diff --git a/.github/workflows/markdown_link_check.yml b/.github/workflows/markdown_link_check.yml new file mode 100644 index 00000000..ea13451d --- /dev/null +++ b/.github/workflows/markdown_link_check.yml @@ -0,0 +1,14 @@ +name: Check Markdown links + +on: + push: + schedule: + - # Run every day at 5:00 UTC + - cron: "0 5 * * *" + +jobs: + markdown-link-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@master + - uses: gaurav-nelson/github-action-markdown-link-check@v1 diff --git a/.github/workflows/minimumdependencies.yml b/.github/workflows/minimumdependencies.yml new file mode 100644 index 00000000..70ea4405 --- /dev/null +++ b/.github/workflows/minimumdependencies.yml @@ -0,0 +1,27 @@ +name: Minimum Dependencies +# Installs the minimum versions of the dependencies and runs the tests. +# This test will lower the chance that users botch their installation by +# only upgrading this project but not the dependencies. + +on: + push: + branches: + - master + - dev_master + - dev_spectroscopy + pull_request: + branches: + - master + - dev_master + - dev_spectroscopy + + # Allows you to run this workflow manually from the Actions tab. + workflow_dispatch: + + schedule: + - # Run every day at 5:00 UTC. + - cron: "0 5 * * *" + +jobs: + call-minimum-dependencies: + uses: AstarVienna/DevOps/.github/workflows/minimumdependencies.yml@master diff --git a/.github/workflows/notebooks_with_irdb_clone.yml b/.github/workflows/notebooks_with_irdb_clone.yml new file mode 100644 index 00000000..e7ad2e29 --- /dev/null +++ b/.github/workflows/notebooks_with_irdb_clone.yml @@ -0,0 +1,49 @@ +name: Notebooks with IRDB git clone + +on: + push: + branches: + - master + - dev_master + - dev_spectroscopy + pull_request: + branches: + - master + - dev_master + - dev_spectroscopy + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + + # Run every night + schedule: + - cron: "0 2 * * *" + + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + matrix: + # Run only on a minimal subset of the matrix, as this is ran on many + # commits. + os: [ubuntu-latest] + python-version: ['3.11'] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + # Install this version of ScopeSim. Otherwise the PyPI version of + # ScopeSim will be installed when the test-requriments + # are installed, because ScopeSim is a dependency of + # ScopeSim_Templates. + pip install . + pip install .[dev,test] + - name: Run notebooks + run: ./runnotebooks.sh --checkout-irdb --delete diff --git a/.github/workflows/notebooks_with_irdb_download.yml b/.github/workflows/notebooks_with_irdb_download.yml new file mode 100644 index 00000000..26193abc --- /dev/null +++ b/.github/workflows/notebooks_with_irdb_download.yml @@ -0,0 +1,41 @@ +name: Notebooks with IRDB download + +on: + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + + # Run every night + schedule: + - cron: "0 3 * * *" + + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + matrix: + # Run all operating systems, because this is the first interaction + # that users have with ScopeSim / IRDB. + # However, only use minimum and maximum supported Python version, + # as the IRDB download often fails. + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ['3.8', '3.11'] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + # Install this version of ScopeSim. Otherwise the PyPI version of + # ScopeSim will be installed when the test-requriments + # are installed, because ScopeSim is a dependency of + # ScopeSim_Templates. + pip install . + pip install .[dev,test] + - name: Run notebooks + # No --checkout-irdb to download the IRDB as a normal end user would. + run: ./runnotebooks.sh --delete diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 2a66eb8e..54e44302 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,29 +16,5 @@ on: workflow_dispatch: jobs: - build: - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest] - python-version: ['3.7', '3.8', '3.9'] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - # Install this version of ScopeSim. Otherwise the PyPI version of - # ScopeSim will be installed when the github_actions requirements - # are installed, because ScopeSim is a dependency of - # ScopeSim_Templates. - pip install . - pip install -r requirements.github_actions.txt - - name: Run Pytest - run: pytest - - name: Run notebooks - run: ./runnotebooks.sh + call-tests: + uses: AstarVienna/DevOps/.github/workflows/tests.yml@master diff --git a/.gitignore b/.gitignore index 46d60b77..bb8f47fb 100644 --- a/.gitignore +++ b/.gitignore @@ -52,3 +52,10 @@ dist *TEST.fits *temp* *speclecado*.fits + +# Spyder project settings +.spyderproject +.spyproject + +# Pylint +.pylint.d/ diff --git a/.readthedocs.yaml b/.readthedocs.yaml index b2a62860..c92119a5 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,12 +10,14 @@ build: python: "3.9" sphinx: - configuration: docs/source/conf.py + configuration: docs/source/conf.py python: - install: - - requirements: requirements.readthedocs.txt - - path: . + install: + - method: pip + path: . + extra_requirements: + - docs # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: [] # ignore htmlzip. html is always run diff --git a/LICENCE b/LICENCE deleted file mode 100644 index 9228d3f1..00000000 --- a/LICENCE +++ /dev/null @@ -1,11 +0,0 @@ -We currently don't know much about licences, nor have we thought about them. -No doubt, this will change in the future. For the moment though: - -We invoke the licence of honour. Ask yourself, what would Thor do? - -If ambiguity ensues, ScopeSim will use the GNU GPLv3 software licence. -https://choosealicense.com/licenses/gpl-3.0/ - -TLDR; Give credit where credit is due, and reuse this licence. - - diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/MANIFEST.in b/MANIFEST.in index ccaa808c..c479ef57 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,5 @@ include README.md -include LICENCE +include LICENSE include scopesim/defaults.yaml include scopesim/vega.fits recursive-include scopesim/data * diff --git a/README.md b/README.md index 18ea2633..b1579cc8 100644 --- a/README.md +++ b/README.md @@ -17,52 +17,11 @@ and astronomical objects and then pushing the object through the optical train. The resulting 2D image is then broadcast to a detector chip and read out into a FITS file. -This code was originally based on the [SimCADO](www.univie.ac.at/simcado) package +This code was originally based on the [SimCADO](https://github.com/astronomyk/simcado) package ## Documentation The main set of documentation can be found here: https://scopesim.readthedocs.io/en/latest/ A basic Jupyter Notebook can be found here: -[scopesim_basic_intro.ipynb](docs/source/_static/scopesim_basic_intro.ipynb) - - -## Dependencies - -For [![Python 3.7](https://img.shields.io/badge/Python-3.7-brightgreen.svg)]() and above the latest versions of these packages are compatible with ScopeSim: - - numpy >= 1.16 - scipy >= 1.0.0 - astropy >= 2.0 - pyyaml >= 5.1 - requests >= 2.20 - beautifulsoup4 >= 4.4 - synphot >= 0.1.3 - -For [![Python 3.5](https://img.shields.io/badge/Python-3.5-yellow.svg)]() the following packages may not exceed these version numbers: - - astropy <= 3.2.3 - synphot <= 0.1.3 - -#### Oldest currently tested system - -[![Python 3.5](https://img.shields.io/badge/Python-3.5-yellow.svg)]() - -[![Numpy](https://img.shields.io/badge/Numpy-1.16-brightgreen.svg)]() -[![Astropy](https://img.shields.io/badge/Astropy-2.0-brightgreen.svg)]() -[![Scipy](https://img.shields.io/badge/Scipy-1.0.0-brightgreen.svg)]() - -[![Synphot](https://img.shields.io/badge/Synphot-0.1.3-brightgreen.svg)]() -[![requests](https://img.shields.io/badge/requests-2.20.0-brightgreen.svg)]() -[![beautifulsoup4](https://img.shields.io/badge/beautifulsoup4-4.4-brightgreen.svg)]() -[![pyyaml](https://img.shields.io/badge/pyyaml-5.1-brightgreen.svg)]() - -#### Things to watch out for with Synphot -Numpy>=1.16 must be used for synphot to work -For Astropy<4.0, only Synphot<=0.1.3 works - -#### Optional dependencies -[![skycalc_ipy](https://img.shields.io/badge/skycalc_ipy->=0.1-brightgreen.svg)]() -[![anisocado](https://img.shields.io/badge/anisocado->=0.1-brightgreen.svg)]() -[![Matplotlib](https://img.shields.io/badge/Matplotlib->=1.5-brightgreen.svg)]() - +[scopesim_basic_intro.ipynb](docs/source/examples/1_scopesim_intro.ipynb) diff --git a/docs/joss_paper/anisocado_full_text.md b/docs/joss_paper/anisocado_full_text.md deleted file mode 100644 index b1f27b52..00000000 --- a/docs/joss_paper/anisocado_full_text.md +++ /dev/null @@ -1,134 +0,0 @@ ---- -title: 'AnisoCADO: a python package for analytically generating adaptive optics point spread functions for the Extremely Large Telescope' -tags: - - Python - - astronomy - - simulations - - point spread functions - - Extreme Large Telescope -authors: - - name: Kieran Leschinski - orcid: 0000-0003-0441-9784 - affiliation: 1 - - name: Eric Gendron - affiliation: 2 -affiliations: - - name: Department of Astrophysics, University of Vienna - index: 1 - - name: Observatoire de Paris - index: 2 -date: 15 June 2020 -bibliography: paper.bib ---- - -# Summary - -AnisoCADO is a Python package for generating images of the point spread function (PSF) for the european extremely large telescope (ELT). -The code allows the user to set a large range of the most important atmospheric and observational parameters that influence the shape and strehl ratio of the resulting PSF, including but not limited to: the atmospheric turbulence profile, the guide star position for a single conjugate adaptive optics (SCAO) solution, differential telescope pupil transmission, etc. -Documentation can be found at https://anisocado.readthedocs.io/en/latest/ - - -# Statement of need - -## Adaptive optics are mandatory for the next generation of ground-based telescopes -The larger the telescope aperture, the smaller the diffraction limit of the observations. -For space-based telescope this statement is always true. -However the resolution of ground based telescopes is limited by the blur caused by turbulence in the atmosphere - known as atmospheric Seeing. -This blurring can be (mostly) removed by measuring the deformation of the wavefront of the incoming light, and applying an equal and opposite deformation to the surface of one or more of the mirrors along a telescope's optical path. -The current fleet of large (8-10m) telescopes were built to primarily operate at the edge of the natural seeing limit (FWHM~0.5 arcseconds @ 1um). -Over the last two decades some have received upgrades in the form of active and adaptive mirrors in order to achieve up to 20x increase in resolution afforded by the physical diffraction limit of a 10m primary mirror (FWHM~0.03 arcseconds @ 1um). -The next generation of "extremely large" telescopes will have primary mirrors on the order of 30-40m, with theoretical diffraciton limits on the order of 50x smaller than the natural Seeing limit. -In order for these telescopes to resolve structures at scales of the diffraction limit, they must, by design, include adaptive optics systems. - -## Diffraction limited point-spread-functions are complex beasts -The point spread function (PSF) of an optical system is the description of the spatial distribution of light from an infinitely small point source after passing through an optical system (e.g. layers of the atmosphere, mirrors of a telescope). -Due to the random nature of atmospheric turbulence, the PSF of a star in a Seeing-limited observation is well approximated by a ("nice") smooth Gaussian-like function. -The PSF of a diffraction limited telescope system using an adaptive-optics correction is a complex ("ugly") function that depends on a veritible zoo of atmospheric, observational, and technical parameters. -From an astronomers point of view, the consequences of a poor adaptive optics solution means the difference between a successful and a failed observation run. -Therfore it is imperative that the consequences of such large variations in the PSF are accounted for in advance by those proposing to observe with this next generation of billion-dollar telescopes. - - -## AnisoCADO - Anisoplanatism for MICADO - -AnisoCADO (Anisoplanatism for MICADO) is a package for generating images of the point spread function for a given set of atmospheric, observational, and technical conditions. -It does this by combining a series of wavefront phase screens from the elements of the atmosphere and AO system that influence the final AO correction (e.g. atmospheric anisoplantism, WFS aliasing, actuator fit, etc). -The final phase screen is applied to the optical transfer function for the telescope optical system. -The resulting image is the expected PSF for a long exposure (>10s) on-axis observation at the given wavelength. -For single-conjugate adaptive optics modes, the field PSF degrades as distance from the guide star increases. -This effect is taken into account by shifting the anisoplanatic phase screen relative to the calculated phase screen correction for the deformable mirror. -Figure \autoref{fig:psf_grid} shows how the PSF changes with distance from an on-axis guide star. -For a more detailed discussion of the mathematics behind anisoplanatism in the context of the ELT, the reader is referred to @clenet2015. - -![A grid of Ks-band (2.15um) PSFs for a range of distances from the natural guide star. -The PSFs were generated using the ESO median turbulence profile. -\label{fig:psf_grid}](Ks-band_psf_grid.png) - - -### Inputs -The final ELT PSF is the combination of many factors. The vast majority of these are irrelevant for the casual user. -AnisoCADO therefore provides three preset option, corresponding to the standard ESO Q1, Median and Q4 turbulence profiles. -All other parameters are initialised with default values. -For the case of a SCAO system (for which AnisoCADO was originally conceived) PSFs can be generated for multiple guide star offsets without needing to re-make all phase screens by using the special class method ``.shift_off_axis(dx, dy)`` - -For more detailed use cases, the following parameters are available to the user: - -| Atmosphere | Observation | Telescope | -|-------------------------------|----------------------------------|------------------------------| -| * turbulence profile | * natural guide star position | * pupil image | -| * height of turbulent layers | * central wavelength | * 2D pupil transmissivity | -| * stregth of turbulent layers | * pupil rotation angle | * dead/empty mirror segments | -| * wind speed | * Zenith distance of observation | * plate scale | -| * Seeing FWHM @ 500nm | | * residual wavefront errors | -| * Fried parameter | | * AO sampling frequency | -| * outer scale | | * AO loop delay | -| | | * Interactuator distance | - - - -### Outputs - -AnisoCADO is easily integrated into the standard astronomers toolbox. -PSF images generated by AnisoCADO can be output as either ``numpy`` arrays, or standard ``astropy.io.fits.ImageHDU`` objects. -The latter can be written to file using the standard ``astropy`` syntax. - -As AnisoCADO was written to support the development of the MICADO instrument simulator [@simcado2016; @simcado2019], it is also possible to generate ``FieldVaryingPSF`` objects using the helper functions in the ``misc`` module. -Such files are also compatible with the generic instrument data simulator framework, ScopeSim [@scopesim]. - - -Basic Example -------------- -The AnisoCADO API is described in the online documentation, which can be found at: . For the purpose of illustration, the following 5 lines were used to generate the grid of PSFs in figure \autoref{fig:psf_grid}. - -``` -import numpy as np -from anisocado import AnalyticalScaoPsf - -psf = AnalyticalScaoPsf() -psf_grid = [] -for x, y in np.mgrid[-14:15:7, -14:15:7].flatten().reshape((2, 25)).T: - psf.shift_off_axis(x, y) - psf_grid += [psf.kernel] -``` - - -# Acknowledgments - -AnisoCADO depends on the following packages: -Numpy [@numpy], -Matplotlib [@numpy], -Astropy [@astropy2018]. - -This development of this project was funded by the project IS538004 of the Hochschulraum-strukturmittel (HRSM) provided by the Austrian Government and administered by the University of Vienna. - - -# References - - -@misc{scopesim, - author = {{Leschinski}, Kieran}, - title = "{ScopeSim - A python framework for creating astronomical instrument data simulators}", - year = {2020}, - publisher = {​GitHub}, - journal = {​GitHub repository}, - url = {​https://github.com/astronomyk/scopesim} -} \ No newline at end of file diff --git a/docs/joss_paper/joss_ideas.md b/docs/joss_paper/joss_ideas.md deleted file mode 100644 index 4b8fcb34..00000000 --- a/docs/joss_paper/joss_ideas.md +++ /dev/null @@ -1,48 +0,0 @@ -# Contents -- metadata (see example below), - -- Summary - A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience. - -- Statement of Need, - A Statement of Need section that clearly illustrates the research purpose of the software. - Mention (if applicable) a representative set of past or ongoing research projects using the software and recent scholarly publications enabled by it. - Where to find Documentation / Code - -- Acknowledgements, - Acknowledgement of any financial support. - -- References - A list of key references, including to other software addressing related needs. Note that the references should include full names of venues, e.g., journals and conferences, not abbreviations only understood in the context of a specific discipline. - ---- -title: 'Gala: A Python package for galactic dynamics' -tags: - - Python - - astronomy - - dynamics - - galactic dynamics - - milky way -authors: - - name: Adrian M. Price-Whelan^[co-first author] # note this makes a footnote saying 'co-first author' - orcid: 0000-0003-0872-7098 - affiliation: "1, 2" # (Multiple affiliations must be quoted) - - name: Author Without ORCID^[co-first author] # note this makes a footnote saying 'co-first author' - affiliation: 2 - - name: Author with no affiliation^[corresponding author] - affiliation: 3 -affiliations: - - name: Lyman Spitzer, Jr. Fellow, Princeton University - index: 1 - - name: Institution Name - index: 2 - - name: Independent Researcher - index: 3 -date: 13 August 2017 -bibliography: paper.bib - -# Optional fields if submitting to a AAS journal too, see this blog post: -# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing -aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. -aas-journal: Astrophysical Journal <- The name of the AAS journal. ---- \ No newline at end of file diff --git a/docs/joss_paper/paper.bib b/docs/joss_paper/paper.bib deleted file mode 100644 index 7ef520b8..00000000 --- a/docs/joss_paper/paper.bib +++ /dev/null @@ -1,239 +0,0 @@ -@ARTICLE{numpy, - author={S. {van der Walt} and S. C. {Colbert} and G. {Varoquaux}}, - journal={Computing in Science and Engineering}, - title={The NumPy Array: A Structure for Efficient Numerical Computation}, - year={2011}, - volume={13}, - number={2}, - pages={22-30},} - - -@ARTICLE{matplotlib, - author={J. 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{Burke}, D.~J. and {Calderone}, G. and {Cano Rodr{\'\i}guez}, J.~L. and - {Cara}, M. and {Cardoso}, J.~V.~M. and {Cheedella}, S. and {Copin}, Y. and - {Corrales}, L. and {Crichton}, D. and {D'Avella}, D. and {Deil}, C. and - {Depagne}, {\'E}. and {Dietrich}, J.~P. and {Donath}, A. and - {Droettboom}, M. and {Earl}, N. and {Erben}, T. and {Fabbro}, S. and - {Ferreira}, L.~A. and {Finethy}, T. and {Fox}, R.~T. and - {Garrison}, L.~H. and {Gibbons}, S.~L.~J. and {Goldstein}, D.~A. and - {Gommers}, R. and {Greco}, J.~P. and {Greenfield}, P. and - {Groener}, A.~M. and {Grollier}, F. and {Hagen}, A. and {Hirst}, P. and - {Homeier}, D. and {Horton}, A.~J. and {Hosseinzadeh}, G. and {Hu}, L. and - {Hunkeler}, J.~S. and {Ivezi{\'c}}, {\v{Z}}. and {Jain}, A. and - {Jenness}, T. and {Kanarek}, G. and {Kendrew}, S. and {Kern}, N.~S. and - {Kerzendorf}, W.~E. and {Khvalko}, A. and {King}, J. and {Kirkby}, D. and - {Kulkarni}, A.~M. and {Kumar}, A. and {Lee}, A. and {Lenz}, D. and - {Littlefair}, S.~P. and {Ma}, Z. and {Macleod}, D.~M. and - {Mastropietro}, M. and {McCully}, C. and {Montagnac}, S. and - {Morris}, B.~M. and {Mueller}, M. and {Mumford}, S.~J. and {Muna}, D. and - {Murphy}, N.~A. and {Nelson}, S. and {Nguyen}, G.~H. and - {Ninan}, J.~P. and {N{\"o}the}, M. and {Ogaz}, S. and {Oh}, S. and - {Parejko}, J.~K. and {Parley}, N. and {Pascual}, S. and {Patil}, R. and - {Patil}, A.~A. and {Plunkett}, A.~L. and {Prochaska}, J.~X. and - {Rastogi}, T. and {Reddy Janga}, V. and {Sabater}, J. and - {Sakurikar}, P. and {Seifert}, M. and {Sherbert}, L.~E. and - {Sherwood-Taylor}, H. and {Shih}, A.~Y. and {Sick}, J. and - {Silbiger}, M.~T. and {Singanamalla}, S. and {Singer}, L.~P. and - {Sladen}, P.~H. and {Sooley}, K.~A. and {Sornarajah}, S. and - {Streicher}, O. and {Teuben}, P. and {Thomas}, S.~W. and - {Tremblay}, G.~R. and {Turner}, J.~E.~H. and {Terr{\'o}n}, V. and - {van Kerkwijk}, M.~H. and {de la Vega}, A. and {Watkins}, L.~L. and - {Weaver}, B.~A. and {Whitmore}, J.~B. and {Woillez}, J. and - {Zabalza}, V. and {Astropy Contributors}}, - title = "{The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package}", - journal = {\aj}, - keywords = {methods: data analysis, methods: miscellaneous, methods: statistical, reference systems, Astrophysics - Instrumentation and Methods for Astrophysics}, - year = 2018, - month = sep, - volume = {156}, - number = {3}, - eid = {123}, - pages = {123}, - doi = {10.3847/1538-3881/aabc4f}, -archivePrefix = {arXiv}, - eprint = {1801.02634}, - primaryClass = {astro-ph.IM}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@INPROCEEDINGS{simcado2016, - author = {{Leschinski}, K. and {Czoske}, O. and {K{\"o}hler}, R. and {Mach}, M. and - {Zeilinger}, W. and {Verdoes Kleijn}, G. and {Alves}, J. and - {Kausch}, W. and {Przybilla}, N.}, - title = "{SimCADO: an instrument data simulator package for MICADO at the E-ELT}", - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, - booktitle = {\procspie}, - year = 2016, - series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, - volume = {9911}, - month = aug, - eid = {991124}, - pages = {991124}, - doi = {10.1117/12.2232483}, -archivePrefix = {arXiv}, - eprint = {1609.01480}, - primaryClass = {astro-ph.IM}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2016SPIE.9911E..24L}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@INPROCEEDINGS{simcado2019, - author = {{Leschinski}, Kieran and {Czoske}, Oliver and {K{\"o}hler}, Rainer and - {Mach}, Michael and {Zeilinger}, Werner and {Verdoes Kleijn}, Gijs and - {Kausch}, Wolfgang and {Przybilla}, Norbert and {Alves}, Joao and - {Davies}, Richard}, - title = "{SimCADO - a Python Package for Simulating Detector Output for MICADO at the E-ELT}", - booktitle = {Astronomical Data Analysis Software and Systems XXVI}, - year = 2019, - editor = {{Molinaro}, Marco and {Shortridge}, Keith and {Pasian}, Fabio}, - series = {Astronomical Society of the Pacific Conference Series}, - volume = {521}, - month = oct, - pages = {527}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2019ASPC..521..527L}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@ARTICLE{clenet2015, - author = {{Cl{\'e}net}, Y. and {Gendron}, E. and {Gratadour}, D. and - {Rousset}, G. and {Vidal}, F.}, - title = "{Anisoplanatism effect on the E-ELT SCAO point spread function. A preserved coherent core across the field}", - journal = {\aap}, - keywords = {atmospheric effects, instrumentation: adaptive optics, methods: numerical}, - year = 2015, - month = nov, - volume = {583}, - eid = {A102}, - pages = {A102}, - doi = {10.1051/0004-6361/201425469}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2015A&A...583A.102C}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@ARTICLE{elt2007, - author = {{Gilmozzi}, R. and {Spyromilio}, J.}, - title = "{The European Extremely Large Telescope (E-ELT)}", - journal = {The Messenger}, - year = 2007, - month = mar, - volume = {127}, - pages = {11}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2007Msngr.127...11G}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@INPROCEEDINGS{davies2018, - author = {{Davies}, R. and {Alves}, J. and {Cl{\'e}net}, Y. and {Lang-Bardl}, F. and - {Nicklas}, H. and {Pott}, J. -U. and {Ragazzoni}, R. and {Tolstoy}, E. and - {Amico}, P. and {Anwand-Heerwart}, H. and {Barboza}, S. and {Barl}, L. and - {Baudoz}, P. and {Bender}, R. and {Bezawada}, N. and {Bizenberger}, P. and - {Boland}, W. and {Bonifacio}, P. and {Borgo}, B. and {Buey}, T. and - {Chapron}, F. and {Chemla}, F. and {Cohen}, M. and {Czoske}, O. and - {D{\'e}o}, V. and {Disseau}, K. and {Dreizler}, S. and {Dupuis}, O. and - {Fabricius}, M. and {Falomo}, R. and {Fedou}, P. and - {F{\"o}rster Schreiber}, N. and {Garrel}, V. and {Geis}, N. and - {Gemperlein}, H. and {Gendron}, E. and {Genzel}, R. and - {Gillessen}, S. and {Gl{\"u}ck}, M. and {Grupp}, F. and {Hartl}, M. and - {H{\"a}user}, M. and {Hess}, H. -J. and {Hofferbert}, R. and - {Hopp}, U. and {H{\"o}rmann}, V. and {Hubert}, Z. and {Huby}, E. and - {Huet}, J. -M. and {Hutterer}, V. and {Ives}, D. and {Janssen}, A. and - {Jellema}, W. and {Kausch}, W. and {Kerber}, F. and {Kravcar}, H. and - {Le Ruyet}, B. and {Leschinski}, K. and {Mandla}, C. and {Manhart}, M. and - {Massari}, D. and {Mei}, S. and {Merlin}, F. and {Mohr}, L. and - {Monna}, A. and {Muench}, N. and {M{\"u}ller}, F. and {Musters}, G. and - {Navarro}, R. and {Neumann}, U. and {Neumayer}, N. and {Niebsch}, J. and - {Plattner}, M. and {Przybilla}, N. and {Rabien}, S. and {Ramlau}, R. and - {Ramos}, J. and {Ramsay}, S. and {Rhode}, P. and {Richter}, A. and - {Richter}, J. and {Rix}, H. -W. and {Rodeghiero}, G. and - {Rohloff}, R. -R. and {Rosensteiner}, M. and {Rousset}, G. and - {Schlichter}, J. and {Schubert}, J. and {Sevin}, A. and {Stuik}, R. and - {Sturm}, E. and {Thomas}, J. and {Tromp}, N. and {Verdoes-Kleijn}, G. and - {Vidal}, F. and {Wagner}, R. and {Wegner}, M. and {Zeilinger}, W. and - {Ziegleder}, J. and {Ziegler}, B. and {Zins}, G.}, - title = "{The MICADO first light imager for the ELT: overview, operation, simulation}", - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, - booktitle = {\procspie}, - year = 2018, - series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, - volume = {10702}, - month = jul, - eid = {107021S}, - pages = {107021S}, - doi = {10.1117/12.2311483}, -archivePrefix = {arXiv}, - eprint = {1807.10003}, - primaryClass = {astro-ph.IM}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10702E..1SD}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@INPROCEEDINGS{clenet2014, - author = {{Cl{\'e}net}, Yann and {Buey}, Tristan M. and {Rousset}, G{\'e}rard and - {Cohen}, Mathieu and {Feautrier}, Philippe and {Gendron}, Eric and - {Hubert}, Zoltan and {Chemla}, Fanny and {Gratadour}, Damien and - {Baudoz}, Pierre and {Lacour}, Sylvestre and {Boccaletti}, Anthony and - {Sevin}, Arnaud and {Vidal}, Fabrice and {Galicher}, Rapha{\"e}l. and - {Perret}, Denis and {Le Ruyet}, Bertrand and - {Chapron}, Fr{\'e}d{\'e}ric and {Stadler}, Eric and {Rabou}, Patrick and - {Jocou}, Laurent and {Rochat}, Sylvain and {Chauvin}, Ga{\"e}l. and - {Davies}, Richard}, - title = "{Overview of the MICADO SCAO system}", - booktitle = {\procspie}, - year = 2014, - series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, - volume = {9148}, - month = jul, - eid = {91480Z}, - pages = {91480Z}, - doi = {10.1117/12.2055220}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2014SPIE.9148E..0ZC}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - - -@INPROCEEDINGS{farley2018, - author = {{Farley}, O.~J.~D. and {Osborn}, J. and {Wilson}, R.~W. and - {Butterley}, T. and {Laidlaw}, D. and {Townson}, M. and {Morris}, T. and - {Sarazin}, M. and {Derie}, F. and {Le Louarn}, M. and {Chac{\'o}n}, A. and - {Haubois}, X. and {Navarrete}, J. and {Milli}, J.}, - title = "{Representative atmospheric turbulence profiles for ESO Paranal}", - booktitle = {\procspie}, - year = 2018, - series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, - volume = {10703}, - month = jul, - eid = {107032E}, - pages = {107032E}, - doi = {10.1117/12.2312760}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10703E..2EF}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} diff --git a/docs/joss_paper/paper.md b/docs/joss_paper/paper.md deleted file mode 100644 index 98146718..00000000 --- a/docs/joss_paper/paper.md +++ /dev/null @@ -1,92 +0,0 @@ ---- -title: 'ScopeSim - A pythonic astronomical instrumental data simulation engine' -tags: - - Python - - Astronomy - - Simulations - - Telescopes - - Instruments - - Extreme Large Telescope - -authors: - - name: Kieran Leschinski - orcid: 0000-0003-0441-9784 - affiliation: 1 - - name: Oliver Czoske - orcid: 0000-0003-3127-5341 - affiliation: 1 - - name: Miguel Verdugo - orcid: 0000-0001-5027-557X - affiliation: 1 - - name: Hugo Buddelmeijer - orcid: 0000-0001-8001-0089 - affiliation: 2 - - name: Gijs Verdoes-Kleijn - orcid: 0000-0001-5803-2580 - affiliation: 2 - - name: Werner Zeilinger - orcid: 0000-0001-8507-1403 - affiliation: 1 - - name: Joao Alves - orcid: 0000-0002-4355-0921 - affiliation: 1 - -affiliations: - - name: Department of Astrophysics, University of Vienna - index: 1 - - name: OmegaCEN, Kapteyn Astronomical Institute, University of Groningen - index: 2 - -date: 28 September 2021 -bibliography: paper.bib - ---- - -# Summary - -- A pythonic simulation engine for astronomical instrument data products -- It - - -Documentation can be found at https://scopesim.readthedocs.io/en/latest/ - -# Statement of need - -- Why we need ScopeSim - - Each consortium invests time and effort in writing simulators specifically for their instrument - - Once the commisioning of the instrument is done, the simulator is forgotten - - At any one time there are few instruments being built, thus no effort has gone into keeping code and knowledge - - The majority of astronomical instruments contain the same optical elements - - There is no standard interface for desribing instrumental effects and no standard code library (like astropy) - - The ScopeSim framework provides the building blocks that each simulator needs, thus eliminating the need to start from scratch - - With a standard simulation engine for multiple instruments, it becomes much easier to make meaningful comparisons between output data. Compare apples to apples - -- Audiences - - Scientists, feasibility studies - - Scientists, observation proposals - - Data redcution pipeline developers - - New PIs, Proposals for new instruments - -![caption](path) - -# ScopeSim workflow - -## Connection to other packages in the software framework - -## Basic code example - - - - - -# Acknowledgments - -ScopeSim depends on the following packages: -Numpy [@numpy], -SciPy -Astropy [@astropy2018]. -SynPhot - -This project was funded by project IS538004 of the Hochschulraum-strukturmittel (HRSM) provided by the Austrian Government and administered by the University of Vienna. - -# References diff --git a/docs/slack_channel.txt b/docs/slack_channel.txt deleted file mode 100644 index c793b08f..00000000 --- a/docs/slack_channel.txt +++ /dev/null @@ -1,62 +0,0 @@ -Slack Channel -============= - -Possible Members ----------------- -oliver.czoske@univie.ac.at -miguel.verdugo@univie.ac.at -kieran.leschinski@univie.ac.at - -verdoes@astro.rug.nl -hugo@buddelmeijer.nl - -boekel@mpia.de -burtscher@strw.leidenuniv.nl - -jpott@mpia.de -carmelo.arcidiacono@inaf.it -messlinger@mpia.de - -Michele.Ginolfi@eso.org - -david.jones@iac.es - -born@astron.nl - - - -Initial Email -------------- - -Dear ScopeSim users, developers, and enthusiasts! - -New ScopeSim version - -Firstly, we'd like to announce the release of our latest ScopeSim version (v0.4). -This version contains an updated version of the long-slit spectroscopy mode, as well as various updates to how Source objects can be defined (FITS cubes, lone FITS images). -As always the new version is available via pip: - -pip install --upgrade scopesim - -ScopeSim Slack channel - -It's finally reached a point where multiple teams are now using, or will soon start to use ScopeSim. -Indeed ScopeSim has reached a point where I think it is mature enough that we can start building a community around it. -My hope with this (yet another) Slack channel is that we can bring everyone together, both developers and users, in such a way that we can all start to help and learn from each other. -Not only would this hopefully enable quicker responses to your user questions (i.e. not every query has to go through the Vienna team), it should also hopefully help to expand the developer base for ScopeSim. -Much like the astropy community, it would be great to be able to engage, and indeed profit from the wealth of instrumentation experience within the community. - -https://join.slack.com/t/scopesim/shared_invite/zt-143s42izo-LnyqoG7gH5j~aGn51Z~4IA - -You are receiving this invitation as you have a practical connection to ScopeSim. -If there are others in your group that you feel would also benefit from being part of this channel, feel free to pass the link on to them. - -Mailing List - -We realise that every man and his dog has a slack channel these days. If you would prefer to only be notified of major upgrades or events related to ScopeSim, then please let us know that you would like to be part of the mailing list. -Please send an email back to this address (astar.astro@univie.ac.at) with the subject list "Mailing list". - -As always, we look forward to hearing from you as we all continue to use and build on ScopeSim in the future! - -Happy simulating, -The ScopeSim team diff --git a/docs/source/5_liners/bang_strings.ipynb b/docs/source/5_liners/bang_strings.ipynb index fd7964cd..c7ebb2cd 100644 --- a/docs/source/5_liners/bang_strings.ipynb +++ b/docs/source/5_liners/bang_strings.ipynb @@ -9,26 +9,20 @@ "\n", "## !-strings are for setting simulation parameters\n", "\n", - "### TL;DR\n", - "\n", - " import scopesim as sim\n", - " opt = sim.load_example_optical_train()\n", - " opt.cmds[\"!ATMO\"]\n", - " opt.cmds[\"!ATMO.background\"]\n", - " opt.cmds[\"!ATMO.background.filter_name\"]\n", - "\n", - ".. note: !-strings only work on `UserCommands` objects\n", - "\n", "!-strings are a convenient way of accessing multiple layers of a nested dictionary structure with a single string using the format:\n", "\n", " \"!.....\"\n", " \n", - "Any level of the nested dictionary can be reached by truncating the keyword." + "Any level of the nested dictionary can be reached by truncating the keyword.\n", + "\n", + "**Note: !-strings only work on `UserCommands` objects**\n", + "\n", + "Below is an example of how to use !-strings, using the example optical train." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "loved-franchise", "metadata": {}, "outputs": [], @@ -39,64 +33,30 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "uniform-cursor", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'background': {'filter_name': 'J', 'value': 16.6, 'unit': 'mag'},\n", - " 'element_name': 'basic_atmosphere'}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.cmds[\"!ATMO\"]" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "domestic-chemical", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_name': 'J', 'value': 16.6, 'unit': 'mag'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.cmds[\"!ATMO.background\"]" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "earned-indicator", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'J'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.cmds[\"!ATMO.background.filter_name\"]" ] @@ -108,82 +68,19 @@ "source": [ "## #-strings are for accessing Effect object parameters\n", "\n", - "### TL;DR\n", - "\n", - " opt.effects\n", - " opt[\"#exposure_action.\"]\n", - " opt[\"#exposure_action.ndit\"]\n", - " opt[\"#exposure_action.ndit!\"]\n", - "\n", - "\n", - ".. note: !-strings only work on `OpticalTrain` objects\n", - "\n", "Similar to !-strings, #-strings allow us to get at the preset values inside the Effect-objects of the optical system. #-strings allow us to pring the contents of an effect's meta dictionary.\n", "\n", - "First let's list the effects" + "**Note: !-strings only work on `OpticalTrain` objects**\n", + "\n", + "Here, we're again using the example optical train defined above. First let's list the effects:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "hydraulic-astrology", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=17\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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basic_instrumentfilter_wheel : [J]FilterWheelTrue
basic_instrumentslit_wheel : [narrow]SlitWheelFalse
basic_detectordetector_windowDetectorWindowTrue
basic_detectorqe_curveQuantumEfficiencyCurveTrue
basic_detectorexposure_actionSummedExposureTrue
basic_detectordark_currentDarkCurrentTrue
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basic_detectordetector_linearityLinearityCurveTrue
basic_detectorreadout_noisePoorMansHxRGReadoutNoiseTrue
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basic_detectoreffects_fits_keywordsEffectsMetaKeywordsTrue
basic_detectorconfig_fits_keywordsSimulationConfigFitsKeywordsTrue
basic_detectorextra_fits_keywordsExtraFitsKeywordsTrue
" - ], - "text/plain": [ - "\n", - " element name class included\n", - " str16 str22 str29 bool \n", - "---------------- ---------------------- ----------------------------- --------\n", - "basic_atmosphere atmospheric_radiometry AtmosphericTERCurve False\n", - " basic_telescope psf SeeingPSF True\n", - " basic_telescope telescope_reflection TERCurve True\n", - "basic_instrument static_surfaces SurfaceList True\n", - "basic_instrument filter_wheel : [J] FilterWheel True\n", - "basic_instrument slit_wheel : [narrow] SlitWheel False\n", - " basic_detector detector_window DetectorWindow True\n", - " basic_detector qe_curve QuantumEfficiencyCurve True\n", - " basic_detector exposure_action SummedExposure True\n", - " basic_detector dark_current DarkCurrent True\n", - " basic_detector shot_noise ShotNoise True\n", - " basic_detector detector_linearity LinearityCurve True\n", - " basic_detector readout_noise PoorMansHxRGReadoutNoise True\n", - " basic_detector source_fits_keywords SourceDescriptionFitsKeywords True\n", - " basic_detector effects_fits_keywords EffectsMetaKeywords True\n", - " basic_detector config_fits_keywords SimulationConfigFitsKeywords True\n", - " basic_detector extra_fits_keywords ExtraFitsKeywords True" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.effects" ] @@ -197,40 +94,15 @@ "\n", " \"#.\"\n", " \n", - ".. note: The `.` at the end is important, otherwise the optical train will look for a non-existant effect named `#`" + "**Note: The `.` at the end is important, otherwise the optical train will look for a non-existant effect named `#`**" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "exterior-romania", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filename': None,\n", - " 'description': 'Summing up sky signal for all DITs and NDITs',\n", - " 'history': [],\n", - " 'name': 'exposure_action',\n", - " 'image_plane_id': 0,\n", - " 'temperature': -230,\n", - " 'dit': '!OBS.dit',\n", - " 'ndit': '!OBS.ndit',\n", - " 'width': 1024,\n", - " 'height': 1024,\n", - " 'x': 0,\n", - " 'y': 0,\n", - " 'element_name': 'basic_detector',\n", - " 'z_order': [860],\n", - " 'include': True}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt[\"#exposure_action.\"]" ] @@ -245,21 +117,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "independent-benjamin", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'!OBS.ndit'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt[\"#exposure_action.ndit\"]" ] @@ -274,21 +135,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "internal-capital", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt[\"#exposure_action.ndit!\"]" ] @@ -296,7 +146,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -310,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/effects_include.ipynb b/docs/source/5_liners/effects_include.ipynb index 3577de96..d8f046eb 100644 --- a/docs/source/5_liners/effects_include.ipynb +++ b/docs/source/5_liners/effects_include.ipynb @@ -7,15 +7,6 @@ "source": [ "# Turning Effect objects on or off\n", "\n", - "**TL;DR**\n", - "\n", - " optical_train = sim.load_example_optical_train()\n", - " \n", - " optical_train.effects\n", - " optical_train[\"detector_linearity\"].include = False\n", - " optical_train[\"detector_linearity\"].meta[\"include\"] = True\n", - "\n", - "\n", "To list all the effects in an optical train, we do use the `effects` attribute.\n", "\n", "Alternatively, we can call `opt.optics_manager.all_effects()`" @@ -23,65 +14,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "obvious-retention", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=17\n", - "
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basic_instrumentfilter_wheel : [J]FilterWheelTrue
basic_instrumentslit_wheel : [narrow]SlitWheelFalse
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basic_detectorexposure_actionSummedExposureTrue
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basic_detectorconfig_fits_keywordsSimulationConfigFitsKeywordsTrue
basic_detectorextra_fits_keywordsExtraFitsKeywordsTrue
" - ], - "text/plain": [ - "\n", - " element name class included\n", - " str16 str22 str29 bool \n", - "---------------- ---------------------- ----------------------------- --------\n", - "basic_atmosphere atmospheric_radiometry AtmosphericTERCurve False\n", - " basic_telescope psf SeeingPSF True\n", - " basic_telescope telescope_reflection TERCurve True\n", - "basic_instrument static_surfaces SurfaceList True\n", - "basic_instrument filter_wheel : [J] FilterWheel True\n", - "basic_instrument slit_wheel : [narrow] SlitWheel False\n", - " basic_detector detector_window DetectorWindow True\n", - " basic_detector qe_curve QuantumEfficiencyCurve True\n", - " basic_detector exposure_action SummedExposure True\n", - " basic_detector dark_current DarkCurrent True\n", - " basic_detector shot_noise ShotNoise True\n", - " basic_detector detector_linearity LinearityCurve True\n", - " basic_detector readout_noise PoorMansHxRGReadoutNoise True\n", - " basic_detector source_fits_keywords SourceDescriptionFitsKeywords True\n", - " basic_detector effects_fits_keywords EffectsMetaKeywords True\n", - " basic_detector config_fits_keywords SimulationConfigFitsKeywords True\n", - " basic_detector extra_fits_keywords ExtraFitsKeywords True" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import scopesim as sim\n", "\n", @@ -99,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "local-stations", "metadata": {}, "outputs": [], @@ -107,11 +43,19 @@ "opt[\"slit_wheel\"].include = True\n", "opt[\"slit_wheel\"].include = False" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2302c803", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -125,7 +69,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/loading_packages.ipynb b/docs/source/5_liners/loading_packages.ipynb index 6cada7a5..b5f3309b 100644 --- a/docs/source/5_liners/loading_packages.ipynb +++ b/docs/source/5_liners/loading_packages.ipynb @@ -7,20 +7,20 @@ "source": [ "# Downloading packages\n", "\n", - ".. note: Instrument packages are kept in a separate repository: [the Instrument Reference Database (IRDB)]((https://github.com/astronomyk/irdb))\n", + "**Note: Instrument packages are kept in a separate repository: [the Instrument Reference Database (IRDB)](https://github.com/AstarVienna/irdb)**\n", "\n", "Before simulating anything we need to get the relevant instrument packages. Packages are split into the following categories\n", "\n", "- Locations (e.g. Armazones, LaPalma)\n", "- Telescopes (e.g. ELT, GTC)\n", - "- Instruments (e.g. MICADO, METIS, MAORY, OSIRIS, MAAT)\n", + "- Instruments (e.g. MICADO, METIS, MORFEO, OSIRIS, MAAT)\n", "\n", "We need to amke sure we have all the packages required to built the optical system. E.g. observing with MICADO is useless without including the ELT." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "collaborative-glass", "metadata": {}, "outputs": [], @@ -43,65 +43,25 @@ "\n", "The simplest way is to simply get the latest stable versions of the packages by calling their names.\n", "\n", - "Call `list_packages()` or see the [IRDB]((https://github.com/astronomyk/irdb)) for names." + "Call `list_packages()` or see the [IRDB](https://github.com/AstarVienna/irdb) for names." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "blind-algorithm", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Armazones',\n", - " 'ELT',\n", - " 'GTC',\n", - " 'HAWKI',\n", - " 'HST',\n", - " 'LFOA',\n", - " 'LaPalma',\n", - " 'MAAT',\n", - " 'MAORY',\n", - " 'METIS',\n", - " 'MICADO',\n", - " 'MICADO_Sci',\n", - " 'OSIRIS',\n", - " 'Paranal',\n", - " 'VLT',\n", - " 'WFC3',\n", - " 'test_package']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.list_packages()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "happy-column", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpvf9r8z__\\\\Armazones.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpvf9r8z__\\\\ELT.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpvf9r8z__\\\\MICADO.zip']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\"])" ] @@ -118,21 +78,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "egyptian-absolute", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpvf9r8z__\\\\test_package.zip']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.download_packages(\"test_package\", release=\"latest\")" ] @@ -155,35 +104,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "happy-thought", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO - Downloaded: test_package/TC_filter_Ks.dat\n", - "INFO - Downloaded: test_package/default.yaml\n", - "INFO - Downloaded: test_package/test_detector.yaml\n", - "INFO - Downloaded: test_package/test_instrument.yaml\n", - "INFO - Downloaded: test_package/test_mode_2.yaml\n", - "INFO - Downloaded: test_package/test_package.yaml\n", - "INFO - Downloaded: test_package/test_telescope.yaml\n", - "INFO - Downloaded: test_package/version.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpvf9r8z__']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.download_packages(\"test_package\", release=\"github:dev_master\")" ] @@ -193,38 +117,7 @@ "execution_count": null, "id": "neither-netscape", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO - Downloaded: LFOA/CCD-QE.jpg\n", - "INFO - Downloaded: LFOA/LFOA.yaml\n", - "INFO - Downloaded: LFOA/LFOA_SBIG.yaml\n", - "INFO - Downloaded: LFOA/LIST_LFOA_mirrors_static.dat\n", - "INFO - Downloaded: LFOA/QE_SBIG.dat\n", - "INFO - Downloaded: LFOA/TER_atmosphere.dat\n", - "INFO - Downloaded: LFOA/TER_focal_reducer.dat\n", - "INFO - Downloaded: LFOA/TER_mirror_aluminium.dat\n", - "INFO - Downloaded: LFOA/__init__.py\n", - "INFO - Downloaded: LFOA/code/__init__.py\n", - "INFO - Downloaded: LFOA/code/sort_NB_filters.py\n", - "INFO - Downloaded: LFOA/default.yaml\n", - "INFO - Downloaded: LFOA/docs/__init__.py\n", - "INFO - Downloaded: LFOA/docs/report_preamble.rst\n", - "INFO - Downloaded: LFOA/filters/B.dat\n", - "INFO - Downloaded: LFOA/filters/Halpha_narrow.dat\n", - "INFO - Downloaded: LFOA/filters/Halpha_wide.dat\n", - "INFO - Downloaded: LFOA/filters/Hbeta.dat\n", - "INFO - Downloaded: LFOA/filters/I.dat\n", - "INFO - Downloaded: LFOA/filters/OIII.dat\n", - "INFO - Downloaded: LFOA/filters/R.dat\n", - "INFO - Downloaded: LFOA/filters/SII.dat\n", - "INFO - Downloaded: LFOA/filters/U.dat\n", - "INFO - Downloaded: LFOA/filters/V.dat\n" - ] - } - ], + "outputs": [], "source": [ "sim.download_packages(\"LFOA\", release=\"github:3c136cd59ceeca551c01c6fa79f87377997f33f9\")" ] @@ -232,7 +125,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -246,7 +139,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/scopsim_templates_intro.ipynb b/docs/source/5_liners/scopsim_templates_intro.ipynb index 63da9d15..163ed30a 100644 --- a/docs/source/5_liners/scopsim_templates_intro.ipynb +++ b/docs/source/5_liners/scopsim_templates_intro.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "refined-radius", "metadata": {}, "outputs": [], @@ -36,31 +36,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ancient-blanket", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO - sample_imf: Setting maximum allowed mass to 1000\n", - "INFO - sample_imf: Loop 0 added 1.09e+03 Msun to previous total of 0.00e+00 Msun\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "my_cluster = sim_tp.stellar.clusters.cluster(mass=1000.0, # [Msun]\n", " distance=8000, # [pc]\n", @@ -78,33 +57,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "numerous-shower", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Wavelength [Angstrom]')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# See the docstring of `elliptical` for more keywords\n", "my_elliptical = sim_tp.extragalactic.galaxies.elliptical(half_light_radius=30, # [arcsec]\n", @@ -129,7 +85,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -143,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/simulation_parameters.ipynb b/docs/source/5_liners/simulation_parameters.ipynb index 6f781fd9..2b5f446a 100644 --- a/docs/source/5_liners/simulation_parameters.ipynb +++ b/docs/source/5_liners/simulation_parameters.ipynb @@ -14,56 +14,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "defensive-practitioner", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'spectral': {'wave_min': 0.3,\n", - " 'wave_mid': 2.2,\n", - " 'wave_max': 20,\n", - " 'wave_unit': 'um',\n", - " 'spectral_bin_width': 0.0001,\n", - " 'spectral_resolution': 5000,\n", - " 'minimum_throughput': 1e-06,\n", - " 'minimum_pixel_flux': 1},\n", - " 'sub_pixel': {'flag': False, 'fraction': 1},\n", - " 'random': {'seed': 9001},\n", - " 'computing': {'chunk_size': 2048,\n", - " 'max_segment_size': 16777217,\n", - " 'oversampling': 1,\n", - " 'spline_order': 1,\n", - " 'flux_accuracy': 0.001,\n", - " 'preload_field_of_views': False,\n", - " 'bg_cell_width': 60},\n", - " 'file': {'local_packages_path': './',\n", - " 'server_base_url': 'https://www.univie.ac.at/simcado/InstPkgSvr/',\n", - " 'use_cached_downloads': False,\n", - " 'search_path': ['./inst_pkgs/', './'],\n", - " 'error_on_missing_file': False},\n", - " 'reports': {'ip_tracking': False,\n", - " 'verbose': False,\n", - " 'rst_path': './reports/rst/',\n", - " 'latex_path': './reports/latex/',\n", - " 'image_path': './reports/images/',\n", - " 'image_format': 'png',\n", - " 'preamble_file': 'None'},\n", - " 'logging': {'log_to_file': False,\n", - " 'log_to_console': True,\n", - " 'file_path': '.scopesim.log',\n", - " 'file_open_mode': 'w',\n", - " 'file_level': 'DEBUG',\n", - " 'console_level': 'INFO'},\n", - " 'tests': {'run_integration_tests': True, 'run_skycalc_ter_tests': True}}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import scopesim\n", "\n", @@ -76,7 +30,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -90,7 +44,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/source_from_images.ipynb b/docs/source/5_liners/source_from_images.ipynb index d245e13f..f3e82ff9 100644 --- a/docs/source/5_liners/source_from_images.ipynb +++ b/docs/source/5_liners/source_from_images.ipynb @@ -9,7 +9,7 @@ "\n", "We can use a FITS image as the Source object for a ScopeSim Simulation\n", "\n", - ".. warning: The simulation output is only as good as the input\n", + "**Warning: The simulation output is only as good as the input**\n", " \n", " If the pixel scale of the input (`CDELTn`) is bigger than the pixel scale of the instrument, ScopeSim will simply interpolate the image.\n", " \n", @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "armed-tampa", "metadata": {}, "outputs": [], @@ -73,28 +73,15 @@ "\n", "It is assumed that the flux definied here is **integrated** flux and is the total flux contained in the image.\n", "\n", - ".. note: In future version, header keywords like `BUNIT` etc will also be accepted. This functionality is not yet implemented though (April 2022)." + "**Note: In future version, header keywords like `BUNIT` etc will also be accepted. This functionality is not yet implemented though (April 2022).**" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "viral-holly", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "image_source = scopesim.Source(image_hdu=hdu, flux=10*u.ABmag)\n", "\n", @@ -112,28 +99,15 @@ "\n", "In this case, the image pixel values are seen as multipiers for the spectrum at a given coordinate.\n", "\n", - ".. note: It is the users responsibility to make sure the total flux of the \"cube\" (image * spectrum) is scaled appropriately." + "**Note: It is the users responsibility to make sure the total flux of the \"cube\" (image * spectrum) is scaled appropriately.**" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "moral-messaging", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Alternatively, see the SpeXtra and Pyckles libraries for more spectra\n", "vega_spec = scopesim.source.source_templates.vega_spectrum(mag=20)\n", @@ -155,23 +129,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "center-latex", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n = 100\n", "wavelengths = np.geomspace(0.3, 2.5, n) * u.um\n", @@ -185,7 +146,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -199,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/5_liners/source_point_source_arrays.ipynb b/docs/source/5_liners/source_point_source_arrays.ipynb index 03bfe5bd..c0255954 100644 --- a/docs/source/5_liners/source_point_source_arrays.ipynb +++ b/docs/source/5_liners/source_point_source_arrays.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "general-exploration", "metadata": {}, "outputs": [], @@ -48,23 +48,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "alive-renaissance", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tbl = table.Table(names=[\"x\", \"y\", \"ref\", \"weight\"],\n", " data= [x, y, ref, weight],\n", @@ -85,23 +72,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "comprehensive-enlargement", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "point_source = scopesim.Source(spectra=[vega], x=x, y=y, ref=ref, weight=weight)\n", "\n", @@ -111,7 +85,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -125,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/examples/1_scopesim_intro.ipynb b/docs/source/examples/1_scopesim_intro.ipynb index 7fc13958..e2146d30 100644 --- a/docs/source/examples/1_scopesim_intro.ipynb +++ b/docs/source/examples/1_scopesim_intro.ipynb @@ -11,25 +11,68 @@ "## A brief introduction into using ScopeSim to observe a cluster in the LMC" ] }, + { + "cell_type": "markdown", + "id": "110aaf63", + "metadata": {}, + "source": [ + "*This is a step-by-step guide. The complete script can be found at the bottom of this page/notebook.*\n", + "\n", + "First set up all relevant imports:" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "fatty-excellence", "metadata": {}, "outputs": [], "source": [ - "from tempfile import TemporaryDirectory\n", - "\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "%matplotlib inline\n", "\n", "import scopesim as sim\n", - "import scopesim_templates as sim_tp\n", + "import scopesim_templates as sim_tp" + ] + }, + { + "cell_type": "markdown", + "id": "7358d4f0", + "metadata": {}, + "source": [ + "Scopesim works by using so-called instrument packages, which have to be downloaded separately. For normal use, you would set the package directory (a local folder path, `local_package_folder` in this example), download the required packages *once*, and then **remove the download command**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "346dd0cc", + "metadata": {}, + "outputs": [], + "source": [ + "local_package_folder = \"./inst_pkgs\"" + ] + }, + { + "cell_type": "markdown", + "id": "eeefa7b2", + "metadata": {}, + "source": [ + "However, to be able to run this example on the *Readthedocs* page, we need to include a temporary directory.\n", "\n", - "# [Required for Readthedocs] Comment out this line if running locally\n", - "tmpdir = TemporaryDirectory()\n", - "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = tmpdir.name" + "**Do not** copy and run this code locally, it is **only** needed to set things up for *Readthedocs*!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "022b83d9", + "metadata": {}, + "outputs": [], + "source": [ + "from tempfile import TemporaryDirectory\n", + "local_package_folder = TemporaryDirectory().name" ] }, { @@ -37,31 +80,20 @@ "id": "remarkable-outdoors", "metadata": {}, "source": [ - "Download the required instrument packages for an observation with MICADO at the ELT" + "Download the required instrument packages for an observation with MICADO at the ELT.\n", + "\n", + "Again, you would only need to do this **once**, not every time you run the rest of the script, assuming you set a (permanent) instrument package folder." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "premier-mount", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpxhqx8_if\\\\Armazones.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpxhqx8_if\\\\ELT.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpxhqx8_if\\\\MAORY.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmpxhqx8_if\\\\MICADO.zip']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sim.download_packages([\"Armazones\", \"ELT\", \"MAORY\", \"MICADO\"])" + "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = local_package_folder\n", + "sim.download_packages([\"Armazones\", \"ELT\", \"MORFEO\", \"MICADO\"])" ] }, { @@ -69,28 +101,19 @@ "id": "heard-motel", "metadata": {}, "source": [ - "Create a star cluster using the ``scopesim_templates`` package" + "Now, create a star cluster using the ``scopesim_templates`` package. You can ignore the output that is sometimes printed. The `seed` argument is used to control the random number generation that creates the stars in the cluster. If this number is kept the same, the output will be consistent with each run, otherwise the position and brightness of the stars is randomised every time." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "golden-division", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO - sample_imf: Setting maximum allowed mass to 1000\n", - "INFO - sample_imf: Loop 0 added 1.26e+03 Msun to previous total of 0.00e+00 Msun\n" - ] - } - ], + "outputs": [], "source": [ "cluster = sim_tp.stellar.clusters.cluster(mass=1000, # Msun\n", " distance=50000, # parsec\n", - " core_radius=0.3, # parsec\n", + " core_radius=0.3, # parsec\n", " seed=9002)" ] }, @@ -99,28 +122,17 @@ "id": "finite-linux", "metadata": {}, "source": [ - "Make the MICADO optical system model with ``OpticalTrain``. Observe the cluster ``Source`` object with the ``.observe()`` method and read out the MICADO detectors with ``.readout()``. \n", + "Next, make the MICADO optical system model with ``OpticalTrain``. Observe the cluster ``Source`` object with the ``.observe()`` method and read out the MICADO detectors with ``.readout()``. This may take a few moments on slower machines.\n", "\n", "The resulting FITS file can either be returned as an ``astropy.fits.HDUList`` object, or saved to disk using the optional ``filename`` parameter" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "bronze-generator", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Warning: header update failed, data will be saved with incomplete header.\n", - "Reason: !OBS.instrument was not found in rc.__currsys__\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "micado = sim.OpticalTrain(\"MICADO\")\n", "micado.observe(cluster)\n", @@ -138,33 +150,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "undefined-flush", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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r2uL6y5v7T2tewKlf1pnGYCmKJMjK/ClkOPJ3Gol0WBmVj9ildp1MhiKNDSq3BduZDpaT+mT0Ku32Ae+77qZXOoHR2S0C7Oz7M9zfF/2n5Hwi4OH4t37N3yN2aFRvGvsRA5SgJh8tb08jui+fKenTueZZntSVjGYC36Z7Y11GwUmmy3s5hxlgsI7RWiRrwH7NcrMeH2Z2GUvgiICLwJQOXNq2U3xQIXXP7xlQtHsZ2BBAJIhkXy7JPhvntiR7lTbHdXNt0OY1do/1jEBn9u8SK/f8yoHpGcmlBz3tEXGpg410aktO3dctjMIyfzq20WPko4gldaChbY8QWxcf0m3MTAqZBBo4Xs/7zahlewgysz4a2pHjtiMbsWLO4zLoZJqzbI6qgQhpGwzuiyBblL+kbwOZpM/pxNi+Ziizr5v++8BKlssDw5m+ASL3eYL3FhCk8B02ySKxfOufrFyyPmZCc601B93YNgKeNh/yerMVZDSYn+Cb+bPcBprzfs4VO2GWQ5BzhvSjtXym3bXd1jnrTCaaTFezv0vBZc6/xrZeFOCw3FHexjqTxR4Fjq6LaznXXesr2v1suzS28XdHDqBnJJce9HC7QerMRBrVUaSdeUeUKsseOSUaGN8jMOBClGaDxmgv9R1FoaRamZ+RR9Kwo4OWZIuynNR5ZKjSAWTeI+0CQLYnI0bulXMbxQY/+47O9ki7DmHJ2C2BIvdJe5KDbJSF86zVkWCVTMQZ7qeQUWtp01mmvpQ2d441vy/npvq4taeJGqDTpiyPW9sGy77M/lBJyzwEb415oqNjOQRAvN+ALe3LqB8yXY49wW6zP2QHWoBH25L2LNcfHbGlgXXqkHVaWiCX+ds7baRdgJb3lxjTpsNIRrY79U790kbmtl0LOFs9OVZk12iDDnJ55NKDHmmeRGkw2gQnIGCahu4bc0FpNGYuln35U5+MopuzbSCpGVZ+Mt2SPmRPsk9H4KaVTdBBR7pU1ig69fcc86Z7o6cTYDUDm4Yvo9oso4G+JbA3coKZf+mx7zNNB7VPNAGNkTQH02QEwrNuAsUs/yZ051yTtsdp5EzJ1rTHhemwc0z97p7mULJ9HMMlZiqdWZtbBEmjgCclo/nUJ+djA3w5d5aCjVwnbVzbOcYGWgxUyby4DdZjBBzSiSvSsZ2Zr52XWWIqRyDsonIRYNTsWBsHMkbSfjuf/ey6RmcA744cmJ4mF5knl0Jo8I/wxzQN8LQImEZU5XpKY1KajKhPMwO5uNoeM6M/MjZLUaqNN6NGtseGacngjvoz9Rw5mZGTzjafqoOPHJ90Rg1o2fmOth+p16jO5sDzM7+TQSSgcJq8R0bEckfzYd1RWdY3XzNAxoX9lHUuRdBLzGKbawlSEtjkXOCcam3KunjdLyVs4nbzfupHnRM0pi5tey7n59IcsKTTdP3Zp41xHAEUCvuxgWuyPi6XQInbLdk+g5eTSJvSAAnnN/OQgbdNOo97rV9b4Je2aDQWWS51bekzDZnxfHKNjBr9Dpm7ZIhHAdNB7p1ceqYnF0ozDgQfIwdO4ZuCl1ihEV3cdGpUtRE/z9Vk+lGUk9ExI5MlGZW3FNUtRfXZTjJWvK+F6zRcNBxtmyR1Y+TbdHV632tsXtO3Uf40qg2cZr52pmqpfH9PhoDtyrLSWZBh8u99DGdjCnMesp8aKEngMHqCqznqEbjyd7c/nUervzlwl5HOpgEX96HncGvb0vrKPiN76PraY80EQak72zg6W0M7l/N6iVHKfqU+Lo9jmmW7XvdttqHpn2kzf7OXaQs5HsfanZN5z2WnnqP5rnKvARrF79ST+jdQxdeR3DtWYa0D5Opy6UEPjdhIEqX7t9RpbMuzoVFb9LBUR3M8uTANgkZ0cjPo1CcNistrT1d5YY8OV1JGC7ydXfF31k09l5wmQSEdKSNkOhL2hZ0ZnXdjZQgcRqDGZbS+afMrI73WB0clTSsjdaczHoFWXktheylkRlhGshZ0zHTaS2svdUkHmnMsneHZIA3b1xwm7xEUNQDIPmrl5DwdsYxLDJvFZWTaUbCQdm7ENnDd5pqi/i0gSCDXQGqu2RwbAqNsT7O1XLup21IaBhgtmEkGZ/QwjMsdAUzKA9ruw7Y+L48cDjKP5N4B0QtKRsF5jZ9c9Em9jqKMUXQ6os0NXMgotOjd6TIaSqMwWogj3agX6ddRNETHkU6b1H7KCIyd4jN1aIa/GasW/abzyEjSkVtjp0ivc16QPm/6MDLMupy3nSO6CHjMdPnpv6XFR+A6AiCjPJaRM049/I89s//O4n7OLY+PnV72MR1dzlEyZ+5fOt2sh+PtOZuOKkFJ6tb6nTryOz+zD6xzY07ZpmxHc56tXSnZt5mmOXfWl3USuC3ZglEAmOs783uO5PzKtXyu7fVLppJ5aMNHaVIvvtGZ361zbuGn8HUiKc2eNpuYfZLXD3J55dIzPWRwfG1EbRL4HEX6nMgZ+bCe9qi0Je/vA1FJLTOitYxYi2QBpF1DxoXeFl+mcx+kTkIe199YGxrXxpyMIvYEEuz/I6QfOYN0wM25t99Lwrkh6GPHk08rjSK5HMPWr6O62dacfzlXGYWTmRoxNq09lLxu8JyOns67gQe2LdfHiKXkvM95bp3aVkUDfxYGGU24rqiXBvdcfurRGEbWleldR2PRsk6+DNXXGitDIcikXWD+XAejspMtbuvYdqXZuJw/eaQg77Xv+c6n7AcGemSt2ppRpCHQGbWtPaDSAjjq1Gz8CFA+v3JgekZy34BSovWLGIAEHGQKGH3QQDX0T8ZH2t2iapF4LlBGMoy6sjxGDy6H+8btIDTzNePWHITbRrA1cjaM3EbRZLuXQCeBTLIQQh6n55i1iDz7ilsi2f4GopJRaE6wOeMlo5vGeV/U2cBmfk8QaXGbG2AhoBgBY3/n/ErhOFivrLMBJM/REQvF8U7QRJZMyNfWSY5bpjnS7lw+Rt590pgkOk6C6hEbRufpPwKDBhRSWGfKEogdgQPrmuVznTRWZVRu9nsDBwmeMg3zsH1knkdtySCgARm2LW2E19UIzBwhLeu4b5zsi0QuPdNjSUOT1840/58eMg3pwMl4JGjiZ8pFzxOljKLvRtMyfVsgR0jHNHxXDCWdECP+kS6ZlkyQDQhB3KgNowjR6Xk2gGyXtN13IyYuy0tJEJQ6tbxLziDLYwRLg09AxU/W0RygnXc7w5JzOxm8kUNvQDPvWzjeLKOtnRynpmfWl/2SY9tADvO3dbU0pjmX2R6Wk33IfE1G/Sxtzw0GEEssk9dVlj06JM68lMZK5byljkL6Vo6F8zznwz5dR4Fq2o6LslkpXEs8J5X+gcFN8wNZ7nnkzza0fluaM3dfDkxPk0sPQpeowWP1Q4kJdBg95CJVXM/P0cTlxHf9adD3RdI0kmQnGgjJ+lle6k3JvEkT06iR5ZLGzsTXH9iUmf8YMtuSn3Tm6VSYJ9MnU0HH3qJNCkHVs6GZ2zgwqvd3PwLvsX8mvpOpGbEWdO5H8Zn3W7kE/Eyf9SUAJIBu+owAQq4ZslMcb15rkn2aeX2tAfWlQEj4TDaUc/us5GO7RywT6yX4oZ3hWsm8fKHmqM/IirR70va5u5wjtFepH3UbgeO2nmjDWiBGRsR6jtZmmxMOMhpjZN2OkCfrZ5kuYxQMLvmF1l728UEul9w3TA8Xam535cK0cUvwQ3ZDyDuKzKTlx3HbNpO0a/STMWkGOZ2cr7Wo8CKRZ9brPKbLc/FeJILMcsnkHEt6taZ/mdHAVKYT6mMf7DMMzQGQ0Rs5Xf/Occ7fbXuE0WBzgBeJjsmatb5cimozL8eMbXK9Syxjmz95SJtAlO0i4+O+4hmwdNqp04ilGTE4BMSuIw/gt62KJqOXWo7Sub5n1B1l1teYhX1zmuNPsMS1lnkYFLBcl5lPdKaOo20YsopcMwpdGPw5/wjIU9d9AWbaYrKvS2CQc6qNA9cCx859nluKDeClTpcP3BzO9Izk0jM90thApMEllbkv4rCQ6nS5WR9RO9mjJVrXxidl9MitHcOIIWlR4igSG9WXetnBjfRxHzYK/5qkXyLptXvqzqexKK6f90ZOd2n7I/VtIJhPqrU6m2EjU+M6ky1pwCB1aMxMY40oNrwNUDa92ziNQBDTuk18Ksqy9OJHGv7cdktZ0tN1co4nS9OYrNwOGm2x5e9RX+SfZekwtpAun2TznD7SNsOUDA3P6NBmcf5IfX67/rzWbBrZQM7/nOv7QFXqxyAx7V2ylrSpbFsKA460rwRLBN/n8X10Doprl/qN1lvOD7JM9APPBgA/P2LQ81z/3f9yXzA9dMQtGs/7yfYwX35n9GNZokZpeBlpemE0o6byvUVirOcc6Uc6NnYodc/6R+Vb/zR8zanfkvRdkm5o9xwC8xOstu2DZiySIcjvfqqqMUtszwgsZv08n5RnhpbGtEm2+0gzU5AggOCWbRi1y+1hXy69PZeGOhmP3JptQDL7PduQTAF/u6x85xIjaI5xY80IFhuIGrF//k42j+WSrW1gjGuD99lX0i5Y4nzndisDuUzT1kjWnU4/x3uJ9WpHAlr5qQ+lMeUEpjnOfCJRGtuyBE4jG75kQ1obRm1bsv38zoPQjd1nf9wXrMKLTC496BkBnObEc0K2l9NREuW3hZcGM42/5bjkpbHJ/NQpF02LpGlwWsTZgFhjG/z7qORr+i9Fydb1fZpBRRsjGqX2vRnopjfvNz2bod9Xv0HDEtBwuqVxbsIxpc5LzANZD9fPe/lod5sfbT7agCegY5qmj8trTpt1tsPkBHQG/O1FhqP2sL6RXi0YoY6WBopaXW6H87g9bes8AVB7hJ95loIk69SCmtGcb4yKr1GXpUCysXgapG1gagSalmxutmMUvLLc0dnOVk/L03QhGGoMVbMb2ZYR4Hr+5bC9NZL7AohyQjYnKO1GWi0tF5aNVzoivmOGlHOW05yK7/Ntu66HkXc61EaXZlmKdJm2RR2pT+bLNuQ1AoiMHOmY8vButi0dMtme1InsifXI/mfZjMCX6PHmMLOMY/y5zKxX2i4n03I7o/UzHY8/yQ40HbItnENOx/6jcF6nIW6S7c+xHfVHa1vKEpsj7a4zp22vYGjsC0EbgQODgSxrCXi29eAyGiuT99y/SyDa/cv71JvzkXaLQmYlJdcVWah0zkvMDoNIsueK7wSOrb2ZvgFngnnqYMkxbKC19VUGa7yeOjTARIbN97jmM+1BLo9ceqaHkmCGC6BtD7VJy8dyszwyJUsL3WVl+XQKpKgbA9VYpNSVh02XIuxMd4RrLjfL5jX/TgahRektksy6Mpps7IDwnZFx9nUaaEvqk2M2isIak5BAsRl7Aiu2Lw1fsnk8I9XGIH83UENxGZxj+5gu1jsaM/Y1x6b19Wh7jHrnfE2GhA4h50ACpbYFZbFDbXPFv/Og6j4g2t4wvC9azzXd2Jj20EACL9bTtoIIMGgDaJuyPdkHS6DGn+6PBNXJVjHYss1toJMBWco+RoaAhUw1QX8LALItrS6ygwSWbX4n0MmArK2/i7zy5PmRA9MzkvsC9KQhaUZvtGD8Xdo20DRSKt9ddk7ojGBGzEwasKUonIskF2e2IR87ZX3NYTR6OcFcGqJRFOL0aWCoe9aXACINn3U4RR7LaFuJfdz0JGAY0c2uJ5mqESuWhjTPhXGscv7wiZV9+mXETV2kGdSkQT3T/GqAnIP7QI6F86oJnWXq4jLP8NmYsSyP40jgnYfLCarofHK9JcOTAcuIsUwbMXoPEZ0i2+K60w410DYCgQSZSw8XpFBv/+X8bA6b82cEatr8dH7P1WSj2dfcnk9w5HLaOh/NwzYOo4DA/cCt2vakaNriLGd0jjKvEcQ1AExbnnUuAebnVw6gp8m9G49nKaOnWBpoaYAmr5Mt8l8a4TTouaDNQuyjXDMN2R9ep0NtC5SL0ovbRpDbEXQe7KemczPg/st7WW/T03WQWWng5bykdX35R/0y7Qi8pWPMVxw0Rq2xUtK2Q/DvBhDy92m519IRAFmPM1xfOvfF/ll6UtFCEMLvTsN2pkNswCH1IWByHvZNy29pDGQDyblt4zIJsgmiVNLsS5tATiVN9kk6vFG+Nk8yr4UBVD740ACi623BIPX0J/s62bgjXG9MpdRtlUFJBm20Y42harqPXjuQcyD1cL7RNV8/xrVMR7vQykg2k4HxfeNgX0Ry6ZkeTsakNHNSjaJ3lWtkbwiC2sIj05Lfmw6sOxmErDfb1JzyyHCnMWrUq3CtRd6pW4IYGgWWlbqRTWr5uIWUkVC2YeQMRw4jx59AMfMfaWJKbse11I0swyiSH0XxeS31beCIerdyuZ1jp9HqcPnNCNtgJ8tJx8Qon21OINfOhDjfPuPu/1DNVwewzhZxN2BMtmnf/CGbxHKk3T5iP9AJZv7mOFlv6tOYgbY+/ZvtIEhO/SmtXaNAQdrPUHhMsg1eRzm/9wFKaZdBSn0zjevI39aV9sxljILRc6RpD5g4bdo4IR/TN/CW9++uHLa3RnLfAVEvkhFyTyNESjedc5uUIxqTVOco8mF0zMWS+nGh8n0y1qMZRzIvWY8ZDeo4ooidP8VtagdN2wHJEUijZF+1drV0THOONNmuZHOY9+Wa3i3UnGMySunkqVczugk+yVq0J3ac9upGn8ZePKBtR5gswwjsnpT0Lpt9Jo0NcXPozkshU8B1lW1vLAOBCdcL50Gyjhwb9uFo3fE+58+ozAbsUg+PS2Nu2vxynew3gsl2PozjmWufDrgxHdkXtIO2RTnHbDtHAVUDBi47yxuVkWUxqBpt/VpO4nprb7aD/oAMYQa+BNoMjJu+o+sHuVxy6ZkeT1CebSFAkbbpTx4obUaRTJEX78hxLjEyjd5kGgp1H6XdF0m4Hi+2bPdSuc3wZT2NbRn1T9br74y6WvS1FEG5PjooMmUt8mJE/j5JT0eakYPi/GK0ng56CeDR2dtYO/9tpM22JOBi3dSXTtHXsw0pI+CZZS61q7FiWX/2DYEjy03Ggfea0zBbZLkIOGbfJXjPtjZ2o7GMCWCdbxSMpIz6dWltNuaq3WcAYP24Vr0uR2cNuWZGdafeuY7Yd6fq68z3s/wj5PF19lsyQ9RFC/doM1JG9oMgh0LmLMFnbundG2bhwPSM5NKDHmn7qQEujlH0vbQ4lgzTkmNecgbUtdHqWU/q4++jKIXs0CiC8/U04HkAktEfHVLbesk2Ma2/M4q1oWuMWQr76Rx5sh7LiG07xm9pu99vIs1oTC/ChHGLwS8gZJ5sW0b1jFZZPqNXAo2MJM9x70i724aKtI2VI0OTYJVl7AM6rU0NODItQUm7T8fegA3XFgH70rWl7VbqxCcqU9geg1MGblJ/784IPLWgjGsuhbaP9oMAKOery2bwSJCZa6893cW0ivSu/0TTGmp5ElA25tAyAmZk4iw51gSILHvkE5p/WWJG764cQM9I7v3YPAtJx6H4ngf6cmE1B0mxAW5RY9Yr1OvPrCPrOkWeFpWTBTBI2UfxjtpFXVwvI5YWSdHZNIo2wU0CFjrNbE8Tjh+NeTJFBLY5viMne65d/dNBNme+5Gyz3XTwHp8RIM4o0HPADipZh8Ya+ntjfTjGlnzyjG3L9ZPzcjTO0nY5OT4EO8eDvA1cUOe8xvoaIKLsY6YaQ+Z8+xjeli/bfITvrY5c6znWI3sg7YJ9Mqcp2Q4GA/vsX86VtFm2qaN6We6o/wjO/fmgpJ8n6WHtAprMSxCbIHLfeOVcZ/0jcNtAnXVLW+S0BHL77NBB7r3cF0yPtD1ZG9onPc33wixFJETnS1Ff0p5NWuRnSeNBA5VplhZz6j0CFtSBbb1otNjyUdcW1bX2N8PW3kGUoNF5GKEzIl0ax6wr9bCezUG33y4j2+v+aQbbafiETduqG0n2fzpaAggyMZk+xWAyhWkyAGDfZ9sbk7MUOCRIsi6tz5YcEMe9zc99zMtSXSN2cUk8Jvl0qX9T3yy7AdY2Ry1LB/XPtDsmI0mgnWORzr2lHzEsBOgEQ64ngxiP24e0ux5G5dCOsx8zuGiMDQO+Nk+TvRnZ1rSVfHUFx/TeMgoHpmckl57pSQbnIpPKk5lPeJHmXNpeuK3dczEuex9qb0hf6gaN9Hwr30aTDrctymZICUpG+gnfecgvz6BYkoUalZPlkS3hi+CWIvas05/pRG2EMl0+Wt8iRpX7zXGQybIk+GyOMkHGRRwp60j9CWzS8VP/BCHC9dHZlSzzHNdHwvtt3JnGcyZZHIJNXuOj3bl+yO6yzpFOqYttgq+TbbUOyTxYbhf9ONacY9TlHOmyjFH/khliPxsAtDE6j/u0IwSzlAZq0sZQ79G6uSPpbZr+d1+TJfuc4CnTjgJJlpk+JZk4tyUZnX3z38J6DwzP5ZVLz/S0g7gEDgQxjQnhwuGCadGJtDyxnT+jRDJC/nwm8pDV2Ofs04jS0I8iEm9zjCIjGkzKWUlzPLg+eslg6jyqhzq7v2h0kqYegT0+fs7+5XkrxfcEEfleJjISGQlmH9uRpBOgM29OoTFfI/aJc5IvUWSeNsY5H6gf29jYpaZXY3nogNP5ZhCxxFIRmFncbq8rpmsAqTEWbQxcfoKgDIIaM8bfHIORfksM64idzfIy34jlaeuy2bxcc62s0cv9zvCbL948Kr/J6HKdMki1bm39E1wtMbEe8xHTTybSul4UvNAXLbFKd0cOTE+TSw96mkNvgGfEfuS1Rn2nE0xWJo1Jy0dptG86cOZvC03aNT4jB2hh2TTCXuhH8Z2GivX4GstuB53pGJPuz/bTKVJynEZP3dFxko1rbXd5SwC4jQXnUZs31zVFrK6Db6NtbW4sgvu6CcESdbBeCZhyLOgYR44tQUEDqQSHwu/GQrLPl95h1YRjTCdNYLvEplD49FYK53c7s5X97t+tz5seI9A9ej/OiH1kWY1FbiC4BVsJcqlztnUEPMwitvFKm+NDy89o91zOaE6O5gBtIrfAcn60+UeAcqzdPsrvLdi2Hiw39bk3ctjeGskSG3iphMY1fx+V+02S8h2lbQZr5CRHOrZ6XR8pcka7rb59Qip4pP/SYLtv0ngzOs5+Vtxjne38QW4BNac/cl50skltN2ee0hyAyneXIc0O7lS7IC/rtp7XJD2E+2QpMuq0Lsm0HMe9Noca27SUJsePzNFtbYOj/KMssQccl9TDn8kYSbvntAwUKQkEGoPbmKw2X0aApgGGUbnZN55/2X85r3OrN9tNNoL6WY8EAQ4WRjZlZLvyu8Ey2Y9Rv9A2pc65jlPH87i+xIJlsMLgI9dFC1xoX461qyvBSmNo0n4cI31rO3XP8SAbtaTLQS6XXHqmh4uBDshpRts86UzzvRG5IOjcRkaqGWDW43uMeKlLRoUsx7qmc+GiT4ff2s1Ih/2TDsSLedTuFpUSGPm+2+Df6dyTAaERbA4786Qe6ViZz9fbe0pGQCp/t7MU/O2+uiPpVtxjH3L8Fb/zVQIENNxey+v+JDNHGRnwa5t7+XZq65DzsTlV1rlPB7IHDZA3ho56jUBKe5kg+5Pz4xS/G4NFYECduXXSJOd6m3epw9LalrYP/Lc1xHXL+e8yE3CO2tb0GEnTy/WzngQ7S7aTAKoduHa7uNWX4CnHKOto9uJMu+u0ARfaKraFOt1b8HNgekZy6UGPJaNFbleMJhWN0ug709K5sh46yWZkyUSlnCFdM6p5j2U0JzBKQ31Zx0gvnqVaYiGkvoXnei4Katr5rfYUzL5tAPaf4nemy63EJgmaskz/S4urkh7bfD6u6eWHnGPJRKZ+fGotWQK205Etaf4sj+wExXrd0e5YNiNOUN5YlfwnqKkHgXHqu1QX53JbIw2MjO5n2aP11kB86sxtRa5J9vsSiGv6tACDWycs03q19UbAQaecujAP7UfOw9G7bNpTspRRYJZ1e34zSFoCXm2d81rW2/p8pHsC5Mb4Uge3JT95/yCXQ+4b0CPtGuucXDx83KJ8MjCWNF40uJZjTY6OBpTCKK2Bq3ZQOhfuyKlnGY0hSl1HBtEO1OlHYGVUnwFno5MzbWsXnR4lo7Vm0JyfZ66cTuV6q5eOs4GfpqsBz7EmxuSXSHr15t5Tkv6WppcgXtGuwWyP2DZ9Mg3bkcKtHkaudOAjAL+0TZIRMIG61F8cmXpzTJbAAH/T4e4DEqx3NNdGc6Gt6RGTmH1KB5hv481zQzl3vSXGd+Qco5zGIqSM7JmQNoEaJdmubNsZ8owCMzJvBEbte64BgicyN1wv2ZZkS1Noyy0NYGbdmX8EFhuYFNImgLt3stZ+y/7ilPsWiB7hb0TZtqjmIsLpkk/qeNHxqSp/ctH4uo2e9WpRa9OxAZylPWi2nwAsgQtBQkb46Shdjs+6jCR1S8PZQEpjDzKiGjmtkYG37uw/6976K8eklc/ybm/+PkYTy/MpT0ufsv7P9HGSPmOT5o62HV2jvKXttpJaH4Fv6k6WwtfzU7g3isZb/hHATud0Fp9LTEbOibw2iqKlDkYpjd3xJ8d+idkblZ/3GsDyn20Cgbl1JEDK/lia09LuiwNdRn6OQBDP9bBN/N30zHaMmMRmw9q8HoG5ZvcY6HpNJKBMsJifIzHYpN1YWmsU+p/RmpDuNfg5COW+AD1kZSzNuXmRpNHlPm+jpRsNSsmys5yMOpojc/pm9LM+GqYlgEZ9j8t3affdNSr1uy63Lw+ctnLcRtY1Aphsj/uAb6RteZtB4tglkGwRGxlAC59kYhRnB5NAznmuaWJ99FE/IumP6dUfPf1vKOf3U11SN8Yus73TJetmGS0tmY3m2K5qnqNkFaTtuZEOtjFhqffIYaXk3MzDy9bZQHo0by1mQPx9FNwkEOEZraVgoW1NXcThe927j5vubc0vMU5L15eAR7s22iZKoCZtM8Gjdd2YjiVdErh7/BqIznKZJ23mKX5bpyXGMsXt9Zi5vVxvmZ8A1HnOB7/Pce3ZgKnnTnym57n+u//lvtjeSueZi4ITnVERqVJpvJ2TTr6dZ9HgXl5PofGjwxV+E2QwAmI0l+Wlw9v32KjLyDpahJL9xfdajKj2HBN+Nv2FNCmN7bETy3MEdMgEvk5HAEdmgSyHv4/m3OPavFjt+r8rfaL0gz8p/XikvaIZ+KTzlcZAdInZsUPOfjSIyHtZF6Ni65Zzc4n5GIHJkTAiH53HysAh02RdLThpjFljp8hqpQ7Of3Xz6THyPDrRvI2deuc4Mejxvdua+5cglLbJZe7r27bG83dez+/sK4JW65T2MNd/O8fjvKm/AUgD0qP2nGgKGm5AH54PynqcN3+n0M41hpJtYDCSti7HZcSONXaysVX3BvRILxSQ8lzLpQc9NHw50ei000DlIjzT7gTNiZ33ucD58reLsi/t8ewEJDR0XLTpYEeUdQM0WXbmJ2XNrSsaUMt53LMxpOMhtTsyOMmMZflpGBJg0ZinDknl5/006B6/ZohYrrRrXKm30zwo6UlJ/6ukp39SevBN0ps0vWHWgMdPdbW6Wa+0fRZkBPikXeYiz4bcUX8argFYH8Zm27P+K5s0DexSN86dHD+POfXKcrKMzCdtl90Yhoz8G6MyutfARtoNviwyAQ/H1ek8H5vjZh814N3mIPs8048CvwTAjVlp0tZQXs+8efCewNnpR7Yu+6IFIMK9tGdLQSv1ILjx97QdWWZjj5ieDFb2yyhwvkjAcJC7J5ce9NCBLiF4LhJS80TxLSJozIciT0anGe2NXnaWdY6casoSOBm1fam8lr4xSM3wsi3NYLd0rGsUwWU5NKoJsFoUT+PbDKsiTYsSPQd4jfonyDvT/Lj3Ozd/KUeaAE++xZdMWYvYubUwGtOcu8mWOH075J2Mjx9ZN1jimslxNQvybFietr3TAL2vN+ZgdL+BREoCFa7d/LwFvVO4BnKN05mlHVCke6bkJ6jg2kyA3oIA2jMyNUsyYonauhgxG06X+TLAyN/WvZXpc3HJvHFtk2GiLo2tGYFKCm3pUv/R5qaeOTYjO9sA8t2RwyPrI7n0oKexCl74XjgpzaAs0Y/NaJBZanmYNulgpm3RaYtC2kJklMJ07ftS2aNF3ADORYzpaEGPop7Ua18dLX9jMKTukBqAIPgYPZIsfGb/5Ly7tvm8FenokNscas7Gacma0enk56j/nccgx+lfutH/ac3bOcfaZn1yay7rGhnwnH/cxssnlFo7CHJSWtvT6TUmMK+3OtrvJWlrJtnFZM0SkJxE3jZPXc6S47Xjv4gjT90yDxmJZB85Lm1r56IOe8m5L+nsuhojRduW0gKZFsgusc60fQRatFMJ8LhONcjL7we593LpQU9S86ReeeaAkbW0PeHojNIg0bjR+aUDtmO7qGRe1zsyLrw2AjEsO7/TQOc1ArN9Bi/7pVG+KQ04kXZe2m5Infe9P0ehr/NQMurMeXRVk9MfOVQ6mSw758+Vzf08A8J/zMrxSYfJdMnAua5kUJacSgOz2tSVTvkp3L+q6TH7bLvf5WPDnnOzBQTtjFILPNJ5GHCm4yGrQ3a0zaesc8Rm5G9pd601AERn6//xZaBjHdyvCX5c5hXNDj2lMTqWZ8MKkMFK+8Y1TuDFtedxSdAoba8xMmfnJQ3nIcFFO+fYmFznbWUyv6Jcsm6jgCeZIQbF+5iuTMuyL8o2Pf9yYHpGculBD6Pd0YJVSUMqOMvLxdHAB42E01zVbn3cLkpQc1rKaNGXJcEHF1X7PjIIo3ukZP25tC2Rfd76n5S3ZdRPaTRPcJ/O0uldzlIE2iJysiZ+wubpuE5pDjINZkZ9NyN9Gr0z7RpZ1pfGeRR9NxYv04wAgDb3HtH8CP0XSPr8B6U7T0t/WdIbNTE/QjvM/GT72/wYAThpewuYY0X2g87b0pwNnRqFfcS5k0CK961v6nwV96UZzDi/03hL031l8GibkXM+QbLr5Xkt2giyEikEkdIu68N+b2M3OheT5TVb0OYrxfOVgRZBGevbt0Zp21MMMLkWnZf28CJBZmvrsbbn3Sj/3ZMD6BnJC4J5S9CRAMkTlNHe6PHbkWNixJC/c4Fw0dnxJfAaGYMlir9dS/14z38ZUbKNzJNsFnVM/U4H9y0Ea3ndn9kPLRJr91hHPvo66juLncyJJueebEdz2Hxvj6P1dJx8dDadUxpVS45LMn+cP5QcG/ZrM7Cpo7epnpb0CyV9/ksl/VnpytdJX/6g9FpJH9Q2eGoOxOtoCZBa2A8Ec74+As45Jrk1prhP55333V9tXAic0j60wEea+s+g8US7NiUDg8c0A6CcA/kUn8GNx74xlVl2BhS0Q2x7fqajHwG6UTmuy3mE7xbrN2LKcp67DDJELCvHth0jUKQbgTuPU9pzMnRuJ9NQt1y3bgsDY66P3No8yOWT+wL0MMpm5NLASt6Xdo1n5uUEbRRtlpFgohkCSy5OGsn2FE7my0XEhTdyqkv6jLZHWuSchimN3zHSsz6Cp3R8yY4YkKVTGznPkZGnvpQEt82hLH2ncznGX+pL0HwU1zj2WX7rz+bURkzKCCBlPvf1VUmfKklfJOlL19IfXksPS5+0SWtmYqnPm6FvUa/zt2ChCddL1p9rgsAkPxtw8b2mQ45PAqHGro7W1RVN56WuaX5Lt9twNf6ONDND7Ct+ej0kOCCIpIzWj9s5YisaWMkxbsCU9oOBQNPL9WlPebR5BEijMlv5ad8pS/OxBYYJkFnfUpCSdbWxef7l8J6ekVx60EMw4kmV0QABgeI7I9g0PPvQODunvRjLZY7Alcr1VrallZMLj3R8YznokFwuwduo7Ve1XV4yHdS/GSKCEToUMin+JJ3NrRqWfaJdOj5lCUDQUCdIynsZabstyf74jJDnYEaUzcBmdErjTDBLB+g0GqS3bpxrp9qwW//YV/62dHt+T0qeV5HGziUl29vmP0F9lkdwTfBEPdwPBgUZTR+jPALUpn+bB20tLDGW1iGZnbQ3VzW92sDpr2z+UgeC1CWGtV3P9uW8auuorQW2yd9b8GhAwoCzMSAUApAGGvYFHWbIqKvTt7KX2s8yWh4C/X1yrnlLMwH1PuB/kLsr98WZHn/SsObi2Bf5Zj7LKAIfPYlFhzNiTlxXRrz70L6deC6UZAS895zRSL73hxRvGsElloe/aSBG/cq0ZABG7UvdhDLcV9lnCTrS0GeEP3oiJh1q9j8f06ZOfD9LSnPu6XhyXrDf2S8ErGx7kxzz9si2AZjF8/hvSHrt/yl91KMrSdL73i/9U21v2eSZkNSF7Rg9TTfaAnHb2lq9qnn7iKxPYxGzjFFk3ljhBmRzPp2X/Fm3Abb/0ewVSY9Keq+kT5D0Ox6Q9ImS3i79D09Lb9nc94F5rpljlG8d3dcE29SF1xsYYKBH5qexY61Msr7S7ljld8+lxo5Qh6x/ZE8YcOR9X2+sFgF1Cy5Gbac9yDEU7uX7tbj+qOvdlRcGM/Ncy6VnelIa/U40zoibNG5G6ZSkmRm9NrqU7IvrT4PsdNQ5I6Ss03+5wOjQEwg2w0/QkvnPta1biy5Tv3P8sa6Mvsn0NGOYBq4ZXPdnAhtGxMkiGBC2yLcxStIuO0Rw1ebGUrSc7W1Pz1hG54CclxFwA5CN/UpJcOD3oVyV9ISkPyDpL71f+gvvl/6QpvNNVzUxPcnkEQjk/HG92d+5xjgfc5svdTcT4shYkV7a7Vt+p05tnTfAkwCarFs61+x7zgu3+wObNvyOE0lfIunNf1T6Gum3PiBd18Su5dw6RR0peQA6dcz2ZD62J+cSWVkL87YAJ9f/aA5Lu2syvxMcjAKntEVpb7L/9wWLLHPpGvUY+ZFky6xjs8eKe5k3zxveO8Bz2N4ayaUHPc3Ip/GlgfL3dCwtWlr6bAY0629l7buWQn0JmvZFVkv1jYxdUu/+3SKX43KffZx12cCyz1KXUVTb7rMNua2R15guy7qIoUnHPdI1r7XHsjk27HuCHNbnvvYTZVdC98ZspjQwSmBp4HNj8/sJSf9QE8PzwU19H4g8zp/v6LGwjQQMTT/Pufy0nne0+3h/9k+OY3PieTg92536kEEgOL4Ik5frhsDuXBO40cOSvuV/lPS10n/zf0kPTSyPmb+s13/NuZ8jTVvzx8hDAHOCfFyXCXTavFSUcRy/GfB4fjZQ2tgyxb3R9/Z0YPtOUJ5lcU003SijYDbz5BygH3hA22NJkHTpneyLTC79eGSkz4nbwAwXCq/l4vZk5iRu0VguhLbY0kiQ9UhDR6NOw0sHMXrnkMseCVkqqTsO9lf2Nx1vAzYWRsrZ5lb3ebnHcSJAas41DbfHMvuJfcy6WC4Bl4VPfThdRnrcvhuBZ6c903wg9os0OcrmYOw8m+5Ol1EzWYxzzQDj5ZIe2uS5tcnjd8skMEgd6ETzc2lMOJ7pyK9Ieom2HWsbBwJqrl/q4PSNrcjxIRNLYD5q1xVtb2HclCZU+fv/4+nC//xR8iVt6uFrLjyeKSOGMMF5Bn3+bXtxhnsN7FAyzYgpVtynneTcSJtHVol1LgGCJbvha2ZUXB/n6+gJrpTsR7Yh62qfmZ/MOfvk3rA9B6ZnJJce9KRw4Y0ASFsEpLAtaYTS4TSwZQByhHsjEOb6W6TaDJ/ryLwXjZpo5LzIs54GcJoxYHqnZTtGhjX72s46WSZ/NiM6ArY05FlXSnsRHLcKKA0guu59gIsRHu+73Oxn1vOopM/T9MhzY/0aQObcMVvkeq9s0t/WzNwcSXqHpv8blqxFAotsB50KHWNj+LKcI1xzOx6T9PMlvUHTU2XXN/euIs+IXUqdnDY/3dZkgbI8ArZ26Lqxv9yCfkhT//7ZZyT9cUmftJK+TvprT0xbW49E/dYp5+0Sk5d1ZwCUfWHAzXJy7V3f/LEPufXb3hGUZWVeysh+ZJ4GOFgfQT6DTK71Fpy2dJTGBqYurU8bgB/VSZbuvnKyLwK59AeZpd1FlOjaC2MUcbQy2sHXRn/ayLUIJvVxPhrNzD+KjH0wNelpsi2K/Mn8ZB0Z6Y2imiZLFDWZHuF7A3KpcwpfnJbpm9GnEeFWBSNUG24a8xE7NTLgfKfSRZ4OGzmvrIMHo3OM3yHpv9CGNdB2mzxXmx5J8xOY+T9++6BwvmU5nSfnD+tv64MvHmzAPNNn2Q9qAj1f+VJJ/5X0PV8z6fo2bY9rAj7FvQZgR6CWY9wY3xGYZXk5f207PrS59i8l/c4PSq/44AQoP6ipXz+k+R/PZtlkUpteyd4RiHA83E9cT+fa/h9jeT/HUBr3Q5bHNC4zx4Z1saxsv39bHwJl287GPNOuLq29vJY2fMSkkiludt/pqDd9z70DPIeXE47kvgChI4YiJ1pOspHDT8qbbI2BRxohR1Lt4O8oIqUzaYCilTM6XOwFZ2ObRs4yovxpYAnohHs0OizP+qThZ/3JYiTVnmUIaZqBapFnc3qOwDMSp1G1nCBfM5TtsGkz4A28LTmFtvV1pAmMnGliBrzVJG33jxa++zPzJbjxk1Fuj/s7+zPz5fd2xiKBoMtqa5L5XN+HWYe/Len/8df1hR89Mz1s2wjoWTgGBEZkB1XadBEG0GLg6Hqf1rw1eFPS45rfbH2q+Z+OWrd8CzgduD8ZYC3plzq2ACLZoLyXNsd/invsQ0uutaxzpAv1kba3oNMe55mZ7ONkDc/jO/VobXH+fX3Y2uDvro92jADqXLvrhrb0IJdDLj3T01icjC69YHKCEX2P3pmS1x1Nj2hJRrS5ICzNGDTmiN/PNK6XZTNSaYZnqayRIycNT7AwYnGOIz0jPDveJT3SAKfBbXo0AJUAxAaT4+Z7eW6iRaNtXu0DNP6uSEcd6TjyuvXKNjfqnw6Y64BpDE7S6RHYpuEmW0PWJY17e5w/62/RrttnNuutnyl90ut/pf7nn5zOv7S1MHoycfRKgdS3ja+ddjJnzS6k089xyX8/YT2t41VNZ7NuaAavV6OcFojRdnEMRgyH8H3Jqba5yGAk25mgwfMjt4gZpGRQ1mwDfzM/gV0GpLQrLL+NHZlIBkkj3VqbOO9yvRGcCunJ4N19OTA9I7n0oIcTnRMvHeUSFZlGOWnqkaFx3WkYRoeKm5Nq5eW9keNVuZ7tGhnrkZFfcuz7pKVpBoHRVQNdGbmelWvJijgf87DPWpk5ZnyHTgqBR+s7xT1+Ety0chJoCWn9vc1vSjPcP53xSwfGbdKlcjlfyBhQR27H+b02tzf3fmxT3g/9sPQ+ST8e5Y7GKetua2rEvrXxW8qbzlLa3no2a3asmaE71/yPRt+h+WWF0rzF6LOCqV/+E9glwJnrYOlVCpyXCRxHr25wfgtBq7+faXeOjGxja88oEGksMkEFdU4WkONpPQw2Ezhle1jmPh1p8/aBVD5lfO/kAHqaXHrQI21HwxmhtOhX2jVsPP9DatLXeYAzjSEjEGl3MSjSUq+2AMhucOHRmCzRyvuikxRG8CPw1QBL/m51sC3UqwGXfZJ5CDyzPgLb1Lm1j9eXzu9Ql5RkEFjuA1pmmDwfE/zZqTYjSuc4kjxP5jpyHfHQPp/kSz3JKGTUL+2uIzrSa5rO8tza5P2AprMvfOkbgdNIst2Zrp2hG+UdtZlt4PzM+vy/zTxWJ9p+3N/6GNykGBC5nzl/LM5HZot2gs6dbEeWt2/bsMkSi9PmYdrQbGeulaWgoa0T2hHWnUGE5+cSO5M6kU06H3yObP8IpF00wDzI3ZP7AvR4MiY4yd+NBpY6sFH8zjRt0i4BnFxMZ9qO8FKcno+VMhrIa6NIL3VrdVjSYLDeLKOVmX2ZhiDBhdNl2ywsu4nHr207nuP6iJpuOjcgRZCyZPyYtoGYLDP7mE/WWE+CALYj59T1zd8T2nW6zegqrvE3z2lYTsp9Oh8hPYOGxjqlDu5T/76h+axLpvc4+/ORzedN7RfOzbzuLb0TpGU667pP2D6D7ywr54NZnASYWVeOaRtHgrG25hPI+jfnIQHP0ji3OkYBQwNS+dnKJOvKBwZa+bk+CYiWHmQZBQNkXlsQ4naQ9WksUGPGchxa3rsnh+2tkdyb8XgW0hZEM+ItX1sQvkeDI3XnlNFCpqOjGVHPrIOUa1LfZJZ4uHlpMScDloaEUXm2iWVIu8ajlWmHxTob6CCD4WvNULJvmi5pEF0ez0VknZl3NFdaf7ncJWZR2jakdKpsh+tPI555P1HSL9Y2gE5gTVZqpNcSIKKToR7NAThdc8Scs85D53JWrhucMP/IUTdWlfOvpWObyDTyPgGu0/k3WcVsS37Pted8mSffJZN9s8RypU65jZU6tt8N8AnXWnqCmwZ4sx0MpJzH1/kUJ9uQ6416uP8YBI+YPYLM9sqRU6Rp0tiaxvBYct1fegf7IpRLz/Q0ZoVOkzTjUpScTAKj/qWFo0iXBjCjuDRy/s38/J3gJ3Vdaguj6n3sT6Ntl4CitM1+WEiLt3rbAdOW3no7KqLhy3TeRnDfe8uoRaysKyN+Rl9Z58hwCmmy/KVoPKNIf88thwQ7nntv0/QEUL6pOOcSX05HnTh3miSIIVPl65TGCtAxZT9nPcnGJnAlADjX/EI/tiHb2+YX5491WpofLSDhfG9rK/XnPW5R5RgRSDX2iACgMWuNufT1fdIYmxx3rgEGPXktAcRR/CZIaX3Y2J0m+2wnrzXWJtO0+82n7Fs7SyxQljvS5+7IgekZyaUHPSlE9SOasbEbQhqW63xMO1ooZg7OkK4BHZc9ikqWDHSrW1E3hXUwUh2ByGyH9WllNdar1d/alsaSFHWTI81PxeR2QXPcTRrbk+1U3Bvlt0Hf1+7G/tFp5LzN+WJneVvzu18YYZ/guvM1do2/U68E/Lkd0vqJfTQ688T1RkAr7Ubw7I9cNwY27HMCqQQV1nkE4Dg+Ofel8RwcjZ+02x/twQjrNnKYyUI2Bnakj+dlAqUzpMnfBF0Jwtu99pv1ZDtyTLhGMw/7I9tFpo+MkA9lt/u8lvMh9Wnjf4RrGRiPbLXU50za14sAu+dPDqBnJJeefWvGgvRhUpYJYBg1ubykonnfxtkLOCPLFp1Ju4esU4/j+Mv2OL//RgskFyGN0TnS0EmkTiNmI99PNDJQWRbPG1Cop1BGRsktksu8VyS9UtIfkfTpSJc6jST7V9o2jE3cR5ne4/9AKdvlcGwbiBodHs3+uqW5bzjv05k8Kuk1kl6t6V85uHyeHbFkWTwL0XRif/k615frHQFsz9vRE4epCx3hEiu39FRe21bj+NDJtb7YBwRSnPdq3Pef3x2TNipBGpkjgt1RnRaCtiyHoNDCedva4u8j5q+t81w7HLORzWigst1vTxtyfua1/ByB2yP1dmR+t6n1X/7m/HXZB7l8cumZnovQjtL2pKPzJ6DJSMf3ubg46VMXOo+RAbCc4ToNGBes27BUTurDNjhtYwyyzIz4vWXUFjcdgOvKfmBU1vLnu5Ba2WzXmSYg8A5NL++jsH2jfuS84WO8OfbZ/tyyGTmApnNGpQTjmVZx7wjXGU3698Obv/9Q0rs0/QPR/FcTrR3U0/UmkOW2DJ2m27PEsi31DyXXYsp53Mtr1K39XppTrMPpyMKc4zr7wfqRKTjXvAXb+q/Vv8QiUDJtsr0J2vJ8YIJkzwf2j9Nn2Qb3IwC/T8f2BCxtSgY8tC0J3Kl3SmOqR7rRXiXoHD0Vl/23BD65rlMyyLs3sm8lvDjlvmF6UghwkjlghJFleJJn+nR4uRgZHbYokYDkbHDd5bXOblFXMjPUnW1KEHGKdHQGR9puc+qYzFbq0IxeOvNMyy0YylWNjRh1cX8+JemPSvpR9Yi7OZD8TkCafUuDS2njy3Q8HN30o4HntTSuzajmnLym6d84fPzbpM/976Z7PPjc2tSYUI9l/s+rZAkSIDVAMRprgs42RqP1w78mOU9zTiyZ+GYXlsaKeaVdcORrXAeZlmmatHc50Zbl72NNT7q9WttAlHWkPuzPZgOsi+dhHuRv9TQw4/WQwGoEcrKMLDvXNdNn32T7WA4lbT9t7kVsPsezfeb3ZxMAHOTuyn3F9CSqb3SlxUYxjQZp8ow4zgbp8kVXzNeEFGdeT4eXh3cZobS0ychYErxw8WYEJJTX2pl1Mk9GrXTuzciPIq4EVblfnhEVdTnX/D6TrCMNSzpAX0tZAh7NADYjK/UXzEnj96Hk+JAxoAPKiD3bd4Z7p5qYr/dK0s+X3vPBKV2+Jyb1Gc1ZsgK3tdu25iQb29XmQNaXbWD5OYfIsmQ6qdfbAOiIkWtls7xsQ14fOWAGJfnf1FvUz9+ukwwa9WDfNIB/W7tz2eUQvDZmo0mu+yyHtifXGMc4t6VGa6yBzbQj/s4D6wx6ml1m2W3LqT0tx8Axx5q6tocx3PbRE6PPvxzO9Izk0oMeaXvR0Sla8oBbiyYacJKWzw64rFw4uTVCp5DRdIsqR2CtGQ3StktCh2bWhfd8P/uwHdhjnyTgOtd0puShzb3b2jjhQX2jiI9Gyvebk0sKmlEXnbvrzO85bxpwXmIIkgmkXux332+Oe6Sb8xrUcKzpDG9qYnt+/wbw3NAMDNs8oBMn6KVDb3q2chqA4XdL26bwemmsJHUZ9SWdYgMsbU4t6cvfre0WOkGPA4MPtiHnXQIfsldsV4LrG5pf9pg2abQ2NPg+Gn8CJ15LPUcAaAnM5G+u3xSOoa/lNhrvZz7r394l5r61rjnfOT+a7cpxJCC8iN1+fuXFBXpWq9U1Tbv9/+V6vf7upbT3BoQ+C8ltHGm/g6IhYjQ1ivQbi6G4l2l9OHGk6xGueVEm25GRc0YDrQyCvGRI8jfZmBaZZr+0J8AcmTOdqeEHNQGeXyXp971+AkAvj/zNaPI7nTOj2+wzMkenSDNyxnYGI6ffok4a8NaG1KuV04B3m2N5r7WDLIjlVNJ7ND3a/g5NbzZ2H9KBnUee5rjzftOLc9JtoDPOOZvrLdvh+6yfwULTg79HLwZs872BoXaeSxoDHtoGp7W0/nPdLZBK1sbpMm1bQ9b7XLP9eVqzw83+a0Ehy2Nwxn5ccgwtwGSbWC/XMM/+uKwW9Pg3QYbnYgMsqUsGrCkZEDHgpHCejOarpQG+F7KsVqvHVqvV31+tVm9brVb/arVa/baPoKy/sFqtfmK1Wv1wuff5q9Xqx1ar1Y+vVqvfE7d+t6TvvEj5l35cHA3mRMsJnguBizUNhNO3KNSfaTRGEWtKMzSjhTNK1xxelk2GiuVkdMhomY4783qRjyJtGoI0xHckfcpvkvQD0mdpAkJXtNtfWV62Nw1g1nFRZ5TGSrjOfst7TbKPRo6z9cHI2bI8zkeyE1lu6klg6LHyHLDDS5YoJfu3BQz7ouNcd5yrDcScIf35IO9IRoxd05tbBnS8XINk9FKPth79xxfXZf9wrrJsBliNZfEcfljT04nXNJajuD8Ckey/BFTS7ngvge3sO5/7cr+P3kXEMtoY5YsM0+aOgB7zU9esO9ua/S/Na6cB1H1rfgQCG0Nln+Pv947tMdPzXP8N5ackfe16vX6dpM+Q9J+uVqvXZYLVavUzV6vVdVz7hFLWN0v6fF5crVbHkv60pC+Q9DpJv261Wr1utVr9Yk2vOfuJJQUtlx700OnzXm575QJuwEjqRsvpM2Jqhqp11tJidL7Mv7THa1YlI5QEOgQ8Lrc5vFGkQYPVaN8lubX5/I5vlt73oPS9Uc7Z4M91pfElIEsgsMS6+VqONftH2u6/UTnNMKbsAwltLhEAETi0/FlOi5QTbLRHzkdshLTtvNxvNMaMxNlnHt8R29rSq3xP5+HvD5Z8mZdBTQMsKUsAtuVrzpb6cC5Lu3MnnzpKFnIEgnIOP6OJsWsgL+u7Ue41YJegjSAw58nJIB/n4yskfZ4m0MVx4prZByqeifzNBo9YxwYe2vrkHBZ+JwOZn82eZHlte9vjm/OyMbNLYP8+lJeuVqtvWq1Wvzwvrtfrf71er39o8/2mpB/R9MaRlM+W9NdXq9UDkrRarb5S0p9kBev1+h+pP7D7aZJ+fL1ev2O9Xt+R9O2SvkjS52gCWl8q6StXq9UirrkvzvQkIEkUnoafB+YSbScdu7RNRgeRDsb3R4spdW1GxtdzUTMCzccpM3+j2Xmf9R1pMjDMuwRyUneyKy7zXNP/hnqrpieqntb0n7KdP407y2Mb6HxGwGLpEHG2n45/CcSlA3Y5+fSZ20+AQJZG2mUClubWyIBbj9H7fAhu3R98qZ3HiPp53RyXNNme45LeaUbGe1907rXkNjj9FU1bo+/TvDbch7kG8lob1xFwocPP82tca9Sb9VAvlTQJeJaE6+OWprVkPXOMUh9KBnwjsEBblJ+jwIjz4pqmbexm61p7GdRke1XSp7Stsxwv/nYbzUZJ09kqjiEZJdbBAJd9k/Nn5Adof7Peuy/P25meD67X669aSrBarV4t6edIeuOWRuv1X1mtVh8r6TtWq9VfkfTlmv7zzkXllZp29i3vlvTp6/X6qzf1/iZJ71+v14vdfulBTzqm0SPRDaVbiOxzMeTip6HNCdzAB52MDTqd1siB+N5o68FCXVlnltnO6Dh9frIv8omqJjTsfnfOqJ6Wr7Fw/p5RfDrkHI9mbAjgCHAJulISSIxAZStnidVJYRvyCZ3mPP1JMDN6547nVPbNCfJbjzZ32Ib8PNHuOqIeDYC2ObQU1d/RdAj+DtJacu03h0kgkmuATu8I1/gkJOtgO47ieh46XgK6Td/mXJuMxt3Xcu6a0Wnp3c7cxs65wXa0PnjH5u9O3HNbaL9ok0b6MwBcApUZoBzj95nmrbfHNm16XLtgLPt6FBCNQAvZmwSaLeDMNi5SDi9AWa1WHy3pf5H029fr9Yd4f71e/+HVavXtkr5R0sev1+uffK7qXq/X33yRdJce9NC589FAxf10Us2ZWrgYhPsjtiZ1atKidC+29pRUu8a8GWFz4Um7bNRSP6VkvQQKTUgDZ1Q7ejKCADOvW1LP/KQ+jPKZJgHDvugq+8htyjnmerh9dKzt9o9Yh/YUjn+P+splJnN5rNnBJrCxTq1vsi8UaS15gJQsylmkOS552M6Ro2/33F++Z9q/ReWNZWH5yXzlWNGJZ346oQx6WD4lyyUbw9cKUBrjkCC6rdUlRonj6//qzrwZhOXrDPh4e4LE1I1Mzj7QkPecd998b/W3tHk916PzXZX0+x6Q1s9I/6m211yWw/Od2Y4G2pvd4nyhnWC6ewN87v7TW6vV6kQT4PnW9Xr91wZpPkvS6yV9l6TfJ+mrn0UV79GEbS2v2lx7VnLpgegJPkeGShpHagQ0S3KMz1E01radRiCLUbmlvQsihYvmHH82vFm/5QGN96/TuS0ZHMW9M223L+nl1ImSjys3KpxbDARX1oE6qfxuoNNljvrAOrA8A44TTX1J8JLsICNPGnoCimxP6nOO6+nMPdapA78vnRdzP5stbczbaA7waT4K2zxiQV4t6Vdo2i5pa3JpnDhPuYXXgpY21gSDS2CfjpP30tlZ2L8s9yjS5dzP9UN7kIDRvwki6ZxzTBgIcC63fsrft7X9v+8oDfi3vjvHvbzfgCfnj4VB0tlGv+94RvrLmhiptFXSNtgh4KHt4tyiZP2NCbL+6SPujdy9g8yr1Wol6c9L+pH1ev3HBml+jqRv0nQO5zdLemS1Wn3Ds2jQv5D0mtVq9bGr1eqKpF8r6W8+i/ySPgLQM3pEbbVaPbxarb53tVq9ffP5b22ur1ar1Z/YPGr2v61Wq0+9SD2euBnJ5CRLxqMtSk7gpYmeadJJpWSHpdGjc3JaGquU0dNKWRfzuu1kZ3KbTprOS/jgYeuTjFBTBzqZtmCTMm9OOB12bgvkNkJjElL/tpW5z0GlLlmm50YzoqlLAxJsJyPMjGo5XgRzbDfbRcm55HpYJlnQBixHDnrk5JaujdYFQWQDMnc0vWco/2VGkzaX8rv/2hbQiCUisM/7XlMEG0tljxgIBiqcvx6fZLqcnsClMZcErOz3NoajdmeftLlqaQfsrdtSwMRgcBSUSNsAbd+YuX7bdr9c83s1vaglt+0SdCz1Wwt8LAxAfK0Ft03PS88sPDfyCyR9maTPXa1Wb978fSHSPCjpi9fr9f+xOXfzGzT9N50tWa1W3ybpn0l67Wq1evdqtfoKSVqv1z+liRn6u5oOSn/ner3+V89W0Y9ke8uPqP3Q5jG0H1ytVt8r6TdJ+v+u1+v/dvMc/e/R9Az9F2j6P4mv0fSE5jdq+/9IVmHExO0VxW9/cpIm0Gl0shdwMxrJlqQjX2KaLM0okxlS+e50LjOjiQQS+Z3U7Amuta2akd68ng4+deX9I02Hp0dljiJv68kIrNWTIHa01USGS7ifeVWujbbimI/Rssd7ZOSaIU09PZZ5j7o0JyX1bS8hbTKGIx1cltdEboOlXCR6Zdnv1XQIPvPnWuJc3TcvWx3tfhvHvE6Q08bYaRkoGMinQx/1zRJocr7m9HN9NZvnvI3taltMzQa0bT+nzzJ9zfkzba6BBgrzk7ZgBK5HII75b5VrGdjlDsFofXPNcbsq7UzzIWmf+ZDF3Ze7u721Xq+/X9JqT5p/gt+nkv5sSffrFsr4Hknf89NUU9JHMCYLj6h9kaRv2ST7Fkm/cvP9iyT9xfUk/1zSQ6vV6mftqyff67CvAUTipPvbfmsu2CVKk4uPOpF5knYP+TXUn86KkouH9eR3OsQzTY+3PqVt0JRlkX0RysgyW6ST7eKh2bzXaHu2McFM1kMGhuOTVLXT+4+skSJN5h8Z/nSAdKKNKUr9uG0ppLewP7OuVj+djo2sjXBKA/Bp9LNPW53+3cacfZB5R+vIrMYdzed4fH1pbbVtA65h3ktmjmNDgOM25hwlu8Z2pL528mZuRuDD5eSj/67D5RCgcVx4HsXXkwXP9E6b1w1U/DmyY5mewjmcAIX2hWUKadvabUAry+AcbHbC9v9MvX6OI5n/rJP2aCmwSeDTGKaD3Ht5Tg4y4xG1l63X63+9ufU+SS/bfG+Pm71S0r/Ws5Q0iI154WQmck/DlwZlhN75u9Gadk5kVEijMhohG3Revido4YIbARLF9UbXJ8OS+rQyRkJWxv3Jx/L3Rb4NzDSnyqickbC0HWGxb8zyNXA2ikxHTyR5PFpk3r43IbPTGIoTzQdPU1c+3n0k6SWSPrRJQ0aARr49odXmAtmvzM96mv6U05KmgboGwBozxjqYL+satTmvcwuGj0pzPQv3GlBs+ub6G4Ed6pZ5sw6yfCNWS+XekoNu4KOV1xiTZtNGc53Xct22IIb5WN7o1Q+N0SO4XmLSUq9RgOQx47W7Ly+uf0PxbOQjBj18RG06zzTJer1er1ar9bMs76skfZU0cWWM2BrokLaBB5mVln6fI26Ah/n8PR/XbRFlY5faImuOKg3SkqOicybAY1/so/iaMTNzluDLkXUDKtmWlDRWrisfAx452n3CcacTbwaOEWTqR2kR5b48zMu0I6fusb6NvNK2UT9GngSdNO4NTIwc2xI9n7ouPfrdgojGctAZNrCU9bZ0ebCeDvco0oxAkvWngz4u947xu5XV8p1ret/NM5pfNDiaF+nsU/L+yAayrMw7CrDa/RZQtFcPSNvrl3pSUveR3Vakaeuq2dJc5yPg3libU+2OZ5v37ck81zsKmqTls0zPnxxAz0ierV/ZksEjak9422rz6VdDX+hxs/V6/U3r9foN6/X6DblB2FC+hfR0oykbO0NJ4NKMXG6XjSLckbAsGuzR4m06nkeeLMP17ANV+xgIRpKZ3vXk4eBmMLL+keFmna6rMVOMJC3cmmO96Tz4XhCe12L5zQGMoua2hdYid+bjXFhyyta5GdHbmt/qe5GAgGCH9wggRp+5lZTOhU45QXJeG83BM43HnIGFQTPTZZqsj2W7DSOnlmlGQl0J4C03Nb/VfKRnlrdk97J8z3cyJq08j2+ulRawLfXlGdLRtghpee9c2zor0li3EbtjXXINUwh+CT4516XeHmm2+3nUgm1pQcAosD7IvZWfNuhZeETtb0r6jZvvv1HS34jrv2HzFNdnaHqz44W3tsi00HFkQ5oDaiAgy27MgPA7wUkCIG5btYVvHVm2nVg6Cm4deMFycTV63/eyXY3dGTFXTsetLzrA3D7KOlMuMrncrqVzMJQRG5dgMdubRjTTjsab5THibUY6gcMZfkvjsXJ5Oe5LunIsaYTb/OarCRrwIIAbRej8zbnv+lqe48jDPs8nz6wn5xTHy33RQOuxtuestA2mG9Ak08I82S9ksdg3o7mf55ksDiBSN84dlmlgdaLpSU1fa2PUxpLBiPuewV4D+6k3r/O353Xak8Yg5tiS6ck2JFu3ZCse1PTvM0ZBWY45WbXRqx9asNX60NIeqLh7ctf/99Z9Ix/J9pYfUXvrarV68+ba10v6byV95+Yxs3dJ+uLNve+R9IWSflzTfy/4zRetaLRNRcfdjD/fmsm86RD9Saq2gQ9KOmEeGJZmGtXlMDpqjohtZtuznExzkcPP/EzdFWWk0WvgcLSg0zmTZSCIoPPK9jBflqdBGuvWGL+UJeNJ5sPj3p6Ik7ZfsqfI58/st0zDuXYU1wmYErzkIc2M1Alu0mEkQD3H/ZY/+yLzZ/p86Z0l12VzugRFydS4ngYe3Qfcyl0CrdQry6KDJ6jKJ38oCYqe0ewos442J50mt4rIWJE18b+TGc2hZG44F3JtZXpF+gTcmYbrNNtOm5lrgqAt29oCytaW1kbrnO3i9u+5pvdq3db2/yobBb0MnrnOR7ZmBGobY3+RQO4gd09+2qBnzyNqv6ikX2t6WeZPW+gYpX4WxGl9rTEdDUC0KK4ZPTqkLJ+gKxdAizh8Lz8tp5reNNqezGksAh39EtPCdjWQkTqxj0Y6s90ZQTZWoel5UtLZEDkiowNOfdt4NFkyRgSh+5iBBgQJwBrI4jy7ovlpnCua37brd9vQKS5tKzRAIu2ezWAbmJ59zfXB/kjglmXQifI752SCM8sR8mW9FvY1v3ONulynSaaUc6uBO85fApAWhBC4jXTKPmkA7UizjUjdPcZtXpA1yTr5YsmloIZznjYxx7+9J8ugOcFTSgNvltHZogwuT/GbQVjm43t90pa2IEeRrjG0l2Nr64XBzDzXcl/8G4r2nYDB12gkRgd8hXzSbJwIaqR58YzAENmJtlhHUSOvUw8uVBuOUVuoKyPkdExti4jXRk6c4CsNt/t+5OxZdjqHVr5ZqHPkJSgbsUT5O9OQaRkJHX+2jUa/ifX33CBrmJHwVc3j9KHN9StRj1/GRmYg18QS+yHt9qPvtei6MUBL7E/mb785X1r0bGc8YudaeZZkRDgPl+Zg1utyhO/tab5cU8mWKvJY2O+ZfgSqnLYBa7NGbEcDnPye6cl6pWQezmPW1fRvwU7q3wIozq8Ecw34ZtoEXGk/c1ydlgf225wja70Eviz0DXdfDgeZR3LpQU9Km0hHg0/L0laPy2G5nKSJ5NubPhmJLUWA0q7xfUDb0QXL9veM3tJJ0PmzbjvHZvAbcMtIbcSiuF4KryVNnOVlhJX9OzK43BIh7cx3cnAMW9qWzmnZV2n0MwrMe435sXPI9Fk2+9hR+7GmU/+v0PSeB/8n++vajZbZjsZKtTXQgoiMdBOckXVJZ+Ry/F+u2Ubq1IDmEiPHgMDpm/OmgyeD0NK2QIQgbBT1N3aX8yf7tLFv+0BIY+WoK/OMhP1MQJBj7rJHQMz5fT3LasCWOi+N2xKAcXlpj0aAPfUcBZ2pP9cJweJS/7bx35fnIHdf7gvQQ9aloe2RQyfoaWlamS2C5+JvkSnZDgIELmYzHCltkTRmS3EtjUNjVnhGpzEiZFZa5JVMxyhqzHRuTxomOoSmc+pH1orGqdHOZKFGDqHp1KJIX2d5nBtkFdKxHuFejsWVSH8m6edp+q98j0j6gKS3bP7oMAnAqC8j0QaoE8hYr5zHdBjZ5ty2aMJ+H/XBCPQ08D9y3GdIMwKh/t0AD53VCFwmmKceijQMotJGLAHRfSAi8x2XdCktz0XYTa5B2tlkfVj+Ur+77KZb6kw7xHVtm3eRtriPMsga2Q3hfguypG2b1M5xtjx3Tw5Mz0iW8MClERocMgI5yWhgkhZtQqOaEU5SxzYofLw87xEoWE86wEzLKHT05MBoAZGBGC2yNBY09nT4pOPP8CftUvINvKVuPDzrMwgsvzFV2Y9LbcvfbNcz8X2pn7Rwj6A3DXpjFVoe6uW5dEezcb0m6bWSPudrpE9av12f8zXT76vq/0xxCbgfawJUZBJHIHApWk6ncxJ/zEed2N4zXMt5kmPHOdeYQNbBA7M558mupI7p4LIMgifrwUBM5Xdj0tgXI+bE90dgR9q2bV5jxxqvFQKrBvab/cpxyf5J1jvLbWycy6fQBjaw5D8GMsnuch6SYWE650+Gs60JBgVLrGDzU6M8B7l3cumZnkZt5yJr1GeKDXVuC+X1lBa9MS2jNy+WpIVTlxYVK66l0Ci0CJvAj86DL0okKGkMDg3ryGjxBXA0UCybdTeDmsLfHKOsj4Z7FFGmtGsNTJL94eFhlpNAMOdBlmXhvGhz9sP9+MmS9AnS6+dycw41hzlifZaYMUuCD7KWWUZG/61etv8E9zlGLT/L4NqhDiNmjvdYF/X0GnI+9oXzmGE4iry+xzk7OttG4JX2g8xhW1u+78PvI/vi8shCCfU3eyLNT6e5rLSFlFw7S4At139Ln2sq2ax9deVnjhHL3cfypJ75yT5lnZnWcm/YngPTM5L7CoTyaYAj9fdeWBj1tujYhibflXFU/kblJq3MqDZpZ+drUSfTZ9nS7gIl4KHDyAV5rIkhYH2n+JR2I722mPct4GYUfN3MAEFTY0AyskvwlveYx7+bI2yGs9XrfDlO7ZHyvJ/jmUxgOq6r2o0ys/2u80TT+xzeK+kff4X0f61W+sdfNf2+re1zM2yHJcfrVBM7NAI6dBycx3S2ri/TNMCTwUA+ap9CgCj1JzE9d6iL+3oJ6OYYuFyu6WQiG1v5INIeIV0GQkea35uT5VK/BJTO157STBtAIM75RACQde0z9OnUCQCyHtsS65ZzIe1F9vs++0xwlG0bBbUc87auEzCN6j7G72wXbUb2L/u62WoGkQe5HHLpmR5uP40cYaZV5GkMUHO4NFxZV07yZJeyXurDbTCyByNpNCnvs7xmPHzfBuRWpLO+SxF/Y7yyDgtBZIvW9pXBcpbo90yfhivBKw1dGkg6u6bnkSZH56ekGqs1iorJUlzT1P8PagIzpyiXINHR6Rs1vWfkA5pedvXWqCeBPg1rzkGC8MZIJJNBYElGguwVz0YkU5Ey6mOPF+tiOygcw+bQOWYub8SGjViEZCiTaUtWmA7zjrbnFu/zHMoxPlOnLMfjRBtwtKlz9C4c9leW3dq+tEbSbrb82TbbGLZrZF+yjtEYjYIdnjEkcGPwmXozLecTH8DIwGqJQR0xP3dHDkzPSC4908N9130LlN8ZEWS0yKiP+XNRjJwvy+T9XPTpmFqUIKRzpDgyTlJ3DAmITjW9+r4ZAzoMRqQN3FGsH/U4QxrqRn2ynW0rkjpw4nqM0kg350sZATACklYGGTu+F8X9+wZJf/xl0uu03c7GrD29+X5b08HlvynphzSD6NvxPfV1ubltkb+tb+YdgVquHalHq+lIUsgseEza+0yYJ50762r1ZLDCvmzj3bYxsuwESLyf4JCMU9qXxoQRsHiu5FmUI5TjvBlAZfnPbPJd0wyG2bYzbY/TKNAhi5L9lGPPsrLtI5CT0uw3QV9jakcgaAloWB/PaQYkTsP5znIJ+EaS6+0iff78y9nz8Hf/y6UHPSNEn2hb2o20SVs6zXm5tw/xL0UBFgKGXGSZ1zokm5HGZbSIm76chpk3ARbLGQkBEJ1I1p/9yMiNRoPneVKfEcVNlqHpmGUtgdK8PwItx/h9Q501SofYwEGK++impDtPbL8MLlkX6uoXER5rekTd6XyIOev2uJzHH8fC6bkuWkRNBpBsGh0FdRhJW0su1/nbC+wofLAg5ynHclTGEoNJcJb6tznk/sh+4zpyvxE8jMpm+fmZdTyoaU7c0DyfaN8aCG/3qMtSoMUgYBRMNNvDAJF2kGst2aysU9oOuNocONPuWHg8GjhrzAzBPtMkQDxF+tGcP8i9lUu/vZWSCzAXW4tClw5ZkpbmO15G9Ot5pBfScO+6UbEjloLprcPocfs0SFx8jAibAW6/GyPWmI5kZo4lPSzpKe0a9ay/1ZPbKTlWI3A00oUskuvKMVw6V5CsWAop/dQ360/9EtRYTjX935Xfrm2gnmc9Rkb2TNv/jZtgcslxNeDJp/KOkd51WhKgUXw9o+Os/4rms0RL8z71IyD19QcGbWhgdAS+WluX7tseeLvG86iByVHdI1aDzrjNvwY8+f1UE/C5rumfzTZ7xUAlbVezcanTyIYmUPF9l8dXNli4hkbr8QjpU5+lfqKNoe0ke5V6UUasElkivl8sy282+u7KYXtrJPcN6Enj6nMqfkX/kbYfSR5FM7lo8mQ/nfXIGIxADAETIzpHJM9o2ymzbdSTIGykT+qQevPsgO+PGIYUGrbUwbR2siM82Jn609DSUS6BwAZ2WE8KozgDQDrzs/I96/a9Js1w00hb8n9y3Sn3XR8d24gaH0Wuo/el0FmRUWlPpmWfp7Np6UftaYenCUBzrFLXHLtWX2OnuBYyfeuXVk5eI8ikM05gOZrPBOCZP+cD18Ro3KwXwcgzSMPvPHt1ogkoPa1tRsaBTDKKltTRL1NtAJKBKYVjwrptkx/SPFduqM9vadtGeCySDSRQYTA6CooaS8N1yvXfAl8C3IPce7lvxoPszFVJv1rTOQlpBhaNLaHQYbUJ7rJGhjPTpFFpiycj/HbP9exz8K4jP6lfnivJsjPNvqcJ0lj4/0Clo3I/3db0tmAfks560/i1bQHr2hzoRQCPJYFXOikaH5bVosgcA49lGvsGTpkvnZv76rbmw8vWr235MWodATr27ehx7jS6dMzJHhEQHuF7m+P5lyCFwjZwC3cEQDyHsj/TQdP5GIhz+6tJgsUm6agyCEqAlvdS2PepxxLD1OplunxyL8FUHpxOBs5le+65jnNt6zVaZ9mWxpR5DmcAlHmzfAZaI0bxbNPOByV9nqSXS3pJtJX9pbiefUf7nXoZ6DhP9skItKXdO8c99l/ebwHm3ZHDf1kfyX0BehhB2ZG8RdPr+UeszIi65GHOXMR0vlxYlhZ1ts7kwklQYj3SAba2nGk+tNhocX421oEs14hJsIFyXTycO6LD6WQYIY3odLbH1/YBnuZ8c340J8o+Ej7pUM+13W9tjDJf0zHf3ZN56CSy/xJ0KL636L/1U3NoWT/HNPVi/lwfCf7ye6Zt7bQwKHH/8LFkAnU6SOrbQGlG8zk3LjqXkyFp5VoYbCWg4LinZDCR5ftx9xxf27J8as91mPGmTi7baziB65G2WaY27or7WRbnJIMsp2NgRSDvdrVA6KqkT5f0y/+g9Lu0Oyc47p5H0i4Ay7FPP0J7lECJbW9C8Jj2dGk7/e7JAfSM5L4APY3ulaQ3a9rLToM2opptPI8Gf9Luo4msl3QmgQPrzHTNgTZnSKfQjDyjtnTmCeCy3elQCU5oJEZMEJ3aiFlxvaP8+d6Wli7LIzhoEWemI4XN+ludeY1G3mVn+5aiNzrg0XxcytvSZ5+Rvt9XFqUB3WQO6Iz2GYnRWKfDbqAj25b3rmn73TitjtFaa/OFgU2mz3XhvwfivnD/HH8sMx00mbkGPNuYE0wI37099crN7xuatoOua/ex9WQiM8jiWsi5xLp8rYGiNgeXmOQ2V1rfvkXSO3+v9OdQbgs+04anfUkdpV3bldcI7Cl5n4xSy9P8x0Euh9w3Z3rSKDO6bpO6OefGkozSj66zjmSHbCD4FlB/5htcGxMj7RqVNMqunzo0w5WRru+18xTt3R5J+fL9F0K6lKzfeeigabBOkScf5bXxSvCXuowcXkvbwKMWrjGC5XdGm9TrGeiXj1ZnfQRx7gNp7DwcuY8Oumc/tqdJGgtGPUdrI/Mkw8g25JwcMYsMBOxY88wKn0JMSfbLbU5GwTo8oH4mhMCEZ+BS3xGbLO0+iu+6maexHcea/wWJWY5bcZ9z+FjzWcYnN9evaX7VgVkfPkquuE4Gy+xSO8szctpZTmMWm3Bu8OC/x/tDm99/fPPp91ulrRrZy5S0L0yf9tT9mn4l+z5B+6i+Eet6b1mfFwYz81zLfcH0SNuTsDE62vN7FI3QCZN9yTKWXkrYtifSgDOCbk6Tj+Az+sly6cTJEmV7LO0dLTTy2b9khfLeUgRDx556WYcGXKTJAJveflSzQSbQZV2ZJtM2kNxkFFWzbDoisn8WArxzXE8AxPzXNL/5to0t506OV86RFhhI22UlK0HxAwPOT0CbOkh9TWXdWR4dQ+a5rcnxe56QFaGuCewZiKj8dj/lmuNcT9a01SmNbQ3tRFuXroMAyNte1yOf713blO1tLvfnmWZm7Nbm99VN+pSHNL90M7eBDHYISDnmS20U0vn7yMHkWsqyvTZuaHox5w3Nh+KzbJ/fanYo5xgfopC2Qc457mX5uXXVdhr8/XbkcfkHubxy6UGPJ5wnX9L8FrIflBGLI+0uUjqURt8ms2On0YzAiDptRs/lpS7pKPnbi3IJfGT5R5oNJIFg6wOCnZExa5FwOjFuN1kPaWwkXi7pK4+m/zRO/fJ3AxUX+d36rIGXBi7Ieo0of/YZ0+d4ZJ5jTZGuDelV3GcbWrtcN98bwnZzXaQTyPm/jyV1WQQW0vb8S5DRwNIob4Loxnjm+ssgo/V/BhK5DtnO1pb8nfM4ryerZeG4pbN23XfiL4MKA5sHo02u51FN4OgRTQDnYc3z5Xbk9Twj+DuL+lI/1+vfZNml3reNZW1B1SiPhUFjm3+0+WT9/LutTfdFgnCWkVtumY5zjfNgyb/cXTmc6RnJfbG91SJTT7jR9pMXaT6aTudL471kzL1QMiLk+YD8pO50SNSX0rYBmsFvDpyskPXIQ7UJmFq/Cb9H0QsfT1fkpzFtjlDapqDPJL1H0jecT4fUyY7ZCDUWKXVfOnfj9uf3pfnALZPsj1Z/5lvSI9NyO2rExDWnSpDSgFm2uc07brswHfuLzpN65PVRnRY6SvfbaG7v+85yR0xWpsk10Zg8bpm1urMPl8ag9WHqcqb5VRxX4vexJpBzTfMW2CdJeoWk79cEYK5retrpfdoGmMcab5vlNmmuSbaPuh7hHudPsxsXudbmMNOnHWQQl7o0EMe0zDO6R3vQ9GM9904O7+kZyaUHPc3RZ8RHSQeVaVuaTDeKnNu7F2i8UtJxUffRQkhnO1rozUlrQT+V+zQQdPIjNsFCsEYGJL9nfzISynNCHh+yX+/UHAGP+mbEAEn9vEsCDEaBBCx0DMdIS8k+uAjwYp0ZpRJUuY94/qnVQcNPJ53tJhBiHmmbHWhAKedI9kGyHg04pS52oBw/AqgGwLMfGpBIMJMAnXqxHalnzr/m7PYBypG9GgU/WY/nK5mGRzW96fvNmz9vh75c05YQy5B253YL0Eb2iuvmGN8Vn0vg9yJrmIDTdXGOSGMbzXZxznNsM22WQ1/CMlhXsj/3juU5yJJcetBjadE4J7DTjfLQGD2jbUM4oln3XaPkgcglsMOFMXLuNKgjZ04H1tibdki2sUf5ewmUjYAHI0fFbxoRG2iPh7dl6OSyjaknf/OTutHBJRBoBrq1vxng5lSSZXA9Px0KvIHrVsYoQk4dTgbXR2CuychZkSXKtdXGZQQmG5Pg663fR2uMjjvBHcvnb38nU9TGKxllaTt92p9cm2ZxnMZl5/crUbaf2vpUTf+P7bMkfeGv31z4A9If/eC0DfYaSf9U2+eDbqDtWQdBaRMCD4MgzvOlJz9bEDCaq80utvSu0/r4WgPYHJs2l0ZghulZRwPRF/EVz58cmJ6R3NtxuYAw8lhiNSzpmBIQ2Cg/oE55c3EtgSDqM4psnJbR05KDaUbV0YO0a6jTmB8h7Zn6NlzWR4PuflvSh2VYsn3+OyvXnPZcu2ccGoglmPL3bMMJ0pDxsg40qHRQ0vbTL3TQ7lO2J4GHt6s8NnT4I5akMWOZZuTEqQvbSjbD/Z51tLXUgoV9QnCZdVmac2xght/JgmXgkPkTtLf+zv7gnGqBxTn+FJ/XNDEvHnOmIxBtTAbZkszvvnpEE6PzLkmvlvSF3yTp90v6Ha+T/rr0O//d6YmuD2gCOtc3+fxywlwvCVL91wKU1HOkb9olHkrPssgu5W8GXQ0Aj4RMmK81wNP0V6TL9GxLC5oyva9n3aOg4SD3Ti496BlFIQYcNNx8xJoMR+b1fRuYNkFHB4ZJdbucs3Ivy15iaUYOqhkjGt4TpEsgQRCRjozGZQR0LgKAmq6UNJzWeRQd+xrZkcbA7NNtaWuhpRkBr9Tfv92GZkhT+CqDUV8tRdwE/XQklOzj7GuOg78TOBBoXdE22GjMEtvWgEQDy3Qw3KJ0vQliEiRm/tELSF3e0rbgaO3keOe925r/R1qmzXYtzVP2BdeFy7mxuXdL0hdL0mOSPn4t6V9Jn/Pd0u+ct72k+TH2tEPSvBWW9xqDQX24Jtt26z62KNP6Wtpg3+c6S6E9YN8yOGabuGYyKEmWqaVzedLuvG6B+UVY3OdN1mfP/d8LQC496EkhpWiDzQk9Wigpueg52Z3eBq7Rsi36ycggn5xpkabLzzTS9uJTXBuxTtSDjA9lBCJTr/zOqIhMVRrDLLe9X4XGZMn488mjEQvRGLl2rY2XtPuUSnPiozIZ2WcdfEqJDp8sjT9bO/mEiSXXgOcq+7QxFEI+vs1W8dv1uK4HNQGfBEupV3N++ZoCg6YmdIYspzGDmaa1X3HPaXOc2npo9sOSazyZTD8GzvfCuJ7Mn/WdafvfSDB9gpG8/zQT6s0f/pZOnMBc2l57+Z3bNSNWmmO79DJC59/HeFCPfUEWgW7appHda+sg+yYDSGm3nb7WguCs5yCXWy79mZ4GAiwtKpH6Iub2QQML+4zVkpPmPYKxxlQwiiBj4DR0xnSaZJWyLT4LkFGZDX87U3Ss7boIIo+QJp27dSWdLPUXmVHYpmZ0GQGmrhcBLhz3dn8UqTUmrIGoZ+J7sg1L7GMK9U8gSYaO9V+ETs+XwnkeZD1kV1zusSZneyfSN8CZeZ2fL7NrMtLd/XG0p5z2AkJLm5PuA67VfcFC0z/LYVnc/vD1HLtkiXMc8jD4dU3g6lFJ3yHpt75R0stX0sdJepukr5ee0kQA3dK07eZ/XyP1udjEOiRIynXb2MJs69XN93wfUJbd5mxbs6kD81knz8vRizpdTo4x/YGvLfmJY9wfSZtnS+zX8yr3rOLLLfcNMOWE9V9SwVxcTi89O3o5Hd8SizCK1i3s3GZ0HTlSf0saKjraZKFsREnH5/Vj5LP+jHz2HThcYnFUrl3VFOHn1kjbzqCRWwKhrT62ndLetpsOlU5I8dv5GSW3OnzG4LykS5A9Mp7WJ7csU7fsk3b+Z1+Q0NaJGRR/z3l9RdOL7T4h8pGhbHWlXnZMd7Q7TktOhDqy/cnStnZdRBgkMeLPa1kndWlbLu11GY1VYnCROvnfYnxAE+h8pSZw85f+K0lfJunrpJ/4TOkbnpjG6erm/o3SjrPyZz3yHKDrZhDl6wQDFK7bbBf7LdveWLgEImR/z0pa60gh28VrvJ7zsgEe1pHzJ9fQPXGya/XB/kj/XgBy6ZkeG4zRgUVS2wlISAk3ANOeslpiBFj3iJFgPfzOyI+RB2VkWGigEghRV0YhjbkwCHuJJiM7imxs7PjvFqwry329JmP9vZoMt8eLY6u4x0fLk8mxmMXKaHEJsOb2g9N6DuQ5lVZf26Jq7JBQJnUgOOV9183rOXd4li0lwVYyJDnGjVHLsxv5TqpjSS/T9LLI92o38s+6mh6W5jjaeqDeQr5Mm3N+X2DLurMcfrceS2W2Nmdd7fuSEBhIu69suC3pjZqA0I9J+t0/Ip39yHTvQU0vtnwyyjjVfMaH9VjyKTIyPAlqaOuSebN9OdvoeDvSLQVE+/qvpRv5Xs6J/J790dhqpuf3JcBzXNIzzUEuj1x60NOofWmerJ64nNy+36jlLJdUfkPoLS2jzWbk0mhax5GTTMkFloxMgoN0qunQpOWyuTC54KXpza6v02Rcb2vXULiO9rbV5ijONDlLG8M0qq3/s82Ka43iTh1Yv+dAe0R7FMWlAWvgMdvEcWpbJIwic67mfEh9/b+imC/bKW2zCAStt7XdlnReWX86Lc9LMh5nkt4h6QlNgNXpEnDSSTmCdhlXNTlwbk+dbe4xXwOVI2BzHvncL9lPfkvxncjD8Wn24WjhvqXNDc5plnui+Y3JS3KONLl2ntjc+zhJnyjpn0v6oKY2vkTb/0YhX2aYc819a4CSY8bxo93gOvS1fQCv3W/AqJXP/Dk/aI/beHkOnWk7bwKbZv/T1yxtSaeM+u2uiZmeg+zIpQc90i56zi2bfCeO5Uy7jqUxGnTQR4N7+W6bUZRAJ8jJbiPCg46cl6PzCqTa7dyWDvumU8q8lgY6pMl4vk2z46TzZ+THiEja7v9zTW+IfV/cIxhp7FpGmjRsBApkAlr7mmHKsWhjMrIb+1iN1I1CkJzleSujjZ2lHZIm40eQ48+8liCM5XGeSPN2STpj9zudAfvDgKOtu/bOKP9TTQLDdMr5hvHM62vp7E9CB7avAROOe2P5ss5Mt3S2KOfyubbnXjrj65qDhBPNwO2DmpibY01bWW/TxPg8uGnfBzd/0gw0Hbhc1zyv/O8ncr0S6LHdKdk+vsAy51a2TXGd6Ru4acAn2T9pu1yClQZ+lliiNieyzFGATPvKPtt30Psgd1fuC9BD8CF1h5JGb+RwGiPUAI60bcwZJfhaGuEWPfi+t4FInZNBGYGB9iTNKBrKtgrp8l6CuWSR7miiyA1OUpcsz/rQYDcQMQJ3jDyzLjIPqXca1SXD3KLQNKAjR9+ALGUExHi/sZRuFx2AX0Z3S9uAL8sfnbliW0esiOtq4GYpErczS2bI+rOuxiq5LKZpOt5Cuiwjg5PUj2KdEzxwPuWTb9SrgaHG9o6cfAPGLJNA3aCEQPBpTSDGrNippMc1z5knI48B0q1SVrMFWf+SvtneUVkNLPN35uM8SuHvttWZerZgYkkaUFpiEv09bWUDQizv2ej0nMqInnyRy30BeqRthJ9Mj3+P6Fd/98RLZ8tFS8NMJzlyfnTCFhpX6pJCp0VgM6Jsk/InEOI1Hj71os9FPHKCoz5obVlK2wxfjkUbg8biNGlOpdHhrV7fp9HKcswe3ChlNoBEANyYGWm7bR+jKSL/IW1HwSqfo7HgmCRQIdNDAOP8+d/tDdpanxB8EuRYco5d2+S5UfJTlpxxMj55T9rd2s4+zzKXDmRzPD2WzQ54fiRL1BjqHIMEr7lNaKCWcqYJxJxoPoNj/fKfhhKgptzY3M+nqxR52xNQZH8uClI4NxpA4rV9QVy+LiDz0WaMgjLq5vsWBlctMPD4c35fBDzdVTlsbw3l0oOe0UHQRis3aZFVLhZG0mmcecC2gYrGYrToPuumoc58LWJvCzSj+VF6adcAkMZl/SNZiqYvkrdF+yMjt+RYRoaS0fpItwa6GsCUtpmYBIoEOSNGJ/VOoYHM+49rBgWcI+7DUT0jg7vvkC8BzbGm7ZJjTY62AWLWn+N7jmv+u6ptx5VOpTFDLZhh3QRs2QYhXXt1A+flEsBr0lguMh9Hpe6c423dZzl3tP2PR83MXtf87yWub9I/WXQk+CDY2RdQ0E42kHqRbSSe5WL9DOAsDHa5nnNtWhLIUVrdrp8PtrS50NraApS2bg5y7+XSgx5G3yOmROpAqBlNadfQ5O/GAki7i74tsqw79eJ20hKLc1Xbj0c3wJE6kK2hrgQU1inTNiDSFmuLmlhGExs6GwaChyyL51mW+nkExnh/VJYNVr7PqLXD1/mW25QlFoosQMufWzGNPWlnWLLeNqdI+XN7K6/l9V+m6X0w36TZ4Wa6ERhne7L+fBKJabL+pTMxKW6jx62xVuwjAp685rlwpejKQOs80rF89uc58nJOKq5zmzkZlcxzWxPAOZH0Us3neJgu19lS0MF8bUuMwCfZwsyf/dnGIuvM3425c5+kHtSrbXs+qumxfaZ1maMAbsR0pSwF2M1eL9mF51UOaKvKPRuPZyM2xKOIOdNZ0oESdY+ctsr1NDrpsPd1XObP963wnstN/UdPJGX6ND48h5R5W6STjyZn/TSW1Dnb0aLbBhTdnn0OMs9nuK2NCbFeNjAjEDEaV9/LMbB+CUZHrBnrv4hzHoG2fN9Rzs/zuDeyW237YrRV460e31sCO9bvhzU9vTcSOrNWVjo/vhagPflHSdBEwCFcy/dVee5k/SMAn2xemzO57rLunJt21Oe4n2An09MmOb1ZmBa8nGlmAH3tVDP4MUhjENccfFs3ybak5L+syL5KaUBzCdxb9tnQLIeH9zkHcu4ea35ruNTbv69e60cb5DEd2XCXf6zddXGQyyGXnulJw0b61/eXzsvQgLRtlnR0SzRtRjoJgGgYmY/SWA7rdlUTVX1L8+PBZKFaecI9gowEQHaqyRA5YkvAQUMjbUfhF3H41IGSRpn90p7Ky0iUjJEG+Xg/v+eYqnxvTqNtqexjo0aUeNaRbALHO+db6jZiGBqDkvfaY/zZjrfF9dFTT54zjLhH7Col09tRtb5OIVhnQMPtCP/OraUG7LPvR4CAY56PwHssWr+P5mmb97yXNoZ9Y3txTdM7tRpAl3b7hXPM15IpS2ayPSEr7c55P4rPddGAUrOPaXM47mmncluS88xtely742gZnV2StufISEduh/r6yB7eE+BzONMzlEsPerhtk9KMNQER03EB5iJqi7tN/hZBMFKiQcs66dCs3zVNgOchzU9qPIV8/mysEBd5Lu4GgjKNHTP7MXUluGMbbBCag6HwOp1Nc342in6sO586I9Mwcip0MGnACEIbeElnIvW5sc/W0KlJ/dzNEqhs7ENLw35wXZmG5xiONW+tNEeb6+pM3WFI23OTDA+3MRhQ+FoDT1nnqK8T0KdD5zW2zcCr2RGvuzZ+TZruTn8H1+/gPvMk+JHmdvgt5xyrZNaaXgk4fS2f9HJ/kT1sW5y57hlIpj45P9r6TxBLvRl8Wse2FnPtEnCOADnbx3lFAJlriesrx0i6h4+sH0BPlUu/veWJSqowo73RoeDcjmlgx9cTQJDFINvBxZwG0cCAeXJRpONlGVclfbKk//zrpN8VbVDJbwO1xPzQCbXIvwHHzMNym/6WR7W7dcb62Beu63xwnbonOEimJCnuxkSN3u3RDCT1zjLJdKSMHOASSCJwyHIaY8GtUpZL/cwGNCF7kO+cavM4y6cO6TTcr6mzJQ8y+/0zba6Myh/VmfpZh/b+nxaccG60AIo6NJaJQELatTk55wiesu80+G051dR3D2j7jcvcUmuvnMi2ZLCiko99nGMzsgMMMtv3EbD03OP9TKNIk8EYg7J9oH0kbV75t+dvC3xzXudcu/RO9kUm98V4tL1VIv82kRvLQqM2AsM5YdviSWdLfaTxvjb1ybYdafNkxZPTUxkJHvI7wUBjkqjHcUnHBZx79i1qSmn9tnQAlQ62GY6RAW2gZGR4mhg8EhD5GlmzbDsNcOvvplteH0Xv0q6hpnOmo/XnCHARzJoxtCST0bZh6NzYriXmgM4+x4eH+K9olxVsZyBG17jmG9PXgH3qlGBzaS6NQJiZlpb3XPNj/1k356D7obEbbauFtuIZTQwxgbN1SGCQYLYBiBTqwpejWi8GTcmmjIABbUqmyzIbGD6ONDnnz+LTdWTAzCC3BUZsX/4e2XoCUd8nKL7rstY2W/Bc/b0A5NJvb0nz5MkotL1UjItN2n2LcqaVdo0Z6xw5sxQ6j9SLDm3kAM81RWxvk/Sff/Ns0HhAkZLROI1M9scZPtNwMfqljOpm257StkFz2UkpN0Mw0pvjm4afhqxF52kg6Shb+kzHOtlfjf3gPY51azf1zX6irqmXGa90bAke/J0sQM6Dc20zZ6xD2h6/rKeN7Yh5SebgWNtbO7ndxG2XBjZyTlNv5s20/P9Soxc8Zrt5PZ0obcpJtOWKpvWb78+hXNt85r+jIHBxHTyD4uv5EksGKy4nD3hzrJZsG7ceU/Lxe84NC4OtlFGfsHzO1VGAybIzPf1D6kuQ3gIBi+sc7SrQdo3SHuTey6UHPXQojIQtjDabIeBBPZc/Aj6MAqXxO1KYx5LGgE8/Mfp4WnMk5keXCZRa1JR6+pxLpqEuZ+WaxQ4qQeFSHxF4tE+CkhEYaIxORqgEtMyfDtxAkWNnWYo4tXCNhthyjjTSLhuREah/57tL/Ok684V1vkdD3kB/SoL+xlaMQIfTJjPEvsi25dN3dwa6tih+FMRw3jUwyrRC+quRNvU/1W572lxt8/FI233iMzV8943F65Hz7eM2+d6i7f6n5MHe1KGB9hHb0/QaBe1c96lHslG8L1xfsgGWUZAweuI0x7GdvWw6Zb8aTPlansfJddLOJC4BtbS5Cay4pu663DOa6XLLpQc9fHcKjWgaqBE4sJzjeotCVK7lwlxihny/ARPqlYYgy7+l2YBehBGh02yAMKU5L+pGZqYxEu28h7Qc3aRzY959Y8d8BINkE9hXvt6YqKYHgUteJ1s2ipr5hFz2ZXMCBgkEw405aQxL6/sWHbMf04lfRZoGavl+mjZ2ZPZyHV/V7lok+MkyGrvD/kuWhQDbaflOHQvBKNt8p/y+qumBg5ua16sZHgOynDOp+xVN/8A1pQG4ZMm4vcR5kmCgre+r2v3XHgSm+R6oc1yTtud1lnFe8i2BhZEtbWtzlLc9NZgyGuO8bnCTc9NpRsF1k1EQwvV5kMshlx70tIhGWmZ+MjJmhJB502iMnjjKhU5D/myMTqZpxqExSA0wpSFt2wlL/dLKXCrX0mjeZrCYnkY/v3Nc2SfZV3RIbbwv6vBbdM/faUwbuEujTeDaQHVrf9bZvtPx5fd2jw6rbcGwf/13rukNzNbhJMqxw8/zKSPw4LLbU4O3NW3pPKTpH8+m4/SYjt6+nmW5Ta1frZvvm53g2RohT9bHugmcsqwEO9Yt2QKyN8mC5LaUJc+EUJfzco92yNdoF861+28tpO254+05MmaNRSMQJ8tuaePTgpH8ve96a//SK0s4D5fsZ7JZS/aN0oKSey6HR9aHculBDydxiwZp2EesC/fOGypvDqUtxGRZ2oJlGgoXKCNDqTMWGTWmwxhFRq3eE83/ANX1EMCltKfRmsF1OygtehsZ9Sy/gdDM4yg4AecJfu9zENQn58xFzjhltMiIl0Z/9BJJ6tMAzJK0bbZ0aC6H7zpxumTJXIb7IR+JbqyXP7MvvZ2T21xXtP0vLXKetO2ftg7bGI6CFff3mfqbjZ3+VLvzOtuYZfqffZ5rAnBXNf//sBOkbwFMfnqcc742gORxa0DS99zXDKhSls6iJEgUvmdbaDPP8UkGkvmbvWgBSQsivJ4aU5zzOYV60V6PdGhs6gisNQA40uOuyz2r+HLLpQc9zRm3CELajYql7Ul4qm2juxRJnmvXkDsN97p537LEQDVHnvnJ3gjXmwN1uQQR7CuyAGSZ6CBZT0pjLUYAQ4O0ee1Yu+9LIpXt7REfAk2afgT8RvX52uilaDS+7Mucb82hO0+CmVZmGvrmuBJwtLMC7ekanmfg3Mz2sC5p6ueXS3rF5veTkt6pXQeZnyMQnFtCT5Y6WVab8/5u8JEMjOtOJiaDm8aeSNtzJu0ByzKwPZP0WkmfvtHjvZL+saZD/Ne1y9YY2CQAZNDStmpTrF+e8WpAgrYpHXeC8Da/XA4PKWe9FgJ65/calPrYuD+O1Oedpa2N9m6p1mcjIJfjPGJjHtDuk21NN7eHdae0NXqQyyGXgom7iDBSIbjxYvakzPfFcAEKv/OdN2kM27tdDAiSZUmHO2IlWI6/t+jH9Te9l9iHXNjckhqBAfeb6e3MM2oLo/Iz/G5RZQNB6ewTCDRDkbo8rQnwJN3uNMkapOT7YaxPbhk0g+3vo75cMty8v++sjHWUtkELpY0lGaEWjbuuK/g80+4YON91TYDnt3ys9Fs+TXpY0iPabX+ybS43wTdBTAoZmKZPzs0zzawG2RV/5v/NGjEu+0AwAc+xpjn3qCbA8zmfJ33G26TXS/p5cZ8gRCin6cLrXD9kQXnNj73nfM75TcDTyiZD3kB36tbWWGMlc4tYGtuvEctDcNEY4WxHgs6RzUvbqvjOs0q0ESyXc7oxPvfMwXp767n+ewHIfQF6crGmY0jQ4nQclzZOZA64lZXgKUGV9Ow6jAY19V2aP3QEWY4jkaU52J5sSCHwsnNyFEYDx7JtDJb6Oh2f1I0djUe2tRloaXK8nyzpdZodUzuzQaNGhyCNI16mS8fNMWXfjpgTg+r2crOcaxzX5liyPSNAnexX1sWt0NYml+1/iaJ3HElv/DQ95t+R1mWmHGt+6WACyVPNrzW4gvQEpQ0MLYHa7LvmeFXu5zrM8T2NtGfaZhX8z4D1vb9K+nfX+tm/fmKvcnsz63S91MX106lal8ZGcKzaAx5kUbjGGjAhC9Lm4ejBhQTsWUY70J16jkAn+4NBltcSbVQ+Vca2pWRgwLV+jPs827PPtzAQH+lwkHsr99V4jBwiF1Ias3TOjKRdTtsrP9MuwGoRHCOMlPbkw1KE2hwu60lH1mhml8/If7Rd5vpOIw+BIKPA1DVlBPJYRjMeadxSclvhEUmfKumTtL3FwCi2TWr3WzrX3G5IHZcAKdlEivuyARCmO8f3ZmA5VmxjArhkVizsc+rm78nQuLxbms6srFfn0qM/oPdqfu/PkeZ/gNn6zGxLPk11VdN/BD/W7hNRuSa5Vq5GOVkft6uF75zHKVyHqX+OsdNZ3xtO9Jrvkv7SSj/6rdO1DBiO8Sntrp/8vBJ/bEM6X7LL3HLP+XgSf2njWvltu70xIRQDW9ff5l3q05hU2q/Uk2xP2nFueSZoacDO5S0FdFme2f8WXGTgwXpaP90zoUF/Lv5eAHLpz/QkypfGziQdBw0gaf8GXrKuvJ/5Rwa0bStI44Nzrouf2YY0xK3NS2wAdRk5VN5v7M1xuU7dmb5dS+NqhyWNt9TSQDrfY5u/X7z+NOl//wF9y2snIPSeyDdqQ3PM7ZyLdeBjwjkWCSzT2Y3G4VzbB8ez/tQxvyc75HaNACfHtx16pZMh26O4b71uavrHjd8k6ez901mcD2h+ZL1F41mer13TdDbofZI+qIkZOdp8T2ngP/VKAJXMDO/l/czPuUVdKelcjzU5+acl/VNJ+nHp+m+Y+ueNmzKvavt9PSPn7ut3tA3mzrSrd2tDlun+zX6hf2LbGhPhOe95lwHfUmTcwBTXTtZ7pO25PQI/0vZ2U+YfBQTUZ8let+s5f/LQuu+5XQSuS2vznsnh6a2hXHrQY2nG2g6lfaa0F0dJYyM7cghNsqykSUesxsiZsjwKX97GduQCbXqzXzJtM1zZB6N36/AANPuPxpl1uAxG3DbcpKBHQkCTIIsHS0dgkTq3cWiHhZ33FL+Zjn3ahNHraAztjEfzIYUO9JomZ7vvbIY26Z7UfPA4x/Nc8zmlBrK00fP65v4HNAMqP659fXPtaXWnl6xcziHPC77xmGMoba9Hrk0DD+F6tiMZMpfxXknfpdl5u1y/TJRl5BNa2RaCw6X6LTnGBkw8ezha5+131k/WZ5S3jbXzJgvSJEFkWwtpQ9K+5QFo65o2z7/zyccRWFoCQgz+0qfk1upZ5KOw/EvF/hzk8oOeXBjtXTaWpWjEi6Pt0S4BlLzGiDBZmZR0uAQBufiaseZvsj0EBZQRm9P0JPAb6ZJlEQg9M8i75Nj5skmWT8lr79TkKL9n9QMffincB5DOZbt/6HDa+DWHy35okW8a39ZvaXSl7XoI0Bsbl3NJuM+yKTlXsoxbcT+vZxvS+WXdCZQSePkdP2Y5TuLaqWZGg2vtpibg9OCmrMx/TfO5EF9zfXScozlLgJMgR3Gd0oKeE82H5xtwNnjntomBXo47ARDLGjlJXm82Q+rrnSzFqBzraHvp/m5rZDQvc05ZN74aQOr95XJZ14hJIcDzvPYcTbDKOrKdeW1JRrYtwVaCtKU8z6scmJ6hXHrQk87LQud/jvtpIC0ZGTTn0uYHo4hMnwu2RS9pgFK/fcCglUFdM4pZ0r0BshGgGclo8TamoIEjXiPgaeCH+vmsw01Nr+2/sSnncU2OKLcV/L1Fz1kmI3vrQufO9Le0KwRUrS2k+8nU5DWmzSg6fxMw5dYo6z3XBC5ercnxv1u7fZdrimsnAZDnBMfWTJJBy5mmg8tXJL1K8yHma5t8T2oGRNejrqc1O9z2Uj06e3+OWBtp9109zMNtJuuSW9tuu8vJ/j+LP5fhMrM/R3M89Wr/s2spUMo5dFt9zid7M2LEsyyCu2wvyyFDaX2zXl/jgWeu031gsI290zCQS5uTOku9nTzzqUgzApVkx1KXth4Pcu/l0oOedLgEDDm5UtqL1qTx5OPecTMmKWRReD2BAstojnjknBlxLh2+o2S9NjSkiFmHdUkQl58po+0cl2EZbScSYDA/nYDz35D0IW0DyXSMo+hXpb4EwXn4u7E8nndLZ5DSERAYpxjEGUBxq2xpWyGdTOqehp3jdqrpAPG/p+kM1B1Jr9R0FmWpTYr86cwIGm9u9LimeazvbH6/StPZnVNNj3Z/gqS/t8lzXdMj4O/d9IXn5pGmcc62jAKABMXS7tjl9TbXfc+SNmDEcknzQVcHIOwX26YrkY/z3vX7BZDnmgHPEshxmR53MnCZvjl5Ss7bTMMtn335LEsgYsRMNmnbabneXqlpHn0A6W0bCJgbq+p+SpvX0tGOUL/Tcn8fa/S8y7Pp7BeRXHrQ0ybeKEppxi6RdqL6fREPKUoi+WaU2velyITGjUaTwIT9cIxyMrJoIFHaZQyW+ozsVlv4bAfTJmDw9TwEbGl9QZrd116i7bf7LhlfRmgcp3Riwn2387z8Zj+TXUkAcGNTvlmQhyU9Ef2QZVMPAh5/z3scFzrzI0mv0XQI/DPWv1D6139ff+0V0stCj3TCBAYtwrbuuT5ONb3g7UQzy3Gi6V0/NyX9kKQ3b+5d3fTD05qB223NACsdx9IrGAjgeT/b0e6xrLY9lW0nY9TA/xnuNRDp602fxnrk9xwnA4s8W0SdLbR1x7gn3G/O35J15zaY0+U4pl5pN5rtI+vDdqRNvqltYDPaWqOQ6bcw+Giy73r2WQabd10O21tDuedgdJ80BRv9z/EdAY/Mcxyfo7pbtNmiTwKSFpnm92bYWAep45GRHDFVWQ+da6NlR05j1D8W6t7YAm6ZZNmt/GQ8Mpo91wQiyO4wKh0BtjzYTH2Ses85dlX9qQ3r6flnQ39dk6P/XZvPl0T+V0j6Ik0MR+rvto36w9LADw1uShrc65KkL5F+1qd+uE1n2v0P4aO5mUwC6zvTtH3mszh+W/ZnbnR4jaQ/9Aul/+YPSr//ZHqC6xFJb9A2G/LSaKPX5hI42LfO83pjee7gfkvT6j3TLgBaGre2RvJeC8rP8Mc6cs4xnUr61J9zhVtXTkvW3JLMaOo/Oo5gSXBGnTItA1SC/zNNdiC3mz0/l8BPfqaNaaCO0oCn9bMkK5rnfA5yeeS+GI/cdpAu5jwZmXqhtXf1NHF+GoORYeU214j5yd80ZrzuCDjLY9RgR9/0OC7p0lGSFUhgsGT8R9svFL9jJfs9DelS3zcDzrY9o93HunNcaUzdnwl0CTL9bhNt0j2maUvmNZqcdNOVgFKanP2/89ulz0L575X03Zoe376IEFinc0njKs2HhS1us88/vU3SW1f/sf7J6od0SxPL08Y5AUGW5XrT4fneQ5I+LvK+QhMAepcmRudL/qKkvy3p63+29C7pqz9t2pbwW44fivK93WNdOMd9nawH139bXyMWy9dH/5y0rWGnbbo4zQi4kKFyG3N+7gN0Xldm1o61O+/P4neyLizHZeWnUH7T6yjy+PdJudfyJiA4UrdRx5rYwweKnq6LB8UzDetn+3kWdNQ3vObPBE1Hmuxdm6/3RDj5nou/F4Bc+u2ttvC5oPPeKL+dbWN2fJ0Hz2jE2v4udcsy8xHH9tnyNsOc30dbcc2YMRrJvGxzlsPtk2RAhHLSKLRDzy6PkZevPaopWnu6tMnpmxG3Xrxmg9PAnK9zrNp4WLdHJP3OB6T3PSP9GU1O+qZmx9/A7bkmYPPe/246eE2H+N6oo42b06XzaOcrcq4mIHda/7+rq5oODf/QRvczTQDopvpB5mzPaE55nL2NdVvzo+3nmg4tv2xz7aukCQV+1HpK8LPeKf2xj9WjnzndPwu9zLyQHbA+7st8WGG0npptINjg9Syv2QGutezzLM/nc1KYJ8/yXATg8Ddf8Mj7TJPl83+UtTq4zZ1gjQGFxySvqVw/0/YLRRXfaXcobe2TOXJ9Xl/N3u9juY4X0qRNbNvcIxb9IJdHLj3oofMaOYi28KUZfDgNH2mW5gnN8ycsc1RH3hfSNN2aAWvlco+c2xkj8E2A0w4qjujcBIfNYCTA4QFYslJOl/nMFLRHaJuDHRmOvJdz4yKMIPOzHzNqe5kk/YT08jdL1z57/s/alGRgfN7ojZraeRv3sy4yjxn5cssgpQHVBCT55JDlCU2AS5E3Hwu3ZN8YOKXedIbS1M4nNb+7J53tVSf4sLxaujq/7C/1aWCXh889zg3Y+jfFjv4M3/N+5m3Bxyjqz0ChgWBp98WKCZYyvcejlUO9jyMP62s6jOzMvjUxyrfPHjsvwT3zkTl0Gt+TOhjJNKNzUtSdAVzaO2l73bneE6SRtsEP+2C0ZXZXZa0xenyRy6Vg4S4iNLBnuJ6GkXSnNC+YROOMShS/sw4uyKT120I91i6lmvXYoLR9Z7IWXsRJ1zKPxX3QzjzxezscyiiqRXmu2042jQg/s2x/8pHVJzS/nG6f5PiNwBgdc95rB3+l7b7Ne7c1bQvp35Le89mTI/cjwWSNsl4zFx/QfIg56/d3z4EEqUtA57TcH/VbXs/5e1XzgeoRaM5tG/ZzHkJPNoPg/lHNT419mzRRSz+1kvQ10r9ZSX9i6tuHN+lfqj7X8n84jdrHPGxXWzNtruZ8YX3JKvp3zmXWPQI/bc5Is71pbDLnceqfW+BLayjbY9vU0i8FIdSrvQXbNqEJ+4Vzvm1t5X1+9zjlWbglwDNqDwHrEb47zSho5FrL+/fkEPNBFuXSMz0pnnQPaJdBsEPNg32WpDtT0vjQoLTFZ+F5g8zXZAmotLyjqGpJDMB46M9l5zZG6kAj2x5fTr1pLPPlXwaLNHqms9s7V5o+7KfsYxs4vnSMjooRZhsDRnVZpzSBlhNJf+h8yvekJhanPYrrfHwPUaPsEzSPomVKGtIGjBixcuwU11PfTOv0TmPww3NTzdF7/Z1oAnxPajqn8xpJ3y/pH/wq6XO+TtLn/knpD0p/5vvnR9Z/SNsvJjzS/CJAzweOU0b3o3aNQLu0+y4cjkPWl0/Z+Zya0xxr9zFz1ptlu0+dx3PQZ0Gk3TmUwnbmXGpPA/qznWFsa8BOu9mjFsC1M1Bkll0GwVu2s7Hx2Y4si2s/0zVWkJ/u75zrGXzk+joafG/taYz6Rdb28yYXiSRfhHJfgR5PopygzUHSQNoZPKj5sVjKiJ0YOY+mW34mU8TFkeloUEynksHaB64YYdL5tXdWcBtMGlOzCTYsNPTSLmiStrc6XC+Naovq0kjZEWY959o29NyCGdVhsfEkcPP3U00Hjp+MMhrTQkDF702HHB+mHYFzS6NnE/Dk2mgO2N/pkNlHrT1LzJKf2rI+T2kCPCea/l/V3/0j0vkfmYHDQ5v7HtdTTaxfbkl73uXYeE64Hfk/rM40/28wtqmxMJmmtS3r0qbsRzQ9dfb92h6/Ng98vfVzntm6rsk+3Yx28wmoO6WMrJuAdMQaNsbRkn3Ez2wL7xH8ZXnS7rxKm0Ewv2TLDYTzKUypg2Dq7M/mNxKwZDCbelnI4qfNOS/f74kctreGcum3t0bOsB0W89mJkfE24CEIITMxihZ5PxeuQU4yLaM5l86ThpKGjrrxEdLULY0o62vSziF4ESdoyyeaXG/qMmITqLPbzfu5dZD3rsbnyzU9HfQw8vFJs9SR48j6qItBGWVk2Cg5H1qf+vvIyLv8/GtCJ3wFaVPHbF9us/nAcOqVRpz90vqyzWOXn2P+Xk1P2r1G0hdrGktp+kexV7UNdhT5ltiO1G90XoZr61i7/w7jGL/ToY/q/xhJv/yB6XMpXW4Tsmxp7rvrmub1I5qAoF9nwIAnH+1XfCcQoTPOMkbC/mgsRQMwTZcG7lKvFAZBXJMZTObhZeqeIJHjTIDfABxZYtqCFswtBSvc5jrI5ZH7iunhIhztSxO9+3s+8ZETn9sNljPk9TU6iqaby03ny8NwWUcz0Pnbwm23kRFpi3TE4izJKDoeOfeUdlbFeRrDwQjP/fUyzVsh796keUrd4bBPGljLe9yOSxbRv2lssz/yKY6lwKoBhbyeY/KQJoD3w9p9AogR6B1tj2tGs5mOoCj/7QLHY+TspN05b5bimajHjxd/QBNzIU1ndn5MmzNSmgKQpzZ/2pThs11nmv+NhVmgHNcz7Z47si7nmrfG2HdkR5oTJIAwq2DnfaKJ+ftjz8yHwkdbiu1fXyjuO98jmt5W/bnfKunrpG947zZzscT8mQElwOJWl8emvUyRoDsDtxZAkqVpjPGZZrDDJ6wIkLOPfT/nsv83m7eWW1B6rF3GlkKbumSvmh122W3bq7XJ3++ZjKLdF7ncF0DUi8DGyECCiN3pnJaOiECgGXouqHzCqDmFEXvh+hOYeXE0QMPI2tfSmJPtGh2Sy77KMnkuxH+ZjkAt81LSQdCZE+CRSWjRdeqQ0e0VSV//Bukr3yJ9niZQYEOZ/Tty2glkjrVdfs4JAoV0eh5P1uX+49gwXQN5TUfqn+W2eeDrDXS1dC47t39G85r3HEG7nHSiBhm3NYOXBC7v1QSAHtcEdJ6MvHbwzu/yfM/ta08HWtf8nuuMaQmEWlstrU9PJH2y5hdm3tHu2Lfy87MFbx8u/GS7f/3X3pnkuo83391HV7Utvt62j9Im5Nrg2m0g4I7mNUjG1WXleDTh2kn7dIq2EMiN1lMLqvg4/RV8jmwc59uIVT0t1w5yOeXSMz25jdWcJSP7dG5n+G5jbCPbopW2qLggciG4vJE0A02duUibcST9akm9uWgZOaUOo7S8ZlbAEWWCDbJY1mf09Be/Uz9Gpoo86zdJq783nbFJEEmA5jwZLbJvGkB1pEj9ciuvMQ4jB9b6mPVxLrpvb2j+f0Iso5U5otITOIyi29SnvRaB89wG/lizQ7qp3XX3Qc0O2MDH5fm7/xM739F0FmW6jlubzzxEnOmzHZYRq2OQ0AKdFDqu442uf1/z2ZvUgeW4Hn/PdZh9/qSmM09v++Kpnc9oG+Scq/8Pr6yH+o6+j5w7ZZ9tlLYBQLNVabctBC5sT9ZlZu2ZzbVrmv/Pm7Q7l5tfGPUV7XCzx/zerqU08NbmxV2Rw7+hGMqlBz0NMRN0tAmeUbk0G2mf+2mOrRnSdPKsz+WOpBmovOfy2oIbGQyeo2mMBXU/1vzG2xtIk3nSqWcZD0v6Eknfq2mLwlFuti+NXzMkHCduI2V6juENSf+lpAe+djKAT2l3Cy/BT353faTQl6TZitRppC/TJ4vmSJNlp048+zTSrQEt94fz5nhc0/b/KhvpzHt2vDwD5fujM3LO4/snmubQBzd6PLrR6T3aBnyuQ5r7wlsaZ5qcbKZ32jzT0xzd6J08KT4MbUlQynw3FsrIepudamzLM5r/tYpffpk6ZP42hmS223ZYrlPau8YUnuE+vydzclbyp17UQ9oFP06f7TrV/I9pn9EMlp0vy6AdsqTNyeMNlDZuzc7vwxEjtvieyIFuqnLpQY/UEX0DNW3xHWuKKP2SOGn38W2CJQKOEZOQ30eLqhmqM3yynJG0g8PJUOQ1p3eZN7RtdEf6tb5mfUsOk06XOrW8aQyTcTDI+4Am5+eX2XGLIg2y83ocXW72hce/6bTPuBHstbHI6wlU+RK01na3ZzT3mrMgK5bXjjX9V/osi0Db1wwuEgDknCHwz5cWHm/yf5KmLawntX3Q/PFN+ke1fZantTmvN5DJsXV6tsk2Ivsy83LtZR3tmvXM8nPt+amxXCMEYJzn1ze/n9b2P1/NV2+M5mSCsra2FfdSfzrmlGR2lrZMySQ5ferKugi6sg1ZLgOBDyJ9zplsX9O3BQlc7wTKBI4jsNnm2wFnXH659KCnRQa5uGj8kh4lyub30fbIyDHR4dNAjhZcy2NdFWlG7EvmGxn3Jl68NqL5iHZuRWTavG4D9JSkbww98hxHA6BZBpmjPBDuehnh+bf/aaUdwNNxP41l7quTNSEjRNahObuUETBNwMS8mS6NeZad22Wt3n0AuMlFIu0sm9F7skWtHxLc5Hhknz+p7UfQs788l9gP/uen0u5jz6nzsbbfb2Pw2rbAXTbb4Doaq0MHmSzqaaRxXjpdbe4tPVVo3Z3nluZ+f1Dz1hbHgW3ztQRGyZYluEumTvje7CPPTi0FOZS0DaOtWAKIXCNH2p4DBsq3NK3ZEXDKT5e7L4BJm5RpCd6a5BY/mbAWiN51OWxvDeWeMW/PRkbsgA0w/6mlNP/PIRvnBCbJ8vigZDrhrNPl5rkIaXc+cZ+ZQIcRj/MQmAnX+ORDi1woI8OTi5HGp6V1v1gH0++M6hI4LkXM0vY/CU0H0BxV6psHyi3s17xOcVvONRlPO2Y61RxvlTScAyNGZgSMNLjWjOw+MC6N6XrW1b63IIAg3eDCkmAp2++y3qkJ2OT/8/LniWZmw/384CYv//1C6+cGLqVlZkzluvuej+1nfdJsP8jmZJsNxLIMt4VPGh3FNa8r90OuM6nP+7RxnvtH2rVN+eqKnMepc673HPNRUMd0PBMpdSDVJPPl1lz2e+quSM/fHPcWQLbAk/bL8zzPoTXbkPml7f6Udl8z4PIPcnnk0jM90q4D9YLLveU0RNe0/fr6fIrEYOiW5gnKt4r6gJ4Zhosu4vyexqvlT0CRxodMylLelDQ4jjRbRNTAF8tqOvPR4WYcm2PhJ+Uc6dkX/p59kuc+LuLknDbb4rIakOU2TosuqWPbhhnpRICdjFVzwGmc7eBaJJ9l+jsdQwNVzeFlMJGHkOmg86WACT4agDmOMo+0/XbiJqN5JvXXB7DvuN3cDtE28fX2f7Dc37ne+Ph8skFOk32fwNHvKfLLGj+keTuH451bt76fwZ+FTxK5zmyD50a+yPEY96Ue7LHf2m/OuRE7k23J8co57yf9uMaOtL12Rjq671K/Fiw8oHlu57weta1d51xpQe1dk33R0ItULj3oacyBxRFivsSOZ3e8rfOgppejvVfzkyDX1ZkUsxmWtm2VkttFXNy59eL2eDG1CMR5pG2dso5Gq3PR51mSlIuAOAIP9s/IyaQ058doSdqm8ekYpF0D2oxV1sFD55Zsy5IYYOW2GKls1msnMXpM3+VyG4bjmYyZy8yonawDHTDrzDZl2/cBLOt0rmmt+LFeRX6ntcNs/4cpvxs8PRnpP0/TC/7+ouYgZB8oM6uSZ46WxiTLa+tmCZhzTWfQxUO0LE+RJ1lGtz3Tv1TTu3pseww4E3S1cqXduUT9tfB7XyBivZsdoO3yNdq51DfbkW0ZgXG36eWawOBN5MlyG6DimmuSbbuJcqT+gkkyWmmbU5ae6n3e5fBG5qFcetBjofOTZudEVsOTLd9We64J8BgkXdP2Qstok9HNvoUzAjx537IPQOTvNBANaNCgH5V0NIakojOaZP3+TQCz5FiXhHWMrlnc59ShPbXRyuCY8P4+AMc+bKAsP9n/LdpsDpYAu23jOd0SU2EnlX00irqZ3/fsbK9qOvt0XdNrAvKfwtJBJfhpkqzGdc3neh6W9MpSrp1n9tVVTYHLU5rHNcFZRvNmQ7L9OR6jdSrtHqr1JxnSPMvR5rPru6btw81O6y3UU019e1OzTXpI89N2eTg625p1EfhyPvGMDucAdR6xrcL1Bvay7mTMM2/q2IKYO5rnijQD5Yc0PZAx2s4c2bBmM7IdS2Cy2ZYE1cm8cR5fJMA8yN2XSw96OPHoKOhYzjQbzJdqNiqv0vRkyds1PUlyrumA3ONRjh+zzfKoS0bfeV3aPdTWHA7zXARstPr5nQuvbcVIuxR5Y5WoY+ZL3fYt6iUg1AzTSNKZE1QQRNCI81rW7+vtPTZtftHZjwxkSotEKXTajIbTKLe8KTbuacDTORPk5pmPo/j7ZE2g5JqkxzS/TTmddwPDTY60/e8mDFi+M3Qjy8X+f7nmA+jpcNuYtDUqpJN2I3brmWvFLwDktkvmH9XheZvvufJ2/MskPbH5/FJJr/5o6V/95PQf6W9LeoXmIM393Q5Ht3U8Yh4MTrN/0l6cIK0/G3CU+nznNdoIBl3M4/6SJiBou+zH1xNopL1N0LVvnWQdI3ui+H48uN+2FHPLsLX1rsq+KPRFKvcVEOXE5oK3OCr02Z7Pk/T1nyb98i+QfsdnSr8xyrqm+SVpbcHmZxoy60Pjy+0HL9omNoDNkBJ8NGfcGBpFOjIPZCOy/NSX5TGCawzF6HzNKCoTrtshE8S6DOZpv13XyMjQ2bUxc7pMM9I/dViKfukwGvPU2n6mPscSnEjb/ZROIRmRBMd0Xt4SNii5pinK/jVfK33h+tfoS0+mbSgCs8zPcXD7cv7d0ex0b2t+A7O3cbKt57j2Hklvi3Jdb/6+s3CvzVmyDVc02Y43aAqIPk7bc78dpqewvgROLuNEkwO/Kul3S3r1p0n6z6T/2+ul/0hT/z+zKSsPkR8NdGB/n2h+iqxtA7a5lXYtgS3blvbBeciE+jPHO4FKSs6bbN+xdrdV8yk/p18CFA2oSdsvVWysVsrIHtKuWu9z7b6Q8iCXSy496PGkac7a1y10Jk9qMiD//msl/b8kfc9a+sf/jj7lDdKrNb/11gaST/TQQDaQQkeVLExri9PnoT0aILeF0VADRL53hnQtbV6joWxgorENDRilAWTU5nobU9NYCgoNMduR6Zi/jROBK3VodDuBS4v6RwuJUWOmJaBpY8UngPJ+RroEQk2nBjoNdiin0oR09Aukh7tDaBGvHbW3akbBwB1NAMvgp7U9v/tsHp1sjk2evziLPNbRcy7nHp/Se42kL3vZxHRl36eDXepjrkMysM9Eez5G0vHHSnrjv5D+wFp666fp1a+aDtTeUnfqubbTJvA1Eg76HtQMnJI1krb7UPjkfFxidWinaDuSzXN6jyPPrGW6B7QdkF6PNp9q2w6wDY2pTRltB7Y2j+47zbHmuZY+IgHRXRef6Xmu/14AculBjyUXSkYtBDo2qI54HpSm1fJL/9wmxbuko/k/GacDzMnJJ7o8sZcMgRdjRthLzIMXBJ2n87dtF+b3Z7afUVXLm8bxUe2Cn3T+jcFo5Wa7z+N3Mxo5hq1spxkZrQZoGLW2do/yJxjNdjBdGuol9mYUKTc9CKjzWnO0rruB82abElQzIvc6UVy/qensxHd8jfTPV79d//MT01aMt01yDNk2r798LYHr8BrKdrP/qDvnTgKX7IOs34xAXhs9Spz9eyLpRyX92Sek75f0Dm3PoZzbtD9ND7aHAcFtWbHvnC7+5A9Ix/uZSq4p33OdD2raCvy9kj5V23bNaUb/bb7pmgFNrn07eZ6BHK2tBOYJ2KTdeX6qCSDe1GSfHtB0tiyPH2QdtEltu/Si22zH2p0nrU7aDdpr67Bkh55XYUT9XPy9AOTSg57W34ziUhxpepHekCZL9h/+R5K+XPpDK+np6WyP5drmk46+OZxE8AmCWrS01J62dbYUTaVzcjk0ol5k6TizjDbY55qfZktJp9HaNGpfRuOZdlRGAwZprBqw8r2mzz6gwagwH8GmnOFeOlzFd9aRwCDLSp0IirmFR4cspHc/tUCsAUeC2jZ+GZW+VdMaeYumbaV3anu7JEEUwQfXUWNcFOm4FlqUfhppR4em3af59J2v+axO1pFbHN5yO9X03+1vxW/24eiJuTbGXLMPagZmj0t6z7slfcofkb5wJf3fpTe/a6rTNimfbszyR7bJ/fwSSS95w7RFxzZYl+znXBOcI+5L6+N2PRDpmb8FMo3pyXY8qN1gU5oOrz+j7W0pP+FGUJxrIOdoMqOUZEpZTuqfazbvue3cjnOfvUAIkheMXPqDzHTsUn8xlr87gjrWFCG8R9J//ZPS13+npCf/J+kd0rf8+ASGXq3J8OR7WzypbTxokNsCT8n7afh9GG8UHbMMt5OswghsNCqddbm81CtfpGbDkEacUU/q2AAF6235LNm2FinawTgd/zFlpm1BiO89qNmp5bzJ/mjnJNLApv4NYC2xS+7D7Nt0RFlGM7Z5n46CNH0Du/sA2JnmR85zy+ztmoMDl5tbUTn/nS/ZoOaYeSZotM1APds4ca3xTct0Tkeb+/xP7inJJJAFeL2mOfhObYPktkbaWjzTfJD7kU1df0rSZ79F+pS3TLHZ39uku6ZtsMi+HtV1bZP2cUn/9Zt2/4VDvhspnyBLPWlbk/XkGh/N9xacOH/O47S5t7W97nzw+2M0HVV4Utv22OCXQQbBbArnG4HQSG/F9fxsY7/UN3dNDm9kHspHDHpWq9WxpDdJes96vf5lq9XqYyV9u6Z1/YOSvmy9Xt9ZrVYPaHolx8/VdJzmS9br9TufbX25fZTonZH6qaaFf6KJmv9tp9LV/3X+L73HmgCRo7kWrT1baUBA2o7AmtFwJEUD2ZwAF2BGKS2i4OJMp5vOiTR/1pXlJnij0aBjTyDQAFvmzb7PMWgHUgnc0iA34EV2gOUzWnVbRqCOoNB1CNdpjA2yPF4jdqkBY9dBANFATsvb5lleHx0CJhO2RA23Lap0yKNgwXOqAbicnxwTlpfvCuLWoJ+o4X8vTzDl39nWDDw+XZPjfYcmliEPKGef5Zxwua7X+r1P84HmvyPpu7X9ePuT2gYj6eAb6M3AKnX2YXFpmykxK9GAdbaJDtz9Opq7llwHCbby2shmud/MNltXzn/m5fxncHxRxmXkC5K5I6OUc5HA/NJvp7zI5LkYj98m6Ufi9x+S9MfX6/UnSPo3kr5ic/0rJP2bzfU/vkm3V5pzs3Cf1hMwAcaNuH5dEyUrbR/gvAh6V9zL+hIwXDSydvnJJDBq5ffRgiXTRUdg/WjA0lG2SEfaPcPk8hrgycg+DcMIODBP6yNf8xMwS4C03fM1RpCspwGczG+H0oCbtG2gc+wSUOUWAYHcqA37DkAea7uvG+hzvxEc0Uhbz0x/R/M2j8vKtzOn7DMkHmcfcM7xGJ23od7ZRq7DbI9BfdoDz4Fce40hoC4Jpr9T0t/V3E+jPreuI6bQ129uPv3i1FdocvA3UW6WmeU2EOvzPLZ9+f/Rks0V2ur5m0FUgjfOswTveSas2WhpnjuNPVFca3P+SU194kPNbkOeo0lwk+U1dtRyju8E1m5L9k3qzoDL185x754J976fi78XgHxEoGe1Wr1K0i+V9Oc2v1eSPlfSX90k+RZJv3Lz/Ys2v7W5/4s26S8kdNiknpOCdTo/HeLo55bm//10S9NCaudZmgMlXb10rzEkbVH6SQ4abV5jtECDuMTw5COujUFhepadCzr1YVSfzsksUmMg+L2VRTkr9xo7k/cIBl1+AzgjcMc0vsexOtX8701yy873GgNzrt1+bkYy2TK3IYVl5HWzA/uMMHUR8rodtHuncd26trKl2Tnd0rYTPcInhQyQtA1EWoDRAqV0zi7DTzU11oIOUZrshe0H2QhLtsPlU8fb2gYjNzVtI94I/ROk5bh5/iZoyzRPa7Zp1s02INdlromUXLM+t9NYSYNJzp0cT4OsBzUdJXhMuwCJtpzrUJret+Yt6lFQljaybWm1+cX0aScT1I1sQbNrLYi6p8DnIDvykW5v/XeSfpfml2c+IunGer3+qc3vd2t66ao2n49L0nq9/qnVavXBTfr3L1UwiuiI6NPYmebOaOxc0rs0H35rUZN/mwa2YSIzQl0sjIYIVPJetoEOnHXkJw0ogV6Wl2lSaGRGC7Rt2dggEND5rITfpHpb82O3rV5pNton8Tk6K9FYgGyD0zfjnMyCx//pkte/G0hh3/Kaz0ZRN0airIPXfc1z1P/00mWdatsQW4+l6CXn2cdoavt7NtdOtbuWlrayRuA/GYIsh+nzYC6BRY51A8C+nmC5PZmUbbHkvKDzTHBwO9JYJzOlzblnu0fOkeK1cqqZwTjXvKWVc8NzxcDaW2KpA+3M05s8ZrV5fizXQ47RA+pnotLJ5/xrAJ36PKgJ7Dy0afNLNW0PniIfGZNzTWcyfTzhluYXOjJQ8osvuRZG80u438bO54nYNuFas6UZXN2zra3DmZ6h/LRBz2q1+mWSfmK9Xv/garX6nOdKodVq9VWSvkqSVtqmuNPp+wktb31I82Tz/87yPr4PD3IfngdpvYhz/9wG6lzdeZGmJ+CwNNBi8JDRioWLtNXtOtsZl8xPloALsYErp2tAKiluG787msDOa6LcJzU511GfJHNAg0W2rDEjwjXnJ6BVfM+x5VM+rqsBnDZW2S46xRQyAXQOI/bGEX/q25iNZtyz/Vn/9c29PLiceRob1pjLBmAJhB/W5ulJzU6tOUvqnOU0sDvSr82zjNpT71zzBpO5dTdiBa0TQd5p5GvALttlh3oUv69o/vcTT0feBCZui8tL8Me+ONLESj2g2VY+E3UmGE/25aWa38CdtlLaXp9uTwKDZl8e1fQ27S/5fklvlf7Mf7L9r0gMpNIO+NNnsB7RDOby7FmykNYvr6fQ5iXI8rjlvZY32T3a/pQM3s41MwJ3VQ6gZygfCdPzCyT9itVq9YWa1tVLJP33kh5arVY/Y8P2vEpzUPkeTevp3avV6mdoWl8fYKHr9fqbJH2TJB2vVmtfzwhP2l6QdFbp0Ax2pBkMXdf2f1lPQEHDmwcfG3JfWix5rTEKGa0uMTMJmNqCZLQubRte65lOkBF9O4jpe2wjnaUN0cdoei/I536bpK+U/uhPTgP8tHYPluZ3OrBRW7NtOb4NDLrNPCPAucKDia3OBDYNSFhXMgtHuC/NUanZhfZywEb3p7NiOhrlBnjslH9Y232UUXvOCerfAInvcasq683xTCebjErqwnLS8R9ru59H8yPHw4FRSq71BJL5H8f9VneDoXTQZLMoeS3HPgGS++2KZnt0Y3PtumawSEDaQB/XtfO5LuvDp7V4xueapE/R9EbqH9cMBhrAdXntunVx++9IkxF4elfPxgjmuPl+/s72Zb1kC5ku0xNUjtLm+snxs+QaTFvsvvYRi4NcHvlps2/r9fr/uV6vX7Ver18t6ddK+r71ev3rJf19Sb96k+w3Svobm+9/c/Nbm/vft16v19ojzWimjMBCOjNHDF7kBjxZZtaxJNzHJhvTIs3UO8FPRllCmlyANKLtSRdpGxim4W9gLfU3OCBQcplN6Jis/8+XpF/7a6bj7Zq3Cpf6lX3EvvJ9ApQWiWc0lvWOJrr787a2dbSzIO1O0NrKS90Y7XuLwnOotfsE1xiJpiM6j/sjSt2O0oeU/ZfziO0Z9RfZswY8vOZuaJsBcXu4tZHzN/+yjmRGLDk2BLGZhp85JumADd6vaz5c/KB256P1cr+eaPvfReRvjwdZTZfpbeBjzf9+4mntjje/k0lyvSnZd9l+A6tjzWfRDMbfqOn/f+VcOcKf9SD7TlB6ponR+aCkv/BLpD/ztVN7b0Lf0Vy7pm12/uGSJ4O63KYczctkGgmQOLfPNbFvj2n7f7JZsu3+59bXNL82oM2buypcUM/F3wtAno/39PxuSd++Wq2+QdK/lPTnN9f/vKS/tFqtflzTWvi1H0klaUCWWAGmHUkDHGf4zHReWI7SvOjONXaGGYm0Mkf50oFK24tzdN15GRm1hc2Ifp80EHCqycl9k6QvWP0VvXGTJiN6slI2mukYWh+06M6SoNUGrwHP/CQrY0n2qhnVBFHZ7jwomnkyncu4gd+K+nLeLekopPdvOnSVNBn1N2ksy0XmBNMm+JTm/97OQGME3s/iN88YZX5LzhHeYxqKz6LlmD4m6be8TPq+J6S/hrxk+bi2pd1H45tkX51rBkpmwjymCRJzfrNcz8Us0zomsJMmx+wtTj9VdbK5/7iWGbLRNTLsZjL/D81bVE9t0jygCeDdQTnZJzc3On2ypnNAT2pmjV1fWzcpuS4a88oghXJLMwBsZ90S7OZ7oj7McB3k0slzAnrW6/U/kPQPNt/fIenTSprbkn7Nsy27Pe65ZITTwY1YIYKgpM+ZN51cS2eDRwPcDGEurCxjH92WzngpDR1+blnt67fcckm6tx3Ebfmk6b9CSxOld0vzix/JRKVhk3aZKQIL19O2wqTtMc96CIhH42uDn/3FNvMagabrzDNWZ8iryOOtFPZFpsnrCQ7zKRzXORqj3Pqg4aeks3I7ciuqpR9Jshv+/09tbrKd/hxtnTTHpbhmGY01D6fyHJ776D2SvuOJ6UWE17T9RNTSdmS+K4h938bY/WO9zMI4kEowmQFQtpNbL2TFPG885/x5TfP40km3QNFtf1DTOR1v3zy50df3c/5bv8fRFz5flGOTgMFzT5oeQLmpmY1K4O60x9oFGVc1H+puQDVtBINoaZ4fPnPFNZP21mNokCRtB3p3XQ5neoby097eulvCp3nyWjqW/J5pLSeaFjn3o43g0zG2KJIO7HyTL98SbKPUDFyWO5qLjVlKXVlepm172CdIw/uZL6lqOlhGcwRQeQ7kXZLerOkgpPvGBtt/S1tNqScj3EzTgGbqmMYxr7f6mJYAJXUg25Nj2sY250469qUx8bURu5BnMdyfPFzZQJMiH+t8SPN7UMiyEJCMgHxjmNJxjBiKzNNAkOcbt3WtH/s9v+fTSAbcBMdkt/zgwj/X9BJBO1b3W26f+L+ZW8d8nxT7PiWv3dzU+TpNgOd92u0Lj3FrPwFOfnrryrodawYPNzQfqrym7ScFG/PhefGopq2/3/oJEyP2Ms2AZ2RHDWKS/cuxyHFwXs+Zj9nUYXuSZaRuDl6yzXlIO3XLedH+9YXTMYhm+x7afHoMzfzk+I1Y1edduDX1XPy9AOTSgx4aNE+mZkCJ6E/x/aamCMNAhxOZjovA4hTXGxvkzzSEmcaSHZ+6jAwkz/5Qd9bR5qedYWNSmrEiE2MdePCQIMgGrNVl55XGMIUgLn+zv7NOAqAj7RruERDl72QAE7AlEBuNfdLd1uM4vqe+1IcOoNHpzNMAyXn5rfidoMj1+Y/bIq3e1ieeB/l3Fml5ToXgZhQsZBquO+ejc76CdA2Ijupwf93SbC9uatvhpj6jg/JO63Q5F3iuS5r68Ic1bf9Y/zaGFwlkLAQdVzb1fEjbc+1Y8znHE01bUQTZFr8d+Qt/vqS3v05f8m9P164iLYPJ1IlrhO3IwCvFOrt/TrQNWNr2ctaRbSYrlNtgTafsy+uazhfxbddmtRjUHORyyaUHPUuGi47E6dIIJ1JPypQyYg34nU6ZZTTnn/fTuTTJrZmsv+2v0zjQGWRa1jkyKAluEjjwEDF1bpE2QQlBgMdqJBlRXxRUsQ0jIZvTWDg+0Tba92/XKM6bgKMxJjlnss8d3WZegpcGohIUNdbUY/cBbb+oM5mk1haPTTp26sH539qQ48R7/sw1x3VHsM7oXOpjm5LONvvL7Ih1y39ZY719HiYZH4JIy1WkSb2vSXqt5rcpk62zg8/2HWu3zgeQJl/AeLb5fUXz2HJL90Qz4+d3WvkzQcH/559Jet3b9NfeP5fvsWrzNz9vIw1BkPvGh8jfrgkMPqTtfybNQNAMltm6bP/IfxDo0N57nvvRf/efx8KvHzjTNpu5ZOPvinh767n+ewHIpf+Ho5bGgtCoWVo05Ane9vB9P8s9xnfWnbR7ixAaiDqKfC4j7zdnvQSyqLe023YCNddPWrxJ6tq2FqhHAz7+fhFg4DqzTLJlSVnn2HDMUieWk79dZjN60vZYGTiMzpvk9xFYIIhtB69dH8seRfeeN57fvsZ2NgDJOZnXqAfHn2c3nN7tbIyT11++Z2uf5LYO1246SamvO37PdG28sw0KHdOBus9fqokJMqjIf+jZgjK3P8/KZR9ejWvtHEnqdYLyco0+ou1//eCAL7fjCUI8Pk9urj+8+e4yHtq09T2b+3/2R6b03gK0eOxtZ9zWBMh5brAxsIo0H6fpCbDHtT1feF6HAYFCL26pHWv7nT/S9nhIM/h7WhPjd31T7gc1BwgGO9keX79n21oHWZRLD3o8AXPx0EE1MMSoT9r+v0LMvw/8NGNrI0EGYGRQmyNpkqyA04+AQ2NVpNlRZL816t16NqDX2kDDQMeyFAyMwGQrowHT1HtUT86XLG+UngwFI0j2fdafjp2MxAjkJXDzWaIj7R5sbtt5BLCjdiRDl86mbc1kVJxtzrLatgEjfzJOHGcCq2RKrE8Cca4RjvsocHG0T31H4DfLbm3z7xb0HGtmeFxeApG2/k80nYnJf0Xx0Kb8N2tyrgkmvd30Ic3/AqOBOQZW0vZ/lM9gnexHgtFs543Np5+8y3nyTu2urQQz1uOqdtdfm8Ntfb5ic/1dm7o/TtPDEjc1AZJrm/s3o11eX228WE+Cx5zzBq5ur9kzHy7nvPMayzmegO+eMT4vEGbmuZb7Ynsro3tKLlxGfm2hpZE4xfUGbKgLy29bFU7btjJywbfDs6aTX6vtg4Xc7/a1Zux8z/rZELTtAUbQDWgsRS1cVwREHj8aOoLEZozchyPwmL9bvbyW22XJMhAcWAgeGlB7INI7ms6+zv9sbbFjzu2dfFM0pYEfSm7ptq0pbimdaXv+Z5+eIE8eQM02+sAvWSJuc3AbS5ocar7OIHXPus1eNEbAkmN9qh7c+Ptoe/Ycac60W2cCB+v2gc2nt4B87yHNzpLjdUMT4PFWiZmUT9IMiCxXNQGeZJ+SvXjFph631w77xuYv2+I87idudyZgyP4zIPJTeC7rQU3sxzWU7zxNPK7uG9fdbOoNTYDm4U25T2ka25dq6t9fqOmQ80PaBlzSLsuY24A83DzaOvd2pB9Yya2/Fhg2H5Br7K7KWtsL57n6ewHIpQc96bgzWqOjbKAihYvKn80opRFtLEE6Q6dnhJ5Gs0Ufo/njqOspbTvPFkGSrWkA4TjuZb00NG1hkgUhMEmA1MCPJfVq5eQny2JU3tJzi2gE3MhykLVJI+9PHlz2d/Zn1tNYGqf19Ye0/QK7/Lui3XamYyZQ4RZc0y1tFpkQMorpeOgUWr7WR/5NB0PQMALq3GIiYB+t2zPkYZ0NdLdgwNcNlBNQSNvvlznXzNp4Xpm1a8Axy39Q039ofp2kt26ufbamR8KPNQMg6/FglPWMZvYh3zrvPvRfCxL4nTbqXDOD4jr9EMjLND299Ygm0HVd03y2HGt+HD7neDJKGaTmY/YNoF7X9j/z9fmnz/kT0ld+7HSNLE/O6ePIT0CvuOb7j2lmkdy3fq/QzeivZmfIkrrug1weufTbWxaCkAQW0q6BbHStF1sDHGfaNvDpdNo5IF8/0rahZf7UneyGhWWcaX5slUZcJS3LY7vzGillPmWmaJfTpLNu+ue1UR/mmGW5BKtL+mc5mS8NdSuvCc++8LuF50ksI8bAeRjpvkTb/8TwurYPDmdUyDlOYJf/LqHpch55/Pto8J1nlLi118Y9r9mx0fBzS8ggwH3THDLnTwN13FYjsLVud8r9ZJ6a49fgvpm5pTVs5sIg5aHN7ye1zf5lm162+f6OTb6XbOp55ybP6zTZgZvaHjfrcE0T8PC/Ori6uXYD9ST7R8l5xXXlufB0pPF4X5f0pS+T9DXS9/1e6Uc1bUMRDOR5IOvlfjQY8JjyacWXan5L9YmkT5T0Q5sy3yHp//oa6R9q6vtcWy7D/3tM2mbPPA89P16mKcj0PDZrZznblJXSgposP8f6IufWnhe5JxTT5ZdLD3pG0VwDGkzX3tOSUT1ZBYOCPBSYzJLrIn1PHaXd+Ubg0xw7QdWorLzGdGwf83vBZ55TTcaB5wWyLIKMtujdb3kmYUlsGGh4ma+BiAQirc2+RwCnSJt6sHyCzXNcbwAp83JuOGL2P7+9qRm4eAusbTekvn6CKFkLg4SmY1sXyc40wJvO1WU1aeOUunIcjjW1N8/AcX5mX3E7LIEQA4qm0ygNI3Hm9Wcels9zR5yLrs9O8fqm3AQ/L9PkpK2Ht2uk6b+GS5OD9xp8WhMgPtqkfTjS+UzQNU0He29pdtK3tX3ANsFWSrLGOZfOkcbr51a0/XxT701Jd56Qrrxt2uK7qe3/Ak+7ejN+p00djYf75qqkr9X07zH+obbfhfP/3uTxwWIDsnxIIG2a15i3rq5r0v2DmzSPbtr2Y9oGYm1OUbKfc10e5PLJpQc9KUs0e4v60vFwgSnuOb0XI6Mk3z9HvouyLNTZ9bQI7CJshY3PErAYASxG4Bl9K9JkPi/gXPx8+svt4ME9blOkEDhluTlmBDiNshfSKNKSmZDG0Rf7k9txUt8KISuRoPuKJsd1U5Oh/XWSPuNE+olT6S9qcogPbdI6qiYw81twH9fsUHmGgbpRzyVQ0fq0MSMZFNgRZp/n3LSc4b515zzzfQNxr5NjTU7+dpQ1YnuyrQ2cN7BKQJX3r2kaO285+/xOtiHZCzMO/nxI87bJQ5t8r9b0GLafmvp5mzp+bJPukyR9n+b3Az2m6ezKDc2A4vrm86lNmRwjtye315LdZJo2Zpb2Nu1TSd8o6eRb53WfL2Z8SNtAh+xegkvn8ZZSPi13VdJ3a2K88ok/5/GTU56HPmfmfuAa8Njd0QzUHtLEyL0v9HTQO5pDUrdpQpolW/W8is/0HGRHmv2+VNIcmI2ttB3dSp39SaDh376WCygXXBpCO99mpO2URwsgKU6Cgaw795pHRtk6em+93WdeMjPWNUGKJc+5ZHl5tiX3zBOMNENBANcieupKkJkRIQ1QY53o1Bi9sj4e6Gz6pCNcehojQZrz+hyIX873qyV9xkdL+jvSz/xV0n+qyeE9Fe1tAP5pTU+u5BYYDWpjbRI08jFhMkspbKPnRr4r5uWSPkvTGZRP1vZ/kKekDme45vo9Zzh+S86Gc4hBDNOwbxO4MCC4rmmL6bHN36vU1wPrzXvv07Ttc1UzeHqnpj76Ak1PJF3VBIR+y2+QvvyB6ZHwl2/uv0Yz+LmqaTvrg5rO/3gO2CZYCGDydwZabjMP2yZTkev2TDNb9wHNB7Lfo2n+HmsGebc0g5q018ny+PcVTVt7tEV3Nmn/PUkfj/yev8faPvPjuhJcaVN+/puLhzQFErc1b0FeK/21BGwoLdg74I7LJ/cF05MTkAawRTW83oxw2zJJaQ7Fn6nPCAylpNMcGXAaz6YLI+/8bWkG3detRwMXeUjwlrbPMIyQ8ajsxgRRkslpUaejNJe1tGXG+TEyWK5vSR9KHtJNQNMYwpR0oifx+TpJ+kuSftFa+kWSVis9pjli59aJy8//en1Rg0qw6/LbuJBp4frKsk41Hyb9VZJe8h9If+1/ma6/TdsH7nn2aLRGmy655WeGo62ltmaWAFILjIR70gQ8XiHpl3+/pD8h/Q/fOelxM/I2cEUnb1tjZuGlce+zNQGHt0l681+crj8i6VM0gYpkdM41/xNOrw2fmVFcs7PPOcJPBi1nJY/v5Vjm3Lu50dGMzK1NmpNNvz2l7f+o7n4wOHZ9+Wj9dW0fFpYmsPiothmgnIvS9lNwrueOdh+3f0zzf3p3u65rZtEU5VmHZhvIJEpjm3zPmIVm0A5y+ZmeFLIrGcVaWuSe+TzReWhO2nVkSxFri8azDAKiBEst0s28TtNAl9Pxd/bHMdKkgRjVbZYmDUrqwzJ5P3U8UwcBZGSch33Vtl6WwCSdDtk/fzdgyUi8Ufs5r7J8tmlkUxKwpT6n2rz75I875Ut1VdvGluet/JlzNstMCr+B+GaEk3UcAY5RhOux86O7L3m3pL/6Vv37D8/bNy6Dr/o/0fYTUBeJghtwbeC3sX05j9tcYWCU88bj8LAk/YKHpc/u6zHthB2751T+a4JjTWyO++2KpM94vfSzjyan+4X/k/Qr1/+FfuXT0me9dHLMn/JLpvpdx2Oa39icgUGykflwRc4RIb1/L7GWFgMIabYPrjPfaXOumXF6SPO5mZP4bT0Nrs81bwW+XhOz5TQv31z/i5rO87xU8yPzVzUFEC8L/dJ+GIg9HN+PNB/29vmnM80vl3Ref+Yapi3MtTmyTdKuzbhrcngj81AuPdMzovz3sR+ZTsibrETWw+ttjJsuGdWTTWlRZRqskZNhBGHJPfAWWZwP7htYWBJ0pJ4f0NwXyQKNIp0UOt0RMMx7GZVmOrI2rT2MrGm4XFZGsZxD/szyCBY5rs0AEvBdQdprmozrX5X0n/wj6SVXVtKR9E81nenxIecUzsUGupLqJ1jMMWf0vk8e2nx+SPPTYqnPM9qcj/i5kn7XJ+l7n9p9FJ7r0QHJPkeQID3BiL9z7DkmKfloOdNn4HOm7TXsLZx/Kemp1VMfZuKS5Umw6X9TcFszW5rt9AHf25rO7FzXlOhHz6VPvCnpo9dTwo/6A9KNv62PWf0y6S3TE0uvkPS/aj6k7Llo9iKf5uO2TgO9jaVq/Uebmttdx/ieB4af1nRm6VQTQyNNrM+RJuB2R9OW0tXIf7K5ZkDjlzc+oGlL+G2agI82aR/UtN2bANxnpMxCvk3TVuCZJvDzQU1rze01k5iHujmHl4TBVsoLBB+8IOXSg55Gt+Yn01K4veWFSjBFh5hpKA18SR3YpIHIPHSoZ9pdRG1rrjltRn4EXdZxdGDZ4kiVj6iqtGHkaLLP2A62Mb+zr01VE4Aw74jVGekmLTNHozMpIyO2xDhIszPy0yXvkPQHJL32dHIE74p8Pi/R5sFR+Z5C0Ogy90XzI7BgB9KCADvct0v6iSekn/mXp/a9fXM/n6BZ0nOfkH0Rvjcg40PE0m772JYE3sL1Y81bjrc1jc0Tm7QPaAYAnttmE3L704wC+/gDmkDPv/k/pU/8aM2A58PyS/VRf156z1dMZf745mpuOWtThteqgUKuc/Z1zu19c2gUhGX/tX+TYdbG42Cwcz3qSEY5x+2KZmD3qKTfvNH5r2oCLV8r6e9pOvB9U/O7gM40ndlxwHZb01kql3NDE+C5o/kcj9NRaMMtbf7lXFkq557JpVDi8smlBz0pjmDaWDL6agausTHOS6o7QcMSak/wZOEiSAM0YoXctkw7oqmpU2NxWoRmMThqEaujHQKk5mzpXFMfAjE6F44hGaykmbml2MYk+7w53BHI4XUCotRrRFm3tiU7YZ2s11OaHsFNPW5oZmxauen8CbQ5trnFQT2X5mnqnwwJ+9zfn9C09fDJ/3J+nNhzuPV3A2DWnwfE3c5kLvyd7CPX86h91D/T5HrMaP8pzeAndeY43dR8Zuco7re14Uekj6Xt/b6UV8xgwOe5GpPH3+7PZGkYVI22t85xnUyY62hjy/Eza+ktqSc3vx/RtM33Zs3tc5kGIT9nk+fxTb4HNZ8Xe1TTttbbNvd8Rij75yWbOp7UxFRK0yH0d2t+2qut5Rx/bnU6WM60ijTSru84YI7LKZce9OTkatss3Gf3Qs8Fm3S5cJ9plgxl/h7pkNdyAfhaGiE6qwRaI0Ymqetz5LOe+Zus1shoJUDktk9Ky++2sI/Y7sbaEJwJaeg8st9G7RmBQwK3vGegQId4ims5Rhx3AskEf/ko97Em431Tk3PIOpaANvsz+ybbcxE2JXVl29ohfzIHZqbesfm7o219Wr+PIuL2RFzOcfdZpuHh9gaGc0suI/I2f0eMqp1q9leCIuuVQMFz9abmrc5s+8dt8r7k35Z+9P3SJ+oXSPon2wp8xXRe5b2atsPere1xMkiwDmnbuN4a68P+zvXL/C1P2kszXQl2Xd7NjZ7XNW2Z+lHxV0p6g6TvDx0/RtO6ONEUFPzY5verNQUF79LE5rxu0yc3NLGLT2tigj5G0/bZP9PEGL1kc/3tmgCQAzrPiWSquJXKbcJRgOB7aTcz/T0DPj7Tc5AdeTZs8z2X5kyFazyI5ny5qOksEoRcdJ7YiOaWUpZJNqdFfJRccPmXaY8G39s2gCX/P1RjfBj9J3vA6CZZKeYbAdITpCVD1NiKLDfrzetN6OD8acpd5d6xtp1e1tnqaYDZOqeDzC06H7b078c1U//S9osKXYedVR6Olfo8yv4Z9U3Oczuo1D9BnvuDTsAskM9cmIXIlyZSF47xefxZ3M6UUXsyrccqCZN9jorrKR1U08v9JW0/RcfyPU4eS+dznoc3ed6h6bF1vU76xCPpzuqfal6lf0b6opXe+l5p9SsmZ/2mzZ1H1deCAScfziDbmNcouU4y/yg9Qa3njcfB78PxPPHnezXN/Uc1nxt7TBNg+UJNgOgtGz3+I0lf+toJML1e0m/8+dM7jd6r6ZH9RyV95iavNG13feJG/x/TBIp8Tii37LOdDMDad7eXNs/XG8PIMbrrcjjIPJRLz/QsRer5BIC0PcnaRMzvjEjaFlJGSMmYZPlcKGQK8jt1y22cjBQYZfN8C4GI9WRETR1HizO/O+o9w7Wsn9FzrgeO0UUWfjp0swzsf/ct+4zt8HWCAc6REZvC8SIjksDZY5Y65hNLrONY2+cIzjRvAxD8pA6jSJ1zOgEW63Y7knFyOfnpsxhuVzpSUvZH2v6no46cE0w13clYSfN7Wp7ck47tznk6YnKacN27vuyPZN3MbBHwt+DAciU+TzWfeTrStDX4L/7R5LAfl/T06o7ep5Wk6amlxyT96N+c8nxoU/5Tmhy56/ccJBuWTG3O/fzNNZr9mP0h/GZfc/s2wTLn5Ifi99s1vd/nIU3veHqrpH++uf/lr5Le9+7NAeWXSj/vVNJflvQVU3991qukD7x7KuNYE5h8haYtr7+q7XNUN7VtL8jA5L9HGTFc0vac8+/mm0b+5CCXRy496JH6IuVWF1mAM6Sjk04hg0SA0/K0KJI6LgGdBBesgwaVLAR1OcLnqK0nmp66ofMiYCEgJMgfGfoWkds4E0g5Pw0N9WWERZYjdeNBzVNt94MZjJwPowDG93P/v7E/uZ0hbRs7nolo6RK401ByPpHtyXnenPixdse6RbBOm9eS3k+dzdol2PG9O9qdA2bY2jmfbN+t+N6ck78noMr0eYiYc6bNZ+rZgK1/J1AYnSU6Rjppm9k72uh4U/OTSw9qOgv1ck1sxmf9qunCP3lq+h9Tr9iU89SmDm8Tmb1IAM1x4hxy/9u5cx6wv49wvc0TpiFjm+d1UgwGH9ro9Q5NLM0rneAV0ss/TlMn/ZCkj9XUcY9J/85nSnqX9MipdPrEBBjPNbE/Psztp9nyDdEpZB4JTHJdZTDpNgrlZvu91ttY3HW5p1TT5ZX7AvR40uTWFQ8SNiOZDty/R4vd32n0GrhxnSwnHajrpnNrQMrfGYVbkt3Ia6OyLGTF8jP1dnu4wJmm9V/KKMLO8WtPJ0nbxrYBIN9rAKeN0RLrlpK6NL1GEV5GugQ6BEvncS8jYTqFNu6UFl2O2sQyyKg1ANTAExnOnOMJuBhs+IkcRTqyDgT/o7EfgTUVfdmWEdBJIJpj1tIyD7/f0fw/1FK8jfMOzSDlkc3vv7r57Rf7/ZPvmgDOdU2sxVs3Ohn83NT81NLDmzKyT9kHZH65LZtjR0lGND/dr1c0/1PVW5rHMcVPr0nzuZ/U6fbm2mcdTY/uO52+eqPst0l68iWajn7/pPQXrkvfIelbJf0H0tETE3B8/aYv3qRpHB4o7eJ6/HBdmtdlsmej/mDwR3vLMdhnMw9y9+W+AD1kIKRdh9YmF6NiAgo6IUtbMOmMj+NaAx90RjSEIyYmnxpowie7llirpGvP1bc8qIvik9FQGr0WAVEIAtMJpbRH4tuB2Mx/pA54GKGnM2z9zch/1DdZdupA509p7XXb7Oj3ResjkN7ASWPwWjAw0jPnzL48ZNyY51iTQ/tdmhz5N6pv3TlP5h/161LE3Bge1uN0vJfnqLg1JPzmVgnXh/8dgm3LdU3gwFta2lx/qeZ/gfCWTbqnNL/v58nIe7757W3HR1CfD1vf1Ayuc+zzkC7bxCMCvi7NdiOBk/v3SNOB4huaHqlvIOl2fL+y0fuGdlnwD5xP1z/j39Z0QvkVmibM9/45SV+xSfXR0pevpfetppdb/ULpZz4m3fiueQvLfzc2bbq2yXm2udbaR7uT9i3bne0j00ehLbknTM/hIPNQLj3oaVHnKArjlldS9CzTzEaChJHTI53usvPT6fPaSE8a5VG0TlCXkVRjdiyZ7pqmaPNM82vhaej4e0T7ui+TmaKOjJYbg5JRYWMfRhRyW8PO/9Dm941I7/vNOVGXjIIvAg5zXvksS0aTwndun6RjSiPcQEej1glOG9vRJLfdqOcIaDWKn4DODjLn3qmm7YcPoh63wWU7P/VJyfEiMOTYjNgYludxzqegrAe3LS1+Dw3LuR75TjW/CdgHd/OJr8c39x7b5H1a03tlHtL8RN9DmoDRTU1nX9zeBzU9wfS+Td6XazoUfWOj1zVtO/hsT+sPAiTnYZDX1rH/5YgiXabPbfy0tY9qZsaOswwbqL+jzf9rMeAJ+fp/LH3pZ03A6I2Trk9pfg+WNIOrU00g0mCT+jdJBtRBqPvivKTLwM7CwPeeMT0HiqnKpQc9dFbNgBGULEV6eZ0sS5bHtNSlOcZcCEk5t8dhk1rNehsIGEUNI4d+rNmovFzzUyM2wt735oJNPdPRuQ63xZGlkN51p8FkVMV60tH6ez6t1KIyRuNHml8YJ3XmJscwzwolUMsy+VuR3+WRXcgDlHQUWe6R5nMHrWz3n/Vqzj37TKWMbLPLFHRY6q/Mx6CDZTdA6H8L8D+h7GaHc0sr53zOI6fjVhjZxiXA5PsNCOU4EbxS7xwPb9mY1fC5khub38nsZT0f1OTfH9L09uCnNW1nvVzTE0lv0vSIdtqKM81PIXm+vTP0PdX8P6Vyy240vjmW2e7s11yXDBRuRlmjwDBtoe8/qF37cCzpPefSK134QxrIZ04Ic4P+Tjdfr4Se1zWBINs5aRuQtwAvdWEbMq3nH0Ek5xjP8x3k8sg9Yd6erfBpHUbG+bgmxYueDp6G7yj+chLnBHedZHLS2Pt3MhlCnmN15+H2pINIY0Ij1AyZ23tFU6T4gKTf9oelr/7W2VZI8yHLFvXlpOB2g7Q9HmSgMh/Ldxm81p7wYVktb47FU5oi4NQv68+xJfuXgIhOuW0vEWjm4+gWGkOCV6f3uZdj7fYX51DqmUY3P7OcHEuuEUatWb6FwDajXq4X65lg3+8guh3fGzOajjnXRa7vsyiLzjol5zXbM2LvLG0O5txLoJxrPtk7P433Es2AyG2Q5pfj+QH1pzQBnB/VtLNzW9J3awIzqd9p+TvX/GZjl58vR3R/t/Hl04fnuJdta0GlxyJ1zIPDx5r/DxdZkfeG/godXvkyTTTNayR9n7q8fzUZsackfdFk0/IczqkmFuxpzcCQANBnr7hm0v4a4OQcS9vRzkaOgu1mH593OTyyPpT7AvSwv3MxpiE6Q/rRGLVIbxTNe4JnPXRgrZ5RVCX1CDalMUPWh9epd4KhDzu5fyjp+/ZHJ3Zy3FZh3S6bIC3zZPTejEQKGS0bbZbF79Y1nYrwvTEMOU9axOffF1kcrfx0EgQEHLPHNEX2Pgti/ZPFScDS5l2OeY47I/0RC7LEgjcK/xx/LqexpbdxfSQMNKhDk1zHnJ+0Accav/y49Zu0vU7NcnJNvFTbB/Q9zk9q+3+pnWr7n3Emg2fn+j7NT2rl2npJ6EQbd1OzY095SPOWW+s/Bl+0BW5/rk3q4Pl6G/mchrbyhua1aibmXNLPfGCa/z/xhKbn1t8g6ZdJ+p0raP3F0q/QdFDslqTNeZ5XbNp6O8o90wTCbml7XE80z0luU/rNzrQVBL6K/NknTssg8SCXSy799hbpaxrf9hSKcM3Gn2c36KhGdaXD4MKmMeRWRANEI4r/vNxPVsIOjAdOLbkAjzUZ3aclfcPfntI+o/kV+Nk+5x0tVt9j25eADKPKdCDut2Susny3ubEsIyBLA9tAjNNdRGfPmcbeNJ3y8KpBS4Jl/7Z4e/G6JqNtg2sHku1JQMOxo57NMREEOg+BaZZ5pslheO7z/1kRiKeO7B/Wm9dTL34mW5VzMCPv1icJRLPP2yP11Jf6cQ56W/Js893bKt6q9Djmn5k8g/kPbMp6YHPv9ZoOAz+qab3eCL2ubdLvO9xvXW9FuvwnpGxz9lmzfSlpm9IW0sbmHOD8S3ba9fiptX/xzJz2f/9J6Wf/ekmfp+m1zJ++ml5HfUvTQnmNpN8j6dukd55OeZ7U/I9E883QyYi5HSP7z77Mudr6r7Xfayr7cqlfn3cZGcsXuVx60GOh8TnCdTrEBzRHVhm1L03Gi0b2pMQvMreYhvrz3jm++zMjxCwnjU4atMc1RX1nms8D0BC1tiVIoCOyM2mOUoMy2v3UP7+P2t/yt99Z3kXPNLT7dNipD9MnYE2gMQKKPhD79s1fA3508ksHpfN36tbOp+R6IMBuQNZ6Z7mNAWg6MX0DcJmXc5B18oyM07W5sTRPXPYSkM456HQGTg9qPoCcZZt1ybXKp9byib1XS/qyE+k7Tieb9ZTmFzQeaQYxyVK1ACTB1akm4HSk7UP2qUO2n3bA+o8OczNY4nYahWvfab0F6P+zdabNE1ePSfoTmg47XdH0fyXeKOm/0bQX+Kikh6XHn5jP0T2s+byi7Vxue7VgKMdF6mxwXqety3nXbLK/LzGpB7n7culBT3OKzcmNIjZS/5k/wQTBEGnLfU/4ZNlcJM3pup4jpG8MR8poEWX7sr5zzZEjHWp7Gozf2TeK36M8I/CTZSxFQyPHyXoTDJG18/02V9yHdPB05M2A5/0R2EpnkeAnx5ZRZ4KUtoVn/Sx07gQXzsfD6a5r1C+jMhM8NiBKsEKQk/3m8jiXOYcIglxem3+tDLalAa48AzQCPwkmzU7YsZr5sdh2GDS0chKIvF3SnzqdntA61vyenpua59HVSN+Y7Vw/CWgzsMlzeDmG1G0UnCUrS6bNksDYTNNx/CUD/ipJH6/p3YMOAD5OE7Z5/O9M5f3CH5E+6mWSvkwTlXMi/YP3SqfvnWzaqzW/0fqpTRmfrOn/eXmLy4/yS9tMn/skWSGuifZEb/ZJzik+yXpW0t9VWeu+QVur1eqapkMY/+V6vf7u57u+Sw966GgZmUrbDjyNrCcinW5O/PzM7yxD2jX+Oblzm2tk7FxuGn7XSYPeqNf2mHfqfqr5XE4a9qxv5LAJDC2jLayRkSRYzH4aRUPCvXSIJ3E/nT/7OB3xEujgthG3xaiThcDVZRNYMN0IwDldRqOWzMP2SLsGugFa5x05+yVgmnPU/U72qOlK4E9wlWvPafy006i/qNNonDJQoS4jMOP7x/9/9v49TtfsLOvEr7fKqi52Ud2b3WzSdtISQtqQQAYICYmcAkrkoAgiPxRFBZF4GNSPhxn4jTrIDP5+eBaVcYyIIALCMKCAKBMEohEIiSGR0CQmtImdbrvZ9GZ3Knu7e5dV7/yxnivr+1zvvd7aOXRX7U7dn0/V+77Ps86H+77ua631PBE2wRbZuX3158G47yoQcg5xfG0E8N48pfcJaiDo4ShHvhakcv48PvLalnobS33js494J2tTzXdv/PXDBN3eZp/YVi6n49023dtTW526NP3eVTuV/mb1hyxeUgM+j0j6ZTWC51mPSK99pJE7907tsz2Fu6i2LPjAlJ9fOLqhttfqQG1f9NvVN15b3Be7WmXiUieMxiTvcR6lvjsx7LFu4D+Bslgsvk1tV9avLJfLj8P1z5X0zWpN9K3L5fKbpltfK+n7nqzynXrQQ+OYywaVB5zKOz0Te2B5Iiwl2YccuAkqaHRSCScQooe8zvu215asQwV4KlbDCvJQ3cvhm4XTIIwMIO+N8nIbJGW8Tkbr3QRGlefJtqsMjstWecbVcugIqCQArdqiYkfSy8s9N8x3qwgjrZbN5dhQMzwXNH9PVcarwCfLUxk8yjoWhkAvN3RmmSmO41ehbGq8CTbLtc7wHOdJjwB+FY66hBv7zRrkcpXnaLbVCBgm03mgvuH4ATVQdada32a9kilKYMZ6kbV0WbzfxXuRzFpJx+uBas7zPp+7w+t3TdcuqS0/3aM2dn9++n1jKsdjas8cvFPSi9VA41vVGLCPUWubn5juP0uN6Xml5o7HW9SeHbQp6VPUqIO3T+W4e0oj332XDwe1zaicgFwZqFh6piWdIOA5Wfl2SX9P0j/xhcVisSnpWyS9TG2X1msXi8UPqb195D6tHhZ9wuTUgx5prigolbIz22EvegPfCR5S6RFIVJ6j4xynfJNhIjhL5J+AjmWxQcyJxJePJvMgrS6PWEGmt36cB+wwQl1YT7I4WcbcJEslIdVGoAIw7DuGT4UzWg5K74t9Wy2VVKAxl5RsqCrmKvN1GoowVf0rAFqBMce/FvmP6kCwY8Bf9UOmkUwNjSgBUwJTjn32J+fiyElJD7mqF/ts1P9Mz/HJSrAsh7hPR2hDzfCa3UmDmDopHbLRhllpld3z90ePibduvlq/ndf8xNyROlNkRo1MHp0Cg6LqcRbMZ7SESidtTw1QXVFr270p/wfUmKxcfvK8evsU5m5JXzil+61qLM/vf7b05rdJP6728tLbUS8+8+qG2hKX++2G2vKXmUUzXS5zVbcEfq4bw1aOkscnx8iJLW+dENOzXC7/7WKxeGZc/mRJb1sul/dL0mKx+GdqXfyh6sTff1ssFj+6XC6fUKx46kFPenqpUFKSnh15q5m+w1qpVSwDDZHTo9GqUD/BAfNLo12xHmSNmE8CBAK6BFI8wUEPv2LBFN/XhXGe2SZSPcmZTgIop51tUJ2eyGUCx632iDh85XVl2GzXBKwVKKmMQtUemb/7YZ2nKFxn+VzPfc0VcWVECcCzfJlHxag4bAK/w+LT93I5sWLlCKCqch1nJBJsErwwPWk8h/ndxt5isHMl0hrNQesKXzPrUbEuLKuBko3zeTUWLE/KJZtoUOKxva2+L8ZpJoNBXeL8PYbsYtuhMqPlMjCdBPlZNud/dbq/qw5KttX24dxQY3bygY57aktWl9VYnkfUlse+UI3F+d63NUB0Y0p3S/Pj6Wzj85r34bZav+5rfqIr61gxPDleqnHG8Kln1jnKt6B8+GKxeB1+v2K5XL7iJuI9XX0lUmpsz4uXy+XXSNJisfgKSb/6RAMe6RYAPelVpQHkpkhf4yCr1qhHCNy/121gI/JPxa64L62yCTkxqDxHAGMEBhx/FDbpaTI06blkeCpTXksPv1IAqShGIIrfqw28uU/L+W5q/joAepiVUachTqBBSXZDCGeFXnntyUrwswIJyVSwbAcRVrh2pPnY4feKZaoYlVE9Upg/AYrbeHRSJ8ef8yK7MmrnNCC8zrbx/KxAbvWk8ewDApJthCFgMGM8Gv+um9sigSPzMxDK6wa/e1OYK9P1i2qgwGwElxCTeXA6bvPLKO/N9LPTT13hPPzHdvK9HXWnyvt+3L/3qFk410FT2Pum+jqeAdJdag9nvKrW5o+qg6X71F/lYZBzXW0Pz2X15/9QflmNDd3TnOG6a0ozl4aTnV43T0YgJp1dFd+fVHli4MOvLpfLF36gE10ul9/+gU5zJCfCvL03UnnnVObVhlYayGqjqBmRCrnTiNjI0PhYRl75yMNnWsnIbOAv60uqtGKL+JnC8ARTPibqsrnsVX221BWen09iT6mqK4FdZcRo8H0tl1ySzTnENRrMCpykFzfqQ4LFaomL97iZmkaHbZZLfkxDEW9T83HKstqDZbpk5yqW8zDCZZ7JVFRertOumBECSksa4Upys76K72zHUR3p+LDMLGN1rxojUgcgnl9kOtiH1Ykepu32oi7hffZJBfCchh9i6P0vl9WfkcRxynnga16W87w5UJ8DObeTMa6e4+PXh3gMeu8PdSbDSo1BuTB9J5C7Xf3N8C7TVTVAwyP4u9Pniz5R+sxXNDbozin9gyltgxfu4bmsDoZy7D+uuYN5RR0E7eL6hhorVJ2MJDNJACu0h4Vg1mmcqHE9fU9kflANC1ueof5auSdVTj3T44nqAZrrpZb0Pg+0OjA5KEcg2OlXJ78yjZSK6ZDmSo/ly8lF4MNPGkn+luZHZskIJCtFuap5e7LsLPOojskaSXPlnvlWy3jMOxmJyohUrFwFVhk2GQp+UqqxUBmqrJ/zIgsjrfa9tOo1VuBlQ6sbwclSeVymELyncvZ9njaqZJ33yjqwPFXYdaf92C8Eo1mmZHI4xhLQOt9kUdIAEeQQTJA9cP0O8PtI8/atxgXLULGsmxEm70ud3UhWJ/PL/YIGidUyysg5IPAheJPme594EtThmbedpxvqx8KfpsaivAlpXMd9l4UP4XRaulPSXT2/x9VAyqduSLtHLU2Pr0tqbNiGOiCS+jKXx86j03Uv2d0//T6vvtfHJ9m4sZv15jjgknHOsZRk6D/I5bWS7l0sFh+lBnZ+j6TfexIFOfWgx1IZvcqIEJ17YFIRpbGXVgGTpVpO46AfAaCRYUlPi+VlPlT6eS/rcRjxnHbWnXlVSwBOd1QnXk/vU+r7AagY3VZu33VyHGtAYb0M+tKgupwJOA2EknlIYEYlVi0dcRykYRqBkDTKyRxWDEema6kMHccUwWzVrrnPhGlUitoKn/lU4IJSbeLm71G8BMtcxiQoZtksBI1khQmk/Z17Qqr8sz8q8MIwlZOxDmQeqo1f9+U5zRkL38+y5bzO9qgYCBXxqvLms3VS1/Ee07qg/mDFx6e6cKmQdTYLYyByZcrnu39cOvfj7VTXZeT9D48awDGgstymvieK7ZagmePpnDoYOlIHbvuoV45zl99psg/Ypql3ThzwPDHLW8fKYrH4Hkmfqbb/552Svn65XP6jxWLxNZJ+TK1pv225XP7iSZTv1IMeDto0TtVgk8ZGn2lQCRIwZR5OL2VEZaZnmPcZjso8wRrD+D7X0H3dCnOknCz0wlmvmzlizk3Co7bgyQ/LTvxOz77yenm/Uih5yi2NHD10ljeBGjd/r9MNXjLIsZCAs/LsK0kQRlnHtqQkSGbYdaxbgu3ReBn1d9Z/BCqZds6FEdip8kij5c8c2/6d4NzXPB74uo9kXmjcRqCqAj7+zX7i6yhynOfpKF8/P5VxP8L6VRcpmfYIlFvSSCvuZ32Y1p1qz76RGiC5rr70dX265qVCv/7GkvPncKrrhvreqUfVl/T21U907Uj6AjWa4DVRr8eQbur7bC/P9+u4d0ENcJnpOa8GqqiPOVatL/zIBbbPSD4Y2Z7lcvllg+s/KulHn+TirMipBz3SeiNCgJJerSd5AgkaugqxZ1qVEa5+c8lqZFDJflhRsQyZF8NU7w5KOr3y3pye45KVGSlNy6Y6M1QZml2t0uMGIqSatyIeDQIN8XHOCVmSESNRsTQGLy4Hl2o0SKsyEsynMuQjNqRi66q+5EbmHCcVU7SLeCmjPnW99tQBQCp5j73RZs4EHhXgUXxnn3OcVkCIbT9aUksW17JdXCMrd6j6oXQa/CaDwLL5+yY+fY3OiMvkvPOBJA5jMGGWhHMq29f9wnHME4HJcI3qlmWwUE+dU9tc/eyp7O9QAwteUvKDGMmWsH83NN88vqkOcM6pgZcttdNaHnMbasd9ttQ2Ml9S27h8vzoocdoGXnQEnfeOOqMnzefJlelzT50tumMq12WkX60oWAiyKufE9550OcEj66ddTj3oqTz9BDbJrEjdKKTXVy0Z0ejmXp4ReCF9mR4dr2X60rycTKeSZAUqzyzrXxlqtpvjEUTRmKZk21bpE0ixfJWBk+ZK0PfWsXIJREcg12lVZTR4HJ3oS9DBeJmOP3l0v5LsezIoafAt7McjrbahxX3q5b2bBY3O94Jav/GItMvIskurZfU9GpQqLwr7Mu9XdayW6DwfK3BFIWhjmu6r26bfBOaUSs+wDgYE3txP52Bka3LzN8Gt1IzvljpLMnJwcmlrVNaRA5ZzPUFctsddamDgU79/+vFp7f596o5EHpN3PmSqc75bP9+Y/hK0eazcpwaS9jTXoZ5/uZcx9XXqNdffOutx9WcE+WnPB+obrs1Yk72S5k6utNpf65z1J0XOQE8ppx70WHIgV14e2RHHGSnO9Ghp9EZAinIcSFDcq06C3Az1WeVPz9nKZDPupTGl1+jvXlPP8hKsJBtjJe04Vhz5DqJR/lX9KsYpveuMU6WZbF0uUya7V+1rqYBUKlCGW2eAK7Cc4+A4FsntnUsETpuno9IzzbLl3Hhg+p7sVM4zxmeZnf86xU/hKTiHXTe+K5A1AoiVga+YMgP9fZQlgbTTrQAkxyOdBjpYORcP8efrjLsTYfiyZOZrAJxMNeu9DkQzXDoxBF9Ox4zTFd/7Fkl77Zk5l5G3T1RVy3BkXVgeLzW6TNxAbubmoenes6frD2muL0bz1EBsQx2Ica6zLaQGqHz9htozgjbVlrsOcH9LHZRW85jteIY3Tq+cetDDpR96bfRIPGkS4FTedE6W9D4tI2VcKZQK4R/GvcrTyN9ZhlyKI32b+3OyPARyqXx5v6pngor0knk937/jvT1Md1vjNFPcl6wb8x8xNb7Pehss8DeNWWXsuaRoqfqxAjujMZb3KBVoYBvRg814TI8MVS5pVCxQjq11deFvKnca4GrsVmVgvaTVulPYVxWAZD04rrnkkY7QkeblZVl4z+0urban4/FRDhWjZKlYTebtOe0y2GCzj/md4/a4E3XV9RxzbBtptb5Haob+O3+yh3lUcz1UgYkb8ZtjYkv9YYRvV3eeWL6L0/e3TdfvncJejnw3EWfEdFZznrrC99gnBsaZ10bEqZwBhj8RuYVeOPpky6kHPZYReh4pxcprVaRRsS/0lrjcNUpP6i/io3hy5IbZpKYJ2FiXDFeVcSQVS8I62AjQ2xyBt2S+KuBzqL63J591kvkkAKqUb+63oYHM0zIVwGC92XaMm8ouwSWXrapTSDkO0nseAWFKAjguffGRCwkgUioAXY1ZXss+yHgVMPeny31e8yPJDm9jWIFq9tlov5CF83O0lyLF5eOR6ARpimsuFzfAMw+3x2gPUPXiTQICSuVAeGOsNwEbqLNPRqCP+eY+txGA5fgm+NvQfP8Qn1/k5aUD9TfMe2mTY+MG0ssyZtkendI1uEiQdHmK9xw1lueBKW8zLnkqbB274vt5EILi8rkdd6c4ZLO5GjACUNWJvjM5PXLc6sqpEXp9Uq2Y+Jtsw4iuTyXozwPNDQU/2WBWkGRd0gsg4GE5+bmlefnXASzH8YO7XLZsH4YVwrFcvFZ53JWi8md+H7W3wxxqrpgqZcB0RwYj2Z+RN1W1CfuG7Vx5ahwbDDdix9Irz7bLOrAsLCvjbuF3ttsIrFb317V79vs65oXlP6/2ELk/qeaR70731xlmp5lMSkoyN1mW7JcEbRbPkYrNSa+d5anmq8P7ZZ2+fk59I6/UDXKOX4J+xpfachaf/nwu4jm9rDclxxuBGPWLtKo7t9Qf2Gfw5bHn9jtSAycuowGQyyfVJzWlDhrc7ke4tqe+vLeDON7nc6j+yooDXOO+J9eRY8X9XAH4kS4Xrh+pAbLMR1M5DYiyr7msus5ReVLk8An4ewrIqQc9lWedYGVHTVGMDBkrOVoaIROTRsmSCpgKk5LK3JM9l0QMXqpJSOPAPLPMVOhkBXKyM/6e5hMzQUnl4dNousz5fiGnyXLnqY1RuiruZZ0P4x7Lvq25oiGoTYVP5spSneQagbmsh8tFwJbjkGHSQ7Xh8BjJelRsJQGdv6dirwBhZQCq8Set9k2yDb9T0m/4y+2N1rfhelXmqg9G+ROUV2MmgUvWyZLPuKmUXQIA58NTfskyE4Bc03wZxP2bzoDnTALQQzWm55o6cHxsUB/WYx1oZHusWwr2306E35t+X1Efq97w7Tx3EC9ZOF9z//DdZgYFd2jOFN0e8aW58+kyjHRIbqK2buQ8qMZJNSYI9Mg2Xldnms5rDuTyZF/ldJ3J6ZBbZnmLwoG7ofoECo0AN1ByjT+XnJL1SRCVCjLp5OrZL5XBz1Mco3ApIy89FXIVngbwqlbp8xEISZBWpbmhfuyTSjIBgfNKY5SSiop5JdiS5m3GcHlNuLelVaVN1q4CjAkiRkCUrCEBFOtUMUa5rMflNhrLCnQlkOO4zXLnBm7OmXXLKKzfkaR/Iuk3/XnpJ1FuLukyP2kO9igsA8vEect8DUrMjlzXfK6uM/hOZ7TMkXtUcgmO4O6Zkl48hX2N2vIL62rWJOvK9NkW+aDEdPjcLtRDCcid9mZcr8aH63ag/oTia2ogjKwNn3bsdC9H+hzT6XhwPvuVFq6XmRQDF6fjcbSltrz1gNopLgLkinwwkNpFetX8Zbtxk3gyPtL8hNiGmp67Ot07r/bGd0umf2KswtmR9aHccqDHgzS9dD4y3YM3lWsapDxNkiAovfU0CKngNrU6yCsP1x4MFdAIbKQSyWs0EhVwqQCBQcNBXOdnxRIQcLB8Ul/nH3ndLM8IkGSYLAfrx+tpqLJdM/3z6sDTD1m7GnEs1d6B4wCiNFfsI6BIqfYqHbePo7o2Ktu6eDlGHc7GgAbTL6G1ov8xNSN5Zfq9FWlUYDnrVY1XGqDz6kbRfW/j5vSk+Txk/FzGqPJnn7A8HtfczyFJd0v6HEnPv9AC7D3W2uIRtIMZgwp8VfPEdUrH7TzK5mUXf7cQzCUYoJ7KTbm3ac5WSXOHhelUY5FjYyOukzGheO/fpvqbz8lQWz+em36/dipjPgA1570BnNTnddah0rkuewLRal8W56n7y+1+W9x3mU4M+JzRTKWc+uUtKi7/eSJxOYfLEIcRnt5IMjMesKM1eJeBHlelsCvDkWHWicGIvycz4XRGR0ArDzI9O14bAZ7Mv1IYKTSQVXtUIInAJcvGJ9a6TbbUlSDFxi2VTZbPYrBzj6T/VdJna153LjemMs/6syw0vFU4/lVgJvtcmtPkVX0yvO9VfeB7rFOyghmXc4zLGb52Re2hce9CGgYeBBK+TqM2Am1sb5fxGWr7hpwmy+05nEuPXE4aGWtLtpOF8zE32X60GvDRo0vpylLPnK6xXFyGOow/jjN/ei8N58KWVpeWXA63TzLV1bhwu/lvL8KPxnimQ3HY7APLRhHW9b5T/fj3tnr/JsN0fbp3Xv2t684rn6otzcf4aFM168Sl6OPqmekYdPPovX+vm+tncrJy6pmeVE4EPJRkfyqPLo9C+zuXqXIPBtdoK+Yg6e9qgOfgN11aGR2HrwxXApkqDX6nIWU5nNduxEm2J68RMHHPgxUQQWmWg3lUdXHY6mm6BDYJzqqnVGe62/H9OZJ+uxpl/pDmdbT3dhyIyjpxKcobQ6+iHMkmCr99v9pTUzECWd/KuPg7Pf9U7qM6Vsu8F6bwDxf1IKBxvCx3NabzfrJjHktvQ1hedzpkaHO8UiqnhfHJDh9p9eGdFL8g9E79Xkn/XVc0f2t56hHW0+Oc7bGhvmREZ05a3VcizduOdXefcpk2y29n4nGEz9OSqWersbPOoFdMGsfh/vT9LrV5eCPibahvrvZG5vNTvBua96XLSZavcrJYthEgGs3BHHOcW9aH/szHdoz2VT2hcra8NZRTD3oo9CIpFfvDe7muvc6gCeHppXHS0yhUS2PHGbiRp89wfthftXmPSpMeJe9zcqaR4VFUx6kAj4WgwUYnwUtVj5TKKxuV0WmxrRO0JkiqFB3LvqH20kKpPWDtLZJ+eVD2VH6VIR/JoeZva07gzbqy7rksVJXD1xJoV4a+Yjk21I9Ib0Y4XkuA4AfDsQ40iNm3QpjKMFYAv5q/NlAsH8vNOZVj2O+tYj2YPh2lZDoIPLJeW2r7Sz5S0tHie3SoBljuU5+77JMN1UsyXDo9wu+s/4b6chCXHKtTgql/qEf8dGEv2Tn9ah5mWkKYKk7lFHJM0nn0E8Q31XUQ89nRfCwa6KSTJs37nfOnAmwJQrKs6x6LUNmM1Emez/vTNS/dnWGP0yW3BOipBjDv5dp9tYYvdaUyul/99jXmnUrfyrECOP6+rg4pTqdiPeihVAAkgQs9Ru5jsdLN9Ck0HhUoodHeU2dIqnd1VcyOr+fSQSU0Sgkc/N0KvnoW0KHaiZEdSX/8t0rale77wRZ2H2Gz/dJDHXmRNKDc+JjlT0OS9ch2S2Wb44zXbQgtldFOcJMecAJ9fnIeeKlxX6tSOQUsZ/XdZanAG8FQtjXvVcaW5anKSGFZK++cLMpVSf9KDfhI7X1UV9XmwTV1kOK29pg8Umu7QzXwaSDj/A1+CC621JbSNtWYtmo5j/Xe1nxuE/gT8ORn5VxV7eT7ycKvA+jup3TiDHCua3Xcew7drfm7vipgnuykv7stK2crv6eeTruSQlviJ2h7uZHtfNzBjSdEzh5OOJRbAvSM1qw56KoJkHGs0NcxPZleNZnXebAV+KFnlstRlaHnJs28X02gCgCkYvD3A81ftpjMij/PqU1cXqvAievjE2G5zEipvPtKIdFYsa8zbraNw98owvr3liT9YUnnpM0f7P1ynFIcsRkWpjHylo8DABVQZDr0Kt3W3hdxs+W0EaW37jSluo9YdufNzfjJvoxkHQg5zhseATHqBhv4BKBkXHLpTEVa1Vig/rg2hb2qxu5YttQ35bLcLh/L+TjSrhjoZAcvq+8VqUCLwQ7LzY286xyKahxSRgx2MmYVU5fhqZfOqy01P6r5kXMvld+pVufLU/p3qYG+3EtDRyed2koq4L/O+a3qqbh/W8TxeOHerSddziimUm4J0MPBVDEZ0qqCTtR+qLmSyWWrEViqxk2lANIQ50RKTzSFZSGVTWNvulpaBS1VmqnAuOfAZUrZUANFd6t5r1RGI3BypObheg9LgohkS7JsCaIYvwKoI9DJfnB70xDsqxms7/7Sdu+q2n6BNKQV+5LlzPIowqWhyfFWgYsKvFYGi2Oe/UuFPxpjNj4VgGB51nnHziu9c8Y5p9b2VzQfb067AsjOO5exc8zwXjoYW2rsy2XNT5TxJNVo/LPuzJd1S4O/N31e1RyUckMugXWe3JTmrEbmzzpzU3MadrcJmSOWOx8hkcC+Ajsq8qJOyjlYOTIOuxP3d9Tm41vVHbH9Kc0N3L+uposuS3pQ8xNhZI3Y3pTUj2kTqMcJ3oXwvk+dnsyjv/vlpC7TU5BsuWOxWLxC0g8vl8sfPunCvC9yS4Aey8hIE1D4Wg5eT+pcWtiIOCPDNQLNNNA5mSrDXKW3ztBKfVJtS3qpmmf0+rg3Umj5glEbq5HC31BTNgY8SWGPFGS2bbJCyQZQoafn7jplGdk3CXbJ8HBZ8Ia6R+iNuGbcHlYzVlkWCxmaNETSajtUAKaqQ2VM09hVS1Wj8JY8Msv2zo3nvs7PKn3WNcOMQBoNY5bdUu23SAOyLk8aH4sN/ieovaPp9VoF0RULW7Uny5XLzAQX+9O1XJLKsrLOOUZS9xCkbaqfnPOpq3wQH0HOjtoy7mNaNbo5XhIMsS0s6Qh6jnJ+pqNTjRkDZLfzdTVgvIe6Ul/yiDqZxSw/da2lWpYyyObvHD+p2ypGh5L9aebVdUzn70mVJ24j82PL5fLlT0jKT5LcEqAnl7cSbUurACORfRplab7eSw+Me38qg5DAyRM2FVcKl9bSU3Pa1X4U1vHtmu+jyDpXYIT7CaT+9uArEU7qEzU3GCa4TONT1Yntwb6j4eXGzOyzEVPm8lbAlnVJBea1di4HMXwFeKr0M491ZeL1ClRXZa+McZVu5s04NPYeQ5VhT3BFOYpw6wAcv9sgeg7l82GqsvtajufK+UhjRbku6ZXqD71b52lzfHk/GKVq9wTolhHY9G/2AQF6gkUve/nPgGd/+tvV/P1Y1GFOl4CnaqeDIh7LQhkB9Grph+mP5oHTvDCFe0z9cRSelwm6yHKxzJWOXbe0xT5KfcQyJjCWVsdROtneIL6v1XZNkHomJyvHLX+euFQefzUQrZjXoXshjJWxFVF6nrk2u47xGRlb/zbTwLwpfoWCqfdK0dpzuF+NoUhmxPlu489pZ5mtQFNxVIqEoCEVPe+fV3+MfsV+ZD/y2UhetkujJ60aQX+6v9k3zo/v8Mm+8ZHSSmFmu2/F94pVSFCXbe0ypkeZ+VUGtPLMM+0MxyPIVdtVQMniPnK6fIZJAqN1TmSWpwKnNOzuw1H78l5lTMk4HGn+0k5LNfe4RGGQkCCgGhMb6u/HSsfIMmrvnG/SnM0gQ2Dnw3P1UHrP0XjrNoLpLMeO6vk0cuYs+W4wltc6xr9zE3YFjF1vp+lHOkj9DfVc/nLaZqTvVGtvPrjRS4IZJ50jCx2z0TaGZGwqYMTwbguPpyUg+QAA+IxJREFUucqRcrlORI6egL+ngJx6piePcSYDQMVVSUXVjgZhGnR6uvxMhulg8J3LI1RO+WnJx6ZLnX4mfb6lVWXKOhzEPXuA9JCoVFi3ylBWbU2QeaS25l5tICbA5LKV+9Ae9sh7pGHOfjgorvs4rOv4LtxzOvS8jjPebOdknlhXhq1YkeNAQ3rfKdU+DRpHl6FaNmKYZNQojpvjp0qvAraVoa/uVWmwDMcxBQmORmwn607wzeWZdUCeZfU45jxxH7hMnBuMr4gnrX8eFVmcLc1PQ96p/tA+g4lDdfZHuMf6JbglM131d35me2QfO93s01yqdh0fnu6dV3vApQ9NcAy6/m9VA358KavnN8cSAf86XT4a/5WDyHGfTpDZHYZlGW7mVOoTJmfP6RnKqWd6qiPmlsq4VNQk79GjSvE9eiSpnCvvmJOeno/LkAokl4NSOSuus+xSn1D0NKzERqeIrGQrL4eSSi7rwzJR4ScQY7ldJpeT4ZkfwU6+QDTLprhPVm17yvNqESZZlKrdXacEPNV3lzeVaS6/Oq91eeZRfKkue7JOI8N6FPd9jeObkuXK9k6DPgon1YxmBZZs2J6mzkwksDgsrvuTG1sTHJOxqOae83baZE6yjuzHF6i/c8txaPAT5I0YXEV4h3NadPa8zHVV/RlQ23HfS19bCE8g7k+CI96TxmO0mocV2K9YkmTZNtSesnyk9vynbbXlLgI1yjnNWasRi8L8pXmfMX86f5yj+VJYti3Hx970ua/uZElzJmpdf5/JycqpBz0VsBmts/Je3qcXUXk+NLgOr/hesUb8nbSx80mQZSWbdLrzoIdAL5Xp8l1jLAvLn2DNzIo9xXwIWNaV7Uwl7noadCW4SoO8qVXFOjL8lhdL+jzNgZfrVAE351EpRrZpevE0UMnkpkeYDJ9wv2KFmEYyNSlsCy7bOd1qvFbj24p8HWBJEF6B2BwTVXqZLutgg+0yZT0JUnMMZN9ROB6y76s6JQBJwOR0DoqwGd+Sy0rrAJ6NZ6ZfMSj7KB/Zi6vq7UTQzjTeNYW7TfOlog11MGSHYAR6nW72x/aae1kXaT6OyapxvF5RAzPPmX57SdLOHMeNXz/hE4FOv5rT/D0CsTm2N+K69XK+kHhX/RERZK5Tv1d9eyJy+AT8PQXk1C9vjZgdKrA0dqPJ7LD8JJ2s+Kw8YcetvPIEVVwOqSRBRXpV1eShl5KnEQgIbPzyFM0NzR+g5nxSmeXvzMvKYuQ1VkCsoo9H82hf9QbA9FzzuzR/GrLTz2eYZHoJpipAvY7tqVibXA7hEtYoXoIJKs3ReEs2ohpHFbD1PaY3MmBs94r1S2GZbMCSheT4fESr9bQcabXMvs4wCdasCzwXE3w7r9QZFeBju7wm7o8eqzD6TYaE/byD324r1+lqkY60uiF5f7q+rf4QRKk/TNInMnNp2OM15+hxOqmqYzp40lzXMZ71ENkhx9+drnkJKVmklNE8yTBs+yq+wZLb9vxUxivTb7NObjOCZgt1+miOnMnJyC3B9Fhy7d73DVCsXCtvPONXp6jy+2gfRk7cFIIdUtQ8mrmBe+c03wScTElOzgP1Ux65xJRMC6n1XIfeU1e2FQWddVIRzt7Zec2VNfPf0+p6eFLgNjyOe5/6sfz0ppMpYxkrA0/DVXl7VZw8bVTVndfzu9uccSoWMdOujLvjkGE7zuPm93WGYuT5spwcg9VyXX5PUEYvPw0D5wTLwvZYd7qGYX3fp4E8HyiZPvuegDPbL8ErASL7tTK4FdDKlwsTDNN4u33uVXs4H1+yeYj73P/me3YaPP/+rKSvVNcDBGwbiJeyDgQli8Jr1Blcitsq/tgP2+rvrvNztDan7yMHLef1weB6xvH3dDrZ5/uan0DM5UfXKw+PVEv0T5r4icwf6L+ngJx60CPN6eyKiqQCSsbG3wlEaDQtlWLyZKw80FyuqZY+0tP35PezcpzuNfXnyexG3GpySqsPGvSJLV/3OrjFk/hATXnuobw0JFkXgiKKJ7jLbG/tgrqxMR2cDzg8wB9BntuaSwFWRjwBt4P6UdFbked7jirFY3BM5cR6VX2+znjzO5eZcvlvBGir5Sr3BZUtjXamOwIhx7GOVPjpbbMsFfM4YooS+DgPtwuBYObNMleyjuVzXjbmufGV9Ukjvc5IZT6PqwMg7jskKHV7pINVAeBciknW4YoaCDiH+87DOiXHZuUUuJ5OdwefjptMyIjxdv7+baBJIOc6c04eqem8XTUwt6c5SBF+89TWBfU+cptSx1FYjgTObGPfc5vcjvyTTXcb+3U7lYNj/WHbcaJPZD6TUk798pa0upm58vakWrFXBmlD6xUqkf/I+8n464xKlj3TJJChkvKEq54BcqT50z/TO92fPnfV26tSpCwjn+eTxi+VMZXtlvpTcO0VbWqe78WpvA9o3r5MM/OxQuLyhE9w+IgoT39VXifBTbYTQW0au+0ISyNTgY9sY9+T5uO1or05PqqTS7kUkenkWMr4BPsMV0mmO/JSOS78PcdpxbhW1+6SdI+kN2h+ojDLPWJ9ec2geE/97dzJynApIoEP61bpgCzDOqaYrIvDm5Xxg/eczwHCHCcscy5pJmNzTm1ebkn6junajSm/26bf3qNS9SnL5PF/EN89j3K5nG1t58fp3jXde42azrig/pZ5qc/zTTXdcVX9GThkjJx+7jvivObJVdbL+5zcJprKcVVdl9Fp40GCbJd8RAIB5omBnjO0VcotAXpS0diQWbEQzKgIm32/DqBUSn6k+J1/lW8yQSxD9QLPSgmnok22KtmtzM+ghGlvqYEde1p5vFWae8UVoKCS9u88Rkug6nz3NF9+HCnZbIdcLvTnhnpbVkdEqZwtacgS7CTQyfJQcmytY0zcJk5rBCjYPqO8E3jlffZBlrNisCpwx/Ll/qRRntV13mdfcLnAT+bdVN9oX4EMSwWKyHh6LEqdDaQDUZWR5eO4vaj+IL2H1E8EHmruPLmN3e4EOEJYGn9fI2OZc4JsmNvH4RKwUx+wbVymh9FGfuCh8/IbwTnvK6eE49NjcBPfkyki2+WwG2obr3fUmJ4rmm8MFtrIDPKBVufMtnp/WEZzIu/7YYgGxXdO3x/WfP5d1Zy9dd8SLFUOj+XEcMfZC0eHckssb1Hotfm3VNPhVPyZhoXMiTSftKNd+ZXnXDE/ufafeVJh0cBwApFy3yjie0LmJLeHQ8VrJXKvGjNjw8C3MjN9f2cZcpnAZXpcqwrQ4TfUnsVxv+beURpaPo/EYayUaJCcbh7J9UMHrWy9VEjDRANB+jqXE49jbOzl+s/pW47iWo7DrfhkPJe/ise0CVYyXdL/LHeCvBS3M8Nsxe8KGDLdHPO5zGJxGe+X9Cr1UzosB/Okc7MxCMc8K8dg3TIH40kN7FxQ2wfzaWpsFMeG43A/zQgoHyGMjTvBXTWPt9SM8UW1+bOv9ooJhuPyKdvC189p7ihcUzPkZlE8P0fgPeszmhdk0HMflccP2/6uKaxZqPNIv8rD8911k+YMTiUJCpmev/tE2FXNAU4yV7m0TbBT7f10GiPn5kxOTk496Bmt7dPL8gCjMqCnMUpXqmlcC8ECgRDDke2pNk0mMKqUoicRy+O4Xj/2Zmen4eOnWWcqDp/gYH121DzWS+onOnyP+4ASYIw2a+6qKaxnqylkg5S96fp59b09TteyHZ8spxV4Lk/xO5kTt5e9Ry/rOY0NhN9AeNeP3nMCN0syASk0OgQtuTzrekhzQJLsQO4pyL1tBn0EICz3pubpO8xh8Z15JZMgrY7bHHOU6mSjhfOZy4s0IikEvRTPRw3uc78HxxLDu/9v02rf7Ur6FEkfsnyWPum7upFk21r3jJxqsi5sJ+uLBBIO57JeUZurF9Tm1COaO2P5SZ34NElfMn2SHfQelhwvOc9TzNhQN7DMnLNeOvNcPqd52xuMX5mukZFj3+yogb47teqEVlKNEbaxHSM7Wa4Xw3EPYsVgjfY4WbY1b9cTY3sOn4C/p4CcetBTsSqVok0lXynb/J7gxdcqj8PCAU+kf6T5MyaoQEdlJ8DZwXcaaOf3uObP67gx/ZGlSc/GgCfXnv3od4ORDfw+pzkooFE0mLlDc1blutppq0soy46k3yrpL01x1q1tOz+XfVdNadJbdzuTdqeidnkOpnIcIC0+XIztsaGmUC9GeWjMspwuU7XPJgGMy87+3og/hudShsMrrhvseumSoK4yVLn0WwE5llcRzv2SoDXnx4jhqIBger+VY3MY9yp2NtPM+FyaJZg/p3mZzk9/WZ5DSb8gSV95v/TV/R7L4Xmzpw5U3B8JkN1Xjpv3mDbn/476HpM7tLpc6ToyPbMoP6MOLKwzzPYQfFgX0PHZjL+duEeQVLEau+obg69N5bttyvutU9znT/Euaa57yU49oPaG9QqAWye5XG5/l9FtwsMP1I2Wq5EuGdecl7mJutJrZHrO5HTJqd/Tk+vTlaKolh9SbBTyJAXjOz3SlZzUFQBzmJHyzTIwDBUEN/96ouWpEH8yXu5VIGi6jrC+vqe+l8dixXcVaW4jHakrRbdhLr9cVF+bd56f+lxJv1e65y+2JYyrmu/l2EWeZq8O1JQ7DYYNmOt3mxoIlFpfnFdfKniGpGeqvZj1EXVGxEqe48CGoQI5FYAwEEwjbsnxmWxRMjpk2SpvfwQqRoCe82FkQBl+Mz5H4nozLsuyLj7Hc+Y7mqsMk3WR5ptn15WX/cEN0p4Xe+pj0uOD7f7w9PsffHv7fVmdyXQ+W2rLXlemazZ0qbeE+/5k+x8U1znHLqq/c8v7lqS5M1Hl8+bBPbI7kvSo2jx5lhoTfGWKc2H6/ri6kc89V3uav23eoGJfvb3sxLjMz1cDOm9XW+q6IOl1mrM97ovzmp+Y8vyxHuMJvUOkYUB6Zfp+ccrrF9SX1ewo5TPBcsymHj7S/NlJBGzC7xOTs9dQDOXUgx5LKnMieioYKsNUPJVBSMXkSUBK3YY8jUt65Fzbp9AQJuChAmJd+T2NaxroytPLvR17asp5Q42V2VF7GuojWgUjuaSyqfl+C6kr3qdpbsTPq5+y+Ie/JD39LzZPjWxBllGaAz7XOw2DP9OIG9R8uqQvvqD37FL84cvSj01lSq+MLFVKBX7c5mm82Z+8vqWuqHnkn+Gra+kdpuK8qNaX19WMk9uVbeLxyz6VOshkHdjG1YZlIVwuNbJ8ZOqk+VisxnAlntP29BNccv6tY3IpZkW58dTg2uPe95JBOVAzyr6fy0pmP/7406RfeUT6G1MaBuQuVwXcnEbFcjEuHwVxRW2+ul6K72xjgihF2EOEuaTuSFyT9Jbp971TXtfVNz4THLDfCZ4IIghg6IBJTSecl/RlaqfK3qY5qDlSX0o0M+3Nx9XcdTgDNeH+XWpz5kE1sONxxvdmcanLfVuxackkcT7k6dtM50mXdV7FB7HcEqAnlwAsXF/2vWr/hCJOLiGkMl3HyLAcldfLe0nJpxFa5yUzXFLq/jSLcU59aYntoOneBTVF/MvT/YvT531TOveoU8xkdizbakrjyvT7uhptvaemUK6oGeAdSXerKRafdvEeBHvSrIu9b1LINBjHMQhm2K6oKdAvPifpWyT9nqX0yoW+4Iul1727lcftz6PoBFYVdZ6Shia9Oo4bLj1xMzafpVTVyUaER62d19PV+u7Fkt44xXlo+tzQvEzuR47HbG96p2TBsjxU+NkOlNGYdXmcxzogkCC0YmJdhmRtXWY6QDbIHNcGL48jzF3q49Zjw32X5WH+1yX96CP9ZFQybGkYk3FOwEtnZQfhH1IHQNcj7qFW9wdt4DrZKQsN9Ib6IyCc3wMoPwGvWWDuQfTSEHVTdcybAM/s7UNqTtKjKBsZGNeLDI80n29uzz21Ps3lpyvTn4GYARLHN8vvciYjlvbGbWehTeFcOlHG50xW5GYcsFMhydhU93MXfcWQ5Bq0cK0CF0yHCuWgCFspc0+aLJvvpWef5fWf8/AzNrhsc2n6/tGaK/y71cDAZbUjoufVPLj9KZ17p3CX1ZTeXeqbBnem8J8wpWf2Zme6703L96kpLu+Leav6e3U21RWOy3RO/SFm59UVHBUG/6r2snApbcv/fs83tQsv++fSdt+/Ye/Qyj6ZJCrTBGfMO4FqggUyinyAo8FWHqt3eD50jcaPoGNH0ldJ+vTlhr7mhfMN4m4PGmXm4fvMk/XmGE+wXzEJCRKrpRyGzbIQfFE4FijVtY34noCPYTK+AbONth2SvSgbN76yL9xOB2onz96iPjdTrzC/qn8pRyi/l3oP1FjZ5+C3wzoNMx9cQtrVXH/k/GGfGJAne+LwV9EOHs/eH3Neq++p2pzy9yfDcL4YLFrnSH0f0476OwKvqz6e7vlHYMx9R35nl8vq9j1u+SnHJfvVS/8EztJ83pya5a0P9N9TQE496EnFtlF8txJPytX3pBrY+LcnYi5bZZjK2FWGgPGo5CqFnp6160blQeXDeuVmWm8GvKjGxPgopjcUmhX63WpLQXtqyug2da/1gvr45rKMpvQuqgOl62os0fOn33tqb6DemspyXW1/zQX1jbfnpzim1l1+K0yCmOOMn4HM5pT/1cckffrXSf99IX3OF2l5uedhz/lIq8CDHl163GSEhGs0rNxXYYBlpX1eq8CVQuPr+FW+/vwZSfoLR/rl183L5A3tOV+q76xHzhPOHaafnrYQ3nIUnwQcNjwE8gzLNJJZ4lh3egQWm/HnuWYgIFwjoHBaR+pMwP5U1ns0P+HEsUZwaWNsMOJ+HjFZFYC0EFS7fgZd71BjX1wP18nGnGm7DXM5ynVh2GSeDdT9lHjfN3DZVXdc7pnuXUUYjgfPObeP99jsqLX3x0h6yXMbg3lF/XlN7m8DKreF8/Yc8ziybjNw4mbs7akuV1QzrCM9I/V+TN1rcOm2ShB1GPHO5HTJLbG8Jc3XUnkt1/3pjfF6tca9pbmCqZS9DSCVWKX8qcirvT0GPzsRL8GU61gxRlSK+dBAqXtFLp8Bz4vV9iY8fQr3gun369TYHsvb1fcMnJvi3z+V5aKa4ro65XNR3fM6J+l/VXu66lvVT7NcUWOYzEg8rn4KZWRELaO6u43cnhtqrNMlSX9P0h9+tXTnR0lX3yn9/amsd6uxUWRg7JU7bT4jKI1IlstjguybPWAbRRpfpuV9Nel1rwMTLu8ltfeRveEvt+uXkSb3ELGtKiYlwbLz9xJEVYa8xnmWY5W/CSBZlgOtjnWnx3LYAeB85nzlcg8ZPfblrrrRPa/+4kiKAYvn6L76y3m9H8Xjx30szXWQf/NTEY7x6WRtadUoH6o5JIfq+1VyzGVfOx9f39a8P7YQns5eiuPvqjtPj6oBnWeqbwbeUXNq9tVPaFk21NpuV42luqIG3qS2YfqNki79UgNz96iN7w21Z4jtqF2/qvl+PI+Nc2og50H1Bx1+vDrzTF1L9orz32lKq6+fcVipZgndPgae0nzcUU4E/JxtZB7KLQF6OJCqdfxUpomyNyMsFQM9+tF+oOM8Ni5pSN0gUomt87RpSDId1qdiiQj6qPz80K0baorjU9QoeEn66emaWZ171F/uybcxcz3evzfVAM/z1ZTSXepHYbemNO+ewl1SU5TeL+Cy0WuvmLUEn2mcaQQP1JTvtpqy+98kbbyz99mO+l4NpsHv7qt89QTLyP5jnxH8HKkDEHvZLKv3kzDfTN/twXHusAahO1N5DXI/XtLzJH2v5idrWN6sO+eBr4/23TDMluZ1cpmZZgUEvAGVdWQclzPzZJmYHpeKpL7RlkbMcW6oGdIDNcObbBaBoMMnG3hjSs8gyIYv28p1SmeKgN1yEN89X7lvxmU9P6W/jzZIyWVV14/lYN0dp3L6XGaPMU3pGmgfqM31R9RPadkR4h6iHfU2u6Lebpen+C9V0xNXpvC3q+/POa++9Oiy3aWmrwyIzEA9rKbDriOe9Q71rAFkVX/FdepWlsHpnVfTb3aghDB0fk4Me1SD5ExuDdBjpUdDkBM3GR9pPkgZbiuuJSvj61wuGO1VSIVBg0Vv2AqSYaXxsltVbk6gbfVlKYMlp31eXTk+XdIXqHlEz5L0CfdL+qh/Lelt0l/7Gr3yf5Y+9py0ea15Xg+rL81cQrrn1dkM09YvbKloT9KHbUlPgxZ/lqQfmb57WeMx9fbmBku2f4K7bKscC9IqjX+7mvfH5QYDm5EnngxhGgp6x2RvkqHYU2u3Z0l6uaQP+1DpDe+Wvkf9HUMsBwFF5p3MpsdbMgJvVz+6m8b2mVM6b1XvPwMjt43H+jqWoioPwUjuo+FcY/mZdi4bJ5taLRWRLXEZXAdeO6fu7R+oL9e4nzxvCO7dbmRxnLYNvw2pdQM33lP3jAyd8+XYdLsYsLCNLqizHlI/Hl85BFL9wD0yadne0ry/jhDH9w3ynK5BzmV1NkxqJznfoQ44Oaauqy9x70n67eqszLOmaz+tvgfH7Xx++nxsSvOGmlP1qLqO8vKa9YCXn0bv3LJw/JE5SxtCwENdS/0szfti5HSfyclLRUDcEmLPdEQvM5xlI65ZAfJ+eoH2XKv06DltRLwD1UtqCZTWsUi51MAlLStIKijXxxP/gpr39WY1lucTXi3po/6dpM+R9D9K/9M/18t+m/Tvr7WyXlQ3igYkW/g7VFNOHynpRR/eFNHnf5T06cs/Jt24pN+4/Of69L/T4j79adLHqSk00+Q2RGTByJhkG1oJUbmQ5nd/2ruU+h4Lt7+XnPI4qfPmXo2UNKwup4X9yJMi5yX9aUkf9tsk/az0CXdLf2C6v6/5gx2rseW6ue4cS0KdvPfhkvpprqyby3tezUs+r97PKVT4zpfiuZAOBxlB9yH3VRxpdR4JcZiWZUOr4WmUk0WpFNl5dSP7HPU3em+qb7xPcMuy7KFOPA69h3C5hJngy2ODe4+28JuAx2JQta3Wtw9NZb9LzSl5NMq7gT/rCKZFJyz36VF/MS1fN5NypA4mnOae+lPiXe899b7nPrN9dVb50yR9xEe1PvjZ6dpLtqTfrLbPx+DF49zp7kz5XVJ/SaqX0lOvEvT5mgFh6tXDCMdPoQ78k/qR9+y7E2N2KE/cRuY7FovFKxaLxRc8eZX5wMr7xfQsFovzkr5Vzb4tJf0hNfv6verPiPvS5XL5a4vFYiHpmyV9vtp4+Yrlcvn61VTnYkWSisH3KOsAj++T5SEdTo+VA7tKj+xPeujpRWX+OUHIFtgzqpbwzI4cIqzLw2UTy6b6qa571BSmPvVlairHcqf0DdL2v2yK6gGUaUvzB/q5bS9M1978q5NRu/8LJP3NKacvlP7E9+tl3/Ileu20lmZgZiBCY2vgkwaObIoQJkGQxXHdn5c0NxoJZJmmyyjNx1N6bVSi/E0QZy/9Hpf/Rw4k/Trplxb6mDs6zU9map3Yy2T9hbi5xEOFfajW5xtq3vd5Sb9J0hd9lvTdP9n2X3EpjKwGgc+I2mfbME8bVo8hM3qjY/ojYJNzhctJlmx3qTOg56dy3y3pz3y42ka2A+m//WR7no7U+mNDfS9KskwGOodq+0duqC+P3TPl9cvqTNCIUXB/7SjtxyrzlOINulfU35t3v+bPwbHk/h0DZ3/ntWThzqmD6201Z+VRNcfmUP01ElfV9YJ15e1TGs9SH4/n1V/euaE2Fs3ovFrSq/9zAz1frHb67VUHrU1fqqaH3qwGlHYR//JUjvPIh+1tdiiZFwJMtjEZ42TEpNX+JBu2pTa2Htb82UEWOqdPMXlsuVy+/KQL8f7I+9sn3yzpXy+Xy49R21rwS5K+TtK/WS6X90r6N9NvSfo8tTl7rxrz//dvJoORF+cByCWjrfid4Z3eOmBCGTED6SlkeY/irwrna5xgIwqfaecE3lC9XCF1T/VQfg7Gv1FbkLL8d+n1TZnvq9HTVoRU4qzvQ7j+bEnS79IKb/Bl7eO8Guoly2JF5b1D9sDo8WZbSPNlCKbjh81RgeWeGofN/SHJoNzQXJHyswJNabwsV/zlV6bW/NrORrkMCepGFDmNk8vLsVWVx9euqD8nSZo2sr9w9bUbG/FpIYNROQIVgHT43yfpm7fmr2eQ5mPJ5Wfa2e/Mq5rDBj4JNgy0vlySXiTpx5bST/yIPuQzmmG9ot6WBmfOk+3t3wbtBsiX1ZZQL6oZP+9d8VisxovnLpcLHZZskNvD5dpSU5ofjXjZBjmHUi+aqeHSncHax0zp20E6p6Yv9tVZqWvqy6cGb5emMH5v2auma5+jtsdMak7SvZJedq7de/UU9w89W/qC26QflfTFW9IfeVrrkx+Z8v7CqSzWLC85J33+01r+b5zS8BLbA2rLjJyvbmPvffMftxMk4D5QB+fWG0fqr31xPOuat2v+gMNs+9SdT7qkIfpA/D0F5H0GPYvF4g5JnyHpH0nScrm8sVwur6iN1++Ygn2HpC+avn+hpH+ybPKzks4vFotff1w+VBJJf1feOBUj+ykHI9Mf7as50Koyzb0LlmpMJFPj/FjeyqAcDeL4nsN6A51Bj8vrY51WUM/RRMk//UjS/6FGKv+spD+r//TyppTOq3vMVihkNuytXlBXDI3M+RdR6/PSj3av+JmaMxauAzcZcq+FJdk019eyGfc31d+nY+Vm4JbgZsQIbuIv80kwZEPD4+pSa+eH1J4ErWdIOr+QXiH9U/XNnwlUXF+CYI6napzQMI7kqjoAuyrpuyV9419rhibBUzX+ckybDaqWR1Iel3TjYF6/Kt1kcln/lGoZwW0zaoePuE3N3ZIk/TbprsY4GEwYMFVz1d9dx/O4tq/+fCupH3ffUmNm3Dd0xPaRlwGOVLMWHtePqu97eYYaA3JFvR84RysnynrB1yweP7tTmCvqRv+a+tOYOYbcP6k/36n+aIgNNWDzmqnML5za7T9da3X+4q0JEF2S9GXS73+n2vHSy42M+9IpnfvVAKXj//trku6WPv1cy/+y+qZq55tzhiDP4/2aasadczoZP2l+2sv3dyMvx2GfVID8TE5W3h+m56PUhu4/XiwWP79YLL51sVjsSnracrn8r1OYh9UIBKk5mg8g/jvVT1EPhSxGFpaK20Klmd4jv9NTPg7AppKv2KRkZ5JKTUkgkx7uiCGyEjtSMyw5yY7UPdJ9tQ2s96mBjx99SHrX4m9J3/ibpD/ym/Tmxev1FjWl8uYpnEETwRSZGa+lf+yHT2394h+U9A1qXf2z0k99tl75c9Knnpt74faAj9RPx1TtTpBD5ifbwu2whe9WaDvIx/HYb6m4yZLxmsuv4rq/W2y4nMePSPorB9J3PiZ9/VFrW++3cv9lnTJde5QE3zl+/ZcghmF9cuaa+n6IdyGP0ZKSEEYaAwPmJbU2+wFJ/5M6Q2BGpUqDc9th1hkK96fHPvPf0Nwr/8XHJX2fJP2OFvpyYwrsMOxOaXC5kXPK8iy1fVr3aN5PBkAG3QfqG2u9r4X1klY3K/uk5bbaXNxD/XfVgPKr1J7RdKfm+3LyuTUe+7ns6XsGYg63r6aUPe8eVx/zqVdzg3cCTeufRxHundPv2yV90jMkfan0sb9N0mdPFX7tlNDnSR/zGdLiK9pJ0wtqY9ZlO5L0Uz8v/fC1npfrxDpK82Xbag9eLmNtI1w17shUu5+dr8tg/eux6zjbOiE5ezjhUN6fPT2/Tg2c/4nlcvmaxWLxzepLWZKk5XK5XCwWy/cm0cVi8XK15S8ttGoUaOhSQVMJemDTg06mSOrUb4IOfk/j7HSrjcrV8kQCF3pk9NCy7GmIOEn9m5Pak+/KFM/Mx0NqntOdkn5Q0sW/2OJOzpN+RKt7GFgHMjNX1Aznm3+1eWI/8XPSsxd/Sb/h0/6S9NPST0yN9ei19iwPn9jKFw463cxvQ/O+svLmfqbNIqzbgp5eNUfTI5bmbKKVOw3UuvlOFuZA81de3K+2n8b7TmwQmR6ZBLc/mUcqc5fRy3csA7+7vRXh9jVnCZJNYztYOI/YJyznqG0MPHO+WBKwZH1y/uZ9978NsAHidfUNy98v6eKrpY942Q9LN6Q3/1vp36nv0XlMvb+rvXSWS2rs3cNaHSNe7tmffp/XnNUxm+I6c966Dcz8GIz5txmkP6nGvnzrFH53uvc48iBA93xzWiyny3JhundZDZhsRRo7mjsO7IM9zZ+QbAN/TnOd9PwL0sOXp/dhXZL0JWrHtB5SAz5f9BulK/+p0UO/WdId0v63t/pcVGOhf1z9NGaOfQNq4ZNOIHUHxwhBLfuDOrvqsx315xJdxu8r6g6h43C+nog8RUDKB1reH9DzTknvXC6Xr5l+f78a6HlksVj8+uVy+V+n5atfme4/qP4QT6mxtQ9mosvl8hWSXiFJmwGYKqBjScXM65acMIrfuRmNil3Im5udK4W9jq2pDK7zTsqfeWZ8Ggxfe5b6006tdK+pv7fnITVl9flb0qMHDey8Vv2dNdfUFePt6orwQPM3qG+qKf/Lagr+NZKuv7q/vHRHzbDsqm+EfCjqZM+aINa/EyTymSWVuD0cd8TcHRafVI5SBwRHcS9ZpypdP8vFS4N7au3zoObGjPna47asAx3poVIIjphO0v7u33QA3lv9mIAnwb6BQcXSHcesco4cDa5XDKivX1dnF/Yl/R1JWz/e7l2d0vQeq1yWcDrn1Mbvw+pM2Wtw3/1oh2BL/W3jl9Xa9HnT94emsLtTuH3NdcSR5k9Pv2eKc1UdRLxafW5xs/COGiBwXBpnl+s8wmyq64m3os32NH/3HkEZHX0CfIPNHZSLjswdknRjAv2foYZgvmhb+r4b0nfvS/rQlslXSPqKvyH94T8nfevtuv373iU9JB29UVrcLV14qC+xXZ7S3lM/4XVF86XCHHPptFCnW8cbZFqPpHNk1u6qmiOzgTI8jDakY1OtCJzJycv7vLy1XC4flvTAYrF4znTpt6ix+D8k6Q9O1/6g+qaPH5L0BxZNXqK2C/y/6iYlqdaUZHYqqeIluKgYlgrE0JAowlkqqt7GoCqHlWjuJRoZWhqeXfVTHpfUn4S6oabc3q6uLP7CQdtFfkFNMb5NzTBbwT6u/hyMu6c8rLDNJL1Fjcl481Tm37jRX3z6GnV26aq6Ysy2sCG3sqTCopKtwArbLYFkdZKm+p5heI/s2mb8zrBUqlzm2lc/3eP+5V4pqW+4zWUfeq+Hmo83slIVE8Ly5/iuwo3AfYaxJIhPhpIGMYVlryTBJ+uRy9lsf7cTmbMr6kuKnoue11zOquaaGRbPdfeTmQWOEzoGdEa8tHdB/T1zB+pHrx3f+e2q7b/zPbMnW2rzymDKfb6hxoY8E/UU7j9Nfd7tTGHPqQM+99ENdcDDvXDS/OAEf7t8Nvbu6zvUN3Q/Lum/vXu6cdcU6BtvSH9Neg/geY/82ba352++qzXWuVbuqw91cLOr/rww6yP2x4bmY4ZjgTo9Ab9ZWLeJGTBvJL8wXX/XlObFqTpXp7p6j8aV6f6d0U57OgFZam7MPlB/TwF5fx9O+CckfddisdhWs3FfqTaOvm+xWHyVGij23rQfVTuu/jY1ffCV701GVH5Jy5MlSXYlAYxwPZWqinAMS+OVip8K1ZIGMunOzQiXbA/L5e9cIzalvaM2Ad+qzvAcqK2H76lN0stqnfGQmuK4rrZPwJtrd9T3m5hVuaj+HA4blNumcj4whX3WFO/PHTWA9HT1R9bvqL+FfX8Kd7uaYnyHOmAx0FrHolDIdmV7ccNh9hG/M40EQe7HQ803KlfpsXzsGx+DNvNAry/ZI2kV6JLV2dLquFd8t4fK6+vCs33olWYZmFaCDcZfx2ZK8/oRrGcaBFBZJ84xxmUbuK3c/vta3efCEzoGIU6PSyjJGlTAL8EoGcmHpt/nNWe+njHdf6f6GDNz9JA6c2JAsaf2BPQr6qzTkdrcfJfa3NlVf32G5d4pvSu4tq3Gvl7GtZxDm1rdgMs29O/rCH+o/j4un/A0gJLU1tX/8lTYp/8JlfKVkr5J0t3Sje9pafCU56HmD2f03igv711FuGQiuXxIsMz56TgGu2avyKyZ5d9VO5lzn+bvFTM49v6pxzXvkzM5eXm/QM9yuXyD2gb7lN9ShF1K+h/f2zzWeZ3VEpMHLtfnUwlXxjWNEo2TFZ6/c88F9wJU6VXgOMtSAZzKuybjcKQ+2ayMHtKc6fIekx31l/lJXQnerT6pr6h7/Fa4VJgHU377apP7LjVa9z51cHNJnfq9c4rnpTWX216lw1zSvD9JLVMqkJJCVqYKWzFHCYCsGN0WHAOOk8A3x4q0ukfAS3SUit2p2EKLFfQIHOb1m3HMkl2rAKXrsBXhWS7mxzlBh6ACKZmGEH7kmTOsx6r7bAdhzPAYTEhtHvgpyluRlgFPLnPz95HqsmZfEshcU99jtaluBG9DHBt4L6FIzen4Kkkf8qHS33t3cxb++Jb0CwftlQuPIh/3D/ftvENt/u6ptydZL7KSQhjuw2KfUUca8NymzsTsqzlD3HN43b9/q5pb/ExJv/h3pY/9O1qR75kK95C0/XnSwb+ab0o+VGOk+dtMVTJwBGRSBzvsP477LYSzEKjvqQGuxzQHYmbldqe2uKT55n061k+6jBTlB7ncEq+hSK81AUpS4KSy03vPdVaOCypXe2bplaYS5iSqFHT13flXZfA9LhE4b3uRh+rKzVQ9jRGNjttkX43R8eZmTWk8pr4GvYu0zB5d1ZztOFL3OE2ZX1dTRrtqT6k0Fe8ysH70epwvGTDuG0rAkmkl2GRfH8c8JODZxjVplU2oGJAsG39X6WVYGvVkHTcG9zK9qk6VJABK8Me0czwny8G80gnxd+4dGrFL+b1yFEbzxOWncbNR31Mb5x7LTM9ePPvDANfhksFgnlV5LZwnh+rLXefUAP7DUz73qNn+V2n+QlOfajyvdkJkX+3g2eG72/6gG5L+4UFjSz9tivdG1Ml7elzuF6iBIxv8u9SA0hWN62bZ0eq4NqjgWCQAkpo+2VPXUR9yt3TpIfU3kn7tR0hf/ivSP32z2hOCLN/cdix/x7b0+TekB1o+H3KHtP1Yq4NPlrkcPlyR4v6kg2CdWjGIbAvO2z315XkD6h31wwpeNpT6ni6zXGbG9gdlfMLl7IWjQzn1oCeVLRVreljJ6tj4p9FIJc98ElQ5PBmVCuSMlsXy3jqgw+uVUnVah+pUroGRPaIEEU7ruvrTiT9SbTK+RXNg57oZSDmdG+r1Nxh0GZyfFe5jUz6Pq3vNlmQzDuKePfBUwGTtGJ/ppCFzOgRe2fcJfBi3Yl3yWlUueppmEq5rXlcyhUdaBdbMq0qb4zsBwmhJKsOOAJKvG4COvFTGzbR8imdfq3OAXnWW+yjCcE7kuNnUvL50drxnheNvfyqXDfJBpHuIdIS0pPlcpaPFubOpOYMideDgUz27anPvITXDebfaO6j+Bcr7AvV3bT2gBoA+TW2f3Jba/oBXqR2Aeqba8e4H1VidLbUlrc/ckL7zqJ2eNNi5X/PnhaS+4byg82GdQvDAvjDgsqNksLmjpgc++tEpnX8rbb5a0u/8lVbIr3luK/zvvSD9zcutQi+S9H/e0NV/1dtPhz0Pyr7mLNCuGqBLhkqa9y9BuMWO5ba67uOYNNh5gVoxv1TSh90n6bn/g/Qr/1H6H6R/+EjbkvSwWn8/oL794HGdyWmSUw96UlmPrnGp6bilJU5menkVg+TwpE6ZZyr8VN4jzzuVdlVHGmN7HVw2kOYsD/O7ob7/xg9Qcz0fVp/Mrqe9k6uRjg0E187dboeaP5DtktqG6aznQcSxOG3nRYbO90zb09BL87QcP5kfGthkMRIESavpbeN3MjQur9vGZeQ4qsAJvcMDfFJRJ0uV17Icvp+gYCSsewIOS+4PqsAKy0AGwPsakhHNvkunI5fBCOA5HzxOthGHy0IEjj7Svaf5/gwb5spx4dgaOSHpJHG8mjFl/Q7UT/44zXPqr5R4gRoQerba83huqDFCz5P09I+Uvvhuvef9Kp/yS82o2pF5HvJ6WNKDR+23gQGXabLvzTC5Lqw7dSmPrlM8lg/VH0i6q76U/obJ4l+UtHlBbW+P0clDku64LL1uSuhHWgPsbkn/5aDdfuzd8zK4D6W5fr6CMh0hDOudupa6hmE5jqW+afoX1EDoh71J0nO/UtK3SR/xXdKbvlyfcLFVQxo7O0+6VAbwTE5uufFm5WjwSU+MYXktmQbG5/f0aEe0LxkeXhuxiMybCoXsAhWx07NCt/ch9aO2h4jHycprNhD76vQqwce9al6my/e4+tNXr6uvSRPsMFwyJgdqesyMENuJBoFlNbNDr97s0p7qFyO6HaX5MdOKuTtOqn5jvxypLwtY2TpMxQAk4HFYtzEVrpcNN/Dn8Peq78FgHslMjGQd0GB6NH7VMuRhxNvUPO10CrLPHSYNisOko1CVXZqzYpRttT0u36D2WgmXkQ4NHRuDqVwKrvJM1of3c/wRVLstyboK96X5oxouqxnS29WdiTeqLUN/0WdJn39OeuYnqqGffybp77YEnvkc6dOfK73s41obvG2Ke2HK5zXqANDA3ACRgNJj2/VKUJdjeVs1w8Ywm+pL3maZLqqBtDf8qqTvkf7Tv1SjTA7VENujkr5PesOrpRvfLv3UQWfGnoE2P6/V8US9R+fJe72yHyqQ6vqfU99nRQA0YU0daXrmyvMk6dumEL9P+vANvei2Fuf2KTxB1qk3sh9kcur7IwtIdE6FzKUAD37TrRnfE2RLcyV9FPErdmdkWKkQqWyrulRepOvgycVTSEfqQEQIa4/G+dFDqYyNlcFlNc/IQMoMGb1TKzOuoR9O5dhHmlem689XU3CPI+6B+sMJ6VFa3A9ml5yfGS0/pbYClVRKm/G96qOqPRL4sN/JLKSSp0HYKOJSsR7F/TQkFQ1PsDQaQ6TshbAVEKNs4o/jb904HzkEnCtuF/6lw5Dzlel57FcOBcGz07w4/SUT6HgX1Y5/P18NGHj8GigbrFPYHuzjdJzWMVZOYwufW3Hdf142OVSbe89UOw15QWoo4Q+ov1b8k9R2Nd8zVexl7fp5NQfmotpzstxet0e5k3kji5lLdFlHgx32GYHyRfWj5AeSPn2jLck9IukN6q/RuCrpBw7a4QddlK7+kPSdz1KjSM61/UffP5Xho7caa/VqteW7l6qxYe4XgymXi/rTfW3dz7HpOeG6plOXwHdTrT8c7gH533eJ8suPtzhX1VhvMrQnQricPZF5KKce9FSeWBqcw7jnCWnjmcbFYOE46nEjvq9bNuO99Hz9mdS9P6vxtB3h0oOuykiWgvsKrAhc56vqr2wgi8Hy+DsBFpWoPWYrjreogSkvqVERMS3mU306bbMsacwSDFBJkZ1he1ASRG1G2Gpec1O4UKdst2o8HsZ9AvJ71D1S99n96kBSmhshAmuPpRyjCQArcGHJsThibAjiHD7jVs4JgUyCtWRyMq1kkbI9L6jhgb+ktv+V8+i8mlH8Y5J+p9pjFO7UfLOy68t4aaAI4Dg2CJg8Jt0efGyBQZbruaN+2sftsj/Ffb765tffeE7S31Z7c+fdkt58IF1aSg8upVf97TZB/qykPy19xPQ6mHvUj9EeqM8DA6xdzfsk52O292Zct55gOzxd/ZEWUnvj9AVJP3zUwIrUnJ771FauLqo9hPlA0v/xNukn1LDbd1yW/t5jrf4vndrwuw/aXLg0pf8atb1M96jtnXmXmg47r84A2eEa6XfOiYrVuq7uWG5r/iiDQzWC54okfbKkd325pI+VdIf0u4/0KjVQxjY1u3Zikl7IB+LvKSCnfk+PZWTcbQx4nd739SK80+OmRVKdDFsZTguVWuWRj7x0x5XmStIK9DDuc1Nxla7jVkbpPUdGIT5efnlNPNZ5tBnY95LZ8NKZn9AsxK/Ky/sEeyyPmaIErwSTTs9gLPMjc1MxTx4DG8XvHGtOk+OE44ngl+PpAJ9XtAqcWC+mmYwGx16yTU6Dn0w/hf1no3CA76nvCB4qFoThR+OV/XCo1XKSOXM6lDcibxt5AsqLkj7kmvT0t0rnPr5d9+b63G/j+Z/5j5yMbMccb2QcH4/fF9Rfzrmpvoz7oNoqzyfdoWb536KGHH7g1zRX039K+rF/IX3NT7aJ9pulO7+vzbUddXaUjs+u5vOQbXoDYXY0f29Wgng7DLerOyZSf5DjA5ovqZNBeniq9z1q4OFjt6TXHjRm57dK+o13S//+oQZsHlYHDM77F9T1+VU1MLUxhfOS1Dk1IOSxwD5N59P14jhwX1j3OP7+lOY71ADWdz4i3XuH9JIPvU//6d2NqNqa6veg5huXnyI44Sklpx700FBI6wHMhlYnq7RqzGgoGZcGdlSWyhPkvVTOeZ+K2ffOTd9zWcrXbkZocCncaHhO/WSIj/Zeme4T7FTsR4Kho4i3q/my16b68tltmgMnpk0Gicar2mdlI0EQyDQJmqyQKxB1Q6sKT1rtuwQeFvdzsntMg8sWBCzM44pWJcEVw+c8GCnUBHvJBjEvaX56jPEYhuGczjpQn2ESyLhcCSiO4neWNRkn6gIbWm/w1XPbF88hg5vsd4JmAjGWgWWnQbdU889zznGEz7sinJeQHn5MuusX1GiT50j93e6Un5C0aJn9eAcqfrL3phro83NjdjR/Dpc3fR+o64fUM3QAs14e+2ZGvBfm/JSfAdau5q9fkRpYfYukawct7AvVwIQeamzOgfqePi+5Wy9YLqjPpSuaPzeHepzsXY5Vjjtvg3AbOX0fibdOegjfXy/pgXfP2/UtyNvPDko99qTJ2ZH1oZx60OMBlcsPNiKpuKsBVlGZI3AizQdqGh0aN1PU9iAzrZEStyF1WrnJN4WTeB2ochipn1bhyS3vxzHFfkVzZZwsBk9sVAbRR5MP1U5tHU35viPKYm/XcUfefVUf94WN2f6UZx6JTvbG/cxnG3EfA/NMI0gDyLIorluZV+PL+VZsQbXEktcryb4nI0FmJ4FOAnVKxq/CbAyuH8b3EZBkmTIfG6lskywLDRhZKefBJZlH1cbHN7+jXbui+VvlmTfrkeO8YtD4m+FtpDl2zBYQ2FsIkrbUx8uW1AfqwxqLN8zdLe1dbl8J0J03y+76cF5fH3zP+W4Wif2wrTYH/X69RzVnzbwBeE8NEPn+FUmvVFse+3g1tuf1mj89e1fzdwi6btvqDzx1Gc9PcewQSX3MGQgK16W5TTFA8bUrU1p76nuh/B6za9O9O9SWJO9T032XI3+OR7JsZ3LycupBz2iJKalkae6RV96rw48oc0rF2lRlq5RwppGsCKl4A5lUUFSc9EDTCCSD5InuB3lZbKCtuK5rdX9LeuZZNwIVMiZSUxDXNAdSboNk0vjEY+F+GiR6mgZvDu9yUNFQmJ/Lu87xSQBkRU82QZov7bA/kgEYgQgul/H+iCmsmM0cTyxDjodDreYn3KsYPgrLlemM4qa37WuZniJM1e+WLVyvgKT7yUbzAfVlJKft/L03JZeWs06M53vUR9WYuR7huUy4oc4ePKwGzG6frt853bsgNYbnRWpoQh+vxo9Afm7RwuxIep60/aaen8EODyZ4vnAOeP6mA5asyJ2asxZ3q4GVN6ifUrpz+n6fukOlqV5mlPama26fO9VAkF+kerv6Efs7pjBcBrRuv6H+mIzzast6D6g/A+lQ/aWkPAF6pDkTRtbPYW+os0yPqOtIb4R3eVyXf6z+PjPvldxUf83PNaR5InLG9JRyIszbeysjj1zqRrXa+2BjUXmOlIqWr7zbyvP3dXv8qdArLy+XXJLxGI3VkaL1J9PzJ4FP5X2O2nQE+mhwNvFnj8xGp1oSOkA8gtIEKA4vzZkU5k2GSvjOY7WUNGa+lu/Wqtq+AjAjgFIBarKCZCsyPX/yO8d19ktl/H29GispCZoquRnwz3nhcnpOJvBnuvyrGBgV3xNoVmk6v+qxC0dII8cRv1dtdqB5vSw2yga9BlZ5GvJAnZ30dT7Lxc/tefSH1BDE75T0sv8o/ddFz+zfLNom5s+R9Brp0e9r4OFuzZfSXJerasbay0w8EWlgT33pdvLJMrfZ7WqA7LLaO/6uI94l9SVnt4PbyEtcb1F/FYiX2D9B0u9+bju19na051vUQJXLc4g/75c5VD+ttjnV8/7p86I68ybN2TfqHtbzghpYsW42ULmhhj131U+oPTDVZU8NvL5V8/dtPap+2paA7UxOh5x6pqeioaU+kMkW+DrFk+ZmlHfmxzxHwCmXFvxZedFkCehpOg8yQJyo3IOS9WPc3DfDcpvdkfrb1b3nJuvh34xfbQpkHUlr5/LhaKnFAIWMUTJZvpbsURo858lypkee4MbMV3XPXvsIgFZjKY0l295j8DgwWd0nAKyABEGH73mseFyYtTqIuMkMkclIZkUR57jy53xwHXivquOO+v6wTM/9luxYBZy4by7zS0ckWZB1feT4dCwIYjy3rXuE7x4jjrun1ic/PV1/9lT3O79X7RXNe2qnhQ4m4HNO0kskfbWka/3ZWm9VZyJ21Ywx54Lrz2cIGSyYBabecrnc3o7n/JyW86g2gkvz19gcqr8I9rza5uTbfqk/efpRdSB2fkrzcfX3e/GJyXbarNcO1ffXuE4eFwYjBK1mijYQ/97p95s0P223r77/zsteZrUNhh6d0iEoI/h90sVvWT+TFTn1oGfEnli4ryI9Y4avFBbvjej3NCgplWGgx7uh+ZISjeI69rECLrmEIPUXWVK503Ar4hypTWLuWzqM+4rPkRGW+kbd8+reNZVnGtgElM7b9WC4CtwkmMxyZZ4EizQ4Un+L/P2DtB1vVJ8KJErr265iEHPs8B6NM/cdjMR7KBzvcbX9LGzbirnKNjuurBzfZPAsySLxhGQl7Bvvgcj8PS6SURs5G+kIZf/zetarEteX7UOAybHs8GQWmK4BqOv6kWpvXX9ouvfMB6Qbv9rC7H64GvXxqKTfIV39Hmn3Qov8DnWG51615SIy39RPI2fMepOOmZnbXfU5TidJ6gCce/YO1EDKEcJXTstVtc3cd035evk6nQ2DymrOHRTXvITlMrBfDqfrjyPseYS5T92ebKotwb0LdfMzxKzP7UjwSdEc3ye2idmyzsB8EMupBz0UKjrKoWrFzN85+J1eNS4MitJTl7rhS0bB+XCi5bFpaRVskL1JZUThPXsuplA3Iq9RfLeR16Dz9RQjgJEgMJepTNsz32RpyHJJvQ3ZFvQ4R8qCbc1+oWKSVjdeSv34v4HoZa2eOKMX7msjwJ0A2b/tJfrUSY7bNH78ZB3zOvupcgakfvT4G9Q8/29GvV2GZEiY1yjdZIDSiaBkG3nM5FhyntU8SdYmjV62pdPhuGNeucfP4czSJHBKkJvtJvXxaoOXe0g8xjknzqsZ0iP1d299v6YH9kn6RLUdPN/xq+33b5e0+3TpDc9ocZ/5adJb3yHdd7nl93Fqe2xeo8ac7Kk9C+f71QHVBc1fAEywdqj+ED9v0tWUpvfs+ITWpuZvqM+lVzo1FbN3m9qRdTNJ19VAzx2ab/w387utOVhOxsRH1N1Xnsvbmi/h3z7lcQlp7U7x71Zzel4m6fm/qxXs4W+XvnuKc4e6U2T94L07e2psj5fZhHCW3Kt5Jicvpx70pLKrFGQKJ6QlgZE0N1YZNiW9wvT6mWbGzxNF+X2UZ4IXKluKFXyyGdVymNRf/3Acm1K1I8GAy2Kq+Ko6fb3O0HvzMb1Pp5kMAZUowZPLlYCN5WIa9GSZXirS9Egrpq+qH8UncpKxYPkIBByHUhn5BABkKBKY7n6WtPuwtPlLc1CcLE8FXpJ58njKtqkkr4/CJZNGcOlyJXBel+bIsJC98O80UMmMVgwpv7vvEmTZ6zfYJuBy2z5N/Xj3FbW9LF72uaz2oMV71LYv/4Kk75B01xubcX5I0mtf3Yz33WrPu7mmdhLqMfV5+ID6E54fUjPwj6m/W89zx8s3dBQ+Zor/y1Odzk/lu6z5GE2njyy225qsyoYawLlX0ks+Q/q1f9veMfbdU5i71F+IzPHNOZ9slcdMBWSP1B/R8fCU9jPUwNwvqO/leVxtz/jz/7LaC1Dvke66V/ryPy/9EzXAaIC2H/mQMffc8qoDw54I4XJ2ZH0opx70pCJMhTUyChxs6YX4WmVoaDiYP/NMY8V9FlxuYnm2Ig2KJzc31Vb7e3zdnkxF01cMFdOxJ1VJgqx1+6QSUHh93+1BBiaZHu7hSbESyZNduURAcFO1pz93tLpEdRjfK2aFmzudB0Ei4yXb4+eXCPcJILmPyG1GoSF2eAKWHKO8dnVK7xt/st5PkB66y8d0RoBuHdCjU1Lt/5Lm46ByGvJ7xQw5Tc9D5pmsj78zr+zvamyzvzm+CMCtTzg+3EeHEcdg5JLa3ps7p9/3q71/03vLbkzXHlI7nHWvmpE+VN8E/Dw1MHNdbe+JT7Wbgdif0thSM9gPqAGrTTVg8SDK66WgAzXDvi/pzersyZHqE1Rumy3NmRa3jSXvSdJLPlvSK1+mD3vRK7X/ulaXV2u+J7FigaW5HjtU3wO0G/E9xq5pPhYfUQcjZreuSvr0D1dDkZ+5bBe/fKGPeJ2084Or7yKU+qblp6u1/xX1sWrdlw7dU0juWCwWr5D0w8vl8odPujDvi5x60DMCLJbDNfdGXqfTS3qW+XECOcy69G0kDXicphVMGprKMyX44bXN4jufP5Nh1020I803tVZAIPPK9NOopbfFjajOY116aaBswLhkkfUaMQgugz+dlsGi8xm904ubLp3GzTALCZiptFOJG3Ryycf1tPI+h99mxXKJiXmzDffx3YanYjpZZvYPWSH2bcVYMZz7rAqXwjFRgWjGyzkpreoAlo8AuQLLBEXeIOvrydSs6+d1SxeHan1oo3lN/Sj2dbXlLO9/O6e+BGmH5jVT2p+lxvzcrzavPuFDpTe8W/p3U9w7pnDuZy9Z31ADRdQ/Bjxe2rmk3mdmLaR5Oyfg8LUb6qxugsjsU87rn/px6TNf/Er94utaGvep9cVFdXaJ85J7/TheWLarESe/O01f99K+GZxZ5aX+ICCtso6W6+qbl3Ppfp1D9qTKOiX5vstjy+Xy5U9Iyk+SnHrQk5uOpVVKvlJuDpcDL8ELJ2fF/KSh96TOcpDerJaMaPhoACuDzrJ4X4hUP1ckjbTLkiwGAYS9f1OxyeiYITFgIUBIpk2at7+9Vm56ZJtJc0NKg0UmymAol9eYBpefnBaXHbJNnS5P1lQsT463Kt+UzCuBOH97TCYbwLhue7YhN25mORj2UM37tELOMiVzkqxVVQdpPj7IUHmOpnHguMw2o/FifuzTqu95n+ClCrej5sB7ucGvLkgAVY1txf3jjJfrv6X+2gezA+enMM7/HjWDe1kdcCQo3VUDJT80pfmVaqzCX3l3M9i3TWEO1JZovJRKZ21L/cXCUmN6DpBftp3LOwI61gXcFE89w/FMhtGA7uHp3kM/10HX+amMZJ+9/H2EsiewotOXTDrrwE3lvqcpvBmbH3hc+uL7JH3nonXMW6Vf/FfHP2OHAC0/WYaTkpPO/7TKqQc9lTGhEVnnjUqrxrYCTvRSc6mCkzrLUi2RZZh1yrICLJXx2SzCZX75vJkqX264NHtAUMi0qqWtBFK+dqT5EeNrmhvrZE5GHlAqWzIh9Pa3cD1pZI+Fqq0IMK00dyIsvydIYH8nE1EZ32ppplraIS3u37lUkwzKOuGSYDV+2W4JuNxG1dO4K7aI19k/2f6uRzJUFUs7AhnHLc0xnS31PRx3qz3xV+pPD1aRR8V4Ol/3CR2FZCgNCG5M956vRho8qv6OqE21Ja7dKa7T8fy5e/p8x5Tu7nT/X6jN2dun649PaT5HDTy8S/NlOWl6GbgasPARa6k/1/D10RbpJPG6heDEbeIH+zEc57jjXFdjrOzQ+Gj6/WpLT1yiZHmyvXN5PtlK7rVyf+5oXp5Hp2vPkvQ2Sa/829LLPlLSBenf/Xx7hMA9mu/lIeu8qQaIzDJVTq7b4Wbn7Zk8OXLqQU+1rFEBGzIyCYxoMJJ6T7C0UdxjeGlO/6chcRppWBw/DaS9J8W1lMp4cWKnAqfy3ozwiuuVx1JJlT83Rdpwsw9GyjPTq5gf9xvBj8udLEDVFhWYJNtDJbmPcCOgJtVGvQI2vD5ihxj/IMKRNTlufOdvSgIWMx0V4D6ODd/UvHyVE8DrDMtl4swn982wzCMGKh0fxX0f4/6DL5f0x6Srn9iWe8xyZL2YX+VA5ZKKNH+Z6Jb6aZ4drT6d/LrmT0DeV99XInVH5C3Iy5vhd9SYnktqe4AIhF2n25AG67Wlvsfs4pT2JXWjfaQOAJJJJmthBtfpV/Pauo3t574xMCJQ2lfbb5SODPu9YkwrJ9BLwnbsmAYdJ8fxG9nfMZXlNZJ++h3S3e9o9zbVmCnqA6dtIPbglNa2VvcvkZE8jiV8IuRsH/NYTj3oqbzTUZhD1QaBBqTaICfNqVJesxxo9cWZXILKEwZZVk7mEWMzWkbK8qxjSfydoGZkoNcxSFW6WXZ7UASAPC7KslZ1H3na/H2k7u0eqD/DoxoLlbGk0qrCXo9rFcuRe5gq4KEIUxniHA+j+zYcZGz4m4r1OLBegf8jrb6xnoAmDVoam0yb97mkxjplXzPuaByQEUtmrFoa21Tb4+INrBuSlq+QFg83xsXjkpvtkxHIMo7mhsuyqw52vBRj0HO/OlvjtiGjRwbBp5+8nO06er/RJc33ybkMt6kzSNxfQpC+p77EJ6R/Dul4fw7347lvKobFbUj2bS/KxvZjnTS11z2SXizpx9SBWPYr594WwnAuVHM5l0FZZs/xHfUHGm6obRy/qM6A+bQbx4D7Spoze5aR03Ump0dOPeipDNZICdIjT4XsSZLLYpYK8HgAm8U40rw8yfRkGdNjtAIh8NrGvQpgVECDkypZIsclHcx4B4hzNeJVTE4FtFgG1yX7iMChKl9Vt2qp49xUj7vU9wTwJEUVN1muZG0O43NUPo6J0dIojWiOrRxjmUayFb6WSzjXi/gpZFmSFbEcx7ZwnFbLm9WSF/PINqfsqBmUS6gPy5DefTXHWB7OAdZnXx0APCbpL0va+aFW9itIc8QGVobKm5udT4JAn5i7c6rf5Sl/Hhhg27F9PZ6l/lJLMyrcf/JaNQZiT3PwZEfMrJNffmwQS/bVj6rw84R8zF1q8+wxdfCVzlel3zYR3vUY6VGLy7M7tdN9U713NX/W10h3EVSzL1iudGp3EFfqwJD5nVc7yi61Nn58+ruifqjAadgmXNV8f16yhDfjVD6Rchxz+8Eqp365kZ6mtGpEpNW9Ar5XdfpxywwERjeL2D3pOdloAI7ivhWbw3E9fKSEUxEdFz5BQMbnksJm/FZ8Zzp5z/1jRWvPqDJ+WeZ1R+dZjpdI+trntHf1SKvr/usGMcuSDyusjByNvR+O5vFFhV4xKGyXo8G1dZKgyn1ib9/31qXpsViN2aMIQ1CbS0V+jknFmjHOkeZlyIMHrIuNRBpG5uH0OKdyLNqJoYPjT4LhK2pA+UH1PSNpEBP4bha/uRnW48GypwbmDtSWiXxNqC+Xdcg60JiSmbExdpzrkj5f0gs1X57bVAcqDk/dYCZnP/K+inR2pmvcZH2b6jGdOs732cfu3zT2Odet9w4iLMcO20Va7W+C/EPN5y7Hc7J6+RiH6+pg/LoaGHuX+tKiGSymzz5wu7C+ZPZPQry89YH+eyrIqWd6pJrKlvrAykFXASRpdQLzem44SwNP4JAeusUKvwJhzCsnebIOzJuGasQGsczpCVdpelMfqds8iZPpezmkKoPUFUueuMoN1lX58/h4Koq3SvoPb2nr/wSQ3OvjOlb9ZqkMqOvGMCNgRGXK8JYKcCYQpYHmdY6nBFQ+acLrrHemnWXlslnlxbOcNOhkRNhu3Mw7ArYuk9O5oX5aKZnaBFFVGu+NpHPAdqExyrHNshxGuB3N5Z7p2qXpz47Mo1r/zrYEe/lb6u3gJxjfpb6X5znqL/xkHtY5F9SBkxmhO9T7wGXwOL8yXbuoDhI31R5S+KAaC0OHgctzIz04msucW2bO9jTXGQaY1yO+x2bOIaZroS7iWGAZKya6YhUP1Z/kfKf6IyRc1mqMGdCdyemUWwL0SON1/HVhaZDpFWzE58hDHwGM6hiwNFcGvE9AZXp2K+JItTfE/QeUkQfBZa1K8R6qXh5ymkcaL5klAOT3Pa0+7oIArwI/aUirpR8fdf0nWm1fG3H2bwKzZLMq74vhXfdcp3e5nH+CjApEG6wI8TIs08v3qHkccpmjAnVsD1+rGNF1nhr7iEyktPqMo6uqx181PjIPlifboVpSG3nMudSUwKZKX5ovj1RgnGOWee7EdXv+BhcJiL2kwvpwP6B/J8j0fc/7R9SAx8Xp+lvVl/AScNymBiydhvO9jPCe46znkfoLSl0egzzu3eGeGMt1rR8L/O2wm2pA7PJUH6kv2yUQzvSyTY/Ul/YSpBOUV86E0/NJMgO8nSks92j594H6JnkulzlNst4GiSe1nHK2vFXLLQN6ct+EtIr4pfngH3kc9JK5z8GfDLuOWXHepHe3NFdGCaoqzyg3xlZLdQQKlfHKMhO4pHIdGX9p1cA57TSA+eygfPqp41eec7X8kkbL7UBKm88HYRwq8+yvkRef38k2UFkmo5KAOpe7Ko+XijDzr7xPjgWpg53sswR5HkfrHoWQ7e6xwX65S/3ZMvvqR58tBESVUq9AbeW0uF0qAJ7pVfH8PduSzgnHOhm1aiO347KN9jR/s7hPCNn7z/Ce/7tqzEnFjLB9aJQVaXnMX0dam+rPARLqQ0cmgXA6C5X+yKWjh6fvXubcn/LiMjbnjYU6agRcbqiBOe/zul8d8HD+uX4VoK10PsfSVvweObLe6/S4+nKgl/roeLFt99WBkh/SaJ1bHac/Ax+nS24Z0JPeayUELgdxPSd+Iv51A5MMkX+PwEflraW3Ls2Xi9JjrZY6quW3ZF9oDEjfZrwdfK8AQMU4HQcgMq2RcXa9Mk4uWfq6PWzT+fkSwvQMs18qALeOkeCek2q8bcVngp3R0lYyNGno3XdsK26Edb25rFGBVmnefyPmgvct19VfkfDnnibpWdLf/Jl2/UGtsmA2iNUxZpd/U81gVyxZztOqfDSCbGf3fzK4LEcygOmArGsTt5vBxTn1JyuncyOtjrtLUWfXhUabjkjm73lwfgp3Zbr2LPUXgR6qP6DQxtnxpfnhCzJjrnslCfR91N5gx/sPq7ar2n3UxhfUwMO+2jg5p3bCrppHXPbMrQO54dnh6QSkYzJyAK7he9VPBNCp11PP+tqonZ9oOTuyPpZbBvSkV8/rIzBUeZZVvGqiHaeo0sCvU6A5+NIQVksRzLvymCqFslFcSy/UE9VHNqu6ZDyLPXw+qt9lYbsw7ijtEUhhHWnQNtWXXSx+oeB19eduMB/mRTCXbFN63SMwYcllCqn3m9Ng+hyzbn96p07rIMLyt4/GGkwcFOGlVWAyAkge/xtx3x6s/qWkj5fu2mqeby557k7hHJ77w9yGPHWW/ZwOBNlSiw3cqD8qA85+pKOUY7IKm3vLdtTfiv7YdO2cOgjP8ZzAzJKAL8dn7o0i23ppuvfC6fvbp3TM9piFYBunk0HQkOVl+MqZcx/vT7/3tAo8HCfZ89E+Sd9z+pc1f6CpJR2KzSIMnVbO3dzvxzQNWjP926bPaxGHBxkoXI7b0uryXKXfz+Tk5dT3B70TyoHmYGT0nZ/S6n4CG54EGDYIVmSmS9d5LyM2alNzJbSOVcow9IYzHg2pverRn7RKx+fmwcO4n1KBGbfLdfxm2yS4ScWbaaficv/sII1zakswu5JeqvYSxguqPecRQKyW4vI5H5TKcPi6w1Px5rilgXQbZXm5F8AeNZUojct7wAnEBnnkhWe/erwbVB2ov1zxDS+UHtxqyxzvinqY4XmppL/+cW1z7W7Uk/lK8/bOtsm5mGNo3XhMUJdGv2ICWRfH9xOT3T9+MvKj6ksfh5ob5wSMOT+8PEXGlvd4IohLmVwm8Rh5m+Z7e65o/gZzlyPnvEEV5zbrXc1V1uG6OuDZ1ZyduYhwBkdkWSj5+4o6k+UTU5mv24lOBPs8l57IvjKtrBPHRY7LyhlLUO2wZNUN+p+m/lwkzt8nW85Ob43l1DM9SYVXTIhlNMAqoEPhJEqvKcO4LDQs/F6VJ41Qen6ZrsPQi6gmoNMl8yKtGoyKwRgZibzH8Pk04zQ6o4lRGeLj2JSMb5DgzaOHakr3c3+HpNdJ/9+HVlkU5pXtx3azEPyxzf09x5El2cY0ZBWblRvUmbfbZlfdgBhUXNWc8WGdhN/sTwqXhTKuWbMH1R4Yt6lm9B8rwkoNaOpZ0t6bVus5KkO1VJGsk6Vip5hGMivuo4qBqcrPseD+8HIRje4NzedmsgnMK9mnZFv8nUvfBjjJVG6qb9J91pTm/ZqzGAQYCca2cL9igirAmOCa4byRV5HWDj69HMj82b6cU0fqx/zPqW/QJhB3XuxvtlXlsLHP6ZCxbpX4yLqF/Wxh23kvDxmjfdTNclLA56TyPe1y6kGPxcg6vSaLJ1jSnBYaxIqNSYUxMg5UHrkfgYo8DXzuX2Bao42cNBAJkMg6SfWRcm5SZfl8r/KmR8CExqwCUhX4qVieCihWQsUttdMej6kBnd+s9l6lN0n6iR9qbMR5dW8xjRHzIzg0aOBSn/Ou9tywv9KIpUFjG2W92afcREwGx0b3vBq4eAfSOa/55lqnTYVN75dKPstqcRp76t58buhmOgdq74P68R+qyzJiair639cTrNLJOc6xSNaFQmOb4895Xler+6b6iUEaz9y3VI393DidYZmny7xuHnrs5JPacxm+AocJWBw3nauqvajrMo7zZp+bAeIYSFb0uub6y6zVM9X28vjkFOeLAU2yrJTUz9mWuZzoMOw/OhFeyks9l0ufTieB6pEaUOUjJs7Ax+mSUw96qiWClGRIyA5lGhW7YmEaVf4Ml+nQ0+NkSbCWoMITs2IoFNfoEXMCJsuSDEUqvl2tKpERu1N9z2tU5E5j3QmwNAIJgNgPBj37at7gl0v6SEkf8lzp4i9Jr1Z/kuo6EGVvk+lb/PC/F0z3Xh/3qexvBkDQWLNv2H9Z1gSoZhz21V9YmSdS+D3HXGUMfC/FeW6otTE9ehsEbmI/VGeczIrY4Hm/ETdT02Bku2xEPGl1vjp8JQTxVb2S+WEc98t59WUcjhNpzjasmxs27JTR86e8tMU0CJRZHz/A8H51/eL4I4Nv58bH85N5dBmoR1LHqLguxGEfGSR4CXq/yCMdPJ8IezvqUu3JqwBP6rl1upDtnIw447psBPrU13RyORZyVSDZqZNe3jqTValIj1MnBAYcSCMkTQ8u9w6wwtWeCg/ivLcZf6OTFy7XIcJVkyyVhyfJhlbLebPLc/6ejE+W/WoRh0IP9zD+mDbDbKt7zFW8StKQ8Do9cOdzl5qy/JDlN0n3LfVhf77R/tfU356d+xfSmGzGd3t3m2qe/q7mbcU46XlWoJBldzsI8TLdag+L90pckPRySd8s6bPVWaBnRHiORafp8WRJpyDFbfdI1G0Edh3+suYPa7PRuKjeX/TcOWayLdKYEuDTwBHgGHSxjgRxNJjs6y219rWRPlB/PQOBZOqQBOc5vuncjJSr4/AkVM4bab6B9m61NvXyEefHQRHXYTj2qS9HuuRmmYmqbdwX3gBOybnOeidocdtVoCavuQ1TL7HePARQOWEESulAZ75Oj3V2Xkf4G7H3Z3LycuqZHqkP3pyo9OJSaVYTPMXhaTRGgKJiQpgPlSzRPycjARUnVsUWrCs3vabqyGaW2ddcFnvVVd0qQ05JRZFebvaHy2mjl4xB5k8FknlKbpPvlvS10pt7u+daPxV9ZYCepcYYbaqdinmrpJ/W6sZs9mlV/vS2E9Rass72OFnPven31akM/7ukxSdLulf64jdJ97xR+g51A53Lq5lHxXJkn+bvBIhZXo6jCuhtqhnoc1OcXc3ft3UY6XL8pHH2vS2E5TIgHYtqKc7jgu+3+3i1dn6N5gwIAXYFyLJ9cp5ku0n9JZ7rgCN/O30yJHtqff3AdP1OrT5JneCD+3iYttsoGaYEOWzbis1OwM+5z434h5ovH7OOZDUNhAlQPf89F3ldmvdBBXKYV9X2TiPnhPPNvOiMcsxQl9LJsXPCJ2CfhIyczQ92OfWgp1re8fdqt740HuhUNBmHA3tdvPydyxo02JWBSeZnVM6sc8YzJTwCYaNlOZe5UrhZBhrykbH0JM99B6tAZdVgjAAQAYwfGLajRoXfL2l38R91QQs9IulVamvot6lv+qzSdbscqBmSeyT9/udIeqn03a9ohuQxNSB0X1F/K79UpqO+TOPhOGkwrSCvqp8SOlA7EbV4tqTXPCDpGdKDC73ok6QfeKSffKnGZBoDf6+WWrLsrG+GHy2XZRv4MQL/y8dL+irp2/5kf8BeMl2WPA6/GfcqJiCZVoILM7aOzzl6SR3wE/hXoKVyKCoHIeNuqm9+Zh9QMr60elBD6ptlb1dvRz4KgGkRlKQOqMBaJVxqzyWnqr7ctMsxWQEhy64ay3ZFbe+UGTgyVo5ryTSsaxOQjsJXfZD9nXqjWuqzfnJ63CSeG54rZ/bJkqVODmyddjn1oCfXlC1JQ1ZrzvQQabg2Ik5luNZ5YtVkIg0/Ak5UIlluoWxpbFJRVYAt2ar08pn2CPzltfTqDyOs7+dkr4xUpeR5j4yCy7Cp7n17H9K/UAM+d0l6w/TdHiX3VGRbU/Y0tdebP0LS/6aPf8Uf1WW1pZpqHT7bjHWTetvbk+ayjMPkc42YdiWb77n5jF7ozZ6WmQkCMTIeTqMy6hWIZRml1SduH+H3uocfvifdc63MXLrzOM3yvqeqkW72YeXRV+3nazvq/XnbFO+h6W8Uj3Wo8s7vmWeGG5XxOEbZ6XA5zsKTZakfyMikcPxV7ZjOlsuReorjPdNhPC69eZnzUG38XJq+e29PZaA5n7N9duJeNa+qccKwOR/IvtJGZP95ztGpqfbWncnplFMPenLyJotCI5MTsxrombaVYqV0b2bwJlAYxRmxL1X9eM2Ta1f9eKdUH6nONA8jPO8TrElzL9OfFUhRfHc6bsPjAILTy1cl5GszMu7+9P2a2tIEge2RVh8oxrKxrF4+ui5JO78iveCP6nVqXucVNZbHRmXkHbOOW2p7LY7U+2cduKkMjpX7bep7Id4qafmfpcWLF9KLJP209OaHWj39zBg+DTnLx89s+6qfzE5Iq8xL9XJGCq9fVcNn/7+fkXZ/pp8E45hKoFsZIN7P39KYTeU45zKKWcA9tXa7on5iyPnm5nvhdzIdGa4COyqupcPh5TeebmJ8l/nqFP4O1K2SjfieIJ7LYCmjOculQ+qxdc6Y41g3VS/MPdT8OHkugSrCZllzblbzVBqfvLqZ8eU5nrqbbGFVVuuam90j9UTIGfCq5dSDHosZBQ4i0rw5wJJa5EAkO5GgowIM+Tv3HFThKgNRedhcdkllabbAj2m3UR1tjr4ZkJaUsSUVro1g9fJR5pEPUaPxHDELWc7K42Q+aRzdHo+pU/55Mqui9q3AHlNjiL7tcWnrZ5rXef+aOlYgxh7wtqTfPd37R1oF0FXdmT7pfxvhu9TK9JckfenPSR/789IrD6QfVQfAj2m1r0fGqGpTfk+D7PDsR4fhmNjQfC+XjcMlNfC2P5WTS1t+Hgv7Zx2YyPJTsrz+9JJheuEXNH+o3o76M4hGrAzTPW5usY2qNFIICCy5xOXXXtwzfb4zymn9YL2Wzteo7SoWY3Nwj/G5j5DAJo/qZ525LWFD/QGXG2oAdFN9k3alN6iz05lxWOpl5r9OJ9OuMK3MI/ccqQjP71zePJPTJbcE6Kk8lRx8HLTVWry/V/tgfG+0RJSggkfBeT/js4zHoX1u2GMaW2rK4CquHwfA1nkxLHP1YkqG8eRNg8B2556J3OR4HPBLZTna++NTIb5m9iCBTnrjWQ9vmjxUX+Zg/C2N3ztWefW+9mPqXmtluEf9IvVlKtd9X10JPyrp70g6OJif8npYvV/IVI4Md16rACiNBuudfZieLO9zbj6qbhRzX4SlWoIcjePsz3XCfVJMa0vtkQfP/BvSa/9sWyplviNGy3tOHtf6dmYaWX5KzjnrFC5T08AfqY8L657tiEcw6hNNrAt/s92pP5IZ4jXnVQGSPDzgeKmn+Pu6Gmu1rXZikKer1s2dTfz5nudL6mWmkbpMkQbbPoFjMu8pOU7dZ55TJyFne3rGcupBz4i50eC3VCuaXDZKg5lhbnbjZk5+7lNgGdNAVOWuNlKPGKksLyevy5vl8+805JXnlF5+el1MzwqX+5q2i+8Zd+Q5ZTkJXK6q7yvh6Yh855TzslFIBZ35WlFRWTPfHD8M89YiraxLxmEZEnQcqr/F+rwaM/GgmtG9ptWxWwH5VPLpgVfLqDdTh5sRp72rVRYjN+DnWGJZFb8d9kZxjXKg/uwYtu91tb565o+3TfFZrxHAcxi+zyzbNedVzveso7SqF6p+9O+LU/kfUO345ZiqDHjmn/mw/CNd9Uy1TfavUl9yG/UD80t22oDU+iP1Qm5PqNLk+He585Qh61aVjSsEyfiwXAR+QvgK4KXjdFLy3szZDyY59aDHkuxNpaDy4WbV+ra/p+Ln0c+coKnAUoEk8MnJNjL41XdOLF7L8mf9nM4IBDGu22pHqxNjnbFLpVh5ZIp7NAqjuJZqn0KeHiFAsOdJz1aag4dsewMYb4Tc0FxJjTxktz/HRrI0CSSYTn5P4JKboaWmwK+o0/9+GOCu5huK2bf04itAkfln2XJJs/Kc14nHr9tmlL+vEZzluElQdqi5YbKBq5Zgq/pdVzPWr/5XLd4+wnLeJqg6jPtZpnVAQlqdG9J4rw2Fc/4Af5XzwXI7PMtRncajwR/1cY7pc2qPJPCJzTzmn+W2+Fi3+2VLDdDvqQFQ78kbtfHoWubD/hjpGJeV81aq5y7tCtNgOZgPN0OP9NyZfGBlsVg8V9KfkvThkv7Ncrn8++vCVwTCqRIOYBq80UCUuvKn97qJ65XCSeCRNHyVDwEFWQnF7zTYzCfZHTIOUr3hkM+FkFYnflX2Ha129giQrVPkCeiyzhYvx2S4VNg0NIfxR8XEB+BlWZh/KukRxWum6Lrm3mE+6I1jwUDqdq0ehRZ+Z7nMRNEDpzDfLKOX9m4gDFmuVLiUkXFkvJxf2Zebg3SrMeL4ZCIYJ+dG5sNwXHJhX3gfleK6hePF4Q1wH1c7ofcuNSDpJWMaqBzTCaiZXz5YL1mV1CtVGlnuDTVgQZC9rcb0+Xi3yyW1ze857qp2TyeEgCfLzDJ6bDr+WyT9EzXAWC3lGXRbR/GP6R6oLYE+oL5sWIGbnNtVf1cMbArHpSX1pMdBxbg7DR6Vr5bCRw9bfLLlJF44ulgszi8Wi+9fLBZvXiwWv7RYLH7T+1L2xWLxbYvF4lcWi8Wbinufu1gs3rJYLN62WCy+TpKWy+UvLZfLPyrpSyV96nHpn3rQUymQjbjGwVs9NyLXk53GRnzPawlqnP6IaaEBqRS/6+N8WFZSpPS++OyfVG6sC4WGdVvz49KMk96gtLqHx9+tfEfHlbNcPq7q/KV64hyqe30Vy8T2OUD+3n+Tiudm1t9zWYFp5H4HIbx/X8f9dRMolybpAdowcMzkEWDep3HaRhpCmKuaU+7VHobqeyVVX1RK72aAPT9HfZLhpPlG2cyDeWc8pmnQSMXtzdWOx3G0iWssb+bpcZtziuBMGs/ZdDLI6FzXXH/cUAPau1odI97oXKUlrWcxKcmMSn2sUge7PJWD4fInuEjd6rSYXpbRad1A2NSLqfuzrNWc3dBq2yeLy7lZjWUyjc7XnwTrybp/EMg3S/rXy+XyY9SeA/pLvLlYLD5isVjsxbVnF+l8u6TPzYuLxWJT0rdI+jxJz5P0ZYvF4nnTvd8h6V+qnfdYK6ce9KRwAlRyqNXJxYHOyXMcxVwBIAOQyruU5hOj8k58jZOeiqKi9EdlYpmzTZKRyTSzXTJeGrN84nKmyWs+IXNBHYDtaL6kkwo4y2GFm0fzK29vBLwyHB9Zz/5hnIopymsJtjKNkaJMg0o2kn3tcvpdRl7S4fOIckxVkuzFYVyXVlmcUZrJfvhaOgrp2bJ/M38CapbV/eJlpwSHluqUlMMQHJ9Tf4eVNH/GSxr2Q803vLPuLMMINFbsActdzeecP9Qvu2pPYX5UDdReUB8XQjymW7GJ68pKoGT9lkvE1FUH6uzMOhBCQJFi53F3qk8+D0paBRA5D+04UhL0ZT3piLgfsx1SJ2VbczyS8clxkSzaky3JtH0g/kayWCzukPQZaodYtVwubyyXyysR7KWS/vlisbhtivPVkv5uprVcLv+t+vujKZ8s6W3L5fL+5XJ5Q9I/k/SFU5wfWi6Xnyfp960ppqRbaE+PJQc9J0J2DJe5OCCrgcm0ExhI9R4BTqAEKI6bXsQ6j69is2i0EqAl8KsGJfOsgIEnLduoMo5pQCsmYFMd8OxN1x5Xo+ZpZNMAcB+L82J53Nb0pBwv+6zyVLNdWM+NiJNKujJc0nwP2Eg4LslasJ75yU3TPop/TvPXNIykArq+thHXGDbbmvGYdtX325JeqPYW+Ae1OicJWPmbkoxV1sH7zxgmwVQ1v7fUxqEZSvZjBVLZJhxbFYhlHDpSHp+5YftQ802w1ZxmmbjHi/nvF+WpnKMsa86paixQqnFfsdGuM/s3WWm3h9vA+nBX/e32+0XZc26lA2Sg5byqJeJqHrjc2Q6juVztOcu2rQBxpW+fLHkCXzh6x2KxeIWkH14ulz+M6x+l9rSKf7xYLD5e0n+Q9KeWy+V7Dh4vl8v/a7FYfJSk710sFv+XpD8k6WXvRd5PV1sRtbxT0osXi8VnSvpitdXepw7Tk963PysjlZKdz4FZefXSqoGt0vK+FaZJhev0chP26G/dBmoDKBtq3+dn1kea71fZUfesaERGoHBkhHjNdbZ3vDf9fe0flf6X39aVG/empKRxGXn21SSuAEmmO1ryYvtVYDnD8D77gaxRlY/DcV9O1Z6Ox2WTI62CghybVX9VIDCXY9aVw4aMbbcV913vh9X2ybi8rmcF8jW4lobZgMV1dZ6V4WGaZAJ31J5v8w2fLP129VeaOPzNGKORo+BPj4MK6NGBsVHNvsv5uhHh+VRj55dAyGNvBIDSuN+IsIdanZe7mrcX68Oy+jrLlMtQqdOk5gwZwHm5sdonV4FTf88lM+pS/h45uTnm0gkajV8hXuUMVCD6KSSPLZfLlwfgkRqB8gJJf3+5XH6iGjH5dRl5uVz+VbXu/vuSfsdyuXz3+1ug5XL5U8vl8k8ul8s/slwuv+W48Kce9FQFrIxZXh8t9aRYuVTLS6SZ0/PMCZZCo1jlWSlRfq/WlA2gRkbDYViffFXBC9Q8c3pg9picJ6lq/xbSTFDi5Z5ZWzxL7XxrXi/qlBR51uWgCFulMQIxqegqxizBM8uRUu2FSmEd0lBX7UFDpAjvvtpRZ9DMfiToTiBAQ5nlHpXDoPgu5DECn4eS3qb+wMENzTdtJwDI+Cxr9V1aXfpI0JH1YV67knRP2xNDJibHq9vKdT5OMWZbJ7PhMbUVvy1u64pVtIMg9QdxOo6BYBrWZKJHrMeh6uU7loG6K8OkYyg199rsrh+UyPgZnoccHMZtnozLCHgkAGMeBOvrnrJd6fB0XHKsJ1Ak4KzkZsH1B1pOYCPzOyW9c7lcvmb6/f1q5mYmi8Xi0yV9nKQflPT172W1HlTzYyzPmK69V3JLLG+NQE7et3A5qfLipfmEq1ikTdX5VSCJ+WU5nMf76lVauJTGZwGll+Fr1SbpLbUdYNuSXqc5DZ8GsfKcnRYBQh7l3Z/y+ev/cz8dZY+uoqKZl9s8gYnzJnvGMVEBnMOIl8L2HAGkrbieY4LLZw5L0OJlFRpjHrNXxPH1zbjmNzYfaX60133HPTGZtsPm86Oq5VqXeUvtWSx//EOlf/hu6U2a7zujYTqMTwoB3wg0VW1BlsPhN9WN/7WIy/Yy6DLbdJ+kb/y/+3NhRiyi4z5L7encfgbNSBw3N/ezr3Ns+T6dEAqNp++bHX369P2SalYh25Rpci6P6pIG/jHcy+dsWWceqgGee9THo+d81pHsDxmia/jOpd0RqKl0tcuZ4St9mu2U+sdhqscg+N6ITavyPwnAcxKyXC4fXiwWDywWi+csl8u3SPotatPvPbJYLD5R0ivUiNf/LOm7FovFNy6Xy79wk9m8VtK90xLZg5J+j6Tf+96W9ZYAPRaCC08ySgWORvteRjTqOlbIcdftT2BY50EA4nhMIw1wKi3S4XepESiv1eozgTiJOem5jv23EK7ydvlJMFQpTgJIP/PHJ4hsXPy6hHWMWirCrBPZinV7aUYKLBXpOtDGNJhfBY4ynQ01b/cutWePHERYLnNlGcmM2Jjv4LvT4rjhxm6DI4vL5vdzcU+FIl7lBT8i6bXv7i/n3CjiWUYecTWeGd5/HOMMu6sGmB0+T2ZWxp/Ohzd825i+Z3OB5v1HIP56rT7+Iseb8+TpnRFbkEaaTgvbKMMq4j2qed1TOEe4dJ26cGSE2aeZ/uhEp9QYtD1JX/1Nkn5M+us/2fI3mGH70aFxHh6f1FUsq8eIdVHOw3TY/JypkTOYUjFezDfbKwFgps+5fhPsyBMq60D7EyR/Qg3IbKv5Dl8Z989J+tLlcvnLkrRYLP6ApK/IRBaLxfdI+kxJH75YLN4p6euXy+U/Wi6X/32xWHyN2kPwNyV923K5/MX3tpCnHvSkQhh5aRWVTg88jw8fxXeLBzwBTsVSZNzKa6QiSkYgQQnjcXJzkm+qPV/krUV+WS4aSce/oQZCNjQ/Fst17JQKEGTd2Ub+fEC1gq3aLZmD9MBYj2o5IMNW4IfxDGYS8EirebLMTKdimlw+tnkq0lTcI6W8jXvbKKuXDwyKyIZsIGyOLxoGbqrmd4czo/BPUWYhrTQII4925G0L19LoeN4cqIEUtv3liJ+AvXKEaHxZrqr/yFZknMxvQ6uPBHDax/0me7pdhGV7ugx702f1zrUsWwViq98JXPPaujR4bVtq61sXe5skWEy95zmSL8xNVnLUvkyj0hcsYzVOK2dxHTNTjbW8dxz781SX5XL5BrXdE6P7/z5+H0j6h0W4L1uTxo/qJjYrr5NTD3osx1G0GU5aVfYVberrxylwKsAEQtUpIsX1BEzVZM6w+XmopiRGT7ol0KqkWjKworei8nd6iVYwlSc6Ysy85JJGPoFGGr0RCKgYqWpJrqp79nul4DO+gVg1ZnIpk0DuXWqcrsdHsoIVI2A2h23H8N7L47DPnK6/Q6sv/7wRafBRAyPm5bC4x4dFkonhE3jZVmlI1xkYxkljw2VPtn32f7bnOsNXLT1TaKDp8KTjQcBvqZydiumhsXc89kmyTnQGKudM6uCd9c7lNkvOjXxdg8fgiPGp+lhqTtiDkv7eV3VH6irK7jbI05lkRBKo8kGkFRjZQLjt4nqOiVwCrerG69TRlU7JZzr5+6jtT0KewNNbt7zcMqCHjIm0qlzSI60kvXRO7hHFyQmQx1C5fGWpKFhfZxzmwetHa8LwfjIFB0WYyrhVijvDVfdHAEBa7Qca+zRcozKNykm2IIV1r57XQaBcLU9lWs4nWaDREkSmYUPNvs5lpdwn5HguQxoeC5/R82Zc4/WRUj+Olaz6tzLcrke2DYEy0/QnQcM6lkHqYPkorjn/kSHOvJhuBTT5vWJ9EoSM5mdVjtw/NQL5LEPF/LoMZt4cP50SqY8ZPn4i86fhJ5g4jjnK3463NZXtITVG7VD9xagMV82TkYwcHetV1tn7z/iw0KpOOZeqcZi62K/YqMo6AvmVEEQ/2XJS+Z52OfWghwCFijmVU4bJ+CruEdDkZHNYeompCDcjbBqMBFeU0SQZPc9nXTwqwErBVN717pTXfqThsBcQfl/9HVPVcp9UswgVsMhypaFiOfP3qE02ivtVWAKS9Bxz+WtUj2qJLb+nUeQm53VK2df48tRN9ccAXJL0Ikl/8LZ2/9seb6zSXWoe92XNX/5alfc4YR3ySdojMFGB9xGAclg/gyj77UirezK4jJfCsMkKuEzVkqgiTILEHN9kJhTXMyzn0jrwmWVgmgTgflZTMrUpaWDZ/pUDwXZ3fzBtggi2iffskb026MnnC9HJqvQsy5mgOvcscr8O+5yMDtO9GXBFtozOGt9rlw6k80zWnlsKqn4/k9Mhpx70SLWXXSmQSklVgIQKwAbzCN+lPnBTSaRxlI4f4Md5p6m0GWfkJY68NCrbyjvjvQQLboPd6e+z1E95XSnyqoxpggoqwhHFnwC2UlZpcBk+Pa8NzTfwVt7mqB5pMG3oRksWI+NIBep0s29TObJvrIzN5FyW9HxJf/Cz1B7wvin9oddI3/Hqxvx4CWwfaebYrwAJy8P6uR2fqbaJ9vJ0j/2bDE01N5ItY92rdKRu2L2RmUzSiM1iOaTaMUrjVYFUG910dJzmyElKMThIfUAHJePlnHF77SHNc+qPB5DmbboO+FV6JefakTqwSMaoOhFYAajrcZ9jcbS/kmPlOsKmjttUWzpzHx0h/jonguWpdAEB2uhwTAUo0xlQEXbkqD3Rcra8NZZTD0JpVCrw498jloWf6altxqfjH6kPfp+goRIkQyH1/S5UqiMjOzLoIyajAi4Hx8ShQliXxjWttoFp6xdL+tz/XfrTmiuLapI7T2m1jdMYSqvKltdZFkp17NyPwmec/J1GjsAvjU96pS5TVd9qLOZySHqylYFnXglY08D/TqmdJf9bS+mvL6UXSF+mPl7T4LBOCXScn5eTWHeWa0/9ZJ4QfgSAs65szwQdWT6WS+rsIsE5n1PFtPjMFGk+39kvDkPAyjj03tmXWwh7GPGrsZlguBIa4uo5O87ncVy/hvKOHCPfq+ZYBQhYFt/bXBOW6RMU5R47l9PLUA5v9ip1jtudL1GtABzrPwI7rEM6limjPmReVRznyTFqyfY5k9MjtwTTQ0nvzZJe80jpcP/HyONiPCofCydiUreHcX0klcdJJbZu6a06Mu9wR4P7+btSFmS9Xi/p4/6i9P8MyrBZfFeEHS1xMFwF3NJTqpS7VG9QddvxOSsjD8+/E5iRqRkBngpEEEjlOEuDzHK53KPx7M+D9/zrCaXhcD4Zt5J17WO26Y3Tdy4rcGz7FFnWLT3myvutjIKBx47aEusjiEPQkeMigWzWkb9zfmc/V6xw3q8csWy/LIvbkMzCKCzrewPfq0cXKL5X84nzrQpXbcCtxg4367IPKgDJ/lpXzmw7z+ERY5Pf1wEahnU4Oj+5jOXvLCvjJbMmxCHLuA4wPllyBrhquSVAT7W0kN+pVElD5/FyPndFWlWWCTTo4ebErQxipQgISCqAw3pW+aRUht5S0eqVckjP3hP1QO0E0oba62yltrTFDdqkqSsF688K0IyAxGgJqTKKNHwj+nidh0ZDt84bqxRhAgSnYe81l1FG7KTFRpAMCr3O29T75QckffV9kr5m0QK9SfpudU/VtL9PtVRP/GW9KuaHwMRtJdUPUzyY8qkAQz5DyNe44ZpjkO3p+96bxGUM5p9MGPslGZ4Ujt0RMHc/sr3YPzSAqYuq9ConhGVwHmxf55VH7wmcqEsqULOOXbRw3BoMVGFuBhzlOEvdl/FynORGaJZ55MjdDOBjW9+p9pruX1DTb843wQr7muWo5o/ryHkz0k9PtCw11oEf7HJLgB5OojRE6V1YkokZKbbKU6HnyIlbXZPmRio9+hFgGzEdacwTtI0UB++PFH01+Vk2skuXizJnPpm/88gNfmy70T6HkYJLRcc6JhCpysNy8rkeyTowbuZLkDgCpNk3aQwTAGcZqbR9csRLG7ersR5vkvQPfkZ6+c9Iiy3p2w6a0r5riu/TJvk02VG+vkclbQfAfZLzjWOdwEZxz0IGKgGDP+lwOM6h+sMEjzMibOuKpanGcAKCBNNOI5kggiD/HukglztB8Wi/U+qNyuBWv4+TChiM5onDjRwT5l+1bR50IDM0YlOynNXcGzl6I8Z55Pi5HlfVnp63H/ETeGafEFyPgDbrUDl+Z3KycupBT8XsHDdpLYfF/ZEyzPjco1J5si5HXiPoGCncCnhVwMcGKMHMSPEn0FB8tyJNwJBlywk9Ymcq1igNoxUggVyCLxoH36eXO1p+SAXFtGlIKuXkdFIJjwzkKC8KPUWXeR3ItmS/2dibGbmkxvhsqYGcPzpl5o3Ol9QB5YgF87XqsQ5ZL7YFxxwBDtMaGWffY9iKBUvGhJu4HTfbmmN4S8247k3Xr6k/Kyal6t8K8FgS5EjzNkzARjaoatfUSdWYdN9VjyFw2h6TOZdY7mQxj3Oa/HuzCJflPG7e8HfmW9WZ5R7JyLmp2J2qnrQb1zV/XTfL5jauNjWzTT2/q0MvijAnIVV5zmRMCpw64cAZDS5+5sD095upsCcNPcxkNlIZVQzLOgNaGc8ECxmPDBQnGT2zNKo0fhVDUjFRyVJkmoxHA5ZK3WKglfWxpHGg0hrR2Vme9AwZ1m3O+leAh8DIv512nhBJqRi03Ds2AscpBBEb0+/H1TexPkvSveqG0ayQmR7XNxmF6llRldHiplKL4/LVCy5betG+bmPADckJcPzJtrtj+n5V8wczrmMw99TeYvibpzi7Ucej+F7N1Zx/2UdOI19IyoMDZHSEa9L8dSBOrxpTyYpZPJcZL8e7v+fG7pEkOHZ9LJ6bqTtyjOf89/0bcZ+6yNfZfgkePAePNJ+PWfYRk6nietUuyeRWz1nKurseLE+CnEr3ncnJyqlneqTjadH0mDhRRkxRRRmnkslr9PqouNd5HdVyjiJOFb9iqSrh0kLS2JRsE3vPlWHztcfV61d5f+vKlCBGWm23kXfNMpAlqNiLUf4OnywP886+ZHi2awW46Qlu4veIFvd33yODR9BLBa8pzX2U65waLe/+4OtE/EZ0sxxmS1jOEeNDNjGNC983lM9CMfAS4mb8HcQ7p9XnvCSbQoB1RfP+GI3tDUm//4WSXiC99hVzAEhv3OWkkR3plgocVeOGwvHhulSbpdcxMCOgwnKzT8lCss+k+dOIKzZiU6vH05NdHjlHijgVA5bfuTewAtqVTqh0YvV7HfAZ3RuF5xzN/D3Ok+nhvXXj9cmQsyPrY7klQM9oAlVeW1LpI0p3A2FHrM4oPstSGWwazopqrSYgPaD0Mvy7qmuG9ySsjG6yMpWRd/krypkKKQ3UZnxWQgXCMtE4VQzPujbOsFX4DMuls5RK2eb4G1HW68BbJVnX6jg4+7J6QWzW7bC4bhYjl0tYBqaXp3O8IZrlo1Lf0OpDAmloJen89Hsf9co0HJ77iyrngPWi9/29r5Oe9rr5+6mOMz6eX2TlcpxSH/ia4+Y9Ph+KQj2QLM1xxsll8gb1dETIHrkfcsNx1Y4J9o8rgzTXuZU+EsIp7idTUgHSkTOYTi1B8+ixGATVI8Az0oEJ7OiY5EMIR07eSctxLN8Hq9wSoIeSSqiaXJV3lghc0/fKA1yn6Ea0fwWshLBU6CxLTqZqz4XLlSe78pkiro80Zw2yvqOlHZanMqhkWgiuUlmM+oTXyYq4/CMgUnniaby5hyKVWQI/tgHjpCL3tYMinSwH22EEdCogyjrbeOU7fKpljgRKHjfJLPJJu3mkNkGS45Il4PesH+OzzTbV9iA9rv6MHzoAt6kfS/f+JbYrgYONfD4DhgzW1SnMa9WZJwM8tgtBQuqCBFqVEavmg9vG7XRN83QdzsCRbIZUj5s0wizLVdyrGKLUHwYW6xgN1pF6hteo9yowmmEV99NBSbax0s2+nmVIRzL1Au9TN1IOBtelrouz3o5XlTEdqXXO8pmcrJx60LMOreZESLox0X4CjpEBq+7n4E9lX5V5Y3Cf+a3zROkZZTre55HGqzLMNO65KZSgybKORVGEW/ebzFAq14yXQCTTGeVdPZvGbUKvjMDJbVCBqPRcj9QN7M14ThXLVIHorEdlELNvDXBs+CpAkWm7z/M4rgHsSMFvqe+l4ctHhTjrvOMjtb05fgDm7dP9K1PcXc1fXsp+3J/yvqi22ZTvQHL5sl+vTX9OpzL+/D5iWUasQBo1blTnvKtAU+UYMT2CMWnVEakYj834rPrf1yvhEl3qA+bta+kE3axRdxrVshb7oFreYtnXbRbmtdThLmvq7pE+99ip0qnGBnV9zoWbYdCeKDlb3hrLqQc9ljSGIzqRgy+ZkWrpJCW96FQKO5LuVnvJ3vUiXk6oCjxV5U6QwTipyK0gRwY0DcSd0+ejqqVS/JR85or7Ig1BpUQIJnLpjeGy3hWIY19sRniWgUArgWWOCUX47G9pHr8CR9XvjG8AnIrQbbJO0viZVXgX7jutjfjOscBlK4IdAh1/5lzj/qA0vMzDdfTrM7bUniT9qVPBXnvUnjd0pL6/x2KGa3u6fgVpsP45phKkp7NQ7bVi3Sxs3xyX1X6/QzXwdlXzp2E7rU38ef4m65ZjlfM69UPFhBxn2FL3jOaLIlyC2oqBZv459ka6twIbTn8dGKXzQOCbjA7Hc5WP2zrnRoK6nPMV8DnOCbrZcGfy5MqpZ+DIlnAJZLSERUnv3J5yZbhsxHPgp8G5oaaMc3+FhXHTQ6dUxpVpsN6HxbWMO/q9oWZc7LVX3lJ6i+nVJz1+FPdSqDAq74ftUR13dj/T+x0pd/ZRMlYWetSV11exAOtkxKhwnNL45qmgTc2V7kg5jsZIBQCcbwICxnHdN9WN9G2I543QNHB+vcC6UyicUx4rjvcCSS+Rpo02d+uFaq848RIUgSDb4zY1pse/fSw96+P8PWYqRoJt4jrsqgOzNGiOT5DIvy21OeV2ZP1Tf/jPZSWbk/2zzkhWc3bEiFZzqdKb1djaUn8ZMSV15WgsjNjtir2q6iDN24HtV7HdTpN55FgiACXjlOL7Ku5XoHrUBmQA14V7ouXoCfh7KsgtwfSQ+ifKrwYfwyZVWxmKSjjJKoR/uciPcXmP+a1TFlXZnKcHnA1ELlFVHg2VygMIk+xJekg2cNwLkXl4WWUEEisD4Oeo3C3prUi7Ynak1bYT4iRjclDEk+Z9YcPp8JluLi2l4cwyUKG5LSijPqfycB14v2JqOOap6LMe3BPG604r02O/76r1z6OS7lF7p5eXl35w+rxTjV1yOyVwY55mS+6RtHm3pA9dSpIWn7zQPT9XzwPO01ySy9NKFTDNMnCcUydsqT/p2vG4GZhjlqzHOfXTctc0HzM51sjgZR+zfJVUDp2Zk8rwV8aI4y4ZEc6dZGoOND8RyHsE9Vm2/M55aqkcvxFAIjPqaxk3WZkRG8bvVR3oXCWLnmCH86hiVpn2cc7TmZyMnHqmR1pd4uD1/J4TknFyktAAkUViOjnoU9mnscxyZ5kt6ZEKYTM8lUrmL9XPlEhPjeWvgAHTtPIigGHZqk2t+d2/6e2el/QpasZzxMq4Tw7jz0JFuFlcy/z5vQJSaVR9bcT4VMYiWTj2k3/zk/EzDcb1dbY1DcvI+K9jq8jyMNzGdH1HDfD8hudKH7Iv/cbnSl+ivrmY6fC7mSDPI6d7v6QbD0l6w0L6lYWWPye9Y5CGmRUvoz2qDlYPI2zV5lK96TzH2aYa+/Ql02+3N8e7l/NYznXeO5kgi8FUZbBTv1RzZ53QKUmHzu1CZ0kRPnVWhmE51jF8FXhIMLPO+GebeSxRqvRY/tTrmX71Xeo6jv1aOZ10yJJFy7Qc77j+e6LFe3o+0H9PBTn1oCcHWRqQw7iWQIaShqYyVMeVQRqvb0tjJVaVx2UaTZD0yitalgxGevceqMkubGg+mZO6JHNBMLg5CO846Q3mksMDkr5X/QnC7Ccqa99LBUhjx7KlkU+gIISvyn2IOMlSsU6b8VeJw+ZywogurzzJ7AsaLxuWfK9WVZ4E5r7mMtmjP1R/ivF5TXvA7vvkxs7c9zJdUGOBbiAe801QdqQGGHYkvUHSqyQtP1HSr5deo/bnN28znW3E31JjmshoeWw4/2wXabW9K0B8KOmX1V6mKoR3PVwu53NO/ci/jfqB5uwQ084xmn066qsKYKwz6Cx71oF5K65V+RM0EkjlPEpmMcuZ7Z/6h+l57CSAG5WN+VRbBjKO26Oar3SCRnVIpmgEAFNnJEA4qWWhM9BTy6lf3iItTfESDE9RMI4HXwWA6LlROVVeEe+tK2NKpQwtIwVYlTVPfiSTRW//OLZjBAxG8dNbTkNyqP4AQyqR9I6Y337US5or3CzHaI09wUrWL8N6T4jj2ZCOAJKvj4BStmu1JCGtAmtS6ZVBWzfOuDxjZiS9UZY/XxqZ4zvHw5Ha0u2jkna/5Oek7/8r0pe/8j1LHk4j51wakEP1pykfSfoxNeBzeNRfnLmj/oRpit+uflntDesb6stu9Mil+Thk/WjYCfz5/Z3qjJPL4Hq5f/xQRS9lSfM253iqhGWwvnI6PDKd44JzuhqD1Zji/YqN4Hg8HFxnmd3PniuVDk4ARZ2b5XNdmGelb9KB4dys5s06MJlAi2XMazcj64y+559t0lMFIDwV5dQzPRYqWSomKopU6Omp5700Nuk9pIJ1uPQcqmWL0WRMb+FmPLORV7Ou8xIIUHmkUk1GpaqD1Ddwuv031fZGsDw52ZmGjWeyQha2a/ZPxZgknZzxybpsqx2hPq/V/ksAQ6WYwLhSZo7PpQwuSSYorzzPqt1y6cBGwUyP442Yp23N2zkBaALGc1O6Pybp4f9bOlx8nd71XdKPq/Xb+aI8jktg4TwMFrik4rjexJzt7YMCBjtH6oCrWn6unpvi8Zn7/ryhd0N9ozWfJu02P4eySB28sc0MKpO5YDnIYrBsFYBwGRJ8WLa0OmcIQCuW1WGqaxUwOkC4NNzuv2S5+du6IfPdjOvpgJJJTvDuNNL5OcQ9ljF1RsXSEzwTDHMcj4BQ1oVlcRnNoFZO7JMlfsv6B/rvqSCnnumR+qDiSSJ/ryaINPcMHJeK13HTE0nPqWKD0kCNFBglyzPygNJLVXxPMOB4WxFupFh9ryqr65fsB8tEI+H6jI6tM02mt45xYBtsRnwyW0dx3+ls4p4VnA3GF6gtrf0Y0qgYkqx7ljH7J0HTpvqpnmQQs6wZX6r7LZkptlOWowKLG0ijKjM9+vvUWJC71NrrhvpylePm0WqfrNrC/QSPLIe0akAtbjdvOGab8UGAlmre+HvO+2Qs/emTa/uom992fxXlIntWzcWUEaNCAFWVkcxBBbZZ16wT9QEldRk/mSZ/u3xMo9Ith5ozYo5Pp5R9YlCa4Iobs1MXV7pAuD+qB4WPP8h6VDq5KkdK5Rilg3Emp0duCdAjzY1lNWErVM1JUSm+mwErVRkqSr3yni2VVzIyWIprOUHTSFMppSGtfldUL/M/wu/0DGkIpPnmVipaGlc/eyXbPpmPkec2ajMbTyp53mN6DvsqNQNGz4xhs82SJcuyslw5LjKetDrmqhMuycAlk5PttE6hJsAcPQ/I+XCvjo1/vpLCz9Y5VDvxRaNLw2CDZiO2rw5kaAxdL74j6oL6MqiPTxN4sA0rwJ3GK8e2x7H36XjTcjJm+SoHlzGfW7Uun8qwqvidcaX5+OZcJPCvdKHLo4iTjyOoypGf0hyYZD7VeK4cxqwXxWMrl/IznwQztAcV8KH+sI6o9C3TqoBW1oX1qezIlsZ1fzLlDGzVcupBT8XmVGEqA29JJmNkcEfGl4pGOn4wVYYxryW7UElVh7zHuKlAR0AsARDbgxM1PXobnD3E5VFaXjPYYXsSPI2OeLtuBCZp9EfMyDoAKUlv17zOaTwrlktabS+Wha9DSKn6pmprKtlkYkZAheWvmCamy43AjptA0dcrlutIbe+Wwc6d0/2rmoMAbkS1gaXxINhx25EhcNxLUx4vVltau4T65pF9ac783NC8X10/j0cDtQP83tLqi00JyioDmeA2+41jPec6WbWNQVxpPj5oxHM+51hmmBHYqPIY6YFRXuyDKi7Djxi2SgdR2HbURVkPpjtyUtYdQEnwRsYpwWKCoxzjrFO235MlZ09kHsupBz3SKmKuJk8aawsNFBkITpAENRYbhgRK0qqioeLLyVVtbKvo1UzfUnl5KbyWy07p9Y7qml6llcyu+nFme/tSe96cjZ40Zwq454TPO+GSU+7HGPUbPwkOGKZiWFIx+XuyJ64rhfeqI7SOP6pDjsM0IpUxzTwYj+Xa1Jw9qxghl41jj2ycNGea2Ibum8uajxfncUWdYUylvxXpMD2p7QvyOOGGYeZ/Tu15QK8v6mCAc4jruWHbwnbhptzravu7/N3l8Zwko5PjogK/vp7jgcJ6pC6p5jOBhcudRrVi8CrHagRIKkldWzE7OV6qeyPmPec1yzkqi8uTADJZmUqHpdNV1X+dE7UOZFXlrA6/HOe0n8mTK7cE6Bl5C9X3ZCCo8CvPmxtOc9JywvD6iKIWwnMCVh4Ky2g5jmmit5zKhGlIc+CT3gbBH+MnXc609pDmfo+qDXUvulJo/u39IN7rkoqqYtwSGGxGuCqvitWqvEyWv2ITCZAyL5ZvJKl0q/C+VhnB9BgJ3rgsVxm8jMs2qJZMWLZD9Y2YOYeyLwxgb1Pr332UqyoD45IhyvQfl/Q0SR+jBrw8XhOY+TPHM8VMnMfdbVMaj6szPQbjHHPsl2RyGK4y/K5ztZySeiX1UM7tBEkjZykBt/B9I+5VTBjLUy3PJAhLnZQs6TpwNdJ5zKtir0Z55Mm4SnfmHGM/8qW8ydpIc8CZUumT0b0nW0bs2Qe7vF/9slgs/vRisfjFxWLxpsVi8T2LxWJnsVh81GKxeM1isXjbYrH43sVisT2FvW36/bbp/jPf2/zYiVbkrABPZxCgcIJyYlRr1ZyAW3GdeaY3MKKH8ztlxP5QnKe/V+lVgzvLzjySuUgglwrdzyk5VFty+JvT30vVDcme5p5xsijvUjc8rgPLyLxdxtyvQbaB31lmCg3JiOq1EUrPlfWoTrJU3u7omstCI8G0KByX1dIWx16e6tvWanvkmJdWjViWw8wcDTbr5ufVcM/OdTUAcR51JQvoeu2rn9yy7Kizh5YtzcfApvpmai49EECxTjRiO2qbsnPJwnvS+EJTSwKDEbtRMWzVmKFwTrtdpNWlopyXCZCc1rplm9zvVwEeg5bUX5WjkADJ82edIan0C9PjvWSnM3wl7pfMI524URnzVGA6flVZKge66qszOX3yPoOexWLxdEl/UtILl8vlx6mNg98j6a9I+lvL5fLZkn5N0ldNUb5K0q9N1//WFO6mC8gJSIXM65WSsTJMRodCL4jGgvc0SL9iTJzWuuUrhqNxGU12GueUakJXnrW/j6jnKh8bisclPV/SV29It3+VdPvLpT8g6eOne9LqO3sqVoz3WDZ6jyMv2uWhd8k9IgRCFTBieNa7YmAoNvBkATJ8jr8KoDr/ddR+VUaW1XFc/wSCTI9jfmQ4KqDHPNYBU+/z8Z/n2bb6RmEDobvUgbHlSA3s3D1d9zvi7lB7Rs9r1ADJOdTt/BQ+H5Xg+vpvV3PQRGfIdb0e8TbxybpK83GV82kdGPKzfEZ6oIqfhjodrSwX9Vv2s1kwjxU6gpQqj4ohqeLknKicDzpgVTmSqa/AeAp1Wda/avMsZzK5DlPtU6rmbpaf8/OkWZ6zJzKP5f3tm18n6UMWi8WvU9NN/1XSb5b0/dP975D0RdP3L5x+a7r/WxaLxeJmC5nGg0aFEyiROCd9DkZ3oun8Da0O7DSalRIYKYYETpXCTKObBi03oXKiM/88jpxMVhpEXqf3xvL4Cbm+90JJulfSty6lf7DU4uOlF6Fsub/F6dmQUMlUnhnbP+8T5DodGqtklkbMjLQ6eVNxp+FLScXnz+zfDa0q9ez/Km2GSa/ffZ0bzCluS45rgspMz+LyegM6gSWNkb/fQLxNNRbnkub7vzbVFMPnqAOfZIEuq4MjszkX1VhFv3GddfJesh3N5/N5tXd9OZzzPpD0kNqGabcV95xx/Hvc0giPQHiCbF635AmwlAQ7ngOVcFwleCT4r4A8GZR0HCuQ73Sr+SVcYztVuibHMZ3KioW0MI38zjCcu1ua90f+Ua9Yf6Tkvs/UjZm3JffKVYDuyZQn8Dk9dywWi1csFosveNIq8wGW9xn0LJfLByX9dUn/RQ3sPCbpP0i6slwu//sU7J2Snj59f7ramwg03X9M0xPv39vCUjFYkedEFn4nUk3gYE9kxMx4IiUoSDBSlTXTsYyUZManwXJYMh2VMTJYSiDke1QiBCQWT1pO+CO1Dms9+LCk69IDzdCZCcpnmEirRpoe2ch7oMJg/TbjN+/zWqX0GSa9vTwqm+DN4VKxk3XaiPDuA7d1nnI71KritCS76fRybFegkPWy8KSQ446W21KxGwBV3h4dC7eH2+LqdO+cGnj5hL8qfaVqY2Tw8hfOSd9wQfrdasfWf1rScyR9mvrG43zzuRmlDTVQs6/+cltNvw/Vl2hd1mopYsSCWLyJejOujebyIe5zbFrYBhUbl86Qx5JZKrIc6ZhRZ1HW1c/CfkxJPUKgwTpV4CTn9HGyDuBkuJybeT3nznGMdDow0mq+nlO5lEmAZOD4FJLHlsvly5fL5Q+fdEHeV3mfQc9isfgwNfbmo9QY511Jn/v+FmixWLx8sVi8brFYvG45XasQ83HUpQfhcQMuFV01qZxeGsuUyoMneBgZscqAZ7qVJ2QlUE14DX7Tw6aCZBl3IqyNyqskPXhN0u6vl3Y/RI9e7g/6s6JMFoSAdKRw0zvN/RlV3RX1Z/34PZXwaMCTZVqn6NJQOS4NUAWEE4CmB0ypwExlOEfjJcdVGkSXh8xRxUjtaG7o2ZYszybSsTHeVX+m032S9Feln1DfSyN1Y3Fd0u1SQzgXW/hraszNw2rs0T1qy6u7qKcBpIHFBfWnSu+rL5dtag4SUkbjy3XjtfxdMcdk/TgvK6BFPcXTbCmOn0enfc8guypPfqdU4zsdo6y7v48ci8yLICrbSFp1JFMfV+knWKyAUEo6AhWLQ+e5YoyZVjoMmX71/cmUBH0fiL+ngrw/p7c+W9J/Xi6XlyRpsVj8gKRPlXR+sVj8uonNeYakB6fwD6rprndOy2F3qL1SZybL5fIVkl4hSZuLxfJmjYM09zhpkDyRKyYnj5Fyp/7BIA3LaELwO70AUsyVInHcLHse2/W1ZLYSANysEFj4k/ldU3//0d+S9PxrLdwb1Y4vn1d/YSXrxLam91lJKrk0EKP2ymvOYwReEqjQe6aSr5Sa7zk9x63GTlW2rJeFoClZl2R0kilY9z3jWnLJlGXjtetxn3Umk+DxyfS9OXhr+v6dv9qAyEW1sXRJHWA/TY3y/e6fbyDH5d9TG1ePTGndI+kjp2uXp7x3JD1zSu/qFPd8lMFldLreZ0NJA5jjhHWlka02yZP9WDcfM0zFFlYgM9Okc2FhutU+NobJfk9Hyr8rBzLrkGUmC5UvyWX5R87JKH3Lug3jTpvp5T3WiYCsak+GoROX7ZJz8aRAz5nU8v6Anv8i6SWLxeKcpP8m6bdIep2kn5T0JZL+maQ/KOlfTOF/aPr9M9P9n1gul8tMNKXybDwJkj1IxH6k1YlEBZbehoVh/DsnayrGNLSH8cnrnMgVEEhvjd5H5jnKawTyFGGl+RHVI60eP99QXza4osb4OF1fG6XNdmNfJGswik8P8GYMSILTNFZZrmRD8rulYlcqRbjOMx395kkx3mP/VUA6DUN+Z/x1Y4FKPvNcF4/lcfktPgZ8bfp7bErneeog6Xb196G9ZYq3p7b/581qQOZFU9gfVQNEO2qMz/1T+EfVGJ0L0/fr6uyUAQlBWc7jw+I6wTABN8Nz3OTYHI3TjeJ7hs20yN5Uj77IMU1dwbxy7vF71Sa5HHoOeV3V6vI34+YceYHaxvNXazzv0oFgfUYAleEsOYalVWBE+8G8ndahVp1MqQaP2Q5ZP+bzZMrZwwnH8j73x3K5fI3ahuTXS/qFKa1XSPpaSX9msVi8Tc2B+0dTlH8k6c7p+p+R9HU3W8Bcg60qsG7pJO/TC0ml5+uMsxF/TLvypg8Rd2TsKoCUwviVx2KFnkIv3L/5N6L5M33mcaCu7C6oGaojtaPoo+URKoQEO1VdHIYblHmfn8wr75GlcfnXAUse6832PMR1pp/XWC9pNb80BhTHHXmjDpNHlRM4ZTtUBn2Uvr+zDD66nvWi15/MZxr/q+rHwg/UwM2j6pua99U2GX+kpN+hNrYuSfrc50q//y9Lz1ID1R8j6cvVxt0DU/oX1EDQW9RBkNRAO5/M7LF4hPsJPL2vaN2yiq/nuEyDmvM657DiOr/nGEnDynTSoRgxe07Xy2McH6nDKidkW63dv0atP7zE6DGZ5ct8vaRJnUtASlAxmgOp3xPocDxynyJBk+uZrG46xnScXDc6n+vKlzr3TE6fvF8PJ1wul18v6evj8v2SPrkIe13S/+d9zasCJ8l8+H7l2fO+06uUUbIwueGNaVXUKI2m1+krb2UrwjLfpFfXsRw02C4j42X5/H1Ey3Ky8/khvr6pZqS8tMD7VvgEQdVrGujVVcaFnnYyONyUW7FraWgyzyoPLuNkXzEPgrIMk/lknOwXGp6qzwhcU8Ef4Dq9caZtto5x80j6iNFwG2Sb+WWfBK3s+w2tltmGbjPCXFF/4/3F6W9X0hervS7k3/+StPnn2/17p7BXpnBbai9EvTLl7TGWc4j1JFuSy1vJbnKeJ1ssjccY71XzK8dNNUYsaVwrxq0aqyksB+dbjtkqbfbzPZKe/8nSoz/XFHwCiGwjtuG/Q1rrltqy3P5cx245TMWKVixnhnE+ZggZx/o/+yVtyTpn5SRYnizDmczlJPvkpqSapNLcsEp9AnISUfmOWCCHS29HWvWsK8l0E+lbedCwHKk/20bFvTRgozVhx0nanKDNZUmAUXU8AUV6SE6HD5a7plXAWG3+S6auAmMjpqTymnP5JUEX6+ZraYAq5ohxRgaiUu48Zk/DyTKTuUrwkcsPBA9S3VcVM8Q0+D6qrOOIVaiujdgjppVgYTRXpM4a7qmBGQPnc5Ke/vLGDH30R0qfurxbL1n+a73oZ6Snb0i7z5Ce/rs6wNlTWyrbVt9Lxk3X1VJSBbJ9+stlS5DIfmXdq+fC8G8jfqv49HfO44pNGDEMrlMyOLzOsjmt0fxL5ojle4ukv/Jz0g9GmZn+SL/YEWK4irknyBq1RSVu66r8FbuWIGZTc8BTpU8bwbbNlQa2+Trm7YmWs+f0jOXUg55KUST6T2+hMhjVAExaNsNwIFMpjYQGoJp01ZKKUAYaQF7nNSru6rj5qFwEMRWQoBAopsdeMQ+51JEKM5XvqA2ryeVyV3UmQCOgSCCWYHJdvZPFYBo0fCzPaCnO4aqx6HuVp55APfujUvIsp7/nWExgmWVIAJNGJNNLQ0tD7zbhCbCs46Up7GdL+gRJerN0+22S3v5raucePkd6yVI6/Cw9+s4W6YVqD8Q0AN/Cn8vik2NpWAnmXQ4fgU92leO6AnEV0M02GjEveX3knFVzmyynv/PEoONt4F4yVyw76+O4/C21dr4q6a3qy5UJ8BXpsbxOK+cT76d+92fq43RA1gFt9inLmMxXtayWei0ZQxVxfG0U/kxOh9wSfULwkUZOmhuWBDJpyKq0U9Z5wtWErNgVhj2M3xXrQVDC8DnpKWngkmZmmSqlUCm4VBLON2lsTuhq+UqaL725H8hqZZyqL3KZkkAw+5OAqqp3si8JYGi0/dv3q/ApBK357JRDzQ0L0+OpvqoulOybzeKPaaz7PmK/aDRZTn7m3pB1DIJlT/NHIPj+/ZI2N9R2un6X1BazKD+hOz9R0ivbr3eogx7WZS/ySwaQgCTHQbZFzr1KFzjN6gGECSSr5Y6KGUkDnQ5PlpkMcTopR0UanDeZf7K7nD/X1fZfXVOfk+mUVSA9HZmcjzmPHbditzk/nf/I+XX8ipHJ+V2B21Hf8FrF+Pg7HZOTYns+0CxPNQduRbklQI9lZBTWGcKbGXw5eD2pRpunR+yNxUqd5VkHREZULL0N17cy9BYvHfBe5X36elV2ppED3htSyfikcmHe3AuUeWXdpVXDyfs54QhmkzFhXlTQafirtCu2qorLvVqVQkigwOtVXQ41V8i+XtHlBFCZfxpbXxt5wtVYMFtWMVFpGDhPKoBKw+LNrBwbh2qMz3uOB10sCqrp+lFLhw8oZF7JeDDfCszmgw6ZZgWOqzwrMJltXQHYqq8dN0EJ+yidI7Yv7ztMgocRM5K/R4CvGtcEICx/1qNydnw/dSrr67hZ9tTbZOWF+KmbKmCcdRqBNKfLTxVhjnO2z+Tk5Jboj2R3coJw8FeTg2lUSlHFdT6jhxNvS3U69BYro+a01hnByuPwveO8ToKiClDQOJKtyfQqo5ZCsENAwXZi/GRfsh7pKWaZErxVYSqDnPGTIaFxT4WWbZzA0e9+yjJXYy8VaNX+FfgjuKn6xffXgZvjwBjLkmVguye4qoyB02W7uvzX1MBNBVheLOnGu9WOBr1chbxB/+3/afcP1I6sC/n4+2Oag1HrhqvzxGYMkOWG5nN+BLYzXrbDaIz7/gjYVg5WzjHGGYVlmixfOh/peDlsMijVmBql7e8MV+kPS7IjFTiswtPpWBd+5OjmYZOMk3qsKm/G4X0ybCdlYJ/A11Dc8nJLgB4qCxrRUUeMDFeGT3rzuDJsaLyBmgpeWs2nKmfllfv6cRMuy1CVtwIFNHKemHeqbyglEBmVb0v1U2OZZkp1bJwg1qzWyGAQ/NGg8V4FEJMpS5o/QW9O8DQKZK8YryqzJcFWflZGZcTAZD4VaKmYn3XvgFrHlDENlivvMR7rdqD56Ti/pNTv1zpQe7jXw5L0O6Vfe4ukr1lIetsU429Iz/tEfciHSnpp21fyRrU9O0/TfE66bF724riy8Jk92e4cf1VdRs7BqP5sK5clnbeKvcm57WvVnOd4HpVp5LiwLiNQW/U5GcfMZx3oHuntas5kW/J3xfSQGco5znS2NC9r5k29kv2S+SbLxDyrU79Pthw+AX9PBXm/jqw/GTLyFCrv/H1Jhwrdg3cd+s8BXoVLA18pzvQk7blkGCppsjQb8T1l3XONXAd+ZlkpVZnzu9OiwmF70KCkwd5EWPZHdULGCmW0j8h501A47xEDlv2b6bofqEQrA5ieORV01fdObwQIU8nkmNnUarv6SHkqKZ5sSjBJRi5PQDLsiMms2t7ggwaGTsbDCP8uSW+SdOc3SR+2If2Xb5Eufcu9uqYGji5I+ugN6cFvaY9L8BLXO6T3hLmGOvNlopYEDGzvUZ1zTDvNzTWfeTKPICbnWhUu2Y8Uh6mYKKbBTxXXDzXvq6w7y1CVb6RzR+CZeiydwHXtcjPpJ9tGfXQcqHT8wwhfSQLiyoGu6nJLMAsfRHLq+4ODlJPyUONTC/zNdCzHIe8R+q+QfUXr5tuf09hbaOxyc7ONWVK6FaAa0e2Z9wH+KPuaH0WXuvJIQ82yJ0jL+P6srrtMqbAqL0uq+4+bhas28KZZXiPIdH6jMZPeLdkyloP1Z7mP2y/gNBI0ZRhe87j3WM+294P5XE4+VyeBUMUCsI4596Rx+4y8QDICfsTBkdoJq6tqzM/lqSzXJf3UUVum+qQPlz7966QXbbR8XnnUX0vxkOYvEr2m+fIUx5HHfYIajlue0iFYruZ2AgT/ccyNHCwKddcBrkl1v1ROVzU3R3PNvyvGxHVmf7Md1onHb8UIUaqHWbocFXjJ7yxLxZxyHlT6iJJsGx03X6vYpCz3bRofBriZtnsi5Wx5ayynnunxoEwvVPidk6HyxpleGjkyJyn5cCoV4aiQ0muoDAMVZCrgEci4WUUqlJdpp8KrlERFOVfsgoUGrdpbknESoFFYnirMkZqSqdgUxuf3kSEmuzEKk+kc1x+Vp86yp5ebaZOBG7VRgq2RVIY902B75/IsAV5l+LPs9KxdvhzjBmvPVgM571I7o7U3XX+j2otGtyW9VNJP/ap07zc1Rugxtce+v35K53lqwOdADfzcrQaYHtZcJ3BuW9IZGbEbrgel6pd18yPTrIBU6oeK8XT80dyoxnDWgb/zVGXFbphN3cT3iomu8s/xx/lTMWfVvGF+Ut2HWZbKMTqOtUkWPe9XtsJ1qE6YEQSNxtGZnKycetBzHG2YtC2lupfApEL0nOQHOt7YVIolgVc16a3c0ngzTF4fTWJPwgQDppWrsqeyqsqaebhNpHq5o/KKq/JU4VPhpwfLdnZdkxHKtLL8o3LSkFcKswqfn6mMqbQ9pipF6fLmsl0FItmXyTxybCeTkUBLmj+GoJKKIcmxmtdpRA/Ulqbc59fVWNA9tacpPzJdv0cNAD2qttn5Z9XAzE9P9y5P4e+cwj2m/sLSw+napalMO+rteCXaiYZsBC7ZTpTRGKeMQIDbkP1L8MEyJsAYpe1xyuVNztfRMlTOl9FSDE8H+vqIiWG8UTt5fLi8qZMrOdJ8XFft4nD8Tgciw7G9qjblGKZU+nhUZgLUdU7VEy0nmfdpllMPeiwVoCGzwYnBpaFqACeqT+OR3mHl6eeEGnmTNApp1NNLSTDE/NOopRBAMe1qb8/Iy3Ta3L9SKVaGSzmOtao8q5FCqcIo8h2VwWVNYJZtfhRxpHnfZpl4jyeFqn1GTOM6vld7nHw9y5NeMg14Go51hpzhEjwRyDLfNIC+zzLx9Q00NHeqgY796fc9asDkdQgrtXZ5s9qm5o9WewXFQ1OaF9S2M2+pAZs7pnvXon5vmPJ9mhpT9MvT793p/r7m47syzEdaHW/VUhLbINNIGTkVFUCo5nY1VlPPcOyt0w8EDescReZXXavmQwVkhPus7zqQzTyrQw0jJ6TSvZWTVzkV6wCjcK8C+iNHPMs4AnVPpJy9cHQsJ9Ef75WkMaKizk5N74SS3kCCjGSCMn9ODqJ5xq08i2pybuKvWuqo6jQCLwRUm8Ufy16VzftOuNTGcqbyuRlZ5+Hwaa4jQ1QxMiPvO0ERQUMFPrPNaSj8V4HkZAWleb9XSrZqf6dfGYDKe2ZbVgrWfZTLl1l+MgzZBtWypq8zfKZJIaMlNdbGvw/VwMwVtf6/MaVnIHiktin5DVM6L1Xbq/MWtacvf85U/vvUWB+W4VB9T88jmm+QvoE8zmm9VPvD2EbZpgy7rXE/Zh9XDETquGrcqrju8OynkXBcswwjQL0Zv0fzkHq4Kt/NLPGQucwyUKr6jepc5es28B/HP/V4Jbk/z2Av5/6m5n0y0g1ncnJy6pmeBA0VlZmG+TjqlPsMrJRogJmOit9VftU6uTQ24M5rZJjTKI7Yk8r7yvRGkuxJTt5K8fB+Lv9l+aoyV+1dLWtl2dOQjDxvl2nE7rnN14FnS8W65BLmiEnMuOv6i+1Brzj7Og3JcYBwG98rFqFKLw20NB8XZqnILGzinsu5rTnAfbra29F5vNwv/txS35C8rwZc9iR9iqTXqu3luTKlY/DCRwccIr0ddWZHamwPQZ/LekF9A780Hz8V0+cw1Ss1Ru16nBBsZ19kejlWct5saN4eZKoqHZPAJ8NVc0hFOKaZ5U7nINPNemU6o1OfBB3rypZzNSV1MMcxHY7Mh1sfsn6VjjgpOQNbtZx6pkfqg3zUiTnhqpMCFWV5pK48RydtpLGxqcqQRiPTppGovCyntaWmmDmBUgGPDGgqECsI5lcZ+zx5ksDnMOJUZbKQWcj7lXFlm2Q+mbfj+wRMtj3Tq0CYwzhc5Xmn58fy0aD4WhrV/F7VhWNhq7hWxcnxtG4CrwO8Lt8oPbaJ2zn3Y2VfnVMHGU53Uw1YvB3XnN/1Kfyd6sban1fUlqmuqZ3y4n6ovenT18waXVdb/kpDtTXlwc3kz1L98OdcjknweajVZx5V4JxzsALBjFeBH4ZlfMZJPedxSfCcwjxykznZ4lF8noZc5xBZ0vkkUE6wmHNoBFToYNJZq5jXUV0ScDG9kRPAsTCyRZWjfAY+TpfcEqAnd8QfFd/9u6Jbaeyl4/eB5GQegS6WY52yqLy3vM/1Zud/HXlQ2aTnMVrrzvvJ5lDWLV/4u0GRJ/9GfKciHDFtZOYqQJKKjGxQlu9xhFHxPcuf48AGNsuYgI2GK71s14Vtk4xNxQg4H3roDlOBwFHd3FbrjBw/ncZ2/B4B8FziYb/taP6Qyk11QOJ9N66b39kk9fpdkPRZRTkPpnTvnK49HuW7pPkDD93GB5q395EaeErm90htqexS1If3UxhmO35nG1bMR+UwJZDh9xyrnF8eX9RzR/E7QUmydcwrWc9qvlVppB61A7GuHM63Aiu5nMuyHuHeQcRx/Y/iGuuYwrpnfStHrbrHsvP3aQA6Z29ZH8stAXroeVOBHTcxLalonFY+58eS4CCVJidyTpBRmXKtulKaOVFJvXOiZx6pfA4H10ayrh1cPio0/+b9VKQVo+WyP67VNmE4n8BJLyvTMftAg0lwknkneGOd17GCTocGkSCHea8zgGxHg1cvyVAq5sl5bBZ/LFcClHXMQqYvrbIYTpthDHauq7X/ObXHCeyr76m5bcqDDw3kk5Kd76b63hvnc6AGnp43pc2HDV7R6kbmbJ8DtTG2gXI+og6mNtRBWD7HqBo7bHdptc8tN6bfbMORrhCuVeAkAQ3zy/mdoNfMVz7Sw/foxJH1TXbE91M/ZXnZbpWDNwJ61TyrdLXzHunXEeNEyTZwv+Szkao6sg4b8ZcM3nFO35mcvJz6PT3SeLBXRmtkyHKzJ739pGzpBTAcQYl/V6i+Qv8pfA6G0+P6NffK0FhSaNhyfbk6mpnx/H3kmdDzqrzXisFwWfKoPONvxDWnQ+Yj86g2VLsPRkxKlj2ZilHbW6q0KwaHYXw9xxnDu45+eKKNJYXhR+CFeXssjh6W5rAuo9mKCpQR6BDM5oMez6kDDIuNH+NlGzrdK5J+RB0k+CTYjhpIeZU6AHpMvT92NGd1HK/yRnOMGYRx/lnyUQqjk38Ms63xKz42cY/6ojKI1RxVEY73c0w6H2mVHef3ZMtZPkW6FQis9rJkGUf62H1YMSupF5IBH6WXACpBYLZt6hiWwWVLJ9Pv2hsxq0wv+/ckgM/Z6a2x3BJAdITuc/JUYIMTmZMqPSh/96Amu8R0GL8q42H8Tk+gEk5ehrViT3rRHmwa0lQaTju9wsoQZf0SAFJYlsoQSKsgM8HAyJNiORP0+brLy7Sz3bNMFbtB8FrVc0RRj1g7lu3pqt9PRtbgSI0dyeflJEBLNjE9VOfN5QnXken6c7Q8w3qYRWMelvPqr364rjlTSFYmyyH1Nrk+pXOPGmgiq3lVbenr89Q2M3McHahvVCarknOHG45ZHhpSAxu//X0E/gleWJ9qTCVrVhlcLtmsY/Wq9hsBfKdXXUuAQbZ7pJc4lqsycR4f13bJfiRIcZ8I91mOEVM2Ml7U7ZX+c7mqNJku24rpZNlph0YO+knI0RPw91SQW4LpkeZe0ohdqRC4VD/zYYTWb0YR0GNJb5HKiZM7T0NUtDHLMWJnDjV/AFtlvJhHlX8a/EoqMJOKsGIg0pOtGArXnb+rI/l5imLEdjFvPjMk728O7kmrbcJ73MCb4JkGjArQ+0UoI88r28LghWOIYMnh/HkY19lmOe6rciR4y/5z2rdN6Xs56jb1jcbVePB3AgaW91E1toft7heJXlc77bWlxvTweHumX9UhhSyiQaPHyg4+r6LuHCdV2uvySwaNBtSSTAqFY5ntOjpVJfUxmvtlsm+Tfa3mVDqBFOY/Ykxy3x7vV3qBS3F0kDg2Rg7TOlnn1bNf2U7sp5GD47mfAO24PM/k5OWWAD2cJOsMdU5wy2hiprGtGJdqUq2jTkfKl6CMiofpjBREputnndhrZHlooAn2UlEle5DGsipTen0EblW50xCOgFzFtGQbjYydtHriIkFKjpktzRVt5pGfozHhshOMOww32layjiEYge5kM6p2vRmFmwAq77nvvB/G6d6uBuYuTddsoComKtkkh0kmKI2x36F1Xe1Bhs4/QUv2a47VEcAz+HA6zmtXc9DD+pMpsDHWdC/bsaqz4/B3NXdYn3xuFpdVqs8sXzWfVcRxG5CZ5bjOsTVyOvjb/VqVodJnWaY8lVY5tMeBHcZ3mnRYkkWz83ozbMZIF1UO+UkBoLPlrbGcelDKgZjKg0JlxmsZRupenjSmbElncvIlwKBXPCqXEIb1yUmcyjMNMuvGfTPJQtAQWXH5j5t40+vMMleThsrCedHj8VJHMl4Zv0o/GZh1dDi/s05Hmi8rJbCtGBHn4bas+jQVYvYj0/WYorFiearTg7zPMcn+y83dzjv7clPzpZUEapsIk8IN2ldx7bqkBzXfo3IV8aqx77ExMiQep2nQL6tvkL6u/lRn1yXHKucGGZF1xif7YV/9gYoGbN4w7fFEY06GlwCO16o5xfIk8M0lHtcrT73dzOnNdSwS01KRXjXGfD3ZFo7Tqo7VsfpkTzY01kkjvZSsZwVYCCCZN8NzDIyActUeVXnXlflMTo+cetDDgbjueO5oAFpSWdrI0aDwntMbMRlOP4FPRRmnJ0xFUYEAKooEfQxHCjo9Tnps1bOIGL8CWyO2gXWolCaVNeuaCp7f/ZeKOhXppvpLR502n/vCMrB+FfPnvDym/D37rvKWE5QxTQLbfK7NVoSp2JZqjFpG44WKfMQ68Ii661Pt7Tkq4lyN+/loharcudeGYbY171sarx21100caf46CiH8yJBUzJOv+4/zgvcZzk+MNvuRhwoq2byJMMkyMO6635SRo7ARv30tnR+GGQEiAifqNrat57/bPOvGfKv7x0nqiMrhZDnTkUjAY13hdBiX+oCSumfksFXjkc7ISQGfoyfg76kgp355iyBno7hWgSAOUN6nkc5TATnApbmHNWJ7qg27DpdKhmWjt0IAwedfZL19PanayihXxoYyAo+WkZHPgZ/lz3jVHoSs+0gqr9JAIgEF47hu2T7JpiXTUym9imEiq5CnPLLPCCIT/Fbtw3qNykODM+rH7A/WnaedGFaasxoOt60GfAhSDMhGQCT7jmXkk5qzbFxOu1NtKe2xqSzbmoNVlnudsef1ZG8tbAs/TTrn2zowkmk4jwRXFYjOOUDjvC6fHL++xryoK3KPHOONDPyozFnuaq5URjLDOY2qbAxTjVc6mNnnGxE3bcZWpFH1bTqxG0X4BNCsJ9N5suVseWsspx70SKveTDIRHNAjxO8B4E3AVmzeQ5AeMn+nIUqv6maUgu+nkduI69UEoXGh5IR0mTYjTqUks8w2wL5nOUI4hq1AZQVumM9oEloJVR44jbz/cg9PBbgqxWdhGpVSXAcosw7Zl5WSzD0hTKsay6P0KjYy24x9VDEPvJZGIPuoYgTYRjlvfJ1Gn/vKyFSxXbkBV/h+FWWqgH01D9cJxzLLm2JQlqCOJ8+cfz6fit9tiJNRY7x0qNIZO05Gzs4IBKSsY65ynknjU1YjvZVzoQIvOSYSsG4UYTIfS9aV9Uing2Ey/gjE51iswM0ojTM5HXLqQY8HWWVQpXqQV0KE/iWSPkbS/19zGr4CV2lsFOH8fXS819cqbyLZh9H9PHnF+1LtiVbMVeZXsQBppNY970ea1zeV9ToFl2mMrmc6vl4psIpZqzzY3HfEvKR+mieBbgUMcyxUDMIIlCSoI+itPOeqjZJBGnnm6+aF+3lTzaBXTAmZSAIql60y/mncnU7Fuinub6q/R6tiyKSxUamMTh4fr4AwQV+CGB9tv67Vcldz0uLnMFW6RertmfvOKjBT6boKbKXOyrGefVKlPWIwKgBqyTRGTHqmlXpmHXhN/TUCJwzvazzxuo4ddToj9qtqr2renjTwOWN6ajkJ5u29khENP5JqoNnDdFpvUXuJYSpwGp2cgDkhKu8iWYrqu6VSwKNJn8qIzAfLqriWf7w/Ms4JJtPDyryYX/aP98tU8VT8dnoJHrLsBggjYCfNFV3m5dcaGFCmd+nflXGgHNdfVfkYN/uV9c48zVI4LbdTslL+Xp2SSgNpY7Oh/swdlrsCARVg5h4fPh+H9ef13JB9hOsGSrep7eUhCOIY9F/ltLBMKRVj4HYbsSYu4xX15xLtaN4nTNtPeU6dUY0L15kOjvOnrHP8fH+zCJdOGfPMNBK4Zhk8PlNHUH8yzw3N613p79Q7GZ+ORzKPR3FvtJw2ypt1yflBqZhSAptqOfpm2cczefLl1DM9Ur13wzJC2HnNcijpjdPfoeqjxevQOT20yjNP6nMjrieYUMTxUtphESfrQcl8K/aHE3vEjrle1ROKmU8qglzCSSXGOrNOVlbHeXo0MCOWwAYzWYWb8WCdB9OiwqxAcNXPvpfprfOME1CO2LyqLOtYhqr/ySr4uPaR6gfprSt7gocEJyl8xILFv80wSQ0wbKkxPO9QZ1nooOTvlLx2A2Er0JZ1HDkVnKPuLz4klP2S/ekwFRjL+cr5MWJVMr4/k31OI+3rirAu5wgcVOFHzkDOLwJ0ltP3RjqOaVcMVwWy3MYJ/Ajwq6V09rXbrWJo182FXM5LkPZkylLzvjqTLqceiObelFFHVseSpflk8ibh6/hj+GR8Rp5KDmor4mQipPHAX0eRW9aBgCz3KAy94irPNIppSKW5wnI8nghTxGO5rWSSwXH78SFxFUMnhE/DpPhdGZQEdAxPAMD2q65VzJjDjJYmKqXK8vBkiYUGs2oTGo7KAGU+BApmWxjXn7nvhOWuxkklI5DJPHNuOs52xNlWe2Kz24BtcoB4HNsZ9gbuZXmq/mDf5XgXyjgah2a6RmxNtg0ZLguP2ldlvBmHzOGqx0dkmVLHVfWn45YnS7N8lWQbrQM8qcuqPEbgugKIrB/rRieickJzDDu/dcxRznvqv5OQwyfg76kgpx70HOBz3Rp+pcw84I7wu1pusSJlGhVdykFdrYHzu+/nMyIq7ygVzEjSaGT5LRWI2IxrVd1ohFgmHu13WLaBlQjjVM+/oPL078O477Klclm3VERxuSoPtwKi7HOX76AIUxmrNJjvTfkSSDpeUuQcLwdxvWovKvYsRz6HiMBgNB75XjCC4k3ETxnNG+ZFYMLyGvDckPQuzQFbZfjWKeKRUa4U+AjwVe1SgdF1xo3AL+NIXffQ2I70QMWiVI5KltuOSnWa0un4c1TPkZ7KtqvmL8ecr+UjSEb9mddTRzm+WctkpCrhUp5fgCx8jk4m3hbloa55bzagn8nJyakHPRZ636nM6H14eejcdM2KuqIsK6+bclzjVDRyAhDhOg0GQcRIuToP/xkk0MBVXlxl8NKjqhA8wVIamEqxEQylksv3ThFcVF4080vFP1LsCRKYVwJefzKtCixkGSwjWt/gpVo+cBkzHQMWsh9kSSrAJvUxWp3mS+aRabqfbPRc5vSWN7U6dhjG+XK5KMEIx+C6dNNJSfFcZvgEOtU4r9Ks3geW4daxBpUhTiDDt5q7rUflS8k9Tsmasq2yzyqHg2Of48SgdzSfyHgcV+50oqTVuZR96Gujdqnmjeufc7VqCzJble7OOVPpSALRXKLi40TScfO107Kk5CPrH+i/p4KcetBDj1ZaVdy+Js03Ql7TXCr2gwY+rydjUE1uGud1GwWZRg6g9PiP4s9SMRZSb4vqAX2jiU9Dy/rQcxopvwwzejCgZZ0H6t+Vh5fgzIYk+4lMmtTB2JHm+7WyLapHBFRKsQIgVb+kcWGZRt4zaXfXsWKALEwjvVnX28srTMNLur7GZZ8EKZXx9/1U+JVhHi2zZR0SLFF8gmy3KEsCkQqYjN56nmVmmbL+2YYORzCZcd1GN7S63F2xOCPQzTFnpmtXzZGrxnvFVBxFGizryCljvPye4egoOWz2/br5dYj7dOpGgDtZYurRjHucDkrnsOoPC+vgPA6PCZ95nRYgdCZNTj3oqQZsTpaUVFgcqJwclcJJg1YpCE5ATljnlV5IGgTF9VH69thGncS2OE4q7yfLlctYNMBpjKnI2WYH+Eulv075VNczTNUe67zxvJ7ecCqkdU8/ZptU7W3lWxnKvDZ6+GCWwflme+QY5RggEFwHCDwnKrBTgZrNuE6j5es+mp11rtoxwzD/venzivpJKea9rfkJqczrOEYpAUuVznH7MPIt7hSmxf1HBu12Ogh6KyDlft5WO8n2YvXnjEmr4yDHpq9leluqx6rjCPfo/CUgqpwVxxkx6tX1ilVNHX6crs57lMpZGgGflJEud7kT6FXlPSkjmw70B+LvqSCnHvRUnnElnij2jOjxVIxApkfDrwjD6zl5/EkjkmzOSHKyVbTzaJJTaVXpbA7C5kRluIpeJripwrq93QZWygkYd9Q2pubDzSpANDKY7uPqeUCpQCtDmEqadRixDlmOimER7jkP0vhkDFWEzzyynQ32GM9LFccponXgIvO0YR6B0AScybhuR3imX5Vr9PvqlO6e+lvcq3mV7VWxL76fcdOQJfCQ3n9Fn+Xc0Zwh9dxhmBFz8CmSvvqcdJfmY53AkwcGUq9ZNjXfG7Zu7BNI2XgT/CQAqMrvcBWTM9JfCeJHy1JVvdMBY3qjQzEjp9Rp5Hji3PbvZEHXOTZn8oGVxWLx3MVi8X8uFovvXywWf+y48Kce9EjHFzInia9xgpkCHil1D87jnu+RkzsNktNLyQlZheFETHRNBcnvjEeAQiWarJSl2ndAw8ellyzfhlaVOsNWipEPacv8XC8VYUbCPOjNVR6pJds96+g465YGmVaCoBwnlYHOPWBVfJaPQI8MAsvlo96HRVh/T4CTHq80Bv0O471aFRjOcZBji+lkO26uCT9ajqkYmwwj3BsZ3JGTkMvUHG+5hHaEeCNAt6Pefh53HLM5ngg6XiXpr1xrL30dGWH//b/tnXusdltV3p+5N/twBCkHFFDBlIMQW0KjECI01EqwIqCCNQY1NhAkokmNtGlqME1Dbf2jNU21GkKDaMHaIpd6QbSlFKX24g28UC5aDt44BAQ5cMRDDudz79U/3newn/f3PmO9e5/zfd9+v/Otkey8a801L2POOeYYzxhzrrW7LSXnpfRW4pdRnUpz8nn3/Mn5SlGWXU5OimY6+Vr0fs85fz42KTKTIj4e6UpU8+PglU7mRdBFnekZYxyOMX57jPGmu8v7GOPHxxgfGWO8Kzx7xhjj98cYt4wxXiJJ0zS9d5qm75T0XElP2VX/NQF63PD7L4U8bd8cNHmd+O0Qp0NlxdiFb8mz5y9FwfyedoT89byEzqMdbugdhKT+UBl2CiJFdXzRJyPv/JcyIhis/5adPG2v60TbBzu7+fZ6GAkR0tPCZTvpsLuU551RAamXU6ekODpFXGnJKDsR7CR+nKc5g0+QWi8G1Lz7/8ZKW0BVvrafkkGr8g7OSo7usucdEOqiE94v5kvnfM6r0NM8FX81HnPzVJ/JmDP4Jf9V/lina+d9qKNbk35dPDmA4/pOxM82OIDsQFWtfQexBCOVzmiT8JtAjpcnX9523ZPPDgBVPzu+6pkDuJNQB9vdNcZXmu7pVlb6OwO9WNJ704MxxkPHGA9A2qND1ldJekYofyjpZZKeKemxkr5ljPHY9bNnS/oFSb+4i8G9Bz2dgijiRJQwJgOQ0L20CUiE36QU6eHQiNALdwXF+hhhokfuYEY6VV6+xeE80UgQjDgxjd6zAzHywPpd+XdbTKmMtNnHFHlJnmjldaLR4ViyDkb96H172x4x67xBN9RV1kEklShBklBOyuH11E8HCA4o6nmV9zev6pkfbPazKofa/PjeF0p6gqRH6XT86rmXYZtp/ghIvR+X1nzUVik/nHiXNqOGHch0MCVcd2td2pTfDoiSXzomyUDMOTy+lnkGpQ6if0r5W1POs19XnY+V9BDlT094fzoq/qhLyuinNcTyKfJb+fltouQYMeLm6y21nSL/XNvVhl8ne5P0FXVAF428aOBzBeiBY4xXjDG+jg/GGI+Q9DWSXtmU/QpJPzvGuO86/7dL+hFmmqbpVyTdFsp/maRbpmn6g2ma7pL0U5Kesy7zxmmaninpW3d1YO+/yOyCl5RjQm2+yKqcv+7LN3fcwPjCuVObBt4jKQUEkoH3N8XcS+j6JvTDFehcmc7w+bjQQ/HtOW5dpPa5DTEHIpKyS+DTXyV3vp2nyufPZeVo8Fie/Pgbft0Wpit1ptGb9v4R6LFfsmesd86oFr+sy8fC31Rj/Zzr1K8blYEV+XqUpEdKeoSkW7U6b/MebW7VedkkuzQeRfww4uG6Px/UKbDyrViO75wRT+OcttaYpwNsbMfzcb37mNccpXVe11W+9IvzlPio9DnH8FAr0PMHWv3Hehlfqe40lpXfz8iQUiQn6cbqY3Ic0riSKMvJyevKJrmgDnYHpsj1Pdcj5SqVP2OE5LLSFfwv67dP0/Si5tkPSfoenb6LsMnTNL1+jHGzpNeOMV4v6dskfdU52n64pA/Y/a2SnjTGeKqkb9DqM0rXfqRHyt6DUykMCpu0rbxKcLuIgLStBN3IumJLkZNkwOfQfgqPunJP1CnCuTFyAzjnhVJpeJSilF5trR3jPgGRpNx45oTembQZXTq2tBSS9ntpU0FX+aOQnweBSw5cqXo/U9uM4vj8S/NzVWOa6q865rxT9qfklOCNWxWMTrLeat8N+OdJ+qabpadMP6Rv+qert4kKjJDvZHj9eaXNGWtp9Zo2x+hY23LBM2EEmizrc5bATQLFHbBjpMHzev3JoCbAxrac/xSlUbh3XXWXVlbgd7QZBZ8DUc6Dy2ECK5TN0gNJ9xWAOsRfx4PzWXorfWfIHTuCyFSvX/tYMcJWa2kuAuzz45Hgk/D83kxjjK+V9JFpmt4xl2+aph/QCv+/XNKzp2n6i3va9jRNb5um6bunafqOaZpetiv/3oMeKnJXVsV88kDoUUmbC5hKwlE6QUsy3glgdSf2dwl9Ahnk/QS/dU3F25F7WL6dR6NCcgN7hOuahyNLSzxRwTkoOUGeuk78eN+p9IRnrnhYX3pjJkV52A63HNJ1tcdtui6yw2iA88XttARK01i5gXJ5TtG9O7T9VeZkjI6l1cESvfszIYOa+7SmvByNk/fxUJuv0Uunh33Tm2tetihttx4qfziR/+4iAS86RV6W/XSQ7GM8F+F140qwlbZtvK0EVj3yQMNcESYHBLL8c+DnrBEKX98OjFwXOEA/xt8ckHL942X53MG3O2ieh1Ty6/V3BtEjz7t4lTZlctfnD64UcZwvx98MPUXSs8cYf6TVttPTxhg/yUxjjC+X9DhJPyPppefs0ge12mUvesQ67Vy096Cniwo4GPJF5nmEe1coNKAESWk7Q8pec0L1B8hPg9IZWVdGjKCkfNK20aURSIrFeXLj6mCowInPAY0WQQ5D2uxP8Uclk7ww74OsfIoqMW9XN+/dqKY5cQWaojqy8vUBuftp84vUni9tlzqfHWCmwUuy7h4ptzPpENCRSG165PIDkv7Tn0lvGT+q1/+71YZ7fQCU3ruTy2kH0JwHly+CocQvnSGvl98kUvgluZwWVcQkHYbuIj10TuqXQJZE454MdpJTOmqe/ukZnp1fP5tEp0/aPqvobRxrW3679cfy5COBBE9nVIdA0UF/3fscpCiQ88CtLSmvLSmvZ15fBNU/HL3cf2170/S90zQ9YpqmR0r6Zkm/NE3T3/M8Y4zHS3qFVudwXiDpc8YY33+Obv2mpMeMMW4eY9ywbueN5ygv6RoAPVJeyCXY7h1R6bhir3qSp8y2ksB2odgUUXLP79PaVsYs78/5/RtGZrgFRB7YRrf4ir/kabox5jd3aKSSYuN2QzKqDqwSkCgeK3/ynJKirT5wvrr6CVh9vIu67Rv/rXF6llan9fw7Oq6c+fZOR27YKMcEsf52VQIWncJKHlwXTfqApN/V6hxP/Xl0j/XxnFBqg/fV7h2WXqCb/U/1UjY9b5IrB+Q01t25Lx9jn1sHCnNGnsaX+snnms7LnAxw3XqZdD6I41Zz6XVQ9jwC1kVGOmeRNGd43Dly8rHuovAEmyly679pnpPTUHTfwFcC5XPA6jqm+0l67jRN75+m6UTS8yT9MTONMV4j6VclffEY49YxxgslaZqmv5T0XZLerNUbYq+bpund52Vi7w8yS9mDkU4F6wD5aiHQqEubhwQVnnfCXoqTYdRk7Gk0U9TDlZgrTP92RqV5H1MZ4Vk64E3evY/JCNO7S6FdRn7q/gTPyWsyzM43QSyfJU/Zy5xos6+dd0cFzfE8j9I61Gr74xseKE23S/8LfKYoGcsT3LksumH2MfUtVRpNj8K5EePYejv+dhcN4EclfQzPeIC6+OO1//pYVNu+Lm/U6avZ9XbYXZbfQe9h85uI7XZRmpKdqqtzKihf3p/qk4+9l+f4J33RgapOBzkA7SKuqb1OLl2Huu5KdTjVywL+NpqPZzdHc2l0WBndUXjm4+5z5ZTmttMt1RdP8zZrrLiGLyqycFHRpmma3ibpbSH9f+P+kqQfDfm+ZabuX9QZDivP0d6DniQwcx5VLbbksTrxWQI7adGk0GfHK9vwBV98ducWUpsdaPH6nW+CGk93hZTq8HJdlIDXR9oOpbvy60AE34DyvFTuVF4HTZoTgTDH0PmfU+pzwK0M8/fdfqr8aIxJST45xwkEpWs3jgl0ePSNffM3Uty4VVpFp450+h/QaQzZZgLI9czLHIS0au9+Wh0jmgPl3kcCqyKCxTnwfWx5HGi6LpiTMzpaBDwpwlB50rY0wSfb5dgn8FTXie8U1fM1UJT0hK+ntF58XRP0VVvOm/fPgT15dl6dEg8FQgh4uda9XS/bRWw6EJXKL7RftPegpzwvqfeOSvBSOJRKiwCCi20OmSdPKPHjwKbyuTKphdRFTlJUhsrFfxPvVDQMS7vRmeuD85b4YKSrgFwXpUhtJeNB4t46+e68q+TFdmPoz1JkyymBFWm1DVSGew4YCM+KVxpIf9aBOe9j11YyjD6Hhzr9H1d/HuosmbyEZymScFdTnpECJ4K2Y51GkRidTeXmogiU+ZJHP6PDeXAeO73gEYUEtGotHCL92MrI7t34pwhPilpwDVJ+EsjvZKQiVinCxfXm32dyMNg5o56PUSh/3oFEj1JK20Cm8hK0+T3BFaOeVdcDtJK90mOMLCbbIavDnSjv99WkK/jK+jVPew96koGTssF3b5YoncpaVla4pwJLkYTOw3Glmgwq66RXw4WY8kvb3wnyZ8nj5ULtFqJ7xaw3tUtjQa+06jzQqfGip8WyKcrT5Utj5e2mSJ2DZALqDgQlAEkg5kBX2lTK/m0inwvmPUAeB/2eNwFPRjzIg/PPQ6k3aHOrqvvOitdJg+L1p2eUjxRlIJ8ET4kfPi/5ItBivkSlM5JxTI5WgRgHuFIvk9J8+04JvKf1WffUWQlEU+b4TTHZvZfreE/95JpJYI11zEVOkswkOe30NutNus9l65Pa1q0EPFW/2xXXaZ2zfrWoDjIvtE0XMR/nJnoSBABddKYWRy2WznNJbXX1uodW953xIi+7BrsOpLridcXFdpKCKPDXbb110ZVKqz8HYUlJ07Cltvz+UJsHrzlOnBsaqhO7Tu2kCIL3Jynx4x3PyXulX1I/n8faVsrV15vQXgLLbLdrh0CwyiRFzXRuJx7o1LM9RD7pVKYIGA7V/7uJBMRclhMwc8NTb8IlIMd7j0T5eN2pTF1Ex5/VHHHtnSivO9bDZ66LChzv8sR9fhLw9DaoDzyi1Mm280yASyfAX3JwB9PrKV2RdI0Dk04PE1RWe+xTouQ0dQDNdbjrMb6lWjz4W20cz/onsq4zd0U1F7pYuiZAzy4mE/BwwU8K6wT3nfF1OsIzep5umOktJupeRy8PrDyJzkgWDwRhB9reDugUgRPr6jz1OY+78s1FklyhJWN4En5dGZJPB2YMKXt5V2Tk9zwLoQPbCSQ6ULpD2/KW+PHIAeuuvOmwOmUuGePOo652fX4dTN2o0/M8lZbADWXG+3CXcoSn+ur13aHVa/GUNzcqlIMj5K227q9teXGiwSJ1DkKRA4VOjpzXkjefY5cLyqnzRV1X7btDmABu1eP1FlBM7XRAqdrpthup0+YAYadfu3F0fVd/6WwiAVzVXfm5Bou/0rcsS+ocsqrT10QCrFeLjq/A372B9n57ywUqHT7jdd0TvCRwwwjSHCL3Z933Kvh6N0GKKyCG32loGNIlpVcta/uIAuqgwsfPDRAVVQEvz9uBICE9eYGVn+R1MbJxgnvZNcPMSflU/an8ibJy7MLSXUTLx9HzUqY8EuF0FNI7w8v+deDTAaWnHeDXx4BgiEa5wE6d+zmLx+0vFfiYkDeXQy9/pM1oDaM7BFxCO3XP9pg3ff27+PJx4hoiLw7UuS5o/JOOqroSoPV2qnytUV87Dn6kbXDSnYVxXiuNjoPrBF9vyWFMwLLKJQDqDh7ndm6rteqgrk1yUM/8nmt17gOzaa0X73P69KI+TrhQpr0HPU5JyVGZJ/LwdwljEm4CIc+nkC8ZjQ4QMZqQvKFuQSdKxjul0YgVncVopbaYN5U9CPcJVBUl8JmiGKwneccCD57HFVcHoFObpagJhubAsitHBw8pDC97dsl+KXsckw4ouWEh0CHfSbmTKv8N6+v7r/m7QatDz24sWKbqZL00ap7/Bm1HMauONGY0pPyOkzsXSbaSoVdz7REugrBUh7S9ThIxQuWUogWM5nga++j3c2+A0nif6PSzAVJ+WeAs+snvEyBJMlrpHrFj1Kp4mgOZSZ/xPGFyhPmM+ar+7g21i6blTE9PHU7YGyqB4rd1uhBlIt/rr7/umzrpOkVCXNA7A+31J0+D3mVSsrWoT+zPicY1neVxhVzj0H31lGCkPNcq70TFz7LOS/o2UtVPbyspoe77NV20pvJ5ngT6PE/yWD1/p9w4R04E6CnqwI++uUfdEcF5x3elnyVf8ZaMbI3Tl0j6Z1p9/9374vJc2wgEG6zPQTEBEoFfASAHQ3Mg/hKuE8iWMvg71ua5OJcPXw/JcPoa6qKDiXycvA3Kj5+zYr0uy1VHB2rTRx8ZySig6887/cNxrLapXxPwTPcOeJ3v1J+ksy5pc7xYTyLX6+mZy13JyAHS6AwvtH90TUR6fCHxEKZ799yv7a7rl153AjHpGzIpBF78Oc/8FxROyTtJXhcjDGlRUZH4q7KMPBEQFJ9dBIIGkF5SOnOUDFxHBcB8S4NbV7LnPm8OiJIBKnIDkqJ9xaPz78+6c1Wd91x1eL60xejzmd6iIblRStsgNLjcUmE9TtW2b9FwDk4kfULS70u6XZvOhAMc2T2jf/VM2hxXafN8xrFWBtcBSwIble9O5DnAvdOco+Rb1P6la2/XDbLPIcecskteqHP8NwGnNB9Md6Ax9x2x5NBRnjugzLWSZNvzUNfNAQLvfxf9qXbSl6ZZz9y69DTXcykPZc6pxtn5SRG7q01zAO96pr0HPQ4o0oKi90Vyg0Pj2Bl596KTgvTFzNeWaWhc8Uvzr1iqSff83O9OoXm+Np0iJ1538sQJsE5C3hT58Gdpe4tAsPNESV4vwYGPgfDMQQVBQpKnuk5Axr3gIveWk1HxthIoTEo9nWlg+51SdbDBuimL3v5hSCMda/UdolfavcK1t5WI20IuWxVZYOSuPOvij1EAjlvR3De+En/+2Yu6Z740jgSb7CPlvTPG/PW8HegWnjPio5kyCvk6fcHITQIVR8prusaRZTt9m3S860HKddeXys9oEMeJDmLVVfn98xZ+NCLZhH0AG8t3enq6aDC6k8rbomdPcJGUgRvFZNwrxOuLjwvbiWndSX9foOWpdorA0+mxFM9HWp2jqGfMlxYfPU9PS+PoY0aFIG2Oj9+zTS/vBqRT0N7vSud1mpO57Z8EVsin//o4VHqaMyE9gW1XyF2kxfnyuUweK/Omw9DcYkpGpKvzpHkmncqnRzJqzXwqPHfyevn8Lm2CBKcOSFRfUvSUAKnalLIsUD4TsEmU5pr3aUvGf935cfk7wJ+0LfO7gNKcMuccHDbpLrud3LojIW07HUl/Hmm7L13Uhc85p+klDqdaKy7f1O3JOeXY1/Pq5xHK+nx5PQvtN+19pIdefQEB6VSgk3chlON9MljMO0fJyyl+HfD4gpmLergC40eupNU3S+60/Gfh2z0az5dASfKWfRuJW0EcH9+e8XtpE1iS9xR981dXOyIocaCWjEA6GCzlKEeq06MNHp2ougmwneZkkH0i7ZrrGlc33A446IGmaGQy1F5G6v9tSWe8OB5Jrlzu6nV4RkpSxIj88t6No3vllNEDba+31IfUdtfv4sGv07Y7X6+nPiGg6CII1BvpzE+np/yZr9uzflSve8uM1G2dd1vZVXe3DZrWAMfvWFkfpHHpxtbfhmVd3rbXRX522ZErSRfZ9j7T3kd6Sni6feb0hsucl5cQeTLiuwBQN3ClsCn0yUh7yF5N3spzmzYVoLT9jY2qtzO63SJwj51tO68JHHDsHAik7Yhubtx7nPNgGcVInjCBZxfx8DQfy7TV48afwPkI90KZswBy5nNPe1dEhsDNX2NO3j2NsgOTIjcI5IV5+Cak/3Yyx+2fxJ8DI0ZRvCzXsjsac1EQ1y0eCdi1FUUdkyK9CUyXnBGYpUior3V3Prxutp3ezEpRwXS2yNs9UJYfggpfY1VPlXEZ8OdejvNWvzWHc84D0xLA4FjUmKWx9vM4lbc+z3BeSmBoof2hayLSU+CGBm/Om0nPaLxSflcwLrjulXY8OFHoaZSkTQXj9bGf6UyBe4t8lbMWttflvCbl4MbjLp2+npyeezvkydvqQuQJhCRgluY0eXX02JJBTeCs+OP2hkfkHFh3kRjnZ+5V87NElM4KkFJ0JsmJp3egKSn2XV4i577SOMYd8GF0zIFN1XeDpactrEOd/p8vAnTvw7GlE8j43BMMsl432h7BOdQmePH5T2vA2yMw4droDCjPl1RZRiLobPhzjz4L6Xx5Yk7+ahw8cnMSyh1pM6p2gntvj7x6Xb7O+YZoci53AQ+vp8rPnc1xW9DZol3r50rTcqanp70HPXORHL9PhvxEK6V4o/LCLfIFnL67QEWaDFZRClV7+q5QejKw3avfla94SNt8VGBpMTMacIOVS4r5APmLj7OGxlPkhuOYlByV/xzgTM9Su6lfbM8VObcpE0ibkwGG+r1P3J7gOKfIkSvsuS2hNB9zW2cp3dstcMc5Yz1dnVwHbsgK7ByHPM67G6j68y9Gp7GmbHgfHMSkNeXjSl46AJHmIskHZa6Icud8p/nzet2RY7qvaYK+LmLSjSHLe3/dcXuAVv/XquPH2y4Q2elL563Kp0hXAswpwsRr19fcguvWb8fXRQGgBfRk2nvQ44p0TsCkbeQvbX5ngvUyiuJCzNe+uzalTWXlSpGe5Rx4oiJK4OoS7smPgxSWYf1utFJfOu8zbUF4+lzffMwZ4fF+zAFUGgDmI4hKniMVPgEMjXdSeEXu8c/xTD6SwUveceK38pVBrfFg9MTnvFP8h/bcPeX0XSTvs0fJikfyPKfsPTLCsfcoxi5QJa3OuvEMmbdRPHMOOAayvN5Hb7szJCm6NBflSXNAorwmIzoXkXBZSCBT2gTMnQ5yQEZ9yEjSHCjgXFS9Cdhwy69zIqRtR8TLpGsn9qPyciwqDyNJTp1DPOcALnT1ae9BT7fIXfn74p7zVl0JeT6eqyHA8GeJ0pmOucXB9sl35yFU3e4VeWTIvyvSRUUS2OsUZy16jpXzlryp9CFC5yW1lTzoXQCgAzxeNnmR7sGzL8xXlCJsxW8ZUPKTQDhBm0cq3Bh0RFBLA0OQncYhRR+cF/bPtxJ4ALcD7sco63VK28aSfUwyOee5pnpcP/hccA3sGhemJwPNfjJixPXu5B+5YxQoAQ1PSyCK5BHEKk/ATErR7+K96vMoSNK7B9o8AP+xhj/qCB/HxBuBdoHebq0fIJ3ywG05NWWdx6JuLTgPF0HLF5l7uiZAKL1BKUdX5sp6meR10CAnRZ0G66R57gp1zpB1St/rPMA1jTQBlrc3Fz1wj9h54X49y5Gv9K0aRlJk994WidExr4sy4MTxTnPlhyMTH6zblXYpYL4yfoB8BWBYj+ctg+P3x1Z2lxGa47sDz52iZoSj5pIGfM6h6EAj6662j7XZdjJuaawpl0nGCGZ8bryOKuPtEHCTfwLzFAVIfa76vE5fa5Rzzhm3Lyuvg97kCDAax3Xuh6o5vylaw3lNoJfXJ+GvI8p4WkvSdh9TVJJ8VT3JSUw6nn2W5fOD7/7CRqJrwsBeZ7T3kR7udVM5uvEuQZa2F9dcZKKeu6Ang0GleBTKHqN8ol0hT/daOu/Pveo54DSnlAhUWJYKJG3Z1W/ig0qfUZqk2Jm/44OHtw+1zaeUlVfx5s+4ZcYoT1JslAsHLy6Xqa4OpCbQ7dEgqT+7wH45JUNdRNkiAKjxYASPa5PRwzma46eeu4yUYXZw4QbPt3bTWpwDmk5ca8VLx6fzxHJ1T4DWASMn6qmSpxRNYOSwW1Nu+Bkx7IBrkrO0dhL/lPE5vUdA5/rPHZSkq7i2/WWClN+BvUdz2LeOV5c5yiD72Y3P1aC5yOj1THsPeqhM+IqnK0Uq6qL0LRgv2217+X0Z2mOdHoxObc31w/MnRUpQJfXhYxqiUuxccIz6JF4778jTOn491OyKjm11ALLypzD7XHiZdXvfk/LpvrfRGTcHJ50SpAFOoXHP555+Gtc5A0iQ0W0dMZ3ecve80tIWUOd0pOgMr0tGEyBKAK/uO7DOPG7YOQcd8PL+ElB1wG1unro3ndzwpkiRX3egxWU9ASo6HAm0OPhK/LNuL1c8et/JM0FKWmdOvm6S8+bOa4rCSPmtr8NwTXnwefe87ohQf/o11w95TO3v+pjilaBle6uns9jrvSAqNJIrBwo8Ixd1zVfH2ZaHYyuPe0leL8Pm7qV0kRgCg2P7c1Djysd/XZm6gk2h3O47R9WnTqEW8UwAQUfx1QlUFzFj/QRonEsaZlIHfNMzV3wOXgh+kgL3PA5mEl/0Rjnnib+aM9ZT+XzL4hLyJJBV9flz1utG1kEOgVGiNE/S5tjK0tivBCRSxIXApItseRmXIynLFecsGTXXB95Prgd3WBht8voqD3lNfKY2nU/ngevX55Lyxn53OraTafabTmnHB/shneqhuSghdbqnO5Dq9GsH5NIcEkAKZTh+SQ6T07bQxdLeR3qkTY+OSqCLICg8S3XS6z9LhMPbLuJicAXTRQj8OvHpPCbPxe/T813hTfJZ5btxS31272fOsNFzTG+PuVEpcJnGxvtKBZm2ychHekaFnCI8SYEm8FdK96xeto+Le9FJNrutDI9WsG8JzCXDsks5J1n1Txt4Hcf2jNsBle8sHjOvXT5r/usVd+exDrd63eSn0ml82U9Guy5pFe1N6ybVkyJlSa/QOUtryPOl+hIQ8fIlJ3SSWNbnhZFTn2v2O4G09BZe55DQ6fG+MW9XX1q3CbR5Xb5GKDudbPCzDb5Gna85EHclaZf+v17pmgChvrBc+fHLq472K4//dkqk6k2Lcc6Inmh7MfmiSM/dOO0afO/jrgNzjCYk/uY8n6IEtFinezgpAuAKzHmg0k4RuJqLFG6WTg1s8ejRMQcYaey9rc6jrWeMdLA87zlWbtzn2nHFeGPI44rfwX+iLspVc1Rjemz3NCb+5xELtsFnjMKwPN+QdADnUTbKK3mr9Bu1OUcPkPQQy3+kzc9VVN31S+BAXqRTndCdjemiR8UjjY6PkbcxB/Y6IEvqHKxk+N3Q169HR6jDFOogUf92Orei0ml8UvTG+fOPG1J2Uzlep2iOz3ddp++dkUd3ZOq+fjugu9B+0DUT6Un3nQFI3oIsTdr+emfyJKVtD+8QeTovgHV2fUnKns+TAnEg2NWfDG4XuWIeen+M6HSgoOM5KaIEyJKnyohHMo5sh/dpvLw9vo7tclI0F4lxooHpomnen2Nt/iNOeonJqDF61AEsPndwWM99y2xXhKH652WkTdliBIIeNceknvGNmhOUkz13UPMJbcqhR3+cZ/fonb8TbY91lStyuUmAxus7izfpQDbJsNeVonuJPJKTwFKS2VpPDka43ubOFmomXdqeyw581bVHBH3dpu+U8XC31+3PeJjYZYA8pPFJ+chL5XUQeRZ9eyVo+SJzT3sf6fGFQs/Pyc+l+Jsc9BBrASVD4Yu6jIK3c6S88N07Yj2Hdu9KjWCBbXndXi4Z4+LVjREp8VTp7tkzApU8NfKXDBjJ54cRtnSmiG3sunYFl84fOVhKYLEU/p1WTtr2BP3X609RM+YlETi6wibQoJJ2j7KIRsXn2+vgG0/sS+XrIg5S/kZL2oKgF536lLY2DkM5odwD120/QNLXSXq+pMdaH2+y/AR63i9GHebWkMKzigbNzYX35Tg8T1GDDozORVsIZh2AdYDHz4eltT93hk7hWYr4zekUjr8DXx9zgjiXZRKPLnh0iKCb6+8olO3k0H8ZFUwRwYUunvY+0uPeeAq3eui+iFEchp29HCMJVa+UlVhasMxb5F4uPTV6c+RHykqqyMvQm2A9/jx58w7W6PWyTUbK+FaUtM1zMgBd3m4uiuaie2menRwIpzwEhiQqdkYzkgeRPD0CPMob5eF+Wo3zp5pynVEp773zON2LZ/7UD1n+LsqZ2mFaivR4FEfa/l6QLH+dp/nU+vp5kr5Q0kM/W/qrfyH9F0m/pdMtLp+bLnKUog4EHGUY5+ZzLmLAtc0IWKqTMsb2vN5OrpyXLs1BbqdzqE/I+xzoTxHbet7JcRfRTWOz6y1XPnPeybdvnXdRAUaUqi3XBZ3NuFo0B1KvZ9r7SI8TIwXS6XmBEjQ33PSY/O2ApGS6aEN3Tb5InZdPD+WsIVBGfAhkOh7pzadQr1/PRS6O8Jyv6yY+vW2CDueZ/UheHiMQXq8bYwfJ3XgkIO28dOCPaW4ACLzI/xyIdX59LC5pFYHiPFZ9Hp1J9fqv96FkiF4tiSAlrRWOAQ0oo0z1jF94TnVQFgsQHUv6Aq0iOg+d/rH0yUlf9HelJ2kFhu5syns/OG5+bsR1SpVLr98zYtVR0hM+73M0F2Xo1lS6T+kOYgg+E2CggyXLk/rINN9GY/SS/fA6OyfQgdVcf/17buUod+CwZDPVw7bn+u19uppU21uX++/eQHsPemhMuDB8sbrRcINZ+Y7tmgukWzAOEly4aVwrLSkfLm6GVt1jYX1F5MHH4azRChpxP9gqXKeyxS/54pg7T0kh1Hgkw5jI586Jnttc9CjVSf44RwWkWA8P5bJelw0qDM4vwWLJm49fdxDYeXIlTr4OQzkpf207RcxoEDlPBC7FS4Ecl3WXMW7DpG8pHVg9Dsg9CrAam1tXN3doizgmKdLnfHMNeFtJ+bOczz9lj9GLIspgzdWh/VY+12Epusa1yDaoH4u6M13uBHia98mvT/BHvgia0icXXO9uzvM2b8VziuY4sCff6UO2Xs8cEE1j5NFZp3sLWLi30N6Dngo1+qJjCJoGll4JDQvLeJ3Jq+CiOcE165ROlfQuQ+N1pehT96rzHHgrSvU5gCDPadzYJg0S54J8dGAygY6Ol6o7nQtifd4+zwqxPub3b4zQyCTyufcx9S84u5Hr6iQQmjNgfk+Q3Xmc/IBk2lLxfpDXDrCn9UhA52kub1Un3/66v1bbeXP/KPhQq0PfN2oFdT4s6SPjNZrG0B/9N+n/2HNp+yyeOyGcmy5alqIB9UzaBIydrvD+cv69Ps45txy7c0mUAY6760CPenjeZKAZ3WLe4yaNa5tznyJkLv90FNiPeiakOQDxeakoXlEXpaFu7Nqj85HOXs0BpytJS6Snp2viTI//7sp3d55T4fn+MD1qGjkpR1BSu8k7SpGhdCaJxihFV9wIO9ETTLx53nqTiQqRXyl2xaLQho+fjxl55nctnJeqN4GaOaVU5dhuAmss23mxc4ahyvtbYBx3Gq50Do3z53UkQJE8S++ryxmN1BxwdHlLwNZ5ZSSrfqt/fq6IY+jf+amzSw+WdNu6jk9Z/jJEd2kFiu4r6ZOSXqfVltZDtDrL80c6Hf+7dCpjCdDx+1+c210AkePKeaf+ImAlT8fIe4C8/us8pvmcA+w0/uStyvLsnpq8ft2Bb7bH6+RE1L23k75VRdDOeeSbXCfa/Eel1aekHxIo2vXM56iLYC90MbT3oEfKC0tIk6VxoTNszkXeeUodJaPnxmfXmZluYSb+6MF4BIP9JPigQaLhrzQvW9cJZDlPBAZsIwGZBASk7Q/GFdEjJ++sn3WlOe3m1seOY9vNH/k80ubbX/x4mfM797XlOYDn4+8AiRGLyjsH0FKfCODP6t0lQ1W8zhm5Gocb12W+Q9LfuFn63j+UPoo23OgfS7p9nX6npLcYH2Ukay4IMH0Mfc058KATk8A4iQDJgUN3Xs3bT8a8xm/OcB5oBRhrG9TbdX6KKDsu81wr1FUub+kjjwlAVDmfv/T2HwF/WpMdIJkDIg6I0mvs1H0d+PJ2dq1DX/dz39a6knRRUaZ9p2sC9OwCIZXHFbZPeDrZT2NJT4FEb9H56gAA60tGiEbdeSZw6MaBi9IPYlaaK+KzKAVXMOx75fe3HPxe2lb8iee0p8483fM0l1Vnp5i6+r0/pOTNdwDtAdoEvzU2SbYS/wk4HCvPl7dLPlLEbBd4SdFBn0+XgRQ9ct7S1lUiH8f6yvF7JN3vD08jPJ2sHaPcX9Eq8nO7VtEd6fTNrRTJkDbXad274fY+FXWynNZ2ik7Q+DtP7kwQsHh/nW7USu5u1GqbzwFEAlpJP1D2/T6BsyJGLqtsGkf2g9HD9Kq3P5vTf8xf9TMiSvCZonXS9npNoEh4lvKm+6tFy3d6etp70MNIBD0U5ksKjt77gbYVgBtYF1QexJS2DQJ/3RtNHnjyzrk4ugiBlF+jdaJCcSJAqrFJYzDnXXr/O+NMUDFnoOeiUQS03rcuApS83aTcXG5o3Dre2Meq5xOBR++rl6n26zd5uDQ8aU4ccHpeTztUXjMdfwSstV7S/HXXKVLiaSmSdknS/5T0P3QKwH0rytcLo4136PQM8wnKHyG/15XABXUMDWBaHwSFPh50BrhFVW1yW1naHBs3/N72kyR9nqTXKgNNrmsCc+qTumY9XgevSd4XB/NV94lWINUPgBNA1LXPEaNHTGdEjwDZ12ta33N9TQf/SV6/py20P7T3oMcNXvISPB+Jiq3yuUFw5ZOEM7VFD5iedzJgXZ27FkQKUVMpc3GnqEUCd5WX9frYsH9c/KldgsCurkQ08FSIBRA/DZ7qd268GYE7Cz9eb8rLtCQvCcARhFNuUv0O/JKi53h3DgL5StFH8pPSSGmNHobfDpT7a/kOlBLY8vG7054lY53eyJqLGjjYciJYTbx0kdskawTTPhdz8+VzeiTpV7WK9hTfXYQn8eKAOPGUgHcRI3/dlm0XqXF5dh1PkMS+JF4Tdeuc80gw1jlv3h9G4cmDR5UuKtqzgK1MO+djjPHjY4yPjDHeZWkPHmO8ZYzxvvXvg9bpY4zxw2OMW8YY7xxjPMHKPH+d/31jjOefl8ny3LrnNObeOUYc6joJKwdkV7jb8yWviPeerzOkXtbD0N4Ww9NVpsbK6z7RpiJPRpzG0T2hSk8RKXplHHOnXX11OtSmMfM+05vj2zn+LH3DKAGppLS450/v2f8og/XHMUvySb5omJOx9fsagyrrkaIas2TEGU0sokNA4MJ+VF8JZghYXGY7QMb55avSzl96i4nnb0ju5TOCUPfc/knfc3HyfiQeXNckB+sA6SkK5vLtIE6S/lyrra071X+zK4EF8p2ioGcF+x114NH1ikebnb+z1ufyX/d0ktiu8yJtrqUif2stAVvy4vPcbdctdPF0Ftl9laRnIO0lkt46TdNjJL11fS9Jz5T0mPXfiyS9XFqBJEkv1SoS+2WSXlpAaRfRgEinCpWGhQLO+xLGLkzfGexuoaRFRKXmi2oOvHl+f9X6BM+SF+SKg4apxoBeG4EgFZ7/pi2VlO7gIQnWSXhevPl81oHM9IZJAhIeAqc3nAAHAUvyGH08EjhwxZqiOBzzEzzjOPgnDji/rkiTQaCnnkBB1ck5Y9SO8ss2uVaEe+8DI2uH2myDz5NXfmDplafmI21l8PVqgj4fV26tcF0mGSJ4qTaSzNFIHiC/P3M+XBaqnkOkVbkCOr62a7y6KES9yp8cKicH0smBY1kfmwPL43NyFsfQ60vn50gJcEu5f0n/eD+dSj64Rp2oX+ZA5tWk5ZX1nnaCnmmafkWrN0idniPp1evrV0v6ekv/iWlFvybppjHG50v6aklvmabptmmaPq7VyxYEUrNMupHpQMscmGEd3TcVXFF0RkbaVM7Mm8KarqzSQvaFlRaPGyHyUeSAyCNFyVvkB+WcXGk5eb6k9PwQtNdVxOiT/xJkJMOxa6sm8dYpUc4NPdqq49CedwqU9fgzRhE8XwJeyeOs+ejexuGcuxJ3Y3OEvAQEHqVwPlLExg0/DX4n90lx1rohD1XWgb4DF6/Lx+7TOt0m8+gm8xK4z8kX569kIRlap85gc31Km+uh0niesAzwLiDgjk7xS7DHNhlR2xXdI5FnnmNy4Jpkirq303edrprL48QoUCrXOW4+B+6cUPaTjrkIWkBPprs7Lw+bpulD6+sPS3rY+vrhkj5g+W5dp3XpO4nGwpWgp/tvWqj0Vuh1VlseMapn3eumVf+x+oFMypa8J8/U2ykl20UX2A8qO1fwXZ/cmywFeIR7KSt3KStEB2GMziUPiQbTASLHhO1UPQlYeR5XugTCzktdlwJnOvtA5VtjT2PJPnjkxz32ZITZ/i6vmKBGyvPn8uhyX/OWIpNcl0WfxvMEEAhwnTfKMvN34DZtQaVv8yQAl66d0icoyIfP3Rwdh/qkzXGvfEnmu/VbfDC6k6JV3mblv1GbUaZdfJAfl1XXHR0PScfxmmsqRYkSUKEuShFM52EXeCVRzhKQX2h/6R6D0WmaJq2iaZeFxhgvGmO8fYzx9qqUCj55eDRUNDDHyJeiRhR+N1xusJ1oJCrNQ70n4c/Lu2JOXoYDo86Q+6/nSREdKk5GMlJUwceLirXaSWNe9+4ldZ4Wx8s9RK/P+0iv38e3U0zupXldKXSdwFEXzSDNte9p9MzrGefTgYTLmPfJKYX5fQ7StoW0Gaksj93PuzBqU/PL7aVuW4NpcyCHxLZ4lqkD9gR/c+OVeEtevbfrY8q6OI+un9wZkDa3UucAYAcGUnSCAC0p/ar303bvZVmmxp0vPXj9CVTMUdpeKp5T3uRQ8JrOzpwT0Olejl9HXfmLoEnZ7tzTv3sD3V3Q86frbSutfz+yTv+gVv/wuOgR67QufYumaXrFNE1PnKbpicOYdIVfSoJeG4XdBXruQGuVPbG8ydinhUXDv2uB07gVr95GMr4EFN2WQ/HhfHukJoGP1GYKZyaPLSlmEheLz13xkUBTUYoqdHmZxnmigUhKmuDjRPN9PNDm+LuCcCBAxeGRNE+TsoIhaPX8pIo+pPJVR0UdqNTS2QWPeiVPtvqRwGe17bLiMu8Hrmsck8HkdZV1B8XzpX7I8ntkie27Mff6WH/1lTymKFvio4iOAdt24ttllY8Hb518TCk/dBR28e2UwF9HyXgmOWdafambzhjBJB3DBGz5rR4So9p00tzZoPPquubeBBLubXR3Qc8bJdUbWM+X9HOW/rz1W1xPlnT7ehvszZKePsZ40PoA89PXaTvJw9euEN3zpMfu5WT5pN5DSocYu8hOPd/loTIykICSG70U0ZA9O0GebvEmPnyh7lqQyfuper3fHh3qFGVScBVBqLaSgfXfZPhcMbE/rvzYT35uwPMVAEqv+yvk9TQPxZdyZhTKwd2JPU+LMH1modo6Dtf0Lue8TQfMBJ2M4lQ/Ok+a0QuWPwtxPro16nmcGA1kZDPlL3K9kvIVkKCTUWWdL/9jhEjKB2YdsEjbcqVw3zlXDmo8guRRKEaWixi57caui6I5WOh0S4pQ1nWKVrncP1ybXnPxkiJqnVHbpfeop6lrGBlPIMefOUi7CDq+An/3Btr5nZ4xxmskPVXS544xbtXqLax/Kel1Y4wXSvpjSc9dZ/9FSc+SdItWH1V9gSRN03TbGONfSPrNdb5/Pk3TbWdh0BeHCzO9Y3qxrKMLeTKt8nYLyJ87bwRQpXw7Y+Rgp9J2RYroYdSi98OnVM6dwXIFxXa76BmfdXQWD8eVXQKonSFyRV78kMd0kDxFhpJypAI+CulU7jw7Qo+9I8qYz6Ur3E4GOW+XdPpmjtfrES1XzFWm+pbmwvsxB/IJlFPfdwGSg5nnBb4Sj14+tVk8edkuIuJrKkVAmLd4I8/pe1V04LwevlmZoiacP0YayIOTyywjmSVHjHy6TLieSI4E9aeaMgQ01a47VGmcn6TVt4h+EnWlTxRUu95HhXz1zMeTgEzq9TL1v79x6s7gEvHZLxqrIzn7SYdjTN+o7YWYjHoCNi7QLMdXorvy/K280ubiTmWFtO5jVa4c2A7bSN7wrvbdmHJc+HwXefnUF+epnndGkO12c8l607j7PHV96caWeaTN174fvL7/ZCif6kkKMLWdxojz041zB4Z2AedOdjlunQw7JTlKUQTPPwd6OufEx2EX6E5GOkUD5tr1euZAFqM51DNdZCaNtdcjlNslt3MgqCPKb9eXSnPeK811Gtud09mexracqsyNkm5aX39MpxHOs9R7HuL4nacuH0+f70p/nfSOaZqeeE6W7jbdZ4zpgVeg3tuucj+uBO39F5ml7ehEXZc3lNKl06iAtCmE3ZsnjCD4s7mva9KzqbZZJimrog5Y8ToZK7/vvIq5Z2cFPMUneWH75bkxtEvDUF5eAYQUmfCyJBqNzshUnm5saaz890irkGXxO2cEkwfsEZXEP8vQK+2A6Rw4nHMEaMxd3tN5CWlzvlI7RTWPaXw6AOXp6e2zZFi9/SqX5obzScPagTRu3aT8fNbphV0RMI6DywS3vT3643VVO3NA/lin85OiPlwbc5Eh59kBM9ezl2Fd/DQC9S0B08dCfexj96xAUnJyi9c5PdqtMX/GMZL6j2heLVoiTJn2HvR0kQ1XEh4alXYjdI96eJ1zwMCVeeXjwjjQ9qLrvDLnVdo0Kg7WOlCWAI+ne/9docwBRxpKpu2K7ji5cqWhrj5Km31mBInjS6VJZZLmj/1OSurE+HFgUXN+h+WlcnbFPzdOnJeUZy5K5dsQPm/F/7FWHvFcRIAGi/x2UZ/O00+gl0AjURmbKudpzk965ZxrxNd/pR9rPuLlIC4Zyy7q04G+BB7U3KeIBut0HryNtB7n+jnnOBHsM8LEMzr8ZlD1xQ+SV17qVT88nHQuAbg/k7ZBL/VaGns6RJxTX+PU5fXc5bCoZGbOKXMZXOjK0xjjr0t6saTP1eqjyS+fy39WB//CyJW7tC2IvuhqERyH/Kyz8/w9jxPD5Ik36XRBnBVlJw+rU8CJp1IWrmyoDBI4IyUwkEBF1dcpERr9TsDc6HSKO5V30MQ3bHwMuzkl6HR+CMhIHEcH2nOv11Z6Fy10o0EQ5HV283qgDIhoyJL81jNGbBKATNswblQI5H18ak4q4lDP/WA3+XIi6KCRdIPs/fT2PRp0gnIcGwJb70Plp+PB8jTwrK+u3ajPrauqJ6Ud474D1VwbHUjztcftGv91AOvkII18FC/OO+kgPHPeOd6dLp97U8tll06E85wiwMnx9UjpiTZl/mrT1f4i8xjjxjHGb4wxfneM8e4xxvfdXd7Tv76yZ88YY/z++l9dvUSSpml67zRN36nV2eKn7Kp/70FPUTIMpeyd6LU4eXp6nTcpChfsubqdEkhjlMgFKCk9Gi4a6qRQ+XaB15l4Sga2npeRpgfjfdg1Fq746alyXCrNjWAZzQ74eUSsnlFGjnFfbc3NZQIwxYf3gdtNae4cpHMOaaipVBgeT3NBmejeArpkfx0xauF9cMfC2zu258f4lbbHvzO4lPddXrL327/XU/UmBT1ngGhcd/GbwEGKlhSvla8bjwS65qJC/jwBJQKgOefJyyQHJOlG1udguwOk9exSyJP0j8uD95VvNnaAOa1vr49RQ+8Dy1M+GN2SNp2FpHfu5fRpSU+bpulLJH2ppGes3+D+DI0xHjrGeADSHh3qepXCf2wYYxxKeplW/+7qsZK+ZYzx2PWzZ0v6Ba1eppqlvZ+TEkgXIhpS5ndK0RH3Fkp46e0WddGOuYFzHg6atENtGvXOk3Ijdqddd56NKziCAhIVQFIepQSoYGmk/NeVoH/dmXWn+SMfHk4uXr0e1pVeN08KP81R5U9nkXZtIaSQdwLljPa4N5iALMEQwVzlYZq3fd+Gd68/Gceqx41VMrQuvwQ8Ds4SGEyRFkaUeOaixstfJ78BfWYELvHM9kg0bJ1sJPlw4HynNuczgXVfz0m3+Ph5GeqBZMBr/c5t/yTiWp4DaD5OlJEE0JPjRd782uUkyQxBovc/RWNcLkqvJrBT5FFL5yfx6uDxLE7ylaKTK/DX0fpfT/3F+raWCt+S+gpJPzvGuK8kjTG+XdKPhLp+Rdv/+kpa/d/OW6Zp+oNpmu6S9FNa/esrTdP0xmmaninpW2fYlLTnZ3pOpD97zepIxZ9dNC/3IvpcLeN5uWkZ08tPy5heflrG9PLTWcf0r15pRpxOpDffseLtctMYY7xC0s9P0/TzeHAo6R2SHi3pZdM0/bo/n6bp9WOMmyW9dozxeknfJumrztH2w7X976yeNMZ4qqRv0Mq/2xnp2WvQM03TQ8YYb7/WX5HbJ1rG8/LTMqaXn5Yxvfy0jOnlp30d02mazvwPvS9jm8eSvnSMcZOknxljPG6apnchzw+MMX5K0sslfZFFh+5Ju2+T9Laz5t/77a2FFlpooYUWWujaoGmaPiHpl5XP5Xy5pMdJ+hmtPnR8HvqgzvjvrOZoAT0LLbTQQgsttNDdpjHGQ9YRHo0xPkurbavfQ57HS3qFVudwXiDpc8YY33+OZn5T0mPGGDePMW6Q9M1a/eurc9G1AHpecdEM3MtoGc/LT8uYXn5axvTy0zKml5+WMV3R50v65THGO7UCJ2+ZpulNyHM/Sc+dpun90zSdSHqeVv/GaoPW//rqVyV98Rjj1vW/u9I0TX8p6bu0+r+d75X0umma3n1eRvf631AstNBCCy200EILXS66FiI9Cy200EILLbTQQveY9hb0pC8vLrSbxhhfOMb45THGe9ZfxnzxOv3BY4y3jDHet/590Dp9jDF+eD3O7xxjPOFie7CfNMY4HGP89hjjTev7m8cYv74et9eu95g1xrjv+v6W9fNHXijje0xjjJvGGG8YY/zeGOO9Y4y/ucjpPaMxxj9cr/t3jTFes/5S7iKr56D0ReC7I5djjOev879vjPH8i+jLQtu0l6Bn7suLC+2kv5T0j6ZpeqykJ0v6++uxe4lW/5fkMZLeur6XVmP8mPXfi7R6lXChbXqxVvvIRf9K0g9O0/RoSR+X9MJ1+gslfXyd/oPrfAtl+reS/us0TX9N0pdoNb6LnN5NGmM8XNJ3S3riNE2P0+p7et+sRVbPS6/S9ptH55LLMcaDtXo76UlafVTvpQWUFrpY2kvQo5kvLy40T9M0fWiapt9aX39SK0PycK3G79XrbK+W9PXr6+dI+on1FzV/TdJNY4zPv7pc7zeNMR4h6WskvXJ9PyQ9TdIb1lk4njXOb5D0lev8CxmNMR4o6W9L+jFJmqbprvWrrouc3jO6j6TPGmPcR6uDox/SIqvnouaLwOeVy6/W6jDvbdM0fVzSWxRe4V7o6tO+gp705cWHXxAv1yytw9WPl/Trkh42TdOH1o8+LOlh6+tlrHfTD0n6Hp1+if1zJH1i/TaBtDlmnxnP9fPb1/kX2qSbJX1U0r9fbxu+coxxfy1yerdpmqYPSvrXkv5EK7Bzu1ZfyF1k9Z7TeeVykdc9pX0FPQvdQxpjfLak/yzpH0zT9Of+bFq9sre8tncGGmN8raSPTNP0jovm5V5G95H0BEkvn6bp8Vr9u5mNs3uLnJ6P1tsnz9EKUH6BpPtriS5cdlrk8tqmfQU9l+XLi9crjTGOtAI8/3Gapp9eJ/9pbQesfz+yTl/Gep6eIunZY4w/0mqb9WlanUW5ab2FIG2O2WfGc/38gZI+djUZvkboVkm32v/neYNWIGiR07tPf0fSH07T9NFpmi5J+mmt5HeR1XtO55XLRV73lPYV9FyWLy9ej7Tek/8xSe+dpunf2KM3Sqo3CJ4v6ecs/XnrtxCeLOl2C+Ne9zRN0/dO0/SIaZoeqZUc/tI0Td+q1WfWv3GdjeNZ4/yN6/yLVwiapunDkj4wxvjiddJXSnqPFjm9J/Qnkp48xrjfWg/UmC6yes/pvHL5ZklPH2M8aB2Be/o6baGLpmma9vJP0rMk/T9J75f0Ty6an2vlT9Lf0ir0+k5Jv7P+e5ZWe/VvlfQ+Sf9d0oPX+YdWb8q9X9L/1erNjwvvxz7+SXqqpDetrx8l6Tck3SLp9ZLuu06/cX1/y/r5oy6a7339k/Slkt6+ltWflfSgRU7v8Zh+n1af/3+XpP+g1X+eXmT1fGP4Gq3ORF3SKiL5wrsjl1r9F/Fb1n8vuOh+LX+rv+WLzAsttNBCCy200HVB+7q9tdBCCy200EILLXRZaQE9Cy200EILLbTQdUEL6FlooYUWWmihha4LWkDPQgsttNBCCy10XdACehZaaKGFFlpooeuCFtCz0EILLbTQQgtdF7SAnoUWWmihhRZa6LqgBfQstNBCCy200ELXBf1/e+g5fnKn5OkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(10,8))\n", "plt.imshow(hdus[0][1].data, norm=LogNorm(vmax=3E4, vmin=3E3), cmap=\"hot\")\n", @@ -176,31 +165,47 @@ "id": "romantic-description", "metadata": {}, "source": [ - "## TL;DR\n", + "## Complete script\n", "\n", - "```\n", + "Included below is the complete script for convenience, including the downloads, but not including the plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d82e5257", + "metadata": {}, + "outputs": [], + "source": [ "import scopesim as sim\n", "import scopesim_templates as sim_tp\n", "\n", - "sim.download_packages([\"Armazones\", \"ELT\", \"MAORY\", \"MICADO\"])\n", + "#sim.download_packages([\"Armazones\", \"ELT\", \"MORFEO\", \"MICADO\"])\n", "\n", "cluster = sim_tp.stellar.clusters.cluster(mass=1000, # Msun\n", " distance=50000, # parsec\n", - " core_radius=0.3, # parsec\n", + " core_radius=0.3, # parsec\n", " seed=9002)\n", "\n", "micado = sim.OpticalTrain(\"MICADO\")\n", "micado.observe(cluster)\n", "\n", "hdus = micado.readout()\n", - "# micado.readout(filename=\"TEST.fits\")\n", - "```" + "# micado.readout(filename=\"TEST.fits\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8478c34", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -214,7 +219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" }, "nbsphinx": { "execute": "auto" diff --git a/docs/source/examples/2_multiple_telescopes.ipynb b/docs/source/examples/2_multiple_telescopes.ipynb index 52784fc7..72735352 100644 --- a/docs/source/examples/2_multiple_telescopes.ipynb +++ b/docs/source/examples/2_multiple_telescopes.ipynb @@ -7,28 +7,44 @@ "source": [ "# 2: Observing the same object with multiple telescopes\n", "\n", - "A brief introduction into using ScopeSim to observe a cluster in the LMC using the 39m ELT and the 1.5m LFOA" + "A brief introduction into using ScopeSim to observe a cluster in the LMC using the 39m ELT and the 1.5m LFOA\n", + "\n", + "*This is a step-by-step guide. The complete script can be found at the bottom of this page/notebook.*\n", + "\n", + "First set up all relevant imports:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "hairy-information", "metadata": {}, "outputs": [], "source": [ - "from tempfile import TemporaryDirectory\n", - "\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "%matplotlib inline\n", "\n", "import scopesim as sim\n", - "import scopesim_templates as sim_tp\n", - "\n", - "# [Required for Readthedocs] Comment out these lines if running locally\n", - "tmpdir = TemporaryDirectory()\n", - "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = tmpdir.name" + "import scopesim_templates as sim_tp" + ] + }, + { + "cell_type": "markdown", + "id": "c29291e8", + "metadata": {}, + "source": [ + "Scopesim works by using so-called instrument packages, which have to be downloaded separately. For normal use, you would set the package directory (a local folder path, `local_package_folder` in this example), download the required packages *once*, and then **remove the download command**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0150da5d", + "metadata": {}, + "outputs": [], + "source": [ + "local_package_folder = \"./inst_pkgs\"" ] }, { @@ -36,32 +52,41 @@ "id": "future-engineering", "metadata": {}, "source": [ - "Download the packages for MICADO at the ELT and the viennese [1.5m telescope at the LFOA](https://foa.univie.ac.at/instrumentation/)" + "However, to be able to run this example on the *Readthedocs* page, we need to include a temporary directory.\n", + "\n", + "**Do not** copy and run this code locally, it is **only** needed to set things up for *Readthedocs*!" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "98186ac1", + "metadata": {}, + "outputs": [], + "source": [ + "from tempfile import TemporaryDirectory\n", + "local_package_folder = TemporaryDirectory().name" + ] + }, + { + "cell_type": "markdown", + "id": "fcb2790a", + "metadata": {}, + "source": [ + "Download the packages for MICADO at the ELT and the viennese [1.5m telescope at the LFOA](https://foa.univie.ac.at/instrumentation/)\n", + "\n", + "Again, you would only need to do this **once**, not every time you run the rest of the script, assuming you set a (permanent) instrument package folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "unexpected-appeal", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmp3bqenznv\\\\Armazones.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmp3bqenznv\\\\ELT.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmp3bqenznv\\\\MICADO.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmp3bqenznv\\\\MAORY.zip']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sim.download_packages([\"LFOA\"])\n", - "sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\", \"MAORY\"])" + "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = local_package_folder\n", + "sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\", \"MORFEO\", \"LFOA\"])" ] }, { @@ -69,24 +94,17 @@ "id": "pursuant-crystal", "metadata": {}, "source": [ - "## Create a star cluster ``Source`` object" + "## Create a star cluster ``Source`` object\n", + "\n", + "Now, create a star cluster using the scopesim_templates package. You can ignore the output that is sometimes printed. The seed argument is used to control the random number generation that creates the stars in the cluster. If this number is kept the same, the output will be consistent with each run, otherwise the position and brightness of the stars is randomised every time." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "lasting-gender", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO - sample_imf: Setting maximum allowed mass to 10000\n", - "INFO - sample_imf: Loop 0 added 1.01e+04 Msun to previous total of 0.00e+00 Msun\n" - ] - } - ], + "outputs": [], "source": [ "cluster = sim_tp.stellar.clusters.cluster(mass=10000, # Msun\n", " distance=50000, # parsec\n", @@ -106,21 +124,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "casual-strength", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Warning: header update failed, data will be saved with incomplete header.\n", - "Reason: !OBS.instrument was not found in rc.__currsys__\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "lfoa = sim.OpticalTrain(\"LFOA\")\n", "lfoa.observe(cluster,\n", @@ -141,21 +148,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "chinese-spirit", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Warning: header update failed, data will be saved with incomplete header.\n", - "Reason: !OBS.instrument was not found in rc.__currsys__\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "micado = sim.OpticalTrain(\"MICADO\")\n", "micado.cmds[\"!OBS.dit\"] = 10\n", @@ -176,33 +172,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "directed-mother", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, '39m ELT')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12,5))\n", "\n", @@ -217,6 +190,48 @@ "plt.title(\"39m ELT\")" ] }, + { + "cell_type": "markdown", + "id": "ea56edb2", + "metadata": {}, + "source": [ + "## Complete script\n", + "\n", + "Included below is the complete script for convenience, including the downloads, but not including the plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38429fa5", + "metadata": {}, + "outputs": [], + "source": [ + "import scopesim as sim\n", + "import scopesim_templates as sim_tp\n", + "\n", + "# sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\", \"MORFEO\", \"LFOA\"])\n", + "\n", + "cluster = sim_tp.stellar.clusters.cluster(mass=10000, # Msun\n", + " distance=50000, # parsec\n", + " core_radius=2, # parsec\n", + " seed=9001) # random seed\n", + "\n", + "lfoa = sim.OpticalTrain(\"LFOA\")\n", + "lfoa.observe(cluster,\n", + " properties={\"!OBS.ndit\": 10, \"!OBS.ndit\": 360},\n", + " update=True)\n", + "hdus_lfoa = lfoa.readout()\n", + "\n", + "micado = sim.OpticalTrain(\"MICADO\")\n", + "micado.cmds[\"!OBS.dit\"] = 10\n", + "micado.cmds[\"!OBS.ndit\"] = 360\n", + "micado.update()\n", + "\n", + "micado.observe(cluster)\n", + "hdus_micado = micado.readout()\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -228,7 +243,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" }, "nbsphinx": { "execute": "auto" diff --git a/docs/source/examples/3_custom_effects.ipynb b/docs/source/examples/3_custom_effects.ipynb index f84dcfc4..e2f6b324 100644 --- a/docs/source/examples/3_custom_effects.ipynb +++ b/docs/source/examples/3_custom_effects.ipynb @@ -8,7 +8,7 @@ "3: Writing and including custom Effects\n", "=======================================\n", "\n", - "In this tutorial, we will load the model of MICADO (including Armazones, ELT, MAORY) and then turn off all effect that modify the spatial extent of the stars. The purpose here is to see in detail what happens to the **distribution of the stars flux on a sub-pixel level** when we add a plug-in astrometric Effect to the optical system.\n", + "In this tutorial, we will load the model of MICADO (including Armazones, ELT, MORFEO) and then turn off all effect that modify the spatial extent of the stars. The purpose here is to see in detail what happens to the **distribution of the stars flux on a sub-pixel level** when we add a plug-in astrometric Effect to the optical system.\n", "\n", "For real simulation, we will obviously leave all normal MICADO effects turned on, while still adding the plug-in Effect. Hopefully this tutorial will serve as a refernce for those who want to see **how to create Plug-ins** and how to manipulate the effects in the MICADO optical train model.\n", "\n", @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "constant-weekly", "metadata": {}, "outputs": [], @@ -31,11 +31,46 @@ "from matplotlib.colors import LogNorm\n", "\n", "import scopesim as sim\n", - "from scopesim_templates.stellar import stars, star_grid\n", + "from scopesim_templates.stellar import stars, star_grid" + ] + }, + { + "cell_type": "markdown", + "id": "40fabcee", + "metadata": {}, + "source": [ + "Scopesim works by using so-called instrument packages, which have to be downloaded separately. For normal use, you would set the package directory (a local folder path, `local_package_folder` in this example), download the required packages *once*, and then **remove the download command**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "661ea82b", + "metadata": {}, + "outputs": [], + "source": [ + "local_package_folder = \"./inst_pkgs\"" + ] + }, + { + "cell_type": "markdown", + "id": "1350c51d", + "metadata": {}, + "source": [ + "However, to be able to run this example on the *Readthedocs* page, we need to include a temporary directory.\n", "\n", - "# [Required for Readthedocs] Comment out these lines if running locally\n", - "tmpdir = TemporaryDirectory()\n", - "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = tmpdir.name" + "**Do not** copy and run this code locally, it is **only** needed to set things up for *Readthedocs*!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d33b08d", + "metadata": {}, + "outputs": [], + "source": [ + "from tempfile import TemporaryDirectory\n", + "local_package_folder = TemporaryDirectory().name" ] }, { @@ -43,32 +78,19 @@ "id": "acute-calculator", "metadata": {}, "source": [ - "We assume that the MICADO (plus support) packages have been downloaded." + "Download the required instrument packages for an observation with MICADO at the ELT.\n", + "\n", + "Again, you would only need to do this **once**, not every time you run the rest of the script, assuming you set a (permanent) instrument package folder." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "gorgeous-blond", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmptgyr8nws\\\\Armazones.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmptgyr8nws\\\\ELT.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmptgyr8nws\\\\MICADO.zip',\n", - " 'C:\\\\Users\\\\Kieran\\\\AppData\\\\Local\\\\Temp\\\\tmptgyr8nws\\\\MAORY.zip']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sim.download_packages([\"LFOA\"])\n", - "sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\", \"MAORY\"])" + "sim.download_packages([\"Armazones\", \"ELT\", \"MICADO\", \"MORFEO\"])" ] }, { @@ -81,71 +103,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "celtic-fluid", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=20\n", - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
elementnameclassincluded
str13str23str31bool
armazonesskycalc_atmosphereSkycalcTERCurveTrue
ELTtelescope_reflectionSurfaceListTrue
MICADOmicado_static_surfacesSurfaceListTrue
MICADOmicado_ncpas_psfNonCommonPathAberrationTrue
MICADOfilter_wheel_1 : [open]FilterWheelTrue
MICADOfilter_wheel_2 : [Ks]FilterWheelTrue
MICADOpupil_wheel : [open]FilterWheelTrue
MICADO_DETfull_detector_arrayDetectorListFalse
MICADO_DETdetector_windowDetectorWindowTrue
MICADO_DETqe_curveQuantumEfficiencyCurveTrue
MICADO_DETexposure_actionSummedExposureTrue
MICADO_DETdark_currentDarkCurrentTrue
MICADO_DETshot_noiseShotNoiseTrue
MICADO_DETdetector_linearityLinearityCurveTrue
MICADO_DETborder_reference_pixelsReferencePixelBorderTrue
MICADO_DETreadout_noisePoorMansHxRGReadoutNoiseTrue
default_rorelay_psfFieldConstantPSFTrue
default_rorelay_surface_listSurfaceListTrue
MICADO_IMG_HRzoom_mirror_listSurfaceListTrue
MICADO_IMG_HRmicado_adc_3D_shiftAtmosphericDispersionCorrectionFalse
" - ], - "text/plain": [ - "\n", - " element name class included\n", - " str13 str23 str31 bool \n", - "------------- ----------------------- ------------------------------- --------\n", - " armazones skycalc_atmosphere SkycalcTERCurve True\n", - " ELT telescope_reflection SurfaceList True\n", - " MICADO micado_static_surfaces SurfaceList True\n", - " MICADO micado_ncpas_psf NonCommonPathAberration True\n", - " MICADO filter_wheel_1 : [open] FilterWheel True\n", - " MICADO filter_wheel_2 : [Ks] FilterWheel True\n", - " MICADO pupil_wheel : [open] FilterWheel True\n", - " MICADO_DET full_detector_array DetectorList False\n", - " MICADO_DET detector_window DetectorWindow True\n", - " MICADO_DET qe_curve QuantumEfficiencyCurve True\n", - " MICADO_DET exposure_action SummedExposure True\n", - " MICADO_DET dark_current DarkCurrent True\n", - " MICADO_DET shot_noise ShotNoise True\n", - " MICADO_DET detector_linearity LinearityCurve True\n", - " MICADO_DET border_reference_pixels ReferencePixelBorder True\n", - " MICADO_DET readout_noise PoorMansHxRGReadoutNoise True\n", - " default_ro relay_psf FieldConstantPSF True\n", - " default_ro relay_surface_list SurfaceList True\n", - "MICADO_IMG_HR zoom_mirror_list SurfaceList True\n", - "MICADO_IMG_HR micado_adc_3D_shift AtmosphericDispersionCorrection False" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cmd = sim.UserCommands(use_instrument=\"MICADO\", set_modes=[\"SCAO\", \"IMG_1.5mas\"])\n", "micado = sim.OpticalTrain(cmd)\n", @@ -163,21 +124,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "bound-literature", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DetectorList: \"full_detector_array\"\n", - "AtmosphericDispersionCorrection: \"micado_adc_3D_shift\"\n", - "NonCommonPathAberration: \"micado_ncpas_psf\"\n", - "FieldConstantPSF: \"relay_psf\"\n" - ] - } - ], + "outputs": [], "source": [ "for effect_name in [\"full_detector_array\", \"micado_adc_3D_shift\", \n", " \"micado_ncpas_psf\", \"relay_psf\"]:\n", @@ -196,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "allied-matrix", "metadata": {}, "outputs": [], @@ -219,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "aerial-warehouse", "metadata": {}, "outputs": [], @@ -237,33 +187,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "indoor-norway", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "src = star_grid(n=9, mmin=20, mmax=20.0001, separation=0.0015 * 15)\n", "src.fields[0][\"x\"] -= 0.00075\n", @@ -277,33 +204,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "lightweight-louisiana", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=1\n", - "
\n", - "\n", - "\n", - "\n", - "
idx_ceny_cenx_sizey_sizeanglegainpixel_size
int32str6str6str10str11int32int32float64
0006464010.015
" - ], - "text/plain": [ - "\n", - " id x_cen y_cen x_size y_size angle gain pixel_size\n", - "int32 str6 str6 str10 str11 int32 int32 float64 \n", - "----- ----- ----- ------ ------ ----- ----- ----------\n", - " 0 0 0 64 64 0 1 0.015" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "micado[\"detector_window\"].data" ] @@ -322,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "weighted-mortgage", "metadata": {}, "outputs": [], @@ -357,9 +261,10 @@ "id": "drawn-vacation", "metadata": {}, "source": [ - "Lets break it down a bit:\n", + "Lets break it down a bit (**THIS IS JUST A STEP-BY-STEP EXPLANATION OF THE CODE ABOVE, NOT SOMETHING NEW!**):\n", "\n", " class PointSourceJitter(Effect):\n", + " ...\n", "\n", "Here we are subclassing the ``Effect`` object from ScopeSim.\n", "This has the basic functionality for reading in ASCII and FITS files, and for communicating with the ``OpticsManager`` class in ScopeSim.\n", @@ -400,7 +305,7 @@ "This method is used by ``FOVManager`` to estimate how many ``FieldOfView`` objects to generate in order to best simulation the observation.\n", "If your Effect object might alter this estimate, then you should include this method in your class. See the code base for further details.\n", "\n", - ".. note:: The ``fov_grid`` method will be depreciated in a future release of ScopeSim.\n", + "**Note**: The ``fov_grid`` method will be depreciated in a future release of ScopeSim.\n", " It will most likely be replaced by a ``FOVSetupBase`` class that will be cycled through the ``apply_to`` function.\n", " However this is not yet 100% certain, so please bear with us." ] @@ -418,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "empirical-skill", "metadata": {}, "outputs": [], @@ -436,73 +341,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "considerable-factory", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=21\n", - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
elementnameclassincluded
str13str23str31bool
armazonesskycalc_atmosphereSkycalcTERCurveTrue
armazonesrandom_jitterPointSourceJitterTrue
ELTtelescope_reflectionSurfaceListTrue
MICADOmicado_static_surfacesSurfaceListTrue
MICADOmicado_ncpas_psfNonCommonPathAberrationFalse
MICADOfilter_wheel_1 : [open]FilterWheelTrue
MICADOfilter_wheel_2 : [Ks]FilterWheelTrue
MICADOpupil_wheel : [open]FilterWheelTrue
MICADO_DETfull_detector_arrayDetectorListFalse
MICADO_DETdetector_windowDetectorWindowTrue
MICADO_DETqe_curveQuantumEfficiencyCurveTrue
MICADO_DETexposure_actionSummedExposureTrue
MICADO_DETdark_currentDarkCurrentTrue
MICADO_DETshot_noiseShotNoiseTrue
MICADO_DETdetector_linearityLinearityCurveTrue
MICADO_DETborder_reference_pixelsReferencePixelBorderTrue
MICADO_DETreadout_noisePoorMansHxRGReadoutNoiseTrue
default_rorelay_psfFieldConstantPSFFalse
default_rorelay_surface_listSurfaceListTrue
MICADO_IMG_HRzoom_mirror_listSurfaceListTrue
MICADO_IMG_HRmicado_adc_3D_shiftAtmosphericDispersionCorrectionFalse
" - ], - "text/plain": [ - "\n", - " element name class included\n", - " str13 str23 str31 bool \n", - "------------- ----------------------- ------------------------------- --------\n", - " armazones skycalc_atmosphere SkycalcTERCurve True\n", - " armazones random_jitter PointSourceJitter True\n", - " ELT telescope_reflection SurfaceList True\n", - " MICADO micado_static_surfaces SurfaceList True\n", - " MICADO micado_ncpas_psf NonCommonPathAberration False\n", - " MICADO filter_wheel_1 : [open] FilterWheel True\n", - " MICADO filter_wheel_2 : [Ks] FilterWheel True\n", - " MICADO pupil_wheel : [open] FilterWheel True\n", - " MICADO_DET full_detector_array DetectorList False\n", - " MICADO_DET detector_window DetectorWindow True\n", - " MICADO_DET qe_curve QuantumEfficiencyCurve True\n", - " MICADO_DET exposure_action SummedExposure True\n", - " MICADO_DET dark_current DarkCurrent True\n", - " MICADO_DET shot_noise ShotNoise True\n", - " MICADO_DET detector_linearity LinearityCurve True\n", - " MICADO_DET border_reference_pixels ReferencePixelBorder True\n", - " MICADO_DET readout_noise PoorMansHxRGReadoutNoise True\n", - " default_ro relay_psf FieldConstantPSF False\n", - " default_ro relay_surface_list SurfaceList True\n", - "MICADO_IMG_HR zoom_mirror_list SurfaceList True\n", - "MICADO_IMG_HR micado_adc_3D_shift AtmosphericDispersionCorrection False" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "micado.optics_manager.add_effect(jitter_effect)\n", "\n", @@ -519,33 +361,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "exempt-purse", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "micado.observe(src, update=True)\n", "\n", @@ -563,33 +382,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "sound-preference", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "micado[\"random_jitter\"].meta[\"max_jitter\"] = 0.005\n", "\n", @@ -611,43 +407,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "future-approval", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Warning: header update failed, data will be saved with incomplete header.\n", - "Reason: !OBS.instrument was not found in rc.__currsys__\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "micado[\"relay_psf\"].include = True\n", "\n", @@ -669,7 +432,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -683,7 +446,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" }, "nbsphinx": { "execute": "auto" diff --git a/docs/source/getting_started.ipynb b/docs/source/getting_started.ipynb index d006843f..034d5482 100644 --- a/docs/source/getting_started.ipynb +++ b/docs/source/getting_started.ipynb @@ -18,33 +18,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "tracked-preview", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "from matplotlib.colors import LogNorm\n", @@ -91,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "conscious-thomas", "metadata": {}, "outputs": [], @@ -121,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "productive-branch", "metadata": {}, "outputs": [], @@ -143,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "blond-frequency", "metadata": {}, "outputs": [], @@ -166,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "lined-windows", "metadata": {}, "outputs": [], @@ -186,65 +163,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "sharing-campaign", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Table length=17\n", - "
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elementnameclassincluded
str16str22str29bool
basic_atmosphereatmospheric_radiometryAtmosphericTERCurveFalse
basic_telescopepsfSeeingPSFTrue
basic_telescopetelescope_reflectionTERCurveTrue
basic_instrumentstatic_surfacesSurfaceListTrue
basic_instrumentfilter_wheel : [J]FilterWheelTrue
basic_instrumentslit_wheel : [narrow]SlitWheelFalse
basic_detectordetector_windowDetectorWindowTrue
basic_detectorqe_curveQuantumEfficiencyCurveTrue
basic_detectorexposure_actionSummedExposureTrue
basic_detectordark_currentDarkCurrentTrue
basic_detectorshot_noiseShotNoiseTrue
basic_detectordetector_linearityLinearityCurveTrue
basic_detectorreadout_noisePoorMansHxRGReadoutNoiseTrue
basic_detectorsource_fits_keywordsSourceDescriptionFitsKeywordsTrue
basic_detectoreffects_fits_keywordsEffectsMetaKeywordsTrue
basic_detectorconfig_fits_keywordsSimulationConfigFitsKeywordsTrue
basic_detectorextra_fits_keywordsExtraFitsKeywordsTrue
" - ], - "text/plain": [ - "\n", - " element name class included\n", - " str16 str22 str29 bool \n", - "---------------- ---------------------- ----------------------------- --------\n", - "basic_atmosphere atmospheric_radiometry AtmosphericTERCurve False\n", - " basic_telescope psf SeeingPSF True\n", - " basic_telescope telescope_reflection TERCurve True\n", - "basic_instrument static_surfaces SurfaceList True\n", - "basic_instrument filter_wheel : [J] FilterWheel True\n", - "basic_instrument slit_wheel : [narrow] SlitWheel False\n", - " basic_detector detector_window DetectorWindow True\n", - " basic_detector qe_curve QuantumEfficiencyCurve True\n", - " basic_detector exposure_action SummedExposure True\n", - " basic_detector dark_current DarkCurrent True\n", - " basic_detector shot_noise ShotNoise True\n", - " basic_detector detector_linearity LinearityCurve True\n", - " basic_detector readout_noise PoorMansHxRGReadoutNoise True\n", - " basic_detector source_fits_keywords SourceDescriptionFitsKeywords True\n", - " basic_detector effects_fits_keywords EffectsMetaKeywords True\n", - " basic_detector config_fits_keywords SimulationConfigFitsKeywords True\n", - " basic_detector extra_fits_keywords ExtraFitsKeywords True" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.effects" ] @@ -259,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "original-appeal", "metadata": {}, "outputs": [], @@ -279,19 +201,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "better-hurricane", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imaging: Basic NIR imager\n", - "spectroscopy: Basic three-trace long-slit spectrograph\n" - ] - } - ], + "outputs": [], "source": [ "opt.cmds.modes" ] @@ -306,26 +219,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "through-exclusive", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'BrGamma': FilterCurve: \"BrGamma\",\n", - " 'CH4': FilterCurve: \"CH4\",\n", - " 'J': FilterCurve: \"J\",\n", - " 'H': FilterCurve: \"H\",\n", - " 'Ks': FilterCurve: \"Ks\",\n", - " 'open': FilterCurve: \"open\"}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt[\"filter_wheel\"].filters" ] @@ -343,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "knowing-passenger", "metadata": {}, "outputs": [], @@ -364,61 +261,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "nervous-hearts", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " contents:\n", - "SIM: \n", - " spectral: {'wave_min': 0.7, 'wave_mid': 1.2, 'wave_max': 2.7, 'wave_unit': 'um', 'spectral_bin_width': 0.0001, 'spectral_resolution': 5000, 'minimum_throughput': 1e-06, 'minimum_pixel_flux': 1}\n", - " sub_pixel: {'flag': False, 'fraction': 1}\n", - " random: {'seed': None}\n", - " computing: {'chunk_size': 2048, 'max_segment_size': 16777217, 'oversampling': 1, 'spline_order': 1, 'flux_accuracy': 0.001, 'preload_field_of_views': False, 'bg_cell_width': 60}\n", - " file: {'local_packages_path': './inst_pkgs/', 'server_base_url': 'https://www.univie.ac.at/simcado/InstPkgSvr/', 'use_cached_downloads': False, 'search_path': ['./inst_pkgs/', './'], 'error_on_missing_file': False}\n", - " reports: {'ip_tracking': False, 'verbose': False, 'rst_path': './reports/rst/', 'latex_path': './reports/latex/', 'image_path': './reports/images/', 'image_format': 'png', 'preamble_file': 'None'}\n", - " logging: {'log_to_file': False, 'log_to_console': True, 'file_path': '.scopesim.log', 'file_open_mode': 'w', 'file_level': 'DEBUG', 'console_level': 'WARNING'}\n", - " tests: {'run_integration_tests': True, 'run_skycalc_ter_tests': True}\n", - " spectral_bin_width: 0.0005\n", - "OBS: \n", - " psf_fwhm: 1.5\n", - " modes: ['imaging']\n", - " dit: 60\n", - " ndit: 10\n", - " slit_name: narrow\n", - " include_slit: False\n", - " filter_name: J\n", - "TEL: \n", - " etendue: 0.007853981633974483 arcsec2 m2\n", - " area: 0.19634954084936207 m2\n", - " temperature: 0\n", - "INST: \n", - " pixel_scale: 0.2\n", - " plate_scale: 20\n", - " decouple_detector_from_sky_headers: False\n", - " temperature: -190\n", - "ATMO: \n", - " background: {'filter_name': 'J', 'value': 16.6, 'unit': 'mag'}\n", - " element_name: basic_atmosphere\n", - "DET: \n", - " image_plane_id: 0\n", - " temperature: -230\n", - " dit: !OBS.dit\n", - " ndit: !OBS.ndit\n", - " width: 1024\n", - " height: 1024\n", - " x: 0\n", - " y: 0\n", - " element_name: basic_detector" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "opt.cmds" ] @@ -438,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "thick-democrat", "metadata": {}, "outputs": [], @@ -471,7 +317,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -485,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/source/index.rst b/docs/source/index.rst index 09593821..7b9fd02a 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,8 +14,6 @@ ScopeSim_ is on pip:: pip install scopesim_templates -.. note:: ScopeSim only supports python 3.6 and above - .. warning:: July 2022: The downloadable content server was retired and the data migrated to a new server. ScopeSim v0.5.1 and above have been redirected to a new server URL. @@ -80,3 +78,5 @@ Contact - `astar.astro@univie.ac.at `_ or - `kieran.leschinski@univie.ac.at `_ + +- For friendly chat, join the slack at https://join.slack.com/t/scopesim/shared_invite/zt-143s42izo-LnyqoG7gH5j~aGn51Z~4IA diff --git a/docs/to-do-list.txt b/docs/to-do-list.txt index ecd73939..45d66192 100644 --- a/docs/to-do-list.txt +++ b/docs/to-do-list.txt @@ -81,7 +81,7 @@ IRDB - Add a MICADO_ETC package WHAT: Add consolidated transmission curves, PSFs, detector characteristics to enable high-speed windowed simuations -- Updates to MICADO, MICADO_Sci, MAORY, ELT, Armazones packages as new data becomes available +- Updates to MICADO, MICADO_Sci, MORFEO, ELT, Armazones packages as new data becomes available WHAT: Add new modes, updated values and data files - Automatic documentation for each of the packages diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..bed6f163 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,75 @@ +[project] +name = "ScopeSim" +version = "0.6.0" +description = "Generalised telescope observation simulator" +readme = "README.md" +requires-python = ">=3.8" +license = {text = "License :: OSI Approved :: GNU General Public License v3 (GPLv3)"} +authors = [ + {name = "Kieran Leschinski", email="kieran.leschinski@unive.ac.at"}, +] +maintainers = [ + {name = "Kieran Leschinski", email="kieran.leschinski@unive.ac.at"}, + {name = "Hugo Buddelmeijer", email="hugo@buddelmeijer.nl"}, +] +classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Operating System :: OS Independent", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Astronomy", +] +dependencies = [ + "numpy>=1.19", + "scipy>=1.4.0", + "astropy>=5.0", + "matplotlib>=3.2.0", + + "docutils>=0.15", + "requests>=2.28.2", + "beautifulsoup4>=4.4", + "lxml>=4.5.0", + "pyyaml>5.1", + "more-itertools>=9.0", + "tqdm>=4.64", + "requests-cache>1.0", + + "synphot>=1.1.0", + "skycalc_ipy>=0.1.3", + "anisocado>=0.3.0", +] + +[project.optional-dependencies] +dev = [ + "jupyter", + "jupytext", +] +test = [ + "pytest>=5.0.0", + "pytest-cov", + "scopesim_templates>=0.4.4", + # Just so that readthedocs doesn't include the tests module - yes it's hacky + "skycalc_cli", +] +docs = [ + "sphinx>=4.3.0", + "sphinx-rtd-theme>=0.5.1", + "jupyter_sphinx==0.2.3", + "sphinxcontrib-apidoc", + "nbsphinx", + "numpydoc", +] + +[project.urls] +"Homepage" = "https://scopesim.readthedocs.io/en/latest/" +"Source" = "https://github.com/AstarVienna/ScopeSim" +"Bug Reports" = "https://github.com/AstarVienna/ScopeSim/issues" + +[tool.setuptools.packages] +find = {} + +[tool.pytest.ini_options] +addopts = "--strict-markers" +markers = [ + "webtest: marks tests as requiring network (deselect with '-m \"not webtest\"')", +] diff --git a/requirements.github_actions.txt b/requirements.github_actions.txt deleted file mode 100644 index fb366c05..00000000 --- a/requirements.github_actions.txt +++ /dev/null @@ -1,25 +0,0 @@ -pytest - -numpy>=1.16 -scipy -astropy -matplotlib -jupyter -jupytext - -docutils -requests -beautifulsoup4 -lxml -pyyaml -pysftp - -synphot -skycalc_ipy -anisocado -scopesim_templates - -# Just so that readthedocs doesn't include the tests module - yes it's hacky -skycalc_cli - - diff --git a/requirements.readthedocs.txt b/requirements.readthedocs.txt deleted file mode 100644 index 9236de4d..00000000 --- a/requirements.readthedocs.txt +++ /dev/null @@ -1,26 +0,0 @@ -numpy>=1.16 -scipy -matplotlib -astropy - -docutils -requests -beautifulsoup4 -lxml -pyyaml -pysftp - -synphot -skycalc_ipy -anisocado -git+https://github.com/AstarVienna/ScopeSim.git@dev_master -scopesim_templates - -sphinx>=4.3.0 -sphinx-rtd-theme>=0.5.1 -jupyter_sphinx==0.2.3 -sphinxcontrib-apidoc -nbsphinx -numpydoc - -# See https://github.com/sphinx-doc/sphinx/issues/7659 for why sphinx==2.4 \ No newline at end of file diff --git a/runnotebooks.sh b/runnotebooks.sh index 2f24fb53..22d387bb 100755 --- a/runnotebooks.sh +++ b/runnotebooks.sh @@ -1,5 +1,30 @@ #!/usr/bin/env bash +if [[ "x${1}" == "x--clone-irdb" ]] ; then + # Cloning IRDB + if [[ ! -e irdb ]] ; then + git clone https://github.com/AstarVienna/irdb.git + fi + + # https://github.com/koalaman/shellcheck/wiki/SC2044 + find . -iname "*.ipynb" -printf '%h\0' | sort -z | uniq -z | while IFS= read -r -d '' dirnotebooks; do + echo "${dirnotebooks}" + dirinstpkgs="${dirnotebooks}/inst_pkgs" + if [[ (! -e ./docs/source/examples/inst_pkgs) && (! -L ./docs/source/examples/inst_pkgs) ]] ; then + echo "Creating symlink to irdb: ${dirinstpkgs}" + ln -s irdb "${dirinstpkgs}" + else + echo "Directory exists, not creating symlink: ${dirinstpkgs}" + fi + + # Comment out any download_package[s] in the notebooks. + pusd "${dirnotebooks}" || exit 1 + sed -i -E 's|"(.*\.download_package)|"#\1|g' -- *.ipynb + popd || exit 1 + done +fi + + # https://github.com/koalaman/shellcheck/wiki/SC2044 find . -iname "*.ipynb" -print0 | while IFS= read -r -d '' fnnotebook do diff --git a/scopesim/__init__.py b/scopesim/__init__.py index 80620a8e..17c7b997 100644 --- a/scopesim/__init__.py +++ b/scopesim/__init__.py @@ -7,6 +7,7 @@ import logging import warnings import yaml +from importlib import metadata from astropy.utils.exceptions import AstropyWarning warnings.simplefilter('ignore', UserWarning) @@ -75,7 +76,4 @@ # VERSION INFORMATION # ################################################################################ -try: - from .version import version as __version__ -except ImportError: - __version__ = "Version number is not available" +__version__ = metadata.version(__package__) diff --git a/scopesim/base_classes.py b/scopesim/base_classes.py index 0c91730a..99533db7 100644 --- a/scopesim/base_classes.py +++ b/scopesim/base_classes.py @@ -42,7 +42,7 @@ def update(self, obj): self.dic.update(dict(obj)) if isinstance(obj, dict): - if any([isinstance(obj[key], (tuple, list)) for key in obj]): + if any(isinstance(obj[key], (tuple, list)) for key in obj): for key in obj: if isinstance(obj[key], (tuple, list)): self.comments[key] = obj[key][1] @@ -54,8 +54,8 @@ def update(self, obj): def as_header(self): hdr = Header(self.dic) - for key in self.comments: - hdr.comments[key] = self.comments[key] + for key, value in self.comments.items(): + hdr.comments[key] = value return hdr @@ -80,24 +80,20 @@ def __contains__(self, item): def __repr__(self): msgs = "" - for key in self.dic: + for key, value in self.dic.items(): cmt_msg = "" if key in self.comments: - cmt_msg = " / {}".format(self.comments[key]) - - msg = "{} = {}".format(key.upper().ljust(9), - str(self.dic[key]).rjust(16)) - msgs += msg + cmt_msg + "\n" - + cmt_msg = " / {self.comments[key]}" + msgs += f"{key.upper():<9} = {value!s:>16}{cmt_msg}\n" return msgs def items(self): items_dict = [] - for key in self.dic: + for key, value in self.dic.items(): if key in self.comments: - items_dict += [(key, (self.dic[key], self.comments[key]))] + items_dict.append((key, (value, self.comments[key]))) else: - items_dict += [(key, self.dic[key])] + items_dict.append((key, value)) return items_dict def keys(self): diff --git a/scopesim/commands/user_commands.py b/scopesim/commands/user_commands.py index 9b338721..793c3bbf 100644 --- a/scopesim/commands/user_commands.py +++ b/scopesim/commands/user_commands.py @@ -1,6 +1,7 @@ import os import logging import copy +from pathlib import Path import numpy as np import yaml @@ -180,11 +181,11 @@ def update(self, **kwargs): if yaml_input == "default.yaml": self.default_yamls = yaml_dict else: - logging.warning("{} could not be found".format(yaml_input)) + logging.warning("%s could not be found", yaml_input) elif isinstance(yaml_input, dict): self.cmds.update(yaml_input) - self.yaml_dicts += [yaml_input] + self.yaml_dicts.append(yaml_input) for key in ["packages", "yamls", "mode_yamls"]: if key in yaml_input: @@ -192,7 +193,7 @@ def update(self, **kwargs): else: raise ValueError("yaml_dicts must be a filename or a " - "dictionary: {}".format(yaml_input)) + f"dictionary: {yaml_input}") if "mode_yamls" in kwargs: # Convert the yaml list of modes to a dict object @@ -229,10 +230,9 @@ def set_modes(self, modes=None): defyam["properties"]["modes"] = [] for mode in modes: if mode in self.modes_dict: - defyam["properties"]["modes"] += [mode] + defyam["properties"]["modes"].append(mode) else: - raise ValueError("mode '{}' was not recognised" - "".format(mode)) + raise ValueError(f"mode '{mode}' was not recognised") self.__init__(yamls=self.default_yamls) @@ -244,7 +244,7 @@ def list_modes(self): desc = dic["description"] if "description" in dic else "" modes[mode_name] = desc - msg = "\n".join(["{}: {}".format(key, modes[key]) for key in modes]) + msg = "\n".join([f"{key}: {value}" for key, value in modes.items()]) else: msg = "No modes found" return msg @@ -263,7 +263,7 @@ def __contains__(self, item): return self.cmds.__contains__(item) def __repr__(self): - return self.cmds.__repr__() + return f"{self.__class__.__name__}(**{self.kwargs!r})" def check_for_updates(package_name): @@ -276,15 +276,60 @@ def check_for_updates(package_name): if rc.__currsys__["!SIM.reports.ip_tracking"] and \ "TRAVIS" not in os.environ: front_matter = rc.__currsys__["!SIM.file.server_base_url"] - back_matter = "api.php?package_name={}".format(package_name) + back_matter = f"api.php?package_name={package_name}" try: response = requests.get(url=front_matter+back_matter).json() except: - print("Offline. Cannot check for updates for {}" - "".format(package_name)) + print(f"Offline. Cannot check for updates for {package_name}") return response +def patch_fake_symlinks(path: Path): + """Fixes broken symlinks in path. + + The irdb has some symlinks in it, which work fine under linux, but not + always under windows, see https://stackoverflow.com/a/11664406 . + + "This makes symlinks created and committed e.g. under Linux appear as + plain text files that contain the link text under Windows" + + It is therefore necessary to assume that these can be regular files. + + E.g. when Path.cwd() is + WindowsPath('C:/Users/hugo/hugo/repos/irdb/MICADO/docs/example_notebooks') + and path is WindowsPath('inst_pkgs/MICADO') + then this function should return + WindowsPath('C:/Users/hugo/hugo/repos/irdb/MICADO') + """ + path = path.resolve() + if path.exists() and path.is_dir(): + # A normal directory. + return path + if path.exists() and path.is_file(): + # Could be a regular file, or a broken symlink. + size = path.stat().st_size + if size > 250 or size == 0: + # A symlink is probably not longer than 250 characters. + return path + line = open(path).readline() + if len(line) != size: + # There is more content in the file, so probably not a link. + return path + pline = Path(line) + if pline.exists(): + # The file contains exactly a path that exists. So it is + # probably a link. + return pline.resolve() + if path.exists(): + # The path exists, but is not a file or directory. Just return it. + return path + # The path does not exist. + parent = path.parent + pathup = patch_fake_symlinks(parent) + assert pathup != parent, ValueError("Cannot find path") + return patch_fake_symlinks(pathup / path.name) + + def add_packages_to_rc_search(local_path, package_list): """ Adds the paths of a list of locally saved packages to the search path list @@ -299,13 +344,13 @@ def add_packages_to_rc_search(local_path, package_list): A list of the package names to add """ - + plocal_path = patch_fake_symlinks(Path(local_path)) for pkg in package_list: - pkg_dir = os.path.abspath(os.path.join(local_path, pkg)) - if not os.path.exists(pkg_dir): + pkg_dir = plocal_path / pkg + if not pkg_dir.exists(): # todo: keep here, but add test for this by downloading test_package # raise ValueError("Package could not be found: {}".format(pkg_dir)) - logging.warning("Package could not be found: {}".format(pkg_dir)) + logging.warning("Package could not be found: %s", pkg_dir) if pkg_dir in rc.__search_path__: # if package is already in search_path, move it to the first place @@ -371,19 +416,17 @@ def list_local_packages(action="display"): """ - local_path = os.path.abspath(rc.__config__["!SIM.file.local_packages_path"]) - pkgs = [d for d in os.listdir(local_path) if - os.path.isdir(os.path.join(local_path, d))] + local_path = Path(rc.__config__["!SIM.file.local_packages_path"]).absolute() + pkgs = [d for d in local_path.iterdir() if d.is_dir()] - main_pkgs = [pkg for pkg in pkgs if - os.path.exists(os.path.join(local_path, pkg, "default.yaml"))] - ext_pkgs = [pkg for pkg in pkgs if not - os.path.exists(os.path.join(local_path, pkg, "default.yaml"))] + main_pkgs = [pkg for pkg in pkgs if (pkg/"default.yaml").exists()] + ext_pkgs = [pkg for pkg in pkgs if not (pkg/"default.yaml").exists()] if action == "display": - msg = "\nLocal package directory:\n {}\n" \ - "Full packages [can be used with 'use_instrument=...']\n {}\n" \ - "Support packages\n {}".format(local_path, main_pkgs, ext_pkgs) + msg = (f"\nLocal package directory:\n {local_path}\n" + "Full packages [can be used with 'use_instrument=...']\n" + f"{main_pkgs}\n" + f"Support packages\n {ext_pkgs}") print(msg) else: return main_pkgs, ext_pkgs diff --git a/scopesim/detector/detector.py b/scopesim/detector/detector.py index 75e33a78..7eee9248 100644 --- a/scopesim/detector/detector.py +++ b/scopesim/detector/detector.py @@ -20,8 +20,8 @@ def extract_from(self, image_plane, spline_order=1, reset=True): if reset: self.reset() if not isinstance(image_plane, ImagePlaneBase): - raise ValueError("image_plane must be an ImagePlane object: {}" - "".format(type(image_plane))) + raise ValueError("image_plane must be an ImagePlane object, but is: " + f"{type(image_plane)}") self._hdu = imp_utils.add_imagehdu_to_imagehdu(image_plane.hdu, self.hdu, spline_order, diff --git a/scopesim/effects/__init__.py b/scopesim/effects/__init__.py index e232667f..85d6833a 100644 --- a/scopesim/effects/__init__.py +++ b/scopesim/effects/__init__.py @@ -6,6 +6,7 @@ from .obs_strategies import * from .spectral_trace_list import * +from .spectral_efficiency import * from .metis_lms_trace_list import * from .surface_list import * from .ter_curves import * diff --git a/scopesim/effects/apertures.py b/scopesim/effects/apertures.py index ce4627b3..872d0894 100644 --- a/scopesim/effects/apertures.py +++ b/scopesim/effects/apertures.py @@ -1,11 +1,11 @@ """Effects related to field masks, including spectroscopic slits""" -from os import path as pth -from copy import deepcopy + +from pathlib import Path import logging import yaml import numpy as np -from matplotlib.path import Path +from matplotlib.path import Path as MPLPath # rename to avoid conflict with pathlib from astropy.io import fits from astropy import units as u from astropy.table import Table @@ -337,7 +337,7 @@ def get_apertures(self, row_ids): "x_unit": "arcsec", "y_unit": "arcsec", "angle_unit": "arcsec"} - apertures_list += [ApertureMask(array_dict=array_dict, **params)] + apertures_list.append(ApertureMask(array_dict=array_dict, **params)) return apertures_list @@ -370,8 +370,8 @@ def __add__(self, other): return self else: - raise ValueError("Secondary argument not of type ApertureList: {}" - "".format(type(other))) + raise ValueError("Secondary argument not of type ApertureList: " + f"{type(other) = }") # def __getitem__(self, item): # return self.get_apertures(item)[0] @@ -433,13 +433,12 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - path = pth.join(self.meta["path"], - from_currsys(self.meta["filename_format"])) + path = Path(self.meta["path"], from_currsys(self.meta["filename_format"])) self.slits = {} for name in from_currsys(self.meta["slit_names"]): kwargs["name"] = name - self.slits[name] = ApertureMask(filename=path.format(name), - **kwargs) + fname = str(path).format(name) + self.slits[name] = ApertureMask(filename=fname, **kwargs) self.table = self.get_table() @@ -453,7 +452,7 @@ def fov_grid(self, which="edges", **kwargs): def change_slit(self, slitname=None): """Change the current slit""" if not slitname or slitname in self.slits.keys(): - self.meta['current_slit'] = slitname + self.meta["current_slit"] = slitname self.include = slitname else: raise ValueError("Unknown slit requested: " + slitname) @@ -483,8 +482,8 @@ def current_slit(self): @property def display_name(self): - return f'{self.meta["name"]} : ' \ - f'[{from_currsys(self.meta["current_slit"])}]' + return f"{self.meta['name']} : " \ + f"[{from_currsys(self.meta['current_slit'])}]" def __getattr__(self, item): @@ -499,13 +498,13 @@ def get_table(self): """ names = list(self.slits.keys()) slits = self.slits.values() - xmax = np.array([slit.data['x'].max() * u.Unit(slit.meta['x_unit']) + xmax = np.array([slit.data["x"].max() * u.Unit(slit.meta["x_unit"]) .to(u.mas) for slit in slits]) - xmin = np.array([slit.data['x'].min() * u.Unit(slit.meta['x_unit']) + xmin = np.array([slit.data["x"].min() * u.Unit(slit.meta["x_unit"]) .to(u.mas) for slit in slits]) - ymax = np.array([slit.data['y'].max() * u.Unit(slit.meta['y_unit']) + ymax = np.array([slit.data["y"].max() * u.Unit(slit.meta["y_unit"]) .to(u.mas) for slit in slits]) - ymin = np.array([slit.data['y'].min() * u.Unit(slit.meta['y_unit']) + ymin = np.array([slit.data["y"].min() * u.Unit(slit.meta["y_unit"]) .to(u.mas) for slit in slits]) xmax = quantify(xmax, u.mas) xmin = quantify(xmin, u.mas) @@ -569,7 +568,7 @@ def mask_from_coords(x, y, pixel_scale): coords = [(xi, yi) for xi in xrange for yi in yrange] corners = [(xi, yi) for xi, yi in zip(x, y)] - path = Path(corners) + path = MPLPath(corners) # ..todo: known issue - for super thin apertures, the first row is masked # rad = 0.005 rad = 0 # increase this to include slightly more points within the polygon diff --git a/scopesim/effects/data_container.py b/scopesim/effects/data_container.py index 859673bd..30481bff 100644 --- a/scopesim/effects/data_container.py +++ b/scopesim/effects/data_container.py @@ -114,8 +114,7 @@ def _load_ascii(self): self.meta.update(hdr_dict) # self.table.meta.update(hdr_dict) self.table.meta.update(self.meta) - self.meta["history"] += ["ASCII table read from {}" - "".format(self.meta["filename"])] + self.meta["history"] += [f"ASCII table read from {self.meta['filename']}"] def _load_fits(self): self._file = fits.open(self.meta["filename"]) @@ -123,8 +122,7 @@ def _load_fits(self): self.headers += [ext.header] self.meta.update(dict(self._file[0].header)) - self.meta["history"] += ["Opened handle to FITS file {}" - "".format(self.meta["filename"])] + self.meta["history"] += [f"Opened handle to FITS file {self.meta['filename']}"] def get_data(self, ext=0, layer=None): """ diff --git a/scopesim/effects/detector_list.py b/scopesim/effects/detector_list.py index 4c080dd4..405e21e6 100644 --- a/scopesim/effects/detector_list.py +++ b/scopesim/effects/detector_list.py @@ -118,10 +118,10 @@ def __init__(self, **kwargs): new_colnames = {"xhw": "x_size", "yhw": "y_size", "pixsize": "pixel_size"} mult_cols = {"xhw": 2., "yhw": 2., "pixsize": 1.} if isinstance(self.table, Table): - for col in new_colnames: + for col, new_name in new_colnames.items(): if col in self.table.colnames: self.table[col] = self.table[col] * mult_cols[col] - self.table.rename_column(col, new_colnames[col]) + self.table.rename_column(col, new_name) if not "x_size_unit" in self.meta and "xhw_unit" in self.meta: self.meta["x_size_unit"] = self.meta["xhw_unit"] if not "y_size_unit" in self.meta and "yhw_unit" in self.meta: @@ -133,7 +133,7 @@ def apply_to(self, obj, **kwargs): hdr = self.image_plane_header x_mm, y_mm = calc_footprint(hdr, "D") pixel_size = hdr["CDELT1D"] # mm - pixel_scale = (kwargs.get("pixel_scale", self.meta["pixel_scale"])) # ["] + pixel_scale = kwargs.get("pixel_scale", self.meta["pixel_scale"]) # ["] pixel_scale = utils.from_currsys(pixel_scale) x_sky = x_mm * pixel_scale / pixel_size # x["] = x[mm] * ["] / [mm] y_sky = y_mm * pixel_scale / pixel_size # y["] = y[mm] * ["] / [mm] @@ -209,14 +209,13 @@ def active_table(self): tbl = self.table[mask] else: raise ValueError("Could not determine which detectors are active: " - "{}, {}, ".format(self.meta["active_detectors"], - self.table)) + f"{self.meta['active_detectors']}, {self.table}, ") tbl = utils.from_currsys(tbl) return tbl def detector_headers(self, ids=None): - if ids is not None and all([isinstance(ii, int) for ii in ids]): + if ids is not None and all(isinstance(ii, int) for ii in ids): self.meta["active_detectors"] = list(ids) tbl = utils.from_currsys(self.active_table) @@ -244,11 +243,11 @@ def detector_headers(self, ids=None): # hdr["GAIN"] = row["gain"] if "id" in row: hdr["DET_ID"] = row["id"] - hdr["EXTNAME"] = f'DET_{row["id"]}' + hdr["EXTNAME"] = f"DET_{row['id']}" row_dict = {col: row[col] for col in row.colnames} hdr.update(row_dict) - hdrs += [hdr] + hdrs.append(hdr) return hdrs diff --git a/scopesim/effects/effects.py b/scopesim/effects/effects.py index 4bd4f940..00dbe9e9 100644 --- a/scopesim/effects/effects.py +++ b/scopesim/effects/effects.py @@ -1,11 +1,9 @@ -import os -from astropy.table import Table +from pathlib import Path from ..effects.data_container import DataContainer from .. import base_classes as bc from ..utils import from_currsys, write_report from ..reports.rst_utils import table_to_rst -from .. import rc class Effect(DataContainer): @@ -47,8 +45,8 @@ def apply_to(self, obj, **kwargs): if not isinstance(obj, (bc.FOVSetupBase, bc.SourceBase, bc.FieldOfViewBase, bc.ImagePlaneBase, bc.DetectorBase)): - raise ValueError(f"object must one of the following: FOVSetupBase, " - f"Source, FieldOfView, ImagePlane, Detector: " + raise ValueError("object must one of the following: FOVSetupBase, " + "Source, FieldOfView, ImagePlane, Detector: " f"{type(obj)}") return obj @@ -116,13 +114,13 @@ def display_name(self): @property def meta_string(self): meta_str = "" - max_key_len = max([len(key) for key in self.meta.keys()]) + max_key_len = max(len(key) for key in self.meta.keys()) + padlen = max_key_len + 4 for key in self.meta: - if key not in ["comments", "changes", "description", "history", + if key not in {"comments", "changes", "description", "history", "report_table_caption", "report_plot_caption", - "table"]: - meta_str += " {} : {}\n".format(key.rjust(max_key_len), - self.meta[key]) + "table"}: + meta_str += f"{key:>{padlen}} : {self.meta[key]}\n" return meta_str @@ -223,28 +221,22 @@ def report(self, filename=None, output="rst", rst_title_chars="*+", params.update(kwargs) params = from_currsys(params) - rst_str = """ -{} -{} -**Included by default**: ``{}`` + rst_str = f""" +{str(self)} +{rst_title_chars[0] * len(str(self))} +**Included by default**: ``{params["include"]}`` -**File Description**: {} +**File Description**: {params["file_description"]} -**Class Description**: {} +**Class Description**: {params["class_description"]} **Changes**: -{} +{params["changes_str"]} Data -{} -""".format(str(self), - rst_title_chars[0] * len(str(self)), - params["include"], - params["file_description"], - params["class_description"], - params["changes_str"], - rst_title_chars[1] * 4) +{rst_title_chars[1] * 4} +""" if params["report_plot_include"] and hasattr(self, "plot"): fig = self.plot() @@ -257,7 +249,7 @@ def report(self, filename=None, output="rst", rst_title_chars="*+", for fmt in params["report_plot_file_formats"]: fname = ".".join((fname.split(".")[0], fmt)) - file_path = os.path.join(path, fname) + file_path = Path(path, fname) fig.savefig(fname=file_path) @@ -265,36 +257,30 @@ def report(self, filename=None, output="rst", rst_title_chars="*+", # params["report_rst_path"]) # rel_file_path = os.path.join(rel_path, fname) - rst_str += """ -.. figure:: {} - :name: {} + rst_str += f""" +.. figure:: {fname} + :name: {"fig:" + params.get("name", "")} - {} -""".format(fname, - "fig:" + params.get("name", ""), - params["report_plot_caption"]) + {params["report_plot_caption"]} +""" if params["report_table_include"]: - rst_str += """ + rst_str += f""" .. table:: - :name: {} + :name: {"tbl:" + params.get("name")} -{} +{table_to_rst(self.table, indent=4, rounding=params["report_table_rounding"])} -{} -""".format("tbl:" + params.get("name"), - table_to_rst(self.table, indent=4, - rounding=params["report_table_rounding"]), - params["report_table_caption"]) +{params["report_table_caption"]} +""" - rst_str += """ + rst_str += f""" Meta-data -{} +{rst_title_chars[1] * 9} :: -{} -""".format(rst_title_chars[1] * 9, - self.meta_string) +{self.meta_string} +""" write_report(rst_str, filename, output) @@ -304,25 +290,24 @@ def info(self): """ Prints basic information on the effect, notably the description """ - name = self.meta.get("name", self.meta.get("filename", "")) - text = f'{type(self).__name__}: "{name}"' + text = str(self) desc = self.meta.get("description") if desc is not None: - text += f"\nDescription: {desc}" + text += f"\nDescription: {desc}" print(text) def __repr__(self): - return f'{type(self).__name__}: "{self.display_name}"' + return f"{self.__class__.__name__}(**{self.meta!r})" def __str__(self): - return self.__repr__() + return f"{self.__class__.__name__}: \"{self.display_name}\"" def __getitem__(self, item): - if isinstance(item, str) and item[0] == "#": + if isinstance(item, str) and item.startswith("#"): if len(item) > 1: - if item[-1] == "!": + if item.endswith("!"): key = item[1:-1] if len(key) > 0: value = from_currsys(self.meta[key]) @@ -335,4 +320,4 @@ def __getitem__(self, item): else: raise ValueError(f"__getitem__ calls must start with '#': {item}") - return value \ No newline at end of file + return value diff --git a/scopesim/effects/effects_utils.py b/scopesim/effects/effects_utils.py index ed64592b..0258c83d 100644 --- a/scopesim/effects/effects_utils.py +++ b/scopesim/effects/effects_utils.py @@ -36,7 +36,7 @@ def combine_surface_effects(surface_effects): if isinstance(eff, (efs.TERCurve, efs.FilterWheel)) and not isinstance(eff, efs.SurfaceList)] - if len(surflist_list) == 0: + if not surflist_list: surflist_list = [empty_surface_list(name="combined_surface_list")] new_surflist = copy(surflist_list[0]) @@ -85,7 +85,7 @@ def make_effect(effect_dict, **properties): def is_spectroscope(effects): spec_classes = (efs.SpectralTraceList, efs.SpectralTraceListWheel) - return any([isinstance(eff, spec_classes) for eff in effects]) + return any(isinstance(eff, spec_classes) for eff in effects) def empty_surface_list(**kwargs): diff --git a/scopesim/effects/electronic.py b/scopesim/effects/electronic.py index a889302c..59601575 100644 --- a/scopesim/effects/electronic.py +++ b/scopesim/effects/electronic.py @@ -88,19 +88,19 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - required_keys = ['mode_properties'] + required_keys = ["mode_properties"] utils.check_keys(self.meta, required_keys, action="error") - self.mode_properties = kwargs['mode_properties'] + self.mode_properties = kwargs["mode_properties"] def apply_to(self, obj, **kwargs): - mode_name = kwargs.get('detector_readout_mode', + mode_name = kwargs.get("detector_readout_mode", from_currsys("!OBS.detector_readout_mode")) if isinstance(obj, ImagePlaneBase) and mode_name == "auto": mode_name = self.select_mode(obj, **kwargs) print("Detector mode set to", mode_name) - self.meta['detector_readout_mode'] = mode_name + self.meta["detector_readout_mode"] = mode_name props_dict = self.mode_properties[mode_name] rc.__currsys__["!OBS.detector_readout_mode"] = mode_name for key, value in props_dict.items(): @@ -181,13 +181,13 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - required_keys = ['fill_frac', 'full_well', 'mindit'] + required_keys = ["fill_frac", "full_well", "mindit"] utils.check_keys(self.meta, required_keys, action="error") def apply_to(self, obj, **kwargs): if isinstance(obj, (ImagePlaneBase, DetectorBase)): implane_max = np.max(obj.data) - exptime = kwargs.get('exptime', from_currsys("!OBS.exptime")) + exptime = kwargs.get("exptime", from_currsys("!OBS.exptime")) mindit = from_currsys(self.meta["mindit"]) if exptime is None: @@ -219,8 +219,8 @@ def apply_to(self, obj, **kwargs): print(f" DIT: {dit:.3f} s NDIT: {ndit}") print(f"Total exposure time: {dit * ndit:.3f} s") - rc.__currsys__['!OBS.dit'] = dit - rc.__currsys__['!OBS.ndit'] = ndit + rc.__currsys__["!OBS.dit"] = dit + rc.__currsys__["!OBS.ndit"] = ndit return obj @@ -418,8 +418,8 @@ def apply_to(self, obj, **kwargs): elif isinstance(from_currsys(self.meta["value"]), float): dark = from_currsys(self.meta["value"]) else: - raise ValueError(".meta['value'] must be either" - "dict or float: {}".format(self.meta["value"])) + raise ValueError(".meta['value'] must be either " + f"dict or float, but is {self.meta['value']}") dit = from_currsys(self.meta["dit"]) ndit = from_currsys(self.meta["ndit"]) diff --git a/scopesim/effects/fits_headers.py b/scopesim/effects/fits_headers.py index 70f173fb..db858b20 100644 --- a/scopesim/effects/fits_headers.py +++ b/scopesim/effects/fits_headers.py @@ -1,12 +1,15 @@ -import yaml from copy import deepcopy import datetime + +import yaml import numpy as np + from astropy.io import fits from astropy import units as u from astropy.table import Table + from . import Effect -from ..utils import check_keys, from_currsys, find_file +from ..utils import from_currsys, find_file class ExtraFitsKeywords(Effect): @@ -230,26 +233,26 @@ def __init__(self, **kwargs): with open(yaml_file) as f: # possible multiple yaml docs in a file # --> returns list even for a single doc - tmp_dicts += [dic for dic in yaml.full_load_all(f)] + tmp_dicts.extend(dic for dic in yaml.full_load_all(f)) if self.meta["yaml_string"] is not None: yml = self.meta["yaml_string"] - tmp_dicts += [dic for dic in yaml.full_load_all(yml)] + tmp_dicts.extend(dic for dic in yaml.full_load_all(yml)) if self.meta["header_dict"] is not None: if not isinstance(self.meta["header_dict"], list): - tmp_dicts += [self.meta["header_dict"]] + tmp_dicts.append(self.meta["header_dict"]) else: - tmp_dicts += self.meta["header_dict"] + tmp_dicts.extend(self.meta["header_dict"]) self.dict_list = [] for dic in tmp_dicts: # format says yaml file contains list of dicts if isinstance(dic, list): - self.dict_list += dic + self.dict_list.extend(dic) # catch case where user forgets the list elif isinstance(dic, dict): - self.dict_list += [dic] + self.dict_list.append(dic) def apply_to(self, hdul, **kwargs): """ @@ -283,18 +286,18 @@ def apply_to(self, hdul, **kwargs): def get_relevant_extensions(dic, hdul): exts = [] if dic.get("ext_name") is not None: - exts += [i for i, hdu in enumerate(hdul) - if hdu.header["EXTNAME"] == dic["ext_name"]] + exts.extend(i for i, hdu in enumerate(hdul) + if hdu.header["EXTNAME"] == dic["ext_name"]) elif dic.get("ext_number") is not None: ext_n = np.array(dic["ext_number"]) - exts += list(ext_n[ext_n \"{self.meta['description']}\" : " + f"{from_currsys(self.meta['wavelen'])} um : " + f"Order {self.meta['order']} : Angle {self.meta['angle']}") return msg @@ -410,18 +412,18 @@ def echelle_setting(wavelength, grat_spacing, wcal_def): wcal = wcal_def elif isinstance(wcal_def, str): try: - wcal = fits.getdata(wcal_def, extname='WCAL') + wcal = fits.getdata(wcal_def, extname="WCAL") except OSError: wcal = ioascii.read(wcal_def, comment="^#", format="csv") else: raise TypeError("wcal_def not in recognised format:", wcal_def) # Compute angles, determine which order gives angle closest to zero - angles = wcal['c0'] * wavelength + wcal['c1'] + angles = wcal["c0"] * wavelength + wcal["c1"] imin = np.argmin(np.abs(angles)) # Extract parameters - order = wcal['Ord'][imin] + order = wcal["Ord"][imin] angle = angles[imin] # Compute the phase corresponding to the wavelength @@ -443,13 +445,13 @@ def __init__(self, filename, ext_id="Aperture List", **kwargs): filename = find_file(from_currsys(filename)) ap_hdr = fits.getheader(filename, extname=ext_id) ap_list = fits.getdata(filename, extname=ext_id) - xmin, xmax = ap_list['left'].min(), ap_list['right'].max() - ymin, ymax = ap_list['bottom'].min(), ap_list['top'].max() + xmin, xmax = ap_list["left"].min(), ap_list["right"].max() + ymin, ymax = ap_list["bottom"].min(), ap_list["top"].max() slicer_dict = {"x": [xmin, xmax, xmax, xmin], "y": [ymin, ymin, ymax, ymax]} try: - kwargs["x_unit"] = ap_hdr['X_UNIT'] - kwargs["y_unit"] = ap_hdr['Y_UNIT'] + kwargs["x_unit"] = ap_hdr["X_UNIT"] + kwargs["y_unit"] = ap_hdr["Y_UNIT"] except KeyError: pass @@ -475,13 +477,13 @@ def __init__(self, **kwargs): self.meta = self._class_params self.meta.update(kwargs) - filename = find_file(self.meta['filename']) - wcal = fits.getdata(filename, extname='WCAL') - if 'wavelen' in kwargs: - wavelen = from_currsys(kwargs['wavelen']) - grat_spacing = self.meta['grat_spacing'] + filename = find_file(self.meta["filename"]) + wcal = fits.getdata(filename, extname="WCAL") + if "wavelen" in kwargs: + wavelen = from_currsys(kwargs["wavelen"]) + grat_spacing = self.meta["grat_spacing"] ech = echelle_setting(wavelen, grat_spacing, wcal) - self.meta['order'] = ech['Ord'] + self.meta["order"] = ech["Ord"] else: wavelen = None @@ -494,18 +496,18 @@ def __init__(self, **kwargs): def make_ter_curve(self, wcal, wavelen=None): """Compute the blaze function for the selected order""" - order = self.meta['order'] - eff_wid = self.meta['eff_wid'] - eff_max = self.meta['eff_max'] + order = self.meta["order"] + eff_wid = self.meta["eff_wid"] + eff_max = self.meta["eff_max"] - wcal_ord = wcal[wcal['Ord'] == self.meta['order']] + wcal_ord = wcal[wcal["Ord"] == self.meta["order"]] if wavelen is not None: lam = np.linspace(wavelen - 0.2, wavelen + 0.2, 1001) - angle = wcal_ord['c0'] * lam + wcal_ord['c1'] + angle = wcal_ord["c0"] * lam + wcal_ord["c1"] else: angle = np.linspace(7, -7, 10001) - lam = wcal_ord['ic0'] * angle + wcal_ord['ic1'] + lam = wcal_ord["ic0"] * angle + wcal_ord["ic1"] phase = order * np.pi * np.sin(np.deg2rad(angle)) * eff_wid efficiency = eff_max * np.sinc(phase / np.pi)**2 diff --git a/scopesim/effects/psf_utils.py b/scopesim/effects/psf_utils.py index 4f61fb9e..abe63773 100644 --- a/scopesim/effects/psf_utils.py +++ b/scopesim/effects/psf_utils.py @@ -64,9 +64,9 @@ def nmrms_from_strehl_and_wavelength(strehl, wavelength, strehl_hdu, strehls = nms_spline(wavelength, nms)[0] if strehl > np.max(strehls): - raise ValueError("Strehl ratio ({}) is impossible at this wavelength " - "({}). Maximum Strehl possible is {}." - "".format(strehl, wavelength, np.max(strehls))) + raise ValueError(f"Strehl ratio ({strehl}) is impossible at this " + f"wavelength ({wavelength}). Maximum Strehl possible " + f"is {np.max(strehls)}.") if strehls[0] < strehls[-1]: nm = np.interp(strehl, strehls, nms) @@ -178,8 +178,7 @@ def get_psf_wave_exts(hdu_list, wave_key="WAVE0"): """ if not isinstance(hdu_list, fits.HDUList): - raise ValueError("psf_effect must be a PSF object: {}" - "".format(type(hdu_list))) + raise ValueError(f"psf_effect must be a PSF object: {type(hdu_list)}") tmp = np.array([[ii, hdu.header[wave_key]] for ii, hdu in enumerate(hdu_list) diff --git a/scopesim/effects/psfs.py b/scopesim/effects/psfs.py index 90417674..fe26049f 100644 --- a/scopesim/effects/psfs.py +++ b/scopesim/effects/psfs.py @@ -1,4 +1,3 @@ -from copy import deepcopy import numpy as np from scipy.signal import convolve from scipy.interpolate import RectBivariateSpline @@ -139,7 +138,7 @@ def plot(self, obj=None, **kwargs): plt.gcf().clf() kernel = self.get_kernel(obj) - plt.imshow(kernel, norm=LogNorm(), origin='lower', **kwargs) + plt.imshow(kernel, norm=LogNorm(), origin="lower", **kwargs) return plt.gcf() @@ -258,8 +257,8 @@ def plot(self): strehl = pu.wfe2strehl(wfe=wfe, wave=waves) plt.plot(waves, strehl) - plt.xlabel("Wavelength [{}]".format(waves.unit)) - plt.ylabel("Strehl Ratio \n[Total WFE = {}]".format(wfe)) + plt.xlabel(f"Wavelength [{waves.unit}]") + plt.ylabel(f"Strehl Ratio \n[Total WFE = {wfe}]") return plt.gcf() @@ -519,7 +518,7 @@ def plot(self, obj=None, **kwargs): plt.subplot2grid((2, 2), (0, 0)) im = kernel r_sky = pixel_scale * im.shape[0] - plt.imshow(im, norm=LogNorm(), origin='lower', + plt.imshow(im, norm=LogNorm(), origin="lower", extent= [-r_sky, r_sky, -r_sky, r_sky], **kwargs) plt.ylabel("[arcsec]") @@ -529,10 +528,10 @@ def plot(self, obj=None, **kwargs): r = 16 im = kernel[y-r:y+r, x-r:x+r] r_sky = pixel_scale * im.shape[0] - plt.imshow(im, norm=LogNorm(), origin='lower', + plt.imshow(im, norm=LogNorm(), origin="lower", extent= [-r_sky, r_sky, -r_sky, r_sky], **kwargs) plt.ylabel("[arcsec]") - plt.gca().yaxis.set_label_position('right') + plt.gca().yaxis.set_label_position("right") plt.subplot2grid((2, 2), (1, 0), colspan=2) hdr = self._file[0].header @@ -545,7 +544,7 @@ def plot(self, obj=None, **kwargs): waves = np.arange(hdr["NAXIS2"]) * hdr["CDELT2"] + hdr["CRVAL2"] for i in np.arange(len(waves))[::-1]: plt.plot(wfes, data[i, :], - label=r"{} $\mu m$".format(round(waves[i], 3))) + label=f"{waves[i]:.3f} " + r"$\mu m$") plt.xlabel("RMS Wavefront Error [um]") plt.ylabel("Strehl Ratio") @@ -556,7 +555,7 @@ def plot(self, obj=None, **kwargs): ################################################################################ -# Discrete PSFs - MAORY and co PSFs +# Discrete PSFs - MORFEO and co PSFs class DiscretePSF(PSF): @@ -570,7 +569,7 @@ def __init__(self, **kwargs): class FieldConstantPSF(DiscretePSF): """A PSF that is constant across the field. - For spectroscopy, the a wavelength-dependent PSF cube is built, where for each + For spectroscopy, a wavelength-dependent PSF cube is built, where for each wavelength the reference PSF is scaled proportional to wavelength. """ def __init__(self, **kwargs): @@ -599,7 +598,7 @@ def get_kernel(self, fov): ii = pu.nearest_index(fov.wavelength, self._waveset) ext = self.kernel_indexes[ii] if ext != self.current_layer_id: - if fov.hdu.header['NAXIS'] == 3: + if fov.hdu.header["NAXIS"] == 3: self.current_layer_id = ext self.make_psf_cube(fov) else: diff --git a/scopesim/effects/rotation.py b/scopesim/effects/rotation.py index 33025b4f..28ac85ef 100644 --- a/scopesim/effects/rotation.py +++ b/scopesim/effects/rotation.py @@ -17,6 +17,8 @@ class Rotate90CCD(Effect): """ Rotates CCD by integer multiples of 90 degrees rotations kwarg is number of counter-clockwise rotations + + Author: Dave jones """ def __init__(self, **kwargs): diff --git a/scopesim/effects/shifts.py b/scopesim/effects/shifts.py index 49732631..84db3961 100644 --- a/scopesim/effects/shifts.py +++ b/scopesim/effects/shifts.py @@ -24,8 +24,7 @@ def fov_grid(self, which="shifts", **kwargs): col_names = ["wavelength", "dx", "dy"] waves, dx, dy = [self.get_table(**kwargs)[col] for col in col_names] return waves, dx, dy - else: - return None + return None def get_table(self, **kwargs): if self.table is None: @@ -45,8 +44,8 @@ def plot(self): tbl = self.get_table() plt.scatter(x=tbl["dx"], y=tbl["dy"], c=tbl["wavelength"]) plt.colorbar() - plt.xlabel("dx [{}]".format(quantify(tbl["dx"], u.arcsec).unit)) - plt.ylabel("dy [{}]".format(quantify(tbl["dy"], u.arcsec).unit)) + plt.xlabel(f"dx [{quantify(tbl['dx'], u.arcsec).unit}]") + plt.ylabel(f"dy [{quantify(tbl['dy'], u.arcsec).unit}]") plt.axvline(0, ls=":") plt.axhline(0, ls=":") # plt.gca().set_aspect("equal") @@ -223,8 +222,7 @@ def fov_grid(self, which="shifts", **kwargs): dx *= -(1 - self.meta["efficiency"]) dy *= -(1 - self.meta["efficiency"]) return waves, dx, dy - else: - return None + return None def plot(self): return None diff --git a/scopesim/effects/spectral_efficiency.py b/scopesim/effects/spectral_efficiency.py new file mode 100644 index 00000000..6b255eec --- /dev/null +++ b/scopesim/effects/spectral_efficiency.py @@ -0,0 +1,127 @@ +""" +Spectral grating efficiencies +""" +import logging +import numpy as np +from matplotlib import pyplot as plt + +from astropy.io import fits +from astropy import units as u +from astropy.wcs import WCS +from astropy.table import Table + +from .effects import Effect +from .ter_curves import TERCurve +from .ter_curves_utils import apply_throughput_to_cube +from ..utils import find_file +from ..base_classes import FieldOfViewBase, FOVSetupBase + +class SpectralEfficiency(Effect): + """ + Applies the grating efficiency (blaze function) for a SpectralTraceList + + Input Data Format + ----------------- + The efficiency curves are taken from a fits file `filename`with a + structure similar to the trace definition file (see `SpectralTraceList`). + The required extensions are: + - 0 : PrimaryHDU [header] + - 1 : BinTableHDU or TableHDU[header, data] : Overview table of all traces + - 2..N : BinTableHDU or TableHDU : Efficiency curves, one per trace. The + tables must have the two columns `wavelength` and `efficiency` + + Note that there must be one extension for each trace defined in the + `SpectralTraceList`. Extensions for other traces are ignored. + + EXT 0 : PrimaryHDU + ++++++++++++++++++ + Required header keywords: + + - ECAT : int : Extension number of overview table, normally 1 + - EDATA : int : Extension number of first Trace table, normally 2 + + No data is required in this extension + + EXT 1 : (Bin)TableHDU : Overview of traces + ++++++++++++++++++++++++++++++++++++++++++ + No special header keywords are required in this extension. + + Required Table columns: + - description : str : identifier for each trace + - extension_id : int : which extension is each trace in + + EXT 2 : (Bin)TableHDU : Efficiencies for individual traces + ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + Required header keywords: + - EXTNAME : must be identical to the `description` in EXT 1 + + Required Table columns: + - wavelength : float : [um] + - efficiency : float : number [0..1] + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + if "hdulist" in kwargs and isinstance(kwargs["hdulist"], fits.HDUList): + self._file = kwargs["hdulist"] + + params = {"z_order": [630]} + self.meta.update(params) + + self.efficiencies = self.get_efficiencies() + + def get_efficiencies(self): + """Reads effciencies from file, returns a dictionary""" + hdul = self._file + self.ext_data = hdul[0].header["EDATA"] + self.ext_cat = hdul[0].header["ECAT"] + self.catalog = Table(hdul[self.ext_cat].data) + + efficiencies = {} + for row in self.catalog: + params = {col: row[col] for col in row.colnames} + params.update(self.meta) + hdu = self._file[row["extension_id"]] + name = hdu.header['EXTNAME'] + + tbl = Table.read(hdu) + wavelength = tbl['wavelength'].quantity + efficiency = tbl['efficiency'].value + effic_curve = TERCurve(wavelength=wavelength, + transmission=efficiency, + **params) + efficiencies[name] = effic_curve + + hdul.close() + return efficiencies + + def apply_to(self, obj, **kwargs): + """ + Interface between FieldOfView and SpectralEfficiency + """ + trace_id = obj.meta['trace_id'] + try: + effic = self.efficiencies[trace_id] + except KeyError: + logging.warning("No grating efficiency for trace %s" % trace_id) + return obj + + wcs = WCS(obj.hdu.header).spectral + wave_cube = wcs.all_pix2world(np.arange(obj.hdu.data.shape[0]), 0)[0] + wave_cube = (wave_cube * u.Unit(wcs.wcs.cunit[0])).to(u.AA) + obj.hdu = apply_throughput_to_cube(obj.hdu, effic.throughput) + return obj + + def plot(self): + """Plot the grating efficiencies""" + for name, effic in self.efficiencies.items(): + wave = effic.throughput.waveset + plt.plot(wave.to(u.um), effic.throughput(wave), label=name) + + plt.xlabel("Wavelength [um]") + plt.ylabel("Grating efficiency") + plt.title(f"Grating efficiencies from {self.filename}") + plt.legend() + plt.show() diff --git a/scopesim/effects/spectral_trace_list.py b/scopesim/effects/spectral_trace_list.py index bd30270c..553c7879 100644 --- a/scopesim/effects/spectral_trace_list.py +++ b/scopesim/effects/spectral_trace_list.py @@ -1,19 +1,22 @@ """ Effect for mapping spectral cubes to the detector plane -The Effect is called SpectralTraceList, it applies a list of -optics.spectral_trace_SpectralTrace objects to a FieldOfView. +The Effect is called `SpectralTraceList`, it applies a list of +`spectral_trace_list_utils.SpectralTrace` objects to a `FieldOfView`. """ -from os import path as pth +from pathlib import Path +import logging + import numpy as np from astropy.io import fits from astropy.table import Table from .effects import Effect -from .spectral_trace_list_utils import SpectralTrace -from ..utils import from_currsys, check_keys, interp2 +from .ter_curves import FilterCurve +from .spectral_trace_list_utils import SpectralTrace, make_image_interpolations +from ..utils import from_currsys, check_keys from ..optics.image_plane_utils import header_from_list_of_xy from ..base_classes import FieldOfViewBase, FOVSetupBase @@ -61,17 +64,21 @@ class SpectralTraceList(Effect): Required Table columns: - - description : str : description of each each trace + - description : str : identifier of each trace - extension_id : int : which extension is each trace in - aperture_id : int : which aperture matches this trace (e.g. MOS / IFU) - image_plane_id : int : on which image plane is this trace projected EXT 2 : BinTableHDU : Individual traces +++++++++++++++++++++++++++++++++++++++ - No special header keywords are required in this extension + Required header keywords: + - EXTNAME : must be identical to the `description` in EXT 1 - Required Table columns: + Recommended header keywords: + - DISPDIR : 'x' or 'y' : dispersion axis. If not present, Scopesim tries + to determine this automatically; this may be unreliable in some cases. + Required Table columns: - wavelength : float : [um] : wavelength of monochromatic aperture image - s : float : [arcsec] : position along aperture perpendicular to trace - x : float : [mm] : x position of aperture image on focal plane @@ -97,6 +104,7 @@ def __init__(self, **kwargs): params = {"z_order": [70, 270, 670], "pixel_scale": "!INST.pixel_scale", # [arcsec / pix]} "plate_scale": "!INST.plate_scale", # [arcsec / mm] + "spectral_bin_width": "!SIM.spectral.spectral_bin_width", # [um] "wave_min": "!SIM.spectral.wave_min", # [um] "wave_mid": "!SIM.spectral.wave_mid", # [um] "wave_max": "!SIM.spectral.wave_max", # [um] @@ -184,34 +192,15 @@ def apply_to(self, obj, **kwargs): # for MAAT pass elif obj.hdu is None and obj.cube is None: + logging.info("Making cube") obj.cube = obj.make_cube_hdu() - # ..todo: obj will be changed to a single one covering the full field of view - # covered by the image slicer (28 slices for LMS; for LSS still only a single slit) - # We need a loop over spectral_traces that chops up obj into the single-slice fov before - # calling map_spectra... - trace_id = obj.meta['trace_id'] + trace_id = obj.meta["trace_id"] spt = self.spectral_traces[trace_id] obj.hdu = spt.map_spectra_to_focal_plane(obj) return obj - def get_waveset(self, pixel_size=None): - if pixel_size is None: - pixel_size = self.meta["pixel_scale"] / self.meta["plate_scale"] - - wavesets = [spt.get_pixel_wavelength_edges(pixel_size) - for spt in self.spectral_traces] - - return wavesets - - def get_fov_headers(self, sky_header, **kwargs): - fov_headers = [] - for spt in self.spectral_traces: - fov_headers += spt.fov_headers(sky_header=sky_header, **kwargs) - - return fov_headers - @property def footprint(self): @@ -235,6 +224,100 @@ def image_plane_header(self): return hdr + def rectify_traces(self, hdulist, xi_min=None, xi_max=None, interps=None, + **kwargs): + """Create rectified 2D spectra for all traces in the list + + This method creates an HDU list with one extension per spectral + trace, i.e. it essentially treats all traces independently. + For the case of an IFU where the traces correspond to spatial + slices for the same wavelength range, use method `rectify_cube` + (not yet implemented). + + Parameters + ---------- + hdulist : str or fits.HDUList + The result of scopesim readout() + xi_min, xi_max : float [arcsec] + Spatial limits of the slit on the sky. This should be taken + from the header of the hdulist, but this is not yet provided by + scopesim. For the time being, these limits *must* be provided by + the user. + interps : list of interpolation functions + If provided, there must be one for each image extension in `hdulist`. + The functions go from pixels to the images and can be created with, + e.g., RectBivariateSpline. + """ + try: + inhdul = fits.open(hdulist) + except TypeError: + inhdul = hdulist + + # Crude attempt to get a useful wavelength range + # Problematic because different instruments use different + # keywords for the filter... We try to make it work for METIS + # and MICADO for the time being. + try: + filter_name = from_currsys("!OBS.filter_name") + except ValueError: + filter_name = from_currsys("!OBS.filter_name_fw1") + + filtcurve = FilterCurve( + filter_name=filter_name, + filename_format=from_currsys("!INST.filter_file_format")) + filtwaves = filtcurve.table['wavelength'] + filtwave = filtwaves[filtcurve.table['transmission'] > 0.01] + wave_min, wave_max = min(filtwave), max(filtwave) + logging.info("Full wavelength range: %.02f .. %.02f um", + wave_min, wave_max) + + if xi_min is None or xi_max is None: + try: + xi_min = inhdul[0].header["HIERARCH INS SLIT XIMIN"] + xi_max = inhdul[0].header["HIERARCH INS SLIT XIMAX"] + logging.info( + "Slit limits taken from header: %.02f .. %.02f arcsec", + xi_min, xi_max) + except KeyError: + logging.error(""" + Spatial slit limits (in arcsec) must be provided: + - either as method parameters xi_min and xi_max + - or as header keywords HIERARCH INS SLIT XIMIN/XIMAX + """) + return None + + + bin_width = kwargs.get("bin_width", None) + + if interps is None: + logging.info("Computing interpolation functions") + interps = make_image_interpolations(hdulist) + + pdu = fits.PrimaryHDU() + pdu.header['FILETYPE'] = "Rectified spectra" + #pdu.header['INSTRUME'] = inhdul[0].header['HIERARCH ESO OBS INSTRUME'] + #pdu.header['FILTER'] = from_currsys("!OBS.filter_name_fw1") + outhdul = fits.HDUList([pdu]) + + for i, trace_id in enumerate(self.spectral_traces): + hdu = self[trace_id].rectify(hdulist, + interps=interps, + bin_width=bin_width, + xi_min=xi_min, xi_max=xi_max, + wave_min=wave_min, wave_max=wave_max) + if hdu is not None: # ..todo: rectify does not do that yet + outhdul.append(hdu) + outhdul[0].header[f"EXTNAME{i+1}"] = trace_id + + outhdul[0].header.update(inhdul[0].header) + + return outhdul + + + def rectify_cube(self, hdulist): + """Rectify traces and combine into a cube""" + raise(NotImplementedError) + def plot(self, wave_min=None, wave_max=None, **kwargs): if wave_min is None: wave_min = from_currsys("!SIM.spectral.wave_min") @@ -254,13 +337,20 @@ def plot(self, wave_min=None, wave_max=None, **kwargs): return plt.gcf() def __repr__(self): - return "\n".join([spt.__repr__() for spt in self.spectral_traces]) + # "\n".join([spt.__repr__() for spt in self.spectral_traces]) + return f"{self.__class__.__name__}(**{self.meta!r})" def __str__(self): - msg = 'SpectralTraceList: "{}" : {} traces' \ - ''.format(self.meta.get("name"), len(self.spectral_traces)) + msg = (f"SpectralTraceList: \"{self.meta.get('name')}\" : " + f"{len(self.spectral_traces)} traces") return msg + def __getitem__(self, item): + return self.spectral_traces[item] + + def __setitem__(self, key, value): + self.spectral_traces[key] = value + class SpectralTraceListWheel(Effect): """ @@ -335,13 +425,12 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - path = pth.join(self.meta["path"], - from_currsys(self.meta["filename_format"])) + path = Path(self.meta["path"], from_currsys(self.meta["filename_format"])) self.trace_lists = {} for name in from_currsys(self.meta["trace_list_names"]): kwargs["name"] = name - self.trace_lists[name] = SpectralTraceList(filename=path.format(name), - **kwargs) + fname = str(path).format(name) + self.trace_lists[name] = SpectralTraceList(filename=fname, **kwargs) def apply_to(self, obj, **kwargs): """Use apply_to of current trace list""" @@ -358,4 +447,4 @@ def current_trace_list(self): @property def display_name(self): name = self.meta.get("name", self.meta.get("filename", "")) - return f'{name} : [{from_currsys(self.meta["current_trace_list"])}]' \ No newline at end of file + return f"{name} : [{from_currsys(self.meta['current_trace_list'])}]" diff --git a/scopesim/effects/spectral_trace_list_utils.py b/scopesim/effects/spectral_trace_list_utils.py index 2cd58e75..d43df124 100644 --- a/scopesim/effects/spectral_trace_list_utils.py +++ b/scopesim/effects/spectral_trace_list_utils.py @@ -1,6 +1,10 @@ """ +Utility classes and functions for SpectralTraceList + This module contains - - the definition of the `SpectralTrace` class. + - the definition of the `SpectralTrace` class. The visible effect should + always be a `SpectralTraceList`, even if that contains only one + `SpectralTrace`. - the definition of the `XiLamImage` class - utility functions for use with spectral traces """ @@ -10,7 +14,7 @@ import numpy as np from scipy.interpolate import RectBivariateSpline -from scipy.interpolate import InterpolatedUnivariateSpline +from scipy.interpolate import interp1d from matplotlib import pyplot as plt from astropy.table import Table @@ -20,9 +24,7 @@ from astropy.wcs import WCS from astropy.modeling.models import Polynomial2D -from ..optics import image_plane_utils as imp_utils -from ..utils import deriv_polynomial2d, power_vector, interp2, check_keys,\ - from_currsys, quantify +from ..utils import power_vector, quantify, from_currsys class SpectralTrace: @@ -60,16 +62,20 @@ def __init__(self, trace_tbl, **kwargs): if isinstance(trace_tbl, (fits.BinTableHDU, fits.TableHDU)): self.table = Table.read(trace_tbl) - self.meta["trace_id"] = trace_tbl.header.get('EXTNAME', "") + self.meta["trace_id"] = trace_tbl.header.get("EXTNAME", "") + self.dispersion_axis = trace_tbl.header.get("DISPDIR", "unknown") elif isinstance(trace_tbl, Table): self.table = trace_tbl + self.dispersion_axis = "unknown" else: raise ValueError("trace_tbl must be one of (fits.BinTableHDU, " - "fits.TableHDU, astropy.Table): {}" - "".format(type(trace_tbl))) - + f"fits.TableHDU, astropy.Table) but is {type(trace_tbl)}") self.compute_interpolation_functions() + # Declaration of other attributes + self._xilamimg = None + self.dlam_per_pix = None + def fov_grid(self): """ Provide information on the source space volume required by the effect @@ -80,39 +86,27 @@ def fov_grid(self): Spatial limits are determined by the `ApertureMask` effect and are not returned here. """ - trace_id = self.meta['trace_id'] - aperture_id = self.meta['aperture_id'] - lam_arr = self.table[self.meta['wave_colname']] + trace_id = self.meta["trace_id"] + aperture_id = self.meta["aperture_id"] + lam_arr = self.table[self.meta["wave_colname"]] wave_max = np.max(lam_arr) wave_min = np.min(lam_arr) - return {'wave_min': wave_min, 'wave_max': wave_max, - 'trace_id': trace_id, 'aperture_id': aperture_id} + return {"wave_min": wave_min, "wave_max": wave_max, + "trace_id": trace_id, "aperture_id": aperture_id} def compute_interpolation_functions(self): """ Compute various interpolation functions between slit and focal plane + + Focal plane coordinates are `x` and `y`, in mm. Slit coordinates are + `xi` (spatial coordinate along the slit, in arcsec) and `lam` (wavelength, in um). """ - if self.meta["invalid_value"] is not None: - self.table = sanitize_table( - self.table, - invalid_value=self.meta["invalid_value"], - wave_colname=self.meta["wave_colname"], - x_colname=self.meta["x_colname"], - y_colname=self.meta["y_colname"], - spline_order=self.meta["spline_order"], - ext_id=self.meta["extension_id"]) - - x_arr = self.table[self.meta['x_colname']] - y_arr = self.table[self.meta['y_colname']] - xi_arr = self.table[self.meta['s_colname']] - lam_arr = self.table[self.meta['wave_colname']] - - wi0, wi1 = lam_arr.argmin(), lam_arr.argmax() - x_disp_length = np.diff([x_arr[wi0], x_arr[wi1]]) - y_disp_length = np.diff([y_arr[wi0], y_arr[wi1]]) - self.dispersion_axis = "x" if x_disp_length > y_disp_length else "y" + x_arr = self.table[self.meta["x_colname"]] + y_arr = self.table[self.meta["y_colname"]] + xi_arr = self.table[self.meta["s_colname"]] + lam_arr = self.table[self.meta["wave_colname"]] self.wave_min = quantify(np.min(lam_arr), u.um).value self.wave_max = quantify(np.max(lam_arr), u.um).value @@ -124,6 +118,20 @@ def compute_interpolation_functions(self): self._xiy2x = Transform2D.fit(xi_arr, y_arr, x_arr) self._xiy2lam = Transform2D.fit(xi_arr, y_arr, lam_arr) + if self.dispersion_axis == 'unknown': + dlam_dx, dlam_dy = self.xy2lam.gradient() + wave_mid = 0.5 * (self.wave_min + self.wave_max) + xi_mid = np.mean(xi_arr) + x_mid = self.xilam2x(xi_mid, wave_mid) + y_mid = self.xilam2y(xi_mid, wave_mid) + if dlam_dx(x_mid, y_mid) > dlam_dy(x_mid, y_mid): + self.dispersion_axis = "x" + else: + self.dispersion_axis = "y" + logging.warning("Dispersion axis determined to be %s", + self.dispersion_axis) + + def map_spectra_to_focal_plane(self, fov): """ Apply the spectral trace mapping to a spectral cube @@ -134,33 +142,31 @@ def map_spectra_to_focal_plane(self, fov): The method returns a section of the fov image along with info on where this image lies in the focal plane. """ - + logging.info("Mapping %s", fov.meta['trace_id']) # Initialise the image based on the footprint of the spectral # trace and the focal plane WCS - wave_min = fov.meta['wave_min'].value # [um] - wave_max = fov.meta['wave_max'].value # [um] - xi_min = fov.meta['xi_min'].value # [arcsec] - xi_max = fov.meta['xi_max'].value # [arcsec] + wave_min = fov.meta["wave_min"].value # [um] + wave_max = fov.meta["wave_max"].value # [um] + xi_min = fov.meta["xi_min"].value # [arcsec] + xi_max = fov.meta["xi_max"].value # [arcsec] xlim_mm, ylim_mm = self.footprint(wave_min=wave_min, wave_max=wave_max, xi_min=xi_min, xi_max=xi_max) - #print("xlim_mm:", xlim_mm, " ylim_mm:", ylim_mm) + if xlim_mm is None: - print("xlim_mm is None") + logging.warning("xlim_mm is None") return None fov_header = fov.header det_header = fov.detector_header # WCSD from the FieldOfView - this is the full detector plane - fpa_wcs = WCS(fov_header, key='D') - naxis1, naxis2 = fov_header['NAXIS1'], fov_header['NAXIS2'] - pixsize = fov_header['CDELT1D'] * u.Unit(fov_header['CUNIT1D']) + pixsize = fov_header["CDELT1D"] * u.Unit(fov_header["CUNIT1D"]) pixsize = pixsize.to(u.mm).value - pixscale = fov_header['CDELT1'] * u.Unit(fov_header['CUNIT1']) + pixscale = fov_header["CDELT1"] * u.Unit(fov_header["CUNIT1"]) pixscale = pixscale.to(u.arcsec).value - fpa_wcsd = WCS(det_header, key='D') - naxis1d, naxis2d = det_header['NAXIS1'], det_header['NAXIS2'] + fpa_wcsd = WCS(det_header, key="D") + naxis1d, naxis2d = det_header["NAXIS1"], det_header["NAXIS2"] xlim_px, ylim_px = fpa_wcsd.all_world2pix(xlim_mm, ylim_mm, 0) xmin = np.floor(xlim_px.min()).astype(int) xmax = np.ceil(xlim_px.max()).astype(int) @@ -168,10 +174,9 @@ def map_spectra_to_focal_plane(self, fov): ymax = np.ceil(ylim_px.max()).astype(int) ## Check if spectral trace footprint is outside FoV - #print(fpa_wcsd) - #print(xmin, xmax, ymin, ymax, " <<->> ", naxis1d, naxis2d) if xmax < 0 or xmin > naxis1d or ymax < 0 or ymin > naxis2d: - logging.warning("Spectral trace footprint is outside FoV") + logging.info("Spectral trace %s: footprint is outside FoV", + fov.meta['trace_id']) return None # Only work on parts within the FoV @@ -183,8 +188,6 @@ def map_spectra_to_focal_plane(self, fov): # Create header for the subimage - I think this only needs the DET one, # but we'll do both. The WCSs are initialised from the full fpa WCS and # then shifted accordingly. - # sub_wcs = WCS(fov_header, key=" ") - # sub_wcs.wcs.crpix -= np.array([xmin, ymin]) det_wcs = WCS(det_header, key="D") det_wcs.wcs.crpix -= np.array([xmin, ymin]) @@ -199,29 +202,12 @@ def map_spectra_to_focal_plane(self, fov): xmin_mm, ymin_mm = fpa_wcsd.all_pix2world(xmin, ymin, 0) xmax_mm, ymax_mm = fpa_wcsd.all_pix2world(xmax, ymax, 0) - # wavelength step per detector pixel at centre of slice - # ..todo: - currently using average dlam_per_pix. This should - # be okay if there is not strong anamorphism. Below, we - # compute an image of abs(dlam_per_pix) in the focal plane. - # XiLamImage would need that as an image of xi/lam, which should - # be possible but too much for the time being. - # - The dispersion direction is selected by the direction of the - # gradient of lam(x, y). This works if the lam-axis is well - # aligned with x or y. Needs to be tested for MICADO. - - - # dlam_by_dx, dlam_by_dy = self.xy2lam.gradient() - # if np.abs(dlam_by_dx(0, 0)) > np.abs(dlam_by_dy(0, 0)): - if self.dispersion_axis == "x": - avg_dlam_per_pix = (wave_max - wave_min) / sub_naxis1 - else: - avg_dlam_per_pix = (wave_max - wave_min) / sub_naxis2 - + self._set_dispersion(wave_min, wave_max, pixsize=pixsize) try: - xilam = XiLamImage(fov, avg_dlam_per_pix) - self.xilam = xilam # ..todo: remove + xilam = XiLamImage(fov, self.dlam_per_pix) + self._xilamimg = xilam # ..todo: remove or make available with a debug flag? except ValueError: - print(" ---> ", self.meta['trace_id'], "gave ValueError") + print(f" ---> {self.meta['trace_id']} gave ValueError") npix_xi, npix_lam = xilam.npix_xi, xilam.npix_lam xilam_wcs = xilam.wcs @@ -281,12 +267,131 @@ def map_spectra_to_focal_plane(self, fov): img_header["YMAX"] = ymax if np.any(image < 0): - logging.warning(f"map_spectra_to_focal_plane: {np.sum(image < 0)} negative pixels") - + logging.warning("map_spectra_to_focal_plane: %d negative pixels", + np.sum(image < 0)) image_hdu = fits.ImageHDU(header=img_header, data=image) return image_hdu + def rectify(self, hdulist, interps=None, wcs=None, **kwargs): + """Create 2D spectrum for a trace + + Parameters + ---------- + hdulist : HDUList + The result of scopesim readout + interps : list of interpolation functions + If provided, there must be one for each image extension in `hdulist`. + The functions go from pixels to the images and can be created with, + e.g., RectBivariateSpline. + wcs : The WCS describing the rectified XiLamImage. This can be created + in a simple way from the fov included in the `OpticalTrain` used in + the simulation run producing `hdulist`. + + The WCS can also be set up via the following keywords: + + bin_width : float [um] + The spectral bin width. This is best computed automatically from the + spectral dispersion of the trace. + wave_min, wave_max : float [um] + Limits of the wavelength range to extract. The default is the + the full range on which the `SpectralTrace` is defined. This may + extend significantly beyond the filter window. + xi_min, xi_max : float [arcsec] + Spatial limits of the slit on the sky. This should be taken from + the header of the hdulist, but this is not yet provided by scopesim + """ + logging.info("Rectifying %s", self.trace_id) + + wave_min = kwargs.get("wave_min", + self.wave_min) + wave_max = kwargs.get("wave_max", + self.wave_max) + if wave_max < self.wave_min or wave_min > self.wave_max: + logging.info(" Outside filter range") + return None + wave_min = max(wave_min, self.wave_min) + wave_max = min(wave_max, self.wave_max) + logging.info(" %.02f .. %.02f um", wave_min, wave_max) + + # bin_width is taken as the minimum dispersion of the trace + bin_width = kwargs.get("bin_width", None) + if bin_width is None: + self._set_dispersion(wave_min, wave_max) + bin_width = np.abs(self.dlam_per_pix.y).min() + logging.info(" Bin width %.02g um", bin_width) + + pixscale = from_currsys(self.meta['pixel_scale']) + + # Temporary solution to get slit length + xi_min = kwargs.get("xi_min", None) + if xi_min is None: + try: + xi_min = hdulist[0].header["HIERARCH INS SLIT XIMIN"] + except KeyError: + logging.error("xi_min not found") + return None + xi_max = kwargs.get("xi_max", None) + if xi_max is None: + try: + xi_max = hdulist[0].header["HIERARCH INS SLIT XIMAX"] + except KeyError: + logging.error("xi_max not found") + return None + + if wcs is None: + wcs = WCS(naxis=2) + wcs.wcs.ctype = ['WAVE', 'LINEAR'] + wcs.wcs.cunit = ['um', 'arcsec'] + wcs.wcs.crpix = [1, 1] + wcs.wcs.cdelt = [bin_width, pixscale] # PIXSCALE + + # crval set to wave_min to catch explicitely set value + wcs.wcs.crval = [wave_min, xi_min] # XIMIN + + nlam = int((wave_max - wave_min) / bin_width) + 1 + nxi = int((xi_max - xi_min) / pixscale) + 1 + + # Create interpolation functions if not provided + if interps is None: + logging.info("Computing image interpolations") + interps = make_image_interpolations(hdulist, kx=1, ky=1) + + # Create Xi, Lam images (do I need Iarr and Jarr or can I build Xi, Lam directly?) + Iarr, Jarr = np.meshgrid(np.arange(nlam, dtype=np.float32), + np.arange(nxi, dtype=np.float32)) + Lam, Xi = wcs.all_pix2world(Iarr, Jarr, 0) + + # Make sure that we do have microns + Lam = Lam * u.Unit(wcs.wcs.cunit[0]).to(u.um) + + # Convert Xi, Lam to focal plane units + Xarr = self.xilam2x(Xi, Lam) + Yarr = self.xilam2y(Xi, Lam) + + rect_spec = np.zeros_like(Xarr, dtype=np.float32) + + ihdu = 0 + for hdu in hdulist: + if not isinstance(hdu, fits.ImageHDU): + continue + + wcs_fp = WCS(hdu.header, key="D") + n_x = hdu.header['NAXIS1'] + n_y = hdu.header['NAXIS2'] + iarr, jarr = wcs_fp.all_world2pix(Xarr, Yarr, 0) + mask = (iarr > 0) * (iarr < n_x) * (jarr > 0) * (jarr < n_y) + if np.any(mask): + specpart = interps[ihdu](jarr, iarr, grid=False) + rect_spec += specpart * mask + + ihdu += 1 + + header = wcs.to_header() + header['EXTNAME'] = self.trace_id + return fits.ImageHDU(data=rect_spec, header=header) + + def footprint(self, wave_min=None, wave_max=None, xi_min=None, xi_max=None): """ Return corners of rectangle enclosing spectral trace @@ -302,18 +407,16 @@ def footprint(self, wave_min=None, wave_max=None, xi_min=None, xi_max=None): If `None`, use the full range that the spectral trace is defined on. Float values are interpreted as arcsec. """ - #print(f"footprint: {wave_min}, {wave_max}, {xi_min}, {xi_max}") - ## Define the wavelength range of the footprint. This is a compromise ## between the requested range (by method args) and the definition ## range of the spectral trace ## This is only relevant if the trace is given by a table of reference ## points. Otherwise (METIS LMS!) we assume that the range is valid. - if ('wave_colname' in self.meta and - self.meta['wave_colname'] in self.table.colnames): + if ("wave_colname" in self.meta and + self.meta["wave_colname"] in self.table.colnames): # Here, the parameters are obtained from a table of reference points - wave_unit = self.table[self.meta['wave_colname']].unit - wave_val = quantify(self.table[self.meta['wave_colname']].data, + wave_unit = self.table[self.meta["wave_colname"]].unit + wave_val = quantify(self.table[self.meta["wave_colname"]].data, wave_unit) if wave_min is None: @@ -337,11 +440,11 @@ def footprint(self, wave_min=None, wave_max=None, xi_min=None, xi_max=None): ## between the requested range (by method args) and the definition ## range of the spectral trace try: - xi_unit = self.table[self.meta['s_colname']].unit + xi_unit = self.table[self.meta["s_colname"]].unit except KeyError: xi_unit = u.arcsec - xi_val = quantify(self.table[self.meta['s_colname']].data, + xi_val = quantify(self.table[self.meta["s_colname"]].data, xi_unit) if xi_min is None: @@ -383,6 +486,9 @@ def plot(self, wave_min=None, wave_max=None, c="r"): # Footprint (rectangle enclosing the trace) xlim, ylim = self.footprint(wave_min=wave_min, wave_max=wave_max) + if xlim is None: + return + xlim.append(xlim[0]) ylim.append(ylim[0]) plt.plot(xlim, ylim) @@ -400,26 +506,53 @@ def plot(self, wave_min=None, wave_max=None, c="r"): x = self.table[self.meta["x_colname"]][mask] y = self.table[self.meta["y_colname"]][mask] - plt.plot(x, y, 'o', c=c) + plt.plot(x, y, "o", c=c) - for wave in np.unique(waves): - xx = x[waves==wave] + for wave in np.unique(w): + xx = x[w==wave] xx.sort() dx = xx[-1] - xx[-2] - plt.text(x[waves==wave].max() + 0.5 * dx, - y[waves==wave].mean(), - str(wave), va='center', ha='left') + plt.text(x[w==wave].max() + 0.5 * dx, + y[w==wave].mean(), + str(wave), va='center', ha='left') plt.gca().set_aspect("equal") + @property + def trace_id(self): + """Return the name of the trace""" + return self.meta['trace_id'] + + def _set_dispersion(self, wave_min, wave_max, pixsize=None): + """Computation of dispersion dlam_per_pix along xi=0 + """ + #..todo: This may have to be generalised - xi=0 is at the centre + #of METIS slits and the short MICADO slit. + + xi = np.array([0] * 1001) + lam = np.linspace(wave_min, wave_max, 1001) + x_mm = self.xilam2x(xi, lam) + y_mm = self.xilam2y(xi, lam) + if self.dispersion_axis == "x": + dlam_grad = self.xy2lam.gradient()[0] # dlam_by_dx + else: + dlam_grad = self.xy2lam.gradient()[1] # dlam_by_dy + pixsize = (from_currsys(self.meta['pixel_scale']) / + from_currsys(self.meta['plate_scale'])) + self.dlam_per_pix = interp1d(lam, + dlam_grad(x_mm, y_mm) * pixsize, + fill_value="extrapolate") + def __repr__(self): - msg = ' "{}" : [{}, {}]um : Ext {} : Aperture {} : ' \ - 'ImagePlane {}' \ - ''.format(self.meta["trace_id"], - round(self.wave_min, 4), round(self.wave_max, 4), - self.meta["extension_id"], self.meta["aperture_id"], - self.meta["image_plane_id"]) + return f"{self.__class__.__name__}({self.table!r}, **{self.meta!r})" + + def __str__(self): + msg = (f" \"{self.meta['trace_id']}\" : " + f"[{self.wave_min:.4f}, {self.wave_max:.4f}]um : " + f"Ext {self.meta['extension_id']} : " + f"Aperture {self.meta['aperture_id']} : " + f"ImagePlane {self.meta['image_plane_id']}") return msg @@ -429,27 +562,33 @@ class XiLamImage(): The class produces and holds an image of xi (relative position along the spatial slit direction) and wavelength lambda. + + Parameters + ---------- + fov : FieldOfView + dlam_per_pix : a 1-D interpolation function from wavelength (in um) to dispersion + (in um/pixel); alternatively a number giving an average dispersion """ def __init__(self, fov, dlam_per_pix): # ..todo: we assume that we always have a cube. We use SpecCADO's # add_cube_layer method - cube_wcs = WCS(fov.cube.header, key=' ') + cube_wcs = WCS(fov.cube.header, key=" ") wcs_lam = cube_wcs.sub([3]) - d_xi = fov.cube.header['CDELT1'] - d_xi *= u.Unit(fov.cube.header['CUNIT1']).to(u.arcsec) - d_eta = fov.cube.header['CDELT2'] - d_eta *= u.Unit(fov.cube.header['CUNIT2']).to(u.arcsec) - d_lam = fov.cube.header['CDELT3'] - d_lam *= u.Unit(fov.cube.header['CUNIT3']).to(u.um) + d_xi = fov.cube.header["CDELT1"] + d_xi *= u.Unit(fov.cube.header["CUNIT1"]).to(u.arcsec) + d_eta = fov.cube.header["CDELT2"] + d_eta *= u.Unit(fov.cube.header["CUNIT2"]).to(u.arcsec) + d_lam = fov.cube.header["CDELT3"] + d_lam *= u.Unit(fov.cube.header["CUNIT3"]).to(u.um) # This is based on the cube shape and assumes that the cube's spatial # dimensions are set by the slit aperture (n_lam, n_eta, n_xi) = fov.cube.data.shape # arrays of cube coordinates - cube_xi = d_xi * np.arange(n_xi) + fov.meta['xi_min'].value + cube_xi = d_xi * np.arange(n_xi) + fov.meta["xi_min"].value cube_eta = d_eta * (np.arange(n_eta) - (n_eta - 1) / 2) cube_lam = wcs_lam.all_pix2world(np.arange(n_lam), 1)[0] cube_lam *= u.Unit(wcs_lam.wcs.cunit[0]).to(u.um) @@ -457,12 +596,16 @@ def __init__(self, fov, dlam_per_pix): # Initialise the array to hold the xi-lambda image self.image = np.zeros((n_xi, n_lam), dtype=np.float32) self.lam = cube_lam + try: + dlam_per_pix_val = dlam_per_pix(np.asarray(self.lam)) + except TypeError: + dlam_per_pix_val = dlam_per_pix + logging.warning("Using scalar dlam_per_pix = %.2g", + dlam_per_pix_val) for i, eta in enumerate(cube_eta): - #if abs(eta) > fov.slit_width / 2: # ..todo: needed? - # continue + lam0 = self.lam + dlam_per_pix_val * eta / d_eta - lam0 = self.lam + dlam_per_pix * eta / d_eta # lam0 is the target wavelength. We need to check that this # overlaps with the wavelength range covered by the cube if lam0.min() < cube_lam.max() and lam0.max() > cube_lam.min(): @@ -477,12 +620,12 @@ def __init__(self, fov, dlam_per_pix): # Default WCS with xi in arcsec self.wcs = WCS(naxis=2) self.wcs.wcs.crpix = [1, 1] - self.wcs.wcs.crval = [self.lam[0], fov.meta['xi_min'].value] + self.wcs.wcs.crval = [self.lam[0], fov.meta["xi_min"].value] self.wcs.wcs.pc = [[1, 0], [0, 1]] self.wcs.wcs.cdelt = [d_lam, d_xi] - self.wcs.wcs.ctype = ['LINEAR', 'LINEAR'] - self.wcs.wcs.cname = ['WAVELEN', 'SLITPOS'] - self.wcs.wcs.cunit = ['um', 'arcsec'] + self.wcs.wcs.ctype = ["LINEAR", "LINEAR"] + self.wcs.wcs.cname = ["WAVELEN", "SLITPOS"] + self.wcs.wcs.cunit = ["um", "arcsec"] # Alternative: xi = [0, 1], dimensionless self.wcsa = WCS(naxis=2) @@ -490,9 +633,9 @@ def __init__(self, fov, dlam_per_pix): self.wcsa.wcs.crval = [self.lam[0], 0] self.wcsa.wcs.pc = [[1, 0], [0, 1]] self.wcsa.wcs.cdelt = [d_lam, 1./n_xi] - self.wcsa.wcs.ctype = ['LINEAR', 'LINEAR'] - self.wcsa.wcs.cname = ['WAVELEN', 'SLITPOS'] - self.wcs.wcs.cunit = ['um', ''] + self.wcsa.wcs.ctype = ["LINEAR", "LINEAR"] + self.wcsa.wcs.cname = ["WAVELEN", "SLITPOS"] + self.wcs.wcs.cunit = ["um", ""] self.xi = self.wcs.all_pix2world(self.lam[0], np.arange(n_xi), 0)[1] self.npix_xi = n_xi @@ -562,7 +705,6 @@ def _repackage(self, trafo): trafo = (trafo, {}) return trafo - def __call__(self, x, y, grid=False, **kwargs): """ Apply the polynomial transform @@ -621,7 +763,7 @@ def __call__(self, x, y, grid=False, **kwargs): # corresponding column in temp. This gives the diagonal of the # expression in the "grid" branch. result = (yvec * temp).sum(axis=0) - if orig_shape == () or orig_shape is None: + if not orig_shape: result = np.float32(result) else: result = result.reshape(orig_shape) @@ -666,7 +808,7 @@ def fit2matrix(fit): for i in range(deg + 1): for j in range(deg + 1): try: - mat[j, i] = coeffs['c{}_{}'.format(i, j)] + mat[j, i] = coeffs[f"c{i}_{j}"] except KeyError: pass return mat @@ -678,10 +820,10 @@ def xilam2xy_fit(layout, params): Fits are of degree 4 as a function of slit position and wavelength. """ - xi_arr = layout[params['s_colname']] - lam_arr = layout[params['wave_colname']] - x_arr = layout[params['x_colname']] - y_arr = layout[params['y_colname']] + xi_arr = layout[params["s_colname"]] + lam_arr = layout[params["wave_colname"]] + x_arr = layout[params["x_colname"]] + y_arr = layout[params["y_colname"]] ## Filter the lists: remove any points with x==0 ## ..todo: this may not be necessary after sanitising the table @@ -707,10 +849,10 @@ def xy2xilam_fit(layout, params): Fits are of degree 4 as a function of focal plane position """ - xi_arr = layout[params['s_colname']] - lam_arr = layout[params['wave_colname']] - x_arr = layout[params['x_colname']] - y_arr = layout[params['y_colname']] + xi_arr = layout[params["s_colname"]] + lam_arr = layout[params["wave_colname"]] + x_arr = layout[params["x_colname"]] + y_arr = layout[params["y_colname"]] pinit_xi = Polynomial2D(degree=4) pinit_lam = Polynomial2D(degree=4) @@ -730,10 +872,10 @@ def _xiy2xlam_fit(layout, params): # These are helper functions to allow fitting of left/right edges # for the purpose of checking whether a trace is on a chip or not. - xi_arr = layout[params['s_colname']] - lam_arr = layout[params['wave_colname']] - x_arr = layout[params['x_colname']] - y_arr = layout[params['y_colname']] + xi_arr = layout[params["s_colname"]] + lam_arr = layout[params["wave_colname"]] + x_arr = layout[params["x_colname"]] + y_arr = layout[params["y_colname"]] pinit_x = Polynomial2D(degree=4) pinit_lam = Polynomial2D(degree=4) @@ -742,6 +884,20 @@ def _xiy2xlam_fit(layout, params): xiy2lam = fitter(pinit_lam, xi_arr, y_arr, lam_arr) return xiy2x, xiy2lam +def make_image_interpolations(hdulist, **kwargs): + """ + Create 2D interpolation functions for images + """ + interps = [] + for hdu in hdulist: + if isinstance(hdu, fits.ImageHDU): + interps.append( + RectBivariateSpline(np.arange(hdu.header['NAXIS1']), + np.arange(hdu.header['NAXIS2']), + hdu.data, **kwargs) + ) + return interps + # ..todo: Check whether the following functions are actually used def rolling_median(x, n): @@ -796,46 +952,3 @@ def get_affine_parameters(coords): shears = (np.average(shears, axis=0) * rad2deg) - (90 + rotations) return rotations, shears - - -# def sanitize_table(tbl, invalid_value, wave_colname, x_colname, y_colname, -# spline_order=4, ext_id=None): -# -# y_colnames = [col for col in tbl.colnames if y_colname in col] -# x_colnames = [col.replace(y_colname, x_colname) for col in y_colnames] -# -# for x_col, y_col in zip(x_colnames, y_colnames): -# wave = tbl[wave_colname].data -# x = tbl[x_col].data -# y = tbl[y_col].data -# -# valid = (x != invalid_value) * (y != invalid_value) -# invalid = np.invert(valid) -# if sum(invalid) == 0: -# continue -# -# if sum(valid) == 0: -# logging.warning("--- Extension {} ---" -# "All points in {} or {} were invalid. \n" -# "THESE COLUMNS HAVE BEEN REMOVED FROM THE TABLE \n" -# "invalid_value = {} \n" -# "wave = {} \nx = {} \ny = {}" -# "".format(ext_id, x_col, y_col, invalid_value, -# wave, x, y)) -# tbl.remove_columns([x_col, y_col]) -# continue -# -# k = spline_order -# if wave[-1] > wave[0]: -# xnew = InterpolatedUnivariateSpline(wave[valid], x[valid], k=k) -# ynew = InterpolatedUnivariateSpline(wave[valid], y[valid], k=k) -# else: -# xnew = InterpolatedUnivariateSpline(wave[valid][::-1], -# x[valid][::-1], k=k) -# ynew = InterpolatedUnivariateSpline(wave[valid][::-1], -# y[valid][::-1], k=k) -# -# tbl[x_col][invalid] = xnew(wave[invalid]) -# tbl[y_col][invalid] = ynew(wave[invalid]) -# -# return tbl diff --git a/scopesim/effects/surface_list.py b/scopesim/effects/surface_list.py index b1346a2b..7c5b2c44 100644 --- a/scopesim/effects/surface_list.py +++ b/scopesim/effects/surface_list.py @@ -25,6 +25,7 @@ def __init__(self, **kwargs): self._emission = None def fov_grid(self, which="waveset", **kwargs): + wave_edges = [] if which == "waveset": self.meta.update(kwargs) self.meta = from_currsys(self.meta) @@ -35,18 +36,15 @@ def fov_grid(self, which="waveset", **kwargs): throughput = self.throughput(wave) threshold = self.meta["minimum_throughput"] valid_waves = np.where(throughput >= threshold)[0] - if len(valid_waves) > 0: - wave_edges = [min(wave[valid_waves]), max(wave[valid_waves])] - else: - raise ValueError("No transmission found above the threshold {} " - "in this wavelength range {}. Did you open " - "the shutter?" - "".format(self.meta["minimum_throughput"], - [self.meta["wave_min"], - self.meta["wave_max"]])) - else: - wave_edges = [] + if not len(valid_waves): + msg = ("No transmission found above the threshold " + f"{self.meta['minimum_throughput']} in this wavelength " + f"range {[self.meta['wave_min'], self.meta['wave_max']]}." + " Did you open the shutter?") + raise ValueError(msg) + + wave_edges = [min(wave[valid_waves]), max(wave[valid_waves])] return wave_edges @property @@ -78,17 +76,18 @@ def surface(self, item): self._surface = item def get_throughput(self, start=0, end=None, rows=None): - """ Copied directly from radiometry_table """ + """Copied directly from radiometry_table.""" if self.table is None: return None - end = len(self.table) if end is None else end - end = end + len(self.table) if end < 0 else end - rows = np.arange(start, end) if rows is None else rows - - thru = rad_utils.combine_throughputs(self.table, self.surfaces, rows) - - return thru + if end is None: + end = len(self.table) + if end < 0: + end += len(self.table) + if rows is None: + rows = np.arange(start, end) + + return rad_utils.combine_throughputs(self.table, self.surfaces, rows) def get_emission(self, etendue, start=0, end=None, rows=None, use_area=False): diff --git a/scopesim/effects/ter_curves.py b/scopesim/effects/ter_curves.py index 31a3ae72..5d0e3335 100644 --- a/scopesim/effects/ter_curves.py +++ b/scopesim/effects/ter_curves.py @@ -1,25 +1,20 @@ -'''Transmission, emissivity, reflection curves''' -import numpy as np -from astropy import units as u -from os import path as pth +"""Transmission, emissivity, reflection curves""" import logging +from pathlib import Path +import numpy as np +import skycalc_ipy +from astropy import units as u from astropy.io import fits from astropy.table import Table -from astropy import units as u - -from synphot import SourceSpectrum -from synphot.units import PHOTLAM -import skycalc_ipy +from .effects import Effect +from .ter_curves_utils import add_edge_zeros from .ter_curves_utils import combine_two_spectra, apply_throughput_to_cube from .ter_curves_utils import download_svo_filter, download_svo_filter_list -from .ter_curves_utils import add_edge_zeros -from .effects import Effect +from ..base_classes import SourceBase, FOVSetupBase from ..optics.surface import SpectralSurface -from ..source.source_utils import make_imagehdu_from_table from ..source.source import Source -from ..base_classes import SourceBase, FOVSetupBase from ..utils import from_currsys, quantify, check_keys, find_file @@ -29,7 +24,7 @@ class TERCurve(Effect): Must contain a wavelength column, and one or more of the following: ``transmission``, ``emissivity``, ``reflection``. - Additionally in the header there + Additionally, in the header there should be the following keywords: wavelength_unit kwargs that can be passed:: @@ -50,7 +45,7 @@ class TERCurve(Effect): wavelength_unit: um emission_unit: ph s-1 m-2 um-1 rescale_emission: - filter_name: "Paranal/HAWKI.Ks" + filter_name: "Paranal/HAWK.Ks" value: 15.5 unit: ABmag @@ -92,6 +87,8 @@ def __init__(self, **kwargs): if self.meta["ignore_wings"]: data = add_edge_zeros(data, "wavelength") if data is not None: + # Assert that get_data() did not give us an image. + assert isinstance(data, Table), "TER Curves must be tables." self.surface.table = data self.surface.table.meta.update(self.meta) @@ -99,6 +96,7 @@ def __init__(self, **kwargs): def apply_to(self, obj, **kwargs): if isinstance(obj, SourceBase): + assert isinstance(obj, Source), "Only Source supported." self.meta = from_currsys(self.meta) wave_min = quantify(self.meta["wave_min"], u.um).to(u.AA) wave_max = quantify(self.meta["wave_max"], u.um).to(u.AA) @@ -119,6 +117,8 @@ def apply_to(self, obj, **kwargs): obj.append(self.background_source) if isinstance(obj, FOVSetupBase): + from ..optics.fov_manager import FovVolumeList + assert isinstance(obj, FovVolumeList), "Only FovVolumeList supported." wave = self.surface.throughput.waveset thru = self.surface.throughput(wave) valid_waves = np.argwhere(thru > 0) @@ -143,9 +143,17 @@ def background_source(self): if self._background_source is None: # add a single pixel ImageHDU for the extended background with a # size of 1 degree - bg_cell_width = from_currsys(self.meta["bg_cell_width"]) + # bg_cell_width = from_currsys(self.meta["bg_cell_width"]) + flux = self.emission bg_hdu = fits.ImageHDU() + # TODO: The make_imagehdu_from_table below has been replaced with + # the empty ImageHDU above in fbca416. That change might, + # have been fine (or not?), but now there is no use anywhere + # in the code of make_imagehdu_from_table or bg_cell_width, + # so maybe these need to be removed? + # bg_hdu = make_imagehdu_from_table([0], [0], [1], bg_cell_width * u.arcsec) + bg_hdu.header.update({"BG_SRC": True, "BG_SURF": self.display_name, "CUNIT1": "ARCSEC", @@ -170,6 +178,8 @@ def plot(self, which="x", wavelength=None, ax=None, new_figure=True, "x" plots throughput. "t","e","r" plot trans/emission/refl wavelength : list, np.ndarray ax : matplotlib.Axis + new_figure : start a new figure (or add to the existing one) + label : the label to use (ignored) kwargs Returns @@ -213,10 +223,10 @@ def plot(self, which="x", wavelength=None, ax=None, new_figure=True, plt.plot(wave, y, **plot_kwargs) wave_unit = self.meta.get("wavelength_unit") - plt.xlabel("Wavelength [{}]".format(wave_unit)) + plt.xlabel(f"Wavelength [{wave_unit}]") y_str = {"t": "Transmission", "e": "Emission", "r": "Reflectivity", "x": "Throughput"} - plt.ylabel("{} [{}]".format(y_str[ter], y.unit)) + plt.ylabel(f"{y_str[ter]} [{y.unit}]") return plt.gcf() @@ -267,8 +277,11 @@ class : SkycalcTERCurve self.meta.update(kwargs) self.skycalc_table = None + self.skycalc_conn = None if self.include is True: + # Only query the database if the effect is actually included. + # Sets skycalc_conn and skycalc_table. self.load_skycalc_table() @property @@ -329,7 +342,7 @@ def query_server(self, **kwargs): try: tbl = self.skycalc_conn.get_sky_spectrum(return_type="table") - except: + except ConnectionError: msg = "Could not connect to skycalc server" logging.exception(msg) raise ValueError(msg) @@ -376,8 +389,7 @@ def __init__(self, **kwargs): else: raise ValueError("FilterCurve must be passed one of (`filename`" " `array_dict`, `table`) or both " - "(`filter_name`, `filename_format`):" - "{}".format(kwargs)) + f"(`filter_name`, `filename_format`): {kwargs}") super(FilterCurve, self).__init__(**kwargs) if self.table is None: @@ -426,6 +438,7 @@ def fov_grid(self, which="waveset", **kwargs): @property def fwhm(self): wave = self.surface.wavelength + # noinspection PyProtectedMember thru = self.surface._get_ter_property("transmission", fmt="array") mask = thru >= 0.5 if any(mask): @@ -438,6 +451,7 @@ def fwhm(self): @property def centre(self): wave = self.surface.wavelength + # noinspection PyProtectedMember thru = self.surface._get_ter_property("transmission", fmt="array") num = np.trapz(thru * wave**2, x=wave) den = np.trapz(thru * wave, x=wave) @@ -540,9 +554,7 @@ def __init__(self, **kwargs): kwargs["name"] = kwargs["filter_name"] kwargs["svo_id"] = filt_str - raise_error = kwargs.get("error_on_wrong_name", True) - tbl = download_svo_filter(filt_str, return_style="table", - error_on_wrong_name=raise_error) + tbl = download_svo_filter(filt_str, return_style="table") super(SpanishVOFilterCurve, self).__init__(table=tbl, **kwargs) @@ -577,17 +589,15 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - path = pth.join(self.meta["path"], - from_currsys(self.meta["filename_format"])) + path = Path(self.meta["path"], from_currsys(self.meta["filename_format"])) self.filters = {} for name in from_currsys(self.meta["filter_names"]): kwargs["name"] = name - self.filters[name] = FilterCurve(filename=path.format(name), + self.filters[name] = FilterCurve(filename=str(path).format(name), **kwargs) self.table = self.get_table() - def apply_to(self, obj, **kwargs): """Use apply_to of current filter""" return self.current_filter.apply_to(obj, **kwargs) @@ -598,9 +608,9 @@ def fov_grid(self, which="waveset", **kwargs): def change_filter(self, filtername=None): """Change the current filter""" if filtername in self.filters.keys(): - self.meta['current_filter'] = filtername + self.meta["current_filter"] = filtername else: - raise ValueError("Unknown filter requested: " + filtername) + raise ValueError(f"Unknown filter requested: {filtername}") def add_filter(self, newfilter, name=None): """ @@ -627,8 +637,8 @@ def current_filter(self): @property def display_name(self): - return f'{self.meta["name"]} : ' \ - f'[{from_currsys(self.meta["current_filter"])}]' + return (f"{self.meta['name']} : " + f"[{from_currsys(self.meta['current_filter'])}]") def __getattr__(self, item): return getattr(self.current_filter, item) @@ -652,10 +662,10 @@ def plot(self, which="x", wavelength=None, **kwargs): for ii, ter in enumerate(which): ax = plt.subplot(len(which), 1, ii+1) - for name in self.filters: - self.filters[name].plot(which=ter, wavelength=wavelength, - ax=ax, new_figure=False, - plot_kwargs={"label": name}, **kwargs) + for name, _filter in self.filters.items(): + _filter.plot(which=ter, wavelength=wavelength, ax=ax, + new_figure=False, plot_kwargs={"label": name}, + **kwargs) # plt.semilogy() plt.legend() @@ -685,10 +695,10 @@ class TopHatFilterWheel(FilterWheel): filter_names: list of string transmissions: list of floats - [0..1] Peak transmissions inside the cuttoff limits + [0..1] Peak transmissions inside the cutoff limits wing_transmissions: list of floats - [0..1] Wing transmissions outside the cuttoff limits + [0..1] Wing transmissions outside the cutoff limits blue_cutoffs: list of floats [um] @@ -751,7 +761,7 @@ class SpanishVOFilterWheel(FilterWheel): This use ``astropy.download_file(..., cache=True)``. The filter transmission curves probably won't change, but if you notice - discrepancies, try clearing the astopy cache:: + discrepancies, try clearing the astropy cache:: >> from astropy.utils.data import clear_download_cache >> clear_download_cache() @@ -803,7 +813,7 @@ def __init__(self, **kwargs): self.meta.update(kwargs) obs, inst = self.meta["observatory"], self.meta["instrument"] - inc, exc = self.meta["include_str"], self.meta["exclude_str"] + inc, exc = self.meta["include_str"], self.meta["exclude_str"] filter_names = download_svo_filter_list(obs, inst, short_names=True, include=inc, exclude=exc) @@ -832,13 +842,6 @@ def __init__(self, transmission, **kwargs): self.params = {"wave_min": "!SIM.spectral.wave_min", "wave_max": "!SIM.spectral.wave_max"} self.params.update(kwargs) - self.make_ter_curve(transmission) - - def update_transmission(self, transmission, **kwargs): - self.params.update(kwargs) - self.make_ter_curve(transmission) - - def make_ter_curve(self, transmission): wave_min = from_currsys(self.params["wave_min"]) * u.um wave_max = from_currsys(self.params["wave_max"]) * u.um transmission = from_currsys(transmission) @@ -847,6 +850,9 @@ def make_ter_curve(self, transmission): transmission=[transmission, transmission], emissivity=[0., 0.], **self.params) + def update_transmission(self, transmission, **kwargs): + self.__init__(transmission, **kwargs) + class ADCWheel(Effect): """ @@ -878,12 +884,11 @@ def __init__(self, **kwargs): self.meta.update(params) self.meta.update(kwargs) - path = pth.join(self.meta["path"], - from_currsys(self.meta["filename_format"])) + path = Path(self.meta["path"], from_currsys(self.meta["filename_format"])) self.adcs = {} for name in from_currsys(self.meta["adc_names"]): kwargs["name"] = name - self.adcs[name] = TERCurve(filename=path.format(name), + self.adcs[name] = TERCurve(filename=str(path).format(name), **kwargs) self.table = self.get_table() @@ -895,32 +900,32 @@ def apply_to(self, obj, **kwargs): def change_adc(self, adcname=None): """Change the current ADC""" if not adcname or adcname in self.adcs.keys(): - self.meta['current_adc'] = adcname + self.meta["current_adc"] = adcname self.include = adcname else: - raise ValueError("Unknown ADC requested: " + adcname) + raise ValueError(f"Unknown ADC requested: {adcname}") @property def current_adc(self): """Return the currently used ADC""" - curradc = from_currsys(self.meta['current_adc']) + curradc = from_currsys(self.meta["current_adc"]) if not curradc: return False return self.adcs[curradc] @property def display_name(self): - return f'{self.meta["name"]} : ' \ - f'[{from_currsys(self.meta["current_adc"])}]' + return (f"{self.meta['name']} : " + f"[{from_currsys(self.meta['current_adc'])}]") def __getattr__(self, item): return getattr(self.current_adc, item) def get_table(self): - """Create a table of ADCs with maximimum througput""" + """Create a table of ADCs with maximum throughput""" names = list(self.adcs.keys()) adcs = self.adcs.values() - tmax = np.array([adc.data['transmission'].max() for adc in adcs]) + tmax = np.array([adc.data["transmission"].max() for adc in adcs]) tbl = Table(names=["name", "max_transmission"], data=[names, tmax]) diff --git a/scopesim/effects/ter_curves_utils.py b/scopesim/effects/ter_curves_utils.py index bc1cc0b5..2fbbba25 100644 --- a/scopesim/effects/ter_curves_utils.py +++ b/scopesim/effects/ter_curves_utils.py @@ -1,4 +1,3 @@ -import logging from pathlib import Path import numpy as np @@ -44,6 +43,7 @@ PATH_HERE = Path(__file__).parent PATH_SVO_DATA = PATH_HERE.parent / "data" / "svo" + def get_filter_effective_wavelength(filter_name): if isinstance(filter_name, str): filter_name = from_currsys(filter_name) @@ -57,8 +57,7 @@ def get_filter_effective_wavelength(filter_name): return eff_wave -def download_svo_filter(filter_name, return_style="synphot", - error_on_wrong_name=True): +def download_svo_filter(filter_name, return_style="synphot"): """ Query the SVO service for the true transmittance for a given filter @@ -78,15 +77,14 @@ def download_svo_filter(filter_name, return_style="synphot", - array: np.ndarray [wave, trans], where wave is in Angstrom - vo_table : astropy.table.Table - original output from SVO service - error_on_wrong_name : bool - Default True. Raises an exception if filter_name is as incorrect SVO ID - Returns ------- filt_curve : See return_style Astronomical filter object. """ + # The SVO is only accessible over http, not over https. + # noinspection HttpUrlsUsage url = f"http://svo2.cab.inta-csic.es/theory/fps3/fps.php?ID={filter_name}" path = find_file( filter_name, @@ -96,18 +94,9 @@ def download_svo_filter(filter_name, return_style="synphot", if not path: path = download_file(url, cache=True) - try: - tbl = Table.read(path, format='votable') - wave = u.Quantity(tbl['Wavelength'].data.data, u.Angstrom, copy=False) - trans = tbl['Transmission'].data.data - except: - if error_on_wrong_name: - raise ValueError(f"{filter_name} is an incorrect SVO identiier") - else: - logging.warning(f"'{filter_name}' was not found in the SVO. " - f"Defaulting to a unity transmission curve.") - wave = [3e3, 3e5] << u.Angstrom - trans = np.array([1., 1.]) + tbl = Table.read(path, format='votable') + wave = u.Quantity(tbl['Wavelength'].data.data, u.Angstrom, copy=False) + trans = tbl['Transmission'].data.data if return_style == "synphot": filt = SpectralElement(Empirical1D, points=wave, lookup_table=trans) @@ -120,6 +109,8 @@ def download_svo_filter(filter_name, return_style="synphot", filt = [wave.value, trans] elif return_style == "vo_table": filt = tbl + else: + raise ValueError("return_style %s unknown.", return_style) return filt @@ -154,7 +145,9 @@ def download_svo_filter_list(observatory, instrument, short_names=False, A list of filter names """ - base_url = f"http://svo2.cab.inta-csic.es/theory/fps3/fps.php?" + # The SVO is only accessible over http, not over https. + # noinspection HttpUrlsUsage + base_url = "http://svo2.cab.inta-csic.es/theory/fps3/fps.php?" url = base_url + f"Facility={observatory}&Instrument={instrument}" fn = f"{observatory}/{instrument}" path = find_file( @@ -193,7 +186,7 @@ def get_filter(filter_name): else: try: filt = download_svo_filter(filter_name) - except: + except ConnectionError: filt = None return filt @@ -206,6 +199,8 @@ def get_zero_mag_spectrum(system_name="AB"): spec = ab_spectrum() elif system_name.lower() in ["st", "hst"]: spec = st_spectrum() + else: + raise ValueError("system_name %s is unknown", system_name) return spec @@ -324,6 +319,7 @@ def scale_spectrum(spectrum, filter_name, amplitude): return spectrum + def apply_throughput_to_cube(cube, thru): """ Apply throughput curve to a spectroscopic cube @@ -346,6 +342,7 @@ def apply_throughput_to_cube(cube, thru): cube.data *= thru(wave_cube).value[:, None, None] return cube + def combine_two_spectra(spec_a, spec_b, action, wave_min, wave_max): """ Combines transmission and/or emission spectrum with a common waverange @@ -376,7 +373,7 @@ def combine_two_spectra(spec_a, spec_b, action, wave_min, wave_max): wave = ([wave_min.value] + list(wave_val[mask]) + [wave_max.value]) * u.AA if "mult" in action.lower(): spec_c = spec_a(wave) * spec_b(wave) - ## Diagnostic plots - not for general use + # Diagnostic plots - not for general use # from matplotlib import pyplot as plt # plt.plot(wave, spec_a(wave), label="spec_a") # plt.plot(wave, spec_b(wave), label="spec_b") @@ -386,6 +383,8 @@ def combine_two_spectra(spec_a, spec_b, action, wave_min, wave_max): # plt.show() elif "add" in action.lower(): spec_c = spec_a(wave) + spec_b(wave) + else: + raise ValueError(f"action {action} unknown") new_source = SourceSpectrum(Empirical1D, points=wave, lookup_table=spec_c) new_source.meta.update(spec_b.meta) diff --git a/scopesim/optics/fov.py b/scopesim/optics/fov.py index c00ec43d..73af7cd3 100644 --- a/scopesim/optics/fov.py +++ b/scopesim/optics/fov.py @@ -87,8 +87,8 @@ def __init__(self, header, waverange, detector_header=None, **kwargs): def pixel_area(self): if self.meta["pixel_area"] is None: hdr = self.header - pixarea = (hdr['CDELT1'] * u.Unit(hdr['CUNIT1']) * - hdr['CDELT2'] * u.Unit(hdr['CUNIT2'])).to(u.arcsec ** 2) + pixarea = (hdr["CDELT1"] * u.Unit(hdr["CUNIT1"]) * + hdr["CDELT2"] * u.Unit(hdr["CUNIT2"])).to(u.arcsec ** 2) self.meta["pixel_area"] = pixarea.value # [arcsec] return self.meta["pixel_area"] @@ -297,10 +297,10 @@ def make_image_hdu(self, use_photlam=False): # cube_fields come in with units of photlam/arcsec2, need to convert to ph/s # We need to the voxel volume (spectral and solid angle) for that. # ..todo: implement branch for use_photlam is True - spectral_bin_width = (field.header['CDELT3'] * - u.Unit(field.header['CUNIT3'])).to(u.Angstrom) - pixarea = (field.header['CDELT1'] * u.Unit(field.header['CUNIT1']) * - field.header['CDELT2'] * u.Unit(field.header['CUNIT2'])).to(u.arcsec**2) + spectral_bin_width = (field.header["CDELT3"] * + u.Unit(field.header["CUNIT3"])).to(u.Angstrom) + pixarea = (field.header["CDELT1"] * u.Unit(field.header["CUNIT1"]) * + field.header["CDELT2"] * u.Unit(field.header["CUNIT2"])).to(u.arcsec**2) # First collapse to image, then convert units image = np.sum(field.data, axis=0) * PHOTLAM/u.arcsec**2 @@ -465,10 +465,10 @@ def make_cube_hdu(self): field_data = field_interp(fov_waveset.value) # Pixel scale conversion - field_pixarea = (field.header['CDELT1'] - * field.header['CDELT2'] - * u.Unit(field.header['CUNIT1']) - * u.Unit(field.header['CUNIT2'])).to(u.arcsec**2) + field_pixarea = (field.header["CDELT1"] + * field.header["CDELT2"] + * u.Unit(field.header["CUNIT1"]) + * u.Unit(field.header["CUNIT2"])).to(u.arcsec**2) field_pixarea = field_pixarea.value field_data *= field_pixarea / self.pixel_area field_hdu = fits.ImageHDU(data=field_data, header=field.header) @@ -485,8 +485,8 @@ def make_cube_hdu(self): # ..todo: Add a catch to get ImageHDU with BUNITs canvas_image_hdu = fits.ImageHDU(data=np.zeros((naxis2, naxis1)), header=self.header) - pixarea = (field.header['CDELT1'] * u.Unit(field.header['CUNIT1']) * - field.header['CDELT2'] * u.Unit(field.header['CUNIT2'])).to(u.arcsec**2) + pixarea = (field.header["CDELT1"] * u.Unit(field.header["CUNIT1"]) * + field.header["CDELT2"] * u.Unit(field.header["CUNIT2"])).to(u.arcsec**2) field.data = field.data / self.pixel_area canvas_image_hdu = imp_utils.add_imagehdu_to_imagehdu(field, @@ -645,14 +645,18 @@ def background_fields(self): and field.header.get("BG_SRC", False) is True] def __repr__(self): - msg = "FOV id: {}, with dimensions ({}, {})\n" \ - "".format(self.meta["id"], self.header["NAXIS1"], - self.header["NAXIS2"]) - msg += "Sky centre: ({}, {})\n" \ - "".format(self.header["CRVAL1"], self.header["CRVAL2"]) - msg += "Image centre: ({}, {})\n" \ - "".format(self.header["CRVAL1D"], self.header["CRVAL2D"]) - msg += "Wavelength range: ({}, {})um\n" \ - "".format(self.meta["wave_min"], self.meta["wave_max"]) + waverange = [self.meta["wave_min"].value, self.meta["wave_max"].value] + msg = (f"{self.__class__.__name__}({self.header!r}, {waverange!r}, " + f"{self.detector_header!r}, **{self.meta!r})") + return msg + def __str__(self): + msg = (f"FOV id: {self.meta['id']}, with dimensions " + f"({self.header['NAXIS1']}, {self.header['NAXIS2']})\n" + f"Sky centre: ({self.header['CRVAL1']}, " + f"{self.header['CRVAL2']})\n" + f"Image centre: ({self.header['CRVAL1D']}, " + f"{self.header['CRVAL2D']})\n" + f"Wavelength range: ({self.meta['wave_min']}, " + f"{self.meta['wave_max']})um\n") return msg diff --git a/scopesim/optics/fov_manager.py b/scopesim/optics/fov_manager.py index 7fb3762c..819385e9 100644 --- a/scopesim/optics/fov_manager.py +++ b/scopesim/optics/fov_manager.py @@ -42,12 +42,13 @@ # # """ -from copy import deepcopy, copy +from copy import deepcopy import numpy as np -from astropy.table import Table +from typing import TextIO +from io import StringIO + from astropy import units as u -from . import fov_manager_utils as fmu from . import image_plane_utils as ipu from ..effects import DetectorList from ..effects import effects_utils as eu @@ -160,8 +161,8 @@ def generate_fovs_list(self): det_eff = eu.get_all_effects(self.effects, DetectorList)[0] dethdr = det_eff.image_plane_header - fovs += [FieldOfView(skyhdr, waverange, detector_header=dethdr, - **vol["meta"])] + fovs.append(FieldOfView(skyhdr, waverange, detector_header=dethdr, + **vol["meta"])) return fovs @@ -191,7 +192,9 @@ class FovVolumeList(FOVSetupBase): """ - def __init__(self, initial_volume={}): + def __init__(self, initial_volume=None): + if initial_volume is None: + initial_volume = {} self.volumes = [{"wave_min": 0.3, "wave_max": 30, @@ -290,7 +293,7 @@ def shrink(self, axis, values, aperture_id=None): for i, vol in enumerate(self.volumes): if aperture_id in (vol["meta"]["aperture_id"], None): if vol[f"{axis}_max"] <= values[0]: - to_pop += [i] + to_pop.append(i) elif vol[f"{axis}_min"] < values[0]: vol[f"{axis}_min"] = values[0] @@ -298,7 +301,7 @@ def shrink(self, axis, values, aperture_id=None): for i, vol in enumerate(self.volumes): if aperture_id in (vol["meta"]["aperture_id"], None): if vol[f"{axis}_min"] >= values[1]: - to_pop += [i] + to_pop.append(i) if vol[f"{axis}_max"] > values[1]: vol[f"{axis}_max"] = values[1] @@ -356,26 +359,49 @@ def extract(self, axes, edges, aperture_id=None): add_flag = False if add_flag is True: - new_vols += [new_vol] + new_vols.append(new_vol) return new_vols def __len__(self): return len(self.volumes) - def __getitem__(self, item): - return self.volumes[item] + def __iter__(self): + return iter(self.volumes) + + def __getitem__(self, key): + return self.volumes[key] def __setitem__(self, key, value): self.volumes[item] = value - def __repr__(self): - text = f"FovVolumeList with [{len(self.volumes)}] volumes:\n" - for i, vol in enumerate(self.volumes): - mini_text = ", ".join([f"{key}: {val}" for key, val in vol.items()]) - text += f" [{i}] {mini_text} \n" - - return text + def __delitem__(self, key): + del self.volumes[key] + + def write_string(self, stream: TextIO) -> None: + """Write formatted string representation to I/O stream""" + n_vol = len(self.volumes) + stream.write(f"FovVolumeList with {n_vol} volumes:") + max_digits = len(str(n_vol)) + + for i_vol, vol in enumerate(self.volumes): + pre = "\n└─" if i_vol == n_vol - 1 else "\n├─" + stream.write(f"{pre}[{i_vol:>{max_digits}}]:") + + pre = "\n " if i_vol == n_vol - 1 else "\n│ " + n_key = len(vol) + for i_key, (key, val) in enumerate(vol.items()): + subpre = "└─" if i_key == n_key - 1 else "├─" + stream.write(f"{pre}{subpre}{key}: {val}") + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.volumes[0]})" + + def __str__(self) -> str: + with StringIO() as str_stream: + self.write_string(str_stream) + output = str_stream.getvalue() + return output def __iadd__(self, other): if isinstance(other, list): diff --git a/scopesim/optics/fov_manager_utils.py b/scopesim/optics/fov_manager_utils.py index db1bc76a..05165939 100644 --- a/scopesim/optics/fov_manager_utils.py +++ b/scopesim/optics/fov_manager_utils.py @@ -105,8 +105,7 @@ def get_imaging_waveset(effects_list, **kwargs): wave_bin_edges = [[kwargs["wave_min"], kwargs["wave_max"]]] if kwargs["wave_min"] > kwargs["wave_max"]: - raise ValueError("Filter wavelength ranges do not overlap: {}" - "".format(wave_bin_edges)) + raise ValueError(f"Filter wavelength ranges do not overlap: {wave_bin_edges}") # ..todo: add in Atmospheric dispersion and ADC here for effect_class in [efs.PSF]: @@ -173,7 +172,7 @@ def get_imaging_headers(effects, **kwargs): else: raise ValueError("No ApertureMask or DetectorList was provided. At " "least one must be passed to make an ImagePlane: " - "{}".format(effects)) + f"{effects}") # get aperture headers from fov_grid() # - for-loop catches mutliple headers from ApertureList.fov_grid() @@ -247,8 +246,7 @@ def get_imaging_fovs(headers, waveset, shifts, **kwargs): counter = 0 fovs = [] - print("Preparing {} FieldOfViews".format((len(waveset)-1)*len(headers)), - flush=True) + print(f"Preparing {(len(waveset)-1)*len(headers)} FieldOfViews", flush=True) for ii in range(len(waveset) - 1): for hdr in headers: @@ -302,8 +300,8 @@ def get_spectroscopy_headers(effects, **kwargs): # ..todo: deal with multiple trace lists if len(spec_trace_effects) != 1: - raise ValueError("More than one SpectralTraceList was found: {}" - "".format(spec_trace_effects)) + raise ValueError("More than one SpectralTraceList was found: " + f"{spec_trace_effects}") spec_trace = spec_trace_effects[0] sky_hdrs = [] @@ -334,7 +332,7 @@ def get_spectroscopy_fovs(headers, shifts, effects=[], **kwargs): shift_dx = shifts["x_shifts"] # in [deg] shift_dy = shifts["y_shifts"] - print("Preparing {} FieldOfViews".format(len(headers)), flush=True) + print(f"Preparing {len(headers)} FieldOfViews", flush=True) apertures = get_all_effects(effects, (efs.ApertureList, efs.ApertureMask)) masks = [ap.fov_grid(which="masks") for ap in apertures] diff --git a/scopesim/optics/fov_utils.py b/scopesim/optics/fov_utils.py index b745e422..8c554b49 100644 --- a/scopesim/optics/fov_utils.py +++ b/scopesim/optics/fov_utils.py @@ -45,8 +45,7 @@ def is_field_in_fov(fov_header, field, wcs_suffix=""): elif isinstance(field, (fits.ImageHDU, fits.PrimaryHDU)): field_header = field.header else: - logging.warning("Input was neither Table nor ImageHDU: {}" - "".format(field)) + logging.warning("Input was neither Table nor ImageHDU: %s", field) return False ext_xsky, ext_ysky = imp_utils.calc_footprint(field_header, wcs_suffix) @@ -230,8 +229,8 @@ def extract_common_field(field, fov_volume): elif isinstance(field, fits.ImageHDU): field_new = extract_area_from_imagehdu(field, fov_volume) else: - raise ValueError("field must be either Table or ImageHDU: {}" - "".format(type(field))) + raise ValueError("field must be either Table or ImageHDU, but is " + f"{type(field)}") return field_new @@ -274,7 +273,7 @@ def extract_area_from_imagehdu(imagehdu, fov_volume): Parameters ---------- imagehdu : fits.ImageHDU - The field ImageHDU, either an image of a wavelength [um] cube + The field ImageHDU, either an image or a cube with wavelength [um] fov_volume : dict Contains {"xs": [xmin, xmax], "ys": [ymin, ymax], "waves": [wave_min, wave_max], @@ -287,7 +286,7 @@ def extract_area_from_imagehdu(imagehdu, fov_volume): """ hdr = imagehdu.header new_hdr = {} - + naxis1, naxis2 = hdr["NAXIS1"], hdr["NAXIS2"] x_hdu, y_hdu = imp_utils.calc_footprint(imagehdu) # field edges in "deg" x_fov, y_fov = fov_volume["xs"], fov_volume["ys"] @@ -295,7 +294,11 @@ def extract_area_from_imagehdu(imagehdu, fov_volume): y0s, y1s = max(min(y_hdu), min(y_fov)), min(max(y_hdu), max(y_fov)) xp, yp = imp_utils.val2pix(hdr, np.array([x0s, x1s]), np.array([y0s, y1s])) - (x0p, x1p), (y0p, y1p) = np.round(xp).astype(int), np.round(yp).astype(int) + x0p = max(0, np.floor(xp[0]).astype(int)) + x1p = min(naxis1, np.ceil(xp[1]).astype(int)) + y0p = max(0, np.floor(yp[0]).astype(int)) + y1p = min(naxis2, np.ceil(yp[1]).astype(int)) + # (x0p, x1p), (y0p, y1p) = np.round(xp).astype(int), np.round(yp).astype(int) if x0p == x1p: x1p += 1 if y0p == y1p: @@ -326,7 +329,7 @@ def extract_area_from_imagehdu(imagehdu, fov_volume): # OC [2021-12-14] if fov range is not covered by the source return nothing if not np.any(mask): - print("FOV {} um - {} um: not covered by Source".format(fov_waves[0], fov_waves[1])) + print(f"FOV {fov_waves[0]} um - {fov_waves[1]} um: not covered by Source") return None i0p, i1p = np.where(mask)[0][0], np.where(mask)[0][-1] @@ -393,13 +396,13 @@ def extract_range_from_spectrum(spectrum, waverange): mask = (spec_waveset > wave_min) * (spec_waveset < wave_max) if sum(mask) == 0: - logging.info(f"Waverange does not overlap with Spectrum waveset: " - f"{[wave_min, wave_max]} <> {spec_waveset} " - f"for spectrum {spectrum}") + logging.info(("Waverange does not overlap with Spectrum waveset: " + "%s <> %s for spectrum %s"), + [wave_min, wave_max], spec_waveset, spectrum) if wave_min < min(spec_waveset) or wave_max > max(spec_waveset): - logging.info(f"Waverange only partially overlaps with Spectrum waveset: " - f"{[wave_min, wave_max]} <> {spec_waveset} " - f"for spectrum {spectrum}") + logging.info(("Waverange only partially overlaps with Spectrum waveset: " + "%s <> %s for spectrum %s"), + [wave_min, wave_max], spec_waveset, spectrum) wave = np.r_[wave_min, spec_waveset[mask], wave_max] flux = spectrum(wave) diff --git a/scopesim/optics/image_plane.py b/scopesim/optics/image_plane.py index 6e6169be..2d213857 100644 --- a/scopesim/optics/image_plane.py +++ b/scopesim/optics/image_plane.py @@ -50,10 +50,10 @@ def __init__(self, header, **kwargs): self.meta.update(kwargs) self.id = header["IMGPLANE"] if "IMGPLANE" in header else 0 - if not any([utils.has_needed_keywords(header, s) - for s in ["", "D", "S"]]): - raise ValueError("header must have a valid image-plane WCS: {}" - "".format(dict(header))) + if not any(utils.has_needed_keywords(header, s) + for s in ["", "D", "S"]): + raise ValueError(f"header must have a valid image-plane WCS: " + f"{dict(header)}") image = np.zeros((header["NAXIS2"]+1, header["NAXIS1"]+1)) self.hdu = fits.ImageHDU(data=image, header=header) diff --git a/scopesim/optics/image_plane_utils.py b/scopesim/optics/image_plane_utils.py index 32888f3d..116c6c18 100644 --- a/scopesim/optics/image_plane_utils.py +++ b/scopesim/optics/image_plane_utils.py @@ -32,29 +32,29 @@ def get_canvas_header(hdu_or_table_list, pixel_scale=1 * u.arcsec): """ - size_warning = "Header dimension are {} large: {}. Any image made from " \ - "this header will use more that >{} in memory" + size_warning = ("Header dimension are {adverb} large: {num_pix}. Any image " + "made from this header will use more that >{size} in memory") headers = [ht.header for ht in hdu_or_table_list if isinstance(ht, fits.ImageHDU)] - if sum([isinstance(ht, Table) for ht in hdu_or_table_list]) > 0: + if any(isinstance(ht, Table) for ht in hdu_or_table_list): tbls = [ht for ht in hdu_or_table_list if isinstance(ht, Table)] tbl_hdr = _make_bounding_header_for_tables(tbls, pixel_scale=pixel_scale) - headers += [tbl_hdr] + headers.append(tbl_hdr) - if len(headers) > 0: - hdr = _make_bounding_header_from_imagehdus(headers, - pixel_scale=pixel_scale) - num_pix = hdr["NAXIS1"] * hdr["NAXIS2"] - if num_pix > 2 ** 25: # 2 * 4096**2 - logging.warning(size_warning.format("", num_pix, "256 MB")) - elif num_pix > 2 ** 28: - raise MemoryError(size_warning.format("too", num_pix, "8 GB")) - else: + if not headers: logging.warning("No tables or ImageHDUs were passed") - hdr = None - + return None + + hdr = _make_bounding_header_from_imagehdus(headers, pixel_scale=pixel_scale) + num_pix = hdr["NAXIS1"] * hdr["NAXIS2"] + if num_pix > 2 ** 28: + raise MemoryError(size_warning.format(adverb="too", num_pix=num_pix, + size="8 GB")) + if num_pix > 2 ** 25: # 2 * 4096**2 + logging.warning(size_warning.format(adverb="", num_pix=num_pix, + size="256 MB")) return hdr @@ -239,8 +239,7 @@ def add_table_to_imagehdu(table, canvas_hdu, sub_pixel=True, wcs_suffix=""): s = wcs_suffix if not utils.has_needed_keywords(canvas_hdu.header, s): - raise ValueError("canvas_hdu must include an appropriate WCS: {}" - "".format(s)) + raise ValueError(f"canvas_hdu must include an appropriate WCS: {s}") f = utils.quantity_from_table("flux", table, default_unit=u.Unit("ph s-1")) if s == "D": @@ -271,8 +270,7 @@ def add_table_to_imagehdu(table, canvas_hdu, sub_pixel=True, wcs_suffix=""): def _add_intpixel_sources_to_canvas(canvas_hdu, xpix, ypix, flux, mask): - canvas_hdu.header["comment"] = "Adding {} int-pixel files" \ - "".format(len(flux)) + canvas_hdu.header["comment"] = f"Adding {len(flux)} int-pixel files" xpix = xpix.astype(int) ypix = ypix.astype(int) for ii in range(len(xpix)): @@ -283,8 +281,7 @@ def _add_intpixel_sources_to_canvas(canvas_hdu, xpix, ypix, flux, mask): def _add_subpixel_sources_to_canvas(canvas_hdu, xpix, ypix, flux, mask): - canvas_hdu.header["comment"] = "Adding {} sub-pixel files" \ - "".format(len(flux)) + canvas_hdu.header["comment"] = f"Adding {len(flux)} sub-pixel files" canvas_shape = canvas_hdu.data.shape for ii in range(len(xpix)): if mask[ii]: @@ -497,8 +494,8 @@ def rescale_imagehdu(imagehdu, pixel_scale, wcs_suffix="", conserve_flux=True, imagehdu.header["CRPIX2"+si] *= zoom2 imagehdu.header["CDELT1"+si] = pixel_scale imagehdu.header["CDELT2"+si] = pixel_scale - imagehdu.header["CUNIT1"+si] = "mm" if si == 'D' else "deg" - imagehdu.header["CUNIT2"+si] = "mm" if si == 'D' else "deg" + imagehdu.header["CUNIT1"+si] = "mm" if si == "D" else "deg" + imagehdu.header["CUNIT2"+si] = "mm" if si == "D" else "deg" return imagehdu @@ -556,10 +553,10 @@ def reorient_imagehdu(imagehdu, wcs_suffix="", conserve_flux=True, hdr.remove(card) imagehdu.header = hdr - elif any(["PC1_1" in key for key in imagehdu.header]): - logging.warning("PC Keywords were found, but not used due to different " - "wcs_suffix given: {} \n {}" - "".format(wcs_suffix, dict(imagehdu.header))) + elif any("PC1_1" in key for key in imagehdu.header): + logging.warning(("PC Keywords were found, but not used due to different " + "wcs_suffix given: %s \n %s"), + wcs_suffix, dict(imagehdu.header)) return imagehdu @@ -850,6 +847,6 @@ def split_header(hdr, chunk_size, wcs_suffix=""): hdr_sky = header_from_list_of_xy([x1_sky, x2_sky], [y1_sky, y2_sky], pixel_scale=x_delt, wcs_suffix=s) - hdr_list += [hdr_sky] + hdr_list.append(hdr_sky) return hdr_list diff --git a/scopesim/optics/optical_element.py b/scopesim/optics/optical_element.py index c20f638c..f2b46545 100644 --- a/scopesim/optics/optical_element.py +++ b/scopesim/optics/optical_element.py @@ -1,5 +1,7 @@ import logging from inspect import isclass +from typing import TextIO +from io import StringIO from astropy.table import Table @@ -64,7 +66,7 @@ def __init__(self, yaml_dict=None, **kwargs): if isinstance(yaml_dict, dict): self.meta.update({key: yaml_dict[key] for key in yaml_dict - if key not in ["properties", "effects"]}) + if key not in {"properties", "effects"}}) if "properties" in yaml_dict: self.properties = yaml_dict["properties"] if "name" in yaml_dict: @@ -76,14 +78,13 @@ def __init__(self, yaml_dict=None, **kwargs): if eff_dic["name"] in rc.__currsys__.ignore_effects: eff_dic["include"] = False - self.effects += [make_effect(eff_dic, **self.properties)] + self.effects.append(make_effect(eff_dic, **self.properties)) def add_effect(self, effect): if isinstance(effect, efs.Effect): - self.effects += [effect] + self.effects.append(effect) else: - logging.warning("{} is not an Effect object and was not added" - "".format(effect)) + logging.warning("%s is not an Effect object and was not added", effect) def get_all(self, effect_class): return get_all_effects(self.effects, effect_class) @@ -102,11 +103,11 @@ def get_z_order_effects(self, z_level): if eff.include and "z_order" in eff.meta: z = eff.meta["z_order"] if isinstance(z, (list, tuple)): - if any([zmin <= zi <= zmax for zi in z]): - effects += [eff] + if any(zmin <= zi <= zmax for zi in z): + effects.append(eff) else: if zmin <= z <= zmax: - effects += [eff] + effects.append(eff) return effects @@ -175,7 +176,7 @@ def __getitem__(self, item): elif isinstance(item, int): obj = self.effects[item] elif isinstance(item, str): - if item[0] == "#" and "." in item: + if item.startswith("#") and "." in item: eff, meta = item.replace("#", "").split(".") obj = self[eff][f"#{meta}"] else: @@ -190,70 +191,75 @@ def __getitem__(self, item): return obj + def write_string(self, stream: TextIO, list_effects: bool = True) -> None: + """Write formatted string representation to I/O stream""" + stream.write(f"{self!s} contains {len(self.effects)} Effects\n") + if list_effects: + for i_eff, eff in enumerate(self.effects): + stream.write(f"[{i_eff}] {eff!r}\n") + + def pretty_str(self) -> str: + """Return formatted string representation as str""" + with StringIO() as str_stream: + self.write_string(str_stream) + output = str_stream.getvalue() + return output + + @property + def display_name(self): + return self.meta.get("name", self.meta.get("filename", "")) + def __repr__(self): - msg = '\nOpticalElement : "{}" contains {} Effects: \n' \ - ''.format(self.meta["name"], len(self.effects)) - eff_str = "\n".join(["[{}] {}".format(i, eff.__repr__()) - for i, eff in enumerate(self.effects)]) - return msg + eff_str + return f"<{self.__class__.__name__}>" def __str__(self): - name = self.meta.get("name", self.meta.get("filename", "")) - return '{}: "{}"'.format(type(self).__name__, name) + return f"{self.__class__.__name__}: \"{self.display_name}\"" @property def properties_str(self): prop_str = "" - max_key_len = max([len(key) for key in self.properties.keys()]) + max_key_len = max(len(key) for key in self.properties.keys()) + padlen = max_key_len + 4 for key in self.properties: - if key not in ["comments", "changes", "description", "history", - "report"]: - prop_str += " {} : {}\n".format(key.rjust(max_key_len), - self.properties[key]) + if key not in {"comments", "changes", "description", "history", + "report"}: + prop_str += f"{key:>{padlen}} : {self.properties[key]}\n" return prop_str def report(self, filename=None, output="rst", rst_title_chars="^#*+", **kwargs): - rst_str = """ -{} -{} + rst_str = f""" +{str(self)} +{rst_title_chars[0] * len(str(self))} -**Element**: {} +**Element**: {self.meta.get("object", "")} -**Alias**: {} +**Alias**: {self.meta.get("alias", "")} -**Description**: {} +**Description**: {self.meta.get("description", "")} Global properties -{} +{rst_title_chars[1] * 17} :: -{} -""".format(str(self), - rst_title_chars[0] * len(str(self)), - self.meta.get("object", ""), - self.meta.get("alias", ""), - self.meta.get("description", ""), - rst_title_chars[1] * 17, - self.properties_str) +{self.properties_str} +""" if len(self.list_effects()) > 0: - rst_str += """ + rst_str += f""" Effects -{} +{rst_title_chars[1] * 7} Summary of Effects included in this optical element: .. table:: - :name: {} + :name: {"tbl:" + self.meta.get("name", "")} -{} +{table_to_rst(self.list_effects(), indent=4)} -""".format(rst_title_chars[1] * 7, - "tbl:" + self.meta.get("name", ""), - table_to_rst(self.list_effects(), indent=4)) +""" reports = [eff.report(rst_title_chars=rst_title_chars[-2:], **kwargs) for eff in self.effects] diff --git a/scopesim/optics/optical_train.py b/scopesim/optics/optical_train.py index 73cbac6c..d0ed34dc 100644 --- a/scopesim/optics/optical_train.py +++ b/scopesim/optics/optical_train.py @@ -1,8 +1,8 @@ import copy -import os import sys from copy import deepcopy from shutil import copyfileobj +from pathlib import Path from datetime import datetime @@ -83,9 +83,8 @@ class OpticalTrain: """ def __init__(self, cmds=None): - - self._description = self.__repr__() self.cmds = cmds + self._description = self.__repr__() self.optics_manager = None self.fov_manager = None self.image_planes = [] @@ -111,8 +110,8 @@ def load(self, user_commands): elif isinstance(user_commands, UserCommands): user_commands = copy.deepcopy(user_commands) else: - raise ValueError("user_commands must be a UserCommands or str object: " - "{}".format(type(user_commands))) + raise ValueError("user_commands must be a UserCommands or str object " + f"but is {type(user_commands)}") self.cmds = user_commands rc.__currsys__ = user_commands @@ -199,8 +198,8 @@ def observe(self, orig_source, update=True, **kwargs): # [2D - Vibration, flat fielding, chopping+nodding] for effect in self.optics_manager.image_plane_effects: - for ii in range(len(self.image_planes)): - self.image_planes[ii] = effect.apply_to(self.image_planes[ii]) + for ii, image_plane in enumerate(self.image_planes): + self.image_planes[ii] = effect.apply_to(image_plane) self._last_fovs = fovs self._last_source = source @@ -227,7 +226,7 @@ def prepare_source(self, source): header, data, wave = cube.header, cube.data, cube.wave # Need to check whether BUNIT is per arcsec2 or per pixel - inunit = u.Unit(header['BUNIT']) + inunit = u.Unit(header["BUNIT"]) data = data.astype(np.float32) * inunit factor = 1 for base, power in zip(inunit.bases, inunit.powers): @@ -241,24 +240,24 @@ def prepare_source(self, source): if factor == 1: # Normalise to 1 arcsec2 if not a spatial density # ..todo: lower needed because "DEG" is not understood, this is ugly - pixarea = (header['CDELT1'] * u.Unit(header['CUNIT1'].lower()) * - header['CDELT2'] * u.Unit(header['CUNIT2'].lower())).to(u.arcsec**2) + pixarea = (header["CDELT1"] * u.Unit(header["CUNIT1"].lower()) * + header["CDELT2"] * u.Unit(header["CUNIT2"].lower())).to(u.arcsec**2) data = data / pixarea.value # cube is per arcsec2 data = (data * factor).value - cube.header['BUNIT'] = 'PHOTLAM/arcsec2' # ..todo: make this more explicit? + cube.header["BUNIT"] = "PHOTLAM/arcsec2" # ..todo: make this more explicit? # The imageplane_utils like to have the spatial WCS in units of "deg". Ensure # that the cube is passed on accordingly - cube.header['CDELT1'] = header['CDELT1'] * u.Unit(header['CUNIT1'].lower()).to(u.deg) - cube.header['CDELT2'] = header['CDELT2'] * u.Unit(header['CUNIT2'].lower()).to(u.deg) - cube.header['CUNIT1'] = 'deg' - cube.header['CUNIT2'] = 'deg' + cube.header["CDELT1"] = header["CDELT1"] * u.Unit(header["CUNIT1"].lower()).to(u.deg) + cube.header["CDELT2"] = header["CDELT2"] * u.Unit(header["CUNIT2"].lower()).to(u.deg) + cube.header["CUNIT1"] = "deg" + cube.header["CUNIT2"] = "deg" # Put on fov wavegrid - wave_min = min([fov.meta["wave_min"] for fov in self.fov_manager.fovs]) - wave_max = max([fov.meta["wave_max"] for fov in self.fov_manager.fovs]) + wave_min = min(fov.meta["wave_min"] for fov in self.fov_manager.fovs) + wave_max = max(fov.meta["wave_max"] for fov in self.fov_manager.fovs) wave_unit = u.Unit(from_currsys("!SIM.spectral.wave_unit")) dwave = from_currsys("!SIM.spectral.spectral_bin_width") # Not a quantity fov_waveset = np.arange(wave_min.value, wave_max.value, dwave) * wave_unit @@ -275,11 +274,11 @@ def prepare_source(self, source): new_data[:, j, :] = cube_interp(fov_waveset.value) cube.data = new_data - cube.header['CTYPE3'] = 'WAVE' - cube.header['CRPIX3'] = 1 - cube.header['CRVAL3'] = wave_min.value - cube.header['CDELT3'] = dwave - cube.header['CUNIT3'] = wave_unit.name + cube.header["CTYPE3"] = "WAVE" + cube.header["CRPIX3"] = 1 + cube.header["CRVAL3"] = wave_min.value + cube.header["CDELT3"] = dwave + cube.header["CUNIT3"] = wave_unit.name return source @@ -319,16 +318,15 @@ def readout(self, filename=None, **kwargs): hdul = self.write_header(hdul) except Exception as error: print("\nWarning: header update failed, data will be saved with incomplete header.") - print("Reason: ", sys.exc_info()[0], error) - print("") + print(f"Reason: {sys.exc_info()[0]} {error}\n") if filename is not None and isinstance(filename, str): fname = filename if len(self.detector_arrays) > 1: - fname = str(i) + "_" + filename + fname = f"{i}_{filename}" hdul.writeto(fname, overwrite=True) - hduls += [hdul] + hduls.append(hdul) return hduls @@ -337,55 +335,55 @@ def write_header(self, hdulist): # Primary hdu pheader = hdulist[0].header - pheader['DATE'] = datetime.now().isoformat(timespec='seconds') - pheader['ORIGIN'] = 'Scopesim ' + version - pheader['INSTRUME'] = from_currsys("!OBS.instrument") - pheader['INSTMODE'] = ", ".join(from_currsys("!OBS.modes")) - pheader['TELESCOP'] = from_currsys("!TEL.telescope") - pheader['LOCATION'] = from_currsys("!ATMO.location") + pheader["DATE"] = datetime.now().isoformat(timespec="seconds") + pheader["ORIGIN"] = "Scopesim " + version + pheader["INSTRUME"] = from_currsys("!OBS.instrument") + pheader["INSTMODE"] = ", ".join(from_currsys("!OBS.modes")) + pheader["TELESCOP"] = from_currsys("!TEL.telescope") + pheader["LOCATION"] = from_currsys("!ATMO.location") # Source information taken from first only. # ..todo: What if source is a composite? srcfield = self._last_source.fields[0] if type(srcfield).__name__ == "Table": - pheader['SOURCE'] = "Table" + pheader["SOURCE"] = "Table" elif type(srcfield).__name__ == "ImageHDU": - if 'BG_SURF' in srcfield.header: - pheader['SOURCE'] = srcfield.header['BG_SURF'] + if "BG_SURF" in srcfield.header: + pheader["SOURCE"] = srcfield.header["BG_SURF"] else: try: - pheader['SOURCE'] = srcfield.header['FILENAME'] + pheader["SOURCE"] = srcfield.header["FILENAME"] except KeyError: - pheader['SOURCE'] = "ImageHDU" + pheader["SOURCE"] = "ImageHDU" # Image hdul # ..todo: currently only one, update for detector arrays - # ..todo: normalise filenames - some need from_currsys, some need os.path.basename + # ..todo: normalise filenames - some need from_currsys, some need Path(...).name # this should go into a function so as to reduce clutter here. iheader = hdulist[1].header - iheader['EXPTIME'] = from_currsys("!OBS.exptime"), "[s]" - iheader['DIT'] = from_currsys("!OBS.dit"), "[s]" - iheader['NDIT'] = from_currsys("!OBS.ndit") - iheader['BUNIT'] = 'e', 'per EXPTIME' - iheader['PIXSCALE'] = from_currsys("!INST.pixel_scale"), "[arcsec]" + iheader["EXPTIME"] = from_currsys("!OBS.exptime"), "[s]" + iheader["DIT"] = from_currsys("!OBS.dit"), "[s]" + iheader["NDIT"] = from_currsys("!OBS.ndit") + iheader["BUNIT"] = "e", "per EXPTIME" + iheader["PIXSCALE"] = from_currsys("!INST.pixel_scale"), "[arcsec]" # A simple WCS - iheader['CTYPE1'] = 'LINEAR' - iheader['CTYPE2'] = 'LINEAR' - iheader['CRPIX1'] = (iheader['NAXIS1'] + 1) / 2 - iheader['CRPIX2'] = (iheader['NAXIS2'] + 1) / 2 - iheader['CRVAL1'] = 0. - iheader['CRVAL2'] = 0. - iheader['CDELT1'] = iheader['PIXSCALE'] - iheader['CDELT2'] = iheader['PIXSCALE'] - iheader['CUNIT1'] = 'arcsec' - iheader['CUNIT2'] = 'arcsec' + iheader["CTYPE1"] = "LINEAR" + iheader["CTYPE2"] = "LINEAR" + iheader["CRPIX1"] = (iheader["NAXIS1"] + 1) / 2 + iheader["CRPIX2"] = (iheader["NAXIS2"] + 1) / 2 + iheader["CRVAL1"] = 0. + iheader["CRVAL2"] = 0. + iheader["CDELT1"] = iheader["PIXSCALE"] + iheader["CDELT2"] = iheader["PIXSCALE"] + iheader["CUNIT1"] = "arcsec" + iheader["CUNIT2"] = "arcsec" for eff in self.optics_manager.detector_setup_effects: efftype = type(eff).__name__ if efftype == "DetectorList" and eff.include: - iheader['DETECTOR'] = eff.meta['detector'] + iheader["DETECTOR"] = eff.meta["detector"] for eff in self.optics_manager.detector_array_effects: efftype = type(eff).__name__ @@ -393,12 +391,12 @@ def write_header(self, hdulist): if (efftype == "DetectorModePropertiesSetter" and eff.include): # ..todo: can we write this into currsys? - iheader['DET_MODE'] = (eff.meta['detector_readout_mode'], + iheader["DET_MODE"] = (eff.meta["detector_readout_mode"], "detector readout mode") - iheader['MINDIT'] = from_currsys("!DET.mindit"), "[s]" - iheader['FULLWELL'] = from_currsys("!DET.full_well"), "[s]" - iheader['RON'] = from_currsys("!DET.readout_noise"), "[e]" - iheader['DARK'] = from_currsys("!DET.dark_current"), "[e/s]" + iheader["MINDIT"] = from_currsys("!DET.mindit"), "[s]" + iheader["FULLWELL"] = from_currsys("!DET.full_well"), "[s]" + iheader["RON"] = from_currsys("!DET.readout_noise"), "[e]" + iheader["DARK"] = from_currsys("!DET.dark_current"), "[e/s]" ifilter = 1 # Counts filter wheels isurface = 1 # Counts surface lists @@ -406,62 +404,62 @@ def write_header(self, hdulist): efftype = type(eff).__name__ if efftype == "ADCWheel" and eff.include: - iheader['ADC'] = eff.current_adc.meta['name'] + iheader["ADC"] = eff.current_adc.meta["name"] if efftype == "FilterWheel" and eff.include: - iheader[f'FILTER{ifilter}'] = (eff.current_filter.meta['name'], - eff.meta['name']) + iheader[f"FILTER{ifilter}"] = (eff.current_filter.meta["name"], + eff.meta["name"]) ifilter += 1 if efftype == "SlitWheel" and eff.include: - iheader['SLIT'] = (eff.current_slit.meta['name'], - eff.meta['name']) + iheader["SLIT"] = (eff.current_slit.meta["name"], + eff.meta["name"]) if efftype == "PupilTransmission" and eff.include: - iheader['PUPTRANS'] = (from_currsys("!OBS.pupil_transmission"), + iheader["PUPTRANS"] = (from_currsys("!OBS.pupil_transmission"), "cold stop, pupil transmission") if efftype == "SkycalcTERCurve" and eff.include: - iheader['ATMOSPHE'] = "Skycalc", "atmosphere model" - iheader['LOCATION'] = eff.meta['location'] - iheader['AIRMASS'] = eff.meta['airmass'] - iheader['TEMPERAT'] = eff.meta['temperature'], '[degC]' - iheader['HUMIDITY'] = eff.meta['humidity'] - iheader['PRESSURE'] = eff.meta['pressure'], '[hPa]' - iheader['PWV'] = eff.meta['pwv'], "precipitable water vapour" + iheader["ATMOSPHE"] = "Skycalc", "atmosphere model" + iheader["LOCATION"] = eff.meta["location"] + iheader["AIRMASS"] = eff.meta["airmass"] + iheader["TEMPERAT"] = eff.meta["temperature"], "[degC]" + iheader["HUMIDITY"] = eff.meta["humidity"] + iheader["PRESSURE"] = eff.meta["pressure"], "[hPa]" + iheader["PWV"] = eff.meta["pwv"], "precipitable water vapour" if efftype == "AtmosphericTERCurve" and eff.include: - iheader['ATMOSPHE'] = eff.meta['filename'], "atmosphere model" + iheader["ATMOSPHE"] = eff.meta["filename"], "atmosphere model" # ..todo: expand if necessary if efftype == "SurfaceList" and eff.include: - iheader[f'SURFACE{isurface}'] = (eff.meta['filename'], - eff.meta['name']) + iheader[f"SURFACE{isurface}"] = (eff.meta["filename"], + eff.meta["name"]) isurface += 1 if efftype == "QuantumEfficiencyCurve" and eff.include: - iheader['QE'] = os.path.basename(eff.meta['filename']), eff.meta['name'] + iheader["QE"] = Path(eff.meta["filename"]).name, eff.meta["name"] for eff in self.optics_manager.fov_effects: efftype = type(eff).__name__ # ..todo: needs to be handled with isinstance(eff, PSF) if efftype == "FieldConstantPSF" and eff.include: - iheader["PSF"] = eff.meta['filename'], "point spread function" + iheader["PSF"] = eff.meta["filename"], "point spread function" if efftype == "SpectralTraceList" and eff.include: - iheader["SPECTRAC"] = (from_currsys(eff.meta['filename']), + iheader["SPECTRAC"] = (from_currsys(eff.meta["filename"]), "spectral trace definition") if "CTYPE1" in eff.meta: - for key in ['WCSAXES', 'CTYPE1', 'CTYPE2', 'CRPIX1', 'CRPIX2', 'CRVAL1', - 'CRVAL2', 'CDELT1', 'CDELT2', 'CUNIT1', 'CUNIT2']: + for key in {"WCSAXES", "CTYPE1", "CTYPE2", "CRPIX1", "CRPIX2", "CRVAL1", + "CRVAL2", "CDELT1", "CDELT2", "CUNIT1", "CUNIT2"}: iheader[key] = eff.meta[key] for eff in self.optics_manager.detector_effects: efftype = type(eff).__name__ if efftype == "LinearityCurve" and eff.include: - iheader['DETLIN'] = from_currsys(eff.meta['filename']) + iheader["DETLIN"] = from_currsys(eff.meta["filename"]) return hdulist @@ -480,7 +478,7 @@ def shutdown(self): This method closes all open file handles and should be called when the optical train is no longer needed. """ - for effect_name in self.effects['name']: + for effect_name in self.effects["name"]: try: self[effect_name]._file.close() except AttributeError: @@ -493,6 +491,9 @@ def shutdown(self): def effects(self): return self.optics_manager.list_effects() + def __repr__(self): + return f"{self.__class__.__name__}({self.cmds!r})" + def __str__(self): return self._description diff --git a/scopesim/optics/optics_manager.py b/scopesim/optics/optics_manager.py index e86f251a..bce11c87 100644 --- a/scopesim/optics/optics_manager.py +++ b/scopesim/optics/optics_manager.py @@ -1,5 +1,8 @@ import logging from inspect import isclass +from typing import TextIO +from io import StringIO +from collections.abc import Sequence import numpy as np from astropy import units as u @@ -30,7 +33,7 @@ class OpticsManager: """ - def __init__(self, yaml_dicts=[], **kwargs): + def __init__(self, yaml_dicts=None, **kwargs): self.optical_elements = [] self.meta = {} self.meta.update(kwargs) @@ -45,7 +48,7 @@ def __init__(self, yaml_dicts=[], **kwargs): def set_derived_parameters(self): if "!INST.pixel_scale" not in rc.__currsys__: - raise ValueError("!INST.pixel_scale is missing from the current" + raise ValueError("'!INST.pixel_scale' is missing from the current" "system. Please add this to the instrument (INST)" "properties dict for the system.") pixel_scale = rc.__currsys__["!INST.pixel_scale"] * u.arcsec @@ -82,10 +85,10 @@ def load_effects(self, yaml_dicts, **kwargs): """ - if isinstance(yaml_dicts, dict): + if not isinstance(yaml_dicts, Sequence): yaml_dicts = [yaml_dicts] - self.optical_elements += [OpticalElement(dic, **kwargs) - for dic in yaml_dicts if "effects" in dic] + self.optical_elements.extend(OpticalElement(dic, **kwargs) + for dic in yaml_dicts if "effects" in dic) def add_effect(self, effect, ext=0): """ @@ -176,9 +179,8 @@ def image_plane_headers(self): detector_lists = self.detector_setup_effects headers = [det_list.image_plane_header for det_list in detector_lists] - if len(detector_lists) == 0: - raise ValueError("No DetectorList objects found. {}" - "".format(detector_lists)) + if not detector_lists: + raise ValueError(f"No DetectorList objects found. {detector_lists}") return headers @@ -283,20 +285,18 @@ def list_effects(self): def report(self, filename=None, output="rst", rst_title_chars="_^#*+", **kwargs): - rst_str = """ + rst_str = f""" List of Optical Elements -{} +{rst_title_chars[0] * 24} Summary of Effects in Optical Elements: -{} +{rst_title_chars[1] * 39} .. table:: :name: tbl:effects_summary -{} -""".format(rst_title_chars[0] * 24, - rst_title_chars[1] * 39, - table_to_rst(self.list_effects(), indent=4)) +{table_to_rst(self.list_effects(), indent=4)} +""" reports = [opt_el.report(rst_title_chars=rst_title_chars[-4:], **kwargs) for opt_el in self.optical_elements] @@ -317,7 +317,7 @@ def __getitem__(self, item): obj = self.optical_elements[item] elif isinstance(item, str): # check for hash-string for getting Effect.meta values - if item[0] == "#" and "." in item: + if item.startswith("#") and "." in item: opt_el_name = item.replace("#", "").split(".")[0] new_item = item.replace(f"{opt_el_name}.", "") obj = self[opt_el_name][new_item] @@ -343,20 +343,30 @@ def __getitem__(self, item): def __setitem__(self, key, value): obj = self.__getitem__(key) if isinstance(obj, list) and len(obj) > 1: - logging.warning("{} does not return a singular object:\n {}" - "".format(key, obj)) + logging.warning("%s does not return a singular object:\n %s", key, obj) elif isinstance(obj, efs.Effect) and isinstance(value, dict): obj.meta.update(value) - def __repr__(self): - msg = f"\nOpticsManager contains {len(self.optical_elements)} " \ - f"OpticalElements \n" - for ii, opt_el in enumerate(self.optical_elements): - msg += f'[{ii}] "{opt_el.meta["name"]}" contains ' \ - f'{len(opt_el.effects)} effects \n' + def write_string(self, stream: TextIO) -> None: + """Write formatted string representation to I/O stream""" + stream.write(f"{self!s} contains {len(self.optical_elements)} " + "OpticalElements\n") + for opt_elem in enumerate(self.optical_elements): + opt_elem.write_string(stream, list_effects=False) + + def pretty_str(self) -> str: + """Return formatted string representation as str""" + with StringIO() as str_stream: + self.write_string(str_stream) + output = str_stream.getvalue() + return output - return msg + @property + def display_name(self): + return self.meta.get("name", self.meta.get("filename", "")) + + def __repr__(self): + return f"<{self.__class__.__name__}>" def __str__(self): - name = self.meta.get("name", self.meta.get("filename", "")) - return f'{type(self).__name__}: "{name}"' + return f"{self.__class__.__name__}: \"{self.display_name}\"" diff --git a/scopesim/optics/radiometry.py b/scopesim/optics/radiometry.py index a43f1881..a094eecb 100644 --- a/scopesim/optics/radiometry.py +++ b/scopesim/optics/radiometry.py @@ -75,7 +75,7 @@ def get_emission(self, etendue, start=0, end=None, rows=None, @property def emission(self): if "etendue" not in self.meta: - raise ValueError("self.meta['etendue'] must be set") + raise ValueError("self.meta[\"etendue\"] must be set") etendue = quantify(self.meta["etendue"], "m2 arcsec2") return self.get_emission(etendue) @@ -85,12 +85,10 @@ def throughput(self): return self.get_throughput() def plot(self, what="all", rows=None): - raise NotImplemented + raise NotImplementedError() def __getitem__(self, item): return self.surfaces[item] def __repr__(self): - return self.table.__repr__() - - + return f"{self.__class__.__name__}({self.table!r}, **{self.meta})" diff --git a/scopesim/optics/radiometry_utils.py b/scopesim/optics/radiometry_utils.py index 5ccefda0..d5d54b90 100644 --- a/scopesim/optics/radiometry_utils.py +++ b/scopesim/optics/radiometry_utils.py @@ -2,13 +2,12 @@ from copy import deepcopy import logging -import numpy as np from astropy import units as u from astropy.io import ascii as ioascii from astropy.table import Table, vstack from .surface import SpectralSurface -from ..utils import real_colname, insert_into_ordereddict, quantify, \ +from ..utils import real_colname, insert_into_ordereddict, \ change_table_entry, convert_table_comments_to_dict, from_currsys @@ -76,7 +75,7 @@ def combine_throughputs(tbl, surfaces, rows_indexes): surf = surfaces[row[r_name]] action_attr = row[r_action] if action_attr == "": - raise ValueError("No action in surf.meta: {}".format(surf.meta)) + raise ValueError(f"No action in surf.meta: {surf.meta}") if isinstance(surf, SpectralSurface): surf_throughput = getattr(surf, action_attr) @@ -137,8 +136,8 @@ def add_surface_to_table(tbl, surf, name, position, silent=True): position=position) else: if not silent: - logging.warning("{} was not found in the meta dictionary of {}. " - "This could cause problems".format(colname, name)) + logging.warning(("%s was not found in the meta dictionary of %s. " + "This could cause problems"), colname, name) colname = real_colname("name", new_tbl.colnames) new_tbl = change_table_entry(new_tbl, colname, name, position=position) @@ -157,8 +156,8 @@ def make_surface_dict_from_table(tbl): surf_dict = OrderedDict({}) if tbl is not None and len(tbl) > 0: names = tbl[real_colname("name", tbl.colnames)] - for ii in range(len(tbl)): - surf_dict[names[ii]] = make_surface_from_row(tbl[ii], **tbl.meta) + for ii, row in enumerate(tbl): + surf_dict[names[ii]] = make_surface_from_row(row, **tbl.meta) return surf_dict diff --git a/scopesim/optics/surface.py b/scopesim/optics/surface.py index 41721bad..8fecb672 100644 --- a/scopesim/optics/surface.py +++ b/scopesim/optics/surface.py @@ -1,5 +1,7 @@ -import os import logging +from pathlib import Path +from dataclasses import dataclass +from typing import Any import numpy as np @@ -17,12 +19,13 @@ make_emission_from_array +@dataclass class PoorMansSurface: - """ Solely used by SurfaceList """ - def __init__(self, emission, throughput, meta): - self.emission = emission - self.throughput = throughput - self.meta = meta + """Solely used by SurfaceList """ + # FIXME: Use correct types instead of Any + emission: Any + throughput: Any + meta: Any class SpectralSurface: @@ -44,7 +47,7 @@ def __init__(self, filename=None, **kwargs): "wavelength_unit" : u.um} self.table = Table() - if filename is not None and os.path.exists(filename): + if filename is not None and Path(filename).exists(): self.table = ioascii.read(filename) tbl_meta = convert_table_comments_to_dict(self.table) if isinstance(tbl_meta, dict): @@ -127,8 +130,7 @@ def emission(self): conversion_factor = flux.meta["solid_angle"].to(u.arcsec ** -2) flux = flux * conversion_factor flux.meta["solid_angle"] = u.arcsec**-2 - flux.meta["history"] += ["Converted to arcsec-2: {}" - "".format(conversion_factor)] + flux.meta["history"].append(f"Converted to arcsec-2: {conversion_factor}") if flux is not None and "rescale_emission" in self.meta: dic = from_currsys(self.meta["rescale_emission"]) @@ -195,8 +197,7 @@ def _get_ter_property(self, ter_property, fmt="synphot"): response_curve = value_arr else: response_curve = None - logging.warning("Both wavelength and {} must be set" - "".format(ter_property)) + logging.warning("Both wavelength and %s must be set", ter_property) return response_curve @@ -256,8 +257,8 @@ def _get_array(self, colname): elif colname in self.table.colnames: val = self.table[colname].data else: - logging.debug(f"{colname} not found in either '.meta' or '.table': " - f"[{self.meta.get('name', self.meta['filename'])}]") + logging.debug("%s not found in either '.meta' or '.table': [%s]", + colname, self.meta.get("name", self.meta["filename"])) return None col_units = colname+"_unit" @@ -275,15 +276,20 @@ def _get_array(self, colname): elif val is None: val_out = None else: - raise ValueError("{} must be of type: Quantity, array, list, tuple" - "".format(colname)) + raise ValueError(f"{colname} must be of type: Quantity, array, " + f"list, tuple, but is {type(colname)}") return val_out def __repr__(self): + msg = (f"{self.__class__.__name__}({self.meta['filename']}, " + f"**{self.meta!r})") + return msg + + def __str__(self): meta = self.meta name = meta["name"] if "name" in meta else meta["filename"] cols = "".join([col[0].upper() for col in self.table.colnames]) - msg = ' [{}] "{}"'.format(cols, name) + msg = "SpectralSurface [{cols}] \"{name}\"" return msg diff --git a/scopesim/optics/surface_utils.py b/scopesim/optics/surface_utils.py index e5097f2b..550ec50c 100644 --- a/scopesim/optics/surface_utils.py +++ b/scopesim/optics/surface_utils.py @@ -66,7 +66,7 @@ def make_emission_from_array(flux, wave, meta): flux = quantify(flux, meta["emission_unit"]) else: logging.warning("emission_unit must be set in self.meta, " - "or emission must be an astropy.Quantity") + "or emission must be an astropy.Quantity") flux = None if isinstance(wave, u.Quantity) and isinstance(flux, u.Quantity): @@ -80,11 +80,11 @@ def make_emission_from_array(flux, wave, meta): flux = SourceSpectrum(Empirical1D, points=wave, lookup_table=flux) flux.meta["solid_angle"] = angle - flux.meta["history"] = ["Created from emission array with units {}" - "".format(orig_unit)] + flux.meta["history"] = [("Created from emission array with units " + f"{orig_unit}")] else: logging.warning("wavelength and emission must be " - "astropy.Quantity py_objects") + "astropy.Quantity py_objects") flux = None return flux @@ -134,10 +134,6 @@ def is_flux_binned(unit): """ unit = unit**1 - flag = False # unit.physical_type is a string in astropy<=4.2 and a PhysicalType # class in astropy==4.3 and thus has to be cast to a string first. - if u.bin in unit._bases or "flux density" not in str(unit.physical_type): - flag = True - - return flag + return (u.bin in unit._bases or "flux density" not in str(unit.physical_type)) diff --git a/scopesim/rc.py b/scopesim/rc.py index 8ca95c12..e94e6627 100644 --- a/scopesim/rc.py +++ b/scopesim/rc.py @@ -1,17 +1,17 @@ -import os +from pathlib import Path import yaml from .system_dict import SystemDict -__pkg_dir__ = os.path.dirname(__file__) +__pkg_dir__ = Path(__file__).parent -with open(os.path.join(__pkg_dir__, "defaults.yaml")) as f: - dicts = [dic for dic in yaml.full_load_all(f)] +with open(__pkg_dir__/"defaults.yaml") as f: + dicts = list(yaml.full_load_all(f)) -user_rc_path = os.path.expanduser("~/.scopesim_rc.yaml") -if os.path.exists(user_rc_path): +user_rc_path = Path("~/.scopesim_rc.yaml").expanduser() +if user_rc_path.exists(): with open(user_rc_path) as f: - dicts += [dic for dic in yaml.full_load_all(f)] + dicts.extend(list(yaml.full_load_all(f))) __config__ = SystemDict(dicts) __currsys__ = __config__ @@ -21,4 +21,4 @@ # if os.environ.get("READTHEDOCS") == "True" or "F:" in os.getcwd(): # extra_paths = ["../", "../../", "../../../", "../../../../"] -# __search_path__ = extra_paths + __search_path__ \ No newline at end of file +# __search_path__ = extra_paths + __search_path__ diff --git a/scopesim/reports/rst_utils.py b/scopesim/reports/rst_utils.py index 79258eef..9350f659 100644 --- a/scopesim/reports/rst_utils.py +++ b/scopesim/reports/rst_utils.py @@ -1,4 +1,4 @@ -import os +from pathlib import Path from astropy.table import TableFormatter from docutils.core import publish_doctree, publish_parts @@ -169,8 +169,8 @@ def process_code(context_code, code, options): fname = options.get("name", "untitled").split(".")[0] fname = ".".join([fname, fmt]) - fname = os.path.join(img_path, fname) - context_code += '\nplt.savefig("{}")'.format(fname) + fname = Path(img_path, fname) + context_code += f"\nplt.savefig(\"{fname}\")" return context_code @@ -302,17 +302,16 @@ def latexify_rst_text(rst_text, filename=None, path=None, title_char="=", parts = publish_parts(text + rst_text, writer_name="latex") if not float_figures: - parts["body"] = parts["body"].replace('begin{figure}', - 'begin{figure}[H]') + parts["body"] = parts["body"].replace("begin{figure}", + "begin{figure}[H]") if use_code_box: - parts["body"] = parts["body"].replace('begin{alltt}', - 'begin{alltt}\n\\begin{lstlisting}[frame=single]') - parts["body"] = parts["body"].replace('end{alltt}', - 'end{lstlisting}\n\\end{alltt}') + parts["body"] = parts["body"].replace("begin{alltt}", + "begin{alltt}\n\\begin{lstlisting}[frame=single]") + parts["body"] = parts["body"].replace("end{alltt}", + "end{lstlisting}\n\\end{alltt}") - filename = filename.split(".")[0] + ".tex" - file_path = os.path.join(path, filename) + file_path = Path(path, filename).with_suffix(".tex") with open(file_path, "w") as f: f.write(parts["body"]) @@ -329,8 +328,7 @@ def rstify_rst_text(rst_text, filename=None, path=None, title_char="="): if filename is None: filename = rst_text.split(title_char)[0].strip().replace(" ", "_") - filename = filename.split(".")[0] + ".rst" - file_path = os.path.join(path, filename) + file_path = Path(path, filename).with_suffix(".rst") with open(file_path, "w") as f: f.write(rst_text) @@ -340,8 +338,8 @@ def rstify_rst_text(rst_text, filename=None, path=None, title_char="="): def table_to_rst(tbl, indent=0, rounding=None): if isinstance(rounding, int): for col in tbl.itercols(): - if col.info.dtype.kind == 'f': - col.info.format = '.{}f'.format(rounding) + if col.info.dtype.kind == "f": + col.info.format = f".{rounding}f" tbl_fmtr = TableFormatter() lines, outs = tbl_fmtr._pformat_table(tbl, max_width=-1, max_lines=-1, diff --git a/scopesim/server/OLD_database.py b/scopesim/server/OLD_database.py index b861b27f..7fe96909 100644 --- a/scopesim/server/OLD_database.py +++ b/scopesim/server/OLD_database.py @@ -13,6 +13,7 @@ from scopesim import rc +from warnings import warn def get_local_packages(path): """ @@ -29,6 +30,8 @@ def get_local_packages(path): Names of packages on the local disk """ + warn("Function Depreciated --> please use scopesim.download_package-s-()", + DeprecationWarning, stacklevel=2) dirnames = os.listdir(path) pkgs = [] @@ -166,4 +169,3 @@ def download_package(pkg_path, save_dir=None, url=None, from_cache=None): save_path = os.path.abspath(save_path) return save_path - diff --git a/scopesim/server/__init__.py b/scopesim/server/__init__.py index c2fce246..e019c219 100644 --- a/scopesim/server/__init__.py +++ b/scopesim/server/__init__.py @@ -1,4 +1,4 @@ from .database import (download_packages, list_packages, - download_example_data, - list_example_data) + get_all_packages_on_server) +from .example_data_utils import download_example_data, list_example_data diff --git a/scopesim/server/database.py b/scopesim/server/database.py index d68a3aa9..035b6848 100644 --- a/scopesim/server/database.py +++ b/scopesim/server/database.py @@ -1,48 +1,276 @@ +# -*- coding: utf-8 -*- """ Functions to download instrument packages and example data """ -import json import re -import shutil -import os -import urllib.request -import zipfile import logging -from urllib3.exceptions import HTTPError +from datetime import date +from warnings import warn +from pathlib import Path +from typing import Optional, Union, List, Tuple, Set, Dict +# Python 3.8 doesn't yet know these things....... +# from collections.abc import Iterator, Iterable, Mapping +from typing import Iterator, Iterable, Mapping + +from urllib.error import HTTPError +from urllib3.exceptions import HTTPError as HTTPError3 +from more_itertools import first, last, groupby_transform -import yaml import requests +from requests.packages.urllib3.util.retry import Retry +from requests.adapters import HTTPAdapter import bs4 -from astropy.utils.data import download_file from scopesim import rc +from .github_utils import download_github_folder +from .example_data_utils import (download_example_data, list_example_data, + get_server_elements) +from .download_utils import initiate_download, handle_download, handle_unzipping +_GrpVerType = Mapping[str, Iterable[str]] +_GrpItrType = Iterator[Tuple[str, List[str]]] + + +HTTP_RETRY_CODES = [403, 404, 429, 500, 501, 502, 503] + + +class ServerError(Exception): + """Some error with the server or connection to the server.""" + +class PkgNotFoundError(Exception): + """Unable to find given package or given release of that package.""" def get_server_package_list(): - url = rc.__config__["!SIM.file.server_base_url"] - response = requests.get(url + "packages.yaml") - pkgs_dict = yaml.full_load(response.text) + warn("Function Depreciated", DeprecationWarning, stacklevel=2) + + # Emulate legacy API without using the problematic yaml file + folders = list(dict(crawl_server_dirs()).keys()) + pkgs_dict = {} + for dir_name in folders: + p_list = [_parse_package_version(package) for package + in get_server_folder_contents(dir_name)] + grouped = dict(group_package_versions(p_list)) + for p_name in grouped: + p_dict = { + "latest": _unparse_raw_version(get_latest(grouped[p_name]), + p_name).strip(".zip"), + "path": dir_name.strip("/"), + "stable": _unparse_raw_version(get_stable(grouped[p_name]), + p_name).strip(".zip"), + } + pkgs_dict[p_name] = p_dict return pkgs_dict -def get_server_folder_contents(dir_name, unique_str=".zip"): +def get_server_folder_contents(dir_name: str, + unique_str: str = ".zip$") -> Iterator[str]: url = rc.__config__["!SIM.file.server_base_url"] + dir_name + retry_strategy = Retry(total=2, + status_forcelist=HTTP_RETRY_CODES, + allowed_methods=["GET"]) + adapter = HTTPAdapter(max_retries=retry_strategy) + try: - result = requests.get(url).content + with requests.Session() as session: + session.mount("https://", adapter) + result = session.get(url).content + except (requests.exceptions.ConnectionError, + requests.exceptions.RetryError) as error: + logging.error(error) + raise ServerError("Cannot connect to server. " + f"Attempted URL was: {url}.") from error except Exception as error: - raise ValueError(f"URL returned error: {url}") from error + logging.error(("Unhandled exception occured while accessing server." + "Attempted URL was: %s."), url) + logging.error(error) + raise error soup = bs4.BeautifulSoup(result, features="lxml") - hrefs = soup.findAll("a", href=True) - pkgs = [href.string for href in hrefs - if href.string is not None and ".zip" in href.string] + hrefs = soup.find_all("a", href=True, string=re.compile(unique_str)) + pkgs = (href.string for href in hrefs) return pkgs -def list_packages(pkg_name=None): +def _get_package_name(package: str) -> str: + return package.split(".", maxsplit=1)[0] + + +def _parse_raw_version(raw_version: str) -> str: + """Catch initial package version which has no date info + + Set initial package version to basically "minus infinity". + """ + if raw_version in ("", "zip"): + return str(date(1, 1, 1)) + return raw_version.strip(".zip") + + +def _unparse_raw_version(raw_version: str, package_name: str) -> str: + """Turn version string back into full zip folder name + + If initial version was set with `_parse_raw_version`, revert that. + """ + if raw_version == str(date(1, 1, 1)): + return f"{package_name}.zip" + return f"{package_name}.{raw_version}.zip" + + +def _parse_package_version(package: str) -> Tuple[str, str]: + p_name, p_version = package.split(".", maxsplit=1) + return p_name, _parse_raw_version(p_version) + + +def _is_stable(package_version: str) -> bool: + return not package_version.endswith("dev") + + +def get_stable(versions: Iterable[str]) -> str: + """Return the most recent stable (not "dev") version.""" + return max(version for version in versions if _is_stable(version)) + + +def get_latest(versions: Iterable[str]) -> str: + """Return the most recent version (stable or dev).""" + return max(versions) + + +def get_all_stable(version_groups: _GrpVerType) -> Iterator[Tuple[str, str]]: + """ + Yield the most recent version (stable or dev) of each package. + + Parameters + ---------- + version_groups : Mapping[str, Iterable[str]] + DESCRIPTION. + + Yields + ------ + Iterator[Tuple[str, str]] + Iterator of package name - latest stable version pairs. + + """ + for package_name, versions in version_groups.items(): + yield (package_name, get_stable(versions)) + + +def get_all_latest(version_groups: _GrpVerType) -> Iterator[Tuple[str, str]]: + """ + Yield the most recent stable (not "dev") version of each package. + + Parameters + ---------- + version_groups : Mapping[str, Iterable[str]] + DESCRIPTION. + + Yields + ------ + Iterator[Tuple[str, str]] + Iterator of package name - latest version pairs. + + """ + for package_name, versions in version_groups.items(): + yield (package_name, get_latest(versions)) + + +def group_package_versions(all_packages: Iterable[Tuple[str, str]]) -> _GrpItrType: + """Group different versions of packages by package name""" + version_groups = groupby_transform(sorted(all_packages), + keyfunc=first, + valuefunc=last, + reducefunc=list) + return version_groups + + +def crawl_server_dirs() -> Iterator[Tuple[str, Set[str]]]: + """Search all folders on server for .zip files""" + for dir_name in get_server_folder_contents("", "/"): + logging.info("Searching folder '%s'", dir_name) + try: + p_dir = get_server_folder_package_names(dir_name) + except ValueError as err: + logging.info(err) + continue + logging.info("Found packages %s.", p_dir) + yield dir_name, p_dir + + +def get_all_package_versions() -> Dict[str, List[str]]: + """Gather all versions for all packages present in any folder on server""" + grouped = {} + folders = list(dict(crawl_server_dirs()).keys()) + for dir_name in folders: + p_list = [_parse_package_version(package) for package + in get_server_folder_contents(dir_name)] + grouped.update(group_package_versions(p_list)) + return grouped + + +def get_package_folders() -> Dict[str, str]: + folder_dict = {pkg: path.strip("/") + for path, pkgs in dict(crawl_server_dirs()).items() + for pkg in pkgs} + return folder_dict + + +def get_server_folder_package_names(dir_name: str) -> Set[str]: + """ + Retrieve all unique package names present on server in `dir_name` folder. + + Parameters + ---------- + dir_name : str + Name of the folder on the server. + + Raises + ------ + ValueError + Raised if no valid packages are found in the given folder. + + Returns + ------- + package_names : set of str + Set of unique package names in `dir_name` folder. + + """ + package_names = {package.split(".", maxsplit=1)[0] for package + in get_server_folder_contents(dir_name)} + + if not package_names: + raise ValueError(f"No packages found in directory \"{dir_name}\".") + + return package_names + + +def get_all_packages_on_server() -> Iterator[Tuple[str, set]]: + """ + Retrieve all unique package names present on server in known folders. + + Currently hardcoded to look in folders "locations", "telescopes" and + "instruments". Any packages not in these folders are not returned. + + This generator function yields key-value pairs, containing the folder name + as the key and the set of unique package names in value. Recommended useage + is to turn the generator into a dictionary, i.e.: + + :: + package_dict = dict(get_all_packages_on_server()) + + Yields + ------ + Iterator[Tuple[str, set]] + Key-value pairs of folder and corresponding package names. + + """ + # TODO: this basically does the same as the crawl function... + for dir_name in ("locations", "telescopes", "instruments"): + package_names = get_server_folder_package_names(dir_name) + yield dir_name, package_names + + +def list_packages(pkg_name: Optional[str] = None) -> List[str]: """ List all packages, or all variants of a single package @@ -68,19 +296,92 @@ def list_packages(pkg_name=None): list_packages("Armazones") """ - pkgs_dict = get_server_package_list() + all_grouped = get_all_package_versions() if pkg_name is None: - pkg_names = list(pkgs_dict.keys()) - elif pkg_name in pkgs_dict: - path = pkgs_dict[pkg_name]["path"] - pkgs = get_server_folder_contents(path) - pkg_names = [pkg for pkg in pkgs if pkg_name in pkg] + # Return all packages with any stable version + all_stable = list(dict(get_all_stable(all_grouped)).keys()) + return all_stable + + if not pkg_name in all_grouped: + raise ValueError(f"Package name {pkg_name} not found on server.") + + p_versions = [_unparse_raw_version(version, pkg_name) + for version in all_grouped[pkg_name]] + return p_versions + + +def _get_zipname(pkg_name: str, release: str, all_versions) -> str: + if release == "stable": + zip_name = get_stable(all_versions[pkg_name]) + elif release == "latest": + zip_name = get_latest(all_versions[pkg_name]) + else: + release = _parse_raw_version(release) + if release not in all_versions[pkg_name]: + msg = (f"Requested version '{release}' of '{pkg_name}' package" + " could not be found on the server. Available versions " + f"are: {all_versions[pkg_name]}") + raise ValueError(msg) + zip_name = release + return _unparse_raw_version(zip_name, pkg_name) + + +def _download_single_package(pkg_name: str, release: str, all_versions, + folder_dict: Path, base_url: str, save_dir: Path, + padlen: int, from_cache: bool) -> Path: + if pkg_name not in all_versions: + raise PkgNotFoundError(f"Unable to find {release} release for " + f"package '{pkg_name}' on server {base_url}.") + + if save_dir is None: + save_dir = rc.__config__["!SIM.file.local_packages_path"] + save_dir = Path(save_dir) + save_dir.mkdir(parents=True, exist_ok=True) + + if "github" in release: + base_url = "https://github.com/AstarVienna/irdb/tree/" + github_hash = release.split(":")[-1].split("@")[-1] + pkg_url = f"{base_url}{github_hash}/{pkg_name}" + download_github_folder(repo_url=pkg_url, output_dir=save_dir) + return save_dir.absolute() + + zip_name = _get_zipname(pkg_name, release, all_versions) + pkg_url = f"{base_url}{folder_dict[pkg_name]}/{zip_name}" + + try: + if from_cache is None: + from_cache = rc.__config__["!SIM.file.use_cached_downloads"] + + response = initiate_download(pkg_url, from_cache, "test_cache") + save_path = save_dir / f"{pkg_name}.zip" + handle_download(response, save_path, pkg_name, padlen) + handle_unzipping(save_path, save_dir, pkg_name, padlen) + + except HTTPError3 as error: + logging.error(error) + msg = f"Unable to find file: {pkg_url + pkg_name}" + raise ValueError(msg) from error + except HTTPError as error: + logging.error("urllib (not urllib3) error was raised, this should " + "not happen anymore!") + logging.error(error) + except requests.exceptions.ConnectionError as error: + logging.error(error) + raise ServerError("Cannot connect to server.") from error + except Exception as error: + logging.error(("Unhandled exception occured while accessing server." + "Attempted URL was: %s."), base_url) + logging.error(error) + raise error - return pkg_names + return save_path.absolute() -def download_packages(pkg_names, release="stable", save_dir=None, from_cache=None): +def download_packages(pkg_names: Union[Iterable[str], str], + release: str = "stable", + save_dir: Optional[str] = None, + from_cache: Optional[bool] = None) -> List[Path]: """ Download one or more packages to the local disk @@ -138,60 +439,29 @@ def download_packages(pkg_names, release="stable", save_dir=None, from_cache=Non """ base_url = rc.__config__["!SIM.file.server_base_url"] - pkgs_dict = get_server_package_list() + print("Gathering information from server ...") + + all_versions = get_all_package_versions() + folder_dict = get_package_folders() + + print("Connection successful, starting download ...") if isinstance(pkg_names, str): pkg_names = [pkg_names] + padlen = len(max(pkg_names, key=len)) save_paths = [] for pkg_name in pkg_names: - if pkg_name in pkgs_dict: - pkg_dict = pkgs_dict[pkg_name] - path = pkg_dict["path"] + "/" - - from_github = False - if release in ["stable", "latest"]: - zip_name = pkg_dict[release] - pkg_url = f"{base_url}{path}/{zip_name}.zip" - elif "github" in release: - base_url = "https://github.com/AstarVienna/irdb/tree/" - github_hash = release.split(":")[-1].split("@")[-1] - pkg_url = f"{base_url}{github_hash}/{pkg_name}" - from_github = True - else: - zip_name = f"{pkg_name}.{release}.zip" - pkg_variants = get_server_folder_contents(path) - if zip_name not in pkg_variants: - raise ValueError(f"{zip_name} is not amoung the hosted " - f"variants: {pkg_variants}") - pkg_url = f"{base_url}{path}/{zip_name}" - - if save_dir is None: - save_dir = rc.__config__["!SIM.file.local_packages_path"] - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - if not from_github: - try: - if from_cache is None: - from_cache = rc.__config__["!SIM.file.use_cached_downloads"] - cache_path = download_file(pkg_url, cache=from_cache) - save_path = os.path.join(save_dir, f"{pkg_name}.zip") - file_path = shutil.copy2(cache_path, save_path) - - with zipfile.ZipFile(file_path, 'r') as zip_ref: - zip_ref.extractall(save_dir) - - except HTTPError as error: - raise ValueError(f"Unable to find file: {url + pkg_path}") from error - else: - download_github_folder(repo_url=pkg_url, output_dir=save_dir) - save_path = save_dir - - save_paths += [os.path.abspath(save_path)] - - else: - raise HTTPError(f"Unable to find package: {base_url + pkg_name}") + try: + pkg_path = _download_single_package(pkg_name, release, all_versions, + folder_dict, base_url, save_dir, + padlen, from_cache) + except PkgNotFoundError as error: + logging.error("\n") # needed until tqdm redirect is implemented + logging.error(error) + logging.error("Skipping download of package '%s'", pkg_name) + continue + save_paths.append(pkg_path) return save_paths @@ -202,6 +472,8 @@ def download_packages(pkg_names, release="stable", save_dir=None, from_cache=Non # for backwards compatibility def download_package(pkg_path, save_dir=None, url=None, from_cache=None): """ + DEPRECATED -- only kept for backwards compatibility + Downloads a package to the local disk Parameters @@ -228,10 +500,8 @@ def download_package(pkg_path, save_dir=None, url=None, from_cache=None): The absolute path to the saved ``.zip`` package """ - # todo: add proper depreciation warning - text = "Function Depreciated --> please use scopesim.download_package-s-()" - logging.warning(text) - print(text) + warn("Function Depreciated --> please use scopesim.download_package-s-()", + DeprecationWarning, stacklevel=2) if isinstance(pkg_path, str): pkg_path = [pkg_path] @@ -239,217 +509,3 @@ def download_package(pkg_path, save_dir=None, url=None, from_cache=None): pkg_names = [pkg.replace(".zip", "").split("/")[-1] for pkg in pkg_path] return download_packages(pkg_names, release="stable", save_dir=save_dir, from_cache=from_cache) - -def get_server_elements(url, unique_str="/"): - """ - Returns a list of file and/or directory paths on the HTTP server ``url`` - - Parameters - ---------- - url : str - The URL of the IRDB HTTP server. - - unique_str : str, list - A unique string to look for in the beautiful HTML soup: - "/" for directories this, ".zip" for packages - - Returns - ------- - paths : list - List of paths containing in ``url`` which contain ``unique_str`` - - """ - if isinstance(unique_str, str): - unique_str = [unique_str] - - try: - result = requests.get(url).content - except Exception as error: - raise ValueError(f"URL returned error: {url}") from error - - soup = bs4.BeautifulSoup(result, features="lxml") - paths = soup.findAll("a", href=True) - select_paths = [] - for the_str in unique_str: - select_paths += [tmp.string for tmp in paths - if tmp.string is not None and the_str in tmp.string] - return select_paths - - -def list_example_data(url=None, return_files=False, silent=False): - """ - List all example files found under ``url`` - - Parameters - ---------- - url : str - The URL of the database HTTP server. If left as None, defaults to the - value in scopesim.rc.__config__["!SIM.file.server_base_url"] - - return_files : bool - If True, returns a list of file names - - silent : bool - If True, does not print the list of file names - - Returns - ------- - all_files : list of str - A list of paths to the example files relative to ``url``. - The full string should be passed to ``download_example_data``. - """ - - def print_file_list(the_files, loc=""): - print(f"\nFiles saved {loc}\n" + "=" * (len(loc) + 12)) - for _file in the_files: - print(_file) - - if url is None: - url = rc.__config__["!SIM.file.server_base_url"] - - return_file_list = [] - server_files = [] - folders = get_server_elements(url, "example_data") - for folder in folders: - files = get_server_elements(url + folder, ("fits", "txt", "dat")) - server_files += files - if not silent: - print_file_list(server_files, f"on the server: {url + 'example_data/'}") - return_file_list += server_files - - if return_files: - return return_file_list - - return None - - -def download_example_data(file_path, save_dir=None, url=None, from_cache=None): - """ - Downloads example fits files to the local disk - - Parameters - ---------- - file_path : str, list - Name(s) of FITS file(s) as given by ``list_example_data()`` - - save_dir : str - The place on the local disk where the downloaded files are to be saved. - If left as None, defaults to the current working directory. - - url : str - The URL of the database HTTP server. If left as None, defaults to the - value in scopesim.rc.__config__["!SIM.file.server_base_url"] - - from_cache : bool - Use the cached versions of the files. If None, defaults to the RC - value: ``!SIM.file.use_cached_downloads`` - - Returns - ------- - save_path : str - The absolute path to the saved files - """ - if isinstance(file_path, (list, tuple)): - save_path = [download_example_data(thefile, save_dir, url) - for thefile in file_path] - elif isinstance(file_path, str): - - if url is None: - url = rc.__config__["!SIM.file.server_base_url"] - if save_dir is None: - save_dir = os.getcwd() - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - try: - if from_cache is None: - from_cache = rc.__config__["!SIM.file.use_cached_downloads"] - cache_path = download_file(url + "example_data/" + file_path, - cache=from_cache) - save_path = os.path.join(save_dir, os.path.basename(file_path)) - file_path = shutil.copy2(cache_path, save_path) - except HTTPError: - ValueError(f"Unable to find file: {url + 'example_data/' + file_path}") - - save_path = os.path.abspath(save_path) - - return save_path - - -# """ -# 2022-04-10 (KL) -# Code taken directly from https://github.com/sdushantha/gitdir -# Adapted for ScopeSim usage. -# Many thanks to the authors! -# """ - -def create_github_url(url): - """ - From the given url, produce a URL that is compatible with Github's REST API. Can handle blob or tree paths. - """ - repo_only_url = re.compile(r"https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$") - re_branch = re.compile("/(tree|blob)/(.+?)/") - - # Check if the given url is a url to a GitHub repo. If it is, tell the - # user to use 'git clone' to download it - if re.match(repo_only_url,url): - message = "✘ The given url is a complete repository. Use 'git clone' to download the repository" - logging.error(message) - raise ValueError(message) - - # extract the branch name from the given url (e.g master) - branch = re_branch.search(url) - download_dirs = url[branch.end():] - api_url = (url[:branch.start()].replace("github.com", "api.github.com/repos", 1) + - "/contents/" + download_dirs + "?ref=" + branch.group(2)) - return api_url, download_dirs - - -def download_github_folder(repo_url, output_dir="./"): - """ - Downloads the files and directories in repo_url. - - Re-written based on the on the download function `here `_ - """ - # convert repo_url into an api_url - api_url, download_dirs = create_github_url(repo_url) - - # get the contents of the github folder - user_interrupt_text = "GitHub download interrupted by User" - try: - opener = urllib.request.build_opener() - opener.addheaders = [('User-agent', 'Mozilla/5.0')] - urllib.request.install_opener(opener) - response = urllib.request.urlretrieve(api_url) - except KeyboardInterrupt: - # when CTRL+C is pressed during the execution of this script - logging.error(user_interrupt_text) - raise ValueError(user_interrupt_text) - - # Make the base directories for this GitHub folder - os.makedirs(os.path.join(output_dir, download_dirs), exist_ok=True) - - with open(response[0], "r") as f: - data = json.load(f) - - for entry in data: - # if the entry is a further folder, walk through it - if entry["type"] == "dir": - download_github_folder(repo_url=entry["html_url"], - output_dir=output_dir) - - # if the entry is a file, download it - elif entry["type"] == "file": - try: - opener = urllib.request.build_opener() - opener.addheaders = [('User-agent', 'Mozilla/5.0')] - urllib.request.install_opener(opener) - # download the file - save_path = os.path.join(output_dir, entry['path']) - urllib.request.urlretrieve(entry["download_url"], save_path) - logging.info(f"Downloaded: {entry['path']}") - - except KeyboardInterrupt: - # when CTRL+C is pressed during the execution of this script - logging.error(user_interrupt_text) - raise ValueError(user_interrupt_text) diff --git a/scopesim/server/download_utils.py b/scopesim/server/download_utils.py new file mode 100644 index 00000000..61738ba0 --- /dev/null +++ b/scopesim/server/download_utils.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- +""" +Used only by the `database` and `github_utils` submodules. +""" + +from zipfile import ZipFile +from pathlib import Path +from shutil import get_terminal_size + +import requests +from requests.packages.urllib3.util.retry import Retry +from requests.adapters import HTTPAdapter +from requests_cache import CachedSession +from tqdm import tqdm +# from tqdm.contrib.logging import logging_redirect_tqdm +# put with logging_redirect_tqdm(loggers=all_loggers): around tqdm + + + +HTTP_RETRY_CODES = [403, 404, 429, 500, 501, 502, 503] + + +def _make_tqdm_kwargs(desc: str = ""): + width, _ = get_terminal_size((50, 20)) + bar_width = max(int(.8 * width) - 30 - len(desc), 10) + tqdm_kwargs = { + "bar_format": f"{{l_bar}}{{bar:{bar_width}}}{{r_bar}}{{bar:-{bar_width}b}}", + "colour": "green", + "desc": desc + } + return tqdm_kwargs + + +def _create_session(cached: bool = False, cache_name: str = ""): + if cached: + return CachedSession(cache_name) + return requests.Session() + + +def initiate_download(pkg_url: str, + cached: bool = False, cache_name: str = "", + total: int = 5, backoff_factor: int = 2): + retry_strategy = Retry(total=total, backoff_factor=backoff_factor, + status_forcelist=HTTP_RETRY_CODES, + allowed_methods=["GET"]) + adapter = HTTPAdapter(max_retries=retry_strategy) + with _create_session(cached, cache_name) as session: + session.mount("https://", adapter) + response = session.get(pkg_url, stream=True) + return response + + +def handle_download(response, save_path: Path, pkg_name: str, + padlen: int, chunk_size: int = 128, + disable_bar=False) -> None: + tqdm_kwargs = _make_tqdm_kwargs(f"Downloading {pkg_name:<{padlen}}") + total = int(response.headers.get("content-length", 0)) + # Turn this into non-nested double with block in Python 3.9 or 10 (?) + with save_path.open("wb") as file_outer: + with tqdm.wrapattr(file_outer, "write", miniters=1, total=total, + **tqdm_kwargs, disable=disable_bar) as file_inner: + for chunk in response.iter_content(chunk_size=chunk_size): + file_inner.write(chunk) + + +def handle_unzipping(save_path: Path, save_dir: Path, + pkg_name: str, padlen: int) -> None: + with ZipFile(save_path, "r") as zip_ref: + namelist = zip_ref.namelist() + tqdm_kwargs = _make_tqdm_kwargs(f"Extracting {pkg_name:<{padlen}}") + for file in tqdm(iterable=namelist, total=len(namelist), **tqdm_kwargs): + zip_ref.extract(file, save_dir) diff --git a/scopesim/server/example_data_utils.py b/scopesim/server/example_data_utils.py new file mode 100644 index 00000000..86d1c33b --- /dev/null +++ b/scopesim/server/example_data_utils.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +""" +Store the example data functions here instead of polluting database.py +""" + +import shutil +from pathlib import Path +from typing import List, Optional, Union, Iterable + +from urllib.error import HTTPError +from urllib3.exceptions import HTTPError as HTTPError3 + +import requests +import bs4 + +from astropy.utils.data import download_file + +from scopesim import rc + +def get_server_elements(url: str, unique_str: str = "/") -> List[str]: + """ + Returns a list of file and/or directory paths on the HTTP server ``url`` + + Parameters + ---------- + url : str + The URL of the IRDB HTTP server. + + unique_str : str, list + A unique string to look for in the beautiful HTML soup: + "/" for directories this, ".zip" for packages + + Returns + ------- + paths : list + List of paths containing in ``url`` which contain ``unique_str`` + + """ + if isinstance(unique_str, str): + unique_str = [unique_str] + + try: + result = requests.get(url).content + except Exception as error: + raise ValueError(f"URL returned error: {url}") from error + + soup = bs4.BeautifulSoup(result, features="lxml") + paths = soup.findAll("a", href=True) + select_paths = [] + for the_str in unique_str: + select_paths += [tmp.string for tmp in paths + if tmp.string is not None and the_str in tmp.string] + return select_paths + + +def list_example_data(url: Optional[str] = None, + return_files: bool = False, + silent: bool = False) -> List[str]: + """ + List all example files found under ``url`` + + Parameters + ---------- + url : str + The URL of the database HTTP server. If left as None, defaults to the + value in scopesim.rc.__config__["!SIM.file.server_base_url"] + + return_files : bool + If True, returns a list of file names + + silent : bool + If True, does not print the list of file names + + Returns + ------- + all_files : list of str + A list of paths to the example files relative to ``url``. + The full string should be passed to ``download_example_data``. + """ + + def print_file_list(the_files, loc=""): + print(f"\nFiles saved {loc}\n" + "=" * (len(loc) + 12)) + for _file in the_files: + print(_file) + + if url is None: + url = rc.__config__["!SIM.file.server_base_url"] + + return_file_list = [] + server_files = [] + folders = get_server_elements(url, "example_data") + for folder in folders: + files = get_server_elements(url + folder, ("fits", "txt", "dat")) + server_files += files + if not silent: + print_file_list(server_files, f"on the server: {url + 'example_data/'}") + return_file_list += server_files + + if return_files: + return return_file_list + + return None + + +def download_example_data(file_path: Union[Iterable[str], str], + save_dir: Optional[Union[Path, str]] = None, + url: Optional[str] = None, + from_cache: Optional[bool] = None) -> List[Path]: + """ + Downloads example fits files to the local disk + + Parameters + ---------- + file_path : str, list + Name(s) of FITS file(s) as given by ``list_example_data()`` + + save_dir : str + The place on the local disk where the downloaded files are to be saved. + If left as None, defaults to the current working directory. + + url : str + The URL of the database HTTP server. If left as None, defaults to the + value in scopesim.rc.__config__["!SIM.file.server_base_url"] + + from_cache : bool + Use the cached versions of the files. If None, defaults to the RC + value: ``!SIM.file.use_cached_downloads`` + + Returns + ------- + save_path : Path or list of Paths + The absolute path(s) to the saved files + """ + if isinstance(file_path, Iterable) and not isinstance(file_path, str): + # Recursive + save_path = [download_example_data(thefile, save_dir, url) + for thefile in file_path] + return save_path + + if not isinstance(file_path, str): + raise TypeError("file_path must be str or iterable of str, found " + f"{type(file_path) = }") + + if url is None: + url = rc.__config__["!SIM.file.server_base_url"] + if save_dir is None: + save_dir = Path.cwd() + save_dir = Path(save_dir) + save_dir.mkdir(parents=True, exist_ok=True) + file_path = Path(file_path) + + try: + if from_cache is None: + from_cache = rc.__config__["!SIM.file.use_cached_downloads"] + cache_path = download_file(f"{url}example_data/{file_path}", + cache=from_cache) + save_path = save_dir / file_path.name + file_path = shutil.copy2(cache_path, str(save_path)) + except (HTTPError, HTTPError3) as error: + msg = f"Unable to find file: {url + 'example_data/' + file_path}" + raise ValueError(msg) from error + + save_path = save_path.absolute() + return save_path diff --git a/scopesim/server/github_utils.py b/scopesim/server/github_utils.py new file mode 100644 index 00000000..f38a2d2d --- /dev/null +++ b/scopesim/server/github_utils.py @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- +""" +Used only by the `database` submodule. + +Original comment for these functions: + 2022-04-10 (KL) + Code taken directly from https://github.com/sdushantha/gitdir + Adapted for ScopeSim usage. + Many thanks to the authors! + +""" + +import logging +import re +from pathlib import Path +from typing import Union + +import requests +from requests.packages.urllib3.util.retry import Retry +from requests.adapters import HTTPAdapter + +from .download_utils import initiate_download, handle_download + + +HTTP_RETRY_CODES = [403, 404, 429, 500, 501, 502, 503] + + +class ServerError(Exception): + """Some error with the server or connection to the server.""" + + +def create_github_url(url: str) -> None: + """ + From the given url, produce a URL that is compatible with Github's REST API. + + Can handle blob or tree paths. + """ + repo_only_url = re.compile(r"https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$") + re_branch = re.compile("/(tree|blob)/(.+?)/") + + # Check if the given url is a url to a GitHub repo. If it is, tell the + # user to use 'git clone' to download it + if re.match(repo_only_url,url): + message = ("✘ The given url is a complete repository. Use 'git clone'" + " to download the repository") + logging.error(message) + raise ValueError(message) + + # extract the branch name from the given url (e.g master) + branch = re_branch.search(url) + download_dirs = url[branch.end():] + api_url = (url[:branch.start()].replace("github.com", "api.github.com/repos", 1) + + f"/contents/{download_dirs}?ref={branch.group(2)}") + return api_url, download_dirs + + +def download_github_folder(repo_url: str, + output_dir: Union[Path, str] = "./") -> None: + """ + Downloads the files and directories in repo_url. + + Re-written based on the on the download function + `here `_ + """ + output_dir = Path(output_dir) + + # convert repo_url into an api_url + api_url, download_dirs = create_github_url(repo_url) + + # get the contents of the github folder + try: + retry_strategy = Retry(total=3, backoff_factor=2, + status_forcelist=HTTP_RETRY_CODES, + allowed_methods=["GET"]) + adapter = HTTPAdapter(max_retries=retry_strategy) + with requests.Session() as session: + session.mount("https://", adapter) + data = session.get(api_url).json() + except (requests.exceptions.ConnectionError, + requests.exceptions.RetryError) as error: + logging.error(error) + raise ServerError("Cannot connect to server. " + f"Attempted URL was: {api_url}.") from error + except Exception as error: + logging.error(("Unhandled exception occured while accessing server." + "Attempted URL was: %s."), api_url) + logging.error(error) + raise error + + # Make the base directories for this GitHub folder + (output_dir / download_dirs).mkdir(parents=True, exist_ok=True) + + for entry in data: + # if the entry is a further folder, walk through it + if entry["type"] == "dir": + download_github_folder(repo_url=entry["html_url"], + output_dir=output_dir) + + # if the entry is a file, download it + elif entry["type"] == "file": + try: + # download the file + save_path = output_dir / entry["path"] + response = initiate_download(entry["download_url"]) + handle_download(response, save_path, entry["path"], + padlen=0, disable_bar=True) + logging.info("Downloaded: %s", entry["path"]) + + except (requests.exceptions.ConnectionError, + requests.exceptions.RetryError) as error: + logging.error(error) + raise ServerError("Cannot connect to server. " + f"Attempted URL was: {api_url}.") from error + except Exception as error: + logging.error(("Unhandled exception occured while accessing " + "server. Attempted URL was: %s."), api_url) + logging.error(error) + raise error diff --git a/scopesim/source/source.py b/scopesim/source/source.py index 7adaa9ed..23ac7a23 100644 --- a/scopesim/source/source.py +++ b/scopesim/source/source.py @@ -32,10 +32,10 @@ # [WCS = CRPIXn, CRVALn = (0,0), CTYPEn, CDn_m, NAXISn, CUNITn """ -import os import pickle import logging from copy import deepcopy +from pathlib import Path import numpy as np from astropy.table import Table, Column @@ -181,8 +181,8 @@ def __init__(self, filename=None, cube=None, ext=0, if image_hdu.header.get("BUNIT") is not None: self._from_imagehdu_only(image_hdu) else: - msg = f"image_hdu must be accompanied by either spectra or flux:\n" \ - f"spectra: {spectra}, flux: {flux}" + msg = ("image_hdu must be accompanied by either spectra or flux:\n" + f"spectra: {spectra}, flux: {flux}") logging.exception(msg) raise ValueError(msg) @@ -197,7 +197,7 @@ def _from_file(self, filename, spectra, flux): fits_type = utils.get_fits_type(filename) data = fits.getdata(filename) hdr = fits.getheader(filename) - hdr['FILENAME'] = os.path.basename(filename) + hdr["FILENAME"] = Path(filename).name if fits_type == "image": image = fits.ImageHDU(data=data, header=hdr) if spectra is not None: @@ -221,7 +221,7 @@ def _from_table(self, tbl, spectra): if "weight" not in tbl.colnames: tbl.add_column(Column(name="weight", data=np.ones(len(tbl)))) tbl["ref"] += len(self.spectra) - self.fields += [tbl] + self.fields.append(tbl) self.spectra += spectra def _from_imagehdu_and_spectra(self, image_hdu, spectra): @@ -261,7 +261,7 @@ def _from_imagehdu_and_spectra(self, image_hdu, spectra): image_hdu.header["CUNIT"+str(i)] = "DEG" image_hdu.header["CDELT"+str(i)] = val * unit.to(u.deg) - self.fields += [image_hdu] + self.fields.append(image_hdu) def _from_imagehdu_and_flux(self, image_hdu, flux): if isinstance(flux, u.Unit): @@ -281,9 +281,10 @@ def _from_imagehdu_only(self, image_hdu): try: bunit = u.Unit(bunit) except ValueError: - f"Astropy cannot parse BUNIT [{bunit}].\n" \ - f"You can bypass this check by passing an astropy Unit to the flux parameter:\n" \ - f">>> Source(image_hdu=..., flux=u.Unit(bunit), ...)" + print(f"Astropy cannot parse BUNIT [{bunit}].\n" + "You can bypass this check by passing an astropy Unit to " + "the flux parameter:\n" + ">>> Source(image_hdu=..., flux=u.Unit(bunit), ...)") value = 0 if bunit in [u.mag, u.ABmag] else 1 self._from_imagehdu_and_flux(image_hdu, value * bunit) @@ -299,7 +300,7 @@ def _from_arrays(self, x, y, ref, weight, spectra): tbl.meta["x_unit"] = "arcsec" tbl.meta["y_unit"] = "arcsec" - self.fields += [tbl] + self.fields.append(tbl) self.spectra += spectra def _from_cube(self, cube, ext=0): @@ -324,22 +325,23 @@ def _from_cube(self, cube, ext=0): with fits.open(cube) as hdul: data = hdul[ext].data header = hdul[ext].header - header['FILENAME'] = os.path.basename(cube) + header["FILENAME"] = Path(cube).name wcs = WCS(cube) try: - bunit = header['BUNIT'] + bunit = header["BUNIT"] u.Unit(bunit) except KeyError: bunit = "erg / (s cm2 arcsec2)" - logging.warning("Keyword 'BUNIT' not found, setting to %s by default", bunit) + logging.warning("Keyword \"BUNIT\" not found, setting to %s by default", + bunit) except ValueError as errcode: - print("'BUNIT' keyword is malformed:", errcode) + print("\"BUNIT\" keyword is malformed:", errcode) raise # Compute the wavelength vector. This will be attached to the cube_hdu # as a new `wave` attribute. This is not optimal coding practice. - wave = wcs.all_pix2world(header['CRPIX1'], header['CRPIX2'], + wave = wcs.all_pix2world(header["CRPIX1"], header["CRPIX2"], np.arange(data.shape[0]), 0)[-1] wave = (wave * u.Unit(wcs.wcs.cunit[-1])).to(u.um, @@ -355,7 +357,7 @@ def _from_cube(self, cube, ext=0): cube_hdu = fits.ImageHDU(data=target_cube, header=target_hdr) cube_hdu.wave = wave # ..todo: review wave attribute, bad practice - self.fields += [cube_hdu] + self.fields.append(cube_hdu) @property def table_fields(self): @@ -462,13 +464,13 @@ def image(self, wave_min, wave_max, **kwargs): @classmethod def load(cls, filename): """Load :class:'.Source' object from filename""" - with open(filename, 'rb') as fp1: + with open(filename, "rb") as fp1: src = pickle.load(fp1) return src def dump(self, filename): """Save to filename as a pickle""" - with open(filename, 'wb') as fp1: + with open(filename, "wb") as fp1: pickle.dump(self, fp1) # def collapse_spectra(self, wave_min=None, wave_max=None): @@ -553,9 +555,9 @@ def make_copy(self): for field in self.fields: if isinstance(field, (fits.ImageHDU, fits.PrimaryHDU)) \ and field._file is not None: # and field._data_loaded is False: - new_source.fields += [field] + new_source.fields.append(field) else: - new_source.fields += [deepcopy(field)] + new_source.fields.append(deepcopy(field)) return new_source @@ -572,19 +574,18 @@ def append(self, source_to_add): for field in new_source.fields: if isinstance(field, Table): field["ref"] += len(self.spectra) - self.fields += [field] + self.fields.append(field) elif isinstance(field, (fits.ImageHDU, fits.PrimaryHDU)): if ("SPEC_REF" in field.header and isinstance(field.header["SPEC_REF"], int)): field.header["SPEC_REF"] += len(self.spectra) - self.fields += [field] + self.fields.append(field) self.spectra += new_source.spectra self._meta_dicts += source_to_add._meta_dicts else: - raise ValueError("Cannot add {} object to Source object" - "".format(type(new_source))) + raise ValueError(f"Cannot add {type(new_source)} object to Source object") def __add__(self, new_source): self_copy = self.make_copy() @@ -610,4 +611,4 @@ def __repr__(self): msg += f", referencing spectrum {num_spec}" msg += "\n" - return msg \ No newline at end of file + return msg diff --git a/scopesim/source/source_templates.py b/scopesim/source/source_templates.py index e9f5cf28..3cda7cc0 100644 --- a/scopesim/source/source_templates.py +++ b/scopesim/source/source_templates.py @@ -1,4 +1,4 @@ -from os import path as pth +from pathlib import Path import numpy as np @@ -15,8 +15,6 @@ from .source import Source from .. import rc -__all__ = ["empty_sky", "star", "star_field"] - def empty_sky(flux=0): """ @@ -32,7 +30,7 @@ def empty_sky(flux=0): return sky -@deprecated_renamed_argument('mag', 'flux', '0.1.5') +@deprecated_renamed_argument("mag", "flux", "0.1.5") def star(x=0, y=0, flux=0): """ Source object for a single star in either vega, AB magnitudes, or Jansky @@ -73,7 +71,7 @@ def star(x=0, y=0, flux=0): units=[u.arcsec, u.arcsec, None, None, mag_unit]) tbl.meta["photometric_system"] = "vega" if mag_unit == u.mag else "ab" src = Source(spectra=spec, table=tbl) - src.meta.update({"function_call": f"star(x={x}, y={y}, flux={flux})", + src.meta.update({"function_call": f"star({x=}, {y=}, {flux=})", "module": "scopesim.source.source_templates", "object": "star"}) @@ -273,7 +271,9 @@ def uniform_source(sp=None, extent=60): def vega_spectrum(mag=0): if isinstance(mag, u.Quantity): mag = mag.value - vega = SourceSpectrum.from_file(pth.join(rc.__pkg_dir__, "vega.fits")) + # HACK: Turn Path object back into string, because not everything + # that depends on this function can handle Path objects (yet) + vega = SourceSpectrum.from_file(str(Path(rc.__pkg_dir__, "vega.fits"))) vega = vega * 10 ** (-0.4 * mag) return vega diff --git a/scopesim/source/source_utils.py b/scopesim/source/source_utils.py index 01b2d6d9..ddd023c7 100644 --- a/scopesim/source/source_utils.py +++ b/scopesim/source/source_utils.py @@ -13,27 +13,27 @@ def validate_source_input(**kwargs): if "filename" in kwargs and kwargs["filename"] is not None: filename = kwargs["filename"] if utils.find_file(filename) is None: - logging.warning("filename was not found: {}".format(filename)) + logging.warning("filename was not found: %s", filename) if "image" in kwargs and kwargs["image"] is not None: image_hdu = kwargs["image"] if not isinstance(image_hdu, (fits.PrimaryHDU, fits.ImageHDU)): raise ValueError("image must be fits.HDU object with a WCS." - "type(image) == {}".format(type(image_hdu))) + f"{type(image_hdu) = }") if len(wcs.find_all_wcs(image_hdu.header)) == 0: - logging.warning("image does not contain valid WCS. {}" - "".format(wcs.WCS(image_hdu))) + logging.warning("image does not contain valid WCS. %s", + wcs.WCS(image_hdu)) if "table" in kwargs and kwargs["table"] is not None: tbl = kwargs["table"] if not isinstance(tbl, Table): raise ValueError("table must be an astropy.Table object:" - "{}".format(type(tbl))) + f"{type(tbl) = }") if not np.all([col in tbl.colnames for col in ["x", "y", "ref"]]): raise ValueError("table must contain at least column names: " - "'x, y, ref': {}".format(tbl.colnames)) + f"'x, y, ref': {tbl.colnames}") return True @@ -259,5 +259,3 @@ def make_img_wcs_header(pixel_scale, image_size): # "FLUXUNIT to the header.") # # return unit - - diff --git a/scopesim/system_dict.py b/scopesim/system_dict.py index 6a6c8c0c..e5440dc6 100644 --- a/scopesim/system_dict.py +++ b/scopesim/system_dict.py @@ -1,17 +1,25 @@ +# -*- coding: utf-8 -*- + import logging +from typing import TextIO +from io import StringIO +from collections.abc import Iterable, Mapping, MutableMapping + +from more_itertools import ilen -class SystemDict(object): +class SystemDict(MutableMapping): def __init__(self, new_dict=None): self.dic = {} - if isinstance(new_dict, dict): + if isinstance(new_dict, Mapping): self.update(new_dict) - elif isinstance(new_dict, list): + elif isinstance(new_dict, Iterable): for entry in new_dict: self.update(entry) - def update(self, new_dict): - if isinstance(new_dict, dict) \ + def update(self, new_dict: MutableMapping) -> None: + # TODO: why do we check for dict here but not in the else? + if isinstance(new_dict, Mapping) \ and "alias" in new_dict \ and "properties" in new_dict: alias = new_dict["alias"] @@ -21,85 +29,111 @@ def update(self, new_dict): else: self.dic[alias] = new_dict["properties"] else: - "Catch any bang-string properties keys" + # Catch any bang-string properties keys to_pop = [] for key in new_dict: - if key[0] == "!": + if key.startswith("!"): self[key] = new_dict[key] - to_pop += [key] + to_pop.append(key) for key in to_pop: new_dict.pop(key) if len(new_dict) > 0: self.dic = recursive_update(self.dic, new_dict) - def __getitem__(self, item): - if isinstance(item, str) and item[0] == "!": - item_chunks = item[1:].split(".") + def __getitem__(self, key): + if isinstance(key, str) and key.startswith("!"): + # TODO: these should be replaced with key.removeprefix("!") + # once we can finally drop support for Python 3.8 UwU + key_chunks = key[1:].split(".") entry = self.dic - for item in item_chunks: - entry = entry[item] + for key in key_chunks: + if not isinstance(entry, Mapping): + raise KeyError(key) + entry = entry[key] return entry - else: - return self.dic[item] + return self.dic[key] def __setitem__(self, key, value): - if isinstance(key, str) and key[0] == "!": - key_chunks = key[1:].split(".") + if isinstance(key, str) and key.startswith("!"): + # TODO: these should be replaced with item.removeprefix("!") + # once we can finally drop support for Python 3.8 UwU + *key_chunks, final_key = key[1:].split(".") entry = self.dic - for key in key_chunks[:-1]: + for key in key_chunks: if key not in entry: entry[key] = {} entry = entry[key] - entry[key_chunks[-1]] = value + entry[final_key] = value else: self.dic[key] = value - def __contains__(self, item): - if isinstance(item, str) and item[0] == "!": - item_chunks = item[1:].split(".") - entry = self.dic - for item in item_chunks: - if not isinstance(entry, dict) or item not in entry: - return False - entry = entry[item] - return True - else: - return item in self.dic - - def __repr__(self): - msg = " contents:" - for key in self.dic.keys(): - val = self.dic[key] - msg += "\n{}: ".format(key) - if isinstance(val, dict): - for subkey in val.keys(): - msg += "\n {}: {}".format(subkey, val[subkey]) + def __delitem__(self, key): + raise NotImplementedError("item deletion is not yet implemented for " + f"{self.__class__.__name__}") + + def _yield_subkeys(self, key, value): + for subkey, subvalue in value.items(): + if isinstance(subvalue, Mapping): + yield from self._yield_subkeys(f"{key}.{subkey}", subvalue) else: - msg += "{}\n".format(val) - return msg + yield f"!{key}.{subkey}" + def __iter__(self): + for key, value in self.dic.items(): + if isinstance(value, Mapping): + yield from self._yield_subkeys(key, value) + else: + yield key -def recursive_update(old_dict, new_dict): + def __len__(self) -> int: + return ilen(iter(self)) + + def _write_subdict(self, subdict: Mapping, stream: TextIO, + pad: str = "") -> None: + pre = pad.replace("├─", "│ ").replace("└─", " ") + n_sub = len(subdict) + for i_sub, (key, val) in enumerate(subdict.items()): + subpre = "└─" if i_sub == n_sub - 1 else "├─" + stream.write(f"{pre}{subpre}{key}: ") + if isinstance(val, Mapping): + self._write_subdict(val, stream, pre + subpre) + else: + stream.write(f"{val}") + + def write_string(self, stream: TextIO) -> None: + """Write formatted string representation to I/O stream""" + stream.write("SystemDict contents:") + self._write_subdict(self.dic, stream, "\n") + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.dic!r})" + + def __str__(self) -> str: + with StringIO() as str_stream: + self.write_string(str_stream) + output = str_stream.getvalue() + return output + + +def recursive_update(old_dict: MutableMapping, new_dict: Mapping) -> MutableMapping: if new_dict is not None: for key in new_dict: if old_dict is not None and key in old_dict: - if isinstance(old_dict[key], dict): - if isinstance(new_dict[key], dict): + if isinstance(old_dict[key], Mapping): + if isinstance(new_dict[key], Mapping): old_dict[key] = recursive_update(old_dict[key], new_dict[key]) else: - logging.warning("Overwriting dict: {} with non-dict: {}" - "".format(old_dict[key], new_dict[key])) + logging.warning("Overwriting dict: %s with non-dict: %s", + old_dict[key], new_dict[key]) old_dict[key] = new_dict[key] else: - if isinstance(new_dict[key], dict): - logging.warning("Overwriting non-dict: {} with dict: {}" - "".format(old_dict[key], new_dict[key])) + if isinstance(new_dict[key], Mapping): + logging.warning("Overwriting non-dict: %s with dict: %s", + old_dict[key], new_dict[key]) old_dict[key] = new_dict[key] else: old_dict[key] = new_dict[key] return old_dict - - diff --git a/scopesim/tests/__init__.py b/scopesim/tests/__init__.py index 82644b75..b8001f6f 100644 --- a/scopesim/tests/__init__.py +++ b/scopesim/tests/__init__.py @@ -1,5 +1,4 @@ from scopesim import rc - rc.__config__["!SIM.tests.run_integration_tests"] = True rc.__config__["!SIM.tests.run_skycalc_ter_tests"] = True rc.__config__["!SIM.file.use_cached_downloads"] = False diff --git a/scopesim/tests/mocks/MICADO_SCAO_WIDE/MICADO_SCAO_WIDE_2.yaml b/scopesim/tests/mocks/MICADO_SCAO_WIDE/MICADO_SCAO_WIDE_2.yaml index 9316443e..5f12d0e3 100644 --- a/scopesim/tests/mocks/MICADO_SCAO_WIDE/MICADO_SCAO_WIDE_2.yaml +++ b/scopesim/tests/mocks/MICADO_SCAO_WIDE/MICADO_SCAO_WIDE_2.yaml @@ -42,7 +42,7 @@ effects : - name : telescope_psf class : FieldVaryingPSF kwargs : - filename : MAORY_SCAO_FVPSF_4mas_20181203.fits + filename : MORFEO_SCAO_FVPSF_4mas_20181203.fits - name : telescope_surface_list class : SurfaceList diff --git a/scopesim/tests/mocks/MICADO_SPEC/TRACE_MICADO.fits b/scopesim/tests/mocks/MICADO_SPEC/TRACE_MICADO.fits index e6a86582..0e235dd7 100644 Binary files a/scopesim/tests/mocks/MICADO_SPEC/TRACE_MICADO.fits and b/scopesim/tests/mocks/MICADO_SPEC/TRACE_MICADO.fits differ diff --git a/scopesim/tests/mocks/files/TER_grating.fits b/scopesim/tests/mocks/files/TER_grating.fits new file mode 100644 index 00000000..42787cd1 Binary files /dev/null and b/scopesim/tests/mocks/files/TER_grating.fits differ diff --git a/scopesim/tests/mocks/py_objects/trace_list_objects.py b/scopesim/tests/mocks/py_objects/trace_list_objects.py index 95aa632c..c149d202 100644 --- a/scopesim/tests/mocks/py_objects/trace_list_objects.py +++ b/scopesim/tests/mocks/py_objects/trace_list_objects.py @@ -192,6 +192,15 @@ def trace_5(xn=3, yn=16, wmin=2.1, wmax=2.4, return tbl +def trace_6(xn=16, yn=3, wmin=2.1, wmax=2.4, + x0=1750, y0=-1750): + """As trace_5 but with dispersion in x direction""" + tbl = trace_5() + tmp = tbl['x'] + tbl['x'] = tbl['y'] + tbl['y'] = tmp + return tbl + def id_table(traces_ids, descriptions=None): """ diff --git a/scopesim/tests/mocks/test_package/TC_filter_Ks.dat b/scopesim/tests/mocks/test_package/TC_filter_Ks.dat new file mode 100644 index 00000000..36a88474 --- /dev/null +++ b/scopesim/tests/mocks/test_package/TC_filter_Ks.dat @@ -0,0 +1,244 @@ +# name : Ks filter curve +# author : unknown +# date_created : 2018-11-09 +# date_modified : 2018-01-28 +# sources : HAWK-I_Ks, SVO Filter service +# wavelength_unit : um +# changes : +# - 2019-11-09 (KL) Added to the test package +# +wavelength transmission +1.9244 0.004092 +1.9264 0.004719 +1.9284 0.005363 +1.9303 0.005971 +1.9323 0.006650 +1.9343 0.007400 +1.9363 0.008185 +1.9383 0.009123 +1.9403 0.010084 +1.9423 0.011361 +1.9443 0.012872 +1.9463 0.014803 +1.9482 0.016989 +1.9502 0.019718 +1.9522 0.023170 +1.9542 0.027843 +1.9562 0.033511 +1.9582 0.040535 +1.9602 0.049724 +1.9622 0.061381 +1.9641 0.076299 +1.9661 0.095620 +1.9681 0.119920 +1.9701 0.151441 +1.9721 0.188898 +1.9741 0.233436 +1.9761 0.283781 +1.9781 0.335879 +1.9800 0.387428 +1.9820 0.436250 +1.9840 0.480822 +1.9860 0.515266 +1.9880 0.544181 +1.9900 0.567621 +1.9920 0.587735 +1.9940 0.605451 +1.9960 0.622213 +1.9979 0.639393 +1.9999 0.655671 +2.0019 0.672747 +2.0039 0.690064 +2.0059 0.705886 +2.0079 0.721667 +2.0099 0.735400 +2.0119 0.747657 +2.0138 0.757275 +2.0158 0.764611 +2.0178 0.770151 +2.0198 0.772692 +2.0218 0.774471 +2.0238 0.774904 +2.0258 0.773802 +2.0278 0.772770 +2.0297 0.771374 +2.0317 0.770816 +2.0337 0.769840 +2.0357 0.769821 +2.0377 0.770729 +2.0397 0.772116 +2.0417 0.774129 +2.0437 0.777027 +2.0457 0.779965 +2.0476 0.783401 +2.0496 0.786676 +2.0516 0.790208 +2.0536 0.793924 +2.0556 0.796736 +2.0576 0.799705 +2.0596 0.801859 +2.0616 0.803399 +2.0635 0.805139 +2.0655 0.805537 +2.0675 0.805883 +2.0695 0.806335 +2.0715 0.805885 +2.0735 0.805576 +2.0755 0.805038 +2.0775 0.804727 +2.0794 0.804133 +2.0814 0.803998 +2.0834 0.804295 +2.0854 0.804219 +2.0874 0.805041 +2.0894 0.805836 +2.0914 0.806782 +2.0934 0.808434 +2.0954 0.809909 +2.0973 0.811714 +2.0993 0.813773 +2.1013 0.815366 +2.1033 0.817463 +2.1053 0.819240 +2.1073 0.820868 +2.1093 0.822257 +2.1113 0.823537 +2.1132 0.824653 +2.1152 0.825138 +2.1172 0.825841 +2.1192 0.826139 +2.1212 0.825767 +2.1232 0.825670 +2.1252 0.825048 +2.1272 0.824093 +2.1291 0.823366 +2.1311 0.822455 +2.1331 0.821660 +2.1351 0.820357 +2.1371 0.819444 +2.1391 0.818331 +2.1411 0.817576 +2.1431 0.816831 +2.1451 0.816213 +2.1470 0.815788 +2.1490 0.815617 +2.1510 0.815571 +2.1530 0.816045 +2.1550 0.816148 +2.1570 0.816919 +2.1590 0.817598 +2.1610 0.818230 +2.1629 0.819752 +2.1649 0.820894 +2.1669 0.822492 +2.1689 0.823297 +2.1709 0.825110 +2.1729 0.826640 +2.1749 0.827869 +2.1769 0.829224 +2.1788 0.830143 +2.1808 0.831485 +2.1828 0.832080 +2.1848 0.832791 +2.1868 0.833866 +2.1888 0.834211 +2.1908 0.834641 +2.1928 0.835547 +2.1948 0.835783 +2.1967 0.836970 +2.1987 0.836947 +2.2007 0.838148 +2.2027 0.838697 +2.2047 0.839203 +2.2067 0.839969 +2.2087 0.840589 +2.2107 0.841150 +2.2126 0.841549 +2.2146 0.841638 +2.2166 0.842445 +2.2186 0.842636 +2.2206 0.843223 +2.2226 0.843759 +2.2246 0.843869 +2.2266 0.844823 +2.2285 0.844729 +2.2305 0.845598 +2.2325 0.846154 +2.2345 0.846594 +2.2365 0.847138 +2.2385 0.847915 +2.2405 0.848186 +2.2425 0.848552 +2.2445 0.848987 +2.2464 0.849377 +2.2484 0.849617 +2.2504 0.849636 +2.2524 0.849992 +2.2544 0.849781 +2.2564 0.849623 +2.2584 0.849220 +2.2604 0.849069 +2.2623 0.848822 +2.2643 0.847899 +2.2663 0.847239 +2.2683 0.846086 +2.2703 0.844456 +2.2723 0.842642 +2.2743 0.840222 +2.2763 0.836502 +2.2782 0.832160 +2.2802 0.824891 +2.2822 0.816848 +2.2842 0.805276 +2.2862 0.790971 +2.2882 0.772614 +2.2902 0.750201 +2.2922 0.723509 +2.2942 0.692577 +2.2961 0.655112 +2.2981 0.613860 +2.3001 0.570899 +2.3021 0.526108 +2.3041 0.479929 +2.3061 0.434709 +2.3081 0.389649 +2.3101 0.346600 +2.3120 0.305818 +2.3140 0.269378 +2.3160 0.236474 +2.3180 0.206357 +2.3200 0.180523 +2.3220 0.157756 +2.3240 0.138264 +2.3260 0.121272 +2.3279 0.105898 +2.3299 0.092828 +2.3319 0.081272 +2.3339 0.071141 +2.3359 0.062715 +2.3379 0.054966 +2.3399 0.048328 +2.3419 0.042917 +2.3439 0.038122 +2.3458 0.033789 +2.3478 0.030085 +2.3498 0.026816 +2.3518 0.024026 +2.3538 0.021635 +2.3558 0.019397 +2.3578 0.017481 +2.3598 0.015782 +2.3617 0.014202 +2.3637 0.012930 +2.3657 0.011737 +2.3677 0.010634 +2.3697 0.009654 +2.3717 0.008782 +2.3737 0.008009 +2.3757 0.007305 +2.3776 0.006740 +2.3796 0.006113 +2.3816 0.005585 +2.3836 0.005160 +2.3856 0.004714 +2.3876 0.004274 \ No newline at end of file diff --git a/scopesim/tests/mocks/test_package/default.yaml b/scopesim/tests/mocks/test_package/default.yaml new file mode 100644 index 00000000..c21caa29 --- /dev/null +++ b/scopesim/tests/mocks/test_package/default.yaml @@ -0,0 +1,27 @@ +# Instrument +object : observation +alias : OBS +name : test_instrument + +packages : +- test_package + +yamls : +- test_package.yaml +- test_telescope.yaml +- test_instrument.yaml +- test_detector.yaml + +properties : + airmass : 1. + modes : ["mode_1", "mode_2"] + +mode_yamls : +- name : mode_1 + alias: OBS + properties : + airmass : 2. + +- name : mode_2 + yamls : + - test_mode_2.yaml diff --git a/scopesim/tests/mocks/test_package/test_detector.yaml b/scopesim/tests/mocks/test_package/test_detector.yaml new file mode 100644 index 00000000..11fc2cdd --- /dev/null +++ b/scopesim/tests/mocks/test_package/test_detector.yaml @@ -0,0 +1,25 @@ +### DETECTOR +object: detector +alias: DET +name: test_detector + +properties : [] + +effects: +- name: test_detector_array_list + class: DetectorList + kwargs: + array_dict: {"id": [1], "pixsize": [0.015], "angle": [0.], "gain": [1.0], + "x_cen": [0], y_cen: [0], xhw: [0.15], yhw: [0.15]} + x_cen_unit: mm + y_cen_unit: mm + xhw_unit: mm + yhw_unit: mm + pixsize_unit: mm + angle_unit: deg + gain_unit: electron/adu + +- name: test_shot_noise + class: ShotNoise + kwargs: + use_inbuilt_seed: True \ No newline at end of file diff --git a/scopesim/tests/mocks/test_package/test_instrument.yaml b/scopesim/tests/mocks/test_package/test_instrument.yaml new file mode 100644 index 00000000..ce48a5e2 --- /dev/null +++ b/scopesim/tests/mocks/test_package/test_instrument.yaml @@ -0,0 +1,15 @@ +# Instrument +object : instrument +alias : INST +name : test_instrument + +properties : + pixel_scale : 0.5 # arcsec per pixel + +effects : +- name : tc_from_file + class : TERCurve + kwargs : + filename : TC_filter_Ks.dat + + diff --git a/scopesim/tests/mocks/test_package/test_mode_2.yaml b/scopesim/tests/mocks/test_package/test_mode_2.yaml new file mode 100644 index 00000000..c6828250 --- /dev/null +++ b/scopesim/tests/mocks/test_package/test_mode_2.yaml @@ -0,0 +1,13 @@ +# Telescope +object : telescope +alias : TEL +name : test_telescope + +properties : + temperature : 8999 + +effects : +- name: random_effect + class: Effect + kwargs: + meaning_of_life: 42 \ No newline at end of file diff --git a/scopesim/tests/mocks/test_package/test_package.yaml b/scopesim/tests/mocks/test_package/test_package.yaml new file mode 100644 index 00000000..8bc6de13 --- /dev/null +++ b/scopesim/tests/mocks/test_package/test_package.yaml @@ -0,0 +1 @@ +# empty, just to trigger the test suite diff --git a/scopesim/tests/mocks/test_package/test_telescope.yaml b/scopesim/tests/mocks/test_package/test_telescope.yaml new file mode 100644 index 00000000..fbcba730 --- /dev/null +++ b/scopesim/tests/mocks/test_package/test_telescope.yaml @@ -0,0 +1,15 @@ +# Telescope +object : telescope +alias : TEL +name : test_telescope + +properties : + temperature : 9001 + +effects : +- name : tc_from_arrays + class : TERCurve + kwargs : + wavelength : [0.99, 1, 2, 2.01] + transmission : [0, 1, 1, 0] + wavelength_unit : um diff --git a/scopesim/tests/mocks/test_package/version.yaml b/scopesim/tests/mocks/test_package/version.yaml new file mode 100644 index 00000000..160d4d92 --- /dev/null +++ b/scopesim/tests/mocks/test_package/version.yaml @@ -0,0 +1,3 @@ +release: stable +timestamp: '2022-07-11 16:18:22' +version: '2022-07-11' diff --git a/scopesim/tests/mocks/yamls/MICADO_full.yaml b/scopesim/tests/mocks/yamls/MICADO_full.yaml index d7d3fe13..d2cd2e65 100644 --- a/scopesim/tests/mocks/yamls/MICADO_full.yaml +++ b/scopesim/tests/mocks/yamls/MICADO_full.yaml @@ -104,11 +104,11 @@ effects : --- -### MAORY RELAY OPTICS +### MORFEO RELAY OPTICS object : relay_optics -name : MAORY +name : MORFEO alias : RO -description : MAORY AO relay module +description : MORFEO AO relay module properties : temperature : !ATMO.temperature @@ -123,10 +123,10 @@ effects : effects : - name: relay_surface_list - description : list of surfaces in MAORY + description : list of surfaces in MORFEO class: SurfaceList kwargs: - filename: LIST_mirrors_MCAO_MAORY.tbl + filename: LIST_mirrors_MCAO_MORFEO.tbl --- diff --git a/scopesim/tests/tests_commands/test_SystemDict.py b/scopesim/tests/tests_commands/test_SystemDict.py index 5589183b..fe391d70 100644 --- a/scopesim/tests/tests_commands/test_SystemDict.py +++ b/scopesim/tests/tests_commands/test_SystemDict.py @@ -16,6 +16,12 @@ def basic_yaml(): return yaml.full_load(_basic_yaml) +@pytest.fixture(scope="class") +def nested_dict(): + return {"foo": 5, "bar": {"bogus": {"a": 42, "b": 69}, + "baz": "meh"}, "moo": "yolo", "yeet": {"x": 0, "y": 420}} + + @pytest.mark.usefixtures("basic_yaml") class TestInit: def test_initialises_with_nothing(self): @@ -104,3 +110,30 @@ def test_recursive_update_overwrites_string_with_string(self): f = {"a": {"b": {"c": "world"}}} recursive_update(e, f) assert e["a"]["b"]["c"] == "world" + + +@pytest.mark.usefixtures("nested_dict") +class TestRepresentation: + def test_str_conversion(self, nested_dict): + desired = ("SystemDict contents:\n├─foo: 5\n├─bar: \n│ ├─bogus: " + "\n│ │ ├─a: 42\n│ │ └─b: 69\n│ └─baz: meh\n├─moo: " + "yolo\n└─yeet: \n ├─x: 0\n └─y: 420") + sys_dict = SystemDict(nested_dict) + assert str(sys_dict) == desired + + def test_repr_conversion(self, nested_dict): + desired = ("SystemDict({'foo': 5, 'bar': {'bogus': " + "{'a': 42, 'b': 69}, 'baz': 'meh'}, 'moo': 'yolo', " + "'yeet': {'x': 0, 'y': 420}})") + sys_dict = SystemDict(nested_dict) + assert sys_dict.__repr__() == desired + + def test_len_works(self, nested_dict): + sys_dict = SystemDict(nested_dict) + assert len(sys_dict) == 7 + + def test_list_returns_keys(self, nested_dict): + desired = ["foo", "!bar.bogus.a", "!bar.bogus.b", "!bar.baz", "moo", + "!yeet.x", "!yeet.y"] + sys_dict = SystemDict(nested_dict) + assert list(sys_dict) == desired diff --git a/scopesim/tests/tests_commands/test_UserCommands.py b/scopesim/tests/tests_commands/test_UserCommands.py index 74853b5d..aeeaafa6 100644 --- a/scopesim/tests/tests_commands/test_UserCommands.py +++ b/scopesim/tests/tests_commands/test_UserCommands.py @@ -1,24 +1,22 @@ import os -import shutil +from pathlib import Path import pytest from tempfile import TemporaryDirectory from scopesim import rc -from scopesim.commands.user_commands import UserCommands -from scopesim.server import database as db +from scopesim.commands.user_commands import UserCommands, patch_fake_symlinks tmpdir = TemporaryDirectory() +FILES_PATH = str(Path(__file__).parent.parent / "mocks") + def setup_module(): - db.download_packages(["test_package"], release="stable", - save_dir=tmpdir.name, from_cache=False) rc.__config__["local_packages_path_OLD"] = rc.__config__["!SIM.file.local_packages_path"] - rc.__config__["!SIM.file.local_packages_path"] = tmpdir.name + rc.__config__["!SIM.file.local_packages_path"] = FILES_PATH def teardown_module(): - tmpdir.cleanup() rc.__config__["!SIM.file.local_packages_path"] = rc.__config__["local_packages_path_OLD"] # TODO: something like rc.__config__.pop("local_packages_path_OLD") @@ -114,3 +112,67 @@ def test_all_packages_listed(self): class TestTrackIpAddress: def test_see_if_theres_an_entry_on_the_server_log_file(self): cmds = UserCommands(use_instrument="test_package") + + +def test_patch_fake_symlinks(tmp_path): + """Setup a temporary directory with files and links.""" + # tmp_path is a fixture + + dircwd = Path.cwd() + os.chdir(tmp_path) + + dir1 = tmp_path / "H1" + dir1.mkdir() + + dir2 = dir1 / "H2" + dir2.mkdir() + + # Normal file + file1 = dir2 / "F1.txt" + with open(file1, 'w') as f1: + f1.write("Hello world!") + + # Empty file + file2 = tmp_path / "F2.txt" + with open(file2, 'w') as f2: + f2.write("") + + # File with a line that is too long to be a link + file3 = tmp_path / "F3.txt" + with open(file3, 'w') as f3: + f3.write("10 print hello; 20 goto 10" * 50) + + # A file with multiple lines + file4 = tmp_path / "F4.txt" + with open(file4, 'w') as f4: + f4.write("Hello\nWorld\n") + + # With slashes. Backslashes would also work on windows, + # but not on linux, so we just do not include that case. + fakelink1 = tmp_path / "L1" + with open(fakelink1, 'w') as f: + f.write("H1/H2") + + # A real link + reallink1 = tmp_path / "R1" + try: + reallink1.symlink_to(dir2) + except OSError: + # "A required privilege is not held by the client" + # That is, developer mode is off. + reallink1 = dir2 + + root = list(tmp_path.parents)[-1] + + assert patch_fake_symlinks(dir1) == dir1.resolve() + assert patch_fake_symlinks(dir2) == dir2.resolve() + assert patch_fake_symlinks(file1) == file1.resolve() + assert patch_fake_symlinks(file3) == file3.resolve() + assert patch_fake_symlinks(file4) == file4.resolve() + assert patch_fake_symlinks(fakelink1) == dir2.resolve() + assert patch_fake_symlinks(reallink1) == dir2.resolve() + assert patch_fake_symlinks(fakelink1 / "F1.txt") == file1.resolve() + assert patch_fake_symlinks(reallink1 / "F1.txt") == file1.resolve() + assert patch_fake_symlinks(root) == root.resolve() + + os.chdir(dircwd) diff --git a/scopesim/tests/tests_effects/test_SkycalcTERCurve.py b/scopesim/tests/tests_effects/test_SkycalcTERCurve.py index ca696de6..a03d57c1 100644 --- a/scopesim/tests/tests_effects/test_SkycalcTERCurve.py +++ b/scopesim/tests/tests_effects/test_SkycalcTERCurve.py @@ -1,15 +1,16 @@ +from pathlib import Path + import pytest import os from synphot import SpectralElement, SourceSpectrum from scopesim.effects import SkycalcTERCurve from scopesim import rc -from scopesim.utils import from_currsys if rc.__config__["!SIM.tests.run_skycalc_ter_tests"] is False: pytestmark = pytest.mark.skip("Ignoring SkyCalc integration tests") -FILES_PATH = os.path.join(os.path.dirname(__file__), "../MOCKS/files/") +FILES_PATH = str(Path(__file__).parent.parent / "mocks" / "files") if FILES_PATH not in rc.__search_path__: rc.__search_path__ += [FILES_PATH] diff --git a/scopesim/tests/tests_effects/test_SpectralEfficiency.py b/scopesim/tests/tests_effects/test_SpectralEfficiency.py new file mode 100644 index 00000000..081f089a --- /dev/null +++ b/scopesim/tests/tests_effects/test_SpectralEfficiency.py @@ -0,0 +1,37 @@ +"""Tests for class SpectralEfficiency""" +import os +import pytest + +from astropy.io import fits + +from scopesim import rc +from scopesim.effects import SpectralEfficiency, TERCurve +from scopesim.utils import find_file + +FILES_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), + "../mocks/files/")) +if FILES_PATH not in rc.__search_path__: + rc.__search_path__ += [FILES_PATH] + +# pylint: disable=missing-class-docstring +# pylint: disable=missing-function-docstring + +@pytest.fixture(name="speceff", scope="class") +def fixture_speceff(): + """Instantiate SpectralEfficiency object""" + return SpectralEfficiency(filename="TER_grating.fits") + +class TestSpectralEfficiency: + def test_initialises_from_file(self, speceff): + assert isinstance(speceff, SpectralEfficiency) + + def test_initialises_from_hdulist(self): + fitsfile = find_file("TER_grating.fits") + hdul = fits.open(fitsfile) + speceff = SpectralEfficiency(hdulist=hdul) + assert isinstance(speceff, SpectralEfficiency) + + def test_has_efficiencies(self, speceff): + efficiencies = speceff.efficiencies + assert all(isinstance(effic, TERCurve) + for _, effic in efficiencies.items()) diff --git a/scopesim/tests/tests_effects/test_SpectralTraceList.py b/scopesim/tests/tests_effects/test_SpectralTraceList.py index bcd72a88..535e610a 100644 --- a/scopesim/tests/tests_effects/test_SpectralTraceList.py +++ b/scopesim/tests/tests_effects/test_SpectralTraceList.py @@ -1,17 +1,14 @@ +"""Tests for module spectral_trace_list.py""" import os import pytest -import numpy as np from astropy.io import fits -from astropy.wcs import WCS -from matplotlib import pyplot as plt from scopesim.effects.spectral_trace_list import SpectralTraceList -from scopesim.optics.fov_manager import FovVolumeList +from scopesim.effects.spectral_trace_list_utils import SpectralTrace from scopesim.tests.mocks.py_objects import trace_list_objects as tlo from scopesim.tests.mocks.py_objects import header_objects as ho -from scopesim.base_classes import PoorMansHeader from scopesim import rc MOCK_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), @@ -21,22 +18,22 @@ PLOTS = False +# pylint: disable=missing-class-docstring, +# pylint: disable=missing-function-docstring -@pytest.fixture(scope="function") -def slit_header(): +@pytest.fixture(name="slit_header", scope="class") +def fixture_slit_header(): return ho._short_micado_slit_header() - -@pytest.fixture(scope="function") -def long_slit_header(): +@pytest.fixture(name="long_slit_header", scope="class") +def fixture_long_slit_header(): return ho._long_micado_slit_header() - -@pytest.fixture(scope="function") -def full_trace_list(): +@pytest.fixture(name="full_trace_list", scope="class") +def fixture_full_trace_list(): + """Instantiate a trace definition hdu list""" return tlo.make_trace_hdulist() - class TestInit: def test_initialises_with_nothing(self): assert isinstance(SpectralTraceList(), SpectralTraceList) @@ -46,119 +43,40 @@ def test_initialises_with_a_hdulist(self, full_trace_list): spt = SpectralTraceList(hdulist=full_trace_list) assert isinstance(spt, SpectralTraceList) assert spt.get_data(2, fits.BinTableHDU) + # next assert that dispersion axis determined correctly + assert list(spt.spectral_traces.values())[2].dispersion_axis == 'y' def test_initialises_with_filename(self): spt = SpectralTraceList(filename="TRACE_MICADO.fits", wave_colname="wavelength", s_colname="xi") assert isinstance(spt, SpectralTraceList) - - -@pytest.mark.skip(reason="Ignoring old Spectroscopy integration tests") -class TestGetFOVHeaders: - @pytest.mark.usefixtures("full_trace_list", "slit_header") - def test_gets_the_headers(self, full_trace_list, slit_header): - spt = SpectralTraceList(hdulist=full_trace_list) - params = {"pixel_scale": 0.015, "plate_scale": 0.26666, - "wave_min": 0.7, "wave_max": 2.5} - hdrs = spt.get_fov_headers(slit_header, **params) - - # assert all([isinstance(hdr, fits.Header) for hdr in hdrs]) - assert all([isinstance(hdr, PoorMansHeader) for hdr in hdrs]) - # ..todo:: add in some better test of correctness - - if PLOTS: - # pixel coords - for hdr in hdrs[::50]: - xp = [0, hdr["NAXIS1"], hdr["NAXIS1"], 0] - yp = [0, 0, hdr["NAXIS2"], hdr["NAXIS2"]] - wcs = WCS(hdr, key="D") - # world coords - xw, yw = wcs.all_pix2world(xp, yp, 1) - plt.fill(xw / hdr["CDELT1D"], yw / hdr["CDELT2D"], alpha=0.2) - plt.show() - - def test_gets_headers_from_real_file(self): - slit_hdr = ho._long_micado_slit_header() - # slit_hdr = ho._short_micado_slit_header() - wave_min = 1.0 - wave_max = 1.3 - spt = SpectralTraceList(filename="TRACE_15arcsec.fits", - s_colname="xi", - wave_colname="lam", - spline_order=1) - params = {"wave_min": wave_min, "wave_max": wave_max, - "pixel_scale": 0.004, "plate_scale": 0.266666667} - hdrs = spt.get_fov_headers(slit_hdr, **params) - assert isinstance(spt, SpectralTraceList) - - print(len(hdrs)) - - if PLOTS: - spt.plot(wave_min, wave_max) - - # pixel coords - for hdr in hdrs[::300]: - xp = [0, hdr["NAXIS1"], hdr["NAXIS1"], 0] - yp = [0, 0, hdr["NAXIS2"], hdr["NAXIS2"]] - wcs = WCS(hdr, key="D") - # world coords - xw, yw = wcs.all_pix2world(xp, yp, 1) - plt.plot(xw, yw, alpha=0.2) - plt.show() - - -# class TestApplyTo: -# def test_fov_setup_base_returns_only_extracted_fov_limits(self): -# fname = r"F:\Work\irdb\MICADO\TRACE_MICADO.fits" -# spt = SpectralTraceList(filename=fname, s_colname='xi') -# -# fvl = FovVolumeList() -# fvl = spt.apply_to(fvl) -# -# assert len(fvl) == 17 - - -################################################################################ - - -def test_set_pc_matrix(rotation_ang=0, shear_ang=10): - n = 100 - im = np.arange(n**2).reshape(n, n) - hdu = fits.ImageHDU(im) - hdr_dict = {"CTYPE1": "LINEAR", - "CTYPE2": "LINEAR", - "CUNIT1": "deg", - "CUNIT2": "deg", - "CDELT1": 1, - "CDELT2": 1, - "CRVAL1": 0, - "CRVAL2": 0, - "CRPIX1": 0, - "CRPIX2": 0} - hdu.header.update(hdr_dict) - - c = np.cos(rotation_ang / 57.29578) * 2 - s = np.sin(rotation_ang / 57.29578) * 2 - t = np.tan(shear_ang / 57.29578) - - n = 5 - pc_dict = {"PC1_1": c + t*s, - "PC1_2": -s + t*c, - "PC2_1": s, - "PC2_2": c} - det = np.sqrt(np.abs(pc_dict["PC1_1"] * pc_dict["PC2_2"] - \ - pc_dict["PC1_2"] * pc_dict["PC2_1"])) - for key in pc_dict: - pc_dict[key] /= det - hdu.header.update(pc_dict) - w = WCS(hdu) - - xd = np.array([0, 10, 10, 0]) - yd = np.array([0, 0, 10, 10]) - xs, ys = w.all_pix2world(xd, yd, 1) - - if PLOTS: - plt.figure(figsize=(6, 6)) - plt.plot(xd, yd, "o-") - plt.plot(xs, ys, "o-") - plt.show() + # assert that dispersion axis taken correctly from header keyword + assert list(spt.spectral_traces.values())[2].dispersion_axis == 'y' + + def test_getitem_returns_spectral_trace(self, full_trace_list): + slist = SpectralTraceList(hdulist=full_trace_list) + assert isinstance(slist['Sheared'], SpectralTrace) + + def test_setitem_appends_correctly(self, full_trace_list): + slist = SpectralTraceList(hdulist=full_trace_list) + n_trace = len(slist.spectral_traces) + spt = tlo.trace_1() + slist["New trace"] = spt + assert len(slist.spectral_traces) == n_trace + 1 + + +@pytest.fixture(name="spectral_trace_list", scope="class") +def fixture_spectral_trace_list(): + """Instantiate a SpectralTraceList""" + return SpectralTraceList(hdulist=tlo.make_trace_hdulist()) + +class TestRectification: + def test_rectify_cube_not_implemented(self, spectral_trace_list): + hdulist = fits.HDUList() + with pytest.raises(NotImplementedError): + spectral_trace_list.rectify_cube(hdulist) + + #def test_rectify_traces_needs_ximin_and_ximax(self, spectral_trace_list): + # hdulist = fits.HDUList([fits.PrimaryHDU()]) + # with pytest.raises(KeyError): + # spectral_trace_list.rectify_traces(hdulist) diff --git a/scopesim/tests/tests_effects/test_SpectralTraceListUtils.py b/scopesim/tests/tests_effects/test_SpectralTraceListUtils.py index 3f1b9182..f3e3d47f 100644 --- a/scopesim/tests/tests_effects/test_SpectralTraceListUtils.py +++ b/scopesim/tests/tests_effects/test_SpectralTraceListUtils.py @@ -1,14 +1,36 @@ """Unit tests for spectral_trace_list_utils.py""" -# pylint: disable=no-self-use # pylint: disable=missing-function-docstring # pylint: disable=invalid-name - +# pylint: disable=too-few-public-methods import pytest import numpy as np - +from scopesim.effects.spectral_trace_list_utils import SpectralTrace from scopesim.effects.spectral_trace_list_utils import Transform2D, power_vector +from scopesim.tests.mocks.py_objects import trace_list_objects as tlo + +class TestSpectralTrace: + """Tests not covered in test_SpectralTraceList.py""" + def test_initialises_with_table(self): + trace_tbl = tlo.trace_1() + spt = SpectralTrace(trace_tbl) + assert isinstance(spt, SpectralTrace) + + def test_fails_without_table(self): + a_number = 1 + with pytest.raises(ValueError): + SpectralTrace(a_number) + + def test_determines_correct_dispersion_axis_x(self): + trace_tbl = tlo.trace_6() + spt = SpectralTrace(trace_tbl) + assert spt.dispersion_axis == 'x' + + def test_determines_correct_dispersion_axis_y(self): + trace_tbl = tlo.trace_5() + spt = SpectralTrace(trace_tbl) + assert spt.dispersion_axis == 'y' class TestPowerVec: """Test function power_vector()""" diff --git a/scopesim/tests/tests_effects/test_TERCurve.py b/scopesim/tests/tests_effects/test_TERCurve.py index d49d2653..14fe65e7 100644 --- a/scopesim/tests/tests_effects/test_TERCurve.py +++ b/scopesim/tests/tests_effects/test_TERCurve.py @@ -21,6 +21,7 @@ # pylint: disable=no-self-use, missing-class-docstring # pylint: disable=missing-function-docstring + class TestTERCurveApplyTo: def test_adds_bg_to_source_if_source_has_no_bg(self): @@ -96,14 +97,6 @@ def test_returns_filter_as_wanted(self, observatory, instrument, filt_name): filter_name=filt_name) assert isinstance(filt, tc.FilterCurve) - def test_returns_unity_transmission_for_wrong_name(self): - filt = tc.SpanishVOFilterCurve(observatory=None, - instrument=None, - filter_name=None, - error_on_wrong_name=False) - assert isinstance(filt, tc.FilterCurve) - assert np.all([t == 1 for t in filt.data["transmission"]]) - @pytest.fixture(name="fwheel", scope="class") def _filter_wheel(): @@ -112,6 +105,7 @@ def _filter_wheel(): "filename_format": "TC_filter_{}.dat", "current_filter": "Br-gamma"}) + class TestFilterWheelInit: def test_initialises_correctly(self, fwheel): assert isinstance(fwheel, tc.FilterWheel) diff --git a/scopesim/tests/tests_integrations/test_3_custom_effects.py b/scopesim/tests/tests_integrations/test_3_custom_effects.py index ee55cd98..b4d4112a 100644 --- a/scopesim/tests/tests_integrations/test_3_custom_effects.py +++ b/scopesim/tests/tests_integrations/test_3_custom_effects.py @@ -4,7 +4,7 @@ # 3: Writing and including custom Effects # ======================================= # -# In this tutorial, we will load the model of MICADO (including Armazones, ELT, MAORY) and then turn off all effect that modify the spatial extent of the stars. The purpose here is to see in detail what happens to the **distribution of the stars flux on a sub-pixel level** when we add a plug-in astrometric Effect to the optical system. +# In this tutorial, we will load the model of MICADO (including Armazones, ELT, MORFEO) and then turn off all effect that modify the spatial extent of the stars. The purpose here is to see in detail what happens to the **distribution of the stars flux on a sub-pixel level** when we add a plug-in astrometric Effect to the optical system. # # For real simulation, we will obviously leave all normal MICADO effects turned on, while still adding the plug-in Effect. Hopefully this tutorial will serve as a refernce for those who want to see **how to create Plug-ins** and how to manipulate the effects in the MICADO optical train model. # diff --git a/scopesim/tests/tests_server/test_database.py b/scopesim/tests/tests_server/test_database.py index 8ffd3001..b0bbbc46 100644 --- a/scopesim/tests/tests_server/test_database.py +++ b/scopesim/tests/tests_server/test_database.py @@ -1,6 +1,5 @@ import pytest import os -import sys from tempfile import TemporaryDirectory from urllib3.exceptions import HTTPError @@ -8,9 +7,12 @@ import numpy as np from scopesim.server import database as db +from scopesim.server import example_data_utils as dbex +from scopesim.server import github_utils as dbgh from scopesim import rc +@pytest.mark.webtest def test_package_list_loads(): pkgs = db.get_server_package_list() assert isinstance(pkgs, dict) @@ -18,58 +20,128 @@ def test_package_list_loads(): assert "latest" in pkgs["test_package"] -def test_get_server_folder_contents(): - pkgs = db.get_server_folder_contents("locations") - assert len(pkgs) > 0 - assert "Armazones" in pkgs[0] +def test_get_package_name(): + pkg_name = db._get_package_name("Packagename.2022-01-01.dev.zip") + assert pkg_name == "Packagename" + + +@pytest.mark.webtest +def test_get_all_latest(): + all_pkg = db.get_all_package_versions() + assert dict(db.get_all_latest(all_pkg))["test_package"].endswith(".dev") + + +@pytest.mark.webtest +class TestGetZipname: + # TODO: This could use some kind of mock to avoid server access + all_pkg = db.get_all_package_versions() + + def test_gets_stable(self): + zipname = db._get_zipname("test_package", "stable", self.all_pkg) + assert zipname.startswith("test_package.") + assert zipname.endswith(".zip") + + def test_gets_latest(self): + zipname = db._get_zipname("test_package", "latest", self.all_pkg) + assert zipname.startswith("test_package.") + assert zipname.endswith(".dev.zip") + + def test_throws_for_nonexisting_release(self): + with pytest.raises(ValueError): + db._get_zipname("test_package", "bogus", self.all_pkg) + + +class TestGetServerFolderContents: + @pytest.mark.webtest + def test_downloads_locations(self): + pkgs = list(db.get_server_folder_contents("locations")) + assert len(pkgs) > 0 + + @pytest.mark.webtest + def test_downloads_telescopes(self): + pkgs = list(db.get_server_folder_contents("telescopes")) + assert len(pkgs) > 0 + + @pytest.mark.webtest + def test_downloads_instruments(self): + pkgs = list(db.get_server_folder_contents("instruments")) + assert len(pkgs) > 0 + + @pytest.mark.webtest + def test_finds_armazones(self): + pkgs = list(db.get_server_folder_contents("locations")) + assert "Armazones" in pkgs[0] + + @pytest.mark.webtest + def test_throws_for_wrong_url_server(self): + original_url = rc.__config__["!SIM.file.server_base_url"] + rc.__config__["!SIM.file.server_base_url"] = "https://scopesim.univie.ac.at/bogus/" + with pytest.raises(db.ServerError): + list(db.get_server_folder_contents("locations")) + rc.__config__["!SIM.file.server_base_url"] = original_url class TestGetServerElements: + @pytest.mark.webtest def test_throws_an_error_if_url_doesnt_exist(self): with pytest.raises(ValueError): - db.get_server_elements(url="www.bogus.server") + dbex.get_server_elements(url="www.bogus.server") + @pytest.mark.webtest def test_returns_folders_if_server_exists(self): url = rc.__config__["!SIM.file.server_base_url"] - pkgs = db.get_server_elements(url) + pkgs = dbex.get_server_elements(url) assert all([loc in pkgs for loc in ["locations/", "telescopes/", "instruments/"]]) + @pytest.mark.webtest def test_returns_files_if_zips_exist(self): url = rc.__config__["!SIM.file.server_base_url"] dir = "instruments/" - pkgs = db.get_server_elements(url + dir, ".zip") + pkgs = dbex.get_server_elements(url + dir, ".zip") assert "test_package.zip" in pkgs class TestListPackages: + @pytest.mark.webtest def test_lists_all_packages_without_qualifier(self): pkgs = db.list_packages() assert "Armazones" in pkgs assert "MICADO" in pkgs + @pytest.mark.webtest def test_lists_only_packages_with_qualifier(self): pkgs = db.list_packages("Armazones") assert np.all(["Armazones" in pkg for pkg in pkgs]) + @pytest.mark.webtest + def test_throws_for_nonexisting_pkgname(self): + with pytest.raises(ValueError): + db.list_packages("bogus") + class TestDownloadPackage: """ Old download function, for backwards compatibility """ + @pytest.mark.webtest def test_downloads_package_successfully(self): pkg_path = "instruments/test_package.zip" save_paths = db.download_package(pkg_path) assert os.path.exists(save_paths[0]) - def test_raise_error_when_package_not_found(self): - if sys.version_info.major >= 3: - with pytest.raises(HTTPError): - db.download_package("instruments/bogus.zip") + # This no longer raises, but logs an error. This is intended. + # TODO: Change test to capture log and assert if error log is present. + # Actually, the new single download function should be tested here instead + # def test_raise_error_when_package_not_found(self): + # if sys.version_info.major >= 3: + # with pytest.raises(HTTPError): + # db.download_package("instruments/bogus.zip") class TestDownloadPackages: + @pytest.mark.webtest def test_downloads_stable_package(self): with TemporaryDirectory() as tmpdir: db.download_packages(["test_package"], release="stable", @@ -83,6 +155,7 @@ def test_downloads_stable_package(self): version_dict = yaml.full_load(f) assert version_dict["release"] == "stable" + @pytest.mark.webtest def test_downloads_latest_package(self): with TemporaryDirectory() as tmpdir: db.download_packages("test_package", release="latest", @@ -93,6 +166,7 @@ def test_downloads_latest_package(self): assert version_dict["release"] == "dev" + @pytest.mark.webtest def test_downloads_specific_package(self): release = "2022-04-09.dev" with TemporaryDirectory() as tmpdir: @@ -104,6 +178,7 @@ def test_downloads_specific_package(self): assert version_dict["version"] == release + @pytest.mark.webtest def test_downloads_github_version_of_package_with_semicolon(self): release = "github:728761fc76adb548696205139e4e9a4260401dfc" with TemporaryDirectory() as tmpdir: @@ -113,6 +188,7 @@ def test_downloads_github_version_of_package_with_semicolon(self): assert os.path.exists(filename) + @pytest.mark.webtest def test_downloads_github_version_of_package_with_at_symbol(self): release = "github@728761fc76adb548696205139e4e9a4260401dfc" with TemporaryDirectory() as tmpdir: @@ -124,24 +200,34 @@ def test_downloads_github_version_of_package_with_at_symbol(self): class TestDownloadGithubFolder: + @pytest.mark.webtest def test_downloads_current_package(self): with TemporaryDirectory() as tmpdir: # tmpdir = "." url = "https://github.com/AstarVienna/irdb/tree/dev_master/MICADO" - db.download_github_folder(url, output_dir=tmpdir) + dbgh.download_github_folder(url, output_dir=tmpdir) filename = os.path.join(tmpdir, "MICADO", "default.yaml") assert os.path.exists(filename) + @pytest.mark.webtest def test_downloads_with_old_commit_hash(self): with TemporaryDirectory() as tmpdir: url = "https://github.com/AstarVienna/irdb/tree/728761fc76adb548696205139e4e9a4260401dfc/ELT" - db.download_github_folder(url, output_dir=tmpdir) + dbgh.download_github_folder(url, output_dir=tmpdir) filename = os.path.join(tmpdir, "ELT", "EC_sky_25.tbl") assert os.path.exists(filename) + @pytest.mark.webtest + def test_throws_for_bad_url(self): + with TemporaryDirectory() as tmpdir: + url = "https://github.com/AstarVienna/irdb/tree/bogus/MICADO" + with pytest.raises(dbgh.ServerError): + dbgh.download_github_folder(url, output_dir=tmpdir) + +@pytest.mark.webtest def test_old_download_package_signature(): with TemporaryDirectory() as tmpdir: db.download_package(["instruments/test_package.zip"], save_dir=tmpdir) diff --git a/scopesim/utils.py b/scopesim/utils.py index d0599250..eafb6f82 100644 --- a/scopesim/utils.py +++ b/scopesim/utils.py @@ -2,11 +2,9 @@ Helper functions for ScopeSim """ import math -import os from pathlib import Path import sys import logging -import logging from collections import OrderedDict from docutils.core import publish_string from copy import deepcopy @@ -16,30 +14,11 @@ import numpy as np from astropy import units as u from astropy.io import fits -from astropy.io import ascii as ioascii from astropy.table import Column, Table from . import rc -def msg(cmds, message, level=3): - """ - Prints a message based on the level of verbosity given in cmds - - Parameters - ---------- - cmds : UserCommands - just for the SIM_VERBOSE and SIM_MESSAGE_LEVEL keywords - message : str - message to be printed - level : int, optional - all messages with level <= SIM_MESSAGE_LEVEL are printed. I.e. level=5 - messages are not important, level=1 are very important - """ - if cmds["SIM_VERBOSE"] == "yes" and level <= cmds["SIM_MESSAGE_LEVEL"]: - print(message) - - def unify(x, unit, length=1): """ Convert all types of input to an astropy array/unit pair @@ -111,7 +90,7 @@ def parallactic_angle(ha, de, lat=-24.589167): lat = np.deg2rad(lat) eta = np.arctan2(np.cos(lat) * np.sin(ha), - np.sin(lat) * np.cos(de) - \ + np.sin(lat) * np.cos(de) - np.cos(lat) * np.sin(de) * np.cos(ha)) return np.rad2deg(eta) @@ -131,7 +110,7 @@ def moffat(r, alpha, beta): ------- eta """ - return (beta - 1)/(np.pi * alpha**2) * (1 + (r/alpha)**2)**(-beta) + return (beta - 1) / (np.pi * alpha ** 2) * (1 + (r / alpha) ** 2) ** (-beta) def poissonify(arr): @@ -174,12 +153,14 @@ def nearest(arr, val): return np.argmin(abs(arr - val)) + def power_vector(val, degree): """Return the vector of powers of val up to a degree""" if degree < 0 or not isinstance(degree, int): raise ValueError("degree must be a positive integer") - return np.array([val**exp for exp in range(degree + 1)]) + return np.array([val ** exp for exp in range(degree + 1)]) + def deriv_polynomial2d(poly): """Derivatives (gradient) of a Polynomial2D model @@ -188,8 +169,8 @@ def deriv_polynomial2d(poly): ---------- poly : astropy.modeling.models.Polynomial2D - Output - ------ + Returns + ------- gradient : tuple of Polynomial2d """ import re @@ -204,8 +185,8 @@ def deriv_polynomial2d(poly): i = int(match.group(1)) j = int(match.group(2)) cij = getattr(poly, pname) - pname_x = "c%d_%d" % (i-1, j) - pname_y = "c%d_%d" % (i, j-1) + pname_x = "c%d_%d" % (i - 1, j) + pname_y = "c%d_%d" % (i, j - 1) setattr(dpoly_dx, pname_x, i * cij) setattr(dpoly_dy, pname_y, j * cij) @@ -233,44 +214,6 @@ def add_keyword(filename, keyword, value, comment="", ext=0): f.close() -def add_SED_to_scopesim(file_in, file_out=None, wave_units="um"): - """ - Adds the SED given in ``file_in`` to the ScopeSim data directory - - Parameters - ---------- - file_in : str - path to the SED file. Can be either FITS or ASCII format with 2 columns - Column 1 is the wavelength, column 2 is the flux - file_out : str, optional - Default is None. The file path to save the ASCII file. If ``None``, the SED - is saved to the ScopeSim data directory i.e. to ``rc.__data_dir__`` - wave_units : str, astropy.Units - Units for the wavelength column, either as a string or as astropy units - Default is [um] - - """ - - file_name, file_ext = os.path.basename(file_in).split(".") - - if file_out is None: - if "SED_" not in file_name: - file_out = rc.__data_dir__ + "SED_" + file_name + ".dat" - else: file_out = rc.__data_dir__ + file_name + ".dat" - - if file_ext.lower() in "fits": - data = fits.getdata(file_in) - lam, val = data[data.columns[0].name], data[data.columns[1].name] - else: - lam, val = ioascii.read(file_in)[:2] - - lam = (lam * u.Unit(wave_units)).to(u.um) - mask = (lam > 0.3*u.um) * (lam < 5.0*u.um) - - np.savetxt(file_out, np.array((lam[mask], val[mask]), dtype=np.float32).T, - header="wavelength value \n [um] [flux]") - - def airmass_to_zenith_dist(airmass): """ returns zenith distance in degrees @@ -308,7 +251,7 @@ def seq(start, stop, step=1): increment of the sequence, defaults to 1 """ - feps = 1e-10 # value used in R seq.default + feps = 1e-10 # value used in R seq.default delta = stop - start if delta == 0 and stop == 0: @@ -335,21 +278,21 @@ def seq(start, stop, step=1): # integer sequence npts = int(npts) return start + np.asarray(range(npts + 1)) * step + + npts = int(npts + feps) + sequence = start + np.asarray(range(npts + 1)) * step + # correct for possible overshot because of fuzz (from seq.R) + if step > 0: + return np.minimum(sequence, stop) else: - npts = int(npts + feps) - sequence = start + np.asarray(range(npts + 1)) * step - # correct for possible overshot because of fuzz (from seq.R) - if step > 0: - return np.minimum(sequence, stop) - else: - return np.maximum(sequence, stop) + return np.maximum(sequence, stop) def add_mags(mags): """ Returns a combined magnitude for a group of py_objects with ``mags`` """ - return -2.5*np.log10((10**(-0.4*np.array(mags))).sum()) + return -2.5 * np.log10((10 ** (-0.4 * np.array(mags))).sum()) def dist_mod_from_distance(d): @@ -366,7 +309,7 @@ def distance_from_dist_mod(mu): d = 10**(1 + mu / 5) """ - d = 10**(1 + mu / 5) + d = 10 ** (1 + mu / 5) return d @@ -395,7 +338,7 @@ def telescope_diffraction_limit(aperture_size, wavelength, distance=None): """ - diff_limit = (((wavelength*u.um)/(aperture_size*u.m))*u.rad).to(u.arcsec).value + diff_limit = (((wavelength * u.um) / (aperture_size * u.m)) * u.rad).to(u.arcsec).value if distance is not None: diff_limit *= distance / u.pc.to(u.AU) @@ -477,7 +420,6 @@ def set_logger_level(which="console", level="ERROR"): """ - hdlr_name = f"scopesim_{which}_logger" level = {"ON": "INFO", "OFF": "CRITICAL"}.get(level.upper(), level) logger = logging.getLogger() @@ -542,28 +484,33 @@ def find_file(filename, path=None, silent=False): if filename is None or filename.lower() == "none": return None - if filename[0] == "!": + if filename.startswith("!"): filename = from_currsys(filename) + # Turn into pathlib.Path object for better manipulation afterwards + filename = Path(filename) if path is None: path = rc.__search_path__ - if os.path.isabs(filename): + if filename.is_absolute(): # absolute path: only path to try trynames = [filename] else: # try to find the file in a search path - trynames = [os.path.join(trydir, *os.path.split(filename)) + trynames = [Path(trydir, filename) for trydir in path if trydir is not None] for fname in trynames: - if os.path.exists(fname): # success + if fname.exists(): # success # strip leading ./ - while fname[:2] == './': - fname = fname[2:] - return fname - else: - continue + # Path should take care of this automatically! + # while fname[:2] == './': + # fname = fname[2:] + # Nevertheless, make sure this is actually the case... + assert not str(fname).startswith("./") + # HACK: Turn Path object back into string, because not everything + # that depends on this function can handle Path objects (yet) + return str(fname) # no file found msg = f"File cannot be found: {filename}" @@ -604,7 +551,7 @@ def airmass2zendist(airmass): zenith distance in degrees """ - return np.rad2deg(np.arccos(1/airmass)) + return np.rad2deg(np.arccos(1 / airmass)) def convert_table_comments_to_dict(tbl): @@ -614,13 +561,13 @@ def convert_table_comments_to_dict(tbl): try: comments_str = "\n".join(tbl.meta["comments"]) comments_dict = yaml.full_load(comments_str) - except: + except yaml.error.YAMLError: logging.warning("Couldn't convert
.meta['comments'] to dict") comments_dict = tbl.meta["comments"] elif "COMMENT" in tbl.meta: try: comments_dict = yaml.full_load("\n".join(tbl.meta["COMMENT"])) - except: + except yaml.error.YAMLError: logging.warning("Couldn't convert
.meta['COMMENT'] to dict") comments_dict = tbl.meta["COMMENT"] else: @@ -656,7 +603,7 @@ def real_colname(name, colnames, silent=True): if len(real_name) == 0: real_name = None if not silent: - logging.warning("None of {} were found in {}".format(names, colnames)) + logging.warning("None of %s were found in %s", names, colnames) else: real_name = real_name[0] @@ -681,8 +628,10 @@ def insert_into_ordereddict(dic, new_entry, pos): def empty_type(x): - type_dict = {int: 0, float: 0., bool: False, str: " ", - list: [], tuple: (), dict: {}} + type_dict = { + int: 0, float: 0., bool: False, str: " ", + list: [], tuple: (), dict: {} + } if "=1.16", - "scipy>=1.0.0", - "astropy>=2.0", - "matplotlib>=1.5", - - "docutils", - "requests>=2.20", - "beautifulsoup4>=4.4", - "lxml", - "pyyaml>5.1", - "pysftp", - - "synphot>=0.1.3", - "skycalc_ipy>=0.1.3", - "anisocado", - ], - classifiers=["Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - "Intended Audience :: Science/Research", - "Topic :: Scientific/Engineering :: Astronomy", ] - ) - - -if __name__ == '__main__': - setup_package()