66454 0.851661 0.0384781 0.5 10 2.56 2.775 1.035 3.752 0.816267 0.0361141 0.5 10 2.568 2.775 1.16379 3.76303 0.787125 0.0382124 0.5 10 2.576 2.775 0.815 3.628 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0.5 10 2.744 2.775 1.55449 3.49257 -0.156924 0.0240437 0.5 10 2.752 2.775 0.98 3.848 0.932046 0.0266546 0.5 10 2.76 2.775 1.67 3.236 -0.173805 0.0258678 0.5 10 2.768 2.775 1.63343 3.4 -0.162743 0.0233001 0.5 10 2.776 2.775 1.635 3.95773 -0.150024 0.0282813 0.5 10 2.784 2.775 2.125 3.76189 -0.234638 0.0226179 0.5 10 2.792 2.775 1.765 3.496 -0.172584 0.0236628 0.5 10 2.8 2.775 2.61736 3.896 -0.267743 0.0205147 0.5 10 2.808 2.775 1.94 3.752 -0.188055 0.0220009 0.5 10 2.816 2.775 1.87138 3.68559 -0.17139 0.0269455 0.5 10 2.824 2.775 1.895 3.88 -0.158139 0.0224118 0.5 10 2.832 2.775 2.025 3.964 -0.181057 0.0221113 0.5 10 2.84 2.775 2.125 3.724 -0.18272 0.021264 0.5 10 2.848 2.775 1.96153 3.94 -0.152425 0.0232059 0.5 10 2.856 2.775 2.05 3.86495 -0.156137 0.0230454 0.5 10 2.864 2.775 2.075 3.69898 -0.158887 0.0208025 0.5 10 2.872 2.775 2.00401 3.85741 -0.144713 0.0167054 0.5 10 2.88 2.775 2.36537 3.944 -0.174352 0.0196092 0.5 10 2.888 2.775 2.32 3.944 -0.172592 0.0234283 0.5 10 2.896 2.775 2.39 3.976 -0.159884 0.0231825 0.5 10 2.904 2.775 2.355 3.908 -0.155461 0.0216822 0.5 10 2.912 2.775 2.475 3.97179 -0.16481 0.023264 0.5 10 2.92 2.775 2.64 3.756 -0.160311 0.0190001 0.5 10 2.928 2.775 2.675 3.8 -0.160795 0.0177616 0.5 10 2.936 2.775 2.72 3.9 -0.159066 0.0193041 0.5 10 2.944 2.775 2.945 3.888 -0.147221 0.0126406 0.5 10 2.952 2.775 3.19182 3.95346 -0.164891 0.0141224 0.5 10 2.96 2.775 3.005 3.992 -0.155544 0.0196346 0.5 10 2.968 2.775 3.41 3.792 -0.152946 0.0153359 0.5 10 2.976 2.775 3.495 3.764 -0.150346 0.0135758 0.5 10 2.984 2.775 3.47934 3.928 -0.151814 0.0161973 0.5 10 2.992 2.775 3.995 3.864 -0.149287 0.0132784 0.5 10 3 2.775 4.835 3.972 0.0585 0.397449 0.5 10 1 2.8 4.33 3.944 0.0814005 0.398739 0.5 10 1.008 2.8 4.73 3.956 0.084 0.390097 0.5 10 1.016 2.8 4.93573 3.548 -0.223131 0.383152 0.5 10 1.024 2.8 4.97496 3.94397 0.100014 0.379902 0.5 10 1.032 2.8 4.815 3.94967 0.129467 0.377752 0.5 10 1.04 2.8 4.805 2.64 0.111769 0.376286 0.5 10 1.048 2.8 4.945 3.828 0.108 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1.384 2.8 4.855 3.976 -0.143561 0.253626 0.5 10 1.392 2.8 4.55948 3.97558 -0.156588 0.258659 0.5 10 1.4 2.8 4.93891 3.87112 -0.151971 0.25038 0.5 10 1.408 2.8 4.995 3.944 -0.16 0.245762 0.5 10 1.416 2.8 4.58322 3.88275 -0.0460562 0.0532757 0.5 10 1.424 2.8 4.94926 3.91541 -0.156228 0.0818547 0.5 10 1.432 2.8 2.8046 3.64093 -0.150465 0.0607374 0.5 10 1.44 2.8 2.96236 3.61252 -0.150277 0.0590722 0.5 10 1.448 2.8 4.99 3.892 -0.0593974 0.0561972 0.5 10 1.456 2.8 3.11646 3.92 -0.148188 0.0611676 0.5 10 1.464 2.8 4.33 3.976 -0.0919327 0.0553948 0.5 10 1.472 2.8 3.405 3.604 -0.150956 0.0597332 0.5 10 1.48 2.8 3.48 3.85386 -0.145533 0.0656466 0.5 10 1.488 2.8 3.6697 3.66776 -0.155492 0.0617298 0.5 10 1.496 2.8 3.86812 3.88069 -0.152747 0.0601069 0.5 10 1.504 2.8 3.92 3.744 -0.153192 0.0668199 0.5 10 1.512 2.8 4.24 3.968 -0.149342 0.0599533 0.5 10 1.52 2.8 4.0046 3.99168 -0.146481 0.0700125 0.5 10 1.528 2.8 4.39778 3.908 -0.14788 0.0754041 0.5 10 1.536 2.8 4.535 3.936 -0.150013 0.0761619 0.5 10 1.544 2.8 4.87222 3.89571 -0.151561 0.0774753 0.5 10 1.552 2.8 4.855 3.696 -0.150022 0.0398491 0.5 10 1.56 2.8 4.875 3.928 -0.146173 0.044432 0.5 10 1.568 2.8 4.87547 3.99638 -0.154381 0.0121857 0.5 10 1.576 2.8 4.275 3.78 -0.147339 0.00743576 0.5 10 1.584 2.8 4.34545 3.91236 -0.142728 0.00843451 0.5 10 1.592 2.8 4.575 3.896 -0.145442 0.00837861 0.5 10 1.6 2.8 4.825 3.912 -0.146564 0.00748147 0.5 10 1.608 2.8 4.85 3.552 -0.139337 0.00565169 0.5 10 1.616 2.8 4.975 3.98919 -0.144861 0.00760869 0.5 10 1.624 2.8 2.96454 0.0222566 -0.531112 0.00649654 0.5 10 1.632 2.8 3.67443 3.71954 -0.049453 0.00584359 0.5 10 1.64 2.8 3.61143 3.71715 -0.0385206 0.00642012 0.5 10 1.648 2.8 4.00072 3.84058 -0.0491711 0.00628813 0.5 10 1.656 2.8 3.83 3.75868 -0.0380829 0.00644882 0.5 10 1.664 2.8 3.19138 3.732 0.000192459 0.0063159 0.5 10 1.672 2.8 4.24 3.756 -0.0384011 0.0066434 0.5 10 1.68 2.8 4.00392 3.90713 -0.0302052 0.00674278 0.5 10 1.688 2.8 3.91496 3.88099 -0.0167635 0.00732973 0.5 10 1.696 2.8 4.97 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2.8 4.835 3.952 -0.253371 0.321783 0.5 10 1.864 2.8 4.81637 3.924 -0.270394 0.311591 0.5 10 1.872 2.8 4.72268 3.98614 -0.286844 0.296235 0.5 10 1.88 2.8 4.7439 3.87491 -0.296165 0.279578 0.5 10 1.888 2.8 4.955 3.92644 -0.171132 0.288086 0.5 10 1.896 2.8 4.895 3.876 -0.18512 0.349386 0.5 10 1.904 2.8 4.8082 3.96201 -0.179193 0.406592 0.5 10 1.912 2.8 4.72482 3.93998 -0.185773 0.444001 0.5 10 1.92 2.8 4.96004 3.94404 -0.198086 0.484604 0.5 10 1.928 2.8 4.88906 3.97814 -0.198303 0.508537 0.5 10 1.936 2.8 4.96791 3.93033 -0.179214 0.519913 0.5 10 1.944 2.8 4.905 3.94244 -0.196251 0.542181 0.5 10 1.952 2.8 4.84 3.99029 -0.208364 0.561733 0.5 10 1.96 2.8 4.93877 3.92923 -0.198444 0.573007 0.5 10 1.968 2.8 4.85616 3.988 -0.218055 0.589375 0.5 10 1.976 2.8 4.855 3.944 -0.210427 0.598092 0.5 10 1.984 2.8 4.78073 3.96829 -0.219325 0.607693 0.5 10 1.992 2.8 4.71771 3.888 -0.214241 0.60461 0.5 10 2 2.8 4.91622 3.99297 -0.201509 0.622574 0.5 10 2.008 2.8 4.91 3.912 -0.211493 0.620693 0.5 10 2.016 2.8 4.89 3.79645 -0.191595 0.607876 0.5 10 2.024 2.8 4.81 3.936 -0.197238 0.609253 0.5 10 2.032 2.8 4.98869 3.77309 -0.185969 0.59046 0.5 10 2.04 2.8 4.88867 3.816 -0.17636 0.572388 0.5 10 2.048 2.8 4.925 3.768 -0.165473 0.555296 0.5 10 2.056 2.8 4.85701 3.92893 -0.160784 0.552298 0.5 10 2.064 2.8 4.86307 3.844 -0.135355 0.515418 0.5 10 2.072 2.8 4.75114 3.90091 -0.132951 0.504115 0.5 10 2.08 2.8 4.94677 3.90542 -0.149495 0.489191 0.5 10 2.088 2.8 4.93 3.848 -0.126495 0.471452 0.5 10 2.096 2.8 4.85487 3.98182 -0.129982 0.459545 0.5 10 2.104 2.8 4.505 3.996 -0.102811 0.44105 0.5 10 2.112 2.8 4.905 3.936 -0.104187 0.432646 0.5 10 2.12 2.8 4.925 3.952 -0.1003 0.419379 0.5 10 2.128 2.8 4.445 3.98597 -0.0609013 0.406743 0.5 10 2.136 2.8 4.905 3.96219 -0.0495052 0.405441 0.5 10 2.144 2.8 4.795 3.98 -4.77135e-05 0.39078 0.5 10 2.152 2.8 4.98 3.98 -0.00550643 0.389903 0.5 10 2.16 2.8 4.75518 3.88014 -0.00818871 0.381751 0.5 10 2.168 2.8 4.7384 3.97472 0.0113597 0.376816 0.5 10 2.176 2.8 4.745 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3.96436 0.21686 0.309677 0.5 10 2.344 2.8 3.045 3.96551 0.213975 0.306535 0.5 10 2.352 2.8 3.61931 3.95145 0.201773 0.301006 0.5 10 2.36 2.8 3.48 3.864 0.213711 0.29573 0.5 10 2.368 2.8 3.37 3.996 0.207931 0.295835 0.5 10 2.376 2.8 4.41 3.99653 0.190346 0.287946 0.5 10 2.384 2.8 3.56369 3.98295 0.206255 0.288977 0.5 10 2.392 2.8 4.18124 3.90899 0.20157 0.282741 0.5 10 2.4 2.8 4.75929 3.97143 0.18914 0.278185 0.5 10 2.408 2.8 4.345 3.924 0.196791 0.275856 0.5 10 2.416 2.8 4.3277 3.996 0.208272 0.27413 0.5 10 2.424 2.8 4.48 3.97617 0.210007 0.268269 0.5 10 2.432 2.8 4.7 3.732 0.206085 0.242234 0.5 10 2.44 2.8 4.57 3.884 0.194147 0.235858 0.5 10 2.448 2.8 4.90596 3.86365 0.190021 0.230357 0.5 10 2.456 2.8 4.9235 3.89574 0.193776 0.217028 0.5 10 2.464 2.8 4.55 3.96178 0.177189 0.158771 0.5 10 2.472 2.8 4.545 3.972 0.184872 0.116294 0.5 10 2.48 2.8 4.857 3.8536 0.176746 0.0439631 0.5 10 2.488 2.8 1.39 3.99594 0.697429 0.0426586 0.5 10 2.496 2.8 2.1422 3.94059 0.596575 0.0392123 0.5 10 2.504 2.8 1.98 3.916 0.619517 0.0416586 0.5 10 2.512 2.8 1.835 3.952 0.642 0.0417729 0.5 10 2.52 2.8 1.225 3.808 0.746877 0.0415139 0.5 10 2.528 2.8 1.37379 3.92171 0.719726 0.0389355 0.5 10 2.536 2.8 1.1907 3.092 0.765155 0.0387318 0.5 10 2.544 2.8 0.855 3.5 0.855268 0.0372679 0.5 10 2.552 2.8 0.795 3.93077 0.874568 0.0338518 0.5 10 2.56 2.8 1.42498 3.38425 0.73467 0.0370018 0.5 10 2.568 2.8 1.005 3.11916 0.825991 0.0358249 0.5 10 2.576 2.8 0.987831 3.74626 0.835703 0.0369746 0.5 10 2.584 2.8 1.165 3.432 0.799021 0.0369301 0.5 10 2.592 2.8 0.995 3.84554 0.842513 0.0388647 0.5 10 2.6 2.8 1.26 3.908 0.787946 0.0384077 0.5 10 2.608 2.8 0.985 3.892 0.85384 0.0346924 0.5 10 2.616 2.8 1.10449 3.47959 0.830602 0.0364881 0.5 10 2.624 2.8 0.795 3.80175 0.914486 0.0339007 0.5 10 2.632 2.8 1.0187 3.78696 0.856383 0.0330596 0.5 10 2.64 2.8 1.055 3.81889 0.854528 0.0333257 0.5 10 2.648 2.8 1.345 3.36 0.798588 0.0269503 0.5 10 2.656 2.8 0.735 3.612 0.954492 0.0314517 0.5 10 2.664 2.8 1.095 3.092 0.856566 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2.552 2.975 1.195 3.73134 0.778234 0.035525 0.5 10 2.56 2.975 1.00493 3.508 0.826753 0.0356718 0.5 10 2.568 2.975 0.773459 3.79477 0.900419 0.0367708 0.5 10 2.576 2.975 0.75 3.34093 0.910716 0.0323662 0.5 10 2.584 2.975 1.08155 3.532 0.817072 0.0316881 0.5 10 2.592 2.975 0.975 3.528 0.852614 0.0352087 0.5 10 2.6 2.975 1.11 3.68 0.820793 0.0312828 0.5 10 2.608 2.975 1.07179 2.11744 0.836135 0.0323061 0.5 10 2.616 2.975 1.135 3.684 0.824738 0.0315036 0.5 10 2.624 2.975 0.87 2.264 0.896168 0.0273789 0.5 10 2.632 2.975 1.215 3.652 0.813972 0.0279434 0.5 10 2.64 2.975 0.61 3.984 0.992 0.0342728 0.5 10 2.648 2.975 0.870702 1.95256 0.910617 0.0290197 0.5 10 2.656 2.975 1.01 3.08128 0.87775 0.0273539 0.5 10 2.664 2.975 0.855 3.136 0.924327 0.0268833 0.5 10 2.672 2.975 0.98 3.8 0.890297 0.0275794 0.5 10 2.68 2.975 0.68 1.38 0.985273 0.0276629 0.5 10 2.688 2.975 2.9 3.892 -0.376868 0.0242448 0.5 10 2.696 2.975 1.39814 3.71051 -0.138153 0.0287979 0.5 10 2.704 2.975 1.38815 3.8 -0.136115 0.031792 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2.975 2.15719 3.74158 -0.162447 0.0282275 0.5 10 2.872 2.975 2.425 3.988 -0.183002 0.0216924 0.5 10 2.88 2.975 2.37876 3.70137 -0.162531 0.024191 0.5 10 2.888 2.975 2.395 3.94463 -0.175343 0.0212217 0.5 10 2.896 2.975 2.505 3.81155 -0.161389 0.0232887 0.5 10 2.904 2.975 2.53323 3.968 -0.152616 0.0207602 0.5 10 2.912 2.975 2.5 3.94788 -0.162788 0.025485 0.5 10 2.92 2.975 2.73 3.812 -0.166917 0.0190965 0.5 10 2.928 2.975 2.67 3.78 -0.157921 0.0163449 0.5 10 2.936 2.975 2.94 3.84 -0.155496 0.0193857 0.5 10 2.944 2.975 3.15 3.876 -0.160628 0.0161231 0.5 10 2.952 2.975 3.20918 3.856 -0.154508 0.0177703 0.5 10 2.96 2.975 3.305 3.80043 -0.158659 0.0177061 0.5 10 2.968 2.975 3.325 3.804 -0.15668 0.0165527 0.5 10 2.976 2.975 3.485 3.98212 -0.154809 0.0197095 0.5 10 2.984 2.975 3.67668 3.90135 -0.154406 0.0188079 0.5 10 2.992 2.975 4.91 3.328 -0.14999 0.0162793 0.5 10 3 2.975 4.02 3.964 -0.0645455 0.3948 0.5 10 1 3 3.285 3.976 -0.0347438 0.394232 0.5 10 1.008 3 3.7 3.996 -0.040524 0.386115 0.5 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0.285331 0.5 10 1.352 3 4.59106 3.944 -0.132519 0.295659 0.5 10 1.36 3 4.96 3.932 -0.122639 0.300475 0.5 10 1.368 3 4.55455 3.99719 -0.132046 0.272472 0.5 10 1.376 3 4.52 3.556 -0.172678 0.257456 0.5 10 1.384 3 4.77 3.728 -0.149953 0.259077 0.5 10 1.392 3 4.88916 3.75933 -0.16677 0.269429 0.5 10 1.4 3 4.835 3.928 -0.169357 0.271066 0.5 10 1.408 3 4.98 3.916 -0.161284 0.293694 0.5 10 1.416 3 2.65774 3.70219 -0.156269 0.0609235 0.5 10 1.424 3 4.605 3.844 -0.0629261 0.0629621 0.5 10 1.432 3 2.835 3.804 -0.16079 0.0638038 0.5 10 1.44 3 2.95 3.72726 -0.154372 0.0689429 0.5 10 1.448 3 4.67393 3.94715 -0.0789679 0.0658556 0.5 10 1.456 3 3.205 3.56 -0.16275 0.0671185 0.5 10 1.464 3 3.475 3.76 -0.150686 0.0604972 0.5 10 1.472 3 3.285 3.99477 -0.155366 0.0771285 0.5 10 1.48 3 3.57 3.796 -0.156 0.0747317 0.5 10 1.488 3 4.785 3.988 -0.105195 0.0668042 0.5 10 1.496 3 3.81642 3.86 -0.153685 0.0780359 0.5 10 1.504 3 4.125 3.948 -0.147394 0.0741028 0.5 10 1.512 3 4.00874 3.89934 -0.157164 0.0812749 0.5 10 1.52 3 4.345 3.976 -0.15314 0.0879784 0.5 10 1.528 3 4.63215 3.90699 -0.147989 0.0742269 0.5 10 1.536 3 4.615 3.968 -0.155859 0.0951041 0.5 10 1.544 3 4.885 3.952 -0.153549 0.0933858 0.5 10 1.552 3 4.95449 3.96359 -0.15662 0.0845481 0.5 10 1.56 3 4.975 3.68024 -0.155238 0.0236288 0.5 10 1.568 3 4.12 3.828 -0.152117 0.00813856 0.5 10 1.576 3 4.11424 3.93539 -0.157729 0.00761606 0.5 10 1.584 3 4.46915 3.77791 -0.149365 0.00809913 0.5 10 1.592 3 4.65439 3.972 -0.149336 0.00890641 0.5 10 1.6 3 4.81157 3.928 -0.149708 0.00890826 0.5 10 1.608 3 4.87182 3.98546 -0.198229 0.00895168 0.5 10 1.616 3 4.3869 0.04 -0.364736 0.0083129 0.5 10 1.624 3 4.94 3.372 -0.264 0.009086 0.5 10 1.632 3 4.905 3.496 -0.271078 0.0106272 0.5 10 1.64 3 4.9 3.984 -0.230175 0.0121484 0.5 10 1.648 3 4.88427 3.92 -0.251566 0.0142387 0.5 10 1.656 3 4.89431 3.96276 -0.223573 0.0140226 0.5 10 1.664 3 4.865 3.93244 -0.259676 0.0159 0.5 10 1.672 3 4.76698 3.98 -0.259887 0.0134975 0.5 10 1.68 3 4.6617 3.98071 -0.292764 0.0143742 0.5 10 1.688 3 4.76264 3.72593 -0.0468377 0.0127825 0.5 10 1.696 3 4.38966 3.5972 -0.23096 0.0151587 0.5 10 1.704 3 4.825 3.816 -0.218832 0.0212545 0.5 10 1.712 3 4.965 3.8261 -0.217848 0.0296715 0.5 10 1.72 3 4.905 3.96901 -0.186982 0.0432391 0.5 10 1.728 3 4.75 3.976 -0.180208 0.0762718 0.5 10 1.736 3 4.86 3.93434 -0.170212 0.101696 0.5 10 1.744 3 4.69246 3.964 -0.144301 0.117687 0.5 10 1.752 3 4.96557 3.94846 -0.128547 0.118232 0.5 10 1.76 3 4.16241 3.98962 -0.108817 0.116086 0.5 10 1.768 3 4.36842 3.97874 -0.0850355 0.114143 0.5 10 1.776 3 4.435 3.96159 -0.0756487 0.109255 0.5 10 1.784 3 4.62 3.964 -0.189679 0.299877 0.5 10 1.792 3 4.93505 3.94303 -0.197258 0.384023 0.5 10 1.8 3 4.375 3.988 -0.126368 0.348985 0.5 10 1.808 3 4.7911 3.96 -0.1104 0.345746 0.5 10 1.816 3 4.99853 3.88855 -0.0961713 0.328539 0.5 10 1.824 3 4.975 3.964 -0.0815155 0.326156 0.5 10 1.832 3 4.98737 3.808 -0.277157 0.297907 0.5 10 1.84 3 4.91356 3.89137 -0.178172 0.329542 0.5 10 1.848 3 4.85167 3.98406 -0.182281 0.401682 0.5 10 1.856 3 4.61645 3.86079 -0.173129 0.432302 0.5 10 1.864 3 4.865 3.948 -0.173754 0.494868 0.5 10 1.872 3 4.93307 3.97846 -0.207954 0.532378 0.5 10 1.88 3 4.96 3.952 -0.212354 0.555256 0.5 10 1.888 3 4.91169 3.75335 -0.188858 0.564182 0.5 10 1.896 3 4.905 3.88116 -0.179125 0.589495 0.5 10 1.904 3 4.765 3.97596 -0.202118 0.61545 0.5 10 1.912 3 4.99856 3.868 -0.203012 0.634623 0.5 10 1.92 3 4.84475 3.8918 -0.189276 0.637051 0.5 10 1.928 3 4.74376 3.96701 -0.21125 0.656819 0.5 10 1.936 3 4.835 3.992 -0.21409 0.67675 0.5 10 1.944 3 4.98 3.91321 -0.206249 0.689185 0.5 10 1.952 3 4.76029 3.78958 -0.209231 0.673802 0.5 10 1.96 3 4.90782 3.94625 -0.216 0.702292 0.5 10 1.968 3 4.985 3.992 -0.198357 0.708681 0.5 10 1.976 3 4.97825 3.84508 -0.241452 0.69615 0.5 10 1.984 3 4.67614 3.988 -0.213504 0.701332 0.5 10 1.992 3 4.875 3.988 -0.216351 0.714274 0.5 10 2 3 4.97015 3.96018 -0.241894 0.7148 0.5 10 2.008 3 4.995 3.968 -0.250986 0.701267 0.5 10 2.016 3 4.935 3.90038 -0.253319 0.681264 0.5 10 2.024 3 4.85499 3.79999 -0.259908 0.650427 0.5 10 2.032 3 4.953 3.83884 -0.246222 0.647656 0.5 10 2.04 3 4.93994 3.99928 -0.274272 0.631047 0.5 10 2.048 3 4.92 3.828 -0.246537 0.612139 0.5 10 2.056 3 4.97 3.904 -0.275043 0.590778 0.5 10 2.064 3 4.92365 3.94692 -0.261903 0.577553 0.5 10 2.072 3 4.68083 3.84867 -0.283957 0.539187 0.5 10 2.08 3 4.9587 3.95781 -0.281495 0.532065 0.5 10 2.088 3 4.59922 3.84891 -0.277589 0.477818 0.5 10 2.096 3 4.98463 3.916 -0.290675 0.455632 0.5 10 2.104 3 4.9 3.9584 -0.28093 0.412877 0.5 10 2.112 3 3.98 3.984 -0.00279756 0.370069 0.5 10 2.12 3 4.98524 3.9962 -0.0118712 0.363972 0.5 10 2.128 3 4.84 3.97945 -0.00965321 0.357069 0.5 10 2.136 3 4.795 3.936 -0.0181998 0.341812 0.5 10 2.144 3 4.365 3.968 0.0453701 0.336548 0.5 10 2.152 3 4.01953 3.992 0.0522133 0.33393 0.5 10 2.16 3 4.91 3.968 0.044 0.329101 0.5 10 2.168 3 4.72873 3.98819 0.0416172 0.324747 0.5 10 2.176 3 3.635 3.992 0.0900392 0.319394 0.5 10 2.184 3 4.03 3.96 0.076 0.312902 0.5 10 2.192 3 3.9662 3.93296 0.09055 0.304677 0.5 10 2.2 3 4.72369 3.99895 0.0734253 0.30164 0.5 10 2.208 3 3.14221 3.93777 0.130966 0.29463 0.5 10 2.216 3 3.41584 3.95509 0.127688 0.293504 0.5 10 2.224 3 4.535 3.988 0.103645 0.290166 0.5 10 2.232 3 4.21673 3.98402 0.107787 0.287444 0.5 10 2.24 3 3.145 3.9998 0.146 0.284207 0.5 10 2.248 3 3.505 3.992 0.15841 0.282753 0.5 10 2.256 3 4.21435 3.96748 0.123164 0.275424 0.5 10 2.264 3 4.1232 3.972 0.139986 0.271243 0.5 10 2.272 3 4.74646 3.96253 0.123975 0.266297 0.5 10 2.28 3 4.54 3.952 0.148282 0.262893 0.5 10 2.288 3 4.19091 3.96873 0.14564 0.260546 0.5 10 2.296 3 4.625 3.9413 0.144572 0.255812 0.5 10 2.304 3 4.375 3.96 0.141292 0.247954 0.5 10 2.312 3 4.86849 3.98413 0.141322 0.250806 0.5 10 2.32 3 4.97702 3.864 0.123672 0.231539 0.5 10 2.328 3 4.885 3.98 0.130461 0.228494 0.5 10 2.336 3 4.96 3.89556 0.117738 0.205371 0.5 10 2.344 3 4.945 3.848 0.108671 0.177078 0.5 10 2.352 3 4.76659 3.93727 0.0985246 0.149056 0.5 10 2.36 3 4.975 3.996 0.10039 0.151457 0.5 10 2.368 3 4.965 3.80622 0.0877877 0.104058 0.5 10 2.376 3 4.69733 3.87487 0.314337 0.0975888 0.5 10 2.384 3 4.48772 3.98369 0.323632 0.0943499 0.5 10 2.392 3 4.595 3.968 0.311078 0.072798 0.5 10 2.4 3 4.75 3.984 0.316 0.0690872 0.5 10 2.408 3 4.86727 3.96148 0.298544 0.0470325 0.5 10 2.416 3 1.585 3.85762 0.621427 0.0453374 0.5 10 2.424 3 1.455 3.696 0.645299 0.0412785 0.5 10 2.432 3 1.29 3.896 0.685692 0.0439066 0.5 10 2.44 3 1.56 3.80081 0.641027 0.0435109 0.5 10 2.448 3 1.42809 3.928 0.670637 0.0433248 0.5 10 2.456 3 1.1 3.78786 0.743374 0.0405215 0.5 10 2.464 3 1.19 3.436 0.726033 0.0392555 0.5 10 2.472 3 0.965 3.948 0.786548 0.0384476 0.5 10 2.48 3 1.24242 3.94337 0.726573 0.0399673 0.5 10 2.488 3 0.905 3.9 0.818916 0.0366234 0.5 10 2.496 3 1.46 3.85488 0.691407 0.0383637 0.5 10 2.504 3 1.145 3.516 0.764 0.0375588 0.5 10 2.512 3 1.19 3.696 0.754694 0.0376362 0.5 10 2.52 3 1.42334 3.98267 0.713214 0.0361853 0.5 10 2.528 3 1.31197 3.45186 0.739324 0.0345184 0.5 10 2.536 3 1.315 3.852 0.741799 0.0330973 0.5 10 2.544 3 0.810271 3.62068 0.874461 0.0342382 0.5 10 2.552 3 0.92 3.46735 0.847766 0.0374466 0.5 10 2.56 3 0.72 3.62 0.913807 0.0337737 0.5 10 2.568 3 0.515 3.028 0.999414 0.0288634 0.5 10 2.576 3 1.15232 3.304 0.79865 0.0285742 0.5 10 2.584 3 1.155 3.55396 0.806382 0.0331026 0.5 10 2.592 3 1.05 3.436 0.834516 0.0321708 0.5 10 2.6 3 0.56 3.276 0.996912 0.032208 0.5 10 2.608 3 1.12 3.164 0.825273 0.0309043 0.5 10 2.616 3 0.91911 3.632 0.88338 0.0298171 0.5 10 2.624 3 0.8 3.568 0.91483 0.0256328 0.5 10 2.632 3 1.105 3.89804 0.843421 0.0288671 0.5 10 2.64 3 1.09 3.012 0.847333 0.026462 0.5 10 2.648 3 0.685 2.408 0.966187 0.0280375 0.5 10 2.656 3 0.959463 3.89157 0.888401 0.0275298 0.5 10 2.664 3 0.78 3.072 0.944622 0.028768 0.5 10 2.672 3 1.74994 3.98489 -0.0456406 0.0238929 0.5 10 2.68 3 0.665 3.328 0.986034 0.0244794 0.5 10 2.688 3 2.87 3.872 -0.387826 0.0238145 0.5 10 2.696 3 1.37109 3.82 -0.153454 0.0330939 0.5 10 2.704 3 2.91951 3.96361 -0.381666 0.0253379 0.5 10 2.712 3 2.905 3.768 -0.369445 0.0242046 0.5 10 2.72 3 1.47 3.544 -0.165581 0.0268364 0.5 10 2.728 3 1.49517 3.81614 -0.147813 0.0250429 0.5 10 2.736 3 1.535 3.364 -0.166933 0.0231428 0.5 10 2.744 3 2.54 3.92 -0.320305 0.0251771 0.5 10 2.752 3 2.21 3.74381 -0.278821 0.0237959 0.5 10 2.76 3 3.085 3.96 -0.343431 0.0236205 0.5 10 2.768 3 1.615 3.65216 -0.167523 0.0269597 0.5 10 2.776 3 1.71 3.336 -0.177674 0.028034 0.5 10 2.784 3 1.68939 3.87749 -0.161105 0.0256962 0.5 10 2.792 3 2 3.592 -0.216127 0.0243623 0.5 10 2.8 3 1.785 3.732 -0.17062 0.0259516 0.5 10 2.808 3 2.245 3.96 -0.239044 0.0253376 0.5 10 2.816 3 1.91 3.732 -0.18311 0.0263231 0.5 10 2.824 3 1.835 3.832 -0.166497 0.0279757 0.5 10 2.832 3 2.02 3.948 -0.188 0.0241222 0.5 10 2.84 3 2.20745 3.616 -0.197322 0.0208166 0.5 10 2.848 3 1.94166 3.99965 -0.157525 0.0235471 0.5 10 2.856 3 2.09092 3.69466 -0.170103 0.0263039 0.5 10 2.864 3 2.12 3.952 -0.167313 0.0262042 0.5 10 2.872 3 2.17 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1.432 3.075 2.885 3.912 -0.152418 0.0683411 0.5 10 1.44 3.075 4.40784 3.95827 -0.0791368 0.0666644 0.5 10 1.448 3.075 3.03868 3.88695 -0.15834 0.0757255 0.5 10 1.456 3.075 3.19 3.864 -0.159936 0.0727362 0.5 10 1.464 3.075 3.3 3.97806 -0.157622 0.0760041 0.5 10 1.472 3.075 3.518 3.60306 -0.165498 0.0725869 0.5 10 1.48 3.075 4.325 3.98 -0.117051 0.0674436 0.5 10 1.488 3.075 3.73 3.832 -0.152977 0.0788953 0.5 10 1.496 3.075 3.74668 3.88859 -0.15165 0.0747013 0.5 10 1.504 3.075 3.94145 3.94619 -0.158583 0.0919845 0.5 10 1.512 3.075 4.18971 3.83177 -0.163027 0.0840306 0.5 10 1.52 3.075 4.61614 3.94707 -0.149799 0.0850071 0.5 10 1.528 3.075 4.77 3.776 -0.161396 0.0926413 0.5 10 1.536 3.075 4.575 3.72591 -0.153131 0.054053 0.5 10 1.544 3.075 4.95887 3.932 -0.159018 0.104991 0.5 10 1.552 3.075 4.8508 3.9315 -0.152293 0.0653362 0.5 10 1.56 3.075 4.98809 3.74591 -0.158976 0.0205078 0.5 10 1.568 3.075 4.93768 3.87022 -0.156063 0.0102594 0.5 10 1.576 3.075 4.34 3.996 -0.197283 0.00944148 0.5 10 1.584 3.075 4.835 3.924 -0.195746 0.00876021 0.5 10 1.592 3.075 4.59 3.93107 -0.20601 0.00959968 0.5 10 1.6 3.075 4.585 3.972 -0.213597 0.0103156 0.5 10 1.608 3.075 4.965 3.924 -0.202245 0.0114877 0.5 10 1.616 3.075 4.81 3.936 -0.225697 0.0131433 0.5 10 1.624 3.075 4.845 3.876 -0.236098 0.0150936 0.5 10 1.632 3.075 4.835 3.892 -0.243585 0.017274 0.5 10 1.64 3.075 4.93547 3.84333 -0.257496 0.0177861 0.5 10 1.648 3.075 4.854 3.9512 -0.251057 0.0201878 0.5 10 1.656 3.075 4.68208 3.98571 -0.270775 0.0186296 0.5 10 1.664 3.075 4.32758 3.99406 -0.298686 0.014816 0.5 10 1.672 3.075 4.81094 3.604 -0.0584892 0.0156174 0.5 10 1.68 3.075 4.585 3.548 -0.215479 0.0175551 0.5 10 1.688 3.075 4.69 3.68 -0.216147 0.022689 0.5 10 1.696 3.075 4.72081 3.8688 -0.210898 0.0334273 0.5 10 1.704 3.075 4.91 3.9856 -0.201139 0.0573878 0.5 10 1.712 3.075 4.85223 3.99378 -0.177232 0.0932235 0.5 10 1.72 3.075 4.855 3.98 -0.152541 0.120859 0.5 10 1.728 3.075 4.805 3.96228 -0.137626 0.130561 0.5 10 1.736 3.075 4.605 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2.208 3.075 3.74629 3.94503 0.119252 0.275534 0.5 10 2.216 3.075 3.5367 3.98936 0.119929 0.269047 0.5 10 2.224 3.075 3.97334 3.98726 0.123801 0.267793 0.5 10 2.232 3.075 4.03 3.956 0.122762 0.262742 0.5 10 2.24 3.075 4.54213 3.924 0.124918 0.258902 0.5 10 2.248 3.075 4.96 3.976 0.117133 0.254679 0.5 10 2.256 3.075 4.63263 3.9981 0.117828 0.250087 0.5 10 2.264 3.075 4.815 3.924 0.107823 0.239956 0.5 10 2.272 3.075 4.82 3.808 0.10532 0.227152 0.5 10 2.28 3.075 4.99923 3.91539 0.112455 0.221435 0.5 10 2.288 3.075 4.69 3.896 0.0888084 0.192673 0.5 10 2.296 3.075 4.805 3.888 0.0911154 0.179684 0.5 10 2.304 3.075 4.865 3.88 0.0797724 0.15331 0.5 10 2.312 3.075 4.88974 3.96039 0.0797478 0.141439 0.5 10 2.32 3.075 3.35876 3.93101 0.338076 0.11425 0.5 10 2.328 3.075 3.55938 3.796 0.330403 0.106003 0.5 10 2.336 3.075 3.65 3.804 0.332 0.103494 0.5 10 2.344 3.075 3.755 3.888 0.337388 0.103458 0.5 10 2.352 3.075 3.785 3.91125 0.329017 0.0965223 0.5 10 2.36 3.075 4.23853 3.89083 0.322407 0.0940644 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2.36741 3.9 -0.170376 0.343122 0.5 10 1.16 3.1 4.82746 3.88997 -0.170346 0.372511 0.5 10 1.168 3.1 4.475 3.972 -0.155427 0.374016 0.5 10 1.176 3.1 4.93 3.968 -0.126 0.380799 0.5 10 1.184 3.1 4.75 3.9 -0.10645 0.378501 0.5 10 1.192 3.1 4.62431 3.97944 -0.102498 0.388771 0.5 10 1.2 3.1 3.555 3.996 -0.0615072 0.369219 0.5 10 1.208 3.1 3.53 3.88266 -0.0487488 0.370674 0.5 10 1.216 3.1 3.67921 3.97937 -0.048459 0.364515 0.5 10 1.224 3.1 4.175 3.7 -0.0542419 0.34822 0.5 10 1.232 3.1 4.13636 3.88509 -0.0481854 0.343489 0.5 10 1.24 3.1 4.365 3.896 -0.0457279 0.339367 0.5 10 1.248 3.1 4.77713 3.359 -0.190128 0.331474 0.5 10 1.256 3.1 4.54315 3.62312 -0.193388 0.329166 0.5 10 1.264 3.1 4.995 3.948 -0.236691 0.325768 0.5 10 1.272 3.1 4.985 3.932 -0.247083 0.327484 0.5 10 1.28 3.1 4.92012 3.8961 -0.247603 0.318632 0.5 10 1.288 3.1 4.59089 3.86871 -0.0679966 0.31748 0.5 10 1.296 3.1 4.89132 3.78106 -0.0562724 0.307604 0.5 10 1.304 3.1 4.805 3.86571 -0.0549918 0.28017 0.5 10 1.312 3.1 4.76953 3.98763 -0.151019 0.290124 0.5 10 1.32 3.1 4.69622 3.98497 -0.157579 0.305795 0.5 10 1.328 3.1 4.945 3.87114 -0.144416 0.304093 0.5 10 1.336 3.1 4.45175 3.8814 -0.121171 0.307218 0.5 10 1.344 3.1 4.85459 3.87893 -0.0984336 0.305365 0.5 10 1.352 3.1 4.71046 3.96436 -0.104092 0.31297 0.5 10 1.36 3.1 4.51885 3.49976 -0.153634 0.266988 0.5 10 1.368 3.1 4.73287 3.70089 -0.158043 0.266534 0.5 10 1.376 3.1 4.55496 3.76529 -0.148477 0.272314 0.5 10 1.384 3.1 4.975 3.89645 -0.140476 0.294532 0.5 10 1.392 3.1 4.96 3.94834 -0.140823 0.315168 0.5 10 1.4 3.1 4.99 3.804 -0.146173 0.15765 0.5 10 1.408 3.1 4.585 3.784 -0.048 0.0658682 0.5 10 1.416 3.1 4.83751 3.84615 -0.0481949 0.067562 0.5 10 1.424 3.1 2.835 3.488 -0.153816 0.0672197 0.5 10 1.432 3.1 4.97913 3.84331 -0.0581404 0.068471 0.5 10 1.44 3.1 3.02 3.956 -0.143721 0.0721675 0.5 10 1.448 3.1 4.93909 3.97292 -0.0653091 0.0708924 0.5 10 1.456 3.1 3.27273 3.87418 -0.140162 0.0723877 0.5 10 1.464 3.1 3.395 3.588 -0.153376 0.0745193 0.5 10 1.472 3.1 3.465 3.7 -0.154896 0.074691 0.5 10 1.48 3.1 4.92733 3.89386 -0.0976418 0.0703846 0.5 10 1.488 3.1 3.8329 3.768 -0.151388 0.083989 0.5 10 1.496 3.1 3.94736 3.74589 -0.153492 0.0857718 0.5 10 1.504 3.1 4.54588 3.44511 -0.163146 0.0780858 0.5 10 1.512 3.1 4.58145 3.58733 -0.160964 0.0837352 0.5 10 1.52 3.1 4.90406 3.624 -0.158687 0.0827698 0.5 10 1.528 3.1 4.65677 3.98 -0.149217 0.104118 0.5 10 1.536 3.1 4.86311 3.912 -0.148935 0.101846 0.5 10 1.544 3.1 4.78496 3.884 -0.14626 0.0670889 0.5 10 1.552 3.1 4.935 3.888 -0.151554 0.0571512 0.5 10 1.56 3.1 4.895 3.784 -0.155456 0.00841288 0.5 10 1.568 3.1 4.31691 3.95353 -0.192603 0.00944402 0.5 10 1.576 3.1 4.49 3.964 -0.185112 0.0098116 0.5 10 1.584 3.1 4.483 3.9624 -0.19938 0.0104588 0.5 10 1.592 3.1 4.83 3.20946 -0.251936 0.0104967 0.5 10 1.6 3.1 4.7 3.44 -0.254616 0.0111884 0.5 10 1.608 3.1 4.76632 3.98905 -0.199415 0.0132574 0.5 10 1.616 3.1 4.89877 3.94604 -0.215679 0.0161836 0.5 10 1.624 3.1 4.92 3.928 -0.222082 0.0184523 0.5 10 1.632 3.1 4.595 3.984 -0.248863 0.0180436 0.5 10 1.64 3.1 4.79177 3.94 -0.242041 0.0210986 0.5 10 1.648 3.1 4.78 3.96 -0.259454 0.0199127 0.5 10 1.656 3.1 4.86858 3.95389 -0.266028 0.0181493 0.5 10 1.664 3.1 4.99 3.992 -0.271907 0.0166712 0.5 10 1.672 3.1 4.76066 3.58 -0.0499231 0.0154184 0.5 10 1.68 3.1 4.81441 3.64753 -0.198824 0.0235286 0.5 10 1.688 3.1 4.69539 3.92831 -0.20473 0.0319062 0.5 10 1.696 3.1 4.95203 3.924 -0.210079 0.0542737 0.5 10 1.704 3.1 4.73 3.98109 -0.180906 0.0838125 0.5 10 1.712 3.1 4.75102 3.97281 -0.167946 0.119196 0.5 10 1.72 3.1 4.33122 3.98283 -0.150602 0.129934 0.5 10 1.728 3.1 4.74689 3.97751 -0.129126 0.135727 0.5 10 1.736 3.1 4.27 3.952 -0.109455 0.125655 0.5 10 1.744 3.1 3.355 3.96 -0.074215 0.120079 0.5 10 1.752 3.1 3.84549 3.96039 -0.0666254 0.120432 0.5 10 1.76 3.1 4.85 3.96 -0.181889 0.330058 0.5 10 1.768 3.1 4.81072 3.98057 -0.169831 0.40671 0.5 10 1.776 3.1 4.92668 3.97335 -0.223831 0.406315 0.5 10 1.784 3.1 4.81021 3.87691 -0.231821 0.365138 0.5 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4.795 3.93531 -0.218854 0.736324 0.5 10 1.96 3.1 4.955 3.968 -0.222215 0.748938 0.5 10 1.968 3.1 4.96804 3.84219 -0.200825 0.733716 0.5 10 1.976 3.1 4.97 3.944 -0.211113 0.740207 0.5 10 1.984 3.1 4.91 3.952 -0.224 0.724758 0.5 10 1.992 3.1 4.905 3.956 -0.23 0.706882 0.5 10 2 3.1 4.8986 3.89575 -0.237039 0.682373 0.5 10 2.008 3.1 4.88787 3.82 -0.230235 0.660099 0.5 10 2.016 3.1 4.80667 3.88552 -0.2478 0.637388 0.5 10 2.024 3.1 4.70819 3.99379 -0.25032 0.6255 0.5 10 2.032 3.1 4.88 3.88168 -0.236316 0.600385 0.5 10 2.04 3.1 4.9471 3.95079 -0.261355 0.580248 0.5 10 2.048 3.1 4.95 3.928 -0.254562 0.551804 0.5 10 2.056 3.1 4.8 3.976 -0.252 0.529514 0.5 10 2.064 3.1 4.95915 3.956 -0.251932 0.506125 0.5 10 2.072 3.1 4.96 3.912 -0.24 0.441427 0.5 10 2.08 3.1 4.885 3.99613 -0.291013 0.400535 0.5 10 2.088 3.1 2.535 3.98165 0.0551508 0.378537 0.5 10 2.096 3.1 3.04883 3.91468 0.0487743 0.367235 0.5 10 2.104 3.1 3.21 3.944 0.0376109 0.360142 0.5 10 2.112 3.1 3.225 3.99212 0.0262928 0.35054 0.5 10 2.12 3.1 4.19 3.98 0.0229396 0.344796 0.5 10 2.128 3.1 2.54 3.988 0.0848112 0.335489 0.5 10 2.136 3.1 2.96 3.912 0.099408 0.326902 0.5 10 2.144 3.1 3.16283 3.97026 0.0939061 0.321121 0.5 10 2.152 3.1 2.78 3.956 0.121977 0.315142 0.5 10 2.16 3.1 2.925 3.98 0.123523 0.311163 0.5 10 2.168 3.1 2.895 3.972 0.126202 0.308502 0.5 10 2.176 3.1 2.65 3.932 0.125104 0.298405 0.5 10 2.184 3.1 3.39 3.9815 0.123551 0.29776 0.5 10 2.192 3.1 3.36109 3.976 0.130271 0.293728 0.5 10 2.2 3.1 3.97666 3.952 0.106359 0.284187 0.5 10 2.208 3.1 3.81901 3.95921 0.140975 0.275504 0.5 10 2.216 3.1 3.72 3.984 0.1358 0.273389 0.5 10 2.224 3.1 3.85229 3.95732 0.125415 0.267935 0.5 10 2.232 3.1 4.07 3.9 0.132375 0.262678 0.5 10 2.24 3.1 4.09782 3.99826 0.121579 0.261643 0.5 10 2.248 3.1 4.50426 3.99803 0.136658 0.256948 0.5 10 2.256 3.1 4.975 3.988 0.132 0.252317 0.5 10 2.264 3.1 4.975 3.984 0.122889 0.247505 0.5 10 2.272 3.1 4.83 3.876 0.123233 0.227787 0.5 10 2.28 3.1 4.675 3.9 0.107514 0.219706 0.5 10 2.288 3.1 4.95586 3.9096 0.0992509 0.211094 0.5 10 2.296 3.1 4.995 3.77744 0.0985062 0.18643 0.5 10 2.304 3.1 4.86368 3.984 0.105871 0.143461 0.5 10 2.312 3.1 4.96009 3.98807 0.0758202 0.139522 0.5 10 2.32 3.1 4.89006 3.96 0.0827152 0.126489 0.5 10 2.328 3.1 3.46719 3.83775 0.339436 0.110666 0.5 10 2.336 3.1 3.58343 3.792 0.336681 0.10715 0.5 10 2.344 3.1 3.52 3.86951 0.346876 0.110602 0.5 10 2.352 3.1 3.98 3.95132 0.327419 0.10222 0.5 10 2.36 3.1 4.01683 3.728 0.334197 0.0947513 0.5 10 2.368 3.1 4.4 3.876 0.321708 0.0919935 0.5 10 2.376 3.1 4.29 3.976 0.332615 0.0920367 0.5 10 2.384 3.1 4.355 3.936 0.328257 0.081236 0.5 10 2.392 3.1 4.71858 3.99887 0.331562 0.0843501 0.5 10 2.4 3.1 4.98 3.948 0.320652 0.0789708 0.5 10 2.408 3.1 4.91 3.988 0.324986 0.0563972 0.5 10 2.416 3.1 1.37222 3.67903 0.665992 0.0423096 0.5 10 2.424 3.1 1.215 3.76984 0.706889 0.0437662 0.5 10 2.432 3.1 1.405 3.86342 0.668962 0.0430112 0.5 10 2.44 3.1 1.50494 3.752 0.657424 0.0424441 0.5 10 2.448 3.1 0.965 3.82 0.784007 0.0386169 0.5 10 2.456 3.1 1.27749 3.75144 0.712407 0.0418936 0.5 10 2.464 3.1 1.315 3.81659 0.709619 0.0404142 0.5 10 2.472 3.1 1.135 3.816 0.752343 0.0391853 0.5 10 2.48 3.1 1.405 3.712 0.698121 0.0390178 0.5 10 2.488 3.1 0.99 3.688 0.79644 0.0360464 0.5 10 2.496 3.1 1.035 3.2 0.791548 0.0382359 0.5 10 2.504 3.1 1.00808 3.784 0.803064 0.0371675 0.5 10 2.512 3.1 1.09628 2.888 0.784051 0.0354424 0.5 10 2.52 3.1 1.19 3.936 0.768699 0.0360202 0.5 10 2.528 3.1 0.545 3.14 0.974761 0.0305376 0.5 10 2.536 3.1 1.31953 3.02362 0.746278 0.0311912 0.5 10 2.544 3.1 0.96 2.916 0.839299 0.0329489 0.5 10 2.552 3.1 0.78931 3.8009 0.894286 0.0356693 0.5 10 2.56 3.1 1.095 3.688 0.813488 0.0323585 0.5 10 2.568 3.1 0.887639 2.69811 0.872769 0.0321905 0.5 10 2.576 3.1 0.57 3.90928 0.986798 0.034219 0.5 10 2.584 3.1 0.795 3.52835 0.907907 0.0341367 0.5 10 2.592 3.1 0.885 3.62 0.884198 0.0343113 0.5 10 2.6 3.1 1.045 2.668 0.842823 0.0264512 0.5 10 2.608 3.1 0.95635 2.28468 0.87173 0.0278043 0.5 10 2.616 3.1 1.06556 2.0054 0.848001 0.0266198 0.5 10 2.624 3.1 0.78 1.888 0.932 0.0247856 0.5 10 2.632 3.1 3.15158 3.81814 -0.433937 0.0260275 0.5 10 2.64 3.1 1.105 3.984 0.848291 0.0264257 0.5 10 2.648 3.1 2.575 3.54873 -0.381492 0.0255838 0.5 10 2.656 3.1 0.64388 2.3071 0.993584 0.027071 0.5 10 2.664 3.1 1.335 3.472 -0.142024 0.0271983 0.5 10 2.672 3.1 3.17 3.97269 -0.401225 0.0266091 0.5 10 2.68 3.1 2.49 3.576 -0.348 0.0255742 0.5 10 2.688 3.1 1.39389 3.69511 -0.13848 0.0271069 0.5 10 2.696 3.1 0.685 2.82686 0.993696 0.0249584 0.5 10 2.704 3.1 2.45 3.86 -0.322479 0.0233004 0.5 10 2.712 3.1 2.65 3.956 -0.335639 0.0258532 0.5 10 2.72 3.1 1.59518 3.08015 -0.176769 0.0255551 0.5 10 2.728 3.1 2.335 3.596 -0.292577 0.0259331 0.5 10 2.736 3.1 2.77363 3.94377 -0.32719 0.0268285 0.5 10 2.744 3.1 2.225 3.896 -0.268037 0.0236714 0.5 10 2.752 3.1 1.58149 3.71719 -0.144751 0.0285553 0.5 10 2.76 3.1 1.73 3.32 -0.172749 0.026394 0.5 10 2.768 3.1 1.7 3.488 -0.164 0.0276208 0.5 10 2.776 3.1 2.18 3.548 -0.231701 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0.00399995 0.368878 0.5 10 1.112 3.15 4.16481 3.552 -0.0106652 0.356552 0.5 10 1.12 3.15 4.30902 3.81521 -0.00922848 0.345554 0.5 10 1.128 3.15 4.97645 3.72748 0.0300651 0.346318 0.5 10 1.136 3.15 4.83 3.148 0.0391901 0.337716 0.5 10 1.144 3.15 4.80667 3.70534 -0.180072 0.36597 0.5 10 1.152 3.15 3.905 3.98 -0.155259 0.377695 0.5 10 1.16 3.15 4.88 3.94 -0.149115 0.391513 0.5 10 1.168 3.15 4.78513 3.92011 -0.119496 0.383275 0.5 10 1.176 3.15 3.685 3.776 -0.0882823 0.389438 0.5 10 1.184 3.15 3.99 3.876 -0.0806183 0.39375 0.5 10 1.192 3.15 4.23 3.96994 -0.0803639 0.384496 0.5 10 1.2 3.15 4.815 3.93521 -0.07821 0.375878 0.5 10 1.208 3.15 3.58 3.984 -0.0356508 0.360758 0.5 10 1.216 3.15 4.09585 3.79668 -0.0475779 0.348074 0.5 10 1.224 3.15 4.68429 3.988 -0.0343085 0.343285 0.5 10 1.232 3.15 4.84 3.792 -0.0361077 0.340529 0.5 10 1.24 3.15 4.89351 3.444 -0.218288 0.345556 0.5 10 1.248 3.15 4.68409 3.97763 -0.218355 0.34079 0.5 10 1.256 3.15 4.87039 3.84 -0.226131 0.338158 0.5 10 1.264 3.15 4.96707 3.97027 -0.237305 0.345651 0.5 10 1.272 3.15 4.61622 3.89698 -0.248532 0.323218 0.5 10 1.28 3.15 4.845 3.96 -0.0443529 0.313377 0.5 10 1.288 3.15 4.4865 3.95677 -0.062492 0.286144 0.5 10 1.296 3.15 4.10708 3.99766 -0.184643 0.289659 0.5 10 1.304 3.15 4.82682 3.9297 -0.151716 0.29454 0.5 10 1.312 3.15 4.97723 3.912 -0.155593 0.311845 0.5 10 1.32 3.15 4.47872 3.976 -0.128294 0.322234 0.5 10 1.328 3.15 4.49 3.976 -0.117623 0.313835 0.5 10 1.336 3.15 4.41987 3.956 -0.102234 0.306868 0.5 10 1.344 3.15 4.9323 3.80334 -0.117613 0.269939 0.5 10 1.352 3.15 4.93856 3.432 -0.145058 0.274643 0.5 10 1.36 3.15 4.935 3.76 -0.129926 0.275714 0.5 10 1.368 3.15 4.76282 3.83391 -0.156268 0.278233 0.5 10 1.376 3.15 4.865 3.89636 -0.142441 0.300898 0.5 10 1.384 3.15 4.725 3.988 -0.14127 0.309757 0.5 10 1.392 3.15 4.88531 3.69625 -0.15704 0.277684 0.5 10 1.4 3.15 4.70114 3.88708 -0.163287 0.245907 0.5 10 1.408 3.15 4.97192 3.8 -0.16687 0.164413 0.5 10 1.416 3.15 4.495 3.916 -0.0619082 0.0692145 0.5 10 1.424 3.15 2.82536 3.70076 -0.157974 0.0755135 0.5 10 1.432 3.15 2.94 3.688 -0.154886 0.0750311 0.5 10 1.44 3.15 4.96457 3.92766 -0.067108 0.0765134 0.5 10 1.448 3.15 3.00955 3.9867 -0.155094 0.0774355 0.5 10 1.456 3.15 3.14904 3.73984 -0.156872 0.077045 0.5 10 1.464 3.15 3.26357 3.74686 -0.156555 0.0775088 0.5 10 1.472 3.15 3.37 3.99189 -0.15321 0.0915806 0.5 10 1.48 3.15 3.695 3.748 -0.159723 0.0856912 0.5 10 1.488 3.15 3.795 3.93354 -0.156245 0.0898521 0.5 10 1.496 3.15 3.86 3.76 -0.15649 0.085597 0.5 10 1.504 3.15 4.06723 3.8482 -0.153316 0.0935891 0.5 10 1.512 3.15 4.05425 3.9954 -0.151989 0.0972226 0.5 10 1.52 3.15 4.33377 3.99432 -0.150116 0.100347 0.5 10 1.528 3.15 4.85036 3.79053 -0.162205 0.103789 0.5 10 1.536 3.15 4.905 3.988 -0.164514 0.0832892 0.5 10 1.544 3.15 4.99602 3.93585 -0.159704 0.110619 0.5 10 1.552 3.15 4.97337 3.9787 -0.150186 0.08529 0.5 10 1.56 3.15 4.895 3.96991 -0.157047 0.0309593 0.5 10 1.568 3.15 4.47985 3.99855 -0.18584 0.0100754 0.5 10 1.576 3.15 4.99 3.16486 -0.231105 0.010465 0.5 10 1.584 3.15 4.93839 3.90354 -0.201892 0.0110335 0.5 10 1.592 3.15 4.89351 3.88665 -0.206311 0.0138199 0.5 10 1.6 3.15 4.87025 3.7282 -0.216373 0.0150456 0.5 10 1.608 3.15 4.71 3.96877 -0.210762 0.0178389 0.5 10 1.616 3.15 4.91293 3.94788 -0.230122 0.0211776 0.5 10 1.624 3.15 4.68574 3.98059 -0.231712 0.0233171 0.5 10 1.632 3.15 4.77 3.972 -0.23841 0.0248951 0.5 10 1.64 3.15 4.74885 3.95998 -0.251349 0.0224488 0.5 10 1.648 3.15 4.63205 3.76738 -0.0675221 0.0171284 0.5 10 1.656 3.15 4.69771 3.73816 -0.0665147 0.0201538 0.5 10 1.664 3.15 4.93833 3.588 -0.189998 0.0223761 0.5 10 1.672 3.15 4.89216 3.72 -0.199448 0.0302726 0.5 10 1.68 3.15 4.98726 3.932 -0.192878 0.0476684 0.5 10 1.688 3.15 4.88853 3.872 -0.205684 0.0639136 0.5 10 1.696 3.15 4.64935 3.95548 -0.169675 0.104506 0.5 10 1.704 3.15 4.96034 3.924 -0.147591 0.12749 0.5 10 1.712 3.15 4.625 3.964 -0.138257 0.141205 0.5 10 1.72 3.15 3.785 3.916 -0.105596 0.124918 0.5 10 1.728 3.15 4.44157 3.932 -0.0999455 0.131469 0.5 10 1.736 3.15 3.855 3.928 -0.0718734 0.123296 0.5 10 1.744 3.15 4.67583 3.87266 -0.171367 0.186632 0.5 10 1.752 3.15 4.98 3.968 -0.186498 0.417506 0.5 10 1.76 3.15 4.96372 3.95498 -0.136 0.405839 0.5 10 1.768 3.15 4.64049 3.996 -0.228083 0.400804 0.5 10 1.776 3.15 4.755 3.9999 -0.242825 0.376078 0.5 10 1.784 3.15 4.515 3.956 -0.0834948 0.355709 0.5 10 1.792 3.15 4.63094 3.91675 -0.0731544 0.344306 0.5 10 1.8 3.15 4.875 3.968 -0.0624926 0.335436 0.5 10 1.808 3.15 4.965 3.8 -0.188146 0.374286 0.5 10 1.816 3.15 4.40721 3.94457 -0.1769 0.436262 0.5 10 1.824 3.15 4.79115 3.924 -0.177172 0.509445 0.5 10 1.832 3.15 4.91291 3.87636 -0.18434 0.553227 0.5 10 1.84 3.15 4.90107 3.94086 -0.169126 0.583585 0.5 10 1.848 3.15 4.95824 3.90303 -0.182221 0.615678 0.5 10 1.856 3.15 4.96733 3.96059 -0.189114 0.64498 0.5 10 1.864 3.15 4.78787 3.9743 -0.179921 0.650978 0.5 10 1.872 3.15 4.975 3.964 -0.191185 0.683454 0.5 10 1.88 3.15 4.98077 3.92462 -0.175019 0.685287 0.5 10 1.888 3.15 4.99446 3.94357 -0.187823 0.71219 0.5 10 1.896 3.15 4.86422 3.84351 -0.203194 0.705427 0.5 10 1.904 3.15 4.98 3.948 -0.212 0.726075 0.5 10 1.912 3.15 4.95185 3.988 -0.219726 0.728223 0.5 10 1.92 3.15 4.99 3.94 -0.187258 0.747644 0.5 10 1.928 3.15 4.94565 3.82052 -0.201973 0.74896 0.5 10 1.936 3.15 4.61955 3.9717 -0.203189 0.745664 0.5 10 1.944 3.15 4.905 3.892 -0.198336 0.759017 0.5 10 1.952 3.15 4.745 3.952 -0.215534 0.756558 0.5 10 1.96 3.15 4.62019 3.93615 -0.215827 0.741183 0.5 10 1.968 3.15 4.915 3.99431 -0.230762 0.744377 0.5 10 1.976 3.15 4.905 3.99098 -0.220687 0.73445 0.5 10 1.984 3.15 4.94067 3.9897 -0.212003 0.716005 0.5 10 1.992 3.15 4.84074 3.992 -0.229501 0.697755 0.5 10 2 3.15 4.97864 3.94691 -0.216096 0.677871 0.5 10 2.008 3.15 4.935 3.992 -0.227269 0.666382 0.5 10 2.016 3.15 4.96 3.924 -0.236411 0.636612 0.5 10 2.024 3.15 4.825 3.98 -0.22951 0.593525 0.5 10 2.032 3.15 4.92693 3.808 -0.252167 0.56694 0.5 10 2.04 3.15 4.81 3.952 -0.257267 0.54065 0.5 10 2.048 3.15 4.785 3.984 -0.229079 0.501137 0.5 10 2.056 3.15 4.83167 3.972 -0.235454 0.481121 0.5 10 2.064 3.15 4.99 3.99447 -0.266017 0.417389 0.5 10 2.072 3.15 3.6 3.94 -0.00999489 0.375464 0.5 10 2.08 3.15 2.26 3.896 0.0768054 0.37005 0.5 10 2.088 3.15 2.27533 3.95507 0.0821287 0.366052 0.5 10 2.096 3.15 2.72 3.968 0.0740347 0.356217 0.5 10 2.104 3.15 2.68 3.996 0.0718648 0.34826 0.5 10 2.112 3.15 3.20749 3.97799 0.0576997 0.335405 0.5 10 2.12 3.15 2.7517 3.996 0.108703 0.325711 0.5 10 2.128 3.15 3.02 3.896 0.104817 0.315637 0.5 10 2.136 3.15 2.97 3.984 0.0935784 0.311746 0.5 10 2.144 3.15 3.225 3.96372 0.105786 0.303274 0.5 10 2.152 3.15 3.40521 3.912 0.0950082 0.295861 0.5 10 2.16 3.15 3.365 3.988 0.0871699 0.290355 0.5 10 2.168 3.15 3.81888 3.876 0.0823179 0.27939 0.5 10 2.176 3.15 3.99467 3.97574 0.0867385 0.274436 0.5 10 2.184 3.15 3.925 3.984 0.0978326 0.271271 0.5 10 2.192 3.15 4.22 3.976 0.094 0.262834 0.5 10 2.2 3.15 4.61 3.996 0.102791 0.257236 0.5 10 2.208 3.15 4.67 3.85998 0.0884383 0.243267 0.5 10 2.216 3.15 4.71933 3.99115 0.106439 0.234869 0.5 10 2.224 3.15 4.825 3.928 0.0795543 0.227296 0.5 10 2.232 3.15 4.59 3.976 0.0774267 0.214201 0.5 10 2.24 3.15 4.815 3.86942 0.0626262 0.187155 0.5 10 2.248 3.15 4.975 3.944 0.0783203 0.193746 0.5 10 2.256 3.15 4.415 3.988 0.0613624 0.137877 0.5 10 2.264 3.15 4.585 3.996 0.0582923 0.126105 0.5 10 2.272 3.15 2.82 3.592 0.342912 0.119933 0.5 10 2.28 3.15 2.885 3.36 0.35 0.108696 0.5 10 2.288 3.15 2.965 3.716 0.351223 0.113764 0.5 10 2.296 3.15 3.095 3.96571 0.345668 0.113576 0.5 10 2.304 3.15 3.1 3.90602 0.341305 0.110645 0.5 10 2.312 3.15 3.335 3.816 0.333338 0.102402 0.5 10 2.32 3.15 3.48 3.87083 0.330944 0.0997516 0.5 10 2.328 3.15 3.38778 3.85123 0.332489 0.0909738 0.5 10 2.336 3.15 3.695 3.88218 0.34237 0.0933011 0.5 10 2.344 3.15 4.06824 3.85459 0.325141 0.0875049 0.5 10 2.352 3.15 4.145 3.836 0.330054 0.0856924 0.5 10 2.36 3.15 4.31786 3.896 0.325051 0.084894 0.5 10 2.368 3.15 4.55441 3.872 0.326083 0.0793265 0.5 10 2.376 3.15 4.98115 3.96092 0.312809 0.0718736 0.5 10 2.384 3.15 4.78152 3.97284 0.322309 0.0735081 0.5 10 2.392 3.15 4.64 3.856 0.306 0.0434488 0.5 10 2.4 3.15 4.955 3.93311 0.367697 0.0424533 0.5 10 2.408 3.15 1.32 3.852 0.679071 0.0417142 0.5 10 2.416 3.15 1.21 3.864 0.705068 0.0425948 0.5 10 2.424 3.15 1.065 3.684 0.742525 0.0357282 0.5 10 2.432 3.15 0.745 3.58094 0.851452 0.0375549 0.5 10 2.44 3.15 1.255 3.676 0.712087 0.0378146 0.5 10 2.448 3.15 0.97 3.19976 0.786046 0.0375232 0.5 10 2.456 3.15 0.77 3.97985 0.853634 0.0370672 0.5 10 2.464 3.15 0.89 3.82 0.821012 0.0383971 0.5 10 2.472 3.15 1.25 3.292 0.7308 0.0363857 0.5 10 2.48 3.15 0.655 2.404 0.91 0.0318466 0.5 10 2.488 3.15 1.015 3.89063 0.797217 0.0382587 0.5 10 2.496 3.15 1 3.39428 0.804567 0.0358429 0.5 10 2.504 3.15 1.1 3.496 0.780997 0.0319515 0.5 10 2.512 3.15 0.82 3.568 0.86491 0.0334222 0.5 10 2.52 3.15 0.83 1.484 0.866083 0.026137 0.5 10 2.528 3.15 0.595 2.988 0.959033 0.0309539 0.5 10 2.536 3.15 0.56 2.852 0.975137 0.0307199 0.5 10 2.544 3.15 0.625 2.988 0.952 0.0316949 0.5 10 2.552 3.15 0.675 2.792 0.94 0.0304112 0.5 10 2.56 3.15 0.925 2.98 0.8626 0.027155 0.5 10 2.568 3.15 0.78 2.384 0.906609 0.0281398 0.5 10 2.576 3.15 0.80085 3.26068 0.902034 0.0289185 0.5 10 2.584 3.15 0.855 3.956 0.892 0.0315024 0.5 10 2.592 3.15 4.31 3.94 -0.517581 0.0269869 0.5 10 2.6 3.15 3.82 3.90209 -0.495165 0.0260662 0.5 10 2.608 3.15 3.89918 3.81202 -0.491041 0.0266412 0.5 10 2.616 3.15 0.605 3.32 0.993028 0.031517 0.5 10 2.624 3.15 3.45 3.864 -0.464438 0.0269973 0.5 10 2.632 3.15 0.645 3.7832 0.984797 0.0278503 0.5 10 2.64 3.15 3.975 3.996 -0.469076 0.0271853 0.5 10 2.648 3.15 3.475 3.94 -0.445619 0.0277063 0.5 10 2.656 3.15 3.42843 3.85475 -0.434571 0.0271181 0.5 10 2.664 3.15 1.32298 3.732 -0.155303 0.0288146 0.5 10 2.672 3.15 2.225 3.38578 -0.338937 0.0261108 0.5 10 2.68 3.15 2.46733 3.412 -0.355366 0.0254111 0.5 10 2.688 3.15 3.64909 3.94994 -0.41452 0.0264261 0.5 10 2.696 3.15 3.335 3.976 -0.399289 0.0280365 0.5 10 2.704 3.15 2.6628 3.792 -0.354836 0.0277234 0.5 10 2.712 3.15 1.585 2.944 -0.190047 0.0260964 0.5 10 2.72 3.15 2.245 3.57641 -0.30336 0.0258999 0.5 10 2.728 3.15 2.03696 3.32556 -0.267122 0.0257442 0.5 10 2.736 3.15 1.53 3.692 -0.16156 0.0309455 0.5 10 2.744 3.15 1.54 3.804 -0.159791 0.0307469 0.5 10 2.752 3.15 2.57 3.864 -0.307527 0.028412 0.5 10 2.76 3.15 1.655 3.35115 -0.175508 0.0279401 0.5 10 2.768 3.15 2.11 3.54 -0.242439 0.0270496 0.5 10 2.776 3.15 1.83 3.468 -0.196795 0.0265005 0.5 10 2.784 3.15 1.7703 3.388 -0.17497 0.0315266 0.5 10 2.792 3.15 1.775 3.652 -0.175985 0.028709 0.5 10 2.8 3.15 1.88308 3.5 -0.185277 0.0298508 0.5 10 2.808 3.15 1.75 3.9445 -0.158963 0.0277298 0.5 10 2.816 3.15 2.03052 3.48858 -0.184533 0.0261018 0.5 10 2.824 3.15 2.095 3.67871 -0.188959 0.0246067 0.5 10 2.832 3.15 1.92 3.66872 -0.16404 0.0285158 0.5 10 2.84 3.15 2.055 3.84294 -0.180204 0.033257 0.5 10 2.848 3.15 2.17842 3.884 -0.174802 0.02265 0.5 10 2.856 3.15 2.16455 3.88 -0.17116 0.0287576 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2.888 3.175 2.6134 3.928 -0.1895 0.0243987 0.5 10 2.896 3.175 2.45 3.94833 -0.176028 0.029651 0.5 10 2.904 3.175 2.54 3.88775 -0.170111 0.0303784 0.5 10 2.912 3.175 2.595 3.988 -0.162159 0.0308861 0.5 10 2.92 3.175 2.835 3.688 -0.168 0.0222924 0.5 10 2.928 3.175 2.67 3.984 -0.170798 0.0231089 0.5 10 2.936 3.175 2.98 3.956 -0.159741 0.0230493 0.5 10 2.944 3.175 2.895 3.98 -0.162657 0.027648 0.5 10 2.952 3.175 3.12364 3.976 -0.162059 0.0273365 0.5 10 2.96 3.175 3.43 3.884 -0.165541 0.0220511 0.5 10 2.968 3.175 3.53547 3.836 -0.161545 0.0231874 0.5 10 2.976 3.175 4.26912 3.47908 -0.156246 0.0219793 0.5 10 2.984 3.175 4.8878 3.344 -0.157229 0.0222053 0.5 10 2.992 3.175 4.01248 3.83761 -0.15596 0.0251373 0.5 10 3 3.175 4.52937 3.8938 -0.129052 0.397596 0.5 10 1 3.2 4.01 3.668 -0.104105 0.401132 0.5 10 1.008 3.2 4.15268 3.77815 -0.103629 0.401227 0.5 10 1.016 3.2 4.26 3.86393 -0.101603 0.408499 0.5 10 1.024 3.2 3.41 3.68 -0.066507 0.398029 0.5 10 1.032 3.2 3.525 3.792 -0.062 0.401014 0.5 10 1.04 3.2 3.87 3.864 -0.0639226 0.390255 0.5 10 1.048 3.2 4.23103 3.94411 -0.0595893 0.388486 0.5 10 1.056 3.2 4.67496 3.60587 -0.0617938 0.381308 0.5 10 1.064 3.2 4.52 3.99276 -0.0689494 0.371435 0.5 10 1.072 3.2 4.895 3.976 0.0577149 0.370005 0.5 10 1.08 3.2 3.47 3.996 0.000893798 0.362003 0.5 10 1.088 3.2 3.725 3.964 0.0114897 0.357152 0.5 10 1.096 3.2 4.74859 3.36 0.0163535 0.35642 0.5 10 1.104 3.2 4.68179 3.67208 0.021971 0.361412 0.5 10 1.112 3.2 4.92546 3.87237 0.0148337 0.352063 0.5 10 1.12 3.2 4.33 3.928 -0.0178645 0.346143 0.5 10 1.128 3.2 4.99505 3.96398 -0.163245 0.347844 0.5 10 1.136 3.2 4.91304 3.908 -0.170324 0.384244 0.5 10 1.144 3.2 4.775 3.828 -0.145311 0.400507 0.5 10 1.152 3.2 4.94 3.948 -0.145859 0.403093 0.5 10 1.16 3.2 3.85727 3.86721 -0.102886 0.394798 0.5 10 1.168 3.2 4.27752 3.948 -0.103066 0.39824 0.5 10 1.176 3.2 3.915 3.82802 -0.0816123 0.389534 0.5 10 1.184 3.2 3.65157 3.94785 -0.0664915 0.383459 0.5 10 1.192 3.2 4.61 3.88 -0.0671362 0.380623 0.5 10 1.2 3.2 4.7156 3.91648 -0.0605565 0.372482 0.5 10 1.208 3.2 3.755 3.89917 -0.0319884 0.350656 0.5 10 1.216 3.2 4.84 3.89072 -0.0366641 0.353657 0.5 10 1.224 3.2 4.95 3.148 -0.209353 0.341966 0.5 10 1.232 3.2 4.85701 3.304 -0.208318 0.348532 0.5 10 1.24 3.2 4.93 3.636 -0.21853 0.352707 0.5 10 1.248 3.2 4.855 3.608 -0.218934 0.346965 0.5 10 1.256 3.2 4.945 3.936 -0.230997 0.339018 0.5 10 1.264 3.2 4.93456 3.73175 -0.0586771 0.315864 0.5 10 1.272 3.2 4.885 3.8482 -0.0396989 0.313773 0.5 10 1.28 3.2 4.40379 3.84061 -0.160728 0.289169 0.5 10 1.288 3.2 4.99628 3.89426 -0.0459945 0.309851 0.5 10 1.296 3.2 4.44854 3.99483 -0.155206 0.308293 0.5 10 1.304 3.2 4.87054 3.99889 -0.176516 0.310133 0.5 10 1.312 3.2 4.70343 3.996 -0.112058 0.318056 0.5 10 1.32 3.2 4.48371 3.99625 -0.113549 0.316608 0.5 10 1.328 3.2 4.88 3.968 -0.0969611 0.31136 0.5 10 1.336 3.2 4.63 3.392 -0.135465 0.279498 0.5 10 1.344 3.2 4.97248 3.52352 -0.144267 0.278608 0.5 10 1.352 3.2 4.80665 3.62569 -0.134197 0.284259 0.5 10 1.36 3.2 4.835 3.76 -0.141037 0.2993 0.5 10 1.368 3.2 4.87632 3.96506 -0.128505 0.318793 0.5 10 1.376 3.2 4.97872 3.78298 -0.14012 0.323308 0.5 10 1.384 3.2 4.92146 3.98725 -0.144987 0.307185 0.5 10 1.392 3.2 4.73456 3.91965 -0.158282 0.257783 0.5 10 1.4 3.2 4.71974 3.892 -0.15639 0.143977 0.5 10 1.408 3.2 4.7588 3.992 -0.0508646 0.0676309 0.5 10 1.416 3.2 4.905 3.824 -0.0536373 0.0736426 0.5 10 1.424 3.2 4.675 3.796 -0.0649924 0.0734198 0.5 10 1.432 3.2 4.18 3.876 -0.087696 0.0712799 0.5 10 1.44 3.2 4.88882 3.95229 -0.0726968 0.0764025 0.5 10 1.448 3.2 3.04591 3.77673 -0.157486 0.074331 0.5 10 1.456 3.2 3.245 3.864 -0.158551 0.0830379 0.5 10 1.464 3.2 3.2213 3.98104 -0.15516 0.0805827 0.5 10 1.472 3.2 3.38609 3.88195 -0.156582 0.0884428 0.5 10 1.48 3.2 3.57 3.77451 -0.158571 0.0871762 0.5 10 1.488 3.2 3.915 3.91718 -0.158941 0.0851497 0.5 10 1.496 3.2 3.78 3.96 -0.153604 0.100242 0.5 10 1.504 3.2 4.15 3.82 -0.159484 0.102713 0.5 10 1.512 3.2 4.57123 3.68412 -0.159956 0.0997316 0.5 10 1.52 3.2 4.645 3.6968 -0.16362 0.100256 0.5 10 1.528 3.2 4.69629 3.76503 -0.16 0.10421 0.5 10 1.536 3.2 4.91 3.83459 -0.155991 0.118932 0.5 10 1.544 3.2 4.98102 3.74408 -0.153179 0.0817579 0.5 10 1.552 3.2 4.88392 3.79114 -0.144754 0.032076 0.5 10 1.56 3.2 4.89862 3.96904 -0.14749 0.0252203 0.5 10 1.568 3.2 4.95239 3.23391 -0.228589 0.012692 0.5 10 1.576 3.2 4.64 3.964 -0.189209 0.0143829 0.5 10 1.584 3.2 4.95332 3.824 -0.195301 0.0159158 0.5 10 1.592 3.2 4.875 3.996 -0.189864 0.0196374 0.5 10 1.6 3.2 4.83 3.848 -0.216939 0.021903 0.5 10 1.608 3.2 4.95762 3.91454 -0.204564 0.0262707 0.5 10 1.616 3.2 4.63 3.968 -0.224014 0.0266897 0.5 10 1.624 3.2 4.885 3.956 -0.22 0.0281607 0.5 10 1.632 3.2 4.81938 3.992 -0.247157 0.0260032 0.5 10 1.64 3.2 4.935 3.98347 -0.249801 0.0205251 0.5 10 1.648 3.2 4.79876 3.684 -0.0673523 0.0237563 0.5 10 1.656 3.2 4.75 3.61476 -0.190115 0.0259082 0.5 10 1.664 3.2 4.84998 3.92799 -0.191515 0.0366043 0.5 10 1.672 3.2 4.965 3.988 -0.192612 0.065479 0.5 10 1.68 3.2 4.90133 3.99229 -0.189197 0.0998281 0.5 10 1.688 3.2 4.59878 3.94703 -0.152296 0.119893 0.5 10 1.696 3.2 4.67472 3.97177 -0.145516 0.14993 0.5 10 1.704 3.2 4.57738 3.98572 -0.13384 0.146596 0.5 10 1.712 3.2 4.61 3.98329 -0.110044 0.153515 0.5 10 1.72 3.2 4.48016 3.97613 -0.0898901 0.147493 0.5 10 1.728 3.2 4.97958 3.99966 -0.0819766 0.152372 0.5 10 1.736 3.2 4.93139 3.77312 -0.170803 0.31073 0.5 10 1.744 3.2 4.9 3.956 -0.190689 0.448283 0.5 10 1.752 3.2 4.94 3.83438 -0.215017 0.41177 0.5 10 1.76 3.2 4.815 3.89877 -0.220398 0.393609 0.5 10 1.768 3.2 4.9 3.996 -0.096 0.393634 0.5 10 1.776 3.2 4.975 3.992 -0.0848496 0.381419 0.5 10 1.784 3.2 4.9912 3.96 -0.0701862 0.357052 0.5 10 1.792 3.2 4.575 3.94 -0.177471 0.35134 0.5 10 1.8 3.2 4.82649 3.92 -0.164439 0.426487 0.5 10 1.808 3.2 4.8109 3.98472 -0.174787 0.501745 0.5 10 1.816 3.2 4.9657 3.93256 -0.157016 0.539434 0.5 10 1.824 3.2 4.84579 3.92463 -0.162121 0.572555 0.5 10 1.832 3.2 4.705 3.9 -0.173914 0.598374 0.5 10 1.84 3.2 4.715 3.992 -0.180691 0.634748 0.5 10 1.848 3.2 4.975 3.98 -0.179946 0.671937 0.5 10 1.856 3.2 4.91 3.79287 -0.185352 0.666178 0.5 10 1.864 3.2 4.71987 3.948 -0.194334 0.685264 0.5 10 1.872 3.2 4.78 3.94399 -0.174249 0.699681 0.5 10 1.88 3.2 4.75008 3.788 -0.200007 0.698752 0.5 10 1.888 3.2 4.785 3.9953 -0.200279 0.735582 0.5 10 1.896 3.2 4.66337 3.89974 -0.181558 0.725283 0.5 10 1.904 3.2 4.975 3.85274 -0.199878 0.75622 0.5 10 1.912 3.2 4.945 3.81928 -0.204 0.757658 0.5 10 1.92 3.2 4.895 3.96212 -0.204786 0.774894 0.5 10 1.928 3.2 4.97 3.804 -0.20953 0.767325 0.5 10 1.936 3.2 4.965 3.98 -0.203682 0.787849 0.5 10 1.944 3.2 4.89466 3.99573 -0.212401 0.779248 0.5 10 1.952 3.2 4.87 3.964 -0.188056 0.757317 0.5 10 1.96 3.2 4.78 3.992 -0.186533 0.733159 0.5 10 1.968 3.2 4.725 3.912 -0.220073 0.721126 0.5 10 1.976 3.2 4.945 3.856 -0.217362 0.710226 0.5 10 1.984 3.2 4.91897 3.952 -0.216248 0.702234 0.5 10 1.992 3.2 4.98384 3.92875 -0.224994 0.683938 0.5 10 2 3.2 4.88516 3.85613 -0.230689 0.646996 0.5 10 2.008 3.2 4.97 3.82 -0.227514 0.619699 0.5 10 2.016 3.2 4.84 3.98 -0.252567 0.594211 0.5 10 2.024 3.2 4.9 3.89324 -0.248942 0.560233 0.5 10 2.032 3.2 4.95126 3.75301 -0.242108 0.514174 0.5 10 2.04 3.2 4.9536 3.96288 -0.23627 0.503767 0.5 10 2.048 3.2 4.90998 3.992 -0.221841 0.437283 0.5 10 2.056 3.2 4.48 3.956 -0.318142 0.389129 0.5 10 2.064 3.2 2.77467 3.984 0.0425095 0.38071 0.5 10 2.072 3.2 2.62 3.99359 0.0616794 0.369404 0.5 10 2.08 3.2 2.47 3.932 0.0745332 0.357241 0.5 10 2.088 3.2 2.57821 3.936 0.0833285 0.352163 0.5 10 2.096 3.2 2.61747 3.988 0.0757612 0.345829 0.5 10 2.104 3.2 2.57 3.96255 0.0766163 0.330682 0.5 10 2.112 3.2 2.735 3.97201 0.0705088 0.323017 0.5 10 2.12 3.2 3.08 3.956 0.06658 0.313619 0.5 10 2.128 3.2 3.3775 3.976 0.0614299 0.301662 0.5 10 2.136 3.2 3.32 3.996 0.0885693 0.287784 0.5 10 2.144 3.2 3.90054 3.86443 0.0896678 0.275209 0.5 10 2.152 3.2 3.66 3.876 0.075749 0.270095 0.5 10 2.16 3.2 3.85639 3.9656 0.0779999 0.267991 0.5 10 2.168 3.2 4.48654 3.936 0.0714484 0.2625 0.5 10 2.176 3.2 4.43 3.98226 0.0841128 0.257595 0.5 10 2.184 3.2 4.82942 3.95553 0.0704967 0.248604 0.5 10 2.192 3.2 4.63713 3.9737 0.0793083 0.233688 0.5 10 2.2 3.2 4.70423 3.97986 0.0693983 0.223485 0.5 10 2.208 3.2 4.92 3.864 0.0698667 0.200772 0.5 10 2.216 3.2 4.825 3.96202 0.0686772 0.176168 0.5 10 2.224 3.2 4.97415 3.91532 0.044935 0.164983 0.5 10 2.232 3.2 4.96 3.828 0.0335636 0.1355 0.5 10 2.24 3.2 2.44156 3.796 0.341678 0.128601 0.5 10 2.248 3.2 2.85681 3.584 0.328564 0.109316 0.5 10 2.256 3.2 2.58 3.824 0.346565 0.119691 0.5 10 2.264 3.2 2.81864 3.67891 0.336263 0.109124 0.5 10 2.272 3.2 2.825 3.65331 0.353319 0.110372 0.5 10 2.28 3.2 2.92 3.85811 0.338602 0.107829 0.5 10 2.288 3.2 3.085 3.992 0.335175 0.108067 0.5 10 2.296 3.2 3.04974 3.86732 0.349674 0.104716 0.5 10 2.304 3.2 3.26 3.94459 0.346768 0.0968162 0.5 10 2.312 3.2 3.305 3.97995 0.344994 0.100193 0.5 10 2.32 3.2 3.305 3.9011 0.341706 0.0927989 0.5 10 2.328 3.2 3.615 3.72871 0.337705 0.0850516 0.5 10 2.336 3.2 3.7 3.90934 0.339271 0.0890711 0.5 10 2.344 3.2 4.055 3.864 0.329526 0.0787975 0.5 10 2.352 3.2 4.1093 3.98744 0.33766 0.0796659 0.5 10 2.36 3.2 4.69915 3.99932 0.323653 0.0690774 0.5 10 2.368 3.2 4.61355 3.86284 0.322318 0.0689208 0.5 10 2.376 3.2 4.855 3.7 0.31967 0.0636749 0.5 10 2.384 3.2 4.98233 3.804 0.309881 0.0546314 0.5 10 2.392 3.2 4.1 3.964 -0.191351 0.0528805 0.5 10 2.4 3.2 4.60289 3.95032 -0.19753 0.052373 0.5 10 2.408 3.2 4.9381 3.77448 -0.183849 0.0506391 0.5 10 2.416 3.2 4.97 3.884 -0.190203 0.0486483 0.5 10 2.424 3.2 4.69 3.83271 -0.16901 0.0480384 0.5 10 2.432 3.2 4.925 3.90027 -0.170571 0.0483823 0.5 10 2.44 3.2 4.91 3.824 -0.155787 0.045373 0.5 10 2.448 3.2 4.835 3.988 -0.16503 0.0440894 0.5 10 2.456 3.2 4.89 3.99268 -0.152245 0.045777 0.5 10 2.464 3.2 0.985 3.532 0.796 0.0352838 0.5 10 2.472 3.2 0.815 3.3 0.852 0.0349079 0.5 10 2.48 3.2 0.87662 3.8493 0.836418 0.0353666 0.5 10 2.488 3.2 0.855739 3.18459 0.846002 0.0339525 0.5 10 2.496 3.2 0.635 3.952 0.93198 0.0338942 0.5 10 2.504 3.2 0.68 3.72525 0.916671 0.0329659 0.5 10 2.512 3.2 0.765 3.568 0.888903 0.032264 0.5 10 2.52 3.2 0.755738 3.416 0.896 0.0323381 0.5 10 2.528 3.2 0.74 3.308 0.909105 0.0321932 0.5 10 2.536 3.2 0.823113 3.4828 0.882191 0.0320926 0.5 10 2.544 3.2 0.99 2.92079 0.837851 0.0289877 0.5 10 2.552 3.2 0.755 3.984 0.914623 0.03175 0.5 10 2.56 3.2 3.86 3.644 -0.523996 0.0265411 0.5 10 2.568 3.2 1.005 3.668 0.844 0.0288723 0.5 10 2.576 3.2 0.664197 3.1903 0.956515 0.0301058 0.5 10 2.584 3.2 3.50563 3.7645 -0.493381 0.0271981 0.5 10 2.592 3.2 4.115 3.92 -0.502782 0.0256907 0.5 10 2.6 3.2 3.16 3.756 -0.466 0.0273252 0.5 10 2.608 3.2 0.63 3.804 0.981325 0.0288079 0.5 10 2.616 3.2 3.57956 3.90239 -0.471608 0.0282867 0.5 10 2.624 3.2 3.99 3.992 -0.477446 0.028343 0.5 10 2.632 3.2 3.545 3.744 -0.452164 0.0268751 0.5 10 2.64 3.2 3.235 3.932 -0.437512 0.0281813 0.5 10 2.648 3.2 3.65769 3.92215 -0.444 0.0278179 0.5 10 2.656 3.2 2.66 3.64 -0.386087 0.0271163 0.5 10 2.664 3.2 3.085 3.808 -0.407432 0.0284029 0.5 10 2.672 3.2 3.175 3.94105 -0.406661 0.0290495 0.5 10 2.68 3.2 3.13 3.94 -0.3944 0.0273436 0.5 10 2.688 3.2 2.3144 3.72352 -0.332489 0.0264903 0.5 10 2.696 3.2 1.41635 3.46508 -0.156598 0.0305655 0.5 10 2.704 3.2 1.44 3.932 -0.143742 0.0317327 0.5 10 2.712 3.2 2.66243 3.76008 -0.340892 0.028316 0.5 10 2.72 3.2 3.745 3.94748 -0.387815 0.0266776 0.5 10 2.728 3.2 2.955 3.89179 -0.344763 0.0280473 0.5 10 2.736 3.2 1.565 3.97597 -0.14654 0.027361 0.5 10 2.744 3.2 1.58554 3.47243 -0.163685 0.0287102 0.5 10 2.752 3.2 1.56053 3.84842 -0.154249 0.0328153 0.5 10 2.76 3.2 2.39202 3.76562 -0.276319 0.0288085 0.5 10 2.768 3.2 1.99334 3.73067 -0.223964 0.0267078 0.5 10 2.776 3.2 2.495 3.876 -0.276756 0.0294593 0.5 10 2.784 3.2 1.775 3.632 -0.176797 0.0290612 0.5 10 2.792 3.2 1.875 3.388 -0.183325 0.0302011 0.5 10 2.8 3.2 2.14 3.812 -0.219902 0.0289193 0.5 10 2.808 3.2 1.92 3.512 -0.167521 0.0281217 0.5 10 2.816 3.2 2.64787 3.98295 -0.254155 0.027971 0.5 10 2.824 3.2 1.98 3.852 -0.179271 0.0310282 0.5 10 2.832 3.2 2.075 3.636 -0.180932 0.0315305 0.5 10 2.84 3.2 2.16341 3.764 -0.185726 0.0287021 0.5 10 2.848 3.2 2.18858 3.97487 -0.189147 0.0290496 0.5 10 2.856 3.2 2.205 3.724 -0.176339 0.0308125 0.5 10 2.864 3.2 2.90644 3.80569 -0.220227 0.021116 0.5 10 2.872 3.2 2.34268 3.6755 -0.165929 0.0285552 0.5 10 2.88 3.2 2.705 3.828 -0.194708 0.0225743 0.5 10 2.888 3.2 2.4 3.96 -0.172335 0.0339693 0.5 10 2.896 3.2 2.585 3.996 -0.174264 0.0271843 0.5 10 2.904 3.2 2.685 3.884 -0.170841 0.0266131 0.5 10 2.912 3.2 2.615 3.792 -0.162439 0.0254384 0.5 10 2.92 3.2 2.755 3.992 -0.161818 0.028328 0.5 10 2.928 3.2 3.22618 3.996 -0.189673 0.0202707 0.5 10 2.936 3.2 3.2149 3.57592 -0.160399 0.0197875 0.5 10 2.944 3.2 2.92743 3.97794 -0.158916 0.0277317 0.5 10 2.952 3.2 3.335 3.84 -0.167543 0.0204844 0.5 10 2.96 3.2 3.46 3.872 -0.152981 0.0207445 0.5 10 2.968 3.2 3.645 3.816 -0.158557 0.0230873 0.5 10 2.976 3.2 4.985 3.22 -0.156317 0.0224558 0.5 10 2.984 3.2 4.19895 3.684 -0.157679 0.0233841 0.5 10 2.992 3.2 4.03 3.80598 -0.153176 0.024161 0.5 10 3 3.2 3.80309 3.76012 -0.0894611 0.403432 0.5 10 1 3.225 4.48171 3.96954 0.00152414 0.405399 0.5 10 1.008 3.225 4.05549 3.98714 -0.0950304 0.401374 0.5 10 1.016 3.225 4.33217 3.95412 -0.094 0.403003 0.5 10 1.024 3.225 4.22 3.992 -0.0936604 0.387973 0.5 10 1.032 3.225 3.615 3.98627 -0.0496496 0.391661 0.5 10 1.04 3.225 3.91335 3.93868 -0.0528702 0.392206 0.5 10 1.048 3.225 4.08144 3.85715 -0.0625478 0.386478 0.5 10 1.056 3.225 3.665 3.98 -0.0407551 0.381556 0.5 10 1.064 3.225 3.005 3.836 -0.000749564 0.374682 0.5 10 1.072 3.225 3.13089 3.83671 0.00784714 0.371162 0.5 10 1.08 3.225 3.59549 3.96 0.0140197 0.352163 0.5 10 1.088 3.225 3.91554 3.9962 0.0100651 0.352593 0.5 10 1.096 3.225 4.715 3.66 0.0169799 0.355129 0.5 10 1.104 3.225 4.63008 3.58406 0.0310962 0.357158 0.5 10 1.112 3.225 4.97596 3.69198 0.0253422 0.35917 0.5 10 1.12 3.225 4.755 3.816 0.0139285 0.343493 0.5 10 1.128 3.225 4.98604 3.83683 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4.72769 3.83416 -0.124503 0.407321 0.5 10 1.152 3.25 4.93005 3.9681 -0.114998 0.40027 0.5 10 1.16 3.25 3.715 3.956 -0.0828328 0.396583 0.5 10 1.168 3.25 4.15 3.888 -0.0779499 0.392255 0.5 10 1.176 3.25 4.935 3.924 -0.0672386 0.386265 0.5 10 1.184 3.25 4.92 3.964 -0.0583788 0.376381 0.5 10 1.192 3.25 4.94 3.924 -0.0527202 0.380229 0.5 10 1.2 3.25 4.85557 3.88927 -0.0453685 0.363534 0.5 10 1.208 3.25 4.37381 3.748 -0.0274286 0.344367 0.5 10 1.216 3.25 4.98972 3.15362 -0.196046 0.35905 0.5 10 1.224 3.25 4.82 3.328 -0.212673 0.358842 0.5 10 1.232 3.25 4.585 3.652 -0.225287 0.352369 0.5 10 1.24 3.25 4.82 3.74144 -0.223228 0.351378 0.5 10 1.248 3.25 4.77292 3.83033 -0.229319 0.337461 0.5 10 1.256 3.25 4.795 3.848 -0.229124 0.310079 0.5 10 1.264 3.25 4.875 3.97119 -0.0469467 0.316304 0.5 10 1.272 3.25 4.94405 3.89924 -0.0431112 0.317927 0.5 10 1.28 3.25 4.235 3.976 -0.168204 0.318144 0.5 10 1.288 3.25 4.32 3.98319 -0.166412 0.318479 0.5 10 1.296 3.25 4.86 3.872 -0.123518 0.322106 0.5 10 1.304 3.25 4.655 3.996 -0.132475 0.326277 0.5 10 1.312 3.25 4.265 3.97257 -0.10849 0.312741 0.5 10 1.32 3.25 4.87518 3.93615 -0.106878 0.301062 0.5 10 1.328 3.25 4.93 3.268 -0.134834 0.290875 0.5 10 1.336 3.25 4.94 3.48 -0.121914 0.282318 0.5 10 1.344 3.25 4.86 3.54 -0.12689 0.289578 0.5 10 1.352 3.25 4.78265 3.976 -0.187712 0.31799 0.5 10 1.36 3.25 4.91 3.804 -0.134801 0.329582 0.5 10 1.368 3.25 4.92234 3.87484 -0.132442 0.340451 0.5 10 1.376 3.25 4.92 3.956 -0.142931 0.295024 0.5 10 1.384 3.25 4.81 3.96654 -0.164027 0.264646 0.5 10 1.392 3.25 4.89257 3.956 -0.153348 0.234853 0.5 10 1.4 3.25 4.80407 3.90482 -0.161087 0.16416 0.5 10 1.408 3.25 4.915 3.96 -0.0441306 0.076161 0.5 10 1.416 3.25 4.445 3.768 -0.0660691 0.0746281 0.5 10 1.424 3.25 4.805 3.788 -0.0646831 0.0750229 0.5 10 1.432 3.25 2.9818 3.82544 -0.15566 0.0795997 0.5 10 1.44 3.25 4.625 3.94595 -0.0799821 0.0778955 0.5 10 1.448 3.25 4.975 3.936 -0.0716364 0.0824073 0.5 10 1.456 3.25 3.395 3.88197 -0.143142 0.0836558 0.5 10 1.464 3.25 4.345 3.992 -0.105731 0.0815545 0.5 10 1.472 3.25 3.58568 3.60055 -0.157391 0.0890762 0.5 10 1.48 3.25 3.66 3.88415 -0.146301 0.0926314 0.5 10 1.488 3.25 3.845 3.988 -0.156071 0.0995861 0.5 10 1.496 3.25 4.115 3.948 -0.157131 0.095874 0.5 10 1.504 3.25 4.34124 3.96899 -0.152203 0.100865 0.5 10 1.512 3.25 4.26 3.836 -0.153756 0.111847 0.5 10 1.52 3.25 4.8089 3.804 -0.151025 0.113009 0.5 10 1.528 3.25 4.855 3.82 -0.15 0.118239 0.5 10 1.536 3.25 4.96217 3.688 -0.159362 0.0906739 0.5 10 1.544 3.25 4.89213 3.93586 -0.146982 0.0932985 0.5 10 1.552 3.25 4.99615 3.56492 -0.155208 0.0154554 0.5 10 1.56 3.25 4.90929 3.98343 -0.153689 0.0233006 0.5 10 1.568 3.25 4.9178 3.41424 -0.214531 0.016194 0.5 10 1.576 3.25 4.45243 3.9819 -0.195959 0.0185704 0.5 10 1.584 3.25 4.62901 3.99521 -0.204234 0.0214523 0.5 10 1.592 3.25 4.88368 3.988 -0.191234 0.0281633 0.5 10 1.6 3.25 4.89761 3.81409 -0.210495 0.0282162 0.5 10 1.608 3.25 4.915 3.868 -0.199263 0.0263278 0.5 10 1.616 3.25 4.75 3.992 -0.222218 0.0331232 0.5 10 1.624 3.25 4.585 3.992 -0.231914 0.02439 0.5 10 1.632 3.25 4.615 3.836 -0.07747 0.0240082 0.5 10 1.64 3.25 4.955 3.892 -0.0816087 0.0278537 0.5 10 1.648 3.25 4.745 3.87836 -0.0690986 0.0307345 0.5 10 1.656 3.25 4.905 3.99427 -0.185145 0.0473049 0.5 10 1.664 3.25 4.865 3.948 -0.19867 0.0724902 0.5 10 1.672 3.25 4.79297 3.96638 -0.190379 0.118251 0.5 10 1.68 3.25 4.9242 3.96 -0.173454 0.150151 0.5 10 1.688 3.25 4.62 3.948 -0.14341 0.149322 0.5 10 1.696 3.25 4.225 3.956 -0.111278 0.154005 0.5 10 1.704 3.25 4.995 3.97171 -0.0947522 0.152547 0.5 10 1.712 3.25 4.08 3.99306 -0.0841179 0.15252 0.5 10 1.72 3.25 4.9052 3.852 -0.174164 0.153115 0.5 10 1.728 3.25 4.885 3.96 -0.157618 0.416799 0.5 10 1.736 3.25 4.91 3.988 -0.181402 0.463375 0.5 10 1.744 3.25 4.94244 3.99795 -0.115975 0.428447 0.5 10 1.752 3.25 4.87786 3.86982 -0.0970325 0.377912 0.5 10 1.76 3.25 4.99023 3.99527 -0.0947022 0.397645 0.5 10 1.768 3.25 4.62714 3.9277 -0.0767459 0.369425 0.5 10 1.776 3.25 4.35663 3.97252 -0.0604651 0.361201 0.5 10 1.784 3.25 4.965 3.9 -0.158 0.397885 0.5 10 1.792 3.25 4.99507 3.88806 -0.181729 0.470791 0.5 10 1.8 3.25 4.915 3.98075 -0.156736 0.533647 0.5 10 1.808 3.25 4.65611 3.928 -0.169938 0.565244 0.5 10 1.816 3.25 4.9 3.972 -0.167902 0.618799 0.5 10 1.824 3.25 4.95 3.94 -0.166516 0.642878 0.5 10 1.832 3.25 4.79 3.9534 -0.191347 0.654641 0.5 10 1.84 3.25 4.845 3.976 -0.195959 0.675065 0.5 10 1.848 3.25 4.85823 3.968 -0.17862 0.704697 0.5 10 1.856 3.25 4.88709 3.94 -0.16467 0.708055 0.5 10 1.864 3.25 4.85946 3.79957 -0.192545 0.714962 0.5 10 1.872 3.25 4.67012 3.96999 -0.17803 0.73368 0.5 10 1.88 3.25 4.72061 3.976 -0.177446 0.747538 0.5 10 1.888 3.25 4.7 3.88418 -0.189219 0.75174 0.5 10 1.896 3.25 4.84574 3.9126 -0.182686 0.771237 0.5 10 1.904 3.25 4.83 3.936 -0.197464 0.780953 0.5 10 1.912 3.25 4.89 3.98 -0.20424 0.790353 0.5 10 1.92 3.25 4.8316 3.98528 -0.179079 0.784108 0.5 10 1.928 3.25 4.995 3.904 -0.194791 0.796583 0.5 10 1.936 3.25 4.92517 3.94013 -0.207537 0.784726 0.5 10 1.944 3.25 4.77864 3.97891 -0.198214 0.764146 0.5 10 1.952 3.25 4.95 3.988 -0.223153 0.750656 0.5 10 1.96 3.25 4.99946 3.96191 -0.19397 0.736592 0.5 10 1.968 3.25 4.85346 3.95305 -0.201338 0.714459 0.5 10 1.976 3.25 4.8438 3.93553 -0.211184 0.695523 0.5 10 1.984 3.25 4.90798 3.97039 -0.201907 0.671638 0.5 10 1.992 3.25 4.8446 3.81866 -0.216375 0.638694 0.5 10 2 3.25 4.97 3.956 -0.223899 0.622828 0.5 10 2.008 3.25 4.96 3.96 -0.235561 0.591469 0.5 10 2.016 3.25 4.855 3.94825 -0.209792 0.539741 0.5 10 2.024 3.25 4.77 3.9 -0.231363 0.491623 0.5 10 2.032 3.25 4.97799 3.98639 -0.216784 0.490041 0.5 10 2.04 3.25 2.40571 3.98211 0.0397952 0.395058 0.5 10 2.048 3.25 2.54541 3.99299 0.0408739 0.387915 0.5 10 2.056 3.25 2.54 3.896 0.0724304 0.376848 0.5 10 2.064 3.25 2.905 3.92 0.0651716 0.364533 0.5 10 2.072 3.25 3.09 3.936 0.0561276 0.353386 0.5 10 2.08 3.25 3.18 3.984 0.057565 0.344468 0.5 10 2.088 3.25 3.38879 3.95838 0.065269 0.32803 0.5 10 2.096 3.25 3.08 3.94244 0.0591223 0.319158 0.5 10 2.104 3.25 3.44 3.912 0.0655139 0.309087 0.5 10 2.112 3.25 3.51368 3.924 0.0573039 0.301448 0.5 10 2.12 3.25 3.98058 3.94847 0.0648206 0.291325 0.5 10 2.128 3.25 3.87019 3.896 0.0564831 0.282897 0.5 10 2.136 3.25 4.11 3.96 0.0599999 0.272663 0.5 10 2.144 3.25 4.06181 3.97745 0.0503449 0.253295 0.5 10 2.152 3.25 4.96553 3.89643 0.068194 0.242574 0.5 10 2.16 3.25 4.825 3.89146 0.0551932 0.232862 0.5 10 2.168 3.25 4.75279 3.848 0.05 0.223044 0.5 10 2.176 3.25 4.885 3.916 0.0522968 0.220272 0.5 10 2.184 3.25 4.96 3.81694 0.0319999 0.191059 0.5 10 2.192 3.25 4.825 3.90796 0.0273854 0.168921 0.5 10 2.2 3.25 2.145 3.87724 0.346478 0.153025 0.5 10 2.208 3.25 2.16455 3.99564 0.337932 0.127212 0.5 10 2.216 3.25 2.715 3.92478 0.327773 0.124382 0.5 10 2.224 3.25 2.435 3.748 0.346467 0.127967 0.5 10 2.232 3.25 2.39754 3.804 0.351777 0.121044 0.5 10 2.24 3.25 2.515 3.88 0.359885 0.118614 0.5 10 2.248 3.25 2.69 3.94343 0.341361 0.118446 0.5 10 2.256 3.25 2.695 3.58 0.354951 0.107324 0.5 10 2.264 3.25 2.755 3.88965 0.354283 0.107588 0.5 10 2.272 3.25 2.78 3.884 0.355036 0.100004 0.5 10 2.28 3.25 2.925 3.884 0.345953 0.0942302 0.5 10 2.288 3.25 3 3.796 0.348 0.0969765 0.5 10 2.296 3.25 3.17204 3.556 0.346982 0.0899041 0.5 10 2.304 3.25 3.5266 3.92928 0.32815 0.0858969 0.5 10 2.312 3.25 3.295 3.756 0.339029 0.0805297 0.5 10 2.32 3.25 3.31625 3.777 0.338834 0.0766838 0.5 10 2.328 3.25 3.625 3.77747 0.341554 0.0817153 0.5 10 2.336 3.25 4.465 2.992 -0.235912 0.0738044 0.5 10 2.344 3.25 4.5 3.508 -0.263799 0.0794995 0.5 10 2.352 3.25 4.25 3.748 -0.262272 0.0751814 0.5 10 2.36 3.25 4.97498 3.79199 -0.266624 0.0749389 0.5 10 2.368 3.25 4.99997 3.95198 -0.269193 0.0724831 0.5 10 2.376 3.25 4.94831 3.804 -0.255889 0.0726778 0.5 10 2.384 3.25 4.86 3.884 -0.249425 0.0717187 0.5 10 2.392 3.25 4.8 3.976 -0.249451 0.0668359 0.5 10 2.4 3.25 4.965 3.81301 -0.237154 0.0581627 0.5 10 2.408 3.25 4.96541 3.22033 -0.197559 0.0591424 0.5 10 2.416 3.25 4.725 3.264 -0.196 0.0585788 0.5 10 2.424 3.25 4.50913 3.596 -0.196235 0.0592703 0.5 10 2.432 3.25 4.765 3.836 -0.208703 0.059982 0.5 10 2.44 3.25 4.995 3.7 -0.199231 0.0576966 0.5 10 2.448 3.25 4.79256 3.78014 -0.192851 0.0520416 0.5 10 2.456 3.25 4.93157 3.94549 -0.197207 0.0522345 0.5 10 2.464 3.25 4.89899 3.896 -0.193262 0.0475705 0.5 10 2.472 3.25 0.86 3.63049 0.842363 0.0350804 0.5 10 2.48 3.25 0.965 2.764 0.817702 0.030363 0.5 10 2.488 3.25 0.91 3.856 0.838135 0.0346164 0.5 10 2.496 3.25 0.775 3.716 0.886746 0.0318439 0.5 10 2.504 3.25 0.625 3.08513 0.943688 0.0332085 0.5 10 2.512 3.25 0.535 3.892 0.986089 0.0349885 0.5 10 2.52 3.25 0.71 2.764 0.915406 0.0296449 0.5 10 2.528 3.25 0.795 3.628 0.891448 0.0329969 0.5 10 2.536 3.25 4.23 3.78734 -0.551301 0.0275598 0.5 10 2.544 3.25 0.66 3.496 0.946 0.0298804 0.5 10 2.552 3.25 0.725 3.044 0.924932 0.0282282 0.5 10 2.56 3.25 0.625 3.916 0.97089 0.0309984 0.5 10 2.568 3.25 4.90659 3.99327 -0.537228 0.0283246 0.5 10 2.576 3.25 3.60651 3.77721 -0.498915 0.0281724 0.5 10 2.584 3.25 4.10209 3.92167 -0.504914 0.0277775 0.5 10 2.592 3.25 4.20618 3.89694 -0.502787 0.0280821 0.5 10 2.6 3.25 2.84 3.58207 -0.44143 0.0276497 0.5 10 2.608 3.25 2.95 3.676 -0.440611 0.0279287 0.5 10 2.616 3.25 2.745 3.668 -0.423379 0.027653 0.5 10 2.624 3.25 3.335 3.716 -0.445498 0.0279717 0.5 10 2.632 3.25 2.9 3.54552 -0.416341 0.027622 0.5 10 2.64 3.25 3.77246 3.85797 -0.448444 0.0281579 0.5 10 2.648 3.25 3.61861 3.8917 -0.434728 0.0277763 0.5 10 2.656 3.25 3.4802 3.932 -0.426558 0.0291916 0.5 10 2.664 3.25 3.23 3.916 -0.410961 0.0287831 0.5 10 2.672 3.25 3.49 3.964 -0.413668 0.0294583 0.5 10 2.68 3.25 2.505 3.752 -0.351403 0.0282634 0.5 10 2.688 3.25 2.575 3.776 -0.34887 0.0288109 0.5 10 2.696 3.25 1.4 3.89538 -0.146162 0.0357326 0.5 10 2.704 3.25 3.01268 3.94 -0.362381 0.0288096 0.5 10 2.712 3.25 2.7114 3.70912 -0.336817 0.028874 0.5 10 2.72 3.25 3.665 3.99277 -0.376441 0.0278649 0.5 10 2.728 3.25 2.095 3.468 -0.2626 0.0277054 0.5 10 2.736 3.25 2.83264 3.98611 -0.326693 0.030274 0.5 10 2.744 3.25 2.63 3.66692 -0.300901 0.0261072 0.5 10 2.752 3.25 1.61 3.712 -0.15903 0.0320479 0.5 10 2.76 3.25 1.895 3.584 -0.208584 0.0272999 0.5 10 2.768 3.25 1.665 3.828 -0.156715 0.0349531 0.5 10 2.776 3.25 1.8163 3.39317 -0.177455 0.0325867 0.5 10 2.784 3.25 1.82644 3.43378 -0.172598 0.0336912 0.5 10 2.792 3.25 1.895 3.308 -0.174389 0.0300512 0.5 10 2.8 3.25 2.58 3.856 -0.252 0.0286722 0.5 10 2.808 3.25 1.9 3.8366 -0.168054 0.0325678 0.5 10 2.816 3.25 1.89176 3.76285 -0.16415 0.0362882 0.5 10 2.824 3.25 2.135 3.912 -0.195007 0.029771 0.5 10 2.832 3.25 2.02 3.812 -0.16608 0.0363458 0.5 10 2.84 3.25 2.065 3.98 -0.166728 0.0349054 0.5 10 2.848 3.25 2.185 3.844 -0.178 0.0330349 0.5 10 2.856 3.25 2.255 3.804 -0.176434 0.0316287 0.5 10 2.864 3.25 2.1623 3.88487 -0.15586 0.030456 0.5 10 2.872 3.25 2.53 3.728 -0.184 0.0223547 0.5 10 2.88 3.25 2.38 3.956 -0.160475 0.0320737 0.5 10 2.888 3.25 2.40048 3.96499 -0.157091 0.032407 0.5 10 2.896 3.25 2.51 3.796 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0.5 10 2.928 3.275 3.04475 3.8478 -0.159355 0.0256712 0.5 10 2.936 3.275 2.91578 3.96947 -0.164603 0.0247286 0.5 10 2.944 3.275 3.075 3.94 -0.161078 0.0267683 0.5 10 2.952 3.275 3.15107 3.968 -0.153452 0.0279973 0.5 10 2.96 3.275 3.455 3.86 -0.158489 0.0245933 0.5 10 2.968 3.275 4.93 3.116 -0.148193 0.0253693 0.5 10 2.976 3.275 3.98535 3.736 -0.155407 0.0242579 0.5 10 2.984 3.275 4.87 3.424 -0.1501 0.0283283 0.5 10 2.992 3.275 4.98 3.504 -0.14581 0.0292611 0.5 10 3 3.275 3.54735 3.876 -0.0857774 0.410279 0.5 10 1 3.3 4.075 3.768 -0.0843012 0.407654 0.5 10 1.008 3.3 4.34 3.764 -0.0914223 0.399668 0.5 10 1.016 3.3 2.925 3.776 -0.0273055 0.398134 0.5 10 1.024 3.3 3.38 3.99845 -0.043287 0.388561 0.5 10 1.032 3.3 3.5 3.86951 -0.041566 0.396038 0.5 10 1.04 3.3 3.655 3.912 -0.0391684 0.382144 0.5 10 1.048 3.3 4.83949 3.77559 -0.0379032 0.377845 0.5 10 1.056 3.3 4.97576 3.96061 -0.036269 0.372592 0.5 10 1.064 3.3 4.705 3.96543 -0.0606978 0.3751 0.5 10 1.072 3.3 4.89 3.992 -0.052 0.361551 0.5 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0.5 10 1.24 3.3 4.78739 3.88991 -0.211739 0.329778 0.5 10 1.248 3.3 4.695 3.96 -0.0636578 0.321135 0.5 10 1.256 3.3 4.70589 3.98472 -0.0420582 0.318349 0.5 10 1.264 3.3 4.275 3.896 -0.161878 0.312679 0.5 10 1.272 3.3 4.99246 3.90597 -0.038079 0.318712 0.5 10 1.28 3.3 4.24887 3.9751 -0.14823 0.336346 0.5 10 1.288 3.3 4.89151 3.76474 -0.12931 0.324879 0.5 10 1.296 3.3 4.305 3.888 -0.105465 0.322818 0.5 10 1.304 3.3 4.63622 3.97698 -0.100247 0.323127 0.5 10 1.312 3.3 4.625 3.99639 -0.10517 0.302727 0.5 10 1.32 3.3 4.99 3.916 -0.0839781 0.288244 0.5 10 1.328 3.3 4.81555 3.46444 -0.127385 0.286137 0.5 10 1.336 3.3 4.99083 3.728 -0.147381 0.307086 0.5 10 1.344 3.3 4.19328 3.97062 -0.153098 0.31715 0.5 10 1.352 3.3 4.58316 3.91053 -0.12681 0.342161 0.5 10 1.36 3.3 4.92649 3.92 -0.126 0.353914 0.5 10 1.368 3.3 4.94715 3.92172 -0.142043 0.280575 0.5 10 1.376 3.3 4.555 3.996 -0.160607 0.251626 0.5 10 1.384 3.3 4.88512 3.77609 -0.15803 0.232807 0.5 10 1.392 3.3 4.95 3.88473 -0.164502 0.178227 0.5 10 1.4 3.3 4.605 3.784 -0.0485787 0.0761308 0.5 10 1.408 3.3 4.935 3.827 -0.0472622 0.0782015 0.5 10 1.416 3.3 4.51027 3.912 -0.0614047 0.0781626 0.5 10 1.424 3.3 4.99815 3.88652 -0.0553474 0.0837213 0.5 10 1.432 3.3 3.07 3.492 -0.156544 0.0832194 0.5 10 1.44 3.3 3.12 3.564 -0.151794 0.0848417 0.5 10 1.448 3.3 3.24 3.868 -0.143986 0.0905202 0.5 10 1.456 3.3 3.25 3.90999 -0.149235 0.0992752 0.5 10 1.464 3.3 3.45568 3.8986 -0.154553 0.0936355 0.5 10 1.472 3.3 3.635 3.852 -0.15458 0.0985756 0.5 10 1.48 3.3 3.785 3.632 -0.152502 0.0984159 0.5 10 1.488 3.3 3.98 3.948 -0.139948 0.0971397 0.5 10 1.496 3.3 4.345 3.604 -0.156862 0.105967 0.5 10 1.504 3.3 4.415 3.796 -0.153137 0.116084 0.5 10 1.512 3.3 4.85 3.428 -0.164187 0.112006 0.5 10 1.52 3.3 4.795 3.684 -0.154483 0.130069 0.5 10 1.528 3.3 4.98901 3.992 -0.14722 0.142072 0.5 10 1.536 3.3 4.81946 3.96757 -0.152369 0.130995 0.5 10 1.544 3.3 4.90359 3.61583 -0.148161 0.0427106 0.5 10 1.552 3.3 4.98772 3.92 -0.154875 0.0446534 0.5 10 1.56 3.3 4.65 3.424 -0.213244 0.0167077 0.5 10 1.568 3.3 4.65316 3.86273 -0.187758 0.0205269 0.5 10 1.576 3.3 4.89992 3.79994 -0.199029 0.0257203 0.5 10 1.584 3.3 4.82282 3.81826 -0.20474 0.0299845 0.5 10 1.592 3.3 4.755 3.956 -0.196054 0.0366603 0.5 10 1.6 3.3 4.67 3.972 -0.196507 0.0338183 0.5 10 1.608 3.3 4.84395 3.97235 -0.206624 0.0361952 0.5 10 1.616 3.3 4.71 3.524 -0.0829625 0.0258056 0.5 10 1.624 3.3 4.99994 3.9628 -0.0878401 0.0281111 0.5 10 1.632 3.3 4.74244 3.76595 -0.0748286 0.0322157 0.5 10 1.64 3.3 4.63 3.768 -0.192353 0.0332048 0.5 10 1.648 3.3 4.97557 3.75645 -0.166798 0.0579784 0.5 10 1.656 3.3 4.575 3.928 -0.17041 0.0860571 0.5 10 1.664 3.3 4.90455 3.98764 -0.184512 0.160066 0.5 10 1.672 3.3 4.73 3.96333 -0.153626 0.163275 0.5 10 1.68 3.3 4.4 3.996 -0.134358 0.161588 0.5 10 1.688 3.3 4.815 3.97928 -0.113073 0.171515 0.5 10 1.696 3.3 4.82 3.956 -0.091753 0.168215 0.5 10 1.704 3.3 4.50879 3.96143 -0.0784272 0.163974 0.5 10 1.712 3.3 4.95 3.912 -0.164641 0.286271 0.5 10 1.72 3.3 4.96 3.988 -0.177085 0.491096 0.5 10 1.728 3.3 4.815 3.964 -0.199172 0.466179 0.5 10 1.736 3.3 4.845 3.99607 -0.203702 0.437366 0.5 10 1.744 3.3 4.81572 3.93658 -0.0993735 0.405487 0.5 10 1.752 3.3 4.63439 3.968 -0.0833083 0.394513 0.5 10 1.76 3.3 4.54517 3.852 -0.0708363 0.359923 0.5 10 1.768 3.3 4.95146 3.936 -0.155203 0.375261 0.5 10 1.776 3.3 4.83791 3.93935 -0.18 0.432103 0.5 10 1.784 3.3 4.90891 3.90406 -0.17154 0.513876 0.5 10 1.792 3.3 4.9513 3.90504 -0.163004 0.570909 0.5 10 1.8 3.3 4.97 3.968 -0.163464 0.620506 0.5 10 1.808 3.3 4.92535 3.90438 -0.156381 0.631135 0.5 10 1.816 3.3 4.895 3.908 -0.177121 0.665517 0.5 10 1.824 3.3 4.88253 3.896 -0.171928 0.684887 0.5 10 1.832 3.3 4.86279 3.99023 -0.166088 0.708596 0.5 10 1.84 3.3 4.75852 3.92282 -0.17203 0.71423 0.5 10 1.848 3.3 4.94424 3.96339 -0.174805 0.748884 0.5 10 1.856 3.3 4.92142 3.88913 -0.175295 0.754529 0.5 10 1.864 3.3 4.97 3.876 -0.171054 0.766094 0.5 10 1.872 3.3 4.82353 3.688 -0.185452 0.749024 0.5 10 1.88 3.3 4.81 3.78109 -0.190945 0.765891 0.5 10 1.888 3.3 4.67 3.968 -0.188836 0.783678 0.5 10 1.896 3.3 4.89819 3.93455 -0.194362 0.801792 0.5 10 1.904 3.3 4.9869 3.86507 -0.19796 0.801516 0.5 10 1.912 3.3 4.92 3.88311 -0.189559 0.804393 0.5 10 1.92 3.3 4.83 3.96 -0.2 0.799028 0.5 10 1.928 3.3 4.975 3.87071 -0.182727 0.785073 0.5 10 1.936 3.3 4.99 3.884 -0.192373 0.772282 0.5 10 1.944 3.3 4.56161 3.976 -0.208068 0.733561 0.5 10 1.952 3.3 4.945 3.976 -0.200673 0.735588 0.5 10 1.96 3.3 4.97226 3.90359 -0.195488 0.716183 0.5 10 1.968 3.3 4.87343 3.856 -0.203727 0.692305 0.5 10 1.976 3.3 4.8343 3.9 -0.207319 0.673874 0.5 10 1.984 3.3 4.93088 3.85727 -0.201019 0.631446 0.5 10 1.992 3.3 4.835 3.916 -0.217901 0.605816 0.5 10 2 3.3 4.98 3.944 -0.201157 0.56957 0.5 10 2.008 3.3 4.99315 3.97565 -0.222341 0.547716 0.5 10 2.016 3.3 4.825 3.98327 -0.215859 0.495546 0.5 10 2.024 3.3 4.93294 3.9521 -0.199244 0.410416 0.5 10 2.032 3.3 2.525 3.936 0.0452867 0.391556 0.5 10 2.04 3.3 2.93676 3.92 0.0537977 0.378402 0.5 10 2.048 3.3 2.89988 3.988 0.0635296 0.368716 0.5 10 2.056 3.3 2.875 3.928 0.0506986 0.359404 0.5 10 2.064 3.3 3.38184 3.96947 0.0296976 0.344222 0.5 10 2.072 3.3 3.45 3.856 0.0541553 0.329032 0.5 10 2.08 3.3 3.47562 3.98049 0.0482171 0.323546 0.5 10 2.088 3.3 3.5 3.9 0.0463828 0.305433 0.5 10 2.096 3.3 4.01 3.94418 0.040465 0.294115 0.5 10 2.104 3.3 3.8 3.948 0.04 0.280789 0.5 10 2.112 3.3 4.25125 3.941 0.048 0.27282 0.5 10 2.12 3.3 4.6886 3.904 0.0409133 0.262646 0.5 10 2.128 3.3 4.53 3.89107 0.0442973 0.252843 0.5 10 2.136 3.3 4.975 3.88 0.0439315 0.241842 0.5 10 2.144 3.3 4.61587 3.9447 0.0358001 0.215304 0.5 10 2.152 3.3 4.69829 3.91943 0.0326324 0.1919 0.5 10 2.16 3.3 4.99882 3.96916 0.0178476 0.17548 0.5 10 2.168 3.3 3.375 3.828 -0.452025 0.151483 0.5 10 2.176 3.3 2.08 3.776 0.363464 0.154965 0.5 10 2.184 3.3 2.40843 3.97474 0.326572 0.13523 0.5 10 2.192 3.3 2.2 3.94 0.364924 0.144524 0.5 10 2.2 3.3 2.27907 3.70725 0.360771 0.133171 0.5 10 2.208 3.3 2.32944 3.74994 0.358152 0.131426 0.5 10 2.216 3.3 2.45756 3.972 0.3571 0.125738 0.5 10 2.224 3.3 2.465 3.904 0.365269 0.115136 0.5 10 2.232 3.3 2.50732 3.7433 0.353537 0.110544 0.5 10 2.24 3.3 2.68 3.84766 0.346931 0.11227 0.5 10 2.248 3.3 2.695 3.972 0.358325 0.105121 0.5 10 2.256 3.3 2.695 3.25473 0.354622 0.0916676 0.5 10 2.264 3.3 3.01813 3.976 0.336925 0.099098 0.5 10 2.272 3.3 2.46 3.356 -0.303957 0.0838628 0.5 10 2.28 3.3 2.90731 3.924 0.352563 0.0985453 0.5 10 2.288 3.3 3.05 3.988 0.358171 0.0949864 0.5 10 2.296 3.3 2.455 3.78754 -0.291586 0.0785117 0.5 10 2.304 3.3 3.255 3.788 0.354002 0.0873659 0.5 10 2.312 3.3 3.3425 3.81832 0.344626 0.0854644 0.5 10 2.32 3.3 4.5742 3.19536 -0.293743 0.0890715 0.5 10 2.328 3.3 2.715 3.664 -0.279814 0.0892644 0.5 10 2.336 3.3 2.97017 3.71213 -0.28905 0.0873735 0.5 10 2.344 3.3 3.63 3.704 -0.294711 0.0845618 0.5 10 2.352 3.3 3.01205 3.872 -0.282166 0.0882856 0.5 10 2.36 3.3 3.815 3.856 -0.298228 0.0903279 0.5 10 2.368 3.3 4.92921 3.83137 -0.314351 0.0870784 0.5 10 2.376 3.3 4.36631 3.90294 -0.288252 0.0824333 0.5 10 2.384 3.3 4.4 3.872 -0.289732 0.0817114 0.5 10 2.392 3.3 4.72777 3.852 -0.283645 0.081247 0.5 10 2.4 3.3 4.77473 3.97978 -0.276421 0.0787153 0.5 10 2.408 3.3 4.8885 3.968 -0.280727 0.0745677 0.5 10 2.416 3.3 4.78 3.94627 -0.272547 0.0707868 0.5 10 2.424 3.3 4.82 3.82285 -0.260927 0.0686969 0.5 10 2.432 3.3 4.49667 3.796 -0.229874 0.0652399 0.5 10 2.44 3.3 4.29664 3.952 -0.229006 0.0660316 0.5 10 2.448 3.3 4.67737 3.82989 -0.234995 0.0561495 0.5 10 2.456 3.3 4.59894 3.99322 -0.23852 0.0521175 0.5 10 2.464 3.3 4.93166 3.91969 -0.232646 0.047902 0.5 10 2.472 3.3 0.545 3.204 0.967169 0.0327877 0.5 10 2.48 3.3 0.58 2.124 0.956 0.0317359 0.5 10 2.488 3.3 0.66 3.468 0.927614 0.0322426 0.5 10 2.496 3.3 0.645 3.868 0.938 0.0349876 0.5 10 2.504 3.3 0.56 2.884 0.976 0.0307597 0.5 10 2.512 3.3 4.305 3.72443 -0.566428 0.0278438 0.5 10 2.52 3.3 0.74 3.156 0.912595 0.0315739 0.5 10 2.528 3.3 4.775 3.9 -0.560767 0.0282601 0.5 10 2.536 3.3 4.73 3.892 -0.554722 0.0283252 0.5 10 2.544 3.3 0.715 3.62 0.934578 0.0292128 0.5 10 2.552 3.3 4.48995 3.81926 -0.5347 0.0279847 0.5 10 2.56 3.3 4.13 3.84236 -0.520973 0.0286607 0.5 10 2.568 3.3 3.935 3.908 -0.508715 0.0289813 0.5 10 2.576 3.3 4.20317 3.93853 -0.508152 0.028975 0.5 10 2.584 3.3 3.89689 3.84151 -0.494763 0.0283564 0.5 10 2.592 3.3 4.905 3.928 -0.507338 0.0275959 0.5 10 2.6 3.3 3.6208 3.90861 -0.470505 0.0290744 0.5 10 2.608 3.3 3.51 3.744 -0.46 0.0284388 0.5 10 2.616 3.3 3.64912 3.9233 -0.461085 0.0289523 0.5 10 2.624 3.3 3.17 3.71076 -0.430453 0.0290838 0.5 10 2.632 3.3 2.90882 3.7144 -0.413185 0.0284199 0.5 10 2.64 3.3 3.37 3.87898 -0.426993 0.0297189 0.5 10 2.648 3.3 3.56919 3.956 -0.431645 0.0293581 0.5 10 2.656 3.3 1.34 3.596 -0.138236 0.0309198 0.5 10 2.664 3.3 2.595 3.724 -0.36249 0.0292972 0.5 10 2.672 3.3 3.24 3.876 -0.396 0.0298651 0.5 10 2.68 3.3 2.56 3.704 -0.347284 0.0294907 0.5 10 2.688 3.3 2.98 3.97289 -0.372986 0.0286244 0.5 10 2.696 3.3 1.44 3.74 -0.149201 0.03347 0.5 10 2.704 3.3 2.51398 3.60718 -0.317454 0.0284358 0.5 10 2.712 3.3 2.83 3.76 -0.337667 0.0292648 0.5 10 2.72 3.3 2.605 3.78775 -0.317812 0.0301933 0.5 10 2.728 3.3 2.315 3.948 -0.28626 0.0287812 0.5 10 2.736 3.3 3.37225 3.9778 -0.344563 0.0286035 0.5 10 2.744 3.3 2.515 3.864 -0.289477 0.0312058 0.5 10 2.752 3.3 1.7 3.228 -0.168482 0.0323788 0.5 10 2.76 3.3 1.71071 3.56404 -0.162176 0.0306968 0.5 10 2.768 3.3 2.45145 3.916 -0.266412 0.0308917 0.5 10 2.776 3.3 1.70598 3.956 -0.153915 0.036014 0.5 10 2.784 3.3 1.695 3.992 -0.145189 0.030581 0.5 10 2.792 3.3 1.89 3.504 -0.160468 0.0285309 0.5 10 2.8 3.3 1.91997 3.63998 -0.170001 0.0345267 0.5 10 2.808 3.3 1.865 3.66002 -0.156223 0.0324037 0.5 10 2.816 3.3 2.1154 3.75502 -0.187421 0.0311364 0.5 10 2.824 3.3 2.07 3.656 -0.167873 0.0330344 0.5 10 2.832 3.3 2.28799 3.864 -0.195385 0.0298028 0.5 10 2.84 3.3 2.19167 3.93969 -0.179677 0.0329966 0.5 10 2.848 3.3 2.1 3.788 -0.162201 0.0321441 0.5 10 2.856 3.3 2.26348 3.80123 -0.162258 0.0333455 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0.417799 0.5 10 1.096 3.45 4.77293 3.95434 -0.128552 0.437552 0.5 10 1.104 3.45 4.015 3.948 -0.100976 0.428064 0.5 10 1.112 3.45 4.45193 3.864 -0.0979075 0.417056 0.5 10 1.12 3.45 3.16634 3.94107 -0.0523998 0.411749 0.5 10 1.128 3.45 3.5684 3.9768 -0.0544266 0.411092 0.5 10 1.136 3.45 3.465 3.972 -0.0432058 0.400193 0.5 10 1.144 3.45 3.915 3.848 -0.0403794 0.393719 0.5 10 1.152 3.45 4.38183 3.73406 -0.0304044 0.38422 0.5 10 1.16 3.45 4.37133 3.82506 -0.0295584 0.37879 0.5 10 1.168 3.45 4.665 3.904 -0.02752 0.380622 0.5 10 1.176 3.45 4.625 3.952 -0.023786 0.358657 0.5 10 1.184 3.45 4.935 3.436 -0.20073 0.352497 0.5 10 1.192 3.45 4.815 3.972 -0.00147414 0.359949 0.5 10 1.2 3.45 4.985 3.72 -0.0728955 0.356589 0.5 10 1.208 3.45 4.89976 3.71908 -0.205209 0.355748 0.5 10 1.216 3.45 4.635 3.99414 -0.0528838 0.34558 0.5 10 1.224 3.45 4.39909 3.92327 -0.0409098 0.338313 0.5 10 1.232 3.45 4.84435 3.92988 -0.043074 0.347496 0.5 10 1.24 3.45 4.77 3.856 -0.031275 0.342259 0.5 10 1.248 3.45 4.96 3.924 -0.139207 0.353166 0.5 10 1.256 3.45 4.745 3.972 -0.128 0.349496 0.5 10 1.264 3.45 4.89253 3.97403 -0.108018 0.33835 0.5 10 1.272 3.45 4.445 3.956 -0.0720342 0.323972 0.5 10 1.28 3.45 4.565 3.98 -0.0728393 0.329289 0.5 10 1.288 3.45 4.97773 3.892 -0.0657942 0.336188 0.5 10 1.296 3.45 4.885 3.84 -0.06 0.29818 0.5 10 1.304 3.45 4.93 3.864 -0.135495 0.323919 0.5 10 1.312 3.45 4.93181 3.98291 -0.13481 0.380266 0.5 10 1.32 3.45 4.82564 3.92851 -0.139175 0.373649 0.5 10 1.328 3.45 4.44 3.88 -0.10642 0.342317 0.5 10 1.336 3.45 4.69091 3.99583 -0.100609 0.356453 0.5 10 1.344 3.45 4.76615 3.76492 -0.113833 0.303266 0.5 10 1.352 3.45 4.81929 3.75045 -0.117057 0.247498 0.5 10 1.36 3.45 4.96264 3.97011 -0.164874 0.222886 0.5 10 1.368 3.45 4.64672 3.99738 -0.149526 0.205622 0.5 10 1.376 3.45 4.87 3.952 -0.144405 0.206146 0.5 10 1.384 3.45 4.99048 3.92838 -0.138151 0.196933 0.5 10 1.392 3.45 4.95 3.772 -0.147568 0.118912 0.5 10 1.4 3.45 2.80165 3.59736 -0.141027 0.0850328 0.5 10 1.408 3.45 4.77 3.81407 -0.05 0.0853645 0.5 10 1.416 3.45 4.91472 3.87214 -0.0474179 0.0916403 0.5 10 1.424 3.45 3.06156 3.63325 -0.137492 0.0953915 0.5 10 1.432 3.45 4.55 3.988 -0.070812 0.0920396 0.5 10 1.44 3.45 3.365 3.608 -0.143524 0.0992228 0.5 10 1.448 3.45 3.35211 3.72968 -0.14084 0.113574 0.5 10 1.456 3.45 3.50169 3.82935 -0.136683 0.114439 0.5 10 1.464 3.45 3.59 3.828 -0.137894 0.121341 0.5 10 1.472 3.45 3.81 3.684 -0.14789 0.119451 0.5 10 1.48 3.45 4.11539 3.62431 -0.148473 0.127845 0.5 10 1.488 3.45 4.095 3.596 -0.143844 0.126945 0.5 10 1.496 3.45 4.4186 3.74288 -0.140656 0.145483 0.5 10 1.504 3.45 4.57319 3.66434 -0.144394 0.159292 0.5 10 1.512 3.45 4.74679 3.79478 -0.142798 0.177523 0.5 10 1.52 3.45 4.94 3.848 -0.141659 0.193493 0.5 10 1.528 3.45 4.76159 3.84 -0.139914 0.12884 0.5 10 1.536 3.45 4.815 3.964 -0.132 0.0806672 0.5 10 1.544 3.45 4.98963 3.90022 -0.143845 0.0675569 0.5 10 1.552 3.45 4.81339 3.79471 -0.175956 0.0345506 0.5 10 1.56 3.45 4.90471 3.82377 -0.174711 0.0459673 0.5 10 1.568 3.45 4.895 3.92 -0.168868 0.0536075 0.5 10 1.576 3.45 4.555 3.976 -0.195975 0.0512708 0.5 10 1.584 3.45 4.875 3.956 -0.188872 0.0505693 0.5 10 1.592 3.45 4.64662 3.7653 -0.0869579 0.0335629 0.5 10 1.6 3.45 4.74649 3.7878 -0.0784202 0.0384895 0.5 10 1.608 3.45 4.94494 3.66 -0.157788 0.0437964 0.5 10 1.616 3.45 4.96 3.952 -0.159222 0.0703723 0.5 10 1.624 3.45 4.83 3.728 -0.168114 0.0955228 0.5 10 1.632 3.45 4.84883 3.944 -0.159021 0.190431 0.5 10 1.64 3.45 4.705 3.968 -0.182756 0.189489 0.5 10 1.648 3.45 4.46 3.97442 -0.11611 0.196234 0.5 10 1.656 3.45 4.98778 3.98623 -0.104446 0.213198 0.5 10 1.664 3.45 4.51548 3.98294 -0.0859232 0.20436 0.5 10 1.672 3.45 4.76793 3.95835 -0.0706204 0.217731 0.5 10 1.68 3.45 4.77 3.9 -0.143708 0.263281 0.5 10 1.688 3.45 4.72095 3.90073 -0.161815 0.498085 0.5 10 1.696 3.45 4.7252 3.92416 -0.11739 0.490846 0.5 10 1.704 3.45 4.94 3.976 -0.189834 0.487155 0.5 10 1.712 3.45 4.945 3.984 -0.0880934 0.465043 0.5 10 1.72 3.45 4.93883 3.9696 -0.0771831 0.443681 0.5 10 1.728 3.45 4.985 3.9 -0.0627609 0.386865 0.5 10 1.736 3.45 4.96504 3.868 -0.161989 0.397035 0.5 10 1.744 3.45 4.98044 3.71235 -0.154514 0.456762 0.5 10 1.752 3.45 4.98849 3.99079 -0.135337 0.547376 0.5 10 1.76 3.45 4.95656 3.75309 -0.148956 0.592943 0.5 10 1.768 3.45 4.79463 3.9837 -0.134952 0.632989 0.5 10 1.776 3.45 4.855 3.996 -0.165556 0.68801 0.5 10 1.784 3.45 4.965 3.872 -0.154425 0.715121 0.5 10 1.792 3.45 4.94 3.956 -0.148887 0.739769 0.5 10 1.8 3.45 4.925 3.952 -0.158978 0.761261 0.5 10 1.808 3.45 4.95 3.948 -0.166207 0.774491 0.5 10 1.816 3.45 4.715 3.952 -0.155957 0.775454 0.5 10 1.824 3.45 4.98276 3.868 -0.16019 0.801265 0.5 10 1.832 3.45 4.8758 3.688 -0.162452 0.785359 0.5 10 1.84 3.45 4.965 3.932 -0.173894 0.815522 0.5 10 1.848 3.45 4.79046 3.88437 -0.161715 0.822538 0.5 10 1.856 3.45 4.91366 3.95666 -0.165578 0.846254 0.5 10 1.864 3.45 4.93393 3.91114 -0.151793 0.832826 0.5 10 1.872 3.45 4.875 3.76576 -0.17625 0.828967 0.5 10 1.88 3.45 4.9 3.78468 -0.172224 0.834793 0.5 10 1.888 3.45 4.92949 3.944 -0.166018 0.842613 0.5 10 1.896 3.45 4.93179 3.91343 -0.176598 0.823357 0.5 10 1.904 3.45 4.93 3.952 -0.157686 0.790976 0.5 10 1.912 3.45 4.94813 3.944 -0.191248 0.780298 0.5 10 1.92 3.45 4.95296 3.916 -0.172301 0.748537 0.5 10 1.928 3.45 4.955 3.868 -0.166475 0.723075 0.5 10 1.936 3.45 4.865 3.904 -0.188 0.697362 0.5 10 1.944 3.45 4.85227 3.86582 -0.190366 0.671271 0.5 10 1.952 3.45 4.69 3.872 -0.19145 0.63587 0.5 10 1.96 3.45 4.95055 3.84103 -0.19859 0.608333 0.5 10 1.968 3.45 4.84899 3.996 -0.181847 0.559959 0.5 10 1.976 3.45 4.94923 3.94178 -0.172954 0.536418 0.5 10 1.984 3.45 4.96 3.8 -0.171766 0.464378 0.5 10 1.992 3.45 4.97 3.95252 -0.184 0.401122 0.5 10 2 3.45 3.605 3.972 0.0094713 0.382933 0.5 10 2.008 3.45 3.97263 3.9621 0.00542315 0.36425 0.5 10 2.016 3.45 3.77865 3.983 0.00868339 0.351449 0.5 10 2.024 3.45 4.15 3.992 0.0142499 0.332797 0.5 10 2.032 3.45 4.28252 3.99327 0.0115824 0.313143 0.5 10 2.04 3.45 4.835 3.9294 0.0137236 0.290673 0.5 10 2.048 3.45 4.805 3.976 -0.00600423 0.270407 0.5 10 2.056 3.45 4.945 3.964 0.0112451 0.259424 0.5 10 2.064 3.45 2.81 3.96 -0.411956 0.240182 0.5 10 2.072 3.45 2.27 3.90471 -0.474208 0.228604 0.5 10 2.08 3.45 2.305 3.94822 -0.457714 0.222201 0.5 10 2.088 3.45 1.785 3.932 -0.530008 0.215006 0.5 10 2.096 3.45 2.06 3.94 -0.502353 0.209164 0.5 10 2.104 3.45 1.69687 3.84549 -0.559228 0.201905 0.5 10 2.112 3.45 1.685 3.988 -0.566 0.199145 0.5 10 2.12 3.45 1.72243 3.932 -0.563479 0.190348 0.5 10 2.128 3.45 1.88 3.964 -0.560535 0.18778 0.5 10 2.136 3.45 2.015 3.856 -0.553493 0.175511 0.5 10 2.144 3.45 1.98 3.776 -0.542313 0.16329 0.5 10 2.152 3.45 1.995 3.724 -0.565547 0.154372 0.5 10 2.16 3.45 2.16778 3.78623 -0.554248 0.148827 0.5 10 2.168 3.45 2.185 3.996 -0.547709 0.147298 0.5 10 2.176 3.45 2.5 3.972 -0.535089 0.139422 0.5 10 2.184 3.45 2.375 3.888 -0.561858 0.130717 0.5 10 2.192 3.45 2.42216 3.78762 -0.561534 0.124195 0.5 10 2.2 3.45 2.335 3.764 -0.550789 0.112564 0.5 10 2.208 3.45 2.375 3.984 -0.552326 0.108134 0.5 10 2.216 3.45 2.61308 3.82646 -0.550072 0.104314 0.5 10 2.224 3.45 2.57398 3.76557 0.369885 0.0968704 0.5 10 2.232 3.45 2.715 3.216 -0.313267 0.0996912 0.5 10 2.24 3.45 2.53 3.57736 -0.322905 0.108955 0.5 10 2.248 3.45 2.79374 3.676 -0.318523 0.106736 0.5 10 2.256 3.45 3.755 2.972 -0.282477 0.113915 0.5 10 2.264 3.45 4.32192 3.11754 -0.262801 0.11201 0.5 10 2.272 3.45 4.12386 3.11109 -0.277053 0.115347 0.5 10 2.28 3.45 4.02689 3.26951 -0.282064 0.109793 0.5 10 2.288 3.45 3.09951 3.4 -0.33101 0.109619 0.5 10 2.296 3.45 3.235 3.528 -0.325758 0.111416 0.5 10 2.304 3.45 3.91 3.00286 -0.306836 0.110981 0.5 10 2.312 3.45 4.285 2.972 -0.308274 0.110329 0.5 10 2.32 3.45 2.25 3.86 -0.419447 0.115741 0.5 10 2.328 3.45 2.15 3.936 -0.414814 0.115372 0.5 10 2.336 3.45 3.22 3.86996 -0.363041 0.101316 0.5 10 2.344 3.45 2.54184 3.652 -0.401168 0.110704 0.5 10 2.352 3.45 2.865 3.42 -0.401318 0.114157 0.5 10 2.36 3.45 2.835 3.804 -0.402234 0.113292 0.5 10 2.368 3.45 3.135 3.764 -0.399323 0.111668 0.5 10 2.376 3.45 3.45 3.36254 -0.396587 0.115746 0.5 10 2.384 3.45 3.7333 3.44647 -0.38506 0.112919 0.5 10 2.392 3.45 4 3.51734 -0.384401 0.114558 0.5 10 2.4 3.45 4.35 3.504 -0.38664 0.116039 0.5 10 2.408 3.45 3.77 3.816 -0.387783 0.114132 0.5 10 2.416 3.45 3.87 3.96 -0.393426 0.12236 0.5 10 2.424 3.45 4.565 3.80647 -0.396798 0.120037 0.5 10 2.432 3.45 4.82779 3.64623 -0.389935 0.11919 0.5 10 2.44 3.45 4.70224 3.332 -0.366864 0.115043 0.5 10 2.448 3.45 4.325 3.74 -0.365826 0.122444 0.5 10 2.456 3.45 4.64229 3.908 -0.366 0.125646 0.5 10 2.464 3.45 4.58124 3.872 -0.364939 0.129735 0.5 10 2.472 3.45 4.86462 3.86318 -0.359461 0.132025 0.5 10 2.48 3.45 4.92 3.912 -0.353999 0.128533 0.5 10 2.488 3.45 4.87 3.916 -0.361596 0.053413 0.5 10 2.496 3.45 0.545 3.692 0.996 0.0321851 0.5 10 2.504 3.45 0.545 3.268 0.996 0.0292453 0.5 10 2.512 3.45 4.99 3.99117 -0.554761 0.0296062 0.5 10 2.52 3.45 4.21103 3.98439 -0.532655 0.029673 0.5 10 2.528 3.45 4.53 3.836 -0.530412 0.0294379 0.5 10 2.536 3.45 3.94302 3.96241 -0.511296 0.0299435 0.5 10 2.544 3.45 4.355 3.992 -0.514892 0.0306718 0.5 10 2.552 3.45 3.76 3.96 -0.494523 0.0296816 0.5 10 2.56 3.45 4.065 3.80941 -0.491747 0.0299429 0.5 10 2.568 3.45 3.645 3.968 -0.472573 0.0293749 0.5 10 2.576 3.45 3.735 3.968 -0.472178 0.0305078 0.5 10 2.584 3.45 3.69485 3.892 -0.463899 0.0304221 0.5 10 2.592 3.45 3.8 3.96944 -0.460443 0.0311143 0.5 10 2.6 3.45 3.29318 3.90914 -0.435683 0.0305475 0.5 10 2.608 3.45 3.18 3.65162 -0.418075 0.0296765 0.5 10 2.616 3.45 3.7 3.856 -0.432152 0.0298408 0.5 10 2.624 3.45 3.15238 3.944 -0.406159 0.0309816 0.5 10 2.632 3.45 3.3 3.88044 -0.407434 0.0313169 0.5 10 2.64 3.45 2.805 3.792 -0.375825 0.0299445 0.5 10 2.648 3.45 2.87 3.68747 -0.370549 0.0293912 0.5 10 2.656 3.45 1.45498 2.83085 -0.161463 0.0305208 0.5 10 2.664 3.45 1.38802 3.396 -0.139821 0.0316502 0.5 10 2.672 3.45 1.50857 2.90403 -0.163698 0.0316025 0.5 10 2.68 3.45 3.065 3.75898 -0.348889 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0.000001 +-0.000001 -0.000002 -0.000002 -0.000001 0.000001 +0.000002 0.000003 0.000002 0.000001 0.000001 +0.000004 0.000016 0.000044 0.000121 0.000233 +0.000406 0.000355 0.000193 -0.000105 -0.000321 +-0.000534 +n1=251 d1=0.008 o1=1 n2=121 d2=0.025 o2=1.5 in=stackedSection.asc data_format=ascii_float diff --git a/TDD/stereoniptomo/viewNipSources.py b/TDD/stereoniptomo/viewNipSources.py index 3d66a56..9fc8c24 100755 --- a/TDD/stereoniptomo/viewNipSources.py +++ b/TDD/stereoniptomo/viewNipSources.py @@ -27,6 +27,16 @@ f.close() +xx=[] +yy=[] +f = open("result.txt","r") +for line in f: + ys,xs = line.split() + xx.append(float(xs)) + yy.append(float(ys)) + +f.close() + z=[] f = open("surfsources.txt","r") for line in f: @@ -36,13 +46,24 @@ z0 = [0]*len(z) -m=[] -m0=[] +m1=[] +m01=[] +i=0 +f = open("modelin1.txt","r") +for line in f: + m1.append(float(line)) + m01.append(0.01*i) + i=i+1 + +f.close() + +m2=[] +m02=[] i=0 -f = open("modelin.txt","r") +f = open("modelin2.txt","r") for line in f: - m.append(float(line)) - m0.append(0.01*i) + m2.append(float(line)) + m02.append(0.01*i) i=i+1 f.close() @@ -50,6 +71,8 @@ plt.xlim(0, 6) plt.ylim(3,0) plt.plot(list(x),list(y),'*') +plt.plot(list(xx),list(yy),'.') plt.plot(list(z),list(z0),'v') -plt.plot(list(m0),list(m),'-') +plt.plot(list(m01),list(m1),'-') +plt.plot(list(m02),list(m2),'-') plt.show() From 159c5eec3670d4e59800449305c0f31eb115ae2c Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 23 Mar 2021 16:49:22 -0300 Subject: [PATCH 49/63] Velocity model output #17 Output the inverted velocity model after sfstereoniptomo finishes --- Mstereoniptomo.c | 18 ++++++++++++++---- TDD/stereoniptomo/SConstruct | 4 ++-- tomography.c | 4 ++-- vfsacrsnh_lib.c | 2 +- 4 files changed, 19 insertions(+), 9 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index d30b2c8..9e32cb8 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -8,9 +8,9 @@ Trace rays from NIP sources to acquisition surface in order to get traveltime cu #include #include "tomography.h" #include "vfsacrsnh_lib.h" -#define MAX_ITERATIONS 50 -#define temp0 10 -#define c0 0.01 +#define MAX_ITERATIONS 100 +#define temp0 5 +#define c0 0.1 int main(int argc, char* argv[]) { @@ -36,13 +36,14 @@ int main(int argc, char* argv[]) float tmis; // data time misfit value float *m0, *t0, *RNIP, *BETA; float x[2]; - sf_file out, shots, vel, angles, pots, m0s, t0s, rnips, betas; + sf_file out, shots, vel, velinv, angles, m0s, t0s, rnips, betas; sf_init(argc,argv); shots = sf_input("shotsfile"); vel = sf_input("in"); out = sf_output("out"); + velinv = sf_output("velinv"); angles = sf_input("anglefile"); m0s = sf_input("m0s"); t0s = sf_input("t0s"); @@ -173,6 +174,7 @@ int main(int argc, char* argv[]) sf_warning("%d/%d (%f)",is+1,ns,otmis); tmis0=100; } /* loop over nipsources */ + sf_putint(out,"n1",ndim); sf_putint(out,"n2",ns); sf_putfloat(out,"d1",1); @@ -181,6 +183,14 @@ int main(int argc, char* argv[]) sf_putfloat(out,"o2",0); sf_floatwrite(ots[0],ndim*ns,out); + sf_putint(velinv,"n1",n[0]); + sf_putint(velinv,"n2",n[1]); + sf_putfloat(velinv,"d1",d[0]); + sf_putfloat(velinv,"d2",d[1]); + sf_putfloat(velinv,"o1",o[0]); + sf_putfloat(velinv,"o2",o[1]); + sf_floatwrite(slow,nm,velinv); + /* TODO */ //updatevelmodel(x,slow,nm,dmis,i); diff --git a/TDD/stereoniptomo/SConstruct b/TDD/stereoniptomo/SConstruct index 3726a55..6a3182d 100644 --- a/TDD/stereoniptomo/SConstruct +++ b/TDD/stereoniptomo/SConstruct @@ -54,10 +54,10 @@ Flow('nipsources','ray','dd type=float | put n1=2 n2=%d'%(ns)) # forward modeling # do a ray-tracing from NIP sources to acquisition surface and measure traveltime -Flow(['tCoordinates','xCoordinates'], +Flow(['tCoordinates','velinv'], ['model','nipsources','angles','t0s','m0s','rnips','betas'], ''' - stereoniptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} m0s=${SOURCES[4]} rnips=${SOURCES[5]} betas=${SOURCES[6]} x=${TARGETS[1]} v0=1.508 + stereoniptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} m0s=${SOURCES[4]} rnips=${SOURCES[5]} betas=${SOURCES[6]} velinv=${TARGETS[1]} v0=1.508 ''') End() diff --git a/tomography.c b/tomography.c index 0df6b01..5b1c921 100644 --- a/tomography.c +++ b/tomography.c @@ -76,7 +76,7 @@ float calculateTimeMissfit(float* s, /* NIP sources matrix */ float currentRayAngle; int i, ir, it; float p[2], t, nrdeg; - int nt=5000, nr=4; //TODO to correct nr + int nt=5000, nr=5; //TODO to correct nr float dt=0.001; raytrace rt; float** traj; // Ray trajectory (z,x) @@ -146,6 +146,6 @@ float calculateTimeMissfit(float* s, /* NIP sources matrix */ } /* Loop over reflection rays */ //} /* Loop over NIP sources */ - tmis = (tmis*tmis)/(2*nr); + //tmis = 100*(tmis*tmis)/(nr); return tmis; } diff --git a/vfsacrsnh_lib.c b/vfsacrsnh_lib.c index 06ea73b..6f0042a 100644 --- a/vfsacrsnh_lib.c +++ b/vfsacrsnh_lib.c @@ -15,7 +15,7 @@ */ -#define MAX 3 +#define MAX 5 #define MIN 0 #define APERTURE 0.5 #define Rnip_MAX 4 From a3de7acaf3121c37dab473322d6f4ffe74c1af17 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 3 Apr 2021 19:50:21 -0300 Subject: [PATCH 50/63] README files to tests directory #17 --- TDD/README.md | 24 ++++++++++ TDD/stereoniptomo/README.md | 36 +++++++++++++-- TDD/stereoniptomo/SConstruct | 2 +- TDD/stereoniptomo/pick.txt | 85 ++++++++++++++++++++++++++++++++---- 4 files changed, 135 insertions(+), 12 deletions(-) create mode 100644 TDD/README.md diff --git a/TDD/README.md b/TDD/README.md new file mode 100644 index 0000000..b8b3648 --- /dev/null +++ b/TDD/README.md @@ -0,0 +1,24 @@ +## Test directory + +### Run unit tests and intergration test + +Run the following command to test: + +```sh +~$ make +``` + +It will run the unit tests of diffmig and diffsimul experiments using unittest python module and it will run optimization tests to get the time of those experiments. + +### Generate GDB version of the programs for debug + +To generate a GDB version of the programs. +Run the following make command: + +```sg +~$ make progname.x +``` + +The program make will generate a GDB version of the program and load the arguments using a GDB script. + +You are ready to debug sfstereoniptomo with GDB. diff --git a/TDD/stereoniptomo/README.md b/TDD/stereoniptomo/README.md index 22278b3..f811d6e 100644 --- a/TDD/stereoniptomo/README.md +++ b/TDD/stereoniptomo/README.md @@ -1,7 +1,37 @@ -This is a experiment for sfstereoniptomo test with GDB. -Run this experiment before test to generate the input. +## Stereoniptomo test -After that, go to the TDD directory, build the program version for +### Picking + +To run this experiment, first do the time picking of the reflectors +in the stacked section using the program _sfipick_ +with the following command: + +```sh +~$ pick.txt +``` + +### Run full experiment + +The file 'pick.txt' will be generated to store time picked points +from stacked section. After that, run full experiment with the command: + +```sh +~$ make +``` + +That command will run the experiment using the points you picked in the +stacked section as a parameter. Those points are associated with the RNIP +and BETA parameters in the 'parameterscube.rsf' file that will be used to +set up the model tracing rays to locate the NIP sources using _sfnipmodsetup_. + +The program _sfstereoniptomo_ does the global optimization of the NIP sources +location throushout forward modeling by a ray-tracing from NIP sources +to acquisition surface and using traveltime as convergence criteria. + +### Generate a GDB version of sfstereoniptomo + +To generate a sfstereoniptomo for GDB, +go to the TDD directory, build the program version for GDB with: ```sg diff --git a/TDD/stereoniptomo/SConstruct b/TDD/stereoniptomo/SConstruct index 6a3182d..50e143a 100644 --- a/TDD/stereoniptomo/SConstruct +++ b/TDD/stereoniptomo/SConstruct @@ -20,7 +20,7 @@ from rsf.proj import * ns = int(ARGUMENTS.get('ns',1)) -# Constant velocity model for test +# Constant gradient velocity model for test Flow('model',None, ''' spike n1=301 d1=0.02 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | diff --git a/TDD/stereoniptomo/pick.txt b/TDD/stereoniptomo/pick.txt index 665b874..dc94a9f 100644 --- a/TDD/stereoniptomo/pick.txt +++ b/TDD/stereoniptomo/pick.txt @@ -1,8 +1,77 @@ -2.33217 3.65906 0 -1.24696 3.68253 0 -2.37043 1.99283 0 -2.35652 2.56389 0 -2.33913 3.12712 0 -1.5913 2.00847 0 -1.47652 2.61473 0 -1.35478 3.12712 0 +2.36 2.44263 0 +1.19826 3.95241 0 +2.31826 4.27705 0 +2.33565 3.47523 0 +2.36348 2.34485 0 +2.36348 1.54302 0 +2.34957 2.72816 0 +2.32174 3.90548 0 +2.31478 4.20274 0 +2.32174 4.33963 0 +1.17391 4.38266 0 +1.60522 1.91069 0 +1.59826 1.96936 0 +1.17391 4.32399 0 +1.60522 1.66819 0 +1.60522 1.73077 0 +1.60522 1.78944 0 +1.6087 1.8442 0 +1.59826 1.51565 0 +1.60522 1.56258 0 +1.6087 1.60952 0 +2.36696 1.83638 0 +1.26087 3.54954 0 +2.36348 2.24707 0 +1.17391 4.45306 0 +2.34957 2.82595 0 +2.34957 2.90417 0 +2.35304 2.64602 0 +1.25391 3.62777 0 +2.3287 3.58866 0 +2.36696 1.72686 0 +2.34261 2.98631 0 +2.34957 3.07236 0 +2.32174 3.97979 0 +1.23652 3.69035 0 +2.32174 4.12842 0 +2.36696 1.63299 0 +1.18087 4.19883 0 +1.18087 4.24967 0 +1.22609 3.7764 0 +2.36348 1.94589 0 +1.47304 2.66558 0 +1.45217 2.7399 0 +1.50087 2.51695 0 +1.48348 2.59518 0 +1.54261 2.3292 0 +1.51478 2.41917 0 +1.57043 2.17666 0 +1.55652 2.25098 0 +1.5913 2.03585 0 +1.58087 2.10235 0 +2.33565 3.38136 0 +1.18783 4.03455 0 +2.33913 3.30313 0 +1.18435 4.12451 0 +2.32174 4.38266 0 +2.31826 4.43742 0 +2.36 2.54824 0 +2.3287 3.82334 0 +1.2087 3.87027 0 +2.32174 4.0502 0 +1.31304 3.31877 0 +1.33043 3.24446 0 +1.33739 3.1897 0 +1.35826 3.1193 0 +1.37913 3.03325 0 +1.39652 2.94329 0 +1.41391 2.87679 0 +1.43826 2.80639 0 +2.36348 2.12973 0 +2.36 2.03194 0 +2.34609 3.22099 0 +2.34609 3.14276 0 +2.3287 3.73729 0 +2.3287 3.66297 0 +1.2887 3.46741 0 +1.29217 3.39309 0 From 29ed8df61b021da5ede988b5728e528a2b4430d8 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 3 Apr 2021 19:51:51 -0300 Subject: [PATCH 51/63] Change tests directory name #17 --- {TDD => test}/Makefile | 0 {TDD => test}/README.md | 0 {TDD => test}/datamisfit/SConstruct | 0 {TDD => test}/gdb_script1 | 0 {TDD => test}/gdb_script2 | 0 {TDD => test}/gdb_script3 | 0 {TDD => test}/gdb_script4 | 0 {TDD => test}/gdb_script5 | 0 {TDD => test}/nipmodsetup/README.md | 0 {TDD => test}/nipmodsetup/SConstruct | 0 {TDD => test}/niptimecurve/README.md | 0 {TDD => test}/niptimecurve/SConstruct | 0 {TDD => test}/stereoniptomo/Makefile | 0 {TDD => test}/stereoniptomo/README.md | 0 {TDD => test}/stereoniptomo/SConscript1 | 0 {TDD => test}/stereoniptomo/SConscript2 | 0 {TDD => test}/stereoniptomo/SConstruct | 0 {TDD => test}/stereoniptomo/ascFormat.sh | 0 {TDD => test}/stereoniptomo/model1/modelin.txt | 0 {TDD => test}/stereoniptomo/model2/parametersCube.asc | 0 {TDD => test}/stereoniptomo/model2/stackedSection.asc | 0 {TDD => test}/stereoniptomo/modelin1.txt | 0 {TDD => test}/stereoniptomo/modelin2.txt | 0 {TDD => test}/stereoniptomo/parametersCube.asc | 0 {TDD => test}/stereoniptomo/pick.txt | 0 {TDD => test}/stereoniptomo/stackedSection.asc | 0 {TDD => test}/stereoniptomo/viewNipSources.py | 0 {TDD => test}/test_diffmig.py | 0 {TDD => test}/test_diffsimul.py | 0 29 files changed, 0 insertions(+), 0 deletions(-) rename {TDD => test}/Makefile (100%) rename {TDD => test}/README.md (100%) rename {TDD => test}/datamisfit/SConstruct (100%) rename {TDD => test}/gdb_script1 (100%) rename {TDD => test}/gdb_script2 (100%) rename {TDD => test}/gdb_script3 (100%) rename {TDD => test}/gdb_script4 (100%) rename {TDD => test}/gdb_script5 (100%) rename {TDD => test}/nipmodsetup/README.md (100%) rename {TDD => test}/nipmodsetup/SConstruct (100%) rename {TDD => test}/niptimecurve/README.md (100%) rename {TDD => test}/niptimecurve/SConstruct (100%) rename {TDD => test}/stereoniptomo/Makefile (100%) rename {TDD => test}/stereoniptomo/README.md (100%) rename {TDD => test}/stereoniptomo/SConscript1 (100%) rename {TDD => test}/stereoniptomo/SConscript2 (100%) rename {TDD => test}/stereoniptomo/SConstruct 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b/test/stereoniptomo/SConscript2 similarity index 100% rename from TDD/stereoniptomo/SConscript2 rename to test/stereoniptomo/SConscript2 diff --git a/TDD/stereoniptomo/SConstruct b/test/stereoniptomo/SConstruct similarity index 100% rename from TDD/stereoniptomo/SConstruct rename to test/stereoniptomo/SConstruct diff --git a/TDD/stereoniptomo/ascFormat.sh b/test/stereoniptomo/ascFormat.sh similarity index 100% rename from TDD/stereoniptomo/ascFormat.sh rename to test/stereoniptomo/ascFormat.sh diff --git a/TDD/stereoniptomo/model1/modelin.txt b/test/stereoniptomo/model1/modelin.txt similarity index 100% rename from TDD/stereoniptomo/model1/modelin.txt rename to test/stereoniptomo/model1/modelin.txt diff --git a/TDD/stereoniptomo/model2/parametersCube.asc b/test/stereoniptomo/model2/parametersCube.asc similarity index 100% rename from TDD/stereoniptomo/model2/parametersCube.asc rename to test/stereoniptomo/model2/parametersCube.asc diff --git a/TDD/stereoniptomo/model2/stackedSection.asc b/test/stereoniptomo/model2/stackedSection.asc similarity index 100% rename from TDD/stereoniptomo/model2/stackedSection.asc rename to test/stereoniptomo/model2/stackedSection.asc diff --git a/TDD/stereoniptomo/modelin1.txt b/test/stereoniptomo/modelin1.txt similarity index 100% rename from TDD/stereoniptomo/modelin1.txt rename to test/stereoniptomo/modelin1.txt diff --git a/TDD/stereoniptomo/modelin2.txt b/test/stereoniptomo/modelin2.txt similarity index 100% rename from TDD/stereoniptomo/modelin2.txt rename to test/stereoniptomo/modelin2.txt diff --git a/TDD/stereoniptomo/parametersCube.asc b/test/stereoniptomo/parametersCube.asc similarity index 100% rename from TDD/stereoniptomo/parametersCube.asc rename to test/stereoniptomo/parametersCube.asc diff --git a/TDD/stereoniptomo/pick.txt b/test/stereoniptomo/pick.txt similarity index 100% rename from TDD/stereoniptomo/pick.txt rename to test/stereoniptomo/pick.txt diff --git a/TDD/stereoniptomo/stackedSection.asc b/test/stereoniptomo/stackedSection.asc similarity index 100% rename from TDD/stereoniptomo/stackedSection.asc rename to test/stereoniptomo/stackedSection.asc diff --git a/TDD/stereoniptomo/viewNipSources.py b/test/stereoniptomo/viewNipSources.py similarity index 100% rename from TDD/stereoniptomo/viewNipSources.py rename to test/stereoniptomo/viewNipSources.py diff --git a/TDD/test_diffmig.py b/test/test_diffmig.py similarity index 100% rename from TDD/test_diffmig.py rename to test/test_diffmig.py diff --git a/TDD/test_diffsimul.py b/test/test_diffsimul.py similarity index 100% rename from TDD/test_diffsimul.py rename to test/test_diffsimul.py From 2eb74feed976986d855673979e18ea76adcf0535 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Sat, 3 Apr 2021 20:19:27 -0300 Subject: [PATCH 52/63] New directories for documentation and resources #17 --- doc/README.md | 1 + res/README.md | 1 + {diagrams => res/diagrams}/crePackage.dia | Bin {diagrams => res/diagrams}/crePackage.png | Bin {diagrams => res/diagrams}/creVelocityInversion.dia | Bin {diagrams => res/diagrams}/creVelocityInversion.png | Bin .../diagrams}/velocityInversionAlgorithm.dia | Bin .../diagrams}/velocityInversionAlgorithm.png | Bin 8 files changed, 2 insertions(+) create mode 100644 doc/README.md create mode 100644 res/README.md rename {diagrams => res/diagrams}/crePackage.dia (100%) rename {diagrams => res/diagrams}/crePackage.png (100%) rename {diagrams => res/diagrams}/creVelocityInversion.dia (100%) rename {diagrams => res/diagrams}/creVelocityInversion.png (100%) rename {diagrams => res/diagrams}/velocityInversionAlgorithm.dia (100%) rename {diagrams => res/diagrams}/velocityInversionAlgorithm.png (100%) diff --git a/doc/README.md b/doc/README.md new file mode 100644 index 0000000..ac9e4d5 --- /dev/null +++ b/doc/README.md @@ -0,0 +1 @@ +### Documentation diff --git a/res/README.md b/res/README.md new file mode 100644 index 0000000..e862fb6 --- /dev/null +++ b/res/README.md @@ -0,0 +1 @@ +### Resources files diff --git a/diagrams/crePackage.dia b/res/diagrams/crePackage.dia similarity index 100% rename from diagrams/crePackage.dia rename to res/diagrams/crePackage.dia diff --git a/diagrams/crePackage.png b/res/diagrams/crePackage.png similarity index 100% rename from diagrams/crePackage.png rename to res/diagrams/crePackage.png diff --git a/diagrams/creVelocityInversion.dia b/res/diagrams/creVelocityInversion.dia similarity index 100% rename from diagrams/creVelocityInversion.dia rename to res/diagrams/creVelocityInversion.dia diff --git a/diagrams/creVelocityInversion.png b/res/diagrams/creVelocityInversion.png similarity index 100% rename from diagrams/creVelocityInversion.png rename to res/diagrams/creVelocityInversion.png diff --git a/diagrams/velocityInversionAlgorithm.dia b/res/diagrams/velocityInversionAlgorithm.dia similarity index 100% rename from diagrams/velocityInversionAlgorithm.dia rename to res/diagrams/velocityInversionAlgorithm.dia diff --git a/diagrams/velocityInversionAlgorithm.png b/res/diagrams/velocityInversionAlgorithm.png similarity index 100% rename from diagrams/velocityInversionAlgorithm.png rename to res/diagrams/velocityInversionAlgorithm.png From e3b494cccdb7e4dadaeb494572075ffd2f59a328 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Mon, 5 Apr 2021 15:05:52 -0300 Subject: [PATCH 53/63] Help message of Makefile in test directory #20 Use the command 'make help' to get help from Makefile --- test/Makefile | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/test/Makefile b/test/Makefile index dc4aebd..f2ed719 100644 --- a/test/Makefile +++ b/test/Makefile @@ -77,3 +77,9 @@ clean: rm *.x .PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x getparameter.x stereoniptomo.x + +help: + @echo "Use 'make' to run unit tests and intergration test" + @echo "Use 'make progname.x' to generate a GDB version of progname for debug" + @echo "Programs available:" + @echo "\tgetparameter.x\n\tnipmodsetup.x\n\tniptimecurve2.x\n\tniptimecurve.x\n\tstereoniptomo.x" From 2bbeb1578c00e38a48af54eaf698dd9b3fd30c49 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 6 Apr 2021 13:52:42 -0300 Subject: [PATCH 54/63] Automate iterative picking with Makefile #20 Use Makefile to automate iterative picking loading. To iterative picking, run the following command: make picking Or run 'make help' to get help about the available commands --- test/stereoniptomo/Makefile | 19 ++++++++++++------- test/stereoniptomo/README.md | 5 ++++- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/test/stereoniptomo/Makefile b/test/stereoniptomo/Makefile index 1380dee..5e88fea 100644 --- a/test/stereoniptomo/Makefile +++ b/test/stereoniptomo/Makefile @@ -17,13 +17,13 @@ PARCUBE=model2/parametersCube.rsf STKSEC=model2/stackedSection.rsf # Extract parameters from stacked section -NP=$(shell (> $(PARCUBE:.rsf=.asc) <$(STKSEC) sfdisfil format="%f " number=n > $(STKSEC:.rsf=.asc) echo "n1=$(N1) d1=$(D1) o1=$(O1) n2=$(N2) d2=$(D2) o2=$(O2) in=$(notdir $(STKSEC:.rsf=.asc)) data_format=ascii_float" >> $(STKSEC:.rsf=.asc) + +picking: stackedSection.asc + scons -f SConscript1 $(PARAM) stackedSection.rsf + pick.txt diff --git a/test/stereoniptomo/README.md b/test/stereoniptomo/README.md index f811d6e..daec06b 100644 --- a/test/stereoniptomo/README.md +++ b/test/stereoniptomo/README.md @@ -7,9 +7,12 @@ in the stacked section using the program _sfipick_ with the following command: ```sh -~$ pick.txt +~$ make picking ``` +This command will convert the stacked section in ascii format to RSF +format and it will run the program _sfipick_ to do iterative picking. + ### Run full experiment The file 'pick.txt' will be generated to store time picked points From 2c306e329e9d8f11bf045b1b44b639dbbd43c64b Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Tue, 6 Apr 2021 15:36:48 -0300 Subject: [PATCH 55/63] Scratch of the velocity update function #20 This velocity updates the velocity gradient to simulate a velocity varying with depth model and evaluate if the NIP sources converges to the reflector interfaces --- Mstereoniptomo.c | 135 +++++++++++++++++----------------- test/stereoniptomo/SConstruct | 4 +- test/stereoniptomo/pick.txt | 79 +------------------- tomography.c | 48 ++++++++---- 4 files changed, 104 insertions(+), 162 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index 9e32cb8..b1faa18 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -36,6 +36,7 @@ int main(int argc, char* argv[]) float tmis; // data time misfit value float *m0, *t0, *RNIP, *BETA; float x[2]; + int iv, nv=10; sf_file out, shots, vel, velinv, angles, m0s, t0s, rnips, betas; sf_init(argc,argv); @@ -111,87 +112,87 @@ int main(int argc, char* argv[]) sf_warning("n1=%d",ns); } - /*sf_putint(xCurve,"n1",2); - sf_putint(xCurve,"n2",ns); - sf_putint(timeCurve,"n1",2); - sf_putint(timeCurve,"n2",ns);*/ + /* Optimized parameters */ + sf_putint(out,"n1",ndim); + sf_putint(out,"n2",ns); + sf_putint(out,"n3",nv); + sf_putfloat(out,"d1",1); + sf_putfloat(out,"o1",0); + sf_putfloat(out,"d2",1); + sf_putfloat(out,"o2",0); + sf_putfloat(out,"d3",1); + sf_putfloat(out,"o3",0); + + /* Velocity models from inversion */ + sf_putint(velinv,"n1",n[0]); + sf_putint(velinv,"n2",n[1]); + sf_putint(velinv,"n3",nv); + sf_putfloat(velinv,"d1",d[0]); + sf_putfloat(velinv,"d2",d[1]); + sf_putfloat(velinv,"o1",o[0]); + sf_putfloat(velinv,"o2",o[1]); + sf_putfloat(velinv,"d3",1); + sf_putfloat(velinv,"o3",0); - for(is=0; is u){ - //for(is=0;is u){ s[is][0]=cnew[0]; s[is][1]=cnew[1]; - //} - Em0 = -fabs(tmis); + Em0 = -fabs(tmis); + } } - } - - //sf_warning("%d/%d (%f)",q+1,MAX_ITERATIONS,otmis); - } /* loop over iterations */ + + } /* loop over iterations */ - sf_warning("%d/%d (%f)",is+1,ns,otmis); - tmis0=100; - } /* loop over nipsources */ + sf_warning("%d => %d/%d (%f)",iv,is+1,ns,otmis); + tmis0=100; + } /* loop over nipsources */ - sf_putint(out,"n1",ndim); - sf_putint(out,"n2",ns); - sf_putfloat(out,"d1",1); - sf_putfloat(out,"o1",0); - sf_putfloat(out,"d2",1); - sf_putfloat(out,"o2",0); - sf_floatwrite(ots[0],ndim*ns,out); + /* TODO: Function to update velocity */ + updatevelmodel(slow, n, o, d, v0, iv); - sf_putint(velinv,"n1",n[0]); - sf_putint(velinv,"n2",n[1]); - sf_putfloat(velinv,"d1",d[0]); - sf_putfloat(velinv,"d2",d[1]); - sf_putfloat(velinv,"o1",o[0]); - sf_putfloat(velinv,"o2",o[1]); - sf_floatwrite(slow,nm,velinv); + sf_floatwrite(ots[0],ndim*ns,out); - /* TODO */ - //updatevelmodel(x,slow,nm,dmis,i); + sf_floatwrite(slow,nm,velinv); + } } diff --git a/test/stereoniptomo/SConstruct b/test/stereoniptomo/SConstruct index 50e143a..82a91e5 100644 --- a/test/stereoniptomo/SConstruct +++ b/test/stereoniptomo/SConstruct @@ -20,12 +20,10 @@ from rsf.proj import * ns = int(ARGUMENTS.get('ns',1)) -# Constant gradient velocity model for test +# Constant velocity model for test Flow('model',None, ''' spike n1=301 d1=0.02 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | - math output="x1*0.05+input" | - smooth | put label1=Depth unit1=km label2=Position unit2=km ''' ) diff --git a/test/stereoniptomo/pick.txt b/test/stereoniptomo/pick.txt index dc94a9f..1c418c5 100644 --- a/test/stereoniptomo/pick.txt +++ b/test/stereoniptomo/pick.txt @@ -1,77 +1,2 @@ -2.36 2.44263 0 -1.19826 3.95241 0 -2.31826 4.27705 0 -2.33565 3.47523 0 -2.36348 2.34485 0 -2.36348 1.54302 0 -2.34957 2.72816 0 -2.32174 3.90548 0 -2.31478 4.20274 0 -2.32174 4.33963 0 -1.17391 4.38266 0 -1.60522 1.91069 0 -1.59826 1.96936 0 -1.17391 4.32399 0 -1.60522 1.66819 0 -1.60522 1.73077 0 -1.60522 1.78944 0 -1.6087 1.8442 0 -1.59826 1.51565 0 -1.60522 1.56258 0 -1.6087 1.60952 0 -2.36696 1.83638 0 -1.26087 3.54954 0 -2.36348 2.24707 0 -1.17391 4.45306 0 -2.34957 2.82595 0 -2.34957 2.90417 0 -2.35304 2.64602 0 -1.25391 3.62777 0 -2.3287 3.58866 0 -2.36696 1.72686 0 -2.34261 2.98631 0 -2.34957 3.07236 0 -2.32174 3.97979 0 -1.23652 3.69035 0 -2.32174 4.12842 0 -2.36696 1.63299 0 -1.18087 4.19883 0 -1.18087 4.24967 0 -1.22609 3.7764 0 -2.36348 1.94589 0 -1.47304 2.66558 0 -1.45217 2.7399 0 -1.50087 2.51695 0 -1.48348 2.59518 0 -1.54261 2.3292 0 -1.51478 2.41917 0 -1.57043 2.17666 0 -1.55652 2.25098 0 -1.5913 2.03585 0 -1.58087 2.10235 0 -2.33565 3.38136 0 -1.18783 4.03455 0 -2.33913 3.30313 0 -1.18435 4.12451 0 -2.32174 4.38266 0 -2.31826 4.43742 0 -2.36 2.54824 0 -2.3287 3.82334 0 -1.2087 3.87027 0 -2.32174 4.0502 0 -1.31304 3.31877 0 -1.33043 3.24446 0 -1.33739 3.1897 0 -1.35826 3.1193 0 -1.37913 3.03325 0 -1.39652 2.94329 0 -1.41391 2.87679 0 -1.43826 2.80639 0 -2.36348 2.12973 0 -2.36 2.03194 0 -2.34609 3.22099 0 -2.34609 3.14276 0 -2.3287 3.73729 0 -2.3287 3.66297 0 -1.2887 3.46741 0 -1.29217 3.39309 0 +1.38261 3.02934 0 +2.33913 3.0528 0 diff --git a/tomography.c b/tomography.c index 5b1c921..cfc5eb0 100644 --- a/tomography.c +++ b/tomography.c @@ -14,12 +14,36 @@ #endif /*^*/ -void updatevelmodel(float* x, float* slow, int nm, float dmis,int i){ -/*< TODO update velocity model >*/ - //float v; - int im; - for(im=0;im*/ +{ + int i, j; + float x, v; + float grad=(ite+1)*0.01; + + for(i=0;i sx=%f sy=%f sa=%f",x[1],x[0],nrdeg); + nrdeg = a[is]; // TODO verify is in degree? for(ir=0;ir tc=%f t=%f tmis=%f dtmis=%f\n",t,ts+tr,ts+tr-t,tmis); } /* Loop over reflection rays */ - //} /* Loop over NIP sources */ - //tmis = 100*(tmis*tmis)/(nr); + /* TODO: Evaluate the best function to calcullate the time misfit */ + tmis = (tmis*tmis)/(nr); return tmis; } From 976a17dd55ab73f67304c2c1b0ef216c6dcde79d Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Mon, 12 Apr 2021 17:27:03 -0300 Subject: [PATCH 56/63] Generalize function to disturb parameters #20 --- Mstereoniptomo.c | 2 +- vfsacrsnh_lib.c | 36 ++++++++++++++++++++---------------- 2 files changed, 21 insertions(+), 17 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index b1faa18..ece9003 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -147,7 +147,7 @@ int main(int argc, char* argv[]) temp=getVfsaIterationTemperature(q,c0,temp0); /* parameter disturbance */ - disturbParameters(temp,cnew,x,1); + disturbParameters(temp,cnew,x,2,0.01); tmis=0; diff --git a/vfsacrsnh_lib.c b/vfsacrsnh_lib.c index 6f0042a..6600d45 100644 --- a/vfsacrsnh_lib.c +++ b/vfsacrsnh_lib.c @@ -51,32 +51,36 @@ float getVfsaIterationTemperature(int iteration,float dampingFactor,float inicia } -void disturbParameters(float temperature, float* disturbedParameter, float* parameter, int ns /*Number of parameters */){ -/*< Perturbar os parâmetros da iteração anterior >*/ +void disturbParameters( float temperature, /* Temperature of this interation in VFSA */ + float* disturbedParameter, /* Parameters disturbed vector */ + float* parameter, /* original parameters vector */ + int np, /*Number of parameters */ + float scale /* Scale to multiply by disturbance */) +/*< Disturb parameters from the previous iteration of VFSA + Note: It receives a parameter vector and distubs it accordingly to +VFSA disturb parameters step. + >*/ +{ float u; float disturbance; int i; - //for(is=0;is= MAX || disturbedParameter[i] <= MIN) { + if (disturbedParameter[i] >= MAX || disturbedParameter[i] <= MIN) { - disturbedParameter[i] = (APERTURE) * getRandomNumberBetween0and1() + parameter[i]; - - } - - //sf_warning("d=%f p=%f i=%d d=%f",disturbedParameter[is][i],parameter[is][i],i,disturbance/100); + disturbedParameter[i] = (APERTURE) * getRandomNumberBetween0and1() + parameter[i]; + } - //} + + } } void nonHyperbolicCRSapp(float t[2*mMAX+1][hMAX], float m0, float dm, float om, float dh, float oh, float t0, float v0, float RN, float RNIP, float BETA){ From c3a318333776432d8e89f508c64515233510c109 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 14 Apr 2021 14:30:49 -0300 Subject: [PATCH 57/63] Modify stereoniptomo strategy #20 Modify stereoniptomo strategy to use VFSA to find the optimal velocity gradient for a linear velocity model varying with depth that best fits the data --- Mstereoniptomo.c | 119 +++++++++++++++++---------------- test/stereoniptomo/SConscript2 | 2 +- test/stereoniptomo/SConstruct | 9 +++ tomography.c | 53 +++++++++------ vfsacrsnh_lib.c | 2 +- 5 files changed, 103 insertions(+), 82 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index ece9003..957b478 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -20,7 +20,7 @@ int main(int argc, char* argv[]) float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 float** s; // NIP source position (z,x) float* cnew; - float** ots; + float* ots; float tmis0=100, otmis=0, deltaE, Em0=0, PM, temp=1, u=0; int ndim; // n1 dimension in shotsfile, should be equal 2 int nshot; // n2 dimensions in shotsfile @@ -32,11 +32,13 @@ int main(int argc, char* argv[]) float v0; // Near surface velocity int ns; // Number of NIP sources int is; // Loop counter for NIP sources - int q; + int q; // Loop counter for VFSA iteration float tmis; // data time misfit value float *m0, *t0, *RNIP, *BETA; float x[2]; int iv, nv=10; + float* gv; + int i, j; sf_file out, shots, vel, velinv, angles, m0s, t0s, rnips, betas; sf_init(argc,argv); @@ -71,8 +73,10 @@ int main(int argc, char* argv[]) if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); s = sf_floatalloc2(ndim,nshot); sf_floatread(s[0],ndim*nshot,shots); - cnew = sf_floatalloc(ndim); - ots = sf_floatalloc2(ndim,nshot); + cnew = sf_floatalloc(1); + ots = sf_floatalloc(1); + gv = sf_floatalloc(1); + gv[0]=0.01; sf_fileclose(shots); /* Anglefile: get initial emergence angle */ @@ -115,7 +119,7 @@ int main(int argc, char* argv[]) /* Optimized parameters */ sf_putint(out,"n1",ndim); sf_putint(out,"n2",ns); - sf_putint(out,"n3",nv); + sf_putint(out,"n3",1); sf_putfloat(out,"d1",1); sf_putfloat(out,"o1",0); sf_putfloat(out,"d2",1); @@ -126,7 +130,7 @@ int main(int argc, char* argv[]) /* Velocity models from inversion */ sf_putint(velinv,"n1",n[0]); sf_putint(velinv,"n2",n[1]); - sf_putint(velinv,"n3",nv); + sf_putint(velinv,"n3",1); sf_putfloat(velinv,"d1",d[0]); sf_putfloat(velinv,"d2",d[1]); sf_putfloat(velinv,"o1",o[0]); @@ -134,65 +138,64 @@ int main(int argc, char* argv[]) sf_putfloat(velinv,"d3",1); sf_putfloat(velinv,"o3",0); - for(iv=0; iv u){ - s[is][0]=cnew[0]; - s[is][1]=cnew[1]; - Em0 = -fabs(tmis); - } - } + tmis=0; + + /* Calculate time missfit through forward modeling */ + tmis=calculateTimeMissfit(s,v0,t0,m0,RNIP,BETA,n,o,d,slow,a,nshot); + + if(fabs(tmis) < fabs(tmis0) ){ + otmis = fabs(tmis); + /* optimized parameters matrix */ + ots[0]=cnew[0]; + tmis0 = fabs(tmis); + } + + /* VFSA parameters update condition */ + deltaE = -fabs(tmis) - Em0; + + /* Metrópolis criteria */ + PM = expf(-deltaE/temp); + + if (deltaE<=0){ + gv[0]=cnew[0]; + Em0 = -fabs(tmis); + } else { + u=getRandomNumberBetween0and1(); + if (PM > u){ + gv[0]=cnew[0]; + Em0 = -fabs(tmis); + } + } - } /* loop over iterations */ + sf_warning("%f => %d/%d (%f)",gv[0],q,MAX_ITERATIONS,otmis); - sf_warning("%d => %d/%d (%f)",iv,is+1,ns,otmis); - tmis0=100; - } /* loop over nipsources */ + } /* loop over iterations */ - /* TODO: Function to update velocity */ - updatevelmodel(slow, n, o, d, v0, iv); + /* Print optimal velocity gradient */ + sf_warning("=> vgrad=%f v0=%f",ots[0],v0); + + /* Generate optimal velocity model */ + for(i=0;i*/ +float calculateTimeMissfit(float** s, /* NIP sources matrix (z,x) pairs */ + float v0, /* Near surface velocity */ + float* t0, /* Normal ray traveltime for each NIP source */ + float* m0, /* Central CMP for each NIP source */ + float* RNIP, /* RNIP parameter for each NIP source */ + float* BETA, /* BETA parameter for each NIP source */ + int *n, /* Velocity model dimension n1=n[0] n2=n[1] */ + float *o, /* Velocity model axis origin o1=o[0] o2=o[1] */ + float *d, /* Velocity model sampling d1=d[0] d2=d[1] */ + float *slow, /* Slowness velociy model */ + float *a, /* Normal ray angle for each NIP source */ + int ns /* Number of NIP sources */) +/*< Return time missfit sum of source-NIP-receiver rays + +Note: This function traces nr reflection rays pairs from each NIP source +position passed though s matrix. It also calculate the difference between +the traveltime of the traced rays with calculated traveltime using CRE +traveltime approximation to calculate the time misfit returned by the function. + >*/ { float currentRayAngle; - int i, ir, it; + int i, ir, it, is; float p[2], t, nrdeg; int nt=5000, nr=5; //TODO to correct nr float dt=0.001; @@ -109,9 +114,11 @@ float calculateTimeMissfit(float* s, /* NIP sources matrix */ x = sf_floatalloc(2); - x[0]=s[0]; - x[1]=s[1]; - nrdeg = a[is]; // TODO verify is in degree? + for(is=0;is Date: Mon, 19 Apr 2021 17:47:47 -0300 Subject: [PATCH 58/63] Run inversion in looping #20 Run the velocity inversion in a loop using the prevous optimal model as input for the next iteration --- Mstereoniptomo.c | 2 +- test/stereoniptomo/SConscript2 | 2 +- test/stereoniptomo/SConstruct | 49 +++++++++++++++++++++------------- test/stereoniptomo/pick.txt | 49 ++++++++++++++++++++++++++++++++-- 4 files changed, 80 insertions(+), 22 deletions(-) diff --git a/Mstereoniptomo.c b/Mstereoniptomo.c index 957b478..46010e7 100644 --- a/Mstereoniptomo.c +++ b/Mstereoniptomo.c @@ -183,7 +183,7 @@ int main(int argc, char* argv[]) } /* loop over iterations */ /* Print optimal velocity gradient */ - sf_warning("=> vgrad=%f v0=%f",ots[0],v0); + sf_warning("(%f)=> vgrad=%f v0=%f",tmis0,ots[0],v0); /* Generate optimal velocity model */ for(i=0;i Date: Wed, 28 Apr 2021 15:35:02 -0300 Subject: [PATCH 59/63] Remove useless experiments Those experiments were related to old strategy of velocity inversion using diffraction simulation. Now, the velocity inversion strategy has changed to stereoniptomo using ray tracying and VFSA --- experiments/README.md | 1 - .../VelocityInversionReport.ipynb | 225 ---------------- .../diffractionSimulation.jpg | Bin 67454 -> 0 bytes .../focusingAnalisysResult.jpg | Bin 47577 -> 0 bytes .../Experiment_velocity_inversion/model.jpg | Bin 32627 -> 0 bytes .../stackedSection.jpg | Bin 31145 -> 0 bytes .../Experiment_velocity_inversion/vdix.jpg | Bin 46466 -> 0 bytes .../velocityInversionResult.jpg | Bin 55660 -> 0 bytes .../algorithmVelocityInversion/README.md | 1 - .../algorithmVelocityInversion/SConstruct | 97 ------- .../algorithmVelocityInversion/__init__.py | 0 .../algorithmVelocityInversion/ascFormat.sh | 35 --- .../diffSimulationAndMigration.py | 250 ------------------ .../algorithmVelocityInversion/inputScons.py | 38 --- experiments/modelingAndStack/SConstruct | 78 ------ experiments/setUpNIPModel/SConstruct | 64 ----- experiments/stereotomo/SConstruct | 66 ----- experiments/stereotomo/ascFormat.sh | 7 - experiments/stereotomo/gdb_script | 2 - experiments/stereotomo/viewFoldGraph.py | 40 --- .../README.md | 1 - .../SConstruct | 97 ------- .../__init__.py | 0 .../ascFormat.sh | 35 --- .../diffSimulationAndMigration.py | 250 ------------------ .../inputScons.py | 38 --- test/Makefile | 14 +- test/test_diffmig.py | 63 ----- test/test_diffsimul.py | 75 ------ 29 files changed, 2 insertions(+), 1475 deletions(-) delete mode 100644 experiments/README.md delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/diffractionSimulation.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/focusingAnalisysResult.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/model.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/stackedSection.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/vdix.jpg delete mode 100644 experiments/algorithmVelocityInversion/Experiment_velocity_inversion/velocityInversionResult.jpg delete mode 100644 experiments/algorithmVelocityInversion/README.md delete mode 100644 experiments/algorithmVelocityInversion/SConstruct delete mode 100644 experiments/algorithmVelocityInversion/__init__.py delete mode 100755 experiments/algorithmVelocityInversion/ascFormat.sh delete mode 100644 experiments/algorithmVelocityInversion/diffSimulationAndMigration.py delete mode 100644 experiments/algorithmVelocityInversion/inputScons.py delete mode 100644 experiments/modelingAndStack/SConstruct delete mode 100644 experiments/setUpNIPModel/SConstruct delete mode 100644 experiments/stereotomo/SConstruct delete mode 100755 experiments/stereotomo/ascFormat.sh delete mode 100644 experiments/stereotomo/gdb_script delete mode 100755 experiments/stereotomo/viewFoldGraph.py delete mode 100644 experiments/velocityInversionCreStackedSection/README.md delete mode 100644 experiments/velocityInversionCreStackedSection/SConstruct delete mode 100644 experiments/velocityInversionCreStackedSection/__init__.py delete mode 100755 experiments/velocityInversionCreStackedSection/ascFormat.sh delete mode 100644 experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py delete mode 100644 experiments/velocityInversionCreStackedSection/inputScons.py delete mode 100644 test/test_diffmig.py delete mode 100644 test/test_diffsimul.py diff --git a/experiments/README.md b/experiments/README.md deleted file mode 100644 index 5f21e89..0000000 --- a/experiments/README.md +++ /dev/null @@ -1 +0,0 @@ -#### Experiments and studies of CRE Velocity Inversion diff --git a/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb b/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb deleted file mode 100644 index 2f85620..0000000 --- a/experiments/algorithmVelocityInversion/Experiment_velocity_inversion/VelocityInversionReport.ipynb +++ /dev/null @@ -1,225 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inversão do modelo de velocidades\n", - "\n", - "Utilizando o modelo de 3 camadas, v1=1.508, v2=1.690, v3=2.000, abaixo:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='model.jpg', width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtemos a seção empilhada a partir do processamento sísmico convencional (Análise de velocidades, correção NMO, etc). A seção empilhada é exibida abaixo:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='stackedSection.jpg', width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fazendo o picking iterativo na seção empilhada, simulamos hipérboles de difração em pontos escolhidos da seção associados a eventos de reflexão" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='diffractionSimulation.jpg', width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Refazemos a análise de velocidades utilizando a continuação de velocidades (velocity continuation) e a focalização de difrações das difrações simuladas como critério de otimização das velocidades." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='focusingAnalisysResult.jpg', width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Este modelo é suavizado antes da inversão de velocidades para estimativa do modelo de velocidades em profundidade " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='vdix.jpg', width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Por fim, o modelo de velocidades em profundidade é obtido a partir do algoritmo descrito no paper _Time-to-depth conversion and seismic velocity estimation using time-migration velocity_ (Cameron, 2008)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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zIriT-mW)SW`+VT8Oc}P{;B`a3paMTvv-y(|HJ)+Wu{Bcwtyi3n@mxF1)>i$ znKbyrZ7Q7F(LC;!ItIADO)kTc^a7#=7yzWs=*PDNnmf-@T*n)+W;5MW11hC0TbuTa z(;pEn%g`~cMT%SC1NxEXeJ*fUs;@$ZdF0Ly;5@?BSe&ib_vXs8FBvBIM4)i2x7l{^ zx_|C)P6QFzAAffv0yPDdCob^A9^gVSnFMM+a#+8O`oCj3-MN2~I47!Bf5E?N)q4Ay zi`o-u?f?ueu=9Ko4v_s_)xy~YbwD}sB-`M94scguWzZpigK1kiaJ_XHE>PHK>!yAi}lWvk{%QxV}BEH*g$c*E+DZCi&*oS2n;mfAwn# zJcVXCqfTFXj-dsX+%J5naAfM3+?@>IdX|!Zu8(?<%Wtf!;uR^6!hJ1L$2V}EjVg51 ziMauCbCUeH8^G5|quSwbprySuN0j&@mEk9)lG$_JYlJ5w7L`ev&Eo$ zqEIGH?+T+@%cvx68T6DGR-PxgZj9PHE2_c3lh@e~MqeVO^5g+oFPhVUdEAPdNXhjyiRl=NMT4sVu--oBEAuDBO5_8T864;0vrr4vXvKG5^5 z>ke@@)#n!9i%i}jg?4%M-VUD`jTE|@krXxc$YM7ae*~eVqU?D3Nb+tFOV?cG9Lw!9Cqf|8rS}11CVftFb0GSa)0yIj z!5d)A<()u`3+(u%m^4m0RpBK*KSg-OSK3Vx1Z+{J(jTRw(Y#_h-(hcw;S3xj0z68P zGVrEN0PZDQ{jS%08yVK&=6w+`1|d@Az#y&WDaW8O9ly0{P+6upz8kiS($al-lY=iH zM%q5cE4N*aV9m#gz!8uHVa#GWIUtxzEzS9_VHk)tDrsVl?v)+;@(=v2jr*o?syEoK z-OOp?+3!Z-w4n+FQos>|LvVr$%xuHpeF$no10$l&l%+uR1o?k00BvdxnC4z9OYXDmu`bthzI13wd~uJ$ zC`t1R!WSbc?g9`jZ7kpeN&!DC23W7pg3+)8pE0nU4M|NNJTy?Xp0b;0+W(Zr=*pc{ zWjhpg&t}t(q0^KOi3@}NGbZTnWjA208>;m0Q!8&Ck~#6|>UZTc^zpep?xd9cc{C3OIIpJvNrh`7!1(-XL;GC;sL0>pyD* z@Z}^r%3F0=F^p${kDMB_%na(zE4>h2vp<-hlQ|B5{Ecq(mqUKrWjG&qr!8F=i@BV~ z=4s%O%dRK~h36NVEIT`*54KLa@%XLb+~iKtJ%3I=UeiZ{PVqaFsjKDLS^f{|910KQ z*!-68Xhc3_E&aAV^j_M%(RV?{BU#_4dTE90W&1ruh=>j#>-N}&E$YJ2P_>ihRX;G5 zcD{s`n<``!z|t}588^?$yV4J2&SD8||CF8WNSqaR6=x!w*hxNur>aeW^4;=rcx;b+ zy~DKn*c~kk+7PGj>GNiTkpkeR<{y5XRhI#x3kml}nE@C&>|YE$5s?H4Qzhu01^IlU z!YOC3!#0XQN1eWD)}y7GkiG-?{WE}&yO{dZB^%y. - -# Madagascar package -from rsf.proj import * - -# Personal Madagascar recipes -from diffSimulationAndMigration import diffmig -from diffSimulationAndMigration import diffsimul -from inputScons import castingParametersFromCmd - -# Parameters dictionaries to the functions -diffSimulParameters = {'numberOfReflectors':2, -'section':'../modelingAndStack/stackedSection.rsf', -'velocities':(1.5,1.6), -'diffSimulatedSection':'diffSimulatedSection'} - -diffMigParameters = {'diffSimulatedSection': 'diffSimulatedSection', -'diffMigratedSection': 'diffMigratedSection', -'v0':1.4, -'nv':100, -'dv':0.01, -'nx':201, -'padx':1000, -'nt':1001, -'tmin':0, -'tmax':4, -'rect1':10, -'rect2':10, -'srect1':1, -'srect2':3, -'vslope':None, -'units':'Km', -'f1':1, -'j3':1, -'dx':0.025, -'x0':0, -'beg1':0, -'frect1':0, -'frect2':0, -'an':1, -'nout':2048, -'vx0':None} - -# substitute parameters into parameters dictionaries -# if some of them was passed through command line -castingParametersFromCmd(ARGLIST,diffSimulParameters) -castingParametersFromCmd(ARGLIST,diffMigParameters) - - -# Diffraction simulation in an iterative picking loop -diffsimul(**diffSimulParameters) - -# Diffraction migration by focusing analysis -diffmig(**diffMigParameters) - -# Velocity inversion with the Camerom algorithm (2008) -Flow('vdix2 vmig2',diffMigParameters['diffMigratedSection']+'-pik', - ''' - dix rect1=20 rect2=20 - vrmsout=${TARGETS[1]} - niter=40 - ''') - -nz=200 -dz=0.01 -Flow('vxz x0 t0','vdix2', - ''' - transp | - cameron2d method=cheb neval=20 nc=1000 nz=%d dz=%g - x0=${TARGETS[1]} t0=${TARGETS[2]} - ''' % (nz,dz),local=1) - - -Flow('vz','vxz', - ''' - put label1=Lateral unit1=km label2=Depth unit2=km n3=1 | - transp - ''') - -End() diff --git a/experiments/algorithmVelocityInversion/__init__.py b/experiments/algorithmVelocityInversion/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/experiments/algorithmVelocityInversion/ascFormat.sh b/experiments/algorithmVelocityInversion/ascFormat.sh deleted file mode 100755 index e7c5172..0000000 --- a/experiments/algorithmVelocityInversion/ascFormat.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -# -# ascFormat.sh (Shell Script) -# -# Purpose: Convert sfipick picked points -# to Madagascar readable ascii format. -# Important! $1=1 cut and format the first collumn of the file (t0 time) -# If $1=2 cut and format the second collumn of the file (m0 CMP) -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 20/08/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -INPUT=$(cat "-" | tr '\t' ' ' | cut -d' ' -f"$1") -NUMBER_OF_POINTS=$(echo "$INPUT" | wc -l) -FORMATED_INPUT=$(echo "$INPUT" | tr '\n' ' ') - -if [ "$1" == "1" ] -then - FILE="t0s" -elif [ "$1" == "2" ] -then - FILE="m0s" -else - echo "Error: $(basename $0): \$1 should be equal to 1 or 2!" - exit 1 -fi - -echo "${FORMATED_INPUT}n1=$NUMBER_OF_POINTS d1=1 o1=0 data_format=ascii_float in=$2" diff --git a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py b/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py deleted file mode 100644 index 0426fa4..0000000 --- a/experiments/algorithmVelocityInversion/diffSimulationAndMigration.py +++ /dev/null @@ -1,250 +0,0 @@ -# coding: utf-8 -# -# diffSimulationAndMigration.py (Madagascar Recipe) -# -# Purpose: Generate diffraction simulated hyperbolas -# in the stacked section using iterative picking. And -# migrate it using focusing measure. The input is a -# stacked section with only diffraction hiperbolas. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo Dirack 01/03/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * -import string, sys, os - -frect=0 - -def diffmig(diffSimulatedSection, - diffMigratedSection, - v0, - nv, - dv, - nx, - padx, - nt, - tmin=0, - tmax=10, - rect1=10, - rect2=10, - srect1=1, - srect2=3, - vslope=None, - units='Km', - f1=1, - j3=1, - dx=1, - x0=0, - beg1=0, - frect1=0, - frect2=0, - an=1, - nout=2048, - vx0=None): - ''' - Diffraction migration by focusing measure - :param diffSimulatedSection: RSF filename, stacked Section with simulated diffractions - :output diffMigratedSection: RSF filename, basename of the outputs - |-> diffMigratedSection+pik: time migrated velocity - |-> diffMigratedSection+dip: dominant slope section - |-> diffMigratedSection+slc: slice of the velocity cube - |-> diffMigratedSection+vlf: velocity cube focusing measure - |-> diffMigratedSection+foc: focusing measure - |-> diffMigratedSection+sem: semblance - ''' - - if diffSimulatedSection == diffMigratedSection: - raise ValueError("Filenames should not be equal") - return False - - if frect==0: - frect1=2*rect1 - - if frect==0: - frect2=2*rect2/j3 - - # dominant slope of stacked section - dip = diffMigratedSection+'-dip' - - # Calculate dominante slope, rect parameter - # are the smooth radius - Flow(dip,diffSimulatedSection,'dip rect1=%d rect2=%d' % (rect1,rect2)) - - # Velocity analisys by focusing - # Stolt Migration. It needs the cosine - # tranform of the data. sfvczo does - # the velocity continuation process - # migrate data multiple times with a - # constant velocity - velcon=''' - pad n2=%d beg1=%d | cosft sign2=1 | put o3=0 | - stolt vel=%g | - vczo nv=%d dv=%g v0=%g | - transp plane=23 | - cosft sign2=-1 | - window n2=%d f1=%d | - put o2=%g | - transp plane=23 - ''' % (padx,beg1,v0,nv,dv,v0,nx,beg1,x0) - - # Velocity continuation of diffraction - vlf=diffMigratedSection+'-vlf' - Flow(vlf,diffSimulatedSection,velcon) - - Flow(vlf+'q',diffSimulatedSection, - ''' - math output="input*input" | %s | clip2 lower=0 - ''' % velcon) - - # j#n is the jump parameter of sfwindow - if j3 > 1: - focus = 'window j3=%d | ' % j3 - else: - focus = '' - - # Focusing analisys - # local varimax is max when the velocity - # collapses a diffraction - focus = focus + ''' - focus rect1=%d rect3=%d | - math output="1/abs(input)" | - cut max1=%g | cut min1=%g - ''' % (frect1,frect2,tmin,tmax) - - # focusing measure - foc=diffMigratedSection+'-foc' - Flow(foc,vlf,focus) - - # semblance is the local varimax measure - # sfmul multplies data - # sfdivn does smooth data division - sem = diffMigratedSection+'-sem' - Flow(sem,[vlf,vlf+'q'], - ''' - mul $SOURCE | - divn den=${SOURCES[1]} rect1=%d rect3=%d - ''' % (rect1,rect2)) - - # Data after picking - pik=diffMigratedSection+'-pik' - - # if vslope, use it to mute data - if vslope: - pick = 'mutter x0=%g v0=%g half=n | ' % (vx0,vslope) - else: - pick = '' - - pick = pick + 'scale axis=2 | pick an=%g rect1=%d rect2=%d | window' % (an,rect1,rect2) - - if j3 > 1: - pick2 = pick + ''' - transp | - spline n1=%d d1=%g o1=%g | - transp - ''' % (nx,dx,x0) - else: - pick2 = pick - - Flow(pik,sem,pick2) - - # slice of picking cube - slc = diffMigratedSection + '-slc' - Flow(slc,[vlf,pik],'slice pick=${SOURCES[1]}') - return True - -def diffsimul( - section, - diffSimulatedSection, - velocities, - numberOfReflectors - ): - ''' - Simulate difraction hyperbolas in the picked points in the stacked section - :param section: RSF filename, stacked section - :output diffSimulatedSection: RSF filename, diffraction simulated section - :param velocities: tuple, velocity layers should equal to the number of reflectors - :param numberOfReflectors: int, number of reflectors to iterative picking - ''' - - if type(velocities) is not tuple: - raise TypeError("velocities parameter should be a tuple") - return False - - if type(numberOfReflectors) is not int: - raise TypeError("numberOfReflectors parameter should be int") - return False - - if numberOfReflectors <= 0: - raise ValueError("numberOfReflectors should be major than 0") - return False - - if section == diffSimulatedSection: - raise ValueError("filenames should not be equal") - return False - - if len(velocities) < numberOfReflectors: - raise ValueError("number of velocities should be equal or major than number of reflectors") - return False - - reflectorsList = [] - # Iterative picking - Loop over reflectors - for i in range(numberOfReflectors): - - reflectorPickedPoints = 'reflectorPickedPoints-%i.txt' % i - t0sFile = 't0s-%i' % i - t0sAscii = 't0s-%i.asc' %i - m0sFile = 'm0s-%i' % i - m0sAscii = 'm0s-%i.asc' % i - returnedSection = 'returnedSection-%i' % i - diffSection = 'diffSection-%i' % i - - # Reflector iterative Picking - Flow(reflectorPickedPoints,section, - ''' - ipick - ''') - - # Next step is done with 'ascFormat.sh' Shell Script - # please check if the script have permissions to execute - # Build t0 coordinates file (pass 1 to generate t0s file) - Flow(t0sAscii,reflectorPickedPoints, - ''' - ./ascFormat.sh 1 %s - ''' % (t0sAscii)) - - Flow(t0sFile,t0sAscii,'sfdd form=native') - - # Build m0 coordinates file (pass 2 to generate m0s file) - Flow(m0sAscii,reflectorPickedPoints, - ''' - ./ascFormat.sh 2 %s - ''' % (m0sAscii)) - - Flow(m0sFile,m0sAscii,'sfdd form=native') - - # Diffraction simulation in stacked section - Flow([returnedSection,diffSection],[section,t0sFile,m0sFile], - ''' - diffsim diff=${TARGETS[1]} aperture=1 - t0=${SOURCES[1]} m0=${SOURCES[2]} v=%g freq=10 verb=y - ''' % (velocities[i])) - - section = returnedSection - reflectorsList.append(diffSection) - - Flow(diffSimulatedSection,reflectorsList, - ''' - add ${SOURCES[1:%d]} - scale=%s - ''' % ( - len(reflectorsList), - ','.join(['1' for i in range(len(reflectorsList))]))) - - return True diff --git a/experiments/algorithmVelocityInversion/inputScons.py b/experiments/algorithmVelocityInversion/inputScons.py deleted file mode 100644 index a4f8447..0000000 --- a/experiments/algorithmVelocityInversion/inputScons.py +++ /dev/null @@ -1,38 +0,0 @@ -# coding: utf-8 -# -# inputScons.py (Madagascar Recipe) -# -# Purpose: Functions to treat the input passed through -# command line in SCons. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo Dirack 04/10/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - - -def castingParametersFromCmd(ARGLIST,param): - ''' - casting of the parameters from - command line - :global ARGLIST: list of parameters passed through comand line in scons - :param param: dictionary, parameters dictionary you want to cast - ''' - - for k, v in ARGLIST: - if k in param.keys(): - if type(param[k]) is float: - param[k] = float(v) - elif type(param[k]) is int: - param[k] = int(v) - elif type(param[k]) is tuple: - param[k] = eval(v) - else: - param[k] = v - - diff --git a/experiments/modelingAndStack/SConstruct b/experiments/modelingAndStack/SConstruct deleted file mode 100644 index 280ff88..0000000 --- a/experiments/modelingAndStack/SConstruct +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Madagascar Script) -# -# Purpose: Modeling and Stacking step. -# -# Site: http://www.dirackslounge.online -# -# Version 1.0 -# -# Programer: Rodolfo A. C. Neves (Dirack) 03/10/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -# Madagascar package -from rsf.proj import * - -# Personal Madagascar recipes -from rsf.recipes.kimodel import multiLayerModel as mlmod -from rsf.recipes.kimodel import kirchhoffNewtonModeling as kinewmod -from rsf.recipes.velocityAnalysis import velocityAnalysis as nmoStack - -xmax = 6.0 -zmax = 2.0 - -layers = ((0.30,0.50,0.20,0.30), - (1.65,1.85,1.55,1.65)) - -velocities = (1.508, - 1.690, - 2.0) - -# Generate multi layer model and data cube -mlmod(interfaces='interfaces', - dipsfile='interfacesDip', - modelfile='mod1', - xmax=xmax, - zmax=zmax, - layers=layers, - velocities=velocities) - -kinewmod(reflectors='interfaces', - reflectorsDip='interfacesDip', - filename='multiLayerDataCube', - velocities=velocities, - nt=1001, - dt=0.004, - ns=201, - ds=0.025, - nh=161, - dh=0.025) - -Flow('dataTransposed','multiLayerDataCube','transp plane=23 | transp plane=34') - -# velocity analysis, NMO correction and stack -nmoStack(dataCube='multiLayerDataCube', - pick='vnmo', - stack='stackedSection', - vrms='vrms', - v0=1.5, - dv=0.01, - nv=100, - vel0=1.5, - rect1=15, - rect2=40, - rect3=3, - dt=0.004) - -# Use aliases to split building -# Call it 'scons alias' to build -# target identified by alias -Alias('model','multiLayerDataCube.rsf') -Alias('stack','velocityCube.rsf') - -End() diff --git a/experiments/setUpNIPModel/SConstruct b/experiments/setUpNIPModel/SConstruct deleted file mode 100644 index 53e3132..0000000 --- a/experiments/setUpNIPModel/SConstruct +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Python) -# -# Objetivo: Exemplo de uso do programa sfrays2. -# -# Site: https://dirack.github.io -# -# Versão 1.0 -# -# Programador: Rodolfo A C Neves (Dirack) 02/12/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# Licença: GPL-3.0 . - -from rsf.proj import * - -# create a model -amp=1 -rad=1 -Flow('model',None, - ''' - math n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 output="1+2*x1+0.5*x2" | - math output="input+%g*exp(-((x1-1.5)*(x1-1.5)+(x2-5)*(x2-5))/(%g*%g))" | - put label1=Depth unit1=km label2=Position unit2=km - ''' % (amp,rad,rad)) - -# plot the model -Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title=Model allpos=y') - -Flow('s1',None, - ''' - math n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1" - ''') - -Flow('s2',None,'spike n1=1 d1=1 o1=1 n2=1 o2=1 d2=1 mag=1') - -Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') - -Flow('anglefile',None,'spike n1=1 mag=180') - -Flow('t0s',None,'spike n1=1 mag=1') - -# do a ray-tracing -Flow('ray',['model','shotsfile','anglefile','t0s'], - ''' - niptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} - ''') - -# do a ray-tracing -#Flow('ray',['model'], -# ''' -# niptomo -# ''') - -# plot the ray -Plot('ray','graph transp=y yreverse=y min1=0 max1=3 min2=0 max2=10 wantaxis=n wanttitle=n scalebar=y plotcol=7 plotfat=3') - -# overlay model and ray -Result('overlay','model ray','Overlay') - -End() diff --git a/experiments/stereotomo/SConstruct b/experiments/stereotomo/SConstruct deleted file mode 100644 index 2c8aaad..0000000 --- a/experiments/stereotomo/SConstruct +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Python) -# -# Purpose: Test sfnipmodsetup in a constan velocity model. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 07/02/2021 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * - -# create a model -amp=1 -rad=1 - -# Constant velocity model for test -Flow('model',None, - ''' - spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.508 | - put label1=Depth unit1=km label2=Position unit2=km - ''' ) - -# plot the model -Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') - -Flow('s2',None,'spike n1=26 d1=1 o1=0 n2=1 o2=1 d2=0 mag=0') - -Flow('shotsfile',['s2','m0s'],'rcat ${SOURCES[1]} axis=2 | transp') - -Flow('anglefile',['beta','t0s','m0s'], - ''' - getparameter t0s=${SOURCES[1]} m0s=${SOURCES[2]} beta=y - ''') - -# do a ray-tracing -Flow(['ray','nipangles'],['model','shotsfile','anglefile','t0s'], - ''' - nipmodsetup shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} nipangles=${TARGETS[1]} - ''') - -# Rotate nipangles -Flow('angles','nipangles','math output="input-180" | put n1=26 d1=1 o1=1') -Flow('nipsources','ray','dd type=float | put n1=2 n2=26') - -# do a ray-tracing from NIP sources -Flow(['tCoordinates','xCoordinates'], - ['model','nipsources','angles','t0s','m0s','rnips','betas'], - ''' - stereoniptomo shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} m0s=${SOURCES[4]} rnips=${SOURCES[5]} betas=${SOURCES[6]} x=${TARGETS[1]} v0=1.508 - ''') - -Flow('raysteste',['model','nipsources','angles'], - ''' - rays2 nt=3000 dt=0.001 shotfile=${SOURCES[1]} a0=13 nr=1 escvar=n - ''') - - -End() diff --git a/experiments/stereotomo/ascFormat.sh b/experiments/stereotomo/ascFormat.sh deleted file mode 100755 index c3babb9..0000000 --- a/experiments/stereotomo/ascFormat.sh +++ /dev/null @@ -1,7 +0,0 @@ -INPUT=$(cat "-" | tr '\t' ' ' | cut -d' ' -f"$2") -NUMBER_OF_POINTS=$(echo "$INPUT" | wc -l) -FORMATED_INPUT=$(echo "$INPUT" | tr '\n' ' ') -FILE="$1" - -echo "${FORMATED_INPUT}n1=$NUMBER_OF_POINTS d1=1 o1=0 data_format=ascii_float in=$1" - diff --git a/experiments/stereotomo/gdb_script b/experiments/stereotomo/gdb_script deleted file mode 100644 index 369cf3f..0000000 --- a/experiments/stereotomo/gdb_script +++ /dev/null @@ -1,2 +0,0 @@ -set args testes.rsf -show args diff --git a/experiments/stereotomo/viewFoldGraph.py b/experiments/stereotomo/viewFoldGraph.py deleted file mode 100755 index 5391246..0000000 --- a/experiments/stereotomo/viewFoldGraph.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# -# viewFoldGraph.py (Python) -# -# Objetivo: Visualizar gráfico de cobertura dos CMP's. -# -# Site: https://dirack.github.io -# -# Versão 1.0 -# -# Programador: Rodolfo A C Neves (Dirack) 27/09/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# Licença: GPL-3.0 . - -import matplotlib.pyplot as plt - -f = open("x.txt","r") -xstr = filter(None,f.read().split('\n')) -x = map(lambda i: float(i),xstr) - -f = open("y.txt","r") -ystr = filter(None,f.read().split('\n')) -y = map(lambda i: float(i),ystr) - -f = open("z.txt","r") -zstr = filter(None,f.read().split('\n')) -z = map(lambda i: float(i),zstr) -z0 = [0]*26 - -#print(len(list(z))) -#print(len(list(z0))) - -plt.xlim(0, 6) -plt.ylim(3,0) -plt.plot(list(x),list(y),'*') -plt.plot(list(z),list(z0),'v') -plt.show() diff --git a/experiments/velocityInversionCreStackedSection/README.md b/experiments/velocityInversionCreStackedSection/README.md deleted file mode 100644 index be9ab2b..0000000 --- a/experiments/velocityInversionCreStackedSection/README.md +++ /dev/null @@ -1 +0,0 @@ -#### Velocity inversion algorithm applied to a CRE stacked section diff --git a/experiments/velocityInversionCreStackedSection/SConstruct b/experiments/velocityInversionCreStackedSection/SConstruct deleted file mode 100644 index 94316db..0000000 --- a/experiments/velocityInversionCreStackedSection/SConstruct +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Madagascar Script) -# -# Purpose: Simulate diffractions in the stacked section and migrate them in -# time using focusing analysis, after that, do velocity inversion -# using the algorithm from Camerom (2008). -# -# Site: http://www.dirackslounge.online -# -# Version 1.0 -# -# Programer: Rodolfo A. C. Neves (Dirack) 19/08/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -# Madagascar package -from rsf.proj import * - -# Personal Madagascar recipes -from diffSimulationAndMigration import diffmig -from diffSimulationAndMigration import diffsimul -from inputScons import castingParametersFromCmd - -# Parameters dictionaries to the functions -diffSimulParameters = {'numberOfReflectors':1, -'section':'stackedSection.rsf', -'velocities':(1.5,1.6), -'diffSimulatedSection':'diffSimulatedSection'} - -diffMigParameters = {'diffSimulatedSection': 'diffSimulatedSection', -'diffMigratedSection': 'diffMigratedSection', -'v0':1.4, -'nv':100, -'dv':0.01, -'nx':201, -'padx':1000, -'nt':1001, -'tmin':0.3, -'tmax':2.296, -'rect1':10, -'rect2':10, -'srect1':1, -'srect2':3, -'vslope':None, -'units':'Km', -'f1':1, -'j3':1, -'dx':0.025, -'x0':0, -'beg1':0, -'frect1':0, -'frect2':0, -'an':1, -'nout':2048, -'vx0':None} - -# substitute parameters into parameters dictionaries -# if some of them was passed through command line -castingParametersFromCmd(ARGLIST,diffSimulParameters) -castingParametersFromCmd(ARGLIST,diffMigParameters) - - -# Diffraction simulation in an iterative picking loop -diffsimul(**diffSimulParameters) - -# Diffraction migration by focusing analysis -diffmig(**diffMigParameters) - -# Velocity inversion with the Camerom algorithm (2008) -Flow('vdix2 vmig2',diffMigParameters['diffMigratedSection']+'-pik', - ''' - dix rect1=20 rect2=20 - vrmsout=${TARGETS[1]} - niter=40 - ''') - -nz=200 -dz=0.01 -Flow('vxz x0 t0','vdix2', - ''' - transp | - cameron2d method=cheb neval=20 nc=1000 nz=%d dz=%g - x0=${TARGETS[1]} t0=${TARGETS[2]} - ''' % (nz,dz),local=1) - - -Flow('vz','vxz', - ''' - put label1=Lateral unit1=km label2=Depth unit2=km n3=1 | - transp - ''') - -End() diff --git a/experiments/velocityInversionCreStackedSection/__init__.py b/experiments/velocityInversionCreStackedSection/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/experiments/velocityInversionCreStackedSection/ascFormat.sh b/experiments/velocityInversionCreStackedSection/ascFormat.sh deleted file mode 100755 index e7c5172..0000000 --- a/experiments/velocityInversionCreStackedSection/ascFormat.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -# -# ascFormat.sh (Shell Script) -# -# Purpose: Convert sfipick picked points -# to Madagascar readable ascii format. -# Important! $1=1 cut and format the first collumn of the file (t0 time) -# If $1=2 cut and format the second collumn of the file (m0 CMP) -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 20/08/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -INPUT=$(cat "-" | tr '\t' ' ' | cut -d' ' -f"$1") -NUMBER_OF_POINTS=$(echo "$INPUT" | wc -l) -FORMATED_INPUT=$(echo "$INPUT" | tr '\n' ' ') - -if [ "$1" == "1" ] -then - FILE="t0s" -elif [ "$1" == "2" ] -then - FILE="m0s" -else - echo "Error: $(basename $0): \$1 should be equal to 1 or 2!" - exit 1 -fi - -echo "${FORMATED_INPUT}n1=$NUMBER_OF_POINTS d1=1 o1=0 data_format=ascii_float in=$2" diff --git a/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py b/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py deleted file mode 100644 index 582190d..0000000 --- a/experiments/velocityInversionCreStackedSection/diffSimulationAndMigration.py +++ /dev/null @@ -1,250 +0,0 @@ -# coding: utf-8 -# -# diffSimulationAndMigration.py (Madagascar Recipe) -# -# Purpose: Generate diffraction simulated hyperbolas -# in the stacked section using iterative picking. And -# migrate it using focusing measure. The input is a -# stacked section with only diffraction hiperbolas. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo Dirack 01/03/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * -import string, sys, os - -frect=0 - -def diffmig(diffSimulatedSection, - diffMigratedSection, - v0, - nv, - dv, - nx, - padx, - nt, - tmin=0, - tmax=10, - rect1=10, - rect2=10, - srect1=1, - srect2=3, - vslope=None, - units='Km', - f1=1, - j3=1, - dx=1, - x0=0, - beg1=0, - frect1=0, - frect2=0, - an=1, - nout=2048, - vx0=None): - ''' - Diffraction migration by focusing measure - :param diffSimulatedSection: RSF filename, stacked Section with simulated diffractions - :output diffMigratedSection: RSF filename, basename of the outputs - |-> diffMigratedSection+pik: time migrated velocity - |-> diffMigratedSection+dip: dominant slope section - |-> diffMigratedSection+slc: slice of the velocity cube - |-> diffMigratedSection+vlf: velocity cube focusing measure - |-> diffMigratedSection+foc: focusing measure - |-> diffMigratedSection+sem: semblance - ''' - - if diffSimulatedSection == diffMigratedSection: - raise ValueError("Filenames should not be equal") - return False - - if frect==0: - frect1=2*rect1 - - if frect==0: - frect2=2*rect2/j3 - - # dominant slope of stacked section - dip = diffMigratedSection+'-dip' - - # Calculate dominante slope, rect parameter - # are the smooth radius - Flow(dip,diffSimulatedSection,'dip rect1=%d rect2=%d' % (rect1,rect2)) - - # Velocity analisys by focusing - # Stolt Migration. It needs the cosine - # tranform of the data. sfvczo does - # the velocity continuation process - # migrate data multiple times with a - # constant velocity - velcon=''' - pad n2=%d beg1=%d | cosft sign2=1 | put o3=0 | - stolt vel=%g | - vczo nv=%d dv=%g v0=%g | - transp plane=23 | - cosft sign2=-1 | - window n2=%d f1=%d | - put o2=%g | - transp plane=23 - ''' % (padx,beg1,v0,nv,dv,v0,nx,beg1,x0) - - # Velocity continuation of diffraction - vlf=diffMigratedSection+'-vlf' - Flow(vlf,diffSimulatedSection,velcon) - - Flow(vlf+'q',diffSimulatedSection, - ''' - math output="input*input" | %s | clip2 lower=0 - ''' % velcon) - - # j#n is the jump parameter of sfwindow - if j3 > 1: - focus = 'window j3=%d | ' % j3 - else: - focus = '' - - # Focusing analisys - # local varimax is max when the velocity - # collapses a diffraction - focus = focus + ''' - focus rect1=%d rect3=%d | - math output="1/abs(input)" | - cut max1=%g | cut min1=%g - ''' % (frect1,frect2,tmin,tmax) - - # focusing measure - foc=diffMigratedSection+'-foc' - Flow(foc,vlf,focus) - - # semblance is the local varimax measure - # sfmul multplies data - # sfdivn does smooth data division - sem = diffMigratedSection+'-sem' - Flow(sem,[vlf,vlf+'q'], - ''' - mul $SOURCE | - divn den=${SOURCES[1]} rect1=%d rect3=%d - ''' % (rect1,rect2)) - - # Data after picking - pik=diffMigratedSection+'-pik' - - # if vslope, use it to mute data - if vslope: - pick = 'mutter x0=%g v0=%g half=n | ' % (vx0,vslope) - else: - pick = '' - - pick = pick + 'scale axis=2 | pick an=%g rect1=%d rect2=%d | window' % (an,rect1,rect2) - - if j3 > 1: - pick2 = pick + ''' - transp | - spline n1=%d d1=%g o1=%g | - transp - ''' % (nx,dx,x0) - else: - pick2 = pick - - Flow(pik,sem,pick2) - - # slice of picking cube - slc = diffMigratedSection + '-slc' - Flow(slc,[vlf,pik],'slice pick=${SOURCES[1]}') - return True - -def diffsimul( - section, - diffSimulatedSection, - velocities, - numberOfReflectors - ): - ''' - Simulate difraction hyperbolas in the picked points in the stacked section - :param section: RSF filename, stacked section - :output diffSimulatedSection: RSF filename, diffraction simulated section - :param velocities: tuple, velocity layers should equal to the number of reflectors - :param numberOfReflectors: int, number of reflectors to iterative picking - ''' - - if type(velocities) is not tuple: - raise TypeError("velocities parameter should be a tuple") - return False - - if type(numberOfReflectors) is not int: - raise TypeError("numberOfReflectors parameter should be int") - return False - - if numberOfReflectors <= 0: - raise ValueError("numberOfReflectors should be major than 0") - return False - - if section == diffSimulatedSection: - raise ValueError("filenames should not be equal") - return False - - if len(velocities) < numberOfReflectors: - raise ValueError("number of velocities should be equal or major than number of reflectors") - return False - - reflectorsList = [] - # Iterative picking - Loop over reflectors - for i in range(numberOfReflectors): - - reflectorPickedPoints = 'reflectorPickedPoints-%i.txt' % i - t0sFile = 't0s-%i' % i - t0sAscii = 't0s-%i.asc' %i - m0sFile = 'm0s-%i' % i - m0sAscii = 'm0s-%i.asc' % i - returnedSection = 'returnedSection-%i' % i - diffSection = 'diffSection-%i' % i - - # Reflector iterative Picking - Flow(reflectorPickedPoints,section, - ''' - ipick - ''') - - # Next step is done with 'ascFormat.sh' Shell Script - # please check if the script have permissions to execute - # Build t0 coordinates file (pass 1 to generate t0s file) - Flow(t0sAscii,reflectorPickedPoints, - ''' - ./ascFormat.sh 1 %s - ''' % (t0sAscii)) - - Flow(t0sFile,t0sAscii,'sfdd form=native') - - # Build m0 coordinates file (pass 2 to generate m0s file) - Flow(m0sAscii,reflectorPickedPoints, - ''' - ./ascFormat.sh 2 %s - ''' % (m0sAscii)) - - Flow(m0sFile,m0sAscii,'sfdd form=native') - - # Diffraction simulation in stacked section - Flow([returnedSection,diffSection],[section,t0sFile,m0sFile], - ''' - diffsim diff=${TARGETS[1]} aperture=1 - t0=${SOURCES[1]} m0=${SOURCES[2]} v=%g freq=10 verb=y - ''' % (velocities[i])) - - section = returnedSection - reflectorsList.append(returnedSection) - - Flow(diffSimulatedSection,reflectorsList, - ''' - rcat ${SOURCES[1:%d]} - scale=%s - ''' % ( - len(reflectorsList), - ','.join(['1' for i in range(len(reflectorsList))]))) - - return True diff --git a/experiments/velocityInversionCreStackedSection/inputScons.py b/experiments/velocityInversionCreStackedSection/inputScons.py deleted file mode 100644 index a4f8447..0000000 --- a/experiments/velocityInversionCreStackedSection/inputScons.py +++ /dev/null @@ -1,38 +0,0 @@ -# coding: utf-8 -# -# inputScons.py (Madagascar Recipe) -# -# Purpose: Functions to treat the input passed through -# command line in SCons. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo Dirack 04/10/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - - -def castingParametersFromCmd(ARGLIST,param): - ''' - casting of the parameters from - command line - :global ARGLIST: list of parameters passed through comand line in scons - :param param: dictionary, parameters dictionary you want to cast - ''' - - for k, v in ARGLIST: - if k in param.keys(): - if type(param[k]) is float: - param[k] = float(v) - elif type(param[k]) is int: - param[k] = int(v) - elif type(param[k]) is tuple: - param[k] = eval(v) - else: - param[k] = v - - diff --git a/test/Makefile b/test/Makefile index f2ed719..a7cb432 100644 --- a/test/Makefile +++ b/test/Makefile @@ -1,7 +1,6 @@ # Makefile # # Purpose: Run all tests with 'make'. -# Or one test as example 'make test_diffmig'. # # Site: https://dirack.github.io # @@ -13,23 +12,14 @@ # # License: GPL-3.0 . -SRC = diffmig diffsimul -DEP = $(addprefix test_, $(SRC)) SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c SRCNIPMODEL2= ../Mniptimecurve2.c ../raytrace.c ../grid2.c ../atela.c SRCGETPARAM= ../Mgetparameter.c SRCSTEREO = ../Mstereoniptomo.c ../tomography.c ../raytrace.c ../grid2.c ../atela.c ../vfsacrsnh_lib.c -all: $(DEP) integ - -test_%: - @echo $@ - @python -m unittest $@ - -integ: - cd ../experiments/modelingAndStack/ && scons - cd ../experiments/algorithmVelocityInversion/ && scons +all: + @echo TODO # Version of the sfnipmodsetup program for GDB (GNU debugger) nipmodsetup.x: $(SRCNIPTOMO) diff --git a/test/test_diffmig.py b/test/test_diffmig.py deleted file mode 100644 index b6b1b2a..0000000 --- a/test/test_diffmig.py +++ /dev/null @@ -1,63 +0,0 @@ -import unittest -import sys,os - -sys.path.append('/usr/lib/scons/') -from rsf.proj import * - -testdir = os.path.dirname(os.environ['PWD'])+'/experiments/algorithmVelocityInversion/' -sys.path.append(testdir) -from diffSimulationAndMigration import diffmig - -class TestDiffMig(unittest.TestCase): - - def setUp(self): - self.diffSimulatedSection = 'diffSimulatedSection' - self.diffMigratedSection = 'diffMigratedsection' - self.param = {'diffSimulatedSection': self.diffSimulatedSection, - 'diffMigratedSection': self.diffMigratedSection, - 'v0': 1.4, - 'nv':100, - 'dv':0.01, - 'nx':201, - 'padx':1000, - 'nt':1001, - 'tmin':0, - 'tmax':4, - 'rect1':10, - 'rect2':10, - 'srect1':1, - 'srect2':3, - 'vslope':None, - 'units':'Km', - 'f1':1, - 'j3':1, - 'dx':0.025, - 'x0':0, - 'beg1':0, - 'frect1':0, - 'frect2':0, - 'an':1, - 'nout':2048, - 'vx0':None} - - self.tmp={} - - def test_diffmig_ok(self): - ''' - Function normally returns True - if succed - ''' - self.assertTrue( - diffmig(**(self.param))) - - def test_diffmig_filenames(self): - ''' - Filenames should not be equal - function should raise a - ValueError - ''' - self.tmp = self.param - self.tmp['diffMigratedSection'] = self.param['diffSimulatedSection'] - self.assertRaises(ValueError, - diffmig,**self.tmp) - diff --git a/test/test_diffsimul.py b/test/test_diffsimul.py deleted file mode 100644 index 7f51e1e..0000000 --- a/test/test_diffsimul.py +++ /dev/null @@ -1,75 +0,0 @@ -import unittest -import sys,os - -sys.path.append('/usr/lib/scons/') -from rsf.proj import * - -testdir = os.path.dirname(os.environ['PWD'])+'/experiments/algorithmVelocityInversion/' -sys.path.append(testdir) -from diffSimulationAndMigration import diffsimul - -class TestDiffSimul(unittest.TestCase): - - def setUp(self): - self.section = 'stackedSection' - self.diffSection = 'diffSimulatedsection' - self.velocities = (1.5,1.6,2.0) - self.numberOfReflectors = 3 - - def test_diffsimul_velocities(self): - ''' - Function should raise a - TypeError when velocities is - not a tuple - ''' - self.assertRaises(TypeError, - diffsimul,self.section,self.diffSection,1,self.numberOfReflectors) - - def test_diffsimul_numberOfReflectors_int(self): - ''' - Function should raise a - TypeError when numberOfReflectors - is not an int - ''' - self.assertRaises(TypeError, - diffsimul,self.section,self.diffSection,self.velocities,2.0) - - def test_diffsimul_numberOfReflectors_positive(self): - ''' - Function should raise a ValueError - if numberOfReflectors is negative - ''' - self.assertRaises(ValueError, - diffsimul,self.section,self.diffSection,self.velocities,-1) - - def test_diffsimul_numberOfReflectors_major_0(self): - ''' - Function should raise a ValueError - if numberOfReflectors is not - major than 0 - ''' - self.assertRaises(ValueError, - diffsimul,self.section,self.diffSection,self.velocities,0) - - def test_diffsimul_filenames_not_equal(self): - ''' - input filenames should not be equal - ''' - self.assertRaises(ValueError, - diffsimul,self.section,self.section,self.velocities,self.numberOfReflectors) - - def test_diffsimul_ok(self): - ''' - Function normally returns True - ''' - self.assertTrue(diffsimul( - self.section,self.diffSection,self.velocities,1)) - - def test_diffsimul_len_velocities(self): - ''' - Number of velocities should be - equal or major than - numberOfReflectors - ''' - self.assertRaises(ValueError, - diffsimul,self.section,self.diffSection,self.velocities,4) From 8b0b7234dd2561eca6f880ad65ddd8b1df970476 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 28 Apr 2021 15:47:16 -0300 Subject: [PATCH 60/63] Remove program sfniptimecurve and sfgetbetaangle Those were test programs and there is no need for them anymore --- Mniptimecurve.c | 141 ---------------------------- Mniptimecurve2.c | 175 ----------------------------------- SConstruct | 2 +- test/Makefile | 20 +--- test/README.md | 3 +- test/datamisfit/SConstruct | 82 ---------------- test/gdb_script2 | 9 -- test/gdb_script3 | 9 -- test/niptimecurve/README.md | 24 ----- test/niptimecurve/SConstruct | 54 ----------- 10 files changed, 6 insertions(+), 513 deletions(-) delete mode 100644 Mniptimecurve.c delete mode 100644 Mniptimecurve2.c delete mode 100644 test/datamisfit/SConstruct delete mode 100644 test/gdb_script2 delete mode 100644 test/gdb_script3 delete mode 100644 test/niptimecurve/README.md delete mode 100644 test/niptimecurve/SConstruct diff --git a/Mniptimecurve.c b/Mniptimecurve.c deleted file mode 100644 index 310c7cc..0000000 --- a/Mniptimecurve.c +++ /dev/null @@ -1,141 +0,0 @@ -/* NIP Point source modeling ray tracer - -Trace rays from NIP sources to acquisition surface in order to get traveltime curves used in NIP tomography. - -*/ - -#include - -#include - -#include "raytrace.h" - -#define DANGLE 15.0 -#define INITIAL_ANGLE 45.0 - -int main(int argc, char* argv[]) -{ - bool verb; // Verbose parameter - int n[2]; // Velocity grid dimensions n[0]=n1, n[1]=n2 - float d[2]; // Velocity grid sampling d[0]=d1, d[1]=d2 - float o[2]; // Velocity grid origin o[0]=o1, o[1]=o2 - float t; // Ray traveltime - float** s; // NIP source position (z,x) - int ndim; // n1 dimension in shotsfile, should be equal 2 - int nshot; // n2 dimensions in shotsfile - int nm; // Number of samples in velocity grid - float* a; // Normal Ray initial angle - int nr=7; // Number of rays to trace - float* slow; // slowness - int im; // loop counter - float v0; // Velocity - float dt=0.001; // ray time sampling - int nt=3000; // Numer of time samples for each ray - int ns; // Number of NIP sources - int it,ir,is; // loop counters - float** traj; // Ray trajectory (z,x) - float x[2]; // Ray initial position - float p[2]; // Ray initial slowness vector - float currentRayAngle; // Ray initial angle - sf_file shots, vel, angles, timeCurve,xCurve; - raytrace rt; - - sf_init(argc,argv); - - shots = sf_input("shotsfile"); - vel = sf_input("in"); - timeCurve = sf_output("out"); - xCurve = sf_output("x"); - angles = sf_input("anglefile"); - - /* Velocity model: get 2D grid parameters */ - if(!sf_histint(vel,"n1",n)) sf_error("No n1= in input"); - if(!sf_histint(vel,"n2",n+1)) sf_error("No n2= in input"); - if(!sf_histfloat(vel,"d1",d)) sf_error("No d1= in input"); - if(!sf_histfloat(vel,"d2",d+1)) sf_error("No d2= in input"); - if(!sf_histfloat(vel,"o1",o)) o[0]=0.; - if(!sf_histfloat(vel,"o2",o+1)) o[1]=0.; - - if(!sf_getbool("verb",&verb)) verb=true; - /* verbose */ - - /* Shotsfile: get m0 shot points */ - if(!sf_histint(shots,"n1",&ndim) || 2 != ndim) - sf_error("Must have n1=2 in shotsfile"); - if(!sf_histint(shots,"n2",&nshot)) sf_error("No n2= in shotfile"); - s = sf_floatalloc2(ndim,nshot); - sf_floatread(s[0],ndim*nshot,shots); - sf_fileclose(shots); - - /* Anglefile: get initial emergence angle */ - if(!sf_histint(angles,"n1",&ns)) sf_error("No n1= in anglefile"); - a = sf_floatalloc(ns); - sf_floatread(a,ns,angles); - - - /* get slowness squared */ - nm = n[0]*n[1]; - slow = sf_floatalloc(nm); - sf_floatread(slow,nm,vel); - - for(im=0;im0){ - t = it*dt; - sf_floatwrite(&traj[it][1],1,xCurve); - }else if(it == 0){ - t = abs(nt)*dt; - sf_floatwrite(&traj[nt][1],1,xCurve); - }else{ - t = -it * dt; - sf_floatwrite(&traj[-it][1],1,xCurve); - } - - sf_floatwrite(&t,1,timeCurve); - - /* Raytrace close */ - raytrace_close(rt); - free(traj); - } - } - -} diff --git a/Mniptimecurve2.c b/Mniptimecurve2.c deleted file mode 100644 index ef91fe6..0000000 --- a/Mniptimecurve2.c +++ /dev/null @@ -1,175 +0,0 @@ -/* New version of NIP Point source modeling ray tracer - -Trace rays from NIP sources to acquisition surface in order to get traveltime curves used in NIP tomography. - -*/ - -#include - -#include - -#include "raytrace.h" - -#define DANGLE 15.0 -#define INITIAL_ANGLE 45.0 -#define DSLOW 0.04 - -void updatevelmodel(int nm, float* slow){ - - int im; - - for(im=0;im0){ - t = it*dt; - sf_floatwrite(&traj[it][1],1,xCurve); - }else if(it == 0){ - t = abs(nt)*dt; - sf_floatwrite(&traj[nt][1],1,xCurve); - }else{ - t = -it * dt; - sf_floatwrite(&traj[-it][1],1,xCurve); - } - - sf_floatwrite(&t,1,timeCurve); - - /* Raytrace close */ - raytrace_close(rt); - free(traj); - - /* Calculate data misfit (t,x) */ - tmis[ir] = td[ir] - t; - xmis[ir] = xd[ir] - x[1]; - } /* Loop over rays */ - - updatevelmodel(nm,slow); - } /* Loop over NIP sources */ - -} diff --git a/SConstruct b/SConstruct index 3b86801..5a7c7f9 100644 --- a/SConstruct +++ b/SConstruct @@ -5,7 +5,7 @@ import bldutil # Put your name programs in progs variable # without 'M' preffix and '.c' extension progs = ''' -nipmodsetup niptimecurve niptimecurve2 stereoniptomo getparameter +nipmodsetup stereoniptomo getparameter ''' try: # distributed version diff --git a/test/Makefile b/test/Makefile index a7cb432..ebda4a2 100644 --- a/test/Makefile +++ b/test/Makefile @@ -13,13 +13,11 @@ # License: GPL-3.0 . SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c -SRCNIPMODEL= ../Mniptimecurve.c ../raytrace.c ../grid2.c ../atela.c -SRCNIPMODEL2= ../Mniptimecurve2.c ../raytrace.c ../grid2.c ../atela.c SRCGETPARAM= ../Mgetparameter.c SRCSTEREO = ../Mstereoniptomo.c ../tomography.c ../raytrace.c ../grid2.c ../atela.c ../vfsacrsnh_lib.c all: - @echo TODO + @echo "No unit and integration tests found, run 'make help' for more details" # Version of the sfnipmodsetup program for GDB (GNU debugger) nipmodsetup.x: $(SRCNIPTOMO) @@ -27,18 +25,6 @@ nipmodsetup.x: $(SRCNIPTOMO) @echo "Program nipmodsetup.x for GDB built" gdb -q --command=gdb_script1 --args $@ -# Version of the sfniptimecurve program for GDB (GNU debugger) -niptimecurve.x: $(SRCNIPMODEL) - gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm - @echo "Program niptimecurve.x for GDB built" - gdb -q --command=gdb_script2 --args $@ - -# Version of the sfniptimecurve2 program for GDB (GNU debugger) -niptimecurve2.x: $(SRCNIPMODEL2) - gcc -g $^ -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm - @echo "Program niptimecurve2.x for GDB built" - gdb -q --command=gdb_script3 --args $@ - # Version of the sfgetparameter program for GDB (GNU debugger) getparameter.x: setup1 $(SRCGETPARAM) gcc -g $(SRCGETPARAM) -I$$RSFSRC/include -I.. -L$$RSFSRC/lib -o $@ -lrsf -lm @@ -66,10 +52,10 @@ stereoniptomo.x: setup1 $(SRCSTEREO) clean: rm *.x -.PHONY: clean nipmodsetup.x niptimecurve.x niptimecurve2.x getparameter.x stereoniptomo.x +.PHONY: clean nipmodsetup.x getparameter.x stereoniptomo.x help: @echo "Use 'make' to run unit tests and intergration test" @echo "Use 'make progname.x' to generate a GDB version of progname for debug" @echo "Programs available:" - @echo "\tgetparameter.x\n\tnipmodsetup.x\n\tniptimecurve2.x\n\tniptimecurve.x\n\tstereoniptomo.x" + @echo "\tgetparameter.x\n\tnipmodsetup.x\n\tstereoniptomo.x" diff --git a/test/README.md b/test/README.md index b8b3648..692c922 100644 --- a/test/README.md +++ b/test/README.md @@ -8,7 +8,8 @@ Run the following command to test: ~$ make ``` -It will run the unit tests of diffmig and diffsimul experiments using unittest python module and it will run optimization tests to get the time of those experiments. +It will run the unit tests and integration tests if they are available. +Run 'make help' for more details and options. ### Generate GDB version of the programs for debug diff --git a/test/datamisfit/SConstruct b/test/datamisfit/SConstruct deleted file mode 100644 index de354f4..0000000 --- a/test/datamisfit/SConstruct +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Madagascar script) -# -# Purpose: Calculate data misfit for pre calculated traveltime curve. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 20/01/2021 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * - -def arr2str(array,sep=' '): - ''' - Convert a tuple array into a string with a given separator - Example: (1,2,3) is converted to '1,2,3' for sep=',' - :param array: tuple, array to convert - :param sep: string, separator - ''' - return sep.join(map(str,array)) - -def buildVectorFile(filename,array): - ''' - Build a 1D vector file using sfspike - :param filename: string, RSF filename - :param array: tuple, array 1D vector - ''' - l=len(array) - s=arr2str(array,',') - - Flow(filename,None, - ''' - spike mag=%s nsp=%d n1=%d k1=%s - '''%(s,l,l,arr2str(range(1,l+1,1),','))) - -# time coordinates -t=(0.472, 0.385, 0.346, 0.334, 0.346, 0.385, 0.472) - -# x coordinates -x=(4.499, 4.711, 4.866, 5, 5.134, 5.289, 5.501) - -# Constant velocity model for test -Flow('model',None, - ''' - spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.4 | - put label1=Depth unit1=km label2=Position unit2=km - ''' ) - -# plot the model -Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') - -# Generate traveltime curve -buildVectorFile('t',t) -buildVectorFile('x',x) - -Flow('s1',None, - ''' - math n1=3 d1=1 o1=1 n2=1 o2=1 d2=1 output="5" - ''') - -Flow('s2',None,'spike n1=3 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') - -Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') - -Flow('anglefile',None,'spike n1=3 mag=0') - -Flow('t0s',None,'spike n1=3 mag=1') - -Flow(['tCoordinates','xCoordinates'], - ['model','shotsfile','anglefile','t0s','t','x'], - ''' - niptimecurve2 shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} tdata=${SOURCES[4]} xdata=${SOURCES[5]} - ''') - -End() diff --git a/test/gdb_script2 b/test/gdb_script2 deleted file mode 100644 index e429bf5..0000000 --- a/test/gdb_script2 +++ /dev/null @@ -1,9 +0,0 @@ -# Command scripts for GNU debugger (GDB) -# -# You can call this script to set up the program niptimecurve.x -# for debugging. Inside GDB session call it: -# source gdb_script2 - -cd niptimecurve -set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=x.rsf >time.rsf -show args diff --git a/test/gdb_script3 b/test/gdb_script3 deleted file mode 100644 index 09788b6..0000000 --- a/test/gdb_script3 +++ /dev/null @@ -1,9 +0,0 @@ -# Command scripts for GNU debugger (GDB) -# -# You can call this script to set up the program niptimecurve2.x -# for debugging. Inside GDB session call it: -# source gdb_script3 - -cd datamisfit -set args < model.rsf shotsfile=shotsfile.rsf anglefile=anglefile.rsf t0s=t0s.rsf x=xCoordinate.rsf tdata=t.rsf xdata=x.rsf >tCoordinate.rsf -show args diff --git a/test/niptimecurve/README.md b/test/niptimecurve/README.md deleted file mode 100644 index 946d450..0000000 --- a/test/niptimecurve/README.md +++ /dev/null @@ -1,24 +0,0 @@ -This is a experiment for sfnipmodel test with GDB. -Run this experiment before test to generate the input. - -After that, go to the TDD directory, build the program version for -GDB with: - -```sg -~$ make nipmodel.x -``` - -Call this program in GDB with: - -```sh -~$ gdb nipmodel.x -``` - -Use the gdb\_script inside gdb session to set up the arguments -of the program: - -``` -(gdb) source gdb_script2 -``` - -You are ready to debug sfniptomo with GDB. diff --git a/test/niptimecurve/SConstruct b/test/niptimecurve/SConstruct deleted file mode 100644 index 0ca9473..0000000 --- a/test/niptimecurve/SConstruct +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# SConstruct (Python) -# -# Purpose: Program sfniptimecurve test in a constant velocity model. -# -# Site: https://dirack.github.io -# -# Version 1.0 -# -# Programer: Rodolfo A C Neves (Dirack) 02/12/2020 -# -# Email: rodolfo_profissional@hotmail.com -# -# License: GPL-3.0 . - -from rsf.proj import * - -# create a model -amp=1 -rad=1 - -# Constant velocity model for test -Flow('model',None, - ''' - spike n1=301 d1=0.01 o1=0 n2=1001 d2=0.01 o2=0 mag=1.5 | - put label1=Depth unit1=km label2=Position unit2=km - ''' ) - -# plot the model -Plot('model','model','grey color=j scalebar=y label1=Depth unit1=km label2=Position unit2=km barlabel=Velocity barunit=km/s barreverse=y title="Constant Velocity Model" allpos=y') - -Flow('s1',None, - ''' - math n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 output="x1+4" - ''') - -Flow('s2',None,'spike n1=2 d1=1 o1=1 n2=1 o2=1 d2=1 mag=0.5') - -Flow('shotsfile',['s2','s1'],'rcat ${SOURCES[1]} axis=2 | transp') - -Flow('anglefile',None,'spike n1=2 mag=0') - -Flow('t0s',None,'spike n1=2 mag=1') - -# do a ray-tracing from NIP sources -Flow(['tCoordinates','xCoordinates'], - ['model','shotsfile','anglefile','t0s'], - ''' - niptimecurve shotsfile=${SOURCES[1]} anglefile=${SOURCES[2]} t0s=${SOURCES[3]} x=${TARGETS[1]} - ''') - -End() From 0f3c089a059c505aa3c159b8789cca75373e7565 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 28 Apr 2021 16:35:36 -0300 Subject: [PATCH 61/63] Correct bugs in tests Move gdb scripts for a directory and correct the bugs in the Makefile to run all the tests correctly --- test/Makefile | 35 +++++++++++-------- test/{ => gdb_scripts}/gdb_script1 | 0 test/{gdb_script4 => gdb_scripts/gdb_script2} | 0 test/{gdb_script5 => gdb_scripts/gdb_script3} | 0 4 files changed, 21 insertions(+), 14 deletions(-) rename test/{ => gdb_scripts}/gdb_script1 (100%) rename test/{gdb_script4 => gdb_scripts/gdb_script2} (100%) rename test/{gdb_script5 => gdb_scripts/gdb_script3} (100%) diff --git a/test/Makefile b/test/Makefile index ebda4a2..2e05469 100644 --- a/test/Makefile +++ b/test/Makefile @@ -12,9 +12,24 @@ # # License: GPL-3.0 . -SRCNIPTOMO= ../Mnipmodsetup.c ../raytrace.c ../grid2.c ../atela.c +DEP= ../raytrace.c ../grid2.c ../atela.c +SRCNIPTOMO= ../Mnipmodsetup.c $(DEP) SRCGETPARAM= ../Mgetparameter.c -SRCSTEREO = ../Mstereoniptomo.c ../tomography.c ../raytrace.c ../grid2.c ../atela.c ../vfsacrsnh_lib.c +SRCSTEREO = ../Mstereoniptomo.c ../tomography.c $(DEP) ../vfsacrsnh_lib.c + +# model files: Stacked section and parameters cube from CRE stack +PARCUBE=model2/parametersCube.rsf +STKSEC=model2/stackedSection.rsf + +# Extract parameters from stacked section +NP=$(shell ( Date: Wed, 28 Apr 2021 16:48:26 -0300 Subject: [PATCH 62/63] Move Version and Changelog files to doc directory --- CHANGELOG.md => doc/CHANGELOG.md | 0 VERSION.md => doc/VERSION.md | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename CHANGELOG.md => doc/CHANGELOG.md (100%) rename VERSION.md => doc/VERSION.md (100%) diff --git a/CHANGELOG.md b/doc/CHANGELOG.md similarity index 100% rename from CHANGELOG.md rename to doc/CHANGELOG.md diff --git a/VERSION.md b/doc/VERSION.md similarity index 100% rename from VERSION.md rename to doc/VERSION.md From f6b9840f902361e3dbc9b18699a1963146b4bd06 Mon Sep 17 00:00:00 2001 From: Rodolfo Dirack Date: Wed, 28 Apr 2021 18:14:42 -0300 Subject: [PATCH 63/63] Update CHANGELOG.md --- doc/CHANGELOG.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/doc/CHANGELOG.md b/doc/CHANGELOG.md index 3466499..cd23d2a 100644 --- a/doc/CHANGELOG.md +++ b/doc/CHANGELOG.md @@ -7,10 +7,30 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## UNRELEASED - Contribute with the next version -## [v0.1.2-alpha.1](https://github.com/Dirack/creVelocityInversion/compare/v0.1.1-alpha.1...develop/0.1.2) - 2020-10-30 +## [v0.1.3-alpha.1](https://github.com/Dirack/creVelocityInversion/compare/v0.1.2-alpha.1...develop/0.1.3) - 2021-04-28 ## RELEASED +## [v0.1.2-alpha.1](https://github.com/Dirack/creVelocityInversion/releases/tag/v0.1.2-alpha.1) (Development) - 2021-04-28 + +## Added + +- Velocity funtion to update the velocity gradient to simulate a velocity varying with depth model +and evaluate if the NIP sources converges to the reflector interfaces [2c306e3](https://github.com/Dirack/creVelocityInversion/commit/2c306e3) + +## Changed + +- Run inversion in looping [#20](https://github.com/Dirack/creVelocityInversion/issues/20) Run the velocity inversion +in a loop using the prevous optimal model as input for the next iteration [87a0567](https://github.com/Dirack/creVelocityInversion/commit/87a0567) + +## Removed + +- Remove program sfniptimecurve and sfgetbetaangle: Those were test programs and there is no need for them anymore +[8b0b723](https://github.com/Dirack/creVelocityInversion/commit/8b0b723) + +- Remove useless experiments from experiments directory: The inversion strategy has changed from diffraction simulation +to ray tracying algorithm and NIP tomography [be73a35](https://github.com/Dirack/creVelocityInversion/commit/be73a35) + ## [v0.1.1-alpha.1](https://github.com/Dirack/creVelocityInversion/releases/tag/v0.1.1-alpha.1) (Development) - 2020-10-07 ## Added