diff --git a/.flake8 b/.flake8 index 6aeb43a9..a656fd97 100644 --- a/.flake8 +++ b/.flake8 @@ -5,6 +5,6 @@ exclude = build, dist, versioneer.py, - doc/source + docs/source max-line-length = 115 ignore: W504,W503,E741,E402,E226 diff --git a/.github/workflows/conda_unit_test.yml b/.github/workflows/conda_unit_test.yml index a1075dec..72a348d3 100644 --- a/.github/workflows/conda_unit_test.yml +++ b/.github/workflows/conda_unit_test.yml @@ -43,6 +43,6 @@ jobs: - name: Test with coverage and pytest shell: bash -l {0} run: | - coverage run --concurrency=thread --parallel-mode -m pytest -vvv + coverage run --concurrency=thread --parallel-mode -m pytest -vvv --ignore=examples coverage combine coverage report diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml new file mode 100644 index 00000000..42c5d728 --- /dev/null +++ b/.github/workflows/publish-docs.yml @@ -0,0 +1,88 @@ +name: Publish Documentation + +# comment for normal +# (For development use) +# on: push + +# comment here and uncomment above to publish docs for _any_ branch +# (For production use) +on: + push: + branches: + - main + +jobs: + build: + env: + ENV_NAME: test + # will define PACKAGE in steps, obtained from GITHUB_REPOSITORY + + if: github.repository_owner == 'bluesky' + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.8] + + steps: + - name: get correct package name from repository + shell: bash -l {0} + run: | + echo "PACKAGE=${GITHUB_REPOSITORY##*/}" >> $GITHUB_ENV + + - name: show that + shell: bash -l {0} + run: | + env | sort | grep hklpy + + - uses: actions/checkout@v2 + + - name: set environment name in YAML file + shell: bash -l {0} + run: | + sed -i.bak "s/name: ${PACKAGE}/name: ${ENV_NAME}/g" environment.yml + head envir* + + - name: Setup Miniconda ${{ matrix.python-version }} + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + channel-priority: true + channels: nsls2forge,conda-forge,defaults + environment-file: environment.yml + mamba-version: "*" + python-version: ${{ matrix.python-version }} + use-only-tar-bz2: true # required for caching + + - name: Install publishing requirements + shell: bash -l {0} + run: | + conda install jupyter nbconvert sphinx sphinxcontrib-napoleon -c defaults -c conda-forge + pip install sphinx-rtd-theme + + - name: Install the package locally + shell: bash -l {0} + run: | + pip install -e . + + - name: Build Docs + shell: bash -l {0} + run: | + # conda info + # conda list jupyter + # conda list nbconvert + env | sort | grep -i CONDA + make -C examples/ + make -C docs/ html + + - name: Deploy documentation to blueskyproject.io. + # We pin to the SHA, not the tag, for security reasons. + # https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/security-hardening-for-github-actions#using-third-party-actions + uses: peaceiris/actions-gh-pages@bbdfb200618d235585ad98e965f4aafc39b4c501 # v3.7.3 + with: + deploy_key: ${{ secrets.ACTIONS_DOCUMENTATION_DEPLOY_KEY }} + publish_branch: master + publish_dir: ./docs/build/html + external_repository: bluesky/bluesky.github.io + destination_dir: ${PACKAGE} + keep_files: true # Keep old files. + force_orphan: false # Keep git history. diff --git a/.gitignore b/.gitignore index 69321b51..dc517e21 100644 --- a/.gitignore +++ b/.gitignore @@ -66,8 +66,11 @@ target/ .ipynb_checkpoints # generated docs -doc/source/generated +docs/source/generated # Microsoft VS Code editor .vscode/ .history/ + +# PyTest cache +.pytest_cache/ diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 358a5a5f..00000000 --- a/.travis.yml +++ /dev/null @@ -1,83 +0,0 @@ -language: python -os: linux -dist: xenial - -cache: - directories: - - $HOME/.cache/pip - -jobs: - fast_finish: true - include: - - python: 3.6 - - python: 3.7 - - python: 3.8 - - python: 3.6 - env: - - BUILD_AND_PUBLISH_DOCS=1 - # Doctr deploy key for bluesky/bluesky.github.io - - secure: "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" - -install: - # Install conda. - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - - # Useful for debugging any issues with conda. - - conda info -a - - env | sort -u - - # Update the yaml file with the Python version from Travis env var. - - sed -i "s/python>=3.6/python=${TRAVIS_PYTHON_VERSION}/g" environment.yml - - cat environment.yml - - # Create a test conda env from the environment.yml file. - - export CONDA_ENV="testenv-${TRAVIS_PYTHON_VERSION}" - - conda env create -n ${CONDA_ENV} --file environment.yml - - conda activate $CONDA_ENV - - conda install -y bluesky -c conda-forge - - # Validate that we installed the requested Python version. - - python -c "import os, sys; sysver='.'.join([str(_) for _ in sys.version_info[:2]]); travis_python=os.getenv('TRAVIS_PYTHON_VERSION'); assert sysver==travis_python, f'Installed Python version {sysver} does not match requested Python version on TravisCI {travis_python}'" - - # Install the package itself. - - pip install -v . - - # Check the list of installed packages. - - conda list - - pip list - -script: - # Running tests - - conda deactivate - - conda activate $CONDA_ENV - - py.test -vv --cov=hkl --cov-report term-missing hkl/tests - - | - if [ ! -z "${BUILD_AND_PUBLISH_DOCS}" ]; then - set -e - # Install deps for building the docs. - pip install --upgrade -r requirements-doc.txt - - # Build the docs. - make -C doc/ html - - # Upload the docs to gh-pages. - - # doctr deploy --deploy-repo bluesky/bluesky.github.io --deploy-branch-name master hklpy - - # A fix for doctr-versions-menu: - pip install pyparsing --upgrade - - DEPLOY_DIR="hklpy/${TRAVIS_TAG:=main}" - - # Avoid updating /index.html in the bluesky/bluesky.github.io repo. - # See https://goerz.github.io/doctr_versions_menu/v0.3.0/command.html#customizing-index-html - # for details. - export DOCTR_VERSIONS_MENU_WRITE_INDEX_HTML=false - - doctr deploy --command=doctr-versions-menu --build-tags --deploy-repo bluesky/bluesky.github.io --deploy-branch-name master ${DEPLOY_DIR} - fi diff --git a/doc/Makefile b/docs/Makefile similarity index 100% rename from doc/Makefile rename to docs/Makefile diff --git a/doc/make.bat b/docs/make.bat similarity index 100% rename from doc/make.bat rename to docs/make.bat diff --git a/doc/source/_static/.gitkeep b/docs/source/_static/.gitkeep similarity index 100% rename from doc/source/_static/.gitkeep rename to docs/source/_static/.gitkeep diff --git a/doc/source/_themes/bootstrap.zip b/docs/source/_themes/bootstrap.zip similarity index 100% rename from doc/source/_themes/bootstrap.zip rename to docs/source/_themes/bootstrap.zip diff --git a/doc/source/calc.rst b/docs/source/calc.rst similarity index 100% rename from doc/source/calc.rst rename to docs/source/calc.rst diff --git a/doc/source/conf.py b/docs/source/conf.py similarity index 100% rename from doc/source/conf.py rename to docs/source/conf.py diff --git a/doc/source/context.rst b/docs/source/context.rst similarity index 100% rename from doc/source/context.rst rename to docs/source/context.rst diff --git a/doc/source/diffract.rst b/docs/source/diffract.rst similarity index 100% rename from doc/source/diffract.rst rename to docs/source/diffract.rst diff --git a/doc/source/engine.rst b/docs/source/engine.rst similarity index 100% rename from doc/source/engine.rst rename to docs/source/engine.rst diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst new file mode 100644 index 00000000..ca941cc8 --- /dev/null +++ b/docs/source/examples/index.rst @@ -0,0 +1,36 @@ +.. _examples: + +Examples +======== + +All examples are available as +`Jupyter notebooks `_ +from the *hklpy* source +code website: https://github.com/bluesky/hklpy/tree/main/examples + +Diffractometer Geometries +------------------------- + +.. toctree:: + :maxdepth: 1 + :glob: + + notebooks/geo_* + +Variations +---------- + +.. toctree:: + :maxdepth: 1 + :glob: + + notebooks/var_* + +Tests and Comparisons +--------------------- + +.. toctree:: + :maxdepth: 1 + :glob: + + notebooks/tst_* diff --git a/docs/source/examples/notebooks/.gitignore b/docs/source/examples/notebooks/.gitignore new file mode 100644 index 00000000..524499c6 --- /dev/null +++ b/docs/source/examples/notebooks/.gitignore @@ -0,0 +1,8 @@ +# ignore any built content from notebooks + +# written by nbconvert from root/examples/*.ipynb +*.rst +*_files/ + +# copied from root/examples/resources/* +resources/ diff --git a/docs/source/examples/notebooks/resources/.keep b/docs/source/examples/notebooks/resources/.keep new file mode 100644 index 00000000..e69de29b diff --git a/doc/source/index.rst b/docs/source/index.rst similarity index 97% rename from doc/source/index.rst rename to docs/source/index.rst index 28c3322b..bab3c464 100644 --- a/doc/source/index.rst +++ b/docs/source/index.rst @@ -33,6 +33,7 @@ Contents: .. toctree:: :maxdepth: 1 + :glob: calc context @@ -40,4 +41,5 @@ Contents: engine sample util + examples/* diff --git a/doc/source/sample.rst b/docs/source/sample.rst similarity index 100% rename from doc/source/sample.rst rename to docs/source/sample.rst diff --git a/doc/source/util.rst b/docs/source/util.rst similarity index 100% rename from doc/source/util.rst rename to docs/source/util.rst diff --git a/environment.yml b/environment.yml index ad8b23d1..0d0f9a8c 100644 --- a/environment.yml +++ b/environment.yml @@ -14,6 +14,7 @@ dependencies: - pip - prettytable - pyepics + - pyRestTable - pip: - coveralls - pytest diff --git a/examples/Makefile b/examples/Makefile new file mode 100644 index 00000000..5977bf9e --- /dev/null +++ b/examples/Makefile @@ -0,0 +1,22 @@ +# create reStructured text from jupyter notebooks +# +# Create the rst files and related resources +# in the documentation directory. + +TARGET_DIR = ../docs/source/examples/notebooks +NOTEBOOKS := $(wildcard *.ipynb) +RSTS := $(subst .ipynb,.rst, ${NOTEBOOKS}) +RESOURCES := $(wildcard resources/*) + +# default rule: just rebuild everything +# TODO: could be smarter and rebuild conditionally +all :: ${RSTS} resources + +# copy all the local resources cited in the notebooks +resources :: #./resources/* + mkdir -p ${TARGET_DIR}/resources/ + cp ${RESOURCES} ${TARGET_DIR}/resources/ + +# rule to build a rst file from a notebook +%.rst: %.ipynb + jupyter nbconvert --output-dir ${TARGET_DIR} --to rst $< diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..47ff8456 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,27 @@ +# Examples + +## file names + +To facilitate automatic sorting in the documentation, a file naming +convention is set forth for the examples. The convention is to prefix +the file names. + +prefix | type of example +------ | ------- +``geo_`` | diffractometer geometry example +``var_`` | variation on a geometry +``tst_`` | tests and comparisons + +## notebook views +The examples can be viewed directly as notebooks in most web +browsers. If an example fails to load properly, try using this +convenient web tool to view: + +TODO: Change these links to `main` branch after PR #52 is merged. + +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/geo_e4cv.ipynb +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/geo_e6c.ipynb +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/geo_k4cv.ipynb +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/tst_e4cv_fourc.ipynb +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/tst_e6c_test_calculations.ipynb +* https://nbviewer.jupyter.org/github/bluesky/hklpy/blob/24-examples/examples/var_e4cv_renamed_axes.ipynb diff --git a/examples/archive/compare.ipynb b/examples/archive/compare.ipynb new file mode 100644 index 00000000..ebe97907 --- /dev/null +++ b/examples/archive/compare.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# _hkl_ trajectory calculation: compare with SPEC results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial setup\n", + "\n", + "### Initialize the MatPlotLib graphics" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import support libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.calc import CalcE6C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the desired _hkl_ trajectory data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['h', 'k', 'l'], dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hkls = pd.read_csv('./hkl_data/hkl.txt', delim_whitespace=True)\n", + "hkls.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the desired _hkl_ trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(12, 6))\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.2)\n", + "\n", + "plt.suptitle('Desired HKL trajectory')\n", + "axes[0].plot(hkls.h)\n", + "axes[0].set_title('h')\n", + "axes[1].plot(hkls.k)\n", + "axes[1].set_title('k')\n", + "axes[2].plot(hkls.l)\n", + "axes[2].set_title('l')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the motor positions calculated by SPEC" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Delta', 'Theta', 'Chi', 'Phi', 'Mu', 'Gamma'], dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spec_motors = pd.read_csv('hkl_data/motors.txt', delim_whitespace=True)\n", + "spec_motors.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the trajectory of the physical motors calculated by SPEC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 2, figsize=(12, 6),\n", + " subplot_kw={'xticks': []})\n", + "fig.subplots_adjust(hspace=0.3, wspace=0.2)\n", + "\n", + "plt.suptitle('Trajectory according to SPEC')\n", + "for ax, key in zip(axes.flat, spec_motors.keys()):\n", + " ax.plot(spec_motors.index, spec_motors[key], label=key)\n", + " ax.set_title(key)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate with ***hklpy***\n", + "\n", + "### Initialize a calculation engine for the 6-circle diffractometer geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "physical axes OrderedDict([('mu', 0.0), ('omega', 0.0), ('chi', 0.0), ('phi', 0.0), ('gamma', 0.0), ('delta', 0.0)])\n", + "pseudo axes OrderedDict([('h', 0.0), ('k', 0.0), ('l', 0.0)])\n", + "omega parameter is CalcParameter(name='omega', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\n" + ] + } + ], + "source": [ + "diffractometer = CalcE6C(engine='hkl')\n", + "diffractometer.wavelength = 13.3 # angstroms\n", + "\n", + "print('physical axes', diffractometer.physical_axes)\n", + "print('pseudo axes', diffractometer.pseudo_axes)\n", + "print('omega parameter is', diffractometer['omega'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decide which diffractometer *mode* to use.\n", + "\n", + "The motor trajectories show `delta` (the diffractometer arm) having the greatest variation, `theta` (also known as `omega`, sample rotation colinear with `delta`), also varies but not at half the value of `delta`, and `gamma` moves a small amount.\n", + "\n", + "Need a mode that only moves three axes. Learn the available modes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['bissector_vertical', 'constant_omega_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_phi', 'lifting_detector_omega', 'lifting_detector_mu', 'double_diffraction_vertical', 'bissector_horizontal', 'double_diffraction_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal', 'constant_mu_horizontal']\n" + ] + } + ], + "source": [ + "print(diffractometer.engine.modes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `bissector_vertical`, we expect `delta` to be twice `theta` so that is not the mode. Continuing along, `lifting_detector_omega` allows the variation seen above." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode is lifting_detector_omega\n" + ] + } + ], + "source": [ + "diffractometer.engine.mode = \"lifting_detector_omega\"\n", + "\n", + "print('mode is', diffractometer.engine.mode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply constraints: restrict the range of allowed positions for some physical motors\n", + "\n", + "Only allow solutions with the values of $\\varphi=0$, $\\chi=-90$, & $\\mu=0$, as in the plots above. Note: with `chi.fit = False`, no solutions were found in the calculations below. Only by adjusting the limits of `chi` to a small range and allowing it to be fit, while keeping the final value `chi.value = -90` were the calculations able to resolve the motor positions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phi CalcParameter(name='phi', limits=(0.0, 0.0), value=0.0, fit=False, inverted=False, units='Degree')\n", + "chi CalcParameter(name='chi', limits=(-90.01, -89.99), value=-90.0, fit=True, inverted=False, units='Degree')\n", + "mu CalcParameter(name='mu', limits=(0.0, 0.0), value=0.0, fit=False, inverted=False, units='Degree')\n" + ] + } + ], + "source": [ + "phi = diffractometer['phi']\n", + "phi.limits = (0, 0)\n", + "phi.value = 0\n", + "phi.fit = False\n", + "\n", + "chi = diffractometer['chi']\n", + "chi.limits = (-90.01, -89.99)\n", + "chi.value = -90\n", + "chi.fit = True\n", + "\n", + "mu = diffractometer['mu']\n", + "mu.limits = (0, 0)\n", + "mu.value = 0\n", + "mu.fit = False\n", + "\n", + "delta = diffractometer['delta']\n", + "delta.limits = (0, 180)\n", + "\n", + "gamma = diffractometer['gamma']\n", + "gamma.limits = (-10, 10)\n", + "\n", + "print('phi', diffractometer['phi'])\n", + "print('chi', diffractometer['chi'])\n", + "print('mu', diffractometer['mu'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample\n", + "\n", + "### Describe the unit cell and give it a name\n", + "\n", + "Define a hypothetical tetragonal crystal." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "lattice = Lattice(a=3.78, b=3.78, c=13.28, alpha=90, beta=90, gamma=90)\n", + "sample = diffractometer.new_sample('tetragonal', lattice=lattice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Orient the sample on the diffractometer\n", + "\n", + "### Find two reflections and identify _hkl_ of each." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pos1 = diffractometer.Position(mu=0.0, omega=71.04, chi=-90.0, phi=0.0, gamma=-1.65, delta=136.7)\n", + "r1 = sample.add_reflection(0, 0, 2, position=pos1)\n", + "\n", + "pos2 = diffractometer.Position(mu=0.0, omega=158.22, chi=-90.0, phi=0.0, gamma=1.7, delta=164.94)\n", + "r2 = sample.add_reflection(1, 0, 1, position=pos2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the UB matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.66011306 0.0298982 0.02223195]\n", + " [ 0.07775045 0.02016382 -0.4725787 ]\n", + " [-0.03081074 1.6618271 0.00533407]]\n", + "from spec:\n", + " [[ 0.03383097 1.66167452 -0.0073293 ]\n", + " [ 1.66007366 -0.03259177 0.0221635 ]\n", + " [ 0.07733505 -0.02730107 -0.47255519]]\n" + ] + } + ], + "source": [ + "sample.compute_UB(r1, r2)\n", + "print(np.array(sample.UB))\n", + "\n", + "spec_ub = [[0.0338309723166807, 1.6616745234937, -0.00732930331262271],\n", + " [1.66007365775423, -0.032591767600211, 0.0221634966739925],\n", + " [0.0773350510852808, -0.0273010739795478, -0.472555187096841]\n", + " ]\n", + "print('from spec:\\n', np.array(spec_ub))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate the motor trajectories from list of _hkl_ positions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode is lifting_detector_omega\n", + "29 (0.268, 0.0, 1.5) PosCalcE6C(mu=0.0, omega=97.28279140606712, chi=-90.0, phi=0.0, gamma=0.054224488409117436, delta=124.95837187080619)\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Solutions are not comparable. 1 < 30", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhkl_motors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"delta\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec_motors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Delta\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 17\u001b[0m f\"Solutions are not comparable. {len(hkl_motors['delta'])} < {len(spec_motors['Delta'])}\")\n", + "\u001b[0;31mValueError\u001b[0m: Solutions are not comparable. 1 < 30" + ] + } + ], + "source": [ + "print('mode is', diffractometer.engine.mode)\n", + "\n", + "hkl_motors = {nm: [] for nm in diffractometer.physical_axis_names}\n", + "\n", + "for seq, (h, k, l) in hkls.iterrows():\n", + " try:\n", + " solutions = diffractometer.forward((h, k, l))\n", + " except ValueError:\n", + " solutions = []\n", + " for sol in solutions[:1]: # only show the first solution\n", + " print(f\"{seq} {h,k,l} {sol}\")\n", + " for nm in diffractometer.physical_axis_names:\n", + " hkl_motors[nm].append(getattr(sol, nm))\n", + "\n", + "if len(hkl_motors[\"delta\"]) < len(spec_motors[\"Delta\"]):\n", + " raise ValueError(\n", + " f\"Solutions are not comparable. {len(hkl_motors['delta'])} < {len(spec_motors['Delta'])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the motor trajectories according to *hkl*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(3, 2, figsize=(12, 6),\n", + " subplot_kw={'xticks': []})\n", + "fig.subplots_adjust(hspace=0.3, wspace=0.2)\n", + "\n", + "plt.suptitle('Trajectory according to hkl')\n", + "for ax, key in zip(axes.flat, diffractometer.physical_axis_names):\n", + " ax.plot(spec_motors.index, trajectory[key], label=key)\n", + " ax.set_title(key)\n", + "plt.show()" + ] + } + ], + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/config.py.out_of_date b/examples/archive/config.py similarity index 100% rename from examples/config.py.out_of_date rename to examples/archive/config.py diff --git a/examples/archive/e6c_sixc.ipynb b/examples/archive/e6c_sixc.ipynb new file mode 100644 index 00000000..f31b28a2 --- /dev/null +++ b/examples/archive/e6c_sixc.ipynb @@ -0,0 +1,512 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python38264bitcondaf8e76b08f7284c68a6b3de15f965a87a", + "display_name": "Python 3.8.2 64-bit (conda)" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# JL124 E6C 6-circle example\n", + "\n", + "Compare with data acquired using SPEC\n", + "\n", + "In _hkl_ *E6C* geometry (https://people.debian.org/~picca/hkl/hkl.html#orge5e0490):\n", + "\n", + "\"E6C\n", + "\n", + "* xrays incident on the $\\vec{x}$ direction (1, 0, 0)\n", + "\n", + "axis | moves | rotation about axis\n", + "--- | --- | ---\n", + "mu | sample | $\\vec{z}$ `[0 0 1]`\n", + "omega | sample | $-\\vec{y}$ `[0 -1 0]`\n", + "chi | sample | $\\vec{x}$ `[1 0 0]`\n", + "phi | sample | $-\\vec{y}$ `[0 -1 0]`\n", + "gamma | detector | $\\vec{z}$ `[0 0 1]`\n", + "delta | detector | $-\\vec{y}$ `[0 -1 0]`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "Huber 6-circle diffractometer at the Advanced Photon Source\n", + "\n", + "\"APS\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "In SPEC *sixc* geometry (https://certif.com/spec_help/sixc.html):\n", + "\n", + "name | mnemonic | description\n", + "----- | ----- | -----\n", + "Delta | del | Detector arm rotation\n", + "Theta | th | Rotates sample circles\n", + "Chi | chi | Sample tilt\n", + "Phi | phi | Sample rotation\n", + "Mu | mu | Diffractometer rotation\n", + "Gamma | gam | Out-of-plane detector rotation\n", + "\n", + "> When the azimuthal angle is set to 90 degrees in the azimuth-fixed modes (3, 6, 9 and 12), the incident angle alpha will be equal to the exit angle beta." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# mapping of axis names between E6C and SPEC\n", + "AXIS_NAME_MAP = dict(\n", + " # remap so we can use the sixc names\n", + " # E6C sixc\n", + " mu='mu', # Diffractometer rotation around vertical axis\n", + " omega='theta', # Rotates chi around horizontal axis\n", + " chi='chi', # Rotates phi around beam axis\n", + " phi='phi', # Sample rotation around horizontal axis (when phi is co-linear with omega)\n", + " gamma='gamma', # Diffractometer rotation around vertical axis\n", + " delta='delta', # Detector arm rotation\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============ ==============================================================================================================================================================================================\nterm value \n============ ==============================================================================================================================================================================================\nSPEC file /home/33id/data/junelee/20130213/LNO1/JL124_1.spc \nscan # 52 \nSPEC scanCmd hklscan 0.5 0.5 0.5 0.5 1.2 1.8 120 1 \ngeometry sixc \nmode Z-Axis with Azimuth fixed and Chi, Phi set to -Sigma, -Tau \nlattice LatticeParameters(a=3.905, b=3.905, c=3.905, alpha=90.0, beta=90.0, gamma=90.0) \nwavelength 0.8265616267 \nreflection 1 Reflections(h=0.0, k=0.0, l=2.0, wavelength=0.8265616267, angles=OrderedDict([('del', 0.003), ('th', 90.0), ('chi', 0.5799999712), ('phi', 239.9999477), ('mu', 12.102), ('gam', 12.9945)])) \nreflection 2 Reflections(h=3.0, k=0.0, l=3.0, wavelength=0.8265616267, angles=OrderedDict([('del', 47.18), ('th', 90.0), ('chi', 0.5799999712), ('phi', 239.9999477), ('mu', 21.77425), ('gam', 15.7375)]))\n[UB] [[ 1.20770271 1.24845482 0.00209558] \n [-1.48561242 0.91180747 0.00324183] \n [-0.01737524 0.02282508 1.65153055]] \n============ ==============================================================================================================================================================================================\n\n" + ] + } + ], + "source": [ + "import pyRestTable\n", + "from spec2nexus.spec import SpecDataFile\n", + "\n", + "specfile = SpecDataFile(\"hkl_data/JL124_1_s52.spc\")\n", + "specscan = specfile.getScan(52)\n", + "\n", + "spec_d = specscan.diffractometer\n", + "spec_d.UB = spec_d.geometry_parameters[\"ub_matrix\"][2]\n", + "\n", + "terms = {\n", + " \"SPEC file\": specfile.specFile,\n", + " \"scan #\": specscan.scanNum,\n", + " \"SPEC scanCmd\": specscan.scanCmd,\n", + " \"geometry\": spec_d.geometry_name,\n", + " \"mode\": spec_d.mode,\n", + " \"lattice\": spec_d.lattice,\n", + " \"wavelength\": spec_d.wavelength,\n", + " \"reflection 1\": spec_d.reflections[0],\n", + " \"reflection 2\": spec_d.reflections[1],\n", + " \"[UB]\": spec_d.UB,\n", + "}\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "for k, v in terms.items():\n", + " tbl.addRow((k, v))\n", + "print(tbl)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import spec2nexus\n", + "\n", + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.diffract import E6C\n", + "from hkl.util import Lattice\n", + "\n", + "from ophyd import (PseudoSingle, SoftPositioner)\n", + "from ophyd import Component as Cpt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SoftPositioner(name='e6c_mu', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "SoftPositioner(name='e6c_omega', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "SoftPositioner(name='e6c_chi', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "SoftPositioner(name='e6c_phi', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "SoftPositioner(name='e6c_gamma', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "SoftPositioner(name='e6c_delta', parent='e6c', settle_time=0.0, timeout=None, egu='', limits=(0, 0), source='computed')\n", + "G0: 12 0 1 -0.002814275157 0.007740448308 0.9999660821 0 0 0 0 0 0 600 0 1 1 143.6\n", + "G1: 3.905 3.905 3.905 90 90 90 1.609010322 1.609010322 1.609010322 90 90 90 0 0 2\n", + " 3 0 3 0.003 90 0.5799999712 239.9999477 12.102 12.9945 47.18 90 0.5799999712 239.9999477\n", + " 21.77425 15.7375 0.8265616267 0.8265616267\n", + "G3: 1.207702707 1.248454819 0.002095582696 -1.485612421 0.9118074731 0.003241829804\n", + " -0.0173752388 0.02282507942 1.651530555\n", + "G4: 2.998037021 0.002160676878 2.996606618 0.8265616267 21.73024942 15.73707597 66.518375\n", + " 59.48580986 -94.28612051 -0.5799999712 -239.9999477 0 0 0 -92.49362136 0 0 0 0 0\n", + " 0 -180 -180 -180 -180 -180 -180 0\n", + "\n" + ] + } + ], + "source": [ + "class This(E6C):\n", + " h = Cpt(PseudoSingle, '')\n", + " k = Cpt(PseudoSingle, '')\n", + " l = Cpt(PseudoSingle, '')\n", + " mu = Cpt(SoftPositioner)\n", + " omega = Cpt(SoftPositioner)\n", + " chi = Cpt(SoftPositioner)\n", + " phi = Cpt(SoftPositioner)\n", + " gamma = Cpt(SoftPositioner)\n", + " delta = Cpt(SoftPositioner)\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position\n", + "\n", + "e6c = This(\"\", name=\"e6c\")\n", + "print(\"\\n\".join(map(str, e6c.real_positioners)))\n", + "import yaml\n", + "print(yaml.dump(specscan.G))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(PosCalcE6C(mu=0.0, omega=-27.97391316589647, chi=-120.00000000000001, phi=0.0, gamma=-30.996525444813518, delta=-38.344980261923986), PosCalcE6C(mu=0.0, omega=-27.97391316589647, chi=-120.00000000000001, phi=0.0, gamma=149.00347455518647, delta=-141.65501973807605))\n", + "bissector_vertical (3, 0, 3) PosCalcE6C(mu=0.0, omega=67.68234833655978, chi=139.5004906975483, phi=129.21870663340866, gamma=0.0, delta=135.36469667311957)\n", + "constant_omega_vertical (3, 0, 3) no solution\n", + "constant_chi_vertical (3, 0, 3) PosCalcE6C(mu=0.0, omega=-26.264973297127604, chi=-120.00000000000001, phi=9.674485199071988, gamma=0.0, delta=-135.3646966731196)\n", + "constant_phi_vertical (3, 0, 3) PosCalcE6C(mu=0.0, omega=31.58777850004291, chi=53.48670337763097, phi=0.0, gamma=0.0, delta=135.36469667311957)\n", + "lifting_detector_phi (3, 0, 3) no solution\n", + "lifting_detector_omega (3, 0, 3) PosCalcE6C(mu=0.0, omega=-32.00865186003745, chi=-120.00000000000001, phi=0.0, gamma=-13.404723805458783, delta=-137.01416647059162)\n", + "lifting_detector_mu (3, 0, 3) no solution\n", + "double_diffraction_vertical (3, 0, 3) no solution\n", + "bissector_horizontal (3, 0, 3) PosCalcE6C(mu=-67.68234833655978, omega=1.7848965847989348e-35, chi=130.49950930245168, phi=-50.78129336659135, gamma=-135.36469667311957, delta=0.0)\n", + "double_diffraction_horizontal (3, 0, 3) no solution\n", + "psi_constant_vertical (3, 0, 3) PosCalcE6C(mu=0.0, omega=62.817281854177445, chi=89.87264321243354, phi=39.10993530284701, gamma=0.0, delta=-135.36469667311948)\n", + "psi_constant_horizontal (3, 0, 3) PosCalcE6C(mu=0.0, omega=38.63519194712502, chi=103.29596815903612, phi=21.080230412620633, gamma=-135.36469667311957, delta=0.0)\n", + "constant_mu_horizontal (3, 0, 3) no solution\n", + "bissector_vertical (0, 0, 2) PosCalcE6C(mu=0.0, omega=23.877900192534586, chi=89.8660776125703, phi=57.12297985830933, gamma=0.0, delta=47.75580038506917)\n", + "constant_chi_vertical (0, 0, 2) no solution\n", + "constant_phi_vertical (0, 0, 2) PosCalcE6C(mu=0.0, omega=23.990373225530274, chi=89.927301791686, phi=0.0, gamma=0.0, delta=47.755800385069186)\n", + "lifting_detector_omega (0, 0, 2) PosCalcE6C(mu=0.0, omega=-27.973913162552734, chi=-120.00000000000001, phi=0.0, gamma=-30.99652544234211, delta=-38.34498026234804)\n", + "bissector_horizontal (0, 0, 2) PosCalcE6C(mu=-23.877900192534685, omega=-5.314172724725106e-53, chi=179.86607761257076, phi=57.12297985823302, gamma=-47.75580038506937, delta=0.0)\n", + "psi_constant_vertical (0, 0, 2) PosCalcE6C(mu=0.0, omega=23.83648693360791, chi=90.1273583552991, phi=-140.89006586063522, gamma=0.0, delta=47.7558003850692)\n", + "psi_constant_horizontal (0, 0, 2) PosCalcE6C(mu=0.0, omega=90.31087139267841, chi=156.07658277896124, phi=-12.86565508698122, gamma=-47.75580038506912, delta=0.0)\n", + "['bissector_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_omega', 'bissector_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal']\n" + ] + } + ], + "source": [ + "# get the UB matrix from the SPEC data\n", + "e6c.UB.put(spec_d.UB[[1,2,0], :])\n", + "# print(e6c.engine.modes)\n", + "e6c.engine.mode = \"lifting_detector_omega\"\n", + "# e6c.calc[\"chi\"].limits = (88, 92)\n", + "# e6c.calc[\"chi\"].fit = False\n", + "# e6c.calc[\"chi\"].value = 240\n", + "e6c.chi.move(240)\n", + "\n", + "# SPEC: del th mu chi phi gam\n", + "# (002) 0.003 90 0.5799999712 239.9999477 12.102 12.9945\n", + "# (303) 47.18 90 0.5799999712 239.9999477 21.77425 15.7375\n", + "# print(e6c.forward(0.5, 0.5, 1.2))\n", + "print(e6c.calc.forward((0, 0, 2)))\n", + "modes = []\n", + "r_hkl = (3, 0, 3)\n", + "for mode in e6c.engine.modes:\n", + " e6c.engine.mode = mode\n", + " try:\n", + " sol = e6c.forward(r_hkl)\n", + " modes.append(mode)\n", + " print(mode, r_hkl, sol)\n", + " except Exception:\n", + " print(mode, r_hkl, \"no solution\")\n", + "r_hkl = (0, 0, 2)\n", + "for mode in modes:\n", + " e6c.engine.mode = mode\n", + " try:\n", + " sol = e6c.forward(r_hkl)\n", + " print(mode, r_hkl, sol)\n", + " except Exception:\n", + " print(mode, r_hkl, \"no solution\")\n", + "print(modes)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: compare in a table, as in e4cv" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class Diffractometer(E6C):\n", + " h = Cpt(PseudoSingle, '')\n", + " k = Cpt(PseudoSingle, '')\n", + " l = Cpt(PseudoSingle, '')\n", + "\n", + " # use the SPEC axis names here\n", + " mu = Cpt(SoftPositioner)\n", + " theta = Cpt(SoftPositioner)\n", + " chi = Cpt(SoftPositioner)\n", + " phi = Cpt(SoftPositioner)\n", + " gamma = Cpt(SoftPositioner)\n", + " delta = Cpt(SoftPositioner)\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sixc = Diffractometer(\"\", name=\"sixc\")\n", + "\n", + "sixc.calc.physical_axis_names = AXIS_NAME_MAP" + ] + }, + { + "source": [ + "```\n", + "sample: JL124_1\n", + "crystal: 3.905 3.905 3.905 90 90 90\n", + "geometry: sixc\n", + "mode: 12 (Z-Axis with Azimuth fixed and Chi, Phi set to -Sigma, -Tau)\n", + "lambda: 0.8265616267\n", + "r1: (0, 0, 2) 0.003 90 0.5799999712 239.9999477 12.102 12.9945\n", + "r2: (3, 0, 3) 47.18 90 0.5799999712 239.9999477 21.77425 15.7375\n", + "Q: (2.99804, 0.00216068, 2.99661) 47.14125 90.089 0.58 239.94275 21.73025 15.7375\n", + "UB: 1.207702707 1.248454819 0.002095582696 \n", + " -1.485612421 0.9118074731 0.003241829804 \n", + " -0.0173752388 0.02282507942 1.651530555\n", + "```" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='JL124_1', lattice=LatticeTuple(a=3.905, b=3.905, c=3.905, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.60901032e+00, -9.85234670e-17, -9.85234670e-17],\n", + " [ 0.00000000e+00, 1.60901032e+00, -9.85234670e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.60901032e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "lattice = Lattice(\n", + " a=3.905, b=3.905, c=3.905,\n", + " alpha=90.0, beta=90.0, gamma=90.0)\n", + "\n", + "# add the sample to the calculation engine\n", + "sixc.calc.new_sample(\n", + " \"JL124_1\",\n", + " lattice=Lattice(\n", + " a=3.905, b=3.905, c=3.905,\n", + " alpha=90.0, beta=90.0, gamma=90.0)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "sixc.calc.wavelength = 0.8265616267 # angstrom\n", + "\n", + "# SPEC motors (in order): del th chi phi mu gam\n", + "# r1: (0, 0, 2) 0.003 90 0.5799999712 239.9999477 12.102 12.9945\n", + "# r2: (3, 0, 3) 47.18 90 0.5799999712 239.9999477 21.77425 15.7375\n", + "\n", + "r1 = sixc.calc.sample.add_reflection(\n", + " 0, 0, 2, \n", + " position=sixc.calc.Position(\n", + " delta=0.003, \n", + " theta=90, \n", + " chi=0.5799999712,\n", + " phi=239.9999477, \n", + " mu=12.102, \n", + " gamma=12.9945,\n", + " )\n", + " )\n", + "r2 = sixc.calc.sample.add_reflection(\n", + " 3, 0, 3, \n", + " position=sixc.calc.Position(\n", + " delta=47.18, \n", + " theta=90, \n", + " chi=0.5799999712,\n", + " phi=239.9999477, \n", + " mu=21.77425, \n", + " gamma=15.7375,\n", + " )\n", + " )\n", + "sixc.calc.sample.compute_UB(r1, r2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-1.2466385 , 1.00614404, 0.14993609],\n", + " [ 0.06162735, -0.16202206, 1.59964532],\n", + " [ 1.015386 , 1.24512539, 0.08699568]])" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "# UB: 1.207702707 1.248454819 0.002095582696 \n", + "# -1.485612421 0.9118074731 0.003241829804 \n", + "# -0.0173752388 0.02282507942 1.651530555\n", + "\n", + "# FIXME: must get same numbers, probably in different rows\n", + "\n", + "sixc.UB.get()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "calc.wavelength is 0.8265616267\n", + "sample is HklSample(name='JL124_1', lattice=LatticeTuple(a=3.905, b=3.905, c=3.905, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-86.88707255123839, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=5.3468811222840165, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=-141.09349844908007, fit=True, inverted=False, units='Degree'), U=array([[-0.77478589, 0.62531858, 0.09318529],\n", + " [ 0.0383014 , -0.10069672, 0.99417965],\n", + " [ 0.63106246, 0.77384549, 0.05406782]]), UB=array([[-1.2466385 , 1.00614404, 0.14993609],\n", + " [ 0.06162735, -0.16202206, 1.59964532],\n", + " [ 1.015386 , 1.24512539, 0.08699568]]), reflections=[(h=0.0, k=0.0, l=2.0), (h=3.0, k=0.0, l=3.0)], reflection_measured_angles=array([[0. , 1.23564414],\n", + " [1.23564414, 0. ]]), reflection_theoretical_angles=array([[0. , 0.78539816],\n", + " [0.78539816, 0. ]]))\n", + "position is DiffractometerPseudoPos(h=0.0, k=0.0, l=0.0)\n", + "sample name is JL124_1\n", + "u matrix is [[-0.77478589 0.62531858 0.09318529]\n", + " [ 0.0383014 -0.10069672 0.99417965]\n", + " [ 0.63106246 0.77384549 0.05406782]] {'sixc_U': {'source': 'PY:sixc.calc.sample.U', 'dtype': 'array', 'shape': [3, 3]}}\n", + "ub matrix is [[-1.2466385 1.00614404 0.14993609]\n", + " [ 0.06162735 -0.16202206 1.59964532]\n", + " [ 1.015386 1.24512539 0.08699568]] {'sixc_UB': {'source': 'PY:sixc.calc.sample.UB', 'dtype': 'array', 'shape': [3, 3]}}\n", + "reflections: [[0. 0. 2.]\n", + " [3. 0. 3.]] {'sixc_reflections': {'source': 'PY:sixc.calc.sample.reflections', 'dtype': 'array', 'shape': [2, 3]}}\n", + "ux is -86.88707255123839 {'sixc_ux': {'source': 'PY:sixc.calc.sample.ux.value', 'dtype': 'number', 'shape': []}}\n", + "uy is 5.3468811222840165 {'sixc_uy': {'source': 'PY:sixc.calc.sample.uy.value', 'dtype': 'number', 'shape': []}}\n", + "uz is -141.09349844908007 {'sixc_uz': {'source': 'PY:sixc.calc.sample.uz.value', 'dtype': 'number', 'shape': []}}\n", + "lattice is [ 3.905 3.905 3.905 90. 90. 90. ] {'sixc_lattice': {'source': 'PY:sixc.calc.sample.lattice', 'dtype': 'array', 'shape': [6]}}\n", + "OrderedDict([('sixc_h', {'value': 0.0, 'timestamp': 1607320176.3933952}), ('sixc_h_setpoint', {'value': 0.0, 'timestamp': 1607320176.393514}), ('sixc_k', {'value': 0.0, 'timestamp': 1607320176.3942313}), ('sixc_k_setpoint', {'value': 0.0, 'timestamp': 1607320176.3943505}), ('sixc_l', {'value': 0.0, 'timestamp': 1607320176.3947651}), ('sixc_l_setpoint', {'value': 0.0, 'timestamp': 1607320176.394884}), ('sixc_mu', {'value': 0, 'timestamp': 1607320203.7948568}), ('sixc_theta', {'value': 0, 'timestamp': 1607320203.7948658}), ('sixc_chi', {'value': 0, 'timestamp': 1607320203.7948737}), ('sixc_phi', {'value': 0, 'timestamp': 1607320203.794881}), ('sixc_gamma', {'value': 0, 'timestamp': 1607320203.7950253}), ('sixc_delta', {'value': 0, 'timestamp': 1607320203.7950404})])\n" + ] + } + ], + "source": [ + "print('calc.wavelength is', sixc.calc.wavelength)\n", + "print('sample is', sixc.calc.sample)\n", + "print('position is', sixc.position)\n", + "\n", + "print('sample name is', sixc.sample_name.get())\n", + "print('u matrix is', sixc.U.get(), sixc.U.describe())\n", + "print('ub matrix is', sixc.UB.get(), sixc.UB.describe())\n", + "print('reflections:',\n", + " sixc.reflections.get(),\n", + " sixc.reflections.describe())\n", + "print('ux is', sixc.ux.get(), sixc.ux.describe())\n", + "print('uy is', sixc.uy.get(), sixc.uy.describe())\n", + "print('uz is', sixc.uz.get(), sixc.uz.describe())\n", + "print('lattice is', sixc.lattice.get(), sixc.lattice.describe())\n", + "print(sixc.read())" + ] + } + ] +} \ No newline at end of file diff --git a/examples/archive/hkl-example.ipynb b/examples/archive/hkl-example.ipynb new file mode 100644 index 00000000..00e4f758 --- /dev/null +++ b/examples/archive/hkl-example.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HKL calculation, compared to SPEC results" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.calc import CalcE6C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the desired HKL trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['h', 'k', 'l'], dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "hkls = pd.read_csv('hkl_data/hkl.txt', delim_whitespace=True)\n", + "hkls.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the motor positions that SPEC calculated" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Delta', 'Theta', 'Chi', 'Phi', 'Mu', 'Gamma'], dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "# The motor positions according to SPEC\n", + "spec_motors = pd.read_csv('hkl_data/motors.txt', delim_whitespace=True)\n", + "spec_motors.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the trajectory of the physical motors" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-04T09:23:14.572867\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig, axes = plt.subplots(3, 2, figsize=(12, 6),\n", + " subplot_kw={'xticks': []})\n", + "fig.subplots_adjust(hspace=0.3, wspace=0.2)\n", + "\n", + "plt.suptitle('Trajectory according to SPEC')\n", + "for ax, key in zip(axes.flat, spec_motors.keys()):\n", + " ax.plot(spec_motors.index, spec_motors[key], label=key)\n", + " ax.set_title(key)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the desired HKL trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-04T09:23:16.337259\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(12, 6))\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.2)\n", + "\n", + "plt.suptitle('Desired HKL trajectory')\n", + "axes[0].plot(hkls.h)\n", + "axes[0].set_title('h')\n", + "axes[1].plot(hkls.k)\n", + "axes[1].set_title('k')\n", + "axes[2].plot(hkls.l)\n", + "axes[2].set_title('l')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize a calculation engine" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mode is bissector_vertical\nphysical axes OrderedDict([('mu', 0.0), ('omega', 0.0), ('chi', 0.0), ('phi', 0.0), ('gamma', 0.0), ('delta', 0.0)])\npseudo axes OrderedDict([('h', 0.0), ('k', 0.0), ('l', 0.0)])\nomega parameter is CalcParameter(name='omega', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\n" + ] + } + ], + "source": [ + "calc = CalcE6C(engine='hkl')\n", + "calc.wavelength = 1.33e1 # not nm, angstroms\n", + "print('mode is', calc.engine.mode)\n", + "print('physical axes', calc.physical_axes)\n", + "print('pseudo axes', calc.pseudo_axes)\n", + "print('omega parameter is', calc['omega'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set some constraints on the physical motors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CalcParameter(name='mu', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='omega', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='chi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='phi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='gamma', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='delta', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nupdated constraints:\nCalcParameter(name='phi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='chi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='mu', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\n" + ] + } + ], + "source": [ + "# First, show the default constraints\n", + "for axis in calc.physical_axis_names:\n", + " print(calc[axis])\n", + "\n", + "if False:\n", + " phi = calc['phi']\n", + " phi.limits = (0, 0)\n", + " phi.value = 0\n", + " phi.fit = False\n", + "\n", + " chi = calc['chi']\n", + " chi.limits = (-90, -90)\n", + " chi.value = -90\n", + " chi.fit = False\n", + "\n", + " mu = calc['mu']\n", + " mu.limits = (0, 0)\n", + " mu.value = 0\n", + " mu.fit = False\n", + "\n", + "print(\"updated constraints:\")\n", + "print(calc['phi'])\n", + "print(calc['chi'])\n", + "print(calc['mu'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Work with a sample" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# from ophyd.hkl.sample import HklSample\n", + "# new_sample supports kwargs (see `help(HklSample)`)\n", + "from hkl.util import Lattice\n", + "lattice = Lattice(a=3.78, b=3.78, c=13.28, alpha=90, beta=90, gamma=90)\n", + "sample = calc.new_sample('sample0', lattice=lattice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Primary reflection" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "r1 = sample.add_reflection(\n", + " 0, 0, 2,\n", + " position=calc.Position(\n", + " mu=0.0, omega=71.04, chi=-90.0, phi=0.0, gamma=-1.65, delta=136.7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Secondary reflection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "r2 = sample.add_reflection(\n", + " 1, 0, 1,\n", + " position=calc.Position(\n", + " mu=0.0, omega=158.22, chi=-90.0, phi=0.0, gamma=1.7, delta=164.94))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate the UB matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 1.66011306 0.0298982 0.02223195]\n [ 0.07775045 0.02016382 -0.4725787 ]\n [-0.03081074 1.6618271 0.00533407]]\nfrom spec:\n [[ 0.03383097 1.66167452 -0.0073293 ]\n [ 1.66007366 -0.03259177 0.0221635 ]\n [ 0.07733505 -0.02730107 -0.47255519]]\n" + ] + } + ], + "source": [ + "sample.compute_UB(r1, r2)\n", + "print(np.array(sample.UB))\n", + "\n", + "spec_ub = [[0.0338309723166807, 1.6616745234937, -0.00732930331262271],\n", + " [1.66007365775423, -0.032591767600211, 0.0221634966739925],\n", + " [0.0773350510852808, -0.0273010739795478, -0.472555187096841]\n", + " ]\n", + "print('from spec:\\n', np.array(spec_ub))" + ] + }, + { + "source": [ + "## Check one (_hkl_) reflection from the test data" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "physical positions= PosCalcE6C(mu=0.0, omega=85.7031, chi=-90.0, phi=0.0, gamma=-0.47, delta=113.6647)\npseudo should be (0.21, 0, 1.5)= OrderedDict([('h', 0.20994021606732546), ('k', 1.336972588470526e-05), ('l', 1.500103655211857)])\n" + ] + } + ], + "source": [ + "# h k l\n", + "# 0.21 0 1.5\n", + "#\n", + "# Delta Theta Chi Phi Mu Gamma\n", + "# 113.6647 85.7031 -90.0000 0.0000 0.0000 -0.4700\n", + "\n", + "calc.physical_positions = calc.Position(\n", + " mu=0.0, omega=85.7031, chi=-90.0, phi=0.0, gamma=-0.47, delta=113.6647)\n", + "print(\"physical positions=\", calc.physical_positions)\n", + "print('pseudo should be (0.21, 0, 1.5)=', calc.pseudo_axes)\n" + ] + }, + { + "source": [ + "## Compute (_hkl_) from this position" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "possible modes ['bissector_vertical', 'constant_omega_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_phi', 'lifting_detector_omega', 'lifting_detector_mu', 'double_diffraction_vertical', 'bissector_horizontal', 'double_diffraction_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal', 'constant_mu_horizontal']\nchosen modes constant_omega_vertical\nwavelength, A 13.3\nConstraints:\nCalcParameter(name='mu', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='omega', limits=(-180.0, 180.0), value=85.7031, fit=True, inverted=False, units='Degree')\nCalcParameter(name='chi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='phi', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\nCalcParameter(name='gamma', limits=(-180.0, 180.0), value=-0.47, fit=True, inverted=False, units='Degree')\nCalcParameter(name='delta', limits=(-180.0, 180.0), value=113.6647, fit=True, inverted=False, units='Degree')\nSolution(s) for (0.21 0 1.5):\n (PosCalcE6C(mu=0.0, omega=85.7031, chi=-89.40746350365764, phi=1.078155321060729, gamma=-0.47, delta=113.66466533035054),)\n" + ] + } + ], + "source": [ + "# FIXME: So far, only works when previous constraints are removed. Wrong mode?\n", + "for axis in \"chi mu phi\".split():\n", + " calc[axis].limits = (-180, 180)\n", + " calc[axis].fit = True\n", + " calc[axis].value = 0.0\n", + "\n", + "# FIXME: pick mode\n", + "# see: https://repo.or.cz/hkl.git/blob/HEAD:/Documentation/sphinx/source/diffractometers/e6c.rst\n", + "# constant_omega_vertical: close but cannot fix phi=0\n", + "# constant_mu_horizontal: close but cannot fix phi=0\n", + "# constant_phi_vertical has right values but omega, chi, delta have wrong sign\n", + "# psi_constant_vertical: close but cannot fix phi=0, needs second psi reflection\n", + "calc.engine.mode = \"constant_omega_vertical\"\n", + "\n", + "print(\"possible modes\", calc.engine.modes)\n", + "print(\"chosen modes\", calc.engine.mode)\n", + "print(\"wavelength, A\", calc.wavelength)\n", + "print(\"Constraints:\")\n", + "for axis in calc.physical_axis_names:\n", + " print(calc[axis])\n", + "print(\"Solution(s) for (0.21 0 1.5):\\n\", calc.forward((0.21, 0, 1.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate the trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "-- hkl (0.21, 0.0, 1.5) --\nSolutions:\n\tPosCalcE6C(mu=0.0, omega=-56.832335627765005, chi=61.23349385814405, phi=-90.22910278033105, gamma=-0.47, delta=-113.66467125553001)\n" + ] + } + ], + "source": [ + "for seq, (h, k, l) in hkls.iterrows():\n", + " print('-- hkl {} --'.format((h, k, l)))\n", + " print('Solutions:')\n", + " for sol in calc.forward((h, k, l)):\n", + " print('\\t{}'.format(sol))\n", + " \n", + " break" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit ('base': conda)", + "language": "python", + "name": "python38264bitbaseconda9c3e3a9452084ea0903e516deca6d551" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/archive/hkl_data/JL124_1_s52.md b/examples/archive/hkl_data/JL124_1_s52.md new file mode 100755 index 00000000..7424357b --- /dev/null +++ b/examples/archive/hkl_data/JL124_1_s52.md @@ -0,0 +1,21 @@ +# README + +First six motors are consistent with (sixc?) geometry but other motors (`33ide:*`) suggest the different geometry used in APS 33-ID-E. +Finally, this 6 motor set is a unique match to *sixc* `('motors': ['del', 'th', 'chi', 'phi', 'mu', 'gam'])`. +Test this by number of parameters in the `G` array. + +Compare Q and O0/P0 for a specific solution to test the UB matrix. + +``` +sample: JL124_1 +crystal: 3.905 3.905 3.905 90 90 90 +geometry: sixc +mode: 12 (Z-Axis with Azimuth fixed and Chi, Phi set to -Sigma, -Tau) +lambda: 0.8265616267 +r1: (0, 0, 2) 0.003 90 0.5799999712 239.9999477 12.102 12.9945 +r2: (3, 0, 3) 47.18 90 0.5799999712 239.9999477 21.77425 15.7375 +Q: (2.99804, 0.00216068, 2.99661) 47.14125 90.089 0.58 239.94275 21.73025 15.7375 +UB: 1.207702707 1.248454819 0.002095582696 + -1.485612421 0.9118074731 0.003241829804 + -0.0173752388 0.02282507942 1.651530555 +``` diff --git a/examples/archive/hkl_data/JL124_1_s52.spc b/examples/archive/hkl_data/JL124_1_s52.spc new file mode 100755 index 00000000..aa62e477 --- /dev/null +++ b/examples/archive/hkl_data/JL124_1_s52.spc @@ -0,0 +1,215 @@ +#F /home/33id/data/junelee/20130213/LNO1/JL124_1.spc +#E 1361139208 +#D Sun Feb 17 16:13:28 2013 +#C JL123_1 User = epix33id +#H0 SR_current SR_fill SR_status SR_mode SR_fb SR_fbH SR_fbV SR_topUp barometer_mbar +#H1 DCM_energy ID_E ID_taperE +#H2 ID_gap ID_E ID_harmonic ID_taperGap ID_taperE +#H3 FE_XBPM_HP FE_XPM_VP FE_XPM_HA FE_XPM_VA DCM_Choice DCM_energy DCM_lambda DCM_theta0 DCM_thetaEnc ID_E_Offset +#H4 COUPLE_ID_to_DCM DCM_mode DCM_omega2 DCM_xtal2_twist DCM_xtal2_up DCM_xtal2_down +#H5 FB_o2_on FB_o2_r FB_o2_sp +#H6 Mirr1_angle Mirr1_y Mirr1_stripe +#H7 Mirr2_angle Mirr2_y Mirr2_stripe +#H8 D3_pos D3_VDC D3_gain D3_bias D3_time D3_suppr D3_dark D3_Amps +#H9 XBPM_X_on XBPM_X_setPoint XBPM_X XBPM_Y_on XBPM_Y_setPoint XBPM_Y +#H10 PF4_thickAl PF4_thickTi PF4_trans PF4_bladeA1 PF4_bladeA2 PF4_bladeA3 PF4_bladeA4 +#H11 PF4_bladeB1 PF4_bladeB2 PF4_bladeB3 PF4_bladeB4 +#H12 sfo_sclr_auto +#H13 I0_VDC I0_gain I0_bias I0_time I0_suppr I0_dark I0_Amps +#H14 I00_VDC I00_gain I00_bias I00_time I00_suppr I00_dark I00_Amps +#H15 I_VDC I_gain I_bias I_time I_suppr I_dark I_Amps +#H16 HSC1t HSC1l HSC1b HSC1r HSC1h HSC1v HSC1h0 HSC1v0 +#H17 HSC2t HSC2l HSC2b HSC2r HSC2h HSC2v HSC2h0 HSC2v0 +#H18 HSC3t HSC3l HSC3b HSC3r HSC3h HSC3v HSC3h0 HSC3v0 +#H19 SFO540_i0 SFO540_i1 SFO540_i2 SFO540_i3 SFO540_i4 SFO540_i5 SFO540_i6 SFO540_i7 +#H20 SFO540_i8 SFO540_i9 SFO540_i10 SFO540_i11 SFO540_i12 SFO540_i13 SFO540_i14 SFO540_i15 +#H21 SFO540_o0 SFO540_o1 SFO540_o2 SFO540_o3 +#H22 SFOk2k_1 SFOk2k_2 SFOk2k_3 SFOk2k_4 SFOk2k-5 SFOk2k_6 SFOk2k_7 SFOk2k_8 SFOk2k_9 SFOk2k_10 +#O0 Delta Theta Chi Phi Mu Gamma wh-slit top wh-slit left +#O1 wh-slit bottom wh-slit right DCM theta Motor 5 Motor 6 Motor 7 Motor 8 Motor 9 +#O2 Motor 10 Motor 11 Motor 12 Motor 13 Motor 14 Motor 15 mono-sl top mono-sl left +#O3 mono-sl bottom mono-sl right GammaScrew baffle Table X Table Y1 Table Y2 Table Y3 +#O4 33ide:m58:c1:m5 33ide:m58:c1:m6 33ide:m58:c1:m7 33ide:m58:c1:m8 ion_ch_vert +#C Sun Feb 17 16:13:28 2013. Globals saved in "/home/33id/data/junelee/20130213/LNO1/autosave/spec_autosave20130217_161328.sav". +#U test of issue #133: support user control line "#U " - appears in header section + +#S 52 hklscan 0.5 0.5 0.5 0.5 1.2 1.8 120 1 +#D Sun Feb 17 20:49:00 2013 +#T 1 (clock) +#G0 12 0 1 -0.002814275157 0.007740448308 0.9999660821 0 0 0 0 0 0 600 0 1 1 143.6 +#G1 3.905 3.905 3.905 90 90 90 1.609010322 1.609010322 1.609010322 90 90 90 0 0 2 3 0 3 0.003 90 0.5799999712 239.9999477 12.102 12.9945 47.18 90 0.5799999712 239.9999477 21.77425 15.7375 0.8265616267 0.8265616267 +#G3 1.207702707 1.248454819 0.002095582696 -1.485612421 0.9118074731 0.003241829804 -0.0173752388 0.02282507942 1.651530555 +#G4 2.998037021 0.002160676878 2.996606618 0.8265616267 21.73024942 15.73707597 66.518375 59.48580986 -94.28612051 -0.5799999712 -239.9999477 0 0 0 -92.49362136 0 0 0 0 0 0 -180 -180 -180 -180 -180 -180 0 +#Q 2.99804 0.00216068 2.99661 +#P0 47.14125 90.089 0.58 239.94275 21.73025 15.7375 0.8000406 1.2 +#P1 0.79996698 0.49999998 7.5737788 0 0 0 0 0 +#P2 0 0 0 0 0 0 4.9999698 2.4999625 +#P3 -0.10019175 2.5002594 163.5625 34.92 -2.55002 -87.37856 -101.46268 -70.889 +#P4 87.99997 2.1800002 92.08 -0.00159 12.55028 +#UIM Image header information from areaDetector +#UIM UIMR: ROI information, UIMC: image counter setup +#UIM Center pixel: 159 157 +#UIMR Name minX sizeX minY sizeY BgdWidth +#UIMR1 diffuse 155 9 153 9 0 +#UIMR2 peak 144 30 142 30 0 +#UIMR3 stop 119 80 117 80 0 +#UIMR4 bkg 79 160 77 160 0 +#UIMC counter STATS# Value-PV +#UIMC1 imtot 5 Total +#UIMC2 immax 5 MaxValue +#UIMC3 imroi1 1 Net +#UIMC4 imroi2 2 Net +#UIMC5 imroi3 3 Net +#UIMC6 imroi4 4 Net +#UIMC7 imsca1 1 MaxValue +#UIMC8 imsca2 2 MaxValue +#UIMC9 imsca3 3 MaxValue +#UIMC10 imsca4 4 MaxValue +#C JL124_1 +#V0 102.139 8 1 4 1 1 1 988.066 +#V1 14.9994 15.2362 0.0110617 +#V2 14.2165 15.2362 3 0.00569699 0.0110617 +#V3 -3.22609 1 14.9994 0.826595 7.57378 7.57407 -15.0001 +#V4 1 1 -0.0151907 -10 10 4.19292 +#V5 0 -0.00499686 0 +#V6 2.49998 19.5 3 +#V7 2.38249 26.209 3 +#V8 1 -1 1.0e+10 0 7 0 0 -1e-10 +#V9 1 -0.585 -0.58421 1 0.52 0.519408 +#V10 0 0 1 0 0 0 0 +#V11 0 0 0 0 +#V12 AutoCount +#V13 0 1000000 10 us 0 4.846e-09 0 0 +#V14 0 1000000 30 us 0 1.103e-09 0 0 +#V15 0 1000000000 30 us 0 8.7e-11 0 0 +#V16 1.05 0.8 -0.55 -0.6 0.5 0.2 -0.7 0.8 +#V17 2.2 2.1 0.8 0.9 3 3 -0.6 0.7 +#V18 10 10 10 10 20 20 0 0 +#V19 -0.01221 -0.021978 -0.031746 -0.041514 -0.031746 -0.031746 -0.031746 -0.026862 +#V20 -0.031746 -0.031746 -0.046398 0.00732601 -0.031746 -0.046398 -0.031746 -0.168498 +#V21 0 0 0 0 +#V22 0 0 0 0 0 0 0 0 0 +#N 31 +#L H K L Delta Theta Chi Phi Mu Gamma Epoch clock I00 PIN scint ctr5 ctr6 ctr7 filters trans corrdet imtot immax imroi2 imroi3 imroi4 imsca1 imsca2 imsca3 imsca4 I0 imroi1 +0.5 0.5 1.2 9.5635 108.86025 0.58 -120 8.844 6.189 17745 5.0000002 1670929 0 0 0 0 0 0 1 0.013510845 1758786 223 158397 984078 1756324 218 218 223 223 1038203 14027 +0.5 0.5 1.205 9.56425 108.85525 0.58 -120 8.8715 6.2235 17751 5.0000002 1673119 0 0 0 0 0 0 1 0.013781481 1766743 235 159828 989398 1764245 204 214 235 235 1041978 14360 +0.5 0.5 1.21 9.56475 108.85025 0.58 -120 8.899 6.2595 17758 5.0000002 1670875 0 0 0 0 0 0 1 0.013713387 1769617 257 159286 989557 1767131 201 216 257 257 1040589 14270 +0.5 0.5 1.215 9.56525 108.84525 0.58 -120 8.926375 6.2955 17764 5.0000002 1673189 0 0 0 0 0 0 1 0.014028728 1821319 237 163301 1015892 1818642 213 223 237 237 1043359 14637 +0.5 0.5 1.22 9.56575 108.84025 0.58 -120 8.954 6.3305 17771 5.0000002 1673473 0 0 0 0 0 0 1 0.013536854 1719528 225 154815 960908 1717035 202 218 225 225 1039828 14076 +0.5 0.5 1.225 9.56625 108.8355 0.58 -120 8.9815 6.365 17777 5.0000002 1669287 0 0 0 0 0 0 1 0.013320269 1724673 231 155651 965023 1722207 210 220 226 231 1038943 13839 +0.5 0.5 1.23 9.56675 108.8305 0.58 -120 9.009 6.401 17783 5.0000002 1670414 0 0 0 0 0 0 1 0.013581104 1737781 229 157033 972664 1735283 212 216 229 229 1042846 14163 +0.5 0.5 1.235 9.5675 108.8255 0.58 -120 9.036625 6.436 17790 5.0000002 1670564 0 0 0 0 0 0 1 0.01359778 1732889 230 156470 971420 1730453 226 226 230 230 1044141 14198 +0.5 0.5 1.24 9.568 108.82075 0.58 -120 9.064125 6.4705 17796 5.0000002 1668472 0 0 0 0 0 0 1 0.013437155 1726455 232 155688 967355 1724061 199 213 232 232 1039729 13971 +0.5 0.5 1.245 9.5685 108.81575 0.58 -120 9.09175 6.5065 17802 5.0000002 1672918 0 0 0 0 0 0 1 0.013640844 1739266 229 156943 974026 1736829 203 215 229 229 1045903 14267 +0.5 0.5 1.25 9.569 108.811 0.58 -120 9.119375 6.5415 17809 5.0000002 1669778 0 0 0 0 0 0 1 0.013372907 1708158 223 154315 955962 1705818 199 212 223 223 1036723 13864 +0.5 0.5 1.255 9.56975 108.80625 0.58 -120 9.147 6.576 17815 5.0000002 1673201 0 0 0 0 0 0 1 0.0137012 1729979 229 155899 968497 1727629 209 212 225 229 1044799 14315 +0.5 0.5 1.26 9.57025 108.8015 0.58 -120 9.17475 6.612 17821 5.0000002 1674947 0 0 0 0 0 0 1 0.013374305 1725323 223 154567 965778 1722910 201 213 223 223 1044615 13971 +0.5 0.5 1.265 9.57075 108.79675 0.58 -120 9.202375 6.647 17828 5.0000002 1673459 0 0 0 0 0 0 1 0.012790736 1647367 218 148185 922260 1645034 190 210 218 218 1040206 13305 +0.5 0.5 1.27 9.5715 108.792 0.58 -120 9.230125 6.6815 17834 5.0000002 1674226 0 0 0 0 0 0 1 0.013371371 1708323 227 154192 957728 1705921 210 225 227 227 1042825 13944 +0.5 0.5 1.275 9.572 108.78725 0.58 -120 9.257875 6.7175 17841 5.0000002 1673740 0 0 0 0 0 0 1 0.013353548 1713602 224 155207 961280 1711219 209 224 224 224 1042794 13925 +0.5 0.5 1.28 9.5725 108.7825 0.58 -120 9.285625 6.7525 17847 5.0000002 1673979 0 0 0 0 0 0 1 0.013296335 1713979 222 155146 961089 1711584 199 221 222 222 1042919 13867 +0.5 0.5 1.285 9.57325 108.77775 0.58 -120 9.313375 6.787 17853 5.0000002 1673313 0 0 0 0 0 0 1 0.01339954 1710246 222 154800 959313 1707930 208 211 222 222 1041379 13954 +0.5 0.5 1.29 9.57375 108.77325 0.58 -120 9.341125 6.822 17860 5.0000002 1672936 0 0 0 0 0 0 1 0.013452479 1703517 230 154137 954245 1701163 205 219 230 230 1039511 13984 +0.5 0.5 1.295 9.57425 108.7685 0.58 -120 9.369 6.858 17866 5.0000002 1675226 0 0 0 0 0 0 1 0.013381488 1713693 220 154519 959550 1711254 201 216 220 220 1045250 13987 +0.5 0.5 1.3 9.575 108.76375 0.58 -120 9.39675 6.8925 17872 5.0000002 1674964 0 0 0 0 0 0 1 0.013346464 1717237 230 155870 963557 1714834 218 218 230 230 1044846 13945 +0.5 0.5 1.305 9.5755 108.75925 0.58 -120 9.424625 6.9275 17879 5.0000002 1673394 0 0 0 0 0 0 1 0.01335524 1709080 230 154687 959221 1706731 212 216 230 230 1041389 13908 +0.5 0.5 1.31 9.576 108.75475 0.58 -120 9.4525 6.9625 17885 5.0000002 1673473 0 0 0 0 0 0 1 0.013347977 1713378 226 154939 961262 1711043 203 220 226 226 1042555 13916 +0.5 0.5 1.315 9.57675 108.75 0.58 -120 9.480375 6.997 17892 5.0000002 1673141 0 0 0 0 0 0 1 0.013251618 1709241 229 154762 959923 1706874 202 215 229 229 1041986 13808 +0.5 0.5 1.32 9.57725 108.7455 0.58 -120 9.50825 7.033 17898 5.0000002 1667775 0 0 0 0 0 0 1 0.01304221 1658190 222 150948 930650 1655849 196 212 222 222 1031497 13453 +0.5 0.5 1.325 9.578 108.741 0.58 -120 9.53625 7.068 17904 5.0000002 1671248 0 0 0 0 0 0 1 0.013378409 1706072 229 154511 957164 1703725 216 216 229 229 1039735 13910 +0.5 0.5 1.33 9.5785 108.7365 0.58 -120 9.564125 7.1025 17911 5.0000002 1673987 0 0 0 0 0 0 1 0.013349605 1711282 227 154660 959376 1708862 204 212 227 227 1042278 13914 +0.5 0.5 1.335 9.57925 108.732 0.58 -120 9.592125 7.1375 17917 5.0000002 1675919 0 0 0 0 0 0 1 0.013292961 1710303 227 155304 959204 1707869 202 214 227 227 1042958 13864 +0.5 0.5 1.34 9.57975 108.7275 0.58 -120 9.620125 7.1725 17923 5.0000002 1677968 0 0 0 0 0 0 1 0.013178222 1714986 222 155271 962660 1712653 208 210 222 222 1044754 13768 +0.5 0.5 1.345 9.5805 108.723 0.58 -120 9.648125 7.207 17930 5.0000002 1670490 0 0 0 0 0 0 1 0.013121667 1708478 224 154501 958965 1706049 192 212 224 224 1037673 13616 +0.5 0.5 1.35 9.581 108.7185 0.58 -120 9.676125 7.242 17936 5.0000002 1673428 0 0 0 0 0 0 1 0.01311587 1713736 232 155432 962262 1711285 201 214 232 232 1043240 13683 +0.5 0.5 1.355 9.58175 108.71425 0.58 -120 9.704125 7.2765 17943 5.0000002 1673571 0 0 0 0 0 0 1 0.013546262 1721983 230 156046 965916 1719587 198 211 230 230 1043166 14131 +0.5 0.5 1.36 9.58225 108.70975 0.58 -120 9.73225 7.3115 17949 5.0000002 1674453 0 0 0 0 0 0 1 0.013306315 1715145 218 155752 962989 1712663 208 217 218 218 1042738 13875 +0.5 0.5 1.365 9.583 108.7055 0.58 -120 9.76025 7.3465 17955 5.0000002 1674041 0 0 0 0 0 0 1 0.013437477 1715161 231 155701 964164 1712826 219 219 231 231 1042532 14009 +0.5 0.5 1.37 9.5835 108.701 0.58 -120 9.788375 7.381 17962 5.0000002 1674471 0 0 0 0 0 0 1 0.013659209 1722098 241 156807 966909 1719674 206 222 241 241 1044570 14268 +0.5 0.5 1.375 9.58425 108.69675 0.58 -120 9.8165 7.416 17968 5.0000002 1673395 0 0 0 0 0 0 1 0.013541489 1715560 241 155165 962766 1713202 199 222 241 241 1042426 14116 +0.5 0.5 1.38 9.585 108.69225 0.58 -120 9.844625 7.451 17974 5.0000002 1676359 0 0 0 0 0 0 1 0.013555808 1716798 225 156228 963491 1714342 205 216 225 225 1044792 14163 +0.5 0.5 1.385 9.5855 108.688 0.58 -120 9.87275 7.4855 17981 5.0000002 1675889 0 0 0 0 0 0 1 0.013288384 1705188 233 154607 958425 1702746 210 210 233 233 1042941 13859 +0.5 0.5 1.39 9.58625 108.68375 0.58 -120 9.901 7.5205 17987 5.0000002 1677876 0 0 0 0 0 0 1 0.013552171 1716436 234 156331 963272 1713982 208 214 234 234 1046253 14179 +0.5 0.5 1.395 9.58675 108.6795 0.58 -120 9.929125 7.555 17994 5.0000002 1675697 0 0 0 0 0 0 1 0.013606178 1707875 225 155206 957739 1705326 203 209 225 225 1044011 14205 +0.5 0.5 1.4 9.5875 108.67525 0.58 -120 9.957375 7.59 18000 5.0000002 1676737 0 0 0 0 0 0 1 0.013305199 1715359 221 155693 962502 1712842 194 213 221 221 1043577 13885 +0.5 0.5 1.405 9.58825 108.671 0.58 -120 9.9855 7.625 18006 5.0000002 1675671 0 0 0 0 0 0 1 0.013537316 1724483 233 157350 969300 1721922 205 218 233 233 1043486 14126 +0.5 0.5 1.41 9.58875 108.66675 0.58 -120 10.01375 7.6595 18013 5.0000002 1676328 0 0 0 0 0 0 1 0.01361469 1729988 234 157134 971490 1727617 204 216 234 234 1043799 14211 +0.5 0.5 1.415 9.5895 108.6625 0.58 -120 10.042 7.6945 18019 5.0000002 1677791 0 0 0 0 0 0 1 0.01349297 1730475 231 158112 972325 1727961 208 214 231 231 1043951 14086 +0.5 0.5 1.42 9.59025 108.65825 0.58 -120 10.070375 7.7295 18025 5.0000002 1678447 0 0 0 0 0 0 1 0.0137881 1741868 233 158773 979239 1739492 207 227 233 233 1046192 14425 +0.5 0.5 1.425 9.591 108.654 0.58 -120 10.098625 7.764 18032 5.0000002 1676488 0 0 0 0 0 0 1 0.013702208 1742708 237 158202 977705 1740083 212 226 237 237 1041511 14271 +0.5 0.5 1.43 9.5915 108.65 0.58 -120 10.126875 7.799 18038 5.0000002 1678318 0 0 0 0 0 0 1 0.01370032 1752903 232 158739 984288 1750437 209 219 232 232 1045669 14326 +0.5 0.5 1.435 9.59225 108.64575 0.58 -120 10.15525 7.8325 18045 5.0000002 1679298 0 0 0 0 0 0 1 0.013670062 1753749 238 159948 985206 1751264 204 224 238 238 1045789 14296 +0.5 0.5 1.44 9.593 108.6415 0.58 -120 10.183625 7.8675 18051 5.0000002 1680380 0 0 0 0 0 0 1 0.013850325 1753936 234 160139 985637 1751362 204 215 234 234 1044813 14471 +0.5 0.5 1.445 9.59375 108.6375 0.58 -120 10.212 7.9025 18057 5.0000002 1679602 0 0 0 0 0 0 1 0.013694096 1759729 240 160237 988108 1757226 213 214 240 240 1045341 14315 +0.5 0.5 1.45 9.59425 108.63325 0.58 -120 10.240375 7.937 18064 5.0000002 1680346 0 0 0 0 0 0 1 0.01392073 1768470 229 161048 992226 1765932 219 220 229 229 1047287 14579 +0.5 0.5 1.455 9.595 108.62925 0.58 -120 10.26875 7.972 18070 5.0000002 1678548 0 0 0 0 0 0 1 0.013828098 1769066 231 161204 993751 1766513 207 225 231 231 1042515 14416 +0.5 0.5 1.46 9.59575 108.62525 0.58 -120 10.297125 8.007 18076 5.0000002 1681089 0 0 0 0 0 0 1 0.01398416 1785775 240 162418 1001671 1783163 204 221 240 240 1048615 14664 +0.5 0.5 1.465 9.5965 108.621 0.58 -120 10.325625 8.0405 18083 5.0000002 1680550 0 0 0 0 0 0 1 0.013884348 1784063 244 162472 1000526 1781513 209 227 244 244 1046142 14525 +0.5 0.5 1.47 9.59725 108.617 0.58 -120 10.354 8.0755 18089 5.0000002 1677568 0 0 0 0 0 0 1 0.014006517 1775902 232 161542 995350 1773261 209 223 232 232 1040587 14575 +0.5 0.5 1.475 9.59775 108.613 0.58 -120 10.3825 8.11 18096 5.0000002 1678906 0 0 0 0 0 0 1 0.013954636 1791534 239 162768 1003793 1788980 203 225 239 239 1045674 14592 +0.5 0.5 1.48 9.5985 108.609 0.58 -120 10.411 8.145 18102 5.0000002 1676995 0 0 0 0 0 0 1 0.014226748 1787985 243 162540 1002076 1785437 221 221 243 243 1042965 14838 +0.5 0.5 1.485 9.59925 108.605 0.58 -120 10.4395 8.18 18108 5.0000002 1679065 0 0 0 0 0 0 1 0.014231116 1804658 249 164298 1011614 1801980 225 230 249 249 1046861 14898 +0.5 0.5 1.49 9.6 108.601 0.58 -120 10.468 8.2135 18115 5.0000002 1680808 0 0 0 0 0 0 1 0.013969773 1795250 237 163265 1006061 1792607 225 229 237 237 1047619 14635 +0.5 0.5 1.495 9.60075 108.597 0.58 -120 10.4965 8.2485 18121 5.0000002 1677594 0 0 0 0 0 0 1 0.014027187 1775460 239 161799 994518 1772857 234 234 239 239 1041834 14614 +0.5 0.5 1.5 9.6015 108.593 0.58 -120 10.525125 8.283 18127 5.0000002 1677709 0 0 0 0 0 0 1 0.014110451 1789869 238 162831 1002748 1787270 215 238 238 238 1043978 14731 +0.5 0.5 1.505 9.60225 108.589 0.58 -120 10.553625 8.318 18134 5.0000002 1677383 0 0 0 0 0 0 1 0.014227621 1794050 237 163256 1005433 1791394 221 228 237 237 1043393 14845 +0.5 0.5 1.51 9.603 108.585 0.58 -120 10.58225 8.3515 18140 5.0000002 1673220 0 0 0 0 0 0 1 0.014504627 1797781 238 163176 1006064 1795123 225 228 238 238 1039944 15084 +0.5 0.5 1.515 9.60375 108.58125 0.58 -120 10.610875 8.3865 18147 5.0000002 1676479 0 0 0 0 0 0 1 0.014129846 1806430 258 164372 1011649 1803694 209 232 258 258 1042545 14731 +0.5 0.5 1.52 9.6045 108.57725 0.58 -120 10.6395 8.4215 18153 5.0000002 1676165 0 0 0 0 0 0 1 0.014057373 1810537 245 165095 1013116 1807790 222 242 245 245 1042300 14652 +0.5 0.5 1.525 9.60525 108.57325 0.58 -120 10.668125 8.455 18159 5.0000002 1678961 0 0 0 0 0 0 1 0.014119097 1822146 247 165522 1019473 1819326 219 228 247 247 1044826 14752 +0.5 0.5 1.53 9.606 108.5695 0.58 -120 10.69675 8.49 18166 5.0000002 1678391 0 0 0 0 0 0 1 0.014340957 1826558 239 166553 1021908 1823844 224 228 239 239 1045258 14990 +0.5 0.5 1.535 9.60675 108.5655 0.58 -120 10.725375 8.525 18172 5.0000002 1676091 0 0 0 0 0 0 1 0.014463654 1824192 245 166799 1019482 1821406 219 227 245 245 1040885 15055 +0.5 0.5 1.54 9.6075 108.56175 0.58 -120 10.754125 8.5585 18178 5.0000002 1677313 0 0 0 0 0 0 1 0.014228512 1837129 255 166901 1027159 1834482 219 224 250 255 1044171 14857 +0.5 0.5 1.545 9.60825 108.55775 0.58 -120 10.78275 8.5935 18185 5.0000002 1675281 0 0 0 0 0 0 1 0.014593452 1836204 245 167784 1025864 1833504 214 229 245 245 1039987 15177 +0.5 0.5 1.55 9.609 108.554 0.58 -120 10.8115 8.627 18191 5.0000002 1676125 0 0 0 0 0 0 1 0.014538914 1848640 240 168450 1033159 1845926 225 235 240 240 1042994 15164 +0.5 0.5 1.555 9.60975 108.55025 0.58 -120 10.84025 8.662 18198 5.0000002 1677884 0 0 0 0 0 0 1 0.014432721 1854960 243 168653 1036902 1852302 218 229 243 243 1045333 15087 +0.5 0.5 1.56 9.6105 108.54625 0.58 -120 10.869 8.697 18204 5.0000002 1674583 0 0 0 0 0 0 1 0.014647376 1845546 258 167430 1031045 1842911 225 231 258 258 1039094 15220 +0.5 0.5 1.565 9.61125 108.5425 0.58 -120 10.89775 8.7305 18210 5.0000002 1676967 0 0 0 0 0 0 1 0.014712184 1862210 246 169144 1041248 1859485 224 229 246 246 1043217 15348 +0.5 0.5 1.57 9.612 108.53875 0.58 -120 10.9265 8.7655 18217 5.0000002 1677897 0 0 0 0 0 0 1 0.014933471 1871358 252 170569 1046299 1868756 223 247 252 252 1045102 15607 +0.5 0.5 1.575 9.61275 108.535 0.58 -120 10.95525 8.799 18223 5.0000002 1673168 0 0 0 0 0 0 1 0.014706393 1856435 248 167840 1035441 1853693 229 237 248 248 1036964 15250 +0.5 0.5 1.58 9.6135 108.53125 0.58 -120 10.984125 8.834 18230 5.0000002 1677574 0 0 0 0 0 0 1 0.01486278 1883832 244 171009 1051736 1881102 223 244 244 244 1044892 15530 +0.5 0.5 1.585 9.61425 108.5275 0.58 -120 11.013 8.869 18236 5.0000002 1676615 0 0 0 0 0 0 1 0.014714443 1875640 244 170060 1047082 1873009 219 227 244 244 1042785 15344 +0.5 0.5 1.59 9.615 108.52375 0.58 -120 11.04175 8.9025 18242 5.0000002 1676648 0 0 0 0 0 0 1 0.01456147 1888443 252 170665 1053272 1885669 217 230 252 252 1043782 15199 +0.5 0.5 1.595 9.616 108.52 0.58 -120 11.070625 8.9375 18249 5.0000002 1675901 0 0 0 0 0 0 1 0.014958999 1896050 251 172869 1057326 1893322 216 239 251 251 1043987 15617 +0.5 0.5 1.6 9.61675 108.51625 0.58 -120 11.0995 8.971 18255 5.0000002 1670668 0 0 0 0 0 0 1 0.01464121 1864649 249 169157 1041304 1862064 226 240 249 249 1033043 15125 +0.5 0.5 1.605 9.6175 108.5125 0.58 -120 11.128375 9.006 18262 5.0000002 1674756 0 0 0 0 0 0 1 0.014849135 1903838 260 173382 1060810 1901159 218 243 260 260 1042889 15486 +0.5 0.5 1.61 9.61825 108.50875 0.58 -120 11.15725 9.0395 18268 5.0000002 1675224 0 0 0 0 0 0 1 0.015000773 1903642 260 173258 1062228 1900853 222 233 260 260 1042013 15631 +0.5 0.5 1.615 9.619 108.505 0.58 -120 11.18625 9.0745 18274 5.0000002 1675705 0 0 0 0 0 0 1 0.015364394 1912279 247 174229 1067019 1909587 234 235 247 247 1043842 16038 +0.5 0.5 1.62 9.62 108.5015 0.58 -120 11.215125 9.108 18281 5.0000002 1678187 0 0 0 0 0 0 1 0.014876676 1920665 250 173957 1069563 1917977 224 236 249 250 1043916 15530 +0.5 0.5 1.625 9.62075 108.49775 0.58 -120 11.244125 9.143 18287 5.0000002 1676853 0 0 0 0 0 0 1 0.01502557 1922215 258 173964 1070514 1919435 225 249 258 258 1042423 15663 +0.5 0.5 1.63 9.6215 108.494 0.58 -120 11.273 9.177 18294 5.0000002 1677575 0 0 0 0 0 0 1 0.015388604 1937614 258 176208 1081389 1934967 233 239 258 258 1045254 16085 +0.5 0.5 1.635 9.62225 108.4905 0.58 -120 11.302 9.2115 18300 5.0000002 1676879 0 0 0 0 0 0 1 0.014943368 1942875 262 177023 1082430 1940124 228 254 262 262 1045882 15629 +0.5 0.5 1.64 9.62325 108.48675 0.58 -120 11.331 9.2455 18306 5.0000002 1677278 0 0 0 0 0 0 1 0.015183468 1947285 259 177091 1083496 1944599 235 235 259 259 1044623 15861 +0.5 0.5 1.645 9.624 108.48325 0.58 -120 11.36 9.2805 18313 5.0000002 1677038 0 0 0 0 0 0 1 0.015251513 1955066 253 176680 1088971 1952406 236 241 253 253 1044421 15929 +0.5 0.5 1.65 9.62475 108.4795 0.58 -120 11.389 9.314 18319 5.0000002 1677777 0 0 0 0 0 0 1 0.015640453 1952639 255 177948 1087513 1949948 238 247 255 255 1044471 16336 +0.5 0.5 1.655 9.62575 108.476 0.58 -120 11.418125 9.349 18326 5.0000002 1676296 0 0 0 0 0 0 1 0.015333591 1954267 260 177566 1088176 1951550 235 241 260 260 1042939 15992 +0.5 0.5 1.66 9.6265 108.4725 0.58 -120 11.447125 9.3825 18332 5.0000002 1677502 0 0 0 0 0 0 1 0.015367228 1963510 253 177623 1091189 1960837 229 245 253 253 1044105 16045 +0.5 0.5 1.665 9.62725 108.46875 0.58 -120 11.47625 9.4165 18338 5.0000002 1680643 0 0 0 0 0 0 1 0.015370244 1970212 252 179073 1095051 1967545 236 245 252 252 1046958 16092 +0.5 0.5 1.67 9.62825 108.46525 0.58 -120 11.50525 9.4515 18345 5.0000002 1679142 0 0 0 0 0 0 1 0.015605448 1972729 260 179014 1097041 1969998 244 244 255 260 1043674 16287 +0.5 0.5 1.675 9.629 108.46175 0.58 -120 11.534375 9.485 18351 5.0000002 1682913 0 0 0 0 0 0 1 0.015431686 1972434 264 178773 1097767 1969729 243 258 264 264 1047844 16170 +0.5 0.5 1.68 9.62975 108.458 0.58 -120 11.5635 9.52 18358 5.0000002 1678058 0 0 0 0 0 0 1 0.015534338 1976944 254 178670 1099100 1974271 237 244 254 254 1043881 16216 +0.5 0.5 1.685 9.63075 108.4545 0.58 -120 11.592625 9.5535 18364 5.0000002 1677085 0 0 0 0 0 0 1 0.015498395 1976224 261 179081 1099755 1973477 241 248 261 261 1043721 16176 +0.5 0.5 1.69 9.6315 108.451 0.58 -120 11.62175 9.5875 18370 5.0000002 1677382 0 0 0 0 0 0 1 0.015321632 1978183 270 178911 1099677 1975501 229 250 270 270 1043492 15988 +0.5 0.5 1.695 9.6325 108.4475 0.58 -120 11.650875 9.6225 18377 5.0000002 1674703 0 0 0 0 0 0 1 0.015884726 2009757 265 182129 1116700 2007036 236 250 265 265 1041063 16537 +0.5 0.5 1.7 9.63325 108.444 0.58 -120 11.680125 9.656 18383 5.0000002 1675093 0 0 0 0 0 0 1 0.015722861 2008603 357 182760 1116667 2005877 233 243 357 357 1045484 16438 +0.5 0.5 1.705 9.63425 108.4405 0.58 -120 11.70925 9.6895 18390 5.0000002 1673115 0 0 0 0 0 0 1 0.015504195 2005206 296 182285 1113910 2002516 233 261 296 296 1044556 16195 +0.5 0.5 1.71 9.635 108.437 0.58 -120 11.7385 9.7245 18396 5.0000002 1671067 0 0 0 0 0 0 1 0.015642206 2005144 271 182480 1112233 2002338 231 271 271 271 1041605 16293 +0.5 0.5 1.715 9.63575 108.4335 0.58 -120 11.767625 9.7585 18402 5.0000002 1676102 0 0 0 0 0 0 1 0.016666683 2014193 401 183344 1118361 2011426 401 401 401 401 1044419 17407 +0.5 0.5 1.72 9.63675 108.43 0.58 -120 11.796875 9.792 18409 5.0000002 1676244 0 0 0 0 0 0 1 0.015846622 2013243 289 182499 1117454 2010408 252 289 289 289 1044008 16544 +0.5 0.5 1.725 9.6375 108.4265 0.58 -120 11.826125 9.827 18415 5.0000002 1676437 0 0 0 0 0 0 1 0.015785326 2017613 264 182623 1119352 2014891 253 261 264 264 1043184 16467 +0.5 0.5 1.73 9.6385 108.42325 0.58 -120 11.855375 9.861 18422 5.0000002 1677696 0 0 0 0 0 0 1 0.015546068 2018794 290 182550 1119331 2016080 235 278 278 290 1043029 16215 +0.5 0.5 1.735 9.6395 108.41975 0.58 -120 11.884625 9.8945 18428 5.0000002 1679247 0 0 0 0 0 0 1 0.016042243 2022595 264 183241 1122428 2019851 243 259 262 264 1044804 16761 +0.5 0.5 1.74 9.64025 108.41625 0.58 -120 11.913875 9.9295 18434 5.0000002 1679503 0 0 0 0 0 0 1 0.015854015 2032214 258 183591 1126924 2029283 240 251 256 258 1045918 16582 +0.5 0.5 1.745 9.64125 108.41275 0.58 -120 11.94325 9.963 18441 5.0000002 1677828 0 0 0 0 0 0 1 0.015738689 2024420 262 183104 1122603 2021585 239 256 262 262 1041764 16396 +0.5 0.5 1.75 9.642 108.4095 0.58 -120 11.9725 9.997 18447 5.0000002 1677932 0 0 0 0 0 0 1 0.015792849 2023898 260 183694 1121491 2021099 230 248 260 260 1043257 16476 +0.5 0.5 1.755 9.643 108.406 0.58 -120 12.001875 10.0305 18454 5.0000002 1677678 0 0 0 0 0 0 1 0.015758387 2030472 266 183605 1124549 2027723 230 247 263 266 1043952 16451 +0.5 0.5 1.76 9.64375 108.4025 0.58 -120 12.031125 10.0655 18460 5.0000002 1677701 0 0 0 0 0 0 1 0.015884053 2035359 264 184259 1127152 2032552 237 242 264 264 1044066 16584 +0.5 0.5 1.765 9.64475 108.39925 0.58 -120 12.0605 10.0995 18466 5.0000002 1677751 0 0 0 0 0 0 1 0.015958608 2037812 269 183951 1127533 2034985 242 251 260 269 1044076 16662 +0.5 0.5 1.77 9.64575 108.39575 0.58 -120 12.089875 10.133 18473 5.0000002 1677861 0 0 0 0 0 0 1 0.016000737 2036047 273 184266 1127561 2033248 243 250 273 273 1041952 16672 +0.5 0.5 1.775 9.6465 108.3925 0.58 -120 12.11925 10.167 18479 5.0000002 1676792 0 0 0 0 0 0 1 0.015911529 2026005 267 183083 1122261 2023265 243 251 267 267 1039435 16539 +0.5 0.5 1.78 9.6475 108.389 0.58 -120 12.148625 10.202 18486 5.0000002 1677679 0 0 0 0 0 0 1 0.015869234 2052574 269 185028 1135030 2049773 241 250 268 269 1045104 16585 +0.5 0.5 1.785 9.6485 108.38575 0.58 -120 12.178 10.2355 18492 5.0000002 1675625 0 0 0 0 0 0 1 0.016092106 2038781 270 184887 1128544 2035960 237 246 263 270 1041753 16764 +0.5 0.5 1.79 9.64925 108.3825 0.58 -120 12.2075 10.2695 18498 5.0000002 1677402 0 0 0 0 0 0 1 0.016047204 2084036 272 188368 1152923 2081230 243 255 272 272 1045665 16780 +0.5 0.5 1.795 9.65025 108.379 0.58 -120 12.236875 10.303 18505 5.0000002 1675161 0 0 0 0 0 0 1 0.015971457 2034065 270 184435 1127464 2031380 236 270 270 270 1041796 16639 +0.5 0.5 1.8 9.65125 108.37575 0.58 -120 12.266375 10.338 18511 5.0000002 1674891 0 0 0 0 0 0 1 0.016092557 2047441 264 184903 1132089 2044594 238 251 264 264 1040046 16737 +#C Sun Feb 17 21:22:00 2013. Globals saved in "/home/33id/data/junelee/20130213/LNO1/autosave/spec_autosave20130217_212200.sav". diff --git a/examples/hkl_data/hkl.txt b/examples/archive/hkl_data/hkl.txt similarity index 100% rename from examples/hkl_data/hkl.txt rename to examples/archive/hkl_data/hkl.txt diff --git a/examples/hkl_data/motors.txt b/examples/archive/hkl_data/motors.txt similarity index 100% rename from examples/hkl_data/motors.txt rename to examples/archive/hkl_data/motors.txt diff --git a/examples/hkl_data/spec_session.txt b/examples/archive/hkl_data/spec_session.txt similarity index 100% rename from examples/hkl_data/spec_session.txt rename to examples/archive/hkl_data/spec_session.txt diff --git a/examples/hkl_data/ub.txt b/examples/archive/hkl_data/ub.txt similarity index 100% rename from examples/hkl_data/ub.txt rename to examples/archive/hkl_data/ub.txt diff --git a/examples/hkl_test.py.out_of_date b/examples/archive/hkl_test.py similarity index 100% rename from examples/hkl_test.py.out_of_date rename to examples/archive/hkl_test.py diff --git a/examples/archive/spec_report.py b/examples/archive/spec_report.py new file mode 100644 index 00000000..cc34b33d --- /dev/null +++ b/examples/archive/spec_report.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python + +""" +Report the diffractometer structure of SPEC data files +""" + +import os +import pyRestTable +from spec2nexus.spec import SpecDataFile + + +SUBDIR = os.path.join(os.path.dirname(__file__), "hkl_data") +SUBDIR = os.path.join("/tmp", "spec_data") + + +def show_ref(refl): + s = ( + f" {refl.h:.2f}" + f" {refl.k:.2f}" + f" {refl.l:.2f}" + ) + s = f"({s.strip()}) " + s += f" wavelength={refl.wavelength:.4f}" + for k, v in refl.angles.items(): + s += f" {k}={v:.4f}" + return s + + +def show_UB(ub): + s = [] + for row in ub: + r = [ + f"{v:2.4f}" + for v in row + ] + s.append(" ".join(r)) + return "\n".join(s) + + +def report(specfile): + specfile = SpecDataFile(specfile) + + # search for scans with hkl in the name + hklscans = [ + specscan + for specscan in specfile.getScanCommands() + if specscan.find("hkl") > 0 + ] + if len(hklscans): + scan_number = int(hklscans[0].split()[1]) + else: + scan_number = specfile.getLastScanNumber() + + specscan = specfile.getScan(scan_number) + + spec_d = specscan.diffractometer + spec_d.UB = spec_d.geometry_parameters["ub_matrix"][2] + + terms = { + "SPEC file": specfile.specFile, + "scan #": specscan.scanNum, + "SPEC scanCmd": specscan.scanCmd, + "geometry": spec_d.geometry_name, + "mode": spec_d.mode, + "lattice": spec_d.lattice, + "wavelength": spec_d.wavelength, + "reflection 1": show_ref(spec_d.reflections[0]), + "reflection 2": show_ref(spec_d.reflections[1]), + "[UB]": show_UB(spec_d.UB), + } + tbl = pyRestTable.Table() + tbl.labels = "term value".split() + for k, v in terms.items(): + tbl.addRow((k, v)) + print(tbl) + + +def main(): + for item in sorted(os.listdir(SUBDIR)): + print(item, "\n" + "="*40) + try: + report(os.path.join(SUBDIR, item)) + except Exception as exc: + print(item, exc) + print("") + + +if __name__ == "__main__": + main() diff --git a/examples/archive/tardis_example.ipynb b/examples/archive/tardis_example.ipynb new file mode 100644 index 00000000..3aeab98c --- /dev/null +++ b/examples/archive/tardis_example.ipynb @@ -0,0 +1,740 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TARDIS Configuration\n", + "\n", + "* using the E6C geometry from libhkl\n", + " * @cmazzoli found that, in this geometry, with the \"lifting_detector_mu\" mode, the following mapping applies:\n", + " \n", + "| libhkl | TARDIS |\n", + "| :---: | :---: |\n", + "| mu | theta |\n", + "| gamma | delta |\n", + "| delta | gamma |\n", + "| phi | None |\n", + "| chi | None |\n", + "| omega | None |\n", + "\n", + "* The diffractometer geometry with angle and axis definitions are depicted below\n", + "\n", + "<--\n", + "< img src=\"6c_diffractometer.png\" width=480 height=320 >\n", + "< img src=\"6c_angle_definitions.png\" width=480 height=320 >\n", + "-->\n", + "\n", + "* *libhkl* documentation of the [E6C (Eulerian 6-circle) geometry](https://people.debian.org/~picca/hkl/hkl.html#orge5e0490)\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Begin by instantiating a calculation engine of the appropriate geometry, and configuring its mode as __lifting_detector_mu__ " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Available modes = ['bissector_vertical', 'constant_omega_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_phi', 'lifting_detector_omega', 'lifting_detector_mu', 'double_diffraction_vertical', 'bissector_horizontal', 'double_diffraction_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal', 'constant_mu_horizontal']\n\nphysical axes = OrderedDict([('mu', 0.0), ('omega', 0.0), ('chi', 0.0), ('phi', 0.0), ('gamma', 0.0), ('delta', 0.0)])\n\npseudo axes = OrderedDict([('h', 0.0), ('k', 0.0), ('l', 0.0)])\n" + ] + } + ], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.calc import CalcE6C\n", + "\n", + "tardis_calc = CalcE6C()\n", + "\n", + "# what modes are available?\n", + "print('Available modes =', tardis_calc.engine.modes)\n", + "print('\\nphysical axes =', tardis_calc.physical_axes)\n", + "print('\\npseudo axes =', tardis_calc.pseudo_axes)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tardis_calc.engine.mode = 'lifting_detector_mu'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, seed the calculation engine with a parameterized sample and wavelength (or energy).\n", + "\n", + "**NOTE**: length units are in Angstrom, angles are in degrees, and energy is in keV." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "HklSample(name='sample1', lattice=LatticeTuple(a=9.069, b=9.069, c=10.39, alpha=90.0, beta=90.0, gamma=119.99999999999999), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n [0., 1., 0.],\n [0., 0., 1.]]), UB=array([[ 7.99999720e-01, 3.99999860e-01, -6.41365809e-17],\n [ 0.00000000e+00, 6.92820080e-01, -6.41365809e-17],\n [ 0.00000000e+00, 0.00000000e+00, 6.04733908e-01]]), reflections=[], reflection_measured_angles=array([], shape=(0, 0), dtype=float64), reflection_theoretical_angles=array([], shape=(0, 0), dtype=float64))\n" + ] + } + ], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "# lattice cell lengths are in Angstrom, angles are in degrees\n", + "lattice = Lattice(a=9.069, b=9.069, c=10.390, alpha=90.0, beta=90.0, gamma=120.0)\n", + "sample = tardis_calc.new_sample('sample1', lattice=lattice)\n", + "\n", + "print(sample)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Energy = 0.7691422970508319 keV\n" + ] + } + ], + "source": [ + "tardis_calc.wavelength = 1.61198 # in Angstrom\n", + "\n", + "# just to check\n", + "print('Energy =', tardis_calc.energy, 'keV')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, apply constraints appropriate for TARDIS' geometry. This includes setting limits on the acceptable ranges of motion, initial (and constant!) values, and whether or not a particular axis should be factored into the fitting function that produces the forward and inverse solutions.\n", + "\n", + "**NOTE**: physical motors should be checked that limits are in place prior to initiating any motion. Note also that none of the calculations below are associated with any physical motors, and that there is no connection between \"limit\" values used in the calculation, and soft-limit values that may be present in a control system for physical motors." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Theta\n", + "mu = tardis_calc['mu']\n", + "mu.limits = (-181, 181)\n", + "mu.value = 0\n", + "mu.fit = True\n", + "\n", + "# we don't have it. Fix to 0\n", + "phi = tardis_calc['phi']\n", + "phi.limits = (0, 0)\n", + "phi.value = 0\n", + "phi.fit = False\n", + "\n", + "# we don't have it. Fix to 0\n", + "chi = tardis_calc['chi']\n", + "chi.limits = (0, 0)\n", + "chi.value = 0\n", + "chi.fit = False\n", + "\n", + "# we don't have it!! Fix to 0\n", + "omega = tardis_calc['omega']\n", + "omega.limits = (0, 0)\n", + "omega.value = 0\n", + "omega.fit = False\n", + "\n", + "# Attention naming convention inverted at the detector stages!\n", + "# delta\n", + "gamma = tardis_calc['gamma']\n", + "gamma.limits = (-5, 180)\n", + "gamma.value = 0\n", + "gamma.fit = True\n", + "\n", + "# gamma\n", + "delta = tardis_calc['delta']\n", + "delta.limits = (-5, 180)\n", + "delta.value = 0\n", + "delta.fit = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take a look at the UB matrix, but thus far, it won't be very interesting" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 7.99999720e-01, 3.99999860e-01, -6.41365809e-17],\n", + " [ 0.00000000e+00, 6.92820080e-01, -6.41365809e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 6.04733908e-01]])" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "tardis_calc.sample.UB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add two, known reflections and the motor positions associated with those hkl values.\n", + "Here, we are using values from @cmazolli's ESRF notes:\n", + "\n", + "```\n", + "(3,3,0): del = 64.449, gam = -0.871, th = 25.285\n", + "(5,2,0): del = 79.712, gam = -1.374, th = 46.816\n", + "```\n", + "\n", + "**NOTE**: the translation of gamma==delta, delta==gamma, and mu==theta is being used" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "r1 = tardis_calc.sample.add_reflection(3, 3, 0, \n", + " position=tardis_calc.Position(gamma=64.449, mu=25.285, chi=0.0, phi=0.0, omega=0.0, delta=-0.871))\n", + "\n", + "r2 = tardis_calc.sample.add_reflection(5, 2, 0,\n", + " position=tardis_calc.Position(gamma=79.712, mu=46.816, chi=0.0, phi=0.0, omega=0.0, delta=-1.374))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[(h=3.0, k=3.0, l=0.0), (h=5.0, k=2.0, l=0.0)]" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "tardis_calc.sample.reflections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now a UB matrix can be computed." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "tardis_calc.sample.compute_UB(r1, r2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 0.31323551, -0.4807593 , 0.01113654],\n", + " [ 0.73590724, 0.63942704, 0.01003773],\n", + " [-0.01798898, -0.00176066, 0.60454803]])" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "tardis_calc.sample.UB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare some libhkl-generated results with those from @cmazolli's notes:\n", + "\n", + "```python\n", + "# Experimentally found reflections @ Lambda = 1.61198 A\n", + "# (4, 4, 0) = [90.628, 38.373, 0, 0, 0, -1.156]\n", + "# (4, 1, 0) = [56.100, 40.220, 0, 0, 0, -1.091]\n", + "# @ Lambda = 1.60911\n", + "# (6, 0, 0) = [75.900, 61.000, 0, 0, 0, -1.637]\n", + "# @ Lambda = 1.60954\n", + "# (3, 2, 0) = [53.090, 26.144, 0, 0, 0, -.933]\n", + "# (5, 4, 0) = [106.415, 49.900, 0, 0, 0, -1.535]\n", + "# (4, 5, 0) = [106.403, 42.586, 0, 0, 0, -1.183]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(PosCalcE6C(mu=38.37622128052063, omega=0.0, chi=0.0, phi=0.0, gamma=90.63030469353308, delta=-1.1613181970939916),)\n" + ] + } + ], + "source": [ + "print(tardis_calc.forward((4,4,0)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(PosCalcE6C(mu=40.21991977757096, omega=0.0, chi=0.0, phi=0.0, gamma=56.09704093977082, delta=-1.083660865503293),)\n" + ] + } + ], + "source": [ + "print(tardis_calc.forward((4,1,0)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change wavelength here to 1.60911 Angstrom.\n", + "Note the difference below in `delta` (TARDIS' gamma axis)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(PosCalcE6C(mu=60.99346591074179, omega=0.0, chi=0.0, phi=0.0, gamma=75.84521749189147, delta=-1.5839501607961701),)\n" + ] + } + ], + "source": [ + "# change wavelength\n", + "tardis_calc.wavelength = 1.60911\n", + "print(tardis_calc.forward((6,0,0)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(PosCalcE6C(mu=26.173823521308144, omega=0.0, chi=0.0, phi=0.0, gamma=53.05207622287554, delta=-0.8437995840438257),)\n(PosCalcE6C(mu=49.892322604056034, omega=0.0, chi=0.0, phi=0.0, gamma=106.32053081067252, delta=-1.423656049079967),)\n(PosCalcE6C(mu=42.54926633295045, omega=0.0, chi=0.0, phi=0.0, gamma=106.31894239326303, delta=-1.1854071532601609),)\n" + ] + } + ], + "source": [ + "tardis_calc.wavelength = 1.60954\n", + "print(tardis_calc.forward((3,2,0)))\n", + "print(tardis_calc.forward((5,4,0)))\n", + "print(tardis_calc.forward((4,5,0)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HKL PseudoPositioner Use\n", + "\n", + "Let's explore the idea of an hkl 'motor'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['theta', 'omega', 'chi', 'phi', 'delta', 'gamma']\n" + ] + } + ], + "source": [ + "from ophyd import Component as Cpt\n", + "from ophyd import (PseudoSingle, EpicsMotor)\n", + "from hkl.diffract import E6C\n", + "\n", + "\n", + "class Tardis(E6C):\n", + " h = Cpt(PseudoSingle, '')\n", + " k = Cpt(PseudoSingle, '')\n", + " l = Cpt(PseudoSingle, '')\n", + " \n", + " theta = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X1}Mtr')\n", + " omega = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X2}Mtr')\n", + " chi = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X3}Mtr')\n", + " phi = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X4}Mtr')\n", + " delta = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X5}Mtr')\n", + " gamma = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X6}Mtr')\n", + " \n", + "# FIXME: hack to get around what should have been done at init of tardis_calc instance\n", + "tardis_calc._lock_engine = True\n", + "\n", + "# re-map Tardis' axis names onto what an E6C expects\n", + "name_map = {\n", + " # tardis: E6C\n", + " 'mu': 'theta',\n", + " 'omega': 'omega',\n", + " 'chi': 'chi',\n", + " 'phi': 'phi',\n", + " 'gamma': 'delta',\n", + " 'delta': 'gamma',\n", + " }\n", + "\n", + "tardis = Tardis(\n", + " '', # no prefix\n", + " name='tardis', # local name\n", + " calc_inst=tardis_calc, # the calc engine setup above\n", + " # energy=tardis_calc.energy, # FIXME: unexpected keyword argument\n", + ")\n", + "tardis.calc.physical_axis_names = name_map\n", + "print(tardis.calc.physical_axis_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TardisRealPos(theta=0.0, omega=0.0, chi=0.0, phi=0.0, delta=0.0, gamma=0.0)" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "tardis.real_position" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "tardis.connected" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Energy = Signal(name='tardis_energy', parent='tardis', value=8.0, timestamp=1607099911.6298892) keV\n" + ] + } + ], + "source": [ + "print('Energy =', tardis.energy, 'keV')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "Item 0: Must be number, not NoneType", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtardis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(self, position, wait, timeout, moved_cb)\u001b[0m\n\u001b[1;32m 834\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpositioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msingle_pos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pseudo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 835\u001b[0m \u001b[0mpositioner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msingle_pos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m return super().move(position, wait=wait, timeout=timeout,\n\u001b[0m\u001b[1;32m 837\u001b[0m moved_cb=moved_cb)\n\u001b[1;32m 838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/positioner.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(self, position, wait, timeout, moved_cb)\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mmotion\u001b[0m \u001b[0mfails\u001b[0m \u001b[0mother\u001b[0m \u001b[0mthan\u001b[0m \u001b[0mtiming\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 352\u001b[0m '''\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoved_cb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmoved_cb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_move\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/positioner.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(self, position, moved_cb, timeout)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_subs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SUB_REQ_DONE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mcheck_value\u001b[0;34m(self, pseudo_pos)\u001b[0m\n\u001b[1;32m 603\u001b[0m pos, high))\n\u001b[1;32m 604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m \u001b[0mreal_pos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpseudo_pos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 606\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_pos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 607\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Apps/anaconda/envs/bluesky_2020_9/lib/python3.8/site-packages/hkl/diffract.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, pseudo)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mpseudo_position_argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpseudo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m solutions = self._calc.forward_iter(start=self.position, end=pseudo,\n\u001b[0m\u001b[1;32m 165\u001b[0m max_iters=100)\n\u001b[1;32m 166\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pseudo to real: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolutions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mposition\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 655\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 656\u001b[0m \u001b[0;34m'''Pseudo motor position namedtuple'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal_position\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 659\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/projects/Bluesky/ophyd/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Apps/anaconda/envs/bluesky_2020_9/lib/python3.8/site-packages/hkl/diffract.py\u001b[0m in \u001b[0;36minverse\u001b[0;34m(self, real)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mreal_position_argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphysical_positions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPseudoPosition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpseudo_positions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Apps/anaconda/envs/bluesky_2020_9/lib/python3.8/site-packages/hkl/calc.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Apps/anaconda/envs/bluesky_2020_9/lib/python3.8/site-packages/hkl/calc.py\u001b[0m in \u001b[0;36mphysical_positions\u001b[0;34m(self, positions)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;31m# Set the physical motor positions and calculate the pseudo ones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis_values_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_units\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Item 0: Must be number, not NoneType" + ] + } + ], + "source": [ + "tardis.move((1,0,1), wait=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "status = _" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "status.done" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.real_position" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.position" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#tardis.set((1,0,2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.h.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.h.read()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.read()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.position" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tardis.real_position" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit ('base': conda)", + "language": "python", + "name": "python38264bitbaseconda9c3e3a9452084ea0903e516deca6d551" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/geo_e4cv.ipynb b/examples/geo_e4cv.ipynb new file mode 100644 index 00000000..43ba978f --- /dev/null +++ b/examples/geo_e4cv.ipynb @@ -0,0 +1,903 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python38264bitcondaf8e76b08f7284c68a6b3de15f965a87a", + "display_name": "Python 3.8.2 64-bit (conda)", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# E4CV : 4-circle diffractometer example\n", + "\n", + "The [IUCr provides a schematic of the 4-circle diffractometer](http://ww1.iucr.org/iucr-top/comm/cteach/pamphlets/2/node14.html) (in horizontal geometry typical of a laboratory instrument).\n", + "\n", + "\n", + "![E4CH geometry](resources/img69.gif)\n", + "\n", + "At X-ray synchrotrons, the vertical geometry is more common\n", + "due to the polarization of the X-rays.\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Load the *hklpy* package (named _`hkl`_)\n", + "\n", + "Since the *hklpy* package is a thin interface to the *hkl*\n", + "library (compiled C++ code), we need to **first** load the\n", + "*gobject-introspection* package (named _`gi`_) and name our\n", + "required code and version.\n", + "\n", + "This is needed _every_ time before the *hkl* package is first imported." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')" + ] + }, + { + "source": [ + "## Setup the *E4CV* diffractometer in *hklpy*\n", + "\n", + "In _hkl_ *E4CV* geometry (https://people.debian.org/~picca/hkl/hkl.html#org7ef08ba):\n", + "\n", + "![E4CV geometry](resources/3S+1D.png)\n", + "\n", + "* xrays incident on the $\\vec{x}$ direction (1, 0, 0)\n", + "\n", + "axis | moves | rotation axis | vector\n", + "--- | --- | --- | ---\n", + "omega | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "chi | sample | $\\vec{x}$ | `[1 0 0]`\n", + "phi | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "tth | detector | $-\\vec{y}$ | `[0 -1 0]`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "\n", + "## Define _this_ diffractometer\n", + "\n", + "Create a Python class that specifies the names of the \n", + "real-space positioners. We call it `FourCircle` here but that\n", + "choice is arbitrary. Pick any valid Python name not already in use.\n", + "\n", + "The argument to the `FourCircle` class tells which *hklpy* base\n", + "class will be used. This sets the geometry. See the\n", + "[*hklpy* diffractometers documentation](https://blueskyproject.io/hklpy/master/diffract.html#hkl.diffract.Diffractometer.calc_class)\n", + "for a list of other choices.\n", + "\n", + "In *hklpy*, the reciprocal-space axes\n", + "are known as `pseudo` positioners while the real-space axes\n", + "are known as `real` positioners. For the real positioners,\n", + "it is possible to use different names than the canonical names\n", + "used internally by the *hkl* library. That is not covered here.\n", + "\n", + "note: The keyword argument `kind=\"hinted\"` is an indication\n", + "that this signal may be plotted.\n", + "\n", + "This demo uses simulated motors.\n", + "To use EPICS motors, import that structure from *ophyd*:\n", + "\n", + "```python\n", + "from ophyd import EpicsMotor\n", + "```\n", + "\n", + "Then, in the class, replace the real positioners with (substituting with the correct EPICS PV for each motor):\n", + "\n", + "```python\n", + "omega = Cpt(EpicsMotor, \"pv_prefix:m41\", kind=\"hinted\")\n", + "chi = Cpt(EpicsMotor, \"pv_prefix:m22\", kind=\"hinted\")\n", + "phi = Cpt(EpicsMotor, \"pv_prefix:m35\", kind=\"hinted\")\n", + "tth = Cpt(EpicsMotor, \"pv_prefix:m7\", kind=\"hinted\")\n", + "```\n", + "\n", + "and, **most important**, remove the `def __init__()` method.\n", + "It is only needed to define an initial position for the simulators.\n", + "Otherwise, this will move these EPICS motors to zero." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from hkl.diffract import E4CV\n", + "from ophyd import PseudoSingle, SoftPositioner\n", + "from ophyd import Component as Cpt\n", + "\n", + "class FourCircle(E4CV):\n", + " \"\"\"\n", + " Our 4-circle. Eulerian, vertical scattering orientation.\n", + " \"\"\"\n", + " # the reciprocal axes are called: pseudo in hklpy\n", + " h = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " k = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " l = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + "\n", + " # the motor axes are called: real in hklpy\n", + " omega = Cpt(SoftPositioner, kind=\"hinted\")\n", + " chi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " phi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " tth = Cpt(SoftPositioner, kind=\"hinted\")\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " \"\"\"Define an initial position for simulators.\"\"\"\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "fourc = FourCircle(\"\", name=\"fourc\")" + ] + }, + { + "source": [ + "## Add a sample with a crystal structure" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.15691131e+00, -7.08403864e-17, -7.08403864e-17],\n", + " [ 0.00000000e+00, 1.15691131e+00, -7.08403864e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.15691131e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "# add the sample to the calculation engine\n", + "a0 = 5.431\n", + "fourc.calc.new_sample(\n", + " \"silicon\",\n", + " lattice=Lattice(a=a0, b=a0, c=a0, alpha=90, beta=90, gamma=90)\n", + " )" + ] + }, + { + "source": [ + "## Setup the UB orientation matrix using *hklpy*\n", + "\n", + "Define the crystal's orientation on the diffractometer using \n", + "the 2-reflection method described by [Busing & Levy, Acta Cryst 22 (1967) 457](https://www.psi.ch/sites/default/files/import/sinq/zebra/PracticalsEN/1967-Busing-Levy-3-4-circle-Acta22.pdf).\n", + "\n", + "### Choose the same wavelength X-rays for both reflections" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fourc.calc.wavelength = 1.54 # Angstrom (8.0509 keV)" + ] + }, + { + "source": [ + "### Specify the first reflection and identify its Miller indices: (_hkl_)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "r1 = fourc.calc.sample.add_reflection(\n", + " 4, 0, 0,\n", + " position=fourc.calc.Position(\n", + " tth=69.0966,\n", + " omega=-145.451,\n", + " chi=0,\n", + " phi=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Specify the second reflection" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "r2 = fourc.calc.sample.add_reflection(\n", + " 0, 4, 0,\n", + " position=fourc.calc.Position(\n", + " tth=69.0966,\n", + " omega=-145.451,\n", + " chi=90,\n", + " phi=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Compute the *UB* orientation matrix\n", + "\n", + "The `compute_UB()` method uses the current wavelength. It always returns 1. Ignore it." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "fourc.calc.sample.compute_UB(r1, r2)" + ] + }, + { + "source": [ + "## Report what we have setup" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================== ===================================================\n", + "term value \n", + "==================== ===================================================\n", + "energy, keV 8.050922077922078 \n", + "wavelength, angstrom 1.54 \n", + "position FourCirclePseudoPos(h=-0.0, k=0.0, l=0.0) \n", + "sample name silicon \n", + "[U] [[-1.22173048e-05 -1.22173048e-05 -1.00000000e+00] \n", + " [ 0.00000000e+00 -1.00000000e+00 1.22173048e-05] \n", + " [-1.00000000e+00 1.49262536e-10 1.22173048e-05]]\n", + "[UB] [[-1.41343380e-05 -1.41343380e-05 -1.15691131e+00] \n", + " [ 0.00000000e+00 -1.15691131e+00 1.41343380e-05] \n", + " [-1.15691131e+00 1.72683586e-10 1.41343380e-05]]\n", + "lattice [ 5.431 5.431 5.431 90. 90. 90. ] \n", + "==================== ===================================================\n", + "\n", + "sample\tHklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-45.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=-89.99901005102187, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=135.00000000427607, fit=True, inverted=False, units='Degree'), U=array([[-1.22173048e-05, -1.22173048e-05, -1.00000000e+00],\n", + " [ 0.00000000e+00, -1.00000000e+00, 1.22173048e-05],\n", + " [-1.00000000e+00, 1.49262536e-10, 1.22173048e-05]]), UB=array([[-1.41343380e-05, -1.41343380e-05, -1.15691131e+00],\n", + " [ 0.00000000e+00, -1.15691131e+00, 1.41343380e-05],\n", + " [-1.15691131e+00, 1.72683586e-10, 1.41343380e-05]]), reflections=[(h=4.0, k=0.0, l=0.0), (h=0.0, k=4.0, l=0.0)], reflection_measured_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]), reflection_theoretical_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]))\n" + ] + } + ], + "source": [ + "import pyRestTable\n", + "\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"energy, keV\", fourc.calc.energy))\n", + "tbl.addRow((\"wavelength, angstrom\", fourc.calc.wavelength))\n", + "tbl.addRow((\"position\", fourc.position))\n", + "tbl.addRow((\"sample name\", fourc.sample_name.get()))\n", + "tbl.addRow((\"[U]\", fourc.U.get()))\n", + "tbl.addRow((\"[UB]\", fourc.UB.get()))\n", + "tbl.addRow((\"lattice\", fourc.lattice.get()))\n", + "print(tbl)\n", + "\n", + "print(f\"sample\\t{fourc.calc.sample}\")" + ] + }, + { + "source": [ + "## Check the orientation matrix\n", + "\n", + "Perform checks with _forward_ (hkl to angle) and\n", + "_inverse_ (angle to hkl) computations to verify the diffractometer\n", + "will move to the same positions where the reflections were identified.\n", + "\n", + "### Constrain the motors to limited ranges\n", + "\n", + "* allow for slight roundoff errors\n", + "* keep `tth` in the positive range\n", + "* keep `omega` in the negative range\n", + "* keep `phi` fixed at zero\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "fourc.calc[\"tth\"].limits = (-0.001, 180)\n", + "fourc.calc[\"omega\"].limits = (-180, 0.001)\n", + "\n", + "fourc.phi.move(0)\n", + "fourc.engine.mode = \"constant_phi\"" + ] + }, + { + "source": [ + "### (400) reflection test\n", + "\n", + "1. Check the `inverse` (angles -> (_hkl_)) computation.\n", + "1. Check the `forward` ((_hkl_) -> angles) computation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "#### Check the inverse calculation: (400)\n", + "\n", + "To calculate the (_hkl_) corresponding to a given set of motor angles,\n", + "call `fourc.inverse((h, k, l))`. Note the second set of parentheses needed by this function.\n", + "\n", + "The values are specified, without names, in the order specified\n", + "by `fourc.calc.physical_axis_names`." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "axis names: ['omega', 'chi', 'phi', 'tth']\n" + ] + } + ], + "source": [ + "print(\"axis names:\", fourc.calc.physical_axis_names)" + ] + }, + { + "source": [ + "Now, proceed with the inverse calculation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4 0 0) ? 4.00 0.00 0.00\n" + ] + } + ], + "source": [ + "sol = fourc.inverse((-145.451, 0, 0, 69.0966))\n", + "print(f\"(4 0 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "#### Check the forward calculation: (400)\n", + "\n", + "Compute the angles necessary to position the diffractometer\n", + "for the given reflection.\n", + "\n", + "Note that for the forward computation, more than one set of angles may be used to reach the same crystal reflection. This test will report the *default* selection. The *default* selection (which may be changed through methods described in the `hkl.calc` module) is the first solution.\n", + "\n", + "function | returns\n", + "--- | ---\n", + "`fourc.forward()` | The *default* solution\n", + "`fourc.calc.forward()` | List of all allowed solutions." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(400) : tth=69.0985 omega=-145.4500 chi=0.0000 phi=0.0000\n" + ] + } + ], + "source": [ + "sol = fourc.forward((4, 0, 0))\n", + "print(\n", + " \"(400) :\", \n", + " f\"tth={sol.tth:.4f}\", \n", + " f\"omega={sol.omega:.4f}\", \n", + " f\"chi={sol.chi:.4f}\", \n", + " f\"phi={sol.phi:.4f}\"\n", + " )" + ] + }, + { + "source": [ + "### (040) reflection test\n", + "\n", + "Repeat the `inverse` and `forward` calculations for the\n", + "second orientation reflection." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "#### Check the inverse calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0 4 0) ? 0.00 4.00 0.00\n" + ] + } + ], + "source": [ + "sol = fourc.inverse((-145.451, 90, 0, 69.0966))\n", + "print(f\"(0 4 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "#### Check the forward calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(040) : tth=69.0985 omega=-145.4500 chi=90.0000 phi=0.0000\n" + ] + } + ], + "source": [ + "sol = fourc.forward((0, 4, 0))\n", + "print(\n", + " \"(040) :\", \n", + " f\"tth={sol.tth:.4f}\", \n", + " f\"omega={sol.omega:.4f}\", \n", + " f\"chi={sol.chi:.4f}\", \n", + " f\"phi={sol.phi:.4f}\"\n", + " )" + ] + }, + { + "source": [ + "## Scan in reciprocal space using Bluesky\n", + "\n", + "To scan with Bluesky, we need more setup." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from bluesky import RunEngine\n", + "from bluesky import SupplementalData\n", + "from bluesky.callbacks.best_effort import BestEffortCallback\n", + "from bluesky.magics import BlueskyMagics\n", + "import bluesky.plans as bp\n", + "import bluesky.plan_stubs as bps\n", + "import databroker\n", + "from IPython import get_ipython\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.ion()\n", + "\n", + "bec = BestEffortCallback()\n", + "db = databroker.temp().v1\n", + "sd = SupplementalData()\n", + "\n", + "get_ipython().register_magics(BlueskyMagics)\n", + "\n", + "RE = RunEngine({})\n", + "RE.md = {}\n", + "RE.preprocessors.append(sd)\n", + "RE.subscribe(db.insert)\n", + "RE.subscribe(bec)" + ] + }, + { + "source": [ + "### (_h00_) scan near (400)\n", + "\n", + "In this example, we have no detector. Still, we add the diffractometer\n", + "object in the detector list so that the _hkl_ and motor positions will appear\n", + "as columns in the table." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 1 Time: 2020-12-17 15:58:41\n", + "Persistent Unique Scan ID: 'c38e296e-8ece-4d4a-98a6-cfe36a547470'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | fourc_h | fourc_k | fourc_l | fourc_omega | fourc_chi | fourc_phi | fourc_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 15:58:41.8 | 3.900 | -0.000 | -0.000 | -146.431 | 0.000 | 0.000 | 67.137 |\n", + "| 2 | 15:58:42.7 | 3.950 | 0.000 | 0.000 | -145.942 | -0.000 | 0.000 | 68.115 |\n", + "| 3 | 15:58:43.6 | 4.000 | 0.000 | 0.000 | -145.450 | -0.000 | 0.000 | 69.099 |\n", + "| 4 | 15:58:44.5 | 4.050 | -0.000 | -0.000 | -144.955 | 0.000 | 0.000 | 70.088 |\n", + "| 5 | 15:58:45.3 | 4.100 | 0.000 | 0.000 | -144.458 | 0.000 | 0.000 | 71.083 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['c38e296e'] (scan num: 1)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('c38e296e-8ece-4d4a-98a6-cfe36a547470',)" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:58:47.836288\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([fourc], fourc.h, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### chi scan from (400) to (040)\n", + "\n", + "If we do this with $\\omega=-145.4500$ and $2\\theta=69.0985$, this will be a scan between the two orientation reflections.\n", + "\n", + "Use `%mov` (IPython *magic* command) to move both motors at the same time." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "possible modes: ['bissector', 'constant_omega', 'constant_chi', 'constant_phi', 'double_diffraction', 'psi_constant']\n", + "chosen mode: constant_phi\n", + "\n", + "\n", + "Transient Scan ID: 2 Time: 2020-12-17 15:58:48\n", + "Persistent Unique Scan ID: '6bf0d200-2424-4a6b-81da-8977c2d4ded0'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+------------+-------------+------------+------------+\n", + "| seq_num | time | fourc_chi | fourc_h | fourc_k | fourc_l | fourc_omega | fourc_phi | fourc_tth |\n", + "+-----------+------------+------------+------------+------------+------------+-------------+------------+------------+\n", + "| 1 | 15:58:49.3 | 0.000 | 4.000 | 0.000 | 0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 2 | 15:58:50.2 | 10.000 | 3.939 | 0.695 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 3 | 15:58:51.3 | 20.000 | 3.759 | 1.368 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 4 | 15:58:52.3 | 30.000 | 3.464 | 2.000 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 5 | 15:58:53.4 | 40.000 | 3.064 | 2.571 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 6 | 15:58:54.3 | 50.000 | 2.571 | 3.064 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 7 | 15:58:55.2 | 60.000 | 2.000 | 3.464 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 8 | 15:58:56.1 | 70.000 | 1.368 | 3.759 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 9 | 15:58:56.9 | 80.000 | 0.695 | 3.939 | -0.000 | -145.450 | 0.000 | 69.099 |\n", + "| 10 | 15:58:57.8 | 90.000 | 0.000 | 4.000 | 0.000 | -145.450 | 0.000 | 69.099 |\n", + "+-----------+------------+------------+------------+------------+------------+-------------+------------+------------+\n", + "generator scan ['6bf0d200'] (scan num: 2)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('6bf0d200-2424-4a6b-81da-8977c2d4ded0',)" + ] + }, + "metadata": {}, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:58:59.952774\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABC0AAALXCAYAAACpVbpOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACYv0lEQVR4nOzdd3zV1f3H8fcnmwxGQkLYJGEFUBAiiXsL7lWt2zrqaK3112m3HbbW1lbtEK2zzmrrQEVw42QqI2EnrAAhIYFACNnn9wfBIgVkJDnfe+/r+Xjkkdzv/d7kfR0nN+/7PeeYc04AAAAAAABBE+U7AAAAAAAAwO5QWgAAAAAAgECitAAAAAAAAIFEaQEAAAAAAAKJ0gIAAAAAAAQSpQUAAAAAAAgkSgsAAAAgQpnZI2ZWbmaFbfT9ms1sTuvHxLb4ngAimznnfGcAAAAA4IGZHSupRtI/nXMj2uD71Tjnkg8+GQBsx5UWAAAAQIRyzr0vqWrnY2aWY2aTzWy2mX1gZkM9xQMASgsAAAAAX/CgpG8558ZI+p6kv+/HYxPMbJaZTTOzc9slHYCIEuM7AAAAAIBgMLNkSUdKet7MdhyOb73vfEm/2s3D1jjnxrV+3c85t9bMsiW9Y2bznXPF7Z0bQPiitAAAAACwQ5SkTc65Ubve4Zx7QdILe3uwc25t6+cSM3tP0mGSKC0AHDCmhwAAAACQJDnnNktabmYXSpJtN3JfHmtm3cxsx1UZ3SUdJWlBu4UFEBEoLQAAAIAIZWbPSPpE0hAzKzWzayVdJulaM5srqUjSOfv47XIlzWp93LuS7nTOUVoAOChseQoAAAAAAAKJKy0AAAAAAEAgUVoAAAAAAIBAiojdQ7p37+4GDBjgOwYASJJmz569wTmX7juHD4zHAIKE8XiA7xgAIGnv43FElBYDBgzQrFmzfMcAAEmSma30ncEXxmMAQcJ4zHgMIBj2Nh4zPQQAAAAAAAQSpQUAAAAQYGY23swWm9kyM7ttN/cfb2bVZjan9ePnPnICQHuIiOkhAAAAQCgys2hJf5N0iqRSSTPNbKJzbsEup37gnDuzwwMCQDujtAAQchobG1VaWqq6ujrfUfYqISFBffr0UWxsrO8oANBuQmFMDvHxeKykZc65Ekkys2clnSNp19ICQIQL1/GY0gJAyCktLVVKSooGDBggM/MdZ7ecc6qsrFRpaamysrJ8xwGAdhP0MTkMxuPeklbvdLtUUv5uzjvCzOZKWivpe865oo4IByA4wnU8Zk0LACGnrq5OaWlpgRyMdzAzpaWlBbrpBoC2EPQxOQzG4939g3W73P5UUn/n3EhJf5H00m6/kdn1ZjbLzGZVVFS0bUoA3oXreExpASAkBXUw3lkoZASAthD08S7o+b5EqaS+O93uo+1XU3zOObfZOVfT+vUkSbFm1n3Xb+Sce9A5l+ecy0tPT2/PzAA8Cfp4dyD5KC0A4ACsXr1aJ5xwgnJzczV8+HDde++9viMBQESKgPF4pqRBZpZlZnGSLpY0cecTzCzTWv8SMLOx2v4av7LDkwKIeO0xJrOmBQAcgJiYGN19990aPXq0tmzZojFjxuiUU07RsGHDfEcDgIgS7uOxc67JzG6WNEVStKRHnHNFZnZj6/0TJH1F0k1m1iRpm6SLnXO7TiEBgHbXHmMypQUAHICePXuqZ8+ekqSUlBTl5uZqzZo1YfMiGQBCRSSMx61TPibtcmzCTl//VdJfOzoXAOyqPcZkSgsAIe2XrxRpwdrNbfo9h/XqrF+cNXyfz1+xYoU+++wz5efvbjF3AIgcvsdkxmMA2M73eCy13ZjMmhYAcBBqamp0wQUX6J577lHnzp19xwGAiMV4DADB0ZZjMldaAAhp+9P2trXGxkZdcMEFuuyyy3T++ed7ywEAQeFrTGY8BoAvCqfXyFxpAQAHwDmna6+9Vrm5ufrOd77jOw4ARCzGYwAIjvYYkyktAOAAfPTRR3riiSf0zjvvaNSoURo1apQmTZr05Q8EALQpxmMACI72GJOZHgIAB+Doo48Wu8kBgH+MxwAQHO0xJnOlBQAAAAAACCRKCwAAAAAAEEiUFgAAAAAAIJAoLQCEpFCYvxwKGQGgLQR9vAt6PgBoK0Ef7w4kH6UFgJCTkJCgysrKQA/KzjlVVlYqISHBdxQAaFdBH5MZjwFEinAdj9k9BEDI6dOnj0pLS1VRUeE7yl4lJCSoT58+vmMAQLsKhTGZ8RhAJAjX8ThkSwszi5Y0S9Ia59yZvvMA6DixsbHKysryHQOtGI+ByMaYDADBEK7jcShPD/m2pIW+QwAAGI8BAADQPkKytDCzPpLOkPSQ7ywAEMkYjwEAANCeQnV6yD2SfiApxXMO/eiFeSqu2Kqc9GQNzEhWTnqSctKT1btrJ0VFme94ANDe7lFAxmMAoau2oUklFVtVXFGj4vIaLauoUXH5Vv3ugkM0ul833/GwnxqbW3TZQ9N10tAM3XBcju84AA7STU/OVo/OCbr97OFefn7IlRZmdqakcufcbDM7fi/nXS/peknq169fu+XJSEnQkvU1mjR/naq3NX5+PCE2Stndk5WTkayB6cnKydheZmR1T1JCbHS75QGAjhK08RhAsDnntKGmQcvKa7aXExU1WlZeo5KKrVqzadvn50WZ1C81UTnpyYo23gAKRbHRUdq8rVEfLN1AaQGEuIamFr27uFwXH+7vNVzIlRaSjpJ0tpmdLilBUmcze9I5d/nOJznnHpT0oCTl5eW1254v/3fKYP3fKYPlnFPV1h2/iLd+/ov4s1Ub9crctZ+fbyb17ZaonPSk1isz/ltsdEuKa6+YANAeAjUeAwiGpuYWrd647b/lxOdXTtRoc13T5+d1io1WTkaS8gZ008XpfZXT+rpoQPdExcfwBk+oK8hO079mrlZDU4viYkJyRjoASfPXbFJdY4sKslO9ZQi50sI59yNJP5Kk1nf2vrfrC2QfzExpyfFKS45XfnbaF+7b1tCskg3by4ydf4F/VFyphqaWz89LTYr7YpnROuWEqSYAgiio4zGAjrG1fvuUjmUVW1Rc/t83bFZUblVj83/7ye7J8RqYkaSzRvb6whs2PTsn8PomjBVkp+mxj1do/ppNGtPf3x87AA7OtJIqSdLYrLQvObP9hFxpEYo6xUVreK8uGt6ryxeON7c4rdm47fNf8jsulZxcWKaNtf+dahIfE6Xs9O3rZYzu100X5vVRSkJsRz8NAAAQweaXVuuFz0q1rHz765Z11XWf3xdlUv+07VNhT8zN+O9aX92T1SWR1yyRaGzW9qJiWkkVpQUQwqaVVGpIjxSlepwVENKlhXPuPUnveY5xwKKjTP3SEtUvLVEnDM34wn1VWxv+W2a0FhpzSzfp1Xnr9Oe3lujKI/rra0dmKT0l3lN6APivUB+PAeyec04fF1dqwtRifbB0gxJiozQoI0UF2WmfLz4+MCNZ/dKY0oEvSk2K09DMFE0rqdQ3TxjoOw6AA9DY3KJZKzbqorw+XnOEdGkRzlKT4pSalKrDB3yxmZ67epMmTC3W398r1kMfLNdFeX319WOy1S8t0VNSAAAQbppbnN4oKtP9U4s1r7Ra6Snxuu20obo0v586c7Un9tGOdS0am1sUG826FkComVdarW2NzSrI9jc1RKK0CDkj+3bV/ZePUXFFjf7xfomenblKT01fqTMP7aUbj8vRsF6dfUcEAAAhqr6pWS9+ukYPvl+ikg1b1T8tUb897xCdP7o3u59hv+Vnpeqxj1doXmm1xvRn61og1EwrqZT03+levlBahKic9GTdecGhuvXkwXrko+V6atpKTZy7VscNTtdNx+coPytVxjZhAABgH2ypa9QzM1bpoQ+Wq3xLvUb07qy/XnqYThvRU9EslokDtOMPnenLKyktgBA0fXmVBvdIVlqy3yUJKC1CXGaXBP349Fx98/iBemLaCj360Qpd/OA0Hdavq246Lkcn5/ZgZW4AALBbG2rq9ehHy/XEJyu1ua5JR+ak6e6LRurogd158wMHLS05XkN6pGhaSZW+cbzvNAD2x/b1LKr0lTF+17OQKC3CRpfEWN184iBdd0y2np+1Wg+8X6Lrn5itgRnJuuHYbJ0zqjd7ZAMAAEnS6qpaPfh+iZ6btVoNzS0aPzxTNx6Xo5F9u/qOhjCTn52qf88uZV0LIMTMX1Ot2oZm5Xvc6nQHSoswkxAbrSuOGKBLxvbTa/PX6f73ivX9f8/Tn95couuOydbFh/dVUjz/2gEAiEQL123WhKnFenXeOkWZdP5hfXT9cdnKSU/2HQ1hqiA7Tf/8ZKXmr6nW6H5MEQFCxY71LPKz/W9ZzF+vYSomOkrnjOqts0f20ntLKnT/e8X69asL9Jd3luqqIwboqiMHeN1rFwAAdAznnGYsr9L9U4v13uIKJcVF69qjs3TNUVnK7JLgOx7C3OfrWpRUUVoAIWR6SZUGZSSru+f1LCRKi7BnZjphSIZOGJKh2Ss3asLUYt379lI9+H6Jvnp4X3392Gz17trJd0wAANDGWlqc3l5UrvvfW6ZPV21SWlKcvnfqYF1RMEBdEtm2FB2je3K8BmUka1pJpW46Psd3HAD7YMd6FueN7u07iiRKi4gypn83/ePKPC1dv0UTppboyWkr9eS0lTp71PbtUgf3SPEdEQAAHKSGphZNnLtWD0wt1tLyGvXp1km/Pme4Lszry7al8KIgO00vfFqqpuYWxbCuBRB4hWuqtbWhWQXZ/tezkCgtItKgHim6+6KR+s6pg/XwB8v1zIxVeuHTNTo5N0M3HZ+jMf39z1sCAAD7Z2t9k56duVoPf1CitdV1GpqZonsvHqUzDunJH4rwqiA7TU9MW6nCtZs1isVegcCbvrxKkgKxCKdEaRHRenftpJ+fNUzfOnGgHv9khR77eIUuuP8TjR2QqhuPz9YJQzLY7gwAgICr2tqgxz9eocc/WaFNtY0am5WqO84/RMcPTuf3OAJhx7oW00oqKS2AEDCtpFI56UlKT/G/noVEaQFJ3ZLidOvJg3X9sdl6dsZqPfRBia55bJZG9O6sey8+jBXFAQAIIOecHv5wue5+Y4m2NTbr5Nweuun4bK6YROCkp8RrYOu6Fjcex7oWQJA1Nbdo5vIqnXtYMNazkCgtsJPEuBhdc3SWrjiiv16es1a/nbRQZ//lQ915waE6a2Qv3/EAAECr6m2N+sG/52pK0XqdnJuhH44fqkGsTYUAK8hO1UufrWVdCyDgitZuDtR6FpLEiIH/ERsdpa+M6aPXbjlaQ3t21ree+Uy/eLlQ9U3NvqMBABDxitZW6+y/fqi3F5brp2fk6h9X5lFYIPAKstNUU9+korWbfUcBsBfTSiolSfnZwblqj9ICe9SzSyc9e32Bvn5Mlh7/ZKUumvCJSjfW+o4FAEBEcs7p2RmrdN7fP1Z9Y4v+dUOBrjsmm3UrEBJ2XtcCQHBNK6lUdnqSMlISfEf5HKUF9io2Oko/OWOYJlw+WiUVW3XGfR/q3UXlvmMBABBRtjU063vPz9NtL8xXflaqXrvlaNauQEjJSElQTnrS57sSAAiepuYWzVqxMVBTQyRKC+yj8SN66pVvHa3eXTvp6sdm6g9TFqmpucV3LAAAwl5xRY3O/dtHeuGzUt168iA9dvVYpSUHY0V3YH8UZKdp5vIqXkMCAbVg3WZtqW+itEDoGtA9SS9840hdfHhf/e3dYl3x8AyVb6nzHQsAgLD16ry1OvsvH6qipl6PXz1Wt548WNFRTAdBaMrPTtOW+iYtWMe6FkAQ7Zi+VZAVrCv5KC2wXxJio3XnBYfqjxeO1GerN+qM+z7UdOYmAgDQphqaWnT7xCLd/PRnGpKZotduOVrHDk73HQs4KDv+EJpewhQRIIiml1Qpu3uSMjoHZz0LidICB+grY/ropW8epZT4GF360HRNmFqslhbnOxYAACFvzaZtuuiBT/TYxyt07dFZ+tcNR6hnl06+YwEHLaNzgrLTk1iMEwig5hanGcurlB+wqSESpQUOwtDMznr55qM0fkSm7nx9ka5/Ypaqaxt9xwIAIGS9u7hcZ9z3gYrLa3T/ZaP1szOHKTaal2sIH/lZaZqxvErNvNkFBMqCtTvWswjW1BCJ0gIHKSUhVn+95DDdftYwTV1SoTP+8oHml1b7jgUAQEhpbnG6+43FuvrRmcrsnKCJ3zpapx3S03csoM0VZKdqS32TFrKuBRAo05dvvwIqP4srLRCGzExfOypLz91whFpanC64/2M9MW2lnKNBBwDgy1RsqdcVD0/XX95Zpq/m9dVL3zxKWd2TfMcC2sWOXQmYIgIEy7SSSmV1T1Jml2CtZyFRWqANHdavm1675RgdkZOmn71UqFv/NUdb65t8xwIAILBmLK/SGfd9oNkrN+qurxyq33/lUCXERvuOBbSbHp0TlNWddS2AIGlucZq+vEr5Ads1ZAdKC7SpbklxevRrh+t7pw7WK3PX6py/faSl67f4jgUAQKA45/TA1GJd8o9pSoqP0UvfPEoX5fX1HQvoEAXZqZrOuhZAYCxct1lb6po+vxIqaCgt0Oaiokw3nzhIT1ybr021DTr7rx/p5TlrfMcCACAQqrc16vonZut3ry/SuOE9NPHmo5Tbs7PvWECHKchO05Y61rUAgmLHlU/5AVyEU6K0QDs6amB3vXbLMRrRu7O+/ewc/fSl+apvavYdCwAAbwrXVOvMv3ygdxeV6xdnDdPfLh2tlIRY37GADrVjoT+miADBMK2kSv3TEgO7vTalBdpVj84JevrrBbrh2Gw9OW2VvnL/J1pdVes7FgAAHco5p6enr9L593+spmanf91whK4+Kktm5jsa0OEyuyRoQFqippVU+Y4CRLzmFqcZyytVEMBdQ3agtEC7i42O0o9Oz9WDV4zRisqtOuO+D/TWgvW+YwEA0CFqG5r0nefm6scvzldBdppeu+UYjenfzXcswKuC7DTNXFGlFta1ALxaVLZZm+uaVJATzKkhEqUFOtCpwzP12reOUd/URF33z1m68/VFampu8R0LAIB2s6y8Ruf+7SO9NGeNvnPKYD32tcOVmhTnOxbgXX52qqq3NWphGetaAD7tuOIpnystgO36pSXqPzcdqUvG9tOEqcW69KHpKt9c5zsWAABtbuLctTr7rx+qsqZBT1yTr1tOGqSoKKaDANLO61owRQTwaVpJpfqlJqpX12CuZyFRWsCDhNho/e78Q/Sni0Zqfmm1Tr/vQ31cvMF3LAAA2kR9U7N+/nKhbnnmMw3r2Vmv3XKMjh7U3XcsIFB6de2k/mmJms5inIA3LS1OM5ZXqSCgu4bsQGkBb84f3Ucv33yUOneK0RUPz9Ck+et8RwIA4KDUNTbrusdn6Z+frNTXj8nSM9cXKLNLgu9YQCAVZKVp+nLWtQB8WVS2RdXbGlWQHdypIRKlBTwb3CNFE28+Wof17apbnvlMby9kgU4AQGhqaGrRN576VB8s3aA/fOVQ/eSMYYqN5qUWsCc71rVYVLbFdxQgIu3Ydjif0gLYu+T4GD1y9eEa1quzbnryU32wtMJ3JAAA9ktTc4u+/exnemdRue44b4QuzOvrOxIQeDv+UJq+nCkigA/Tl1eqb2on9Q7wehYSpQUConNCrP55zVjlZCTr6/+c9XnrBwBA0DW3OH33+bl6vbBMPz9zmC7L7+87EhASenftpH6pibzuAzxoaXGavrxKBQHeNWQHSgsERtfEOD157Vj16Zaoax+bqdkrN/qOBADAXrW0OP3ohXl6ec5a/WD8EF1zdJbvSEBIyc9KZV0LwIPF67doU21j4KeGSJQWCJi05Hg9fV2+0lPi9bVHZ6hwTbXvSAAA7JZzTr+YWKTnZpXqlpMG6RvHD/QdCQg5Bdlp2lTbqCXlrGsBdKQdO/fkZwV75xCJ0gIBlNE5QU99vUCdE2J1+cPTtahss+9IAAB8gXNOv520UE9MW6kbjs3W/508yHckICTlt261OK2YKSJAR5pWUqU+3Tqpb2qi7yhfitICgdS7ayc9/fV8xcdE6fKHpmtZeY3vSAAAfO5Pby7RPz5YrquO6K/bThsqM/MdCQhJfbolqk+3TppWUuU7ChAxtq9nUan8EFjPQqK0QID1T0vSU9cVSJIue2iaVlZu9ZwIAADpb+8u01/eWaaLD++rX5w1nMIC7c7MxpvZYjNbZma37eZ+M7P7Wu+fZ2ajfeQ8UAXZaZqxgnUtgI6ytLxGG2sbVZAd/KkhEqUFAm5gRrKevC5f9U0tuvQf07Vm0zbfkQAAEeyhD0r0hymLde6oXrrjvEMUFUVhgfZlZtGS/ibpNEnDJF1iZsN2Oe00SYNaP66XdH+HhjxIBdlpqtraoKVcWQt0iB079hSEwCKcEqUFQsDQzM568tp8ba5r1GX/mKb1m+t8RwIARKAnp63Ub15bqNMPydQfLxypaAoLdIyxkpY550qccw2SnpV0zi7nnCPpn267aZK6mlnPjg56oHYsBMjWp0DHmFZSqd5dQ2M9C4nSAiFiRO8uevyasarYUq/LHpquDTX1viMBACLI87NW66cvFerk3Azd89XDFBPNSyh0mN6SVu90u7T12P6eE1h9UxPVu2snTV9OaQG0N+ecpi+v+nwR3FDAb1yEjNH9uumRrx2u0o21uvyh6dpU2+A7EgAgAkycu1Y//M88HTOou/566WjFxfDyCR1qd5f07Lr4w76cIzO73sxmmdmsioqKNgnXVgqy0zStpErOsa4F0J6WlteoamtDyEwNkSgtEGLys9P0jyvzVLJhq658ZIY21zX6jgQACGOTC8v0f/+ao8MHpOrBK/KUEBvtOxIiT6mkvjvd7iNp7QGcI+fcg865POdcXnp6epsHPRj52amsawF0gM/XswiRnUMkSguEoGMGpev+y0ZrwdrNuvrRmdpa3+Q7EgAgDL27qFzfeuZTHdqnix7+2uHqFEdhAS9mShpkZllmFifpYkkTdzlnoqQrW3cRKZBU7Zxb19FBD8YRre/6sq4F0L6mlVSqV5cE9U3t5DvKPqO0QEg6KbeH7rvkMH22aqOue3yW6hqbfUcCAISRj5Zt0A1PztaQzBQ9dvVYJcfH+I6ECOWca5J0s6QpkhZKes45V2RmN5rZja2nTZJUImmZpH9I+oaXsAehT7dO29e1KKnyHQUIW845TS+pUkF2Wkht101pgZB1+iE99aeLRmna8kpd/8Rs1TdRXAAADt6M5VW67vFZykpL0hPX5KtLp1jfkRDhnHOTnHODnXM5zrk7Wo9NcM5NaP3aOee+2Xr/Ic65WX4T7z8zU35WqqaVVLKuBdBOlpXXqHJrQ0gtwilRWiDEnXtYb915/iF6f0mFbn76MzU2t/iOBAAIYXNWb9I1j81Uz64JevK6fHVLivMdCYgYBdlpqtzaoGWsawG0i8/XswihRTglSguEga8e3k+/Ome43lywXrf+a46aKC4AAAegcE21rnx4ulKT4vT0dQVKT4n3HQmIKDv+kJq2nCkiQHuYtrxKPbskqF9qou8o+4XSAmHhyiMG6MenD9Vr89bpB/+Zp5YWLisEAOy7Jeu36MpHZig5PkZPfz1fmV0SfEcCIk7f1E7q1SWBxTiBdrB9PYvKkFvPQpJYVQph4/pjc1TX2KI/vblECbHRuuPcESH3PyQAoOMt37BVlz00XTFRpqe/XqA+3ULrHSggXJiZ8rPT9MHSCjnneB0HtKHiihptqGlQflZorWchcaUFwsy3Thyobxyfo6enr9KvXl3AQk4AgL1aXVWrS/8xTS0tTk9/PV8Duif5jgREtILsVG2oaVBxxVbfUYCwMq11Z55QW89C4koLhBkz0/fHDVFdY4se+Wi5EmKj9YNxQ2jqAQD/Y131Nl360DTVNjTrma8XaGBGiu9IQMT7fF2LkkoNzEj2nAYIH9NKKpXZOUH900LvakKutEDYMTP97MxcXZrfT/e/V6z73l7mOxIAIGDKt9Tpsn9M16atjfrnNWM1rFdn35EASOqXmqjMzqxrAbQl55ymlVQpPzs1JN/M5UoLhCUz02/OGaH6xhb9+a0lSoiN0g3H5fiOBQAIgKqtDbr8oekq21ynf14zViP7dvUdCUArM1NBdqo+Kq5kXQugjZRs2KoNNfUhOTVE4koLhLGoKNNdXzlUZx7aU797fZEe+2i570gAAM+qaxt1+UPTtbKyVg9dlae8AaG3IBkQ7gqy01SxpV4lG1jXAmgLO65cCtXSgistENaio0x//uooNTS16PZXFig+NlqXjO3nOxYAwIMtdY268tEZWlZeowevHKMjc7r7jgRgN/J3WtciJ511LYCDNa2kShkp8RoQgutZSFxpgQgQGx2lv1x6mI4bnK4fvzhfL35W6jsSAKCD1TY06ZrHZqpoTbX+eulhOn5Ihu9IAPZgQFqienSO1/TW3Q4AHDjnnKaXVKogOy1kp1tRWiAixMdE64ErxuiI7DR997m5em9xue9IAIAO0tLi9M2nPtXslRt1z8WjdOrwTN+RAOzF9nUt0jStpJLt64GDtHzDVpVvCd31LCRKC0SQhNho/ePKPA3ukaJvPztHq6tqfUcCAHSAe99eqncXV+iXZw/XmYf28h0HwD7Iz0pT+ZZ6LWddC+CgTGu9Yik/O3TXcAq50sLMEsxshpnNNbMiM/ul70wIHUnxMZpw+Ri1OKebnpqtusZm35GAkMV4jFDwzqL1uvftpbpgdB9dXtDfdxwA+6ig9Q+s6cuZIgIcjOnLK5WeEq/s7km+oxywkCstJNVLOtE5N1LSKEnjzazAbySEkgHdk3TPV0epcM1m/eylQi47BA4c4zECbWXlVt367BwN69lZd5w3ImTn8gKRKKt7kjJS4j/f9QDA/nPOaVqIr2chhWBp4barab0Z2/rBX53YLyfl9tAtJw7U87NL9cyM1b7jACGJ8RhBtq2hWTc++anMTBMuH6OE2GjfkQDsBzNTPutaAAdlRWWt1m+uV35W6E4NkUKwtJAkM4s2szmSyiW96ZybvptzrjezWWY2q6KiosMzIvi+ffJgHTs4XbdPLNKc1Zt8xwFCEuMxgsg5p5+8OF+LyjbrnotHqV+IbvEGRLqC7FSt31yvFZWsQwYciB1XKoXyIpxSiJYWzrlm59woSX0kjTWzEbs550HnXJ5zLi89Pb3DMyL4oqNM9108Shmd4/WNJ2ersqbedyQg5DAeI4ienL5KL3y2Rt8+aZBOYGtTIGTt+ENrOlNEgAMyvaRS3ZPjlZMeuutZSCFaWuzgnNsk6T1J4/0mQajqmhinCZeP0YatDbrl2c/U1NziOxIQkhiPERSfrtqoX71SpBOGpOuWEwf5jgPgIGR3T1L3ZNa1AA7E9vUsqpSfnRrS61lIIVhamFm6mXVt/bqTpJMlLfIaCiFtRO8u+s25I/TRskrd/eYS33GAkMF4jKCp2FKvbzz5qXp26aR7vnqYoqJC+0UaEOnMTAXZqZpWUsW6FsB+WllZq7LNdSE/NUQKwdJCUk9J75rZPEkztX0O9aueMyHEXZTXV5eM7af73yvW5MIy33GAUMF4jMBoam7Rt575VBtrG3T/5aPVJTHWdyQAbaAgO01lm+u0qop1LYD9MX359iuUjsgO7UU4JSnGd4D95ZybJ+kw3zkQfm4/e5gWrK3W956fq8E9kpWdnuw7EhBojMcIkj9MWaxpJVW6+8KRGt6ri+84ANrIjneJp5VUqn9aaM/LBzrStJIqdU+OU04Y/E0TildaAO0iPiZaf798jOJionTjk7O1tb7JdyQAwD54ff46PfB+ia4o6K8LxvTxHQdAG8pJ37GuRZXvKEDI2L6eRaXys9JCfj0LidIC+ILeXTvpL5ccpmXlNfrhf+YxfxIAAm5Z+RZ97/m5OqxfV/3szGG+4wBoY2am/OxUTS+p5HUZsI9WV23Tuuo6FYTB1BCJ0gL4H0cN7K7vjRuiV+et0yMfrfAdBwCwBzX1TbrhidlKiI3W3y8brbgYXtYA4aggO01rq+u0umqb7yhASNix4044LMIpUVoAu3XTcTkaN7yHfjtpoWYs53JEAAga55x+8O+5Wr5hq/5y6WHq2aWT70gA2klB1vZ3i9n6FNg300oqlZYUp4EZob+ehURpAeyWmekPF45U/9REffPpT1W+uc53JADATh76YLkmzS/TD8cP1ZE53X3HAdCOBmYkKy0pTtOWU1oAX8Y5p+nLq5SfnRoW61lIlBbAHnVOiNWEK8aopq5J33jqUzU0tfiOBACQ9Elxpe6cvEinjcjU9cdm+44DoJ2ZmQqy0zS9pIp1LYAvUbpxm9Zs2hY2U0MkSgtgrwb3SNFdXzlUs1Zu1G8nLfQdBwAi3rrqbbr56U81IC1Rf7hwZNi8iwRg7/KzU7Vm0zaVbmRdC2BvPmmdRpWfRWkBRIyzRvbStUdn6bGPV+jlOWt8xwGAiNXQ1KJvPPWp6hqb9cAVY5QcH+M7EoAOsuNdY9a1APZuekmVUpPiNChM1rOQKC2AfXLbaUM1dkCqbvvPfC0q2+w7DgBEpN+8tkCfrdqku74yUgMzUnzHAdCBBmUkKzUpTtNKWCAd2JtpJZXKz0pVVFT4XIlIaQHsg9joKP31ssOUkhCjG5+Yreptjb4jAUBEeeHTUv3zk5W6/thsnXFoT99xAHQwM1N+VipXWgB7sbqqVms2bVN+64474YLSAthHGSkJ+vtlo1W6cZu++9wctbSwEBQAdISitdX60QvzVZCdqh+MG+I7DgBPCrLTtGbTNq2uqvUdBQik6cu3X4lUkBM+61lIlBbAfskbkKqfnpGrtxaW6/6pxb7jAEDYq65t1E1PfqquibH6yyWjFRPNSxcgUu1Y12LHH2YAvmhaSaW6JcZqcJhNoeQ3P7CfrjpygM4Z1Ut/fGOx3l9S4TsOAIStlhan/3tujtZVb9PfLxuj9JR435EAeDQoI1ndEmOZIgLswbSSSo0Ns/UsJEoLYL+ZmX53/iEanJGibz/7mUo3cokiALSHv767TO8sKtfPzxymMf27+Y4DwLOoKFN+VhqlBbAbpRtrVbpx2+dXJIUTSgvgACTGxWjCFWPU1Ox005Pbt98DALSddxeX689vLdH5h/XW5QX9fccBEBAF2akq3biNN42AXUxv3VmH0gLA57K6J+lPXx2l+WuqdfvEIt9xACBsrK6q1a3PztGQHim647xDZBZel7kCOHD5O9a1YOtT4AumlVSqa2KshvQIr/UsJEoL4KCcMqyHbj5hoJ6duVrPzljlOw4AhLy6xmbd+ORstTinB64Yo05x0b4jAQiQIT1S1JV1LYD/MW15pcYOCL/1LCRKC+Cg/d8pg3XMoO76+cQizSvd5DsOAIQs55x++lKhitZu1j1fHaX+aUm+IwEImO3rWqSygwiwk+1bAYfnehYSpQVw0KKjTPdefJjSk+N105Ofqmprg+9IABCSnp6xSv+eXapbThqkk3J7+I4DIKDys9K0qqpWazZt8x0FCITprVce5Wenek7SPigtgDaQmhSn+y8frYqaen372c/U3OJ8RwKAkPLZqo26fWKRjhucrm+fNMh3HAABVvD5uhZMEQGk7etZdOkUq9zMzr6jtAtKC6CNHNqnq359znB9sHSD/vzmEt9xACBkVNbU6xtPfaoenRN078WjFB2G83EBtJ2hmSnq0imWxTiBVtOXV2lsVniuZyFRWgBt6quH99PFh/fVX99dpjcXrPcdBwACr6m5Rd965jNVbW3QhMvHqGtinO9IAAJux7oW05ZzpQWwdtM2raysDdv1LCRKC6DN3X72cB3ap4u+8685Wr5hq+84ABBof3xjiT4urtRvzh2hEb27+I4DIETkZ6dpZWWt1lWzrgUi2/TW8i4/KzzXs5AoLYA2lxAbrb9fNlox0aYbn5it2oYm35EAIJAmF5ZpwtRiXZrfTxfm9fUdB0AIKWhdcJApIoh000uq1DkhRrk9w3M9C4nSAmgXfbol6r5LDtOS8i2647WFvuMAQOCUVdfp+/+eq5F9uugXZw3zHQdAiMnN7KwunWI1jcU4EeGmlVRqbFZaWK8HRWkBtJNjBqXr68dk66npq/TB0grfcQAgMJxz+uF/5qmp2eneiw9TfEy070gAQkxUlOnwAamUFohoZdV1WlFZ+/mVR+GK0gJoR985ZbBy0pP0g3/P0+a6Rt9xACAQ/jVztaYuqdBtpw3VgO5JvuMACFEF2alaUVmrsuo631EAL3asZxHOi3BKlBZAu0qIjdbdF43S+s11+vUrC3zHAQDvSjfW6jevLdQR2Wm6oqC/7zgAQtiOP9Sms4sIItS0kkqlhPl6FhKlBdDuRvXtqpuOz9Hzs0v19kK2QQUQuVpanH7w73lyzumurxwatvvJA+gYuT07KyUhhikiiFjTSqo0dkBqWK9nIVFaAB3ilpMGaWhmim57Yb421Tb4jgMAXjw5faU+Lq7UT84Ypr6pib7jAAhx0VGm/KxUdhBBRFq/uU7LN2wN+6khEqUF0CHiY6J190UjtXFrg34xsch3HADocCs2bNXvJi3SsYPTdclYtjcF0DYKstNUsmGr1m9mXQtElh1XGFFaAGgzw3t10bdOHKSX56zV5MJ1vuMAQIdpaXH6/r/nKiba9PsLDpFZeF/GCqDj5Gdt/4ONKSKINNNKqpQSH6NhvcJ7PQuJ0gLoUN84IUeH9O6in7xYqMqaet9xAKBDPPLRcs1csVG/OGu4enbp5DsOgDAyrFdnpcTHaBpTRBBhppdU6vCs8F/PQqK0ADpUbHSU/njhSG2pa9LPXi6Uc853JABoV8UVNfrDlMU6OTdDF4zu7TsOgDATHWU6amB3vbuoXC0tvK5CZCipqFHJhq06emB331E6BKUF0MGGZKbo1lMGadL8Mr0yj2kiAMJXU3OLvvvcXHWKi9Zvz2daCID2MX5Epso212lu6SbfUYAOMaVo+46E40dkek7SMSgtAA+uPyZbo/p21c9fLlT5FhaOAhCeHvygRHNWb9KvzhmhjJQE33EAhKkThmYoJso0uajMdxSgQ0wuKtOhfbqoV9fImHJJaQF4EBMdpbsvGqltDc368QvzmSYCIOwsLtuie95cqtNGZOqsQ3v6jgMgjHXpFKsjB3bXlMIyXlMh7K3dtE1zV2/SuOGRcZWFRGkBeJOTnqzvjxuitxaW6z+frvEdBwDaTGNzi77z3BylJMToN+eOYFoIgHY3fnimVlTWavH6Lb6jAO3qjdYriiJlaohEaQF4dc1RWRo7IFW/fKVI66q3+Y4DAG3i7+8Wq2jtZt1x3gilJcf7jgMgApwyrIfMpMmFTBFBeJtcVKZBGcnKSU/2HaXDUFoAHkVFmf5w4aFqanb64X+YJgIg9BWuqdZf3lmqc0b10vgRTAsB0DHSU+J1eP9USguEtcqaes1YXhVRV1lIlBaAd/3TkvTj04fq/SUVenbmat9xAOCA1Tc163vPz1VqUpx+efZw33GAkGdmqWb2ppktbf3cbQ/nrTCz+WY2x8xmdXTOoBg3IlOLyrZoZeVW31GAdvH2wnK1OEXUehYSpQUQCJfl99eROWn6zasLtLqq1nccADgg9729VIvKtujOCw5R18Q433GAcHCbpLedc4Mkvd16e09OcM6Ncs7ldUy04Bk3vIckaQq7iCBMTS4qU59unTS8V2ffUToUpQUQAFFRpru+cqjMTD/49zy1tDBNBEBombN6k+5/r1gXjumjE4f28B0HCBfnSHq89evHJZ3rL0rw9emWqBG9OzNFBGFpS12jPly6QeOHZ0bcAteUFkBA9OmWqJ+ekatPSir1xLSVvuMAwD6ra2zWd5+box6dE/Szs4b5jgOEkx7OuXWS1Po5Yw/nOUlvmNlsM7t+T9/MzK43s1lmNquioqId4vo3fnimPl21Ses31/mOArSpdxdXqKG5ReMibD0LidICCJSvHt5Xxw9J152vL9KKDczHBBAa/vTmEhVXbNXvLzhUnRNifccBQoqZvWVmhbv5OGc/vs1RzrnRkk6T9E0zO3Z3JznnHnTO5Tnn8tLT09skf9DsWKDwDaaIIMxMKSxT9+R4je6326VtwhqlBRAgZqY7zz9UsdGm7/97rpqZJgIg4GatqNI/PijRpfn9dOzg8PwjCGhPzrmTnXMjdvPxsqT1ZtZTklo/l+/he6xt/Vwu6UVJYzsqf9AMzEhRTnqSJlNaIIzUNTbr3cXlOnV4D0VHRdbUEInSAgiczC4Juv3s4Zq5YqMe/Wi57zgAsEe1DU363vNz1btrJ/349FzfcYBwNFHSVa1fXyXp5V1PMLMkM0vZ8bWkUyUVdljCABo/IlPTSqq0cWuD7yhAm/hg6QbVNjRrfITtGrIDpQUQQOcd1lunDOuhu6Ys1rLyGt9xAGC37pq8WCsqa/WHr4xUcnyM7zhAOLpT0ilmtlTSKa23ZWa9zGxS6zk9JH1oZnMlzZD0mnNuspe0ATF+eE81tzi9tXC97yhAm5hcWKbOCTEqyE7zHcULSgsggMxMd5w3Qolx0fru83PV1NziOxIAfMHHxRv02Mcr9LUjB+iInMh8EQW0N+dcpXPuJOfcoNbPVa3H1zrnTm/9usQ5N7L1Y7hz7g6/qf0b0buzenftpClFlBYIfY3NLXp70XqdnNtDcTGR+ee7t2dtZkeZ2ZtmtsTMSsxsuZmV+MoDBE1GSoJ+fc4IzV29SQ+8z/8aaF9m9qtdbkeb2VO+8iDYauqb9IN/z1NW9yT9cPxQ33EA4AvMTKcO76H3l1Zoa32T7zjAQZmxvEqbahsjcteQHXxWNQ9L+pOkoyUdLimv9TOAVmeN7KUzDumpe95aokVlm33HQXjrZ2Y/kiQzi9f2hdyW+o2EoLrjtYVau2mb/njhoeoUF+07DgD8j/HDM9XQ1KL3Fofn1q6IHJMLy5QQG6VjB0XuYtc+J6BWO+de9/jzgZDw63NHaPrySn33ubl66ZtHKTY6Mi8LQ7u7WtJTrcXFCZJed8792XMmBND7Syr0zIxVuuHYbI3pn+o7DuCdmZ2/t/udcy90VBb8V96AVKUlxWlyUZnOOLSn7zjAAWlpcZpSVKbjB2dE9JsEHV5amNno1i/fNbM/SHpBUv2O+51zn3Z0JiDIUpPidMd5h+iGJ2brb+8u060nD/YdCWFkpzFZku6V9ICkjyRNNbPRjMnYWfW2Rv3wP/M0MCNZ/3cKYxHQ6qy93Oe0/bUuOlh01PYpIhPnrFVdY7MSYiP3Dz6Ers9Wb1L5lnqNj+CpIZKfKy3u3uV23k5fO0kndmAWICSMG56p8w7rrb++s0wn5/bQiN5dfEdC+Nh1TN4oaVjrccZkfMGvX12g8i31euHyMfwBALRyzl29L+eZ2VXOucfbOw/+a9zwTD0zY7U+Lt6gE4f28B0H2G9TisoUG206YWiG7yhedXhp4Zw7YV/OY2AHvuj2s4bro2Ub9N3n5mrit45SfAx/MODgMSZjX729cL3+PbtUN58wUCP7dvUdBwhF35bEONqBjszprpT4GE0uLKO0QMhxzmlyYZmOzOmuLp1ifcfxKsiT47/tOwAQJF0SY/X7Cw7V4vVbdO9brI+IDseYHME2bm3QbS/M19DMFN1y0iDfcYBQZb4DRJq4mCidlJuhtxaWs308Qs6isi1aVVUb8VNDpGCXFgzswC5OGJqhi/L6aMLUYn22aqPvOIgsjMkR7BcTi7Rxa4PuvmhkxO4RD7QB5ztAJBo3PFNVWxs0cwWvmxBaJheWyUw6ZRhXCQX5lQcDO7AbPz1zmDI7J+i7z89VXWOz7ziIHIzJEer1+es0ce5a3XLSIA3vxXo6wEGg/PXguCHpio+J0pSiMt9RgP0ypahMhw9IVffkeN9RvAtyacHADuxG54RY3fWVkSqp2Kq731jsOw4iB2NyBNpQU6+fvFSoQ3p30U3H5/iOA4S6j3wHiESJcTE6bnC6JheWqaWF/h2hYfmGrVpUtkXjhjM1RAp2acHADuzB0YO66/KCfnrow+WauaLKdxxEBsbkCOOc089eKlRNXZPuvmikYqOD/JIB8M/MfmtmXXe63c3MfrPjtnPuZi/BoPEjMlW2uU7z1lT7jgLskx1XBo0bztQQyWNpwcAOHJwfnZarPt066XvPz1VtQ5PvOAhxjMnY1cS5a/V6YZn+75TBGtwjxXccIBSc5pzbtOOGc26jpNP9xcEOJw3toZgo0+RCpoggNEwuLNMhvbuoT7dE31ECwefbJgc0sJtZXzN718wWmlmRmbGiPSJSUnyM/vCVkVpZWavfv77IdxyEvv0ekxmPw1f55jr9/OUiHdavq64/Ntt3HCBURJvZ55PPzayTJCajB0CXxFgdkZOmyYXr5BxTRBBs66q3ac7qTewashOfpcWBDuxNkr7rnMuVVCDpm2Y2rJ0yAoFWkJ2mq48aoMc/WamPl23wHQeh7UDGZMbjMOSc049emK+6xmb98cKRio5iORNgHz0p6W0zu9bMrpH0pqTHPWdCq/EjMrWislZL1tf4jgLs1RtF6yWJ9Sx24rO0OKCB3Tm3zjn3aevXWyQtlNS7XZMCAfaDcUOV1T1J3//3PG2pa/QdB6Frv8dkxuPw9O/ZpXp7Ubl+MH6octKTfccBQoKZmaRnJP1GUq6k4ZJ+7Zy7y2swfO6UYT1kJnYRQeBNKSrTwIxkDczgd/AOXkqLthrYzWyApMMkTW/rjECo6BQXrT9eOFLrqrfpD1PYTQT7ry3GZMbj8FC+pU6/enWBxmal6uojB/iOA4QMt33OwUvOucnOue85577rnJviOxf+KyMlQWP6dWNdCwRa1dYGTV9epfFcZfEFMT5+qHPOmdlLzrkxkiYfyPcws2RJ/5F0q3Nu827uv17S9ZLUr1+/g4kLBN6Y/t105RED9PgnK3ThmL46pE8X35EQQg52TGY8Dh+/m7RI9Y0tuvP8QxTFtBBgf00zs8OdczN9B8HujR+Rqd+8tlCrKmvVL40FDhE8by1cr+YWx3oWu/A5PWSamR1+IA80s1htf4H8lHPuhd2d45x70DmX55zLS09PP5icQEj4zqmD1T05Xj95ab6a2Ycc+++AxmTG4/DxcfEGvfjZGt14XLaymRYCHIgTJH1iZsVmNs/M5pvZPN+h8F871ghgigiCakphmXp37aThvTr7jhIoPkuLAxrYWy9jfljSQufcn9o9JRAiOifE6qdn5GpeabWenrHKdxyEnv0ekxmPw0dDU4t+9lKh+qUm6hsnDPQdBwhVp0nKkXSipLMkndn6GQHRNzVRw3t11mRKCwRQTX2TPli6QeOGZ2r7Syzs4GV6SKvTDvBxR0m6QtJ8M5vTeuzHzrlJbZIKCGFnj+yl52at1l2TF2n88Eylp7DTGvbZgYzJjMdh4qEPS1RcsVWPXn24EmKjfccBQhWXOYaA8cMzdfebS1S+uU4ZnRN8xwE+9+6icjU0tzA1ZDd8Xmnh9vCx9wc596FzzpxzhzrnRrV+8AIZkGRm+tU5I1TX2Kzfvb7QdxyElv0ekxmPw0Ppxlrd9/ZSjR+eqROGZPiOA4Sy1yS92vr5bUklkl73mgj/Y8cfhFMWrPecBPiiyUVl6p4cpzH9u/mOEjg+r7R4TdtfEJukBElZkhZr+6r1AA5QTnqybjg2R399d5kuyuurguw035EQGhiTI9TtExcoykw/P2uY7yhASHPOHbLzbTMbLekGT3GwBwMzkpXdPUlvFJXpioL+vuMAkqS6xma9t6hcZ4/qrWgWwv4f3q60cM4d0vru3CHOuUGSxkr60FceIJx884SB6tOtk372UqEamlp8x0EIYEyOTG8uWK+3Fq7Xt08apF5dO/mOA4QV59ynkg5o0Xm0HzPTuBGZ+qS4UptqG3zHASRJHy3boK0NzUwN2QOf00O+gIEdaDud4qL1y7OHa2l5jR75aLnvOAhBjMnhb1tDs26fWKTBPZJ1zdFZvuMAIc/MvrPTx/fM7GlJFb5z4X+NH56pphantxeW+44CSJImF5YpJSFGR3CF9G55mx5iZt/Z6WaUpNFiYAfazEm5PXTqsB66962lOmtkL/XmXVTsBWNy5Pnru0u1ZtM2PXfDEYqNDsx7GEAoS9np6yZtn3b3H09ZsBeH9uminl0SNLmoTBeM6eM7DiJcU3OL3ly4Xifn9lBcDL+Pd8fnP5WUnT7itX1gP8djHiDs7Jij/suJRZ6TIAQwJkeQZeVb9OD7JbpgdB+NzUr1HQcIC865XzrnfinpT5Ludc495Zyr850L/8vMNG54pt5fUqGt9U2+4yDCzVhepU21jRo3vIfvKIHl7UqL1kFdZpay/aar8ZUFCFd9uiXqlpMG6feTF+nthet1Ui6DIXaPMTlyOOf0s5eK1Ck2Wj86fajvOEDYMLMRkp6QlNp6e4Okq5xzhV6DYbfGj8jUYx+v0NQlFTr9kJ6+4yCCTS4qU0JslI4dnO47SmB5u9LCzEaY2WeSCiUVmdns1sEeQBu69ugsDcpI1i8mFmlbQ7PvOAgoxuTIMXHuWn1SUqkfjB+q7snxvuMA4eRBSd9xzvV3zvWX9N3WYwigwwekKi0pTpMLy3xHQQRraXGaUlSm4wanKzHO58aeweZzeggDO9AB4mKi9OtzR6h04zb9/b1lvuMguBiTI8Dmukb9+tWFGtm3qy4Z2893HCDcJDnn3t1xwzn3nqQkf3GwN9FRppNze+idReWqb+JNHfgxp3ST1m+uZ9eQL+GztGBgBzpIQXaazj+styZMLVZxBVf9Y7cYkyPA3VMWq2prvX5zzgj2gQfaXomZ/czMBrR+/FQSW3gF2PgRmaqpb9LHxZW+oyBCTSkqU0yU6cShTOHeG5+lBQM70IF+dHquEmKj9fOXC+Wc8x0HwcOYHObml1briWkrdUVBfx3Sp4vvOEDYMLMnWr/8QFK6pBckvSipu6SrfeXClztyYJqS42M0hSki8MA5pymFZTpyYHd16RTrO06gdXhpwcAO+JGeEq8fjBuij5ZV6pV563zHQUAwJkeG5hann740X6lJ8frOqUN8xwHCzRgz6y/pKkm3SzpF0kmSfiWJS5oCLD4mWicOzdAbC9aruYU3dNCxFq/fohWVtRo/nKkhX8bHah87D+wnaPtgvmOUYGAH2tGl+f31/OxS/frVBTp+SLo6J9DqgjE5Ejw7c5Xmllbr3otH8W4O0PYmSJosKVvSrJ2O7xhPs32Ewr4ZPyJTE+eu1cwVVSrITvMdBxFkcmGZzKRThjE15Mv4mB6yY2Afqu0D+8zWz7P1xYEeQBuLjjL95twR2lBTrz+9scR3HAQDY3KY21BTr9+/vkhHZKfp7JG9fMcBwo5z7j7nXK6kR5xz2Tt9ZDnnKCwC7rjB6YqPiWIXEXS4yYVlyuvfTekp7OT1ZTq8tGBgB/w6tE9XXZ7fX//8ZIUK11T7jgPPGJPD3+8mLdK2xmb9+tzhMuPiGaC9OOdu8p0B+y8pPkbHDk7XlKIy1vxCh1mxYasWlW3ROKaG7BNvC3EysAP+fO/UIUpNitNPXypUC3M4IcbkcDW9pFL/+bRUXz8mWwMzUnzHAYBAGj88U+uq6zSvlDdz0DGmFG2/sofSYt/43D0EgCddEmP149NzNWf1Jv1r1mrfcQC0g8bmFv3s5UL17tpJ3zpxkO84ABBYJ+VmKDrKNLmIKSLoGJOLyjSid2f1TU30HSUkUFoAEeq8w3orPytVd76+SJU19b7jAGhjj3y4XEvW1+iXZw9Xp7ho33EAILC6JsbpiOw0TSlkigja3/rNdfps1SZ2DdkPlBZAhDLbvijn1vom3fn6It9xALShtZu26Z63lurk3B46mVXJAeBLjRuRqZINW7WsvMZ3FIS5N1qv6Bk/gtJiX1FaABFsUI8UXXdMtp6fXaqZK6p8xwHQRn71ygI5Of3irGG+owBASBg3rIfMxC4iaHeTi8qUk57EWlP7gdICiHC3nDRQvbt20k9fLFRjc4vvOAAO0ruLyjW5qEzfOnEQc2UBYB9ldE7Q6H7dWNcC7Wrj1gZNK6liAc79RGkBRLjEuBj94qxhWrx+ix77aIXvOAAOQl1js34xsUg56Un6+jHsWAsA+2P88EwVrd2s1VW1vqMgTL21cL2aWxxTQ/YTpQUAnTKsh04amqE/v7VE66q3+Y4D4AD9/d1lWlVVq1+fO0JxMfyKB4D9sePd7ylcbYF2MqWoTL26JOiQ3l18RwkpvKIBIDPT7WcPV3OL069fXeA7DoADUFJRowlTS3TuqF46Mqe77zgAEHL6pSUqt2dn1rVAu6ipb9L7Szdo3IhMmZnvOCGF0gKAJKlvaqK+deJATZpfpvcWl/uOA2A/OOf085eLFB8bpR+fkes7DgCErPHDMzV71UaVb6nzHQVhZuriCjU0tbDV6QGgtADwua8fm63s9CT9YmKR6hqbfccBsI9enbdOHy7boO+PG6KMlATfcQAgZI0fkSnnpDcXrPcdBWFmclGZ0pLilDcg1XeUkENpAeBz8THR+vU5I7Syslb3v1fsOw6AfbClrlG/fnWBRvTurMvy+/uOAwAhbXCPZGV1T2KKCNpUXWOz3lm4XqcO76HoKKaG7C9KCwBfcNTA7jp7ZC/dP7VYyzds9R0HwJf485tLVVFTr9+cewgvhADgIJmZxg3P1CfFlaqubfQdB2Hi4+IN2trQzFanB4jSAsD/+OkZuYqLjtIvJhbJOec7DoA9KFpbrcc+Xq5Lx/bTqL5dfccBgLAwfkSmmlqc3l7EFBG0jcmFZUqJj2Gh7ANEaQHgf2R0TtB3Tx2s95dUaNJ8Lo8EgqilxelnLxWqW2KcfjBuqO84ABA2Du3dRT27JDBFBG2iqblFby5YrxNzM9iO/ADxTw3Abl1R0F/De3XWr14tUk19k+84AHbx3KzV+nTVJv349Fx1SYz1HQcAwkZUlOnUYT00dUmFaht4DYSDM2NFlTbWNrJryEGgtACwWzHRUfrNuSNUvqVe97y5xHccADup2tqgOycv0tgBqTp/dG/fcQAg7Iwbkan6phZNXVzhOwpC3JTCMsXHROm4Iem+o4QsSgsAe3RYv266+PB+evTjFVq4brPvOABa/f71Raqpa9Kvzx0hMxbfBIC2NnZAqrolxmpKEVNEcOBaWpymFK3XcYPTlRgX4ztOyKK0ALBXPxw/RF06xeqnLxWqpYVFOQHfZq2o0r9mrda1R2dpSGaK7zgAEJZioqN0yrAeenthuRqaWnzHQYiat6ZaZZvrNH4EU0MOBqUFgL3qmhinH502VLNXbtS/Z5f6jgNEtKbmFv30pUL16pKgW04a5DsOAIS18SMytaW+SR8Xb/AdBSFqcmGZYqJMJw3t4TtKSKO0APClLhjdR4cP6Kbfvb5QG7c2+I4DRKzHPl6hRWVb9POzhispnstMAaA9HZnTXcnxMUwRwQFxzmly4TodkZPGgtkHidICwJeKijL9+twR2lzXpLumLPIdB4hIZdV1+vObS3TCkHSNG847NgDQ3hJio3XC0Ay9UbRezUyRxX5asr5GKyprNY5dQw4apQWAfTI0s7OuOWqAnpmxWp+u2ug7DhBxfv3aAjW1OP3ybBbfBICOMm54D1VubdCsFVW+oyDETC4sk5l06jDeaDhYlBYA9tmtJw9WZucE/eTFQjU1sygV0FHeX1Kh1+at080nDFS/tETfcQAgYhw/JENxMVGazBQR7KfJRWUa06+bMjon+I4S8igtAOyzpPgY/eKsYVq4brP++clK33GAiFDf1Kyfv1yorO5Juv64bN9xACCiJMfH6NhB3fVG0Xo5xxQR7JtVlbVauG4zu4a0EUoLAPtl/IhMHTOou/781hJVsSgn0O4e/WiFVlTW6pdnD1d8TLTvOAAQccYNz9SaTdtUuGaz7ygIETsWb2U9i7ZBaQFgv5iZfn7mMNU2NOtPby72HQcIaxVb6vXXd5bp5NwMHTs43XccAB3MzC40syIzazGzvL2cN97MFpvZMjO7rSMzRoKTc3soOso0uWid7ygIEZOLyjS8V2f1TWVKZ1ugtACw3wb1SNHl+f309PRVWlTGuw5Ae7n7jcWqb2rWj0/P9R0FgB+Fks6X9P6eTjCzaEl/k3SapGGSLjGzYR0TLzJ0S4pTQXaqJheyrgW+XPnmOs1euVHjucqizVBaADggt548WCkJsfr1qwuY4wm0g8I11frXrNW66ogByk5P9h0HgAfOuYXOuS+7rHGspGXOuRLnXIOkZyWd0/7pIsv44ZkqrtiqZeVbfEdBwE1ZsF6SWM+iDVFaADgg3ZLidOvJg/TRskq9tbDcdxwgrDjn9KtXF6hbYpy+ddIg33EABFtvSat3ul3aegxt6JRh2/8A5WoLfJkphWXK7p6kgRm84dBWKC0AHLDLC/prYEay7nhtgRqa2AIVaCuTC8s0Y3mVvnPKYHXpFOs7DoB2ZGZvmVnhbj729WoJ282x3V4CaWbXm9ksM5tVUVFx4KEjUGaXBB3Wrytbn2KvNtU26JOSSo0bkSmz3f2viQNBaQHggMVGR+mnZ+RqRWWtHv94he84QFioa2zWHZMWamhmii4+vK/vOADamXPuZOfciN18vLyP36JU0s6DRR9Ja/fwsx50zuU55/LS01ncd3+NH56pwjWbtbqq1ncUBNRbC8vV3OJYz6KNUVoAOCjHD8nQ8UPSdd/bS1VZU+87DhDyHvlouUo3btPPzhymmGh+TQP4UjMlDTKzLDOLk3SxpImeM4WlHdtXvtG6ZgGwqylFZerZJUGH9uniO0pY4dUQgIP20zOGqbaxWXe/ucR3FCCklW+u09/eWaZThvXQUQO7+44DwDMzO8/MSiUdIek1M5vSeryXmU2SJOdck6SbJU2RtFDSc865Il+Zw9mA7kkampmiV+ft9kIWRLjqbY16f0mFxg1nakhbo7QAcNAGZiTrioL+enbGKi1cxxaowIH64xuL1dDcwhanACRJzrkXnXN9nHPxzrkezrlxrcfXOudO3+m8Sc65wc65HOfcHf4Sh7+L8vrqs1WbNHtlle8oCJgnp61UfVOLLszr4ztK2KG0ANAmbj15kDp3itWvXmELVOBAFK6p1vOzS3X1UVnK6p7kOw4AYDcuHttXXRNjdf97Jb6jIEDqGpv16EfLddzgdA3vxdSQtkZpAaBNdE2M03dOGaxPSiqZ6wnsJ+ecfvlKkVIT43TziQN9xwEA7EFiXIyuOmKA3lq4XkvWb/EdBwHx/OxSbahp0I3H5fiOEpYoLQC0mUvH9tOgjGT9dtJC1Tc1+44DhIxJ88s0c8VGfffUIeqcwBanABBkVx05QJ1iozVharHvKAiApuYWPfh+sUb17aqC7FTfccISpQWANhMTHaWfnTlMKytr9dhHK3zHAUJCXWOzftu6xelX2eIUAAIvNSlOF4/tq4lz1mrNpm2+48CzSYVlWl21TTcdn8MCnO2E0gJAmzp2cLpOHJqhv7yzTBVb2AIV+DIPf7hcazZt08/PGqboKF7sAEAouO6YbEnSQx+wtkUkc87p/veKlZOepFNye/iOE7YoLQC0uZ+ckau6xmb96c3FvqMAgbZ+c53+9u4yjRveQ0fmsMUpAISK3l076exRvfTsjNXauLXBdxx4MnVJhRau26wbjstRFG88tBtKCwBtLic9WVcdOUDPzlytorXVvuMAgfWHKYvV1OzY4hQAQtCNx+VoW2OzHv9khe8o8GTC1GJldk7QuaN6+44S1igtALSLW04cpK5sgQrs0bzSTfr37FJdffQA9U9ji1MACDWDe6To5NweeuzjFaptaPIdBx3s01UbNa2kStcdk6W4GP6sbk/80wXQLrokxuo7pw7R9OVVmlJU5jsOECjOOf3qlQXqnhynm09gi1MACFU3HZ+tTbWNenbGat9R0MEmvFesLp1idfHYfr6jhD1KCwDt5pLD+2pIjxTdMWmh6hrZAhXY4dV56zRr5UZ979QhSmGLUwAIWWP6p2rsgFQ99EGJGptbfMdBB1lWvkVvLFivq47or+T4GN9xwh6lBYB2s2ML1NVV2/QoW6ACkrZvcXrn64s0rGdnXZjHFqcAEOpuOj5Ha6vrNHHOWt9R0EEemFqihNgoXXXkAN9RIgKlBYB2dfSg7jo5t4f++s5SlW+p8x0H8O4f75ewxSkAhJHjh6RraGaKJkwtVksL63iFu3XV2/TSnDX6al5fpSXH+44TEUKutDCzR8ys3MwKfWcBsG9+ckauGppbdPeUJb6joI0xJu+fsuo6/f29Yp02IlMF2Wm+4wAA2oCZ6cbjcrS0vEbvLCr3HQft7OEPlqvFSdcdk+07SsQIudJC0mOSxvsOAWDfZXVP0teOHKDnZq9W4Rq2QA0zj4kxeZ/dNWWRmlvY4hQAws2Zh/ZUn26d9Pf3lrFrWhjbVNugp2es0lmH9lTf1ETfcSJGyJUWzrn3JVX5zgFg/9x84iB1S4xjC9Qww5i87+as3qQXPl2ja4/J4oUOAISZmOgoff2YbH26apNmrtjoOw7ayT8/WanahmbdeHyO7ygRJeRKCwChqUunWH331MGasaJKrxeyBSoiy/YtTouUnhKvb7LFKQCEpYvy+io1KU4Tphb7joJ2sK2hWY99vEInDs3Q0MzOvuNElLAtLczsejObZWazKioqfMcBIOniw/tpaGaKfssWqBGF8ViaOHetPl21Sd8fN4St0QAgTHWKi9bVRw7QO4vKtXDdZt9x0Maem7VaVVsbdONxXGXR0cK2tHDOPeicy3PO5aWnp/uOA0BSdJTp52cOU+nGbXr4w+W+46CDRPp4vK1h+xanI3p31ldG9/EdBwDQjq44or8S46L1AFdbhJXG5hY9+H6JxvTvpsMHdPMdJ+KEbWkBIJiOHNhdpw7rob+9u0zlm9kCFeHvwfdLtK66Tj8/c7ii2OIUAMJa18Q4XTq2n16Zt06rq2p9x0EbeW3eOq3ZtE03HZcjM36Xd7SQKy3M7BlJn0gaYmalZnat70wA9s+PT89VY3OL7pqy2HcUHCTG5L1bV71NE6YW64xDempsVqrvOACADnDtMVmKMumhD0p8R0EbcM7p/veKNSgjWScOzfAdJyKFXGnhnLvEOdfTORfrnOvjnHvYdyYA+2dA9yRdc1SW/j27VPNL2QI1lDEm791dkxer2TnddtpQ31EAAB2kZ5dOOndUb/1r1mpV1tT7joOD9O7ici1ev0U3HpfDFZOehFxpASA8fPPEgUpLitMvXyliC1SEpU9XbdSLn63R19niFAAizg3HZau+qUWPf7zCdxQcpAnvlahXlwSdPaqX7ygRi9ICgBedE2L1vXFDNGvlRr02f53vOECbamlx+tUrC5SREq9vHM8WpwAQaQZmpOjUYT30+CcrVVPf5DsODtCsFVWasaJK1x2Trdho/nT2hX/yALy5KK+vcnt21u8mLWILVISViXPXas7qTfrB+KFKYotTAIhINx6Xo+ptjXp2xirfUXCAJkwtVtfEWF08tq/vKBGN0gKANzu2QF2zaZv+8T6LVSE81DY06c7XF+nQPl10/mG9fccBAHhyWL9uKshO1UMfLFdDU4vvONhPS9Zv0VsLy/W1IwcoMY43IHyitADg1RE5aRo/PFN/f69Y69kCFWHggaklKttcp5+fOYwFuwAgwt10/ECVba7TS3PW+I6C/TRharE6xUbrqiMG+I4S8SgtAHj349Nz1dzi9PvJi3xHAQ7K2k3b9MD7xTrz0J7KG8AWpwAQ6Y4d1F3DenbWhKnFamlh4fFQUbqxVhPnrNXFY/uqW1Kc7zgRj9ICgHf90hJ1zdFZeuHTNZq7epPvOMAB+/3kRXJO+tHpub6jAAACwMx04/E5KqnYqjcWrPcdB/vooQ+WS5KuOybbcxJIlBYAAuKbJ+Soe3K8fvXqArZARUiavXKjXp6zVjccm63eXTv5jgMACIjTR2SqX2qi7p9azGucEFC1tUH/mrlaZ4/qxe/zgKC0ABAIKQmx+v64wZq9cqNemccWqAgt27c4LVKPzvG64bgc33EAAAESEx2l64/N1tzVmzStpMp3HHyJxz9eoW2NzbqR3+eBQWkBIDC+MqavhvfqrDsnLdS2BrZAReh4ac4azS2t1g/Z4hQAsBtfGdNH3ZPjdP/UYt9RsBe1DU16/JMVOjm3hwb3SPEdB60oLQAExo4tUNdW1+lBtkBFiNha36TfT16kkX276txRbHEKAPhfCbHRuvqoLL2/pEJFa6t9x8EePDtjtTbVNuqm41nLIkgoLQAESn52mk4/JFMTphZrXfU233GAL/XA1GKt31zPFqcAgL26vKC/kuNjNGEqb8wEUUNTix76oERjB6RqTH92AAsSSgsAgfOj03LV7JzumrzYdxRgr0o31uqB90t0zqheGtO/m+84AIAA69IpVpfl99Nr89ZqZeVW33Gwi4lz12ptdZ1uOp61LIKG0gJA4PRNTdR1R2fpxc/W6LNVG33HAfbo95MXy0z64fihvqMAAELANUdnKSYqSv/4gKstgqSlxemBqcUampmi44ek+46DXVBaAAikb5wwUOkpbIGK4Jq1okqvzF2rG47NUS+2RAMA7IMenRN0wZjeem5WqSq21PuOg1ZvLyrX0vIa3XhcjsyY6hk0lBYAAik5PkbfHzdEn63apJfnrPUdB/iClhanX76yQJmdE3TDcSzWBQDYd18/JluNzS169KPlvqNAknNO97+3TH26ddKZh/b0HQe7QWkBILC+MrqPDundRb+fvEh1jWyBiuB4ac4azV9TrR+eNkSJcWxxCgDYd9npyTptRKaemLZSW+oafceJeDNXbNSnqzbp68dkKyaaP4+DiH8rAAIrKsr0kzNyta66Tg9/yLsRCIa6xmb9YcpijezTReeMZItTAMD+u/G4HG2pa9LT01f5jhLx7n9vmVKT4nRRXl/fUbAHlBYAAq0gO02nDOuh+98r1oYa5n7Cv4c/XK511XX68em5bHEKADggh/bpqqMGpunhD5ervomrSX1ZuG6z3l1coauPHKBOcdG+42APKC0ABN5tpw3VtsZm3fvWUt9REOE21NTr/veKdeqwHsrPTvMdBwAQwm46bqDKt9TrxU/X+I4SsR6YWqzEuGhdcUR/31GwF5QWAAIvJz1Zl+X309MzVmlZeY3vOIhg97y1RHWNzbrtNLY4BQAcnKMGpumQ3l30wPslam5hp7SOtrqqVq/MW6dLx/ZT18Q433GwF5QWAELCt08apE6x0brz9YW+oyBCLSvfomdmrNZl+f2UnZ7sOw4AIMSZmW48LkfLN2zVlKIy33Eizj8+KFGUSdcek+U7Cr4EpQWAkJCWHK9vnJCjtxaW6+PiDb7jIALd+foiJcZG65aTBvmOAgAIE+NHZGpAWqImTC2Wc1xt0VE21NTrXzNX69xRvdWzSyffcfAlKC0AhIxrjspS766d9NtJC9XCZZToQB8Xb9BbC8v1jRMGKi053nccAECYiI4y3XBcjuaVVuvj4krfcSLG4x+vUENzi244Ltt3FOwDSgsAISMhNlrfHzdEhWs26+W5LFqFjtHS4vTbSQvVu2snXX3UAN9xAABh5vzRvZWREq/73yv2HSUi1NQ36fGPV+jUYT00MCPFdxzsA0oLACHl7JG9dEjvLvrD5MWqa2SLMLS/l+euUeGazfrB+CFKiGU7NABA24qPidY1R2fpw2UbNL+02necsPfsjFXaXNekG4/L8R0F+4jSAkBIiYoy/fj0XK2trtMjHy33HQdhrq6xWX+YvFiH9umisw7t5TsOACBMXZbfTykJMZowlast2lN9U7P+8UGJCrJTdVi/br7jYB9RWgAIOUfkpOnk3B76+7vFqqyp9x0HYezhD5drbXWdfnx6rqKizHccAECYSkmI1RUF/TWpcJ2Wb9jqO07YevmztVq/uV43HT/QdxTsB0oLACHpttOGaltjs+55a6nvKAhTG2rqdf97xTplWA8VZKf5jgMACHNXH5Wl2OgoPfh+ie8oYamlxWnC+8Ua1rOzjh3U3Xcc7AdKCwAhaWBGsi4d209Pz1ilZeU1vuMgDN371lJta2zWbacN9R0FABAB0lPideGYPvrP7FKVb67zHSfsvLFgvUoqturG43NkxtWToYTSAkDIuvXkQeoUG607X1/kOwrCzLLyGj09Y5Uuy++nnPRk33EAABHi+mOz1dTSoodZt6tNOed0/9Ri9UtN1OkjMn3HwX6itAAQstKS4/WNE3L01sL1+oS9zdGG7nx9kRJjo/Xtkwb5jgIAiCD905J0+iE99fS0Vdpc1+g7TtiYVlKluas36evHZismmj+BQw3/xgCEtGuOylKvLgn67aSFamlxvuMgDHxSXKm3Fq7XN04YqLTkeN9xAAAR5sbjcrSlvklPTlvpO0rYuH9qsbonx+nCMX18R8EBoLQAENISYqP1/fFDNH9NtSbOXes7DkJcS4vTHZMWqHfXTrr6qAG+4wAAItCI3l107OB0PfLhCtU1NvuOE/IK11Tr/SUVuvqoLCXERvuOgwNAaQEg5J0zsrdG9O6sP0xZzC93HJSX565R4ZrN+v64IbywAQB4c+Nx2dpQU69/zy71HSXkTZharOT4GF1e0N93FBwgSgsAIS8qyvTj03O1ZtM2PfrRCt9xEKLqGpv1h8mLdUjvLjp7ZC/fcQAAEeyI7DSN7NtV979XrMqaet9xQtbslRs1af46XZbfT106xfqOgwNEaQEgLByZ010n52bo7+8u45c7DsgjHy3X2uo6/fj0XEVFsRUaAMAfM9Mvzhqmyq31+tqjM1VT3+Q7UshZsn6LrnlspvqmJuqG43J8x8FBoLQAEDZuOy1XtY3Nuvftpb6jIMRU1tTr7+8W6+TcHjoiJ813HAAANLpfN/39stFasG6zrv/nLKbA7ofVVbW64uHpio+J0pPX5is1Kc53JBwESgsAYWNgRrIuHdtPT01fpeKKGt9xEELufXuptjU267bThvqOAgDA504c2kN/vPBQfVxcqVufnaNmdkr7Uhtq6nXlIzO0raFZ/7x2rPqmJvqOhINEaQEgrHz75EHqFButO19f5DsKQkRxRY2emr5Kl47tp4EZyb7jAADwBecd1ke/OGuYJheV6ScvzpdzFBd7sqWuUVc9MkPrqrfp0asP19DMzr4joQ1QWgAIK92T43XT8Tl6c8F6TSup9B0HIeB3kxapU2y0bj15kO8oAADs1tVHZemWEwfq2ZmrddeUxb7jBFJdY7Oue3yWFpdt0f2Xj9GY/qm+I6GNUFoACDvXHp2lnl0S9NtJC9XCZZTYi0+KK/XWwvX6xgk5SkuO9x0HAL7AzC40syIzazGzvL2ct8LM5pvZHDOb1ZEZ0XH+75TBuiy/n+5/r1gPvl/sO06gNDW36FvPfKbpy6t090UjdcKQDN+R0IYoLQCEnYTYaH1/3BDNK63WK/PW+o6DgGppcfrtpIXq1SVB1xyV5TsOAOxOoaTzJb2/D+ee4Jwb5ZzbY7mB0GZm+tU5I3TGoT3120mL9Pys1b4jBYJzTj96Yb7eXLBet581TOeM6u07EtoYpQWAsHTuqN4a0buz7pq8mNW2sVsT567V/DXV+v74IUqIjfYdBwD+h3NuoXOOuQD4XHSU6c8XjdIxg7rrthfm642iMt+RvLvz9UV6fnapvn3SIH2NNyHCEqUFgLAUFWX68em5WrNpmx79aIXvOAiYusZm/WHKYo3o3VnnjOQdGQAhz0l6w8xmm9n1ezrJzK43s1lmNquioqID46EtxcVEacLlY3RI7y66+ZnPInoNrwlTi/XA+yW68oj+rE0VxigtAIStI3O66+TcDP393WWqrKn3HQcB8uhHK7Rm0zb9+PRcRUWZ7zgAIpiZvWVmhbv5OGc/vs1RzrnRkk6T9E0zO3Z3JznnHnTO5Tnn8tLT09skP/xIio/Ro187XP1SE3Xd47NUuKbad6QO96+Zq3Tn64t01sheuv2s4TLj93m4orQAENZuO22oahubdd/bS31HQUBU1tTr7+8u08m5GToyp7vvOAAinHPuZOfciN18vLwf32Nt6+dySS9KGtteeREc3ZLi9MS1Y9WlU6yuemSGlm/Y6jtSh5lcWKYfvTBfxw5O190XjuQNiDBHaQEgrA3MSNElY/vqqemrVFxR4zsOAuDet5eqtrFZt52W6zsKABw0M0sys5QdX0s6VdsX8EQE6Nmlk564dntHdflD01VWXec5Ufv7uHiDbnnmM43s21UTLh+tuBj+pA13/BsGEPZuPXmwEmKj9fvXF/mOAs+KK2r01PRVunRsPw3MSPYdBwD2yszOM7NSSUdIes3MprQe72Vmk1pP6yHpQzObK2mGpNecc5P9JIYP2enJeuzqsare1qgrH5muTbUNviO1m/ml1fr647M0oHuiHv3a4UqMi/EdCR2A0gJA2OueHK+bjs/RGwvWa3oEL1aF7SuMd4qN1rdZrAtACHDOveic6+Oci3fO9XDOjWs9vtY5d3rr1yXOuZGtH8Odc3f4TQ0fDunTRQ9eOUYrNtTq6sdmqrahyXekNldcUaOrHp2hrolx+uc1+eqaGOc7EjoIpQWAiHDt0Vnq2SVBv520UC0tzncceDCtpFJvLlivm47PUffkeN9xAABoU0fmdNd9lxymuas36cYnP1VDU4vvSG1mXfU2XfnwDJmkJ6/LV2aXBN+R0IEoLQBEhITYaH1/3BDNLa3WK/PW+o6DDtbS4vTbSQvVs0uCrj2aPdwBAOFp/IhM3Xn+oXp/SYW++/zcsHijZuPWBl3x8Axt3taox68Zq6zuSb4joYNRWgCIGOeO6q3hvTrrrsmLVdfY7DsOOtAr89ZqXmm1vj9uiBJio33HAQCg3Vx0eF/ddtpQvTJ3rW5/pUjOhW5xsbW+SVc/NlOrqmr1j6vyNKJ3F9+R4AGlBYCIERVl+snpuVqzaZse+3iF7zjoIHWNzbpr8mIN79VZ547q7TsOAADt7sbjcnTDsdn65ycrdc9bobnte31Ts258crbmlW7SXy85TAXZab4jwRNKCwAR5ciB3XXS0Az97Z1lqtoavqtr478e/WiF1mzapp+ckcs+7gCAiHHbaUN1UV4f3fv2Uj0eYm/WNLc4fee5ufpg6QbdecGhOnV4pu9I8IjSAkDE+dHpQ1Xb2Kz73g7Ndx6w7ypr6vX3d5fp5NwMHZnT3XccAAA6jJnpt+cdolOH9dAvJhbp5TlrfEfaJ845/fzlQr02b51+cnquLsrr6zsSPKO0ABBxBmak6OLD++rJaStVUlHjOw7a0X1vL1VtY7NuO22o7ygAAHS4mOgo3XfJYSrITtV3n5urdxeX+470pf785hI9NX2VbjwuR18/Ntt3HAQApQWAiPR/pwxWQmy0fj95ke8oaCfFFTV6avoqXTK2rwZmpPiOAwCAFwmx0frHlXka2jNFNz05W7NXVvmOtEePfLhc972zTF/N66sfjh/iOw4CgtICQETqnhyvm47P0ZSi9ZqxPLi/vHHgfv/6IiXERuvWkwf7jgIAgFcpCbF67Oqx6tmlk65+dKYWlW32Hel/vPhZqX716gKNH56pO84bITPWocJ2lBYAItY1R2WpZ5cE3fHagrDYxxz/Nb2kUm8sWK+bjs9R9+R433EAAPCue3K8nrh2rBLjYnTlwzO0uqrWd6TPvbNovb7//DwdmZOmey4epZho/kzFf/FfA4CI1SkuWt87dYjmllbrlXlrfcdBG2lpcbpj0kL17JKga47K8h0HAIDA6NMtUf+8dqzqm1p0+cPTVbGl3nckzVxRpZue/FS5PTvrwSvzlBAb7TsSAobSAkBEO++w3hrWs7PumrxYdY3NvuOgDbwyb63mlVbre6cOUac4XvgAALCzwT1S9OjVh6t8c72ufGSGNtc1esuycN1mXfPYTPXu2kmPXX24kuNjvGVBcIVkaWFm481ssZktM7PbfOcBELqiokw/PSNXazZtC7k9zIMgaONxXWOz7pq8WMN7ddZ5h/X2HQcAgEAa3a+bJlwxRsvKt+i6x2d5eeNmVWWtrnxkhpLjY/TEdflKYzon9iDkSgszi5b0N0mnSRom6RIzG+Y3FYBQduTA7jppaIb++u4yVW1t8B0nZARxPH7s4xVas2mbfnJ6rqKiWMALAIA9OW5wuv500SjNXFGlm5/+VE3NLR32s8u31Onyh6ersblFT1w7Vr27duqwn43QE4rX34yVtMw5VyJJZvaspHMkLfCaCkBI+9HpQzXung9039tLdfvZw33HCRWBGo+rtjbob+8s00lDM3TkwO4+IgAAEFLOGtlLm7Y16mcvFerGJ2drZJ+uHfJzX5u/Thtq6vXUdflsS44vFYqlRW9Jq3e6XSopf9eTzOx6SddLUr9+/TomGYCQNTAjRRcf3ldvL1qv204byiJQ+yZQ4/EHSytU19SsH50+tN1+BgAA4eaKgv7avK1Rf3pzid5aWN4hPzMlPkYPXDFGh/Xr1iE/D6EtFEuL3V3v+z97FTrnHpT0oCTl5eWxlyGAL/WD8UP10zOGUVjsu0CNx+eM6q0jstOU0TmhvX4EAABh6ZsnDNSNx+XIuY75synKjGmc2GehWFqUSuq70+0+ktirEMBB69Ip1neEUBO48ZjCAgCAAxMdZdr9+xGAXyG3EKekmZIGmVmWmcVJuljSRM+ZACASMR4DAACgXYXclRbOuSYzu1nSFEnRkh5xzhV5jgUAEYfxGAAAAO0t5EoLSXLOTZI0yXcOAIh0jMcAAABoT6E4PQQAAAAAAEQASgsAAAAAABBIlBYAAAAAACCQKC0AAAAAAEAgUVoAAAAAAIBAorQAAAAAAACBRGkBAAAAAAACidICAAAAAAAEEqUFAAAAAAAIJEoLAAAAAAAQSJQWAAAAAAAgkCgtAAAAAABAIFFaAAAAAACAQKK0AAAAAAAAgURpAQAAAAAAAonSAgAAAAAABBKlBQAAAAAACCRKCwAAAAAAEEiUFgAAAAAAIJAoLQAAAAAAQCBRWgAAAAAAgECitAAAAAAAAIFkzjnfGdqdmVVIWtmOP6K7pA3t+P19CdfnJYXvc+N5hYb+zrl03yF8YDw+KOH63HheoSXcnhfjcfsKt/9eduB5hZ5wfW7h9Lz2OB5HRGnR3sxslnMuz3eOthauz0sK3+fG80KkC+f/VsL1ufG8Qku4Pi+0j3D974XnFXrC9bmF6/PaFdNDAAAAAABAIFFaAAAAAACAQKK0aBsP+g7QTsL1eUnh+9x4Xoh04fzfSrg+N55XaAnX54X2Ea7/vfC8Qk+4PrdwfV5fwJoWAAAAAAAgkLjSAgAAAAAABBKlBQAAAAAACCRKCwAAAAAAEEiUFgAAAAAAIJAoLQAAAAAAQCBRWgAAAAAAgECitAAAAAAAAIFEaQEAAAAAAAKJ0gIAAAAAAAQSpQUAAAAAAAgkSgsAAAAAABBIlBYAAAAAACCQKC0AIIKZ2XgzW2xmy8zstt3cb2Z2X+v988xs9E73dTWzf5vZIjNbaGZHdGx6AAAAhDtKCwCIUGYWLelvkk6TNEzSJWY2bJfTTpM0qPXjekn373TfvZImO+eGShopaWG7hwYAAEBEobQAgMg1VtIy51yJc65B0rOSztnlnHMk/dNtN01SVzPraWadJR0r6WFJcs41OOc2dWB2AAAARIAY3wE6Qvfu3d2AAQN8xwAASdLs2bM3OOfSfeeQ1FvS6p1ul0rK34dzektqklQh6VEzGylptqRvO+e27vpDzOx6bb9KQ0lJSWOGDh3aZk8AAA5GgMbjDsfrYwBBsrfxOCJKiwEDBmjWrFm+YwCAJMnMVvrO0Mp2c8zt4zkxkkZL+pZzbrqZ3SvpNkk/+5+TnXtQ0oOSlJeX5xiPAQRFgMbjDsfrYwBBsrfxmOkhABC5SiX13el2H0lr9/GcUkmlzrnprcf/re0lBgAAANBmKC0AIHLNlDTIzLLMLE7SxZIm7nLORElXtu4iUiCp2jm3zjlXJmm1mQ1pPe8kSQs6LDkAAAAiQkRMDwEA/C/nXJOZ3SxpiqRoSY8454rM7MbW+ydImiTpdEnLJNVKunqnb/EtSU+1Fh4lu9wHAAAAHDRKCwAhp7GxUaWlpaqrq/MdZa8SEhLUp08fxcbG+o6yR865SdpeTOx8bMJOXztJ39zDY+dIymvPfACCLxTG5FAYjwHgYIXreExpASDklJaWKiUlRQMGDJDZ7taJ9M85p8rKSpWWliorK8t3HABoN0EfkxmPAUSKcB2PWdMCQMipq6tTWlpaIAfjHcxMaWlpgW66AaAtBH1MDqfx2MyGmNmcnT42m9mtZvZrM5vXeuwNM+vlOyuAjheu4zFXWgAISUEdjHcWChkBoC0EfbwLer595ZxbLGmUJJlZtKQ1kl6UtNE597PW47dI+rmkGz3FBOBR0Me7A8lHaQEAAACEnpMkFTvnVu5yPEmS85AHANoF00MA4ACsXr1aJ5xwgnJzczV8+HDde++9viMBQESK4PH4YknP7LhhZneY2WpJl2n7lRb/w8yuN7NZZjaroqKig2ICiCTtMSZTWgDAAYiJidHdd9+thQsXatq0afrb3/6mBQsW+I4FABEnEsfj1q2mz5b0/I5jzrmfOOf6SnpK0s27e5xz7kHnXJ5zLi89Pb1jwgKIKO0xJlNaAMAB6Nmzp0aPHi1JSklJUW5urtasWeM5FQBEnggdj0+T9Klzbv1u7nta0gUdnAcAJLXPmMyaFgBC2i9fKdKCtZvb9HsO69VZvzhr+D6fv2LFCn322WfKz89v0xwAEGp8j8kRNB5foi9ODRnknFvaevNsSYu8pAIQGL7HY6ntxmRKCwA4CDU1Nbrgggt0zz33qHPnzr7jAEDEipTx2MwSJZ0i6YadDt9pZkMktUhaKXYOAeBZW47JlBYAQtr+tL1trbGxURdccIEuu+wynX/++d5yAEBQ+BqTI2k8ds7VSkrb5RjTQQB8QTi9RmZNCwA4AM45XXvttcrNzdV3vvMd33EAIGIxHgNAcLTHmExpAQAH4KOPPtITTzyhd955R6NGjdKoUaM0adIk37EAIOIwHgNAcLTHmMz0EAA4AEcffbScc75jAEDEYzwGgOBojzGZKy0AAAAAAEAgUVoAAAAAAIBA8lZamNmFZlZkZi1mlrfT8bFmNqf1Y66ZnbfTfZeY2Xwzm2dmk82su5/0AHwLhUuBQyEjALSFoI93Qc8HAG0l6OPdgeTzeaVFoaTzJb2/m+N5zrlRksZLesDMYswsRtK9kk5wzh0qaZ6kmzswL4CASEhIUGVlZaAHZeecKisrlZCQ4DsKALSroI/JjMcAIkW4jsfeFuJ0zi2UJDPb9XjtTjcTJO34J26tH0lmVimps6Rl7Z8UQND06dNHpaWlqqio8B1lrxISEtSnTx/fMQCgXYXCmMx4DCAShOt4HMjdQ8wsX9IjkvpLusI519R6/CZJ8yVtlbRU0je9hQTgTWxsrLKysnzHAACIMRkAgiJcx+N2nR5iZm+ZWeFuPs7Z2+Occ9Odc8MlHS7pR2aWYGaxkm6SdJikXto+PeRHe/nZ15vZLDObFeSmCQAAAAAA7F67XmnhnDv5IB+/0My2Shqh7VND5JwrliQze07SbXt57IOSHpSkvLy8YE7qAQAAAAAAexS4LU/NLKt10U2ZWX9JQyStkLRG0jAzS2899RRJC72EBAAAAAAA7c7bmhatW5n+RVK6pNfMbI5zbpykoyXdZmaNklokfcM5t6H1Mb+U9H7rfSslfc1LeAAAAAAA0O587h7yoqQXd3P8CUlP7OExEyRNaOdoAAAAAAAgAAI3PQQAAAAAAECitAAAAAAAAAFFaQEAAAAAAAKJ0gIAAAAAAAQSpQUAAAAAAAgkSgsAAAAAABBIlBYAAAAAACCQKC0AAAAAAEAgUVoAAAAAAIBAorQAAAAAAACBRGkBAAAAAAACidICAAAAAAAEEqUFAAAAAAAIJEoLAAAAAAAQSJQWAAAAAAAgkCgtAAAAAABAIFFaAAAAAACAQIrxHQAAAADAlzOzIZL+tdOhbEk/l9Rb0lmSGiQVS7raObepwwMCQDvgSgsAAAAgBDjnFjvnRjnnRkkaI6lW0ouS3pQ0wjl3qKQlkn7kLyUAtC1KCwAAACD0nCSp2Dm30jn3hnOuqfX4NEl9POYCgDZFaQEAAACEnoslPbOb49dIer2DswBAu6G0AAAAAEKImcVJOlvS87sc/4mkJklP7eFx15vZLDObVVFR0f5BAaANUFoAAAAAoeU0SZ8659bvOGBmV0k6U9Jlzjm3uwc55x50zuU55/LS09M7KCoAHBxKCwCIYGY23swWm9kyM7ttN/ebmd3Xev88Mxu9y/3RZvaZmb3acakBIOJdop2mhpjZeEk/lHS2c67WWyoAaAeUFgAQocwsWtLftP0du2GSLjGzYbucdpqkQa0f10u6f5f7vy1pYTtHBQC0MrNESadIemGnw3+VlCLpTTObY2YTvIQDgHYQ4zsAAMCbsZKWOedKJMnMnpV0jqQFO51zjqR/tl5qPM3MuppZT+fcOjPrI+kMSXdI+k4HZweAiNR6JUXaLscGeooDAO2OKy0AIHL1lrR6p9ulrcf29Zx7JP1AUsvefggLvwEAAOBAUVoAQOSy3RzbdfG23Z5jZmdKKnfOzf6yH8LCbwAAADhQlBYAELlKJfXd6XYfSWv38ZyjJJ1tZiskPSvpRDN7sv2iAgAAIBJRWgBA5JopaZCZZZlZnKSLJU3c5ZyJkq5s3UWkQFK1c26dc+5Hzrk+zrkBrY97xzl3eYemBwAAQNjzVlqY2YVmVmRmLWaWt5v7+5lZjZl9b6djY8xsfuvWe/eZ2e4uWwYA7APnXJOkmyVN0fYdQJ5zzhWZ2Y1mdmPraZMklUhaJukfkr7hJSwAAAAiks/dQwolnS/pgT3c/2dJr+9y7H5t33Jvmra/kB6/m3MAAPvIOTdJ28fTnY9N2OlrJ+mbX/I93pP0XjvEAwAAQITzdqWFc26hc27x7u4zs3O1/Z29op2O9ZTU2Tn3SeuL6H9KOrcDogIAAAAAAA8Ct6aFmSVJ+qGkX+5yV29tXxBuh91tzQcAAAAAAMJEu04PMbO3JGXu5q6fOOde3sPDfinpz865ml2WrNiXrfl2/tnXa/tUEvXr12/fAgMAAAAAgMBo19LCOXfyATwsX9JXzOwuSV0ltZhZnaT/aPtWezvsbmu+nX/2g5IelKS8vLw9lhsAAAAAACCYfC7EuVvOuWN2fG1mt0uqcc79tfX2ltYt96ZLulLSX7yEBAAAAAAA7c7nlqfnmVmppCMkvWZmU/bhYTdJekjbt94rFjuHAAAAAAAQtrxdaeGce1HSi19yzu273J4laUQ7xgIAAAAAAAERuN1DAAAAAAAAJEoLAAAAAAAQUJQWAAAAAAAgkCgtAAAAAABAIFFaAAAAAACAQKK0AAAAAAAAgURpAQAAAAAAAonSAgAAAAAABBKlBQAAAAAACCRKCwAAAAAAEEiUFgAAAAAAIJAoLQAAAAAAQCBRWgAAAAAAgECitAAAAAAAAIFEaQEAAAAAAAKJ0gIAAAAAAAQSpQUAAAAAAAgkSgsAAAAAABBIlBYAAAAAACCQKC0AAACAEGBmQ8xszk4fm83sVjO70MyKzKzFzPJ85wSAthTjOwAAAACAL+ecWyxplCSZWbSkNZJelJQo6XxJD3gLBwDthNICAAAACD0nSSp2zq3cccDMPMYBgPbB9BAAAAAg9Fws6RnfIQCgvVFaAAAAACHEzOIknS3p+f183PVmNsvMZlVUVLRPOABoY5QWAAAAQGg5TdKnzrn1+/Mg59yDzrk851xeenp6O0UDgLZFaQEAAACElkvE1BAAEYLSAgAAAAgRZpYo6RRJL+x07DwzK5V0hKTXzGyKr3wA0NbYPQQAAAAIEc65Wklpuxx7Udu3PgWAsMOVFgAAAAAAIJAoLQAAAAAAQCB5Ky3M7EIzKzKzFjPL2839/cysxsy+13o70cxeM7NFrY+7s+NTAwAAAACAjuLzSotCSedLen8P9/9Z0uu7HPujc26opMMkHWVmp7VjPgAAAAAA4JG3hTidcwslycz+5z4zO1dSiaStO51fK+nd1q8bzOxTSX06IisAAAAAAOh4gVvTwsySJP1Q0i/3ck5XSWdJensv51xvZrPMbFZFRUWb5wQAAAAAAO2rXUsLM3vLzAp383HOXh72S0l/ds7V7OF7xkh6RtJ9zrmSPX0T59yDzrk851xeenr6wT0RAAAAAADQ4dp1eohz7uQDeFi+pK+Y2V2SukpqMbM659xfW+9/UNJS59w9bZMSAAAAAAAEkbc1LfbEOXfMjq/N7HZJNTsKCzP7jaQukq7zkw4AAAAAAHQUn1uenmdmpZKOkPSamU35kvP7SPqJpGGSPjWzOWZGeQEAAAAAQJjyuXvIi5Je/JJzbt/p61JJ/7vVCAAAAAAACEuB2z0EAAAAAABAorQAgIhmZuPNbLGZLTOz23Zzv5nZfa33zzOz0a3H+5rZu2a20MyKzOzbHZ8eAAAA4Y7SAgAilJlFS/qbpNO0fb2gS8xs2C6nnSZpUOvH9ZLubz3eJOm7zrlcSQWSvrmbxwIAAAAHhdICACLXWEnLnHMlzrkGSc9KOmeXc86R9E+33TRJXc2sp3NunXPuU0lyzm2RtFBS744MDwAAgPBHaQEAkau3pNU73S7V/xYPX3qOmQ2QdJik6W0fEQAAAJGM0gIAItfudmRy+3OOmSVL+o+kW51zm3f7Q8yuN7NZZjaroqLigMMCAAAg8lBaAEDkKpXUd6fbfSSt3ddzzCxW2wuLp5xzL+zphzjnHnTO5Tnn8tLT09skOAAAACIDpQUARK6ZkgaZWZaZxUm6WNLEXc6ZKOnK1l1ECiRVO+fWmZlJeljSQufcnzo2NgAAACJFjO8AAAA/nHNNZnazpCmSoiU94pwrMrMbW++fIGmSpNMlLZNUK+nq1ocfJekKSfPNbE7rsR875yZ14FMAAABAmKO0AIAI1loyTNrl2ISdvnaSvrmbx32o3a93AQAAALQZpocAAAAAAIBAorQAAAAAAACBRGkBAAAAAAACidICAAAAAAAEEqUFAAAAAAAIJEoLAAAAAAAQSJQWAAAAAAAgkCgtAAAAAABAIFFaAAAAAACAQKK0AAAAAAAAgURpAQAAAAAAAonSAgAAAAgBZjbEzObs9LHZzG41s1Qze9PMlrZ+7uY7KwC0FUoLAAAAIAQ45xY750Y550ZJGiOpVtKLkm6T9LZzbpCkt1tvA0BYoLQAAAAAQs9JkoqdcyslnSPp8dbjj0s611coAGhrlBYAAABA6LlY0jOtX/dwzq2TpNbPGbt7gJldb2azzGxWRUVFB8UEgINDaQEAAACEEDOLk3S2pOf353HOuQedc3nOubz09PT2CQcAbYzSAgAAAAgtp0n61Dm3vvX2ejPrKUmtn8u9JQOANkZpAQAAAISWS/TfqSGSNFHSVa1fXyXp5Q5PBADthNICAAAACBFmlijpFEkv7HT4TkmnmNnS1vvu9JENANqDt9LCzC40syIzazGzvN3c38/Maszse7u5b6KZFXZMUgAAACAYnHO1zrk051z1TscqnXMnOecGtX6u8pkRANqSzystCiWdL+n9Pdz/Z0mv73rQzM6XVNOOuQAAAAAAQADE+PrBzrmFkmRm/3OfmZ0rqUTS1l2OJ0v6jqTrJT3X7iEBAAAAAIA3+1RamFm6pB9KGiYpYcdx59yJbR3IzJJaf9YpknadGvJrSXdLqm3rnwsAAAAAAIJlX6eHPCVpoaQsSb+UtELSzC97kJm9ZWaFu/k4Zy8P+6WkPzvnvjAFxMxGSRronHtxXwKb2fVmNsvMZlVUVOzLQwAAAAAAQIDs6/SQNOfcw2b2befcVElTzWzqlz3IOXfyAWTKl/QVM7tLUldJLWZWJ6lZ0hgzW9GaO8PM3nPOHb+Hn/2gpAclKS8vzx1ADgAAAAAA4NG+lhaNrZ/XmdkZktZK6tMegZxzx+z42sxul1TjnPtr66H7W48PkPTqngoLAAAAAAAQ+vZ1eshvzKyLpO9q+zoTD0n6v4P5wWZ2npmVSjpC0mtmNuVgvh8AAAAAAAgv+3SlhXPu1dYvqyWd0BY/uHVtir2uT+Gcu30Px1dIGtEWOQAAAAAAQDDt6+4h9+3mcLWkWc65l9s2EgAA/9/e3QfZXdV5Hn9/JItKFgyWQXkwQhSYQQYhtCiusCIwCLsKPo5uORNhaqLswOowujIyrqKOsqwugrpgFqHQcXQZEMElwgArPpWiiURICAhkRsmA2j6DD0TMd/+4v9abeDu56e7b99fd71fVrXvP+Z3zu99fVerUzbfPgyRJktT/8pDHAAcDdzevg4DHA3+e5P0DiUySJEmSJM1p/W7E+TTg+VX1CECSC4F/Ao4Fbh9QbJIkSZIkaQ7rd6bFnsD8rvJ8YI+q+g3w8JRHJUkCIMmXmvcHk/ys6/Vgkp8NOz5JkiRpkPqdaXEusDrJzUCAI4F3J5kP3Dig2CRpzquq5zbvOw87FknS1EmyEPgLYG+6fpNX1SnDikmS2qjf00M+kmQFcBidpMVbqur+5vKbBhWcJOl3kuwAPJHNf9x+Z3gRSZIm4Wrgi3T+APibIcciSa3V7+khAY4GFlfVO5IsSnJYVX1tsOFJkgCSnA68DfgesKmpLjobI0uSZp6dqurNww5Cktqu3+Uh/4vOj+TnA+8AHgSuBJ45oLgkSZt7PbB/Vf1w2IFIkqbE/01yQlWtGHYgktRm/SYtnlVVS5LcClBVP06y4wDjkiRt7j7gp8MOQpI0OUkepDNTLsBbkjwM/LopV1XtMsz4JKlt+k1a/LpZS13w242DNm29iyRpspKc0XxcD9yc5Fq6Tm2qqv85lMAkSRPixsqStH36PfL0AuAqYLckfwd8CXj3wKKSJI3ZuXl9B7gB2LGrzh++kjRDJbmpnzpJmuv6PT3k40lW0dmMM8BJVbVuoJFJkqiqs7vLSXbpVNeDQwpJkjQJSR4DzAeekGRXOr+tAXYB9hhaYJLUUv0uD4HOjvVfbPo8NsmSqvrGYMKSJHVLMgJcSjO7IslPgVOqatVQA5Mkba/XAm+gk6BYxe+SFj8DPjSkmCSptfo98vSdwGuAe2n2tWjenz+YsCRJW7gE+M9V9UWAJM+lk8TwyFNJmkGq6nzg/CT/paou6L6W5NFDCkuSWqvfmRavAJ5aVRsHGYwkaVwPjiUsAKrqS80O9JKkmek1dPaN6/YVYMn0hyJJ7dVv0mINsAD4/uBCkSRtxdeSfBj4BJ2Zbn9C5zSRJQAu15OkmSHJk4A96Sy3PoTN97TYaWiBSVJL9Zu0eA9wa5I1bH7U3osGEpUkaUsHN+9v26L+ObhcT5JmkuPozLLYC3gfm+9p8ZYhxSRJrdVv0uIy4L8DtwObBheOJKmXqjpqa9eTLK2qy6YrHknSxDRj9WVJXlpVV47XznFdkjr6TVr8YMuNgiRJrfJ6OglmSdIMsLWERcNxXZKAR/XZblWS9yQ5PMmSsddAI5MkbY9su0mPTskLktyV5J4kZ/a4niQXNNdv6x77t9VXkjQpExrXJWm26XemxSHN+7O76lxDLUntUdtusrkkOwAfAo4FNgBfT3JNVd3R1ex4YN/m9SzgQuBZffaVJE3cdo/rkjQb9ZW02NZaaknS0E3kL3KHAfdU1XqAJJ8ETgS6Ew8nAh+tqgK+mmRBkt2BvfvoK0maOGdaSBJ9Ji2SPI7OjvVHNlWfB95RVT8dVGAzxdmfWcsd9/9s2GFImiIH7LELb3vh04cdxkR8eQJ99gTu6ypvoDObYltt9uyz77RyPJZmnxk8Jk+FiYzrreGYLM0uwxyP+93T4hLgQeAVzetnwKWDCkqStLkk706yoKu8a5J3jZWr6rSJ3LZH3ZbTkcdr00/fzg2SZUlWJlk5Ojq6nSFK0uw00XG9mfF2RZI7k6xr9px7RpKvJLk9yWeS7DINjyBJ06LfPS2eWlUv7SqfnWT1AOKZceZw9l/S9Dq+qt4yVqiqHyc5AfjbSdxzA/DkrvJewP19ttmxj75jsS4HlgOMjIwMbI2247GkGWai4/r5wHVV9bIkOwI7ATcAb6yqzyc5BXgT8NZBBd4Px2RJU6XfmRa/TPLcsUKSfwf8cjAhSZJ62CHJo8cKSR4LPHor7fvxdWDfJPs0P3xfCVyzRZtrgD9rThF5NvDTqnqgz76SpPFt97jezKA4EvgIQFVtrKqfAPsDX2ia3QC8tOcNJGkG6nemxeuAjzZ7WwD8GFg6mJAkST38PXBTkkvpLMM4BbhsMjesqkeSnAZcD+wAXFJVa5O8rrl+EbACOAG4B/gFcPLW+k4mHkmaYyYyri8GRoFLkzwDWAW8HlgDvAi4Gng5m8+Ek6QZrd/TQ74JPGNsfVxVbbarTpKlVTWpH8+SpN6SBPgEcBtwDJ39JN5ZVddP9t5VtYJOYqK77qKuzwX8Zb99JUnbNolxfR6wBDi9qm5Jcj5wJp2ExwVJ/hudWW8bx/neZcAygEWLFk3Fo0jSwPU70wL4/WRFl9czyb/4SZJ6q6pK8umqOhS4btjxSJImZxLj+gZgQ1Xd0pSvAM6sqrcCfwyQZD/gP4zzvdOyx5AkTaV+97TYFs+RlqTB+mqSZw47CEnSlNnucb2qvgvcl2T/pupo4I4kuwEkeRSdjTwvGucWkjTjTFXSYrsztUlenmRtkk1JRnpcX5TkoSRv7KrbMcnyJN9qjnlykyFJc8VRwFeS3JvktuZYu9uGHZQkacImOq6fDny8aXsw8G7gVUm+BdxJ5ySnSwcVtCRNt+1aHrIVE5lpsQZ4CfDhca6fB3x2i7qzgO9X1X5NJvnxE/heSZqJjh92AJKkKTWhcb2qVgNb/sHv/OYlSbPOVCUtvry9HapqHUBnH6LNJTkJWA/8fItLpwB/0PTfBPxge79XkmYo1x5L0uziuC5JfegraZHk3cC5zTnQJNkV+Ouq+luAqjptqgJKMh94M3As0L00ZEHz8Z1JngfcC5xWVd+bqu+WpBa7ls4P3ACPAfYB7gKePsygJEkT5rguSX3od0+L48cSFgBV9WPghG11SnJjkjU9XidupdvZwHlV9dAW9fOAvYAvV9US4CvAe7fy3cuSrEyycnR0dFuhSlKrVdUfVdVBzfu+wGHAl4YdlyRpYhzXJak//S4P2SHJo6vqYYAkjwUeva1OVXXMBGJ6FvCyJOcCC4BNSX4FfAj4BXBV0+4fgT/fynd7pJOkWauqvuFpIpI0eziuS1Jv/SYt/h64KcmldKaxnQJcNoiAquqIsc9J3g48VFUfbMqfAZ4H/D+aI54GEYMktU2SM7qKjwKWAE4jk6QZynFdkvqzzaRFOjtlfgK4DTiGzrq7d1bV9ZP54iQvBj4ALASuTbK6qo7bRrc3Ax9L8n46g/rJk4lBkmaQnbs+P0JnLfSVQ4pFkjR5juuS1IdtJi2qqpJ8uqoOBa6bqi+uqqv43VKP8dq8fYvyt4EjpyoGSZopqupsgCQ7d4q/t++PJGkGcVyXpP70uxHnV11jJ0nDk+TAJLcCa4C1SVYlOXDYcUmSJsZxXZL60++eFkcBr03ybeDndJaIVFUdNLDIJEndlgNnVNXnAJqjn5cDzxliTJKkiXNcl6Q+9Ju0OH6gUUiStmX+2A9bgKq6Ocn8YQYkSZoUx3VJ6kO/SQuPDJWk4Vqf5K3Ax5ryq4F/HmI8kqTJcVyXpD70m7S4lk7iIsBjgH2Au4CnDyguSRKQ5GNV9afAF4G9gU/RGYs/jycoSdKM47guSdunr6RFVf1RdznJEuC1A4lIktTt0CRPAZbS2V8o/G72W4YWlSRpohzXJWk79DvTYjNV9Q1PE5GkaXERneOmFwMru+rHfuQuHkZQkqQJc1yXpO3QV9IiyRldxUcBS4DRgUQkSfqtqroAuCDJhVV16rDjkSRNjuO6JG2ffmda7Nz1+RE6e1xcOfXhSJJ68YetJM0ujuuS1J9+97Q4GyDJzp1iPTTQqCRJkiRJ0pz3qH4aJTkwya3AGmBtklVJDhxsaJIkSZIkaS7rK2kBLAfOqKqnVNVTgL9u6iRJkiRJkgai36TF/Kr63Fihqm4G5g8kIkmSJEmSJPrfiHN9krcCH2vKrwb+eTAhSZIkSZIkbWOmRZKxJMUXgYXAp4CrgCcAJw82NEmSJEmSNJdta6bFoUmeAiwFjgICVHMtgwxMkiRJkiTNbdtKWlwEXAcsBlZ21Y8lLxYPKC5JkiRJkjTHbXV5SFVdUFV/CFxSVYu7XvtUlQkLSZIkSZI0MH2dHlJVpw46EEmSJEmSpG79HnkqSZIkSZI0rUxaSJIkSZKkVjJpIUmSJEmSWsmkhSRJkiRJaiWTFpIkSZIkqZVMWkiSJEmSpFYyaSFJkiTNEEkWJLkiyZ1J1iU5PMnBSb6aZHWSlUkOG3ackjRV5g07AEmSJEl9Ox+4rqpelmRHYCfgcuDsqvpskhOAc4HnDTFGSZoyJi0kSZKkGSDJLsCRwGsAqmojsDFJAbs0zR4H3D+UACVpAExaSJIkSTPDYmAUuDTJM4BVwOuBNwDXJ3kvneXfzxlahJI0xdzTQpIkSZoZ5gFLgAur6hDg58CZwKnAX1XVk4G/Aj7Sq3OSZc2eFytHR0enK2ZJmpShJS2SvDzJ2iSbkoz0uL4oyUNJ3thV96oktye5Lcl1SZ4wvVFLkiRJQ7MB2FBVtzTlK+gkMZYCn2rq/hHouRFnVS2vqpGqGlm4cOHAg5WkqTDMmRZrgJcAXxjn+nnAZ8cKSebR2XjoqKo6CLgNOG3QQUqSJEltUFXfBe5Lsn9TdTRwB509LP59U/d84O4hhCdJAzG0PS2qah1Akt+7luQkYD2dKW+/rW5e85P8kM5mQ/cMPFBJkiSpPU4HPt6cHLIeOBm4Gji/+SPfr4BlQ4xPkqZU6zbiTDIfeDNwLPDbpSFV9eskpwK300lm3A385Vbus4xmwF60aNEgQ5YkSZKmRVWtBrZcWv0l4NDpj0aSBm+gy0OS3JhkTY/XiVvpdjZwXlU9tMW9/g2dTYYOAfagszzkb8a7iWv2JEmSJEma2QY606KqjplAt2cBL0tyLrAA2JTkV8AtzT3vBUhyOZ3dkiVJkiRJ0izUuuUhVXXE2OckbwceqqoPJtkDOCDJwqoapbN8ZN2QwpQkSZIkSQM2zCNPX5xkA3A4cG2S67fWvqrup7N05AtJbgMOBt498EAlaRZK8vgkNyS5u3nfdZx2L0hyV5J7kpzZVf8/ktzZHEF9VZIF0xa8JEmS5oyhJS2q6qqq2quqHl1VT6yq43q0eXtVvberfFFV/WFVHVRVL6yqH05v1JI0a5wJ3FRV+wI30WO5XZIdgA8BxwMHAK9KckBz+QbgwOYI6m+xlT2GJEmSpIkaWtJCkjRUJwKXNZ8vA07q0eYw4J6qWl9VG4FPNv2oqn+qqkeadl8F9hpsuJIkSZqLTFpI0tz0xKp6AKB5361Hmz2B+7rKG5q6LZ0CfHa8L0qyLMnKJCtHR0cnEbIkSZLmmtZtxClJmhpJbgSe1OPSWf3eokddbfEdZwGPAB8f7yZVtRxYDjAyMlLjtZMkSZK2ZNJCkmaprR07neR7SXavqgeS7A58v0ezDcCTu8p7Afd33WMp8B+Bo6vKZIQkSZKmnMtDJGluugZY2nxeClzdo83XgX2T7JNkR+CVTT+SvAB4M/CiqvrFNMQrSZKkOcikhSTNTecAxya5Gzi2KZNkjyQrAJqNNk8DrgfWAZdX1dqm/weBnYEbkqxOctF0P4AkSZJmP5eHSNIc1BwZfXSP+vuBE7rKK4AVPdo9baABSpIkSTjTQpIkSZIktZRJC0mSJEmS1EomLSRJkiRJUiuZtJAkSZIkSa1k0kKSJEmSJLWSSQtJkiRJktRKJi0kSZIkSVIrmbSQJEmSJEmtZNJCkiRJkiS1kkkLSZIkSZLUSiYtJEmSJElSK5m0kCRJkiRJrWTSQpIkSZIktZJJC0mSJEmS1EomLSRJkiRJUiuZtJAkSZIkSa1k0kKSJEmSJLWSSQtJkiRJktRKJi0kSZIkSVIrmbSQJEmSJEmtNG/YAUiSJEnqT5IFwMXAgUABpwBvAPZvmiwAflJVB09/dJI09UxaSJIkSTPH+cB1VfWyJDsCO1XVn4xdTPI+4KdDi06SptjQlockeXmStUk2JRnpqt87yS+TrG5eF3VdOzTJ7UnuSXJBkgwnekmSJGl6JdkFOBL4CEBVbayqn3RdD/AK4BNDCVCSBmCYe1qsAV4CfKHHtXur6uDm9bqu+guBZcC+zesFgw9TkiRJaoXFwChwaZJbk1ycZH7X9SOA71XV3b06J1mWZGWSlaOjo9MRryRN2tCSFlW1rqru6rd9kt2BXarqK1VVwEeBkwYVnyRJktQy84AlwIVVdQjwc+DMruuvYiuzLKpqeVWNVNXIwoULBxupJE2Rtp4esk+TPf58kiOauj2BDV1tNjR1kiRJ0lywAdhQVbc05SvoJDFIMo/OLOb/M6TYJGkgBroRZ5IbgSf1uHRWVV09TrcHgEVV9cMkhwKfTvJ0oNf+FbWV715GZykJixYt2r7AJUmSpJapqu8muS/J/s2M5aOBO5rLxwB3VtWG8e8gSTPPQJMWVXXMBPo8DDzcfF6V5F5gPzqZ5b26mu4F3L+V+ywHlgOMjIyMm9yQJEmSZpDTgY83J4esB05u6l+JG3BKmoVad+RpkoXAj6rqN0kW09lwc31V/SjJg0meDdwC/BnwgWHGKkmSJE2nqloNjPSof820ByNJ02CYR56+OMkG4HDg2iTXN5eOBG5L8k066/ReV1U/aq6dClwM3APcC3x2msOWJEmSJEnTZGgzLarqKuCqHvVXAleO02clcOCAQ5MkSZIkSS3Q1tNDJEmSJEnSHGfSQpIkSZIktZJJC0mSJEmS1EomLSRJkiRJUiuZtJAkSZIkSa1k0kKSJEmSJLWSSQtJkiRJktRKJi0kSZIkSVIrmbSQJEmSJEmtZNJCkiRJkiS1kkkLSZIkSZLUSiYtJEmSJElSK5m0kCRJkiRJrWTSQpIkSZIktZJJC0mSJEmS1EomLSRJkiRJUiuZtJCkOSjJ45PckOTu5n3Xcdq9IMldSe5JcmaP629MUkmeMPioJUmSNNeYtJCkuelM4Kaq2he4qSlvJskOwIeA44EDgFclOaDr+pOBY4HvTEvEkiRJmnNMWkjS3HQicFnz+TLgpB5tDgPuqar1VbUR+GTTb8x5wH8FaoBxSpIkaQ4zaSFJc9MTq+oBgOZ9tx5t9gTu6ypvaOpI8iLgX6vqm4MOVJIkSXPXvGEHIEkajCQ3Ak/qcemsfm/Ro66S7NTc44/7jGMZsAxg0aJFfX61JEmSZNJCkmatqjpmvGtJvpdk96p6IMnuwPd7NNsAPLmrvBdwP/BUYB/gm0nG6r+R5LCq+m6POJYDywFGRkZcSiJJkqS+uTxEkuama4ClzeelwNU92nwd2DfJPkl2BF4JXFNVt1fVblW1d1XtTSe5saRXwkKSJEmaDJMWkjQ3nQMcm+RuOieAnAOQZI8kKwCq6hHgNOB6YB1weVWtHVK8kiRJmoNcHiJJc1BV/RA4ukf9/cAJXeUVwIpt3GvvqY5PkiRJAmdaSJIkSZKkljJpIUmSJEmSWsmkhSRJkiRJaiWTFpIkSZIkqZVMWkiSJEmSpFYaWtIiycuTrE2yKclIV/3eSX6ZZHXzuqip3ynJtUnubPqdM6zYJUmSpGFIsiDJFc1v4nVJDm/qT09yV/M7+dxhxylJU2WYR56uAV4CfLjHtXur6uAe9e+tqs8l2RG4KcnxVfXZQQYpSZIktcj5wHVV9bLmN/FOSY4CTgQOqqqHk+w23BAlaeoMLWlRVesAkvTb/hfA55rPG5N8A9hrYAFKkiRJLZJkF+BI4DXQ+U0MbExyKnBOVT3c1H9/aEFK0hRr654W+yS5Ncnnkxyx5cUkC4AXAjdNe2SSJEnScCwGRoFLm9/KFyeZD+wHHJHklub38zN7dU6yLMnKJCtHR0enM25JmrCBJi2S3JhkTY/XiVvp9gCwqKoOAc4A/qHJKo/dcx7wCeCCqlq/le92UJYkSdJsMg9YAlzY/Fb+OXBmU78r8GzgTcDl6TGduaqWV9VIVY0sXLhwGsOWpIkb6PKQqjpmAn0eBsamtq1Kci+d7PHKpsly4O6qev827rO8acvIyEhtbxySJElSy2wANlTVLU35CjpJiw3Ap6qqgK8l2QQ8gc6sDEma0Vq3PCTJwiQ7NJ8XA/sC65vyu4DHAW8YWoCSJEnSEFTVd4H7kuzfVB0N3AF8Gng+QJL9gB2BHwwjRkmaakPbiDPJi4EPAAuBa5Osrqrj6Gwu9I4kjwC/AV5XVT9KshdwFnAn8I1mxtsHq+ri4TyBJEmSNO1OBz7enByyHjiZzjKRS5KsATYCS5tZF5I04w3z9JCrgKt61F8JXNmjfgPQ31EjkiRJ0ixUVauBkR6XXj3NoUjStGjd8hBJkiRJkiQwaSFJkiRJklrKpIUkSZIkSWolkxaSJEmSJKmVTFpIkiRJkqRWMmkhSZIkSZJayaSFJEmSJElqJZMWkiRJkiSplUxaSJIkSZKkVjJpIUmSJEmSWsmkhSRJkiRJaiWTFpIkSZIkqZVMWkiSJEmSpFYyaSFJkiRJklrJpIUkSZIkSWolkxaSJEmSJKmVTFpIkiRJkqRWMmkhSZIkSZJayaSFJEmSJElqJZMWkiRJkiSplUxaSJIkSZKkVjJpIUmSJEmSWsmkhSRJkiRJaiWTFpIkSZIkqZVMWkiSJEmSpFYyaSFJkiRJklrJpIUkSZIkSWolkxaSJEmSJKmVTFpIkiRJkqRWMmkhSZIkSZJaaWhJiyQvT7I2yaYkI131eyf5ZZLVzeuiHn2vSbJmeiOWJEmSJEnTad4Qv3sN8BLgwz2u3VtVB/fqlOQlwEMDjEuSJElqpSQLgIuBA4ECTgGOA/4CGG2avaWqVgwlQEmaYkObaVFV66rqru3pk+TfAmcA7xpMVJIkSVKrnQ9cV1V/ADwDWNfUn1dVBzcvExaSZo227mmxT5Jbk3w+yRFd9e8E3gf8Yls3SLIsycokK0dHR7fVXJLmlCSPT3JDkrub913HafeCJHcluSfJmVtcO725tjbJudMTuSTNXUl2AY4EPgJQVRur6idDDUqSBmygSYskNyZZ0+N14la6PQAsqqpD6Myq+IckuyQ5GHhaVV3Vz3dX1fKqGqmqkYULF07+YSRpdjkTuKmq9gVuasqbSbID8CHgeOAA4FVJDmiuHQWcCBxUVU8H3jtdgUvSHLaYzhKQS5s/8F2cZH5z7bQktyW5ZLxEtCTNRANNWlTVMVV1YI/X1Vvp83BV/bD5vAq4F9gPOBw4NMm/AF8C9kty8yDjl6RZ7ETgsubzZcBJPdocBtxTVeuraiPwyaYfwKnAOVX1MEBVfX+w4UqS6OxHtwS4sPkD38/pJJ0vBJ4KHEznD4Dv69XZmciSZqLWLQ9JsrD56x5JFgP7Auur6sKq2qOq9gaeC3yrqp43vEglaUZ7YlU9ANC879ajzZ7AfV3lDU0ddJLJRyS5pVnK98yBRitJgs44vKGqbmnKVwBLqup7VfWbqtoE/G86Seff40xkSTPR0E4PSfJi4APAQuDaJKur6jg66/TekeQR4DfA66rqR5P5rlWrVv0gybcnHfT4ngD8YID3H5bZ+lwwe5/N55oZnjIdX5LkRuBJPS6d1e8tetRV8z4P2BV4NvBM4PIki6uqtuyQZBmwrCk+lGS7NmHeTrPt30q32fpsPtfMMtuea1rG46lSVd9Ncl+S/ZsN7Y8G7kiy+1giGngxnVP6tmoafh/D7Pv3Msbnmnlm67PNpucadzxOj9+X2k5JVlbVyLDjmGqz9blg9j6bz6V+NYmD51XVA0l2B26uqv23aHM48PYmoUySvwGoqvckuY7O8pCbm2v3As+uqqHON57N/1Zm67P5XDPLbH2umaTZ5+1iYEdgPXAycAGdpSEF/Avw2q4kxtDM1n8vPtfMM1ufbbY+15aGNtNCkjRU1wBLgXOa9157DX0d2DfJPsC/Aq8E/lNz7dPA84Gbk+xH58fzbMn0S1JrVdVqYMv/pPzpEEKRpGnRuj0tJEnT4hzg2CR3A8c2ZZLskWQFQFU9ApwGXA+sAy6vqrVN/0uAxUnW0Nmgc2mvpSGSJEnSZDjTYmosH3YAAzJbnwtm77P5XOpLc0rT0T3q7wdO6CqvAFb0aLcRePUgY5yg2fxvZbY+m881s8zW59JgzNZ/Lz7XzDNbn222Ptdm3NNCkiRJkiS1kstDJEmSJElSK5m0kCRJkiRJrWTSQpIkSZIktZJJC0mSJEmS1EomLSRJkiRJUiuZtJAkSZIkSa1k0kKSJEmSJLXS/weAbd7f00CAZgAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "print(\"possible modes:\", fourc.calc.engine.modes)\n", + "print(\"chosen mode:\", fourc.calc.engine.mode)\n", + "\n", + "# same as orientation reflections\n", + "%mov fourc.omega -145.4500 fourc.tth 69.0985\n", + "\n", + "RE(bp.scan([fourc], fourc.chi, 0, 90, 10))" + ] + }, + { + "source": [ + "### (_0k0_) scan near (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 3 Time: 2020-12-17 15:59:00\n", + "Persistent Unique Scan ID: '680df0da-cfbe-40ed-80f8-877ed9c1bb6b'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | fourc_k | fourc_h | fourc_l | fourc_omega | fourc_chi | fourc_phi | fourc_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 15:59:01.2 | 3.900 | 4.100 | 0.000 | -126.651 | 43.568 | 0.000 | 106.695 |\n", + "| 2 | 15:59:02.2 | 3.950 | 4.100 | -0.000 | -126.178 | 43.933 | 0.000 | 107.641 |\n", + "| 3 | 15:59:03.6 | 4.000 | 4.100 | 0.000 | -125.697 | 44.293 | 0.000 | 108.605 |\n", + "| 4 | 15:59:04.5 | 4.050 | 4.100 | -0.000 | -125.206 | 44.648 | 0.000 | 109.586 |\n", + "| 5 | 15:59:05.3 | 4.100 | 4.100 | -0.000 | -124.706 | 45.000 | 0.000 | 110.585 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['680df0da'] (scan num: 3)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('680df0da-cfbe-40ed-80f8-877ed9c1bb6b',)" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:59:07.493331\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([fourc], fourc.k, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### (_hk0_) scan near (440)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 4 Time: 2020-12-17 15:59:08\n", + "Persistent Unique Scan ID: '659a52b8-9c94-4893-9292-d488747ea842'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | fourc_h | fourc_k | fourc_l | fourc_omega | fourc_chi | fourc_phi | fourc_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 15:59:08.6 | 3.900 | 3.900 | 0.000 | -128.558 | 45.000 | 0.000 | 102.883 |\n", + "| 2 | 15:59:09.2 | 3.950 | 3.950 | 0.000 | -127.627 | 45.000 | 0.000 | 104.745 |\n", + "| 3 | 15:59:09.9 | 4.000 | 4.000 | -0.000 | -126.675 | 45.000 | 0.000 | 106.647 |\n", + "| 4 | 15:59:10.5 | 4.050 | 4.050 | -0.000 | -125.703 | 45.000 | 0.000 | 108.593 |\n", + "| 5 | 15:59:11.2 | 4.100 | 4.100 | 0.000 | -124.706 | 45.000 | 0.000 | 110.585 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['659a52b8'] (scan num: 4)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('659a52b8-9c94-4893-9292-d488747ea842',)" + ] + }, + "metadata": {}, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:59:12.843490\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAALPCAYAAACUgDDgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACM2UlEQVR4nOzdd3jV5f3/8dc7i7DDSMIIUxDCkhEQqq0LW1drq7VVq4KoaFtb7a6142trl53aZa0ynKi1VqtWC9ZVG4QwZAUFwgorIWFDIOP9+yOhv5QyAuSc+4zn47rOlXw+nzNe59LrzuF97vt9m7sLAAAAAAAglqWEDgAAAAAAAHAsFDAAAAAAAEDMo4ABAAAAAABiHgUMAAAAAAAQ8yhgAAAAAACAmEcBAwAAAAAAxLykLGCY2RQzKzOzJc30fC+b2XYze+GQ89PMbLWZLWy4DW+O1wMAAAAAINkkZQFD0jRJFzTj8/1M0rVHuPY1dx/ecFvYjK8JAAAAAEDSSMoChru/Kamy8TkzO6VhJsU8M3vLzAYex/O9KmlXc+cEAAAAAAD1krKAcQQPSPqCu4+S9FVJv2+m5/2hmS0ys1+ZWYtmek4AAAAAAJJKWugAscDM2kj6gKSnzezg6RYN1y6T9P3DPGyDu3/kGE99h6TNkjJUXyD5xhGeCwAAAAAAHAUFjHopkra7+/BDL7j7XyT95USe1N03Nfy638ymqn5mBwAAAAAAOE4sIZHk7jslrTazKyTJ6p12ss9rZl0PPp+kj0tqll1PAAAAAABINubuoTNEnZk9IelsSZ0lbZH0PUn/lPQHSV0lpUua4e5NWu5hZm9JGiipjaQKSTe4+ytm9k9J2ZJM0kJJt7j77mZ9MwAAAAAAJIGkLGAAAAAAAID4whISAAAAAAAQ85KuiWfnzp29d+/eoWMASBLz5s3b6u7ZoXPEA8ZnANHE+Nx0jM8Aou1IY3TSFTB69+6toqKi0DEAJAkzWxs6Q7xgfAYQTYzPTcf4DCDajjRGs4QEAAAAAADEPAoYAAAAAAAg5lHAAAAAAAAAMS/pemAAiD/V1dUqLS1VVVVV6ChHlJmZqby8PKWnp4eOAgBRw/gMALEpHsZn6fjH6LguYJhZD0kPS+oiqU7SA+5+b9hUAJpbaWmp2rZtq969e8vMQsf5H+6uiooKlZaWqk+fPqHjAEDUMD4DQGyK9fFZOrExOt6XkNRI+oq750saK+nzZjYocCYAzayqqkqdOnWK2cHXzNSpU6eYr3ADQHNjfAaA2BTr47N0YmN0XBcw3H2Tu89v+H2XpGJJ3cOmAhAJsTz4SrGfDwAiJdbHv1jPBwCREg/j3/FmjOsCRmNm1lvSCEnvHObaZDMrMrOi8vLyqGcDAAAAAAAnJyEKGGbWRtIzkm53952HXnf3B9y9wN0LsrOzox8QQEKora3ViBEjdMkll4SOAgA4BGM0AMSm5hyf476AYWbpqi9ePObufwmdB0Diuvfee5Wfnx86BgDgMBijASA2Nef4HNcFDKtfMPOQpGJ3/2XoPAASV2lpqV588UXdeOONoaMAAA7BGA0Asam5x+e43kZV0hmSrpW02MwWNpz7lru/FC4SgEi6629LtWzj/6wUOymDurXT9z46+Kj3uf3223XPPfdo165dzfraAJAoQo3PEmM0ABxNIo3PcT0Dw93/5e7m7sPcfXjDjeIFgGb1wgsvKCcnR6NGjQodBQBwCMZoAIhNkRif430GBoAk05RKb3N7++239fzzz+ull15SVVWVdu7cqWuuuUaPPvpo1LMAQKwKMT5LjNEAcCyJND7H9QwMAIiGH//4xyotLdWaNWs0Y8YMnXvuuXwwBoAYwRgNALEpEuMzBQwAAAAAABDzWEICAMfh7LPP1tlnnx06BgDgMBijASA2Ndf4zAwMAAAAAAAQ8yhgAAAAAACAmEcBA0BccPfQEY4q1vMBQKTE+vgX6/kAIFLiYfw73owUMADEvMzMTFVUVMTsIOzuqqioUGZmZugoABBVjM8AEJtifXyWTmyMpokngJiXl5en0tJSlZeXh45yRJmZmcrLywsdAwCiivEZAGJTPIzP0vGP0RQwAMS89PR09enTJ3QMAMAhGJ8BIDYl6vjMEhIAAAAAABDzKGAASFrurt37a0LHAAAcRvmu/aEjAABiDAUMAElrVflunXbXP/TK0s2howAAGllVvlvn/eJ1TX17degoAIAYQgEDQNIqXFWh2jrXwC5tQ0cBADTYtueAbpg2V+mpKRqfnxs6DgAghtDEE0DSKiypULf2merZsVXoKAAASQdq6vTZx+Zp4/YqPX7T6erB+AwAaIQZGACSUl2da3ZJpcae0klmFjoOACQ9d9d3n1ui2SWV+uknh6qgd8fQkQAAMYYCBoCk9H7ZLlXuOaBxfTuFjgIAkPTQv1Zrxtz1+vw5p+gTI/JCxwEAxCAKGACSUuGqCknSuFMoYABAaK8Wb9EPXyrWhUO66CvnDwgdBwAQoyhgAEhKhasq1KNjS+V1YH01AIRUvGmnvvjEAg3p1l6//NRwpaSwrA8AcHgUMAAknbo61zurK1k+AgCBle/arxunF6lNZpr+dF2BWmakho4EAIhh7EICIOks27RTO/ZVs3wEAAKqqq7V5EeKVLFnv56++QPq0j4zdCQAQIyjgAEg6cwuaeh/0bdz4CQAkJzcXV//8yItWLdd918zUkPz2oeOBACIAywhAZB0CldVqE/n1nzbBwCB/OafK/X8uxv1tY8M0AVDuoaOAwCIExQwACSVmto6zVldqbH0vwCAIF5YtFG/nPm+LhvRXZ87+5TQcQAAcYQCBoCksmzTTu3aX0P/CwAI4N312/WVp95VQa8O+vHlQ2XGjiMAgKajBwaApFK4qr7/xdi+HQMnAYDksmnHPt30cJGy27bQH68dpRZp7DgSDWaWKqlI0gZ3v8TMnpQ0oOFylqTt7j48UDwAOC4UMAAklcKSCvXLaaOctvS/AIBo2XugRjdOL9LeA7V69MbT1alNi9CRksltkooltZMkd//0wQtm9gtJOwLlAoDjxhISAEmjurZOc1dXMvsCAKKors51+4yFKt60U7+5eoROzW0bOlLSMLM8SRdLevAw10zSpyQ9Ee1cAHCiKGAASBqLN+zQngO1bJ8KAFH0s3+8p38s26JvXzxI5wzICR0n2fxa0tcl1R3m2gclbXH3FYd7oJlNNrMiMysqLy+PYEQAaDoKGACSBv0vACC6ni5arz+8vkpXn95T15/RO3ScpGJml0gqc/d5R7jLVTrK7At3f8DdC9y9IDs7OyIZAeB4UcAAkDRml1RoQG5b1l4fJzO7wsyWmlmdmRU0On++mc0zs8UNP89tdO11M3vPzBY23PjaFUgyc1ZX6lvPLtYZ/Trpro8NZseR6DtD0sfMbI2kGZLONbNHJcnM0iRdJunJcPEA4PhRwACQFA7U1KlozTa2Tz0xS1T/QffNQ85vlfRRdx8qaYKkRw65/hl3H95wK4tCTgAxYl3FXt38SJF6dGil3189SumpfOSMNne/w93z3L23pCsl/dPdr2m4PF7ScncvDRYQAE4Au5AASArvlm7Xvupaje1LAeN4uXuxpP/59tTdFzQ6XCop08xauPv+KMYDEGN2VlVr0vS5qnPpoYmj1b5VeuhI+F9XiuadAOIQBQwASaFwVYXM6H8RQZdLWnBI8WKqmdVKekbS3e7uYaIBiJaa2jrd+vgCrdm6R4/ccLr6dG4dOhIkufvrkl5vdDwxVBYAOBkUMAAkhcJVFcrv0k5ZrTJCR4lJZjZLUpfDXLrT3Z87xmMHS/qppA83Ov0Zd99gZm1VX8C4VtLDh3nsZEmTJalnz54nmB5ArLj7xWK9+X65fnLZUJbsAQCaHQUMAAmvqrpW89Zt07Vje4WOErPcffyJPM7M8iQ9K+k6d1/V6Pk2NPzcZWaPSxqjwxQw3P0BSQ9IUkFBATM0gDj2yOy1mvbvNbrxzD66cgwFSQBA86OjEoCEt2Dddh2oqdM4+l80KzPLkvSipDvc/e1G59PMrHPD7+mSLlF9I1AACeqtFeX6v+eX6ryBObrjovzQcQAACYoCBoCEV1hSoRSTxtD/4oSY2SfMrFTSOEkvmtkrDZduldRP0ncO2S61haRXzGyRpIWSNkj6U4DoAKJgZdlufe6x+eqf00b3XjVCqSlslwoAiAyWkABIeLNXVWhI9/Zql0kn/BPh7s+qfpnIoefvlnT3ER42KqKhAMSEbXsO6Ibpc9UiLUUPTihQmxZ8tAQARA4zMAAktH0HarVw/XaWjwBAMztQU6ebH52nTTuq9MdrC5TXoVXoSACABEcBA0BCm79umw7U1mks3fABoNm4u77918Was7pSP/vkMI3q1SF0JABAEqCAASChFa6qUGqKaXRv+l8AQHP501sleqqoVF88t58uHd49dBwAQJKggAEgoRWWVGho9/asywaAZjJz2Rb9+O/LdfHQrrp9/Kmh4wAAkggFDAAJa8/+Gr27frvGsXwEAJrFso07dduMBRravb1+fsVpSmHHEQBAFFHAAJCwitZuU02d08ATAJpB2a4q3Th9rtplpuvB6wrUMiM1dCQAQJJhTjWAhFW4qkLpqaaC3jSXA4CTUVVdq5senqdte6v19C3jlNMuM3QkAEASooABIGEVllTotLwstcpgqAOAE+Xu+tqfF+nd9dt1/zWjNKR7+9CRAABJiiUkABLSrqpqLdmwg/4XAHCS7n11hf727kZ944KBumBIl9BxAABJjAIGgIQ0d02laul/AQAn5W/vbtSvZ63Q5SPzdMtZfUPHAQAkOQoYABJS4aoKZaSmaGQv+l8AwIlYuH67vvr0uxrTu6N+dNkQmbHjCAAgLAoYABJSYUmFRvTMUmY6XfIB4Hht3L5PN04vUk67Frr/2lFqkcZYCgAIjwIGgISzY2+1lm7cSf8LADgBe/bX6IbpRdpfXaspE0arY+uM0JEAAJDELiQAEtA7qyvkLvpfAMBxqq1z3TZjod7bvFNTJo5W/9y2oSMBAPAfzMAAkHBml1SqRVqKhvfMCh0FAOLKPS8v16ziLfruJYN09oCc0HEAAPgvFDAAJJzCkgoV9O7Amm0AOA5PFa3XH98s0TVje2rCB3qHjgMAwP+ggAEgoWzbc0DFm3ZqbB+WjwBAU80uqdCdzy7WB/t31vc+OpgdRwAAMYkCBoCE8s7qCkmigScANNHaij265dF56tmxlX579Uilp/LxEAAQm/gLBSChFK6qUMv0VA3LywodBQBi3o591Zo0ba4k6aEJo9W+ZXrgRAAAHBkFDAAJ5WD/i4w0hjcAOJqa2jrd+vh8ravcq/uvGaXenVuHjgQAwFHF/Sd8M5tiZmVmtiR0FgBhbd29X+9v2c3yEQBogu+/sExvrdiqH358qMay7TQAIA7EfQFD0jRJF4QOASC82SUN/S/4IA4ARzX932v0cOFaTf5QX31qdI/QcQAAaJK4L2C4+5uSKkPnABBe4aoKtWmRpqHd24eOAgAx6433y3XX35ZqfH6uvnHBwNBxAABosrgvYDSFmU02syIzKyovLw8dB0CEFJZUaHTvDkqjgz4AHNaKLbt062PzNaBLO9175XClprBdKgAgfiTFp3x3f8DdC9y9IDs7O3QcABGwZWeVSsr30P8CAI6gcs8B3TC9SC3SU/XghAK1bpEWOhIAAMeFv1wAEsL/73/ROXASAIg9+2tqdcsj87R5Z5WenDxW3bNaho4EAMBxS4oZGAASX+GqCrXLTNOgbu1CRwGAmOLuuvPZJZqzplI/v+I0jejZIXQkAABOSNwXMMzsCUmFkgaYWamZ3RA6E4DoKyyp0Jg+nVjPDQCH+OObJfrzvFLddl5/fey0bqHjAABwwuJ+CYm7XxU6A4CwNm7fp7UVe3XduN6howBATHll6Wb99OXlumRYV90+vn/oOAAAnJS4n4EBAP+//wUNPAHgoCUbduj2GQs1LC9LP7/iNJkxQw0AEN8oYACIe4WrKpTVKl0Du7QNHQUAYkLZzird9HCRslql60/XjlJmemroSAAAnLS4X0ICAIUlFTq9T0el0P8CAFRVXaubHi7Sjn3VevqWccpplxk6EgAAzYIZGADi2vrKvSrdto/lIwAgqa7O9ZWn39WiDTt075UjNLhb+9CRAABoNhQwAMS1woP9L07pHDgJAIT361dX6MVFm/TNCwbq/EG5oeMAANCsKGAAiGuzV1WoU+sMnZrbJnQUAAjquYUbdN+rK3TFqDxN/lDf0HEAAGh2FDAAxC13V2FJhcb27UR3fQBJbf66bfranxdpTJ+O+uEnhjImAgASEgUMAHFrbcVebdpRpbGn0P8CQPIq3bZXkx8uUpd2mbr/mlHKSOPjHQAgMbELCYC49Z/+FzTwBJCkdu+v0Y3Ti7S/pk4zJheoY+uM0JEAAIgYChgA4lbhqgplt22hU7Jbh44CAFFXW+e67YkFWlG2W1Mnjla/nLahIwEAEFHMMQQQlw72vxhH/wsASeonfy/Wq8vL9H8fHaQPnZodOg4AABFHAQNAXFpVvkflu/ZrHP0vACShJ+eu05/eWq0J43rp2nG9Q8cBACAqKGAAiEv0vwCQrApXVejOZ5foQ6dm6zuXDAodBwCAqKGAASAuzS6pUNf2merVqVXoKAAQNWu27tFnH5un3p1b67dXj1BaKh/lAADJg796AOKOu+sd+l8ASDI79lZr0vS5MkkPTShQu8z00JEAAIgqdiEBEHdWlO3W1t0HNJb+FwCSxIGaOn32sXlaX7lXj904Vr06sfsSACD5UMAAEHcKV9H/AkDycHfd+exi/XtVhX5xxWka06dj6EgAAATBEhIAcadwVYW6Z7VUj470vwCQ+H7/+io9Pa9Ut53XX5ePygsdBwCAYChgAIgrdXWu2asr2D4VQFJ4buEG/eyV9/SJEd11+/j+oeMgDplZqpktMLMXGp37gpm9Z2ZLzeyekPkA4HiwhARAXFm+eZe2761m+QiAhDd3TaW+9vQijenTUT+5fChNi3GibpNULKmdJJnZOZIulTTM3febWU7IcABwPJiBASCuFJY09L9gBgaABLZm6x5NfrhIeR1a6oFrR6lFWmroSIhDZpYn6WJJDzY6/VlJP3H3/ZLk7mUhsgHAiaCAASCuFK6qUK9OrdQtq2XoKAAQEdv2HND10+ZKkqZMHK2sVhmBEyGO/VrS1yXVNTp3qqQPmtk7ZvaGmY0+3APNbLKZFZlZUXl5eRSiAsCxsYSkmWzdvV+f+dM7oWNE1Zg+HfWDjw8JHQNJpLbO9c7qCl08tGvoKAAQEftranXzI/O0Yfs+PX7j6erdme1ScWLM7BJJZe4+z8zObnQpTVIHSWMljZb0lJn1dXdv/Hh3f0DSA5JUUFDwX9cAIBQKGM0kLcXUJ4k+ZJRu36tH31mrL57XX9ltW4SOgySxbONO7aqqYfkIgITk7vrGnxdpzppK3XfVCBX0ZrtUnJQzJH3MzC6SlCmpnZk9KqlU0l8aChZzzKxOUmdJTLMAEPMoYDSTrFYZuv/aUaFjRM2yjTt10X1v6bXlZfrU6B6h4yBJFJZslSQaeAJISL+atUJ/XbhRX/vIAH3stG6h4yDOufsdku6QpIYZGF9192vM7BZJ50p63cxOlZQhaWuonABwPOiBgROS37Wtume11MziLaGjIIkUrqpQ3+zWymmXGToKADSrZ+aV6r5XV+hTBXn63NmnhI6DxDZFUl8zWyJphqQJhy4fAYBYxQwMnBAz0/j8HD1ZtF5V1bXKTKc7OiKrprZOc9ds06XD+VYSQGIpXFWhb/5lkc7o10k//ATbpaL5ufvrkl5v+P2ApGtC5gGAE8UMDJyw8/JzVVVdp7dXMusQkbd4ww7t3k//CwCJZWXZbt38SJF6dWqt339mlNJT+WgGAMCR8FcSJ+z0vh3VpkWaZrGMBFEwu6RSkjSW/hcAEkTF7v26ftocZaSlaOrE0WrfMj10JAAAYhoFDJywFmmpOuvUbM0qLlNdHUsnEVmFJRU6NbeNOrdh1xsA8a+qulY3PVyksp379eCE0erRsVXoSAAAxDwKGDgp4wflqHzXfi3asCN0FCSw6to6Fa2pZPYFgIRQV+f6ylPvasH67fr1p4dreI+s0JEAAIgLFDBwUs4ZkKPUFNOsZSwjQeQsKt2uvQdq2T4VQEL42T/e04uLN+mOCwfqwqFdQ8cBACBuUMDASclqlaGCXh3og4GIKlxVIUk6nQIGgDg3Y846/eH1VfrM6T110wf7ho4DAEBcoYCBk3b+oFwt37xL6yv3ho6CBFVYUqGBXdqqY+uM0FGSkpldYWZLzazOzAoanT/fzOaZ2eKGn+c2upZhZg+Y2ftmttzMLg+THogdb60o151/XaKzTs3WXR8bzHapAAAcJwoYOGnn5edKkl5lFgYiYH9NrYrWbGP71LCWSLpM0puHnN8q6aPuPlTSBEmPNLp2p6Qydz9V0iBJb0QjKBCr3tu8S597dL7657TRb68eoTS2SwUA4Ljx1xMnrU/n1uqX00azistCR0ECWrhuu/bX1NH/IiB3L3b39w5zfoG7b2w4XCop08wObhMzSdKPG+5X5+5bo5MWiD1lu6o0adpctcxI1ZSJo9U2k+1SAQA4ERQw0CzG5+dqdkmFdlZVh46CBFNYUiEz6fQ+FDBi3OWSFrj7fjPLajj3AzObb2ZPm1luwGxAMPsO1OrG6UWq3HNAUyaOVreslqEjAQAQtyhgoFmcPyhHNXWuN94rDx0FCaZwVYUGd2un9q34xjKSzGyWmS05zO3SJjx2sKSfSrq54VSapDxJb7v7SEmFkn5+hMdONrMiMysqL2f8QGKprXPdNmOBlmzYod9cNUJDurcPHQkAgLhGAQPNYniPDurUOoPdSNCsqqprtWDddpaPRIG7j3f3IYe5PXe0x5lZnqRnJV3n7qsaTldI2ttwXpKeljTyCK/7gLsXuHtBdnZ2M70bIDb8+KVi/WPZFn33kkEaP4hJSAAAnCwKGGgWqSmmcwbm6LXlZaqurQsdBwli/tptOlBbRwPPGNWwVORFSXe4+9sHz7u7S/qbpLMbTp0naVm08wEhPVK4Rg/+a7UmfqC3Jp7RJ3QcAAASAgUMNJvx+bnaWVWjuWsqQ0dBgigsqVBqiml0746hoyQ1M/uEmZVKGifpRTN7peHSrZL6SfqOmS1suOU0XPuGpP8zs0WSrpX0lagHBwJ5bXmZvvf8Uo3Pz9F3LhkUOg4AAAkjLXQAJI4P9u+sjLQUzVpWpg+c0jl0HCSAwlUVGtK9PR37A3P3Z/X/l4M0Pn+3pLuP8Ji1kj4U4WhAzFm6cYdufXy+BnVrp3uvHKHUFAsdCQCAhMEMDDSb1i3SdMYpnTSzeLPqZ5ADJ27vgRq9W0r/CwDxY/OOKt0wrUjtWqbroQmj1boF3xMBANCcKGCgWY0flKv1lfu0omx36CiIc/PWblN1rdP/AkBc2L2/RpOmzdXu/TWaMnG0cttlho4EAEDCoYCBZnXewPou6zOXsRsJTk7hqgqlpZgKenUIHQUAjqqmtk5feHy+3tuyS7/7zEjld20XOhIAAAmJAgaaVZf2mRqW116vsp0qTlJhSYWG5bVnCjaAmObu+v4Ly/Tae+X6/qWDddapbAcMAECkUMBAsxufn6sF67erfNf+0FEQp3bvr9Gi0h0sHwEQ86a8vUYPF67V5A/11WdO7xU6DgAACY0CBprd+PxcuddvIweciLlrKlVb5xrXl91sAMSufyzdrLtfXKYLh3TRNy8YGDoOAAAJjwIGml1+17bqntVSM1lGghM0e1WF0lNNo+h/ASBGLSrdrttmLNSwvCz98lPDlcJ2qQAARBwFDDQ7M9P4/By9taJcVdW1oeMgDhWWVGhEjw5qmZEaOgoA/I/SbXt1w/QidWqToQevK2CsAgAgSihgICLGD8pVVXWd3l65NXQUxJmdVdVasmGHxtL/AkAM2llVrRumFamqulZTJ45WdtsWoSMBAJA0KGAgIk7v00ltWqRpFstIcJzmlFSqzqVxfSlgAIgt1bV1+vxj87WqfLfuv2aU+ue2DR0JAICkQgEDEZGRlqKzTs3WrOIy1dV56DiII4UlFcpIS9GInlmhowDAf7i7vvvcEr21Yqt+dNlQndGPJsMAAEQbBQxEzPhBOSrftV+LNuwIHQVxpHBVhUb17KDMdNaUnwgzyzazn5vZS2b2z4O30LmAePfHN0v0xJz1uvWcfvpUQY/QcQAASEoUMBAx5wzIUWqKadYylpGgabbvPaDizTs1jv4XJ+MxScWS+ki6S9IaSXNDBgLi3UuLN+knf1+uj57WTV8+/9TQcQAASFoUMBAxWa0yVNCrA30w0GSzSyrlLgoYJ6eTuz8kqdrd33D3SZLGhg4FxKv567bpS08uVEGvDvrZJ4exXSoAAAFRwEBEnT8oV8s379L6yr2hoyAOzC6pUMv0VJ2WlxU6Sjyrbvi5ycwuNrMRkvJCBgLi1bqKvbppepG6tM/UA9cVsLQNAIDAKGAgos7Lz5UkvcosDDTB7JIKFfTuoIw0hqaTcLeZtZf0FUlflfSgpC+FjQTEnx17q3X9tDmqqXNNnThaHVtnhI4EAEDS418JiKg+nVurX04bzSouCx0FMa5i934t37xLY9k+9aS4+wvuvsPdl7j7Oe4+yt2fD50LiCcHaup0y6PztL5ynx64dpT6ZrcJHQkAAEhKCx3gZJnZBZLulZQq6UF3/0ngSDjE+PxcPfhWiXZWVatdZnroOIhR76yulCQKGCfJzO47zOkdkorc/blo5wHijbvrjr8sVmFJhX796eE6nTEJAICYEdczMMwsVdLvJF0oaZCkq8xsUNhUONT5g3JUU+d6473y0FEQwwpXVahVRqqG5bUPHSXeZUoaLmlFw22YpI6SbjCzX4eLBcSH3/5zpZ6ZX6ovjT9VHx/RPXQcAADQSFwXMCSNkbTS3Uvc/YCkGZIuDZwJhxjeo4M6tc5gNxIcVWFJhUb37qj01HgfloLrJ+lcd/+Nu/9G0nhJ+ZI+IenDQZMBMe65hRv0i5nv67KR3fXF8/qFjgMAAA4R70tIukta3+i4VNLph97JzCZLmixJPXv2jE4y/EdqiuncgTl6ZelmVdfW8Q9U/I+yXVVaWbZbnxzFZhnNoLuk1qpfNqKG37u5e62Z7Q8XC4htc1ZX6mtPL9LYvh31k8uGyYztUhGemQ109+VmNvJw1919frQzAUBI8V7AONynC/+fE+4PSHpAkgoKCv7nOiJv/KBcPT2vVHPXVOoDp3QOHQcxZnZJff+Lcaw1bw73SFpoZq+rfoz8kKQfmVlrSbNCBgNi1eqtezT5kSLldWyp+68ZxU5IiCVfVv2XcL84zDWXdG504wBAWPFewCiV1KPRcZ6kjYGy4Cg+2L+zMtJSNGtZGQUM/I/CVRVq2yJNg7u1Cx0l7rn7Q2b2kuqX2Jmkb7n7wXHxa+GSAbGpcs8BXT91jlLMNHXiaGW1YrtUxA53n9zw85zQWQAgFsT7VwxzJfU3sz5mliHpSklsFxiDWmWk6YxTOmlm8Wa5MwkG/212SYXG9OmoNJYXnTSrn/d+nqTT3P2vktLMbEzYVEBsqqqu1eSHi7RxR5X+dF2BenVqHToScERm9gEzu9rMrjt4C50JAKItrv+14O41km6V9IqkYklPufvSsKlwJOMH5Wp95T6tKNsdOgpiyOYdVVq9dY/GncLykWbye0njJF3VcLxL9bs1AWjE3fX1Py9S0dpt+uWnTtOoXh1CRwKOyMwekfRzSWdKGt1wKwgaCgACiPclJHL3lyS9FDoHju28gbm6U0s0c9kWnZrbNnQcxIjCkq2SpLH0v2gup7v7SDNbIEnuvq1hhhqARn458309/+5Gff2CAbpkWLfQcYBjKZA0yJnGCiDJRbSAYWaXHe26u/8lkq+P2NKlfaaG5bXXq8Vb9Plz2J4O9QpXVah9y3QN6kr/i2ZSbWapamhobGbZkurCRgJiy9NF6/Wbf67UlaN76LNnnRI6DtAUSyR1kbQpdBAACCnSMzA+epRrLokCRpIZn5+rX816X+W79iu7bYvQcRADCksqdHqfjkpJYcvCZnKfpGcl5ZjZDyV9UtK3w0YCYse/V27VHX9ZrDP7ddYPPj6E7VIR08zsb6r/zNxW0jIzmyPpP1tiu/vHQmUDgBAiWsBw9+ubcj8zm+Du0yOZBbFhfH6ufjnzfb22vEyfGt3j2A9AQivdtlfrK/dp0hl9QkdJGO7+mJnNU30jT5P0cXcvDhwLiAkry3bp5kfnqW92a/3+mpFKp3EwYt/PQwcAgFgSK3+5bwsdANGR37Wtume11MziLaGjIAbMLqmUJBp4Nr8tkt6S9G9JLc1sZOA8QHDlu/Zr4tS5apGWqikTR6tdZnroSMAxufsb7v6GpHWS3ml0PEfS2rDpACD6YqWJJ/M3k4SZaXx+jp4sWq+q6lplpqeGjoSACldVqEOrdJ2aQ1PX5mJmP5A0UdIqNfTBaPh5bqhMQGhV1bW66eEibd29X09OHqe8Dq1CRwKO19OSPtDouLbh3OgwcQAgjFiZgUFH5SQyflCuqqrr9PbKraGjICB31+ySCo3t24n+F83rU5JOcfez3f2chhvFCyStujrXl55cqHdLt+veK0fotB5ZoSMBJyLN3Q8cPGj4nR2mACSdWClg8K+XJHJ6n05q0yJNs1hGktTWV+7Thu37WD7S/JZIygodAogVP31luf6+ZLPuvChfHxncJXQc4ESVm9l/Gnaa2aWS+CYIQNKJlSUkb4cOgOjJSEvRWQOyNau4TD+sc759T1KFJfWfu8b1pYDRzH4saYGZLRGd6pHkHn9nnf74RomuHdtLN5xJs2DEtVskPWZmv204LpV0bcA8ABBEVAoYZvYjSfe4+/aG4w6SvuLu35Ykd781GjkQO8bn5+jFRZu0aMMODWc6b1IqXFWhzm1aqF9Om9BREs10ST+VtFhSXeAsQDBvvF+u7zy3ROcMyNb3PjqI7VIR19x9laSxZtZGkrn7rsbX2dEPQLKI1hKSCw8WLyTJ3bdJuihKr40YdM6AHKWmmGYtYxlJMnJ3FZZUaGzfjvyjovltdff73P21g93qGzrWA0lj+ead+vxj83Vqblv95uqRSmO7VCQId999aPGiATv6AUgK0fqLnmpmLQ4emFlLSS2Ocn8kuKxWGSro1YE+GElq9dY92rJzP/0vImOemf3YzMaZ2ciDt9ChgGgp21mlSVPnqnWLVE2ZWKA2LWJltSwQUUf8NsDMUs1sgZm90HD8f2a2wcwWNtz4UhFA3IjWX/VHJb1qZlNVv+PIJNVPc0YSO39Qru5+sVjrK/eqR0e2tEsmhSUVkuh/ESEjGn6ObXSObVSRFPYeqNEN04u0fV+1nrp5nLq2bxk6EhAtR9vR7zZJxZLaNTr3K3f/eWQjAUDzi/gMDKufH/6EpLsl5UsaLOkH7n5PpF8bse28/FxJ0qvMwkg6hasqlNuuhfp0bh06SsJptHXqOWyjimRSW+f64hMLtXTjDv326hEa0r196EhANB12BoaZ5Um6WNKD0Y0DAJER8RkY7u5m9ld3HyXp5Ui/HuJHn86t1S+njWYVl2niGXSHTxburtkllTqzXyf6X0SAmbWX9D1JH2o49Yak77v7jnCpgMj74YvFmlW8Rd+/dLDOHZgbOg4QbUfa0e/Xkr4uqe0h5281s+skFam+sf62CGY7qrv+tlTLNu4M9fIATtCgbu30vY8OjvrrRqsHxmwzGx2l10IcGZ+fq9klFdpZVR06CqJkZdlubd1N/4sImiJpl6RPNdx2SpoaNBEQYdP/vUZT3l6tSWf00XXjeoeOAzQ7M/uRmWU1Ou5gZncfPD7cjn5mdomkMnefd8ilP0g6RdJwSZsk/eIIrznZzIrMrKi8vPzk3wQANINo9cA4R9LNZrZW0h7VT3Nzdx8WpddHjDp/UI7uf2OV3nivXB89rVvoOIiC/9//onPgJAnrFHe/vNHxXWa2MFQYINJeLd6iu/62VOcPytWdF+eHjgNEyoXu/q2DB+6+raH55reP8pgzJH2s4X6ZktqZ2aPufs3BO5jZnyS9cLgHu/sDkh6QpIKCgqP12DgpIb7BBRC/oraNquorvedK+qikSxp+IskN79FBnVpnsBtJEilcVaHuWS3VoyPN9SJkn5mdefDAzM6QtC9gHiBiFpfu0BeeWKDB3drr3iuHKzWFZWlIWMe9o5+73+Huee7eW9KVkv7p7teYWddGd/uEpCWRCAwAkRCtGRgRq9oivqWmmM4dmKNXlm5WdW2d0lOjVVNDCHV1rndWV+qcATn0v4icWyQ93NALQ5K2SZoQMA8QEesr9+r6aXPVoVWGHppQoFYZbJeKhNacO/rdY2bDG55njaSbmyMgAERDtP7av6j6QdJUP4Wtj6T3VL8jCZLc+EG5enpeqeauqdQHTmFZQSJ7v2yXKvcc0Ni+HUNHSVju/q6k08ysXcPxf3VGM7MJ7s421ohr2/ce0ISpc1RdW6cZk09XTrvM0JGAiGm0o98iSeNV/3n6B+7+SlOfw91fl/R6w+/XNn9KAIiOqBQw3H1o42MzGymqvWjwwf6dlZGWolnLyihgJLjCVQ39L2jgGXGHFi4auU0n/q0dEFxVda1uerhIpZX79OiNp6tfzqGbKwCJhR39AOD/CzJf393nS2JXEkiSWmWk6YxTOmlm8Wa5s9ookRWuqlCPji2V16FV6CjJjLU7iFt1da6vPPWu5q7Zpl9++jSN6cNsLiQNdvQDAEVpBoaZfbnRYYqkkZLYjwn/MX5Qrl57tlwrynbr1Fy+TUtEB/tffGRwbugoyY4qIeLWj14q1ouLN+nOi/J1yTB2rkJSYUc/AFD0emA0/hdpjep7YjwTpddGHDhvYK7u1BLNXLaFAkaCWrZpp3bsq2b5SHjMwEBcmvKv1XrwX6s18QO9deMH+4SOA0TbhaEDAEAsiFYPjLskycza1h/67mi8LuJHl/aZGpbXXrOKt+jz5/QLHQcRMLukof9FX/qcBPZ26ADA8fr74k36wYvL9JHBufrOJYPYxQjJiNlzAKAo9cAwsyFmtkD1+0wvNbN5ZjYkGq+N+DE+P1cL129X+a79oaMgAgpXVahP59bq0p7dAiLJzH5kZlmNjjuY2d0Hj9391iDBgBM0b22lbn9yoUb0yNK9V45QagrFCySlFyW90PDzVUklkv4eNBEABBCtJp4PSPqyu/dy916SvtJwDviP8fm5cpdeW14WOgqaWU1tneasrtTYviwfiYIL3X37wQN33ybponBxgBO3qny3bphepG5ZLfXghNHKTE8NHQkIwt2Huvuwhp/9JY2R9K/QuQAg2qJVwGjt7q8dPGjYi7p1lF4bcSK/a1t1z2qpmcVbQkdBM1u6cad27a+h/0V0pJpZi4MHZtZSUouj3B+ISeW79mvi1DlKNdO060erY+uM0JGAmMGOfgCSVbSaeJaY2XckPdJwfI2k1VF6bcQJM9P4/Bw9WbReVdW1fNOWQAob+l+M7cuWh1HwqKRXzWyq6tdMT5I0PWwk4PjsPVCjG6bPVfmu/ZoxeZx6deI7DyQ3dvQDgHoRnYFhZgcLFm9Jypb0F0nPSuos6fpIvjbi0/hBuaqqrtPbK7eGjoJmVLiqQv1y2iinLf0vIsnqOxs+IeluSfmSBkv6gbvfEzQYcBxqaut06+MLtGTDDv32qpEa3iMrdCQgFrRtdGuh+l4YlwZNBAABRHoGxigz6yVpgur3rzb9/y7KdOHC/zi9Tye1aZGmWcVbdF5+bug4aAbVtXWau6ZSl4/MCx0l4bm7m9lf3X2UpJdD5wGOl7vru88v1T+Xl+nujw/R+EH8HQAkdvQDgIMi3QPjftV/iB4oqUjS3Iaf8xp+Av8lIy1FZw3I1qziMtXVsWNYIlhUukN7D9TS/yJ6ZpsZ66IRl37/+io9/s46ffbsU3TN2F6h4wAxgx39AKBeRAsY7n6fu+dLmuLufRvd+rh730i+NuLX+fm5Kt+1X4s27AgdBc1gdkP/i9P70P8iSs6RVGhmq8xskZktNrNFoUMBx/LsglL97JX39PHh3fS1Dw8IHQeINezoBwCKUhNPd/9sNF4HieHsAdlKTTHNWraFtc8JYHZJhQbktlWnNmyEESUXhg4AHK9/r9yqr/95kcb17aR7PnmaUlJYZQoc4n929DMzutsCSDrR2kYVaLKsVhkq6NVBs9hONe4dqKlT0ZptLB+JLj/CDYhJyzfv1M2PzFPfzm10/7WjlJHGRxPgMErM7Dtm1rvh9m2xox+AJMSnBMSk8wflavnmXVpfuTd0FJyEd0u3a191rcb2pYARRS9KeqHh56uSSiT9PWgi4Ag27diniVPmqnWLNE29frTat0wPHQmIKezoBwD/jQIGYtLBHUheZRZGXCtcVSEzaWxf+l9Ei7sPdfdhDT/7Sxoj6V+hcwGH2llVreunztXu/TWaev1odctqGToSEIsa7+j3f5LOl3SepO+LHf0AJCEKGIhJfTq3Vr+cNppVXBY6Ck5C4aoK5Xdpp6xWGaGjJC13ny/ppHYlMbMrzGypmdWZWUGj8+c3dMJf3PDz3Ibzbc1sYaPbVjP79cm9EySSAzV1+uyj87SybLfuv2aU8ru2Cx0JiFXs6AcAjUSliSdwIsbn5+rBt0q0s6pa7TKZVhxvqqprNW/dNl3LVohRZWZfbnSYImmkpPKTfNolki6T9MdDzm+V9FF339iwnd8rkrq7+y5Jwxtlmqf6ac+A3F3ffGaR3l5ZoV9ccZrO7N85dCQgZrn7fZLuM7M/0BQfAJiBgRh2/qAc1dS53njvZP/thRAWrNuuAzV1Gkf/i2hr2+jWQvW9MC49mSd092J3f+8w5xe4+8aGw6WSMs3sv7abMbP+knJUv34b0C/+8b7+smCDvnL+qbp8VF7oOEBcoHgBAPWYgYGYNbxHB3VqnaFZxVv00dO6hY6D41RYUqEUk8bQ/yKq3P0uqX4ZR/2h747SS18uaYG77z/k/FWSnnR3dkKBHn9nnX772kpdNaaHbj23X+g4AAAgzlDAQMxKTTGdOzBHryzdrOraOqWnMmEonsxeVaEh3duz/CfKGpZyPCKpY8PxVkkT3H3JMR43S1KXw1y6092fO8ZjB0v6qaQPH+bylZKuPcpjJ0uaLEk9e/Y82ssgzv1z+RZ9+6+Ldc6AbP3g0iEyo/8gAAA4PvyLEDFt/KBc7ayq0dw1laGj4DjsO1CrBeu3sXwkjAckfdnde7l7L0lfaTh3VO4+3t2HHOZ2rOJFnuq39LvO3Vcdcu00SWnuPu8or/uAuxe4e0F2dnZT3h/i0KLS7fr8Yws0uFt7/fbqkUqjIA0AAE4AnyAQ0z7Yv7My0lI0axm7kcSTeWu3qbrWNfYUChgBtHb31w4euPvrklpH4oXMLEv1PTbucPe3D3OXqyQ9EYnXRvxYV7FXk6bNVac2GXpoYoFat2DyJwAAODEUMBDTWmWk6cx+nTWzeLNYQh8/Cku2KjXFNLo3/S8CKDGz75hZ74bbtyWtPpknNLNPmFmppHGSXjSzVxou3Sqpn6TvNNoyNafRQz8lChhJbdueA5o4dY6qa13Trh+jnLaZoSMBAIA4RgEDMW98fq7WV+7TirJo9SLEySpcVaFhee3Vhm9ao8bMHmn49S1J2arftvRZSZ0lXX8yz+3uz7p7nru3cPdcd/9Iw/m73b21uw9vdCtr9Li+7r78ZF4b8auqulY3Plyk0u379OCEAvXLaRM6EgAAiHP86wIx77z8HOlZaeayLTo1t23oODiGPftrtKh0hyZ/qG/oKMlmlJn1kjRB0jmSTNLBaUt0S0RU1da5vvTkQs1ft02/u3oks7EAAECzYAYGYl5uu0wNy2uvWcVbQkdBExSt3aaaOtdYGnhG2/2SXpY0UFKRpLkNP+c1/ASi5ocvFuvvSzbrzovyddHQrqHjAACABEEBA3FhfH6uFq7frvJd+0NHwTEUrqpQeqqpoHeH0FGSirvf5+75kqY0LN04eOvj7kyHQdQ8+FaJpry9Wtef0Vs3fpD/9QAAQPOhgIG4MD4/V+7Sa8vZjSTWFZZU6LS8LLXKYIVaCO7+2dAZkLxeWrxJP3ypWBcO6aJvXzwodBwAAJBgKGAgLuR3bavuWS01k2UkMW1XVbWWbNihcWyfCiSduWsqdfuTCzWyZwf96tPDlZpC6xUAANC8KGAgLpiZxufn6K0V5aqqrg0dB0cwd02lautc4+h/ASSVlWW7deP0IuVltdSD1xUoMz01dCQAAJCAKGAgbowflKuq6jq9vXJr6Cg4gsJVFcpITdHIXvS/AJJF2a4qTZw6R+mppmnXj1GH1hmhIwEAgARFAQNx4/Q+ndSmRRq7kcSwwpIKjeiZxbevQJLYs79GN0wrUsXuA5oycbR6dmoVOhIAAEhgFDAQNzLSUnTWgGzNKi5TXZ2HjoND7NhbraUbd9L/AkgSNbV1uvXx+Vq6cYd+95kRGpaXFToSAABIcBQwEFfOz89V+a79WrRhR+goOMQ7qyvkLvpfAEnA3fWd55botffKdffHh+rcgbmhIwEAgCRAAQNx5ewB2UpNMc1axjKSWFNYUqEWaSka3jMrdBQAEfbbf67UE3PW6/PnnKKrT+8ZOg4AAEgSFDAQV7JaZWh07w70wYhBhasqVNC7g1qk0f8CSGTPzCvVL2a+r8tGdNdXPzwgdBwAAJBEKGAg7ozPz9Xyzbu0vnJv6ChoULnngJZv3sXyESDBvbWiXN94ZpHO6NdJP7l8mMwsdCQAAJBEKGAg7pyXX7/WmlkYseOdkgpJooEnkMCWbdypzz46X/1y2ugP14xSRhofIQAAQHTF7acPM7vCzJaaWZ2ZFYTOg+jp07m1+uW00avFZaGjoEFhSYVapqeyCwGQoDZu36frp81RmxZpmnr9aLXLTA8dCQAAJKG4LWBIWiLpMklvhg6C6Bufn6vZJRXaWVUdOgokzS6p73+RnhrPQwqAw9mxr1oTp87R3v21mjZptLq2bxk6EgAASFJx+68Ndy929/dC50AY5w/KUU2d6433ykNHSXpbd+/X+1t2s3wESED7a2p1yyPztHrrHt1/7SgN7NIudCQAAJDE4raAcTzMbLKZFZlZUXk5/+BNBMN7dFCn1hn0wYgBsw/2v6CBJ5BQ6upc3/jzIhWWVOinlw/TGf06h44EAACSXEwXMMxslpktOczt0uN5Hnd/wN0L3L0gOzs7UnERRakppnMH5ui15WWqrq0LHSepFa6qUJsWaRravX3oKACa0c/+8Z7+unCjvvaRAbpsZF7oOAAAAEoLHeBo3H186AyIXeMH5erpeaWau6ZSHziFbwZDKSyp0OjeHZRG/wsgYTw6e63+8PoqXTWmpz539imh4wAAAEiK8RkYwNF8sH9nZaSlaNYydiMJZcvOKpWU76H/BZBAZi3bou8+t0TnDszRDy4dLDMLHQkAAEBSHBcwzOwTZlYqaZykF83sldCZEF2tMtJ0Zr/Omlm8We4eOk5S+v/9L5gBAySCheu369Yn5mtI9/b67dUjmFkFAABiStx+MnH3Z909z91buHuuu38kdCZE3/j8XK2v3KcVZbtDR0lKhasq1C4zTYO6sTMBEO/WVuzRDdPmKrttCz00YbRaZcT0KlMAAJCE4raAAUjSefk5kqSZy9iNJITCkgqN6dNJqSlMMQfiWeWeA5o4da5q3TXt+jHKbtsidCQAAID/QQEDcS23XaaG5bVnO9UANm7fp7UVe+l/AcS5qupa3Th9rjZs36cHryvQKdltQkcCAAA4LAoYiHvj83O1cP12le/aHzpKUilcdbD/BQUMIF7V1rlum7FAC9Zv172fHq6C3h1DRwIAADgiChiIe+Pzc+Uuvbac3UiiqbCkQh1apWtgl7ahowA4Ae6uH7ywTK8s3aLvXDxIFw7tGjoSgAgws1QzW2BmLxxy/qtm5mZGJ24AcYMCBuJefte26p7VUjNZRhJVhasqdHqfTkqh/wUQlx58a7Wm/XuNbjizjyad2Sd0HACRc5uk4sYnzKyHpPMlrQuSCABOEAUMxD0z0/j8HL21olxV1bWh4ySF9ZV7tWH7PvpfAHHqb+9u1A9fKtbFQ7vqzovyQ8cBECFmlifpYkkPHnLpV5K+Lol96AHEFQoYSAjjB+WqqrpOb6/cGjpKUigsqe9/MZb+F0DceaekQl956l2N7t1Bv/jUacyiAhLbr1VfqKg7eMLMPiZpg7u/e7QHmtlkMysys6Ly8vLIpgSAJqKAgYRwep9OatMijd1IomT2qgp1ap2hU3PZrQCIJyvLdummh4uU17Gl/nRdgTLTU0NHAhAhZnaJpDJ3n9foXCtJd0r67rEe7+4PuHuBuxdkZ2dHMCkANB0FDCSEjLQUnTUgW7OKy1RXx2zISHJ3FZZUaGzfTjLjm1sgXpTtrNKEKXOVkZaq6dePUVarjNCRAETWGZI+ZmZrJM2QdK6kRyT1kfRuw/k8SfPNrEuokABwPChgIGGcn5+r8l37tWjDjtBREtrair3atKNKY+l/AcSN3ftrdP20udq294CmThytHh1bhY4EIMLc/Q53z3P33pKulPRPd7/c3XPcvXfD+VJJI919c8isANBUFDCQMM4ekK3UFNOsZSwjiaSD/S/G0f8CiAvVtXX6/GPztXzzLv3u6pEamtc+dCQAAIATQgEDCSOrVYZG9+5AH4wIK1xVoey2LXRKduvQUQAcg7vrzmcX6433y3X3x4fonIE5oSMBCMDdX3f3Sw5zvre70wEdQNyggIGEMj4/V8s379L6yr2hoySkg/0vxtH/AogL9726Uk8VleoL5/bTVWN6ho4DAABwUihgIKGcPyhXkpiFESGryveofNd+jaP/BRDzni5ar1/Nel+XjeyuL59/aug4AAAAJ40CBhJKr06t1S+njV4tLgsdJSHR/wKID2++X647/rJYZ/brrJ9cNowZUwAAICFQwEDCGZ+fq9klFdpZVR06SsKZvapCXdtnqlcndjAAYtXSjTv02UfnqV9OG/3hmpHKSONPPQAASAx8qkHCOX9QjmrqXG+8Vx46SkJxd82m/wUQ09ZX7tXEqXPVrmW6pl0/Rm0z00NHAgAAaDYUMJBwhvfooE6tM+iD0cze37JbFXsOaCz9L4CYVLF7v66bMkcHaur08KQx6tI+M3QkAACAZkUBAwknNcV07sAcvba8TNW1daHjJIzCVfW7rNH/Aog9ew/UaNL0Im3cvk8PTShQ/9y2oSMBAAA0OwoYSEjjB+VqZ1WN5q6pDB0lYRSWVCivQ0v16Ej/CyCWVNfW6XOPzdfi0u2676oRKujdMXQkAACAiKCAgYT0wf6dlZGWolnL2I2kOdTVud5ZXamxzL4AYoq765vPLNbr75Xr7o8P1UcGdwkdCQAAIGIoYCAhtcpI05n9Omtm8Wa5e+g4cW/55l3avrea5SNAjPnZK+/pmfmlun18f119es/QcQAAACKKAgYS1vj8XK2v3KcVZbtDR4l7hSUVkqRxNPAEYsa0t1fr96+v0lVjeuq28/qHjgMAABBxFDCQsM7Lz5EkzVzGbiQnq3BVhXp1aqVuWS1DRwEg6YVFG3XXC8t0/qBc/eDSwWxtDAAAkgIFDCSs3HaZOi2vPdupnqTaOtc7qytYPgLEiH+v2qovP/muRvXsoN9cNUJpqfwpBwAAyYFPPUho4/NztXD9dpXv2h86StxatnGndlXVsHwEiAHLNu7UzQ/PU69OrfTghAJlpqeGjgQAABA1FDCQ0M7Lz5W79NpydiM5UYUlWyWJGRhAYOsr92ri1Dlq3SJN0yeNUVarjNCRAAAAoooCBhJafte26p7VUjNZRnLCCldVqG92a+W0ywwdBUhalXsOaMLUOaqqrtXDN4yhHw0AAEhKFDCQ0MxM4/Nz9NaKclVV14aOE3dqaus0d802Zl8AAe09UKNJ0+aqdNs+PThhtE7NbRs6EgAAQBAUMJDwxg/KVVV1nd5euTV0lLizeMMO7d5P/wsglOraOn3+sflaVLpd9105QmP6dAwdCQAAIBgKGEh4p/fppDYt0tiN5AQUllRIksYyAwOIOnfXt/6yWK+9V67vXzpEFwzpEjoSAABAUBQwkPAy0lJ01oBszSouU12dh44TVwpXVejU3Dbq3KZF6ChA0vnFP97X0/NK9cXz+uuasb1CxwEAAAiOAgaSwvn5uSrftV+LNuwIHSVuHKipUxH9L4AgHi5co9++tlJXju6hL43vHzoOAABATKCAgaRw9oBspaaYZi1jGUlTLSrdrn3VtfS/AKLspcWb9L3nl2p8fo7u/vgQmVnoSAAAADGBAgaSQlarDI3u3YE+GMehcFV9/4sxfShgANFSuKpCt89YqBE9svSbq0YqLZU/0wAAAAfxyQhJY3x+rpZv3qX1lXtDR4kLs1dXaGCXturYOiN0FCApFG/aqckPF6lnp1aaMnG0Wmakho4EAAAQUyhgIGmcPyhXkpiF0QT7a2rr+1+wfASIitJtezVx6hy1apGq6ZPGKKsVhUMAAIBDUcBA0ujVqbX657TRq8VloaPEvIXrtmt/TR0NPIEo2LbngCZMmaO9B2o1fdIYdc9qGToSAABATKKAgaRyXn6uZpdUaGdVdegoMa2wpEJm0un0vwAiat+BWk2aPlfrt+3Tg9cVaGCXdqEjAQAAxCwKGEgq5w/KUU2d6433ykNHiWmFqyo0uFs7tW+VHjoKkLBqaut06+PztXD9dt135XCdzownAACAo6KAgaQyvEcHdWqdQR+Mo6iqrtWCddtZPoL/MLMrzGypmdWZWUGj8+eb2TwzW9zw89xG165qOL/IzF42s85h0scmd9edzy7Rq8vL9P1Lh+iCIV1DRwIAAIh5FDCQVFJTTOcOzNFry8tUXVsXOk5Mmr92mw7U1tHAE40tkXSZpDcPOb9V0kfdfaikCZIekSQzS5N0r6Rz3H2YpEWSbo1e3Nj3q5nv68mi9frCuf107dheoeMAAADEBQoYSDrjB+VqZ1WN5q6pDB0lJr3xfrlSU0yje3cMHQUxwt2L3f29w5xf4O4bGw6XSso0sxaSrOHW2sxMUjtJGw99fLJ6ZPZa3ffPlfpUQZ6+fP6poeMAAADEDQoYSDof7N9ZGWkpmrWM3Uga21lVrW8+s0h/fLNEH+zfWW0z6X+B43K5pAXuvt/dqyV9VtJi1RcuBkl66HAPMrPJZlZkZkXl5Ynfm+blJZv03eeW6LyBOfrRJ4aqvr4DAACApqCAgaTTKiNNZ/brrJnFm+XuoePEhH8u36IP//JNPVW0Xjef1Vf3XzMqdCREmZnNMrMlh7ld2oTHDpb0U0k3Nxynq76AMUJSN9UvIbnjcI919wfcvcDdC7Kzs5vt/cSid0oq9MUZCzW8R5Z+e/VIpaXyJxgAAOB4pIUOAIQwPj9X/1xephVlu3VqbtvQcYLZtueAvv/CMj27YINOzW2jP157hk7rkRU6FgJw9/En8jgzy5P0rKTr3H1Vw+nhDc+5quE+T0n6ZjPEjFvLN+/UjQ8XKa9DS02ZMFotM1JDRwIAAIg7FDCQlM7Lz5GelWYu25K0BYy/L96k7zy3RNv3VuuL5/XX5885RS3S+EcVms7MsiS9KOkOd3+70aUNkgaZWba7l0s6X1JxgIgxYcP2fZo4Za5apqfq4Ulj1KF1RuhIAAAAcYn5q0hKue0ydVpe+6TcTnXr7v363GPz9NnH5iu3Xaaeu/UMffn8Uyle4IjM7BNmVippnKQXzeyVhku3Suon6TtmtrDhltPQ2PMuSW+a2SLVz8j4UYjsoW3fe0ATpszRnv01mj5pjPI6tAodCQAAIG4xAwNJa3x+rn45632V79qv7LYtQseJOHfX8+9u1P89v1R79tfqax8ZoMkf6qt01uHjGNz9WdUvEzn0/N2S7j7CY+6XdH+Eo8W0fQdqNWnaXK2r2Kvpk8Yov2u70JEAAADiGv9yQdIaPyhX7tJryxN/N5ItO6t008NFum3GQvXq1FovfvFMff6cfhQvgAipqa3TF55YoAXrt+vXVw7XuFM6hY4EAAAQ95iBgaQ1sEtbdc9qqZnFW/Sp0T1Cx4kId9fT80r1gxeW6UBNnb59cb6uP6OPUlPYuhGIFHfXd55bolnFW3TXxwbroqFdQ0cCAABICBQwkLTMTOPzc/Rk0XpVVdcqMz2xekBs2L5P33xmkd5asVVjenfUTz85TH06tw4dC0h4v561Qk/MWa/Pn3OKJnygd+g4AAAACYP540hq4wflqqq6Tm+v3Bo6SrOpq3M9OnutPvzLNzRv7TZ9/9LBmjF5LMULIAoenb1W9766Qp8claevfnhA6DgAAAAJhRkYSGqn9+mkNi3SNKt4i87Lzw0d56StrdijbzyzSLNLKnVmv8768WVD1aMjux4A0fDyks367nNLdM6AbP34sqEyY6kWAABAc6KAgaSWkZaiswZka1ZxmX5Y50qJ094QtXWuaf9eo5+9slzpKSn6yWVD9enRPfgHFBAlc9dU6oszFmhYXpZ+95mRNMgFAACIAAoYSHrn5+fqxUWbtGjDDg3vkRU6znFbWbZb33hmkeat3aZzBmTrR5cNVdf2LUPHApLG+1t26YZpc5WX1VJTJo5Wqwz+tAIAAEQCn7KQ9M4ekK3UFNOsZVviqoBRU1unP721Wr+a9b5apqfql586TZ8Y0Z1ZF0AUbdy+TxOmzFGL9FRNnzRGHVtnhI4EAACQsChgIOlltcrQ6N4dNKt4i776kfhourd880597elFWrxhhz4yOFc/+PgQ5bTNDB0LSCrb9x7QhClztLuqRk/ePI5+MwAAABHGIl1A0vj8XC3fvEvrK/eGjnJUB2rq9OtZ7+ujv/mXNm7fp99dPVL3XzOK4gUQZVXVtbpxepHWVuzVH68bpUHd2oWOBAAAkPDitoBhZj8zs+VmtsjMnjWzrNCZEL/OH1S/A8ms4i2BkxzZ4tId+thv/6Vfz1qhC4d01T++9CFdPKwrS0aAKKuprdMXnligeeu26ZefPk0fOKVz6EgAAABJIW4LGJJmShri7sMkvS/pjsB5EMd6dWqt/jltYrKAUVVdq3teXq6P//5tVe45oAeuHaX7rhqhTm1ahI4GJB1313eeW6qZy7boe5cM0iXDuoWOBAAAkDTitgeGu/+j0eFsSZ8MlQWJYfygXP3pzRLtrKpWu8z00HEkSfPXbdPX/7xIK8t264pRefr2xYPUvlVsZAOS0b2vrtATc9bps2efooln9AkdBwAAIKnE8wyMxiZJ+vuRLprZZDMrMrOi8vLyKMZCPBmfn6uaOtcb74X/f2TfgVrd/cIyXf6Hf2vv/hpNnzRGP7viNIoXQECPv7NOv561QpePzNPX46ThLwAAQCKJ6RkYZjZLUpfDXLrT3Z9ruM+dkmokPXak53H3ByQ9IEkFBQUegahIAMN7ZKlT6wzNKt6ij54Wblr47JIKfeOZRVpbsVefOb2nvnnhQLWNkRkhQLL6x9LN+vZfF+vsAdn6yeVD6T0DAAAQQEwXMNx9/NGum9kESZdIOs/dKUzgpKSmmM4dmKNXlm5WdW2d0lOjO0Fp9/4a/fTvy/XI7LXq2bGVHr/pdJoDAjGgaE2lvvDEAg3t3l6//8zIqI8NAHAyzCxVUpGkDe5+iZn9QNKlkuoklUma6O4bQ2YEgKaK209hZnaBpG9I+pi7x/bel4gb4wflamdVjeauqYzq6761olwf+dWbevSdtZp0Rh+9fPsHKV4AMWDFll26YXqRumW11JSJo9UqI6br/gBwOLdJKm50/DN3H+buwyW9IOm7QVIBwAmI2wKGpN9KaitpppktNLP7QwdC/Ptg/87KSEvRrGVlUXm9Hfuq9Y0/L9K1D81Ri7QUPX3zOH33o4P4RxIQAzbt2KfrpsxRRlqKHp40hp1/AMQdM8uTdLGkBw+ec/edje7SWhKzmAHEjbj9V5K79wudAYmnVUaazuzXWTOLN+s7l+RHdJ37q8VbdOezS1S2q0q3nHWKbh/fX5npqRF7PQBNt2NvtSZMmaNdVTWaMXmsenRsFToSAJyIX0v6uuq/9PsPM/uhpOsk7ZB0zuEeaGaTJU2WpJ49e0Y0JAA0VTzPwAAiYnx+rtZX7tOKst0Ref5tew7oS08u1A3Ti9S+Zbqe/dwZ+uaFAyleADGiqrpWNz1cpNVb9+iBa0dpSPf2oSMBwHEzs0sklbn7vEOvufud7t5D9U3wbz3c4939AXcvcPeC7OzsCKcFgKahgAEc4rz8HEnSzGVbmv25/754k87/1Rv627sb9cXz+uv5L5yh03pkNfvrADgxtXWu22Ys0Jw1lfrlp4brA/3oRQMgbp0h6WNmtkbSDEnnmtmjh9zncUmXRzsYAJwoChjAIXLbZeq0vPaaVdx8BYzyXfv1ucfm6bOPzVduu0w9f+uZ+vL5p6pFGrMugFjh7vruc0v0ytIt+u4lg4JupwwAJ8vd73D3PHfvLelKSf9092vMrH+ju31M0vIgAQHgBMRtDwwgksbn5+qXs95X+a79ym574o373F3Pv7tR//f8Uu3ZX6uvfWSAJn+oL9swAjHoN/9cqcfeWaebz+qrSWf2CR0HACLlJ2Y2QPXbqK6VdEvgPADQZBQwgMMYPyhXv5j5vl5bXqZPje5xQs+xeUeVvv3XxZpVXKbhPbL0s08OU//ctsd+IIComzFnnX45831dNrK7vnnBwNBxAKBZufvrkl5v+J0lIwDiFgUM4DAGdmmr7lktNbN4y3EXMNxdT88r1Q9eWKYDNXX69sX5uv6MPkpNidyOJgBO3MxlW/StZxfrQ6dm66eXD4vo7kMAAAA4cRQwgMMwM50/KFcz5q5TVXVtk3cIKd22V3f8ZbHeWrFVY/p01E8vH6Y+nVtHOC2AEzVvbaVufXy+hnRvrz98ZiTLuwAAAGIYn9SAIzgvP0dV1XV6e+XWY963rs71yOy1+siv3tS8tdv0/UsHa8ZNYyleADFsZdku3TC9SF3bZ2rKxNFq3YKaPgAAQCzj0xpwBKf36aQ2LdI0q3iLzsvPPeL91lbs0TeeWaTZJZU6s19n/fiyoerRsVUUkwI4Xpt3VOm6h+YoLSVFD086XZ3bnHizXgAAAEQHBQzgCDLSUnTWgGzNKi7TD+tcKYf0sKitc0379xr97JXlSk9J0U8vH6pPFfRg/TwQ43bsq9aEKXO0s6pGMyaPVc9OFBwBAADiAQUM4CjOz8/Vi4s2adGGHRreI+s/51eW7dbX//yu5q/brnMGZOtHlw1V1/YtwwUF0CRV1bW66eEilWzdrakTx2hI9/ahIwEAAKCJKGAAR3H2gGylpphmLdui4T2yVFNbpz+9tVq/mvW+Wqan6lefPk0fH96dWRdAHKitc33pyYWas7pS9145XGf27xw6EgAAAI4DBQzgKLJaZWh07w6aVbxFl5zWVV97epEWb9ihCwZ30fc/Plg5bTNDRwTQBO6u/3t+qf6+ZLO+fXG+Lh3ePXQkAAAAHCcKGMAxjM/P1d0vFuuS+/6l9i3T9burR+qioV2YdQHEkd+9tlKPzF6ryR/qqxs/2Dd0HAAAAJwAtlEFjuGCIV3UOiNVFw3tqplfPksXD+tK8QKIIxW79+uBN0v0iRHd9c0LBoaOAwAAgBPEDAzgGPI6tNKi//uIUlMoWgDxqFObFnru1jPVPavl/+wmBAAAgPhBAQNoAooXQHzr07l16AgAAAA4SSwhAQAAAAAAMY8CBgAAAAAAiHkUMAAAAAAAQMyjgAEAAAAAAGIeBQwAAAAAABDzKGAAAAAAAICYRwEDAAAAAADEPAoYAAAAAAAg5lHAAAAAAAAAMY8CBgAAAAAAiHkUMAAAAAAAQMyjgAEAAAAAAGIeBQwAAAAAABDzKGAAAAAAAICYRwEDAAAAAADEPAoYAAAAAAAg5lHAAAAAAAAAMY8CBgAAAAAAiHkUMAAAAAAAQMyjgAEAAAAAAGIeBQwAAAAAABDzKGAAAAAAAICYRwEDAAAAAADEPAoYAAAAAAAg5lHAAAAAAAAAMc/cPXSGqDKzcklrI/gSnSVtjeDzx5Jkeq9Scr3fZHqvUmTfby93z47QcycUxudmlUzvVUqu95tM71VifI4JjM/NKpneq5Rc7zeZ3qsU+fd72DE66QoYkWZmRe5eEDpHNCTTe5WS6/0m03uVku/9Jqtk+u+cTO9VSq73m0zvVUq+95uskum/czK9Vym53m8yvVcp3PtlCQkAAAAAAIh5FDAAAAAAAEDMo4DR/B4IHSCKkum9Ssn1fpPpvUrJ936TVTL9d06m9yol1/tNpvcqJd/7TVbJ9N85md6rlFzvN5neqxTo/dIDAwAAAAAAxDxmYAAAAAAAgJhHAQMAAAAAAMQ8ChgAAAAAACDmUcAAAAAAAAAxjwIGAAAAAACIeRQwAAAAAABAzKOAAQAAAAAAYh4FDAAAAAAAEPMoYAAAAAAAgJhHAQMAAAAAAMQ8ChgAAAAAACDmBS9gmNkFZvaema00s28e5rqZ2X0N1xeZ2chG17LM7M9mttzMis1sXHTTAwAAAACAaAhawDCzVEm/k3ShpEGSrjKzQYfc7UJJ/RtukyX9odG1eyW97O4DJZ0mqTjioQEAAAAAQNSFnoExRtJKdy9x9wOSZki69JD7XCrpYa83W1KWmXU1s3aSPiTpIUly9wPuvj2K2QEAAAAAQJSELmB0l7S+0XFpw7mm3KevpHJJU81sgZk9aGatIxkWAAAAAACEkRb49e0w57yJ90mTNFLSF9z9HTO7V9I3JX3nf17EbLLql5+odevWowYOHHhSoQGgqebNm7fV3bND54gHnTt39t69e4eOASBJMD4DQPwJXcAoldSj0XGepI1NvI9LKnX3dxrO/1n1BYz/4e4PSHpAkgoKCryoqOjkkwNAE5jZ2tAZ4kXv3r3F+AwgWhifASD+hF5CMldSfzPrY2YZkq6U9Pwh93le0nUNu5GMlbTD3Te5+2ZJ681sQMP9zpO0LGrJAQAAAABA1ASdgeHuNWZ2q6RXJKVKmuLuS83slobr90t6SdJFklZK2ivp+kZP8QVJjzUUP0oOuQYAAAAAABJE6CUkcveXVF+kaHzu/ka/u6TPH+GxCyUVRDIfAAAAAAAIL3gBAwCOpbq6WqWlpaqqqgod5YgyMzOVl5en9PT00FEAIGoYnwEA0UQBA0DMKy0tVdu2bdW7d2+ZHW5jorDcXRUVFSotLVWfPn1CxwGAqGF8BgBEU+gmngBwTFVVVerUqVNMfjiWJDNTp06dYvobSACIBMZnAEA0UcAAEBdi9cPxQbGeDwAiJdbHv1jPBwBoOgoYANBEtbW1GjFihC655JLQUQAAh2CMBoDERwEDAJro3nvvVX5+fugYAIDDYIwGgMRHAQMAmqC0tFQvvviibrzxxtBRAACHYIwGgOTALiQA4spdf1uqZRt3NutzDurWTt/76OCj3uf222/XPffco127djXrawNAogg1PkuM0QCQLJiBAQDH8MILLygnJ0ejRo0KHQUAcAjGaABIHszAABBXmvJNXHN7++239fzzz+ull15SVVWVdu7cqWuuuUaPPvpo1LMAQKwKMT5LjNEAkEyYgQEAx/DjH/9YpaWlWrNmjWbMmKFzzz2XD8YAECMYowEgeVDAAAAAAAAAMY8lJABwHM4++2ydffbZoWMAAA6DMRoAEhszMAAAAAAAQMyjgAEAAAAAAGIeBQwAAAAAABDzKGAAiAvuHjrCUcV6PgCIlFgf/2I9HwCg6ShgAIh5mZmZqqioiNkPoe6uiooKZWZmho4CAFHF+AwAiCZ2IQEQ8/Ly8lRaWqry8vLQUY4oMzNTeXl5oWMAQFQxPgMAookCBoCYl56erj59+oSOAQA4BOMzACCaWEICAACAmLP3QE3oCACAGEMBAwAAADFl844qjf/FG3pq7vrQUQAAMYQCBgAAAGLG3gM1uvHhudqxr1rDerQPHQcAEEPogQEAAICYUFfn+vKT72rZxp16cEKBBnZpFzoSACCGMAMDAAAAMeHn/3hPLy/drG9dlK9zB+aGjgMAiDEUMAAAABDcM/NK9fvXV+mqMT10w5nsbAIA+F8UMAAAABDU3DWVuuMvi/WBUzrp+5cOkZmFjgQAiEEUMAAAABDM+sq9uvmReereoaV+/5mRSk/l4ykA4PD4CwEAAIAgdlVV64bpc1Vb53poQoGyWmWEjgQAiGEUMAAAABB1NbV1+sITC1RSvkd/+MxI9c1uEzoSACDGsY0qAAAAou7uF4v1+nvl+tEnhuoD/TqHjgMAiAPMwAAAAEBUPTp7rab9e40mndFHV5/eM3QcAECcoIABAACAqPnXiq363vNLdc6AbN15cX7oOACAOEIBAwAAAFGxqny3PvfYPPXLbqP7rhqh1BS2SwUANB0FDAAAAETctj0HdMO0uUpPTdGDEwrUNjM9dCQAQJyhiScAAAAi6kBNnT772Dxt3F6lJyafrh4dW4WOBACIQxQwAAAAEDHuru8+t0SzSyr1608P16heHUNHAgDEKZaQAAAAIGIe+tdqzZi7Xree008fH9E9dBwAQByjgAEAAICImLVsi374UrEuGtpFXz7/1NBxAABxjgIGAOCYzGyKmZWZ2ZJG564ws6VmVmdmBYfc/w4zW2lm75nZR6KfGEBoxZt26rYZCzSkW3v94orhSmHHEQDASaKAAQBoimmSLjjk3BJJl0l6s/FJMxsk6UpJgxse83szS41CRgAxonzXft04vUhtMtP0p+sK1DKDIQAAcPIoYAAAjsnd35RUeci5Ynd/7zB3v1TSDHff7+6rJa2UNCYKMQHEgKrqWk1+pEgVe/brwetGq0v7zNCRAAAJggIGAKC5dZe0vtFxacM5AAnO3fX1Py/SgnXb9etPD9fQvPahIwEAEggFDABAczvcQnc/7B3NJptZkZkVlZeXRzgWgEj7zT9X6vl3N+prHxmgC4Z0DR0HAJBgKGAAAJpbqaQejY7zJG083B3d/QF3L3D3guzs7KiEAxAZLyzaqF/OfF+Xjeyuz519Sug4AIAERAEDANDcnpd0pZm1MLM+kvpLmhM4E4AIenf9dn3lqXdV0KuDfnzZUJmx4wgAoPkFL2CY2QUN2+ytNLNvHua6mdl9DdcXmdnIQ66nmtkCM3sheqkBILmY2ROSCiUNMLNSM7vBzD5hZqWSxkl60cxekSR3XyrpKUnLJL0s6fPuXhsqO4DI2rh9n258uEjZbVvoj9eOUos0dhwBAERGWsgXb9hW73eSzlf9lOO5Zva8uy9rdLcLVf/tXX9Jp0v6Q8PPg26TVCypXVRCA0AScverjnDp2SPc/4eSfhi5RABiwZ79NbpxepH2HajVYzeerk5tWoSOBABIYKFnYIyRtNLdS9z9gKQZqt9+r7FLJT3s9WZLyjKzrpJkZnmSLpb0YDRDAwAAJLu6OteXnlyo5Zt36jdXj9CpuW1DRwIAJLjQBYymbLV3tPv8WtLXJdUd7UXocg8AANC87nnlPf1j2RZ9++JBOmdATug4AIAkELqA0ZSt9g57HzO7RFKZu8871ovQ5R4AAKD5PF20Xve/sUpXn95T15/RO3QcAECSCF3AaMpWe0e6zxmSPmZma1S/9ORcM3s0clEBAAAwZ3WlvvXsYp3Rr5Pu+thgdhwBAERN6ALGXEn9zayPmWVIulL12+819ryk6xp2IxkraYe7b3L3O9w9z917Nzzun+5+TVTTAwAAJJG1FXt08yNF6tGhlX5/9Silp4b+KAkASCZBdyFx9xozu1XSK5JSJU1x96VmdkvD9fslvSTpIkkrJe2VdH2ovAAAAMlqZ1W1bphepDqXHpo4Wu1bpYeOBABIMkELGJLk7i+pvkjR+Nz9jX53SZ8/xnO8Lun1CMQDAABIejW1dfr8Y/O1ZusePXLD6erTuXXoSACAJBS8gAEAAIDY9oMXlumtFVv1k8uGatwpnULHAQAkKRYuAgAA4IgeKVyj6YVrdeOZfXTlmJ6h4wAAkhgFDAAAABzWWyvK9X9/W6bzBubojovyQ8cBACQ5ChgAAAD4HyvLdutzj81X/5w2uveqEUpNYbtUAEBYFDAAAADwX7btOaAbps9Vi7QUPTihQG1a0DYNABAef40AAADwHwdq6nTzo/O0aUeVnrhprPI6tAodCQAASczAAAAAQAN3153PLtac1ZX62SeHaVSvDqEjAQDwHxQwAAAAIEl64M0SPT2vVF88t58uHd49dBwAAP4LBQwAAABo5rIt+snLy3Xx0K66ffypoeMAAPA/KGAAAAAkuaUbd+i2GQs0tHt7/fyK05TCjiMAgBhEAQMAACCJle2q0k3Ti9QuM10PXleglhmpoSMBAHBY7EICAACQpKqqa3XTw/O0bW+1nr5lnHLaZYaOBADAEVHAAAAASELurq/9eZHeXb9d918zSkO6tw8dCQCAo2IJCQAAQBK699UV+tu7G/WNCwbqgiFdQscBAOCYKGAAAAAkmeff3ahfz1qhy0fm6Zaz+oaOAwBAk1DAAAAASCIL1m3TV59+V2N6d9SPLhsiM3YcAQDEBwoYAAAASWLD9n266eF5ym3XQvdfO0ot0thxBAAQP2jiCQAAkAT27K/RjdOLtL+6Vk/cdLo6ts4IHQkAgONCAQMAACDB1da5bpuxUO9t3qkpE0erf27b0JEAADhuLCEBAABIcPe8vFyzirfou5cM0tkDckLHAQDghFDAAAAASGBPFa3XH98s0TVje2rCB3qHjgMAwAmjgAEAAJCgZpdU6M5nF+uD/Tvrex8dzI4jAIC4RgEDAAAgAa3Zuke3PDpPPTu20m+vHqn0VD72AQDiG3/JAAAAEsyOfdW6YfpcSdJDE0arfcv0wIkAADh5FDAAAAASSE1tnW59fL7WVe7V/deMUu/OrUNHAgCgWbCNKgAAQAK562/L9NaKrbrn8mEa27dT6DgAADQbZmAAAAAkiOn/XqNHZq/V5A/11adG9wgdBwCAZkUBAwAAIAG88X657vrbUo3Pz9U3LhgYOg4AAM2OAgYAAECcW7Fll259bL5OzW2re68crtQUtksFACQeChgAAABxrGL3fk2aPlct0lP10MTRat2CFmcAgMTEXzgAAIA4tb+mVrc8Ok9bdu7Xk5PHqntWy9CRAACIGGZgAAAAxCF317f+skRz12zTz684TSN6dggdCQCAiKKAAQAAEIfuf6NEz8wv1W3n9dfHTusWOg4AABFHAQMAACDOvLJ0s+55ZbkuGdZVt4/vHzoOAABRQQEDAAAgjizZsEO3z1ioYXlZ+vkVp8mMHUcAAMmBAgYAAECcKNtZpZseLlJWq3T96dpRykxPDR0JAICoYRcSAACAOLDvQK1uerhIO/ZV6+lbximnXWboSAAARBUFDAAAgBhXV+f6ytMLtWjDDv3xmlEa3K196EgAAEQdS0gAAABi3D2vvKeXFm/WHRcO1IcHdwkdBwCAIChgAAAAxLAn5qzT/W+s0mdO76mbPtg3dBwAAIKhgAEAABCj3ny/XN/+6xKddWq27vrYYHYcAQAkNQoYAAAAMei9zbv0ucfmq39OG/326hFKS+VjGwAgufGXEAAAIMaU7azS9VPnqHWLVE2ZOFptM9NDRwIAIDh2IQEAAIghew/U6IbpRdq+r1pP3TxO3bJaho4EAEBMYAYGAABAjKitc902Y6GWbtyh31w1QkO6s10qAAAHUcAAAACIET98sVgzl23Rdy8ZpPPyc0PHAQAgpgQvYJjZBWb2npmtNLNvHua6mdl9DdcXmdnIhvM9zOw1Mys2s6Vmdlv00wMAADSP6f9eoylvr9b1Z/TWxDP6hI4DAEDMCVrAMLNUSb+TdKGkQZKuMrNBh9ztQkn9G26TJf2h4XyNpK+4e76ksZI+f5jHAgAAxLx/Lt+iu/62VOPzc/Tti/k4AwDA4YSegTFG0kp3L3H3A5JmSLr0kPtcKulhrzdbUpaZdXX3Te4+X5LcfZekYkndoxkeAADgZC3ZsEO3Pr5Ag7q1071XjlBqioWOBABATApdwOguaX2j41L9bxHimPcxs96SRkh653AvYmaTzazIzIrKy8tPNjMAAECz2LRjn26YPldZLdP10ITRat2CDeIAADiS0AWMw33F4MdzHzNrI+kZSbe7+87DvYi7P+DuBe5ekJ2dfcJhAQAAmsvu/TWaNK1Ie/bX6qGJo5XbLjN0JAAAYlroMn+ppB6NjvMkbWzqfcwsXfXFi8fc/S8RzAkAANBsamrrdOvj8/X+ll2aMnG08ru2Cx0JAICYF3oGxlxJ/c2sj5llSLpS0vOH3Od5Sdc17EYyVtIOd99kZibpIUnF7v7L6MYGAAA4Me6u//vbUr3+Xrl+cOkQnXUqs0MBAGiKoDMw3L3GzG6V9IqkVElT3H2pmd3ScP1+SS9JukjSSkl7JV3f8PAzJF0rabGZLWw49y13fymKbwEAAOC4PPSv1Xp09jrd/KG+uvr0nqHjAAAQN0IvIVFDweGlQ87d3+h3l/T5wzzuXzp8fwwAAICY9PKSzfrhS8W6cEgXfeOCgaHjAAAQV0IvIQEAxAEzm2JmZWa2pNG5jmY208xWNPzs0HA+3cymm9liMys2szvCJQdix7vrt+v2JxfotLws/erTw5XCdqkAABwXChgAgKaYJumCQ859U9Kr7t5f0qsNx5J0haQW7j5U0ihJNzdsdw0krdJte3XD9CJ1btNCf7quQJnpqaEjAQAQdyhgAACOyd3flFR5yOlLJU1v+H26pI8fvLuk1maWJqmlpAOSDrvNNZAMdlZVa9K0udpfU6upE0cru22L0JEAAIhLFDAAACcq1903SVLDz5yG83+WtEfSJknrJP3c3Q8tfgBJobq2Tp97dL5Kyvfoj9eMUv/ctqEjAQAQtyhgAACa2xhJtZK6Seoj6Stm1vdwdzSzyWZWZGZF5eXl0cwIRJy769vPLtG/Vm7Vjy4bqg/06xw6EgAAcY0CBgDgRG0xs66S1PCzrOH81ZJedvdqdy+T9LakgsM9gbs/4O4F7l6QnZ0dldBAtPzhjVV6smi9bj2nnz5V0CN0HAAA4h4FDADAiXpe0oSG3ydIeq7h93WSzrV6rSWNlbQ8QD4gmBcWbdQ9L7+nj53WTV/58Kmh4wAAkBAoYAAAjsnMnpBUKGmAmZWa2Q2SfiLpfDNbIen8hmNJ+p2kNpKWSJoraaq7LwoQGwhi3tpt+vJT76qgVwfd88lhMmO7VAAAmkNa6AAAgNjn7lcd4dJ5h7nvbtVvpQoknbUVe3TTw0Xq1j5TD7BdKgAAzYoZGAAAAM1g+94Dun7aXNW5a+r1Y9SxdUboSAAAJBQKGAAAACfpQE2dbn5knkor9+mBawvUp3Pr0JEAAEg4LCEBAAA4Ce6ubz6zSO+srtS9Vw7XmD4dQ0cCACAhMQMDAADgJNz36kr9ZcEGffn8U3Xp8O6h4wAAkLAoYAAAAJygZxeU6lez3tflI/P0hXP7hY4DAEBCo4ABAABwAt4pqdA3/rxYY/t21I8vG8p2qQAARBgFDAAAgOO0qny3Jj8yT3kdW+qP1xQoI42PVAAARBp/bQEAAI5D5Z4DmjRtrtJSTNMmjlH7VumhIwEAkBTYhQQAAKCJqqprddPDRdq8o0pPTB6rnp1ahY4EAEDSoIABAADQBHV1rq8+/a7mrd2m3109UiN7dggdCQCApMISEgAAgCb45cz39cKiTfrGBQN18bCuoeMAAJB0KGAAAAAcw1Nz1+u3r63UVWN66Jaz+oaOAwBAUqKAAQAAcBRvr9yqbz27WB/s31nfv3QI26UCABAIBQwAAIAjWLFll255dJ5OyW6j331mpNJT+egEAEAo/BUGAAA4jPJd+3X9tLnKTE/VlOtHq10m26UCABASBQwAAIBD7DtQqxsfLtLW3fv10IQCdc9qGToSAABJj21UAQAAGqmrc33pyYVaVLpdf7xmlIblZYWOBAAAxAwMAACA//LTl5fr5aWb9e2LB+nDg7uEjgMAABpQwAAAAGjw2Dtr9cc3S3TduF6adEbv0HEAAEAjFDAAAAAkvf5emb773FKdMyBb371kENulAgAQY47aA8PM/uXuZ5rZLkne+JIkd/d2EU0HAAAQBcWbdurWxxdoQG5b/fbqkUpju1QAAGLOUQsY7n5mw8+20YkDAIgkM8uWdJOk3mr0N8DdJ4XKBIS2ZWeVJk2bqzYt0jRl4mi1bkGPcwAAYlGT/0KbWaqkXP33B951kQgFAIiY5yS9JWmWpNrAWYDg9uyv0aRpc7VzX7WeumWcurTPDB0JAAAcQZMKGGb2BUnfk7RFUl3DaZc0LEK5AACR0crdvxE6BBALautct81YoOJNO/XQhNEa3K196EgAAOAomjoD4zZJA9y9IpJhAAAR94KZXeTuL4UOAoT2gxeWaVZxmX5w6WCdMzAndBwAAHAMTS1grJe0I5JBAACR06gZs0n6lpntl1QtmjIjSU19e7Wm/XuNbjizj64d1zt0HAAA0ATH2oXkyw2/lkh63cxelLT/4HV3/2UEswEAmgnNmIH/b9ayLfrBC8v04UG5+tZF+aHjAACAJjrWHmFtG27rJM2UlNHoHB+GASDOmNmrTTkHJKolG3boC08s0JDu7fXrK4crNcVCRwIAAE10rG1U72p8bGbt6k/7roimAgA0KzPLlNRaUmcz66D6pSOS1E5St2DBgCjauH2fJk2bq46tM/TghAK1ymC7VAAA4klTdyEpkDRVDbMuzGyHpEnuPi+C2QAAzedmSbervlgxT/+/gLFT0u8CZQKiZldVtSZNm6t9B2r1yGdPV05btksFACDeNPWrhymSPufub0mSmZ2p+oIG26gCQBxw93sl3WtmX3T3+xpfM7MWgWIBUVFTW6dbH1+glWW7NfX60RrQhVWwAADEo2P1wDho18HihSS5+78ksYwEAOLPxMOcK4x2CCBa3F3ffX6p3ni/XHd/fIg+2D87dCQAAHCCmjoDY46Z/VHSE6rfhu/Tqt+VZKQkufv8COUDADQDM+siqbuklmY2Qv/dA6NVsGBAhD341mo9/s46ffbsU3TlmJ6h4wAAgJPQ1ALG8Iaf3zvk/AdUX9A4t7kCAQAi4iOqn32RJ+kX+u8eGN8KlAmIqL8v3qQf/b1YFw/rqq99eEDoOAAA4CQ1qYDh7ucc7bqZTXD36c0TCQDQ3BrG6Olmdrm7P3Ok+zGeI1EsWLdNtz+5UCN6ZOkXV5ymFLZLBQAg7jW1B8ax3NZMzwMAiKCjFS8aMJ4j7q2v3KubHi5SbrtM/em6AmWmp4aOBAAAmkFzFTBO+GsNM7vAzN4zs5Vm9s3DXDczu6/h+qKDfTea8lgAwHHja2rEtR37qnX9tLmqrnVNmThandqwyQ4AAImiuQoYfiIPMrNUSb+TdKGkQZKuMrNBh9ztQkn9G26TJf3hOB4LADg+JzSeA7HgQE2dPvvoPK2t2KP7rxmlfjltQkcCAADNKPQMjDGSVrp7ibsfkDRD0qWH3OdSSQ97vdmSssysaxMfCwA4PszAQFxyd337r4v171UV+sllwzTulE6hIwEAgGbW1F1IjuXtE3xcd0nrGx2XSjq9Cffp3sTHRtVdf1uqZRt3howA4AQM6tZO3/vo4NAxYsWJjudAUL9/fZWeKirVF8/rr8tH5YWOAwAAIqBJMzDM7EdmltXouIOZ3X3w2N1vPcHXP9w3fYdOXz7SfZry2PonMJtsZkVmVlReXn6cEQEgcURwPAeCef7djfrZK+/pEyO660vj+4eOAwAAIqSpMzAudPdvHTxw921mdpGkb5/k65dK6tHoOE/SxibeJ6MJjz2Y9wFJD0hSQUFBxNZ38w0ugDgQqfEcCKJoTaW++vS7GtO7o35y+VCZsQoKAIBE1dQeGKlm9p823mbWUlJztPWeK6m/mfUxswxJV0p6/pD7PC/puobdSMZK2uHum5r4WADAf4vUeA5E3eqte3TTw0XqntVSf7x2lFqksV0qAACJrKkzMB6V9KqZTVX9Mo1Jkqaf7Iu7e42Z3SrpFUmpkqa4+1Izu6Xh+v2SXpJ0kaSVkvZKuv5ojz3ZTACQ4CIyngPRtnX3fk2cOkdmpqkTR6tD64zQkQAAQIQds4Bh9XMxn5C0SNJ41fee+IG7v9IcAdz9JdUXKRqfu7/R7y7p8019LADg8CI9ngPRsvdAjW6YXqQtO6v0xE1j1btz69CRAABAFByzgOHubmZ/dfdRkl6OQiYAQAQwniMR1Na5vvjEQi0u3a4/XlugET07hI4EAACipKk9MGab2eiIJgEARAPjOeKWu+t7zy/RrOItuutjg3X+oNzQkQAAQBQ1tQfGOZJuNrO1kvaoftqxu/uwiCUDAEQC4zni1v1vlOjR2et081l9de243qHjAACAKGvyNqoRTQEAiBbGc8Sl5xZu0E9fXq6PndZN3/jIwNBxAABAAE0tYHhEUwAAooXxHHHn36u26qtPv6uxfTvqZ1cMU0qKhY4EAAACaGoB40XVf+g1SZmS+kh6T9LgCOUCAEQG4zniynubd+nmR+apd6fW+uO1BWqRlho6EgAACKRJBQx3H9r42MxGSro5IokAABHDeI54snlHla6fOkct01M1bdIYtW+ZHjoSAAAIqKm7kPwXd58viS72ABDnGM8Rq3ZVVWvi1Dnasa9aU68fre5ZLUNHAgAAgTVpBoaZfbnRYYqkkZLKI5IIABAxjOeIB9W1dfrcY/O1smy3pkwcrcHd2oeOBAAAYkBTe2C0bfR7jerXUD/T/HEAABHGeI6Y5u765jOL9daKrfrZJ4fpQ6dmh44EAABiRFN7YNwlSWbWtv7Qd0c0FQAgIhjPEet+NfN9PTO/VF8af6quKOgROg4AAIghTeqBYWZDzGyBpCWSlprZPDMbEtloAIDmxniOWDZjzjrd98+V+nRBD33xvH6h4wAAgBjT1CaeD0j6srv3cvdekr7ScA4AEF8YzxGTXnuvTHf+dYnOOjVbd39iiMwsdCQAABBjmlrAaO3urx08cPfXJbWOSCIAQCQxniPmLC7doc8/Nl8Du7TV7z4zUumpJ7RJGgAASHBNbeJZYmbfkfRIw/E1klZHJhIAIIIYzxFT1lfu1fXT5qpDqwxNnThabVo09aMJAABINkf9isPMDn7AfUtStqS/SHpWUmdJ10c2GgCguTCeIxZt33tAE6bOUXVtnaZPGq2cdpmhIwEAgBh2rK85RplZL0kTJJ0jySR5wzUWpwJA/GA8R0ypqq7VTQ8XqbRynx698XT1y2l77AcBAICkdqwCxv2SXpbUV1JRo/MHP/j2jVAuAEDzYjxHzKirc33lqXc1d802/fbqERrTp2PoSAAAIA4cdQmJu9/n7vmSprh730a3Pu7Oh10AiBMnO56b2RQzKzOzJY3OdTSzmWa2ouFnh0bXhplZoZktNbPFZsbaAPzHj14q1ouLN+nOi/J1ybBuoeMAAIA40aQ23+7+2UgHAQBE3kmM59MkXXDIuW9KetXd+0t6teFYZpYm6VFJt7j7YElnS6o+wddFgpn69mo9+K/VmviB3rrxg31CxwEAAHGEfcoAAMfk7m9Kqjzk9KWSpjf8Pl3Sxxt+/7CkRe7+bsNjK9y9Nho5EdteXrJJ339hmT4yOFffuWSQzGi/AgAAmo4CBgDgROW6+yZJaviZ03D+VEluZq+Y2Xwz+/qRnsDMJptZkZkVlZeXRyEyQpm3tlK3zVioET2ydO+VI5SaQvECAAAcHwoYAIDmlibpTEmfafj5CTM773B3dPcH3L3A3Quys7OjmRFRVFK+WzdOL1K3rJZ6cMJoZaanho4EAADiEAUMAMCJ2mJmXSWp4WdZw/lSSW+4+1Z33yvpJUkjA2VEYOW79mvC1DlKMdO060erY+uM0JEAAECcooABADhRz0ua0PD7BEnPNfz+iqRhZtaqoaHnWZKWBciHwPYeqNEN0+eqfNd+PTRxtHp1ah06EgAAiGNpoQMAAGKfmT2h+t1EOptZqaTvSfqJpKfM7AZJ6yRdIUnuvs3MfilpriSX9JK7vxgkOIKpqa3TFx5foCUbduiBaws0vEdW6EgAACDOUcAAAByTu191hEtH6m3xqOq3UkUScnd99/mlenV5me7++BCNH5QbOhIAAEgALCEBAADN6vevr9Lj76zTZ88+RdeM7RU6DgAASBAUMAAAQLN5dkGpfvbKe7p0eDd97cMDQscBAAAJhAIGAABoFv9euVVf//MijevbSfd8cphSUix0JAAAkEAoYAAAgJO2fPNO3fzIPPXp3Fr3XztKLdJSQ0cCAAAJhgIGAAA4KZt27NP1U+eqVYtUTbt+jNq3TA8dCQAAJCB2IQEAACdsZ1W1rp86V7uqavTUzePULatl6EgAACBBMQMDAACckAM1dfrso/O0smy3/nDNSA3q1i50JAAAkMCYgQEAAI6bu+ubzyzS2ysr9IsrTtMH+2eHjgQAABIcMzAAAP+vvXuPsqo88zz+fSxALoKAcpOLqICKaBCL0sSVmVbEoMmIml5pSdKUoGElI7GTjpOQtu1JWtNtpzOt8dLpsQNSpL3ESaIwhkQRzWSlY6RALoKIICqUICCKhjtVvPNHnWSV5BQUVVTtc+p8P2uxztl7v3uf38vLemvz1N77SEftfz39Kj9b+hZfGz+CT18wKOs4kiSpBFjAkCRJR+XhFzZw33PruG7sYKZfOizrOJIkqURYwJAkSU327CtbuG3uSi45sw93XD2KiMg6kiRJKhEWMCRJUpOsqNnBTQ8tZeSAHtz32TF0KPM0QpIktR3PPCRJ0hFt2L6bqbOrOemETsy8vpxux/sccEmS1LY8+5AkSYf13q79XP/gIg7UJR6dVkHf7p2zjiRJkkqQBQxJktSovQfq+MKcxdTs2MNDN17IsL4nZB1JkiSVKG8hkSRJeR08mPjqj5exZMN73PWZ0Ywd2jvrSJIkqYRZwJAkSXnd8fPV/GLl29x65dl88rwBWceRJEklzgKGJEn6EzN/8zqz/vN1plw8lBs/fnrWcSRJkixgSJKkD5v/0mbu+PnLTDinP3/7yZFZx5EkSQIyLGBERO+IWBARa3OvvRppNyEi1kTEuoiY0WD9P0fEKxGxIiIej4iebRZekqR2qvqNd/nKj5cxZkgv7r5uNGXHRdaRJEmSgGyvwJgBLEwpDQcW5pY/JCLKgPuBK4CRwKSI+MOvghYAo1JK5wGvAt9sk9SSJLVTr23byRfmLGZQzy78cHI5nTuWZR1JkiTpj7IsYEwEqnLvq4Cr87SpANallNanlPYDj+b2I6X0dEqpNtfud8Cg1o0rSVL7tfX3e6mctYgOxwWzp1TQq1unrCNJkiR9SJYFjH4ppc0Aude+edoMBDY2WK7JrTvUVOAXjX1QREyLiMURsXjbtm0tiCxJUvuza18tN8xezPad+5lZOZYhJ3XNOpIkSdKf6NCaB4+IZ4D+eTbd2tRD5FmXDvmMW4Fa4KHGDpJSegB4AKC8vDw11k6SpFJTW3eQ6Q+/yKpN7/Pvk8v5yOCeWUeSJEnKq1ULGCmlyxrbFhFbImJASmlzRAwAtuZpVgMMbrA8CNjU4BiVwKeAcSklCxOSJB2FlBK3zV3Jc2u28Z1rRjHu7H5ZR5IkSWpUlreQzAMqc+8rgbl52lQDwyPitIjoBFyX24+ImAB8A7gqpbS7DfJKktSu3P/cOh5ZtJGbLjmDz114atZxJEmSDivLAsadwPiIWAuMzy0TEadExHyA3EM6pwNPAauBx1JKq3L73wd0BxZExLKI+Le27oAkScXqp0tq+N7Tr3LN+QO55fIzs44jSZJ0RK16C8nhpJS2A+PyrN8EXNlgeT4wP0+7Ya0aUJKkduo3a9/hGz9dwcXDTuKfPn0eEfkeOSVJklRYsrwCQ5IktbHVmz/gi/+xhGF9T+AHn7+ATh08FZAkScXBsxZJkkrEph17mPJgNScc34EHp4ylR+eOWUeSJElqMgsYkiSVgPf3HGDKg9Xs2lfL7KljGXBil6wjSZIkHZXMnoEhSZLaxv7ag3zxR0tY/85OZk+p4Kz+PbKOJEmSdNQsYEiS1I6llPj6T5bz/Prt/MtnPsLFw07OOpIkSVKzeAuJJEnt2D8/tYYnlm3if3ziTK4dMyjrOJIkSc1mAUOSpHbqoRfe5F9/9RqTKobw3//sjKzjSJIktYgFDEmS2qGFq7dw2xMrufSsvtw+8RwiIutIkiRJLWIBQ5Kkdmb5xh1Mf3gpowaeyL2TzqdDmT/uJUlS8fOMRpKkdmTD9t3cUFXNyd07MbNyLN2O93ndkiSpfbCAIUlSO/HOzn1MnvUCtQcTs6dU0Kf78VlHkiRJOmYsYEiS1A7s2lfL1NnVvP3BXmZWjuWMPidkHUmSJOmY8rpSSZKK3IG6g3zpoRdZtekDHvjLC7jg1F5ZR5IkSTrmvAJDkqQillLiGz9Zwa9f3cY/XDOKcWf3yzqSJElSq7CAIUlSEbvzl6/ws6Vv8bXxI/iLsUOyjiNJktRqLGBIklSkZv7mdf73/1vP5y8awvRLh2UdR5IkqVVZwJAkqQjNW76J2598mQnn9OfbV40iIrKOJEmS1KosYEiSVGR+u+4dvvbYMipO683d142m7DiLF5Ikqf2zgCFJUhFZtel9pv1oCaeffAL/Prmczh3Lso4kSZLUJixgSJJUJDa+u5vrH6ymR+cOzJ46lhO7dMw6kiRJUpuxgCFJUhHYvnMfk2ctYn/tQaqmVjDgxC5ZR5IkSWpTFjAkSSpwu/bVMnV2NZt27GHW9eUM79c960iSJEltzgKGJEkF7EDdQW56+EVeeut97vvsGC44tXfWkSRJkjLRIesAkiQpv5QSM376Er9as41/vPZcxo/sl3UkSZKkzHgFhiRJBeq7T63hpy/W8NXLRjCpYkjWcSRJkjJlAUOSpAL04H++zg9+9RqfvXAIN48blnUcSZKkzFnAkCSpwDy5YhN//+TLXD6yH7dPHEVEZB1JkiQpcxYwJEkqIL997R3++sfLKT+1F/dMOp+y4yxeSJIkgQUMSZIKxqpN7zNtzhKGntyVH04eS+eOZVlHkiRJKhgWMCRJKgAb393N9Q9W071zB2ZPqeDErh2zjiRJklRQLGBIkpSxd3ftp3LWIvYdqKNqagWn9OySdSRJkqSC0yHrAJIklbLd+2uZOruat3bs4aEbL2REv+5ZR5IkSSpIXoEhSVJGDtQd5KaHXmRFzQ7umXQ+5UN7Zx1JkiSpYHkFhiRJGUgp8c2fvcRza7bxnWtG8Ylz+mcdSZIkqaB5BYYk6YgiYlZEbI2IlQ3W9Y6IBRGxNvfa65B9hkTEzoi4pe0TF77vPb2Gnyyp4a/GDedzF56adRxJkqSCZwFDktQUs4EJh6ybASxMKQ0HFuaWG7oL+EXrRys+Vb99g/ufe41JFUP4ymXDs44jSZJUFCxgSJKOKKX0a+DdQ1ZPBKpy76uAq/+wISKuBtYDq9ogXlH5+YrNfOv/rmL8yH7cPvEcIiLrSJIkSUXBAoYkqbn6pZQ2A+Re+wJERDfgG8C3j3SAiJgWEYsjYvG2bdtaNWwheP617Xz1x8sYM6QX9046nw5l/hiWJElqKs+cJEnH2reBu1JKO4/UMKX0QEqpPKVU3qdPnzaIlp3Vmz9g2pzFDDmpKzMry+ncsSzrSJIkSUXFbyGRJDXXlogYkFLaHBEDgK259RcCfx4R3wV6AgcjYm9K6b6sgmat5r3dVM5aRLfjOzBnagU9u3bKOpIkSVLRsYAhSWqueUAlcGfudS5ASunjf2gQEd8CdpZy8eK9XfuZPGsRew/U8ZMvfYxTenbJOpIkSVJR8hYSSdIRRcQjwPPAmRFRExE3UF+4GB8Ra4HxuWU1sHt/LVOrqql5bw8/rBzLiH7ds44kSZJUtLwCQ5J0RCmlSY1sGneE/b517NMUh9q6g0x/eCnLN+7gXz93ARWn9c46kiRJUlGzgCFJ0jGWUuJvHn+JZ1/Zyh1Xj2LCqP5ZR5IkSSp63kIiSdIx9i8LXuWxxTXcfOkwPn/RqVnHkSRJahcsYEiSdAz96Pk3uPfZdVw3djBfHT8i6ziSJEntRmYFjIjoHRELImJt7rVXI+0mRMSaiFgXETPybL8lIlJEnNz6qSVJatz8lzbzd/NWcdnZfbnj6lFERNaRJEmS2o0sr8CYASxMKQ0HFuaWPyQiyoD7gSuAkcCkiBjZYPtg6p98v6FNEkuS1Ijfrd/OVx5dxvmDe3LvpDF0KPMiR0mSpGMpy7OriUBV7n0VcHWeNhXAupTS+pTSfuDR3H5/cBfwdSC1Yk5Jkg7rlbc/4AtzFjO4dxdmVo6lS6eyrCNJkiS1O1kWMPqllDYD5F775mkzENjYYLkmt46IuAp4K6W0/EgfFBHTImJxRCzetm1by5NLkpTz1o49VM5aRNdOZcy54UJ6deuUdSRJkqR2qVW/RjUingHyfXfcrU09RJ51KSK65o5xeVMOklJ6AHgAoLy83Ks1JEnHxHu79jN55gvs3l/H//niRxnYs0vWkSRJktqtVi1gpJQua2xbRGyJiAEppc0RMQDYmqdZDTC4wfIgYBNwBnAasDz3gLRBwIsRUZFSevuYdUCSpEbs2V/HDVXVbHxvD3OmVnBW/x5ZR5IkSWrXsryFZB5QmXtfCczN06YaGB4Rp0VEJ+A6YF5K6aWUUt+U0tCU0lDqCx1jLF5IktpCbd1BvvzIiyzduIPv/8VoLjr9pKwjSZIktXtZFjDuBMZHxFrqv0nkToCIOCUi5gOklGqB6cBTwGrgsZTSqozySpJESom/fWIlz6zeyt9fdQ5XnDsg60iSJEkloVVvITmclNJ2YFye9ZuAKxsszwfmH+FYQ491PkmS8rlrwas8Wr2RL186jL/86NCs40iSJJUMv6RekqQm+tHv3uSeZ9fxmfJB/PX4EVnHkSRJKikWMCRJaoJfrtzM381dybiz+vIP15xL7iHSkiRJaiMWMCRJOoJFr7/LzY8uY/Tgntz32TF0KPPHpyRJUlvzDEySpMNY8/bvubGqmkG9ujCzcixdOpVlHUmSJKkkWcCQJKkRb+3YQ+WsRXTuWMacqRX07tYp60iSJEklywKGJEl57Ni9n8pZi9i1r5aqqRUM6tU160iSJEklLbOvUZUkqVDtPVDHDVWL2bB9N1VTKzh7QI+sI0mSJJU8CxiSJDVQW3eQ6Q8v5cUN73HfpDF89IyTso4kSZIkvIVEkqQ/Silx29yVPLN6C9/6b+fwyfMGZB1JkiRJORYwJEnKufuZtTyyaCM3XXIGlR8bmnUcSZIkNWABQ5Ik4KEX3uT7C9fy5xcM4pbLz8w6jiRJkg5hAUOSVPKeWvU2tz2xkkvO7MM/XnsuEZF1JEmSJB3CAoYkqaRVv/EuNz+ylHMH9eT+z42hY5k/GiVJkgqRZ2mSpJL16pbfc8Psagb27MKD14+laye/nEuSJKlQWcCQJJWkTTv2UDlrEcd3LKNqagW9u3XKOpIkSZIOwwKGJKnkvL/7AJWzFrFzby1VUyoY3Ltr1pEkSZJ0BF4rK0kqKXsP1HHjnGre3L6b2VPHMvKUHllHkiRJUhNYwJAklYzauoPc/MhSFr/5HvdOOp+PnXFy1pEkSZLURN5CIkkqCSklbpu7iqdf3sL//NRIPnXeKVlHkiRJ0lGwgCFJKgn3LFzHI4s28KU/O4PrLz4t6ziSJEk6ShYwJEnt3iOLNnDXM69y7ZiBfP0TZ2YdR5IkSc1gAUOS1K5t37mP2598mf86og//9OnziIisI0mSJKkZfIinJKldO+mE43n4CxcxvO8JdCyzbi9JklSsLGBIktq90YN7Zh1BkiRJLeSvoiRJkiRJUsGzgCFJkiRJkgqeBQxJkiRJklTwLGBIkiRJkqSCZwFDkiRJkiQVPAsYkiRJkiSp4FnAkCRJkiRJBc8ChiRJkiRJKngWMCRJkiRJUsGzgCFJkiRJkgqeBQxJkiRJklTwLGBIkiRJkqSCZwFDkiRJkiQVPAsYkiRJkiSp4FnAkCRJkiRJBc8ChiRJkiRJKngWMCRJkiRJUsGzgCFJkiRJkgqeBQxJkiRJklTwLGBIkiRJkqSCZwFDkiRJkiQVPAsYkiRJkiSp4GVWwIiI3hGxICLW5l57NdJuQkSsiYh1ETHjkG1fzm1bFRHfbZvkkiRJkiSprWV5BcYMYGFKaTiwMLf8IRFRBtwPXAGMBCZFxMjctkuAicB5KaVzgO+1VXBJkiRJktS2sixgTASqcu+rgKvztKkA1qWU1qeU9gOP5vYD+BJwZ0ppH0BKaWvrxpUkSZIkSVnpkOFn90spbQZIKW2OiL552gwENjZYrgEuzL0fAXw8Ir4D7AVuSSlV5/ugiJgGTMst7oyINceiA404GXinFY9fSEqpr1Ba/S2lvkLr9vfUVjpuu7NkyZJ3IuLNVvyIUvp3XUp9hdLqbyn1FZyfJUkNtGoBIyKeAfrn2XRrUw+RZ13KvXYAegEXAWOBxyLi9JRS+pMdUnoAeKCJn9kiEbE4pVTeFp+VtVLqK5RWf0upr1B6/S1UKaU+rXn8UhrnUuorlFZ/S6mvUHr9lSQdXqsWMFJKlzW2LSK2RMSA3NUXA4B8t4DUAIMbLA8CNjXY9rNcwWJRRBykvkq/7diklyRJkiRJhSLLZ2DMAypz7yuBuXnaVAPDI+K0iOgEXJfbD+AJ4FKAiBgBdKK0LqmUJEmSJKlkZFnAuBMYHxFrgfG5ZSLilIiYD5BSqgWmA08Bq4HHUkqrcvvPAk6PiJXUP9yzMt/tIxlok1tVCkQp9RVKq7+l1Fcovf6WqlIa51LqK5RWf0upr1B6/ZUkHUYUxv/5JUmSJEmSGpflFRiSJEmSJElNYgFDkiRJkiQVPAsYTRARnSNiUUQsj4hVEfHtPG16RcTjEbEi13ZUg20TImJNRKyLiBltm/7oHYP+vhERL0XEsohY3LbpmyciyiJiaUQ8mWdbRMQ9ufFbERFjGmwrqrGFFvW16MYVjtjfsyLi+YjYFxG3HLKt6Ma2FDk/Oz87PxfnuILzsyTp6FnAaJp9wKUppY8Ao4EJEXHRIW3+BliWUjoPmAx8H+p/OAP3A1cAI4FJETGyrYI3U7P728AlKaXRRfTd7X9F/YNi87kCGJ77Mw34ARTt2EIz+tpAsY0rHL6/7wI3A99ruLKIx7YUOT87Pzs/1yu2cQXnZ0nSUbKA0QSp3s7cYsfcn0OffjoSWJhr/wowNCL6ARXAupTS+pTSfuq/MWVi2yRvnhb2t+hExCDgk8APG2kyEZiT+3v5HdAzIgZQhGPbgr4WpSP1N6W0NaVUDRw4ZFPRjW2pcn52fsb5uSg5P0uSmsMCRhPlLnNcBmwFFqSUXjikyXLg2lzbCuBUYBAwENjYoF1Nbl1Ba0F/of5k+umIWBIR09oockvcDXwdONjI9sbGsBjH9m6a11covnGFI/e3McU4tiXL+dn5ucGy83NxjCs4P0uSmsECRhOllOpSSqOpPwmsaHhPcc6dQK/cSeWXgaVALRD5DteKUY+JFvQX4OKU0hjqL++8KSL+S9ukPnoR8Slga0ppyeGa5VmXDrO+ILWwr1BE4wpN7m+ju+dZV7BjW+qcn52fD+H8XMDjCs7PkqTm65B1gGKTUtoREb8CJgArG6z/AJgC9Q/aAl7P/ekKDG5wiEHAprbK21LN6C8ppU25160R8Tj1l3v+um2TN9nFwFURcSXQGegREf+RUvp8gzY15B/DTo2sL1Qt6WuxjSs0rb+NafTvQYXL+fmP652fnZ8LeVzB+VmS1ExegdEEEdEnInrm3ncBLgNeOaRNz4jolFu8Efh17iSyGhgeEafltl8HzGuz8M3Qkv5GRLeI6J5r0w24nAYn1oUmpfTNlNKglNJQ6sfm2TwnUPOAyVHvIuD9lNJmimxsW9LXYhtXaHJ/G1NUY1vKnJ+dn3F+LqpxBednSVLzeQVG0wwAqqL+ydfHAY+llJ6MiC8CpJT+DTgbmBMRdcDLwA25bbURMR14CigDZqWUVmXRiaPQ7P4C/YDH63/pRwfg4ZTSL9u6Ay11SF/nA1cC64Dd5H6zWaRj+yea0lfaybjCh/sbEf2BxUAP4GBEfAUYmfvPXtGPbYlwfnZ+dn5uB+MKzs+SpCOLlLxtUJIkSZIkFTZvIZEkSZIkSQXPAoYkSZIkSSp4FjAkSZIkSVLBs4AhSZIkSZIKngUMSZIkSZJU8CxgSJIkSZKkgmcBQ5IkSZIkFbz/D5EojEi1QnzLAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([fourc], fourc.h, 3.9, 4.1, fourc.k, 3.9, 4.1, 5))" + ] + } + ] +} \ No newline at end of file diff --git a/examples/geo_e6c.ipynb b/examples/geo_e6c.ipynb new file mode 100644 index 00000000..4c1be6fb --- /dev/null +++ b/examples/geo_e6c.ipynb @@ -0,0 +1,915 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python38264bitbaseconda9c3e3a9452084ea0903e516deca6d551", + "display_name": "Python 3.8.2 64-bit ('base': conda)", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# E6C : 6-circle diffractometer example\n", + "\n", + "The 6-circle diffractometer can be considered as a 4-circle\n", + "diffractometer with two additional rotations that rotate the \n", + "sample and detector separately about the $\\vec z$ axis.\n", + "\n", + "![Schematic of a 6-circle diffractometer](resources/6-circle-schematic.png)\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Load the *hklpy* package (named _`hkl`_)\n", + "\n", + "Since the *hklpy* package is a thin interface to the *hkl*\n", + "library (compiled C++ code), we need to **first** load the\n", + "*gobject-introspection* package (named _`gi`_) and name our\n", + "required code and version.\n", + "\n", + "This is needed _every_ time before the *hkl* package is first imported." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')" + ] + }, + { + "source": [ + "## Setup the *E6C* diffractometer in *hklpy*\n", + "\n", + "In _hkl_ *E6C* geometry (https://people.debian.org/~picca/hkl/hkl.html#orge5e0490):\n", + "\n", + "* xrays incident on the $\\vec{x}$ direction (1, 0, 0)\n", + "\n", + "axis | moves | rotation axis | vector\n", + "--- | :--- | :---: |:---\n", + "mu | sample | $\\vec{z}$ | `[0 0 1]`\n", + "omega | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "chi | sample | $\\vec{x}$ | `[1 0 0]`\n", + "phi | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "gamma | detector | $\\vec{z}$ | `[0 0 1]`\n", + "delta | detector | $-\\vec{y}$ | `[0 -1 0]`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "\n", + "## Define _this_ diffractometer\n", + "\n", + "Create a Python class that specifies the names of the \n", + "real-space positioners. We call it `SixCircle` here but that\n", + "choice is arbitrary. Pick any valid Python name not already in use.\n", + "\n", + "The argument to the `SixCircle` class tells which *hklpy* base\n", + "class will be used. This sets the geometry. See the\n", + "[*hklpy* diffractometers documentation](https://blueskyproject.io/hklpy/master/diffract.html#hkl.diffract.Diffractometer.calc_class)\n", + "for a list of other choices.\n", + "\n", + "In *hklpy*, the reciprocal-space axes\n", + "are known as `pseudo` positioners while the real-space axes\n", + "are known as `real` positioners. For the real positioners,\n", + "it is possible to use different names than the canonical names\n", + "used internally by the *hkl* library. That is not covered here.\n", + "\n", + "note: The keyword argument `kind=\"hinted\"` is an indication\n", + "that this signal may be plotted.\n", + "\n", + "This demo uses simulated motors.\n", + "To use EPICS motors, import that structure from *ophyd*:\n", + "\n", + "```python\n", + "from ophyd import EpicsMotor\n", + "```\n", + "\n", + "Then, in the class, replace the real positioners with (substituting with the correct EPICS PV for each motor):\n", + "\n", + "```python\n", + "mu = Cpt(EpicsMotor, \"pv_prefix:m42\", kind=\"hinted\")\n", + "omega = Cpt(EpicsMotor, \"pv_prefix:m41\", kind=\"hinted\")\n", + "chi = Cpt(EpicsMotor, \"pv_prefix:m22\", kind=\"hinted\")\n", + "phi = Cpt(EpicsMotor, \"pv_prefix:m35\", kind=\"hinted\")\n", + "gamma = Cpt(EpicsMotor, \"pv_prefix:m7\", kind=\"hinted\")\n", + "delta = Cpt(EpicsMotor, \"pv_prefix:m8\", kind=\"hinted\")\n", + "```\n", + "\n", + "and, **most important**, remove the `def __init__()` method.\n", + "It is only needed to define an initial position for the simulators.\n", + "Otherwise, this will move these EPICS motors to zero." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from hkl.diffract import E6C\n", + "from ophyd import PseudoSingle, SoftPositioner\n", + "from ophyd import Component as Cpt\n", + "\n", + "class SixCircle(E6C):\n", + " \"\"\"\n", + " Our 6-circle. Eulerian.\n", + " \"\"\"\n", + " # the reciprocal axes are called: pseudo in hklpy\n", + " h = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " k = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " l = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + "\n", + " # the motor axes are called: real in hklpy\n", + " mu = Cpt(SoftPositioner, kind=\"hinted\")\n", + " omega = Cpt(SoftPositioner, kind=\"hinted\")\n", + " chi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " phi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " gamma = Cpt(SoftPositioner, kind=\"hinted\")\n", + " delta = Cpt(SoftPositioner, kind=\"hinted\")\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " \"\"\"Define an initial position for simulators.\"\"\"\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "sixc = SixCircle(\"\", name=\"sixc\")" + ] + }, + { + "source": [ + "## Add a sample with a crystal structure" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.15691131e+00, -7.08403864e-17, -7.08403864e-17],\n", + " [ 0.00000000e+00, 1.15691131e+00, -7.08403864e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.15691131e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "# add the sample to the calculation engine\n", + "a0 = 5.431\n", + "sixc.calc.new_sample(\n", + " \"silicon\",\n", + " lattice=Lattice(a=a0, b=a0, c=a0, alpha=90, beta=90, gamma=90)\n", + " )" + ] + }, + { + "source": [ + "## Setup the UB orientation matrix using *hklpy*\n", + "\n", + "Define the crystal's orientation on the diffractometer using \n", + "the 2-reflection method described by [Busing & Levy, Acta Cryst 22 (1967) 457](https://www.psi.ch/sites/default/files/import/sinq/zebra/PracticalsEN/1967-Busing-Levy-3-4-circle-Acta22.pdf).\n", + "\n", + "### Choose the same wavelength X-rays for both reflections" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sixc.calc.wavelength = 1.54 # Angstrom (8.0509 keV)" + ] + }, + { + "source": [ + "### Find the first reflection and identify its Miller indices: (_hkl_)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "r1 = sixc.calc.sample.add_reflection(\n", + " 4, 0, 0,\n", + " position=sixc.calc.Position(\n", + " delta=69.0966,\n", + " omega=-145.451,\n", + " chi=0,\n", + " phi=0,\n", + " mu=0,\n", + " gamma=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Find the second reflection" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "r2 = sixc.calc.sample.add_reflection(\n", + " 0, 4, 0,\n", + " position=sixc.calc.Position(\n", + " delta=69.0966,\n", + " omega=-145.451,\n", + " chi=90,\n", + " phi=0,\n", + " mu=0,\n", + " gamma=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Compute the *UB* orientation matrix\n", + "\n", + "The `compute_UB()` method always returns 1. Ignore it." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "sixc.calc.sample.compute_UB(r1, r2)" + ] + }, + { + "source": [ + "## Report what we have setup" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================== ===================================================\n", + "term value \n", + "==================== ===================================================\n", + "energy, keV 0.8050922077922078 \n", + "wavelength, angstrom 1.54 \n", + "position SixCirclePseudoPos(h=-0.0, k=0.0, l=0.0) \n", + "sample name silicon \n", + "[U] [[-1.22173048e-05 -1.22173048e-05 -1.00000000e+00] \n", + " [ 0.00000000e+00 -1.00000000e+00 1.22173048e-05] \n", + " [-1.00000000e+00 1.49262536e-10 1.22173048e-05]]\n", + "[UB] [[-1.41343380e-05 -1.41343380e-05 -1.15691131e+00] \n", + " [ 0.00000000e+00 -1.15691131e+00 1.41343380e-05] \n", + " [-1.15691131e+00 1.72683586e-10 1.41343380e-05]]\n", + "lattice [ 5.431 5.431 5.431 90. 90. 90. ] \n", + "==================== ===================================================\n", + "\n", + "sample\tHklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-45.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=-89.99901005102187, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=135.00000000427607, fit=True, inverted=False, units='Degree'), U=array([[-1.22173048e-05, -1.22173048e-05, -1.00000000e+00],\n", + " [ 0.00000000e+00, -1.00000000e+00, 1.22173048e-05],\n", + " [-1.00000000e+00, 1.49262536e-10, 1.22173048e-05]]), UB=array([[-1.41343380e-05, -1.41343380e-05, -1.15691131e+00],\n", + " [ 0.00000000e+00, -1.15691131e+00, 1.41343380e-05],\n", + " [-1.15691131e+00, 1.72683586e-10, 1.41343380e-05]]), reflections=[(h=4.0, k=0.0, l=0.0), (h=0.0, k=4.0, l=0.0)], reflection_measured_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]), reflection_theoretical_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]))\n" + ] + } + ], + "source": [ + "import pyRestTable\n", + "\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"energy, keV\", sixc.calc.energy))\n", + "tbl.addRow((\"wavelength, angstrom\", sixc.calc.wavelength))\n", + "tbl.addRow((\"position\", sixc.position))\n", + "tbl.addRow((\"sample name\", sixc.sample_name.get()))\n", + "tbl.addRow((\"[U]\", sixc.U.get()))\n", + "tbl.addRow((\"[UB]\", sixc.UB.get()))\n", + "tbl.addRow((\"lattice\", sixc.lattice.get()))\n", + "print(tbl)\n", + "\n", + "print(f\"sample\\t{sixc.calc.sample}\")" + ] + }, + { + "source": [ + "## Check the orientation matrix\n", + "\n", + "Perform checks with _forward_ (hkl to angle) and\n", + "_inverse_ (angle to hkl) computations to verify the diffractometer\n", + "will move to the same positions where the reflections were identified.\n", + "\n", + "### Constrain the motors to limited ranges\n", + "\n", + "* allow for slight roundoff errors\n", + "* keep `delta` in the positive range\n", + "* keep `omega` in the negative range\n", + "* keep `gamma`, `mu`, & `phi` fixed at zero\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sixc.calc[\"delta\"].limits = (-0.001, 180)\n", + "sixc.calc[\"omega\"].limits = (-180, 0.001)\n", + "\n", + "for nm in \"gamma mu phi\".split():\n", + " getattr(sixc, nm).move(0)\n", + " sixc.calc[nm].fit = False\n", + " sixc.calc[nm].value = 0\n", + " sixc.calc[nm].limits = (0, 0)\n", + "sixc.engine.mode = \"constant_phi_vertical\"" + ] + }, + { + "source": [ + "### (400) reflection test\n", + "\n", + "1. Check the `inverse` (angles -> (_hkl_)) computation.\n", + "1. Check the `forward` ((_hkl_) -> angles) computation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "#### Check the inverse calculation: (400)\n", + "\n", + "To calculate the (_hkl_) corresponding to a given set of motor angles,\n", + "call `fourc.inverse((h, k, l))`. Note the second set of parentheses needed by this function.\n", + "\n", + "The values are specified, without names, in the order specified\n", + "by `fourc.calc.physical_axis_names`." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "axis names: ['mu', 'omega', 'chi', 'phi', 'gamma', 'delta']\n" + ] + } + ], + "source": [ + "print(\"axis names:\", sixc.calc.physical_axis_names)" + ] + }, + { + "source": [ + "Now, proceed with the inverse calculation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4 0 0) ? 4.00 0.00 0.00\n" + ] + } + ], + "source": [ + "sol = sixc.inverse((0, -145.451, 0, 0, 0, 69.0966))\n", + "print(f\"(4 0 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "#### Check the forward calculation: (400)\n", + "\n", + "Compute the angles necessary to position the diffractometer\n", + "for the given reflection.\n", + "\n", + "Note that for the forward computation, more than one set of angles may be used to reach the same crystal reflection. This test will report the *default* selection. The *default* selection (which may be changed through methods described in the `hkl.calc` module) is the first solution.\n", + "\n", + "function | returns\n", + "--- | ---\n", + "`sixc.forward()` | The *default* solution\n", + "`sixc.calc.forward()` | List of all allowed solutions." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(400) : tth=69.0985 omega=-145.4500 chi=0.0000 phi=0.0000 mu=0.0000 gamma=0.0000\n" + ] + } + ], + "source": [ + "sol = sixc.forward((4, 0, 0))\n", + "print(\n", + " \"(400) :\", \n", + " f\"tth={sol.delta:.4f}\", \n", + " f\"omega={sol.omega:.4f}\", \n", + " f\"chi={sol.chi:.4f}\", \n", + " f\"phi={sol.phi:.4f}\",\n", + " f\"mu={sol.mu:.4f}\",\n", + " f\"gamma={sol.gamma:.4f}\",\n", + " )" + ] + }, + { + "source": [ + "### (040) reflection test\n", + "\n", + "Repeat the `inverse` and `forward` calculations for the\n", + "second orientation reflection." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Check the inverse calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0 4 0) ? 0.00 4.00 0.00\n" + ] + } + ], + "source": [ + "sol = sixc.inverse((0, -145.451, 90, 0, 0, 69.0966))\n", + "print(f\"(0 4 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "### Check the forward calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(040) : tth=69.0985 omega=-145.4500 chi=90.0000 phi=0.0000 mu=0.0000 gamma=0.0000\n" + ] + } + ], + "source": [ + "sol = sixc.forward((0, 4, 0))\n", + "print(\n", + " \"(040) :\", \n", + " f\"tth={sol.delta:.4f}\", \n", + " f\"omega={sol.omega:.4f}\", \n", + " f\"chi={sol.chi:.4f}\", \n", + " f\"phi={sol.phi:.4f}\",\n", + " f\"mu={sol.mu:.4f}\",\n", + " f\"gamma={sol.gamma:.4f}\",\n", + " )" + ] + }, + { + "source": [ + "## Scan in reciprocal space using Bluesky\n", + "\n", + "To scan with Bluesky, we need more setup." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from bluesky import RunEngine\n", + "from bluesky import SupplementalData\n", + "from bluesky.callbacks.best_effort import BestEffortCallback\n", + "from bluesky.magics import BlueskyMagics\n", + "import bluesky.plans as bp\n", + "import bluesky.plan_stubs as bps\n", + "import databroker\n", + "from IPython import get_ipython\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.ion()\n", + "\n", + "bec = BestEffortCallback()\n", + "db = databroker.temp().v1\n", + "sd = SupplementalData()\n", + "\n", + "get_ipython().register_magics(BlueskyMagics)\n", + "\n", + "RE = RunEngine({})\n", + "RE.md = {}\n", + "RE.preprocessors.append(sd)\n", + "RE.subscribe(db.insert)\n", + "RE.subscribe(bec)" + ] + }, + { + "source": [ + "### (_h00_) scan near (400)\n", + "\n", + "In this example, we have no detector. Still, we add the diffractometer\n", + "object in the detector list so that the _hkl_ and motor positions will appear\n", + "as columns in the table." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 1 Time: 2020-12-17 15:57:10\n", + "Persistent Unique Scan ID: 'c425fe19-f854-4327-9cdc-8fe06f8fb812'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| seq_num | time | sixc_h | sixc_k | sixc_l | sixc_mu | sixc_omega | sixc_chi | sixc_phi | sixc_gamma | sixc_delta |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| 1 | 15:57:10.8 | 3.900 | -0.000 | -0.000 | 0.000 | -146.431 | 0.000 | 0.000 | 0.000 | 67.137 |\n", + "| 2 | 15:57:12.2 | 3.950 | 0.000 | 0.000 | 0.000 | -145.942 | -0.000 | 0.000 | 0.000 | 68.115 |\n", + "| 3 | 15:57:13.6 | 4.000 | 0.000 | 0.000 | 0.000 | -145.450 | -0.000 | 0.000 | 0.000 | 69.099 |\n", + "| 4 | 15:57:15.1 | 4.050 | -0.000 | -0.000 | 0.000 | -144.955 | 0.000 | 0.000 | 0.000 | 70.088 |\n", + "| 5 | 15:57:16.6 | 4.100 | 0.000 | 0.000 | 0.000 | -144.458 | 0.000 | 0.000 | 0.000 | 71.083 |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "generator scan ['c425fe19'] (scan num: 1)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('c425fe19-f854-4327-9cdc-8fe06f8fb812',)" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:57:20.455013\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([sixc], sixc.h, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### chi scan from (400) to (040)\n", + "\n", + "If we do this with $\\omega=-145.451$ and $\\delta=69.0966$, this will be a scan between the two orientation reflections.\n", + "\n", + "Use `%mov` (IPython *magic* command) to move both motors at the same time." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "possible modes: ['bissector_vertical', 'constant_omega_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_phi', 'lifting_detector_omega', 'lifting_detector_mu', 'double_diffraction_vertical', 'bissector_horizontal', 'double_diffraction_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal', 'constant_mu_horizontal']\n", + "chosen mode: constant_phi_vertical\n", + "\n", + "\n", + "Transient Scan ID: 2 Time: 2020-12-17 15:57:21\n", + "Persistent Unique Scan ID: '9a2303fe-abeb-4e21-a2d3-df2b13e49fb2'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| seq_num | time | sixc_chi | sixc_h | sixc_k | sixc_l | sixc_mu | sixc_omega | sixc_phi | sixc_gamma | sixc_delta |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| 1 | 15:57:21.7 | 0.000 | 4.000 | 0.000 | 0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 2 | 15:57:23.2 | 10.000 | 3.939 | 0.695 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 3 | 15:57:24.8 | 20.000 | 3.759 | 1.368 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 4 | 15:57:26.1 | 30.000 | 3.464 | 2.000 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 5 | 15:57:27.5 | 40.000 | 3.064 | 2.571 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 6 | 15:57:28.8 | 50.000 | 2.571 | 3.064 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 7 | 15:57:30.1 | 60.000 | 2.000 | 3.464 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 8 | 15:57:31.5 | 70.000 | 1.368 | 3.759 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 9 | 15:57:33.6 | 80.000 | 0.695 | 3.939 | -0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "| 10 | 15:57:34.9 | 90.000 | 0.000 | 4.000 | 0.000 | 0.000 | -145.450 | 0.000 | 0.000 | 69.099 |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "generator scan ['9a2303fe'] (scan num: 2)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('9a2303fe-abeb-4e21-a2d3-df2b13e49fb2',)" + ] + }, + "metadata": {}, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:57:38.047589\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "print(\"possible modes:\", sixc.calc.engine.modes)\n", + "print(\"chosen mode:\", sixc.calc.engine.mode)\n", + "\n", + "# same as orientation reflections\n", + "%mov sixc.omega -145.4500 sixc.delta 69.0985\n", + "\n", + "RE(bp.scan([sixc], sixc.chi, 0, 90, 10))" + ] + }, + { + "source": [ + "### (_0k0_) scan near (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 3 Time: 2020-12-17 15:57:38\n", + "Persistent Unique Scan ID: '5fcf5644-d749-4f98-aaf7-3f8bee897cdc'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| seq_num | time | sixc_k | sixc_h | sixc_l | sixc_mu | sixc_omega | sixc_chi | sixc_phi | sixc_gamma | sixc_delta |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| 1 | 15:57:39.3 | 3.900 | 4.100 | 0.000 | 0.000 | -126.651 | 43.568 | 0.000 | 0.000 | 106.695 |\n", + "| 2 | 15:57:40.7 | 3.950 | 4.100 | -0.000 | 0.000 | -126.178 | 43.933 | 0.000 | 0.000 | 107.641 |\n", + "| 3 | 15:57:42.0 | 4.000 | 4.100 | 0.000 | 0.000 | -125.697 | 44.293 | 0.000 | 0.000 | 108.605 |\n", + "| 4 | 15:57:43.4 | 4.050 | 4.100 | -0.000 | 0.000 | -125.206 | 44.648 | 0.000 | 0.000 | 109.586 |\n", + "| 5 | 15:57:45.0 | 4.100 | 4.100 | -0.000 | 0.000 | -124.706 | 45.000 | 0.000 | 0.000 | 110.585 |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "generator scan ['5fcf5644'] (scan num: 3)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('5fcf5644-d749-4f98-aaf7-3f8bee897cdc',)" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:57:48.401942\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([sixc], sixc.k, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### (_hk0_) scan near (440)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 4 Time: 2020-12-17 15:57:49\n", + "Persistent Unique Scan ID: 'aa116e92-4999-4996-ba94-f8ee96854c89'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| seq_num | time | sixc_h | sixc_k | sixc_l | sixc_mu | sixc_omega | sixc_chi | sixc_phi | sixc_gamma | sixc_delta |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "| 1 | 15:57:49.6 | 3.900 | 4.100 | 0.000 | 0.000 | -126.651 | 46.432 | 0.000 | 0.000 | 106.695 |\n", + "| 2 | 15:57:50.8 | 3.950 | 4.050 | 0.000 | 0.000 | -126.669 | 45.716 | 0.000 | 0.000 | 106.659 |\n", + "| 3 | 15:57:51.9 | 4.000 | 4.000 | 0.000 | 0.000 | -126.675 | 45.000 | 0.000 | 0.000 | 106.647 |\n", + "| 4 | 15:57:53.1 | 4.050 | 3.950 | 0.000 | 0.000 | -126.669 | 44.284 | 0.000 | 0.000 | 106.659 |\n", + "| 5 | 15:57:54.4 | 4.100 | 3.900 | 0.000 | 0.000 | -126.651 | 43.568 | 0.000 | 0.000 | 106.695 |\n", + "+-----------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+\n", + "generator scan ['aa116e92'] (scan num: 4)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('aa116e92-4999-4996-ba94-f8ee96854c89',)" + ] + }, + "metadata": {}, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-17T15:57:57.049132\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([sixc], sixc.h, 3.9, 4.1, sixc.k, 4.1, 3.9, 5))" + ] + } + ] +} \ No newline at end of file diff --git a/examples/geo_k4cv.ipynb b/examples/geo_k4cv.ipynb new file mode 100644 index 00000000..6c98f212 --- /dev/null +++ b/examples/geo_k4cv.ipynb @@ -0,0 +1,883 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python38264bitcondaf8e76b08f7284c68a6b3de15f965a87a", + "display_name": "Python 3.8.2 64-bit (conda)", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# K4CV : 4-circle kappa diffractometer example\n", + "\n", + "The kappa geometry replaces the traditional $\\chi$-ring on a 4-circle\n", + "diffractometer with an alternative kappa stage that holds the phi stage. The kappa stage is tilted at angle $\\alpha$ (typically 50 degrees) from the $\\omega$ stage.\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Load the *hklpy* package (named _`hkl`_)\n", + "\n", + "Since the *hklpy* package is a thin interface to the *hkl*\n", + "library (compiled C++ code), we need to **first** load the\n", + "*gobject-introspection* package (named _`gi`_) and name our\n", + "required code and version.\n", + "\n", + "This is needed _every_ time before the *hkl* package is first imported." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')" + ] + }, + { + "source": [ + "## Setup the *K4CV* diffractometer in *hklpy*\n", + "\n", + "In _hkl_ *K4CV* geometry (https://people.debian.org/~picca/hkl/hkl.html#org723c5b9):\n", + "\n", + "![K4CV geometry](resources/k4cv.png)\n", + "\n", + "For this geometry there is a special parameter $\\alpha$, the angle between the kappa rotation axis and the $\\vec{y}$ direction.\n", + "\n", + "axis | moves | rotation axis | vector\n", + "--- | :--- | :---: | :---\n", + "komega | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "kappa | sample | $\\vec{x}$ | `[0 -0.6428 -0.7660]`\n", + "kphi | sample | $-\\vec{y}$ | `[0 -1 0]`\n", + "tth | detector | $-\\vec{y}$ | `[0 -1 0]`" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "\n", + "## Define _this_ diffractometer\n", + "\n", + "Create a Python class that specifies the names of the \n", + "real-space positioners. We call it `KappaFourCircle` here but that\n", + "choice is arbitrary. Pick any valid Python name not already in use.\n", + "\n", + "The argument to the `KappaFourCircle` class tells which *hklpy* base\n", + "class will be used. This sets the geometry. See the\n", + "[*hklpy* diffractometers documentation](https://blueskyproject.io/hklpy/master/diffract.html#hkl.diffract.Diffractometer.calc_class)\n", + "for a list of other choices.\n", + "\n", + "In *hklpy*, the reciprocal-space axes\n", + "are known as `pseudo` positioners while the real-space axes\n", + "are known as `real` positioners. For the real positioners,\n", + "it is possible to use different names than the canonical names\n", + "used internally by the *hkl* library. That is not covered here.\n", + "\n", + "note: The keyword argument `kind=\"hinted\"` is an indication\n", + "that this signal may be plotted and appear in the live table.\n", + "\n", + "This demo uses simulated motors.\n", + "To use EPICS motors, import that structure from *ophyd*:\n", + "\n", + "```python\n", + "from ophyd import EpicsMotor\n", + "```\n", + "\n", + "Then, in the class, replace the real positioners with (substituting with the correct EPICS PV for each motor):\n", + "\n", + "```python\n", + "komega = Cpt(EpicsMotor, \"pv_prefix:m41\", kind=\"hinted\")\n", + "kappa = Cpt(EpicsMotor, \"pv_prefix:m22\", kind=\"hinted\")\n", + "kphi = Cpt(EpicsMotor, \"pv_prefix:m35\", kind=\"hinted\")\n", + "tth = Cpt(EpicsMotor, \"pv_prefix:m7\", kind=\"hinted\")\n", + "```\n", + "\n", + "and, **most important**, remove the `def __init__()` method.\n", + "It is only needed to define an initial position for the simulators.\n", + "Otherwise, this will move these EPICS motors to zero." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from hkl.diffract import K4CV\n", + "from ophyd import PseudoSingle, SoftPositioner\n", + "from ophyd import Component as Cpt\n", + "\n", + "class KappaFourCircle(K4CV):\n", + " \"\"\"\n", + " Our kappa 4-circle. Eulerian, vertical scattering orientation.\n", + " \"\"\"\n", + " # the reciprocal axes are called: pseudo in hklpy\n", + " h = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " k = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " l = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + "\n", + " # the motor axes are called: real in hklpy\n", + " komega = Cpt(SoftPositioner, kind=\"hinted\")\n", + " kappa = Cpt(SoftPositioner, kind=\"hinted\")\n", + " kphi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " tth = Cpt(SoftPositioner, kind=\"hinted\")\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " \"\"\"Define an initial position for simulators.\"\"\"\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "k4cv = KappaFourCircle(\"\", name=\"k4cv\")" + ] + }, + { + "source": [ + "## Add a sample with a crystal structure" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.15691131e+00, -7.08403864e-17, -7.08403864e-17],\n", + " [ 0.00000000e+00, 1.15691131e+00, -7.08403864e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.15691131e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "# add the sample to the calculation engine\n", + "a0 = 5.431\n", + "k4cv.calc.new_sample(\n", + " \"silicon\",\n", + " lattice=Lattice(a=a0, b=a0, c=a0, alpha=90, beta=90, gamma=90)\n", + " )" + ] + }, + { + "source": [ + "## Setup the UB orientation matrix using *hklpy*\n", + "\n", + "Define the crystal's orientation on the diffractometer using \n", + "the 2-reflection method described by [Busing & Levy, Acta Cryst 22 (1967) 457](https://www.psi.ch/sites/default/files/import/sinq/zebra/PracticalsEN/1967-Busing-Levy-3-4-circle-Acta22.pdf).\n", + "\n", + "### Choose the same wavelength X-rays for both reflections" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "k4cv.calc.wavelength = 1.54 # Angstrom (8.0509 keV)" + ] + }, + { + "source": [ + "### Find the first reflection and identify its Miller indices: (_hkl_)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "r1 = k4cv.calc.sample.add_reflection(\n", + " 4, 0, 0,\n", + " position=k4cv.calc.Position(\n", + " tth=-69.0966,\n", + " komega=55.4507,\n", + " kappa=0,\n", + " kphi=-90,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Find the second reflection" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "r2 = k4cv.calc.sample.add_reflection(\n", + " 0, 4, 0,\n", + " position=k4cv.calc.Position(\n", + " tth=-69.0966,\n", + " komega=-1.5950,\n", + " kappa=134.7568,\n", + " kphi=123.3554\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Compute the *UB* orientation matrix\n", + "\n", + "The `compute_UB()` method always returns 1. Ignore it." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "k4cv.calc.sample.compute_UB(r1, r2)" + ] + }, + { + "source": [ + "## Report what we have setup" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================== ===================================================\n", + "term value \n", + "==================== ===================================================\n", + "energy, keV 8.050922077922078 \n", + "wavelength, angstrom 1.54 \n", + "position KappaFourCirclePseudoPos(h=0.0, k=-0.0, l=0.0) \n", + "sample name silicon \n", + "[U] [[ 1.74532925e-05 -6.22695871e-06 -1.00000000e+00] \n", + " [ 0.00000000e+00 -1.00000000e+00 6.22695872e-06] \n", + " [-1.00000000e+00 -1.08680932e-10 -1.74532925e-05]]\n", + "[UB] [[ 2.01919115e-05 -7.20403894e-06 -1.15691131e+00] \n", + " [ 0.00000000e+00 -1.15691131e+00 7.20403894e-06] \n", + " [-1.15691131e+00 -1.25734128e-10 -2.01919115e-05]]\n", + "lattice [ 5.431 5.431 5.431 90. 90. 90. ] \n", + "==================== ===================================================\n", + "\n", + "sample\tHklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-160.36469500932463, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=-89.99893826046727, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=19.635304987561902, fit=True, inverted=False, units='Degree'), U=array([[ 1.74532925e-05, -6.22695871e-06, -1.00000000e+00],\n", + " [ 0.00000000e+00, -1.00000000e+00, 6.22695872e-06],\n", + " [-1.00000000e+00, -1.08680932e-10, -1.74532925e-05]]), UB=array([[ 2.01919115e-05, -7.20403894e-06, -1.15691131e+00],\n", + " [ 0.00000000e+00, -1.15691131e+00, 7.20403894e-06],\n", + " [-1.15691131e+00, -1.25734128e-10, -2.01919115e-05]]), reflections=[(h=4.0, k=0.0, l=0.0), (h=0.0, k=4.0, l=0.0)], reflection_measured_angles=array([[0. , 1.57081338],\n", + " [1.57081338, 0. ]]), reflection_theoretical_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]))\n" + ] + } + ], + "source": [ + "import pyRestTable\n", + "\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"energy, keV\", k4cv.calc.energy))\n", + "tbl.addRow((\"wavelength, angstrom\", k4cv.calc.wavelength))\n", + "tbl.addRow((\"position\", k4cv.position))\n", + "tbl.addRow((\"sample name\", k4cv.sample_name.get()))\n", + "tbl.addRow((\"[U]\", k4cv.U.get()))\n", + "tbl.addRow((\"[UB]\", k4cv.UB.get()))\n", + "tbl.addRow((\"lattice\", k4cv.lattice.get()))\n", + "print(tbl)\n", + "\n", + "print(f\"sample\\t{k4cv.calc.sample}\")" + ] + }, + { + "source": [ + "## Check the orientation matrix\n", + "\n", + "Perform checks with _forward_ (hkl to angle) and\n", + "_inverse_ (angle to hkl) computations to verify the diffractometer\n", + "will move to the same positions where the reflections were identified.\n", + "\n", + "### Use `bissector` mode\n", + "\n", + "where `tth` = 2*`omega`\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "k4cv.calc.engine.mode = \"bissector\"" + ] + }, + { + "source": [ + "### (400) reflection test\n", + "\n", + "1. Check the `inverse` (angles -> (_hkl_)) computation.\n", + "1. Check the `forward` ((_hkl_) -> angles) computation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Check the inverse calculation: (400)\n", + "\n", + "To calculate the (_hkl_) corresponding to a given set of motor angles,\n", + "call `k4cv.inverse((h, k, l))`. Note the second set of parentheses needed by this function.\n", + "\n", + "The values are specified, without names, in the order specified\n", + "by `k4cv.calc.physical_axis_names`." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "axis names: ['komega', 'kappa', 'kphi', 'tth']\n" + ] + } + ], + "source": [ + "print(\"axis names:\", k4cv.calc.physical_axis_names)" + ] + }, + { + "source": [ + "Now, proceed with the inverse calculation." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4 0 0) ? 4.00 -0.00 -0.00\n" + ] + } + ], + "source": [ + "sol = k4cv.inverse((55.4507, 0, -90, -69.0966))\n", + "print(f\"(4 0 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")\n" + ] + }, + { + "source": [ + "### Check the forward calculation: (400)\n", + "\n", + "Compute the angles necessary to position the diffractometer\n", + "for the given reflection.\n", + "\n", + "Note that for the forward computation, more than one set of angles may be used to reach the same crystal reflection. This test will report the *default* selection. The *default* selection (which may be changed through methods described in the `hkl.calc` module) is the first solution.\n", + "\n", + "function | returns\n", + "--- | ---\n", + "`k4cv.forward()` | The *default* solution\n", + "`k4cv.calc.forward()` | List of all allowed solutions." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(400) : tth=-69.0985 komega=55.4507 kappa=0.0000 kphi=-90.0010\n" + ] + } + ], + "source": [ + "sol = k4cv.forward((4, 0, 0))\n", + "print(\n", + " \"(400) :\", \n", + " f\"tth={sol.tth:.4f}\", \n", + " f\"komega={sol.komega:.4f}\", \n", + " f\"kappa={sol.kappa:.4f}\", \n", + " f\"kphi={sol.kphi:.4f}\"\n", + " )" + ] + }, + { + "source": [ + "### (040) reflection test\n", + "\n", + "Repeat the `inverse` and `forward` calculations for the\n", + "second orientation reflection." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "### Check the inverse calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0 4 0) ? -0.00 4.00 0.00\n" + ] + } + ], + "source": [ + "sol = k4cv.inverse((-1.5950, 134.7568, 123.3554, -69.0966))\n", + "print(f\"(0 4 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "### Check the forward calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(040) : tth=-69.0985 komega=-1.5939 kappa=134.7551 kphi=-57.3291\n" + ] + } + ], + "source": [ + "sol = k4cv.forward((0, 4, 0))\n", + "print(\n", + " \"(040) :\", \n", + " f\"tth={sol.tth:.4f}\", \n", + " f\"komega={sol.komega:.4f}\", \n", + " f\"kappa={sol.kappa:.4f}\", \n", + " f\"kphi={sol.kphi:.4f}\"\n", + " )" + ] + }, + { + "source": [ + "## Scan in reciprocal space using Bluesky\n", + "\n", + "To scan with Bluesky, we need more setup." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from bluesky import RunEngine\n", + "from bluesky import SupplementalData\n", + "from bluesky.callbacks.best_effort import BestEffortCallback\n", + "from bluesky.magics import BlueskyMagics\n", + "import bluesky.plans as bp\n", + "import bluesky.plan_stubs as bps\n", + "import databroker\n", + "from IPython import get_ipython\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.ion()\n", + "\n", + "bec = BestEffortCallback()\n", + "db = databroker.temp().v1\n", + "sd = SupplementalData()\n", + "\n", + "get_ipython().register_magics(BlueskyMagics)\n", + "\n", + "RE = RunEngine({})\n", + "RE.md = {}\n", + "RE.preprocessors.append(sd)\n", + "RE.subscribe(db.insert)\n", + "RE.subscribe(bec)" + ] + }, + { + "source": [ + "### (_h00_) scan near (400)\n", + "\n", + "In this example, we have no detector. Still, we add the diffractometer\n", + "object in the detector list so that the _hkl_ and motor positions will appear\n", + "as columns in the table." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 1 Time: 2020-12-18 11:24:51\n", + "Persistent Unique Scan ID: 'e92af3e2-031a-4059-98f7-56640f7d5193'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | k4cv_h | k4cv_k | k4cv_l | k4cv_komega | k4cv_kappa | k4cv_kphi | k4cv_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 11:24:51.8 | 3.900 | -0.000 | -0.000 | 56.431 | -0.000 | -90.001 | -67.137 |\n", + "| 2 | 11:24:52.8 | 3.950 | 0.000 | 0.000 | 55.942 | 0.000 | -90.001 | -68.115 |\n", + "| 3 | 11:24:53.9 | 4.000 | 0.000 | -0.000 | 55.451 | 0.000 | -90.001 | -69.099 |\n", + "| 4 | 11:24:55.0 | 4.050 | 0.000 | 0.000 | 54.956 | 0.000 | -90.001 | -70.088 |\n", + "| 5 | 11:24:56.0 | 4.100 | 0.000 | 0.000 | 54.458 | 0.000 | -90.001 | -71.083 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['e92af3e2'] (scan num: 1)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('e92af3e2-031a-4059-98f7-56640f7d5193',)" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-18T11:24:58.341223\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([k4cv], k4cv.h, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### (_hk0_) scan from (400) to (040)\n", + "\n", + "Scan between the two orientation reflections. Need to\n", + "keep $\\varphi\\ge0$ to avoid big jumps during the scan." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 2 Time: 2020-12-18 11:24:59\n", + "Persistent Unique Scan ID: 'e3c4a20d-cc93-48e7-8302-cb919c033210'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | k4cv_h | k4cv_k | k4cv_l | k4cv_komega | k4cv_kappa | k4cv_kphi | k4cv_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 11:24:59.5 | 4.000 | 0.000 | 0.000 | -124.550 | -0.000 | 90.000 | -69.099 |\n", + "| 2 | 11:25:00.2 | 3.556 | 0.444 | 0.000 | -117.539 | -9.305 | 92.995 | -61.065 |\n", + "| 3 | 11:25:00.9 | 3.111 | 0.889 | 0.000 | -110.558 | -20.863 | 96.749 | -54.612 |\n", + "| 4 | 11:25:01.9 | 2.667 | 1.333 | -0.000 | -103.581 | -34.906 | 101.425 | -50.011 |\n", + "| 5 | 11:25:02.9 | 2.222 | 1.778 | 0.000 | -96.678 | -51.202 | 107.118 | -47.592 |\n", + "| 6 | 11:25:03.6 | 1.778 | 2.222 | 0.000 | -90.012 | -68.872 | 113.784 | -47.592 |\n", + "| 7 | 11:25:04.2 | 1.333 | 2.667 | -0.000 | -83.768 | -86.675 | 121.238 | -50.011 |\n", + "| 8 | 11:25:04.8 | 0.889 | 3.111 | 0.000 | -78.039 | -103.647 | 129.267 | -54.612 |\n", + "| 9 | 11:25:05.4 | 0.444 | 3.556 | -0.000 | -72.737 | -119.520 | 137.795 | -61.065 |\n", + "| 10 | 11:25:06.1 | -0.000 | 4.000 | 0.000 | -67.504 | -134.756 | 147.045 | -69.099 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['e3c4a20d'] (scan num: 2)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('e3c4a20d-cc93-48e7-8302-cb919c033210',)" + ] + }, + "metadata": {}, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-18T11:25:07.700070\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "k4cv.calc.engine.mode = \"constant_phi\"\n", + "k4cv.calc[\"kphi\"].limits = (0, 180)\n", + "\n", + "RE(bp.scan([k4cv], k4cv.h, 4, 0, k4cv.k, 0, 4, 10))" + ] + }, + { + "source": [ + "### (_0k0_) scan near (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 3 Time: 2020-12-18 11:25:08\n", + "Persistent Unique Scan ID: '11b49a7d-304c-4f51-8bc8-55e41b8987cf'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | k4cv_k | k4cv_h | k4cv_l | k4cv_komega | k4cv_kappa | k4cv_kphi | k4cv_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 11:25:08.9 | 3.900 | -0.000 | 0.000 | -66.523 | -134.756 | 147.045 | -67.137 |\n", + "| 2 | 11:25:09.8 | 3.950 | -0.000 | -0.000 | -67.012 | -134.756 | 147.045 | -68.115 |\n", + "| 3 | 11:25:10.8 | 4.000 | -0.000 | 0.000 | -67.504 | -134.756 | 147.045 | -69.099 |\n", + "| 4 | 11:25:11.7 | 4.050 | -0.000 | 0.000 | -67.998 | -134.756 | 147.045 | -70.088 |\n", + "| 5 | 11:25:12.7 | 4.100 | -0.000 | 0.000 | -68.496 | -134.756 | 147.045 | -71.083 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['11b49a7d'] (scan num: 3)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('11b49a7d-304c-4f51-8bc8-55e41b8987cf',)" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-18T11:25:15.025361\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([k4cv], k4cv.k, 3.9, 4.1, 5))" + ] + }, + { + "source": [ + "### (_hk0_) scan near (440)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "Transient Scan ID: 4 Time: 2020-12-18 11:25:15\n", + "Persistent Unique Scan ID: '4b5af333-201a-404b-b8c6-4c5f5d14a88a'\n", + "New stream: 'primary'\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| seq_num | time | k4cv_h | k4cv_k | k4cv_l | k4cv_komega | k4cv_kappa | k4cv_kphi | k4cv_tth |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "| 1 | 11:25:16.4 | 3.900 | 4.100 | 0.000 | -122.253 | -61.940 | 111.095 | -106.695 |\n", + "| 2 | 11:25:17.4 | 3.950 | 4.050 | 0.000 | -122.615 | -60.940 | 110.715 | -106.659 |\n", + "| 3 | 11:25:18.3 | 4.000 | 4.000 | 0.000 | -122.985 | -59.941 | 110.339 | -106.647 |\n", + "| 4 | 11:25:19.0 | 4.050 | 3.950 | -0.000 | -123.366 | -58.946 | 109.965 | -106.659 |\n", + "| 5 | 11:25:19.6 | 4.100 | 3.900 | -0.000 | -123.755 | -57.952 | 109.593 | -106.695 |\n", + "+-----------+------------+------------+------------+------------+-------------+------------+------------+------------+\n", + "generator scan ['4b5af333'] (scan num: 4)\n", + "\n", + "\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('4b5af333-201a-404b-b8c6-4c5f5d14a88a',)" + ] + }, + "metadata": {}, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-18T11:25:21.417878\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "RE(bp.scan([k4cv], k4cv.h, 3.9, 4.1, k4cv.k, 4.1, 3.9, 5))" + ] + } + ] +} \ No newline at end of file diff --git a/examples/hkl-example.ipynb b/examples/hkl-example.ipynb deleted file mode 100644 index 582de4ba..00000000 --- a/examples/hkl-example.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# HKL calculation, compared to SPEC results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# from ophyd.hkl.diffract import E6C\n", - "from ophyd.hkl.calc import CalcE6C" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the desired HKL trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "hkls = pd.read_csv('hkl_data/hkl.txt', delim_whitespace=True)\n", - "hkls.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get the motor positions that SPEC calculated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# The motor positions according to SPEC\n", - "spec_motors = pd.read_csv('hkl_data/motors.txt', delim_whitespace=True)\n", - "spec_motors.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the trajectory of the physical motors" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(3, 2, figsize=(12, 6),\n", - " subplot_kw={'xticks': []})\n", - "fig.subplots_adjust(hspace=0.3, wspace=0.2)\n", - "\n", - "plt.suptitle('Trajectory according to SPEC')\n", - "for ax, key in zip(axes.flat, spec_motors.keys()):\n", - " ax.plot(spec_motors.index, spec_motors[key], label=key)\n", - " ax.set_title(key)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the desired HKL trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(3, 1, figsize=(12, 6))\n", - "fig.subplots_adjust(hspace=0.4, wspace=0.2)\n", - "\n", - "plt.suptitle('Desired HKL trajectory')\n", - "axes[0].plot(hkls.h)\n", - "axes[0].set_title('h')\n", - "axes[1].plot(hkls.k)\n", - "axes[1].set_title('k')\n", - "axes[2].plot(hkls.l)\n", - "axes[2].set_title('l')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize a calculation engine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc = CalcE6C(engine='hkl')\n", - "calc.wavelength = 1.33 # nm\n", - "print('mode is', calc.engine.mode)\n", - "print('physical axes', calc.physical_axes)\n", - "print('pseudo axes', calc.pseudo_axes)\n", - "print('omega parameter is', calc['omega'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set some constraints on the physical motors" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "phi = calc['phi']\n", - "phi.limits = (0, 0)\n", - "phi.value = 0\n", - "phi.fit = False\n", - "\n", - "chi = calc['chi']\n", - "chi.limits = (-90, -90)\n", - "chi.value = -90\n", - "chi.fit = False\n", - "\n", - "mu = calc['mu']\n", - "mu.limits = (0, 0)\n", - "mu.value = 0\n", - "mu.fit = False\n", - "\n", - "print('phi', calc['phi'])\n", - "print('chi', calc['chi'])\n", - "print('mu', calc['mu'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add a sample to work with" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# from ophyd.hkl.sample import HklSample\n", - "# new_sample supports kwargs (see `help(HklSample)`)\n", - "from ophyd.hkl.util import Lattice\n", - "lattice = Lattice(a=3.78, b=3.78, c=13.28, alpha=90, beta=90, gamma=90)\n", - "sample = calc.new_sample('sample0', lattice=lattice)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primary reflection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "r1 = sample.add_reflection(0, 0, 2, \n", - " position=calc.Position(mu=0.0, omega=71.04, chi=-90.0, phi=0.0, gamma=-1.65, delta=136.7))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Secondary reflection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "r2 = sample.add_reflection(1, 0, 1,\n", - " position=calc.Position(mu=0.0, omega=158.22, chi=-90.0, phi=0.0, gamma=1.7, delta=164.94))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate the UB matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sample.compute_UB(r1, r2)\n", - "print(np.array(sample.UB))\n", - "\n", - "spec_ub = [[0.0338309723166807, 1.6616745234937, -0.00732930331262271],\n", - " [1.66007365775423, -0.032591767600211, 0.0221634966739925],\n", - " [0.0773350510852808, -0.0273010739795478, -0.472555187096841]\n", - " ]\n", - "print('from spec:\\n', np.array(spec_ub))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate the trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "for seq, (h, k, l) in hkls.iterrows():\n", - " print('-- hkl {} --'.format((h, k, l)))\n", - " print('Solutions:')\n", - " for sol in calc.calc((h, k, l)):\n", - " print('\\t{}'.format(sol))\n", - " \n", - " break" - ] - } - ], - "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": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/hkl-verify-e6c.ipynb b/examples/hkl-verify-e6c.ipynb deleted file mode 100644 index ce2d4ea0..00000000 --- a/examples/hkl-verify-e6c.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Non-exhaustive test of E6C calculations: verify orientation, U, UB, and rotation directions\n", - "\n", - "#### with the aid of Yong Chu's mental math\n", - "#### the TL;DR is that it appears to function as documented and as expected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from ophyd.hkl.calc import CalcE6C\n", - "from ophyd.hkl.util import Lattice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize the calculation engine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc = CalcE6C(engine='hkl')\n", - "calc.engine.mode = 'constant_chi_vertical'\n", - "calc.wavelength = 1. # nm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the crystal lattice" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lattice = Lattice(a=1, b=1, c=1, alpha=90, beta=90, gamma=90)\n", - "sample = calc.new_sample('sample0', lattice=lattice)\n", - "\n", - "print('lattice', sample.lattice)\n", - "print('physical axes', calc.physical_axes)\n", - "print('pseudo axes', calc.pseudo_axes)\n", - "print('omega parameter is', calc['omega'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the UB matrix from two reflections" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# checking orientation of delta\n", - "r1p = calc.Position(mu=0.0, omega=30.0, chi=0.0, phi=0.0, gamma=0., delta=60.)\n", - "r1 = sample.add_reflection(0, 0, 1, position=r1p)\n", - "r2p = calc.Position(mu=0.0, omega=120.0, chi=0.0, phi=0.0, gamma=0, delta=60.)\n", - "r2 = sample.add_reflection(1, 0, 0, position=r2p)\n", - "sample.compute_UB(r1, r2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sample.U" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sample.UB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### pause to contemplate life and calculate some motor positions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc.physical_positions = calc.Position(mu=0.0, omega=30.0, chi=90.0, phi=0.0, gamma=0, delta=60.)\n", - "print('pseudo should be (0,1,0)=', calc.pseudo_axes)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# checking orientation of delta\n", - "calc.physical_positions = calc.Position(mu=30.0, omega=0.0, chi=0.0, phi=0.0, gamma=60., delta=0.)\n", - "print('pseudo should be (0,1,0)=', calc.pseudo_axes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc.physical_positions = calc.Position(mu=0, omega=30., chi=-90.0, phi=0.0, gamma=0., delta=60.)\n", - "print('pseudo should be (0,-1,0)=', calc.pseudo_axes)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "\n", - "calc.physical_positions = calc.Position(mu=0.0, omega=-60.0, chi=0.0, phi=0.0, gamma=0, delta=60.)\n", - "print('pseudo should be (-1,0,0)=', calc.pseudo_axes)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Diffracting upside-down now\n", - "#### Note that omega and phi only need to sum to +-120, which reflects what the inverse calculations from the library give" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc.physical_positions = calc.Position(mu=0.0, omega=-50.0, chi=0.0, phi=-70.0, gamma=0, delta=-60.)\n", - "print('pseudo should be (1,0,0)=', calc.pseudo_axes)\n", - "\n", - "calc.physical_positions = calc.Position(mu=0.0, omega=-100.0, chi=0.0, phi=-20.0, gamma=0, delta=-60.)\n", - "print('pseudo should be (1,0,0)=', calc.pseudo_axes)\n", - "\n", - "calc.physical_positions = calc.Position(mu=0.0, omega=100.0, chi=0.0, phi=-220.0, gamma=0, delta=-60.)\n", - "print('pseudo should be (1,0,0)=', calc.pseudo_axes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc.physical_positions = calc.Position(mu=0.0, omega=45.0, chi=45.0, phi=0.0, gamma=0, delta=90.)\n", - "print('pseudo should be (0,1,1)=', calc.pseudo_axes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "solutions = calc.calc((1,0,0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for sol in solutions:\n", - " print(sol.positions.omega + sol.positions.phi)" - ] - } - ], - "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": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/resources/3S+1D.png b/examples/resources/3S+1D.png new file mode 100755 index 00000000..4dcb210c Binary files /dev/null and b/examples/resources/3S+1D.png differ diff --git a/examples/resources/4S+2D-photo.jpg b/examples/resources/4S+2D-photo.jpg new file mode 100755 index 00000000..d9cf9278 Binary files /dev/null and b/examples/resources/4S+2D-photo.jpg differ diff --git a/examples/resources/4S+2D.png b/examples/resources/4S+2D.png new file mode 100755 index 00000000..2ce49a6b Binary files /dev/null and b/examples/resources/4S+2D.png differ diff --git a/examples/resources/6-circle-schematic.png b/examples/resources/6-circle-schematic.png new file mode 100755 index 00000000..e40bc4d7 Binary files /dev/null and b/examples/resources/6-circle-schematic.png differ diff --git a/examples/resources/LNO_LAO_s14.dat b/examples/resources/LNO_LAO_s14.dat new file mode 100644 index 00000000..8cb36139 --- /dev/null +++ b/examples/resources/LNO_LAO_s14.dat @@ -0,0 +1,257 @@ +#F LNO_LAO +#E 1276730676 +#D Wed Jun 16 18:24:36 2010 +#C LNO_LAO User = epix33bm +#H0 SR_current SR_fill SR_status SR_mode SR_fb SR_fbH SR_fbV SR_topUp barometer_mbar +#H1 SR_BM_HPOS SR_BM_VPOS SR_BM_HANG SR_BM_HANG +#H2 slits_wt slits_wl slits_wb slits_wr +#H3 Mir1_alpha Mir1_y Mir1_y1 Mir1_y2 Mir1_bender +#H4 DCM_energy DCM_lambda DCM_theta0 DCM_thetaEnc DCM_mode Mir_use Mir_alpha +#H5 Mir2_alpha Mir2_y Mir2_y1 Mir2_y2 Mir2_bender +#H6 mue_sclr_auto mue_sclr_freq +#H7 PF4_thickAl PF4_thickTi PF4_trans PF4_bladeA1 PF4_bladeA2 PF4_bladeA3 PF4_bladeA4 PF4_bladeB1 PF4_bladeB2 PF4_bladeB3 PF4_bladeB4 +#H8 I0_VDC I0_gain I0_bias I0_time I0_suppr I0_dark I0_Amps +#H9 I00_VDC I00_gain I00_bias I00_time I00_suppr I00_dark I00_Amps +#H10 MUE540_i0 MUE540_i1 MUE540_i2 MUE540_i3 MUE540_i4 MUE540_i5 MUE540_i6 MUE540_i7 +#H11 MUE540_i8 MUE540_i9 MUE540_i10 MUE540_i11 MUE540_i12 MUE540_i13 MUE540_i14 MUE540_i15 +#H12 MUE540_o0 MUE540_o1 MUE540_o2 MUE540_o3 +#H13 HSC_I0h HSC_I0v +#H14 HSC1t HSC1l HSC1b HSC1r HSC1h HSC1v HSC1h0 HSC1v0 +#H15 HSC2t HSC2l HSC2b HSC2r HSC2h HSC2v HSC2h0 HSC2v0 +#H16 HSC3t HSC3l HSC3b HSC3r HSC3h HSC3v HSC3h0 HSC3v0 +#H17 dark1 dark2 dark3 dark4 dark5 dark6 dark7 dark8 +#H18 dark9 dark10 dark11 dark12 dark13 dark14 dark15 dark16 +#H19 darkToTime darkCoTime darkRing +#H20 powderMode powderWidth powderMotor powderVelo +#H21 rockON rockCenter rockWidth +#O0 2-theta theta chi phi antheta an2theta z-axis m_1_8 +#O1 xt1 yt1 yt2 yt3 m_2_5 zt1 samx samz +#O2 DCM.theta wst wsl wsb wsr m22 +#C Wed Jun 16 18:25:04 2010. do ./matrix150.mac. +#C Wed Jun 16 18:26:02 2010. Mode reset from 5 to 0. +#C Wed Jun 16 18:26:18 2010. Mode reset from 0 to 3. +#C Wed Jun 16 18:27:18 2010. Freezing Phi at 0. +#C Wed Jun 16 18:27:39 2010. do ./align.mac. + +#S 14 hklscan 1.00133 1.00133 1.00133 1.00133 2.85 3.05 200 -400000 +#D Wed Jun 16 18:47:18 2010 +#M 400000 (I0) +#G0 0 0 1 0 0 1 0 0 0 0 0 0 50 0 0.1 0 68 68 50 -1 1 1 3.13542 3.13542 0 463.6 838.8 +#G1 3.781726143 3.791444574 3.79890313 90.2546203 90.01815424 89.89967858 1.661462253 1.657219786 1.65396364 89.74541108 89.98229138 90.10024173 0 0 2 1 1 3 38.09875 19.1335 90.0135 0 0 0 65.644 32.82125 115.23625 48.1315 0 0 1.239424258 1.239424258 +#G3 -1.658712442 0.09820024135 -0.000389705578 -0.09554990312 -1.654278629 0.00242844486 0.0002629818914 0.009815746824 1.653961812 +#G4 1.001328179 1.001328179 2.999452893 1.239424258 29.28146688 29.4104177 0 89.84344949 0.7 0 0 0 0 0 0 0 -180 -180 -180 -180 -180 -180 -180 -180 -180 0 +#Q 1.00133 1.00133 2.99945 +#P0 65.644 32.82125 115.23625 48.1315 -0.001 -0.16 2.5 -0.2 +#P1 -3.4480499 0.49927508 0.030010083 0.499275 1.749425 -113.52071 -946.48814 0 +#P2 11.508019 2.9999781 30.000019 2.9999781 29.999854 1230.0415 +#C LNO_LAO +#V0 102.36 4 1 4 1 1 1 1 986.893 +#V1 -0.695912 0.254987 11.1785 11.1785 +#V2 2.99998 30 2.99998 29.9999 +#V3 5.50001 -6.18921e-06 3.7125 -3.71251 16 +#V4 9.99906 1.23942 0.149966 11.4038 0 1 5.50001 +#V5 5.78578 29.8071 33.7125 25.9017 15.2093 +#V6 0 16960 +#V7 0 0 1 0 0 0 0 0 0 0 0 +#V8 4.98413 100000000 0 1 5.23e-07 785.198 4.98413e-08 +#V9 2.78144 1000000 0 1 -2.9e-09 1 2.78144e-06 +#V10 0.129426 2.78144 4.98413 0.01221 -0.0610501 -0.0610501 0.119658 -0.031746 +#V11 -0.0757021 -0.0512821 0.393162 0.031746 -0.021978 -0.031746 -0.0512821 0.026862 +#V12 5 5 5 0 +#V13 2.1 0.5 +#V14 0.9 1.7 1.1 2.3 4 2 0.3 -0.1 +#V15 9.5 9.5 9.5 9.5 19 19 0 0 +#V16 0.5 1.1 0.5 1.4 2.5 1 0.15 0 +#V17 1000000 785.198 1 0.09375 0.0520833 1.39583 7.20833 203185 +#V18 0 0 0 0 0 0 0 0 +#V19 216 97 100 +#V20 0 0 theta 0.4 +#V21 OFF 0 0 +#N 27 +#L H K L Epoch I0 I00 harmonic W Fluor corrdet filters trans piltot TempC ccdtot scan_bar Energy SampK ContK vortot livet realt icr ocr sca1 seconds signal +1.00133 1.00133 2.85 1387 400000 217383 0 2030 2070 1989 0 1 0 0 0 157633 9.9990631 0 0 0 0 0 0 0 0 0.780109 1989 +1.00133 1.00133 2.851 1390 400000 220156 0 2150 2186 2112 0 1 0 0 0 163578 9.9990631 0 0 0 0 0 0 0 0 0.79094 2112 +1.00133 1.00133 2.852 1393 400000 219835 1 2210 2245 2163 0 1 0 0 0 161868 9.9990631 0 0 0 0 0 0 0 0 0.789425 2163 +1.00133 1.00133 2.853 1396 400000 216589 1 2313 2353 2261 0 1 0 0 0 157376 9.9990631 0 0 0 0 0 0 0 0 0.777549 2261 +1.00133 1.00133 2.854 1399 400000 215071 0 2225 2285 2176 0 1 0 0 0 156314 9.9990631 0 0 0 0 0 0 0 0 0.772036 2176 +1.00133 1.00133 2.855 1402 400000 214735 2 2389 2440 2331 0 1 0 0 0 154578 9.9990631 0 0 0 0 0 0 0 0 0.771171 2331 +1.00133 1.00133 2.856 1406 400000 212312 2 2370 2413 2317 0 1 0 0 0 152642 9.9990631 0 0 0 0 0 0 0 0 0.762595 2317 +1.00133 1.00133 2.857 1409 400000 212443 1 2621 2673 2564 0 1 0 0 0 154863 9.9990631 0 0 0 0 0 0 0 0 0.763323 2564 +1.00133 1.00133 2.858 1412 400000 210209 4 2682 2734 2625 0 1 0 0 0 150505 9.9990631 0 0 0 0 0 0 0 0 0.755646 2625 +1.00133 1.00133 2.859 1415 400000 212158 1 2689 2734 2637 0 1 0 0 0 152799 9.9990631 0 0 0 0 0 0 0 0 0.762536 2637 +1.00133 1.00133 2.86 1418 400000 213132 2 2964 3027 2887 0 1 0 0 0 154230 9.9990631 0 0 0 0 0 0 0 0 0.767447 2887 +1.00133 1.00133 2.861 1421 400000 215394 2 2836 2887 2782 0 1 0 0 0 155455 9.9990631 0 0 0 0 0 0 0 0 0.773786 2782 +1.00133 1.00133 2.862 1425 400000 217312 0 3122 3166 3060 0 1 0 0 0 157487 9.9990631 0 0 0 0 0 0 0 0 0.780963 3060 +1.00133 1.00133 2.863 1428 400000 217329 3 3262 3328 3192 0 1 0 0 0 157812 9.9990631 0 0 0 0 0 0 0 0 0.781131 3192 +1.00133 1.00133 2.864 1431 400000 219117 1 3423 3497 3356 0 1 0 0 0 161200 9.9990631 0 0 0 0 0 0 0 0 0.788173 3356 +1.00133 1.00133 2.865 1434 400000 218155 4 3490 3559 3411 0 1 0 0 0 158784 9.9990631 0 0 0 0 0 0 0 0 0.784488 3411 +1.00133 1.00133 2.866 1437 400000 216532 1 3612 3675 3544 0 1 0 0 0 160833 9.9990631 0 0 0 0 0 0 0 0 0.778812 3544 +1.00133 1.00133 2.867 1441 400000 213366 0 3681 3765 3598 0 1 0 0 0 150249 9.9990631 0 0 0 0 0 0 0 0 0.76729 3598 +1.00133 1.00133 2.868 1444 400000 213725 1 3715 3786 3642 0 1 0 0 0 153936 9.9990631 0 0 0 0 0 0 0 0 0.768794 3642 +1.00133 1.00133 2.869 1447 400000 216163 4 3881 3941 3798 0 1 0 0 0 154307 9.9990631 0 0 0 0 0 0 0 0 0.777622 3798 +1.00133 1.00133 2.87 1450 400000 218890 1 4303 4387 4205 0 1 0 0 0 156193 9.9990631 0 0 0 0 0 0 0 0 0.787592 4205 +1.00133 1.00133 2.871 1453 400000 218829 1 4271 4346 4187 0 1 0 0 0 154483 9.9990631 0 0 0 0 0 0 0 0 0.787632 4187 +1.00133 1.00133 2.872 1457 400000 220899 5 4467 4547 4359 0 1 0 0 0 158051 9.9990631 0 0 0 0 0 0 0 0 0.795457 4359 +1.00133 1.00133 2.873 1460 400000 223974 2 4842 4939 4729 0 1 0 0 0 162056 9.9990631 0 0 0 0 0 0 0 0 0.807066 4729 +1.00133 1.00133 2.874 1463 400000 223853 3 5001 5097 4895 0 1 0 0 0 160299 9.9990631 0 0 0 0 0 0 0 0 0.806878 4895 +1.00133 1.00133 2.875 1466 400000 219871 4 4924 5011 4824 0 1 0 0 0 157386 9.9990631 0 0 0 0 0 0 0 0 0.792261 4824 +1.00133 1.00133 2.876 1469 400000 218463 3 4983 5073 4876 0 1 0 0 0 153268 9.9990631 0 0 0 0 0 0 0 0 0.786813 4876 +1.00133 1.00133 2.877 1473 400000 217604 3 5094 5177 5002 0 1 0 0 0 157895 9.9990631 0 0 0 0 0 0 0 0 0.78404 5002 +1.00133 1.00133 2.878 1476 400000 220096 5 5570 5683 5464 0 1 0 0 0 159735 9.9990631 0 0 0 0 0 0 0 0 0.793091 5464 +1.00133 1.00133 2.879 1479 400000 220094 5 5629 5744 5499 0 1 0 0 0 161287 9.9990631 0 0 0 0 0 0 0 0 0.793177 5499 +1.00133 1.00133 2.88 1482 400000 223850 6 5961 6074 5832 0 1 0 0 0 162744 9.9990631 0 0 0 0 0 0 0 0 0.807608 5832 +1.00133 1.00133 2.881 1486 400000 220236 4 5910 6001 5768 0 1 0 0 0 162567 9.9990631 0 0 0 0 0 0 0 0 0.793738 5768 +1.00133 1.00133 2.882 1489 400000 220269 4 6219 6321 6077 0 1 0 0 0 161879 9.9990631 0 0 0 0 0 0 0 0 0.793964 6077 +1.00133 1.00133 2.883 1492 400000 217361 4 6208 6316 6091 0 1 0 0 0 158162 9.9990631 0 0 0 0 0 0 0 0 0.783397 6091 +1.00133 1.00133 2.884 1495 400000 215963 3 6489 6594 6352 0 1 0 0 0 157681 9.9990631 0 0 0 0 0 0 0 0 0.778338 6352 +1.00133 1.00133 2.885 1498 400000 215559 7 6569 6711 6437 0 1 0 0 0 161554 9.9990631 0 0 0 0 0 0 0 0 0.776965 6437 +1.00133 1.00133 2.886 1501 400000 217476 5 6766 6872 6621 0 1 0 0 0 158440 9.9990631 0 0 0 0 0 0 0 0 0.783961 6621 +1.00133 1.00133 2.887 1505 400000 219494 4 6939 7079 6814 0 1 0 0 0 159418 9.9990631 0 0 0 0 0 0 0 0 0.791666 6814 +1.00133 1.00133 2.888 1508 400000 220449 4 7403 7537 7262 0 1 0 0 0 160905 9.9990631 0 0 0 0 0 0 0 0 0.795713 7262 +1.00133 1.00133 2.889 1511 400000 216764 5 7434 7555 7267 0 1 0 0 0 158275 9.9990631 0 0 0 0 0 0 0 0 0.782138 7267 +1.00133 1.00133 2.89 1514 400000 216278 3 7466 7615 7314 0 1 0 0 0 158145 9.9990631 0 0 0 0 0 0 0 0 0.780219 7314 +1.00133 1.00133 2.891 1518 400000 215009 13 7598 7753 7431 0 1 0 0 0 153781 9.9990631 0 0 0 0 0 0 0 0 0.775617 7431 +1.00133 1.00133 2.892 1521 400000 216584 10 7693 7836 7530 0 1 0 0 0 154757 9.9990631 0 0 0 0 0 0 0 0 0.781495 7530 +1.00133 1.00133 2.893 1524 400000 216772 6 8300 8426 8109 0 1 0 0 0 155548 9.9990631 0 0 0 0 0 0 0 0 0.782269 8109 +1.00133 1.00133 2.894 1527 400000 215287 10 8451 8572 8262 0 1 0 0 0 150606 9.9990631 0 0 0 0 0 0 0 0 0.777158 8262 +1.00133 1.00133 2.895 1530 400000 212983 7 8267 8394 8100 0 1 0 0 0 155053 9.9990631 0 0 0 0 0 0 0 0 0.76928 8100 +1.00133 1.00133 2.896 1534 400000 212760 9 8492 8667 8294 0 1 0 0 0 155905 9.9990631 0 0 0 0 0 0 0 0 0.768586 8294 +1.00133 1.00133 2.897 1537 400000 210507 10 8683 8848 8493 0 1 0 0 0 154364 9.9990631 0 0 0 0 0 0 0 0 0.760678 8493 +1.00133 1.00133 2.898 1540 400000 208856 13 8841 9014 8641 0 1 0 0 0 151760 9.9990631 0 0 0 0 0 0 0 0 0.754732 8641 +1.00133 1.00133 2.899 1543 400000 208609 15 8990 9144 8790 0 1 0 0 0 149256 9.9990631 0 0 0 0 0 0 0 0 0.749576 8790 +1.00133 1.00133 2.9 1546 400000 207046 10 9301 9463 9084 0 1 0 0 0 146938 9.9990631 0 0 0 0 0 0 0 0 0.744602 9084 +1.00133 1.00133 2.901 1549 400000 207183 17 9478 9641 9267 0 1 0 0 0 150086 9.9990436 0 0 0 0 0 0 0 0 0.745229 9267 +1.00133 1.00133 2.902 1553 400000 209064 7 9680 9849 9477 0 1 0 0 0 151895 9.9990631 0 0 0 0 0 0 0 0 0.751224 9477 +1.00133 1.00133 2.903 1556 400000 206119 10 9901 10052 9680 0 1 0 0 0 149265 9.9990631 0 0 0 0 0 0 0 0 0.741661 9680 +1.00133 1.00133 2.904 1559 400000 206220 13 10184 10363 9953 0 1 0 0 0 149130 9.9990436 0 0 0 0 0 0 0 0 0.741984 9953 +1.00133 1.00133 2.905 1562 400000 208649 17 10401 10567 10143 0 1 0 0 0 149428 9.9990436 0 0 0 0 0 0 0 0 0.750146 10143 +1.00133 1.00133 2.906 1565 400000 212450 16 10986 11185 10746 0 1 0 0 0 153086 9.9990631 0 0 0 0 0 0 0 0 0.763383 10746 +1.00133 1.00133 2.907 1569 400000 211765 22 11289 11466 11015 0 1 0 0 0 152995 9.9990631 0 0 0 0 0 0 0 0 0.761721 11015 +1.00133 1.00133 2.908 1572 400000 213365 23 11441 11608 11151 0 1 0 0 0 152739 9.9990631 0 0 0 0 0 0 0 0 0.76681 11151 +1.00133 1.00133 2.909 1575 400000 211170 23 11616 11801 11343 0 1 0 0 0 154123 9.9990631 0 0 0 0 0 0 0 0 0.759248 11343 +1.00133 1.00133 2.91 1578 400000 211016 12 11855 12063 11579 0 1 0 0 0 152547 9.9990631 0 0 0 0 0 0 0 0 0.7587 11579 +1.00133 1.00133 2.911 1581 400000 209611 20 11911 12113 11627 0 1 0 0 0 149865 9.9990631 0 0 0 0 0 0 0 0 0.754012 11627 +1.00133 1.00133 2.912 1585 400000 210007 19 12390 12596 12083 0 1 0 0 0 150941 9.9990631 0 0 0 0 0 0 0 0 0.756436 12083 +1.00133 1.00133 2.913 1588 400000 211884 24 12632 12848 12315 0 1 0 0 0 154443 9.9990631 0 0 0 0 0 0 0 0 0.762308 12315 +1.00133 1.00133 2.914 1591 400000 213157 16 12858 13076 12603 0 1 0 0 0 155177 9.9990631 0 0 0 0 0 0 0 0 0.76676 12603 +1.00133 1.00133 2.915 1594 400000 213056 31 13078 13287 12755 0 1 0 0 0 156111 9.9990631 0 0 0 0 0 0 0 0 0.76633 12755 +1.00133 1.00133 2.916 1597 400000 215861 23 13948 14153 13629 0 1 0 0 0 156116 9.9990631 0 0 0 0 0 0 0 0 0.776286 13629 +1.00133 1.00133 2.917 1601 400000 213724 33 13943 14197 13596 0 1 0 0 0 152412 9.9990631 0 0 0 0 0 0 0 0 0.769135 13596 +1.00133 1.00133 2.918 1604 400000 213182 24 14119 14332 13798 0 1 0 0 0 150199 9.9990631 0 0 0 0 0 0 0 0 0.767139 13798 +1.00133 1.00133 2.919 1607 400000 215570 29 14738 14975 14360 0 1 0 0 0 155035 9.9990631 0 0 0 0 0 0 0 0 0.775939 14360 +1.00133 1.00133 2.92 1610 400000 217483 27 15081 15311 14700 0 1 0 0 0 157602 9.9990631 0 0 0 0 0 0 0 0 0.78316 14700 +1.00133 1.00133 2.921 1613 400000 217325 25 15389 15646 15070 0 1 0 0 0 156302 9.9990631 0 0 0 0 0 0 0 0 0.782463 15070 +1.00133 1.00133 2.922 1617 400000 215089 30 15188 15408 14798 0 1 0 0 0 153816 9.9990631 0 0 0 0 0 0 0 0 0.774464 14798 +1.00133 1.00133 2.923 1620 400000 214769 29 15774 16032 15425 0 1 0 0 0 151230 9.9990631 0 0 0 0 0 0 0 0 0.774272 15425 +1.00133 1.00133 2.924 1623 400000 213650 42 15870 16101 15462 0 1 0 0 0 148617 9.9990631 0 0 0 0 0 0 0 0 0.769825 15462 +1.00133 1.00133 2.925 1626 400000 216081 52 16558 16801 16153 0 1 0 0 0 151208 9.9990631 0 0 0 0 0 0 0 0 0.778844 16153 +1.00133 1.00133 2.926 1629 400000 215730 33 16461 16733 16077 0 1 0 0 0 153621 9.9990631 0 0 0 0 0 0 0 0 0.777123 16077 +1.00133 1.00133 2.927 1633 400000 218245 49 17327 17622 16901 0 1 0 0 0 156028 9.9990631 0 0 0 0 0 0 0 0 0.786618 16901 +1.00133 1.00133 2.928 1636 400000 220118 53 18076 18349 17607 0 1 0 0 0 158185 9.9990631 0 0 0 0 0 0 0 0 0.793727 17607 +1.00133 1.00133 2.929 1639 400000 220084 48 18265 18538 17785 0 1 0 0 0 160188 9.9990631 0 0 0 0 0 0 0 0 0.793627 17785 +1.00133 1.00133 2.93 1642 400000 217624 52 18346 18631 17873 0 1 0 0 0 157400 9.9990631 0 0 0 0 0 0 0 0 0.784698 17873 +1.00133 1.00133 2.931 1645 400000 214837 45 18488 18749 18032 0 1 0 0 0 157717 9.9990436 0 0 0 0 0 0 0 0 0.7747 18032 +1.00133 1.00133 2.932 1649 400000 215193 54 18614 18871 18129 0 1 0 0 0 156889 9.9990436 0 0 0 0 0 0 0 0 0.775885 18129 +1.00133 1.00133 2.933 1652 400000 213608 49 18635 18914 18190 0 1 0 0 0 150302 9.9990631 0 0 0 0 0 0 0 0 0.770149 18190 +1.00133 1.00133 2.934 1655 400000 215055 50 19326 19602 18820 0 1 0 0 0 151733 9.9990631 0 0 0 0 0 0 0 0 0.775882 18820 +1.00133 1.00133 2.935 1658 400000 215759 68 19673 19995 19174 0 1 0 0 0 152803 9.9990436 0 0 0 0 0 0 0 0 0.778406 19174 +1.00133 1.00133 2.936 1662 400000 213819 56 19763 20057 19246 0 1 0 0 0 150830 9.9990631 0 0 0 0 0 0 0 0 0.770496 19246 +1.00133 1.00133 2.937 1665 400000 213390 46 20271 20550 19731 0 1 0 0 0 146384 9.9990631 0 0 0 0 0 0 0 0 0.766337 19731 +1.00133 1.00133 2.938 1668 400000 211587 48 20319 20611 19770 0 1 0 0 0 146389 9.9990631 0 0 0 0 0 0 0 0 0.759232 19770 +1.00133 1.00133 2.939 1671 400000 212885 55 20485 20735 19957 0 1 0 0 0 149270 9.9990631 0 0 0 0 0 0 0 0 0.764017 19957 +1.00133 1.00133 2.94 1674 400000 212916 57 21251 21562 20663 0 1 0 0 0 149071 9.9990631 0 0 0 0 0 0 0 0 0.764424 20663 +1.00133 1.00133 2.941 1678 400000 210837 78 21510 21805 20930 0 1 0 0 0 151622 9.9990631 0 0 0 0 0 0 0 0 0.757636 20930 +1.00133 1.00133 2.942 1681 400000 214362 59 22406 22682 21845 0 1 0 0 0 151414 9.9990631 0 0 0 0 0 0 0 0 0.770007 21845 +1.00133 1.00133 2.943 1684 400000 213817 79 22698 22976 22067 0 1 0 0 0 151040 9.9990631 0 0 0 0 0 0 0 0 0.768148 22067 +1.00133 1.00133 2.944 1687 400000 210987 70 22781 23055 22183 0 1 0 0 0 151607 9.9990631 0 0 0 0 0 0 0 0 0.758462 22183 +1.00133 1.00133 2.945 1690 400000 213053 91 23090 23373 22450 0 1 0 0 0 153447 9.9990631 0 0 0 0 0 0 0 0 0.765139 22450 +1.00133 1.00133 2.946 1694 400000 213665 77 24230 24570 23499 0 1 0 0 0 154583 9.9990631 0 0 0 0 0 0 0 0 0.767756 23499 +1.00133 1.00133 2.947 1697 400000 216307 100 25232 25577 24528 0 1 0 0 0 151875 9.9990631 0 0 0 0 0 0 0 0 0.777474 24528 +1.00133 1.00133 2.948 1700 400000 216110 93 26050 26383 25361 0 1 0 0 0 152211 9.9990631 0 0 0 0 0 0 0 0 0.776829 25361 +1.00133 1.00133 2.949 1703 400000 219055 86 27007 27314 26245 0 1 0 0 0 159551 9.9990631 0 0 0 0 0 0 0 0 0.788317 26245 +1.00133 1.00133 2.95 1706 400000 215357 86 26502 26788 25765 0 1 0 0 0 156283 9.9990631 0 0 0 0 0 0 0 0 0.773619 25765 +1.00133 1.00133 2.951 1710 400000 215481 117 27350 27679 26630 0 1 0 0 0 155238 9.9990631 0 0 0 0 0 0 0 0 0.774448 26630 +1.00133 1.00133 2.952 1713 400000 214113 129 27341 27648 26515 0 1 0 0 0 156620 9.9990631 0 0 0 0 0 0 0 0 0.769567 26515 +1.00133 1.00133 2.953 1716 400000 216518 133 28683 28944 27815 0 1 0 0 0 160495 9.9990631 0 0 0 0 0 0 0 0 0.778673 27815 +1.00133 1.00133 2.954 1719 400000 216595 99 29317 29608 28490 0 1 0 0 0 156540 9.9990631 0 0 0 0 0 0 0 0 0.77879 28490 +1.00133 1.00133 2.955 1722 400000 218921 127 30529 30878 29665 0 1 0 0 0 155579 9.9990631 0 0 0 0 0 0 0 0 0.787203 29665 +1.00133 1.00133 2.956 1726 400000 215519 150 30683 31072 29766 0 1 0 0 0 155383 9.9990436 0 0 0 0 0 0 0 0 0.775293 29766 +1.00133 1.00133 2.957 1729 400000 215747 139 31496 31846 30615 0 1 0 0 0 153778 9.9990631 0 0 0 0 0 0 0 0 0.776396 30615 +1.00133 1.00133 2.958 1732 400000 213750 135 31794 32129 30831 0 1 0 0 0 151323 9.9990631 0 0 0 0 0 0 0 0 0.76879 30831 +1.00133 1.00133 2.959 1735 400000 212146 185 31937 32257 30975 0 1 0 0 0 147423 9.9990631 0 0 0 0 0 0 0 0 0.76297 30975 +1.00133 1.00133 2.96 1738 400000 210970 156 32698 33083 31720 0 1 0 0 0 153923 9.9990631 0 0 0 0 0 0 0 0 0.758818 31720 +1.00133 1.00133 2.961 1742 400000 208587 144 33096 33414 32078 0 1 0 0 0 150333 9.9990436 0 0 0 0 0 0 0 0 0.750901 32078 +1.00133 1.00133 2.962 1745 400000 208911 176 33920 34180 32814 0 1 0 0 0 150866 9.9990631 0 0 0 0 0 0 0 0 0.752252 32814 +1.00133 1.00133 2.963 1748 400000 211119 164 35575 35964 34435 0 1 0 0 0 152393 9.9990631 0 0 0 0 0 0 0 0 0.759916 34435 +1.00133 1.00133 2.964 1751 400000 213216 188 36779 37085 35596 0 1 0 0 0 154675 9.9990631 0 0 0 0 0 0 0 0 0.767351 35596 +1.00133 1.00133 2.965 1754 400000 213402 197 37970 38319 36812 0 1 0 0 0 152879 9.9990631 0 0 0 0 0 0 0 0 0.767994 36812 +1.00133 1.00133 2.966 1758 400000 215771 193 39882 40208 38650 0 1 0 0 0 157268 9.9990631 0 0 0 0 0 0 0 0 0.776675 38650 +1.00133 1.00133 2.967 1761 400000 217550 241 41629 41977 40288 0 1 0 0 0 157653 9.9990631 0 0 0 0 0 0 0 0 0.783304 40288 +1.00133 1.00133 2.968 1764 400000 217611 248 42759 43124 41364 0 1 0 0 0 160806 9.9990631 0 0 0 0 0 0 0 0 0.783547 41364 +1.00133 1.00133 2.969 1767 400000 215152 277 43407 43691 41965 0 1 0 0 0 158655 9.9990631 0 0 0 0 0 0 0 0 0.774783 41965 +1.00133 1.00133 2.97 1770 400000 215958 299 45489 45769 43885 0 1 0 0 0 155037 9.9990631 0 0 0 0 0 0 0 0 0.777939 43885 +1.00133 1.00133 2.971 1774 400000 217670 333 48374 48695 46679 0 1 0 0 0 155338 9.9990631 0 0 0 0 0 0 0 0 0.784347 46679 +1.00133 1.00133 2.972 1777 400000 215211 355 48478 48758 46688 0 1 0 0 0 150590 9.9990631 0 0 0 0 0 0 0 0 0.775751 46688 +1.00133 1.00133 2.973 1780 400000 215181 379 50751 51059 48838 0 1 0 0 0 152894 9.9990631 0 0 0 0 0 0 0 0 0.775556 48838 +1.00133 1.00133 2.974 1783 400000 212711 400 51677 51974 49805 0 1 0 0 0 150698 9.9990631 0 0 0 0 0 0 0 0 0.762322 49805 +1.00133 1.00133 2.975 1786 400000 211557 462 54487 54610 52339 0 1 0 0 0 150365 9.9990631 0 0 0 0 0 0 0 0 0.759017 52339 +1.00133 1.00133 2.976 1790 400000 211378 488 57374 57598 55148 0 1 0 0 0 150422 9.9990436 0 0 0 0 0 0 0 0 0.75856 55148 +1.00133 1.00133 2.977 1793 400000 214318 533 61246 61425 58885 0 1 0 0 0 151825 9.9990631 0 0 0 0 0 0 0 0 0.768513 58885 +1.00133 1.00133 2.978 1797 400000 216369 70 22154 22468 66376.766 100 0.32458647 0 0 0 155213 9.9990631 0 0 0 0 0 0 0 0 0.77578 21545 +1.00133 1.00133 2.979 1800 400000 217489 83 23774 24091 71361.57 100 0.32458647 0 0 0 158603 9.9990631 0 0 0 0 0 0 0 0 0.77994 23163 +1.00133 1.00133 2.98 1804 400000 219115 88 25777 26078 77224.415 100 0.32458647 0 0 0 157628 9.9990436 0 0 0 0 0 0 0 0 0.786028 25066 +1.00133 1.00133 2.981 1807 400000 222183 119 28343 28706 84753.995 100 0.32458647 0 0 0 166118 9.9990631 0 0 0 0 0 0 0 0 0.798067 27510 +1.00133 1.00133 2.982 1810 400000 221754 125 30690 31030 91722.863 100 0.32458647 0 0 0 161898 9.9990631 0 0 0 0 0 0 0 0 0.796118 29772 +1.00133 1.00133 2.983 1813 400000 219409 136 32499 32838 97219.087 100 0.32458647 0 0 0 163035 9.9990631 0 0 0 0 0 0 0 0 0.787591 31556 +1.00133 1.00133 2.984 1817 400000 221519 170 36013 36395 107629.26 100 0.32458647 0 0 0 166324 9.9990631 0 0 0 0 0 0 0 0 0.795573 34935 +1.00133 1.00133 2.985 1820 400000 221891 208 40216 40527 119940.31 100 0.32458647 0 0 0 163124 9.9990631 0 0 0 0 0 0 0 0 0.797351 38931 +1.00133 1.00133 2.986 1823 400000 219515 270 44249 44551 131647.51 100 0.32458647 0 0 0 158346 9.9990631 0 0 0 0 0 0 0 0 0.788354 42731 +1.00133 1.00133 2.987 1826 400000 224576 390 52461 52784 155634.95 100 0.32458647 0 0 0 161605 9.9990631 0 0 0 0 0 0 0 0 0.807836 50517 +1.00133 1.00133 2.988 1829 400000 223951 492 61113 61325 180900.95 100 0.32458647 0 0 0 161164 9.9990631 0 0 0 0 0 0 0 0 0.805589 58718 +1.00133 1.00133 2.989 1834 400000 219756 78 23273 23543 211300.32 200 0.10726912 0 0 0 159390 9.9990631 0 0 0 0 0 0 0 0 0.789307 22666 +1.00133 1.00133 2.99 1837 400000 217329 132 31100 31453 281367.08 200 0.10726912 0 0 0 157109 9.9990631 0 0 0 0 0 0 0 0 0.780257 30182 +1.00133 1.00133 2.991 1840 400000 217157 231 41394 41737 373182.88 200 0.10726912 0 0 0 159044 9.9990631 0 0 0 0 0 0 0 0 0.779956 40031 +1.00133 1.00133 2.992 1844 400000 219481 426 57399 57698 513344.37 200 0.10726912 0 0 0 158676 9.9990631 0 0 0 0 0 0 0 0 0.788631 55066 +1.00133 1.00133 2.993 1848 400000 216519 82 26551 26841 742286.22 300 0.034818105 0 0 0 155277 9.9990631 0 0 0 0 0 0 0 0 0.777877 25845 +1.00133 1.00133 2.994 1851 400000 214736 219 40560 40942 1125994.6 300 0.034818105 0 0 0 155484 9.9990631 0 0 0 0 0 0 0 0 0.771727 39205 +1.00133 1.00133 2.995 1856 400000 212993 87 26558 26850 2055366.7 400 0.012563695 0 0 0 152934 9.9990631 0 0 0 0 0 0 0 0 0.765927 25823 +1.00133 1.00133 2.996 1860 400000 212673 135 32026 32365 7635100.7 500 0.0040778768 0 0 0 148809 9.9990631 0 0 0 0 0 0 0 0 0.765014 31135 +1.00133 1.00133 2.997 1867 400000 212702 280 46010 46319 2.9112342e+08 800 0.00015247829 0 0 0 154952 9.9990631 0 0 0 0 0 0 0 0 0.765802 44390 +1.00133 1.00133 2.998 1870 400000 212709 6 9616 9783 61746497 800 0.00015247829 0 0 0 152313 9.9990631 0 0 0 0 0 0 0 0 0.765923 9415 +1.00133 1.00133 2.999 1874 400000 215083 566 61699 61812 3.8816019e+08 800 0.00015247829 0 0 0 157259 9.9990631 0 0 0 0 0 0 0 0 0.774605 59186 +1.00133 1.00133 3 1877 400000 212531 131 28126 28471 1.793698e+08 800 0.00015247829 0 0 0 158718 9.9990631 0 0 0 0 0 0 0 0 0.765256 27350 +1.00133 1.00133 3.001 1881 400000 210612 90 25258 25560 56075941 700 0.00043742467 0 0 0 150228 9.9990631 0 0 0 0 0 0 0 0 0.758256 24529 +1.00133 1.00133 3.002 1887 400000 212932 11 11921 12101 2856788.9 500 0.0040780053 0 0 0 158781 9.9990631 0 0 0 0 0 0 0 0 0.766295 11650 +1.00133 1.00133 3.003 1892 400000 210836 28 14863 15094 1156984.5 400 0.012563695 0 0 0 155849 9.9990631 0 0 0 0 0 0 0 0 0.758871 14536 +1.00133 1.00133 3.004 1896 400000 208124 67 20011 20292 560024.73 300 0.034818105 0 0 0 153364 9.9990631 0 0 0 0 0 0 0 0 0.749535 19499 +1.00133 1.00133 3.005 1899 400000 208376 14 11159 11342 313802.26 300 0.034818105 0 0 0 154939 9.9990631 0 0 0 0 0 0 0 0 0.750745 10926 +1.00133 1.00133 3.006 1904 400000 211018 76 22310 22585 202350.87 200 0.10726912 0 0 0 149825 9.9990631 0 0 0 0 0 0 0 0 0.755339 21706 +1.00133 1.00133 3.007 1907 400000 212226 42 15902 16131 144505.7 200 0.10726912 0 0 0 152900 9.9990436 0 0 0 0 0 0 0 0 0.759828 15501 +1.00133 1.00133 3.008 1910 400000 212296 24 12210 12361 111131.7 200 0.10726912 0 0 0 151527 9.9990631 0 0 0 0 0 0 0 0 0.760047 11921 +1.00133 1.00133 3.009 1913 400000 209527 8 9632 9798 87984.313 200 0.10726912 0 0 0 148857 9.9990631 0 0 0 0 0 0 0 0 0.750712 9438 +1.00133 1.00133 3.01 1916 400000 208222 11 7826 7982 71409.18 200 0.10726912 0 0 0 154517 9.9990631 0 0 0 0 0 0 0 0 0.746763 7660 +1.00133 1.00133 3.011 1921 400000 208400 47 19339 19599 58095.459 100 0.32458647 0 0 0 150084 9.9990631 0 0 0 0 0 0 0 0 0.747176 18857 +1.00133 1.00133 3.012 1924 400000 209593 31 15753 15981 47466.551 100 0.32458647 0 0 0 151502 9.9990436 0 0 0 0 0 0 0 0 0.751307 15407 +1.00133 1.00133 3.013 1927 400000 212445 29 13687 13903 41073.801 100 0.32458647 0 0 0 155487 9.9990631 0 0 0 0 0 0 0 0 0.761379 13332 +1.00133 1.00133 3.014 1930 400000 212388 21 11838 12036 35611.466 100 0.32458647 0 0 0 153023 9.9990631 0 0 0 0 0 0 0 0 0.761351 11559 +1.00133 1.00133 3.015 1933 400000 213902 11 10405 10548 31390.711 100 0.32458647 0 0 0 155386 9.9990631 0 0 0 0 0 0 0 0 0.767174 10189 +1.00133 1.00133 3.016 1937 400000 211560 12 8774 8902 26430.553 100 0.32458647 0 0 0 152932 9.9990631 0 0 0 0 0 0 0 0 0.75907 8579 +1.00133 1.00133 3.017 1940 400000 210798 9 7581 7688 22841.371 100 0.32458647 0 0 0 153925 9.9990631 0 0 0 0 0 0 0 0 0.755881 7414 +1.00133 1.00133 3.018 1944 400000 208867 45 19645 19861 19130 0 1 0 0 0 151642 9.9990436 0 0 0 0 0 0 0 0 0.74935 19130 +1.00133 1.00133 3.019 1947 400000 211771 50 18029 18309 17572 0 1 0 0 0 155935 9.9990631 0 0 0 0 0 0 0 0 0.759381 17572 +1.00133 1.00133 3.02 1951 400000 211499 48 16130 16343 15733 0 1 0 0 0 154153 9.9990631 0 0 0 0 0 0 0 0 0.758782 15733 +1.00133 1.00133 3.021 1954 400000 213865 32 14689 14891 14316 0 1 0 0 0 151453 9.9990631 0 0 0 0 0 0 0 0 0.767057 14316 +1.00133 1.00133 3.022 1957 400000 215906 22 13372 13594 13003 0 1 0 0 0 152339 9.9990436 0 0 0 0 0 0 0 0 0.774726 13003 +1.00133 1.00133 3.023 1960 400000 216466 19 12229 12439 11913 0 1 0 0 0 152965 9.9990436 0 0 0 0 0 0 0 0 0.777313 11913 +1.00133 1.00133 3.024 1963 400000 219405 15 11558 11766 11301 0 1 0 0 0 157917 9.9990436 0 0 0 0 0 0 0 0 0.788551 11301 +1.00133 1.00133 3.025 1966 400000 215564 14 10139 10365 9915 0 1 0 0 0 154074 9.9990436 0 0 0 0 0 0 0 0 0.773809 9915 +1.00133 1.00133 3.026 1970 400000 215655 11 9282 9433 9065 0 1 0 0 0 155111 9.9990631 0 0 0 0 0 0 0 0 0.77446 9065 +1.00133 1.00133 3.027 1973 400000 213613 7 8603 8773 8423 0 1 0 0 0 154231 9.9990436 0 0 0 0 0 0 0 0 0.767254 8423 +1.00133 1.00133 3.028 1976 400000 214817 9 7891 8044 7697 0 1 0 0 0 154939 9.9990631 0 0 0 0 0 0 0 0 0.771787 7697 +1.00133 1.00133 3.029 1979 400000 216578 15 7523 7664 7332 0 1 0 0 0 154421 9.9990631 0 0 0 0 0 0 0 0 0.77903 7332 +1.00133 1.00133 3.03 1982 400000 213631 8 6924 7046 6754 0 1 0 0 0 152217 9.9990631 0 0 0 0 0 0 0 0 0.76721 6754 +1.00133 1.00133 3.031 1986 400000 213598 5 6484 6600 6357 0 1 0 0 0 155069 9.9990631 0 0 0 0 0 0 0 0 0.767029 6357 +1.00133 1.00133 3.032 1989 400000 211047 4 6019 6132 5903 0 1 0 0 0 149947 9.9990631 0 0 0 0 0 0 0 0 0.75835 5903 +1.00133 1.00133 3.033 1992 400000 213140 2 5622 5732 5518 0 1 0 0 0 151626 9.9990631 0 0 0 0 0 0 0 0 0.766317 5518 +1.00133 1.00133 3.034 1995 400000 213250 2 5299 5392 5197 0 1 0 0 0 151245 9.9990436 0 0 0 0 0 0 0 0 0.767067 5197 +#C Wed Jun 16 18:57:54 2010. Scan aborted after 185 points. diff --git a/examples/resources/img69.gif b/examples/resources/img69.gif new file mode 100644 index 00000000..dee5da3a Binary files /dev/null and b/examples/resources/img69.gif differ diff --git a/examples/resources/k4cv.png b/examples/resources/k4cv.png new file mode 100755 index 00000000..a9ef92ca Binary files /dev/null and b/examples/resources/k4cv.png differ diff --git a/examples/tardis_example.ipynb b/examples/tardis_example.ipynb deleted file mode 100644 index 6ef7a93b..00000000 --- a/examples/tardis_example.ipynb +++ /dev/null @@ -1,690 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TARDIS Configuration\n", - "\n", - "* using the E6C geometry from libhkl\n", - " * @cmazzoli found that, in this geometry, with the \"lifting_detector_mu\" mode, the following mapping applies:\n", - " \n", - "| libhkl | TARDIS |\n", - "| :---: | :---: |\n", - "| mu | theta |\n", - "| gamma | delta |\n", - "| delta | gamma |\n", - "| phi | None |\n", - "| chi | None |\n", - "| omega | None |\n", - "\n", - "* The diffractometer geometry with angle and axis definitions are depicted below\n", - "\n", - "\n", - "\n", - "\n", - "* see [here](https://people.debian.org/~picca/hkl/diffractometers/e6c.html) for further documentation on libhkl\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Begin by instantiating a calculation engine of the appropriate geometry, and configuring its mode as __lifting_detector_mu__ " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available modes = ['bissector_vertical', 'constant_omega_vertical', 'constant_chi_vertical', 'constant_phi_vertical', 'lifting_detector_phi', 'lifting_detector_omega', 'lifting_detector_mu', 'double_diffraction_vertical', 'bissector_horizontal', 'double_diffraction_horizontal', 'psi_constant_vertical', 'psi_constant_horizontal', 'constant_mu_horizontal']\n", - "\n", - "physical axes = OrderedDict([('mu', 0.0), ('omega', 0.0), ('chi', 0.0), ('phi', 0.0), ('gamma', 0.0), ('delta', 0.0)])\n", - "\n", - "pseudo axes = OrderedDict([('h', 0.0), ('k', 0.0), ('l', 0.0)])\n" - ] - } - ], - "source": [ - "from hkl.calc import CalcE6C\n", - "\n", - "tardis_calc = CalcE6C()\n", - "\n", - "# what modes are available?\n", - "print('Available modes =', tardis_calc.engine.modes)\n", - "print('\\nphysical axes =', tardis_calc.physical_axes)\n", - "print('\\npseudo axes =', tardis_calc.pseudo_axes)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "tardis_calc.engine.mode = 'lifting_detector_mu'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, seed the calculation engine with a parameterized sample and wavelength (or energy).\n", - "\n", - "**NOTE**: length units are in Angstrom, angles are in degrees, and energy is in keV." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HklSample(name='sample1', lattice=LatticeTuple(a=9.069, b=9.069, c=10.39, alpha=90.0, beta=90.0, gamma=119.99999999999999), ux=Parameter(name='ux', limits=(-180.0, 180.0), value=0.0, fit=True, units='Degree'), uy=Parameter(name='uy', limits=(-180.0, 180.0), value=0.0, fit=True, units='Degree'), uz=Parameter(name='uz', limits=(-180.0, 180.0), value=0.0, fit=True, units='Degree'), U=array([[ 1., 0., 0.],\n", - " [ 0., 1., 0.],\n", - " [ 0., 0., 1.]]), UB=array([[ 7.99999720e-01, 3.99999860e-01, -6.41365809e-17],\n", - " [ 0.00000000e+00, 6.92820080e-01, -6.41365809e-17],\n", - " [ 0.00000000e+00, 0.00000000e+00, 6.04733908e-01]]), reflections=[], reflection_measured_angles=array([], shape=(0, 0), dtype=float64), reflection_theoretical_angles=array([], shape=(0, 0), dtype=float64))\n" - ] - } - ], - "source": [ - "from hkl.util import Lattice\n", - "\n", - "# lattice cell lengths are in Angstrom, angles are in degrees\n", - "lattice = Lattice(a=9.069, b=9.069, c=10.390, alpha=90.0, beta=90.0, gamma=120.0)\n", - "sample = tardis_calc.new_sample('sample1', lattice=lattice)\n", - "\n", - "print(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Energy = 0.7691422970508319 keV\n" - ] - } - ], - "source": [ - "tardis_calc.wavelength = 1.61198 # in Angstrom\n", - "\n", - "# just to check\n", - "print('Energy =', tardis_calc.energy, 'keV')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, apply constraints appropriate for TARDIS' geometry. This includes setting limits on the acceptable ranges of motion, initial (and constant!) values, and whether or not a particular axis should be factored into the fitting function that produces the forward and inverse solutions.\n", - "\n", - "**NOTE**: physical motors should be checked that limits are in place prior to initiating any motion. Note also that none of the calculations below are associated with any physical motors, and that there is no connection between \"limit\" values used in the calculation, and soft-limit values that may be present in a control system for physical motors." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Theta\n", - "mu = tardis_calc['mu']\n", - "mu.limits = (-181, 181)\n", - "mu.value = 0\n", - "mu.fit = True\n", - "\n", - "# we don't have it. Fix to 0\n", - "phi = tardis_calc['phi']\n", - "phi.limits = (0, 0)\n", - "phi.value = 0\n", - "phi.fit = False\n", - "\n", - "# we don't have it. Fix to 0\n", - "chi = tardis_calc['chi']\n", - "chi.limits = (0, 0)\n", - "chi.value = 0\n", - "chi.fit = False\n", - "\n", - "# we don't have it!! Fix to 0\n", - "omega = tardis_calc['omega']\n", - "omega.limits = (0, 0)\n", - "omega.value = 0\n", - "omega.fit = False\n", - "\n", - "# Attention naming convention inverted at the detector stages!\n", - "# delta\n", - "gamma = tardis_calc['gamma']\n", - "gamma.limits = (-5, 180)\n", - "gamma.value = 0\n", - "gamma.fit = True\n", - "\n", - "# gamma\n", - "delta = tardis_calc['delta']\n", - "delta.limits = (-5, 180)\n", - "delta.value = 0\n", - "delta.fit = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can take a look at the UB matrix, but thus far, it won't be very interesting" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 7.99999720e-01, 3.99999860e-01, -6.41365809e-17],\n", - " [ 0.00000000e+00, 6.92820080e-01, -6.41365809e-17],\n", - " [ 0.00000000e+00, 0.00000000e+00, 6.04733908e-01]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tardis_calc.sample.UB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add two, known reflections and the motor positions associated with those hkl values.\n", - "Here, we are using values from @cmazolli's ESRF notes:\n", - "\n", - "```\n", - "(3,3,0): del = 64.449, gam = -0.871, th = 25.285\n", - "(5,2,0): del = 79.712, gam = -1.374, th = 46.816\n", - "```\n", - "\n", - "**NOTE**: the translation of gamma==delta, delta==gamma, and mu==theta is being used" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "r1 = tardis_calc.sample.add_reflection(3, 3, 0, \n", - " position=tardis_calc.Position(gamma=64.449, mu=25.285, chi=0.0, phi=0.0, omega=0.0, delta=-0.871))\n", - "\n", - "r2 = tardis_calc.sample.add_reflection(5, 2, 0,\n", - " position=tardis_calc.Position(gamma=79.712, mu=46.816, chi=0.0, phi=0.0, omega=0.0, delta=-1.374))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(3.0, 3.0, 0.0), (5.0, 2.0, 0.0)]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tardis_calc.sample.reflections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now a UB matrix can be computed." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tardis_calc.sample.compute_UB(r1, r2)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.31323551, -0.4807593 , 0.01113654],\n", - " [ 0.73590724, 0.63942704, 0.01003773],\n", - " [-0.01798898, -0.00176066, 0.60454803]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tardis_calc.sample.UB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare some libhkl-generated results with those from @cmazolli's notes:\n", - "\n", - "```python\n", - "# Experimentally found reflections @ Lambda = 1.61198 A\n", - "# (4, 4, 0) = [90.628, 38.373, 0, 0, 0, -1.156]\n", - "# (4, 1, 0) = [56.100, 40.220, 0, 0, 0, -1.091]\n", - "# @ Lambda = 1.60911\n", - "# (6, 0, 0) = [75.900, 61.000, 0, 0, 0, -1.637]\n", - "# @ Lambda = 1.60954\n", - "# (3, 2, 0) = [53.090, 26.144, 0, 0, 0, -.933]\n", - "# (5, 4, 0) = [106.415, 49.900, 0, 0, 0, -1.535]\n", - "# (4, 5, 0) = [106.403, 42.586, 0, 0, 0, -1.183]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(PosCalcE6C(mu=38.37622128052063, omega=0.0, chi=0.0, phi=0.0, gamma=90.63030469353308, delta=-1.1613181970939916),)\n" - ] - } - ], - "source": [ - "print(tardis_calc.forward((4,4,0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(PosCalcE6C(mu=40.21991977757096, omega=0.0, chi=0.0, phi=0.0, gamma=56.09704093977082, delta=-1.083660865503293),)\n" - ] - } - ], - "source": [ - "print(tardis_calc.forward((4,1,0)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Change wavelength here to 1.60911 Angstrom.\n", - "Note the difference below in `delta` (TARDIS' gamma axis)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(PosCalcE6C(mu=60.99346591074179, omega=0.0, chi=0.0, phi=0.0, gamma=75.84521749189147, delta=-1.5839501607961701),)\n" - ] - } - ], - "source": [ - "# change wavelength\n", - "tardis_calc.wavelength = 1.60911\n", - "print(tardis_calc.forward((6,0,0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(PosCalcE6C(mu=26.173823521308144, omega=0.0, chi=0.0, phi=0.0, gamma=53.05207622287554, delta=-0.8437995840438257),)\n", - "(PosCalcE6C(mu=49.892322604056034, omega=0.0, chi=0.0, phi=0.0, gamma=106.32053081067252, delta=-1.423656049079967),)\n", - "(PosCalcE6C(mu=42.54926633295045, omega=0.0, chi=0.0, phi=0.0, gamma=106.31894239326303, delta=-1.1854071532601609),)\n" - ] - } - ], - "source": [ - "tardis_calc.wavelength = 1.60954\n", - "print(tardis_calc.forward((3,2,0)))\n", - "print(tardis_calc.forward((5,4,0)))\n", - "print(tardis_calc.forward((4,5,0)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# HKL PseudoPositioner Use\n", - "\n", - "Let's explore the idea of an hkl 'motor'" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;31m# re-map Tardis' axis names onto what an E6C expects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mname_map\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'mu'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'theta'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'omega'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'omega'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'chi'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'chi'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'phi'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'phi'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gamma'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'delta'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'delta'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'gamma'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mtardis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mphysical_axis_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname_map\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m/home/dchabot/Dropbox/workspace/hklpy/hkl/calc.py\u001b[0m in \u001b[0;36mphysical_axis_names\u001b[1;34m(self, axis_name_map)\u001b[0m\n\u001b[0;32m 231\u001b[0m '''\n\u001b[0;32m 232\u001b[0m \u001b[1;31m# make sure re-map names are 1-to-1 with the engine's expectations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis_name_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mphysical_axis_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 235\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axis_name_map\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mphysical_axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "from ophyd import Component as Cpt\n", - "from ophyd import (PseudoSingle, EpicsMotor)\n", - "from hkl.diffract import E6C\n", - "\n", - "\n", - "class Tardis(E6C):\n", - " h = Cpt(PseudoSingle, '')\n", - " k = Cpt(PseudoSingle, '')\n", - " l = Cpt(PseudoSingle, '')\n", - " \n", - " theta = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X1}Mtr')\n", - " omega = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X2}Mtr')\n", - " chi = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X3}Mtr')\n", - " phi = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X4}Mtr')\n", - " delta = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X5}Mtr')\n", - " gamma = Cpt(EpicsMotor, 'XF:31IDA-OP{Tbl-Ax:X6}Mtr')\n", - " \n", - "# FIXME: hack to get around what should have been done at init of tardis_calc instance\n", - "tardis_calc._lock_engine = True\n", - "\n", - "tardis = Tardis('', name='tardis', calc_inst=tardis_calc, energy=tardis_calc.energy)\n", - "\n", - "# re-map Tardis' axis names onto what an E6C expects\n", - "name_map = {'mu': 'theta', 'omega': 'omega', 'chi': 'chi', 'phi': 'phi', 'gamma': 'delta', 'delta': 'gamma'}\n", - "tardis.calc.physical_axis_names = name_map" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "Item 0: Must be number, not NoneType", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtardis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m/home/dchabot/Dropbox/workspace/ophyd_hkl/ophyd/pseudopos.py\u001b[0m in \u001b[0;36mposition\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 294\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mposition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[1;34m'''Pseudo motor position namedtuple'''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal_position\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 298\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/dchabot/Dropbox/workspace/hklpy/hkl/diffract.py\u001b[0m in \u001b[0;36minverse\u001b[1;34m(self, real)\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minverse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m \u001b[0mpseudo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_calc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 101\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPseudoPosition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpseudo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/dchabot/Dropbox/workspace/hklpy/hkl/calc.py\u001b[0m in \u001b[0;36minverse\u001b[1;34m(self, real)\u001b[0m\n\u001b[0;32m 319\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mphysical_positions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreal\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 322\u001b[0m \u001b[1;31m# self.update() # done implicitly in setter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpseudo_positions\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/dchabot/Dropbox/workspace/hklpy/hkl/calc.py\u001b[0m in \u001b[0;36mphysical_positions\u001b[1;34m(self, positions)\u001b[0m\n\u001b[0;32m 244\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mphysical_positions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[1;31m# Set the physical motor positions and calculate the pseudo ones\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 246\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_geometry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis_values_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_units\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 247\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: Item 0: Must be number, not NoneType" - ] - } - ], - "source": [ - "tardis.position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print('Energy =', tardis.energy, 'keV')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.move((1,0,1), wait=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "status = _" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "status.done" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.real_position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#tardis.set((1,0,2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.h.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.h.read()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.read()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tardis.real_position" - ] - } - ], - "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": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/tst_e4cv_fourc.ipynb b/examples/tst_e4cv_fourc.ipynb new file mode 100644 index 00000000..1680f185 --- /dev/null +++ b/examples/tst_e4cv_fourc.ipynb @@ -0,0 +1,556 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.8.2 64-bit (conda)", + "metadata": { + "interpreter": { + "hash": "65c0bedf0ae9353f4900a680b37c84df15903be9a31564afd11c054f1ae7fe55" + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Compare E4CV 4-circle orientation with SPEC fourc\n", + "\n", + "Following the E4CV example (consult the example for geometry\n", + "details), compare the orientation matix and\n", + "positioning operations with *hklpy* (and *libhkl*) and *SPEC*.\n", + "\n", + "Information from a SPEC data file will be used for the comparison.\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "In SPEC *fourc* geometry (https://certif.com/spec_help/fourc.html):\n", + "\n", + "name | mnemonic | description\n", + "----- | ----- | -----\n", + "2theta | tth | Detector arm rotation\n", + "Omega | om | Rotates sample circles\n", + "Chi | chi | Sample tilt\n", + "Phi | phi | Sample rotation\n", + "\n", + "The provided SPEC data file names these motors: `tth`, `th`, `chi`, `phi`\n", + "so this example will use the same names to help the comparison." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# mapping of axis names between hklpy and SPEC\n", + "AXIS_NAME_MAP = dict(\n", + " # E4CV fourc\n", + " tth='tth', # Detector arm rotation\n", + " omega='th', # Rotates chi around horizontal axis\n", + " chi='chi', # TODO: Rotates phi around beam axis # TODO: is this correct?\n", + " phi='phi', # Sample rotation around horizontal axis (when phi is co-linear with omega)\n", + ")" + ] + }, + { + "source": [ + "## Read the SPEC scan from the data file\n", + "\n", + "The SPEC file provides all the information needed here. The\n", + "[*spec2nexus*](https://github.com/prjemian/spec2nexus) \n", + "(python) package can read the file and parse the content into useful \n", + "structures, including deducing the diffractometer geometry in many cases." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "import pyRestTable\n", + "from spec2nexus.spec import SpecDataFile\n", + "\n", + "specfile = SpecDataFile(\"resources/LNO_LAO_s14.dat\")\n", + "specscan = specfile.getScan(14)\n", + "\n", + "spec_d = specscan.diffractometer\n", + "spec_d.UB = spec_d.geometry_parameters[\"ub_matrix\"][2]\n", + "\n", + "terms = {\n", + " \"SPEC file\": specfile.specFile,\n", + " \"scan #\": specscan.scanNum,\n", + " \"SPEC scanCmd\": specscan.scanCmd,\n", + " \"geometry\": spec_d.geometry_name,\n", + " \"mode\": spec_d.mode,\n", + " \"lattice\": spec_d.lattice,\n", + " \"wavelength\": spec_d.wavelength,\n", + " \"reflection 1\": spec_d.reflections[0],\n", + " \"reflection 2\": spec_d.reflections[1],\n", + " \"[UB]\": spec_d.UB,\n", + "}\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "for k, v in terms.items():\n", + " tbl.addRow((k, v))\n", + "print(tbl)" + ], + "cell_type": "code", + "metadata": {}, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============ =======================================================================================================================================================\nterm value \n============ =======================================================================================================================================================\nSPEC file LNO_LAO \nscan # 14 \nSPEC scanCmd hklscan 1.00133 1.00133 1.00133 1.00133 2.85 3.05 200 -400000 \ngeometry fourc \nmode Omega equals zero \nlattice LatticeParameters(a=3.781726143, b=3.791444574, c=3.79890313, alpha=90.2546203, beta=90.01815424, gamma=89.89967858) \nwavelength 1.239424258 \nreflection 1 Reflections(h=0.0, k=0.0, l=2.0, wavelength=1.239424258, angles=OrderedDict([('tth', 38.09875), ('th', 19.1335), ('chi', 90.0135), ('phi', 0.0)])) \nreflection 2 Reflections(h=1.0, k=1.0, l=3.0, wavelength=1.239424258, angles=OrderedDict([('tth', 65.644), ('th', 32.82125), ('chi', 115.23625), ('phi', 48.1315)]))\n[UB] [[-1.65871244e+00 9.82002413e-02 -3.89705578e-04] \n [-9.55499031e-02 -1.65427863e+00 2.42844486e-03] \n [ 2.62981891e-04 9.81574682e-03 1.65396181e+00]] \n============ =======================================================================================================================================================\n\n" + ] + } + ] + }, + { + "source": [ + "## Plot the (_hkl_) trajectories in the scan" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-12-16T22:22:40.342986\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# plot the h, k, & l vs. point number\n", + "fig, axes = plt.subplots(3, 1, figsize=(12, 6))\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.2)\n", + "\n", + "plt.suptitle('Desired HKL trajectory')\n", + "axes[0].plot(specscan.data[\"H\"])\n", + "axes[0].set_title(\"h\")\n", + "axes[1].plot(specscan.data[\"K\"])\n", + "axes[1].set_title(\"k\")\n", + "axes[2].plot(specscan.data[\"L\"])\n", + "axes[2].set_title(\"l\")\n", + "plt.show()" + ] + }, + { + "source": [ + "## Setup the *E4CV* diffractometer in *hklpy*" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.diffract import E4CV\n", + "from hkl.util import Lattice\n", + "\n", + "from ophyd import (PseudoSingle, SoftPositioner)\n", + "from ophyd import Component as Cpt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class Diffractometer(E4CV):\n", + " h = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " k = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " l = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + "\n", + " # use the SPEC axis names here\n", + " th = Cpt(SoftPositioner, kind=\"hinted\")\n", + " chi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " phi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " tth = Cpt(SoftPositioner, kind=\"hinted\")\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fourc = Diffractometer(\"\", name=\"fourc\")\n", + "fourc.calc.physical_axis_names = {\n", + " # E4CV: local\n", + " 'omega': 'th',\n", + " 'chi': 'chi',\n", + " 'phi': 'phi',\n", + " 'tth': 'tth',\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='LNO_LAO', lattice=LatticeTuple(a=3.781726143, b=3.791444574, c=3.79890313, alpha=90.2546203, beta=90.01815424, gamma=89.89967858), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.66146225e+00, -2.89938471e-03, 5.11196668e-04],\n", + " [ 0.00000000e+00, 1.65721725e+00, 7.34922202e-03],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.65394723e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "# add the sample to the calculation engine\n", + "fourc.calc.new_sample(\n", + " specfile.specFile,\n", + " lattice=Lattice(\n", + " a=spec_d.lattice.a, \n", + " b=spec_d.lattice.b, \n", + " c=spec_d.lattice.c,\n", + " alpha=spec_d.lattice.alpha, \n", + " beta=spec_d.lattice.beta, \n", + " gamma=spec_d.lattice.gamma)\n", + " )" + ] + }, + { + "source": [ + "## Test *hklpy* with the UB orientation matrix from *SPEC*\n", + "\n", + "Set the UB matrix as provided in the SPEC data file." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[-1.65871244e+00 9.82002413e-02 -3.89705578e-04]\n", + " [-9.55499031e-02 -1.65427863e+00 2.42844486e-03]\n", + " [ 2.62981891e-04 9.81574682e-03 1.65396181e+00]]\n", + "[[-9.55499053e-02 -1.65427875e+00 2.42825603e-03]\n", + " [ 2.63161907e-04 9.81566638e-03 1.65396189e+00]\n", + " [-1.65871254e+00 9.82003048e-02 -3.89644168e-04]]\n", + "(002) : PosCalcE4CV(th=23.915206114844626, chi=89.91480547663566, phi=99.11611601380724, tth=47.83041222968925)\n", + "(113) : PosCalcE4CV(th=42.33129428600627, chi=115.20291094237979, phi=48.133061440101486, tth=84.66258857201254)\n" + ] + } + ], + "source": [ + "# get the UB matrix from the SPEC data\n", + "# SPEC's UB first row moved (via numpy slicing) to last row for hklpy\n", + "fourc.UB.put(spec_d.UB[[1,2,0], :])\n", + "print(spec_d.UB)\n", + "print(fourc.UB.get())\n", + "\n", + "# calculate angles with hklpy using the SPEC UB matrix\n", + "fourc.engine.mode = \"bissector\"\n", + "fourc.calc[\"phi\"].limits = (-50, 100)\n", + "fourc.calc[\"tth\"].limits = (-2, 180)\n", + "print(\"(002) :\", fourc.forward((0, 0, 2)))\n", + "print(\"(113) :\", fourc.forward((1, 1, 3)))" + ] + }, + { + "source": [ + "Define a custom repoting function to format the output table." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def add_ref_to_table(tbl, r):\n", + " sol = fourc.forward((r.h, r.k, r.l))\n", + " nm = f\"{r.h:.0f} {r.k:.0f} {r.l:.0f}\"\n", + " for sm in AXIS_NAME_MAP.values():\n", + " row = [f\"({nm})\", sm]\n", + " row.append(f\"{getattr(sol, sm):.5f}\")\n", + " row.append(f\"{r.angles[sm]:.5f}\")\n", + " tbl.addRow(row)" + ] + }, + { + "source": [ + "For each of the orientation reflections used in the SPEC file, report the computed motor positions for each reflection for E4CV and SPEC.\n", + "\n", + "In this case, the two reflections cannot be reached if the same\n", + "diffractometer is used for both. Set the mode for each reflection." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======= ===== ========= =========\n", + "(hkl) motor E4CV SPEC \n", + "======= ===== ========= =========\n", + "(0 0 2) tth 47.83041 38.09875 \n", + "(0 0 2) th 23.99932 19.13350 \n", + "(0 0 2) chi 90.01350 90.01350 \n", + "(0 0 2) phi 0.00000 0.00000 \n", + "(1 1 3) tth 84.66259 65.64400 \n", + "(1 1 3) th 42.33129 32.82125 \n", + "(1 1 3) chi 115.20291 115.23625\n", + "(1 1 3) phi 48.13306 48.13150 \n", + "======= ===== ========= =========\n", + "\n" + ] + } + ], + "source": [ + "# Compare these angles with those from SPEC\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"(hkl) motor E4CV SPEC\".split()\n", + "r1, r2 = spec_d.reflections\n", + "fourc.calc[\"tth\"].limits = (-2, 180)\n", + "\n", + "fourc.engine.mode = \"constant_phi\"\n", + "fourc.phi.move(0)\n", + "add_ref_to_table(tbl, r1)\n", + "\n", + "fourc.engine.mode = \"bissector\"\n", + "add_ref_to_table(tbl, r2)\n", + "\n", + "print(tbl)\n", + "\n", + "# FIXME: (113) `th` values do not match." + ] + }, + { + "source": [ + "## Setup the UB orientation matrix using *hklpy*\n", + "\n", + "Compute the UB matrix using *hklpy* (& *libhkl*)." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "========= ===================================================\n", + "term value \n", + "========= ===================================================\n", + "SPEC [UB] [[-1.65871244e+00 9.82002413e-02 -3.89705578e-04] \n", + " [-9.55499031e-02 -1.65427863e+00 2.42844486e-03] \n", + " [ 2.62981891e-04 9.81574682e-03 1.65396181e+00]]\n", + "E4CV [UB] [[-9.55498634e-02 -1.65427875e+00 2.42844498e-03] \n", + " [ 2.63111155e-04 9.81585901e-03 1.65396189e+00] \n", + " [-1.65871254e+00 9.82002627e-02 -3.89705597e-04]]\n", + "========= ===================================================\n", + "\n" + ] + } + ], + "source": [ + "fourc.calc.wavelength = 1.239424258 # Angstrom\n", + "\n", + "refs = [\n", + " fourc.calc.sample.add_reflection(\n", + " r.h, r.k, r.l, \n", + " position=fourc.calc.Position(\n", + " tth=r.angles[\"tth\"],\n", + " th=r.angles[\"th\"],\n", + " chi=r.angles[\"chi\"],\n", + " phi=r.angles[\"phi\"],\n", + " )\n", + " )\n", + " for r in spec_d.reflections\n", + "]\n", + "\n", + "fourc.calc.sample.compute_UB(*refs)\n", + "\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"SPEC [UB]\", spec_d.UB))\n", + "tbl.addRow((\"E4CV [UB]\", fourc.UB.get()))\n", + "print(tbl)" + ] + }, + { + "source": [ + "Report the results, as before, and compare with table above." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================== =========================================================================\n", + "term value \n", + "==================== =========================================================================\n", + "energy, keV 1.0003370452024831 \n", + "wavelength, angstrom 1.239424258 \n", + "position DiffractometerPseudoPos(h=-0.0, k=0.0, l=0.0) \n", + "sample name LNO_LAO \n", + "[U] [[-5.75094968e-02 -9.98327391e-01 5.92267768e-03] \n", + " [ 1.58361191e-04 5.92337392e-03 9.99982444e-01] \n", + " [-9.98344947e-01 5.75094251e-02 -1.82553939e-04]] \n", + "[UB] [[-9.55498634e-02 -1.65427875e+00 2.42844498e-03] \n", + " [ 2.63111155e-04 9.81585901e-03 1.65396189e+00] \n", + " [-1.65871254e+00 9.82002627e-02 -3.89705597e-04]] \n", + "lattice [ 3.78172593 3.7914443 3.79890295 90.25465556 90.01815877 89.89967654]\n", + "==================== =========================================================================\n", + "\n", + "sample\tHklSample(name='LNO_LAO', lattice=LatticeTuple(a=3.781725931569308, b=3.79144430103082, c=3.798902949497184, alpha=90.25465555509926, beta=90.01815876717824, gamma=89.89967653973522), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-90.01045975373877, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.3393464183946019, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=93.2969283549115, fit=True, inverted=False, units='Degree'), U=array([[-5.75094968e-02, -9.98327391e-01, 5.92267768e-03],\n", + " [ 1.58361191e-04, 5.92337392e-03, 9.99982444e-01],\n", + " [-9.98344947e-01, 5.75094251e-02, -1.82553939e-04]]), UB=array([[-9.55498634e-02, -1.65427875e+00, 2.42844498e-03],\n", + " [ 2.63111155e-04, 9.81585901e-03, 1.65396189e+00],\n", + " [-1.65871254e+00, 9.82002627e-02, -3.89705597e-04]]), reflections=[(h=0.0, k=0.0, l=2.0), (h=1.0, k=1.0, l=3.0)], reflection_measured_angles=array([[0. , 0.44139322],\n", + " [0.44139322, 0. ]]), reflection_theoretical_angles=array([[0. , 0.44081129],\n", + " [0.44081129, 0. ]]))\n" + ] + } + ], + "source": [ + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"energy, keV\", fourc.calc.energy))\n", + "tbl.addRow((\"wavelength, angstrom\", fourc.calc.wavelength))\n", + "tbl.addRow((\"position\", fourc.position))\n", + "tbl.addRow((\"sample name\", fourc.sample_name.get()))\n", + "tbl.addRow((\"[U]\", fourc.U.get()))\n", + "tbl.addRow((\"[UB]\", fourc.UB.get()))\n", + "tbl.addRow((\"lattice\", fourc.lattice.get()))\n", + "print(tbl)\n", + "\n", + "print(f\"sample\\t{fourc.calc.sample}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======= ===== ========= =========\n", + "(hkl) motor E4CV SPEC \n", + "======= ===== ========= =========\n", + "(0 0 2) tth 38.08407 38.09875 \n", + "(0 0 2) th 19.12616 19.13350 \n", + "(0 0 2) chi 90.01350 90.01350 \n", + "(0 0 2) phi 0.00000 0.00000 \n", + "(1 1 3) tth 65.63700 65.64400 \n", + "(1 1 3) th 32.81850 32.82125 \n", + "(1 1 3) chi 115.20291 115.23625\n", + "(1 1 3) phi 48.13305 48.13150 \n", + "======= ===== ========= =========\n", + "\n" + ] + } + ], + "source": [ + "# Compare these angles with those from SPEC\n", + "# fourc.calc[\"phi\"].limits = (-1, 100)\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"(hkl) motor E4CV SPEC\".split()\n", + "r1, r2 = spec_d.reflections\n", + "fourc.calc[\"tth\"].limits = (-2, 180)\n", + "\n", + "fourc.engine.mode = \"constant_phi\"\n", + "fourc.phi.move(0)\n", + "add_ref_to_table(tbl, r1)\n", + "\n", + "fourc.engine.mode = \"bissector\"\n", + "add_ref_to_table(tbl, r2)\n", + "\n", + "print(tbl)" + ] + } + ] +} \ No newline at end of file diff --git a/examples/tst_e6c_test_calculations.ipynb b/examples/tst_e6c_test_calculations.ipynb new file mode 100644 index 00000000..ae8288a7 --- /dev/null +++ b/examples/tst_e6c_test_calculations.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Non-exhaustive test of E6C calculations\n", + "\n", + "Verify *hklpy* (from its interface to the *hkl* library) computations\n", + "of orientation, U, UB, and rotation directions.\n", + "\n", + "With the aid of Yong Chu's mental math.\n", + "\n", + "[TL;DR](https://www.merriam-webster.com/dictionary/TL%3BDR) appears to function as documented and as expected\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples\n", + "\n", + "### Import the Python libraries needed" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import gi\n", + "gi.require_version('Hkl', '5.0')\n", + "from hkl.calc import CalcE6C\n", + "from hkl.util import Lattice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize the calculation engine" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calc = CalcE6C(engine='hkl')\n", + "calc.engine.mode = 'constant_chi_vertical'\n", + "calc.wavelength = 1. # Angstrom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the crystal lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "lattice LatticeTuple(a=1.0, b=1.0, c=1.0, alpha=90.0, beta=90.0, gamma=90.0)\nphysical axes OrderedDict([('mu', 0.0), ('omega', 0.0), ('chi', 0.0), ('phi', 0.0), ('gamma', 0.0), ('delta', 0.0)])\npseudo axes OrderedDict([('h', 0.0), ('k', 0.0), ('l', 0.0)])\nomega parameter is CalcParameter(name='omega', limits=(-180.0, 180.0), value=0.0, fit=True, inverted=False, units='Degree')\n" + ] + } + ], + "source": [ + "lattice = Lattice(a=1, b=1, c=1, alpha=90, beta=90, gamma=90)\n", + "sample = calc.new_sample('sample0', lattice=lattice)\n", + "\n", + "print('lattice', sample.lattice)\n", + "print('physical axes', calc.physical_axes)\n", + "print('pseudo axes', calc.pseudo_axes)\n", + "print('omega parameter is', calc['omega'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the UB matrix from two reflections" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "# define the wavelength\n", + "calc.wavelength = 1.0\n", + "\n", + "# checking orientation of delta\n", + "r1p = calc.Position(mu=0.0, omega=30.0, chi=0.0, phi=0.0, gamma=0., delta=60.)\n", + "r1 = sample.add_reflection(0, 0, 1, position=r1p)\n", + "r2p = calc.Position(mu=0.0, omega=120.0, chi=0.0, phi=0.0, gamma=0, delta=60.)\n", + "r2 = sample.add_reflection(1, 0, 0, position=r2p)\n", + "sample.compute_UB(r1, r2)" + ] + }, + { + "source": [ + "### Show the computed **U** matrix" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -3.74939946e-33, 6.12323400e-17],\n", + " [ 0.00000000e+00, 1.00000000e+00, 6.12323400e-17],\n", + " [-6.12323400e-17, -6.12323400e-17, 1.00000000e+00]])" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "sample.U" + ] + }, + { + "source": [ + "### Show the computed **UB** matrix" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 6.28318531e+00, -3.84734139e-16, 0.00000000e+00],\n", + " [ 0.00000000e+00, 6.28318531e+00, 0.00000000e+00],\n", + " [-3.84734139e-16, -3.84734139e-16, 6.28318531e+00]])" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "sample.UB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate various (_hkl_) given motor positions\n", + "\n", + "#### (010)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (0,1,0)= OrderedDict([('h', 1.7187070131469975e-16), ('k', 0.9999999999999998), ('l', 1.7919353632379053e-16)])\n" + ] + } + ], + "source": [ + "calc.physical_positions = calc.Position(mu=0.0, omega=30.0, chi=90.0, phi=0.0, gamma=0, delta=60.)\n", + "print('pseudo should be (0,1,0)=', calc.pseudo_axes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (0,1,0)= OrderedDict([('h', 5.729023377156659e-17), ('k', 0.9999999999999999), ('l', 6.123233995736765e-17)])\n" + ] + } + ], + "source": [ + "# checking orientation of delta\n", + "calc.physical_positions = calc.Position(mu=30.0, omega=0.0, chi=0.0, phi=0.0, gamma=60., delta=0.)\n", + "print('pseudo should be (0,1,0)=', calc.pseudo_axes)" + ] + }, + { + "source": [ + "#### (0 -1 0)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (0,-1,0)= OrderedDict([('h', 0.0), ('k', -0.9999999999999998), ('l', 5.672885640905521e-17)])\n" + ] + } + ], + "source": [ + "calc.physical_positions = calc.Position(mu=0, omega=30., chi=-90.0, phi=0.0, gamma=0., delta=60.)\n", + "print('pseudo should be (0,-1,0)=', calc.pseudo_axes)\n" + ] + }, + { + "source": [ + "#### (-1 0 0)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (-1,0,0)= OrderedDict([('h', -0.9999999999999999), ('k', 0.0), ('l', 2.291609350862664e-16)])\n" + ] + } + ], + "source": [ + "\n", + "calc.physical_positions = calc.Position(mu=0.0, omega=-60.0, chi=0.0, phi=0.0, gamma=0, delta=60.)\n", + "print('pseudo should be (-1,0,0)=', calc.pseudo_axes)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diffracting upside-down now\n", + "\n", + "Note that omega and phi only need to sum to +/-120\n", + "($\\omega+\\varphi = \\pm |120|$), which reflects what\n", + "the inverse calculations from the library give." + ] + }, + { + "source": [ + "#### (100)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (1,0,0)= OrderedDict([('h', 1.0), ('k', 0.0), ('l', 5.729023377156662e-17)])\npseudo should be (1,0,0)= OrderedDict([('h', 1.0), ('k', 0.0), ('l', 5.729023377156662e-17)])\npseudo should be (1,0,0)= OrderedDict([('h', 1.0), ('k', 0.0), ('l', 5.729023377156662e-17)])\n" + ] + } + ], + "source": [ + "calc.physical_positions = calc.Position(mu=0.0, omega=-50.0, chi=0.0, phi=-70.0, gamma=0, delta=-60.)\n", + "print('pseudo should be (1,0,0)=', calc.pseudo_axes)\n", + "\n", + "calc.physical_positions = calc.Position(mu=0.0, omega=-100.0, chi=0.0, phi=-20.0, gamma=0, delta=-60.)\n", + "print('pseudo should be (1,0,0)=', calc.pseudo_axes)\n", + "\n", + "calc.physical_positions = calc.Position(mu=0.0, omega=100.0, chi=0.0, phi=-220.0, gamma=0, delta=-60.)\n", + "print('pseudo should be (1,0,0)=', calc.pseudo_axes)" + ] + }, + { + "source": [ + "#### (011)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "pseudo should be (0,1,1)= OrderedDict([('h', 3.4374140262939965e-16), ('k', 1.0), ('l', 1.0)])\n" + ] + } + ], + "source": [ + "calc.physical_positions = calc.Position(mu=0.0, omega=45.0, chi=45.0, phi=0.0, gamma=0, delta=90.)\n", + "print('pseudo should be (0,1,1)=', calc.pseudo_axes)" + ] + }, + { + "source": [ + "### Verify that $\\omega+\\varphi = \\pm 120$ is kept." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# calculate all allowed combinations of motor positions, given hkl\n", + "solutions = calc.forward((1,0,0))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "expecting either 120 or -120 (approximately): 119.9999999269113\nexpecting either 120 or -120 (approximately): -119.9999999269113\n" + ] + } + ], + "source": [ + "for sol in solutions:\n", + " print(\"expecting either 120 or -120 (approximately):\", sol.omega + sol.phi)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit ('base': conda)", + "language": "python", + "name": "python38264bitbaseconda9c3e3a9452084ea0903e516deca6d551" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/var_e4cv_renamed_axes.ipynb b/examples/var_e4cv_renamed_axes.ipynb new file mode 100644 index 00000000..ea26f1f1 --- /dev/null +++ b/examples/var_e4cv_renamed_axes.ipynb @@ -0,0 +1,521 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python38264bitcondaf8e76b08f7284c68a6b3de15f965a87a", + "display_name": "Python 3.8.2 64-bit (conda)", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Rename the axes of a 4-circle diffractometer\n", + "\n", + "Following the E4CV example, this example will repeat those same steps but using different names for the motor axes.\n", + "\n", + "E4CV name | local name\n", + "--- | ---\n", + "omega | theta\n", + "chi | chi\n", + "phi | phi\n", + "tth | ttheta\n", + "\n", + "## How's it done?\n", + "\n", + "This change is made by:\n", + "\n", + "1. Defining the `FourCircle` class using our local names\n", + " (instead of the canonical E4CV names)\n", + "1. Writing a dictionary to the `fourc` object that maps\n", + " the canonical E4CV names to our local names:\n", + "\n", + "```python\n", + "fourc.calc.physical_axis_names = {\n", + " # E4CV: local\n", + " 'omega': 'theta',\n", + " 'chi': 'chi',\n", + " 'phi': 'phi',\n", + " 'tth': 'ttheta',\n", + " }\n", + "```\n", + "\n", + "----\n", + "\n", + "Note: This example is available as a\n", + "[Jupyter notebook](https://jupyter.org/) from the *hklpy* source\n", + "code website: https://github.com/bluesky/hklpy/tree/main/examples" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "source": [ + "## Load the *hklpy* package (named _`hkl`_)\n", + "\n", + "As done in the E4CV example.\n", + "\n", + "This is needed _every_ time before the *hkl* package is first imported." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import gi\n", + "gi.require_version('Hkl', '5.0')" + ] + }, + { + "source": [ + "## Define _this_ diffractometer\n", + "\n", + "Following the E4CV example, create a `FourCircle` class,\n", + "but use our own names for the motors.\n", + "\n", + "E4CV name | local name\n", + "--- | ---\n", + "omega | theta\n", + "chi | chi\n", + "phi | phi\n", + "tth | ttheta\n", + "\n", + "Use the new motor names when constructing the `FourCircle` class. Otherwise, everything else in this step the same as in the E4CV example." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from hkl.diffract import E4CV\n", + "from ophyd import PseudoSingle, SoftPositioner\n", + "from ophyd import Component as Cpt\n", + "\n", + "class FourCircle(E4CV):\n", + " \"\"\"\n", + " Our 4-circle. Eulerian, vertical scattering orientation.\n", + " \"\"\"\n", + " # the reciprocal axes are called: pseudo in hklpy\n", + " h = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " k = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + " l = Cpt(PseudoSingle, '', kind=\"hinted\")\n", + "\n", + " # the motor axes are called: real in hklpy\n", + " theta = Cpt(SoftPositioner, kind=\"hinted\")\n", + " chi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " phi = Cpt(SoftPositioner, kind=\"hinted\")\n", + " ttheta = Cpt(SoftPositioner, kind=\"hinted\")\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " \"\"\"Define an initial position for simulators.\"\"\"\n", + " super().__init__(*args, **kwargs)\n", + "\n", + " for p in self.real_positioners:\n", + " p._set_position(0) # give each a starting position" + ] + }, + { + "source": [ + "### Create an instance of the diffractometer.\n", + "\n", + "As in the E4CV example, create an instance of the diffractometer. Note that the instance still has the canonical E4CV names, as reported by libhkl." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "motor names: ['omega', 'chi', 'phi', 'tth']\n" + ] + } + ], + "source": [ + "fourc = FourCircle(\"\", name=\"fourc\")\n", + "print(\"motor names:\", fourc.calc.physical_axis_names)" + ] + }, + { + "source": [ + "## Switch to **our** motor names\n", + "\n", + "This is the magic step, *map* the canonical libhkl names onto\n", + "the names we want to use. This is done using a Python dictionary. They keys are the canonical names, the value of key is the local name. *All* axes\n", + "must be in the dictionary, even if the names remain the same." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "motor names: ['theta', 'chi', 'phi', 'ttheta']\n" + ] + } + ], + "source": [ + "fourc.calc.physical_axis_names = {\n", + " # E4CV: local\n", + " 'omega': 'theta',\n", + " 'chi': 'chi',\n", + " 'phi': 'phi',\n", + " 'tth': 'ttheta',\n", + " }\n", + "\n", + "print(\"motor names:\", fourc.calc.physical_axis_names)" + ] + }, + { + "source": [ + "## Add a sample with a crystal structure" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "HklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=0.0, fit=True, inverted=False, units='Degree'), U=array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), UB=array([[ 1.15691131e+00, -7.08403864e-17, -7.08403864e-17],\n", + " [ 0.00000000e+00, 1.15691131e+00, -7.08403864e-17],\n", + " [ 0.00000000e+00, 0.00000000e+00, 1.15691131e+00]]), reflections=[])" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "from hkl.util import Lattice\n", + "\n", + "# add the sample to the calculation engine\n", + "a0 = 5.431\n", + "fourc.calc.new_sample(\n", + " \"silicon\",\n", + " lattice=Lattice(a=a0, b=a0, c=a0, alpha=90, beta=90, gamma=90)\n", + " )" + ] + }, + { + "source": [ + "## Setup the UB orientation matrix using *hklpy*\n", + "\n", + "Define the crystal's orientation on the diffractometer using \n", + "the 2-reflection method described by [Busing & Levy, Acta Cryst 22 (1967) 457](https://www.psi.ch/sites/default/files/import/sinq/zebra/PracticalsEN/1967-Busing-Levy-3-4-circle-Acta22.pdf).\n", + "\n", + "### Choose the same wavelength X-rays for both reflections" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fourc.calc.wavelength = 1.54 # Angstrom (8.0509 keV)" + ] + }, + { + "source": [ + "### Find the first reflection and identify its Miller indices: (_hkl_)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "r1 = fourc.calc.sample.add_reflection(\n", + " 4, 0, 0,\n", + " position=fourc.calc.Position(\n", + " ttheta=69.0966,\n", + " theta=-145.451,\n", + " chi=0,\n", + " phi=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Find the second reflection" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "r2 = fourc.calc.sample.add_reflection(\n", + " 0, 4, 0,\n", + " position=fourc.calc.Position(\n", + " ttheta=69.0966,\n", + " theta=-145.451,\n", + " chi=90,\n", + " phi=0,\n", + " )\n", + ")" + ] + }, + { + "source": [ + "### Compute the *UB* orientation matrix\n", + "\n", + "The `compute_UB()` method always returns 1. Ignore it." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "fourc.calc.sample.compute_UB(r1, r2)" + ] + }, + { + "source": [ + "## Report what we have setup" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================== ===================================================\n", + "term value \n", + "==================== ===================================================\n", + "energy, keV 8.050922077922078 \n", + "wavelength, angstrom 1.54 \n", + "position FourCirclePseudoPos(h=-0.0, k=0.0, l=0.0) \n", + "sample name silicon \n", + "[U] [[-1.22173048e-05 -1.22173048e-05 -1.00000000e+00] \n", + " [ 0.00000000e+00 -1.00000000e+00 1.22173048e-05] \n", + " [-1.00000000e+00 1.49262536e-10 1.22173048e-05]]\n", + "[UB] [[-1.41343380e-05 -1.41343380e-05 -1.15691131e+00] \n", + " [ 0.00000000e+00 -1.15691131e+00 1.41343380e-05] \n", + " [-1.15691131e+00 1.72683586e-10 1.41343380e-05]]\n", + "lattice [ 5.431 5.431 5.431 90. 90. 90. ] \n", + "==================== ===================================================\n", + "\n", + "sample\tHklSample(name='silicon', lattice=LatticeTuple(a=5.431, b=5.431, c=5.431, alpha=90.0, beta=90.0, gamma=90.0), ux=Parameter(name='None (internally: ux)', limits=(min=-180.0, max=180.0), value=-45.0, fit=True, inverted=False, units='Degree'), uy=Parameter(name='None (internally: uy)', limits=(min=-180.0, max=180.0), value=-89.99901005102187, fit=True, inverted=False, units='Degree'), uz=Parameter(name='None (internally: uz)', limits=(min=-180.0, max=180.0), value=135.00000000427607, fit=True, inverted=False, units='Degree'), U=array([[-1.22173048e-05, -1.22173048e-05, -1.00000000e+00],\n", + " [ 0.00000000e+00, -1.00000000e+00, 1.22173048e-05],\n", + " [-1.00000000e+00, 1.49262536e-10, 1.22173048e-05]]), UB=array([[-1.41343380e-05, -1.41343380e-05, -1.15691131e+00],\n", + " [ 0.00000000e+00, -1.15691131e+00, 1.41343380e-05],\n", + " [-1.15691131e+00, 1.72683586e-10, 1.41343380e-05]]), reflections=[(h=4.0, k=0.0, l=0.0), (h=0.0, k=4.0, l=0.0)], reflection_measured_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]), reflection_theoretical_angles=array([[0. , 1.57079633],\n", + " [1.57079633, 0. ]]))\n" + ] + } + ], + "source": [ + "import pyRestTable\n", + "\n", + "tbl = pyRestTable.Table()\n", + "tbl.labels = \"term value\".split()\n", + "tbl.addRow((\"energy, keV\", fourc.calc.energy))\n", + "tbl.addRow((\"wavelength, angstrom\", fourc.calc.wavelength))\n", + "tbl.addRow((\"position\", fourc.position))\n", + "tbl.addRow((\"sample name\", fourc.sample_name.get()))\n", + "tbl.addRow((\"[U]\", fourc.U.get()))\n", + "tbl.addRow((\"[UB]\", fourc.UB.get()))\n", + "tbl.addRow((\"lattice\", fourc.lattice.get()))\n", + "print(tbl)\n", + "\n", + "print(f\"sample\\t{fourc.calc.sample}\")" + ] + }, + { + "source": [ + "## Check the orientation matrix\n", + "\n", + "Perform checks with _forward_ (hkl to angle) and\n", + "_inverse_ (angle to hkl) computations to verify the diffractometer\n", + "will move to the same positions where the reflections were identified.\n", + "\n", + "### Constrain the motors to limited ranges\n", + "\n", + "* allow for slight roundoff errors\n", + "* keep `ttheta` in the positive range\n", + "* keep `theta` in the negative range\n", + "* keep `phi` fixed at zero\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "fourc.calc[\"ttheta\"].limits = (-0.001, 180)\n", + "fourc.calc[\"theta\"].limits = (-180, 0.001)\n", + "\n", + "fourc.phi.move(0)\n", + "fourc.engine.mode = \"constant_phi\"" + ] + }, + { + "source": [ + "### (400) reflection\n", + "#### Check the inverse calculation: (400)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4 0 0) ? 4.00 0.00 0.00\n" + ] + } + ], + "source": [ + "sol = fourc.inverse((-145.451, 0, 0, 69.0966))\n", + "print(f\"(4 0 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "#### Check the forward calculation: (400)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(400) : ttheta=69.0985 theta=-145.4500 chi=0.0000 phi=0.0000\n" + ] + } + ], + "source": [ + "sol = fourc.forward((4, 0, 0))\n", + "print(\n", + " \"(400) :\", \n", + " f\"ttheta={sol.ttheta:.4f}\", \n", + " f\"theta={sol.theta:.4f}\", \n", + " f\"chi={sol.chi:.4f}\", \n", + " f\"phi={sol.phi:.4f}\"\n", + " )" + ] + }, + { + "source": [ + "### (040) reflection\n", + "#### Check another inverse calculation: (040)" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0 4 0) ? 0.00 4.00 0.00\n" + ] + } + ], + "source": [ + "sol = fourc.inverse((-145.451, 90, 0, 69.0966))\n", + "print(f\"(0 4 0) ? {sol.h:.2f} {sol.k:.2f} {sol.l:.2f}\")" + ] + }, + { + "source": [ + "Continue the E4CV example on your own..." + ], + "cell_type": "markdown", + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/requirements-doc.txt b/requirements-doc.txt deleted file mode 100644 index 3626add6..00000000 --- a/requirements-doc.txt +++ /dev/null @@ -1,8 +0,0 @@ -doctr -doctr-versions-menu -ipython -matplotlib -numpydoc -sphinx -sphinx-copybutton -sphinx_rtd_theme diff --git a/requirements-test.txt b/requirements-test.txt deleted file mode 100644 index 3beb4784..00000000 --- a/requirements-test.txt +++ /dev/null @@ -1,9 +0,0 @@ -attrs>=19.3.0 -bluesky -codecov -flake8 -ophyd -pyepics -PyGObject -pytest -pytest-faulthandler