diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index f243aed0e..d3045885d 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,10 +17,10 @@ jobs: uses: actions/checkout@v4 # Setup Python - - name: Set up Python 3.10 + - name: Set up Python 3.11 uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.11' # install package - name: Install dependencies diff --git a/MANIFEST.in b/MANIFEST.in index 736577b70..cf1c2ac79 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -5,11 +5,13 @@ include src/exojax/data/atom/HITRAN_molparam.txt include src/exojax/data/clouds/drag_force.txt include src/exojax/data/clouds/ammonia_liquid_density.csv include src/exojax/data/abundance/AAG2021.dat +include src/exojax/data/abundance/xspec_abundance.txt include src/exojax/data/atom/NIST_Atomic_Ionization_Energies.txt include src/exojax/data/testdata/spectrum.txt include src/exojax/data/testdata/spectrum_ch4.txt include src/exojax/data/testdata/spectrum_ch4_new.txt +include src/exojax/data/testdata/spectrum_ch4_trans.txt include src/exojax/data/testdata/spectrum_co.txt include src/exojax/data/testdata/moldb_vald.pickle include src/exojax/data/testdata/lpf_test_ref.txt diff --git a/README.md b/README.md index ee236da81..5e19698ab 100644 --- a/README.md +++ b/README.md @@ -7,20 +7,21 @@ Differentiable spectral modelling of exoplanets/brown dwarfs/M dwarfs using JAX! Read [the docs](http://secondearths.sakura.ne.jp/exojax/develop) 🐕. -In short, ExoJAX allows you to do gradient based optimizations and HMC-NUTS samplings using the latest database. +In short, ExoJAX allows you to do gradient based optimizations, HMC-NUTS, and SVI using the latest database. ExoJAX is at least compatible with -- PPLs: [NumPyro](https://github.com/pyro-ppl/numpyro), [blackjax](https://github.com/blackjax-devs/blackjax) -- Optimizers: [JAXopt](https://github.com/google/jaxopt), [optax](https://github.com/google-deepmind/optax), [bayeux](https://github.com/jax-ml/bayeux) +- PPLs: [NumPyro](https://github.com/pyro-ppl/numpyro), [blackjax](https://github.com/blackjax-devs/blackjax), [bayeux](https://github.com/jax-ml/bayeux) +- Optimizers: [JAXopt](https://github.com/google/jaxopt), [optax](https://github.com/google-deepmind/optax) - +
ExoJAX Classes - Databases: *db (mdb: molecular, adb: atomic, cdb:continuum, pdb: particulates) -- Opacity Calculators: opa (i.e. Voigt profile) +- Opacity Calculators: opa (Voigt profile, CIA, Mie, Rayleigh scattering etc) - Atmospheric Radiative Transfer: art (emission w, w/o scattering, refelction, transmission) +- Spectral Operator: sop (planet rotation, instrumental boradening) - Atompsheric Microphysics: amp (clouds etc)
@@ -29,87 +30,19 @@ ExoJAX is at least compatible with See [this page](http://secondearths.sakura.ne.jp/exojax/develop/tutorials/get_started.html) for the first step! -## Functions +## Real Examples (external) -
Voigt Profile :heavy_check_mark: - -```python3 -from exojax.spec import voigt -nu=numpy.linspace(-10,10,100) -voigt(nu,1.0,2.0) #sigma_D=1.0, gamma_L=2.0 -``` - -
- -
Cross Section using HITRAN/HITEMP/ExoMol :heavy_check_mark: - -```python -from exojax.utils.grids import wavenumber_grid -from exojax.spec.api import MdbExomol -from exojax.spec.opacalc import OpaPremodit -from jax import config -config.update("jax_enable_x64", True) - -nu_grid,wav,res=wavenumber_grid(1900.0,2300.0,200000,xsmode="premodit",unit="cm-1",) -mdb = MdbExomol(".database/CO/12C-16O/Li2015",nu_grid) -opa = OpaPremodit(mdb,nu_grid,auto_trange=[900.0,1100.0]) -xsv = opa.xsvector(1000.0, 1.0) # cross section for 1000K, 1 bar -``` - - - -
- - - -
Do you just want to plot the line strength at T=1000K? - -```python -mdb.change_reference_temperature(1000.) # at 1000K -plt.plot(mdb.nu_lines,mdb.line_strength_ref,".") -``` - -
- -
Emission Spectrum :heavy_check_mark: - -```python -art = ArtEmisPure(nu_grid=nu_grid, pressure_btm=1.e2, pressure_top=1.e-8, nlayer=100) -F = art.run(dtau, Tarr) -``` - - - -
- -
Transmission Spectrum :heavy_check_mark:
-
Reflection Spectrum :heavy_check_mark:
- -## Installation - -``` -pip install exojax -``` - -or - -``` -python setup.py install -``` - -
Note on installation w/ GPU support - -:books: You need to install CUDA, JAX w/ NVIDIA GPU support. - -Visit [here](https://github.com/google/jax) for the installation of GPU supported JAX. - -
+- :star: [exojaxample_WASP39b](https://github.com/sh-tada/exojaxample_WASP39b) : An example of HMC-NUTS for actual hot Saturn (JWST/ERS, NIRSPEC/G395H) +- :star: [exojaxample_jupiter](https://github.com/HajimeKawahara/exojaxample_jupiter) : An example of HMC-NUTS for actual Jupiter reflection spectrum ## References [![paper](https://img.shields.io/badge/paper_I-ApJS_258_31_(2022)-orange)](https://iopscience.iop.org/article/10.3847/1538-4365/ac3b4d) - Paper I: Kawahara, Kawashima, Masuda, Crossfield, Pannier, van den Bekerom, [ApJS 258, 31 (2022)](https://iopscience.iop.org/article/10.3847/1538-4365/ac3b4d) +- Paper II: in prep + + ## License diff --git a/documents/conf.py b/documents/conf.py index a1e6b7087..8f1f1417a 100644 --- a/documents/conf.py +++ b/documents/conf.py @@ -13,17 +13,18 @@ import sphinx_rtd_theme import os import sys -sys.path.insert(0, os.path.abspath('~/exojax')) + +sys.path.insert(0, os.path.abspath("~/exojax")) # -- Project information ----------------------------------------------------- -project = 'ExoJAX' -copyright = '2020-2024, ExoJAX contributors' -author = 'ExoJAX contributors' +project = "ExoJAX" +copyright = "2020-2024, ExoJAX contributors" +author = "ExoJAX contributors" # The full version, including alpha/beta/rc tags -release = '1.5.0' +release = "1.6.0" # -- General configuration --------------------------------------------------- @@ -31,16 +32,19 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinxemoji.sphinxemoji', - ] +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinxemoji.sphinxemoji", +] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- @@ -48,14 +52,14 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] -html_logo = '_static/logo.png' +html_static_path = ["_static"] +html_logo = "_static/logo.png" -#html_theme_options = { +# html_theme_options = { # 'style_nav_header_background': '#333', -#} -html_css_files = ['header.css'] +# } +html_css_files = ["header.css"] diff --git a/documents/developers/doc.rst b/documents/developers/doc.rst index e8c856c24..4c98ea5dd 100644 --- a/documents/developers/doc.rst +++ b/documents/developers/doc.rst @@ -23,9 +23,11 @@ Generates the up-to-date documents of tutorials The following commands automatically run the tutorial notebooks and generate rst: +``documents/tutorials/`` + + .. code:: sh - cd documents/tutorials/ python jupyter2rst.py exe If you just want to generate rst without executing notebooks, try this: diff --git a/documents/developers/pytest.rst b/documents/developers/pytest.rst index 0a0dd8108..b6ee2b877 100644 --- a/documents/developers/pytest.rst +++ b/documents/developers/pytest.rst @@ -1,35 +1,31 @@ Test codes for developers ============================== -ExoJAX has many test codes in the ``tests`` directory. The ``test`` directory contains several types of the collection of ``pytest`` code. +ExoJAX has many test codes in the ``tests`` directory. ExoJAX has three test categories. + +Unit Tests +----------------- +``tests/unittests``: Tests in this category are automatically executed by GitHub Actions +when a pull request is made to the develop or master branch. +Therefore, items that need to be downloaded from external sites or take more than 10 seconds to run should not be included in this category. +Tests that take a long time but are considered unit tests should be placed in ``integrations/unittests_long``. + +Integration Tests +----------------- +``tests/integration``: This category is for testing the behavior of multiple integrated functions. Tests that have a long execution time, +involve external downloads, or depend on the status of external servers should be included here if they are to be part of automated testing. +ntegration tests also include comparisons with other codes or outputs, ensuring higher reliability. +However, since changes in the counterpart code can occur, the tests do not always succeed. + +- ``tests/integration/comparison/transmission`` : An example of a transmission comparison with calculations done by Y. Kawashima using a different method. +- ``tests/integration/comparison/twostream``: A comparison code with the radiative spectrum calculations performed by petitRADTRANS. +- ``tests/integration/comparison/clouds``: A comparison with cloud models from VIRGA. + +End-to-end Tests +----------------- +``tests/endtoend``: In ExoJAX, codes like HMC-NUTS that require long execution times are often used in the final application. +Therefore, such tests belong to the end-to-end category. However, due to the long execution times, these tests are not run frequently. -- ``tests/unittests``: the collection of the unit tests. The GitHub action runs the test code in this directory. -- ``tests/integration``: the collection of the test codes that need longer time to run than the code in ``unittest``. - -test/unittests ---------------------- - -We recommend to write the unit test code in ``tests/unittests`` directory before pull-request and to perform the unit tests before your submission of the pull-request: - -.. code:: sh - - cd exojax/test/unittests - pytest - - -test/integration/unittest_long ----------------------------------- - -In essence, these are the unit tests that need longer time than the code in ``unittest``, sometimes including downloading the data. - -test/integration/comparison ---------------------------- - -The code for the comparison with external data, packages, etc - -- ``transmission/comparison_with_kawashima_transmission.py``: comparison with Yui Kawashima's computation of the transmission spectrum -- ``twostream/comparison_petitRADTRANS_*.py``: comparison with pRT -- ``nonair/nonair_co_hitran_comp.py``: non-air broadening comparison with ``radis`` diff --git a/documents/index.rst b/documents/index.rst index b35c235c1..412baa2a4 100644 --- a/documents/index.rst +++ b/documents/index.rst @@ -6,7 +6,9 @@ ExoJAX ================================== -Version 1.5 (:doc:`userguide/history`) +Version 1.6 (:doc:`userguide/history`) + +Note: Paper II will be under peer review. We plan to release version 2.0 at the time of acceptance of the paper II. `ExoJAX `_ provides an auto-differentiable high-resolution spectrum model for exoplanets/brown dwarfs using `JAX `_. ExoJAX enables a fully Bayesian inference of the high-dispersion data to fit the line-by-line spectral computation to the observed spectrum, @@ -19,6 +21,11 @@ So, the notable features of ExoJAX are summarized as - **Easy to use the latest molecular/atomic data in** :doc:`userguide/api`, **and** :doc:`userguide/atomll` - **A transparent open-source project; anyone who wants to participate can join the development!** +.. admonition:: For a more geek-oriented explanation + + ExoJAX is a spectral model based on the `Differentiable Programming (DP) `_ paradigm! + ExoJAX aims to provide building blocks for retrieval code, much like Minecraft |:bricks:|. + |:green_circle:| If you have an error and/or want to know the up-to-date info, visit `ExoJAX wiki `_. Or use `the discussions form `_ on github or directly raise `issues `_. @@ -49,6 +56,13 @@ Contents exojax/exojax.rst +ExoJAX example (exojaxample) +--------------------------------- + +- |:ringed_planet:| `exojaxample_WASP39b `_ : An example of HMC-NUTS for actual hot Saturn (JWST/ERS, NIRSPEC/G395H) + +- |:ringed_planet:| `exojaxample_jupiter `_ : An example of HMC-NUTS for actual Jupiter reflection spectrum + References --------------------- @@ -57,12 +71,12 @@ References `ApJS 258, 31 (2022) `_ (Paper I) +- Kawahara et al., 2024, in prep. (Paper II) - License & Attribution --------------------- -Copyright 2021-2023, Contributors +Copyright 2021-2024, Contributors - `Hajime Kawahara `_ (@HajimeKawahara, maintainer) - `Yui Kawashima `_ (@ykawashima, co-maintainer) @@ -71,11 +85,13 @@ Copyright 2021-2023, Contributors - Dirk van den Bekerom (@dcmvdbekerom) - Daniel Kitzmann (@daniel-kitzmann) - Brett Morris (@bmorris3) -- Erwan Pannier (@erwanp) and `RADIS `_ community +- Erwan Pannier (@erwanp) and Nicolas Minesi (@minouHub) from `RADIS `_ community - Stevanus Nugroho (@astrostevanus) - Tako Ishikawa (@chonma0ctopus) - Yui Kasagi (@YuiKasagi) - Shotaro Tada (@sh-tada) +- Ko Hosokawa (@KoHosokawa) +- Hibiki Yama ExoJAX is free software made available under the MIT License. See the ``LICENSE``. diff --git a/documents/tutorials.rst b/documents/tutorials.rst index fe35ac4a0..aeb050f82 100644 --- a/documents/tutorials.rst +++ b/documents/tutorials.rst @@ -32,14 +32,19 @@ Transmission Spectra tutorials/Transmission_beta.rst +|:ringed_planet:| An example of HMC-NUTS for actual hot Saturn (JWST/ERS, NIRSPEC/G395H) can be found `here `_ . + + Reflection Spectrum ------------------------------------ .. toctree:: :maxdepth: 1 - tutorials/Jupiter_cloud_model_using_amp.rst - tutorials/Jupiter_Hires_Modeling.rst + tutorials/jupiters/Jupiter_cloud_model_using_amp.rst + tutorials/jupiters/Jupiter_Hires_Modeling.rst + +|:ringed_planet:| An example of HMC-NUTS for actual Jupiter reflection spectrum can be found `here `_ . Molecular/Atomic/Continuum Databases --------------------------------------- @@ -51,6 +56,7 @@ Molecular/Atomic/Continuum Databases tutorials/branch.rst tutorials/Fortrat.rst tutorials/CIA_opacity.rst + tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.rst Multi Molecule/Segments Mdb and Opa Handler @@ -118,5 +124,7 @@ Others tutorials/hjerting.rst tutorials/pure_absorption_rt.rst tutorials/voigt_function.rst + tutorials/Cross_Section_using_Discrete_Integral_Transform.rst + tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.rst diff --git a/documents/tutorials/Ackerman_and_Marley_cloud_model.ipynb b/documents/tutorials/Ackerman_and_Marley_cloud_model.ipynb index e583764ed..65ba9a2ce 100644 --- a/documents/tutorials/Ackerman_and_Marley_cloud_model.ipynb +++ b/documents/tutorials/Ackerman_and_Marley_cloud_model.ipynb @@ -14,7 +14,7 @@ "Here, we try to compute a cloud opacity using Ackerman and Marley Model.\n", "Although `atmphys.AmpAmcloud` can easily compute the parameters of the AM model,\n", "we here try to run the methods one by one.\n", - "We consider enstatite (MgSiO3) and Fe clouds. \n" + "We consider enstatite (MgSiO3). \n" ] }, { @@ -53,22 +53,32 @@ "shell.execute_reply": "2022-10-30T01:22:40.210931Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-02 10:27:38.117849: W external/xla/xla/service/gpu/nvptx_compiler.cc:765] The NVIDIA driver's CUDA version is 12.2 which is older than the ptxas CUDA version (12.6.20). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n" + ] + } + ], "source": [ "from exojax.utils.constants import kB, m_u\n", "from exojax.atm.atmprof import pressure_layer_logspace\n", "from exojax.utils.astrofunc import gravity_jupiter\n", "\n", - "Parr, dParr, k = pressure_layer_logspace(log_pressure_top=-4., log_pressure_btm=6.0, nlayer=100)\n", + "Parr, dParr, k = pressure_layer_logspace(\n", + " log_pressure_top=-4.0, log_pressure_btm=6.0, nlayer=100\n", + ")\n", "alpha = 0.097\n", - "T0 = 1200.\n", - "Tarr = T0 * (Parr)**alpha\n", + "T0 = 1200.0\n", + "Tarr = T0 * (Parr) ** alpha\n", "\n", "mu = 2.3 # mean molecular weight\n", "R = kB / (mu * m_u)\n", "rho = Parr / (R * Tarr)\n", "\n", - "gravity = gravity_jupiter(1.0, 1.0)\n" + "gravity = gravity_jupiter(1.0, 1.0)" ] }, { @@ -89,7 +99,16 @@ "shell.execute_reply": "2022-10-30T01:22:40.215660Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database for solar abundance = AAG21\n", + "Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, arXiv:2105.01661\n" + ] + } + ], "source": [ "from exojax.utils.zsol import nsol\n", "\n", @@ -118,11 +137,9 @@ }, "outputs": [], "source": [ - "from exojax.atm.psat import psat_enstatite_AM01, psat_Fe_AM01, _psat_Fe_solid\n", + "from exojax.atm.psat import psat_enstatite_AM01\n", "\n", - "P_enstatite = psat_enstatite_AM01(Tarr)\n", - "P_fe_sol = psat_Fe_AM01(Tarr)\n", - "#P_fe_sol = _psat_Fe_solid(Tarr) #considers only solid Fe\n" + "P_enstatite = psat_enstatite_AM01(Tarr)\n" ] }, { @@ -147,8 +164,7 @@ "source": [ "from exojax.atm.amclouds import compute_cloud_base_pressure\n", "\n", - "Pbase_enstatite = compute_cloud_base_pressure(Parr, P_enstatite, MolMR_enstatite)\n", - "Pbase_Fe_sol = compute_cloud_base_pressure(Parr, P_fe_sol, MolMR_Fe)\n" + "Pbase_enstatite = compute_cloud_base_pressure(Parr, P_enstatite, MolMR_enstatite)\n" ] }, { @@ -172,7 +188,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -190,10 +206,6 @@ "plt.axhline(Pbase_enstatite, color=\"gray\", alpha=0.7, ls=\"dotted\")\n", "plt.text(500, Pbase_enstatite * 0.8, \"cloud base (enstatite)\", color=\"gray\")\n", "\n", - "plt.plot(Tarr, P_fe_sol / MolMR_Fe, label=\"$P_{sat}/\\\\xi$ (Fe)\", color=\"black\")\n", - "plt.axhline(Pbase_Fe_sol, color=\"black\", alpha=0.7, ls=\"dotted\")\n", - "plt.text(500, Pbase_Fe_sol * 0.8, \"cloud base (Fe)\", color=\"black\")\n", - "\n", "plt.yscale(\"log\")\n", "plt.ylim(1.e-4, 1.e5)\n", "plt.xlim(0, 3000)\n", @@ -229,7 +241,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "['H2O', 'CO2', 'O3', 'N2O', 'CO', 'CH4', 'O2', 'NO', 'SO2', 'NO2', 'NH3', 'HNO3', 'OH', 'HF', 'HCl', 'HBr', 'HI', 'ClO', 'OCS', 'H2CO', 'HOCl', 'N2', 'HCN', 'CH3Cl', 'H2O2', 'C2H2', 'C2H6', 'PH3', 'COF2', 'SF6', 'H2S', 'HCOOH', 'HO2', 'O', 'ClONO2', 'NO+', 'HOBr', 'C2H4', 'CH3OH', 'CH3Br', 'CH3CN', 'CF4', 'C4H2', 'HC3N', 'H2', 'CS', 'SO3', 'C2N2', 'COCl2', 'SO', 'CH3F', 'GeH4', 'CS2', 'CH3I', 'NF3']\n", "['H2O', 'CO2', 'O3', 'N2O', 'CO', 'CH4', 'O2', 'NO', 'SO2', 'NO2', 'NH3', 'HNO3', 'OH', 'HF', 'HCl', 'HBr', 'HI', 'ClO', 'OCS', 'H2CO', 'HOCl', 'N2', 'HCN', 'CH3Cl', 'H2O2', 'C2H2', 'C2H6', 'PH3', 'COF2', 'SF6', 'H2S', 'HCOOH', 'HO2', 'O', 'ClONO2', 'NO+', 'HOBr', 'C2H4', 'CH3OH', 'CH3Br', 'CH3CN', 'CF4', 'C4H2', 'HC3N', 'H2', 'CS', 'SO3', 'C2N2', 'COCl2', 'SO', 'CH3F', 'GeH4', 'CS2', 'CH3I', 'NF3']\n" ] }, @@ -238,8 +249,6 @@ "output_type": "stream", "text": [ "/home/kawahara/exojax/src/exojax/spec/molinfo.py:64: UserWarning: db_HIT is set as True, but the molecular name 'MgSiO3' does not exist in the HITRAN database. So set db_HIT as False. For reference, all the available molecules in the HITRAN database are as follows:\n", - " warnings.warn(warn_msg, UserWarning)\n", - "/home/kawahara/exojax/src/exojax/spec/molinfo.py:64: UserWarning: db_HIT is set as True, but the molecular name 'Fe' does not exist in the HITRAN database. So set db_HIT as False. For reference, all the available molecules in the HITRAN database are as follows:\n", " warnings.warn(warn_msg, UserWarning)\n" ] } @@ -247,15 +256,12 @@ "source": [ "from exojax.atm.amclouds import mixing_ratio_cloud_profile\n", "from exojax.spec.molinfo import molmass_isotope\n", - "from exojax.atm.mixratio import vmr2mmr\n", + "from exojax.atm.atmconvert import vmr_to_mmr\n", "fsed = 3.\n", "muc_enstatite = molmass_isotope(\"MgSiO3\")\n", - "MMRbase_enstatite = vmr2mmr(MolMR_enstatite, muc_enstatite,mu)\n", + "MMRbase_enstatite = vmr_to_mmr(MolMR_enstatite, muc_enstatite,mu)\n", "MMRc_enstatite = mixing_ratio_cloud_profile(Parr, Pbase_enstatite, fsed, MMRbase_enstatite)\n", - "\n", - "muc_Fe = molmass_isotope(\"Fe\")\n", - "MMRbase_Fe = vmr2mmr(MolMR_Fe,muc_Fe,mu)\n", - "MMRc_Fe = mixing_ratio_cloud_profile(Parr, Pbase_Fe_sol, fsed, MMRbase_Fe)" + "\n" ] }, { @@ -274,12 +280,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "114.975746 77426.445\n" + "104.674515\n" ] } ], "source": [ - "print(Pbase_enstatite, Pbase_Fe_sol)" + "print(Pbase_enstatite)" ] }, { @@ -303,7 +309,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -316,7 +322,6 @@ "plt.figure()\n", "plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3])\n", "plt.plot(MMRc_enstatite, Parr, color=\"gray\", label=\"MMR (enstatite)\")\n", - "plt.plot(MMRc_Fe, Parr, color=\"black\", ls=\"dashed\", label=\"MMR (Fe)\")\n", "\n", "\n", "\n", @@ -372,7 +377,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -445,8 +450,7 @@ "source": [ "from exojax.atm.condensate import condensate_substance_density, name2formula\n", "\n", - "deltac_enstatite = condensate_substance_density[name2formula[\"enstatite\"]]\n", - "deltac_Fe = condensate_substance_density[\"Fe\"]\n" + "deltac_enstatite = condensate_substance_density[name2formula[\"enstatite\"]]\n" ] }, { @@ -564,7 +568,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -649,7 +653,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -658,7 +662,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -739,7 +743,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/spec/dtau_mmwl.py:14: FutureWarning: dtau_mmwl might be removed in future.\n", + "/home/kawahara/exojax/src/exojax/spec/dtau_mmwl.py:13: FutureWarning: dtau_mmwl might be removed in future.\n", " warnings.warn(\"dtau_mmwl might be removed in future.\", FutureWarning)\n" ] } @@ -747,8 +751,7 @@ "source": [ "from exojax.spec.layeropacity import layer_optical_depth_cloudgeo\n", "\n", - "dtau_enstatite = layer_optical_depth_cloudgeo(Parr, deltac_enstatite, MMRc_enstatite, rg, sigmag, g)\n", - "dtau_Fe = layer_optical_depth_cloudgeo(Parr, deltac_Fe, MMRc_Fe, rg, sigmag, g)\n" + "dtau_enstatite = layer_optical_depth_cloudgeo(Parr, deltac_enstatite, MMRc_enstatite, rg, sigmag, g)\n" ] }, { @@ -767,8 +770,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/utils/grids.py:142: UserWarning: Resolution may be too small. R=907.6757560767178\n", - " warnings.warn('Resolution may be too small. R=' + str(resolution),\n" + "/home/kawahara/exojax/src/exojax/utils/grids.py:144: UserWarning: Resolution may be too small. R=907.6757560767178\n", + " warnings.warn(\"Resolution may be too small. R=\" + str(resolution), UserWarning)\n" ] }, { @@ -776,7 +779,7 @@ "output_type": "stream", "text": [ "xsmode = lpf\n", - "xsmode assumes ESLOG in wavenumber space: mode=lpf\n", + "xsmode assumes ESLOG in wavenumber space: xsmode=lpf\n", "======================================================================\n", "The wavenumber grid should be in ascending order.\n", "The users can specify the order of the wavelength grid by themselves.\n", @@ -788,8 +791,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 63/63 [00:17<00:00, 3.55it/s]\n", - "100%|██████████| 63/63 [00:20<00:00, 3.09it/s]\n" + "100%|██████████| 63/63 [00:25<00:00, 2.45it/s]\n" ] } ], @@ -798,8 +800,8 @@ "from exojax.spec.unitconvert import wav2nu\n", "\n", "N = 1000\n", - "wavelength_start = 5000.0 #AA\n", - "wavelength_end = 15000.0 #AA\n", + "wavelength_start = 5000.0 # AA\n", + "wavelength_end = 15000.0 # AA\n", "\n", "\n", "margin = 10 # cm-1\n", @@ -808,26 +810,22 @@ "nugrid, wav, res = wavenumber_grid(nus_start, nus_end, N, xsmode=\"lpf\", unit=\"cm-1\")\n", "\n", "\n", - "\n", "from exojax.spec.opacont import OpaMie\n", + "\n", "opa_enstatite = OpaMie(pdb_enstatite, nugrid)\n", - "opa_Fe = OpaMie(pdb_Fe, nugrid)\n", "\n", - "rg=1.e-4 #0.1um\n", - "#beta0, betasct, g = opa.mieparams_vector(rg,sigmag) # if you've already generated miegrid \n", - "beta0, betasct, g = opa_enstatite.mieparams_vector_direct_from_pymiescatt(rg,sigmag) # uses direct computation of Mie params using PyMieScatt\n", - "beta0_Fe, betasct_Fe, g_Fe = opa_Fe.mieparams_vector_direct_from_pymiescatt(rg,sigmag) # uses direct computation of Mie params using PyMieScatt\n", + "rg = 1.0e-4 # 0.1um\n", + "# beta0, betasct, g = opa.mieparams_vector(rg,sigmag) # if you've already generated miegrid\n", + "beta0, betasct, g = opa_enstatite.mieparams_vector_direct_from_pymiescatt(\n", + " rg, sigmag\n", + ") # uses direct computation of Mie params using PyMieScatt\n", "\n", "\n", "from exojax.spec.layeropacity import layer_optical_depth_clouds_lognormal\n", "\n", "dtau_enstatite_mie = layer_optical_depth_clouds_lognormal(\n", " dParr, beta0, deltac_enstatite, MMRc_enstatite, rg, sigmag, gravity\n", - ")\n", - "dtau_Fe_mie = layer_optical_depth_clouds_lognormal(\n", - " dParr, beta0_Fe, deltac_Fe, MMRc_Fe, rg, sigmag, gravity\n", - ")\n", - "\n" + ")" ] }, { @@ -844,9 +842,9 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGxCAYAAACDV6ltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAABVsUlEQVR4nO3de1hUdf4H8PeZYRjuNxEQRdBUvKComKR20aTQzLxU6xYVWmnuamtRbrmVbtZmbmXu/tay2pRtt9KuZmlWkuaNNFG8pOIlBG8gqNxhrt/fHwdmGGAQcPAg5/16nnlm5pzPnPnMYeC8OVdJCCFAREREpGIapRsgIiIiUhoDEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqZ6b0g20dVarFWfPnoWvry8kSVK6HSIiImoCIQRKS0sRHh4Ojeby638YiC7j7NmziIiIULoNIiIiaoFTp06hS5cul61jILoMX19fAPIM9fPzU7gbIiIiaoqSkhJERETYluOXw0B0GTWbyfz8/BiIiIiIrjFN3d2FO1UTERGR6jEQERERkeoxEBEREZHqcR8iIqIrZLFYYDKZlG6DSFV0Oh20Wq3LpsdARETUQkII5OXloaioSOlWiFQpICAAYWFhLjlPIAMREVEL1YShkJAQeHl58eStRFeJEAIVFRU4f/48AKBTp05XPE0GIiKiFrBYLLYw1KFDB6XbIVIdT09PAMD58+cREhJyxZvPuFM1EVEL1Owz5OXlpXAnROpV8/vnin34GIiIiK4AN5MRKceVv38MRERERKR6DERERNQuTZ06FRMnTlS6DcX89a9/xcCBAxV7f0mSsGbNGsXev7kYiIiI6JoRFRWFpUuXNqn2H//4B1JTU1u1n7bs6aefRlpaWqu/j7Pgde7cOYwdO7bV399VeJSZAiwlJTAcOwYA0IWFQde5s8IdERG1HxaLBZIkwd/fX+lWWqSmf43mytZZ+Pj4wMfHx0VdNV9YWJhi790SXEOkAEtJCcp/+hHlP6XBeDJb6XaISEVKS0uRlJQEb29vdOrUCW+++SZGjhyJJ554wlZjMBjw9NNPo3PnzvD29kZ8fDw2b97sMJ3PP/8c/fr1g16vR1RUFN544w2H8VFRUXj55Zfx0EMPwcfHB5GRkVi7di0KCgowYcIE+Pj4YMCAAdi9e7fD67Zt24abbroJnp6eiIiIwJ/+9CeUl5cDAEaOHImcnBw8+eSTkCTJtkNtamoqAgICsHbtWvTt2xd6vR65ubn1NplZrVb8/e9/R48ePaDX69G1a1f87W9/czqvNmzYgBtvvBEBAQHo0KED7rzzTpw4ccI2/uTJk5AkCatWrcLw4cPh4eGBmJgY/PTTT7aazZs3Q5IkrFu3DgMGDICHhwduuOEGHDx40FbjrP9Lly7hoYceQmBgILy8vDB27Fgcq/5nuqCgAGFhYXjllVds09mxYwfc3d1ta4XqrrmpmR+vvPIKQkNDERAQgIULF8JsNmPu3LkICgpCly5dsHLlSof58Mwzz6BXr17w8vJC9+7d8cILL9iO6kpNTcWLL76Iffv22X4mNWvl6m4yO3DgAG699VZ4enqiQ4cOmDFjBsrKyur19/rrr6NTp07o0KEDZs2adfXOAi+oUcXFxQKAKC4udtk0DadOifO/7ynOT+4sytZ97LLpEtHVU1lZKQ4dOiQqKysdRxjKnN+MzamtaFptMz366KMiMjJSbNy4URw4cEBMmjRJ+Pr6ijlz5jjUDB8+XGzZskUcP35cvPbaa0Kv14ujR48KIYTYvXu30Gg0YuHChSIrK0usXLlSeHp6ipUrV9qmERkZKYKCgsTy5cvF0aNHxR/+8Afh5+cnxowZIz755BORlZUlJk6cKPr06SOsVqsQQojjx48Lb29v8eabb4qjR4+K7du3i0GDBompU6cKIYS4cOGC6NKli1i4cKE4d+6cOHfunBBCiJUrVwqdTieGDx8utm/fLo4cOSLKy8tFcnKymDBhgq2nP//5zyIwMFCkpqaK48ePi61bt4r33nvP6bz67LPPxOeffy6OHTsm9u7dK8aPHy/69+8vLBaLEEKI7OxsAUB06dJFfPbZZ+LQoUPi0UcfFb6+vqKwsFAIIcSmTZsEANGnTx/x/fffi/3794s777xTREVFCaPR2Gj/d911l+jTp4/YsmWLyMzMFImJiaJHjx62161bt07odDrxyy+/iJKSEtG9e3fx5JNP2vpfsGCBiI2NtT1PTk4Wvr6+YtasWeLIkSPi/fffFwBEYmKi+Nvf/iaOHj0qXnrpJaHT6cSpU6dsr3vppZfE9u3bRXZ2tli7dq0IDQ0VixcvFkIIUVFRIZ566inRr18/28+kokL+7gIQX375pRBCiLKyMtGpUycxefJkceDAAZGWlia6desmkpOTHfrz8/MTM2fOFIcPHxZff/218PLyEu+++67Tn5HT30PR/OU3A9FlMBARUUOc/iFe4Of89r97HGtfDnNeu+IOx9rF3Rqua4aSkhKh0+nEp59+ahtWVFQkvLy8bIEoJydHaLVacebMGYfXjh49WsybN08IIcT9998vbrvtNofxc+fOFX379rU9j4yMFA888IDt+blz5wQA8cILL9iGpaenCwC2YPPII4+IGTNmOEx369atQqPR2OZzZGSkePPNNx1qVq5cKQCIzMxMh+G1A1FJSYnQ6/WNBqDLKSgoEADEgQMHhBD2QPTqq6/aakwmk+jSpYstMNQEolWrVtlqLly4IDw9PcXq1aud9n/06FEBQGzfvt02rLCwUHh6eopPPvnENuyPf/yj6NWrl7j//vtF//79RVVVlW1cQ4EoMjLSFuiEECI6OlrcdNNNtudms1l4e3uLjz92vmx67bXXRFxcnNP3qVE7EL377rsiMDBQlJXZQ/y6deuERqMReXl5Dv2ZzWZbzb333iumTJnitBdXBiJuMiMiUonffvsNJpMJQ4cOtQ3z9/dHdHS07fmBAwdgsVjQq1cv2z4oPj4++Omnn2ybiw4fPowRI0Y4THvEiBE4duwYLBaLbdiAAQNsj0NDQwEA/fv3rzes5vIL+/btQ2pqqsP7JiYmwmq1Iju78d0L3N3dHd6vrsOHD8NgMGD06NGNTqe2Y8eO4b777kP37t3h5+eHqKgoAEBubq5D3bBhw2yP3dzcMGTIEBw+fNhpTVBQEKKjox1q6vZ/+PBhuLm5IT4+3jasQ4cO9V73+uuvw2w249NPP8WHH34IvV7f6Gfq16+fw75JoaGhDj8TrVaLDh062H4mALB69WqMGDECYWFh8PHxwfPPP19vHlzO4cOHERsbC29vb9uwESNGwGq1Iisry6G/2mec7tSpk0MvrYk7VRMRudJfzjofJ9W5tMDc443U1vl/9YkDLe+pGcrKyqDVapGRkVHvUgjN3UFXp9PZHtfs79PQMKvVanvvxx57DH/605/qTatr166Nvpenp2ejJ+mrucxDc4wfPx6RkZF47733EB4eDqvVipiYGBiNxmZP63Iu178zJ06cwNmzZ2G1WnHy5EmHcNOQ2vMfkH8GDQ2r+Zmkp6cjKSkJL774IhITE+Hv749Vq1bV22fMVRrrpbUxEBERuZK79+VrWrvWie7du0On0+GXX36xBYzi4mIcPXoUN998MwBg0KBBsFgsOH/+PG666aYGp9OnTx9s377dYdj27dvRq1evK7qe1ODBg3Ho0CH06NHDaY27u7vDWqim6tmzJzw9PZGWloZHH330svUXLlxAVlYW3nvvPdt82LZtW4O1P//8s23+mc1mZGRkYPbs2fVqaub5pUuXcPToUfTp08fp+/fp0wdmsxk7d+7E8OHDHXrq27cvAMBoNOKBBx7AlClTEB0djUcffRQHDhxASEjIZT9fU+3YsQORkZF47rnnbMNycnIcapryM+nTpw9SU1NRXl5uW0u0fft2aDQahzWUSuImMyIilfD19UVycjLmzp2LTZs24ddff8UjjzwCjUZjWzvRq1cvJCUl4aGHHsIXX3yB7Oxs7Nq1C4sWLcK6desAAE899RTS0tLw0ksv4ejRo/jPf/6Df/3rX3j66aevqL9nnnkGO3bswOzZs5GZmYljx47hq6++cggXUVFR2LJlC86cOYPCwsImT9vDwwPPPPMM/vznP+ODDz7AiRMn8PPPP+P9999vsD4wMBAdOnTAu+++i+PHj+PHH39ESkpKg7XLli3Dl19+iSNHjmDWrFm4dOkSHn74YYeahQsXIi0tDQcPHsTUqVMRHBzc6Ekje/bsiQkTJmD69OnYtm0b9u3bhwceeACdO3fGhAkTAADPPfcciouL8c9//tN2JFjd971SPXv2RG5uLlatWoUTJ07gn//8J7788kuHmqioKGRnZyMzMxOFhYUwGAz1ppOUlAQPDw8kJyfj4MGD2LRpEx5//HE8+OCDtk2nSmMgIiJSkSVLlmDYsGG48847kZCQgBEjRqBPnz7w8PCw1axcuRIPPfQQnnrqKURHR2PixIkOa5UGDx6MTz75BKtWrUJMTAzmz5+PhQsXYurUqVfU24ABA/DTTz/h6NGjuOmmmzBo0CDMnz8f4eHhtpqFCxfi5MmTuO6669CxY8dmTf+FF17AU089hfnz56NPnz6YMmWK0/1TNBoNVq1ahYyMDMTExODJJ5/Ea6+91mDtq6++ildffRWxsbHYtm0b1q5di+Dg4Ho1c+bMQVxcHPLy8vD111/D3d290X5XrlyJuLg43HnnnRg2bBiEEFi/fj10Oh02b96MpUuX4r///S/8/Pyg0Wjw3//+F1u3bsXbb7/drPnSmLvuugtPPvkkZs+ejYEDB2LHjh144YUXHGruvvtujBkzBqNGjULHjh3x8ccf15uOl5cXvvvuO1y8eBHXX3897rnnHowePRr/+te/XNbrlZKq9wQnJ0pKSuDv74/i4mL4+fm5ZJrG06dR/Pc/AhYjvCbNgnfCeJdMl4iunqqqKmRnZ6Nbt24OYeJaU15ejs6dO+ONN97AI488onQ715STJ0+iW7du2Lt3r9NLZGzevBmjRo3CpUuXEBAQcFX7U4PGfg+bu/zmPkRK6Xm7fO8d3HgdEZEL7d27F0eOHMHQoUNRXFyMhQsXAoBtMwyRWjEQERGpzOuvv46srCy4u7sjLi4OW7durbeJh0htGIiUYq0+FbmVWyyJ6OoZNGgQMjIylG6jXYiKisLl9joZOXLkZWuobeBO1Ur5+W1gyxtAcfNObkVERESux0BEREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHQERERE6NHDkSTzzxhNJtELU6BiKldLgO6NgL0Hkp3QkRqczUqVMhSRJmzpxZb9ysWbMgSZLtumRffPEFXnrppavcIdHVx0CklOhxQL/JgE/zLk5IROQKERERWLVqFSorK23Dqqqq8NFHH9ku4goAQUFB8PX1VaJFoquKgYiISIUGDx6MiIgIfPHFF7ZhX3zxBbp27YpBgwbZhtXdZGYwGPD000+jc+fO8Pb2Rnx8PDZv3nwVOydqHQxEREQq9fDDD2PlypW25ytWrMC0adMafc3s2bORnp6OVatWYf/+/bj33nsxZswYHDt2rLXbJWpVvJaZUnb8EzBWANe9DmCY0t0QkQtcWv0JrBUVV/19NV5eCJzyu2a/7oEHHsC8efOQk5MDANi+fTtWrVrldI1Pbm4uVq5cidzcXISHhwMAnn76aWzYsAErV67EK6+80uLPQKQ0BiIiIhexVlTAWlamdBtN1rFjR4wbNw6pqakQQmDcuHGNXvX+wIEDsFgs6NWrl8Nwg8GADh06tHa7RK2KgYiIyEU0XsocNXol7/vwww9j9uzZAIBly5Y1WltWVgatVouMjAxotVqHcT4+Pi3ugagtYCAiInKRlmy2UtqYMWNgNBohSRISExMbrR00aBAsFgvOnz+Pm2666Sp1SHR1MBAREamYVqvF4cOHbY8b06tXLyQlJeGhhx7CG2+8gUGDBqGgoABpaWkYMGAAxo0bdzVaJmoVDERERCrn5+fX5NqVK1fi5ZdfxlNPPYUzZ84gODgYN9xwA+68885W7JCo9bX7QFRUVISEhASYzWaYzWbMmTMH06dPV7otIiLFpKamNjp+zZo1tsd1jzjT6XR48cUX8eKLL7q+MSIFtftA5Ovriy1btsDLywvl5eWIiYnB5MmTlT8iIiASMFcBbh7K9kFERETtPxBptVp4VR+BYTAYIISAEELhrgD0nSDf+4Yq2wcREREpf6bqLVu2YPz48QgPD4ckSQ6ramssW7YMUVFR8PDwQHx8PHbt2tWs9ygqKkJsbCy6dOmCuXPnNnqeDSIiIlIfxQNReXk5YmNjnZ7/YvXq1UhJScGCBQuwZ88exMbGIjExEefPn7fVDBw4EDExMfVuZ8+eBQAEBARg3759yM7OxkcffYT8/Pyr8tmIiIjo2qD4JrOxY8di7NixTscvWbIE06dPt11fZ/ny5Vi3bh1WrFiBZ599FgCQmZnZpPcKDQ1FbGwstm7dinvuuafBGoPBAIPBYHteUlLSxE/STD+/BZgqgR6LwUt3EF272sQmeCKVcuXvn+JriBpjNBqRkZGBhIQE2zCNRoOEhASkp6c3aRr5+fkoLS0FABQXF2PLli2Ijo52Wr9o0SL4+/vbbhEREVf2IZyxmgGLCRDW1pk+EbUqnU4HAKhQ4NplRCSr+f2r+X28EoqvIWpMYWEhLBYLQkMddzwODQ3FkSNHmjSNnJwczJgxw7Yz9eOPP47+/fs7rZ83bx5SUlJsz0tKSlovFBHRNUur1SIgIMC2+d7LywuSJCncFZE6CCFQUVGB8+fPIyAg4LInFW2KNh2IXGHo0KFN3qQGAHq9Hnq9vvUaIqJ2IywsDAAc9mkkoqsnICDA9nt4pdp0IAoODoZWq623E3R+fr7LZgARUUtJkoROnTohJCQEJpNJ6XaIVEWn07lkzVCNNh2I3N3dERcXh7S0NEycOBEAYLVakZaWZrs6MxGR0rRarUv/MBPR1ad4ICorK8Px48dtz7Ozs5GZmYmgoCB07doVKSkpSE5OxpAhQzB06FAsXboU5eXltqPOiIiIiK6U4oFo9+7dGDVqlO15zQ7NycnJSE1NxZQpU1BQUID58+cjLy8PAwcOxIYNG+rtaH3N8etcfekO7q9ERESkNEnwJBqNKikpgb+/P4qLi5t1RejGGE+fRvGXawAAXkPi4D2M5yEiIiJypeYuv9v0eYiIiIiIrgYGIiIiIlI9BiKl/PIesOOfQNEppTshIiJSPQYipZgqAWMFL91BRETUBjAQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqKX7pDtXyDgH0lYBWp3QnREREqsdApJTY38v3fuHK9kFERETcZEZERETEQERERESqx01mSslIBUwVQM+FSndCRESkegxESjGUyJfusJqV7oSIiEj1uMmMiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPR5kpxTMQcPPgpTuIiIjaAAYipQx6UL7npTuIiIgUx01mREREpHoMRERERKR63GSmlMwPAVMl0KuT0p0QERGpHgORUiouyJfusJiU7oSIiEj1uMmMiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPR5kpRe8LaNzkGxERESmKS2OlxE2T7/07K9sHERERcZMZEREREQMRERERqR43mSll/2rAXAVE82r3RERESmMgUkpZvnzpDrNR6U6IiIhUj5vMiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1eJSZUtw8ACEAiZmUiIhIaQxEShk6Q74PiFC2DyIiIuImMyIiIiIGIiIiIlI9bjJTysHPAbMBiObV7omIiJTGQKSUkjPVl+4wKN0JERGR6nGTGREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpnioC0aRJkxAYGIh77rlH6VbsNG7yTZKU7oSIiEj1VBGI5syZgw8++EDpNhzd8Efg5qeBgK5Kd0JERKR6qghEI0eOhK+vr9JtEBERURuleCDasmULxo8fj/DwcEiShDVr1tSrWbZsGaKiouDh4YH4+Hjs2rXr6jdKRERE7ZbiZ6ouLy9HbGwsHn74YUyePLne+NWrVyMlJQXLly9HfHw8li5disTERGRlZSEkJAQAMHDgQJjN5nqv/f777xEeHt7qn6FFDq+Vz1Ldm1e7JyIiUprigWjs2LEYO3as0/FLlizB9OnTMW3aNADA8uXLsW7dOqxYsQLPPvssACAzM9Nl/RgMBhgM9stplJSUuGzaDi6dlC/dYapsnekTERFRkym+yawxRqMRGRkZSEhIsA3TaDRISEhAenp6q7znokWL4O/vb7tFRHANDhERUXvXpgNRYWEhLBYLQkNDHYaHhoYiLy+vydNJSEjAvffei/Xr16NLly6Nhql58+ahuLjYdjt16lSL+yciIqJrg+KbzK6GjRs3NrlWr9dDr9e3YjdERETU1rTpNUTBwcHQarXIz893GJ6fn4+wsDCFuiIiIqL2pk0HInd3d8TFxSEtLc02zGq1Ii0tDcOGDVOwMyIiImpPFN9kVlZWhuPHj9ueZ2dnIzMzE0FBQejatStSUlKQnJyMIUOGYOjQoVi6dCnKy8ttR50RERERXSnFA9Hu3bsxatQo2/OUlBQAQHJyMlJTUzFlyhQUFBRg/vz5yMvLw8CBA7Fhw4Z6O1pfc4Y/Lt/z0h1ERESKk4QQQukm2rKSkhL4+/ujuLgYfn5+Lpmm8fRpFH+5BgDgNSQO3tz8R0RE5FLNXX636X2IiIiIiK4GxTeZqVbWt4DFAPTmJjMiIiKlMRAp5cKx6kt3VCjdCRERkepxkxkRERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeD7tXSvxM+d4/Qtk+iIiIiIFIMVp3+V7DlXRERERK49KYiIiIVI9riJRy/AfAbAT6RCrdCRERkeq1KBAZDAbs3LkTOTk5qKioQMeOHTFo0CB069bN1f21X+cPy5fuMJYr3QkREZHqNSsQbd++Hf/4xz/w9ddfw2Qywd/fH56enrh48SIMBgO6d++OGTNmYObMmfD19W2tnomIiIhcqsn7EN11112YMmUKoqKi8P3336O0tBQXLlzA6dOnUVFRgWPHjuH5559HWloaevXqhR9++KE1+yYiIiJymSavIRo3bhw+//xz6HS6Bsd3794d3bt3R3JyMg4dOoRz5865rEkiIiKi1tTkNUSPPfYYdDodLBYLtmzZgqKiIqe1ffv2xejRo13RHxEREVGra/Zh91qtFrfffjsuXbrUGv0QERERXXUtOg9RTEwMfvvtN1f3QkRERKSIFh12//LLL+Ppp5/GSy+9hLi4OHh7ezuM9/Pzc0lz7dr1jwJCAP5dlO6EiIhI9VoUiO644w4A8pFnkiTZhgshIEkSLBaLa7prz3Re8r1Gq2wfRERE1LJAtGnTJlf3QURERKSYFgWiW265xdV9qM9vmwGLEejDs3sTEREp7YquZVZRUYHc3FwYjUaH4QMGDLiiplQhb3/1pTtKle6EiIhI9VoUiAoKCjBt2jR8++23DY7nPkRERER0LWnRYfdPPPEEioqKsHPnTnh6emLDhg34z3/+g549e2Lt2rWu7pGIiIioVbVoDdGPP/6Ir776CkOGDIFGo0FkZCRuu+02+Pn5YdGiRRg3bpyr+yQiIiJqNS1aQ1ReXo6QkBAAQGBgIAoKCgAA/fv3x549e1zXHREREdFV0KJAFB0djaysLABAbGws3nnnHZw5cwbLly9Hp06dXNogERERUWtr0SazOXPm2K5mv2DBAowZMwYffvgh3N3dkZqa6sr+iIiIiFpdiwLRAw88YHscFxeHnJwcHDlyBF27dkVwcLDLmmvXBicDEIBfZ6U7ISIiUr0rOg8RIF+uw9PTE4MHD3ZFP+rh4S/fa6/4R0BERERXqEX7EAHA+++/j5iYGHh4eMDDwwMxMTH497//7creiIiIiK6KFq2emD9/PpYsWYLHH38cw4YNAwCkp6fjySefRG5uLhYuXOjSJtulnO3ypTv6dle6EyIiItVrUSB6++238d577+G+++6zDbvrrrswYMAAPP744wxETXEmQ750h+FepTshIiJSvRZtMjOZTBgyZEi94XFxcTCbzVfcFBEREdHV1KJA9OCDD+Ltt9+uN/zdd99FUlLSFTdFREREdDU1eZNZSkqK7bEkSfj3v/+N77//HjfccAMAYOfOncjNzcVDDz3k+i6JiIiIWlGTA9HevXsdnsfFxQEATpw4AQAIDg5GcHAwfv31Vxe2R0RERNT6mhyINm3a1Jp9EBERESmmxechIiIiImovmhyIZs6cidOnTzepdvXq1fjwww9b3JQqDLwfuP4RwJcXwyUiIlJakzeZdezYEf369cOIESMwfvx4DBkyBOHh4fDw8MClS5dw6NAhbNu2DatWrUJ4eDjefffd1uz72udVfc03N3dl+yAiIqKmB6KXXnoJs2fPxr///W+89dZbOHTokMN4X19fJCQk4N1338WYMWNc3igRERFRa2nWmapDQ0Px3HPP4bnnnsOlS5eQm5uLyspKBAcH47rrroMkSa3VZ/tzaidgMQH9rlO6EyIiItVr8aXWAwMDERgY6Mpe1OXULsBYDlRNULoTIiIi1eNRZkRERKR6DERERESkegxEREREpHoMREoTSjdARERELQ5EZrMZGzduxDvvvIPS0lIAwNmzZ1FWVuay5tSBiYiIiEhpLTrKLCcnB2PGjEFubi4MBgNuu+02+Pr6YvHixTAYDFi+fLmr+2yHeIoCIiKitqJFa4jmzJmDIUOG4NKlS/D09LQNnzRpEtLS0lzWXLvW/x5g8EOAb5jSnRAREaleiwLR1q1b8fzzz8Pd3fGyE1FRUThz5oxLGnOlSZMmITAwEPfcc4/SrVST5CDkFw64eSjdDBERkeq1KBBZrVZYLJZ6w0+fPg1fX98rbsrV5syZgw8++EDpNoiIiKiNalEguv3227F06VLbc0mSUFZWhgULFuCOO+5wVW8uM3LkyLYX1M5lAqd+hii/oHQnREREqteiQPT6669j+/bt6Nu3L6qqqnD//ffbNpctXrzYpQ1u2bIF48ePR3h4OCRJwpo1a+rVLFu2DFFRUfDw8EB8fDx27drl0h5cTZIA5KYDJzYDpeeVboeIiEj1WnSUWUREBPbt24fVq1dj3759KCsrwyOPPIKkpCSHnaxdoby8HLGxsXj44YcxefLkeuNXr16NlJQULF++HPHx8Vi6dCkSExORlZWFkJCQZr+fwWCAwWCwPS8pKbmi/hskSYCklR+L+pseiYiI6OpqdiAymUzo3bs3vvnmGyQlJSEpKak1+rIZO3Ysxo4d63T8kiVLMH36dEybNg0AsHz5cqxbtw4rVqzAs88+2+z3W7RoEV588cUW99skkgRI1SvnLKbWfS8iIiK6rGZvMtPpdKiqqmqNXprNaDQiIyMDCQkJtmEajQYJCQlIT09v0TTnzZuH4uJi2+3UqVOuatdOkgBN9RoiBiIiIiLFtWgfolmzZmHx4sUwm82u7qdZCgsLYbFYEBoa6jA8NDQUeXl5tucJCQm49957sX79enTp0qXRsKTX6+Hn5+dwczmNBtDoAADC1DbCJRERkZq1aB+iX375BWlpafj+++/Rv39/eHt7O4z/4osvXNKcq2zcuFHpFhxIGg3gVn0OJ0OFss0QERFRywJRQEAA7r77blf30mzBwcHQarXIz893GJ6fn4+wsDZ8BmitFtBWByJjpbK9EBERUcsC0cqVK13dR4u4u7sjLi4OaWlpmDhxIgD5pJFpaWmYPXu2ss01QtJqgS5Dgc5xEF2uV7odIiIi1WtRILqaysrKcPz4cdvz7OxsZGZmIigoCF27dkVKSgqSk5MxZMgQDB06FEuXLkV5ebntqLO2SHJ3ly/bAUBo9Ap3Q0RERC0KRN26dYMkOb9a+2+//dbihuravXs3Ro0aZXuekpICAEhOTkZqaiqmTJmCgoICzJ8/H3l5eRg4cCA2bNhQb0frtkTS6WyPhdGoYCdEREQEtDAQPfHEEw7PTSYT9u7diw0bNmDu3Lmu6Mtm5MiREEI0WjN79uw2vYmsLkmrhaTVQOT9Cuup74EJ4+07WRMREdFV16JANGfOnAaHL1u2DLt3776ihtRC4+cHy9aNsJrLILK3QOqZcPkXERERUato0XmInBk7diw+//xzV06y3dL6+wPBPSCsEsT+r5Ruh4iISNVcGog+++wzBAUFuXKS7ZbWzw8IjgYAmPd+A5ScU7gjIiIi9WrRJrNBgwY57FQthEBeXh4KCgrw1ltvuay59swtLAwIjAS8g2EsOAv3T5OB5G+4LxEREZECWhSIas75U0Oj0aBjx44YOXIkevfu7Yq+2j33qCjATQfETIZhfyq8c3dC+m4eMO4NpVsjIiJSHUlc7hAulSspKYG/vz+Ki4tdfl2z4q+/gfHkSeDCcfiVfAC9P4A/pgMdo136PkRERGrT3OV3i/Yh2rNnDw4cOGB7/tVXX2HixIn4y1/+AiPPq9Nk+l695AcdeqDUayLMEz+0hyGLGai4qFxzREREKtKiQPTYY4/h6NGjAOSTME6ZMgVeXl749NNP8ec//9mlDbZn+p495E1nAEToYJTsvwBrVZU88uBnwNL+wMa/AuUXFOuRiIhIDVoUiI4ePYqBAwcCAD799FPccsst+Oijj5CamsrD7ptB0mjgm3g73II7AAAsRUUo+eYbmAsKgKxvAWMZsO1NORh9/wLw209AZZGyTRMREbVDLQpEQghYrVYAwMaNG3HHHXcAACIiIlBYWOi67lRA4+4Ov3HjoPHyAgCYzuXh0qrVKNZPhnHUMoiwAYCpHNjxT+CDu4DFkcCKsY4TMVYo0DkREVH70aKjzIYMGYKXX34ZCQkJ+Omnn/D2228DkC+82pavIdZWaf384HfnOJR8sw7WCjncGHNPwZgLuIX+CV49yuF+IQ3SuUygKBfw8Le/WAjgH7GAuxfQaSAQPlC+7xQLePGcUERERE3RoqPM9u/fj6SkJOTm5iIlJQULFiwAADz++OO4cOECPvroI5c3qpTWPMqsLmEyoerwYVTu3QtLSanDOG1gIDxjB0Dn7wGtlwZS8HXyiNI84A0nR6UFRAKx9wGj5lW/QfWPupEL8xIREbUHzV1+u/Sw+6qqKmi1WuhqXc39Wnc1A1ENYbXCcOw4KvdkwFzYwA7VWg3cAgOhDQ6GW3Aw3Hx0cDOdhebiIeBcJnA2E7iULdfeMAsY84r8uPi0vDbJq0PDt8hhwHW3yrVWC1B6Th6u87waH5uIiMhlmrv8btEms1OnTkGSJHTp0gUAsGvXLnz00Ufo27cvZsyY0ZJJUi2SRgOP6F7Q9+oJU04OKvbshenMGXuBxQpz4QWYCy/AgCzbYI23P9w63g2362dA6+sON3MetJ26wbY+qOICYDUDZfnyra7hj9sDUclZYGmM/FjnVR2aguzhqWciMODe6n5MQO7PtcJVEKBtP6GYiIjavxYFovvvvx8zZszAgw8+iLy8PNx2223o168fPvzwQ+Tl5WH+/Pmu7lOVJEmCe1QU3KOiYMo/D9OZ0zAXFMJ8oRCWS5cAq+PKPWt5OYzl5TCezLFPQ3ca2qBf5TVJgYFw+/0maD2t0JhL5YBUcbH6/gLQdZh9YlXFgEYHWE2AqQIorgCKT9nH+3ayB6LSc8B/7nRsXu8PeFcHpJh7gBtmysNNVcCBTwCvYMcA5REAaFx6aT0iIqIma1EgOnjwIIYOHQoA+OSTTxATE4Pt27fj+++/x8yZMxmIWoEuNAS60BDbc2E2w3zxIiyFhTAXFlavMSqEMBgcXidMZpjzz8Ocf95huKTTQfLQQ+PhB8kjBBoPD0jn9NAU/QxJr5efP7ALGq0VkqUMGks5JEsppKpLcngKH2ifmNkABPeyBywIwFAs3y7+BkQOt9eWnwfWPl7/A0oawDMIGDINuPV5eVhlEfD984CbB+Cmr76v9Ti0HxA1Qq61mIGcbXVqa71G5wXoPFr+AyAionatRYHIZDJBr9cDkA+7v+uuuwAAvXv3xrlzvGr71SC5uUEXEgJdSK2QJASsZWUwFxTCcqE6KBUUwlJSYt+huqbWZIIwmWAtLWve++qrQ9Tps9DsXwtJ7wGNhx7S9cvke3cdNJIJkrUCGksZJGsZpJAe9s12QgA9b7evlaq4CBhKAGEFKgoBS60znRtKgL3/dd7MkIftgchQAnwwwXntgCnA5Hflx2YDsKSvk/CkB6JuBG5+2v7ab5+VNwHWrtNVBy3/CKD7Lfba07sBjZvzYMa1YEREbVKLAlG/fv2wfPlyjBs3Dj/88ANeeuklAMDZs2fRoUMHlzZITSdJErS+vtD6+gLdu9mGC6MR5gsXqtciFcBy8RKsVZUQVQZYqyrrbXprjDAYYDEYgOKSZjR2FhqPnXJ48vSA5JcMKUQPjYenHKLctJBggAaVkHyDoCkpgcbDA3D3gXTrC3KAMVfVv+80sFZjViCkH2CurF9nNcuhpIa5Sg5fzngG2h9bzMDOt53X9kx0DESp4+TpNyTyRmDaOvvz5TcCpsqGw1PH3kDCAnvt1iXydOuuJXPTA97BQPeR9toC+SzyDYY9HmFIRNSgFgWixYsXY9KkSXjttdeQnJyM2NhYAMDatWttm9Ko7ZDc3aHr1Am6Tp3qjRNCyGuLqqpgrayCMFTBWlUlP6+qgjAYHJ9XhyhhMNZb6+SUELBWVgGVVbAUNeUF2+Q7rQYafRAkTw9o9B7yJj4vPaDVQnJzg2TQAj/vhOSmlYfduNw+TqMBtG7yOAhIGg2kggJ5vARg6iZIViMkYQaEEZLVZA9RfuG1mwduerrhQGY2AJ0HO3xO+IXL+0nZ6irlsAbU32R3MVs+G3lDKi85Pt+5vOEd4QEgtD/wh2325x9PkTdVNiQ4Gpi9y/58VRJwKafh8OQTAoxdbK/d+z+gvKDhAOfu4xgMzx8GjOXyplCNG6DRApJWvte4AUH2wI6qYvmoRknjWGe7Z4gjotbXokA0cuRIFBYWoqSkBIGB9v+mZ8yYAa/qMy7TtUGSJEju7oC7O7TNOK2AEALCYKgVlKpgNRggKithrTJUBysDRFWt55VVEM25+K/FKp+osqIClhZ8tmaRJHuw0pRBcjshByqtBtBGQ9K6AVoNJK0bJC95QS2HLS2krVshabVy/fD3bKEMGo0czgAAJjmUnTkjj9dqgUkfQbKYAGGCJEyQrEb5sdUg73Re26AHgaqihoNZYJRjrd5P3km9ZjxqBVepzia7wqPyrSH+XR0D0S//Bs7ubbjWqwPw51ohbN1TQM72hmvdPIHn8+zPP3sEOP5Dw7UAsKDIHoq+eEy+rI1GUx2YaoctDfCHdPkkpQCw6RXg6IY6AcvN/tp7UwHPALk24z/AiR/rT6/mtbe+YD/R6dHvgdwdcq1t2rWC38Ake+3ZvcC5/fWnW/PaqBH2NZJFp4BLJ+uEwVrTDYwC3L3lWkOpHCQd5oHGHji1em6eJWqmFgUiQF4gZmRk4MSJE7j//vvh6+sLd3d3BiKVkCQJkocH4OEBbTNeJywWea1T3fBUb+2UweG5MJlb7bPIjQn5PUxmuOzEXC2lkSBpq4Bt78kBTKsBtN3lAKapDmUetUKZVgspLa06aLlB6r/IvnZM0kCSBCTJAggLJI0EZGdDqgllN74CyVRmC2WwmiDVrDHTe8Nh3Uz0uOrNklV1glmVvNN6bT4hcqASFnntT+17tzrntRKNxF1J47iGyFgm76zfWH2NSyeBc/uc19asuQPk4HJojfPaG1PsISf7JyD9X85re95urz2yDtjymvPa6ZuAztWB6OBn8sWcnZm6Tt6/DQAyPwK+beRC2kmfAT1vkx/v/RD4ek7DoVCjBcb/E4geI9ce+wH47rmGQ6GkBW6eC/RMkGtP75Y/m0NdreA38D6g281ybeFxOVDXDW410+4+Coi4Xq4tzZd/Fg3VSVogLAYI6SPXGkqBnPTqujprISUt4BsG+HeWa81GoCjH+VpLNw97mBZC/n7U/f5Ru9aiQJSTk4MxY8YgNzcXBoMBt912G3x9fbF48WIYDAYsX77c1X1SOyFptZC8vORrt9Vau3g5wmyWQ5LRAGE2A1YrhNkCWMwQFmv1vQXCYgEsFnmcVb4XFrM8rO64Buusteqrp2tu9fVTjqwCwloTzgyXr281ZcCuZbXWjgVDcgu1BzG9FpJnTShzg/Ttt/a1Yx2nQgp7tDrEyTXyWrTq+6yj1UFPC+mWf0AapYFbcBAkrSTv82W1yAska515P24JkPBinaAlfx8gLIDW3V47/E/yKR/qhbLq6dasbQGAmLuBkL7ytOrWWy2Ol8uJHF49DXP9OlGntkNPoNeYhuusFkDva6/1DJQ3adb9TNbq5261N7lK8metmVZdtRfiVpP91pDawyuLgMKshusA+UCIGmX58ho4ZyKutwei4lON74un87IHoksnGw97o56zB6JLOcBH9zqvvfFJIOGv9h7+NcR57dDHgDv+Lj8uy7dfAUCqEwo1bkDs7+21hjLgrWHO11r2uA0Y/YJca7UC/53gfK1l58HAiDn2ntY9JYezemsNtUDQdcDgB+21Py+Xf5YNrV30CQV6Jdprj34vH8DS0FpLva/jUcQFRx2nW7sXrR7w6WivrbmuZu21ltdQoGxRIJozZw6GDBmCffv2OexEPWnSJEyfPt1lzRHVkNzcoPVxA+B92VpXE0LIAcxiAcxmCKtVvq8dsmrdNxrYzBYIk9F2lF/NDWZznWGtvEasqawCwmoCTGj1cKbx9UHQAw9A0jdyZnTfUPnWFGEx8q0put0k35qi9zj51hSxU+RbU8RNlW9NET9DvgH2tRm1g1bt8NT/XqBHQq3xVsfQ5x9hr+1+C5D8TZ3wWCv4hdfaZy6sP3DX/zlOt3ag7FwrfAR0ldeyNRQKhUU+hUYNryCg36T6obAmKAZEyp/ZapEXth37yAtsi1E+CMJqks+hpnGTQ6bZKAe5krONz1ONVj7I4cJx+az+NYS1ej7UCo7mKnnhf2onUHkRKM51Pt3gXnJt1nr5ddlbnNdaTMDQKiBjpfyev/zbeW33UfKlmTYvAiCArW84r+0yVA5E36TI83DPf5zX1uyT+Pl0eT/ArHXOa4O6A3/aC3x8v3ygyqmdDddp3eX9K+fsAz6YKF+T8+IJ+/i+E+VN2AqHpxYFoq1bt2LHjh1wd3d3GB4VFYUztc+oTNRENaEDVqv9scXiOLz2vS2k1PyxqjXcYoGwCvtwhxp5uLBY5KPrhH26jU/byeuqa+Rp1NRUT09Uv85qqTVe8Q1ybZa1vBzCaJTXMNVlMdsXTHVvEI5HBlZclBc89Wqr539wD3vtpZPyvji1x9d+TUS8fV+c/EPySUjr1qD6ec9EwK36b+LpDPkPfr33r3484Hf2NVQnt8ub9mpPq/ZtyCP2TXDH04CT2+rU1Xo8/E/2TUTHfgAOf12/puY28lkguK9cm7VBPsWFs3k8eoH9AIIj64Ht/3BSK+RLBdWsGcr6Vt4E11Cvwgrc8RrQVz5tC45+B3wxveFehVVeOxiXbJ8P/5vs/MuUuAgY9kf5ce7PwIpE57WjngduSqn+Gf8KvNNIMO43WV5DpfeRvwv/nei8NjgaSPybvPm4qgj4/BHntQAw9jUgMFL+7m54tvHa+D8AHa6T58vW1xuv7TUGCO4pP97zgfM1hTU9B0bKj499J/9uOKPRyTcAOLtHnh/OWIxy2APqhyEA+G2THNQUvsJBiwKR1WqFxVJ/Ne3p06fh6+vbwCuopUTNH2lr3QV39R8NJ6GhdrAQFkvj02hJaKi98K/9OoeFf1NDg9Xx0P+aP4ioCRB17rVu9l9Eq0U+k7azWp2nff8Wq0leWDZUByGvKvYIkGstplpn5m6g1iNAXg1dU1t41HkPXh3k/5AB+Zf+7F77+Lq13sHyHyVAngcntzYwzeqevIMdTz1wdIN9oVO7HpB30q59gszDa+U/Us767Xm7vfbg54C5AhIEIAnbPQBI3h3k1fZajXxk3653gMoLck3tegi5NuGF6lX5EqQfXwaKc6qnZ4W7jxmaN/4qfwbvEOCpw/YeUu9w/t+n3g+Yd8r+/LOH5T+wDZG0wIKL9uffPQcc+abhWgB4vgDQVIecbW/KZ1l35pmT9kC05z+N/xfeK9EeiI58A/z8lvPavpPsgShnO7BtifPa2PvsgShvf+M9XP+ofUF56WTj82F4rZOplhcAp352Xlt7IWosr7/wq81UaX9sNTe+AK69abDuwQH1aq2OtVLN5psGbm7u8tohQF6T4RMmr6mw1dR63HmwPVAXnwZCYxzHo9bjfhPt+3FVXAS63dLwNCWNvLanZo2fsULehAupgX4loMsQ+5pEq0Xe1Odsuh2j5c17NUY+C/n3soH54N8F6DvBXpv4ivy3raFa7w7yWsca4/8JWAz2vym1/4brvIDOcfbau/8t1106Kf/tFgKImax4GAJaGIhuv/12LF26FO++K5/oTpIklJWVYcGCBbjjjjtc2mB7JIRA+doPYNy7CehyA6DzlANC4VGIC7/J4aB28KhZUHWNl//4A/IfmYIsOCwgay/Uom6UF2yAvAr4XKbzBWv3WwDf6kPNLxyTd1Kst3CHfN/jViCwm326xzfWr6l53vM2+Xw6AHDhhLwQdhZGet4OhA+Sa4tOAvtWO5+B190KRFSf3qEsD9jzX+e13W4CIqtP3lhxCdi90nlt1xvs5/MxlgH7G1n4dY6TP59GgmQxA0fXyn+/IOR7CZD3SJZ/blLPwfIO0RYjkPGdvQaQQ0NNvd8NkPpOrP7jKiAdfLm6VqBmD2dbrX88pJtvqt7+r4H0298AYaxVI+zvETEU0t2Tq/dV0EBa9hpgLHGsqemp82BIj8ywT3fpW0CJkzW/IXrgoVr7MRS+AVxwsvkgUA+MGWN/fmIhkHfJsaZmmWets8mwsQVg7YUfUL3/gq7hP+R1j7zyDJAvQ1N3gVKzMKq9i31AhLw5oe5Cx+F11Tr2lhdyDS2kJI2870WNTgPlzVr1FoDVz2vvZxQRD8TPdL4A9LGfqBWRI+SzvtceX/s9atYEAPIanTuXOg8NIbU2aXUfCUz5n/N5ENq/1nRvAaZtaLiuZiFcu3Z2hpP5qwE8/Oy1UTfKAdRZv1KtQz0ihjqG4MaE9AaebmQfqtr8uwB/cHIkZV1eQUDy2qbVunsB96xoWq1Ga9+XqSlqn3D2cgY90PTaXrdfvqZGzZrGrjc0/TVXSYsC0euvv44xY8agb9++qKqqwv33349jx44hODgYH3/8sat7bHfMBQWo/Ox1eQGt6wT41fxHdwLI2eH8hWEx9kBUdl4+nNeZ8EH2QFRVJB/l4Uzt/9KMFY1vazfX2o/EYpR3wmyIJCDBAuh08kJV7wZIlYCmerled4Ed5A0pvJO8A65HOfCb2UkQEEBUR0gxA+Q/fBe9IZ2xyMGkes0DJEleW6GRgIHdIQ2+VV4QFp+CVP5JdY1GPuJKU71mAwBiB0OKnyLXluVD0n0r19bUVC9QJa0G6JMAjJgFSZLkcwYFbWt4oSNp5AVIfPV/aaYqwH1z/ZqahUDnwUDcaLnWagUM05wv/IJ7AdXnAAMATJ4L+5Exdf/76wyE1zq/0uRX5Z9fQ9P2CgJqbw6v+e+voX7d66wRvvs9eZ+NhqZb++SYgLxQtZqdLADrHLuY9Knzz+Z4LBzwwGcNfycbMmFZ02tHz5dvTTHsj/ZNNpfTnP2MeiU67hzbmK43NH2hE9pXvjVFYKRjmGqMT0fHnW4bo/cB9D0uXwfIaxNqbyYlcgFJiJbt1GA2m7F69Wrs27cPZWVlGDx4MJKSkuDp2cgOkdegkpIS+Pv7o7i4GH7NOE9PY4ynT6N47q2AsQLS4PshhfWWF7QXj8lraDQaeUGr0cibImr+s+1+s7zZQaOFdOk3oOCIfQFdfZSCVP1fPaKGQ/LtKC8winMhXciqXrDXHFFQfbSPRl4IS74h8uPyfEgXjlUfTVBdr631OLSPvbayCFJRdvUmLG11H27V5/LRygvhmj9ahjL5yA1n//3p/eyHvFpM9pP6ObvxHCtERNSI5i6/m72GyGQyoXfv3vjmm2+QlJSEpKSkFjVKMs9Bg+F9x+8vX3hF4ptRex2A4ZetAgD4+QGhXZtWq/eRb02h1dlPmEdERHQVNPvfbJ1Oh6oqJ9dqIiIiIroGtWi7w6xZs7B48WKYzW3kXClEREREV6BFO1X/8ssvSEtLw/fff4/+/fvD29vxZHlffPGFS5ojIiIiuhpaFIgCAgJw9913u7oXIiIiIkW0KBCtXNnIuVyoaWLulg8jrjm5HxERESmmWfsQWa1WLF68GCNGjMD111+PZ599FpWVlZd/IdXn11m+hpDO4/K1RERE1KqaFYj+9re/4S9/+Qt8fHzQuXNn/OMf/8CsWbNaqzciIiKiq6JZgeiDDz7AW2+9he+++w5r1qzB119/jQ8//BBWq/XyLyZH5zKB07uBqlKlOyEiIlK9ZgWi3Nxch2uVJSQkQJIknD3byKUeqGHZW+TrgFU28Ro7RERE1GqaFYjMZjM8PBz3edHpdDCZTC5tioiIiOhqatZRZkIITJ06FXq9/SKNVVVVmDlzpsO5iHgeIiIiIrqWNCsQJScn1xv2wAMPuKwZIiIiIiU0KxDx/ENERETUHrXoWmZERERE7QkDEREREaleiy7dQS7Q567qS3d0VLoTIiIi1WMgUkpglHyv81K0DSIiIuImMyIiIiKuIVJM/q+A1QxU9VK6EyIiItXjGiKlnEgDsr7lpTuIiIjaAAYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUr12H4iKioowZMgQDBw4EDExMXjvvfeUbomIiIjamHZ/2L2vry+2bNkCLy8vlJeXIyYmBpMnT0aHDh2Ubo2IiIjaiHYfiLRaLby85LNBGwwGCCEghFC4KwDRdwBWC+AdrHQnREREqtfmN5lt2bIF48ePR3h4OCRJwpo1a+rVLFu2DFFRUfDw8EB8fDx27drlML6oqAixsbHo0qUL5s6di+DgNhBCOvQAOkYD7t5Kd0JERKR6bT4QlZeXIzY2FsuWLWtw/OrVq5GSkoIFCxZgz549iI2NRWJiIs6fP2+rCQgIwL59+5CdnY2PPvoI+fn5Tt/PYDCgpKTE4UZERETtW5sPRGPHjsXLL7+MSZMmNTh+yZIlmD59OqZNm4a+ffti+fLl8PLywooVK+rVhoaGIjY2Flu3bnX6fosWLYK/v7/tFhER4bLP4qAwCzh/CDCUtc70iYiIqMnafCBqjNFoREZGBhISEmzDNBoNEhISkJ6eDgDIz89HaWkpAKC4uBhbtmxBdHS002nOmzcPxcXFttupU6dap/mj3wGH1gIVF1pn+kRERNRk1/RO1YWFhbBYLAgNDXUYHhoaiiNHjgAAcnJyMGPGDNvO1I8//jj69+/vdJp6vR56vb5V+yYiIqK25ZoORE0xdOhQZGZmKt0GERERtWHX9Caz4OBgaLXaejtJ5+fnIywsTKGuiIiI6FpzTQcid3d3xMXFIS0tzTbMarUiLS0Nw4YNU7AzIiIiupa0+U1mZWVlOH78uO15dnY2MjMzERQUhK5duyIlJQXJyckYMmQIhg4diqVLl6K8vBzTpk1TsGsiIiK6lrT5QLR7926MGjXK9jwlJQUAkJycjNTUVEyZMgUFBQWYP38+8vLyMHDgQGzYsKHejtZEREREzkiiTVzHou0qKSmBv78/iouL4efn55JpGk+fRvG7iwCrBV4Jd8N75G0umS4RERHJmrv8bvNriNqtjn3ke72Psn0QERHRtb1TNREREZErcA2RUi7+Jl/t3thH6U6IiIhUj4FIKUe+AYwVQDxPD0BERKQ0bjIjIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLV42H3Suk+CrCaAM8gpTshIiJSPQYipYT1l+89fJXtg4iIiLjJjIiIiIhriJRSfEq+dIepr9KdEBERqR4DkVJ+/VK+dMeQIUp3QkREpHrcZEZERESqx0BEREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHw+6VEnUjYDEDnoFKd0JERKR6DERKCR8s33v4KdsHERERcZMZEREREdcQKaX0HCCsgClG6U6IiIhUj4FIKQc+lS/dMShW6U6IiIhUj5vMiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9XjYvVIibgCsJsAjQOlOiIiIVI+BSCkRQ+V7T39l+yAiIiJuMiMiIiLiGiKllBfKl+4wG5XuhIiISPUYiJSy7yP50h2xfZXuhIiISPW4yYyIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPh90rpXMcYDECej+lOyEiIlI9BiKlRI6Q770Cle2DiIiIuMmMiIiIiGuIlFJVBAgBWExKd0JERKR6DERK2fOBfOmOmJ5Kd0JERKR63GRGREREqsdARERERKrHQERERESqx0BEREREqsdARERERKqniqPMoqKi4OfnB41Gg8DAQGzatEnploiIiKgNUUUgAoAdO3bAx8dH6TbsOsUCZiPg7qt0J0RERKqnmkDU5nS7Rb73DlK2DyIiImr7+xBt2bIF48ePR3h4OCRJwpo1a+rVLFu2DFFRUfDw8EB8fDx27drlMF6SJNxyyy24/vrr8eGHH16lzomIiOha0eYDUXl5OWJjY7Fs2bIGx69evRopKSlYsGAB9uzZg9jYWCQmJuL8+fO2mm3btiEjIwNr167FK6+8gv379zt9P4PBgJKSEodbqzCWA8YywGpunekTERFRk7X5QDR27Fi8/PLLmDRpUoPjlyxZgunTp2PatGno27cvli9fDi8vL6xYscJW07lzZwBAp06dcMcdd2DPnj1O32/RokXw9/e33SIiIlz7gWrsfh/Y8S+g+EzrTJ+IiIiarM0HosYYjUZkZGQgISHBNkyj0SAhIQHp6ekA5DVMpaWlAICysjL8+OOP6Nevn9Npzps3D8XFxbbbqVOnWvdDEBERkeKu6Z2qCwsLYbFYEBoa6jA8NDQUR44cAQDk5+fb1i5ZLBZMnz4d119/vdNp6vV66PX61muaiIiI2pxrOhA1Rffu3bFv3z6l2yAiIqI27JreZBYcHAytVov8/HyH4fn5+QgLC1OoKyIiIrrWXNOByN3dHXFxcUhLS7MNs1qtSEtLw7BhwxTsjIiIiK4lbX6TWVlZGY4fP257np2djczMTAQFBaFr165ISUlBcnIyhgwZgqFDh2Lp0qUoLy/HtGnTFOyaiIiIriVtPhDt3r0bo0aNsj1PSUkBACQnJyM1NRVTpkxBQUEB5s+fj7y8PAwcOBAbNmyot6N1mxPSF7AYAPc2dDkRIiIilZKEEELpJtqykpIS+Pv7o7i4GH5+fi6ZpvH0aRR/uQYA4DUkDt7cvEdERORSzV1+X9P7EBERERG5QpvfZNZuWQyAAGC1Kt0JERGR6jEQKWXnO4CxAoh+XelOiIiIVI+bzIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPV42L1SgnsCZgPg7qV0J0RERKrHQKSUXmPle++OyvZBRERE3GRGRERExDVEShHVl+zgtXWJiIgUx0CklPR/yZfu6PE6gOFKd0NERKRq3GRGREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHQERERESqx8PulRLYTb50h85T6U6IiIhUj4FIKX3Gy/c+Icr2QURERNxkRkRERMRARERERKrHTWZK2b0CMFUCPRYq3QkREZHqMRApZcjD8n1AF2X7ICIiIgYiJbgFBcFv3B0AAG1AgLLNEBEREQOREjReXtB37650G0RERFSNO1UTERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6vFq95chhAAAlJSUKNwJERERNVXNcrtmOX45DESXUVpaCgCIiIhQuBMiIiJqrtLSUvj7+1+2ThJNjU4qZbVacfbsWfj6+kKSJJdNt6SkBBERETh16hT8/PxcNt1rCecB54HaPz/AeQBwHgCcB4Dr54EQAqWlpQgPD4dGc/k9hLiG6DI0Gg26dOnSatP38/NT7Ze/BucB54HaPz/AeQBwHgCcB4Br50FT1gzV4E7VREREpHoMRERERKR6DEQK0ev1WLBgAfR6vdKtKIbzgPNA7Z8f4DwAOA8AzgNA+XnAnaqJiIhI9biGiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgUghy5YtQ1RUFDw8PBAfH49du3Yp3VKzLVq0CNdffz18fX0REhKCiRMnIisry6Fm5MiRkCTJ4TZz5kyHmtzcXIwbNw5eXl4ICQnB3LlzYTabHWo2b96MwYMHQ6/Xo0ePHkhNTW3tj9ckf/3rX+t9vt69e9vGV1VVYdasWejQoQN8fHxw9913Iz8/32Ea1/LnB4CoqKh680CSJMyaNQtA+/wObNmyBePHj0d4eDgkScKaNWscxgshMH/+fHTq1Amenp5ISEjAsWPHHGouXryIpKQk+Pn5ISAgAI888gjKysocavbv34+bbroJHh4eiIiIwN///vd6vXz66afo3bs3PDw80L9/f6xfv97ln7chjc0Dk8mEZ555Bv3794e3tzfCw8Px0EMP4ezZsw7TaOi78+qrrzrUtNV5cLnvwNSpU+t9tjFjxjjUtOfvAIAG/y5IkoTXXnvNVtOmvgOCrrpVq1YJd3d3sWLFCvHrr7+K6dOni4CAAJGfn690a82SmJgoVq5cKQ4ePCgyMzPFHXfcIbp27SrKyspsNbfccouYPn26OHfunO1WXFxsG282m0VMTIxISEgQe/fuFevXrxfBwcFi3rx5tprffvtNeHl5iZSUFHHo0CHxf//3f0Kr1YoNGzZc1c/bkAULFoh+/fo5fL6CggLb+JkzZ4qIiAiRlpYmdu/eLW644QYxfPhw2/hr/fMLIcT58+cdPv8PP/wgAIhNmzYJIdrnd2D9+vXiueeeE1988YUAIL788kuH8a+++qrw9/cXa9asEfv27RN33XWX6Natm6isrLTVjBkzRsTGxoqff/5ZbN26VfTo0UPcd999tvHFxcUiNDRUJCUliYMHD4qPP/5YeHp6infeecdWs337dqHVasXf//53cejQIfH8888LnU4nDhw4oOg8KCoqEgkJCWL16tXiyJEjIj09XQwdOlTExcU5TCMyMlIsXLjQ4btR++9HW54Hl/sOJCcnizFjxjh8tosXLzrUtOfvgBDC4bOfO3dOrFixQkiSJE6cOGGraUvfAQYiBQwdOlTMmjXL9txisYjw8HCxaNEiBbu6cufPnxcAxE8//WQbdsstt4g5c+Y4fc369euFRqMReXl5tmFvv/228PPzEwaDQQghxJ///GfRr18/h9dNmTJFJCYmuvYDtMCCBQtEbGxsg+OKioqETqcTn376qW3Y4cOHBQCRnp4uhLj2P39D5syZI6677jphtVqFEO3/O1B3QWC1WkVYWJh47bXXbMOKioqEXq8XH3/8sRBCiEOHDgkA4pdffrHVfPvtt0KSJHHmzBkhhBBvvfWWCAwMtM0DIYR45plnRHR0tO357373OzFu3DiHfuLj48Vjjz3m0s94OQ0tDOvatWuXACBycnJswyIjI8Wbb77p9DXXyjxwFogmTJjg9DVq/A5MmDBB3HrrrQ7D2tJ3gJvMrjKj0YiMjAwkJCTYhmk0GiQkJCA9PV3Bzq5ccXExACAoKMhh+Icffojg4GDExMRg3rx5qKiosI1LT09H//79ERoaahuWmJiIkpIS/Prrr7aa2vOrpqatzK9jx44hPDwc3bt3R1JSEnJzcwEAGRkZMJlMDr337t0bXbt2tfXeHj5/bUajEf/73//w8MMPO1wMub1/B2rLzs5GXl6eQ7/+/v6Ij493+LkHBARgyJAhtpqEhARoNBrs3LnTVnPzzTfD3d3dVpOYmIisrCxcunTJVnOtzJfi4mJIkoSAgACH4a+++io6dOiAQYMG4bXXXnPYVHqtz4PNmzcjJCQE0dHR+MMf/oALFy7YxqntO5Cfn49169bhkUceqTeurXwHeHHXq6ywsBAWi8Xhjz8AhIaG4siRIwp1deWsViueeOIJjBgxAjExMbbh999/PyIjIxEeHo79+/fjmWeeQVZWFr744gsAQF5eXoPzomZcYzUlJSWorKyEp6dna360RsXHxyM1NRXR0dE4d+4cXnzxRdx00004ePAg8vLy4O7uXm8BEBoaetnPVjOusZq28PnrWrNmDYqKijB16lTbsPb+HairpueG+q39eUJCQhzGu7m5ISgoyKGmW7du9aZRMy4wMNDpfKmZRltRVVWFZ555Bvfdd5/DRTv/9Kc/YfDgwQgKCsKOHTswb948nDt3DkuWLAFwbc+DMWPGYPLkyejWrRtOnDiBv/zlLxg7dizS09Oh1WpV9x34z3/+A19fX0yePNlheFv6DjAQkUvMmjULBw8exLZt2xyGz5gxw/a4f//+6NSpE0aPHo0TJ07guuuuu9ptutzYsWNtjwcMGID4+HhERkbik08+aVML6avl/fffx9ixYxEeHm4b1t6/A9Q4k8mE3/3udxBC4O2333YYl5KSYns8YMAAuLu747HHHsOiRYuu+UtY/P73v7c97t+/PwYMGIDrrrsOmzdvxujRoxXsTBkrVqxAUlISPDw8HIa3pe8AN5ldZcHBwdBqtfWONMrPz0dYWJhCXV2Z2bNn45tvvsGmTZvQpUuXRmvj4+MBAMePHwcAhIWFNTgvasY1VuPn59fmQkdAQAB69eqF48ePIywsDEajEUVFRQ41tX/W7enz5+TkYOPGjXj00UcbrWvv34Ganhv7HQ8LC8P58+cdxpvNZly8eNEl34228rekJgzl5OTghx9+cFg71JD4+HiYzWacPHkSQPuYBzW6d++O4OBgh++9Gr4DALB161ZkZWVd9m8DoOx3gIHoKnN3d0dcXBzS0tJsw6xWK9LS0jBs2DAFO2s+IQRmz56NL7/8Ej/++GO91ZoNyczMBAB06tQJADBs2DAcOHDA4Q9DzR/Ovn372mpqz6+amrY4v8rKynDixAl06tQJcXFx0Ol0Dr1nZWUhNzfX1nt7+vwrV65ESEgIxo0b12hde/8OdOvWDWFhYQ79lpSUYOfOnQ4/96KiImRkZNhqfvzxR1itVltgHDZsGLZs2QKTyWSr+eGHHxAdHY3AwEBbTVudLzVh6NixY9i4cSM6dOhw2ddkZmZCo9HYNiVd6/OgttOnT+PChQsO3/v2/h2o8f777yMuLg6xsbGXrVX0O9CsXbDJJVatWiX0er1ITU0Vhw4dEjNmzBABAQEOR9lcC/7whz8If39/sXnzZodDJisqKoQQQhw/flwsXLhQ7N69W2RnZ4uvvvpKdO/eXdx88822adQccn377beLzMxMsWHDBtGxY8cGD7meO3euOHz4sFi2bFmbOez8qaeeEps3bxbZ2dli+/btIiEhQQQHB4vz588LIeTD7rt27Sp+/PFHsXv3bjFs2DAxbNgw2+uv9c9fw2KxiK5du4pnnnnGYXh7/Q6UlpaKvXv3ir179woAYsmSJWLv3r22I6heffVVERAQIL766iuxf/9+MWHChAYPux80aJDYuXOn2LZtm+jZs6fDIddFRUUiNDRUPPjgg+LgwYNi1apVwsvLq97hxm5ubuL1118Xhw8fFgsWLLhqh1w3Ng+MRqO46667RJcuXURmZqbD34eao4V27Ngh3nzzTZGZmSlOnDgh/ve//4mOHTuKhx566JqYB419/tLSUvH000+L9PR0kZ2dLTZu3CgGDx4sevbsKaqqqmzTaM/fgRrFxcXCy8tLvP322/Ve39a+AwxECvm///s/0bVrV+Hu7i6GDh0qfv75Z6VbajYADd5WrlwphBAiNzdX3HzzzSIoKEjo9XrRo0cPMXfuXIdz0AghxMmTJ8XYsWOFp6enCA4OFk899ZQwmUwONZs2bRIDBw4U7u7uonv37rb3UNqUKVNEp06dhLu7u+jcubOYMmWKOH78uG18ZWWl+OMf/ygCAwOFl5eXmDRpkjh37pzDNK7lz1/ju+++EwBEVlaWw/D2+h3YtGlTg9/95ORkIYR86P0LL7wgQkNDhV6vF6NHj643by5cuCDuu+8+4ePjI/z8/MS0adNEaWmpQ82+ffvEjTfeKPR6vejcubN49dVX6/XyySefiF69egl3d3fRr18/sW7dulb73LU1Ng+ys7Od/n2oOT9VRkaGiI+PF/7+/sLDw0P06dNHvPLKKw6BQYi2Ow8a+/wVFRXi9ttvFx07dhQ6nU5ERkaK6dOn1/untz1/B2q88847wtPTUxQVFdV7fVv7DkhCCNG8dUpERERE7Qv3ISIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiVXr55Zdxww03KN0GEbURDEREpEr79u3DwIEDlW6DiNoIBiIiUiUGIiKqjYGIiNq9jIwM3HzzzfD09MSgQYOwc+dOnDhxAgMHDsSECRMgSVKDt7Vr1yrdOhFdJbzaPRG1a0eOHMHQoUMxZ84cTJ06FZmZmZg9ezbOnz+P0tJSVFZWwmQyoaysDD179sT69esxaNAgAEBwcDDc3NwU/gREdDUwEBFRuzZ69Gh07twZH3zwgW3YvffeiwMHDuDIkSO2Yenp6RgxYgRKSkrg4+OjRKtEpCD+60NE7VZOTg5+/PFH7Nmzx2G4Tqert//Q/v37ERUVxTBEpFLch4iI2q3MzEy4ubmhf//+DsP37t3bYCAaMGDAVeyOiNoSBiIiarc0Gg2sViuMRqNt2Pr163HkyJF6gejkyZOIjo6+yh0SUVvBQERE7VZcXBx0Oh3mzp2L3377DWvXrsX06dMBoF4gslqtyMnJwZkzZ8BdK4nUhztVE1G79r///Q/z5s1DYWEhhg4diuHDh2PlypXIy8tzqPv2228xY8YMXLp0CSUlJdBo+P8ikZowEBEREZHq8V8gIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlK9/wcKB1bJ6fdw5wAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -855,7 +853,7 @@ ], "source": [ "fig = plt.figure()\n", - "ax=fig.add_subplot(121)\n", + "ax=fig.add_subplot(111)\n", "plt.plot(dtau_enstatite, Parr, color=\"C1\", ls=\"dashed\", label=\"geometric approximation\")\n", "plt.plot(np.median(dtau_enstatite_mie,axis=1), Parr, color=\"C3\", label=\"Mie\",alpha=0.5,lw=2)\n", "plt.legend()\n", @@ -864,16 +862,6 @@ "plt.ylabel(\"Pressure (bar)\")\n", "#plt.xscale(\"log\")\n", "plt.gca().invert_yaxis()\n", - "\n", - "ax=fig.add_subplot(122)\n", - "plt.plot(dtau_Fe, Parr, color=\"C2\", ls=\"dashed\", label=\"geometric approximation\")\n", - "plt.plot(np.median(dtau_Fe_mie,axis=1), Parr, color=\"C4\",alpha=0.5, label=\"Mie\",lw=2)\n", - "\n", - "plt.legend()\n", - "plt.yscale(\"log\")\n", - "plt.xlabel(\"$d\\\\tau$\")\n", - "#plt.xscale(\"log\")\n", - "plt.gca().invert_yaxis()\n", "plt.show()" ] }, @@ -925,11 +913,11 @@ "outputs": [], "source": [ "from exojax.spec.layeropacity import layer_optical_depth_CIA\n", - "from exojax.atm.mixratio import mmr2vmr\n", + "from exojax.atm.atmconvert import mmr_to_vmr\n", "\n", "mmrH2 = 0.74\n", "molmassH2 = molmass_isotope(\"H2\")\n", - "vmrH2 = mmr2vmr(mmrH2, mu, molmassH2)\n", + "vmrH2 = mmr_to_vmr(mmrH2, mu, molmassH2)\n", "dtaucH2H2 = layer_optical_depth_CIA(\n", " nugrid,\n", " Tarr,\n", @@ -958,7 +946,7 @@ }, "outputs": [], "source": [ - "dtau = dtaucH2H2 + dtau_enstatite_mie+ dtau_Fe_mie\n" + "dtau = dtaucH2H2 + dtau_enstatite_mie\n" ] }, { @@ -975,7 +963,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1005,7 +993,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1035,7 +1023,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1058,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2022-10-30T01:22:50.402987Z", @@ -1087,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2022-10-30T01:22:50.743739Z", @@ -1099,7 +1087,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1141,7 +1129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.19" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/Ackerman_and_Marley_cloud_model.rst b/documents/tutorials/Ackerman_and_Marley_cloud_model.rst index f7efce1d2..3d3b1b8de 100644 --- a/documents/tutorials/Ackerman_and_Marley_cloud_model.rst +++ b/documents/tutorials/Ackerman_and_Marley_cloud_model.rst @@ -4,7 +4,7 @@ Ackerman and Marley Cloud Model Here, we try to compute a cloud opacity using Ackerman and Marley Model. Although ``atmphys.AmpAmcloud`` can easily compute the parameters of the AM model, we here try to run the methods one by one. We consider -enstatite (MgSiO3) and Fe clouds. +enstatite (MgSiO3). .. code:: ipython3 @@ -49,11 +49,9 @@ Vapor saturation pressures can be obtained using atm.psat .. code:: ipython3 - from exojax.atm.psat import psat_enstatite_AM01, psat_Fe_AM01, _psat_Fe_solid + from exojax.atm.psat import psat_enstatite_AM01 P_enstatite = psat_enstatite_AM01(Tarr) - P_fe_sol = psat_Fe_AM01(Tarr) - #P_fe_sol = _psat_Fe_solid(Tarr) #considers only solid Fe Computes a cloud base pressure. @@ -63,7 +61,6 @@ Computes a cloud base pressure. from exojax.atm.amclouds import compute_cloud_base_pressure Pbase_enstatite = compute_cloud_base_pressure(Parr, P_enstatite, MolMR_enstatite) - Pbase_Fe_sol = compute_cloud_base_pressure(Parr, P_fe_sol, MolMR_Fe) The cloud base is located at the intersection of a TP profile and the @@ -79,10 +76,6 @@ vapor saturation puressure devided by VMR. plt.axhline(Pbase_enstatite, color="gray", alpha=0.7, ls="dotted") plt.text(500, Pbase_enstatite * 0.8, "cloud base (enstatite)", color="gray") - plt.plot(Tarr, P_fe_sol / MolMR_Fe, label="$P_{sat}/\\xi$ (Fe)", color="black") - plt.axhline(Pbase_Fe_sol, color="black", alpha=0.7, ls="dotted") - plt.text(500, Pbase_Fe_sol * 0.8, "cloud base (Fe)", color="black") - plt.yscale("log") plt.ylim(1.e-4, 1.e5) plt.xlim(0, 3000) @@ -107,41 +100,36 @@ profile. from exojax.atm.amclouds import mixing_ratio_cloud_profile from exojax.spec.molinfo import molmass_isotope - from exojax.atm.mixratio import vmr2mmr + from exojax.atm.atmconvert import vmr_to_mmr fsed = 3. muc_enstatite = molmass_isotope("MgSiO3") - MMRbase_enstatite = vmr2mmr(MolMR_enstatite, muc_enstatite,mu) + MMRbase_enstatite = vmr_to_mmr(MolMR_enstatite, muc_enstatite,mu) MMRc_enstatite = mixing_ratio_cloud_profile(Parr, Pbase_enstatite, fsed, MMRbase_enstatite) - muc_Fe = molmass_isotope("Fe") - MMRbase_Fe = vmr2mmr(MolMR_Fe,muc_Fe,mu) - MMRc_Fe = mixing_ratio_cloud_profile(Parr, Pbase_Fe_sol, fsed, MMRbase_Fe) + .. parsed-literal:: ['H2O', 'CO2', 'O3', 'N2O', 'CO', 'CH4', 'O2', 'NO', 'SO2', 'NO2', 'NH3', 'HNO3', 'OH', 'HF', 'HCl', 'HBr', 'HI', 'ClO', 'OCS', 'H2CO', 'HOCl', 'N2', 'HCN', 'CH3Cl', 'H2O2', 'C2H2', 'C2H6', 'PH3', 'COF2', 'SF6', 'H2S', 'HCOOH', 'HO2', 'O', 'ClONO2', 'NO+', 'HOBr', 'C2H4', 'CH3OH', 'CH3Br', 'CH3CN', 'CF4', 'C4H2', 'HC3N', 'H2', 'CS', 'SO3', 'C2N2', 'COCl2', 'SO', 'CH3F', 'GeH4', 'CS2', 'CH3I', 'NF3'] - ['H2O', 'CO2', 'O3', 'N2O', 'CO', 'CH4', 'O2', 'NO', 'SO2', 'NO2', 'NH3', 'HNO3', 'OH', 'HF', 'HCl', 'HBr', 'HI', 'ClO', 'OCS', 'H2CO', 'HOCl', 'N2', 'HCN', 'CH3Cl', 'H2O2', 'C2H2', 'C2H6', 'PH3', 'COF2', 'SF6', 'H2S', 'HCOOH', 'HO2', 'O', 'ClONO2', 'NO+', 'HOBr', 'C2H4', 'CH3OH', 'CH3Br', 'CH3CN', 'CF4', 'C4H2', 'HC3N', 'H2', 'CS', 'SO3', 'C2N2', 'COCl2', 'SO', 'CH3F', 'GeH4', 'CS2', 'CH3I', 'NF3'] .. parsed-literal:: /home/kawahara/exojax/src/exojax/spec/molinfo.py:64: UserWarning: db_HIT is set as True, but the molecular name 'MgSiO3' does not exist in the HITRAN database. So set db_HIT as False. For reference, all the available molecules in the HITRAN database are as follows: warnings.warn(warn_msg, UserWarning) - /home/kawahara/exojax/src/exojax/spec/molinfo.py:64: UserWarning: db_HIT is set as True, but the molecular name 'Fe' does not exist in the HITRAN database. So set db_HIT as False. For reference, all the available molecules in the HITRAN database are as follows: - warnings.warn(warn_msg, UserWarning) The followings are the base pressures for enstatite and Fe. .. code:: ipython3 - print(Pbase_enstatite, Pbase_Fe_sol) + print(Pbase_enstatite) .. parsed-literal:: - 114.975746 77426.445 + 104.62701 Here is the MMR distribution. @@ -151,7 +139,6 @@ Here is the MMR distribution. plt.figure() plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3]) plt.plot(MMRc_enstatite, Parr, color="gray", label="MMR (enstatite)") - plt.plot(MMRc_Fe, Parr, color="black", ls="dashed", label="MMR (Fe)") @@ -216,7 +203,6 @@ We need the substance density of condensates. from exojax.atm.condensate import condensate_substance_density, name2formula deltac_enstatite = condensate_substance_density[name2formula["enstatite"]] - deltac_Fe = condensate_substance_density["Fe"] Let’s compute the terminal velocity. We can compute the terminal @@ -323,7 +309,7 @@ Then, :math:`r_g` can be computed from :math:`r_w` and other quantities. .. parsed-literal:: - + @@ -366,14 +352,6 @@ These processes can be reprodced using ``AmpAmcloud``, which uses from exojax.spec.layeropacity import layer_optical_depth_cloudgeo dtau_enstatite = layer_optical_depth_cloudgeo(Parr, deltac_enstatite, MMRc_enstatite, rg, sigmag, g) - dtau_Fe = layer_optical_depth_cloudgeo(Parr, deltac_Fe, MMRc_Fe, rg, sigmag, g) - - - -.. parsed-literal:: - - /home/kawahara/exojax/src/exojax/spec/dtau_mmwl.py:14: FutureWarning: dtau_mmwl might be removed in future. - warnings.warn("dtau_mmwl might be removed in future.", FutureWarning) The Mie scattering can be computed using ``OpaMie``. @@ -384,8 +362,8 @@ The Mie scattering can be computed using ``OpaMie``. from exojax.spec.unitconvert import wav2nu N = 1000 - wavelength_start = 5000.0 #AA - wavelength_end = 15000.0 #AA + wavelength_start = 5000.0 # AA + wavelength_end = 15000.0 # AA margin = 10 # cm-1 @@ -394,15 +372,15 @@ The Mie scattering can be computed using ``OpaMie``. nugrid, wav, res = wavenumber_grid(nus_start, nus_end, N, xsmode="lpf", unit="cm-1") - from exojax.spec.opacont import OpaMie + opa_enstatite = OpaMie(pdb_enstatite, nugrid) - opa_Fe = OpaMie(pdb_Fe, nugrid) - rg=1.e-4 #0.1um - #beta0, betasct, g = opa.mieparams_vector(rg,sigmag) # if you've already generated miegrid - beta0, betasct, g = opa_enstatite.mieparams_vector_direct_from_pymiescatt(rg,sigmag) # uses direct computation of Mie params using PyMieScatt - beta0_Fe, betasct_Fe, g_Fe = opa_Fe.mieparams_vector_direct_from_pymiescatt(rg,sigmag) # uses direct computation of Mie params using PyMieScatt + rg = 1.0e-4 # 0.1um + # beta0, betasct, g = opa.mieparams_vector(rg,sigmag) # if you've already generated miegrid + beta0, betasct, g = opa_enstatite.mieparams_vector_direct_from_pymiescatt( + rg, sigmag + ) # uses direct computation of Mie params using PyMieScatt from exojax.spec.layeropacity import layer_optical_depth_clouds_lognormal @@ -410,17 +388,6 @@ The Mie scattering can be computed using ``OpaMie``. dtau_enstatite_mie = layer_optical_depth_clouds_lognormal( dParr, beta0, deltac_enstatite, MMRc_enstatite, rg, sigmag, gravity ) - dtau_Fe_mie = layer_optical_depth_clouds_lognormal( - dParr, beta0_Fe, deltac_Fe, MMRc_Fe, rg, sigmag, gravity - ) - - - - -.. parsed-literal:: - - /home/kawahara/exojax/src/exojax/utils/grids.py:142: UserWarning: Resolution may be too small. R=907.6757560767178 - warnings.warn('Resolution may be too small. R=' + str(resolution), .. parsed-literal:: @@ -436,8 +403,7 @@ The Mie scattering can be computed using ``OpaMie``. .. parsed-literal:: - 100%|██████████| 63/63 [00:17<00:00, 3.55it/s] - 100%|██████████| 63/63 [00:20<00:00, 3.09it/s] + 100%|██████████| 63/63 [00:17<00:00, 3.57it/s] The difference of the geometric approximation and Mie scattering is a @@ -446,7 +412,7 @@ bit. .. code:: ipython3 fig = plt.figure() - ax=fig.add_subplot(121) + ax=fig.add_subplot(111) plt.plot(dtau_enstatite, Parr, color="C1", ls="dashed", label="geometric approximation") plt.plot(np.median(dtau_enstatite_mie,axis=1), Parr, color="C3", label="Mie",alpha=0.5,lw=2) plt.legend() @@ -455,16 +421,6 @@ bit. plt.ylabel("Pressure (bar)") #plt.xscale("log") plt.gca().invert_yaxis() - - ax=fig.add_subplot(122) - plt.plot(dtau_Fe, Parr, color="C2", ls="dashed", label="geometric approximation") - plt.plot(np.median(dtau_Fe_mie,axis=1), Parr, color="C4",alpha=0.5, label="Mie",lw=2) - - plt.legend() - plt.yscale("log") - plt.xlabel("$d\\tau$") - #plt.xscale("log") - plt.gca().invert_yaxis() plt.show() @@ -490,11 +446,11 @@ Let’s compare with CIA .. code:: ipython3 from exojax.spec.layeropacity import layer_optical_depth_CIA - from exojax.atm.mixratio import mmr2vmr + from exojax.atm.atmconvert import mmr_to_vmr mmrH2 = 0.74 molmassH2 = molmass_isotope("H2") - vmrH2 = mmr2vmr(mmrH2, mu, molmassH2) + vmrH2 = mmr_to_vmr(mmrH2, mu, molmassH2) dtaucH2H2 = layer_optical_depth_CIA( nugrid, Tarr, @@ -511,7 +467,7 @@ Let’s compare with CIA .. code:: ipython3 - dtau = dtaucH2H2 + dtau_enstatite_mie+ dtau_Fe_mie + dtau = dtaucH2H2 + dtau_enstatite_mie .. code:: ipython3 diff --git a/documents/tutorials/CIA_opacity.ipynb b/documents/tutorials/CIA_opacity.ipynb index be6ae3ee7..d43a5d986 100644 --- a/documents/tutorials/CIA_opacity.ipynb +++ b/documents/tutorials/CIA_opacity.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-10-20T05:38:57.455380Z", @@ -23,7 +23,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "xsmode assumes ESLOG in wavenumber space: mode=lpf\n", + "xsmode = lpf\n", + "xsmode assumes ESLOG in wavenumber space: xsmode=lpf\n", + "======================================================================\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", + "Your wavelength grid is in *** descending *** order\n", + "======================================================================\n", "H2-H2\n" ] }, @@ -31,18 +37,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/utils/grids.py:123: UserWarning: Resolution may be too small. R=433.86018742134854\n", - " warnings.warn('Resolution may be too small. R=' + str(resolution),\n" + "/home/kawahara/exojax/src/exojax/spec/unitconvert.py:63: UserWarning: Both input wavelength and output wavenumber are in ascending order.\n", + " warnings.warn(\n", + "/home/kawahara/exojax/src/exojax/utils/grids.py:144: UserWarning: Resolution may be too small. R=433.86018742134854\n", + " warnings.warn(\"Resolution may be too small. R=\" + str(resolution), UserWarning)\n" ] } ], "source": [ "from exojax.utils.grids import wavenumber_grid\n", "\n", - "nus, wav, res = wavenumber_grid(5000, 50000, 1000, unit=\"AA\")\n", + "nus, wav, res = wavenumber_grid(5000, 50000, 1000, unit=\"AA\", xsmode=\"lpf\")\n", "from exojax.spec import contdb\n", "\n", - "cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus)\n" + "cdbH2H2 = contdb.CdbCIA(\".database/H2-H2_2011.cia\", nus)" ] }, { @@ -54,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-10-20T05:39:03.305164Z", @@ -65,11 +73,11 @@ }, "outputs": [], "source": [ - "from exojax.spec.hitrancia import logacia\n", + "from exojax.spec.hitrancia import interp_logacia_vector\n", "import jax.numpy as jnp\n", "\n", "Tfix = jnp.array([1000.0, 1300.0, 1600.0])\n", - "lc = logacia(Tfix, nus, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac)\n" + "lc = interp_logacia_vector(Tfix, nus, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac)" ] }, { @@ -81,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-10-20T05:39:03.612694Z", @@ -93,23 +101,21 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", - "plt.style.use('bmh')\n", + "plt.style.use(\"bmh\")\n", "for i in range(0, len(Tfix)):\n", - " plt.plot(wav[::-1], lc[i, :], lw=1, label=str(int(Tfix[i])) + \" K\")\n", + " plt.plot(wav, lc[:, i], lw=1, label=str(int(Tfix[i])) + \" K\")\n", "plt.axvspan(22876.0, 23010.0, alpha=0.3, color=\"green\")\n", "plt.xlabel(\"wavelength ($\\\\AA$)\")\n", "plt.ylabel(\"absorption coefficient ($cm^5$)\")\n", @@ -163,7 +169,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.19" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/CIA_opacity.rst b/documents/tutorials/CIA_opacity.rst index 4fdd733f2..29585005c 100644 --- a/documents/tutorials/CIA_opacity.rst +++ b/documents/tutorials/CIA_opacity.rst @@ -5,23 +5,30 @@ CIA coefficient from exojax.utils.grids import wavenumber_grid - nus, wav, res = wavenumber_grid(5000, 50000, 1000, unit="AA") + nus, wav, res = wavenumber_grid(5000, 50000, 1000, unit="AA", xsmode="lpf") from exojax.spec import contdb - cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus) - + cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nus) .. parsed-literal:: - xsmode assumes ESLOG in wavenumber space: mode=lpf + xsmode = lpf + xsmode assumes ESLOG in wavenumber space: xsmode=lpf + ====================================================================== + The wavenumber grid should be in ascending order. + The users can specify the order of the wavelength grid by themselves. + Your wavelength grid is in *** descending *** order + ====================================================================== H2-H2 .. parsed-literal:: - /home/kawahara/exojax/src/exojax/utils/grids.py:123: UserWarning: Resolution may be too small. R=433.86018742134854 - warnings.warn('Resolution may be too small. R=' + str(resolution), + /home/kawahara/exojax/src/exojax/spec/unitconvert.py:63: UserWarning: Both input wavelength and output wavenumber are in ascending order. + warnings.warn( + /home/kawahara/exojax/src/exojax/utils/grids.py:144: UserWarning: Resolution may be too small. R=433.86018742134854 + warnings.warn("Resolution may be too small. R=" + str(resolution), UserWarning) logacia can provide an absorption coeffcient as a function of @@ -29,12 +36,11 @@ temperature .. code:: ipython3 - from exojax.spec.hitrancia import logacia + from exojax.spec.hitrancia import interp_logacia_vector import jax.numpy as jnp Tfix = jnp.array([1000.0, 1300.0, 1600.0]) - lc = logacia(Tfix, nus, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac) - + lc = interp_logacia_vector(Tfix, nus, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac) plotting… @@ -42,9 +48,9 @@ plotting… import matplotlib.pyplot as plt - plt.style.use('bmh') + plt.style.use("bmh") for i in range(0, len(Tfix)): - plt.plot(wav[::-1], lc[i, :], lw=1, label=str(int(Tfix[i])) + " K") + plt.plot(wav, lc[:, i], lw=1, label=str(int(Tfix[i])) + " K") plt.axvspan(22876.0, 23010.0, alpha=0.3, color="green") plt.xlabel("wavelength ($\\AA$)") plt.ylabel("absorption coefficient ($cm^5$)") diff --git a/documents/tutorials/Comparing_HITEMP_and_ExoMol.ipynb b/documents/tutorials/Comparing_HITEMP_and_ExoMol.ipynb index bb5f273c3..b2a73d8d1 100644 --- a/documents/tutorials/Comparing_HITEMP_and_ExoMol.ipynb +++ b/documents/tutorials/Comparing_HITEMP_and_ExoMol.ipynb @@ -20,10 +20,9 @@ }, "outputs": [], "source": [ - "from exojax.spec.lpf import auto_xsection\n", "from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural\n", "from exojax.spec.exomol import gamma_exomol\n", - "from exojax.spec import api \n", + "from exojax.spec import api\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] @@ -53,8 +52,8 @@ "outputs": [], "source": [ "# Setting wavenumber bins and loading HITEMP database\n", - "wav=np.linspace(22930.0,23000.0,4000,dtype=np.float64) #AA\n", - "nus=1.e8/wav[::-1] #cm-1" + "wav = np.linspace(22930.0, 23000.0, 4000, dtype=np.float64) # AA\n", + "nus = 1.0e8 / wav[::-1] # cm-1" ] }, { @@ -68,9 +67,21 @@ "shell.execute_reply": "2024-01-16T23:36:32.624298Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radis engine = vaex\n", + "Downloading 05_HITEMP2019.par.bz2 for CO (1/1).\n", + "Download complete. Parsing CO database to /home/kawahara/exojax/documents/tutorials/CO-05_HITEMP2019.hdf5\n" + ] + } + ], "source": [ - "mdbCO_HITEMP=api.MdbHitemp('CO',nus, isotope=1, gpu_transfer=True) # we use istope=1 for comparison" + "mdbCO_HITEMP = api.MdbHitemp(\n", + " \"CO\", nus, isotope=1, gpu_transfer=True\n", + ") # we use istope=1 for comparison" ] }, { @@ -89,7 +100,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/utils/molname.py:178: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.\n", + " warnings.warn(\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n", + " warnings.warn(\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n", " warnings.warn(\n" ] }, @@ -98,6 +113,7 @@ "output_type": "stream", "text": [ "HITRAN exact name= (12C)(16O)\n", + "radis engine = vaex\n", "Molecule: CO\n", "Isotopologue: 12C-16O\n", "Background atmosphere: H2\n", @@ -105,18 +121,6 @@ "Local folder: CO/12C-16O/Li2015\n", "Transition files: \n", "\t => File 12C-16O__Li2015.trans\n", - "# i_upper i_lower A nu_lines gup jlower jupper elower Sij0\n", - "0 84 42 1.155e-06 2.405586 3 0 1 66960.7124 3.811968898414225e-164\n", - "1 83 41 1.161e-06 2.441775 3 0 1 65819.903 9.663028103692631e-162\n", - "2 82 40 1.162e-06 2.477774 3 0 1 64654.9206 2.7438392479197905e-159\n", - "3 81 39 1.159e-06 2.513606 3 0 1 63465.8042 8.73322833971394e-157\n", - "4 80 38 1.152e-06 2.549292 3 0 1 62252.5793 3.115220404216648e-154\n", - "... ... ... ... ... ... ... ... ... ...\n", - "125,491 306 253 7.164e-10 22147.135424 15 6 7 80.7354 1.8282485593637477e-31\n", - "125,492 474 421 9.852e-10 22147.86595 23 10 11 211.4041 2.0425455665383687e-31\n", - "125,493 348 295 7.72e-10 22147.897299 17 7 8 107.6424 1.9589545250222689e-31\n", - "125,494 432 379 9.056e-10 22148.262711 21 9 10 172.978 2.0662209116961706e-31\n", - "125,495 390 337 8.348e-10 22148.273111 19 8 9 138.3903 2.0387827253771594e-31\n", "Broadening code level: a0\n" ] }, @@ -124,14 +128,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/anaconda3/lib/python3.10/site-packages/radis-0.15-py3.10-linux-x86_64.egg/radis/api/exomolapi.py:607: AccuracyWarning: The default broadening parameter (alpha = 0.07 cm^-1 and n = 0.5) are used for J'' > 80 up to J'' = 152\n", + "/home/kawahara/exojax/src/radis/radis/api/exomolapi.py:685: AccuracyWarning: The default broadening parameter (alpha = 0.07 cm^-1 and n = 0.5) are used for J'' > 80 up to J'' = 152\n", " warnings.warn(\n" ] } ], "source": [ - "emf='CO/12C-16O/Li2015' #this is isotope=1 12C-16O\n", - "mdbCO_Li2015=api.MdbExomol(emf,nus, gpu_transfer = True)" + "emf = \"CO/12C-16O/Li2015\" # this is isotope=1 12C-16O\n", + "mdbCO_Li2015 = api.MdbExomol(emf, nus, gpu_transfer=True)" ] }, { @@ -156,10 +160,11 @@ "outputs": [], "source": [ "from exojax.spec import molinfo\n", - "molecular_mass=molinfo.molmass(\"CO\") # molecular weight\n", - "Tfix=1300.0 # we assume T=1300K\n", - "Pfix=0.99 # we compute P=1 bar=0.99+0.1\n", - "Ppart=0.01 #partial pressure of CO. here we assume a 1% CO atmosphere (very few). " + "\n", + "molecular_mass = molinfo.molmass(\"CO\") # molecular weight\n", + "Tfix = 1300.0 # we assume T=1300K\n", + "Pfix = 0.99 # we compute P=1 bar=0.99+0.1\n", + "Ppart = 0.01 # partial pressure of CO. here we assume a 1% CO atmosphere (very few)." ] }, { @@ -175,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:33.581042Z", @@ -186,9 +191,11 @@ }, "outputs": [], "source": [ - "#mdbCO_HITEMP.ExomolQT(emf) #use Q(T) from Exomol/Li2015\n", - "qt_HITEMP=mdbCO_HITEMP.qr_interp(1,Tfix)\n", - "qt_Li2015=mdbCO_Li2015.qr_interp(Tfix)" + "# mdbCO_HITEMP.ExomolQT(emf) #use Q(T) from Exomol/Li2015\n", + "from exojax.utils.constants import Tref_original\n", + "\n", + "qt_HITEMP = mdbCO_HITEMP.qr_interp(1, Tfix, Tref_original)\n", + "qt_Li2015 = mdbCO_Li2015.qr_interp(Tfix, Tref_original)" ] }, { @@ -210,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:33.882875Z", @@ -221,10 +228,22 @@ }, "outputs": [], "source": [ - "Sij_HITEMP=line_strength(Tfix,mdbCO_HITEMP.logsij0,mdbCO_HITEMP.nu_lines,\\\n", - " mdbCO_HITEMP.elower,qt_HITEMP,mdbCO_HITEMP.Tref)\n", - "Sij_Li2015=line_strength(Tfix,mdbCO_Li2015.logsij0,mdbCO_Li2015.nu_lines,\\\n", - " mdbCO_Li2015.elower,qt_Li2015,mdbCO_Li2015.Tref)" + "Sij_HITEMP = line_strength(\n", + " Tfix,\n", + " mdbCO_HITEMP.logsij0,\n", + " mdbCO_HITEMP.nu_lines,\n", + " mdbCO_HITEMP.elower,\n", + " qt_HITEMP,\n", + " Tref_original,\n", + ")\n", + "Sij_Li2015 = line_strength(\n", + " Tfix,\n", + " mdbCO_Li2015.logsij0,\n", + " mdbCO_Li2015.nu_lines,\n", + " mdbCO_Li2015.elower,\n", + " qt_Li2015,\n", + " Tref_original,\n", + ")" ] }, { @@ -255,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:33.942835Z", @@ -266,12 +285,18 @@ }, "outputs": [], "source": [ - "gammaL_HITEMP = gamma_hitran(Pfix,Tfix, Ppart, mdbCO_HITEMP.n_air, \\\n", - " mdbCO_HITEMP.gamma_air, mdbCO_HITEMP.gamma_self) \\\n", - "+ gamma_natural(mdbCO_HITEMP.A) \n", + "gammaL_HITEMP = gamma_hitran(\n", + " Pfix,\n", + " Tfix,\n", + " Ppart,\n", + " mdbCO_HITEMP.n_air,\n", + " mdbCO_HITEMP.gamma_air,\n", + " mdbCO_HITEMP.gamma_self,\n", + ") + gamma_natural(mdbCO_HITEMP.A)\n", "\n", - "gammaL_Li2015 = gamma_exomol(Pfix,Tfix,mdbCO_Li2015.n_Texp,mdbCO_Li2015.alpha_ref)\\\n", - "+ gamma_natural(mdbCO_Li2015.A) " + "gammaL_Li2015 = gamma_exomol(\n", + " Pfix, Tfix, mdbCO_Li2015.n_Texp, mdbCO_Li2015.alpha_ref\n", + ") + gamma_natural(mdbCO_Li2015.A)" ] }, { @@ -285,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:34.163292Z", @@ -297,8 +322,8 @@ "outputs": [], "source": [ "# thermal doppler sigma\n", - "sigmaD_HITEMP=doppler_sigma(mdbCO_HITEMP.nu_lines,Tfix,molecular_mass)\n", - "sigmaD_Li2015=doppler_sigma(mdbCO_Li2015.nu_lines,Tfix,molecular_mass)" + "sigmaD_HITEMP = doppler_sigma(mdbCO_HITEMP.nu_lines, Tfix, molecular_mass)\n", + "sigmaD_Li2015 = doppler_sigma(mdbCO_Li2015.nu_lines, Tfix, molecular_mass)" ] }, { @@ -313,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:34.211118Z", @@ -324,9 +349,9 @@ }, "outputs": [], "source": [ - "#line center\n", - "nu0_HITEMP=mdbCO_HITEMP.nu_lines\n", - "nu0_Li2015=mdbCO_Li2015.nu_lines" + "# line center\n", + "nu0_HITEMP = mdbCO_HITEMP.nu_lines\n", + "nu0_Li2015 = mdbCO_Li2015.nu_lines" ] }, { @@ -338,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:34.214579Z", @@ -353,15 +378,15 @@ "from exojax.spec.lpf import xsvector\n", "\n", "numatrix_HITEMP = init_lpf(mdbCO_HITEMP.nu_lines, nus)\n", - "xsv_HITEMP=xsvector(numatrix_HITEMP, sigmaD_HITEMP, gammaL_HITEMP, Sij_HITEMP)\n", + "xsv_HITEMP = xsvector(numatrix_HITEMP, sigmaD_HITEMP, gammaL_HITEMP, Sij_HITEMP)\n", "\n", "numatrix_Li2015 = init_lpf(mdbCO_Li2015.nu_lines, nus)\n", - "xsv_Li2015=xsvector(numatrix_Li2015, sigmaD_Li2015, gammaL_Li2015, Sij_Li2015)\n" + "xsv_Li2015 = xsvector(numatrix_Li2015, sigmaD_Li2015, gammaL_Li2015, Sij_Li2015)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-01-16T23:36:34.479190Z", @@ -373,7 +398,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAGaCAYAAACGxkvMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWjUlEQVR4nOzdd3xUVfrH8c+dSTJJIAUIJfQOCoQmvUZUFOsqIK4KCC42UBBWF3ddy8+GvRcUaSsWUFTADgEFaUqRDtITAiEJKaRn5v7+mGSSMQmkTDIEvu/Xa8zcc8+555kxJHnmnHuOYZqmiYiIiIiIiFQJi7cDEBERERERuZAoCRMREREREalCSsJERERERESqkJIwERERERGRKqQkTEREREREpAopCRMREREREalCSsJERERERESqkJIwERERERGRKqQkTEREREREpAopCRMREblArFy5EsMwWLRokbdDERG5oCkJExGRIgzDKNVj5cqVFern6aef5rrrrqN+/foYhsHjjz9ebL3FixczdOhQGjZsiM1mo3HjxgwfPpzt27cXW//rr7+mW7du+Pv707RpUx577DFyc3OL1EtKSmLChAnUrVuXGjVqEBkZyaZNm4rUMwyDiRMnFil/5plnMAyDcePG4XA4SnydgwcPdnvfateuTY8ePfjwww/P2K60NmzYwL333kv37t3x9fXFMIwKX1NERCqPj7cDEBGRc8/8+fPdjufNm8ePP/5YpPyiiy6qUD//+c9/aNCgAV27duX7778vsd62bduoVasWDzzwAGFhYRw/fpwPP/yQnj17snbtWjp37uyq++2333LDDTcwePBg3njjDbZt28ZTTz1FXFwc77zzjquew+Hg6quvZuvWrfzzn/8kLCyMt99+m8GDB/P777/Tpk2bM8b+3HPP8e9//5sxY8bwwQcfYLGc+XPNxo0b8+yzzwJw8uRJ5s2bx/jx49m7dy/PPfdcad6uEn3zzTd88MEHRERE0LJlS/bu3Vuh64mISCUzRUREzuK+++4zK+NXxsGDB03TNM2TJ0+agPnYY4+Vuu3x48dNHx8f86677nIrv/jii83OnTubOTk5rrJ///vfpmEY5q5du1xln376qQmYCxcudJXFxcWZoaGh5i233OJ2TcC87777XMfPP/+8CZijR4827Xb7WWMdNGiQ2aFDB7eytLQ0s3HjxmaNGjXM7Ozs0r3oEhw/ftxMT083TfPM/6+ioqKKvObKcPr06Uq9vohIdafpiCIi4jXNmzcvd9t69eoRGBhIUlKSq2znzp3s3LmTCRMm4ONTMNnj3nvvxTRNt3uhFi1aRP369bnxxhtdZXXr1mXkyJF89dVXZGVlFdvvyy+/zEMPPcRtt93G7NmzzzoCVpLAwEB69+5NWloaJ0+eLNc18tWvX5+AgIBS17fb7TzyyCM0aNCAGjVqcN1113H06FG3Or/88gsjRoygadOm2Gw2mjRpwpQpU8jIyHCrN3bsWGrWrMn+/fsZNmwYQUFB3HrrrRV6PSIi5ztNRxQRkQqLj48vVb2goCBsNlu5+0lKSiInJ4fjx4/z6quvkpKSwpAhQ1znN2/eDMAll1zi1q5hw4Y0btzYdT6/brdu3YokUT179mTmzJns3buXTp06uZ177bXXmDp1Kn//+9+ZM2dOuROwfAcOHMBqtRIaGgpAeno66enpZ21ntVqpVatWuft9+umnMQyDhx9+mLi4OF599VUuu+wytmzZ4krmFi5cSHp6Ovfccw916tRhw4YNvPHGG0RHR7Nw4UK36+Xm5jJ06FD69+/Piy++SGBgYLljExG5ECgJExGRCqtbt26p6s2ePZuxY8eWu5/evXuzZ88eAGrWrMl//vMfxo8f7zofGxsLQHh4eJG24eHhHDt2zK3uwIEDi60HcOzYMbckbOnSpRw+fJhbbrmFefPmYbVayxS73W53Javx8fG88847bNq0iWuvvdaVtDz//PM88cQTZ71Ws2bNOHToUJn6LywxMZFdu3YRFBQEQLdu3Rg5ciTvv/8+999/PwAzZsxwG12bMGECrVu35pFHHuHIkSM0bdrUdS4rK4sRI0a47nkTEZEzUxImIiIV9uOPP5aqXocOHSrUz+zZs0lJSeHAgQPMnj2bjIwM7Ha7a0Qqf6pccaNt/v7+pKSkuI4zMjJKrFf4WvlOnDgBQIsWLcqcgAHs3r3bLVk1DIOrr76aDz/80FU2evRo+vfvf9ZrlWXqYXFGjx7tSsAAhg8fTnh4ON98840rCSvcR1paGhkZGfTt2xfTNNm8ebNbEgZwzz33VCgmEZELiZKwcvj555954YUX+P3334mNjWXx4sXccMMNldbfs88+yxdffMHu3bsJCAigb9++zJgxg3bt2rnqZGZmMnXqVD755BOysrIYOnQob7/9NvXr16+0uERE8l122WVV0k+fPn1cz0eNGuVanfHFF18EChKH4u7nyszMdEssAgICSqxX+Fr5xowZw7Fjx3jmmWcICwtjypQpZYq9efPmvP/++xiGgb+/P23atKFevXpudVq2bEnLli3LdN3y+OvKj4Zh0Lp1a7fRtSNHjvDf//6Xr7/+mlOnTrnVT05Odjv28fGhcePGlRaviMj5RklYOaSlpdG5c2fGjRvndkN3ZVm1ahX33XcfPXr0IDc3l0ceeYQrrriCnTt3UqNGDQCmTJnCsmXLWLhwISEhIUycOJEbb7yRNWvWVHp8IiLHjx8vVb2QkJAKj+Lkq1WrFpdeeikfffSRKwnLn0oYGxtLkyZN3OrHxsbSs2dP13F4eLhr+uJf64HzPrLCfHx8+Oyzz7jyyiuZOnUqoaGh3HHHHaWOt0aNGmdNVk+fPs3p06fPei2r1VrqKaDlYbfbufzyy0lMTOThhx+mffv21KhRg5iYGMaOHVtkbzObzVbh++NERC4kSsLK4aqrruKqq64q8XxWVhb//ve/+fjjj0lKSqJjx47MmDGDwYMHl6u/7777zu14zpw51KtXj99//52BAweSnJzMrFmzWLBgAZdeeingnLJz0UUXsW7dOnr37l2ufkVESqu4e7CKU9F7wv4qIyPDbVSmS5cuAPz2229uCdexY8eIjo5mwoQJbnV/+eUXHA6HWwKxfv16AgMDadu2bZH+/P39+frrr4mMjOQf//gHoaGh/O1vf/PY63nxxRer5J6wffv2uR2bpsmff/5JREQE4NyXbe/evcydO5fRo0e76pV22qmIiJyZkrBKMHHiRHbu3Mknn3xCw4YNWbx4MVdeeSXbtm076+afpZH/B0ft2rUB+P3338nJyXH7hLV9+/Y0bdqUtWvXKgkTkUpX2feExcXFFZm6d+jQIZYvX+62EmKHDh1o3749M2fO5K677nLdu/XOO+9gGAbDhw931R0+fDiLFi3iiy++cJXHx8ezcOFCrr322hJXcQwODua7775jwIAB3HLLLSxbtsxthcaKqKp7wubNm8f06dNd94UtWrSI2NhYHn74YQDX+2aapquNaZq89tprFepXRESclIR52JEjR5g9ezZHjhxxTWWZNm0a3333HbNnz+aZZ56p0PUdDgeTJ0+mX79+dOzYEXBOA/Lz83MtcZyvfv36pZ4iJCJSEeW9J2z+/PkcPnzYtSz7zz//zFNPPQXA7bffTrNmzQDo1KkTQ4YMoUuXLtSqVYt9+/Yxa9YscnJyeO6559yu+cILL3DddddxxRVXMGrUKLZv386bb77JnXfe6bqHDJxJWO/evbnjjjvYuXMnYWFhvP3229jt9rOORtWtW5cff/yRfv36ccMNN7B8+XLXyNvgwYNZtWqVWwJTWuW9J+zw4cPMnz8fcI4CAq73sVmzZtx+++1u9WvXrk3//v254447OHHiBK+++iqtW7fmH//4B+D8IK9Vq1ZMmzaNmJgYgoOD+fzzz4vcGyYiIuXkzZ2izweAuXjxYtfx0qVLTcCsUaOG28PHx8ccOXKkaZqmuWvXLhM44+Phhx8utr+7777bbNasmXn06FFX2UcffWT6+fkVqdujRw/zoYce8uwLFpEL0n333WdWxq+MQYMGlfhzMCoqylXvscceMy+55BKzVq1apo+Pj9mwYUNz1KhR5h9//FHsdRcvXmx26dLFtNlsZuPGjc3//Oc/ZnZ2dpF6iYmJ5vjx4806deqYgYGB5qBBg8yNGzcWqQeY9913X5HyXbt2mWFhYWbt2rXN7du3m6Zpmt27dzcbNGhQ5HV26NChLG9NmURFRZX4Pg4aNKhIvY8//ticPn26Wa9ePTMgIMC8+uqrzcOHD7tdc+fOneZll11m1qxZ0wwLCzP/8Y9/mFu3bjUBc/bs2a56Y8aMMWvUqFFpr01E5HxkmGY5PqoTF8Mw3FZH/PTTT7n11lvZsWNHkSWMa9asSYMGDcjOzubAgQNnvG6dOnWK3HQ9ceJEvvrqK37++WdatGjhKl+xYgVDhgzh1KlTbqNhzZo1Y/LkyWVewUtERMonNTWV2rVr8+qrr3Lfffd5OxwRETlHaTqih3Xt2hW73U5cXBwDBgwoto6fnx/t27cv9TVN02TSpEksXryYlStXuiVgAN27d8fX15fly5dz0003AbBnzx6OHDnitpyziIhUrp9//plGjRq5pvWJiIgURyNh5XD69Gn+/PNPwJl0vfzyy0RGRlK7dm2aNm3Kbbfdxpo1a3jppZfo2rUrJ0+eZPny5URERHD11VeXub97772XBQsW8NVXX7ntDVZ4qed77rmHb775hjlz5hAcHMykSZMA+PXXXz3wikVERERExFOUhJXDypUriYyMLFI+ZswY5syZQ05ODk899RTz5s0jJiaGsLAwevfuzRNPPEGnTp3K3J9hGMWWF17qOX+z5o8//thts+YGDRqUuT8REREREak8SsJERERERESqkLa3FxERERERqULVIgk7ffo0jz32GFdeeSW1a9fGMAzmzJlT6vZJSUlMmDCBunXrUqNGDSIjI9m0aVPlBSwiIiIiIlKCarE6Ynx8PE8++SRNmzalc+fOrFy5stRtHQ4HV199NVu3buWf//ynazPOwYMH8/vvv9OmTZsyXevYsWMEBQWVeJ+WiIiIiIic/0zTJDU1lYYNG2KxlHFsy1sblJVFZmamGRsba5qmaW7cuLHIRpFn8umnn5qAuXDhQldZXFycGRoaat5yyy1liuPo0aNn3WRZDz300EMPPfTQQw899LhwHkePHi1TTmGaplktRsJsNlu5V/lbtGgR9evX58Ybb3SV1a1bl5EjR/K///2PrKwsbDZbqa4VFBQEwNGjRwkODi5XPCIiIiIiUv2lpKTQpEkTV45QFtUiCauIzZs3061btyJDhD179mTmzJns3bu3xGXjs7KyyMrKch2npqYCEBwcrCRMRERERETKdZtStViYoyJiY2MJDw8vUp5fduzYsRLbPvvss4SEhLgeTZo0qbQ4RURERETkwnDeJ2EZGRnFTjf09/d3nS/J9OnTSU5Odj2OHj1aaXGKiIiIiMiF4byfjhgQEOA2pTBfZmam63xJbDZbqe8XExERERERKY3zfiQsPDyc2NjYIuX5ZQ0bNqzqkERERERE5AJ23idhXbp0YdOmTTgcDrfy9evXExgYSNu2bb0UmYiIiIiIXIjOq+mIsbGxJCcn06pVK3x9fQEYPnw4ixYt4osvvmD48OGAc/PnhQsXcu2111b6dMOcnBzsdnul9iEiFWO1Wl0/M0REREQqW7VJwt58802SkpJcqxkuWbKE6OhoACZNmkRISAjTp09n7ty5HDx4kObNmwPOJKx3797ccccd7Ny5k7CwMN5++23sdjtPPPFEpcWbkpJCfHx8sfejici5x2azERYWpu0nREREpNJVmyTsxRdf5PDhw67jL774gi+++AKA2267jZCQkGLbWa1WvvnmG/75z3/y+uuvk5GRQY8ePZgzZw7t2rWrlFhTUlKIiYmhZs2ahIWF4evrW679A0Sk8pmmSU5ODsnJycTExAAoERMREZFKZZimaXo7iOoiJSWFkJAQkpOTz/hH2oEDB/D19aVx48ZKvkSqCdM0iY6OJicnh5YtW3o7HBERETnHlTY3KM55vzBHVcvJySErK4uQkBAlYCLViGEYhISEkJWVRU5OjrfDERERkfOYkjAPy1+EQzf5i1Q/+f9utZiOiIiIVCYlYZVEo2Ai1Y/+3YqIiEhVUBImIiIiIiJShZSEiYiIiIiIVCElYSIiIiIiIlVISZiIiIiIiEgVUhImHnfo0CEMw+DKK68ssc7KlSsxDIO7777bVTZ27FgMw2DdunUANG/eHMMwSv04dOgQQKnqFjZ48GBX+dKlS0uMuVevXq56K1eudDv31+v7+PgQHh7ODTfcwM8//+xWd8uWLTz66KP07t2bevXqYbPZaNmyJffee69rs+Di7N27l5EjRxIWFkZAQACdO3fmnXfeobit/rZs2cIjjzzC0KFDqVu3LoZhMHjw4BKvDXDq1CmmTZtG69atsdls1K1bl+HDh7Njx44zthMRERGRsvHxdgAiJZk8eTJJSUluZXPmzOHw4cM88MADhIaGup0rfFynTh0mTpxYpv58fHz48MMPueaaa4qc27FjBxs2bMDHx4fc3Nxi2xfuMzMzky1btvDVV1/x9ddf8+mnnzJixAgA7r77btavX0/Pnj0ZNWoUNpuN9evX884777Bw4UJ++eUX2rdv73btnTt30rdvXzIyMhg5ciQNGzZk2bJl3HvvvezcuZM33njDrf6XX37Js88+i5+fH23btiU+Pv6Mrz0hIYE+ffqwb98++vTpw/XXX09sbCyff/453377LStWrKBXr16lfi9FRERE5AxMKbXk5GQTMJOTk0usk5GRYe7cudPMyMiowsjOLQcPHjQBc+jQoSXWiYqKMgHzrrvucpWNGTPGBMy1a9eW2G7QoEEmYB48eLDEOoDZrl27Usebf81rr73W9PX1NePi4orUmTJlimmxWMyrr77aBMyoqKhS9fn++++bgNm8eXNX2euvv27u27evSN3nnnvOBMxhw4YVOTdw4EATML/55htXWVZWljlgwAATMH/99Ve3+tu3bzd///13Mzs724yNjTUBc9CgQSW+B/fdd58JmA8++KBb+a+//mparVbz4osvNu12e4ntzxf69ysiIiKlVZrcoCSajiiSZ9y4ceTk5DB//ny38pycHP73v/9xxRVX0Lhx4zJfs0aNGhw6dIiTJ08CMGnSJFq3bl2k7rRp0wgICGDVqlVu5Xv37uXnn38mMjKSq666ylXu5+fH//3f/wHw/vvvu7Xp0KED3bp1K/Wm4V999RUWi4UnnnjCrbxPnz5ce+217Ny5s0hcIiIiIlI+SsJE8vTu3ZuLL76Y2bNnu5UvWbKEkydPMm7cuApd/2wbARuGga+vLz4+7rOE8+8/u+KKK4q06d+/PzVq1KhwgnT8+HHCwsKoWbNmkXMtWrQAYMWKFRXqQ0REREScdE9YFbv2jdWcTM3ydhhnVDfIxpJJ/St8nT///JPHH3+82HP5i2hUlvj4+BL7bt++PaNGjSr23Lhx45g2bRobN26kR48eAMyaNYs6depw/fXXs3z58jLFMXfuXNLS0mjRogVhYWFnrLto0SJSUlJc947l27dvHwBt2rQp0sZqtdKiRQt27txJbm5ukQSutMLCwoiLi+P06dNFErGDBw8CzhE5EREREak4JWFV7GRqFsdTMr0dRpXYv39/keltVSUhIaHEvq+//voSk7Dbb7+d6dOn8+GHH9KjRw+OHTvG999/z8SJE/Hz8ztjn4UTv8zMTLZu3cp3332HxWLhhRdeOGPbo0ePcv/99xMQEOCaYpgvOTkZgJCQkGLbBgcH43A4SE1NpVatWmfspyRXXXUVs2fP5oknnnCLdf369a4VI/+6SIqIiIiIlI+SsCpWN8jm7RDOylMxDh06lO+++67YcytXriQyMtIj/RSnXbt27N69u8zt6tWrx9VXX80nn3zCK6+8wty5c7Hb7aWailg48bNarYSFhXH99dczdepUBgwYcMZ2w4YNIy4ujnnz5tGuXbsyx11RTz75JN999x0vvvgia9eupXfv3sTGxrJo0SIuvvhi/vjjDywWzV4WERER8QQlYVXME9P8pHKNGzeOL7/8ks8//5zZs2fTvXt3IiIiztquPIlfQkICQ4YMYceOHbzzzjvcdtttRerkj4Dlj4j9VUpKCoZhEBQUVKa+C2vcuDEbN27kscce49tvv2XDhg00adKEJ598kubNmzNq1Cjq1atX7uuLiIiISAElYSJ/MWzYMMLDw3n44YeJiYnh7bffrpR+8hOwrVu38tZbb3HXXXcVWy//XrD8e8MKs9vtHDx4kBYtWpT7frB8jRo14oMPPihSnj/F8pJLLqnQ9UVERETESfOLRP7CarUyevRoYmJi8Pf355ZbbvF4H4UTsDfeeIN77723xLqDBg0C4IcffihybvXq1aSlpbnqeJrdbueTTz7Bx8eHm266qVL6EBEREbnQKAkTKcaDDz7I4sWL+f777wkNDfXotRMTE7nsssvYunUrr732GhMnTjxj/Xbt2jFw4ECioqL49ttvXeXZ2dk8+uijANx5550ViiknJ4eMjAy3MofDwbRp09izZw+TJk2iYcOGFepDRERERJw0HVHOS2daoh7g7rvvpkGDBiWer1evHjfccIPnAwNuvPFGtmzZQvv27UlMTCw2zsmTJ7slf2+//Tb9+vXjhhtu4OabbyY8PJxly5axY8cOJk6cSN++fd3a7969m+eeew7AlVzt3r2bsWPHuurMmTPH9fzEiRN06NCBK664ghYtWpCdnc3333/P7t27ufrqq3n22Wc99vpFRERELnRKwuS8dKYl6gFuuOGGMyZhlSl/j7Tdu3eXGOPYsWPdkrAOHTqwfv16/vOf/7Bs2TLS0tJo27Ytb731Fvfcc0+R9sePH2fu3LluZSdOnHArK5yEhYSEcP3117NmzRqWLl2Kr68vHTt25P3332fcuHFaGVFERETEgwzTNE1vB1FdpKSkEBISQnJyMsHBwcXWyczMdC2U4O/vX8URikhF6N+viIiIlFZpcoOS6ONtERERERGRKqQkTEREREREpAopCRMREREREalCSsJERERERESqkJIwERERERGRKqQkTEREREREpAopCRMREREREalCSsJERERERESqkJIwERERERGRKqQkTEREREREpAopCRMREREREalCSsJERERERESqkJIwERERERGRKqQkTKQSHDp0CMMwGDt2rLdDKbM5c+ZgGAZz5szxdigVtnLlSgzD4PHHH/d2KCIiIiIuSsLEo/KTjzM9mjdv7u0wz2svvvgihmGwfft2b4ciIiIiIsXw8XYAcn5q1aoVt912W7HnQkNDqzaYC8xXX31Fq1at6Nixo7dDEREREZFiKAmTStG6dWtNAfOCkydP8uuvvzJ58mRvhyIiIiIiJdB0RPGq5557DsMwuPvuu0s8d88997iVL1myhMjISEJCQggICKBz5868/PLL5ObmutUrfF/Wrl27uOaaawgNDaVWrVrccsstxMfHA7B27VqGDBlCcHAwtWrV4s477yQtLa3YeGfPnk2vXr2oWbMmNWvWpFevXhW+d+qrr77CMAxefPFFt/JXX30VwzBo3LixW3lmZib+/v5ERkYWudbSpUtxOBxcf/31FYqpcGw9e/YkMDCQunXrMm7cOE6cOFGknmEYDB48mJiYGEaPHk2DBg2wWCysXLnSVae07112djZvvPEGQ4cOpUmTJthsNurVq8eNN97I5s2bi40zIyODf/3rXzRp0gR/f386duzI+++/75H3QERERMTTlISJVz300ENceumlvPfee3z55Zeu8g0bNvDf//6Xiy++mJdfftlV/vLLL3Pdddfxxx9/8Pe//5377ruPjIwMpk6dyogRIzBNs0gfBw8epG/fvmRlZXHnnXfSuXNnPvnkE2644QZWr17NkCFDqFmzJhMmTKBVq1bMmjWLSZMmFbnO/fffz7hx44iJiWH8+PGMHz+emJgY7rjjDh544IFyvwcDBw7EYrEQFRXlVp5/HBMTw759+1zla9euJSsrq9gk7MsvvyQsLIx+/fqVO558n3/+OSNGjKB169ZMnjyZTp06MXv2bPr378+pU6eK1E9ISKBPnz788ccfjBo1igkTJhAcHAyU7b1LTExk8uTJZGVlMWzYMKZMmcLgwYP55ptv6Nu3Lxs3bnSr73A4uO6665gxYwa1atXigQceoHfv3kyZMoWXXnqpwu+DiIiIiMeZUmrJyckmYCYnJ5dYJyMjw9y5c6eZkZFR8oXWvGGaL7Y/++Ojm4u2/ejm0rVd84Z7u8yU8rUro4MHD5qA2apVK/Oxxx4r9vHtt9+6tYmOjjbr1Klj1q5d24yOjjZTUlLMVq1amTabzdy6daur3p9//mn6+PiY9erVM48cOVLw0jIzzf79+5uAOW/evCKxAOarr77qKnc4HOawYcNMwAwNDTW//PJL17ns7GwzIiLC9PHxMY8fP+4qX7VqlQmYF110kZmUlOQqT0xMNNu2bWsC5s8//1yk7zFjxpTqfevWrZsZFBRk5uTkmKZpmna73QwNDTWHDBliAuZ7773nqvvoo48W6c80TTMtLc0MCAgwx44dW6o+SzJ79mzX+/bdd9+5nfvXv/5lAubEiRPdyvPr33HHHWZubq7bubK+d5mZmWZ0dHSRuLZv327WrFnTvOyyy4qN98orr3Tr+48//jD9/PxMwHzsscdK9dpL9e9XRERExCxdblASjYR5Q1YqpB47+yM9vmjb9PjStc1KdW9nmuVrV0779+/niSeeKPbx3XffudVt1KgRs2bNIjExkdtuu417772X/fv38/zzzxMREeGqt2DBAnJzc5k6dSpNmjRxldtsNmbMmAFQ7PS2Vq1acf/997uODcNg1KhRAHTt2tVt6p6vry/Dhw8nNzeXnTt3usrnzp0LwOOPP05ISIirvFatWjz22GMl9l1akZGRpKam8ttvvwGwefNmkpKSuPPOO2natCkrVqxw1Y2KiiIgIIBevXq5XePHH38kIyPDY1MRL7vsMoYOHepW9u9//5vQ0FDmzZuHw+FwO+fn58fzzz+P1Wp1Ky/re2ez2WjUqFGReDp06EBkZCQ///wzOTk5rvJ58+YB8PTTT7v13alTJ26//fayvGQRERGRKqGFObzBFgRBDc9eLzCs+LLStLUFuR8bRvnaldPQoUOLJFtncv3113P33Xfz7rvvAjBs2DC3xAlw3Q80ePDgIu379OmDv78/W7ZsKXIuIiICwzDcysLDwwHo0qVLkfr5544dO1aqvvOnBRbXd2lFRkby0ksvERUVRe/evV1TES+99FIiIyNd72V6ejobNmxgwIAB+Pn5uV3jq6++IiAggCuuuKLccRQ2YMCAImU1a9akS5curFy5kgMHDtC6dWvXuRYtWhAWVvR7tjzv3ZYtW3j++edZvXo1x48fd0u6AOLj413/n7Zu3UqNGjXo1q1bsa9h1qxZZ36hIiIiIlWs2iRhWVlZ/Pe//2X+/PmcOnWKiIgInnrqKS6//PKztv3pp594+umn2bZtG7m5ubRt25ZJkyZ571PyvhOdj/L4+yfla2cLgqm7yte2ivztb39zJWETJxZ9f1JSUgCoX79+kXOGYVC/fn1iYmKKnMu/L6kwHx+fs54r/Id/SkoKFouFunXrFqlfv359DMNwxVceAwYMwGq1EhUVxfTp04mKiqJDhw7Uq1ePyMhI5s6dy86dO4mJiSE7O7vI/WB2u52lS5dy2WWXERgYWO44CivufS5cnpycXKr6ZX3vfv31Vy699FIArrjiCtq0aUPNmjUxDIMvv/ySrVu3kpWV5aqfnJzsNjJamphEREREvKnaJGFjx45l0aJFTJ48mTZt2jBnzhyGDRtGVFQU/fv3L7Hd119/zQ033ECfPn14/PHHMQyDzz77jNGjRxMfH8+UKVOq8FVISZKSkvjHP/5BjRo1sNvtTJo0ic2bNxMUVDAyl58wnThxgmbNmrm1N02TEydOFJtUeUJwcDAOh4OTJ09Sr149t3NxcXGYplmhvoODg+nevTtr1qwhIyOD1atXM3r0aKBgtCgqKso1OvfXJOzXX3/l5MmT3HDDDeWO4a+KWwWxcHnhqYVAkdHGfGV9755++mmysrL45ZdfivzbXrduHVu3bnUrCwkJ4eTJk2V6DSIiIiLeVC3uCduwYQOffPIJzz77LC+88AITJkxgxYoVNGvWjIceeuiMbd98803Cw8NZsWIFEydO5L777mP58uW0atWqwkuLi+dMmDCBI0eO8Nprr/HCCy+wf/9+7rvvPrc6Xbt2BXBb9jzf+vXryczMLHZ6oSecqe/8sor2HRkZSXp6Om+//TYpKSmu0aCmTZvSqlUrVqxYQVRUFDVq1KBHjx5ubb/66issFgvXXHNNhWIo7JdffilSdvr0abZs2UJwcDAtW7Ys1XXK+t7t37+f2rVrF0nA0tPT2bRpU5FrdO7cmbS0tGLPFfcaRERERLytWiRhixYtwmq1MmHCBFeZv78/48ePZ+3atRw9erTEtikpKdSqVQubzeYq8/HxISwsjICAgEqNW0pn1qxZLFy4kBEjRjB+/HgmTpzINddcw/z581mwYIGr3t///nd8fHx4+eWX3e7Xys7O5uGHHwacI6aVYcyYMQA88cQTblPnkpOTeeKJJ9zqlFf+6NaMGTOwWCxu91BFRkayYsUKNm7cSL9+/fD19XVr+9VXX9GnT58iI01nkp6ezu7duzly5Eix53/66Se+//57t7Knn36apKQkRo8ejcVSuh8fZX3vmjVrxqlTp9ixY4erzG63M23atGJHvPKnFf/73//Gbre7yrdt28b8+fNLFaOIiIhIVaoW0xE3b95M27Zti0z36tmzJ+C8ib+ke0IGDx7MjBkzePTRRxkzZgyGYbBgwQJ+++03Pvvss0qP/UL1559/8vjjj5d4/l//+hf+/v7s3buXBx54gCZNmjBz5kzX+Q8//JCIiAjuuece+vTpQ4sWLWjVqhUzZsxg6tSpREREMHLkSGrUqMGSJUvYs2cP119/PbfddlulvJ6BAwcyadIk3njjDTp27MhNN92EaZp8/vnnREdHc//99zNw4MAK9dG/f398fX05efIkXbt2pVatWq5zkZGRfPDBB67nhe3YsYM///zT7UOK0tiwYQORkZEMGjSo2FGqa665hmuvvZbhw4fTvHlz1q1bR1RUFK1ateLJJ58sdT9lfe8mTZrEDz/8QP/+/Rk5ciT+/v6sXLmSmJgYBg8eXCTWMWPGsGDBAr777ju6du3KVVddRWJiIh9//DFXXHEFS5cuLdP7IiIiIlLpPLtafuXo0KGDeemllxYp37FjhwmY7777boltT58+bY4cOdI0DMO1l1FgYKDb3lAlyczMNJOTk12Po0ePemafsPNY4b25zvQ4deqUmZWVZXbr1s20WCzmqlWrilzrhx9+MA3DMHv37u3aP8s0TfOrr74yBw0aZAYFBZk2m83s1KmT+dJLL7nVKRxLcXt1RUVFlbh/VP6+U7Nnzy5y7sMPPzR79OhhBgYGmoGBgWaPHj3MDz/8sMT3obT7hOXr27evCZhTp051Kz927JjrvVu7dq3buaefftoEzL1795apr/z3YNCgQW7lhV//l19+afbo0cMMCAgw69SpY44dO9aMjY0tcq3irvNXpX3vTNM0Fy1aZHbr1s0MDAw0w8LCzJEjR5r79+83x4wZYwLmwYMH3eqnpaWZDz30kNmoUSPTZrOZF198sTlz5swz/n8uzoX+71dERERKryL7hBmmaZoVTeSioqJYvnw5a9asITo6mvj4eAIDA6lbty6dOnVi0KBBXHPNNTRo0KBc12/VqhXt2rXjm2++cSs/cOAArVq14pVXXmHy5MnFts3NzeWJJ55gz5493HjjjdjtdmbOnMmmTZv48ccf6d27d4n9Pv74467pUoUlJyeXuAhDZmYmBw8epEWLFvj7+5f+RYqUU69evUhNTXXb10zKR/9+RUREpLRSUlIICQk5Y25QknJPR0xLS+P111/n/fff5/Dhw+Tncv7+/tSuXZuMjAy2b9/OH3/8wUcffYSvry/XXnstU6ZMoV+/fmXqKyAgwG1J6nyZmZmu8yWZOHEi69atY9OmTa57WEaOHEmHDh144IEHWL9+fYltp0+fzoMPPug6TklJKXHao4g3xMbGsnHjRtc9cSIiIiJy7ivXwhzvvvsurVu35t///jfBwcH83//9H8uXLyc5OZn09HSio6NJSEggJyeH3bt3M3fuXEaOHMkPP/zAwIEDufHGGzl48GCp+wsPDyc2NrZIeX5Zw4bFb0KcnZ3NrFmzuPrqq90WEfD19eWqq67it99+Izs7u8R+bTYbwcHBbg+Rc0l4eDgOh4Nnn33W26GIiIiISCmVKwmbNGkSl19+OX/88QdbtmzhkUceITIy0m1PJ3DuG9S2bVtuv/125s+fz4kTJ3jvvffYunVrmVYt69KlC3v37i2yGW7+KFZJS4MnJCSQm5vrtmJavpycHBwOR7HnREREREREKku5krAdO3Ywb948OnbsWKZ2AQEB3Hnnnezdu9e1rHRpDB8+3HUvV76srCxmz55Nr169XFMEjxw5wu7du1116tWrR2hoKIsXL3Yb8Tp9+jRLliyhffv2WqZeRERERESqVLnuCWvbtm2FOrVarbRo0aLU9Xv16sWIESOYPn06cXFxtG7dmrlz53Lo0CFmzZrlqjd69GhWrVrluj/NarUybdo0/vOf/9C7d29Gjx6N3W5n1qxZREdH87///a9Cr0NERERERKSsqsU+YQDz5s3j0UcfZf78+Zw6dYqIiAiWLl161r2Z/v3vf9OiRQtee+01nnjiCbKysoiIiGDRokXcdNNNVRS9iIiIiIiIk0eWqL9QlGYZyvwlrps3b66pjiLVTEZGBocOHdIS9SIiInJWFVmivsz3hDkcDrZv386xY8eKnMvJyeHnn38u6yXPK1arFXC+FyJSveT/u83/dywiIiJSGcqUhB0+fJhOnToRERFBkyZNuO6660hISHCdT0xMJDIy0uNBVie+vr7YbDaSk5PRIKNI9WGaJsnJydhsNnx9fb0djoiIiJzHynRP2EMPPUTDhg1ZtmwZSUlJTJs2jX79+rFixQrXXl1KPCAsLIyYmBiio6MJCQnB19cXwzC8HZaIFMM0TXJyckhOTub06dM0atTI2yGJiIjIea5MSdiqVav4/vvvad68OQA//PADd911FwMGDCAqKgqbzaZkA1xzQuPj44mJifFyNCJSGjabjUaNGmlTdhEREal0ZUrC0tPTsdlsrmOLxcL777/PPffcw8CBA1mwYIHHA6yugoODCQ4OJicnRxtCi5zjrFarpiCKiIhIlSlTEtauXTt+++032rdv71b+zjvvcO+993LNNdd4NLjzga+vr/64ExERERERlzItzHHjjTeWONr19ttvM2rUKN0TJiIiIiIicgbaJ6wMKrIXgIiIiIiInD+qdJ8wERERERERKT8lYSIiIiIiIlWoTAtznMmyZcv44YcfSElJoVmzZnTu3Jlu3brRrFkzT3UhIiIiIiJS7XkkCZsxYwaPPPKI26Ic+fuFhYaG0qVLF7p160a3bt245ZZbPNGliIiIiIhIteSRhTmaNWtGamoqs2fPplOnThw+fJgtW7awZcsWNm/ezO7du8nNzcUwjGq9Z5YW5hAREREREahYbuCRkbDExETGjRvH9ddfD0DLli2JjIx0nc/Ozmbbtm1s2bLFE92JiIiIiIhUWx5Jwjp37kxaWlqJ5/38/OjevTvdu3f3RHciIiIiIiLVlkdWR5w8eTJLliwhISHBE5cTERERERE5b3kkCRs+fDi33HIL1157LTExMZ64pIiIiIiIyHnJY/uEtWzZkh07dtCuXTtGjhzJu+++y4YNG8jKyvJUFyIiIiIiItWeR1ZHfOihh3jppZf466UMw8BqtdK+fXu6detG9+7dmTRpUkW78xqtjigiIiIiIlCx3MAjSViDBg0wDIPFixcTERFBdHQ0W7ZsYdOmTWzevJnNmzcTHx+vJepFREREROS84PUl6tPT05kwYQK9e/cGoG3btrRt25aRI0e66hw9epTNmzd7ojsREREREZFqyyNJ2CWXXEJcXNwZ6zRp0oQmTZp4ojsREREREZFqyyMLc0yfPp0lS5YQHR3ticuJiIiIiIictzyShO3atYtBgwYxdOhQduzY4YlLioiIiIiInJc8sjCHxWLBMAxM08RisdC3b18uvfRSunXrRrdu3WjcuLEnYvU6LcwhIiIiIiJwDizM8dlnn7Flyxa2bNnC5s2bWb16NatXr8YwDADCwsLo2rUr3bt35+mnn/ZElyIiIiIiItWSR0bC/urkyZOuhCw/Odu7dy+maWqJehERERERqfa8PhK2Y8cOYmJi6NevHzVq1KBu3bpcfvnlXH755a46GRkZ/PHHH57oTkREREREpNryyMIczzzzDNdffz3Jyckl1gkICKBXr16e6E5ERERERKTa8kgStmbNGq666ioaNmxY7Pm9e/dy0003sWbNGk90JyIiIiIiUm15JAk7fvw4bdu2LfF827Zt2bFjB6+++qonuhMREREREam2PJKEhYSEkJKScsY6ffv2ZcOGDZ7oTkREREREpNrySBLWo0cPvv32WxwOR4l16tWrR1xcnCe6ExERERERqbY8koTdeeedHD58mAcffLDEOvv378ff398T3YmIiIiIiFRbHknCbrjhBkaOHMnrr7/ONddcw/bt293OL1++nK+++ooePXp4ojsREREREZFqyyP7hAF89NFHhIaGMnPmTL799luaN29O8+bNiYuLY9euXZimyT//+U9PdSciIiIiIlIteWQkDMBqtfLuu++yatUqbrjhBk6ePElUVBQ7duygefPmfPLJJ26bN4uIiIiIiFyIDNM0zcq6eHJyMqZpEhoaWlldVKmUlBRCQkJITk4mODjY2+GIiIiIiIiXVCQ38Nh0xOKEhIRU5uVFRERERESqHY9NRxQREREREZGzK1cSduWVV7Jx48ZydZiWlsZzzz3HW2+9Va72IiIiIiIi1Vm5krCTJ0/Su3dvIiMjmT17NsnJyWdts27dOiZOnEizZs34v//7P+rXr1+erkVERERERKq1ci/MMXfuXJ544gkOHTqExWKhXbt2dO/enfr16xMaGkpmZiaJiYns2bOH3377jdTUVKxWK6NGjeKpp56iadOmnn4tlU4Lc4iIiIiICFQsN6jQ6oimafLNN98we/ZsVq5cSWJiYpE6FouFiIgI/va3v3HnnXcSHh5e3u68TkmYiIiIiIiAF5Owv9q1axfR0dEkJCQQEBBA3bp16dChg0dWSczKyuK///0v8+fP59SpU0RERPDUU0+Veu+xTz/9lFdffZU//vgDX19fLr74Yp566ikuvfTSUsegJExEREREROAcWqL+oosu4qKLLvLkJV3Gjh3LokWLmDx5Mm3atGHOnDkMGzaMqKgo+vfvf8a2jz/+OE8++STDhw9n7Nix5OTksH37dmJiYiolVhEROXc5HCZPLt3J2v0JPP23jlzSvLa3QxIRkQtMpW7W7CkbNmygV69evPDCC0ybNg2AzMxMOnbsSL169fj1119LbLtu3Tr69u3LSy+9xJQpUyoUh0bCRESqvzlrDvL4kp0ANK0dyE8PDsLPRzu2iIhI2VQkN6gWv3UWLVqE1WplwoQJrjJ/f3/Gjx/P2rVrOXr0aIltX331VRo0aMADDzyAaZqcPn26KkIWEZFz0J9xqTz77W7X8ZHEdD7deMSLEYmIyIWoWiRhmzdvpm3btkUyzJ49ewKwZcuWEtsuX76cHj168Prrr1O3bl2CgoIIDw/nzTffrMyQRUTkHJNjdzDl061k5Trcyl9f8Sfp2bleikpERC5E1SIJi42NLXZVxfyyY8eOFdvu1KlTxMfHs2bNGh599FH+9a9/8emnn9KlSxcmTZrEe++9d8Z+s7KySElJcXuIiEj19OPOE2yLce5r2apuDYa0rwfAydQsZq855MXIRETkQlMtkrCMjAxsNluRcn9/f9f54uRPPUxISOCDDz5g2rRpjBw5kmXLlrlWRzyTZ599lpCQENejSZMmFXwlIiLiLRsOFmyjMv2qi5g+rD0Ww3k8e80hqsEt0iIicp6oFklYQEAAWVlZRcozMzNd50tqB+Dr68vw4cNd5RaLhZtvvpno6GiOHCn5XoDp06eTnJzsepzp3jMRETm3bT5yyvW8R/PatK4XRP82dQGIP51F9KniP9ATERHxtGqRhIWHhxMbG1ukPL+sYcOGxbarXbs2/v7+1KlTB6vV6nauXj3nNJRTp04V1xQAm81GcHCw20NERKqfzBw7O445p5S3rleTkEBfALo0LtjH8o/oZK/EJiIiFx6P7ROWnZ3Nl19+ycaNG0lKSsJutxepYxgGs2bNKvO1u3TpQlRUFCkpKW6J0Pr1613ni2OxWOjSpQsbN24kOzsbPz8/17n8+8jq1q1b5nhERKR62RaTTK7DOd2wa5NQV3lE44Lnf0QncXVE0fuPRUREPM0jSdjhw4e5/PLL2b9//xnn1Jc3CRs+fDgvvvgiM2fOdO0TlpWVxezZs+nVq5frXq0jR46Qnp5O+/btXW1vvvlm1q1bx9y5c/nHP/4BOKcxfvTRR1x88cUljqKJiMj5o/BUxG7NarmeR2gkTEREvMAjSdiUKVP4888/uf322xk3bhyNGzfGx8djg2z06tWLESNGMH36dOLi4mjdujVz587l0KFDbknd6NGjWbVqlVsieNddd/HBBx9w3333sXfvXpo2bcr8+fM5fPgwS5Ys8ViMIiJy7tp0OMn1vFvTgiSsXrA/DYL9OZ6SyfaYZBwOE0v+ah0iIiKVxCOZ0ooVKxgyZAhz5871xOWKNW/ePB599FHmz5/PqVOniIiIYOnSpQwcOPCM7QICAlixYgUPPfQQH374IWlpaXTp0oVly5YxdOjQSotXRETODaZpsilvJCzI5kObejXdzndqHMLxnZmkZuVyMCGNVnVrFncZERERj/FIEuZwOOjatasnLlUif39/XnjhBV544YUS66xcubLY8nr16jFnzpzKCUxERM5pMUkZxKU6V9jt3CS0yEhX58Yh/LjzBADbopOVhImISKXzyOqIvXr1YteuXZ64lIiIiEdtPpLket6taWiR850KLc6xNTqpyHkRERFP80gS9txzz7FixQoWLVrkicuJiIh4TOEkrGuhRTnyRTQqWJxjmxbnEBGRKuCR6YjLli0jMjKSm2++mUGDBtGtW7di99QyDINHH33UE12KiIiUyr64VNfzDg2L/m6qVcOPJrUDOJqYwY5jKeTaHfhYq8U2miIiUk15JAl7/PHHXc9XrlxZ4r1ZSsJERKSqHUpIA5yLctStaSu2TkTjUI4mZpCRY+dAfBpt6wdVZYgiInKB8UgSFhUV5YnLiIiIeFRWrp2YUxkANA+rgWEUv/x8u/pBLCMWgAMnlYSJiEjl8kgSNmjQIE9cRkRExKOOJqbjyNs6snlYjRLrNasT6Hp+OG/kTEREpLJo0ruIiJy3Dsanu563OEMS1rxOwblDSsJERKSSeWQkLN+aNWuYM2cOW7ZsISUlheDgYLp27cro0aPp37+/J7sSERE5q0PxBQlVi7DAEuu5JWGFEjcREZHK4LEkbMqUKbz++uuYpnPeh2EYmKbJ77//zqxZs3jggQd4+eWXPdWdiIjIWR0olIQVTrT+KiTQl1qBvpxKz9F0RBERqXQemY44d+5cXnvtNdq0acNHH33EsWPHyM3NJTY2lgULFtC2bVtee+015s2b54nuRERESsV9JKzkJAygWV6Sdiw5k8wce6XGJSIiFzaPJGHvvPMOjRs3Zv369dxyyy00aNAAwzCoX78+o0aNYt26dTRq1Ii3337bE92JiIiUSv79XbUCfQkN9Dtj3eaFFuc4mqgpiSIiUnk8koTt2LGDm266iZCQkGLPh4SEcNNNN7Fjxw5PdCciInJWGdl2YpMzgTOvjJivmdviHErCRESk8lTZ6ogl7c0iIiJSGQqvcni2qYgAzcO0TL2IiFQNjyRhHTp04PPPP+f06dPFnk9NTeXzzz+nQ4cOnuhORETkrA4Wvh/sDIty5GumZepFRKSKeCQJu+uuu4iOjqZPnz58/vnnxMfHAxAfH8+iRYvo27cv0dHR3HPPPZ7oTkRE5KwKJ2GlmY5YePXEw5qOKCIilcgjS9TfcccdbN68mTfffJORI0cCYLFYcDgcAJimyaRJkxgzZownuhMRETmrsqyMCM7FO4L8fUjNzNVImIiIVCqP7RP2+uuvM2LEiGI3ax4zZgwDBgzwVFciIiJnVdaRMMMwaF6nBttikok5lUF2rgM/nyq7dVpERC4gHkvCAAYMGKBkS0REzgmH85aZrxtko6atdL/umtUJZFtMMg4Tjp5Kp1XdmpUZooiIXKD0EZ+IiJx3MnPsnEzNAqBxrYBStys8bVErJIqISGVREiYiIued/P3BABqFlj4Jc1shMV6Lc4iISOUo13TEcePGYRgGzzzzDPXr12fcuHGlamcYBrNmzSpPlyIiIqUWcyrD9bxRGUbCmhSqG5OUcYaaIiIi5VeuJGzOnDkYhsHDDz9M/fr1mTNnTqnaKQkTEZGqEJNUMIrVuAwjYQ0L1S2cyImIiHhSuZKwgwcPAtCoUSO3YxERkXNBdDlHwhqE+GMxwGHCsWQlYSIiUjnKlYQ1a9bM7dgwDEJDQwkODi6xTWpqKqdOnSpPdyIiImXiNh0xNLDU7XytFuoH+xObnKmRMBERqTQeWZijRYsWvPbaa2es8/rrr9OiRQtPdCciInJG0UnlGwmDgoU8EtKyycyxezQuERER8FASZpompmmetY6IiEhVyB/FCgnwLfUeYfnc7gvT4hwiIlIJqmyJ+ujoaIKCgqqqOxERuUDl2h0cT3EuUV+W5enzFR45O6YkTEREKkG57gkDePLJJ92OV65cWWw9u93O0aNH+eSTT+jdu3d5uxMRESmVE6lZ2B3O2RdlnYoIWiFRREQqX7mTsMcff9z13DAMVq5cWWIiBtCwYUNmzJhR3u5ERERKxX1RjrInYYWXtNdImIiIVIZyJ2FRUVGA816vSy+9lLFjxzJmzJgi9axWK7Vr16Z9+/ZYLFU2+1FERC5QbnuEVXAkLFpJmIiIVIJyJ2GDBg1yPX/ssccYPHiwW5mIiIg3VHQkrGGov+u5RsJERKQylDsJK+yxxx7zxGVEREQqrPCKho1rlX6PsHxB/r4E+/uQkpmr1RFFRKRSeGR+4K+//sqDDz7I8ePHiz0fGxvLgw8+yLp16zzRnYiISImiT5V/j7CCds7kLTYp07XIh4iIiKd4JAl76aWXWLJkCQ0aNCj2fHh4OEuXLuWVV17xRHciIiIlyh+9CvC1UivQt1zXaJQ3JTHXYXIyNctjsYmIiICHkrCNGzfSv3//M9YZOHCgRsJERKRSmabpuo+rUa0ADMMo13UauW3YnH6GmiIiImXnkSQsLi6ORo0anbFOgwYNiIuL80R3IiIixUpIyyYzxwGUb1GOfG57hSVlVjguERGRwjyShIWGhnLkyJEz1jl8+DA1a9b0RHciIiLFivHA/WB/basNm0VExNM8koT17t2bxYsXc/To0WLPHzlyhC+//JK+fft6ojsREZFiFV7NsCIjYY20YbOIiFQijyRhDz74IOnp6fTr14958+YRGxsLOFdFnDt3Lv369SMjI4OpU6d6ojsREZFiFR61Ks9Gzfnc7wlTEiYiIp7lkX3CBg4cyMsvv8zUqVO54447ADAMA9N0LutrsVh47bXXGDhwoCe6ExERKZanRsLCatrws1rItjs0EiYiIh7nkSQM4IEHHiAyMpJ3332XjRs3kpycTGhoKD179uTuu++mY8eOnupKRESkWJ7YIwzAYjEID/XncEK67gkTERGP81gSBhAREcHbb7/tyUuKiIiUWv5ImI/FoF6Qf4Wu1Sg0gMMJ6aRm5ZKSmUOwf/n2HBMREfkrj9wTJiIici6IOeXc0ys81B+rpXx7hOVzW6Zeo2EiIuJBHkvCcnNzeeWVV+jZsyfBwcH4+BQMsm3ZsoV7772XvXv3eqo7ERERN6mZOaRk5gIVux8sn1ZIFBGRyuKRJCwjI4PIyEimTZvG4cOHCQ4Odi3KAdCiRQtmz57NvHnzyt1HVlYWDz/8MA0bNiQgIIBevXrx448/lvk6l19+OYZhMHHixHLHIiIi5x73RTkCK3w9rZAoIiKVxSNJ2DPPPMOaNWt49tlnOX78OHfeeafb+ZCQEAYNGsT3339f7j7Gjh3Lyy+/zK233sprr72G1Wpl2LBhrF69utTX+OKLL1i7dm25YxARkXOXpzZqLu4aSsJERMSTPJKEffrpp0RGRvLQQw9hGAaGUXQefsuWLTly5Ei5rr9hwwY++eQTnn32WV544QUmTJjAihUraNasGQ899FCprpGZmcnUqVN5+OGHyxWDiIic2wonSo09MB1R94SJiEhl8UgSduTIES655JIz1gkKCiI5Oblc11+0aBFWq5UJEya4yvz9/Rk/fjxr167l6NGjZ73G888/j8PhYNq0aeWKQUREzm2eHgkLDylYXVH3hImIiCd5JAkLCgoiLi7ujHX2799P3bp1y3X9zZs307ZtW4KDg93Ke/bsCTgX/jiTI0eO8NxzzzFjxgwCAir+i1lERM490R7aqDmfv6+VsJo2QNMRRUTEszyShPXu3ZslS5aQlJRU7PmjR4/yzTffMHDgwHJdPzY2lvDw8CLl+WXHjh07Y/upU6fStWtXRo0aVaZ+s7KySElJcXuIiMi5qfBIWHhoxfYIy5c/ohaXmkV2rsMj1xQREfFIEvbPf/6TU6dOMWTIENasWUNurnOJ4PT0dJYvX87QoUPJzc3lwQcfLNf1MzIysNlsRcr9/f1d50sSFRXF559/zquvvlrmfp999llCQkJcjyZNmpT5GiIiUjXyR6vqBdmw+ViLr5STAWvfBsdfEirThKzTRao3ykvmTBOOJ2d6NF4REblw+Zy9ytkNHDiQN998kwceeMBttCsoKAgAq9XK22+/Tffu3ct1/YCAALKysoqUZ2Zmus4XJzc3l/vvv5/bb7+dHj16lLnf6dOnuyWOKSkpSsRERM5BmTl2TqY6f0+UeD9YTgbMvxGO/AqnDsFVM8AwIDcLlk2FEzvgjm/Bt2AU7a/L1DetU/Gl70VERDyShAHcc889DB48mHfffZf169eTmJhIcHAwvXr14t5776VDhw7lvnZ4eDgxMTFFymNjYwFo2LBhse3mzZvHnj17eO+99zh06JDbudTUVA4dOkS9evUIDCz+l6rNZit2BE5ERM4tx0pzP9ivbzoTMIAtH0Gfe6FWc/jqPti20Fm+dDL87V1Xk4baK0xERCqBx5IwgIsuuojXXnvNk5cEoEuXLkRFRZGSkuK2OMf69etd54tz5MgRcnJy6NevX5Fz8+bNY968eSxevJgbbrjB4zGLiEjVcduoubiRsPRE+PV153PDCqO/diZgAP0egN3LICcdtn4MXW6FFgOc19Iy9SIiUgk8ck9YSUzTZN++faVaQv5Mhg8fjt1uZ+bMma6yrKwsZs+eTa9evVxTBI8cOcLu3btddUaNGsXixYuLPACGDRvG4sWL6dWrV4ViExER7yucIBW7R9jqVyArb3GlrrdC40LT4xt0gqueLzheU/BhYuGRMC1TLyIinuKRkbAvvviCL7/8ktdee41atWoBcOjQIa699lp27twJwIgRI/joo4+wWku4WfoMevXqxYgRI5g+fTpxcXG0bt2auXPncujQIWbNmuWqN3r0aFatWoVpmgC0b9+e9u3bF3vNFi1aaARMROQ8ccaRsJRY2JD3IZ7VBoMeLnqBzrfAquch+Qj8+SMc3w4NOtK4lqYjioiI53lkJOydd95hy5YtrgQMYMqUKezYsYPIyEgiIiJYuHAhH374Ybn7mDdvHpMnT2b+/Pncf//95OTksHTp0nIvey8iIucPt42aQ/9yn++vb0Bu3sqGPe6EkMZFL2D1gb4T3dsAIQG+BPo5PzzUSJiIiHiKR5KwnTt3ujZOBueiF8uWLePmm2/mp59+YsOGDVx00UUVSsL8/f154YUXiI2NJTMzkw0bNjB06FC3OitXrnSNgp2JaZq8+eab5Y5FRETOLdEljYTZc+CPT5zPrTYYcIatUrreBgF5HyZuXwRJRzEMw3VfWExSRql+x4iIiJyNR5KwxMREGjRo4DpevXo1ubm53HLLLQD4+vpy+eWXs3//fk90JyIi4iZ/JCw00JeatkIz7f/8CdITnM/bXQU1wkq+iF8N6DnB+dyRC5vmAbimJGblOog/ne3x2EVE5MLjkSQsODiYhIQE13FUVBQWi4UBAwa4ynx9fUlLS/NEdyIiIi65dgfHU5zTDYssT78/quB5l7+f/WKXjAMM5/MdX4Bp0rhWwfTGo6fSKxitiIiIh5Kw9u3bs2TJEhISEkhKSmLBggV0797d7R6xw4cPU79+fU90JyIi4nIiNQu7wzlNsEgSdtUMuHMF9J0ErYac/WJBDaB5f6jdCjreBPYct8U5orVMvYiIeIBHVke8//77GTFiBI0bN3aNeD311FNuddatW0e3bt080Z2IiIiL26Icf10Z0TCcy9EXXpL+bG7+H/iHONuC20hYtEbCRETEAzyShN1000289dZbruXiR40axdixY13nV61aRUpKCldeeaUnuhMREXGJSSpIjIqMhJVHQKjboUbCRETE0zyShAHcc8893HPPPcWeGzRoEKdOnfJUVyIiIi5uGzX/dSTMA5SEiYiIp3nknjARERFvcduoOX+PsM3/gw+vgjWvQ+qJ8l88YT+1j6927RUWnajpiCIiUnEeGwkTERHxhuji7gnbtRSO/Op8NO8HQWVcGMrhgA+HQvQGjJCmNA59g71xaUQnZeBwmFgshgdfgYiIXGg0EiYiItVa/khYgK+VWoG+kJ0OB/KWpq/ZAMK7lv2iFgv45Y2qJR+hZ82TAGTnOog/neWJsEVE5AKmJExERKot0zQ5lpeENaoVgGEYcGAl5Dr3DaPtUGdCVR5thrqeDmST6/lR3RcmIiIVpCRMRESqrYS0bDJzHEChlRH3fFNQod2w8l+8bUES1jF9neu5lqkXEZGKUhImIiLVVpE9whwO2Puds8AnAFoOKv/F67RybtoMNEjeSjCnAa2QKCIiFackTEREqi33lREDIHYzpDnv36JVJPhWcMn6vNEwi2lnoGUboJEwERGpuEpNwkzTZN++fRw9erQyuxERkQtUkT3C/lxecLLNFRXvoNCUxEjrZkAjYSIiUnEeScK++OILRo8e7bYh86FDh4iIiKB9+/Y0b96cUaNGYbfbPdGdiIgIUMxIWOEkrPWQinfQtC/4BQEQadmKgUNJmIiIVJhHkrB33nmHLVu2UKtWLVfZlClT2LFjB5GRkURERLBw4UI+/PBDT3QnIiICuE8NbFzDDjG/OQ/C2kJo04p34OPnuq+stpFKB+MQMaece4WJiIiUl0eSsJ07d9KzZ0/XcWpqKsuWLePmm2/mp59+YsOGDVx00UVKwkRExKMOJTiTMJuPhXp1wmDKDrjhHRgw1XOdtLoUfALYFtADKw6y7Q5Oaq8wERGpAI8kYYmJiTRo0MB1vHr1anJzc7nlllsA8PX15fLLL2f//v2e6E5ERAS7w+RIXhLWrE4gFosBQQ2gy9+h8yjPddT5Fnj4EIvav8pWszUARxK1OIeIiJSfR5Kw4OBgEhISXMdRUVFYLBYGDBjgKvP19SUtLc0T3YmIiHA8JZNsu3OPsGZ1alReR36B4OtP00J9HE5QEiYiIuXnkSSsffv2LFmyhISEBJKSkliwYAHdu3d3u0fs8OHD1K9f3xPdiYiIcDi+4IO95nUCK72/FmEFfRyK14eKIiJSfh5Jwu6//36OHTtG48aNadq0KbGxsdxzzz1uddatW0fnzp090Z2IiIjrfjCAwVlR8M0/Yc93kF05o1TN6tTAip1GnORggpIwEREpP48kYTfddBNvvfUWHTp0oG3btsyYMYOxY8e6zq9atYqUlBSuvPJKT3QnIiLC4UKJ0EUnv4MNM+Hjm+HUIc935nDQfMV9bLFNYI7f8259i4iIlJWPpy50zz33FBn9yjdo0CC3PcREREQq6lBeImQjm9C4Dc7CoIZQ7yLPd2axYD0dS5CRQZARQ2b8EUzTxDAMz/clIiLnPY+MhJXENE327dvH0aNHK7MbERG5AOUvjtHXZw8We6azsPWlUFmJUatLXU+75m4h/nR25fQjIiLnPY8kYV988QWjR492G+06dOgQERERtG/fnubNmzNq1CjsdrsnuhMRkQucaZquJGxYwI6CE60vq7xOCyVhgyx/uEbiREREysojSdg777zDli1b3FZDnDJlCjt27CAyMpKIiAgWLlyozZpFRMQjTqZmkZHj/GCvL1udhYYFWg6uvE4bdiPLJwiAfpbtHDqZUnl9iYjIec0jSdjOnTvp2bOn6zg1NZVly5Zx880389NPP7FhwwYuuugiJWEiIuIR+SsjhpNAo5zDzsJGl0BArTO0qiCrD8kN+gJQyzhN5pFNldeXiIic1zyShCUmJtKgQQPX8erVq8nNzeWWW24BnBs1X3755ezfv98T3YmIyAUufyrgQOsfBYWth1R6v0ahPkKP/VLp/YmIyPnJI0lYcHAwCQkJruOoqCgsFgsDBgxwlfn6+pKWpvnzIiJScflLxA+0bC0obFX5SVhop6Gu5y2S11d6fyIicn7ySBLWvn17lixZQkJCAklJSSxYsIDu3bu73SN2+PBh6tev74nuRETkAncoIR0rdgZYtjsL/EOhUbdK79e3TnMOG40AaJezGzMzudL7FBGR849HkrD777+fY8eO0bhxY5o2bUpsbGyRPcPWrVtH586dPdGdiIhc4A4npGFicG/uFOx9JsEld4DFWiV976nRA4A4QjkVs69K+hQRkfOLRzZrvummm3jrrbeYNWsWAKNGjWLs2LGu86tWrSIlJYUrr7zSE92JiMgFzDRNDsWn48DCkZAeWIdGVmn/O5v+nRmb+7HfbMhnlpb0PHsTERERNx5JwgDuueeeIqNf+QYNGuS2h5iIiEh5xSZncjorF4DW9WpWef8hjdqyf5Oz/0MJafRsUbvKYxARkerNI9MRRUREqsreE6mu523qV30S1jyshuv5gZNacEpERMrOYyNhAGvWrGHOnDls2bKFlJQUgoOD6dq1K6NHj6Z///6e7EpERC5Q+06cpoexm7aWaLrUvLnK+29TaPRt34lUcNir7H40ERE5P3gsCZsyZQqvv/46pmkCYBgGpmny+++/M2vWLB544AFefvllT3UnIiIXqH1xqYy0rmSEz8+w/ENo8g0071dl/TcKDaCOXy5jHF9w2eEdsLA93Py/KutfRESqP49MR5w7dy6vvfYabdq04aOPPuLYsWPk5uYSGxvLggULaNu2La+99hrz5s3zRHciInIB23s81bVJs+kbCI26V2n/hmHQrH5t/m5dwcXmPsz9UWDPqdIYRESkevNIEvbOO+/QuHFj1q9fzy233EKDBg0wDIP69eszatQo1q1bR6NGjXj77bc90Z2IiFygTNPEcnIn9Y0kAIzm/cHXv8rjaNsghNWOjs4Ysk9D9MYqj0FERKovjyRhO3bs4KabbiIkJKTY8yEhIdx0003s2LHDE92JiMgF6lhyJpfkbiooaDXEK3G0rR/Ez/aIgoI/l3slDhERqZ6qbHVEwzCqqisRETlP7TuRyiDLHwUFrS/zShztGgTxi6NwEvajV+IQEZHqySNJWIcOHfj88885ffp0sedTU1P5/PPP6dChgye6ExGRC9TBYye5xLIHgLSARlCnlVfiaFO/JicJZbujubMgdiukxHolFhERqX48koTdddddREdH06dPHz7//HPi4+MBiI+PZ9GiRfTt25fo6OgSN3MWEREpDcfBX7AZzo2SM5sNBi/Nsqhb00atQF+WO7oVFO79ziuxiIhI9eORJervuOMONm/ezJtvvsnIkSMBsFgsOBwOwHkj9aRJkxgzZownuhMRkQtUg7jVruc1Owz1WhyGYdC2fhA/HerGAz5fOAv3fgeX3OG1mEREpPrw2D5hr7/+OiNGjCh2s+YxY8YwYMAAT3UlIiIXINM06ZDxGxiQixVbm0ivxtOuQRDzDzbnhBnqXK3xwErITge/QK/GJSIi5z6PLswxYMAAZs2axe+//86+ffv4/fff+eCDDzySgGVlZfHwww/TsGFDAgIC6NWrFz/+ePYbob/44gtuvvlmWrZsSWBgIO3atWPq1KkkJSVVOCYREak6x5LSmZ17BcvtXdkV0A38g70aT9v6QZhYWG7v6ixw5MLxbV6NSUREqgePJGFWq5Vbb73VE5cq0dixY3n55Ze59dZbee2117BarQwbNozVq1efsd2ECRPYtWsXt912G6+//jpXXnklb775Jn369CEjI6NSYxYREc/ZcSyVufahjM/5J99EvOHtcGjXIAiAj+yX8WmzJ+GhA9C0l5ejEhGR6sAj0xGDg4Np0qSJJy5VrA0bNvDJJ5/wwgsvMG3aNABGjx5Nx44deeihh/j1119LbLto0SIGDx7sVta9e3fGjBnDRx99xJ133llpcYuIiOf8EZ3set65Saj3AsnTtp4zCdthtmBRVi1u9i9+r0wREZG/8shIWM+ePdm6dasnLlWsRYsWYbVamTBhgqvM39+f8ePHs3btWo4ePVpi278mYAB/+9vfANi1a5fHYxURkcrxR0xBEtapcaj3AskTEuhL41oBAGyPSSHX7vByRCIiUl14JAl7/PHHWbFiBfPmzfPE5YrYvHkzbdu2JTjYff5/z549AdiyZUuZrnf8+HEAwsLCPBKfiIhULvPQavyP/oIVO2E1/WgY4u/tkICCEbmMHDv74vL2yjRN7wUkIiLVgkemI/74448MHjyYO+64gzfeeIMePXpQv359jL/s32IYBo8++miZrx8bG0t4eHiR8vyyY8eOlel6M2bMwGq1Mnz48DPWy8rKIisry3WckpJSpn5ERMQzspY/x0zzFxJtNfm/+u8X+f3iLV0ah7LsD+cmzXG/L+Gi7J/h+B9w9xqweHTtKxEROY94JAl7/PHHXc9///13fv/992LrlTcJy8jIwGazFSn39/d3nS+tBQsWMGvWLB566CHatGlzxrrPPvssTzzxRNmCFRERz0pPxC/aee9vilmDps1aeTmgAoXvTau/Zx6krnMexPwGTXp6JygRETnneSQJi4qK8sRlShQQEOA2IpUvMzPTdb40fvnlF8aPH8/QoUN5+umnz1p/+vTpPPjgg67jlJSUSl2AREREirF7GRbTDsB3jh5EnAOLcuTr2CgYiwEOE5bZe9OevCRs51dKwkREpEQeScIGDRrkicuUKDw8nJiYmCLlsbHOKSANGzY86zW2bt3KddddR8eOHVm0aBE+Pmd/6TabrdgROBERqULbPnM9/c7ekxsbnzurEAb6+dC2fhC7j6fyUVIHHvT3wXDkOpOwK56Cc2TapIiInFs8MmF9zZo1PPjgg64FL/4qNjaWBx98kHXr1pXr+l26dGHv3r1F7slav3696/yZ7N+/nyuvvJJ69erxzTffULNmzXLFISIiVSzlGObBXwA46KjPiaAO1As6NxblyNclb2Qu0VGD5PB+zsLkoxC90XtBiYjIOc0jSdjLL7/MkiVLaNCgQbHnw8PDWbp0Ka+88kq5rj98+HDsdjszZ850lWVlZTF79mx69erlmiJ45MgRdu/e7db2+PHjXHHFFVgsFr7//nvq1q1brhhERMQLti3CwLna4Jf2/ufUVMR8XQrFtDX40oITWz+p+mBERKRa8Mh0xI0bNzJkyJAz1hk4cCA//vhjua7fq1cvRowYwfTp04mLi6N169bMnTuXQ4cOMWvWLFe90aNHs2rVKsxCywNfeeWVHDhwgIceeojVq1ezevVq17n69etz+eWXlysmERGpAn8UTEX80tGPkefA/mB/VXhxjq9zLmGQbyDkpMP2z2HoM+B7bo3ciYiI93kkCYuLi6NRo0ZnrNOgQQPi4uLK3ce8efN49NFHmT9/PqdOnSIiIoKlS5cycODAM7bL30T6+eefL3Ju0KBBSsJERM5VJ3bAiW0AbHa05rDZwG3U6VzRpl5NAnytZOTY2XAsGy66Dv74BDKTYO+30OFv3g5RRETOMR6ZjhgaGsqRI0fOWOfw4cMVuhfL39+fF154gdjYWDIzM9mwYQNDhw51q7Ny5Uq3UTAA0zRLfKxcubLc8YiISCUrNAq22N4PP6uFbk1reTGg4vlYLXRq5Fws5GhiBoltbio4ueVjL0UlIiLnMo8kYb1792bx4sUcPXq02PNHjhzhyy+/pG/fvp7oTkRELgRdbiW1y53Em8Ess/ema9NQAvys3o6qWL1b1nY9j8pqD8GNnQdJRyA320tRiYjIucojSdiDDz5Ieno6/fr1Y968ea6l42NjY5k7dy79+vUjIyODqVOneqI7ERG5ENRty3eNJ9M7600SCKFvqzBvR1Sifq0LYluzPxGGvQB3Lod714KPnxcjExGRc5FH7gkbOHAgL7/8MlOnTuWOO+4AwDAM19RAi8XCa6+9dtb7t0RERApbuz+B3LxfVX1a1fFyNCXr2rSW676w1X/GY468CkN7hImISAk8koQBPPDAA0RGRvLuu++yceNGkpOTCQ0NpWfPntx999107NjRU12JiMgFwDRN1h5IAMDf13JOLsqRz8/HQq+WtVm55yRxqVn8GXeaNvWDvB2WiIicozyWhAFERETw9ttve/KSIiJyofn1DTBNjjS9gdjkTAB6NK+Nn49HZtBXmv6tw1i55yQAq/+Md0/CTu6Fum29FJmIiJxrzu3faCIicmHJOg0/vwg/Pkr43D7YcC5qcS7fD5avcIxr/ox3Ptm9DGZGwls9IGG/lyITEZFzjZIwERE5d2ya59xfC/g9oB9ZOBe1OJfvB8vXvkEQdWo44113IJFcuwMS/oRjm5wVNrzvxehERORcoiRMRETODfYcWPuW6/DZ5MsBqBXoS8eGwd6KqtQsFoO+easkns7KZfPRJOh6O/j4Oytsmgfpid4LUEREzhlKwkRE5NywbRGkRANwMjySP7IbAnD5xfXxsVaPX1eD2tZ1Pf9223EIrO1MxABy0mCd7psWERElYSIici4wTVjzmuvwE78bXc+v6hjujYjK5fKL6uNrdS5N/+32WBwOE/o9AJa8dbDWvwcZSd4LUEREzglKwkRExPt2fgUndwHgaNyTmYfrAxBk86Fv63P/frB8IYG+9M+bkhibnMnmo6cgtAl0vsVZISsFNureMBGRC52SMBER8S57Lqz4P9fh9lb/IDUzF4AhF9XD5mP1VmTlcnVEQ9fzZX8cdz7pPwWMvF+5a9+CzGQvRCYiIucKJWEiIuJdm+c7VxEEaNqXBQntXKeu7NjAS0GV3+UXF0xJ/GZb3pTEOq2g00hnhYxT8MvLXoxQRES8TUmYiIh4mQm2EACyL/0v3+88AUCAr5VBbet5M7ByCQnwZUAb5wIdx1PypiQCRD4CVpvz+YaZzmRMREQuSErCRETEuy4ZBw9sgevf5tukppxKzwHgsovrE+BXvaYi5ru6U8FiIp9vinE+qdUMet8NrS6F8T9CQC0vRSciIt6mJExERLwvsDZ0vZWP1h1xFd3aq6kXA6qYKzrUp0ZeArl4UwzJGc7Ekkv/C7cvhgYdvRidiIh4m5IwERE5J+w5nsqGQ87NjNvUq0mvFrW9HFH5Bfn7cmO3xgBk5Nj5/Hfn/mdYfbwYlYiInCuUhImISNXb/gUsvgfS4l1FC9Yfdj2/tVdTDMPwRmQeM7pPM9fz+esOOxfo+KucDNgfVYVRiYjIuUBJmIiIVK2UWFj2IGxdAG90h+QY0rJy+SLv3qkAXys3dm/s5SArrk39IPq2cu5xdjA+jV/+jHevcGg1vNMPPhoBJ3Z4IUIREfEWJWEiIlJ1HA748u6ClQFbDISQRsxde4jULOfeYNd1bkiwv68Xg/Sc0X2au57PWn3Q/eT+KEjcD44c+PxOyE6v2uBERMRrlISJiEjVWfcWHFjpfB7UEK59jZTMHN5bdQAAiwETBrX0XnwedtlF9WgUGgDAz3tPsuFgYsHJgf+Eehc7n8fthO/+5YUIRUTEG5SEiYhI1Tj4C/z0eN6BAX97FwJr8+Hqg67VA//WtTGt6tb0Woie5mO18MBlbVzHz3+3G9PMuzfM1x+GzwbfQOfxprnwx0IvRCkiIlVNSZiIiFS+U4fgs9HgcE45pN8D0HIQSenZzPrFOU3Px2LwwJA2JV+jmrqxayNa13Mmlr8dPsWK3XEFJ+u1h2EvFhx/PRGif6/iCEVEpKopCRMRkcqVmQwf/x0y8qbitb4MhvwXgFd+3Ou6F2zEJU1oWifQW1FWGh+rhWlXtHUdP//dHnLtjoIKXW+FLrc5n+dmwsejIOkIIiJy/lISJiIilScnExbcDHF5q//VaQ03zQKLld8PJzJvnXNZ+gBfK5Mube3FQCvX0A4N6Nw4BIA9J1KZ+csB9wrXvAzN+jmfp8U5V0xM+8tqiiIict5QEiYiIpXHxwYNuzmfB9aBWz6BgFCycu08/Pk28m+PmnpFWxrmLWBxPjIMgyeu74glb+uzV3/cx59xqQUVfGxw8/+gdivn8cndsHtp1QcqIiJVQkmYiIhUHsOAoU/DkMfgti8gzHnP1xvL/+TPuNMARDQOYWzf5l4Msmp0aRLKnQOcKz9m2x38c9Ef2Atv4BxYG25b5Fw18rLHoftYr8QpIiKVT0mYiIh4lsPhfmwYMOBBaNgFgKjdcby18k8ArBaD526MwMd6Yfw6evDytrQIqwHA5iNJvPD9HvcKtVvCvWuh/xQvRCciIlXlwvitJyIiVePETninr3M5+mIcTkjjgU82u01DvLhhcBUG6F3+vlZeGB6BNW9e4rur9vP11mPulQJCizbcsRiObqj8AEVEpEooCRMREc/Y8SV8cBmc3AUf3wLHNrudPpWWzT/m/UZKpnM1xKEd6nPPoFZeCNS7Lmlem/9cfZHr+KFFW/kjOqnkBgdWwuf/gNnDYP1MXBmsiIhUW0rCRESkYrLT4Ov7YeEYyElzltVuAYFhriopmTmM/nADe0847wNrWbcGL47ojGEY3ojY68b2bc7w7o0ByMxxcPusDew8llJ85fUzwZHjfHz7T1h0B6QnVmG0IiLiaUrCRESk/A7+Au8OgE1zC8o6Dodx30NoEwCS03O4Y/ZGtsUkA1AvyMaHY3oQ5O/rjYjPCYZh8NQNHenZvDYAyRk53PrBOnbFFpOIjZwLfSYWHO9YDG/3hj3fVVG0IiLiaUrCRESk7FJPwOJ7YO41kLjfWeYbCNe+Djd9AH7OTZePJqZz07u/8vvhUwDUruHHR3f2onne4hQXMn9fKx/e0YNuTUMBOJWew8h31/Lz3pPuFa2+zhUmR8wFf2ddTp+Aj2+GT27Vxs4iItWQYZqaXF5aKSkphISEkJycTHDwhXMjuYiIm4M/w4JRBVMPARr3hBvegbCCDZfXH0jgvgWbiT+dBUCdGn7MG9+TDg1Dqjric1pKZg6jZ21gy9EkwLli5H+vuZjRfZoVna6ZEgtfT4I/fywo8wmA3ndD5H/A6lN1gYuIXOAqkhtoJExERMomvAv45m2sbAuBa15xTj/MS8By7Q5e/mEPt7y/zpWAtaxbg8X39lMCVoxgf18+urMXl19cHwC7w+Sxr3cwYf7vJKZl/6VyONy6EG58H2rUc5blZjgXQVECJiJSbWgkrAw0EiYiF5zEg86l0Tvf7F7+22w4sR0G/Qtq1nUVbz5yiv98uZ0dhRaZ6Ne6Dm/9vRuhgX5VFXW1ZHeYzPhuNzN/PuAqqxtk4z9XX8R1nRsWHRXLTIZVz8OG92Hcd9CoW8G5nAw4dRjqta+i6EVELjwVyQ2UhJWBkjARuSBknYadX8HWj+HQL2DxhQe2QkijEpscS8rg9eX7+PS3o64V1K0Wgwcvb8vdg1q59sWSs/thx3H+9cU2t1Gw3i1r8+9hF9OpcTEjiWkJUKOOe9nWT2DxXdC0D3S/Ay6+Hnz9KzlyEZELi5KwKqIkTETOW9npznu9dn4JO792v98LoO/9cMX/FWkWk5TBB78c4KP1R8jOdbjKLwoP5tkbO9GlSWjlxn2eikvJ5JHF2/lp1wm38isurs+kS9sUn4wVNusKOLq+4Ng/BNoNcyZjLSOVkImIeICSsCqiJExEzjtbPnYmXgdWOe8t+qvaraDrrc7RlEDncup2h8na/Ql8+ttRvtkWi91R8GskyObDA5e1YWzf5vhYddtxRa3YfYLHv97JkcR0t/KeLWozpk9zhlxUD39fq3sj04T178Hvs+Hk7qIX9asJbYdC2yuh1aVQI6xoHREROSslYVVESZiIVFumCafjIKi+e/mCm2HvX/absoVAxxuhy9+hcQ/Iuxdp9/EUFm+K4cstMZxIyXJr4u9rYWzfFtw9qKXu/fKwrFw7n208yhsr/iQu1f19D7L5cFWnBtzQtRG9W9TBUnjap2nCkXXw+xzYvQyyU4te/Pq3nUl24TYX6AbaIiJlpSSsiigJE5FqwTQh5Rgc/8O5al7+IzMZ/nWkYGVDcC6wsXQy1KxfaHRkCPj6k5ljZ92BBH7eG8+qvXHsP5lWpKvaNfwY3acZt/duRp2atqp7jRegzBw7X2yK4cM1B/kz7nSR8w2C/YlsX5eBberSt3UYIQGFNsPOyYQDK533+u1Z5vxeAJiyA0IaF9TbsRiW/58z+W7Sw7kSZt32YKtZqa9NRKQ6UhJWRZSEicg5KSvVuUJe/F44uQfi9xU/6gEwdhk0719wnJ4Ipw5CeFeSM+1siU5i85FT/H74FBsOJpJV6D6vfD4Wg8Ht6nFjt0Zc2r6Y6XBSqRwOk7UHEli8OYZvt8WSlm0vUsdiQJcmofRtFUbXpqF0aRJakCTbc+DwrxC7Bfo94N7w+3/D2jeLdlqrOdTrAPUvhvodILwz1G7p8dcmIlKdKAmrIkrCRKTK2HMgLR7STsLpE5B0BJKPQnK0M4nqPragbk4GPB0OnOHHuX8oNOwCA/+J2awfx5Iz2XM8hd3HU9lzPJXtMcnFjnTlsxjQrWktruvSkGsiGlK7hqYcngsysu38tOsEizfHsHpfPNn2oklzvqa1A+nSJJT24UG0bxBE2/pBNAoNcF/6fslk2PIR2LNLvA4A7a+BUR+5l/0+x/l9Vqu5c3QtsI6mNorIeU1JWBVREiYiZZKbDdmnnY/MZMhMgawU51d7FnQb7V4/6hnY8aUz8cpILPm6nUbCTe+7l70aAUmHAQNCm+IIa0tqcBtiAtuzx9Ka3Rm1OJyYwZHEdI4kpnM6K/es4YeH+DOobV0GtS1mepucczKy7aw7mMDPe0/yy774Yqcs/lVNmw+t6tagaZ0aNK0dQNPagTQL8aGl/SBhyduwxO2AEzshbpf7ipl9JsLQpwuOHQ54JhxyMwvKrDbn5tLBjSC4ofMRFA4XXes+BVJEpJqqSG7gU0kxeVxWVhb//e9/mT9/PqdOnSIiIoKnnnqKyy+//KxtY2JimDJlCj/88AMOh4PIyEheeeUVWrbUVAoRKYbD4dyIODutIInKOp13nFro+WnoPwXqtito++dP8Pk/nOfONJpgtRVNwtLiIX7PWcNLizvI73tPkpyRQ2JaNidSMgkKfZAYfz+2ZYRxNNUg8XjhvpPyHiXztRpc3DCErk1C6do0lG5Na9G4VkDRDYLlnBXgZyWyXT0i29UDIDY5g81HnNNLtxxN4o/o5CLTS09n5bI1Opmt0clFrudnbUb9kLbUD7qFBs38aOOfSDuO0DT3II7AnlhjU6gV6EdooC/+6cfdEzBwftBw6pDzUVh4Z/ckbM938OU9UKOuc6XGwNrOJfX9Q/MeIRAQCgG1oc1lFX2bRETOCdUmCRs7diyLFi1i8uTJtGnThjlz5jBs2DCioqLo379/ie1Onz5NZGQkycnJPPLII/j6+vLKK68waNAgtmzZQp06dUpsKyLnMNOE3Cznsuo5GeCwQ2gT9zp//gQJ+wtGn7JS8x55zzNTMLNSyI34OwmXPMjprBxOZ9lJy8ik34IBpQrjR+sAjte1YRgGhgHhCQlceqZRrHz2LN5ZvpN0u5XMHDsZOXYGxDiINGwkW2uRZISSaIQQ5wgmNjeYgzm1iDbDiDHDOHa4DpkfbvjLBevmfc05Y7c+FoOGoQG0qluDdg2Cad8giHYNgmhZtwY2H93bdT4JDwkgvFMAwzqFA5Bjd/Bn3Gn2nkh1TUPdczyVY8kZFDcnJtvu4GhiBkcTC29dEO58bAH4xVUa5pfNMNuDtPY5SRNLPPVIpI49npCckwTYU9yuuyneBx9rEjVtPtS0+RCSfBxbRqJz9PdMH0IEhsFD+93LvroPdi11LrvvVwP8Ags9rwG+ecdNe0GHv7m33fsDWKzg45/3sBV89Q0oOLb4aFqliHhctZiOuGHDBnr16sULL7zAtGnTAMjMzKRjx47Uq1ePX3/9tcS2zz//PA8//DAbNmygR48eAOzevZuOHTvy0EMP8cwzz5Q6Dk1HlPOKaToTF9MOjty8h72gzOJTdP+gk3udU5IcDmd9t7aFyuq2hzqtCtplphTcZ2LPdt7v5Pa10PPL/885hSnfji/hlxedq7vlZEBuBmaOM/EyCt0DlR3cjD0jfyEr15nQpGTk0uWXCTQ6+fNZ34qPcofw79zxbmW7bGMJMM5yXwxwb/b9fOPo7TruYBzkLd/XScPf+TCdX1PNQFIIJNUMJJVAUs0Aljj6klPoszALDhxUbG8tP6uFukE26gXbCA/xp2ntGjSrE0jT2s5HeIi/9u8SN1m5dqJP5U1VTXBOVz2ckM7RxHROpGaSlH7mxP5sAsikgXGKcCOBuiTxvaMHmRSspDncuopJPosJI4UaRmaJ1znh25jn2yzA39eCv68Vf18Lw/dMo0XiLyW2yXey3d851v9ZfK0W/HwMfK0Wmr7dEsNecn8uI+fDxdcVHMdsco7c5SdsFl+w+uR99Sv03Beue8P5Nd+fy53bBrjV+0sbqy/UbADN+7nHcWyLc7TRsILFkvfVx5lIGta8r5aCkcR8DodzBN9Vp1BdJZciFXLeT0dctGgRVquVCRMmuMr8/f0ZP348jzzyCEePHqVJkyYltu3Ro4crAQNo3749Q4YM4bPPPitTEpYv87f/4Vcjb4nnv/wAK3xk7zjc+UM6j+X4NiyxmwvqGlDcx4+OgDo42l/rOjYxse79BuP0CeexWXCm4L95betHYG/YvVAQOfhsnV9w/Je2hRvntBmGI6hBQbynDuJz4Ce32IrL2U3DSlbXcW6X8z38M9aTu9zrYTprmHk1TRN7aAuyW1/l1jZgy2yMrOS8F1q4vsN1JUyTzJZXkFO/i6utJT2empvezQ/UVc/1Nf85kNJzCnZbLUxMTBNs0b8S+OeSonXzvpp5X3P9w4jr+S9XO4A6W2fin7gDTNP5Gk0Tw3TkXcfheiQ1ieRkm1swzbx3woSLfrwdw7QX1MVZN7+9kXe8r9ujJNXt7gotOH4THTb8y1mPgvqFn+e3/fbKX3BgwWGCwzTptPMl2h2Yi4WSb+AHOFqrN19HvIXDYbra3r7pZsIyDpyxHcC3De/j5zDna7U7TGpmneCxP/911nYA9xwexAGjGbkOB3aHydDsDUzP2eZWp7g/G04lJ3Ptm6vdyl71zaXRGQZ3Mk1fUgkgk6KLTMy3O6c6p2PjtBngllCl489pM4B0bMSa7qPpO8wWDM5+pVSv9a/+moBZDAgO8CU0wJeQAF9CAv0IKXQcGuhLaKAf9YJs1A/2p16QjdBAX00hlDKx+VhpVbcmreoWvwx9Zo6dk6lZnEjJ5ESK82tcahZJ6dmcSs/mVHoOp9KcX5PSs8kttHk3QAb+HDTDOWiGF3v9RfZBLLIPAsCfLEI5TbCRTghpBBtpBJNOiJFGRo6NzzdFu7Vt6mPFtDSghpFJIFnUIBOLUfT31OIdyTyzdU2hEpND/qVIwIBJi3axzhqI1TCwWgx6mVt5OauYTbCLMSZxDBaLFavF2XZ4widcnvTZWdsdDOrOgvZvYjEM1yj7uK3jqJux/6xt17V6gG3NxmIYYBgGNbNOcPMvQ4ut68CCaTgTOtOwsHLgJ5wOboNhgMUwaBjzLRdvfxHTVS8/cTPAsGAazq85/mHsGDIPS/4pDJpseYmQ2F9dbUwMZ1vD4mqPYZDcaDBxHe90nsqLq/nK+zHseXviufq0/KW9QUKHsWTW74qR19Iv9TB1Nr/tTFJd9Y1C7Quukdjzn+Dr/BvNAAKO/kxAdN73SN77Tv5ZI78W2IMakd7ptrxqzp4Dty/AevpYwXuD4f7nYV4MuY36kNukt6tPcrMI2PR+QZ/5dV39Gq5+ci+6ATMo3BWXJXE/1v0/FVz/L21c76aPjdzOt7n9f7cc+RVL4p8lt8l7vY6Qpjiauc82s+76CiPHffP44tgb98SsXejD2IwkfPZ9c9Z2ALntr3eOZOdHdXIX1mObztrO9A/G3u5atzLr/uUYp2PP2tYR1h5Ho0vcyny2zDtru6y0jLPWKUm1SMI2b95M27Zti2SYPXv2BGDLli3FJmEOh4M//viDcePGFTnXs2dPfvjhB1JTUwkKCipTPP4//gt/29n/yOnyhT9JFFx7onUx03wXnrXdH44WXJftfgP8Z35P0dNy9ntF3si9gZdyRxbESha7/R86azuA0cvS2Gi2dx0PtWzkPb+z/zGZYfrRfVlTt7KnfWZxq8/ys7b93n4Jd+W4/wG82vYijY34s7Z9bnUSH9kLpn21NI6xwlbM0srFuGp9B6LNeq7jsdbveNz37P/YDjgaMGyd+6eTs32XEGndeta2Xx608MSK5m5lf9rW4mOcORkCeOPbTUQV+sOmr2U7C/wOnbUdwNTPtmCnIBP5l89JLvI5e59H41N44Xv377kr/XIJK8UgypbDiXx84IjrOIxkHvM/Q4NCjpxMZo9ZsLx6rAWyfH3Iwo8M/Mg0/cjARia+ZGLLO/bjlFn03/HHuUNYae/CaQJINQM5TQApBHDaDAD/YGoEBhDs70twgA9DA3ypafOlps1KDZsPObYnqeFnpZbNhxo2HyyG4UqgHaYzMTVNE4fpTMjzE1XzL8cWw8Di+jvAcP5RBfhYDQJ8rQT4WQnwteZ9sl9wHOBrxeZjcd98V8QL/H2tNKkdSJPagWeta5omqVm5JKXlkJSRzenMXFKzcjmdmUtadi6pmbmczj/OKjh3OiuXjBw7mTkBZOYEcyzHzoEce5GE7q8ezp3wlxITGznUIJNAI5MaOB8nCXGrZcHk+ZybsRk52MjBRrbz4TrOexg5HMoK5KRZsEl2giWD077++JN9xp/fDtNg1T73qcm9fZJL9ZfXkaQc3v/loFvZTX4Z1C3Fz98fd8cza0fBh6DhJHBzCT9/LeR/UOhcqGfGd3vYZxYswHKzdRczfI+dtc9Y8yS3zVrvVvaW70autm4uoUWBb4748K9fLnYr22b7kSDj7H/c/ntnE5Y5CuLtZuzlC9uCs7YDGLDuEk5T8D09xWcRD/h8cdZ2mxytufGbRm5li/3epqvlzxJaFHg190ZezS1YMCeIdLb5P1mqeG/81s4ms63r+GrLOt7ye/2s7VLNADp9UdetbIbPTG72WXnWtkvtvZiY476NxVrbNMKNs0+5fyjnH3xmj3QdtzGi+dFWur9HB30Oxyn4gHO8dRmP+n50hhZOex2NuCLb/W/Keb7PMtC6rYQWBT7IvYqncm93KzvkP+Ws7WxZ5Z9QWC2SsNjYWMLDi36Cll927FjxPyASExPJyso6a9t27doVOQ/OxUCysgp+8KakpBRbT8qv8HSyfKZZ6OOwMrQ1S9OoBFXRtriRpzO1tZuGcwSrmDo5pg+nzJo4MHBgYObVcz4smGZ+uYEFk8K7CMWaddjqaIkdS97Dit10Ps/F6irbYxb9YGOpvTcbHe0Ktc1rn//cdB5vdLj/m0ohkEnZE8nGh5xCj2zTh1yszuf4YMeHk9YwAvM+OfaxGPxqRDLIOgQfq4F/XmLifDinI9l8rNh8nWWj8877+1rzkqtOhAT45j33dT2v6e+DVcmNiMcZhuH89+bvS1POnrSdTa7dQWaug8wce96jmOe5zuc5ducjO9dBrsMkJzevrNDzbLtJbl69w/a7ybY78o5NsvPKc+1m3jUcOEywO0waOZyj+nbTZJujB/0c852zBOx2DDMHa960bD9ynT/JjFx8Kbp/23z75UQ5uuCDHV/seV9z8TXy2mHHj1xizLAibT+zD6KuIxlr3m8GKw6s2LFiYjUKyvab7glCDj78bO+Ud97EYuS3LXwd5/NM3D8AzjJ9iTNDsRT6bWRx/XbBVZZpFl051XKmLTMKKe734NlmauT76+/H4v6mKEu/ldkO8v6+KW/bcscr56pqcU9Yq1ataNeuHd984z6MeeDAAVq1asUrr7zC5MmTi7Q7evQoTZs2ZcaMGTz0kHv2/eGHHzJ+/Hg2b95Mly5diu338ccf54knnihS/vpz/yLA34aBWeI/ChOTNYFDyDGcGblpQrPs/TTPKfpJiQP3nOO0JZhNAX3c6nTJ3EiwI8l1XKTXvIKjvi047NfGVWwx7fRJj3JrYBTzDGBHQHdSrLVcpbVz42ibtb2YV+fezjQs/FZzsNv5Zll7qJdzrEigBvk/SJw/KpN9wzjg36HQeYOL0n/D18wqmLrg+rFaMGRuYnDcrxlJvvVdbX3NTJpn7nKvj+GcLlGovYlBtK01dovNNf0hyJ5ErZy4grp5UxYM1zQE57HD4kuCrYmrnWFAUPZJ/Mws1xQBw3D+uiqYDuF85Fpt5PjULHhLDPCzZ7jOF34YeVMX8vvIf28K91vQXzHn8nqxWAyshvOrkTciUzAyY7ieW/Kmu1gtxhnPWwqXFalbcN5V1+JcCMJqseR9NQp9tWC1FhxbDUOjPiJSrTnyEjV7oaTNbnd+dThMcvPKHabzeX79XHtBmZk3Bd+RN/3ckTfiTuERd4qOvJuFRuj/OmLvbG46b911TYl378NV5shvT6HrF+6zoG3hY/c+cU3PL5gej/Oe4bwX4vxN7cBhWMkx/PNuWXA2qZGT6Ez0TEfe7QDOxA+H89YAAwdgkmqtTZY10NXO15FBnaxoMMnrM6++6UyInVP4ndc9FNABh+Hj6rd2ViyhOced/bj+NC74/5EvwwjkcMBFFC5unrGdAMfpQrdP4Lqu8/U7j477NOGEXxNXQ4sjl4j0dXnHZqFuCm6fyL/WTltXTluDXa+1Tm4cbbN3uOo53y/3P+kNTOyGD2sDI93K22Vto35ujCuZdl00/zp514zzCWe7f3e3tv3SfsJmnn0q725bBMd8C2ZJ1XCk0Cv97PdoA6wJHEKWJcB13CT7AK2yzz4FON1Skw2BA93KIjI2UNuRcNa2R32as992kVvZoLRvz5r8ZmRmMvlfz52/94QFBAS4jUjly8zMdJ0vqR1QrrYA06dP58EHH3Qdp6Sk0KRJE8bcM71Ub/Q/ipT0LqZWaVWkbd8KtL2mnO0uOXuVEnU/e5USVeS1ioiIlJ/FYmDBwFcLjXpRz3K261KBPrtVoG2fs1cp0bVnrwLcV6SkVwX6rEjbs28rBVD0JiJvxXv2tikpKUz+13Plunq1SMLCw8OJiYkpUh4b67zRrmHDhsW2q127NjabzVWvLG0BbDYbNputxPMiIiIiIiJlVS3WKe7SpQt79+4tck/W+vXrXeeLY7FY6NSpE7/99luRc+vXr6dly5ZlXpRDRERERESkIqpFEjZ8+HDsdjszZ850lWVlZTF79mx69erlWhnxyJEj7N69u0jbjRs3uiVie/bsYcWKFYwYMaJqXoCIiIiIiEiearEwB8DIkSNZvHgxU6ZMoXXr1sydO5cNGzawfPlyBg503oQ3ePBgVq1a5baXVWpqKl27diU1NZVp06bh6+vLyy+/jN1uZ8uWLdStW7ekLovQZs0iIiIiIgIXwGbNAPPmzePRRx9l/vz5nDp1ioiICJYuXepKwEoSFBTEypUrmTJlCk899RQOh4PBgwfzyiuvlCkBExERERER8YRqMxJ2LtBImIiIiIiIQMVyg2pxT5iIiIiIiMj5QkmYiIiIiIhIFVISJiIiIiIiUoWUhImIiIiIiFQhJWEiIiIiIiJVqNosUX8uyF9IMiUlxcuRiIiIiIiIN+XnBOVZbF5JWBkkJCQA0KRJEy9HIiIiIiIi54KEhARCQkLK1EZJWBnUrl0bgCNHjpT5jZbzQ0pKCk2aNOHo0aPaK+4Cpe8B0feA6HtA9D0gAMnJyTRt2tSVI5SFkrAysFict9CFhIToH9wFLjg4WN8DFzh9D4i+B0TfA6LvAYGCHKFMbSohDhERERERESmBkjAREREREZEqpCSsDGw2G4899hg2m83boYiX6HtA9D0g+h4QfQ+IvgcEKvZ9YJjlWVNRREREREREykUjYSIiIiIiIlVISZiIiIiIiEgVUhImIiIiIiJShZSEiYiIiIiIVCElYWeRlZXFww8/TMOGDQkICKBXr178+OOP3g5LqtDp06d57LHHuPLKK6lduzaGYTBnzhxvhyVVaOPGjUycOJEOHTpQo0YNmjZtysiRI9m7d6+3Q5MqsmPHDkaMGEHLli0JDAwkLCyMgQMHsmTJEm+HJl709NNPYxgGHTt29HYoUgVWrlyJYRjFPtatW+ft8KQKbdq0ieuuu47atWsTGBhIx44def3118t0DZ9Kiu28MXbsWBYtWsTkyZNp06YNc+bMYdiwYURFRdG/f39vhydVID4+nieffJKmTZvSuXNnVq5c6e2QpIrNmDGDNWvWMGLECCIiIjh+/Dhvvvkm3bp1Y926dfoD7AJw+PBhUlNTGTNmDA0bNiQ9PZ3PP/+c6667jvfee48JEyZ4O0SpYtHR0TzzzDPUqFHD26FIFbv//vvp0aOHW1nr1q29FI1UtR9++IFrr72Wrl278uijj1KzZk32799PdHR0ma6jJerPYMOGDfTq1YsXXniBadOmAZCZmUnHjh2pV68ev/76q5cjlKqQlZXFqVOnaNCgAb/99hs9evRg9uzZjB071tuhSRX59ddfueSSS/Dz83OV7du3j06dOjF8+HD+97//eTE68Ra73U737t3JzMxk9+7d3g5HqtioUaM4efIkdrud+Ph4tm/f7u2QpJKtXLmSyMhIFi5cyPDhw70djnhBSkoKbdu2pW/fvixatAiLpfyTCjUd8QwWLVqE1Wp1+4TT39+f8ePHs3btWo4ePerF6KSq2Gw2GjRo4O0wxIv69u3rloABtGnThg4dOrBr1y4vRSXeZrVaadKkCUlJSd4ORarYzz//zKJFi3j11Ve9HYp4SWpqKrm5ud4OQ6rYggULOHHiBE8//TQWi4W0tDQcDke5rqUk7Aw2b95M27ZtCQ4Odivv2bMnAFu2bPFCVCJyLjBNkxMnThAWFubtUKQKpaWlER8fz/79+3nllVf49ttvGTJkiLfDkipkt9uZNGkSd955J506dfJ2OOIFd9xxB8HBwfj7+xMZGclvv/3m7ZCkivz0008EBwcTExNDu3btqFmzJsHBwdxzzz1kZmaW6Vq6J+wMYmNjCQ8PL1KeX3bs2LGqDklEzhEfffQRMTExPPnkk94ORarQ1KlTee+99wCwWCzceOONvPnmm16OSqrSu+++y+HDh/npp5+8HYpUMT8/P2666SaGDRtGWFgYO3fu5MUXX2TAgAH8+uuvdO3a1dshSiXbt28fubm5XH/99YwfP55nn32WlStX8sYbb5CUlMTHH39c6mspCTuDjIwMbDZbkXJ/f3/XeRG58OzevZv77ruPPn36MGbMGG+HI1Vo8uTJDB8+nGPHjvHZZ59ht9vJzs72dlhSRRISEvjvf//Lo48+St26db0djlSxvn370rdvX9fxddddx/Dhw4mIiGD69Ol89913XoxOqsLp06dJT0/n7rvvdq2GeOONN5Kdnc17773Hk08+SZs2bUp1LU1HPIOAgACysrKKlOcPNwYEBFR1SCLiZcePH+fqq68mJCTEdd+oXDjat2/PZZddxujRo1m6dCmnT5/m2muvRWtcXRj+85//ULt2bSZNmuTtUOQc0bp1a66//nqioqKw2+3eDkcqWf7f/rfccotb+d///ncA1q5dW+prKQk7g/DwcGJjY4uU55c1bNiwqkMSES9KTk7mqquuIikpie+++04/A4Thw4ezceNG7Rl3Adi3bx8zZ87k/vvv59ixYxw6dIhDhw6RmZlJTk4Ohw4dIjEx0dthihc0adKE7Oxs0tLSvB2KVLL83/v169d3K69Xrx4Ap06dKvW1lISdQZcuXdi7dy8pKSlu5evXr3edF5ELQ2ZmJtdeey179+5l6dKlXHzxxd4OSc4B+dPSk5OTvRyJVLaYmBgcDgf3338/LVq0cD3Wr1/P3r17adGihe4RvUAdOHAAf39/atas6e1QpJJ1794dcP48KCx/nYiyTFNWEnYGw4cPx263M3PmTFdZVlYWs2fPplevXjRp0sSL0YlIVbHb7dx8882sXbuWhQsX0qdPH2+HJFUsLi6uSFlOTg7z5s0jICBASfkFoGPHjixevLjIo0OHDjRt2pTFixczfvx4b4cplejkyZNFyrZu3crXX3/NFVdcUaE9o6R6GDlyJACzZs1yK//ggw/w8fFh8ODBpb6WFuY4g169ejFixAimT59OXFwcrVu3Zu7cuRw6dKjImy/ntzfffJOkpCTXJx1Llixx7Yw+adIkQkJCvBmeVLKpU6fy9ddfc+2115KYmFhkc+bbbrvNS5FJVbnrrrtISUlh4MCBNGrUiOPHj/PRRx+xe/duXnrpJX0CfgEICwvjhhtuKFKev1dYcefk/HLzzTcTEBBA3759qVevHjt37mTmzJkEBgby3HPPeTs8qQJdu3Zl3LhxfPjhh+Tm5jJo0CBWrlzJwoULmT59epluUzBM3U18RpmZmTz66KP873//49SpU0RERPB///d/DB061NuhSRVq3rw5hw8fLvbcwYMHad68edUGJFVq8ODBrFq1qsTz+jF6/vvkk0+YNWsW27ZtIyEhgaCgILp3786kSZO47rrrvB2eeNHgwYOJj49n+/bt3g5F/r+9ew+K6j7/OP5eQAFRvNQr1SAFwYqIxpgYo7h4pagzxoy20dAwIVQrIwUmNQkxgqZqJmJspBN1SutlUGJijAnG2FgLgaqIRqCNk1hR0YqXSCBAvSCw5/eHw/5cd0FExDZ+XjM7I9/znO95zv7h8PC93Wdr1qxhy5YtFBcXU1VVRY8ePRg/fjxJSUn4+fk96PSkjdTW1rJ8+XI2bNjA+fPn8fb2JiYmhri4uLvqR0WYiIiIiIhIG9LkVRERERERkTakIkxERERERKQNqQgTERERERFpQyrCRERERERE2pCKMBERERERkTakIkxERERERKQNqQgTERERERFpQyrCRERERERE2pCKMBERERERaXUlJSVERUXh4+ODu7s7vr6+JCUlcePGjUbvKS8vZ8GCBQQEBODu7s4jjzxCbGwslZWVNnH79u1j1KhRdOrUid69e/Pyyy9TV1dnvZ6cnIzJZLL7eHh4tOhdampqGDp0KCaTicLCwhb1cSsVYSIiIiIi0mJms5mNGzfatX/zzTdYLBbWr1/PsWPHWL16NevWrSMxMbHRvs6fP8/58+dJSUnhq6++YuPGjezZs4eoqChrTFFREeHh4YSFhVFQUMC2bdv45JNPeOWVV6wxL730EhcuXLD5DBo0iJkzZ7boHRcuXIiXl1eL7nXEZBiG0Wq9iYiIiIjIQ8VsNhMZGUlkZOQdY1euXMnatWs5depUs/v/4IMPeO6557hy5QouLi4kJiayd+9eDh8+bI3JzMxk1qxZfPvtt3Tq1Mmuj6KiIoYOHUpOTg5jxoyxtqelpbFq1SpOnz5N//79iY2NZf78+Tb3fvbZZyQkJPDhhx8SGBhIQUEBQ4cObXb+jrjc090iIiLyg2exWNixYwelpaVMnToVX1/fB52SiPyPqqyspFu3bnd9j6enJy4uN0uXmpoa3NzcbGLc3d25fv06X375JWaz2a6PtLQ0/P39bQqwLVu2sHjxYv7whz8wbNgwCgoKiI6OxsPDg+effx6AS5cuER0dzc6dO+nQocNdvm3jNB1RREREmjRjxgxmzpxJXFwcwcHBNn99FhFpruLiYlJTU5k7d26z7ykrK+ONN97gV7/6lbVt8uTJHDhwgIyMDOrr6yktLWXp0qUAXLhwwa6P69evs2XLFpspjQBJSUmsWrWKGTNm4OPjw4wZM4iPj2f9+vUAGIZBZGQk8+bN47HHHmvJKzdKRZiIiIg0Kj8/n8zMTDIzMykvL2fs2LEsXrz4QaclIg/Q8uXL6dixo/WTm5vLvHnzbNrOnj1rc09paSlhYWHMnDmT6OjoZj2nqqqKKVOmMGjQIJKTk63tkyZNYuXKlcybNw9XV1f8/f0JDw8HwMnJvrz56KOPqK6uto5uAVy5coWTJ08SFRVlk/fvfvc7Tp48CUBqairV1dW8+uqrd/sV3ZHWhImIiEijMjMziY2N5fTp0wBs376dxMRE/vWvfz3gzETkQSkvL6e8vNz685w5c3jmmWeYMWOGta1///7W6YPnz5/HbDYzcuRINm7c6LBQul11dTWTJ0+mQ4cO7Nq1y276Idwcqbpw4QJdu3alpKSEQYMGkZ+fz4gRI2zixo8fj6enJx999JG17dKlS/Tu3Zv09HSeeOIJm3hnZ2d8fHyYPn06mZmZmEwm67X6+nqcnZ2ZM2cOmzZtuuN7NEYjYSIi8oNWUlKCyWRq1oLx/2bZ2dmYTCabvwbfC8MwGD58OJMmTWoybsyYMRiGwa5du6ioqGDDhg0MHz7cJub48eO4uLjw7rvvtkpuIvLfrVu3bvj5+Vk/7u7u9OzZ06atoQArLS3FbDYzfPhwNmzY0KwCrKqqikmTJtG+fXs++eQThwUYgMlkwsvLC3d3dzIyMujXrx+PPvqoTczp06fJysqym4rYq1cvvLy8OHXqlE3efn5++Pj4ALBmzRqKioooLCyksLCQ3bt3A7Bt2zaWLVt219/brbQxh4iIyH+B7OxsQkNDSUpKarVCqymbN2/m6NGjHDx4sMm4Ll26EBMTw7Rp0wAICAhg3759NjEBAQE8++yzLFmyhIiICIc7k4nIw6ehAPP29iYlJYXLly9br/Xu3dsaM378eDZv3szjjz9uLcCuXr1Keno6VVVVVFVVAdCjRw+cnZ2Bm7sshoWF4eTkxI4dO3jzzTd5//33rdcb/PnPf6ZPnz787Gc/s8tvyZIlxMbG0rlzZ8LCwqipqeHIkSNUVFSQkJDAI488YhPfsWNHAHx9fenbt+89fTcqwkRERB4yFouF5ORkxowZw8iRI5uMra6uZtWqVZhMJgzDYNiwYfz4xz+2i1u4cCHp6emsWbOG11577X6lLiL/Q/bu3UtxcTHFxcV2RUvDiqja2lqOHz/O1atXATh69CiHDh0CwM/Pz+aehm3k4ea28cuWLaOmpobg4GA+/vhju0LLYrGwceNGIiMj7YozgBdffJEOHTqwcuVKfvvb3+Lh4UFQUBBxcXGt8fpNUhEmIiLykPnss88oKSlpVrG0fPlyLl26RHx8PGvWrKGwsNBhXFBQEEOGDOGPf/wjr776arOmHInID0N2drbD9uacHda/f39u3aLCbDbTnC0r/va3v90xxsnJiX//+99NxsyePZvZs2ffsS+wz/Ve6H9IERGxUVFRgbOzM1OnTrVpLywsxGQyYTKZKC4utrlmNptxd3enpqaGGzdukJqayuTJk+nXrx+urq707NmTGTNmUFBQYHNfbm4uJpOJF154wWEu3377Le3ateOpp56yu5aTk8O0adPo3r07rq6uDBgwgEWLFln/mtocze3j1vVYR44cYeLEiXTq1InOnTvz9NNPU1JS4rD/uro6VqxYga+vL25ubvj5+bFixQpOnTpls04tOTmZ0NBQ4Ob0mIbv2WQyOez7bnJwZMOGDZhMJp555pkm40pKSli9ejU9e/ZkyZIl+Pn5ceLECa5du+YwftasWZw5c4asrKxm5yIi8jBSESYiIja6du1KcHAwubm51NfXW9tv/cX61n9fv36dvLw8nnzySVxdXSkvLycuLo6amhrCw8OJj4/HbDaze/duRo0aZXPG1OjRo+nfvz8ffvgh169ft8slIyODuro6IiIibNrXrl2L2Wxm//79TJkyhdjYWPr27cuyZcuYOHEiN27cuON7tqSPw4cPExISQvv27Zk7dy6PPfYYO3fuZMKECQ7zf+GFF0hMTAQgJiaGsLAwVq9ebTfVxWw2W7dOHjt2LElJSdZPly5d7imH2xmGQVZWFgEBAXTt2rXJ2JdffpmamhqWLl1Kp06dGDx4MPX19fzjH/9wGP/kk08C2K0ZExGR2xgiIiK3SUhIMADj0KFD1rZp06YZ/v7+Rr9+/Yxnn33W2r5v3z4DMJYuXWoYhmFcv37dOHfunF2fX331ldGxY0djwoQJNu2LFi0yAGPbtm129wwfPtxo37698d1331nbjh07Zri4uBjBwcFGWVmZTfyKFSsMwEhJSbG2nT592gCM559/vsV9ZGVlGYABGO+9955NfEREhAEYGRkZNu1//etfDcAYOnSoceXKFWv7+fPnjV69etnl1PCMpKQku++hpTk4cuzYMQMw5syZ02Tc/v37DcAYNGiQUVdXZxiGYSQlJRmAsW7dOof3VFZWGoAREhJyxzxERB5mGgkTERE7DVPjGubc19fXk5OTQ2hoKKGhoQ5HxcxmMwCurq4ON24IDAwkNDSUnJwcamtrre0No1zp6ek28V9//TVffvkl4eHhdOvWzdq+fv166urqSE1N5Uc/+pHNPQsXLqRHjx5kZGQ0+X4t7SMkJISf//znNm0NUylvHeG79X0WL15Mhw4drO19+vThN7/5TZP5NeVucnDk3LlzwM3tmRtjGAbx8fEApKSkWBe0BwUFATS6LszT0xM3NzfrM0RExDFtzCEiInZCQkJwdnYmKyuLV155hYKCAiorKxk3bhxXr15l8+bNfP311/z0pz8lKysLd3d3m8MuCwsLeeutt/j73//OxYsXbYougLKyMvr06QOAv78/jz/+OHv27KGsrIzu3bsD/1/E3D4VMS8vD4C//OUvDqe9tWvXjm+++abJ92tpH7efjwVYd/z6/vvvbdqLioqAm1Mub+dojVtz3U0Ojnz33XcAdtMcb7V161by8/OZOHGizW5jgwcPBhovwuDm+UFlZWV3zENE5GGmIkxEROx4enry6KOPsn//fmpra8nKysJkMhEaGmrdtCIrKwtvb2/y8/MZO3Ys7du3B+DAgQOMGzcOgEmTJjFgwAA6duyIyWRi586dFBUVUVNTY/O8iIgI8vPz2bZtGzExMRiGwZYtW+jatStTpkyxiS0vLwe4p4MyW9qHp6enXVvDgaS3rp+Dm4eNOjk5WYvKWzU1CtWaOTji7u4O0Oj6sWvXrll3N0xJSbG55ufnh5ubG//85z+xWCwOd0C8du2azcifiIjYUxEmIiIOhYaGcvjwYfLz88nOziYwMJAePXoA4OPjQ1ZWFgMGDKC2ttY6fRGwntuSm5trNwqUl5dnHSG61S9+8QsSEhJIT08nJiaGnJwczpw5w9y5c3F1dbWJbShCqqqqWnwocGv00ZxnWCwWysrKrN9bg0uXLt2XZzZHQy4NhejtUlJSrFs6BwcHN9rPiRMnCAgIsGmzWCxUVlYSGBjYStmKiPwwaU2YiIg41FBYff755+Tm5lpHtwDGjRtHdna2dc1Yw3owgJMnT9KtWze7Auzq1ascPXrU4bO6d+9OWFgYeXl5FBcXW6ciPvfcc3axDdMeG6YUtkRr9HEnDQXM/v377a4dOHDArq1h3VVzRrPuRWBgIE5OThw/ftzu2oULF3jrrbfw8PAgKirK4aehwHI0JfHEiRNYLBbr2jEREXFMRZiIiDg0evRoXFxcWLt2LdXV1TZFWGhoKGVlZfzpT3/Cw8ODESNGWK95e3tTUVHBsWPHrG319fW89NJLXL58udHnNaz9SktL44MPPsDHx8fh2qn58+fj4uLCggULOHv2rN3177//3u48svvRx53MmTMHgKVLl9qcq3Xx4kXeeecdu/iGzUfudLDoverSpQtDhgzhyJEjWCwWm2uvvfYa//nPf1i0aBFpaWkOP1FRUYDjIuzQoUPAzW32RUSkcZqOKCIiDnXs2JERI0Zw8OBBnJycbH6xbhglu3z5MpMnT6Zdu3bWawsWLODzzz9n9OjRzJo1Czc3N7KzsyktLcVsNpOdne3wedOmTaNz5868/fbb1NbWEhsbi8lksosbPHgw7777Lr/+9a8JCAggPDwcX19fqqurOXXqFF988QWRkZGsW7eu0XdrjT7uZMKECcyePZutW7cSFBTE9OnTqamp4f333+eJJ54gMzPTZk3VwIED8fLy4r333sPV1ZW+fftiMplYsGABnTt3bnEejjz99NMkJSWRl5fHqFGjACgoKGDTpk34+/uTkJDQ6L1NjYTt3bsXFxcXu4O+RUTElkbCRESkUQ3F1rBhw2x20/Py8sLf3x+wnYoIMHXqVLZv385PfvIT0tPT2bp1KwMHDiQ/Px9vb+9Gn+Xm5sbMmTOtOyk6morYIDo6moMHDzJ9+nTy8vL4/e9/z/bt2ykrKyM+Pt7uMOT71cedbNq0iTfeeAOLxUJqaiq7d+8mLi6ORYsWAbabbDg7O7Njxw5GjhxJRkYGixcv5vXXX6eiouKe87jdiy++iIuLi82xAAkJCVgsFt555x3rJiuONLZD4tWrV9m5cydTp07Fy8ur1XMWEfkhMRmGYTzoJERERB4maWlpREdHW0fjHoSIiAg+/fRTzpw50yqbkzS80xdffEFISEgrZCgi8sOlIkxEROQ+uXjxIr169bKZVllaWspTTz3FuXPnOH36NP369XsguZ05c4aBAwfy+uuvk5iYeE991dXV4e/vT1BQEB9//HErZSgi8sOlNWEiIiL3yZtvvsmnn37KmDFj6NmzJ2fPnmXXrl1UV1eTnJz8wAowuLmByqZNm1plu/yzZ8/yy1/+0u5gbRERcUwjYSIiIvfJnj17ePvttykqKqKiogI3NzeGDBnC/PnzmT179oNOT0REHhAVYSIiIiIiIm1IuyOKiIiIiIi0IRVhIiIiIiIibUhFmIiIiIiISBtSESYiIiIiItKGVISJiIiIiIi0IRVhIiIiIiIibUhFmIiIiIiISBtSESYiIiIiItKGVISJiIiIiIi0IRVhIiIiIiIibej/AHWsLSxH6C0uAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -383,14 +408,14 @@ } ], "source": [ - "fig=plt.figure(figsize=(10,4))\n", - "ax=fig.add_subplot(111)\n", - "plt.plot(wav[::-1],xsv_HITEMP,lw=2,label=\"HITEMP2019\")\n", - "plt.plot(wav[::-1],xsv_Li2015,lw=2,ls=\"dashed\",label=\"Exomol w/ .broad\")\n", - "plt.xlim(22970,22976)\n", - "plt.xlabel(\"wavelength ($\\AA$)\",fontsize=14)\n", - "plt.ylabel(\"cross section ($cm^{2}$)\",fontsize=14)\n", - "plt.legend(loc=\"upper left\",fontsize=14)\n", + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_subplot(111)\n", + "plt.plot(wav[::-1], xsv_HITEMP, lw=2, label=\"HITEMP2019\")\n", + "plt.plot(wav[::-1], xsv_Li2015, lw=2, ls=\"dashed\", label=\"Exomol w/ .broad\")\n", + "plt.xlim(22970, 22976)\n", + "plt.xlabel(\"wavelength ($\\AA$)\", fontsize=14)\n", + "plt.ylabel(\"cross section ($cm^{2}$)\", fontsize=14)\n", + "plt.legend(loc=\"upper left\", fontsize=14)\n", "plt.tick_params(labelsize=12)\n", "plt.savefig(\"co_comparison.pdf\", bbox_inches=\"tight\", pad_inches=0.0)\n", "plt.savefig(\"co_comparison.png\", bbox_inches=\"tight\", pad_inches=0.0)\n", diff --git a/documents/tutorials/Comparing_HITEMP_and_ExoMol.rst b/documents/tutorials/Comparing_HITEMP_and_ExoMol.rst index 981bfb996..f37c37b83 100644 --- a/documents/tutorials/Comparing_HITEMP_and_ExoMol.rst +++ b/documents/tutorials/Comparing_HITEMP_and_ExoMol.rst @@ -3,10 +3,9 @@ Comparing HITEMP and ExoMol .. code:: ipython3 - from exojax.spec.lpf import auto_xsection from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural from exojax.spec.exomol import gamma_exomol - from exojax.spec import api + from exojax.spec import api import numpy as np import matplotlib.pyplot as plt @@ -20,28 +19,43 @@ not exist, moldb will try to download it from HITRAN website. .. code:: ipython3 # Setting wavenumber bins and loading HITEMP database - wav=np.linspace(22930.0,23000.0,4000,dtype=np.float64) #AA - nus=1.e8/wav[::-1] #cm-1 + wav = np.linspace(22930.0, 23000.0, 4000, dtype=np.float64) # AA + nus = 1.0e8 / wav[::-1] # cm-1 .. code:: ipython3 - mdbCO_HITEMP=api.MdbHitemp('CO',nus, isotope=1, gpu_transfer=True) # we use istope=1 for comparison + mdbCO_HITEMP = api.MdbHitemp( + "CO", nus, isotope=1, gpu_transfer=True + ) # we use istope=1 for comparison + + +.. parsed-literal:: + + radis engine = vaex + Downloading 05_HITEMP2019.par.bz2 for CO (1/1). + Download complete. Parsing CO database to /home/kawahara/exojax/documents/tutorials/CO-05_HITEMP2019.hdf5 + .. code:: ipython3 - emf='CO/12C-16O/Li2015' #this is isotope=1 12C-16O - mdbCO_Li2015=api.MdbExomol(emf,nus, gpu_transfer = True) + emf = "CO/12C-16O/Li2015" # this is isotope=1 12C-16O + mdbCO_Li2015 = api.MdbExomol(emf, nus, gpu_transfer=True) .. parsed-literal:: - /home/kawahara/exojax/src/exojax/utils/molname.py:178: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname. + /home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname. + warnings.warn( + /home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname. + warnings.warn( + /home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname. warnings.warn( .. parsed-literal:: HITRAN exact name= (12C)(16O) + radis engine = vaex Molecule: CO Isotopologue: 12C-16O Background atmosphere: H2 @@ -49,24 +63,12 @@ not exist, moldb will try to download it from HITRAN website. Local folder: CO/12C-16O/Li2015 Transition files: => File 12C-16O__Li2015.trans - # i_upper i_lower A nu_lines gup jlower jupper elower Sij0 - 0 84 42 1.155e-06 2.405586 3 0 1 66960.7124 3.811968898414225e-164 - 1 83 41 1.161e-06 2.441775 3 0 1 65819.903 9.663028103692631e-162 - 2 82 40 1.162e-06 2.477774 3 0 1 64654.9206 2.7438392479197905e-159 - 3 81 39 1.159e-06 2.513606 3 0 1 63465.8042 8.73322833971394e-157 - 4 80 38 1.152e-06 2.549292 3 0 1 62252.5793 3.115220404216648e-154 - ... ... ... ... ... ... ... ... ... ... - 125,491 306 253 7.164e-10 22147.135424 15 6 7 80.7354 1.8282485593637477e-31 - 125,492 474 421 9.852e-10 22147.86595 23 10 11 211.4041 2.0425455665383687e-31 - 125,493 348 295 7.72e-10 22147.897299 17 7 8 107.6424 1.9589545250222689e-31 - 125,494 432 379 9.056e-10 22148.262711 21 9 10 172.978 2.0662209116961706e-31 - 125,495 390 337 8.348e-10 22148.273111 19 8 9 138.3903 2.0387827253771594e-31 Broadening code level: a0 .. parsed-literal:: - /home/kawahara/anaconda3/lib/python3.10/site-packages/radis-0.15-py3.10-linux-x86_64.egg/radis/api/exomolapi.py:607: AccuracyWarning: The default broadening parameter (alpha = 0.07 cm^-1 and n = 0.5) are used for J'' > 80 up to J'' = 152 + /home/kawahara/exojax/src/radis/radis/api/exomolapi.py:685: AccuracyWarning: The default broadening parameter (alpha = 0.07 cm^-1 and n = 0.5) are used for J'' > 80 up to J'' = 152 warnings.warn( @@ -77,10 +79,11 @@ i.e. the partial pressure = pressure. .. code:: ipython3 from exojax.spec import molinfo - molecular_mass=molinfo.molmass("CO") # molecular weight - Tfix=1300.0 # we assume T=1300K - Pfix=0.99 # we compute P=1 bar=0.99+0.1 - Ppart=0.01 #partial pressure of CO. here we assume a 1% CO atmosphere (very few). + + molecular_mass = molinfo.molmass("CO") # molecular weight + Tfix = 1300.0 # we assume T=1300K + Pfix = 0.99 # we compute P=1 bar=0.99+0.1 + Ppart = 0.01 # partial pressure of CO. here we assume a 1% CO atmosphere (very few). partition function ratio :math:`q(T)` is defined by @@ -90,9 +93,11 @@ Here, we use the partition function from HAPI .. code:: ipython3 - #mdbCO_HITEMP.ExomolQT(emf) #use Q(T) from Exomol/Li2015 - qt_HITEMP=mdbCO_HITEMP.qr_interp(1,Tfix) - qt_Li2015=mdbCO_Li2015.qr_interp(Tfix) + # mdbCO_HITEMP.ExomolQT(emf) #use Q(T) from Exomol/Li2015 + from exojax.utils.constants import Tref_original + + qt_HITEMP = mdbCO_HITEMP.qr_interp(1, Tfix, Tref_original) + qt_Li2015 = mdbCO_Li2015.qr_interp(Tfix, Tref_original) Let us compute the line strength S(T) at temperature of Tfix. @@ -110,10 +115,22 @@ we need to use float32 for jax. .. code:: ipython3 - Sij_HITEMP=line_strength(Tfix,mdbCO_HITEMP.logsij0,mdbCO_HITEMP.nu_lines,\ - mdbCO_HITEMP.elower,qt_HITEMP,mdbCO_HITEMP.Tref) - Sij_Li2015=line_strength(Tfix,mdbCO_Li2015.logsij0,mdbCO_Li2015.nu_lines,\ - mdbCO_Li2015.elower,qt_Li2015,mdbCO_Li2015.Tref) + Sij_HITEMP = line_strength( + Tfix, + mdbCO_HITEMP.logsij0, + mdbCO_HITEMP.nu_lines, + mdbCO_HITEMP.elower, + qt_HITEMP, + Tref_original, + ) + Sij_Li2015 = line_strength( + Tfix, + mdbCO_Li2015.logsij0, + mdbCO_Li2015.nu_lines, + mdbCO_Li2015.elower, + qt_Li2015, + Tref_original, + ) Then, compute the Lorentz gamma factor (pressure+natural broadening) @@ -138,12 +155,18 @@ and the natural broadening .. code:: ipython3 - gammaL_HITEMP = gamma_hitran(Pfix,Tfix, Ppart, mdbCO_HITEMP.n_air, \ - mdbCO_HITEMP.gamma_air, mdbCO_HITEMP.gamma_self) \ - + gamma_natural(mdbCO_HITEMP.A) + gammaL_HITEMP = gamma_hitran( + Pfix, + Tfix, + Ppart, + mdbCO_HITEMP.n_air, + mdbCO_HITEMP.gamma_air, + mdbCO_HITEMP.gamma_self, + ) + gamma_natural(mdbCO_HITEMP.A) - gammaL_Li2015 = gamma_exomol(Pfix,Tfix,mdbCO_Li2015.n_Texp,mdbCO_Li2015.alpha_ref)\ - + gamma_natural(mdbCO_Li2015.A) + gammaL_Li2015 = gamma_exomol( + Pfix, Tfix, mdbCO_Li2015.n_Texp, mdbCO_Li2015.alpha_ref + ) + gamma_natural(mdbCO_Li2015.A) Thermal broadening @@ -152,8 +175,8 @@ Thermal broadening .. code:: ipython3 # thermal doppler sigma - sigmaD_HITEMP=doppler_sigma(mdbCO_HITEMP.nu_lines,Tfix,molecular_mass) - sigmaD_Li2015=doppler_sigma(mdbCO_Li2015.nu_lines,Tfix,molecular_mass) + sigmaD_HITEMP = doppler_sigma(mdbCO_HITEMP.nu_lines, Tfix, molecular_mass) + sigmaD_Li2015 = doppler_sigma(mdbCO_Li2015.nu_lines, Tfix, molecular_mass) Then, the line center… @@ -163,9 +186,9 @@ this shift is quite a bit. .. code:: ipython3 - #line center - nu0_HITEMP=mdbCO_HITEMP.nu_lines - nu0_Li2015=mdbCO_Li2015.nu_lines + # line center + nu0_HITEMP = mdbCO_HITEMP.nu_lines + nu0_Li2015 = mdbCO_Li2015.nu_lines We use Direct LFP. @@ -175,22 +198,21 @@ We use Direct LFP. from exojax.spec.lpf import xsvector numatrix_HITEMP = init_lpf(mdbCO_HITEMP.nu_lines, nus) - xsv_HITEMP=xsvector(numatrix_HITEMP, sigmaD_HITEMP, gammaL_HITEMP, Sij_HITEMP) + xsv_HITEMP = xsvector(numatrix_HITEMP, sigmaD_HITEMP, gammaL_HITEMP, Sij_HITEMP) numatrix_Li2015 = init_lpf(mdbCO_Li2015.nu_lines, nus) - xsv_Li2015=xsvector(numatrix_Li2015, sigmaD_Li2015, gammaL_Li2015, Sij_Li2015) - + xsv_Li2015 = xsvector(numatrix_Li2015, sigmaD_Li2015, gammaL_Li2015, Sij_Li2015) .. code:: ipython3 - fig=plt.figure(figsize=(10,4)) - ax=fig.add_subplot(111) - plt.plot(wav[::-1],xsv_HITEMP,lw=2,label="HITEMP2019") - plt.plot(wav[::-1],xsv_Li2015,lw=2,ls="dashed",label="Exomol w/ .broad") - plt.xlim(22970,22976) - plt.xlabel("wavelength ($\AA$)",fontsize=14) - plt.ylabel("cross section ($cm^{2}$)",fontsize=14) - plt.legend(loc="upper left",fontsize=14) + fig = plt.figure(figsize=(10, 4)) + ax = fig.add_subplot(111) + plt.plot(wav[::-1], xsv_HITEMP, lw=2, label="HITEMP2019") + plt.plot(wav[::-1], xsv_Li2015, lw=2, ls="dashed", label="Exomol w/ .broad") + plt.xlim(22970, 22976) + plt.xlabel("wavelength ($\AA$)", fontsize=14) + plt.ylabel("cross section ($cm^{2}$)", fontsize=14) + plt.legend(loc="upper left", fontsize=14) plt.tick_params(labelsize=12) plt.savefig("co_comparison.pdf", bbox_inches="tight", pad_inches=0.0) plt.savefig("co_comparison.png", bbox_inches="tight", pad_inches=0.0) diff --git a/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.ipynb b/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.ipynb index 70d638ebc..46887cc6d 100644 --- a/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.ipynb +++ b/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.ipynb @@ -53,12 +53,13 @@ "outputs": [], "source": [ "from jax import config\n", - "config.update('jax_enable_x64', True)" + "\n", + "config.update(\"jax_enable_x64\", True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:46.881094Z", @@ -73,32 +74,50 @@ "output_type": "stream", "text": [ "xsmode = dit\n", - "xsmode assumes ESLIN in wavenumber space: mode=dit\n" + "xsmode assumes ESLIN in wavenumber space: xsmode=dit\n", + "======================================================================\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", + "Your wavelength grid is in *** descending *** order\n", + "======================================================================\n", + "radis engine = vaex\n" ] } ], "source": [ - "from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural\n", + "from exojax.spec.hitran import line_strength\n", + "from exojax.spec.hitran import doppler_sigma\n", + "from exojax.spec.hitran import gamma_hitran\n", + "from exojax.spec.hitran import gamma_natural\n", "from exojax.utils.grids import wavenumber_grid\n", + "from exojax.utils.constants import Tref_original\n", "from exojax.spec import api\n", "\n", "# Setting wavenumber bins and loading HITRAN database\n", - "nus, wav, resolution = wavenumber_grid(1900.0, 2300.0, 350000, unit=\"cm-1\", xsmode=\"dit\")\n", - "mdbCO = api.MdbHitran('CO', nus, isotope=0, gpu_transfer=True) #here we use all of the isotopes in DIT.\n", + "nus, wav, resolution = wavenumber_grid(\n", + " 1900.0, 2300.0, 350000, unit=\"cm-1\", xsmode=\"dit\"\n", + ")\n", + "mdbCO = api.MdbHitran(\n", + " \"CO\", nus, isotope=1, gpu_transfer=True\n", + ") # here we use the isotope=1 (12C16O) in DIT.\n", "\n", "# set T, P and partition function\n", - "Mmol = 28.01 # molecular weight\n", + "Mmol = mdbCO.molmass\n", "Tfix = 1000.0 # we assume T=1000K\n", - "Pfix = 1.e-3 # we compute P=1.e-3 bar\n", - "Ppart = Pfix #partial pressure of CO. here we assume a 100% CO atmosphere.\n", - "qt = mdbCO.qr_interp_lines(Tfix) #use all isotopes as a partition function\n", + "Pfix = 1.0e-3 # we compute P=1.e-3 bar\n", + "Ppart = Pfix # partial pressure of CO. here we assume a 100% CO atmosphere.\n", + "qt = mdbCO.qr_interp_lines(\n", + " Tfix, Tref_original\n", + ") # use all isotopes as a partition function\n", "\n", "# compute Sij, gamma_L, sigmaD\n", - "Sij = SijT(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt)\n", - "gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, \\\n", - " mdbCO.gamma_air, mdbCO.gamma_self) \\\n", - "+ gamma_natural(mdbCO.A)\n", - "sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol)\n" + "Sij = line_strength(\n", + " Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original\n", + ")\n", + "gammaL = gamma_hitran(\n", + " Pfix, Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self\n", + ") + gamma_natural(mdbCO.A)\n", + "sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol)" ] }, { @@ -110,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:51.085989Z", @@ -122,17 +141,18 @@ "outputs": [], "source": [ "from exojax.spec.set_ditgrid import ditgrid_log_interval\n", - "sigmaD_grid=ditgrid_log_interval(sigmaD)\n", - "gammaL_grid=ditgrid_log_interval(gammaL)\n", + "\n", + "sigmaD_grid = ditgrid_log_interval(sigmaD)\n", + "gammaL_grid = ditgrid_log_interval(gammaL)\n", "\n", "# we can change the resolution using res option\n", - "#sigmaD_grid=set_ditgrid(sigmaD,res=0.1)\n", - "#gammaL_grid=set_ditgrid(gammaL,res=0.1)" + "# sigmaD_grid=set_ditgrid(sigmaD,res=0.1)\n", + "# gammaL_grid=set_ditgrid(gammaL,res=0.1)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:51.241333Z", @@ -144,24 +164,22 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEDCAYAAAAx/aOOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABBT0lEQVR4nO29e5wdZZXv/V17d+dKCCH3EJLYXrgkjEAiJF5GjDgDvqh4mYPieM55R08UGc/LOHPeUeYdVOboMGfOx2HOKEZUXuUcQFQuKgcYkUQIAwkkASQhBkPnQud+6XSa3Lp773X+2FWd2tVVtatq1+6u3r2+n09/eu+qp1atenbtWrueX631iKpiGIZhGC6FoXbAMAzDyBcWGAzDMIwqLDAYhmEYVVhgMAzDMKqwwGAYhmFUYYHBMAzDqCK3gUFE7hCRfSKyISN7JRF5wfn7RRY2DcMwmhHJax6DiPwh8Dpwp6ouyMDe66p6Wv2eGYZhNDe5vWNQ1SeBQ95lIvJGEXlURNaJyCoROXeI3DMMw2hachsYQrgd+IKqLgT+CrgtwbZjRGStiKwWkasb4p1hGEYT0DLUDsRFRE4D3g78VETcxaOddR8Bbg7YbKeq/rHzeq6q7hSRNmCFiLykqq822m/DMIzhxrAJDFTubg6r6oX+Fap6P3B/1MaqutP53y4ivwEuAiwwGIZh+Bg2Q0mqegTYKiJ/AiAV3hpnWxGZJCLu3cUU4B3Ayw1z1jAMYxiT28AgIvcAzwDniEiHiHwa+CTwaRF5EdgIfCimufOAtc52K4FbVNUCg2EYRgC5fVzVMAzDGBpye8dgGIZhDA25FJ+nTJmi8+bNS7dx9x6YMCNTf4wRhp1DxlBRx7m3bt26A6o6NQs3chkY5s2bx9q1a9NtvPLv4T1fztYhY2Rh55AxVNRx7onI9qzcsKEkwzAMowoLDIZhGEYVFhgMwzCMKiwwGIZhGFVYYDAMwzCqsMBgGIZhVJHLx1WNfLJueyer2w+yuG0yC+dOqrk8ykb38V6eaT/I9NPH8Nl3vxGA5U+8yr4jJ1jSNpkJY1sj7a3b3sl96zsQYP6siWzY1cWB7pNMnTC6/727rvNYT6Stu9fs4JENu5k/83QmjG3lw13H2b29k+VPvMrW/a/TNvW0fh/jHKdr78oFM7n20jk1l8chqI+T9LthJMECgxGLdds7+eT3V9PTV2ZUS4G7PrOYhXMnhS6PsnGit+xZ2sXjv9sLQMlZ/GJH5aI+ujXY3rrtnXzi9mfoKcUr51IQQn27e80ObnzgJQBW/f4AApRbO7j12af7/dmy/yiP/24vxUKBvlL0cfrtAVx76ZzQ5XEI6mMgdr8bRlLyGRi691QSPdKwbVWlTJ6RKaVth7hOD6BFEIXSihUw78zQ5ZE2ws4638BmmL3StkN8Xg4kOnvDbI15voMbWo5VLXubvMwXCho80FrjOP32xvzbw3BsdujyOAT2McTud2MYkZPrVz4Dw4QZ6TNPV2JZqw2guL2T77SvprevTGtLgXcuXQxzJ4Uuj7JxsreM97d+0bkAlzw3Eu4dQ5C94vZObtuS/I4hyNaJcTu41fkl7+632Hof/1L+aJU/xQIUCwVKpejj9Nv7xjsugEvnhC6PQ2AfQ+x+N4YRdV2/bszMjVxWV120aJFaSYz8MXI0hjvZfdFfmMZgDD71lcRYp6qLsnDDAoNh+LFzyBgqchIY7HFVwzAMowoLDIZhGEYVFhgMwzCMKiwwGIZhGFVYYDAMwzCqsMBgGIZhVGGBwTAMw6jCAoNhGIZRhQUGwzAMowoLDIZhGEYVNQODiIwRkWdF5EUR2SgiXwto80UReVlEfisij4vIXM+6koi84Pz9IusDMAzDMLIlTnXVk8BSVX1dRFqBp0TkEVVd7WnzPLBIVY+JyHXAfwOucdYdV9ULM/XaMAzDaBg17xi0wuvO21bnT31tVqqqW2x+NRCv0LxhGIaRO2JpDCJSFJEXgH3AY6q6JqL5p4FHPO/HiMhaEVktIldH7GOZ027t/v3747hlGIZhNIBYgUFVS85w0GzgEhFZENRORP4UWAT8o2fxXKcU7LXArSLyxpB93K6qi1R10dSpU5Mcg2EYhpEhiWZwU9XDIrISuALY4F0nIpcDfwO8W1VPerbZ6fxvF5HfABcBr9bptzGIxJ0QptZEPv7JeYJsRbV1100aN6rmRDy12m7e0101Oc/itslAZSKeD3cdZ5bPr7vX7ODe53ZUTSzkPdakk/DEaZ9mAiSbtMfIgpqBQUSmAr1OUBgLvA/4B1+bi4DvAleo6j7P8knAMVU9KSJTgHdQEaaNYULQRPRhF/Sgdu7yE72eeTLpYuXmffx42ZIBF/Owtl/74AJufmjjgGlB4dTUnf59hrUtCPQ5u1j1+wMI0FoUEKGvVEZbO1hyUWe/b3ev2cGN/dNydvH47/ZSLBToK1WO9T8umcfyJ9v77QGRwcFrL6x93H5P2tYw4hBnKGkmsFJEfgs8R0VjeEhEbhaRDzpt/hE4Dfip77HU84C1IvIildlMb1HVlzM+BqOBrG4/SE9fmbJCb1+Z1e0HE7Vzl/vpLekAW1FtH9mwm56+gRd6IHSfYW39u1BnH72O/6VytW+PbNhd1b5Upr9tb1+ZRzfuqVrvb+/Hvz6ofdx+T9rWMOJQ845BVX9LZfjHv/wmz+vLQ7Z9GrigHgeNoWVx22RGtRT6J513h1zitnOX+3+9txZlgK2otlcumMlz2w7R01vGHzoKQuA+w9p67xiAqjuGUqlMsVDt25ULZvb/sgcoFqBYKFAqVY71ivkz+u8Y3PZR+O0FtY/b70nbGkYcbM5noyYjT2O4k1lX31zll2kMxqCQkzmfLTAYhh87h4yhIieBwWolGYZhGFVYYDAMwzCqsMBgGIZhVGGBwTAMw6jCAoNhGIZRhQUGwzAMowoLDIZhGEYViYroGSOHehOm1m3v5L71HWzZ282hoz20TT0tNKktysYtj2zitUPHeMv0CXQd72X66WO47JxpgUltcX1pLRYY1VLgmrfN4ZwZExIfp5vs5tYmWtI2uT9JLmlfub4J8JGLK9OY1JuoZsluRr1YYDAGUG9RtnXbO/nE7c/QUzqVPLll/9HAwnlRNv7d8qdxTew54hbs7eJXL+8dUDgviS8uL3a8RGtRKJU19nFWF9Rz7VQyq0e3Jusrv2/3rn2NglPIL20xPCuoZ2RBPgND955KBmAatq2qlOszUlPadojr9ABaBFEorVgB885MtP3n5UDg2VVasTKWrdK2Q3yheCCyTRzfonzpx3+cEefQmOc7uKHlWOC6pH0V6lvKfndt1vPZGUNMTq5f+QwME2akL0mwEitnUCfF7Z18p311f1G2dy5dDAl+dRa3d3LbloG/0luLwruWLollq7i9k3955dQdgx/3jqGWb2G+eH0ql7X6OCPOoRPjdnCr744B6L9jSNJXft9aikLBKeSXpt9dm/V8dsYQU9f168bM3MhnYDCGlIVzJ3HXZxanHqdeOHcS9yxbUpfGsHDuJH7yubfXrTEE+VKPxuAWvMtCY/D6lpXGUO9nZxhgRfQMYyB2DhlDhRXRMwzDMPKIBQbDMAyjCgsMhmEYRhU1A4OIjBGRZ0XkRRHZKCJfC2gzWkTuFZEtIrJGROZ51n3ZWb5ZRP44Y/8NwzCMjIlzx3ASWKqqbwUuBK4QkcW+Np8GOlX1TcA/Af8AICLnAx8H5gNXALeJSDEj3w3DMIwGUDMwaIXXnbetzp//UaYPAT9yXv8MeK+IiLP8x6p6UlW3AluASzLx3DAMw2gIsTQGESmKyAvAPuAxVV3ja3IW8BqAqvYBXcBk73KHDmdZ0D6WichaEVm7f//+RAdhGIZhZEesBDdVLQEXisgZwAMiskBVN2TpiKreDtwOlTyGLG0b8cii+Jpro/t4L8+0H2R0S4E3T5/ARy6enaiG0Or2g0waN4oNuyp1iCaMbuHXm/aCCJefOy12QplbpO5Ad6XW0tQJo5kwuoVn2g+mKsh395odPLJhN1cumAlUEt2mnz6Gz777jUC65LSg403SX3FsW7KbkYREmc+qelhEVlLRC7yBYSdwNtAhIi3AROCgZ7nLbGeZkTOyKL7m2jjRW65a/uy2Tn66roN7/lNtm66Nk73lAeOVLlv2vR6raF1UAb0KyQryeQvorfq9t45TF4//bi/FQiFxAbyw443bX3FsW0E9Iylxnkqa6twpICJjgfcBv/M1+wXwH5zXHwNWaCWl+hfAx52nlt4AvBl4NiPfjQxZ3X6Qnr4yZYXevjKr2w+mthFEXJuujVq3jBrD5ur2g/SGBoVTxD3mRzbsDl1XKldsJO2/sONN+xkE2a7nMzVGJnE0hpnAShH5LfAcFY3hIRG5WUQ+6LT5ATBZRLYAXwS+BKCqG4GfAC8DjwLXO8NSRs5Y3DaZUS0FigKtLQUWt01ObUMC1sW16dqodWJKDJuL2ybTWgzypppCzGN2h4+CKBYqNpL2X9jxpv0MgmzX85kaIxOrlWT0YxqDYyviHDKNwWgoOamVZIHBMPzYOWQMFTkJDFYSwzAMw6jCAoNhGIZRhQUGwzAMowoLDIZhGEYVFhgMwzCMKiwwGIZhGFVYYDAMwzCqSFQryWhO6k2C8ie1uQlffltR+wlK8po/a+KA5LOghDV/Mpi3jbseqpPPovb34a7jzPL45k1qu/bSOYF9cPeaHYmS3Wr1eZx9JsWS3Yy4WGAY4dRbaC24cF4XKzfv48fLllRd0MP2E1U4z1vgDggsiuctOBdUOO/eta9REOkvcHfTVfO5+aGNofvT1g6WXNTJwrmTAgvn+S/U3jZxCurV6vM4+0yKFdQzkmBDSSOceguthRXO6y1pla2o/UQVzvO2DyuK57UX1KavpFUF7h7ZsDtyf6XyKd/9hfOCCun5l9UqqFerz+PsMylWUM9IggWGEU69hdbCCue1FqXKVtR+ogrneQvchRXF89oLatNSlKoCd1cumBm5v2LhlO/+wnlBhfT8y2oV1KvV53H2mRQrqGckwWolGaYxDNAY7mTW1Tf32zONwRg0clIryQKDYfixc8gYKnISGGwoyTAMw6gin08lde+pRM40bFsFK7N1xxhh2DlkDBU5OffyGRgmzEh/K78SGwYw6sPOIWOoqOvcuzEzN/IZGIyGM1iCc9S2k8aNovNYT9X/JDOYrdveyfInXmXfkRO8Ycp4th44WjVjHMSbUc0vVn/8aBf/48617DtygmveNiex+BsmRLvHmbTPTYg2BpuagUFEzgbuBKZTmYP9dlX9Z1+b/wJ80mPzPGCqqh4SkW1AN1AC+rISR4z0DFZSW9S2bnKZQNV/F2/SWpida777NG4KxYsdXf3rnt3WOSCpLewYgxLiprbs5bG+vY7dSqJZ3AtyWLJbr5M34U3Yi9PnluxmDAVxxOc+4C9V9XxgMXC9iJzvbaCq/6iqF6rqhcCXgSdU9ZCnyXuc9RYUcsBgJbVFbetehv3/+23V8Gt1+0ECXOjHn9QWZissac5LkgSzsGQ3dw9J+9yS3YyhoGZgUNXdqrreed0NbALOitjkE8A92bhnNILBSmqL2tY98cT3v99WDb8Wt02mJeLs9Se1hdkKS5rzkiTBLCzZzXW1kLDPLdnNGAoS5TGIyDzgSWCBqh4JWD8O6ADe5N4xiMhWoJPKj8LvqurtIbaXAcsA5syZs3D79u3JjsTFnkGPhWkM1XaqNYa7+B/lj5nGYAw+OcljiB0YROQ04Ang66p6f0iba4A/VdUPeJadpao7RWQa8BjwBVV9MmpfluBmDCl2DhlDRU4CQ6wENxFpBe4D7goLCg4fxzeMpKo7nf/7gAeAS9K5ahiGYQwGNQODiAjwA2CTqn4zot1E4N3Azz3LxovIBPc18EfAhnqdNgzDMBpHnDyGdwCfAl4SkRecZTcCcwBUdbmz7MPAr1T1qGfb6cADldhCC3C3qj6agd9GCoZSV/DbcMfb67V13/oOtuzt5tDRHs4cPwqAk33lVNrALQ9v4tGNe/jSmN08efil0EJ+cbl7zQ7ueKodRPizd7yBc2ZMqLv/71vfEVuDiWvTtAbDT83AoKpPMfChkaB2PwR+6FvWDrw1pW9Ghgxl7oLfRtAEOWlsDZi0Z/+p3yRJ8w9ueXgTy59sB+B3Ld3cvXNH/7qkuQfgz2eAGx94idaiUCpr6v73Hm+tPI+4Ni2fwQjCiuiNEIYyd8FvI+xxh6S2ssw/eHTjntB1afosaN+9Ja2r/73Hm0X+geUzGGFYYBghDGXugt9G2EmX1FaW+QdXzJ8Rui5p7kHYvluLUlf/e483i/wDy2cwwrD5GEYQpjFEc0pjeIAnZ/8n0xiMwScnj6taYDAMP3YOGUNFTgKDDSUZhmEYVVhgMAzDMKqw+RianDzqCkE1keL66a9r5K+JBPTXT1rSNpkJY1sjbYbVIfLvJ67WEHYcYfWTTG8w8ogFhiYmj7kLQfMufPUD87n5oY01/QzKXfDOu9BSEEqqlDxzNAgwujXYZthcB7u6jg/MkaB2PkNYf4fN0VBrroioPrWcBqOR2FBSE5PH3IWgeRce2bA7lp9BuQtV8y6UTgUFFyXcZthcBx2dxwNzJOL4F3QcYXM0WE6DkVcsMDQxecxdCJp34coFM2P5GZS7UDXvQlEo+s5oIdxm2FwHsyeNDcyRqJXPENbfYXM0WE6DkVfscdUmxzSGFBrDyr9nXdvnTGMwBp+cPK5qgaEJySoYTBo3ipWb96WasCZOMIjrf61gEHXRv/e5HVWT97hj/pET3/jOoVpCdNL+dhPprpg/gy+9/7y6Py+byKeJyElgMPG5ychKcPYXuktSlC6O4Bwmlvr9v+mq+Xz1FxtCBeewY/QXsXt2Wyc/XdfBn719Xn+xPK/gHHUsUUL0TVfFE85dvMX6lj/Zzp4jJ3h0457Un1eYgF4PJkQbpjE0GVkJzkH3kXGL0sURnOMKuI9s2B0tOMcUlt39+ovl1TqmsGJ9Xv+S9Ld//795ZX9dn1eYgF4PJkQbFhiajKwE56ATI25RujiCc1wB98oFM6MF55jCsrtff7G8WscUVqyvIMmEcxf//i97y9S6Pq8wAb0eTIg2TGNoIvzj+knGh73bPvB8B68dOsbitskc7SnF1hi8Njbs6uJA90kOH+vhZF+ZJW2TOXKyL7bG4C+Od8a4Uf22rnnbnFgF6VyNoaevzK6u4/T0KW+bN4nRrcXoYwrRGLbs7eZkX5mJY1t5ZW83c84cx9UXzY6tnbi4GsOcM8fRdby3XwNJW7DP1Rjmzzw9dh/Xop5zyaiDnGgM+QwM58zWtcuvT7fxtlUw713ZOjQM2NV1nPvXd1AqK8WC8JGLZzNr4thE2/aVB54L7z13OhecNbEuGwDFgvDRmD7t6jrOz9a9hteUCBREKCc8vl1dx/np2tcGDGe1RNkIOIeijk+c40vS5y/trCS69dtIeXxe/+5b10HJ+T4n6e8om2nPKSMldVy/ZOmNTS4+T5iR/lf/SkbkHcMDK7fwzd7NlBWKAjLxHK5/z5sSb+tn3ckp/M/3XFqXDahcPAsxfXJtBdlQkh3fAyu38E99A21F2gg4h2odX9I+/28/WMOqvgNVy9Icn98/170k/V3LZppzykhJXdevGzNzo6bGICJni8hKEXlZRDaKyP8T0OYyEekSkRecv5s8664Qkc0iskVEvpSZ50YV9YwLD7WuEGSrxedM2qSwxW2TA48rbR8FTQ2UxUQ+lvRm5Ik4dwx9wF+q6noRmQCsE5HHVPVlX7tVqnqVd4GIFIFvA+8DOoDnROQXAdsaKfE+b37XZxYnevbcO458wVkT69YVPnLx7NS6gmvLzRdYeu50Dh/rYefh44wd1cLl505LPH7ujr0v+8M2nnhlP691HmPm6WPoPtnHpHGjuH99B0AsWwvnTuKmq+bzyIbdTB4/ig07uzjeV+b00S2MaimwpG1y/9M7cey5/eomvbVNGc+vN+3tn9QH4Nsrt8T+LBfOncQ9y5b0J73NnzWR+9Z3cP/6jtR6w8K5k6rOqaQ+GcOXmoFBVXcDu53X3SKyCTgLiHNxvwTYoqrtACLyY+BDMbc1QvBejP3P0Ne61fdmMt++qn3A0MiDL+ziGx++oOaz/a6N7z+1NVRX2LSnu2ZxN/fiPXn8KH754i68T4YWC1AsFOjtO8GWfa9TkIo24BJk12vvwRd2AZXn+7/x4QsA+p/533PkJJv2dHP3mh186MJZvHn6hMALXlBftxSEMpXHZnc67V7s6AJgVFH46gcXxBJsr720Enj9ORc3PvBS/51Ja1H4k0Vnx7q4L5w7qT/hzpt7cc+zO7j8vOmJM9b9Nv35JSZKNy+JNAYRmQdcBKwJWL1ERF4EdgF/paobqQSQ1zxtOoDAAWsRWQYsA5gzJ5vszWbE+wUtiFDW6gnmaz3tE5S85ueRDbtDA0NcG1DbJ/8F0U+pDKXyqap4ZYWeknL3mh3ct75jQOJVlL2w5/uVSjCsqsLqrAvt65KGHntPSfnbn29AVWMnhwX55tqPOt4w/LkXZYVfvbyX37yyP3UVVm9uQ09vmZt+voFygmM0hhex8xhE5DTgPuAGVT3iW70emKuqbwX+BXgwqSOqeruqLlLVRVOnTk26+YjB+wUtl5WCxJ9gPip5zUuUrhDXBtT2KU4yVrEgA3SLsIqpUfauXDAz8riCbIb2dVFoCchtcCmVNVFyWC0dJ6pCbBBhuRf1JKt59YZCYeAPEqO5iHXHICKtVILCXap6v3+9N1Co6sMicpuITAF2Amd7ms52lhkpcb+gvX1lWhPe0rvb9vSW8RfTPm/GBEa1FGrqClE2oDL8895zp/fXNIry6coFM/vLOLgUOPVrudUzLOPmRvxsXQelUjkw6PjtXX3hLA4e7RlQQ+iOp9rZsv9o1bZVVVjbq481qK+hUqzv1y/vrQqS7vBXmI9BuL4F+VUsCKgmEn9dvWH5E6/y+Ka9/cOF9QjIXr3BHVZz+8VE6eajZh6DiAjwI+CQqt4Q0mYGsFdVVUQuAX4GzAWKwCvAe6kEhOeAa51hplBGcoJbnOJlYW3iFL+r1SZJJdRabbxCqPeC6vXdm5zlVkL1twEC2wXpAW5l1VoB7u41O7jjqXYQ4fJzp1XZ3PXgTXzlyAfZd+QEb5gyPjC4ePcZt9prnMQ8/zH42yctwFerCmvaKq1R+7UCfHUwXBLcROSdwCrgJej/kXgjMAdAVZeLyJ8D11F5guk48EVVfdrZ/v3ArVSCxB2q+vVaTo20wBAlJsd5kidKTAYixWTvl3jznm5u+vmGUDF5VEshcozae5F8/Hd7B0ya01KUmsXvvAQJyWHH49UDWgoDBVvvcQKhReLWbe/k377/Rb7Z+7Eq+wUhsYC7bnsn13z3abxzHbm5Ci1F4ZqYorKLtwAfwIwJo9n7+knQZCK11z+vSJ3mGINsmkhdBzkJDHGeSnqKgY+l+9t8C/hWyLqHgYdTedfEBAWDKDHZf2GrFQy83Pvcjv5f9is372Pr/tdpm3oal50zja/+sjIkIEJNOz19ZZY/8SoXnn1GoK2bfrGBvoCCcy6VdZX1J3rL3PLIJi47Z1r/MXl/eU8Y3VJ1EfTyyIbd/b+k3buVjTu7TomjjmB773M7mD9rIkvaJvPDZ7b1X6w+evHsKiH15l9uZPrpYwBoP3CUqwIOwRVwH9u0l8++qy3wrsV/97C/+2RVUIBTw2R9JeWuNTv6pzYNunj653B48IXqUdg93SdPfTauvbWvhQYI/91BmEi9YvM+lp4zLdZwoP/uIEykDgrWRn7JZ0mMJr9jCHvapUBF2FNnTNn9teV/VBIRemMKwDCw7HX/8srwdSbUY8sVcr1BJcre1RfO4pe/3U0ppDxF4LFyKqv4mkvmcP/6jlCd5IaWn3Fr38cC1pzCP/9zWHnuOBSdzzx8nuhkVD1h5flh4b87WDR3EutfOxwZzKPuEoPuDrw6kHjO7TC/DB/D5Y7ByA7319Wuw8f7f1WhSqEgCOHBoCBCqVx5RLLH86s7Cu8FMqx1lr8J6rEVdGHy23OF5PkzT+d7q9oJu5bNOmMMe46cHBg0BEShWKzcMXz04tnc+utXBojfcSkrnOwtc+uvX+GGy98SWp47Dq6vPR579ZTPdp9ium99R/+v+aC7g2e3ddJSFC6ZN4m12zsD7xh7+ip3VDd9YP6Ai3nU3cHHL5nD/FkTufmhjf2PNgf5ZQEin1hgaDBhCVItxVNPrgSNw3575ZZTj0rGvOq6TxZNP30Mv960N/XF+uoLZ7H1wNH+pK20tBSEpedO4zHfkztxGNVS4M/ePo+Nu49UCb/fXrklNChAJXGtXFYKUHU3UHCGysrlcn828A2Xv4WntxyItAcw5bRRHHi9Z8BypZI89/SrB3jvudMpFiXy13ctyo69Z9oPcvHZZ6S2A1BSuGfNDqCiP1x2zrRA/0ol5d3nTOOvrzyvfxhsxeZ9Ve1e7OjiT5Y/PUB/8D615b07KJWVWWeM5dpLK+L5fes7+u8iigXhZ+s66CsFa0FGPrDA0EDChoxKZeWaS87mrDPGhv5qmjRuVM0xf4A3TR3P+NEtA54sevL3+0OHSoI464wxtE09rf8inCSRzYsAl58/nQvPPqNfO/jN5n2xh1jeNHU8l7ZNDr1YLG6bzKiiBNqrPNlZubMqCLzvvOmc6C0xtrVYCZRAX5mqhLG/u/oC/vbnGwKHpdzhou9+ahGb93TzjYdf5vWTpQHtSuXK2Pwo59f3c9s6B/SZd8a3Dbu6eHbrIbbsez2wD/pKWvk1X4Dpp49h5+ETVeu9j7DedNV8Vm7exxMBfexNknvs5b20BtwduI+buhnOUDl/bv7lxqofBkFJcnEeYXXbfdTRNHYdPs49z+6omaxoDC0WGBqI91bbP2T00Rq/kjqP9YSOl7u0FoV/+NhbB9jxfmFffO0wv3p5b4iFU1z/njdXPenj2og73OK9WH3O91SLt4bPmogL4qiWQuDx+I/NtbdxZxcv7eyirJWA9N7zprPq9/v7L06uH26g9A9prG4/yPXveVO/kN19vJdn2g/yxr7T+MaSC6ru4lyfojO1B/76DpojGoKfWPKjCtdeOpfFbZMjH2G99tI5A55YGmArwL+wR1QXzp3ETR+YH6iZ+B+K8PZN1KO43tIa963vCPwsLDDkBwsMDaTeZLTRrYUBv9gFeOPU8bRNPS3ysULvF9F7UXQpFoQP/MHMyGf0F86dxA2Xv4U17QcHznkMSAHK5epEtKiLAgwsW/G5P2xLXBzPX7/HGwg+9+43Drg4uUHOO6QR9Ku2n5WrIKA/ohLRvAlyA+yFHMO9n307tzyyiee2dQ5Y763YunDuJL737xcN2N7Ll95/HgAPvrCTvUdODvisvUlycf1LmiQX127UZ2HkA3sqqcHUk+zjzVF4pv1g/2OLae0EJaLF3T5Oslpc3PyEsICUhKT9G6t9jHPIm4i2pG1yaNJd3GOodYeR1F6SpLq4/iU9b+LYNRHaR06eSrLA0ADSnvD+ZDPvM+yDGQyCMpvrCQZZX1iyuKBE2kh4DnnzDS47Z1rdF/WgIJyHAOH3z4JEA8hJYLChpIzxP9sdV1SrFqrxjD13sXLzPn68bEmiC3GQcPzTdR01q2v6t3V1jv6S1wkyl1173rHqOD7EObak/Zu1DZfqobEufvXy3gE5Dkn88o/r12PLq2G82PESrcXKY8/19FuWn6XXblafh5ENsaurGvHwCs5JKk96t/MLkr0lTVTBMqwCahx//Nu6/91y070Jj83//Hy91TjT9m/WNlyC8g3S2g3KhajHVtB5VG+/ZflZeu1m9XkY2WCBIWPSTofo3c4/rWVrUTKZqjOOP2FTdBak4kfS6SeznnIyi+kms5yyMqhkdpqpPl2//OWy67EVdB7V229ZTx/q2rUpRPOFaQwNwDSGgfZMY4jvl2kMI3gYKScagwWGOggqbBfnxPY+beSf5zdpMAh6cumyc6bFCgbeL/mE0S2B28e9OPkvGEBdF5C0fZsUfxBc3DaZhe3LU51DboDo6Stz5ERv/1zVaZ9Y8laX3XrgaF2Bxy3XfeHZZ3C0p1RV/DCNPe+TVECsgntx7Q74PEZSoLDAEM5wCAz+Ms9xRVl3uxO90TnJrUWpKTjHsRVWBC1J0bdaAqjflr8oXq1y3WHHlbRvk+IX2t3jXLHwGWZdfXMiW1FF74TkxeOi7CUVpGslvyW1F3buJP2cg+wGfR4jSozOSWDI51NJ3XsqHZSGbatgZbbuBFHadojr9ABa9CwsVgq1lVasgHlnRm8Xo+dLK1aG2kliK8if0rZDfF4OxD4Doo4r1JbnfVSfBNlL07dJCeo/UdCt2xKff2Oe7+CGlmOh65P6nqW9Gc9v5YaW3sg2SexFnTv1fD5hn0eWn3nuGaTrVy3yGRgmzEj/q38lg3LHUNzeyXfaK1m3RedXrZvF+c6liyHkF467Xa0aRK1F4V1Ll4TaiWtrVEuBdwX4U9zeyW1bkt0xhB2X31bQHUOQD7WOK2nfJsXdj1tTyj3OP3nDM4nPoRPjdnBrjTuGJL5H2av1efjZczzeHUNce2HnTtLPOchu0OeR5Weee+q6ft2YmRs2lFQHpjEE2zKNwTSGtJjGkI+hJAsMCchTeQv3yaUkpSXy/LRRXp5KWbe9k9KKb1BcemPdfmT5tJJrz/28syhzkXXZjEZd0PNybgwKOQkMNYeSRORs4E5gOpV8p9tV9Z99bT4J/DWVu+Zu4DpVfdFZt81ZVgL6snJ8sKknOzNYJE6f0ezPjnarn0YFhzxnNOcl89X14zo9wHfaV+cmI9pvb9XvD9SVxZx1VnQjReO8nBsjjTgJbn3AX6rq+cBi4HoROd/XZivwblW9APg74Hbf+veo6oXDNShAfdmZ7rZ+0mY0B2VH15rxK88ZzXnJfPX2UZ4yooPs1ZPFnHVWtP/cyvJzzMu5MdKoGRhUdbeqrndedwObgLN8bZ5WVbd28GpgdtaODjX1ZGe624pvedqM5qDs6KAM3KBt85jRnJfMV+/nlKeM6CB79WQxZ50V7T+36jnOMNtDfW6MNBJpDCIyD3gSWKCqR0La/BVwrqp+xnm/Feik8iP1u6rqv5twt1sGLAOYM2fOwu3btyc4DA8ZaAxhwqf3da3b2Vse3sSDL+xkzpnjuHjOpH6RecGs0/uFxCiNIUxUXrl5X/+4MDBAY/CP9Xq3b5synmfaDzK6pcAZ40YBp0TDsGPzZ+J6RW1IJjD7x4oHS2BOgqsxbDn/CwN0mDTDP14x+mSp8jP9jLGjUonSrsYwf+bpvHrgKFv3v05rscColkLi0t+uxuDa6C2V6S0p3Sd6edO007j6otmJjtufae+K0lC/MB2ljTXdsFJONIbYgUFETgOeAL6uqveHtHkPcBvwTlU96Cw7S1V3isg04DHgC6r6ZNS+hlJ8ziK5KiqhqJGJa0mn40yauFZr/1H4x4pvump+/xzYeRs73vXgTSxdtySTMfOsE9+iPpOk9mrNIpfmuC35rU5yEhhiFdETkVbgPuCuiKDwB8D3gQ+5QQFAVXc6//cBDwCX1Ot0I6ka00wx9g7w6MY9oevi6AphmkSVnQB/wqqqhlHruIKqfUbtPwr/WPEjG3bnduy4o/N4ZmPmUdpPGi0j6jNJai9Ia/CSVk8L8i+rqrqN0DGMgdQMDCIiwA+ATar6zZA2c4D7gU+p6iue5eNFZIL7GvgjYEMWjjeKqjHNFGPvAFfMnxG6Lo6uEKZJVNkJ8CesqmoYtcaCg6p9Ru0/Cv9Y8ZULZuZ27Hj2pLGZjZlHaT9ptIyozySpvSCtwUua4w7zL6uquo3QMYyB1BxKEpF3AquAlwD398WNwBwAVV0uIt8HPgq4wkCfqi4SkTYqdwlQeTT2blX9ei2nhjqPIYuxb7/GkDR3IW3iWpTGUG/iml9jqCcHw68x5Go4YOXfs67tc5k9l5914ps3uezwsR5O9pVTTy/qzWeYOLaVV/Z2M+fMcYk1hjD/wJLfEpGToSRLcHOo9yLlFQbTfkHrSVzzZx2nDWx5LJE96AScQ1keR5altRthz82SvmL+DN43f4aV6h5MchIY8lkraZCpN4nGn3yURgSsJ3HNK/bdu/Y1Co5gnlQ8z+M0nHkgy+PIcvrORtjzPjix/Ml2vvfUVlRtOtCRhs3gRv1JNH6BMY0IWE/imlfs6/MK5gnF8zxOw5kHsjyOLKfvbIQ9/4MTpbJNBzoSscBA/Uk0foExjQhYT+KaV+xr8QrmCcXzrKdubJbkpCyPI0icrUdIzdqe/8GJYsGmAx2JmMbgYBpDsC3TGCqYxmAaw6CQE41hRAWGLJ6KCStVHedpo7A5ndumjGfj7iORgSDo6aAD3SeZOmE0E0a39G8fp0rmcC2RPWjEOIeyeDLG//QOVJ7gSXtxz9qe92mq3lKZM8eP4s3TJ2TiXz1+BdltmvMvJ4FhxIjPWWTehmed1q6UGiYwQ1d/mzCxOe40nHGqbgaJ1XBqUp2k4uBgTcOZB7LMvq31mWY13WZae4EZ2/uP8uy2zkz9qzeDeSSdf4PJiNEYssi8jcwErpHRHCUwewkSm6P2G+RH1DEFidV9dYiDWWSKDxeyzL6t9ZkmtZ21vVoPPWTlX73nykg6/waTERMYssi8jcwErpHRHCUwewkSm6P2G+RH1DEFidUtdYiDWWSKDxeyzL6t9ZkmtZ21vVoPPWTlX70ZzCPp/BtMml5jiBp/jKsxhGX/xtEYvPt4bOOeflFvzuTxkRpDUEVJt7LqG6aMZ+uBo4xuKfDm6RNqagxBtpq9SmpdJNAY6tEa/JrA4WM9HDrak3osP2t7fo3Brb46/fQxXDx3Ul3+ebWxtA9seO36z79hmx2dE42hqQNDFkkw/rHRJFUiw3UF+MaHL4gUmv1j2e74aW9fsvHtrKtSjojEophfzkYmvrUUpT9RMe2salna82sOxQIUC4W6/LNZ3wLISWDIp/jcvafSQWnYtgpWVl6Wth3iOj2AFkEUSitWwLwzE5krbTvE5+VAVU/FtePdP1A1cDfm3x6GY8HzGfVvF/TpeJbFOaYgW2n7ospeHX2aezznUBRZ9kXQeQZASttZ2xvzfAc3tBwbuKIO/7I8LwNtD8dzNOa512jyGRgmzEhfCG8l/dsWt3fynfbV9PaVaW0p8M6liyHhL4fi9k5u21J9x/CumHa8+xf/HcM7LoCQOwZ3u57eMmV88zL3nVo2KsYxBdmKs12cY0rbp7nHcw5FkWVf+M8z9xd+qZTOdtb2Tozbwa0Bdwz1+JfleRlke1ieozHPvWBuzMyNph5KgmySYOpJ0EmbuBb0vDykGz/NuiplUyYWeUlwO9+oxLeoWfWGyp6rObiaWhb+NUoPGLbnaE6GkpoyMLglk9MmrrmiWtwTNm3imn86RFfQdgW5Iyf7Yn+pawnMIz5pLQkpvpyNTHzLYmrMvCe+uSXmsxCjg2wPGzE6J4Ehn0NJdbCr63hi4Sko+UYgliiWNnGt1jSc7jZQXTE1yBe/Ldd3F0taayyDkfiWtirpcEh8809jm6ZCcZTtpp8OtAE0XR6DOy1jvYlrcZOY0iau+ZOlouirkbjjt+W3aUlrjWUwEt/ykkjXiMS3oGls00x7GmbbpgNNTtMFBndaxnoT19x3caa/TJO45k+WiqKlRuKO35Y/jciS1hrLYCS+5SWRrhGJb0HT2Ao2HehQYhqDg2kM4cdUa99Nh2kMkZjG0EByojHEmfP5bOBOYDqVO7zbVfWffW0E+Gfg/cAx4D+q6npn3X8A/j+n6X9V1R/VcqrRZbfTlClOc/H3X1y9VVnTXvzTBq+o40lbabZpyXDe8LwHinrnjAYLFJmSk8AQR3zuA/5SVdeLyARgnYg8pqove9pcCbzZ+bsU+A5wqYicCXwFWEQlqKwTkV+oamcWzqchzVSIaQRmv4Bbhqpidd5tIJ7AnFYgjzqetJVmjXCGoxj9YkdXKsHXxOjmpOYQt6rudn/9q2o3sAk4y9fsQ8CdWmE1cIaIzAT+GHhMVQ85weAx4IpMjyAhaaZCTCMw+wVcf1DwE0dgTiuQRx1P2kqzRjjDVYxOI/iaGN2cJBKfRWQecBGwxrfqLOA1z/sOZ1nY8iDby0RkrYis3b9/fxK3EhEkxjVCYPYLuC0RAiDEE5jTCuRRx5O20qwRznAVo9MIviZGNyexxWcROQ14Avi6qt7vW/cQcIuqPuW8fxz4a+AyYIyq/ldn+d8Cx1X1v0ftyzQG0xiGFNMYEtkzjSFDcqIxxAoMItIKPAT8q6p+M2D9d4HfqOo9zvvNVILCZcBlqvrZoHZhZBEY0jxV439KKGjqzKiKqEHbest017r4h5V0zuLiH7cPDDIJDH6ynPc464ubO1/55PGj2HrgKNNPH8Nl50xLbdsNFIeP9dJ9opc3TTuNqy+aXVd58hETKHISGGqKz84TRz8ANgUFBYdfAH8uIj+mIj53qepuEflX4Bsi4vb0HwHZfuMCSJO5WysTGaKn3qy1LUQLzGHZy1kIzJa9PLT4Bd+0wrFrK0sBNVA8potfvbw3le0ge89u6+TZbZ2p7JkYPTTE0RjeAXwKWCoiLzh/7xeRz4nI55w2DwPtwBbge8DnAVT1EPB3wHPO383OsoaSJnM3biZy2NSbcbaNEpjDspczEZgte3lI8Qu+WUxlmZWAGiUep7GdtT0To4eGmncMjm4QqZxqZTzq+pB1dwB3pPIuJa7I1NtXpuj8WnbLA9cSmN0ywGGETb0ZZ1t/6WOvL34bQXcMaUS8JH1gNAZX8HXvGLKYytJbrroee1cumFn12LSXNLaztucer/9uPEsxOqu+bCaaMvM5SmNwX3unyrzmbZWhIVdQvuycaYEag3dsE6gSgsOm3fRrDG7pba/w7BeS42oMtQRk0xhS0mCNIa0o67Xlz5ivx6ZfY+jpK3PkRC+IcNbEMYmFZK/GcOjoSYoFoaWlwOiWYip7fo3B9XN0S4Ezxo2qS4zPWterm5xoDE0bGIKIqwUETbsZJ2HNJWysMmr/lqSWIxoQGFyyng406zHyYM2hQppz9JrvPh2a+5NWcwhK0Esy5W6U7SHXHHISGJquiF4UWegIcRLW4lZBjbNNGJakNjzxf255GyPPUiNY3X4wMiE0reaQZcKf37ZpDhVGVGCIW9E0SkeIk7AWNlYZtX9LUhsZ+D+3vCVsRSWspTlHoxJC02oOWSb8+W1bAlyFfA4lnTNb1y4P1LJrs20VzHtX6OpdXcfp6DzO2NYiWw8c5ejJPhacNRGA3+/r5s3TJnCB8z5s29mTxgKwafcRAKZNGMO+7hP9r4/3lpg9aSyzJo6N3H/cbWodi7ud/72RkhrnUL1k+Tl5z6c051AQL+3sYsPOLloKwpjWIuNGF+s6R9du6+TwsR7Gthbrtufa3LT7CMdOlgAYN7rIeTNPz+Scb0R/JqKOc0+W3mgaQyiesttBAnPcOZfhlCjnis5xhKksxSzLTh4iGqgxNJpGzO993/oOtuzt5tDRHtqmnlZ38psrdB882jPgu5U2AS7LzHCv3UFPfsuJxtC0U3v6Bd4XO04JamGJal68Ipz38buohLMsk9RMXDaSkrV4GiT0btl/NNPkN+93K0sxup4kQtfukAvRQ0jTaQzu1J5pBOY466OEqSyT1ExcNpKStXgaVYk16+S3tDYbJUaPdCG66QKDO7VnGoE5znpX9goSpvzilcTYJgwTl42kZC2eRlViTZv8FkWexOiRLkSbxhCBaQwjFNMYquyZxjDyNIamCgzrtndy+OGvsWLGp2OdGGFlsaOqoIZtkybz1C78OWUYBwYvjbiwuVnN3qzjerKus87i9mZJP9N+kOmnj+Gz735jJt+nQQkUOQkMTSM+uyLU56WLu7bvqCk+RWUyh1VBrZX9bOKykRfynhUd5GNW9ryVWKGLlZv38eNlS4Z/VvQg0jQaQ9IKllGZzGFVUGtlP5u4bOSFvGdFB/mYlT0/vSW1rOiENE1g8ItQtYSiqEzmsGk2a2U/m7hs5IW8Z0UH+ZiVPb8U3VoUy4pOSNNrDFFisH9mNVfA8s685r73tvFvEzYumqT6qWkMOaJJNQb/OZ92Yh9XY+g+0UfnsR7eMn0CXcd7U8385vXRW6HYFaa9U98msedqDKOdmhz1Tl/q9zULPSSQnGgMTRUYgKqOTZpwFqUhuHMpxJ0FzTSEYUyTBAYv/kSwequRNlpvcL+jLmn8DaruKuR85recBIamGUoKImnCWRrdoda+TUMw8kCWs8hB4/UG/8/VNP4GVXe1md/i0dSBIWnCWRrdoda+TUMw8kBSDa4WjdYbBugEKfwNqu4qKW357Ta73lBzKElE7gCuAvap6oKA9f8F+KTztgU4D5iqqodEZBvQDZSAvri3OVlO1JM04SxNbkMYpiEMU5pwKAmqZ5HLIgHMmwDqn42w3pyGrDSRdds7Wf7Eq+w7cqJujSHK1xGnMYjIHwKvA3cGBQZf2w8Af6GqS53324BFqho8CWwIWYnPEC4Wh72PunCbeDxCaNLA4CXLqUb9NrPKQPb6OGF0S3/CWtrMa69/h4/1ZCJIB/laV3/mJDDUTHBT1SdFZF5Me58A7qnLo5T4E9zuXfsaUJ20Vut9lLhcS0w2cdkYLgRVJG1EJVZIX+U0zB50paruGmbvxY6uugXpRvTnUJOZxiAi44ArgPs8ixX4lYisE5FlNbZfJiJrRWTt/v37E+/fL671BYjHtd5Hicu1xGQTl43hQlBF0kZVYk1rM6qyK6SbZjTMXr2CdCP6c6jJUnz+APBvqnrIs+ydqnoxcCVwvTMsFYiq3q6qi1R10dSpUxPv3C+utQSIx7XeR4nLtcRkE5eN4UJQRdJGVWJNazOqsiukm2Y0zF69gnQj+nOoiZXH4AwlPRSlMYjIA8BPVfXukPVfBV5X1f9ea39pp/bc1XWcE79/gq2nXcR5M08HTk2/Gfd91JSLtabStKk1m4QGT+2ZB9zpMSH91LJhNrOactPr4+higdc6j3Pa6BbmTRmfeppR178TvSX6ysrZk8YyqrWY2bFDnf2Zk6k9MymiJyITgXcDf+pZNh4oqGq38/qPgJtjGZwwI5UAs3t7J4d3H2HFjE9zxkUVMfl3Eyti8CxnnG+Wbxvv+3XbO3mu/SBFp71fTJ7la1/rvTFMWUnTi8/+c3Xd9k4eqPMpmzCbaYXdIHur2w/S7fi4JeFTS0Hfz/6nljpOsGRUeiE6zPa3kj4FVte5d2PK7QZSMzCIyD3AZcAUEekAvgK0AqjqcqfZh4FfqepRz6bTgQdExN3P3ar6aGae+wgSny1T2TBq04hMXv/3qd7vT63M6DQitz8zOgsh2mvbK0jXO9XoYFNTY1DVT6jqTFVtVdXZqvoDVV3uCQqo6g9V9eO+7dpV9a3O33xV/XojDsAlSHy2TGXDqE0jMnn936esq5vmOTPatZ1lpvlg0zSZz0His2UqG0ZtGpHJ6/8+ZV3dNM+Z0a7tLDPNB5umKqLnT3DbvKe7aipPq3ZqxGIEJLj58VcO9VYVrmc4KcvvT5CPh4/1cOhoD2eOH8UZ40YByRLrvJnRb5gyng07u0CEy8+dVnfimz/pLVa/5iTBrakCA9DfsZaQZqRmBAYGl6yrsDaCqJnfXJL63ahKrK5tfwJcqH85CQxNM5TkxxLSDCM5w2FsPGrmN5c01V0HS29I499g07SBwRLSDCM5w2FsPGrmN5c01V0HS29I499gk0keQx5ZOHcSd31mcdUY5zkzJkS+N4yRzsK5k7hn2ZJMq7Bmjfe77dUbXNJoIwvnTuLez769IZVYvX2ahXYzGDSVxrBueyelFd+guPTGXHe6kXNGsMYwXAkSpl3yeCEOrcbavjwXGkPT3DG4gtR1eoDvtK82MdkwRghxxOg8JZhFVWNdsfB4LqonNI3G4BWk8i7sGIaRHY0QoxtJVDXWjs7jQ+RVNU0TGFxBKivByDCM4UEjxOhGElWNdfakfBTfbJqhJFeQKq1YwTuX5uOW0TCMxtMIMbqR+AV+r8Ywq/2FoXYPaKLAAJUOZ96ZkJMTwDCMwWHh3Ek1i2R+e+WW3DyB6PfXFaMP79nLGW2dQ+5jUwUGwzAMP1lXes0arxg9raWL2763esiF8nwGhu49lUcG07BtVaWmuWGkxc6hpqK07RDX6QG0CKJQWrGiMrKQE0rbDvF5OQAtsLjwMpR/MuQ+5jMwpJyoBxgRk6wYDcbOoaaiuL2T77SvprevTGtLgXcuXZyr4ebi9k5u21K5Y7ih5Wfcxr/jXal8HMSJegzDMIYzQVUQ8oRXjL5gz0Tuef/QD3VZYDAMo+mpJU4PNf3+rZyei7uZmnkMInKHiOwTkQ0h6y8TkS4RecH5u8mz7goR2SwiW0TkS1k6bhiGYTSGOAluPwSuqNFmlape6PzdDCAiReDbwJXA+cAnROT8epw1DMMwGk+cOZ+fBA6lsH0JsMWZ+7kH+DHwoRR2DMMwjEEkq5IYS0TkRRF5RETmO8vOAl7ztOlwlhmGYRg5JgvxeT0wV1VfF5H3Aw8Cb05qRESWAcsA5syZk4FbhmEYRhrqvmNQ1SOq+rrz+mGgVUSmADuBsz1NZzvLwuzcrqqLVHXR1KlT63XLMAzDSEmsiXpEZB7wkKouCFg3A9irqioilwA/A+YCReAV4L1UAsJzwLWqujHG/vYD2xMcR6OYAhwYaidiYH5mz3Dx1fzMluHs51xVzeRXdc2hJBG5B7gMmCIiHcBXgFYAVV0OfAy4TkT6gOPAx7USbfpE5M+Bf6USJO6IExQcu7m4ZRCRtVnNiNRIzM/sGS6+mp/ZYn5WqBkYVPUTNdZ/C/hWyLqHgYfTuWYYhmEMBU0zUY9hGIaRDRYYorl9qB2IifmZPcPFV/MzW8xPYorPhmEYxsjB7hgMwzCMKiwwGIZhGFU0fWCoVeFVREaLyL3O+jVOzoa77svO8s0i8sfOsjEi8qxTAmSjiHzN0/6HIrLVU2n2wpz6KSLydRF5RUQ2ich/zqmfqzx9uUtEHsypn+8VkfWOn0+JyJty6udSx88NIvIjEYld+SBrPz3riiLyvIg85Fn2BsfGFsfmqJz6+edOe5VKUm9sBtnPu5y2G6RSLbu1poOq2rR/VPInXgXagFHAi8D5vjafB5Y7rz8O3Ou8Pt9pPxp4g2OnCAhwmtOmFVgDLHbe/xD42DDw8/8G7gQKzvtpefTTZ/c+4N/n0U8qiZzneez+MG9+UvkR+BrwFmfdzcCnh8pPz3ZfBO6mkkDrLvsJlXwogOXAdTn18yJgHrANmDKU3/cafr7fOS8EuCdOfzb7HUOcCq8fAn7kvP4Z8F4REWf5j1X1pKpuBbYAl2iF1532rc5fvQr+YPt5HXCzqpYBVHVfTv0EQEROB5ZSqcOVRz8VON15PRHYlUM/JwM9qvqKs+4x4KND5SeAiMwG/i/g+64RZ5uljg0cm1fnzU8AVX1eVbfF9G0o/XzYOS8UeJZKeaJImj0wxKnw2t9GVfuALipfotBtndu1F4B9wGOqusbT7usi8lsR+ScRGZ1TP98IXCMia6VSETdu0cOh6E+oXBgeV9UjOfXzM8DDUqkM8Cnglhz6eQBoERE3W/ZjVNcyG3Q/gVuB/xcoe9ZPBg47NsL2lQc/62FI/HSGkD4FPFrLwWYPDA1BVUuqeiGVyHuJiLg1pL4MnAu8DTgT+Ouh8bBChJ+jgRNaSan/HnDHELkIRPrp8gkqt8BDSoSffwG8X1VnA/8/8M0hchEI9tP5tfhx4J9E5FmgGygNlY8ichWwT1XXDZUPcWgyP28DnlTVVbXsNXtgiFPhtb+NI8ZNBA7G2VZVDwMrcWa4U9Xdzh3bSSoXiEvy6CeVXxn3O68fAP4gp37iiHqXAP87po+D6qeITAXe6rl7uBd4e978dN4/o6rvUtVLgCepaCND5ec7gA+KyDYqQylLReR/Oduc4RHGI6syD6Gf9TDoforIV4CpVDSI2tQSIYbzH5VaUO1URBpX5Jnva3M91SLPT5zX86kWedqpiEZTgTOcNmOBVcBVzvuZzn+hclt3S079vAX4M+f1ZcBzefTTWfY54Ed5/dydfR3glKj7aeC+vPnpvJ/m/B8NPA4sHSo/fdteRrVY+lOqxefP59FPz/JtJBOfB7s/PwM8DYyN7WOSL9xw/KOiyL9CRb3/G2fZzcAHnddjnBNxCxVhps2z7d84220GrnSW/QHwPPBbYANwk6f9CuAlZ/n/wnk6JId+nkHlF/hLwDNUfvHmzk9n/W+AK3L+uX/Y6csXHX/bcurnPwKbnPY3DGV/+mxfRvWFrM2xscWxOTqnfv5nKnfffVQeOPh+Tv3sc9q/4PzdVMs/K4lhGIZhVNHsGoNhGIaREAsMhmEYRhUWGAzDMIwqLDAYhmEYVVhgMAzDMKqwwGAYhmFUYYHBMAzDqOL/AATp/spkFjnyAAAAAElFTkSuQmCC\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "#show the grids\n", - "plt.plot(sigmaD,gammaL,\".\")\n", + "# show the grids\n", + "plt.plot(sigmaD, gammaL, \".\")\n", "for i in sigmaD_grid:\n", - " plt.axvline(i,lw=1,alpha=0.5,color=\"C1\")\n", + " plt.axvline(i, lw=1, alpha=0.5, color=\"C1\")\n", "for i in gammaL_grid:\n", - " plt.axhline(i,lw=1,alpha=0.5,color=\"C1\")" + " plt.axhline(i, lw=1, alpha=0.5, color=\"C1\")" ] }, { @@ -173,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:51.586306Z", @@ -184,8 +202,9 @@ }, "outputs": [], "source": [ - "from exojax.spec import initspec \n", - "cnu,indexnu,pmarray=initspec.init_dit(mdbCO.nu_lines,nus)" + "from exojax.spec import initspec\n", + "\n", + "cnu, indexnu, pmarray = initspec.init_dit(mdbCO.nu_lines, nus)" ] }, { @@ -197,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:51.733878Z", @@ -209,19 +228,20 @@ "outputs": [], "source": [ "from exojax.spec.dit import xsvector\n", - "xs=xsvector(cnu,indexnu,pmarray,sigmaD,gammaL,Sij,nus,sigmaD_grid,gammaL_grid)" + "\n", + "xs = xsvector(cnu, indexnu, pmarray, sigmaD, gammaL, Sij, nus, sigmaD_grid, gammaL_grid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Also, we here try the direct computation using LPF for the comparison purpose" + "Also, we here try the direct computation using Direct-LPF for the comparison purpose" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:02:53.826846Z", @@ -231,18 +251,11 @@ }, "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████| 56/56 [00:08<00:00, 6.96it/s]\n" - ] - } - ], + "outputs": [], "source": [ - "from exojax.spec.lpf import auto_xsection\n", - "xsv=auto_xsection(nus,mdbCO.nu_lines,sigmaD,gammaL,Sij,memory_size=30) " + "from exojax.spec.opacalc import OpaDirect\n", + "opa = OpaDirect(mdbCO, nus)\n", + "xsv = opa.xsvector(Tfix, Pfix, Ppart)" ] }, { @@ -254,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:03:01.887461Z", @@ -268,19 +281,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n" + "/tmp/ipykernel_809841/4022811313.py:11: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend(loc=\"upper left\")\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -292,7 +304,7 @@ "plt.legend(loc=\"upper right\")\n", "plt.ylabel(\"Cross Section (cm2)\")\n", "ax = fig.add_subplot(212)\n", - "#plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label=\"precomputed\")\n", + "# plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label=\"precomputed\")\n", "plt.plot(nus, xsv - xs, lw=2, alpha=0.5)\n", "plt.ylabel(\"LPF - DIT (cm2)\")\n", "plt.legend(loc=\"upper left\")\n", @@ -323,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.19" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.rst b/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.rst index 7dc86e119..94243f9b3 100644 --- a/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.rst +++ b/documents/tutorials/Cross_Section_using_Discrete_Integral_Transform.rst @@ -18,38 +18,56 @@ errors): .. code:: ipython3 from jax import config - config.update('jax_enable_x64', True) + + config.update("jax_enable_x64", True) .. code:: ipython3 - from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural + from exojax.spec.hitran import line_strength + from exojax.spec.hitran import doppler_sigma + from exojax.spec.hitran import gamma_hitran + from exojax.spec.hitran import gamma_natural from exojax.utils.grids import wavenumber_grid + from exojax.utils.constants import Tref_original from exojax.spec import api # Setting wavenumber bins and loading HITRAN database - nus, wav, resolution = wavenumber_grid(1900.0, 2300.0, 350000, unit="cm-1", xsmode="dit") - mdbCO = api.MdbHitran('CO', nus, isotope=0, gpu_transfer=True) #here we use all of the isotopes in DIT. + nus, wav, resolution = wavenumber_grid( + 1900.0, 2300.0, 350000, unit="cm-1", xsmode="dit" + ) + mdbCO = api.MdbHitran( + "CO", nus, isotope=1, gpu_transfer=True + ) # here we use the isotope=1 (12C16O) in DIT. # set T, P and partition function - Mmol = 28.01 # molecular weight + Mmol = mdbCO.molmass Tfix = 1000.0 # we assume T=1000K - Pfix = 1.e-3 # we compute P=1.e-3 bar - Ppart = Pfix #partial pressure of CO. here we assume a 100% CO atmosphere. - qt = mdbCO.qr_interp_lines(Tfix) #use all isotopes as a partition function + Pfix = 1.0e-3 # we compute P=1.e-3 bar + Ppart = Pfix # partial pressure of CO. here we assume a 100% CO atmosphere. + qt = mdbCO.qr_interp_lines( + Tfix, Tref_original + ) # use all isotopes as a partition function # compute Sij, gamma_L, sigmaD - Sij = SijT(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt) - gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, \ - mdbCO.gamma_air, mdbCO.gamma_self) \ - + gamma_natural(mdbCO.A) + Sij = line_strength( + Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original + ) + gammaL = gamma_hitran( + Pfix, Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self + ) + gamma_natural(mdbCO.A) sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol) - .. parsed-literal:: xsmode = dit - xsmode assumes ESLIN in wavenumber space: mode=dit + xsmode assumes ESLIN in wavenumber space: xsmode=dit + ====================================================================== + The wavenumber grid should be in ascending order. + The users can specify the order of the wavelength grid by themselves. + Your wavelength grid is in *** descending *** order + ====================================================================== + radis engine = vaex DIT uses a grid of sigmaD, gammaL, and wavenumber. @@ -58,21 +76,22 @@ set_ditgrid.ditgrid_log_interval makes a 1D grid for sigmaD and gamma. .. code:: ipython3 from exojax.spec.set_ditgrid import ditgrid_log_interval - sigmaD_grid=ditgrid_log_interval(sigmaD) - gammaL_grid=ditgrid_log_interval(gammaL) + + sigmaD_grid = ditgrid_log_interval(sigmaD) + gammaL_grid = ditgrid_log_interval(gammaL) # we can change the resolution using res option - #sigmaD_grid=set_ditgrid(sigmaD,res=0.1) - #gammaL_grid=set_ditgrid(gammaL,res=0.1) + # sigmaD_grid=set_ditgrid(sigmaD,res=0.1) + # gammaL_grid=set_ditgrid(gammaL,res=0.1) .. code:: ipython3 - #show the grids - plt.plot(sigmaD,gammaL,".") + # show the grids + plt.plot(sigmaD, gammaL, ".") for i in sigmaD_grid: - plt.axvline(i,lw=1,alpha=0.5,color="C1") + plt.axvline(i, lw=1, alpha=0.5, color="C1") for i in gammaL_grid: - plt.axhline(i,lw=1,alpha=0.5,color="C1") + plt.axhline(i, lw=1, alpha=0.5, color="C1") @@ -84,29 +103,26 @@ needed. These can be computed using init_dit. .. code:: ipython3 - from exojax.spec import initspec - cnu,indexnu,pmarray=initspec.init_dit(mdbCO.nu_lines,nus) + from exojax.spec import initspec + + cnu, indexnu, pmarray = initspec.init_dit(mdbCO.nu_lines, nus) Then, let’s compute a cross section! .. code:: ipython3 from exojax.spec.dit import xsvector - xs=xsvector(cnu,indexnu,pmarray,sigmaD,gammaL,Sij,nus,sigmaD_grid,gammaL_grid) + + xs = xsvector(cnu, indexnu, pmarray, sigmaD, gammaL, Sij, nus, sigmaD_grid, gammaL_grid) -Also, we here try the direct computation using LPF for the comparison -purpose +Also, we here try the direct computation using Direct-LPF for the +comparison purpose .. code:: ipython3 - from exojax.spec.lpf import auto_xsection - xsv=auto_xsection(nus,mdbCO.nu_lines,sigmaD,gammaL,Sij,memory_size=30) - - -.. parsed-literal:: - - 100%|███████████████████████████████████████████████████████████████████████████████████| 56/56 [00:08<00:00, 6.96it/s] - + from exojax.spec.opacalc import OpaDirect + opa = OpaDirect(mdbCO, nus) + xsv = opa.xsvector(Tfix, Pfix, Ppart) The difference is <~ 1%. @@ -119,7 +135,7 @@ The difference is <~ 1%. plt.legend(loc="upper right") plt.ylabel("Cross Section (cm2)") ax = fig.add_subplot(212) - #plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label="precomputed") + # plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label="precomputed") plt.plot(nus, xsv - xs, lw=2, alpha=0.5) plt.ylabel("LPF - DIT (cm2)") plt.legend(loc="upper left") @@ -128,7 +144,8 @@ The difference is <~ 1%. .. parsed-literal:: - WARNING:matplotlib.legend:No handles with labels found to put in legend. + /tmp/ipykernel_809841/4022811313.py:11: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. + plt.legend(loc="upper left") diff --git a/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.ipynb b/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.ipynb index 2f08471ce..fdc22afe7 100644 --- a/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.ipynb +++ b/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.ipynb @@ -35,12 +35,13 @@ "outputs": [], "source": [ "from jax import config\n", + "\n", "config.update(\"jax_enable_x64\", True)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "6fbc7d76", "metadata": { "execution": { @@ -55,33 +56,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "xsmode assumes ESLOG in wavenumber space: mode=lpf\n", - "Somehow wmin and wmax was not given for this database. Reading from the files\n", - "Somehow wmin and wmax was not given for this database. Read 3.40191, 14477.377142 directly from the files\n", - "Added HITRAN-{molecule} database in /home/kawahara/radis.json\n" + "xsmode = modit\n", + "xsmode assumes ESLOG in wavenumber space: xsmode=modit\n", + "======================================================================\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", + "Your wavelength grid is in *** descending *** order\n", + "======================================================================\n", + "radis engine = vaex\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", - "from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural\n", + "from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural\n", "from exojax.spec import api\n", "from exojax.utils.grids import wavenumber_grid\n", + "from exojax.utils.constants import Tref_original\n", "\n", "# Setting wavenumber bins and loading HITRAN database\n", - "nus,wav,R = wavenumber_grid(1900.0,2300.0,350000,unit=\"cm-1\")\n", - "mdbCO=api.MdbHitran('CO',nus, isotope=1) #use isotope=1 12C-16O\n", + "nus, wav, R = wavenumber_grid(1900.0, 2300.0, 350000, unit=\"cm-1\", xsmode=\"modit\")\n", + "mdbCO = api.MdbHitran(\"CO\", nus, isotope=1) # use isotope=1 12C-16O\n", "\n", "# set T, P and partition function\n", - "Mmol=28.01 # molecular weight\n", - "Tfix=1000.0 # we assume T=1000K\n", - "Pfix=1.e-3 # we compute P=1.e-3 bar\n", - "Ppart=Pfix #partial pressure of CO. here we assume a 100% CO atmosphere." + "Mmol = mdbCO.molmass\n", + "Tfix = 1000.0 # we assume T=1000K\n", + "Pfix = 1.0e-3 # we compute P=1.e-3 bar\n", + "Ppart = Pfix # partial pressure of CO. here we assume a 100% CO atmosphere" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "f2619bbb", "metadata": { "execution": { @@ -93,15 +99,17 @@ }, "outputs": [], "source": [ - "qt=mdbCO.qr_interp(1,Tfix) #isotope=1\n", + "qt = mdbCO.qr_interp(1, Tfix, Tref_original) # isotope=1\n", "\n", - "#computes logsij0 etc in device\n", + "# computes logsij0 etc in device\n", "mdbCO.generate_jnp_arrays()\n", "# compute Sij, gamma_L, sigmaD\n", - "Sij=SijT(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt)\n", - "gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, \\\n", - " mdbCO.gamma_air, mdbCO.gamma_self) \\\n", - "+ gamma_natural(mdbCO.A)" + "Sij = line_strength(\n", + " Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original\n", + ")\n", + "gammaL = gamma_hitran(\n", + " Pfix, Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self\n", + ") + gamma_natural(mdbCO.A)" ] }, { @@ -114,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "777f9ffa", "metadata": { "execution": { @@ -127,9 +135,10 @@ "outputs": [], "source": [ "from exojax.spec.hitran import normalized_doppler_sigma\n", - "dv_lines=mdbCO.nu_lines/R\n", - "nsigmaD=normalized_doppler_sigma(Tfix,Mmol,R)\n", - "ngammaL=gammaL/dv_lines" + "\n", + "dv_lines = mdbCO.nu_lines / R\n", + "nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R)\n", + "ngammaL = gammaL / dv_lines" ] }, { @@ -142,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "fa0390c9", "metadata": { "execution": { @@ -156,12 +165,12 @@ "source": [ "from exojax.spec.set_ditgrid import ditgrid_log_interval\n", "\n", - "ngammaL_grid = ditgrid_log_interval(ngammaL)\n" + "ngammaL_grid = ditgrid_log_interval(ngammaL)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "2be8e7ff", "metadata": { "execution": { @@ -178,25 +187,23 @@ "Text(0, 0.5, 'normalized gammaL')" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAGwCAYAAACJjDBkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAByTElEQVR4nO3de1xUdf4/8NcMcREEBNFBFMRbXgLFCyJaauWKZSldVkJdXTPNykvR7irmvd+K5aburhZpWdvmhXUz82vqRmiZQYpcTEs0TcQLoIQyiNyE8/uDZmKYGZgZ5sw5M/N6Ph48lDNnzvkc5vaez+f9eX8UgiAIICIiIiKzKKVuABEREZE9YhBFREREZAEGUUREREQWYBBFREREZAEGUUREREQWYBBFREREZAEGUUREREQWuEfqBjiy+vp6XLt2Dd7e3lAoFFI3h4iIiEwgCALKy8sRFBQEpdJ4fxODKBFdu3YNwcHBUjeDiIiILHD58mV06dLF6O0MokTk7e0NoOFB8PHxkbg1REREZAq1Wo3g4GDt57gxDKJEpBnC8/HxYRBFRERkZ1pKxWFiOREREZEFGEQRERERWYBBFBEREZEFGEQRERERWYBBFBEREZEFGEQRERERWYBBFBEREZEFGEQRERERWUAWQdSmTZsQGhoKDw8PREVF4fjx483uv2vXLvTp0wceHh4IDw/H/v37dW5fsWIF+vTpAy8vL/j5+WHMmDE4duyYzj6lpaWYMmUKfHx80K5dO8ycORO3b9/W2ef777/HAw88AA8PDwQHB+PNN9+0zgUTERGR3ZM8iEpJSUFCQgKWL1+O7OxsDBgwADExMbh+/brB/dPT0xEfH4+ZM2ciJycHsbGxiI2NxenTp7X73Hvvvdi4cSNOnTqFo0ePIjQ0FGPHjsWNGze0+0yZMgU//PADUlNTsW/fPhw5cgSzZ8/W3q5WqzF27Fh07doVWVlZWLt2LVasWIHNmzeL98cgIiIi+yFIbOjQocJLL72k/b2urk4ICgoSkpKSDO4/adIkYfz48TrboqKihOeff97oOcrKygQAwpdffikIgiD8+OOPAgAhMzNTu8+BAwcEhUIhXL16VRAEQXj77bcFPz8/obq6WrvPwoULhd69e5t8bZrzlpWVmXwfIiIikpapn9+S9kTV1NQgKysLY8aM0W5TKpUYM2YMMjIyDN4nIyNDZ38AiImJMbp/TU0NNm/eDF9fXwwYMEB7jHbt2mHIkCHa/caMGQOlUqkd9svIyMDIkSPh5uamc56zZ8/i5s2bBs9VXV0NtVqt80NE8lVYVon0CyUoLKuUuilEZIckDaJKSkpQV1cHlUqls12lUqGoqMjgfYqKikzaf9++fWjbti08PDywfv16pKamIiAgQHuMjh076ux/zz33wN/fX3scY+fR3GZIUlISfH19tT/BwcHNXT4RSSglswAj1hzC5C3HMGLNIaRkFkjdJCKyM/dI3QCxPPjgg8jNzUVJSQm2bNmCSZMm4dixY3rBkzUlJiYiISFB+7tarW4IpMqLAcUd0c5LROYpVldh3e6jaC/8ukEA1u/+BqODRkDl46G3b0HpHYT4e+rdRkQOqrzcpN0kDaICAgLg4uKC4uJine3FxcUIDAw0eJ/AwECT9vfy8kLPnj3Rs2dPDBs2DL169cL777+PxMREBAYG6iWu3717F6WlpdrjGDuP5jZD3N3d4e7urn9DzseAF998ieSi5uYdxLtc0d9+LA/w89T+fvpaGdLOFEMAkA7g4b4qhAX52q6hRCSNiiqTdpM0iHJzc8PgwYORlpaG2NhYAEB9fT3S0tIwd+5cg/eJjo5GWloaXn75Ze221NRUREdHN3uu+vp6VFdXa49x69YtZGVlYfDgwQCAQ4cOob6+HlFRUdp9XnvtNdTW1sLV1VV7nt69e8PPz8+8Cx04FfDxNu8+RCQaN3UVdmQeRb3w2zYXhQLxUSOAX3ubitVVmPnFUdQLYdp9dpxW4LPR+r1VRORg1OUAVrW4m+TDeQkJCZg+fTqGDBmCoUOHYsOGDaioqMCMGTMAANOmTUPnzp2RlJQEAFiwYAFGjRqFt956C+PHj8fOnTtx4sQJbemBiooK/PWvf8WECRPQqVMnlJSUYNOmTbh69Sp+//vfAwD69u2LcePGYdasWUhOTkZtbS3mzp2LZ555BkFBQQCAyZMnY+XKlZg5cyYWLlyI06dP4+9//zvWr19v/kV6qwBvHyv8tYjIGlTeQMKTSizefRp1ggAXhQKrnwyDqnOIdp8L10tQLDT5wiQAP1d5Q9W5vY1bTEQ2JXi2vA9kEETFxcXhxo0bWLZsGYqKihAREYGDBw9qk7gLCgqgVP6W/z58+HBs374dS5YsweLFi9GrVy/s2bMHYWEN3xZdXFyQl5eHf/3rXygpKUH79u0RGRmJb775Bvfdd5/2ONu2bcPcuXPx8MMPQ6lU4qmnnsI//vEP7e2+vr744osv8NJLL2Hw4MEICAjAsmXLdGpJEZH9iosMwch7OyC/5A5CAzzRybeNzu3dArygVECvtyo0wLQ3VyJyfApBEISWdyNLqNVq+Pr6oqysDD4+7IkisjcpmQV6vVVxkSEt37EFhWWVuFhSgW4BXnrBGxFJz9TPb8l7ooiI5Kql3ipLpGQWIHH3KdQLgFIBJD0ZbpXAjIhsT/JlX4iI5KyTbxtE92hvlQCqsKxSG0ABDUOFi3efNljsk4VAieSPPVFERDZysaRCJ8cKAOoEAfkld3SCNPZWEdkH9kQREdmIJlm9sabJ6ub0VhGRtBhEERHZSCffNkh6MhwuioZISpOs3rgXqrneKiKSFw7nERHZEEsrEDkO9kQREdlYc8nqpvRWWYrJ6kTWxZ4oIiKZYWkFIvvAnigiIhliaQUi+WNPFBGRg2NpBSJxsCeKiMjBsbQCkTgYRBEROTiWViASB4fziIicAEsrEFkfe6KIiJyEVKUViBwVe6KIiAiAOKUViBwZgygiItLq5NuGwRORiTicR0RERGQBBlFEREREFmAQRURERGQBBlFEREREFmAQRURERGQBBlFEREREFmAQRURERGQBBlFEREREFmAQRUROo7CsEukXSlBYVil1U4jIAbBiORE5hZTMAiTuPoV6AVAqgKQnwxEXGSJ1s4jIjrEniogcXmFZpTaAAoB6AVi8+zR7pIioVRhEEZHDu1hSoQ2gNOoEAfkld6RpEBE5BAZRROTwugV4QanQ3eaiUCA0wNPsYzGviog0GEQRkcPr5NsGSU+Gw0XREEm5KBRY/WQYOvm2Mes4KZkFGLHmECZvOYYRaw4hJbNAjOYSkZ1QCIIgtLwbWUKtVsPX1xdlZWXw8fGRujlETq+wrBL5JXcQGuBpdgBVWFaJEWsO6QwLuigUOLroQbOPRUTyZurnN2fnEZHT6OTbxuKAp7m8qpaOWVhWiYslFegW4MWAi8iBMIgiIjKBJq+qaU9US3lVLK1A5LiYE0VEZAJL8qpYWoHIsbEniojIRHGRIRh5bweT86paMwRIRPLHIIqIyAzm5FVZOgRoCPOqiOSHw3lERCJhaQUixyaLIGrTpk0IDQ2Fh4cHoqKicPz48Wb337VrF/r06QMPDw+Eh4dj//792ttqa2uxcOFChIeHw8vLC0FBQZg2bRquXbum3eerr76CQqEw+JOZmQkAyM/PN3j7d999J84fgYgcUlxkCI4uehA7Zg3D0UUPmp1UzrwqIvmSPIhKSUlBQkICli9fjuzsbAwYMAAxMTG4fv26wf3T09MRHx+PmTNnIicnB7GxsYiNjcXp06cBAHfu3EF2djaWLl2K7Oxs7N69G2fPnsWECRO0xxg+fDgKCwt1fp577jl069YNQ4YM0Tnfl19+qbPf4MGDxftjEJFD6uTbBtE92ls0DGfpkjWsrE4kPsmLbUZFRSEyMhIbN24EANTX1yM4OBjz5s3DokWL9PaPi4tDRUUF9u3bp902bNgwREREIDk52eA5MjMzMXToUFy6dAkhIfrfAmtra9G5c2fMmzcPS5cuBdDQE9WtWzfk5OQgIiLComtjsU0iai1LinyyrAJR65j6+S1pT1RNTQ2ysrIwZswY7TalUokxY8YgIyPD4H0yMjJ09geAmJgYo/sDQFlZGRQKBdq1a2fw9r179+KXX37BjBkz9G6bMGECOnbsiPvvvx979+5t9nqqq6uhVqt1foiIWsPcvCoO/xHZjqSz80pKSlBXVweVSqWzXaVSIS8vz+B9ioqKDO5fVFRkcP+qqiosXLgQ8fHxRqPJ999/HzExMejSpYt2W9u2bfHWW29hxIgRUCqV+OSTTxAbG4s9e/boDA02lpSUhJUrVxq9XiIiS5hTWoFlFYhsx6FLHNTW1mLSpEkQBAHvvPOOwX2uXLmC//3vf/jPf/6jsz0gIAAJCQna3yMjI3Ht2jWsXbvWaBCVmJiocx+1Wo3g4GArXAkROTtTSytYs6wCwNIKRM2RNIgKCAiAi4sLiouLdbYXFxcjMDDQ4H0CAwNN2l8TQF26dAmHDh0y2gv1wQcfoH379kYDo8aioqKQmppq9HZ3d3e4u7u3eBwiIrFohv8W7z6NOkGwuKwCwNwqopZImhPl5uaGwYMHIy0tTbutvr4eaWlpiI6ONnif6Ohonf0BIDU1VWd/TQD1008/4csvv0T79u0NHksQBHzwwQeYNm0aXF1dW2xvbm4uOnXqZMqlEZHEnHl2WmvLKgDMrSIyheTDeQkJCZg+fTqGDBmCoUOHYsOGDaioqNAmeU+bNg2dO3dGUlISAGDBggUYNWoU3nrrLYwfPx47d+7EiRMnsHnzZgANAdTTTz+N7Oxs7Nu3D3V1ddp8KX9/f7i5uWnPfejQIVy8eBHPPfecXrv+9a9/wc3NDQMHDgQA7N69G1u3bsV7770n6t+DiFqPPSjmVVY3hLlVRC2TPIiKi4vDjRs3sGzZMhQVFSEiIgIHDx7UJo8XFBRAqfytw2z48OHYvn07lixZgsWLF6NXr17Ys2cPwsLCAABXr17VzqJrWprg8OHDGD16tPb3999/H8OHD0efPn0Mtu3111/HpUuXcM8996BPnz5ISUnB008/bcWrJyJrM9aDMvLeDvzwN4O1c6uIHJHkdaIcmbbOxJVz8PHxlro5RE4hM78UL27L1tv+zpTBGBLqJ0GL7NdnuVeRtD9Pm1uV+GgfTIzoLHWziESnVpfDt8u9LdaJkrwnyinkfAx4eUjdCiKn0Ke6FlPvuYjG3w4VUKD31Z+BkpZzH+k3EwE8NKoWt+7Uop2nK7zvFgAnpG4VkQ1UVJm0G4MoWxg4FWBPFJFNeAMI9dLvQfFmD4pFvH/9IXIq6nIAq1rcjUGULXirAG8u+0JkKxMfCMTQ/v1MKk7ZWk3rKLGuEpEDEEzL/WMQRUQOqbWz00zRdBbgEwM749Ocq049K5DImUhaJ4qIyF4ZmgX4SfZV1lUiciIMooiILGCojlJTmrpKROSYGEQREVlAU0epOayrROTYGEQREVlAs0adi6IhknJRKPDUoM46v1u6Zh0R2QcW2xSRtthmC8W6iMh+FZZV6swCbPq7mOflLEAicZj6+c3ZeURErdB0FqAUswKTngzHyHs7MKgisjEGUUREdsTQrMBFn5yC4td17lhagch2mBNFRGRHDM0KFACWViCSAIMoIiI7YsqsQJZWILINBlFERHak6axAJYCmMRVLKxDZBnOiiIjsTFxkCEbe20E7C/DIuRtYvPu0dsFlMUsrcFYg0W8YRBGRQ3C2D/fGswCbBlViXb+hWYFMYCdnxiCKiOweP9zFL61gaFbg4t2nMfLeDk4RtBIZwpwoIrJrxj7cOTvNugzNCmQCOzk7BlFEZNf44W4bhmYFMoGdnB2DKCKya/xwtw1DawVybUBydsyJIiK7pvlwt9XsNGdmqwR2InvBIIqI7B4/3G3HFmsDEtkLBlFE5BCk+nB3ttIKRPQbBlFERBYyVFph5L0dGFQROQkGUUREFjBUWmHRJ6egUMCp61URORPOziMisoCh0goCwHpVRE6EQRQRkQUMlVZoivWqiBwbgygiIgs0rZukBNA0pmK9KiLHxpwoIiILNS2tcOTcDZvVq+KsQCLpMYgiImqFxqUVbFWvirMCieSBQRQRkRWJXa+KswKJ5IM5UUREdoSzAonkg0EUEZEd4axAIvlgEEVEZEc4K5BIPpgTRURkZ6ScFUhEv2EQRUR2ydmn+EsxK9AQZ38cyLkxiCIiu2Noir+zz0YTe1agIXwcyNkxJ4qI7IqhKf6cjWZ7fByIZBJEbdq0CaGhofDw8EBUVBSOHz/e7P67du1Cnz594OHhgfDwcOzfv197W21tLRYuXIjw8HB4eXkhKCgI06ZNw7Vr13SOERoaCoVCofOzZs0anX2+//57PPDAA/Dw8EBwcDDefPNN6100EVnE0BR/zkazPT4ORDIIolJSUpCQkIDly5cjOzsbAwYMQExMDK5fv25w//T0dMTHx2PmzJnIyclBbGwsYmNjcfr0aQDAnTt3kJ2djaVLlyI7Oxu7d+/G2bNnMWHCBL1jrVq1CoWFhdqfefPmaW9Tq9UYO3YsunbtiqysLKxduxYrVqzA5s2bxflDEJFJDE3x52w02+PjQAQoBEEQWt5NPFFRUYiMjMTGjRsBAPX19QgODsa8efOwaNEivf3j4uJQUVGBffv2abcNGzYMERERSE5ONniOzMxMDB06FJcuXUJISMN4fWhoKF5++WW8/PLLBu/zzjvv4LXXXkNRURHc3NwAAIsWLcKePXuQl5dn8D7V1dWorq7W/q5WqxEcHIyysjL4+Pi0/McgIpOkZBbozUZjLo7t8XEgR6VWq+Hr69vi57ekPVE1NTXIysrCmDFjtNuUSiXGjBmDjIwMg/fJyMjQ2R8AYmJijO4PAGVlZVAoFGjXrp3O9jVr1qB9+/YYOHAg1q5di7t37+qcZ+TIkdoASnOes2fP4ubNmwbPk5SUBF9fX+1PcHCw0TYRkeXiIkNwdNGD2DFrGI4uetBmH9yFZZVIv1DCvJ9fSfU4EMmFpLPzSkpKUFdXB5VKpbNdpVIZ7e0pKioyuH9RUZHB/auqqrBw4ULEx8frRJPz58/HoEGD4O/vj/T0dCQmJqKwsBDr1q3Tnqdbt25659Hc5ufnp3euxMREJCQkaH/X9EQRkfXZejYaZ6IZJsWsQCK5cOgSB7W1tZg0aRIEQcA777yjc1vjYKd///5wc3PD888/j6SkJLi7u1t0Pnd3d4vvS0TyZWwm2sh7OzCAIHJikg7nBQQEwMXFBcXFxTrbi4uLERgYaPA+gYGBJu2vCaAuXbqE1NTUFnOSoqKicPfuXeTn5zd7Hs1tROQ8WjMTjUOARI7LakHU9evXsXr1arPu4+bmhsGDByMtLU27rb6+HmlpaYiOjjZ4n+joaJ39ASA1NVVnf00A9dNPP+HLL79E+/btW2xLbm4ulEolOnbsqD3PkSNHUFtbq3Oe3r17GxzKIyLHZelMtJTMAoxYcwiTtxzDiDWHkJJZIGIricjWrBZEFRYWYunSpWbfLyEhAVu2bMG//vUvnDlzBi+88AIqKiowY8YMAMC0adOQmJio3X/BggU4ePAg3nrrLeTl5WHFihU4ceIE5s6dC6AhgHr66adx4sQJbNu2DXV1dSgqKkJRURFqamoANCSNb9iwASdPnsTPP/+Mbdu24ZVXXsHUqVO1AdLkyZPh5uaGmTNn4ocffkBKSgr+/ve/6wwDEpFzaLrorynr07EYJZHjkzwnKi4uDjdu3MCyZctQVFSEiIgIHDx4UJvEXVBQAKXyt1hv+PDh2L59O5YsWYLFixejV69e2LNnD8LCwgAAV69exd69ewEAEREROuc6fPgwRo8eDXd3d+zcuRMrVqxAdXU1unXrhldeeUUnQPL19cUXX3yBl156CYMHD0ZAQACWLVuG2bNni/wXISI5Mnd9uuaGAM3No+L6dETyZLU6USdPnsSgQYNQV1dnjcM5BG2diSvn4OPjLXVziOxWsboKBaV3EOLvCZWPh9TNMUmxugoTNh7VCaRcFAp8NneEWdfwWe5VrN5/RjsrcPGjfTExorMILSYiDbW6HL5d7m2xTpTkPVFOIedjwMs+3viJ5Ob0tTKknSmGACAdwMN9VQgL8pW6WS1SAXg/rAxpZ65DgAAFFHi4b0eozpmeF1VeXYv8oxcR7/Lbtvz/HUJ5RSi83V2t32gialBRZdJuJgdRLeUC3bhxw9RDOZ+BUwH2RBGZrVhdhZlfHEW9EKbdtuO0Ap+NNq83RyphADqMrsLl0koE+7cxu815+aX4+G623vYRnQdjSGjzE1zssfeOSDbU5QBWtbibyUFUTk5Oi/uMHDnS1MM5F28V4M1lX4jMdeF6CYqFJsGCAPxc5Q1V55Zn3cqByhtQWTj61iXEF78oLuoNCXYOCQW8jedGNRQG/YGFQYksJZi2BqTJQdThw4ctbgsRkSU0pQWaBhHOssitZlZg0/XpLJkVyMKgRNbHnCgiki1LgghHI+WsQCJqnkVB1JUrV7B3714UFBRoay9paNaeIyKyBnODCGuSS2kBc9anc/beOyJbMjuISktLw4QJE9C9e3fk5eUhLCwM+fn5EAQBgwYNEqONROTkpFjk1l4XHGbvHZHtmF0naujQoXjkkUewcuVKeHt74+TJk+jYsSOmTJmCcePG4YUXXhCrrXZHWyeqhToTRCQvhWWVGLHmkF5vztFFD9pNMFJYVunUvXdErWHq57fZPVFnzpzBjh07Gu58zz2orKxE27ZtsWrVKkycOJFBFBHZPUfIK2LvHZH4zF47z8vLS5sH1alTJ1y4cEF7W0lJifVaRkQkEUsXHDZFYVkl0i+UONwaelwrkJyR2T1Rw4YNw9GjR9G3b188+uijePXVV3Hq1Cns3r0bw4YNE6ONREQ2JVZekSP31DhC7x2RucwOotatW4fbt28DAFauXInbt28jJSUFvXr14sw8IrIKOeTVWHtWoDn1m+Rw/ebirEByRmYHUd27d9f+38vLC8nJyVZtEBE5Nzn11lgzr8jUnho5Xb85OCuQnFGrim3evn0b9fX1Ots4C42ILOXI1bZN6amx9+uXsqYXkRTMTiy/ePEixo8fDy8vL/j6+sLPzw9+fn5o164d/PyaXxCTiKg5zfXW2DtNT42LoiFj3VBPjSNcfyffNoju0Z4BFDkFs3uipk6dCkEQsHXrVqhUKigUipbvRERkAkfPq2mpp0bM67fHPCsiuTM7iDp58iSysrLQu3dvMdpDRE5MyrwaWwUZzeVZcVYgkX0xO4iKjIzE5cuXGUQRkSikyKuRU5Ah5axAIjKP2UHUe++9hzlz5uDq1asICwuDq6urzu39+/e3WuOIyDnZstq2HIMMKWYFEpH5zA6ibty4gQsXLmDGjBnabQqFAoIgQKFQoK6uzqoNJCISk6MHGY6eZ0YkJbODqGeffRYDBw7Ejh07mFhORHbP0YMMOdRvYlI7OSqFIAhCy7v9xsvLCydPnkTPnj3FapPDMHUVaCKSVkpmgV6Q4WiJ14VllZLUb5JTvhmRqUz9/Da7J+qhhx5iEEVEDkXKIpEnL9/E8fxSDA31x4Bg8Wrt2TLPTEOO+WZE1mR2EPX444/jlVdewalTpxAeHq6XWD5hwgSrNY6IHJ9chnqkCDJe/U8uPsm+qv39qUGd8dakCJu2QUyOnm9GZHYQNWfOHADAqlWr9G5jYjkRmcOZh3pOXr6pE0ABwCfZVzEtuquoPVK25Oj5ZkRmL/tSX19v9IcBFBGZythQT2FZpbQNs5Hj+aUGt5/Iv2njlojHlKVuiOxZqxYgJiKylLMP9QwN9Te4fUioY/RCaXBRYnJkFgVRmZmZOHz4MK5fv476+nqd29atW2eVhhGRY3P2oZ4BwX54alBnvZwoRxnKa0yKfDMiWzA7iFq9ejWWLFmC3r1769WJYs0oIjKVHOoXSe2tSRGYFt0VJ/JvYkion00DKLkk9BPZM7PrRKlUKrzxxhv44x//KFKTHAfrRBHpMvTBLVX9ImcOIpw5oZ/IFKLViVIqlRgxYkSrGkdEzsfYB7cUQz3OHESwdhOR9Zg9O++VV17Bpk2bxGgLETkoOc3Ek1NbpNBcQj8Rmcfsnqg//elPGD9+PHr06IF+/frpFdvcvXu31RpHRI5BTjPx5NQWKTh7Qj+RNZndEzV//nwcPnwY9957L9q3bw9fX1+dHyKipjQf3I1J9cEtp7ZIQQ61mwrLKpF+ocRpev/IcZmdWO7t7Y2dO3di/PjxYrXJYTCxnOg3clrkV6q2yCmZnQsSExln6ue32UFU165d8b///Q99+vRpdSMdHYMocnZNgwY5zcSzdVsYPDT8zUesOaQ3lHh00YOSB5VEjYk2O2/FihVYvnw5PvjgA3h6Okf3NxGZr7nZeHJph63aYs6MODn1Vlmbs+ejkeMxOyfqH//4Bw4cOACVSoXw8HAMGjRI58cSmzZtQmhoKDw8PBAVFYXjx483u/+uXbvQp08feHh4IDw8HPv379feVltbi4ULFyI8PBxeXl4ICgrCtGnTcO3aNe0++fn5mDlzJrp164Y2bdqgR48eWL58OWpqanT2USgUej/fffedRddI5EzkMgNOLu0wdUZcSmYBRqw5hMlbjmHEmkNIySywYSvF5+z5aOR4zO6Jio2NtWoDUlJSkJCQgOTkZERFRWHDhg2IiYnB2bNn0bFjR73909PTER8fj6SkJDz22GPYvn07YmNjkZ2djbCwMNy5cwfZ2dlYunQpBgwYgJs3b2LBggWYMGECTpw4AQDIy8tDfX093n33XfTs2ROnT5/GrFmzUFFRgb/97W865/vyyy9x3333aX9v3769Va+fyBHJpcdBLu0wZUacM9RvYpV6cjRm50RZW1RUFCIjI7Fx40YAQH19PYKDgzFv3jwsWrRIb/+4uDhUVFRg37592m3Dhg1DREQEkpOTDZ4jMzMTQ4cOxaVLlxASYjgHYe3atXjnnXfw888/A2joierWrRtycnIQERFh0bUxJ4qclVxyX+TSDqDlZPb0CyWYvOWY3v12zBqG6B6O9eVNqtw4IlOZ+vlt9nCeNdXU1CArKwtjxozRblMqlRgzZgwyMjIM3icjI0NnfwCIiYkxuj8AlJWVQaFQoF27ds3u4++vv6r6hAkT0LFjR9x///3Yu3dvs9dTXV0NtVqt80PkjOQwjV4O7Wg8lT8uMgRHFz2IHbOG4eiiB/WSyp1pqKuTbxtE92jPAIrsntnDeXV1dVi/fj3+85//oKCgQCePCABKS0tNPlZJSQnq6uqgUql0tqtUKuTl5Rm8T1FRkcH9i4qKDO5fVVWFhQsXIj4+3mg0ef78efzzn//UGcpr27Yt3nrrLYwYMQJKpRKffPIJYmNjsWfPHkyYMMHgcZKSkrBy5Uqj10vkyJomRMdFhmDkvR1s3uMgl3aYm1gv9VCXIye0E4nF7CBq5cqVeO+99/Dqq69iyZIleO2115Cfn489e/Zg2bJlYrTRYrW1tZg0aRIEQcA777xjcJ+rV69i3Lhx+P3vf49Zs2ZptwcEBCAhIUH7e2RkJK5du4a1a9caDaISExN17qNWqxEcHAyUFwMKLqlAjqNYXYWC0jsI8feEyscDn+Vexer9Z7QBw+JH+2JiRGd0UgKdOgJAGVBeJnq75NKOYnUV1u0+ivaaYUQBWL/7G4wOGgGVj4fR+8X1ccPouf1wubQSwf5toPJxA8oNf0G0JmN/NyKnVV5u0m5mB1Hbtm3Dli1bMH78eKxYsQLx8fHo0aMH+vfvj++++w7z5883+VgBAQFwcXFBcXGxzvbi4mIEBgYavE9gYKBJ+2sCqEuXLuHQoUMGe6GuXbuGBx98EMOHD8fmzZtbbG9UVBRSU1ON3u7u7g53d3f9G3I+BryMv3ES2ZPT18qQdqYYAoB0ACN6BiD/fAniXX7bJ/9/h1BeEQpvd1djh7G68upa5B+9KHk7AKDm5h3Eu1zR334sD/BrfnhO9esPShp+L6+uxa07tWjn6SrKdcjp70YkGxVVJu1mdhBVVFSE8PBwAA1DXmVlDd/qHnvsMSxdutSsY7m5uWHw4MFIS0vTzvqrr69HWloa5s6da/A+0dHRSEtLw8svv6zdlpqaiujoaO3vmgDqp59+wuHDhw3OqLt69SoefPBBDB48GB988AGUypbTw3Jzc9GpUyezrhEAMHAq4ONt/v2IZKZYXYWZXxxFvRCm3bY9D6g3sO+IzoMxJNTPZm3Lyy/Fx3ezJW8HALipq7Aj86heQnt81AigmZ6opj7LvYrVX4jbQySnvxuRbKjLAaxqcTezg6guXbqgsLAQISEh6NGjB7744gsMGjQImZmZhnthWpCQkIDp06djyJAhGDp0KDZs2ICKigrMmDEDADBt2jR07twZSUlJAIAFCxZg1KhReOuttzB+/Hjs3LkTJ06c0PYk1dbW4umnn0Z2djb27duHuro6bb6Uv78/3NzccPXqVYwePRpdu3bF3/72N9y4cUPbHk2P1r/+9S+4ublh4MCBABoWVt66dSvee+89s68R3irAm7PzSH7MzYO5cL0ExYL+B6tCAQhNAobOIaGAt+1ya7qE+OIXxUW9wMXW7QAAlTeQ8KRSL79J1dn0CuWFZZV4ZX8W6jV/bwFI2F+Mof37WTVnSU5/NyLZEEyb0GF2EPXEE08gLS0NUVFRmDdvHqZOnYr3338fBQUFeOWVV8xuZ1xcHG7cuIFly5ahqKgIEREROHjwoDZ5vKCgQKeXaPjw4di+fTuWLFmCxYsXo1evXtizZw/Cwhq+GV+9elU7i65paYLDhw9j9OjRSE1Nxfnz53H+/Hl06dJFZ5/GFR9ef/11XLp0Cffccw/69OmDlJQUPP3002ZfI5EcNA2YLFmGxFi9o7880htvHjgrae0fuSVmtzah3VY1rqT+uxHZs1bXicrIyEBGRgZ69eqFxx9/3FrtcgisE0VSaSlgWjiuD944mGdR/SRj9Y5sUfvH2Bp4Uq/PJ8a6eLaucSVl7SbODCS5EW0BYjIdgyiSgikBkxKG85hMLewol0AFgOSL+ooZ7LRUoNMRcGFmkiPRFiA2VnBSoVDAw8MDPXv2RLdu3cw9LBFZqPG3eAB6S4e8cSBPL2Cqh+E8JlMLO9py8V7A8JIoiZ+cAhoNLUq1TIqYw25S1bgCbNM75AxL3ZBjs2jtPIVCgaYdWJptCoUC999/P/bs2QM/P87sILKmlobpnru/m94HurGAyZw8Jmt+oFpyLEOBSj0A2Mm6eK1h64AVsF3vkFzWNiSylNnLvqSmpiIyMhKpqakoKytDWVkZUlNTERUVhX379uHIkSP45Zdf8Kc//UmM9hI5rZTMAoxYcwiTtxzDiDWH8O7XF/S+xb/3zUWDS4cseqSP3tInz4/sYXAZksZLlRg6b0pmgVntbnw8S49laEkUJSCLZVJsvbRM08dHjOMb6h0S43zOtNQNOSazc6LCwsKwefNmDB8+XGf7t99+i9mzZ+OHH37Al19+iWeffRYFBea92Toa5kSRtRjKuzGW1zT7ge54/+hFixK/rZmA3vR4ms/Kxm845h6raX4QAElyhowluIs97GaLHiJbL4TsDHlfZH9Ey4m6cOGCwQP6+Pjg559/BgD06tULJSUl5h6aiIwwNpxlaJhuxv2hmHF/qN4HekvDQoZ6IAzlUzU33NJcfpahb2vmDN0Yyw8SO2fI1NIQYg+72Sp/SOzhyaakzPsiai2zg6jBgwfjz3/+Mz766CN06NABAHDjxg385S9/QWRkJADgp59+algzjoiswpL6TNaoS9RcArol+VlNmfvhbChQETN4aalnzpaJ0I5cN6rpY8iSB2QvzA6i3n//fUycOBFdunTRBkqXL19G9+7d8dlnnwEAbt++jSVLlli3pUROzNgHW1xkCCYMCLLKt3hzArUj5260GFxo8rMaH0+BhqCsXmg5d0jqD1Jr9MxZky17iKTsHWLJA7InFtWJqq+vxxdffIFz584BAHr37o3f/e53Jq0/50yYE0XWJnbejbH8lJOXbyIz/yYiQ/3Q0cejVflZhj6crVFNvSXmBmXGcoMM9cyJVQCzKSnzh2xV8sCWBUaJjBEtJwoAlEolxo0bh3HjxlncQCJqnqEPLbHzbgz1QLSmjEJz+VkaYgyZOeoSN1L1ELHkAZFhFgVRRCQuKYc0Ggdqhoa0DA3TWZqfZe1kdlOWuDE1KLPFEKolbJ0/ZMuCmLZOaidqLQZRRDIjpyrOxpLNZ99vuIyCucGFtZPZDQVMrcljMtbzI0UBTENsEWzbsneIiyGTvWEQRSQzchrSMNYzYGkZBVOPb2kyuzWWuGkaqMklYGrK2FI4Xu73YHBXP6u1mSUPiIxjEEUkM3Ia0mipZ6C1H3DNDZkN6+ZvNJnd3IDJ2FCjLRLaxWKsF2/u9hyrtp0lD4iMM2l2nlqtNvmAnIX2G87OI0vJbRaW2LMCmx7fUDL75m8u6t3P1IDJUMV2a1dntzVDM9kas3bbbVGR3RB7CmzJcZj6+W1SEKVUKqFQKFraDQBQV1dneisdHIMoMsTUb9VSfGjJ4QPL2BI3MHHYz5QlbsxZRkes5U6soXGwbYiYbWfJA3JkVi1xcPjwYe3/8/PzsWjRIvzxj39EdHQ0ACAjIwP/+te/kJSU1MpmEzk2Y0GKFOUMmpJLQrs1ktlb+tuZm9AuV5r8oaz8m5i/M8dmQ8AseUDUwKQgatSoUdr/r1q1CuvWrUN8fLx224QJExAeHo7Nmzdj+vTp1m8lkQMwFqTcqqxtyO+ReLhCLh9YYiezN3cOKWtAWaqTbxs8NqANKmru2iRvydjzuE+gNypq6qzaMyWn/EAiQ8yuWO7p6YmTJ0+iV69eOtvPnTuHiIgI3Llzx6oNtGcczqPG5FgBuzE5DZ3YIifM2Dmkyv2xBlu0vaXnsbW/CEiZH0jOS7SK5cHBwdiyZQvefPNNne3vvfceFx0maoahb9VKQBa9P4C8avTYYpq73GtAWcLctluS12ToeQz89kXA2sPALHlAcmZ2ELV+/Xo89dRTOHDgAKKiogAAx48fx08//YRPPvnE6g0kchSGgpS/jOttcEaYtYcrTP2wlOoDS6qcMHsOmFrL0rymps9jQwn51v4i0LSKPssdkFxYtADx5cuX8c477yAvLw8A0LdvX8yZM4c9UU1wOI8MMTTVXszhiuY+LOXwgSSHGYGOytjja42hW83z2NNNiSfeTrfJMDCfK2QrVi1xQJZhEEVSlzNo7sOyaQVwuZQz4BR262gu4DCW17RkfF+M79/J7L+9LfKWjD1Xdr8YbfWEdiLRcqIA4JtvvsG7776Ln3/+Gbt27ULnzp3x73//G926dcP9999vcaOJHIk535rFGlYyNuMuK/+mbMsZcAp767VUrsJYXtP/+/wMVu8/Y3ZAbYthYGPPldi300VJaCcyhdLcO3zyySeIiYlBmzZtkJ2djerqagBAWVkZVq9ebfUGEtkjYx9ihWWVNm2H5sOyMReFQq9wJfBb8GJLxtrHKeyt01zwnH6hBEBDwOFioIiypc/VTr5tEN2jvUkBVGFZJdIvlJh1DkPPFUA/od3WrzFybmYHUf/v//0/JCcnY8uWLXB1ddVuHzFiBLKzs63aOCJ71VwPiy1pkoA1H5aaoZbBXf1sEry09GFprH3shWodQwGHAsD8nTmYvOUYRqw5BAA4uuhBLBnfV+/+jQMuawclKZkFGLHmkLYdKZkFJt2v6XPF0IeXFK8xcm4W1Yn68ccfERoaCm9vb5w8eRLdu3fHzz//jH79+qGqqkqsttod5kQ5L7nl+hjKuZIqoV2KtfmcUePHVwlA+PVHQ/N8BKD3XFWgoe5Ta/LljD3O9pjQTs5HtJyowMBAnD9/HqGhoTrbjx49iu7du5vdUCJHJGXNJVPLBYiZx2JudXZnLjUglsaP7y8V1Zi7PUfndk2vTXSP9nolCwRA57FL/OQUvNzvweCufiY9TsYC6OaGGf3bmjZLtPFzRS51zch5md0TlZSUhI8//hhbt27F7373O+zfvx+XLl3CK6+8gqVLl2LevHlitdXusCfKcUk9684YuUwBl3t1dmdjSg+Q5rlqKODSMOU51dy5AOv3erEXk8Rg6ue32TlRixYtwuTJk/Hwww/j9u3bGDlyJJ577jk8//zzDKDIKRjL6TCU/2NOsm1rySWZHTCck6OEbgAFMIfFVkzJPdM8Vw3ly2mY8pwy1tv0+feFAGAwr6k1z1lzX2OWJLUTGWNxnaiamhqcP38et2/fRr9+/dC2bVtrt83usSfK8Rj7lv2XR3pLvoiwsd6fHbOGIbpHe5u2BdDPuTJWnZ09UbZjaq9N48fOkI3xA+Hf1s1gT6yh14iG5rXR3DAjYHm9qpbIpaeW5E+0YpsfffQRIiMj0bev7oyOqqoq/Oc//8G0adMsa7EDYhDleOQ8TCW3ZHZNm2xZnZ2sp7CsEln5NzF/Z47Zw2/NBWGNn5OmBFzWSGjXbJfb64PkS7ThvD/+8Y8YOnSo3jp5ZWVlmDFjhvktJbIjch6mkrJcgLEhkqZDLXGRITi66EHsmDUMRxc9yABKxjr5tsFjA4IsGn7TPM7GyidoXhtNn7ONaY598rLppRaaK5/Q3DAjh/bIUmb3RCmVSvztb3/DkiVL8Je//AUrVqwAABQXFyMoKAh1dXVitNMusSfKMUk1TMVkdpJKS0nnxoaMTe39KSyrxOffF+L/fX5G7xiaXt6WnlstnUuMXi9yXKIu+zJ16lQMHz4cTzzxBE6fPo1///vfFjfUKZQXAwomzzqKuD5uGD23Hy6XViLYvw1UPh4IVAYiaX+eNrBKfLQPOinLgPIyq5zzs9yrWL3/jDZQWfxoX0yM6IxidRUKSu8gxN8TKh8PAEAnJdCpIwBY7/zGFKursG73UbTXfDAJwPrd32B00Ahte8j+aZ5TxeoaqBQ39QKV7h7lQHmtwfutf7Tl10YnJfB4dyW2NDk2gN+KW7Xw3LpSUIr2wk29+57OO4srnm4I8ffUaUvT/fi8JR3l5SbtZnZPlIuLCwoLC9GxY0cUFBRgwoQJUCgUSE5OxvDhw9kT1Yg2kv2/ZfDx4gvTXpVX1+LWnVq083SFt7trq/ez5Pxbj17UKZSogAIjerbHt+dLIKAhT+XhviqEBfla7bymuHzzDj7JvqK3/alBXRDsx6VbHNHpa2VIO3MdAgQooMDDfTu2+Lwz9bWhe2zd4qAaj4Z1Qhs3F71jGXqdANAeR/Ma6dreEz8V38aRn26YfGxyPuqKKvg+vsr6ieVKpRJFRUXo2LEjAODOnTuYMmUK0tLSUFFRwSCqEW0QdeUcfHy8pW4OWcBYD5AtZeaX4sVt+ksqGUpm/2yubb9JF6urMGHjUb2eCVu3g2yrWF2l0xMrxrHbuCnx7IeZzSa1N309fpZ7VdvTZKxK+2dzRwCA3vO2pWOTc1Gry+Hb5V7rD+ctX75cp5yBp6cnPv30UyxfvhxHjhyxrLWOzlsFeDMnyt4UllXilf1ZqBf8GjYIQML+Ygzt38+ms3m6hPjiF8VFnTd8JfQXEIYA/FzlDVVn25UzUHkDCU8q9WbcqTozt8SRqbwBlUjxReNjJzzZRn/pmkbDe01fjxMfCMTQ/v2M52/9+hqJ7tFe53lr7Nihod1RUVNnUiV1cjCCaT3pZs/OW758OTw99Q++cuVKHD582NzDAQA2bdqE0NBQeHh4ICoqCsePH292/127dqFPnz7w8PBAeHg49u/fr72ttrYWCxcuRHh4OLy8vBAUFIRp06bh2rVrOscoLS3FlClT4OPjg3bt2mHmzJm4ffu2zj7ff/89HnjgAXh4eCA4OBhvvvmmRddH9knOiwgvfKSPLBYQBjjjztmJWbyy8XPrH5MH6g3VGXo9Nlc0tPFrxJRjx76dbvZCyeRcTOqJ2rt3Lx555BG4urpi7969RvdTKBR4/PHHzWpASkoKEhISkJycjKioKGzYsAExMTE4e/asdsiwsfT0dMTHxyMpKQmPPfYYtm/fjtjYWGRnZyMsLAx37txBdnY2li5digEDBuDmzZtYsGABJkyYgBMnTmiPM2XKFBQWFiI1NRW1tbWYMWMGZs+eje3btwNoGIobO3YsxowZg+TkZJw6dQrPPvss2rVrh9mzZ5t1jWSfNOUMmg5VWTtQMYWhde7aebqKum6YOQsIc+0752SLmZma51ZhWaVZr0dT1q9s7tjAbz1TmnILI+/twOc56TApJ6pxHpRSabzzSqFQmJ0TFRUVhcjISGzcuBEAUF9fj+DgYMybNw+LFi3S2z8uLg4VFRXYt2+fdtuwYcMQERGB5ORkg+fIzMzE0KFDcenSJYSEhODMmTPo168fMjMzMWTIEADAwYMH8eijj+LKlSsICgrCO++8g9deew1FRUVwc3MD0LDkzZ49e5CXl2fwPNXV1aiurtb+rlarERwczBIHdkyq4pBSlzOQc2V2kgcpilda8nq0pEq7EkC9gX3EqqRO8mPVEgf19fUG/99aNTU1yMrKQmJionabUqnEmDFjkJGRYfA+GRkZSEhI0NkWExODPXv2GD1PWVkZFAoF2rVrpz1Gu3bttAEUAIwZMwZKpRLHjh3DE088gYyMDIwcOVIbQGnO88Ybb+DmzZvw8/PTO09SUhJWrlxpyqWTxEwNUgz1AInNnG/3YvUAGRvKXHMgj9/OCUDzw91iPR8seT2a+hppfGxPNyWeeDtd7/r+3+dnsHr/GX55IC2zc6KsqaSkBHV1dVCpVDrbVSoVioqKDN6nqKjIrP2rqqqwcOFCxMfHa6PJxrMLNe655x74+/trj2PsPJrbDElMTERZWZn25/Llywb3I2mZs4Aw4JyLCMu5MjvJg6HniC2Gu8V8PWqOPSDYr8VK6qxyToCJPVH/+Mc/TD7g/PnzLW6MtdXW1mLSpEkQBAHvvPOO6Odzd3eHu7u76OchyxkLUm5V1spimEqKb/eGGMonMVaZXYocMZKeKTlHYjO1R9kSmp4pQ5XUpXhNkjyZFEStX7/epIMpFAqzgqiAgAC4uLiguLhYZ3txcTECAwMN3icwMNCk/TUB1KVLl3Do0CGdMc3AwEBcv35dZ/+7d++itLRUexxj59HcRvZJ7sNUzp7MTvZFiuFuDVsltY/v30lbK06DXx5Iw6Qg6uLFi6Kc3M3NDYMHD0ZaWhpiY2MBNORcpaWlYe7cuQbvEx0djbS0NLz88svabampqYiOjtb+rgmgfvrpJxw+fBjt27fXO8atW7eQlZWFwYMHAwAOHTqE+vp6REVFafd57bXXUFtbC1dXV+15evfubTAfiuyDoSDFUM0lqb5pSvnt3pRZd1J+aJI8STEz01iPshhffOTQ40byZdHaedaUkJCA6dOnY8iQIRg6dCg2bNiAiooKzJgxAwAwbdo0dO7cGUlJSQCABQsWYNSoUXjrrbcwfvx47Ny5EydOnMDmzZsBNARQTz/9NLKzs7Fv3z7U1dVpc5j8/f3h5uaGvn37Yty4cZg1axaSk5NRW1uLuXPn4plnnkFQUBAAYPLkyVi5ciVmzpyJhQsX4vTp0/j73/9ucq8cyZOUw1RMZidn0Ph5DkCU4TZbD3vzywMZY1EQdeXKFezduxcFBQWoqanRuW3dunVmHSsuLg43btzAsmXLUFRUhIiICBw8eFCbxF1QUKBTVmH48OHYvn07lixZgsWLF6NXr17Ys2cPwsLCAABXr17V1rKKiIjQOdfhw4cxevRoAMC2bdswd+5cPPzww1AqlXjqqad0cr98fX3xxRdf4KWXXsLgwYMREBCAZcuWsUaUA5B7zSXAtoGKLb/Vk2Nr/DzXpGQLsP5wmxTD3vzyQIaYvXZeWloaJkyYgO7duyMvLw9hYWHIz8+HIAgYNGgQDh06JFZb7Y6pdSZIXKy51Lz0CyWYvOWY3vYds4YhuoftlpAh+2boed6YtWtISVXDTUPMpHaSnlXrRDWWmJiIP/3pT1i5ciW8vb3xySefoGPHjpgyZQrGjRvXqkYTWZschqmYzE7OwNDzvDFrD7c5elI72Qez60SdOXMG06ZNA9BQW6myshJt27bFqlWr8MYbb1i9gUSWYs0l09Y1M7Q2HxNnyVyGnueNiRGYN64ZJeYafo3J5X2F5MHsnigvLy9tHlSnTp1w4cIF3HfffQAaimcSyYWz11wyJw+LibPUWk2f5woAUDR8WRA7MDf0XB95bweHSGoneTM7iBo2bBiOHj2Kvn374tFHH8Wrr76KU6dOYffu3Rg2bJgYbSSyiJTDVE0DFVsns1tSVJSJs9RaTZ/nAEQPzA091xd9cgqKX1/7jpDUTvJldmL5zz//jNu3b6N///6oqKjAq6++ivT0dPTq1Qvr1q1D165dxWqr3WFiufSkSD41J19CrGR2Y8niCoXuMKLYC8YSic3Yc70xR0tqJ/GJlljevXt37f+9vLyQnJxsWQuJWkGuNZfMLRcgVu+P3IuKkvMRazaboed6U46U1E7y0qpim7dv30Z9fb3ONva4kNia6+kxpeq2mOSSL8G170hOxJzN1vS5rkRDbarGL0Oxkto1r2mWO3BeZgdRFy9exNy5c/HVV1+hqqpKu10QBCgUCtTV1Vm1gUSNNdfTc+TcDcmnHcspX4Jr35Ec2KKYa9Pn+pFzN2z2PLdlUjvJj9lB1NSpUyEIArZu3QqVSgWFopk5rURWZqynJyv/piyqbnPtOyJdtuqdbfxct9Xz3NZJ7SQ/ZgdRJ0+eRFZWFnr37i1Ge4iaZaynBwZyIqTK9+Had0S/kap31hbPc0MBogDIoogu2YbZxTYjIyNx+fJlMdpC1CJjhSEHd/XTK/Rn7TdqQ8X8jBX4a1wEUGws/kdyJodirmIV4mypwCggThFdkg+ze6Lee+89zJkzB1evXkVYWBhcXV11bu/fv7/VGkfOpbUz7sQcRjPU0wNA8hwsQD7J7ETGOOoSLVIltZN8mF0n6rvvvsPkyZORn5//20EUCiaWG8A6UaYzd8adMWLUXTK0sKoS0BtClKrmkrEFjln/iZydrV4bjd93DCW1MyfK/ohWJ+rZZ5/FwIEDsWPHDiaWk1VYc8adGHkQhnp66gHdr5uQrvdHymR2Iks1/nIEwK6XaJEiqZ3kwewg6tKlS9i7dy969uwpRnvICcl9xp2xwpWGeqKs3W0v16KiRK3RuOdZ8zVcgOMs0cIaUs7D7MTyhx56CCdPnhSjLeSkDCVntjTjzpYMJcYmPRUuerJsSmYBRqw5hMlbjmHEmkNIySwAII9kdiJLNe15bpxDZO1JEVIntRt7DZPjMLsn6vHHH8crr7yCU6dOITw8XC+xfMKECVZrHDkHY8NRmhl3Yn6LNPQt0dA2Yz09YvX+WLKAMJE9MNTz3JijLNFiiyKjJD2zg6g5c+YAAFatWqV3GxPLyVL2MOPOUL6VWLVojA1xrjmQxxo0ZNdaWutO7CVaANsMsXHWrHMwO4hqulYekSVMXeNOrG+Rhr4lJn5ySmcIUcoghQsIk6Nq2vOsAABFQ4FKWwy3Nf3ytHBcH4R38bXJwsgsd+B4zAqiamtr0aZNG+Tm5iIsLEysNpGDM7duC2fccQFhcixNvxwBsMlwm6EvT0kH8gCIX0OKs2Ydk1lBlKurK0JCQjhkRxaTS56Avc644wLC5CiafjmyxfO4uXwsWyyMzNeq4zF7OO+1117D4sWL8e9//xv+/v5itIkcmFzyBIx9SwQgapBirBfOWGDFBYTJ2YiZr9RSPpbYNaTI8ZhdsXzgwIE4f/48amtr0bVrV3h5eencnp2dbdUG2jNtxdMr5+Dj4y11c2ShWF2FCRuP6vX2fDZ3BFQ+HlY7R0HpHYT4e7Z4zGJ1FS6XViLYv412X0PbrNUuQ9f+0kM9sPHQeW1gtfjRvpgY0dlq5yWyF5/lXsXq/WdEfS18lnsVSfvzUGfgo8/a70VNNX1vMue9imxLrS6Hb5d7rV+xPDY2tjXtck45HwNefIEAgArA+2FlSDtzHQIEKKDAw307QnXOOvVTTl8rQ9qZYggA0gE83FeFsCBflFfX4tadWrTzdIW3+29lOVS//qAEzW6zhpqbdxDvckVv+y9ff4l4l99+z//fIZRXhOq0k8jRlVfXIv/oRdFfCxMBPDSq4f2gWF2Fb8//Isp7UVNN35v6BPogr0it915FMlFRZdJuZvdEkenYE2WcGL09cu/pMdQ+JX5NaG/inSmDMSTUz1ZNI5JcZn4pXtymP5Ih9mtBrJ7npudo+tpvSuxeMDKPaD1RGllZWThz5gwA4L777sPAgQMtPZTj81YB3lyAuDGVN6Cychxz4XoJioUmb7YCsOzQLxA02wUgYX8xhvbvZ/M8BZU3kPCk0qQZd51DQgFv5lGQ8+gS4otfFBdt/lpo/F4kVj6WwfempgTg5ypvqDq3t9p5qRUE0yYUmR1EXb9+Hc888wy++uortGvXDgBw69YtPPjgg9i5cyc6dOhg7iGJrEKq2krmvPFyxh2RYVKXBDC39Io5WkpoB1iuxF6ZPZwXFxeHn3/+GR999BH69u0LAPjxxx8xffp09OzZEzt27BClofZIO5zXQncgWU9KZoFJPT1HFz1olTfn5t54zQmuCssqOeOOCNK8FgrLKjFizSHR3icA/fem2IFB2JNzTSdg5BJO8mHq57fZQZSvry++/PJLREZG6mw/fvw4xo4di1u3blnUYEfEIMp6WhOQNH3zstabVXNvvEfO3RDtWy2Rs7DF8iwAkH6hBJO3HNPbvmPWMET3sN7wWtP3Jn55ki9TP78tWval6aLDQEMhTi4JQ2KQa20lYzWvsvJvyqKgKJE9s9XyLIDtlmgxVGC0uYXPSf7MDqIeeughLFiwADt27EBQUBAA4OrVq3jllVfw8MMPW72B5NyMVTi/VVmLNw7kSbp0jLE33qZVzwGucUdkDlsuzwI4dj4WiUtp7h02btwItVqN0NBQ9OjRAz169EC3bt2gVqvxz3/+U4w2khMz1tuz5kCeXmBVWFZp07Zp3nhdFAoAvy2eOrirH5QK3X2ZNEpkOlOWZ7H26z0uMgRHFz2IHbOG4eiiB20WxBj7omjr9zOyjNk9UcHBwcjOzsaXX36JvLyGbwZ9+/bFmDFjrN44IrnPuDM2VMiFR4ksJ8XyLIA0S7TIZSkssoxFdaIUCgV+97vf4Xe/+52120Okw1A3u7EZd9bq6TF3xp2hN16ucUdkuaav+6Zs0bPb+LUOwKbr+bHn2n5YFESlpaUhLS0N169f10sm37p1q1UaRo5NrrWVjHWtj7y3g9kz7rjwKJHlGr/uv796C28eOGuznt3GX6Q0I/MCHDMfi1rH7BIHK1euxKpVqzBkyBB06tQJCoVu8senn35q1QbaM5Y4MEzOtZWMTXXeGD8Q83fmiFpHhoiMs1U5AEOlSxoT63XPcgfyIlqJg+TkZHz44Yf4wx/+0KoGknOSe08PZ9wRyZOtygE0l9QOOFY+FrWe2bPzampqMHz4cKs1YNOmTQgNDYWHhweioqJw/PjxZvfftWsX+vTpAw8PD4SHh2P//v06t+/evRtjx45F+/btoVAokJubq3N7fn4+FAqFwZ9du3Zp9zN0+86dO6123c7K3NpKnHFHRI2lZBZgxJpDmLzlGEasOYSUzAKrHl/zRcoYW+VjpV8oQWFZpc7/SX7MDqKee+45bN++3SonT0lJQUJCApYvX47s7GwMGDAAMTExuH79usH909PTER8fj5kzZyInJwexsbGIjY3F6dOntftUVFTg/vvvxxtvvGHwGMHBwSgsLNT5WblyJdq2bYtHHnlEZ98PPvhAZ7/Y2FirXLczM/QG1VJPj7WY+mZkaKqzseCK3xyJbMcW5QCavtYVADRZK7bKx9IEicOTDmF4kngBI7We2TlRCxYswEcffYT+/fujf//+etXL161bZ/KxoqKiEBkZiY0bNwJoqIYeHByMefPmYdGiRXr7x8XFoaKiAvv27dNuGzZsGCIiIpCcnKyzb35+Prp164acnBxEREQ0246BAwdi0KBBeP/997XbFAoFPv3001YFTsyJMszQMiwj7+0g6tpV5lY9N4Z5C0TSsdXyLIDuax2AQ+djkT7RcqK+//57bVDSuAcIgF6SeXNqamqQlZWFxMRE7TalUokxY8YgIyPD4H0yMjKQkJCgsy0mJgZ79uwx+bxNZWVlITc3F5s2bdK77aWXXsJzzz2H7t27Y86cOZgxY0az11hdXY3q6mrt72q12uJ22SO51layVtVzgHkLRFKyZTkAQ0u0AI6Zj0WWMzuIOnz4sFVOXFJSgrq6OqhUKp3tKpVKW8SzqaKiIoP7FxUVWdyO999/H3379tXL81q1ahUeeugheHp64osvvsCLL76I27dvY/78+UaPlZSUhJUrV1rcFnvW2vXtANuvcbfmQB4EAwnufIMikiepywGIvTxLS0VGmYcpPxbViXIUlZWV2L59O5YuXap3W+NtAwcOREVFBdauXdtsEJWYmKjTU6ZWqxEcHGzdRsuQ3Ht6pKp6TkTWJ1Uh2+ZmFlurDU2DRAUAKABBYB6mXEkWRAUEBMDFxQXFxcU624uLixEYGGjwPoGBgWbt35L//ve/uHPnDqZNm9bivlFRUXj99ddRXV0Nd3d3g/u4u7sbvc2Ryb2nR4qq50QkHkdenqVpkAjYJh+LLCNZEOXm5obBgwcjLS1Nm7xdX1+PtLQ0zJ071+B9oqOjkZaWhpdfflm7LTU1FdHR0Ra14f3338eECRPQoUOHFvfNzc2Fn5+fUwZJLZH7+naAbaueE5HtOOLyLMbysUh+JB3OS0hIwPTp0zFkyBAMHToUGzZsQEVFBWbMmAEAmDZtGjp37oykpCQADTMDR40ahbfeegvjx4/Hzp07ceLECWzevFl7zNLSUhQUFODatWsAgLNnzwJo6MVq3GN1/vx5HDlyRK/OFAD83//9H4qLizFs2DB4eHggNTUVq1evxp/+9CfR/hb2zB7Wt9O0s/HvXN+OyL454/IsYia2k/kkDaLi4uJw48YNLFu2DEVFRYiIiMDBgwe1yeMFBQVQKn8rZTV8+HBs374dS5YsweLFi9GrVy/s2bMHYWFh2n327t2rDcIA4JlnngEALF++HCtWrNBu37p1K7p06YKxY8fqtcvV1RWbNm3CK6+8AkEQ0LNnT6xbtw6zZs2y9p9A1uTa0yP3qudEJL6m7wONO77FSh+Q+ouX2IntZD6z60SR6ey5ThTXtyMiOTP2PtCYGPWjpGKohhTf38QjWp0ocnxy7+nh+nZEJJdyALbKybJVYjuZh0EU6TF3fTtbz7gzlpugWd+OM+6IHJ8cygHYMifLlontZDoGUaTHHnp6bF31nIjkR8pyALbOyZJLYjvpYhBFeqTq6TGWayWHqudEJE9SlQOQYokWvr/JD4MoB2SNKbC27ukxlshuyWwUzrgjIrFzlaTKyWr8/marfCwyjrPzRCTF7DxrzaprjrVn3BmbdbL7xWg88XY6Z6MQkVlslauUklnQbE6WmOUHbJmP5Yw4O09OyosBxR3RT1OsrsK63UfRXhN0CMD63d9gdNAIfPfzL1i9/4w2uFr8aF9MjOisvV9B6R2E+HtC5ePR4nk6KYFOHQGgDCgva3W7rxSUor1wU3ejAJw+e87g9qsF+egU6tfq8xKR49F7H2ys0XuiKe91LYnr44bRc/vhcmklgv0bvthp/q/ycQPKi1p9DkNseY1Oq7zcpN0YRNlCzseAl/hP5pqbdxDvckVve9k3PyD/dCHiXX7blv+/QyivCMWlX+4g7UwxBADpAB7uq0JYkK/obW2sT3Utpt5zUScxUwEFHq44h5J7Lutt7331Z6DE1aZtJCL7YOx9UGefY3mAn3WG2lS//qDkt981/xeLra/RKVVUmbQbgyhbGDgV8PEW/TRu6irsyDyqN/x1X7f78HHuab39+/mFYcnXp1Ev/FbxfcdpBT4bbf43GHN7sxrzBhDqdRVJ+/O0uVaJj/ZBYERnhPrrb/f+tQeNiKgpQ++DjbkoFIiPGgGI2EvT+P0QgMXvjcbI4RodnrocwKoWd2MQZQveKsBb/JwolTeQ8KRSL/E77N4O+EVxVS+4qm7TEcVCk2ExAfi5yhuqzqZX+W0Ym/+hVXlYEx8IxND+/fRyrYxtJyIypOn7oKFcJVVnsXOVfhA1V0nqa3QKgmm9eEwsF5FUy74YSvxunACpeZGNvLdDq5cRaG4pAmPVzbmAJhGJrfH7IGCb+lGG3g8bs/bEGCmu0VkwsdwJWKN+UmtLFphb3fxWZS3eOJDHBTSJSFTG6keJ+SXO1rWjpLhG0sUgyk5Zq35Sa4u3mVvdfM2BPAhNAitbLxtDRM6p6fvmwnF9EN7F12rBhhzW8xP7GkmXUuoGkPmMLRBcWFZp0fE6+bZBdI/2Fr3ANNXNXRQNo/9Nq5s3pgTQdPBY882MiEhMht43kw7kYfKWYxix5hBSMgtafY6m74cKAL/+1ybLtNjiGkkXe6LskC1W8zanO9jUocK/jOuNNw7mcQFNIrK55obarNkrLuV6fra6RvoNgyg7JPZq3pZUPTd1qLCdpysX0CQim2tpqM2aX0SlylWy5TVSAwZRdkjM1byNDRWOvLeD0dl2LbW1cbu4gCYRSaHp+2ZTYveK2yJXSeprdEYscSAisUscWHsNOwBIv1CCyVuO6W3fGD8Q83fmcB07IrJrmvfN76/ewpsHzup8ERVrpnBzpQ/EmKUsxTU6GpY4cAKGhtBay9zZduwaJiJ7onnfjO7RHhMGBNmkV9zWuUpSXKOzYhBFOowNFWpm2zEpnIgcReMvomLmK0mZq2ToGr3cXFBRU8eyB1bAIMqBWOtNQKzCnEREcmRJ3T1zyCFXqfE1arDgcesxJ0pEtlz2xZIZdZYEXWLkYRERSaW5paus/R4nVa5SczlZzG01jDlRTsSSGXWWfvMSIw+LiEgqtqi7pyFVrlJzOVnMbW0dBlEOwNz16/oEehsNuvhCIiJnInbdPWOM5WMBsHpuVnM5WcxtbR0GUQ7A3Bl1mfk3OdOOiAji1t0zReNRAc1KWQKsm69kLCeLua2txyDKAZg7oy4ylDPtiIg0pCoC3DQVo/F3W2uPEDS+Rk83Je7U1DO31QoYRDkIc2bUDQj240w7IqJGpMj3bC5XCbD+CAFzWq2PQZQdau36dc1tJyIi22ipfpQtRgjEzsdydAyi7Iwls+qMffvgtxIiIuk0TcVQAIACEATb5CvZIh/L0bFOlIisXSfKlvVMiIjINhrX3wNgkxGC5mpHAfxsYZ0oB2TLeiZERGQbTUcFOvm2QWFZJdIvlIg2tGbrfCxHxSDKjkhVz4SIiGynadrGwnF9EN7F12a1owB+tphKKXUDyHSa8XMXRcPoNWfVERE5FkMrUCQdyMPkLccwYs0hpGQWWOU8TT9PFAB+/S8/W8zAnig7w1l1RESOq7lhNjFrR9kyH8uRMIiyQ5xVR0TkmFoaZhO7dpStlqJxFAyiiIiIZMLYEi0atshVYukD07HEgYi0UySvnIOPj7fUzSEiIjtRrK7C5dJKnCkqw6ZDF7SrSyQ+2gcTIzqLet4JG482m3D+2dwRUPl4iNYGOVCry+Hb5V75lzjYtGkT1q5di6KiIgwYMAD//Oc/MXToUKP779q1C0uXLkV+fj569eqFN954A48++qj29t27dyM5ORlZWVkoLS1FTk4OIiIidI4xevRofP311zrbnn/+eSQnJ2t/LygowAsvvIDDhw+jbdu2mD59OpKSknDPPRb8yXI+Brwc+wlHRETWo/r1Z8g9QOyoWty6U4t2nq7wvlsAnBDvvDU37yDe5Urz+xzLA/wcfOZeRZVJu0kaRKWkpCAhIQHJycmIiorChg0bEBMTg7Nnz6Jjx456+6enpyM+Ph5JSUl47LHHsH37dsTGxiI7OxthYWEAgIqKCtx///2YNGkSZs2aZfTcs2bNwqpVq7S/e3r+9oSoq6vD+PHjERgYiPT0dBQWFmLatGlwdXXF6tWrzb/QgVMB9kQREZEFvH/9ARp6igpK78DTzQV3auoQ4u9p1V4hN3UVdmQ23xMVHzUCcPCeKKjLAaxqcTdJh/OioqIQGRmJjRs3AgDq6+sRHByMefPmYdGiRXr7x8XFoaKiAvv27dNuGzZsGCIiInR6kQAgPz8f3bp1M9oTFRERgQ0bNhhs14EDB/DYY4/h2rVrUKlUAIDk5GQsXLgQN27cgJubm0nXZ+2K5URE5Lwa5yppiJGnlJJZ0OxSNM6QEyX7iuU1NTXIyspCYmKidptSqcSYMWOQkZFh8D4ZGRlISEjQ2RYTE4M9e/aYff5t27bh448/RmBgIB5//HEsXbpU2xuVkZGB8PBwbQClOc8LL7yAH374AQMHDjR4zOrqalRXV2t/V6vVZreLiIioqab1ozSsXfYAYOkDc0gWRJWUlKCurk4nUAEAlUqFvLw8g/cpKioyuH9RUZFZ5548eTK6du2KoKAgfP/991i4cCHOnj2L3bt3N3sezW3GJCUlYeXKlWa1hYiIqCXN1Y8SY4kWY6UPSJfkieVSmD17tvb/4eHh6NSpEx5++GFcuHABPXr0sPi4iYmJOj1larUawcHBrWorERFRc/WjbLVEC2tH6ZMsiAoICICLiwuKi4t1thcXFyMwMNDgfQIDA83a31RRUVEAgPPnz6NHjx4IDAzE8ePH9c6jaYMx7u7ucHd3b1VbiIiImjJWP8pWS7SwdpRhkq2d5+bmhsGDByMtLU27rb6+HmlpaYiOjjZ4n+joaJ39ASA1NdXo/qbKzc0FAHTq1El7nlOnTuH69es65/Hx8UG/fv1adS4iIiJLxEWG4OiiB7Fj1jB89tJw7Jg1DEcXPSh6ANM0H0v49Qf4LSersKxS1DbIlaTDeQkJCZg+fTqGDBmCoUOHYsOGDaioqMCMGTMAANOmTUPnzp2RlJQEAFiwYAFGjRqFt956C+PHj8fOnTtx4sQJbN68WXvM0tJSFBQU4Nq1awCAs2fPAmjoQQoMDMSFCxewfft2PProo2jfvj2+//57vPLKKxg5ciT69+8PABg7diz69euHP/zhD3jzzTdRVFSEJUuW4KWXXmJPExERScbQsl+Nh9nE6JFqLh8LECcny15IGkTFxcXhxo0bWLZsGYqKihAREYGDBw9qk7gLCgqgVP7WWTZ8+HBs374dS5YsweLFi9GrVy/s2bNHWyMKAPbu3asNwgDgmWeeAQAsX74cK1asgJubG7788kttwBYcHIynnnoKS5Ys0d7HxcUF+/btwwsvvIDo6Gh4eXlh+vTpOnWliIiIpNZ0mG3WA90w4/5uVg1oWlrPz1Y5WXLEZV9ExDpRREQklsKySoxYc0gvuGHtqNaTfZ0oIiIispyxYTbWjrIdBlFERER2qLlhNlvVjiosq0T6hRKnLXvAIIqIiMgOacoeJH5yCvVNbrNFnhLLHkhY4oCIiIhaJy4yBN8mPoTZD3SH8tdIxha1o1j2oAF7ooiIiOxYJ982WDy+L2bcH2qzPCWWPWjAIIqIiMgBNM5Z0tSO8nJzQUVNnfZfa+UrsexBAwZRREREDqRxrlJT1spXaroMjaGyB47eCwWwTpSoWCeKiIhsyVjtqMZcFAocXfSgVYKcwrJKhyx7wDpRRERETqalXCXAuvlKLZU9sPdgqiUMooiIiBxES7lKgLj5So2HEpUKYOG4Pgjv4mv1nCy5YBBFRETkIJrmKjUlZr5S07IH9QKQdCBPZx9HqyHFIIqIiMiBNF6ixdNNiTs19dp/xcxXMmUoUYwlaaTEIIqIiMjBNM1VsgVThhIBx6ohxYrlRERETkKT9F1YVqnzf2vQDCW6KBTN7udINaTYE0VEROQEbLHWXeOhxO+v3sKbB87q5GY5Wg0p1okSkbbOxJVz8PHxlro5RETkpIrVVZiw8WizFcY/mzsCKh8Pq5/3cmkl2rgpUVlTj2D/NlY/hxjU6nL4drmXdaJkIedjwEv+TxoiInJMNTfvIN7lSvP7HMsD/Kw7zKb69Uej/GotLt+pRTtPVwDArV//7+3uatXztlpFlUm7MYiyhYFTAfZEERGRRNzUVdiR2XxPVHzUCEDEXqLPcq9i9RdnDA4nLn60LyZGdBbt3GZTlwNY1eJuDKJswVsFeHPZFyIikobKG0h4UtnsWneqzuLVbiosq8Qr+7NQL/jp3ygACfuLMbR/P/nkSgmm9cgxiCIiInICjZO+bb3WXUs1pOy17AGDKCIiIich1Vp3LdWQsteyB6wTRURE5KRSMgswYs0hTN5yDCPWHEJKZoEo52laQ0oBQFNOyp7LHrDEgYi0JQ5amCJJRERka4VllRix5pBO75CLQoGjix4ULaApLKuUZDjRXKZ+fnM4j4iIyAkZylMSOzepueFELzcXVNTUaf8Vc3jRWhhEEREROSFDeUq2zk1qXEW9KWtWUhcLc6KIiIicUNM8JVvnJhWWVRoNoICG4G7x7tNWW9tPDOyJIiIiclJNyx7YcvispbIHgPxLHzCIIiIicmJN85RspaWyB4D8Sx9wOI+IiIhsrulwYlP2UPqAPVFEREQkicbDiZ5uStypqdf+K8fSB00xiCIiIiLJGBtOtEUl9dZiEEVERESy0rj0gQLArAe6YXz/TrKrH8WK5SJixXIiIiLzGKqk3pgt6keZ+vnNxHIiIiKSjZZKH8ipfhSDKCIiIpINTemD5mjqR0mNQRQRERHJhqb0QXMBilzqRzGIIiIiIlmJiwzBt4kPYfYD3fV6peRUP4qJ5SJiYjkREVHrFJZV6tSRskX9KLtJLN+0aRNCQ0Ph4eGBqKgoHD9+vNn9d+3ahT59+sDDwwPh4eHYv3+/zu27d+/G2LFj0b59eygUCuTm5urcXlpainnz5qF3795o06YNQkJCMH/+fJSVlensp1Ao9H527txplWsmIiIi03TybYPoHu0xINgP0T3aAwDSL5Tg5OWbSL9QImmCuaR1olJSUpCQkIDk5GRERUVhw4YNiImJwdmzZ9GxY0e9/dPT0xEfH4+kpCQ89thj2L59O2JjY5GdnY2wsDAAQEVFBe6//35MmjQJs2bN0jvGtWvXcO3aNfztb39Dv379cOnSJcyZMwfXrl3Df//7X519P/jgA4wbN077e7t27az7ByAiIiKTNa4fpWGLkgfGSDqcFxUVhcjISGzcuBEAUF9fj+DgYMybNw+LFi3S2z8uLg4VFRXYt2+fdtuwYcMQERGB5ORknX3z8/PRrVs35OTkICIiotl27Nq1C1OnTkVFRQXuuachrlQoFPj0008RGxtr8vVUV1ejurpa+7tarUZwcDDKrpyDj4+3ycchIiIiXcXqKkzYeNRg+QMXhQKfzR0BlY+HVc6lVpfDt8u9LQ7nSdYTVVNTg6ysLCQmJmq3KZVKjBkzBhkZGQbvk5GRgYSEBJ1tMTEx2LNnT6vaovkjaQIojZdeegnPPfccunfvjjlz5mDGjBlQGFkoEQCSkpKwcuVK/RtyPga8rPPAEhEROaOam3cQ73LF+O3H8gA/K83Yq6gyaTfJgqiSkhLU1dVBpVLpbFepVMjLyzN4n6KiIoP7FxUVtaodr7/+OmbPnq2zfdWqVXjooYfg6emJL774Ai+++CJu376N+fPnGz1WYmKiTpCn6YnCwKkAe6KIiIgs5qauwo5M4z1R8VEjACv1REFdDmBVi7s59dp5arUa48ePR79+/bBixQqd25YuXar9/8CBA1FRUYG1a9c2G0S5u7vD3d1d/wZvFeDN2XlERESWUnkDCU8qsXj3adQ1ykTSlDxQdbZiTpRgWo+WZEFUQEAAXFxcUFxcrLO9uLgYgYGBBu8TGBho1v7NKS8vx7hx4+Dt7Y1PP/0Urq6uze4fFRWF119/HdXV1YYDJSIiIhJVXGQIRt7bweYlD4yRrMSBm5sbBg8ejLS0NO22+vp6pKWlITo62uB9oqOjdfYHgNTUVKP7G6NWqzF27Fi4ublh79698PBoufsvNzcXfn5+DKCIiIgk1LTkgZRFNyUdzktISMD06dMxZMgQDB06FBs2bEBFRQVmzJgBAJg2bRo6d+6MpKQkAMCCBQswatQovPXWWxg/fjx27tyJEydOYPPmzdpjlpaWoqCgANeuXQMAnD17FkBDL1ZgYKA2gLpz5w4+/vhjqNVqqNVqAECHDh3g4uKC//u//0NxcTGGDRsGDw8PpKamYvXq1fjTn/5kyz8PERERyZkgsX/+859CSEiI4ObmJgwdOlT47rvvtLeNGjVKmD59us7+//nPf4R7771XcHNzE+677z7h888/17n9gw8+EADo/SxfvlwQBEE4fPiwwdsBCBcvXhQEQRAOHDggRERECG3bthW8vLyEAQMGCMnJyUJdXZ1Z11ZWViYAEMrKysz+uxAREZE0TP385rIvIuKyL0RERPbHbpZ9ISIiIrJHDKKIiIiILMAgioiIiMgCDKKIiIiILMAgioiIiMgCDKKIiIiILMAgioiIiMgCDKKIiIiILCDpsi+OTlPHVLOsDBEREcmf5nO7pXrkDKJEVF5eDgAIDg6WuCVERERkrvLycvj6+hq9ncu+iKi+vh7Xrl2Dt7c3FAqF1Y6rVqsRHByMy5cvO+RyMo5+fQCv0RE4+vUBjn+Njn59AK/RUoIgoLy8HEFBQVAqjWc+sSdKREqlEl26dBHt+D4+Pg77ogAc//oAXqMjcPTrAxz/Gh39+gBeoyWa64HSYGI5ERERkQUYRBERERFZgEGUHXJ3d8fy5cvh7u4udVNE4ejXB/AaHYGjXx/g+Nfo6NcH8BrFxsRyIiIiIguwJ4qIiIjIAgyiiIiIiCzAIIqIiIjIAgyiiIiIiCzAIEoiR44cweOPP46goCAoFArs2bNH5/bi4mL88Y9/RFBQEDw9PTFu3Dj89NNPOvtUVVXhpZdeQvv27dG2bVs89dRTKC4u1tmnoKAA48ePh6enJzp27Ig///nPuHv3rtiXZ5XrGz16NBQKhc7PnDlzdPaR6vqSkpIQGRkJb29vdOzYEbGxsTh79qzOPtZ6fL766isMGjQI7u7u6NmzJz788EOxLw+A9a6x6WOoUCiwc+dOnX3kfI2bN2/G6NGj4ePjA4VCgVu3bukdp7S0FFOmTIGPjw/atWuHmTNn4vbt2zr7fP/993jggQfg4eGB4OBgvPnmm2JeGgDrXV9oaKjeY7hmzRqdfaS4PqDlaywtLcW8efPQu3dvtGnTBiEhIZg/fz7Kysp0jmPPr0VTr1Gur0VTnqfPP/88evTogTZt2qBDhw6YOHEi8vLydPaR5DEUSBL79+8XXnvtNWH37t0CAOHTTz/V3lZfXy8MGzZMeOCBB4Tjx48LeXl5wuzZs4WQkBDh9u3b2v3mzJkjBAcHC2lpacKJEyeEYcOGCcOHD9fefvfuXSEsLEwYM2aMkJOTI+zfv18ICAgQEhMT7eL6Ro0aJcyaNUsoLCzU/pSVlcni+mJiYoQPPvhAOH36tJCbmys8+uijojw+P//8s+Dp6SkkJCQIP/74o/DPf/5TcHFxEQ4ePGgX1ygIggBA+OCDD3Qex8rKSru5xvXr1wtJSUlCUlKSAEC4efOm3nHGjRsnDBgwQPjuu++Eb775RujZs6cQHx+vvb2srExQqVTClClThNOnTws7duwQ2rRpI7z77rt2cX1du3YVVq1apfMYNj6GVNdnyjWeOnVKePLJJ4W9e/cK58+fF9LS0oRevXoJTz31lPYY9v5aNOUaBUG+r0VTnqfvvvuu8PXXXwsXL14UsrKyhMcff1wIDg4W7t69KwiCdI8hgygZaBpknD17VgAgnD59Wrutrq5O6NChg7BlyxZBEATh1q1bgqurq7Br1y7tPmfOnBEACBkZGYIgNAQySqVSKCoq0u7zzjvvCD4+PkJ1dbXIV/UbS65PEBqCqAULFhg9rlyuTxAE4fr16wIA4euvvxYEwXqPz1/+8hfhvvvu0zlXXFycEBMTI/Yl6bHkGgVB//FvSs7X2Njhw4cNBhk//vijAEDIzMzUbjtw4ICgUCiEq1evCoIgCG+//bbg5+en87xcuHCh0Lt3b3EuxAhLrk8QGoKo9evXGz2uXK5PEJq/Ro3//Oc/gpubm1BbWysIgv2/Fg1peo2CYD+vRVOu7+TJkwIA4fz584IgSPcYcjhPhqqrqwEAHh4e2m1KpRLu7u44evQoACArKwu1tbUYM2aMdp8+ffogJCQEGRkZAICMjAyEh4dDpVJp94mJiYFarcYPP/xgi0sxyJTr09i2bRsCAgIQFhaGxMRE3LlzR3ubnK5P023u7+8PwHqPT0ZGhs4xNPtojmFLllyjxksvvYSAgAAMHToUW7duhdCoPJ2cr9EUGRkZaNeuHYYMGaLdNmbMGCiVShw7dky7z8iRI+Hm5qbdJyYmBmfPnsXNmzet1PqWWXJ9GmvWrEH79u0xcOBArF27VmeYRC7XB5h2jWVlZfDx8cE99zQsH2vvr0Vj+zS+Rg17eC22dH0VFRX44IMP0K1bNwQHBwOQ7jHkAsQypPkgSkxMxLvvvgsvLy+sX78eV65cQWFhIQCgqKgIbm5uaNeunc59VSoVioqKtPs0fkJpbtfcJhVTrg8AJk+ejK5duyIoKAjff/89Fi5ciLNnz2L37t0A5HN99fX1ePnllzFixAiEhYVpz2+Nx8fYPmq1GpWVlWjTpo0Yl6TH0msEgFWrVuGhhx6Cp6cnvvjiC7z44ou4ffs25s+frz2OXK/RFEVFRejYsaPOtnvuuQf+/v46j2O3bt109mn8WPv5+bWy9S2z9PoAYP78+Rg0aBD8/f2Rnp6OxMREFBYWYt26dQDkcX2AaddYUlKC119/HbNnz9Zus/fXYlOGrhGwj9dic9f39ttv4y9/+QsqKirQu3dvpKamagN3qR5DBlEy5Orqit27d2PmzJnw9/eHi4sLxowZg0ceeUTnW4O9MvX6Gr8BhIeHo1OnTnj44Ydx4cIF9OjRQ4qmG/TSSy/h9OnTer1ojqQ117h06VLt/wcOHIiKigqsXbtW+8YtF47+OLbm+hISErT/79+/P9zc3PD8888jKSlJVsuJtHSNarUa48ePR79+/bBixQrbNs5KWnON9vBabO76pkyZgt/97ncoLCzE3/72N0yaNAnffvutzqiGrXE4T6YGDx6M3Nxc3Lp1C4WFhTh48CB++eUXdO/eHQAQGBiImpoavZk0xcXFCAwM1O7TdKaU5nfNPlJp6foMiYqKAgCcP38egDyub+7cudi3bx8OHz6MLl26aLdb6/Exto+Pj4/Nvvm25hoNiYqKwpUrV7TDunK+RlMEBgbi+vXrOtvu3r2L0tJS2bwWW3N9hkRFReHu3bvIz88HIP31AS1fY3l5OcaNGwdvb298+umncHV11d5m769Fjeau0RC5vRZbuj5fX1/06tULI0eOxH//+1/k5eXh008/bbbtmtua26c118cgSuZ8fX3RoUMH/PTTTzhx4gQmTpwIoCEIcXV1RVpamnbfs2fPoqCgANHR0QCA6OhonDp1SucNPjU1FT4+PujXr59tL8QIY9dnSG5uLgCgU6dOAKS9PkEQMHfuXHz66ac4dOiQ3lCGtR6f6OhonWNo9tEcQ0zWuEZDcnNz4efnp+3BkPM1miI6Ohq3bt1CVlaWdtuhQ4dQX1+vDfyjo6Nx5MgR1NbWavdJTU1F7969RR3qssb1GZKbmwulUqkdxpTq+gDTrlGtVmPs2LFwc3PD3r179Xou7P21CLR8jYbI5bVoyfNUaJgYpw0AJXsMLU5Jp1YpLy8XcnJyhJycHAGAsG7dOiEnJ0e4dOmSIAgNMysOHz4sXLhwQdizZ4/QtWtX4cknn9Q5xpw5c4SQkBDh0KFDwokTJ4To6GghOjpae7tmyufYsWOF3Nxc4eDBg0KHDh1sUgKgtdd3/vx5YdWqVcKJEyeEixcvCp999pnQvXt3YeTIkbK4vhdeeEHw9fUVvvrqK53pwnfu3NHuY43HRzMl989//rNw5swZYdOmTTabVm2Na9y7d6+wZcsW4dSpU8JPP/0kvP3224Knp6ewbNkyu7nGwsJCIScnR9iyZYsAQDhy5IiQk5Mj/PLLL9p9xo0bJwwcOFA4duyYcPToUaFXr146JQ5u3bolqFQq4Q9/+INw+vRpYefOnYKnp6foJQCscX3p6enC+vXrhdzcXOHChQvCxx9/LHTo0EGYNm2a5NdnyjWWlZUJUVFRQnh4uHD+/HmdfZpOj7fX16Ip1yjn12JL13fhwgVh9erVwokTJ4RLly4J3377rfD4448L/v7+QnFxsSAI0j2GDKIkoplO3PRn+vTpgiAIwt///nehS5cugqurqxASEiIsWbJEb9p+ZWWl8OKLLwp+fn6Cp6en8MQTTwiFhYU6++Tn5wuPPPKI0KZNGyEgIEB49dVXdaa8yvX6CgoKhJEjRwr+/v6Cu7u70LNnT+HPf/6zTp0oKa/P0LXh1xosGtZ6fA4fPixEREQIbm5uQvfu3XXOISZrXOOBAweEiIgIoW3btoKXl5cwYMAAITk5Wairq7Oba1y+fHmL+/zyyy9CfHy80LZtW8HHx0eYMWOGUF5ernOukydPCvfff7/g7u4udO7cWVizZo1dXF9WVpYQFRUl+Pr6Ch4eHkLfvn2F1atXC1VVVZJfnynXaOy9CIBw8eJF7XHs+bVoyjXK+bXY0vVdvXpVeOSRR4SOHTsKrq6uQpcuXYTJkycLeXl5OseR4jFU/HoBRERERGQG5kQRERERWYBBFBEREZEFGEQRERERWYBBFBEREZEFGEQRERERWYBBFBEREZEFGEQRERERWYBBFBEREZEFGEQREcmQQqHAnj17pG4GETWDQRQRERGRBRhEERE5iZqaGqmbQORQGEQRkSzs27cP7dq1Q11dHQAgNzcXCoUCixYt0u7z3HPPYerUqfjll18QHx+Pzp07w9PTE+Hh4dixY4d2v82bNyMoKAj19fU655g4cSKeffZZ7e+fffYZBg0aBA8PD3Tv3h0rV67E3bt3tbcrFAq89957eOKJJ+Dp6YlevXph79692ts//PBDtGvXTucce/bsgUKh0P6+YsUKREREYOvWrQgJCUHbtm3x4osvoq6uDm+++SYCAwPRsWNH/PWvf9X7mxQWFuKRRx5BmzZt0L17d/z3v//Vuf3y5cuYNGkS2rVrB39/f0ycOBH5+fna2//4xz8iNjYWf/3rXxEUFITevXs39xAQkZkYRBGRLDzwwAMoLy9HTk4OAODrr79GQEAAvvrqK+0+X3/9NUaPHo2qqioMHjwYn3/+OU6fPo3Zs2fjD3/4A44fPw4A+P3vf49ffvkFhw8f1t63tLQUBw8exJQpUwAA33zzDaZNm4YFCxbgxx9/xLvvvosPP/xQL5hZuXIlJk2ahO+//x6PPvoopkyZgtLSUrOu7cKFCzhw4AAOHjyIHTt24P3338f48eNx5coVfP3113jjjTewZMkSHDt2TOd+S5cuxVNPPYWTJ09iypQpeOaZZ3DmzBkAQG1tLWJiYuDt7Y1vvvkG3377Ldq2bYtx48bp9DilpaXh7NmzSE1Nxb59+8xqNxG1QCAikolBgwYJa9euFQRBEGJjY4W//vWvgpubm1BeXi5cuXJFACCcO3fO4H3Hjx8vvPrqq9rfJ06cKDz77LPa3999910hKChIqKurEwRBEB5++GFh9erVOsf497//LXTq1En7OwBhyZIl2t9v374tABAOHDggCIIgfPDBB4Kvr6/OMT799FOh8Vvr8uXLBU9PT0GtVmu3xcTECKGhodq2CIIg9O7dW0hKStI595w5c3SOHRUVJbzwwgvatvbu3Vuor6/X3l5dXS20adNG+N///icIgiBMnz5dUKlUQnV1tcG/GRG1DnuiiEg2Ro0aha+++gqCIOCbb77Bk08+ib59++Lo0aP4+uuvERQUhF69eqGurg6vv/46wsPD4e/vj7Zt2+J///sfCgoKtMeaMmUKPvnkE1RXVwMAtm3bhmeeeQZKZcPb3smTJ7Fq1Sq0bdtW+zNr1iwUFhbizp072uP0799f+38vLy/4+Pjg+vXrZl1XaGgovL29tb+rVCr069dP2xbNtqbHjY6O1vtd0xN18uRJnD9/Ht7e3tr2+/v7o6qqChcuXNDeJzw8HG5ubma1l4hMc4/UDSAi0hg9ejS2bt2KkydPwtXVFX369MHo0aPx1Vdf4ebNmxg1ahQAYO3atfj73/+ODRs2IDw8HF5eXnj55Zd1hrEef/xxCIKAzz//HJGRkfjmm2+wfv167e23b9/GypUr8eSTT+q1w8PDQ/t/V1dXndsUCoU210qpVEIQBJ3ba2tr9Y5n6BjNHdcUt2/fxuDBg7Ft2za92zp06KD9v5eXl8nHJCLzMIgiItnQ5EWtX79eGzCNHj0aa9aswc2bN/Hqq68CAL799ltMnDgRU6dOBQDU19fj3Llz6Nevn/ZYHh4eePLJJ7Ft2zacP38evXv3xqBBg7S3Dxo0CGfPnkXPnj0tbm+HDh1QXl6OiooKbbCSm5tr8fGa+u677zBt2jSd3wcOHAigof0pKSno2LEjfHx8rHZOIjIdh/OISDb8/PzQv39/bNu2DaNHjwYAjBw5EtnZ2Th37pw2sOrVqxdSU1ORnp6OM2fO4Pnnn0dxcbHe8aZMmYLPP/8cW7du1SaUayxbtgwfffQRVq5ciR9++AFnzpzBzp07sWTJEpPbGxUVBU9PTyxevBgXLlzA9u3b8eGHH1p8/U3t2rULW7duxblz57B8+XIcP34cc+fO1V5bQEAAJk6ciG+++QYXL17EV199hfnz5+PKlStWawMRGccgiohkZdSoUairq9MGUf7+/ujXrx8CAwO1U/SXLFmCQYMGISYmBqNHj0ZgYCBiY2P1jvXQQw/B398fZ8+exeTJk3Vui4mJwb59+/DFF18gMjISw4YNw/r169G1a1eT2+rv74+PP/4Y+/fv15ZZWLFihaWXrmflypXYuXMn+vfvj48++gg7duzQ9rZ5enriyJEjCAkJ0eaOzZw5E1VVVeyZIrIRhdB0QJ+IiIiIWsSeKCIiIiILMIgiIiIisgCDKCIiIiILMIgiIiIisgCDKCIiIiILMIgiIiIisgCDKCIiIiILMIgiIiIisgCDKCIiIiILMIgiIiIisgCDKCIiIiIL/H8gm7mtx1EqFQAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "#show the grids\n", + "# show the grids\n", "plt.plot(mdbCO.nu_lines, ngammaL, \".\")\n", "for i in ngammaL_grid:\n", " plt.axhline(i, lw=1, alpha=0.5, color=\"C1\")\n", @@ -214,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "87f63886", "metadata": { "execution": { @@ -228,7 +235,7 @@ "source": [ "from exojax.spec import initspec\n", "\n", - "cnu, indexnu, R, pmarray = initspec.init_modit(mdbCO.nu_lines, nus)\n" + "cnu, indexnu, R, pmarray = initspec.init_modit(mdbCO.nu_lines, nus)" ] }, { @@ -241,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "48675923", "metadata": { "execution": { @@ -254,7 +261,8 @@ "outputs": [], "source": [ "from exojax.spec.modit import xsvector\n", - "xs=xsvector(cnu,indexnu,R,pmarray,nsigmaD,ngammaL,Sij,nus,ngammaL_grid)" + "\n", + "xs = xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, nus, ngammaL_grid)" ] }, { @@ -267,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "683b3ab5", "metadata": { "execution": { @@ -277,24 +285,16 @@ "shell.execute_reply": "2022-10-30T01:15:36.220465Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 13/13 [00:02<00:00, 5.74it/s]\n" - ] - } - ], + "outputs": [], "source": [ - "from exojax.spec.lpf import auto_xsection\n", - "sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol)\n", - "xsv=auto_xsection(nus,mdbCO.nu_lines,sigmaD,gammaL,Sij,memory_size=30)" + "from exojax.spec.opacalc import OpaDirect\n", + "opa = OpaDirect(mdbCO, nus)\n", + "xsv = opa.xsvector(Tfix, Pfix, Ppart)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "8169def7", "metadata": { "execution": { @@ -307,26 +307,24 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "fig=plt.figure(figsize=(10,5))\n", - "ax=fig.add_subplot(211)\n", - "plt.plot(nus,xs,lw=1,alpha=0.5,label=\"MODIT\")\n", - "plt.plot(nus,xsv,lw=1,alpha=0.5,label=\"Direct LPF\")\n", + "fig = plt.figure(figsize=(10, 5))\n", + "ax = fig.add_subplot(211)\n", + "plt.plot(nus, xs, lw=1, alpha=0.5, label=\"MODIT\")\n", + "plt.plot(nus, xsv, lw=1, alpha=0.5, label=\"Direct LPF\")\n", "plt.legend(loc=\"upper right\")\n", "plt.ylabel(\"Cross Section (cm2)\")\n", - "ax=fig.add_subplot(212)\n", - "plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label=\"MODIT\")\n", + "ax = fig.add_subplot(212)\n", + "plt.plot(nus, xsv - xs, lw=2, alpha=0.5, label=\"MODIT\")\n", "plt.ylabel(\"LPF - DIT (cm2)\")\n", "plt.legend(loc=\"upper left\")\n", "plt.show()" @@ -342,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "9f86a45b", "metadata": { "execution": { @@ -355,41 +353,31 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "fig=plt.figure(figsize=(10,5))\n", - "ax=fig.add_subplot(211)\n", - "plt.plot(nus,xs,lw=2,alpha=0.5,label=\"MODIT\")\n", - "plt.plot(nus,xsv,lw=1,alpha=0.5,label=\"Direct\")\n", + "fig = plt.figure(figsize=(10, 5))\n", + "ax = fig.add_subplot(211)\n", + "plt.plot(nus, xs, lw=2, alpha=0.5, label=\"MODIT\")\n", + "plt.plot(nus, xsv, lw=1, alpha=0.5, label=\"Direct\")\n", "plt.legend(loc=\"upper right\")\n", - "plt.xlim(2050.8,2050.9)\n", + "plt.xlim(2050.8, 2050.9)\n", "plt.ylabel(\"Cross Section (cm2)\")\n", - "ax=fig.add_subplot(212)\n", - "plt.plot(nus,xsv-xs,lw=2,alpha=0.6,label=\"MODIT\")\n", + "ax = fig.add_subplot(212)\n", + "plt.plot(nus, xsv - xs, lw=2, alpha=0.6, label=\"MODIT\")\n", "plt.legend(loc=\"upper left\")\n", "plt.ylabel(\"Difference (cm2)\")\n", - "plt.xlim(2050.8,2050.9)\n", - "#plt.yscale(\"log\")\n", + "plt.xlim(2050.8, 2050.9)\n", + "# plt.yscale(\"log\")\n", "plt.savefig(\"fine_grid.png\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "785f3acf", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -408,7 +396,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.19" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.rst b/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.rst index bbdde3c61..fecb72e04 100644 --- a/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.rst +++ b/documents/tutorials/Cross_Section_using_Modified_Discrete_Integral_Transform.rst @@ -16,45 +16,53 @@ errors): .. code:: ipython3 from jax import config + config.update("jax_enable_x64", True) .. code:: ipython3 import matplotlib.pyplot as plt - from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural + from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural from exojax.spec import api from exojax.utils.grids import wavenumber_grid + from exojax.utils.constants import Tref_original # Setting wavenumber bins and loading HITRAN database - nus,wav,R = wavenumber_grid(1900.0,2300.0,350000,unit="cm-1") - mdbCO=api.MdbHitran('CO',nus, isotope=1) #use isotope=1 12C-16O + nus, wav, R = wavenumber_grid(1900.0, 2300.0, 350000, unit="cm-1", xsmode="modit") + mdbCO = api.MdbHitran("CO", nus, isotope=1) # use isotope=1 12C-16O # set T, P and partition function - Mmol=28.01 # molecular weight - Tfix=1000.0 # we assume T=1000K - Pfix=1.e-3 # we compute P=1.e-3 bar - Ppart=Pfix #partial pressure of CO. here we assume a 100% CO atmosphere. + Mmol = mdbCO.molmass + Tfix = 1000.0 # we assume T=1000K + Pfix = 1.0e-3 # we compute P=1.e-3 bar + Ppart = Pfix # partial pressure of CO. here we assume a 100% CO atmosphere .. parsed-literal:: - xsmode assumes ESLOG in wavenumber space: mode=lpf - Somehow wmin and wmax was not given for this database. Reading from the files - Somehow wmin and wmax was not given for this database. Read 3.40191, 14477.377142 directly from the files - Added HITRAN-{molecule} database in /home/kawahara/radis.json + xsmode = modit + xsmode assumes ESLOG in wavenumber space: xsmode=modit + ====================================================================== + The wavenumber grid should be in ascending order. + The users can specify the order of the wavelength grid by themselves. + Your wavelength grid is in *** descending *** order + ====================================================================== + radis engine = vaex .. code:: ipython3 - qt=mdbCO.qr_interp(1,Tfix) #isotope=1 + qt = mdbCO.qr_interp(1, Tfix, Tref_original) # isotope=1 - #computes logsij0 etc in device + # computes logsij0 etc in device mdbCO.generate_jnp_arrays() # compute Sij, gamma_L, sigmaD - Sij=SijT(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt) - gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, \ - mdbCO.gamma_air, mdbCO.gamma_self) \ - + gamma_natural(mdbCO.A) + Sij = line_strength( + Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original + ) + gammaL = gamma_hitran( + Pfix, Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self + ) + gamma_natural(mdbCO.A) MODIT uses the normalized quantities by wavenumber/R, where R is the spectral resolution. In this case, the normalized Doppler width @@ -64,9 +72,10 @@ with the normalized gammaL and q = R log(nu). .. code:: ipython3 from exojax.spec.hitran import normalized_doppler_sigma - dv_lines=mdbCO.nu_lines/R - nsigmaD=normalized_doppler_sigma(Tfix,Mmol,R) - ngammaL=gammaL/dv_lines + + dv_lines = mdbCO.nu_lines / R + nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R) + ngammaL = gammaL / dv_lines MODIT uses a grid of ngammaL, and wavenumber. set_ditgrid.ditgrid_log_interval makes a 1D grid (evenly log spaced) for @@ -78,10 +87,9 @@ ngamma. ngammaL_grid = ditgrid_log_interval(ngammaL) - .. code:: ipython3 - #show the grids + # show the grids plt.plot(mdbCO.nu_lines, ngammaL, ".") for i in ngammaL_grid: plt.axhline(i, lw=1, alpha=0.5, color="C1") @@ -110,39 +118,33 @@ can be computed using init_dit. cnu, indexnu, R, pmarray = initspec.init_modit(mdbCO.nu_lines, nus) - Let’s compute the cross section! .. code:: ipython3 from exojax.spec.modit import xsvector - xs=xsvector(cnu,indexnu,R,pmarray,nsigmaD,ngammaL,Sij,nus,ngammaL_grid) + + xs = xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, nus, ngammaL_grid) Also, we here try the direct computation using LPF for the comparison purpose .. code:: ipython3 - from exojax.spec.lpf import auto_xsection - sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) - xsv=auto_xsection(nus,mdbCO.nu_lines,sigmaD,gammaL,Sij,memory_size=30) - - -.. parsed-literal:: - - 100%|██████████| 13/13 [00:02<00:00, 5.74it/s] - + from exojax.spec.opacalc import OpaDirect + opa = OpaDirect(mdbCO, nus) + xsv = opa.xsvector(Tfix, Pfix, Ppart) .. code:: ipython3 - fig=plt.figure(figsize=(10,5)) - ax=fig.add_subplot(211) - plt.plot(nus,xs,lw=1,alpha=0.5,label="MODIT") - plt.plot(nus,xsv,lw=1,alpha=0.5,label="Direct LPF") + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(211) + plt.plot(nus, xs, lw=1, alpha=0.5, label="MODIT") + plt.plot(nus, xsv, lw=1, alpha=0.5, label="Direct LPF") plt.legend(loc="upper right") plt.ylabel("Cross Section (cm2)") - ax=fig.add_subplot(212) - plt.plot(nus,xsv-xs,lw=2,alpha=0.5,label="MODIT") + ax = fig.add_subplot(212) + plt.plot(nus, xsv - xs, lw=2, alpha=0.5, label="MODIT") plt.ylabel("LPF - DIT (cm2)") plt.legend(loc="upper left") plt.show() @@ -156,23 +158,22 @@ There is about 1 % deviation between LPF and MODIT. .. code:: ipython3 - fig=plt.figure(figsize=(10,5)) - ax=fig.add_subplot(211) - plt.plot(nus,xs,lw=2,alpha=0.5,label="MODIT") - plt.plot(nus,xsv,lw=1,alpha=0.5,label="Direct") + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(211) + plt.plot(nus, xs, lw=2, alpha=0.5, label="MODIT") + plt.plot(nus, xsv, lw=1, alpha=0.5, label="Direct") plt.legend(loc="upper right") - plt.xlim(2050.8,2050.9) + plt.xlim(2050.8, 2050.9) plt.ylabel("Cross Section (cm2)") - ax=fig.add_subplot(212) - plt.plot(nus,xsv-xs,lw=2,alpha=0.6,label="MODIT") + ax = fig.add_subplot(212) + plt.plot(nus, xsv - xs, lw=2, alpha=0.6, label="MODIT") plt.legend(loc="upper left") plt.ylabel("Difference (cm2)") - plt.xlim(2050.8,2050.9) - #plt.yscale("log") + plt.xlim(2050.8, 2050.9) + # plt.yscale("log") plt.savefig("fine_grid.png") .. image:: Cross_Section_using_Modified_Discrete_Integral_Transform_files/Cross_Section_using_Modified_Discrete_Integral_Transform_18_0.png - diff --git a/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.ipynb b/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.ipynb index e376d93c8..d1a1dd139 100644 --- a/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.ipynb +++ b/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "6fbc7d76", "metadata": { "execution": { @@ -51,20 +51,18 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-03-15 08:31:41.511257: E external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n", - "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "xsmode = premodit\n", - "xsmode assumes ESLOG in wavenumber space: mode=premodit\n" + "xsmode assumes ESLOG in wavenumber space: xsmode=premodit\n", + "======================================================================\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", + "Your wavelength grid is in *** descending *** order\n", + "======================================================================\n", + "radis engine = vaex\n" ] } ], @@ -73,7 +71,7 @@ "from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural\n", "from exojax.spec import api\n", "from exojax.utils.grids import wavenumber_grid\n", - "\n", + "from exojax.utils.constants import Tref_original\n", "# Setting wavenumber bins and loading HITRAN database\n", "nu_grid, wav, R = wavenumber_grid(1900.0,\n", " 2300.0,\n", @@ -84,7 +82,7 @@ "mdbCO = api.MdbHitran('CO', nu_grid, isotope=isotope)\n", "\n", "# set T, P and partition function\n", - "Mmol = 28.01 # molecular weight\n", + "Mmol = mdbCO.molmass\n", "Tfix = 1000.0 # we assume T=1000K\n", "Pfix = 1.e-3 # we compute P=1.e-3 bar\n", "Ppart = Pfix #partial pressure of CO. here we assume a 100% CO atmosphere.\n" @@ -101,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "id": "87f63886", "metadata": { "execution": { @@ -112,27 +110,42 @@ } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# of reference width grid : 8\n", + "# of temperature exponent grid : 2\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "uniqidx: 100%|██████████| 4/4 [00:00<00:00, 21236.98it/s]\n" + "uniqidx: 100%|██████████| 6/6 [00:00<00:00, 23109.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Premodit: Twt= 1000.0 K Tref= 400.0 K\n", + "Premodit: Twt= 1000.0 K Tref= 296.0 K\n", "Making LSD:|####################| 100%\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ "from exojax.spec import initspec\n", "\n", "Twt = 1000.0\n", - "Tref = 400.0\n", + "Tref_broadening = Tref_original\n", "dit_grid_resolution = 0.2\n", "lbd, multi_index_uniqgrid, elower_grid, ngamma_ref_grid, n_Texp_grid, R, pmarray = initspec.init_premodit(\n", " mdbCO.nu_lines,\n", @@ -140,9 +153,10 @@ " mdbCO.elower,\n", " mdbCO.gamma_air,\n", " mdbCO.n_air,\n", - " mdbCO.line_strength_ref,\n", + " mdbCO.line_strength_ref_original,\n", " Twt=Twt,\n", - " Tref=Tref,\n", + " Tref=Tref_original,\n", + " Tref_broadening=Tref_broadening,\n", " dit_grid_resolution=dit_grid_resolution,\n", " diffmode=0,\n", " warning=False)\n" @@ -158,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "id": "08c80693", "metadata": {}, "outputs": [], @@ -167,7 +181,7 @@ "\n", "molecular_mass = mdbCO.molmass\n", "nsigmaD = normalized_doppler_sigma(Tfix, molecular_mass, R)\n", - "qt = mdbCO.qr_interp(isotope, Tfix)\n", + "qt = mdbCO.qr_interp(isotope, Tfix, Tref_original)\n", " " ] }, @@ -181,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "id": "48675923", "metadata": { "execution": { @@ -194,16 +208,15 @@ "outputs": [], "source": [ "from exojax.spec.premodit import xsvector_zeroth\n", - "\n", - "xs = xsvector_zeroth(Tfix, Pfix, nsigmaD, lbd, Tref, R, pmarray, nu_grid,\n", + "xs = xsvector_zeroth(Tfix, Pfix, nsigmaD, lbd, Tref_original, R, pmarray, nu_grid,\n", " elower_grid, multi_index_uniqgrid, ngamma_ref_grid,\n", - " n_Texp_grid, qt)\n", + " n_Texp_grid, qt, Tref_broadening)\n", " \n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "id": "8169def7", "metadata": { "execution": { @@ -216,14 +229,12 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -239,10 +250,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "785f3acf", "metadata": {}, "outputs": [], + "source": [ + "from exojax.spec.opacalc import OpaDirect\n", + "opa = OpaDirect(mdbCO, nu_grid)\n", + "xsv = opa.xsvector(Tfix, Pfix, Ppart)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3fc6cb2d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 5))\n", + "ax = fig.add_subplot(211)\n", + "plt.plot(nu_grid, xs, lw=1, alpha=0.5, label=\"PreMODIT\")\n", + "plt.plot(nu_grid, xsv, lw=1, alpha=0.5, label=\"Direct LPF\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.ylabel(\"Cross Section (cm2)\")\n", + "ax = fig.add_subplot(212)\n", + "plt.plot(nu_grid, xsv - xs, lw=2, alpha=0.5, label=\"PreMODIT\")\n", + "plt.ylabel(\"LPF - PreMODIT (cm2)\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38070c9c", + "metadata": {}, + "outputs": [], "source": [] } ], @@ -262,7 +316,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.19" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.rst b/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.rst index bf3a30bee..e0319fc1d 100644 --- a/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.rst +++ b/documents/tutorials/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform.rst @@ -24,7 +24,7 @@ errors): from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural from exojax.spec import api from exojax.utils.grids import wavenumber_grid - + from exojax.utils.constants import Tref_original # Setting wavenumber bins and loading HITRAN database nu_grid, wav, R = wavenumber_grid(1900.0, 2300.0, @@ -35,23 +35,23 @@ errors): mdbCO = api.MdbHitran('CO', nu_grid, isotope=isotope) # set T, P and partition function - Mmol = 28.01 # molecular weight + Mmol = mdbCO.molmass Tfix = 1000.0 # we assume T=1000K Pfix = 1.e-3 # we compute P=1.e-3 bar Ppart = Pfix #partial pressure of CO. here we assume a 100% CO atmosphere. -.. parsed-literal:: - - 2023-03-15 08:31:41.511257: E external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error - WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.) - - .. parsed-literal:: xsmode = premodit - xsmode assumes ESLOG in wavenumber space: mode=premodit + xsmode assumes ESLOG in wavenumber space: xsmode=premodit + ====================================================================== + The wavenumber grid should be in ascending order. + The users can specify the order of the wavelength grid by themselves. + Your wavelength grid is in *** descending *** order + ====================================================================== + radis engine = vaex We need to precompute some quantities. These can be computed using @@ -64,7 +64,7 @@ section. from exojax.spec import initspec Twt = 1000.0 - Tref = 400.0 + Tref_broadening = Tref_original dit_grid_resolution = 0.2 lbd, multi_index_uniqgrid, elower_grid, ngamma_ref_grid, n_Texp_grid, R, pmarray = initspec.init_premodit( mdbCO.nu_lines, @@ -72,9 +72,10 @@ section. mdbCO.elower, mdbCO.gamma_air, mdbCO.n_air, - mdbCO.line_strength_ref, + mdbCO.line_strength_ref_original, Twt=Twt, - Tref=Tref, + Tref=Tref_original, + Tref_broadening=Tref_broadening, dit_grid_resolution=dit_grid_resolution, diffmode=0, warning=False) @@ -83,15 +84,25 @@ section. .. parsed-literal:: - uniqidx: 100%|██████████| 4/4 [00:00<00:00, 21236.98it/s] + # of reference width grid : 8 + # of temperature exponent grid : 2 .. parsed-literal:: - Premodit: Twt= 1000.0 K Tref= 400.0 K + uniqidx: 100%|██████████| 6/6 [00:00<00:00, 23109.11it/s] + +.. parsed-literal:: + + Premodit: Twt= 1000.0 K Tref= 296.0 K Making LSD:|####################| 100% +.. parsed-literal:: + + + + Precompute the normalized Dopper width and the partition function ratio: .. code:: ipython3 @@ -100,7 +111,7 @@ Precompute the normalized Dopper width and the partition function ratio: molecular_mass = mdbCO.molmass nsigmaD = normalized_doppler_sigma(Tfix, molecular_mass, R) - qt = mdbCO.qr_interp(isotope, Tfix) + qt = mdbCO.qr_interp(isotope, Tfix, Tref_original) Let’s compute the cross section! The current PreMODIT has three @@ -110,10 +121,9 @@ should use xsvector_zeroth. .. code:: ipython3 from exojax.spec.premodit import xsvector_zeroth - - xs = xsvector_zeroth(Tfix, Pfix, nsigmaD, lbd, Tref, R, pmarray, nu_grid, + xs = xsvector_zeroth(Tfix, Pfix, nsigmaD, lbd, Tref_original, R, pmarray, nu_grid, elower_grid, multi_index_uniqgrid, ngamma_ref_grid, - n_Texp_grid, qt) + n_Texp_grid, qt, Tref_broadening) @@ -132,3 +142,28 @@ should use xsvector_zeroth. .. image:: Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform_files/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform_10_0.png +.. code:: ipython3 + + from exojax.spec.opacalc import OpaDirect + opa = OpaDirect(mdbCO, nu_grid) + xsv = opa.xsvector(Tfix, Pfix, Ppart) + +.. code:: ipython3 + + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(211) + plt.plot(nu_grid, xs, lw=1, alpha=0.5, label="PreMODIT") + plt.plot(nu_grid, xsv, lw=1, alpha=0.5, label="Direct LPF") + plt.legend(loc="upper right") + plt.ylabel("Cross Section (cm2)") + ax = fig.add_subplot(212) + plt.plot(nu_grid, xsv - xs, lw=2, alpha=0.5, label="PreMODIT") + plt.ylabel("LPF - PreMODIT (cm2)") + plt.legend(loc="upper left") + plt.show() + + + +.. image:: Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform_files/Cross_Section_using_Precomputation_Modified_Discrete_Integral_Transform_12_0.png + + diff --git a/documents/tutorials/Fitting_Telluric_Lines.ipynb b/documents/tutorials/Fitting_Telluric_Lines.ipynb index 986d86bcc..5dab56694 100644 --- a/documents/tutorials/Fitting_Telluric_Lines.ipynb +++ b/documents/tutorials/Fitting_Telluric_Lines.ipynb @@ -80,7 +80,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -138,7 +138,15 @@ "shell.execute_reply": "2024-01-16T23:36:59.221044Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-02 10:15:49.948219: W external/xla/xla/service/gpu/nvptx_compiler.cc:765] The NVIDIA driver's CUDA version is 12.2 which is older than the ptxas CUDA version (12.6.20). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", @@ -224,7 +232,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -280,7 +288,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -319,21 +327,1283 @@ "name": "stdout", "output_type": "stream", "text": [ + "radis engine = vaex\n", + "Using /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O\n", + "\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_1\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_1.header\n", + "END DOWNLOAD\n", + " Lines parsed: 317529\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_2\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_2.header\n", + "END DOWNLOAD\n", + " Lines parsed: 42175\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_3\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_3.header\n", + "END DOWNLOAD\n", + " Lines parsed: 27542\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_4\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_4.header\n", + "END DOWNLOAD\n", + " Lines parsed: 56399\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_5\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_5.header\n", + "END DOWNLOAD\n", + " Lines parsed: 10616\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_6\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_6.header\n", + "END DOWNLOAD\n", + " Lines parsed: 6320\n", + "PROCESSED\n", + "\n", + "Data is fetched from http://hitran.org\n", + "\n", + "BEGIN DOWNLOAD: H2O_7\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + " 65536 bytes written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.data\n", + "Header written to /home/kawahara/exojax/documents/tutorials/downloads__can_be_deleted/H2O/H2O_7.header\n", + "END DOWNLOAD\n", + " Lines parsed: 23192\n", + "PROCESSED\n", "xsmode = lpf\n", - "xsmode assumes ESLOG in wavenumber space: mode=lpf\n", + "xsmode assumes ESLOG in wavenumber space: xsmode=lpf\n", "======================================================================\n", - "We changed the policy of the order of wavenumber/wavelength grids\n", - "wavenumber grid should be in ascending order and now \n", - "users can specify the order of the wavelength grid by themselves.\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", "Your wavelength grid is in *** descending *** order\n", - "This might causes the bug if you update ExoJAX. \n", - "Note that the older ExoJAX assumes ascending order as wavelength grid.\n", "======================================================================\n", "OpaPremodit: params automatically set.\n", - "Robust range: 148.362692491353 - 337.48243799560873 K\n", - "Change the reference temperature from 296.0K to 163.08464046497667 K.\n", + "default elower grid trange (degt) file version: 2\n", + "Robust range: 149.42336577900824 - 361.77172380843604 K\n", "OpaPremodit: Tref_broadening is set to 212.1320343559642 K\n", - "OpaPremodit: gamma_air and n_air are used. gamma_ref = gamma_air/Patm\n", + "OpaPremodit: gamma_air and n_air are used. gamma_ref = gamma_air/Patm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/set_ditgrid.py:52: UserWarning: There exists negative or zero value.\n", + " warnings.warn(\"There exists negative or zero value.\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "# of reference width grid : 18\n", "# of temperature exponent grid : 4\n" ] @@ -342,16 +1612,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/spec/set_ditgrid.py:52: UserWarning: There exists negative or zero value.\n", - " warnings.warn(\"There exists negative or zero value.\")\n", - "uniqidx: 100%|████████████████████████████████████████████████████████████████████████| 29/29 [00:00<00:00, 3263.52it/s]\n" + "uniqidx: 100%|██████████| 29/29 [00:00<00:00, 2410.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Premodit: Twt= 282.92333337743037 K Tref= 163.08464046497667 K\n", + "Premodit: Twt= 294.0349975006009 K Tref= 168.89218925939926 K\n", "Making LSD:|####################| 100%\n" ] } @@ -402,7 +1670,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -521,7 +1789,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABkEAAAHDCAYAAACTTb1hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddZwd1fn48c/M1d27bllNNu7uCTESJAR3aKF4kaI1vDgVKFBatBR3lyCBECLE3XUl6+52Zeb3x7m6kgRKv/xKn/frlVd27947M3fkzJzznPMczTRNEyGEEEIIIYQQQgghhBBCiJ8Y/cfeACGEEEIIIYQQQgghhBBCiP8ECYIIIYQQQgghhBBCCCGEEOInSYIgQgghhBBCCCGEEEIIIYT4SZIgiBBCCCGEEEIIIYQQQgghfpIkCCKEEEIIIYQQQgghhBBCiJ8kCYIIIYQQQgghhBBCCCGEEOInSYIgQgghhBBCCCGEEEIIIYT4SZIgiBBCCCGEEEIIIYQQQgghfpIkCCKEEEIIIYQQQgghhBBCiJ8kCYIIIYQQQgghjoimadx1111H9N7c3Fwuuuii77yOgoICNE3jhRde+M6fFUIIIYQQQojOJAgihBBCCCHE/4gXXngBTdNYv379D7K8lStXctddd1FfX/+DLO/7qKys5Oabb2bkyJHExMTgdDoZMGAAF198MStWrPjRtksIIYQQQgjx/wfrj70BQgghhBBCiP8ObW1tWK2hKsTKlSu5++67ueiii0hISIh47549e9D1/2yfq7Vr17JgwQKampo499xzufLKK3E4HOTn5/PBBx/wwgsvsHTpUmbOnPkf3Q4hhBBCCCHE/78kCCKEEEIIIYQ4Ik6n84jf63A4/oNbAnV1dZx66qlYrVY2b97MkCFDIv5+33338cYbbxAVFfUf3Q4hhBBCCCHE/98kHZYQQgghhBD/wy666CJiYmIoKSnh1FNPJSYmhtTUVH7zm9/g8/ki3hs+J8hdd93Fb3/7WwD69u2LpmlomkZBQQHQdU6Q2tpafvOb3wTTVsXFxTF//ny2bNnyvbb7qaeeoqysjEcffbRLACSwreeddx4TJ06M+K65ubld3nvXXXehaVqX11955RXGjx9PVFQUSUlJnHvuuRQVFUW8Z9++fZxxxhmkp6fjdDrJzs7m3HPPpaGhIfieL7/8kqOOOoqEhARiYmIYPHgwt9566/f63kIIIYQQQojvRkaCCCGEEEII8T/O5/Nx3HHHMXnyZB566CG++uorHn74Yfr3789VV13V7WdOP/109u7dy+uvv84jjzxCSkoKAKmpqd2+Py8vjw8++ICzzjqLvn37UlFRwdNPP82sWbPYuXMnmZmZ32mbP/74Y6Kiojj99NO/25c9Qvfffz933HEHZ599NpdddhlVVVU8/vjjzJw5k02bNpGQkIDb7ea4446jo6ODa6+9lvT0dEpKSvjkk0+or68nPj6eHTt2cOKJJzJq1CjuueceHA4H+/fv59tvv/2PbLcQQgghhBAikgRBhBBCCCGE+B/X3t7OOeecwx133AHAlVdeybhx43juued6DIKMGjWKcePG8frrr3Pqqad2O8Ii3MiRI9m7d2/EPCEXXHABQ4YM4bnnnguu+0jt3r2bwYMHY7PZIl5vamqio6Mj+HtUVBQul+s7LbuwsJA//OEP3HfffREjNk4//XTGjh3LE088wa233srOnTvJz8/n7bff5swzzwy+78477wz+/OWXX+J2u/nss8+CgSIhhBBCCCHE/x1JhyWEEEIIIYTgyiuvjPh9xowZ5OXl/WDLdzgcwQCIz+ejpqYmmBpq48aN33l5jY2NxMTEdHn9ggsuIDU1Nfjv97///Xde9nvvvYdhGJx99tlUV1cH/6WnpzNw4ECWLFkCQHx8PABffPEFra2t3S4rMGH8hx9+iGEY33lbhBBCCCGEEP8eCYIIIYQQQgjxP87pdHZJY5WYmEhdXd0Ptg7DMHjkkUcYOHAgDoeDlJQUUlNT2bp1a8T8GUcqNjaW5ubmLq/fc889fPnll3z55Zffe1v37duHaZoMHDgwIqCSmprKrl27qKysBNRcKDfddBP//Oc/SUlJ4bjjjuMf//hHxPc555xzmD59Opdddhm9evXi3HPP5a233pKAiBBCCCGEEP9HJB2WEEIIIYQQ/+MsFst/fB0PPPAAd9xxB5dccgn33nsvSUlJ6LrODTfc8L0CAkOGDGHLli14PJ6IlFijRo3q8TPdTX4OdJkA3jAMNE3js88+63bfhI9Aefjhh7nooov48MMPWbRoEddddx0PPvggq1evJjs7m6ioKJYtW8aSJUtYuHAhn3/+OW+++SZHH300ixYt+j/Z90IIIYQQQvwvkyCIEEIIIYQQ4nvpKajQnXfeeYc5c+bw3HPPRbxeX1//vebKOPHEE1m9ejXvv/8+Z5999hF9JjExkfr6+i6vFxYWRvzev39/TNOkb9++DBo06LDLHTlyJCNHjuT2229n5cqVTJ8+naeeeor77rsPAF3XmTt3LnPnzuWvf/0rDzzwALfddhtLlixh3rx5R7TtQgghhBBCiO9H0mEJIYQQQgghvpfAhOPdBRY6s1gsmKYZ8drbb79NSUnJ91r3VVddRa9evbjxxhvZu3dvl793Xheo4EZDQwNbt24NvlZWVsb7778f8b7TTz8di8XC3Xff3WU5pmlSU1MDqHlJvF5vxN9HjhyJruvBydlra2u7bMeYMWMAIiZwF0IIIYQQQvxnyEgQIYQQQgghxPcyfvx4AG677TbOPfdcbDYbJ510UjA4Eu7EE0/knnvu4eKLL2batGls27aNV199lX79+n2vdSclJfH+++9z0kknMXr0aM4991wmTpyIzWajqKiIt99+G4DevXsHP3Puuefy+9//ntNOO43rrruO1tZWnnzySQYNGhQxOXv//v257777uOWWWygoKODUU08lNjaW/Px83n//fa644gp+85vf8PXXX/OrX/2Ks846i0GDBuH1enn55ZexWCycccYZgJqjZNmyZSxYsIA+ffpQWVnJE088QXZ2NkcdddT3+u5CCCGEEEKIIydBECGEEEIIIcT3MnHiRO69916eeuopPv/8cwzDID8/v9sgyK233kpLSwuvvfYab775JuPGjWPhwoXcfPPN33v9U6dOZfv27fz1r39l4cKFvPnmmxiGQVZWFkcddRTPPPMMM2bMCL4/OTmZ999/n5tuuonf/e539O3blwcffJB9+/ZFBEEAbr75ZgYNGsQjjzzC3XffDUBOTg7HHnssJ598MgCjR4/muOOO4+OPP6akpITo6GhGjx7NZ599xpQpUwA4+eSTKSgo4F//+hfV1dWkpKQwa9Ys7r77buLj47/3dxdCCCGEEEIcGc3sbpy4EEIIIYQQQgghhBBCCCHEfzmZE0QIIYQQQgghhBBCCCGEED9JEgQRQgghhBBCCCGEEEIIIcRPkgRBhBBCCCGEEEIIIYQQQgjxkyRBECGEEEIIIYQQQgghhBBC/CRJEEQIIYQQQgghhBBCCCGEED9JEgQRQgghhBBCCCGEEEIIIcRPkvXH3oAjYRgGpaWlxMbGomnaj705QgghhBBCCCGEEEIIIYT4EZmmSVNTE5mZmeh6z+M9/iuCIKWlpeTk5PzYmyGEEEIIIYQQQgghhBBCiP+PFBUVkZ2d3ePf/yuCILGxsYD6MnFxcT/y1gghhBBCCCGEEEIIIYQQ4sfU2NhITk5OMH7Qk/+KIEggBVZcXJwEQYQQQgghhBBCCCGEEEIIAXDYKTRkYnQhhBBCCCGEEEIIIYQQQvwkSRBECCGEEEIIIYQQQgghhBA/SRIEEUIIIYQQQgghhBBCCCHET5IEQYQQQgghhBBCCCGEEEII8ZMkQRAhhBBCCCGEEEIIIYQQQvwkSRBECCGEEEIIIYQQQgghhBA/SRIEEUIIIYQQQgghhBBCCCHET5IEQYQQQgghhBBCCCGEEEII8ZMkQRAhhBBCCCGEEEIIIYQQQvwkSRBECCGEEEIIIYQQQgghhBA/SRIEEUIIIYQQQgghhBBCCCHET5IEQYQQQgghhBBCCCGEEEII8ZMkQRAhhBBCCCGEEEIIIYQQQvwkSRBECCGEEEIIIYQQQgghhBA/SRIEEUIIIYQQQgghhBBCCCHET5IEQYQQQgghhBBCCCGEEEII8ZMkQRAhhBBCCCGEEEIIIYQQQvwkSRBECCGEEEIIIYQQQgghhBA/Sd8rCPKPf/yD3NxcnE4nkydPZu3atYd8f319Pddccw0ZGRk4HA4GDRrEp59++r02WAghhBBCCCGEEEIIIYQQ4khYv+sH3nzzTW666SaeeuopJk+ezKOPPspxxx3Hnj17SEtL6/J+t9vNMcccQ1paGu+88w5ZWVkUFhaSkJDwQ2y/EEIIIYQQQgghhBBCCCFEtzTTNM3v8oHJkyczceJE/v73vwNgGAY5OTlce+213HzzzV3e/9RTT/GXv/yF3bt3Y7PZvtdGNjY2Eh8fT0NDA3Fxcd9rGUIIIYQQQgghhBBCCCGE+Gk40rjBd0qH5Xa72bBhA/PmzQstQNeZN28eq1at6vYzH330EVOnTuWaa66hV69ejBgxggceeACfz9fjejo6OmhsbIz4J4QQQgghhBBCCCGEEEII8V18pyBIdXU1Pp+PXr16Rbzeq1cvysvLu/1MXl4e77zzDj6fj08//ZQ77riDhx9+mPvuu6/H9Tz44IPEx8cH/+Xk5HyXzRRCCCGEEEIIIYQQQgghhPh+E6N/F4ZhkJaWxjPPPMP48eM555xzuO2223jqqad6/Mwtt9xCQ0ND8F9RUdF/ejOFEEIIIYQQQgghhBBCCPET850mRk9JScFisVBRURHxekVFBenp6d1+JiMjA5vNhsViCb42dOhQysvLcbvd2O32Lp9xOBw4HI7vsmlCCCGEEEIIIYQQQgghhBARvtNIELvdzvjx41m8eHHwNcMwWLx4MVOnTu32M9OnT2f//v0YhhF8be/evWRkZHQbABFCCCGEEEIIIYQQQgghhPghfOd0WDfddBPPPvssL774Irt27eKqq66ipaWFiy++GIALL7yQW265Jfj+q666itraWq6//nr27t3LwoULeeCBB7jmmmt+uG8hhBBCCCGEEEIIIYQQQgjRyXdKhwVwzjnnUFVVxZ133kl5eTljxozh888/D06WfvDgQXQ9FFvJycnhiy++4MYbb2TUqFFkZWVx/fXX8/vf//6H+xZCCCGEEEIIIYQQQgghhBCdaKZpmj/2RhxOY2Mj8fHxNDQ0EBcX92NvjhBCCCGEEEIIIYQQQgghfkRHGjf4zumwhBBCCCGEEEIIIYQQQggh/htIEEQIIYQQQgghhBBCCCGEED9JEgQRQohuNLR6+C/IFiiEEEIIIYQQQgghhDgECYIIIUQnS/dWMfqeRdz54Y4fe1OEEEIIIYQQQgghhBD/BgmCCCFEJw99sQeAl1cX/shbIoQQQgghhBBCCCGE+HdIEEQIITrRtR97C4QQQgghhBBCCCGEED8ECYIIIURnmkRBhBBCCCGEEEIIIYT4KZAgiBBCdCIhECGEEEIIIYQQQgghfhokCCKEEJ1IOiwhhBBCCPFD8PoMCmtafuzNEEIIIYT4nyZBECGEEEKI/yJr8mooqm39sTdD/H+grsXNTW9uZkNh3Y+9KUKIHvzy5Q3M+ss3fL697MfelJ+UTQfrOPeZVWwvafixN+WIrc2v5cPNJT/2ZgghhBD/kyQIIoT4r2Sa5g+2rOX7qnjoiz34DLXMH27JQgjxw9pR2sA5z6xmxp+X/Nib8l9p5f5q3t9UTO7NC7n29U0R95KWDi+Vje10eH0/4hZ+N5MfWMx7m0o448mVeHwGR/3pa3JvXsj+yub/yPpWHqjmzXUH/yPLFuKnavHuSgCeXZ7/I2/JT8u1r29idV4tpz3x7ff6fH2ru8e/GYaJx2cc0XKeWXaAN9YevlxsaPVw9tOruP6NzYy+e1G379lf2cTb64si7k1biurJvXkhH20pPew6tpc08JcvdvPxllJM0wzWbURXeVXNrMmrAVS98oesWwohhPj/k/XH3gAhhDhS+yubeH1tEZ9sLaWisYNfHzOIa+cO/LeXe8FzawHokxzNWRNykGdgIcT/r7YU/ff0eO3JjtIG/vT5Hn5z7CBGZSf8IMts9/hw2iyHfM/W4nrO/+ea4O8fbynFYdV56KzR/PGz3Ty19AAAp43N4pFzxgDQ3OGlsKaF4ZnxP8h2/tDcYY10W4rqKa5rA2DeX5fy2uWTmdY/hVdWF3L7B9vRNNhz73zs1iPrA+XxGQy87TMAjhqQwkuXTOL8Z9X+y0yIYsbA1B/424j/dXUtbuKibFh+onlJvf8lDdIVje2kxjjQf8Dj4DNMmto9JETbf7BlBso7j++779c31x3k9+9u49YThnDFzP4A1DR38K9v8zlrfA53fLid3eVNfPOb2bgcPTeZ7Cxt5IFPdwNwwqgM4pw2QDWqlzW0k5kQxdNLD/DgZ7sjPtfQ5uGfy/O4/9NdfHXTLFJiHBGBkZRYB3MGp2GaJqf8QwV5rnt9E0cNSCHJ1f0+fPDTXTy9LC/4+7Wvbwr+vPWuY4Pb9v8TwzAjzrOyhjYSouxE2Q99Pz+cjQfr6J8aQ3xU99+5prmDox9eCsDF03N5dc1B3F6DzHgni26aRUynY97u8fHqmoMsGJlBerzz39o2IYT4vjqXmeK7kyDIf7Md74O7BTQdNAtgguE9ss8GWnk1zf95HQj8HPZaxM9h7/G2+9etge4/jRqKwWKDqKTASg6zfrOb1whtV2B66iP+Oex/NLV8d6t6Lfz7hn9PtMj/TRNMg9qmFlbn13LUgOTQA6NuBZ8HGkvAFg2xGep1d7P63fSp91hs4HWrdbTXg88NzoTIdVusYLGDt0P9Pbg/Ou3vzvu/u9faG8DTptZteMHTCtHJapmeVrVthlct3zTD9lE4/2u6RS3LGQeuNPB1QGOZet001Do0Xb1H09XrPo//dU2tR7OE/mZ4w46rGXmcD3dcw7bTY5h8ubOChdsqMIHxgKlr7Fi8lisXw4XTcpmWZVPbZY8BoKHDoKS6nnqfk+EZLuITUiCpr397/duWmAslGzhOX4sDDzG794A1ndnt++mjt2Giw/Z28HmpbTfAYiPJ6d/ndhdYnZCQo45vdDKUbVH7PCoJ4rOhYrtan7dd/a9p6rjrFnVumv7Gq/B9GTiugd8x1T7V/defI07tqqgk6DUcOpqgYgdVNTXYY5OJHzAVmiswKndTXN1AVpwVi6cZLA61PnczOOOD55yBhq5pan2OOEgZpLbJGQ8VO6C5IrQNVgdEJarXAtsffs3pltD2x2VCQh+1T7zt6ntb/ee8blXHqdcwqN4PbbVQXwjRKWo/xmdDWx2kDgF7tP868ahzuWI7NJWp/Wz41D/dAq21avlWp1pX9kSoP6iOlelTr5kGJPRWyzVNqNoFTeVqH9qiQLeBKwUyRkNzpfp8ykC1fGuUum4bS6G+KFTWxqarbS7fprbJ2662wRatjo89BmoPqP3mTICyzWpbA+Wn3QXJA8HToq41n1utLyoJMsdAXQFU7VHrM3zqGATLDEL73OqEjFFquy12te7WGnUMfW61jemjoKUSqvdCay0dPpO9pXUMzojDbrP590N06Bg64yBjTOi6dSWDz+vfbxXqXApc4xYrOGLVPoxJU9+1cgd0NKttc8Sq7TG84EpVZUt7Q7DMDV0LFrVfbFH+9Rrq2Fvs6lzoNUItH6CuEOry1bmoW9U+zhittsvuUvuqoQTKt6rlWBzgbVPnakyaem9LFVTtVsuwOtU5FFivuyX0feJzoGafui5Sh6j93lYPlTvV+Wl41fexOtX5ZnWqcyltuPquTn8Dev1Bda9sb+SL7cV8sOEgl87ox4R+vdT2ahZ1DFIGgCNebVtTmVqHv6xIbjBIo44qIhvlWzq8tHt8JMc4Qi8aPnVtOeLV8Qu+uVr9zZWq9mGr6glJYl+IOfKG7cqmdpJdjh4bLFfuLePelxZy9tg0hqdamZDlwmsY3POvDURpHfx1/yIaTRcTph7NrSePBuDP733LpuJmXrv2WLRu71chbq+B3arT9+aPyaCWu86exrEjc9R1aI9h1eatrC71cf2CCei6xmtrDhJDK2laPVZ8eLGwLs9Oa2szK5Z9ySStgwZcrNtcyXlbF/OPc0fxyNpmNu4v5YqTZnLKtJFHvG++F9NUx8LnCZW3PeyDdbvzufKFVfTXmkmmEQ9Wtm+EwdpBkrQm2k07j/xzNwPPG8UHH25lnKZTSQKPL97L1Nw4UqJgUE5G6NnH5warA9PbgWZ14DNMBt72GVa82PCyYn81q/NqcODGg5V7P9nJohtn/Wf3R7iOJlUOtNaqbY1KUOWrbun6vuZKtf9i0kPPZlW7wdPuv840dU26UtXv35dpqucOq9N/jz4EnyeynNMtPR7bIHeruhe2N6hnjYZiVRZ42lQZl9TPf39yHn5ZnbfbNA+/zYC3pQ5LUwmaaagyLSoxVD4DGIa6f9ljvts2+FU3q7ItxWXnmtc2sXCbShdV8McFh/+wt0OV4dYoiE5S62+tVfvMYlPb+j2365ACx91iV/c+n0ets7VWHavg87ymnhG8HcTSShPReDuPLDBNdV9oqVbnqi1KbXdbvVqOLdpfnrnUfajz+Q7kl1aRbanB1lrlf360qnPN40+Z2Fqjnu9ALUu3quU4E9RyfW71POxKAWDRjnKueHkDZ47pxUPnTjj0fmgoho5GiOmlnlfa69U+N011Xmg6eN3UdJhc9foOSswUnrr6JEbGNqn1RyWoc6ilEp/FyZKCDvKrW5jcLykyQG7495v/nK1q6qCx3XPkx6wbv393GwAPfLqbaf1TGJEVz6Nf7ePl1YX8Y8mB4PuW76vi+BEZPS7nr1/uDf58/rOr+eTaGQC8vraIW9/fxi3zh3QJgATct3AXAHMfXso5E3Iil7toL3MGp/Ht/pqI1296azNxThvj+yTyi2m5EX8LD4DY8RBLKz50TDRG3fUFeQ8s+PEbz0xTXSetNawsaueeN5dRaibx7o0nYJhw3KPLADhvUm8ePP273XPb3V6+3bCR+z/aghcLJWYKG/8wPxgIMQyT7aUNDMuI47Pt5cHPPf9tAZlUE6u1UtKQwp8+2829p44ILdfjY8Ydb5CpVfPiwlieu/5MBqZG+csauzqPD8XrVs/OrTXq/mTz123s0epZNC5LPZN521U9wavqnrhSVbkQlah+bixRdQV3M6qdw1/2htfTHHGQNkQtL9BGUJcfqq/HZ6ltCJQPutX/XO5R73f562K1eWr54c/oFrsqa3WregaPSlD3KZt/Xzhi1TNzoIxMH6m2PzpF1YXa6tT9KjZDLb+pzF8XivWvx6fWndhXLad6r1qP13/P03RV3nta1PK9HWobrE71vG8a/vYq//4I1AWSB6j6W1sd2JxqfY3Fah9ZbOq9HU2h5y5XKtTsV39rbwy1g5hmqK3HYlPlXsogdQxKN0JLFYa7Fa/Xg93uoKCyHtM06Zsap75newPYXP56XqKqO3Q0heqyjli1Le31/nqvv06rWVQ5G5+ttrGuQH1Pd4vaVm+7WkZshirP2xvUcUoeCIZH7cPm8tD9OtAmoFv9dUp/vcVi89/XHOpz7ha1Pnerv+3EF1nXTxuq2gQCz2Vt9f42BiPUlhF4ryNWLd/Tppbj61B1rMRc9bdAfcrn9p9vWmgZoD4TqN9Z7Gr/BOrigfaV+GxV72yrVZ8N1JcDx8sR629/c6vlGF7VDqBpap/bokPLtzpVu1FtnvpezZXqfa4UtZ2mod5ni/J/vzjImaz2fdUu8HZQ09CIS3PjtNvUdhheVc80DLUed7O6D5o+f53Tf18O7Ad/XSbY3qLbwDR44estrGxI4rwT5jF3VN/vVEYKRTP/C8b9NTY2Eh8fT0NDA3FxcT/25vx/o/7rx4gxm7EeQSVGfDcvriygrs1NVkIUZ43POfwHfiAFNS18uLkkGB46aVQm/VJdfLOnisRoO2NyEr7zMtu9vmDv1jPGZpOTFP3DbfD/gZL6Nr7aVUHdIYasA0zvn8LE3KTg7+9uLKaoLpQz/5ihvXrsyfvoYlWBGZWVQEa8k/WFtdS0qPXdMHcQPsPk8SX7APjVnAE9XnOGaXKwtpWshChslv+D69LqAG8HDW0enl+pUixcNC0Xh9XCv77Nx+MzSI9zcvq4bNYV1DI8I46EaDtN7R6e+zaUkuHiaX177CnVkw6vj6Z2Lykx/0YD0uFEJ8OgY2H7e8EGOjzt//5yE3PVA0tt3mHfGuSMh6zxcODrI/9MIDgYCID9X95us8ZD+RZViQqIy1DBC39l5vMd5ewub2RkVjxzh/Q69PI0DUaeBcXroPZHTucx+Hj1MFqysef32KNh6Emw7d2eOwdYHRENk7UtHXgNk7TYrj38yhvbecOfAuiSM04irv8U2PDikW9z7nT1sHxApbAyMXls8b7gn6+ePQB7eJmhW1XjWUNxl0VtL2ngq90V5BvpPHb/A+rYtFTzx4fux+N2c921vyY+vR/UHICtb7F0bxUbD9Yz57RLGTNhOhSugrxvut9OTYMx56tg4WF8saOcX768gVvnD+aiIV7s0YkQ2wtaaqCtFl9sFnffczNJWtNhl1VqJrPUOp3zfB8FX5s/IoMNDGXBSWcT7+pazhTWtHDaY4uZMywT39Z3ydVVg8b1Rw9E0zRM0+Sxr/dhmhpRw47nknPO5Jx7n2OGdzW6ZjAgNYb9Vc1UmQlE0UGM1tZlHUnRdmr99x6fqfOrM+bhcCVCn6mw9wv1ppSBbPv6TRbvruBL33g+uu08LFU7VKOGLUqVYxa7CkjaXapS1Z2mctj5oapMBrhSYPjpkQEs08Tc+zmPvvQ2mvbdy5R9RjY5WiVOzU2JmcJN1/+Op558hERPGX2TXeTXtLDF6M8vfnEFf37hbebom7FpXlb5hpPpaKOPN48W08mGqGm8d+VU1Tj/XQMJzZVQtEYFJnxedb6nDIT+R6vga9h3pa4Adn+iKraddHh9kDkOx9DjQdfZs309n7z+JBZNXdMjMuOZN1SVbV5/I2rg/l3b6qak0cOw0ZOw9J+jKsc9cbeo8qatVpWt8dmqorvtbWiuUts8YJ5qkI/ppa4vV4oKPB9cDQk5NOVvoKq6mo+2qnQ20XYrM2bOY402mqK6dm45aRTWsDKgfvsitq/6gsG9YnHadWIcVt5YV4TLbuWkURldA4Sjzobk/ofe700VULBcBUZ9HtV4M/BYcMSo4P7ez9X7kgdA6mC+3X6ANQtfQNcMrp49AI/PINpmQRt1lmqQ3/+V6gAAqsFl9LnqXlm6Se0v3aIaZawOFRAOD7C2N/LNF++yau0aorQOzp2cy/Oryygzk1ljDOFPFx/H7MFp3X+Pg6uhcCWmt526Vg8uuwWH1QIWK6v2VbAmXzUap8Y4WDBtLAnjzwg28P87nlyyjzcXLeXxiTWMTPQ3CsaksnjDLrYV13L5jH647N33L3z4q/18akwmKrU/n9w0V71Yf1CVIy3VR7YB9mgYcaY6VxuKoaGIrTu28fWGHQAkRtuZPyKdtFgnbR4fu8oaGZYZh9P6HXrUDzuFe175nLg6dVxvOGYoZE+AfnP8DUWN6nr0uVXjXdh1WdvSwTd7q5jSLxmX3UqM04phmDy/soBWd9f78IJRWQycejJm5S6+WbeZLcX1AOwy+vCVMY6FN8xmSJpL3TfLNoWeZ2JSefiDVVSSSKmZzFpjCB3YewycmaZJdbMbwzTpFRe6v+fevJAo2hmsFaNb7Tx+0y/42ROLKW4y/IEDdT3+7byxnDw6s9tlL91bxS/+tRYNAxPVievAAydg0TUm3f8VlU0dwfcm0UgLTmbrW0jXalllDKPcTKKXVkeemYEPC3E048VKKw5A4+tfz2L5vmr+8NGObte/4fZ5wY4Pn28v49evfMt0fTun9jXoaKqlsLYl+N4aM44zjpnDmJx4VVZFJ0PvKeq+9G+oKjtIbNVGrE1FbCqoYVhuFi6XSy03ZTCkDg4FIit3wYGv8bbWY9G1iOegFtOJFQOHpu65b3tn8fjp/fhkWyXXnnMSiYH6hmHQnL+Omppqeo86Cs0ZzxMvvETNvtXEdrqP15hxJB11GTfOV8GUh77Yw5NL9nDdvKEUVtXj3vYhmVo1XqzEaaF9VZS1gIevOjP4+0dLV7N/0bPoWtf0aKapcdWll+HsN0011Hpa1XNy3jfq3nEk7K4jf+8PoNXtxeMzj7juF6hnjs1JZNag/4MRoJqm6mrfsb7hNQxMk/+bOni4QEdU1AjjA9XNzByYyrJ9VQDMHpTKmJzE/9tt+o5a3V4OVLWQkxjVZbRec4cXp03/t9scfYaJrnHYDk7/19w+A8MwcdosVDa1Y9E1kl0OPD6Dtfm1tLi9TO2fTGObl3c2FjN/eDqDeqlnRsM02VvRRG6KK+JeW9/q5oVVBSRG2bsEq/8dJiZ//3o/PtNk1okXMnbKnB9s2T8FRxo3kCDIf7FfPvYWeeV1XHd0P04amY5hGJS3GGTGRx3+w0FmKLIdiPSbBsFRA6aBafgorGkmO8GJVcMf+bSFehUFIpV1haqikzKoU68rLXJ9Eb38eyoEw0cNdPrZf8q2e3zYLahe7GGvq59RUVVNB7sLEzUE3abrYBp4fD6+3l3B9P7JxNgtlNa3Ut/qYVivKDBNTnviW5pR+/HLG2cRHGXTVKEaAe0uyBwLBStCmzzs5FBU2mJX66/aE4qUa/4bh+FTPZPri9TWDjqOX7y8jbKGdhKjrDS2udEw/f12DPomRVFU24KOQb+UaO4/dTh2C2iBkR6lm1SFKHeG6iHSUKwqsE3lUFfAk1u8fFiRguHf13efNIy0OCf9U2OC++vrXRVsKa7nonGJxFRuwGZ26l0V20tFtw2fP/rtU+v0tOE+uIFNtVYGDh1Nkt6qIvyBxruyrerzA4/x9xzsfLzN7o8x0NTu4YVv8/liRzkakBht5fKj+vKtvzeq5n+f2lcGI/V8fOjkRY2gxaPhc7eRrVXhw0KVGc9My1ZOG5NFn+TQw/6W4nq8PpPX9mrkmRn+SofaSh0DDZNnfz4Oi83K5c+vJlrrYF5aE+cNteHqPw2K1kZ8m1fWFLK0MYPhegEnjcqkqsVLeeJ4JveOISMjS107FTvU9+w1XF0vge/s7VC/Gz61fwM9FzTd3xveP5qnvV5VnJsqguvdW9HES9taSdEa6Mk+I5siM5VbZySwsbSd5Xn1WDBoMZ2cO7U/k3JiefWdtzhugIuJuUkU1bWyfF8V/UbPZMqIweq8LlgGPi/NHV5+ucxGtRmPBlx6VC5njctSvUZMU/W2qQhV2N6vTOPNLbXY8fjPbLiqn6ooVzd1kJTVD5tmqt4O1ft6/A5BGaNYdNDksSUFnDOpDxdOSKejYjd76jWGRzdi6ei0H3oNUw2LTeWRr2t6aNRJYGTJkYhJVT2R2upUIwCo3qC9RqiRL/aY0Cit7vSeorbF6lDHM+xYdstiUw0QFpvaTneLqrwGeuX43LB/cZePtbi9PLashIaoPtw23hORBqG4rpWnNjSxyRiIhsEDR9kY4qzDmj0ePSZZLbdgRWgEQuf9ljVeNfwFRocZXnXs6gpC79MtqmGztUY1FOpWVRYGGs2iEqD31FAvIUz/9/GoUSTtDXhShmJzJavv2F3DfdoQNWrN0wrF67vdfUZ0MmbacCyGm9o2H/FRNiwHV6q/mSYt1gRa4gfw+zfXo2NwWloFJ43OQAuM+KvN4/El+4J5taf1T2FSWMDVTOzDn7+tZ1xOAseMzlU9tlqrMcu2oHV3j0vM5WBFFbcvbabJjEbDZN7gJK6ekuoftdLpsSx9hBoZZPqgeB3bD1bx1S51ztzw2/sgPgtz27s89uYnAJw6Jovc3H7QVEGr28szy1Wwr1esk/MuvJKWbR/z7DIVGJ8/IoOvdlUwfcxwxqQ7VAMXwORfqkbNHpgNxTz2lzsA2GQMZKy+D5fDzrmX3MC/nv4rpuGlb0Yq+WVVuE0bTUTRjp1mMwoLBv20MtxY2WHmMl7f2+N6AOg/lxvOPh4OrlK9oPrOBHs097+9jKjNL2KgYdVC126Ky8HPp/ShrtXNi6sKAPCYVk684l4+eOYu4q0erpo/Ec0Rw6Pvhq6bFtOpGqG0wzdEnDllCLsKS0mJcTC2dyJPLT1Au38ekfOmDaZXlImJSV5VC72yconJGRUKoE68NDSaKbhDTVj/nLpOLFZ/T0d/mRSfBeMuDI3kLN9O+Zq3g0E5AEdyb/KrW8jUVOPviNwMmhzpfLOrjFac2PEyL9dG4cGuDQrtph2n1rWTQXWfE0kpVOdUVnwUJQ2RjUvRNivRDgsZOf2Ze/5veu5t7/OqRvdAD8aOZlj7TPdlC6hnqV7D1c8F30L+MlrdXt7fVEJOUjQF1So1WWaCkzfXq+eoK67+HdG9BvLHO6/BqblpMqOJ1VQniJNHZ+LxmXy2vYwiIw0T0DWTeJqDDWYTRwxh+pk3dB/McbfA+n9FBmGGn4pZtYctG1aSEuMgOzHUuaS0oY231heRFuvg/El9MDGpa/Xwkv9c7E67aWf06b9hfmabeobKGMUjd13TbZDLY1qpJIERqVYuGBVLc4eXWIdVNSqMu1CdL91pruTg4qf4ZkcJMU4rC0ZmqMABwIjTadv5OQeKykiNdQQbiv+1spDGtg6qzXhiaAueJ5cdP4WY+GT27NjIZ9vLSIy2c9b4bKIT01UwZvWTXcsxiw0mXubv+e+D9f/iiYWrI9K5BRimzjLHLD6488Ku36OpnC9efJBd5Y0RL58/qTfFdW3BRqd6M4Y4WtE1g/PnjCNtztXqntRcpZ6V00d1GQlzIP8Aqe2FxCVnqHtVJxfdeh9jdFV23jBXjZr1+Az+8c1+ADLio1RvfkeMasCrKww+IwQ62yRG2/nFVbeoUUqr/6F6pOqW0Kie6n3dP4vYnF06gYR3gKk246kwEzHQOC7LQ2lZMdVmPFVmPE/MT0SzOsAZR21ZPpvaM5gzegBW/30wwOMz0DR4e0MxFY1qXelxTmKdNuadfimOzJGw9c3I+3zgsHR4eG5FPtVmfPBZtNJMpMaMo5ForPjorVWSqtVHfO7CKX1Yk1/LnoquwfLKsddztH0njXu/5YSRGRHpDgP7E6DZjOJd3ww2/fG80IdNE5rKWfj+S2zbm8daYwgbzYG8etkUpg9QAbHxN7/GGZZlRGtdyyK3aeVzYxJ5ZiaPnTuGU8ZkhS3apN1jYNE1jn9sGeVVNZxpWUoHdt72zeKuU0byydYy1uaHAtpDtIMcb1nbZT2mqaFpJruM3uSbGZxgWcPo7AQ+LbLyoW86D583iZ1ljbzyzTau7lvJkoI21ppDCNSnnr1wAscMU4He/jd/xLXW94HQ+YnNSVuHm6eXqJEo43onMjM8jaEzXl2X1shGz4j9WLZZ9YTOmawCcWGK923h3ZcfJzveQZzTxo6yBhKibFw0rS9tHh9WXcM29Hj1zNjewP6P/8InW0owTL3bgEJPtAHzuP6in6l1blrEO+++DkBsUjpDZ5/D2vcei3h/sxmFEzdWzUeh0YvhoyZwyVmnc+rtf+doyybWGUNJTEphQP3yiM+NzUlkU1EdAH1y+zP/jEvJX/oKJRWVbC+qxm1asWtdA3oDesVx4lmXwe5PQ6MmDiX3KBUQCozC7SxjtKpPdNLu8fHephJKGr3UJ44kN6qVxSUWzhiTzvyEIp5cmo8Fg9PGZNA3JVTfLTMT8aWNIKNuPQ2NDTy9sozVxlCitQ76aBX8bmosSS4HJPZR5VZA5hg2VFu4/RNVxvXTyhioF3P93IEUVLewt6KZ5WUaKVoDPlNnt9mbR2bqXVKJQShA0eL2EmXzB67js/2jCfwjU1ypavRHeGC41zBV5yleF1kGOmJDdTBgW0kD7+5qYZ0xhKcunEBOvF090xZGlnOgyrovd1bQe9oZjCAPmsr5Zk8lX1c4uXWqS5U10cmq7aKhWAWCAxlADK+6h1Ts6FJWG7EZnPZuA16s2PHgwYKBjo7BOX2aGZIWTZ+hE0jzVeCt2MWXOytojcqgPWUkZwxx4ClYhd2i47BZ1fpaqlSdyBGrnsnDj03GaKjeExo56Al7RotKVHXUcOkjID6bvfv3U+51ERsXz4j0GGyeBnzudk56YR9WfDhx88iJWdgdTi59t4ij9G3YNS9lZjK/u+Q8kqNtamRRxc7QsvsfrTINNJWr7XL4R/YYPgyfj62FlQz1bOepr7YT47Bx0bRcyqL6s+FAOXNTG4lxWPFYo9GyJ2B1xlLe7MVu0UiKtobaRQD2hs2jlDtdjTAKqNkfantIH6mehXRbqI5afzBU/wwXl8ktK7zE1G1n7phB/HlNO6laA3dPNtlV1siW4nrWG4Nx4iaaDkrMZCbqezhrfDb3bk/E295MEk3000u5cmZ/Gts9vLb2ILuM3vTWKlV7Xs4kaop2cdHoGIaPHKeufcMX2UGy13A1qqalmgNVzRQ2mczuG4Nuc6h9WrQOr2Hw9yX7g5t+/vV/Ji1V0tKGO9K4gaTD+i9lGCZflLkAF9cudlPmiA7mIwXYcfdxPP9tPg8t2svUfsk8dcH479zTG1TEtv+tn/p/U5Wf3fceT22Lm/Q4J7qu4fYa/OWL3Ty7PFDYqgL68fPGcvSQNFwOK26veiAI5KE2TbPHKPD9C3fyzZ4qPr9hZrcpNgprWpj1l2+Cvwd6/ny4uYTHvtrHq5dPJiMsEGSaJn1vUd9hct8k3vzlVMbc+Tktbh9QypD0WHaXq5vopL5W3vrlVDaZlcHPN8T256g/fU1Te+ChZ5R66OzbC9KGqQpBzuRQpT1c5thuvyPpIynesYLTXymk8osywF+It3Z96/aw56ONVfDOsw04bTq7752vXuw7E3QrOypaOeOVEp78+SnM6Z8Gho93v1rGQ+VN+AhVHM77qAloYt/9E4I9JS55pABw8Nj2VnQGs/3UaqLb/A3GqYMwBp2Abu8+uHb/BicvriqEFRV8eM10RvdN8H/HURCXRYXHSUL6wFBl+zBM0+Sz7eX84aMdVDVFA/04d2ION88fQkK0nRlhAe/zn13NygNqB633DVEvhrVTbDVDvSJnspX3N5cwuW8yQzNiiXXYWLJHHec15iyKze5vItNeDpzX/cGE1eVwXznkHXM0ZvEGDK8nuB+3NcWw1BjNcL2Aj7eWUmfG8qLPRZ99UUzKhWkDUjlt7M/ZU97EcY8uIz3Oyapbjj6iHhGl9W1sLqpn/ojx6v3r/xVsPN/jmsBrPhvXWd8Lvn+70ZdGM5op+i6+Msax0+wDaJy7LPCOUIVu8yowV7YwQ88h5sBeshKjWLG/msqmDv74jZUXh/XjnKdXE+3L4bmhm/h8byPfGgsIVMJ+u9zgt8uL2H//fKwWnbameqp2rSOvqplPimy84xsFpEd8n6H5n7CuQFUQn1g1Fjc2LPi43lXG1dN6Yc2dCgdDuftN08Rnmnyxoxwj4Xyu/Xo70Jc718CcWcN5rTqWJ785wOnjRvDX1M+CnzvgHM62jvGcOiFLPUhufy80AiShN4xRlebG5ma+3riLBe5FXXoQNbZ7aGzzkJ0YTWVTOzfs6MPKYjfZmpszLRs4Zmgvnt9p8I7PAEYAGuO1PfxhWH3X+Q6sDugfdhIXrAgFQfrNhqI17DeziG/OI9VpqIpS7lHg79m+6kANKbEOrnplAw6rhSHpsSTHxHBN5hAS6ndzoKqZqqYOxvdJ5NnleRSZOXzePILEFZ9wzYwsFu+q5NnKodSaWVSSECwbTl5uYicdNx6unxvLDfMGotXmdd8LK7EP5gDVk7W62c0t723j+BHpnDl+AqWF+2lf+zx9U1xoKQNh+Gn+FFq7/UOeY6CxhI27D/Ba+1BuGzmMRJcd0zTZU9HE/upmFu2oYFLfOdz+8XbAA5TTL8VFSo2Nm4dUMq636k1lxvTCM/gUDta2EmO3kFa0zh8UD2lxezl9kY+KqBb+evZoLnllPdDBuqOdpFjbeHlVIW82x0N6FDsNlY6ptmI/a7/K52PfVBpw8UtLIVGaSb6RTppWT01zWIPJwGN5Yl88TxbtgSJ4YfAA7FadS18o5pfWfcwd0ouRWWEj0OwuGH0utz63lhVGqJK3cTf8ebfJyMRBPH96DinFX6k/aDoMXhBqrOsznT22/RTseIFcvRyjsQzd6sBbWxBclsdnBM+pF1cVssQ3hjmWzeqPuz/lrbUHyTfS6auX89l2lXrm1DWDuHuSSce2DzlxdCaZm16BPtN6SMmos23FJ8H1jdVV4LKlw82OL1/E9PeGyy9TjZEbzYGsNoZFHJc4WujARgf2wwZBWkt2wI62UKVY09i8r5A++zdSrRkErtZCoxcZWi3VLR28s6GIzIQoSswUYmgjXmthw9rlRGkd2B3RaJOuAF0nenkBrZWqUXOHmcsJJ51D9cK7AdWDNJHmbhtq3lkdet7SdS0YAAEoKK/GlxiN22fw8dZS2FrK9XOrQgGx2jxVQQ2knfB5VWNHc5Xa35OvUtdJWz2sedqf0m0b7FsE6aMw2xt4Y91B1hhDSaKJKX1iOOWC6xl7z6JgA1hL8ghOO+18/vrnrymqVRXjv188l8fuviq4ncVmKtlaVbcBEIDhdV9RAaT3Ssdqd0JDAQAHjTR665W0ery0erxU79rJxLoq4pJ66LWf/w0UrVO9gUecrr5LTwEQgH1fqs40Pg8UrqS4rpV3NhbjMa1UNdfRYdpZvtfFcL0g+JFte/YxIcqFU3PTajr4l+945utrGaQXBycS/so3ju1mv7AVmWRQy4mWVazbvpspE1ZiGeAvm01/QNZihbIteFobeW9HPWVV1Zw4MpMBfMBHm0vIr2lhr5HN2b0rmTU4lbpWD59uVddUZVMHK/ZXs74wbGRPD5yam/2rF/Kx5wC94p0MH1MUDIDsMHIjvus/fKcC8GG5h48q8rBicNPAcib3TVadYnoIgqz9+j1WrsunxExhaeNoli3ZwX0zo1Vj1fb3+GRDESX16lzJTohm1uBUmtv9PbJ9Ku3ZQK2YeZaNbN2znz5JJcHy49vmDOqW5/GrOTrW4vURAZCDta28t0mNaLs6+VvswxeooFhLNbGuKJ6pG0er6UQDLPiYpO+mv17KcY7tocBfmOK8nREBkN1Gb4boB3ktbFLqD33TyTczcNHGBZYveW3JRm4Ye1Clt9jymgo81BXA8FODn9myew9fvfxnLJrBVbP64xi+IOIZvrKmjpFa6H5Y3qaBpy0YjKw0E/lHzSxu/kLn42umMyInSaUQ2f8l6llJlXN1rW7VkJM7XW0HsDLhJNZVWLlu7gA008SsL6TekkJi9TrViDdkgWpQWvuMCsZpGu3OVP60rok1vqmUmCm0EwrgLS4ySWYotcRiotN/YWj/6YzGQIe1zZwV5ebWCSowEwgaxzpsNHWojlCf+ibTu76SEXo+i594jb9eOJOKwt18srWUpg5Vzq82hjGvj4UB/nrT676jmalvxYOVFcYIwjs/ndzXJLXoXUAFbeJo5aXVqs6YZ2SQrVVFNDAntRWyYeNSHJqbM5Yk8POBHs7rGxkIqjNjSdSamGvZBIQFQXa8R3PxTvbty8OpwUzLVmp9sTy3Ij8YBJmhb+s2AAJg17zM0zfwT196l7+d88xq1ubXcvrYLPKqmvlZ9FZumpDMCysLuMjyBXd+GLgrmWRr1VSaCYzTIzv4JLvs1LS4g9f5IK2YXlodUTYL0weksKV4P0O0g1Q0jqKsrpnTLMuZ5HCgOepo6ohml9kHgPzqZqAXrW4vc3U1OnZgr3hV1sbngD2aKKBv2wfkr/6QjQfrqGrq4Ixx2aqBt70B9nwacS1EKNmgymRQI7AmXhYc0dXa2sJLLzxJtOZjWU0cG8xBnGlZRn2bh4+2lJBXrToUXGxbTXzWeKjN45MtJVSaibzlm0UCLaRrNfTRKhmodx31Gq6jcC2ggiDPvvMJif7Tqqm2nLKtkSO03aaNlHk3kNW8jQNrPqGPXkHz9oVsHtKPqbpquJ2o76KkuU/E5+yOaLQRp0HRvwAoLDjAm0/eFZGJIHrgTLz7Q+t733cUM/Rt6JWNeHd8xOaiegakqXlItpc0kp0YRWJ4r3rdAuMvUh0h+s5Qnek6d2KKToYhJ6iORvnL2V3eiIFG78Qo/rkijwIjna+NMTRWxwQ/snoj3IsdL4OZrm9H31IAqNFwqbEOni52sMFsIFuLZ6azjs99s6glDkyo02J5afVaFTgbsgB2fYy3tpDCmlaO+9LEa3gAta/qzRgG6sXsq2jm5i2p7DAn0IEdB25seGkmmsuX7eD1MzNUQKN0EwBVzR28uqaQJjOKWK2NnMRodQ6O/XmwjHd7DTq8PmITesOWN0L7Y+Cx6lzNHKfS0uYtVfsuYwyYBsbuT7nxlZV8bkzGgxUDnRkvVpP3wAm4fQZvLd3ONFcpA1Jj1Hleuokvlq7itYpsVr7TzPqbT+fVN17h9YIsWogiadmnXDGjH88csDJ86jEkZLopaNA4c0B2qL6eOQYGzVfXTljDelHabLaY3Y/a2l4AFABr68nWGvjHsEb2VDTxlnc8pfs1blvtZrrVxRhzN2/7ZlFJIutvO4cUT5lKMW11wNa31CjvISeohnFzvtp/7lb1HJn3DfnVLfyltB/einIu7t/M1H7JMOESCjpimP3QN0A0qsNpPVDPxdNzWbSjghIzN7it0z8O/NSfEl8Kg/RiNhoDefPZcoZnxrHwulMgqT8cWMxa6zj+8ZXGzEEjuPSok7p87z9+uotnlnlx0A/ox8nmSm5c3MxHhosk0si3bubCKbnctrKNz4x6/3Ypn10/g6GZYQ3ZqUNg3XPgSsHMncHCbWVM6ZesslKk+Uct2mMwHbGMunsRI7PiefbCCWpOp8Q++Mq2sq+iCdeQo8lu3UWbsxcj3o5W90TSeXa1Wo3F9DFg7cfYNC9veudQRthobGC7ry/Pr4UO1LUdSyv99FKe8ncwqzbj+cKYhObvgmwU6EAa7240MTbqrPzNZeyp8nDti8t4Zdg6NUpowFw4uJrtew/w1e4KHveexpz6Jv55w9lUtxnoZQW8smglS32jmarvoNRMITXl3x/h+r9KgiD/pXRd44NrpnOqf6K08AAIwPA/fBH8eVVeTXCite4mRWtz+xh65+f+Qm0GO0sbWV9Yy50fdl+ID7nj8yPaxvDJ2L6PQPDl8hl9WZ1Xy57yJl6/YgpnPBkZ0c+9eSGvXz6F69/YDMDUB79m853HBIfyhedSXZNfS2FNiz8AogQCIABr82t5b2Pkg1j4JHUBl7+0nq9/PYu0uHhah19CtMNKTJd3HYKm8XxBMpU09viWaLuFVnc3vcGAdo/BlqJ6RuckgC0KwzBZ8Dc1KuXi59fx6mWTmT4ghV9/3Qr+Rs5YpzUskAM3vbWFx88by7biyN7qBjoF1c0M83cgqet7Esc/tIxhGXH8bHIfvtlbyTVzBgQDTZ/4K/wAp/zj22BQyjDhL1udPPnNAeAA0wck8+LFkyLSPXRWWt/GnR/uCPZy7pfi4v7TRjK1f3K373/t8imACoAFjv/hrMmvYU1+DTEZg4KvtZvffZLGhdvK+NcXdmZZdjEgNYacpGg8pgV3WLEaaAYorGmlsKaVtzcU8+m2cr7cqb5feWM7a/NrmdwvORgYuffUEVwwJfLBfNWBGs4L3Jn9TtH30Vcvx2W38nGbC4P+bDH6M9rfS7HRjGatOYR1vsHBIf2d/fnMUfzuna3B9ooWU/X+fMvfuxagFQenPaGuOTexXLVrJG3+YfqdHffoMmYPTmPZngoW1KnrqMnsPq2OEfZ5j/8c9WHhby1zefxLMGhmy2x1vv5zRWTaqkff3Bax/r9/vT/YI/i9jSX89mdTyKheTW3aJE5+00OLezM3vLmZT649ihHOsAbpsPQnJ/xjLcV1bWzI7cu9w8upSp7I7l1bSfOEGnpOGZ3Fe5vLWOlTFaI2UzU6fLmrgg4zkCpBbVcbDr7eU4nLYeXFLU0kaU1cOKUPq0tbcMeUMK53Il7D4MC+Rqb6vLgcFtbXx3D/lolsLqoHRnDvKcO54+kdwKf0ZGeZKkMO6pv5eW4TGw6qoF2HP/Dcjh0TnVIzhedWqAacZpxdHuhAw426Nzy2eB+PLd7HKfoWHpgVzbP+kQSXHdWPA1XN/O3LvSx+PXKbvtpVwW/e3sLodAdzqksZk53Ays0Onnl5IXedNIyfTxlGcV0bNz73LZN7Defz3RYKTCvv3Ptlt98r0HAZkFfdQj6D+WRvAWNyEqhrdXP7lwV89m4o4HW9ZT/XHt0/Yrj2s8vz2GOOhVYPl7wQGiny8NJiTu4LdW1u2nCQXxYqi7eYA9jiGxD8/V+++djw0oqTCyyLKKptCQbyjdSh/OXvS4Lvvej5df6f1LmxeHcFi3dXBEehFbbZmXVLaN8luezUtoQq2NvqHBz73H5eHNGggid2F6am8fTSAwzPjGN8n0SKmjXqUQW0t/oA9v1fkhfWi3ZfRTMD02LZWdZIiSeGPDOTOWzG9JdIzW7Vo6sv4SOjNB5a28alVh9vrS/i4mlW4vd1f2yaO7x8vbH7VHI7i7s2+F577AienjSPy19ez/HD07loWi66rvG3xfv465d7WeSbwLGW0LHZYvQnXaulzoxliH6QHEst63eqOSlSXA7G9m7gm11dR09l9krlX2UTGaEVML1+O8X1bawxZjBe20u81oKtSZURbbakYFApLSWFAn8QZHzfNM6Z1Jt/+BsMnUnZ6HU7u6yns0Awfb+RxQC9hFV5NayihoywtCvbSxpZvFtt8ynOwfSt2q3yeg8/VfUoLFLnjdvq4r7PCzhmWC816XhMqgpo7fkMr9eDtXg95e2qvCwy0/jFzy7k+BGqkW79Hcfzlwe+IU2royla3UM+uHo6n+8o57jh6Wg2Bw67gw63avSbPnEChetD10+JmUKWFgrMBXqCNzoySYq2oGrvUE4SSWZTRPqw/VtWMDLNhq33RDUSNFyggaBqj3/+g0J8hsmStn5kZvehtNHLXZ/tJ55W5uibuHx6b+LrD4K3ndK6Jp7fUMN2Yzg7zT4kaE00mi5acFJjxpGqNTBUL6S6rp49+aoxtdJMZNe9J/DIMyVQpu5DL3qPpY7OPcI0+g0YxDcH2lhgWc3ubWsZ7g+C1K95hdaqQjJnX075wX288c1+lvjGMEp388m2Uk4Zk0V+TQumqVFoprO5eD2b/al8wh1JACTAV7aFA8CB6mYSolRjZpWZwJfGBDxYGaPvZ60xJPh+NzY2mIMB+P2+BK6s3sDZs5K7v9sbPjZs3gzA176x1BBPE6pB7fq5A2lo81BS30apmUymVkNxfSuvrlH7s8O0Byv6O8xc5pqbqGxsY21+6FxZYwxlmF7ItuIGxkbvZktxPc/sslNLLJN1FcCyaV62bNnAxOELoDaPRTvLWVafSpEZmYpxzmmXcuCDB6GpCrOxBC0+2/+FVR7zovWR956tRj+G6AcjXpsyfgKXZCdyxwfbKTFT6K+V4mmuwVd9gOe+2klucjQLRqJ6pyeolLdLVywPplF7a30RF1g/V4H7KBV03753f/Dva40hfLo8nhMse4LrrDQT8Pif/078xyoA/nH+OBaMOoWv12+P2L6WugpcSeoe9/A2B4+XqvL0xVUFbLzjGB5a18ET3yzj7+eNY8GoDAzDpMNnEDXxcmgux+dK55rXtrG4tpLUWAe3zBnQKV2SRk2nOaOCp0LYGbKkbSApK5eyw+xDnZnEIK2VnA5Vnn3rG8FeM4daM44Rej7pWi01+9fx+rrIfV1lxvP3/DjOtuxlszEcHxaWGF07gG2+8xiqG9v45O8qCGLHSyPRpKDqIHlmBguNKVgweGPoSpbtq6I1fx1xmhu3aWOn2Ydb9uqkDU5irvsb/7oT+Mg3jYstn5OtVane5DFp0FpLTcE2VuyLTDOWq1WQHJhQ3DSD5d0aYyiT9V1dtjla66C3VtFlUFNghMd7m0oYqh3k/P4dJETFk5UQBfUtxNJKtlZFDG1Mt2znoJFGCg2kuBy0un0kumzEOW3BtLsAFs0giSacNjv2tEGMyamGok1UNc3D2lRCktZElN2Fw6pztGcT49hHqZlMUa0q659blscwXV2zE6bNVUHnMLEJocayorpWOrw+HINnw57P1Ogjn1cFfTvrPMJ23T9h+nVgd/GPdz4nWuug3ozhY2NaxLkVCIAAvLt8C5dMa6eguARQ6S+9WKkmnmozng7sDCRU915jDGWkpYhoM9SrzeltwtvWhMVmJ9GfYjMw6qixRLWBrDWGkEAzW4z+LJ05gK+WRT5HNlUUEBUW9OrlKY6oyhho6FGR94nOqZijY2KDNXevaaHQTGesrYQUbwOLdlSwt7KJTQfr6Z8aw9aSeqJsFn758/NVtobAHAXhcyo5Ot+XCI22yRpPYVExV2xt5XRLaMTKB8ZRXT8DNPpbItoJ1Wmrmjuoau7w19ug2EzjtbbIDgs1pqoL7Spv5OrHN3LphBHs+Hoj64zBeDud/LWo9366vYwd5sTgvaGD0H1ilTGc3LdgtLaHKzLK2FcZej593TeXMfp+qNtNc1x/YsKC3Cf/fQW7y5vY/NuJJARe1PTQ/grMn5IxmrfXF/HMG8u4anZ/bnpLB7ruk/s/3cWGwjqiSlqo1Uvpl+JixmA79D2Rx99sYJ+p7i0T/rgCyAVUFgiAZ5bn8Y0vjkf3bwgu7/fvbiXvwbCUe7oeMTKqtL6NWU9sp3P9+LxJvXl9bWS5WWfGBp8dq0Lflm+9Q/iWwcFlTLh/MT+b3Jv7T/MHuYeepLItJPXjrXVFeAyD8yb2RrdHs1vLZfnyPFrcXpZ7RzNet7Imv4bJfZP45/p6Hvii+9Hyz39b0O3rATXEs8oI3U92lDayNr+WSX1H8OzeaO7/WJWdS/dW8eXOcq6aPYBXVxdy36kjSItzstDfThQ4P972zQ4uqwX1nPzS6gI6CO+kosx/bDlLfjObzASn6kxrd8G0awF4f1MJN72lRkslu+yMzknguV9MQNM0vtpZQVO7l5UHajj9iZX87byx9E2O4dsqF19tr+LNLa146Tl9qA8LHxtTiaGtm/py6LsENBHZUThQFgayXyhasO1j2kOr/K9Fc9uu3lyfNZh51mge32rB3F1BiZmCDwtfVSVw/r82sPJADVEkkqzNoNhMY5uvL2dP7PP/XVqx/yYSBPkvNiYngbtOGsZdHx++kh4w6i7VoJ8Z76S0IbI3zY7SRnJvXtjdx35Uzy4P9bzqHAAJ6NxAPPfhpRw7vBfvbiwJjkIJCB9F0p1AgXo4Rz+8lIm5iawrUI2O503K4cZjBvU4Seza/Fp0TfWc/mRrKflhD4idFfxxAT7D5G+L91HZ1M6tJwxlf2VzsDEaVCP8aP8cIf1ujawU/uyfayJ+f+is0ZTWt0VM3vfxllI+3lLKZUd1zU9+oNkeDIK8u7GYisYOKhqrWLJH9ex9ZfXBYLAjMLonoN3jw2mzcO0bm4I3PlDBqDfWFfHzTg38oEYcvbyqgL98sYcWtxo+fdXs/lwzZ0DE8PeeHDe8a08tgPevnsaAtBhK6tt45fEvg0P0l/pGE1/SzBj/pnfw3UdJXfv6JjT6U+VL4MyqpeyvasYgnfCHH7ObQEEgABK+nLW3zQtOBHjHB9uZPSiVnKRothU3cNLfV3RZhtpmdQNucXuDFe+1xpBgEESlc9OC2zB9QHKXyRVPG5vF798NBUE83dwSvJ1eq0Q1CDx01mhOH5vFiv3VXPgvNcT/QFULB6r812unRX110ywW/G05HV6DBaMyIKy+Hh6kCV/f1/sbaGvrZnhUp/36ZljQBmDaq81MShnCmuq2iPee+PgK3j6mjYm6Ok+ve2Mnizoil/VyQTyJ/Sfwt5f2cYpeTF891FD84ZYSfGHb5w47bzqfQ4HfP95aSo2ZTZLWxEurC2k2o/hn3ubg+wZqxSywqAaQ5707aQgLp97RQyC6OzVmHBsOhh6yy/0NmLrNyclDMmnZFuol6jVD19R7V0/jzg+3s72k+4BsIACyw8hl+b4q9lQ00UrXNCEBW8rbmWOFzcX1HDDUuXLXxzsj7lObDgL0PMloT0x0isxU/vHNfhKibLQRGWAz0Phkq0rNMqlvEqFiuOt12GbaWVugjq0nbKTcORNyupxPHqx4sGLRNXxYaPX4aPX4cNmtLHiia3qL7tb5/uYSjhnaiyd2tAKheaYW3TiTCfd9FfHeduws3l3B0r2VVPhiKdy9jmV7qyLeM1VX179ZvQ+sFrb6G2A7THuwN/eineV0kBJM81fZ1EFpQxs+wwy+Fq6JaDYb/RmjH2Brcb1qhE8dHEyZ2djWwde7ytl0IFAKdNXdRLVWq434aBtv/XJqxOvXzR3IdXMHUlLfxvV/aucoi2oobDBdLDHGYsXLEP0ghgEr9qvGquqWDr7sJgACkBjjpA0nW81+TEctq8mMok1T39XTrBqutLBRjVpY+qNjRmZjs+hMP/1atm1Yzi/Ovpi//Pku0rROKQW6sc3oGzHiEqCsMfSMtfJAqDFu4WcLmdoviS3FDZwZu4W4ptAzzvKCVl7aXshLqwq5aFouN2VEEwcU1TTx7sZihqbHkRCtypZmaxLHDQ81ICe67Hzkm0o0HfwuRd1nk2Mc/Gxy6J4b23skHftVRTguITLd2QrfSOK0FoZqB4PzqwBoFhs2R2ifzR/dm5IdxRD2aLVs8ccsAwb228KCS26P3Dnhc/LkLYG2Op5efoBX2rMoJRAkSKMYyDareH5lPr8aUorF185b64vYbw5kjanKnCYz1OCw0RzEBFTjV1LdVnSvShGQnZaE02YhIT6BDv8jSDPqc/vvn49F1zBNlQPaabNw8qOtmFVrqKosVw3tmoUPFq+gvs3NSYnL2HtANdw1EEot8uFm9ZobGy3dXEv/rkAAqtnfMWG5MZJ8M52DZhpb/nAsu8oaOfeZ0HNvnRlLeWM7y7fuY9akrqMnvM3VdLg76DDtnDp9NK+uPYjFn4bqscX7GO0fsdhgxgRTqgU/a3NRcL96Jv1kayk73l5IVdg8BwDZmZmsKx8M+/bQJ8XFkj2VNDGQ1cZQKs0Easw4LrJ+QWNDLXjaKdm/jZ1ljeSZw5naL5krZvXj4ufXcdXs/pwwpg/Xv5/JUO0gH325hFPOvICl337Lxk+fIzUtnY5G9Rz3jm8W7aadOeOH885GkzMtS4Pbc9KYbKYNSOHnk3tz5e2qESuvrIq3P1pKjGawr7KZ7SUNjIj5AiZcCpqGzd1AB6phlZYGTEy0mgOqZ6nPQ1utOub5RjorjRFoGOQZGfTT1UkWfm4GXPPaRq55DRy4uSrsmejVNQe5LDmLvy3ey7qwUXK1LW4+314WnJT7129v5vgR6cFOYYN6xbDoxln86dNdLN5dicOq8+yFExiTk8AJI9U99b6FO/lwcynXzOnP9P4pnN+pPhCumnie9J0c/D0j7Nj7/M9ldcRgmhp2zcvL/lEbH/umcpJFNeK0m3bqieUZn+oBPDwzjh2loeeJ1FgH7145jYRoO9Fh86X0culU+qLBn740NSGG7XUWMhJjaOkzF/a9QVyH2uc1xAWfE1/ea2Fvygz2Gq2sNobRRDSFZhp9NZUCmJg0Wkp3BrcVQqPexuj76YjxP6t1NGHTvBimzkEjLSII8rlvEn20CobqhaTSgBHWEOwJS98WQyvnJu5mWEY69J6C3V8unGL5NiI9bW9dNXY6nQ7O/dnPMXUb73+5BNja5ZiY9hjoPQXXBvVs0VZXTly7vxExLhe7tQ6b1kEq9aRq9dS0qnvLosVfMNeiUskljJjXZbk2Z2RXvfLGdvpkjIb8paoXeXO5Sk3UUKJGamVNUPf+QEqdhN4qnQyoEcLpI6g+uIsMYI+ZExEACdhgDGK4VoDd64X2evJK1ba2mg4K/rgg2Oaw38wkz8ikn66CFmdMGkDhrjpoCQVBDNOkvKqK2FgVNPCaFhxJWVDXQEObeu5oMF2sNNVk5laLTpQr8jvHlke2FYSn0QTwmRo2ZzdBiTCOqNAyfVj4+/lj2bIkD6ry2etv7G9xe9laUg/Alo4Mrljh4qm+VjUhvd4pq4Kt6xx06Db+tSKfp5YeoLIpFug6X9X7V09jc1E9L64s4Jez+nPLe6qzQVZCFINjU6AscoRtd/XcNbfOZXVeDde/sZkvfRN4Z6uLYrOV279oBeZGvHda/2Reu3wKd3+8gw9XTvcvM9QQ/LfzVODz129txuNT14uBHhEAAdWxbrUxjCozgenxM5kY9rdAp9RvC5sJhhr897LKpnbcXoPsxGiW7q3it++oa6dzm820/snBDBGBjl+jNLWP86pbuP1vG2jEBZ3qDwEGOhVmIqk0UNApQG+Y8NhX+3jkq72kxjpYc8tcdGvo+N1TNoHAs7/NorHi90eT7LJjtegMzYiN6FzcQhSf+KbgxdJN3Tvy/v3qmoPMGJjKQ4v2oAHXzh1Ie34xv3tX7YPb3t/OUQNSWLG/mim66uDZRDQ7jFzG6Af4/eI63vbt50h8fsMMjn80FHAbnZPA7QuGsnBrGXsrmoL79uynVzF3SBqLd1dGfH51Xi2r81TZtWhn98/q4cLPS6ObuhrAnIe+AVSgY9agVJJj7AxMiw1+f4CaFjdf767k6IeXctsJQ7n8pVDAZ09FU7CNBQYA/ek5HX/IwbDj//xFE7n4BdVZ6bPrZzD/sdA+inVauWhaLsuWjGKmRW3TUt8o7j55eETnhFvmD+HBzyI7rQPsMPtyxZdu+FLd51OZS33YNR/Y5204KfY/E+554ORu2xrFkZMgyH+5i6b3ZeagVE5/ciXnTMjh0hl9mXS/GlZ59ez+zB2axhtri3h7Q+Tohs4BkMPJf/AEalvcjO/UWNOdAw+cQEVjO797Z2uw4eI/Yc2tc5n8QNc8+KAKw9fXFnX7t56Ep8XqbHhmHO9cOY3r3tgU0YgdCIAAvL62KLjOL26YyeD0WHyGyaaDdfRLjeHsp1d1WS7Ap9fNYFhmXPBh8J8XTgDAomvceExotMLY3om8e9U0nl2Wx+c7ynlmWR67y5siGkF6ctrYLHQNNh6s45s9kY1p/1zRNd3N23UDOWlEPWSOwbeta05qUD21txXXU9bpXNpSVE/fFFdEACTg9g+2dwmC7Cpr5Ob3trGlqB6Acb0TePD0UQxOP8QkpZ04bRYeOWc0VU0dDOwVyw1vbObGeQMZ60+bM7iXlc98kzhK38YqYziVJDJbC41Uau8U0T9SJnpEGq3u99ShVTZ1sLM0sgF6xp+X9PDukO5GUrSFNcYcNzqHnZvVz+lxTp77xcSIUVy/nNkPm0Vnx93HMexONXKs2uy+52B3JvdNQtc1Zg5KjXg4CCg0etFHryDPNpDdtx+P02Zh+e/mUN3sZlhmHH+/3463retExOG2lbXg0nouq8JHw4Uz0VlT3f0x/ctXBTwwtplPtpbS4OsmhR3wt7CJGjvzYeG44b3QNQ2fux0CHeItNgoeWMAt723j3Y3FeHyh22t4g7OVyEpX+MihtiNsTJvSL4lLpvclyWVnxf5qCmta+XiTl2ijg1IzmRMtq6n2p2zKTk3k0mMHc+u20OjA8HWO653IJ9fOoLnDS0VjO6ZpMu+vy7qs040tmLM7PIgCcNXs/v4RX3C4IOCh5CZHU1DTSmK0jbpWVal99bLJvLexBJtF4411RXiw4jNMalrcwUaaeUPT+GpXpRrFVtNCQU0Lm4rqOHFkJuuMwd2uK7zy5gv7PvefNiIYBBmRFcej54xl3l9V49ot84dQ+tXX4IM95U0s21fFLu8oAFx2C1mJUeytCFXa1xhDGagVU2EmMVQv5MtdFXiILP9SYlRjgNdncPWrG1m0swKv/3r2+ucg6RwAAfD4tzkwT0lo5I8Nn9HOjlLVAOM1LehhvTsDo7x6OtcaTFXB9/hMGHQsZI2nrsXNU8sO8NLKQto8iWRryZxp6XqO9ES3HvpRMyshir1mDkf5AxeBbfNixW3aqGtTPTFrzDiStZ5HT1osqjLVgZ3NxgAs+KgjluS4aGiGjlb1WUtYEMRiC50HToeqXEwcN46J48YBKoXSHMtm9hlZwcpNd+rMWKb3jYGD3Vc02zy+YNoWr2Gw3P9stHzLXhb0U9vtM03WF4WeQV5YWUCRvpcLetcFKzu7yhvpnRiN17Twq+NGdOkFdstpk1hfUMcpY7qfxNdmD1XYo2MiQ1keazR7vMmqR3UY3WbHag+dL7rFgs2iRQRBAvblHYhMYeTz4PW42VPeRP+0GJwH17C7vBG316CZro0/tYEeqXmFOFDBkzr/OfmnM0byly/2MrV/Mh/7R4oFAtEer4HHrc4Tm0M1Rken9qbDX9/0YGXGwJTgSFRNA6eurqFLZg1m4zsxlNS1QVM5m5d/RL3/nNu0+wBGq7qWVMqmyLu8T7dx0bRB1K/qeh86lAozkQRaghMAdxYY0ZeWlMj6K+cx4b6vKDTTGdwrlvgoG1P6JbP//vl8vqOcZ5flsbNY7av2DrdKI9Ypv39huapEeyxR3LZgGLecMJSL79gc/HtgQupB2alQWhjxWd1/zlh0jfF9ElmHk3ZP6Dp81TuP0weksLZMvW+p/xmz0XRhonPAVOm5ms0oNeqttYa9hcXB/fDn4wYxvk9SxKTWxWYqQzlIbaV630cLPyJHN6muKot4D0CM08rI4SPZtbOAof6e8Onxals0TaNPr2SozMdXuDo4eukd3yzYvZQRWfEqzV5MKt4WFZBbZQxjnK5GQ87qyGBg0jAeuf83wfljAvMFmuh8ZEzjBl2NbPjFUf15c8ECfv7PNV3qPp0bIFs9XipKCoCuwZMrX9kY/LndYwQbgAD2VjRz9asb+HSbClI+dNZoxvg7Q6XGqmv0sXPH8ti5oZEYV87qz1NLDxAuM96JzzSpaIwMZoXfF71YiLJZ2HXvAh6/dxG+DtVxy21aIxqHvJ2Cv3edPJzLXlzPkPRY3uwU+LZbdVpMJy6tnYNmGklRgH8TzpnYl9nOXKYPSKa+KLKhaEifTJ6fNZGLn1/HN3uq+GYPwJTg30vNFDWysVGVC5V5W/2vJ7POGIKOQbZFnZdD3duBYXQ0qWPUgIv09AwIO2QVZgIJmrqXx2stNLV7+e3bWzhlTBaD0mM4St/GYO0gBjrHDkpCi8+EvrNoi1oB5PU4P59uj8Lae6L/59U9voeEHKLt/nt8UxVOj7ov+GKzwLIttNOAqI5KTNOkj6bqpvW4cDiiuizXERU5+fn7m0p4dN2nvDlJY3IiKv1iXBZse9s/t4AGiX3Ir27htc01POsbxbH6Pobphfwqtx4rkG5R23XT6bOYOGEyAI/c9i6aZtJkRrPcGEUfSwWpFjVfZFS1aqifM1I1QPdLcZFX3cKTP5/IN6tNKFDpHDWbHavVFixtA/NwlFXVYJrq3mJaHPRKTKA1rI9CIBBzw7yBAMRERe6HFnfXuTwCc7KAema1RR+67ml1hO1HXefEUZnsWNk1AAqqw90mcyDsqubYR5exv7I58u+/nU3v+Aw0R4yam6VBBdGqWr3c80nPHVx33H0cLoeVsb0TuXi66sh43qTeVDa1E+uw8fI7VbR2qoLPH9uPSxbMZJx/9LWuQa84J6eMyWLp3ire29h5LSFnT8jmz2eqdLGjsxN43gx1Ynr0nDGcOjaUgnFsTkKwDltidpeqR/WG32dmc9Y/N/DUz8dx/Ag12i3gV2/tYMFxag6Rvy/ey6OfhTroXjw9t8eRC0/9fBzHDktnW0kDp4TVDcPrIs10vTbCvXLpZC59zo0dL010Pa6PfKWCS1VNHVz0wjpeOtbfIQmTgobQd/jTGaOCc2sBXDg1lwun5rKhsI66FjeXvbSe/f6RKP+6aAKLd1Xy6hoVZIyyWfjr2aO56tXQQbnyldCIlOu6ybQSuOcEUs9adI0qI5EXvcfSGvas1TfFxec3zGBfRTMHqppxWPXgPWfF7+eQnRjNqWMy+WBzKe9fPS3YjjLRPw9ieGfp8ACIVdeCdZYjtfC6o3h3Qwn44/QmOl/cMJP+qS7KGtq7tIXUtLh5b1PJIZeZX93CZWEBkO51rZs+ePrIYCBx5z3HsWxvVXC//O74wcwZksbKm4/Gomv0inPy2+MGU97Qzh9OGoZF16hpcfP414PY6B2IHS8JsTH8Yloumw7W8cHmUn4xtQ+/nNWfC6b2Cba5AJw/uTevrek0utLfzaxzBpeA2YNTJQDyA5AgyE9Av9QYNt95bPD38IoEwPg+SfzlrNE8+Nkunl7afQqL7vxyVj+W7qni5Usno2kayTEO8h88gQ2FdaTFOtl4sI4Yh5Wjh6ShafD41/vpm+LComtkJkTxymWTj2g9bq+BYZr4DFPl7POrb3Xz6bZybn1/G8kuO1fM7BeMoO69bz52q84zF4znipfVjeG44b14+oIJHPWnrymui2xcvemYQVw3dyCvrinktvdVQ8vqW+ayrqCWvikuTBOGZcbx/Lf53LdwF2N7J/DeVdNobPMSHx2qvDx74QT63rKwy9Dozo57dBm77jmedzcWc/sH24ntZoKwgF5xquKy4vdz2FfRzOzBPU9wNL5PItbZ/fl8h6oALdtbFdFA1l1hCgQLyxcunkRFYzu/eXsLy/f1HKBaVtjG+1MmceOfI3tYzBiYEvxc55twIK3Lz59bw6VHdR3SGBBII9Pu8fG3xft4ZlkeXsMkxmHl9/OH8LNJvVVvme/otLHZwZ8333lMROOQpqnUAB+GDSG2hLXgeDpV4n4oR9oIfM8nh+/x/8qlkzlqoHqgdHsN7nl4F/jbywKV0JtPGEbzFx+gawYZ2QNgswpwWS0aTpuFpy8Yz1NLD/DHsCBTtN0aLDMeX7yPT79qZpa+JZgnfve9x+M1TKJsFm59b1uwgThQ4QaYMySN1bfMZcqDKihp0TU+MaYSb7Rw9JjhwdE8aXFO0uJCDSqBW3tglMofTx/Jze+Fcqt27lkdsPqWuSS6bF3mmRmTk+BPJRXpL2eOCvYa8mLhk62l/uV/9/PMh879p40kJcbBzpJ6Fj2pXu+bohrqHjhtBHefPJxPlq3m4NfLg+sMMNE4ekgaX/sfHlvM0AO5+xC35Ld+OZVJfbtOVD0hN4l2j4/3N5WwzBiN1b9XA70VdVsUcVFWPGZo2XHRUVR1GmAT47ASk6q+Q2Ak2o23hyoR3rBefjMG9+LtiyLvMwlRti49XDLSM/n0zBmc8LfIiScDbpk/hF9My8Vu0btc8x9uLqGsoZ1p/ZOD+bvfWFcU0ZjktFspuEttx+q8GpY990HEMhbtLGeHMZIp/ZJYnacauBKibdS3eiKWEzg+Z4zLxmrRmT04lW/2VPHgaaMYkBbDUz8fx6aien4+pQ+PfaODj+DkuwHLfjeHJXuq+M3bqsz8zbGDeGgRrGI4k7VQD1MDPTiC86ubZgZft1p07jp5OIt2VkSUR50bXQMC10ar20e03Ro83l4stHt9wdESPnTeuXIaNz1RHOy5C3DBUYOoX9U1MB/YFx6fQZNH49lFe3huRX4wheTonATmZcXgW/8dgiD64R81n798Bsuf96dm0q0EYoUd/sklQQVH3KYNu9Z1tAmApluCAbRvjDHB15NiVRDE16FOemtYIEAPayy2O7oGTitJ5E3fHFI59GgQD1ZG5KSwq+vtN6i7npitdeVATnBy5TIjclSjB2uXNEsH61ppJ4qBvbo21vxscp+IkR+dWcICUo7oGDRCwftohwO8XUf/Wax2NEto31gsVix6116/QR1NKpd5fRG4W/h2fw2biuooqGlhwchM9vg7m7R00xgRGG2xp7CYJLs6p9ssMRTcp67zsyfkoGkaTe0evtlTFdynHkPlEgewOdVy49P68LpvUjCI0j+1+6Slk/om8Y3poqq5if3F5XyzPhTsamqow+5TDVfnTBtMzO6ttDWGAlW6zUlstCMsg/WhVZqJ6Bi85ZvNTH0ro7UDh3y/MyqalBgHfz5jFLd/uJ1fHxvqGGO1qAa4af1TuP71jRj5umrk87Z3CYI0NattjoqKRtc1dDRSB03CvS8/4nqKjY1nSafUQA57aFmxThstphOfqRp5a81YqkigX6qLZf4G9Cb/aLCEXjmEZ9xrxUlVUwNGUwWldS34TJ2bT5nI+D5d72s3njKF7Z9sCAYm4rTIkdN7jdDznobG388fx5wHyuhotbLDyOVXSaHGK6tT/fztbnXfN0ydYjOFcjOJpg4PsW214ErB0l6PDxUI3mgMJMtSzVvLtzHKlxMMgADMHpbFcz9fwJ8+3x0W/AcjRgUeX7lsMm6vwec7ysOekzVW+oaTojUQrXWQrVUFGzcaieLNK6aQEG0P660acrA28mYdCIBcP3cgJ43uPtgZ7ub5Q/jtcYOx6BrFda1sKKzj5NGZaJrG/MeWsyssFWT4fd6HhT+eMRKA6GgXTf4gSAd2PFipNuOJpp37fz6Hhg749dtbOGFkOhNzk1j2uzk4rN2XEe/4ZjJML6SIkZxuCz372uw2fjEtF4A9dZHXqsMVz7Qe0uJCKEXPi19v5heD59NUqYJhC31TaCEqmHILIK5NBdYWrVPrrjNjOG/GcN59dyipNLDRHEgdccHgVCytPLRoD03tXt7eUMyy385hgq7SoI3MiicjMRaGnAS6jmmLDDR0ZlpDZZ7VFnqGtlv1YNYCn0Xdn1oShwAVWLzNWLz+e1dULD7dCYSuB4u7mVa3jzhNveeCn13c7bodrsgRDvn+e82/1tcyfq4Na0ejKrsDkys3FEFUAl/vrqDen64n0Ji6dm8Jk3sbmC01aEBWdmh069u+WRxl2cY3vjGA6mTW6m7ErNxJq0eV0S6XOl6vXT6FDYV1HDe8F46WHPzTWKBbHZhhzw2GLQa89XS0NtPREe3flw5iYlwRU2leMK0vvtRhnDku27+PI+9lHZ6ukXuPFnrG6NCcOO12lvlG9djpIVCeQGi0lNXRfRBkkzkw+HPnAAiEMlP86dQF6D4L+sY/U9viZlN7K+FBPiA46qzYTCWqhwwJabHq+EQ5bMH9ssI3gmitgwlJvUlyhcryCWHlbrKr546At8wfwjkTQ8c3vO73pzNGRgRAAHKSopncN4kDVc2M7zOQN3Z4aMXJML2QYjMFm0ULjhSByKBvgInOo4u7nyuupwBIjMPKhFzVOW90TgIXTcvlhZXqvWWmKjvaTEfEiKUrZvbjmWWhdrHXLp9MSowjmNrrvEm9uWJmP3aVNWLRNX758oaIdS7bW8WCfflcmFREa4eXwubBgJVd9xxPlL37YzS+j2rg/uvZo4OjWOKjbPzhpOGcMiaL8X0Sg202fztvbLcBjyPxxhVT+NvifSwP69PntOl8/etZaJrGiKx41QmAru2Gj5wzhofOGt1t+vJ1t81j+h+/xu2LvJZW/P5ofvbP1Ryo6j7LSU5SFEW1bYzOSeDNK6Zgs+hYdI2i2lY2rnYQrXWQkNEv2DaRkxRN3gMn8OTSA/zliz3dLhMig/x9kqMprAmVCDfPH8IFU/rw1a6KLinTe8U5gp0AbjpmEGeMy+a1NQc5fkQ60XZrRIaRwDyUmQmh8vuaOQMilhea90ellnbY1L579NyxEfsy2m5lZFY820oasFt1Lp/Rr9t2O4DHzh3DJS+sZ3hmHB/96qiwOZrFD0GCIP9Dbpk/lFvmD6WsoY13NxQzLDOO0dkJJETbg2lDuvQqnB+Z9kTTNCb4o8G9kyNv+tfNHcj30TmdUkBCtJ3zJ/fm/MmhIYtXzOwXsY3HDk/n/tNGoKEF3/fZ9TO48c3NfLVLNTL+6YyRnDNR/a1zA0HnCsRlM/px2YxQA354ACRgya9ns2J/Nbd/sL3L38INvfNz+vj3UWACwe4ECs7sxGiyE7t/kAo3OieB1FhHl3QED501mjPHZzMiM55b3w81JCd2+g694py8fOlkapo7Djmy58Y3IwMgX944k4G9Ynnh2/wuKdjG9k5gdHYCL6wswOMzI3qdpcTYWfKb2Yz0p2K74c3NnDMhh1vf30aB/2Z13PBe3H3yiGDvvX/XkeRItIYFQc6b1IdfTOvD/spmXl97kJ9N7sPVr25kfJ9ENhQePhVKcL2dfu8pCHLc8F64HFbe26h6NAQaaLMTo7oE8OxWnU+vm8GAtJiI1zISXXiCQRArV83uz9FD0jj50xNw4Obu2ASVvscwGZIe519veo+pwwCiHVZ2mX1o8Lk42/oN643BEenIrprdPxgE6ZymLD3eScEfF2AYJoZpMuC2z6gm/rABQ4C/nzeOg7WtjM5J4OOtpcG0Xd5uhtcH1hVQ8McFNLR50DVwWC0Muv2zLu8fmhHHN7+ZzU1vbcZXFFqmgc6G2+exv7KZc55Z3eXhvLujp+mWYIUhxhnWi9xf2dI0DbtVIzE2msBjTZwrCvyH1Wm38K+LQgPAn12Wx2efVfjzonZdY6zTyiXT+zIxt6cEROpYnDQ6k4+3lHZpwNQc0cQ6bREN679dMJKn11Rx9ewBnRcVZNE1XAkpat4CIgNScdFdRxFcMbMfJrD5YD0f7ZxGrlZOVvY4hmVGVrw33XEMY/090RxWvcd0d6eM6Tq5b98UF0ZN6PglukIPpOEVswC3z6AVJzMGppIe5+SDzaU8ft5Y7vl4J+7q0H46anA6j1wcGvr/1M/HU9nYEbzHHT8ig+NHqJ5vdqsW3gkTgF9M7UNyjIMzx2eTHuekvs3NzEGpPLRIVeLCz+PB6XFcNL0vF03vmoYwMyGKpy8Yz6oDNeDPshXIJrv5zmNYvq+aa1/fRJTNgs+rllnf6iElxkGz/x7TOSWF024hI8HJATOLBtNFvL8xMd7l7LbhdlBGIlSqEQcPP72Wvaaq/A7PjOOmYwZx9JA01mzfw+rDdbQKY7EePt1gn7REAqEyp90avF5UT3/1i6FZ6TBtwQaLzjSLlUfPHcsv/hWZoiwu2kkL0OZvZApP7RS+bTZr13MooHNv584yk2OxHOLz4J97qtMl3uExqG1x89LqAiDUqPLa5ZP5+9f78eR3/5jejp3+qYducOuObgkP+kTeb+8/bQSXvr4bm80OYY8sutUWMZpHt1ixWfQejgKY7hbyt68iuXINTpvOpiJ1D93nbwSqb/NgmDqnjOsdvAcGtPkb0pubmrDb1UacPzM0Yi9wb79h3iAa2jwMs7uhALaVhBo5A72gYxxWdofNSdXT6NLMhCgszhhww7a9kR2FPO1NmKbajqNHZLNxX+QBtNgc2A5zfleb8cFOF2/45mCgsWBUJvYDu7qUJZ3Z/KlXzp6Yw+njsrptlEhy2bl6zkCW5VtVWpi8JTDoeDWJql9bq7rutbD85V5HEk/4TuYMfTk5/nQ9FpuDVcZwmswo5llU45Q9bBRQjMMa0as0MGrr5NFZPPueukZq/Tn0LQk5UB7qKerVHbh9BpWVZdS1umnHwfSB3Xf6yUpNZTvgbmvG4/URQ+RzUXhAUdPU/cpjieIb/1wU4fvJZVPnaqAXuN0ZBS0aDaaL51bkc8PwRjxNlfjcbXhMK3FJqTT4s7TF0UpbY2TA2+4PoiZEqW142XsMiVoTV8WE7ll2q87JozNpaPNwh7+usNYcCiacqK8CLZTybMawXCb3U410o7Pj2dJpnr7uDM+M4/rvUOcKNKp1rmfYLJHnc/h9/uq5g1jgvw/bwhp5O7BxzynDuetDAwsG8+NczOudyBnjQ4Gp+Kier4k64vjWGEmyYcdutRAYC2UNG7EY44os22Kjo3BYLVwwpU9EmquAwAjGsvIyaG+kttlNu2kPBlprw1KLBJ6RaurVfm7BSVqsOu/DjR3YG/LW01urjOiN29YRGr119JA06DcbXP4AjeXQZYFPD5W/trDryqbruP1lhKGp9+j+a9XibcfiU+e/1enC7LQOzd1CQ5uHWFqxaBoDcroPjEVFRfOxbypWfOiY5Pl78zcRxY7SWkb3blHpAAPczXg7Wmjq8AbnTWz1z4PX0tRIU3MTur+3Qmpy6DouJYW3fHOCv3eYNnymSWv5/mDaqlj/8U2Pd6r0uIAeFhTSbU403RoM0Ot2J3ihraMDT4e6bgzd3uXZIisxlllh2QYslsjG/e2l3V1bOot8E5iq72S1ZRJn2XQ2moPw+KzMtXRtoLc7XOw1shmkF5OPuj7CR618H7//QAWdb7CqOrGvm7rPImMCw8xCdpm9eegwHQXDO4itNweDqTHd/6z90iWTeHZ5Hn86Y1TwPVFhaeqev3gihmFyxcsbuOvk4V3mqEyJCR2nrITu2yxevnQyPsPkoUV7+MI/n0JghMLSm2YdNi35kVowKoPMeCcXTs1F0yK37a6ThzOlXzLlDW3kVbfw7up2GswYBqTFsL+ymVPHZPK74wYTH2XjL1/s4fMbZjAkPY7KplD2geGZcfRNcfk7y5rBtpdAg76iUdagfg6MTO0pABIuPCAVH2XDbtW7dHQ7eXQmGwvrgsGczq47egA3HTuYotrWLqMm0uOcxDpDx3VIeiw3HjPoiNpHNE3Daun+famxDvbeP5+Ln18bTJF+0uhM0uOdvHfVdNbk19Av1cWGwjrKGzr42ZTelNW3MyIrrtt190l2cYtvFglaC71dkR1odV3jmjkDuHh6Lic9voIOrxHRRvK74wdz9ewB3DxfzZW28WAdp/vTxl88PZcrZ6k5P04Zk8W8ob348+e7eXGVun/85czRfLC5hFmDUjlpVCa6rvHxtaGOspqm8d7V0yisaWFKv54D8AEWXWNoRlywU0Fq2LnY+bntiZ+N44+f7+aKGf3ISYyif6qLxnYvS34zmwc+3RUMihw9pFeXABWotGzi3ydBkP9BGfFR/Oro7xew+LF1V4B27vUY67Txz19M7PK+H0puiovcFBefbC0NNl5DKJIePlQwPCLdk+8z6mHtrXNZm1/LOWE5oecNVROdnT+5N+dOzKGkvo2PtpRy3qTu814mxzj4+ZTevLL6IOdNykHXND7fXs7U/skRk50DjMqOD/Y6vWh63y5BkFcvm0xRbVuXG/V1cwcyf0Q6sc7Qg+qHm0v5cLPqkdcrzsHdJ48ITuz6fyk9zgH+jjljcuIZkh7HkPQ4ThylKhAFf1zAkt2VXdI8hes8qXGg1/YaYyiTtN0s8feECvfAaSM5b1IOd0VMYqkcNSCFX87qH0x/8OE104PzvnRmsVqDjVAe00JKjINYp41WnLTixGW38PrlU3hj3UHuWDCs22V05vI/uJWSwpPek7v0XM5NcfH65VOCo5e6E+hlGuA7gihIostOov+B8OVLJqP558557I/dT8zcWXiF++0rp3LWU5E93OOcNnonR/Pe1dO59MEdwU5043NTSI5xkBzjYP3t8/hiR3lwpBioPMe5lNNkRgV7groc1mA5FO0IPeja7ZG3U1tYmp2MxLhgo26HN3J/xDqtbDQH0dmDp4+kqLaV6+YOPKJ5ccJ7XZaZycHc3rrViUXXIhrH+/VK4P2ru66zszzXGGrrG9lp9iYtrMk8Nqrr8dc0jStn9cc0TfreUk6emcmF/sp6IF1av1QXiS47J47KYNWBmm4DHYdy1az+vP1eqCdWgivUGBfntEW0L7/mnYvp75EzMiuea+YMCPbGeeeqadz00NZgA2RaQmTjqNNm6RLkD+guC1D/sABlYLSWN6yXVEQAyXXoQO9xw9OZ0i+Z5/3t+PFOGwV3qHvLSaMzg4H7Pz5XD/lqDo7mDm8wLVbnwKvdYg32GgxvyI912vnGN4bZls186xtB/oMn0O4xWLJ8KXv97ZZeLAzqFcNNxwzi2GHpwXuVpbsc1odwuHRYADFOW3CS0zmTJ7B9eTEnjspE32EN7vC0+Gh8zW3g6/6+qukWZg1K5b2rpwUrQi9dMom9qz4B1ChEALsjdP6Gj4yw2HvuDdndqLTwNBqjeqce9ntaHU46Rw46vAZr80N5+E001t46l7Q4J1P7JfPwP3dDYdcJezuwBXt9fhcRI0EcTtAsYKqGrIn9Uth297F88UkTeet3hH3GHjGax2a1Yj1EEGT9qm/4drnq9GDrVPkrrmulxe3Fi87Vs/vz8FmjeWNdEYnRNjYU1tE/Ko3yr5fjbW+h2aMaHjOTErqsY0xOAu9fPZ0PFy8jvyDyb07/8R3TO/S5K2b246ywRtrOBmT3oiNvP401kRPpujvacfuvqeSYKJUjP4zdZsViPXT5bKDzqm8uGjCpXwojs+K59YShvPzSOmp7zrwIgMUVqnx3FwAJSI934kWnze2Gip2hCXgzx0FiLu1t6qZnCRsFpc5cLSIfue4f4RN+voePBLHoGobNFRyp1eZvFI2yW7hy9mCKl6tn0kbTRWqsCgy/s6GYn03ujW1PDDTDwbIKvIaJoVnJTuw+NUlSgj/vv7ud+saG4ITkAeH3s0CJ5/F17eUNkY2roMqvJJedtjb1vSpq6iip1vCZJm57PM9dMo373lsHB8GJm4rKyogxSzZ/8DBwz60hnhoznn7dBCXPnZhDYrSN/qkxwRzigXK4yp+yMiYmdP/58FdHccMbm1i4rYzjR2QE075N6JPIX84aHXw2fOzcsd+r7nA4PjO0X2PDUgmFz52Uk5rIscPSufPDHRjoJET3XG4eTngjdnj5GRsbORIkyn9NHzOsV7dBkEZ/2hqn5oa2Wmpa3DQTxX2njuD2D7ZjoPOlPoNjjOWU1aigbJRVox44emhGxBQ69582gpFZ8cRq7Xz45Htomkk07cHgX0ebauSKddjQYtLUnDF+muXQ+8IIC4KEpxg0LXaCkWf/cQ0EQay+VnSfapi1OVygdbrPeNupb24lSuvAYbWi9TCnRZTNEkxNFxDrtNLRYWPJnkqGj2vDGl6+dTRT6w8UtWsOpvRLoj5f7YO4xj00NjYGv7PN1n3w5/q5AylftQHcUFTTHBzV5IrvOvrLEp5y0eYAPVQG6fYoaAWPuwOPV+0n3WLF2ikI0rksPpJnD1PT2WnmstOXS6bmDDZgt/WQJtnmdLHCGEGRmcZBm+rMYrX11DVDpfsMzNN2OEVGGjl6JQVm1zpxO45u6wrdbmPEvUKdT4HyauagVGYOigw+h48sibZZmNwvmd33Ht/l/g2RHY4yErp/Bgl0bu1uxEqs08a9pwzvcc7D8IwTAS9dMgkTIjq4TMpN4h/nj+t2GQGBtoWHF+2hyJ/C77NLJpEa4whu4zVzBkT06k8MK8/C616aprHutnnBbBahtp5Qnc6HhVtPGHLIbQoID4LEHSJo/Pvjh5CVEMUpYzNJi3Xyq9c28snWMq6Z05+bjlXpfnOSotl0xzHkVTdz9tOrsWiaeiYI69T3+Q0ze1rF9/Lkz8ezfF81GwrruGq2CjbER9s41t/RckBa6L4WHpzqLMZhpY446sw4hjm7v16j7VYW/3o2AO9tLA6OoOlchxzXO5EDD5zQbaool8PKnScN52BtK61uH9MHpHS5Djob1zsxOArkSLx/tUpZ/+b6Iu49dUSP78tJio44dxdeNwNQ59uhmkwCWQ1OG3v4EaDi8CQIIsT39PrlUyisaeV3727lYv8QboCPfjWdk/9+6PzQj5wzmhvf3MLIrCOfgyGcpmlM7pcc0eAbXhHRdY2cpOguw/U6u+/Ukdx36siw31Wh3TkIEkhHE7D73uPZVtJAlM3CsIw4dF1jYFoME/okst4/ciIlxsFNYXOadJ6/4YIpffjt8YOJcx6659R/wryhaTQ29SWxeS81ZhxXje2+Mdbspm/PWeOz0TQ1aqjzfBSBXturjOFsYFDExNkBNouGpmndphOZPzKDvimubiP/nVnCes0lxUZz/qTeEdur6xqT+iZ1m0KpJ+G9Vzp6qABMPURKgu70dEPv6T4fqNinxjpUbyZfD2/sQUY3o4nCgyRT+qfS4h/lnhT2MJ8S46BPUmQjxi6zN02+KOrNGC6zqmGo9rDeMa6wwEe7JbLiGV45s4U1InX+3uEBwmi7hVZ/2qGegpc9cdpC59NnvolcYlXzv1j8vaLjomwEulxGOY+s8VSzRfGFoQLKKWFzMThtPT86RKSh8/8/Z0gaa2+bG+wZ8/h5Y/EaZrcVrEM5e2IOdXszaNulAlWusH0XFxW5TeFTdw/yB3ADjYjxUTY1T4S/g29S3OFH4AWEB0E+901idE4CZ0/I6fK+8AbL8F59un74gFZ02HXYUxAxPiaKDlSgPTw1V+d3261asCIX3nBosVjYbPbngDeTJqLQNI0ouwV7WPBuSGYSj18zs0uFIrwh9UhYDtM7FtTD/+u+o7Hi46m+6WyeM4wom4V7dr0e3OEOm412i7XnMsFfJoann0yLc7DPEnluOMK+Y3gv/vDAZWfdjQTxhKXqUoGBQx9b3dY1COLxGcEREgB9UlzBlIGappEaF91tIq5oh73HUbSHEr6NDrsdU9OCJ43FYsFmtWDttFyLzYEe9jmLxcqh2l/37gz1nu3cMP3ORpWKxouN1FgnmqYFy7rjR2SwO7+IzwFvRwut/nVkJPf8nGTEdy0nLf5tTYlxsPx3c4hxWINB9p44oqLpANqbVMeWVlOlZ1A5rtUOiot2kG/pR0rY5AEWizXiXtwT0/90cN3cgUzrr56n9CPokWl3HdkzYrTdgoEezMntLtnKwZpWti76mnpbOpm56lksvEd/YLLn8JFqmq4zZ3AqxXtD8wh2bmjU7NHBoH4rjmDnCWdYeezGSkK0nZvnD+HB00dis+g88FeVB7u+Qd1L7HZbj/eA+NhYDFNH1wyqyrvmAJ87NJ0l/ja0kdlqHwXSCXVWGzcMF6Ec3NgcnDMmh83LVJqRmvoGmv2N1wlxqufvc5cexTP3v0qr20t1ZSk5YYcqMDLGErbtT/5sXHBujnA2f8qyQA94CJUlNc3qhhweBAGVPuOvZ49hdX5NMAhy+cx+5CZHc86EHDSN7zUKrDvht5fxfRKZbEuFAvV7ZIAi7GebgySXyiBgmN0/cx1KIE3N7+cPoX5TKOVNeIA2NlrdkwKB68DcTYFUMuHS45yUN0KHacehuamtLKGorpUOUhiRFc91Rw9gZ1kTu3ZVgRX2l9bQ3OFlR0kdSahzPrxn7fmTegefYwLLdNHGKD2P0doB9u9V22mzaDDuF4RHULTDjAZEC50zdlt4EMQBgQRG/vfo/s4Gps+L4VNXqd1uV+kiwxg+D01N6ppy2C1g7f54RHfqnT62dwIXT+/LM2/45zioayAjO2xHeDuo81+rsTGxPHvFVB5/txXfprW4fQbNjf5OgLbun59GZcdz4zGDuGeTA9ywKU+dy01mFK7Ero384enBrFY7Fl0PDUi0OPz7woPXHxzXdAvWTkGOziNDjmQUasTx07TgM3l3HR/2GdmMdVhpJIZtZgxx/mNhtdl6DIKcPSEnOI9EOJUyNfL1RcYEEs0mBg0eyRcXTeT8Z1cHJ0X+LsyorteJ4xCdqaLC6g/R9sD53X3ZnOSyc8n0vrh9PvqlHLoc6m5ERKzTys8m92Hu0F6UNbRzoKqZY4b2orKpg4rGdmYOSuXppQdo8/eBs+gaMwam0OE1gqP1L56eyw1zjywgBJHBjMCoi56Ef+/uOvwFyoYHThvJre9vY1CfbCgOdc4KZB45nJiw59RDtYNE2S1cPjM0QuKOE4cxb2ivLplMEl12xruSWHfbvOD3+NmUPizaWcHo7O/X1nQoTpuFY4b14phhh5+X9lDC94PzMB1KIDITRHpc17LuUHNlWHSN5y+e9B238Mg5bRaunTuQa79jZpzw8/PYYb14fe3Bbs+9v58/jr0VTcFnSPHvkSCIEN+Tpmnkprh4q9PEf6OyE7j31BHBIfDhAo3bpmmSEGVnYK/uc1QfqYm5SfzhpGHkJv8wFaLAzX1y3yTW5KsH3KEZcVx2VGTqFqfNEpwkK0DXNd65ahqgJqbq3MMvvILYP9V1yCj5f9o/fzGR8552s903hTIziXt7uPEO6ibneu+k6OANLtBgHRA+wXp3ARAI7ePuhpravkPPPi2sYfHRn00kym4JVhiB7zVL+5EMkz1SsQ4rTR1eJvSQxulI5kux6nznIEi0vettLXxIsMthD2ZTtnVqnJ0+IJnbFwzlyW8OUNPiBjSKzTS0sL7/4Q1XDqvOh77pZGnVZLgie/+EV+itEfnZIx++wxvveydFs7u8ie8jfPh7eM9em38S6DPGZ5O/So1qch6ix3s4W08N+UfQuw4iz6fwXuuapnVJw3Gk4pz2YGKU8Am/HVYLFq37k97l6Hp9W8J7th+i8bvL5zQzWNndbfZm66WTDjtS57sGQax6eKW8h/dYrSoIUhuZf9dusUQMVbFbtOBxDG/It+gWQOsy8WN4wO74UTndVihs9ihW+oZjoHFW0oFgKoCeHEkjMcDZk/qyu7yR6f1TgpVU3WIJdpC1WS20h+2/Vb7h6JoRnL8gcEzD5xZz2a1onQLO4YEAM2x5NmvP54Gnm8dld0QQxBJxbD2mFZsWmQZT66axyO0zgo3RAFYtclttPdybXI7v13kg/L7jdDow0YIlscW//XqnstlqtWEJ+5xusfV4XgJ4vT01B4XRrcR10+svPkaVVx6vD5//3IuP7fn5xunoppEi7HjnJB1ZgDPKGUUj4GmtB1Tu++hOuaqsVis7rMOw+1qYalEt8Jqmd2mI6yy8VAoPyIcfd13TIn4PftZ16J6KAU6bBR+6ml/PNPlyZwX7KgP3kjzsseo+HJ4KLvC8EBEc1S08c+EE3vmiiYqV/pHGeuS5FuV0BIMg7dhZdatKJejodF8JzA0SKH/s/r+3+UeloPd8vTlsFjyaDQcd1NZUdvm7puu8f/U0thY3cLK/MainSVk1qy2YvgbUfAPXzx3IeUv9qddamnDbXf5N8u8fi1U1vLu9xEbMOkDweIefv/NHZnAo4Q09gXK4xe3FbVrJiOnakKPrGpNykzhlTCatbh+zBqWiaRp/OnNUl/f+O8KL93evmsbbH1bin689ond/eKoqh82K3arz7c1Ho6Ed0UjVcHeeOIxLpveld3I0z24JD8yHpTjTrdgtenCen0AQxOWwcseJw/hiRzlr/XWUv503ltRYB288sQTcNazbofLHd5g2BqbFBOseE25WDc4O3NzwxmY6qppI0tU9eUhYqrzw55ZeKUnU15Rjx8sUXY2A37JiIYkaeK3RXdJfHe7ZSAsr323hZVf4vcf/Hmtg//s8mD51L3HYbWidgu2az0Nza5t/Pzl7fGgIfzY+bWwW95wynFinjYWLE6EW6puayDAN1hfWsqWogTPHZ1NvqiBIXIyqq3pis9ABr8+kvV7NTRO8Zvxev3wK9y3cGexU57D7v4ehHkz2mdmc2E3jeHgQxOJwYdXNYBBE9weXfD4fXp8qV3RLN0GQTs8a/4+9846Tozjz/q+6J23Q7kpa7SpLCGWhhASSyEEgMAbjCJgjGeMccZSNCT6fwfbZxmdj4wg4HRjf2ecXY2GiMUYGDIgsspAISghWabW7013vH5Oqerqnu2d6dmZ2f9/PRzDboaq6ulI/Tz3P47cxIZt64ZeR6asr53TjmfVbiq7cLEdq1g259VGsxBryHQdOKFKCnHfoVFxwxDRs29WH/33olbzL7F1oxi7ZjIOzSvvPnzAbb//hP/CBw6fhx0rsCj/2Nk/EXdYibEdhc5a6UcqJ2jaCuHK6+ORg3gXcLEFy88H4jiaM72jKKzZHtiTyLivfuWQifn1rIQ0hMuPMz885CL0DVkm3zm6o320tAZ7vG++cj6c378Zh072FzTl37Zf/+Unc8dJibJftOP2gSSVdAars19mCdyyegLameKgxtLstVRSHRUW1MDly5hj89wXLPV2B1gNqewuyL27FtNH43KpZmDe+bUgGBz9q1hj86vyD827MVTpbkyWtakg4qAQhpAr827LJOGx6J1qTMYxuSeD7dzynuWYQQuDo2V2R5HWei2/5KPnzxw8LbXa/n8fukPu+dCz+8dx2vL3EBD5YGKaJZ6W3ewwg4z/5/33sMHQ0x/P+NtW6yAXk+ot1MBYbz6Fn/OF48NwjS8Zayd3ttgs0zIRuKov73AJc/XibN8HdJH6w+J+PHIJ/bXgDpx9UvEseAB6JLcBc/A1P2t5BfMvY6Fwk8D5l4XjtncWVxabh/JgUAu8/fBpOWjAOl/3pSdz97Dbs7bc0hY1U6tgwBF6U4/CiHIfTHMJtdfesGYthj0yhRezDZuFthv6Ndy7AB371L7z/MN0vahDUltOLJF6WY2BLgYlZ1wiqzqEpGWzqV9vo6cumYOu/MjtnjVJBkQGcuWwy/vjwK9rupahQ35nz/Xnp/VpcFGPqbnc/n/4qzh6aCLBq1y0w/K8Poow0XZQpQggk4oYWZ0AVettQBdnuH13qx3ws4V4v8ZjI+LcHcHb8Rddr0tJETGQFWAGVZpe/Y37RMVWxYBgGhPJ3D1qwWzZhGTJKkNw5dcdrImbAcCgWDOUdSKEqDL0/Lgbc3GEp9RmPmbpyxUXJG3NxGVIs+NZ3s7vdA+jPEAa1VCnH+831a9vRk2LxBAxTUdTEY3jdGI0k3Heo9vf3ux5XMWNx13benA1qbkkJy5LolzFX93v5Z3ARHrj1DT+amzLKkvRApuy5AKpG1g2TLQ3ETANpmNggJ2AFskoQQwSydMqh7vhUa9k0TdhpXWl2k7Uc/xbQ6ioVN/LK1ide7VEUIBn2vpFRJKhKkBzquADDQNw00NHahLwI0CFYbG1KImee1Nacyj+TWg8SosgyIplMwgLQl1WClBIYCyEgYnEg3ZeP3aBiGAYWTx6JxYqrilXzxuIPD7+COeP0tY9lS21Xd8yMIRU3sWLWeOC5R7Bnzx70ZQdNzeollgKwt0gJkmu3Jx4wDn+Y9UrRhiA31LWdqoweyFrMuBEzDXzv9MW+aVfCKYsm4JGXe/KWJeq60stVVSKrmB3X7u7KzA/DEHl3k+p4ruYNIwZTqONroSznH7Yfzj9sP+wbsLBl5z5MyW4Ca+8Yjf6tr+OVVzJx60QsqSnEc+5dkxjAbU9twVFGpm8bponuthT++ukjtA0z2ZMAkJ/LAKAFGddUbgJ2w8cdlmoJIhTFh+pGK3dFTvFk22nAtgCR6UNOpaQh0+jdlymTUUKRrwrBP3jktLzFQ3tLM7AD2LO3D5A27nkuY+n2zxdehxiTsfwelVXUNSfj2Cpb8MjLb+LQ6Ttd81yx/+i8excASGTnsH3ZoOgSoqiPAhml25+t5YjBwryULuDMbfpK7duKURszrnAMw9TbDOCiFPGfC6Syns2teY+aNQbr1xfPTxLCVQkSLzGWTRrVjBXTRmPtC4X5cvHkkUjGTFz2tgMQMw38/B59HZWLN7RoUgfWfeV4tCTNUEqQtC2xTuqeIJIldtmnlDWT02KoEsIqSHO0at8ohffg577IC3XTVZD1dVBrDiATw+5RmXEHVSrOohMhBL5z2qLA15dLWO8Ng43qyjmIDEQI4evlpJERQuBwj1hpJFqoBCGkCgghNEXAJ1c2ZgwWoLyYJV50t6XwjgNLKx4GiyCuKICCq4Uc6m0XHjcTR80ag9N/AjxtTcay+AiM9tDSv33xBNz/4o68j9KYS72W8vntRN2ZqwouH/rKcdi9L12Wv/ikQ+uwWFHchWVm9whXS5oczxtTcV+6Ke/L2Q0z4DtSURf6HzxyGlafOEc7n1CEil678se1N+Hqs5bgfdc+gDvWb4V7iHRHWR2WDXrAZRP/ax2O2cZGrI/pFlCqtcX8Ce345+pjy7LI0TfBCvzeOhIA8Pnsx4RUBPFuu7PcUJtoQo2d4LOz/2unHoCLT55b8qOrXIRiwVH0/qR0fVVuY5ipWQAEL6czJWefUWlviqOndwDzJnQAm7P3G+GWXcKj7bkpU2KGKLYmy7aLFdNGQ76kKvDcy60qAhJx97Esptz7fNsyiO13FF1jCROxrBlXGCGxE23XqzA0yw0JwJaqoCS3QzuOOePakLZsdLYm88dzqBZEEIrCJFlKgOUuEHnGnojRYidGtEyAGSu4SbJdrreSHSXSz6Wp4yVc8WoXfqgqliLXA1kBnW07FTFJGGZBQB8zTTyYWIpm28IT9lScGbvdkUkA8z3DvU00pfQ2l4bpEIjo7O5LFx3zU9K60dysC3QXTh2D119+I7972UJGOWDZUlNqQhiBlXyA7k99r6HsPjdMaNHokVHEBBUiJUwjP8Zn5iwdqzejSIinCuvS3Nxju1iqqWO84Riz1LgRhtK3VaGwDYF3OWKwpLJWmP37MkoF4dEG8uWLJYA00LNzd9E54TJ3X3ryPBwwoR0nL9StMmwpNSVmbp3V1tKMfQD29O7Dvnh2p73i6jEn3HXGI8lZGiViBq4N4Vrj7BVT8MSrOzFPjgayoWckBEb7uGqrJqcdNAkxQ+RdmmgWrOpaSRHeB9tdHwx1DtcUUEJoFl1uO+1TcTOvAAGA9pGjsW0rsOeNzGQ7a6K+i7s/K/IwhY1DjcewyHhOK4PbejWnJNetwrIuulzmYN+xQHkmdY0oNEuQrOIlazk1MNCPEdm5NJlIFK8PpQ17IKMEkSWUMEII/Pvb5mHHngFtl/HIrILD2vka0g/9Jn9834AFY1/GwiQXsyBuGvl63Pz6m9lnKt0ezFgcNoDerBLk+HljXce1uGnkN6bFTaHNcbGYCQtA256XkG7OKV3NovWUs/4DKcRFsRJESgkp3edYded67vpkCRexAHDglI68EuTyd8zHyQsKY5TbelzNo705/PrJzSqu1IYd1c1vlEqQclHX1VEEgT518QRcdedzWDmnMtdNbiQV5WIQKxqio45nQeUyhEQBlSCEkCKG4jyUCwKfc51Q7mSr3peIGVg+bbTrOQDobE1g++5+TB7VjO+etgi2LfMCWbcgmm6KES/i2gdiQbAyqiWhmcOG4ZjZXThmdhcWTuzAQVNHatZL1aAHpd3BCQ/3Rn7M6h6Bp7fswskLioOHqcJ8v93U7m/D/R7nu1M/cBMxE6+jHf+w52OU4yN1/oR2vHXBOEwZ3VyRwtHNlQpQ+MB6s20memQLNsquskyIVeWR34elEKIqChBAFzwEdbPkxq7kWHRkf4eJTbIhNhld2IbtMqMcLaWw+p8PH4InXu1BcsezWJ9VgrjFAioHtx2OMUMUxWrIxSn65fkH47v/+Rcgu0HcMGO47JR5uORPT+DjxxR2VanBWhMelhHqx7RsHu3aT6QwC7EmPKwZgqAKYIVhaH9LCN3VWLY9GIbATR8/DFJKmIYoEoqrShwpVKFU6XEzF7hd5WZ7OQCJf08kEDN0IbCTZCKO36WPwntid3nmIRz9OO4lcCxz/pKKRKHIJV22LpxDSTyRQMxQLEFicexFCvfbi+Fmf2UHUYJ49N1EPKYFaxdGrKT/7hndrXjecczpLiYILc26Mt4w4zDMGKSdcb0ihcgEBZdSs/IRwii5CxgAxrc34aYzD0O7w+3FxtRsbLI34kV7LM5MPlB0nwUjsBJECJEZD93DYqBvb2bXtpkqzLnvPXgy/m/dq44+lHWJpr4fh6XcxFEtSL+Goutiypjf3lQsrM25hbT7s657fOYIIxaHBNC3b0/xOZc5rL05jvMdrlsBwLZlkbULAHS0NmMzgL29vbCaM+0tpsW+8bDCKsPSCAC++rbMBohv/+zZ/DGJTBygWtGajOEcJaahqtRS3QOqSicR4cYoNd2YU5CsnfNf044e3YltyFj+AEAypSs244lkvn8cZDytZOPdf2VWIdGctf4AgHhWIeG0MAT8LQ+EZgmiBAJXY4lk+03OTac90JdfCycTiaL637OvH/19WSWNjyXKWSumFh3ryMYX6+0bwJ6+wtjdO2Ch1c5YxuXafF/aylsy7d2bUWa61YOKYRiwUYhv5vXtlYjp31YqcTOjBLGlzL9fw4xBCL2+Y4536RWwXafY/aiErhzOkYbpcN/jbwkCAB84Yn+8sG0P3rZoQn4jXA43wXmfR3yjoLh9SyZLuMNS5/woBfnOmHZuMRzcUOcOt9icYelsTeKBL68MtdEwKGp7rgcFUiMzFN1bkfqFShBCSBHl7jKtZy46aS6OndON5ftllBblzrWl7nOu7dua4rjjs0flBdHqh/upiyZg4469mN7Vio/9NutmKIRgS10seArKQhI3Dfzi3IMiSSsKRJmL31+9/2C8uXfAdWefKqgp9fELeASldryi3G5/pwu4uOY+wkB+96CjARmGwA/ee2DJcgQhbbt/NOWDYhsJXGOdgCBWLTnUp9frrXYLfc09UgXl6E+OxK/Tx2Evkrg8RHySx8wD0GfFsEn6mytP72rF9K5W3Hb3c/ljzvgU5eKmTDGFixIke1ncNNCUiOVD7JimgbNXTMExs7u0+E2qssvLMiIe0z/69rlcoylBKrAEMR2WIOoYakPou9iV/pzpZyL7W28n6nVSKv3GZ2d6LnD7h2N/ytxbKFgmJo3qDstFMJSKx12VIyrCIcX2dBnkI3jyIq0s+4VwzPTZ+WevqY+bcVNXPplmDIV458XP4xWbQcuqhEWUMOOQ2VgAfsKl2WPbsMZxrBxFY2uzPn4Lw4QwzMI7zgrbpne14uXNqkDdLLI0ciIEcMCE4sCk0ojhHjvjAs6tvVgwQ7kTMUzDUwmSz7OpsGlj2bTR+N+PHIIH/roJvRsyqqScwlBty87na29O5R2haUJzTUHttks+c21OwBfzUSbkgg2n+/YWnTNE8HqxpNQsIXOtPq8E2bcPiZyAVt2p7yFAFWX2vXy6mlJJYEJHeW6lqoG6sUB1h6i2gUqfX0WUUDpJEU4JMnZMN9Yrf6ccShAJA7YsuLjL51tqvMi20VZRmOXym3Pc5mA/JYjqekmNgaIoQXIC4Nz5ZDbulEC2HhxtfyCdxs/uWo8TTBTFKAnCyJYmbAawp9/CQGFgx5t7BzDCzlhI5ebdjqYEXsgqQQayykw/S5DcOi2nvPC6Xh0/Eqahrf9zbq5sWUjHNM0ihXfMobANtLFIac8LsuN0keIUwOuyDU/JKZrlRu6bLeFjCdLeFMeP/m2J6zk3a+K9/cUWjmE4ZnY3vvbnp7BwUgcefOkNraxuqFN2EBevQbGVhK897yDMGx8+QHcUliBAOE8LYVCbWFAre+IOLUHIYFKdEYEQ0tAMxXkoFTdx9Kyu/C6XcncclFIQucnM21Jx153mhiHwqZUzcWSZPk5VuW21Fne1plwlSNeIlKcrrpLulBxYbqtvR+e48rRF+OCR03DGwboPWdXFkvoBX46LryBYHsKv3KI84xu7/Lx139212z9hePkQR7ini5sC29GOvUiF+uizYOIZOQm9CO5uTnMtE1Jx4/VMbukIUfwRYWqm5vqObyEEJo1q1nbdqYHRnYGO89co9aV+9NmKmxx9F2/5H4aqMFUIQ3v/TksQL8G6U1CitpuDpo5EMmZg8qjiILdAxpovhwUTfSj8rVoEJGKGJih0c8mUSsZdY4Xo6GOOl7C3HJd5ALC1ZRZekZ242/IOsLynfSYesGfl/zZTrZqbkXg8lg+q7caAx2B0p7Uo/7tkP1B3nZeh6HRzleTHiGa9PxumqQkbc0qKi06ai6VTC252MhYYfru/vY4rO5Bd2m7GEiT42BREgNAjdcHwgZNHFgIXo2B5pcY2MJwxCJS5TbXm0OY8l/afs7LIKUH8LEGQLYscKFazhlEmzxvfrgs0s/XU1pKpi4H+fsis5RG0d+KhBKlQka3GLfCKB1Ir1LLFYmobUPpkhGsYPe6HYz5X8kwEsCydME53ddOU0vu0hB6PJUepsSjnDqtJc4eVL2CotJz3GJqiMV50TU5Zn1OCxEwDwogV1b8BG2ZW+xnW3SYAtGTHvv60rY3dfWmrYJGXLdPx87oxILNKjawSxG+8LR6X3NuPuq6Im4b2PZXbhGPbqiWIWTS2xhztJJBCXBi4+ROH4/zD9sOlp8wDAMyboI8Z/7Jn4VfW8RhADKm4gRFZF42TR2UsCOMua5zdMphy083d43wXpXkYJo9uxgNfXonfvH9Z/piXtTgAjO8o9JUo+7f6To+a1aW5gwxKVEqQaqG27yjdhw8n3rZoPEakYvi35d4xQgmJmqEpOSOEVISr8HeIUe5CL8htq+ZlPsaOnd3le60W/K/M9VMsxE72emFsu78QWX2qJ+ypADIfI5WgBf70+WBdMNFl967jA+7o2V1YfeKcoh27MUPgCXsqNstR2Nc6Oe+L1s1dRxTYHn02V66TF47DYdM78dnjZ5aVvqZ8qJKrq0DlUAOjOz74wyjNEo4P7qCU+pD0QnOxElJIYXsMCm5td6fRViResBVBniq883Jplki14i5rEe60FiGRdO+jquAq1lRQNqoKCU35UoEliKq4EUZxkHPNlU9AqwnVDV5bcxPef9g0vH3RBNcB+JpzD8byaaNw44dWlCynaQhNGe1mZZZKxPwtQRzty8v1jChz+T5t7EjcaB2Fh6T3OHD6sinY0HYwbrEOwu3WgYg1dxT57i9njaBa7ZRS3Gs7zcvYdV5OTJBkvFgAKzUXQJk0x7an8B0tWHWxu7WgCE0u7xJDJoQ7rKIEXbCl4bqLXz2WE+Koig5hOndXq1ZBiptE1RLLZXzKWfXkrBZ9lTbZtEW6MiXI2xdP0MaJXD3l3P1JawAyVyZ1XPQYIyvdrapaN0XlHjEqVKWz6kpIre8oLUHUpavTHaHU2pl/np2jO7V5PZUonncGXJQgJdeB2XNJ9Bedcuuzfhtr1LpT1wIiVlzXuWsTWSUIjFim7Trns4yNS+aeMjaoJLObdNKWjbRVGNclgPRAOluWzLN2tiYxfWxmXWz1ZxVDfjFBnJtVPPqPug5Lxg3NDVJOuZG2JV58fU823WJ3WM42FGizmzAwd3wbvvLWuXml5NIpI3HKwgn5S9R5uzkRw/ffuxjHzO7Ct96d2UxgKu+kR7Zgk92F/7MO9c8bQGuq8M6uOfcgfO/0RXjP0kmB7i3FyJaEZmVSaum6ePJIXP6O+fjvC5ZXnK/KqYvHY8HEdnxsiAeyJpVx5WmLcN+XjsX0rtIusgmJErrDIoQU8fkTZuGdP1qL8w6dWuuiVI1SO1lLUSoQco4vvWUOlk8bjdMO8l/IlmuR0hvvyP+O0nx5sLjiHQvw5T8+jg8cPs3zGlWofae9COvlJLwSwA1RKQzNHUHpj7ePHT0DCdPE8fO6cctVv8+XKghx08Ct9lIAwL8nk7jy9EV4dNObWKbEkImSjhZ3QXPOD3AyZuLXyq6wsEQVi6NSwiixAOATx85wPa7tOgzQp3OUM2yo5QxvCeKhBFHa8Tp7f1gwsT02HyfiH/les122Y1vz0sI9qsDJoxyJmIF1MvPB6iWAbW+KY3pXK9KWjfauSfhv60C8iVacYtxbKLe2w7f83c6GKIwCQpgwzYLDKBuGa1DnIhxCI00R1TEZ8VGTgPaJcGP+xHZc/4EVnkpGtZyaqzaXsiTjcc0tj2tRne6w1PglUhRcsZT53X3UrDG44h3zFfdMxc/VnIjhfz9yCJZ9PbPTNxEztB228bhZljJQFSSVFP5qiq/wCtdy3OTFHW5UhBnX3c6ornkc860hgEfs/TESu7BeTsaxxkNYYx+Mk8x/lsyzU4nd5SZIsUMqQfyE8xYMnLFscvEJbU7M9A3VrYxToafNoapAXxPweluC5PERpucFnFZG2GqLOAw5kC1D8HpJxU2888DJ2LRufTbdTD0ls+7+LCtdWA+q5faMCVKhJYjm4q++BGiq4VEiMQiWIMr6N+ZQYmvxMwIoXkSyDR0tSWzdmR23HDGtpMxY8xXdV6otZeeKhHBxT+RmCeJXTuG+flEtrwrusMzs37m2GdPO59PJzoRAeUq1pmw/GLBlkRVf/0DWFZe2CSaZic+RtdDysz5xfuN4tR/1OyZmGNoUF8+OQY++8mYhXTNWNNY7rU698tLmUpc6E0LgrYsm4G9PZK/PluYrb50LIGPVcNSswiY3dUx4VXbiFjvjVvjHZ7m7wNLyUp50+bTRnjE5vnDCbHxjzXp87dQDfNNUnyOHX6wPpzV7FDQnYvjTxw6LPN164m2LxuMba9bjsOmd/hcTV4QQaE5QJE0GF7Y4QkgRS6aMwqOXHp83+R2KlGvtou7a8WLK6Bacd2iwHf9ubiOC8GbTFNxpLcJWORIfa0AT3EmjmvHL9x1c8hpVCZJGDJtkd4mrg6G5JvIRpDQlTHxyZUaIfotSqiCo1jnJmIHWZAyHVHGR/KEj9sfTm3fhkP1H4+s3FzxjV+J+S5Vz6vEWamcJogqwgwg73zJ/rOtx3fVC8DoqR/ir+ZkPqbD0yk3dAblTtuAhORP7mS2AEpP619ZxOCpWsNRQTfW9BHmqktdL4WsYAjd/4nAYAvjrk1vwuMwpMj0UNhVYDknNPY3IB1kFMnXjFhi9qLyO45oQ24wDB57tWw4/NwdC6M/pJlgb09aEty+ZCDzinc6r5nhHvoWyWjCQj+pS5m5sIQRODyDscLom0WIFxBJlKQNlUCWIKmQvY6wpRxAYc8R/ME0ThmEUupOmBHG6XBG40y5Yh6y3JsOGgZ2yBW1iDzaa7i4eLjhiGtZv3oVTFo3Hy3+5s+i8BSPQpot8EX2eu605ibZUsWBfHUvygdHVcTZWXDc5NB/8ah9xmXecY5+vIUg2PWllg9MbccDKCWXDvWNDc6uXSTdnKdDfP4B7ntmKOdCFhp7zS4h4JG5obp8itKqIAkNTXrvHBImyzOrUWxT/R7VQCrKOMQw0t3YAOSWIo09LSA9LEO81fU5pknNJ5VW+/CG/+EBKu1WVPpoVY/ZZizYqZMdCZ9sXijssvzh3biSy/WD3vjSeeHWndm5gIBsYXal/M2s5ItN92fL4WII4y+tlCaLEGnNOt64x0Eyz6DqnMttrk5mEUL4v3Nuz+lyWLN3m1XeiTo2r5rmvP1Wak4V8SikqPnTkNJy8cFzoGEKrT5yNF7btwdIpI0PdVy/Ysr6/bztbk3jk4uNDzdWEkNozdCWchJCKcPtYHkoECd6qcuFxM/HAhh04af54z2tkGTEs1DV6GP/fEAKPZHdsl2tNUu9U46mMkJYExQS3BMnnMwi7PUe2JHDteQdj6859mhIkqkBzpsfH+2BjxLyF3i/IcdhfvIrtsuDGzCtQofrBHSomSBnS37CKGwAYkDHERRqvCXfFn9AChOfcZxS7BIsp15micM6r7avt1s1XdY6Eywefl6uneAUB7FWlhxC6OywJoVsXeAmAnDtnq2DJJKD3EbfdsYZh4vzDp+F6FyXIM/ZEvCDHozk2u+ieHJn3nPPRHkmxYcA9lraqGDSE3tfi8ViZliDFgmg3hMMFWljKUZw4fckLIwbDEHkliChh5eMcY3PPeYN1FCaI7WhyvNMcI1Jx/OTsjKXW1Wvc3WGFmdv9dqHbXsI+JYuc0i2muWxyxCBQLCQScdVaoPT7LRLe+gnTs/UsrewufDOWV/KGtcZwxhYCgFRWSJ62LOxNpwHDaX3g4Q6rQkuQeLxY4F0vqL06oQaA9nm35aI2LadbVy1mTsA8R3SMAl59DQCQdOwsltK9D5TamLB7IGO71yl6is65ra1824a6EUFdi6oKp1wbdPS7XKB4Z78ZIXox33ixKM2gNGeVIHsH0nh++27tnG0Vx8rJlTWdDqaQLFaCuF+vbgjLrEMKrdHtuQzTLFLAxJxWax7NxlYdSnqUX1V259YZXq2weJ4OzmHTO/G2ReOxdOqoktcJITBxZHOotAHgg0fuH/qeemCj3YXJxlY8jvovv5+VDSGk/qAShBAyLJk7rg1/f3Z74Ou93OqodDSFd/sihMBHj94fr+/ux/5jyvOHOVR9klbjqSoJUg3oO9NLoe4KGky3306B1UgPN1lhUf3CGzV0v6YqFJwfvH+1l2Ke3ICnZcENndfHSbkxQcrZAS9UgXJAAfyvrZXYX7yKHeZc9zQVAV3OGsIQothVhqELs/PHPcrR3hTHJ46ZDiFEoKC9an14Bf2uSEmrSWhNbfftQVNH4/7XU8DezN8xj3ycgpKw1jhetCRMoDdbNENoddoT78QovKZdb5gxmB6C1d1owno5GQsd51ULlDSMQlj2iHZjeynuVSWXIUTeHQmQ2f0fdhMB4FCSlRpH1WcrI9BvOUJq5w7iTNBdtRyqNVexJYgbe9CEZ+QkLAjiysel71gwMDJE4Gy/dYD0smBQXd1kfxsl4lao/WfkiMKuZF2B4KYEcbrDKl3e3Hu0s0oQQ3UlFPYda8+etQRJZt65KWwsxnO5Qhby8FgfeMXpCYqqFK63tduelkl4RXZisxyFj2prmHCuqcqhaB7WrACD5TlyVMHaVlXQAcCRM8fAftrNQsl7jGmSvW4h0bOFchHM+5RTa8PaWlRVgmQtQRzp2zmRjUv9jxS7svmHX9Mm497PL20rm24hz1y99lu5YOw+eQa0BEnEDJx7yFTs7ktj4sgmbWZy64uZIPH68XjcqQTx719e8bXUdq5uNHFD/ZYIOzPGTQPf0+JMEQD4s70cE+U2bDMm+F9MCCEhoRKEEDIs+cSxMxAzBU48YFzFaf3gvYtx3b0bcMkp7gJLPz63yn2nKCnPZVkpVCF0kY/yQIS3BInKGiMIqkDugAltmDe+OLh7ULSPUM0SpHZWYlpgdIfQpA+JooDPXj71ne5+glJOLCFNcRNQmNODVjwkZ2KS4VHXmiWIyKYtigJrq/lpSpAS5bjw+FmBylgSdYdvBUJDVUBhCEOTQpyxbArO65yMW6++IXPe4z0WBU8t053bd96zEBu27wHuzpWtwJTRzTDTBXHZ6/HxWGu1oEu8gYOMpzPlMwxPgZGXAknd3a/6s6+WIDKHqiSc2tmCbVsKO6HjsRhWzunGtfduyASy3BEszaBKEN3qwv85X5dtGC0KblzKsfCLO1znGIapjdt6QONgSpBQuLzPtx84GWNGJF0udsfPZZv0aDOqgDk3vqpKoaKd3Mp4llLiRmiCardd8kXHSpfXzCtBrHzZ8vGBQvZhNwWN6pbKELnA6O6Cf5XKLUGUtlZn7rBsmLjROgqAPm5rVhkRWh5LxQ7N6ZZSc00WsJ66xhTiNCQcgdG/+a4FuPw/ri+6p9R4EbdKKUGKD/mu9TRlqqJo1Nxhmdnz+jNbIieI986jHCvHWMw7PdvOWR66uMPKrjX8NnYU1UmJ8l96yrzCZepaxu39C6PosNPqNKgbNdfDSv1LH0sQdYz0mstJOPqQwPNyAppdXNgRQkilUAlCCBmWtCRjkSkf3rpgPN66wNtNVjWotx2E1cDp2icK9N13ZUyBAavd9BAgVBs1308c42+9VApV4K/6uo5qJ305qIqrmEfgWpXmAEqQRAkhgBN1A/xHjw5mpq8rbqIJjK4K9nO7FJ3uRABoQlTNr3dE71C1JPDqr37C2VJoXccwHC5SDMTMOF6ToxFHGlaTe8wd5y7icmOUvOPATPD0K+8uHPvV+Qdjc88+zB7bhs2vFhQFsZiJZ+VErU5ipun5/nOCkzGtutBbU+wYZl4zGdWYIqS7qlkIgQe+vBJp20ZrMoY3FFdqiUQSnz9hFuaMG4FjZnfjN9+4NlBeR8zsBp7Ppl9iINXdYfm/qz9Yh+FE835MEBnLznJigjgVGyIW19uN4a18jUTJ7ZLGWSumhkyi9HN7KkHU2AtGsQDW2V/Uv9Ud7Lqg2s1VkDNoceny5t69bVsQyMxBeS/+Id+xm2sl993lxXXhVa5yUWNf1NsyTnVz5xxr3X5XjDL4FG0QKEPx0tVZmAPSjjgOHc0JzB7bhp4tr2vHSylBbNvNWWCueC6utXzapaZMVa2tTJc2UWQ5VbD49KIc62anRa1KLnai2i/iDgsbv/7grKegc1evUXD95NbfhWEU9VHnGsir2aiKCq/yCM3FlSh5rVo+L7egpDxYm4SQakAlCCGEkLqkGotfQxHglLNjuJxdXoMREySHvvM/unzVYNKlPpqrjdD8aHu/v7s/dzRMU+hBsBXUXadhLEFUIVFQJapZhbpTFQs5d1imYWhCtRPmjcWnjytYxqhPGVUcIS/DGCMiBaYqfBEOSxAhBExT4HfWkZAQOMorMLpDgFKeBZgbEofPGJP/y3SxMlMFIsI0PceC9qY4lnaOxGVvm6cd13fXF5QgUUlPS70lzQoh1QEA6JcxxONxNCdiOO0g/wDrKkfPHoMHs0qQkjvgQypBdqMZD9kzMMHMKEFEOe/XMGEaIi/0Mw1DH+tLBMOuVkyuSgT9bkiPHbW6cjRzTcwQ+cDu/c36Bg9V8GgqVg3Cw/Ve4by3kNuNnEBZ5IM+m3m7gdBuf9S8sr/dlOh6YHSPHeIVKgHimmK+zixBPAaEcuJzBMpPef4iSxBNUB2snhItIzF3XBu27erD7PEdxRe49KlSm2GsUkGZy3CHpSlB4ol87C87WShrbswrsljM3Su8R+1y1rReayRAUYIo5Y4nwilBnP086Lt8KnUgDHsrnpBT8RlXJYhZZOXptDoNNDZ7KTaUtPOWIF7usLTyUWxPCCH1DpUghBDSgNTbDsJqIBRXCW2pGHbuS6O5wgB0mguCMnYMh6n2ZfuNwpOv7cThM913qFcDbfNyhfIVVeCvCb5qqAQxA5Zj8ujSASTVmAfVjglilthF7odXPxcuOw9jhtAKePVZSzzTqpbgNp9XVOmoQklHTBDDNGAIAZlTArlYwmQvdPxZHfcKqgAq985VQXrGEsT9/S+bNhpnn3lIcZqG/ry5IXGwLQH7YeKH6bfBhsAnyhyDgwqu3dwz+aEKU8tydyZE1vVaxv2LacY0YV1bLvCMC35K7rBv6m5rAXZgBE7was+eGZW+3vYSProEfY+ZBn5pHQcTNr6ZSGmXa8o+1arBRxDotAjza8NFsXyMGNL5IocbR9WsS1qCuFjFFKVV4diZ0CxB6ksJcuj+owEA08a0aMeFEV4hEYStLbPQL/+GJ+WUovYgNeuTgHWebMPxc8dmfsddLEVdFBellOIPxxdjLl5yPedWDb5KEC34t4mfWicBAL4Zc4kJUpRWtg5KrEHKiQkSK7GZpKAULtR/wlGvfooX57sL+i77jRRus1dkb3Kz2iqeT8vJy6s7q4/lt/lJrQO6w4qW4eD1gBAy+FAJQgghpC55ypiJqViHXbIJ3z1tEf79pifx7fcsqihNLbZFWCETEEr79Jv3L0Palp5xKapBzChDcOCBKvBXP/KcwScHE/Ujv9THux8xozwliF1WTBBVsBau7tqbPFx+ubjD8lNs6DFBovmw3K+zxfW46RlyOxzO4NTOHcnqe/SKPeIUUjv9hpddNscTqsLhnABPFYgYpukdbNkrqLyHVURk7rACvqcpo5oxobMDrclY2eOZl3ud4gvVMSyoEsR/B78fwjSArA980xCaHL8Nuzzvq1TZ7ORxuR/6EQ9tQeh3tVdgdPVd5HaFxwyBNDJKB6ebIs2yzUOgHywweumKcyobNDdcoS1BjKLfbsJvTdlfNXdYyv11Jt8b3ZrEo5cejyZHH9f6a4RCyV5zBK62TtaUmIVsVAV4wDxTbYXfLnO1qwurEvPBFtGJ6TKGhEgXnXMbmzwV8Tm09iXQj3j2cHHfCWs5lb3J/xoH8RLtOZ23BCnk7Qyk7usOK2Bg9GIU12xuliDCLHseVFuG51zk8lxeudWbMpMQQkhpyhq1r7rqKkydOhWpVArLli3D/fffH+i+66+/HkIInHrqqeVkSwghZBjxlDEDD9iz8HvrSBw7pxt3fe5oLJkysqI0TcUFRizEB9QGO7O78PlY8DgbMdMYVAUIoAu6K3WH9b5D9wMAHDVrDOLKaqFeAqObAWKCeKFWTaLaShCzWMjox3fesxBTRzfj2+9e6Hpe3XGac4eVUQJ4l08LbhqRIOuACe343umL8L8f0a0YVCuuStAFYYZD6GFoAmgvxY7hcB+iBaGNEFMVDmXLqQrnTcP0jjPg6ZdcFY7Vbgd5zDTw108fgf/76KFlp6EHVy51nWLVEVBhZSmfM7GQisYcxTFzgrkWjEKhKLXfmfTCWhz4dWmvmCDqWJJrb+oGAedmAfWNmLGCaxw9kHzpfDLX+LnDclhwqRYrIdu/mzsnwzTyVmTKycJPD+1WORakKnFt/Kk/4WlbKl60MaAshUQAJKSrAiSbq5J/wHqKKVZLbu7OXN5dqTFmyeSRsLwCM7u1X582rfYRVbmotSmRs2x0fwfSy2cZ4Cq49yMW865bSxbHBEnEQipBnNYZZbQf1/HXMAMpJPtlprw3Wcs90vbK0yVtj/dbbcva4cz+Xa21LgIhZAgSevV1ww034MILL8Qll1yChx56CAsXLsSqVauwdevWkvdt2LABn/3sZ3H44YeXXVhCCCEZZnSNqHURqk6vaMI/7PnoQXSLYFUAKhKlXSap3Gwvww3po7Extl9kZakG6gdna7IyBczKud24+3NH42dnL9UEUs7AmIOJWo6gCgU/4iECo1tlyPY1xU1ABdI7DpyIuz53NGZ0u/dzN3dYHc1xGCWVD+4CmEp526IJOHDySIcwIZqYILpVgKH/bQhN+OBl0eO02IguJoiOmm5+N69y3jBjngHpvQSuepDwYkFZpYSx14mbRmUCUIcCy/MyzXIq2HNKxXd/uZYgUlNYGdrL85ZtSk2hOK49I4T92NHTQ+WtttHcr/CKyuLr01K1OvCoFxfBrN6vHG6KUm3YZHfhBXs8YqoSRFWmuGRjCm/Bunu59OsNZe4OHy/FcP1dlIdXf9OKVaE7LI84KvVMYCuukJSS55eleBECmP8uYPqxQGtXyTRzlHKr+cmVMzwDXbvVg59yTm1TqtWIW/t09peCYr1kBiXzd6OUZaTMKkHU5ypyQeUbE8TZ74OO6eo9LlZbphnI9eF11ir8wToMz8kJgcqXwy1tr1ZYlgtGUpI/fexQnLxwPH5wxuJaF4UQMgQJPVt+5zvfwQUXXIDzzjsPc+fOxdVXX43m5mb84he/8LzHsiyceeaZuOyyyzBt2rSKCkwIIQRYOacLl50yD//z4WI/8kOFaBzq6MRjJn6VPg6/SR8LI57yvyFLP+J4DaMjdQVRLc47dCpOPGAsFk+qzGoGyMTWiJkG7KYOrLP3x332nIrcUFWK+v1tRqSMCRcTpBxLEEVxE5HyQRUk5HYzf+Wtc7E2djCkFPi7Nb/oHrXoUbvwKSpfOcFT3NLRdmbr7i8MYWjCWq/dmE4PJbGILEFKucNyGyZiphFaWKIJezUlSFTusAYPI2D5tbgMAZ9T3U1edswXobcloaS5OeEeBF5AaALa1W+Zg9suPAIXHjczXN5KU8o9S1hLPrfm/5LsLvzhaW1UHLMoXsKtYsw08D/2EfiTfYgW5FsXlLvk4+O/v/gGhwWKGs8r5DvWklL+cFrHaMJ2D/dhrjvEQxDXdt7X/3oCcLekiYKS04SX4sqPzhnApIPdkwzpDqu9KY4xbU0exXNx0eTzPlVhvmqB2tasrGWy6RYpyHJllyXUIGUI403T0JTIbpSKleMXI67cmCDaPV7usAI0iz1owktyLPS+pq4jguc5b3yby5WAKMe1LinJgokd+P4ZizFpVPDNaoQQEpRQX4L9/f148MEHsXr16vwxwzCwcuVKrF271vO+r371q+jq6sL555+Pv//977759PX1oa+vL//3zp07wxSTEEKGPEIInHPI1FoXo+EwDYHX0Q4gOkuCeuOSk+dFnqaAwF12ZkdWGKVB1BiKJYXqiqUSvGJJuFGOaF8PjB5RPArlA11K4InLVqElGcMWcyx+YJ3q6sJDVSpGaQmSL5Pijasq7rAcliCGMLTd8l475wdSo7W/o1KCOFuD+p5F/v+FawzT2x2WlzRHd4cVXjlQT+giqBJKkDKCMOvusMrtY7pwWip1/GzyANc7pMMSxBQC051WmgHelXT5Hb6LFuejKoe8hI9uLnpKxTbQgiQr1xkebbWQT7iYIM4KUIXVYWOCeMUrKSqnmq6nJUhlY6fhUZZ6ppT1TCWU3Oiijf0R1ZPLu/ObDzzzLsMdlvq+EzED15x7EGwpMa65IPPI5eccIwPVehnvxhACEqJIqa9dY3jPPX5rGqcVT1RKEJi6O6y11jx8KnCKyrzs5SZQWeeecMBYfHrJUiye7L6xSHi5TCOEEFKXhJott2/fDsuy0N3drR3v7u7G5s2bXe+555578POf/xw//elPA+dz+eWXo729Pf9v0qRJYYpJCCGEuBIkkHIpGkRmETnqc9dSCSKSrfirtRQ3Wcuj29Ef4qV6BiovganFMYlICaIIFN+7fDJakoW68PRhrlANSxBdaBONJYgzXoH2phzusLxeo4w34/r00Uqa1RFYqMIgt6LEzLj3bnxPv+TKCSP6mCDR29p5EzS4su4CzD/daWNatOcwfHYmB0EIQyvHU5v3eF5r+LXBkFZRObdcYf3Mu+Vte+x+1u5T+1i2DcdLuLZSlT5xl4DOAGC4tKywMUGcbVzdcR7W5Zmel2pd5nTtoz6P+zhRseubAHFH6g1nbKaoKG0JEn2ebq6b/Nwjeo21bsf92rRTIXD07C4cO6fb3V2bcKhBcjFBSlRa2Fg5mXuc40Qx6mM5Lbr85lNnPwrsDsvnHlMImEraL8vOQOkW4WlBWjg+eXQLjpnd7XodABi0BCGEkIaiqquvXbt24ayzzsJPf/pTdHYGn5xWr16Nnp6e/L9NmzZVsZSEEELqkYg86miogiVnwNcgjGqpXTyMWjKjqxWzukfg4P1G1TQIpGkIPCmn4jk5schXfRjSpZyRl+BDR2Zcep66aHzge1SBXSVl1tLU3ECFX8pVzRIky0aMBQD0yJZKUy38Ms2iGBmaANpDkGMIgd0ouDSJavd10c5ZVbjpck0mzoSXEiSAgFpzJxWurJ5UY5B14V5rniNwdrAHKApc7cJv378cR80ak//bK+6Kb16aIFI/92Zv2vM+zYNSWTk7lVFZJUjIdup2dRBLEPW4W0yQYndY7koQTWEXgTusony1mCAhFREe1h/OPIwgiroKh05doN8YwlO/oPfl0pbyVliqMXqiUvq6tbmYnzsnj7nSTTHj7+EtgHWRUZg93NyQlRqyfbxaeZRJaHXthqbgdyg1/JUgTkuQkAVEQTmr52voFrEh+pLa7rxaljqu+7W+cpRPhBBCakeo7VKdnZ0wTRNbtmzRjm/ZsgVjx44tuv7555/Hhg0bcPLJJ+eP2XbGRUIsFsPTTz+N/fffv+i+ZDKJZDIZpmiEEEKIL6r1RxgXC987fRGuv38TLq6Cq6lGIGYa+MsnD6+5JYz6/ioR5J+8YByuvPUZT/cGXrzzwImYNbYN8ye0B75HFbJEtqNVEUQE/QBXhQTV1mP9TS7BBKsNz8iJuKSCdPzcYcUCWIIYipCnOR5De3P11pfj25vQ0zuAaZMnAs+8rJ2LxUq9Jy8FjhrYWhVENYbQpUe24L+tY7APScd2Yu/y626//PMY257Cmcum4q7ns/eUaW2lFc/R1jyDI8PfGimYwsfNciJiSxCPcqh62dyzlLKSVJWumiWIUfr9FgdG9xUt6vdXMI56CfEzAmC1UP5Ko0otQXRlUWMoQbT3WWFMFJVzD90Pj73Sg7fMH+eTfUSKa5eym74xzjzydmu/fm3ayyWdoc9ruWsd6gMApV2IlS2MD2GV5Vw3Cx+L3GIlSPgyuvVFYejxmMIoQTRLNU9LH9USKZyFDyGEkPomlBIkkUhgyZIluP3223HqqacCyCg1br/9dnzsYx8run727Nl47LHHtGMXXXQRdu3ahe9973t0c0UIIcSTlXO6ce29GzChwz0wZTmU8nVeirctmoC3LZoQWTkakXr40FM/wCuxquhoTmDt6mNDu0SLmQYWTeoIdY9ZYhdluag7IINagug7a6N/l2qK+5DAQzJkcGjXRFXXMXpgdCGE1h68lJqmURAGn3bQpOh2Fbsce9eHvwproBe/frLYcqCUBZXn+9Bc8QdzJxWGatuBSIiMAgQoendeiDKExKqRgZ97G++MdQG4mrWXgE0IZ7tzEdYFeAThsr07rCWIW0YW/NuMWt85RYOhbRbQr1fHzETMvf+59bDiQM9+2+b1VGKKsDqstY9XTIsil1sB4tG4uVQKVxaHYrcBMEpYBlVCazKGH5+11PWcprSPSgniGhPE53169pvw7rC8xhHDq00KFAbpXNolB+1y68lHyF/ChZtp+ClBwio//dPIlMnQ+uKo1jCbGwqV6PXk+nxdusyNEtuHEEJIhtCOcy+88EKcc845WLp0KQ4++GBceeWV2LNnD8477zwAwNlnn40JEybg8ssvRyqVwgEH6MEEOzo6AKDoOCGEEKLyhRNmY/bYETh6dldkaQbZOU7qF92dWWUCpETJnfnRoSpB7Hil7qEyaDtHlccYJO9GrqgyAzuiggiH8EX/W2j9eYSHaxUhBHqRwlP2FFhj5wPxVCRlc92939qZFZe8CAB4SXZju2zHy7ITby8lyPMKzuqxAztKv/zVpB+FGDq6EqTETZpbsWCDtGoVVn7cHcduZ6UcpVqzWUJhAAQTS7qlH4XgV7UE8XYXV1qhWhQI2SMmiJ9lpdMSxO/6kjFBwrZ/D2WpYQhY6mWKUNXL+qDSmELlKPlqjWYJMEibIXTLxWjydCu7nzssT0sBl7T8heEebUpdy+TT0Hts3h1WqdTLfTdClExYt4rQ279/YPnirIKgxj5x63OGYWhj1+oTZwdL2Fkmr/0HqhLcL9ZLg/RjQgghGUIrQU477TRs27YNF198MTZv3oxFixZhzZo1+WDpGzdubJiPM0IIIfVLU8LE6QdPjjRNTYjOuarh0N9fY3x4xkwTv0kfCxM23pKMxqpJ/ejuT4wMdM9g6keiUsbowjezaBd1zDTw6ZUzsbc/jUmjml3TyO2ov8U+CP8+49iKy/Q3ayGONB/BHViGT/lcm0YMv7aOy5SjpCWI+3FDczekusaq/7a/Szbhr1Zhl7duyVJi7A0YQF27JVHoV2UrQbSNvwKqUqQlGS+6PHOL1Cw2XN22BHgGt/4SRewlqcUE8XL7ouTpUnczulu1v71jgpQui/N5/IKCO6stFiu8g7BCR+FpyeBdJm93WJW9F91tWP33Y8BZf4OzbqpOTBAXN22+7rC8EnOzBCnTHZabkk7kYoJILe1SgdGDKo2d+MVe0uchbwstj5v1P8uxBHFVzjreXTy4JYhueedvIbcvWTqurduQEMZlKiGEkMEltBIEAD72sY+5ur8CgLvuuqvkvddee205WRJCCCEVoyo+OluHZ5DzRkYVQA2WJUelmIbANmQUFR1N7sLUsBgC+FH6FJiw8amAlg2DqQSJyhJE3YEpTFMXPGX78idXzgicXhSKz4flDDyanoYRiXAWJaXy9hKeqQIjTRBV58LTjXYX/tc+Qjumy35LKYTCW4IYTe34q7UUfYjjvWULqXVBufpOvvXuRa537DNadVdBbmUrszhR6HiDxARRN66p1nX3felY7O5Lo2uE3s7VMVh1Sej3rpy71P12VzvLG1d2nIetGqdFmXJGu07rb17C0QrHEC2POu/HObRg8jXYPBLVeOdW33HTZ072GptdLYJKl9MztpAWE8QjrQCB0cuuJl9LENUKUc9EVU66UbQxNmAhTztoEu58ehsWTepwdz1mmDAME3+xDkYcaRyYGh0o3eKEvJWdv0ofhxFiLz6Y8lOCFNJYNLEd0yZNxQeOmFZeeQghhFSdspQghBBCSCNiGgK//9AK9Fs2OpqpBGk01I/NRrEEiZsGvv72+ehLW+hqi8YVkxACfci0X7VOSgVNHUwtSNqOyh2WKhwyHX8HjYVSIIrd9QBgIfzu4ZKWIF7HVfc8JWIZ1Bu2685ib+EztDMiyGV6ygJ4Uk4FUIGiy2HRIZW8RziUl3+0DsUs8TJSzQv0crjFSi5zd3YUAnJLswRxv0azBFEeoLsthW6X67WYIGq8I+31FmfmFIb675p37DiPl//Jqgcj1wrhXaZqucPS2nd99+McQeP5VIuoPExEGxPExeqrzGJqijHVGsklv1JzfPnzgo+7J82IT6+vuE/9OaspqBJt1byxuOVTR2DK6GY8+Pj6ovMDqdEwhMDTMmMtHmZu9xoOVAwBvI52vC79LTrU9LrbU/j0yfMCl4UQQsjgQyUIIYSQYcXSqaNqXQRSJinlg7stIquKweC9y6J166Z+7wdVBnntQo0KoYhnrKiUIOpvw4C+sT2gEkTZOltLxVmpvL0EQ3r8AlOJkVvfCkC3t6+71Clxc1nKnkKCoQOK5/PSXa0JD8EkAGyQ47BBjsNJhq5ITyrj08zuVjyzZTdOWTTeN2+3+orGHZa/pYapjiUB4iypbVK1xvOP8eFUOATPB9Bjgrgr2YLlrbcpR5lMtQ14KEHKjjmTzSOAy616Q939P2jB3D3iuFSUpEsfiPvEtPDUbHgE6y4H4ab4EMIx3+UsQapgCmIIwC6VrPfmg5hPf3CO4UGHNSEEZo0dkb1JT+Nf9ixMT7Q44jEFf3a1Cr2D1QdPz6ixkpAQQkg4qAQhhBBCSEPQ3hzHL993MJoSpuaPfrihCqVUQUAtA6OrRKUE0XbnF8UECaj8UYoSlSWIH265mGb4vDXho2HkgzjXuyWIU7D0pbfMhiHS+b9Lu8PS/gqUn+p+rXw3ebpwXC2jl3Igd4VpCFi2xLzxbflzN37oEDzxag+W71eem5ZylTkqtvQXuKv9KEj/MDzcYWmBhF3ucwqgwypNVCWIWwD30njUgzOPAAq4SgXy3gqZ+kUTfNfEEqQyxVMepexNcROnLBxfFLMieJmK68FLoJ7P3istN9MEIdCfVjQT2WuEtOBF+W2zdDtU+5tTIeynOFVdaWUOlBETxHHPdtkOUwjPdZB/espvj2vKVoJUebMJIYSQyqEShBBCCCENwxEzx9S6CDVH/d4PKhCstn7ETwBUDqrwTZiGHlcisNCjASxBvIJWG7ov9oISpN4FLZnyTRzZhJs/eTjaUnE8+cwzhbMBA6MHlZepSpB4GcomwBF/xlG/fkLYdRcfh30DuovF9qY4Dtm/tC/5UrgJWcNiBYgJIkMqQdSd6PFKLEH8xgtHWUxF2GonWp1X++TtUY4S1inegdErE8hrQuR678Y5ah0TJKJxWx13pne1YtwhZwTJ3COt4nbgW06PSVgdX9R+IaWAEHn7PwDAK4n90IzH8Lpsw2ix01GmMuvJ57ZSMWFCu8Mqo4ymw9pEIjM+mtrYFTrZXIFcD2trDZ8kgsa7IoQQUh80xhYUQgghhBACQP/QDv7xX92Pc6FIeDpbkwCAMSOSFSaq7jQvb0dpLSxBZubceCiUzDuA33mjLDdRtSFX5aYh0JbKuK0r5YZIRXebFex9jVSUD+W+Y62uDWjty6lo/PBR+yMVN3DhcTMBACNS8craepVMuDS3UR5tRlVqBFOCFH7HQwjEi2OC+FzvtBwxTPwmvRI3pI/GiBH+fvq1ezVLBnUcKaUEcVfwGBVaIBpa+67vfpzD8BiHBouo6kl9v9tTU4GuOQHu8RibXdq+KFOs4hXzRHNnly3H7AOW4vr00bgttcqlUGWOfb4xQXSLRJW4jzssp3K0rHfpSENCZC1BvPNxY4fMzMuvii7fa8NYgujWXYFvI4QQUiNoCUIIIYQQ0kDoLmwKkoBSotRqxwRR+e0Fy3DRHx7HhcfPrCwhh/BRldEGDZarCm0Ha5fmIft34runLcTG13vx3dsyFhC5gN0DMoa4SKMnOR7tfa+WLJcaf0DfQV7fkhYJgWTMwHdPW1Q4GNRvunYu2Dvubkvh+2csRmsqVsE71syMtGI4g61/4YTZ+MxxMwPF0KglmnVWgGoJElReVfYkK7AE8dW3FF0v8PG3HYqevQOYPLrZt5xaUh6KtWLrFPfshUB+cK3YHZbqmquGlmlhEJoQvBZljsgSpIwx1MvC0U2Y7zf2SI/AG0HiJeXSPnXxRIxsOQEHTGjHb75xY6j8PfHru4a3Qjjm436wYMmSoZz+45zrJQRMhyVIkHT/YB2OecYGbDPm4WTk6s5j7g0awwqOsazO52ZCCCFUghBCCCGENBS6O6zalcOLmd0j8LsPrag4HVX4YghTE1cE3ZFs1yhQytsXT8RtT27J/517Z2/5wNfw7KZX8MaL69DzdFYJEiA4q7oDPapAwX+1luJ481+4w1qMT5WZxhrrYBxpPIIH7Fk4wnwUALBo8kh894JVWtyeoMFjhcvu5yCcvNA/AHlJHAI1Ta7l4mKr3hUgAGCpAncPhZItw7nDSsVNPHjRSpiGcATMLn1fkRLELyMXJcjZy6f6ls89KfeYILJUO1Tu6UcCJvqy5SirCIV0NW9YjSEwrXng58jyDG+F42kJ4nK8XFdh3pYgan5G9lqBo2e7WzKUazHj1w5VxYfzuf1isxVbgpShBHH8nXOHpbdL/3R2oRn/tOdiPBSrvQD3+VrKUPFBCCENBZUghBBCCCENhKclSAmBv11lD6jV0DVou7ZNR2D0gNLIWilBnHnnyj5vSjfmTenGtRseLVzo8Sjquw0StDksnzjzHfj0byfiolMXlZ3GejkZ661JGIsdhYNCFAnHtJ21JYSFtXItoseKcFqCVPdzSW2hN3/icIxIRZPfcfPGwVr/MABv4WN/rOC6LWjMnNGtxa6/1DHJrccVJe3zcosEixVoH4RDwZU/XuQOy3182Y6R2Gi3Y5dsxvsq1IKIBnJrl0NoQvCIgpSHK0E0qZQzoHjGa3KzBPFJyuO43qTUfuQ0Ryovfd/7QigwnfNuaHdYZSiKTEMfUQrusMp7YkudlyNoW1SCEEJIY9EYqy9CCCGEEAJAF1qYAT/An2k+ELtkM+6xDqhSqaqBouwRujssBBSmpOK1ENplaEp45+0V70O/xt11TlS7sU84YBwevuytOHPZlApTEg63MS67pAN+cnjt2q8+er6aorHKVh9CEcrNHd+GSaPCuXsC3JWQs8YVYmd4VWVvshNrrINxY/rIilwdhRGkAgEsR4piiJT/DpwKLuVEqQJol/3Dno9H5f4VCzyNcHLtuqDmipuIKsqzHZSRt9uYXW4pvdpUTFhK2gGUIGXHQyp9Xg3c7nxuf0sQZ17hyyhjTS5lChf/6RPHzsj/thSvZHaQduCXjYcSixBCSH1CSxBCCCGEkAZCtwQJ9tG91xyBG6wT0Ugf6ZqiwLHjNKg7rJVzuvHuJROxdOrISMsWhEP278TbFo3HokkdxSfVHdVegjbNF7+pnoiohNEpifRdyy4CQiOgAFKri0E1Bcn/dAolnX7wGwVVeeMlvJbIWPNUip9w06nUCOsOqxKFmKdirShNDyskrW2UXYyisjQKmlu+xit+HuFQbAW6xysmiIvw37cPeETtClSWQJYgZSpBfO7TLUG8Lfz87gXKs5oQ8Sb83ZqPw83HABQsQYJsALn7c0fjkZffxFsXjMN/3f5s5n5FYzxgpPzz9zmvVkEpF3uEEELqAypBCCGEEEIaCC8liL/jp8b6QFfFLcKpBAkojTMNgW+9e2GEpQqOaQh87/TFrud0WazXbmPluOKSKeizDya6/3oXS5CAcQWMkkLqKqKWz9AFZdW2BKmWwzZTVZx5VeWgeYtzKjX8rq5ceJpPS3PFVkLJprVRw/W6ii1BGtAUROu7tRh7oqonteoDzoUygJVe4aBfkPAA47wHQa6pliWIpkw1nP04pCVIGWU0hcBGWYiDIiEy1npK1l71M3l0MyaP1i3rLClxm3UgZohXgCZ/y1g/5ZaadzqAUoUQQkhtqb+vKEIIIYSQkFzxjvkAgE+vnFnjklQfVY4Q1I9/qXghUWDA9r8obJpCEUQLwxEYPfLsBpUgbp9U4UpvvEO5vmrFqoDSwt2gShA9/kA0JQuC9j5gwFa6S8zH733lRNE3iytLVZB6uSOTg6QFKdpB7us+y1HeiNxhGdp71guRbup0va6UlVD4snjnX6/orvgGy71g9HUjgrpC0+7xstILbwnS0eS+9zSQ4jeYuUiAa8KjPpfTKs6vPzjXBeVYQmWy0Dd7CAHElLJMHFnsMssLKYHH5TT8wT4clpEIXR4nhhBYYx2MZ+yJeK11TsXpEUIIqS60BCGEEEJIw3P6wZNx/LyxGNVS+UdtveMZqLSEPLPaos4XMR5TsBHbZEdkaeou3A3d2qBBBIieaFYeXsF3gevSxyOJAZwVK+xmrX+XOsXvxjD8lT7OWwf1OR2WOZolSB1a3gRBE9R61Hl1dKMu7z+k8qAoLnoFWk9dyOyeyd3WAiyJtyqnlDFWjU1Uofa1kueoFWo/bOQg0KVdoXne5HHYzeWf+zhxwryx2LarDwd5uGT0Coyu5xfAWqQCS5BSw4A6/hW5ofQZG2VMt8Iox5LIEHrMqdxv0xD4w0cOQdqW6GgOvu5Tx/YgzcDvEiEyLgXXy8n4CEVrhBBS93CkJoQQQsiQYDgoQIDyLEE+v2oW/vHcdpx7yNSqlOlvWILJ1ig8IyfiPyJKU7P8cOzGr7UeYHSFbU2zjPC4xhACb6Ate32NgxP7oAqpXN1haX94l79WO+XVvJwKt8Z1h+WveOpuHxz3LWFjejiFrVEFRveyBngdbXobUM6pfv4r1WEEjWVUT2jWM+bQUIIEHVu82q2rEsQjzdlj2zB7bMA8PA1BgpQ3mrbVI1vQLvYU8lbc6jndWfn1h/6WcY7ry3CHZejRVKRS04snuyuWSqFvpvDHN3B8AysGCSFkONJ4KzFCCCGEkGGMFqhWFdaVuGfK6BY8dNFx+FSV3IX1IYF1cjr2IjqhqipgMQ1DCyxbK8HDr89fhqVTRuLqf1tSUTqGcBe46tcUftfKTZQfS6ZkhFAHTlGEUa7usIKVX48/MKj+sLQyaJYgVa5wEYkWpDgRQ3Nj5P7Jd+SMMfj8CbPw2wuWRVEIT8IqQZwBhit5BZ6Wc9rY6RDuarqSYK7cApVFy7+ipAYNtc4aUYmTwyvYfVlphXGHlauz9km+6Vqmu1unQJYgEbSn/2etwHNygiNdb2WqbzB4w8B9dsFFVDn9xxB6/7Qr7DhRW781oHEXIYQMa2gJQgghhBDSQKgC9JiyM9eyS3/dV9MVSzV2s2tKj5AxBarFYTM6cdiMTv8LfQgbE0QX5NaPIPJnZy/F3c9uw8zWPtxybeaY245oz934Ja5zCqariXAoFlVBWSO6MAIcbcyjyg1D4CNHTY80XzchY5FCy0cSWdzfq2AJ4tgLqMVQUZWOZedcjB6TpEEI2HfrnaCKWP0m9+d1jwniUTeHfhJI9wJNHZ7Z3GIdhDHiTYxr9lCUROG3KQAvyW6MEzu0Y6UCkPsqQZx/l6EEEUI41heiIh2W7g7LP6GO5njJ87QEIYSQxqJxVzKEEEIIIcMQLZyECK4EaTS0wOgOIepQigkSRAmixzWon+X7yJYE3rZoApJxxV2Zq798bRu2Z3pqAO/BFC7pShA4wvk2Jnpw79q2mbB6pKJXX4EiyisOgfrO46aBg/cbpWRXnf42qNZNEaEK/BtVIQjA0aiCvVOvecbdEqTw+/XUVOyTCcTnvxOIp4Cm0m6bnpJTcLe9sISyw7/ey1dQFe6TcCocHPGciiymfGKCwOEqsQwFfsYdlqIcr3CZE/T2K94xH2cum4yjZnaVvI46EEIIaSxoCUIIIYQQ0kCogig1Jsiy/Ubhfx9+BW2pobG8UwWRTpdEjS540N1beew21jz3ROeSpxr4BR1WBfKlBKllBS+OhFrlC8gqRQXRg3sPJqXdoQVKwXF9JZYgujsf95ggV55+oK5oDWCpVXlZIku2qnjWX4Ohj6Hh73GcKT6ktJ8VSw7E7CUXYmpna4gSliyI7yXl9hGBgmIgowRxKDq0sdsRq8dHOSOlhJSqgtkscbU7TndYmd/ldx5ViVIqldMPnozTA6RXj/MxIYQQb4bGVzIhhBBCyDBBj5VR+OOSU+Zh/65WnLxgfA1KVQWk6g5LP9XoLiiC+Xh3d0NTj375DT8liEcsBiflCCqjQd+tLKJ2HF8LtJggg1mZxXVXLCwtXb/FsQcqafMe7pxKBDxX+9j4jmZgVwXZu5akcRABFZj1TjnWPV6WO2kXUzG9noBpXSNClc+nJP5XRPBunJYbAGCYJQKjB7Ds0AKRlxUTpDgweiVoSufGbc6EEELKhEoQQgghhJAGQrOQUIQS7U1xfPToaP37B6UaMmOv58yciz6/wUR3v+Tlrkf9XZ2d6VFh+Lm78nLtVXRZbeIP6C7mBlfJVKW46NpbGNwd/C6WIGHd4BRZgpRfGi1AvPrbSzkCvT20JE3866KVSMXD72IvLot3TJJ6Raszo/I6qBlljaHu11ku705tT+Uq6b3mUemS3h+sw/B2855C/lWaF9T+U2QJ4pOn83HKUdRk8lctQSpTkNuaDqT+5lJCCCHVpTFWX4QQQgghBIAuAHAqB4YSWkyQkAFZ6x5V6OrxDvVg6Oqt9ffsukDZzR1SUP8zNfo0Ee7C8UHJOhINoov1RY1ci9mVvP8sRU28AuF7EGWis3hOi5HO1iRak5XvHdTKUnFqg4MmBG/gcVd734HvcT/u1mV1BVt5hLG0eUmOxctyjFqC8jJVbnOzsjA1K0THPOxXXumICVJOYHQUu8NKxiqYJ1QlSMTNeQjYDxJCyJCHShBCCCGEkAailIXE0MJbeNLoj63Ha/B/mJhZ3+6w/AK9ixKCNP06fwuZaqAJx8sI3luPaAGNB1F47SZILRKe+rrDCrfjPGhaXumU2uEeZd01ovLWqFJdDDblvFPhISqx3aydEGyMK4XXfV4uoPR4G2Vl6aA4Jkip+FVBnlN3h1X52Dq2vQlzx7WVfb/qDivq1ty4vYMQQoYPQ2OVTwghhBAyTDA0QfHQ/ezWLEEMASELjtgbWRgHON5bAOlVPKb6Za+/5bu2C9pF0GUE3IWtWy9EUbKA1MZoAkD1dg/7WedUi7RIFB0LG0uiSOlZQZv3VKx5xAfJnBqEBtEgQ5ha95W8h1pTlmWU13VWf9Ehze1a2UoQ9+OqsiPHf52xWB87IlIaO/MySyhTw1rNlRNTRkIfI1efNKei8UxW0RKEEEJI/dO4KxlCCCGEkGGIEU5+3rCoj2YKASGtmpUlasLG+Bjf3qTeXI0iVYQqXHbzXx9UeOqnTKkeNYy5Uq0g7INsCXKrtQSvytF4rnlR0TmvHfVeFAdGL7/8mkWMh97DWT6tf1bpc7nSAM+DhW7F1bgxQTTlcVBLEC/LDNtlLtKUbaGKptzn5Rqx+NgpC8fjgAkdhXsjWgw4R6NSMUH8xnUJqbXzcqz7bLvyNPQyFWBMEEIIGX4wMDohhBBCSAOh+zYfuh/xmrGEAWxrngHgTrwmR9esTJER0DXLf1+wHDv29GPSKAMPZY+ZdegOy69NBhVclYrTUE10w5zBVoLY/teUgb7zvfpt5gm5H56w9sMpRlPROWHqdTpgtpROrMj9XQVKELVNeQjCnbJcQ9eQDGvCWq3VK2W5OPO6zEVxqSuLwpSswP5jWsPdUIW1gFM5VyomjF89WnblMUGcZapUoWtXS+lMCCGkIaAShBBCCCGkgZDKR3wyXn8C8aiwY83536Yh0JsYhZ+l34peJPCNGpYrCoLuNF+xf0bhs+G1bYXr61AQ6Rc3QBeCewuhhJe7oqrjFJQPnqCsRzZjBHZVlIabXLCeLMacwtPtbXNCXV+R4FOzulLbVImAz8NE0RwEfbd/49aFrgQJqJT1eF43tWUlCtw/fexQvPxGL+ZPbA91X7Vi16iYmrLQ22LKDcvWx9Fy5q6JI5vQlDCVSq/sOekOixBChjdD98uZEEIIIWQI0t4Ux4ppo3H4jE50jUjWujhVY2f7LDxu74ebrWV5QcxepCCHwPI1rPBKF2jXn+TGz9++bglSSglSG+Gz830MZg3PPPpMvGCPx545Z0Sccm1igrhlpdboM/ZE2PBzq+SM0VF+n1dv9VTWFSlB4H7dMEQYhXdlNnJMkHLmDY93v3TKKJdLy+9vCyZ24C3zx4UrWyYj998VUOQOq9Rz+eTptLoox5WVEALXvW+5kkboJDyJmcO7bxNCyHCEliCEEEIIIQ2EEAL//YHl/hc2OiKG2+wlAKLzd14vaEF0AzybtgO3Dt1h+bn30t+f9/MOxs5mj4xrky+A9xx2AA6e8yVMGdXsf7EH7pYgNYxz4sC5g9zPJY2zv1cSB8Az/k4Jy4By4kcMXTzciVWV6C2xyhlbvJ63fWKxJVMtFLiaC66o5kinKzqtK4TLY+nUkfipFs+jvDJWa16INbBSjxBCSHlw5CeEEEIIIXWHVARh5lATRGqCWf/L9cCw1ShQZejC5eLzgWOCGOHqJTJE5YK68rMW2K+zpSIh5i6jrThdNb0aK86cgkvLzy+/o7yVxQTxKEeJd64JsavWHuqwI7ugvqmGVkaXowTxekeJYoVlTaqmKhZLTgVkib7gk2fXiBROVC1cylQ6qGUotw2OyVrNLpzYjs7WzO8TDhhbVlqEEEIaF1qCEEIIIYSQukOVkxqGGMQoDdVHF1iFswSpRw8ehs/zqAL5UvLvmlmC1Mh1VFSsTy4E7DcxW2xCSvRnDlbBVU65OLO37dK9uegdVCBh1ixBlH6kiWMNp9Klfqxoao3ad+Omnxuz+sUoRykY4tVXU3HmlZqnlVMleTnSqXQDwqiWBPZmf5erzDSUSa/c0tz4wRW4bu0GfOCIaWhOxPDyG3sxb3y4GCyEEEIaHypBCCGEEEJIRcgqqCi0HchDTA6pKjWCWEloKoY6FMr6uYIJKvzS06mN9YIhjLqs41KkRQL32IvRZvRimngVgH+w+mrhllOxO6zSaRQHKq/AHZYyeGh9TXUl5NzgPkSCgUdBe1MCiyZ2AAA6mhO1LUwF6G2oQksQ94tDph4tkbngCmfsESA9j/4XCjWwT3lpTO1swSUnz8v/3d4UvQKkwaYNQggZllAJQgghhBBCKsLPu02laZqGqEoetUI3nAhnCVKPAnq/MgVVgmjXDaIORHU3NnhxD6qL0ISPNbYEcfxt+WtBHH9GZAmiKdnU9B0xQeqo7mqPwFGzurI/B6kuqjDY6+808E0h0i/rtsrwsHKqMFH9rwofRlXOOJWhQTF8lOyEEEJIUIbGKp8QQgghhAwpVOuSoSaI1ALaBrEEqXOhrJ8ALrjQURHq1UrYVYf160ehyF59prbP5GyzvoHRnTFBKjAF87SIKdHvwiopy6FhdLoN2B/c8IoHE/gev2tL/FUt1DGyXGWF87ZFk0ZWUqSS6ZfblNRnMxvYJRshhJDaQyUIIYQQQgipiEGxBGkcsaEvQhQEOUE8hGguferQN5ifAE49X+o91iomSL0Hni+LOlKcFcUE8R0wonOHpVlRqco61ejILBGIfbgrQRBMcVTv6K80eiWIoY1xg0PU4+VvL1iGeRM6Kk5HRat3s0xLELXfxpIVlogQQshwpnFXMoQQQgghpC5I23bkaUpFUFppcNZ6Q99pHiQmSH27wwolZC8hANd27Q+iNkIWKQwaR0TtRTWCJgfLtzgv57GVc7p90ojOEkS3ovJwjVUiJgjd7yh9YZCUILIKda61qcD9oTx3WM9t3R34vkrQ491UXmeH7N9ZcRpOhNJ8yg+MHsPN1jLcYh0ExJsiKhkhhJDhCGOCEEIIIYSQivBz8V8OWmD0IbM9P4PQAqP7X68+fz0qQUK5jSlxqS64r81eLWE0stjb3aJlMOOc+NXdlNHNWDy5tMsdpwIsqrag14Oq6PCOCTJU3EGVjWK1BnNwduFPH9OCR1+NNk0vBVgpwrnDKlybiA1Sf4vAEqQaCicVLcxTmf3YEALPyEn534QQQki5UAlCCCGEEEIq4gsnzMY31qzH+w7dL7I0nQYDQyswuhroNUhMEFVgW3/owi2/F1UiFkPNAsC7Wws0MvXaZsa2p3yvcb77StqCHlRZSdNwv8aZX7WErrVS8oUmlgDmv6vwexA4YuYYdPV1Y+LI6Hb9hx1znff4obp4O2LGmOAFq4AoYoJUG63ey9zMoCtS6vM5CSGENAZUghBCCCGEkIr40JHTcMIBYzF1dHNkaTrjBgwhHUhoV0Wa7Miov8CwUbmuqguDnyEiZBN1bj1UCmd5jQosWbzvVYWzTvdb1VdQ1EVbD0rnjEHNLmYIHDChPdpEtf4Q9KbgL0m1/hjdGq2yyMtaQ59Hosos2plWKDN3uQpmGmYRQgiJCipBCCGEEEJIRQghsF9nS6RpDiXLDyeGvrXV93pV2GXFR1SjSBURRjAlSgVG19yE1WanvCEMvJqchhF4BG/K1pqUIQpqFkcmgqyc7u8qswQp1IPa8koFldb+qlI7jFpQTkpjlHjfXoRR7sZNA+cdkrGETEbsDstrzNTbcJkKhhLn/mXPKitNz7zKjQmiWWZFVRpCCCHDkbJmy6uuugpTp05FKpXCsmXLcP/993te+9Of/hSHH344Ro4ciZEjR2LlypUlryeEEEIIIcQp9hlKSpGw7naEAVyfPhq/t46ATESrbIoCXeBeQTp1sOVXGALb4hPxm/Sx+K11bE3KEAVaVQ5iTJCxbaXdXQWJQSARnWWGMDUnWK7lcPZBo6wg2sE4ecF4LJrYgSMHyWVSQzLtKMCIAVNWRJakriSoztjS3hRHe1M88vQ9+0wErqZKcZ89p+I0tNheFcQEyVHPVm2xQRxnCSGElEfokfqGG27AhRdeiEsuuQQPPfQQFi5ciFWrVmHr1q2u1991110444wzcOedd2Lt2rWYNGkSjj/+eLzyyisVF54QQgghhAxN5FDSejgQmkurIO6wBDZjNF6WXdUrVAWEClwfMDB6rXy/G0JkFCEYiX7Ea1KGsLgJBnV9UvXr8ufnLMU7Fk/AR46eXvK6UpZAOYqUEpUoQbS4CR7XOC1PDHfrkSjYf0wrjprVBTPWGG2rJrR0Aod/JqMMiYiw1ndABXFbIu5vQSxBqmE5F0XbF8o8Xm4/NrRXV39KkM8ePxOzukdEGhONEEJIdQg9E33nO9/BBRdcgPPOOw9z587F1VdfjebmZvziF79wvf43v/kNPvKRj2DRokWYPXs2fvazn8G2bdx+++0VF54QQgghhAwP5BCKCqIJr8wAgdE9ftcLUQV5roddvgICB00dVetilMU+xLF8v9H4t2VTNIHjYFTrsXO68Z3TFqE1Wbm35WLLjArcYXkIXtXd9UWBsgejwtonVj+PRibqXfVaTJDoA6MDACYuBZpHA13zwt1XJnqw9+jbbBCrrTCUba0SXn81qHzsmBm45dNHoL2Zik1CCKl3Qq1S+/v78eCDD2L16tX5Y4ZhYOXKlVi7dm2gNPbu3YuBgQGMGuX9cdHX14e+vr783zt37gxTTEIIIYQQ0uAUGYIMHR1IaPcemhC4DoVAUQmmjCq7d/FCUzIZAmccPBmJmNFwypB/2AfgJ0stYMKB2JKufXwVN4IIVotidFRQfq9n15QgjkvUphe1IBgHfwDYvQUYXdpihkSLHkQ8qCVIyHc/47hw11dIOc8UhqjbfvmWINVV9hBCCBk+hJqJtm/fDsuy0N3drR3v7u7G5s2bA6XxhS98AePHj8fKlSs9r7n88svR3t6e/zdp0qQwxSSEEEIIIQ2OPZTdYWlxCgJYgtS53CdMzAkpTM9zulCvNoJ7IQyYhsB7lk7Cfp31F3+lFHvQBCw8Deic4QiaXMNClUGRAqyCB/AKMyNVayxH+9X/jrjyWkYD3XMb76U0OLogPSh1/o6qrDSOYgZWLTjLLaLRwGMZIYSQ+mJQvy6uuOIKXH/99fjDH/6AVMo7aN7q1avR09OT/7dp06ZBLCUhhBBCCKk1Q9gQRI99EUAypBuC1LcUyKt099uzsVWOxI7Wmd73ai5ravWc9V2/briVWJTh/qd+cD5RBUoQpR5Mra95C8X1ptd47YEUo42hgWOCVKkwofGY/UT0fVyNPxKJJYgaE6TMCq1HF1iEEEIak1DusDo7O2GaJrZs2aId37JlC8aOHVvy3v/8z//EFVdcgdtuuw0LFiwoeW0ymUQymQxTNEIIIYQQMoRwGoKMbknUpiBVIKzFQ70rPoJwr30A7sUB+Irp/R71ehmMUrkWokYZR4seELyxnqnIbU4lMUE0haP7p6/TZVY9uQ8j0aDHfQnYnuq+HTRWHy+3iI3wbIQQQhqDUDN7IpHAkiVLtKDmuSDnK1as8Lzvm9/8Jv793/8da9aswdKlS8svLSGEEEIIGRY43WGdc8hUvG3ReHz/jMU1KlF0hN0Rq+6EHcryoHpwhzVUMLz8QDUAxULPaAKjJ+MFJYg6ujgtAwYzHg0ZHMqxjKof4bu/HWRko6XyyN95z6KKk1NLTksQQgghtSaUJQgAXHjhhTjnnHOwdOlSHHzwwbjyyiuxZ88enHfeeQCAs88+GxMmTMDll18OAPjGN76Biy++GL/97W8xderUfOyQ1tZWtLa2RvgohBBCCCFkqJKKm/je6Y2vAAF0F1imGSQmSDn+7BuPIK7Bqk89lKFy1DgtdfVEAfzaFQmpKxBGm4aBOXMWwBrYhwnjJ3rk51CCDK7HaDIIlBcYvVqlCYdXa9TcVUU1dir98+2LJ1ScnFDSK1cJUj/KKEIIIY1OaCXIaaedhm3btuHiiy/G5s2bsWjRIqxZsyYfLH3jxo3ajpsf/ehH6O/vx7ve9S4tnUsuuQSXXnppZaUnhBBCCCFDEjmEA6OHdbOiW4I0tkCoVOn1WCk1EkQ3eP3mEI1sCVIk0K2g/EJg1Xs/lf/thlM4Wx/KOBIlhmYJEvCmOrFG8xrz1ePlKhjKyTcMUQRGZ28khBASFaGVIADwsY99DB/72Mdcz911113a3xs2bCgnC0IIIYQQMow5c9kUXLf2Jaya113rokSOFqcgSEwQRRiViteHYK4a1IXFS4MpDAD3ItetEiRAUYoEupWW3+d+p8KNMUGGHoY2ttRRf/DgrQvG46ZHXwUQTHkgaqU0DkG5SpV6HcoIIYQ0HmUpQQghhBBCCKkmI1sSuG/1sUNyV7YqsAr6fPPGt2HLzj7MGjuiWsWKBOHj72hce8rznLZbuwGEevVMYwsLne++ug/jFIo3dt0RVzRJetCxpXYNYfqYgttwT6VNlS1BImEoW3QSQghpOKgEIYQQQgghdclQVIAAjl3JAYX9f/rYYbClRDxADJF65BfnLsW6jW9i1byxnteIBtutXc8YWkyQxqrLoi5RZQFvUWB0WoIMObSYIAHnFVnLfnPQ+Ri59ivYvieNBSuO9708uj4erdIiitQSypw3qiURQYqEEEKGK1SCEEIIIYQQMoiUI+w3DQGzwYTZKsfM7sYxs0u7NtMUQhREB2bplJG46+lt2jG9XTVWuyl2RxV9+dUN6qUDsXMn+1BAt5RogP7Q2oXTPvdDbNu5D5NGt/heHlSxU3RfA9RFzDRw9+eORtq20Zyg+IoQQkj5cBYhhBBCCCFkENEDnVPYn0OPlVLDgjQYFxwxDa3JGA6fOSZ/rGaB5aOgKC76YDcGNr6hhqpgNYMOLjV2MZWKmyUVIGrpyo1j41TxRf7IEbnDmjy6OZJ0CCGEDG+oBCGEEEIIIWQQUS1BGllWHTVavTSo269akIyZOPfQ/bRjmrVRg8n0i3anV/0BBjs/MtholiBlvF6B+rMJkppbRbZZQgghxA9+XRBCCCGEEDKIaPEaDLOGJakvtMDoNSzHUKCRhaJysMXNRUoPusMaauhKwWAiEDUmiKhzxVi9BkYf9L5MCCGElICWIIQQQgghhAwiMjECG+0u7EUKIpasdXEipRIf85qgkW7CKsKo27gWAdqHEa9+MQLGTKmnmiPlow0tgceW+lQsuFG3sT2kXesSEEIIIXn4dUEIIYQQQsggYhgG/tc+Amvsg+t2B28tEFpMENZLJdSrUDRIqaQwscEeW/Wy5HG2NUGLpKGGan0X2EiqztuBpsarU8uvvlhbrYtACCGE5KEShBBCCCGEkEFEjQNCWX8BzU0YLUEqol4DowdxjzO2PYU34R0QOqqSeCKCWYmQxkEbT8oYW+rTIii8iy8nRbqTiAKZ53i9ZTr+YR2A36WPijRdQgghpBzoDosQQgghhJBBRLVyoMVDAa1e6nRnc6NQ7zEMSpGKmzho6kgMbBykDBu4rkgwyhpz675dVK6sc+o8RNTuq4SBB+TsaNMkhBBCyqQ+twgRQgghhBAyRDE0//S1K0dVqOR5hGoJMtQqZnARdVuXwcqSMM2qliLofvd6qjlSAYZqNRH+rdanJUj09CY7a10EQgghpGrQEoQQQgghhJBBRNASxBXNg9Mg1gvfwGASrLbrRujMxjEkMMpRCipmEgJ11CbdKHO8dLqn29k0CTdZy7FdtuNTERSLEEIIqSdoCUIIIYQQQsggoio+qAMpYBoMjB4ZDV5/9RrYnTQmeoyc8G3rhCMORTJm4JilC6IrVJSU2d+FU7UjBJ6TE/EmRkRQKEBGHGOEEEIIqQRaghBCCCGEEDKIqO6whpqwX1YQ0Fx14VSvgb0bh/psVzJguYZYtyA1RrP+KKNtzTziPZgxdzHEmFnRFapSqtBJ7IiVFtSBEEIIqSeoBCGEEEIIIWQQUXe5DxVZr5QCQki8mZpYdhrCoJuwyGD9RQItUoYG6nsMqojTMJMQEw6MsET1SUsyWvHQgE0tCCGEkPqBShBCCCGEEEIGETEELUF+Yp2EEejFaYkxZaeh+u1HBRYlpI4J2N6r3S2C7lAvchdEGhJtaClnbKnDcTqKlulM450HTsR9L+zA0bPLH8dVBiw7knQIIYSQKODXBSGEEEIIITViqChBepHCVoxEawU7ibXgxYP4lTIkBd1Ku4oZtW9j22QHAODV5P6Brg8cvLpsal8nZPBQ3euV19uHR3tJxU381xmL8fbF5Vv0qbx1wTgAwIyu1kjSI4QQQiqBliCEEEIIIYQMIqrf9aES+uIzx83E3c9uw1krppSdhqG5wxoiFVMHVF+h4M/11tFoRS9WJsYHur76ZQ4mCq99zZEo0JXNZbzVOuhDjci88e2494vHYHRrotZFIYQQQmgJQgghhBBCyGDSny64CEnFzRqWJDo+fuwM3PihQ9CcqGCPlWBMkGpg1kFVWjDRg9bAsuS3HDAWhhBYMKGjquUiwwND0zaX0yHqoBNVgcEIXD6+ownJ2NCY5wghhDQ2tAQhhBBCCCFkEBnf0ZT/HTe5JylHxbu1iSvtzfFaFwExQyBtSxw+ozPQ9d1tKXz0qOkwq+TKazCEv6SeCN+ONDd5Q1QpOyRdARJCCCEeUAlCCCGEEELIINKSjOH+Lx2LRIwKEBWjRr7BhqogcNXcsXizdwBzx7bVuij4xxePwZOv7sRRs4IGXBZVU4BkUg/oDmtoyr6HHRWPLUO1IQzNoY8QQghxhUoQQgghhBBCBpmutlSti1B31C4OyNCUBM4ZV3vlR47uthS666jNB33j9RBPhVQO3esRQgghhNvPCCGEEEIIITVH1Ch4xVC1BMnTkALgIf5OyKBSKyuzqkKfboQQQkgohuBqgBBCCCGEENJwKEK9luamEheSUNTMwqYCpF3d5Ck/HlZoFj1DRCESjfKWHYEQQsjwge6wCCGEEEIIITWnbUQbJs4+GEIYGDVy5KDlaw91OWBDKkGq+1L8BMijmhPYsbcfB00dvHZIqocQAlNGtWB3Xxpzxo4IeNdQHxgIIYSQ4QWVIIQQQgghhJC64F3/9uFBz9O2q2t1UDMSLUD/HqB9Yq1LEp6OycCrD1cteekj4D5z2RTsS1to6aBF0tBA4O2LJ2R+xhpQKVglqOYhhBAynKAShBBCCCGEEDJskUPVN9LyjwD2ABBvQEF+1xzAiAEjumuSvWkItCT4qTxkaMi4ONXHnHkc7HU34MWmBbUuCiGEEFJ1uLIjhBBCCCGEDFveMn8cfv/K41g0saPWRYkWM5b514gIAYyZWetSMHjIkCG8EmTAGvrv/vy3HYebJs3Ae+dMqHVRCCGEkKrToKtiQgghhBBCCKmccWPG4CNHTYfJ3eLDhrFtKWwIciHbxNCgjPc4fUxLsDbSwKTiJt61bP9aF4MQQggZFKgEIYQQQgghhAxfph4Gc2AP0DWv1iUhg8SqeWNx3/YOzOj2CZLdiEHlSTGqEiQZLDD6ARPa0bJwAka2xKtUqMqYMroFr9S6EIQQQkgDQSUIIYQQQgghZPgSTwFz31brUpBBpClu4KhZXf4XUgkydFh4OtC3C2geFfiW/Tpbqligyjhoagda5nSjuy1V66IQQgghDQGVIIQQQgghhBBChhEB4z20jKluMcjgMWq/cNfXeTwYAYF549trXQxCCCGkYaAShBBCCCGEEELI8CHeXPr8gWcDuzYDnXUQnJ3UBoOiEkIIIWQowZmdEEIIIYQQQsjwYcJSYOer3kqO9gmZf2T40n0A8MaLwMiQFiSDRdcc4KV7Q7n3IoQQQoYzVIIQQgghhBBCCBk+xBLA/HfVuhSknjFjwLy317oU3rR2ASs+AsQHL26JlAJCSPTJxKDlSQghhEQFI70RQgghhBBCCCGENBKp9oyyZpC40ToSr8hO/N46YtDyJIQQQqKCliCEEEIIIYQQQgghxJNX0YkbraMgRK1LQgghhISHliCEEEIIIYQQQgghxJNrzj0Ik0Y14YYPrKh1UQghhJDQ0BKEEEIIIYQQQgghhHhy9Owu/H32MbUuBiGEEFIWZVmCXHXVVZg6dSpSqRSWLVuG+++/v+T1N954I2bPno1UKoX58+fj5ptvLquwhBBCCCGEEEIIIYQQQgghQQmtBLnhhhtw4YUX4pJLLsFDDz2EhQsXYtWqVdi6davr9ffeey/OOOMMnH/++Xj44Ydx6qmn4tRTT8Xjjz9eceEJIYQQQgghhBBCCCGEEEK8EFJKGeaGZcuW4aCDDsIPfvADAIBt25g0aRI+/vGP44tf/GLR9aeddhr27NmDm266KX9s+fLlWLRoEa6++upAee7cuRPt7e3o6elBW1tbmOISQgghhBBCCCGEEEIIIWSIEVRvEComSH9/Px588EGsXr06f8wwDKxcuRJr1651vWft2rW48MILtWOrVq3CH//4R898+vr60NfXl/+7p6cHQOahCCGEEEIIIYQQQgghhBAyvMnpC/zsPEIpQbZv3w7LstDd3a0d7+7uxvr1613v2bx5s+v1mzdv9szn8ssvx2WXXVZ0fNKkSWGKSwghhBBCCCGEEEIIIYSQIcyuXbvQ3t7ueT6UEmSwWL16tWY9Yts2duzYgdGjR0MIMShl2LlzJyZNmoRNmzbRBRchEcA+RUi0sE8REi3sU4RED/sVIdHCPkVItLBPERIttehTUkrs2rUL48ePL3ldKCVIZ2cnTNPEli1btONbtmzB2LFjXe8ZO3ZsqOsBIJlMIplMasc6OjrCFDUy2traOBASEiHsU4REC/sUIdHCPkVI9LBfERIt7FOERAv7FCHRMth9qpQFSA4jTIKJRAJLlizB7bffnj9m2zZuv/12rFixwvWeFStWaNcDwK233up5PSGEEEIIIYQQQgghhBBCSBSEdod14YUX4pxzzsHSpUtx8MEH48orr8SePXtw3nnnAQDOPvtsTJgwAZdffjkA4JOf/CSOPPJIfPvb38ZJJ52E66+/Hv/617/wk5/8JNonIYQQQgghhBBCCCGEEEIIUQitBDnttNOwbds2XHzxxdi8eTMWLVqENWvW5IOfb9y4EYZRMDA55JBD8Nvf/hYXXXQRvvSlL2HGjBn44x//iAMOOCC6p6gCyWQSl1xySZFbLkJIebBPERIt7FOERAv7FCHRw35FSLSwTxESLexThERLPfcpIaWUtS4EIYQQQgghhBBCCCGEEEJI1ISKCUIIIYQQQgghhBBCCCGEENIoUAlCCCGEEEIIIYQQQgghhJAhCZUghBBCCCGEEEIIIYQQQggZklAJQgghhBBCCCGEEEIIIYSQIcmQUoLcfffdOPnkkzF+/HgIIfDHP/5RO3/ppZdi9uzZaGlpwciRI7Fy5Urcd999rmn19fVh0aJFEEJg3bp12rlHH30Uhx9+OFKpFCZNmoRvfvObRfffeOONmD17NlKpFObPn4+bb745qsckZNCIok9NnToVQgjt3xVXXKFdwz5FhgtRzVN//vOfsWzZMjQ1NWHkyJE49dRTtfMbN27ESSedhObmZnR1deFzn/sc0um0ds1dd92FAw88EMlkEtOnT8e1114b8dMSUn0q7VN33XVX0RyV+/fAAw/kr+M8RYYLUcxTzzzzDN72trehs7MTbW1tOOyww3DnnXdq13CeIsOFKPrUQw89hOOOOw4dHR0YPXo0PvCBD2D37t3aNexTZLjg16dUPvShD0EIgSuvvFI7vmPHDpx55ploa2tDR0cHzj///KI+xbUfGS5E0af+4z/+A4cccgiam5vR0dHhem89zFNDSgmyZ88eLFy4EFdddZXr+ZkzZ+IHP/gBHnvsMdxzzz2YOnUqjj/+eGzbtq3o2s9//vMYP3580fGdO3fi+OOPx5QpU/Dggw/iW9/6Fi699FL85Cc/yV9z77334owzzsD555+Phx9+GKeeeipOPfVUPP7449E9LCGDQFR96qtf/Spee+21/L+Pf/zj+XPsU2Q4EUWf+p//+R+cddZZOO+88/DII4/gH//4B9773vfmz1uWhZNOOgn9/f249957cd111+Haa6/FxRdfnL/mxRdfxEknnYSjjz4a69atw6c+9Sm8//3vxy233FK9hyekClTapw455BBtfnrttdfw/ve/H/vttx+WLl0KgPMUGV5EMU+99a1vRTqdxh133IEHH3wQCxcuxFvf+lZs3rwZAOcpMryotE+9+uqrWLlyJaZPn4777rsPa9aswRNPPIFzzz03nwb7FBlO+PWpHH/4wx/wz3/+01Wud+aZZ+KJJ57Arbfeiptuugl33303PvCBD+TPc+1HhhNR9Kn+/n68+93vxoc//GHXe+tmnpJDFADyD3/4Q8lrenp6JAB52223acdvvvlmOXv2bPnEE09IAPLhhx/On/vhD38oR44cKfv6+vLHvvCFL8hZs2bl/37Pe94jTzrpJC3NZcuWyQ9+8IPlPxAhNabcPjVlyhT53e9+1/Me9ikyXCmnTw0MDMgJEybIn/3sZ5733HzzzdIwDLl58+b8sR/96Eeyra0t388+//nPy3nz5mn3nXbaaXLVqlVlPg0htaeStV+O/v5+OWbMGPnVr341f4zzFBmulNOntm3bJgHIu+++O3/Nzp07JQB56623Sik5T5HhSzl96sc//rHs6uqSlmXlr3n00UclAPnss89KKdmnyPDFq0+9/PLLcsKECfLxxx8vkkc8+eSTEoB84IEH8sf+8pe/SCGEfOWVV6SUXPuR4Us5fUrlmmuuke3t7UXH62WeGlKWIGHo7+/HT37yE7S3t2PhwoX541u2bMEFF1yAX/3qV2hubi66b+3atTjiiCOQSCTyx1atWoWnn34ab7zxRv6alStXavetWrUKa9eurdLTEFJ7vPoUAFxxxRUYPXo0Fi9ejG9961uayRv7FCHuuPWphx56CK+88goMw8DixYsxbtw4nHjiidqOo7Vr12L+/Pno7u7OH1u1ahV27tyJJ554In8N+xQZbpSap3L86U9/wuuvv47zzjsvf4zzFCHuuPWp0aNHY9asWfjlL3+JPXv2IJ1O48c//jG6urqwZMkSAJynCPHCrU/19fUhkUjAMAqim6amJgDAPffcA4B9ihAV27Zx1lln4XOf+xzmzZtXdH7t2rXo6OjIW/wCwMqVK2EYRt4VHdd+hBTw61NBqJd5atgpQW666Sa0trYilUrhu9/9Lm699VZ0dnYCAKSUOPfcc/GhD31IGxBVNm/erL00APm/cybeXtfkzhMylCjVpwDgE5/4BK6//nrceeed+OAHP4ivf/3r+PznP58/zz5FiE6pPvXCCy8AyPiPvuiii3DTTTdh5MiROOqoo7Bjxw4AlfWpnTt3ore3t6rPR8hg4zdPqfz85z/HqlWrMHHixPwxzlOE6JTqU0II3HbbbXj44YcxYsQIpFIpfOc738GaNWswcuRIAJynCHFSqk8dc8wx2Lx5M771rW+hv78fb7zxBr74xS8CAF577TUA7FOEqHzjG99ALBbDJz7xCdfzmzdvRldXl3YsFoth1KhRvv0ld67UNVz7kaGGX58KQr3MU8NOCZLzLXbvvffihBNOwHve8x5s3boVAPD9738fu3btwurVq2tcSkIah1J9CgAuvPBCHHXUUViwYAE+9KEP4dvf/ja+//3vo6+vr4alJqR+KdWnbNsGAHz5y1/GO9/5TixZsgTXXHMNhBC48cYba1lsQuoWv3kqx8svv4xbbrkF559/fg1KSUjjUKpPSSnx0Y9+FF1dXfj73/+O+++/H6eeeipOPvnkvMCWEKJTqk/NmzcP1113Hb797W+jubkZY8eOxX777Yfu7m7NOoQQAjz44IP43ve+h2uvvRZCiFoXh5CGZ6j1qWE3a7a0tGD69OlYvnw5fv7znyMWi+HnP/85AOCOO+7A2rVrkUwmEYvFMH36dADA0qVLcc455wAAxo4diy1btmhp5v4eO3ZsyWty5wkZSpTqU24sW7YM6XQaGzZsAMA+RYiTUn1q3LhxAIC5c+fmr08mk5g2bRo2btwIoLI+1dbWlnexQMhQIeg8dc0112D06NE45ZRTtOOcpwjR8fueuummm3D99dfj0EMPxYEHHogf/vCHaGpqwnXXXQeA8xQhTvzmqfe+973YvHkzXnnlFbz++uu49NJLsW3bNkybNg0A+xQhOf7+979j69atmDx5MmKxGGKxGF566SV85jOfwdSpUwFk+oJzM0w6ncaOHTt8+0vuXKlruPYjQ4kgfSoI9TJPDTsliBPbtvM70v/rv/4LjzzyCNatW4d169bh5ptvBgDccMMN+I//+A8AwIoVK3D33XdjYGAgn8att96KWbNm5U28V6xYgdtvv13L59Zbb8WKFSsG45EIqSlqn3Jj3bp1MAwjb4LKPkVIadQ+tWTJEiSTSTz99NP58wMDA9iwYQOmTJkCINNfHnvsMW1xf+utt6KtrS2vPGGfIsMZt3lKSolrrrkGZ599NuLxuHaO8xQhpVH71N69ewGgaIe6YRh5a0bOU4SUxut7qru7G62trbjhhhuQSqVw3HHHAWCfIiTHWWedhUcffTQv01u3bh3Gjx+Pz33uc7jlllsAZPrCm2++iQcffDB/3x133AHbtrFs2bL8NVz7ERKsTwWhbuapyEKs1wG7du2SDz/8sHz44YclAPmd73xHPvzww/Kll16Su3fvlqtXr5Zr166VGzZskP/617/keeedJ5PJpHz88cdd03vxxRclAPnwww/nj7355puyu7tbnnXWWfLxxx+X119/vWxubpY//vGP89f84x//kLFYTP7nf/6nfOqpp+Qll1wi4/G4fOyxx6pdBYRESqV96t5775Xf/e535bp16+Tzzz8vf/3rX8sxY8bIs88+O58H+xQZTkQxT33yk5+UEyZMkLfccotcv369PP/882VXV5fcsWOHlFLKdDotDzjg5zdO0QABAABJREFUAHn88cfLdevWyTVr1sgxY8bI1atX59N44YUXZHNzs/zc5z4nn3rqKXnVVVdJ0zTlmjVrBr1OCKmEqNZ+t912mwQgn3rqqaI8OE+R4USlfWrbtm1y9OjR8h3veIdct26dfPrpp+VnP/tZGY/H5bp166SUnKfI8CKKeer73/++fPDBB+XTTz8tf/CDH8impib5ve99L3+efYoMJ0r1KTemTJkiv/vd72rHTjjhBLl48WJ53333yXvuuUfOmDFDnnHGGfnzXPuR4UQUfeqll16SDz/8sLzssstka2trPr1du3ZJKetnnhpSSpA777xTAij6d84558je3l759re/XY4fP14mEgk5btw4ecopp8j777/fMz03JYiUUj7yyCPysMMOk8lkUk6YMEFeccUVRff+7ne/kzNnzpSJRELOmzdP/vnPf476cQmpOpX2qQcffFAuW7ZMtre3y1QqJefMmSO//vWvy3379mn5sE+R4UIU81R/f7/8zGc+I7u6uuSIESPkypUriwS6GzZskCeeeKJsamqSnZ2d8jOf+YwcGBgoKsuiRYtkIpGQ06ZNk9dcc021H5+QyIlq7XfGGWfIQw45xDMfzlNkuBBFn3rggQfk8ccfL0eNGiVHjBghly9fLm+++WbtGs5TZLgQRZ8666yz5KhRo2QikZALFiyQv/zlL4vyYZ8iw4VSfcoNN4Ht66+/Ls844wzZ2toq29ra5HnnnZcX1ubg2o8MF6LoU+ecc45rGnfeeWf+mnqYp4SUUkZnV0IIIYQQQgghhBBCCCGEEFIfDPuYIIQQQgghhBBCCCGEEEIIGZpQCUIIIYQQQgghhBBCCCGEkCEJlSCEEEIIIYQQQgghhBBCCBmSUAlCCCGEEEIIIYQQQgghhJAhCZUghBBCCCGEEEIIIYQQQggZklAJQgghhBBCCCGEEEIIIYSQIQmVIIQQQgghhBBCCCGEEEIIGZJQCUIIIYQQQgghhBBCCCGEkCEJlSCEEEIIIYQQQgghhBBCCBmSUAlCCCGEEEIIIYQQQgghhJAhCZUghBBCCCGEEEIIIYQQQggZklAJQgghhBBCCCGEEEIIIYSQIUms1gUIgm3bePXVVzFixAgIIWpdHEIIIYQQQgghhBBCCCGE1BApJXbt2oXx48fDMLztPRpCCfLqq69i0qRJtS4GIYQQQgghhBBCCCGEEELqiE2bNmHixIme5xtCCTJixAgAmYdpa2urcWkIIYQQQgghhBBCCCGEEFJLdu7ciUmTJuX1B140hBIk5wKrra2NShBCCCGEEEIIIYQQQgghhACAbwgNBkYnhBBCCCGEEEIIIYQQQsiQhEoQQgghhBBCCCGEEEIIIYQMSagEIYQQQgghhBBCCCGEEELIkIRKEEIIIYQQQgghhBBCCCGEDEmoBCGEEEIIIYQQQgghhBBCyJCEShBCCCGEEEIIIYQQQgghhAxJqAQhhBBCCCGEEEIIIYQQQsiQhEoQQgghhBBCCCGEEEIIIYQMSagEIYQQQgghhBBCCCGEEELIkIRKEEIIIYQQQgghhBBCCCGEDEmoBCGEEEIIIYQQQgghhBBCyJCEShBCCCGEEEIIIYQQQgghhAxJqAQhhBBCCCGEEEIIIYQQQsiQhEoQQgghhBBCCCGEEEIIIYQMSagEIYQQQgghhBBCCCGEEELIkIRKEEIIIYQQQgghhBBCCCGEDEmoBCGEEEIIIYQQQgghhBBCyJCEShBCCCGEEEIIIYQQQgghhAxJylKCXHXVVZg6dSpSqRSWLVuG+++/v+T1b775Jj760Y9i3LhxSCaTmDlzJm6++eayCkwIIYQQQgghhBBCCCGEEBKEWNgbbrjhBlx44YW4+uqrsWzZMlx55ZVYtWoVnn76aXR1dRVd39/fj+OOOw5dXV34/e9/jwkTJuCll15CR0dHFOUnhBBCCCGEEEIIIYQQQghxRUgpZZgbli1bhoMOOgg/+MEPAAC2bWPSpEn4+Mc/ji9+8YtF11999dX41re+hfXr1yMej5dVyJ07d6K9vR09PT1oa2srKw1CCCGEEEIIIYQQQgghhAwNguoNQrnD6u/vx4MPPoiVK1cWEjAMrFy5EmvXrnW9509/+hNWrFiBj370o+ju7sYBBxyAr3/967AsyzOfvr4+7Ny5U/tHCCGEEEIIIYQQQgghhBAShlBKkO3bt8OyLHR3d2vHu7u7sXnzZtd7XnjhBfz+97+HZVm4+eab8ZWvfAXf/va38bWvfc0zn8svvxzt7e35f5MmTQpTTEIIIYQQQgghhBBCCCGEkPICo4fBtm10dXXhJz/5CZYsWYLTTjsNX/7yl3H11Vd73rN69Wr09PTk/23atKnaxSSEEEIIIYQQQgghhBBCyBAjVGD0zs5OmKaJLVu2aMe3bNmCsWPHut4zbtw4xONxmKaZPzZnzhxs3rwZ/f39SCQSRfckk0kkk8kwRSOEEEIIIYQQQgghhBBCCNEIZQmSSCSwZMkS3H777fljtm3j9ttvx4oVK1zvOfTQQ/Hcc8/Btu38sWeeeQbjxo1zVYAQQgghhBBCCCGEEEIIIYREQWh3WBdeeCF++tOf4rrrrsNTTz2FD3/4w9izZw/OO+88AMDZZ5+N1atX56//8Ic/jB07duCTn/wknnnmGfz5z3/G17/+dXz0ox+N7ikIIYQQQgghhBBCCCGEEEIchHKHBQCnnXYatm3bhosvvhibN2/GokWLsGbNmnyw9I0bN8IwCrqVSZMm4ZZbbsGnP/1pLFiwABMmTMAnP/lJfOELX4juKQghhBBCCCGEEEIIIYQQQhwIKaWsdSH82LlzJ9rb29HT04O2trZaF4cQQgghhBBCCCGEEEIIITUkqN4gtDssQgghhBBCCCGEEEIIIYSQRoBKEEIIceG1nl7c+/x2vNbTW+uiEEIIIYQQQgghhBBCyiR0TBBCCBnq3PDARqz+38dgS8AQwOXvmI/TDppc62IRQgghhBBCCCGEEEJCQksQQghReK2nN68AAQBbAl/638dpEUIIIYQQQgghhBBCSANCJQghhCi8uH1PXgGSw5ISG7bvrU2BCCGEEEIIIYQQQgghZUMlCCGEKOzX2QJD6MdMITC1s7k2BSKEEEIIIYQQQgghhJQNlSCEEKIwrr0Jl79jfn5wNAB8/R0HYFx7Uy2LRQghhBBCCCGEEEIIKQMqQQghxA3h+D8hhBBShzyy6Q389O/P45FNb9S6KIQQF17r6cW9z29nfLmIYb0SQgghJAyxWheAEEJqzWs9vXhx+x7s19kCAK6B0Y+YOYbWIISQukAdszgulcdQqcN3/egf+NdLb+b/Xj5tFPYf04JjZnfh2DljI89vqNQbIYPFDQ9szK8rDQFc/o75OO2gybUuVsOj1qsA8MUTZ+ODR+5fk7KEGRfPv+Z+/O3pbWhOmfj4MTNwwRG1KTMhhBAyHBFSSul/WW3ZuXMn2tvb0dPTg7a2tloXp7b8/Hhg0321LgUh0TLrLcAZ/+1+bu1VwC1fAgBYHrebFWRtORM2PTIyK8sHZgqw9lWSgjftk4FPPwZctRx4/TmgbTzw5kvRpT/rJODpP1eejpkAEi1Ar8tuZSMOXLwd+MUJwMsPAHY6c1yYQOdMYNtT5efb0gXMPQUYPR1YsxqAMu3FW4BkK/DZZwrHvjEVGNgHGCYQbwY+96x7ul/tBOwBoLUb6O0BhAAu2pw59+9jAKs/k/eercX3Tl4B9LwC9GwsPjdmTmXPm6NjCvCpRzO///sM4IW/AQN7Kk9XxUwAS84F7v9J6etiTUC6gp2aRhyALLSLHK3d+ru7bBQg1Q4skH/frd3AxIOA9Te555HqAPa9WXxcmECqrdBux8wBtj0NwC6+dvIKYNP9jjJkibe413/TSGDMbGDjWvdymQngK9vyf/b9+3iY/Zl0BmDiT6esq2+B2s+PB3ZsAJpH5dt1OtGK3x59D46591yMSb+CZOuY7DmZqacvv1q14nz+d+tw40OvqKMAzlk+BQdMbMNhfz4WlgTOaf0JTj94si6cunIBIGVmrK0Tln39NmzZ2YfLYr/Au8TfsFM24684CLPFRrRhLzbKsVh1yIHo/9cv0WMn8TfzEBza8hrGJfuBUVO9512FG/+1ET+441n0Dlg4YHwH7ni60Ba/8U4KcwkpxWs9vTj0ijvyG2uATJy5e754dDRKxO/OB3a+AggDaBoF7NmSOd41F9j6pPs9Y+YAb2wA0vuQmSOzc1luHRaGb83IrHGaRgJf2JApj3Ndk+oAvvhSZgx9cyPyc3Ju7VoGbvUKAKvfMhsfDKFUcFNe5I61JEzs6bd8FRs3PLARX/ifwnO4jYuPbHoDP737Bdz02Oai+5Mm8N5lU3Hq4vG47cktuPqu5zEggQXj2/CnTxwOALjs/x7Hb+57CZYEjpo5Bj8/72DXsjyy6Q28/7oHsG33gHa8OW7g/MP2w2dWzQ5WMcOEM3+yFve/uAMTRjahszWJx1/twZxxbfjhvy2JTsm/9irgli8js75pBcYeABz7FWDqYcA/fwSs+RK09WT7FODTjxb+/s+ZwO4t7mkbMcC2oH3XuDFpeVZ+VKbYz4gVr79LXitR+JgWQFNH5rtq3AJ3OZYRA5a+z/tbomMKsK8H6NvlvcZXESYQbwL6dwcrcygMuK7/q4Uw3b8pMiczY796XpjZKvG6Z4gR1TdzIDzam+fxqDEAM5aRLwCZb2vDBI7+ErDio4OQf/0TVG9AJUij8YsTvAUlJDLUTkFvSIPA7LcCp//G/dw/fwSs+WLmnXiNVkL7X2BKpumSB9sCISQqypln0gBM5UY79x1mAmaqAxgzq/gD86D3A5sfC7aBouhD1wAuDeli6d+78wpf5RM4/y837lo2YBrKBdn/WQB6jVbc9+4Hceyfjyh8/CdaM0K+nNDsiil5pZUFwIKAma3VnMK6SL9t5Y4LWDCQcPlINIxCvUojo9uU2fttuCjDhQlcsgP42tiMkmTGSuDpvwDSzii3PvpP77r682eAB37mLAFw7v8Dnvw/4P6fwmuScirwjWxd2nbht0ruuET2Xbg1OiMOzDgOePpmAEDa0tvmVtmOMaIHALAdbYhLCx2xPZk6KbWZAcgIgh76lfKxagBN7RnB6T9/BDz4S2BbVmhb8qM/AF4KxyDk3ieQEeju3pp5p7k1yqUjkReAxJqA/Y/OCLP++hW9zF1zga3r4SosGTMH2P5Mpo3kBBhmArAGEMmHtJkADjwbOOnb2kYSAJl+9KVXMr+vWq4LD+ItwLQj8+9fY/ZbM/93UyLPeguw+fFi4fcJVwDLP5wRfucE9bmPdw0BnHA50n+9GLAL5/PuClZ9HfjbN4F9O6HVpzABM54V5jsxgFgSSPeh6B2s+np4gUF+XBOZ5332r5lNED5YcMincj8q3VhTZSwAfS0TYe+/Eq0b74xkY01+zV1qPT1pGbDliWLhZbwFGNibSwXpbCG3ox1Nsg9Nog8GJPoQx87PbgokwP7x3c/jir+sh1QsSTqa45pCA5ni4goPhe9rPb1YcfkdRcfXrj4GAPD7Bzfhxn+9jJt2nY4m9OFBOQunpy/Rrn0k9j40i314GDPwnoHLitJKmAL9VvG48NsLlhUUNJeOhAW7SO7ZKxNIif58fUsAsVzDM1PAkrOBp2/J9F0jFk4Jpm46EWZ2/siOlV8dHUhonjYSEHZ/ptxmYR+aBLDJmIyuJhste17NCP6kzGymyG18ySngnPNFrAlvyia09u+ABQOmaRfGEjOV+b/Vhz1WDClk+rANgf+wzsJnjeuRFP3oQwJNZr/WRwcgYOTeQ1bOnE/Xdc4aZGE5IWRY4Pr9OGZO6e+NYQKVIEMZKkKqSpFgfBCF30E6YzllcaZbV8L8UgqQLD13/Rda7vgKTL+CK+8q/8w5iQ+Kn9tTCeJU6Ivi0/WGs7h+77yu2wQZFKjsDU7U/aXceUa9z4YS2E1k/pZGAqbdr6WVhoCAhEPfEJzcbt0X/w7c8G+AlQYWnQHrgZ/DhoHPz7kN//nUMTDzJbLdnzEElq0La7Qyx1uAkVOAreshYXuP4T75a0qYMLi8KwuFJxfwCrgngFgKSI4A3n0NcN0pZQv6K6lbt2LlsAAIO+Oyx498/eXqw0xkhPpNo9yt5746RhNwV4Ncldgy+w7K3CABkW146vuZ/VZgzzZ3ZWIISzeJQt9VlidF5XQbc8KN2Qaw6muFHchOZr/VwyrO0YeD5Nk+2d2qEchYCLhZf2bx2+BSlTWv09rsv8/IKGjc1qL/MT6vUFPTtVAQgJZ6L6XWmVHMu6WGv4rSzCZs2YBphpifXMpRcq0NvW1blq4c98o3l6azD9lZTbUJA2gdo1uKbrgH+MNHgJ6X8krkpzERU7AFKQxkxm8lwWcwAZPlNqREP2wIWDOOR9PZvyuk9/Pj8dIb+3Dk9i8Ule93scswWbyGUdgFAQmBwnNZdvaZZUYZbSjH92WVFg9hBp6w98PZxl8hkbH6nDPwKy2PB2MXQAiJVKoJiYHXtQ0SfrgqwYFC3/jWjMxFav3lyFlBV4jfPGZZmbbnOo7PegvwzC35MVpLxiNNIQoKlsB1JQrvyuu8y88hCb8ZglGqabHeGo8AXT/QvVG9e8/vx1hTwRvFMIZKkKHOL06AtXFt+UIN4kq1P1ZK5uuVd4VlcX0mRxo1W9gEUIAAwE2Pvop/XX85Lor9MpAipFQ9qgv/sgVJHu/ATfECVL9OQ43ipYSDLh+k6i1anj7nva71Kk5YqMSpjFoqe+sVdZcq4OgqPmNoWfm44CmYcNyrKUCy5dGOC6Ufh+gsrqeEAVzyhma1YAFAVlhuZ/UQOYF4vgqjXF1q9Z15skgVAWWUR8OlHJ7v0mwCrPJdw9X0uT2wBPSdtuMXAq8+AoyeBnxkbabt/Ouaiqw7Aj2y39eqVP52mcR8+3QAZUdoQbByXorMTuhA79i3zCLjftKxo95PYK+NLT7KibzgGplyhx0T/dYuEpl+VLK7qe/U9QIUj9fxVuDLr2SE4teeVDjuXJNe2q4lmVeuKekW5ee2vq7Sd4WfAsntz0BruzLKW9VxKcicDGXuUcqaTnXgs1P/FxevPwVt2F3Y2a/cXzSfZsm16xwDZjMSF79WOHDFVFj73kBfOoa56V/mD18fuwxLzachsoZeubJ45ePGTjuFVmNf/nrLBv7Nugj/tOcCAJ6LvRemqcy/ZmZ/ghnQvEiKEmXRLOkEcOmbhXM5V69qWh7J+H4nhcA5FlgAtqbbMdbsCZWOnVU8SQR/F1JklWMB8FvDRUEI/U3pNBxjZ6j+Xerl1uh7otLvwihkIb7jYL3IX1xwtgnXb4gK363zeevl+YtemSrHCbgOyt3j+e0FBOprvuX0mp8nrwDet6bMlIcOVIIMcd7xw3/gN5vfhqQYKHSkXK8LIcgMQpABsGhRU0EnD1Jmz4Ha4z63yT5/wm+QUxONWLhd0UeDz4dhyUFYub9SAZ+b4D/Qval24IseOwcd/L9HXsHH/3sdfhP/GlaIJyteZOYWwBV9sAX90FWuV39GNfEPikAsgMAvd12oOimRV+iPbY8P/mrg+UhuY8wglsXZrlwvcDvuJOQit5y2XOr1+Qq4QuTjlpeaRskPctcK1c+7LYC98tKyKrP+fYU/zkzLGRuc+Sdagf2OzPx+7lZIqx+Qnq/GuzyVopRLrYeq5BUFQdqsmQSkFdzXNrJWJ3W0cs8LCnPvp31yxm/3vjcyF3TNBZJtwJbHy/bPPRjznG1nXCsX3pPegVyzdxv8Iihn6DZdNG8KINGcca9lpQHYJYX4vuvFMEVxFNxrrM/vuvcgVwcSimWSEYO002WXM1+2gy4A5p2qKUAkMm35dmspfjftcvz8peP0Z5CFcvsKT0vUq5ugvhwkgN50DAkj7b85SC2XS5starpBhT7OAlWbIH3MZZ7IWxQo11iytDsytz7YH29D8qJNuP2pzbhj/VZ8Yvtl6HzlNhgS6LUyipC74p/EJGNbRjER5tkUcqpi5/22DWyUYzAJ2/LP42yPRc9aglDfUpf2uCtApLcLxkJGlY0xEorSotx1jQNbBrN4LIsKBZ1OSq7rA5Yl0D1ecoVKx1u4jC3VqCOXcua/9/VsA83nYcsWuq7c2rJawIhkaUVZRbFucfvedyRZdC6onK1EPmEI+m1aE0m4S4E852F1TnMpqwVgc9siTPzM36IsYcNCJcgQ5vanNqPlN6dgqfl0+AWWx2DiKScLItQdJAFsqQHA7Z5Q15dLpYNzpWXzEApFRW4BU1KO6iE89GoeWn0FtAR5racXP/7G54NZggwmQYXKAdLxe6xBUXZERUQfKUUfTiHTLVJ2lVhQllqH5n9UOuYFfc/O/CrJsxJKrhrD3xPpWFVigAk8h0VB0DapCsXK/BgF3OvOyn7MR/YxKTN9RwhkFCCGCbR0AZ0zge3PoP/1FxCvJF5DJQzWuqOa+Lwrvw83W9ap0geAbcRhJpqBvp3QnqRpZCaeg0u7cRvzyunDOZdkZQkMZGG9k7dqESbQMgbYvXlQ59+KlHoCEDkFiJkEEiOA3a8hbQ0Athy89VOJfmrJrNVYgPp0KgwkgL60iaRZ/vgjBIDUyIKSDvq4mlMy5sdAVDBnlRqvvATDJYQjKn1mC2bt+Wlkm4MaHqEIylH6neXGChlCED4AA+kZq3D59iPxq82F2CD3pT6CLvkmgGwsaFEogw1gQJqICasq8V9y0Sac+gd1PPOj+DojM36k9/kq5+tfglQgjBVOZAjXn6GoybdflOusAGn5yRrckJ5/VF4ev/u9ylbVdxVwje+pcIjqu9yRZqA5LIJ83cYzr+9l1zLBoRCrx7GrjLYpAdxvzcK1M6/C1WcfVI1SNRxB9QaDPh+Qypn0h3eWpwABAJlZtEjo/9Q/pPLPL61BGUQc5Qp6T+45q4paXwieX2QTZS7fKr2Lku0jdzxIudT71evW3wRcf6ZvOcY9dS0uStSZAgRwdKLK0snVl7PdVvP9Vo2oyiodzx4y3aJ6c7TB/OvzaKfS+X4rfQ+O8VX7Vyq/Wr17l3rxLU+Je/JjeERjn2s6zvqsNmEG/bBzGJR/Jeou6t2MuY+NHiuV3RpvAtOOAtrGI53uR4/VlBH21IJGGwvdkBlhmY3iRymabx3ncgF86xELwF5LwO7rQdGT9b4BVQHiOvYqJ3L9JFQfrqBuVCFMLjBvj0zC2r150Offit6vBAYkMgqQkVOBtvHYF+9Av20OrpC8RJ2ZIqMICVKluSKnITKBsCUQq0ABYiM7lva+oS8rlPKYUBRisnBNLpZAKEq1Ha8x3Ws94kju2bmfwLnGGiwb4gqQMHUeRKiRlgIDMuO8L4wC5FV7FAZeW4/Y1ie0c38ZOBhWtqUaQi+DASAhrIqELS7x0PNIuD9zmPZQlHyiBYg3Z1z/lRiNyhkSSz1L4DTKzLsmAi/H+recW2uy3olyzguQlvasLmsft+vL/j6q9NnUbwzXglUJl3zVb4OiOhyMsgWdw6LIym0edFk75tuOS75F6816o4yyCQBTsBlPvbazGiUa0lAJ0mj84gRM63+88h0lVRig6pLBfDZloeM1/1RlUVPtZyzxAVfufdopP0XIP38ErPlidmemT56NjNR/RyowJjoywGJosOud7zlahkJ9BpyjBaozNLYZ+9CbRkaYOmoaMGoatlstaBL91XMjMUwQQMYVkDIfuq4Nch9+DTAXmABaRD+ENscXf2aU/FBXSNulzxflH0GbzFlqGRJok3vryu1YUGKQ2NXbn7G+aR6NV6wO2DDq6oMvbPyQmJQQtr8LIz+0OnAIJt2s9AxkhK59SOSvqQmObzYJYN5jAePkNTiBHy837sRb0GM3eY4vAhIpkQ71Ll+3RmA0dqJlz4t4t3Gndm62sQGGS2a511Xp/GyKgsWHE+f45PrIAbbUa/ele4HmzqwViCwSF6jfsWGVGlG11UZt8m6CXE95dQPM+ZHibIcedRW5DKVc3NZvg5mvs30Mh/bi0XGKxsehXg8Oxpo9+J9959e6GA1HPa2JSRCkDYGM/83B/pfz+6n+yxH0+GD888rXrzyl7lOfx42i4zLjl9VC9p8F2LntK7I4r3LqS73e+VstU5A8PHGZSNTL3fLL3+p4zhyWnakP/eIShVDOCQC2UP65vDe3MoQhl4Sz7UM5Xilh2qXvO/LBqwrKTbNUu60Vtcy7FkRV384kbDvTP8u9P3T+SgLaWl6Za8KUp5Y0SDFDk0z3ZISpALDjBci+XWhCf+mbSDikz4d9A33Q5YRTtgT6jSYgG4uiHAFGrYS7jSpgU2mVu5He+hTQ+zrGDLyCFtH4fdYQVWoTsvA/tx30hgTidn9V3BmVTbbMZsiv+CG/VpKA7N+DFvR6XlJOGxpr9iApBmBKYJb5Mi4xrwUAXGJei6XGM65jRpSbE7xes9peLY/8BDxOqKgCaDsNbH8GSPfqwnjHPwu1GaPrqh+Wg0tdDraQP2jygyrYh0sz9dSC1AbX8dNNIZH90ykvCTL+1srKOoi8K/Ax5bibHCbMPGRL97Kp9Wl4nItiuvN6H0GfwXNZH/F7Hi3fAH64ItpEhziMCdKgfPjXD+Ivj28etPyWG09CSoH75Jz8sfeZN+MDxp/QK5NYbX8Q/7Tn5s+da6zBB43/w5uiFScOfGvQyvk+82bY0sC19glF58411sAQNn5hvaXo3GWxX8CWBi6zzi0692j8PLSgDxvlGBhCogO7EYMF07DRZ8WREv3YjFGwpcA4sQNxw4JlAzMHfqul81D8/WjHXkgD+NrA2VoZH4+diyazHz12Mw4c+Jnvcz4aPw9SCixM/wIPxd8PKQWWpH+K38UvwRI8Cwlg+sBv8WDsAgghceDAz3B97DIsEM9jGzpw5MB/ac/+dvF3JDEAU9gQWXPuzB4gA/FcQE1kj9sCEgK7ZROWpH+aKQsEFg78Apea1+Is46+QAjhz4Cu42LwWhrBx4sC3cK6xBqtjv0Zampg3cB2+9a75ePfSyb7PWoqB/5iE3QM2Fu/7CYDM+z/HWIMkBtCBXYjDzn+J+H0r5nZs9dsGXpOjsV5MwocGPovlxpO4zvw6YrCxXk5Cq+jFRLE97wuzlG/Q3M5Ckd3J1W+beFO2av3iztgnYQipvRMnf4t/AhOxPfOHy4P4PZudXaAZRlapA+AhzIANgaV4BoYAZIA6suzMfe8ZuAwA8Hz8vTAMYJ9t4nfyaLxVrEWHsaeQLwwYjmVIzhW4zFdQ1vd29nwYX8Y2MjqynP9PYSi+mLPZCsPdPal6rFoBEp35hMGyC88ksp3PyB63YOAb9nvxJePX+XeHMp7Bzv4ntwjYjja0Yw8SRjD3DbbzR/am3PvQFqbZ8lk2sEmOwSSxDX2IQ0qBFrM//74sCJyVvig/1zwTPxPx7HbHXNvwChoahtx4lnv2XNVZVqH9OQN95u7xTDO3IA9QebkxVTgX0tn3bGevUZ8xX2aZeefVsABxa7O2GYd53FeBN15E+v6fwCxz1Wjnyh2ingaLMONOtfFrZ85rgWzfVzYeANn6zW4WyI0NlYxJYbAkIA0DMXWAqQOc83Ij4dUuvJ7FFoBpJmBZ/TBk0TA9aPi1OVuZ34YCaj3bdrBxTo0lkbtHFbIYRmbuNLP/z9WnaeY2WgkISBjIxKCIC/fYOxYyfVKNdxI16jOHmRMbgfx3AgxslqMxQWyrb6G8l89+n+vzlwccu8POK1GNRW75DtYcVw9YyKwR8987ovC9WWp9aCvTck6Raju+xSwAe+wk4tJCk6nHhsl9UwLQAuDkxq0g44tE5vtNHRtsO/PNA6XsznnPOS7mviWLjufKk1OwCBR99+W+i3PlyeUnlH+5cVPaMj/G5gsotMcvas+WDdgQeF22YUX6R3ifeTM+Z/w3EoYFGyJjQZbN2MhZfCnfQiZkvk5zz1pkrWgU3p0N9++i3Boh/4zZOaQPMXzTei9ONP6JsWIHxmIHdssmbMYozBAvwzAkNllj8BSm4BjxMAzYMCHz39U77RR6kYCUAjvQhjnGJgiRseK93P43fNT4Azqwp1C/BvBXayn2E69hP2xG0tDnKRsCUspMXUhgn4zjHrkAh4jH0YI+yOz9H0lfmL/n2fiZiBkStg08IGfh9PQl+XPnGmvwZfOXMHL1lCuIAHqtBGKmhQSsTNvJvk+htOPct9Z22Q5LmBhv7ChsXs1EX8u8Q2X94vymBDLrYeF4b/0wkTIs2AD6bBMpYeX7TK4dpKWBuFnY/J5735YNpGEgYdhI2wKGKTPeUswU8JUtLi1geMHA6MOA/7xlPX5w5/O1LsawpJTS5JKs4P+S9PuKzpWrpBkMzjP/gvcat2EcXgcEsA0deFV2Ypp4Fe1iD3bLFLbb7XgAs12fLYebwizHMvEUhJD4pz0XXa0J3H/RcRWX+7WeXqy4/A7PZzrf+DPGih2FBYYs7F7KLZat7BZACwbusBbjw8oE65bmR4w/ot3YnV0gCfQhjphMIyksLfaYLQXSwkSvHcce0YTbrCW41Dqv7Ge9OvZtHCEeQcywsUs24zWZWajEhF1YiEnAEgJCCpjChg3gFXsMvmB9EJ8yboQh7LwSA8i8r0+bv8N88Xy+/M6FlC2BAZi43jqmqPzXxy7Lp3lD7FIsNp+DlAJ7RRI7ZQvaxB4kZBpCSEhbwDBspKUJQ0j0yTj2ihTG2a8DBmBJA5YUSBhW/gMsF/BZ0ZkAyHzo98PEP+R8fGjgMxlFkZFRFG20xuDo9PcAZPrVRfFfZj4UZGZhKYWBfhlDTNgQ0s4IDLIr2N12CrvQjFFiF5JioOhDIrf2Vxc8u2QzEmIAlhRoEv0QAAakAVPYMKEviqRyn0CmXmOw8n9n6jqGu+UifGjgQvww9h0YwoYtDUwVr+H39tH5MSL37uaJDUiJft+gwDaAfhlHXAzkBe0P2TPxnoFL89dcFrsGbxH/REKk0YrejJLFWfacQkMCPWjF83I8npJT8BbxT4ww9iIGG2mYMGFjp2xGr0xgjHgTO+wRWJH+UVG5/hz7Ql5Z6uSy2C/wHnEnDCFhiMxHiA0DaWkiIdIQUmoLR686yL03gczHXRox9Mok0jCRxABaRS+kBB6Smfp4KP5+jMBePCMnYZTYiTFGT75PuH1kWwC2221YJ2fgMONRpDCg1Z2Tfmlip2zGBozL1//VsW/jUPEY4oYFGya2yA5IaWCM8QZS6AekRBoxvGyPQZPRh/FiR6Qf+/k2iMyiv1P0ZNq5AMRBFwCbH4O16Z+hXASpzz8gEtgcG4/+8csxXW7IpJW9KLeLNSpZmfohq7bffpiIw8q3/1yfSwsTyax6zUZmfLGEiRT6ayacdfuYcqIqQXZYLXhWTsyPx9fHLsPBsWezrlok0sj0/4QcyG92qCVuchSv/mLZBYW2gD4Oe92jKjyQnf8tmNhtpdAh9mQUcjnBM4qVjvWEBLBHJrDJ7sJYsQPtxt7McRuQRkZYos6ZADQBqCULdVXJey8SSGXz83oHtvLevJ5LKgnn0u7LCvK9BHnOedh5rppCUL/0JYC9dhwbZTd2oRlLzWcC1fmbVhNazH2QtsDt8sC8oEfd9JRbD3xo4LMAgPUtFyBlAPjyq7jxXxtx0R8fR1+69AC93HgSV5g/xnjjdRh2RsAic+9IEZaFaScSOaFVAgcMXIs/x76AWeYmbLfbsHzgajwZPxtNRrpI+K2OzVJpS375OwWYfmUr1f7CtBUJYJ8dQ5ORLt5Mo/wOuumqGqjrHFPo9Qu/fMtQgqjv0Jl2kI0jhuE9DjjzcMvb2VYy452AKaT2LH71Xe47CbNpAQAGbEBmlZF5wSzclRa557MBpLPfLDHYsKRAD1pgQeS/yzfIcfiI8Ue0it7M2hiZ752Y8hL7pYHdaMbzcjzeM3Apbo5/ATONTeiVCfxDzsdsbMRoYyfStoFmox/P2+PxloFv4PrYZTjIfBoCwPP2OFyUfj/uk3OwTDyFr8V+hinGFli2wCNyOq60342LzWuxv3gNMWHl34+zr+yUTfg/6zAcJNZjuvkqDNhI2wYgBAxhw0IsvwYeb2xDHBYsKfBbayUutc7D1fH/hC0NXGediAvN3+EN0ZIfF38Y+w72E6+hBy2YIrYigTQ6xO58nfbJBNIw8KrsxD/tuegWO3CEeARJI419SOD36SMBAMvEk5hkbkMcaTxi74/Tsmv188y/4DPGDWgy+vMuE3fLZjSJPpjZdaSJjJJg4cAv0JaKYee+jCLpL/HPY5LYim3owHXpE3CtfQL+Fv8Exog3YcPAJtmFEwe+ma+r62OXYa7YgJfRhcvS5xbJVq6PXYaF4jmYhsTrsg0b7W7MEhvRKnoz8bcAPGNPhJQCc8xNrt99bnjJq5YbT+Ji81rsks04La2nsUw8hXPNv2ArOorkRLn3lZvXro59G0vEM5nNxWI3AIE0DDxpT8EYvAkI4Ofpk3CNfaLr/SrOedHJXfFPoMvowd+shRCQeE2OxmXWubjEvBanxe7EVrsdX0h/GOeaf8Ey8ST2IoXPpD+q1fUy8RS+HbsKu9CsvZ+7srKH3Dryr9ZS3G/PxWdj16NF9EMi09a/l35X/lmATDvoEm/i++l3YD0m4wrzx+g23sAL1jiMNd7AVtmBEwe+iUfj70OzsQ/P2JOwGynY0sgreq6PXYbutjj2+/w/gF+ckFkQnv/XEm91eBBUbxAbxDKRiPnsqtk4c/kUvOXKu/FGb9r/BhIZpZQAboqRHKUUHM6JZsMVJ+G1nl5s2L4XUzubMa69CTO+9GcMRGHf58JTcgqekxPQavRiHxL5yflcYw0+GPsTEkjjfjkXl5Z4PgCaRZCT++Sc/KJ66+5+/Pju5/HBI/avqNzj2pvwjXfOxxf/5zHX9boVS8GyDKQh8Ig1HaPETozH60iKjHuD3IfXAAS2yXbcbxcrb1SeklPwoJyJhfZzWUGKQL+MoxdJmHI3zKxCIrfzYqfVhCfFfthgj8UaeXBFz3qfnItu8QbGyDfRIvahBX3YIdswGjshhA0LAn0igddlG5pEH5rkPhgCeApT8E97Lk63LylK8wE5F2+gLS/86ZHN2CDHYr7xIsxsjaaFiTutxVgjlxXd/6WOb+CF7RmBjBDAgIyhX8Twx/Rh+UXGqbF7kJBp7BsxCejdgVZ7J3qsFvSbcTTLPuwxmvC61Yo2sxe7ZROa7X0YYexFGiZisBFDOrPAkIAUArvsJuwTCSSQxmZ7NADgOvsEnID78YQ9VeuD19onYLn1JBbiWbQavUgIC7vsJjyDSZgqN6PD2I3XZTP6Eccr1hh81343zjHWYLLYirH26xipWLYMSAN9Io6kHIDIbme3IbKLfBtCCPTKBGKwYAgJaQO2srN/QJqQWYl9PBPmFf0yhgFhZj9w0ngTrTCVz2m3BV+Of9pzMcLsRVxY6LUT+VgNXh9yljTwAsZhktyKJtEHCWAHMguEMa1x7OlLY4M9Fi+LMXjTbsUS8TRaRR+AwseYJU0IIWFDYg9S+JN1CDahC5PFVhjCRp9MoBcGdqEJI7IuKUxh4w77QFznovwFgJPS3/B8xkvS78MGcxyOEI9gsXgWTUY/dtnN2CJGYqrcjIToB5CxThO2DemwRgEyO2Zi2Trts2MwDRtWVkmVkGkMCBNbrXa0m3vzggLVIu/S2DU4w74DwrAyO3OEXfTxvc9O4FPWJ3CuuQYWYkgLG70yie2yHWPFDrRk69FCRgEHAfTIVvzFKvSpzRiFHrQiIdOIizSa0Yd+EcdmexRGiV1IoB+9SGKE0YtRcmegnXZuH9Vux1TLmAEZQx/ieSWmAQAv3AUYmSVjbtcZXNJS08yxT8YBIRE3TUxefBxw0n8Ca6+C+eo6wE7jNXQgYe9Dh9iNdHZ3XN5iLJtGWMHtHjuFrWIk+mUMs4xNmXID2CObMELsRb9tQBgShpRICxMxmYYUwD4ZA2IpDMDE1oFmTBJbkRRWSUGF+rxRCLZUoZLfcwtkdqqJWBO+Pvdm/M9Dr+TP3bjgJ1i++4vAKw8DRgxWrBk7dqfRafQgDkt75xmrOgHTkHlFmNuz+u0wDYMA0CdjiIuMQDGzoxCZccxxrWWY6LGa0WHuRr8dw9/lQhwmHkWL0V9SAdJnm7jePgYHifUZAYCUeLZpAZa/98vADWfB2vdGZpeeelMA1LpzCneC1k8YoZsNoBcp3GgfjWvsE3GpeS0ObX31/7P33mFyXNeZ9+/eqs4905MDMBF5BgABAiSCSJBgEkFREiiKImXTsihbmQ7rsJZsS5a06/0s2yvZXq0VVt4VJZsOIi2L8lKEMjMBMIEkgEHkBGAwOfRM56665/ujunsyAFKU1wHv85CD6amucOvWrXvPe9734E8NUi1T+MljKUHmkdbF8ym251L3drFA3vzg5nwCXwo7nD1+zA86FrN686LRhaSA+cdwUWgNqpAqmTAB/MopvFuVp+idh9lEWPGaHCzS4udVWcYmfXrOubyhhJ94KpvZbZMRH7ZysTAIiqO0c6fzab7p+zSOaC/hgqUzdV0gowMMuDU8y9o5a4zL8l8r/Xv+fOBm/1/z2MduAOBdV7Twrita+FHXIF99/FUyjkvXwPQCUmS/6eQxvQnjeIlcn7G/xo3meXJiUWElCZElLz6CZLDU3DZWzO07s1W0caI8yzrAe6d/ES8wFbAUTmU77tRpjEDKBAioPD7lzYFKikzlZRAri1JdIZcZhUvxHATol1qqiRMiR0oChFR2UZLPCx577+yLedYuROoJkMVHiJmaIsV2cfFStX3zU37/BVF8FmGGAAHlEcACtpIlyQrwrN9EXfy4BIuPDcU5gisamZUMNHu/addGacE2LkorRtxyqizPaaGYsKZL44sqJbwU73FRPSWAg0JEoZWQFj8KIaRyiMzM/8733i5ex2vF7DnK/OubTUaB1xZGaXpo4Jb8n7Df92GqJIGjLOImTFhlKVOZ0j5cgePSRKsaIkUIF02YLEHJ4yjNuFtGsCxGd+WbCW/5AIEDj/DCYBeXcYoqmcJWXta+CxgsFAYprFGOSisACUJ0zyI1PmP/H4yj2We284f2X5EgBMC7nU+xX32YoMqXtgVvTf8J5/38of1XTFvl/POmv2L/gTP0WQ20MIxCyItNBj9hlcFfWPe4BU0BKG51/ph7zD526CNUqimWyxh+5s6Bz5g6qtQ07iyWbnbA+65Z2f8wM05+2fd5VnMOG5cpwrPmtIKDRS8NpfXid32/QzMjPOhcO2cN+Sm5jzvsxygjVfqsS1o5TBtXcgIXmDRRTqkmVpmzlOsUgmLEjfKSWklVxMcLn3wzL52Z4As/Osm5nkaCJleKsQBcm/8f3KP38V57H700zLmWd8+7tvl4t/Mpvun7NM0yzFect3OMFv7c+gIRlaHHrSGo8yQIc6fzaR7GI6frmADAp1kypjQ7XvWr161kbUM5v/J3L7LfdPIW8yeLfueAdHDAWTyGMp+gOCCdrFTnqGNyzr1pUcP0SR0vmVV00brk92fjfOtkgMdkc+l9NxvF37UypXNfKpH3gHTwMXMvtoaQrUkXitYNU8UyGcco+KE7k7yAA79t/z1BlWNSonOuBeABs3sOyXTMauao28pHnd8sJUUDXJZfmPxQxLudT/HMh673fvmlfedtg0tYiEskyL9xNMZCvPipm+cEy4+ei/Po8RF2r63lhg5vML32T35M7/jSHqkXwpbmCr5171V87nvH+Mpjp8nNGzRtPbeI5Tu3LOdzd25mIJ7mH58/yyOHB8jkXa5ZVcvJ4WkOdI/PGXhnT6IU0FYVYnAqQ/o8GU3FwOJPi7AP8i6srI1ycjixoNBbLGizsi7Cvdet4lf+9kXSPyMWoqHcj+MK16+t40/u3Ax497cxFipt8zt71vHfvnvsdR/jV69byVOnR3mhL77gb/tNJ/dY++YQIFAgZxx4r72PBj0+M6N7A/BH3z3G2zctm3ONrwd3XdnCNWtqFzwD76aZmrM19A0JT7ob+Z5so4NertRd7LZeIkAeozSCYlzKeMWsWPCimo/9ppP9ppMv2Z/nav0KjlgEVZ5sIbAJhUm50mQkwIiuJGamiVCByE8XOuqSVm7hAGnjZ0RV4FcOCULE1Eygvs+t5S3On/Ap6z5utfeTc20GqVywr2jA4v73b2dTcyUT//1T6IQwZsrplxoa1DhnpJYy0lSpaWxcVul+xF14/r9/awedy2Ls/KMfIzCHAIGZScadgaepjgSYDjaRHz1OmZUiLQFSKogYqLCSGNEMShV+lSfqpqnSU2DyKG0BLqI0KQkQV1FscRhXZfRKPeBN2JYiGT9cmFR8SH+HsGSZVmFEFFnlIy0BkoR41Gzme7INEUVShTjormOzPkUFr84sdJTCEkGUwhXlqVugdN8NCgcLrYvy1hwat5TlbitDFh+5WYE/SxmOm2Z+bLZwvX6BNj1IUoIMysJ7BlAesPizd28uje1Df/Yn9I07nJLl7FaH8BUIo1mnBcwEGaKSxlWarPhwsUoD72ffeZm3z+4yfvDjKs6+2sVl+lVccjiFrFwBpgkVFqeGAVONXd3Gw+mb+UzmjwBFigDHTAsvyBq2qBOs0334cVDq/ASpAt60ooq1DeU8fmqEU8Mzfbom4ocUHGI1te4EzdYoy8WzpEuYEA42fuUQ1FkQKQXTEI2jFFMSIUzGuwblBUUsJUyZAOMqRoMZo8JKkjE+JqRsznn5LUWvNDCqYpS7CfyWS14sLOXOIg00g6oaEUUlUxil+Il7OQdMJ3dbP8BShnxBGeOIJlMosltcYP7xOzdy15Ut/LdPepNYn3LodhvJKD/lylt4jUsZVWqKbrOMtaoPW5vSfb0QEVFaVONl6gZVbo6qxUGjxCO2XKXJK7u042GpwBKXCtH4q1bA9CBuehLfeVJEpfSf8lQ7hR7pC1V5BdYBdt7r/ffwbxN8+buMpnwEVA5XbEIqjVMImjriUbEXU8h2dtChixYecXawXndTK5NUqmkv+1K5TEsYoxQRlcUgpCXAlFRSxTRGKcr9QbQVIucoBqWSeibRSrCYaxM3P0g8+9qBOUTOxQZhlyKpWORz8IKvWttYjZv43J2b+cWdrTzXM8EVbZVsaq4E9kHPk/D9T2JNDZEvPDNhlfXUa4U9O2JhF7Kbi+c733ZOYI6twk9LhAhe4NsRC4NmwkSptSbnBFxnb21Zwliojccza0g7ntpssX1S+K4j8Iysp1ca+LT7Ph7mY6yzzrCBk9C+C8oacDKJgqWIwV7EQmip855NgMwmBWFh25zvnp5vm9lw0Yya8tL8JHTb51h9ZQs8/FucfX4ftc45vCduBsVhcMF7YN7PdGFMmL/NbGKkGHyeHaCbXQ9gNolUzIqNqlQp+CmFTjN7CmHEwkWRw0dE57AsHwRj+NNTOI6FT8mchID5KPZRVQjcPu2u58vuXg5IBw/7fofV+hxG1KLWUBfC7LFkfi9TikJw1Qu6nnFr2e38BV+0P8+N1guIsomXr4WxQiBfWeTFW25bGIwypRBgsa3TEsCPQx/1502ymo/sIpWpb+hoKM0PAL76+Gn+/mAfsbCPkG1xfGiarwd/hVfHvHfLI2Y763Qfa6wzjJoYWXyAYpXqR3naWW9uJ378BaWr97tFkhAJE/LGzVlBwrBf8U8rP8ub19dz/IoW+Lv7yU6fpc+t4G/dG9ljHWAdZwioPDZugXQPEyVFhByuKIxSWBhcLBxRaOXNpCYlwk/MZjp0D+0yiItFTnzYODjYVKjEnLHXOg8BMnu8cIEcNnnXJmJlS0lARXjBZE1Mp0vfLRJCjrIQI/gvQIDMfoaWUivOXw8vNf4vNWYYimXajRfk0Z720ZJMiUz1mTx+PaNcF+b288XGp9nnsxQ5rvHmvt00UM0UFYW1SVpsAvPe4aI1h4JbqTCTtDBIOd4czFFByknOIUA85bdNSHJYypAT7SVCFRJGFN58Ki/ePEuHqwnoFCY55ikulmiz2e/q2YTexZDZnrJQl+bDs/dX3FdRfW2UIoufU+5yvidXAtBLAwF1lmfcThrVGC2MkMGHheDDwVWanPjokwbq1QQpgnwl/3bAW49HdZpg7UZuve7N0N4CV3wInnGZOPC3mPEuFAaD53AwLmUkJERIZYmoDPWFIPjn3TuJhXwcyK8G5iZ4fsJ5/8xcGvj6sj8gk3c5cK55TjsckA6+UvYr/Pd3bWJL+2U8cniQytwUeWUzamKUqTRThPlC/nZa1SB32T9BFVqqGFO4z3gxh1+yvssd+jFqVHzOHHiKMGOmnFY95A3250FVxAfAeDIP4JFqSi1IzLNl7nvhWVnHAafzvIHyIvabTiat8tL8OKKzrJM+fNph0kRwlcVLahXfXv1ZXvhF735vaq7kr+7ZBs+8g0eODHPfqblJkfeZPdheNgotlUFW1Zfx42Mj573WIvaZbXMC6n1WHT1uA+92PjUnoH6r88f8PZ57w/9+79bSe+Krj59m3+FB9mxo4APXrFzwexHJnMPH/vGVizqnC6FND9HgS+K4C+9NC8M0bbiavzh2GbwBSd6vJXH5fOvU//4795biVQ8818f3jwzhD95GYviHPDRYsyABEwfeETjIMdXKUWsjd3Y28M1CotL8eMVsgmN+UvRSJE97deinjp/9R8YlEuTfCWYHyxtjoTkTYIDHfud6XjozwZ/uO8bTp8fPSx7YwMr6KNetq2Xj8gq2tFaW9v1bN6/jt272Mn3mqxReOjMxb/HtncuvXL+aX7l+9eu6riKJ0j2aoDzg4wddgySzDrdtbuJTezcAsOfPHuPYUIJY0OLzd23mUN8kX1jEJux3b1nH6aFpHnqpH0tr7rqiubSP2VjsOoro+q+3cPf/eoanXh1/XddzPrRWhfmHD1913m0+cM1KvvTY6dLL/bVgXX3Uu3/AZx46zNee6V2wzUGzjv3SuWAALg7msycBbxS+9cJZ7r3u9fWP2Vj0GeiepNvWfKJ/htVfp/uo0gledlewyXoVF42L5kWziiqdYJ3pYz9LvwSL+LrZQyXTbLJOe8E1HG+Bi7f0yItFRvmYdkOU6TRRk/ImlK8zSSziV/z9L+0k+8j/JT44yJPOZazRZ70FgbHxaZc8FgfEO/fiy/hWez/1rjfh/f23rONg9zhvXl8/px5L5fLVDA5GeXHMYrM+NYcIe9j+GCusAVqDaX4xfIgDkzPZEVtaKkpjTc9nb+Wpv/gm3xlpW3QSuboiytVVGVSogZHRIeqZRCnBJy5+nUOLcEbVMUEZK+mnX9UybcJ0WL3kjIWjgwQkj0+5hCUDGmL1K9huV/K3vR6ROTsAOR87rCMkTIjjqoUaJlEISQnSSwPLZYRGPcb+fCc79FEOu+2s1z3UMkEeq5CJKIh4VmPGWGitcUUXLJkgr2wG3Cpa7VG0UhyXRhoYp5JpbOWW/EMtcQkoT4mAUvRLDZ917uaAdHDQdPBx+37a9GBpkTIbRXJ5NuybPskn7j/ELdZ+xgqqoOKicEHmnQTolxr8ON45iQIlc+4j7btorHiKVdYhQEiYIH7tMu6GiFkpwirLoFQx5FawwddPZ+M53vNzN3LiL/6C3JjNMdNSCkRtV118mIdYp/uW6tYAPHTvmxaMtbPH4ciA4tR3DlEhCaZ1hFGTZ1hVUGcmUcpQSYJpQkybIPVqEkExZCo5KctpYpQaK86kRHGMRbWK41cuedHEdIqw5LC0iwiEdY71ZQ5/unsj3z8yVHpOnn/MIfr4dwg6ebLiI4+PKOmCasGglVAjk9xSM8TpeCtHHe8Z2K66Sj1yXMoQFFGVJic2g24loYoa/tPW1cSu9J7F3//lu3jubw4Tyw5QqadJFginahUng5/vuFehlWF9gZibHUBh3r0uZlIW14uCt2DP4imovIxlzx5qQsq9GkrKwchMuD4vNglCREkzHWmlunwZTqyVVCJDzEovmVlZPCcH7RG/CqbsOso69kDDxgXfcbGIqiQj4lkwrqPPUyoZQWsvSO6g0WKWLMRabIM8ml63nnHKqNAJHnR3k5QQd9qPkhY/56SGGjVJGSksbTOSD5JXPgaoIkmICjNNmZsjW9bGAxOd3GLtp5IkiBBVM3ZZxbYvtrOa1c6zVQKvdbifs31BSQYz5IOetY0IpFUAbYcJFtp1U/PCeQsiUL4MZ7QXR/xMqQgBHAwurmi0EqYJEysoGRWFzFoUVqHoUvFajFKlAAZcfGBusetUgDGKrPZjjCpYAYFoSn7ZUvjpikVIu1TX1nPXL3+Tb/7P30UNPXmeQCAkCdNLQ4k4uNX5Yx7mY6wuC3sbVa2AyXOMZyyq1BT+C5z47Oes2AeEgjXgrPOYbbU2O8joFsimwKzxeX6bLXY9xeBrKBzhro5m/uz662ctfBX+aIypiTjVanrOfllsX6JxoaRmy2EzQiVVJk5EZwt3fUZZkcdHiGxBweD9LeX6URYlq7hSuyiPeDomrQV7QUUZSSwFPlyyWKhCcFsX3oiiFMOmgoaAUFbX5u3o3CtksPCTv7ASSjyriVGpKL13AP6rew+flK+zzjpT2nb+OLkYQTm/vspiyhWFd881wogpKykcP+r8Jl/k87w5cISbqkbo+c+3cvCTnyYuEfpMPX6Vp4kRylQKXbhTpjA++pVDXCKLJqycD+014Qtu84FrVs4JZMFcG1kRhV/lyIifhITwKS9rOiFBylQKu0CCDFFBvRnHrx3Sxs+gqiZKmi5pZb/byQcC36fCZ/P7N61bcDwwDPpb+ETmTgCukUOMqzLKJUVIZclIgOPSwnZ11FMAKa8nTksE8NonbXzkle29F1H8mXsXv6G/SYseZlzKQCCmk3OUN8XAfPH5EzxiZXaiiOD1aVdbaGXoVzXUmDg1enrO82NBiZSbT1rYuLioCysGxXsvoqSkeHQK1k2GmToAqrAtaiEhPHtMmd9/XTRZ8XtJGjpA0W9SO3ncQnt+wbmdW/R+tnCyZKM351wXeWkVj7kYKehdt9euOdF008g3zXW8XT9FTnxU6Wn8BWvZYhso5Sn+VuRO0rDtdvKvPsnE0BliOllqlyK5nDY+LC1EyQAw7MaIWhl84nj9RLz+4qIZoQI/eZor60kQwEy/QIVOzmmv2eevS9cwM1dSzNTfWYr0ESAlARISpFpPzVEQeaSvJiM2CoNWhldcL6u9T+pKwc9j0sJRpw2l4HJ1CoAhqeSc1LBBd+Mnzwo1CAompIz7ZiUnFhMTK8aPwdBhj9QvXkuwoqC08AhupYQEIc5KDb2mkU36VIncCK6+lqvX1fG9h44suOeznRs2LCvnYx+5ld954CU4d3bBttaKXdC+CYAXPvlmjvyvB/jx8Coi2WHW0jcnsRKgU/VQpycX7McUfPledRsp02n8OExJmFo1SUwnec5ds6SjQktVkPfsaCuNPV99/DQ1P5rCoBZNzLvNfpJKmSp9/3yB8oPrPsaBnnFwZmIvB806jIJWPYwtYyQJEZE0I6qSXlPHc7Ju0RgTO+/l0ORRONW94E/2VR/h47d08onC73v+/DGODSaWPK8i5gfUZ1tez4/nvNv5FFtaKvjWrDjh/HfEYu8MmEk4ve1/PsXQdHbB39c2RHEcw9nJ9AIFYtin2bmimsl0nj0bGnjvySmyA/BgdvF7Y48c5cVPfYEfdQ3ywW88vyBBeTYqQzaRgM14MkdqXqJyfXmAA793I2//H0/w8rmpJfYAO1ZU8Wd3bS7FNR8/MTKH8Pnjd26cQzgUlZdwJfD7/P2fPQZDc+/VfWYP7/il/8JdzZXcVfhsa3vVayaSAjZkF+GCPnrdqte0n0uYi0skyH8gbGqu5G8+sPMN2998lcKii+834BizCZTFXij7fuPaOb/f0NHAb928jgee6+M7h86xflk5772qvXSufzIviDgfF7qO+z+4k/V/sI9k7g2URAB3XNF84Y3wJhhfffz0koqQPevrODGU5NXRmUzqHSuq+PsPztz7T+3dwDefP7vgGi5k19VYHsCazp73ZfRacXr4wi/41432XQSrruDAkz8ufaSVYdxE6dB9DBYmnMvUKB26j4q2zdTXtvCha67nh0eH+OQiE8MiRBSZgvVUhUrgK9TfAE2fW8P95s1s10fZaHVz1q3hWVl33gyD86GUsdH9BOea38qjZw+y2zoEwDmpQSkhKzZPmMv43qwJ4uwMlmK2+WITG37u7/jBMz3UfveXFyiBilLl34k+yq1NhuDerQuUZkVc9etf56//+jk4srAw1/1V93L1zgz6wNfpMi2goZY4WmXRIoxRzorLdxMaT2HOxonlE6CErPER1RnGTYA0EaKk8eFy3G1iYy7Bni3LOP7BWwEvcL73L59etA0HpZpmPUK324BfexPZKSJ0mwZqrUkywXrIe1kgHbqXzZwgplNMmTAjqpJamaRCJ7BESBIgKp4NVQ4fccIEJU9ChTD+MsTNQiFAGSRPGSlvUSyglZCQEEYUQZ/i/vRNc6Tln3Xu5uP2/aVFSnnQ4o6tzezdvGzRcal6w41ccW0TvU/0gPIyEjPiJ1xo12JQ0/OEF5rUCBaeVVOZznL58kq+9YG55OvK1ItMkuNls5Isfla6/US1Z1XmxyFCigYtuOEaKG8EYM2qtRzJpPny5J4514MLH+GhJYM779yyfNHrmjMON7+F6cf+kpWJo5xxaxjWFbiiGVYVtMoAYZ2m363Gp1yMmmLMlPEP5nqmJcwOfYRxt4x2a5BhXYsbbKAxfQxdyEDP4SMmWS+DWQdo7thGc2ly62FrSyXxqtU4Q4cIqDx+PKuWrPjwq7y3+MfmHVuaiF33p3zjmW546Ci3WPsBxXHTjA+HKSKUS5JRKoiRoKW+mnDblpmLFmH11e/ghR9+kzY1VIqCZPAzKVFuv/FqjvRPwYnHZ76yaKsWbLewCJEvZSM6ylv+G9G4ypDH5lF3Mwelg4/ob1OtprGUISU2Z6WWRjVOFG8hGvJbULWC3On9GK0xwqKExOwAoxbIKYuTpolXs8t46/ApglWvzFmwAwRMiulZ486X7c+zUx8hVCgaOCERKkgWagQsrsYoBbNUkIQKoYC4G6FD9dKmBxmSCpISZJoQjuiCfYouqHS8YHuENHllMxJuoaxxA984+2ZazSBr7LMY0eSMjdYGixlrGwUlC6KZYLdN3oBPu4VAPjhGYReKqSyV9QuFLF7xFBIZ5UOJF3jzF5RjRXJCRKOUIW38BEK1S/SCAtp3wf4vkglUY2cmKSdVIGs0opSnusEQlwgVTJdsVPLiBQWLxI4q3OGs2Njk8anFr+VCJEgpWxbwacNpu42861LmTtGo8mjxsuctXFwsNMZTJ/nLIezZH9751lvJ/s03UPPyQYrnkMMmrcP0OvVz3ruH936X9QXSkfJGdCBCPuuAyBwbmPn2ULMJi9n/NoWgpYVXx8JWBhcpfV4MZuaUzXHTwkuyit28SIs14tlAzesF87OUi0FOW0F7TYT2hnGYl/kXM9OckyBVTJ+37Q3eeeSNhSKDUoo8NkGypFSIpAlRqadLliV58dErng1JFVMF4hy05dnIFTOxS+8YYIooSnm2c0GVw4efoORw8FQDAZUnIWFEoEyniJuoN9ZYEx5BOnUOo2xyKIxRGD1TiHaG/PMUGAbPmmiUCp4yG+fYV+w3nShrFtnGjAqu2EZ5o0uKumL7zA4qL0VOmVmfPWx2zlEPfzLwcd6y+j4Qb6ttb7mH//NUL98fr2WPOkitniCG4OBZtOaxsQrh+Sqfi8peDIU4g/fvWvGati+iMRbi2tXVPHZyDKWE77hX06KGSvPKuEQKalXFpIng1w4xkrjaYsDEsJVLiCzHTAsHTAf3mT383KYmdtRH2bFzkTnmjo8yumyCA49Y7NBHedxsJk0XV1gniEuUccqJkSCHD20FSOYgojMIeP1GcpTpFCfcFh4x29HKsN90cky34heHSYlSoRKECvY5rkjBPihTunFuYQyenfEPhULylsuoiTFhojSqccIqW7rPDhZ+z4inhGJ/z+HHMnn8lkfY542Fv0CWL/ZulgIB6KILapeitZpX60wXlMaOaFRBCWUKyQT2rJqDRaVM0eazeCxXdMleqc5KQ+MmCFeTOH2Q49lKHnF3cJ/ZQ7sewG8c2higvKBsmU/uzyZy5pNIRbhAUoJYOIBNFj+94q0LJiVKXEXZYY56dndAQvw4+AhJBr9yqWMUjjzESNlGJlSCiGQ91aW4KCXkjI2rbTAuBdEFaEXaBKgskFSu0rhikSBEVnzegeJnCFU0M1qYq6MokV/zC4DnxasT6IhmRCooJ0WsYIM7m4AqFSHHGyP8Ks8ZaQUD1XoaRwClUcjMLEV59dW6aGWf2TbnXVQMun/Z93kv2E94jg31e+191DJBwoS5z51LItxn9qAc+M91I6WxpgiTHGeaMFMmQg8NNMg4tnKpUEkel3par343btblocu95KN/fqmfxfDWjQ0EfJq3bGwsrfXu3tHCN59fSIL8/PaWOb+v/+BXcc5M8NCXP7EgsbK4Lp2tUCjiGC0ccDvYZh0jJzanZDnVTBPTSeIS4VnpWLCObq4M8s0Pv2lBRvwHrlkJ09fw0tkpPtN925y/fca9h5W1Ya5ZXcP/XrGVB547Syzsw9aK+w+cYT42NMX40nuu4KuPn+a+p7rpj2cxoqmyEvSauoIFljdW9Zo6qnSCpvLIkln6AXvx6mPzP9/3n67l0985zH1P9y66PcB7d7Tymds28NXHT19Ukuz7drYuTs5cJBpjIQ78/o0Lzuve3Sv5z3vWlX6fnyi9ANMbiEfX8JkX55I0n3HvQSm4Y9kywIvpnf6jW/nq46f5P092MzC1kHx5/zUrSsm0DzzXx9ee7AYF77uqvbSW+86v7eL2Lz7FC32Tpe+trY9y5xXNC5K3YaHDyIUUF9/7jWv5+a8+w9OnZ5KkF1vfFvf7w6NDjCayXL+ujhND03z/yBBNFaFFE5S/ePdWPvPPR+mb5ejTUhWas069hNeOSyTIJfy7xbvmBbLeSBz5L3v43PeO8fWne5jKzhAJt2xo4JHDg695f691MPvANSt566ZlixYE/9TbNyypzFnsGh58/izV0QDXra1lf/c4z/ZMLHncL79nK5uaK/nMQ4f562d6cfDY/d+4aQ1//sOTixJDyyqC3NxRv+jADnDrZY0Xfd1vBIxoOiwvM332hPOj4R9SOX2Sys7rIRbixs56PvWdI57v7CLo1D2A4qRZTqfuI0wWv3IYcCvoV3UoJXzE+U0+Jfdxk/08zc7wnO9vWF7O4f7FsxLaqoIksu4cazQA2ndR1f08DWquEqlY10HwMmxmK1kOdnyMP3jb+gu+wCfTObrPowTaUVnFnra6BVYL8/GuK5rYtwgJcsfWJpCTTJogVTrBo+5mbrBeICh5AirHcGg1bZJm9fXv4WzXGvzPfJlyvOzwftdmlXUO8EifSYlQrydQ1VuhfmYy9/jJpaXDfVJHmz3JNo4xIhU8b9awVZ9gm3WMXlNHR+cm2O9tu013EbNSHHXbPCLDZIlZSYZMpZfVJnlsZXDQnDLL+LHZwhZ1gjY96AXTQlVU5BMEyZEWPwGVw0++lLU2SBV9UocvtJxj6bnPfZEIKZIgd2xt5g/etv68925dYxljDGEpQ1yi5LE8Wy9C1Mm4F6RShTokAiGVxUETj3XSsOH6Bftzo40lRQfAR+yHaGWQQakiRhK/yiPAVKCR2PrbvC917uWs2sGBx+f2swPSwS+/qQ3GUzSdDXJTRz23bVl+3rFpMVx+9Vvo/0k/zdlBnnTrvaxb+/NstNIkJURAOwQlw5gpo4dGtuoTiAijVPI19y3skQO8vV0IlVdy/JUkK9QAYZXFJy5W4V5mYysJF69nNtp34ez+Pab//v00qnFcFGMmhlIGG5ekhJBYC7GgJ8PvnyhOVBU55eOQu6pUl6JIvN1c9irhqqa5hMDgKwS7/hGlFAdNB83KGzOOmDbqGEcffpA3rdjOuYpOApMvlfrI/GxQA15xemUXiKs8ljKMmXLyWPhVngkp45hpIkGYo6aNbt1IjGTJSKMQvvUIEJ9F2NYw/iq2OJyRKiI6gxHBN2sRO5sIMGhcpZiSMK+yjDBp8tNZgvPbduoc/kCYryd3l8ad/aaTdboPSzx1W0TlsJWnWrBnBZgMXl2VmJo5b9dR+LXDoFvNUWnjD+2/IqrSfMV5O2UqRYsapkP1kpAQDTKNhSIpITxlWIi8aDrNOIGAj5vXN3Cmq45vO1dzk36Ocp3CFHK4HRQGCxuHYhA7LZ7Nma1cLO0paeJEqCRRspqCuWRBMaBStKUqEiApE8DxlTGUD1CjpgibDH7t4IiNpVxyWCA2aRWgrKx6xmZsSWjKc0OMmBA55SvQPt6YoAp9wxGLKj1d6ENe0E0KarFi8CdpghilKVMOgiywTLpYAqTYAA6axoiiJlbJ4IRFOp7Ap1OIKM+aTSxs5REJtu2Hcm9hzDNfwnLSc0gJZp2LRoiVlfP25mWcmqpekAwDQNVKfPUduNkzWNnR0r6K2eNFBUbRT77Y03PYnDW1tKhhT1VWIK78yssGzxg/Ee0VxEyIH58yTEoZA1JFuxrgUbOZHYEBlmdOEFa5QgB0YcBRF37zVBgG8pmFSqrOveROPE6z1YdTsOlbqrh7iYjXgqDJicWUhL3segkCnu9+VFL4tUsOmzhRImTI4iPpBolYmdK7LCkBkipIpUxjFbz4c/hQCJNEGTExLtOvkibIc2YNDWqMNoYIkiOjfIyYClylqWAaU9nu9eGpc2AFSBgLv85TLqnSOFcMPKeVH584+JRLgiD3uzdxjIXz5wgZLyPb2J71o6KkfgSPgHNFIWpmzHNRqFkB59l9tvjTKRhZjbtlnKFuTlBu7+Zl8Lb7Z05i572kMieR7z9ERKehkDVvUGQkwBQRoiZFQOfxa8V7d7Yyaq9csLZYDHMUnK8DVdEAMJP40aYG6JWGklI2JUEcvFplaRNgRFUQkiwpglQTZ5QKeqQBVVCTrr3tY0sfrH0XW9thy5Gn2N/XWVJkP+zsYK0+Q7lKUaOmeEbWc12gH58zTkJCDFJFSHIEdI4xKSeg8yiRUsJWjzQQJstR08rN1gEiKkPcRMgr2yNtlMYV4yl3iqoI5VlYqkLh7KK9X17ZPOTu4kP6IYI6T9KKMZYPUqkS2AVFUxECTEuEJF49kgrSFO2HHHRJaVkcS2bs5MSrR6YtHAx58QL4ol2UGCxlyIpNVvmJ4o1vk26UkJX17HvUbOWILtSZkJJqTpTCiCKvbJxADH+4GnZ8hL/P3cxnu2pK5/+I2Y4oT5mqRQoFnb0eXqzLVSL9pKiSmKsCLDaEUYohU4tPOZ4lmoIu08pR08YefYBd9ssovPpPp6QJHy5npJY36SOUqzSkJwgE46VgckwS+JRD3A3jswyWcQloh5QJENQ5qtS0Z2MsqmCNpeimgaz4qNOT9Ls1aL8Pf6KfaNBmPFtOWLKEVQYp3P/iPRQ8QiwjNpMSYZIyhqSSTZzCy1nwarw4qMLY4b0FsmJhlKaOCTIqgEuSomI8KzaCIqRyGBRxE2GbPkaPaVjSZWA2AQJzbaiP07JgTVbc5oN7ryc8+502/ipRM81xU8UpmugTr17fKjlLhU7QyhDrdrxlznuwpWpxNdkHrlmxYH6+qbmSd25ZPqf22FKJTH3jqfMmVvas/gVGpjIwMJMI2UkvW+1T5IxHgCxTY1jKcNhdgcJlr34KgK+5t+DTXkzivGPgrZ/jbx44BN0LiZ6Hm36Ta27dxA1Q2sdAPL0oCXL7lqZCm3gKid/65iH0S15CZase5pRZVrIAbtXD9Jo6bl5ft+Rp3bCujv/xo1MLPr9+3cLvfPrtG/jQtSvn2H0//PIAK+ui3L6lqXQvi+c2O/bz0ftfoH8yU9pXS1XopyJAznde82MM8xOlF+DWz3Hw5XPw4osL/nRy6x8Qfcfcuc754l7F+wPnj/1966NX8aOuwSUTOefjgtcwD3/7gZ0XjL0V9/uenW2l3zc1V5bO+aX++Byipviev6GjoWTBNd/N4xJeHy6RIJdwCa8TRWuw+QPePzzbd0Gp251blnPliqqfajCbXxBcAZ+dJde7GGXObHszmCuRn4/t7VWl/X1q74YFL9IPXLOyRAxlHJf68iC/dsPq0rXdf7CP3DwJyU+7iLsYPNczlzDYYR0FWDDhVGn4/chj2N1PwI6P0BgL8Ue3b+T3vnUYV7xsxPdf0877rmpneCrDt7/0CCJChUoySoxKmfasJVQFIlJaMS/mJwosSYAAvOvKliUtwmLDz3J9oIsfZTuIqRSWMpyTGuIS5mrrCLjwf5iZeH742pUX9RIfT+b52nkmrAfr72TPzvMH48GbTG5pqVj0JU7PKYJuki63hXY9yDRhzkiUZYxSlzpJQq0lOvAy/pHDnJIGVnGGBjXONCFy+AAhTIZJIp4VR3pszrGfOjW65HkZ0WyKxOmZtBmRGAAjEiNGgivKJqlriJUm98NU8LCzo+RRepP9PC+77ZyghXeax6jXE+SwGZYKHnKv5j6zp2T9tNw5gd8kUbqGtOvZjuSMTUDnAcFRml7XyxQ6ZS9bVB00W4a+d/OyC7a5Uoo+6uiVOurVJI6xGNDVngWU0jTIOD7lCeTLVQqFYVQqCcZavUzBeeipu5Evu00ckA4+bX0NEeHr7h7ebD3rFWEnyAPOtfxGqAeOfBvarob2XVxWlYbHF44fG6+6lTfHQtwz67PXrBpUmlhVHY+fraZKJ7jf94dYyvCYexnbwwNUZuJMqzD3Od4z/V57H0rBY87lXsE7t4Ndlymmho9wyEzSYI0TJluo6+IFwkPVTZQqo89pkCepPvEg4wGbbN4HAhGdxojmuNsEKDbqBIy/CsDwlLfo6JM66sw426yFxJtVvQaq2hccKpd3iZEs6Ac81KsJj3yyy2HqnBc4UHMDtjATpCsGXSzP5AFHNA4WtnJxsBmVGD3SQI800KjGWK+7GZcysuJHUGQLgUwRRU7ZlNt4R6pqJxntJTz2IumC2mi2/cfsnxqhx63H0RZNMkyTNYZqeTvs/OjcC267ikD7Lqb7dkJhYX2MFror30Rg4lGShHjS3che/YSXsVqwiMoZr6hqUOWISwRXNFV6iqiVodeto486OlUPFSTQCK1qkLiKeiY8SohIGi2ul/U+R8Kg0FqRyLnsOzyI8Bbu0fs8okwsMgQIq0yBdBHy+LwAfcHiSCnBFMiaPDbTEqZST2MKyo3Zyo/5RXhNwdZj0nhZ2DGdZhybuAmDVohk8CmnpAwZkQoSEiQWqIXGyxZ9bEoobySvA0R1Bi8Qa+NTWTQGR2xSBPGrfKktNEIOG0uZQj8q3FclZLDJiQ89qz5A8VrmkyDzlSGKmUxcg6bbbWA5DmSmKDPTnCOKFq82jWNsssqmXiZxlYUvXDlD9gy+AoWMakGVAlBetq5Xv8Vy4lweGeev79yxeJs0bASlCE5+BzNyGhdVsDh0S8RiVmyUyiNSDJb5yeIjqjKMSDm1agqNi6WKJJAQ0blC0NOzPByXMpIEucI6UbDz2E5DXYKqs+cIyYSXiV5Q+pQyvdFMSJiAcgiQwyqqRuapqFCKoHbIFmxP7PMUbVNAUOXIYZFXfuIS4hRNDJnKEuHqlzxhlSGl/MQlwmzbyFXqDGGTBe2RYcdpoU0GyWg/xlX4LJeECTJJFEsZhqWSg2Yd2wK9tOSHedZdx4Qqp10PECHLJFGqmCZOGWON11JeIHjM2Bly6UlSEgQFIckSVE5hNNPkxS4o1ixSeKqv4Opr4fjcJIghKqlVSQYkRlb5ybuaNmuYkHgJK654mfk58ZHBT1SlPbJJefc9qJwF41teLM/OS2kSOsyR/NwxfLH3dVt1hCeVcMS0s9k6yYSUMW3CVFgJaiSOoy2Oum00RKtoZWZevub3vztnzuy3FF/6hS0XHcC5EN56WSP/9KKXXLJNH6VND/Gou7mklB2jnIDkadFDBFWew247dWqSqEozJjFeNKt4U3QQOq7gD247v5VvEd/66FU8/9h3CB/o4sBUC/vMdtbqM0xJGBQ0R8E2eRyEKcJESWNpw1lTS1zCbAqP05qcSbLpEi/Yvn1FFWuTL5KLh4lLGCOKlAriMw7VKk5UZTCiCiZB3jitRJXG5KNuC1GVYYd1hD7qiAUstBVmKu+n0kxjWXPHMoXXL+MqQlqg0gciQjYn5EU8JW6RmC+drSIhQQRFFO/vKQmSVQEq3CkilkvK+JkkSowkSnn1qrLaT8DkyGoflnGxtSEnNpOUEZQM5SqNoMhhkxE/OWwcsdCWzyON23cRGWqGrhll+37TyR77ILZ2OWtqWWENoiSPpZhTC8UVyCkftrhoNaOkcFWR+DWUSRrRmv3uOrYE+iHr7X+76iKuosRNmIjOMEEZNWqKUSknQYg/c97Fb5V9nzLbEKqsoXXkCKOmnLgKU2ESRKwMSlxP+SF+HKXBQFhnPeNhpciIjaWEWjNJF60MupXsipzFX94O2WmyxpDNjBDVabLGR1AvrH2kEO/dJzAhUR43mxinnKutV/BLnmShfl5IFRRtEqJPahkw1Z5S0mhq9SR+5XDSXU6VTlClpjCiSeOnxRrmqNu2KFEL57ehDipFzl18TBegZzS1YH2X9cU4RSUPuteWbGnvsB5jlTkLSni+Z4K3bpr5zpmJxevFnp1ILzpPX7z22EIoNX82MBdXtFYttEp/ppeJAy9yaFRTzRRWwTp4jHJWcZaaqJ8mHeL3ty9mubc4ooXkpPmIBBaGQC8UVynic3du5mzZCtShZziRrFhgAdwRHGN5ZWTJc3otZFLxvM5neT9/38X9PPXxG36mgfPXShLMx3hyoaoDYF1D2aKfF+/P+WyqLoQLJXL+tPhpXXHOR9T8LJO7/yPiEglyCZfwU2L+gFeUur33r/ZzYiSFpZhjH/XOLctL2f0/7WD2WuV6Pw1+7y3rLrjNfFJlNk78t7fwmYcO89BL56gtC/A7e9b+zAkQWDgRW2rC+TV3D7+4upX26pk2XKp9G2Mh3M3LUK88hV85PODsLilK3mU/SgXJOZGf+TUyZgfCFkNb9dKTJ8oamI6to2PUU7MU7byWKTjja2fErShter5J1XysqD3PMbm4YHwRS77ERZis6KRdP0StmpzTbu+19yF9z0H9agLNm/ly13reYT3OW9R+anWcERNjjApq1CT1aoJBU0V5anqOH27QWtpBvHNZFCtQz6kJL4POUp7NwClZzsoqL9W4OLn/6uON/N9XBtiuuijTaX7gbCWuolypjtGvasF4tlOPuptLC5ui9dMVka9iOZMEjCeP9otDVGdw0OTET5xwKVNok9XLdlUzx8ZjNi72/m1treRX3LfQooaolmly2ldq2y/Zn6fGihe8/2FawvjJc9bUsNUdh4GXPRJjFqo33MDBf/Y6sFJzn6Epmcka8z6d6ckXu4B4XRDDy2W7+KizpUSAuKL5unsLVwT+EddoujMVvNl6FoBeaUBEWK972C5dHJAORqq305k6xTbrGAkTosxK4xayp8dNGW3x3gX+yt6xBfwR8sFq9mda2KB7SgqmQ7KaPuppqFc0FkiNt25axj8dGsCIptUaJicLiTc73gdqXn/d+VFM1qHix1+gUiXmEDoRv03g8g9Cz5OEk31MmHDBP3tGTTDfxkLhka+W8pQhSQnxDffNHDVtfNj6NrutQ/SYBo6Ydlr0MGOU4xiLmJWiiRGCKk/cDRNWFrHyRlCaYD7Os6aVq9XL2MowJSGC5Et1aCicSw5NUgWokTjLrFHGa7cTjdV5Rbpn97ed9wLwuR2UFtbXBjSBs1N8b3QbEZUmpLIkVIS8sanS04y75fRTwxrOYuMyThnfMVfxdvUUMRK06mEOOp3cZf2oUI9Hc5v9JC+6q3mZlZSZJOusvoIHu0WENHEiRPDIpSmrmVzay+S/R+9jj32QXlNPhZUkQNarq6AEPy4OnvVPmTJYysUxFlnlxxKXoMrRqobIi0Ww4Inu1YzShVoys/sYoIRpidAvNbTrIUQplutR0uLnoOmgkmm2WifJYXsEJ5Z3lwORxcm72ejcy8lzU1Qnv0dQZdFisHHIiJ+T0oSgqNVxEqqMMplG4wXUixnNxexm8GojZZVXW0bEI5XsebZZ80mx2QFEj0zw4ShNuU5htd8Efgtr+BSh6ZfJYTPgVtNkjRCSLFllc8pdTrsuJ1IgGmnYQDo+giU5zwYPzZSEEBSxQmFkg4LFlF1FtO+Cow9RqRKclToa1AQ+8l5NAlSpWHpG/F6NEmNK2eggDFON33WotjxblikJESEDqqijKiip8Gpt5MSHRlhHH1tSR3nVrSGkUvhVnmLp5mIRaoNFkjBfzN/MXv0kneHxxdU+ImRbruHxkVe4wXrxgkochTcWjFnVTLuQEn8pMAwQJoNWQsqxCGkvQDFFhJT4qbGTaDGccWuxtdDECC4WfW4dq6x+cuLjtCzjGK00yhgbdDc5Y2MidZwZD9KqBqnTkxw0HVypjtGkRzjsruCr5m38LzvlvWzGXyXni3HQbWCPdZCAymNkJtipELL4CZFjUKqJLl/Pb9Vpordv46P3P893X5lRYmdrOkkbP2p8jIDJMqUq6TfVVJCgXk2gFGSMj0EqsZVn8eXDC4a6aOxCPRyrkOGvCp142oSJqwi5yHKYnGnbpd7XW1sr+RtRbLC7OS1NVMg0TdZoIVAfwDGatAowntO0ziLGi3PmH3QNcVNHfSnx6I2aN89OWDloOmlQE+y2DtErDTxv1nCDfoFGNY5YIUbzAerUBBGVISEh8lis0IM07HwXsdbLX9Nxt7ZUQmIzkVOHaRt9iH6poU/qaVFDvCl5BNef55ypxlKGBEGM0QRUniY1SrBpM2/b8n5q3E56x5PknFVcv66OTc5h0k82c258iqzxkdM+IpJhXJXjkzxR8iSMp9oIqrxHnRYUQBl8nu1nNE+0uZOykI01+CIHzxk6pZtyKzWnFkZxpA1ohwYzzjGaiZWVURYJw7mTOPksWXEJqjwWM0QCeNZRdXoCDYybMpIqRJmkCFgOcTdERgeISZKAyhM3EbSGjOsjqjWIwdJCthCUj6o0ChdXFChFygQ4zIqSWtfomRDPjR31fPLbc+1995lt1DHOTusoGdcmYnnatyIBXDzvtAQoJ4EF5FGkVYC88RHSWZQ42MoQljRVOoGsuolVK2/hLwcTdGQn+L0X1hNXEd6jvk+aAKZQR6+WOF3SSu7tX4SeRwj3PUM2UMs/JztpVYMIireZp4loh1FTwXFZznbrOLZyShVdFEKcMnwmT0RnaDYjfIc3saphNWX2EImqDs49/yhtOklOfER0ek4No9kKTIUQ0jkey2+mS1rppIe4iRDRGdImQJWeLiWHjJsy4qqMZ6WDo24bd1iPMiDVVDFFix7GVqaQtOVZZqUlQBeti9g4hXjLxga+8vjSyWfs+DDVAR98/8SCPymgbX5NoKoVDPjP8aC7ZYEt7R3WY/RKA7XzXhKyxNzhfFOKiwn0hnznr+iUdxepMbrzo5jytVT83W+z3BrlsNteIkD82iG//m5++a2/vfB758Ftm5fxtad6Fny+1Jr2YuMqTWaAs9E6vjx144K2/lzsRzC2sDbtbFwsmfTT4l9z4FwtMWM5H3/2Lxn3+n+FnzVRcwkeLpEgl3AJPwM0xkJ8/7euK/1+MfK4n+ZYb9RLoHtWHZH5SOV++qLoiylIftbY2jq3vZeS5yoFwV33LvDbLrbvQDzN06dHaa/xfD63RMYZi9l8YfzqBRLmd9mP0spCSyjwAkDvvrKZvz24UHJbRPB8k8eqlVgnD3iWEvhnvkOOiqjFjVt34kQ7XnNfW2yRVMRrIVOKWPQl3r6LukPfIj2LAIGZdvs9/Rz0PEms7Wp+adU48W7DtITwK4cKnUSJwo+DX+UZUpU4m39+jh3We97UxqMn56pDAG7d2MCdK5p4+tUwR8wwe+yDNMkIljLsc7bRFK5jZ2FptKm5kv95dyXb9/fw8HeOcsRt46i0cbv1eCnwfoBOpt0QfdTPOc4B6eDF5T9P09QLhIYPUaGT2LgFW5Qw33HfRB/1XKNeolUPEypfixqQBYzYr99QWNhfZJsXlQeg5hAgAAdNB6tUvxdMJUCf1FGhElSoJFOZsoKR2lw0xkJ8tpBt8113Bx+2vs099j66TT0vyFq2qOPcY+8jnl5J+fp3zPnuz3KC2lIVLmXlu+IRWe+zHiGo8jydqeOItLNTeX34ebOmVBNkve7mgNtB2K+JJfuY9hmsfJoRiXkkIqNEdZp8NoRvsYWLUkxb5Xx5/EqutQ7NUTDttg7xDXcP3PonpbGjGFxS/cKIVDAisYXEW0wtXGU+/NvE+p8nXr2S0yOJOYTO8swIFS/+Pf6WK9HLNhPqexFkxgpr9sLeC6IqpiVESOXwckM9HDVtxYsqXhoA+2Qb4sJ11iF84pDB51nOaJds+03QuRcGX2a46c1w9hHQijEpI4CDVl7hal/BqsoFpiRCp+4lh81J00Ttso2QHoeBlxaQbkWUFtbd/cSTNewz27lSd7FVHycvmjM00WYG0ZbQbIaZkjAJQti4tKhhXjBrsJSwNTREW3KAbmmkTQ9hPMqBdj1ArcRZoc+V2ipt/CTVjB1WQLIEp7px6z3V2+waUikJoJUQIIsPb2xK4hUPzouFi0VA53DF9ZQ3Rb/2gmWIEa+iSUFwgpplmSLKq9sSUDlWWIOkXD+WChIXHxaGLfokfpWnx9RTrlLYyiVhQsSJsDybPP+qsXCjl4UcXjLNbNKvekEzCTJGOcvUGKPEOOPW0SijmAJBaGFAZuxiEhLCwSKm00yYCONShg/HU5+JJqC8WhJFMqQYNCwGEOcrHdISIKoy3gGqVuDkDSP00sAEfu2QMCHSKoDPOAXigRki4Of+lvRfXEfF+CFcLEZNOZYyXgICijyKnC5j8XzCeXBynJTl1EgcX+HmTJoIlTqBjUuCEAfMOi7Xpzy7FVPD/e5N3K2/T4WVKtnTlKu0R5YwQxDnxSKssuSNZ8fUoCa4oX6KhC6jXvWRw7OsKyNBgDwZZYNoEoSoIEGbHuSQrGJFbIxA2yLZ9u276BmY4jL1ELoQ0ptfz2QxTPga6MvCFdYJ+k0Nn3XuBuAj9rfZZE5RY00xJFU8Y9ZzvX6BVVY/2UANY3kfaRFCkmWQKhoYJ6yzpCSIpYSgyvOM4z07dUywTvcRdCsZlOVs1ifJGW8kSqsAGKjSU9y1fIKyiiZvbKhagXXuOFHlFdyuNFOstfqxMGTEj085lKkUkxLllCzneitJYJmXePPFu7fOnWs75Tz99KOExv6R5dYYJ90m+qnlevUCBk3G9RGy8jTIJKOUM0o5DUzgoPDheo9UoVZMvlDvBYFynaLbbaRu/S183oJ9F5hvNcZCfPDadp54vJe36ydZbo0y6UYZ17VETIaoSrFKziK5igXE+M96zlxMWMk8sZ9l00HS8UQpKCoiWNqQj9QzMqHx6zxJCXJE2tigu2mvjhBr27LkeL4k2nfB0GFqu76HqzI86m4uJcNcr1/EcnPkpYKc8uwBpwlRK5PU+dKEa5oJb7iRt87fZ4/3vps2IZqsUSZMlKQKMW6iNOgxnGAV2VQOGxdtDCFdVPko8q7FRqubTGgjjT//P+Hh3yaZzfCou41N1snSO7ZUC2aWgi+gHbKuH3PZu8i8+iPGcn4EH7W4oPK4FBRyIp6NmI4XyHCbfmqIFVQUoybGCdXMFRwnp2wG3BrKdJKwZLG1YULKiCnPNmhcokRUhqQEqSFOVvmZdKO42mIF55gmTJ+pY7U/VWqexRJUfrUjQ/jEGEk3SMxKIkZAz8whtIKc2IxLGWUqgQBpgiUSsNxNUmPFPQWesjjutnCZlaHR7WHVxo2MparpOPRPXKtf4nmzpmRtlDJBnpCN/JfWQ1RPeUH8KbuKP054yoViUpRCCu/nAD2mkSvleIlsd7BQQJWKM045UyZKRvlplDFuOfFhDv5CgPirL6AA12gsJdgYUuLHr5zSu7lo85giwLiJsl73oIxw1LRxlDbew/eJ6jQOFq+YdhrVOAkVokca6JJWlPK2fVB28zHrb9lg9ZAVm3HK8JMnKhkOmnVMEmW76pqT8PTND+/k/3v46Hkflf7JNL/3lnb+dBESZFE0bORwXSUH+ubO7IvBeaWED85bE4f9ixsohvwXeoucH6+eJ54Ai1s/0f0EoRe+SoVO0O/WEFQ5OuhDKaHP1HHdyBPwTHihovg8eK2qC7jIuErnXvqCV3Ogf67S5IB0cKZzFU0rqi/q3H5W5Me/BUymc4t/njp/XZM3Mu51Cf9x8dONcJdwCZdwUdjUXMkv71ror/mvDe01S6sBUrnzv5T+taI4+b9QhuT5sl7+4dk+rvrsj/n5rx7gqs/+mH94tg+q2hlof+eiEuYHnN30MXeCp4EPXtPOUx+/nl+9YfV5z6dnNLX0Hye6Kc8OMGiqSEjYy5aTMINSRXl2gOUy9Lr62mLt9NbLGnjo3jfxudl1SX5KRBtWLdluwyvv8Aptn/oh20YeYJXq5ym5jB+5l2NhqFWThMkyaKpoq/B5NRhmZe0Xg8+zsWFZOX9591Zo3MQ66Wa97qbX1POsrKPX1LNed7NWuhfYQi2LedULBMWHrYfYMOt7R9x2rigb51p9yPPMn4XB8BpiFdUclxZEvEzzaQnziqwgqtIcNW182d3LiFRQV+7nHe+4a07W9B+/cyO/cdPa13T/DhYs3/qkbg4BUtxpgpBXVNBdy935T/CAsxu/ciA9seQ+Z09ClfKcoIuKkOLvsdDiMvPGWIidK6vf2Emq0jQc+zrvtfexz9nG3flPsM/ZxiZ9ilCyn5X6HFvU8dLmW/UJrtUvMk2EI8bLrj07kSaRc7DyCTKFQtx35z/B1509ZPHjN0ss2kQYccJcax0qKZiK3wP4+dB+Gieen/OVb330Kt765ps4vfYDDIXX0qqHuFIdo1UPEa/opPymjy309wdIjzNkL+cFWVv6yIgmp31M++o8MqJ+AxqDUPSXLzTRrN0YNFN4CgEHiykVY0Qq+HX7QX7dfpBRKviGs4fHzCYvI1YUb2oKMFHwUwfPI33CRGYC1TvvpXvlLzBMBU+4Gzli2kgRICcWSnnFfvNY5LGJkMFF44jNaVnO5NgQZKcXtV9bgPZdOHUbuMN6jC3qODXEiRPhuGnmBM2EJOdlcRPgpFmOAjpUH616iMtWLKe9Yys3Xr2Dyo7dZC0vUK/E0KjG6dQ9hMiRMEGSRAhZOSLKIxEjKoOtDVOBBpKRllLbt+phJiXKd9w38ZJZSR67kH0LL5sVDEslWfwMU0FWfDhYGJnx/te6YEmmrMJzJEy60VIgB7xxQlDYuITJErayOA2b+O38vYxKDBsHHw5Pmo08bjYxaaJU6gRpCRBo3nRhJcjAy2QcQ4804mCRET9xouTFRikhJDmmVIiEhEjqkGfzhUJUkdDw9j8m5WTER0hlPWUfZYyZKFkV8Oy8CocrBpdcZjJuZ5QwXs0FHy5Pu52kpiZhogf33GEOmdWMESNCmpQKMmSqqNAJIpLGLq+fsf3qeZK6EMRVlFG3HL9yKSNNRvlJS4CEhL1M6IGXzt8unXtJNm7jcn2KDH76TD0uimo9jYvFcbeZHDY7dRcJCdEvNaWs7jOqjmE3hqP8njoIXVJyFNvBVYppE0bbNv5ghGUbrmbXqloa3EEmJcpBs44nzQYmTDlKeSR/nDBnpYYMPm5Uz7HNOsb0iltLqqk56H6Claf/mhgJFJ5ip4j5aSulcxJFRX6QDb4B+k0NfuXQoXo5IB2kJEDEcsmJjzxeEWu/cug3NfiCQVpCGfwmR1oFkEJWuE/yGDQ5K0qtmuR263F2Wkd4zGzmuLQQ8FvcUv4qL5rV+JTDBt3NYdPON9yb8eHQNvBdUn0veGND42VkAjXUqjiVTFGhU4W6CQq/ckhLgGFTgZ88l1unSOiyOYWB58y1lSJGku+Yq3nOXcNlVjfVxHmFFcTdMLZlSJoACYIEyZPDR5+p9ciOQvvllU1O/DjKe6YThDCiWWX10zjyBMs7dlzUfOv6Pe/krTff5ClIjE2FlSBlAhyTZhyx8GsHVdU+J6njXwo3dDRwa9mrBPNT/MTdTEA57NRH8CmXY6YJKzFEizXEYdPOFBE0wmHTjluxAl78G0/d91ohgqpeQa9pYL3u4Vetb7Fe93BG6hBfiHbfKAfddXzPbCOHzyPXcqvIDJ2AZ764cH9tV0MgSpM1yrBbwY9kKykToEon0OXLyAZrSBvPyjConVnPhlBpJUm6AXyhiLfvzr04m+9mg+4hSdCru4E3F8yJjaNsHPHMrvKiaYhCLNlHwvh4wmzEVkJO+zwVEp6iIqCcQm0RQ0oCxEPLiaoMYZVh0FQyocpYwxmmCTFJlJdY4RU5x0IKNTs89UiUMpUirB0svPE3a3yUWWlyYvOqLMMvDpU6Qa5phzdfKOCuK1t4+nev5+8+sIOnf/d6rqqKszyQ5ZRqwnEtfFrmqEkFCKssDWqCNEFGpYyUBAipDI2MEbXSJCXIiMToNzV0VGvC2UISUvsuBibTbFNd1KpJEhIqzbnDOsOVqos6Nelt27mXx+rfs0ARPUWEp2QDSULcbj2OTzm4KPLYqEIGiIVQRoopwrxi2mnVQ3zB+hw88yXqnH6y2ER1ihAZ8tg4WIX6Kcwap4W82PRTR5IgXbTyNXMLLWqIqE7jE5cUATIS4DmzplAnKY2IYr/p5GvmFjpUL5V6mlFThlJCjZpCIzwva0gQYtJE59Q2Klr4TGec8z4mjrv0e71ohzUH7btYu31h/RDwgvP7TeespCkPh85MLrr9S2fi5z23C6FiifUBnIeEOPoQuv95JiXKCZoBr17KkKny6sRNLSzKfjH43J2beejeN/HJWzveuDVt+y7Syxa32kwt27FQUX4JC5Bfon8vqhK6hEt4g3FJCXIJl3AJJTTGQly1soqnTo8v+NtTp8b+zcrzitnpz/d4Fgjjqdyiqof5Xqng1Un53W+9UiqQbgR+71uHuebjv0R1J/DswhoI8wP8N3XU8V9u2zAnKPzZeb6Ws3FF23kW1JVtWBtvp+rlxwmlz5GWACGdJR1ahrXxdqhsW/q7F8C/iMx05728NH0Mji/MuP+m9VZ+s2OEzE/+tBCED5CQILV6kpz4CKocKDhj6pluvpnKgZcW2OssacU18DJVMkmzf5ozufKCPZTQ7J+mSvwLbKEOnZlERHGtdYg2PUiPaeBb7jUAfNj6NsvMOGMszPR5U6SfkLHoNg10Wj1eXQV8dJsGoipDp+7ha+4tbFlewe7Na7mr/adv8+JiYzGVUytD+JXDV5y3L1De/JreX6pjMRsD8TSffeQYAJ2qhxGJ8ai7mTdbz7JTe8/Noca7eEfN2KJ2Wj8rZPMLJ8YZ/BjJU8ckPu0uqAnS4zYWgvye0m372NkSATKnJpADv28/5hXmnY8LKJjexaMkXvxHovPaYWtLFaukl68dG6eXegaoplHGSEyOEO95YaGNSOdeEnmXyAs/5HplkSZIA2NErQzPu2to2PEhGHyF3KmfMGHCRMmQwMsG1WpmMeFlghsaGMdRmnFTRn7VLdiJOAx6z90Z6lj3jv9c6nudfX9DtmuCAVWGz7hMEaFcksRVGYnRc4QL9/nQmUm+4PwS77MeoVP10itZNulXyYgfrQzTEiFG0itsasKcpY4oKZJnXia++ueJXUxf6X6CePeLVDNBnY5z0PWy8PdYB0lIiG4aiEmSKjXFTusoI24lx1QzVWaKFdMnoX4LTeu20zR0mESPhevokgWK1z4KR1mEQmWkk3kiKs1yRgmpLP2mhtoNd1DevAmezNKmBxg2nuezUoI4ig/Z36FdDRCTJMv0OPc7N7HbOoRngKQIkyGis0yZMNMqTD0TUKjXIApGpJLDqp3t5ggxnS7U1FCedZkIKRXACkSIdV7PXdl+ogNpbGXIip/rrEMcMB18V3awxu1je6CPWPostP/6+du0YSM9zz3LNusYR0wbw1RwFYdZbo3Sb2p4mZUkTIhVup8qNc20CRLQDsGC3VQeLyCXJERSgjSoca+IulGcpJl1cgYfOQLioJQU6jd4dSyKqhAXT02igJDKYSmXYaoItFwOyV7ofBsrBh4gbzRJHSJCmjoF/aaGOFHsik6qimTPkX8iHarneVezRZ/EEYWrNdMSwodL1tg06RyMd5+/XQZfITT0IoPiEUA+5ZCWIFk8kqZSJwBFL/XYODzo7AZgt/Uifhz6VD1+9yxVVq5kOVioDoOFISQ5/Nqhz6ljeOXPcdW23XD0IZxwLZ91vMznT1n3EbDzZMQL9vpNnpz20WvqWWENkHMt+vwraVrs/IcOY8V7yRaILUcsIirt2RjO29Qt5HgrBVXuOOHmy/hR8BfoO3KQPfZBbpLn2OQ7iz9Yzg9kB62Jw1jKMClRHql+L3+Y+1OczBS2ruCMqeRlVnKZnCasM4TI8WyulSkitOohmmSEZ/U6vuzs5fLLfEQmu2l//vlSBnsd42gtHHTX0a4HSGRcwgAi5Go3MHJihOVqhAgZpk2YamuanNhMEaFHGrlcn0Abl3DizEJbwSJEeLVsC0eljxhJgm6esM5SJxMkdYhRt4IaK05Q8uSVRdb4aNRjxCUCAlXKO6ZXF8erU4NAnDBlwSBBe6kS9IsjYGteNo3UWnFyrl3Kcs4pH8+5a7giEruwoutnhfJG7GgfbYlBygtBZVA0MYpfcvS43vznDusxlqsRcviIJF2o6bgwAbsohGz7DXz5RI5fsx9kufLquTzkXs3a4JOMZSZpUwOsVZ5i+rQsI0CWdC5McLHdPfNFGDvNy247VTrBlXIMSxm63BbW2gP4p/qIFezUBMhje5aABWVBQDvY8wKsgjBCJRUkUTgY4xHapkCAp8VPFj+DCagfPklZfpKr9RA1ahIjGkfbTLo+KqxkiVTIio8EISqzo0QCPo5lqjguzWyRE1Ra00yYMqaIcE3FGEEpx5ke9Qh/8TMg1ay2zuIvhPL95Dlo1rFZn8IYl1o9SUySpAkwYaKkpuJEB1+ZE4ydk0HduZfhTA2rXvwKZVZqjp2hARyxvL6vXCZNOWMqhhho0BNYuIQKavQMAVIqSGrsHKnl6wgXiOrm/KskdIJeU0eH1cc+Zxt/bu7gi/bnudo6gh3ZXcrob7En4Omn2a662GB1lxJM3mU/SiNjBRLIq6GSIEyNmsBSXpH5gHKoYZI/cu7mvTziqVnHKrADhlXRLE7Sxq/yJF0/IZ1DleYBAIq82IRUllVyljQ+btH7+UW9j07dS1wiHDKrSEiIDquPsEkTxFuHdOoelAh71EE26ZP0GK/O2q3qAD4czwpRstSqOEoJb33bu/j12rI58/ymyvPP9+/Y2rSgruVshBdRayymfJiN53om5hAQdeWLPlHUlQfOe24XwmR68cTJX3pTG3/w9iXqS1atgPr1cLqXbbqLJCEQaNcDAIzWvonG16ACmY2fheriiZOL16H8txwv+ZfEaykQfwmX8EbjkhLkEi7hEuZgTcNiBjn/9tEYC/HWTcu49bJlVIb9i26z2PqzezRZIkCKcEXOr9aYh3dva14Q3L7ryhae+d3ruWZ1zZzPL2g9tfNe4uFmkqlUKTM1ISGSqRTxcPPi2aKvAT+TLP55OO+kW4SB2qu5z9nDKVnObn2ITeo0CYKclVomJcoKfY5/fmmAsTXvWnQBfkNHA//1to0LJqFTmTyZvJfT50HI5A1TmYWT9eI5KiX0mAYeM5vZaR3hdutx6nScV1JVDNfvKmV3gXfvmtZtZ9INloKNf+PexCmzzKtDIUG6pBWAPbe+s7Q4/WnbvG986b7YxyLqELwAvnXFexYtzt09mpwpGlko6Fo87yJi7ZcXsrJfTwDkdUAM3St/ga87e9hjH+R+3x+yxz7Io+5mcuE6/H4/EybCm61nebP1LL3SwISJsM06xjq8+jl51+BbcTXfmEWAFPE1s4czq38RFrOcwVMwTa65c0nl14i9iL/w0GHyrzxETBIMUI0rmgGqiUkC55XvePVHZkMpUlOT+EyO5dYYZaRIqyBxN8yWskmqEycByKoQE1LGKOVEyJT6YPFOGDQ58WGUV0T4FE3kRk4yNtBXOlQzw9SOPVvqe7FC8UgRoUvaeMasp0vaFvhFF5+LLmnliGnzLHwKNms58VHJFJZyGTKVDKkqDIpKnWDcRDDHf7B4Fu8i7bZ84If00sgDzm6iKk05CWIqSYMe50V3FcNSgU85+MSh1ppgLWcI6ywvDEP+1Sfh6EPQsJFUqB5Rak6xV4OiTGcIk2O6bCVDpooBqhkyVeSr1tCUOUV1NMjv3rKOfWZ7qehl0dP7ZVnJOalhgGompAytDI+6m3nUvZwylaJGT6HFkFDhAingKUosDCkJMmJibFEn0UoYdcvIik1AOfjFxSjFsBtjqmozmZe+RePAD3jM3cy3nF1MEyZGkjXqDJs4zQla+L/ZzaSmJy+cjT10mI3O0RKhk5Igfsthwo2SVzbTJkSbGqTW5xVLt5TwgruaXqknX1A42LheNj4B9rlehnZIZ+mRBgalAq1UiYwToZAFrUpWWAAB8nRLA4dNG0kJcrM+SKD7R7D+HTi16ylnmgqd5IhpIykhlBJOSDNfdvdSnzpeKIgOoDBj3VTKNFaBrDCisXGJqQRV1jRusHLxOhqzMf4q/lycBCF82iWPRb9Uc1qWYylDuUpiYThumjlGG9fWxGlVg9SqKfqlhoz4SeoQWfGRU3apTotGcMRiWnl2L7Zy+WHXMANVV0DnXh6p+DmUEj5l3UeH1Ue320gvDQy41QStPKvoZ6U1wHPuGk7RTGzy2OLnX7+B0eormFBelratXETpOSqQYnBTF+qSpCXgBQIjNbzv7vcwtP6ekr3gVN7ijFOOnh4koYKlz68afcC7tnANkyZKvZ7gSnWMej3BpInyiqxkiKrSOGcpQyNj7JcOjrb8Atk1b4PCu/SItFOn43SoXqIqw1fc27C23u11msFX8I8c5kF3Ny/Iao6YFrTysucNmnKS1Otxuk0jR2knE6xZOgjfvouOhiidqodJE+VlVhaIKkOQPENUMmmivCQrGDJVBFQePw79ppZRYoybMq8qjmhP4SUWObwgdjZYDdUrL6w0moXm3GlW6AEecHbzY9kCeFnOh007SUKoQPR1EgpvANbfRiRWy2W+s0QKhbarVZwaK4EKlpHFzx3WYzzoXsv33StpVYNUZc96bfC6Mp4V2b4XWad653zaqgYZtet5xaxglR4gKzbPmPU86m6mXsfx1a1c0gon5wpJwnOUvlGVwWSnOJeP4C/opNKFOjhaCUY0BkVEZfApx1NnDh0muP9/0MywR1zgqcvQYOHiVy5Z42OaMC6KgM6TzrkE/CFaAknSxk+aAHljEbUyJQLEKyTuEJYMPhyM8iGi2Ka7qLGmSIeXUxkro6POT70vjeVmvMLbKK9/KMGSQu0l4xJSWVqVZ7vrKouMBEgRIEqK1bqfcnehNewctO+iIuyjTKcwBSUbhXN1xPIsHFEkJIStXGpkkqjyimgnJIgj3vZJCXrdVsFQeG2pD8daL6eq/XKqdIIut4UKneCP7a9QpRM85a4HX1npvTV7vp0w3r/vM3swoogTIS9e0XeNIUCOaYkggK0MGfFx2LRzi3WADt2H+CLYvgBM9hG2hAGpZtitJGR5hH7RrlDQjEsZk0RISJAqPc0qfY5base5ztdFSoIcMqt40N3NZ9x7GDdROq0+MuL3arwJrKOPa61DtIRypPEX5v2tvGhWYyuHTquHlPiJqSQrkocWzPOzztIZ71taKriho+G8BcaXsqn+3J2b+dXrFi8cXh6am/98Y0f9otvdsMTnF4ttbVWLfr738vPUl9z5UYIdN7M8UCCbRAiTIahyDFGFf9NtP9U5vdG4UPH3Szg/ioTdbLwe++1LuITXg0skyCVcwiXMwW1LFAt7LYWx/7WjeYnsm8WyciJL+KWG/fq8GTpFFCeyi6ExFuLWyxrn2CFta1984ljC/i/B0X/mgLuOQfG2HZQqDrjr4Og/e3//V47zTrrbd1HedjnHpBWDJqKygDBGjI/nP8hXnLcxIVHusB5lqvvFi1+AN17GgBtbUJ9hRGIMurEZm5VZ56iU8CXnNr7s7qVCJ0p2RodNO19yb2P95Vfxux95/1yJ9dBhKoafLQUbv+DezpfdvQybilJA/o2e5C1FKoGnDrnP7OG2zcsW2G7Fdv/aoqRZe02kVLRZUFyrD/ER2ytg+oxZT7/UoJ/6czJd+1iYb/wzws57qb7x1/m62VMKsrmi+Z5sw2x9HwObf420ClKORwgNSSWnaOZZs65UvP76dXXErv9PDHbes2D3llJeTaClSMSd93LVL3ySd82bsIO3WJ/a/P6F3xFDzldBh9XHpInyBfd2Jk2UDquPnC82x8aliHJ3nLz2kzBhksqzXjnEGjKhWk9103gZquVKanSciKRxlVdbwaC8wEVheW8pFy0Gn3KolQkCUz1U6AR/4dzBXzh3AIJ+6s+J/+QvSsfWWjFKJQ+61/IF93YedK9llEr0rArexWdXRLFedxOnjEPuClBe5rtPecW0PSKkisdlEykTpFUPEzZTS9zceajfQGDjXq7oWFkYBxRxiXpZtBKiTQ0RUDkSEkKUwojmmLSQMkGa9TD5XMHuQYRE9WZ8hVoJMIuyE8HJ5+horqN9eT3N1WW0L69n29pCAcmBl/nQtSu5bs/tJZuO7aqLndYRJk2UZ2Udh007IZWlmWHW6x7W6266pJUzrmcxVCOThHSGnNigYEzKGJUYbdYQFi5ZApylDlEWObFJqgDjppyc9lMeP4YeP02X28Kn3Xv4nmzjJ+5mMvjo1L1cZp1m0kT5lHMP3c3vuHDwdOw0ZTXL+VHN3XSqHtr1AA87O3iGDeSMZ4m1pkoRUHmUtumTOr7s7uUZWU+f1JMTm7DKEsZrW4Wn6pgyYTqsPgRNvtD/BLCU19pKeZYxKfEDnqXRMsZ4lWW8aFaRIEQ6Z2DwFVLHH+Uls7qkFCgGiFv1EHvUgbnXs/427ECQTqsHHw6TEiVJgEqVQGPIi01cogvG9QXo3Ev/uvcCUE0cnzKMSIwySeEWasn0SzXHpIX2Gz/AFfar3GY/Sc5YVDPFGussQ6aSDD4C5AtaoIKFWKGGRFyiDJtKbrYOknniL6F9F7nmN7GOPrZbXXS5LRyjmazx8Y9cQ7bgW58Xi2+YPSQkSJ2zhA1I+y4CG/dyXFqwlPEKPssMKerO6vM5sUkSYJgK3GAMkqO8dGaCuiP3lcbTnLKxU2NcZR1h3ES5O/8Jxk2Uq6wj5B0Xu7IFu6GjQHIJInCKJlLip10NlJ6NXlNPqx7ifdY+1mQOkTr+KF9ybuMFWcMA1UxKlC5ppUmN8IFr2qlef0PpXR4N2nTqXgaoYZgqssrPuJSRFj9+laeGOAGV5zGzGXP1byxuK1jAqrpybqkdYb3uLp3bq2YZ5VaeK3UXSoQhqUIrQ5gsOWzq9QR+cYjoDOMmCgpGpRxBl6yzDo6FyPa/srQKZRHEgj4alrUB0KJmasbVqwlqiJMIt/4/tVDJOC7Tro0UCkULigkTxg1V0xTKUMME23QX63U3mbI27DU3QXry9dlhNV5GuTvOPfa+EtGRFZvd1iEqdZKQynHKNJasudbrHibW3Em0on7x4zVehkTqqGHCq1EhGo1QwwRT/kaeNBtJECQjPjSChfHqEuEnJQFEKXxOysuEqt8AkWo2WadpZoSs2OjCs1xsG1u7+MkTlyiTEiXkt8Afpnf5rXTRRkr8lOk0duHdkxerYK1mEbZyiB3glVQVjXqMaqYA4cB0LTpSRSBcQdYfw0qPEXcjPG42UaM80tBVmkNmJcNSgUZYqc8RJosfhwRBAuQJqDxVET/BimXnH/96niTS9xPGpcw7N2OTJUBebKyCXaGDRbdpZFCq8Ks8y/UoYTLYyjBJlLT4yWPhVy690kB98vhMNln7LtbsvpuRppuIq2hp/txr6vmI85tz3lvP93rWrPtNJz3SyB77IF+0P0+yYNGYUz50YX7hVw69Uk9SguSx8CmH7dYx3qIPUBX2E95yF8RawDhQv54EQSwtzJg1eokQObEJkMdF42KRlgA1VTXUrdpKaO11BHwWCQmV6pRU6QRPuuv5vlzJg+5uumjlGC2crb4KCVWyRZ8iJzaPmc2MUcYps5xBqSKsssRNlLX10QW3YKkg+tUrq/jWR72EnPl1LYvQapHC6LNQHl7cjmoqPdeCa74V8my7rp8GryvA3fMkqb4XOJqrZUzKKVcpfMrlRXcNwWWdVJ948PWNNz8j/EeIl/ys8TOxKruES7gIXCJBLuESLmEO/iMw80tlzp8ZTy/4LJlzF9nSy8BZagL74Wvbec+OFv73e7eWJrKLoWi1NaNL8Ky2BuILz6MEMWSr1xFVafqlthCUriWq0mSr1y0aWP3XhvNOurufoDpxkt9b9jxb1HF85MljM2Ji7NEH2GB185B7NSNSQePIExc/IR54iUjr5RwpBNWKC7Ijpp1w6+ULMjobYyE+eM0KFJ6XbiNjpUDRANWs1700xAIL6/2MnSYbrCllj1P4/pfdvfjKG7h3k7zhk7ylSKXZ2NpaOccP+q4rW5bctjEW4mN7vGKzIoo2PUirGuSIaecL7u1eG6pB3JHT/3L2Hd1P0DjxPJ9vfWZOYfS7muPE2rdSHfGRE5uYStKkRtiguzli2vi0cw/7TSfb26vY1FzJQDzN944MLtj979yy9oKLvn94to8HlrAYWGzsQGmqrSTHClmQv2p9iwqd4JhpocpKLgygiRBcdzPZshb8Ok8jY1TqaRIS5OODNxKvWAfPfInowEFygWpCOocRzSgxTpomMvi8egsIBsWjZhNxN8Ja6ywVJHjIvYoD0sEB6eCIaadWTaIPP1hSZwy65TzoXjun3z7oXsuQO6MOLD67nbqHWhVnRGI8Zi5nxJSXbDhy2JSTYoU+x2W8SoMep6GynMDmuy6uoGX7LtjxETZc9VY+v/YwLWoEn3I4ZFbRZVoJ6axHdimIS4QpwnSoPur1OH7Jgx3wMpQHX6G19wE0hrSxmSbk9ZtClr5xc9D7NNHN72D5r/+Q6OZ3wNARCERLgdUPXbOS/3rbet6nH+EO61EmTZQKneAZdz0fcz7EPmcbu61DbFYnqSGOEmFEVZJxbcI6RxlpjFI87l7GY2YzIbIEyTFFBBuHdmuAtPExQgUZ8ZNTNr2mjqDOI5afqErzPv0I6+gjqjKkxQsIj5lyKnSC91n7aCj3Xzh42rkXdv0mf/ifPsy7tzUTrlzG92Q7D7rXcoomwuQ4OSE4vjLykWV81rmbA9JBj2kgYYJklRcsnJBoKVgZkixhncNv8jSqUbQSJkwZebE9IgSvIDCFAtMOmqzY+LXDCs55GcFmI3rNjTB2ivKqWvbJdkYkVsoC/5Z7DeMmym7rkJdlPKv/BKLV2JZH4KQlQKVK4ogXzOozBTuFgZcv2Ndi7ZczKREy+IlImrXqLDVWnH6p4Tl3DZMSoVP18soP/pp8asqrp6Ki9EkdZ90a1lpnAEWvqWdKIqQkQB4fWXzksYlLmKhOM2miVIW9LNwbO+o5RgsH3Q6iKk0tk9RZk7yTx7yi9xIkh4/f0A/QYfUxZC9qhgVA9YYbKVt7LQPUYEQVlGGK/KyaMznxMUkEH4aTZjkEKyBcjbXvY+yxD5bqLIlAnRUn72ra9DD3+/6QKp3wiL3cBAy8xLQJePWG8Hz7I6S4wjpBcxlzno3vudv4SP1RqnseIbx2N0pRUoh4SQiKfqmlYmqmlhM7P0q4ZQvvjzzFGvoYoJpH3c3EVIqc8tFjvILEYbKsVz0z48VSGHiZyyoy1BBnve7hGXc9T5jLOJcvI6AcWq1BtuljDJsKnjNrOGRWYeOwxh4EEWxlcMSiRk3h4CmNRqWckMpysmr3a6vhsfOj+MNlvNfeR62K8xfOHXzfvZJaNelZlJX5ofuJi9/fG4mBlxmRCn7ieqRxkxrBxWKfu40RXcdz6WUlQmK5GuUz4zczcPV/9dr+datXFtYaC5AjkBllbX2Ur7i3lea3y9WIRzQEY4sfb+AlApFyRqlkttJ3lEpivhyX65P80FxJRvz4lBfcT0oIWzlYGKZMGEdZntKsfReBcAVKQblOEVEZT8mnPIrXiFe7SQNJFaJ84y0E191MvOla9px4G88Zr56Xr6A8yRmLtAowLmUFlZaNyiVZpfuZkjBnqGPErWSrdYLceD/ZXIaULuesW0NA57lCH8cIZFSAaRMiQxALYdQtR+HVC/HjECWNjSEuEXyhQmLP+e7NwEvgC4FSJN0ArrbIG10Yrz01W9r1yJwBqSJJiLQE0EoYM2V0m0aUguVqjDwWu1pDhFu3zDnmQNUV/NqrOxfMn3foLqrWX1d6dseT2dJ3jtECRrjaOgJieN6sQRtDUDlovOexUk2TIsS4G8WHoVylqbBzBBrWQKgKGjbAqhsJRStoL4dgwU4LPLvChARRSgiqHBohg59nzTr0pndB515euup/8teZa+iw+kqq433ONj7q/Cb/x30LH/zFX+CmW97J737k/ay650s8PLaMNH58OLzZehZQfNa5m0/k38+IVHJjZy3VG25ccAs2NsUWvTV7NjaW/r1YvUal4I9uPz9RsZQSYzG75fn1Ys63PngteM0BbhGGwmt41PW2K6qoxiinz7fKm5f9v1LLLYL/CPGSfwn8W6mbewn/vnCpJsglXMIlLMDn7tzML+5s5bmeCa5oe+N9NP9fY3bR5zmfp3MLPitmxc+2xLKUoq0mvGSx+Cvbqi7KD/R8VltLTm4bN8HJ5wBVClhuV13cYT0G2cTFFR7+V4Al648oBQMvs9Y+x4Ay7DedHDCd7NVPcKvdw8PODu4ze6hdvZXd61IXPyFu3ETT4P28pXac7rGZ+gxvqR2mKeNA490LvnJ9bIgdlx8n7vaSPRvnYLyec1RTSYL3Vh2msjcA8xc2nXsZq72BA91zyagD0oG+Zj21tQuzwX5aFBdJS9WYAa9Z5/hBXwDFxZlC6DH1gPIKmKpv0aKG6TENRKLlRP6lFiSDr5A99A+sHRjnmwV7r3v0Pu4ceIjcQ4/gmACjBUsjjdChelive9guXRyQDj54jWf7tdgzBzA2vfDZn40iYbkUFuWC6jcQAK4I93K06yjnVDXLGKOzo5PAytaFAbT2XcR7XyQ7PcoJ0+R57nOaDquPXqeBoy2fZmfqDJlMgh+lGtmpsuSVzWHTTkoC3GUPYtCcMxVYynDQdDCkqrhDHiWkslypuvgat7BddVGhEzxr1rEyUrjuho30t9dx4Mzc/nFAOuhvb2N9wwzRdteVLcQTqxk6PsmjfZXstl4EFI+ZTdSrCTaYV1EKYiS5XJ9klHKmoiuoVsoL8l1MtnP3E7zw7BNkXz3GWamhT9YzLWH22Aepk0l0obh4XtuEyDJFiAbGSasA8dorCddvgG/fizI5MuJjSFVSKQnSKoBfHPwqjyUKatbAjo94xyz+7Ppnz0apeJ4Fq49Vqp9aK86XnBmCs0taccQiphIkVcgLposfo3WpiKsrmhO0cFP1KNaEIScWtjiIgrDkSGs/P3S2sM9s5+PW/XTqPqarN5MJ1sHRPt6un6RGT5MxNmkV5FnTUbJVe0dlN9WJTmBhgGV+3ypi7W0f4/DTD7Pjuw/yjLuePqmjRQ3jI89Pat/DePlaDozGCpeuqNRJcuKpQ05JE3VqggA5lIazppIGPU5UMijECxQqj4QzApYSlBjC2jDqlnFUtVMuSV5lGVVmmjdXjxIL+aH9HcRevJ9PVZ7icLySx5zLWa97+KT9DZQSHjObud1OzdSAEoFVN+JGW0m9+I9EVQYLl26Wg8BLrGJFx9XnVQkA8MwXiRz7Hr3SyLCp4krVRURnGDKV9Es1GfyEVI6V9NOuBxhNxRhe834e7xpml3qR5XqMpAkSUjmShHjS3cge6yBBcjzjdhImR0wlUALbK6eJhT2f9cZYiHe84y7+6VuKD1kP0aYH8bt5YlaKU65n9bSTw2ywunnFbWcguJLzhdtjK7YSOHY/ASuPiCKvbK8GDQoXzwKqUhJMEyKicrib74bTD7N27AB/424p2fw9JpvZK09RbqUIiMOUClMnk6S1HwlW4ihoHH2SXhp43qxhqz7Bdt2FZQyVfuE9P/cLrHI6+EUFW1qvp+6pP4DTP6Z6aoD3LavhmXNR0NDltFKhE8QkQfcrz9C6/keeGqTnSaYnR/hBaiXbdBdBk6dST+EW+lOZSqMEeqWeBEHc5++HqePnJVen0p4lHSJs011s1cdJECJhgsR0mno1TjcNPOju5uPW31BBAjDk8ZFUIYLkCoXTbZISYlKijFJBoGLda1Nu9DxJJNlHCs92cJvuwlKGB5zd7I0eIdb1DxD0/b9RgzRsJH5uiqh6kjgRpiRMuUoRVWleDm7joIxxk3qutLlQrKH3OuuBPfNF8tOjfN3ZM6fW2ItmFW9OH6U6MA4F0qxP6qhVk8QO/x3xug8QW6x9lCYwdowjZh1lKlX4Xj3TEubWzKPUlUeRSfAph7zYpMSPrQ1KwKdcBkwZubL1NI6d8saY1BgGG0tyhFW2oO5SKPEsmxyxyQerWR8y+LZdA21Xc+T0KFdyH1vUcWpV3FMbGIuc9mNE8ZTZyCZ1ilY9hCjlvR+UjRFFhTVNQPKkczBwbhizvIl/kJt4J48RIU1WBTjmtrJCD7BKzoISqnSiQDZ7ys8ySTNAFUHyWFXLPZLjfONf4yZ4+RF8xgFLcdatodaK4zd5REPSBAhaeWzj0Kl7ccTmgKyjUY2xXI+znDH6pYZJiRLVaULZUe94xbpf3U8wNjDFe/W+kgJkgGomTZTfa3yexqNfKz23VZGZ+hM3q4M0WaMcdVto0OOst/rwqzy5WTVKwpKlnCQ+y/EUb6IxjsPgq4fJSz1r7GHvHZ6dpiw7jLEMyiiy4kOJEFFZRBTThJignMOmjem17yR2nacKPPjEae4ze7hJniuRN7OtUDN5wy/v8qwWnz49yiNmG0GyNKthAPqkrjRHuO2yZVx1ZfOit+DGjvpF61POt6KaX9dyS2vlBefyi9UGOV+A/rWsD14LXlMtjvZdlKdyXLv/IZSCp836kqJrrOcFxnb8J6r/lRUc//ceL7mES/j3iktKkEu4hEtYFP+emfmKJWTCFaGFtUIaYyH+6PaNWIUop6UU/9/tXpHzV0eTi+7nYuuFzLYdKqJIsCyJgZeJBm3+0czN2P6WuZZo0L5w5uu/IixZC2PkGLmpUR51N3OcFjp0L37t0u9W064HeZ/1CJ87UctA5/suPmAw8DIkhmj2Tc2xS2j2TUFiaPF2a7yM8OQpKrof5tF4Hb/jfIhJE2VPWTeVZVHIJRcqUdp3cSy4OBF1PLjpZxbgKNaYWdewOMmyWN8+H4o2cF208mXXswRbrkYKmaAjfMXdi3Xtb144yPhGYaIbNXmGrLFL9Um6pBWfyaMn+xifGOewaWOAavqkji5po4YJ7rAeY7vq4qUzccB75hbjK776xKvnVWAtRZ4UEfQtMp0qKBpyvnJaCuqjFj1EzlfuBdzn94WC1V2X21Lyr3+ZlXS5LeyxD9J55n649b9zau0HCZPhHDUcNu1ohMv1SYZNBQNSxVOykSfNZbTqISZVlAfc3YwFWujUffyx/RV2WkeIS5QrOlYSvP4/e4GI9l1cdtWti17bxqtuXXCusbYtVO96H1oZ0hJAxCvGbkRhtGZcyknjx1YOZ00d3+ipIPvKQwvroCyBeO+LBA//HRlj0yf1tKghNugeJkwUv8579kxWlmkTKmUu+3B5zqwjWlnrsVLljRh/DFdpyklxSFZx2ixDK+MVjg5WwC9/b+6Bd3wEOt42R1FXEfbxNfcWXjKrqFWTdBR87Iv2WCdlOSjFgKliSKoI6ywpCZLBD+L5lr9TPcayyefw+3z4IhWU+yEtAdLKT9IEadeD7NEHiKsIEyaKGeshvGwt/2iu9SzGxKHO8orEf6wwDnVYfVSvuvy1ZaMXUL3+eva767nDerT0TPdLLf/7dBkj1VeWtlMI3aae49JSaOdhaohzXFoYMJVkVICfuJdjlCaocpSrhFcLBF0iBotJBD7LJSRZHpdNuKIJ6wyJirWl80/lHYanMhwxbZ4fPJ6qQUSRWHkrZeuunyG923fBzo9ybjKNH4ewSuNiMWwq6ZJWIqQvWqWWzrm06UHqGCdFkLzR1KtxVtNPSHlqq5zyMWIqAFiXeZl7f24vnctiOJWrOEEzfVLHIVmFUsIx08K3nat5TtbxJXcvQ61vo+ZNd1NZ1wRjp0vHvevKFv7yFy5n/ZrV1PuzRKwMw24MtOIa/RJ+y6HHrafJGmXl6E+WvoCeJyl77gvY2nDCbSKhQoUsdFP4v0bjkVOTEiVUtYxY2xZo2IivqoX1NZrtqguAR9wd9Ec6ccQioPKsY0ZhlVn7dsZ9RTs8Sj9z+FBKiCrPKk0Kxaf9Z5/2EjNQOIOvYAYPl9QY95k9pT48ZQpkxjNfBBF6y7fyaeceXjErqNcT1KtJBqWKOFEmJEKXtPKou5komQvXPdj5UYbrr55jg1lOCr9yOE4zE+K9Ly+3TvJH9ldZa53Fpwxxu5okIfKFecIhs5I8NqPECKg8R6WNTWMPX1yNoyJEyLdfx33OHgLKoUUN4YqmS1o5nY6QaLjydT3LbwiUYoV7mloVp8u0eDWhTAu1Ks7GzEGuUS/NUR7fYT1K9ejB13+88mUEtffczYZWQLAMJ5fjnfoxnnHXc9B0MCIxRqSC8aWSFeo3QOfbWK+7aVHDuKJpUcOs193kr/gALN/KLdYBpiVMP9UEdJ6QZMkof6F2SJro9GlY71k0xTf+Iv1uJUqBp2sTtBhQkBebCaKETAK0DUf+CYC1mZf4uP03tOkhnjIbecFdQ0KHcYxGK+EG/Tx+5XDaLCMXrCFFiBBZpgkXirS7OGLhKJt/OFPFjR11jJpykoRIEWCECiZMBL92mDYR772LwhWFi/e81jCFP1ZPOFLpkRwXSFIJ+S0sbRhxYygNjrFJ6LCnplP1pCWAXzlMSDm6fi1VOsEhs5phKkgSpFpNgYJ+qeGrAyuZPvbjmTnx4CusffaT3GPvY5+zjY85H+IZdz3rdTf1uR545YHS87O1tbI0LztDHT9wtrKf9UQljV/lEPHqohg0IooyUviUU6rv0W9qGKSSclJU9/wz+aEu6H+eyb4j5PNZ8q5L3IRxRWErt1D3yvXqTuDVS+pcNqPK2NZWxT163xzV8T16X+nvs5u1vSZSUM7MzBla1HBpTG3bevOS8/7XYkU1u67lxZIV/+ashnqepPqFv2RFMMkDzm4+5nyoZMtaLRPee+JfkR1WEf+e4yWXcAn/XnFJCXIJl3AJ/+FwRVtVSWZbhAK2LiIThqVVC69FbrwYigTL733rMK7IHIJlSTRsJNx4GcuJwqwMn2WbbyS8bce/Kqnw64II6VAdp85MElFpVquzWMpw2LTTZVrZaz1JqxrECOdXzCyCjONyfCgBFBc7wvGhBJURP4tW1TjybXLpOMfSMSJk+FXLU0EcnQ4hdoaGsqlF23t+MelZl/Yzx/HBxILPzte3l0LRBq5T9WDQCwqj71pTQ/X0if+fvTePk6uss//fz73VtXV1V3VXr+m1ktDpJfvSSUNCQgAJggIhyCiowQ0Rx3H8OQp+Z0adcRTn6zh+dQRciQ6MCwFZBIIgJqQhSWdPp5esvWTpfanuqq7q6rrP8/vjVldS6e5soKJT52W/MLfq7vc+99bnfM45MCT/NJ2rGaUMTr+Jo/WtrNM3U62aKBZdnBLZpKXncaxPi1uxnK2OCmLaHuSkn+nAfn91Ef9TdyJh8QrY0zbATXMnv57GyZOpTuGkxGfLVvxte3n9wBHyOaM+6jhwhBk538VdsiDx2CmJPm02/tODCftRozeQWjQPt02Hlq0URY7RhKBHuTmpsikWXSglQBM0y2JzPfRRrrXTZBTjFy46rvoXKrpe4LquYygRxjZzuVlULj3TyTv+g/yBp+rHxQ88NJU3tG8FEX+IBrmXCr0dO6OAYKucS5gUVuj1WJB0qkyEUFTSwun86/BdZJGvPzBKl3SbP34ZIgs/HobJTdX5XXAR7SqHxaqZGr0RqTQOqSLcYoQVJQ7SPNnmzVZ1K6K/BRmOEMFCFoMQC3rNsJrB0Gx7eGIX+bgiJIbx58VXjPWsV5tYY6mjQrbFu1pHdAcvRJfhEmFyRT9WOYZNH6PTyOSUyGaa6qVQ60FH0hOxozkzaNWyEZHTnDSyydUGKJUdzLSc4pXoIjbJpXwt6yhX2HQ+cbUP8YbAEBb6pBWXMMchXUg68q9nmTv1sizp8t0Obp6bR0FjL6dUFu0qlzpZQY3ewEI1EzAbBZooodEopUK08VHLi6QTRAAnZDaKNF6X82iUpazR6nCIsNmVjIERu1vG7xkhwKYizNRPYZVjbFHzaYqWkHEsiLc6gtdp5XjhrTy817zev6n9IJ6/pKHoO7qbjrV3JlyL/s3fJa3lJSwiSo/KYFCmkqv102bk4NM60fc9ceHO+ppPoY1GKTn1PYSADdE1VGuNXKftwalHGDEc9GtptMlcwsLKLNpxDx/m+M6NvHQ6hzz6KNEGeTa6nA1yDcu0RpQS8QYFgB1HQRyFn6zysXpWTsLqvU4rlC2hq7sRY7QDNEE+fXTgxSEjNIlSAlEHtxWVTb79rbWM1P2M1p4gfzDW4NNO817jTdDNznUNGFOCKKYdnEuE+J/eHCqaXyOt6laoupXqvU/wcHYjrbKXUq0ba/8oncFM3CpIEAepKkREpEBrLSmjQTYb85mlnaBGmB3Mm4xqbrHtYnQkwB9+8R2eNFYCENI3s7gkA9/ij9DeN4Kj/xnsyiw6jqvRNkWr0QS47OGzrk07y7S9aMh4BkOuGGCUFLbI+bxkLGWdvpkq9yh2S9oFr3XvdZ+lcfdpbrDUUah6iIgUOmUGpVoXO2Q5Dka5Um/EpZlB2G0yB6/NxeCogUJDCIWbIMM4SSeIJhQfKTiJM+XCat8E+FbQJitoen0DVaqFAtFLgeilWHTzZHQlBXPvocbnvbRlvl3oOEDU30mPcrPRWBV/9jxgeZzs4TZ6NS+Pjt0Sn36HZQsL2h+DbacvzuLwXNz0LVJe/AfWDjzJsVEPr6lFrBa7mW3rxOIt5/QwwBm1zEZjFZqAn4QOTT5m+1YQHYkgeCVGwZnzi1h5OS0zhzfkFczTjpIlhkBBP2loSpEmRrASxepyx5d1omOIJjWTPNmHRYugAwiIKugkkwGZCqMCMSpxO7vxvfgPeE/uRDoNGkby2C4rma230G+kUaJ1U6h6sAqDiLTQoEqZnmtBD2voHfuZIU6TIgxOySz6RTpWNcbVYi9XDLlxza3gP3uqmdnxPEu1RmxE0XQ7hbYQzSO55IgB8rR+nIRRCFIYw4gMm/txtipjMijFaOlqNh21slRvwqMCSE0jIJ30CA/pKojSBP0yjdycHAoKptEnvMzv3EuPTMcvUpkmeikQfTwbXU6TKqF3DNLGX3T7j5MS6ifLboGzXkuz8CP9pxmzZDHejpbvdvDQ7XN48Ol6fmq8GwF82bIBi2aOARYRJY0RemU66VoIM4nEzF4aVVYzUD2mXkpjBDEyhhENEQlGsWsKHYWNCHYxFs8GGSfZ24xsQDG/7wXY3gG5s5nXdZBU934e91fHVcdrLHUQhZ/JNQnv0/kDu/lKyV7qT4pY3hqs0zezTt+CkFCatfq8t8KUqvi3CZekxPhzo+MA4ajBhpHlceXNDlUBBtxh2YIzLf0v/zduEkkk8Y5AUgmSRBJJ/K/D+Av3+ACocZ5i31nznKtaeDv8QO9cUkztA9fwi48vo/aBay7sxepbQUfGIp4+J5vg6T2n6MhY9GcN1nxb4FvBsRkf4qgqYJF2GI8IxFUbK7V9HFUFvGQsQ3D+UMAJ6D9GyN83aTB6yN8H/ccmnW1MpbBPzkxQQbSqPPr9AQL27EmPtzOmojgXDusf95Hb0huctEj/8RXTL/mH1bgSRCqNOyybecDyREIw+lUt/4/Irp9fUjDsW0LN/Rxd/GU2GispEL1xb/JHordwfO5nOURJnDiAM3kWg7hQSiTYC9TMmLzQdL7fVvluBw/cWD7l55MSn10HGat/FrcK0IE37oftVgGi9c9NVEXkz0PZMybsxzajitKiQrOzs+sg9iMv0iBLaVe58etYCLARwSEi8SLqiLRTobfjVgEiJ/djpBcghYZQEn341KQFkkvxhh4PMx33Xg8qO+/Va5mvmffSoEqlReaTLQa5QjtF9tgU4c7norWWLEuIH8hb2WispFTroETrwqMFORR08JKxlAbpw4HZpe+wO/AWlpFXtpjp9oCp6hIC+o4yYvEwgBmqnC5GcIsgr8pFHBI+GOmD/uMX3Jzx54UANsg18dDnJaIZjxagwSilTeUBZrE0W/NzyshiHzMZUVaiwsKItKEhsYkoz/SXssWfy155BWlaCJsaA03wSnQRL6tqdqgKXp/5Bcifx+rQq5TmuBnLuII9lFMh2uKdzp9vvxK/SJ2QZ3Sxx3itvoXTMQJEF2YX6w5ZxZyxA/x0VRiBGVQLUKW14ldOTqps/MpJnuhHCGiUpVSKVjpVBlFSEJjkmQWFDkSViIel20WUVBXGjytuL/KmUWl2d3bW461ajSYUczkWt08Z7wRNFwEzVHw8M2H7I1j2PYGhNDpVJrtkGc/K5XRLD6v0fbgJYLXZoe/oBY+De6AJ3TuDFpnLu/SdVIh2BmUqCijRO7HKMZpkCbNEO5kpUQzdTn/LXvJi99mmaDVVWiv36C+Z9o1nESDjUMDHttjpyFyc+IEQ7Go+zmf61vELeR1hmYJTjJod3doYw9LB6A0P4V792cm3Xyn8USt/MObTTDGLOUSqHiYgHUilAQqLMJBCkCZCBHBQIjoJ9PeY103pclhwF65wBzODe0nv2Y1dM2iWxQRxoIAgDtplDvYxPzbNoFR0ki5M0jddjFAqOlFp06gNFqBQ/J1lI39n2QgIHmyZR0flPThW3M+jxq304ubvLBtZp29mm1FFMyWsrUzFWf1hs7AtBN7DG/m67wBNspR/HfsQHcqLQ4xiU2OkEqZaawIEdX4PI7YLkwb5A7u5vSqVdpnLTlVOSNqodvsZkk4ytQAF9MXzAgTgUiHSx3oodBocUwXsMsrI0QcZlg5sYowSlzRtb/LnXjIBkNO3kxq9gY3GqoS8C3GBkOM/BdojaXECBMxnT0RZIRrBm1fKTszpO6lg+pwa7ENt0PrG5a2stZaRwCDHRj1YNYNlWiNWzeDYaAbBkSDHe4Px9zRDaexQFbx/aTHOlCl6N7c9jLH7cZ40VsbHs3aVy5PGSuShVxChfjYYNwICTUmiwkKfSmcEu0mApHqwZRSa90RrLdNPPEMR3RiaBcmZ5geLUHjxgxD4cXFEFfB6W5ix5pchOkrqjGW0yDzWWOoYlg4Ox/ItdCE5Lb30i3TSRAhQWIwQUXQipJgh38LCiLQjkPi0LhzBk0iri/pTfjrwElEpeMQwMhrCYoxSpp1AF5KISiGChWjMLkqGBghgM8fK83XN+1ZwwjqDdpWLRRq4tSB2OUq38JjXujZGj+HmONOo7x6j41QrJVmpWEUUD0EEiqByEMTGUq2RK/VG0qef3dihINOHcuVzh2Uz37T8gHX6ZjzCZESGUqcn3D93LinmjQfMd499a47x/pJhZOYMQhmVCKGjIfHECKvY0pEIAtjwiCC5YgArY4SUDWGxM6bZkUKnV6YTklacIsLZbSwjyk69MZ0R4WBa+VKco32w93F48Quw4wfMXPl+brvvX7h5Tj4b5Bo2Rav5sGUTrxf/+Mz7dGst7H2C8tx0bEs+SF0sc22jsQpQfLVoH/kDuy94O0ypiv/fhrw5HJ31CR47y3oMzLHoUN57cc2//S//N24SSSTxjkBSCZJEEkn8r8Tb1X3zdviBXqoX667W/gnF7jMezX/5L9HeqtUEX9iAS4RAgZ9UZoqTWEWULdH57FAVk1oanR8CISOkEiJTC1CoetCFpF+60GQEJlviTd8iuvm7VL/2I5yM0qEyyRf9uPUgv46u4oa5/0TNJGt6qzZpl4vUKciXm+ZeYscqcGLAtIYSQmFljGxtkH3RmXzPWMuX9Q1ME71EI1lY/1RdWdu+T9WoMaHpXRMwd2gLacUW/vN4YvFxh6pAGHDvSl/C/bV4CgXXhdQy966cAQK+8WJzwvQpiU8liaR4qNDb2RQ901G4zNJIJKUwwXLJ/L6iPmUuO1SiXd8OVcHBlDFqlILc2ZzqDVLVtgVQtKtccrV+UgnRqTIJKSslWlfC9T1fO4qn28+3G2bh1grIp4+Snn1Y1ddZ+L4vTdjsix2P+oOjCKFiP/jhk/ozzNJOIIAmo4QjFJoZERIWp/Xhat8M+bMu/CNWKdLcWXygOovPbE+lWppd4+kE2SIXUKm1Ui0aKdU6OSCn0zaSR3swjUbl4+u+A0wnZm+XOQPNUkenzGC61gEIwqRQJk5gDwcYzptOWqbvgvsJ5vMi1Wbhv3/xP3TgjR/ffNXHIC7WWOp4OVqNV/czKFPpJoM0Rpirt9BtZPA47+Ldxg7StCDztaMMqjRy9AGy8NMr3ByUPr5qrI+vb6XtEOx9jpbeAD/vnI9bmPkJQldUYNpxfVC8xFC/B/fCGy5qHxLQ8AzO0T4yl9/DF7c4WIxZxJqz/CbSPN2sVp28+eBH6G/4A9q+vXSeHqBJlcbzQ7IYoNjr4CFeYXe/lf1yJjYZNQtzZ61GE4oxdEZlCk5tFE1IpqleejU3S2UTQoDLbs6R73bw1SuO4Ww9yWGjkA68LBVmpk+VamNax6uQlWpeP33H0N15vNpTTKoI0SB9eLQA3WRQSicRZSHqLcOaOeP8x0Eps/DiuI593S+yTDSgAJc2iqE0lNCYpZ/k723PY7c5sEWHCQ+ewKvcbKcybmdURQuVoi2+vZNhgnpx28OMnNjLtw6ZQcoLxSHs2hj9Mo1RkcKYtFCht5M79Bvg81PugnPaLDIa9/IhNpGj+zliFJCpDZMWIyoUAgvQozwclQVUa81kjEYg/04AXmvuwtE2gALyGcGW6aFY6+aonMYeVcZCcZhirZtoRgkM9VCutRPGip9UUgnh1fyM6BU8ZlxHtdZEgegFTF/87bKC1t4RamZ4ue2293Hy2WZQUCh6+dT0ThYWpZNWfvMEQtYx2oMSblBwTBXgkQFcWogZ0mz+yBJ+9qssWgpvo6rm3VOf320PQ2c9s2fMJctl5aQ/wnRNIEeHSRtuJlsOkqaPIBF0qQxyxQDZ+hCRlEIENuaLI5xU2RwwfCzWDxNVOh2BNHqy51DWcWByVcJ5tiX9yE62GVXAmbwLgH/L+h35jc7LU1W8HZgiE2qTrGa6y0JRmmDn3XYO2+dTFt6H9/AJM3Oh9KrLW1/HAQLhKA8Zd/MZ8RS6kAwqF981budfLfsIKZP4RkKH8LJe28QCwwML75pS3eCyW6jU2hIsjKq0NhxWjYFwlArRSkDaSNdHGFI2FCZJOEQquQVzYKA1njfkDJ5gYUobo1GJ0jTCSscqooCZJ1FAL0dlAQHlIE0L0Zu/knz7GM1jxUzXXqRfuligHWGa6seuR3jdmMOnop/jy/oGFmhHkF0nMUYitKkCpNLI1QaIKAt9pNEucygRHeS7dCLtO7ldO0qDLGW/NgMhFS4RQqkoNhFFyRFGtRSU0rAIg7BMIUWLYpyuhwXrLtg1n+9x8AHtVabpfQQNO3Z9jOmygxR9jKBhR2kCpwzh0QKc6JJUjpwgIJ1M0/o4KvN5WS0hkyFutmxndm66meszjqrbIDJCyuBpnL1dXKk1YGWMEWwcksXMqfnkxO1xO8hv2gAtmzhsKeVAz0kWiUMIrKTrIRxEUEBYpSBQpIgoGSJAWNkwCV/JoDUHt28eY+EATUN9lGjdWAnE1a0K6FdpMZsxKwcNHw1NXUzPD5PvLTGbIwLm+++8ogxWlA3zwsEOiM2f8FOhYz9YU6HqVpZkLOK7pf38vqmbZ/dBhWxj5FQHp5q7WF16oRsiCQB8K/BmhuC51yZ89JOT0/hI5mLyJ5ktiSSSSOJSkVSCJJFEEv9r8XZ13/yp/UDFFPYnl+GK8o5EftMGrsno4YXoMjpVJulihCwxRJ1RjkcLsFQ0obhEUuGmb2GZfhVz9RasaoydqhyrGmOu3oI+/Sq46VuTzuYuXUiWO41UEaJA9JIqQkSUhUOUTNm5+VZt0i4X4xZW52IkIiedfj70B0cBKKGLCCnsMsqo0Nt5IuVrVOjt7DLKwGK7qE76twVCw930K76Z/mSCN/lD6U/i6qwjddqsSWe7+28+wOo1t//RNuv+VTOm9lkWGl49SLNRjEcL8Lf603i0AM2ymEw9OFFF41vBUefkeTJHnfPN4q9vBTKninhaN9CjMjipskBBsdbNiLSxU5UzIm1UaO1Ms4bY0uvGHbML+2L0XjZFq4kcfBb/5u9e9r5nptrine87VAVKaPiVi5CyMc02Qq4YwFAaOVo/EasbZl53cZ73vhWQP5cFsj7uy31IFRMSdm7Ra7le20WeGKBV5rFFzmcQFw3Sx3ZZwf9pmZvQHS4iw1TprehIuvEQVRoztVNkMkwoFDLVNReJGt20ahqULnaq8rgipFR0olW8l0qthT7lYY+aRRknWawf5qSRxXGRj6E0XmQpe+VMpmsdlGqdJgGCm0Hlihf8wSTVZva8Sniwgwdb5vGYcSY/oc4opxc3xXSyxlJHemb2+S1PJkNrrZln5MpldXkutQ9cw2c/dg93v/8uVodeNQmk/Hnkux1UiVYyGaCXDDYaK/mesTbu0Z1jdJOrulFKUSy6ydf6GFMWzh5tBGBRCk1TtMo8gspOntbPsHSwTt/Ml/J3myqAvDmw/RHKPOBZcAspxYvxKxc1egNfsWzgg6WD2Obccub6qbwF+zX/wOkrv8pGYxUeLRBXQLXIfE6IXFoKb7twUdm3gr60MrZu3UyR6AJgBDtSgU0H9BQiyoI1GsAfGKE3JZcxwyyGmrkDXazTt7DRWMVGYyVCTF101Cbp9g+Eo5SLNj6pP0OO5udVYyGHKSKVUUaEnWZZbJI/2x+ZfKFC4FZB1hYMUqG1s9MooxsP6YzEj7+OIqJ06qWPYq2b3Ix07J5pAPQ1/J6+2sc4GRtTB5ULx+BhIsrCo8YtfM9Yy6PGLXRLD8rfSYpFZxQrSpk2VUoJRrHidqRQKdoojh1DMH3xl2lN8X2+M7ud9y+Zhq9sNrPy0qmw9YHQE69fpegrW8cPO2ayTt/MP6X8HFA8GP0Eu4wypmsdXK3tJ0cM4FcuMquuOf/5BQh00VL/Jp/dns66xhX8V6OTkcAw2WoAj25m2IxgI6osGGiMoWORo+z0uwlgp0prpVh0E1QODqsi6qWPXx5NYWQseuF1n4PxQn2N3sA2o4rvGWtplKUUW4f/dM/SyTBFJtRj8ka0G/4VXLk4Gn5JwYHv4mj4pfnhis9dPmmTNwd90d1UikTSolK04SxZQFA42BStjls7rrHsPP9YV/MpnMULuS+3kSHl4nvGWoaUi0/mNmLPLcOtj3KLXktQOPl1dCVhbBRq3TgZZVClEm4/AJ5Sc3zxrQBHJgiBTYvSZXh4Xl6JX6UiEEgglTBZYog0LcRTxip497/TV3YHWw8c4bjMJ1MLUEonmfoQfiOVT0U/x1LRhF+4sDGGdbSfkLLRKvPpI50no6s4qgrIFn7ayUUt/3tsuoYt1EWl1spKbS8geMK4nmZZRDCqMaY07NoYQio6yKRTZpCqhRkzdPRpMSus8zUctGzF27aJHJfGacNLoyil08jEoYVxMoquSdpkDiOxc9Gn0tCNMF5tiKMyH5uIUqM14NM6GXMVUpQmEpUnMZWZ3aKTYzPtrFwiDEogl382kTA5G0oSyJjFS8dGaZClNKli7FoElPnWM4aGgY4Ws1sUKKxiDIGpPmV0mLHuJqIeH6/LBWTjJ0VEkWiMKDtjpGCgs9mYT47mZ6W+n0rRxhudGn1zP2rmhC29F5qex7/5uzzwVD0fFqYd1oboGq5u/9hZ+XECIkFea+7iqode429/sY9n9p2mWjRRpbXSqvL4+BbHefPmkrg4jDcRJJFEEkm8HbgsEuT73/8+paWl2O12li5dSl3dxYWj/fKXv0QIwa233no5q00iiSSSSAIoypictCmcYvpfFFproWM/nsVreVktBWBIOfGTikuEGJQuhFCTFpQutFyXOweVN58cbZDbtK3kaIOovPm43DmT2wbEpO650+cRdebRgZcumckxVcjXffunlLm/HTZpl4OplCDOy7Dhykw1MzQ2qWqejK7iMMWkqjC6kKSqMIcppmv6Oqi85S1t80VDKSKRMM7QaQLKzveMtQSUHWfoNJFIGG9qihmqehZ0ISZVd+xq7Z90FbtbByadPo4fbDk2QQXy6JbzBKrnzsY25xYWV85gGn3oQjKNPhZXzEgs6J6F8eM+cXos2H7bw6Q3/5qNxiraVU68m3iLXIBHM9UCudoAS0QzudoAaU4HTncWqSKcYLM1bu8w2r73soMmzw4zXa9twilGaVIltMkc1FiIStFGlWilW3r4Q38m/tQLWP2djdLlpGdms8ZSx6B08cXovdQZ5UzXOnAwymFVyBY5PyEHBmCbrDCL33lzQAisMoSOJIIFpQSaAF0pNCFJtU1+v0yKbQ/jPbyRSl9xfJ3jZNJKfR/Fna9wps/UVFBFDAt52gAB5eC/jLVUl0+nROsmqnTSCWIXEbpkBjtVOUXT8vnszA5eXavFSDVBKBJFqcT8BJcIU0wnNhHl5Wg1Wnjw0s+fUrDgLvOvZSv5A7upmeHFO37tneMln5ZVwFPGygSbnKeMldhS09Ed6WSJIYpEFw41ikUYsRjhMzSdEBIdFf+8S2WQKkYRCGbmmsHUdB2Epuchfx7D13yd1rSFuEWAuRznOn03Y5ZUM6tlvKjnWwGly7m35DQAg9IVtyhzaqM0GD7y3LYz9llToWUro/XPsk7fTLbw8ztjCb14OCILGFMaDiNImhhBR5JGkJTh03SqTPapK2ILMM95hWhDCBW3DzsXQsA31p5juVnzKWzFC1hjqWMUG3VGOS4Rpke5eTh6C0dkAS7CjGVXTFSNjSNW6My2SfzKhYNR5motaEIyqqxmcR8NhMZVtmOU5njwLoud+44DjOz4OSDiBFcXmYSV1SwmxrBDVfAD4xb0lBRSNEG7dzkpwqBQ9JAiDNq9y0m3p/Dp7N0ITF980xtf8Y3x5+QLn+f0b7/OZ3ek83+bsvh9h42jR5p4uXY7hx/7JGz7fvy8HrLPo1GWxPNAZOynahAn/TKNXtx0yQzcImCGr58P+SYpuiumdAHTQsfoO4ZbjzCGhQ6VyYBMx6WNMChdNBvFGNIsrr4mF9GtPOQIP0Hs7JZlaCgqRMvFkWxnI1aovyt9P4PSFc/XcGsB6nPeA07vnzX0d6qQZm/VtbzmuI79B+s5uedl9h+s5zXHdZdOvp4N3wosPQ3cYKljU7Sau8b+kU3RatZZNpPVuZXMq+7hv9WNpt0dWeQUz8KtglMfn9ZaCA2SU72Ov1+RzetLd/L3K7LJqV4HowGco30UZZjvi7miH7OibiaGuEQIf3iMkejZTSQCqWBYOenBQ5Hopl3mMKhSkUInRUTxMMwJlcett72PfLeDQ/Z5vGlU4RcuctQAKZpBQNoRuuJhy7ep0RvwEKDQJQilFvA/8jrayAUETaqEjcYqsvOK+LvrrmB1eS4dETsNgTQaZQnZwo9X+JmttfAD41ZaZR5S6ESVjtIEGQQ4TRanjUxcjhRcoz0XHvu6DkLbm7R7l/OPxsdQSuHQRuMh305GcYpRnoyuokmVcJhSou4SdAy2yAVsk1VESGGGOM2PBhfjL1o1qTVjhz9EZDSEEAqlIDM1hdWe7qm3K38eA9LFm0YVCsEC7ShCmfkfMnZubETQhXm+NMBQOsM46ZBmA4QMDzMUNrha2xsPsx/DQggbA9KFQ0SYL47QJTPJEMNUam08aazisH2+uQ3L7oOK9xCtf47HU77Gmth1ukGuQSkzP87c1rmMjEXpf2MDSzCbGMbz6EDRKEsxlEoW7y8BU72fX/JvviSSSCKJ8+CS7bB+9atf8bnPfY5HH32UpUuX8p3vfIcbbriBQ4cOkZOTM+V8ra2tfP7zn2fFiqSXXxJJJJHEW0F7/+Qv1Cf6Q385AXhTQSlYcDe7m7tYp22Ih/YWiy6yhR8hFDtkJQ+dW1C6mOU6PPjTy1CnjxIUDlJlCJFeRpHDM7ltQMcBAF5vD2MNahhKIyjseDMz8WU5zc+nKAS8HTZpl4pxC6tzcXLg0q+L8QL3dlnJdir5sthAhj5MugqiaxJPNIB9xf3wp/Iw7j/OmLKwc1yRon0NXUh2GmWsVEHcIyf4xtrb+NLTBzGUQheCr6+dPek1cjlKqg5/iIdeap4wffwH7lTh4fhWEGn/OsVaFwUxC6VIyrIJ4dvjGD/uZ1+NQsDCkoz4cUg3+qk4p4O2lE6iUieq6XgcVgo96TikwJqeRwgrjV1ZE2x6minBVpRy2UGT+W4HP1kVpn3rLynX22k2ilFArj5ASFmxizFytEEajFJGpA3LxQRVj2Pbw7gHmrDOvoWMA0f5W/E0LhHiuMynQPThEUE0YONZxflxRItqoCgDOuux2E3bCzU0SLHWha4kEWEhbMsiz1t8JhfhQug/Dj3NLPdE+ak8Q7o0qRJSVYjM4UYCaiX9pJs2ZCodjxYkjRHma0f5tP40M6VE2sAaGSOKheNGHporm+uumEVJaoQ0TxEE90FrGlTdihr4Kev0zTTK0riFzjqxhZCwc0gW8Zhcw7p0QdXlWtKVLqcvOMrInpfxHNpCmlUzcw7OJufy5uDMn8ttvmLqnqpHYhadblv7PqzZyzj8h8cpFb8nX/SjoRhTOinCiEVaE/+zESFXH+SIUUCXyEQXirKKOTiLi8xzkDcXgIPH2vjas99nu6rgm5Y+nFqYw0YRPz4yk//whybcZ7/ceTJecGqTuWbnuOqjUmsxO9arP3z+Y9B1kKy+XXQrCz3KTanooEXmcb12El1FkMAYOkppWEUEK1HeNKoSCMiAcsZDc7czkQT52q1VXFuRO+kY4bbpZPoWcPLYKWZpJzilsuK5DOvZhE/rYEC6cJ1PsVS6nKPlfo4cf4Rrtd0IAUHpQGoCq4oyiJ0oGpYxSUOXoNtyM3eWFkPDb7CFe9loXJNAjpbLdlbp+3i3vp0d0RjxRQWhuR8mMthC9+5jZAonQ6SSThDZe5RRq4Y3K49r3nU/9mgFQsAyfQHewxth67cZDfQx2N3PDWIHfs3Fxlh4+gP642S07iOQ4SBGh+HLSqVKa6VJFiPRWKo1Uq010ypz2K1mIZUw7ZMMl5knM3RoajKiYz/H9RlsNGZRozdQrZooFl0MKhd5DBFUVlpkPrnaAKkqxIBIo0NlMtOiyBDDFDOubFGkEqJQ9DBO79ksly699Y+MUjuUayoDxdPoQrLNqOK7eyvYe8Mx3Bc7Hv2RMJlNbIc/xA+3tPCZWMVAKfjh6y1U1Ey8Hy8a2x+BxufjhWUwr71lshFf3zFWV3RTd3cZffW5KKGR7bZCqH/q8VqpODGatuXfSbMZoOnmc3bbw8Ac9mfnUlT7JVbr7ZwwsvgdiykTJygQfeySV5A+2Iuz62Ds+aQYSvPxQl8h1+l76JVphISd54wreS9voiPxOUJU5Pdiz24HivFlpbKTCu7lOaxalC6ZQbfIINMYZrneQLk8iTuniLTpaziafS2PPS1ZpjWilIgrg2xLPojbaGG4+TVe7nThEjrtKpcC0YumFMuzQxzsamNYOBkwXGTqw9hUFE2EKKGLB6Kf4LGSXSbBEd+XKZA7G3/Fnfz+1b1UiDZsIsqY0hkULnTpJCIsZIkhqrUmLEKxYu5MQmFFm4qwUtuLTZhKqGNqGleLvYy2B+Hqj5xZ/raHCTe/TGdXJwXCQrf0ABANWpi97REsfccmV2ArhXPWKurqwlSKVgylYWga4RipmykCCKFi70mCCDqjIgWrGsOnd9Ar3aSnZuMdPECh6CWidJSyoAsZy7XKotDoZaZ+Gqk6GcVKoyqZmMuz7D6G33gy/p614ayciv5gxPw/pcs5ftqPOvAI6/QtsfGlG1DxcVwXIlm8vwRM9X7+/uriZGZKEkkk8bbhkttDv/3tb/Pxj3+ce+65h8rKSh599FGcTic//elPp5zHMAzuuusuvvrVrzJ9+vS3tMFJJJFEEv/b8Vdth+VbQV9wlP43NqBiPyRMC5ZVFBWV8pEZw+y6237hAPlzIQTDgz3sajxGUDhihIaDXU3HGB7smfzg5c2hO2LB2teY0K3oGGimO2IxO6bPgz+1TZqaohh6OTXS8TBoTZhd/hV6O4eNQn4jV3DYKKRcb8e590dvcYsvAZW3EJ1/16SKlOj8D0DlLdy5pJjaB67hFx9fRu0D10x5jSwqmXg+BGcRDZNgqtD583antWzFv/m7vH7gCG2xUN42mcvrB46YNlSTdGqefdzHl59A+FXdit2ic2/6mwxK0/ZjULpYqe/DohnUGnPQnR7cjhSsqZngnYEjcIL3z4wmWFkv05r4x6pe3A7rlPt8MVjt6eY2bxtNRjEvyaVM1zoYlg4cIoJbjBBVGgu1w1TrzYgs39Qd7VOgpGopN8yZxvtmRLkyK8TzcjndyoOfVJZkBCadJ27/ljcHlnwUtzuDTD2EpiSG0DhuTGMgJQ+il2CHVXUrODJwjvbxiat96EKwVDRxh74FW5oX5cxigX6UYtGFoTQ0IRlTOhEsOFWYYtFFatcu8lw61tRMBrOrOTT3C/QGRmna+wbf3trDwWNtCYW+09NvBwQr9X2s1V/nk/ozCKE4KH104KVGazLVDpcKIaBlK69teoolj4f59e7T/GTrUZoPNUFoMHEsjBUW71xSzBsPmqG1bzy4mjuXFNMXHOWlY6NsMeYTlmaFVBMKI2ZXIoCx2FWnAQJJvn2UhRlhlkzPYoalJ269hW8FHRXr+VpDFsv0Br5p+UE8HP0A0zEmscLYf2KA1w/3Aoos/Ggxiyot9u9A+CLsinJnY/VdhZZTToP0kSrC3KDvJFvzM6YsDOLCUBY0IVFKI6AcrHC2sVg7jKE0SkQ3lVoLm6LVNDNxvLm2PJs5Be6pizf58ygrLeHmGTpFvlmcIDeuEPBoATbJZThnrbrgIO6tWo1Cw48Lq4ri0kJEpc5eORO/cpIqwkTRECh+85tfx9RrgnSHJWFc2C4raVIlKKUSVqkUnLRdwcDAAF7MfJhtsoomVYqbYUZGo3DF9XirruXmedO4ae400+5mwV1gsdPnnsMOo5zrLbtxqwA7VAUVoo0MLchxI48B6Yp3+ecP7GZtpYunjFV8MXqvSSiKYUq0bjYaK/li9F6ejK5ittZKutF3/vObPw/r2BBAnDCWCPbJmYxIC6mMMltrRSl4Xc6jTeawUDuKc6yPCoefLPw0qVIejt5KUDlYqjUxQ5ymUZVgO/nmJSs3TtivYFC4yI8pA8dDv5fQxFB/zyXZ8/2xcK5NbMuul7ld35xgQ3m7tpnWXS9f/kqUZNi3JqGwDPCYcSNRdym0bKVj3yv884FMbthbw7e39tB9dM9EC8lxxMYpWmtBxggQaZj/rvkUHZX38MPXWxgVVk4YWbi1EFVaKyFsnFJeSrRu/OllZwjg0quwLXw/7SqXsLJQoPWTTpBS0Um7yuFZeRXfDV6PCA3A1m9Day35bgebyp6nTD/FYaOQ59RVjEg7/VoaUaVj1cZwqBG46Vs0283zrJTAowUYlKb1n9HyBoT9DA+Y2TsHDR91soJTKhurGIOxEe6wbGZE2TgmptFq5MafNRYM1mg7CEgnzLz+wtaTvhU0FH2AQeniDstm8ugjRRiMKZ0B4eK4nIabIB8s6OQjK2awcMkK9EV3s8WYz0ytg1FlYZusYrMxn1zNjzMtPZGgaq1F69hHnuinQZbwsqyOjS0gQ344vnny+8e3gkjhlQCUiE56lJt+mYYUAhtj5ntYbHAylMZJmc2gctGDB5QgUwwjQv3YdI2MDC/9Kp0ukcl2oxKHw4lHBDlKAbqS2EWEkyqLR6K38PmyrgR1928e+Uc6/cH4uLFe2xT/LK7MxRx7n5KrKBA91GgNFIieOAEC8IUbZyWL95eAsxXGZ+PTq2f+ybcliSSS+OvFJZEgkUiE3bt3c911151ZgKZx3XXXsW3btinn+5d/+RdycnL46Ec/elHrGR0dZWhoKOEviSSSSCIJE5O9JCZ0i/+FY6hlD0qphB8SO1QF29LWUJThxDt06NIXqhQ9XZ2Ux0KqxwmNcq2d3q7OyYtMXQeJdhyc0K24KVqN0dFgdtq9g7C4NHPidcGFA7+nwp1Litl7wzE+6q2nySjmADMwlMYBZtBkFBOtf25qr/q3G74VjCz4OB4VIEMbJp8+MrRhPCrAyIKPm0VUf4iW3mC8g/VScCGeyJeVOsFuC6BqWvrU62p8FrXncdwqQAdeDKXRgRe3CsCex6Hx2Ulnu3NJMW88ECs4P7B6IpnjysWekU+V1sLf6k9TpbUwqFwMSpfpz51WyHB+DbiLYDQAGaWUhevZt+YY//X+Bfzi+jF+dPUIs2eUTCx6XypyZ6P7riJNmCqk4zKfWfoJrIxxOBYcmy38eG3gTM24+CJfzad4zXEdf/jld+io38zJ1kMIi40vzGwn54rFzLhqHTlFM7lD3xzP0gASuy5jAdqRgZOMSUUIO2FlJUMbIjzUy1B4LK72uiBKl5se+K5cVodeZd+qvXyvooF3z83DffUnMbxluAmQLfwAeAjiEiECysGYsHCVOEgk0Es3HmzFi8jNzuaZfad50lhFFgOU0MHupmOEOw/Hj5FZXFlJFn6u0fZRpbXxZNQsDO+QVXzdt9/stL/U81e6nL6cauprf8uHhJm5kk8fg6cP8cap6JSd6AnF0W0PY+x+nDeNKr5irOc4BUTRsSARyrQSCpMSH4+kMrt2XWO9vNmfxj8cruLFAx209J4hslp6g2yXFeTTFydAvhi9l21GFVfqjZSF9yVsz6tNXVRpLQSUg148nLmLFX3CgzPdM6k9SwJ8K+Cmb1Gw+mNkaAGmiT5ETNEyiIt+w8WIsKGUwBAaWmomrjQPC4o9rJyVzfxiD+N2NpNZYf2+uYdbvv8m/9+v9034bPxc4PBgJ4LXZWdxsZt7tE3U6A1sN6q49bb3mWTCBdRT+U0bWJUTJCrNjudemU4/aXhEgIhI4Q2jigHScBPgdm0L/Q1/MAlVzzS+Pv0Ay2L30DLRxFdL9tJHBi/JZfHlCwGFo0ewhnsm5MMcVUWMWT2TP0NLl0PNp/DoowgBr0QXxbOl7rBspkXm8rJaRnpGljl/ay20bGX29GK+f/cCvjZvkGGchJQNlwixRtvB0pjf/qISD3bLBSztSpfjnXMtd+hbzhCUKFwizBFZgEUY6ERpUD4aZCklWjfpdg09GsLBKKNYY7kEJfTixk3QzPQRXWZGxYWur3PgrVqNP2bdNk6ErNc2caXeYJJdf0YVyKRoraWw7WnOtkwzVTyCwrbfXL59V839tJdNVGntUBX05a9kNNDPU43meLBUNOEWAR7tqjQbVs5nidWy1bxXVn7B/G/LVmitpaU3SAWtNKkS9jCLcWWPQnBUFdCjPHiLZ525z/Ln4R5o4s7iAd6Qc+nGw5iyUKW1MahcvGgso0GW4HfNMAPiW7bCi/9AWbgea+W7OX7DBubMKMGpjZIph7EIA01JWnuH2fPrr8cbVnaoCrYZVfFMI6EknNqNN3CElw2TNKjRG9horOR3xhL6hgJY5RjlWjslohunNsoJmU0IOzoGt1pqUd1NJml/EYpL871G4VBhQsLOEVnAbjWLbulGIOlV6YwOdqL7W6GzHu/wYT7kG0zIBJmtt2LxXYWLcOK5SZ+GHntp2iIX8D1jLQ3Sh0cLMEoKZM2aktyNN50IYhaBHqTScIhRJILY/9CEBBRB7AwpJ2GsWMUYNiMMjgyyau4iZ/V9OKs/zPLlKzHcxeSJfqq1ZkZFCm0yl+NMY8UVXhaXT4e9j8MLn2fgp3eQ3/FKwm+FNZY6HrZ8m4/oLyb81sp3O/jE1b4pj/HcAs8Fz0MSZzBZI9A3b79E5X8SSSSRxAVwSSRIb28vhmGQm5ubMD03N5fOzs5J56mtreUnP/kJP/rRxXeMfuMb38Dtdsf/ioqKLmUzk0giiST+qnHBbvG/cKSXLuCpswiQcfzzAQ99ZesuqMCYFF0HSR86NCmhkT50eHJCQ0nkrJsndCtukGswZt10yV3tf2zEr4vYvzXgobf448Ft0wl4ZuEXrniQ67aY73W3c8af9BiEtn6f8kkUKeGt3+dXO9u56qHX+MCPdnDVQ6/xq53tUy7ncjJB8t0O7ls5Y8L0+lND7D8x1XwKXUao1psTVBvVejOajHA+6uXcbtwzizTzHLbmfogC0RvrPOzlWWM5fuECFJ8/Moe5f5jPcyIWGmxLh5IrsR95kSUH/onZYwdI82SbBMh4B+3lwrcC14LbWVSSwX36syzRmgkrK0Ok4mYEpzZKt/TQNwrhwY6LXmyHP8QPX29BKYUQgnaZw+GuYfRgNzllS0i74f/grP4wxd5UKrXW+Hy3Lph25pi11sLxzYyGhgngoBsPPcosOOeJfoz+tosPIx5X7Sy4C/wnSOvYRo7sMUO9gaHQGG0qjyz81GgN5DKAlaiZgSEz6MZDWNk41esn2lFP++kulmkNsSwJQYVoY5bWTkfOmfMxXlwRcYM0wfWVOfzi48v4r7sW4MtyTb6tF4FDdtN3fY2lLh4ovilaza7m4/Q1/P6iluGyWxDC9EA/zjT6ZDoSgRCKbplBwHCio1CAFIIoOkPKSZ4YoEZv4EljFV9qmcdw82vQWosvK5WvWjYwW2uJW1stFU3spII5y28yCZ9tD8fXn5Nup1h0U6030yBLaVe5MYuqXPRp83B11EF/y0Xti7fqWm4oNNCFRCnBmLBgSI0c3U9Q2XhOXskxmYcz1MHAKPy4LYfaw10812Zlo7GSdfoW7tFfmnL5T+05Nfk4Ecsy2JO6nB8diNDRfpQbYjk4dVRM/P5k2P4IND2Ps3guLrebE0Y2Y1goEt3kiX7qjHIOU0ydUc4BNYNsBrhi/zdNAnDBXUzPSuWnCw7x+tKd/HTBIYoznTx5js3ceK0yLasgwYJuh6ogqGy4xRkyq8Mf4s1jvabaZPsj0LEfZ8lC3p3TzwnyMJSGRwRIY4StagFrK1Nx+xaZY5FSYHeDEHgPb+SqwCZaZT5vyDnsMsq40VLHQyk/oFJrY8BdAa7E36OTnttUGwtLMuIKWiEE1Tlj2MUY7UY2Bhau0fayTt/CGBYMPRWyrkB6SrGLCKv0fXxSfxalFPvVDLYblbwrL4B7oOmSlRv5A7u5vSqVlxNCv+tYMfeKqYOi/5zoOECWyzZpJpDXZb14EnkSTJVhZuRUcir/OtwiwN/qT8eton5qrKE9ffHkhfOzCZDx59l4MPjex5lb/294tCBB5SBXDNAgS9GRFIhessQQ8qrP4h5sThhfAGy6RpvKo0kW49GCGGdlAF2pN6Ivusu0dHJ4oO8oe7w3sWzfGl558Wnqj7VhT/fi1MM0GsW8qWazwygncvA5Fpz+ZZwg3qEq4plGmb27iAyewlYwh/fMy+cOfUs880oTkjqjnDeYjU2NkU6QVMI8YVzPEVnICHZQgg5/mL7g6EWdg/zGx7g/ay9hYadLZjBdO00qI2yRCyjVzLrOTwJX8uKBDvy1P4TXv0Va4Dg/kLfGVUEr0jop8wiwpSWSgpnTGcudR7MspkprjTVstNIq89g9koc/r3pKosaXlYoAWmU+EZFCgejGIUZRCPwqlRFpIxrLDJom+hlTOmFlQxcGnSqTSMFimHkd1HyKkYWfoKNiPX8YLaeuI4qLMFYxxl7jCt419i2ejK7iFjafUSV2HsR56g36pSvht0K/dLFcb+Cewo7E97LWWpYNbeKUyoodkyzW6VvizRkHTg1e1LlI4gwu2AiURBJJJPEWcVnB6BeL4eFhPvjBD/KjH/2IrKysi57vwQcfxO/3x/9OnDjxR9zKJJJIIom/PPw1vyR6Z1/H3OXvnjBdKszgwovJFDgXSpIy5xZ+dg6h8TO5Bsuc905ezK+5n77ZH5k4HczpNfdf+nb8kTGZbc1bQv489FRvQvj0ePeinur909l3bH+Ego5XaT5HkdJsFJN98nc0P/N/kbHaiFTw4FP1UwaWt/dNnqnT1h887yZ4UlMmnb5rKvLEOxO73UFEWRJUGxFlwW53gPcy5P2dB/C37uF/6hJJnlLRiZsAbSqPHaoCBXxmeyrPaSYRsmfYw7NtVlqOHOTAGy+Z1ktvlQCJr3w5vuvvpcodZgyd1+U8BmQaWZqfFGXQL9KpM8rpyFp+0d5sfQ2vsUxrYKOxKhZGLLEyxsnS203yprWWjoxFfLt7CW4RjBccnt5z6sx57zjASNQgqgSdKjPe0R1SNqxEcagQeCcSW5Oisx72PgGd9YxGDfyhCKNRw5x+5BX6bAVsNubjFkEKRQ92ESGChajS0YTkkComTArFopuRYJDO/gHcKsAdls0U04lLC/OyUW3m7IyjtZbVoVeZMd2HuuIG0n0LqB58kelvfgHriTfOhJtfht/drPB+PFogTjaMe56/aVQxcmjzhTu8az6Fs/rDfN13gDssWzho+AhjJYrOqLKSJfx49SEiykKEFBSCqLLQpnIZFSnk02eOI7IiXtjMH9jN9XlBepSHp42r2W5U8WnfaXbebWd1+cRi93UVubTJXLqlm1X6vninf7HoIvP0FsL2LLNL+yKw59dfZ/D0IXN5eBiTOl59iBFl47TKogMvAZyEpQVt+CQLxeG47VmFaCOLQUrE5M1g45gwTsQKt3051azbO49oTCnWJs3MiCU08aWnD045jsWX0bGfPanLeWrXCZr9Fv7R+Bhb5Hyi6FiJIoQZHH+CHF6Sy5iVl4ZVj/38K10OC+7COdJBsX8XzpEO9nlvmtCAANBolBCq+hvqzvmsjXw0IwL9x/nB68e4MkZG//Dfv0B33UbTwkhJcnLyeDBrC4scXRQ7DbwuB1+Y2c7sDONM8dS3wsyl6ThAeLCD3W0DvCiXstFYiVNEsCnTYC1TDbH1wFGzKeJ84eSxYzz9+k9ww/JlvG/RNG5YvoySGbMp8aTwhHwXf5DzAUGR1o1FGfzn0DX0Xf2vvDH9MyilqBRtVGmtgOBk1b0sef+XyC8oPe+5Pt+2zL7qZj7xxX/nxjkFLJg9m7lz5rMwW/xZQ9GnhsJZvJDb1r4vobnitrXvw1m8kAtrKKfGVBlmPcMR3L4FSLQEyzBdiKnt/87KBElA6XKwpeHqMIkmgGwxSKvK47iahpNRfKKD7BMvT1ie31POlrYwN1jq8IohWmUOh1Qx2WKIWy21rJg70ySuWmtBSfoWfpr/uy+FDwlTyeVWAcJDvbwSXcR35B0cNHykaSGajCLSWjbx1ML9aBC3vmuXuTzeWcAbXSkcP9XFwtR+lk7PjG9Sg/ThEuFYfkeECCk0yBJu0Wu5QjtJWFlpVbkoFEMtey/uJPQfxzt6Ckeah1o1h06ZyWL9MHdZXqFV5hJRKfi00zgJER3uYSwaJTDQzQ1iR9xarjcQpvP4PlN1evb7YN4cjqYvY4ucn2AV9ahxC48Yt9IfGLtgeHszxbTJHFzaKBqSQZmKnQhWzSCoHBgiBZuIUCS6cWphXogu46TMZng4APlzE5pjPrMtFS9DhISVEWWnj3SWiiYEmIRebJvJqyLirSBTC8QtsNZrm8jUAkSzKykqPkv10VoLe58gEI4mWPeCihMh//7SofOP4UlMiikbgZJIIokk3gZcEgmSlZWFrut0dXUlTO/q6iIvL2/C948dO0Zrayvvec97sFgsWCwWfv7zn/Pcc89hsVg4duzYpOux2Wykp6cn/CWRRBJJJJGIv+aXxLN//J2NkcjY5S2w5n7cqz7D2oUFCZPXLizAveozUxIadVOoBqYsfL8D8LZeF0rhnXPthMJXHRV451x72aHal74dkrHsCgbPUaQMChfdjpkIEkksCTxW2zrposJRY9LpkbHzq1qqSye/JhdPZTeWO5uUxR9iLLOMCq2dGq2BCq2dscwyUhZ/6MKe3ZNBaFj2PcEXtccTPNqv0g7gEQFaZH7C1z+7PZWjBe/ltwc6OB0rdutC0tjUSEfGoktf/xToC46yxZ9LCDtLRBOFeg+90k0LeYxIG0PCRfr0BRdNYBaFj+BXptJBFxKv8BNRFtJ9C8yu24797GrtZ3usi7ZKMzv+FYmKnl4tl2eiyxlULmZrLUwTvQihiGAhmDX/4s9B/lwIdDG85ftsawvw45NFbGsLMLzl+wAYnulUaa2x4NVsBlUqAenAIgxAxKa50ISkK5KCUoJqvZks/ISEnYPSR8qVn0pUsex9AgD7Nf9A7gd/zJv5H+Jwd5DeI3W8VLuLX/UUn+l2vkR4h5q5tiQlToDoQrJUNLGLyou3+ek4wHR7gHfPyeO+whZyXDqHZBESgVVEMRAMkEYUHQOdEFYKtR6saiy+Pk1AZtU18ZySaTd/iTl/8xW+tcTP126rImP2tWbA+d4nTMLnrIJ3vtvBiquvoVXlky0G41Zk2cJPlhhkwF58UapB/+bvEjn4LJui1Xwxei/fiN5NBAsKQUhZOa2ycKsALhHmtPIyomyEVUpc2XWHZTMALxnLzrueCeNErHB7yD6PJTTFC74deBmULoRQGEpNyEI5dxl9ZXfw7/usOAnTi7mOrxjr+V70dtpVDiu1fazU9lHty+Kn8w+ZBfx3/WvCsUwg9qZAf041h+zzJpS9HzPWcLL0dk4f3k3Hpu+glFk0vEGv49GuSjoq1ptESFcDKtBDR1jnQNBN3ZAH2dEAR15JzHooXQ55czgccHBQ+qjRG1irv06J6MRPKsPKSZ7Wz4BMNZsizofx4jiQZtUo8qaR1r0b+o/TmPsehFBoSMKkMKqsFIgeiulkm1HJx7bYUUIQUDa6lQe7GOOWjDa83XXmtZg/99LssM4q1OcP7GZWTipl+R6cWaXmmPanepZeEgR0HODO7PbE5ors9pgK5PKtFKfMMAO8hzfy7sJwnAip0Zr40cqQeewns/87H6Ff8V5YcDcLR3dxS/Zp2mQO1XozSikejt5Ckywm7+RLhHuOnhkvfCvoC5q2UyPSRp9KxyaidKkM3NnTmO0eY2FxxhkFCqYCQyrBbL0lTjpuilazSS6lQrTh0QIcNHy8opZC5XtYWORm5wft/Ou8fvzKFX8+96p0TrS3MBL0kz59EVfqDXGSfyYnqNLacDrT2JrzAVpVPtO1DnpVOg2qlEGVihCCzCkaNhLQWgtOL8dd80kJnGKxaGY7VfRKN9NEHzkMojTBLE4wV2+hTpazs+RjNMlirrfsZi7H6MBLnVFOvz9AAFviOfCtYMhTQZXWSjqJDSZCQFbKyJRWjuN2WEoJRoSDVpnLmErBIiQRUuiTaaSKMGFpIaAcBHBiKI1NcikAWngAf8tuHny6Pt4cs17bxHTtNCeNbF6Xc8kWfv7OspHPZtURqvqbM/dz1W2kVVxHxFvJGksdT6R8jTWWOiLeSjKrroeq285saEwJpS+6m52caRIaJ0LMYPcLjOFJJJFEEkn8yXFJJIjVamXRokX8/vdnpPJSSn7/+99TU1Mz4fvl5eXU19ezb9+++N973/terrnmGvbt25e0uUoiiSSSSGJSHO+dvCv/rfyY6PCHeHrPqYRpT+89dd4urUsufP+1wbcCb9W1k9qvXYxX/duGfNO+ZzJFStTmoVFO9GT+ce3xSc/tdRWTW6isrsg57ybMK8rg9nNItNsXFkwdfB8Le36pJwc3pkLATZCXenLMwuDlHLu8OVhsdgr1PgLKzveMtQSUHbc2wqBMpUmVJHxdKmjuHCZdBBmUrng4e7HWRf3Ghy59/ZOhtZbGN17gSWMVYWXFpYWJSo3dqow2mYtTG+XGGTazgHWR3c7u6Yv5YKk/bgfyg+gt9JJB5A/fYqR9DwdVKYMjY/Eu2oazzn+8rpI3h0jN37FJLqUXDyqWVRFUdg6pYiwzLo1ACEcN+oKj8eKdUoq+4CijgT5mDm0n66zA6FaVh0UzGJSuuEVWCBvPRJdjZ4xsbZAs/PTiZlC56MTLvaWnz6xs3GZmwV10ZCzi+f2n+OHrLTTKEpQS5IgBnokHXF8GhMYcy0mWlE/n+3JtPHfjyQX7cKvgxSu8XLkM9pympbOHTcMz2CdnYhOROCWZKYbwSyf7jekgIKIs1KvpPBldRY3ewKMz68jv32UWnxweM8Oh6lqOOOfzwnO/5uUXnqauvpnDA3LSIudqTzd3FPnZKcuxMkaN1oCVMXbJcjIDRy6c27TtYbQDv6bJKI5bn1SINvwijVOGF0PozBSnqNDbGVZ2LDG7lWFS4/Yu3dLDeMELzFyad89JbAybdJyIFW5nhfdzZczyZ5zc9WgBlBKJGTeTIUaigJmhtdFYSU2saLpBruFZwzxmpVoXVxnbcaZYzAL++LFsreX4Kz/k+TYLPz5ZxPNtFjKPPJmQszOOhSUZk2Yj6UIwNO+j/KhndkLRcFO0mp8aa+LP7GhkhGCU2H5J3IzQHRaMyYnkc0flPXz99CJTESOama21EMTBDlnB79VC6mPZAufmxEx2fIDErIjM6Yz5OzjSdIBK0Uq28LNDVnBQ+kDAVXo9mb11LKEJoRQpwty+NEYIdR0zQ7fBVKRdjhKytZbh5tc4aJtLx/y/Nber44CpKnunIX+u+d+9T5A/sNtsrhjYHSdo459fBqbKMJuVl8bx3iAHTw2xw6hgu1HFl4v3sjr06uWpF30rYNl9kDmdtLEeFutHiEidXjJIEyMEcDJoOFFDnQnjhbt0Ps2yGKc2ikRjVFlYqjWREu5j2OUj0HHozHUFTD/5DELAk9FVeLQAxXRRIjr5pP4sK7X9bDOq2CDXkD//Otx2k6TwOq1YXJm4RSB+/280VtGt3IwOdOK2WZiz/GbusGzhIcsPyNCCHEur5viIlRVdT7BGr+O4zMdNgKu1/QgEcvYdk1p7TYBS9OVUc+3Jj/NKdBFz9RauZj9DIpVuw02p3kUxXRTqpprl08b/R84VizlEMYeNQpzaKEtEM37h4tfRVQyotMTne8tWqvR2shhECDihcjilsvik/iz/nvsqae6sKc/luB2WEIqDho9RrETRiCotprI0VYd2IjFFimmx94DlCXpx01nyXk5YZyQQIGssdfzBWMDrzKNB+rAyRoQU5GA7//U/z5jq2Px58caCq+eWMTdLZ7rXwdwsnavnlk1y/SnIn4u36lq+uKY8PnWHqqBB+kySVXD+MTyJJJJIIok/OS7ZDutzn/scP/rRj/jZz35GU1MT9913H8FgkHvuuQeAD33oQzz44IMA2O12Zs+enfDn8XhIS0tj9uzZWK3Wt3dvkkgiiSSS+KvAH4N82N02MKGLVSnY0za1quOSC99/pfiz268pxWjhlRNsWnaoCrTpK7h57kQ1qlSTk2Zv5Zz+x/vm8+z9V/JPN1Xw7P1X8h/vm3/e77fseplKrQU/qZxU2fhJpVJroWXXy+edb0p0HMDwlvFCdFk8YLhCb+fF6DIseZUJ+RgANVoTi9VB/NKFRzMLLV+M3mtm4bRtwr/5u5e3HePY9jAjdT/j+y3TqBBt5Gn9nDSyGRDpzOQEzbKYl6PVeIaPQKj/4rudOw4wzTLEuN3KDlXBQVlKf/dJDtXv4qkXXuK3z22M+8WffV0UZsTUFL4VzMxJ4yMFJ2iQpQyRCsAQqaTPuRE3IxdvQdNxgCE9kw3RNfFAWJuIsiG6hsiYxB44SWmJj6ekGR68Vc6jVeYBilQRji+mTeXhx7TlsIsIXTKDnarczARIIInM4sqveor53Df/i92/eihmD7aSjcZK2lQuS7UGwlu/f0FLkQmI5VBQ8R6uKrCw8247n/3YPay/bj4Lg7VxMuKCqPkU/owKBrvasKoxcuhnuVZPUDkIYSeIg4hKwSrGyNMG2GWUcVQV4MT0q/dLFyvlDrPwmD/vjM2ZP8THttiJKo1i0Y2VMb589IrJCZ/c2bjm30alVyNdmPd6uhihwqthm3PLRSl9UtK8pIkwS0UTS0UTs/UW6oxyTpKDBclRVUCbzCWMjUHlYr+amWDvskXONxUYsYrurQumsbIsO2Ed1b7Jn2e01tKx75VJyd0r9QZ+tDJ0QUXfrNA+KkVbwrw1ekOcpBlQaYxg5+BJPzuH3AkEyEjdz9jTPsCTUfO6fTK6koMnh1inb55AhHQPhcl3O7htQeLYeeuCaQQjBo/JNXFV0bi9WrwA2HeMvtSZbDMqsWpRfHQyUz9NoyzmUPEHJthRtvQG2a4qyKeP6eIU0+ijR6ajoeJqGb90kVr3PXjh/5v64Gx72CzYn128vOlb+O2F3G7ZQo3WSI9y0yB9HGA6z0SXk4LBggNf5T7Ls/SSwU+i76ZHeXCJELa+BsLH35y4zIuBELD3CVpe+QEff93Jzc/Bld94jef2xcjPzvp3niVWzC4NMPd5y7+fIUDOJtMuA5NlmP1kVRjriTf4UstcnoyahN4SrYmDp4YYGYu+pV3hpm9hyZuDbnORoplWTsWimwrRxj7K6KtanzBeeF12Zs8sZVO0Gg1JtjaEApqH7fzuhODg3m3s6VHmMcifizPFwjd8+6kSrQxKF/PEMd5n2UK51s5mYz47lBnyPvfAvzFa/6ypfhKCbMsI28+5/5+Sq7Bl5EFnPasdx3hPSZSs7DxysrL4Rv8qulQGThHGI0yyVAgI4KAXD2XWHgh0TX4MzkaMQFXAV431HDYKma53MJtjpOqjjCorUmgMyDS+Ju/hyQX7mFn/n7w3rz9uSaoLST59/Le6caKCsOsgzoO/QGASQx14kWiUap1Mk6enVIGMY/xNoVR0glKEsWLRFEFpJySsjBrWWOC8YqecRURZKNB6CSoH3uv+Du/saxHAPdpL3Kpvpcko5qvGerYZVaYtKSlk4adYdHOLtpV/a8g6o44tXQ6hfpzGEPn04jSGzPeXCde7qZSitZY5he741KWiiSqtFYXgY8un/1Wq9ZNIIokk/pJhudQZ7rzzTnp6evjnf/5nOjs7mT9/Pps2bYqHpbe3t6Npf9SokSSSSCKJJP7KMV6ofuos5cZbJR/6pwiL7A9Gzjvff7xvPh+qKWFX6wCLSzP+1xEg48h3O/58P+Z8K+iWvcCOCR/1eKu5dpaTfz7wWrzzDzhvF/VbOafzii7y+621FLX9hojw0ySLaVe5FIsusoWforbfQOvUnZBTIm8Ox1UJX9kDT2hfixccv2Ks54WFggU9w/xsu0kA6ULw8at95BnbuC2/l387vSheaNkg10AUZh16BWyW8/vqXwCBcJRy0cZsvYUdRgV+4cJtBKjWmymhi69E1yO64PO5gzgvUPiIo/84oUj0jB0OW9BQ1BnlrNL38R7tTdwiyDajCoBlWiPbZSUAI5GzCqpKccPichYf3UmnfzpRdwlXWHtxppwAx9yLJ2Xy5qCnz6K5eS/vYmd8cpMqodUxyhx7L74V9/KfGYvY3TrA3/4CJIIPWzYxqFycUNlc5+0nf2ATUakT1XSOG3n04WZtuYPilHZwzDuL0BCMtO/hN/uClNMe76wHqNJaaJSlIATTOl6FrNRLUxWNqy6W3QettXhbtlKj7QZlQG7V5PlIk6G1lqH+HuqMCuaJI8zXjwOKQ7KQXDVIEDteMYyVCGNKJ4iTOqMSIRRVWhtrK1OxzYgRFeP3QctWWo71soSjFItuCkQPp1R2nNCcMP74VtA3EmGk/39IFQoVC8sN9Z+mL60M74WOS82nsOfPpSzwfdZ1baFRlnDQ8MXt1XbJWbSrHHQhaTJKEMJUfKSLEYaUk3QxQpXWwkZjVfzeemrPqQmKwweerufqsuzE7W/Zir91D19ryGL7JBkcgyr1ok6D12Xn7tIh9rRtAcMsolarJopFN4vEIYRQBHGggNYjDXQ8+2vee8v7oOE3jA508mQ0MfQaA+6zPMu79e3siJ7Zrl2tA+Sk2ydVU36opoR79E3x8UgXkvXaJvLXfNZUDji91OkLOcYpimQvQWEnRw6SqQVM7ugcO0pfVir36Jso0boYVqnYtUEKRS+9ykOdNLfpk/ozqD4/5FxctlKHP0RLbxBfVipp3kL09igehs0u8hhBDLBcO4B1qJ2l9jQ+ErwFqWA2rQgJsr8T9+BJgp5SvOPkwMWidDn+lt3s3v1KfNipFk0c39XAc0tu5L3zpr0zLbHG783ND4E/ltG56oG3JU/qziXFXF2WTWvvCKVZTvL7d3Gww8722BBUrUybuDaZQ0vhbVS9lePTWktL2EnrSBalWgfX6PsIqxSGcdJglFLprqBw/LvbHobOeuxlN9N0qIl3iZ00yWIkGlWiFQS0yVw6DxylpOr38VB7394n+ILlBJ39Qyh/kCHlQBfmNi8VTXxSf4Yczc/p/A/jW3YfbPs+0u5hh0rMptohKxjLscLJLQDYLDq24rkMdLVxr/YsHhVgVLORoiSz9JMMKSc/NG6gmC4shzfB7Jsv/Exv2cqscARNwBKaOMAM5qmjOIXEzjAB4SA7zUmaM4f69J/j7DsB0VGmW2xcW5LLa23l5Kk+SrQuXprxHG5VAvlnjbdKEUjx0oP5nKxWTWhCEVQOhp0lZJznXO6K2dCW084SvZmIsnBIFeOWATxaAB+d2PQII9JKSDio0loZUk5eiC5jdUYP+U0bTLUtgIABlYZLhLlHewllakxQCpwijI7BIGnMoo3+g6+Rf9VNsP0R6GqA7HIoWASndpv/3v6I+dwcR/7ceF5YTsF74+d5nb4FUDTKUr40SYNOEkkkkUQSf15cMgkC8OlPf5pPf/rTk362efPm8867YcOGy1llEkkkkUQS/8vwdpMPmamTh2pmpl5YlXjRhe8k/mjwZU1eFCzNcpLvdvCNtXP40tMHMZRCF4Kvr519XtLmj35OOw7gNvrpUe54kdT8gbyZhUa/2UF4GdYe3swQ97zwhYSC40f0TWRW/TtVbgdLVoXOFJXcDnhhG2WuEbPx8qy6wyFKcFjbof/45e9jzaewjX6XNUefYFO0mmaKKVftoJ2xBVkqmvipsYbbi8TFF7GqbsUW/BmzT7ZiKIGGokJrp0e6CSoHx1QBhjIbbsbVIDAJ8SXMTk2HVUctuIucqtU4x+1cOg5cVGYEYB731lq+UryX+pPZtKscikU36/TN/Ka1lGkfuB9v6XLyAUU/1bFO0FaZx+tqHiWiC2e4G6slTCCqsckwveK/kvkyxX1vgl5lKiHGswHy5xI4spPbtc00SrMbuUprZSV76SUDhcAjAoSveDe2S82WyY+RLbFwef+OTWhKUqj3mfkEF2PxEysS7tWu4SXpZrGlmV6ZRqoYpUR006kyGVDp5GiD9Kt03lBzaFNm4PeKslzKcpykla9OvP5Ll/Nacxd9tT/m7yy9nFJZ/L/oOgCu1Btj1kfXJm5Hay3G7sfpUW66VAYdeMlXfWhIjN1PQKrtwvdY6XIKVo9y+Bf/yfX6LtIJMqYsHKWQjcbK+H07Hna8yNFNaPQM4TKeRXI2plIc3jT3rPFICIYGelHKnfDd8XVtM6r4+RYHtTXnUYO0bAUhmH79Jwi+8DDrOrZQIdsoFt1cJepJ00Y4Kgt4KGoW7Nfpm4ns+m/6ZnrxIpiqZ00pNaEev7g0Y0o1paXuB9yX28ijXaYF1j3aJj6RfZBp1t+Bmk1gsIvwiWO4lZ2gZsdQGt3CQ790Udn/hnk9nZ33UvtPfCZzB9/tWwooPqBexa0HSTVCrNO34GKEXM2PlpoJmdMn3wkwl9lay8E3fsvXDm5nu6pgmWjiH2fbyPHOI7X/IGW0c1KZyp0avYF+3MzQBnE4nHwp5zDNbR08aayM55KMqhRe6ZvGHXU/w9lx4JII5Ibiu3jSGKNGb4gX+LcZVXx3eypLVi16R3eMj0YNwmMG9hSdKeLJLwsJzRVu8/mmPf9aQk6ORSgzFP1y7TdjFmSvt4UpRgEClKkKbJClVGmtzDj0AxA3JKwj322nSmvllMqiXeWyUBwmVYToUZ5Ydk8qnYNhvBAfZ+ybH6I0ZQBpH+F0+jyaO/2st2xCQzKMk6eMVXx8RYz0y5/H0J6XqRaRBDVjtWhitL0BhDKJJ3cRpDiwjvkp1ToZVC5OGlnM0k8iUHhEgPdrv6dfpBMtW2PmoFwIQuDtrmPjAsXWA0dJVwGOUMRceRSHpkgVEQ475lJMF862LeDIAN9KsLnwHtrFgJzNf8p1fEXfwB1dmyH3PYljbf8xUnKv4KnWAtbpmykQvQA0qxKmp6ZC/+S5sOammc0SPq0DtwoQwso+OROXCLGYwxhoRJVOVOjoGHFbtZflUtYutoKS8VyRx4wbaRSlrNM3817tDaxalDqjnArNT0jY2CnLaZIlrLHspHDsCpPoaHrebAhwZJr2dwWLTCVI0/PmisaJkPH93fsEtoO/5G91C8Wim3F7wh2qIrEpI4kkkkgiiXcELosESSKJJJJIIok/Bd7OQvWikgzOqQMjhOl1nsQ7H91D4Smn57sdE7tK3wEFpbAti43nWF1gwEpbA+mXucz8pg3clb6fx/3VbJBrWK9t4m73fvKbNsCy+yYqdqpuxR55gs9l7+Q/uokXdf8+eyd2zzSouvUt7aO7ZAHW2QEyDhxliWqOF/Z2qAqWqqZY+HAsAPtiz4lSOIsXcmX38wx2n2BAuUglhFfz06kyUQiKRRclejcbpVmonpT46jhAS2+AB1vmsX0PaM+/xjfWzuHOBXedIUIuhoiKBZVnOq0JhfF1+hYqRCud/lGzEIZZwDHzIRSPGrewQ1XwFX0DY5qV9Aw3kagNuzaDT5b78J1OZaSvE2nPweVbkRDkrAdHEYe/Q6XWSrvKJYsBcjQ/3TIz3rm+tOguanzeqbZ6csT29+Abv+Wphl/h1gLk00e/1oV1dgkLr7k4Ym5kLMr/7GsHzE7fCtFGJgGsYoxUFWZUs/JCdBkuESKVMI2ylKVak0mAOOwTjnuHP8QPX2/hM3pvvOi4Q1WgCbh3uc+0CzuX1Og4gK4JGmIZEYbS6BBmsPhqLXjR59dbdS2zKn+P1vRbMhjC0Cw8Gy1NuG8/zEtcbW3mSDiHw8qXQLis08+oMKbCBMVh6XLSW3Zzh/5KfN6zCRCAJaKB1t6lU49lsUD5vpxq/u3UQu7VnuGjlkZ0ZeDQRulUmRxVBfF9wDCJEGP3E1C2hNG+PtbpmxPWv07fjJ6ex0sDZ4Lex1WY7f0T7QXXa5vIO92Et3odH69Yz/W9I5RmrTbHo1jRsCelgJlsxqobPBldFR+37rBsRoQSSSBaa6H/OI7MfJr6SqgQrezjClKNERbrh7EogyFS2SznU+r1THm8x9GRsYivHdzOMr2BJTHi4WsHq/j+3f+I5fV/oaLnCJ6x1ykW3Yxgx5edhjbzQ4wEBjHqd5KFizXaDjOXRDnYpcppkXkEwgEu1el/VmgfAhJsw8avmf6GP5Cfn/any9m6WMRyY/a0W2iT0yjRuln4yg+Zfj1vixokAS1byReCH68MU1/byJtGFbuoNEPRD2+EoUOXrlqMhZe3pS+iRfZwtWUfdiIEsOMmSJkw1S1yyHbGoilGnnn3PsH6nBYe67oCBeTog7TKPB41TJLhDssWCiNHE9cXHgTDIKB72N6lYZDJQo4xgAu/SmPWrf9w5n5WivTMbK7U9ybcg3foW3AHh8DlpW/J39PhDzPz0A/RwoMMKhduAggNhpQTOxFcIkS+1ofN68N1639e3HEpXQ6d9SwMPk95eTqdp7sJDwWJ6in0GzbS9DC+7lcYQ6c/JY1MGYWBFnZpc/htz2w8WoC/FU8zKFxsDRSwqvMw9tbaM9eEdyYp9c9yg3aSAnFmTPcyhLWrHjKnzoUdf1dvlXn0aG7ytAFW6ftokflYMOjFTS79OMQYA8rGQVmKjuIb0/fjLr0XSpfj84fQhKmKPaNwe4Zi1cVN+naC2KmTFWhIZuutWGe/F3fJAug8ECdA+nKqOWSfx6zwfvP5M5lSMkbe2/f8X2pipPL/i66Lv5Mk80CSSCKJJN55SJIgSSSRRBJJ/K/AuAf1g0/XI5UZ8P2NtXPeEcXyJC6MuphFwrnY1TrAvKKMBLuTd8Q5jVko7WxKJG92UYm+aAE4LyMXbfsjjNY/yxND8+JBzhvkGrQh+GL9s2aH7tl2DQCly+kLjtK+5zus0zfHrHK6aO8XzHjXJ/C+DYWshdVXc4VbcaTTzx8O900oBi+fmXVp5ySm4JjmtpMl0hhSqQx39YIg5oMOAsHswnSuWbGAw/b5kxJffWllPNgywvaYhY5U8KWnD3L1A9eQv4BLyigB0BfdHT+f44WVOyxbKBw9Ev/qopIMHqeERuNMEb2dHJ4ehmJpoI30oKtGensaeB5FQJVzYleEWYXFCVk7kcIr2Wjs5TOWjfEu2oPSLL4bSmMXlZddYOkLhNnVeIwbLO20yVw68NIULSHjwBFm5HzXLAadrxibN4fOvhFu1zYDgo3GKtbrL1Gg9YISWEWUNpnLV4318cL6jdoOBoULhA7SMIuTZ117Lb1BKjjTda0LyVLRxAf/5gOsnjfN/P655ytvDv6+ETzamWyYcSJhyFOF92KVPq21zHEMEMlzwXAQw5bO7L5WlqomdqgK1mubqNDaUanZ7B+cySCuBMKlSmuhUrSelwSZTHHo9i1iUckuaNuSoAyAMwonp/U8FsOx4zey52XKGcQmoowpHZuI0CLz+Wb0A/FljRdZNQnvKkqB3NnoaWWII4njghCCqpvu40vpCyeoMBdPktWlCYllzntNAhbO3IPj45CSZLpshDSDLummSZUAJnHWLd2UaUZiPoBSsOJzHD3tZ9mxjWwzqmiklHX6ljgBMqycDOIyLZJq3j318cG01tmuKuIEiKE0FNBX/3u8N/4b4T/8B/kd+7lF7UW4i0hZ/CFYdh/H33yBwfomZol2FupHOG7k85BxFxWijTWWOrBVX7ySLAavy85Xi/dx4NRgwjWuCSgeGoFpqy9peX90nJMbs0NVsFQ2QfsW8up+ZpJAbycREstNWQ3Me/9dzIhW8EEB8/QmOMyZ3JRLWadSYHeTn25HE4pBmYpHg3aVi1Qa87SjRLDQ4ryBqnNUdS29Adp6Atyub46HnjepEmr0Bgali7IclxlCPp7lsvXboBQjBVfyuwOnKBdtjKEzKlLQlGROpsGVxgvAffH9dasgK+ZeAQcaqVZNlIhursozsOQkOk4AAMEfSURBVLm8vCEWcNd/m8+bj2hFfDhvFBetZGt+osrCIKkoBaOkYGOMYO8J9vz66yx835cu7tjkzjbVdC27GAymkqmFqTWq+FT0c7yp3U+WGMJAo2HUS8W0IlLDXbxwMswGuYa/FU/HM0GaKWZWThW+s8fnZfdx6lA9N1me55ThjSlpDpGj+TmRtRKfI3PKc3nmXR0ajVIeEI8zU+vArkUIYcOuRtE1SUA5GCu7CVf6KmblpeE79Vw8sya/dDnfWDuHZ3/za4wYEVItm8jS/LgYJYg5TmULP73KTUnVUvOZJwSEh3httIyPPR5Gqh1oAn68sozVjsMTlJJnyPvEfdAEF1QjJ5FEEkkk8edBkgRJIokkkkjifw3eiWqBJC4O1ZMU4MC0afnVzvYJ5NafPLz9XPhW4AW+sbZ9gk2Xt+oyt01JTuVfx2PHEoutPzXW8MH8EnxT5Dkcss/jyehK/u6sgvr/i66jwD6fmsvbkjOIFa7SgCvyCnn9SE88VHm8kFt7tJcO/4VDnieDVdPIEiFSHQbNI5mkCAMdRckVVZSvvBlatlLjs4F7YjHlkGM+22ViFpChlJkvMeMSCml5cyB/Lt7S5QnncxeV3HuVD3dpTvyr+W4HkcIr2dM+GJ/WIH2k6yO0Drv4sF5PseikEy+/M5bg0QI0GD5+9vRB1gSfwW3Toeb+uJ3HONIJxoOhLUJdVGj2pGjZymj9s5Tr7YxIW5xU2SDX8GWxAcu+J8Cecn4SpLOeaT1baRd+epSbaq2JoHDQY2SQrQ/gVua2LhWmEiigHKRpIV6KLsXlXMttmS14W7aaRc3c2eBbwazwfnZqwQTruCv1RmosTcC0yQufQuA0hiYEi2PA3xhDFwzfBeIqn5GxKMcX/B+KIsew7HuCGZxknb6FtbxOidbFFmM+lQXzGRw8OoFwaZA+mjlzT1+04rB0Ob7rYdqr/8bR0RCbTpvnc/y+uSg7ldLlOIOjXLP3y+TRR4qQBJUDgaJCtJlkqQFCKHTMnCC33QxO9lZdS+ZVgzje/BYFqhchBKErP4+36lq8cFEqzMeMd/PxBatxT/bhsvugZSvu7Y/QXlTDo8fLEqygXlcLWOruIaXvrG762HXnzQix/bfm8TWUxkxOEBU6QikytGHc0QDRoguMXi1b8fb2sVQcjRMgxaKLYr2bsZSreKP2NXYe83CbpuEUAudQFykxYsNbtZqeF79Jqj5KUNoICRvVWlMsH6aYir5m6Dp4ycqNWXlpIOCp9gqUhDv0LSwq8ZBWfu/br6x4q+g4QCAcnZgbE4Wrw42mHdjbuc2ly03CubOe/ScG+cyWvSyhiWN6A3OW38zq8txLz02JFba9e5/g4/mtvNFZxMFoKe/Sd5KujWCg0ykzKXFEE8YL//Fd/LzVg4tZLBKHyFJ+mlQJO1QFa9QOFuhHKFj9VVOdFttmABbczb7sOzi476tcZaknXQtx2CjgWbmc0r5O3l23kRww743YsVvIVsrnpdCn55A91IfdXchz4ho+s/2MBehP5Y3Izk18xNZO1LBwSnnJZBiLZnDQ8NGHm5mcZLj+JfqqlsZzSi50bPwtu9lz8AR9Ip0jspAvRu9lvbaJAdJwKFO95mCUZttc0soX4j69kfVsihMgJVoXzdES7Cvun6D0HHEV4Te8WDWDZaoRjwjQLd3YCt+Fz5d13nN555JirnMc5lDXTPb/4QpmcppMMcyYCjEqLPQoN4Yzm52HTvBktJ2dVPDjldexOvRqXAF455JiFqkqGt54gYqeNhaKQ7jFCN3KTaoIM1tr4aD08bRxNUsPbcabagOl6Mup5qP/HY6P4VLBR7fY2XV3Nd5ztrmv4TVu17bEyfti0cU6fQt/d81Mrvxzv4MmkUQSSSQxKZIkSBJJJJFEEv+r8GcN+E7isjGvKIPbFxbw1FnBvLcvLCAn3c5tD78ZD0WXCh58apIg4j8T3lbireZ+HP4Q2hsTQ+DtK+6nA9i1/xRCCBaVZMTX5ctKnVAP1gRvu1VD2qyVBI/msK7jWUyFwJni2aTB1lMhlovBkVcwy8kajtR0KjPSGdHSmZ4xDWd2rOw6biM1CVKt+qTTz9tdPxnOKnRe6HzuPzGQQIBAohWRTYyRKsLY1VhCqPZ67SVobIaFawGYFd7PHfpmIqQglcCtBclWAxSu/ihleWmmPcf2R+IkwkVDCDxamP3SRYYWIF/20SG8fFnfQIXejiq90lzm+ZA/F/vRV5mV66K7U0cXkunqNB59mAGZRju5BLHHczQq9HY2RavZoSrY8UITXxewcYFiYfD5+DZ5Dz/JirlX8N29lUCMYBq3wRo+PPl+KkVv9lJ2nHP6d6gK+rIFeRcqmMYIkATLNDGDhwqupUp7nqvZTy9uDkofL6ulLIj2TEq41OgNKMO8wXQh+MKNs/jmS80XrTg8pbIZ6jxMjQbFojt+31ysnYo31cYcT4ROv52d0keTLOHDlk182LIJoqZa7Gu3VPH9tGN4Dz8PIcwsoK6DrC6fw+hJD+ExiT1Fw1aea15XSk4ILN/dNjBh3YpJ8k7OhhBgd5M7Zna0j1tB5dNHgeghoHJwVt125vuxnJP80uXcdtv7OP3sv7BQNJOlDXPSyOG4yCdTDlGht5O+/ydQ9PmpD4wQzO97gXV6R/w++4r+GPPEURztf6Cxz4pb2enAS6oMUzTazenf/B+m/f0W8gd2o3kijPhtGMJCnuqnWHQjEXSKLPRphROtcc6HmC0TC+5iFvCj5tcYCEmyxvJwprxDf47nzWFI97GjMXE/d6gKhsqryMl2vf3rrPkUfQ2/p/4Xj3O/Zl4rbxpVfHeLg9qay8xNKV0ODc+Qbw2z7Kqb2d3kJH1wC26CdONhOHs+mRkZCc+RE/YrqBS/BxS75ax4cdskRbuxzn7vGaIhphRkxefoyFgEu15mlbaXUawcNjLYr2bi0QK8ZCylvastUbUZs6VyBk7g1E6DMvBnVPCZTYkZaEtFEx4tQONYPmViDCdhUrUwbxhVHKaYQekiWx8krFIYGS/oXwRBNdJ5BJC0SVOZ9GV9A9V6Mz3KQ50sJ1/0UaT1MGOkDjWajkcFWGZpTFAQerQAzr0/grMVhK21lKSO8THjbj4jnkIXkkHl4lHjVv7hxBsw45YLPre8LjtXHnmKLFsrPWMeUgkjBNhElLSC2XyntZRKEVPhyQo+vsVB3d3r8MZUtr/a2c4Xn5as1wR3WDZjU2O8aiwE4CbLdlCgodAEpGdmQ8d+qLmfbQdOo9ibsC1KwXajgpt8085MbK1l+slnaEbx5Fnk/R36Fub3vQCtWe88YjOJJJJIIokkCZJEEkkkkUQSSfxl4D/eN58P1ZQk2LS8eaw3gRAAkMBjta186aap7Wn+lHg7ibepQuBfP9zDA0/Vx7sXBfDQ7XO4M/o8+QOtfN3XxZ62bNpUDiWim3/zHSD/jX+GjNIJxc6LRiwgmwVm8PJw82vYT7eBEGQxkGARdEnEQ2c9HH0VXLn0aZm0RjOYqR/GHe3FJgJQNM8kSVq2moWUKYopwYgx6fS3ElZ6Idu1qWzbwLTeGCCNsLSSqw0wXxxlI6tYr21ijWUnVH7ALIxtexjv0VdZVJLB620OZmnt7JLlXF1ix3f65+C8zgxRHw9qvRQSpHQ5zs56FnUd58WuYgaFi7kcp8xygpHSd12cp3yssJO/9dvcRgfh0HHsoW5aIxn8o/ExgLhlTKHeQ5NRjEcLxO2lltDE1gNHmXHdGty5s2Ok1zwWhgapu9uWaG+2/RGzODWZ9ZBvBYZlAHhzwkfRohq4kJJBKfyech7cOZZgmfaFk1fxU0stqVoYpTQ68PLpyhDB/Bp2NKQkLGKHqkDIM0qLr6+dzZ1LinnvvGkXJj5jgc1fapnLEpFCseimQPTEP55X5L7wuNFaC1u/TaYzBZU3l981GTSpEn4WXRMnQtxWC3fnuWHvRnOeBXeZ99jex0G3Ynhm4k+ZRsrYafjdP4ERgQV3T1hVf3B0wjRzemTS6UD8WtFrf8p9lmfpUW4kGrO1FnqUh6Hy+8k5u1AYyzkBuDMb/CVdyO4eekMp2LQIhtQ4wAzcRoDZJ38H21MnWgCeBWeKhcUlGTzVAihoI59bXU0MDBwnlWKK9G42RatpUiU8oD9O4eBxRh7/GzpDVtr9KfSouaQqM4/karWfPtws8E3D5c6ZYI1zXozn/cT2Na3tTdJsBmhXQMmVl65w+FPAt4Ju2QvsmPBRj7eamZeaR3SROGSfR1T9zzm5KerSiPRzUXUrLae7aHvjN9ylDSFQnPAsYXZakNnRw+C9e0IoejMKhSCgHDQoH3foWyjMsJNStADnHTHLqdZaQMGCu3ituYutW7+AWwTIUHlsji6gSZUghKJctXOHZTNPRldx+mzVZmsthAYhwwfuQvCfZKi/h3u0wzSqM5aKQigGpYtcvR+vGCKKheNGHkGcDEoXQiiUgjwxQIYYvrjradvDiI495GgBhqNOSujkJst2TsksRpSVpXozv46uoslu4e+jdXDwv7kxXdEwnJqgIFzPJmh8PlFBqBSRoquoEM/Ez6MuJBWijW8dLuH7i8Nc8Opp+A3BUwcJRqK4lCCipWBVY0gUsrOZUmFjo7EKIcx9NZTisH0+NT4vHf4QDzxVz1LRRFVsrHEzzEptHyFhj+dVZYtBvj3tNdwn7DDzutimT37sJkze9jDO4Q66ZnyYHYeyAfN5cPWMLJyDPzPfj5IkSBJJJJHEOw5JEiSJJJJIIokkkviLwbyijASbFl9W6gT7GYAf1x7nnuWl7wg1yNuNc9UIAFd+47WEY6CAB5+u56alR3EdeR63JZeN8ja2ywqWaU1cE/gNNLwJZ3dhvwV0ZCziVPdv0YVEIuglw2RiYrgk4qH/OAS6eL3fzZ4eyOcQY1oXjvRs5qUOmJ+PB+TGCqaTFRt8WanxcNRxvJWw0ouxXZvMtm08RL1HuelSGXTg5WpjP4V6D99N+S69uBksex/uVZ8xZ2h9A/qP41t6L+k1VzDUuo8qywhpcthUxzQ+B9NXQsV7LqzaOBexotvJvGsZ7D7CEmGG2R82isixei7Nc9+VS/j0YUIjfoalg33MBBIVEgcNHynTZnPg1HCCFdKbRhXVZwe7+1aYYcQtWynLgUO987CefBNvaNAsyE+xTW+J6PKt4MRpP0o1J0xer23CqY1yXObTL9IZlC6KnRGceWkIwhOsrh6876OMRGQC4XFB4vOswGalmtA1SbsybdXiYeXtFew/MXB+W6rxLvTiGrxVt5Kb0k7NgVfYZlTxs+gabtFruTXyAiPPbcdZNN8kQMaPpW4l1NvKr9uz+Er0Vr5i2cCdzlYcWaWTkk6ZqbYJ0z6iv8jC0/XAAxO3bVxRkjeXUT2VbDFIlvAzpJz0KA89yk1JdwO0uM4UT8e3be8THOoaZvtJG9kUUqJ10y09PG1cDZjX1rA7D9f51BjKLE77GFde7CTDkc1w/zWkNj3DUq2Z70XXxnMeHjLu5sO8xLuOv4Y1msoj0fvjGSCnjGymZ1ooM/xYwvXgmHdpBc6zicrWWjMXR4vl45z7+TsIb/cYejGYFd7PTiGJxgrnS0XTW8pAAvP59PNWD/fpbfFQ7Gd6F/OTwkMwmmLeR+NjX2st3u46Mpffw49eb2GttplKrZ3iVIM0pxUs2pkckBgR35GxiB++/n3W6S0cNHwMCXNbd6gKltKERw9w0PAhhCLTFbuPxtVBDo/5b00HdyEZg91cre2jkrZ4lo9Sgiq9FReh2B4pOvACipXaPoQwi/eZWbm4rBdR3mmtZeTEXmoHsxiWhayx1KEpyXGZh1cNsVjv5YXoMtNOLwBXe4dZ5DyBNTUdZ7A9riBcr23CowUY9sUI7bMweuAZ1ljq2BStNskSbRNrLHWUGJ10+isuTIIgGAv0k61GsOrRuO1XDQfx0k857XxZ3RN/+RNnqVt3tfajMMmjRllKoyrlXv05CvQ+kLBJLuX2RQVUGm+S4z8IAX/cDm1xaeZES0NgUek543D6NMb6Wuht2w2siU/uO7qbsfwwKenTSCKJJJJI4p2HS/QDSCKJJJJIIokkknjnIN/t4OMrfBOmS2VaMP21It/toGaGl3y3Y0J+xDikgh69gIirgM6BIcppA6CcNjoHhoi4CswO1MtFzadgwV0cfOO3/PDfv0Dt4a64T3iDLOUx40bgMqy3Km+hO3Mh1r5GBqWLQ6qIfukiNNRDd+ZCqLzF/F7pcrOA1LF/0sXkux3ctqAgYdqtC6ZdFjHW4Q/FCRA4E7Le4Q8lfG/cti1hd0QroGg4K9j8deYRkE6EUDhUBG2c1AEovQoyp0PHAbwuO76bP0+aJxva3gQEWJ1m0XTZfZdePI15nt++Zx759MW7dA8wnacag/QFwhdeRqx458+ooD0o6JYeWsmjQfqo0RtYKkzFxzajimaKKVt2IztURbwbeLy7O64OatkaL0C+NlrG4794nG0//SKP/+Jx9vSo83Y1jxdpz8ZFF2lbtlI0dpwrY9sMxAt1/dJFv0jnoOHDowVoH7Hi7a7jJ6vC8fVpAh5aO4d5RRnxe/GiEVMG5LvtXBnLAPmesZaNxkpAUKm1ArCrdaIFVRyttRD2w4rP0Vd6Iyf2vIyuCbYZVdToDTSpEg6rIiwYjEQMyPSdKdx3HGDEM5NfjSymXGvniZSvUa618+uRxYx4Zp4hV87CopIMzjnUKDRm9m+G7Y/Q4Q/x5jEz/4ftj5hKJaFB10HS/YfokhkMKfO87JZlNEgf2Sd/Z2ZrnINDncMcODmInQiZWoAno6t41LjFDHkHdsgqXBnZ51djjCsvSpeT5rBT7LGR5rBjm3srvXjQhORqbX9CBosADCycUmZnt0cL0GQUM6qloOsWLFYHeErN7v3xQvilYLzw7VsBK79g/nf8+n+noWUr+QO7+cbaOejijN3b19fOJn9g9xkC+u3EtofxHt7InOU387C8nW1GFVfqjWYG0ltYZ0tvkBI6CWInqOwUiW7WaltoKbwNVnzO/NL4NR+7N1evuZ1vf/F+lk7PptBjx57qxl/+Po5bpjNS9zMzhDt2jbX0BtkuK3gyugqPFqCIbtZY6viyviF+fW2Qa6gQ7diPvGjeF0qZz6/QIH051bxZ+BH8OHH17GOuV8UtBZeKJipFK/PFEVwixJtyNu0yh8X6YWZwmlKtkwJ6mFZQSvHSW8DpvXAeklIcL7yVJ6Mr8QsXA9JFthikTJwiTx9gt1HGV431gDkmtpzuJOCtZMziYlO0mg685NPHGksdg9LFibL1ic+i7Y+S2/4izUaxSaRg2vI1GcXcbNmO7/h/X/ikVd2K1WYlQw8waKRxgBl4GcKvuThpZOHSQqzXNp21T2f+r4jt/3ZZyWPSfA/RheSkkU1I2LlPf5YTe1/l9foWWkRhTI1lEqrjwezjRTINU1U7YXy/6VucKL2dG/S6+Has1zbxLr2Ok6W3w03fuvA+JpFEEkkk8SdHUgmSRBJJJJFEEkn8ReOe5T5+XNvyJ+1WfSdhKjWMJiDdN5/D3go2njCLu9erXehC8mR0FXfMu5GqvLS3tO6+QJhdTce4QW+f4BO+nk00U8xtt915aQViITgRtrMpWo1HC5BPH5mxYqQ17CBnvMAzbiUyBRHQ4Q/x9FkZMgBP7z3F52+YdclESEtvcILtWjxk/Zxl/cf75vPuOXl89Ge7AWiiBCUFHi3AdqOK7aqCL+sbsOpjBKSTsLBSeeIJmBFTguTNMYtYHQfMQlvbm9DfQkQqgvZCbLZsnJdrs+FbwaFjvXxY+yolWlf8nA1KFx4tQOdQ5MIdurHi3ejhOhplCRJBDgNUaa00yFLTnkSZHcz/UNZDxegBlsYUJ8ZZ3d0jkWXm8mIWSH3BUT62xR7PAsijj60HBCVVS6fcpqns4S7q/AqBWwVZXD4d1dxAhWxjtt5Cv3SRqQWoi1ayQa5hqWpCNDcyu3A+q2fl8EbN4ree8TOufOmuY87ym/nOZjuQqKJZKppYXHrl1MuIFWt/1VPMg0/XswQ3NfrvgCq2GVXcqO9gttaCn1RSHR7obznT7Z43h+OqhK/sgSdSvhY/N1+OrmdxoZh0XBgvDp6thiq/9R+wGS/QXbeRHz7fyGPGGu7RN3FfbiM51evi4ehD7kZyT29GKRgilYXiML3CQ0/hu3Cd00HuP76LX57IwCVs8Wt0vJBaIduYrbVSdusXSMtuvzjbn3OUF97AEYaKF9Dbtosy/SQY8B21ji/rG5ijt9CXU8N32pfFLYjQoF3mUjVrMYx2QXqhWby+FAurlq0m2TM+Xo3fv7FMCPY+fubf7xR01kNnPXcuuIurH7jm/2fvzOPbKu90/z1HjndbtuVNWWwrIYutOE5iktjBToIJJUAoZKFMC9PCtLRDGTozve0tMJ07nXvbQjszbaedlha6QAtlgCxQSgmFhASb2HF2784m2VnkTbbleI913vvHkWTJS2I7duJ03u/nk4+xdHT288r8nvf3PIP3fNthfVxKzpzcDhZ7kS9gvGBREkW52dhbVrGg9ximE9vgKD7rxfGS6DzIBbWHlwY2EKV0+6znQoKUwXM+wvV0vPdD+u1VFGvJqGgkNL5Bk4hFVQTLU2KZ61nO+x3sf890ayHcHnSY9weyOeAZ81cZarhg/hwWj+0hDeXsCVvPF17uRRMHyFEv8cOZoSQMnGep0s2b7jxyDZWsUY4Tr7roj0pj/pqv8cfjDj5Z/wzzDBfQhMrF+CWkLLtl+P01GpZ8THE9HHwbcMMsQzOxSicG3CjAAS0DCBSF3RcqCMm4h5eq5vF3yg4cigk0XShc0HsM8AtjjzZjCI/lExFd7DpfTYknLyNS6UGNmUN4kGFQDBwNIQhfcBsny0sI77nAJlGIQdF4fyCbXdoq7uQAqjLYCSYYzB6bEzs4Lq9Sqsk1VPKhexkxaidGdyfrDUeIFy46COd3dgv/OCeKKD9BdUxZbrZCjJZl/K64LuBvqz+7V/K4ZdmVj08ikUgk1wUpgkgkEolEIrmhMRvD+Madi3j23RqEpzg35kLoDcrxs22U2ltZmRZH1pxYnt2SGZgJ4rFrMi1Ood/Vw0t/wPc/6W6h8pLYwJest8JVnqO+sjdZodbQqMUO8wn/SlwJwfMMRA6xjLoiQmBML+DFao0n/IotLiWSxMQkvVjlP6N6lILP4bq2YcKQEFcIch6F8Yas91zyK84IhRhDJ8VuK/fcu5W/qfwN0XX1vD+QTYcSxZ1zgzHadume6jmP6Revpx3MS+ir+AO9DWfo72qj0hVCMM1cOAdx0dsp2LBlXMfgxXr296h+NiXeIpFLRJLScQjs0ZcvonkEmtbufra51wGw1bCPeNpYbIB71t/OMTWdtSG13NTlwHWpg9WGSvZ7ZtuvUqpZbagaLJx5ttV95D0+q7T7grNT1UaqB1J1n/fLHM+YClYj4dluStt7vK9FsthgYy4XCFEv8cbAOl/h3StMfPJiP0YmMePHO+M8LY/9uT18Y1sZH51s8W1v7QLT5a2wLPk4XD28+fx/sQIlQEAx42SxaqNfBPEH7Raqz6Xx3eAy5h59xfdZd1AbD6v/J0CceljdxcCc/ztqnspI59rhepjn367iDkMp61V9jPl540oeTX8YM4CiEGPo5YSIoVnEoKGwWLWhCIi2LBtWKDwbOp8MVQ+l9gY2r1KqUYDPLInClHkbJmsKMIZxZeg44fndEqXRb1Soc4WwwHCOPcpXCVYvUeG2sCY2jq/HXeLfjgUTo3bSISLJmx9PW48bw6V+wrud+rqvNNveH0XRO9bMWThis7GdbtFzhdoOe571rOmXC2Je4hFoXsG8DMzzPOfPew+Zl0zu9jz2ZYDehYJ3mx77qOTMiYlE9iJCzu3njYF1AOSqlT7ruZtqXofEqMD1ekRZl/0IZ8r3U+EetHP6fNCfAL2Tabstnf/q7MVkKyS4u99zCPpYb8ZJGfOIcXeyylDD2+rTxKoX2e3O5hP5ngyu1tP0tjt4/qANzZP9UaKl84Kjjq9HniReaceq2liknCVJbaVdRNG9+p+JW7iOkp0/ZWWQiQjRR6jST0eLg4vtzUQtKhjzORoUkBW+xB/oJ4gog0o3wTwsdrFGO0642ke1O4UotRcybqd72aOseu+nvjHDoZhwaZHD7927/x1KnmPm0Zd5cUEhdeHthHedxRRpJjzlbv2ev9LzY8kHRaGvPYjemj/RpYQRofXgUiIB+NbAwwGLKwx2nHptEr3fbd5Or1VCt6a8JAwYlS7CRS/pip366DuxDjlvVxznFQVTUyn5+evQPj6MQXGjCQN5+eswNZVKAUQikUimKVIEkUgkEolEckPz2sF6nv1Tja/gPXTG/l8a/+v1Y2z363DYsnwW//GppaxZkMBhexuKAstTYwP+B/5hdVdgsVPZBRRc9b5EmOfTWPUxSWpbgE+4VbWjRCURmbxg/Cu15HOTBb5e+SKXTmuYaUFFkDprFrND+6G+WJ/Z7S0yjDLjcswBp2NgvNkT/ttWFOErwvzz+f9msXoQV8GDiDkP6rOcw4P1WeLeoHNP0LPt/V/QXX8egSBRcSFEKv/p3grA6qJ3yJoTg8l627BtXxZ7Eca2KoIX38tLR/SZr95w729aW3TbrSudIEcZ3ZcG+Fb9Ml9wL25dCFmZcInU4HNkm+PAdgy6nRj768nM28iP94UBgkNk8KU8iz67u6NWt1VLyyPafoQNQX8O6CiKVbuGzzIegQkLE2l5hHf1EXvkZcw4CVEuIVCoFqkBi6kKxM/oHl/h+3LYCj0m9nk4XD3YWrr43tYlNHX0cu7o+yxMzuCmlXddeTUtXWhC0W2iPPkBm/mIxaqNcPqoEqlUaXrA8tNn4NfGWsI9ReyoipIRPfujj/8K5nxt1G0OPde2li5+497gE0DcQuXX7g3c3tLt6xoInxFEx81PcPbge6hoKBrcnBqj3wMRIQGFW/9Q6lJPYP1Ww16WzI5hQc7fjr0QPpJQ6u286GkjeM4yjCENBLXUEK1206mFsTB1JuFRMSwPU/j57TM427qQxqYG/r02iUVKHesM1aSmWLBkzhvfQOLZfsXHf+Tbf/ipL5vpm9YWFt+ycXp1gHjxy2fx70gDArNlJgu/MdzZ1Uf3kfeIqd1HVLB6ddsTgvCF61AOHSVHHSyI56rV3B1aq3fc+a/be48c2UGFe5FPKLaqNk6LmQRziRSliVItnRNhy8hteA33yYOsVPT1pmt15ARVkaC1E6JeIpE2etVg2rQoypb8E5/zPjvWTXQ4f8UWdR/C0zm3SqlmjXqM3rBkTnUprFHLCFX66RXBHNYWcnv/Kc59UMYWw8f0iSBOM5N5XCBRbaXnTClRG/7PuE7NAwNvc+/cN6GtGRJuJjQmmbDmGiIbqkl0t9EuIjmgWEm95a8wrtvC2f3vkOMvKnhEhmEdhLZCXbRa9hChxT9jYZ8TQmMg5W79fI9F0LIXcbFmD4eqT7NIDcMtVLqUMIxaZ8B4NxJem0T/714v8bTRo4TQJVSilG4SRDvJxuF5R95x2RIfMfJ3i2f/Cwp/wKUkQVdQLBEDLmY0v6JbrE3HZ1oikUgkUgSRSCQSiURy4+Jw9QR0QHh5akc5axYk/MV1gxw/2xYggABsP3Kez+amkjUnlo1Zw4+3p/Cn3DFCsbO38KewcfRi51gwWrIJm12K41w9KgIzTm4POqTP+M77Foy3SA8+65jH09o5Zf0UtQ0Xye3YRVyEk966SjrUGMKSbiLKu+woMy7HHHA6BsYbEOy/7RKPtYgCzIkNhVn3YMx5zNPd4Dk/3mPw+JI7u/qoq7OxUHWhIugixHck3hn/nz788qCIMFaEgGUPsTwtj/139PiJZgV6wdrTnXBZPFZKJUcGX/J1L8RU6YJOr0tfj6MMGsr97G38LXX81mkv4uz5C9R7BBBvt9Kv8nr1WbVDCuWTicl6G/ek/ZamekG7iMQh4vlfiQf5YTMUa3qx9J+sLUQtmsRitaLA0Zc5UvoRW49m+eylfrm2l7ujToMWMyY7lYU9eiiyNwckXavDoGj0iRmEK710EeYrAJaIdI6ZbmJ1+AWofJNZjgp+55nlDrpnv+qGbzg+gJIInxh3JSKCDcNFVnUX4cGrYUBAcARHuuL4/bF6clQNTajMnb+QuYvmgvO03iHhPa9DQqlzDZVoQmXJrBgWJo3Tus97L/tfM6+FXvo9kLQY9c2nCFLcqEIQqfYQ1H0OwjIgLAZneTE1dW284V7rC0jfNbCSmjOpvDCnmajk8Qlijthsvl1ZQo5ayQqlGoOi8Z1KKz/YmK13zExHvOdu77PgOqv/97onp7TA+9rBep7a0cvj6gWCFI2CDDOZt17F9iz5mOxFfNPawncq9YK4QVG4b9OnCE+oD8gk8pG0GDIuEXvhKE8oO0hRGgGF5wb0PKoM1U6uoVIXaM1LiDx7lPuD9pHuriNG7UQRGulqHTPEAGFqPx1aBF1KKNHHfoUrcT7GEAPkPo6hqw9O/FAXkEU1KUoTQghE/AKMLcd8AshpMZNOQgmreJWZAwaagBZiqXSnYVXtZCs1JDgr4E9fh7v+7crnxCvCVr9NaMMxmJUNj+zULbrO7CFEBU0bICFE8Oj8GYQvTIQ/fZ25F9sp0QZFhQMiHauow3J2Z2AHoaebhrAY+oOj0C62EtTdTtDJ9yEyaWxdREJwsa2ZRWr9sL9dqt0pPttF3+IMdnn6d7m4PWLlKqWarYa9tBBLkxaHAxMzhZMVqcZhYqx+Dw7a/j2zOZMHRupobSiHzkZmGM3EpORAfQl0NuqvSxFEIpFIpiVSBJFIJBKJRHLDcrlQ8JHyGm50Su2tI75+yN5G1pzYYTZZlDzHLMcH/G4gsNipDMCT4yx2DsMz03rRhsc4cfwCsw/9wHMtFFJSLJgihs+uHBNVb+ndHsse4qaVd3ITgH0mzp3fwNB+hnOamTMnmsmur8Fy+5dGLTb4Mgy2l6OhB5w+M1LA6Rjwhqz7C1CXC1kfbdvGFXePvhHvdbAX4T78Ms3EEi26CVEu0ajF4sTIVsNecOs1psjQCfwZ71dUNxvDAkUz4xiLNh4/eeUPe1ilViGEbsV0QKRzQXXS3qtgDtN0kSo5Uy94BdjbeGbo+83udnb2sr2qC6NiGswNoZr03MehLWlqrYJKnmOuoZnkxVk4gxJZHJeAsa2aX82qpWVGJ7Fh6uQKIABpebjsR+jf8wqfVRy8KDawgmrKi6rIXr8U41jsYgDTxRM8YznOU2eyaNci2RBUSp2WxGlmcZM4R5rS4AuqB2hLWAlLZkLxTwkxzaPDmAt+97Qr6/OEpBT7xLixEHX8V6N3lGz8Gi77ETqK32CLGss291oOiHQOnqjmV+FHCO9zQtzcwZX5WYSl5/bQses7KELDHBsBC9de/X3gJ/J1F/4Xve0X6NCMNCkxxGkdDDhbCHc1Ifou8VFdDxEINhs+IlVt9B0fQPmMS6we5754w7O9AohbqBRr6X+R31MTxeHq8eTb6OdoQKjsqWpgZuXu8Xe9ebEVQkM5i2/ZyA82Zgfa5tnrIdQ4/L6y5GO05LO67knqTlYwU2nhPwe2Dhb/3enkKNV8/uzHsKiA8JWfY925b3Bzew0HtHSCxQBx6kX6lSBq3XM4zUwiRQ+fDdqFofRDWPO4L+R9u3sdXwnaxiylGYAfD2zlv1r/RLqhjjYRhU0kE0YfK9Qa+i+FEjXgRFGSqHSnEaN2ss29lm2s5dviBcIr38IQN/fK3+mKonf3DPTBrGz63HBy+7PENR8kubMFTQiEIQIRkUB451k48HNorMAZPI8D2mBXRY5azeaMCMKjQgPFTE83TdvHv+F4ewgzMDJLaSGp9xhhBV8f21jaWIGp8yQvDBFqGUAf5waSKSEj4CP+l9Fr3fdOmYNvv1NNhmoHFCo1/bzdlGhk7QKL3v3oKPN1BA2dWKMJeHKkSTUlz+ndm8segr6LendqSg6ERA3r6pRIJBLJ9EGKIBKJRCKRSG5YLhcK/pcYjL4yLW7E129Oix3ZJmuORu/8u3jx9LyA5V8UG/iH+fMJGUexcxh+M60/SRF9zbH0XtIInRFLyOLciRcs4+ZC6xlwlOGs3E1taBYpJw4w0NZECMG4RCQChcN17UR39V02yHvCeRFDGDFk/cjlQ9avtO1R7TYcZQSperHmvBqPWej5GHXuUCKVXu40HKCdSHqsDxE+0cLgJCDQPfC91iQAe2sacAuVVKWJ5fW1zL39i4MFL1uhbqnjtTLzK4Q5OvowKp3DbFZaKz/EvPrKtlATxlvISr+H8JzHCPcKNOYlhNe8Q4oxCMIsUzKrt3LOZ/jzwEk2BJWSrtXhwESbFklHazPG5XeMbZvmJVgaynmeffy5Huq0JFQE6Uod1SKFSrfFd31KSWd5qqcLKvdx/Z5+c0/A6nYcPc/X7nh47M+JV2S9TEeJs+sSCaorQFfRBPS1OQjX2gLFHj+Rrrr4T5SXn2dAqAQpGpli0fhycLyz0dG7MPRn7Wa9C+nP/0yQs44yt4UTpGAWTlChVYtk1oUKumIX0U4k85VzGBQtIJwdPGKSZebY9wW9Y2aVnwDizToJD149rvVcUzwZIN3hZlpmzCT+0gWfndpUPBO2li5WMCTHQalmc+1eXVSfyDYVRe9MY4iV2xVypfbs2k75yUaSUTgv4oe9XyLSqY9WsHq+B+MX5hJb9jqmIBsXOzroJ4gBglAQxOMinD5C6UchxLdf3bV7WaS0B6z3H4JeI7r9HE0immLNiobKYtXGJYIIdXcSJNwkx8US4wwcL88riSSGB2N0HB/e2TKUtDy96A/sCVtP4Ud7+eLZ54hVOrkoZtBNCMcGbqK2OYUtwS7mtB+nN9LM+bPn+Zy6y2cRlqNWMmfWMhBdeq6NF3sR3fVHON4eghACRRF0ijDo7SG5ehehY7HDEhohMxeTPyeXl/aCd/jwCiEW1UEOVQGdlkO7PM3GMO5eYua7f6qmWqTqmS1qJyVuKzet+wz9hmpoKtWF+qTFwDiyxISmd5QlZ+r3kWrQv9uSMwffl0gkEsm0Q4ogEolEIpFIbli8M+5HCgX/S5xdmzUnli3LZw0TO4BRbLI+Q1e/G8GBgPeEgKo5D5I773ISwhXwFiw9hbIQUxohcRbdN97r+z0Rcr8M5iXY3v8Fhw//EDcqRtVGk5ZME4MB7KVaOqvGUBybjCDrQ/bW4YUR4LC9bUQLslG37bEhea05ZbjdRkK9fmGSM2l3dhPj51/vnV3frYWiKIJ3B1ax6gqB4VPJ4bo2YNAGa6thL6Cwzb0WgBRDI0fq2zB6Raq0vEEBRDUMsyhK7Tg8zGZF1eDzYwlqnyj2In32cvo9gzN2hYCwGHCU0SuCcHX2Ex7WS5S9aPD9SQq8tcRH8FuxgXStjlS1kdmimYPqIo4a8pkz1uP1LBdd+APyYxyccQmSRSuKIhBJS/jt+VtY4a5GVQTPbsrUBYBW/RjGXOy7HEIjJPNeFi29G8OOCtxCYFAUFt73dULc74DQMKYt5XfFdWQodrYa9npsfxppEjHELrvbV3z0x1m5m/KiP7Lfr8jLeHNw/HM4KkooEfrs/e/OLWMucDLUyomu+GHPmBaaQkTSPGZW7hkMgMbk66jxZi6NC1shUZUHhhX3cw2VesfMwKrpF6TsGddtLZ08Zcvy5Zg8YzmOZSqEEFsh1rNHWW2o9F13gENkEB13Co6+PLFt+ouw3t+vIIC4PvxPWovep11LY7+wAgSIvYoiAIXk6KWD1816H4bTHxDa3oBBGWCvO4tGEcfdQSWkiUYuKQb+5M5h08p8vUDuyUH6VNCbNGpGjoiFFCiHWWg4hyE4kqZFj3Ku7AwKbhQN8pIHCOo9DUEhzA1txyVSfffRVsM+WoQRrJ+AkKCxTUDI/TLOyt2Uvfoyi1QnUUo3XSKULkL59cBdxKh6wPt5RyOmBYvp1MKodncGiLbtWiRlJ+zkf2Jz4Hl0lNHX5sCmJbNQPct5EU+xSOKiCOfrroNQ/NyVx9Lcx6H4ZxQ0fMA/Z67mX8sHJ4BUi1TuMjVR11xHCRmX7fL0WmO9ubPaJ4AcEOmU/P6ox4JwAQWc8ImxY84Sy33cdx85E1dSG5rFwt7jmC5zX0kkEonk+iNFEIlEIpFIJDc03hn3o4WC/6XxH59aymdzUzlkb+PmtFiy5sTyQuHpEZc9ZG/jriXmceVZjAtPoQwYtDfyvnYVhTJHbDZP2bL4imEb0XQhBOzTlhKjdvpmUQP0zV49tVZJHpRRrInGnZOtKFys2cPOj8LRPEU+TcCbO1/nrjXdRC3Sw+oTgroDRIEXtcFiebNm5BAZ17XTaXihSO/HWqnqM923udcBDIpUMCiAaO7AmcpCELWogE2mFA76FdLv2/QporzC0NQchG5l4hdOvrC3H5OjDMf5Ol5wzOXX7jv1wu/ZX2CJj9Tv8UnCbAzjR6u6OHPIxGzRjEHRMAsn/1gSyYp1PWMfw9LyOFL6EQmuXxElwgFBm4hEbSjjR6uWkpD5iF8Oyyu6OGnJH3ux73LkPg7AAzBC15MuLJmA5E9Y2LZrJ38ftI1ZSgsA/1K3jh9+eoSuE3sR3bV7AwrhXrFtvB0Bjthsvl1RQo6hkhVCvzdftkWzev5aKpx1AaHJ3hnmc4M6Sew9z7rELvY2JXOBONq1SHINlSgabNr0qfF/vzRWMMvxAS4xf1iewkzHBxAfMf1EEEcZ3ZcGfAIIQImWztM2+JWxlvChgeJXi6JgbKsif8l8fnw0A9DHgRfW9mD0dhpMZCzw5l9Y8od3o3nf9z/39iL6zh5DoCBQuD9oLxVuC8Vuq0/srdTS2LggFFPnSbCF+OyleqNScbR0E6NcZJFyli7C6dTCiVS7QSik5OkB497tGNuq6UqcQ1WjAQR0KFFcipxFuDGO5X2HWLTERMuMVOIvBenWcZZ7AAiuL+aL5lNEOXYwR2kCBJWahUXnL2C8ZezWfbWhWbjF71mg1tMtwmgUMaiKfo7dQsWhmECDU4aN3LnMzHs1RwjX+gJE25/ardwUOyTXJjmTi6crWGkopEkzUi+SMCga1Voqzng3Ee3H9eyoK93z5iUU799LZNtrrFLW+Yk+ezGGxfD4g5u4PXTpFbs8H1iRwvqwpdQ23sSPP5gRYHX16L4wSh9aiclzb908SrftsCwxjwCyp28BX3i5F00cGBRV/AU3iUQikUwrpAgikUgkEonkhmdYvsFfOFlzdPHDy+VssgZDQgcLzN/dvHhyhCKPpYZ/voPv59FXfD7b48XW0uWrd3UQQTRdWFW7L1PAO4u6byDjmhQPs1Njh4esT2RGeFoedRdc5KjbEALfsaxSK6mPvh9rWp4+a3yIKJCjVONAL0gFKYIX1o6jSD4F+Ae/K4pgm3utTwBxC1Xv5FAgOvYUVL4J4abB2bHeWdgN5XoXgOf6PZA2UiF9hDDaycKzXf8Q3By1mn+dc5HyBpVKLQ3Q666H69pJMoYyqbKTvYhlWjlHtEgOqot8tmd/rb2LvWXVmK+vs3I3Z8qLCSacDiIAcBGJCRfOQ79jwXwTpraQQVHSE0rsfw29jGQpc1mKfwqKCjmPDe96KnlOn/GevITMASd7hnxUALZD72Geawp8hoUgfOE6Dh7sDVj+EBmELwwZVyH8kL2VEpHuE0DMtFAsrFSdFAgxKICAHpxcQwqE1EPdfkKWPEBY7BZWtRwkuqEEW7eRT8a0MTehnnHfl0mLCQG2hNdRU1lNsZZOridPIWTevSN2w1x3kjM5I1IpORL4crGWjm12BtbkcQbVXwnP98RyWyGlD4VQPJBOXEspmZcqwVIw8YKy1xbNks/Ffo32rm5iIkL1zKKhAgiAEBiyH2J7zVFy1EouamFsCCol1d0AKHQRSozaiRpuhp52vZjv+R50JOTx5Il8vmT4A4sNNu4QpfQqwTRpccyLD6PAXQx4RBDPZ2ZufJo7avfR3tVLTMRcohauhcIfQE8b4ZFJpMSFQ2sQzEjSrSLNS8A0j7iin3CHeg4XEfzZvQKramdHVRqmjUMEicuwsPc4fUot8cpFKrUUfu6+j3SljvuD9tIsYjisLcChmCgpb+C2u7awMdOBozJQtC0ZKdfGks+5/Xvp04y0EEupR0TbathLb2eMLj6P4Z4/brDyo5aVbDXsC+giA4V/qV/KD2evJneM46Rp8XoIa0ETgV2xbiE4EbqUXMtgV+xI3/XDEAJn4kqPAKK/NJKoIpFIJJLphRRBJBKJRCKRSG4whuZJZM2JZXlKDEfq233LLE+J8Qklk5WNMQxv8PXQApXfLP+JsLD3OPcb9nJeJGCiHbPaS7xo873vtUv64rmdUGzXLbSmEF/Q+RALq3GfR1shZmMoJZqVXEMlKz3F2QOalb82hgQU5byiQGvlh6R0dNM/5yFOhC5lQe8xTE2lV/Z9n0L8g99LtAxyhmQd5KjVbNr0KYzuerBX6ZZTQ0JzfeGxlnxfMd3sKaYH4C2me7oOJhNvEPNgl5TgX+qXIiDg+rzhXkv67Azd/38y8AhB0XEJxKrnfF0PuiXTQdLPvgLzvjKmVXXYjmAS7VSTSr1IIkVpJB4XigIm0Y77yO8hMlhf2E+s9L+GGlzWUmZUFHXkEGBv1kpiBigqc+pK+FvDac6LBOpFIilKE08aXmFW7UyY99Tg5zyz9k3W23hmc32AcPvC2h5M4VFjFz1thZhanKxSTvnuTVURbDXso1JLo0ZJ8VU6vaJquxYJfZ0cMd3N1vfmoYljwAxWKfHkGio5ZIsksfMkX35knM+dJR8s+SxOLuJ50x7aeg4SG6YStWjss/avOZZ83EFtwP5hbw3MyYU54xSAx4LnXDg+/iMnqn+PisaPNSubTCk8kHZ167S9/wsO1bX5npGb62uw3P6l4effko8J2LRpPm/ufJ1VaiU9Wgi3Bx3mhHs254mnXYvko7KTpH76IUwdtfrnlj1IaGw2ysc/pYx5LNLOEqp24tIi2Hjpu3w4rxpL0x792ch5bPC7E4gKVokKjdI7VBrKITIJblrPxY422p0XiYmYrYsjR1+BhnKOXJpDiMtAtNJNFN2sMxyjWRip1FKHCxKjYS+i78P/IFF18auBu6gWqb5noFnEkKC0c1GEU63pr/cW/pR7Y7v5gYjkIIOi7d8YdpEWXzBs3XMjL/H37vsAfSx1CxVQMEUG68c+hvu+1N7q6wLz7yLzhtSP+Vg9jJQhpxCYH2dr6RrRJnAkoaf29NhEFYlEIpFMH6QIIpFIJBKJRHID4T9z3VuMX7MggWNn2wOWO37WhcM12DEwGdkYw7hcQXKixT17EaYT28hOjeUpWxYblBJiRSfJxjDud+0Dtz4r/NE1FsKb9+sh6teASRGSFAVTUyn58830nlJ9xdm8+fG6sDHkfIYfeR7D2WP0Zz+EyXqbJwPkNogIGey0mWIBaDS858NfpCkeSMfUUsrnL5V5rKw8s3172n0h9wt7j2PqadeFEe9s4CsV09PvmZJjsLV0BdjEeUN2AZ8A4u1smdTCryd7xNjTTmbeRn68LwwQ/E7cyScXmzG2VY1N5LIXkdR9gnpFUOm2+AKLtxr2Ei9cJKouYjqqoVOFO58dtr6rvqe918r/2vlfs+RMOPoKcd1nSFRd7BtYxovaBv7F8CKzglqI0CID16coevZDQzkP5Dzm2zdd+DsOjU36cmMRQhSFpc532GpwsM09aKXzt4Y3+VSQndcH1lFChk8AKXZbMSjQscjK1p1awH3hLcQqiuDF2kRuOdsW0Ik3ZtLyiKrbT1TICNk405CufveIr3f3T13osyM2mz9X/wYV/dkr0dI5uKOCNQsSJvz95ezq41BdG3rJG0DhUF0b0d7MohHQn43HubTnezS0m2g700642scKUcNBdRHFbiumjnn8dXKwbyKA2V7E1xY20nSqHk1VsLvNDKgG/sawi9D870N1ROCzMlKmxJH/AtNN/KEpnjMHD/qE5SViEQXmJXSf2EfcqQ9oExHUk8hMpYUMpY6fue/lEBks6D0GtuDLPyP2Ii7W7OH9hnBs2jpe1DaQo1axkHpSgpp4Y2AdAKqicUBL5y4OMOfkbwlKzmRLxhy+U5lKsZbO3xh28bdJVSRWvxg4bgtBctYnmN0XyfYj531jaUjiPEx5W8c8OWK0DlfQh4rxWEI6XD0csreOsKLAXy3xEWO2Dx3PshKJRCKZHkgRRCKRSCQSieQGYejMdU3A0zsq+M9PLw34H3HQZySOd6bk1eyXf2fKVVG5Ezobsc15kAOnQxEKRCp93BI2wD2RHdxitBOctQzTibf1GbPW+yblGMbCVQtJaXk4u/pI2vMjhCJ8s5KVM9txrvwHTH5F0dcO1lP7/knS1Wa2Vx9l0+b5PLBiCu2hJoC57TDmvuOwSLer2QjAfWCPH+xqyXmMPbu2U/7qywyI33NQ0cjM20jBrVsGV+RXTHf1XqJyzmewnv09RtuuwODySWZhzzFy1Gpf5gHoNbGVSjUpShMChQuKHord3Z8zeRu25ENDGYTFUHDrFopye/yEiLt0IcFx/MpFckcZ4TOCiMq8i9iyU6wS1b6C/WOGt7CEdRPcLyDMOOoqrvqe9hdCav+kz2b3v2aVb8JAP6XuRcSonXxP/QWpaiPvDORwd3QUEf6WeUO6hMw5j+lZJk2lemB9T/u4gnjCZwRxc2os222A0D+6KDmKGZcEi1vtPKHswKBoFLutHCKDF9b00HvJjSaGb+OASPdNIT9kn6AIYi8aPRtnumErZGFv/4hF3jEV2ie4zZ7KAz4BxKBovkD61soPMZvH0QnkxV5ES/kHbHOv81n21YtESrV0ssp3XzZjxtx2GDqPE+RsoF4JI0L00qWEYhZODoh0/vbES0B8QEj2zYvmUu8q44PmbL418PDIQoHQRs2U2DU/kcSyPxHVWUKx+16feDer6Df0zbpIX5+bNi2CGLWLGcJNlwgDBdYZjvHJTDOmJuXK50gIWgbC+JM7x2cJJ4RCmNJHvwjitqSL/KzRSrFb7/K7PbmLIBEMAz0szt/IDzZme8arAv24HMcD72XP9v8jDVZQRUOZfj1tTS721DRSsGHLyPs1hKw5sXx9YTOJp/cGdJFtNexFcQMUXGENOv4TR0Y4FQF/J43HPtRsDGPTsllsP3Le99p9y2b+RWfSSSQSyY2OFEEkEolEIpFIbhCGzlwHXezA0xVyPWYkjtSZcnXFeoXeATfPf2RDkO4r6iY0vsUt0Y2YgwxwwjND2T+L5AbB4er1hFIPzkoWQtDgGpyV7HD18OT2cgR3skroM9V37nidNQse1wtztsLpcexCDGZ9+ONnh+Zw9fCFfaE8rupFzQGh8ui+MIpyh+Sa5DzGkfo2+ve8ghCvUq1oBC++l+VTJIBgK8TUeZJvWlv4dkU1JSKdHLWKRdSzznAMEDw3cB8Aqw1VZJx9BdRlk1j4VfROHnsRxGYjvBV2e5H+enLmlVfhsdRZnpZHqnU3m2v30jdbpW8gg5uOv01IfySk3QKaNpgJMpn3jDd0OuexQQFENQzOchcC4iyINDcdLe2sUGowKBp1WhLf1h5hc8opGGo+47nefeVv0X7yEOEJqUTFJOgCyEj32mgIAcsexAK8UONnQRXzSXCUsTGmgWbjTIKCglhlfcTXbeKMWYmq9I5YMPVy83hyU7x4s3CGZuPA9X+OR6KhHFNDOb9cu55H94UF2pKdeFu/9yZbBPELkP+Ne4OvS8cq6kjpSICZYyt6ByAEzoRVwKkAyz4AZ8LK4V0J3nva89/dSgjRA22gRaOqGsHiEqlqIz8L+gEregygbPJtxyvUpdz7z9wRm83CkYQC73jm6QAZminx6qlgPhvUT6zaCX6NOAmKC63dAWHJvKXls1XZi6JAhZZGtTuVL4d/QGL7+7Dy/135frLkE93dz+qSl8GtC3wHRDrKADw/dx/ujioWKVFoisJWw176QkyweONgxwsMjt3+z/oQnJW7aSx7n2KP3d8qpRqK3iFrTgwm621Xvnb2Ih6J+Jh3UALywLYa9rHFsJfWygzMq++67CqGWx4GMtLfSWPtkHO4eth59HzAa28evcDX7lgohRCJRCKZpkgRRCKRSCQSieQGISLYMOLrc+KmMPz8MozUmfLU9vKrsi7Beh8dzl+xRd2HELoFzSLq0QRobje4+8F1FtY9qS8/UrjtdMVeRGrHYb6trWOFMjgr+ZBIZ0PHIbBHQ1oeh+ytvtKwVwTKNVTSses7mBMjxlcMvs7YWrpYQWBmyM1UDQv/drh62Ho0i98Fve1b9v6jSym6Y4pC4BUFetqZM2smudVHEW5YRD0PB+1CAL91b+CASMegKHxjSb/elRI6Y/LuNfMSaCjnzPvP8/SZJboIo1Tz3bllzI2P8OUFjBWT9TZ9VvvRV6CxHPo74ab1cPe/60XKo69MvhDiDZ2ueou+/kv0uCHMoBHyp69DWJx+rsxZRPZ2cL/5BPWNArdQaSCeN5YdwygUMA8/n68Z7kape5cUtQL3iSqMi9ay+JZx5mf4XacACyqPZVdoex1z1FaImo25+XWfyGJKywvIIxnKluWzxt8FMlQAgcGf01UI8dyfBT0fUPrQ1sE8ohNvD74/mdiLoKedkMx7AwLkraKOv02qIipm68TOkSWfhd27fZZn3kJ6rqGSBckjiJqK4ntOjlyaQ2FNGPOZxc2GE7jc4VSRxhzRyK0zqgmLuXVQ1LDk4+zqw6H1YYrNDuywGkkoGCVTopoUXhrYgFW1sdWwzxMG3kSzMNIwczEJ0SFYW05TLVLRhEqq2ghAVV880UHRhI7xtJist5GZ1w5F74AbDAp8bWk/QSRzpL6d29WD3K4e5LxI4Hf2GB7PXRDQqRjASK/bi+iu3evLO4LB77LNtXsv24Hjw1HGqcZOnwDiv46thn0YXVXA5UWQkSaOeLlSl8eVvndGm5RyrTpwJRKJRDJ+pAgikUgkEolEcoNwOY/2KQs/vwwjFQE04DdFdp6+O33Ez1yRtDwMXX1w4odsNexDQ2GxaqNfM0BUEoTG6Ms1lA/ODr9REIKoRQV8kUbKi6oYECpBiuDRfAtRi5J8RTJliOXPAZHOSlGNIrTplSXgLYJD4D75FX0TnQdHLEAmOEth3p2+j9hauvissitALPlr5d1hYsmk4dnfjiPv0aZFkmuoZIlyGgHUiWRuL7iNT6TleDoEFEjxyzCZpO07u/o4cvhHbDHsZYWoJkVp5EidgnH150YvOPrTUK7/c5T5Zmn3fvgfiE4XQQaV7vA5nN3/DuboEEzLHhzMkZms+yctD6reoqvsbd7ozORbAw/zraAXud/5NhFL7tHftxVCYwUz++uIs8yn2biYLLWTyLYiiLQOm0HucPWwc+fr5KgmZolmDIpGVXUVpo3/gnki+zjUgqrkOX3cSL9H/9lxDuyF+u9CQPHPeMC8hDVP3uobS5s6ejl39H2yZtQze1H0+PfBcVzvEkjLC7QO9Np/jcX67Frj3Z+jr2A6sY3cuMPQatNfm4ouNL+ussAA+QRdAJnos1f8M0wN5SzJ28iP9+kSwUHS+VKeBdOJbdBRG5irlJYHjjK6zx7lj8cvsEKpI1lp47zbRLDqZpGox6BotCTmMKfjArz6ach9nNeaU3hqR5/eEfn2Hr0jMqF+9G45hk9qyFGrEELhRbGBVaLaEwbeDMCf3SsoyL4Xd3MlcMqXc/O9IN1erk5L4vSiL2EdY94GQMGGLWTNidFFiYEmwrvOciahgMOaRq5aCYCGQrTSSUNH/6j5KSMiBEfVTA6IiICXD5FB+MKQMeWCOKMW8J0L3T4BxItXCMmITmf2FdYxUhg66H2YO76cOzFbO791y0wQiUQiubGQIohEIpFIJBLJDcKV/qd7SsLPr7A/IxUYfll0hkfy0ia2L56i+jb3Ov4+aBsAYaKXcBVQjRCTAhcb9ADlZQ9Nv+Lh5bDkg72IgpATZH36wcHZ1U2lQJJP0JkTG3jeVnm6RroHBBd7eomaLlkCI81m95/1DoSc2+8TQGCwgLXl3H5IjPKtI+XES2wIKmXXwEpe1DbwsLqLDUGlzD7xIsz7X1O2/+FdfcQeeZmbPVZNpVo6b2preP5SGVHnTuiF86novLEVUttwkTfcaz3FzhYA/nNgKzMbLrI6fAwdTp7Z+r0179Fx8iAniz5k4EwLc+ijRwmh9IMiXMoxStxWNm3+FA8se3DMocRjouQ5+s6X80ZnJu1E8oRhB+1Esq0zk786X05IyXPQekYv8puzCE1azJyOc3oxPSwWGishbm7AcTor95CjVtKuRXJQXYRZOElVG+kt/Cls/Nr49m9oB4Z/aHvOY7Dv+xDtKaP2tENjhU9YMi8D8zz9mpvbDpPl/lC3J1ImkA1jztLzH3Zt5wv7Qn3Wgb9c20tBSPv0FXI993zfB9+ht+EMoTNUQtb/09SMPf7nYGiA/CRY4hUsSuLj3OzBSQJth+HoKAvnfpkz4h2iy7aRpLQzS23hnYEcFohzPpH2jbOzeGqmg+DuVrpLX2LnsYVonjFOE/Dmzte5Z2kt4XNGt9AbOqlBCIVcQyXrFyTyQfXg69FKN1bVRlBLFX1nj1GpWXyCsgMTaJCqNpLa9D6YN4/rvJist2FqOczFnhk09g1gbK8iWz2pb5duFqs2erUQko0hvs8ECHlthweFHj8ccTfz9yV7hm3vf9+5EJN13pj2rVjL4IDoG/G9Q2QQt/jWK67DbAzj0XwLzxfaAl4XwNnWnqsSQcaTHyKRSCSS6YEUQSQSiUQikUhuEKZbEOdoBQZtSNjouFAUWso/IF3pBCCaLiKVPlyE09PTQ7Ci6IHoMJipMB0EgbFQ/DO9yLrsQUxpeeQCcBt4bYwcZZD7ZV9xzJtREaN2Uuy28qOKdHLUar7p/COLG8r12dHXu4DqL4TU7Q8UDYp/SnRcAgcJLHodIoPouFODM+BLniPKtssngAD6zwH4R9suKAmfsnB0k/U27kn7LU31g1ZNj66xgFLDWedFYiJCiZqK+0tRiG8uJV25GPByulJHfHMrzLu8zQsAaXnsqWmk9fBvmMcxUtSPaBHRHGYBnVoY6YZ6dg2spESkc2B7OWueKpjcsUJonDev51un03nCsMNXIP4X98OsMVdjcZ6C/i5YeCeExdHdbMcZlEiCcoHQnjZIsgbO8PfYxblEpO+ePyDSeVjdxRdPvgMlEWO/D0ayoPJuq6ddF0S8HSLRs/VOjaTFetbF0VfoLn2JMxdcmI2hescATLwDwtP1U/7qy6zAygHSWUE15UVVZH36wbF1/Vwn9tQ0ElbXhjfFqKemkYK0Kd6ovYiLPb209biJDTNcneib+2XfvWDGI2x5743LXE+TtQDtTzu4SDjt7khuNRxDABdEPDOVFj4XtIuWoCXMTE6j09nqs2/0ihOb1X109sYTfhnbsKGdIAdEOhmana2uV5kf1Ml5EY+GymLFRoLiovvCCaKijMSqLh5mV8Az8va8t4h0lELygvF9J9iLqDjXyp+rm1mu1DKLFuaHh/Nx1xwU3CTg4hPJnZg+/jbY3+UXUY/x7Ls1CIH+XWRtYfG8VH1dfts97LlnhjI7ZuzjjxhFsFUVxiU2PJJn4ZdFtmFdq0+8epSu/oGryjC7Hh24EolEIpk46vXeAYlEIpFIJBLJ2HC4ethxJDCIc8fR8zhcPddpj/QCgxro3nR1lhBpeVyaYeT+oL30iSCaRAxuFIJw0xeaqBcs4yyQ/1V9eUfZ1R3ANMTb8bOIejYEldKuRfo6KUq0dHZUddFX/pY+c306kJY3aDfkb9dlzsIouvjl2l4MHosvb7iyUXTpM+QBhAYZ9/BbsSFgtb8Td0LGPfr7U0XJc8w1NLNkcRYpy9bz8PqlpJ19i/eKSnj98AV+XXiKPbu2T/520/JISk7m/qC99DODYs1KPzO4P2gvScnJYyr6ekPnKzQL8WoHl4RKvNqBhopLiWTXwEpi1E5WKdUI4LC9bXKPIfdxLmZ93tep5LUxW6VU05H1eci4T+/WuuvfONIsKKs4Rv3RDzh25gInSIH0TwYWbIUgKiaB7EQCuode1Dbwy5bFdNcd0QvYY2EkGyJLvi6ihMXoApwlH9b+b/1nT7tu75aWx56w9fypzEHHu/+PE//9T9haOq/aAqo2NIv9biu5hkqeMOwg11DJfncGJ0KXTnidU42zcjetRb/hnEigWLNyTiTQWvQizsrdU7dRexEVH/+RRz8KZ82BlTz6UTgVH/9x7Nd9JNLy9GtsK9S7f4aKYyNgbjvMJ9IT0ISBBEM7sVzE+zWnAFF0Y+yoBdNNGLIfQkGw1bCPJww72GrYhwIYsi/fqTiSvaVAIbbnLKlKAxoqDky8MbAOBMS3HibSvJD8JfPZEHRQ75YS6fw4p4vMmyz6s9bTPvZzZS/iYs0evl0RzwF3OkbRhVHtorELFi5cxJLMpWT+1bf0wn7HBVzl7+LY9SOE0LsTc9RKdlR1cbG9eTBM3kNc0wE9CH3o8QkGRagrkN53fMR1vPDZbN1qbAzrgMGOjaGFLwE8ub38qv9+MhvDyJ1nkgKIRCKR3ABIEUQikUgkEonkBmGk2ZVCwJG6SS5ujgNvgcG/yH1VlhD2Iua6T9OkGWkhlmZiqNFSaNciiNWcuoe/5ikeLXtQn7l9o5D7ZX2fbYWDhSr/Wckeb3rvOT1BakAhG/TiU7TSyQXz+snNqLgaPDO369t7udjTO3hsnuJjQcgJSh8K4dVHcyh9KISCkBOBRcjcxzGu+8qI95Fx3Vcg9/Gp2W8/e6TwT/2COWYzIbbd1NfbEEJQqqWz322lvOgdXHt/POai25iwFzGjqZxmEUOzMALQLIw0ixhmNJWPqZDprNjNCqpRELhEOA2YEICKwIyTGlIodltRFG/WzOTtvhfD2f2+zJefuDdT7Cn0B50tHtzPyt18VHaSOi2Jg2IRdVoSZafP47IfCVyZJR9n5Hx+15A6LAfg1+4N2OZsuno7L3uRLpwmL8ERm83+0y04YrN9RXJn5W6+sC+UOqF3mwngzboQfZmrICLYwAGR7hOK3ELlgEgnPHia/u+4vQj34ZcRKGxzr+Un7s1sc69FAO7DL1+dKDEaxT+ju/Qlvl0ZT4k2KPp+pzKe7tKX9E66iTKaUDsSnjF58S0bmX/LJ1FnhKMpCsFiAIOioQmVDsIJ8qj/JuttxOU9wmylhVy1ktlKC3F5D2Oy3nbZXfKK3f6oisCQMI8YYywG9O60WlJJTU1jhtEMQrB8RT7pBZ/h8zfHcPzWY3zSeHpQ4LPkj+0Z8RzjfzelUiLSURTBH7RbOOReQKKhHWdtMe+Wn6fm6H6ITKJz3p3s7MxgQ1Ap3wv6BbkG3bIuWumkPvrmYedzoTmaXEPlMBEjN6haH0fHMBgZmirZatg7bB3JbUf0zsmG8isfp4c1CxL43OrUYa9PiTgskUgkkmmLtMOSSCQSiUQiuUEYzR5iMm3+J8KkWkI4ygifEUTVvC/Qe6oQg6IBChGmmbgjejlnSCM6cTkm72ze620HNV4uZx/lh35OH+ewvY2XX/s9uYZKVgp9xv0BzcoD+Y/D9Z556gm9rjhd5ytcjmbXZbIVkqseHvl4PUWxB1bkDb+P7EUjes5fNfYivRvAmw8BoCi4XQ0IMYNKzYKiCEq0DNK1Oqh6G0JnTN5+OMro7B3guYF7Wanq17VeJLHNvY6M3irCxxBgbo4J437DXuIVF9UiFQ0Vo9LFzWot3YRQraXyotgAQq85Lk+duP/9iHjsq0q0wMwXVYPPdxwCNQEcZdjrWmnXEkEFt1BRFYGJNjpsRzBaAq2O9rvTR8wBUBWIs9469nteUQKzarx4Osf2uJL4wrN7fPkcz2zO5AFLPo7z7ayghhSl0feR2TTSWvkh5tVjsCgbCVshBkfHiB0zQWcVGIiafuOYo4wgVRdAhub53KZW6edxCmy8OnsHhn2faUJ//aripsdqseVno/Zacwq1Ra+iibvYoB5gmeE00aIbTVUoclth0Z1YPF1qBYuS6DsXQ+8lTc9OWZR0xV0aKVMiL/9WQmOaCHWUcY/TTu+lczxmUglf+SX9Q56x0GjJx7jv+8NFnbFcE1shNJTjTFzJd97rBaBEy0AoChnUUe620KWEYRZODCeq+HjBRpTcx/jWgQO+IPbZopmD6iJKNCsPWIdnc/TPXk2J+yi5hkpwD9qE2Q42Ylp375j2M9qyHOXAR2w17PWtI1etxnKuFmYE6ZlIY+C1g/U8ub18RHsumBpxWCKRSCTTEymCSCQSiUQikdwg3JwWNyyIXAGy0ya5uDkBJi2UPTkTZ/RCCl85So6qYUYPRn63eSU/aE6Fk4LSj3v55doFFIxU5LwRSMvjYu0+2ru6L5s5YTaGsTErjA9r1+M+PlhAnbl0/fSw3misoK/8Lbbb5lOi6UXcEi2d7VW7mN/9FiEwKHh4BZ+RZmH7FazNaXmDxzYkZH1SEUK3j/Hfl6TFDCx9EOfu93UBxJ3BKqWaWLVLt+WazM6b5EwM0QtRao4GFMZVBQzZD0J48BVXYYoI4ZZkjdamdmxaMi4lEqPWxUK1nn4RxGLVzipRzUHSeWZzph5i3DqJgpIQRC0qYJMphYN+hdz7Nn2KqIR6EAJXTz8dh9/Aqnazzb0WgBRDIy3EkmhMH6bgKqNUJD+9MmV897yf2Ojs6qM2NIuFvccx9bpwLtjKF17u9WUEaAKe3lHBmidvxdy1n/sNexEo/OfAVgDuN+zDcm4n2KMnNtYoCnPPvcn9BgdvuNf5CsL6emth5ufGv84pR9AekzGsI+eASMcVA3GjlpSvgtwvY4jeTe6JlwMK57mGSt1a6gqdFaPisdgaJtTC8OspBIQacXb18dSOcjRxFw+ru0BVcGrR9CtBBGsDxKldxEUG611q9iI4+gohpjRC4izQatM7FUZa/xCGTSBoOwy2E7DsQULq9hPSagPX2eHrsheB5uZiv0Z7Vzfhlbuv2HniQ1Gg14VD9PpeWqVUs9WwDxC86L7TJ8yiQUPNGe5ecJAcVQ9iny2aMSgaZuHk1g2bRnwubS1dlIh0hJsAAf/fT1j54aZszGPYTZP1NuJuaaf14xfZathLDjXcm9pP+IyIMdvTOVw9lxVAAGbHXt13aUBQ/HT4XpZIJBLJqEgRRCKRSCQSieQGwWwM49ktmTy1vRwN3df0mS2Zf1n/423Jx7H/HXLUSp+VzyJNDwcXbjggMgB4dF8YpQ+txHS922AmwJ5d2ykvOsWAUAlSNDLFdgo2bBm+oK0QZ3c/548dZY46WCi/cOwDnFYnpvDg6zuDPGkxJ8+7MNbVskpU+4qWRqWTU3HrsApt0DbHK4Bo7sHXhuY2+ItaIwVbTyYjnTfPDOu43lnEFL3DE4YdBCmCzLy7Ma4b4fpc5fZN9iK+aW3hO5VWijV9lvM/WVswRYSM7ZgdZZhnpRI1eyHJZ8vZ3djHRcKoE8kIIegSofy/rFZMmaGYIuonX1DyrOuBNEboBNPDhs86OmghFhC+wuo29zoUYENE97Bp2NmpscOEXoC/K7hp/PvnCY4ve/Vl3OL3lCoaS/I2UmI3owlbwKJuIWit/BBr49tkp8bytC2LAyIdg6LwpTwL4T0fjLmwPRLhM4LITo1lu00/OFWB7NQYwmdM1/8dV0juriVHgRI/ISRHqSapuxZMy6Zkq/4dBN7CeYlm5YHZqye2wuKf0X32KN+uXDjEYquaX814Se+48tgQAvo9rSh0H3mPzyltpKiNpBvqadUiiVK7iaKbNiWSlNgQjNWvQfdZ6OvUP+stzHtEkbHeL74JBENFX82t519B4Lo8y+3pW8AX9oWygmpWH3mZzLz2kb9HhuLZn9SaPeSo4ZRo6WQodhKUNva69evqFWYdiol2LZKIU3/iGUs7v7PHcJBFzBRO8k0XmRn8Z+CxYZuwxEegoAtZ3uvoFiolWjr2lu4x/81SsGELzjkxBH30fUJnnCckyADLvjjmZ/CQvfWKcl13/8Qzp147WO8Ry/w6yq4iaF0ikUgkU8t0/atLIpFIJBKJRDICk2o9NUlM6kzIoRY7AkrIYJWoDrDWcAvBidCl5FpMk3MQ1whn5W7Ki/7Ifk/w8yqlGoreIWtOzPCZvIqC+/DLbFFbfLY03hm77sOVsObz1+cgvFjysXXMpfjQqwFFy2K3lZtu+gxW45nB4t3QAqH3NS9jtAmbCrz378KeY5giQynYsIX0/gocbV2YYyMwb9gy+bZcfrkDP9iY7XmeCzwzwcfY4ZScCeYlRDqO03XhJBcwYaaFve6lWFU7dpJYExmHyf4nCI/XA8GnSDQcrRPMHB3Ct7W1rPCzgjog0nnqrkVEpTgC98dWiFlR2Lx8FtuPnPe9vGX5rAl1sThcPXx+byh/Z9DFQzMt/GKfjVIROmzZHLWa2b1BkJyJZdkSfhCbPcSWLUm3gJrI+RMClj2IBXi+Zg9tPQeJDVOJWuRncTTdMC+Bs0fZYtjrEZ/1sWeLYS9gHrMV0biwFeJ0dFAi0lkxpHDeWvkhZvPEbMNGt9hyj2yxlZZHeFcf1iM/5CblPL3aDFLVJpo0I//X/Tk2qAf49KUKMKaC6xyExsCyB3HEZmM73YIlPhvzMvRxbjy2Yf7ji60QZ+JKvYMp/jimE9t8Vm7e97zdTAfQbcpG/R4Z5RijgG86/8j71TtRcLPXvQyrasOKPeD75rGgt4hvHWDG3Dz+cU4U9dF3Eme9FXP1i3qmEgxaCnowG8N48s5F7Nm1M6DTLVetJi2+YGznw4MpIoS+GSq9l/QssJBxfHa0zjIvBkUhLX5iRmsOV49PAAG/jrIFCdPi7zKJRCKRDEeKIBKJRCKRSCQ3GJNmPTUJ+M+EVIAn71zEl9bOm/gKR7DY8c4oxY0e8uwpOkzbUOHRsBfRXbvXJ4DA4HFtrt07YgdAZGgQypC5rIrn9enAzWlxfGXIbN9Skc6P0mJhvHmzV7LNmgL8798ctZpvWlvoL/2IwjK7p1Onmfze77I8QZncLgq/LhgzDD7PxrzB96+EZ3+OlH5Ef0MzZlRUBFbVTqWWxmKDnZkNp8GUBqYY6Gm/5gb4psXryZ9/jkunBwuhX5nXyJfW3A0MGScayumteY/oUynABt/LF459QDe1hEfoAfJjug62QmxnnKxUTvnuS1URbDXsI12ro4YUSjS9q2yVUs3jaRcwzt08eM+5ehD+z11a3sTvR7/9jarbT1TItbu/J0xaHmcuuKDs52w17GWlqPbkpCjYZm/COhX7riikdhzmEUNzQOH8bwy7SOlIgJnjK54DPout1UMstlYbqnTbuVEEAz3svB1R9P9IN9Rzxp3Mz933AdChRDGwYAO01UL0LLDex2vNKTz58z0I9PH52S2ZPLDswfEJXJZ8Pfy9oZw9Yes9IscBVAV+uXY9BQ0fQOsZsN7HLyrjArqZvN8j69t7GfO0gLQ8Ftftp87ZRU1jN9UiFSt2hvZhzY0PZ0ZwP/R1ErXs3sFr7xU+HMd1UXdIztOXQirIXtjIv5+wUuLX6WaufjEgL2pUPJlTZ8qLOVIfRJ02k1S1ieXvP8/czPIxrWO0zjLQBZDvbl484b+lbC1dPgHEi1uIcXW6SCQSieTaMj3+70kikUgkEolEcsMxdCakAJ55twYU+NKaCQohI1jsOLv6+LvfH/V1hni5GhuL60LlTkIu9XNA5AS8fECkE3JpF1TuDCwkCUH4ys8RF93I6qJ3WCmqCVIEcXkPE74oaVrMIDcbw/jVul7KizSfvdev1vXqRaBW4ev2uFizh7aj7+uhxN4OEO/+e4LRgeHhxd7lpsD2a+j9W6Kls71yF3cEfUibtpIXtQ08rO6iv+IPuAo+g3EyC7+XO55xbMdZuZvCspO0aSuJUTtp1yKxqjbWGY6xJKaPEFM2KKougFyjzpqh+zdweh/Ffp1PuXWFOCtTR5yxfumik62GMwgBL2obWKVUs1ndh7uhDWITxy7iKApz6nay1eBkm18Ox98a3uRTQXZeH1ind5gp1aw2VJJxy2A+zJRZ3Iw1nHuaYLIWsP2P1XzFsI1ZSgsAP3FvJW+EIOxJIS2PqIZyHkvax88bM/i1ewN/Y9jF3yZVERWzdcLnymS9jcy8dvAbQzPz7r5ix0TBhi30On6HszmU3k48n9Ww3Hwnkfd9CkqeA6HhiM32CSCgf0U9ub2cNU8VjL8Y3nqG3vYLPH/QhuYRyjUBL3xkY/X8C4QmzscRm80LhXuGffQg6cQtHodQZC/iTJOLmsZuDIpGhmL35fbkGipZJapZMz+OlHX/PNiFMvQa5Dw22CXnT2MFVL/Nzen38MNNDw92uvl3j1xpTG+soP/Qb6lvDOKNgXv1Z1irJqH+LWb3nST45s9ecR0+C1G/5/kbGxaxZHbMVXfSei2/hma0TbSzRCKRSCRTjxRBJBKJRCKRSCQTYqSZkADfe7eGT2bNnJTZkALBnNgwVIWAbV2NjcV1w3QTIXU7eFjVC7xeHlZ3EeKsgdTNI36sYMMWVig1tHf16kHqd2wZzNW43tiLKAg5QdanH+JE6FIW9B7D1FSq75+nQPXawXrOF9WhoqGhMsuUElhUVhQ4+gpnWrp42rbEF1783bPPMzc+ItA2axJxVuxmBTW6nQx6R4BR7WTXwEqsqp3vq7/gAiZ2DaxgVmszxulWtPZ0FrVrEVSLVPCEEGuoLItsxRg0A5oqQNMgefGU7vuIlniX6XxKfW8Hm0bofJoRFU9/Uyf3B+0lXatDRXATZwlzC4jLGdcxmCKDUQgUTVRFIc4YyeJ2O08o3syXjXpB3F6Es7OXp3b0Ta7FjWdGe8XpOr5dEU+JSCdH8YRzN4xtRvs1x2NN9sU1FpSPFQQCBYVH11gmZE02JuxF0NNO4sqtPHT2HKudu5lriiZxzlZdxLuK52/EMXQM+xOatopZcw1w9D3OO5uo0xL5hwMR9Myq54Gcx/SOo0PvIZgR8FEB2A69h3muaXznyXofHc5fsUXdixCDnSub1b10GBIJtd6Hs2I3K5WaYaH1X8ibO/Zr4wmL/38V8RwQq3lEfZe1hmMIt6KLv8nnmRcfhjk2YvAzyZkjr2uka5K0WP/Z04657TDmeXm+60v6PYPvXw4h6JoRT/MQgaVJGOmaoRA8xkkA19RC9No22kkkEolknEgRRCKRSCQSiUQyIUaaCQl64fBqLSGGzsbetGwWbx69gFuIq7axuG7kPAa9l9jQ8AoM4Os02BB0EDI+M8xXHUXRC6gN5UQFq0SFRulWUSXPDc7sv574Bfma0vLIBeA2iAjx5Vo4YrPZufN1cvyC3d/c+TprFjwecP26Lw1wpL7NN6FYCDhS30ayMWRk3/5JwBwTxmq/nBmraqNdi6RGpPIJ5SDnRQJuofI7cSf/GHdKt32ZTiKIEIQvXEf1IT1Iutht9dg+aZzrMnApOJL44CiYEX7VReTLMWrnhOM40XEJHBCBXWEHRDrpLXWsrC5hlv/+mJegnT2KS3GSIhpYrVYSzCV6CGEgJoMg631j36mALqo/+mbxR2XeSfKMs2xsb6DZODNQVLQV4ghZMvkWN40V9JW/xXbbfEqE/syWCL3raH73W3rGwfV+lofiESYLOhvpzlxMy4xZxF86T3jzK9CTNDXCpOM4hMXwv+pzmXn8JxgUjeNnXVwQ9/MfKcUTe/78uswQboSignAPisijdZl5x7ZQI66+AYqd4cykGaHACqp5eofC+vCTmOx/IvF8E6uUrABRYpVSTdLp4xCeO75rm5aHoasP5cSPAmzIFBTdvistD3PX7oBxC0AFvpR2AWylYxJALtbs4duV8b7PV4k01ojj3B+0l1R3A7UNkVQ5OkhVm1h98mNmzkodXw6MJV//59mWrwtwUcHYr2FyJlr0Qra/cjQgc2qHto6CvGUQHjzm3ZkKC1FbS9ewv33EJPztI5FIJJKpQ4ogEolEIpFIJJIJ4Q0/febdmoDXFeXqLCFGChx98+gFdnw5l+5+bdoEwk8E47qvENzUyYaKP3C7OIRBEQQv/iTGdV8ZvrAQ0NMK9kJ99mzOY7oAUv02JFmvvx2WX65FAN7fHcfpqSghR60MtEMyVNJb+FOwrtI/LwRnZt/HG0erA4pdbwysJX12BtapOE5bIaaIECw334k49C64QQiFteox7qUIF5HUi0QMisa/p+zH2OYcfSb09cKSjwnYtGk+O3e8zlbDXuJxEaJcol8z0NTmIny2lfD4ND0UfayB6+PA4erhye3lvmKgJuDJHeV654Q5C6OtkH+Y18iPTif5PrNKqSZG7eTYpRRm+a/Mk0Mhyp4h0tBHsLjEDMVNlxbC6YVfGl8OhacQXJDGkA6Ap6HkOULb65ijtkLw7ABR0RSbjfr2nsntOktazMnzLox1tawS1b7nwKh2cjJuHYvHMiv+etDZCJ2NhM+/nRTv2HP05anbnjmLhuN/JurYrzD4iaZRx35FQ0gsyVmfGP86PWKOraWTp2xZvi6zZ87+Akt85MhijieXg2UP6j+r3qbCvYg3xFr+1vAWjwW9xV53Hd21MZhm3kRwYzNbDfsCMke2GvYRHBQ3/gB5WyEfn3Tyhnstfx80aEP2Y/dWbgUo/ikmcxaZeRuh6B1wwyEyeGFtD6YTf9THqCs9J0JQF51NiZ+b5AGRToZm5zN8wK2GY7w4sIFqkcpypZb2JhfRsxcSOYFx+LXmlOFdgGlj/LBnfJu11IT7+GDm1Myl6zFZl457XyYbS3zEX0aHqkQikfwP4gZLk5RIJBKJRCKRTCc+uXTmcAeIq6xZjxY42t2vkTvPdMMKIF6Wf+pplqWaWGyOZFmqieWfenrkBRsroLFSFzx62mHf9/WfSVb99caKa7nb46fVxizHB7i0yAA7JJeIZKbjg4D9NxtDKRXpvsKnN2A92RgyNfvm6bKZmxhBsdtKrqGSRWo9i9R60gwN2LRkfuLeTLsWyULHW/S2Xxh/QfMa8cCKFL6y/iZAQVEEvSKYYHWARi0G26x7dUGgp10XQiZZUDpc1zbibOgjdW16MdaSz18l2lmlVAP4RLBit5XZy24ftj6zMRRFUZghBlAUgfB0l8zuPzWxHbQXERWsMscURVSwOih4pN8DxtnQcU4XFcNi9Fn2xjCe2ZyJwdM9MFldZx0xi3z32ROGHb5zcDFm0VWtd8pwlEFkEix7KHDsWfaQ/ro3I2IyEYKWpgY2BJXSrkX6nr8NQaU4mxomfO92XxrQ71O/LrPDde10Xxq4/AcbyrnY3szZpNuIVTtJV+powUiPCGZD0EGi4xIg5zH6F38aEGw17OMJww5dEEHQt/ivxic42gpx1R3lzKF3SVfqAt66QzmA+/DLer6PrZCCRUk89OkH+fcVLo6tO0pBzwf6gmMZoyz5mKNDyFGrh7yhEBMdySXNwP1B+/j7oG20EEudlgj2/SN/33g7ZkbA4eph587XUdHHcxW9C9Dh6hnDyRhcx/ljH/i+EwyKxoVjH4xrHVPFVI0VEolEIpk6pAgikUgkEolEIpkwI1pCoFtCTBSvzZY/V9tdMq0oeY4QVWCMCCVEFXphdiSSFuvF2rA4ulvsnHVepLvFDmFxY/dVn0q8dl1D80m8hbG4ufTOvwuj2hlQBDcqnfTOv2tw/xWF4LMf87C6K6DY9bBhF8FnPx57GPZ48BToUzsOoyjgFiqJtGFAw64lE6n08IRhB1bVRqMWQ4fBNPn7MFnYi8i8VMZ291oaRRzhah8VmoWfu+9lRn0Rzq6+QSFkks+lGKUw7Xs5LY/krE/wN7POBhT/Zy9bT9ac2GHHYTqxDUtEP5eUIM5qCdhJpk2LQDv8u9Gfk9Hws2tj7f/WhQ6v4JHzGKgGiJ4NcRb93LzzNSh5jgdWpFD05K28+mgORU/eqlt7lTwHxT8d59nxoChkXipDgQCRT1Fg8aWyqbm/r5bkTL0TwnueNLf+M+cx/fWp6IpqrCDNbafanUKM2skThh3EqJ1Uu1NIddsnJvp6u8zc6wIEqDfca7HN3jSysJL7ZVj2ILby/fygsJmNBzN9YoyGyglS9e691GUA1IRmsc29jllKM7lqJbOUZra511EbmjW+fVUUOlqbMQo9E6efGRRrVsLo4+6gEgiJ1M+/Jd/XyTbHFEVU9zlwndWvyxhFF1NkKN+0tpDrEUJWq9Xck5VM2IJ8QtVLhIleADQUwpR+gkNCfefK4eph/+kWnJW7A+3GhuCs3OPrAvyJezPFbiur1EpaKz8c8ymZjHVMJQ+sSGHHl3P55t3p7PhybmDWlUQikUimHdIOSyKRSCQSiUQyYa6ZJcR1dn6aNLx2VkPtrWB4JojHV/3I69+lv+IYbqFyXtEIXpzK8k89Nnzd1xpvwc3fZsm/8JyWR+XpFord/QE2V8VuK6vmPEiuxeT7XHNFCXcElbJrYKVfVkopLQP3EzVVORxpeUQB3z37PI1nTxIlOqnS0tinLeV2w0GfFc1P3Fv5r+xl199+bDQcx3GJCEBgUDTqtCQc6Of292UXUct3sPC+b/CAx3psMrk5LW5YLpACZKf5CRxpedyRuZ+sOZ2cd/Vz662fHy6AADjK6GuoQnS3UqmlcEQsJEVpJEFxYW+7SFj1LkLHYvcDgZZG3uW9opujTBc8wk2DQkhYDHQ7fc+iOeexwRnd/s/sRBCCqJgEvpq4lzpnF/UiiRSlkfz4TqJi1k3P+8qbK2EvGhRANPeU5coAkLSYyKUwc6CG8AYbDsWEGScxZguRSwsmJvpa8jHF9XDwbXzjj1uoHCKDOOutMMqsfUdsNk/ZsshRdeHEoGjUa0ksW7wYc2yEbqsGYCskrtk54jpim0vBNo5g9LQ8ou1HWGl4k34RRLMwMltpJlgZ4LwWz6y+zsDzf/QVXfwAMM4Zz1mBtDwWA8+b9lAfrZBsXIbpxDa42EhfbBLd7Y3MFs3MVJwEJ84nOCUDGsr5w1uv8/clEaykmtWGSjLzNlLgOQ8Bx2kvIrXjMCWaNaALUNXg8x2HwB595ftoMtYxxbx2sN5nB6gAz27JlEKIRCKRTGOkCCKRSCQSiUQimTBeS4ind1RMWmj5aN0lh+1txEV2YYmPuDEtJ4YKIDD4cxQhxFm5m8KykySjF7bNwklD2SlSrbsxWW+7hjs/Cv5CSN1+vVDqzQmxFbKwt5+DpA8rQC7oPQa2YF94bkJQN79zr9RngCt60fHP7pX8Y1D31BZegbkzWklerIc/mxfkE/7n5wlv7abDE8meNz9+epzrUThy1kVH+btsMRh5Y2CdXy7BXhIUF/u0LJ7cXs6apwom/bkxG8PYvHwW24+c9722efmswO14CunJMZEkR7vBXQmMfD37L2nYtCR+7r4v4DhQoC0kBfPVCAaWfH3W+sn39d+t9wUKd9b7wDQv8Fkc6ZkdL4pCd/0Rup3nAa/4o9DtvEB3/RHCp1vWjJchgmaA9dFUPI8e0fcOnqPzWDWuvrMYQ1Qily6a+LlHv0d/ubaX8iLBgFAJUgQvrO257LNga+miREtnhaKPW2acXMBETkiwbqvmHZMUhaXOd3gsqIbzIt4ncD0W9BZLnYtg3ufGta/GkCBcplkcaA5ipuLEoGhUaBbe1Nbwq9BaXcDznnvXWV38iLNA6urxXxuPCGy1FUKLQc+AaT1Dl5pMsTueJKWNDKUON4BpHrVHP6bv/G/5OzUJg6Kx322l5CMbK6jUA8/9EYKoRQVsMqVw0O9vg/s2fYqohPqxCX+TsY4pZGgekgB9nF2QcGP+fSKRSCT/A5iQCPLTn/6Uf/u3f6OhoYGsrCx+8pOfsHLlyhGXfeGFF/jtb39LRYXevpqdnc13v/vdUZeXSCQSiUQikdxYPLAihTULErC3dE9KaPlI3SUK8MR/H/VlBDyz+Qaccek8ped55DyGw9WDrcUj6OQ8Bq1n9Pf9sRfRXbuXNi0SoepWOg7FRLsWQXftXkwRIdd9Jiyg74NXAFENg/ukKJiaStm2TFBYNliAfGPZMUxNyuDMYU+xa5EphQtv/V8MihuBgYX3fX1qi132In02tXEO4XEWUjQ3ruZKup0XiFAFZ7VEVASJp7fhrJwzLYUQh6uHt4872GpoH/ZeguIiQWlHCMUnIm7MmtzinMPVw86j5wNee/PoBb52x0J9HBhPId1ehKriE0BAn/mNG54MeoUYd9PYZ9Xnftm3LWdXH7WhWSzsPa7Pdo9MCuwQ8RfyPOvvK3+LnmNvEWaAkMx7r6oID9DZO0AzsWgoGBQNDYVmYujsHWBamvwNvW4wcufXVGy3p51I83wioz2ZLT3tVyeE2osoCDlByifvZ2/fQtaF1HJT17HLrtMSH0GOOiiApKqN1LpTmVHwDWg7HJCDEd7nJCumjx87LfxG28Aj6i6+YjpAeN/IHSKXJTmTlHuX0L3redovNOMWKg3E8+gaC+GLcvSx0G/cIs6ij7vgs8kCxiWEULcfWm3Q1UQ9yZxu7mSOouelVIlUaLhIyKmD/PfZWG436N9R9SIJgFVqJfXR92Mduj3Pc/RAGiP8bTDG7+3JWMcUcsjeOupkjckeZyUSiUQyOYxbBHnttdf46le/ys9//nNWrVrFj370I+644w5qa2tJTEwctvzevXv59Kc/zerVqwkNDeV73/sen/jEJ6isrGTWrFmTchASiUQikUgkkuuL2Rg2abMfvd0lT20vR4NBux1PxUET8OSOG3DGpXUT2ArZs2s7X9gXiuYRdH65tpeCsLjhBV4hiI5LIFY9x3631TczfrWhiui4+dd9JqyP0SxzhICeVpZ3VTJv/Qaq5jxIxtlXMNp2QaR1cP+9xS6KaFgxk3OufmYbg0lOqJ9akccb7uwtiJc8h9j/G1BCeGNgHTWkIITCVsM+ug/8dvqITn7YWrqoFim8MbAOq2pjq2EfK0U1KUoTzcLIXvdSajwFw6mInrC1dAWIlQBuIbC3dGP2FovHWkiPngmNp0lX6nwiCEC6UsfMSJWwuHFY/niyCvb0LaDs1Zdxi99TqmisybyJ5alxw58d734IwWuGu5ld/1tf59K5pXfzwNi3PBwhMGQ/RFXtm9xhKGW2aPZ1OhVk3zd9nmN/hAi8bl78ztOk4xVewmL03/2tyiYqvHjWuadvAV/4s4YmqvmuAr9cu4CCy6zT3HaYb1pb2FEVCQrUulPZnBGh39P+93C3EyKTiJ1/O19tb+aRnoPEhiUQFfOIPr74d26MBUs+lDyHxdBEQ1oGfeFmHk5OxihOAEn6+8U/05f1jlv+gtV4Le/sRVzs6aW7s49QNZY9TaEsUNt9XS2lWjpbDftw9ASBIjgv4pmltDBLaSFFaWK7tpYHrLdedhOT8bfBZP59MVkoowyo0zHiRyKRSCQ64xZBfvCDH/Doo4/yyCOPAPDzn/+cd955h1//+tc8+eSTw5Z/5ZVXAn7/5S9/yfbt29m9ezef/exnJ7jbEolEIpFIJJK/eDzqx0glHSHgSF0bdy+ZXoWRy5KWh7Orj/JXX2YFVg6QzgqqKS+qIuvTD2IaWixTFIyii6B5azlQmwDoM+PXzIvHKBzXv9piK9TDinvacSauHJxtbyvU8xicp6GpCpKsGEUXued+BcKtd8M0VkLc3IDsgYqP/8i3K+Mp0dLJUav5ZucfWQxTN+O81xXYESAE/aEJNAuFapE6WIh3Qw7l4y9oXgMs8RGUigxKRAarRDV/H7SNWUozAP85sNV3DIoCy1NHyOGYhO2PmgnUOs5C+t3/zsDeH7NhzyswQEA2TOiqz8G6r4x9xxSFizV7eH5fOCvUwTDy7x8L4QVjM1HJSmA3ljEM0vJwuHqo+eX/JtWg+ULMa9/8NxwLvj/xIqwlH5O9iC0ZEVRVJ3OBOGaKVjZnRExLYQ24fMfNVO2vELrg0dM+vHMoLGZiwosQOBNX8oWXe333qCbg0X1hlD60EtNI6/Rsc/EtGzFn9dLQ0U+c9dZBUQ8Gz4HjOJizdGupfd8nKsQvQN5eNL59thVC9R9oOnWE5xoz+I17gz4Oui9gjHXD0Zf1cdUzbjlis7GdbsESn43Z+3mvEDIWAsbcLTyi7mJr0F6atBjqhW55BbBdW0tBWBVW1cEbA+tYqeoi6yylmc+sShn5ufAGpo90r3jPy1j3c5qSnRo7PA9pisZZiUQikUwO4xJB+vv7OXz4ME899ZTvNVVVWb9+PcXFxWNaR3d3N5cuXSIuLm7UZfr6+ujr6/P93tHRMZ7dlEgkEolEIpHcwDhcPTy1o3zYDPOhTMcJ1FeiNjSL/e6jAUHh+91WVoYuJXfowp4C3n/8uTfg5R+cSOSvVs4ZuYB3LWmsgOq3ORKRx9Y/96KJA6gKbFsmWN71NiRm6FkKPe3gOgfG2YM/0+8ZDDq2F3GxZo9PAAEo0dL5TmU1z5v2EAWTX3gdaaZ7ciaGtQvZ/vJRFGVQfSslnfBVyyA8eHL3YRLwdk09ub0cgGi6AOggwreM1z7O3HZ4UJiY5O2PmAnUOrjcMMFhFIzrvkJwUycbKv7A7eKQLl4s2IhxPAIIQFoe5adb2GL4FSB8WQ0phibKZ3ye8nozz/5izzB7vZ7Cn3KHoZRdAysDRJjewp/Cxq9N7CR5i+rzUkk1hdPWoxEbZiEqJmFqraVuNBQlUACBwK6LiYi+lnxqT7egiQMBL7uF4EToUnItpuGfcRzXRZe0PPpdPbhauhjo6MXmXoRVOYLRcVzfL+8/mJwA+cYKLtW8x+HWBH7j3gDo4+COql3MTztJSJRJt0y0buK15hSe+sUeXzfhM5szeWA8XSAjjLkgaNJiaMFIqee1XEMlM5PNmCJCmJuZi3oMDIrGWZHIkllGVmsfgn3m8GNVFN+97YjNHnz2/bvDxsFYx49ridkYxrNbMn1/q/jG2WmyfxKJRCIZzrhEkJaWFtxuN0lJSQGvJyUlUVNTM6Z1fOMb32DmzJmsX79+1GWeeeYZ/vVf/3U8uyaRSCQSiUQi+QthJIudoShAdtqNN+PSEh8xYlB4WvwIyQATKeBdS5IW4+q9ROEHR1lBsK+zpbDsFPPWb8CYusxn79J3ah89Daf1jIXkxYEZC0JQF51NiRa4+mItnfpoBetUiD0jFeEs+ZiATZvn89QOXVTwFrZM1uvvQX85VqnVbFH30SyMhCgD9IsZ/N+UYwxkZQTOYp+C2dejZgJ5CqF7ahqH27+FnBi+L55C9/JPPc25C3s413oRt1D568rl/HLXdgoWJo59/22FRLfXcB6BPlqAt7WsofYgH547iRAZgN4Z8PSOCjZ0vcksxwf8zq0LIKB3o6hu+IbjAyiJmFg2iF+HQ9SiAqImo8PhL5GpsOCyFbKwt3/EbqUFvcfAFjz8njJnga2QI699h+8fD/GJBLoVYSWZeRsp8O9mmKwA+aTFNJvXEtteysPqLl7UNrBKqSZa6aQ1cj7mkEtg3YQjNtsngMDg/bvmyVvHXoAfMuauUqoxql383H0vAIoiKNEywA33N+7FtfgTLE9bzvPRe6iPvn9wTDn6ysgdcp7f9U6TksHuPmsLi2/ZOK7z8trB+mFCw3TJA5vsPDSJRCKRTC0TCkafKM8++yz//d//zd69ewkNDR11uaeeeoqvfvWrvt87OjqYM2ccHrASiUQikUgkkhuWkSx2hrJ5+awbr+BgK8SsKPyvBc1cOj1ot/PVBU2jztK/rN3Q9caST6WWzn53//DOljkP6iKNvYiK03VU1QVzARMzcZIRXsfiZL+Z0pZ8THE9qG/vGXaccdZb4Rpf5xupsOVw9bBz5+tsUfcBgo+0pYDCExldxIl2aHwb1Dp9hv0UFtxH9OyfgP0bR1+mu+i/fAKIQdH4Z/VFyouiyA5dihHGJoQ0VjC/dS/btflEKt2eMHLoFmGkN3zAIlZSQoZv8ZupovuCDWPmvSxaejcGv86Whfd9nRD3OyC00bd3Oaaiw+Evkamw4FIUTE2l/HLtAh7dF+a7pi+s7cHUdHzkbXru2zO7f8gWVfE9MrmGSva7rRTvs7GCSqIWFUxugLwln5CuPmoqGtgQVEq6VocDE+1aJJGxCRCTAEJcPodnLGOV554zWQt8Y66iCIrdVs8p8wgg6PaLDED2xX6MtkKiFhUMhqAb/Y5zhM4XR2w2364sIUetZIWifzd8p9LKDzZ67LvGwNCuUJ/gM43ywKZjXolEIpFIRkYdz8Lx8fEYDAYaGxsDXm9sbCQ5Ofmyn/33f/93nn32Wf785z+zZMmSyy4bEhJCdHR0wD+JRCKRSCQSyf8MvBY7qqc2qDA4l9vLm0cv4HD1XOtduzoUhe7Sl0g6vY1it5WfuDdT7LaSdHo73aUvjVgM9Z4Lg+e9ALuhaUBEsIEDIt1XsHYLlQMinfBg1We5sr2yiwuYcAuVC5jYXtXFxZo9euEMdHGo7fDIx+nvwz9FOFw97D/dEnA/mY1h5M4zTZvzPBq2li7SsQOCbe51VIk0jGonrpgMiEyCxnI4+f5g1sI1Lrjr9m9Wcg2VPGHY4SkkZ3AidOnwhdPyoO8iwfZCWrVIHrz0TardKdwedJj51NHR2jz2/U9aTEjmvdyZ0ESK0oRbqCTSTkHYKardKb6weBic4R+ctRlyHuOBFSkUPXkrrz6aQ9GTt+qzznMeg9zHJ3YSLtfhMN4ga8n48JzjgpATlD4UwquP5lD6UMhgJ9IoAkVtaBZvuNeRoLTxz0EvsdWw1ycS5Bgqeattnr6g4/jkXVtbIUEtVbQrkXRrIaSqjaxQaohRO7GfPadvS1F8wvhQys63j207ng4t/zG3RMtAQRd6hAhc+QGRjjspY3zHaSvEWbmHEi3wu6FYS6e18sMxj+mXE3wkEolEIhkv4+oECQ4OJjs7m927d3PfffcBoGkau3fv5u/+7u9G/dz3v/99vvOd7/Dee+9x8803X9UOSyQSiUQikUj+Z+AtfoxURhrXzNdpRGfvAGKIpCM8r4/W2/HAihQWJUdx0N7GirRYsuZMHxuwrn43q5RBay+DorFKqaa7PwdUQV1XMEa1k2K3lQMinVVKNbmGSuq7Zg3aXHmKcg9Y8lnz5K2DHRhTaOHkZTpbrYwFS3wENaRS5U7zhaCrGnw+qBsGVEjKhK6m4Z0I13D/SkWg/VupSB+5k6nkOehpoz8tn7gTNr4X9AscmChzW1hisBOjLhnXrHpndz/1rX/CO4K0YETphSi1FzGgP4Pe+9Gy4k5M1tsCViFGHHkmwPUIGZfoeDttLPmYbIXkqof1zA7vNRllfPHaFm4WH5GgukinjpWqfg8Xu60o5Q7ujT2td4OMdg3He20VhY7WZoyik3BDHxGily4llDUcp+vsDFzzH8SYlocZ+MaGRTzzbqAd+fffreWTWTOv/J3o16niHXN7C39KUnctO9y3ceB44PfLKqWavkvp4ztORSG14zCPGJoDvhv+xrCLlI4EmFkwplNiiY8YMXx8WnRCSiQSieSGY1ydIABf/epXeeGFF3jppZeorq7mscceo6uri0ceeQSAz372swHB6d/73vf453/+Z37961+TlpZGQ0MDDQ0NdHZ2Tt5RSCQSiUQikUj+YnC4enxBz6MxbSyhxoPjOCEpy9iurQ2YGb9DW0tIyjJ9pu8IvHawnk0/28+336lm08/289rB+mu846OzsPc4qw2VAZ0tqw1Vut++ojDjkssngIA+q7jYbWXGpY7BWf3ezATP7OTceaZBAWQKLZycFR+wc+frw6xWHK6eQF//aYzZGMZ9mx7gkMfayaAo3LfpU3rodpsNOs7BpR5f0PP1YJhIplaPvKDzFCRZCX/ov0lMWUSq2sQKpYaTpCKSs4icMY7/dbUX0V27lzcG1lIvkjAoGvUiiefc97IgMZL7g/bxhGEHqw2VzL35Tj5576d8H33tYD23PLuHz7xwgFue3TOtnjfJOPEL6PaFlqsG/ffLWJGZjWH8cm0vDcRToVkwKt0sV07gFvo9mKNWUh998+Q+U0IQ33WSlYYaWrVIupRQEkU7N6kXQBOcc3b7jiVztnHYx8fVIeHp4LhYs4dLe54lqbuW8BlB3LnEHNBlondJVZEcM7qV+Wjrj4pJ4LGkKjpEJD9xb6ZDRPK3SVX62HQ15002TkkkEolkgow7E+SBBx6gubmZ//N//g8NDQ0sXbqUXbt2+cLS6+vrUdXBP1Cfe+45+vv72bp1a8B6/uVf/oVvfetbV7f3EolEIpFIJJK/OA7ZW0esc3izMaabJdSYMWdhtBXyxTULOF5Ug0FxIzDw6BoLRnECzMNnJHsFIe/50AQ8uaN8enii24swNZWSmbeRH+8LAwSHyOBLeRZMTaUQaiQ+cz2lx3oDPlYq0jFlhg6KG97MBI8QQt1+vVg5xRZOjo4+ctRKhMAn0riFoLXyQ8x9o+QFTEOGdQq5K8HWDnEWiJ6tCyE97SP69k81zso95HhEMv9OoNbKDzGvvitwYesm/fqXPEd4QirmjnOEGgTZxjZCU1eO73oIQfjCdSiHjgYIMFbVTmpmLjc1VeIMSiQmIpSoOzwCiL0IZ2cvT+3om9YZBJJx4L3fj76i/4yzQKtN/7fswdGfB3sRBSEnyPr0g9Q2XOT03v/gJvUCCcJFitLEDm0tD1hvndx9bazA4KojWFwiVW1CEwpNSgwDmspcg4MZZ/8IM6OhoZyF0QuHZUXlqtVk1J8CdfmYnpXXmlM4X1SHioaGytK89RT45afcTBWrDVVk5t09rEvqitiLoKedxJVb+cf2Zh7uOUhsWAJRMVvHNRbZWrqG/S0g4IbsApVIJBLJ9WdCweh/93d/N6r91d69ewN+t9vtE9mERCKRSCQSieR/KMooRe//e6+VeQlR0z6selQ8RZ+Co6+wdJHGGS2JuWoTcT0fwKKRC3KH69qGF4EEHKlr4+4l1/kcOI5DWAwFt26hKLcnMEi85DkQGibrbWxefoztR877PrZ5+SxM1qWD6/GzZ3FeOI19IJa0oDZMM+dNnYWTrRCzMZQSTc+rwI2vSB9UdgyyVt8wVkX+ll45ajXftLaweF6q/qZq0IUQr8AE1+64in/G3HNHKdECO4FUDb54bicU2yH3y4PLp+VBQzmn9r1KYUcSLiWFJZwhq6OC0MSbxrfflnxM9iK+aW3hO5VWirV0ctVqvmMpw3h2L0QmER4XpYttftk0jpAlVxc6LZm2dF8aoKW1i/hLA4TPuEwZpPhn0FAOyx7EBKxWS9lzyz9QXPQSK9RqblZqMeU9PPn3Q9JiWkw309+0lzgu0qpEESF6uaQE4dIimGM06YKwowxTQzm/XLveF/buu7fbI0DJvuKmHK4edu58nRxVFwhT1UaKCveS9Zn7KGgq5dg6jfauXsIXPoipo1Y/J/7Pqhd70WDmjT9+OThR+75PVIinAyfnscHPjIER7bCQdlgSiUQimRgTEkEkEolEIpFIJJKpIjs1dkQf8NvSk/4iCpG2lk4O17VRJxTsShPZqbFYRllWjFIsmhZZyuYsvbBuL8Kcljd4bTyzgLHk68W2o+cDPvbm0Qt87Y6FgdcyLY+d7+7C7CjHLVROKhofdSSz6dYpKtgrCqamUj6zch6vHNADgVeKalKUJsrPQXz+QkxTs+VJxeHq8QkgOoIdVV2kmpoH8wq81l5TaC02GuEzgvjiGgsH9ym4hcCgKDy6xkJ4z+nhC9uLaD11kBMXZ5BuqKdOS6KMudT1JrK++iBz+Dqkf3JsHSGeY158y0Z+sDHbI9AVYK5+EY6+rC+z7EH9p7dLYNmDmGKzUd/eEyCE3JDWexIdz32wJ2w9xw7/CZX6wa6HK4mCDeW+cawA6O5IQnM0ERwcgqXnA7AnTa6gaMknpKuP+soqEgztxAsX/coMWkQ0VSKVRMtthCYthuRMOPoKBT0fUPrQVhpcfVjO1RI+I+Ly3S1+OCv3kKMOdmg9LHaxIaiUDlsqpigDUcEQFRoFESFwwmNNObR7wzuujPQ8el+zF3Gxp5e2HjexYQaiJqMbbWoaAyUSiUTyP4BxZ4JIJBKJRCKRSCRTidkYxrNbMn3e5KoCz27OvPEFEHsRF2v28JQtizpPTkGdSOJpWxYXa/YMzkj34+a0uGE1HwXITpsG4egeX3mvEAIEFsbS8rC1dI06s96fU6XvUnf+PHVaEgfFIuq0JOrOn+dU6btTuu/LtHLPPqmkKE3MUpp5w72WE6FLp2a7k8zQ8yuEQrTSSX30zThis9l/ugVHbLZ+PabQWmxEcr8Myx6kIOQEpQ+F8OqjOZQ+FEJByAm9WDt0ZrnjOO3trcSpnXRrITgw4RYq/+p+mIo2AwM170Fjxdi27TcT3WwMG8yZ6WmHZQ/BTesHrdd82y/D3HaYZzZnYvCcJ5/1njejRnJjIQTOxJU8/5ENFb3rQUXjhY9sOBNXjiwKeu5bHMdxKRFUXHDRXfoS4TOCiLz33wm+8zv6co6ySd9dU+dJMmLcVLgtnBYzcRFBDyFUahY6Wps9qeB5PgHPdPCHWE/8TO9sGaMAgr2I1I7DAR1aL2ob+LN7JbPs26G+RO/aaLXpAuGyB/V/nnHe4eqhYv87+nfW5Tr17EVUfPxHHv0onDUHVvLoR+FUfPzHEb/nRmNEOyzB2LNPJBKJRCLxQ3aCSCQSiUQikUimHQ+sSGHNgoRAi6UbncqdXOzoR4hUDOpgToGmQWdbM1GVO4cVlLyC0FPby9HQZzA9s2UaCUJ+Vla+LA+/wpglPmKYd/2wmfX2IlzVe2jXIkHVBQmHYqJdi6Sj+kNIjJoaC6e0PMK7+rj/yA+ZqbQAcF4koCo3jt1KRLAh4HdFERS7rQxcnMtPn9mDQBfNnt2SyQOW/GvfQuS5biZbIbnq4WH3RyAKpmA3NVokcWonZs2JQzHxL4YXSVGbcBkXYUpaPLbtjjQ73U8YAWDf9/X9ibNA6mqo3AkN5Tyw7EHWPHnr4NjTdlgvBidn3jA5MRIPlnwc+98J6Hrw5tI0uDJGz7pIy2NPTSOtH/yGmUoLfxLxxOU9QoH/fesVfydrbLIXgeM4ppuWcfDQSWbSQocIp58ZLFbtRMfdPvj8puXp463rrP57nGXs+yEEUcZ4vrgmKaBDKy9/HcHN5+nrdNJeX0tUiDpoGyYEhMVwaO9bfHTqN6holGhWNplSeIARLLGKf0b32aN8u3IhJZoutJRo6XynsppfzXiJcEfZyPZaQxjT94dEIpFIJGNEdoJIJBKJRCKRSKYlvhnc06Xgf7WYbiLBeYjHDG9S7LbyE/dmit1WHjO8RbzzEJhuGvFjD6xI4eOnCnj10Rw+fqqAB1akXOMdvwJpefrMYc3j++5XjDMbw0aeWe9/TYUgKTGZGLUz4LzEqJ0kJiZNaeHeFBFCfmIvF0QCxZqVHdpa/snaohe+bwC6+t0Bv5doGRwQ6fzXh6d9M6gF8OT28sGOkGvNZe6PAMxLMIbNIDOslWp3Cg5MLOEMdweV0C+CIOdvr27//QUQe9Hg/miec2jdpP88+grmtsOD3SNeuyzzkolvW3J98HY9uANzaUo0Kykdh0btSnC4evjCvlDcKJwX8dSJJB7dF4bD1aMv4O2Cm8yxSQgwZxEpeslOjeUn7q3858BWWkQMtyS7MYYEBdhM0Wob/GyrbewdFpZ8MC8ZsUPrSMjNHLnQx0e1Tfy8TGNPmKdjqrGCQzVnOH2q1tdRU6Kl8+bO1/WOkBE6zDp7B4adHk1AZ6972LKjMabvD4lEIpFIxojsBJFIJBKJRCKRSK4FyZnMMJrJ6juLtbWOA+50rGodWbG9zDDO0Weaj4LZGDb9Cj+2wsHil39B2VuM88wOvmJXj6IwO7SfGfPWcqA2AdALlWvmxTM71DF1Fk72Ijj6Cklz5nPHwtm0d/Xy8MJlmCJCrn2I+AQZaab00DwdPL8ftrexMes63ENDBYfLzZ6PTGJOCqzomIGroYYYpZMuEUZMymL9ukzW/vhZtgX8vuxBOPoK3aUv0XL0feIvXRif1ZBkeiEE/XNu4YDoDXj5gJZO/5zQUUUMW0sXn1PeRUVQ77EuvJkq7C2r9PFrtEDwq8ETeg5guf1L/CBWz7JZ0LsM04ltekaJd2z1CnPrnhz83fvaWO5T/w4tSwiEB+NSIjhT9mcECdSLRAyKbhuW9aBuG/bOiSruCGoEDRyKiYfVXcSondRH34916DaTMwnpG2D1iaPgxteBs9pQRUjK0st+1w3lL7IrVCKRSCTXBSmCSCQSiUQikUgk1wIhIP+rxDaU843yt/iK+yRhBgjJ/Bu9KDQt0s7HgaL4Cm/OBVupDc1iYe9xTH5B014uK+J4iomP35pH3tk2DtnbuDktlqw5sYPFxqnA6+m/7EGi0vL00F5vMfx6WEdNAO9M6ad3VPhsbR5YMZvfl54dtuy1jAMBfDPIveHSAYJDQzkkLQ4sIgvhu2cSXv8HjEobAijVMphrTMcyGdfD7xo7YrOxnW7BEp+N2bu/lnz2hK0n7Mi/ISjnPNBzy9cDbZAkNw6WfGpPtyA4EPCyAE6ELiXXYhrxYwt7j3NBtROvuKjULFRrqeQaKumo+RBnbyymplIIi/FtY1IQQv8eMC/Rs2zAM2bepgeUO8r0fw2eoPJlD+r3cEsXCxds1YWSCQgh2ArBdQ6tsQ6Bwjb32gDbsA4bCCEwqp3sGlhJjNqJGSc5QVW8515JnPXW4etWFIyii/wl86GsipWimiBFkL/kJoyia9yD0bScBCCRSCSSGw4pgkgkEolEIpFIJNcCb7EsLQ8q/4hyqR9mBEPOY9d3v66SMy1dPH3wKCVaLzlqNd+1dDE3PmJC68qa4xE/php7EfS6Amf4+xcFvULIDYD/TOkFPUdBaebV0sBuEEWBHEM12E5fu+NqrIDqtyH9nsBz3FCuvw6B++L579f/62lyO9twiXA6iKBCS+N82SlSrasYuWQ9DjyC22vNKTz1iz1oAlQFntmsZ6Y4O3t5/iMbX/FErQjghY9spOf2yCLsDcq4cyXsRZiaSonLe4SPCveyIagUBqDYbWVW8Yt8WKwwd0kuyxPaJ1dZvNyYk5an//N2qJmXDLuHf7l2PQXGxvGJt2l5XKzdB/XHiex2UKXdFWAbliHqmHPyt2ihsZRo91CipfOEsgOHYgIN7p4fqlvGGfOGrRdgOYWkpqvUDcSRGtSKKUG5fJi6RCKRSCRTiMwEkUgkEolEIpFIriFHXv8uR+taqHB0crSuhSOvf/d679LEEALngq08fWYJOWolTxh2kKNW8rRtCc4FW8dejFOUwZBhf7yz9qeihWFoSLaXqfD6vwZ483NMkaGYmkr51bpeVM9pUxX41dpeffb6tWwHSVqsCyA97Tgrd7P/dAvOyt16Z0j6Pfr7Qzj37n9gbXqbU9pM3tNWcl4kYFVttGkRdNfuHXvuwWh4OkCe2lHuK4prAp7eUYEjNhtHRx9b1H2cF/EUa1bOi3g2q/torfzw6rYruW6MO1fCcRzCYijYsIVbHvondg2sZENQKZsNH5GguIiki8KykzgTV177Yr4lH3K/POI9/Oi+MBwZj4xL5Nyzazu/LjzFwSaFc90qaw3HWKVUA7BKqcaq2NFCYwhOWcGmTZ8iV63GoGhoQmXeTQu5edHc0cfKtDyONAtO1pbTc6qQk7XlHGkWUgCRSCQSyXVDdoJIJBKJRCKRSCTXCNfeH9Nf8Ra7BlbyoraBh9VdbKh4C1diJMZ1X7neuzc+LPkUl12gRPSyQujFMW9gbok7nbstM8e2Hr8ODGdX36CtVlPp1M0avlyh8EYu0gmhF3A5wcGHVnIidCkLeo9hairT7Xuupbjjmdm+Z9d2yl99mQHxew4qGpl5Gym4dcvw5e1FzKh8g34tiJ+77/VZ8mw17MOq2ohV5+sF6qu8PraWroCuAAC3ELRWfsjcc29Sg+AN9zrf9u837MNybifYo2/se+N/Ih4R9YEVecNzJUbL9TBnga2QPbu288JHZxAilXStjsWqjSi6OSAy2O+2st7Vh8nbNXatD2uUe9je0j3mjiVn5W7Ki/7IfreVH4qtPKzu4v6gvTwW9BYrtWpSlCYEgpNZT2JdfRcP2Iu4a0039dH3E2e9Ve8AuYxI7azcTWHZSZJJwoEJs3DSUHaKVOtuTNbbrvYUSCQSiUQybqQIIpFIJBKJRCKRXAtKnmOg/A8+AQTQfw7A/PI/QOiMG8say1ZIXLOTVcopnwBiUDRWKdXENl8Cm2nsBcK0PPbUNA4vlsui8/hQFL3TIiwGU1MpuephPYw8LEZ//RoHgzhcPXxhXyiPq/q9MSBUHt0XRtFI9lKOMsKMCZxqH3zpgEgHNzyT+D6RF+tg6QjiyXiwFZLo7BzxrcSz7xLe7yQu7xEO7QsDBIfI4Et5FsJ7PtDzGOT9eGPRUO7L0DCn5Q3ec/YiPT8jOXP4GJWWh7Orj/JXX2YRkVgNduJpJ4puLhKOhoKiQErHIZhZcI0PSGfcFl9DsRfRXbuX/W6rz/7K+130+aA/EW9w0SHC+Yl7K3nWW31deVGLCgZD0I1+9oEQ+Gx41t+mRSJUcAsVh2Ki3dPRZYoIkc+SRCKRSK45UgSRSCQSiUQikUiuBULDMXM9L57PDHj5RW0DW2fOwiS067RjE6ShnOX1f2arYYBtfjPntxr2sry+CMI/MTYRxFaIs7t/xGJ56ZzdmMKDp3y2tcPVg62lC0t8xI2d/TAk7BjjbP0nXHsvflshTkcHK6gOEMluporWSjCbowKva3Im0eYlNH14itzT+8CtiyBLU4zMTZ6rF6yvdv8VhZBz+1mlGH3FX9Ctf0RfJyy4nYKcLeywtnHQ3saKNE9GjT3phrNIk6CHjDeUBwaGewUQ7/sjUBuaxX73UbYa9pKu1BGh9HJGzKRRxLJYtbHE2EPUon+5boV8szGMTctmsf3Ied9r9y2bObaxy1YIDeWEL1zHwYO9AW8pwAAGFEBRFB5dY9HX2XoZ+0AY/mwIQXRcArHqOZ/QskqpZrWhiui4+fJZkkgkEsl1QYogEolEIpFIJBLJtSD3cUyuHpSDewJDqwHT+r+HG634riiE9jaTl2xku6cWpwB5yRqhvc1j7zpQFLpr9/JZpT2gWP7Xyrt018ZgWn7H1Oy/x8rlteYUntxejvDs/7NbMnkgoX5kq5wbAW/4uL0QXPV6J0jy4mtfsFUUzGe2s9XQzDb32gB7q+QzVTDzbwKXt+SDrZDHb72J/44IJrfsfVaKaoLOa+yxbKTAnDQYWj9R0vII7+pj9ZGXfSKLtzhryH4IrLfx2sF6X96CLzR9hZy1fkPiveePvqL/q9sPrTb9tWUPjvpMWOIjUBSYpbTQLIygKDQLI+dEAolaG0kXG3DZj2C8HiKIRzTeeTRQwHjz6AWetjqvLBorCvS6MJlD+MadaXzv3Ro0AX9j2MUTcQcwRC/EGWJlgdqEqecDXQAcr32gomAUXWTmbeQ/94YCcFCk86U8C0Zx4pp3pEkkEolEAjIYXSKRSCQSiUQiuWaYjWE8uyXT90e4il50vyG7D5IWw7KHMM9K5dfLavlo1UF+vawW86xUWPbQiMHXI5KWR3RcAhuCSmnXIvmJezPtWiQbgg4SHZcwdcV7ReFizR527njdJ0oJYOeO17lYs+fGLdTZi6Cnne7IOZyNWkp35BzdCutqQ8XHi6MMtasZGDrrW+ivO8qGf8ZzTd48dsEnhg0IlRc+sk3ONbEVYooIITNvI6sNVTxh2MFqQxWZeXdjigjBWfHByKHprp6r267k+pGWpwserrNg+0j/eRkBBPRx+n9n9dEvZhCiDFCtpaCh349HxAJeH1hHX/3Ra/9MATSU4z78MiuoDnj5ZqpwH37ZZ/81Kml5YMmn4uM/8uGunWgCHlF38eWYYnpDTHzRtpatVfk8XpXBmZYuKPwBlDw38ro8NlnD8AjIzaYVurIMoKD/bsmXnSASiUQiuS7IThCJRCKRSCQSieQa8sCKFBYlRwXa7dyIeIKvsRdh+OA7GC/VYZhhgPX/ND7hwl6EUXThMN9OzPnzPKHswKBoOMzryRFdeqFtKoQQIajrCibHUInw6wrIMVRS17WQxTdioc5TlDzSLPiorB+3uIBB0VizRLCcEbz7p5iQ2GSq6kPJNVSyUui2WNXCwt2xvSN/IC2PugsutqjPAQr1IpEUpYkUpZH66C8P5hFMFEUBWyEFi/JZocyjvauXmIhQohbqXSaOkCVXHTgtmZ70DbjpvaQROkMl5EoL24tYnqjium0rlVoqVXt2c4ehFDRwKCZqRSqG7GXXp5hvXkLk2aPcb9gb0M10v2EfkaHJo1p8+eOIzebblSXkqJWsUKrJVmupcIXx89bbKPHYxJVo6TxtgxfnFxJ68v3hdnReAWSkLhFLPg5XD0/9Ys8wQXHNk7fKZ0kikUgk1wUpgkgkEolEIpFIJNeQ1w7WD7dfWpFyvXdrwuypaSSsvg0hPLncNY0UpI1jBULgTFzJ//pzL4+rO3yWWF+rX82aNSGYpqrQqCgEX+qgXYsMKNK3a5EEX+q4MTtBhMClRPBR2VGK/bz4Katk3vplGK9l0Tb3y4Sbi9hy6Y9UVddzQYljJq2sz0gifOUDo4oxZmMoNSgIv/4cRVFINl6xdH1l/OyRooAok0W3Rzr6Cix7EFNsNurbeyYeOC2ZftiLsL3/Cw7XGagTM0lVGsl+/xdYbmfke7D4Z3o3xbIHMablsdpeRHRDBDuqVpKu2Imlk3+ytly/cO+0PMKBbNcvoG4fK0U1qUoT2akxhK/83Jj2ydbSRYmWzgpFH/MaRSw7PJZ1/pRo6RxNuYnc8Au6DVdXH7WhWSzsPY6pqfSyOUO2li4pKEokEolkWiFFEIlEIpFIJBKJ5BrhcPX4BBDQjYKe3F7OmgUJN2RhyFm5m9aiFxHEUy+SSFEaUT5+EeecGEzW28a8Hoerd8QA7QZXhu5xPxWk5WHq6iOm4mXMOHEoJsw4QQVT5n3XLfT4qlAUGhocPgEE9JniuOGuhgaMaddY2EnLY3FDOfO7j9HjdhBmgJB5qaOfW3sRwWc/Zpt7LStU/X6oF0kcdKdzx9mPYSoLz5VvYjaV88zmu3l6RwVuITAoCt/dvBhz9YsgNMh9fGq2LZka7EV0l77Eobo2trnX+URBUbeXpNKXCIfL3k/Oyt101+7FvPR2Hl8K7sMvEzJnKUZL9qAN1HUSQiy3w8wPvkPvpXN6d8v6L415XyzxEeSog+OtQ4lntJHBGb8Sok9z5GAhhbtfZkD8noOKRmbeRgq8QfMj5CdFBBtGXF94sHRkl0gkEsn1QYogEolEIpFIJBLJNeKQvXWEhAQ4bG9jY9YNJoLYi+g+8FsEIqDAuNWwl+4Dvx37TGlFYe65N7k/qIE3BgYDtO8P2oflXC3M/NyUHYLJehtrlhygv6KK2aIZg6Ixf3HOuAScaYUQOBNWcUDMCHj5gEjHmXDp2tv3ePJJOsNnYx+IJS2ojRBvPslI94YQ1EVnI4YIYoL/3969x0Vd5f8Df31mhtsIDDDcBlTAJRUBETUQwnJdElutNCt/1npbu7k9ql3LNnd7dNtH6a6ltWW33b5a2/bV2so2M/zhHQRBEeQikr8EVATkfhEF4XN+fwwzMDIgyMBweT0fDx7jnM+Z+ZwPw3vOeN5zzgHOOU9HcF/bb1jCJ/xh/SbZcivgFgD4Rev3PjiXgsXhwMTf/Z/25eqKtwN53wNBd/ft3DTwSrJQ2dBsfH8C2pOCMxqyoS7J6vx3GPU7oDAJOUd24ZtTDciV/SEdz8CLwRUIuX2VaX0rL5lnp1LCTmU+2dAd3amteCMgC38umIwUOQhRijw8N6EMb+br+yNJEjgqT4IEYJq/KyovNCMx6wy825LFLUKBRw7a49iYfe0zQq5zubnV7Lkbm+Vet5eIiMgSmAQhIiIiIhogUhdLLA3FlZdQkmWSAAE6DDAiW7/xdQ+/may2UWGsmxq41F421k0NtU0//3elMAlTPSQ0hkxBpcoT2pZLUHtI/bcPyQCY4O0ECVdNkm2SBIz3dhrYhrQlHL49fRlFxa1oFVVQSjL86i5jYTf7k7jUnkaUMtdkOa8oZS40tRKgi+hbmzp+Y11uBRRK/S0AzFwD5O7EpbT/YGfZKWxtnYuVynjovE7BM+J+YMbqvp2bBp53KM43+iD1p85JwfN+gRjjrTX7sPY9M8oQ0TZj4vXcYGyaPw06QyVrvj8UJgEZ/0ajWocKGx+4X7sIdca/e9auwiSgNBvj3Efhvehw/GQ/Bf7us6GrTscb1z7GiaJqfNV6BxQA1i8KhU7jgOSKMFTLOxGpOmXcF2WZIh6njthg5pz7zJ4zwH0UFBK4tBwREQ0aTIIQEREREQ2QMa7mZ3uM7qJ8UPMOxSjnCUj7yXST6zQRBHVkONDTZaxKTqLWNQibjjWb7M2xufxWfBhuC03Jyf4ZcDTMCnBwgdrDH2qFEpDVgIOLdZe66YvSbGhLs/HchEhszPcwFj83vhzan1L1mxub28i4PwiBC1dtUVT8k+n+JMW5uOAfgtHmvkVflgOngnjUyBNNEmtBchGcCuIBV3Xf2h8w0/i6V3pGtO9v0LbBc0nMX/Bx8vOIU6YhVnEcSknGh2UReDRoRfvgNw0dATMR4HYFUsJ+06QggIDpcUAXSxBev2dGq1AgRQ4aHPtZtCVACioasK4gDEflIMxQ5GF9wEkE9CQRIoR+JhQAbUEixnsC+RVhsL3ahHHuo6ANnAbfsb+Bv7vaeK0Trp5EmqIB8S0RcFE0QIdKzFCdwp6iCAS6TjMbGzqNA9bfF9p5aTlr//6IiGjEYhKEiIiIiGiADKslQgJmQgtgw6JzWPd1NmTA+O1hbXAvNnrXhaHuxB4IoTEuf9QqFJAFUFdVDs3UuP5pvxD6hMeVmvYNfjskRqy91M1N0U1G4/kMeP38H0RK7UuUef18CI2TvaHWTR64tkgSyi6Vmt2f5PZLZRgdZGb6k5Ch9AmB68UaRIo8Y/tdFZeh9AnR78vRF22v7/6m8Xjk86uQRSoUEvDPO8ZjdkEiKi/WYmvrXGMCpFUo8D+tc3HnYBj8pt5L2QKdpMCGRfM6vUd1t8/L9XtmKCUZUYo8+LvPHugr6KwkC43XWowJEEC/gfmfCoBPNPnml/jqqEMScf/pMmT/73X7fPxyEaI61i9MgvZSGtyDf4W3T7riKekblEhaQAY0UgOqcg9AF/1rs6dafOtY3D7eA4UVjSZJFSIiImtgEoSIiIiIaIAMxyVC+jzQJQSc3TzwgCoBQgjjBut+qktwdruz/5IRkmRMgJS4TkPBzxUIcG/7VnNB4tBco8w/Bmcv1kJkfYj7lQcRIfIwViqDgISC0QsRPJAzW4SAJmg2UvNMExepIgjOQcHmX1ddGByv1mHmZA8g6xQiRB5UksDMyYFw1EiALqzPbar0jMCqf7UvFyYLYNUhexz/TQR0QmCl4juTwe+VyvjBMfhNvScpgLzvsTgIuH3divb3qLxtXe/zUpAIXVkOVvqcx/8Ut89g+q3vef3jvEIGbjaVOd6hOCv8cPSEaXGKHISC0ZMQ3JNl7woSUdnYjEcO2eNJhf7vvEUo8OghB6SN2Qet2rb9GtuWkPvV1GnYlb2lw2bqWtQJR3g7dz/jT6dxYPKDiIgGBYW1G0BERERENFIYlghRtg2wD5clQnQaB0T9Qntz1yFJ0FTnIdj5KvQL1QCAhGDnq9BU5/VfMqLkJODggh3lYxG9fj8e+kcqotfvx47ysfqZICUn++e8/akgETqNPb6W74CvVIEoRS58pQp8I98Bb41d+zJfAyFgJgIj7sKiqb4mxYum+iIw4i7zA8lts3Omekh4NMwGD07zwaNhNpjqIVlsdk5+aT2ufxYhgJ9K62F3ZhfiVGmIb4nAw9deRHxLBOKUaVBn/KPP5yUrmLFan+jI+x6tye8j52INWpPfb0+AmNvnpSwHTdnf4WiJbDKDKbVERlP2d0BZzgBfxHUCZkIbPBuK694WlZIEt+Bf9ixBI0lozD+IZVK8ScJvqfQjGvMPmr7nts2S01Wn48XgCqTKwXi39T6kysG4b9IoaB3tLXp5RERE/YUzQYiIiIiIBhCXCOms8VoLcmvtIUOCUpIhQ0JunT38rrWg/+bISGg8dwLfZlyGgH6wUwD49psvcXd4PtRjwvvtzP1GkqC9lIbnpwg0ZUsQEJAgYe2UZmgvpVnlG+xvPTgFy6L8cLywGtP9XRE2xrXryobZOVeqoG44D7Xion7j8lHO7cf7QpKgLU9FpKQ1DnADQKSUh1/kH4DUXIH4lghsk+cCgP62BQg69T1gb8PN0YeiGavxbUYxdClfYpL4Dy5IMo7p7sTCrl5LrxAUV1yGpigXkVL7kmzOUgMu6mIR4BUysO2/XkEidJKEheG++PpEsbF4QbgPdNXpQJW4cZwLAVdFA+a2Jfy2yXOxQhGPuao0uCqiTZONBYn6xM+VGoTcNh+b5k9r67tm62fGZHyurzfU9k8iIqIRhzNBiIiIiIgGWJ9mTgw3QiBTOw85sj/8FGW4VToNP0UZclr9cVI7r/+Ww9JNRkVDExYpD+k37IZ+MHyR8hAqG5qBgdw/w1L8Y/QzKSp/QPg4L4yeOhfh47wwtfIH/UwKKw1Uho1xxaqZ47pPgADG9qMsF7BRA2Oj9bdluZZpvxDw9tYhSplr8ppHKXPh5OSEy7r2BIjBNnku6gPm9n0/ErKKk+er8YeiKJP9hv5QFIWT56vNPyBgJhxmPomjcjCilLl4SvkNopS5SJWDYT/zSesuhQUAkoT60/tRnLnXpPhi5l7Un97fs0RhWQ5syvNwunUsXBQNeEr5DVwUDTjdOhY25Xmms10kyThrDv4x7X1Xdbo+YakLG5r7JxER0YjDJAgREREREVlP26Cii6IBRbIXjomJKJK94KJo0C9b1F+Djv4xuOB3HwCB+5WH8JTyG9yvPARA4ILfwqH5zebCJKAkC3D0gr2LDmPd1LB30QGOXvrywiRrt7B7hUn6gVWvYOBaI3AuWX/rFawv72v7JQkacRm3T74F0W0D3NHKXNw++RY4oAktXeQ5rrVykHeoSiuswgqF6bJPKxTxOF7YRRKkIBG66nQsXPgg5Lb6slBgwcIH9QP/A7mknDlCoLxFjRkK00RepCIXFS3qniUkvEJQrItFjeQIHSqhlGToUIkayREXdbH6fU8M/GOA8N8AV2pQmbsPyT9XoDJ3n/73EDBTPzvK2okhIiKiHuByWEREREREZD2FSQi9loUE2RFQwLjpbq3siNBrWUChe/8kJAoSMcHbCe+0zsIzqv/AVyoHAPy95X5s8XZqH+QbSkqy9Lcz1wBFyfqlpNwCgPCHgYx/648P5uRO254gAPTtdh4N1F0AHNwssydI27VPRSL8gpQoanGDn6oKWg8JuBIIbXE2ViqBra3ts0F+q4yHT8kZwP3evp2bBl5BIu69+CXOmln2adzFzUDBg51jXJKAgkTcUi5Q0iFxckvB50CDZP33BEmCh6oRtcIRUcpcRIg8KCUZdcIR7qrGns0ECZiJelUIapJfwgzVKYwW5VBKMvJa/FAXtgq4fsaWEDhRLpC473O0iC9wTJIRGjMfs/1j9IlJ0YMluIiIiKyMM0GIiIiIiMh6hICTiwcWBY/C0Vb9prtHW4OxKHgUnFw8+m+pFUmC7fkjmCQVmRQHSUWwPX+k/zZk70/eofqEB6BPgCiU+ltAX+4dar229YRhTxAHF30CRKHU3zq46Mst8Zr4x+BEucCZ/Gxc+X+JOJOfjRPlAvj1RtiF3ovVXqfwW2U8AH0C5AmvU7ALvZf7gQxFRz+E57kf0aydZLLPS7N2EjzP/Qgc/bDzY/xjUCuNQnPOd6iRHfFu632okR3RnPMdaqVR1k8i+sfAaeJszNApTGZxROoUcJo4u8ftU55PNjv7TnU+pVPdysZmJGadgXfb+VqEAo8ctG+fETIU3yuJiGjE4UwQIiIiIiKynraB75Db5mNzx013DUvP9NcAm38MynOO4n7VQVySNTghJmCqlI/7VQdR0bIcTtYe7LwZATP138w2zGIxfFPbcH+wf1vbMBPkSk3n9ltiJgiAytx9OJx1Bjp4oQRa6EQlSrLOwC94H7QzVsMTwB+zv8PTrWfgoATsQu9nAmSoctYBmjG43cMee6MUONQ0AXfY5SOw2B4oH6M/fr2U91F3Nh3xLRH6/TKkb6CUZMS3RMD3bDo0du8DUb8b+GvpoMR1Go5e3IE4VZlxFseei36Y7DoNZq6os8Ik+NWlo1Y4AlL77Ls64YixdceBQmeTZEq+fRiq5Z2IVJ0CZKBE0mKZIh6njthg5pz7rJ8YIiIi6gEmQYiIiIiIyHoMS6n4x0AHtG8Wr4lpP94fCpPg3ZiPNFmDCuiXf6mAKyRZQkRjvn7wfagN7l2fAAHabw17GQzmazLMBOmq/X1NiKW8j8a8VNTIziZLr9XIjmg99BZQ+CMw703Y5e+GnWEmzYzVXPJnqJr3pv61y/g3Aov/i0C3AKC4QH9szl+6jAUBgTzhZ1xqqlUokCf8AGQPXNu7UXB8DzRtszgMiTyNogEFx/dA96sFN34Cw+y7SY14PdcPKXIQohR5+HNwRefZdwWJCD6fgTRFgzExpEMlZqhO4fS5sZjUcBXafrtSIiIiy2EShIiIiIiIrKe7geX+HLAvyYLaRgU55g+4kPQjlFIrLsAL2pgVUF/ZO/j3zzCnQ0LJhH8/J5Qspb/bX3UWjnU/I1jhiv+03oFUEYRIKQ/3Kw/BofEiUHwF2L3WdCmx3Wv1e5IwATI0Gf52Dm4Aas/r/z3rha5j2zsUmqYWPHA2AQIC54QXxkpl8FNegvO4O62/pNwPzyGwKA//Vx7XKZEXmP8P4GqSPvnTnQ6z7zbdaPZdWQ40BfGYFHArHjsTgaekb1AiaeEh12Ki4hzqCjOhDYnt32smIiKyACZBiIiIiIho5PEOBXSTMRvArRiL6isyXB0UcJroBeDhwZ8wMMdaCSVL6e/2a38Bx4IjcEe1SbE7qmFv7wCo7ID8H4EJdwG/3qhPgBjuD4XfH/WdJEFTnYdo71Yklyr1RZAQ7d0KTXUe4D/Vyg0E3JrOY5ayDh+03GtM5K1WfQe3ploAQTd+gt7MvvMKASp/Rszli1ipjDfuQaJWXEW+PBZBo2zaZ58RERENYkyCEBERERHRyNNh/wynibP1e4AMpf0zqPe8QmAzfRn8s5PxQNFBRIg8+Ell8PcLgI2TDFQX6hMeDm7Aob/pbyfcBZTlAkc/4N4gQ1HbcljQjAHcAoCqAv19oNvElo+vH37tJaNS6QltqwpqlWKAGnwDwQugqjqLMPkCJlUVIbU1CJMURQhzaYLKPRAIXnDj5+htslH7C6irEvCkyznsr/XBRWiR3+qHR3U/Q3PhEBAw7aYvh4iIaKAwCUJERERERCPPUN8/g3qvLbkV4B0Kn72v4+q1C7C3UcAu9nGg5KT+9Z6xWp8AMSyJ9euN+gSIkK3deuotQwIEAMIf1r++hrKuEiFC6OuWZkOd9z3Uiov6v4Wgu/Wzx6w9Q0wIYOYauJZm44Xs7/BM6xk4KAG70JX90z5JAkqyUFJ7FVX1TZAgQxYK/CrIE7r6EwDsLXs+IiKifsIkCBERERERjTxDff8M6hM7lRJ2KmV7QdST+tvCJNM9QQqTOANkqCrJ0t8aEiBA+23Gv83v+2OYIXalBnANADSjgdoL+vuG49ZkOL9/DOzyd8PO8LfaX3+j/jGoLUhH/okcCKGBr1SBIOkcFD+loXb2o9D4T+V7JRERDQlMghARERER0cgz1PfPoJvT3fJIgOnsIMNsIYB/E0NR274/vUp0Gl5zBxf9fYVSnwhxcBlcfwtHPzBN1vXjcm3n7QJRLjQAJPigApIkIAsJ35do8ZtZg+B3QURE1ANMghAREREREdHw193ySImbAEcv87MGBtPgN/XczSQ6hdAnPK7UdE6GObgMjlkPRz8A8r7XL9E1Y3X7fcDyiZDCJPjVpeMvrbNwn/IwlJKMGuGIi8Id9rn/i8pcb2iDf2XZcxIREfUDJkGIiIiIiIho+OtueaTETYDtKC6PNtJJkmkCBDBNhkmS1ZoGoHMCBGi/7Y9EiBBoHnMbgqSd8FOUoUj2Qgm0qJEdEawoRN3ZDCZBiIhoSGAShIiIiIiIiIa/m1keqeNxGv4G+15BQjZNgBgY7gvZsucLmInaXW9irioN8S0R2CbPRaSUhyhlLk4Jf8y1adTPlGGMEBHRICcJYe1e/Mbq6uqg0WhQW1sLZ2dnazeHiIiIiIiIiGh4K0xCY+qn2Jhlj62tc43FkVIe1k4ow/SJ4wCvEOtvGE9ERCNWT/MGipt58i1btsDf3x/29vaIjIxEWlpat/W/+uorTJw4Efb29ggNDcXu3btv5rRERERERERERDQQhIA6cjkmLlgLZdtSYAoAs+cuxPRZ9zIBQkREQ0avl8PasWMH1qxZgw8//BCRkZF4++23ERcXh/z8fHh6enaqn5ycjCVLlmD9+vWYP38+vvjiCyxYsAAnTpxASEiIRS6CiIiIiIiIiIgsqC3BsdgfuH28BworGuHvroZO4wDgF1ZtGhERUW/0ejmsyMhI3HrrrXjvvfcAALIsY8yYMXjqqafwwgsvdKq/ePFiXL58Gbt27TKWzZgxA1OmTMGHH37Yo3NyOSwiIiIiIiIiIiIiIjLoad6gVzNBmpubkZ6ejnXr1hnLFAoFYmNjkZKSYvYxKSkpWLNmjUlZXFwcdu7c2eV5mpqa0NTUZLxfW1sLQH9RREREREREREREREQ0shnyBTea59GrJEhFRQVaW1vh5eVlUu7l5YXTp0+bfUxpaanZ+qWlpV2eZ/369Xj11Vc7lY8ZM6Y3zSUiIiIiIiIiIiIiomGsvr4eGo2my+O93hNkIKxbt85k9ogsy6iqqoJWq4XUthlXf6urq8OYMWNw/vx5LsFFZAGMKSLLYkwRWRZjisjyGFdElsWYIrIsxhSRZVkjpoQQqK+vh4+PT7f1epUEcXd3h1KpRFlZmUl5WVkZvL29zT7G29u7V/UBwM7ODnZ2diZlLi4uvWmqxTg7O/ONkMiCGFNElsWYIrIsxhSR5TGuiCyLMUVkWYwpIssa6JjqbgaIgaI3T2hra4tp06Zh3759xjJZlrFv3z5ERUWZfUxUVJRJfQBISEjosj4REREREREREREREZEl9Ho5rDVr1mD58uWYPn06IiIi8Pbbb+Py5ctYuXIlAGDZsmXw9fXF+vXrAQDPPPMM7rjjDrz11luYN28etm/fjuPHj+Pjjz+27JUQERERERERERERERF10OskyOLFi1FeXo6XXnoJpaWlmDJlCuLj442bn587dw4KRfsEk+joaHzxxRd48cUX8ac//Qm33HILdu7ciZCQEMtdRT+ws7PDyy+/3GlZLiK6OYwpIstiTBFZFmOKyPIYV0SWxZgisizGFJFlDeaYkoQQwtqNICIiIiIiIiIiIiIisrRe7QlCREREREREREREREQ0VDAJQkREREREREREREREwxKTIERERERERERERERENCwxCUJERERERERERERERMPSsEqCHD58GHfffTd8fHwgSRJ27txpcvyVV17BxIkTMWrUKLi6uiI2Nhapqalmn6upqQlTpkyBJEnIzMw0OZaVlYWZM2fC3t4eY8aMwd/+9rdOj//qq68wceJE2NvbIzQ0FLt377bUZRINGEvElL+/PyRJMvnZsGGDSR3GFI0UluqnfvjhB0RGRsLBwQGurq5YsGCByfFz585h3rx5UKvV8PT0xNq1a9HS0mJS5+DBg5g6dSrs7OwQGBiIbdu2WfhqifpfX2Pq4MGDnfoow8+xY8eM9dhP0UhhiX7qp59+wr333gt3d3c4OzsjJiYGBw4cMKnDfopGCkvE1IkTJ3DnnXfCxcUFWq0Wjz32GBoaGkzqMKZopLhRTHX0xBNPQJIkvP322yblVVVVePjhh+Hs7AwXFxesWrWqU0zxsx+NFJaIqddffx3R0dFQq9VwcXEx+9jB0E8NqyTI5cuXERYWhi1btpg9Pn78eLz33nvIzs5GUlIS/P39MWfOHJSXl3eq+/zzz8PHx6dTeV1dHebMmQM/Pz+kp6dj48aNeOWVV/Dxxx8b6yQnJ2PJkiVYtWoVMjIysGDBAixYsAA5OTmWu1iiAWCpmHrttddQUlJi/HnqqaeMxxhTNJJYIqa+/vprLF26FCtXrsTJkydx5MgRPPTQQ8bjra2tmDdvHpqbm5GcnIxPP/0U27Ztw0svvWSsU1BQgHnz5uGXv/wlMjMz8fvf/x6PPPII9uzZ038XT9QP+hpT0dHRJv1TSUkJHnnkEQQEBGD69OkA2E/RyGKJfmr+/PloaWnB/v37kZ6ejrCwMMyfPx+lpaUA2E/RyNLXmLp48SJiY2MRGBiI1NRUxMfHIzc3FytWrDA+B2OKRpIbxZTBt99+i6NHj5od13v44YeRm5uLhIQE7Nq1C4cPH8Zjjz1mPM7PfjSSWCKmmpub8cADD2D16tVmHzto+ikxTAEQ3377bbd1amtrBQCxd+9ek/Ldu3eLiRMnitzcXAFAZGRkGI+9//77wtXVVTQ1NRnL/vjHP4oJEyYY7z/44INi3rx5Js8ZGRkpHn/88Zu/ICIru9mY8vPzE5s3b+7yMYwpGqluJqauXbsmfH19xT//+c8uH7N7926hUChEaWmpseyDDz4Qzs7Oxjh7/vnnRXBwsMnjFi9eLOLi4m7yaoisry+f/Qyam5uFh4eHeO2114xl7KdopLqZmCovLxcAxOHDh4116urqBACRkJAghGA/RSPXzcTURx99JDw9PUVra6uxTlZWlgAgzpw5I4RgTNHI1VVMXbhwQfj6+oqcnJxO4xGnTp0SAMSxY8eMZT/++KOQJEkUFxcLIfjZj0aum4mpjrZu3So0Gk2n8sHSTw2rmSC90dzcjI8//hgajQZhYWHG8rKyMjz66KP417/+BbVa3elxKSkpuP3222Fra2ssi4uLQ35+Pqqrq411YmNjTR4XFxeHlJSUfroaIuvrKqYAYMOGDdBqtQgPD8fGjRtNprwxpojMMxdTJ06cQHFxMRQKBcLDw6HT6XDXXXeZfOMoJSUFoaGh8PLyMpbFxcWhrq4Oubm5xjqMKRppuuunDP773/+isrISK1euNJaxnyIyz1xMabVaTJgwAZ999hkuX76MlpYWfPTRR/D09MS0adMAsJ8i6oq5mGpqaoKtrS0UivahGwcHBwBAUlISAMYUUUeyLGPp0qVYu3YtgoODOx1PSUmBi4uLccYvAMTGxkKhUBiXouNnP6J2N4qpnhgs/dSIS4Ls2rULjo6OsLe3x+bNm5GQkAB3d3cAgBACK1aswBNPPGHyhthRaWmpyYsGwHjfMMW7qzqG40TDSXcxBQBPP/00tm/fjgMHDuDxxx/HG2+8geeff954nDFFZKq7mDp79iwA/frRL774Inbt2gVXV1fMmjULVVVVAPoWU3V1dbhy5Uq/Xh/RQLtRP9XRJ598gri4OIwePdpYxn6KyFR3MSVJEvbu3YuMjAw4OTnB3t4emzZtQnx8PFxdXQGwnyK6XncxNXv2bJSWlmLjxo1obm5GdXU1XnjhBQBASUkJAMYUUUd//etfoVKp8PTTT5s9XlpaCk9PT5MylUoFNze3G8aL4Vh3dfjZj4abG8VUTwyWfmrEJUEMa4slJydj7ty5ePDBB3Hp0iUAwLvvvov6+nqsW7fOyq0kGjq6iykAWLNmDWbNmoXJkyfjiSeewFtvvYV3330XTU1NVmw10eDVXUzJsgwA+POf/4xFixZh2rRp2Lp1KyRJwldffWXNZhMNWjfqpwwuXLiAPXv2YNWqVVZoJdHQ0V1MCSHw5JNPwtPTE4mJiUhLS8OCBQtw9913GwdsichUdzEVHByMTz/9FG+99RbUajW8vb0REBAALy8vk9khRASkp6fjnXfewbZt2yBJkrWbQzTkDbeYGnG95qhRoxAYGIgZM2bgk08+gUqlwieffAIA2L9/P1JSUmBnZweVSoXAwEAAwPTp07F8+XIAgLe3N8rKykye03Df29u72zqG40TDSXcxZU5kZCRaWlpQWFgIgDFFdL3uYkqn0wEAJk2aZKxvZ2eHcePG4dy5cwD6FlPOzs7GJRaIhoue9lNbt26FVqvFPffcY1LOforI1I3+P7Vr1y5s374dt912G6ZOnYr3338fDg4O+PTTTwGwnyK63o36qYceegilpaUoLi5GZWUlXnnlFZSXl2PcuHEAGFNEBomJibh06RLGjh0LlUoFlUqFoqIiPPvss/D39wegj4XrvwzT0tKCqqqqG8aL4Vh3dfjZj4aTnsRUTwyWfmrEJUGuJ8uy8Rvpf//733Hy5ElkZmYiMzMTu3fvBgDs2LEDr7/+OgAgKioKhw8fxrVr14zPkZCQgAkTJhineEdFRWHfvn0m50lISEBUVNRAXBKRVXWMKXMyMzOhUCiMU1AZU0Td6xhT06ZNg52dHfLz843Hr127hsLCQvj5+QHQx0t2drbJh/uEhAQ4OzsbkyeMKRrJzPVTQghs3boVy5Ytg42Njckx9lNE3esYU42NjQDQ6RvqCoXCOJuR/RRR97r6/5SXlxccHR2xY8cO2Nvb48477wTAmCIyWLp0KbKysoxjepmZmfDx8cHatWuxZ88eAPpYqKmpQXp6uvFx+/fvhyzLiIyMNNbhZz+insVUTwyafspiW6wPAvX19SIjI0NkZGQIAGLTpk0iIyNDFBUViYaGBrFu3TqRkpIiCgsLxfHjx8XKlSuFnZ2dyMnJMft8BQUFAoDIyMgwltXU1AgvLy+xdOlSkZOTI7Zv3y7UarX46KOPjHWOHDkiVCqVePPNN0VeXp54+eWXhY2NjcjOzu7vXwGRRfU1ppKTk8XmzZtFZmam+Pnnn8Xnn38uPDw8xLJly4znYEzRSGKJfuqZZ54Rvr6+Ys+ePeL06dNi1apVwtPTU1RVVQkhhGhpaREhISFizpw5IjMzU8THxwsPDw+xbt0643OcPXtWqNVqsXbtWpGXlye2bNkilEqliI+PH/DfCVFfWOqz3969ewUAkZeX1+kc7KdoJOlrTJWXlwutVivuu+8+kZmZKfLz88Vzzz0nbGxsRGZmphCC/RSNLJbop959912Rnp4u8vPzxXvvvSccHBzEO++8YzzOmKKRpLuYMsfPz09s3rzZpGzu3LkiPDxcpKamiqSkJHHLLbeIJUuWGI/zsx+NJJaIqaKiIpGRkSFeffVV4ejoaHy++vp6IcTg6aeGVRLkwIEDAkCnn+XLl4srV66IhQsXCh8fH2Frayt0Op245557RFpaWpfPZy4JIoQQJ0+eFDExMcLOzk74+vqKDRs2dHrsl19+KcaPHy9sbW1FcHCw+OGHHyx9uUT9rq8xlZ6eLiIjI4VGoxH29vYiKChIvPHGG+Lq1asm52FM0UhhiX6qublZPPvss8LT01M4OTmJ2NjYTgO6hYWF4q677hIODg7C3d1dPPvss+LatWud2jJlyhRha2srxo0bJ7Zu3drfl09kcZb67LdkyRIRHR3d5XnYT9FIYYmYOnbsmJgzZ45wc3MTTk5OYsaMGWL37t0mddhP0UhhiZhaunSpcHNzE7a2tmLy5Mnis88+63QexhSNFN3FlDnmBmwrKyvFkiVLhKOjo3B2dhYrV640DtYa8LMfjRSWiKnly5ebfY4DBw4Y6wyGfkoSQgjLzSshIiIiIiIiIiIiIiIaHEb8niBERERERERERERERDQ8MQlCRERERERERERERETDEpMgREREREREREREREQ0LDEJQkREREREREREREREwxKTIERERERERERERERENCwxCUJERERERERERERERMMSkyBERERERERERERERDQsMQlCRERERERERERERETDEpMgREREREREREREREQ0LDEJQkREREREREREREREwxKTIERERERERERERERENCwxCUJERERERERERERERMPS/wfetlVilWJEcgAAAABJRU5ErkJggg==\n", + "image/png": "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", "text/plain": [ "
" ] @@ -597,22 +1865,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████| 300/300 [00:11<00:00, 26.28it/s]" + "100%|██████████| 300/300 [00:18<00:00, 16.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2.40380537e+02 4.17598205e-01 2.22089558e+22 5.07222559e-01\n", - " 1.29431648e+00]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "[2.40385347e+02 4.17650105e-01 2.22152182e+22 5.07222111e-01\n", + " 1.29427452e+00]\n" ] } ], @@ -662,7 +1923,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -707,7 +1968,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -716,7 +1977,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -753,7 +2014,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -762,7 +2023,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -817,7 +2078,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/documents/tutorials/Fortrat.ipynb b/documents/tutorials/Fortrat.ipynb index abbd5fe44..151abdc94 100644 --- a/documents/tutorials/Fortrat.ipynb +++ b/documents/tutorials/Fortrat.ipynb @@ -31,9 +31,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kawahara/exojax/src/exojax/utils/molname.py:133: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.\n", " warnings.warn(\n", - "/home/kawahara/exojax/src/exojax/spec/api.py:153: UserWarning: nurange=None. Nonactive mode.\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n", + " warnings.warn(\n", + "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n", + " warnings.warn(\n", + "/home/kawahara/exojax/src/exojax/spec/api.py:233: UserWarning: nurange=None. Nonactive mode.\n", " warnings.warn(\"nurange=None. Nonactive mode.\", UserWarning)\n" ] }, @@ -42,8 +46,24 @@ "output_type": "stream", "text": [ "HITRAN exact name= (12C)(16O)\n", + "radis engine = vaex\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.def\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.pf\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.states.bz2\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__H2.broad\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__He.broad\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__air.broad\n", + "Note: Caching states data to the vaex format. After the second time, it will become much faster.\n", + "Molecule: CO\n", + "Isotopologue: 12C-16O\n", "Background atmosphere: H2\n", - "Reading CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2\n", + "ExoMol database: None\n", + "Local folder: CO/12C-16O/Li2015\n", + "Transition files: \n", + "\t => File 12C-16O__Li2015.trans\n", + "\t\t => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2\n", + "\t\t => Caching the *.trans.bz2 file to the vaex (*.h5) format. After the second time, it will become much faster.\n", + "\t\t => You can deleted the 'trans.bz2' file by hand.\n", "DataFrame (self.df) available.\n" ] } @@ -146,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:03:27.522525Z", @@ -158,14 +178,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABPcAAAF9CAYAAABoCY3bAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZhcZZ03/r/PqVP71l29pjvpdHcSQAIBlARZTFgTCfoEVLbJyOKjDxllfFh0BviqqKMBVBwc9cFBGgLDyOKAYHAEJZBOIEEBiSQC2Tq9pdNrdVV17XXOqd8f+d33dKWXdKqr17xf19UXdNVJ1V11upbzOZ9FyWazWRAREREREREREdGMo071AoiIiIiIiIiIiCg/DO4RERERERERERHNUAzuERERERERERERzVAM7hEREREREREREc1QDO4RERERERERERHNUAzuERERERERERERzVAM7hEREREREREREc1QDO4RERERERERERHNUNpUL4AOM00THR0d8Hq9UBRlqpdDRERERERERERTKJvNYmBgAFVVVVDVkfPzGNybJjo6OjBv3rypXgYREREREREREU0jbW1tmDt37ojXM7g3TXi9XgCHd5jP55vi1Ux/2WwWnZ2dAIDKysqCZztO9O1PpOP9uZnu65vu+PwRzTx83RIREU0OfubSZItEIpg3b56MGY2Ewb1pQrwp+Hw+BvfGIJvNIhaLATj8nE1EAGsib38iHe/PzXRf33TH549o5uHrloiIaHLwM5emytH+1jhQg4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwj4iIiIiIiIiIaIZicI+IiIiIiIiIiGiGYnCPiIiIiIiIiIhohmJwbwyee+45XHLJJQgEAlAUBc3NzcNu19raiquuugrFxcVwu91YunQpDh48OLmLJSIiIiIiIiKi4waDe2MQi8WwfPlyfPe73x1xm76+Ppx33nkoKirCK6+8gvfeew/f+ta3YLfbJ3GlRERERERERER0PNGmegEzwec//3kAwK5du0bc5r777kNdXR0eeughedmCBQsmfG1ERERERERERHT8mraZe0888QRuuukmnHnmmbDb7VAUBRs2bBj137z11ltYvXo1ioqK4Ha78fGPfxzPPPPMpKx348aN+OhHP4rPfvazKC8vx9KlS/Hcc89Nyn0Tjdf27duhKAouu+yyKbn/LVu24NOf/jSqqqqgKAqef/75KVkHERERERER0UwzbYN73/jGN/DQQw+hpaUFc+bMOer2r732Gs4991y8/vrruOqqq7Bu3Tp0dnbi6quvxv333z/h6z1w4AAefPBBLFmyBC+//DKuvvpqXHnlldiyZcuE3zfReDU0NODaa6/Fpk2b0NHRMen3H4vFcNppp+HnP//5pN83ERERERER0Uw2bYN7Dz/8MJqbm9HT04N169aNuq2u6/jSl74EVVWxZcsWPPTQQ7j//vvx17/+FSeccALuuusutLS05PybO+64A4qijPpzLEzTxNKlS3H33XfjjDPOwNe+9jV86lOfyinTJZqOotEonn76adxyyy244IILhmTIrl69Gtdff738/bXXXsMpp5wCwzAKtoZLL70U3/ve93DFFVcU7DaJiIiIiIiIjgfTNrh38cUXY/78+WPa9tVXX8X+/fvxd3/3dzj99NPl5X6/H3fddRfS6TQee+yxnH9z++2344MPPhj151hUVlbixBNPzLnsIx/5CFpbW4/pdogm2zPPPIPKykosW7YMa9euxSOPPIJsNiuvr66uzpn6vGLFCiSTSfzlL3/JuZ3169fD4/GM+sPXAxEREREREVFhzYqBGps3bwYArFy5csh1q1atAgA0NjbmXF5WVoaysrKCreGcc87Bvn37ci7bs2fPmAOURFOloaEBa9euBQBcfvnluOmmm9DY2Ijzzz8fwOHg3tatW+X2qqrC4XCgt7c353bWrVuHq666atT7qqqqKuziiYiIiIiIiI5zsyK4t3fvXgDAokWLhlxXWVkJj8cjt8lHMBhEa2sr9u/fDwB4//33EQqFUFNTg0AgAAC49dZbce655+L+++/HmjVr8Morr2Djxo1DgopCKpVCKpWSv0cikbzXR5Sv3bt3Y9u2bbIU1+PxYM2aNWhoaMgJ7g3O3NuxYwcikQg+9rGP5dxWIBCQrwciIiIiIiIimhzTtiz3WITDYQCHy3CH4/P55Db5+O1vf4szzjgDn/vc5wAAl112Gc444wz89re/lducddZZ+PWvf42GhgaceuqpePDBB/HrX/8a55xzzrC3ec8998Dv98ufefPm5b0+onw1NDRg6dKlOYHxtWvX4tlnn5WvmerqakSjUUQiEZimidtuuw1XXHEFysvLc26LZblEREREREREk29WZO5NtBtuuAE33HDDUbe74oorxjwQ4M4778Rtt90mf49EIgzw0aTSdR2PP/447rjjjpzLV65cCZfLhSeffBLr1q1DdXU1AKC9vR2PPvooOjs78Ytf/GLI7bEsl4iIiIiIiGjyzYrgnsjYGyk7LxKJoLi4eDKXdFR2ux12u32ql0HHsRdffBFdXV045ZRTsGvXrpzrli9fjoaGhpzg3u233449e/agsbERqjo06Xc8ZbnRaDSnZ+WBAwewY8cOBAIB1NTU5HWbRERERERERMeDWRHcEyWFe/fuHdIHrLOzE9FoFMuWLZuKpRFNWw0NDQCASy65ZMRt3nvvPSxZsgR2ux0tLS1obGxEdXU1Dh06VNC1vP3227jgggvk7yKr9frrr5f9AImIiIiIiIhoqFkR3FuxYgXuuece/OEPf8A111yTc93LL78styGi/7Fx48Yxb5tMJuX/Z7PZgq/l/PPPn5DbJSIiIiIiIprtZsVAjYsuugj19fX41a9+hR07dsjLw+Ew1q9fD5vNhuuuu27qFkhERERERERERDQBpm3m3sMPP4zXX38dALBz50552ebNmwEA5513Hr74xS8CADRNw8MPP4xVq1Zh+fLluOaaa+D1evHss8+ipaUFP/rRj1BbWzsVD4OIiIiIiIiIiGjCTNvg3uuvv47HHnss57I33ngDb7zxhvxdBPcA4IILLsDrr7+Ou+++G08//TQymQxOPfVU3Hfffbj66qsnbd1ERERERERERESTZdoG9zZs2HDMjfSXLVuG3//+9xOzICIiIiIiIiIiomlmVvTcIyIiIiIiIiIiOh4xuEdERERERERERDRDMbhHREREREREREQ0QzG4R0RERERERERENEMxuEdERERERERERDRDMbhHREREREREREQ0QzG4R0RERERERERENEMxuEdERERERERERDRDMbhHRDQNbd++HYqi4LLLLpuS+//pT3+KZcuWwev1ory8HJdffjl27949JWshIiIiIiKikTG4R0Q0DTU0NODaa6/Fpk2b0NHRMen3/+abb+LLX/4y3nzzTfzxj39EJpPBypUrEYvFJn0tRERERERENDIG94iIpploNIqnn34at9xyCy644AJs2LAh5/rVq1fj+uuvl7+/9tprKC0thWEYBVvDf/7nf+KGG27A4sWLcdppp2HDhg1obW3FO++8U7D7ICIiIiIiovFjcI+IaJp55plnUFlZiWXLlmHt2rV45JFHkM1m5fXV1dU4ePCg/H3FihVIJBJ48803c25n/fr18Hg8o/60traOaU3hcBgAEAgECvAIiYiIiIiIqFAY3KMpMdX9xADg5z//OWpra+FwOHDWWWfhz3/+85SthWiwhoYGrF27FgBw+eWX49ChQ2hsbJTXV1dXo729Xf6uqiqcTie6u7tzbmfdunXYsWPHqD9VVVVHXY9pmrjllltw7rnn4pRTTinQoyQiIiIiIqJC0KZ6AXR8Ev3EnnvuOXR0dIwpwFBITz/9NG677Tb84he/wFlnnYUHHngAq1atwu7du1FeXj6pa5kKuq4jHo8DAFwuFzSNbwXTxe7du7Ft2zZZiuvxeLBmzRo0NDTg/PPPBzA0c2/Hjh0IhUI4++yzc24rEAgUJNPuK1/5Cnbt2oXXX3993LdFREREREREhcXMPZp006Gf2I9//GN86Utfwo033oiTTz4Zv/jFL+ByufDII48U7D6mo2g0ig8++ACNjY14/vnn8Zvf/AZbtmyRJZc09RoaGrB06VIsWrRIXrZ27Vo8++yzcj9VV1cjGo0iEonANE3ceuutWLt2LSorK3NuqxBluTfffDNefPFFvPbaa5g7d27hHzARERERERGNC9N1aNId2U/s29/+Nu68804oigLgcODiwIEDcvvB/cTOPfdcefm//du/4ac//an8d8N5//33UVNTk3NZOp3GO++8gzvvvFNepqoqLr74Ymzfvr1QD3Paeffdd7F9+3aEw2Gk02kAgKIo6OjoQCKRwKpVq5jBN8V0Xcfjjz+OO+64I+fylStXwuVy4cknn8S6detQXV0NAGhvb8ejjz6Kzs5OvPDCC0Nub926dbjqqqtGvc+Rsmaz2SxuvvlmPP/889i8eTPq6uryfFREREREREQ0kXgkT5PuyH5iN910ExobG3NKDrdu3Sq3H6mf2Oc//3l8+tOfRnl5+YgBvuECF729vTAMAxUVFTmXV1RU4MMPPxzPQ5u2/vKXv+Cll15CJpPJuTybzSKdTuPDDz/E0qVLj4uS5OnsxRdfRFdXF0455RTs2rUr57rly5ejoaEhJ7h3++23Y8+ePdiyZQt8Pt+Q2xtPWe5dd92FF154AS+88AK8Xi86OzsBAH6/H06nM6/bJCIiIiIiosJjcI8mVSH7iRUXF6O4uBhz5swZNXvveBeNRrFt27Yhgb3B4vE4Ojs7GdybYg0NDQCASy65ZMRt3nvvPSxZsgR2ux0tLS1obGyUwb5CevzxxwFAvi6FRx99FDfccEPB74+IiIiIiIjyw+AeTaqR+oldeeWV+NnPfga/35/TT8zj8YzYTyzfstzS0lJYLBZ0dXXlXN7V1TXkPmaD3t5eRKPRUbcxTROHDh3CySefzNLcKbRx48Yxb5tMJidwJcDBgwcZOCciIiIiIpoBeBRPk6bQ/cTyLcu12Wz42Mc+hk2bNuHyyy8HcDi4tWnTJtx8883jfJTTTzqdhq7ro25jsVjQ29uLeDw+bHknEREREREREU1PDO7RpCl0P7HxlOXedtttuP7663HmmWdi2bJleOCBBxCLxXDjjTfm/wCnIV3X0d7eDqvVOuq0YU3TEI1GjxoEJCIiIiIiIqLphcE9mjTTqZ/Y1VdfjZ6eHnzrW99CZ2cnTj/9dLz00ktDhmzMdPF4HF1dXbDb7TAMA7quI5vNQlVVmKYJ4HDWntVqZfklERERERER0QzE4B5NmunUTwwAbr755llZhjuYrusYGBiAaZqw2WwyM0/TNBnos1qtsFgscLlc7LdHRERERERENMOoU70AIppYIlPPZrPBYrFAVVXY7XYoigJFUaBpGgzDgKIosNlsU71cIiIiIiIiIjoGDO4RzWKapsHlcgEAMpkMAMDtdmPevHlwu90ya89isSAWi6G3t3cql0tEREREREREx4jBPaJZLJFIIJVKwTRNGcQDDgf9xGVutxtutxvA4QnFHKpBRERERERENHOwwRbRLGUYBtra2mC321FcXIx0Og2HwwFFURCPx+H1epFMJmXwz+12I5FIIJ1Os/ceERERERER0QzBI3iiWSqTySAajcIwDGQyGVitVmiahgULFmDhwoVIpVLYtGkTYrEYPB4PNE1DNBqFqjKhl4iIiIiIiGim4FE80Sylqir6+/sRj8eRTqcRCoWQyWSwaNEiVFRUoLS0FJWVlXC5XMhkMnKirmmaU710IiIiIiIiIhojZu4RzVKZTAa6rsvJuDabDS6XCw6HA8Dh4F88Hkcmk4GmaUilUszcIyIiIiIiIppheBRPdBxQFAUWiyUncJdOp6HrOjRNg6qq0DQNuq4jnU5P4UqJiIiIiIiI6FgwuEc0S4kee7quwzRNGciz2WxTvTQiIiIiIiIiKhAG94hmKdM04fF4YLfbYZom7HY7PB6P7Klns9mgaZrstyfKcxn8Ixpq+/btUBQFl1122ZTc/4MPPoglS5bA5/PB5/Ph7LPPxu9///spWQsREREREU0vDO4RzVKqqiKVSkHTNFRUVMBmsyGdTsvSXBH8czgcME0TDocjJ/hHRP+joaEB1157LTZt2oSOjo5Jv/+5c+fi3nvvxTvvvIO3334bF154IdasWYO//e1vk74WIiIiIiKaXhjcI5qlBgYGkEgkkMlkEAwG4XK54Pf7ZfBOVVWk02nYbDaUlpYOCf4R0WHRaBRPP/00brnlFlxwwQXYsGFDzvWrV6/G9ddfL39/7bXXUFpaCsMwCraGT3/601i9ejUWLVqEE044Ad///vfh8Xjw5ptvFuw+iIiIiIhoZuJRPNEsZBgGenp6YLFY4Ha74XK5kMlk4PF4ZNmtaZqw2WzQdR39/f3QdR02m42Ze0RHeOaZZ1BZWYlly5Zh7dq1eOSRR5DNZuX11dXVOHjwoPx9xYoVSCQSQwJv69evh8fjGfWntbX1qOsxDANPPfUUYrEYzj777MI9UCIiIiIimpG0qV4AERVeJpNBPB6HqqqIRqMwTRMulwtVVVXQtMMv+8GZe36/HwMDA0gmk8zcIzpCQ0MD1q5dCwC4/PLLcdNNN6GxsRHnn38+gMPBva1bt8rtVVWF0+lEd3d3zu2sW7cOV1111aj3VVVVNeJ1O3fuxNlnn41kMgmPx4Pf/OY3OPnkk/N8VERERERENFvwKH4M7rnnHpx55pnwer2oqKjAVVddhebm5pxtnnvuOVxyySUIBAJQFGXI9USTSVVVxGIxpNNpFBUVwWazweFwoKioSG5jmiZ8Ph9sNhtCoRAymQySySRCodCUrft4Fo1GsXv3buzevRvRaHSql0P/f7t378a2bdtkcM/j8WDNmjVoaGiQ2xyZubdjxw6EQqEhWXWBQAALFy4c9UcE34dz4oknYseOHfjTn/6Ef/iHf8D111+P999/v8CPmIiIiIiIZhoG98agsbER//iP/4g//elPeOmllxAMBnHppZdC13W5TSwWw/Lly/Hd7353CldKdJhpmtA0Del0Gn19fTAMQw7OEGw2G7xeL0zThNPphNvthqqq6OjoyPnbpomj6zo6OjrwyiuvoKGhAc888wyeeeYZbNiwAR988MFUL49wOGtv6dKlWLRokbxs7dq1ePbZZxEOhwEcDu5Fo1FEIhGYpolbb70Va9euRWVlZc5tjbcs12azYeHChfjYxz6Ge+65B6eddhp+8pOfTMwDJyIiIiKiGYNluWPw0ksv5fz+y1/+EvX19Xj//fexZMkSAMDnP/95AMCuXbsmfX1ER1JVFbquQ9M0+Hw+JBKJIcMyNE1DVVUV9u7di3g8DkVRUFRUhGg0inQ6PWoGEY1fOBzGpk2b8MEHHwwJpvb19eG1117DvHnz4PF4pmiFpOs6Hn/8cdxxxx05l69cuRIulwtPPvkk1q1bh+rqagBAe3s7Hn30UXR2duKFF14YcnvjLcs9kmmaSKVSY96eiIiIiIhmp2l79P7EE09g69ateOedd7Bz506k02k8+uijuOGGG0b8N2+99RbuvvtubNu2DZlMBqeeeipuu+22ox5MHSuRrREIBAp6u0SFIjL3kskk+vv7oWnasMMyioqK4HQ6YRgGioqKEIlEEA6H2Xdvgum6jm3btmHXrl05gxkGCwaD6OrqYnBvCr344ovo6urCKaecMuTEzfLly9HQ0JAT3Lv99tuxZ88ebNmyBT6fb8jtBQKBvD837rzzTlx66aWoqanBwMAAfvWrX2Hz5s14+eWX87o9IiIiIiKaPaZtcO8b3/gGWlpaUFpaijlz5qClpWXU7V977TWsWrUKDocD11xzDbxeL5599llcffXVaGtrw+23316QdRmGga997WtYvXo15s6dW5DbnO16e3vR3t6OkpISzJkzhxlhk0BVVWQyGaiqiuLiYpmNd2TQzjRN+P1+KIqCdDoNt9sNn8/HibkTLBKJYM+ePSMG9oDD7zXsvTe1RF+9Sy65ZMRt3nvvPSxZsgR2ux0tLS1obGyUwb5C6u7uxnXXXYdDhw7B7/djyZIlePnll0ddGxERERERHR+mbZTl4YcfxqJFizB//nzce++9uPPOO0fcVtd1fOlLX4KqqtiyZQtOP/10AMC3vvUtLFu2DHfddRc+97nPYf78+fLf3HHHHbjvvvtGXcORB97ZbBbr1q1Da2sr3njjjfwf3HFk69at2L59O5LJJCwWC+rr67F69Wr4/f6pXtqsNjAwgFQqBcMwEAwGUVRUBL/fPyRoZ7PZYLPZkEqloCgKdF2Xl9HESSQSRy2nVBQFhw4dwuLFixkQnyIbN24c87bJZHICV4KcAR5ERERERESDTdsjxosvvnjM27766qvYv38/brzxRhnYAwC/34+77roLN9xwAx577DF861vfktfdfvvto5b4HimbzeLLX/4yXnnlFWzZsgVlZWVj/rfHq97eXmzbtk0e9Oq6jj179sDn82HVqlUMWEwQXdfR09MDi8UCt9uNbDaLTCYDj8czYtAum81CUZRRM8mocJLJJAzDGHUbm82GYDCIeDw+bIknERERERERETCNg3vHYvPmzQAONzk/0qpVqwAcnng7WFlZ2ZgDdNlsFl/5ylfwu9/9Do2NjZg3b974Fnyc2Lt377DZLHv37sXZZ5/NnoUTJJ1OwzRNlJWVIZ1OQ9d1KIqCqqqqIQHVdDqNTCYDp9OJTCYDu92OTCbDgRoTSNd1tLS0QNM0ZDKZYQOqmqbB6XQikUhwcjERERERERGNalYcve/duxcAsGjRoiHXVVZWwuPxyG3y8ZWvfAVPPvkkNm7cCKfTic7OTgCHm6OLTKhgMIjW1lbs378fAPD+++8jFAqhpqZm2CBWKpXKKcuLRCJ5r2860nUd/f39w16XSqWQSCQmeUXHD5vNBsMw0NPTA5fLhUwmg+rqapSWlg7ZVlVVhMNhxGIx+P1+hMNhZLNZDtSYQPF4HF1dXbBardB1Hel0esg2VqsVAGC32xlkJSIiIiIiolHNiiN4Mb12pD5uPp9PbpOPBx98EKFQCJ/4xCcwZ84c+bNt2za5zW9/+1ucccYZ+NznPgcAuOyyy3DGGWfgt7/97bC3ec8998Dv98uf2ZYNGI/HkUgk4HQ6h1yXzWaP2m+Mxi+bzcI0TVgsFlgslmG3MU0TPp8PbrcbqVSKAzUmga7riEQiMAwDFosFiqIAAFwul/xdVVV5PfsfEhERERER0WhmRXBvomWz2WF/zj//fLnNDTfcMOw2I/X1u/POOxEOh+VPW1vb5DyYSaTrOrxeb04WmMVigaZpaGlpYbnhBBGltjabDRaLBXa7Hel0etgMscEDNQzDQCqV4kCNCRaJRGS5rQjkORwO1NTUwOl0wmKxQFVVWCwWGIYx7H4jIiIiIiIiEmZFvZfI2BspOy8SiaC4uHgyl3RUdrsddrt9qpcxYWw2GzRNg6IocDqdiMfjUBQFHo8HTqeTgwImkKqqiMViSCQS8Hq9suR7tFJbDtSYHLqu49ChQzJ4emRmZSaTgcViQVFRETKZDFKpFIPgRERERERENKpZkbkneu0N11evs7MT0Wh02H58NHFM04TH44HD4YDD4YCmaTkliAxYTBzx3DudTiSTSXg8Hvj9/mFLbQcP1FBVVQ7WYLbYxEin00gkEjK4Z7FY4HQ64fP55EATu92OWCyGVCrFnntERERERER0VLMiuLdixQoAwB/+8Ich17388ss529DkUFUV6XQaNptNTmkVWWHxeBymabL0c4KIrMlUKgXTNJFIJGC1Wod9vsVAjWg0CofDgWg0inA4zIEaE0RVVfT39yOdTsNisUDXdXg8Hnzyk5/EBRdcgLlz58LpdMJut8Nms8HpdPJ1QkRERERERKOaFUfwF110Eerr6/GrX/0KO3bskJeHw2GsX78eNpsN11133dQt8Dhkmib8fr/MSLLZbHA4HHA6nfB4PDL4RxNHVVVomjZqoI4DNSZXOp2Gruuw2+1wu93wer1wu90oKipCcXExfD4fHA4HgMMTc10uF/cFERERERERjWra1ns9/PDDeP311wEAO3fulJdt3rwZAHDeeefhi1/8IgBA0zQ8/PDDWLVqFZYvX45rrrkGXq8Xzz77LFpaWvCjH/0ItbW1U/Ewjls2mw1WqxWJRAKmacIwDCiKIn9o4oissOrqarjdblitVui6jnQ6PaTEc/BADUVRoOs6B2pMEkVR5PAM4H+yXcWE3EQiga6uLkSjUbhcrileLREREREREU1XeQf33n//ffzsZz/DW2+9hVAoBMMwhmyjKAr279+f1+2//vrreOyxx3Iue+ONN/DGG2/I30VwDwAuuOACvP7667j77rvx9NNPI5PJ4NRTT8V9992Hq6++Oq810PipqioDRaLfmGEYcLvdDCBNEJvNBsMw0NnZCY/HA8MwUFVVNerzrSgKNE1jL8QJJiYYi/56uq5D0zTYbDaYpgm3243+/n7oui77ILa0tCAQCLD3HhEREREREQ0rr6PFxsZGfPKTn0QqlYKmaaioqBj2wHM8kzc3bNiADRs2HNO/WbZsGX7/+9/nfZ9UOCJLbOHChUgkEujr60M8HofL5ZKB4HQ6LUsQqbAMw4Bpmkct6RT7qb6+XgaaRsryo/ETJ0JEMK+srAylpaWyB6XH48kJtNpsNkSjUe4PIiIiIiIiGlFeR4t33HEHdF3Hww8/jOuvvx4Wi6XQ66IZTmQoRSIRWK1WAIDT6YTVaoVpmojH44hEIvD5fFO80tmnt7cX/f39sFgs0DQN5eXlsFgsI5bl6rqO1tZWGVA6WpYf5UfXdXR0dMBiscDj8QA4HIT1eDxyCMr8+fPx/vvvy0A4AESjUQ44ISIiIiIiohHldcT417/+Fddccw2+8IUvMLBHw4rFYkilUujr68PBgwfhdrsBAKlUCsDhQF93dzfLQAtMBJBE1qyu6zh48KDs40ZTJ51OY2BgAKqqIpFIIBaLwTRNOU0aALxeLyoqKuB0OuVrQ2T5EREREREREQ0nr8w9t9uN8vLyQq+FZgld19Hc3AxFUVBXV4dwOAxd19HZ2Yl4PA6LxQKXy8VywwmQTqdhGAbKysoQCoUAABaLJSeAdOT2mqZh0aJFMAwDuq7DMAzulwmgqioikQjS6TSKiooQjUbhcDhQVFSUs42Yput0OpFIJJBOp5m5R0RERERERCPK6+h99erV2Lp1a6HXQrNEOp1GOp2Gx+OB1WqFxWJBOByG3W6HaZrwer2IxWI5k0KpMESZbU9PDxRFQTabxdy5c1FaWjri9jabTfZEjMViMqjECa2FJfrq9ff3Ix6PQ9M0OByOnKw8sY2u6+jv788ZtkFEREREREQ0nLwiKz/84Q8RCoXw1a9+FfF4vNBrohlOBIyi0SgymQyi0SisVitcLhdM00RfX58cFsCgxcRRVRWapo1aOq9pGubOnYtoNIpYLAa32w2v14v29naWTBeYqqoyI7K4uBiapg3JyhvLNkTHu+3bt0NRFFx22WVTvRTce++9UBQFt9xyy1QvhYiIiIiOY2PK3LvwwguHXObxePDzn/8cGzZswAknnDDsYARFUbBp06bxr5JmFE3TUFtbi/379yMUCsHlcqG6uhpdXV3QNA1er5flhhNEBIaqq6thGAYCgcCQMttoNIrOzk5YrVbZ362srEwO4BABJpbmFtZYsvKYuUd0dA0NDbj22mvx3HPPoaOjA1VVVVOyjrfeegv//u//jiVLlkzJ/RMRERERCWM6ct+8efOI10WjUfzlL38Z9jpFUfJaFM0e4m8gm80yaDEJxHMshmiEQiFUV1cjnU6jra0NbW1t+PDDDxGJRKCqKioqKnD++ecjm81yYu4EU1UVqVQKqqqitLRU9pwcKXOPQfCpFQqF0NnZCb/fj7KyMga6p4loNIqnn34amzZtQn9/PzZs2IC77rpLXr969WqUlZXhscceAwC89tpruPLKK9HV1VXQAWDRaBRr167FL3/5S3zve98r2O0SEREREeVjTEeMpmnm9WMYxkSvn6YhMVAjnU7D7/fLwFIqlWK54RTo7OzEE088gaeeegpvvPEG+vr6kMlkkEql0NbWhq1bt7IEdxKEQiEkEglkMhkEg0G4XC74/f5RM/dYvj413n77bWzYsAHPPfccHn/8cTz//PMIh8NTvSwC8Mwzz6CyshLLli3D2rVr8cgjj8jp4ABQXV2NgwcPyt9XrFiBRCKBN998M+d21q9fD4/HM+pPa2vriOv4yle+gssuuwwXX3xx4R8kEREREdExYioCFdyRAzU8Hg/C4TAsFgsz9ybYkWW5TqcT27dvRywWG3b7bDaLQ4cOobKyEvX19dB1XWbvsSy3cHRdR0dHBywWC9xuNwAgk8nA4/HkZEiKzD2bzYbi4mLEYjEGwSdRNBrF+++/j82bNyORSAA4vJ/ef/99uFwurFy5kq+JKdbQ0IC1a9cCAC6//HLcdNNNaGxsxPnnnw/gcHBv8MAvVVXhdDrR3d2dczvr1q3DVVddNep9jVTu+9RTT+Evf/kL3nrrrXE8EiIiIiKiwsnrKOXCCy/EDTfcgOuuu27EbZ544gk88sgjePXVV/NeHM1MgwdqeDweRKNR2Gw2GIYBm80Gv9+PgYEBJJNJBi0K7MiyXDEoYzSGYUDXdTQ1NbEsd4Kk02kMDAxAVVXEYjGYpgmXy4WqqqqcYJFpmrJ/qWEYcLvd8Pl8DIJPgg8++AB/+MMfEAqFhlyXzWaxd+9efPzjH0cgEJj8xREAYPfu3di2bRs2bNgA4HDv3zVr1qChoSEnuDc4c2/Hjh0IhUI4++yzc24rEAjktS/b2trwf//v/8Uf//hHOByOvB8LEREREVEh5RVZ2bx5M5qbm0fdpqWlBY2NjfncPM1wYqCG0+lEIpGA0+nEvHnz4Pf7YbPZEAqFkMlkkEwmhz2QpsIwTXNM06wtFgusVuskrOj4paoqIpEI0uk0ioqKYLPZ4HA4UFRUlLOdCIynUikYhoFUKiUvo4kTjUaxefPmUd+PEomEzOajqdHQ0IClS5di0aJF8rK1a9fi2WeflWXT1dXViEajiEQiME0Tt956K9auXYvKysqc28q3LPedd95Bd3c3PvrRj0LTNGiahsbGRvzbv/0bNE1jOxIiIiIimhITVl8Ui8UYMDiO+f1+LF68WJYYAkBHRwd6enrgdruhqiqy2Sw6OjpQWlrKUrcCEaW08+bNQzKZPGqfMEVRYLFYYBgGy3InkOil19/fj3g8Dk3T4HA4RszIy2azUBQlp5cYTZzm5mb09fUddTtmGk8dXdfx+OOP44477si5fOXKlXC5XHjyySexbt06VFdXAwDa29vx6KOPorOzEy+88MKQ28u3LPeiiy7Czp07cy678cYbcdJJJ+Gf//mfCzq0g4iIiIhorMZ85H7kGexQKDTsWW3DMNDW1oZnn30WtbW1414gzVwiq0GoqqpCS0uLvK6srAyGYTCIVEA2m02W46qqikQiAVVVRwwiWa1WKIqCnp4eRKNRWK1WluVOgMFTcIuKioadlAscDs5mMhk4nU5kMhnY7XZkMhm+RiaQGAB0tIyr0V5HNPFefPFFdHV14ZRTTsGuXbtyrlu+fDkaGhpygnu333479uzZgy1btshS98HyLcv1er045ZRTci5zu90oKSkZcjkRERER0WQZ89FibW0tFEUBcDjb5yc/+Ql+8pOfjLh9NpvFD3/4w/GvkGaN0tJSlJWV4dChQwCAgwcPMohUYLFYDKlUCuFwGKlUCgDgcrnkdFwAMlNPURSoqgpFUTAwMACr1cps2wlimib8fj8URUEmk4HP5xu2l56qqgiHwxgYGIDH40EkEkE2m2XG2ASKRCLo7OyEw+FAMpkcdhuLxcLy6CnW0NAAALjkkktG3Oa9997DkiVLYLfbZWsQEewjIiIiIprNxhzcu+6662SZ2OOPP47TTjsNp59++pDtLBYLAoEALrzwQnzyk58s5FppFhAlS6ZpMgumwEQGkqIoqK6uRl9fH2KxGHRdl1lHhmHAarXK4J7FYkE2m0UymUR1dTUcDgfLcieAzWaD1WqVmZSJRAIlJSVDgkWifFfXdfT29nKq9CQQ073tdjtM00Q6nR6yjdVqhc/ng91un4IVEgBs3LhxzNuOFKSdKJs3b57U+yMiIiIiOtKYj9zFdDoAaGxsxI033oivfvWrE7EmmiVEgMhms0HTNJYcTjARpHC5XDJYkclkoOs6LBYLFEWBx+NBZWUlWlpaZH89ETg6ePAgnE4ny3IniCj7HC0LT5Tv2mw2FBcXIxaLDVu+S4UjWgOIwKs4iSWC3KqqwmKxwDRNJJPJYUs8iYiIiIiIplJeEZUDBw4Ueh00y4TDYTQ3N8tARW1tLex2O8LhMGKxGPx+P8LhMEsOC0hkfLW0tCCVSqG3txe6rssgn8VigcPhgNPplFMdTdOUwQuaOL29vejp6ZF9KMvKymCxWIYEtk3TlMEjwzDgdruHLd+lwtB1Hfv27YPFYpHl6iLAJ96XVFWFzWZDOp3Ghx9+iEAgwJMRREREREQ0rfAIhQpOlIcmEgl4PB5Eo1E0Nzejrq5OBi5SqRQDFxPIMAyZsedyuWCaphyykclkZEmuqqowDAM2mw319fVQFIVluQWm6zo6OjpkIFXXdRw8eBB1dXVDsiNFX7dUKgVFUaDrOnu9TaB4PI6Ojg5ks1kZBAcO9wcFgP7+flkqbbVa0dPTg3g8zuw9IiIiIiKaVvI6cq+vrz/qNqqqwufz4cQTT8QVV1yBq666Kp+7ohlIlId6PB5YrVZ4PB4kEgkAYOBiAolgXH19PQ4dOoRIJCLLDU3ThNPpxKJFi5DNZtHX1wdFUWC1WpFOp2EYBg4cOACHw8Gy3AITz291dTV6e3tlFmVVVdWIwdPBQVaaOLquIx6Pw2q1IpvNyuCepmlwu93o7e2VffjEexYREREREdF0k1dwzzRNmY0CHD4QKi0tlWWAAFBVVYXu7m7s2LEDzzzzDB5++GG8+OKLDBgcB0TALhqNysw9p9Mp9z0DFxNjcFluMplEKpVCNpuVAbvS0lIsW7YMsVgMoVBI9jsc/F+HwzHVD2PWEfulo6ND9tSrrq6W2WGDDQ7Qip6IzKKcOGJgSTQalcFuMck4nU7L1w4AmbHHzzAiIiIiIppu8mp2tmPHDsyZMwcXXnghtm3bhlQqhY6ODqRSKWzbtg0XXXQRqqqq0Nraij179mD16tXYtGkT7r///kKvn6YhTdNQW1sLp9OJRCIBp9OJ2tpamKYpAxdVVVWor6+Xgzao8EQvPcMw4HA44PP5oKoqSkpKUFFRgVgshmQyiWw2C5vNBq/Xi5qaGixatIj7ZYKoqgpN00bscSgCgU1NTejo6EBTU5PMcKXCSyQSSKVSctiJpmkoKSnBRRddhLPPPhsVFRUoKiqCy+WCx+ORwVkiIiIiIqLpJK/g3j//8z8jlUrhD3/4Az7+8Y9DURQAhzOyPv7xj+Oll15CMpnEHXfcgYULF+LXv/415s+fj6eeeqqgi6fpy+/3Y/HixfLH7/czcDHBRHbXwoULMWfOHDgcDtnnLZVKyZ57YhuXyyV77zkcDkQiESSTSQwMDLBcuoAGZ+ONNagtsqPZj3LiiCxXu92O4uJi2O12uN1uBAIB2Gw2BAIB2ZdS/JeIiIiIiGg6yuto5YUXXsDq1atHPNixWCxYvXo1XnjhBQCAw+HAhRdeiH379uW/UppxNE2Dy+ViOeEkEQG5WCwm+x6KSZ9Wq1WWdwJAUVERrFYrDMOApmmIx+Po7+9HS0sLgsEgJ4IW0LEEtUV5tNPphKqqcDqdsmyaCiudTiMajSKbzcIwDNjtdqiqCo/HI19LmqYhk8nANE1kMhlZxktERERERDSd5HX0HolEEIlERt0mHA4jHA7L34frL0XHF/YTm1iapiEQCKClpQWdnZ1IJpNwuVxQVRUOhwN2u11uK4Y8iEEC2WwWmqZh3rx5UBQFwWAQc+bM4X4pEMMwYJrmUTPxVFVFOBxGLBaD3+9HOBxGNptl1tgEUFUVsVhM9gAVLQTmz58vMys9Hg8GBgZkP0qPx8NsSiIiIiIimnbyOmI8+eST8eSTT6KpqWnY65uamvDUU0/h5JNPlpe1trairKwsv1XSrMCy3Iml6zqCwSD8fj/cbjfsdjsMw0AikUAsFkNpaSlcLteQf2eaJrLZLCwWCywWC9xut8z8o/Hr7e1FT08PNE2Dpmmorq4esSzXNE34fD643W6kUim43W74fD4GlCaAaZqw2Wwyc8/hcKCiogIejwcAZH89m82G0tJS2Gw2mQ1LREREREQ0neSVlnPXXXfhc5/7HE4//XR88YtfxLnnnovy8nJ0d3fjjTfeQENDA6LRKO666y4Ah7OE/vCHP2DlypUFXTzNXKKnmGhkT+OXTqeRSCRgtVphmiacTieSySQAwOVyYeHChTITb3DJocigjMfj6OjoQDabRVVVFYOuBSCm5Ireh7qu4+DBg6irqxv2+RXloKlUCoqiyOA390XhieCdpmnwer1IJBLQdV0G70TwT9d19Pf3y5JcBlqJiIiIiGi6ySu495nPfAYPP/wwbrnlFjzwwAP4yU9+Iq/LZrPweDz493//d3zmM58BAMTjcTQ0NGDx4sWFWTXNSOJAurq6Gr29vQAOZzX19vaisrJyilc38yUSCfT09CASiSAYDMosMJvNBpfLhaKiIrmtaZqy5FDsF4vFAtM05YAcGj9R/iz+5jOZDCwWC6qqqmSgNRQKoaenB2VlZTJrTJSK6ro+lcuf1Y4WvBsu+MfMPSIiIiIimo7ybqj1hS98AZ/97Gfxwgsv4K9//SsikQh8Ph9OO+00rFmzBn6/X25bVFSENWvWFGTBNHPZbDZYLBa0trbKA2TDMNDR0YHS0lL2dxsHXdfR3t4Oj8cjMyJ1XUcmk4HVapWTc4XBJYdutxuZTAZutxt1dXWwWq1IpVLshVgAInjU0dEhn/Pq6mp4PB50dnZiz549ePfdd5FMJmGz2XDmmWfC4XBg0aJFch8ahsF9MQHEFGlVVVFaWopoNJoTvGPmHhERERERzRTjOlr0+/247rrrCrUWmuU0TUNVVRVaWlrk72VlZQxeFIDokVdWVoaioiJ0d3cjmUyiuLhYPr+DM44GBy4SiQQURUE2m0U6nUYymZQTQ6lwVFWFpmmIRCL43e9+h0OHDuUMHUomk9i6dSs+/vGPIxqNIh6PIxaLwel0IpFIDNsvkfIXCoWQSCSQyWQQDAZRVFQEv9/PzD0iIiIiIppxGE2hSVVaWoqysjIcOnQIAHDw4EH2dysA0ZftyCnWkUgEdrt9SMbR4My94uJi9Pb2IhaLoaWlBW63GzU1NQy2FsCRE6JN08Tbb7+NcDiMTCYzZPtMJoPOzk54PB4kk0m43W643W60t7fD7/dznxSIyKYUA2SAw8/94KA2M/eIiIiIiGimyPtIMZ1O4/nnn8dbb72FUCg07GAERVHQ0NAwrgXS7GOxWKCqKlRV5YFygWiahkAggNbWVgwMDCAej8Nms6G8vByxWGzYzD2fzwfgcFBDURT4/X7U1tbCNE0Eg0HMmTOHwaRxEsGh1tZWaJome0yOJhKJoKqqSj7/IhDL7NbCSafTGBgYgKqqiMViME0TLpcrpxciM/eIhtq+fTvOOeccrF69Gr/73e8m/f6//e1v4zvf+U7OZSeeeCI+/PDDSV8LERER0XSS15FiS0sLLrnkEuzfvx/ZbHbE7RjcoyOJg+VFixbJy9jfbfx0XUcwGEQgEIDL5UJPTw/S6TT6+voQCARyyg2B3KmshmEglUqhrKwMbrdbTt3lPiks0zQRjUbHtJ1hGDIgqOs6s1sLTFVVRCIRpNNpFBUVIRqNwuFwDBk6w8w9olwNDQ249tpr8dxzz6GjowNVVVWTvobFixfjlVdekb/zc4qIiIgoz+Derbfein379uHzn/88vvCFL2Du3Ln8ckVjIoJKAwMDcDgc7O9WICKzy+Vyobe3VwYhnE7nkHLDwbLZrMyiFGW58Xicfd4KZHBZ7sDAADo6Okbd3mKxAAAnFk8wEbjr7+9HPB6HpmkjDp1h5t7USyaTiEaj8Hg8cDgcU72c41Y0GsXTTz+NTZs2ob+/Hxs2bMBdd90lr1+9ejXKysrw2GOPAQBee+01XHnllejq6pLvbYWgaRoqKysLdntEREREs0FeEblXX30VF110kfwCRzRWg8tHk8kkHA4H+7sVgMgwampqQiQSga7rMtPIYrHklBsCh4NOmUxGBv+Ki4sRDAZhsVjg9/vZ561ABpfl6ro+pCfikSwWiwy4ij59InuPmZSFc7RJuQAz96aL5uZmbN++HfF4HH6/H2eddRbmzZs31cs6Lj3zzDOorKzEsmXLsHbtWnz729/GnXfeKU9GVFdX48CBA3L7FStWIJFI4M0338S5554rL1+/fj3Wr18/6n29//77qKmpGfa6vXv3oqqqCg6HA2effTbuueeeEbclIiIiOl7kdaRomibOOOOMQq+FjgOifLSkpERm7rG/W+Goqiqn3iqKgnQ6jXnz5qG0tHTIduFwGLFYDF6vF729vVAUBQsWLIDX64VpmizNLbB0Og3TNOWB8JEtDQZnthiGgaamJpblTpCjTcoFmLk3HXR1deGFF15AKBQCAHR0dCASieDv/u7vmME3BRoaGrB27VoAwOWXX46bbroJjY2NOP/88wEcDu5t3bpVbq+qKpxOJ7q7u3NuZ926dbjqqqtGva+Ryn3POussbNiwASeeeCIOHTqE73znO/jEJz6BXbt2wev1juPREREREc1seR2lnHXWWfjggw8KvRY6DojyUbfbDU3TZI+3dDo91Uub0UQQYsGCBSgpKYHT6YSiKFAUZdhyKDFQw2azobe3F4ZhIJvNor+/H4lEAuFwWJZQU/7Efqmrq4OmabBYLFAUBaqqDim9FfsrnU4jlUpN0YpnPzEpV9M0+Hw+uN3uYUvXj8zc03WdmXuTKBqN4o9//KMM7AGH90lHRwcOHjw4dQs7Tu3evRvbtm2TwT2Px4M1a9bk9FWurq7O2Tc7duxAKBTC2WefnXNbgUAACxcuHPVnpJNKl156Ka688kosWbIEq1atwn//938jFArhmWeemYBHPT1t374diqLgsssum7I1HDx4EH//938vv2+ceuqpePvtt6dsPURERJRncO/ee+/Fq6++iv/6r/8q9HpolhMHy/v27cOBAwewb98+edBM+ROBuEgkIgNDFosFDodj2OCpzWaTGXputxtFRUVwOBw4dOgQ9u/fL4dzMGtvfERZdF9fH+LxOKxWK+x2O+x2OwDkBPyy2SzsdjusViui0SgWLFiAuro6LFq0CJqmMQBeIOl0GoZhoLq6Gna7HZqmwWq1DildF5l7NpsNZWVlsNlszNybJG1tbfjtb3+L/fv3D7nOMAwMDAxMwaqObw0NDVi6dGnOMKy1a9fi2WefRTgcBnA4uBeNRhGJRGCaJm699VasXbt2SH+89evXw+PxjPrT2to6pnUVFRXhhBNOwL59+wr3YKc5MdRk06ZNR+3jOhH6+/tx7rnnwmq14ve//z3ef/993H///SguLp70tRAREdH/yOvI/Xe/+x0uuOACXH311VixYgU++tGPwufzDdlOURR885vfHPcip9o999yDZ599Frt374bL5cKKFSvwgx/8ALW1tTnbtba24mtf+xr++Mc/Ip1O4+STT8bzzz+P6urqqVk4HTdEL8OmpiZ0d3dD13WUlZUhkUjAYrEMCUhomoaqqiq0tLQAOBzIEBlMNTU1ME2T5dIFEIvFkEql0N/fL/vt2Ww2ZDIZWK1WzJs3D4lEAn19fdB1HYZhwOPxwDRNhEIheL1eDp0pMHGCoaOjQwbwqqurh5Sui+xW4HBAye12w+fzMXNvgiWTSWzatAnt7e0jbuPxeCZxRaTrOh5//HHccccdOZevXLkSLpcLTz75JNatWye/67S3t+PRRx9FZ2cnXnjhhSG3N56y3CNFo1Hs378fn//858f4aGa26TDU5L777sO8efPw6KOPysvq6uoKcttERESUv7yO2r/97W/L/9+8eTM2b9487HazJbjX2NiIf/zHf8TSpUuRSqXw9a9/HZdeeil27twpAx99fX0477zz8MlPfhKvvPIKioqK8P7778sMneNZKBRCT0+PzH4R00M5LKBwRC/DoqIixONxhEIhxOPxYXuJCaWlpSgrK8OhQ4cAHD4bX1paKgMY7Lk3Prquo7m5GYqioK6uDl1dXXIyKwA4HA6oqori4mL09fVBURQZ+Eun0/J1w6EzEyObzcI0TVgslmEPekU2bCqVgqIoMsOYQdaJ9d5776G1tXVIT0phuJMVNLFefPFFdHV14ZRTTsGuXbtyrlu+fDkaGhpygnu333479uzZgy1btgx74jcQCCAQCOS1lq997Wv49Kc/jfnz56OjowN33303LBYLrr322rxub6aZDkNNfvvb32LVqlW48sor0djYiOrqanz5y1/Gl770pQI9SiIiIspHXkeLr732WqHXMa299NJLOb//8pe/RH19Pd5//30sWbIEwOEzmXV1dXjooYfkdgsWLJjUdU5Hb7/9NrZv345EIgGr1YozzzwTmqahtbWVwwIKSJTeBgIBRCIRRCIRGIaBeDyO8vLyEZ/fwUENRVGQTCYRi8WQTqeZLTZOYp+4XC5Zkmu1WuFyueB2u2GxWGC325FMJmU5rsvlgmmaSCaTmDdvHvx+P4fOFJjYL3a7HYZhwGKxyMuGe36z2awsm6aJlUwmsWvXrlGfa5vNhpaWFga8J5Hoq3fJJZeMuM17772HJUuWwG63o6WlRQZ9Cq29vR3XXnst+vr6UFZWhvPOOw9vvvkmysrKCn5f09F0GGrS1NSEBx98ELfddhvuuusuvPXWW/jqV78Km82G66+/fhyPjoiIiMYjr2/GK1asKPQ6hnjiiSewdetWvPPOO9i5cyfS6TQeffRR3HDDDSP+m7feegt33303tm3bhkwmg1NPPRW33XbbUb/AHCvRX2bwmeeNGzfik5/8JD772c9i69atmD9/Pu6880585jOfKeh9zyShUAhbt27FwMAAstksEokENm/ejFNPPXWqlzbriN5uovG/rusjZiQJ6XQamUwGTqcTmUwGXq8XkUgE+/btg8fj4cHzOInyz9bWViSTSYTDYdnfzeFwwOFw4JxzzkE8Hse2bduQSCSgKApM04RpmnA6nUOGznB/jJ+qqohEIojFYvD7/fL9/MhssCNfH3a7XWZVcj9MjHA4jHg8PuL1qqrCarWis7MT8Xh82KwwKryNGzeOedtkMjmBKwGeeuqpCb396UwMNdmwYQOA3KEmg4N7Yx1qkm/2pGmaOPPMM2Xm3xlnnIFdu3bhF7/4BYN7REREU2ja1rZ84xvfwEMPPYSWlhbMmTPnqNu/9tprOPfcc/H666/jqquuwrp169DZ2Ymrr74a999/f8HWZRgGvva1r2H16tWYO3euvPzAgQN48MEHsWTJErz88su4+uqrceWVV2LLli0Fu++ZZs+ePYhEIjlZGKZporW1FbW1tRwWUECit1tvby+6u7thsVjgdrtHHKgBHD5QDofDiEajsNlsCIfDUBQF9fX1KCkpQTAYhK7rU/BoZhfTNBGLxZDNZpHJZBAOhxEOh1FaWopAIIDKykoZODJNE4ZhQFEUtLW1cejMBDBNEx6PBy6XSwa1hytdH/z6cDgciEajCIfDLAmdQNFoFPF4fMTn2GazwWq1Ih6P872JjjvTZajJnDlzcPLJJ+dc9pGPfGTMQ1CIiIhoYuR9lKLrOv71X/8Vy5Ytg8/ny8lk2LFjB7785S9jz549eS/s4YcfRnNzM3p6erBu3bqjruVLX/oSVFXFli1b8NBDD+H+++/HX//6V5xwwgm466675OAA4Y477oCiKKP+HCmbzWLdunVobW2VZ04F0zSxdOlS3H333TjjjDPwta99DZ/61KdyynSPJ7quIxQKDXudKDPMZrMIhUKwWCwMXIzD4N5u8+fPh6qqME0TLpcLsVgMkUhk2INlMTDA7XYjkUhA0zT4/X44nU74fL4Rg4I0NiLDq7q6GqqqoqioCC6XC1arFRaLBbW1tdA0TQabRIDPbrfDZrOxDHSCJBIJGUTKZrNwOp3w+/1D3oMGvz5SqRQHakwwXdfR0tICVVVHzTg2DENOOSY6XoihJn/3d3+Xc/ngoSYAcoaa/PM//zM6Ozvx05/+dMjtrVu3Djt27Bj1Z6Sy3HPPPRe7d+/OuWzPnj2YP39+IR4qERER5Smvb8eJRAIrV67Etm3bZAP+WCwmr6+rq8Ojjz6KQCCA733ve3kt7OKLLx7ztq+++ir279+PG2+8Eaeffrq83O/346677sINN9yAxx57DN/61rfkdbfffvuoJb5Hymaz+PKXv4xXXnkFW7ZsGdLfpbKyEieeeGLOZR/5yEewbdu2Md/HbJJOp6GqKmw225AAkaqqUBQFBw4cQDabhdVqlSVydOxEEE5MWfX7/ejv70c6nYbf7x8xIDF4YEA2m0U2m4Wu60ilUuy5VwCiLLelpQUDAwMycCeyKkVJoaqqiEajSKfTsFqtSKVSMvinKAqHzhSQrutob2+Hy+VCNpuVWWKnnXbakOeWAzUmVzweR39/P7xeLxKJBOLxODKZDDRNk8NPBg9B4X6g48l0Gmpy66234pxzzsH69etx1VVX4c9//jMeeuih4/ZkNhER0XSR15Hi+vXr8cYbb+Dee+/F17/+dXznO9/Bv/zLv8jr/X4/VqxYgZdffjnv4N6xENN6V65cOeS6VatWATg88XawsrKyMTdgzmaz+MpXvoLf/e53aGxsxLx584Zsc84552Dfvn05lx3PZzJFSZsoCx18OXD4OS0pKYHf70c8HkdzczMWL17M4EUeRMAhEokgk8lgYGBABvNGykoaTFEUWK1W2Gw2JBIJHDhwAE6nkz33CkQEs0UvvWw2mxMkSqfTcnK06CmWyWRw4MABOBwODp0pIDGFWPQFczqdcLvdcDqdI/6bwQFWmliiNF28ZhwOB+bMmYNgMIhUKgWr1Qq32w1VVZFOp+FwOKZ6yUSTYjoNNVm6dCl+85vf4M4778R3v/td1NXV4YEHHpCDPoiIiGhq5HXk/vTTT+OCCy7AP/3TPwHAsCWs9fX1ePfdd8e3ujHau3cvAOT0IREqKyvh8XjkNvn4yle+gieffBIbN26E0+lEZ2cngMNnPsUB96233opzzz0X999/P9asWYNXXnkFGzduHBJUFFKpFFKplPw9Eonkvb7pSAQsXC4XksmkDPBZrVYAQFdXFxYvXgzg8AE2BwbkT9M0BAIBNDc3o7u7G7quw+PxQNd1xGIxnH766cM+r+L5rq+vl9lJ8XgcNTU1ME2TE1rHSTy/dXV10HUd/f39yGQysNlsMstyJCKTkgpruGEaqqoOW7Y++PUhgq/MoJw4olw6mUzCYrFA0zQ5gEa8FiwWy7DfN4hmu+k01AQAPvWpT+FTn/rUhN8PERERjV1eRyitra244oorRt3G6/XKBr8TTdzPSGWdPp9vXGt58MEHAQCf+MQnci5/7bXX5ISys846C7/+9a/x//1//x++8Y1v4IQTTsCvf/1rnHPOOcPe5j333IPvfOc7ea9pprBYLLKXWDabhaIoMqtvz549zEwqAF3XEQwG4fP5coYylJaWwuPxjJiVNHiaKwD09fXJMnvTNJFIJBjIGAcxwTgUCsngtt/vl9msIqBks9mgaRoymQysVqt8zhctWiSD4aJUmvtifETZuqIoSCaT8Hg8o5ati9eHCOzxfWpiiPJ1UbaeTqfhcrlgt9sB/E+w2zRNRKNRtgwgIiIiIjpCXkeKXq8X3d3do26zf//+MZe9TndjzaC54oorjhr0FO68807cdttt8vdIJDJsue9MJQIWhmHA6XQiGo3mNEoXfd5o/ETWYyAQwMDAAHp6emQwaNGiRWM6CBb7JplMIhaLsedeAYgJxp2dnQiFQrI01+Vy5UxnFQM1RF8+p9MJq9WKgYEBeL1eGYTivhg/MW01kUhAVVUkEgmUlJTwuZ1i6XQa0WgUhmHIILcIcM+ZMwdbt25FIpGAoijIZrMsyyUiIiIiOkJe03I//vGPY+PGjSNOQ21ra8N///d/Y/ny5eNZ25iJjL2RsvMikci0G9Zgt9vh8/lyfmYT0zRRVlaGOXPmyMCEaIIugno1NTWoq6vDokWLoGkaJ7PmaXDPvcETbo9Wvja47HDu3Lmora1FMpnEvn37EAwGEQgEmCmWJzHB2DRNuN1u2O12OJ1OeDweZDKZnGCdCFTYbDaUlpbK7KVQKISmpibuiwmgqqrscTiSwa+Pqqoq1NfX831qgqiqiv7+fsTjcdkXMZPJoL6+HsXFxbBarfIExGj7jIiIiMZn+/btUBQFl1122ZTc/8DAAG655RbMnz8fTqcT55xzDt56660pWQvRTJPXt+Svf/3r6O/vx0UXXYQ33nhDNhqPx+PYtGkTVq1aBV3XczLTJpLotTdcX73Ozk5Eo9Fh+/HRxBEZMqlUSvZOElMPNU2Dw+FAMpmUAyA4hTJ/oudeb28vurq6oOs67HY77HZ7TrDvSKLssKmpCe3t7WhubobD4cDChQtRUlKCYDDIIQJ5Es+7mLYqSnGj0ShM00RVVZUM1pmmKfeFmHJsGAaKioqwYMEC7osCSqfTUBQFNTU1qKmpwcKFC2XALhqNYt++fWhpaUEymcx5fXR0dKCpqUlOzKXCEj1a7XY7XC4XPB4PXC4XHA5HTtm6aZpygi73AxERUeE1NDTg2muvxaZNm9DR0THp9//FL34Rf/zjH/Ef//Ef2LlzJ1auXImLL74YBw8enPS1EM00eQX3li9fjp/97GfYuXMnli9fjvXr1wM4XK67cuVK7Nu3D//v//0/fOxjHyvoYkeyYsUKAMAf/vCHIde9/PLLOdvQ5FJVVWbtWSwWOBwO2O12eDweRCIRZiYVgOi5V1RUBJfLBVVVkc1mkUgkEIlExpTpIvr0ORwOuN1u+Hy+UQODNLrBgaGuri6EQiEZcHU4HCgqKpLbisw9TdNQXFwMi8WCVCoFr9cLTdPgdru5LwokkUigp6cHBw4cQEdHB/r7+wEAf/3rX/H444/jmWeewZNPPoknn3wSbW1tU7za45OiKDkZeqJs3eFwwDRNOByOow6kISKaCFOd0VRbWwtFUYb8fOUrX5mS9UyWqX7eDcPAN7/5TdTV1cHpdGLBggX4l3/5l1nZXigajeLpp5/GLbfcggsuuAAbNmzIuX716tW4/vrr5e+vvfYaSktLYRhGQe4/kUjg2WefxQ9+8AMsX74cCxcuxLe//W0sXLhQ9sAnopHlXd/yD//wD/jrX/+Km2++GUuXLsWCBQtwxhlnYN26dXj33XfxxS9+sZDrHNVFF12E+vp6/OpXv8KOHTvk5eFwGOvXr4fNZsN11103aeuh/ylpW7hwIWpqalBWVianHmqaBkVRmJlUICLwI8q7PR6PzBob3NttuH8nyg6rq6tRWVmJdDqNcDiMcDjMbMoCsVgsME0T8XhcBlAH75MjM/cMw4CqqmhqasKBAwewb98+ZowVgK7raG9vh8fjgdvtRiwWQ3t7O9577z288sor6OnpQSaTQSqVQmtrK7Zs2YJsNosFCxagqqoKCxYsYFnuBBEngDKZDADI6cQ2m21I2brNZssZSENENFmmOqPprbfewqFDh+TPH//4RwDAlVdeOelrmUxT/bzfd999ePDBB/Gzn/0MH3zwAe677z784Ac/wE9/+tNJX8tEe+aZZ1BZWYlly5Zh7dq1eOSRR3KCmNXV1TkZdCtWrEAikcCbb76Zczvr16+Hx+MZ9UcM9BtM13X5XXkwp9OJ119/vcCPdupNdeB6y5Yt+PSnP42qqiooioLnn39+2O1+/vOfo7a2Fg6HA2eddRb+/Oc/T+5CaczySpXasmULfD4fTj/9dPzkJz8p9JoAAA8//LB8Ee/cuVNetnnzZgDAeeedJwOImqbh4YcfxqpVq7B8+XJcc8018Hq9ePbZZ9HS0oIf/ehHqK2tnZB10vDEwVokEoHT6ZTluXa7HfF4HPF4HLW1tUMyk5i9d+wG99wTQSQAsjH94KBQKBRCT08PysrK4PF4cqaBhsNhZDIZtLW1wel0oqamhvsjT4MDp8lkEqqqyp8jAxODgxfFxcUYGBhAIpGYwtXPTuI9pqSkBEVFRTh06BD++te/jhis6+joQGVlJQ4dOsRpuRMsFArBMAzYbDbZr7W0tFQGwQcHv0XQj5l7RDSZREbTpk2b0N/fjw0bNuCuu+6S169evRplZWV47LHHABzOaLryyivR1dUlh8mN15GDCu+9914sWLBgVlcnTYfnfdu2bVizZo0MwNTW1uLJJ5+clQGOhoYGrF27FgBw+eWX46abbkJjYyPOP/98AIeDe1u3bpXbq6oKp9M5ZNDmunXrcNVVV416X1VVVUMu83q9OPvss/Ev//Iv+MhHPoKKigo8+eST2L59OxYuXDjORzf9iMD1c889h46OjmGfk4kUi8Vw2mmn4Qtf+AI+85nPDLvN008/jdtuuw2/+MUvcNZZZ+GBBx7AqlWrsHv3bpSXl0/qeuno8jr1fcEFF+Chhx4q9FpyvP7663jsscfw2GOP4S9/+QsA4I033pCXHRm9v+CCC/D666/j3HPPxdNPP40HH3wQFRUVeOqpp3D77bdP6FppKDEptK+vDwcOHJCBPcMw4HK5oCgKPvjgA+zfv5+ZSeMkeu4Fg0H09vbKHm/iS43oJfaHP/wBjz32GH7zm99gw4YNOV9KRC8rp9OJuro6ZlOOkwi4xmIxKIqCdDqNVCols/SOzNzz+Xxwu91yurTT6URtbS0HzhSQCBDt3bsXTU1N2Llz56jPqa7rMpOMJo6u6+jo6IDFYoHH44HX64VhGHLozJFl6+K1wMw9IppMU53RdKR0Oo0nnngCX/jCF446QG0mmw7P+znnnINNmzZhz549AA638nj99ddx6aWXTsAjnjq7d+/Gtm3bZHDP4/FgzZo1aGhokNsc+Xzv2LEDoVAIZ599ds5tBQIBLFy4cNSfkRII/uM//gPZbBbV1dWw2+34t3/7N1x77bWz7nN/qkugAeDSSy/F9773PVxxxRUjbvPjH/8YX/rSl3DjjTfi5JNPxi9+8Qu4XC488sgjBVsHFU5eaTnl5eVD0mULbcOGDUP+yI9m2bJl+P3vfz8xC6IxE5NCFUVBXV0dgsEg+vv75TABccBsGAYMw5jVX0omg+i55/P5kEqloOs6VFVFRUUFent78Z//+Z8IBoM5AaVEIoHGxkZ89KMfRX19PeLxOLLZLCwWi5zsmkgkmE2ZJxFwFT0lTdNEZWUlAAwJTIhAYCqVgqIoMAwDdrtdZvwlk8mc6bo0frFYDMlkctRtVFWF1WpFdXW1LBPVdZ2viQJLp9MYGBiAqqqIxWIwTRMul0sOnRFZrczcI6KpNNUZTUd6/vnnEQqFcMMNNxzbA5lhpsPzfscddyASieCkk06CxWKBYRj4/ve/L9c1WzQ0NGDp0qU5QyjXrl2LK6+8Ej/72c/g9/tRXV2NaDSKSCQCj8eDW2+9FWvXrpXfcYX169fLnvwjef/991FTUzPk8gULFqCxsRGxWAyRSARz5szB1Vdfjfr6+sI80GniyMD1t7/9bdx5553yuLi6uhoHDhyQ2w8OXJ977rny8vE810eTTqfxzjvv4M4775SXqaqKiy++GNu3bz/m26OJl9cRyiWXXILNmzcjm80yMENDiPI3j8cDq9WKsrIymKaJUCiEZDIJu90OADJLTEzV5UFzfsTzLSayikCdoihoa2uTvw/375qamjAwMACLxYL+/n5ZQi32HwNK+REBV6fTKSdDB4NBzJkzZ8Q+iIqiQFVV6LoOh8OB/v5+dHZ2wuFwsES6AMT7y/z587Fr166jbq8oCjKZDJqamliWO4FUVUUkEkE6nUZRURGi0WjO0JnBmXsejwfJZJKZe0Q0qURGk0g6GJzRNDjINNaMpkAgMO41NTQ04NJLL530Mr7JNF2e92eeeQb/+Z//iV/96ldYvHgxduzYgVtuuQVVVVU5mVUzma7rePzxx3HHHXfkXL5y5Uq4XC48+eSTWLduHaqrqwEA7e3tePTRR9HZ2YkXXnhhyO0VIojtdrvhdrvR39+Pl19+GT/4wQ+O8VFNb9MhcH00vb29MAwDFRUVOZdXVFTgww8/zOs2aWLldbR477334uyzz8b/+T//B/fdd19BPqRo9hCZSNFoFB6PB9FoFF6vFxUVFeju7oau67BYLMhms0in08xMGifxvLW3t0PTNKiqilQqhf7+fsRisVH/7cDAAIqLi2G1WmG1WpFIJHDgwAH23BundDqNaDQqg9k2mw2ZTAaZTGbI37oIXFRXV6O3txemaaKrqwvV1dWYO3duTmCQ+yN/IvurubkZvb29R91e0zQZ8KaJI0rV+/v7EY/HoWlaztAZcX0mk0EymYTFYmHmHhFNqumS0SS0tLTglVdewXPPPTe+BzbNTZfn/etf/zruuOMOXHPNNQCAU089FS0tLbjnnntmTXDvxRdfRFdXF0455ZQhJ0CXL1+OhoaGnODe7bffjj179sg+/EcaTzD15ZdfRjabxYknnoh9+/bh61//Ok466STceOONed3edDRdAtc0++R1pPj3f//3KCoqwiOPPIInnngCdXV1qKioGJLFpygKNm3aVJCF0syhaRpqa2vR3NyMRCIBp9OJyspKdHV1wW63o6ioCMFgELFYDC0tLXC73QwkjUMsFkM8Hkc4HIaiKPD5fHA4HGhrazvqvzUMA5WVlfB4PMhms0gkEqipqYFpmgwojUMikUB3dze6urpgtVphGAY0TYPVapXlhoIYPtPa2gpVVWGapiz/dDgcsNlsLJEuIPH8KooybEYrANjtdng8HiQSCSxYsABWq5VluRNEnIxQVRWlpaWIRqM5mXkic89qtcLn88nXAjP3iGgyTMeMpkcffRTl5eVTNmFzMkyn5z0ejw/5zLFYLLPqJJPoq3fJJZeMuM17772HJUuWwG63o6WlBY2NjfL5L6RwOIw777wT7e3tCAQC+OxnP4vvf//7sFqtBb+vqTJdAtdHU1paCovFgq6urpzLu7q6hqyDpoe8jlDExFoASKVS+PDDD4dNzWTJ7vHL7/fjxBNPRCwWg9vtltkXolwxlUrB7XajtraWgaRxEJlIotl8d3c3IpEIBgYGEI1Gj/rvFUVBLBZDKBRCZ2cnnE6nzJxhQCk/uq6jvb0dPp8PwWAQ4XBY9uCrqKhAaWlpzvaapqGqqgotLS0ADgf7SkpKEIvFEA6HYZomM1sLIJ1OQ1EUVFVVobu7G9lsFtlsFrquI5vNyuwwAHA4HLJEurW1FU6nk2W5EyQUCiGRSCCTySAYDKKoqCindH3wZwd77hHRZJtOGU3A4ffERx99FNdff/2s/n42nZ73T3/60/j+97+PmpoaLF68GO+++y5+/OMf4wtf+EJetzcdbdy4cczbHq1n8XhdddVVRw3EzmTTKXB9NDabDR/72MewadMmXH755QAOvwdt2rQJN998c163SRMrr08FfqmmowmHw2hubpbN0CsrK+X/e71edHZ2QlVVeDweAGAgKU9H9jcUB71iWIno9zYcMTwjkUhA13W4XC6oqoquri4UFxfD7XYzkJEHsU9KS0sxMDAAwzCQzWZhtVrlBOMjlZaWoqysDIcOHYKmaYjFYshkMmhra2OJdIEkEgn09PSgr69PTslVVVX+jRcXF8vhJ4qiIB6Pw263j7jPaPwGT8p1u90AMKR0fXDPPa/Xy8y9SaLrujxZVFlZKXsgEh1vplNGEwC88soraG1tnVWBpeFMp+f9pz/9Kb75zW/iy1/+Mrq7u1FVVYWbbroJ3/rWtwp+XzT7TafAdTQaxb59++TvBw4cwI4dOxAIBGSm32233Ybrr78eZ555JpYtW4YHHngAsVhsVpVJzyY8WqSCE9lkiURC9txraWmRgbxMJgOn0wlFUTAwMMDMpHEQ/Q3D4TCy2SyKiopkECkUCsEwDFnqOZjFYpGXu1wulJWVIZ1Oo7e3F5FIBCUlJaitrWVAKQ9in/T39yORSEBVVaiqKgeejBTEFkEkXddlZmtdXZ3MaGJma/5ENqXL5UJXVxdM08zJCBPB1WQyiUQiIYf+AId7nrAsd2IcbVIuwMy9qRAOh7Fp0ybs2bMHpmnC7Xbj3HPPxZlnnjnVSyOadNMpowk4nN0zUkuJ2WQ6Pe9erxcPPPAAHnjggQm9Hzo+TKfA9dtvv40LLrhA/n7bbbcBAK6//nrZD/Dqq69GT08PvvWtb6GzsxOnn346XnrppSFDNmh6yOsI5cILL8QNN9yA6667bsRtnnjiCTzyyCN49dVX814czUxHZpOJAJ/FYkEqlYKiKHISJTOTxkf0N9yzZw+6urqQzWZRXl6O7u5uWVYogkbiy2A2m4XFYoGmaVAUBX19fXKibigUgtvt5r4YB1GC29TUhO7ubpimierq6pxA35HS6bQMeicSCVgsFrnf3G73qEFBOjrx/InssKKiImQyGaiqing8Lvu4iMtUVZXvUSzLnThHm5QrthF/+2IbZu5NHF3X8frrr2PXrl3yMyMcDmPr1q1YuHAhM/iIiIjGYToFrs8///wxnSy4+eabWYY7Q+T17Xjz5s1obm4edRsRZabjz+BpuZlMBtFoVA4NEH0YU6kU7HY76urqUFJSgmAwCF3Xp3jlM5fFYoHL5UIsFkNzc7Mse3a5XNA0TZbgitJbwzBgtVrlvxHTXU3TlBlmzc3N3Cd5EH0li4qKUFZWBo/Hg4GBAbhcrpxeYoOpqopwOIxoNAqXy4VkMonOzk60tbVh37590HWdQaVxEJlfBw4cwMDAACKRCEzThNVqRWlpqSz3FNOmk8kkBgYGZCCcJobIykun0+jr65OtBAa/RkzThN/vh8/ng2EY8Pl8I76OaPza29vxt7/9LefLfjabRSwWQ1NT0xSujIiIiIhGM2GnvmOx2KyaakNjJ7LJRBaS0+mUGS/V1dUyyJfJZOTBmsisoWMzuAQaODzl0263I5PJyImg4vkuKSlBXV0d7HZ7ThYfcLjRqsfjQXV1NWw2W04JKR0b8bwFAgF4PB6ZQSkyxIYL0pmmCZ/PB7fbLQPfLD0sPJHtJcpr4/E4nE4nLrroIpx99tkoLy+H3++Hy+WCy+WCxWJBdXU1qqqqUF9fD03T+JoooMFZeaWlpfL5HZyVZ7PZYLVakUgkYJomEonEiK8jGh9d17F7926kUqkh15mmiZ6eHp7wISIiIpqmxlzj1dramvN7KBQachkAGIaBtrY2PPvss6itrR33Amlm8vv9WLx4sRyiAQAdHR1obW2VAYtUKoWuri4kk0n4fD4erOVBBJJEtovf70ckEoFhGAAOZ/SJgzGLxQKbzQZFUWTZoegv1tXVhXg8jr6+PgQCAcRiMe6TPInMVVFuKLLuRiupFf8mlUrBMAzouo6ysjIsWLBAlrOzLDd/4rmbN28egsEgAMjMVlGGGwgEZC9KEVxKp9Msy51Aot+qOMng9/vh9XqHDWqLrG/xXyq8eDyOnp6eYYPYDocDwWAQ8Xh82IbeVFgcaEJERETHasxHirW1tTlfrn/yk5/gJz/5yYjbZ7NZ/PCHPxz/CmnG0jQtJxhRVVWFvXv3Ih6PywDToUOHkE6nYbfbEYvF4Pf7p3DFM8/gQFIymUQwGISiKNB1HdlsVpYe+nw++Hw+RCIRWK1WOQnXNE25rd1ux8DAAILBIBwOBwdq5Glwz73e3l7YbDbU1NSgtLT0qL3zstmsDC6JwGA8HufAmXESZbn79u1DNBqFYRhwuVxwu91yeIYIwGYyGVitVmQymZzeh1R4iUQC0WhUZlA6nU74/f6cv3XxmrHb7TAMAxaLhT0oJ0gwGERfX1/OwCUAcLlcsNvtCIVCiEajDO5NMDHQZO/evdB1HQ6HA4sXL0ZtbS0DfURERDSiMX8zvu6666AoCrLZLB5//HGcdtppOP3004dsZ7FYEAgEcOGFF+KTn/xkIddKM1xRURGcTqcsxW1vb4fFYkF9fT0ymQyam5uxePFiHrAdAxFIam5uRm9vr2xMLyZ7isBFSUkJLrroIoRCIWzbtk0OdzBNE6qqoqamBsFgUG7v8/k4oTVPoueex+NBKBRCMplEe3u7LI0eLkg3eKBGJpOB1+tFJBLBvn374PF4OHCmAAzDkAEhkR0Zj8dRU1MDl8slhwBFIhGkUilYrVZYrVbMmzdPDqDhtNzCGTzBOJvNIh6PQ1VVnHbaaTnPrxi6IU7+hMNheTkVjq7raGpqkpneItAtrhM9KRsbG3Heeedh3rx5U7ncWUvXdWzbtg1/+9vfZHA1Go3iT3/6E9566y14vV6cd955nFxMREQ0Btu3b8c555yD1atX43e/+92k3/+WLVvwwx/+EO+88w4OHTqE3/zmN7j88suPeZuxGvMRihiHDACNjY248cYb8dWvfjWvO6XjgzgQFhkxomxUURQkk0lYrVZ4vV5YrVbY7XYkEgkeOB8jEUjy+XyypFM0qI9EIshms3A6nTLzq7KyEna7XWbwie1jsRiSySQURYHb7UYgEGApaJ7S6bT8W/Z4PFAUBfF4HJFIZEjgQhADNWKxGLxer5x2XF9fD8MwGGgdp3Q6DUVR4PV6oShKzvCYhQsXysCdmMQqMvd0XUdbW5vM/GNZbuGk02kZ/AYAp9MJt9sNp9OZs92Rnxsejwc+n4/9KAssHo/LQUyGYcjs+mw2i0wmg2w2C13XcfDgQfzpT39CWVkZHA7HVC971olEItizZ8+wf9+maSIcDmPLli2cXExERDQGDQ0NuPbaa/Hcc8+ho6MDVVVVk3r/sVgMp512Gr7whS/gM5/5TN7bjFVeR4oHDhwY153S7BcOh9Hc3CyDe7W1tbIUNJVKIZvNyoMFEURi6eGxG9xzTwSVDMPAwMAAgMOlhqLEU2TqDc5Ocjgc8Hq9GBgYQH9/P4DDWXsDAwPcH3my2WzIZrPo6+sDAJlB6fV6hwQuBDFQA4DMkBHbq6rKwPc4iWndwWAQqVQKiqLIknVxgCzKoMXEXJG5xCDSxBguI29wv0Nh8EAN8VoYKQOW8hcMBtHb2wvTNGV2q2masFgsMgCbzWaRTCbR1dWFcDjM4N4E6OnpQSwWG3WbeDyOjo4OBveIiIhGEY1G8fTTT2PTpk3o7+/Hhg0bcNddd8nrV69ejbKyMjz22GMAgNdeew1XXnklurq6CtaW59JLL8Wll1467m3GKq+6loGBATQ1NcmSDeHpp5/G2rVr8b//9//GX/7yl4IskGaewRNcxcTc5uZmWe4j+oqJA7YDBw4gGAwiEAgweHGMRNCio6MDVqsViqIgFoshm83C5XLJwITH45EluIOzk1KpFMLhMEpLS1FXVwdFUdDc3CwHa3B/HLtYLIZ0Oo1wOIy+vj6oqgqbzSbLDoczeKDGkYHvcDgsr6f8xGIxxONxJJPJnCw+ACNOvzVNE4qiYP78+airq5MZfpyWWxjiRIPL5ZKl6H6/f8RgKgdqTBxd13Ho0CHZJ9discjM+iPfswzDQDKZZFn0BBCZwkd7bk3T5OcBERHRUTzzzDOorKzEsmXLsHbtWjzyyCNyiBsAVFdX4+DBg/L3FStWIJFI4M0338y5nfXr18Pj8Yz6M9yg2amQ15H7P/3TP+GJJ55AV1cXrFYrAODBBx/EzTffLJ+wp556Cu+88w5OOumkwq2WZgSRTebxeGC1WuHxeGTjdNFXLJVKAQDsdjtqampgmiZLD/OgaRqqqqpw4MABWCwWlJaWQtM0ORFUPJciCy8ej0PXdVitVqiqikwmg1QqJbMzioqKoCgKe+7lSQS2FUVBeXk5wuGwzMobLXAhKIoCq9UKm80mA99Op5M998ZB7JN0Og2LxSKD3B6PR/aYBIYO1Bhc4m6z2ZBMJmX2MY3fWIZpAByoMRlE1rf4exfvQzU1NWhqakI8Hs/ZXgQAqbDi8Ti6u7thtVqh67qcen8km83G4WNERERH0dDQgLVr1wIALr/8ctx0001obGzE+eefD+BwcG/r1q1ye1VV4XQ60d3dnXM769atw1VXXTXqfU12ue9I8jr12tjYiIsvvhgul0tedu+996K6uhpbtmzBM888w2m5xzGRZSSCedFoFDabDQ6HA+FwGNFoFFarFfF4HIZhyB5K4oCNjo1o/C/KmxOJhBwAkEgkYJom5s+fLw+EbTYbXC4XVFWFoihIp9PYt28fOjs7EQ6H5ZAO7o9jJ54zv98Pp9MJi8UCwzAQj8dl0G6kf6dpGurr61FRUYGioiK43W7U1NSgpKQEwWBQZr7SsRncA9Fut8Nms8n3pdLSUvk5JjLJHA4HTNOUwaZgMIj9+/czm7WAxDANj8cDr9eLVCqFWCyGuXPnDnl+B5fviqnqkUiEmWMFpKoq+vv7ZQBctBI47bTT4PP5crIlVVXNCYpT4QSDQfT09CCbzcJms42Ypep2uxlcJSIiGsXu3buxbds2GdzzeDxYs2YNGhoa5DZHZu7t2LEDoVAIZ599ds5tBQIBLFy4cNSf6XJ8kNe340OHDqGurk7+/sEHH6CtrQ1f/epXcd555+Fzn/sc/tf/+l/YsmVLwRZKM4emaaitrZUluU6nE7W1tVBVFT6fD263G7quw+l0QlEUDAwMsPQwT+IgWWQa9fb2Ih6Pywwlh8OBiooKeDweAIDL5UJZWZkcoAEcDg6KbA2RLSACstwfx0Y8Z5FIRPZwExlhg0WjUezbtw8tLS1IJpNyYENTUxMOHjyIzs5O2Gw2+Hw+Br7HSZQ7Dx4W4HA4UFxcnPNhLHpTOhwOVFZWyhMUxcXFqK+vRyAQYJC1QMTfc0lJCaqqqlBXV4eysrJhe1KKgRoej0cO1BhLFiyNndgfNptNBlxFAEmcPBL9EFVVhcPhmDZfYmcL8f5vGIZszSCe98GG60tJREREuRoaGrB06VIsWrRIXrZ27Vo8++yzCIfDAA4H96LRKCKRCEzTxK233oq1a9eisrIy57ZmfVluKpXKOehvbGyEoihYuXKlvKy+vh6//e1vx79CmpH8fj8WL16cMy1XBDpEQ3tFUZDJZNDW1sbSwzyJrCRR+unxeNDc3AzDMGSJra7r8mBA0zQsXLgQbW1tsmm3xWKB1+tFVVUV+vv7EYvFoKoqamtruT+Okch6bGpqQm9vL2w2G2pqalBaWopEIoGWlhYcOHAAH374ocw+qqiokOnhwOGDN1EmLfr3cbjJ+Ihgt2gHAAAVFRUoLS2Vvw8eamIYhgw0uVyuIdmxfF2Mjwhmt7a2ys+GkSYRc6DGxItEIojH47KXm9PplMMy7HY7HA6H/NwWE9j5/BdWPB5Hf38/3G43UqmUHGhit9vlgCwA8rsTERERDU/XdTz++OO44447ci5fuXIlXC4XnnzySaxbtw7V1dUAgPb2djz66KPo7OzECy+8MOT2ZlJZbl5HKHPnzsV7770nf3/xxRcRCASwZMkSeVlfX5/MFqLjk2jOfSRFUaCqqszeq6urQyaTYY+3PIjJrJFIBF6vF/F4HA6HA8lkEuFwWAaFBme5iJJPwzDgdrvR1dWFSCSCiooKJJPJEfcbHZ2u6wgGg/B4PAiFQkgmk2hvb0cqlUJHRwe6urqGZH61tbVhy5YtWLRoEerr66HrOnw+H1paWrBv3z54PB4GvsdBBOPKysoQCoWQzWaH7WU1eKiJoigwDAOKoqClpUX2wBopAEX5EYNjRK9eXdfR0dGB/v5+VFdX5wRfxQRjZk4Wlq7r6O7uhtPplENnMpkM5s6di5KSEpSXl6Ozs1MGmxwOB3w+HzMnJ4CYzi0yjF0uFzRNk4FX8Tph5h4REdHIXnzxRXR1deGUU07Brl27cq5bvnw5GhoacoJ7t99+O/bs2YMtW7bIE/2DBQIBBAKBvNYiqrWEAwcOYMeOHQgEAqipqRnzNmOV19HipZdeip///Of42te+BofDgZdeegnXXXddzjZ79uw55sXQ7Da4r1g8Hkc2m4XFYoHdbpclvMyKOTaxWAyGYWBgYABdXV0ysKcoCgKBAEzTRDqdzjkYENkApmmiv79fliN++OGH0DQNixYtkkMIFi9ezP1xDAb3d/N6vdA0DdFoFPv370d/f3/OhCYhm82is7MTFRUVOHToEFRVRTAYhNPpxMKFCxn4HicRAI/FYnIYg81mg2EYw77fZLNZmaFEE2O4IRmRSATPPvssDhw4IN+jzjnnHHzsYx+Tnxu6rssAHz8rCiOdTmNgYAB2u10OznC5XFi4cCEcDgdOOukkNDU1AQCcTqcMdDPAVFhiwIw4wSamFWezWbjdbiSTSVmua7fb+bdPREQ0AtFX75JLLhlxm/feew9LliyB3W5HS0sLGhsbZbCvkN5++21ccMEF8vfbbrsNAHD99ddjw4YNY95mrPL6dnDnnXdi48aN+PGPfwwAmDNnDr773e/K67u7u/HGG2/g5ptvzufmaZYaXIolGni73W4AhyPWLPU5NoMns5aUlOT06hHZfOXl5UP6U4lgnpi419nZCYvFIocFJBIJFBcXswQxDzabDaqqYmBgAE6nE6ZpQtM0tLS0jBosymQy8nrTNGVfOLfbDdM0GfgeB1HaHA6HkclkcoKugwMU6XRaTvPOZDIy2DF//nzY7XZks1lOaS2QwUMy/H4/+vv7EYlE0NXVJbdJJpN444035MmGsZTw0rFTVRW9vb2yjUAmk4GmaSgqKgIAeL1eVFZWyuw9RVGGZIPT+Oi6jpaWFtjtdnkCwm63o6KiAuFwWH62m6Ypn3/+/RNRoYRCIfT09KCsrEy+9xPNZBs3bhzztqIH/UQ5//zzj5owMJZtxiqvI5TKykr87W9/w6ZNmwAcTm8cnMLY29uLH/7wh1i1alVBFkmzj6qqcLlcsFgsCIVC8Hg87PF2jESgweFwwGazYe7cueju7pa9DK1WKzKZzJB+bYN7i6VSKWiaJgOrhmEglUohHA7D5/PxAOIYifLPnTt34tChQ9A0DQ6HA5lM5qj/1jRN1NfXI5VKwWq1IplMIplMIp1OM/Cdp8EB8OLiYvT19SGRSMjJxUcGvcPhMAYGBuDxeBCJRKDrupw+nUwm4Xa7uR8KQEwmzmazMrDd19c3ZLtUKoX29vYpWOHxQ3yOWCwWqKoqXxfis2VwCw232y2D5czcKxyRPanrupxUrKoqioqKUFVVhfb2dhiGAavVKkt1xf4hIspHMplEZ2cnPvjgA+zevVueVDj33HNx5plnTvXyiChPeUdSnE4nPvWpTw173cknn4yTTz4570XR7CQyXhYtWpRTSjpc/ys6OnH2XmQg9ff3ywNlMTzA5XKhqqoqJ2g6uLeYOLjWdR0WiwXRaBQulwvl5eWYO3cug63HSNd19Pf3w+l05uyHo7FYLAiHwzLwFI/HZXYZA9/5E4ELl8slA95iwM9w5eoiu7i3txeapsHj8SAYDMqS93nz5nE/FIAoQRRDBLq7u0fcVpRVsyx3YgwepqEoCtxuN+x2u7z+yEEzbrebPfcKbGBgQA65Epn1brcb8+fPh8PhQHl5OSKRCKxWK9sFENG4NTc349VXX0VHR0fOMVgikcCWLVuwcOFCZvARzVA89UqTRgSV+vr60N7ejg8//BDhcBhOpxPpdBrNzc1sln4MNE1DbW2tnOqZSqWQSCSQSCTg9/ths9ngcDhG/IA+8iBBTElUVRXZbBbt7e1yVDiNjei553a7UV1djfLyciiKAovFMuK/URQFdrsduq7zgLnARLBOvNeIgJLT6Ry1XL2srEwGV4uLi1FfX49AIIBgMMj3qHHSdR3t7e3weDxIJBKjBvZEJpmu62hqakJHRweamprk5HUan8HDNFRVlRO6S0tL4XK5AOSeDBKZ3SwLLRxRkmuxWODxeGR23rx582TWvd1ulxNyDcOApml8/idQNBrF7t27sXv3boRCIcTjcb7vT7JQKIS9e/ciFApN9VJmna6uLrz00ksyI/hI4mQDEc1MPO1Nk0bTNMydOxetra0YGBiQX2JDoRCqqqrY4y1PhmHIgIXNZpMBvuLiYhQXFw8JGA3uLZZIJGCz2eB2u1FVVYWenh45cTeRSHCoxjESAdJEIoG+vj6k02lEIpGcIIWYhij2i6qq8sD6pJNOgmma6O7uRjKZhMfjkYFv7of8GIYhh8yIzD3DMEYtVzcMQwbNvV6vzGZi78PxE9mUXq/3qCcPrFYrOjs7+XxPEFEO6nA4ZIaxGKZx5HOuKAqnFU+AdDqNaDQqJ0dbrVaoqirbYqTTadkmQJx88Hg8PBE0QXbt2oXNmzcjFArJ6dAVFRUoKyvD4sWLMX/+/Kle4qyVTCbR29uLDz/8ELt27WKZ6ATQdR07duxAX1/fqFnA6XR6Elc1++m6jkgkgkwmg2g0iq6uLtTU1GDu3LlTvTSahfiNmSaV0+lEWVkZysrK0N3djVQqhVgshmAwiOLiYp6NPgain5gI0Ikm9YqiyICGCGYMNri3mMvlQjqdlpNDo9EovF6v7OPHYMax0TQN1dXV2LdvHzKZDFRVlZNXxYGxaIguDqYdDgcURUEqlcKBAwegaRr6+vpQWlrK/TBO6XRaDpwJBALyy5WiKKOWq4vX0OAJouy5VxgiyL1//35EIpFRtxXTv10uF2pqaqBpmsyw5Oth/MRnRjqdRnl5OUKhEFwuV0629+B2GgJPxBWOqqqIxWIyeJpIJOB0OjF//nz5mRGNRmUv1lQqNWQYEBXGwYMH8corr+ScdBAnOZubm/Hee+/hwgsvxLJly6ZwlbNTW1sbtmzZgvb29pzm9olEAq+99hrLRAskHo/j0KFDRz1JM7g1A41POBzGn//8Z+zbtw+hUEgGTi0WC04//fQRW5wR5YvfzGhSif5JHR0dSKVSckKf1WrFggULeLBwDMQBrt/vRywWkwdcuq7LbLDhDO4tFgwGoaoqMpkMWltbkclk4PP5kEgkZOYYgxljFw6H0dLSAgAoLy+H1WpFf38/DMOQPa0URYHH45FBJlGSKyYiqqoqBziI5vXcD/kZnEkZj8dlCUpFRQVKS0uH/TfZbFbuJ6fTiWAwiEOHDrHnXoGJv/+RsgdEKXtfX58cLON2u+F2u3kiqEAGDzYxDANFRUXwer05WWEi6C0y/Pi5UFji81jsA5Ep5vF4AEB+pouMPqvVKntOcqBG4ei6jj//+c+jZhOnUils27YNJ5xwAgNNBZRMJvHqq6+ira1t2DLReDyOlpYWPucFEAwGEQqFjpqFzeBeYei6jm3btmHHjh1DsiENw8C7776L008/nRl8VFA89UdTQgwasNlsCAQC7GeVB3HQ1dvbi1gshr6+PsRiMVitVllGKAKAgw3uLVZSUoJsNgur1YqFCxfKjMr9+/cjGAwiEAgwmDFGIpMyk8nA7XYjHo+jo6MDqqpC0zRYLBYZ2CsvL885oBMH0zU1NViwYAHq6+uRTCaxb98+7odx0DQNxcXFiMfjGBgYQCqVgtPphNVqHbLt4HJ1VVVht9sRjUbh8/nYc6+ARDalGGxy5EkI0VtM0zRks1mEw2E4HA45qXVgYIDDfgpk8GCTbDYre1EODtxpmib/9puamvh+VGCqqiKVSkFVVZSVlcmTPczMm1zt7e1obm4+6nbxeBxdXV0Tv6DjyN/+9je0traOOlwvk8lM4opmJ5ExbxiG/D46nOEqfig/H3744bCBPcE0TRw4cGCSV0WzHb+d0aQSpTzz5s1DW1sbysrKAECWh7LUZ+w0TYPP58O7776LZDIpe4npug6v14tEIiH7vQ02uLdYKpWCpmnwer3yw9zhcKCmpgamaSIYDGLOnDncJ2MwOJMym82iu7sb4XAY2WwWFotF/litVhlcEr33LBYLbDYbkskkNE1Db28vfD4f5s+fj0wmw/2QJ13X0dPTA7vdDovFIkvQRSbY4OdTlKvHYjH4/X709fUhlUphwYIFcp+xHHH8bDYb4vE4enp65PAekcFnsVhkT0rx2kgmk/D5fHA6nTLDVfRDpPyJwSYulwvZbBbxeByqquK0007L+fsWGd4lJSUyc4/vR4UTCoWQSCTk+3xRUVHOsB8RBB88KZoDNQpL13Xs3r1bfh8a7QSOaZpy2AyNXzKZxM6dO4/aQ7KkpGSSVjR7dXZ2oqmpCcDhzHhR1XBk9rzdbuffeAEkk0n85S9/OWr/QmZgU6GN+5tZNBrFnj17EIvF8IlPfKIQa6JZTJSDtre3IxqNIhgMoqysjP2s8iACFw6HAyUlJTLwkE6nEQwGZWbfkV+aBvcWy2azspG3yG7yer3w+XwwTZO93o6BeF77+/vR398vA3aZTAbZbBamacLr9aK8vDynT6IIaDgcDkQiEXR1dSEej2PhwoXw+XzIZDLcD3kS2XgiQ9LtdmNgYADhcPioQW+v1wsA2L9/v8ymqaqq4ntUgYgy9MEBPb/fj0gkIgN+wOFMvtbWVhnc4z4ojHQ6jVAoJPtbOZ1OuN3uIYFT8Zni8/lkRgffjwpD13V0dHTAYrHA7XYDOJyhNLjsWZRODwwMIJPJwG63c6BGgYmTDWOhaRpLFguovb0dwWBw1BYNdrudz/k46bouj9XFZ65oPSImoQOHg36BQIDPdwG0t7ejt7c3Z4DecPx+/ySuio4Heef9Nzc3Y82aNSguLsbSpUtxwQUXyOveeOMNnHzyydi8eXMh1kizkKqq8Hg8sFgsSCQSsFqtqK2t5cHCMUin0zJgJLK/ROZXSUmJzFQaqbxHNPC2WCyIxWJoa2tDMpmUH/zRaFQGrOjoRPlab2+v7CkpMizEROJAIIAVK1bg7LPPRnl5Ofx+v+wjpus6/H4/Fi5cCL/fj+7ubkQiEYTDYe6HPIkA6uDXh9vtzsmMGbytCHobhoFUKgWLxcLyuAJLp9NywIyYYCyCGyUlJXC5XDmZfGI7KiwxTGNgYACKoiCRSCAWiw35exevi2g0KrP2xIkLGh8xrVhVVfn8m6aZM+xncBuNiooKOUGX70uFEwwG5d/14Pcc0fdzMKvVyhLRAhGtTEQ26kjcbjezm8YpHo+jv78fTqdTVpCI4N6RJbp2u53v7+Ok6zoOHDiATCYz6t+2yKAkKqS8Iimtra34+Mc/jr6+PqxZswadnZ3Yvn27vP6ss85Cb28vnnzySZx//vmFWivNAuJsf319PQYGBmC1WpFMJkfttUHDE8MCRDlhNBqFoigy+8jj8QwbxBD7oLq6Gt3d3TBNE9lsFhUVFTKoFwwG4fF4GHA9BqJ8zePxIBQKIRaLIR6PywNhEShyOBxwOBzydxF8Mk0TTqcTdrsdgUAAzc3N2LdvHzwej5wUSscmFovJPm0iI6a0tBSBQGDEL68ia8wwDKiqigULFsBisSCbzbJ1QAGI9ytFUeByuWTvVb/fLw/gLBaLHCigaRrq6urkyQgxTID7YHzEIIf+/n7E43HZn/LIzwtN01BbW4v3338fBw4ckD1aRfk65U+8FpLJJEpKSmR/ycGDA0zThN/vh6IocuCVyKyn8dN1HYcOHZI9PkWWvaZpMAwjZ6K3OBDnwXhhiICT2+2W5aFHlkSLPsUsEx2/TCYjM/ay2SwcDoesShCfp4ZhwDAMDuwZp87OTjQ3N8vWIxaLZdjjXDEoiaiQ8vqLuvvuu9Hf34/Gxkb813/9Fy655JKc6zVNwyc+8Qm88cYbBVkkzR6iLHffvn3Yv38/urq6ZOmoOINHY6NpGubOnYtMJgNVVVFcXAyHwyGngtrt9iHN0QHIQNPBgweRSqVkVlMikZAZZAy2Hrt0Oi3L1bxeL9xuN0zTlKVU4oBBZMJomia/bBmGAUVR0NbWhv379+PAgQNwOBxYuHAhSkpKOMghD6J5dDgcRmlpKbxerzx4GG4gw5EDNdxuNxRFQTgchq7riMVizKAsgFAohIGBAaTTacRiMWiaBr/fj0AggOLiYgQCAZSUlMhAuNVqRXNzMzo6OtDU1ARd17kPCkBkhGmahtLSUmiaNmJGmBjQ5PP5UF5ejkwmw8/rAhD99lKpFHp7e+Uk6COnFVutViQSCdkqgw3vC0d8bosBV2LYT1FREex2e87rQVEUuN1uBpoKJBKJIBKJQFEUOBwO+Hw+OByOnCwyTdPkexPlTwxPSiaT8gRNIBCAz+eT7+PihOZwGas0dqKHZzQalUPD7Ha7/G4piMA1TxZQoeV16vvll1/GFVdcgXPOOWfEbebPn49XX30174XR7CYCGqIs1OPxsFl9HjKZjAweJRIJeL1e+Twmk8lhgxiapqGqqgotLS0A/mcylshwEmW98Xgczc3NWLx4MffJGIgS0IGBAVnqYLfbZTZAWVkZSktL5YHb4D5K4gypyBowDENOCGXvw/yIgzZN0+DxeOSQGb/fP+yXqSMHaohhKKFQSPa2nDdvHvfBOOi6Lsv/FUWBqqqyVHrZsmVwOBzo6+tDMpmE1WqVk3VFFh8VjujlJvpc+f1+2eLhSOl0Gr29vWhvb0cqlYLdbsfcuXOxaNEivh7yNJZ+e4MN7kFJhaOqqnzPUVVVfh6Xl5fDbrcjlUrJadLiwJxZTeM3uCRX9MW1Wq2yt6oItIpp0jyRkD9d19HS0pIzWMxut6OyshLl5eXo6OiQLRlUVZUBbspPZ2ennIArkieAw+XO4oS+wJMFNBHy+lYWDAZRW1s76jbZbBapVCqfm6dZTAQoTjjhBBw8eFD2jevp6UFxcTE/UI6BODgAIANAqqqipKREHqSNdEaotLQUZWVlshylt7dXZpUtWrRInrFmUGnsNE1DWVkZdu7ciY6OjpwvrIqiIJlMygM3UVrocDhQXFyMUCiETCaD2tpamKYpy9XFVFen08nXxjESZeuxWAw9PT1yaMNIAxmOHKjhdDqRTCZRVFQEr9fLKaEFIAKuotee1WqFrutwOp0ygCdO+gwuWa+urobVamVZbgGJTI54PC5LDYfL9AYOvzb27t2L/v5+WdKVSCRGPcFLoxvcb0/02nO5XDn99sR24mDcMAx5cM7XQGH09vYiGAzK59btdsuTQWJiugjsic9xBprGTwwx8fv9sNvtMqPM4XDInrdiEJbog0j5Ee81uq5D13X5/d7j8WDOnDkoKyuD3W6H1WoF8D9Z3QxgHztxAtMwDDidTmQyGei6DofDAVVV5fEAANnXk881FVpeZbkVFRXYu3fvqNvs3LkTNTU1eS2KZpdQKIQPP/wQhw4dgqqqsNlsiMfjKCoqkgfMoVAIqVQKsVhsqpc7Y6TTaRiGgdLSUnl2ub+/H9FoFF1dXQAwakBInE3KZDKw2WwIBALw+/3o7e1FKpXiQI1jpOu6bFg8+Eyc6OWTSCRQXl4OTdNkIMlmsyEUCsEwDGQyGezZswddXV2IxWJQFEXuA/Y+PHaih5iYAp1MJmV25XCOHKiRSCQAAIFAQPavFAfVlB8R2BblQSKDb6S/bRHca21tZVluAYmJ9S6XCy6XSw5zGC7TGwBaWlrQ398PwzBkj9ZIJIJgMDgFq58dxECTdDqN0tJS2Gy2If32Bm8nplzGYjFEIhH2aSoAcSAOAC6XC06nE1arFQ6HA/Pnz0dFRQUAyJMRnCBaWKlUCgMDA7InmcfjgcfjkX3fxER1DngYn4GBAbS1tSEWiyGTyciTCfPnz0c8HpeD9Gw2GzweD//OxyGdTiMej8uMU+BwdVR9fb38PiNOXlosFgb1aELk9e3gkksuwYsvvoj33ntv2Ou3bt2KV199FatXrx7X4qaLf/3Xf8XixYvh8XhQVFSECy+8EH/6059ytnnuuedwySWXIBAIQFEUNDc3T81ip5m3334bGzZswG9+8xv8x3/8B/77v/8bNpsNwWBQ9nwrLy/HokWL5PPGs6JjI3rn9fb2wul0yv5UojfPaMRZ/3nz5sHj8WDu3LnyLJ6iKOjt7WVQ6RiJrCS3242qqqoh/XpcLpfMDLPZbDnZlWJCaDKZZKP0AtF1HT09PbDb7bJ/kmEYMhtyJIOnSGuaJg+sI5EIg90FICbzDf47H6kXZSaTkRMsqXDS6TRCoRCSySQAwOl0wu12D5vpres69u7dK/uwDh46IP49HTtRFu1yuWAYBoqKioYdaCIGn6TTafT09MjJufycGD/xmS2+N4n+h2VlZaisrMRHP/pRVFRUwO12y+9bbreb34kKQGQOR6NRGXRyuVw4+eST5eev3W5HcXGxzG6iYydKckXw1Gq1wuVyYd68eXA4HOju7pa94JLJJGKxGEpLS1kqmicRSE2lUvJ9wu1246STTkJZWRksFous6rHb7aioqOBzTQWXV3DvG9/4BpxOJ5YvX47vf//72LdvHwDg97//Pb75zW/ik5/8JEpLS/H1r3+9oIudKvPnz8ePf/xj/PWvf8W2bduwcOFCrFq1Cn19fXKbWCyG5cuX47vf/e4UrnR6CYVC2LJlC8LhsPwStWvXLuzYsQPFxcWoqamRHyqiLxYzY8ZO0zSUl5fLL/lWq1VO1RMfICM9lyKDpq2tDdFoFAcPHkQymUQ0GpX7g46NKAPVdV1OfhP7JpvN5gSGRN9DwzDkRFdN0+D1elFVVSWHP4jXBIPex04MyBClPW63G8lkEuFwWAZdo9GofA2IgPeCBQtQXV2NRYsWwev1ore3F/v370dfXx8CgQBfG+OQTqeRSqXkxGjRV7KoqEhOjPZ4PHA4HDKo4XA4UFtbi7q6OixcuJDN1QtAZIOJifUic2+4bLB4PC6zxoD/Ce6JA2/Kz+Cy6Gw2O2JZ9LEMPqFjo6oqotEoLBYLfD4fFEWB0+nESSedBE3T5Ak5h8MhW2OwRcb4De4BV1RUJANOgUAAZWVlKC8vR1FREQKBADObximdTiMajcphYlarVf69A4eDUQ6HAxaLBaqqwuVyyc9ZOjZHBlJFJmRNTQ28Xi8Mw5DZ8jabTQb9+FxToeX1F1VbW4uXX34Z11xzDb75zW/KHiyf+tSnkM1mUVNTg//6r//CnDlzCr3eKfGZz3wm5/cf/ehH+OUvf4ldu3ZhxYoVAIDPf/7zAIBdu3ZN+vqmqz179mBgYCDnsmw2i46ODtTV1cFut8PtdiMWi7G3WB7C4TC6u7tlbzyR6eJ2u2Xq/VgOABwOB5LJJPr6+hAOh2UGhwgqcaDG2Giahvnz56O3t1cG40T2l8PhgMfjycm2KCoqkj053G43urq6ZJlWNBqF1+uFw+GAzWZj78M8iBJc0dBYDG7w+/0wTRPvvfcetm/fjnQ6LQc66LqO1tbWnAPo8vJyuFwu9twrgIGBAbS2tsoAqygFKioqyulFabPZ4PP5EI/HZbB88D7kZ8T4DB6mkclk4PV6RxymAUCe8Bk8Rb2qqorBvTwNLovOZrOyjOu0004b8t5imqY8aZfJZODz+eDz+Zi5VwChUEhmjamqioqKCpSUlMj+n6IPos1mk4M2jvwcp2M3OOBkGEZODzhRFir6kum6LjO76diJALY4wRyLxeB0OjF//nzYbDb5nTMQCCAajcLlcg1pDUBjM1wgVVVVGUgVvfdEEoDb7WbwegKJYXhlZWXH3d903kcoZ511Fvbu3YuNGzfiT3/6E4LBIHw+H8466yysWbNm3G/ETzzxBLZu3Yp33nkHO3fuRDqdxqOPPoobbrhhxH/z1ltv4e6778a2bduQyWRw6qmn4rbbbsNVV101rrUMlk6n8dBDD6G4uBinnnpqwW53ttF1XfZ9O1IqlUJTUxPcbjfi8Tg0TUM0GoXH42EZ6BiJSWOiX8/u3bsxMDAAm80mzz6P9iVUZDU5nU6ZSQMcnuCqaRr6+vpQVVXFCcbHIBwOo6WlBQMDA/KgbXDA+shsC9M0YbfbYZom+vv75aS+1tZWeRAn+pIxoHHsREakmEjs8XhQWloKi8WCLVu24N1335XTWCORCLZu3YqTTz5Z/nuRSSYafouJ1Hw95EfXdTQ1NcmDZNEzVFEUVFZWyoCqOJAWfYA8Hg+CwSC6uro4sbhAjmWYhgi6Ds4cHjzchPvi2B1LWbTNZpPZleJEXklJCT8PxknXdezbtw/xeFy+16fT6ZzXwcDAALq6upBMJuFyuWQWJbMmx2e0gNP/j70/D5LkLO/E8U9mVVZlZd3V1ffd0zPSjDRoRhaIQwazxhZ4NzAYY8Q6DLbD3nXsBmwYNsJc5vA3whgC22vMendB5jBrbLBZY1iHELBCIAkJEGaEpJFGMz19H9Vd95GVmZWZ9fujf88zWdXV3VU9PQea9xOhGLq7uqh+M/M9nudzEHtMURQ0Gg0Eg0FRUL0MFIvFFque/v5+LmATO75QKPBZjFjzAr1jr/vau06SukrMI1cOjz/+OB555BHU63WEQiG87GUvwx133HGtP9ZVw2Xtyvx+P17/+tfj9a9//WF9Hsb73vc+LC4uIp1OY3h4GIuLi3u+/tvf/jbuvvtuqKqKe+65B9FoFF/+8pfxpje9CcvLy3jnO995WZ/noYcewmte8xrU63UMDQ3hm9/8JlKp1GW95/MZZNJKC7QXtIkKh8PX6NP99IPky6FQCKVSib16yAfJdV1mw3SCLMsolUqo1WrMSlJVFZqmsXSuVCpx6IPA3qBiK8maKUCDDNCj0SgzxgheuVU4HMbW1hYURcHs7Cyy2Sw2NzdRrVYRCoVEQaNH2LaNubk5lEolpNNpVKtVuK7LwQDeOYm6rIVCAbVaDTMzM7BtG5IkYWVlhRtXosh6eaCGArFZfT4fd6+ps03PhKIoiMViqNVqqFarmJ6eRiQSEezJQ0AvrDFg+7pR6jexYamwV6vVBPPgAPCGZMTjcWay7nXY8yYsClw+dF1HJpNBKBSC67rsuUehV16JnaZpHOQjCk2Xj70KTqKgeniwbRtra2vsFQls73eogO1Nno9Go9y8FGN9MLQzgb33tXfd9P4r9pOHA9u2kc/nkcvlUKlU8MADD3CgiWmaeOCBBzA7O3vDMPiu2yf43nvvxcLCAra2tvB7v/d7e77Wtm387u/+LmRZxne/+1188pOfxJ/+6Z/iiSeewLFjx/Ce97xnR3HwXe96FyRJ2vM/L+644w6cOXMG3/ve9/Ca17wGv/Zrv4ZsNnvof/fzCa7rIhqNtnyPzNEjkQhGR0eFt9gBQf5tpVIJhmFAlmVmFwH7HwAorTUcDsO2bYRCIfh8PpbjUjS7YFJ2BxozRVFQqVSQy+WYFRYMBvke9y7k1DWlRYnkufQaVVUxMTGBVCqFfD4vnoseQB6ffr8fqVQKw8PDkGUZm5ubO5oNhEajAcdxcPHiRaytrWFxcZFlK8Jz7/JBflVkH0AHO5KeA63PBBVhJUliJkc0GhW+rJeJXlhjBGIZNJtNAGApnWjQHQzeMA2SRbc3fwh0vweDQQ4ZEM/A5aNcLvNhvNlsIhwOI5lMcqOhXWJH8377Oi7QG9oLTuFwmAtOsizvKKgCEAXVA8KyLFQqFWb8UkruyMgI/H7/jvWWCthirHtHOxOYCqdUSKU5n9Q6gpF6eCiVSrj//vvxuc99Dv/wD/+A++67jwt7hHq9josXL16jT3j1ceBTimVZ+MpXvoIf/vCHKBaLLV4sBEmS8Nd//dcHev9XvepVXb/2gQcewNzcHH7rt34Lp06d4u/H43G85z3vwW/+5m/ic5/7HN7//vfzz975znfuKfFtRygUwuzsLGZnZ3HnnXfi6NGj+MxnPvO8CQ05bFDqIRnl6roOANz9NwwDpmmiXC4Lb7EDwO/3Y2pqCs899xzW19dZYitJElRVRSwWg+M4XHBqBxUHTdPkYnaj0cD6+josy8LAwIBIqOwBlKS3uLjI40n3MLEfaUNF6MTcI6ZSpVJBNBplbyXxXPQGKiTVajVsbW3Btu2umjHedYzGXXjuHQ78fn9L15TkuN5QIO8zEY1GUavVYJomFhYW+BAyMjIiDteXgV5ZYxRqoqoq6vU6p1qeOnVKsPYOiF5k0Z2uF31f4GCwbZtTQnVdZ5b9xMQEJ1fKsoxarcZrOcm72iV2Ar3BW3CiYpOmaRgZGYHruqKgeogghY5hGOjr60OpVIKqqrwOt6+3grl3cHTDBKYEXfI3FGN9+bBtGz/84Q/xxBNP7Nq4J1Ad4kbAgVaoxcVF/MIv/ALm5ua4k9sJl1Pc6wUPPvggAOAXf/EXd/zs7rvvBgB85zvfafl+f38/+vv7D/z/2Ww2d1SGBS6BuhSVSgWO48C2bTiOw0WQWq2G1dVV9pGp1WpwHEcEavQI0zThOA6q1SoMw0AwGMTAwACq1eoOSS2l4ZKvHrD9jMqyDNu2mYlBRT3DMESgRpegzVGpVILruszYUxQFrusinU4jnU63/I63a0rsS6/nHiWIkjRXPBfdw+/3I5lMQtd1VCoVNu7e73cqlQpOnDgB13XhOA6Wl5eZXSA89y4P5MNKDGEK4vIe3NqZBPtJFQV6R69hGvR6MgFXFAWJREKw9g6IXmXR3kANwzAQiUREoMZlggpMqqryPr49JZTmIlo7VFXF4OAgh20IHAz7FZyooBoMBnnvIwqqBwPJn03TRDabRSKRYAsfYOd6SzJRMbf0DmICu64LVVU5LIOYwHQtGo0G8vk8EonErmxtge5x7tw5nDlzZt/CHgD09fVdhU90feBAs+Xv//7v48KFC/iN3/gN/PZv/zbGxsau6cR7/vx5AMDRo0d3/GxoaAiRSIRfcxD8wR/8AV772tdibGwM+Xwef/VXf4WVlRW84Q1v4Nfk83ksLS1hbm4OAHD27FkUi0WW1bXDNM2W4mC5XD7w57seQR0h8iihbhwVjhzHQTKZxMTEBJaXl7GwsICpqSkhA+0S5CmWyWS4iERJTJZlwbZtzM7OolqtIpvNolar4emnn4ZpmojH43jBC14Av9+PmZkZ6LqOZrPJ/8Xjcd7MkvxHXJO9Yds2KpUKYrEYm8+T56SiKB1ZkN5nJB6PY2Njgz33qtUqarUa8vm8CJo5AGzbxtbWFiKRCMuaiSWwG4LBICqVCs6fP8/ydFmWYRgGAoGAKLJeJqizTXIr0zRhGAZ3toGdTALqbs/MzEBRFA6pEXPSwdELawwAPwO2bSMSicBxHDQaDaysrCCdTovr0CN6lUWLQI3Dh7fA1N/fj1KptCMltBOrybZt0Wy4TOxXcKJCtmmaLFcXBdXe0clvj4LFaO6QZRmmaUKWZfYmFmyy3uE4zp5M4G6uhUDvyGQy+N73vtcV0SocDmNwcPAqfKrrAwfalT3wwAP4+Z//eXzuc5877M9zIJBMIR6Pd/x5LBbj1xwEa2truOeee7C5uYlUKoUXvvCFeOihh3D8+HF+zVe/+lX81m/9Fn/9b//tvwWAXRN+P/zhD+NDH/rQgT/T9Q5vt7lSqbR47VECK01ywWAQuq7vy6wRuASvp5iiKKhWqwiHw9A0DaOjo6jVapifn8c3vvENVKtV9i6RJAkbGxsol8uYnZ3F5uYmZFlGoVBgD4hisSgCBHoEJX8ODAxgYWEB9XqdC6S7FUnJ9xAA0/XD4TCnxZVKJfFMHBCWZbGvSbFYRLlc3rOw5/P5OASFIMsykskkAoEAisUiNE0TRdbLQLlcRi6X442v3+9HKBTiZwDozNyjoquYky4fxBojg+9arbYnawzYviY+nw+NRoOlciRTF0XW3nGQMA1aB8Sh+3CwX4EJEKymK4FuihzeQjYlq4v5vnfsJX+mOVuwyQ4HjUZjTyYwKUj2uhYCvcG2bTzxxBMol8vc+N3rvHTs2LGWvebzHQe6q1zXxenTpw/7s1y3+PznP7/va37zN3+zJw+/d7/73XjHO97BX5fLZYyPjx/k412X8HabCY7jsESO/MnogNDf3w/XdYUMtEuQpxiZtpK/g+u6WF9fBwDMzc3t6GiQFGtlZQXJZBLA9oFBURT237Msi30QRTGjO5DcPJvNQlVVLhYRow/YeTDz+h7SdSmVSlhaWkKxWEQ4HEZfXx90XRfPRY+gsaUDHN3bhHY7CUmS4DgOZFnG6OgoVFVlmS4l5wocHLZtY319Hc1mE67r8v3ezkDysmUSiQSq1SokSUKxWMTW1hZUVRXJ0ZcBajL09fUhkUjAtm1Uq1VsbW3BcZyOgTGyLMNxHGYgO44DXdfFofuA6FUWnc1msbW1Bb/fD7/fj/7+fvh8PlFYPSC6ZdEIP7LDx34FJ28T2vuvQO+gJoJlWUin0ygWiy3yZ8EmOzx4x3pgYICbwV5vQ/o57Wu810Kgd2xsbHAAZ7PZZAuk3azijh49ekOtlwf6S++8804888wzh/1ZDgxi7O3GziuXy1zIuF7gTTZ9PkOWZQQCAfZXosMdLR6O48Dv9+9g9d1ID+FB4Pf7ceTIEeTzeVy8eBG6rkOSJIRCITSbTWxubu5JVXZdF9lsFqdPn+ZESl3XMTg4iGw2y4VCge7g9/sxMjKC+fl5NJtNBAIByLKMSqUCSZLQ19e363i2L0bk90ahNJFIRHi99Qg6kOm6Dtu24boujx0dICh10jv+zWYTq6urLbLc4eFhxONxVKtVUWQ9IKioRLJOKphqmraDLUOMbzpoGIaBRCKBaDQqQk0uE8REWlpagt/vRzabZZ8gn8+Ho0eP4uUvf3mLCoI8hAKBAGq1GoBtg/trbcfy04peZNF0AHddl71xV1dXMT09LQ7gB0Q3jCZAMPeuBPbz2/MmQzuOw0VssffpHd4mguM4vIbS/dvtcyCwP2isAXQca5pLCoUCdF2H3++HqqpiLjkgbNvGuXPnUKvV4Pf70Wg04DgON8pWV1dbXk9z942EA7Wg/uRP/gQPPPAA/vEf//GwP8+BQF57nXz1NjY2UK1WO/rxCVw50GI8OzuL0dFRZoKRMTcATE5OYmhoCPF4HLZtI5/PM+NGYH/Yts0Mimg0isHBQYRCIQwPD8OyrH1/v16v48KFC1hdXcXGxgb8fj8Mw+CJsF6vc2dEYH+Qv55pmmg0GuxfSEWk9o6/ZVmccgxsH7wTiQQmJiYwMjLCvn3ValU8Fz2CwhuomUAFJWB7oQ8GgxgbG2OWJbFivNfIdV32GVMUBZFIhA8aAr2B7l9KK1NVFYqiMFvS+zpifLuui1qthmaziUQiAUVREI1GxTU4JFSrVayvr3OhqVKp4Cc/+Qkee+yxljmfGEyqqmJkZIQPLoJ10Du8suhoNArTNFGr1XYtlJLdw+joKKfe+3w+cQC/DLQzmigNuv1+9jL3kskk/H6/YO5dJtrl0OFwuEUO7ZWsU/BeuVwWY34AeJsIzWZzRxOh2+dAYH+YprnvWIu55PCwsbGB+fl5/pr29CdOnMDAwMAOxm8oFEI0Gr3aH/Oa4kC7g3/5l3/BK1/5SrzpTW/CK17xCtx+++0dtcySJOEP//APL/tD7odXvOIV+PCHP4xvfOMbuOeee1p+dv/99/NrBK4eSKZIenhg+wGjA51pmty1Ixmpoig4cuSI2LR2AQrUoA7o+vo6crkcVFVFuVzetyBHfibNZhOyLMPn86Fer/PXtDgJJmV3oEMbUcNJBkphGZ1kV9TFrlQq0DSND3LA9rNCSbkiUKN36LqObDbLbGBiDhPzwhvgQInRPp8PgUAAExMTUBQFkiRhZWUF+Xxe+L1dJsgbtFar8aZ2aGiIE6HbQZszkoFeuHCBQ4NGRkbENTggLMuCJEkYHR3Fk08+ucOjhjriL3zhCzkIrD3VW7AODo5OsmjHcfYM0yD2Hh0QR0dHdySvC3SP/RhN3tcJ5t7hge5jv9/Pac/tMtBODKd2L0SB/WHbNhYXF6GqKqty2r1VvSx5y7IQj8dFCvcB4DgO1tfXoaoqZFnedazFXHI4sG0by8vLvG6SF3A0GsXk5CSefPJJBINBDqwCwAnzNxIOdFr84Ac/yP/7wQcfxIMPPtjxdVeruPfzP//zmJmZwRe+8AW8/e1vx6lTpwBsy3T/+I//GIFAAG95y1uu+OcQuIRarQbTNJHL5eA4DsLhMKrVKh+0w+Ew1tfXWZ41PT0NTdOE5KpLUGBAOBxmFguNpSzL7Gu4m8EoecJNTEwgEokgkUjg4sWLTHNOp9MsGxIH6f1Bvoeu6yIUCrGxrq7rGBgY6Ci78i74+XyeC64XL15kFl+nlF2B7uA4DiRJQjAYRCAQQKPRYHZeX18fs/VoPmo2m5AkCaurq3xdQqEQyuWy8Hu7TBiGwYeNcDiMRqOBer0OTdNanot2WRb5HooxPxzU63VsbW2xh2En1Go1bG1tcXFPeI8dHtpl0cTKDofDmJ6e3jMVVJZltjARODi6lUWL+/5w4WWhZrNZtoPxslA7ea6KMe8d2WwWi4uL7Kc9OjoKn8/X0kQQKdyHg2KxiPX1dW78dhprcV8fHizL4gIq/UvEoIGBAfYwp3WSggpJfXCj4EA75m9/+9uH/Tl24N5778XDDz8MAHjyySf5e1RIvOuuu/A7v/M7ALYv3r333ou7774bL3/5y3HPPfcgGo3iy1/+MhYXF/Gxj30MU1NTV/wzC2zDtm0sLCxw0a5UKsG2bWxsbPDDCIB99shIV3judQ8K1JBlmdNAFUXB2NgYJEnC8vIyJElixpIXVNCgBZ28l+LxOAYGBrCxsYHNzU1MTU0JxliX8HrsUREJ2F6IqtUqTp8+3dGo3rIsZvdtbGxAURRMTk5ibW0NxWIRR48eFYEaB0Cj0QAAfjaA7e4dzTmvfOUr4fP58IMf/ABbW1tcUPLS+V3XRb1ex8DAAKeDiubDwUCb2cHBQW4g2LaNdDrdMpbtSaK5XA6maeL48eOciCY8mA4GryS0UCjwM9IOKnATi16wDq4MNjc3sbGxAdd18cwzzyAcDuOWW27BHXfcwcw8us9nZmY4qZhCtMT93zvoGdA0Dc1msyPLhiDu+8NFNyzUds/VWCwm2GQ9wuvTCWxLRjc2Nnb16RThJQeHbdvY2tpiFirtGdvH2ssWBrCrmkdgf1QqFSwvL8M0Td6f0NpJHs7U2Ae2550bId+gHQfaHVwNievDDz+Mz33ucy3fe+SRR/DII4/w11TcA4BXvvKVePjhh/GBD3wAX/ziF9FoNHDy5El85CMfwZve9KYr/nkFLsFrnk4Je6VSCcFgkJlNGxsb0DSNK+ok043FYqJz1AX8fj+GhoYwPz/PXlSNRgNra2vs+wBcWrCJldRsNlkipygKCoUCNjc3oes6Zmdn2ROxVqvtGSsu0Aq/34/JyUnMz8+jUCiwxJOuTSfZleu6TBWnhSoajfK1IYhAjd7gPcCRTBrYbiZomsZyUDpgNBoN9kr0+XyYmZmBoijMNguHwwiHwwgGg+I6HBCRSASyLGNzcxM+nw+NRgPpdBpDQ0Mtr/Me7gzDYDl0pVIR0ujLBK3LiUQCKysrHZndqqoiFouxLC4WiwnWwSGCZNGRSATPPPMMz03NZhPVahXf//738cQTT+DOO+/Ez/3cz+1g+glZ+uXBsiwUi0WWbIVCIYTD4Y7rs7jvDx+O43A4TCcWqmCTXT46MSQ7+XSK8JLLh5dFRv7AnUJJeglREtgdJDenJj0RKUiBZlkW7/OpeUmBhpqmXeNPf3Vx3T7Bn/3sZ/HZz362p9950YtehPvuu+/KfCCBrkHm6eQXVq1WoSgKNE1DrVZDsVjkolMoFEK1WoUsy1BVVTDFukSpVMLi4iIsy+LDbqVSgWVZMAyDWXsUDU5FDm9UOMkTI5EIFhcXsbGxgUKhgHq9zt1SwRjrDqVSCRcvXuTx9/l8SCaTHCLQ6UBAzwnRyJvNJnsw0SGQwjlEQaN7eCXSlHKr6zocx0Gj0eDrYVkWs2GI2u+6LsrlMvtNBoNBlMtl+P1+IVO/DJBPG/l6+nw+hMPhHfNK++GOGN1CGn35oELRhQsXsLm52dLwAbbTo/v7+5kRThBsmsNDvV5HJpPB+fPnd22eGYaBhx9+GJOTkxgfH7/Kn/D5DfK5rVarSCQSHNbQaX0WbJvDRTabxdbWFtth9Pf3c0HJO5/TcyGKqAdDtz6d7Sz5UqnE3xfoDrIss91Uf38/yuXyjlCSXtjCAnuDEp5t24Zt21AUBbIsMykon8/DMAyoqsr7dFVVcfTo0RturLv6a5eWlgCAteT0dTeYmJg42CcT+KmF3+/H1NQU5ubmUCwWoWkaRkdHkclkmJ2Uz+dRq9WwuroK27YxMDAgvGS6BMmeqehTKBSY5UKMF/Kroi7p+Pg4szEAcAGJUoZSqRTm5ub4PROJBGKxmGAqdQEKN1lbW+PkYsMwmJkXj8f3PBBIkgRFUTiheGlpiWnlCwsLoqDRI7wSaSrYUQHPK2trB12HfD6PbDYLWZahaRqKxSIKhQKGhoZE8+GA2NjYwMbGBhdHqahN7LB2eBnHQhp9uCDrC0mSOCnacRzufBuGgZmZGe50CzbN4YAOeSTd2guO4+BHP/oRe4MKWe7hwJvWurm5iUQisev6LNg2hwevVJSaNqurqzvki90WAAX2xn4MSaC1eN1oNBCNRkXxukcUi0VuwGezWSSTyR0BML2whQX2Bklya7UaF67D4TAmJycBbFtdhEIhDkuSJAmzs7M7FCI3ArqaLaempiBJEp555hkcO3aMv94PkiTtm9op8PyG95DmDQ8wTROaprXQZw3DEEyxLkDU+Xg8jmazia2tLVQqFTiOg4mJCe68UfItSaMpAZRCBizLwlNPPYWBgQEUi0WEQiH09/fDMAxmVwrG2P4gphixkyhMhlhi/f39HcfQ66dEQTO6rmN0dBTr6+toNpuYmpqCZVmioNED6FDw9NNPY2NjA6ZpMjtD0zT23wgEAvD7/SzLJf/DdDqNUCiExcVF6LqOmZkZVCoVBINBhMPha/zX/fSB0s1s24bP52NGaidz43apEBWThDT68kEhTBRi4jgO2zTQc5HP5+E4Dp599lmMjY3h1ltv5d/3FscFegetE6RU2O8QXSgUmKEgZLmXj27SWr2vFWybw0M3UtFuC4ACe6PbAqkoXl8e6H4lFQKFtrXPJ16GZDgc5rlEMCR7g23buHjxIjP1yMpofHycG5OVSoXDDAEgHA7jpptuuiHn7K7+4re85S2QJAnxeLzlawGBTiBmGRWgqtVqiwFmKBTC5uYmAPDBwnEcqKoqPB+6ADFfSqUSSqUSe/g4joOVlRVeOCRJ4vH2MpdIDuc4DrM4HMeBpmkYGxtDJpPhlFfBVNofFG5i2zbK5TJLD6l4tNfv0cENAHK5HNLpNHvFAdtSuVAoJAoaPYDCe6iY3Ww2+cBQr9cxMTEBTdPYF5RCUOh6EWOJnhmfz4dUKiWuwQFBRQ2fz8cdV5/Ph9nZ2R0+KO1SoUKhwH6sQhp9eajX69jc3MTm5iabTpP/qjdkg6RGDz30EKampngtmZiY4GdCrNO9gywaMplMV+wYsg0QOBx0k9bqfa1g2xweupGKdusVJ7A7ui2QiuL15YPu1/7+fhSLRV4b2+9XCuYh5ZTf79/B7hPYH9lsFmtrayzbT6VSbBNC80u5XIZlWUilUqhWq9A0rUUifSOhq6e43fuuVy88gRsL7YEakUgEpVIJPp8Ptm2jXq9zcdi2bZZmCbP07kCy53PnzqFarUKSJKRSKaiqimw2C8uyuKBHxqMve9nLUKlU8OCDDzKrjMxfx8bGkMvlYBgGZFlGNBpFIpHA8ePHEYlErvWfe93D7/fjyJEjyOfzuHDhAur1OqLRKMbHx9Hf39+VjIoKHoZhcMGVOnvValUUNHqArutc3AuFQszQIKPd2dnZFnkbpRUT43JhYQF+vx/5fJ6ZeuIaHByyLKNQKPA9TU2fm2++uWNCpTdQQ1VVNJtNZLNZFItFIY0+ILxJua7rthSNVFWFpmlYWlriQjiwzRzLZDJIJBLY2tpqYVCSn6hAb/AWVUk6B4D3Rl6QXE7Icg8H3XqRAZ39yATb5vLQTZhGt9dHoDN6CdMQxevLA92vW1tb3EQeGxvr6G0ognkuDzQvNJtNVm/kcjkMDQ1hZmYGfr+f9/LeIqqqqjdsEVXsDgQOHZ0CNUgGFAgEkEwmkc1mUavVoCgKb3bD4bA4uHWJeDyOEydOoFarYWNjg6nI5GVFaa2UfhuNRtHX14e+vj5sbW1BURQObygUCqjVarAsC6urq2g2m4jH45ibm8PU1BQzdgV2BxWpw+EwZFlGo9HA6uoqyuUyRkdH95Xl2raNWCyGxcVFzM/Pc8GpXC5D0zTxXPSIRqOBSqXC5ruBQICLGNTJo46qbdsoFAothzeS8Pp8PhSLRUQiEXENDgg6cASDQZYHRSKRjrJcr78bsO1pE41GMTs7K6TRlwGvlQNteCVJ4mfC7/dzcAD96zgOKpUKd8CbzSYqlQpc18XJkyfFs9AjiImnKAofuskio9lsolgs8mt9Ph9baghZ7uGhGy8yYGeTIRKJiBCZy0AvXnrUYPD5fMKHu0dcTpiGKF4fHN16GwIimOcgIMktna1IrXb77bejr68PwPY1IAuedDp9wxdRxe5M4NBBzLKFhQXU63WEQiEMDg5C13VO3COZ99GjR9nzanx8XBzcukSpVMLCwgJc10WlUkGxWOSOBiXi0kFsfHycZYixWAy1Wo29IRqNBrOSgG3j+0QiAU3TUK/XhQdiF6BADcMwkEqlmDpu2zYv6J3gleXKsox8Po9QKITZ2VmUy2Uuigv0BgrUoNAM27Y5OdrbyfN2VKPRKG8GZmZmoCgKarUa1tfXd021FOgeiqIgGo3C5/PBcRyWne8Gr29oIBBAMBjkop9gLfUOaritrq6iXq9zsTUajSIQCEDTNJb/ExRFQalUguu6guFxCKhUKlhZWYFpmuwnGQwGEYvFUK1W+XVkpyGsbw4XvRSYRIjM4aFbqWi73ypdGzHfHwz7FZxE8fryQKFUQ0ND8Pl8SCaTaDQawtvwCoBSzg3DQF9fH0qlEsLhcEvRmsKSGo0G8vn8nmFJNwK6mjF/+7d/+0BvLkkS/vqv//pAvyvw0414PI5bbrmFqbIAMD8/zxtbig6nje3a2hps20Y0GhVssX1Anob5fB66rsO2bS7WkaEoBQT4fD5mHLXLEEnSOzU1hVKpBMdxWDKdy+UwMjLCElGxudodZFQfDodhGAanUPb19WF6enpfGRXJ5GzbhqqqUFUVm5ubME2TjWJFkbV7FItFZgnXajUEg0G2CPB28jox94LBICqVCsLhMFZXVyFJEvr6+qDrurgGB4SmaVBVFcvLy3zAO3r06A6/PeDShnliYgKSJPE1LJVKaDabwrbhgKAQgX/9139lxrYkSajX6xgdHUUwGOQGELH6gsEgDMNAvV6HYRjsgei67g27YT4oyAzcMAz4fD40m000m00oigK/349UKgVd1/n+pxAskuXOzs6y9EsUPHrHQcMaqMAqCq0HR7dS0U5sMvq+QHfwrp97+aOK4vXlo16vY2trC4VCgZtdqVSqo7dhJBKBLMvsOSy8DXuDN+U8m80ikUi0+Ba2h5sA2DUs6UbBgTz3uoUo7t3YoA4pAPaT8XalDcPgQzMl4IhD9P4gg3pd19FoNKBpGktx6UBGC3s4HEYsFgNwqZhhWRZqtVpLumutVgOw7b9E9OZSqcRmpQK7gwI1gO1QjEKhAGB7oS+VSrv6U1mWhUajgVAoxOmhtVoN+XwelUoF0WgUqqoiEAgIxlKX8CYiBoNBZuzZts0d092Ye1TYLhaLWF9fh67rmJ2dRTAY5A2wuAa9gyTrFFhCXxuGgXK5jHK5jKGhISQSCd4wk78bscaWl5cRCoUwPj4uxv8AIG8gWZYRiUS4KRQKhXDTTTcBAObm5pjVR5L0SqWCZDIJv9+PcrnMyfbPPPMMjh07JppwXcKyLBiG0WJBYhgGF/eokE3jS7YA5PNZq9WgqqrwJT4geg1rECyyw0O3UlHBJrt8tK+f+/mjigT0g8G2bSwuLnIzvl6vo1Kp4AUveMEOb0PLstDX14dEIsGNNcF87x62bWN5eZn94MmL1lu488p2a7UaXNeFpmk3dBhPV3/1/Pz8lf4cAs9z0KaINlfUmXvmmWeYaus4DiKRiDhE7wOSHVK0OjHDKpUKdF1nZgVJ2mgCpBRESi0mZl+lUuGC1PDwMGq1GqcXC5+x/UGsmM3NTdRqNZYRkl+e158qm81ifX0dw8PDHDRDnepyuYxKpYLV1VVYloVEIgHXdUWYQw+gQ1wymcTGxgYcx0Gz2eRgDe+GoBNzj0yPR0ZGsLi4iK2tLWiahkajIQ7VB0S1WoXjOBgeHkYgEIDruiiVSvjqV7+KxcVFZr2+5CUvgaqq3OWuVCowDANjY2NIJpMwDAP5fB7Dw8NiTuoRlmWhVCqhXC6zByXJ1BOJBHt/NhoNDqAhf1xFUZDNZlGtVtFoNBCNRtFoNEQTrgeQFy4VT6mZE4vFMDQ0hEwmwxYOJKOj5O5UKoWlpSUOmBEF7t7Ra1iDYJFdGewlFRVssstDJ5bYbv6o3TL8BDojm81icXERkiRBlmUMDAwgkUjsKNp5rXeEZ+rBkM1msbKyAgAwDIMtWryFO5qv6dxUrVZ5b3OjoquneHJy8kp/DoHnOQKBAHw+H/uLUbfa5/OxH9/a2hqSyaQ4RO8DSmfd2NhALpfjzrLP54Pf7+fiHiUVU+eTDL1pMad01vHxccRiMSwsLGB5eRnBYBCDg4PCzLhLECtGURTEYjFIkgRJkqBpGuLxOBzHwYULF/DUU0/h3LlzbKp++vRpZlUSaywej2NmZobZlPl8XoQ59ACaZyjBLBgMwrIsDjtp3xC0e+6ZpoloNApFUZBOpzE/P48LFy4gEomIQ/UB4TgOarUatra2EIlEIEkSyuUycrkcv6ZUKuGhhx7CC1/4Qu5y67qO1dVVpFIphMNh9hQVB5DeUalUuEAkSRJM00QwGGQmq9/vx7Fjx7C2toZsNgufzwdVVREMBvk9SEZKCezCsqF72LbN93uz2eRgh4mJCZw4cYILqJIkIRQKcdiGruvI5/NIpVLM3BMF7svDfmEagGCRHSZ6LSQJNtnBQIqeeDyOdDrdogxpR68MP4FL8Er8JUmCZVnI5XIYGBgQ43fIoLEmGxFVVZmB7W3MUKNeJOVegtgdCFwxeD3e/H4/s2GA7QU8Ho8jGAxiYGAAW1tbKJfL6OvrE4WMLkBd/bW1Nei6jlgshsnJSaysrKBWqyGRSPAGarduMxUBaVKMxWKoVCq8QBmGIdgZXYB8qqLRKKcOU6CGoij4x3/8R2xtbbX8jm3beOyxx3D77bd39KEkA1kR5tAbaJ45f/48S9xSqRQnSXs7ebul5c7NzUFRlJaAE8uyxKH6ACCvMZ/PB9d1kcvlEAgEUC6Xd7y2Xq+jUCggm822MIt1XQcAIUk8IEhCRA0dOlRrmtZiuxCPxzE0NMSBJ67rcvDGkSNHsLKywvORsGzoDaVSCdVqFbFYjH3zJEnC8PAw+vr6cPz4cSwuLrJdBo1zo9HgUDIqtooCd+/otcAkWGSHh/ZCElllUNAbQbDJLg+dCnayLOP8+fMtxZBeGH4CO0H35ODgIMrlMkzTRLPZ7CgBpXt3ZmaGiRX7eXALXIJXbksBkZqm7Wi0i6TcnRB3l8AVAaW5UnFvamoK6XQa/f39WF9f54eVJrp6vd7i0SewOyidtVQqoa+vj423M5kMp+XWajVEo9EWjzGv5xUVNSRJwtLSEi/wsiwjlUqxZEtsrvZHIBBAs9lEtVpl83mS0V68eJH9DNvhui6Wl5eRTqf52pAPGXlMiDCH3kHsomazCdu2US6XEQwGWZpLaGfu1Wo1ZjZRYUNVVU4HFYfq3qHrOjY2NhCJRJBIJDA/P9/C2PPCdV1O0wW2r08oFEI+n8f6+rqQJB4QxODw+XwIhUJQFIUPJJOTkzyetFYfPXqUDyLEzltdXYXruiiXy4hEIgiHw6IJ1wOoeUmMekVREAqFkEqlAGyHzlCYD809VOQTDJvLx0GZSoJFdnmgQpKiKCiXy3juuedQLpchyzLOnj2Ll7zkJfjZn/1ZAIJNdjnwFuxs20Ymk0GtVkO1WuU5h8Za+MBdHrz3KXnuEVuyHUKWe3nolJLbSW4rknJ3QuzMBA4dlOZar9cRiURQrVaxsLCAm266qUUK4ff7oes6nn32WU5QpN8VhYzd4S2GapoGWZaxurqKTCYDn8+HeDzOiXDtHmO0ABWLRQ7XsG0bPp+P/y2Xy4hGo4Ip0wOIok/JoPF4HKFQCAsLC3v+nq7rGBkZYUnv4uIiLly4ANM0RZjDAWDbNp577jnU63VOkVZVFZqmwbbtlk5eO3OPUiqpaEFBJoZhwLIs4Xt4QFCAT6PRQLFY3PV1iqIgHA5jbGyMGw8rKytIJBI8Hwn2ZO8gFhJJQW3bZuuFSCTS8jpiWhJGR0f5mQkEAojFYhgYGMCJEyegqupV/1t+GmEYBubm5lhWS8zum2++uSXsKhKJsG9QIBCApmlYXV0VDJvLRCemkmVZPM/sFqghWGSXD8uyMDc3h/n5eVSrVf6+67qo1+t4+OGHcfz4cSQSCcEmuwzQvanrOubm5nY0lG3bxiOPPMJjLQpOB0P7XEL39F57Elp3b+RC00FBRbtqtYpisYhUKtWSkgu0huiRdcKNnpQLiOKewBUALTSRSIR93+gB9aaDAuAOtt/vR71eRzKZFF4++4DSWYmVVCwWuRgBALVaDT6fb0daUCAQQDQaxdbWFnc1isUiYrEY0uk0crkcisUifD4fAoGACNToEtlsFpubmyyZIhZfJpPZ93cty8LCwgIzlEKhEGZmZrCysoJsNotkMgld10VhqUvouo5MJsPBDJVKhUMCKESAQAc2Cg6oVCpcOI9EItA0ja+l8D08GIiRalnWnoU9AMwcuHjxIrNYgW1ZdTAYFJLEA8Lv9yOVSrGnZDAYZC/WdtlKoVDA2toaFz1UVcXg4OAOWZE4qHSParWKWq3G84njOMyQ9Pp/0vUhZmW5XEY8HhcMm8tEO1NpZWUFi4uLyOVyiMfjuOOOOzA9Pd3yO4JFdjhYWFjA2bNnd7UXsSwLFy9exK233irYZJeBQCCAlZUV/OQnP9n1NaZpYmFhAadOnbp6H+x5hva5pNFooFAotHjTepHNZrG1tcWqtP7+/hZrDIHdUa1W8eSTT2JrawuFQgHNZhP5fL4lJBLonIQuSdINnZQLiOKewBUAPXx0KKa0T1VVOR1U0zTous6yN8uyYJqm8PLpAhSooes6FhcX0Wg0WL5pWRaCwSAn5Xrpy+2+h4qioK+vD/V6HaZpwjAMNpq+kb0KeoHXXNd1XViWBcMwmCm2F2RZRrPZ5N8lGWgsFsPExASWl5f5ACIKS92DDtCNRgOyLLMMrt2Dw3VdZs44joNoNMobiEwmA1mWkUgkRLDMZYAYSblcjhs6e72WCnoA2AORUqcFk/hgsG0bq6ur3LSh+cYbtgQA5XIZ58+fR7VaRbPZhCRJmJ+fRzQaxerqqmB5HBCqqqJer6NUKnHgVTAYbJFxEYNeURSes2g9OX/+vBj7y4BXGlcsFjl5EQDW19extraGO++8EzfffDMXlgSL7PJhGAZ++MMf7usbTHtVwSY7ODKZDC5cuLDv6ygAQvjAHQzt9ymlxyuKsuO13rMBKalWV1cxPT0t7ut9sLCwgG9+85tYW1tr+b7jOLxHobNtr0noNwrECV7g0OH3+zE1NcU+VaFQCFNTU5BlGbFYDOFwGLZtcypcKBTijohgi/UGOgDTodg0TWaPtTOVALDvIbEvyJfjwoULKJVK3FUyDIMDNYTfzO6grlE6nYau6zAMA7IsY2BggNOLdwMVUaempjAzM4ORkRFYloVSqYRKpbJvop/ATpAsemNjg5kwPp8PqVRqhwcHNSEo0IQ8O/r6+jA5OcnM1mg0ygxL8Sz0BmIk7eY76X2d67rI5/OYmprCyMgIZmdnEY1Gkc1mMTc3h1wuh1QqJdaGHqHrOjY3N5m5HQ6HmRXjPWTkcjn2hSPfyXK53CKnE+gddLij0CTyluyG/dhsNq/CJ7wxUK1WdxwWaY6///778ZnPfAaPP/54CztnZGQE09PT6O/vFyyyHlEqlVAqlfZ8jSzLGB0dvUqf6PkJ27bx4x//mIOn9kIikWixX1hbW8PFixfZh1vg8OBllFHxz+fz3fCMsv2QyWTw9a9/fcdcTaBzqRckfQYgzk3/f4g7TOCKIB6P45ZbbmlJy6UFxDRNSJIESZLQaDSwvr4Oy7K4ICKwNyhQg+SHjuMgm81y521gYICZAJ0YeDTGtm3DNE02hW02m9B1HQMDAyJQo0sEAgH4fD5O+KRDXCaTgWEYaDabXLgAtjunzWYTPp+PpdN0nZrNJur1OhYXF2EYBoaGhlhOJ3wou4Nt29B1nRd4Si0OBAKIx+MdN7CSJHGB3HEcxONxAGh5LVkLiGehN9D80Q2Llfw+z58/zw0fKpQTc0947vWOcrmMcrmMRqPBxW5FUTA2NtYyjhTGBID/JWaxYHkcHNRIi0QibElCYT8E7x6J/pVlGcFgELOzs7xuiPW4d1CaPRVWd0O5XMZ3v/tdTE1NCRbZIaBare5bcAqHw9A0TbDJLgMUWrUfFEVpSScW6B3tXpySJLEUtB2CUdY7bNvGmTNnkM1m93ydVwUipM+dIZh7AlcMFPjQ/oDRYZqMvSmNEoBgi3WB9kANYFv6Q+EL+XwejUaDJUDtv0u+hwCYXaYoCh8gisUiJEmCYRg7/A0EWkFSZ2B7bMPhMBKJBCqVChzHYWalLMuQJAnAdiGDDnPRaBQ+nw9zc3Ps2zcwMMBFW1mWEYlE+FAnsDeq1Sps20YsFkMymeT0uFKptKOYQYs/dVZJPre+vg7TNNFoNLggUi6XxbNwGaBChRf0PADbB49gMNhyj1P4QzweRyQSQTweF89Bj7Btm1l7dFg2DAMTExMYGhpqea03XMMLv98vWB6XAV3X0Wg0YBgGqtUqJElCOBxumYuo+BcMBlm2G4vFoCgKarUabNtGrVYTc9ABUK/XsbCw0FUBRNd1rK+vAwAXYAV7sncQM2y/saN7WbDJDo6trS3k8/l9X0fhe94C1cTEBGZnZ9kXV2BvkBfn/Pw81tbWUCgU2PZlL1DqtiCv7A2af/eT8lPKvFf6TF97LUhuZNy4ZU2Bqw5vd07XdTSbTf4vHo+z55joTu+N9kAN6o5S58Ln80GSpI7MPYoWr1Qq0DQN9XodlUoFg4ODqFarXDCMRCIIh8NCIt0F0uk0hoeHUavVEI/Hoes6L+YA+ICgqipc12U2n8/n4/CYeDzOxQ7DMBCJRFCr1URSa4+ghKzNzU3IsoxGo8Hek+2yKmJdLi0tcQHW7/djbW0NpVIJjuNAkiQsLCxAVVWMj4+LZ6FHNBoNZhzReAJgP0MqgjebTQ57mJiY4GbDysoK8vk8YrGY8Nw7AGgtDYfD3O12HKejATjZBHgbQtSIEzgYbNtmCRE9B36/H2NjYy0sGlmWYZpmS/iJbdtIpVI4e/YsDMMQc9ABYNs2FhcXUa/XuXm5F2iOIu9ix3GYBSL2pN1jY2MDc3NzALb3peSD64W3uSNwMNi2jQsXLsBxHGY77gZKoxdhMQdDJy9Ox3Fw9OjRjkU7mi8E87p75PN5FAqFPV/j9/u5EdkpTENIn7dxY//1AlcVXjNSWZY5ZSgSiXBqqzjA7Q8K1DBNExcvXoSu62zETRumcDi8wzAdAKeG2raNXC7X4v9DPn0DAwMYHh7G8PAwVFW9Fn/iTxVqtRp0XUc+n8f6+job7fr9fgSDQVQqFU5vomh3WZZZCp3L5TAzM8PBJo1GA5qmwXVdkdTaI1RVxeTkJObm5tigXlVV9kL0oj1gRpZlhMNhNBoNjI6OsufH1NQULMsSktAeYds2nnvuOZYh0kGZWHrpdJoZSTRvSZKE1dVVnqNCoRDK5TK2trZEceMAIBuMra0tPoD09fUhEAjsOGRQM4g896gJAUAcUA4IXdexuroKSZJ4zAFgcnKyZfxoXWg0Gsjn80gkEohEIsjn80ilUjyHiTmoN5CHLa3H+yU9+3w+hMNhzM/Pc7OOfONEkbs70LxP+xyv56QXkiTxfSwKIQcD+anSmFEh1e/3t0gXJUliqwsRFnMwkGIqHo8jnU7z/blbUi41jyn1nPznxdm2M8huivaJu7H3vIo0IX3eHeJpFrgmoEQ48t+zLAvRaFQEanSJcDiM4eFhrKysIJFIoNlsolarwbIsXnj8fv+OhYQmQDJXpxCTcDjMUtDV1VVkMhkUCgUcOXKEPcgEdoIWpEKhgFAoxB5VJOkhJiXJn/1+Py9aVMg2TRPz8/MAtg954XAYqVQKoVBIdLd7RC6Xw+LiIgKBABRFYcl5p0I3cClghqRYhUIBqVSKrxfJSSkcSBw2ugdJLKiwSgcP8j+kZ0FRFMRiMT4Aen3f6vW68Ny7TJAklNgdwWCwo7xTVVUEAgG2A6BGkKqq4oByQNi2jUqlAtd1mdVNXqve16ytrcHv9yMWi8F1XTQaDV6P4/E4F8TFHNQb6vU6stksF+iowE3zUHuCN4WQxeNxto6JRqOIRqNdBaAIbM/71LgnLzJS5dC9DWwXnILBIO9TA4EAarUaF7JFk39/ENOJ1lfXdVl94723aT6n+aO/vx+yLHNDQYTF7I9OjMdEIrGrJLdWq8E0TeRyOWSzWQwNDYmz7R7Y2NjA/Pw8q2joLNWO9nMt7RmF9LkV4i4TuGqgTenRo0dhWRYHCAwNDSGbzbYYegvsjlKphLm5OVy8eBGFQgGDg4NwHIcN6BVF2VV+4rouYrEYAPCi3mw2+QBumiZ7c9RqNRHksA+omyfLMkKhEDRNQ6VSYTkiAIRCId7ERqNR1Ov1FrkPbXwBcEplLpdDIpFAOp0WgRpdggqtpmkiEomgXC7DcRwYhrGnB4d3M0BjL0kSF1qBbS8/UdToHbquo1wu87NABe5YLIZ4PI5sNstMA5LEjY+PQ1EUOI6D5eVl3kiL4kbv0HUduVyOD9qUCj0wMLBjDOPxOGKxGEtbHMdBJBKBz+cTB5QDYnNzk1O7a7UaNE1DMpnc4f3ZLi2i54ASjEXBo3eQjI4k/rZtQ5Ik9jL0+XzY3NxsCbuiwgjZZCiKgkajgVgsJsa9B5imyY17WnspTMYb1qMoCvsPp1IpLC0tCQl6lyCPwvZiaTweZ4sLWndd14WiKLyHoTnFNE0xp3SBTpJc13Vx6623dvQqJDsGSZIwPT2NUqnE/vICO2HbNpaXl+E4DkKhEO9V2uHz+aCqKu/ZRZjG7rix/3qBqwrqzuVyOZRKJWxsbCAYDKJUKjFLhsyPRSGjM2jRqFarzAjL5/NQVRW1Wg3RaJQXeJrkvJ0lugbexGIKHHBdF5qmodlstmwSxES5O2jT6rouDMNg9iR57FFhb3x8HLfeeits28Z3vvMd9jckmdDQ0BByuRySySSA7S6hoigYGBgQSa1dglIRQ6EQSqUSfD4fSw13k1R5A2ZM04SiKLxZ0zQNPp8PxWJRSKMPAJKlkCSXDhqKouBnfuZnEIvFsLm5iUwmg0ajAdu2oaoqy3KpWUGeoqK40TtI9uzz+RCNRnnd6JSaKMsyhoeHec6n+UuWZXFAOQAMw8C5c+f4fqUG3PDwcMv47yYtGhoaQrPZFAWPA4IabzSvN5tNLtiNj49jdXW1pQnq8/mQTCaZOe8ddzH3dw/ycaZkbmpqUjGJrgUVWGlfIyTovYGY8cR4J5XH6OgoW42QisTn82F8fByxWEwUUQ+ATpJc2jd2Ku6RZJfm+UQiIc5Se8CyLPYqr1ar3AAgNqrXpzyRSPDZicI0iGW9urqK6elpsUeEKO4JXEWQkTQtLFRIKhQKiMfjoqDUBWjRIIlUrVZDPp9HPp/nwobjONja2sLIyMiukxwFmZDZejqdxuLiIgqFAkt7dV0XbKV9QP6HpVKJWQAUaELdUlmWEYvF0NfXx7Iseg1tzJaWltj0O5VKIRqNikCNHhEIBNBsNlGpVGAYBidQnjhxoqPHGHApYIZYNVTUIJ+9bpK7BDqDxp+8QEk2kUgkMDQ0BMuy4PP5EIlE+HnxynKJDUteluIg0jv8fj9CoRAftsmMutMYEiOh0WhwGE29XoemaZAkSRxQekS1WkWtVmN2PLHqd7uHSYZE64Nt26LgcRkIBAIoFApYXFxsKXJQsICmaS3SXLJgkGVZjPsBQQEm1Bio1+vs6ZxKpVCv1/kQr6oqN92oeBIKhaCqqmBpd4F8Po9cLsd2C7TfPHLkCLLZLIrFIr82Fovh1ltv5d8T93Zv6DWExOsvT4Wovc5jNzoqlQqWl5dhGAafTakJ4C1cA8DQ0BDv1UWYxu4QIyBwVREKhdDf38+HO2JuWJaF8fFxUVDaB8S8o0VmcXGRD3CqqnIyqyzL6O/v3zHJeTtOtPGi7mkqleIUos3NTUxNTYmOdRcol8uc8BQOh1kK6rouCoUCEokEG6iTBKXRaLDkh+RAlJ7baDQQjUa5+BoKhcR16BF0UJYkiWW6neaUTjJ1n8+Her2OjY0NSJLERVnBKO4NlUqFC97elNx2/ypiVtLrJicnoWkab44TiQSi0ag4iBwAJNki9qOqquxh2A5d11GpVLhDbts2TNPE+fPnOZ1dHFC6h6qqsCyLG5m6rkNV1R1m315jdq9dQ7VaZS9iAOxRLAoe3cG2bRSLRWZ2AJcCM6gRSqFWtGdqNBqo1Wqi0HRA0H3rLVSTNDccDiMWi7GNCTGhyGtPJLh2D5Lk0n6f5o2+vj5mt/f19fE6G4vFeD4Sc0pv2E2SK0JIDgfUECBFAQUKknzcazUVCARw9OhRHncRprE7xJ0pcFVB7JqlpSW4rotiscibKF3XkUgkRCFjD/j9fkxNTeHs2bPY2NgAAA4AqFQqXFhKJpNIJBI7ft/LVIpGo2zISxswYmnoui4YS13AMAycOXOGE3HJby8YDPJ/mqaxpx6xmeh1VOybmZlBtVrF0tISCoUCAoEAbr31VpYJidTi/UHdf9u2EQwGYds2DMNAuVzGbbfd1nFO6SRTtyyLmZSzs7PM5hAHvO5BGzYCHfSosC3L8o5Ct+M4zLCkjV2z2UQqleJnSVyD7kHXoNlscvo2MZc6odFosDyaGJSSJIl14ICQZRmJRAKVSgWlUokP3O0WAbIso1wuo1arIRwOo1arAQA36wT742CoVqscTEIS/2azif7+fsTjcW4q096HxrharYpC0wHhldV5A5SOHz/ORVTy8aSiq67ronjSIyi0JBwOc1iSJEkYHR3lcAxvyAbNOYJR1juoINrX14dEIsHr414hJDSvTExMsOWUSH/uDMuyUKlUuJnoui5s24amabz39vv93Gjf7VwrwjRaIe4ygWsC13V5ExCNRllHf+TIEWaPCXRGOByGoihIJBIIBAKoVCool8uwbZtTQneb5NqZSrFYDOVyGfl8Hj6fD7FYDM1mk9k1gq20N6rVKstMSBLtZd/5fL6Ww5w3rTgej6NWq3GiYqVSgd/vZ9r5M888g/7+fmbuidTivUGFa2IfUdEoHA7vmwZHslHbthEKhTAzM4Pl5WVks1kkk0nBKO4RxBAmVioV8cj7jRgF3kJ3KBRCJBJBPp9HJpNBIBBAIpFo8WIR16B7WJaFXC6Her3OBwxqEnQ6ZFBBnGRexAaZnJzkQCDbtsUBpUtUKhUUi0XIssz+nTTPeEGpxIVCAbquw+/3I5lMinCxy4RpmigWi6jX6wC2i93hcBi33HILwuEwKpUKNjY22JeV/Pi2trZEoemA8I43sK3UIR9harZRgFIgEOCgH5Hg2jvIr9m2bW4o096R5mrvv2LdPBgOUhDtVcZ7I4MkudSMof0HBSGRuoxk5zSGooC6N8QICFxV0IM3Pj6O5eVlNiQtl8swTROBQADHjh0ThYw9kM1msby8zJMZJVIGg0FmQRILox1eplKz2WQG39jYGFZWVlAsFjE0NISRkREoiiKYMvuA5J65XI6LrlRcpWKRd2NFBznbtlEoFNgDi/xTVFVFMplEpVJBvV5nCbYosu4P13X5sECFPbrH9wrUoKaCruvsy6RpGiYnJ7G8vIxcLod4PC4YxT2A7nHvhi0YDCISiXDyJBWKAoEAYrEYdF1HtVrF9PQ0IpEIHwSBS2nF4hp0D1mWUSwWUS6XeXMcCoUwOzvb8ZBh2zYikQgfGmkeW15eZpm0YHl0B6/UKBwOcwEpEonsKNrROk5+lCTHpbVjdnYWkiSxTYNYj/cHHcaBS6bssiwjHo/zHPSiF70Ia2tr2NzcZFsA+rcXlo7ANkgi5/P5EAwGYZomDMNgmS6Fj+m6DsMw0Gg0MDY2xtJckeDaPQKBACRJgmEY3DQIhUI8d3ibZrTuuq7Lc4eYU64chIy3e7RLckulUot117PPPss2IcCltVJVVVFA3QfiThO4qqBD3crKCqrVKrLZLJuqx+NxNBoNUcjYA96EIPKUIXknbUypmLFbQQO45EnmOA5UVYWmadzNJmmjaZqCKbMPVFXFzMwMVlZWkM1mIUkSp4QCQH9/P9LpdEtIAG2kKDSjWq1icnISgUAAuq4jn8/DMAxOmKMCn9iA7Q1Zljk0gPwM2z3eqtUqCoUCkskkF2bpICjLMgqFAifnEpNSUP0PBtM0W8zqqVBH/pNU2Gs0GjAMAwB4k0fXb2NjA8lkEpqm8UFQoDtQejSZT5PsfGhoqOM8oqoqH8xp80xSXoHeQN5jlJBL493J+9N1XcTjcUiSxGz6WCzGc0+5XOYALbEedweSehEbj5gd3qRnKmCTkgEA+x2eP39eyBZ7hGVZKJVKcByHA940TcPw8DAUReF0VloTNE3D7OwsVFUVCa49IpvNtliJeH1pvU0zmjdIli7mlN6xV0G0U9H/IDLeGxVej07arxiGwf95PTu9TTFRQN0fYhQErgkomaxUKjHjJpFIsOGuKGR0BiUEpdNpLCwscCGPJKDe8IBOsh5voIZpmlBVFZVKBYuLiyyNcF0XKysrmJycFEyZfZDL5bC0tMSeVpZlodlsIpFIcLiG90DXztwjmVYkEkEoFMLm5iaq1SqCwSAbfws5YncgyXkoFEKtVuMDHVH5n3rqKTz66KPQdR2RSAQvfelLcfToUf59em29Xsfc3BwMw8DQ0BBSqRSq1apoOvQAXddRKpVaWMSUBE22C1TopgN2rVaDaZpYWFiA3+9HPp/nbiz5MsXjcTH+PYKK3bZtIxqNthQzvJBlGalUCrquo1arMetsenoawWBQsDx6ADUKdF3nOTwej3Nh2wuy0vAGDfT19aHRaMA0TeRyOWSzWQwNDYn1uEuQlxsA9lH1+XwsW/Si2WzyoZ2+FugdtI+s1WrMgFdVFaOjo6jVaiiVShzcVq1WoWkaFz9Egmv3yOVy+NGPfsTMPEmSUK1WuUlPShGvOoTOA7TGijmle1CzvtuCqPA17B5kt+I4Dntykv+pYRiIRCIol8s75OWigLo/xBMtcFVBB4OZmRmsrq5CURSWDRWLRUiSJCj5e4AWmmw2y4wM8iyhTrRlWcx+aYc3UCMej6NcLqNSqUDXdWiahomJCYRCIZRKJeHztg9s28bc3BxqtRr7xxAjkvwkI5FISzR7J+aeYRhYWFgAAJRKJWiahv7+fn4mNE0TG7AuUK/XUalUuKNNh2bLsvCDH/wADz30EAzDgCRJKJfLeOCBB9DX18fzEf2erusYGRnB2toaHMfh4qtoOvQGYgZLkgRVVeE4DssT6efeQ4iXbUy/S6EbYvx7B60VJM0iadxuayv5jsViMQ4vcV2XU9UNwxBrc5egwwddg0AgsG8wEjFr6KCyuLgISZIwPT2NUqnEDR+B/VEsFlGpVFqkiMlksiVpkQoe1WoVPp+PD4fBYBAzMzMtnmVi3tkbJK9TFAWhUAjZbBbA9hhvbm4ikUggGAyiXC6zr6Sqqnx9RIJrd/DuOUn6TOuq3+9nBYhpmpBlGel0mqW65KEt5pTesFdBVDQCLg/k0ek4DhqNBuLxOGKxGKamplAul9mayHXdFhWUKKDuDzFzClxV0OGCpIe0ANGBu6+vTxQy9oDf78fIyAjm5+f5IFatVlEsFuH3+xGPx/eUURG7iSSMzWYTsVgM0WgUjUYDmUyGpVgLCwvw+XyiwLcLaMOkqip3TknKqaoq/+tNd+pU0KCDBS1yxHYi+ajA/rBtG8899xx3AekeTiQS+PGPf8wsDgDcIcxms1haWmrxZ8rlckin0+x7SMVXkmCIzUN3ILZevV5nA/VEIoFUKrWrRJ0OITMzM5ymS4yPZrMpCks9giwbaH5ptwhoB81NruvyPOYNOBFyue5BRQwaU1VVEQwGO77W2/CkQ7qu69B1HYlEgr0PRXG7O9i2jeXlZfZwDoVCsG0bsVisZS0mqSIVlYBLstz19XVxaOwBtBeKRqOo1+uIxWIwDINldqOjo8hkMjzXt0tFxUG9O5CM0TRNVKtV9t7r7+/HyMgIQqEQF0wajQby+TwSiQTi8TjvY4i5mkgkRBF1H9i23XNBVPgadod2j85qtcqqpWq1inq9zpYKAHaooAT2xu6mXAICVwB+vx+pVArlchmFQgGFQoF9OChVUWBvUGiDaZos5YnFYujv70ckEsHMzAwzNtpBCZTUNSW20sDAAMtCbdvG2NgYLMvCwsICbNu+Bn/l9Q86OESjUfj9fpTLZUiSBAAcBkDjTPAWNJLJJHtJTE5OIpFI8OGZuoWRSERchy6g6zoymQx8Ph8HaZTLZZw7d66lsNeOpaUlOI4DAFxoJWm1pmlcuA0EAqLp0CWIxUFpqwTyANpNok6hG5VKBc1mE7IswzAMDjVJpVJi/LuEbdu4cOECh8TQIXuvzbF3bkqn0yyZSSaTmJmZQSqVQj6fF/PQPrBtG5ubmwiHwywPrdVqGBgY2CEJBS7JuC5evIi1tTVcvHiR559yuYxarYZyubwn61LgEizLQrFYZEYT3dOdwkwAsKcTrd2CjdM7AoEAr7m6rsM0TcRiMZw4cQIDAwMIBoMtc71t27taxwjsDpqTge1CNDUQyENYlmUumITDYWiahkajgUgkwmFJ58+fx/z8PM6fP8/XQaAzLMti9piiKEilUuyjuhu8Ml4A/PtinFtBvqjAdjOM9nzAtsQ/GAxyU4s89UgF5U3KnZiYwOzsLH9fYBtipyxwVUH+Gv39/ejr68Pa2hpWVlYQDocxNTXFnRLhbdUZZCQajUbhOA7W1tZ4EqSCEBmid5Ll+v1+jI2NYWlpCZZlcQdqfX2d/TvItyMUCgm2wB7w+/2YmpriYoRt27Btm73FyOvHG2zSiblHEjjyaaLE11gsJgI1eoDjONB1nce3XC5z4W43lEolpFIpTExMwO/3Y2hoCGtra8jlcryZo0OfQHfwsguCwSBkWYbjODAMAwMDA7tK1Ov1OiRJQrFYRCaTQb1ex8DAAAYHB4UPU4/QdR1bW1uIRCLMoKTx3G38yEaAihuRSAS6riMajSIcDvM8JeahvUG+uIODg8zGpgbaXuNGawj5DqVSKTz55JMiZKBHUEo0NXW8zGHvAbtdtk6WAcFgULBuDgDHcaAoCsLhMCfeyrKMUCgEVVV3zPW0NxJMp+5BrDzaa1JABjUfiXXtDRnQNK3FGkagexwkkVX4GnYHsoiq1+sIBoOc+Hz8+HHe71mWhXQ6jWKx2KKCEkm5+0PcbQJXFbRoh8NhLn4UCgX2F+vr6xML+x7wjp+u61AUBZVKBclkEs1mk5MnJyYmdpXneqPGy+UylpaWsLq6inA4jEQiAV3Xsba2hmQyKaRw+8C2bU4u0zQNsiyj0Wggl8shFovt6E7vVtAol8solUqoVCr8bBCrTwRq7A9N05BOp9nrh+j8exX3qPOdzWaZaUNmvgCQz+cRi8XYt0Y0HboDJXfXajUAYJm53+9vYS51KnTTITwYDGJxcRHNZpN9sERhqTdIksQG1MT23i1MA9jeMFerVei6jlAohEgkws8IAOG51yXI8+rixYtc2AuFQqhUKnwg94LW9GAwyGxXOryIkIHeYVkWKxJs24brunBdd0dKNLH7vJ5OyWQSiqKgVqvxuIt7fn9ks1lsbW0hEAjwHF8sFhEMBjE5Ock2C51CHnoNLLhRQTJG8qaldPNoNIpQKIRwOMxNTcuykEgk2C4mkUiIImqPOEgi60FkvDcqqFBdr9fZd19RFBSLRZaSExuPvPhc1xVJuV1CjITAFUexWMTW1hbLRslfQ5Zl5HI5AOAN7dLSEqanp8XCvgtoIzQ/P8/G25ZloVwuY2hoCENDQ1AUZVd/H3qPZrOJhYUF9jYIhUJIpVLs76PrOgYGBkTHaQ+QubGu6xwYQOnF5LnXDXPP7/ezDyJ1vjVNQ71eRz6fRyQSEddhH/j9fvT19cEwDNTrdQDYVz5IhVZKJa5UKlwYj8Vi2Nrags/nw8DAgAh06AF0LSRJgmEYcBwHmqaxfJ3gLXQnEgmUSiWYpoloNMohJsLz8GDQNA3BYBDPPfccp4EeO3asoywUuHSQ0TQNzWYTuq5DlmVMT09jfn4e6+vrgj3WAyjoihK7ibnUaf6QZZnlt/F4HKVSCbZtQ1VVxONxXs/F/NMdbNuGYRhsQWIYxo7GgtfvidK7HcdBPB4XjMkeQWNJXre6rqPRaGBwcBB9fX3I5/NIJpMtcz35q9LhXDCd9oeXEVwqldjGaGxsjH8ObO/vKanbG1wiiqi9gebr/v5+TjFvNBp7JrLS7whfw73hLVT7fD72RSX7kMnJSVy8eBHZbLYlQd6blNvLdbkRIe42gSuKxx9/HI8++iibY95xxx08yVFHNR6PIxQKsY5eUMh3hzdQg4pKPp8P1WoVpVIJsixjZGSkRZJL3TxKI/KOP3WufT4fF6E0TUMymcTx48d54yuwE2QiHQ6HuTBkWRYzJhOJBHebCJ1CBEzT5ITieDzObKVKpbKvrFRgG4ZhYG5ujuXkVFTaDd5UVpp/dF3H6uoqe1KGw2FRXDoAbNtmWbNhGLBtG6ZpIp1O72DuxeNxSJLEc1O5XMbc3ByCwSAfTqrVqihw9wjbtqHrOoLBIBRFQaPRgK7rHZljwCWfMsMwAGyzu8mWQbDHekM2m0Umk2EWfSwW48ThTvOH9zkgX8RwOAxFUVAulwWDrEdsbm7yuqrrOjRNQywWa7lnye+pXb44MDDA/p7inu8O3rGkIrWqqhgeHuammGEYLXN9LBZDLBZjP2HBdNof1BReXl7muZzGMBqN8s93kz+LImpvoHNRoVBAPB7nAtJeczBdgwsXLmBlZYUL2ysrK3wueO1rX3u1/oTrFlSg6+vrY3apLMuYmprivQcAtsTxWuOQfyGtjWQBINbGVoinugv8+Z//Oe69914sLi7C7/fj9ttvx4c//GHceeedLa9bWlrCf/2v/xXf/OY3YVkWTpw4ga985SsYHR29Rp/82qJYLOKhhx5iT7J6vY4HH3wQt912G2ZmZmCaJh8ASfrWni4qsBPpdBrDw8OcprexscHFPK8ZdKVSQSaTwcrKClZWVuC6LtLpNG6//Xb4/X4cOXIEKysraDQasG0bjUYDpmnCtm2kUinMzc1hampKpOXuAmJAEvuR5Gu0EBHzwrvodGLuybKMpaUl6LrOC97GxgYkSUJfXx90XReS0H1AckJN0+Dz+ZDP52GaJv+83SRdkiSWpiwtLXFzwStDJDamKC71Bl3Xsbm52cIakCSphcEKgOWi5DdJ49/+OoHeQanRw8PDLH+jYAdVVXe8vhN7jJLAKVhGsMf2h/fwTWNULBaRTCYxOTnZcdzanwNiKfT39wsGWY8wDAPnzp3jNZeUDcPDwy2NBbrf2+WLmqZhZWWFfeLEPb8/yDuL9vEUJqYoCluKRCKRjvc4sH3Qp5+LVOi9YVkW2/EA26n01WoVt912GytFOsmfRRG1dxy0GPrYY4/hgx/8IIdFeCFJkmjY45JnXrFYRK1WQ7PZRCwWY2IDgB1WFV5WZCqVwtLSklgb94AYjS4wOTmJP/uzP8Ps7CxM08R/+2//DXfffTfm5uZ4gcrlcrjrrrvw6le/Gt/61reQSCRw9uzZPeWRz3c899xznBhEcF0XFy9ehGmaCAQCvAkuFAowDAOyLOPcuXOiqLQHqNNsWRaWlpbg8/nQ19eHUCiEiYkJOI6Dn/zkJzh79izq9XpLYaNUKqHRaGBychKbm5swTRPZbJYlLI7jMMuDilSiqLQ3SPJDG1oqFFWrVZw+fbqjFDEQCCCZTKJSqfCGNxKJsA8FXUsKJBAb3r0RiUTg9/tRLBa5kEHpW7Sh8vl8vLGi4p63I0jm3/l8Huvr65BlGYlEYlfvSoHdUS6Xee6XJIlDfzrdw96USlmWceTIEQDAysoK6vV6S2K0mIu6A1kEUKiGz+dDPB7f9TDXiT3m9/uRzWZZui5Mq/cHHUCi0Sizhx3HwdjY2L4seLJoILuNfD4vGGQ9olqtolarIRQK8XyiKMqOwx812drlixREI4zauwftWYgp6ff7eZ8/NjbGBRFvKj2hnYUjGKq7g7yBgW11TTweZyk5FURkWYZpmpBlmQkTlmUxo08UUbtDL955uVwOm5ubkCQJ8Xgcf/7nf45Xv/rV+L3f+z2kUilYloVbbrllV0uMGw1kAaKqKlvnEMmhVqvhtttuQyAQQKlUQrVaRSKR4L2kLMscyinWxr0hRqIL/Mqv/ErL1x/72MfwqU99Ck899RRe8YpXAAA+8pGPYHp6Gp/85Cf5dXRIuRFh2zaKxWLHnxmGgUajwQu467ooFouQZZm9xsRBrjNo0Wk0GtA0DaVSiePaBwcHsbCwAAB49tlnO3qONRoNrK2tob+/H67rcpE1kUiw19LY2Bhs22aWh9gAdAbJyCcnJ5kBSVLnYDDIRsdeuK7L/nokiXYcB1NTU/x7lUoFkiTxIUUEauwPr6cSyaXJxJvu39HRUWxubvLXPp8PkiRhdHQUqqpCkiSsrKwgkUhA0zQsLi6iWCzi6NGjgj3ZA2gD5jgOZFmG67poNBqc/ExoDxLw+/3QdZ2DTOiALRKje4PjOFhYWOBAnlwuh2g0itnZWayurmJwcHBHoamdPVar1dgfyzAMYVrdJYjNXS6XOX01EomwX1An0D09MzPDsmld15mZLzz3ugcd9mq1GmKxGDPy0ul0y+s6BVsZhoGNjQ1h1N4DyDvL7/dzc9J1XQwPDyMej3NBhAI3yCeuv7+f5yfBwtkfNM7kn2oYBkzTRCKRYEkucKnQ2mg0kM/nW4IJRBG1e9DeZL9i6De/+U08/vjjaDQaeOyxx3Dq1Cnk83m8/vWvZ9WN2Lu3gixAarUaHMdBJBJBKBTC5OQkgG1LEG/DYHNzk+9jIrUQAUKwq3fHdat/+d//+3/jP/7H/4g77riDY5I/+9nP7vk7P/zhD/FLv/RLSCQSCIfDePGLX4wvfelLh/q5LMvCJz/5SSSTSZw8eZK//7WvfQ2333473vCGN2BgYAAvfOEL8X/+z/851P/vnyaQz8Nuk9rExAQmJiYQjUaZVePz+ZDL5aBpGk+uAq2gia1arbKPCVH0aVFfWFjYM0yAGAXj4+OIRCIYGxvjAAiSSVNi3G4+QQKXqOVzc3MoFovMGgC201rJlN4LKqSSNI5MkguFAjKZDGq1GhKJBBKJBCqVChf2hCR0b2xsbGBjY4ODYSixVVVV3pyRuT0ATiNWFAWrq6tYW1vD3NwcTNPkjmAgEOAxJ/aYmJP2B80fXjaBoigYGBjYwWIlKWgwGOT/XSgUmJFM/onlcplZHgJ7w1ukmJycRDweRz6fx7e//W186Utfwt/8zd/gqaee6vi7Xhalbdvo7+/H9PQ0jhw5gv7+fmFa3QVoTjdNk9fkQCDAfobtIBndxYsXsba2hosXL6LZbEJVVRQKBZimiWq1Ku7/LrC+vs6hSpubm1AUBaOjozvW4XZ7DNu24fP5YBiGuOd7AN3ro6OjvA8NBoMYHBxEOp2G4zjQdZ0DN0gZsrq6yhYMxMKZmZlBKpVCPp/fNwzrRgP5GhIDkvaGANDf38+MXyq0xmIxhMNhNBoNRCIRljLmcjnMzc2xr6TYU3aG129vt/l3Y2ODC3vAdgH28ccfx4te9CI8/PDDmJubQ7PZFHv3NpCMn8a0VquxJVEoFIIsy7vex9Tk3drawoULFzA/P8/KM7E2tuK6vePe9773YXFxkf3FFhcX93z9t7/9bdx9991QVRX33HMPotEovvzlL+NNb3oTlpeX8c53vvOyPs9DDz2E17zmNajX6xgaGsI3v/lNpFIp/vn8/Dz+x//4H3j3u9+N973vffh//+//4Y1vfCO+/e1v4+Uvf/ll/X//NIIeYGJ+EehQ3Wg00Gg0UC6XuYNE3ahSqYRYLHagh9UwDPzkJz/B5uZmS5ABcDhGplf6/fcDFS2I3SVJEncvaAG/cOHCvu9jWRaWl5dRrVbZ9LXZbCIYDLJMOhwOY2ZmRixMHeBNl8zlcqjX61w8oqQ+8rrygtLNyGNP0zSEQiEsLy+zYS91qUqlEsbGxpBOpzv6ZAlsg3yuiAlpmiZc10Wz2WQvD/J8I+mb3+9n2RbJbunn5XIZwWAQjUaDix2CPdk9aM63bZvHMRgM7mDPeKWguq7DdV3u4FIntlqtIpfLCUZHjyBvJkmSsLGx0XJY3tra4v3LyMgIv55YlI1GA4qiwLIsZlEK0+ruoOs6CoUCkskkVFXlxkGhUEAkEsGLX/xi3Hrrrfu+D+2ZhPl99zAMA2fPnoXP50MikeB7npo9XnRi7jmOA1VVhVF7D6AC6draGgDwGKqqymsmAA7cqFQqHF4yMjLCTByRCr03aF9iGAZCoRDvFQcHB1mS6C20ZrNZ3r/QHC+kjN2jk9/e0NAQisUiIpEIVFXF0tISF/a8+MVf/EX83//7fzE3N4ebb74ZExMTLdYub3/726/mn3Ldwcsupb06KTamp6dZ/bTbfbyysiLY1V3guh2Ne++9F0ePHsXk5CT+5E/+BO9+97t3fa1t2/jd3/1dyLKM7373uzh16hQA4P3vfz9e9KIX4T3veQ9+9Vd/lWmfAPCud70LH/nIR/b8DF6vsjvuuANnzpxBLpfDpz71Kfzar/0avv/97/OBxXVd3HnnnfjABz4AADh9+jQeeughfPKTn7whi3t0uKMUStu2IUlSC8W5UCigXq8D2GbGVKtV+P1+hMPhA21kv/71r+Mtb3kLstnsjp8dhpHplX7/bkBBGOvr66hWq1zgo2Kfruv7fg6fz4dyuQxN0xCJRJDP51EsFhGNRgGAWTLpdFpMmLvAexgGwIcBYiyRuXGncAAymG42m3BdF5VKBYqiIJFIoNFocGqlruvMZhUelLuD5NGxWAybm5t8UPMW8I4fP46pqSk89NBDqNfrkCSJD3/j4+PsmZjJZHhDAQDRaJSlFeJw3R2KxSLLcl3XbQnW8MIrBXVdl1kzdKCen59n2ZE4jHQPRVHg9/vRaDTgOE5HFkylUsGPf/xjZlPS4ZGsNGht8fv92NraEsXVLkFj6Loutra2eK9AzJv77rsPhmHg5ptvZmk0zV8TExM8Zy0tLaGvr0+Y3/cAYs4Tu8N1XQ4Ra79v25l7NEfF43E8++yzQiLaA2iep+Cker2Oubk5jIyMYGpqiuWglmUhnU6jWCxycJ6Qi3YH13URiURYbkv+qdFolPei3kIrFa9HR0eRTqeFlLEHdPLbKxaL+O53vwvTNBEMBnH69Old/fT/9V//FY899hiCwSCeeOIJBAKBltTXG7m41y7jt20bzWYTQ0ND6OvrQz6fR39//773cTwe56+p0C3QiutWlvuqV72qpRi3Fx544AHMzc3h3//7f8+FPQCIx+N4z3veA8uy8LnPfa7ld975znfimWee2fM/L0KhEGZnZ3HnnXfi3nvvhSzL+MxnPsM/Hxoawk033dTyO8ePH8fS0lKPf/nzC3RAIFYMLSy1Wo2p+PV6HRcvXoTP58Ps7CxOnDhxoELG2972NrzxjW/E+vo6bzbov8MovF3p9+8W8Xgcp06d4k4GpYIGg0FerL1BAe0IBoOoVqsYGRnB+Pg4BgcHEY1GMTQ0hFqtxj413Uh8b1QQbX99fb3lAE2LVSwWY4+IdsiyzOmu1H1qNpsYHx9HOBzmzUQ0GkUkEmEPSnEdOsPLkiTPq0AgwCmfVBBPJpNQFIXZejQv1et12LaNcrkM0zTR19eH4eFhAGDpy9jYmCiudgFiUXpZwMSW3A3EqiRZHElzm80mUqkUIpEI4vG4kEV3Cdd1Wdq/25whSRJyuRwnQ9PhkYqsFEhAfqBCLrc/bNvG5uYmF+Ham4DEULjvvvvwt3/7t7zHJJnR/Pw81tbWkMvl0Gw2EY/HoaoqUqkUp74K7A5iDFMRA0BHxjDQytyj4p9hGCiVSkIi2gPIS4/W0Xg8jv7+fiSTSS5IE0ObrC3i8TiSySQ3foRcdH94rXiISEAWOu2SRGoak9UR/b6QMnYHmkOo+ajrOn7yk59gbm4OKysruHjxIv7lX/4F58+f73jOuu+++/Dbv/3buP/++3HffffxmM/Pz+PixYvX4C+6fkB7uMHBQS6OBgIBDA4OIplMtuzx9ruPl5aWUKlUhKJmFzwvZtAHH3wQwDYdth133303AOA73/lOy/f7+/vR399/4P/PZrPJRRUAeOlLX7pDDvncc891XaB8voH8quigTd3pZrMJv9/PkgcyfaUK/IULF9BoNHDkyJGeD9OZTAbveMc7MDg4eEX+piv9/t2gVCrh7NmzWF9f547z4OAg+5c0m02USiWeGL3sU2CbtddsNlGv17G0tARFUXhDRdHtqqpy2qII1OgMv9+PkZERzM/P88GAksoSiQSz+DotOhSqAYC7TpIkccFPkiS4rotwOAxFUbjAJ65DZ5C0Std1ZtxR8VWWZYTDYZb5E6NJURTYts2hNNlslkM5YrEYNjY2uDDVaDSwsrLCxtQCu4PCTGjOVxQFjUajo/9qe6BGOBxGpVLBhQsXoGkaEokEF8CFLLp75PN5LC8vc6OmEwKBABqNBhcuyO7BMAx+Rnw+H4LBIILBIEKhkJiD9gHJ4qampnD27NldX+e6LjY3N/Hggw9ieHh4h8zItm0MDAyIe79HULp5pVJBqVRi9UEn9rzXEqDRaCAWizFzjNZmRVFgmqa453cBsWuowU1M4VgshnQ6zWPXHtZTr9c5YEYkX+4P27axuLjIDfdSqcQBVel0mhUF5FtG6yml0+u6LqSMPYAKSBTosLKy0qLCoTnj/PnzUFWV1WcA2BPxlltuQaFQwOTkpBhjD7xjS+oZWZaRy+VgWRaSySQAiPv4EPC8GJHz588DAI4ePbrjZ0NDQ4hEIvyag+AP/uAP8NrXvhZjY2PI5/P4q7/6K6ysrOANb3gDv+b3f//38bKXvQx/+qd/il/+5V/Gt771LXzta1/bUVQkmKbZUhykqOfnC1zXRX9/PxRFQbFY5IMEGb+apolisYhKpcIMgWAwyA/tQZIpf/VXfxUPPvjgFUspvtLvvx9s28bc3FzLhooKdQC4IEdMGGIT0gEbACeUUcfJy5ih3wMusfsGBgbEoWIXeCXmZCZNXdW9mJPUMTVNk2XVlmXhwoULkGUZMzMzHCpASa3icLc7bNtGJpOBbdt8wKBnggqsxOyLRCKoVCrsA0cHwmg0ilqthmKxiM3NTWY0BYNBxONxccjrEnRgM00TzWYTjUYD0WiU07m98AZqRKNRZDIZyLKMI0eOcEHQ7/ejWCxC0zQhi+4ClmVhbm6Og5CoeEeNNWD7GlHRzjuexGal9YEO7KZpotFoCLncPvAGY+RyuT1f67ouisUiVldXYVkW+vr6mGlJBcK1tTVx7/eASqWCYrEIWZahaVqLl2o7OhWcEokEJEnC+fPneZ86MjIi7vld4PV429zc5D0n+e21zxc0n3uZkNTgEZ57uyObzbLnfLVaRSKRQCAQaGFHAq3rKXk2A9v7I8uy0N/fz/e6kDJ2BnlpU0PdW9jz7l9ofXQcp+VnmqbhrrvuwjPPPIM77rhDFKo98I4tsB1+JEkS0uk079dPnjzJMn1xH18enhd3HF383ZhesViMX3MQrK2t4Z577sHm5iZSqRRe+MIX4qGHHsLx48f5NXfeeSf+4R/+Ae9973vxvve9D8eOHcM//MM/4KUvfWnH9/zwhz+MD33oQwf+TNc7aPNEDCai1noXomKxyP400WgUkiRd1mH6E5/4BN74xjfioYcewsmTJ7ngQrhcr4Mr/f77geji5A9jmiYb0QPbhzMq6NEhjeRxJBcl7ytJkjA6OgpVVTEwMID19XVOK9I0jdkCIlCjM7yBGnSfu66LkZERKIqC8fFxANjzHqbAB8uy2ISaulSjo6NYXV1FLpdDPB4Xh7s9oOs6MpkMWwDQGNK9T3JDkmIFAgHE43FUq1WYpoloNMpFQEpapMPJ2NiYKK72CPK+ojGXZRnxeHzH+FGxlVjwgUCAzao1TUM+n+fit0B30HUdpmkimUxifX2d1wRiFFAxG9heLyqVChe+g8Egbr75ZmxtbcF1Xd5wLywsCP+xLuE4DqrValdSTgqcKZfLWFpaaikoeZtFAvuD2E0+n4+9ySRJ4rm/E2iv5C3+tSsdBHZHu8cbFTmy2SxCoRDPF7quw+/3Y2ZmpqXBT2sxpZLG43Gx1rahnR1JjRbyHPOek7zrKTXVotHoDl9DERSzO6jYnEqlkMlk0Gw2W85VBCpk09oKgM+4zWYTX/jCF/D4449jZmYGg4ODLWfFP/uzP7uqf9P1Ahrbvr4+hEIhVglMTU1xIzEUCon7+JAgdmpd4POf/3xXr3v961+P17/+9V299t3vfjfe8Y538NflcpkLAs8nULeOJKO00XIch5OQV1ZWOOb9cg7Tf/d3f4dvfOMbUFUVDz74YMvG+DCMTK/0+++HQCCAUCgE27ah6zoUReFFf2BgAPF4HE8//TQajUaLJHpsbAyrq6sctkEFkLW1Naiqypuvvr4+GIaBSCQCTdOEz9geoIUqGo2ygXe9Xkcmk0E8HkelUmHpSaffpYWsXq8zyyYUCsE0TdRqNQDg6ySwP+hQFg6HYRgGFEVhWbO38+qVAlHhaG5uDoqiIJ/PIxwO46abbsLW1hZ0XUe9XkckEhHF1S5RLpexsbGBcDjMhetqtYq+vr4d40dFVF3XEQwGoaoqJEmCaZrsf9XX18eF2IOwuW80aJoGWZaxtLSEcrnMQVayLHPoDLGGV1dX8ZWvfAX9/f144QtfCNu2sbGxwfYCgUAAQ0ND0DRNyOW6gFeSDqDjodALWZY7mrI7joPFxUVmNIl7f3/QPEPehOStGolEOq7B5BVHyen9/f1wHAfNZhOzs7PMdKV1Xoz7/iArkenpaU7aHh4e5jW3vYAdCAQ6ppKKtfYSOrEjbdtGJBLZcU7yrqehUAihUAjxeByapiGVSmFpaUkExewDKjZvbm6iXC7D5/Nx05iUdn6/n/0Oad9Ja2yz2cTy8jKOHTuGZrOJ+fl5bG5utgRq3KjwzgNE7iFLIjoPBQIBlEolcR8fAp4Xo0IFiN3YeeVymbXc1wtIFvN8BW2IZmdnOeGpWCxycm4oFGJT3XA4zJ3WYrGIvr6+Ay3w733ve/GhD30I73rXuzpKMS4XV/r99wMFA5imiUwmA+BSyjB5FhBjgAqqZEhKbD1vkYP+t67rCIfDHPVOkrj19XWR1LoLaBOwtLTE0miSI7qui1qthttuu63jPSzLMkqlEssR8/k8yuUy4vE41tfX0Ww2US6XMTw8jFQqJQ53+4CCM6iYQcVt2jBQsAkx9yRJgs/nY48+Ykw6jsOSxUQiwYdFge7RaDTQaDQ4pZg2wqqqtrzOK9EgKwZgu0i7sLDQ4n8ofCe7B/nkGYbBa0EwGOT7mzrgm5ubsCwLtVoNuVwOxWKRlQheFk48Hue9ihj/vVGv17lIQTYYQKsk2gvypWxnNem6Dl3XkUgkxL3fJWRZRqFQYCVDtVpFPB7v6HlFbCia25vNJlZXVzExMQFJkngtpmAOwQzpDG/Ks5cF2e7Rudv4dUolFanQrfCyI4ntKEkStra2OI2YCqbt66ksy7jtttsAQPgadgkqNmcyGRSLRUiSxKw78na2bbvFy7bZbHLjrNls4j/9p/+EkZERjI6OHjgc8vkKSta2bZvPnt5kbQDiPj4kPC9GhLz2zp8/j5/5mZ9p+dnGxgaq1Spe9KIXXYuPdsOCih/lchmKosBxHIRCIS7kAdvd01qtxsUlx3FQLBYPXIi1LAtvetObrljh7Uq/fzeIx+M4ffo0MpkMp4spioJqtcrJbjTRaZoGYHvBIkrz5uYm8vk8XNfFkSNHIMsystksXvziF0PXdZRKJWxsbCAej2N6epqTWrspLBmGgZ/85CfY3NzcIYV57Wtfe2UGpAcc5ufzBmrQoUtVVfZM0jRtVy+I9kCNWCyGcrmMhYUF+Hw+jI+Po1QqsYzuejncXa/X17Zt1Ot1qKrKRSIqapumyewNy7IQDodRKBRYEkden8PDw9yNrdVqWF5eBgD09fVB13VRXO0S4XCYCxR0MAmHwzsObF6JBgXQrK6uoq+vj/0PS6USyuUyYrGYCBXoEoZhsCyX/CcNw0C1WkWj0UAmk0GtVmOP35tvvhnNZhPr6+sYHR3lIpMkSVhZWUE+n0csFoNhGEICswfocE2eq15GBwX7eNNuJUnioJ/Nzc0WVtPg4CA0TWP5kRj7/UHzCe07A4EAwuHwjqYCvbZSqUCWZVQqFbiuywE+2WxWsMi6hNccPxQKtexPvPO1twhITWdiWlqWhUgk0uJffK33OdcbHMfhgpIsy+jr60NfX19LIbR9PaV9KF0X4Wu4P6jY7LouFEVhXz2aT2666SYEAgGcPXuWVVHeRFcAPF+nUilRqG6DN1mb/PTIhovGar/7mOYaVVXFfbwPnhcj8opXvAIf/vCH8Y1vfAP33HNPy8/uv/9+fo3A1YOXbm+aJvx+P2KxGJuQkgl+tVrlFEta+HspKHnx1re+FV/84hfxnve854r8TVf6/bsBpeWurq4im81ClmWMjIwgGo1icXGRu0xUtDhx4gSmpqbw6U9/Gp/4xCc4Ebcdf/d3f8eFcLoWuVwOIyMjXfkffv3rX8db3vIWZLPZHT+jA8+1xJX4fCQrL5fLCAaDME0TwWAQjUaDDw+d4A3UaDabqNVqzD4jz45IJIJarQbDMNgL8Voe7q7n67u+vo5iscg+HNRMALYPIAMDA3zvRiIRlllQk2Fzc5P9Pfx+PzPAJyYmOHRDbCK6A83nFBITiUQwNja241lol2oZhgEAvCmmwmuz2eSDojho7w8q3NXrdTSbTTz33HP48pe/zAExBGIbfOADH+DvVSoVXLx4kYtMoVAI5XIZW1tbQgKzD+jgQYVR8rml4l4oFGKPJgCc4t2pAeTz+ZBKpfDkk08K+VGXIAN2am5Go9FdlTEUPGBZFtLpNIrFIsvBBIusO+zGFKNmmHe+LpVKXASkRk8ymeSmmyhi7w5vQQQABgYGoCgKYrEYS9Cpcb+b9BmA8DXsAlRYsm0b2WwWiqLwfEJ+tH6/H6urq+wprCgKr63kof22t70N8Xgcs7OzeMlLXoIXvvCFOHXqFCzLwsc//nF87nOfu8Z/6dVHu3ckjfXIyMiOZO3d7uPd5hFxH3fG82K38PM///OYmZnBF77wBbz97W/HqVOnAGwXQv74j/8YgUAAb3nLW67th7yB0E63z+fzAMBdUpJs+Xw+DA4OolqtsozicgI1HMfBRz/6Udx///14wQtesCPw4nKNTK/0++8Hb1ouFSgqlQrW19fZr03TNASDQdRqNfh8Phw7dgzDw8P4m7/5G5w+fRqvetWrEI1GIcsyVFVFPB6H4zhoNBq8yaUCiGmaKJVK7NO0F972trfhjW98I97//vdjcHDwio7DQXAlPl+tVuM0yVqtxlJnGtfdjLwJ5Pnmui6zWg3DQKPRgKZp7PlzPRQ2rtfrSxsxSZJYikjyWp/Px9JOYJttOTk5ibNnz6Jer7OXGHnyEXw+HxRFQa1WQygUEqyxLmHbNi5cuNBSSHIch03t5+bmYBgGRkdHOTGNQFLQcrkMTdNYYpdKpeDz+YT/ZxcgL0naFEuShH/5l3/Bbbfdhje84Q147Wtfi9XVVSwsLGBhYWHH76uqysUnCtQYGBgQnntdgJo1GxsbXMQjxkcwGEQymYTjOGzbQEwPYtV7fd6IESXkR92B9ptki2GaJlRVxcTEBKsXvCC5OTUg4vE4jzNJoQWLbG+0M2wqlQq2tra4mE3ztTd4rNls8hng5MmTUFVVeGjtAW9BhPYnGxsbGBkZ2bUQ2mw2OTyPIHwNuwPtGSkhl5o0FMAWi8WgaRoGBwexvr7e0qwhOwzbtvGXf/mXKJVKWFpawsLCAv7pn/4Ja2trAMB70RsNNF8MDg5ic3MTpmmy4iafz+9bpOvUTKB5RNzHnXHdjsq9996Lhx9+GADw5JNP8vcefPBBAMBdd92F3/md3wGwfWi79957cffdd+PlL3857rnnHkSjUXz5y1/G4uIiPvaxj7GeW+DKgx5kotuTWTE9zASfz4ehoSFkMhlOax0fHz9wZ+nJJ5/E6dOnAQBPPfXUof5NV+P994M3LZeSmVzXRT6f55Qh4JK5aygUQiwWg9/vR7FYxN13341kMolgMMhpokNDQ9jc3OSUOcMwWozAw+FwVxuBTCaDd7zjHddV4ceLw/58tm3j7NmzvGg7jsPBAIFAAKqq7nr/en2WiGlGv0vMytHRUd7o7iYvupq4Xq+vZVk8doVCgTdlxIghliQhGo1icnKSvSVJNjoyMsJdQSoilctllkWLzfD+0HWdzaMDgQAzC7a2tvD3f//3WF9fB7BtF/CSl7wEiqK0eI1tbW0hm83CNE3U63UMDQ1xoMDKygri8bi4BnuA1gdKvyXvsV/8xV9k/85arYaBgQEsLi62HABpvUilUsxqXVxc5A65kMB0BwrVILmWJEkYGBjguYY8nLwFWAoWoAITJRsLGV13oLR0YjNRQFgnvz1ge01QFAX1ep1Z2YlEQkihe0C7OT6FUSWTSei6zvO1ZVkoFovMzKYmJgXDCQ+t3eEtiGSzWWY80Rzu3ZPQa4nx7vP5eG8kfA27AynHaA8CbO8XJUliqwQA7A9PvrSKoiCdTmNkZATr6+swTRMnTpzAb/zGb3BI5qOPPoq3vvWt+KM/+qNr9vddS1DDKpvNolqtQlEURKNROI6DSqXCRTpd1/eU8Pf39/OcTZ7aAp1x3c6gDz/88A766iOPPIJHHnmEv6biHgC88pWvxMMPP4wPfOAD+OIXv4hGo4GTJ0/iIx/5CN70pjddtc8tcOlAXa1WOfCBDmnNZhPhcJgNS/v7+2GaJmKxGCe3JhKJAx2m/+Zv/uaKJg5f6fffD5SoalkWyuUyJ9+GQiH09fUhn8/z4aDZbEJRFN6cvu51r8P8/Dz6+/vRbDa5UwpcYioNDg5ia2sL1WqVi0vDw8NdFZZ+9Vd/FQ8++CCOHDlyRcfgoDjsz6frOtbX11uSPg3DgCzL+x4I2qnnpVIJjUaDC9z9/f0wDIMlcoFA4JqHmlyv15dSt3RdRzweZ2kEJX0mEokWBiUVXh3HYaYMWQVQwAl5JxYKhWsuJ/9pA42Xpmlsnjw/P88eb8C2dPfRRx/F6dOnUSgU4Pf7mT1JEurFxcXrznPyeocsy6hWqzBNk1MVb731Vpw/fx633norNE3D1NQUMpnMjpAHSZKQz+dRKpX48B0MBlEul3nTLdiru8OyLJimyYx6GltN03DbbbdhbW0NmUyGw6ponejEXrr11ltRLpdb9k9i7HdHuVxGsViE67rsxQSAJXa7zRkU+GPbtpBCXwZo/SR7He98TWxs8tgqlUp8/wsvuL1BBRGyZwkEAhgYGGjxKCN0GmcAHMIhfA33h2EYWFxc5PNrvV6HaZoYHh5miygaY2okUHgeqWwCgQCSySRSqVRLofolL3kJ/uIv/gJ/+Id/uMM67PmOarWKM2fOYGVlBVtbW7w2Hj16FEePHm0p0nl9PPeS8JumKZov++C6fbo/+9nP4rOf/WxPv/OiF70I991335X5QAJdw+/3Y2pqCnNzcygWi9A0DSMjI5wKWigUeHF/9tln4ff7cfToUab0HzlyZIdsqxtMTk4ilUrhtttuw6lTp3D69GmcOnUKpmkeitfBlX7//UBpuZVKBYVCgZOG+/r6OMSBTNQVRWlh8/3FX/wFfuEXfgFPPvkkxsfHmeH0/e9/H/V6Hb/8y78MABzvns/nObW1m8LSJz7xCbzxjW/EQw89hJMnT+6QLL/97W+/MoPSJa7E56PFneS4xJScnJwEcImhV61WsbGxwQVU76bKdV00Gg3erJHn3sbGBiRJwtGjR6+LQIfr9frSM6AoCizLYrkbFfji8XjHDQBZAxCDY3l5GYZhIBaLoVarYWlpCZIkiUCNHqBpGjRNw/z8PLOXyMuwHd5QBwDMNCN2HoVqXC+ekz8NILknJYb6/X68/vWvxxe/+EVks1k899xzkGUZCwsLWFpaguu6eOlLX8prRCgUYjk6SRSLxSIKhYKQcu0DWZaxtbXFBT4AUBQFfX19GBkZwdjYGNbW1rC5uQlZlrn5RqwnL3upXC5jbGwMi4uLvH8SY98Ztm1jc3MT4XAY1WoVW1tb0HUdsizjn//5n3Hs2DHcddddLfuXTgnFpmkKKXQP8IZkEBu1VquhVquxAT6FaZAE2jAMRCIR9t4mGaTwgtsJrwyRggZDoRBGR0ehquqOAp3ruohEIrzeRqNRTkZvJ1qIMe6MarXKDOp6vc7F0Ntuuw19fX38OmJhkyWP3+9HPB5HpVJBNBplll/7NTp69Ciefvrpa/XnXRMsLCzgm9/8JiucvLhw4QIGBweRTqeZ8LCb9FZI+HuHGBmBKwra6JK/CR24NzY2uFtKslHqKgUCARw7dqxnptL8/Dx+/OMf48yZM/jxj3+ML33pS4fqdXCl378bxONxHD16FJlMhv3ZNjY2EAgEUKlUWOpMybnk1fHFL34RTzzxBBRFwfnz57l7QsWQ173udVhaWgIA9lgi+nQ3hY2/+7u/wze+8Q2oqooHH3yQrzuwfe2vdXHvsD+fpmkcplGpVGAYBvsUVioVTt577rnn8Oyzz7LEc3BwEHfddRcfLnRd5yJIs9lkHUVExAAAhytJREFUv0ligAC4LphL1+v1JfYLjTuxvahjPTY21jJmlmVxp5CkF8SMzOVy2NzcRLFYRL1ex+zsrAjU6AG2baNcLsPn8/HmdzdQ8XV4eLhjOiuxK+lQIoob+0OWZQ7pURQFjUYDTzzxBM6ePYu5uTk888wzaDQafDgBgBe/+MXcVEun0wiHw5xcHAgEhJSrS2SzWWaP0Rzk9/vR398PTdOg6zob4RMkSUKtVoNlWYhGowC2C4KmafKz451nBXbCsiw4jsPSRbK1IBuYH/3oR8jlcnj5y1/O1jydTNvT6TT7PQsW2f7wMmyI2WuaJpaWljAyMtIyX7dLoPv6+liKLrzgOsPraUjyRWq8dyrQ1et1VKtVLpCGQiHE43FuDHiJFmKMO4OK+sTMo3H2WtEQO5X89qgxRjZJtVoNL3vZyzA9PY3jx4/jFa94Be644w6MjIzgL//yL/GqV73qWv15Vx2ZTAZf//rXkclkOv682Wwim83ijjvu4Dlkt6RcIeHvHWJUBA4dZHBMXbtqtYrFxUVm41G3IxwOQ9M0mKaJfD7Pm6tGo3Egpszk5CQmJyfxute9jr93mF4HV/r9u0Eul8OPfvQjZugRSyMcDqPZbMJxHOTzeaiqykb2APChD30Ib37zm3H33XcjGAxyt/vkyZPsr2RZFsLhMDNoHMeBqqq80djrWrz3ve/Fhz70IbzrXe/aNSX2WuKwP5/f78fx48dRKBRw4cIFllk1m00UCgVsbGxgfn6eD9GEpaUlPProo5ienmYWR6FQQDAYRCQSQbFYRCQSgW3b/LvXQ7f1er++dO9T0Zrml3ZPDlmWUSqVUKlUoKoqF4/oddQRXFtbQzabZQ+haz3+Pw0oFouoVqtIJBLI5/N7FvcoUGm3dFZZlpFIJLj5ILA/XNdlXyBixtx333345V/+Zfz3//7foWkavvWtb2F9fR35fL7Fo1XTNKysrCAUCrE/Fsm/hJRrb9B+hxjYdCghT0nvmHm9mgBwIqO30DQ4OIi1tbWW/ZNgDncGBZlcuHABxWKx5We0FszPz2NxcRGnT5/Gv/t3/67l5xQ+4PP54Pf7BYusC3gZNsB2Wr0syzh+/Dgcx+nYCPBKoOk9hBfc7vAWoMlXW5KkjsXT3ZKLb7vtth1zj8DuIL88wzBgGAbi8ThGRkZa9ru0xrquC9u24boudF3H4uIi+vv7Ua1W8c53vhOWZSGfz+Pv//7v8b73vY/X1Fe/+tV4//vfj5MnT+LkyZO4+eabr9Wfe0Vh2zb+9V//FVtbW3u+TpIkfub3SsoVEv7eIUZF4NDRHqhBdHCfz8fMAvK6isViWF5ehmVZSKVSGBkZ4U7fYTy4V9rr4Gp6KVBaLvkR6LrOBTjypqLNFR2WaXNqWRb+zb/5N3BdF+VyuSWZjzZniUQC5XKZWU/RaLRrY2nLsvCmN73puiz8AIf/+UqlEp555hmsr6+zJCIWiyEYDKJYLGJ1dbXF08qL1dVV7gaSNJTYepZlIRaLYWhoiJ+X6yEt93q9vhQioCgKFEVh759Go4FKpbLj81LRgzz1KK24WCyyrCKZTEJRFKytraFcLiMajV7z8f9pAAU5lEqllsTcTggGgy3PhzedNRgMsiTxepGl/zSAkueI4UFr7Ote9zrEYjHYtg1d11GpVHbMTVRspf9N/n30ryh07A4K0aAiKI1tJBJBMpkEcInxQTJQ+rfTmFIQjXf/JA4yu4OCA/byR3VdF2fOnMGpU6eQSqV2hA+QBCyfzwsW2T6gNTcejyMajcI0Tfh8PkSj0R0sd/rXmwbtNcgXXnB7g0KRACCVSqGvr29HEbQ9ubid8dROtBBr6U4YhoGnn34akiQhmUzCsiwAYJYpge5vAGyBQSnQiqJgYmICkUgEMzMzzEhzXRfnzp3DmTNncObMGfzgBz/Apz71KWxubj5vPZ03NjawsLDQ4nfdCeFwuKtnXkj4e4d4ugUOHZ0CNSg9sZPXFRUwSC5EX/f64JLstx2H5XVwpd+/m/9/13URCoWQzWbZn43o+LIss5yTUopp0vz1X/91PPDAA3jNa16DaDSKfD6PWq2GhYUFmKaJ2dlZRKNRZp7RoaLbtNy3vvWt+OIXv4j3vOc9V3wcDoLD/HxUZF1dXQUANtols/RyubxrYQ8Asy4pLVeSJOi6zoEmuq6jr68PY2NjiMVi0DTtmm/ErtfrK8sydF1nlhhtbP1+P4LB4I7NBXl9kq8bPQerq6uwLIsDOFzXxejoKCYmJhCPx695WvFPAxqNBh/e9kMoFIKqqhgcHORrtry8jHA4vKPoIYob+8O2bVy8eBG1Wg2SJPFB+sUvfjEee+wxvPnNb+YCnveZoPVClmWMj49D0zQ0m002vhdSrv1BzQLyG/Oy86i5QA2gSqXCDD9Kp28vftDhURxk9kc2m0Uul+tqznEcB4uLi0in0y3hA5SyPjs7K1hkXcAryQ2FQnzPAztVBnQW8KZBh8NhNsgXXnCdQZ6GIyMjyGQy3AyjIAfvWtgN40k0CvbG+vo6tra2ePxIhZNOp1vGqVgssm0CFV3JQ7VarTLjD7jk7Ums1uPHj+PNb34zv9ductWfdti2jeeee46b7rspOCRJ4nkA6OyFSkQT8jcUEv7uIUZG4NBBgRoLCwu8ARgcHMRzzz3X4nWlKApUVYXP52PWx8rKCiYnJw/04EYiEZw4cYKDLk6fPn2oXgdX+v33A6XlUtGCJkNVVdlvLxqNIhAIcNQ4wXEc/NM//RN++MMfYnR0FMD2dUomk6hWq3jrW98KSZL4eg0PD/N/3RQ2HMfBRz/6Udx///14wQtesCNw4c/+7M8OdzB6xGF+Pupc0/Wgw5i3W70XiKF38eJFANtS61QqBcMwWKa1vLyM9fV1TE5O4siRI9c0KRe4fq8vFbsDgQCKxSKazSZL0r2ek97X02Y4n89z6tbMzAwbgufzed6wLS0tXRdpxdc7bNvG4uIiwuEwHMdBNpvd9bXUhGg0GizLpWRFSp9uNBq7HhgFdoLYMBTsQ3JDx3Hw+c9/Hk899RROnDiBbDaLbDbbIhMiyRzNaTQP+Xy+5y2z4LBBTFWab1RVZcsRkst5PYfpgE37n3K5zN+n6yYOMnvDtm2sra3tsL7YC+l0ekf4AEnZNU3j6yEKIJ2xmwSUgqfaVQZ+v7+jEb6qqsILbg+0J+VKksR7xF5SQgXjaX/Yts37lUajwQWp4eFhDA0NtbxubW2NvVMpJZdgmiZM00Q6ne5qjL1efs8n6LqOTCbD+4rdinvkD79fkZpCwISEvzeImVTgiiAej+OWW27hDa1t2yiVSqjVamwwHQwG4ff7oWkaHMdBOp1GvV4/8EH6gQcewBNPPIEnnngCf/u3f4t3v/vdh+p1cKXffz9Q0ZRMo13X5W4cfU0Gx+SdR3j66ae5I5LJZDhZlII3SqUScrkc/H4/xsfHsb6+jkwmg0Kh0FVx6cknn8Tp06cBAE899VTLz64Hr4/D/HxU1COGl2maLAcNhUI7vH/aQew+TdPYiNcr5zJNE5qm8eb5epBRXK/XV5ZlfgapcdBsNhGNRhGPx3dl7gUCAUSjUU4lVhQF4XAYpVIJjUaDTZWj0aiQsnQBr9TKm4LbCVQY3tzcRDAYBAAuSlHBVZZlxONxfk7EwW9v0NxjmiYzD1zXxcbGBk6dOgVZlvHMM8+gWq2y9QIAZosFg0GUSiVks1kEAgHEYjE2ZRf3/97QdR2GYTCLiYqiqqryAaTdDsCbYullJPT397P/mzjI7A0K0wgGg9ww3guKoiCZTO4IH4hEIiyxA9BzAeVGQrsEtFKpYGtri0N5xsbGWvaKexnhE66H/eH1BG8BtdFooFAoMNs3nU7vWAsF4+nyQP54rutywS4cDuOWW25pITZYlsVWL3S/tzfyi8UipqambugxJvsPYu0S07QdkiR1FUTZzj4VzZfuIEZG4IrB7/fzw0deYgA4rRK4NBEEg0GsrKzA5/NhYWEBPp+v5wLfXXfdhbvuuou/Pmyvgyv9/t2ACqLpdBr5fB7FYhG6rnP3ORwOM/vLy1q677778NBDDyGTyfCCHwgEkE6nsbm5Cdu2EYvFEAgEsLW11XNx6dvf/vYV+XsPC4f5+fx+P44cOYJSqcShGH6/Hz6fD9lstsUwvROCwSCq1SpmZ2e5C7iwsMCSumg0CkmSOGDGNM1rvpBdr9eXvPKq1SoajQZUVeX730v5J5DPJ7A9DwUCAdTrdczNzaFSqSAcDiMWiyGXy8Hn82FgYEBIWbqALMuoVCq7el9REcnL2iuVSjh16hQUReG03EQigWg0yuEc4oDdHYjBTbYWFI700Y9+FK961at4fZifn8f999/PKa1k7dBsNnnsy+UyisUi+vr6hJSrSzQaDfh8Pk4jDgaDLYfDdjuAer0OwzCwsrLSwkjw+XwsVxIHmb0RCASg6zouXrzYMueQz1X7GhwOh9FoNLC+vr6DeTY9PY35+Xmsr68zu0yM90542TWyLCOfzyMcDnPw1MrKCuLxeMu+n9QgqqqyET75qAovuJ2gMYtEIqjVaujv72cvuE6FfsF4ujy4rssM4HQ6zWFr3gI0cKkpT2tre1FakiQYhsEBhzcqdF3nfYjP5+NmbnuBj9ZEwm7+nAAE+/QAuLFnUYGrBqrik8eYJEmwbRuGYXAHNhQKYWxsDJZlHcpCf6W9Dq62l0KpVMK5c+ewuLgI0zTZt6dcLqNarXKogOu6vMElkKcYyYJyuRxqtRr/nqqqHO1O8glN07ouLi0vL2N8fPyK/N2HgcP+fOVyGYVCocXfze/3o1Qqsc8Gsci8xb5QKIRQKATLsrC0tIRgMIh8Po9QKISjR49ifX0dpVIJiqK0SF2u9UJ2PV5f27Zx4cIFNkOnQkUwGISiKBgZGdlxz3rnIZJlUcGJ2JfEuCFJjGVZ18U1uJ5B408sVgLN9dFoFJVKhTvefr8fpmliaWmpJaE1lUrB7/djY2ODJY2HtR48n2EYBubn5+Hz+RAKhfjgQc0HwvDwMAYGBrCwsMA+TiQDjUajUBQFiUQCpVIJ+XwesVhMMJn2Qb1eR6lUQrVa5fu7r68PU1NT/JpOzD2fzwfDMLjZkEgkoOs6fD6f8CLrEjQ/02GQ7vtwOMwKB2B7HiK7kvbwASqmdGKXiflmd7iuy2um3+/v2ATw+vOFw2EuBAIQXnC7gMasUqmwiiMejyOdTvfU6BWMp+6wtbUFwzC44UhzxNraGo4dO8avozk8l8uhVCq1SHIpjMq27Rt6jMn713Vdth+ybbsjO5f2gQSSkHstKsiLX7BPe4cYHYGrCnrIadE6duwYyuUycrkcxsbGWE56JSfIK+11cCXen1KviBWQy+UAXJKaUFGpr68PhmHsYO7ddNNNiEajmJycxMjICEZHR9nv4Mtf/jL+83/+zxztTteol+LS5OQkUqkUbrvtNpw6dYr/sywLH//4x/G5z33u0MekFxzm5zMMA2fOnOFDL4WbhMNhXoyoqNdsNtnYGAB7UNBmgDbIVEwlnyZgu4CYTCavi4Xsery+uq5ja2sLqqqiXC7zuMViMQwODiKdTu/6u5QkKkkSH8RVVUW9XmcfJsdx+JB9PVyD6xnEgmn3V5FlGZqmIZ1Oo9lsciGWiuI011BCa7lchs/n4+RiVVWZXXmjbpi7AZl5k10AFav/w3/4D3jXu97V8tyqqorl5WU8+OCD+LVf+zXEYjH4fD7Mzc21JK2Xy2V+vgSTqTNs28bTTz/NgVbApUMLeeoBnZl7FDB2/vz5FsbNzMwMp0ULSfruoPH0ziO076HmjLfBSQ24dpZTOp1mpYiiKMwuE/PNTlAhdWJiggumxEJtb4J18udzXRcnT55kSxLBxmmFd8yA7Xm9UqlgcnJy1zHyXhNqLNN+MxAIoFwut4SZ3Ohj7IVt25ifn0e5XOYGI+Gf/umf8NKXvhQ/+7M/C+DSHO7z+aBpGhdPAXBDkwgXFCLzpS99CRcuXMDw8DDe/OY3o6+v7+r+gVcZGxsbOH/+PAC0EE0URWmxTiB2uledV6vVdhTxxsbGdrDbBfu0O4iVS+CKgbqiVNzw+/0YHR1lnytKDdU0DYuLi5ibm0M8HufunliELoEWkng8jkAggEKhgEKhwCbQlOCUz+eZfeTd2J4/fx5f+cpX8P3vfx/z8/N4/PHHkc/nAWx3uX/3d38XsixzQisxm9oNknfD/Pw8fvzjH+PMmTP48Y9/jC996UtYW1sDgK58Fa40DvPz0UGaFvhms4lyuQzXddFoNOC6Lo+/LMtIpVLIZrN8PajbPTMzw76J1WoVi4uLaDQaHAZBzJnrAdfr9SXqvt/v5w3Wbga+ANiHhvzJaNNLBT1KPVMUBalUSvgBdQnq0NKBme71QCCAVCqFSCSCTCbD6az0DExPT/P3tra2kM1mYZomDMPg5GLBXtof5BtWr9db2Ev//M//DMuy8OSTT7Y8txQ8k0wm0Ww2W7rr1FwbGBiApmmCybQHyDwc2A7RkCSJWaxe7Oa5t1equph79gaFvziOwwEyiqJwwqU3mIcUCZ3CNyjQTRRB9kc7E09RFF4z25tg7f58lIoeCoU6HuRFEXvnmMXjcWQyGdTrdcTj8Y5jtBs7UtO0jmEmN/oYe7GxsYGnn356R2EP2G7iP/LIIzh+/HhLEA/NN9FolG0YAOATn/gE/st/+S8YHBzE+vo6Xv7yl6NQKODYsWOYm5vD//f//X947LHHMD09fbX/zKsCCgK0bZuluBRYB4DtcqgpMDExwQ0xIq+0F/GIRSnYp71DjI7AFUGpVGJPjUAggLGxMfh8PiwtLQG41AVZX1/nDjclEFF3Tzy8l0ByQjroptNpNBqNls613+/HwMAAqtXqDubekSNH8Ou//uuYmZkBsM0gO3/+PD760Y/iV37lV6DrOst1aeEaGRnZEQW/GyYnJzE5OYnXve51/L1HH30Ub33rW/FHf/RHhz4eveIwPx915jY2NlpSJemA4fP5+ICXTCaZAVmv19nL0HEczM/PQ1VVlEolGIaBcrmMYDDIPhUUFnE9SBKvx+uraRr6+vqQyWTgOA5kWYaiKCz57LQBkGWZg30osKFSqWBxcZEliQC4sJpOp4UfUJcgqSFt4oDtZ+XIkSOwbZsPHmQZQBIOkqnLsswFpWKxyMnFgjm5P2q1Gh+eqciRSCTw0pe+FH19fXjDG94AYLsY9fnPfx4f/OAH8Uu/9EstzYjx8XGey5aXl/l6CSbT7iiXyyiXy2g0GjAMg+9/uo8Ju3nuxePxFo+her3OHnLCi2xv5HI5LC0tsZ2ILMsIBAI4cuQIstksNjY22BJDkiTIssx7pvbwgWg0itXVVVEE2QOdmHg+n49DYNohvOB6R/uY6brO6yohm81ifX0dw8PDSCQSu7IjAewaZiLu7UusayI5dIJpmlhYWOCwR2rsRyIRPnORFUMul4OiKNja2sLHP/5xjIyM4MyZMzyPv/71r8d73/tefOELX7iKf+XVA9kbEFnBdV1mOtJc6/P5oKoqZFlGIpHgBgoVtWnNTCQSzPQTDN+DQd7/JQICvYGq8KSZr9frWFlZwcDAAMuvEokEYrEYMpkMisUiVFVFf38/xsfHkUqlOHBDYBuUlNtsNnHu3Dlsbm7y4l8sFmEYBmRZZjZfJBLZYSidTqfR39/PjMp4PI7f+I3fwDe+8Q0OedB1HdVqFbVaDUtLSx073d3iJS95Cf7iL/4CH/vYxy73z78iOOjnU1UVJ06c4LSsYDDIxetQKIRgMAhN01gaR4cKuvfpIEfSUGKNDQ0N8cJI3nHxeLyF/n894VpfX5Kh06bCsiwoigLHcdgovR0UqBEOh5nlFIvFMD4+Dtd1USwW2QOO3oN8367Ha3C9gIobuq4zW4bSVm+55RacOHGCZW8+n4+fAy9bzLZtxONxRCIRRKNRLlYJ7A3btvHUU0+hWCzy+Gqahv7+/h3raCAQwO233443vvGN+Na3vtWSaLe8vIy1tTUsLy8DAEvdKdhEbKhbYds21tfXualDDEhaH7wH6HbmHr1OURQuzNJ84zhOixeZmHt2wrZtPPvss8xW0jQNwWAQsixjYmICL3jBCxAMBuE4Ds9FkUgEoVCImwpra2u4ePEiTNNEpVJBKpXCzMwMUqkU8vn8Ze19no+g+7C/vx/T09PsKSlJEu9TFhYW9h03rxecqqpIpVK7JmreyHBdlwOqgsEgMpkMvvrVr+LTn/40vvrVr+Izn/kMvvOd77RckyNHjvC871X7kARSzCWXoOs6q0/2AvnDr6yssHUOqT1oDid5qaqqsCwLjz32GD74wQ/y9yORCD70oQ/h4YcfvqJ/07WELMuoVqvw+XyIRqN839KZk76m/eH09DSvkbQ2nj9/HvPz8zh//jxs227x25ubm0Oz2RSN3i4hRkjg0NFu5EpmubFYDP39/VhfX+eJQNM0NvA+d+4cAoEAH7y9nW+BbZkVbf5jsRjK5TIXj2iR0TQNjUaDD9IEYsZ4jV9t28b4+DiWl5cRiUSQzWaRTCZhmiYKhQKHnxw7dmzf5GJiaLbj6NGjePrppw93IA6Aw/58sViMO3IUJACAmTMAWBZRr9e5c02bAgrQoAKsLMsYHBxEJpPBxsYGLMvC+Pj4ddOpuh6vr2EYOHfuHKcVU1FvenqaJZ3t8AZqOI4D0zTR398PTdP47/P7/SJQowfYto1nnnmGAwVo3L3hGTS+XsNqv9/PslxJkrC4uIitrS2kUikuMBHrVbCXdgd5T5Ic2u/3o16vo1gs7jhok/9NNBrF2toaKpUKBgYGWpgh9Du5XA6FQkFI5naBZVlcBPL5fFy0jsVi7LVH6MTcs20bqVQKZ8+eZcbYrbfeygFZIlBjd1iWxUVRsrYAtvdIqVSKm2W01yRGfadxpMKS8NzbG2R4TywaaijTuLWHYuyVfkkqFHGPt8I7ZjRW+XwejzzyyI4ketu28YMf/AAvf/nLudBEMkgaSzHOu8O2bZTL5X1fl0gkYFkWkyioERkIBBCLxbC8vIxqtQoA3EQwTXNH2u7o6Ci2trauyN9yPaBYLKJer7OnciQSQSKRwMDAALLZLKrVKgdBjo2NsUpmNziOg8XFRcHwPSDEyiVw6PBKSNsXFdoAA9t+WZFIBIODg8jn89B1HcPDw4hGo1hZWUE8Hu96c5XNZvHpT38ajz76KDY2NgAAQ0NDeOlLX4rf/M3fRH9//2X9TVf6/bv9DEtLS6jVaggEApAkiZlKyWQStVqNqeLtUolIJIKbb74Zo6OjGB8fx/j4OILBIL71rW/h5MmTKJVKALbl1MQyIE+Jbg7WkUgEJ06cwOnTp3Hq1CmcPn0aIyMj+Mu//Eu86lWvuuJjsx8O8/Plcjn84Ac/4LRhWvCpsEFMmP7+ftx9990oFov43ve+x9eHih+lUon99eiZsCyLgwR0XUcikbguDtbX4/UtFovI5/MIh8OcsuW6LkzThKIoe25iyffD7/fDMAz2rATArBqSFXTrO3mjQtd1rK+vs88VNQUoVCYQCLBfTaVS4eI2AE5t1XUdwKVr6rouZmZmmI0jDtt7o1qt8saa/PSi0SjS6XTLc3vLLbfgwoULePTRR3H8+HFomgZd15FOp3HkyBEAwMrKCur1OiYmJlCv18WGehdQg5IYCZTW2l7YAzp77vn9/h2yuXK5jLGxMRGosQ8qlQqWlpZgmiaz3xVFwfT0NGKxGKcOe5uSJFtsl+VSY05Iv/ZGu09ef38/hoaGOEymvXhE54BardbiZUj39NzcnLjH2+BNDI1EItjc3OTzRidYlgVd12EYRkdJ+djYGC5evMjXRozzJRQKBdTr9T1fQ4QJCvuq1WrM3KP71jRNFItFAMBHP/pRhMNhlMtlnDt3Drfeeiu/1+Li4vM2UIP89mRZZtZeqVSC4zgoFAocGhgKhXhf6FXWdGoEkJIskUgIv70DQIyQwKGDJKReae7U1BSzCmZmZvgA7pWV0mYgHA4z+6+bh/iHP/whXv3qV0PTNLzqVa/i+PJMJoOPf/zj+JM/+RPcf//9uOOOOw7091zp9+8Gtm0zhVySJJYN0oFC13VO+BwaGtrRFXnggQfw+OOP47777sMDDzyAlZUV7gzeeuut+MIXvoCpqSlO0k0mkxgZGYGiKF1NqA888ACeeOIJPPHEE/jbv/1bvPvd72aT2le/+tV4//vfj5MnT+LkyZO4+eabr8wg7YHD+ny2bWNubq6lUEcFCMMwuINNRVYK1PD5fC3JoNQBz+VyvCAuLy9DkiQcPXoU9XodsizjyJEjnJx2LXE9Xt9AIABZlpmFB1xKqtwNdB8fOXIEjUYDsVgMi4uLmJ+fB7D9bC0sLEBVVRw/fhzJZBLhcLjjgV3gEshnkiSKwHZBuL+/n/2wiP0Zj8eZFUnPkK7rCIfDmJmZQbFY5CJgo9EQjIN9IMsyj7vXYyyRSOAb3/gGnn76aX5un3rqKX5ujx8/jq997WsYGxvDi1/8YlSrVU6MJr89stUQG+qdcF2XUz9pvg6FQjv89oBW5l4ikeCmkJdlT4wxajKIQI3OIPlWrVbj5iax944dO9Zyn5LXHjXUyA/U6wNH4W0i3GF3dDK81zSNC9Gdikd+v3/fQAdxj7fCW0C9cOHCnoU9AjVn2n31arUaVlZW4DgOfD4fxsbG9lXg3CiwbRtnz57dV0JOia/UnCQrHWoo0xkqHo/jt37rt5BOp3nub9+3f+1rX+Pk3ecbiHjSaDS4aQJss+82NzdbFGPApf0iwVvUjsfjXDcAIIKODgixeglcEZDXEh3oaCPl3ViVSiVYloVSqQTTNNlfZmlpCdPT010/xG9/+9vxxje+Ef/zf/7PHZuFZrOJ3/u938Pb3vY2PProowf6W670+3cDy7LgOA4mJyextraGTCbDRuhUWCLpSSdz47vuugu33347Tp8+zV5Kzz33HJaXl7G+vo75+Xk89thjKJVK+F//639xspxpml0drO+66y7cdddd/LXrujh37hzOnDmDM2fO4Ac/+AE+9alPYXNz85r4aB3W57Msi8e9VCqxHDQYDHJyrtdLDAAzlyqVCpvLkidWJBJBoVBAsVhEo9FANBqFZVmoVCrQdR3BYLArWfSVxvV4fWOxGIaGhvDcc88xW2xwcBA33XQT+0q2H9C8htWyLCOfzyMUCmF6epqloFNTUyiXy3jmmWfYv2ZqauqaX4PrFZqmIRaL4eLFixzQEAwG2TOS0tK9zCViHZNvE3Cp6E2sY1mWBbOjCxCLoK+vD5VKhT3GBgYGMDU1hZ/7uZ/j15bLZXziE5/AhQsXkMlkMD8/j+9973v49Kc/jS9/+ct8EAyHwyKpeB/IssyG9+S1FAgEcPPNN++4V13XRTwehyRJ3FSgtFHv4UVVVaytrbFMVARq7ATtGSm0yivJpSaMd8/p/bf9PnZdF4VCAcPDw0L6tQc6Gd4Xi0Us/v/aO/Mwqapz3b81z0PX0PNMgxLsgPOEAg5wrkOiMYIaUcijHr1x1kSNuYommsQpJnpizokRnKLmxMTruSGKIogDKiIoKGM3PY/V1TXummvfPzrfokZoZuj+fs/jo+7aVb1r1V5rr/Wt73vf9vaiwaNkMlnQ0MHtdguzPb7Hd5IZQK2qqhIbjrtDrVbDYDBAr9eLDQKSsohEIqJ6ak+rocYygUAgK1mikGu5QqGA0WgUG1yUSZZpaNjf3w+lUony8nJcc801MBqNRe/jRx999IB/r0NBMpnE9u3b4fP5RMAuHA7D6XSisbERGzZsEGslkgUhOSmikHv2hAkTEAgEsH79ejY62gu4lZgDBnXmQlAphU6ng16vF26jwEhUv7KyctSd+Msvv8SSJUsK7gIqFArcdtttOPbYY/f6exzozx8NlNXo9XpFW1F2BjAi5EoT2Hg8jkQikRfky9Ubo8FXq9VCo9HA6/WKTAJgpDyrrq5urxbWSqUSkydPxuTJk3H55ZeL4/39/fvWEPuJvb0+rVYLg8EAi8WC4eFhodmRmZlBO3q0mCC9Q8roo/ucSqvJhIDKFoeHh/e4LPpgczj8vn6/H5FIRGRFknHJ8PAwnE7nbgMSpA1Ck+JMzb1oNIpIJAKtVotIJHJY/gaHE7RjC0BoXMViMZSWlorFNfUJs9kMSZKQTCbFDi1l3wAjJaaZLtTMrqEMgWAwCFmWkUqlYLFYUF5enneuWq3G8ccfj8rKSqEVRxp89fX18Pv9QsOVA6u7hgIeKpUKsixDrVZn6YplQs9YyvCLRCJwOp1wu93YsGGDWLwcffTR8Pl8eXrFnDm5k8xyaNK4JXMlmrvkygDodDqYzWaRLVxbWwu1Wo1EIoH29nbo9Xro9Xou/SpCrosrzWkqKipgtVoLBo/I6Co38BQKhQpqco/3Nqf20mq16OvrEzpuu0Kr1UKSJGzfvl1kW5eUlIjP4zYujNfrFRrBlNlbKMBHmWItLS1ZjsRKpRJTp05FZ2enkFyg8WW8tbEkSejv74fBYBAbgrFYDE6nE6lUColEQhgbKRQKYZhG871CWcG0Ocxuz3sPtxBz0KBBr7GxEZIkiSwnjUYjJmA2mw12ux0ul2vUn1teXo7PPvusaDngZ599hrKysr2+7gP9+aNBrVajuroabW1tGBoaEgYafr9fLNKsVitisRgCgUBBp1Ais3yLHBZpkUJp5S6XC8FgcL9nLB3odtpXdnd9VHJOWXWUXk5luBSQcLvdcLlcWannmToTtKhQq9UYGhqCy+VCZWUlurq60N/fD5vNtkdl0YcLB+v3JYfQoaEh0fZ0HwcCAUydOrVge2WOQZnSAOFwWLh4kXMlZYJQgO9I+Q0ONiStAEC44NIGBGV60ASYXHVVKhXMZrPYUCBh9kAgAJ1OJ9zVOLNj9/j9fpEFT9mPxcZ/EgGnzCfKfmpoaBDjGZlEcMncriHjC9pgIG2mrq4ulJeXF7xXqU2pj+QuXii7m0Xwi5Mp2ZIpA9DY2Cgy93JlAGj8jsfjGBwcFKXnFHgiWRhu79GRWapYLHgUiUSy2poCT2azmY0eCkDtFQwG0dfXVzDYlIvVahUaZ+FwGOl0Gs3NzUIugHUk80kmk9ixY4eowqH1EM0jKQhF66pwOIx4PA6n0wm73S7mJg6HA0NDQ+jr6xP3caYx23ghEAiIrD0K4sfjcfT09IgNXJ1OB2CkzJnGC2qnXANO2mChTQA2Oto7uIWYg0ZuSdzw8DBMJhMqKipESZzRaERlZeUefe4dd9yB6667DmvXrsXZZ58tAgz9/f1Yvnw5/vjHP+Kxxx7b6+s+0J8/WgwGA2w2G2KxGCwWCyRJQldXlyidLbQrkgll9BkMBsRiMZFVRjsuVDZtNBrR3t4OpVKJtra2PGHq8Q6VU5lMJhFQTSQSYnFH2of0ACM3V3LYokU4/aNSqURZtdVqRSKRQElJCTQaDU98iyBJEjwejyipoFT/srIyUUoLjGSBDQ8PZy0qcqUBEokEenp6xH0uSRL0ej2XJu4BJBtAgW7aESdosa3RaGC1WoUZTUNDA8xmM3w+H0KhkPh9aDLNWQe7JplM4quvvhILaBrfaVFSTCuSxh0qqQsEAjAajQiHw/D7/SILigOrhaFsA9KFpCBTdXU1AOTdq7SA0el0os1JzJ0Ew2nxUlFRgd7eXhbBLwK5ztN8h0q8YrGYqF4oZmDS3d2dlYFDwRCv18uZqrsgV/CenqF+vx8lJSUiyL1x40bU1tbC5XKhq6urYFvr9Xo2esghmUyK9iJzDJofkgZnLgqFAhaLRVQ5kV6nwWAoWOY43tuYCAQCaG9vh9FoRCwWQywWgyzLwgEagNj0osz2XJ3OyspKJBIJxGIx+P1++Hw+NDQ07LKNzznnHLS2tqK1tfVgft0DSjKZxMDAgDDmikQiiEajsNlssFgsIsCn1+uhUCiES3xdXZ1oJ6ooy9XWM5vNHKDeB7inM4cECoLQwoIi+gMDA4jFYujv7x911tiPfvQjuN1u/OY3v8Hvf//7LHH9448/HkuWLMHcuXP3+loP9OePlkgkIh4k5EREZaKpVArRaBSSJMHhcAgdmkyUSiX8fr9wfKLAXm1tLbxeL/x+PxQKhXC+rK6uRjwe58VdDh6PB+3t7aIt0+m0cFwlQfrM0vJ4PC4yUX0+n1h8uFwumM1mYeqwadMmpNNpOBwO+P1+yLJ82LjlHo6oVCro9XqhIUmTB3Jo3bRpEz7++GOEQiHodDqceuqpmDJling/SQOQ5h5l4pSUlMDhcEChUPCCbxTQZEuWZfGPRqPJKtHKXWxT8I8C4pFIROiuRiIR8Uygki+e1BVGkiShZ0PGJZIkobS0tKBuWG6QSavVQpZleDweJBIJqNVqoaHIgdXiSJKEwcFB2O128eylYFOhezXTbdFms4nMSZvNlrV4kWVZ6JayCH5h+vv7EQwGRQawXq+H0WjMKv3PlMmwWCxi0ZlIJPIycCgAzpmqxckVvKc5jVqtRigUwvbt29HW1iZM86ZNmwa32w232y3Gd3rW+v1+NnrIgUpybTYbZFlGa2trVsk5PVczN+3VajUqKiqEEVIsFoPJZBKb8rlljqwjOYLX60UwGBSZeyqVCjqdDlOmTMGGDRvE/D1XriWTVCqF9vZ2oY9Ic8xdtfHFF18Mj8dzIL/aQYc2dRsaGjAwMIBwOAwAmDBhAmw2GxKJBHp7e4VslN1uR11dXZbZSDHjHQq8coB67+BWYg4amSVxNBHr6emB1+sVWR40YdNoNHsUVJo3bx7mzZuHRCIhBlCXy1UwyLU3HOjP3x20s2e1WhGJRMSOCLl5RiIRUeZcjHQ6DavVCgBiogWMTJbj8TgcDodwjauvr4fZbEYikdirxd3uruVIhVyLKcuRtMUog4zKOPV6vdBDJP28wcFBkVU5PDyMlpYWlJSUwOfzCU0gmliQG9fh4pZ7uEGl44ODg1AoFFCpVCJ4FIlEsHnzZrz77rsIBoNiQrx06VKRRZkpDUAOuxSgNZvN8Pv9CAQCQr+GKY7H40E0GhW72vRbVFZWirLc3MU2ZRa0tbVBqVSK0nTqP2ROw5kdu4fKmklLVaPRYMqUKQWz9nKDTMPDw4jH4yKDkjL3KKjNDnXFIQkA0n5LJpPQ6XQF79VMt0UyTtJoNEJHyOPxCCMZKlFiEfx8otEotm7dmlW6T3q1maVehTL3aIG5bds2MVaVlZWxgckoyM0Es9lsYv4YDAbR1tYmNr0TiQTWrl2LGTNmIJFIFAw8sdFDNlSSGw6HEQwGs7RmVSoVSkpK4PV6s/Q8NRoNysvL0dbWlhUUSafTBcsceYNmZP7u8Xggy7LQCKayXK1WiwkTJqCvr0+Y5BXS6STNT0mSxJhhtVqRSqV22cY/+tGPDuZXPSjQGNvT0yPW8DRnJqPMVCqFQCAAg8EAg8GQV7pcyHhncHBQzO05QL13FBfmYpj9DA0Era2t6OrqwubNmxGLxaDT6USWjNVqFUESyjIYDZFIBB9++CG2b9+OiooKVFRUiMBbNBrFCy+8sF++g0ajyfv8zs5O/PCHP9wvn18MaguHwwGz2YzS0lJYrVaoVCpIkgSj0YjS0lI0NTUJoehctFotUqkU3nrrLbz88stYvnw5vF4vBgcHEY/Hs3R/WlpaRCYTpU3vCTqdDps2bdpfX3+f2LRpExYvXozNmzcDADZv3owbbrgBP/zhD/Hee+/t0WfRTlVVVZXQLSTdHlqsURo6BZXUajUqKyuFLgoF8KikizJolEol7Ha70E3s7e3Fpk2b4Pf793ub7AnUt7755pu81/Zn39oT1Go1ysrKhGYKlffr9Xp89dVXeP3110V2DBGPx7FmzRpEo1G0trZiYGAAw8PDCIfDkCQJwWBQlFxLkpTl4N3W1iYmg8xOkskkOjs7xTitUCiE9gqNRUD+YjuZTIrJYGZpejgcRn9/vxAT58yOXUPZ3OFwWIhWU7ZvIShbLBKJ4LHHHhMTbaPRCI1GA7vdDp1OB4/Hg5aWFgwNDcHhcIz7RWEupB9JCxg6ZjAYCi5AMt0WZVmGTqdDLBaDWq1GQ0MDnE6nMAbK1DHbkznQeCAUConKA9KIJJf0CRMmiPs0czOhpKREZNDnbjjSgpzbvDi5gvd2ux2hUAiyLMNsNqO/vz/P+CidTiMYDGJoaChrHMkNPHF7Z5fkAiObZeQ4r9frhTsraTpTwE6hUIh2bWxshMPhEIkSpGmYSCT2eg4/FqENGY1GI3T2tFotXC4X4vG4qL6pqKgQm4yk07ljxw709PRgeHhYBKlCoRCSySQkSRKlpY888gguvvhinHrqqTj11FNx8cUX49FHH8Xg4OCh/voHhEwzNK1WC5PJBI/Hg02bNgnjKJ1OB6VSWTAZJtN4h6SnyPHZbDZDr9fD4XCIsZoZHTxjYw4JmU6Ver1eTHp9Pp/QPhhtxsDWrVsxZ84cdHR0QKFQYPr06XjllVeEdp/f78fChQtx1VVXHZDv4vV68fzzz+O55547IJ8P7NQl6O/vx+DgoAgiZboT6XQ6+P1+oeEDAFOmTMGHH34Ih8OBzs5OXHnllfD7/aisrERvby/efPNNPPLII9BoNOju7oZWq4XT6UQ6nR6VW+7tt99eMEMvlUrhV7/6FZxOJwDgiSeeOGBtsyveeustXHTRRcKh8+9//zuuuuoqTJ06Fel0GrNnz8ayZctw1llnjerzKEDR2dkpsk8pU0mj0YhFMunqEXa7HQaDQWRM9vX1wWQyobGxEQMDAyKYQYGPw8Utt1DfevXVV1FRUQHgwPetYiSTSbGoSKVSkCQJvb29iMViu5wA+Hw+oatCk41IJILOzk5Eo1HY7XbE43E21Bgl8XhclMiR9hWVElHmKpCfuUdiyY2NjaKEt62tDVu3bkUikUB5ebnYFefMjsLQRpksy0I3lX4DklzIhcYpj8eDlStX4rrrroNCoUB7e3vWuFVaWioy99ihLh+Px4NgMAgAYsFNTvS540Tm4j1Tf8xut4vyZzLRYDONXUMbkLRAJx3PiRMnZm0AFMrco4AI6cbJsix0D1nXqTi5gvdWqxUej0dsaBbLpkkkEnC73VlOl263m800csjcuKdsJbo/lUoldDodrFZrQZOkRCIBq9Wa5UacTqdRX1+PlpYWlhXJIRgMoru7W5inUSCUstxJl9zr9cJut8NsNhfV6VSr1di+fTuGh4eh1+vh9Xpx1llnwWg04pxzzsGkSZMAjFRG/e53v8OvfvUrvP322zjhhBMOZRPsVzweDwYHB4WmKd3DJpNJZFYHg0EYjUYh+5FpiAQUNt6hCjMeJ/Ye7u3MQaOYU6VOpxNOQwaDAWazGSaTadQPpLvvvhvHHHMMPv/8c/h8Ptx6662YPn06Vq5cidra2n2+7jfffHOXJaYHQyA10y03Ho+L8jcqe6DSh2AwiGOOOUYEODZv3iwyju655x643W68+uqrUKlUaGtrw6OPPorXX38dN9xwA3p7e1FRUYH6+nrIsgy/379b3cPf/va3mDp1atZCHhhJdd+0aRNMJtMhLc/9+c9/jh//+Mf4xS9+gVdffRVXXHEFbrjhBjz00EMARtrkV7/61aiDewBEMIkyMKg0SKPRCBc5rVablTVGO7HkUEyTt1QqJRbmlK2aSqVgMpkOC7fcQn3r9NNP3299a2+RJAn9/f1i8huPxyFJUl4GQS6ksUJjkEKhgCRJqK+vRzgcRjgcFsK+bKixe0jHExhZeFNwicSqKRshd7FNvxtlS3q9XiEO3tnZKT6HNd+KQwsRrVYrspgoQLR06dKCi+5UKoVt27bhm2++KejGSOOXzWaDTqdjh7oC0OYO3eekdSjLcsEMGVrMZGq9kZNrroh4dXU12tvbeVFehN7eXpHVQZljNpsN/f39QuMWgChTVyqVcLlcCIVCYjOZjGao3C6VSrGu0y4gvT0KgFIJKDkMS5JU8H0KhUJUNXDgqTjUvgMDA/D7/VCpVGJuKEkSXC4XGhsbsW3bNuFiTmNNIBAQxoTkRkxZxQDrSGaSTCbR3t4OYGR8IE1DlUolSvppowWAWFulUqk8nU7aqAd2tvFdd92FSy+9FH/4wx/y2l2WZVx//fW46aabsHr16oP4rQ8cmUkOKpVKbGJRtnQsFhNVMQqFQpgPBgIBEagutvHV3NwMAGy6sw9wSzEHjWJOlR6PBxqNBk1NTaipqUEymRTpuKPh448/xrvvvguXywWXy4X/+Z//wf/+3/8bZ5xxBlasWLHPdfoXX3yxeKAW42A8RA0GA8rKymCxWESZIDnnAjtTog0GQ8HspTVr1uDmm2/G4OAglEolotEoLrroIjzzzDMi2JpMJhEMBsU5u3PLfeihh/DHP/4Rjz/+eFaATKPRYMmSJfjWt751YBpjlHz99deibHTu3LmYP38+vv/974vXf/CDH2Dx4sWj/jwq7bHZbAgGg8LMxOfzQaVSoaysDOFwOC9zj7JptFotSkpK4PF4EA6HRcYMBUepRJGc/8gJ+VAFlg5039oXSHCXBKdp4rUrqB0pIE9ab7QzToEqh8MhtGp4YlEcMoDp6emBJEliYpyrj1JI4J6kGHp7eyFJEpqamoQGZTgcRjQaFRloHFjNh0r54/G4cNumEqMLLrhgt88sYGRnnFwwSccs1wWT2z8begY4nU4MDAwIY56ysjJMnDgxb5zInfeQ26Lb7caGDRuy9LLovbwozycajeKbb76BUqnMMsHILNmi9vP5fMLEgbJwSkpKYLPZsHnzZhGYslqtwlCJdZ0KU8h5ddq0afB6vdixYwc6OzsLvi8YDGL79u0ceNoN1L79/f3wer0iY0+tVgvDhpKSEhEcIdRqNUwmk5B7yQyKUBIA60juhDbDyJiENMVJr5O0a6ktjUYjampq0NnZmaXTWVlZKdZGlNEqSRI2bNiAxYsXF7yvFQoFbrvtNhx77LGH4JsfGDweD7q6ugBAJOlQ9dKOHTuQTqeFTiT1e5vNBpvNJhIfaOMr13gnkUiIyhyWZtk7WHOPOSRkOlWWl5cjlUrhm2++wapVq/D1119jy5Yto9Yai0QiWQ8thUKBZ555BhdeeCFmzJiBrVu37tO1VlRU4G9/+5vIasj954svvtinzx8tlBVAKeVUmkt6D4lEAuFwOC99nx420WhUlNySq5nBYMDw8DBCoRBqa2uRTqexZcsWpFKpLLfcYnpjd999N1577TXccMMNuPPOOwtq/R1q6PvToiDzIWGxWPZI047cJb1eL3w+HwKBgNDQowwAo9GY9QADdpqZmEwm8fvZbDbU1NSIz6MMGVq4dHV1QavVHtLA0oHuW3sL7bJm6hju7t7TaDQiEAhka70FAgF0dHRAlmU4nU5xzzQ1NWHKlCk8sSgCBX0o45QmyLk6bbmZe3S+3W7HhAkTRKlXMpmE0WiEXq8XWkEcWC0OlUTTJFmr1aKhoaHoMysQCOC5557D3XffDQAi25u0g6LRKMrKymAwGODz+bj9C0Bl+qQNSZlgxx9/vJCh2B2pVEqIiJNe1uDgIFpaWsSinLU+s6G2pgx50quleQyNRSTwTsZUVBJmMBgQDAZFm1utVpE9xrpOhcnV2yP9LLfbDYVCgS1btmQFnAjaxDSZTMIkorq6GkB24Gm83+PUvlTZQfNIMgmrrq6G0WhEIBAQciIkA6BWq1FeXo6GhgZMmDABbrdbbOyzrmE+Wq0W4XAYXq9X6PvSfUr6qfF4HHa7Pc8ULxfSiaMNNqPRCIfDscusvM8++wxlZWUH6NsdXGiMpXuVNMZLSkoQDAbFBi0wYn5XUVEh3HMzjY9oTRsKhcSYrtVq0dPTI0x3UqkUurq6xu0YsbfwjI05aNDO6sSJEyFJkhBVDwQCojyOIvV74pZ79NFH4/PPP8fkyZOzjj/99NMAgO985zv7dN3HH3881q5di+9+97sFXx9NhsT+gFKcu7u7IUmSMBOgh7bZbIbVas0KKgHA2WefDbVajUAggB07duDYY48VEzK/3w+LxQKHw4FoNCqy92jyYDAYdluWdeKJJ2Lt2rX40Y9+hBNOOAEvv/zyYbMrW19fj23btmHChAkAgNWrV2eVk3Z0dAj9uNFCiwYq46RdVhLZzX2AATsfYuQeFYvF4Ha7hUZfMpkUO1upVAoulwuRSGS3ZdEHmgPdt/YW2nml0nQSR6bygEJQP6Wy3GQyCavVivb2dmzfvh2xWAxNTU1IJpPw+XyQJAlqtRqTJk3i4F4RKCOJxiDKHjMajVljRmbmnt1uF0YEpEVZV1eHzs5ODA0NQaPRwOFwHDZjyOFKIBBAT08PDAYD7Ha7eKYqFIqizyzKWCIXY8pY8Hg8QkerqalJZFYy+ZDjYjweF9kKSqVS6KXmPicz5UjodUmSIEmS0MclvVxZlsUxLknPRq/XIxKJwOfziYWgQqHIK+0kHdDcLByXy4X+/n5RKkrVI5ylWpzcQJFKpYLX68XatWuxZs0asYDPxWAwoKqqSgTCKbhaKPA0nu9xag+NRoNwOCxkLEgPzmazQavVore3VwT/aBOHNmAooy9TozyzjJp1JEdIJpPw+/1iU5cy+JRKJQwGgygZpXlfZul+plMumXIMDAwgGAxCrVbDaDTi6quvxk033YSNGzfi7LPPFoG8/v5+LF++HH/84x/x2GOPHeJW2D9kjrHBYFDIClGpc2VlJcLhMGRZFmscWZahUCiy5BNIbiqz/LasrAydnZ08Ruwj3FLMQYMCHENDQwiFQhgeHoYsyygpKREisna7PUt/bDQd+qKLLsIrr7yC+fPn57329NNPI51O4w9/+MNeX/edd95ZVFcEGMnuWbFixV5//migHb5EIgGj0YhkMgmDwSC08Sgt2ul0Zj3E77vvPrFIyzTgMBqNkCQJ69atwzHHHCOyx0ggORaLoaenR+y+7m5iYDab8fzzz+PVV1/FOeecs9vyyIPF9ddfn3UtxxxzTNbr//znP/dIb4/ux+rq6qzdJlmWReak2WzOeoBlQiUXVBZNAuwUdPL7/dBqtejq6hK6iLsqiz7QHOi+tbdQCr9KpYLD4RDOcMBIhl5uFh/1AXJzbW1thVKphNfrhcFgQGNjIzo7O8WELRQKHRaGJoc7Ho8Hfr9fCNZTJiUtQAjScVMoFCKILUkShoaG4Ha7EQ6HRft6vV5YrVahlcXtXxgqX0kmk2KhAoyM88WeWWazGTqdDhaLBXfeeafIXigtLYXZbEY4HMb27dvhdDq5pKsINNZYLBbo9XooFApEIhEEg8GC85VCZbllZWUiI4cWkWSAwiLihSG91ExnxkKmDrRhHI/H4XK54PP5hO5brnB7WVkZNBoN678VgebsdJ+Gw2H4/X6kUikEAoGC71EoFNBoNCK4lBt4YkONnVD7dnd3i3bVaDQikGqz2VBZWSk0UlOplHAedTgcCAQCGBwczCrrp40z1pHMhowHLRaLmKOkUimRyfvZZ59lyYZkOuXmmj20t7eLcmoygbzjjjswbdo0/OY3v8Hvf/97se5QqVQ4/vjjsWTJEsydO/cQt8L+odAYS9q/VCpuMpkwNDSUVbVRVVUFl8slPsfv96Orqyuv/JZciTk4vfeM797OHFQoKNLR0YFIJCKykyh7zG63IxAIwGKx7JFb7j333LPLLIPf//73+P3vf7/X133GGWfs8vNNJhNmzJix158/GuLxOPx+vyhNiUQiQsybUsMLXeMtt9yC/v5+2Gw2mEwmrFu3DoFAQOykXn755aiurkZvb69w7KLBWpIklJaW7tHE4LLLLsP06dOxdu1a1NXV7e9m2GOuv/76Xf52Dz/88B59HmXfbdmyRWiMkaCsxWKBRqMpmM5Peh8kxGuxWEQmJQCRuUoldkajMass+lAtrg9039pbfD4fQqEQJEmCz+cDMLL4o00CKk2XZVnsGNL3oCzbTMduq9WKuro67NixQ0zWDgdDk8MdCpZmah6qVKq84DaVUUciERGYJc09+v2cTicsFgsGBwehUqlEwInbvzAmkynLlIrGDYPBUPSZRWLXPp8PkyZNEs+PkpIS6HQ6ACOZBhQ04fbPJ5VKIRqNik01WoxbLJZRL0BoUyJTc2/atGmwWq1sNlAEWkyq1WoRFCFphsz7M3Mjgco/TSZTQdfL+vp6eL1ezlItAjlgdnR0IBqNiowmn89XtFqFzAoouJSrJ5mbqTOe73G1Wg2r1YovvvhCVG9QllNZWRkaGhpgs9kQDoehUCigVqsRiUSg1+thMBhgMpny3Igzy6hZR3InsVgMw8PDYg4iy7KoJKOxO9NdW61WFx0z1q1bJ+b5g4ODAEY2lefNm4d58+aJbHgAcLlc0Gg0h/Kr73eocoYCzna7XWToUTUN6fkaDAZR3USazMDOhBUqvw2FQkLDj4PT+w63FnNQMRgMcLvdwk20t7cX6XQaGo0GBoNBOBftiVvueIDE/sPhMGw2G/x+vwiEOp1OGAwGoZnn9XrR3t6Ozs5OdHV1IRqNQqVSoaGhAS6XS7iEUhZNb2+vMG4gzQO73Y7S0lI0NzeP2tiEqK6uFvoqYxFKPacMsdxFBWWfZkK/H9nCkwFHXV0denp6RCp7Z2cnQqEQbDbbqMuixxvk0kVtkkqlRCkuTSAUCgUqKiowMDAgBPCpfC7TLVej0SAajWZpsCgUisPG0ORIQKFQiHJQpVIJu92OysrKouem02lIkgSz2YzGxkYMDQ3B7/cLnT4qeWdDjV2jVCpRWVmJdDotghiUBV+MeDwOi8WCqVOnCjOarq4uDA4Owu125zlhjvfMmlySySRaW1vFOEPBvdraWhgMhlGX5cZiMQwODsLhcGQtzqlcmoNN2SSTSQwMDMBoNIqsG3LLLSSBkbuRYLFYkEqlsoTbo9EoBgcHkUqlOEu1CMlkUmhDUube4OAgQqEQdDpdQb09YGSOVFJSArvdLu7tiooKhMPhgpk645VkMonBwUER5KPSfpPJBKfTib6+PjH+UiBFr9fD6XRCluU8N+JQKJRXRs3zx5F23r59O4Cdeu9kqNPf3w+Hw5ElGxIKhRCNRpFIJPKccilbm6ptaL6ZiUaj2WO5nyMJutcoq47uw1AohJ6eHiSTSeh0Ouj1ephMJqjVarjdblFWrlarC5boh0IhtLa2cnB6PzB+eztzSCCtCL/fj+HhYUiSBIfDIbIQjjrqKADYI7fc8QAZMiQSCeHSShPUaDQKl8uFtrY2KJVKrF69GsPDw1nvTyQS2LRpk5hMKZVKMQCbTCaoVCoRcO3q6kJ1dXXWbiszAj2YysrKhF5PNBqFUqkUWX0ajSZvMZyZmj44OCh0+WiCAECUvsTjcYTD4T0qix5P0KSASssVCoUIzNEuYSQSyQrAUjCbxhRaoJO+CgVeS0pKYDQaRWC8rq6ONxmKQBNmut9TqZTQz8s19aHfjBaE9Hskk0k4nU6EQiFRjkvizBTY4/YvDOmPhUIhkZ1KWWTFoDGop6dH3P8AhDlQeXk5mpubxQKe2z8bSZLQ19cHrVYLm80mDK22bt2K1tZWlJeX46yzzkJNTY14T6GyXJfLBZ1OB5vNJjT3aGHDwaZ8qDyOgkm0qVZdXY0JEyYUbJ9MKQaa39Azlp7TqVSKtZ12AY3bmfdpNBpFf38/VCoVlEpl1kYmBT0ocEIyAJFIBJIkFczUoY3M8Qg5nZO8AukS6vV6lJaWIh6PI5lM5jnNx2IxeL1e9PX1ZbkRU6Cby56zIQkQnU4nKg1IBooysTNlQ8j8LplM5jnl2u12lJWVoaenB5IkQZZluFwusTEz1kkmk+jq6srKaAQg5tH0XCQtfWrH7u5uNDQ05JlpZEpT0GY9B6f3HW4t5qBCaf6tra0YGBiAXq/P0vIYHBwUO36H2kzgcIJE0Pv6+qDT6USmEqWX06SXzDaKMTAwgMmTJ4vgHjBSpphp5DA8PIxEIoHOzk709/fz75BBJBKB1+vF8PCwaCdy4SU9xEKQEC8tCru7uzE0NAS9Xp+1O0VaKpQVsqdl0eMB2sX2+/2IRCIik5ICQlRmmJkhmemkWMhVl5xeSVOFJip87xeHJswU2CNNz0KLCdJoCYfDsFgsGBgYEM6XlIGQqR80efJkEdjmTZ58KIMsEAgISQbKRGptbRVlzQCEDEPuIpoyKE0mExobGxEMBoWLK2fWFCcajQpDDVrYpNNpUWa0dOlSnH/++bvMXs8NNhVa2HCwaSdU5RGJRMT8R5blguNz5kYC3cepVAoWiwXd3d0iO/WYY44Rz3HWdiqMVqvNM2dQq9Vic8ZgMIj5JjnRU9AkFouJDGzKvGEzjWwikQh6e3vh9XqFhAhtUgaDQTEXySwZpYAqGe9QuShV2dTX13NpfwHIkZhc4ckIw2AwiHsyM9u3mFMumaxRhYfZbB4zLrijgZyCbTYbXC6XeA5SVjqZqVESCq1Rc+Vackv+aUwOBAIcnN4PcI9nDiqU5u9yuaBSqRAKhdDd3S12RqLRKEKh0B655Y4HyCnX7/cjnU5DrVajtLRUOL2lUilotdpdBvaAkSDGwMCAmLApFAo4HA5h5NDR0YFgMIhwOIy6ujpEIhH+Hf4F7ViR1iG1OZlkyLIMt9stUs5zF9JWqxXASNaBTqcTmQVGoxFKpVIECqurq8XkYvLkycJBjdkJZaxSUI8mxqSV4nA4cNppp2HdunXo6+sTEw/SeWtsbEQikcDAwIDYtfX7/ejo6EBVVRW6u7uhVCoPuaHJ4Q4ZyWSWZ0UikbzzMjVaEomEyJ6h3y8SiaC0tFQEZDdt2gS32y0yx7j9s4nH4wiFQlAqlSgpKYFKpcLAwAB6enrQ29srXMGtVis6OjrExs0JJ5wg3P9I8JtK1W02G3bs2AGn0yncQ8d7Zk0upG9LC5xC9PX14eWXX8Zpp52GM844o2BZbjKZLBhs4oVNYUhaBIAQa5dlGZ2dnXn6npkbCTSup9NpmEymrDLovr6+LG041nbKh0wDqI0sFgv6+vpExhKALKd6WZah0+lw1FFHobe3F729vUJzz2g0clZZBslkEu3t7cJEgzbbaYwhaSKqCFEqlXC5XMIwg+SNMt2IM+HS/p2Q9IfP5xMa2TqdDmazOavPkzYcleCS5EKmU67H40F/f7/47EQigf7+fqRSKaGROJaJRCIFTUYSiYTIrqaqDNKHLGSmkVvyH41GEQgEUFFRgfb2dq4c2Ee4xZiDCu2qUlZGe3s7ent74Xa7hUgs6RqM1i13rEMZAclkUkyOksmkKMcaGBgQO3ujgVxGSX8vFouJSYJKpYLVaoVKpcLQ0BAqKyuFo+t4/x0yMwKUSiU0Go1oO8okGBwcRGNjY96ElVLQY7GYyHJyu92YMGGCcCYmzUOv14t4PC7E1Tm4kQ2ZaBiNRlitVvh8PsRiMZEFbDAYRKm/y+XC0NCQEFfXarWIRqNoaWkR97jL5YLJZEJlZSU6OjrQ0dEhspYOtaHJ4YzRaBQOz+l0GkqlEhaLBZIkQZIkEcwGsjVaqHTU7XajuroaiUQC7e3tIkuvr68PkUhEjEnc/vnQAlCSJASDQTGRJlKpFLZt25b1HtJspd12KgFTqVQiy4ayuDmzpjC5QuLFiEaj+OijjzB58mSh15TrlhsMBvMWNtXV1ejq6kIkEuGFTQapVAqxWEy0i1KphMFgEON67kYaldhFo1Eh8RKNRsWYpFQq0d7eDqfTydpORaB5J+lfeb1eDA0Nic132lRLJBJZ5hqU8ZSrJ1lRUcFZZRnQBgHp7fn9fsTjcaTTaTgcDtTV1cFms4nnIc0NrVYrdDodfD4frFZrlhsx/WZUSs2l/SNj8caNG4WGdTgchk6nEzqpJpNJ6DJnBvK8Xq/QbM4MYvX09IjnJ2ma19TUjIu5SmaCgyzLCAaDwmSktbVVVNNkau0BENqEmRQq+ae1D1cO7Dtj8w5kDlsoyOH3+0U5KU0UNBqNcM7dE7fcsQ5NAmiiqlQqRUCDdAmMRqNwbdoVNJGoqKgQpS4+nw+JREIsXBQKhZhM+/1+WK1W/h2ws0SFnIUpSEdlhRaLBWq1Oi+TIBPK8qNdwFgshs7OTiiVSlRVVYkd8rKyMjgcDp6cFSGRSECSJGF6QQsNCryazWYYjUbU1dXhq6++QjAYFLvj5PhHGUtUZjcwMCA+v6qqSuxGcoCjMKSrQqVYAPIWenRepkYLlX/q9XrxDKBFoCzLCIfDItBHAT5u/3xoAkwaTKM5v7+/XwSzqZyLMlTJSEmSJCiVynGfWVOIYDCIgYGBgqX9uZCr+sknn5z3Gjm8Zi5sIpEINBoNGhoaAIwEz/me31mCTplhgUAAZrMZtbW1BeeIhQw17HY7FAqF0M+KRqMAAJvNBp1Ox9pOBcgVvKcsYMq4pgA3beqQ1idVkDgcjqx7Ox6Pi8/mrLKd80nKJqPsR5PJBJ1Oh66uLphMJvT09IjjwMjY4XA4smQsSB9bkiQufc4hFAoJnUfK4E0kEqLiidZXmdloFASktSg9K5uamtDb2ys+K9MB1mw2CxOwsdrW8XgcPp9PjJ9kptHX14fh4WE4nU4hfaNUKkUQkIw0Mtsmt+Q/GAzC7/eLNSpXDuwb3GLMQYVK5tra2jAwMCB2SwKBACRJgtFoZLfcHChLg0wDKIOPhIztdjtKSkrg8/mEgUAhdDodjEajyBLw+/0i+4/+oYwZ+mz+HXZCgbsdO3ZAoVAIfQkKsgIjtveZqedEPB4XpROJRAIWiwWBQADbt29HLBZDU1MTkskk/H4/vF4vtFotLBYLT84KoNVqoVarhetZ5kLD5/PBZrOhrq5O7NRmTsRIF66qqkosWNra2vDNN9+I0rnh4WF0d3cLww4OcBSGyoOMRiNSqZT4PaxWa5b2JE3qMl3nPB4PwuEwfD6f0AmiUmvaTU+n0xxgKoIkSWJhl06nRxXcA0YCJUajEW63G7FYDN3d3XA6nWIRQ2VJnFmTD5XRkWnAaOjv7xc6ZbluuSRLQiWKsixj+/btQu6BM7ZHkCQJHR0dIvOaxpnd3Z+ZJXYAsn4z2mCj0nYeZ/LJXXxTgCOzbBGAMHGgZzK1e66WIW0icFbZCKQzHg6HxZzdbrfDZrMJ3bJQKIRgMCjm/+l0Wmx6kUNpZmYkJU9w6fNOUqmUCByl02mhCUkGJkqlMs8gIh6Pw263Z7lr09yGslfJHZ36QSgUEqXnY5Vikgckh5OZ0ef1egEAJSUl8Pv94v1Ebsm/w+GA1WqF1WrlwPR+gFuMOahQnT056lDpBIkfU6miy+XiDv0v1Go1JkyYgN7e3iyXskQiIQSLh4eHxSQgc0KrUqnEhItMOVQqFTweD4aGhqDRaGA0GmGz2USJQCwWQ2VlJSorK1FRUcGC9hm4XC7Y7Xb09/fDZDIhGAyKnX/aeS2EUqkUEzkyFFAqlWhsbERPTw8GBgYQDAbFRDgSibBbbhHS6bS4rwnK1jAYDDAajVn3LP0uCoVCGMl0dHSIMgBy9qNsStLcGxoags1m4wBHEchtkgxNKBvvqKOOymqvXLfQeDwOpVKZpbHX1tYGh8MBrVaLyZMnY3h4mDVXdgFtBEiSNOrAHjAyDg0MDIhMcGCkfE6n04n+QGL5TDZUMkfGF6PB7/cjmUzmleVWVlairq5OaAuRZANl3Yz3wEcmyWQSwWBQzE8ooJ2ZzZRJIY1D0ohramoSwVm/3y+qIDiQnU+hxbfT6UQ6nUY4HBYZNhRMIlMZAAW1DMnwirPKRu5pKk+mTLJEIiF03qhySa/XIxAIiGATjRXU9rmZkUajEdXV1WhtbeXnJyBcWjUajZh7pFIpEYjKvC8zNx9pIyHTXdtkMmW1byQSQWVlJYLBoDCsGuttnaudbLFYxHybgp7ASLtTe9F5FotFzNlzS/79fj8MBgNUKlWWyRSvffYe5e5PYTK54YYboFAo8PTTT2cd/9vf/oZzzz0XDocDCoUCbW1th+YCD3Moi4N0I8idLxQKic7f0dExav248YLT6cS0adNgtVpFCYTRaBQBjVgsJnb/Mh8umcEmKkEcGhqC0+kUO32SJGF4eBg6nQ5Wq1WUU3g8HmzZskXsujAQmXWJREJoXQEjbZupE5kLGWqYTCaxSLHZbKK8KJ1Ow+fzwWKxYMKECUK7TKPRjPkJw56STqfh9/tFmwMQZQAU+CZoIkxl54lEQuh/kCkKZfcplUrEYjEEg8GscgumMNSmFJRQKBQoKSkp6hwny7LY6U4mk7DZbDAYDIhGo0JLKxQKYdOmTUILjjVXikMB1dGUiBK0yAF2ZjbRTvzw8LDYibfZbEJvkp/FI2Q6VtIiRaFQQKPRiLEnExpPCrVfJBJBV1eXKK0uLS2FWq3OCnwUe5aMNygrJB6PIxqNijFbluWC7UO/U2trK3p6etDa2gpZlkWgBIAogaZNHyab3MW30+mETqcDMFKKV1VVJTJt7HY7dDod0um0cE4HkPU+WqRTIDCRSIhA1XhcvMfjcXi9XkiSJOYsGo0GCoVCtFd9fb3YlI/H4xgaGkIqlRIGbMPDw4jFYlnt6Pf7s8aV8f78pOxHg8GA8vJylJSUwGazQavVorS0VPx3MpnEtm3bhOayUqmE2+3G0NAQWlpaMDQ0BIfDgXA4nNW+kyZNwre+9S00NjZiypQpY76tM7WTSY7I4XCgsbERDodDzAmp35PUisFgEG0NZJf86/V6OBwOAIDFYslrc1777B0c3NsD/t//+39YvXo1Kisr814Lh8M488wz8eCDDx6CKztyoIF0x44diEQiGB4eFgKcVqsVgUAA7e3t2LBhAweVMvD7/RgaGhLaBTqdDpIkQa/Xw2azCecs+kelUkGj0aC6uloITwMjO6pU+maxWIRDMek2+Xw+eDwedHZ2AoAQieUF3s7dVnKeBEYCHPSQikQiCAQCWcElItNQg8pISS8uGAxmvU4luRUVFZg8efKYnzDsKdFoFBqNRpRuUrkVBezUarWYRNBOI2V7aLVaUQba2NiIiooKJJNJsdFAxgQ2mw0Oh4MDHEWgxTMwog1G2isU4M4k04hGpVIJVz+v1wu/349gMAiTyQSNRgNJkkSgL5VKoauri9u+ADS5psxJghyhaRc8F7VajerqalRWVqKpqQkWiwUejwctLS3weDwii5UDTIWhbCZ6nlJQr1B7k/5nIpGAWq1GU1MTGhoa0NDQIAKqZrM5y8GeAx/5UEkcbcYAEHpOxdqHZAIoCEuBQVo4ZmaYcSA7n0KLbwpoUGCKMrUTiQS0Wq2otonFYrBYLFnvo+y8+vp6UZkwnrPKqBQ8EomIOQxl51mtVhGUo2oCtVot2leSJCQSCXEvy7KM+vp6AEBbW5vQl+Pn58jY0d/fj66uLrS1tcHv94sNmYGBgaJtQ0knmYGrwcFBtLS05LUvAOj1+jF/H2dqJ1ssFmHCZbPZ0NLSAq/XC1mWYbfbRdAv87zq6uosvb3cQL9KpRImU9TmXq93XN+/+8LYvhv3I/39/bjhhhuwdOlSXHjhhXmvz58/HwCwcePGg31pRyxUJipJEqLRKHbs2AGlUomSkhIkEgkuS/kXtItK2UqBQADRaBQKhQLhcFhoaiiVShHooB0/Eq2nEhYK5rW2tgqnXJpEDA0NwWw2C7codsvNJh6PizJomiSo1WpoNBqEQiGxE5hZLpoLTSzIKKClpQXRaBTl5eWoqqpCe3s7tm7dCpPJhJKSEnbLLQBlK0mSJH4DWviVlZXB7XaL34Amx1qtVugGUQCPsvXIUEOj0aC2thaxWIx1P3ZDPB7H4OBglg5TIUc0oLBOiyzLwhQoGo3CbreL/sVmGruGngf034RKpRJli2azGR6Pp+D7u7q6hFNuZnk0aUAFAgEhaD3e9ZoyIZduyrKhgJNarYbdbsfw8LDQ9KRzKPCt1WoRDodF9lghV+Kamhr09/eP+8BHJqRzSNlKtHlmsViyFouZeDweDA4Oiow/WphnOuNSxh6XiBYmV29PkiRR5UH/HY1GhbYbZbQqlUrodDqR/Z47hphMJjaMAeDz+UQ5J7mUU8knAGEkkOn8nEgkhGkDgDyX51wDlPF+T9PYQZm+iURClOlXV1eLoDMAsflCJfuhUEjc23q9HjqdTsxb7Ha7aF8KtI6HKg8yHrHZbHC5XGLDpK+vDz09PWLN6fF4hJQUlTnTb0BQoD/TObuyshKdnZ15JlPj9f7dVw7bFnvppZfwwQcfYO3atdiwYQPi8TgWL16MBQsWFH3PmjVrcP/99+Pjjz9GIpFAc3Mzbr/9dsydO3efr2fhwoW4+eab0dzcvM+fNZ6hjjpx4kSkUil0d3dDoVBAr9djYGAABoMBZWVl0Ov13LH/BQ2q5A4qyzLS6bQQradJFWkBkaYEZSNR0I8mEZkliVTuYjKZhDYZiX2zW242tNtK/00ZHJRlJEmSyKDMJVMHiN4nSRIqKyvR09MjfietVisyNI1Go8ic5CD3CMlkEn19fUKkl8pB7Xa7uP9J4BuAyNajxQeVvnm9XvT29iISiaC0tBRWqxW9vb1oa2uDRqNBPB6Hy+XiAEcR0uk0BgYGhN5eJBJBMpkUAtQUtK6qqhKBVdJXJVMfu90Oi8UCn8+HcDgstFbYTGPXkJkGlckRVB6q0+lgMBhgtVqFqDUZCGSWIFJmEzmG0uexDllhAoEAhoaGxHMUGNmsIXdFp9OJtrY2hMNhADu1QY1GIxwOBzo6OhCNRoUrcSgUEpttJpNJ6LlmBrjHO6RzSJItZrMZfr8f0Wi0oNZkMplET0+PCLBStgnJYFD2MGWKsPFAYXL19srLy8Vzk0ysyH0+Go2KAHcoFBJjSe4Y4vf7haHGeDaMSSaT6OzsFGMBBf5NJhPsdjsAiLVPrvMzmR4VcnlmM41saN1EVUp9fX0i+EnmdtQ2mZsvVErq8/nQ19cHk8kEk8kEq9UKAFntq9frhZneWCfXUZg09aj/k5kGnbt161bodDqhMVvsPqQ5CSU98P27fzhsZ20/+9nP0N7eDpfLhYqKCrS3t+/y/BUrVmDOnDnQ6/W47LLLYLFY8Prrr2PevHno7OzEHXfcsdfX8vTTTyMcDu/TZzAj0AOIzB9CoRDsdjuqqqpEwIN2Trhjj0BZecFgENFoNMt1lZwlM0s7o9EokskkamtrYbFYROkzAOHEN2HCBJElEAwGRYkFlXWlUimxcOEF3gikm0eltBQ0JfHYXbVRpqkAAAwNDcHlcqGkpATBYFD8BmTQQUE+zpzMJh6Pw+fziR1TKtMiUx4AqKyszNIVo7azWCwIh8MIhUJoaGiAVqsVO7uUaaBUKmEwGBAMBqHX6zmDpgiUAanX65FMJoUMgE6nw2uvvYaenh4AI9kZ06dPL7hAISOHVColJBgcDodYsHDbF4dK+qkslAJ1er0ebrcbGo0GXq83K5sbGBmHamtrhb5TV1cX/H4/SkpKEAqFhA7ZaA0jxgvJZBK9vb0iI1iWZZE1TJsygUBAbJCpVCpYLBYolUpIkiRKvGjxGIvF4PF4EIvFoNfrUVNTg3A4zMGPHCh7t7u7G8PDw0JfMhwO45VXXsG3v/1tnHTSSUImgwTzq6qq4PF4kEgkoNFoxEZNZjCV1hVsPJBNIbF7tVqNoaEhABBZlMBIBg5JKgAQWTfkSpr7mZQxP54NY+LxuHjeUUkujbuUfZe59sksRafxJhAIiMAK6RlSRhqbaYyQGeyMRqMi04zm65ltk7v5QtrmKpVKVCc0NzdDrVZnZZvV19dDkqRD/E0PPJkluZnOzZMnT0ZHRwf6+/vFmjGZTO523U7jQaZzdldXF4/J+5HDVnPv2WefRVtbGwYHB3H99dfv8txkMolrr70WSqUSq1atwn/913/h8ccfx5dffolJkybhpz/9aV5w8O677xaucMX+AYDNmzfj5z//OZ5//vmCWlrMnkFlEl6vVwym9FCLRqOIRqPo6OgQOhLcsXe65VKaMg2cCoUC6XRaLNQikYgYYM1mM4466ihMnjwZJSUlsFgsMJlMsFgsUCgU6OjogCRJGBgYQDgcRjgchtvthizLImX61FNPxbe//e1xv8AgSAsPQJa7aiqVQk1NDSZOnCgyIYtBi3DKYopGo6IEkUxlnE4nFAqFyJxk7aWdKJVKeL1e9Pf3i8AFtSmVwNHuN5CfuUeLQ61WK7RDyKUYGNHJKisrg8PhQHV1NY466ii+/wtAWWLAzhIutVqNDRs2oKurS/wmoVAIH374oQjsUakcGTkEAgEx3judTvFabW0tt30RKKgdCAREJjeVRVssFpjNZsRiMfFcACCy9hQKBbq7u9HT04P29nZYrVYYDAb4fD5RWsQ6ZPnQpkJmoJRKb81mM4455hghe0ESGNQ/EomECFaTeQ+VPhfScyJNMm77kbm91+tFPB7PM46JxWJYs2YN/vSnP+GDDz4AsHMTrbu7W+jhlpaWora2NkuoXavVore3l40HClBIb4+yoGiThjSaaZygTXmdTodYLJZnyiNJUl7J6HjV86SkBipVJI1gCtZlBjVyS8xramqydFIzTQfYTCMbyj6lqgDaSFGpVKiqqhJtQ2MMab1ZrVb4fD5Rxj9hwgS43e6sstLxZsKTWZJbW1sr2oSSG5LJpNisKi0thdPpRF1dHWpra9HU1JS3LipUQu73+8VmO9+/+85hGzk555xzRn3ue++9h5aWFixcuBDTpk0Tx202G376059iwYIFeP7553HfffeJ1+64445dlvgSn3zyCQYHB9HU1CSOpVIp3HLLLXj22Wexfv36UV8ns3MgdTqdqKqqwvDwMAKBgCiHO/roo4WbqMlkOtSXe9jgdDpx2mmnYfXq1RgeHoYkSUIzj1wmyTGUDAL0ej2MRiMUCoUoywIgNBBisRh0Oh0sFovQU6mqqhK6ZRzMzkatVqO0tBRbt24Vu6skmN7Z2QmlUgmn01m0LDcz49JisSAQCGD79u1ip5C05EpLSzE4OAgAnDmZA2UnUTCJ2ox2t8k4g8jN3PP5fAiFQmhvbxeBbp1OJ4KETqcTQ0NDomxraGiIM2gKYDQaRWCUJmpUsp4LZUs2NjYKzZvBwUF4PB4hN9DU1CTc1bq7u4UoO7d9NslkElu3bkUikRD6QFSKq9VqUVJSAgBZ+oeZQVXaEMqEstCSySTi8TicTidrNuVAmwrhcBhKpVJk3NCCu6mpCeXl5XjzzTcxNDQkMitJeD2znEmr1UKWZZG5WkzPidseYl6zq8V0NBrFRx99hMmTJ2dt7NB9DyArczIcDmP79u1wOp1CW5I0zsZzWxOFyjupkkCj0YhMG5JQoKw9SZKQTqeFKUTmfUyfyyV3EFqzCoUiK1uash4pqFGoxLyzsxM6nQ5Op1MEA71eL9xud15m5Hi+pykzjOZ0wMh4UF5eDkmS0NHRITYTaf5C7a9Wq+H1ejE4OAi32y3m7UqlMi/brK2tDU6nc8xr7hUqybVarejp6UE0GhXPOY1Gg/LycmzduhWDg4Pi3JKSkqy+njvG+P1+kSHMY/L+YUy02sqVKwEAs2fPznttzpw5AID3338/67jb7Ybb7d7tZ1900UU44YQT8j5zwYIFWLhw4V5e8fiCFg1k6R6Px8XDnyL+NIAODQ2JstyamhqUl5cf6ss/bHA6nWhubsYXX3wh9Alod1qWZWi1WqFXRUEnStenVOlYLCYyY3p7e0UfcLlcGB4eRjqdRmdnJzZu3Air1Qq73c4L7H9BmjGBQADBYFBkL1HpUCAQwNSpUws+jJRKJfx+P8LhMCwWCwYGBqBUKlFXV4eenh4EAgHU1NSgu7sbQ0NDqK2tRU1NjXBIY0YgV77Ozk5EIhGRSWMymaDT6YRJAJGZuef1esXurdFohM/nQyKRQE1NjchykiQJwWAQVqt13JcP7QrKzBgYGBDZq8Wg4FFra6uYTJORg06nQ3t7OzweD6xWq3DptlqtkCSJ2z4HSZLQ398PvV4Pi8UiTB0oW0yv10OpVKKpqQlbt26Fz+dDMpnMyuDLvN+7urqg0WiEhmUgEIBGo2FDjRzi8bgITNPzlbSvKGObgqyU3Q2M3PsdHR1Z5UzJZBKlpaWQJElk8VBWCAc/skmlUvD5fBgeHt7lebFYDG1tbfjWt74ltG3p9yITCAqcAhB9iAOp+RQTu6fNGwrgkSkPGTyQRIzBYMgy4jAYDKKEMbekcby1NwXsSPtarVYjGo2itLQUZWVlkCRJBDUKlZiTVE5JSUmW6UAoFGIzjQwy9fZUKpVo70QiAZPJlOXgTIEm0vylscbn8yEQCKC8vBz19fVIp9N5bTweDDWKleQ2NTWhq6tLZO06nU74/X5s2rRJSFLkljQTuSXkNOdgI7v9x5hotW3btgEAJk6cmPcaCcHSOXuK3W7P2g0ERkqSKioqsrL5qMy0paUFAPDNN9/A5/OhtrYWDocj73Mp64ogDYZAILBX13m44vf7sXnzZvh8PtjtdkycOBHxeFwsIlpbWwGMZNx0dnaKnUFZlrF161YRnMpFlmUEg0EAEGYQ+5MD/fl7it/vx5YtW9DT0wOv1yt2TK1Wa5ZLHzk7kb24z+cTkyufz4dUKoVIJIKvv/5aPOCsVis8Ho9YWJBuH+lUhEIhTJ48WfwOh1vb5HIgri+ZTGLjxo1ob2/PK5klDRoKqFK5XGdnJ4aGhkQpBTn0DQ0NCeezWCwmyo3S6TTcbjcCgYAYMwKBwEF/uB3uvy8w0i4UjFapVNDpdCJTgxYPwEhWBwU49Ho9/H4/lEqlEK6nsnYK2BqNRgQCAVgsFjE+e71eeDwe8ZkMMDAwgG3btol7eVdQv5AkSSxmAKCiogIqlQpOpxPd3d2ifKu6ulp8Jrd9NuQ6TONMLBYTZkkqlQpWqxV+vx99fX2IxWJIpVJZGzsKhQLbtm0TYzwAlJaWChMlyiaJRCKiJG88aArtjkAgIBbRVCpL2TSkgRUIBCBJktCUpOy+4eFhlJSUwGQyCWfR0tJS9Pb2oru7GwaDAVVVVUilUtixY4cQdh/vbZ9MJrF+/XoMDw+Le3VXZDrZ9/b2ik3N0tJSGAwG9Pb2QqfTiQyzwcFBhEIhxGIxkQU13sugib6+PnR0dIjNsYGBAQwODopNeApw9Pf3Q5IkmEwmUf7odrvFeqisrAxVVVWQJAmBQEDon6XTaaFROZ6QJEnM18m4gcyo6B6nZ55WqxX3MlUglJWVQaVSoaurC1arVYzTFHgKBALityB9z/F4T3u9XrS3t4tnZDAYFBqpDocDqVQqq200Gg06OzvF+F1aWory8nIEAgFRGULrpcw2pmoDChqOxftZkiR4PB5oNBo4HA7xDKPXBgcHYbFYxLjq8/lgs9lgtVphNBqRTqfFfIXw+/1Clos2HPv7+9Hf38/3726gdsyUCCnEmAjuUWCsWHYRTXgPJG+++WZWJt/5558PAEUdfn/5y1/igQceyDteU1NzwK6RYRiGYRiG2T9cd911h/oSGAC/+tWvDvUlMAzDMMwBJxgM7rKibkwE9w42bW1teccWLFgwKg0/4p577sHtt98u/j+dTgsturES/adSw87OTmEjzjDM4Q/3XYY58uB+yzBHJtx3GebIg/stczChyqrKyspdnjcmgnsUvSyWnRcIBITQ9OECCSlnklv+O1agWnqGYY4suO8yzJEH91uGOTLhvsswRx7cb5mDxWg08MeEHSZp7RXS1evr60MoFCqox8cwDMMwDMMwDMMwDMMwRzJjIrg3Y8YMAMCyZcvyXnv77bezzmEYhmEYhmEYhmEYhmGYscKYCO6dffbZaGxsxJ///GesX79eHPf7/Xj44Yeh1Wpx1VVXHboLHKfodDrcf//9eeXHDMMc3nDfZZgjD+63DHNkwn2XYY48uN8yhyMKeXd+uoeIZ599Fh9++CEAYMOGDfjiiy9w+umno6mpCQAwffp0XHPNNeL8FStWYM6cOdDr9bjssstgsVjw+uuvo729HY899hjuuOOOQ/I9GIZhGIZhGIZhGIZhGOZAcdgG9xYsWIDnn3++6OtXX301lixZknXss88+w/3334+PP/4YiUQCzc3NuP322zFv3rwDfLUMwzAMwzAMwzAMwzAMc/A5bIN7DMMwDMMwDMMwDMMwDMPsmjGhuccwDMMwDMMwDMMwDMMw4xEO7jEMwzAMwzAMwzAMwzDMEQoH95j9zpo1a3DeeefBbrfDZDLhlFNOwV/+8pdDfVkMM+aor6+HQqEo+M/MmTPzzo/FYnjwwQcxceJE6PV6VFZW4rrrrsPAwEDRv/Hyyy/jpJNOgslkQklJCS644AJ88cUXRc/n/s8wO3nppZfw7//+7zjhhBOg0+mgUCjy9IIzCQQCuP3221FXVwedTof6+nr8+Mc/RigUKnh+Op3GU089hebmZhgMBrjdblx++eVobW0t+jfefvttzJgxAxaLBVarFbNmzcLy5cuLnr9161bMnTsXLpcLBoMBU6dOxTPPPANWdWHGKnvSbxctWlT0OaxQKNDW1lbwfQe6H+7pWMIwRzrd3d148sknMXv2bNTW1kKr1aK8vByXXHIJPv3004Lv4WcuM9ZgzT1mv8KuxQxz8Kivr4fP58Ott95a8LUFCxaI/0+n0zjvvPPw9ttv45RTTsGMGTOwbds2/P3vf0dDQwM++eQTuN3urM946KGH8LOf/Qx1dXW45JJLEAwG8eqrryIej2P58uU4/fTTs87n/s8w2dTX16O9vR0ulwsmkwnt7e1YvHhxVt8kwuEwpk+fjvXr12P27Nk49thjsW7dOixbtgwnnngiVq1aBb1en/Wea6+9Fs8++yymTJmC888/Hz09PfjLX/4Cs9mMTz75BBMnTsw6/6WXXsL8+fPhdruF2dhrr70Gj8eDv/zlL/j+97+fdf4333yD0047DZFIBHPnzkVlZSX+8Y9/4Ouvv8aNN96Ip556av82GMMcBuxJv120aBEeeOABXH311aivr897/dZbb4Xdbs86dqD74d6MJQxzpHP33Xfj17/+NSZMmICZM2fC7XZj27ZteOONNyDLMv785z9nmWzyM5cZk8gMs59IJBLyhAkTZJ1OJ69bt04c9/l88qRJk2StViu3tbUdugtkmDFGXV2dXFdXN6pzn3vuORmAfPnll8vpdFocf+aZZ2QA8nXXXZd1/tatW2W1Wi1PmjRJ9vl84vi6detknU4nT548WU6lUuI493+Gyeedd94R9/0vf/lLGYC8ePHigufed999MgD5rrvuyjp+1113yQDkhx9+OOv4e++9JwOQzzzzTDkWi4njS5culQHIs2fPzjrf6/XKdrtddrlccmdnpzje2dkpu1wu2eVyyYFAIOs9Z555pgxAXrp0qTgWi8XkM844QwYgf/zxx6NvDIY5QtiTfnv//ffLAOQVK1aM6rMPRj/c07GEYcYCr7/+urxy5cq846tWrZI1Go1cUlIiR6NRcZyfucxYhIN7zH7j7bfflgHICxcuzHttyZIlMgD5gQceOARXxjBjkz0J7p166qkygLwAWzqdlhsbG2WTySRLkiSO33PPPTIA+fnnn8/7rAULFsgA5Pfff18c4/7PMLtmV0GCdDotV1ZWymazWQ6FQlmvhUIh2Ww2y42NjVnHL7/88rx+SMycOVMGILe3t4tj//mf/1m0Hy5atCivv2/ZskUGIM+aNSvv/JUrVxbt7wwzltjfwb0D3Q/3ZixhmLHO7NmzZQDymjVrZFnmZy4zdmHNPWa/sXLlSgDA7Nmz816bM2cOAOD9998/mJfEMGOeWCyGJUuW4OGHH8bTTz9dUFckGo3i008/xVFHHYW6urqs1xQKBc4991yEw2F8/vnn4vie9mfu/wyz92zbtg09PT04/fTTYTKZsl4zmUw4/fTT0drais7OTnF85cqV4rVc9kcf3dX506dPh8lk4j7NMP9i1apV+PWvf41HH30Ub7zxRlHNrgPdD/dmLGGYsY5GowEAqNVqAPzMZcYuHNxj9hvbtm0DgDy9AQAoLy+H2WwW5zAMs3/o6+vDwoULce+99+Kmm27CKaecgpNOOgktLS3inJaWFqTT6YJ9E9jZZzP757Zt22A2m1FeXj7q8zNfy4T7P8Psml31n8zjdF44HEZvby8aGhqgUql2e/7u/saenq9SqdDQ0IC2tjYkk8ndfDuGGfvcf//9uPvuu/GTn/wEF198MWpqavDCCy/knXeg++GejiUMM9bp6OjAu+++i4qKCjQ3NwPgZy4zduHgHrPf8Pv9AACbzVbwdavVKs5hGGbfWbhwIZYvX47+/n6Ew2GsW7cO8+fPx5o1a3D22WcjGAwCGF3fzDyP/ntPz9/d3+D+zzCF2dM+urd9uth79rZPp9NpMc4wzHhk6tSpeO6559Da2opIJIIdO3bgqaeegkKhwIIFC/Dmm29mnX+g++HejA0MM1ZJJBKYP38+YrEYfv3rX4vAHD9zmbGK+lBfAMMwDLN33H///Vn/P23aNJEp8OKLL+KPf/wjbr/99kNxaQzDMAwz5rn44ouz/r++vh433ngjJk+ejHPPPRc/+9nP8J3vfOcQXR3DjF/S6TQWLFiAVatW4dprr8X8+fMP9SUxzAGHM/eY/QbtNBTbDQwEAkV3IxiG2X/8+7//OwDgo48+AjC6vpl5Hv33np6/u7/B/Z9hCrOnfXRv+3Sx9+xtn1YoFLBYLAVfZ5jxzNlnn40JEyZgw4YNon8BB74f7s3YwDBjjXQ6jR/+8If485//jCuvvBJ/+MMfsl7nZy4zVuHgHrPf2JWOR19fH0KhUFFtA4Zh9h8ulwvAiEYIADQ2NkKpVBbV2Cmk8zFx4kSEQiH09fWN+vzM1zLh/s8wu2Z3Oli5fc5kMqGiogI7duxAKpXa7fm7+xt7en4qlcKOHTvQ0NAgBMoZhsmGnsWSJIljB7of7ulYwjBjjXQ6jYULF+L555/H5ZdfjiVLlkCpzA558DOXGatwcI/Zb8yYMQMAsGzZsrzX3n777axzGIY5cJBjbn19PQDAYDDgpJNOwpYtW9De3p51rizLeOedd2AymXDCCSeI43van7n/M8zeM3HiRFRWVuKjjz4SQXkiHA7jo48+QkNDA2pqasTxGTNmiNdyoT535plnZp0P7J8+/eGHHyIcDnOfZpgihMNhfP311zCZTCLIBxz4frg3YwnDjBUosPfCCy9g3rx5ePHFF4saYPAzlxmTyAyzn0gkEnJjY6Os0+nkdevWieM+n0+eNGmSrNVq5R07dhyy62OYscSmTZvkcDhc8Hh5ebkMQH7//ffF8eeee04GIF9++eVyOp0Wx5955hkZgHzddddlfc6WLVtktVotT5o0Sfb5fOL4unXrZJ1OJ0+ePFlOpVLiOPd/htk1v/zlL2UA8uLFiwu+ft9998kA5Lvuuivr+F133SUDkB9++OGs4++9954MQD7zzDPlWCwmji9dulQGIM+ePTvrfK/XK9tsNtnlcsmdnZ3ieGdnp+xyuWSXyyUHAoGs95x55pkyAHnp0qXiWCwWk8844wwZgPzRRx/tURswzJHGrvptIBCQt2zZkndckiT58ssvlwHICxcuzHrtYPTDPR1LGGYskEql5KuvvloGIF966aVyIpHY5fn8zGXGIgpZluWDGEtkxjgrVqzAnDlzoNfrcdlll8FiseD1119He3s7HnvsMdxxxx2H+hIZZkywaNEiPPHEEzjzzDNRV1cHk8mErVu3YunSpUgkErjnnnvw8MMPi/PT6TTOO+88vP322zjllFMwY8YMbN++HX/7299QX1+PTz/9FG63O+tvPPTQQ/jZz36Guro6XHLJJQgGg3j11VcRj8exfPlynH766Vnnc/9nmGyeffZZfPjhhwCADRs24IsvvsDpp5+OpqYmAMD06dNxzTXXABjJFjj99NPx5ZdfYvbs2TjuuOPwxRdfYNmyZTjxxBPx/vvvw2AwZH3+tddei2effRZTpkzB+eefj97eXrz22mswm81YvXo1Jk2alHX+Sy+9hPnz58PtdmPevHkAgNdeew0ejwevvfYaLr300qzzv/76a5x++umIRCKYN28eKioq8I9//ANff/01brzxRjz11FMHpN0Y5lAy2n7b1taGxsZGnHjiiZg8eTLKy8vR39+Pd999F11dXWhubsaKFSvgdDqzPv9A98O9GUsY5khn0aJFeOCBB2A2m3HLLbcULF+96KKLMG3aNAD8zGXGKIc6usiMPT799FP53/7t32Sr1SobDAb5pJNOkl999dVDfVkMM6ZYuXKlPHfuXHnixImy1WqV1Wq1XF5eLn/3u9+V33777YLviUaj8qJFi+QJEybIWq1WLi8vl6+55hq5r6+v6N956aWX5BNOOEE2GAyyzWaTzzvvPHnt2rVFz+f+zzA7oSyCYv9cffXVWef7fD751ltvlWtqamSNRiPX1tbKd9xxR97uPpFKpeTf/va38pQpU2SdTic7nU553rx58vbt24te0z//+U/5jDPOkE0mk2w2m+UZM2bI77zzTtHzN2/eLH//+9+XHQ6HrNPp5ObmZvk//uM/sjKAGWYsMdp+6/f75R/96EfyiSeeKLvdblmtVssWi0U+6aST5EceeUSWJKno3zjQ/XBPxxKGOdLZXb9FgQxcfuYyYw3O3GMYhmEYhmEYhmEYhmGYIxQ21GAYhmEYhmEYhmEYhmGYIxQO7jEMwzAMwzAMwzAMwzDMEQoH9xiGYRiGYRiGYRiGYRjmCIWDewzDMAzDMAzDMAzDMAxzhMLBPYZhGIZhGIZhGIZhGIY5QuHgHsMwDMMwDMMwDMMwDMMcoXBwj2EYhmEYhmEYhmEYhmGOUDi4xzAMwzAMwzAMwzAMwzBHKBzcYxiGYRiGYRiGYRiGYZgjFA7uMQzDMAzDMIcFixYtgkKhwMqVKw/1pRTljTfegEKhwMcff3yoL2W/4vV6YbPZ8JOf/ORQXwrDMAzDMHsIB/cYhmEYhmEYZhQkEgn85Cc/wZw5c3Daaacd6svZJZIk4fHHH8cVV1yBo48+GkqlEgqFAm1tbQXPdzgcuPnmm/G73/0O7e3tB/diGYZhGIbZJzi4xzAMwzAMwzCj4MUXX8S2bduOiOy2gYEB3HnnnXjllVcQjUZRUlKy2/fceuutSKfT+MUvfnEQrpBhGIZhmP0FB/cYhmEYhmEYZhQ888wzqKmpwaxZsw71pewWl8uFZcuWYWhoCG1tbTjxxBN3+x6n04n/9b/+F1555RUEAoGDcJUMwzAMw+wPOLjHMAzDMAzzL4aHh6FSqXDBBRdkHV+/fj0UCgUUCgW2b9+e9drMmTNhMBgQi8UAAPF4HE899RTmzJmDmpoa6HQ6lJaW4nvf+x7WrVuX9d4XX3wRCoUCDz74YMHr+eKLL6BQKPCDH/wg6/jAwABuu+02NDU1QafTweVy4ZJLLsHGjRvzPqO+vh719fUIhUK45ZZbUFlZCZ1Oh29/+9v461//mnf+zJkzoVAoCl7PggUL8ko7lyxZAoVCgSVLluB//ud/cPLJJ8NoNKKqqgr/5//8H6TTaQDA888/j6lTp8JgMKC2thaPPvpowb9B/OlPf0JzczP0ej2qqqpw2223IRgMFjz3q6++wmWXXYaKigpotVrU1dXhpptuwtDQUNZ5bW1tUCgUWLBgATZt2oSLL74YTqdzl+WqxMaNG/H555/jkksuKdo+q1atwkUXXYSysjLodDrU1NTge9/7Hj788ENxTqau4OLFi9Hc3AyDwYCGhgb87ne/AwDIsozHH38cRx11FPR6PSZOnIgXXnhhl9eXi9lsxrnnnguHw7FH75s7dy7C4TD++7//e4/exzAMwzDMoYODewzDMAzDMP+ipKQEU6dOxQcffIBUKiWOr1ixouB/R6NRfPLJJzj11FOh0+kAjBgT3HrrrYjFYjjvvPNw2223YebMmVi6dClOO+00rFmzRrz/e9/7HkwmE15++eWC1/Piiy8CAObPny+OtbS04Pjjj8eTTz6JCRMm4KabbsJ5552Ht956C6eccgo+/fTTvM9JJBKYPXs2li1bhksuuQRXXnklWlpaMHfuXCxbtmwvWyubv//975g7dy4aGxtx/fXXw2w24xe/+AXuu+8+PProo7j55psxdepUXHfddUin0/jJT35SNGD1xBNP4Oabb8aJJ56IW2+9FRUVFXjyyScxe/ZsJBKJrHPffPNNnHTSSXjzzTcxc+ZM3HrrrWhubsbTTz+NU089FcPDw3mfv337dpxyyikYHBzEggULcPXVV0Or1e7y+y1fvhwAcMoppxR8/be//S1mzpyJd955B+eeey7uuOMOnHXWWfjyyy8LBlGffPJJ3HbbbTjuuONw3XXXIZFI4JZbbsGzzz6LG2+8EY8++ijOOOMM/PCHP8TQ0BCuvvpqrFq1apfXuD849dRTAez8vgzDMAzDHAHIDMMwDMMwjOD222+XAciffvqpOHbhhRfKkyZNkmtqauTLL79cHF++fLkMQH7wwQfFsWg0Knd1deV97saNG2Wz2Syfc845WcevvPLKvL8ny7KcTCblsrIyuby8XE4mk+L4aaedJqtUKvmtt97KOn/Lli2yxWKRm5ubs47X1dXJAOTvfve7ciwWE8ffffddGYA8Z86crPNnzJghF5siXn311TIAeceOHeLY4sWLZQCyRqORP/vsM3E8EAjIpaWlstFolMvLy+WWlhbxWkdHh6zVavOu9f7775cByFqtVv7yyy/F8XQ6LV9xxRUyAPmxxx4Txz0ej2y1WuWqqiq5ra0t67NeeeUVGYB84403imM7duyQAcgA5Pvuu6/gdyzGpZdeKgOQt23blvfa+vXrZaVSKVdWVma1DV17d3d33nd0OBwF28Rms8mTJk2SBwYGxGuffPKJDEC+8MIL9+iaM5kzZ07eb1eMkpISuba2dq//FsMwDMMwBxfO3GMYhmEYhsmA9NTee+89AEAqlcKqVaswa9YszJo1q2AW38yZM8UxnU6HqqqqvM+dMmUKZs2ahVWrVmVln1FW3ksvvZR1/rJly9Df34/LLrsMKpUKALBu3Tp8/PHHuPrqqzFnzpys8ydNmoRrr70WGzZsKFie+5vf/CYrO+3ss89GXV1dVibhvnDllVdm6bpZLBZccMEFkCQJN9xwAxobG8VrNTU1mD59Or755hskk8m8z7rqqqvw7W9/W/y/QqHAww8/DJVKhSVLlojjL7zwAgKBAH75y1+irq4u6zMuu+wyHHfccXj11VfzPr+8vBz33nvvHn2/rq4uAEBZWVnea//5n/8pjCjq6+uzXlMoFKisrMx7zy233FKwTfx+P+6991643W7x2sknn4zGxkZ8+eWXe3TNe0tZWRl6enogy/JB+XsMwzAMw+wb6kN9AQzDMAzDMIcTZ555JlQqFVasWIG7774b69atg9/vx1lnnQVJkvDCCy9g06ZNmDx5MlasWAGDwYCTTz456zPWr1+PRx55BB9++CH6+vrySkk9Hg8qKioAjATZKioq8Oqrr+KJJ56AWj0yPaNgX2ZJ7ieffAIA6O/vx6JFi/KuffPmzeLfxxxzjDhut9vR0NCQd351dTVWr169p01UkGnTpuUdo+9Y7LVUKoX+/v68YOgZZ5yRd35dXR1qamrw9ddfIx6PQ6vVivb49NNP0dLSkveeaDQKj8cDj8cDl8sljk+dOnW3Zbi5DA0NQaVSwWKx5L322WefAQBmz5496s/bm/bKLLlev3493njjjaxz6uvrsWDBglFfQzEcDgeSySR8Pt+oXHYZhmEYhjm0cHCPYRiGYRgmA6vViuOOOw4fffQREokEVqxYAYVCgVmzZkGSJAAjGXt1dXX47LPPMGPGjKxA0ccff4yzzjoLwEiwZ+LEiTCbzVAoFHjjjTfw5ZdfCvMNAFCpVLjiiivw+OOP4+2338b555+PUCiEN954A9/61rdw3HHHiXO9Xi8A4B//+Af+8Y9/FP0O4XA46/9tNlvB89RqtTC82FesVmvBz9/da7mBT6Bwdhwdb2trQzAYhNPpFO3xH//xH7u8tnA4nBXcK/b5u8JgMCCVSiGRSECj0WS95vf7oVAoRHBuNOxNe2VmOa5fvx4PPPBA1jkzZszYL8G9SCQCADAajfv8WQzDMAzDHHg4uMcwDMMwDJPDrFmzsGbNGnz22WdYuXIlpkyZIsokGxoasGLFCkycOBGJREKU8RIPPfQQYrEYPvjgA0yfPj3rtU8++aRgaeX8+fPx+OOP46WXXsL555+P119/HZIkZWXtATuDPk899RRuvPHG/fmVBUrliGpLMpkUwSbC7/cfkL+ZS39/f9HjCoVCZM9Re2zYsCErU3F3FHO73RX0+3u93rzgoN1uhyzL6O3tLViSfSBYsGDBfgnkFcLr9cJisQiTGIZhGIZhDm9Yc49hGIZhGCYHCtgtW7YMH3zwgcjEA4CzzjoLK1euFJp8mXp7wIibrcPhyAvsSZKEL774ouDfmzp1Kpqbm/F//+//RTAYxEsvvQSFQoEf/OAHWedR+e/+KqUtBJVhdnd3Zx1Pp9MHTfPtgw8+yDvW3t6Ozs5OTJkyRWRKHoz2IJqbmwEAW7ZsyXvtpJNOAoD95jx8KAmHw+jq6hLfl2EYhmGYwx8O7jEMwzAMw+Qwffp0qNVqPPPMMwgGg1nBvVmzZsHj8eBPf/oTTCZTlokEMKINNzw8jK+//locS6VSuPPOOzE4OFj0b86fPx+RSAS/+93v8N5772HGjBmoqanJOuekk07CySefjFdeeQWvvfZa3mek02m8//77e/u1AUB8n0zjCgB44oknsGPHjn367NHywgsv4KuvvhL/L8syfvrTnyKVSmVlqy1cuBAWiwX33ntvVnsTkiQJXb59ZcaMGQCQpXtHXH/99VCpVPjZz36G9vb2rNdkWUZPT89+uYaDwdq1a5FKpcT3ZRiGYRjm8IfLchmGYRiGYXIwm8048cQTsXr1aiiVyqxAB2X1DQ4OYs6cOXn6azfddBOWLVuG6dOnY+7cudDr9Vi5ciW6u7sxc+ZMrFy5suDfvOKKK3D33XfjgQceQDqdzivJJV555RXMmjULl112GZ588kkcd9xxMBgM6OjowOrVqzE4OIhoNLrX333hwoV45JFHsGjRIqxfvx4TJkzA559/jo0bN2LGjBn7HDwcDXPmzMGpp56Kyy67DG63G8uXL8fnn3+OU045BTfddJM4z+1245VXXsGll16KqVOn4t/+7d9w9NFHIxaLoa2tDe+//z5OO+00vPXWW/t8TWeffTYsFgveeecd/PjHP856rbm5GU8++SRuvvlmTJkyBRdddBHq6urQ19eHVatW4fzzz8eTTz65z9ewp9x5553weDwARkqX6ZjZbAYAXHPNNXkZpu+88w4A4KKLLjp4F8owDMMwzD7BwT2GYRiGYZgCzJo1C6tXr8axxx4Lu90ujldWVmLSpEnYunVrXkkuAFxwwQX461//iocffhgvvfQSjEYjzjrrLPz973/Hgw8+WPTvVVVV4ayzzsK7774LvV6P73//+wXPa2howLp16/DEE0/gjTfewOLFi6FSqVBRUYEzzzyz6PtGS1lZGVasWIE77rgDy5Ytg1qtxqxZs/DJJ5/gF7/4xUEJ7t1+++34zne+gyeffBLbt2+Hw+HALbfcgp///Od5Lrfnn38+1q1bh0cffRTvvvsu3nnnHZhMJlRXV2PhwoW48sor98s1mc1mXHnllfiv//ov9Pb25pln3HjjjTjmmGPw+OOP45///CdCoRBKS0tx8sknY+7cufvlGvaUv/71r3mZhK+//rr475kzZ+YF915++WVMmzZNlBozDMMwDHP4o5BlWT7UF8EwDMMwDMMwhztbtmzBMcccg0WLFuHee+891Jez33n33Xdx7rnn4vnnn8dVV111qC+HYRiGYZhRwsE9hmEYhmEYhhklN9xwA/77v/8bO3bsEK69Y4UzzjgDoVAIa9euFa7JDMMwDMMc/nBZLsMwDMMwDMOMkgceeABlZWVoa2sbU46yXq8XZ599Ni688EIO7DEMwzDMEQZn7jEMwzAMwzAMwzAMwzDMEQpvyzEMwzAMwzAMwzAMwzDMEQoH9xiGYRiGYRiGYRiGYRjmCIWDewzDMAzDMAzDMAzDMAxzhMLBPYZhGIZhGIZhGIZhGIY5QuHgHsMwDMMwDMMwDMMwDMMcoXBwj2EYhmEYhmEYhmEYhmGOUDi4xzAMwzAMwzAMwzAMwzBHKBzcYxiGYRiGYRiGYRiGYZgjFA7uMQzDMAzDMAzDMAzDMMwRyv8H1DXrGY159r4AAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -177,26 +195,29 @@ "for i, udv in enumerate(np.unique(dv.values)):\n", " mask = dv == udv\n", " mdf = mdb.df[mask]\n", - " ax.plot(mdf[\"nu_lines\"].values,\n", - " mdf[\"Sij0\"].values,\n", - " \".\",\n", - " alpha=0.3,\n", - " #alpha=0.01 + 0.005 * i,\n", - " color=\"gray\")\n", + " ax.plot(\n", + " mdf[\"nu_lines\"].values,\n", + " mdf[\"Sij0\"].values,\n", + " \".\",\n", + " alpha=0.3,\n", + " color=\"gray\",\n", + " )\n", " ax.text(\n", - " np.sum(mdf[\"nu_lines\"].values * mdf[\"Sij0\"].values) /\n", - " np.sum(mdf[\"Sij0\"].values), 1.e2*np.max(mdf[\"Sij0\"].values),\"$\\\\Delta \\\\nu=$\"+str(udv))\n", + " np.sum(mdf[\"nu_lines\"].values * mdf[\"Sij0\"].values)\n", + " / np.sum(mdf[\"Sij0\"].values),\n", + " 1.0e2 * np.max(mdf[\"Sij0\"].values),\n", + " \"$\\\\Delta \\\\nu=$\" + str(udv),\n", + " )\n", "\n", - "for mic in [0.5,1,2,3,4,5,10,20]:\n", - " x = 1.e4/mic\n", - " plt.axvline(x,alpha=0.2,color=\"gray\")\n", - " #plt.text(x,1.e-210,str(mic)+\" $\\\\mu$m\",rotation=\"90\")\n", - " plt.text(x,1.e-39,str(mic)+\" $\\\\mu$m\",rotation=\"90\")\n", + "for mic in [0.5, 1, 2, 3, 4, 5, 10, 20]:\n", + " x = 1.0e4 / mic\n", + " plt.axvline(x, alpha=0.2, color=\"gray\")\n", + " plt.text(x, 1.0e-39, str(mic) + \" $\\\\mu$m\", rotation=\"vertical\")\n", "plt.yscale(\"log\")\n", - "plt.ylim(1.e-41,1.e-13)\n", + "plt.ylim(1.0e-41, 1.0e-13)\n", "plt.tick_params(labelsize=14)\n", - "plt.xlabel(\"wavenumber (cm-1)\",fontsize=14)\n", - "plt.ylabel(\"line strength\",fontsize=14)\n", + "plt.xlabel(\"wavenumber (cm-1)\", fontsize=14)\n", + "plt.ylabel(\"line strength\", fontsize=14)\n", "plt.savefig(\"co_dnu.png\", bbox_inches=\"tight\", pad_inches=0.1)\n", "plt.show()" ] @@ -210,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:03:28.460492Z", @@ -222,14 +243,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -243,32 +262,25 @@ " vdf = mdb.df[mask]\n", " plt.plot(vdf[\"nu_lines\"].values, vdf[\"Sij0\"].values, \".\", color=\"black\")\n", " if i < 10:\n", - " plt.text(np.nanmean(vdf[\"nu_lines\"].values),\n", - " 8 * np.nanmax(vdf[\"Sij0\"].values),\n", - " \"$\\\\nu_{lower}=$\" + str(vl),\n", - " fontsize=12)\n", + " plt.text(\n", + " np.nanmean(vdf[\"nu_lines\"].values),\n", + " 8 * np.nanmax(vdf[\"Sij0\"].values),\n", + " \"$\\\\nu_{lower}=$\" + str(vl),\n", + " fontsize=12,\n", + " )\n", " mask = (dv == 2) * (dJ == -1) * (mdb.df[\"v_l\"] == vl)\n", " vdf = mdb.df[mask]\n", " plt.plot(vdf[\"nu_lines\"].values, vdf[\"Sij0\"].values, \".\", color=\"gray\")\n", "\n", "for mic in [2.3, 2.5, 2.7]:\n", - " x = 1.e4 / mic\n", + " x = 1.0e4 / mic\n", " plt.axvline(x, alpha=0.2, color=\"gray\")\n", - " #plt.text(x,1.e-210,str(mic)+\" $\\\\mu$m\",rotation=\"90\")\n", - " plt.text(x, 1.e-60, str(mic) + \" $\\\\mu$m\", rotation=\"90\")\n", + " plt.text(x, 1.0e-60, str(mic) + \" $\\\\mu$m\", rotation=\"vertical\")\n", "\n", - "plt.text(3800.0,\n", - " 1.e-25,\n", - " \"$\\\\Delta J$ = -1, P-branch\",\n", - " color=\"gray\",\n", - " fontsize=14)\n", - "plt.text(4380.0,\n", - " 1.e-25,\n", - " \"$\\\\Delta J$ = 1, R-branch\",\n", - " color=\"black\",\n", - " fontsize=14)\n", + "plt.text(3800.0, 1.0e-25, \"$\\\\Delta J$ = -1, P-branch\", color=\"gray\", fontsize=14)\n", + "plt.text(4380.0, 1.0e-25, \"$\\\\Delta J$ = 1, R-branch\", color=\"black\", fontsize=14)\n", "plt.yscale(\"log\")\n", - "plt.ylim(1.e-61, 1.e-13)\n", + "plt.ylim(1.0e-61, 1.0e-13)\n", "plt.xlim(3500, 4620)\n", "plt.tick_params(labelsize=14)\n", "plt.xlabel(\"wavenumber (cm-1)\", fontsize=14)\n", @@ -286,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:03:30.383358Z", @@ -316,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-03-14T12:03:30.392304Z", @@ -328,14 +340,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -377,7 +387,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.9" }, "vscode": { "interpreter": { diff --git a/documents/tutorials/Fortrat.rst b/documents/tutorials/Fortrat.rst index 7814250ea..d0e0fdf4f 100644 --- a/documents/tutorials/Fortrat.rst +++ b/documents/tutorials/Fortrat.rst @@ -15,17 +15,37 @@ optional_quantum_states=True option in api.MdbExomol. .. parsed-literal:: - /home/kawahara/exojax/src/exojax/utils/molname.py:133: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname. + /home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname. warnings.warn( - /home/kawahara/exojax/src/exojax/spec/api.py:153: UserWarning: nurange=None. Nonactive mode. + /home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname. + warnings.warn( + /home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname. + warnings.warn( + /home/kawahara/exojax/src/exojax/spec/api.py:233: UserWarning: nurange=None. Nonactive mode. warnings.warn("nurange=None. Nonactive mode.", UserWarning) .. parsed-literal:: HITRAN exact name= (12C)(16O) + radis engine = vaex + => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.def + => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.pf + => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.states.bz2 + => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__H2.broad + => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__He.broad + => Downloading from http://www.exomol.com/db/CO/12C-16O/12C-16O__air.broad + Note: Caching states data to the vaex format. After the second time, it will become much faster. + Molecule: CO + Isotopologue: 12C-16O Background atmosphere: H2 - Reading CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2 + ExoMol database: None + Local folder: CO/12C-16O/Li2015 + Transition files: + => File 12C-16O__Li2015.trans + => Downloading from http://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2 + => Caching the *.trans.bz2 file to the vaex (*.h5) format. After the second time, it will become much faster. + => You can deleted the 'trans.bz2' file by hand. DataFrame (self.df) available. @@ -83,26 +103,29 @@ So, we have 12 different :math:`\Delta \nu`. Let’s plot them. for i, udv in enumerate(np.unique(dv.values)): mask = dv == udv mdf = mdb.df[mask] - ax.plot(mdf["nu_lines"].values, - mdf["Sij0"].values, - ".", - alpha=0.3, - #alpha=0.01 + 0.005 * i, - color="gray") + ax.plot( + mdf["nu_lines"].values, + mdf["Sij0"].values, + ".", + alpha=0.3, + color="gray", + ) ax.text( - np.sum(mdf["nu_lines"].values * mdf["Sij0"].values) / - np.sum(mdf["Sij0"].values), 1.e2*np.max(mdf["Sij0"].values),"$\\Delta \\nu=$"+str(udv)) + np.sum(mdf["nu_lines"].values * mdf["Sij0"].values) + / np.sum(mdf["Sij0"].values), + 1.0e2 * np.max(mdf["Sij0"].values), + "$\\Delta \\nu=$" + str(udv), + ) - for mic in [0.5,1,2,3,4,5,10,20]: - x = 1.e4/mic - plt.axvline(x,alpha=0.2,color="gray") - #plt.text(x,1.e-210,str(mic)+" $\\mu$m",rotation="90") - plt.text(x,1.e-39,str(mic)+" $\\mu$m",rotation="90") + for mic in [0.5, 1, 2, 3, 4, 5, 10, 20]: + x = 1.0e4 / mic + plt.axvline(x, alpha=0.2, color="gray") + plt.text(x, 1.0e-39, str(mic) + " $\\mu$m", rotation="vertical") plt.yscale("log") - plt.ylim(1.e-41,1.e-13) + plt.ylim(1.0e-41, 1.0e-13) plt.tick_params(labelsize=14) - plt.xlabel("wavenumber (cm-1)",fontsize=14) - plt.ylabel("line strength",fontsize=14) + plt.xlabel("wavenumber (cm-1)", fontsize=14) + plt.ylabel("line strength", fontsize=14) plt.savefig("co_dnu.png", bbox_inches="tight", pad_inches=0.1) plt.show() @@ -124,32 +147,25 @@ Let’s go deeper! Expand this for :math:`\Delta \nu=2` (K-band feature). vdf = mdb.df[mask] plt.plot(vdf["nu_lines"].values, vdf["Sij0"].values, ".", color="black") if i < 10: - plt.text(np.nanmean(vdf["nu_lines"].values), - 8 * np.nanmax(vdf["Sij0"].values), - "$\\nu_{lower}=$" + str(vl), - fontsize=12) + plt.text( + np.nanmean(vdf["nu_lines"].values), + 8 * np.nanmax(vdf["Sij0"].values), + "$\\nu_{lower}=$" + str(vl), + fontsize=12, + ) mask = (dv == 2) * (dJ == -1) * (mdb.df["v_l"] == vl) vdf = mdb.df[mask] plt.plot(vdf["nu_lines"].values, vdf["Sij0"].values, ".", color="gray") for mic in [2.3, 2.5, 2.7]: - x = 1.e4 / mic + x = 1.0e4 / mic plt.axvline(x, alpha=0.2, color="gray") - #plt.text(x,1.e-210,str(mic)+" $\\mu$m",rotation="90") - plt.text(x, 1.e-60, str(mic) + " $\\mu$m", rotation="90") + plt.text(x, 1.0e-60, str(mic) + " $\\mu$m", rotation="vertical") - plt.text(3800.0, - 1.e-25, - "$\\Delta J$ = -1, P-branch", - color="gray", - fontsize=14) - plt.text(4380.0, - 1.e-25, - "$\\Delta J$ = 1, R-branch", - color="black", - fontsize=14) + plt.text(3800.0, 1.0e-25, "$\\Delta J$ = -1, P-branch", color="gray", fontsize=14) + plt.text(4380.0, 1.0e-25, "$\\Delta J$ = 1, R-branch", color="black", fontsize=14) plt.yscale("log") - plt.ylim(1.e-61, 1.e-13) + plt.ylim(1.0e-61, 1.0e-13) plt.xlim(3500, 4620) plt.tick_params(labelsize=14) plt.xlabel("wavenumber (cm-1)", fontsize=14) diff --git a/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.ipynb b/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.ipynb index 25a7bce51..921325789 100644 --- a/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.ipynb +++ b/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.ipynb @@ -13,11 +13,20 @@ "id": "919b8f40", "metadata": {}, "source": [ - "Tako Ishikawa \n", - "last update: 2022/04/25 \n", + "Tako Ishikawa, Hajime Kawahara \n", + "last update: 2024/07/14 \n", "created: : 2022/04/22 " ] }, + { + "cell_type": "markdown", + "id": "79db9dfd", + "metadata": {}, + "source": [ + "This notebook demonstrates how to use Kurucz database, not using `opa`.\n", + "Currently, `opa` cannot be used for metal lines yet." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -30,102 +39,87 @@ "shell.execute_reply": "2023-03-14T12:03:47.159139Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/dtau_mmwl.py:14: FutureWarning: dtau_mmwl might be removed in future.\n", + " warnings.warn(\"dtau_mmwl might be removed in future.\", FutureWarning)\n" + ] + } + ], "source": [ "from exojax.utils.grids import wavenumber_grid\n", - "from exojax.spec.rtransfer import pressure_layer\n", - "from exojax.spec import moldb, molinfo, contdb\n", "from exojax.spec import atomll\n", - "from exojax.spec.exomol import gamma_exomol\n", - "from exojax.spec import SijT, doppler_sigma\n", - "from exojax.spec import planck\n", - "import matplotlib.pyplot as plt\n", - "import jax.numpy as jnp\n", - "from jax import vmap, jit\n", - "import numpy as np\n", + "from exojax.spec.hitran import doppler_sigma, line_strength \n", "from exojax.spec.initspec import init_lpf\n", - "from exojax.spec.rtransfer import dtauCIA, dtauHminus\n", "from exojax.spec.lpf import xsmatrix\n", - "from exojax.spec.rtransfer import dtauM" + "from exojax.spec.layeropacity import layer_optical_depth, layer_optical_depth_Hminus, layer_optical_depth_CIA\n", + "import matplotlib.pyplot as plt\n", + "from jax import vmap, jit\n", + "import numpy as np" ] }, { "cell_type": "markdown", - "id": "4f426536", + "id": "a8b3c068", "metadata": {}, "source": [ - "T-P profile " + "Sets a wavenumber grid" ] }, { "cell_type": "code", "execution_count": 2, - "id": "1e0809e3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:03:47.173672Z", - "iopub.status.busy": "2023-03-14T12:03:47.173232Z", - "iopub.status.idle": "2023-03-14T12:03:47.842601Z", - "shell.execute_reply": "2023-03-14T12:03:47.842309Z" - } - }, + "id": "70a32d3d", + "metadata": {}, "outputs": [ { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "xsmode = lpf\n", + "xsmode assumes ESLOG in wavenumber space: xsmode=lpf\n", + "======================================================================\n", + "The wavenumber grid should be in ascending order.\n", + "The users can specify the order of the wavelength grid by themselves.\n", + "Your wavelength grid is in *** ascending *** order\n", + "======================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/unitconvert.py:63: UserWarning: Both input wavelength and output wavenumber are in ascending order.\n", + " warnings.warn(\n" + ] } ], "source": [ - "#Assume ATMOSPHERE\n", - "NP=100\n", - "T0=3000. #10000. #3000. #1295.0 #K\n", - "Parr, dParr, k=pressure_layer(NP=NP)\n", - "H_He_HH_VMR = [0.0, 0.16, 0.84] #typical quasi-\"solar-fraction\"\n", - "Tarr = T0*(Parr)**0.1\n", - "\n", - "mmw=2.33 #mean molecular weight\n", - "\n", - "PH = Parr* H_He_HH_VMR[0]\n", - "PHe = Parr* H_He_HH_VMR[1]\n", - "PHH = Parr* H_He_HH_VMR[2]\n", - "\n", - "fig=plt.figure(figsize=(6,4))\n", - "plt.plot(Tarr,Parr)\n", - "plt.plot(Tarr, PH, '--'); plt.plot(Tarr, PHH, '--'); plt.plot(Tarr, PHe, '--')\n", - "plt.plot(Tarr[80],Parr[80], marker='*', markersize=15)\n", - "plt.yscale(\"log\")\n", - "plt.xlabel(\"temperature (K)\")\n", - "plt.ylabel(\"pressure (bar)\")\n", - "plt.gca().invert_yaxis()\n", - "plt.show()\n" + "#We set a wavenumber grid using wavenumber_grid.\n", + "nu_grid, wav, reso = wavenumber_grid(10380, 10430, 4500, xsmode=\"lpf\", unit=\"AA\", wavelength_order=\"ascending\") " ] }, { "cell_type": "markdown", - "id": "aea1c5cb", + "id": "4f426536", "metadata": {}, "source": [ - "Wavenumber" + "Sets a T-P profile and partial pressures" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "ebeb0daa", + "execution_count": 4, + "id": "1e0809e3", "metadata": { "execution": { - "iopub.execute_input": "2023-03-14T12:03:47.845203Z", - "iopub.status.busy": "2023-03-14T12:03:47.844915Z", - "iopub.status.idle": "2023-03-14T12:03:47.847074Z", - "shell.execute_reply": "2023-03-14T12:03:47.846831Z" + "iopub.execute_input": "2023-03-14T12:03:47.173672Z", + "iopub.status.busy": "2023-03-14T12:03:47.173232Z", + "iopub.status.idle": "2023-03-14T12:03:47.842601Z", + "shell.execute_reply": "2023-03-14T12:03:47.842309Z" } }, "outputs": [ @@ -133,15 +127,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "xsmode = lpf\n", - "xsmode assumes ESLOG in wavenumber space: mode=lpf\n" + "rtsolver: ibased\n", + "Intensity-based n-stream solver, isothermal layer (e.g. NEMESIS, pRT like)\n" ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "wls,wll = 10350, 10450\n", - "nugrid_res = 0.01\n", - "nus, wav, reso = wavenumber_grid(wls, wll, int((wll-wls)/nugrid_res), unit=\"AA\", xsmode=\"lpf\")" + "from exojax.spec.atmrt import ArtEmisPure\n", + "nlayer = 100\n", + "T0 = 3000.0 # 10000. #3000. #1295.0 #K\n", + "alpha = 0.1\n", + "Tlow = 500.0\n", + "Thigh = 4500.0\n", + "art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=nlayer)\n", + "art.change_temperature_range(Tlow, Thigh)\n", + "Tarr = art.powerlaw_temperature(T0, alpha)\n", + "Parr = art.pressure\n", + "dParr = art.dParr\n", + "\n", + "H_He_HH_VMR = [0.0, 0.16, 0.84] # typical quasi-\"solar-fraction\"\n", + "PH = Parr * H_He_HH_VMR[0]\n", + "PHe = Parr * H_He_HH_VMR[1]\n", + "PHH = Parr * H_He_HH_VMR[2]\n", + "\n", + "fig = plt.figure(figsize=(6, 4))\n", + "plt.plot(Tarr, Parr, label=\"$P_\\mathrm{total}$\")\n", + "plt.plot(Tarr, PH, \"--\", label=\"$P_\\mathrm{H}$\")\n", + "plt.plot(Tarr, PHH, \"--\", label=\"$P_\\mathrm{H_2}$\")\n", + "plt.plot(Tarr, PHe, \"--\", label=\"$P_\\mathrm{He}$\")\n", + "plt.plot(Tarr[80], Parr[80], marker=\"*\", markersize=15)\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"temperature (K)\")\n", + "plt.ylabel(\"pressure (bar)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -155,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "b8c660c5", "metadata": { "execution": { @@ -172,6 +202,14 @@ "text": [ "Reading Kurucz file\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/atomllapi.py:616: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " ionE = float(\n" + ] } ], "source": [ @@ -179,9 +217,9 @@ " kuruczlines: fullpath to the input line list obtained from Kurucz linelists (http://kurucz.harvard.edu/linelists/):\n", " For a example in this notebook, gf2600.all downloaded from (http://kurucz.harvard.edu/linelists/gfall/) is used.\n", "\"\"\"\n", - "\n", + "from exojax.spec.moldb import AdbKurucz\n", "kuruczlines = '.database/gf2600.all'\n", - "adbK = moldb.AdbKurucz(kuruczlines, nus)" + "adbK = AdbKurucz(kuruczlines, nu_grid)" ] }, { @@ -194,26 +232,24 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "38848b1b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:03:52.810196Z", - "iopub.status.busy": "2023-03-14T12:03:52.807406Z", - "iopub.status.idle": "2023-03-14T12:03:53.559679Z", - "shell.execute_reply": "2023-03-14T12:03:53.559925Z" + "execution_count": 6, + "id": "86449c95", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/moldb.py:577: FutureWarning: Deprecated Use `atomll.interp_QT_284` instead\n", + " warnings.warn(warn_msg, FutureWarning)\n" + ] } - }, - "outputs": [], + ], "source": [ "#Computing the relative partition function,\n", - "\n", - "qt_284=vmap(adbK.QT_interp_284)(Tarr)\n", - "\n", - "qt_K = np.zeros([len(adbK.QTmask), len(Tarr)])\n", - "for i, mask in enumerate(adbK.QTmask):\n", - " qt_K[i] = qt_284[:,mask] #e.g., qt_284[:,76] #Fe I\n", - "qt_K = jnp.array(qt_K)" + "qt_284 = vmap(adbK.QT_interp_284)(Tarr)\n", + "qt_K = qt_284[:, adbK.QTmask]\n", + "qr_K = qt_K / adbK.QTref_284[adbK.QTmask]" ] }, { @@ -226,28 +262,46 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "22a89200", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:03:53.648100Z", - "iopub.status.busy": "2023-03-14T12:03:53.597831Z", - "iopub.status.idle": "2023-03-14T12:03:53.960502Z", - "shell.execute_reply": "2023-03-14T12:03:53.960195Z" - } - }, + "execution_count": 7, + "id": "691a1d33", + "metadata": {}, "outputs": [], "source": [ - "gammaLM_K = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\\\n", + "# volume mixing ratio (VMR) for e-\n", + "vmre = 10**-7 # assume an arbitrary uniform value here\n", + "\n", + "from exojax.atm.idealgas import number_density\n", + "narr = number_density(Parr, Tarr)\n", + "number_density_e = vmre * narr" + ] + }, + { + "cell_type": "markdown", + "id": "ce6f6bd9", + "metadata": {}, + "source": [ + "Applies JIT to gamma, sigma, and line strength" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4fab0998", + "metadata": {}, + "outputs": [], + "source": [ + "gammaLM_K = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None,0)))\\\n", " (Tarr, PH, PHH, PHe, adbK.ielem, adbK.iion, \\\n", " adbK.dev_nu_lines, adbK.elower, adbK.eupper, adbK.atomicmass, adbK.ionE, \\\n", - " adbK.gamRad, adbK.gamSta, adbK.vdWdamp, 1.0)\n", + " adbK.gamRad, adbK.gamSta, adbK.vdWdamp, 1.0, number_density_e)\n", "\n", "sigmaDM_K = jit(vmap(doppler_sigma,(None,0,None)))\\\n", " (adbK.nu_lines, Tarr, adbK.atomicmass)\n", "\n", - "SijM_K = jit(vmap(SijT,(0,None,None,None,0)))\\\n", - " (Tarr, adbK.logsij0, adbK.nu_lines, adbK.elower, qt_K.T)" + "#from exojax.utils.constants import Tref_original\n", + "\n", + "SijM_K = jit(vmap(line_strength,(0,None,None,None,0,None)))\\\n", + " (Tarr, adbK.logsij0, adbK.nu_lines, adbK.elower, qr_K, adbK.Tref)" ] }, { @@ -260,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "7e7a6b1b", "metadata": { "execution": { @@ -272,7 +326,7 @@ }, "outputs": [], "source": [ - "numatrix_K = init_lpf(adbK.nu_lines, nus)" + "numatrix_K = init_lpf(adbK.nu_lines, nu_grid)" ] }, { @@ -285,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "9c88daf5", "metadata": { "execution": { @@ -322,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "f6ec440c", "metadata": { "execution": { @@ -347,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "c2516180", "metadata": { "execution": { @@ -359,8 +413,10 @@ }, "outputs": [], "source": [ + "mmw = 2.33 # mean molecular weight\n", + "\n", "xsm_K = xsmatrix(numatrix_K, sigmaDM_K, gammaLM_K, SijM_K)\n", - "dtaua_K = dtauM(dParr, xsm_K, VMR_Fe*np.ones_like(Tarr), mmw, g)" + "dtaua_K = layer_optical_depth(dParr, xsm_K, VMR_Fe * np.ones_like(Tarr), mmw, g)" ] }, { @@ -373,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "d0fa6469", "metadata": { "execution": { @@ -393,14 +449,15 @@ } ], "source": [ - "cdbH2H2=contdb.CdbCIA('.database/H2-H2_2011.cia', nus)\n", + "from exojax.spec.contdb import CdbCIA\n", + "cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid)\n", "\n", "vmrh=H_He_HH_VMR[0]\n", "vmre=vmrh*1e-5\n", "vmrH2=H_He_HH_VMR[2] #(0.74*mmw/molinfo.molmass(\"H2\")) #VMR\n", "\n", - "dtau_Hm = dtauHminus(nus, Tarr, Parr, dParr, vmre, vmrh, mmw, g)\n", - "dtaucH2H2=dtauCIA(nus,Tarr,Parr,dParr,vmrH2,vmrH2,\\\n", + "dtau_Hm = layer_optical_depth_Hminus(nu_grid, Tarr, Parr, dParr, vmre, vmrh, mmw, g)\n", + "dtaucH2H2=layer_optical_depth_CIA(nu_grid,Tarr,Parr,dParr,vmrH2,vmrH2,\\\n", " mmw,g,cdbH2H2.nucia,cdbH2H2.tcia,cdbH2H2.logac)\n" ] }, @@ -414,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "272be220", "metadata": { "execution": { @@ -439,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "1fe063a6", "metadata": { "execution": { @@ -452,20 +509,18 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "from exojax.plot.atmplot import plotcf\n", - "plotcf(nus, dtau_K, Tarr, Parr, dParr)\n", + "plotcf(nu_grid, dtau_K, Tarr, Parr, dParr)\n", "#plt.savefig(path_fig + 'dtau_K.pdf')\n", "plt.show()" ] @@ -480,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "e21ae092", "metadata": { "execution": { @@ -490,54 +545,24 @@ "shell.execute_reply": "2023-03-14T12:04:02.109467Z" } }, - "outputs": [], - "source": [ - "from exojax.spec import planck\n", - "from exojax.spec.rtransfer import rtrun\n", - "sourcef = planck.piBarr(Tarr, nus)\n", - "\n", - "F0_K = rtrun(dtau_K, sourcef)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1ae2a2db", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:04:02.142542Z", - "iopub.status.busy": "2023-03-14T12:04:02.134326Z", - "iopub.status.idle": "2023-03-14T12:04:02.248763Z", - "shell.execute_reply": "2023-03-14T12:04:02.248440Z" - } - }, "outputs": [ { "data": { + "image/png": "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", "text/plain": [ - "(200000.0, 1500000.0)" + "
" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, "output_type": "display_data" } ], "source": [ + "F0_K = art.run(dtau_K, Tarr)\n", + "\n", + "fig=plt.figure(figsize=(5, 3))\n", "plt.plot(wav[::-1], F0_K)\n", - "plt.ylim(0.2e6, 1.5e6)" + "plt.show()" ] }, { @@ -545,13 +570,13 @@ "id": "e4d59866", "metadata": {}, "source": [ - "# Comparison with Fe I lines of VALD3\n", + "## Comparison with Fe I lines of VALD3\n", "(c.f. [Forward modeling of the emission spectrum using VALD3](http://secondearths.sakura.ne.jp/exojax/tutorials/metals.html))" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "id": "9399c9ac", "metadata": { "execution": { @@ -568,6 +593,14 @@ "text": [ "Reading VALD file\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/atomllapi.py:616: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " ionE = float(\n" + ] } ], "source": [ @@ -577,13 +610,14 @@ " For more details of VALD data access, please see \"Forward modeling for metal line.ipynb\" (https://github.com/HajimeKawahara/exojax/blob/master/examples/tutorial/Forward%20modeling%20for%20metal%20line.ipynb)\n", "\"\"\"\n", "\n", + "from exojax.spec.moldb import AdbVald\n", "valdlines = '.database/vald2600.gz'\n", - "adbV = moldb.AdbVald(valdlines, nus)" + "adbV = AdbVald(valdlines, nu_grid)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "id": "79da55bf", "metadata": { "execution": { @@ -593,33 +627,40 @@ "shell.execute_reply": "2023-03-14T12:04:12.164443Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kawahara/exojax/src/exojax/spec/moldb.py:267: FutureWarning: Deprecated Use `atomll.interp_QT_284` instead\n", + " warnings.warn(warn_msg, FutureWarning)\n" + ] + } + ], "source": [ - "qt_V = np.zeros([len(adbV.QTmask), len(Tarr)])\n", - "\n", - "for i, mask in enumerate(adbV.QTmask):\n", - " qt_V[i] = qt_284[:,mask]\n", - "qt_V = jnp.array(qt_V)\n", + "qt_284 = vmap(adbV.QT_interp_284)(Tarr)\n", + "qt_V = qt_284[:, adbV.QTmask]\n", + "qr_V = qt_V / adbV.QTref_284[adbV.QTmask]\n", "\n", - "gammaLM_V = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\\\n", + "gammaLM_V = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None,0)))\\\n", " (Tarr, PH, PHH, PHe, adbV.ielem, adbV.iion, \\\n", " adbV.dev_nu_lines, adbV.elower, adbV.eupper, adbV.atomicmass, adbV.ionE, \\\n", - " adbV.gamRad, adbV.gamSta, adbV.vdWdamp, 1.0)\n", + " adbV.gamRad, adbV.gamSta, adbV.vdWdamp, 1.0, number_density_e)\n", "sigmaDM_V = jit(vmap(doppler_sigma,(None,0,None)))\\\n", " (adbV.nu_lines, Tarr, adbV.atomicmass)\n", - "SijM_V = jit(vmap(SijT,(0,None,None,None,0)))\\\n", - " (Tarr, adbV.logsij0, adbV.nu_lines, adbV.elower, qt_V.T)\n", + "SijM_V = jit(vmap(line_strength,(0,None,None,None,0,None)))\\\n", + " (Tarr, adbV.logsij0, adbV.nu_lines, adbV.elower, qr_V, adbV.Tref)\n", "\n", - "numatrix_V = init_lpf(adbV.nu_lines, nus)\n", + "numatrix_V = init_lpf(adbV.nu_lines, nu_grid)\n", "\n", "xsm_V = xsmatrix(numatrix_V, sigmaDM_V, gammaLM_V, SijM_V)\n", - "dtaua_V = dtauM(dParr, xsm_V, VMR_Fe*np.ones_like(Tarr), mmw, g)\n", + "dtaua_V = layer_optical_depth(dParr, xsm_V, VMR_Fe * np.ones_like(Tarr), mmw, g)\n", "dtau_V = dtaua_V + dtau_Hm + dtaucH2H2\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "9fb41a59", "metadata": { "execution": { @@ -632,56 +673,45 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "from exojax.plot.atmplot import plotcf\n", - "plotcf(nus, dtau_V, Tarr, Parr, dParr)\n", + "plotcf(nu_grid, dtau_V, Tarr, Parr, dParr)\n", "#plt.savefig(path_fig + 'dtau_V.pdf')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "e74a034e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:04:12.707940Z", - "iopub.status.busy": "2023-03-14T12:04:12.707656Z", - "iopub.status.idle": "2023-03-14T12:04:12.809486Z", - "shell.execute_reply": "2023-03-14T12:04:12.809167Z" - } - }, + "execution_count": 22, + "id": "0a214e29", + "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "F0_V = rtrun(dtau_V, sourcef)\n", + "F0_V = art.run(dtau_V, Tarr)\n", "\n", + "fig=plt.figure(figsize=(5, 3))\n", "plt.plot(wav[::-1], F0_K, label='Kurucz')\n", "plt.plot(wav[::-1], F0_V, '--', label='VALD', lw=2., alpha=.8)\n", - "plt.ylim(0.2e6, 1.5e6)\n", "plt.legend()\n", "#plt.savefig(path_fig + 'comp_F0_KV.pdf')\n", "plt.show()" @@ -689,16 +719,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "3d174472", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-14T12:04:12.819689Z", - "iopub.status.busy": "2023-03-14T12:04:12.819249Z", - "iopub.status.idle": "2023-03-14T12:04:13.019222Z", - "shell.execute_reply": "2023-03-14T12:04:13.019486Z" - } - }, + "execution_count": 23, + "id": "9dcccb61", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -724,7 +747,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0b7c4d1f", + "id": "7e574e34", "metadata": {}, "outputs": [], "source": [] @@ -746,7 +769,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.rst b/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.rst index 3eba79e5c..37c5a486d 100644 --- a/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.rst +++ b/documents/tutorials/Forward_modeling_for_Fe_I_lines_of_Kurucz.rst @@ -1,74 +1,100 @@ Forward modeling of the emission spectrum using Fe I line list from Kurucz ========================================================================== -| Tako Ishikawa -| last update: 2022/04/25 +| Tako Ishikawa, Hajime Kawahara +| last update: 2024/07/14 | created: : 2022/04/22 +This notebook demonstrates how to use Kurucz database, not using +``opa``. + .. code:: ipython3 from exojax.utils.grids import wavenumber_grid - from exojax.spec.rtransfer import pressure_layer - from exojax.spec import moldb, molinfo, contdb from exojax.spec import atomll - from exojax.spec.exomol import gamma_exomol - from exojax.spec import SijT, doppler_sigma - from exojax.spec import planck + from exojax.spec.hitran import doppler_sigma, line_strength + from exojax.spec.initspec import init_lpf + from exojax.spec.lpf import xsmatrix + from exojax.spec.layeropacity import layer_optical_depth, layer_optical_depth_Hminus, layer_optical_depth_CIA import matplotlib.pyplot as plt - import jax.numpy as jnp from jax import vmap, jit import numpy as np - from exojax.spec.initspec import init_lpf - from exojax.spec.rtransfer import dtauCIA, dtauHminus - from exojax.spec.lpf import xsmatrix - from exojax.spec.rtransfer import dtauM -T-P profile + +.. parsed-literal:: + + /home/kawahara/exojax/src/exojax/spec/dtau_mmwl.py:14: FutureWarning: dtau_mmwl might be removed in future. + warnings.warn("dtau_mmwl might be removed in future.", FutureWarning) + + +Sets a wavenumber grid .. code:: ipython3 - #Assume ATMOSPHERE - NP=100 - T0=3000. #10000. #3000. #1295.0 #K - Parr, dParr, k=pressure_layer(NP=NP) - H_He_HH_VMR = [0.0, 0.16, 0.84] #typical quasi-"solar-fraction" - Tarr = T0*(Parr)**0.1 - - mmw=2.33 #mean molecular weight - - PH = Parr* H_He_HH_VMR[0] - PHe = Parr* H_He_HH_VMR[1] - PHH = Parr* H_He_HH_VMR[2] - - fig=plt.figure(figsize=(6,4)) - plt.plot(Tarr,Parr) - plt.plot(Tarr, PH, '--'); plt.plot(Tarr, PHH, '--'); plt.plot(Tarr, PHe, '--') - plt.plot(Tarr[80],Parr[80], marker='*', markersize=15) - plt.yscale("log") - plt.xlabel("temperature (K)") - plt.ylabel("pressure (bar)") - plt.gca().invert_yaxis() - plt.show() + #We set a wavenumber grid using wavenumber_grid. + nu_grid, wav, reso = wavenumber_grid(10380, 10430, 4500, xsmode="lpf", unit="AA", wavelength_order="ascending") + +.. parsed-literal:: + xsmode = lpf + xsmode assumes ESLOG in wavenumber space: xsmode=lpf + ====================================================================== + The wavenumber grid should be in ascending order. + The users can specify the order of the wavelength grid by themselves. + Your wavelength grid is in *** ascending *** order + ====================================================================== -.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_4_0.png +.. parsed-literal:: + /home/kawahara/exojax/src/exojax/spec/unitconvert.py:63: UserWarning: Both input wavelength and output wavenumber are in ascending order. + warnings.warn( -Wavenumber + +Sets a T-P profile and partial pressures .. code:: ipython3 - wls,wll = 10350, 10450 - nugrid_res = 0.01 - nus, wav, reso = wavenumber_grid(wls, wll, int((wll-wls)/nugrid_res), unit="AA", xsmode="lpf") + from exojax.spec.atmrt import ArtEmisPure + nlayer = 100 + T0 = 3000.0 # 10000. #3000. #1295.0 #K + alpha = 0.1 + Tlow = 500.0 + Thigh = 4500.0 + art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=nlayer) + art.change_temperature_range(Tlow, Thigh) + Tarr = art.powerlaw_temperature(T0, alpha) + Parr = art.pressure + dParr = art.dParr + + H_He_HH_VMR = [0.0, 0.16, 0.84] # typical quasi-"solar-fraction" + PH = Parr * H_He_HH_VMR[0] + PHe = Parr * H_He_HH_VMR[1] + PHH = Parr * H_He_HH_VMR[2] + + fig = plt.figure(figsize=(6, 4)) + plt.plot(Tarr, Parr, label="$P_\mathrm{total}$") + plt.plot(Tarr, PH, "--", label="$P_\mathrm{H}$") + plt.plot(Tarr, PHH, "--", label="$P_\mathrm{H_2}$") + plt.plot(Tarr, PHe, "--", label="$P_\mathrm{He}$") + plt.plot(Tarr[80], Parr[80], marker="*", markersize=15) + plt.yscale("log") + plt.xlabel("temperature (K)") + plt.ylabel("pressure (bar)") + plt.gca().invert_yaxis() + plt.legend() + plt.show() .. parsed-literal:: - xsmode = lpf - xsmode assumes ESLOG in wavenumber space: mode=lpf + rtsolver: ibased + Intensity-based n-stream solver, isothermal layer (e.g. NEMESIS, pRT like) + + + +.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_7_1.png | Load a Kurucz line list for neutral iron (Fe I) @@ -81,9 +107,9 @@ Wavenumber kuruczlines: fullpath to the input line list obtained from Kurucz linelists (http://kurucz.harvard.edu/linelists/): For a example in this notebook, gf2600.all downloaded from (http://kurucz.harvard.edu/linelists/gfall/) is used. """ - + from exojax.spec.moldb import AdbKurucz kuruczlines = '.database/gf2600.all' - adbK = moldb.AdbKurucz(kuruczlines, nus) + adbK = AdbKurucz(kuruczlines, nu_grid) .. parsed-literal:: @@ -91,39 +117,61 @@ Wavenumber Reading Kurucz file +.. parsed-literal:: + + /home/kawahara/exojax/src/exojax/spec/atomllapi.py:616: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead + ionE = float( + + Relative partition function .. code:: ipython3 #Computing the relative partition function, - - qt_284=vmap(adbK.QT_interp_284)(Tarr) - - qt_K = np.zeros([len(adbK.QTmask), len(Tarr)]) - for i, mask in enumerate(adbK.QTmask): - qt_K[i] = qt_284[:,mask] #e.g., qt_284[:,76] #Fe I - qt_K = jnp.array(qt_K) + qt_284 = vmap(adbK.QT_interp_284)(Tarr) + qt_K = qt_284[:, adbK.QTmask] + qr_K = qt_K / adbK.QTref_284[adbK.QTmask] + + +.. parsed-literal:: + + /home/kawahara/exojax/src/exojax/spec/moldb.py:577: FutureWarning: Deprecated Use `atomll.interp_QT_284` instead + warnings.warn(warn_msg, FutureWarning) + Lorentzian width, Doppler width, and Line strength .. code:: ipython3 - gammaLM_K = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\ + # volume mixing ratio (VMR) for e- + vmre = 10**-7 # assume an arbitrary uniform value here + + from exojax.atm.idealgas import number_density + narr = number_density(Parr, Tarr) + number_density_e = vmre * narr + +Applies JIT to gamma, sigma, and line strength + +.. code:: ipython3 + + gammaLM_K = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None,0)))\ (Tarr, PH, PHH, PHe, adbK.ielem, adbK.iion, \ adbK.dev_nu_lines, adbK.elower, adbK.eupper, adbK.atomicmass, adbK.ionE, \ - adbK.gamRad, adbK.gamSta, adbK.vdWdamp, 1.0) + adbK.gamRad, adbK.gamSta, adbK.vdWdamp, 1.0, number_density_e) sigmaDM_K = jit(vmap(doppler_sigma,(None,0,None)))\ (adbK.nu_lines, Tarr, adbK.atomicmass) - SijM_K = jit(vmap(SijT,(0,None,None,None,0)))\ - (Tarr, adbK.logsij0, adbK.nu_lines, adbK.elower, qt_K.T) + #from exojax.utils.constants import Tref_original + + SijM_K = jit(vmap(line_strength,(0,None,None,None,0,None)))\ + (Tarr, adbK.logsij0, adbK.nu_lines, adbK.elower, qr_K, adbK.Tref) Initialization of LPF. .. code:: ipython3 - numatrix_K = init_lpf(adbK.nu_lines, nus) + numatrix_K = init_lpf(adbK.nu_lines, nu_grid) Stellar parameters @@ -151,21 +199,24 @@ Cross section and delta tau .. code:: ipython3 + mmw = 2.33 # mean molecular weight + xsm_K = xsmatrix(numatrix_K, sigmaDM_K, gammaLM_K, SijM_K) - dtaua_K = dtauM(dParr, xsm_K, VMR_Fe*np.ones_like(Tarr), mmw, g) + dtaua_K = layer_optical_depth(dParr, xsm_K, VMR_Fe * np.ones_like(Tarr), mmw, g) Delta tau for CIA and Hminus .. code:: ipython3 - cdbH2H2=contdb.CdbCIA('.database/H2-H2_2011.cia', nus) + from exojax.spec.contdb import CdbCIA + cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) vmrh=H_He_HH_VMR[0] vmre=vmrh*1e-5 vmrH2=H_He_HH_VMR[2] #(0.74*mmw/molinfo.molmass("H2")) #VMR - dtau_Hm = dtauHminus(nus, Tarr, Parr, dParr, vmre, vmrh, mmw, g) - dtaucH2H2=dtauCIA(nus,Tarr,Parr,dParr,vmrH2,vmrH2,\ + dtau_Hm = layer_optical_depth_Hminus(nu_grid, Tarr, Parr, dParr, vmre, vmrh, mmw, g) + dtaucH2H2=layer_optical_depth_CIA(nu_grid,Tarr,Parr,dParr,vmrH2,vmrH2,\ mmw,g,cdbH2H2.nucia,cdbH2H2.tcia,cdbH2H2.logac) @@ -186,13 +237,13 @@ Contribution function using exojax.plot.atmplot .. code:: ipython3 from exojax.plot.atmplot import plotcf - plotcf(nus, dtau_K, Tarr, Parr, dParr) + plotcf(nu_grid, dtau_K, Tarr, Parr, dParr) #plt.savefig(path_fig + 'dtau_K.pdf') plt.show() -.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_26_0.png +.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_29_0.png Perform a radiative transfer. Here, the source function is the Planck @@ -200,32 +251,19 @@ function (multiplied by pi). .. code:: ipython3 - from exojax.spec import planck - from exojax.spec.rtransfer import rtrun - sourcef = planck.piBarr(Tarr, nus) + F0_K = art.run(dtau_K, Tarr) - F0_K = rtrun(dtau_K, sourcef) - -.. code:: ipython3 - + fig=plt.figure(figsize=(5, 3)) plt.plot(wav[::-1], F0_K) - plt.ylim(0.2e6, 1.5e6) - - - - -.. parsed-literal:: - - (200000.0, 1500000.0) - + plt.show() -.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_29_1.png +.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_31_0.png Comparison with Fe I lines of VALD3 -=================================== +----------------------------------- (c.f. `Forward modeling of the emission spectrum using VALD3 `__) @@ -238,8 +276,9 @@ VALD3 `__) For more details of VALD data access, please see "Forward modeling for metal line.ipynb" (https://github.com/HajimeKawahara/exojax/blob/master/examples/tutorial/Forward%20modeling%20for%20metal%20line.ipynb) """ + from exojax.spec.moldb import AdbVald valdlines = '.database/vald2600.gz' - adbV = moldb.AdbVald(valdlines, nus) + adbV = AdbVald(valdlines, nu_grid) .. parsed-literal:: @@ -247,56 +286,67 @@ VALD3 `__) Reading VALD file +.. parsed-literal:: + + /home/kawahara/exojax/src/exojax/spec/atomllapi.py:616: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead + ionE = float( + + .. code:: ipython3 - qt_V = np.zeros([len(adbV.QTmask), len(Tarr)]) + qt_284 = vmap(adbV.QT_interp_284)(Tarr) + qt_V = qt_284[:, adbV.QTmask] + qr_V = qt_V / adbV.QTref_284[adbV.QTmask] - for i, mask in enumerate(adbV.QTmask): - qt_V[i] = qt_284[:,mask] - qt_V = jnp.array(qt_V) - - gammaLM_V = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\ + gammaLM_V = jit(vmap(atomll.gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None,0)))\ (Tarr, PH, PHH, PHe, adbV.ielem, adbV.iion, \ adbV.dev_nu_lines, adbV.elower, adbV.eupper, adbV.atomicmass, adbV.ionE, \ - adbV.gamRad, adbV.gamSta, adbV.vdWdamp, 1.0) + adbV.gamRad, adbV.gamSta, adbV.vdWdamp, 1.0, number_density_e) sigmaDM_V = jit(vmap(doppler_sigma,(None,0,None)))\ (adbV.nu_lines, Tarr, adbV.atomicmass) - SijM_V = jit(vmap(SijT,(0,None,None,None,0)))\ - (Tarr, adbV.logsij0, adbV.nu_lines, adbV.elower, qt_V.T) + SijM_V = jit(vmap(line_strength,(0,None,None,None,0,None)))\ + (Tarr, adbV.logsij0, adbV.nu_lines, adbV.elower, qr_V, adbV.Tref) - numatrix_V = init_lpf(adbV.nu_lines, nus) + numatrix_V = init_lpf(adbV.nu_lines, nu_grid) xsm_V = xsmatrix(numatrix_V, sigmaDM_V, gammaLM_V, SijM_V) - dtaua_V = dtauM(dParr, xsm_V, VMR_Fe*np.ones_like(Tarr), mmw, g) + dtaua_V = layer_optical_depth(dParr, xsm_V, VMR_Fe * np.ones_like(Tarr), mmw, g) dtau_V = dtaua_V + dtau_Hm + dtaucH2H2 + +.. parsed-literal:: + + /home/kawahara/exojax/src/exojax/spec/moldb.py:267: FutureWarning: Deprecated Use `atomll.interp_QT_284` instead + warnings.warn(warn_msg, FutureWarning) + + .. code:: ipython3 from exojax.plot.atmplot import plotcf - plotcf(nus, dtau_V, Tarr, Parr, dParr) + plotcf(nu_grid, dtau_V, Tarr, Parr, dParr) #plt.savefig(path_fig + 'dtau_V.pdf') plt.show() -.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_33_0.png +.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_35_0.png .. code:: ipython3 - F0_V = rtrun(dtau_V, sourcef) + F0_V = art.run(dtau_V, Tarr) + fig=plt.figure(figsize=(5, 3)) plt.plot(wav[::-1], F0_K, label='Kurucz') plt.plot(wav[::-1], F0_V, '--', label='VALD', lw=2., alpha=.8) - plt.ylim(0.2e6, 1.5e6) plt.legend() #plt.savefig(path_fig + 'comp_F0_KV.pdf') plt.show() -.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_34_0.png +.. image:: Forward_modeling_for_Fe_I_lines_of_Kurucz_files/Forward_modeling_for_Fe_I_lines_of_Kurucz_36_0.png .. code:: ipython3 diff --git a/documents/tutorials/Forward_modeling_for_metal_line.ipynb b/documents/tutorials/Forward_modeling_for_metal_line.ipynb index 1f45fe92d..2133ffbc2 100644 --- a/documents/tutorials/Forward_modeling_for_metal_line.ipynb +++ b/documents/tutorials/Forward_modeling_for_metal_line.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "Tako Ishikawa, Hajime Kawahara \n", - "created: : 2021/07/20,  last update: 2022/10/22 \n", + "created: : 2021/07/20,  last update: 2024/06/14 \n", "\n", " - sp = uspecies[i] - MMRmetalMod = mods_uspecies_list[i] #add_to_deal_with_individual_elemental_abundance - MMR_X_I = MMR_uspecies_list[i] *10**MMRmetalMod #modify this into individual elemental abundances shortly... (tako) - mass_X_I = atomicmass_uspecies_list[i] - - dtau_each = dtauM(dParr, xsm, MMR_X_I*jnp.ones_like(dParr), mass_X_I, g) - # Note that the same mixing ratio is assumed for all atmospheric layers here... - dtauatom = dtauatom + dtau_each - # <----process - i = i+1 - xi = [i, dtauatom] - return xi, null - def f_dtaual(xi0): - xi, null = scan(floop, xi0, None, length) - return xi - - length = len(uspecies) - dtauatom_init = jnp.zeros_like(xsm) - xi_init = [0, dtauatom_init] - - dtauatom = f_dtaual(xi_init)[1] - return(dtauatom) - - -def dtauM_vald(dParr, g, adb, nus, cnu, indexnu, pmarray, SijM, gammaLM, sigmaDM, \ - uspecies, mods_uspecies_list, MMR_uspecies_list, atomicmass_uspecies_list, dgm_sigmaD, dgm_gammaL): - """Compute dtau caused by VALD lines from cross section xs (DIT) - Args: - dParr: delta pressure profile (bar) [N_layer] - g: gravity (cm/s^2) - adb: adb instance made by the AdbVald class in moldb.py - nus: wavenumber matrix (cm-1) [N_nus] - cnu: cont (contribution) jnp.array [N_line] - indexnu: index (index) jnp.array [N_line] - pmarray: (+1,-1) array [len(nu_grid)+1,] - SijM: line intensity matrix [N_layer x N_line] - gammaLM: gammaL matrix [N_layer x N_line] - sigmaDM: sigmaD matrix [N_layer x N_line] - uspecies: unique elements of the combination of ielem and iion [N_UniqueSpecies x 2(ielem and iion)] - mods_uspecies_list: jnp.array of abundance deviation from the Sun [dex] for each species in "uspecies" [N_UniqueSpecies] - MMR_uspecies_list: jnp.array of mass mixing ratio in the Sun of each species in "uspecies" [N_UniqueSpecies] - atomicmass_uspecies_list: jnp.array of atomic mass [amu] of each species in "uspecies" [N_UniqueSpecies] - + dParr: delta pressure profile (bar) [N_layer] + g: gravity (cm/s^2) + adb: adb instance made by the AdbVald class in moldb.py + nus: wavenumber matrix (cm-1) [N_nus] + cnu: cont (contribution) jnp.array [N_line] + indexnu: index (index) jnp.array [N_line] + pmarray: (+1,-1) array [len(nu_grid)+1,] + SijM: line intensity matrix [N_layer x N_line] + gammaLM: gammaL matrix [N_layer x N_line] + sigmaDM: sigmaD matrix [N_layer x N_line] + uspecies: unique elements of the combination of ielem and iion [N_UniqueSpecies x 2(ielem and iion)] + mods_uspecies_list: jnp.array of abundance deviation from the Sun [dex] for each species in "uspecies" [N_UniqueSpecies] + MMR_uspecies_list: jnp.array of mass mixing ratio in the Sun of each species in "uspecies" [N_UniqueSpecies] + atomicmass_uspecies_list: jnp.array of atomic mass [amu] of each species in "uspecies" [N_UniqueSpecies] + Returns: - dtauatom: optical depth matrix [N_layer, N_nus] - + dtauatom: optical depth matrix [N_layer, N_nus] + """ - zero_to_ones = lambda arr: jnp.where(arr!=0, arr, 1.) + zero_to_ones = lambda arr: jnp.where(arr != 0, arr, 1.0) + def floop(xi, null): i, dtauatom = xi # process----> - #test220208 dgm_sigmaD = dgml_sigmaD[i] + # test220208 dgm_sigmaD = dgml_sigmaD[i] sp = uspecies[i] - cnu_p = padding_2Darray_for_each_atom(cnu[None,:], adb, sp).reshape(cnu.shape) - indexnu_p = jnp.array(\ - padding_2Darray_for_each_atom(indexnu[None,:], adb, sp).reshape(indexnu.shape)\ - , dtype='int32') + cnu_p = padding_2Darray_for_each_atom(cnu[None, :], adb, sp).reshape(cnu.shape) + indexnu_p = jnp.array( + padding_2Darray_for_each_atom(indexnu[None, :], adb, sp).reshape( + indexnu.shape + ), + dtype="int32", + ) sigmaDM_p = zero_to_ones(padding_2Darray_for_each_atom(sigmaDM, adb, sp)) gammaLM_p = zero_to_ones(padding_2Darray_for_each_atom(gammaLM, adb, sp)) SijM_p = padding_2Darray_for_each_atom(SijM, adb, sp) - #test220207 dgm_sigmaD_p = dgmatrix(sigmaDM_p) - #test220207 dgm_gammaL_p = dgmatrix(gammaLM_p) - xsm_p = xsmatrix(cnu_p, indexnu_p, pmarray, sigmaDM_p, gammaLM_p, SijM_p, nus, dgm_sigmaD, dgm_gammaL) + # test220207 dgm_sigmaD_p = dgmatrix(sigmaDM_p) + # test220207 dgm_gammaL_p = dgmatrix(gammaLM_p) + xsm_p = xsmatrix( + cnu_p, + indexnu_p, + pmarray, + sigmaDM_p, + gammaLM_p, + SijM_p, + nus, + dgm_sigmaD, + dgm_gammaL, + ) xsm_p = jnp.abs(xsm_p) - MMRmetalMod = mods_uspecies_list[i] #add_to_deal_with_individual_elemental_abundance - MMR_X_I = MMR_uspecies_list[i] *10**MMRmetalMod #modify this into individual elemental abundances shortly... (tako) + MMRmetalMod = mods_uspecies_list[ + i + ] # add_to_deal_with_individual_elemental_abundance + MMR_X_I = ( + MMR_uspecies_list[i] * 10**MMRmetalMod + ) # modify this into individual elemental abundances shortly... (tako) mass_X_I = atomicmass_uspecies_list[i] - - dtau_each = dtauM(dParr, xsm_p, MMR_X_I*jnp.ones_like(dParr), mass_X_I, g) + + dtau_each = layer_optical_depth( + dParr, xsm_p, MMR_X_I * jnp.ones_like(dParr), mass_X_I, g + ) # Note that the same mixing ratio is assumed for all atmospheric layers here... dtauatom = dtauatom + dtau_each # <----process - i = i+1 + i = i + 1 xi = [i, dtauatom] return xi, null @@ -254,7 +266,8 @@ def f_dtaual(xi0): xi_init = [0, dtauatom_init] dtauatom = f_dtaual(xi_init)[1] - return(dtauatom) + return dtauatom + def ditgrid(x, dit_grid_resolution=0.1, adopt=True): """DIT GRID (deplicated). @@ -272,8 +285,10 @@ def ditgrid(x, dit_grid_resolution=0.1, adopt=True): warn_msg = " Use `set_ditgrid.ditgrid_log_interval` instead" warnings.warn(warn_msg, FutureWarning) from exojax.spec.set_ditgrid import ditgrid_log_interval + return ditgrid_log_interval(x, dit_grid_resolution, adopt) + def dgmatrix(x, dit_grid_resolution=0.1, adopt=True): """DIT GRID MATRIX (alias) @@ -287,6 +302,7 @@ def dgmatrix(x, dit_grid_resolution=0.1, adopt=True): grid for DIT (Nlayer x NDITgrid) """ warn_msg = " Use `set_ditgrid.ditgrid_matrix` instead" - warnings.warn(warn_msg, FutureWarning) - from exojax.spec.set_ditgrid import ditgrid_matrix + warnings.warn(warn_msg, FutureWarning) + from exojax.spec.set_ditgrid import ditgrid_matrix + return ditgrid_matrix(x, dit_grid_resolution, adopt) diff --git a/src/exojax/spec/dtau_mmwl.py b/src/exojax/spec/dtau_mmwl.py index ce9183c38..8a219c89a 100644 --- a/src/exojax/spec/dtau_mmwl.py +++ b/src/exojax/spec/dtau_mmwl.py @@ -2,7 +2,6 @@ """ - import jax.numpy as jnp from exojax.spec.hitrancia import interp_logacia_matrix from exojax.spec.hminus import log_hminus_continuum @@ -13,79 +12,87 @@ warnings.warn("dtau_mmwl might be removed in future.", FutureWarning) -def dtauCIA_mmwl(nus, Tarr, Parr, dParr, vmr1, vmr2, mmw, g, nucia, tcia, - logac): + +def dtauCIA_mmwl(nus, Tarr, Parr, dParr, vmr1, vmr2, mmw, g, nucia, tcia, logac): """dtau of the CIA continuum. - (for the case where mmw is given for each atmospheric layer) + (for the case where mmw is given for each atmospheric layer) Args: - nus: wavenumber matrix (cm-1) - Tarr: temperature array (K) - Parr: temperature array (bar) - dParr: delta temperature array (bar) - vmr1: volume mixing ratio (VMR) for molecules 1 [N_layer] - vmr2: volume mixing ratio (VMR) for molecules 2 [N_layer] - mmw: mean molecular weight of atmosphere [N_layer] - g: gravity (cm2/s) - nucia: wavenumber array for CIA - tcia: temperature array for CIA - logac: log10(absorption coefficient of CIA) + nus: wavenumber matrix (cm-1) + Tarr: temperature array (K) + Parr: temperature array (bar) + dParr: delta temperature array (bar) + vmr1: volume mixing ratio (VMR) for molecules 1 [N_layer] + vmr2: volume mixing ratio (VMR) for molecules 2 [N_layer] + mmw: mean molecular weight of atmosphere [N_layer] + g: gravity (cm2/s) + nucia: wavenumber array for CIA + tcia: temperature array for CIA + logac: log10(absorption coefficient of CIA) Returns: - optical depth matrix [N_layer, N_nus] + optical depth matrix [N_layer, N_nus] """ narr = number_density(Parr, Tarr) lognarr1 = jnp.log10(vmr1 * narr) # log number density lognarr2 = jnp.log10(vmr2 * narr) # log number density logg = jnp.log10(g) ddParr = dParr / Parr - dtauc = (10**(interp_logacia_matrix(Tarr, nus, nucia, tcia, logac) + - lognarr1[:, None] + lognarr2[:, None] + logkB - logg - - logm_ucgs) * Tarr[:, None] / mmw[:, None] * ddParr[:, None]) + dtauc = ( + 10 + ** ( + interp_logacia_matrix(Tarr, nus, nucia, tcia, logac) + + lognarr1[:, None] + + lognarr2[:, None] + + logkB + - logg + - logm_ucgs + ) + * Tarr[:, None] + / mmw[:, None] + * ddParr[:, None] + ) return dtauc - def dtauM_mmwl(dParr, xsm, MR, mass, g): """dtau of the molecular cross section. - (for the case where mmw is given for each atmospheric layer) + (for the case where mmw is given for each atmospheric layer) Note: - opfac=bar_cgs/(m_u (g)). m_u: atomic mass unit. It can be obtained by fac=1.e3/m_u, where m_u = scipy.constants.m_u. + opfac=bar_cgs/(m_u (g)). m_u: atomic mass unit. It can be obtained by fac=1.e3/m_u, where m_u = scipy.constants.m_u. Args: - dParr: delta pressure profile (bar) [N_layer] - xsm: cross section matrix (cm2) [N_layer, N_nus] - MR: volume mixing ratio (VMR) or mass mixing ratio (MMR) [N_layer] - mass: mean molecular weight for VMR or molecular mass for MMR [N_layer] - g: gravity (cm/s2) + dParr: delta pressure profile (bar) [N_layer] + xsm: cross section matrix (cm2) [N_layer, N_nus] + MR: volume mixing ratio (VMR) or mass mixing ratio (MMR) [N_layer] + mass: mean molecular weight for VMR or molecular mass for MMR [N_layer] + g: gravity (cm/s2) Returns: - optical depth matrix [N_layer, N_nus] + optical depth matrix [N_layer, N_nus] """ return opfac * xsm * dParr[:, None] * MR[:, None] / (mass[:, None] * g) - - def dtauHminus_mmwl(nus, Tarr, Parr, dParr, vmre, vmrh, mmw, g): """dtau of the H- continuum. - (for the case where mmw is given for each atmospheric layer) + (for the case where mmw is given for each atmospheric layer) Args: - nus: wavenumber matrix (cm-1) - Tarr: temperature array (K) - Parr: temperature array (bar) - dParr: delta temperature array (bar) - vmre: volume mixing ratio (VMR) for e- [N_layer] - vmrH: volume mixing ratio (VMR) for H atoms [N_layer] - mmw: mean molecular weight of atmosphere [N_layer] - g: gravity (cm2/s) + nus: wavenumber matrix (cm-1) + Tarr: temperature array (K) + Parr: temperature array (bar) + dParr: delta temperature array (bar) + vmre: volume mixing ratio (VMR) for e- [N_layer] + vmrH: volume mixing ratio (VMR) for H atoms [N_layer] + mmw: mean molecular weight of atmosphere [N_layer] + g: gravity (cm2/s) Returns: - optical depth matrix [N_layer, N_nus] + optical depth matrix [N_layer, N_nus] """ narr = number_density(Parr, Tarr) # number_density_e: number density for e- [N_layer] @@ -94,10 +101,12 @@ def dtauHminus_mmwl(nus, Tarr, Parr, dParr, vmre, vmrh, mmw, g): number_density_h = vmrh * narr logg = jnp.log10(g) ddParr = dParr / Parr - logabc = (log_hminus_continuum(nus, Tarr, number_density_e, - number_density_h)) - dtauh = 10**(logabc + logkB - logg - - logm_ucgs) * Tarr[:, None] / mmw[:, None] * ddParr[:, None] + logabc = log_hminus_continuum(nus, Tarr, number_density_e, number_density_h) + dtauh = ( + 10 ** (logabc + logkB - logg - logm_ucgs) + * Tarr[:, None] + / mmw[:, None] + * ddParr[:, None] + ) return dtauh - diff --git a/src/exojax/spec/exomol.py b/src/exojax/spec/exomol.py index 89f7dfba5..1370ba503 100644 --- a/src/exojax/spec/exomol.py +++ b/src/exojax/spec/exomol.py @@ -1,44 +1,50 @@ import numpy as np +from exojax.utils.constants import Tref_original from exojax.utils.constants import hcperk, ccgs -def Sij0(A, g, nu_lines, elower, QTref): - """Reference Line Strength in Tref=296K, S0. +def line_strength_from_Einstein_coeff(A, g, nu_lines, elower, QTref): + """Reference Line Strength in Tref=296K, S0 from Einstein coefficient. Note: - Tref=296K + This function is not used in other codes in ExoJAX. + But it can be used for the conversion of the line strength from the original ExoMol form + into HITRAN form. Args: - A: Einstein coefficient (s-1) - g: the upper state statistical weight - nu_lines: line center wavenumber (cm-1) - elower: elower - QTref: partition function Q(Tref) - Mmol: molecular mass (normalized by m_u) + A: Einstein coefficient (s-1) + g: the upper state statistical weight + nu_lines: line center wavenumber (cm-1) + elower: elower + QTref: partition function Q(Tref) + Mmol: molecular mass (normalized by m_u) Returns: - Sij(T): Line strength (cm) + Line strength (cm) """ - Tref = 296.0 - S0 = -A*g*np.exp(-hcperk*elower/Tref)*np.expm1(-hcperk*nu_lines/Tref)\ - / (8.0*np.pi*ccgs*nu_lines**2*QTref) - return S0 + line_strength_ref = ( + -A + * g + * np.exp(-hcperk * elower / Tref_original) + * np.expm1(-hcperk * nu_lines / Tref_original) + / (8.0 * np.pi * ccgs * nu_lines**2 * QTref) + ) + return line_strength_ref def gamma_exomol(P, T, n_air, alpha_ref): """gamma factor by a pressure broadening. Args: - P: pressure (bar) - T: temperature (K) - n_air: coefficient of the temperature dependence of the air-broadened halfwidth - alpha_ref: broadening parameter + P: pressure (bar) + T: temperature (K) + n_air: coefficient of the temperature dependence of the air-broadened halfwidth + alpha_ref: broadening parameter Returns: - gamma: pressure gamma factor (cm-1) + gamma: pressure gamma factor (cm-1) """ - Tref = 296.0 # reference tempearture (K) - gamma = alpha_ref*P*(Tref/T)**n_air + gamma = alpha_ref * P * (Tref_original / T) ** n_air return gamma @@ -48,9 +54,9 @@ def gamma_natural(A): 1/(4 pi c) = 2.6544188e-12 (cm-1 s) Args: - A: Einstein A-factor (1/s) + A: Einstein A-factor (1/s) Returns: - gamma_natural: natural width (cm-1) + gamma_natural: natural width (cm-1) """ - return 2.6544188e-12*A + return 2.6544188e-12 * A diff --git a/src/exojax/spec/hitranapi.py b/src/exojax/spec/hitranapi.py index 5008d7512..eb934fdd8 100644 --- a/src/exojax/spec/hitranapi.py +++ b/src/exojax/spec/hitranapi.py @@ -12,10 +12,10 @@ def molecid_hitran(molec): """molec id from Hitran/Hitemp filename or molecule name or molecid itself. Args: - molec: Hitran/Hitemp filename or molecule name or molec id itself. + molec: Hitran/Hitemp filename or molecule name or molec id itself. Return: - int: molecid (HITRAN molecular id) + int: molecid (HITRAN molecular id) """ try: hitf = molec.split('_') diff --git a/src/exojax/spec/hitrancia.py b/src/exojax/spec/hitrancia.py index 25b295faf..4a4406c37 100644 --- a/src/exojax/spec/hitrancia.py +++ b/src/exojax/spec/hitrancia.py @@ -3,58 +3,57 @@ from jax import jit, vmap -HITRAN_DEFCIA = \ - { - 'H2-CH4 (equilibrium)': 'H2-CH4_eq_2011.cia', - 'H2-CH4 (normal)': 'H2-CH4_norm_2011.cia', - 'H2-H2': 'H2-H2_2011.cia', - 'H2-H': 'H2-H_2011.cia', - 'H2-He': 'H2-He_2011.cia', - 'He-H': 'He-H_2011.cia', - 'N2-H2': 'N2-H2_2011.cia', - 'N2-He': 'N2-He_2018.cia', - 'N2-N2': 'N2-N2_2018.cia', - 'N2-air': 'N2-air_2018.cia', - 'N2-H2O': 'N2-H2O_2018.cia', - 'O2-CO2': 'O2-CO2_2011.cia', - 'O2-N2': 'O2-N2_2018.cia', - 'O2-O2': 'O2-O2_2018b.cia', - 'O2-air': 'O2-Air_2018.cia', - 'CO2-CO2': 'CO2-CO2_2018.cia', - 'CO2-H2': 'CO2-H2_2018.cia', - 'CO2-He': 'CO2-He_2018.cia', - 'CO2-CH4': 'CO2-CH4_2018.cia', - 'CH4-He': 'CH4-He_2018.cia' - } +HITRAN_DEFCIA = { + "H2-CH4 (equilibrium)": "H2-CH4_eq_2011.cia", + "H2-CH4 (normal)": "H2-CH4_norm_2011.cia", + "H2-H2": "H2-H2_2011.cia", + "H2-H": "H2-H_2011.cia", + "H2-He": "H2-He_2011.cia", + "He-H": "He-H_2011.cia", + "N2-H2": "N2-H2_2011.cia", + "N2-He": "N2-He_2018.cia", + "N2-N2": "N2-N2_2018.cia", + "N2-air": "N2-air_2018.cia", + "N2-H2O": "N2-H2O_2018.cia", + "O2-CO2": "O2-CO2_2011.cia", + "O2-N2": "O2-N2_2018.cia", + "O2-O2": "O2-O2_2018b.cia", + "O2-air": "O2-Air_2018.cia", + "CO2-CO2": "CO2-CO2_2018.cia", + "CO2-H2": "CO2-H2_2018.cia", + "CO2-He": "CO2-He_2018.cia", + "CO2-CH4": "CO2-CH4_2018.cia", + "CH4-He": "CH4-He_2018.cia", +} def read_cia(filename, nus, nue): """READ HITRAN CIA data. Args: - filename: HITRAN CIA file name (_2011.cia) - nus: wavenumber min (cm-1) - nue: wavenumber max (cm-1) + filename: HITRAN CIA file name (_2011.cia) + nus: wavenumber min (cm-1) + nue: wavenumber max (cm-1) Returns: - nucia: wavenumber (cm-1) - tcia: temperature (K) - ac: cia coefficient + nucia: wavenumber (cm-1) + tcia: temperature (K) + ac: cia coefficient """ # read first line - com = filename.split('/')[-1].split("_")[0] + com = filename.split("/")[-1].split("_")[0] print(com) - f = open(filename, 'r') + f = open(filename, "r") header = f.readline() info = header.strip().split() nnu = int(info[3]) nu = [] for i in range(0, nnu): column = f.readline().strip().split() - #print(column) + # print(column) nu.append(float(column[0])) f.close() - f = open(filename, 'r') + f = open(filename, "r") tcia = [] for line in f: line = line.strip() @@ -65,12 +64,12 @@ def read_cia(filename, nus, nue): tcia = np.array(tcia) nu = np.array(nu) ijnu = np.digitize([nus, nue], nu) - nucia = np.array(nu[ijnu[0]:ijnu[1]+1]) + nucia = np.array(nu[ijnu[0] : ijnu[1] + 1]) # read data data = np.loadtxt(filename, comments=com) - nt = data.shape[0]/nnu + nt = data.shape[0] / nnu data = data.reshape((int(nt), int(nnu), 2)) - ac = data[:, ijnu[0]:ijnu[1]+1, 1] + ac = data[:, ijnu[0] : ijnu[1] + 1, 1] return nucia, tcia, ac @@ -79,44 +78,49 @@ def interp_logacia_matrix(Tarr, nu_grid, nucia, tcia, logac): """interpolated function of log10(alpha_CIA) Args: - Tarr (1D array): temperature array (K) - nu_grid (1D array): wavenumber array (cm-1) - nucia: CIA wavenumber (cm-1) - tcia: CIA temperature (K) - logac: log10 cia coefficient + Tarr (1D array): temperature array (K) + nu_grid (1D array): wavenumber array (cm-1) + nucia: CIA wavenumber (cm-1) + tcia: CIA temperature (K) + logac: log10 cia coefficient Returns: - logac(Tarr, nus) + logac(Tarr, nus) Example: - >>> nucia,tcia,ac=read_cia("../../data/CIA/H2-H2_2011.cia",nus[0]-1.0,nus[-1]+1.0) - >>> logac=jnp.array(np.log10(ac)) - >>> interp_logacia_matrix(Tarr,nus,nucia,tcia,logac) + >>> nucia,tcia,ac=read_cia("../../data/CIA/H2-H2_2011.cia",nus[0]-1.0,nus[-1]+1.0) + >>> logac=jnp.array(np.log10(ac)) + >>> interp_logacia_matrix(Tarr,nus,nucia,tcia,logac) """ - def fcia(x, i): return jnp.interp(x, tcia, logac[:, i]) + + def fcia(x, i): + return jnp.interp(x, tcia, logac[:, i]) + vfcia = vmap(fcia, (None, 0), 0) mfcia = vmap(vfcia, (0, None), 0) inus = jnp.digitize(nu_grid, nucia) return mfcia(Tarr, inus) + @jit def interp_logacia_vector(T, nu_grid, nucia, tcia, logac): """interpolated function of log10(alpha_CIA) Args: - T (float): temperature (K) - nu_grid: wavenumber array (cm-1) - nucia: CIA wavenumber (cm-1) - tcia: CIA temperature (K) - logac: log10 cia coefficient + T (float): temperature (K) + nu_grid: wavenumber array (cm-1) + nucia: CIA wavenumber (cm-1) + tcia: CIA temperature (K) + logac: log10 cia coefficient Returns: - vector logac(T, nus) + vector logac(T, nus) """ - def fcia(x, i): return jnp.interp(x, tcia, logac[:, i]) + + def fcia(x, i): + return jnp.interp(x, tcia, logac[:, i]) + vfcia = vmap(fcia, (None, 0), 0) inus = jnp.digitize(nu_grid, nucia) return vfcia(T, inus) - - diff --git a/src/exojax/spec/lbderror.py b/src/exojax/spec/lbderror.py index 7815e3129..b481c71a8 100644 --- a/src/exojax/spec/lbderror.py +++ b/src/exojax/spec/lbderror.py @@ -3,7 +3,6 @@ from jax import vmap from jax import grad import numpy as np -import warnings import matplotlib.pyplot as plt import pkg_resources @@ -240,7 +239,7 @@ def default_elower_grid_trange_file(version): def optimal_params(Tl, Tu, diffmode=2, version=2, makefig=False, filename=None): - """derive the optimal parameters for a given Tu and Tl, + """derive the optimal parameters for a given Tu and Tl, which satisfies x % (1% if filename=None) precision within [Tl, Tu] Args: @@ -308,8 +307,9 @@ def optimal_params(Tl, Tu, diffmode=2, version=2, makefig=False, filename=None): return dEarr[k] * (diffmode + 1), Trefallow[ind[0]], Twtallow[ind[0]] if i == len(dEarr) - 1: - raise ValueError("could not find the optimal trange. use a different degt file or regenerate a new one.") - + raise ValueError( + "could not find the optimal trange. use a different degt file or regenerate a new one." + ) def _draw_map(value, ax, Trefarr, Twtarr, Tmax_view): diff --git a/src/exojax/spec/limb_darkening.py b/src/exojax/spec/limb_darkening.py index 47a21e62c..487b0c4a7 100644 --- a/src/exojax/spec/limb_darkening.py +++ b/src/exojax/spec/limb_darkening.py @@ -8,12 +8,12 @@ def ld_kipping(q1, q2): (arxiv:1308.0009) Args: - q1: U(0,1) - q2: U(0,1) + q1: U(0,1) + q2: U(0,1) Returns: - u1: quadratic LD coefficient u1 - u2: quadratic LD coefficient u2 + u1: quadratic LD coefficient u1 + u2: quadratic LD coefficient u2 """ sqrtq1 = jnp.sqrt(q1) return 2.0 * sqrtq1 * q2, sqrtq1 * (1.0 - 2.0 * q2) diff --git a/src/exojax/spec/lpf.py b/src/exojax/spec/lpf.py index 0a5cfc184..14df7d789 100644 --- a/src/exojax/spec/lpf.py +++ b/src/exojax/spec/lpf.py @@ -1,92 +1,120 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -"""Custom JVP version of the line profile functions used in exospectral -analysis.""" +"""Line Profile Functions method for opacity calculation.""" from jax import jit, vmap import jax.numpy as jnp from exojax.special.faddeeva import rewofz, imwofz from exojax.special.faddeeva import asymptotic_wofz from jax import custom_jvp - -# exomol from exojax.spec.exomol import gamma_exomol from exojax.spec.hitran import line_strength, doppler_sigma, gamma_natural - # vald -from exojax.spec.atomll import gamma_vald3 - -import warnings - +from exojax.spec.atomll import gamma_vald3, interp_QT_284 def exomol(mdb, Tarr, Parr, molmass): - """compute molecular line information required for MODIT using Exomol mdb. + """Computes molecular line information required for MODIT using Exomol mdb. Args: - mdb: mdb instance - Tarr: Temperature array - Parr: Pressure array - molmass: molecular mass + mdb: mdb instance + Tarr: Temperature array + Parr: Pressure array + molmass: molecular mass Returns: - line intensity matrix, - gammaL matrix, - sigmaD matrix + line intensity matrix, + gammaL matrix, + sigmaD matrix """ qt = vmap(mdb.qr_interp)(Tarr) - SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))(Tarr, mdb.logsij0, - mdb.dev_nu_lines, - mdb.elower, qt, mdb.Tref) - gammaLMP = jit(vmap(gamma_exomol, - (0, 0, None, None)))(Parr, Tarr, mdb.n_Texp, - mdb.alpha_ref) + SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))( + Tarr, mdb.logsij0, mdb.dev_nu_lines, mdb.elower, qt, mdb.Tref + ) + gammaLMP = jit(vmap(gamma_exomol, (0, 0, None, None)))( + Parr, Tarr, mdb.n_Texp, mdb.alpha_ref + ) gammaLMN = gamma_natural(mdb.A) gammaLM = gammaLMP + gammaLMN[None, :] - sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))(mdb.nu_lines, Tarr, - molmass) + sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))(mdb.nu_lines, Tarr, molmass) return SijM, gammaLM, sigmaDM def vald(adb, Tarr, PH, PHe, PHH): - """Compute VALD line information required for LPF using VALD atomic database (adb) - + """Computes VALD line information required for LPF using VALD atomic database (adb) + Args: - adb: adb instance made by the AdbVald class in moldb.py - Tarr: Temperature array - PH: Partial pressure array of neutral hydrogen (H) - PHe: Partial pressure array of neutral helium (He) - PHH: Partial pressure array of molecular hydrogen (H2) + adb: adb instance made by the AdbVald class in moldb.py + Tarr: Temperature array + PH: Partial pressure array of neutral hydrogen (H) + PHe: Partial pressure array of neutral helium (He) + PHH: Partial pressure array of molecular hydrogen (H2) Returns: - SijM: line intensity matrix - gammaLM: gammaL matrix - sigmaDM: sigmaD matrix - + SijM: line intensity matrix + gammaLM: gammaL matrix + sigmaDM: sigmaD matrix + """ # Compute normalized partition function for each species - qt_284 = vmap(adb.QT_interp_284)(Tarr) + qt_284 = vmap(interp_QT_284, (0, None, None))(Tarr, adb.T_gQT, adb.gQT_284species) qt = qt_284[:, adb.QTmask] - + qr = qt / adb.QTref_284[adb.QTmask] + # Compute line strength matrix SijM = jit(vmap(line_strength,(0,None,None,None,0,None)))\ - (Tarr, adb.logsij0, adb.nu_lines, adb.elower, qt, adb.Tref) - + (Tarr, adb.logsij0, adb.nu_lines, adb.elower, qr, adb.Tref) # Compute gamma parameters for the pressure and natural broadenings - gammaLM = jit(vmap(gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\ - (Tarr, PH, PHH, PHe, adb.ielem, adb.iion, adb.dev_nu_lines, adb.elower, adb.eupper, adb.atomicmass, adb.ionE, adb.gamRad, adb.gamSta, adb.vdWdamp, 1.0) + gammaLM = jit( + vmap( + gamma_vald3, + ( + 0, + 0, + 0, + 0, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + ), + ) + )( + Tarr, + PH, + PHH, + PHe, + adb.ielem, + adb.iion, + adb.dev_nu_lines, + adb.elower, + adb.eupper, + adb.atomicmass, + adb.ionE, + adb.gamRad, + adb.gamSta, + adb.vdWdamp, + 1.0, + ) # Compute doppler broadening - sigmaDM = jit(vmap(doppler_sigma,(None,0,None)))\ - (adb.nu_lines, Tarr, adb.atomicmass) + sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))( + adb.nu_lines, Tarr, adb.atomicmass + ) return SijM, gammaLM, sigmaDM + def vald_each(Tarr, PH, PHe, PHH, \ - qt_284_T, QTmask, \ - logsij0, nu_lines, ielem, iion, dev_nu_lines, elower, eupper, atomicmass, ionE, gamRad, gamSta, vdWdamp, Tref, ): + qt_284_T, QTmask, QTref_284, \ + logsij0, nu_lines, ielem, iion, dev_nu_lines, elower, eupper, atomicmass, ionE, gamRad, gamSta, vdWdamp, Tref): """Compute VALD line information required for LPF for separated each species Args: @@ -96,6 +124,7 @@ def vald_each(Tarr, PH, PHe, PHH, \ PHH: partial pressure array of molecular hydrogen (H2) [N_layer] qt_284_T: partition function at the temperature T Q(T), for 284 species QTmask: array of index of Q(Tref) grid (gQT) for each line + QTref_284: partition function at the reference temperature Q(Tref), for 284 species logsij0: log line strength at T=Tref nu_lines: line center (cm-1) in np.array (float64) ielem: atomic number (e.g., Fe=26) @@ -110,27 +139,65 @@ def vald_each(Tarr, PH, PHe, PHH, \ vdWdamp: log of (van der Waals damping constant / neutral hydrogen number) (s-1) Returns: - SijM: line intensity matrix [N_layer x N_line] - gammaLM: gammaL matrix [N_layer x N_line] - sigmaDM: sigmaD matrix [N_layer x N_line] - + SijM: line intensity matrix [N_layer x N_line] + gammaLM: gammaL matrix [N_layer x N_line] + sigmaDM: sigmaD matrix [N_layer x N_line] + """ # Compute normalized partition function for each species qt = qt_284_T[:, QTmask] + qr = qt / QTref_284[QTmask] # Compute line strength matrix + SijM = jit(vmap(line_strength,(0,None,None,None,0,None)))\ - (Tarr, logsij0, nu_lines, elower, qt, Tref) + (Tarr, logsij0, nu_lines, elower, qr, Tref) + SijM = jnp.nan_to_num(SijM, nan=0.0) # Compute gamma parameters for the pressure and natural broadenings - gammaLM = jit(vmap(gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\ - (Tarr, PH, PHH, PHe, ielem, iion, dev_nu_lines, elower, eupper, atomicmass, ionE, gamRad, gamSta, vdWdamp, 1.0) + gammaLM = jit( + vmap( + gamma_vald3, + ( + 0, + 0, + 0, + 0, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + ), + ) + )( + Tarr, + PH, + PHH, + PHe, + ielem, + iion, + dev_nu_lines, + elower, + eupper, + atomicmass, + ionE, + gamRad, + gamSta, + vdWdamp, + 1.0, + ) # Compute doppler broadening - sigmaDMn=jit(vmap(doppler_sigma,(None,0,None)))\ - (nu_lines, Tarr, atomicmass) - sigmaDM = jnp.where(sigmaDMn != 0, sigmaDMn, 1.) + sigmaDMn = jit(vmap(doppler_sigma, (None, 0, None)))(nu_lines, Tarr, atomicmass) + sigmaDM = jnp.where(sigmaDMn != 0, sigmaDMn, 1.0) return SijM, gammaLM, sigmaDM @@ -150,7 +217,7 @@ def ljert(x, a): ljert provides a L(x,a) function. This function accepts a scalar value as an input. Use jax.vmap to use a vector as an input. """ r2 = x * x + a * a - return jnp.where(r2 < 111., imwofz(x, a), jnp.imag(asymptotic_wofz(x, a))) + return jnp.where(r2 < 111.0, imwofz(x, a), jnp.imag(asymptotic_wofz(x, a))) @custom_jvp @@ -159,7 +226,7 @@ def hjert(x, a): combination of rewofz and real(asymptotic wofz). Args: - x: + x: a: Returns: @@ -167,23 +234,23 @@ def hjert(x, a): Examples: - hjert provides a Voigt-Hjerting function w/ custom JVP. + hjert provides a Voigt-Hjerting function w/ custom JVP. - >>> hjert(1.0,1.0) - DeviceArray(0.30474418, dtype=float32) + >>> hjert(1.0,1.0) + DeviceArray(0.30474418, dtype=float32) - This function accepts a scalar value as an input. Use jax.vmap to use a vector as an input. + This function accepts a scalar value as an input. Use jax.vmap to use a vector as an input. - >>> from jax import vmap - >>> x=jnp.linspace(0.0,1.0,10) - >>> vmap(hjert,(0,None),0)(x,1.0) - DeviceArray([0.42758358, 0.42568347, 0.4200511 , 0.41088563, 0.39850432,0.3833214 , 0.3658225 , 0.34653533, 0.32600054, 0.3047442 ],dtype=float32) - >>> a=jnp.linspace(0.0,1.0,10) - >>> vmap(hjert,(0,0),0)(x,a) - DeviceArray([1. , 0.8764037 , 0.7615196 , 0.6596299 , 0.5718791 ,0.49766064, 0.43553388, 0.3837772 , 0.34069115, 0.3047442 ],dtype=float32) + >>> from jax import vmap + >>> x=jnp.linspace(0.0,1.0,10) + >>> vmap(hjert,(0,None),0)(x,1.0) + DeviceArray([0.42758358, 0.42568347, 0.4200511 , 0.41088563, 0.39850432,0.3833214 , 0.3658225 , 0.34653533, 0.32600054, 0.3047442 ],dtype=float32) + >>> a=jnp.linspace(0.0,1.0,10) + >>> vmap(hjert,(0,0),0)(x,a) + DeviceArray([1. , 0.8764037 , 0.7615196 , 0.6596299 , 0.5718791 ,0.49766064, 0.43553388, 0.3837772 , 0.34069115, 0.3047442 ],dtype=float32) """ r2 = x * x + a * a - return jnp.where(r2 < 111., rewofz(x, a), jnp.real(asymptotic_wofz(x, a))) + return jnp.where(r2 < 111.0, rewofz(x, a), jnp.real(asymptotic_wofz(x, a))) @hjert.defjvp @@ -191,8 +258,7 @@ def hjert_jvp(primals, tangents): x, a = primals ux, ua = tangents dHdx = 2.0 * a * ljert(x, a) - 2.0 * x * hjert(x, a) - dHda = 2.0 * x * ljert(x, a) + 2.0 * a * hjert(x, a) - 2.0 / jnp.sqrt( - jnp.pi) + dHda = 2.0 * x * ljert(x, a) + 2.0 * a * hjert(x, a) - 2.0 / jnp.sqrt(jnp.pi) primal_out = hjert(x, a) tangent_out = dHdx * ux + dHda * ua return primal_out, tangent_out @@ -204,12 +270,12 @@ def voigtone(nu, sigmaD, gammaL): function. Args: - nu: wavenumber - sigmaD: sigma parameter in Doppler profile - gammaL: broadening coefficient in Lorentz profile + nu: wavenumber + sigmaD: sigma parameter in Doppler profile + gammaL: broadening coefficient in Lorentz profile Returns: - v: Voigt funtion + v: Voigt funtion """ sfac = 1.0 / (jnp.sqrt(2) * sigmaD) @@ -222,12 +288,12 @@ def voigt(nuvector, sigmaD, gammaL): """Custom JVP version of Voigt profile using Voigt-Hjerting function. Args: - nu: wavenumber array - sigmaD: sigma parameter in Doppler profile - gammaL: broadening coefficient in Lorentz profile + nu: wavenumber array + sigmaD: sigma parameter in Doppler profile + gammaL: broadening coefficient in Lorentz profile Returns: - v: Voigt profile + v: Voigt profile """ sfac = 1.0 / (jnp.sqrt(2.0) * sigmaD) @@ -241,12 +307,12 @@ def vvoigt(numatrix, sigmaD, gammas): """Custom JVP version of vmaped voigt profile. Args: - numatrix: wavenumber matrix in R^(Nline x Nwav) - sigmaD: doppler sigma vector in R^Nline - gammaL: gamma factor vector in R^Nline + numatrix: wavenumber matrix in R^(Nline x Nwav) + sigmaD: doppler sigma vector in R^Nline + gammaL: gamma factor vector in R^Nline Return: - Voigt profile vector in R^Nwav + Voigt profile vector in R^Nwav """ vmap_voigt = vmap(voigt, (0, 0, 0), 0) return vmap_voigt(numatrix, sigmaD, gammas) @@ -257,13 +323,13 @@ def xsvector(numatrix, sigmaD, gammaL, Sij): """Custom JVP version of cross section vector. Args: - numatrix: wavenumber matrix in R^(Nline x Nwav) - sigmaD: doppler sigma vector in R^Nline - gammaL: gamma factor vector in R^Nline - Sij: line strength vector in R^Nline + numatrix: wavenumber matrix in R^(Nline x Nwav) + sigmaD: doppler sigma vector in R^Nline + gammaL: gamma factor vector in R^Nline + Sij: line strength vector in R^Nline Return: - cross section vector in R^Nwav + cross section vector in R^Nwav """ return jnp.dot((vvoigt(numatrix, sigmaD, gammaL)).T, Sij) @@ -273,97 +339,12 @@ def xsmatrix(numatrix, sigmaDM, gammaLM, SijM): """Custom JVP version of cross section matrix. Args: - numatrix: wavenumber matrix in R^(Nline x Nwav) - sigmaDM: doppler sigma matrix in R^(Nlayer x Nline) - gammaLM: gamma factor matrix in R^(Nlayer x Nline) - SijM: line strength matrix in R^(Nlayer x Nline) + numatrix: wavenumber matrix in R^(Nline x Nwav) + sigmaDM: doppler sigma matrix in R^(Nlayer x Nline) + gammaLM: gamma factor matrix in R^(Nlayer x Nline) + SijM: line strength matrix in R^(Nlayer x Nline) Return: - cross section matrix in R^(Nlayer x Nwav) + cross section matrix in R^(Nlayer x Nwav) """ return vmap(xsvector, (None, 0, 0, 0))(numatrix, sigmaDM, gammaLM, SijM) - - -from exojax.spec.make_numatrix import make_numatrix0 -import numpy as np -import tqdm - - -def auto_xsection(nu, nu_lines, sigmaD, gammaL, Sij, memory_size=15.): - """compute cross section. - - Warning: - This is NOT auto-differentiable function. - - Args: - nu: wavenumber array - nu_lines: line center - sigmaD: sigma parameter in Doppler profile - gammaL: broadening coefficient in Lorentz profile - Sij: line strength - memory_size: memory size for numatrix0 (MB) - - Returns: - numpy.array: cross section (xsv) - - Example: - >>> from exojax.spec.lpf import auto_xsection - >>> from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural - >>> from exojax.spec import moldb - >>> import numpy as np - >>> nus=np.linspace(1000.0,10000.0,900000,dtype=np.float64) #cm-1 - >>> mdbCO=moldb.MdbHit('~/exojax/data/CO','05_hit12',nus) - >>> Mmol=28.010446441149536 # molecular weight - >>> Tfix=1000.0 # we assume T=1000K - >>> Pfix=1.e-3 # we compute P=1.e-3 bar - >>> Ppart=Pfix #partial pressure of CO. here we assume a 100% CO atmosphere. - >>> qt=mdbCO.qr_interp_lines(Tfix) - >>> Sij=SijT(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt) - >>> gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self) + gamma_natural(mdbCO.A) - >>> sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) - >>> nu_lines=mdbCO.nu_lines - >>> xsv=auto_xsection(nus,nu_lines,sigmaD,gammaL,Sij,memory_size=30) - 100%|████████████████████████████████████████████████████| 456/456 [00:03<00:00, 80.59it/s] - """ - NL = len(nu_lines) - d = int(memory_size/(NL*4/1024./1024.)) - if d > 0: - Ni = int(len(nu)/d) - xsv = [] - for i in tqdm.tqdm(range(0, Ni+1)): - s = int(i*d) - e = int((i+1)*d) - e = min(e, len(nu)) - numatrix = make_numatrix0(nu[s:e], nu_lines, warning=False) - xsv = np.concatenate( - [xsv, xsvector(numatrix, sigmaD, gammaL, Sij)]) - else: - NP = int((NL*4/1024./1024.)/memory_size)+1 - d = int(memory_size/(int(NL/NP)*4/1024./1024.)) - Ni = int(len(nu)/d) - dd = int(NL/NP) - xsv = [] - for i in tqdm.tqdm(range(0, Ni+1)): - s = int(i*d) - e = int((i+1)*d) - e = min(e, len(nu)) - xsvtmp = np.zeros_like(nu[s:e]) - for j in range(0, NP+1): - ss = int(j*dd) - ee = int((j+1)*dd) - ee = min(ee, NL) - numatrix = make_numatrix0( - nu[s:e], nu_lines[ss:ee], warning=False) - xsvtmp = xsvtmp + \ - xsvector(numatrix, sigmaD[ss:ee], - gammaL[ss:ee], Sij[ss:ee]) - xsv = np.concatenate([xsv, xsvtmp]) - - if(nu.dtype != np.float64): - warnings.warn('The wavenumber grid is not np.float64 but '+str(nu.dtype),UserWarning) - if(nu_lines.dtype != np.float64): - warnings.warn('The line centers (nu_lines) are not np.float64 but '+str(nu.dtype),UserWarning) - - - return xsv - diff --git a/src/exojax/spec/make_numatrix.py b/src/exojax/spec/make_numatrix.py index 78d799d5e..942af0d23 100644 --- a/src/exojax/spec/make_numatrix.py +++ b/src/exojax/spec/make_numatrix.py @@ -8,22 +8,22 @@ def make_numatrix0(nu, hatnu, warning=True): """Generate numatrix0. Note: - Use float64 as inputs. + Use float64 as inputs. Args: - nu: wavenumber matrix (Nnu,) - hatnu: line center wavenumber vector (Nline,), where Nm is the number of lines - warning: True=warning on for nu.dtype=float32 + nu: wavenumber matrix (Nnu,) + hatnu: line center wavenumber vector (Nline,), where Nm is the number of lines + warning: True=warning on for nu.dtype=float32 Returns: - numatrix (Nline,Nnu) + numatrix (Nline,Nnu) """ - if (nu.dtype != np.float64 and warning): - warnings.warn('wavenumber grid is not np.float64 but ' + str(nu.dtype), - UserWarning) - if (hatnu.dtype != np.float64 and warning): - warnings.warn('line center is not np.float64 but ' + str(nu.dtype), - UserWarning) + if nu.dtype != np.float64 and warning: + warnings.warn( + "wavenumber grid is not np.float64 but " + str(nu.dtype), UserWarning + ) + if hatnu.dtype != np.float64 and warning: + warnings.warn("line center is not np.float64 but " + str(nu.dtype), UserWarning) numatrix = nu[None, :] - hatnu[:, None] return jnp.array(numatrix) @@ -32,11 +32,11 @@ def divwavnum(nu, Nz=1): """separate an integer part from a residual. Args: - nu: wavenumber array - Nz: boost factor (default=1) + nu: wavenumber array + Nz: boost factor (default=1) Returns: - integer part of wavenumber, residual wavenumber, boost factor + integer part of wavenumber, residual wavenumber, boost factor """ fn = np.floor(nu * Nz) @@ -49,16 +49,16 @@ def subtract_nu(dnu, dhatnu): """compute nu - hatnu by subtracting an integer part w/JIT Args: - dnu: residual wavenumber array - dhatnu: residual line center array + dnu: residual wavenumber array + dhatnu: residual line center array Returns: - difference matrix + difference matrix """ jdnu = jnp.array(dnu) jdhatnu = jnp.array(dhatnu) - dd = (jdnu[None, :] - jdhatnu[:, None]) + dd = jdnu[None, :] - jdhatnu[:, None] return dd @@ -71,9 +71,9 @@ def add_nu(dd, fnu, fhatnu, Nz): fnu: integer part of wavenumber fhatnu: residual wavenumber Nz: boost factor - + Returns: - an integer part readded value + an integer part readded value """ jfnu = jnp.array(fnu) @@ -87,16 +87,16 @@ def make_numatrix0_subtract(nu, hatnu, Nz=1, warning=True): """Generate numatrix0 using gpu. Note: - This function computes a wavenumber matrix using XLA. Because XLA does not support float64, a direct computation sometimes results in large uncertainity. For instace, let's assume nu=2000.0396123 cm-1 and hatnu=2000.0396122 cm-1. If applying float32, we get np.float32(2000.0396123)-np.float32(2000.0396122) = 0.0. But, after subtracting 2000 from both nu and hatnu, we get np.float32(0.0396123)-np.float32(0.0396122)=1.0058284e-07. make_numatrix0 does such computation. Nz=1 means we subtract a integer part (i.e. 2000), Nz=10 means we subtract 2000.0, and Nz=10 means we subtract 2000.00. + This function computes a wavenumber matrix using XLA. Because XLA does not support float64, a direct computation sometimes results in large uncertainity. For instace, let's assume nu=2000.0396123 cm-1 and hatnu=2000.0396122 cm-1. If applying float32, we get np.float32(2000.0396123)-np.float32(2000.0396122) = 0.0. But, after subtracting 2000 from both nu and hatnu, we get np.float32(0.0396123)-np.float32(0.0396122)=1.0058284e-07. make_numatrix0 does such computation. Nz=1 means we subtract a integer part (i.e. 2000), Nz=10 means we subtract 2000.0, and Nz=10 means we subtract 2000.00. Args: - nu: wavenumber matrix (Nnu,) - hatnu: line center wavenumber vector (Nline,), where Nm is the number of lines - Nz: boost factor (default=1) - warning: True=warning on for nu.dtype=float32 + nu: wavenumber matrix (Nnu,) + hatnu: line center wavenumber vector (Nline,), where Nm is the number of lines + Nz: boost factor (default=1) + warning: True=warning on for nu.dtype=float32 Returns: - numatrix0: wavenumber matrix w/ no shift + numatrix0: wavenumber matrix w/ no shift """ fnu, dnu, Nz = divwavnum(nu, Nz) @@ -104,26 +104,3 @@ def make_numatrix0_subtract(nu, hatnu, Nz=1, warning=True): dd = subtract_nu(dnu, dhatnu) numatrix0 = add_nu(dd, fnu, fhatnu, Nz) return numatrix0 - - -if __name__ == '__main__': - import matplotlib.pyplot as plt - from exojax.utils.grids import wavenumber_grid - from exojax.spec import moldb - import time - import numpy as np - nu_grid, wav, res = wavenumber_grid(22920, 23000, 1000, unit='AA') - mdbCO = moldb.MdbExomol('.database/CO/12C-16O/Li2015', nu_grid, crit=1.e-46) - ts = time.time() - numatrix = make_numatrix0(nu_grid, mdbCO.nu_lines) - print(np.median(numatrix)) - te = time.time() - print(te - ts, 'sec') - - ts = time.time() - numatrixc = make_numatrix0_device(nu_grid, mdbCO.nu_lines) - print(np.median(numatrix)) - te = time.time() - print(te - ts, 'sec') - - print(np.sum((numatrixc - numatrix)**2)) diff --git a/src/exojax/spec/mie.py b/src/exojax/spec/mie.py index 0f5b7ef38..946b67661 100644 --- a/src/exojax/spec/mie.py +++ b/src/exojax/spec/mie.py @@ -49,7 +49,7 @@ def compute_mie_coeff_lognormal_grid( Nsigmag = len(sigmag_arr) Nrg = len(rg_arr) Nmiecoeff = 7 - miegrid = np.zeros((Nrg, Nsigmag, Nwav, Nmiecoeff), dtype=np.complex128) + miegrid = np.zeros((Nrg, Nsigmag, Nwav, Nmiecoeff)) for ind_sigmag, sigmag in enumerate(tqdm(sigmag_arr)): for ind_rg, rg_nm in enumerate(tqdm(np.array(rg_arr) * cm2nm)): diff --git a/src/exojax/spec/modit.py b/src/exojax/spec/modit.py index 1222978a6..0033b1530 100644 --- a/src/exojax/spec/modit.py +++ b/src/exojax/spec/modit.py @@ -5,6 +5,7 @@ * The concept of "folding" can be understood by reading `the discussion `_ by D.C.M van den Bekerom. * See also `DIT for non evenly-spaced linear grid `_ by D.C.M van den Bekerom, as a reference of this code. """ + import warnings import numpy as np import jax.numpy as jnp @@ -25,11 +26,11 @@ from exojax.spec.hitran import gamma_hitran # vald -from exojax.spec.atomll import gamma_vald3, interp_QT284 +from exojax.spec.atomll import gamma_vald3, interp_QT_284 +from exojax.utils.constants import Tref_original -def calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, - log_ngammaL_grid): +def calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, log_ngammaL_grid): """Compute cross section from LSD in MODIT algorithm The original code is rundit_fold_logredst in `addit package `_ ). MODIT folded voigt for ESLOG for reduced wavenumebr inputs (against the truncation error) for a constant normalized beta @@ -42,12 +43,12 @@ def calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, nu_grid: linear wavenumber grid log_gammaL_grid: logarithm of gammaL grid - Note: - When you have the error such as: - "failed to initialize batched cufft plan with customized allocator: + Note: + When you have the error such as: + "failed to initialize batched cufft plan with customized allocator: Allocating 8000000160 bytes exceeds the memory limit of 4294967296 bytes." consider to use moditscanfft.calc_xsection_from_lsd, instead. - + Returns: Cross section in the log nu grid """ @@ -62,20 +63,23 @@ def calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, # fftvalsum = jnp.sum(til_Slsd*til_Voigt,axis=(1,)) # return jnp.fft.irfft(fftvalsum)[:Ng_nu]*R/nu_grid # ----------------------------------------------- - vk = fold_voigt_kernel_logst(jnp.fft.rfftfreq(2 * Ng_nu, 1), - jnp.log(nsigmaD), log_ngammaL_grid, Ng_nu, - pmarray) - fftvalsum = jnp.sum(fftval * vk, axis=(1, )) + vk = fold_voigt_kernel_logst( + jnp.fft.rfftfreq(2 * Ng_nu, 1), + jnp.log(nsigmaD), + log_ngammaL_grid, + Ng_nu, + pmarray, + ) + fftvalsum = jnp.sum(fftval * vk, axis=(1,)) return jnp.fft.irfft(fftvalsum)[:Ng_nu] * R / nu_grid @jit -def xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, S, nu_grid, - ngammaL_grid): +def xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, S, nu_grid, ngammaL_grid): """Cross section vector (MODIT) Notes: - Currently due to #277, we recommend to use + Currently due to #277, we recommend to use modit_scanfft.xsvector_scanfft instead of xsvector. However, this will be changed when cufft fixes the 4GB limit. @@ -84,7 +88,7 @@ def xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, S, nu_grid, indexnu: index by npgetix for wavenumber R: spectral resolution pmarray: (+1,-1) array whose length of len(nu_grid)+1 - nsigmaD: normaized Gaussian STD + nsigmaD: normaized Gaussian STD gammaL: Lorentzian half width (Nlines) S: line strength (Nlines) nu_grid: linear wavenumber grid @@ -96,20 +100,17 @@ def xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, S, nu_grid, log_ngammaL_grid = jnp.log(ngammaL_grid) lsd_array = jnp.zeros((len(nu_grid), len(ngammaL_grid))) - Slsd = inc2D_givenx(lsd_array, S, cnu, indexnu, jnp.log(ngammaL), - log_ngammaL_grid) - xs = calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, - log_ngammaL_grid) + Slsd = inc2D_givenx(lsd_array, S, cnu, indexnu, jnp.log(ngammaL), log_ngammaL_grid) + xs = calc_xsection_from_lsd(Slsd, R, pmarray, nsigmaD, nu_grid, log_ngammaL_grid) return xs @jit -def xsmatrix(cnu, indexnu, R, pmarray, nsigmaDl, ngammaLM, SijM, nu_grid, - dgm_ngammaL): +def xsmatrix(cnu, indexnu, R, pmarray, nsigmaDl, ngammaLM, SijM, nu_grid, dgm_ngammaL): """Cross section matrix for xsvector (MODIT) Notes: - Currently due to #277, we recommend to use + Currently due to #277, we recommend to use modit_scanfft.xsmatrix_scanfft instead of xsmatrix. However, this will be changed when cufft fixes the 4GB limit. @@ -136,11 +137,12 @@ def xsmatrix(cnu, indexnu, R, pmarray, nsigmaDl, ngammaLM, SijM, nu_grid, def fxs(x, arr): carry = 0.0 nsigmaD = arr[0:1] - ngammaL = arr[1:Nline + 1] - Sij = arr[Nline + 1:2 * Nline + 1] - ngammaL_grid = arr[2 * Nline + 1:2 * Nline + NDITgrid + 1] - arr = xsvector(cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, - nu_grid, ngammaL_grid) + ngammaL = arr[1 : Nline + 1] + Sij = arr[Nline + 1 : 2 * Nline + 1] + ngammaL_grid = arr[2 * Nline + 1 : 2 * Nline + NDITgrid + 1] + arr = xsvector( + cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, nu_grid, ngammaL_grid + ) return carry, arr val, xsm = scan(fxs, 0.0, Mat) @@ -157,19 +159,19 @@ def exomol(mdb, Tarr, Parr, R, molmass): R: spectral resolution molmass: molecular mass wavmask: mask for wavenumber #Issue 341 - + Returns: line intensity matrix, normalized gammaL matrix, normalized sigmaD matrix """ - qt = vmap(mdb.qr_interp)(Tarr) - SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))(Tarr, mdb.logsij0, - mdb.dev_nu_lines, - mdb.elower, qt, mdb.Tref) - gammaLMP = jit(vmap(gamma_exomol, - (0, 0, None, None)))(Parr, Tarr, mdb.n_Texp, - mdb.alpha_ref) + qt = vmap(mdb.qr_interp, (0, None))(Tarr, Tref_original) + SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))( + Tarr, mdb.logsij0, mdb.dev_nu_lines, mdb.elower, qt, Tref_original + ) + gammaLMP = jit(vmap(gamma_exomol, (0, 0, None, None)))( + Parr, Tarr, mdb.n_Texp, mdb.alpha_ref + ) gammaLMN = gamma_natural(mdb.A) gammaLM = gammaLMP + gammaLMN[None, :] ngammaLM = gammaLM / (mdb.dev_nu_lines / R) @@ -180,12 +182,12 @@ def exomol(mdb, Tarr, Parr, R, molmass): def setdgm_exomol(mdb, fT, Parr, R, molmass, dit_grid_resolution, *kargs): warn_msg = " Use `modit.set_ditgrid_matrix_exomol` instead" warnings.warn(warn_msg, FutureWarning) - return set_ditgrid_matrix_exomol(mdb, fT, Parr, R, molmass, - dit_grid_resolution, *kargs) + return set_ditgrid_matrix_exomol( + mdb, fT, Parr, R, molmass, dit_grid_resolution, *kargs + ) -def set_ditgrid_matrix_exomol(mdb, fT, Parr, R, molmass, dit_grid_resolution, - *kargs): +def set_ditgrid_matrix_exomol(mdb, fT, Parr, R, molmass, dit_grid_resolution, *kargs): """Easy Setting of DIT Grid Matrix (dgm) using Exomol. Args: @@ -212,10 +214,10 @@ def set_ditgrid_matrix_exomol(mdb, fT, Parr, R, molmass, dit_grid_resolution, Tarr_list = fT(*kargs) for Tarr in Tarr_list: SijM, ngammaLM, nsigmaDl = exomol(mdb, Tarr, Parr, R, molmass) - set_dgm_minmax.append( - minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) + set_dgm_minmax.append(minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) dgm_ngammaL = precompute_modit_ditgrid_matrix( - set_dgm_minmax, dit_grid_resolution=dit_grid_resolution) + set_dgm_minmax, dit_grid_resolution=dit_grid_resolution + ) return jnp.array(dgm_ngammaL) @@ -235,14 +237,13 @@ def hitran(mdb, Tarr, Parr, Pself, R, molmass): normalized gammaL matrix, normalized sigmaD matrix """ - qt = vmap(mdb.qr_interp_lines)(Tarr) - SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))(Tarr, mdb.logsij0, - mdb.dev_nu_lines, - mdb.elower, qt, mdb.Tref) - gammaLMP = jit(vmap(gamma_hitran, - (0, 0, 0, None, None, None)))(Parr, Tarr, Pself, - mdb.n_air, mdb.gamma_air, - mdb.gamma_self) + qt = vmap(mdb.qr_interp_lines, (0, None))(Tarr, Tref_original) + SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))( + Tarr, mdb.logsij0, mdb.dev_nu_lines, mdb.elower, qt, Tref_original + ) + gammaLMP = jit(vmap(gamma_hitran, (0, 0, 0, None, None, None)))( + Parr, Tarr, Pself, mdb.n_air, mdb.gamma_air, mdb.gamma_self + ) gammaLMN = gamma_natural(mdb.A) gammaLM = gammaLMP + gammaLMN[None, :] ngammaLM = gammaLM / (mdb.dev_nu_lines / R) @@ -250,16 +251,17 @@ def hitran(mdb, Tarr, Parr, Pself, R, molmass): return SijM, ngammaLM, nsigmaDl -def setdgm_hitran(mdb, fT, Parr, Pself_ref, R, molmass, dit_grid_resolution, - *kargs): +def setdgm_hitran(mdb, fT, Parr, Pself_ref, R, molmass, dit_grid_resolution, *kargs): warn_msg = " Use `modit.set_ditgrid_matrix_hitran` instead" warnings.warn(warn_msg, FutureWarning) - return set_ditgrid_matrix_hitran(mdb, fT, Parr, Pself_ref, R, molmass, - dit_grid_resolution, *kargs) + return set_ditgrid_matrix_hitran( + mdb, fT, Parr, Pself_ref, R, molmass, dit_grid_resolution, *kargs + ) -def set_ditgrid_matrix_hitran(mdb, fT, Parr, Pself_ref, R, molmass, - dit_grid_resolution, *kargs): +def set_ditgrid_matrix_hitran( + mdb, fT, Parr, Pself_ref, R, molmass, dit_grid_resolution, *kargs +): """Easy Setting of DIT Grid Matrix (dgm) using HITRAN/HITEMP. Args: @@ -286,18 +288,37 @@ def set_ditgrid_matrix_hitran(mdb, fT, Parr, Pself_ref, R, molmass, set_dgm_minmax = [] Tarr_list = fT(*kargs) for Tarr in Tarr_list: - SijM, ngammaLM, nsigmaDl = hitran(mdb, Tarr, Parr, Pself_ref, R, - molmass) - set_dgm_minmax.append( - minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) + SijM, ngammaLM, nsigmaDl = hitran(mdb, Tarr, Parr, Pself_ref, R, molmass) + set_dgm_minmax.append(minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) dgm_ngammaL = precompute_modit_ditgrid_matrix( - set_dgm_minmax, dit_grid_resolution=dit_grid_resolution) + set_dgm_minmax, dit_grid_resolution=dit_grid_resolution + ) return jnp.array(dgm_ngammaL) @jit -def vald_each(Tarr, PH, PHe, PHH, R, qt_284_T, QTmask, \ - ielem, iion, atomicmass, ionE, dev_nu_lines, logsij0, elower, eupper, gamRad, gamSta, vdWdamp, Tref): +def vald_each( + Tarr, + PH, + PHe, + PHH, + R, + qt_284_T, + QTmask, + QTref_284, + ielem, + iion, + atomicmass, + ionE, + dev_nu_lines, + logsij0, + elower, + eupper, + gamRad, + gamSta, + vdWdamp, + Tref, +): """Compute atomic line information required for MODIT for separated EACH species, using parameters attributed in VALD separated atomic database (asdb). Args: @@ -308,6 +329,7 @@ def vald_each(Tarr, PH, PHe, PHH, R, qt_284_T, QTmask, \ R: spectral resolution [scalar] qt_284_T: partition function at the temperature T Q(T), for 284 species QTmask: array of index of Q(Tref) grid (gQT) for each line + QTref_284: partition function at the reference temperature Q(Tref), for 284 species ielem: atomic number (e.g., Fe=26) iion: ionized level (e.g., neutral=1, singly ionized=2, etc.) atomicmass: atomic mass (amu) @@ -328,14 +350,52 @@ def vald_each(Tarr, PH, PHe, PHH, R, qt_284_T, QTmask, \ """ # Compute normalized partition function for each species qt = qt_284_T[:, QTmask] + qr = qt / QTref_284[QTmask] # Compute line strength matrix - SijM = jit(vmap(line_strength,(0,None,None,None,0,None)))\ - (Tarr, logsij0, dev_nu_lines, elower, qt, Tref) + SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))( + Tarr, logsij0, dev_nu_lines, elower, qr, Tref + ) # Compute gamma parameters for the pressure and natural broadenings - gammaLM = jit(vmap(gamma_vald3,(0,0,0,0,None,None,None,None,None,None,None,None,None,None,None)))\ - (Tarr, PH, PHH, PHe, ielem, iion, dev_nu_lines, elower, eupper, atomicmass, ionE, gamRad, gamSta, vdWdamp, 1.0) + gammaLM = jit( + vmap( + gamma_vald3, + ( + 0, + 0, + 0, + 0, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + ), + ) + )( + Tarr, + PH, + PHH, + PHe, + ielem, + iion, + dev_nu_lines, + elower, + eupper, + atomicmass, + ionE, + gamRad, + gamSta, + vdWdamp, + 1.0, + ) ngammaLM = gammaLM / (dev_nu_lines / R) # Do NOT remove NaN because "set_ditgrid_matrix_vald_each" makes good use of them. # ngammaLM = jnp.nan_to_num(ngammaLM, nan = 0.0) @@ -362,31 +422,136 @@ def vald_all(asdb, Tarr, PH, PHe, PHH, R): """ gQT_284species = asdb.gQT_284species T_gQT = asdb.T_gQT - qt_284_T = vmap(interp_QT284, (0, None, None))(Tarr, T_gQT, gQT_284species) - - SijMS, ngammaLMS, nsigmaDlS = jit(vmap(vald_each, (None, None, None, None, None, None, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, )))\ - (Tarr, PH, PHe, PHH, R, qt_284_T, \ - asdb.QTmask, asdb.ielem, asdb.iion, asdb.atomicmass, asdb.ionE, \ - asdb.dev_nu_lines, asdb.logsij0, asdb.elower, asdb.eupper, asdb.gamRad, asdb.gamSta, asdb.vdWdamp, asdb.Tref) + qt_284_T = vmap(interp_QT_284, (0, None, None))(Tarr, T_gQT, gQT_284species) + + SijMS, ngammaLMS, nsigmaDlS = jit( + vmap( + vald_each, + ( + None, + None, + None, + None, + None, + None, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + None, + ), + ) + )( + Tarr, + PH, + PHe, + PHH, + R, + qt_284_T, + asdb.QTmask, + asdb.QTref_284, + asdb.ielem, + asdb.iion, + asdb.atomicmass, + asdb.ionE, + asdb.dev_nu_lines, + asdb.logsij0, + asdb.elower, + asdb.eupper, + asdb.gamRad, + asdb.gamSta, + asdb.vdWdamp, + asdb.Tref, + ) return SijMS, ngammaLMS, nsigmaDlS -def setdgm_vald_each(ielem, iion, atomicmass, ionE, dev_nu_lines, logsij0, elower, eupper, gamRad, gamSta, vdWdamp, Tref, \ - QTmask, T_gQT, gQT_284species, PH, PHe, PHH, R, fT, dit_grid_resolution, *kargs): +def setdgm_vald_each( + ielem, + iion, + atomicmass, + ionE, + dev_nu_lines, + logsij0, + elower, + eupper, + gamRad, + gamSta, + vdWdamp, + Tref, + QTmask, + T_gQT, + gQT_284species, + PH, + PHe, + PHH, + R, + fT, + dit_grid_resolution, + *kargs +): warn_msg = " Use `modit.set_ditgrid_matrix_vald_each` instead" warnings.warn(warn_msg, FutureWarning) - return set_ditgrid_matrix_vald_each(ielem, iion, atomicmass, ionE, - dev_nu_lines, logsij0, elower, eupper, - gamRad, gamSta, vdWdamp, Tref, QTmask, T_gQT, - gQT_284species, PH, PHe, PHH, R, fT, - dit_grid_resolution, *kargs) - - -def set_ditgrid_matrix_vald_each(ielem, iion, atomicmass, ionE, dev_nu_lines, - logsij0, elower, eupper, gamRad, gamSta, - vdWdamp, Tref, QTmask, T_gQT, gQT_284species, PH, - PHe, PHH, R, fT, dit_grid_resolution, *kargs): + return set_ditgrid_matrix_vald_each( + ielem, + iion, + atomicmass, + ionE, + dev_nu_lines, + logsij0, + elower, + eupper, + gamRad, + gamSta, + vdWdamp, + Tref, + QTmask, + T_gQT, + gQT_284species, + PH, + PHe, + PHH, + R, + fT, + dit_grid_resolution, + *kargs + ) + + +def set_ditgrid_matrix_vald_each( + ielem, + iion, + atomicmass, + ionE, + dev_nu_lines, + logsij0, + elower, + eupper, + gamRad, + gamSta, + vdWdamp, + Tref, + QTmask, + T_gQT, + gQT_284species, + PH, + PHe, + PHH, + R, + fT, + dit_grid_resolution, + *kargs +): """Easy Setting of DIT Grid Matrix (dgm) using VALD. Args: @@ -419,35 +584,56 @@ def set_ditgrid_matrix_vald_each(ielem, iion, atomicmass, ionE, dev_nu_lines, set_dgm_minmax = [] Tarr_list = fT(*kargs) for Tarr in Tarr_list: - qt_284_T = vmap(interp_QT284, (0, None, None))(Tarr, T_gQT, - gQT_284species) - SijM, ngammaLM, nsigmaDl = vald_each(Tarr, PH, PHe, PHH, R, qt_284_T, \ - QTmask, ielem, iion, atomicmass, ionE, \ - dev_nu_lines, logsij0, elower, eupper, gamRad, gamSta, vdWdamp, Tref) - floop = lambda c, arr: (c, - jnp.nan_to_num(arr, - nan=jnp.nanmin(arr), - posinf=jnp.nanmin(arr), - neginf=jnp.nanmin(arr))) + qt_284_T = vmap(interp_QT_284, (0, None, None))(Tarr, T_gQT, gQT_284species) + QTref_284 = jnp.array(interp_QT_284(Tref, T_gQT, gQT_284species)) + + SijM, ngammaLM, nsigmaDl = vald_each( + Tarr, + PH, + PHe, + PHH, + R, + qt_284_T, + QTmask, + QTref_284, + ielem, + iion, + atomicmass, + ionE, + dev_nu_lines, + logsij0, + elower, + eupper, + gamRad, + gamSta, + vdWdamp, + Tref, + ) + floop = lambda c, arr: ( + c, + jnp.nan_to_num( + arr, nan=jnp.nanmin(arr), posinf=jnp.nanmin(arr), neginf=jnp.nanmin(arr) + ), + ) ngammaLM = scan(floop, 0, ngammaLM)[1] - set_dgm_minmax.append( - minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) + set_dgm_minmax.append(minmax_ditgrid_matrix(ngammaLM, dit_grid_resolution)) dgm_ngammaL = precompute_modit_ditgrid_matrix( - set_dgm_minmax, dit_grid_resolution=dit_grid_resolution) + set_dgm_minmax, dit_grid_resolution=dit_grid_resolution + ) return jnp.array(dgm_ngammaL) def setdgm_vald_all(asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, *kargs): warn_msg = " Use `modit.set_ditgrid_matrix_vald_all` instead" warnings.warn(warn_msg, FutureWarning) - return set_ditgrid_matrix_vald_all(asdb, PH, PHe, PHH, R, fT, - dit_grid_resolution, *kargs) + return set_ditgrid_matrix_vald_all( + asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, *kargs + ) -def set_ditgrid_matrix_vald_all(asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, - *kargs): +def set_ditgrid_matrix_vald_all(asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, *kargs): """Easy Setting of DIT Grid Matrix (dgm) using VALD. - + Args: asdb: asdb instance made by the AdbSepVald class in moldb.py PH: partial pressure array of neutral hydrogen (H) [N_layer] @@ -474,16 +660,38 @@ def set_ditgrid_matrix_vald_all(asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, dgm_ngammaLS_BeforePadding = [] lendgm = [] for i in range(asdb.N_usp): - dgm_ngammaL_sp = set_ditgrid_matrix_vald_each(asdb.ielem[i], asdb.iion[i], asdb.atomicmass[i], asdb.ionE[i], \ - asdb.dev_nu_lines[i], asdb.logsij0[i], asdb.elower[i], asdb.eupper[i], asdb.gamRad[i], asdb.gamSta[i], asdb.vdWdamp[i], asdb.Tref, \ - asdb.QTmask[i], T_gQT, gQT_284species, PH, PHe, PHH, R, fT, dit_grid_resolution, *kargs) + dgm_ngammaL_sp = set_ditgrid_matrix_vald_each( + asdb.ielem[i], + asdb.iion[i], + asdb.atomicmass[i], + asdb.ionE[i], + asdb.dev_nu_lines[i], + asdb.logsij0[i], + asdb.elower[i], + asdb.eupper[i], + asdb.gamRad[i], + asdb.gamSta[i], + asdb.vdWdamp[i], + asdb.Tref, + asdb.QTmask[i], + T_gQT, + gQT_284species, + PH, + PHe, + PHH, + R, + fT, + dit_grid_resolution, + *kargs + ) dgm_ngammaLS_BeforePadding.append(dgm_ngammaL_sp) lendgm.append(dgm_ngammaL_sp.shape[1]) Lmax_dgm = np.max(np.array(lendgm)) # Padding to unity the length of all the DIT Grid Matrix (dgm) and convert them into jnp.array - pad2Dm = lambda arr, L: jnp.pad(arr, ((0, 0), (0, L - arr.shape[1])), - mode='maximum') + pad2Dm = lambda arr, L: jnp.pad( + arr, ((0, 0), (0, L - arr.shape[1])), mode="maximum" + ) dgm_ngammaLS = np.zeros([asdb.N_usp, len(PH), Lmax_dgm]) for i_sp, dgmi in enumerate(dgm_ngammaLS_BeforePadding): dgm_ngammaLS[i_sp] = pad2Dm(dgmi, Lmax_dgm) @@ -491,8 +699,9 @@ def set_ditgrid_matrix_vald_all(asdb, PH, PHe, PHH, R, fT, dit_grid_resolution, @jit -def xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, - nu_grid, dgm_ngammaLS): +def xsmatrix_vald( + cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nu_grid, dgm_ngammaLS +): """Cross section matrix for xsvector (MODIT) for VALD lines (asdb) Args: @@ -509,8 +718,9 @@ def xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, Return: xsmS: cross section matrix [N_species x N_layer x N_wav] """ - xsmS = jit(vmap(xsmatrix, (0, 0, None, None, 0, 0, 0, None, 0)))(\ - cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nu_grid, dgm_ngammaLS) + xsmS = jit(vmap(xsmatrix, (0, 0, None, None, 0, 0, 0, None, 0)))( + cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nu_grid, dgm_ngammaLS + ) xsmS = jnp.abs(xsmS) return xsmS @@ -529,8 +739,7 @@ def precompute_dgmatrix(set_gm_minmax, dit_grid_resolution=0.1, adopt=True): """ warn_msg = " Use `set_ditgrid.precompute_modit_ditgrid_matrix` instead" warnings.warn(warn_msg, FutureWarning) - return precompute_modit_ditgrid_matrix(set_gm_minmax, dit_grid_resolution, - adopt) + return precompute_modit_ditgrid_matrix(set_gm_minmax, dit_grid_resolution, adopt) def minmax_dgmatrix(x, dit_grid_resolution=0.1, adopt=True): @@ -564,6 +773,7 @@ def dgmatrix(x, dit_grid_resolution=0.1, adopt=True): warn_msg = "Deprecated Use `set_ditgrid.ditgrid_matrix` instead" warnings.warn(warn_msg, FutureWarning) from exojax.spec.set_ditgrid import ditgrid_matrix + return ditgrid_matrix(x, dit_grid_resolution, adopt) @@ -583,6 +793,7 @@ def ditgrid(x, dit_grid_resolution=0.1, adopt=True): warn_msg = "Deprecated Use `set_ditgrid.ditgrid_log_interval` instead" warnings.warn(warn_msg, FutureWarning) from exojax.spec.set_ditgrid import ditgrid_log_interval + return ditgrid_log_interval(x, dit_grid_resolution, adopt) diff --git a/src/exojax/spec/moldb.py b/src/exojax/spec/moldb.py index 429fa9037..50f4007df 100644 --- a/src/exojax/spec/moldb.py +++ b/src/exojax/spec/moldb.py @@ -1,19 +1,21 @@ -"""Molecular database (MDB) class. +"""Atomic database (MDB) class. """ + import numpy as np import jax.numpy as jnp import pathlib -import vaex import warnings from exojax.spec import atomllapi, atomll from exojax.utils.constants import Tref_original -from exojax.spec import api -__all__ = ['AdbVald', 'AdbSepVald', 'AdbKurucz'] + +__all__ = ["AdbVald", "AdbSepVald", "AdbKurucz"] explanation_states = "Note: Couldn't find the hdf5 format. We convert data to the hdf5 format. After the second time, it will become much faster." explanation_trans = "Note: Couldn't find the hdf5 format. We convert data to the hdf5 format. After the second time, it will become much faster." -warning_old_exojax = 'It seems that the hdf5 file for the transition file was created using the old version of exojax<1.1. Try again after removing ' +warning_old_exojax = "It seems that the hdf5 file for the transition file was created using the old version of exojax<1.1. Try again after removing " + +warnings.warn("moldb module will be renenamed to adb in future.", FutureWarning) class AdbVald(object): @@ -42,25 +44,39 @@ class AdbVald(object): gamRad (jnp array): log of gamma of radiation damping (s-1) #(https://www.astro.uu.se/valdwiki/Vald3Format) gamSta (jnp array): log of gamma of Stark damping (s-1) vdWdamp (jnp array): log of (van der Waals damping constant / neutral hydrogen number) (s-1) + gQT_284species (jnp array): partition function grid of 284 species + T_gQT (jnp array): temperatures in the partition function grid + QTref_284 (jnp array): partition function at the reference temperature Q(Tref), for 284 species Note: - For the first time to read the VALD line list, it is converted to HDF/vaex. After the second-time, we use the HDF5 format with vaex instead. + For the first time to read the VALD line list, it is converted to HDF/vaex. After the second-time, we use the HDF5 format with vaex instead. """ - def __init__(self, path, nurange=[-np.inf, np.inf], margin=0.0, crit=0., Irwin=False, gpu_transfer=True, vmr_fraction=None): + def __init__( + self, + path, + nurange=[-np.inf, np.inf], + margin=0.0, + crit=0.0, + Irwin=False, + gpu_transfer=True, + vmr_fraction=None, + engine="vaex", + ): """Atomic database for VALD3 "Long format". Args: - path: path for linelists downloaded from VALD3 with a query of "Long format" in the format of "Extract All", "Extract Stellar", or "Extract Element" - nurange: wavenumber range list (cm-1) or wavenumber array - margin: margin for nurange (cm-1) - crit: line strength lower limit for extraction - Irwin: if True(1), the partition functions of Irwin1981 is used, otherwise those of Barklem&Collet2016 - gpu_transfer: tranfer data to jnp.array? - vmr_fraction: list of the vmr fractions of hydrogen, H2 molecule, helium. if None, typical quasi-"solar-fraction" will be applied. + path: path for linelists downloaded from VALD3 with a query of "Long format" in the format of "Extract All", "Extract Stellar", or "Extract Element" + nurange: wavenumber range list (cm-1) or wavenumber array + margin: margin for nurange (cm-1) + crit: line strength lower limit for extraction + Irwin: if True(1), the partition functions of Irwin1981 is used, otherwise those of Barklem&Collet2016 + gpu_transfer: tranfer data to jnp.array? + vmr_fraction: list of the vmr fractions of hydrogen, H2 molecule, helium. if None, typical quasi-"solar-fraction" will be applied. + engine: "vaex" or "pandas" Note: - (written with reference to moldb.py, but without using feather format) + (written with reference to moldb.py, but without using feather format) """ self.dbtype = "vald" @@ -71,61 +87,112 @@ def __init__(self, path, nurange=[-np.inf, np.inf], margin=0.0, crit=0., Irwin=F self.margin = margin self.crit = crit if vmr_fraction is None: - self.vmrH, self.vmrHe, self.vmrHH = [0.0, 0.16, 0.84] #typical quasi-"solar-fraction" + self.vmrH, self.vmrHe, self.vmrHH = [ + 0.0, + 0.16, + 0.84, + ] # typical quasi-"solar-fraction" else: self.vmrH, self.vmrHe, self.vmrHH = vmr_fraction # load vald file - print('Reading VALD file') - if self.vald3_file.with_suffix('.hdf5').exists(): - valdd = vaex.open(self.vald3_file.with_suffix('.hdf5')) + print("Reading VALD file") + if self.vald3_file.with_suffix(".hdf5").exists() and engine == "vaex": + import vaex + + valdd = vaex.open(self.vald3_file.with_suffix(".hdf5")) + elif self.vald3_file.with_suffix(".hdf5").exists() and engine == "pytables": + import pandas as pd + + valdd = pd.read_hdf(self.vald3_file.with_suffix(".hdf5")) else: print( - "Note: Couldn't find the hdf5 format. We convert data to the hdf5 format.") - valdd = atomllapi.read_ExAll(self.vald3_file) # vaex.DataFrame + "Note: Couldn't find the hdf5 format. We convert data to the hdf5 format." + ) + valdd = atomllapi.read_ExAll( + self.vald3_file, engine=engine + ) # vaex.DataFrame pvaldd = valdd.to_pandas_df() # pandas.DataFrame # compute additional transition parameters - self._A, self.nu_lines, self._elower, self._eupper, self._gupper, self._jlower, self._jupper, self._ielem, self._iion, self._gamRad, self._gamSta, self._vdWdamp = atomllapi.pickup_param( - pvaldd) + ( + self._A, + self.nu_lines, + self._elower, + self._eupper, + self._gupper, + self._jlower, + self._jupper, + self._ielem, + self._iion, + self._gamRad, + self._gamSta, + self._vdWdamp, + ) = atomllapi.pickup_param(pvaldd) # load the partition functions (for 284 atomic species) pfTdat, self.pfdat = atomllapi.load_pf_Barklem2016() # Barklem & Collet (2016) self.T_gQT = jnp.array(pfTdat.columns[1:], dtype=float) - self.gQT_284species = jnp.array(self.pfdat.iloc[:, 1:].to_numpy( - dtype=float)) # grid Q vs T vs Species - self.QTref_284 = np.array(self.QT_interp_284(Tref_original)) + self.gQT_284species = jnp.array( + self.pfdat.iloc[:, 1:].to_numpy(dtype=float) + ) # grid Q vs T vs Species + self.Tref = Tref_original + self.QTref_284 = np.array( + atomll.interp_QT_284(Tref_original, self.T_gQT, self.gQT_284species) + ) # identify index of QT grid (gQT) for each line self._QTmask = self.make_QTmask(self._ielem, self._iion) # Line strength: input shoud be ndarray not jnp array - self.Sij0 = atomll.Sij0(self._A, self._gupper, self.nu_lines, - self._elower, self.QTref_284, self._QTmask, Irwin) # 211013 + self.Sij0 = atomll.Sij0( + self._A, + self._gupper, + self.nu_lines, + self._elower, + self.QTref_284, + self._QTmask, + Irwin, + ) # 211013 ### MASKING ### - mask = (self.nu_lines > self.nurange[0]-self.margin)\ - * (self.nu_lines < self.nurange[1]+self.margin)\ + mask = ( + (self.nu_lines > self.nurange[0] - self.margin) + * (self.nu_lines < self.nurange[1] + self.margin) * (self.Sij0 > self.crit) + ) self.masking(mask) if gpu_transfer: self.generate_jnp_arrays() - + # Compile atomic-specific data for each absorption line of interest ipccd = atomllapi.load_atomicdata() self.solarA = jnp.array( - list(map(lambda x: ipccd[ipccd['ielem'] == x].iat[0, 4], self.ielem))) + list(map(lambda x: ipccd[ipccd["ielem"] == x].iat[0, 4], self.ielem)) + ) self.atomicmass = jnp.array( - list(map(lambda x: ipccd[ipccd['ielem'] == x].iat[0, 5], self.ielem))) + list(map(lambda x: ipccd[ipccd["ielem"] == x].iat[0, 5], self.ielem)) + ) df_ionE = atomllapi.load_ionization_energies() self.ionE = jnp.array( - list(map(atomllapi.pick_ionE, self.ielem, self.iion, [df_ionE, ] * len(self.ielem)))) + list( + map( + atomllapi.pick_ionE, + self.ielem, + self.iion, + [ + df_ionE, + ] + * len(self.ielem), + ) + ) + ) def masking(self, mask): """applying mask. Args: - mask: mask to be applied. self.mask is updated. + mask: mask to be applied. self.mask is updated. """ # numpy float 64 Do not convert them jnp array @@ -144,16 +211,17 @@ def masking(self, mask): self._gamSta = self._gamSta[mask] self._vdWdamp = self._vdWdamp[mask] - if(len(self.nu_lines) < 1): - warn_msg = "Warning: no lines are selected. Check the inputs to moldb.AdbVald." + if len(self.nu_lines) < 1: + warn_msg = ( + "Warning: no lines are selected. Check the inputs to moldb.AdbVald." + ) warnings.warn(warn_msg, UserWarning) - def generate_jnp_arrays(self): """(re)generate jnp.arrays. Note: - We have nd arrays and jnp arrays. We usually apply the mask to nd arrays and then generate jnp array from the corresponding nd array. For instance, self._A is nd array and self.A is jnp array. + We have nd arrays and jnp arrays. We usually apply the mask to nd arrays and then generate jnp array from the corresponding nd array. For instance, self._A is nd array and self.A is jnp array. """ # jnp arrays @@ -183,10 +251,10 @@ def Atomic_gQT(self, atomspecies): Returns: gQT: grid Q(T) for the species """ - atomspecies_Roman = atomspecies.split( - ' ')[0] + '_' + 'I'*int(atomspecies.split(' ')[-1]) - gQT = self.gQT_284species[np.where( - self.pfdat['T[K]'] == atomspecies_Roman)][0] + atomspecies_Roman = ( + atomspecies.split(" ")[0] + "_" + "I" * int(atomspecies.split(" ")[-1]) + ) + gQT = self.gQT_284species[np.where(self.pfdat["T[K]"] == atomspecies_Roman)][0] return gQT def QT_interp(self, atomspecies, T): @@ -194,27 +262,27 @@ def QT_interp(self, atomspecies, T): Collet (2016) are adopted. Args: - atomspecies: species e.g., "Fe 1" - T: temperature + atomspecies: species e.g., "Fe 1" + T: temperature Returns: - Q(T): interpolated in jnp.array for the Atomic Species + Q(T): interpolated in jnp.array for the Atomic Species """ gQT = self.Atomic_gQT(atomspecies) QT = jnp.interp(T, self.T_gQT, gQT) return QT - def QT_interp_Irwin_Fe(self, T, atomspecies='Fe 1'): + def QT_interp_Irwin_Fe(self, T, atomspecies="Fe 1"): """interpolated partition function This function is for the exceptional case where you want to adopt partition functions of Irwin (1981) for Fe I (Other species are not yet implemented). Args: - atomspecies: species e.g., "Fe 1" - T: temperature + atomspecies: species e.g., "Fe 1" + T: temperature Returns: - Q(T): interpolated in jnp.array for the Atomic Species + Q(T): interpolated in jnp.array for the Atomic Species """ gQT = self.Atomic_gQT(atomspecies) QT = atomllapi.partfn_Fe(T) @@ -225,44 +293,44 @@ def qr_interp(self, atomspecies, T): Barklem & Collet (2016) are adopted. Args: - T: temperature - atomspecies: species e.g., "Fe 1" + T: temperature + atomspecies: species e.g., "Fe 1" Returns: - qr(T)=Q(T)/Q(Tref): interpolated in jnp.array + qr(T)=Q(T)/Q(Tref): interpolated in jnp.array """ - return self.QT_interp(atomspecies, T)/self.QT_interp(atomspecies, Tref_original) + return self.QT_interp(atomspecies, T) / self.QT_interp( + atomspecies, Tref_original + ) - def qr_interp_Irwin_Fe(self, T, atomspecies='Fe 1'): + def qr_interp_Irwin_Fe(self, T, atomspecies="Fe 1"): """interpolated partition function ratio This function is for the exceptional case where you want to adopt partition functions of Irwin (1981) for Fe I (Other species are not yet implemented). Args: - T: temperature - atomspecies: species e.g., "Fe 1" + T: temperature + atomspecies: species e.g., "Fe 1" Returns: - qr(T)=Q(T)/Q(Tref): interpolated in jnp.array + qr(T)=Q(T)/Q(Tref): interpolated in jnp.array """ - return self.QT_interp_Irwin_Fe(T, atomspecies)/self.QT_interp_Irwin_Fe(Tref_original, atomspecies) + return self.QT_interp_Irwin_Fe(T, atomspecies) / self.QT_interp_Irwin_Fe( + Tref_original, atomspecies + ) def QT_interp_284(self, T): - """interpolated partition function of all 284 species. + """(DEPRECATED) interpolated partition function of all 284 species. Args: - T: temperature + T: temperature Returns: - Q(T)*284: interpolated in jnp.array for all 284 Atomic Species + Q(T)*284: interpolated in jnp.array for all 284 Atomic Species """ - list_gQT_eachspecies = self.gQT_284species.tolist() - listofDA_gQT_eachspecies = list( - map(lambda x: jnp.array(x), list_gQT_eachspecies)) - listofQT = list(map(lambda x: jnp.interp( - T, self.T_gQT, x), listofDA_gQT_eachspecies)) - QT_284 = jnp.array(listofQT) - return QT_284 + warn_msg = "Deprecated Use `atomll.interp_QT_284` instead" + warnings.warn(warn_msg, FutureWarning) + return atomll.interp_QT_284(T, self.T_gQT, self.gQT_284species) def make_QTmask(self, ielem, iion): """Convert the species identifier to the index for Q(Tref) grid (gQT) @@ -275,12 +343,13 @@ def make_QTmask(self, ielem, iion): Returns: QTmask_sp: array of index of Q(Tref) grid (gQT) for each line """ + def species_to_QTmask(ielem, iion): - sp_Roman = atomllapi.PeriodicTable[ielem] + '_' + 'I'*iion - QTmask = np.where(self.pfdat['T[K]'] == sp_Roman)[0][0] + sp_Roman = atomllapi.PeriodicTable[ielem] + "_" + "I" * iion + QTmask = np.where(self.pfdat["T[K]"] == sp_Roman)[0][0] return QTmask - QTmask_sp = np.array( - list(map(species_to_QTmask, ielem, iion))).astype('int') + + QTmask_sp = np.array(list(map(species_to_QTmask, ielem, iion))).astype("int") return QTmask_sp @@ -309,6 +378,7 @@ class AdbSepVald(object): L_max (int): maximum number of spectral lines for a single species gQT_284species (jnp array): partition function grid of 284 species T_gQT (jnp array): temperatures in the partition function grid + QTref_284 (jnp array): partition function at the reference temperature Q(Tref), for 284 species """ def __init__(self, adb): @@ -318,8 +388,7 @@ def __init__(self, adb): adb: adb instance made by the AdbVald class, which stores the lines of all species together """ - self.nu_lines = atomll.sep_arr_of_sp( - adb.nu_lines, adb, trans_jnp=False) + self.nu_lines = atomll.sep_arr_of_sp(adb.nu_lines, adb, trans_jnp=False) self.QTmask = atomll.sep_arr_of_sp(adb.QTmask, adb, inttype=True).T[0] self.ielem = atomll.sep_arr_of_sp(adb.ielem, adb, inttype=True).T[0] @@ -341,6 +410,7 @@ def __init__(self, adb): self.gQT_284species = adb.gQT_284species self.T_gQT = adb.T_gQT + self.QTref_284 = adb.QTref_284 class AdbKurucz(object): @@ -368,20 +438,29 @@ class AdbKurucz(object): vdWdamp (jnp array): log of (van der Waals damping constant / neutral hydrogen number) (s-1) """ - def __init__(self, path, nurange=[-np.inf, np.inf], margin=0.0, crit=0., Irwin=False, gpu_transfer=True, vmr_fraction=None): + def __init__( + self, + path, + nurange=[-np.inf, np.inf], + margin=0.0, + crit=0.0, + Irwin=False, + gpu_transfer=True, + vmr_fraction=None, + ): """Atomic database for Kurucz line list "gf????.all". Args: - path: path for linelists (gf????.all) downloaded from the Kurucz web page - nurange: wavenumber range list (cm-1) or wavenumber array - margin: margin for nurange (cm-1) - crit: line strength lower limit for extraction - Irwin: if True(1), the partition functions of Irwin1981 is used, otherwise those of Barklem&Collet2016 - gpu_transfer: tranfer data to jnp.array? - vmr_fraction: list of the vmr fractions of hydrogen, H2 molecule, helium. if None, typical quasi-"solar-fraction" will be applied. + path: path for linelists (gf????.all) downloaded from the Kurucz web page + nurange: wavenumber range list (cm-1) or wavenumber array + margin: margin for nurange (cm-1) + crit: line strength lower limit for extraction + Irwin: if True(1), the partition functions of Irwin1981 is used, otherwise those of Barklem&Collet2016 + gpu_transfer: tranfer data to jnp.array? + vmr_fraction: list of the vmr fractions of hydrogen, H2 molecule, helium. if None, typical quasi-"solar-fraction" will be applied. Note: - (written with reference to moldb.py, but without using feather format) + (written with reference to moldb.py, but without using feather format) """ self.dbtype = "kurucz" @@ -392,52 +471,94 @@ def __init__(self, path, nurange=[-np.inf, np.inf], margin=0.0, crit=0., Irwin=F self.margin = margin self.crit = crit if vmr_fraction is None: - self.vmrH, self.vmrHe, self.vmrHH = [0.0, 0.16, 0.84] #typical quasi-"solar-fraction" + self.vmrH, self.vmrHe, self.vmrHH = [ + 0.0, + 0.16, + 0.84, + ] # typical quasi-"solar-fraction" else: self.vmrH, self.vmrHe, self.vmrHH = vmr_fraction # load kurucz file - print('Reading Kurucz file') - self._A, self.nu_lines, self._elower, self._eupper, self._gupper, self._jlower, self._jupper, self._ielem, self._iion, self._gamRad, self._gamSta, self._vdWdamp = atomllapi.read_kurucz( - self.kurucz_file) + print("Reading Kurucz file") + ( + self._A, + self.nu_lines, + self._elower, + self._eupper, + self._gupper, + self._jlower, + self._jupper, + self._ielem, + self._iion, + self._gamRad, + self._gamSta, + self._vdWdamp, + ) = atomllapi.read_kurucz(self.kurucz_file) # load the partition functions (for 284 atomic species) pfTdat, self.pfdat = atomllapi.load_pf_Barklem2016() # Barklem & Collet (2016) self.T_gQT = jnp.array(pfTdat.columns[1:], dtype=float) - self.gQT_284species = jnp.array(self.pfdat.iloc[:, 1:].to_numpy( - dtype=float)) # grid Q vs T vs Species - self.QTref_284 = np.array(self.QT_interp_284(Tref_original)) + self.gQT_284species = jnp.array( + self.pfdat.iloc[:, 1:].to_numpy(dtype=float) + ) # grid Q vs T vs Species + self.Tref = Tref_original + self.QTref_284 = np.array( + atomll.interp_QT_284(Tref_original, self.T_gQT, self.gQT_284species) + ) # identify index of QT grid (gQT) for each line self._QTmask = self.make_QTmask(self._ielem, self._iion) # Line strength: input shoud be ndarray not jnp array - self.Sij0 = atomll.Sij0(self._A, self._gupper, self.nu_lines, - self._elower, self.QTref_284, self._QTmask, Irwin) # 211013 + self.Sij0 = atomll.Sij0( + self._A, + self._gupper, + self.nu_lines, + self._elower, + self.QTref_284, + self._QTmask, + Irwin, + ) # 211013 ### MASKING ### - mask = (self.nu_lines > self.nurange[0]-self.margin)\ - * (self.nu_lines < self.nurange[1]+self.margin)\ + mask = ( + (self.nu_lines > self.nurange[0] - self.margin) + * (self.nu_lines < self.nurange[1] + self.margin) * (self.Sij0 > self.crit) + ) self.masking(mask) if gpu_transfer: self.generate_jnp_arrays() - + # Compile atomic-specific data for each absorption line of interest ipccd = atomllapi.load_atomicdata() self.solarA = jnp.array( - list(map(lambda x: ipccd[ipccd['ielem'] == x].iat[0, 4], self.ielem))) + list(map(lambda x: ipccd[ipccd["ielem"] == x].iat[0, 4], self.ielem)) + ) self.atomicmass = jnp.array( - list(map(lambda x: ipccd[ipccd['ielem'] == x].iat[0, 5], self.ielem))) + list(map(lambda x: ipccd[ipccd["ielem"] == x].iat[0, 5], self.ielem)) + ) df_ionE = atomllapi.load_ionization_energies() self.ionE = jnp.array( - list(map(atomllapi.pick_ionE, self.ielem, self.iion, [df_ionE, ] * len(self.ielem)))) + list( + map( + atomllapi.pick_ionE, + self.ielem, + self.iion, + [ + df_ionE, + ] + * len(self.ielem), + ) + ) + ) def masking(self, mask): """applying mask Args: - mask: mask to be applied. self.mask is updated. + mask: mask to be applied. self.mask is updated. """ # numpy float 64 Do not convert them jnp array @@ -456,15 +577,17 @@ def masking(self, mask): self._gamSta = self._gamSta[mask] self._vdWdamp = self._vdWdamp[mask] - if(len(self.nu_lines) < 1): - warn_msg = "Warning: no lines are selected. Check the inputs to moldb.AdbKurucz." + if len(self.nu_lines) < 1: + warn_msg = ( + "Warning: no lines are selected. Check the inputs to moldb.AdbKurucz." + ) warnings.warn(warn_msg, UserWarning) def generate_jnp_arrays(self): """(re)generate jnp.arrays. Note: - We have nd arrays and jnp arrays. We usually apply the mask to nd arrays and then generate jnp array from the corresponding nd array. For instance, self._A is nd array and self.A is jnp array. + We have nd arrays and jnp arrays. We usually apply the mask to nd arrays and then generate jnp array from the corresponding nd array. For instance, self._A is nd array and self.A is jnp array. """ # jnp arrays @@ -494,10 +617,10 @@ def Atomic_gQT(self, atomspecies): Returns: gQT: grid Q(T) for the species """ - atomspecies_Roman = atomspecies.split( - ' ')[0] + '_' + 'I'*int(atomspecies.split(' ')[-1]) - gQT = self.gQT_284species[np.where( - self.pfdat['T[K]'] == atomspecies_Roman)][0] + atomspecies_Roman = ( + atomspecies.split(" ")[0] + "_" + "I" * int(atomspecies.split(" ")[-1]) + ) + gQT = self.gQT_284species[np.where(self.pfdat["T[K]"] == atomspecies_Roman)][0] return gQT def QT_interp(self, atomspecies, T): @@ -505,27 +628,27 @@ def QT_interp(self, atomspecies, T): Collet (2016) are adopted. Args: - atomspecies: species e.g., "Fe 1" - T: temperature + atomspecies: species e.g., "Fe 1" + T: temperature Returns: - Q(T): interpolated in jnp.array for the Atomic Species + Q(T): interpolated in jnp.array for the Atomic Species """ gQT = self.Atomic_gQT(atomspecies) QT = jnp.interp(T, self.T_gQT, gQT) return QT - def QT_interp_Irwin_Fe(self, T, atomspecies='Fe 1'): + def QT_interp_Irwin_Fe(self, T, atomspecies="Fe 1"): """interpolated partition function This function is for the exceptional case where you want to adopt partition functions of Irwin (1981) for Fe I (Other species are not yet implemented). Args: - atomspecies: species e.g., "Fe 1" - T: temperature + atomspecies: species e.g., "Fe 1" + T: temperature Returns: - Q(T): interpolated in jnp.array for the Atomic Species + Q(T): interpolated in jnp.array for the Atomic Species """ gQT = self.Atomic_gQT(atomspecies) QT = atomllapi.partfn_Fe(T) @@ -536,44 +659,44 @@ def qr_interp(self, atomspecies, T): Barklem & Collet (2016) are adopted. Args: - T: temperature - atomspecies: species e.g., "Fe 1" + T: temperature + atomspecies: species e.g., "Fe 1" Returns: - qr(T)=Q(T)/Q(Tref): interpolated in jnp.array + qr(T)=Q(T)/Q(Tref): interpolated in jnp.array """ - return self.QT_interp(atomspecies, T)/self.QT_interp(atomspecies, Tref_original) + return self.QT_interp(atomspecies, T) / self.QT_interp( + atomspecies, Tref_original + ) - def qr_interp_Irwin_Fe(self, T, atomspecies='Fe 1'): + def qr_interp_Irwin_Fe(self, T, atomspecies="Fe 1"): """interpolated partition function ratio This function is for the exceptional case where you want to adopt partition functions of Irwin (1981) for Fe I (Other species are not yet implemented). Args: - T: temperature - atomspecies: species e.g., "Fe 1" + T: temperature + atomspecies: species e.g., "Fe 1" Returns: - qr(T)=Q(T)/Q(Tref): interpolated in jnp.array + qr(T)=Q(T)/Q(Tref): interpolated in jnp.array """ - return self.QT_interp_Irwin_Fe(T, atomspecies)/self.QT_interp_Irwin_Fe(Tref_original, atomspecies) + return self.QT_interp_Irwin_Fe(T, atomspecies) / self.QT_interp_Irwin_Fe( + Tref_original, atomspecies + ) def QT_interp_284(self, T): - """interpolated partition function of all 284 species. + """(DEPRECATED) interpolated partition function of all 284 species. Args: - T: temperature + T: temperature Returns: - Q(T)*284: interpolated in jnp.array for all 284 Atomic Species + Q(T)*284: interpolated in jnp.array for all 284 Atomic Species """ - list_gQT_eachspecies = self.gQT_284species.tolist() - listofDA_gQT_eachspecies = list( - map(lambda x: jnp.array(x), list_gQT_eachspecies)) - listofQT = list(map(lambda x: jnp.interp( - T, self.T_gQT, x), listofDA_gQT_eachspecies)) - QT_284 = jnp.array(listofQT) - return QT_284 + warn_msg = "Deprecated Use `atomll.interp_QT_284` instead" + warnings.warn(warn_msg, FutureWarning) + return atomll.interp_QT_284(T, self.T_gQT, self.gQT_284species) def make_QTmask(self, ielem, iion): """Convert the species identifier to the index for Q(Tref) grid (gQT) @@ -586,10 +709,11 @@ def make_QTmask(self, ielem, iion): Returns: QTmask_sp: array of index of Q(Tref) grid (gQT) for each line """ + def species_to_QTmask(ielem, iion): - sp_Roman = atomllapi.PeriodicTable[ielem] + '_' + 'I'*iion - QTmask = np.where(self.pfdat['T[K]'] == sp_Roman)[0][0] + sp_Roman = atomllapi.PeriodicTable[ielem] + "_" + "I" * iion + QTmask = np.where(self.pfdat["T[K]"] == sp_Roman)[0][0] return QTmask - QTmask_sp = np.array( - list(map(species_to_QTmask, ielem, iion))).astype('int') + + QTmask_sp = np.array(list(map(species_to_QTmask, ielem, iion))).astype("int") return QTmask_sp diff --git a/src/exojax/spec/molinfo.py b/src/exojax/spec/molinfo.py index 08848358c..45af971d5 100644 --- a/src/exojax/spec/molinfo.py +++ b/src/exojax/spec/molinfo.py @@ -34,22 +34,22 @@ def molmass_isotope(simple_molecule_name, db_HIT=True): """provide molecular mass for the major isotope from the simple molecular name. Args: - molecule: molecular name e.g. CO2, He - db_HIT: if True, use the molecular mass considering the natural terrestrial abundance and mass of each isotopologue provided by HITRAN (https://hitran.org/docs/iso-meta/) + molecule: molecular name e.g. CO2, He + db_HIT: if True, use the molecular mass considering the natural terrestrial abundance and mass of each isotopologue provided by HITRAN (https://hitran.org/docs/iso-meta/) Returns: - molecular mass + molecular mass Example: - >>> from exojax.spec.molinfo import molmass - >>> print(molmass("H2")) - >>> 2.01588 - >>> print(molmass("CO2")) - >>> 44.0095 - >>> print(molmass("He")) - >>> 4.002602 - >>> print(molmass("air")) - >>> 28.97 + >>> from exojax.spec.molinfo import molmass + >>> print(molmass("H2")) + >>> 2.01588 + >>> print(molmass("CO2")) + >>> 44.0095 + >>> print(molmass("He")) + >>> 4.002602 + >>> print(molmass("air")) + >>> 28.97 """ molmass_isotope, abundance_isotope = molmass_hitran() @@ -92,19 +92,19 @@ def mean_molmass_manual(simple_molecule_name): else: if k + 1 < len(listmol): if listmol[k + 1].islower(): - em = EachMass[listmol[k] + listmol[k + 1]] + em = element_mass[listmol[k] + listmol[k + 1]] ignore = True else: - em = EachMass[i] + em = element_mass[i] else: - em = EachMass[i] + em = element_mass[i] tot = tot + em mean_molmass = tot return mean_molmass -EachMass = { +element_mass = { 'H': 1.00794, 'He': 4.002602, 'Li': 6.941, diff --git a/src/exojax/spec/opacalc.py b/src/exojax/spec/opacalc.py index 03f06057f..c056523e3 100644 --- a/src/exojax/spec/opacalc.py +++ b/src/exojax/spec/opacalc.py @@ -52,14 +52,14 @@ def __init__( dit_grid_resolution=None, allow_32bit=False, wavelength_order="descending", - version_auto_trange=2 + version_auto_trange=2, ): """initialization of OpaPremodit Note: If auto_trange nor manual_params is not given in arguments, use manual_setting() - or provide self.dE, self.Tref, self.Twt and apply self.apply_params() + or provide self.dE, self.Twt and apply self.apply_params() Note: The option of "broadening_parameter_resolution" controls the resolution of broadening parameters. @@ -116,9 +116,53 @@ def __init__( print("OpaPremodit: initialization without parameters setting") print("Call self.apply_params() to complete the setting.") + def __eq__(self, other): + """eq method for OpaPremodit, definied by comparing all the attributes and important status + + Args: + other (_type_): _description_ + + Returns: + _type_: _description_ + """ + if not isinstance(other, OpaPremodit): + return False + + eq_attributes = ( + (self.mdb == other.mdb) + and (self.diffmode == other.diffmode) + and (self.ngrid_broadpar == other.ngrid_broadpar) + and (self.wavelength_order == other.wavelength_order) + and (self.version_auto_trange == other.version_auto_trange) + and all(self.nu_grid == other.nu_grid) + ) + eq_attributes = self._if_exist_check_eq(other, "dE", eq_attributes) + eq_attributes = self._if_exist_check_eq(other, "Tref", eq_attributes) + eq_attributes = self._if_exist_check_eq(other, "Twt", eq_attributes) + eq_attributes = self._if_exist_check_eq(other, "Tmax", eq_attributes) + eq_attributes = self._if_exist_check_eq(other, "Tmin", eq_attributes) + eq_attributes = self._if_exist_check_eq(other, "Tref_broadening", eq_attributes) + + return eq_attributes + + def _if_exist_check_eq(self, other, attribute, eq_attributes): + if hasattr(self, attribute) and hasattr(other, attribute): + return eq_attributes and getattr(self, attribute) == getattr( + other, attribute + ) + elif not hasattr(self, attribute) and not hasattr(other, attribute): + return eq_attributes + else: + return False + + def __ne__(self, other): + return not self.__eq__(other) + def auto_setting(self, Tl, Tu): print("OpaPremodit: params automatically set.") - self.dE, self.Tref, self.Twt = optimal_params(Tl, Tu, self.diffmode, self.version_auto_trange) + self.dE, self.Tref, self.Twt = optimal_params( + Tl, Tu, self.diffmode, self.version_auto_trange + ) self.Tmax = Tu self.Tmin = Tl self.apply_params() @@ -201,7 +245,7 @@ def broadening_parameters_setting(self): "Unknown mode in broadening_parameter_resolution e.g. manual/single/minmax." ) - def compute_gamma_ref_and_n_Texp(self, mdb): + def compute_gamma_ref_and_n_Texp(self): """convert gamma_ref to the regular formalization and noramlize it for Tref_braodening Notes: @@ -212,39 +256,42 @@ def compute_gamma_ref_and_n_Texp(self, mdb): mdb (_type_): mdb instance """ - if mdb.dbtype == "hitran": + if self.mdb.dbtype == "hitran": print( "OpaPremodit: gamma_air and n_air are used. gamma_ref = gamma_air/Patm" ) - self.n_Texp = mdb.n_air + self.n_Texp = self.mdb.n_air reference_factor = (Tref_original / self.Tref_broadening) ** (self.n_Texp) - self.gamma_ref = mdb.gamma_air * reference_factor / Patm - elif mdb.dbtype == "exomol": - self.n_Texp = mdb.n_Texp + self.gamma_ref = self.mdb.gamma_air * reference_factor / Patm + elif self.mdb.dbtype == "exomol": + self.n_Texp = self.mdb.n_Texp reference_factor = (Tref_original / self.Tref_broadening) ** (self.n_Texp) - self.gamma_ref = mdb.alpha_ref * reference_factor + self.gamma_ref = self.mdb.alpha_ref * reference_factor def apply_params(self): - self.mdb.change_reference_temperature(self.Tref) + # self.mdb.change_reference_temperature(self.Tref) self.dbtype = self.mdb.dbtype - # broadening + # sets the broadening reference temperature if self.single_broadening: print("OpaPremodit: a single broadening parameter set is used.") self.Tref_broadening = Tref_original else: self.set_Tref_broadening_to_midpoint() - self.compute_gamma_ref_and_n_Texp(self.mdb) + # self.gamma_ref, self.n_Texp are defined with the reference temperature of Tref_broadening + self.compute_gamma_ref_and_n_Texp() + # comment-1: gamma_ref at Tref_broadening (is not necessary for Tref_original) + # comment-2: line strength at Tref (is not necessary for Tref_original) self.opainfo = initspec.init_premodit( self.mdb.nu_lines, self.nu_grid, self.mdb.elower, - self.gamma_ref, + self.gamma_ref, # comment-1 self.n_Texp, - self.mdb.line_strength_ref, - self.Twt, + self.mdb.line_strength(self.Tref), # comment-2 + self.Twt, Tref=self.Tref, Tref_broadening=self.Tref_broadening, Tmax=self.Tmax, @@ -288,9 +335,9 @@ def xsvector(self, T, P): nsigmaD = normalized_doppler_sigma(T, self.mdb.molmass, R) if self.mdb.dbtype == "hitran": - qt = self.mdb.qr_interp(self.mdb.isotope, T) + qt = self.mdb.qr_interp(self.mdb.isotope, T, self.Tref) elif self.mdb.dbtype == "exomol": - qt = self.mdb.qr_interp(T) + qt = self.mdb.qr_interp(T, self.Tref) if self.diffmode == 0: return xsvector_zeroth( @@ -375,10 +422,12 @@ def xsmatrix(self, Tarr, Parr): ) = self.opainfo if self.mdb.dbtype == "hitran": - qtarr = vmap(self.mdb.qr_interp, (None, 0))(self.mdb.isotope, Tarr) + qtarr = vmap(self.mdb.qr_interp, (None, 0, None))( + self.mdb.isotope, Tarr, self.Tref + ) elif self.mdb.dbtype == "exomol": - qtarr = vmap(self.mdb.qr_interp)(Tarr) - + qtarr = vmap(self.mdb.qr_interp, (0, None))(Tarr, self.Tref) + if self.diffmode == 0: return xsmatrix_zeroth( Tarr, @@ -521,6 +570,29 @@ def __init__( else: warnings.warn("Tarr_list/Parr are needed for xsmatrix.", UserWarning) + def __eq__(self, other): + """eq method for OpaDirect, definied by comparing all the attributes and important status + + Args: + other (_type_): _description_ + + Returns: + _type_: _description_ + """ + if not isinstance(other, OpaModit): + return False + + eq_attributes = ( + (self.mdb == other.mdb) + and (self.wavelength_order == other.wavelength_order) + and all(self.nu_grid == other.nu_grid) + ) + + return eq_attributes + + def __ne__(self, other): + return not self.__eq__(other) + def apply_params(self): self.dbtype = self.mdb.dbtype self.opainfo = initspec.init_modit(self.mdb.nu_lines, self.nu_grid) @@ -548,12 +620,12 @@ def xsvector(self, T, P, Pself=0.0): cont_nu, index_nu, R, pmarray = self.opainfo if self.mdb.dbtype == "hitran": - qt = self.mdb.qr_interp(self.mdb.isotope, T) + qt = self.mdb.qr_interp(self.mdb.isotope, T, Tref_original) gammaL = gamma_hitran( P, T, Pself, self.mdb.n_air, self.mdb.gamma_air, self.mdb.gamma_self ) + gamma_natural(self.mdb.A) elif self.mdb.dbtype == "exomol": - qt = self.mdb.qr_interp(T) + qt = self.mdb.qr_interp(T, Tref_original) gammaL = gamma_exomol( P, T, self.mdb.n_Texp, self.mdb.alpha_ref ) + gamma_natural(self.mdb.A) @@ -562,7 +634,7 @@ def xsvector(self, T, P, Pself=0.0): nsigmaD = normalized_doppler_sigma(T, self.mdb.molmass, R) Sij = line_strength( - T, self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower, qt, self.mdb.Tref + T, self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower, qt, Tref_original ) ngammaL_grid = ditgrid_log_interval( @@ -687,6 +759,29 @@ def __init__(self, mdb, nu_grid, wavelength_order="descending"): self.mdb = mdb self.apply_params() + def __eq__(self, other): + """eq method for OpaDirect, definied by comparing all the attributes and important status + + Args: + other (_type_): _description_ + + Returns: + _type_: _description_ + """ + if not isinstance(other, OpaDirect): + return False + + eq_attributes = ( + (self.mdb == other.mdb) + and (self.wavelength_order == other.wavelength_order) + and all(self.nu_grid == other.nu_grid) + ) + + return eq_attributes + + def __ne__(self, other): + return not self.__eq__(other) + def apply_params(self): self.dbtype = self.mdb.dbtype self.opainfo = initspec.init_lpf(self.mdb.nu_lines, self.nu_grid) @@ -713,18 +808,18 @@ def xsvector(self, T, P, Pself=0.0): numatrix = self.opainfo if self.mdb.dbtype == "hitran": - qt = self.mdb.qr_interp(self.mdb.isotope, T) + qt = self.mdb.qr_interp(self.mdb.isotope, T, Tref_original) gammaL = gamma_hitran( P, T, Pself, self.mdb.n_air, self.mdb.gamma_air, self.mdb.gamma_self ) + gamma_natural(self.mdb.A) elif self.mdb.dbtype == "exomol": - qt = self.mdb.qr_interp(T) + qt = self.mdb.qr_interp(T, Tref_original) gammaL = gamma_exomol( P, T, self.mdb.n_Texp, self.mdb.alpha_ref ) + gamma_natural(self.mdb.A) sigmaD = doppler_sigma(self.mdb.nu_lines, T, self.mdb.molmass) Sij = line_strength( - T, self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower, qt, self.mdb.Tref + T, self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower, qt, Tref_original ) return xsvector_lpf(numatrix, sigmaD, gammaL, Sij) @@ -749,14 +844,14 @@ def xsmatrix(self, Tarr, Parr): from exojax.spec.exomol import gamma_exomol from exojax.spec.hitran import gamma_hitran from exojax.spec.hitran import line_strength - from exojax.spec.atomll import gamma_vald3 + from exojax.spec.atomll import gamma_vald3, interp_QT_284 from exojax.spec.lpf import xsmatrix as xsmatrix_lpf numatrix = self.opainfo vmaplinestrengh = jit(vmap(line_strength, (0, None, None, None, 0, None))) if self.mdb.dbtype == "hitran": - vmapqt = vmap(self.mdb.qr_interp, (None, 0)) - qt = vmapqt(self.mdb.isotope, Tarr) + vmapqt = vmap(self.mdb.qr_interp, (None, 0, None)) + qt = vmapqt(self.mdb.isotope, Tarr, Tref_original) vmaphitran = jit(vmap(gamma_hitran, (0, 0, 0, None, None, None))) gammaLM = vmaphitran( Parr, @@ -772,14 +867,14 @@ def xsmatrix(self, Tarr, Parr): self.mdb.nu_lines, self.mdb.elower, qt, - self.mdb.Tref, + Tref_original, ) sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))( self.mdb.nu_lines, Tarr, self.mdb.molmass ) elif self.mdb.dbtype == "exomol": - vmapqt = vmap(self.mdb.qr_interp) - qt = vmapqt(Tarr) + vmapqt = vmap(self.mdb.qr_interp, (0, None)) + qt = vmapqt(Tarr, Tref_original) vmapexomol = jit(vmap(gamma_exomol, (0, 0, None, None))) gammaLMP = vmapexomol(Parr, Tarr, self.mdb.n_Texp, self.mdb.alpha_ref) gammaLMN = gamma_natural(self.mdb.A) @@ -790,17 +885,17 @@ def xsmatrix(self, Tarr, Parr): self.mdb.nu_lines, self.mdb.elower, qt, - self.mdb.Tref, + Tref_original, ) sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))( self.mdb.nu_lines, Tarr, self.mdb.molmass ) elif (self.mdb.dbtype == "kurucz") or (self.mdb.dbtype == "vald"): - qt_284 = vmap(self.mdb.QT_interp_284)(Tarr) - qt_K = jnp.zeros([len(self.mdb.QTmask), len(Tarr)]) - for i, mask in enumerate(self.mdb.QTmask): - qt_K = qt_K.at[i].set(qt_284[:, mask]) # e.g., qt_284[:,76] #Fe I - qt_K = jnp.array(qt_K) + qt_284 = vmap(interp_QT_284, (0, None, None))( + Tarr, self.mdb.T_gQT, self.mdb.gQT_284species + ) + qt_K = qt_284[:, self.mdb.QTmask] # e.g., qt_284[:,76] #Fe I + qr_K = qt_K / self.mdb.QTref_284[self.mdb.QTmask] vmapvald3 = jit( vmap( gamma_vald3, @@ -850,8 +945,8 @@ def xsmatrix(self, Tarr, Parr): self.mdb.logsij0, self.mdb.nu_lines, self.mdb.elower, - qt_K.T, - self.mdb.Tref, + qr_K.T, + Tref_original, ) sigmaDM = jit(vmap(doppler_sigma, (None, 0, None)))( self.mdb.nu_lines, Tarr, self.mdb.atomicmass diff --git a/src/exojax/spec/opacont.py b/src/exojax/spec/opacont.py index 78ee3478d..d62f5a0bc 100644 --- a/src/exojax/spec/opacont.py +++ b/src/exojax/spec/opacont.py @@ -15,7 +15,7 @@ from jax import vmap import numpy as np -__all__ = ["OpaCIA"] +__all__ = ["OpaCIA", "Opahminus", "OpaRayleigh", "OpaMie"] class OpaCont: diff --git a/src/exojax/spec/optgrid.py b/src/exojax/spec/optgrid.py index 59beae097..e5d68ba17 100644 --- a/src/exojax/spec/optgrid.py +++ b/src/exojax/spec/optgrid.py @@ -52,9 +52,9 @@ def optelower( ) = opa.opainfo nsigmaD = normalized_doppler_sigma(Tmax, mdb.molmass, R) if mdb.dbtype == "exomol": - qt = mdb.qr_interp(Tmax) + qt = mdb.qr_interp(Tmax, opa.Tref) elif mdb.dbtype == "hitran": - qt = mdb.qr_interp(isotope, Tmax) + qt = mdb.qr_interp(isotope, Tmax, opa.Tref) Tref_broadening = Tref_original xsv_master = xsvector_zeroth( diff --git a/src/exojax/spec/pardb.py b/src/exojax/spec/pardb.py index 13bd3c16c..560f25630 100644 --- a/src/exojax/spec/pardb.py +++ b/src/exojax/spec/pardb.py @@ -308,3 +308,4 @@ def mieparams_cgs_at_refraction_index_wavenumber_from_miegrid(self, rg, sigmag): if __name__ == "__main__": pdb = PdbCloud("NH3") pdb.load_miegrid() + diff --git a/src/exojax/spec/response.py b/src/exojax/spec/response.py index 4879e8724..9ef4f7a09 100644 --- a/src/exojax/spec/response.py +++ b/src/exojax/spec/response.py @@ -4,6 +4,7 @@ * response is a response operation for the wavenumber grid spaced evenly on a log scale. """ + from jax import jit import jax.numpy as jnp from jax.lax import scan @@ -15,8 +16,8 @@ @jit def ipgauss_ola_sampling(nusd, nus, folded_spectrum, beta, RV, varr_kernel): """Apply the Gaussian IP response using OLA + sampling to a spectrum F. - - + + Args: nusd: sampling wavenumber nus: input wavenumber, evenly log-spaced @@ -24,13 +25,14 @@ def ipgauss_ola_sampling(nusd, nus, folded_spectrum, beta, RV, varr_kernel): beta: STD of a Gaussian broadening (IP+microturbulence) RV: radial velocity (km/s) varr_kernel: velocity array for the rotational kernel - + Return: response-applied spectrum (F) """ Fgauss = ipgauss_ola(folded_spectrum, varr_kernel, beta) return sampling(nusd, nus, Fgauss, RV) + @jit def ipgauss_ola(folded_spectrum, varr_kernel, beta): """Apply the Gaussian IP response to a spectrum F using OLA. @@ -46,22 +48,22 @@ def ipgauss_ola(folded_spectrum, varr_kernel, beta): x = varr_kernel / beta kernel = jnp.exp(-x * x / 2.0) kernel = kernel / jnp.sum(kernel, axis=0) - + ndiv, div_length, filter_length = ola_lengths(folded_spectrum, kernel) F0_hat, kernel_hat = generate_zeropad(folded_spectrum, kernel) ola = olaconv(F0_hat, kernel_hat, ndiv, div_length, filter_length) - + edge = int((len(kernel) - 1) / 2) F = ola[edge:-edge] - + return F @jit def ipgauss_sampling(nusd, nus, spectrum, beta, RV, varr_kernel): """Apply the Gaussian IP response + sampling to a spectrum F. - - + + Args: nusd: sampling wavenumber nus: input wavenumber, evenly log-spaced @@ -69,13 +71,14 @@ def ipgauss_sampling(nusd, nus, spectrum, beta, RV, varr_kernel): beta: STD of a Gaussian broadening (IP+microturbulence) RV: radial velocity (km/s) varr_kernel: velocity array for the rotational kernel - + Return: response-applied spectrum (F) """ Fgauss = ipgauss(spectrum, varr_kernel, beta) return sampling(nusd, nus, Fgauss, RV) + @jit def ipgauss(spectrum, varr_kernel, beta): """Apply the Gaussian IP response to a spectrum F. @@ -91,11 +94,12 @@ def ipgauss(spectrum, varr_kernel, beta): x = varr_kernel / beta kernel = jnp.exp(-x * x / 2.0) kernel = kernel / jnp.sum(kernel, axis=0) - #F = jnp.convolve(F0, kernel, mode='same') + # F = jnp.convolve(F0, kernel, mode='same') F = convolve_same(spectrum, kernel) return F + @jit def sampling(nusd, nus, F, RV): """Sampling w/ RV. @@ -107,7 +111,7 @@ def sampling(nusd, nus, F, RV): RV: radial velocity (km/s) Returns: - sampled spectrum + sampled spectrum """ return jnp.interp(nusd * (1.0 + RV / c), nus, F) @@ -128,14 +132,15 @@ def ipgauss_variable_sampling(nusd, nus, spectrum, beta_variable, RV): Return: response-applied spectrum (F) """ + def convolve_ipgauss_scan(carry, arr): nusd_each = arr[0] beta_each = arr[1] dvgrid = c * (jnp.log1p(1.0 - nus / nusd_each)) - kernel = jnp.exp(-(dvgrid + RV)**2 / (2.0 * beta_each**2)) + kernel = jnp.exp(-((dvgrid + RV) ** 2) / (2.0 * beta_each**2)) kernel = kernel / jnp.sum(kernel) return carry, kernel @ spectrum - + mat = jnp.vstack([nusd, beta_variable]).T _, F_convolved = scan(convolve_ipgauss_scan, 0, mat) - return F_convolved \ No newline at end of file + return F_convolved diff --git a/src/exojax/spec/rtransfer.py b/src/exojax/spec/rtransfer.py index ab4e478b1..b0af74bf2 100644 --- a/src/exojax/spec/rtransfer.py +++ b/src/exojax/spec/rtransfer.py @@ -39,6 +39,7 @@ from jax.scipy.integrate import trapezoid + @jit def trans2E3(x): """transmission function 2E3 (two-stream approximation with no scattering) @@ -302,13 +303,19 @@ def rtrun_emis_scat_lart_toonhm( """Radiative Transfer for emission spectrum using flux-based two-stream scattering LART solver w/ Toon Hemispheric Mean with no surface. Args: - dtau (_type_): _description_ - single_scattering_albedo (_type_): _description_ - asymmetric_parameter (_type_): _description_ - source_matrix (_type_): _description_ + dtau (2D array): Optical depth matrix, dtau (N_layer, N_nus) + single_scattering_albedo (2D array): Single scattering albedo (N_layer, N_nus) + asymmetric_parameter (2D array): Asymmetric parameter (N_layer, N_nus) + source_matrix (2D array): Source matrix (N_layer, N_nus) Returns: - _type_: _description_ + tuple: A tuple containing: + - spectrum (1D array): Emission spectrum in the unit of [erg/cm2/s/cm-1] if using piBarr as a source function. + - cumTtilde (2D array): Cumulative transmission function. + - Qtilde (2D array): Scattering source function. + - trans_coeff (2D array): Transmission coefficients. + - scat_coeff (2D array): Scattering coefficients. + - reduced_piB (2D array): Reduced source function. """ trans_coeff, scat_coeff, reduced_piB, zeta_plus, zeta_minus, lambdan = setrt_toonhm( dtau, single_scattering_albedo, asymmetric_parameter, source_matrix @@ -337,14 +344,20 @@ def rtrun_emis_scat_lart_toonhm_surface( """Radiative Transfer for emission spectrum using flux-based two-stream scattering LART solver w/ Toon Hemispheric Mean with surface. Args: - dtau (_type_): _description_ - single_scattering_albedo (_type_): _description_ - asymmetric_parameter (_type_): _description_ - source_matrix (_type_): _description_ - source_surface: source from the surface (N_nus) + dtau (2D array): Optical depth matrix, dtau (N_layer, N_nus) + single_scattering_albedo (2D array): Single scattering albedo (N_layer, N_nus) + asymmetric_parameter (2D array): Asymmetric parameter (N_layer, N_nus) + source_matrix (2D array): Source matrix (N_layer, N_nus) + source_surface (1D array): Source from the surface (N_nus) Returns: - _type_: _description_ + tuple: A tuple containing: + - spectrum (1D array): Emission spectrum in the unit of [erg/cm2/s/cm-1] if using piBarr as a source function. + - cumTtilde (2D array): Cumulative transmission function. + - Qtilde (2D array): Scattering source function. + - trans_coeff (2D array): Transmission coefficients. + - scat_coeff (2D array): Scattering coefficients. + - piB (2D array): Reduced source function. """ trans_coeff, scat_coeff, piB, zeta_plus, zeta_minus, lambdan = setrt_toonhm( dtau, single_scattering_albedo, asymmetric_parameter, source_matrix @@ -359,7 +372,6 @@ def rtrun_emis_scat_lart_toonhm_surface( return spectrum, cumTtilde, Qtilde, trans_coeff, scat_coeff, piB - @jit def rtrun_reflect_fluxadding_toonhm( dtau, @@ -370,27 +382,28 @@ def rtrun_reflect_fluxadding_toonhm( reflectivity_surface, incoming_flux, ): - """Radiative Transfer for reflected spectrum the flux adding solver w/ Toon Hemispheric Mean with surface. + """Radiative Transfer for reflected spectrum using the flux adding solver w/ Toon Hemispheric Mean with surface. Args: - dtau: layer optical depth (Nlayer, N_nus) - single_scattering_albedo: single scattering albedo (Nlayer, N_nus) - asymmetric_parameter: assymetric parameter (Nlayer, N_nus) - source_matrix: source term (Nlayer, N_nus) - source_surface: source from the surface (N_nus) - reflectivity_surface: reflectivity from the surface (N_nus) - incoming flux: incoming flux F_0^- (N_nus) + dtau (2D array): Layer optical depth (N_layer, N_nus) + single_scattering_albedo (2D array): Single scattering albedo (N_layer, N_nus) + asymmetric_parameter (2D array): Asymmetric parameter (N_layer, N_nus) + source_matrix (2D array): Source term (N_layer, N_nus) + source_surface (1D array): Source from the surface (N_nus) + reflectivity_surface (1D array): Reflectivity from the surface (N_nus) + incoming_flux (1D array): Incoming flux F_0^- (N_nus) Returns: - _type_: _description_ + 1D array: Reflected spectrum in the unit of [erg/cm2/s/cm-1] if using piBarr as a source function. """ trans_coeff, scat_coeff, reduced_piB, zeta_plus, zeta_minus, lambdan = setrt_toonhm( dtau, single_scattering_albedo, asymmetric_parameter, source_matrix ) - + Rphat, Sphat = solve_fluxadding_twostream( trans_coeff, scat_coeff, reduced_piB, reflectivity_surface, source_surface ) + return Rphat * incoming_flux + Sphat @@ -401,13 +414,13 @@ def rtrun_emis_scat_fluxadding_toonhm( """Radiative Transfer for emission spectrum (w/ scattering) using flux-based two-stream scattering the flux adding solver w/ Toon Hemispheric Mean with surface. Args: - dtau (_type_): _description_ - single_scattering_albedo (_type_): _description_ - asymmetric_parameter (_type_): _description_ - source_matrix (_type_): _description_ + dtau (2D array): Optical depth matrix, dtau (N_layer, N_nus) + single_scattering_albedo (2D array): Single scattering albedo (N_layer, N_nus) + asymmetric_parameter (2D array): Asymmetric parameter (N_layer, N_nus) + source_matrix (2D array): Source matrix (N_layer, N_nus) Returns: - _type_: _description_ + 1D array: Emission spectrum in the unit of [erg/cm2/s/cm-1] if using piBarr as a source function. """ _, Nnus = dtau.shape source_surface = jnp.zeros(Nnus) @@ -425,17 +438,23 @@ def rtrun_emis_scat_fluxadding_toonhm( def setrt_toonhm(dtau, single_scattering_albedo, asymmetric_parameter, source_matrix): - """sets some coefficients for rtrun assming Toon Hemispheric Mean + """Sets some coefficients for radiative transfer assuming Toon Hemispheric Mean. Args: - dtau (_type_): _description_ - single_scattering_albedo (_type_): _description_ - asymmetric_parameter (_type_): _description_ - source_matrix (_type_): _description_ + dtau (2D array): Optical depth matrix, dtau (N_layer, N_nus) + single_scattering_albedo (2D array): Single scattering albedo (N_layer, N_nus) + asymmetric_parameter (2D array): Asymmetric parameter (N_layer, N_nus) + source_matrix (2D array): Source matrix (N_layer, N_nus) Returns: - _type_: _description_ + trans_coeff (2D array): Transmission coefficients. + scat_coeff (2D array): Scattering coefficients. + reduced_piB (2D array): Reduced source function. + zeta_plus (2D array): Zeta plus coefficients. + zeta_minus (2D array): Zeta minus coefficients. + lambdan (2D array): Lambda coefficients. """ + gamma_1, gamma_2, mu1 = params_hemispheric_mean( single_scattering_albedo, asymmetric_parameter ) @@ -483,41 +502,3 @@ def settridiag_toohm( ) return diagonal, lower_diagonal, upper_diagonal, vector - - -################################################################################## -# Raise Error since v1.5 -# Deprecated features, will be completely removed by Release v2.0 -################################################################################## - - -def dtauM(dParr, xsm, MR, mass, g): - warn_msg = "Use `spec.layeropacity.layer_optical_depth` instead" - raise ValueError(warn_msg) - - -def dtauCIA(nus, Tarr, Parr, dParr, vmr1, vmr2, mmw, g, nucia, tcia, logac): - warn_msg = "Use `spec.layeropacity.layer_optical_depth_CIA` instead" - raise ValueError(warn_msg) - - -def dtauHminus(nus, Tarr, Parr, dParr, vmre, vmrh, mmw, g): - warn_msg = "Use `spec.layeropacity.layer_optical_depth_Hminus` instead" - raise ValueError(warn_msg) - - -def dtauVALD(dParr, xsm, VMR, mmw, g): - warn_msg = "Use `spec.layeropacity.layer_optical_depth_VALD` instead" - raise ValueError(warn_msg) - - -def pressure_layer( - log_pressure_top=-8.0, - log_pressure_btm=2.0, - NP=20, - mode="ascending", - reference_point=0.5, - numpy=False, -): - warn_msg = "Use `atm.atmprof.pressure_layer_logspace` instead" - raise ValueError(warn_msg) diff --git a/src/exojax/spec/toon.py b/src/exojax/spec/toon.py index 3fd953b3a..3212c4ae9 100644 --- a/src/exojax/spec/toon.py +++ b/src/exojax/spec/toon.py @@ -10,7 +10,7 @@ def zetalambda_coeffs(gamma_1, gamma_2): - """computes coupling coefficients zeta and lambda coefficients for Toon-type two stream approximation + """computes coupling coefficients zeta and lambda coefficients for Toon-type two stream approximation Args: gamma_1 (_type_): Toon+89 gamma_1 coefficient @@ -26,8 +26,9 @@ def zetalambda_coeffs(gamma_1, gamma_2): return zeta_plus, zeta_minus, lambdan -def reduced_source_function_isothermal_layer(single_scattering_albedo, gamma_1, - gamma_2, source_function): +def reduced_source_function_isothermal_layer( + single_scattering_albedo, gamma_1, gamma_2, source_function +): """computes reduced source functions (pi \mathcal{B}) for the isothermal layer Args: @@ -35,8 +36,8 @@ def reduced_source_function_isothermal_layer(single_scattering_albedo, gamma_1, gamma_1 (_type_): Toon+89 gamma_1 coefficient gamma_2 (_type_): Toon+89 gamma_2 coefficient source_function (_type_): pi B(tau) - - + + Returns: _type_: reduced source function for the isothermal layer """ @@ -45,12 +46,14 @@ def reduced_source_function_isothermal_layer(single_scattering_albedo, gamma_1, return coeff * source_function -def reduced_source_function(single_scattering_albedo, - gamma_1, - gamma_2, - source_function, - source_function_derivative, - sign=1.0): +def reduced_source_function( + single_scattering_albedo, + gamma_1, + gamma_2, + source_function, + source_function_derivative, + sign=1.0, +): """computes reduced source functions (pi \mathcal{B}^+ or -) Args: @@ -70,27 +73,56 @@ def reduced_source_function(single_scattering_albedo, def params_eddington(single_scattering_albedo, asymmetric_parameter, mu0): - gamma_1 = (7.0 - single_scattering_albedo * - (4.0 + 3.0 * asymmetric_parameter)) / 4.0 - gamma_2 = -(1.0 - single_scattering_albedo * - (4.0 - 3.0 * asymmetric_parameter)) / 4.0 + """computes Toon+89 parameters for Eddington approximation + + Args: + single_scattering_albedo (_type_): single scattering albedo + asymmetric_parameter (_type_): asymmetric parameter + mu0 (_type_): cosine of the incident angle + + Returns: + _type_: gamma_1, gamma_2, gamma_3, mu1 + """ + gamma_1 = ( + 7.0 - single_scattering_albedo * (4.0 + 3.0 * asymmetric_parameter) + ) / 4.0 + gamma_2 = ( + -(1.0 - single_scattering_albedo * (4.0 - 3.0 * asymmetric_parameter)) / 4.0 + ) gamma_3 = (2.0 - 3.0 * asymmetric_parameter * mu0) / 4.0 mu1 = 0.5 return gamma_1, gamma_2, gamma_3, mu1 def params_quadrature(single_scattering_albedo, asymmetric_parameter, mu0): + """computes Toon+89 parameters for Quadrature approximation + + Args: + single_scattering_albedo (_type_): single scattering albedo + asymmetric_parameter (_type_): asymmetric parameter + mu0 (_type_): cosine of the incident angle + + Returns: + _type_: gamma_1, gamma_2, gamma_3, mu1 + """ s3 = jnp.sqrt(3.0) - gamma_1 = s3 * (2.0 - single_scattering_albedo * - (1.0 + asymmetric_parameter)) / 2.0 - gamma_2 = single_scattering_albedo * s3 * (1.0 - - asymmetric_parameter) / 2.0 + gamma_1 = s3 * (2.0 - single_scattering_albedo * (1.0 + asymmetric_parameter)) / 2.0 + gamma_2 = single_scattering_albedo * s3 * (1.0 - asymmetric_parameter) / 2.0 gamma_3 = (1.0 - s3 * asymmetric_parameter * mu0) / 2.0 mu1 = 1.0 / s3 return gamma_1, gamma_2, gamma_3, mu1 def params_hemispheric_mean(single_scattering_albedo, asymmetric_parameter): + """computes Toon+89 parameters for Hemispheric Mean approximation + + Args: + single_scattering_albedo (_type_): single scattering albedo + asymmetric_parameter (_type_): asymmetric parameter + + Returns: + _type_: gamma_1, gamma_2, mu1 + """ gamma_1 = 2.0 - single_scattering_albedo * (1.0 + asymmetric_parameter) gamma_2 = single_scattering_albedo * (1.0 - asymmetric_parameter) mu1 = 0.5 diff --git a/src/exojax/spec/twostream.py b/src/exojax/spec/twostream.py index f013345ea..469150a96 100644 --- a/src/exojax/spec/twostream.py +++ b/src/exojax/spec/twostream.py @@ -11,12 +11,14 @@ from jax.lax import scan -def solve_fluxadding_twostream(trans_coeff, scat_coeff, reduced_source_function, reflectivity_bottom, source_bottom): +def solve_fluxadding_twostream( + trans_coeff, scat_coeff, reduced_source_function, reflectivity_bottom, source_bottom +): """Two-stream RT solver using flux adding Args: - trans_coeff (_type_): Transmission coefficient - scat_coeff (_type_): Scattering coefficient + trans_coeff (_type_): Transmission coefficient + scat_coeff (_type_): Scattering coefficient reduced_source_function : pi \mathcal{B} (Nlayer, Nnus) reflectivity_bottom (_type_): R^+_N (Nnus) source_bottom (_type_): S^+_N (Nnus) @@ -24,40 +26,43 @@ def solve_fluxadding_twostream(trans_coeff, scat_coeff, reduced_source_function, Returns: Effective reflectivity (hat(R^plus)), Effective source (hat(S^plus)) """ + nlayer, _ = trans_coeff.shape - pihatB = (1.0 - trans_coeff - scat_coeff)*reduced_source_function + pihatB = (1.0 - trans_coeff - scat_coeff) * reduced_source_function # bottom reflection - Rphat0 = scat_coeff[nlayer-1, :] + trans_coeff[nlayer-1, :]**2 * \ - reflectivity_bottom/(1.0 - scat_coeff[nlayer-1, :]*reflectivity_bottom) - Sphat0 = pihatB[nlayer-1, :] + trans_coeff[nlayer-1, :] * \ - (source_bottom + pihatB[nlayer-1, :]*reflectivity_bottom) / \ - (1.0 - scat_coeff[nlayer-1, :]*reflectivity_bottom) + Rphat0 = scat_coeff[nlayer - 1, :] + trans_coeff[ + nlayer - 1, : + ] ** 2 * reflectivity_bottom / ( + 1.0 - scat_coeff[nlayer - 1, :] * reflectivity_bottom + ) + Sphat0 = pihatB[nlayer - 1, :] + trans_coeff[nlayer - 1, :] * ( + source_bottom + pihatB[nlayer - 1, :] * reflectivity_bottom + ) / (1.0 - scat_coeff[nlayer - 1, :] * reflectivity_bottom) def f(carry_ip1, arr): Rphat_prev, Sphat_prev = carry_ip1 scat_coeff_i, trans_coeff_i, pihatB_i = arr - denom = 1.0 - scat_coeff_i*Rphat_prev - - Sphat_each = pihatB_i + trans_coeff_i * \ - (Sphat_prev + pihatB_i*Rphat_prev) / denom - Rphat_each = scat_coeff_i + trans_coeff_i**2 * Rphat_prev/denom - + denom = 1.0 - scat_coeff_i * Rphat_prev + Sphat_each = ( + pihatB_i + trans_coeff_i * (Sphat_prev + pihatB_i * Rphat_prev) / denom + ) + Rphat_each = scat_coeff_i + trans_coeff_i**2 * Rphat_prev / denom RS = [Rphat_each, Sphat_each] return RS, 0 # main loop arrin = [ - scat_coeff[nlayer-2::-1], - trans_coeff[nlayer-2::-1], - pihatB[nlayer-2::-1] + scat_coeff[nlayer - 2 :: -1], + trans_coeff[nlayer - 2 :: -1], + pihatB[nlayer - 2 :: -1], ] RS, _ = scan(f, [Rphat0, Sphat0], arrin) + return RS -def solve_lart_twostream(diagonal, lower_diagonal, upper_diagonal, vector, - flux_bottom): +def solve_lart_twostream(diagonal, lower_diagonal, upper_diagonal, vector, flux_bottom): """Two-stream RT solver given tridiagonal system components (LART form) Args: @@ -68,12 +73,12 @@ def solve_lart_twostream(diagonal, lower_diagonal, upper_diagonal, vector, flux_bottom: bottom flux FB Note: - Our definition of the tridiagonal components is - an F+_(n+1) + bn F+_n + c_(n-1) F+_(n-1) = dn + Our definition of the tridiagonal components is + an F+_(n+1) + bn F+_n + c_(n-1) F+_(n-1) = dn Notice that c_(n-1) is not cn Returns: - _type_: cumlative hat{T}, hat{Q}, spectrum + _type_: cumlative hat{T}, hat{Q}, spectrum """ nlayer, Nnus = diagonal.shape @@ -96,8 +101,10 @@ def f(carry_i_1, arr): # main loop arrin = [ - diagonal[1:nlayer, :], lower_diagonal[0:nlayer - 1, :], - upper_diagonal[1:nlayer, :], vector[1:nlayer, :] + diagonal[1:nlayer, :], + lower_diagonal[0 : nlayer - 1, :], + upper_diagonal[1:nlayer, :], + vector[1:nlayer, :], ] _, stackedTQ = scan(f, [That0, Qhat0], arrin) That, Qhat = stackedTQ @@ -118,14 +125,15 @@ def solve_twostream_pure_absorption_numpy(trans_coeff, scat_coeff, piB): """solves pure absorption limit for two stream Args: - trans_coeff (_type_): transmission coefficient + trans_coeff (_type_): transmission coefficient scat_coeff (_type_): scattering coefficient piB (_type_): pi x Planck function Returns: - _type_: cumlative transmission, generalized source, spectrum + _type_: cumlative transmission, generalized source, spectrum """ import numpy as np + Qpure = np.zeros_like(trans_coeff) nlayer, Nnus = trans_coeff.shape for i in range(0, nlayer - 1): @@ -138,7 +146,7 @@ def solve_twostream_pure_absorption_numpy(trans_coeff, scat_coeff, piB): def contribution_function_lart(cumT, Q): - """computes the contribution function from LART cumlative transmission and generalized source + """computes the contribution function from LART cumlative transmission and generalized source Args: cumT (_type_): cumlative transmission @@ -162,31 +170,31 @@ def set_scat_trans_coeffs(zeta_plus, zeta_minus, lambdan, dtau): Returns: _type_: transmission coefficient, scattering coeffcient """ - trans_func = jnp.exp(-lambdan * - dtau) # transmission function (Heng 2017, 3.58) - denom = zeta_plus**2 - (zeta_minus * trans_func)**2 + trans_func = jnp.exp(-lambdan * dtau) # transmission function (Heng 2017, 3.58) + denom = zeta_plus**2 - (zeta_minus * trans_func) ** 2 trans_coeff = trans_func * (zeta_plus**2 - zeta_minus**2) / denom scat_coeff = (1.0 - trans_func**2) * zeta_plus * zeta_minus / denom + return trans_coeff, scat_coeff -def compute_tridiag_diagonals_and_vector(scat_coeff, trans_coeff, piB, - upper_diagonal_top, diagonal_top, - vector_top): +def compute_tridiag_diagonals_and_vector( + scat_coeff, trans_coeff, piB, upper_diagonal_top, diagonal_top, vector_top +): """computes the diagonals and right-handside vector from scattering and transmission coefficients for the tridiagonal system Args: scat_coeff (_type_): scattering coefficient of the n-th layer, S_n trans_coeff (_type_): transmission coefficient of the n-th layer, T_n piB (): Planck source function, piB - upper_diagonal_top (_type_): a[0] upper diagonal top boundary + upper_diagonal_top (_type_): a[0] upper diagonal top boundary diagonal_top (_type_): b[0] diagonal top boundary - vector_top (_type_): vector top boundary + vector_top (_type_): vector top boundary Notes: In ExoJAX 2 paper, we assume the tridiagonal form as -an F_{n+1}^+ + b_n F_n^+ - cn F_{n-1}^+ = dn Returns: - jnp arrays: diagonal (bn) [Nlayer], lower dianoals (cn) [Nlayer], upper diagonal (an) [Nlayer], vector (dn) [Nlayer], + jnp arrays: diagonal (bn) [Nlayer], lower dianoals (cn) [Nlayer], upper diagonal (an) [Nlayer], vector (dn) [Nlayer], """ Sn_minus_one = jnp.roll(scat_coeff, 1, axis=0) # S_{n-1} @@ -208,18 +216,9 @@ def compute_tridiag_diagonals_and_vector(scat_coeff, trans_coeff, piB, # vector hatpiB = (1.0 - trans_coeff - scat_coeff) * piB hatpiB_minus_one = jnp.roll(hatpiB, 1, axis=0) - vector = rn_minus * hatpiB - rn * (Tn_minus_one - - Sn_minus_one) * hatpiB_minus_one + vector = rn_minus * hatpiB - rn * (Tn_minus_one - Sn_minus_one) * hatpiB_minus_one # top bundary vector = vector.at[0].set(vector_top) return diagonal, lower_diagonal, upper_diagonal, vector - - -def sh2_zetalambda_coeff(): - raise ValueError("not implemented yet.") - - -# if __name__ == "__main__": -# test_tridiag_coefficients() diff --git a/src/exojax/spec/unitconvert.py b/src/exojax/spec/unitconvert.py index 454bed712..94ad8faa9 100644 --- a/src/exojax/spec/unitconvert.py +++ b/src/exojax/spec/unitconvert.py @@ -58,6 +58,7 @@ def wav2nu(wav, unit): except KeyError: raise ValueError("unavailable unit") + def _both_ascending_warning(): warnings.warn( "Both input wavelength and output wavenumber are in ascending order.", diff --git a/src/exojax/special/__init__.py b/src/exojax/special/__init__.py index f1b5e4f2b..6fd7a4e3d 100644 --- a/src/exojax/special/__init__.py +++ b/src/exojax/special/__init__.py @@ -1,6 +1,6 @@ __all__ = [] -__version__ = '1.4.0' +__version__ = '1.6' __uri__ = '' __author__ = 'ExoJAX collaborators' __email__ = 'divrot@gmail.com' diff --git a/src/exojax/special/erfcx.py b/src/exojax/special/erfcx.py index 5a97af9cd..f7809e277 100644 --- a/src/exojax/special/erfcx.py +++ b/src/exojax/special/erfcx.py @@ -4,28 +4,58 @@ @jit def erfcx(x): - """erfcx (float) based on Shepherd and Laframboise (1981) - - Scaled complementary error function exp(-x*x) erfc(x) + """Scaled complementary error function exp(-x*x) based on Shepherd and Laframboise (1981) + Args: x: should be larger than -9.3 Returns: - jnp.array: erfcx(x) + jnp.array: Scaled complementary error function exp(-x*x) Note: - We acknowledge the post in stack overflow - (https://stackoverflow.com/questions/39777360/accurate-computation-of-scaled-complementary-error-function-erfcx). + We acknowledge the post in stack overflow + (https://stackoverflow.com/questions/39777360/accurate-computation-of-scaled-complementary-error-function-erfcx). """ a = jnp.abs(x) b = (a - 2.0) / (a + 2.0) q = (-a * b - 2.0 * (b + 1.0) + a) / (a + 2.0) + b - p = (((((((((( - (5.92470169e-5 * q + 1.61224554e-4) * q - 3.46481771e-4) * q - - 1.39681227e-3) * q + 1.20588380e-3) * q + 8.69014394e-3) * q - - 8.01387429e-3) * q - 5.42122945e-2) * q + 1.64048523e-1) * q - - 1.66031078e-1) * q - 9.27637145e-2) * q + 2.76978403e-1) + p = ( + ( + ( + ( + ( + ( + ( + ( + ( + (5.92470169e-5 * q + 1.61224554e-4) * q + - 3.46481771e-4 + ) + * q + - 1.39681227e-3 + ) + * q + + 1.20588380e-3 + ) + * q + + 8.69014394e-3 + ) + * q + - 8.01387429e-3 + ) + * q + - 5.42122945e-2 + ) + * q + + 1.64048523e-1 + ) + * q + - 1.66031078e-1 + ) + * q + - 9.27637145e-2 + ) * q + 2.76978403e-1 q = (p + 1.0) / (1.0 + 2.0 * a) d = (p + 1.0) - q * (1.0 + 2.0 * a) diff --git a/src/exojax/special/expn.py b/src/exojax/special/expn.py index 7611b1569..5db972d52 100644 --- a/src/exojax/special/expn.py +++ b/src/exojax/special/expn.py @@ -8,10 +8,10 @@ def E1(x): the first order, E1. Args: - x: input + x: input Returns: - The exponential integral of the first order, E1(x) + The exponential integral of the first order, E1(x) """ A0 = -0.57721566 A1 = 0.99999193 @@ -32,9 +32,12 @@ def E1(x): x3 = x**3 x4 = x**4 x5 = x**5 - ep1A = -jnp.log(x)+A0+A1*x+A2*x2+A3*x3+A4*x4+A5*x5 - ep1B = jnp.exp(-x)/x *\ - (x4+B1*x3+B2*x2+B3*x+B4) /\ - (x4+C1*x3+C2*x2+C3*x+C4) + ep1A = -jnp.log(x) + A0 + A1 * x + A2 * x2 + A3 * x3 + A4 * x4 + A5 * x5 + ep1B = ( + jnp.exp(-x) + / x + * (x4 + B1 * x3 + B2 * x2 + B3 * x + B4) + / (x4 + C1 * x3 + C2 * x2 + C3 * x + C4) + ) ep = jnp.where(x <= 1.0, ep1A, ep1B) return ep diff --git a/src/exojax/special/faddeeva.py b/src/exojax/special/faddeeva.py index 263d55366..a1707f385 100644 --- a/src/exojax/special/faddeeva.py +++ b/src/exojax/special/faddeeva.py @@ -1,7 +1,7 @@ """Faddeeva (wofz= w of z) functions (real and imag parts), an asymptotic form of wofz. Note: - We adopt ncut=27 as the summation of n in Algorithm 916. For Sigma1, we use ncut=8 because higher order than 9 does not affect the results. + We adopt ncut=27 as the summation of n in Algorithm 916. For Sigma1, we use ncut=8 because higher order than 9 does not affect the results. """ from jax import jit @@ -9,16 +9,69 @@ import jax.numpy as jnp from exojax.special.erfcx import erfcx -an = jnp.array([ - 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., - 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12., 12.5, 13., 13.5 -]) - -a2n2 = jnp.array([ - 0.25, 1., 2.25, 4., 6.25, 9., 12.25, 16., 20.25, 25., 30.25, 36., 42.25, - 49., 56.25, 64., 72.25, 81., 90.25, 100., 110.25, 121., 132.25, 144., - 156.25, 169., 182.25 -]) +an = jnp.array( + [ + 0.5, + 1.0, + 1.5, + 2.0, + 2.5, + 3.0, + 3.5, + 4.0, + 4.5, + 5.0, + 5.5, + 6.0, + 6.5, + 7.0, + 7.5, + 8.0, + 8.5, + 9.0, + 9.5, + 10.0, + 10.5, + 11.0, + 11.5, + 12.0, + 12.5, + 13.0, + 13.5, + ] +) + +a2n2 = jnp.array( + [ + 0.25, + 1.0, + 2.25, + 4.0, + 6.25, + 9.0, + 12.25, + 16.0, + 20.25, + 25.0, + 30.25, + 36.0, + 42.25, + 49.0, + 56.25, + 64.0, + 72.25, + 81.0, + 90.25, + 100.0, + 110.25, + 121.0, + 132.25, + 144.0, + 156.25, + 169.0, + 182.25, + ] +) @jit @@ -32,15 +85,17 @@ def rewofz(x, y): y: Returns: - jnp.array: Real(wofz(x+iy)) + jnp.array: Real(wofz(x+iy)) """ xy = x * y exx = jnp.exp(-x * x) - f = exx * (erfcx(y) * jnp.cos(2.0 * xy) + - x * jnp.sin(xy) / jnp.pi * jnp.sinc(xy / jnp.pi)) + f = exx * ( + erfcx(y) * jnp.cos(2.0 * xy) + x * jnp.sin(xy) / jnp.pi * jnp.sinc(xy / jnp.pi) + ) y2 = y * y Sigma23 = jnp.sum( - (jnp.exp(-(an + x)**2) + jnp.exp(-(an - x)**2)) / (a2n2 + y2)) + (jnp.exp(-((an + x) ** 2)) + jnp.exp(-((an - x) ** 2))) / (a2n2 + y2) + ) Sigma1 = faddeeva_sigma1(exx, y2) f = f + y / jnp.pi * (-jnp.cos(2.0 * xy) * Sigma1 + 0.5 * Sigma23) return f @@ -57,16 +112,16 @@ def imwofz(x, y): y: Returns: - jnp.array: Imag(wofz(x+iy)) + jnp.array: Imag(wofz(x+iy)) """ wxy = 2.0 * x * y exx = jnp.exp(-x * x) - f = -exx * erfcx(y) * jnp.sin(wxy) + x / jnp.pi * exx * jnp.sinc( - wxy / jnp.pi) + f = -exx * erfcx(y) * jnp.sin(wxy) + x / jnp.pi * exx * jnp.sinc(wxy / jnp.pi) y2 = y * y Sigma1 = faddeeva_sigma1(exx, y2) - Sigma45 = jnp.sum(an * (-jnp.exp(-(an + x)**2) + jnp.exp(-(an - x)**2)) / - (a2n2 + y2)) + Sigma45 = jnp.sum( + an * (-jnp.exp(-((an + x) ** 2)) + jnp.exp(-((an - x) ** 2))) / (a2n2 + y2) + ) f = f + 1.0 / jnp.pi * (y * jnp.sin(wxy) * Sigma1 + 0.5 * Sigma45) return f @@ -83,14 +138,13 @@ def asymptotic_wofz(x, y): y: real y Returns: - jnp.array (complex): wofz(x+iy) + jnp.array (complex): wofz(x+iy) """ z = x + y * (1j) a = 1.0 / (2.0 * z * z) - q = (1j) / (z * jnp.sqrt(jnp.pi)) * (1.0 + a * (1.0 + a * - (3.0 + a * 15.0))) + q = (1j) / (z * jnp.sqrt(jnp.pi)) * (1.0 + a * (1.0 + a * (3.0 + a * 15.0))) return q @@ -110,12 +164,14 @@ def rewofzx(x, y): """ xy = x * y exx = jnp.exp(-x * x) - f = exx*erfcx(y)*jnp.cos(2.0*xy)+x*jnp.sin(xy) / \ - jnp.pi*exx*jnp.sinc(xy/jnp.pi) + f = exx * erfcx(y) * jnp.cos(2.0 * xy) + x * jnp.sin(xy) / jnp.pi * exx * jnp.sinc( + xy / jnp.pi + ) y2 = y * y Sigma1 = faddeeva_sigma1(exx, y2) Sigma23 = jnp.sum( - (jnp.exp(-(an + x)**2) + jnp.exp(-(an - x)**2)) / (a2n2 + y * y)) + (jnp.exp(-((an + x) ** 2)) + jnp.exp(-((an - x) ** 2))) / (a2n2 + y * y) + ) f = f + y / jnp.pi * (-jnp.cos(2.0 * xy) * Sigma1 + 0.5 * Sigma23) return f @@ -137,20 +193,27 @@ def h_bwd(res, g): g: g Returns: - jnp.array, jnp.array: g* partial_x h(x,y), g* partial_y h(x,y) + jnp.array, jnp.array: g* partial_x h(x,y), g* partial_y h(x,y) """ V, L, x, y = res - return (2.0 * (y * L - x * V) * g, - 2.0 * (x * L + y * V) * g - 2.0 / jnp.sqrt(jnp.pi)) + return ( + 2.0 * (y * L - x * V) * g, + 2.0 * (x * L + y * V) * g - 2.0 / jnp.sqrt(jnp.pi), + ) rewofzx.defvjp(h_fwd, h_bwd) def faddeeva_sigma1(exx, y2): - Sigma1 = exx * (7.78800786e-01 / (0.25 + y2) + 3.67879450e-01 / - (1. + y2) + 1.05399221e-01 / (2.25 + y2) + 1.83156393e-02 / - (4. + y2) + 1.93045416e-03 / (6.25 + y2) + 1.23409802e-04 / - (9. + y2) + 4.78511765e-06 / - (12.25 + y2) + 1.12535176e-07 / (16. + y2)) + Sigma1 = exx * ( + 7.78800786e-01 / (0.25 + y2) + + 3.67879450e-01 / (1.0 + y2) + + 1.05399221e-01 / (2.25 + y2) + + 1.83156393e-02 / (4.0 + y2) + + 1.93045416e-03 / (6.25 + y2) + + 1.23409802e-04 / (9.0 + y2) + + 4.78511765e-06 / (12.25 + y2) + + 1.12535176e-07 / (16.0 + y2) + ) return Sigma1 diff --git a/src/exojax/special/j0.py b/src/exojax/special/j0.py index 1198580a3..15e41f082 100644 --- a/src/exojax/special/j0.py +++ b/src/exojax/special/j0.py @@ -1,19 +1,74 @@ import jax.numpy as jnp -RP = jnp.array([-4.79443220978201773821E9, 1.95617491946556577543E12, - - 2.49248344360967716204E14, 9.70862251047306323952E15]) -RQ = jnp.array([1., 4.99563147152651017219E2, 1.73785401676374683123E5, 4.84409658339962045305E7, 1.11855537045356834862E10, - 2.11277520115489217587E12, 3.10518229857422583814E14, 3.18121955943204943306E16, 1.71086294081043136091E18]) -DR1 = 5.78318596294678452118E0 -DR2 = 3.04712623436620863991E1 -PP = jnp.array([7.96936729297347051624E-4, 8.28352392107440799803E-2, 1.23953371646414299388E0, - 5.44725003058768775090E0, 8.74716500199817011941E0, 5.30324038235394892183E0, 9.99999999999999997821E-1]) -PQ = jnp.array([9.24408810558863637013E-4, 8.56288474354474431428E-2, 1.25352743901058953537E0, - 5.47097740330417105182E0, 8.76190883237069594232E0, 5.30605288235394617618E0, 1.00000000000000000218E0]) -QP = jnp.array([-1.13663838898469149931E-2, -1.28252718670509318512E0, -1.95539544257735972385E1, -9.32060152123768231369E1, - - 1.77681167980488050595E2, -1.47077505154951170175E2, -5.14105326766599330220E1, -6.05014350600728481186E0]) -QQ = jnp.array([1., 6.43178256118178023184E1, 8.56430025976980587198E2, 3.88240183605401609683E3, - 7.24046774195652478189E3, 5.93072701187316984827E3, 2.06209331660327847417E3, 2.42005740240291393179E2]) +RP = jnp.array( + [ + -4.79443220978201773821e9, + 1.95617491946556577543e12, + -2.49248344360967716204e14, + 9.70862251047306323952e15, + ] +) +RQ = jnp.array( + [ + 1.0, + 4.99563147152651017219e2, + 1.73785401676374683123e5, + 4.84409658339962045305e7, + 1.11855537045356834862e10, + 2.11277520115489217587e12, + 3.10518229857422583814e14, + 3.18121955943204943306e16, + 1.71086294081043136091e18, + ] +) +DR1 = 5.78318596294678452118e0 +DR2 = 3.04712623436620863991e1 +PP = jnp.array( + [ + 7.96936729297347051624e-4, + 8.28352392107440799803e-2, + 1.23953371646414299388e0, + 5.44725003058768775090e0, + 8.74716500199817011941e0, + 5.30324038235394892183e0, + 9.99999999999999997821e-1, + ] +) +PQ = jnp.array( + [ + 9.24408810558863637013e-4, + 8.56288474354474431428e-2, + 1.25352743901058953537e0, + 5.47097740330417105182e0, + 8.76190883237069594232e0, + 5.30605288235394617618e0, + 1.00000000000000000218e0, + ] +) +QP = jnp.array( + [ + -1.13663838898469149931e-2, + -1.28252718670509318512e0, + -1.95539544257735972385e1, + -9.32060152123768231369e1, + -1.77681167980488050595e2, + -1.47077505154951170175e2, + -5.14105326766599330220e1, + -6.05014350600728481186e0, + ] +) +QQ = jnp.array( + [ + 1.0, + 6.43178256118178023184e1, + 8.56430025976980587198e2, + 3.88240183605401609683e3, + 7.24046774195652478189e3, + 5.93072701187316984827e3, + 2.06209331660327847417e3, + 2.42005740240291393179e2, + ] +) PIO4 = 0.78539816339744830962 SQ2OPI = 0.79788456080286535588 @@ -22,28 +77,28 @@ def j0(x): """Bessel function of the 1st kind, order=0. Args: - x: x + x: x Returns: - J0 + J0 """ - x = jnp.where(x > 0., x, -x) + x = jnp.where(x > 0.0, x, -x) z = x * x - ret = 1. - z/4. + ret = 1.0 - z / 4.0 p = (z - DR1) * (z - DR2) p = p * jnp.polyval(RP, z) / jnp.polyval(RQ, z) ret = jnp.where(x < 1e-5, ret, p) # required for autograd not to fail when x includes 0 - xinv5 = jnp.where(x <= 5., 0., 1./(x+1e-10)) + xinv5 = jnp.where(x <= 5.0, 0.0, 1.0 / (x + 1e-10)) w = 5.0 * xinv5 z = w * w p = jnp.polyval(PP, z) / jnp.polyval(PQ, z) q = jnp.polyval(QP, z) / jnp.polyval(QQ, z) xn = x - PIO4 p = p * jnp.cos(xn) - w * q * jnp.sin(xn) - ret = jnp.where(x <= 5., ret, p * SQ2OPI * jnp.sqrt(xinv5)) + ret = jnp.where(x <= 5.0, ret, p * SQ2OPI * jnp.sqrt(xinv5)) return ret diff --git a/src/exojax/test/data.py b/src/exojax/test/data.py index ca2948aa8..4be95e03c 100644 --- a/src/exojax/test/data.py +++ b/src/exojax/test/data.py @@ -28,6 +28,7 @@ SAMPLE_SPECTRA_CO = "spectrum_co.txt" SAMPLE_SPECTRA_CH4 = "spectrum_ch4.txt" #exojax version 1.0 SAMPLE_SPECTRA_CH4_NEW = "spectrum_ch4_new.txt" #generate_methane_spectrum.py +SAMPLE_SPECTRA_CH4_TRANS = "spectrum_ch4_trans.txt" #generate_methane_trans.py #sample transmission spectra SAMPLE_TRANSMISSION_CH4 = "transmission_ch4.txt" diff --git a/src/exojax/test/generate_methane_spectrum.py b/src/exojax/test/generate_methane_spectrum.py index 56889a4b0..d8362f6e7 100644 --- a/src/exojax/test/generate_methane_spectrum.py +++ b/src/exojax/test/generate_methane_spectrum.py @@ -1,11 +1,12 @@ -""" Reverse modeling of Methane emission spectrum using PreMODIT, precomputation of F0 grids +""" Geneartes Methane emission spectrum using PreMODIT, precomputation of F0 grids """ + #!/usr/bin/env python # coding: utf-8 import numpy as np import matplotlib.pyplot as plt from exojax.utils.grids import wavenumber_grid -from exojax.spec.atmrt import ArtTransPure +from exojax.spec.atmrt import ArtEmisPure from exojax.spec.api import MdbExomol from exojax.spec.opacalc import OpaPremodit from exojax.spec.contdb import CdbCIA @@ -20,94 +21,84 @@ from exojax.test.data import SAMPLE_SPECTRA_CH4_NEW if __name__ == "__main__": - #given gravity, temperature exponent, MMR + # given gravity, temperature exponent, MMR g = gravity_jupiter(0.88, 33.2) alpha = 0.1 MMR_CH4 = 0.0059 vsini = 20.0 RV = 10.0 T0 = 1200.0 - - #obs grid + + # obs grid Nx = 1500 - nusd, wavd, res = wavenumber_grid(16370., - 16550., - Nx, - unit="AA", - xsmode="modit") - + nusd, wavd, res = wavenumber_grid(16370.0, 16550.0, Nx, unit="AA", xsmode="modit") + Nx = 7500 - nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='premodit') - + nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, np.max(wavd) + 10.0, Nx, unit="AA", xsmode="premodit" + ) + Tlow = 400.0 Thigh = 1500.0 - art = ArtTransPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) + art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100) art.change_temperature_range(Tlow, Thigh) Mp = 33.2 - - Rinst = 100000. + + Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) - + ### CH4 setting (PREMODIT) - mdb = MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nurange=nu_grid, - gpu_transfer=False) - print('N=', len(mdb.nu_lines)) + mdb = MdbExomol( + ".database/CH4/12C-1H4/YT10to10/", nurange=nu_grid, gpu_transfer=False + ) + print("N=", len(mdb.nu_lines)) diffmode = 1 - opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[Tlow, Thigh], - dit_grid_resolution=0.2) - + opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[Tlow, Thigh], + dit_grid_resolution=0.2, + ) + ## CIA setting - cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) + cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) mmw = 2.33 # mean molecular weight mmrH2 = 0.74 - molmassH2 = molinfo.molmass_isotope('H2') - vmrH2 = (mmrH2 * mmw / molmassH2) # VMR - - gravity_btm = 2478.57 - radius_btm = RJ - + molmassH2 = molinfo.molmass_isotope("H2") + vmrH2 = mmrH2 * mmw / molmassH2 # VMR + - #settings before HMC + # settings before HMC vsini_max = 100.0 vr_array = velocity_grid(res, vsini_max) - - + # raw spectrum model given T0 def flux_model(T0, vsini, RV): - #T-P model + # T-P model Tarr = art.powerlaw_temperature(T0, alpha) - gravity = art.gravity_profile(Tarr, mmw, radius_btm, gravity_btm) - #molecule + # molecule xsmatrix = opa.xsmatrix(Tarr, art.pressure) mmr_arr = art.constant_mmr_profile(MMR_CH4) dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, g) - - #continuum + + # continuum logacia_matrix = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, - mmw, g) - + dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, mmw, g) + dtau = dtaumCH4 + dtaucH2H2 F0 = art.run(dtau, Tarr) Frot = convolve_rigid_rotation(F0, vr_array, vsini=vsini, u1=0.0, u2=0.0) mu = ipgauss_sampling(nusd, nu_grid, Frot, beta_inst, RV, vr_array) - + return mu - - -#test and save -#mu = flux_model(T0, vsini, RV) -#import matplotlib.pyplot as plt -#plt.plot(nusd, mu) -#plt.show() -#np.savetxt(SAMPLE_SPECTRA_CH4_NEW, np.array([nusd, mu]).T, delimiter=",") + + +# test and save +mu = flux_model(T0, vsini, RV) +import matplotlib.pyplot as plt +plt.plot(nusd, mu) +plt.show() +np.savetxt(SAMPLE_SPECTRA_CH4_NEW, np.array([nusd, mu]).T, delimiter=",") diff --git a/src/exojax/test/generate_methane_trans.py b/src/exojax/test/generate_methane_trans.py index 1a824ad1b..b58f2566d 100644 --- a/src/exojax/test/generate_methane_trans.py +++ b/src/exojax/test/generate_methane_trans.py @@ -1,111 +1,98 @@ -""" Reverse modeling of Methane emission spectrum using PreMODIT, precomputation of F0 grids +""" Geneartes Methane transmission spectrum using PreMODIT """ + #!/usr/bin/env python # coding: utf-8 import numpy as np import matplotlib.pyplot as plt from exojax.utils.grids import wavenumber_grid -from exojax.spec.atmrt import ArtEmisPure +from exojax.spec.atmrt import ArtTransPure from exojax.spec.api import MdbExomol from exojax.spec.opacalc import OpaPremodit -from exojax.spec.contdb import CdbCIA -from exojax.spec.opacont import OpaCIA -from exojax.spec.response import ipgauss_sampling_slow, ipgauss, sampling -from exojax.spec.spin_rotation import convolve_rigid_rotation -from exojax.utils.grids import velocity_grid from exojax.utils.astrofunc import gravity_jupiter - -from exojax.spec import molinfo from exojax.utils.instfunc import resolution_to_gaussian_std -from exojax.test.data import SAMPLE_SPECTRA_CH4_NEW +from exojax.test.data import SAMPLE_SPECTRA_CH4_TRANS +from exojax.utils.constants import RJ, Rs +from exojax.utils.astrofunc import gravity_jupiter +from exojax.spec.specop import SopInstProfile +import jax.numpy as jnp if __name__ == "__main__": - #given gravity, temperature exponent, MMR - g = gravity_jupiter(0.88, 33.2) - alpha = 0.1 - MMR_CH4 = 0.0059 - vsini = 20.0 - RV = 10.0 - T0 = 1200.0 - - #obs grid + + from jax import config + + config.update("jax_enable_x64", True) + + # obs grid Nx = 1500 - nusd, wavd, res = wavenumber_grid(16370., - 16550., - Nx, - unit="AA", - xsmode="modit") - + nusd, wavd, res = wavenumber_grid(16370.0, 16550.0, Nx, unit="AA", xsmode="modit") + Nx = 7500 - nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='premodit') - - Tlow = 400.0 - Thigh = 1500.0 - art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) + nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, np.max(wavd) + 10.0, Nx, unit="AA", xsmode="premodit" + ) + + T_fid = 500.0 + Tlow = 490.0 + Thigh = 510.0 + + art = ArtTransPure(pressure_top=1.0e-10, pressure_btm=1.0e1, nlayer=100) art.change_temperature_range(Tlow, Thigh) - Mp = 33.2 - - Rinst = 100000. + + Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) - + ### CH4 setting (PREMODIT) - mdb = MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nurange=nu_grid, - gpu_transfer=False) - print('N=', len(mdb.nu_lines)) - diffmode = 1 - opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[Tlow, Thigh], - dit_grid_resolution=0.2) + mdb = MdbExomol( + ".database/CH4/12C-1H4/YT10to10/", nurange=nu_grid, gpu_transfer=False + ) + + print("N=", len(mdb.nu_lines)) + diffmode = 0 + opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[Tlow, Thigh], + dit_grid_resolution=0.2, + allow_32bit=True, + ) ## CIA setting - cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) - opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) - mmw = 2.33 # mean molecular weight - mmrH2 = 0.74 - molmassH2 = molinfo.molmass_isotope('H2') - vmrH2 = (mmrH2 * mmw / molmassH2) # VMR - - #settings before HMC - vsini_max = 100.0 - vr_array = velocity_grid(res, vsini_max) - - + # cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) + mu_fid = 2.2 + # settings before HMC + radius_btm = RJ + gravity_btm = gravity_jupiter(1.0, 1.0) + + vrmax = 100.0 # km/s + sop = SopInstProfile(nu_grid, vrmax) + # raw spectrum model given T0 - def flux_model(T0, vsini, RV): - #T-P model - Tarr = art.powerlaw_temperature(T0, alpha) - - #molecule + def flux_model(mmr_ch4, rv): + # T-P model + Tarr = T_fid * np.ones_like(art.pressure) + mmw_arr = mu_fid * np.ones_like(art.pressure) + + gravity = art.gravity_profile(Tarr, mmw_arr, radius_btm, gravity_btm) + mmr_arr = art.constant_mmr_profile(mmr_ch4) + + # molecule xsmatrix = opa.xsmatrix(Tarr, art.pressure) - mmr_arr = art.constant_mmr_profile(MMR_CH4) - dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, g) - - #continuum - logacia_matrix = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, - mmw, g) - - dtau = dtaumCH4 + dtaucH2H2 - F0 = art.run(dtau, Tarr) - Frot = convolve_rigid_rotation(F0, vr_array, vsini=vsini, u1=0.0, u2=0.0) - Frotgauss = ipgauss(nu_grid, Frot, vr_array, beta_inst) - mu = sampling(nusd, nu_grid, Frotgauss, RV=RV) - - return mu - - #test and save - mu = flux_model(T0, vsini, RV) - + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) + + Rp2 = art.run(dtau, Tarr, mmw_arr, radius_btm, gravity_btm) + mu = sop.sampling(Rp2, rv, nusd) + + return jnp.sqrt(mu) * radius_btm / Rs + + # test and save + mmw_ch4_fid = 0.007 + rv_fid = 10.0 + Rp_over_Rs = flux_model(mmw_ch4_fid, rv_fid) + import matplotlib.pyplot as plt - - plt.plot(nusd, mu) - plt.show() - np.savetxt(SAMPLE_SPECTRA_CH4_NEW, np.array([nusd, mu]).T, delimiter=",") - + + plt.plot(nusd, Rp_over_Rs) + plt.savefig("sample_trans.png") + np.savetxt(SAMPLE_SPECTRA_CH4_TRANS, np.array([nusd, Rp_over_Rs]).T, delimiter=",") diff --git a/src/exojax/test/generate_rt.py b/src/exojax/test/generate_rt.py index 1f69e2e3c..e6e1674ad 100644 --- a/src/exojax/test/generate_rt.py +++ b/src/exojax/test/generate_rt.py @@ -5,7 +5,9 @@ from exojax.test.data import TESTDATA_CO_HITEMP_LPF_EMISSION_REF from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_EMISSION_REF - +from exojax.test.data import TESTDATA_CO_EXOMOL_PREMODIT_EMISSION_REF +from exojax.test.data import TESTDATA_CO_HITEMP_PREMODIT_EMISSION_REF + from exojax.test.emulate_mdb import mock_mdb from exojax.spec.opacalc import OpaDirect from exojax.spec.opacalc import OpaModit @@ -15,69 +17,64 @@ config.update("jax_enable_x64", True) -testdata_modit={} -testdata_modit["exomol"]=TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF -testdata_modit["hitemp"]=TESTDATA_CO_HITEMP_MODIT_EMISSION_REF +testdata_modit = {} +testdata_modit["exomol"] = TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF +testdata_modit["hitemp"] = TESTDATA_CO_HITEMP_MODIT_EMISSION_REF + +testdata_lpf = {} +testdata_lpf["exomol"] = TESTDATA_CO_EXOMOL_LPF_EMISSION_REF +testdata_lpf["hitemp"] = TESTDATA_CO_HITEMP_LPF_EMISSION_REF -testdata_lpf={} -testdata_lpf["exomol"]=TESTDATA_CO_EXOMOL_LPF_EMISSION_REF -testdata_lpf["hitemp"]=TESTDATA_CO_HITEMP_LPF_EMISSION_REF def gendata_rt_modit(db): nu_grid, wav, res = mock_wavenumber_grid() - art = ArtEmisPure(nu_grid, - pressure_top=1.e-8, - pressure_btm=1.e2, - nlayer=100) + art = ArtEmisPure(pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100, nu_grid=nu_grid) art.change_temperature_range(400.0, 1500.0) Tarr = art.powerlaw_temperature(1300.0, 0.1) mmr_arr = art.constant_mmr_profile(0.1) gravity = 2478.57 - #gravity = art.constant_gravity_profile(2478.57) #gravity can be profile + # gravity = art.constant_gravity_profile(2478.57) #gravity can be profile mdb = mock_mdb(db) - #mdb = api.MdbExomol('.database/CO/12C-16O/Li2015',nu_grid,inherit_dataframe=False,gpu_transfer=False) - #mdb = api.MdbHitemp('CO', art.nu_grid, gpu_transfer=False, isotope=1) - opa = OpaModit(mdb=mdb, - nu_grid=nu_grid, - Tarr_list=Tarr, - Parr=art.pressure, - dit_grid_resolution=0.2) + # mdb = api.MdbExomol('.database/CO/12C-16O/Li2015',nu_grid,inherit_dataframe=False,gpu_transfer=False) + # mdb = api.MdbHitemp('CO', art.nu_grid, gpu_transfer=False, isotope=1) + opa = OpaModit( + mdb=mdb, + nu_grid=nu_grid, + Tarr_list=Tarr, + Parr=art.pressure, + dit_grid_resolution=0.2, + ) xsmatrix = opa.xsmatrix(Tarr, art.pressure) - dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, - gravity) + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) F0 = art.run(dtau, Tarr) np.savetxt(testdata_modit[db], np.array([nu_grid, F0]).T, delimiter=",") - + return nu_grid, F0 def gendata_rt_lpf(db): nu_grid, wav, res = mock_wavenumber_grid() - art = ArtEmisPure(nu_grid, - pressure_top=1.e-8, - pressure_btm=1.e2, - nlayer=100) + art = ArtEmisPure(pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100, nu_grid=nu_grid) art.change_temperature_range(400.0, 1500.0) Tarr = art.powerlaw_temperature(1300.0, 0.1) mmr_arr = art.constant_mmr_profile(0.1) gravity = 2478.57 - #gravity = art.constant_gravity_profile(2478.57) #gravity can be profile + # gravity = art.constant_gravity_profile(2478.57) #gravity can be profile mdb = mock_mdb(db) - #mdb = api.MdbExomol('.database/CO/12C-16O/Li2015',nu_grid,inherit_dataframe=False,gpu_transfer=False) - #mdb = api.MdbHitemp('CO', art.nu_grid, gpu_transfer=False, isotope=1) + # mdb = api.MdbExomol('.database/CO/12C-16O/Li2015',nu_grid,inherit_dataframe=False,gpu_transfer=False) + # mdb = api.MdbHitemp('CO', art.nu_grid, gpu_transfer=False, isotope=1) opa = OpaDirect(mdb=mdb, nu_grid=nu_grid) xsmatrix = opa.xsmatrix(Tarr, art.pressure) - dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, - gravity) + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) F0 = art.run(dtau, Tarr) np.savetxt(testdata_lpf[db], np.array([nu_grid, F0]).T, delimiter=",") - + return nu_grid, F0 @@ -93,19 +90,19 @@ def gendata_rt_lpf(db): plt.plot(nus, F0_hitemp) plt.plot(nus, F0_exomol_lpf, ls="dashed") plt.plot(nus, F0_hitemp_lpf, ls="dashed") - + fig.add_subplot(212) plt.plot(nus, 1.0 - F0_exomol / F0_exomol_lpf, label="diff exomol") plt.plot(nus, 1.0 - F0_hitemp / F0_hitemp_lpf, label="diff hitemp") plt.legend() plt.show() - + import matplotlib.pyplot as plt + plt.plot(nus, F0_exomol_lpf) plt.plot(nus, F0_exomol, ls="dashed") plt.show() - print( "to include the generated files in the package, move .txt to exojax/src/exojax/data/testdata/" ) diff --git a/src/exojax/test/generate_xs.py b/src/exojax/test/generate_xs.py index c4f45d368..e58c61dac 100644 --- a/src/exojax/test/generate_xs.py +++ b/src/exojax/test/generate_xs.py @@ -1,7 +1,6 @@ from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_XS_REF from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_XS_REF from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_XS_REF_AIR - import numpy as np from exojax.spec.modit import xsvector from exojax.spec.hitran import line_strength @@ -23,6 +22,8 @@ from jax import config +from exojax.utils.constants import Tref_original + config.update("jax_enable_x64", True) testdata = {} @@ -52,25 +53,29 @@ def gendata_xs_modit_exomol(): cont_nu, index_nu, R, pmarray = init_modit(mdbCO.nu_lines, nu_grid) qt = mdbCO.qr_interp(Tfix) - gammaL = gamma_exomol(Pfix, Tfix, mdbCO.n_Texp, - mdbCO.alpha_ref) + gamma_natural(mdbCO.A) + gammaL = gamma_exomol(Pfix, Tfix, mdbCO.n_Texp, mdbCO.alpha_ref) + gamma_natural( + mdbCO.A + ) dv_lines = mdbCO.nu_lines / R ngammaL = gammaL / dv_lines nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R) - Sij = line_strength(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref) + Sij = line_strength( + Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original + ) ngammaL_grid = ditgrid_log_interval(ngammaL, dit_grid_resolution=0.1) - xsv = xsvector(cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, - nu_grid, ngammaL_grid) + xsv = xsvector( + cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, nu_grid, ngammaL_grid + ) - #import matplotlib.pyplot as plt - #plt.plot(nus,xsv) - #plt.yscale("log") - #plt.show() + # import matplotlib.pyplot as plt + # plt.plot(nus,xsv) + # plt.yscale("log") + # plt.show() - np.savetxt(TESTDATA_CO_EXOMOL_MODIT_XS_REF, - np.array([nu_grid, xsv]).T, - delimiter=",") + np.savetxt( + TESTDATA_CO_EXOMOL_MODIT_XS_REF, np.array([nu_grid, xsv]).T, delimiter="," + ) return nu_grid, xsv @@ -90,52 +95,57 @@ def gendata_xs_modit_hitemp(airmode=False): Pself = Pfix filename = TESTDATA_CO_HITEMP_MODIT_XS_REF - -# #### HERE IS Temporary + # #### HERE IS Temporary nu_grid, wav, res = mock_wavenumber_grid() mdbCO = mock_mdbHitemp(multi_isotope=False) - #from exojax.spec import api - #mdbCO = api.MdbHitemp('CO', nus, gpu_transfer=True, isotope=1) - #print(len(mdbCO.nu_lines)) - #print(np.min(mdbCO.nu_lines),np.max(mdbCO.nu_lines)) + # from exojax.spec import api + # mdbCO = api.MdbHitemp('CO', nus, gpu_transfer=True, isotope=1) + # print(len(mdbCO.nu_lines)) + # print(np.min(mdbCO.nu_lines),np.max(mdbCO.nu_lines)) Mmol = mdbCO.molmass cont_nu, index_nu, R, pmarray = init_modit(mdbCO.nu_lines, nu_grid) qt = mdbCO.qr_interp(mdbCO.isotope, Tfix) - gammaL = gamma_hitran(Pfix, Tfix, Pself, mdbCO.n_air, mdbCO.gamma_air, - mdbCO.gamma_self) + gamma_natural(mdbCO.A) + gammaL = gamma_hitran( + Pfix, Tfix, Pself, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self + ) + gamma_natural(mdbCO.A) dv_lines = mdbCO.nu_lines / R ngammaL = gammaL / dv_lines nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R) - Sij = line_strength(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref) + Sij = line_strength( + Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, Tref_original + ) cont_nu, index_nu, R, pmarray = init_modit(mdbCO.nu_lines, nu_grid) ngammaL_grid = ditgrid_log_interval(ngammaL, dit_grid_resolution=0.1) - xsv = xsvector(cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, - nu_grid, ngammaL_grid) + xsv = xsvector( + cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, nu_grid, ngammaL_grid + ) - #import matplotlib.pyplot as plt - #plt.plot(nus,xsv) - #plt.yscale("log") - #plt.show() + # import matplotlib.pyplot as plt + # plt.plot(nus,xsv) + # plt.yscale("log") + # plt.show() np.savetxt(filename, np.array([nu_grid, xsv]).T, delimiter=",") return nu_grid, xsv + if __name__ == "__main__": nlh, xlh = gendata_xs_lpf("hitemp") nle, xle = gendata_xs_lpf("exomol") nme, xme = gendata_xs_modit_exomol() nmh, xmh = gendata_xs_modit_hitemp(airmode=True) - #gendata_xs_modit_hitemp(airmode=True) + # gendata_xs_modit_hitemp(airmode=True) import matplotlib.pyplot as plt + fig = plt.figure() fig.add_subplot(211) plt.plot(nle, xle) plt.plot(nlh, xlh) plt.plot(nme, xme, ls="dashed") plt.plot(nmh, xmh, ls="dashed") - + fig.add_subplot(212) plt.plot(nme, 1.0 - xme / xle, label="diff exomol") plt.plot(nmh, 1.0 - xmh / xlh, label="diff hitemp") diff --git a/src/exojax/utils/__init__.py b/src/exojax/utils/__init__.py index 473b53d4d..05233ce0a 100644 --- a/src/exojax/utils/__init__.py +++ b/src/exojax/utils/__init__.py @@ -1,6 +1,6 @@ __all__ = [] -__version__ = '1.4.0' +__version__ = '1.6' __uri__ = '' __author__ = 'ExoJAX contributors' __email__ = 'divrot@gmail.com' diff --git a/src/exojax/utils/constants.py b/src/exojax/utils/constants.py index 5547d3a89..1b502ec0a 100644 --- a/src/exojax/utils/constants.py +++ b/src/exojax/utils/constants.py @@ -50,13 +50,3 @@ # opfac = bar_cgs/(m_u (g)). m_u: atomic mass unit. bar_cgs: 1 bar in cgs = 1.e6 dyn/cm2 # obtained as opfac = bar_cgs/m_u(in g) = 1.e6/(m_u(in kg)*1.e3) = 1.e3/m_u(in kg), m_u(in kg) = scipy.constants.m_u. opfac = 6.022140858549162e+29 - -if __name__ == "__main__": - #derivation of Gcr - from astropy.constants import G as G_astropy - from astropy.constants import M_sun - from astropy import units as u - day = 24 * 3600 * u.s - Gu = (G_astropy * M_sun / day).value - Gcr_val = Gu**(1.0 / 3.0) * 1.e-3 - print(Gcr_val) diff --git a/src/exojax/utils/grids.py b/src/exojax/utils/grids.py index 95f2ac828..022d4a8b4 100644 --- a/src/exojax/utils/grids.py +++ b/src/exojax/utils/grids.py @@ -1,5 +1,6 @@ -"""generate various grids +"""generates various grids """ + from exojax.spec.unitconvert import nu2wav, wav2nu from exojax.utils.instfunc import resolution_eslog, resolution_eslin from exojax.utils.constants import c @@ -8,8 +9,8 @@ import warnings -def wavenumber_grid(x0, x1, N, xsmode, wavelength_order="descending", unit='cm-1'): - """generating the recommended wavenumber grid based on the cross section +def wavenumber_grid(x0, x1, N, xsmode, wavelength_order="descending", unit="cm-1"): + """generates the recommended wavenumber grid based on the cross section computation mode. Args: @@ -19,12 +20,12 @@ def wavenumber_grid(x0, x1, N, xsmode, wavelength_order="descending", unit='cm-1 xsmode: cross section computation mode (lpf, dit, modit, premodit) wavlength order: wavelength order: "ascending" or "descending" unit: unit of the input grid, "cm-1", "nm", or "AA" - + Note: - The wavenumber (nus) and wavelength (wav) grids are in ascending orders. + The wavenumber (nus) and wavelength (wav) grids are in ascending orders. Therefore, wav[-1] corresponds to the wavelength of nus[0]. - ESLIN sets evenly-spaced linear grid in wavenumber space while ESLOG sets - evenly-spaced log grid both in wavenumber and wavelength spaces. + ESLIN sets evenly-spaced linear grid in wavenumber space while ESLOG sets + evenly-spaced log grid both in wavenumber and wavelength spaces. Returns: nu_grid: wavenumber grid evenly spaced in log space in ascending order (nus) @@ -33,7 +34,7 @@ def wavenumber_grid(x0, x1, N, xsmode, wavelength_order="descending", unit='cm-1 """ print("xsmode = ", xsmode) _check_even_number(N) - grid_mode = check_scale_xsmode(xsmode) + grid_mode = check_grid_mode_in_xsmode(xsmode) grid, unit = _set_grid(x0, x1, N, unit, grid_mode) nu_grid = _set_nus(unit, grid) @@ -43,6 +44,7 @@ def wavenumber_grid(x0, x1, N, xsmode, wavelength_order="descending", unit='cm-1 resolution = grid_resolution(grid_mode, nu_grid) return nu_grid, wav, resolution + def _warning_wavelength_order(wavelength_order): """this is temporary special warning on wavelenght order @@ -57,9 +59,9 @@ def _warning_wavelength_order(wavelength_order): def _set_grid(x0, x1, N, unit, grid_mode): - if grid_mode == 'ESLOG': + if grid_mode == "ESLOG": grid = np.logspace(np.log10(x0), np.log10(x1), N, dtype=np.float64) - elif grid_mode == 'ESLIN': + elif grid_mode == "ESLIN": grid, unit = _set_grid_eslin(unit, x0, x1, N) else: raise ValueError("unavailable xsmode/unit.") @@ -74,18 +76,18 @@ def _check_even_number(N): def _set_nus(unit, grid): - if unit == 'cm-1': + if unit == "cm-1": nus = grid - elif unit == 'nm' or unit == 'AA': + elif unit == "nm" or unit == "AA": nus = wav2nu(grid, unit) return nus def grid_resolution(grid_mode, nus): - if grid_mode == 'ESLOG': + if grid_mode == "ESLOG": resolution = resolution_eslog(nus) minr = resolution - elif grid_mode == 'ESLIN': + elif grid_mode == "ESLIN": minr, resolution, _ = resolution_eslin(nus) warn_resolution(minr) return resolution @@ -96,7 +98,7 @@ def _set_grid_eslin(unit, x0, x1, N): return np.linspace((x0), (x1), N, dtype=np.float64), unit else: cx0, cx1 = wav2nu(np.array([x0, x1]), unit) - unit = 'cm-1' + unit = "cm-1" return np.linspace((cx0), (cx1), N, dtype=np.float64), unit @@ -108,7 +110,7 @@ def velocity_grid(resolution, vmax): vmax: maximum velocity (or Vsini) allowed (km/s) Returns: - 1D array: delta velocity grid + 1D array: delta velocity grid """ dv = delta_velocity_from_resolution(resolution) Nk = (vmax / dv) + 1 @@ -122,7 +124,7 @@ def delta_velocity_from_resolution(resolution): Args: resolution : spectral resolution - Note: + Note: See also [#294](https://github.com/HajimeKawahara/exojax/issues/294) and exojax/tests/figures/functions/delta_velocity_comp.py Returns: @@ -139,58 +141,52 @@ def warn_resolution(resolution, crit=700000.0): crit: critical resolution """ if resolution < crit: - warnings.warn('Resolution may be too small. R=' + str(resolution), - UserWarning) + warnings.warn("Resolution may be too small. R=" + str(resolution), UserWarning) -def check_scale_xsmode(xsmode): - """checking if the scale of xsmode assumes ESLOG(log) or ESLIN(linear) +def check_grid_mode_in_xsmode(xsmode): + """checking if the scale of grid_mode assumes ESLOG(log) or ESLIN(linear) Args: - xsmode: xsmode + xsmode: xsmode, (lpf, dit, modit, premodit) Return: - ESLOG/ESLIN/UNKNOWN + grid_mode (ESLOG/ESLIN/UNKNOWN) """ + def _add_upper_case(strlist): return strlist + [x.upper() for x in strlist] - eslog_list = _add_upper_case(['lpf', 'modit', 'premodit', 'presolar']) - eslin_list = _add_upper_case(['dit']) + eslog_list = _add_upper_case(["lpf", "modit", "premodit", "presolar"]) + eslin_list = _add_upper_case(["dit"]) if xsmode in eslog_list: - print('xsmode assumes ESLOG in wavenumber space: mode=' + str(xsmode)) - return 'ESLOG' + print("xsmode assumes ESLOG in wavenumber space: xsmode=" + str(xsmode)) + return "ESLOG" elif xsmode in eslin_list: - print('xsmode assumes ESLIN in wavenumber space: mode=' + str(xsmode)) - return 'ESLIN' + print("xsmode assumes ESLIN in wavenumber space: xsmode=" + str(xsmode)) + return "ESLIN" else: warnings.warn("unknown xsmode.", UserWarning) - return 'UNKNOWN' + return "UNKNOWN" -def check_eslog_wavenumber_grid(nus, - crit1=1.e-5, - crit2=1.e-14, - gridmode='ESLOG'): +def check_eslog_wavenumber_grid(nus, crit1=1.0e-5, crit2=1.0e-14): """checking if wavenumber_grid is evenly spaced in a logarithm scale (ESLOG) or a liner scale (ESLIN) Args: - nus: wavenumber grid - crit1: criterion for the maximum deviation of log10(nu)/median(log10(nu)) from ESLOG - crit2: criterion for the maximum deviation of log10(nu) from ESLOG - gridmode: ESLOG or ESLIN + nus: wavenumber grid + crit1: criterion for the maximum deviation of log10(nu)/median(log10(nu)) from ESLOG + crit2: criterion for the maximum deviation of log10(nu) from ESLOG Returns: - True (wavenumber grid is ESLOG) or False (not) + True (wavenumber grid is ESLOG) or False (not) """ q = np.log10(nus) p = q[1:] - q[:-1] - w = (p - np.mean(p)) + w = p - np.mean(p) val1 = np.max(np.abs(w)) / np.median(p) val2 = np.max(np.abs(w)) - return (val1 < crit1 and val2 < crit2) - - + return val1 < crit1 and val2 < crit2 diff --git a/src/exojax/utils/indexing.py b/src/exojax/utils/indexing.py index 721d035d3..15b26fa68 100644 --- a/src/exojax/utils/indexing.py +++ b/src/exojax/utils/indexing.py @@ -10,6 +10,36 @@ import jax.numpy as jnp +def get_smooth_index(xp, x): + """get smooth index + + Args: + xp: x grid + x: x array + Returns: + float: smooth index + """ + findex = jnp.arange(len(xp), dtype=float) + smooth_index = jnp.interp(x, xp, findex) + return smooth_index + +def get_value_at_smooth_index(array, smooth_index): + """get value at smooth index position (e.g. cloud base) from an array + + Args: + array (float): array, such as log pressures or temperatures + smooth_index (float): smooth index + + Returns: + float: value at cloud base + """ + + ind = smooth_index.astype(int) + # ind = jnp.clip(ind, 0, len(array) - 2) + res = smooth_index - jnp.floor(smooth_index) + return (1.0 - res) * array[ind] + res * array[ind + 1] + + def getix(x, xv): """jnp version of getix. @@ -94,7 +124,6 @@ def uniqidx(input_array): """ N, _ = np.shape(input_array) - # uniqvals = np.unique(input_array, axis=0) uniqvals = unique_rows(input_array) uidx = np.zeros(N, dtype=int) uidx_p = np.where(input_array == uniqvals[0], True, False) diff --git a/src/exojax/utils/instfunc.py b/src/exojax/utils/instfunc.py index fe610c4bf..59bdbe52f 100644 --- a/src/exojax/utils/instfunc.py +++ b/src/exojax/utils/instfunc.py @@ -17,37 +17,44 @@ def resolution_to_gaussian_std(resolution): spectral resolution. Args: - resolution: spectral resolution R + resolution: spectral resolution R Returns: - standard deviation of Gaussian velocity distribution (km/s) + standard deviation of Gaussian velocity distribution (km/s) """ return c / (2.0 * np.sqrt(2.0 * np.log(2.0)) * resolution) -def resolution_eslog(nu): +def resolution_eslog(nu_grid): """spectral resolution for ESLOG. Args: - nu: wavenumber bin + nu_grid: wavenumber bin Returns: - resolution + resolution """ - return (len(nu) - 1) / np.log(nu[-1] / nu[0]) + return (len(nu_grid) - 1) / np.log(nu_grid[-1] / nu_grid[0]) -def resolution_eslin(nu): +def resolution_eslin(nu_grid): """min max spectral resolution for ESLIN. Args: - nu: wavenumber bin + nu_grid: wavenumber bin Returns: - min, approximate, max of the resolution + min, approximate, max of the resolution """ - resolution = ((nu[-1] + nu[0]) / 2.0) / ((nu[-1] - nu[0]) / len(nu)) - return nu[0] / (nu[1] - nu[0]), resolution, nu[-1] / (nu[-1] - nu[-2]) + resolution = ((nu_grid[-1] + nu_grid[0]) / 2.0) / ( + (nu_grid[-1] - nu_grid[0]) / len(nu_grid) + ) + return ( + nu_grid[0] / (nu_grid[1] - nu_grid[0]), + resolution, + nu_grid[-1] / (nu_grid[-1] - nu_grid[-2]), + ) + def nx_from_resolution_eslog(nu0, nu1, resolution): """Compute the number of wavenumber grid for a given resolution for ESLOG @@ -61,4 +68,3 @@ def nx_from_resolution_eslog(nu0, nu1, resolution): int: the number of wavenumber grid for a given resolution """ return int(resolution * np.log(nu1 / nu0)) + 1 - diff --git a/src/exojax/utils/isotopes.py b/src/exojax/utils/isotopes.py index e26e0cfe8..ed7a19e49 100644 --- a/src/exojax/utils/isotopes.py +++ b/src/exojax/utils/isotopes.py @@ -14,7 +14,6 @@ """ import numpy as np -from exojax.utils import isodata import pkgutil from io import BytesIO import pandas as pd @@ -24,7 +23,7 @@ def molmass_hitran(): """molar mass info from HITRAN_molparam.txt - + Returns: dict: molmass_isotope, abundance_isotope @@ -35,15 +34,20 @@ def molmass_hitran(): >>> molmass_isotope["CO"][1] # molar mass for CO isotope number = 1 >>> abundance_isotope["CO"][1] # relative abundance for CO isotope number = 1 >>> molmass_isotope["CO"][0] # mean molar mass for CO - + """ - path = pkgutil.get_data('exojax', 'data/atom/HITRAN_molparam.txt') - df = pd.read_csv(BytesIO(path), sep="\s{2,}", engine="python", skiprows=1, \ - names=["# Iso","Abundance","Q(296K)","gj","Molar Mass(g)"]) + path = pkgutil.get_data("exojax", "data/atom/HITRAN_molparam.txt") + df = pd.read_csv( + BytesIO(path), + sep="\s{2,}", + engine="python", + skiprows=1, + names=["# Iso", "Abundance", "Q(296K)", "gj", "Molar Mass(g)"], + ) molmass_isotope = {} abundance_isotope = {} for i in range(len(df)): - if ("(" in df["# Iso"][i]): + if "(" in df["# Iso"][i]: molname = df["# Iso"][i].split()[0] tot = 0.0 tot_abd = 0.0 @@ -54,32 +58,31 @@ def molmass_hitran(): tot_abd = tot_abd + df["Abundance"][i] molmass_isotope[molname].append(df["Molar Mass(g)"][i]) abundance_isotope[molname].append(df["Abundance"][i]) - if (i == len(df) - 1 or "(" in df["# Iso"][i + 1]): + if i == len(df) - 1 or "(" in df["# Iso"][i + 1]: molmass_isotope[molname].insert(0, tot / tot_abd) return molmass_isotope, abundance_isotope - def get_isotope(atom, isolist): """get isotope info. Args: - atom: simple atomic symbol, such as "H", "Fe" - isolist: isotope list + atom: simple atomic symbol, such as "H", "Fe" + isolist: isotope list Return: - iso: isotope list, such as "1H", "2H" - mass_number: mass_number list - abundance: abundance list + iso: isotope list, such as "1H", "2H" + mass_number: mass_number list + abundance: abundance list """ iso = [] mass_number = [] abundance = [] - for j, isol in enumerate(isolist['isotope']): + for j, isol in enumerate(isolist["isotope"]): if exact_molname_exomol_to_simple_molname(isol) == atom: - iso.append(isolist['isotope'][j]) - mass_number.append(isolist['mass_number'][j]) - abundance.append(float(isolist['abundance'][j])) + iso.append(isolist["isotope"][j]) + mass_number.append(isolist["mass_number"][j]) + abundance.append(float(isolist["abundance"][j])) return iso, mass_number, abundance @@ -87,20 +90,14 @@ def get_stable_isotope(atom, isolist): """get isotope info. Args: - atom: simple atomic symbol, such as "H", "Fe" - isolist: isotope list + atom: simple atomic symbol, such as "H", "Fe" + isolist: isotope list Return: - iso: stabel isotope such as "1H", "2H" - mass_number: mass_number - abundance: abundance + iso: stabel isotope such as "1H", "2H" + mass_number: mass_number + abundance: abundance """ iso, mass_number, abundance = get_isotope(atom, isolist) jmax = np.nanargmax(abundance) return iso[jmax], mass_number[jmax], abundance[jmax] - - -if __name__ == '__main__': - isolist = isodata.read_mnlist() - print(get_isotope('H', isolist)) - print(get_stable_isotope('H', isolist)) diff --git a/src/exojax/utils/jaxstatus.py b/src/exojax/utils/jaxstatus.py index 1450641b5..5d067e5b5 100644 --- a/src/exojax/utils/jaxstatus.py +++ b/src/exojax/utils/jaxstatus.py @@ -15,7 +15,7 @@ def check_jax64bit(allow_32bit): how_change_msg += ' config.update("jax_enable_x64", True)'+"\n" if not config.values["jax_enable_x64"] and allow_32bit: - msg = "JAX is 32bit mode. We recommend to use 64bit mode. \n" + msg = "JAX uses 32bit mode. \n" warnings.warn(msg+how_change_msg) elif not config.values["jax_enable_x64"]: msg = "JAX 32bit mode is not allowed. Use allow_32bit = True or \n" diff --git a/src/exojax/utils/memuse.py b/src/exojax/utils/memuse.py index 51abf06cc..ffc6e5cbf 100644 --- a/src/exojax/utils/memuse.py +++ b/src/exojax/utils/memuse.py @@ -10,7 +10,7 @@ def device_memory_use(opa, art=None, nfree=None, print_summary=True): nfree (int, optional): the number of free parameters. Defaults to None. print_summary (bool): printing summary Defaults to True. Raises: - ValueError: method not implemented yet + ValueError: method not implemented yet Returns: float: estimated device memory use @@ -29,16 +29,27 @@ def device_memory_use(opa, art=None, nfree=None, print_summary=True): if opa.method == "premodit": ngrid_broadpar = opa.ngrid_broadpar ngrid_elower = opa.ngrid_elower - devmemuse, memdict = premodit_devmemory_use(ngrid_nu_grid, - ngrid_broadpar, - ngrid_elower, - nlayer=nlayer, - nfree=nfree, - precision=precision) + devmemuse, memdict = premodit_devmemory_use( + ngrid_nu_grid, + ngrid_broadpar, + ngrid_elower, + nlayer=nlayer, + nfree=nfree, + precision=precision, + ) memcase, info = memdict - _print_summary_premodit(opa, nfree, print_summary, nlayer, - ngrid_nu_grid, ngrid_broadpar, ngrid_elower, - devmemuse, memcase, info) + _print_summary_premodit( + opa, + nfree, + print_summary, + nlayer, + ngrid_nu_grid, + ngrid_broadpar, + ngrid_elower, + devmemuse, + memcase, + info, + ) else: raise ValueError("unknown method.") @@ -46,9 +57,18 @@ def device_memory_use(opa, art=None, nfree=None, print_summary=True): return devmemuse -def _print_summary_premodit(opa, nfree, print_summary, nlayer, ngrid_nu_grid, - ngrid_broadpar, ngrid_elower, devmemuse, memcase, - info): +def _print_summary_premodit( + opa, + nfree, + print_summary, + nlayer, + ngrid_nu_grid, + ngrid_broadpar, + ngrid_elower, + devmemuse, + memcase, + info, +): explanation = ["(less important)", ""] if print_summary: print("Device memory use prediction:", opa.method) @@ -57,22 +77,23 @@ def _print_summary_premodit(opa, nfree, print_summary, nlayer, ngrid_nu_grid, print("# of the elower grids:", ngrid_elower, explanation[memcase]) print("# of the layers:", nlayer, explanation[1 - memcase]) print("# of the free parameters:", nfree, explanation[1 - memcase]) - print(info + " : required device memory = ", devmemuse / (1024.)**3, - "GB") + print(info + " : required device memory = ", devmemuse / (1024.0) ** 3, "GB") -def premodit_devmemory_use(ngrid_nu_grid, - ngrid_broadpar, - ngrid_elower, - nlayer=None, - nfree=None, - precision="FP64"): +def premodit_devmemory_use( + ngrid_nu_grid, + ngrid_broadpar, + ngrid_elower, + nlayer=None, + nfree=None, + precision="FP64", +): """compute approximate required device memory for PreMODIT algorithm Notes: - This method estimates the major device moemory use for PreMODIT. In Case 0 (memcase=0), the use is limited by FFT/IFFT by modit_scanfft - while in Case1 (memcase) by LBD. - + This method estimates the major device moemory use for PreMODIT. In Case 0 (memcase=0), the use is limited by FFT/IFFT by modit_scanfft + while in Case1 (memcase) by LBD. + Args: ngrid_nu_grid (int): the number of the wavenumber grid @@ -87,7 +108,7 @@ def premodit_devmemory_use(ngrid_nu_grid, Returns: float: predicted required device memory (byte) - (str, str): memory computation case (memcase), info + (str, str): memory computation case (memcase), info """ info = "opacity " factor_case0 = 4 # FFT and InvFFT w/ the same size of buffer @@ -131,9 +152,7 @@ def premodit_devmemory_use(ngrid_nu_grid, n_elower = 20 nlayer = None nfree = 10 - memuse, case = premodit_devmemory_use(n_nu_grid, - n_broadpar, - n_elower, - nlayer=nlayer, - nfree=nfree) - print(case) \ No newline at end of file + memuse, case = premodit_devmemory_use( + n_nu_grid, n_broadpar, n_elower, nlayer=nlayer, nfree=nfree + ) + print(case) diff --git a/src/exojax/utils/mollabel.py b/src/exojax/utils/mollabel.py index 28230130f..f680e76b0 100644 --- a/src/exojax/utils/mollabel.py +++ b/src/exojax/utils/mollabel.py @@ -2,7 +2,7 @@ def molecule_color(simple_molecule_name): - """return the individual matplotlib color label (CN) for a given molecule simple name + """return the individual matplotlib color label (CN) for a given molecule simple name Args: simple_molecule_name (str): simple molecule name, "H2O" @@ -10,19 +10,21 @@ def molecule_color(simple_molecule_name): Examples: >>> format_molecule_color("H2O") "C1" - + Returns: str: CN label, such as C1 for "H2O" based on HITRAN identifier. If the molecule does not exist in the HITRAN identifiers, return gray """ from radis.db.classes import get_molecule_identifier + try: i = get_molecule_identifier(simple_molecule_name) - color = "C"+str(i) + color = "C" + str(i) except: color = "gray" return color + def molecules_color_list(simple_molecule_name_list): """return the individual matplotlib color label (CN) for a given molecule simple name list @@ -32,16 +34,20 @@ def molecules_color_list(simple_molecule_name_list): Returns: str: CN label, such as C1 for "H2O" """ - + return [molecule_color(molecule) for molecule in simple_molecule_name_list] + def molecules_color_lists(simple_molecule_name_lists): - return [[molecule_color(molecule) for molecule in molecules] for molecules in simple_molecule_name_lists] + return [ + [molecule_color(molecule) for molecule in molecules] + for molecules in simple_molecule_name_lists + ] def format_molecule(simple_molecule_name): """Format a given molecule string with subscript numbers and convert it to LaTeX syntax. - + This function takes in a molecule string, where elements are represented by their symbols and the number of atoms is represented by a subscript number following the symbol. The function converts the molecule string to a LaTeX string, with subscript numbers formatted correctly @@ -61,14 +67,17 @@ def format_molecule(simple_molecule_name): >>> format_molecule("CO") "$\\mathrm{CO}$" """ - formatted_molecule = re.sub(r'([A-Z][a-z]?)([0-9]+)', r'\1_\2', simple_molecule_name) + formatted_molecule = re.sub( + r"([A-Z][a-z]?)([0-9]+)", r"\1_\2", simple_molecule_name + ) latex_molecule = f"$\\mathrm{{{formatted_molecule}}}$" return latex_molecule + def format_molecules_list(molecules): """ Format a list of molecule strings with subscript numbers and convert them to LaTeX syntax. - + This function takes in a list of molecule strings, where elements are represented by their symbols and the number of atoms is represented by a subscript number following the symbol. The function converts each molecule string to a LaTeX string, with subscript numbers formatted correctly @@ -86,10 +95,11 @@ def format_molecules_list(molecules): """ return [format_molecule(molecule) for molecule in molecules] + def format_molecules_lists(molecules_lists): """ Format a list of lists of molecule strings with subscript numbers and convert them to LaTeX syntax. - + This function takes in a list of lists of molecule strings, where elements are represented by their symbols and the number of atoms is represented by a subscript number following the symbol. The function converts each molecule string to a LaTeX string, with subscript numbers formatted correctly @@ -105,7 +115,10 @@ def format_molecules_lists(molecules_lists): >>> format_molecules_lists([["H2O", "CH4", "CO"], ["H2O", "CH4", "CO"]]) [["$\\mathrm{H_2O}$", "$\\mathrm{CH_4}$", "$\\mathrm{CO}$"], ["$\\mathrm{H_2O}$", "$\\mathrm{CH4}$", "$\\mathrm{CO}$"]] """ - return [[format_molecule(molecule) for molecule in molecules] for molecules in molecules_lists] + return [ + [format_molecule(molecule) for molecule in molecules] + for molecules in molecules_lists + ] if __name__ == "__main__": @@ -114,9 +127,9 @@ def format_molecules_lists(molecules_lists): for molecule in simple_molecule_name_list: print(format_molecule(molecule)) print(format_molecules_list(simple_molecule_name_list)) - + print(molecules_color_list(simple_molecule_name_list)) - + simple_molecule_name_lists = [["H2O", "CH4", "CO"], ["H2O", "NH3", "CO"]] print(format_molecules_lists(simple_molecule_name_lists)) print(molecules_color_lists(simple_molecule_name_lists)) diff --git a/src/exojax/utils/molname.py b/src/exojax/utils/molname.py index cda5d6f0d..2f9d42d81 100644 --- a/src/exojax/utils/molname.py +++ b/src/exojax/utils/molname.py @@ -9,24 +9,25 @@ - To get the recommended ExoMol database, use radis.api.exomolapi.get_exomol_database_list("CO2","12C-16O2") """ + from radis.db.classes import get_molecule import re import warnings -def exact_molecule_name_from_isotope(simple_molecule_name, - isotope, - dbtype="hitran"): + +def exact_molecule_name_from_isotope(simple_molecule_name, isotope, dbtype="hitran"): """exact isotope name from isotope (number) Args: simple_molecular_name (str): simple molecular name such as CO isotope (int): isotope number starting from 1 - dbtype (str): "hitran" or "exomol" + dbtype (str): "hitran" or "exomol" Returns: str: HITRAN exact isotope name such as (12C)(16O) for dbtype="hitran", 12C-16O for "exomol" """ from radis.db.molparam import MolParams + mp = MolParams() exact_molname = mp.get(simple_molecule_name, isotope, "isotope_name") if dbtype == "hitran": @@ -34,6 +35,7 @@ def exact_molecule_name_from_isotope(simple_molecule_name, elif dbtype == "exomol": return exact_molname_hitran_to_exomol(exact_molname) + def exact_molecule_name_to_isotope_number(exact_molecule_name): """Convert exact molecule name to isotope number @@ -41,22 +43,18 @@ def exact_molecule_name_to_isotope_number(exact_molecule_name): exact_molecule_name (str): exact exomol, hitran, molecule name such as 12C-16O, (12C)(16O) Returns: - int: molecular number, isotope number (or None, None) + int: molecular number, isotope number (or None, None) """ from radis.db.molparam import isotope_name_dict - #check exomol exact name - keys = [ - k for k, v in isotope_name_dict.items() if v == exact_molecule_name - ] + # check exomol exact name + keys = [k for k, v in isotope_name_dict.items() if v == exact_molecule_name] if len(keys) == 0: - #check hitran exact name - exact_hitran_molecule_name = exact_molname_exomol_to_hitran( - exact_molecule_name) + # check hitran exact name + exact_hitran_molecule_name = exact_molname_exomol_to_hitran(exact_molecule_name) print("HITRAN exact name=", exact_hitran_molecule_name) keys = [ - k for k, v in isotope_name_dict.items() - if v == exact_hitran_molecule_name + k for k, v in isotope_name_dict.items() if v == exact_hitran_molecule_name ] if len(keys) == 1: molnumber = keys[0][0] @@ -72,52 +70,72 @@ def exact_molname_exomol_to_simple_molname(exact_exomol_molecule_name): """convert the exact molname (used in ExoMol) to the simple molname. Args: - exact_exomol_molecule_name: the exact exomol molecule name + exact_exomol_molecule_name: the exact exomol molecule name Returns: - simple molname + simple molname Examples: - >>> print(exact_molname_exomol_to_simple_molname("12C-1H4")) - >>> CH4 - >>> print(exact_molname_exomol_to_simple_molname("23Na-16O-1H")) - >>> NaOH - >>> print(exact_molname_exomol_to_simple_molname("HeH_p")) - >>> HeH_p - >>> print(exact_molname_exomol_to_simple_molname("trans-31P2-1H-2H")) #not working - >>> Warning: Exact molname trans-31P2-1H-2H cannot be converted to simple molname - >>> trans-31P2-1H-2H + >>> print(exact_molname_exomol_to_simple_molname("12C-1H4")) + >>> CH4 + >>> print(exact_molname_exomol_to_simple_molname("23Na-16O-1H")) + >>> NaOH + >>> print(exact_molname_exomol_to_simple_molname("HeH_p")) + >>> HeH_p + >>> print(exact_molname_exomol_to_simple_molname("trans-31P2-1H-2H")) #not working + >>> Warning: Exact molname trans-31P2-1H-2H cannot be converted to simple molname + >>> trans-31P2-1H-2H """ + old_name = "exojax.utils.molname.exact_molname_exomol_to_simple_molname" + new_name = "radis.api.exomolapi.exact_molname_exomol_to_simple_molname" + warnings.warn( + old_name + " will be replaced to " + new_name + ".", + FutureWarning, + ) try: - t = exact_exomol_molecule_name.split('-') - molname_simple = '' - for ele in t: - alp = ''.join(re.findall(r'\D', ele)) - num0 = re.split('[A-Z]', ele)[1] - if num0.isdigit(): - num = num0 - else: - num = '' - molname_simple = molname_simple + alp + num - return molname_simple + molname_simple = _molname_exomol_to_simple_molname_no_exception( + exact_exomol_molecule_name + ) except: - print('Warning: Exact molname ', exact_exomol_molecule_name, - 'cannot be converted to simple molname') + print( + "Warning: Exact molname ", + exact_exomol_molecule_name, + "cannot be converted to simple molname", + ) return exact_exomol_molecule_name + return molname_simple + + +def _molname_exomol_to_simple_molname_no_exception(exact_exomol_molecule_name): + t = exact_exomol_molecule_name.split("-") + molname_simple = "" + for ele in t: + alp = "".join(re.findall(r"\D", ele)) + num0 = re.split("[A-Z]", ele)[1] + if num0.isdigit(): + num = num0 + else: + num = "" + molname_simple = molname_simple + alp + num + + # HDO exception + if molname_simple == "HHO": + molname_simple = "H2O" + return molname_simple + def exact_molname_hitran_to_simple_molname(exact_hitran_molecule_name): """convert exact hitran molname (16C)(13C)(17O) to simple molname, CO2. Args: - exact_hitran_molecule_name (str): exact_hitran_molecule_name, such as (16C)(13C)(17O) + exact_hitran_molecule_name (str): exact_hitran_molecule_name, such as (16C)(13C)(17O) Returns: str: simple molecue name, such as CO2 """ - molnum, isonum = exact_molecule_name_to_isotope_number( - exact_hitran_molecule_name) + molnum, isonum = exact_molecule_name_to_isotope_number(exact_hitran_molecule_name) return get_molecule(molnum) @@ -157,18 +175,19 @@ def exact_molname_hitran_to_exomol(exact_molecule_name_hitran): from collections import defaultdict import re - matches = re.findall(r'\((.*?)\)(\d*)', exact_molecule_name_hitran) + + matches = re.findall(r"\((.*?)\)(\d*)", exact_molecule_name_hitran) result = defaultdict(int) for item, freq in matches: - if freq == '': + if freq == "": freq = 1 result[item] += int(freq) # Format the string, exclude 1 from the counts - result_string = '-'.join([ - f'{key}{value}' if value > 1 else key for key, value in result.items() - ]) + result_string = "-".join( + [f"{key}{value}" if value > 1 else key for key, value in result.items()] + ) return result_string @@ -176,37 +195,37 @@ def exact_molname_hitran_to_exomol(exact_molecule_name_hitran): def e2s(exact_exomol_molecule_name): warnings.warn( - "e2s will be replaced to exact_molname_exomol_to_simple_molname.", - FutureWarning) + "e2s will be replaced to exact_molname_exomol_to_simple_molname.", FutureWarning + ) return exact_molname_exomol_to_simple_molname(exact_exomol_molecule_name) def split_simple(molname_simple): - """split simple molname. + """splits simple molname. - Args: - molname_simple: simple molname + Args: + molname_simple: simple molname - Return: - atom list - number list + Return: + atom list + number list Example: - >>> split_simple("Fe2O3") - >>> (['Fe', 'O'], ['2', '3']) + >>> split_simple("Fe2O3") + >>> (['Fe', 'O'], ['2', '3']) """ atom_list = [] num_list = [] tmp = None - num = '' + num = "" for ch in molname_simple: if ch.isupper(): if tmp is not None: atom_list.append(tmp) num_list.append(num) - num = '' + num = "" tmp = ch elif ch.islower(): tmp = tmp + ch @@ -233,26 +252,27 @@ def simple_molname_to_exact_exomol_stable(molname_simple): return "1H3-16O_p" from exojax.utils import isotopes, isodata + isolist = isodata.read_mnlist() atom_list, num_list = split_simple(molname_simple) - molname_exact = '' + molname_exact = "" for j, atm in enumerate(atom_list): iso = isotopes.get_stable_isotope(atm, isolist) molname_exact = molname_exact + iso[0] + num_list[j] if j < len(atom_list) - 1: - molname_exact = molname_exact + '-' + molname_exact = molname_exact + "-" return molname_exact -if __name__ == '__main__': +if __name__ == "__main__": - print(simple_molname_to_exact_exomol_stable('Fe2O3')) - print(simple_molname_to_exact_exomol_stable('CH4')) - print(simple_molname_to_exact_exomol_stable('NaOH')) - print(simple_molname_to_exact_exomol_stable('H3O_p')) + print(simple_molname_to_exact_exomol_stable("Fe2O3")) + print(simple_molname_to_exact_exomol_stable("CH4")) + print(simple_molname_to_exact_exomol_stable("NaOH")) + print(simple_molname_to_exact_exomol_stable("H3O_p")) - print(e2s('12C-1H4')) - print(e2s('23Na-16O-1H')) - print(e2s('HeH_p')) - print(e2s('trans-31P2-1H-2H')) # not working + print(e2s("12C-1H4")) + print(e2s("23Na-16O-1H")) + print(e2s("HeH_p")) + print(e2s("trans-31P2-1H-2H")) # not working diff --git a/src/exojax/utils/progbar.py b/src/exojax/utils/progbar.py index e91bc8664..69f8b90ca 100644 --- a/src/exojax/utils/progbar.py +++ b/src/exojax/utils/progbar.py @@ -1,5 +1,5 @@ def print_progress(i, total_interation, desc="", bar_length=20): - """print ptqdm like rogress bar + """prints a tqdm-like progress bar Args: i (int): step number starting from 0 @@ -9,7 +9,7 @@ def print_progress(i, total_interation, desc="", bar_length=20): """ progress = i / total_interation filled_length = int(progress * bar_length) - bar = '#' * filled_length + '-' * (bar_length - filled_length) - print(f'\r{desc}|{bar}| {progress:.0%}', end='') + bar = "#" * filled_length + "-" * (bar_length - filled_length) + print(f"\r{desc}|{bar}| {progress:.0%}", end="") if i == total_interation: print() diff --git a/src/exojax/utils/recexomol.py b/src/exojax/utils/recexomol.py deleted file mode 100644 index 9e23c8de7..000000000 --- a/src/exojax/utils/recexomol.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Get Recommendation from ExoMol.""" -from urllib.request import HTTPError, urlopen -from bs4 import BeautifulSoup -import numpy as np - -def get_exomol_database_list(molecule, isotope_full_name): - """Parse ExoMol website and return list of available databases, and - recommended database. - - Args: - molecule: str - isotope_full_name: str, isotope full name (ex. ``12C-1H4`` for CH4,1). Get it from - - Returns: - database list - database recomendation - - Example: - databases, recommended = get_exomol_database_list("CH4", "12C-1H4") - >>> ['xsec-YT10to10', 'YT10to10', 'YT34to10'], 'YT34to10' - - Note: - This function is borrowed from radis (https://github.com/radis/radis by @erwanp). See https://github.com/radis/radis/issues/319 in detail. - """ - from exojax.utils.url import url_Exomol_iso - url = url_Exomol_iso(molecule, isotope_full_name) - try: - response = urlopen(url).read() - except HTTPError as err: - raise ValueError(f'HTTPError opening url={url}') from err - - soup = BeautifulSoup( - response, features='lxml' - ) # make soup that is parse-able by bs - - # Recommended database - rows = soup.find_all( - 'a', {'class': 'list-group-item link-list-group-item recommended'} - ) - databases_recommended = [r.get_attribute_list('title')[0] for r in rows] - databases_recommended = list(np.unique(databases_recommended)) - - # All others - rows = soup.find_all( - 'a', {'class': 'list-group-item link-list-group-item'}) - databases = [r.get_attribute_list('title')[0] for r in rows] - - assert len(databases_recommended) <= 1 - - databases = databases + databases_recommended - - return list(np.unique(databases)), databases_recommended[0] - - -if __name__ == '__main__': - db, db0 = get_exomol_database_list('CO', '12C-16O') - assert db0 == 'Li2015' diff --git a/src/exojax/utils/url.py b/src/exojax/utils/url.py index 8569af687..80186e2e7 100644 --- a/src/exojax/utils/url.py +++ b/src/exojax/utils/url.py @@ -5,7 +5,7 @@ def url_virga(): - """return URL for VIRGA refractive index data from ZENODO + """returns URL for VIRGA refractive index data from ZENODO Returns: URL for VIRGA refractive index data @@ -15,7 +15,7 @@ def url_virga(): def url_HITRAN12(): - """return URL for HITRAN 12 parfile. + """returns URL for HITRAN 12 parfile. Returns: URL for HITRAN 12 parfile @@ -25,7 +25,7 @@ def url_HITRAN12(): def url_HITRANCIA(): - """return URL for HITRAN CIA ciafile. + """returns URL for HITRAN CIA ciafile. Returns: URL for HITRAN CIA file @@ -35,7 +35,7 @@ def url_HITRANCIA(): def url_HITEMP(): - """return URL for HITEMP bz2 parfile. + """returns URL for HITEMP bz2 parfile. Returns: URL for HITEMP bz2 file @@ -45,7 +45,7 @@ def url_HITEMP(): def url_HITEMP10(): - """return URL for HITEMP2010. + """returns URL for HITEMP2010. Returns: URL for HITEMP2010 db @@ -55,7 +55,7 @@ def url_HITEMP10(): def url_ExoMol(): - """return URL for ExoMol. + """returns URL for ExoMol. Returns: URL for ExoMol db @@ -65,7 +65,7 @@ def url_ExoMol(): def url_Exomol_iso(molecule, isotope_full_name): - """return URL for ExoMol for isotope. + """returns URL for ExoMol for isotope. Returns: URL for ExoMol for isotope @@ -80,7 +80,7 @@ def url_Exomol_iso(molecule, isotope_full_name): def url_developer_data(): - """return URL for data in exojax. + """returns URL for data in exojax. Returns: URL for ExoJAX diff --git a/src/exojax/utils/zsol.py b/src/exojax/utils/zsol.py index 7e71e759c..f6a006de7 100644 --- a/src/exojax/utils/zsol.py +++ b/src/exojax/utils/zsol.py @@ -3,132 +3,270 @@ - Solar abundance data - AAG21 = Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, arXiv:2105.01661 """ + import numpy as np +from exojax import data +from exojax.spec.molinfo import element_mass AAG21 = { - 'H': [12.00, 0.00, 8.22, 0.04], - 'He': [10.914, 0.013, 1.29, 0.18], - 'Li': [0.96, 0.06, 3.25, 0.04], - 'Be': [1.38, 0.09, 1.32, 0.03], - 'B': [2.70, 0.20, 2.79, 0.04], - 'C': [8.46, 0.04, 7.39, 0.04], - 'N': [7.83, 0.07, 6.26, 0.06], - 'O': [8.69, 0.04, 8.39, 0.04], - 'F': [4.40, 0.25, 4.42, 0.06], - 'Ne': [8.06, 0.05, -1.12, 0.18], - 'Na': [6.22, 0.03, 6.27, 0.04], - 'Mg': [7.55, 0.03, 7.53, 0.02], - 'Al': [6.43, 0.03, 6.43, 0.03], - 'Si': [7.51, 0.03, 7.51, 0.01], - 'P': [5.41, 0.03, 5.43, 0.03, ], - 'S': [7.12, 0.03, 7.15, 0.02, ], - 'Cl': [5.31, 0.20, 5.23, 0.06], - 'Ar': [6.38, 0.10, -0.50, 0.18], - 'K': [5.07, 0.03, 5.08, 0.04], - 'Ca': [9, 6.30, 0.03, 6.29, 0.03], - 'Sc': [3.14, 0.04, 3.04, 0.03], - 'Ti': [4.97, 0.05, 4.90, 0.03], - 'V': [3.90, 0.08, 3.96, 0.03], - 'Cr': [5.62, 0.04, 5.63, 0.02], - 'Mn': [5.42, 0.06, 5.47, 0.03], - 'Fe': [7.46, 0.04, 7.46, 0.02], - 'Co': [4.94, 0.05, 4.87, 0.02], - 'Ni': [6.20, 0.04, 6.20, 0.03], - 'Cu': [4.18, 0.05, 4.25, 0.06], - 'Zn': [4.56, 0.05, 4.61, 0.02], - 'Ga': [3.02, 0.05, 3.07, 0.03], - 'Ge': [3.62, 0.10, 3.58, 0.04], - 'As': [np.nan, np.nan, 2.30, 0.04], - 'Se': [np.nan, np.nan, 3.34, 0.03], - 'Br': [np.nan, np.nan, 2.54, 0.06], - 'Kr': [3.12, 0.10, -2.27, 0.18], - 'Rb': [2.32, 0.08, 2.37, 0.03], - 'Sr': [2.83, 0.06, 2.88, 0.03], - 'Y': [2.21, 0.05, 2.15, 0.02], - 'Zr': [2.59, 0.04, 2.53, 0.02], - 'Nb': [1.47, 0.06, 1.42, 0.04], - 'Mo': [1.88, 0.09, 1.93, 0.04], - 'Ru': [1.75, 0.08, 1.77, 0.02], - 'Rh': [0.78, 0.11, 1.04, 0.02], - 'Pd': [1.57, 0.10, 1.65, 0.02], - 'Ag': [0.96, 0.10, 1.20, 0.04, ], - 'Cd': [np.nan, np.nan, 1.71, 0.03], - 'In': [0.80, 0.20, 0.76, 0.02], - 'Sn': [2.02, 0.10, 2.07, 0.06], - 'Sb': [np.nan, np.nan, 1.01, 0.06], - 'Te': [np.nan, np.nan, 2.18, 0.03], - 'I': [np.nan, np.nan, 1.55, 0.08], - 'Xe': [2.22, 0.05, -1.95, 0.18], - 'Cs': [np.nan, np.nan, 1.08, 0.03], - 'Ba': [2.27, 0.05, 2.18, 0.02], - 'La': [1.11, 0.04, 1.17, 0.01], - 'Ce': [1.58, 0.04, 1.58, 0.01], - 'Pr': [0.75, 0.05, 0.76, 0.01], - 'Nd': [1.42, 0.04, 1.45, 0.01], - 'Sm': [0.95, 0.04, 0.94, 0.01], - 'Eu': [0.52, 0.04, 0.52, 0.01], - 'Gd': [1.08, 0.04, 1.05, 0.01], - 'Tb': [0.31, 0.10, 0.31, 0.01], - 'Dy': [1.10, 0.04, 1.13, 0.01], - 'Ho': [0.48, 0.11, 0.47, 0.01], - 'Er': [0.93, 0.05, 0.93, 0.01], - 'Tm': [0.11, 0.04, 0.12, 0.01], - 'Yb': [0.85, 0.11, 0.92, 0.01], - 'Lu': [0.10, 0.09, 0.09, 0.01], - 'Hf': [0.85, 0.05, 0.71, 0.01], - 'Ta': [np.nan, np.nan, -0.15, 0.04], - 'W': [0.79, 0.11, 0.65, 0.04], - 'Re': [np.nan, np.nan, 0.26, 0.02], - 'Os': [1.35, 0.12, 1.35, 0.02], - 'Ir': [np.nan, np.nan, 1.32, 0.02], - 'Pt': [np.nan, np.nan, 1.61, 0.02], - 'Au': [0.91, 0.12, 0.81, 0.05], - 'Hg': [np.nan, np.nan, 1.17, 0.18], - 'Tl': [0.92, 0.17, 0.77, 0.05], - 'Pb': [1.95, 0.08, 2.03, 0.03], - 'Bi': [np.nan, np.nan, 0.65, 0.0], - 'U': [np.nan, np.nan, -0.54, 0.03], + "H": [12.00, 0.00, 8.22, 0.04], + "He": [10.914, 0.013, 1.29, 0.18], + "Li": [0.96, 0.06, 3.25, 0.04], + "Be": [1.38, 0.09, 1.32, 0.03], + "B": [2.70, 0.20, 2.79, 0.04], + "C": [8.46, 0.04, 7.39, 0.04], + "N": [7.83, 0.07, 6.26, 0.06], + "O": [8.69, 0.04, 8.39, 0.04], + "F": [4.40, 0.25, 4.42, 0.06], + "Ne": [8.06, 0.05, -1.12, 0.18], + "Na": [6.22, 0.03, 6.27, 0.04], + "Mg": [7.55, 0.03, 7.53, 0.02], + "Al": [6.43, 0.03, 6.43, 0.03], + "Si": [7.51, 0.03, 7.51, 0.01], + "P": [5.41, 0.03, 5.43, 0.03], + "S": [7.12, 0.03, 7.15, 0.02], + "Cl": [5.31, 0.20, 5.23, 0.06], + "Ar": [6.38, 0.10, -0.50, 0.18], + "K": [5.07, 0.03, 5.08, 0.04], + "Ca": [6.30, 0.03, 6.29, 0.03], + "Sc": [3.14, 0.04, 3.04, 0.03], + "Ti": [4.97, 0.05, 4.90, 0.03], + "V": [3.90, 0.08, 3.96, 0.03], + "Cr": [5.62, 0.04, 5.63, 0.02], + "Mn": [5.42, 0.06, 5.47, 0.03], + "Fe": [7.46, 0.04, 7.46, 0.02], + "Co": [4.94, 0.05, 4.87, 0.02], + "Ni": [6.20, 0.04, 6.20, 0.03], + "Cu": [4.18, 0.05, 4.25, 0.06], + "Zn": [4.56, 0.05, 4.61, 0.02], + "Ga": [3.02, 0.05, 3.07, 0.03], + "Ge": [3.62, 0.10, 3.58, 0.04], + "As": [np.nan, np.nan, 2.30, 0.04], + "Se": [np.nan, np.nan, 3.34, 0.03], + "Br": [np.nan, np.nan, 2.54, 0.06], + "Kr": [3.12, 0.10, -2.27, 0.18], + "Rb": [2.32, 0.08, 2.37, 0.03], + "Sr": [2.83, 0.06, 2.88, 0.03], + "Y": [2.21, 0.05, 2.15, 0.02], + "Zr": [2.59, 0.04, 2.53, 0.02], + "Nb": [1.47, 0.06, 1.42, 0.04], + "Mo": [1.88, 0.09, 1.93, 0.04], + "Ru": [1.75, 0.08, 1.77, 0.02], + "Rh": [0.78, 0.11, 1.04, 0.02], + "Pd": [1.57, 0.10, 1.65, 0.02], + "Ag": [0.96, 0.10, 1.20, 0.04], + "Cd": [np.nan, np.nan, 1.71, 0.03], + "In": [0.80, 0.20, 0.76, 0.02], + "Sn": [2.02, 0.10, 2.07, 0.06], + "Sb": [np.nan, np.nan, 1.01, 0.06], + "Te": [np.nan, np.nan, 2.18, 0.03], + "I": [np.nan, np.nan, 1.55, 0.08], + "Xe": [2.22, 0.05, -1.95, 0.18], + "Cs": [np.nan, np.nan, 1.08, 0.03], + "Ba": [2.27, 0.05, 2.18, 0.02], + "La": [1.11, 0.04, 1.17, 0.01], + "Ce": [1.58, 0.04, 1.58, 0.01], + "Pr": [0.75, 0.05, 0.76, 0.01], + "Nd": [1.42, 0.04, 1.45, 0.01], + "Sm": [0.95, 0.04, 0.94, 0.01], + "Eu": [0.52, 0.04, 0.52, 0.01], + "Gd": [1.08, 0.04, 1.05, 0.01], + "Tb": [0.31, 0.10, 0.31, 0.01], + "Dy": [1.10, 0.04, 1.13, 0.01], + "Ho": [0.48, 0.11, 0.47, 0.01], + "Er": [0.93, 0.05, 0.93, 0.01], + "Tm": [0.11, 0.04, 0.12, 0.01], + "Yb": [0.85, 0.11, 0.92, 0.01], + "Lu": [0.10, 0.09, 0.09, 0.01], + "Hf": [0.85, 0.05, 0.71, 0.01], + "Ta": [np.nan, np.nan, -0.15, 0.04], + "W": [0.79, 0.11, 0.65, 0.04], + "Re": [np.nan, np.nan, 0.26, 0.02], + "Os": [1.35, 0.12, 1.35, 0.02], + "Ir": [np.nan, np.nan, 1.32, 0.02], + "Pt": [np.nan, np.nan, 1.61, 0.02], + "Au": [0.91, 0.12, 0.81, 0.05], + "Hg": [np.nan, np.nan, 1.17, 0.18], + "Tl": [0.92, 0.17, 0.77, 0.05], + "Pb": [1.95, 0.08, 2.03, 0.03], + "Bi": [np.nan, np.nan, 0.65, 0.0], + "U": [np.nan, np.nan, -0.54, 0.03], } -def nsol(database='AAG21'): - """provide solar abundance dictionary. +def nsol(database="AAG21"): + """provides solar abundance dictionary. + + Args: + database: name of database, default to AAG21. + + Note: + AAG21 Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, arXiv:2105.01661 + AG89 Anders E. & Grevesse N. (1989, Geochimica et Cosmochimica Acta 53, 197) (Photospheric, using Table 2) + AGSS09 Asplund M., Grevesse N., Sauval A.J. & Scott P. (2009, ARAA, 47, 481) (Photospheric, using Table 1) + F92 Feldman U.(1992, Physica Scripta 46, 202) + AE82 Anders E. & Ebihara (1982, Geochimica et Cosmochimica Acta 46, 2363) + GS98 Grevesse, N. & Sauval, A.J. (1998, Space Science Reviews 85, 161) + WAM00 Wilms J., Allen A. & McCray R. (2000, ApJ 542, 914) + L03 Lodders K (2003, ApJ 591, 1220) (Photospheric, using Table 1) + LPG09photo Lodders K., Palme H., Gail H.P. (2009, Landolt-Barnstein, New Series, vol VI/4B, pp 560-630) (Photospheric, using Table 4) + LPG09proto Lodders K., Palme H., Gail H.P. (2009, Landolt-Barnstein, New Series, vol VI/4B, pp 560-630) (Proto-solar, using Table 10) + + Returns: + number ratio of elements for solar abundance + + Example: + >>> nsun=nsol() + >>> print(nsun["Fe"]) + >>> 2.6602622265852853e-05 + """ + available_databases = _available_abundance_databases() + if database not in available_databases: + raise ValueError( + f"database {database} is not available. Available databases are {available_databases.keys()}" + ) + + print("Database for solar abundance = ", database) + print(available_databases[database]) + if database == "AAG21": + return _nsol_aag21() + else: + return _nsol_from_xspec(database) + + +def _available_abundance_databases(): + database_available = {} + database_available["AAG21"] = ( + "Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, arXiv:2105.01661" + ) + database_available["AG89"] = ( + "Anders E. & Grevesse N. (1989, Geochimica et Cosmochimica Acta 53, 197) (Photospheric, using Table 2)" + ) + database_available["AGSS09"] = ( + "Asplund M., Grevesse N., Sauval A.J. & Scott P. (2009, ARAA, 47, 481) (Photospheric, using Table 1)" + ) + database_available["F92"] = "Feldman U.(1992, Physica Scripta 46, 202)" + database_available["AE82"] = ( + "Anders E. & Ebihara (1982, Geochimica et Cosmochimica Acta 46, 2363)" + ) + database_available["GS98"] = ( + "Grevesse, N. & Sauval, A.J. (1998, Space Science Reviews 85, 161)" + ) + database_available["WAM00"] = "Wilms J., Allen A. & McCray R. (2000, ApJ 542, 914)" + database_available["L03"] = ( + "Lodders K (2003, ApJ 591, 1220) (Photospheric, using Table 1)" + ) + database_available["LPG09photo"] = ( + "Lodders K., Palme H., Gail H.P. (2009, Landolt-Barnstein, New Series, vol VI/4B, pp 560-630) (Photospheric, using Table 4)" + ) + database_available["LPG09proto"] = ( + "Lodders K., Palme H., Gail H.P. (2009, Landolt-Barnstein, New Series, vol VI/4B, pp 560-630) (Proto-solar, using Table 10)" + ) + return database_available + + +def _nsol_from_xspec(database): + """ + + Notes: + reference: https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/node116.html + """ + import pkg_resources + import pandas as pd + + filename = "data/abundance/xspec_abundance.txt" + file_path = pkg_resources.resource_filename("exojax", filename) + df = pd.read_csv(file_path, comment="#", delimiter=",") + nsol = df.set_index("El")[database].to_dict() + total_sum = sum(nsol.values()) + nsol = {key: value / total_sum for key, value in nsol.items()} + + return nsol - Args: - database: name of database. + +def _nsol_aag21(): + """provides solar abundance dictionary from AAG21. + + Args: + database: name of database. Returns: - number ratio of solar abundance + number ratio of elements for solar abundance Example: - >>> nsun=nsol() - >>> print(nsun["Fe"]) - >>> 2.6602622265852853e-05 + >>> nsun=nsol() + >>> print(nsun["Fe"]) + >>> 2.6602622265852853e-05 """ # compute total number allab = 0.0 for atm in AAG21: val = AAG21[atm] if val[0] is np.nan: - allab = allab+10**val[2] + allab = allab + 10 ** val[2] else: - allab = allab+10**val[0] + allab = allab + 10 ** val[0] nsun = {} for atm in AAG21: val = AAG21[atm] if val[0] is np.nan: - nsun[atm] = 10**AAG21[atm][2]/allab + nsun[atm] = 10 ** AAG21[atm][2] / allab else: - nsun[atm] = 10**AAG21[atm][0]/allab + nsun[atm] = 10 ** AAG21[atm][0] / allab return nsun -if __name__ == '__main__': - nsun = nsol() - print(nsun['Mg']) - print(nsun['Si']) - print(nsun['O']) - print(nsun['Fe']) +def mass_fraction(atom, number_ratio_elements): + """mass fraction of atom + + Notes: + X = mass fraction of hydrogen + Y = mass fraction of helium + Z = mass fraction of metals + For definition, see https://en.wikipedia.org/wiki/Metallicity#Mass_fraction + + Args: + atom: atom name, such as "H", "He", "C", "O", "Fe", etc. + number_ratio_elements: element number ratio of abundance, when n = nsol(), X, Y, Z are solar abundance Xsol. Ysol, Zsol. + + Returns: + mass fraction of atom + """ + weighted_sum_mass = _sum_mass_weighted_elements(number_ratio_elements) + return element_mass[atom] * number_ratio_elements[atom] / weighted_sum_mass + + +def mass_fraction_XYZ(number_ratio_elements): + """mass fraction of hydrogen, helium, metals, i.e. well known symbols in astronomy X, Y, Z + + Notes: + X = mass fraction of hydrogen + Y = mass fraction of helium + Z = mass fraction of metals + For definition, see https://en.wikipedia.org/wiki/Metallicity#Mass_fraction + + Args: + number_ratio_elements: element number ratio of abundance, when n = nsol(), X, Y, Z are solar abundance Xsol. Ysol, Zsol. + + Returns: + float: X, Y, Z (mass fraction of H, He, metals) + """ + + X = mass_fraction("H", number_ratio_elements) + Y = mass_fraction("He", number_ratio_elements) + Z = 1.0 - X - Y + + return X, Y, Z + + +def _sum_mass_weighted_elements(number_ratio_elements): + sum_element = sum( + [ + element_mass[atom] * number_ratio_elements[atom] + for atom in number_ratio_elements + ] + ) + return sum_element diff --git a/tests/README.md b/tests/README.md index c9a1f1114..aff697d14 100644 --- a/tests/README.md +++ b/tests/README.md @@ -8,17 +8,12 @@ We frequently test the codes in this category when writing code. ## integration Integration test. Longer computation time and/or use real molecular database are allowed. -### integration/comparison -Internal comparison of opacity/radiative transfer codes -We recommend to test the code in this category before the Pull Request. - ## endtoend -Endtoend test. It takes much longer time than integration tests/unit tests. - -To test opacity calculators, use auto/*.py +End-to-end test. It takes much longer time than integration tests/unit tests. -### endtoend/manual_check - not test, but check various things - To check opacity calculators manually, use auto/*.py +## figures +Some codes for making figures +## benchmark +codes for benchmarking diff --git a/tests/endtoend/jaxopt/optimize_spectrum_JAXopt_test.py b/tests/endtoend/jaxopt/optimize_spectrum_JAXopt_test.py index 0f79f1d94..d26a2c204 100644 --- a/tests/endtoend/jaxopt/optimize_spectrum_JAXopt_test.py +++ b/tests/endtoend/jaxopt/optimize_spectrum_JAXopt_test.py @@ -12,75 +12,69 @@ from exojax.spec.atmrt import ArtEmisPure from exojax.spec.specop import SopRotation from exojax.spec.specop import SopInstProfile -from exojax.utils.instfunc import R2STD +from exojax.utils.instfunc import resolution_to_gaussian_std import jax.numpy as jnp def test_jaxopt_spectrum(fig=False): np.random.seed(1) - specdata = pkgutil.get_data('exojax', 'data/testdata/spectrum.txt') + specdata = pkgutil.get_data("exojax", "data/testdata/spectrum.txt") dat = pd.read_csv(BytesIO(specdata), delimiter=",", names=("wav", "flux")) wavd = dat["wav"].values flux = dat["flux"].values - nusd = jnp.array(1.e8 / wavd[::-1]) + nusd = jnp.array(1.0e8 / wavd[::-1]) sigmain = 0.05 norm = 40000 nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) # wavenumber grid setting Nx = 1500 - nus, wav, resolution = wavenumber_grid(np.min(wavd) - 5.0, - np.max(wavd) + 5.0, - Nx, - unit="AA", - xsmode="premodit") - - instrument_resolution = 100000. - beta_inst = R2STD(instrument_resolution) - mmw = 2.33 #mean molecular weight + nus, wav, resolution = wavenumber_grid( + np.min(wavd) - 5.0, np.max(wavd) + 5.0, Nx, unit="AA", xsmode="premodit" + ) + + instrument_resolution = 100000.0 + beta_inst = resolution_to_gaussian_std(instrument_resolution) + mmw = 2.33 # mean molecular weight mmrH2 = 0.74 molmassH2 = molinfo.molmass_isotope("H2") - vmrH2 = (mmrH2 * mmw / molmassH2) #VMR - Mp = 33.2 #fixing mass... - + vmrH2 = mmrH2 * mmw / molmassH2 # VMR + Mp = 33.2 # fixing mass... # art - art = ArtEmisPure(nus, pressure_top=10**-8, pressure_btm=10**2, nlayer=100) - - #mdb/cdb - mdbCO = MdbExomol('.database/CO/12C-16O/Li2015', - nus, - crit=1.e-46, - gpu_transfer=True) - cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus) - + art = ArtEmisPure(nu_grid=nus, pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100) + + # mdb/cdb + mdbCO = MdbExomol( + ".database/CO/12C-16O/Li2015", nus, crit=1.0e-46, gpu_transfer=True + ) + cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nus) + # opa opa = OpaDirect(mdbCO, nus) opacia = OpaCIA(cdbH2H2, nus) # spectral operators vsini_max = 100.0 - sos_rot = SopRotation(nus, vsini_max) + sos_rot = SopRotation(nus, vsini_max) sos_ip = SopInstProfile(nus, vsini_max) def model_c(params, boost, nu1): Rp, RV, MMR_CO, T0, alpha, vsini = params * boost - g = 2478.57730044555 * Mp / Rp**2 #gravity + g = 2478.57730044555 * Mp / Rp**2 # gravity u1 = 0.0 u2 = 0.0 - #T-P model// + # T-P model// Tarr = art.powerlaw_temperature(T0, alpha) def obyo(nusd): - #CO + # CO xsm_CO = opa.xsmatrix(Tarr, art.pressure) mmr_profile = art.constant_mmr_profile(MMR_CO) - dtaumCO = art.opacity_profile_xs(xsm_CO, mmr_profile, - mdbCO.molmass, g) - #CIA + dtaumCO = art.opacity_profile_xs(xsm_CO, mmr_profile, mdbCO.molmass, g) + # CIA logacia = opacia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, - mmw, g) + dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, mmw, g) dtau = dtaumCO + dtaucH2H2 F0 = art.run(dtau, Tarr) / norm @@ -94,7 +88,8 @@ def obyo(nusd): return model import jaxopt - boost = np.array([1.0, 10.0, 0.1, 1000.0, 1.e-3, 10.0]) + + boost = np.array([1.0, 10.0, 0.1, 1000.0, 1.0e-3, 10.0]) initpar = np.array([0.8, 9.0, 0.1, 1200.0, 0.1, 17.0]) / boost def objective(params): @@ -102,14 +97,15 @@ def objective(params): g = jnp.dot(f, f) return g - gd = jaxopt.GradientDescent(fun=objective, maxiter=1000, stepsize=1.e-4) + gd = jaxopt.GradientDescent(fun=objective, maxiter=1000, stepsize=1.0e-4) resolution = gd.run(init_params=initpar) params, state = resolution model = model_c(params, boost, nusd) - resid = np.sqrt(np.sum((nflux - model)**2) / len(nflux)) + resid = np.sqrt(np.sum((nflux - model) ** 2) / len(nflux)) if fig: import matplotlib.pyplot as plt + plt.plot(nusd, nflux) plt.plot(nusd, model, ls="dashed") plt.show() diff --git a/tests/endtoend/metals/Kurucz_linelist_test.py b/tests/endtoend/metals/Kurucz_linelist_test.py index 5a99b6497..a6697f66f 100644 --- a/tests/endtoend/metals/Kurucz_linelist_test.py +++ b/tests/endtoend/metals/Kurucz_linelist_test.py @@ -8,14 +8,15 @@ import os -filepath_Kurucz = '.database/gf2600.all' +filepath_Kurucz = ".database/gf2600.all" if not os.path.isfile(filepath_Kurucz): import urllib.request + try: url = "http://kurucz.harvard.edu/linelists/gfall/gf2600.all" urllib.request.urlretrieve(url, filepath_Kurucz) except: - print('could not connect ', url) + print("could not connect ", url) def test_Kurucz_linelist(): @@ -24,42 +25,40 @@ def test_Kurucz_linelist(): from exojax.spec import atomll from exojax.spec.atmrt import ArtEmisPure from exojax.spec.opacalc import OpaDirect - import jax.numpy as jnp - from jax import vmap, jit import numpy as np from exojax.spec.lpf import xsmatrix wls, wll = 10350, 10450 wavenumber_grid_res = 0.01 - nus, wav, reso = wavenumber_grid(wls, wll, int((wll-wls)/wavenumber_grid_res), unit="AA", xsmode="lpf") + nus, wav, res = wavenumber_grid( + wls, wll, int((wll - wls) / wavenumber_grid_res), unit="AA", xsmode="lpf" + ) NP = 100 - T0 = 3000. + T0 = 3000.0 alpha = 0.1 - art = ArtEmisPure(nu_grid=nus, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=NP) + art = ArtEmisPure(nu_grid=nus, pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=NP) Parr = art.pressure Tarr = art.powerlaw_temperature(T0, alpha) - H_He_HH_VMR = [0.0, 0.16, 0.84] #typical quasi-"solar-fraction" - mmw = 2.33 #mean molecular weight + H_He_HH_VMR = [0.0, 0.16, 0.84] # typical quasi-"solar-fraction" + mmw = 2.33 # mean molecular weight adbK = moldb.AdbKurucz(filepath_Kurucz, nus, vmr_fraction=H_He_HH_VMR) - Rp = 0.36 * 10 #R_sun*10 - Mp = 0.37 * 1e3 #M_sun*1e3 + Rp = 0.36 * 10 # R_sun*10 + Mp = 0.37 * 1e3 # M_sun*1e3 g = 2478.57730044555 * Mp / Rp**2 - print('logg: ' + str(np.log10(g))) - - VMR_Fe = atomll.get_VMR_uspecies(np.array([[26,1]])) - - opa = OpaDirect(mdb=adbK,nu_grid=nus) - - xsmatrix = opa.xsmatrix(Tarr, Parr) - mmr_arr = art.constant_mmr_profile(VMR_Fe) - dtaua_K = art.opacity_profile_xs(xsmatrix, mmr_arr, mmw, g) + print("logg: " + str(np.log10(g))) + VMR_Fe = atomll.get_VMR_uspecies(np.array([[26, 1]])) - assert np.isclose(np.sum(dtaua_K), 6644.0303) +# See Issue #539 +# opa = OpaDirect(mdb=adbK, nu_grid=nus) +# xsmatrix = opa.xsmatrix(Tarr, Parr) +# mmr_arr = art.constant_mmr_profile(VMR_Fe) +# dtaua_K = art.opacity_profile_xs(xsmatrix, mmr_arr, mmw, g) +# assert np.isclose(np.sum(dtaua_K), 6644.0303) -if __name__ == '__main__': +if __name__ == "__main__": test_Kurucz_linelist() diff --git a/tests/endtoend/metals/VALD_MODIT_test.py b/tests/endtoend/metals/VALD_MODIT_test.py index 0eee85c7b..3ef6eabe7 100644 --- a/tests/endtoend/metals/VALD_MODIT_test.py +++ b/tests/endtoend/metals/VALD_MODIT_test.py @@ -14,56 +14,74 @@ Please rename the file sent by VALD ([user_name_at_VALD].[request_number_at_VALD].gz) to "vald4214450.gz" if you would like to use the code below without editing it. """ -path_ValdLineList = '.database/vald4214450.gz' +path_ValdLineList = ".database/vald4214450.gz" import numpy as np import jax.numpy as jnp from exojax.spec import moldb, atomll, contdb, molinfo, initspec, planck from exojax.spec import api -from exojax.spec.rtransfer import pressure_layer, dtauVALD, rtrun_emis_pureabs_fbased2st +from exojax.spec.rtransfer import rtrun_emis_pureabs_fbased2st +from exojax.atm.atmprof import pressure_layer_logspace +from exojax.spec.layeropacity import layer_optical_depth_VALD as dtauVALD from exojax.spec.dtau_mmwl import dtauM_mmwl, dtauHminus_mmwl, dtauCIA_mmwl from exojax.utils.grids import wavenumber_grid from exojax.utils.instfunc import resolution_to_gaussian_std -from exojax.spec.modit import vald_all, xsmatrix_vald, exomol, xsmatrix, setdgm_vald_all, setdgm_exomol +from exojax.spec.modit import ( + vald_all, + xsmatrix_vald, + exomol, + xsmatrix, + setdgm_vald_all, + setdgm_exomol, +) from exojax.spec.response import ipgauss_sampling from exojax.spec.spin_rotation import convolve_rigid_rotation from exojax.utils.grids import velocity_grid + def test_VALD_MODIT(): - #wavelength range + # wavelength range wls, wll = 10395, 10405 - #Set a model atmospheric layers, wavenumber range for the model, an instrument + # Set a model atmospheric layers, wavenumber range for the model, an instrument NP = 100 - Parr, dParr, k = pressure_layer(NP = NP) - Pref=1.0 #bar - ONEARR=np.ones_like(Parr) + Parr, dParr, k = pressure_layer_logspace(NP=NP) + Pref = 1.0 # bar + ONEARR = np.ones_like(Parr) Nx = 2000 nus, wav, res = wavenumber_grid(wls - 5.0, wll + 5.0, Nx, unit="AA", xsmode="modit") - Rinst=100000. #instrumental spectral resolution - beta_inst=resolution_to_gaussian_std(Rinst) #equivalent to beta=c/(2.0*np.sqrt(2.0*np.log(2.0))*R) + Rinst = 100000.0 # instrumental spectral resolution + beta_inst = resolution_to_gaussian_std( + Rinst + ) # equivalent to beta=c/(2.0*np.sqrt(2.0*np.log(2.0))*R) - #settings before HMC + # settings before HMC vsini_max = 100.0 vr_array = velocity_grid(res, vsini_max) - #atoms and ions from VALD - adbV = moldb.AdbVald(path_ValdLineList, nus, crit = 1e-100) #The crit is defined just in case some weak lines may cause an error that results in a gamma of 0... (220219) + # atoms and ions from VALD + adbV = moldb.AdbVald( + path_ValdLineList, nus, crit=1e-100 + ) # The crit is defined just in case some weak lines may cause an error that results in a gamma of 0... (220219) asdb = moldb.AdbSepVald(adbV) - #molecules from exomol - mdbH2O = api.MdbExomol('.database/H2O/1H2-16O/POKAZATEL', nus, crit = 1e-50, gpu_transfer=True)#,crit = 1e-40) - mdbTiO = api.MdbExomol('.database/TiO/48Ti-16O/Toto', nus, crit = 1e-50, gpu_transfer=True)#,crit = 1e-50) - mdbOH = api.MdbExomol('.database/OH/16O-1H/MoLLIST', nus, gpu_transfer=True) - mdbFeH = api.MdbExomol('.database/FeH/56Fe-1H/MoLLIST', nus, gpu_transfer=True) + # molecules from exomol + mdbH2O = api.MdbExomol( + ".database/H2O/1H2-16O/POKAZATEL", nus, crit=1e-50, gpu_transfer=True + ) # ,crit = 1e-40) + mdbTiO = api.MdbExomol( + ".database/TiO/48Ti-16O/Toto", nus, crit=1e-50, gpu_transfer=True + ) # ,crit = 1e-50) + mdbOH = api.MdbExomol(".database/OH/16O-1H/MoLLIST", nus, gpu_transfer=True) + mdbFeH = api.MdbExomol(".database/FeH/56Fe-1H/MoLLIST", nus, gpu_transfer=True) - #CIA - cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus) + # CIA + cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nus) - #molecular mass + # molecular mass molmassH2O = molinfo.molmass_isotope("H2O") molmassTiO = molinfo.molmass_isotope("TiO") molmassOH = molinfo.molmass_isotope("OH") @@ -71,45 +89,56 @@ def test_VALD_MODIT(): molmassH = molinfo.molmass_isotope("H") molmassH2 = molinfo.molmass_isotope("H2") - #Initialization of MODIT (for separate VALD species, and exomol molecules(e.g., FeH)) - cnuS, indexnuS, R, pmarray = initspec.init_modit_vald(asdb.nu_lines, nus, asdb.N_usp) + # Initialization of MODIT (for separate VALD species, and exomol molecules(e.g., FeH)) + cnuS, indexnuS, R, pmarray = initspec.init_modit_vald( + asdb.nu_lines, nus, asdb.N_usp + ) cnu_FeH, indexnu_FeH, R, pmarray = initspec.init_modit(mdbFeH.nu_lines, nus) cnu_H2O, indexnu_H2O, R, pmarray = initspec.init_modit(mdbH2O.nu_lines, nus) cnu_OH, indexnu_OH, R, pmarray = initspec.init_modit(mdbOH.nu_lines, nus) cnu_TiO, indexnu_TiO, R, pmarray = initspec.init_modit(mdbTiO.nu_lines, nus) - #sampling the max/min of temperature profiles - fT = lambda T0,alpha: T0[:,None]*(Parr[None,:]/Pref)**alpha[:,None] - T0_test=np.array([1500.0, 4000.0, 1500.0, 4000.0]) - alpha_test=np.array([0.2,0.2,0.05,0.05]) - res=0.2 + # sampling the max/min of temperature profiles + fT = lambda T0, alpha: T0[:, None] * (Parr[None, :] / Pref) ** alpha[:, None] + T0_test = np.array([1500.0, 4000.0, 1500.0, 4000.0]) + alpha_test = np.array([0.2, 0.2, 0.05, 0.05]) + res = 0.2 - #Assume typical atmosphere + # Assume typical atmosphere H_He_HH_VMR_ref = [0.1, 0.15, 0.75] - PH_ref = Parr* H_He_HH_VMR_ref[0] - PHe_ref = Parr* H_He_HH_VMR_ref[1] - PHH_ref = Parr* H_He_HH_VMR_ref[2] - - #Precomputing dgm_ngammaL - dgm_ngammaL_VALD = setdgm_vald_all(asdb, PH_ref, PHe_ref, PHH_ref, R, fT, res, T0_test, alpha_test) - dgm_ngammaL_FeH = setdgm_exomol(mdbFeH, fT, Parr, R, molmassFeH, res, T0_test, alpha_test) - dgm_ngammaL_H2O = setdgm_exomol(mdbH2O, fT, Parr, R, molmassH2O, res, T0_test, alpha_test) - dgm_ngammaL_OH = setdgm_exomol(mdbOH, fT, Parr, R, molmassOH, res, T0_test, alpha_test) - dgm_ngammaL_TiO = setdgm_exomol(mdbTiO, fT, Parr, R, molmassTiO, res, T0_test, alpha_test) - - - T0 = 3000. + PH_ref = Parr * H_He_HH_VMR_ref[0] + PHe_ref = Parr * H_He_HH_VMR_ref[1] + PHH_ref = Parr * H_He_HH_VMR_ref[2] + + # Precomputing dgm_ngammaL + dgm_ngammaL_VALD = setdgm_vald_all( + asdb, PH_ref, PHe_ref, PHH_ref, R, fT, res, T0_test, alpha_test + ) + dgm_ngammaL_FeH = setdgm_exomol( + mdbFeH, fT, Parr, R, molmassFeH, res, T0_test, alpha_test + ) + dgm_ngammaL_H2O = setdgm_exomol( + mdbH2O, fT, Parr, R, molmassH2O, res, T0_test, alpha_test + ) + dgm_ngammaL_OH = setdgm_exomol( + mdbOH, fT, Parr, R, molmassOH, res, T0_test, alpha_test + ) + dgm_ngammaL_TiO = setdgm_exomol( + mdbTiO, fT, Parr, R, molmassTiO, res, T0_test, alpha_test + ) + + T0 = 3000.0 alpha = 0.07 - Mp=0.155 *1.99e33/1.90e30 - Rp=0.186 *6.96e10/6.99e9 - u1=0.0 - u2=0.0 - RV=0.00 - vsini=2.0 - - mmw=2.33*ONEARR #mean molecular weight + Mp = 0.155 * 1.99e33 / 1.90e30 + Rp = 0.186 * 6.96e10 / 6.99e9 + u1 = 0.0 + u2 = 0.0 + RV = 0.00 + vsini = 2.0 + + mmw = 2.33 * ONEARR # mean molecular weight log_e_H = -4.2 - VMR_H = 0.09 + VMR_H = 0.09 VMR_H2 = 0.77 VMR_FeH = 10**-8 VMR_H2O = 10**-4 @@ -120,66 +149,125 @@ def test_VALD_MODIT(): adjust_continuum = 0.99 - ga=2478.57730044555*Mp/Rp**2 - Tarr = T0*(Parr/Pref)**alpha - PH = Parr* VMR_H - PHe = Parr* (1-VMR_H-VMR_H2) - PHH = Parr* VMR_H2 - VMR_e = VMR_H*10**log_e_H + ga = 2478.57730044555 * Mp / Rp**2 + Tarr = T0 * (Parr / Pref) ** alpha + PH = Parr * VMR_H + PHe = Parr * (1 - VMR_H - VMR_H2) + PHH = Parr * VMR_H2 + VMR_e = VMR_H * 10**log_e_H - #VMR of atoms and ions (+Abundance modification) - mods_ID = jnp.array([[26,1], [22,1]]) + # VMR of atoms and ions (+Abundance modification) + mods_ID = jnp.array([[26, 1], [22, 1]]) mods = jnp.array([A_Fe, A_Ti]) VMR_uspecies = atomll.get_VMR_uspecies(asdb.uspecies, mods_ID, mods) - VMR_uspecies = VMR_uspecies[:, None]*ONEARR + VMR_uspecies = VMR_uspecies[:, None] * ONEARR - #Compute delta tau + # Compute delta tau - #Atom & ions (VALD) + # Atom & ions (VALD) SijMS, ngammaLMS, nsigmaDlS = vald_all(asdb, Tarr, PH, PHe, PHH, R) - xsmS = xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD) + xsmS = xsmatrix_vald( + cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD + ) dtauatom = dtauVALD(dParr, xsmS, VMR_uspecies, mmw, ga) - #FeH + # FeH SijM_FeH, ngammaLM_FeH, nsigmaDl_FeH = exomol(mdbFeH, Tarr, Parr, R, molmassFeH) - xsm_FeH = xsmatrix(cnu_FeH, indexnu_FeH, R, pmarray, nsigmaDl_FeH, ngammaLM_FeH, SijM_FeH, nus, dgm_ngammaL_FeH) - dtaum_FeH = dtauM_mmwl(dParr, jnp.abs(xsm_FeH), VMR_FeH*ONEARR, mmw, ga) - - #H2O + xsm_FeH = xsmatrix( + cnu_FeH, + indexnu_FeH, + R, + pmarray, + nsigmaDl_FeH, + ngammaLM_FeH, + SijM_FeH, + nus, + dgm_ngammaL_FeH, + ) + dtaum_FeH = dtauM_mmwl(dParr, jnp.abs(xsm_FeH), VMR_FeH * ONEARR, mmw, ga) + + # H2O SijM_H2O, ngammaLM_H2O, nsigmaDl_H2O = exomol(mdbH2O, Tarr, Parr, R, molmassH2O) - xsm_H2O = xsmatrix(cnu_H2O, indexnu_H2O, R, pmarray, nsigmaDl_H2O, ngammaLM_H2O, SijM_H2O, nus, dgm_ngammaL_H2O) - dtaum_H2O = dtauM_mmwl(dParr, jnp.abs(xsm_H2O), VMR_H2O*ONEARR, mmw, ga) - - #OH + xsm_H2O = xsmatrix( + cnu_H2O, + indexnu_H2O, + R, + pmarray, + nsigmaDl_H2O, + ngammaLM_H2O, + SijM_H2O, + nus, + dgm_ngammaL_H2O, + ) + dtaum_H2O = dtauM_mmwl(dParr, jnp.abs(xsm_H2O), VMR_H2O * ONEARR, mmw, ga) + + # OH SijM_OH, ngammaLM_OH, nsigmaDl_OH = exomol(mdbOH, Tarr, Parr, R, molmassOH) - xsm_OH = xsmatrix(cnu_OH, indexnu_OH, R, pmarray, nsigmaDl_OH, ngammaLM_OH, SijM_OH, nus, dgm_ngammaL_OH) - dtaum_OH = dtauM_mmwl(dParr, jnp.abs(xsm_OH), VMR_OH*ONEARR, mmw, ga) - - #TiO + xsm_OH = xsmatrix( + cnu_OH, + indexnu_OH, + R, + pmarray, + nsigmaDl_OH, + ngammaLM_OH, + SijM_OH, + nus, + dgm_ngammaL_OH, + ) + dtaum_OH = dtauM_mmwl(dParr, jnp.abs(xsm_OH), VMR_OH * ONEARR, mmw, ga) + + # TiO SijM_TiO, ngammaLM_TiO, nsigmaDl_TiO = exomol(mdbTiO, Tarr, Parr, R, molmassTiO) - xsm_TiO = xsmatrix(cnu_TiO, indexnu_TiO, R, pmarray, nsigmaDl_TiO, ngammaLM_TiO, SijM_TiO, nus, dgm_ngammaL_TiO) - dtaum_TiO = dtauM_mmwl(dParr, jnp.abs(xsm_TiO), VMR_TiO*ONEARR, mmw, ga) - - #Hminus - dtau_Hm = dtauHminus_mmwl(nus, Tarr, Parr, dParr, VMR_e*ONEARR, VMR_H*ONEARR, mmw, ga) - - #CIA - dtauc_H2H2 = dtauCIA_mmwl(nus, Tarr, Parr, dParr, VMR_H2*ONEARR, VMR_H2*ONEARR, mmw, ga, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac) - - #Summations - dtau = dtauatom + dtaum_FeH + dtaum_H2O + dtaum_OH + dtaum_TiO + dtau_Hm + dtauc_H2H2 + xsm_TiO = xsmatrix( + cnu_TiO, + indexnu_TiO, + R, + pmarray, + nsigmaDl_TiO, + ngammaLM_TiO, + SijM_TiO, + nus, + dgm_ngammaL_TiO, + ) + dtaum_TiO = dtauM_mmwl(dParr, jnp.abs(xsm_TiO), VMR_TiO * ONEARR, mmw, ga) + + # Hminus + dtau_Hm = dtauHminus_mmwl( + nus, Tarr, Parr, dParr, VMR_e * ONEARR, VMR_H * ONEARR, mmw, ga + ) + + # CIA + dtauc_H2H2 = dtauCIA_mmwl( + nus, + Tarr, + Parr, + dParr, + VMR_H2 * ONEARR, + VMR_H2 * ONEARR, + mmw, + ga, + cdbH2H2.nucia, + cdbH2H2.tcia, + cdbH2H2.logac, + ) + + # Summations + dtau = ( + dtauatom + dtaum_FeH + dtaum_H2O + dtaum_OH + dtaum_TiO + dtau_Hm + dtauc_H2H2 + ) sourcef = planck.piBarr(Tarr, nus) F0 = rtrun_emis_pureabs_fbased2st(dtau, sourcef) Frot = convolve_rigid_rotation(F0, vr_array, vsini, u1, u2) wavd = jnp.linspace(wls, wll, 500) - nusd = jnp.array(1.e8/wavd[::-1]) + nusd = jnp.array(1.0e8 / wavd[::-1]) mu = ipgauss_sampling(nusd, nus, Frot, beta_inst, RV, vr_array) - mu = mu/jnp.nanmax(mu)*adjust_continuum + mu = mu / jnp.nanmax(mu) * adjust_continuum + + assert np.all(mu != 0) + assert np.all(abs(mu) != np.inf) + assert np.all(~np.isnan(mu)) + - assert (np.all(mu != 0)) - assert (np.all(abs(mu) != np.inf)) - assert (np.all(~np.isnan(mu))) - if __name__ == "__main__": test_VALD_MODIT() diff --git a/tests/endtoend/metals/opacity_Fe_test.py b/tests/endtoend/metals/opacity_Fe_test.py index 999210b87..16d3eafc3 100644 --- a/tests/endtoend/metals/opacity_Fe_test.py +++ b/tests/endtoend/metals/opacity_Fe_test.py @@ -18,32 +18,32 @@ import pytest import numpy as np from exojax.spec import moldb, atomll -from exojax.spec.lpf import auto_xsection from exojax.spec.hitran import line_strength, doppler_sigma import matplotlib.pyplot as plt from exojax.utils.constants import m_u import os -filepath_VALD3 = '.database/vald2600.gz' +filepath_VALD3 = ".database/vald2600.gz" if not os.path.isfile(filepath_VALD3): import urllib.request from exojax.utils.url import url_developer_data + try: - url = url_developer_data()+'vald2600.gz' + url = url_developer_data() + "vald2600.gz" urllib.request.urlretrieve(url, filepath_VALD3) except: - print('could not connect ', url_developer_data()) + print("could not connect ", url_developer_data()) -path_fig = './' +path_fig = "./" -out_suffix = '_pytest' +out_suffix = "_pytest" # H, He, H2 #pure[1.0, 0.0, 0.0] #test[0.05, 0.005, 0.1] #Solar[0.0, 0.16, 0.84] H_He_HH_VMR = [0.0, 0.16, 0.84] # wavenumber range for opacity calculation (Covering whole wavelength ranges of both IRD and CARMENES) -nus = 1e8/np.arange(12200, 11800, -0.01, dtype=np.float64) +nus = 1e8 / np.arange(12200, 11800, -0.01, dtype=np.float64) # wavenumber range for LineList being taken into account (Taking all (except for 1e5–1e6) lines in the line lists (VALD3, Kurucz) into consideration) -nus4LL = 1e8/np.arange(1e5, 1500.0, -0.01, dtype=np.float64) +nus4LL = 1e8 / np.arange(1e5, 1500.0, -0.01, dtype=np.float64) pf_Irwin = False # if True, the partition functions of Irwin1981 is used, otherwise those of Barklem&Collet2016 @@ -56,124 +56,260 @@ nu0 = adbFe.nu_lines # REFERENCE VALUES for T=2995,P=0.1 (wavelength=VAC) -REFS = np.array([[1.5435075e-12, 2692.6172, 1742.1658], - [1.5435075e-12, 2692.6172, 6689.1265], - [1.5435075e-12, 2692.6172, 1246.5763], - [1.5435075e-12, 2692.6172, 534.4775], - [1.5435075e-12, 2692.6172, 2664.4116]]) +REFS = np.array( + [ + [1.5435075e-12, 2692.6172, 1742.1658], + [1.5435075e-12, 2692.6172, 6689.1265], + [1.5435075e-12, 2692.6172, 1246.5763], + [1.5435075e-12, 2692.6172, 534.4775], + [1.5435075e-12, 2692.6172, 2664.4116], + ] +) # ------- # [81, 110, 148, 200, 270, 365, 493, 666, 900, 1215, 1641, 2000, 2217, 2500, 2750, 2995, 3250, 3500, 3750, 4000] -@pytest.mark.parametrize('T', [2995, ]) +@pytest.mark.parametrize( + "T", + [ + 2995, + ], +) # [0.000001, 0.000010, 0.000100, 0.001000, 0.010000, 0.100000, 1.000000, 10.000000, 100.000000, 1000.000000] -@pytest.mark.parametrize('P', [0.1, ]) +@pytest.mark.parametrize( + "P", + [ + 0.1, + ], +) def test_opacity_Fe_vald3(T, P): PH = P * H_He_HH_VMR[0] PHe = P * H_He_HH_VMR[1] PHH = P * H_He_HH_VMR[2] - qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T)) + Qr_T = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp("Fe 1", T)) # ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes. - Sij = line_strength(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt, adbFe.Tref) + Sij = line_strength( + T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, Qr_T, adbFe.Tref + ) sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol) - gammaL = atomll.gamma_vald3(T, PH, PHH, PHe, adbFe.ielem, adbFe.iion, - adbFe.dev_nu_lines, adbFe.elower, adbFe.eupper, adbFe.atomicmass, adbFe.ionE, - adbFe.gamRad, adbFe.gamSta, adbFe.vdWdamp, enh_damp=1.0) + gammaL = atomll.gamma_vald3( + T, + PH, + PHH, + PHe, + adbFe.ielem, + adbFe.iion, + adbFe.dev_nu_lines, + adbFe.elower, + adbFe.eupper, + adbFe.atomicmass, + adbFe.ionE, + adbFe.gamRad, + adbFe.gamSta, + adbFe.vdWdamp, + enh_damp=1.0, + ) val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)]) - diff = np.abs(REFS[0, :]-val) + diff = np.abs(REFS[0, :] - val) print(diff) - assert(diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3) + assert diff[0] < 1.0e-11 and diff[1] < 1.0e-3 and diff[2] < 1.0e-3 + + # assert(diff[0]<1.e-11 and diff[1]<0.1 and diff[2]<1.0 ) #allow longer wavenumber # [81, 110, 148, 200, 270, 365, 493, 666, 900, 1215, 1641, 2000, 2217, 2500, 2750, 2995, 3250, 3500, 3750, 4000] -@pytest.mark.parametrize('T', [2995, ]) +@pytest.mark.parametrize( + "T", + [ + 2995, + ], +) # [0.000001, 0.000010, 0.000100, 0.001000, 0.010000, 0.100000, 1.000000, 10.000000, 100.000000, 1000.000000] -@pytest.mark.parametrize('P', [0.1, ]) +@pytest.mark.parametrize( + "P", + [ + 0.1, + ], +) def test_opacity_Fe_uns(T, P): PH = P * H_He_HH_VMR[0] PHe = P * H_He_HH_VMR[1] PHH = P * H_He_HH_VMR[2] - qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T)) + Qr_T = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp("Fe 1", T)) # ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes. - Sij = line_strength(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt, adbFe.Tref) + Sij = line_strength( + T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, Qr_T, adbFe.Tref + ) sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol) - gammaL = atomll.gamma_uns(T, PH, PHH, PHe, adbFe.ielem, adbFe.iion, - adbFe.dev_nu_lines, adbFe.elower, adbFe.eupper, adbFe.atomicmass, adbFe.ionE, - adbFe.gamRad, adbFe.gamSta, adbFe.vdWdamp, enh_damp=1.0) + gammaL = atomll.gamma_uns( + T, + PH, + PHH, + PHe, + adbFe.ielem, + adbFe.iion, + adbFe.dev_nu_lines, + adbFe.elower, + adbFe.eupper, + adbFe.atomicmass, + adbFe.ionE, + adbFe.gamRad, + adbFe.gamSta, + adbFe.vdWdamp, + enh_damp=1.0, + ) val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)]) - diff = np.abs(REFS[1, :]-val) + diff = np.abs(REFS[1, :] - val) print(diff) - assert(diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3) + assert diff[0] < 1.0e-11 and diff[1] < 1.0e-3 and diff[2] < 1.0e-3 # [81, 110, 148, 200, 270, 365, 493, 666, 900, 1215, 1641, 2000, 2217, 2500, 2750, 2995, 3250, 3500, 3750, 4000] -@pytest.mark.parametrize('T', [2995, ]) +@pytest.mark.parametrize( + "T", + [ + 2995, + ], +) # [0.000001, 0.000010, 0.000100, 0.001000, 0.010000, 0.100000, 1.000000, 10.000000, 100.000000, 1000.000000] -@pytest.mark.parametrize('P', [0.1, ]) +@pytest.mark.parametrize( + "P", + [ + 0.1, + ], +) def test_opacity_Fe_KA3(T, P): PH = P * H_He_HH_VMR[0] PHe = P * H_He_HH_VMR[1] PHH = P * H_He_HH_VMR[2] - qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T)) + Qr_T = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp("Fe 1", T)) # ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes. - Sij = line_strength(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt, adbFe.Tref) + Sij = line_strength( + T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, Qr_T, adbFe.Tref + ) sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol) - gammaL = atomll.gamma_KA3(T, PH, PHH, PHe, adbFe.ielem, adbFe.iion, - adbFe.dev_nu_lines, adbFe.elower, adbFe.eupper, adbFe.atomicmass, adbFe.ionE, - adbFe.gamRad, adbFe.gamSta, adbFe.vdWdamp, enh_damp=1.0) + gammaL = atomll.gamma_KA3( + T, + PH, + PHH, + PHe, + adbFe.ielem, + adbFe.iion, + adbFe.dev_nu_lines, + adbFe.elower, + adbFe.eupper, + adbFe.atomicmass, + adbFe.ionE, + adbFe.gamRad, + adbFe.gamSta, + adbFe.vdWdamp, + enh_damp=1.0, + ) val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)]) - diff = np.abs(REFS[2, :]-val) + diff = np.abs(REFS[2, :] - val) print(diff) - assert(diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3) + assert diff[0] < 1.0e-11 and diff[1] < 1.0e-3 and diff[2] < 1.0e-3 # [81, 110, 148, 200, 270, 365, 493, 666, 900, 1215, 1641, 2000, 2217, 2500, 2750, 2995, 3250, 3500, 3750, 4000] -@pytest.mark.parametrize('T', [2995, ]) +@pytest.mark.parametrize( + "T", + [ + 2995, + ], +) # [0.000001, 0.000010, 0.000100, 0.001000, 0.010000, 0.100000, 1.000000, 10.000000, 100.000000, 1000.000000] -@pytest.mark.parametrize('P', [0.1, ]) +@pytest.mark.parametrize( + "P", + [ + 0.1, + ], +) def test_opacity_Fe_KA4(T, P): PH = P * H_He_HH_VMR[0] PHe = P * H_He_HH_VMR[1] PHH = P * H_He_HH_VMR[2] - qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T)) + Qr_T = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp("Fe 1", T)) # ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes. - Sij = line_strength(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt, adbFe.Tref) + Sij = line_strength( + T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, Qr_T, adbFe.Tref + ) sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol) - gammaL = atomll.gamma_KA4(T, PH, PHH, PHe, adbFe.ielem, adbFe.iion, - adbFe.dev_nu_lines, adbFe.elower, adbFe.eupper, adbFe.atomicmass, adbFe.ionE, - adbFe.gamRad, adbFe.gamSta, adbFe.vdWdamp, enh_damp=1.0) + gammaL = atomll.gamma_KA4( + T, + PH, + PHH, + PHe, + adbFe.ielem, + adbFe.iion, + adbFe.dev_nu_lines, + adbFe.elower, + adbFe.eupper, + adbFe.atomicmass, + adbFe.ionE, + adbFe.gamRad, + adbFe.gamSta, + adbFe.vdWdamp, + enh_damp=1.0, + ) val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)]) - diff = np.abs(REFS[3, :]-val) + diff = np.abs(REFS[3, :] - val) print(diff) - assert(diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3) + assert diff[0] < 1.0e-11 and diff[1] < 1.0e-3 and diff[2] < 1.0e-3 # [81, 110, 148, 200, 270, 365, 493, 666, 900, 1215, 1641, 2000, 2217, 2500, 2750, 2995, 3250, 3500, 3750, 4000] -@pytest.mark.parametrize('T', [2995, ]) +@pytest.mark.parametrize( + "T", + [ + 2995, + ], +) # [0.000001, 0.000010, 0.000100, 0.001000, 0.010000, 0.100000, 1.000000, 10.000000, 100.000000, 1000.000000] -@pytest.mark.parametrize('P', [0.1, ]) +@pytest.mark.parametrize( + "P", + [ + 0.1, + ], +) def test_opacity_Fe_KA3s(T, P): PH = P * H_He_HH_VMR[0] PHe = P * H_He_HH_VMR[1] PHH = P * H_He_HH_VMR[2] - qt = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp('Fe 1', T)) + Qr_T = np.ones_like(adbFe.A) * np.float32(adbFe.qr_interp("Fe 1", T)) # ↑Unlike the case of HITRAN (using Qr_HAPI), we ignored the isotopes. - Sij = line_strength(T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, qt, adbFe.Tref) + Sij = line_strength( + T, adbFe.logsij0, adbFe.nu_lines, adbFe.elower, Qr_T, adbFe.Tref + ) sigmaD = doppler_sigma(adbFe.nu_lines, T, Amol) - gammaL = atomll.gamma_KA3s(T, PH, PHH, PHe, adbFe.ielem, adbFe.iion, - adbFe.dev_nu_lines, adbFe.elower, adbFe.eupper, adbFe.atomicmass, adbFe.ionE, - adbFe.gamRad, adbFe.gamSta, adbFe.vdWdamp, enh_damp=1.0) + gammaL = atomll.gamma_KA3s( + T, + PH, + PHH, + PHe, + adbFe.ielem, + adbFe.iion, + adbFe.dev_nu_lines, + adbFe.elower, + adbFe.eupper, + adbFe.atomicmass, + adbFe.ionE, + adbFe.gamRad, + adbFe.gamSta, + adbFe.vdWdamp, + enh_damp=1.0, + ) val = np.array([np.sum(Sij), np.sum(sigmaD), np.sum(gammaL)]) - diff = np.abs(REFS[4, :]-val) + diff = np.abs(REFS[4, :] - val) print(diff) - assert(diff[0] < 1.e-11 and diff[1] < 1.e-3 and diff[2] < 1.e-3) + assert diff[0] < 1.0e-11 and diff[1] < 1.0e-3 and diff[2] < 1.0e-3 -if __name__ == '__main__': - test_opacity_Fe_vald3(2995., 0.1) - test_opacity_Fe_uns(2995., 0.1) - test_opacity_Fe_KA3(2995., 0.1) - test_opacity_Fe_KA4(2995., 0.1) - test_opacity_Fe_KA3s(2995., 0.1) +if __name__ == "__main__": + test_opacity_Fe_vald3(2995.0, 0.1) + test_opacity_Fe_uns(2995.0, 0.1) + test_opacity_Fe_KA3(2995.0, 0.1) + test_opacity_Fe_KA4(2995.0, 0.1) + test_opacity_Fe_KA3s(2995.0, 0.1) diff --git a/tests/endtoend/reverse/reverse_lpf.py b/tests/endtoend/reverse/reverse_lpf.py index fd27b1066..3bf96e231 100644 --- a/tests/endtoend/reverse/reverse_lpf.py +++ b/tests/endtoend/reverse/reverse_lpf.py @@ -9,7 +9,6 @@ from exojax.spec import contdb from exojax.spec.api import MdbExomol from exojax.spec import molinfo -from exojax.utils.grids import velocity_grid from exojax.utils.grids import wavenumber_grid from exojax.utils.instfunc import resolution_to_gaussian_std from exojax.spec.opacalc import OpaDirect @@ -28,38 +27,36 @@ config.update("jax_enable_x64", True) -dat = pd.read_csv('spectrum.txt', delimiter=',', names=('wav', 'flux')) -wavd = dat['wav'].values -flux = dat['flux'].values +dat = pd.read_csv("spectrum.txt", delimiter=",", names=("wav", "flux")) +wavd = dat["wav"].values +flux = dat["flux"].values nusd = wav2nu(wavd, unit="AA") sigmain = 0.05 -norm = 40000. +norm = 40000.0 nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) -nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 5.0, - np.max(wavd) + 5.0, - 1500, - unit='AA', - xsmode="premodit") +nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 5.0, np.max(wavd) + 5.0, 1500, unit="AA", xsmode="premodit" +) -art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) +art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.0e-5, pressure_btm=1.0e2, nlayer=100) art.change_temperature_range(400.0, 1500.0) -instrumental_resolution = 100000. +instrumental_resolution = 100000.0 beta_inst = resolution_to_gaussian_std(instrumental_resolution) Mp = 33.2 # fixing mass... -mdbCO = MdbExomol('.database/CO/12C-16O/Li2015', - nu_grid, - crit=1.e-46, - gpu_transfer=True) + +mdbCO = MdbExomol( + ".database/CO/12C-16O/Li2015", nu_grid, crit=1.0e-46, gpu_transfer=True +) opa = OpaDirect(mdb=mdbCO, nu_grid=nu_grid) -cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nu_grid) +cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nu_grid) opacia = OpaCIA(cdbH2H2, nu_grid) mmw = 2.33 # mean molecular weight mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +molmassH2 = molinfo.molmass_isotope("H2") +vmrH2 = mmrH2 * mmw / molmassH2 # VMR # sos vsini_max = 100.0 @@ -68,12 +65,12 @@ def model_c(nu1, y1): - Rp = numpyro.sample('Rp', dist.Uniform(0.4, 1.2)) - RV = numpyro.sample('RV', dist.Uniform(5.0, 15.0)) - MMR_CO = numpyro.sample('MMR_CO', dist.Uniform(0.0, 0.015)) - T0 = numpyro.sample('T0', dist.Uniform(1000.0, 1500.0)) - alpha = numpyro.sample('alpha', dist.Uniform(0.05, 0.2)) - vsini = numpyro.sample('vsini', dist.Uniform(15.0, 25.0)) + Rp = numpyro.sample("Rp", dist.Uniform(0.4, 1.2)) + RV = numpyro.sample("RV", dist.Uniform(5.0, 15.0)) + MMR_CO = numpyro.sample("MMR_CO", dist.Uniform(0.0, 0.015)) + T0 = numpyro.sample("T0", dist.Uniform(1000.0, 1500.0)) + alpha = numpyro.sample("alpha", dist.Uniform(0.05, 0.2)) + vsini = numpyro.sample("vsini", dist.Uniform(15.0, 25.0)) gravity = gravity_jupiter(Mp, Rp) # gravity in the unit of Jupiter u1 = 0.0 u2 = 0.0 @@ -85,12 +82,10 @@ def model_c(nu1, y1): def obyo(y, tag, nusd, nus): # CO xsm_CO = opa.xsmatrix(Tarr, art.pressure) - dtaumCO = art.opacity_profile_xs(xsm_CO, mmr_arr, opa.mdb.molmass, - gravity) + dtaumCO = art.opacity_profile_xs(xsm_CO, mmr_arr, opa.mdb.molmass, gravity) # CIA logacia = opacia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, mmw, - gravity) + dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, mmw, gravity) dtau = dtaumCO + dtaucH2H2 F0 = art.run(dtau, Tarr) / norm Frot = sos_rot.rigid_rotation(F0, vsini, u1, u2) @@ -99,42 +94,41 @@ def obyo(y, tag, nusd, nus): numpyro.sample(tag, dist.Normal(mu, sigmain), obs=y) - obyo(y1, 'y1', nu1, nu_grid) + obyo(y1, "y1", nu1, nu_grid) rng_key = random.PRNGKey(0) rng_key, rng_key_ = random.split(rng_key) num_warmup, num_samples = 300, 600 -#kernel = NUTS(model_c, forward_mode_differentiation=True) +# kernel = NUTS(model_c, forward_mode_differentiation=True) kernel = NUTS(model_c, forward_mode_differentiation=False) mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples) mcmc.run(rng_key_, nu1=nusd, y1=nflux) posterior_sample = mcmc.get_samples() -pred = Predictive(model_c, posterior_sample, return_sites=['y1']) +pred = Predictive(model_c, posterior_sample, return_sites=["y1"]) predictions = pred(rng_key_, nu1=nusd, y1=None) -median_mu1 = jnp.median(predictions['y1'], axis=0) -hpdi_mu1 = hpdi(predictions['y1'], 0.9) +median_mu1 = jnp.median(predictions["y1"], axis=0) +hpdi_mu1 = hpdi(predictions["y1"], 0.9) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 6.0)) -ax.plot(wavd[::-1], median_mu1, color='C0') -ax.plot(wavd[::-1], nflux, '+', color='black', label='data') -ax.fill_between(wavd[::-1], - hpdi_mu1[0], - hpdi_mu1[1], - alpha=0.3, - interpolate=True, - color='C0', - label='90% area') -plt.xlabel('wavelength ($\AA$)', fontsize=16) +ax.plot(wavd[::-1], median_mu1, color="C0") +ax.plot(wavd[::-1], nflux, "+", color="black", label="data") +ax.fill_between( + wavd[::-1], + hpdi_mu1[0], + hpdi_mu1[1], + alpha=0.3, + interpolate=True, + color="C0", + label="90% area", +) +plt.xlabel("wavelength ($\AA$)", fontsize=16) plt.legend(fontsize=16) plt.tick_params(labelsize=16) plt.savefig("spectrum.png") plt.close() -pararr = ['Rp', 'T0', 'alpha', 'MMR_CO', 'vsini', 'RV'] -arviz.plot_pair(arviz.from_numpyro(mcmc), - kind='kde', - divergences=False, - marginals=True) +pararr = ["Rp", "T0", "alpha", "MMR_CO", "vsini", "RV"] +arviz.plot_pair(arviz.from_numpyro(mcmc), kind="kde", divergences=False, marginals=True) plt.savefig("corner.png") plt.close() diff --git a/tests/endtoend/reverse/reverse_modit.py b/tests/endtoend/reverse/reverse_modit.py index e79799795..ca9bcc61c 100644 --- a/tests/endtoend/reverse/reverse_modit.py +++ b/tests/endtoend/reverse/reverse_modit.py @@ -1,4 +1,5 @@ """ Reverse modeling of Methane emission spectrum using MODIT + works with ExoJAX v1.6 """ #!/usr/bin/env python @@ -34,10 +35,11 @@ # loading data filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + SAMPLE_SPECTRA_CH4_NEW) + "exojax", "data/testdata/" + SAMPLE_SPECTRA_CH4_NEW +) dat = pd.read_csv(filename, delimiter=",", names=("wavenumber", "flux")) -nusd = dat['wavenumber'].values -flux = dat['flux'].values +nusd = dat["wavenumber"].values +flux = dat["flux"].values wavd = nu2wav(nusd, wavelength_order="ascending") sigmain = 0.05 norm = 20000 @@ -45,31 +47,35 @@ # wavenumber setting Nx = 7500 -nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='modit', - wavelength_order="ascending") +nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, + np.max(wavd) + 10.0, + Nx, + unit="AA", + xsmode="modit", + wavelength_order="ascending", +) # Atmospheric RT setting Tlow = 400.0 Thigh = 1500.0 -art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) +art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100) art.change_temperature_range(Tlow, Thigh) Mp = 33.2 # instrument -Rinst = 100000. +Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) ## CH4 setting -mdbCH4 = api.MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nu_grid, - crit=1.e-30, - Ttyp=273.0, - gpu_transfer=True) -print('N=', len(mdbCH4.nu_lines)) +mdbCH4 = api.MdbExomol( + ".database/CH4/12C-1H4/YT10to10/", + nu_grid, + crit=1.0e-30, + Ttyp=273.0, + gpu_transfer=True, +) +print("N=", len(mdbCH4.nu_lines)) #### T profile range to be used (this is used to determine gammaL grid in MODIT) T0_test = np.array([1100.0, 1500.0, 1100.0, 1500.0]) @@ -77,25 +83,24 @@ vmapped_powerlaw_temperature = vmap(art.powerlaw_temperature, (0, 0)) Tarr_list = vmapped_powerlaw_temperature(T0_test, alpha_test) -opa = OpaModit(mdb=mdbCH4, - nu_grid=nu_grid, - Tarr_list=Tarr_list, - Parr=art.pressure) +opa = OpaModit(mdb=mdbCH4, nu_grid=nu_grid, Tarr_list=Tarr_list, Parr=art.pressure, allow_32bit=True) ## CIA setting -cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) +cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) mmw = 2.33 # mean molecular weight mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +molmassH2 = molinfo.molmass_isotope("H2") +vmrH2 = mmrH2 * mmw / molmassH2 # VMR # check dgm if True: from exojax.plot.ditplot import plot_dgmn + Tarr = art.powerlaw_temperature(1300.0, 0.1) SijM_CH4, ngammaLM_CH4, nsigmaDl_CH4 = modit.exomol( - mdbCH4, Tarr, art.pressure, res, opa.mdb.molmass) + mdbCH4, Tarr, art.pressure, res, opa.mdb.molmass + ) plot_dgmn(art.pressure, opa.dgm_ngammaL, ngammaLM_CH4, 0, 6) plt.show() @@ -110,12 +115,10 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): # CH4 xsmatrix = opa.xsmatrix(Tarr, art.pressure) mmr_profile = art.constant_mmr_profile(MMR_CH4) - dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_profile, - opa.mdb.molmass, gravity) + dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_profile, opa.mdb.molmass, gravity) # CIA logacia = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, mmw, - gravity) + dtaucH2H2 = art.opacity_profile_cia(logacia, Tarr, vmrH2, vmrH2, mmw, gravity) # RT dtau = dtaumCH4 + dtaucH2H2 @@ -126,18 +129,18 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): def model_c(y1): - Rp = numpyro.sample('Rp', dist.Uniform(0.4, 1.2)) - RV = numpyro.sample('RV', dist.Uniform(5.0, 15.0)) - MMR_CH4 = numpyro.sample('MMR_CH4', dist.Uniform(0.0, 0.015)) - T0 = numpyro.sample('T0', dist.Uniform(1000.0, 1500.0)) - alpha = numpyro.sample('alpha', dist.Uniform(0.05, 0.2)) - vsini = numpyro.sample('vsini', dist.Uniform(15.0, 25.0)) + Rp = numpyro.sample("Rp", dist.Uniform(0.4, 1.2)) + RV = numpyro.sample("RV", dist.Uniform(5.0, 15.0)) + MMR_CH4 = numpyro.sample("MMR_CH4", dist.Uniform(0.0, 0.015)) + T0 = numpyro.sample("T0", dist.Uniform(1000.0, 1500.0)) + alpha = numpyro.sample("alpha", dist.Uniform(0.05, 0.2)) + vsini = numpyro.sample("vsini", dist.Uniform(15.0, 25.0)) u1 = 0.0 u2 = 0.0 # T-P model// Tarr = art.powerlaw_temperature(T0, alpha) mu = frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini) - numpyro.sample('y1', dist.Normal(mu, sigmain), obs=y1) + numpyro.sample("y1", dist.Normal(mu, sigmain), obs=y1) rng_key = random.PRNGKey(0) @@ -151,29 +154,28 @@ def model_c(y1): # SAMPLING posterior_sample = mcmc.get_samples() -pred = Predictive(model_c, posterior_sample, return_sites=['y1']) +pred = Predictive(model_c, posterior_sample, return_sites=["y1"]) predictions = pred(rng_key_, y1=None) -median_mu1 = jnp.median(predictions['y1'], axis=0) -hpdi_mu1 = hpdi(predictions['y1'], 0.9) +median_mu1 = jnp.median(predictions["y1"], axis=0) +hpdi_mu1 = hpdi(predictions["y1"], 0.9) # PLOT fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 6.0)) -ax.plot(wavd[::-1], median_mu1, color='C0') -ax.plot(wavd[::-1], nflux, '+', color='black', label='data') -ax.fill_between(wavd[::-1], - hpdi_mu1[0], - hpdi_mu1[1], - alpha=0.3, - interpolate=True, - color='C0', - label='90% area') -plt.xlabel('wavelength ($\AA$)', fontsize=16) +ax.plot(wavd[::-1], median_mu1, color="C0") +ax.plot(wavd[::-1], nflux, "+", color="black", label="data") +ax.fill_between( + wavd[::-1], + hpdi_mu1[0], + hpdi_mu1[1], + alpha=0.3, + interpolate=True, + color="C0", + label="90% area", +) +plt.xlabel("wavelength ($\AA$)", fontsize=16) plt.legend(fontsize=16) plt.tick_params(labelsize=16) -pararr = ['Rp', 'T0', 'alpha', 'MMR_CH4', 'vsini', 'RV'] -arviz.plot_pair(arviz.from_numpyro(mcmc), - kind='kde', - divergences=False, - marginals=True) +pararr = ["Rp", "T0", "alpha", "MMR_CH4", "vsini", "RV"] +arviz.plot_pair(arviz.from_numpyro(mcmc), kind="kde", divergences=False, marginals=True) plt.show() diff --git a/tests/endtoend/reverse/reverse_modit_hitemp.py b/tests/endtoend/reverse/reverse_modit_hitemp.py index 89d9da196..e2a4c747d 100644 --- a/tests/endtoend/reverse/reverse_modit_hitemp.py +++ b/tests/endtoend/reverse/reverse_modit_hitemp.py @@ -1,4 +1,5 @@ """ Reverse modeling of CO/HITEMP using MODIT + works with ExoJAX v1.6 """ #!/usr/bin/env python @@ -10,56 +11,55 @@ from numpyro.infer import MCMC, NUTS import numpyro import numpyro.distributions as dist -from jax import random import pandas as pd import numpy as np import matplotlib.pyplot as plt import jax.numpy as jnp -from exojax.spec import rtransfer as rt +from jax import random +from exojax.atm.atmprof import pressure_layer_logspace +from exojax.spec import initspec +from exojax.spec.layeropacity import layer_optical_depth +from exojax.spec.layeropacity import layer_optical_depth_CIA from exojax.spec import modit from exojax.spec import api, contdb -from exojax.utils.grids import wavenumber_grid -from exojax.spec.rtransfer import rtrun_emis_pureabs_fbased2st, dtauM, dtauCIA, wavenumber_grid +from exojax.spec.rtransfer import rtrun_emis_pureabs_fbased2st from exojax.spec import planck from exojax.spec.response import ipgauss_sampling from exojax.spec.spin_rotation import convolve_rigid_rotation -from exojax.utils.grids import velocity_grid from exojax.spec import molinfo +from exojax.utils.grids import wavenumber_grid +from exojax.utils.grids import velocity_grid from exojax.utils.constants import RJ from exojax.utils.instfunc import resolution_to_gaussian_std -import numpy as np -from exojax.spec import initspec -dat = pd.read_csv('spectrum_co.txt', delimiter=',', names=('wav', 'flux')) -wavd = dat['wav'].values -flux = dat['flux'].values -nusd = jnp.array(1.e8 / wavd[::-1]) +dat = pd.read_csv("spectrum_co.txt", delimiter=",", names=("wav", "flux")) +wavd = dat["wav"].values +flux = dat["flux"].values +nusd = jnp.array(1.0e8 / wavd[::-1]) sigmain = 0.05 norm = 20000 nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) NP = 100 -Parr, dParr, k = rt.pressure_layer(NP=NP) +Parr, dParr, k = pressure_layer_logspace(nlayer=NP) Nx = 5000 -nus, wav, res = wavenumber_grid(np.min(wavd) - 5.0, - np.max(wavd) + 5.0, - Nx, - unit='AA', - xsmode='modit') -Rinst = 100000. +nus, wav, res = wavenumber_grid( + np.min(wavd) - 5.0, np.max(wavd) + 5.0, Nx, unit="AA", xsmode="modit" +) +Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) -molmassCO = molinfo.molmass_isotope('CO') +molmassCO = molinfo.molmass_isotope("CO") mmw = 2.33 # mean molecular weight mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +molmassH2 = molinfo.molmass_isotope("H2") +vmrH2 = mmrH2 * mmw / molmassH2 # VMR # Mp = 33.2 -mdbCO = api.MdbHitemp('.database/CO', nus, crit=1.e-30, gpu_transfer=True) -cdbH2H2 = contdb.CdbCIA('.database/H2-H2_2011.cia', nus) -print('N=', len(mdbCO.nu_lines)) +mdbCO = api.MdbHitemp(".database/CO", nus, crit=1.0e-30, gpu_transfer=True) +cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nus) +print("N=", len(mdbCO.nu_lines)) # Reference pressure for a T-P model Pref = 1.0 # bar @@ -71,43 +71,58 @@ # Precomputing gdm_ngammaL def fT(T0, alpha): - return T0[:, None] * (Parr[None, :] / Pref)**alpha[:, None] + return T0[:, None] * (Parr[None, :] / Pref) ** alpha[:, None] T0_test = np.array([1100.0, 1500.0, 1100.0, 1500.0]) alpha_test = np.array([0.2, 0.2, 0.05, 0.05]) res = 0.2 vmrCO_ref = 4.9e-4 -dgm_ngammaL = setdgm_hitran(mdbCO, fT, Parr, Parr * vmrCO_ref, R, molmassCO, - res, T0_test, alpha_test) +dgm_ngammaL = setdgm_hitran( + mdbCO, fT, Parr, Parr * vmrCO_ref, R, molmassCO, res, T0_test, alpha_test +) # check dgm if False: from exojax.plot.ditplot import plot_dgmn - Tarr = 1300. * (Parr / Pref)**0.1 - SijM_CO, ngammaLM_CO, nsigmaDl_CO = modit.hitran(mdbCO, Tarr, Parr, - Parr * vmrCO_ref, R, - molmassCO) + + Tarr = 1300.0 * (Parr / Pref) ** 0.1 + SijM_CO, ngammaLM_CO, nsigmaDl_CO = modit.hitran( + mdbCO, Tarr, Parr, Parr * vmrCO_ref, R, molmassCO + ) plot_dgmn(Parr, dgm_ngammaL, ngammaLM_CO, 0, 6) plt.show() -#settings before HMC +# settings before HMC vsini_max = 100.0 vr_array = velocity_grid(res, vsini_max) + def frun(Tarr, MMR_CO, Mp, Rp, u1, u2, RV, vsini): g = 2478.57730044555 * Mp / Rp**2 VMR_CO = MMR_CO * mmw / molmassCO - SijM_CO, ngammaLM_CO, nsigmaDl_CO = modit.hitran(mdbCO, Tarr, Parr, - Parr * VMR_CO, R, - molmassCO) - xsm_CO = modit.xsmatrix(cnu, indexnu, R, pmarray, nsigmaDl_CO, ngammaLM_CO, - SijM_CO, nus, dgm_ngammaL) + SijM_CO, ngammaLM_CO, nsigmaDl_CO = modit.hitran( + mdbCO, Tarr, Parr, Parr * VMR_CO, R, molmassCO + ) + xsm_CO = modit.xsmatrix( + cnu, indexnu, R, pmarray, nsigmaDl_CO, ngammaLM_CO, SijM_CO, nus, dgm_ngammaL + ) # abs is used to remove negative values in xsv - dtaumCO = dtauM(dParr, jnp.abs(xsm_CO), MMR_CO * ONEARR, molmassCO, g) + dtaumCO = layer_optical_depth(dParr, jnp.abs(xsm_CO), MMR_CO * ONEARR, molmassCO, g) # CIA - dtaucH2H2 = dtauCIA(nus, Tarr, Parr, dParr, vmrH2, vmrH2, mmw, g, - cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac) + dtaucH2H2 = layer_optical_depth_CIA( + nus, + Tarr, + Parr, + dParr, + vmrH2, + vmrH2, + mmw, + g, + cdbH2H2.nucia, + cdbH2H2.tcia, + cdbH2H2.logac, + ) dtau = dtaumCO + dtaucH2H2 sourcef = planck.piBarr(Tarr, nus) F0 = rtrun_emis_pureabs_fbased2st(dtau, sourcef) / norm @@ -118,15 +133,10 @@ def frun(Tarr, MMR_CO, Mp, Rp, u1, u2, RV, vsini): # test if False: - Tarr = 1200.0 * (Parr / Pref)**0.1 - mu = frun(Tarr, - MMR_CO=0.0059, - Mp=33.2, - Rp=0.88, - u1=0.0, - u2=0.0, - RV=10.0, - vsini=20.0) + Tarr = 1200.0 * (Parr / Pref) ** 0.1 + mu = frun( + Tarr, MMR_CO=0.0059, Mp=33.2, Rp=0.88, u1=0.0, u2=0.0, RV=10.0, vsini=20.0 + ) plt.plot(wavd, mu) plt.show() @@ -134,26 +144,26 @@ def frun(Tarr, MMR_CO, Mp, Rp, u1, u2, RV, vsini): def model_c(nu1, y1): - Rp = numpyro.sample('Rp', dist.Uniform(0.4, 1.2)) - RV = numpyro.sample('RV', dist.Uniform(5.0, 15.0)) - MMR_CO = numpyro.sample('MMR_CO', dist.Uniform(0.0, 0.015)) - T0 = numpyro.sample('T0', dist.Uniform(1000.0, 1500.0)) - alpha = numpyro.sample('alpha', dist.Uniform(0.05, 0.2)) - vsini = numpyro.sample('vsini', dist.Uniform(15.0, 25.0)) + Rp = numpyro.sample("Rp", dist.Uniform(0.4, 1.2)) + RV = numpyro.sample("RV", dist.Uniform(5.0, 15.0)) + MMR_CO = numpyro.sample("MMR_CO", dist.Uniform(0.0, 0.015)) + T0 = numpyro.sample("T0", dist.Uniform(1000.0, 1500.0)) + alpha = numpyro.sample("alpha", dist.Uniform(0.05, 0.2)) + vsini = numpyro.sample("vsini", dist.Uniform(15.0, 25.0)) g = 2478.57730044555 * Mp / Rp**2 # gravity u1 = 0.0 u2 = 0.0 # T-P model// - Tarr = T0 * (Parr / Pref)**alpha + Tarr = T0 * (Parr / Pref) ** alpha # line computation CO mu = frun(Tarr, MMR_CO, Mp, Rp, u1, u2, RV, vsini) - numpyro.sample('y1', dist.Normal(mu, sigmain), obs=y1) + numpyro.sample("y1", dist.Normal(mu, sigmain), obs=y1) rng_key = random.PRNGKey(0) rng_key, rng_key_ = random.split(rng_key) num_warmup, num_samples = 300, 600 -#kernel = NUTS(model_c, forward_mode_differentiation=True) +# kernel = NUTS(model_c, forward_mode_differentiation=True) kernel = NUTS(model_c, forward_mode_differentiation=False) mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples) @@ -161,29 +171,28 @@ def model_c(nu1, y1): # SAMPLING posterior_sample = mcmc.get_samples() -pred = Predictive(model_c, posterior_sample, return_sites=['y1']) +pred = Predictive(model_c, posterior_sample, return_sites=["y1"]) predictions = pred(rng_key_, nu1=nusd, y1=None) -median_mu1 = jnp.median(predictions['y1'], axis=0) -hpdi_mu1 = hpdi(predictions['y1'], 0.9) +median_mu1 = jnp.median(predictions["y1"], axis=0) +hpdi_mu1 = hpdi(predictions["y1"], 0.9) # PLOT fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 6.0)) -ax.plot(wavd[::-1], median_mu1, color='C0') -ax.plot(wavd[::-1], nflux, '+', color='black', label='data') -ax.fill_between(wavd[::-1], - hpdi_mu1[0], - hpdi_mu1[1], - alpha=0.3, - interpolate=True, - color='C0', - label='90% area') -plt.xlabel('wavelength ($\AA$)', fontsize=16) +ax.plot(wavd[::-1], median_mu1, color="C0") +ax.plot(wavd[::-1], nflux, "+", color="black", label="data") +ax.fill_between( + wavd[::-1], + hpdi_mu1[0], + hpdi_mu1[1], + alpha=0.3, + interpolate=True, + color="C0", + label="90% area", +) +plt.xlabel("wavelength ($\AA$)", fontsize=16) plt.legend(fontsize=16) plt.tick_params(labelsize=16) -pararr = ['Rp', 'T0', 'alpha', 'MMR_CO', 'vsini', 'RV'] -arviz.plot_pair(arviz.from_numpyro(mcmc), - kind='kde', - divergences=False, - marginals=True) +pararr = ["Rp", "T0", "alpha", "MMR_CO", "vsini", "RV"] +arviz.plot_pair(arviz.from_numpyro(mcmc), kind="kde", divergences=False, marginals=True) plt.show() diff --git a/tests/endtoend/reverse/reverse_premodit.py b/tests/endtoend/reverse/reverse_premodit.py index 722e13bff..25ef869ac 100644 --- a/tests/endtoend/reverse/reverse_premodit.py +++ b/tests/endtoend/reverse/reverse_premodit.py @@ -1,5 +1,7 @@ -""" Reverse modeling of Methane emission spectrum using MODIT +""" Reverse modeling of Methane emission spectrum using PreMODIT + works with ExoJAX v1.6 """ + #!/usr/bin/env python # coding: utf-8 import numpy as np @@ -40,10 +42,11 @@ # loading the data filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + SAMPLE_SPECTRA_CH4_NEW) + "exojax", "data/testdata/" + SAMPLE_SPECTRA_CH4_NEW +) dat = pd.read_csv(filename, delimiter=",", names=("wavenumber", "flux")) -nusd = dat['wavenumber'].values -flux = dat['flux'].values +nusd = dat["wavenumber"].values +flux = dat["flux"].values wavd = nu2wav(nusd, wavelength_order="ascending") sigmain = 0.05 @@ -51,42 +54,45 @@ nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) Nx = 7500 -nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='premodit', wavelength_order="ascending") +nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, + np.max(wavd) + 10.0, + Nx, + unit="AA", + xsmode="premodit", + wavelength_order="ascending", +) Tlow = 400.0 Thigh = 1500.0 -art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) +art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.0e-5, pressure_btm=1.0e2, nlayer=100) art.change_temperature_range(Tlow, Thigh) Mp = 33.2 -Rinst = 100000. +Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) ### CH4 setting (PREMODIT) -mdb = MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nurange=nu_grid, - gpu_transfer=False) -print('N=', len(mdb.nu_lines)) +mdb = MdbExomol(".database/CH4/12C-1H4/YT10to10/", nurange=nu_grid, gpu_transfer=False) +print("N=", len(mdb.nu_lines)) diffmode = 0 -opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[Tlow, Thigh], - dit_grid_resolution=0.2) +opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[Tlow, Thigh], + dit_grid_resolution=0.2, +) ## CIA setting -cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) +cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) mmw = 2.33 # mean molecular weight mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +molmassH2 = molinfo.molmass_isotope("H2") +vmrH2 = mmrH2 * mmw / molmassH2 # VMR -#settings before HMC +# settings before HMC vsini_max = 100.0 vr_array = velocity_grid(res, vsini_max) @@ -95,15 +101,14 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): g = gravity_jupiter(Rp=Rp, Mp=Mp) # gravity in the unit of Jupiter - #molecule + # molecule xsmatrix = opa.xsmatrix(Tarr, art.pressure) mmr_arr = art.constant_mmr_profile(MMR_CH4) dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, g) - #continuum + # continuum logacia_matrix = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, - mmw, g) - #total tau + dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, mmw, g) + # total tau dtau = dtaumCH4 + dtaucH2H2 F0 = art.run(dtau, Tarr) / norm Frot = convolve_rigid_rotation(F0, vr_array, vsini, u1, u2) @@ -112,7 +117,8 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): import matplotlib.pyplot as plt -#g = gravity_jupiter(0.88, 33.2) + +# g = gravity_jupiter(0.88, 33.2) Rp = 0.88 Mp = 33.2 alpha = 0.1 @@ -136,23 +142,23 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): def model_c(y1): - Rp = numpyro.sample('Rp', dist.Uniform(0.4, 1.2)) - RV = numpyro.sample('RV', dist.Uniform(5.0, 15.0)) - MMR_CH4 = numpyro.sample('MMR_CH4', dist.Uniform(0.0, 0.015)) - T0 = numpyro.sample('T0', dist.Uniform(1000.0, 1500.0)) - alpha = numpyro.sample('alpha', dist.Uniform(0.05, 0.2)) - vsini = numpyro.sample('vsini', dist.Uniform(15.0, 25.0)) + Rp = numpyro.sample("Rp", dist.Uniform(0.4, 1.2)) + RV = numpyro.sample("RV", dist.Uniform(5.0, 15.0)) + MMR_CH4 = numpyro.sample("MMR_CH4", dist.Uniform(0.0, 0.015)) + T0 = numpyro.sample("T0", dist.Uniform(1000.0, 1500.0)) + alpha = numpyro.sample("alpha", dist.Uniform(0.05, 0.2)) + vsini = numpyro.sample("vsini", dist.Uniform(15.0, 25.0)) u1 = 0.0 u2 = 0.0 Tarr = art.powerlaw_temperature(T0, alpha) mu = frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini) - numpyro.sample('y1', dist.Normal(mu, sigmain), obs=y1) + numpyro.sample("y1", dist.Normal(mu, sigmain), obs=y1) rng_key = random.PRNGKey(0) rng_key, rng_key_ = random.split(rng_key) num_warmup, num_samples = 500, 1000 -#kernel = NUTS(model_c, forward_mode_differentiation=True) +# kernel = NUTS(model_c, forward_mode_differentiation=True) kernel = NUTS(model_c, forward_mode_differentiation=False) mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples) @@ -161,32 +167,31 @@ def model_c(y1): # SAMPLING posterior_sample = mcmc.get_samples() -pred = Predictive(model_c, posterior_sample, return_sites=['y1']) +pred = Predictive(model_c, posterior_sample, return_sites=["y1"]) predictions = pred(rng_key_, y1=None) -median_mu1 = jnp.median(predictions['y1'], axis=0) -hpdi_mu1 = hpdi(predictions['y1'], 0.9) +median_mu1 = jnp.median(predictions["y1"], axis=0) +hpdi_mu1 = hpdi(predictions["y1"], 0.9) # PLOT fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 6.0)) -ax.plot(wavd[::-1], median_mu1, color='C0') -ax.plot(wavd[::-1], nflux, '+', color='black', label='data') -ax.fill_between(wavd[::-1], - hpdi_mu1[0], - hpdi_mu1[1], - alpha=0.3, - interpolate=True, - color='C0', - label='90% area') -plt.xlabel('wavelength ($\AA$)', fontsize=16) +ax.plot(wavd[::-1], median_mu1, color="C0") +ax.plot(wavd[::-1], nflux, "+", color="black", label="data") +ax.fill_between( + wavd[::-1], + hpdi_mu1[0], + hpdi_mu1[1], + alpha=0.3, + interpolate=True, + color="C0", + label="90% area", +) +plt.xlabel("wavelength ($\AA$)", fontsize=16) plt.legend(fontsize=16) plt.tick_params(labelsize=16) plt.savefig("pred_diffmode" + str(diffmode) + ".png") plt.close() -pararr = ['Rp', 'T0', 'alpha', 'MMR_CH4', 'vsini', 'RV'] -arviz.plot_pair(arviz.from_numpyro(mcmc), - kind='kde', - divergences=False, - marginals=True) +pararr = ["Rp", "T0", "alpha", "MMR_CH4", "vsini", "RV"] +arviz.plot_pair(arviz.from_numpyro(mcmc), kind="kde", divergences=False, marginals=True) plt.savefig("corner_diffmode" + str(diffmode) + ".png") -#plt.show() +# plt.show() diff --git a/tests/endtoend/reverse/reverse_premodit_blackjax.py b/tests/endtoend/reverse/reverse_premodit_blackjax.py index 6304c76d3..4f2396220 100644 --- a/tests/endtoend/reverse/reverse_premodit_blackjax.py +++ b/tests/endtoend/reverse/reverse_premodit_blackjax.py @@ -1,5 +1,7 @@ -""" Reverse modeling of Methane emission spectrum using MODIT +""" Reverse modeling of Methane emission spectrum using PreMODIT + works with ExoJAX v1.6 """ + #!/usr/bin/env python # coding: utf-8 import numpy as np @@ -37,10 +39,11 @@ # loading the data filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + SAMPLE_SPECTRA_CH4_NEW) + "exojax", "data/testdata/" + SAMPLE_SPECTRA_CH4_NEW +) dat = pd.read_csv(filename, delimiter=",", names=("wavenumber", "flux")) -nusd = dat['wavenumber'].values -flux = dat['flux'].values +nusd = dat["wavenumber"].values +flux = dat["flux"].values wavd = nu2wav(nusd, wavelength_order="ascending") sigmain = 0.05 @@ -48,42 +51,45 @@ nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) Nx = 7500 -nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='premodit', wavelength_order="ascending") +nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, + np.max(wavd) + 10.0, + Nx, + unit="AA", + xsmode="premodit", + wavelength_order="ascending", +) Tlow = 400.0 Thigh = 1500.0 -art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) +art = ArtEmisPure(nu_grid=nu_grid, pressure_top=1.0e-5, pressure_btm=1.0e2, nlayer=100) art.change_temperature_range(Tlow, Thigh) Mp = 33.2 -Rinst = 100000. +Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) ### CH4 setting (PREMODIT) -mdb = MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nurange=nu_grid, - gpu_transfer=False) -print('N=', len(mdb.nu_lines)) +mdb = MdbExomol(".database/CH4/12C-1H4/YT10to10/", nurange=nu_grid, gpu_transfer=False) +print("N=", len(mdb.nu_lines)) diffmode = 0 -opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[Tlow, Thigh], - dit_grid_resolution=0.2) +opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[Tlow, Thigh], + dit_grid_resolution=0.2, +) ## CIA setting -cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) +cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) mmw = 2.33 # mean molecular weight mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +molmassH2 = molinfo.molmass_isotope("H2") +vmrH2 = mmrH2 * mmw / molmassH2 # VMR -#settings before HMC +# settings before HMC vsini_max = 100.0 vr_array = velocity_grid(res, vsini_max) @@ -92,15 +98,14 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): g = gravity_jupiter(Rp=Rp, Mp=Mp) # gravity in the unit of Jupiter - #molecule + # molecule xsmatrix = opa.xsmatrix(Tarr, art.pressure) mmr_arr = art.constant_mmr_profile(MMR_CH4) dtaumCH4 = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, g) - #continuum + # continuum logacia_matrix = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, - mmw, g) - #total tau + dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, mmw, g) + # total tau dtau = dtaumCH4 + dtaucH2H2 F0 = art.run(dtau, Tarr) / norm Frot = convolve_rigid_rotation(F0, vr_array, vsini, u1, u2) @@ -109,7 +114,8 @@ def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): import matplotlib.pyplot as plt -#g = gravity_jupiter(0.88, 33.2) + +# g = gravity_jupiter(0.88, 33.2) Rp = 0.88 Mp = 33.2 alpha = 0.1 @@ -139,42 +145,41 @@ def g(Rp, RV, MMR_CH4, T0, alpha, vsini): def logprob_fn(x): logpdf = stats.norm.logpdf( - nflux, - g(x["Rp"], x["RV"], x["MMR_CH4"], x["T0"], x["alpha"], x["vsini"]), - 1.0) - return jnp.sum(logpdf) + nflux, g(x["Rp"], x["RV"], x["MMR_CH4"], x["T0"], x["alpha"], x["vsini"]), 1.0 + ) + return jnp.sum(logpdf) step_size = 1e-3 -inverse_mass_matrix = jnp.array([0.1, 1., 0.001, 100., 0.001, 1.]) +inverse_mass_matrix = jnp.array([0.1, 1.0, 0.001, 100.0, 0.001, 1.0]) nuts = blackjax.nuts(logprob_fn, step_size, inverse_mass_matrix) # Initialize the state initial_position = { "Rp": 0.5, "RV": 10.0, - "MMR_CH4":0.01, - "T0":1200.0, - "alpha":0.12, - "vsini":12.0 - } + "MMR_CH4": 0.01, + "T0": 1200.0, + "alpha": 0.12, + "vsini": 12.0, +} state = nuts.init(initial_position) # Iterate rng_key = random.PRNGKey(0) -#warmup = blackjax.window_adaptation( +# warmup = blackjax.window_adaptation( # blackjax.nuts, # logprob_fn, # 100, -#) +# ) -#state, kernel, _ = warmup.run( +# state, kernel, _ = warmup.run( # rng_key, # res.x, -#) -#print(state) -#print(kernel) +# ) +# print(state) +# print(kernel) for _ in range(100): _, rng_key = random.split(rng_key) diff --git a/tests/endtoend/reverse/reverse_premodit_transmission.py b/tests/endtoend/reverse/reverse_premodit_transmission.py index ce6dec73c..87917cd65 100644 --- a/tests/endtoend/reverse/reverse_premodit_transmission.py +++ b/tests/endtoend/reverse/reverse_premodit_transmission.py @@ -1,5 +1,6 @@ -""" Reverse modeling of Methane transmission spectrum using MODIT +""" Reverse modeling of Methane transmission spectrum using PreMODIT """ + #!/usr/bin/env python # coding: utf-8 import numpy as np @@ -14,17 +15,15 @@ from exojax.spec.atmrt import ArtTransPure from exojax.spec.api import MdbExomol from exojax.spec.opacalc import OpaPremodit -from exojax.spec.contdb import CdbCIA -from exojax.spec.opacont import OpaCIA -from exojax.spec.response import ipgauss_sampling -#from exojax.spec.spin_rotation import convolve_rigid_rotation -from exojax.spec import molinfo from exojax.spec.unitconvert import nu2wav from exojax.utils.grids import wavenumber_grid -from exojax.utils.grids import velocity_grid from exojax.utils.astrofunc import gravity_jupiter from exojax.utils.instfunc import resolution_to_gaussian_std -#from exojax.test.data import SAMPLE_SPECTRA_CH4_NEW +from exojax.test.data import SAMPLE_SPECTRA_CH4_TRANS +from exojax.utils.constants import RJ, Rs +from exojax.utils.astrofunc import gravity_jupiter +from exojax.spec.specop import SopInstProfile + from jax import config @@ -40,153 +39,145 @@ # loading the data filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + SAMPLE_SPECTRA_CH4_NEW) + "exojax", "data/testdata/" + SAMPLE_SPECTRA_CH4_TRANS +) dat = pd.read_csv(filename, delimiter=",", names=("wavenumber", "flux")) -nusd = dat['wavenumber'].values -flux = dat['flux'].values +nusd = dat["wavenumber"].values +radius_ratio = dat["flux"].values wavd = nu2wav(nusd, wavelength_order="ascending") -sigmain = 0.05 -norm = 20000 -nflux = flux / norm + np.random.normal(0, sigmain, len(wavd)) -Nx = 7500 -nu_grid, wav, res = wavenumber_grid(np.min(wavd) - 10.0, - np.max(wavd) + 10.0, - Nx, - unit='AA', - xsmode='premodit', wavelength_order="ascending") +sigmain = 0.0001 +radis_ratio_obs = radius_ratio + np.random.normal(0, sigmain, len(wavd)) +Nx = 7500 +nu_grid, wav, res = wavenumber_grid( + np.min(wavd) - 10.0, + np.max(wavd) + 10.0, + Nx, + unit="AA", + xsmode="premodit", + wavelength_order="ascending", +) + +T_fid = 500.0 Tlow = 400.0 -Thigh = 1500.0 -art = ArtEmisPure(nu_grid, pressure_top=1.e-8, pressure_btm=1.e2, nlayer=100) +Thigh = 700.0 + +art = ArtTransPure(pressure_top=1.0e-10, pressure_btm=1.0e1, nlayer=100) art.change_temperature_range(Tlow, Thigh) -Mp = 33.2 -Rinst = 100000. +Rinst = 100000.0 beta_inst = resolution_to_gaussian_std(Rinst) ### CH4 setting (PREMODIT) -mdb = MdbExomol('.database/CH4/12C-1H4/YT10to10/', - nurange=nu_grid, - gpu_transfer=False) -print('N=', len(mdb.nu_lines)) +mdb = MdbExomol(".database/CH4/12C-1H4/YT10to10/", nurange=nu_grid, gpu_transfer=False) + +print("N=", len(mdb.nu_lines)) diffmode = 0 -opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[Tlow, Thigh], - dit_grid_resolution=0.2) +opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[Tlow, Thigh], + dit_grid_resolution=0.2, + allow_32bit=True, +) ## CIA setting -cdbH2H2 = CdbCIA('.database/H2-H2_2011.cia', nu_grid) -opcia = OpaCIA(cdb=cdbH2H2, nu_grid=nu_grid) -mmw = 2.33 # mean molecular weight -mmrH2 = 0.74 -molmassH2 = molinfo.molmass_isotope('H2') -vmrH2 = (mmrH2 * mmw / molmassH2) # VMR +# cdbH2H2 = CdbCIA(".database/H2-H2_2011.cia", nu_grid) +mu_fid = 2.2 +# settings before HMC +radius_btm = RJ -#settings before HMC -vsini_max = 100.0 -vr_array = velocity_grid(res, vsini_max) +vrmax = 100.0 # km/s +sop = SopInstProfile(nu_grid, vrmax) -print("ready") +def frun(T0, MMR_CH4, Mp, Rp, RV): + gravity_btm = gravity_jupiter(Rp, Mp) -def frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini): - g = gravity_jupiter(Rp=Rp, Mp=Mp) # gravity in the unit of Jupiter - #molecule - xsmatrix = opa.xsmatrix(Tarr, art.pressure) + # T-P model + Tarr = T0 * jnp.ones_like(art.pressure) + mmw_arr = mu_fid * np.ones_like(art.pressure) + + gravity = art.gravity_profile(Tarr, mmw_arr, radius_btm, gravity_btm) mmr_arr = art.constant_mmr_profile(MMR_CH4) - dtaumCH4 = art.opacity_profile_lines(xsmatrix, mmr_arr, opa.mdb.molmass, g) - #continuum - logacia_matrix = opcia.logacia_matrix(Tarr) - dtaucH2H2 = art.opacity_profile_cia(logacia_matrix, Tarr, vmrH2, vmrH2, - mmw, g) - #total tau - dtau = dtaumCH4 + dtaucH2H2 - F0 = art.run(dtau, Tarr) / norm - Frot = convolve_rigid_rotation(F0, vr_array, vsini, u1, u2) - mu = ipgauss_sampling(nusd, nu_grid, Frot, beta_inst, RV, vr_array) - return mu + + # molecule + xsmatrix = opa.xsmatrix(Tarr, art.pressure) + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) + + Rp2 = art.run(dtau, Tarr, mmw_arr, radius_btm, gravity_btm) + mu = sop.sampling(Rp2, RV, nusd) + + return jnp.sqrt(mu) * radius_btm / Rs import matplotlib.pyplot as plt -#g = gravity_jupiter(0.88, 33.2) -Rp = 0.88 -Mp = 33.2 -alpha = 0.1 + +# g = gravity_jupiter(0.88, 33.2) +Rp = 1.0 +Mp = 1.0 MMR_CH4 = 0.0059 -vsini = 20.0 RV = 10.0 -T0 = 1200.0 -u1 = 0.0 -u2 = 0.0 -Tarr = art.powerlaw_temperature(T0, alpha) -Ftest = frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini) - -Tarr = art.powerlaw_temperature(1500.0, alpha) -Ftest2 = frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini) - -plt.plot(nusd, nflux) -plt.plot(nusd, Ftest, ls="dashed") -plt.plot(nusd, Ftest2, ls="dotted") -plt.yscale("log") -plt.show() +T0 = 500.0 +radius_ratio_test = frun(T0, MMR_CH4, Mp, Rp, RV) +fig = plt.figure() +ax = fig.add_subplot(211) +plt.plot(nusd, radis_ratio_obs) +ax = fig.add_subplot(212) +plt.plot(nusd, radius_ratio_test, ls="dashed") +plt.savefig("spectrum_reverse.png") def model_c(y1): - Rp = numpyro.sample('Rp', dist.Uniform(0.4, 1.2)) - RV = numpyro.sample('RV', dist.Uniform(5.0, 15.0)) - MMR_CH4 = numpyro.sample('MMR_CH4', dist.Uniform(0.0, 0.015)) - T0 = numpyro.sample('T0', dist.Uniform(1000.0, 1500.0)) - alpha = numpyro.sample('alpha', dist.Uniform(0.05, 0.2)) - vsini = numpyro.sample('vsini', dist.Uniform(15.0, 25.0)) - u1 = 0.0 - u2 = 0.0 - Tarr = art.powerlaw_temperature(T0, alpha) - mu = frun(Tarr, MMR_CH4, Mp, Rp, u1, u2, RV, vsini) - numpyro.sample('y1', dist.Normal(mu, sigmain), obs=y1) + RV = numpyro.sample("RV", dist.Uniform(5.0, 15.0)) + Rp = numpyro.sample("Rp", dist.Uniform(0.8, 1.2)) + Mp = numpyro.sample("Mp", dist.Uniform(0.8, 1.2)) + MMR_CH4 = numpyro.sample("MMR_CH4", dist.Uniform(0.0, 0.01)) + T0 = numpyro.sample("T0", dist.Uniform(Tlow, Thigh)) + mu = frun(T0, MMR_CH4, Mp, Rp, RV) + numpyro.sample("y1", dist.Normal(mu, sigmain), obs=y1) rng_key = random.PRNGKey(0) rng_key, rng_key_ = random.split(rng_key) num_warmup, num_samples = 500, 1000 -#kernel = NUTS(model_c, forward_mode_differentiation=True) +# kernel = NUTS(model_c, forward_mode_differentiation=True) kernel = NUTS(model_c, forward_mode_differentiation=False) mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples) -mcmc.run(rng_key_, y1=nflux) +mcmc.run(rng_key_, y1=radis_ratio_obs) mcmc.print_summary() # SAMPLING posterior_sample = mcmc.get_samples() -pred = Predictive(model_c, posterior_sample, return_sites=['y1']) +pred = Predictive(model_c, posterior_sample, return_sites=["y1"]) predictions = pred(rng_key_, y1=None) -median_mu1 = jnp.median(predictions['y1'], axis=0) -hpdi_mu1 = hpdi(predictions['y1'], 0.9) +median_mu1 = jnp.median(predictions["y1"], axis=0) +hpdi_mu1 = hpdi(predictions["y1"], 0.9) # PLOT fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 6.0)) -ax.plot(wavd[::-1], median_mu1, color='C0') -ax.plot(wavd[::-1], nflux, '+', color='black', label='data') -ax.fill_between(wavd[::-1], - hpdi_mu1[0], - hpdi_mu1[1], - alpha=0.3, - interpolate=True, - color='C0', - label='90% area') -plt.xlabel('wavelength ($\AA$)', fontsize=16) +ax.plot(wavd[::-1], median_mu1, color="C0") +ax.plot(wavd[::-1], radis_ratio_obs, "+", color="black", label="data") +ax.fill_between( + wavd[::-1], + hpdi_mu1[0], + hpdi_mu1[1], + alpha=0.3, + interpolate=True, + color="C0", + label="90% area", +) +plt.xlabel("wavelength ($\AA$)", fontsize=16) plt.legend(fontsize=16) plt.tick_params(labelsize=16) plt.savefig("pred_diffmode" + str(diffmode) + ".png") plt.close() -pararr = ['Rp', 'T0', 'alpha', 'MMR_CH4', 'vsini', 'RV'] -arviz.plot_pair(arviz.from_numpyro(mcmc), - kind='kde', - divergences=False, - marginals=True) +pararr = ["Rp", "T0", "alpha", "MMR_CH4", "vsini", "RV"] +arviz.plot_pair(arviz.from_numpyro(mcmc), kind="kde", divergences=False, marginals=True) plt.savefig("corner_diffmode" + str(diffmode) + ".png") -#plt.show() +# plt.show() diff --git a/tests/integration/api/api_premodit_to_direct.py b/tests/integration/api/api_premodit_to_direct.py index 1fa3e9cf3..925728237 100644 --- a/tests/integration/api/api_premodit_to_direct.py +++ b/tests/integration/api/api_premodit_to_direct.py @@ -6,15 +6,90 @@ from exojax.utils.grids import wavenumber_grid from exojax.spec import api from exojax.spec import molinfo -from exojax.spec.lpf import auto_xsection from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural, line_strength_numpy -from exojax.spec.exomol import gamma_exomol from exojax.spec.opacalc import OpaPremodit, OpaDirect +import numpy as np +import matplotlib.pyplot as plt + from jax import config config.update("jax_enable_x64", True) -import numpy as np -import matplotlib.pyplot as plt + +from exojax.spec.lpf import xsvector, make_numatrix0 +import tqdm +def auto_xsection(nu, nu_lines, sigmaD, gammaL, Sij, memory_size=15.): + """computes cross section . + + Warning: + This is NOT auto-differentiable function. + + Args: + nu: wavenumber array + nu_lines: line center + sigmaD: sigma parameter in Doppler profile + gammaL: broadening coefficient in Lorentz profile + Sij: line strength + memory_size: memory size for numatrix0 (MB) + + Returns: + numpy.array: cross section (xsv) + + Example: + >>> from exojax.spec.lpf import auto_xsection + >>> from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural + >>> from exojax.spec import moldb + >>> import numpy as np + >>> nus=np.linspace(1000.0,10000.0,900000,dtype=np.float64) #cm-1 + >>> mdbCO=moldb.MdbHit('~/exojax/data/CO','05_hit12',nus) + >>> Mmol=28.010446441149536 # molecular weight + >>> Tfix=1000.0 # we assume T=1000K + >>> Pfix=1.e-3 # we compute P=1.e-3 bar + >>> Ppart=Pfix #partial pressure of CO. here we assume a 100% CO atmosphere. + >>> qt=mdbCO.qr_interp_lines(Tfix) + >>> Sij=SijT(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt) + >>> gammaL = gamma_hitran(Pfix,Tfix, Ppart, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self) + gamma_natural(mdbCO.A) + >>> sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) + >>> nu_lines=mdbCO.nu_lines + >>> xsv=auto_xsection(nus,nu_lines,sigmaD,gammaL,Sij,memory_size=30) + 100%|████████████████████████████████████████████████████| 456/456 [00:03<00:00, 80.59it/s] + """ + NL = len(nu_lines) + d = int(memory_size/(NL*4/1024./1024.)) + if d > 0: + Ni = int(len(nu)/d) + xsv = [] + for i in tqdm.tqdm(range(0, Ni+1)): + s = int(i*d) + e = int((i+1)*d) + e = min(e, len(nu)) + numatrix = make_numatrix0(nu[s:e], nu_lines, warning=False) + xsv = np.concatenate( + [xsv, xsvector(numatrix, sigmaD, gammaL, Sij)]) + else: + NP = int((NL*4/1024./1024.)/memory_size)+1 + d = int(memory_size/(int(NL/NP)*4/1024./1024.)) + Ni = int(len(nu)/d) + dd = int(NL/NP) + xsv = [] + for i in tqdm.tqdm(range(0, Ni+1)): + s = int(i*d) + e = int((i+1)*d) + e = min(e, len(nu)) + xsvtmp = np.zeros_like(nu[s:e]) + for j in range(0, NP+1): + ss = int(j*dd) + ee = int((j+1)*dd) + ee = min(ee, NL) + numatrix = make_numatrix0( + nu[s:e], nu_lines[ss:ee], warning=False) + xsvtmp = xsvtmp + \ + xsvector(numatrix, sigmaD[ss:ee], + gammaL[ss:ee], Sij[ss:ee]) + xsv = np.concatenate([xsv, xsvtmp]) + + return xsv + + nus, wav, res = wavenumber_grid(22980.0, 23030.0, @@ -30,7 +105,7 @@ Ppart = P * vmr Mmol = molinfo.molmass("CO") -logsij0 = np.log(mdb.line_strength_ref) +logsij0 = np.log(mdb.line_strength) sigmaD = doppler_sigma(mdb.nu_lines,T,Mmol) qt = mdb.qr_interp(mdb.isotope, T) gammaL = gamma_hitran(P,T, Ppart, mdb.n_air, mdb.gamma_air, mdb.gamma_self) + gamma_natural(mdb.A) @@ -48,7 +123,7 @@ opad = OpaDirect(mdb=mdb, nu_grid=np.array(nus)) -logsij0 = np.log(mdb.line_strength_ref) +logsij0 = np.log(mdb.line_strength) qt = mdb.qr_interp(mdb.isotope, T) Sij = line_strength(T,logsij0,mdb.nu_lines,mdb.elower,qt, mdb.Tref) #Sij = line_strength(T,logsij0,mdb.nu_lines,mdb.elower,qt) @@ -61,4 +136,7 @@ ax.plot(1.0e8/np.array(nus), opad.xsvector(T, P), c='C3',ls="dotted") ax.set_xlim(22985, 23025) ax.set_yscale('log') -plt.show() \ No newline at end of file +plt.show() + + + diff --git a/tests/integration/api/api_pytables_test.py b/tests/integration/api/api_pytables_test.py new file mode 100644 index 000000000..67adcabfb --- /dev/null +++ b/tests/integration/api/api_pytables_test.py @@ -0,0 +1,7 @@ + +from exojax.spec.api import MdbHitemp, MdbExomol + +mdb = MdbExomol("CO/12C-16O/Li2015", nurange=[4000.0, 4100.0], engine="vaex") +mdb = MdbExomol("CO/12C-16O/Li2015", nurange=[4000.0, 4100.0], engine="pytables") +Mdb_reduced = MdbHitemp("CO", nurange=[4000.0, 4100.0], isotope=1, elower_max=3300.0, engine="vaex") +Mdb_reduced = MdbHitemp("CO", nurange=[4000.0, 4100.0], isotope=1, elower_max=3300.0, engine="pytables") diff --git a/tests/integration/api/download_h2s.py b/tests/integration/api/download_h2s.py new file mode 100644 index 000000000..7616d6fc1 --- /dev/null +++ b/tests/integration/api/download_h2s.py @@ -0,0 +1,5 @@ +from exojax.spec.api import MdbExomol +from exojax.utils.grids import wavenumber_grid +nu_grid, wav, res = wavenumber_grid(22900,23000,10000, xsmode="premodit", unit="AA") +mdbH2S = MdbExomol(".database/H2S/1H2-32S/AYT2", nurange=nu_grid, gpu_transfer=False) +#mdbCO = MdbExomol(".database/CO/12C-16O/Li2015", nurange=nu_grid, gpu_transfer=False) \ No newline at end of file diff --git a/tests/integration/comparison/premodit/fig_elower_grid_error.py b/tests/integration/comparison/premodit/fig_elower_grid_error.py new file mode 100644 index 000000000..6e29d99c9 --- /dev/null +++ b/tests/integration/comparison/premodit/fig_elower_grid_error.py @@ -0,0 +1,63 @@ +from exojax.spec.lbderror import single_tilde_line_strength_zeroth +import matplotlib.pyplot as plt +import numpy as np + + +def make_fig(): + """ + Figure for the error of the line strength due to the grid of the lower energy level. + """ + ttyp, tref, tlist, x = compute_line_strength_err(dE=300.0) + ttyp, tref, tlist, x1 = compute_line_strength_err(dE=100.0) + + fig = plt.figure(figsize=(7, 3)) + plt.plot(1.0 / tlist, x, color="k", label="$\Delta E=300 \mathrm{cm}^{-1}$") + plt.plot( + 1.0 / tlist, + x1, + color="red", + ls="dashed", + label="$\Delta E=100 \mathrm{cm}^{-1}$", + ) + plt.ylim(-0.02, 0.03) + plt.axhline(0.0, color="k") + plt.axhline(0.01, color="gray", alpha=0.5) + plt.axhline(-0.01, color="gray", alpha=0.5) + plt.plot(1 / tref, 0.0, "o", color="green", alpha=0.5) + plt.plot(1 / ttyp, 0.0, "o", color="red", alpha=0.5) + plt.text( + 1 / tref, 0.0015, "$T_\mathrm{ref}$", color="green", alpha=1.0, fontsize=14 + ) + plt.text(1 / ttyp, 0.0015, "$T_\mathrm{wp}$", color="red", alpha=1.0, fontsize=14) + plt.xlabel("Temperature [K]", fontsize=14) + plt.ylabel("Line strength error", fontsize=14) + plt.legend() + plt.savefig("fig_elower_grid_error.png", bbox_inches="tight", pad_inches=0.1) + plt.show() + + +def compute_line_strength_err(dE=300.0): + """ + Compute the error of the line strength due to the grid of the lower energy level. + + Args: + dE (float, optional): The interval of the Egrid. Defaults to 300.0 cm-1. + + Returns: + _type_: _description_ + """ + p = 1.0 / 2.0 + ttyp = 1.0 / 1200.0 + tref = 1.0 / 500.0 + # tref = 1.0 / 1200. + # ttyp = 1.0 / 500.0 + + tlist = 1.0 / np.linspace(100, 2000, 100) + x = single_tilde_line_strength_zeroth(tlist, ttyp, tref, dE, p) + print(np.array([1 / tlist, x]).T) + + return ttyp, tref, tlist, x + + +if __name__ == "__main__": + make_fig() diff --git a/tests/integration/comparison/premodit/fig_tablexs_error.py b/tests/integration/comparison/premodit/fig_tablexs_error.py new file mode 100644 index 000000000..59abdeae1 --- /dev/null +++ b/tests/integration/comparison/premodit/fig_tablexs_error.py @@ -0,0 +1,98 @@ +""" +This code is a snippet to check the tabulated cross section error. +See Issue #491 for more details. +""" +import numpy as np +import matplotlib.pyplot as plt +import h5py + + +def read_petitRadtrans_highres_xs_data( + h5file="12C-16O__HITEMP.R1e6_0.3-28mu.xsec.petitRADTRANS.h5", +): + """ + Read the high resolution cross section data from petitRadtrans. + This code is a snippet to check the temperature grid of the pRT hihg-res cross section data. + + + Last check in June 10th in 2024 (HK). + 12C-16O__HITEMP.R1e6_0.3-28mu.xsec.petitRADTRANS.h5 is the file used. 4.8GB in size (0.3-28micron). + Temeprature grid = [ 81.14113605 109.60677358 148.0586223 200. 270.16327371 + 364.9409723 492.96823893 665.90956631 899.52154213 1215.08842295 + 1641.36133093 2217.17775249 2995. ] indicating delta log10t = 0.13059631 + Pressure Grid = [1.e-06 1.e-05 1.e-04 1.e-03 1.e-02 1.e-01 1.e+00 1.e+01 1.e+02 1.e+03] + + """ + with h5py.File(h5file, "r") as f: + print(f.keys()) + p = f["p"][:] + t = f["t"][:] + print(t) + print(p) + log10t = np.log10(t) + print(log10t[1:] - log10t[:-1]) + + +def make_fig_tabulate_crosssection_error( + p0=1.0e0, p1=1.0e1, t0=899.52154213, t1=1215.08842295, output="fig_tablexs_error.png" +): + from exojax.utils.grids import wavenumber_grid + + nu, wav, res = wavenumber_grid(22910, 22960, 2000, xsmode="lpf", unit="AA") + + #tc = 10 ** ((np.log10(t1) + np.log10(t0)) / 2.0) #log interpolation + tc = (t1 + t0) / 2.0 #linear interpolation + pc = 10 ** ((np.log10(p1) + np.log10(p0)) / 2.0) + + print(p0,p1,pc) + print(t0,t1,tc) + + from exojax.spec.api import MdbHitemp + from exojax.spec.opacalc import OpaDirect + + mdb_co = MdbHitemp("CO", nurange=[nu[0], nu[-1]]) + mdb_h2o = MdbHitemp("H2O", nurange=[nu[0], nu[-1]]) + + f, (ax, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1]}) + cp = ["C0", "C1"] + mol = ["H2O", "CO"] + lwi = [1.0, 0.5] + for i, mdb in enumerate([mdb_h2o, mdb_co]): + opa = OpaDirect(mdb, nu) + xs = opa.xsvector(tc, pc) + xs00 = opa.xsvector(t0, p0) + xs11 = opa.xsvector(t1, p1) + xs01 = opa.xsvector(t0, p1) + xs10 = opa.xsvector(t1, p0) + + #avexs = (xs00 + xs11) / 2.0 + avexs = (xs00 + xs10 + xs01 + xs11) / 4.0 + ax.plot(nu, xs, color=cp[i], label="direct (" + mol[i] + ")", lw=lwi[i]) + ax.plot( + nu, + avexs, + color=cp[i], + ls="dashed", + label="grid interpolation " + "(" + mol[i] + ")", + lw=lwi[i], + ) + ax2.plot(nu, avexs / xs - 1.0, color=cp[i], label=mol[i], lw=lwi[i]) + ax.legend() + ax2.set_ylim(-0.7,0.7) + ax2.legend() + ax2.axhline(0.0, color="k") + ax.set_ylabel("cross section [cm2]") + ax2.set_xlabel("wavenumber [cm-1]") + ax2.set_ylabel("relative error") + plt.tight_layout() + plt.savefig(output, bbox_inches="tight", pad_inches=0.1) + #plt.show() + + + +if __name__ == "__main__": + # read_petitRadtrans_highres_xs_data() + make_fig_tabulate_crosssection_error(p0=1.0e0, p1=1.0e1, t0=899.52154213, t1=899.52154213, output="fig_tablexs_error_difp.png") + make_fig_tabulate_crosssection_error(p0=1.0e0, p1=1.0e0, t0=899.52154213, t1=1215.08842295, output="fig_tablexs_error_dift.png") + make_fig_tabulate_crosssection_error(p0=1.0e0, p1=1.0e1, t0=899.52154213, t1=1215.08842295, output="fig_tablexs_error.png") + \ No newline at end of file diff --git a/tests/integration/comparison/twostream/comparison_petitRADTRANS_CIA.py b/tests/integration/comparison/twostream/comparison_petitRADTRANS_CIA.py index 7b90331c9..af8b95567 100644 --- a/tests/integration/comparison/twostream/comparison_petitRADTRANS_CIA.py +++ b/tests/integration/comparison/twostream/comparison_petitRADTRANS_CIA.py @@ -13,9 +13,11 @@ from exojax.spec.layeropacity import layer_optical_depth, layer_optical_depth_CIA from exojax.spec.atmrt import ArtEmisPure -from petitRADTRANS import Radtrans +from petitRADTRANS.radtrans import Radtrans from exojax.spec import molinfo -import petitRADTRANS.nat_cst as nc +from petitRADTRANS import physical_constants as cst +from petitRADTRANS.config import petitradtrans_config_parser +petitradtrans_config_parser.set_input_data_path(r'~/database/petitRADTRANS/input_data') from exojax.utils.instfunc import resolution_to_gaussian_std from exojax.utils.grids import velocity_grid @@ -75,7 +77,7 @@ def frun(T0, alpha, logg, logvmr): nlayer=200, nu_grid=nus[k], rtsolver="ibased", - nstream=8) + nstream=6) dtaum = [] for i in range(len(mul.masked_molmulti[k])): @@ -110,15 +112,15 @@ def frun(T0, alpha, logg, logvmr): def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): - atmosphere = Radtrans(line_species = mols, - continuum_opacities = ['H2-H2', 'H2-He'], - wlen_bords_micron = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], - mode = 'lbl') + radtrans = Radtrans(pressures = np.logspace(-3, 2, 200), + line_species = mols, + gas_continuum_contributors = ['H2-H2', 'H2-He'], + wavelength_boundaries = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], + line_opacity_mode = 'lbl') - pressures = np.logspace(-3, 2, 200) - atmosphere.setup_opa_structure(pressures) - temperature = T0*(pressures)**alpha - temperature = np.clip(temperature, 500, None) + pressures_bar = radtrans.pressures * 1e-6 + temperatures = T0*(pressures_bar)**alpha + temperatures = np.clip(temperatures, 500, None) molmass_list = [] @@ -136,21 +138,24 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): mmrHe = vmrHe * molmassHe / mmw mass_fractions = {} - mass_fractions['H2'] = mmrH2 * np.ones_like(temperature) - mass_fractions['He'] = mmrHe * np.ones_like(temperature) + mass_fractions['H2'] = mmrH2 * np.ones_like(temperatures) + mass_fractions['He'] = mmrHe * np.ones_like(temperatures) for i in range(len(mols)): - mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperature) + mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperatures) - MMW = mmw * np.ones_like(temperature) - gravity = 1e1**(logg) + mean_molar_masses = mmw * np.ones_like(temperatures) + reference_gravity = 1e1**(logg) - atmosphere.calc_flux(temperature, mass_fractions, gravity, MMW) + frequencies, flux, _ = radtrans.calculate_flux(temperatures=temperatures, + mass_fractions=mass_fractions, + mean_molar_masses=mean_molar_masses, + reference_gravity=reference_gravity, + frequencies_to_wavelengths=False) - ld = nc.c/atmosphere.freq/1e-4 # [um] + ld = cst.c/frequencies/1e-4 # [um] nus = 1.0e4 / ld # [cm^{-1}] - f = atmosphere.flux #[erg cm^{-2} s^{-1} Hz^{-1}] - f = f * nc.c #[erg/s/cm^2/cm^{-1}] + f = flux * cst.c #[erg cm^{-2} s^{-1} Hz^{-1}] => [erg/s/cm^2/cm^{-1}] nus = nus[::-1] f = f[::-1] @@ -163,8 +168,8 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): ld_min = 23000. ld_max = 24000. mols_exojax = ['CO'] -db_exojax = ['ExoMol'] -mols_petit = ['CO_all_iso'] +db_exojax = ['HITEMP'] +mols_petit = ['CO'] T0 = 995.56 alpha = 0.09 diff --git a/tests/integration/comparison/twostream/comparison_petitRADTRANS_narrow_R70000.py b/tests/integration/comparison/twostream/comparison_petitRADTRANS_narrow_R70000.py index 3878b9dc7..0eed43af4 100644 --- a/tests/integration/comparison/twostream/comparison_petitRADTRANS_narrow_R70000.py +++ b/tests/integration/comparison/twostream/comparison_petitRADTRANS_narrow_R70000.py @@ -13,9 +13,11 @@ from exojax.spec.layeropacity import layer_optical_depth, layer_optical_depth_CIA from exojax.spec.atmrt import ArtEmisPure -from petitRADTRANS import Radtrans +from petitRADTRANS.radtrans import Radtrans from exojax.spec import molinfo -import petitRADTRANS.nat_cst as nc +from petitRADTRANS import physical_constants as cst +from petitRADTRANS.config import petitradtrans_config_parser +petitradtrans_config_parser.set_input_data_path(r'~/database/petitRADTRANS/input_data') from exojax.utils.instfunc import resolution_to_gaussian_std from exojax.utils.grids import velocity_grid @@ -75,7 +77,7 @@ def frun(T0, alpha, logg, logvmr): nlayer=200, nu_grid=nus[k], rtsolver="ibased", - nstream=8) + nstream=6) dtaum = [] for i in range(len(mul.masked_molmulti[k])): @@ -110,15 +112,15 @@ def frun(T0, alpha, logg, logvmr): def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): - atmosphere = Radtrans(line_species = mols, - continuum_opacities = ['H2-H2', 'H2-He'], - wlen_bords_micron = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], - mode = 'lbl') + radtrans = Radtrans(pressures = np.logspace(-3, 2, 200), + line_species = mols, + gas_continuum_contributors = ['H2-H2', 'H2-He'], + wavelength_boundaries = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], + line_opacity_mode = 'lbl') - pressures = np.logspace(-3, 2, 200) - atmosphere.setup_opa_structure(pressures) - temperature = T0*(pressures)**alpha - temperature = np.clip(temperature, 500, None) + pressures_bar = radtrans.pressures * 1e-6 + temperatures = T0*(pressures_bar)**alpha + temperatures = np.clip(temperatures, 500, None) molmass_list = [] @@ -136,21 +138,24 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): mmrHe = vmrHe * molmassHe / mmw mass_fractions = {} - mass_fractions['H2'] = mmrH2 * np.ones_like(temperature) - mass_fractions['He'] = mmrHe * np.ones_like(temperature) + mass_fractions['H2'] = mmrH2 * np.ones_like(temperatures) + mass_fractions['He'] = mmrHe * np.ones_like(temperatures) for i in range(len(mols)): - mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperature) + mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperatures) - MMW = mmw * np.ones_like(temperature) - gravity = 1e1**(logg) + mean_molar_masses = mmw * np.ones_like(temperatures) + reference_gravity = 1e1**(logg) - atmosphere.calc_flux(temperature, mass_fractions, gravity, MMW) + frequencies, flux, _ = radtrans.calculate_flux(temperatures=temperatures, + mass_fractions=mass_fractions, + mean_molar_masses=mean_molar_masses, + reference_gravity=reference_gravity, + frequencies_to_wavelengths=False) - ld = nc.c/atmosphere.freq/1e-4 # [um] + ld = cst.c/frequencies/1e-4 # [um] nus = 1.0e4 / ld # [cm^{-1}] - f = atmosphere.flux #[erg cm^{-2} s^{-1} Hz^{-1}] - f = f * nc.c #[erg/s/cm^2/cm^{-1}] + f = flux * cst.c #[erg cm^{-2} s^{-1} Hz^{-1}] => [erg/s/cm^2/cm^{-1}] nus = nus[::-1] f = f[::-1] @@ -164,7 +169,7 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): ld_max = 14700. mols_exojax = ['H2O'] db_exojax = ['ExoMol'] -mols_petit = ['H2O_main_iso'] +mols_petit = ['1H2-16O__POKAZATEL'] T0 = 995.56 alpha = 0.09 diff --git a/tests/integration/comparison/twostream/comparison_petitRADTRANS_wide_R7000.py b/tests/integration/comparison/twostream/comparison_petitRADTRANS_wide_R7000.py index adb4b46ce..b516a8772 100644 --- a/tests/integration/comparison/twostream/comparison_petitRADTRANS_wide_R7000.py +++ b/tests/integration/comparison/twostream/comparison_petitRADTRANS_wide_R7000.py @@ -13,9 +13,11 @@ from exojax.spec.layeropacity import layer_optical_depth, layer_optical_depth_CIA from exojax.spec.atmrt import ArtEmisPure -from petitRADTRANS import Radtrans +from petitRADTRANS.radtrans import Radtrans from exojax.spec import molinfo -import petitRADTRANS.nat_cst as nc +from petitRADTRANS import physical_constants as cst +from petitRADTRANS.config import petitradtrans_config_parser +petitradtrans_config_parser.set_input_data_path(r'~/database/petitRADTRANS/input_data') from exojax.utils.instfunc import resolution_to_gaussian_std from exojax.utils.grids import velocity_grid @@ -75,7 +77,7 @@ def frun(T0, alpha, logg, logvmr): nlayer=200, nu_grid=nus[k], rtsolver="ibased", - nstream=8) + nstream=6) dtaum = [] for i in range(len(mul.masked_molmulti[k])): @@ -110,15 +112,15 @@ def frun(T0, alpha, logg, logvmr): def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): - atmosphere = Radtrans(line_species = mols, - continuum_opacities = ['H2-H2', 'H2-He'], - wlen_bords_micron = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], - mode = 'lbl') + radtrans = Radtrans(pressures = np.logspace(-3, 2, 200), + line_species = mols, + gas_continuum_contributors = ['H2-H2', 'H2-He'], + wavelength_boundaries = [(ld_min - 5.)*1e-4, (ld_max + 5.)*1e-4], + line_opacity_mode = 'lbl') - pressures = np.logspace(-3, 2, 200) - atmosphere.setup_opa_structure(pressures) - temperature = T0*(pressures)**alpha - temperature = np.clip(temperature, 500, None) + pressures_bar = radtrans.pressures * 1e-6 + temperatures = T0*(pressures_bar)**alpha + temperatures = np.clip(temperatures, 500, None) molmass_list = [] @@ -136,21 +138,24 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): mmrHe = vmrHe * molmassHe / mmw mass_fractions = {} - mass_fractions['H2'] = mmrH2 * np.ones_like(temperature) - mass_fractions['He'] = mmrHe * np.ones_like(temperature) + mass_fractions['H2'] = mmrH2 * np.ones_like(temperatures) + mass_fractions['He'] = mmrHe * np.ones_like(temperatures) for i in range(len(mols)): - mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperature) + mass_fractions[mols[i]] = mmr[i] * np.ones_like(temperatures) - MMW = mmw * np.ones_like(temperature) - gravity = 1e1**(logg) + mean_molar_masses = mmw * np.ones_like(temperatures) + reference_gravity = 1e1**(logg) - atmosphere.calc_flux(temperature, mass_fractions, gravity, MMW) + frequencies, flux, _ = radtrans.calculate_flux(temperatures=temperatures, + mass_fractions=mass_fractions, + mean_molar_masses=mean_molar_masses, + reference_gravity=reference_gravity, + frequencies_to_wavelengths=False) - ld = nc.c/atmosphere.freq/1e-4 # [um] + ld = cst.c/frequencies/1e-4 # [um] nus = 1.0e4 / ld # [cm^{-1}] - f = atmosphere.flux #[erg cm^{-2} s^{-1} Hz^{-1}] - f = f * nc.c #[erg/s/cm^2/cm^{-1}] + f = flux * cst.c #[erg cm^{-2} s^{-1} Hz^{-1}] => [erg/s/cm^2/cm^{-1}] nus = nus[::-1] f = f[::-1] @@ -164,7 +169,7 @@ def run_petit(ld_min, ld_max, mols, mols_exojax, T0, alpha, logg, logvmr): ld_max = 16500. mols_exojax = ['H2O', 'CH4'] db_exojax = ['ExoMol', 'HITEMP'] -mols_petit = ['H2O_main_iso', 'CH4_Hargreaves_main_iso'] +mols_petit = ['1H2-16O__POKAZATEL', '12C-1H4__Hargreaves'] T0 = 995.56 alpha = 0.09 diff --git a/tests/integration/modit_scanfft/modit_scanfft_test.py b/tests/integration/modit_scanfft/modit_scanfft_test.py index ba662a1ae..371651218 100644 --- a/tests/integration/modit_scanfft/modit_scanfft_test.py +++ b/tests/integration/modit_scanfft/modit_scanfft_test.py @@ -20,34 +20,40 @@ def test_xs_exomol(): mdbCO = mock_mdbExomol() Tfix = 1200.0 Pfix = 1.0 - #Mmol = molmass_isotope("CO") + # Mmol = molmass_isotope("CO") Mmol = mdbCO.molmass - + cont_nu, index_nu, R, pmarray = init_modit(mdbCO.nu_lines, nus) qt = mdbCO.qr_interp(Tfix) - gammaL = gamma_exomol(Pfix, Tfix, mdbCO.n_Texp, - mdbCO.alpha_ref) + gamma_natural(mdbCO.A) + gammaL = gamma_exomol(Pfix, Tfix, mdbCO.n_Texp, mdbCO.alpha_ref) + gamma_natural( + mdbCO.A + ) dv_lines = mdbCO.nu_lines / R ngammaL = gammaL / dv_lines nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R) - Sij = line_strength(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref) + Sij = line_strength( + Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref + ) ngammaL_grid = ditgrid_log_interval(ngammaL, dit_grid_resolution=0.1) - xsv = xsvector_scanfft(cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, nus, - ngammaL_grid) + xsv = xsvector_scanfft( + cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, nus, ngammaL_grid + ) filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + TESTDATA_CO_EXOMOL_MODIT_XS_REF) + "exojax", "data/testdata/" + TESTDATA_CO_EXOMOL_MODIT_XS_REF + ) dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) assert np.all(xsv == pytest.approx(dat["xsv"].values)) def test_rt_exomol(): from jax import config + config.update("jax_enable_x64", True) import jax.numpy as jnp - from exojax.spec import rtransfer as rt - from exojax.spec import molinfo + from exojax.atm.atmprof import pressure_layer_logspace + from exojax.spec.modit import exomol from exojax.spec.modit_scanfft import xsmatrix_scanfft from exojax.spec.layeropacity import layer_optical_depth @@ -55,43 +61,49 @@ def test_rt_exomol(): from exojax.spec.planck import piBarr from exojax.spec.modit import set_ditgrid_matrix_exomol from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF - + nus, wav, res = mock_wavenumber_grid() mdb = mock_mdbExomol() - Parr, dParr, k = rt.pressure_layer(NP=100, numpy = True) + Parr, dParr, k = pressure_layer_logspace(NP=100, numpy=True) T0_in = 1300.0 alpha_in = 0.1 - Tarr = T0_in * (Parr)**alpha_in - Tarr[Tarr<400.0] = 400.0 #lower limit - Tarr[Tarr>1500.0] = 1500.0 #upper limit - + Tarr = T0_in * (Parr) ** alpha_in + Tarr[Tarr < 400.0] = 400.0 # lower limit + Tarr[Tarr > 1500.0] = 1500.0 # upper limit + molmass = mdb.molmass MMR = 0.1 cont_nu, index_nu, R, pmarray = init_modit(mdb.nu_lines, nus) - def fT(T0, alpha): return T0[:, None]*(Parr[None, :])**alpha[:, None] + def fT(T0, alpha): + return T0[:, None] * (Parr[None, :]) ** alpha[:, None] + dgm_ngammaL = set_ditgrid_matrix_exomol( - mdb, fT, Parr, R, molmass, 0.2, np.array([T0_in]), np.array([alpha_in])) - + mdb, fT, Parr, R, molmass, 0.2, np.array([T0_in]), np.array([alpha_in]) + ) + g = 2478.57 SijM, ngammaLM, nsigmaDl = exomol(mdb, Tarr, Parr, R, molmass) - xsm = xsmatrix_scanfft(cont_nu, index_nu, R, pmarray, nsigmaDl, ngammaLM, SijM, - nus, dgm_ngammaL) - dtau = layer_optical_depth(dParr, jnp.abs(xsm), MMR * np.ones_like(Parr), molmass, g) + xsm = xsmatrix_scanfft( + cont_nu, index_nu, R, pmarray, nsigmaDl, ngammaLM, SijM, nus, dgm_ngammaL + ) + dtau = layer_optical_depth( + dParr, jnp.abs(xsm), MMR * np.ones_like(Parr), molmass, g + ) sourcef = piBarr(Tarr, nus) F0 = rtrun_emis_pureabs_fbased2st(dtau, sourcef) filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF) + "exojax", "data/testdata/" + TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF + ) dat = pd.read_csv(filename, delimiter=",", names=("nus", "flux")) - - + # The reference data was generated by # # >>> np.savetxt("modit_rt_test_ref.txt",np.array([nus,F0]).T,delimiter=",") # - residual = np.abs(F0/dat["flux"].values - 1.0) + residual = np.abs(F0 / dat["flux"].values - 1.0) print(np.max(residual)) - assert np.all(residual < 1.e-4) + assert np.all(residual < 1.0e-4) return F0 @@ -104,39 +116,51 @@ def test_rt_vald(): from exojax.spec.modit_scanfft import xsmatrix_vald_scanfft from exojax.spec.planck import piBarr from exojax.test.data import TESTDATA_VALD_MODIT_EMISSION_REF + from exojax.atm.atmprof import pressure_layer_logspace + mdb = mock_mdbExomol() - + adb = mock_mdbVALD() asdb = moldb.AdbSepVald(adb) - Parr, dParr, k = rt.pressure_layer(NP=100) + Parr, dParr, k = pressure_layer_logspace(NP=100) T0_in = 3000.0 alpha_in = 0.1 - Tarr = T0_in * (Parr)**alpha_in + Tarr = T0_in * (Parr) ** alpha_in - nus, wav, res = wavenumber_grid(15030.0, - 15045.0, - 2000, - unit='AA', - xsmode="modit") + nus, wav, res = wavenumber_grid(15030.0, 15045.0, 2000, unit="AA", xsmode="modit") cnuS, indexnuS, R, pmarray = init_modit_vald(asdb.nu_lines, nus, asdb.N_usp) - def fT(T0, alpha): return T0[:, None]*(Parr[None, :])**alpha[:, None] - H_He_HH_VMR_ref = [0.0, 0.16, 0.84]#[0.1, 0.15, 0.75] - PH_ref = Parr* H_He_HH_VMR_ref[0] - PHe_ref = Parr* H_He_HH_VMR_ref[1] - PHH_ref = Parr* H_He_HH_VMR_ref[2] - dgm_ngammaL_VALD = set_ditgrid_matrix_vald_all(asdb, PH_ref, PHe_ref, PHH_ref, R, fT, 0.2, np.array([T0_in]), np.array([alpha_in])) + def fT(T0, alpha): + return T0[:, None] * (Parr[None, :]) ** alpha[:, None] + + H_He_HH_VMR_ref = [0.0, 0.16, 0.84] # [0.1, 0.15, 0.75] + PH_ref = Parr * H_He_HH_VMR_ref[0] + PHe_ref = Parr * H_He_HH_VMR_ref[1] + PHH_ref = Parr * H_He_HH_VMR_ref[2] + dgm_ngammaL_VALD = set_ditgrid_matrix_vald_all( + asdb, + PH_ref, + PHe_ref, + PHH_ref, + R, + fT, + 0.2, + np.array([T0_in]), + np.array([alpha_in]), + ) g = 1e5 mmw = 2.33 - ONEARR=np.ones_like(Parr) - VMR_uspecies = atomll.get_VMR_uspecies(asdb.uspecies)[:, None]*ONEARR + ONEARR = np.ones_like(Parr) + VMR_uspecies = atomll.get_VMR_uspecies(asdb.uspecies)[:, None] * ONEARR SijMS, ngammaLMS, nsigmaDlS = vald_all(asdb, Tarr, PH_ref, PHe_ref, PHH_ref, R) - #xsmS = xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD) - xsmS = xsmatrix_vald_scanfft(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD) - dtauatom = rt.dtauVALD(dParr, xsmS, VMR_uspecies, mmw*ONEARR, g) + # xsmS = xsmatrix_vald(cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD) + xsmS = xsmatrix_vald_scanfft( + cnuS, indexnuS, R, pmarray, nsigmaDlS, ngammaLMS, SijMS, nus, dgm_ngammaL_VALD + ) + dtauatom = rt.dtauVALD(dParr, xsmS, VMR_uspecies, mmw * ONEARR, g) sourcef = piBarr(Tarr, nus) F0 = rt.rtrun_emis_pureabs_fbased2st(dtauatom, sourcef) @@ -145,19 +169,20 @@ def fT(T0, alpha): return T0[:, None]*(Parr[None, :])**alpha[:, None] # >>> np.savetxt("modit_rt_test_vald_ref.txt",np.array([nus,F0]).T,delimiter=",") # filename = pkg_resources.resource_filename( - 'exojax', 'data/testdata/' + TESTDATA_VALD_MODIT_EMISSION_REF) + "exojax", "data/testdata/" + TESTDATA_VALD_MODIT_EMISSION_REF + ) ref = pd.read_csv(filename, delimiter=",", names=("nus", "flux")) - residual = np.abs(F0/ref["flux"].values - 1.0) + residual = np.abs(F0 / ref["flux"].values - 1.0) print(np.max(residual)) # Need to regenerate TESTDATA_VALD_MODIT_EMISSION_REF because we modified the layer definition of RT - #assert np.all(residual < 0.03) + # assert np.all(residual < 0.03) return F0 - - + + if __name__ == "__main__": test_xs_exomol() test_rt_exomol() - #test_rt_vald() + # test_rt_vald() diff --git a/tests/integration/moldb/mdbexomol_test.py b/tests/integration/moldb/mdbexomol_test.py index 4d5ecbdcd..324cd4241 100644 --- a/tests/integration/moldb/mdbexomol_test.py +++ b/tests/integration/moldb/mdbexomol_test.py @@ -1,6 +1,6 @@ import pytest from exojax.spec.api import MdbExomol - +from exojax.utils.constants import Tref_original def test_moldb_exomol(): mdb = MdbExomol(".database/CO/12C-16O/Li2015", @@ -13,8 +13,10 @@ def test_moldb_exomol_interp(): nurange=[4200.0, 4300.0], crit=1.e-30) T = 1000.0 - qr = mdb.qr_interp(T) - print(qr) + qr = mdb.qr_interp(T,Tref_original) + ref = 3.5402875 + + assert qr == pytest.approx(ref) if __name__ == "__main__": test_moldb_exomol() diff --git a/tests/integration/moldb/quantum_states_filter_exomol.py b/tests/integration/moldb/quantum_states_filter_exomol.py index 9b5e1aef9..937ac389e 100644 --- a/tests/integration/moldb/quantum_states_filter_exomol.py +++ b/tests/integration/moldb/quantum_states_filter_exomol.py @@ -49,7 +49,7 @@ mdb.activate(mdb.df, load_mask) plt.plot(1.e4 / mdb.nu_lines, - mdb.line_strength_ref, + mdb.line_strength, "+", color="black", label="activated lines") diff --git a/tests/integration/moldb/quantum_states_filter_hitemp.py b/tests/integration/moldb/quantum_states_filter_hitemp.py index 37832f2c2..d2875a5bd 100644 --- a/tests/integration/moldb/quantum_states_filter_hitemp.py +++ b/tests/integration/moldb/quantum_states_filter_hitemp.py @@ -24,7 +24,7 @@ load_mask = (mdb.df["vu"] - mdb.df["vl"] == 3) mdb.activate(mdb.df, load_mask) plt.plot(1.e4 / mdb.nu_lines, - mdb.line_strength_ref, + mdb.line_strength, "+", color="black", label="activated lines") diff --git a/tests/integration/moldb/quantum_states_filter_hitran_co.py b/tests/integration/moldb/quantum_states_filter_hitran_co.py index 4986327e3..6514e1fc8 100644 --- a/tests/integration/moldb/quantum_states_filter_hitran_co.py +++ b/tests/integration/moldb/quantum_states_filter_hitran_co.py @@ -30,7 +30,7 @@ load_mask = (mdb.df["vu"] - mdb.df["vl"] == 2) mdb.activate(mdb.df, load_mask) plt.plot(1.e4 / mdb.nu_lines, - mdb.line_strength_ref, + mdb.line_strength, "+", color="black", label="activated lines") diff --git a/tests/integration/opacalc/opacalc_test.py b/tests/integration/opacalc/opacalc_test.py index 213f86073..d779b6cde 100644 --- a/tests/integration/opacalc/opacalc_test.py +++ b/tests/integration/opacalc/opacalc_test.py @@ -1,14 +1,13 @@ """ Notes: - These tests are classified as the integration test because it - sometimes ends up jaxlib.xla_extension.XlaRuntimeError: RESOURCE_EXAUST due to limited resources. + These tests are classified as the integration test because it + sometimes ends up jaxlib.xla_extension.XlaRuntimeError: RESOURCE_EXAUST due to limited resources. """ import pytest from exojax.test.emulate_mdb import mock_wavenumber_grid from exojax.test.emulate_mdb import mock_mdb - from exojax.spec.opacalc import OpaPremodit from exojax.spec.opacalc import OpaModit from exojax.spec.opacalc import OpaDirect diff --git a/tests/integration/premodit/checkindex.py b/tests/integration/premodit/checkindex.py index 842bbd703..4a7637bd3 100644 --- a/tests/integration/premodit/checkindex.py +++ b/tests/integration/premodit/checkindex.py @@ -29,7 +29,7 @@ mdbH2O_orig.elower, mdbH2O_orig.alpha_ref, mdbH2O_orig.n_Texp, - mdbH2O_orig.line_strength_ref, + mdbH2O_orig.line_strength, Twt=Tgue, Tref=1000.0, Tref_broadening=1000.0, diff --git a/tests/integration/premodit/line_strength_comparison_exomol.py b/tests/integration/premodit/line_strength_comparison_exomol.py index 7d9510fdf..e4b73d867 100644 --- a/tests/integration/premodit/line_strength_comparison_exomol.py +++ b/tests/integration/premodit/line_strength_comparison_exomol.py @@ -35,7 +35,7 @@ unit='AA', xsmode="premodit") -diffmode = 2 +diffmode = 0 Ttest = 1200.0 P = 1.0 @@ -52,7 +52,7 @@ xsv = opa.xsvector(Ttest,P) #tries manual computation of xsvector below -qt = mdb.qr_interp(Ttest) +qt = mdb.qr_interp(Ttest, opa.Tref) dE = opa.dE NE = len(elower_grid) @@ -80,8 +80,8 @@ from exojax.spec.modit import xsvector from exojax.spec.initspec import init_modit -mdb.change_reference_temperature(Tref_original) -qt = mdb.qr_interp(Ttest) +#mdb.change_reference_temperature(Tref_original) +qt = mdb.qr_interp(Ttest, Tref_original) cont, index, R, pmarray = initspec.init_modit(mdb.nu_lines, nus) Sij = line_strength(Ttest, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, mdb.Tref) gammaL = gamma_exomol(P, Ttest, mdb.n_Texp, mdb.alpha_ref) diff --git a/tests/integration/premodit/line_strength_comparison_exomol_water.py b/tests/integration/premodit/line_strength_comparison_exomol_water.py index 43f05098d..e56c5635f 100644 --- a/tests/integration/premodit/line_strength_comparison_exomol_water.py +++ b/tests/integration/premodit/line_strength_comparison_exomol_water.py @@ -49,7 +49,7 @@ lbd_coeff, multi_index_uniqgrid, elower_grid, \ ngamma_ref_grid, n_Texp_grid, R, pmarray = opa.opainfo -qt = mdb.qr_interp(T) +qt = mdb.qr_interp(T, opa.Tref) dE = opa.dE NE = len(elower_grid) @@ -67,10 +67,9 @@ # We need to revert the reference temperature to 296K to reuse mdb for MODIT #=========================================================================== -mdb.change_reference_temperature(Tref_original) -qt = mdb.qr_interp(T) +qt = mdb.qr_interp(T, Tref_original) cont, index, R, pmarray = initspec.init_modit(mdb.nu_lines, nus) -Sij = line_strength(T, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, mdb.Tref) +Sij = line_strength(T, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, Tref_original) gammaL = gamma_exomol(P, T, mdb.n_Texp, mdb.alpha_ref) dv_lines = mdb.nu_lines / R ngammaL = gammaL / dv_lines diff --git a/tests/integration/premodit/line_strength_comparison_hitemp.py b/tests/integration/premodit/line_strength_comparison_hitemp.py index 79975cc24..55e470fc6 100644 --- a/tests/integration/premodit/line_strength_comparison_hitemp.py +++ b/tests/integration/premodit/line_strength_comparison_hitemp.py @@ -54,7 +54,7 @@ xsv = opa.xsvector(Ttest,P) #tries manual computation of xsvector below -qt = mdb.qr_interp(mdb.isotope, Ttest) +qt = mdb.qr_interp(mdb.isotope, Ttest, opa.Tref) dE = opa.dE NE = len(elower_grid) @@ -82,10 +82,9 @@ from exojax.spec.modit import xsvector from exojax.spec.initspec import init_modit -mdb.change_reference_temperature(Tref_original) -qt = mdb.qr_interp(mdb.isotope, Ttest) +qt = mdb.qr_interp(mdb.isotope, Ttest, Tref_original) cont, index, R, pmarray = initspec.init_modit(mdb.nu_lines, nus) -Sij = line_strength(Ttest, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, mdb.Tref) +Sij = line_strength(Ttest, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, Tref_original) gammaL = gamma_hitran(P, Ttest, 0.0, mdb.n_air, mdb.gamma_air, mdb.gamma_self) + gamma_natural(mdb.A) @@ -99,7 +98,7 @@ Smodit = (np.sum(Slsd_modit, axis=1)) ## also, xs -Sij = line_strength(Ttest, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, mdb.Tref) +Sij = line_strength(Ttest, mdb.logsij0, mdb.nu_lines, mdb.elower, qt, Tref_original) cont_nu, index_nu, R, pmarray = init_modit(mdb.nu_lines, nus) ngammaL_grid = ditgrid_log_interval(ngammaL, dit_grid_resolution=0.1) xsv_modit = xsvector(cont_nu, index_nu, R, pmarray, nsigmaD, ngammaL, Sij, nus, diff --git a/tests/integration/premodit_lpf/CH4_Gascellmodel_2408rev_test.py b/tests/integration/premodit_lpf/CH4_Gascellmodel_2408rev_test.py new file mode 100644 index 000000000..4001ba0ff --- /dev/null +++ b/tests/integration/premodit_lpf/CH4_Gascellmodel_2408rev_test.py @@ -0,0 +1,262 @@ +# multi wavelength range +# Create the model data of absorption separating each lines +# 2024/08/29 created based on "CH4Gascellmodel_repeat_2407rev.py" +##opapremodit, gridboost + +from exojax.utils.grids import wavenumber_grid +from exojax.spec.api import MdbHitemp +from exojax.spec.hitran import line_strength +from exojax.spec.specop import SopInstProfile +from Trans_model_1Voigt_HITEMP_nu_2408rev_test import ( + Trans_model_MultiVoigt_test, + create_mdbs_multi, + calc_dnumber_isobaric, +) +import jax.numpy as jnp +import numpy as np +from exojax.utils.constants import Tref_original +import matplotlib.collections as mc +from jax import config + +config.update("jax_enable_x64", True) +import matplotlib.pyplot as plt +import matplotlib.cm as cm + + +# Calculate the Line strength S(T) +def S_Tcalc(nu, S_0, T): + logeS_0 = jnp.log(S_0) + qr = mdb.qr_interp_lines(T, Tref_original) + return line_strength(T, logeS_0, nu, mdb.elower, qr, Tref_original) + + +wmin = 1621.78 +wspan = 0.15 +adjustrange = 0.2 +Resolution = 0.0025 +nu_offset = 0.00 +Twt = 1000 +gridboost = 10 + +# model spectra setting +VMR = 1 # volume mixing ratio +kB = 1.380649e-16 +L = 50 * 1 # cm +P0 = 0.423 # @296K +# Tarr = jnp.array([1000, 1000, 1000]) +Tarr = jnp.array([720.5, 720.0, 727.2, 734.3, 733.8, 731.4, 729.7, 725.2]) + 273.15 +# Tarr =jnp.array([320,383,414,427,427,414,385,324]) +273#CH3-9 of 162178-93 at 2023/07/24/15:21:02 +# Tarr =jnp.array([581, 664, 702, 721, 720, 705, 668, 590]) +273#CH3-9 of 162141-51 at 2023/07/25/17:02:00 + +roundorder = 5 # digit to round up +Nx = round(((wspan + 2 * adjustrange) / Resolution + 1)) # it should be even number +start_idx = round(adjustrange / Resolution) # start index in wav array for triminng +slicesize = round((wspan / Resolution + 1)) + +Nx_boost = Nx * gridboost +ngas, P_total = calc_dnumber_isobaric(Tarr, P0, Tref_original) +P_self = P_total * VMR +nMolecule = ngas * VMR + +wmax = round(wmin + wspan, 5) +wav_n = round(wspan / Resolution + 1) # equal to the specified amount of data point +wav_adjust = jnp.linspace(wmin - adjustrange, wmax + adjustrange, Nx) +nu_adjust = 1e7 / wav_adjust[::-1] + +nu_min = 1e7 / wmax +nu_max = 1e7 / wmin + +# generate the wavenumber&wavelength grid for cross-section +nu_grid, wav, res = wavenumber_grid( + nu_min - adjustrange, + nu_max + adjustrange, + # jnp.max(wavd), + Nx_boost, + unit="cm-1", + xsmode="premodit", + wavelength_order="ascending", +) + +sop_inst = SopInstProfile(nu_grid) + +# Read the line database +mdb = MdbHitemp( + ".database/CH4/", + nurange=nu_grid, + gpu_transfer=False, # Trueだと計算速度低下 +) # for obtaining the error of each line + +# line amount for Voigt fitting +linenum = 1 +# Calculate the line index in the order of the line strength at T=twt +S_T = S_Tcalc(jnp.exp(mdb.nu_lines), mdb.line_strength_ref_original, Twt) + +strline_ind_array = jnp.argsort(S_T)[::-1][:linenum] +strline_ind_array_nu = jnp.sort(strline_ind_array) + +mdb_weak, nu_center_voigt, mdb_voigt = create_mdbs_multi(mdb, strline_ind_array_nu) +# opa = create_opapremodit(mdb_weak, nu_grid, Tarr) +from exojax.spec.opacalc import OpaPremodit + +opa = OpaPremodit( + mdb=mdb_voigt, + nu_grid=nu_grid, + diffmode=0, # i-th Taylor expansion is used for the weight, default is 0. + auto_trange=(np.min(Tarr), np.max(Tarr)), + # manual_params=(160, Tref_original, np.max(Tarr)), +) # opacity calculation +print(opa.Tref, opa.Tref_broadening) +#import sys +#sys.exit() +#opa.Tref = Tref_original +#opa.Tref_broadening = Tref_original + + +# Fixed values definition +nspec = 1 # Number of spectra is 1, so loops over nspec can be removed +valrange = 1.0 # Value range, adjust as needed +nu_span = 0.5 # Span of nu, adjust as needed + +# Parameter definitions with fixed values +offrange = 0.1 + +alphas = jnp.ones((linenum)) # Fixed value as an array of ones +gamma_selfs = mdb_voigt.gamma_self +ns = mdb_voigt.n_air +# Polynomial coefficients for nspec = 1 +coeffs = [ + { + "a": 0.0, # Fixed value + "b": 0.0, # Fixed value + "c": 0.0, # Fixed value + "d": 1.0, # Fixed value + } +] + +wavmodel = wav_adjust[start_idx : start_idx + slicesize] +nu_model = 1e7 / wavmodel + + +xsmatrix_opa, xsmatirx_lpf = Trans_model_MultiVoigt_test( + nu_offset, + alphas, + mdb_voigt.gamma_air, + gamma_selfs, + ns, + Tarr, + P_total, + P_self, + L, + nMolecule, + nu_grid, + nu_model, + mdb_voigt, + opa, + sop_inst, +) + +fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 9)) +# plot the measured spectra + +ax.plot( + wavmodel, + xsmatrix_opa[::-1], + "-", + alpha=1.0, + linewidth=2, + color="C0", + label="opapremodit cross-section", +) + + +ax.plot( + wavmodel, + xsmatirx_lpf[::-1], + "-", + alpha=1.0, + linewidth=2, + ls="dashed", + color="C1", + label="lpf cross-section", +) + + +# Plot range +ymin = 0.0 +ymax = 1.01 +yspan = ymax - ymin +# plt.ylim(ymin, ymax) +plt.xlim(wmin, wmax) +# plt.xlim(wmin - adjustrange, wmax + adjustrange) + + +# plot the line centers +wavlinecenter1 = 1 / mdb.nu_lines * 1.0e7 # convert to wavelength (descending) +wavlines1 = [ + [(wavlinecenter1[i], ymax - yspan * 0.05), (wavlinecenter1[i], ymax)] + for i in range(linenum) +] +lc1 = mc.LineCollection( + wavlines1, colors="gray", linewidths=1, linestyle="-", label="line centers" +) +ax.add_collection(lc1) + +# plot setting +plt.grid(which="major", axis="both", alpha=0.7, linestyle="--", linewidth=1) +ax.grid(which="minor", axis="both", alpha=0.3, linestyle="--", linewidth=1) +ax.minorticks_on() +plt.xlabel("wavelength (nm)", fontsize=16) +plt.ylabel("Transmittance", fontsize=16) +ax.legend(loc="lower right", bbox_to_anchor=(1, 0), fontsize=8, ncol=4) +ax.get_xaxis().get_major_formatter().set_useOffset( + False +) # To avoid exponential labeling +plt.tick_params(labelsize=16) +plt.text( + 0.99, + 1.01, + "Line N=" + str(len(mdb.nu_lines)), + fontsize=10, + va="bottom", + ha="right", + transform=ax.transAxes, +) + +# Read the Wavelength range as str +wavmin_str = str(wmin).replace(".", "") +wavmax_str = str(wmax).replace(".", "") + +direc = "Results/TransSpectra/" +Fname = ( + "TEST_" + + "CH4-VMR1_L=" + + str(L) + + "cm_T1000K_P1430_" + + wavmin_str + + "-" + + wavmax_str + + "_00025_hitemp_gamma-air_linenum" + + str(linenum) + + "_VSmeasure_adjustrange_opa-Tbroad-Tref-Treforig" +) + +# save the Graph +plt.savefig( + direc + Fname + "_eachline_wong2019_2408rev_VScrosssection.jpg", bbox_inches="tight" +) +print("Saved .jpg as ", Fname, "_eachline.jpg") + +# plt.show() +plt.clf() # clear the plot +plt.close() # close the plot window + +""" +#Save the plot data as txt file +Modeldirectry = "Results/TransSpectra/" +f = open(Modeldirectry + Fname + ".txt",'w') +for i in range(wavmodel.size): + f.write(str(wavmodel[i]) + "," +str(transmodel_all[i]) + "\n") + #f.write(str(wavmodel[i]) + "," +str(noisemodel[i]) + "\n") +f.close() +print("Saved .txt as ", Fname) +""" diff --git a/tests/integration/premodit_lpf/README.md b/tests/integration/premodit_lpf/README.md new file mode 100644 index 000000000..1f9d1308b --- /dev/null +++ b/tests/integration/premodit_lpf/README.md @@ -0,0 +1,2 @@ +# OpaPremodit vs LPF using methane +This test compares OpaPremodit and LPF, made by KoHosokawa. See #520 and #521 (2024. September 2nd) diff --git a/tests/integration/premodit_lpf/Trans_model_1Voigt_HITEMP_nu_2408rev_test.py b/tests/integration/premodit_lpf/Trans_model_1Voigt_HITEMP_nu_2408rev_test.py new file mode 100644 index 000000000..9bb896a1e --- /dev/null +++ b/tests/integration/premodit_lpf/Trans_model_1Voigt_HITEMP_nu_2408rev_test.py @@ -0,0 +1,178 @@ +# calculate the Transmission model with Voigt×1 + cross-section +# updated 2024/08/06 + + +# Import the modules +from exojax.utils.constants import Patm, Tref_original # [bar/atm] +from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran +from exojax.spec import initspec, voigt +from exojax.spec.lpf import xsmatrix as lpf_xsmatrix +from jax import jit, vmap +import numpy as np +from jax import config +import copy + +config.update("jax_enable_x64", True) +# from HMC_defs_8data_nsep import gamma_hitran_nsep + + +# multiply the alpha at Sij +def Trans_model_MultiVoigt_test( + nu_offset, + alphas, + gamma_refs, + gamma_selfs, + ns, + Tarr, + P_total, + P_self, + L, + nMolecule, + nu_grid, + nu_data, + mdb_voigt, # masked mdb for Voigt fitting + opa, + sop_inst, +): + + Lbin = L / len(Tarr) # spliting the bins + # include the offset to wavenumber grid(it is shifted to the oppsite direction of actual offset) + nu_data_offset_grid = nu_data + + # create the pressure array + P_total_array = np.full(len(Tarr), P_total) + P_self_array = np.full(len(Tarr), P_self) + + # cross-section matrix of weak lines at each Temperature channel + xsmatrix_opa = opa.xsmatrix(Tarr, P_total_array) + + # Calculation for Voigt fitting + # doppler width + doppler_array = jit(vmap(doppler_sigma, (None, 0, None)))( + mdb_voigt.nu_lines, Tarr, mdb_voigt.molmass + ) + # partition function + Tref_array = np.full(len(Tarr), Tref_original) + qt = vmap(mdb_voigt.qr_interp_lines)(Tarr, Tref_array) + + # line strength + SijM = jit(vmap(line_strength, (0, None, None, None, 0, None)))( + Tarr, + mdb_voigt.logsij0, + mdb_voigt.nu_lines, + mdb_voigt.elower, + qt, + Tref_original, + ) + + # test by HK + P_self_array = np.zeros_like(P_self_array) + gamma_selfs = np.zeros_like(gamma_selfs) + + # Lorentz width + gamma_L_voigt = jit(vmap(gamma_hitran, (0, 0, 0, None, None, None)))( + P_total_array, + Tarr, + P_self_array, + ns, + gamma_refs, + gamma_selfs, + ) + + # create wavenumber matrix + nu_matrix = initspec.init_lpf(mdb_voigt.nu_lines + nu_offset, nu_grid) + + # cross section + xsmatrix_lpf = lpf_xsmatrix(nu_matrix, doppler_array, gamma_L_voigt, SijM * alphas) + + # summing up all layers + xsmatrix_opa_alllayer = xsmatrix_opa.sum(axis=0) + xsmatrix_lpf_alllayer = xsmatrix_lpf.sum(axis=0) + + # downsampling along to the instrumental resolution, include the effect of offset, trim the adjust range region + xsmatrix_opa_specgrid = sop_inst.sampling( + xsmatrix_opa_alllayer, 0, nu_data_offset_grid + ) + xsmatrix_lpf_specgrid = sop_inst.sampling( + xsmatrix_lpf_alllayer, 0, nu_data_offset_grid + ) + + return xsmatrix_opa_specgrid, xsmatrix_lpf_specgrid + + +def create_mdbs_multi(mdb, strline_ind_array_nu): + # Create the mdb copy and remove the strongest line + mdb_weak = copy.deepcopy(mdb) + + # Ensure strline_ind is a 1D array + strline_inds = np.array(strline_ind_array_nu).flatten() + nu_centers = mdb_weak.nu_lines[strline_inds] + + # nu_centers = [mdb_weak.nu_lines[i] for i in strline_inds] + + # Create a mask: Set True for indices not included in strline_ind_array + mask = np.ones_like(mdb_weak.nu_lines, dtype=bool) + mask[strline_inds] = False # Set positions corresponding to strline_ind to False + + # Apply the mask + mdb_weak.apply_mask_mdb(mask) + + # Check if the mdb_weak has one less data point + if len(mdb_weak.nu_lines) != len(mdb.nu_lines) - strline_inds.size: + raise ValueError( + "mdb_weak does not have the correct number of data points after removing the strongest line." + ) + + # Create the mdb class only including the line for Voigt fitting + mdb_voigt = copy.deepcopy(mdb) + mdb_voigt.apply_mask_mdb(~mask) + strline_inds = np.atleast_1d(strline_inds) + mdb_voigt.logsij0 = mdb_voigt.logsij0[strline_inds] + + # Check if the mdb_voigt contains only the strongest line + if len(mdb_voigt.nu_lines) != strline_inds.size: + raise ValueError("mdb_voigt does not contain only the strong lines.") + if not np.all(np.isin(mdb_voigt.nu_lines, nu_centers)): + raise ValueError("The strong line in mdb_voigt does not match nu_center_array.") + return mdb_weak, nu_centers, mdb_voigt + + +def calc_dnumber_isobaric(T_array, P0, T0): + """ + #Calculate the number density at each region + Assumption(if there is "i" Temperature regions with same volume) + V1 = V2 =...= Vi = V_total / i + P1 = P2 =...= Pi = P_total + ∴ n1 * T1 = n2 * T2 = .... ni * Ti, + ni = T1 * n1 / Ti + + Also, the Total amount of molecule is equal to the sum of each amount of molecule + n_total * V_total = n1 * V1 +...+ ni * Vi + n_total * i * V1 = (n1 + T1 / T2 * n1 + T1 / T3 * n1 +...+ T1 / Ti * n1) * V1 + n_total * i = n1 * T1 * (1 / T1 + 1 / T2 +...+1 / Ti) + ∴ n1 = n_total * i / (T1 * Tinv_sum), (Tinv_sum = 1 / T1 + 1 / T2 +...+1 / Ti) + + P_total = P1 = n1 * k_b * T1 + = n_total * i /(T1 * Tinv_sum) * k_b * T1 + = n_total * i * k_B /Tinv_sum + + """ + + from exojax.atm.idealgas import number_density + from exojax.utils.constants import kB + + # Calculate the total number density and volume at the initial condition by PV=n*k_b*T + n_total = number_density(P0, T0) # P[bar]. n=P/k_b*T + # V_total = (n_total * kB * T0) / P0 + nlayer = len(T_array) # number of Temperature layer + + Tinv_sum = 0 + for i in range(nlayer): + Tinv_sum += 1 / T_array[i] + n_array = n_total * nlayer / (T_array * Tinv_sum) + + # Calculate the pressure at given Temperatures(1e-6:conversion factor of the Pa to bar) + P_total = nlayer * kB * 1.0e-6 * n_total / (Tinv_sum) + + # print(nlayer/Tinv_sum) + return n_array, P_total diff --git a/tests/integration/unittests_long/lpf/lpf_test.py b/tests/integration/unittests_long/lpf/lpf_test.py index c116f3459..914a810cc 100644 --- a/tests/integration/unittests_long/lpf/lpf_test.py +++ b/tests/integration/unittests_long/lpf/lpf_test.py @@ -23,7 +23,6 @@ testdata = {} testdata["exomol"] = TESTDATA_CO_EXOMOL_LPF_XS_REF testdata["hitemp"] = TESTDATA_CO_HITEMP_LPF_XS_REF - testdata_spectrum = {} testdata_spectrum["exomol"] = TESTDATA_CO_EXOMOL_LPF_EMISSION_REF testdata_spectrum["hitemp"] = TESTDATA_CO_HITEMP_LPF_EMISSION_REF diff --git a/tests/integration/unittests_long/premodit/optgrid_test.py b/tests/integration/unittests_long/premodit/optgrid_test.py index d31c456d2..3da4e50c5 100644 --- a/tests/integration/unittests_long/premodit/optgrid_test.py +++ b/tests/integration/unittests_long/premodit/optgrid_test.py @@ -29,6 +29,7 @@ def test_optelower_hitemp(): print("optimal elower_max=",Eopt,"cm-1") assert Eopt == pytest.approx(11659.3718) + if __name__ == "__main__": - #test_optelower_exomol() + test_optelower_exomol() test_optelower_hitemp() \ No newline at end of file diff --git a/tests/integration/unittests_long/premodit/premodit_xsection_test.py b/tests/integration/unittests_long/premodit/premodit_xsection_test.py deleted file mode 100644 index b28f5d160..000000000 --- a/tests/integration/unittests_long/premodit/premodit_xsection_test.py +++ /dev/null @@ -1,119 +0,0 @@ -""" short integration tests for PreMODIT cross section - - Note: - This tests compares the results by PreMODIT with thoses by MODIT. - If you are interested more manual comparison, see integrations/premodit/line_strength_comparison_*****.py - ***** = exomol or hitemp, which compares cross section and line strength, starting from molecular databases. - -""" -import pytest -import pkg_resources -import pandas as pd -import numpy as np -from exojax.spec.opacalc import OpaPremodit -from exojax.test.emulate_mdb import mock_mdb -from exojax.test.emulate_mdb import mock_wavenumber_grid - -#The following data can be regenerated by tests/generate_xs.py -from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_XS_REF -from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_XS_REF_AIR - -from jax import config - -config.update("jax_enable_x64", True) - -testdata = {} -testdata["exomol"] = TESTDATA_CO_EXOMOL_MODIT_XS_REF -testdata["hitemp"] = TESTDATA_CO_HITEMP_MODIT_XS_REF_AIR - - -@pytest.mark.parametrize("db, diffmode", [("exomol", 0), ("exomol", 1), - ("exomol", 2), ("hitemp", 0), - ("hitemp", 1), ("hitemp", 2)]) -def test_xsection_premodit(db, diffmode): - - ### DO NOT CHANGE ### - Ttest = 1200 #fix to compare w/ precomputed xs by MODIT. - ##################### - Ptest = 1.0 - mdb = mock_mdb(db) - nu_grid, wav, res = mock_wavenumber_grid() - opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[500.0, 1500.0]) - xsv = opa.xsvector(Ttest, Ptest) - filename = pkg_resources.resource_filename('exojax', - 'data/testdata/' + testdata[db]) - dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) - res = np.max(np.abs(1.0 - xsv / dat["xsv"].values)) - print(res) - assert res < 0.012 - return opa.nu_grid, xsv, opa.dE, opa.Twt, opa.Tref, Ttest - - -@pytest.mark.parametrize("db, diffmode", [("exomol", 0), ("hitemp", 2)]) -def test_xsection_premodit_for_single_broadening(db, diffmode): - - ### DO NOT CHANGE ### - Ttest = 1200 #fix to compare w/ precomputed xs by MODIT. - ##################### - Ptest = 1.0 - mdb = mock_mdb(db) - nu_grid, wav, res = mock_wavenumber_grid() - opa = OpaPremodit(mdb=mdb, - nu_grid=nu_grid, - diffmode=diffmode, - auto_trange=[500.0, 1500.0], - broadening_resolution={ - "mode": "single", - "value": None - }) - xsv = opa.xsvector(Ttest, Ptest) - filename = pkg_resources.resource_filename('exojax', - 'data/testdata/' + testdata[db]) - dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) - res = np.max(np.abs(1.0 - xsv / dat["xsv"].values)) - print(res) - assert res < 0.06 # < 6% (HITEMP) / 4% (ExoMOL) diff from exact broadening parameters using MODIT - return opa.nu_grid, xsv, opa.dE, opa.Twt, opa.Tref, Ttest - - -if __name__ == "__main__": - #comparison with MODIT - import matplotlib.pyplot as plt - - #1200K MODIT/PreMODIT single gammaL is same - - #db = "hitemp" - db = "exomol" - - diffmode = 0 - nus, xs, dE, Twt, Tref, Tin = test_xsection_premodit(db, diffmode) - #nus, xs, dE, Twt, Tref, Tin = test_xsection_premodit_for_single_broadening( - # db, diffmode) - filename = pkg_resources.resource_filename('exojax', - 'data/testdata/' + testdata[db]) - - dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) - fig = plt.figure() - ax = fig.add_subplot(211) - #plt.title("premodit_xsection_test.py diffmode=" + str(diffmode)) - plt.title("diffmode=" + str(diffmode) + " T=" + str(Tin) + " Tref=" + - str(np.round(Tref, 1)) + " Twt=" + str(np.round(Twt, 1)) + - " dE=" + str(np.round(dE, 1))) - ax.plot(nus, xs, label="PreMODIT", ls="dashed") - ax.plot(nus, dat["xsv"], label="MODIT") - plt.legend() - plt.yscale("log") - plt.ylabel("cross section (cm2)") - ax = fig.add_subplot(212) - ax.plot(nus, 1.0 - xs / dat["xsv"], label="dif = (MODIT - PreMODIT)/MODIT") - plt.ylabel("dif") - plt.xlabel("wavenumber cm-1") - plt.axhline(0.01, color="gray", lw=0.5) - plt.axhline(-0.01, color="gray", lw=0.5) - #plt.ylim(-0.05, 0.05) - plt.legend() - plt.savefig("premodit" + str(diffmode) + ".png") - plt.show() diff --git a/tests/manual_check/comparison/comparison_getE.py b/tests/manual_check/comparison/comparison_getE.py deleted file mode 100644 index ff358e5c6..000000000 --- a/tests/manual_check/comparison/comparison_getE.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from exojax.dynamics.getE import getE -# make example by PyAstronomy -from PyAstronomy.pyasl.asl import MarkleyKESolver as MKE - -m = MKE() -print(m.getE(M=0.5, e=0.3)) -marr = np.linspace(0.0, 4*np.pi, 1000) -ea = [] -for meach in marr: - ea.append(m.getE(M=meach, e=0.3)) -ea = np.array(ea) - -fig = plt.figure() -ax = fig.add_subplot(211) -plt.plot(marr, ea) -plt.plot(marr, getE(marr, 0.3)) - -ax.set_ylabel('Eccentric anomary') - -ax = fig.add_subplot(212) -plt.plot(marr, ea-getE(marr, 0.3)) - -plt.xlabel('Mean anomary') -ax.set_ylabel('Difference') -plt.savefig('getE.png') -plt.show() diff --git a/tests/manual_check/comparison/dit_hitran_CO.py b/tests/manual_check/comparison/dit_hitran_CO.py deleted file mode 100644 index 08895bbe2..000000000 --- a/tests/manual_check/comparison/dit_hitran_CO.py +++ /dev/null @@ -1,97 +0,0 @@ -from jax import config -from exojax.spec.hitran import normalized_doppler_sigma -from exojax.spec.set_ditgrid import ditgrid_log_interval -from exojax.spec import api -from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural -from exojax.spec.lpf import auto_xsection as lpf_xsection -from exojax.spec import initspec -from exojax.spec.dit import xsvector as dit_xsvector -from exojax.spec.lpf import xsvector as lpf_xsvector -from exojax.spec import make_numatrix0 -import numpy as np -import tqdm -import jax.numpy as jnp -from jax import vmap -import seaborn as sns -import matplotlib.pyplot as plt -plt.style.use('bmh') - -nus = np.linspace((4000), (4500.0), 3000000, dtype=np.float64) -mdbCO = api.MdbHitran('~/exojax/data/CO/05_hit12.par', nus) - -Mmol = 28.010446441149536 -Tref = 296.0 -Tfix = 1000.0 -Pfix = 1.e-3 - -# USE TIPS partition function -Q296 = np.array([107.25937215917970, 224.38496958496091, 112.61710362499998, - 660.22969049609367, 236.14433662109374, 1382.8672147421873]) -Q1000 = np.array([382.19096582031250, 802.30952197265628, 402.80326733398437, - 2357.1041210937501, 847.84866308593757, 4928.7215078125000]) -qr = Q1000/Q296 - -qt = np.ones_like(mdbCO.isoid, dtype=np.float64) -for idx, iso in enumerate(mdbCO.uniqiso): - mask = mdbCO.isoid == iso - qt[mask] = qr[idx] - -Sij = line_strength(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref) -gammaL = gamma_hitran(Pfix, Tfix, Pfix, mdbCO.n_air, - mdbCO.gamma_air, mdbCO.gamma_self) -# + gamma_natural(A) #uncomment if you inclide a natural width -sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol) - -cnu, indexnu, pmarray = initspec.init_dit(mdbCO.nu_lines, nus) -sigmaD_grid = ditgrid_log_interval(sigmaD, dit_grid_resolution=0.1) -gammaL_grid = ditgrid_log_interval(gammaL, dit_grid_resolution=0.1) - -xs_dit_lp = dit_xsvector(cnu, indexnu, pmarray, sigmaD, - gammaL, Sij, nus, sigmaD_grid, gammaL_grid) -wls_dit = 100000000/nus - -#ref (direct) -d = 10000 -ll = mdbCO.nu_lines -xsv_lpf_lp = lpf_xsection(nus, ll, sigmaD, gammaL, Sij, memory_size=30) - -config.update('jax_enable_x64', True) -xs_dit_lp_f64 = dit_xsvector( - cnu, indexnu, pmarray, sigmaD, gammaL, Sij, nus, sigmaD_grid, gammaL_grid) - - -# PLOT -llow = 2300.4 -lhigh = 2300.7 -tip = 20.0 -fig = plt.figure(figsize=(12, 3)) -ax = plt.subplot2grid((12, 1), (0, 0), rowspan=8) -plt.plot(wls_dit, xsv_lpf_lp, label='Direct', - color='C0', markersize=3, alpha=0.99) -plt.plot(wls_dit, xs_dit_lp, ls='dashed', color='C1', alpha=0.7, label='DIT') - -plt.ylim(1.1e-28, 1.e-17) -# plt.ylim(1.e-27,3.e-20) -plt.yscale('log') - -plt.xlim(llow*10.0-tip, lhigh*10.0+tip) -plt.legend(loc='upper right') -plt.ylabel(' cross section', fontsize=10) -#plt.text(22986,3.e-21,"$P=10^{-3}$ bar") -plt.xlabel('wavelength [$\AA$]') - -ax = plt.subplot2grid((12, 1), (8, 0), rowspan=4) -plt.plot(wls_dit, np.abs(xs_dit_lp/xsv_lpf_lp-1.)*100, - lw=1, alpha=0.5, color='C1', label='MODIT (F32)') -plt.plot(wls_dit, np.abs(xs_dit_lp_f64/xsv_lpf_lp-1.)*100, - lw=1, alpha=1, color='C2', label='MODIT (F64)') -plt.yscale('log') -plt.ylabel('difference (%)', fontsize=10) -plt.xlim(llow*10.0-tip, lhigh*10.0+tip) -plt.ylim(0.01, 100.0) -plt.xlabel('wavelength [$\AA$]') -plt.legend(loc='upper left') - -plt.savefig('comparison_dit.png', bbox_inches='tight', pad_inches=0.0) -plt.savefig('comparison_dit.pdf', bbox_inches='tight', pad_inches=0.0) -plt.show() diff --git a/tests/manual_check/comparison/hi_dynamic_range/errCO_DIT.py b/tests/manual_check/comparison/hi_dynamic_range/errCO_DIT.py deleted file mode 100644 index 312bf9eb7..000000000 --- a/tests/manual_check/comparison/hi_dynamic_range/errCO_DIT.py +++ /dev/null @@ -1,118 +0,0 @@ -import numpy as np -import tqdm -import jax.numpy as jnp -from jax import vmap -import seaborn as sns -import matplotlib.pyplot as plt -plt.style.use('bmh') -from exojax.spec import make_numatrix0 -from exojax.spec.lpf import xsvector as lpf_xsvector -from exojax.spec.dit import xsvector as dit_xsvector -from exojax.spec import initspec -from exojax.spec.lpf import auto_xsection as lpf_xsection -from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural -from exojax.spec import api -from exojax.spec.set_ditgrid import ditgrid_log_interval -from exojax.spec.hitran import normalized_doppler_sigma - - -def comperr(Nnu,plotfig=False): - - nus=np.linspace(1.e7/2700,1.e7/2100.,Nnu,dtype=np.float64) - - mdbCO=api.MdbHitran('~/exojax/data/CO/05_hit12.par',nus) - - Mmol=28.010446441149536 - Tref=296.0 - Tfix=1000.0 - Pfix=1.e-3 # - - #USE TIPS partition function - Q296=np.array([107.25937215917970,224.38496958496091,112.61710362499998,\ - 660.22969049609367,236.14433662109374,1382.8672147421873]) - Q1000=np.array([382.19096582031250,802.30952197265628,402.80326733398437,\ - 2357.1041210937501,847.84866308593757,4928.7215078125000]) - qr=Q1000/Q296 - - qt=np.ones_like(mdbCO.isoid,dtype=np.float64) - for idx,iso in enumerate(mdbCO.uniqiso): - mask=mdbCO.isoid==iso - qt[mask]=qr[idx] - - Sij=line_strength(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt,mdbCO.Tref) - gammaL = gamma_hitran(Pfix,Tfix,Pfix, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self) - #+ gamma_natural(A) #uncomment if you inclide a natural width - sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) - sigmaD_grid=ditgrid_log_interval(sigmaD) - gammaL_grid=ditgrid_log_interval(gammaL) - - - cnu,indexnu,dLarray=initspec.init_dit(mdbCO.nu_lines,nus) - - xs_dit_lp=dit_xsvector(cnu,indexnu,dLarray,sigmaD,gammaL,Sij,nus,sigmaD_grid,gammaL_grid) - wls_dit = 100000000/nus - - #ref (direct) - d=10000 - ll=mdbCO.nu_lines - xsv_lpf_lp=lpf_xsection(nus,ll,sigmaD,gammaL,Sij,memory_size=30) - - - dif=xs_dit_lp/xsv_lpf_lp-1. - med=np.median(dif) - iju=22940. - ijd=26400. - limu,limd=np.searchsorted(wls_dit[::-1],[iju,ijd]) - std=np.std(dif[::-1][limu:limd]) - - return med,std,ijd,iju,wls_dit,xs_dit_lp,xsv_lpf_lp,dif - - -if __name__=="__main__": - import matplotlib - m,std,ijd,iju,wls_dit,xs_dit_lp,xsv_lpf_lp,dif=comperr(200000) - m1,std1,ijd1,iju1,wls_dit1,xs_dit_lp1,xsv_lpf_lp1,dif1=comperr(3000000) - - #PLOT - plotfig=True - if plotfig: - matplotlib.rcParams['agg.path.chunksize'] = 100000 - llow=2300.4 - lhigh=2300.7 - tip=2.0 - fig=plt.figure(figsize=(12,3)) - ax=plt.subplot2grid((12, 1), (0, 0),rowspan=8) - plt.plot(wls_dit1,xsv_lpf_lp1,label="Direct",color="C0",alpha=0.3,markersize=3) - plt.plot(wls_dit,xs_dit_lp,color="C1",lw=1,alpha=0.3) - plt.plot(wls_dit1,xs_dit_lp1,color="C2",lw=1,alpha=0.5,ls="dashed") - - plt.xlim(ijd,iju) -# plt.xlim(llow*10-tip,lhigh*10+tip) - - plt.ylim(1.1e-35,1.e-17) -# plt.ylim(1.e-27,3.e-17) - plt.yscale("log") - -# plt.xlim(llow*10.0-tip,lhigh*10.0+tip) - plt.legend(loc="upper right") - plt.ylabel(" cross section",fontsize=10) - #plt.text(22986,3.e-21,"$P=10^{-3}$ bar") - plt.xlabel('wavelength [$\AA$]') - - ax=plt.subplot2grid((12, 1), (8, 0),rowspan=4) - plt.plot(wls_dit,np.abs((dif)*100),alpha=0.2,color="C1") - plt.plot(wls_dit1,np.abs((dif1)*100),alpha=0.5,color="C2") - - plt.ylabel("difference (%)",fontsize=10) - plt.xlim(ijd,iju) -# plt.xlim(llow*10-tip,lhigh*10+tip) - - plt.ylim(0.1,10000.0) -# plt.ylim(-10*100*std,10*100*std) - plt.yscale("log") - plt.xlabel('wavelength [$\AA$]') - plt.legend(loc="upper left") - - plt.savefig("fig/comparison_dit.png", bbox_inches="tight", pad_inches=0.0) - plt.savefig("fig/comparison_dit.pdf", bbox_inches="tight", pad_inches=0.0) - plt.show() diff --git a/tests/manual_check/comparison/hi_dynamic_range/errCO_MODIT.py b/tests/manual_check/comparison/hi_dynamic_range/errCO_MODIT.py deleted file mode 100644 index 4237c1fd3..000000000 --- a/tests/manual_check/comparison/hi_dynamic_range/errCO_MODIT.py +++ /dev/null @@ -1,128 +0,0 @@ -import numpy as np -import tqdm -import jax.numpy as jnp -from jax import vmap -import seaborn as sns -import matplotlib.pyplot as plt -plt.style.use('bmh') -from exojax.spec import make_numatrix0 -from exojax.spec.lpf import xsvector as lpf_xsvector -from exojax.spec.modit import xsvector as modit_xsvector -from exojax.spec import initspec -from exojax.spec.lpf import auto_xsection as lpf_xsection -from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural -from exojax.spec import api -from exojax.spec.set_ditgrid import ditgrid_log_interval -from exojax.spec.hitran import normalized_doppler_sigma - -#F64/F32 -from jax import config -config.update("jax_enable_x64", True) - -def comperr(Nnu,plotfig=False): - - nus=np.logspace(np.log10(1.e7/2700),np.log10(1.e7/2100.),Nnu,dtype=np.float64) - -# nus=np.logspace(np.log10(3000),np.log10(6000.0),Nnu,dtype=np.float64) - mdbCO=api.MdbHitran('~/exojax/data/CO/05_hit12.par',nus) - - Mmol=28.010446441149536 - Tref=296.0 - Tfix=1000.0 - Pfix=1.e-3 # - - #USE TIPS partition function - Q296=np.array([107.25937215917970,224.38496958496091,112.61710362499998,\ - 660.22969049609367,236.14433662109374,1382.8672147421873]) - Q1000=np.array([382.19096582031250,802.30952197265628,402.80326733398437,\ - 2357.1041210937501,847.84866308593757,4928.7215078125000]) - qr=Q1000/Q296 - - qt=np.ones_like(mdbCO.isoid,dtype=np.float64) - for idx,iso in enumerate(mdbCO.uniqiso): - mask=mdbCO.isoid==iso - qt[mask]=qr[idx] - - Sij=line_strength(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt,mdbCO.Tref) - gammaL = gamma_hitran(Pfix,Tfix,Pfix, mdbCO.n_air, mdbCO.gamma_air, mdbCO.gamma_self) - #+ gamma_natural(A) #uncomment if you inclide a natural width - sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) - - cnu,indexnu,R,dq=initspec.init_modit(mdbCO.nu_lines,nus) - nsigmaD=normalized_doppler_sigma(Tfix,Mmol,R) - ngammaL=gammaL/(mdbCO.nu_lines/R) - ngammaL_grid = ditgrid_log_interval(ngammaL) - - xs_modit_lp=modit_xsvector(cnu,indexnu,R,dq,nsigmaD,ngammaL,Sij,nus,ngammaL_grid) - wls_modit = 100000000/nus - - #ref (direct) - d=10000 - ll=mdbCO.nu_lines - xsv_lpf_lp=lpf_xsection(nus,ll,sigmaD,gammaL,Sij,memory_size=30) - - - dif=xs_modit_lp/xsv_lpf_lp-1. - med=np.median(dif) - iju=22940. - ijd=26400. - limu,limd=np.searchsorted(wls_modit[::-1],[iju,ijd]) - std=np.std(dif[::-1][limu:limd]) - - return med,std,R,ijd,iju,wls_modit,xs_modit_lp,xsv_lpf_lp,dif - - -if __name__=="__main__": - import matplotlib - m,std,R,ijd,iju,wls_modit,xs_modit_lp,xsv_lpf_lp,dif=comperr(200000) - m1,std1,R1,ijd1,iju1,wls_modit1,xs_modit_lp1,xsv_lpf_lp1,dif1=comperr(400000) - - print(m,std,R) - print(m1,std1,R1) - - - - - #PLOT - plotfig=True - if plotfig: - matplotlib.rcParams['agg.path.chunksize'] = 100000 - llow=2300.4 - lhigh=2300.7 - tip=2.0 - fig=plt.figure(figsize=(12,3)) - ax=plt.subplot2grid((12, 1), (0, 0),rowspan=8) - plt.plot(wls_modit1,xsv_lpf_lp1,label="Direct",color="C0",alpha=0.3,markersize=3) - plt.plot(wls_modit,xs_modit_lp,color="C1",lw=1,alpha=0.3,label="R="+str(R)) - plt.plot(wls_modit1,xs_modit_lp1,color="C2",lw=1,alpha=0.5,label="R="+str(R1),ls="dashed") - - plt.xlim(ijd,iju) -# plt.xlim(llow*10-tip,lhigh*10+tip) - - plt.ylim(1.1e-35,1.e-17) -# plt.ylim(1.e-27,3.e-17) - plt.yscale("log") - -# plt.xlim(llow*10.0-tip,lhigh*10.0+tip) - plt.legend(loc="upper right") - plt.ylabel(" cross section",fontsize=10) - #plt.text(22986,3.e-21,"$P=10^{-3}$ bar") - plt.xlabel('wavelength [$\AA$]') - - ax=plt.subplot2grid((12, 1), (8, 0),rowspan=4) - plt.plot(wls_modit,np.abs((dif)*100),alpha=0.2,color="C1",label="R="+str(R)) - plt.plot(wls_modit1,np.abs((dif1)*100),alpha=0.5,color="C2",label="R="+str(R1)) - - plt.ylabel("difference (%)",fontsize=10) - plt.xlim(ijd,iju) -# plt.xlim(llow*10-tip,lhigh*10+tip) - - plt.ylim(0.1,10000.0) -# plt.ylim(-10*100*std,10*100*std) - plt.yscale("log") - plt.xlabel('wavelength [$\AA$]') - plt.legend(loc="upper left") - - plt.savefig("fig/comparison_modit.png", bbox_inches="tight", pad_inches=0.0) - plt.savefig("fig/comparison_modit.pdf", bbox_inches="tight", pad_inches=0.0) - plt.show() diff --git a/tests/manual_check/comparison/modit_hitran_CO.py b/tests/manual_check/comparison/modit_hitran_CO.py deleted file mode 100644 index cfd59e077..000000000 --- a/tests/manual_check/comparison/modit_hitran_CO.py +++ /dev/null @@ -1,106 +0,0 @@ -""" MODIT HITRAN CO - -""" -from jax import config -from exojax.spec.hitran import normalized_doppler_sigma -from exojax.spec.set_ditgrid import ditgrid_log_interval -from exojax.spec import api -from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural -from exojax.spec.lpf import auto_xsection as lpf_xsection -from exojax.spec import initspec -from exojax.spec.modit import xsvector as modit_xsvector -from exojax.spec.lpf import xsvector as lpf_xsvector -from exojax.spec import make_numatrix0 -import numpy as np -import tqdm -import jax.numpy as jnp -from jax import vmap -import seaborn as sns -import matplotlib.pyplot as plt -plt.style.use('bmh') - -nus = np.logspace(np.log10(4000), np.log10(4500.0), 3000000, dtype=np.float64) -mdbCO = api.MdbHitran('~/exojax/data/CO/05_hit12.par', nus) - -Mmol = 28.010446441149536 -Tref = 296.0 -Tfix = 1000.0 -Pfix = 1.e-3 - -# USE TIPS partition function -Q296 = np.array([107.25937215917970, 224.38496958496091, 112.61710362499998, - 660.22969049609367, 236.14433662109374, 1382.8672147421873]) -Q1000 = np.array([382.19096582031250, 802.30952197265628, 402.80326733398437, - 2357.1041210937501, 847.84866308593757, 4928.7215078125000]) -qr = Q1000/Q296 - -qt = np.ones_like(mdbCO.isoid, dtype=np.float64) -for idx, iso in enumerate(mdbCO.uniqiso): - mask = mdbCO.isoid == iso - qt[mask] = qr[idx] - -Sij = line_strength(Tfix, mdbCO.logsij0, mdbCO.nu_lines, mdbCO.elower, qt, mdbCO.Tref) -gammaL = gamma_hitran(Pfix, Tfix, Pfix, mdbCO.n_air, - mdbCO.gamma_air, mdbCO.gamma_self) -# + gamma_natural(A) #uncomment if you inclide a natural width -sigmaD = doppler_sigma(mdbCO.nu_lines, Tfix, Mmol) - -cnu, indexnu, R, pmarray = initspec.init_modit(mdbCO.nu_lines, nus) -nsigmaD = normalized_doppler_sigma(Tfix, Mmol, R) -ngammaL = gammaL/(mdbCO.nu_lines/R) -ngammaL_grid = ditgrid_log_interval(ngammaL) - -xs_modit_lp = modit_xsvector( - cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, nus, ngammaL_grid) -wls_modit = 100000000/nus - -#ref (direct) -d = 10000 -ll = mdbCO.nu_lines -xsv_lpf_lp = lpf_xsection(nus, ll, sigmaD, gammaL, Sij, memory_size=30) - -config.update('jax_enable_x64', True) - -xs_modit_lp_f64 = modit_xsvector( - cnu, indexnu, R, pmarray, nsigmaD, ngammaL, Sij, nus, ngammaL_grid) - - -# PLOT -llow = 2300.4 -lhigh = 2300.7 -tip = 20.0 -fig = plt.figure(figsize=(12, 3)) -ax = plt.subplot2grid((12, 1), (0, 0), rowspan=8) -plt.plot(wls_modit, xsv_lpf_lp, label='Direct', - color='C0', markersize=3, alpha=0.3) -plt.plot(wls_modit, xs_modit_lp, lw=1, - color='C1', alpha=1, label='MODIT (F32)') -plt.plot(wls_modit, xs_modit_lp_f64, lw=1, - color='C2', alpha=1, label='MODIT (F64)') - - -plt.ylim(1.1e-28, 1.e-17) -# plt.ylim(1.e-27,3.e-20) -plt.yscale('log') - -plt.xlim(llow*10.0-tip, lhigh*10.0+tip) -plt.legend(loc='upper right') -plt.ylabel(' cross section $(\mathrm{cm}^2)$', fontsize=10) -#plt.text(22986,3.e-21,"$P=10^{-3}$ bar") -plt.xlabel('wavelength [$\AA$]') - -ax = plt.subplot2grid((12, 1), (8, 0), rowspan=4) -plt.plot(wls_modit, np.abs(xs_modit_lp/xsv_lpf_lp-1.)*100, - lw=1, alpha=0.5, color='C1', label='MODIT (F32)') -plt.plot(wls_modit, np.abs(xs_modit_lp_f64/xsv_lpf_lp-1.) * - 100, lw=1, alpha=1, color='C2', label='MODIT (F64)') -plt.yscale('log') -plt.ylabel('difference (%)', fontsize=10) -plt.xlim(llow*10.0-tip, lhigh*10.0+tip) -plt.ylim(0.01, 100.0) -plt.xlabel('wavelength [$\AA$]') -plt.legend(loc='upper left') - -plt.savefig('comparison_modit.png', bbox_inches='tight', pad_inches=0.0) -plt.savefig('comparison_modit.pdf', bbox_inches='tight', pad_inches=0.0) -plt.show() diff --git a/tests/manual_check/f32/lnmoment_amcloud.py b/tests/manual_check/f32/lnmoment_amcloud.py deleted file mode 100644 index d34aaecb9..000000000 --- a/tests/manual_check/f32/lnmoment_amcloud.py +++ /dev/null @@ -1,55 +0,0 @@ -"""comparison of the errors for exp(-9/2 log^2 sigmag) for FP32 jax - -Note: - this code was used to determine the optimal function of exp(-9/2 * log^2 sigmag) used in layeropacity.layer_optical_depth_clouds_lognormal - The conclusion is that we should use f(sig) in the following code. -""" - -import jax.numpy as jnp -import numpy as np - -def gnp(sig): - logs = np.log(sig) - return np.exp(-4.5*logs**2) - -def f0(sig): - return (sig**(-4.5*jnp.log(sig))) - -def f(sig): - return sig**(jnp.log(sig**-4.5)) - -def g(sig): - logs = jnp.log(sig) - return jnp.exp(-4.5*logs**2) - - -if __name__ == "__main__": - arr = np.logspace(0,3,10001) - - print(len(arr[f0(arr)>0.0])) - print(len(arr[f(arr)>0.0])) - print(len(arr[g(arr)>0.0])) - print(np.min(arr[f(arr)>0.0])) - print(np.max(arr[f(arr)>0.0])) - farr = f(arr) - print(np.min(farr[farr>0.0])) - print(np.max(farr)) - - import matplotlib.pyplot as plt - fig = plt.figure() - ax = fig.add_subplot(211) -# plt.plot(arr,np.log(arr)) - plt.plot(arr,gnp(arr)) - plt.xscale("log") - plt.yscale("log") - plt.axhline(1.0,color="gray",ls="dashed") - ax = fig.add_subplot(212) - plt.plot(arr,g(arr)/gnp(arr)-1.0,label="exp",alpha=0.3) - plt.plot(arr,f0(arr)/gnp(arr)-1.0,label="sig**(-4.5*jnp.log(sig))",alpha=0.3) - plt.plot(arr,f(arr)/gnp(arr)-1.0,label="sig**(jnp.log(sig**-4.5))",alpha=0.7) - plt.xscale("log") - plt.ylim(-2.e-5,2.e-5) - plt.legend() - plt.show() - - diff --git a/tests/manual_check/xs/Ttyp_demo.py b/tests/manual_check/xs/Ttyp_demo.py deleted file mode 100644 index 0fe23e54a..000000000 --- a/tests/manual_check/xs/Ttyp_demo.py +++ /dev/null @@ -1,42 +0,0 @@ -from exojax.spec.lpf import auto_xsection -from exojax.spec import line_strength, doppler_sigma, gamma_natural -from exojax.spec.exomol import gamma_exomol -from exojax.spec import api -import numpy as np - -def demo(Tfix,Ttyp,crit=1.e-40): - """reproduce userguide/moldb.html#masking-weak-lines - - Args: - Tfix: gas temperature - Ttyp: Ttyp for line strength criterion - crit: line strength criterion - - Returns - nus, xsv - - """ - - nus=np.linspace(1000.0,10000.0,900000,dtype=np.float64) #cm-1 - mdbCO=api.MdbExomol('.database/CO/12C-16O/Li2015',nus, Ttyp=Ttyp, crit=crit) - Mmol=28.010446441149536 # molecular weight - Pfix=1.e-3 # we compute P=1.e-3 bar - qt=mdbCO.qr_interp(Tfix) - Sij=line_strength(Tfix,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt,mdbCO.Tref) - gammaL = gamma_exomol(Pfix,Tfix,mdbCO.n_Texp,mdbCO.alpha_ref)\ - + gamma_natural(mdbCO.A) - # thermal doppler sigma - sigmaD=doppler_sigma(mdbCO.nu_lines,Tfix,Mmol) - #line center - nu0=mdbCO.nu_lines - xsv=auto_xsection(nus,nu0,sigmaD,gammaL,Sij,memory_size=30) - return nus, xsv - -if __name__=="__main__": - Tfix=2000.0 - Ttyp=1000. - nus,xsv=demo(Tfix,Ttyp,crit=1.e-40) - import matplotlib.pyplot as plt - plt.plot(1.e4/nus,xsv) - plt.yscale("log") - plt.show() diff --git a/tests/unittests/api_radis/README.md b/tests/unittests/api_radis/README.md new file mode 100644 index 000000000..4453a33c3 --- /dev/null +++ b/tests/unittests/api_radis/README.md @@ -0,0 +1,2 @@ +# Unit tests for consistency with RADIS/api +See #530 \ No newline at end of file diff --git a/tests/unittests/api_radis/exomolapi_test.py b/tests/unittests/api_radis/exomolapi_test.py new file mode 100644 index 000000000..40e05860a --- /dev/null +++ b/tests/unittests/api_radis/exomolapi_test.py @@ -0,0 +1,25 @@ +import pytest + +from radis.api.exomolapi import check_code_level + + +@pytest.mark.parametrize("bdat_list", [["a1"], ["a0", "a1"], ["a1", "a0"]]) +def test_check_bdat_a1(bdat_list): + bdat = {} + bdat["code"] = bdat_list + assert check_code_level(bdat) == "a1" + + +@pytest.mark.parametrize("bdat_list", [["a0"]]) +def test_check_bdat_a0(bdat_list): + bdat = {} + bdat["code"] = bdat_list + assert check_code_level(bdat) == "a0" + + +@pytest.mark.parametrize("bdat_list", [["a0", "a1", "a2"]]) +def test_check_bdat_no_code_level(bdat_list): + """a2 is not a valid code level""" + bdat = {} + bdat["code"] = bdat_list + assert check_code_level(bdat) == None diff --git a/tests/unittests/api_radis/molecular_name_test.py b/tests/unittests/api_radis/molecular_name_test.py new file mode 100644 index 000000000..7b3ce1bfb --- /dev/null +++ b/tests/unittests/api_radis/molecular_name_test.py @@ -0,0 +1,15 @@ +from radis.api.exomolapi import exact_molname_exomol_to_simple_molname + + +def test_exact_molname_exomol_to_simple_molname(): + assert exact_molname_exomol_to_simple_molname("12C-1H4") == "CH4" + assert exact_molname_exomol_to_simple_molname("23Na-16O-1H") == "NaOH" + assert exact_molname_exomol_to_simple_molname("HeH_p") == "HeH_p" + + +def test_exact_molname_exomol_to_simple_molname_HDO(): + """test for HDO + See https://github.com/HajimeKawahara/exojax/issues/528 + """ + + assert exact_molname_exomol_to_simple_molname("1H-2H-16O") == "H2O" diff --git a/tests/unittests/atm/am01_test.py b/tests/unittests/atm/am01_test.py index fa1c1d4ce..f18fac7a4 100644 --- a/tests/unittests/atm/am01_test.py +++ b/tests/unittests/atm/am01_test.py @@ -3,6 +3,9 @@ from exojax.atm import vterm import jax.numpy as jnp import pytest +from exojax.atm.amclouds import sigmag_from_effective_radius +from exojax.atm.amclouds import effective_radius +from exojax.atm.amclouds import get_rg def test_viscosity(): T = 1000.0 # K @@ -10,32 +13,51 @@ def test_viscosity(): def test_pressure_scale_height_for_Earth(): - g = 980.0 #cm^2/s - T = 300.0 # K + g = 980.0 # cm^2/s + T = 300.0 # K mu = 28.8 - - assert atmprof.pressure_scale_height(g, T, mu) == pytest.approx(883764.8664527453) + ref = 883764.8664527453 + + assert atmprof.pressure_scale_height(g, T, mu) == pytest.approx(ref) def test_terminal_velocity(): - g = 980. + g = 980.0 drho = 1.0 - rho = 1.29*1.e-3 # g/cm3 - vfactor, Tr = viscosity.calc_vfactor(atm='Air') + rho = 1.29 * 1.0e-3 # g/cm3 + vfactor, Tr = viscosity.calc_vfactor(atm="Air") eta = viscosity.eta_Rosner(300.0, vfactor) r = jnp.logspace(-5, 0, 70) vfall = vterm.terminal_velocity(r, g, eta, drho, rho) - assert jnp.mean(vfall)== pytest.approx(328.12296) + assert jnp.mean(vfall) == pytest.approx(328.12296) + + +def _am01_test_param_set(): + rw = 1.0e-4 + fsed = 2.0 + alpha = 2.0 + sigmag = 2.0 + + rg_ref = 2.0695821e-05 # computed from get_rg + reff_ref = 6.879041e-05 # computed from effective_radius + return rw, fsed, alpha, sigmag, rg_ref, reff_ref + + +def test_get_rg(): + rw, fsed, alpha, sigmag, rg_ref, _ = _am01_test_param_set() + assert get_rg(rw, fsed, alpha, sigmag) == pytest.approx(rg_ref) + + +def test_effective_radius(): + _, _, _, sigmag, rg_ref, reff_ref = _am01_test_param_set() + assert effective_radius(rg_ref, sigmag) == pytest.approx(reff_ref) + + +def test_sigmag_from_effective_radius(): + rw, fsed, alpha, sigmag_ref, rg, reff = _am01_test_param_set() + val = sigmag_from_effective_radius(reff, fsed, rw, alpha) + assert val == pytest.approx(sigmag_ref) + if __name__ == "__main__": - from exojax.utils.astrofunc import gravity_jupiter - g = gravity_jupiter(1.0,1.0) - print(g) - T=500. - mu=28.00863 - H = atmprof.pressure_scale_height(g, T, mu) - print(H) - import numpy as np - from exojax.utils.constants import RJ - dq = np.log(10**1) - np.log(10**-9) - print(np.exp(H*dq/RJ)) \ No newline at end of file + test_sigmag_from_effective_radius() diff --git a/tests/unittests/atm/atmconvert_test.py b/tests/unittests/atm/atmconvert_test.py new file mode 100644 index 000000000..1e8f70f77 --- /dev/null +++ b/tests/unittests/atm/atmconvert_test.py @@ -0,0 +1,66 @@ +import pytest +from exojax.atm.atmconvert import mmr_to_vmr +from exojax.atm.atmconvert import vmr_to_mmr +from exojax.atm.atmconvert import mmr_to_density + + +def test_mmr2vmr(): + # Test case 1 + mmr = 0.02 + molecular_mass = 18.01528 # H2O + mean_molecular_weight = 28.97 # Earth's atmosphere + expected_vmr = 0.03216158727480228 + assert mmr_to_vmr(mmr, molecular_mass, mean_molecular_weight) == pytest.approx( + expected_vmr, rel=1e-3 + ) + + +def test_vmr2mmr(): + # Test case 1 + vmr = 0.03216158727480228 + molecular_mass = 18.01528 # H2O + mean_molecular_weight = 28.97 # Earth's atmosphere + expected_mmr = 0.02 + assert vmr_to_mmr(vmr, molecular_mass, mean_molecular_weight) == pytest.approx( + expected_mmr, rel=1e-3 + ) + + +def test_mmr_to_density_g_per_L(): + mmr = 0.02 + molmass = 18.01528 # H2O + Parr = 1.0 # bar + Tarr = 300.0 # K + expected_density = 0.014444939134608615 + assert mmr_to_density(mmr, molmass, Parr, Tarr, unit="g/L") == pytest.approx( + expected_density, rel=1e-3 + ) + + +def test_mmr_to_density_g_per_cm3(): + mmr = 0.02 + molmass = 18.01528 # H2O + Parr = 1.0 # bar + Tarr = 300.0 # K + expected_density = ( + 1.4444939134608615e-05 + ) + assert mmr_to_density(mmr, molmass, Parr, Tarr, unit="g/cm3") == pytest.approx( + expected_density, rel=1e-3 + ) + + +def test_mmr_to_density_invalid_unit(): + mmr = 0.02 + molmass = 18.01528 # H2O + Parr = 1.0 # bar + Tarr = 300.0 # K + with pytest.raises(ValueError, match="unit is not correct"): + mmr_to_density(mmr, molmass, Parr, Tarr, unit="invalid_unit") + +if __name__ == "__main__": + test_mmr2vmr() + test_vmr2mmr() + test_mmr_to_density_g_per_L() + test_mmr_to_density_g_per_cm3() + test_mmr_to_density_invalid_unit() \ No newline at end of file diff --git a/tests/unittests/atm/cloud_base_test.py b/tests/unittests/atm/cloud_base_test.py new file mode 100644 index 000000000..076aa3224 --- /dev/null +++ b/tests/unittests/atm/cloud_base_test.py @@ -0,0 +1,53 @@ +"""Unit tests for exojax.atm.amclouds.(cloud base relationals) +""" + +import pytest +import numpy as np + + +def _default_cloud_setting(): + from exojax.atm.atmprof import pressure_layer_logspace + from exojax.atm.psat import psat_enstatite_AM01 + from exojax.utils.zsol import nsol + + Parr, dParr, k = pressure_layer_logspace( + log_pressure_top=-4.0, log_pressure_btm=6.0, nlayer=100 + ) + alpha = 0.097 + T0 = 1200.0 + Tarr = T0 * (Parr) ** alpha + n = nsol() # solar abundance + MolMR_enstatite = np.min([n["Mg"], n["Si"], n["O"] / 3]) + P_enstatite = psat_enstatite_AM01(Tarr) + return Parr, Tarr, MolMR_enstatite, P_enstatite + + +def test_get_pressure_at_cloud_base(): + """test get_pressure_at_cloud_base""" + + Parr, Tarr, MolMR_enstatite, P_enstatite = _default_cloud_setting() + from exojax.atm.amclouds import smooth_index_base_pressure + from exojax.atm.amclouds import get_pressure_at_cloud_base + + smooth_index = smooth_index_base_pressure(Parr, P_enstatite, MolMR_enstatite) + + Pbase_enstatite = get_pressure_at_cloud_base(Parr, smooth_index) + + assert Pbase_enstatite == pytest.approx(104.62701, 1.0e-3) + + +def test_get_value_at_cloud_base_value_is_temperature(): + """test get_value_at_cloud_base using value = temperatures""" + from exojax.atm.amclouds import smooth_index_base_pressure + from exojax.utils.indexing import get_value_at_smooth_index + + Parr, Tarr, MolMR_enstatite, P_enstatite = _default_cloud_setting() + smooth_index = smooth_index_base_pressure(Parr, P_enstatite, MolMR_enstatite) + Tbase_enstatite = get_value_at_smooth_index(Tarr, smooth_index) + ref = 1884.2233 + assert Tbase_enstatite == pytest.approx(ref) + + +if __name__ == "__main__": + test_get_pressure_at_cloud_base() + test_get_value_at_cloud_base_value_is_temperature() diff --git a/tests/unittests/atm/layer_test.py b/tests/unittests/atm/layer_test.py index a3a3637c0..403b313c4 100644 --- a/tests/unittests/atm/layer_test.py +++ b/tests/unittests/atm/layer_test.py @@ -1,6 +1,8 @@ import pytest import numpy as np from exojax.atm.atmprof import pressure_layer_logspace +from exojax.atm.atmprof import pressure_upper_logspace +from exojax.atm.atmprof import pressure_lower_logspace from exojax.atm.atmprof import pressure_scale_height @@ -17,7 +19,43 @@ def test_log_pressure_is_constant(): assert np.all(np.abs(1.0 - pressure[1:] * k / pressure[:-1]) < 1.0e-5) -def test_atmoshperic_height_for_isothermal_with_analytic(): +def test_pressure_upper_logspace(): + pressure, dParr, k = pressure_layer_logspace( + log_pressure_top=-3.0, + log_pressure_btm=2.0, + nlayer=6, + mode="ascending", + numpy=False, + ) + p_upper = pressure_upper_logspace(pressure, k) + ref = np.array([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5]) + assert np.all(np.log10(p_upper) - ref < 1.0e-5) + + +def test_pressure_lower_logspace(): + pressure, dParr, k = pressure_layer_logspace( + log_pressure_top=-3.0, + log_pressure_btm=2.0, + nlayer=6, + mode="ascending", + numpy=False, + ) + p_lower = pressure_lower_logspace(pressure, k) + ref = np.array([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]) + assert np.all(np.log10(p_lower) - ref < 1.0e-5) + + +def test_pressure_scale_height_earth(): + gravity_earth = 980.665 # cm/s2 + T = 288.15 # K + mu_earth = 28.9644 + H = pressure_scale_height(gravity_earth, T, mu_earth) + ref = 843465.7516276574 # cm (8.4km) + + assert H == pytest.approx(ref) + + +def test_atmospheric_scale_height_for_isothermal_with_analytic(): from exojax.utils.grids import wavenumber_grid from exojax.spec.atmrt import ArtTransPure from jax import config @@ -46,9 +84,13 @@ def test_atmoshperic_height_for_isothermal_with_analytic(): ) # n log(k) normalized_radius_theory = 1 / (1 + H_btm / radius_btm * dq) res = 1.0 - (normalized_radius_lower - 1.0) / (normalized_radius_theory - 1.0) + assert np.all(np.abs(res[:-1]) < 1.0e-11) if __name__ == "__main__": test_log_pressure_is_constant() - test_atmoshperic_height_for_isothermal_with_analytic() + test_atmospheric_scale_height_for_isothermal_with_analytic() + test_pressure_upper_logspace() + test_pressure_lower_logspace() + test_pressure_scale_height_earth() diff --git a/tests/unittests/atm/lorentz_lorenz_test.py b/tests/unittests/atm/lorentz_lorenz_test.py new file mode 100644 index 000000000..2777a8a69 --- /dev/null +++ b/tests/unittests/atm/lorentz_lorenz_test.py @@ -0,0 +1,13 @@ +from exojax.atm.lorentz_lorenz import refractive_index_Lorentz_Lorenz + +import jax.numpy as jnp + +def test_refractive_index_Lorentz_Lorenz(): + polarizability = 1e-24 # cm^3 + number_density = 1e19 # cm^-3 + expected_refractive_index = jnp.sqrt((1.0 + 2.0 * 4.0 * jnp.pi * polarizability * number_density / 3.0) / + (1.0 - 4.0 * jnp.pi * polarizability * number_density / 3.0)) + + calculated_refractive_index = refractive_index_Lorentz_Lorenz(polarizability, number_density) + assert jnp.isclose(calculated_refractive_index, expected_refractive_index), \ + f"Expected {expected_refractive_index}, but got {calculated_refractive_index}" diff --git a/tests/unittests/atm/smooth_index_test.py b/tests/unittests/atm/smooth_index_test.py new file mode 100644 index 000000000..9cb9fe959 --- /dev/null +++ b/tests/unittests/atm/smooth_index_test.py @@ -0,0 +1,53 @@ +import jax.numpy as jnp +from jax import grad +from exojax.atm.amclouds import get_smooth_index, get_value_at_smooth_index + +def a_from_searchsorted(x): + a = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0]) + ind = jnp.searchsorted(a, x) + return a[ind] + + + +def searchsorted_is_null_derivative(): + xarr, farr, dfarr = check_derivative(a_from_searchsorted) + import matplotlib.pyplot as plt + plt.plot(xarr,farr,".",label="searchsorted(x)") + plt.plot(xarr,dfarr,".",label="grad(searchsorted(x))") + plt.legend() + plt.xlabel("x") + plt.savefig("searchsorted.png") + plt.show() + +def check_derivative(a_from_func): + d_a_from_func = grad(a_from_func) + xarr = jnp.linspace(1.1, 4.9, 100) + farr = [] + dfarr = [] + for x in xarr: + farr.append(a_from_func(x)) + dfarr.append(d_a_from_func(x)) + farr = jnp.array(farr) + dfarr = jnp.array(dfarr) + return xarr,farr,dfarr + + +def a_from_smooth_index(x): + a = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0]) + smooth_index = get_smooth_index(a, x) + return get_value_at_smooth_index(a, smooth_index) + +def smooth_index_is_not_null_derivative(): + xarr, farr, dfarr = check_derivative(a_from_smooth_index) + import matplotlib.pyplot as plt + plt.plot(xarr,farr,".",label="smoothindex(x)") + plt.plot(xarr,dfarr,".",label="grad(smoothindex(x))") + plt.legend() + plt.xlabel("x") + plt.savefig("smoothindex.png") + plt.show() + + +if __name__ == "__main__": + searchsorted_is_null_derivative() + smooth_index_is_not_null_derivative() \ No newline at end of file diff --git a/tests/unittests/atom/loadatom_test.py b/tests/unittests/atom/loadatom_test.py index aa7a2f2d5..d45f3d3aa 100644 --- a/tests/unittests/atom/loadatom_test.py +++ b/tests/unittests/atom/loadatom_test.py @@ -16,7 +16,7 @@ def test_barklem(): def test_nsol(): nsun = nsol() - assert nsun['Mg'] == 3.272837539539687e-05 + assert nsun['Mg'] == 3.275853193332226e-05 if __name__ == '__main__': diff --git a/tests/unittests/constants/const_test.py b/tests/unittests/constants/const_test.py new file mode 100644 index 000000000..c6d101136 --- /dev/null +++ b/tests/unittests/constants/const_test.py @@ -0,0 +1,22 @@ +from exojax.utils.constants import opfac, bar_cgs, Gcr +from scipy.constants import m_u +import pytest + + +def test_opfac(): + kg2g = 1.0e3 + val = bar_cgs / (m_u * kg2g) + + assert opfac == pytest.approx(val) + + +def test_Gcr(): + from astropy.constants import G as G_astropy + from astropy.constants import M_sun + from astropy import units as u + + day = 24 * 3600 * u.s + Gu = (G_astropy * M_sun / day).value + Gcr_val = Gu ** (1.0 / 3.0) * 1.0e-3 # km/s + + assert Gcr == pytest.approx(Gcr_val) diff --git a/tests/unittests/dynamics/test_getE.py b/tests/unittests/dynamics/test_getE.py deleted file mode 100644 index 8e947f6a5..000000000 --- a/tests/unittests/dynamics/test_getE.py +++ /dev/null @@ -1,18 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from exojax.dynamics.getE import getE - - -def test_getE(): - refs = np.array([0.0000000e+00, 1.6939898e+00, 2.8723323e+00, 3.9681163e+00, 5.3427000e+00, - 9.4048452e-01, 2.3150694e+00, 3.4108529e+00, 4.5891953e+00, 1.7166138e-05]) - marr = np.linspace(0.0, 4*np.pi, 10) - ea = getE(marr, 0.3) - print(ea) - print(refs) - print(np.sum((ea-refs)**2)) - assert np.sum((ea-refs)**2) < 1.e-12 - - -if __name__ == '__main__': - test_getE() diff --git a/tests/unittests/spec/presolar/presolar_test.py b/tests/unittests/experimental/presolar/presolar_test.py similarity index 94% rename from tests/unittests/spec/presolar/presolar_test.py rename to tests/unittests/experimental/presolar/presolar_test.py index 6388fb09c..4b84b6dde 100644 --- a/tests/unittests/spec/presolar/presolar_test.py +++ b/tests/unittests/experimental/presolar/presolar_test.py @@ -7,10 +7,10 @@ import numpy as np from exojax.utils.grids import wavenumber_grid -from exojax.spec.presolar import optimal_mini_batch -from exojax.spec.presolar import lbd_olaform -from exojax.spec.presolar import _reshape_lbd -from exojax.spec.presolar import shapefilter_olaform +from exojax.experimental.presolar import optimal_mini_batch +from exojax.experimental.presolar import lbd_olaform +from exojax.experimental.presolar import _reshape_lbd +from exojax.experimental.presolar import shapefilter_olaform from exojax.utils.constants import Tref_original def _example_filter(N, filter_length): @@ -150,4 +150,4 @@ def test_shapefilter_olaform(): #test_vmap_unbiased_lsd_simple() - #test_shapefilter_olaform() \ No newline at end of file + #test_shapefilter_olaform() diff --git a/tests/unittests/spec/presolar/shapefilter_test.py b/tests/unittests/experimental/presolar/shapefilter_test.py similarity index 87% rename from tests/unittests/spec/presolar/shapefilter_test.py rename to tests/unittests/experimental/presolar/shapefilter_test.py index 76b2e3fa7..5659e7378 100644 --- a/tests/unittests/spec/presolar/shapefilter_test.py +++ b/tests/unittests/experimental/presolar/shapefilter_test.py @@ -1,7 +1,7 @@ import numpy as np -from exojax.spec.shapefilter import compute_filter_length -from exojax.spec.shapefilter import generate_voigt_shape_filter -from exojax.spec import normalized_doppler_sigma +from exojax.experimental.shapefilter import compute_filter_length +from exojax.experimental.shapefilter import generate_voigt_shape_filter +from exojax.spec.hitran import normalized_doppler_sigma from exojax.spec.molinfo import molmass_isotope from jax import vmap import pytest @@ -49,4 +49,4 @@ def test_generate_voigt_shape_filter_vmapped(fig=False): if __name__ == "__main__": #test_compute_filter_length() #test_generate_voigt_shape_filter(fig=True) - test_generate_voigt_shape_filter_vmapped(fig=True) \ No newline at end of file + test_generate_voigt_shape_filter_vmapped(fig=True) diff --git a/tests/unittests/install/func.py b/tests/unittests/install/func.py deleted file mode 100644 index 0db855403..000000000 --- a/tests/unittests/install/func.py +++ /dev/null @@ -1,22 +0,0 @@ -import pytest -import jax.numpy as jnp -from jax.lax import scan - - -def test_cuDNN(): - # convolution requires cuDNN - v = jnp.array([1, 9, 1]) - s = jnp.array([0, 0, 0, 0, 1, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1]) - c = jnp.convolve(s, v, mode='same') - res = c-jnp.array([0., 0., 0., 1., 11., 19., 2., 0., - 2., 18., 2., 0., 0., 0., 1., 9., ]) - assert jnp.sum(res**2) == 0.0 - - -def test_vaex(): - import vaex - - -if __name__ == '__main__': - test_cuDNN() - test_vaex() diff --git a/tests/unittests/spec/api/api_eq_method_test.py b/tests/unittests/spec/api/api_eq_method_test.py new file mode 100644 index 000000000..c2600a106 --- /dev/null +++ b/tests/unittests/spec/api/api_eq_method_test.py @@ -0,0 +1,19 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +import copy + +def test_eq_Hitemp(): + mdb_orig = copy.deepcopy(mock_mdbHitemp(multi_isotope=True)) + mdb = mock_mdbHitemp(multi_isotope=True) + assert mdb_orig == mdb + +def test_eq_Exomol(): + mdb_orig = copy.deepcopy(mock_mdbExomol()) + mdb = mock_mdbExomol() + assert mdb_orig == mdb + + + +if __name__ == "__main__": + test_eq_Hitemp() + test_eq_Exomol() + \ No newline at end of file diff --git a/tests/unittests/spec/api/api_line_strength_test.py b/tests/unittests/spec/api/api_line_strength_test.py new file mode 100644 index 000000000..50f74d2ec --- /dev/null +++ b/tests/unittests/spec/api/api_line_strength_test.py @@ -0,0 +1,38 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +import numpy as np +import pytest + + +def test_line_strength_exomol(): + mdb = mock_mdbExomol() + assert pytest.approx(np.sum(mdb.line_strength_ref_original)) == 3.260386610389642e-22 + + +def test_line_strength_exomol_t(): + mdb = mock_mdbExomol() + Tref = 1200.0 + #mdb.change_reference_temperature(Tref) #old + mask = np.isfinite(mdb.line_strength(Tref)) + val = np.sum(mdb.line_strength(Tref)[mask]) + assert pytest.approx(val) == 1.2823972e-20 + + +def test_line_strength_hitemp(): + mdb = mock_mdbHitemp() + assert pytest.approx(np.sum(mdb.line_strength_ref_original)) == 3.2168443e-22 + + +def test_line_strength_hitemp_t(): + mdb = mock_mdbHitemp() + Tref = 1200.0 + #mdb.change_reference_temperature(Tref) + mask = np.isfinite(mdb.line_strength(Tref)) + val = np.sum(mdb.line_strength(Tref)[mask]) + assert pytest.approx(val) == 1.2651083e-20 + + +if __name__ == "__main__": + #test_line_strength_exomol() + #test_line_strength_exomol_t() + #test_line_strength_hitemp() + test_line_strength_hitemp_t() diff --git a/tests/unittests/spec/api/molinfo_test.py b/tests/unittests/spec/api/molinfo_test.py index 698342ed8..6d45fbe08 100644 --- a/tests/unittests/spec/api/molinfo_test.py +++ b/tests/unittests/spec/api/molinfo_test.py @@ -1,16 +1,25 @@ import pytest from exojax.spec.molinfo import molmass_isotope from exojax.spec.molinfo import isotope_molmass +from exojax.spec.molinfo import molmass #deprecated def test_isotope_molmass(): assert isotope_molmass("(12C)(16O)") == pytest.approx(27.994915) assert isotope_molmass("12C-16O") == pytest.approx(27.994915) + def test_mean_molmass(): assert molmass_isotope("CO") == pytest.approx(27.994915) -if __name__ == "__main__": - test_isotope_molmass() - test_mean_molmass() - #test_read_HITRAN_molparam() \ No newline at end of file + +def test_molmass_CO(): + assert molmass("CO") == pytest.approx(27.994915) + + +def test_molmass_H2O(): + assert molmass("H2O") == pytest.approx(18.010565) + + +def test_molmass_CH4(): + assert molmass("CH4") == pytest.approx(16.0313) diff --git a/tests/unittests/spec/atmrt/artabs_test.py b/tests/unittests/spec/atmrt/artabs_test.py new file mode 100644 index 000000000..49c38a86e --- /dev/null +++ b/tests/unittests/spec/atmrt/artabs_test.py @@ -0,0 +1,43 @@ +import pytest +from exojax.spec.atmrt import ArtAbsPure +from exojax.utils.grids import wavenumber_grid +import jax.numpy as jnp + + +def test_artabs_run_at_toa(): + """Test the run method of ArtAbsPure at TOA + Note: + why the answer is exp(-3.5*2)? This test assumes the d(log10 P) = 1 + and pressure = [-3,-2,-1,0,1] this is (log) center of the layers. + We integrate the dtau over d log10 P = -3.5 to 0 with dtau = 1 for each layer. + 3 layers + 0.5 layer = 3.5 layers. we get tau = 3.5, + but the observer is lcocated at the top of the atmosphere. + then we need to multiply 2. + + """ + nu_grid, wav, res = wavenumber_grid(22990.0, 23000.0, 2, xsmode="premodit") + art = ArtAbsPure(pressure_top=1.0e-3, pressure_btm=1.0e1, nlayer=5, nu_grid=nu_grid) + dtau = jnp.ones((art.nlayer, len(nu_grid))) + incf = jnp.ones_like(nu_grid) + ps = 1.0e0 # bar + f = art.run(dtau, pressure_surface=ps, incoming_flux=incf, mu_in=1.0, mu_out=1.0) + + assert f[0] == pytest.approx(jnp.exp(-3.5 * 2)) + + +def test_artabs_run_at_ground(): + nu_grid, wav, res = wavenumber_grid(22990.0, 23000.0, 2, xsmode="premodit") + art = ArtAbsPure(pressure_top=1.0e-3, pressure_btm=1.0e1, nlayer=5, nu_grid=nu_grid) + print(art.pressure) + dtau = jnp.ones((art.nlayer, len(nu_grid))) + incf = jnp.ones_like(nu_grid) + deltalogp = 0.3 + ps = 10 ** (deltalogp) # bar + f = art.run(dtau, pressure_surface=ps, incoming_flux=incf, mu_in=1.0, mu_out=None) + + assert f[0] == pytest.approx(jnp.exp(-(3.5 + deltalogp))) + + +if __name__ == "__main__": + test_artabs_run_at_toa() + test_artabs_run_at_ground() diff --git a/tests/unittests/spec/atmrt/emispure_test.py b/tests/unittests/spec/atmrt/emispure_test.py new file mode 100644 index 000000000..a47ce9692 --- /dev/null +++ b/tests/unittests/spec/atmrt/emispure_test.py @@ -0,0 +1,139 @@ +""" unit test for ArtEmisPure with OpaPremodit + +Note: + The original file was from integration/unittests_long/premodit/premodit_spectrum_test.py + These tests takes relatively long time to run. So, one of the database is tested at a time (when pytest). + +""" + +import pytest +import pkg_resources +from jax import config +import pandas as pd +import numpy as np +from exojax.test.emulate_mdb import mock_mdb +from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF +from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_EMISSION_REF +from exojax.spec.opacalc import OpaPremodit +from exojax.test.emulate_mdb import mock_wavenumber_grid +from exojax.spec.atmrt import ArtEmisPure + +config.update("jax_enable_x64", True) + + +@pytest.mark.parametrize( + "db, diffmode", + [ + ("hitemp", 0), + ], +) +def test_rt_for_single_broadening_parameters(db, diffmode, fig=False): + """compares PreMODIT+single broadening with MODIT, so difference is should be small but not very small, 0.0322 + + Args: + db: exomol or hitemp + diffmode: 0, 1, or 2 + fig: True or False + + Returns: + nu_grid, F0, reference F0 + + """ + + nu_grid, wav, res = mock_wavenumber_grid() + + art = ArtEmisPure( + pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100, nu_grid=nu_grid + ) + art.change_temperature_range(400.0, 1500.0) + Tarr = art.powerlaw_temperature(1300.0, 0.1) + mmr_arr = art.constant_mmr_profile(0.1) + gravity = 2478.57 + + mdb = mock_mdb(db) + opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[art.Tlow, art.Thigh], + broadening_resolution={"mode": "single", "value": None}, + ) + xsmatrix = opa.xsmatrix(Tarr, art.pressure) + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) + F0 = art.run(dtau, Tarr) + + if db == "hitemp": + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + TESTDATA_CO_HITEMP_MODIT_EMISSION_REF + ) + elif db == "exomol": + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF + ) + + dat = pd.read_csv(filename, delimiter=",", names=("nus", "flux")) + residual = np.abs(F0 / dat["flux"].values - 1.0) + print(np.max(residual)) + assert np.all(residual < 0.033) + return nu_grid, F0, dat["flux"].values + + +@pytest.mark.parametrize("db, diffmode", [("exomol", 1)]) +def test_rt(db, diffmode, fig=False): + """compares PreMODIT with MODIT, so difference is very small, 0.005 + + Args: + db: exomol or hitemp + diffmode: 0, 1, or 2 + fig: True or False + + Returns: + nu_grid, F0, reference F0 + + """ + nu_grid, wav, res = mock_wavenumber_grid() + + art = ArtEmisPure( + pressure_top=1.0e-8, pressure_btm=1.0e2, nlayer=100, nu_grid=nu_grid + ) + art.change_temperature_range(400.0, 1500.0) + Tarr = art.powerlaw_temperature(1300.0, 0.1) + mmr_arr = art.constant_mmr_profile(0.1) + gravity = 2478.57 + + mdb = mock_mdb(db) + opa = OpaPremodit( + mdb=mdb, nu_grid=nu_grid, diffmode=diffmode, auto_trange=[art.Tlow, art.Thigh] + ) + + xsmatrix = opa.xsmatrix(Tarr, art.pressure) + dtau = art.opacity_profile_xs(xsmatrix, mmr_arr, opa.mdb.molmass, gravity) + + F0 = art.run(dtau, Tarr) + + if db == "hitemp": + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + TESTDATA_CO_HITEMP_MODIT_EMISSION_REF + ) + elif db == "exomol": + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + TESTDATA_CO_EXOMOL_MODIT_EMISSION_REF + ) + + dat = pd.read_csv(filename, delimiter=",", names=("nus", "flux")) + residual = np.abs(F0 / dat["flux"].values - 1.0) + print(np.max(residual)) + assert np.all(residual < 0.005) + return nu_grid, F0, dat["flux"].values + + +if __name__ == "__main__": + # nu, F, Fref = test_rt_for_single_broadening_parameters("exomol", 0) + nu, F, Fref = test_rt("exomol", 1) + # nu, F, Fref = test_rt("hitemp", 1) + + import matplotlib.pyplot as plt + + plt.plot(nu, F, label="F") + plt.plot(nu, Fref, label="Fref") + plt.show() diff --git a/tests/unittests/spec/modit/modit_test.py b/tests/unittests/spec/modit/modit_test.py index 0ef5ca246..936012316 100644 --- a/tests/unittests/spec/modit/modit_test.py +++ b/tests/unittests/spec/modit/modit_test.py @@ -14,6 +14,8 @@ from jax import config +from exojax.utils.constants import Tref_original + config.update("jax_enable_x64", True) @@ -25,7 +27,7 @@ def test_xs_exomol(): Mmol = mdbCO.molmass cont_nu, index_nu, R, pmarray = init_modit(mdbCO.nu_lines, nus) - qt = mdbCO.qr_interp(Tfix) + qt = mdbCO.qr_interp(Tfix, Tref_original) gammaL = gamma_exomol(Pfix, Tfix, mdbCO.n_Texp, mdbCO.alpha_ref) + gamma_natural(mdbCO.A) dv_lines = mdbCO.nu_lines / R @@ -92,5 +94,5 @@ def fT(T0, alpha): if __name__ == "__main__": -# test_xs_exomol() + test_xs_exomol() test_rt_exomol() diff --git a/tests/unittests/spec/opa/opadirect_call_test.py b/tests/unittests/spec/opa/opadirect_call_test.py new file mode 100644 index 000000000..15398e465 --- /dev/null +++ b/tests/unittests/spec/opa/opadirect_call_test.py @@ -0,0 +1,21 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +from exojax.utils.grids import wavenumber_grid +from exojax.spec.opacalc import OpaDirect + +def test_opadirect_hitemp_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa = OpaDirect(mdb, nu_grid=nus) + + assert isinstance(opa, OpaDirect) + +def test_opadirect_exomol_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbExomol() + opa = OpaDirect(mdb, nu_grid=nus) + + assert isinstance(opa, OpaDirect) + +if __name__ == "__main__": + test_opadirect_hitemp_call() + test_opadirect_exomol_call() diff --git a/tests/unittests/spec/opa/opadirect_eq_test.py b/tests/unittests/spec/opa/opadirect_eq_test.py new file mode 100644 index 000000000..a6435d7bd --- /dev/null +++ b/tests/unittests/spec/opa/opadirect_eq_test.py @@ -0,0 +1,28 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp +from exojax.spec.opacalc import OpaDirect +from exojax.utils.grids import wavenumber_grid +import copy + + +def test_opadirect_eq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaDirect(mdb, nu_grid=nus) + opa = copy.deepcopy(opa_orig) + + assert opa == opa_orig + +def test_opadirect_neq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaDirect(mdb, nu_grid=nus) + + nus, wav, res = wavenumber_grid(12920.0, 13100.0, 20000, unit="AA", xsmode="premodit") + opa = OpaDirect(mdb, nu_grid=nus) + + assert opa != opa_orig + + +if __name__ == "__main__": + test_opadirect_eq() + test_opadirect_neq() diff --git a/tests/unittests/spec/opa/opamodit_call_test.py b/tests/unittests/spec/opa/opamodit_call_test.py new file mode 100644 index 000000000..9842d18ce --- /dev/null +++ b/tests/unittests/spec/opa/opamodit_call_test.py @@ -0,0 +1,21 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +from exojax.utils.grids import wavenumber_grid +from exojax.spec.opacalc import OpaModit + +def test_opamodit_hitemp_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa = OpaModit(mdb, nu_grid=nus, allow_32bit=True) + + assert isinstance(opa, OpaModit) + +def test_opamodit_exomol_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbExomol() + opa = OpaModit(mdb, nu_grid=nus, allow_32bit=True) + + assert isinstance(opa, OpaModit) + +if __name__ == "__main__": + test_opamodit_hitemp_call() + test_opamodit_exomol_call() diff --git a/tests/unittests/spec/opa/opamodit_eq_test.py b/tests/unittests/spec/opa/opamodit_eq_test.py new file mode 100644 index 000000000..bf0b75bb4 --- /dev/null +++ b/tests/unittests/spec/opa/opamodit_eq_test.py @@ -0,0 +1,28 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp +from exojax.spec.opacalc import OpaModit +from exojax.utils.grids import wavenumber_grid +import copy + + +def test_opamodit_eq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaModit(mdb, nu_grid=nus, allow_32bit=True) + opa = copy.deepcopy(opa_orig) + + assert opa == opa_orig + +def test_opamodit_neq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaModit(mdb, nu_grid=nus, allow_32bit=True) + + nus, wav, res = wavenumber_grid(12920.0, 13100.0, 20000, unit="AA", xsmode="premodit") + opa = OpaModit(mdb, nu_grid=nus, allow_32bit=True) + + assert opa != opa_orig + + +if __name__ == "__main__": + test_opamodit_eq() + test_opamodit_neq() diff --git a/tests/unittests/spec/opa/opapremodit_call_test.py b/tests/unittests/spec/opa/opapremodit_call_test.py new file mode 100644 index 000000000..37aaa674e --- /dev/null +++ b/tests/unittests/spec/opa/opapremodit_call_test.py @@ -0,0 +1,21 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +from exojax.utils.grids import wavenumber_grid +from exojax.spec.opacalc import OpaPremodit + +def test_opapremodit_hitemp_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True) + + assert isinstance(opa, OpaPremodit) + +def test_opapremodit_exomol_call(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbExomol() + opa = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True) + + assert isinstance(opa, OpaPremodit) + +if __name__ == "__main__": + test_opapremodit_hitemp_call() + test_opapremodit_exomol_call() diff --git a/tests/unittests/spec/opa/opapremodit_eq_test.py b/tests/unittests/spec/opa/opapremodit_eq_test.py new file mode 100644 index 000000000..2f6652030 --- /dev/null +++ b/tests/unittests/spec/opa/opapremodit_eq_test.py @@ -0,0 +1,26 @@ +from exojax.test.emulate_mdb import mock_mdbHitemp +from exojax.spec.opacalc import OpaPremodit +from exojax.utils.grids import wavenumber_grid +import copy + + +def test_opapremodit_eq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True) + opa = copy.deepcopy(opa_orig) + + assert opa == opa_orig + +def test_opapremodit_neq(): + nus, wav, res = wavenumber_grid(22920.0, 23100.0, 20000, unit="AA", xsmode="premodit") + mdb = mock_mdbHitemp(multi_isotope=True) + opa_orig = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True, auto_trange=[300.0,1000.0]) + opa = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True, auto_trange=[300.0,1200.0]) + + assert opa != opa_orig + + +if __name__ == "__main__": + test_opapremodit_eq() + test_opapremodit_neq() diff --git a/tests/unittests/spec/opa/sideeffect_test.py b/tests/unittests/spec/opa/sideeffect_test.py new file mode 100644 index 000000000..3a30efc65 --- /dev/null +++ b/tests/unittests/spec/opa/sideeffect_test.py @@ -0,0 +1,41 @@ +""" +See Issue #510, #515 +""" + +import copy +from exojax.test.emulate_mdb import mock_mdbHitemp, mock_mdbExomol +from exojax.utils.grids import wavenumber_grid +from exojax.spec.opacalc import OpaPremodit +import copy + + +def test_sideeffect_call(): + nus, wav, res = wavenumber_grid( + 22920.0, 23100.0, 20000, unit="AA", xsmode="premodit" + ) + mdb = mock_mdbHitemp(multi_isotope=True) + opa1 = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True, auto_trange=[500.0, 1000]) + opa1_orig = copy.deepcopy(opa1) + opa2 = OpaPremodit( + mdb, nu_grid=nus, allow_32bit=True, auto_trange=[500.0, 1200] + ) # used the same mdb used in opa1 + print(opa1 == opa1_orig) + assert opa1 == opa1_orig + +def test_sideeffect_call_exomol(): + nus, wav, res = wavenumber_grid( + 22920.0, 23100.0, 20000, unit="AA", xsmode="premodit" + ) + mdb = mock_mdbExomol() + opa1 = OpaPremodit(mdb, nu_grid=nus, allow_32bit=True, auto_trange=[500.0, 1000]) + opa1_orig = copy.deepcopy(opa1) + opa2 = OpaPremodit( + mdb, nu_grid=nus, allow_32bit=True, auto_trange=[500.0, 1200] + ) # used the same mdb used in opa1 + print(opa1 == opa1_orig) + assert opa1 == opa1_orig + + +if __name__ == "__main__": + test_sideeffect_call() + test_sideeffect_call_exomol() \ No newline at end of file diff --git a/tests/integration/unittests_long/premodit/lbderror_test.py b/tests/unittests/spec/premodit/lbderror_test.py similarity index 93% rename from tests/integration/unittests_long/premodit/lbderror_test.py rename to tests/unittests/spec/premodit/lbderror_test.py index c206753e2..700fe4fc3 100644 --- a/tests/integration/unittests_long/premodit/lbderror_test.py +++ b/tests/unittests/spec/premodit/lbderror_test.py @@ -127,15 +127,16 @@ def test_optimal_params(): Tl_in = 500.0 #K Tu_in = 1200.0 #K diffmode = 2 - dE, Tl, Tu = optimal_params(Tl_in, Tu_in, diffmode) - assert dE == pytest.approx((diffmode+1)*750.0) - assert Tl == pytest.approx(1153.6267095763965) - assert Tu == pytest.approx(554.1714566743503) - + dE, Tl, Tu = optimal_params(Tl_in, Tu_in, diffmode) + assert dE == pytest.approx(2475.0) + assert Tl == pytest.approx(1108.1485374361412) + assert Tu == pytest.approx(570.9650338563875) + if __name__ == "__main__": #test_weight_points_dE() #test_single_tilde_line_strength() #test_single_tilde_line_strength_first() - test_worst_tilde_line_strength_first() + #test_worst_tilde_line_strength_first() + test_optimal_params() \ No newline at end of file diff --git a/tests/unittests/spec/premodit/opa_xsvector_test.py b/tests/unittests/spec/premodit/opa_xsvector_test.py new file mode 100644 index 000000000..6774202b7 --- /dev/null +++ b/tests/unittests/spec/premodit/opa_xsvector_test.py @@ -0,0 +1,90 @@ +""" unit tests for PreMODIT cross section + + Note: + This tests compares the results by PreMODIT with thoses by MODIT. + If you are interested more manual comparison, see integrations/premodit/line_strength_comparison_*****.py + ***** = exomol or hitemp, which compares cross section and line strength, starting from molecular databases. + +""" + +import pytest +import pkg_resources +import pandas as pd +import numpy as np +from exojax.spec.opacalc import OpaPremodit +from exojax.test.emulate_mdb import mock_mdb +from exojax.test.emulate_mdb import mock_wavenumber_grid + +# The following data can be regenerated by tests/generate_xs.py +from exojax.test.data import TESTDATA_CO_EXOMOL_MODIT_XS_REF +from exojax.test.data import TESTDATA_CO_HITEMP_MODIT_XS_REF_AIR + +from jax import config + +config.update("jax_enable_x64", True) + +testdata = {} +testdata["exomol"] = TESTDATA_CO_EXOMOL_MODIT_XS_REF +testdata["hitemp"] = TESTDATA_CO_HITEMP_MODIT_XS_REF_AIR + + +@pytest.mark.parametrize( + "db, diffmode", + [ + ("exomol", 0), + ("exomol", 1), + ("exomol", 2), + ("hitemp", 0), + ("hitemp", 1), + ("hitemp", 2), + ], +) +def test_xsection_premodit(db, diffmode): + + ### DO NOT CHANGE ### + Ttest = 1200 # fix to compare w/ precomputed xs by MODIT. + ##################### + Ptest = 1.0 + mdb = mock_mdb(db) + nu_grid, wav, res = mock_wavenumber_grid() + opa = OpaPremodit( + mdb=mdb, nu_grid=nu_grid, diffmode=diffmode, auto_trange=[500.0, 1500.0] + ) + xsv = opa.xsvector(Ttest, Ptest) + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + testdata[db] + ) + dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) + res = np.max(np.abs(1.0 - xsv / dat["xsv"].values)) + # print(res) + assert res < 0.012 + return opa.nu_grid, xsv, opa.dE, opa.Twt, opa.Tref, Ttest + + +@pytest.mark.parametrize("db, diffmode", [("exomol", 0), ("hitemp", 2)]) +def test_xsection_premodit_for_single_broadening(db, diffmode): + + ### DO NOT CHANGE ### + Ttest = 1200 # fix to compare w/ precomputed xs by MODIT. + ##################### + Ptest = 1.0 + mdb = mock_mdb(db) + nu_grid, wav, res = mock_wavenumber_grid() + opa = OpaPremodit( + mdb=mdb, + nu_grid=nu_grid, + diffmode=diffmode, + auto_trange=[500.0, 1500.0], + broadening_resolution={"mode": "single", "value": None}, + ) + xsv = opa.xsvector(Ttest, Ptest) + filename = pkg_resources.resource_filename( + "exojax", "data/testdata/" + testdata[db] + ) + dat = pd.read_csv(filename, delimiter=",", names=("nus", "xsv")) + res = np.max(np.abs(1.0 - xsv / dat["xsv"].values)) + # print(res) + assert ( + res < 0.06 + ) # < 6% (HITEMP) / 4% (ExoMOL) diff from exact broadening parameters using MODIT + return opa.nu_grid, xsv, opa.dE, opa.Twt, opa.Tref, Ttest diff --git a/tests/unittests/spec/premodit/optgrid_fast_test.py b/tests/unittests/spec/premodit/optgrid_fast_test.py new file mode 100644 index 000000000..cc89da582 --- /dev/null +++ b/tests/unittests/spec/premodit/optgrid_fast_test.py @@ -0,0 +1,25 @@ +""" unittest for exojax.spec.premodit.optgrid + +Note: + for the complete test, use integration/unittests_long/premodit/optgrid_test.py + +""" + + +from exojax.spec.optgrid import optelower +import pytest +from jax import config +config.update("jax_enable_x64", True) + + +def test_optelower_exomol_fast(): + from exojax.test.emulate_mdb import mock_mdbExomol + from exojax.test.emulate_mdb import mock_wavenumber_grid + + nu_grid, wav, reso = mock_wavenumber_grid() + Tmax = 1020.0 #K + Pmin = 0.1 #bar + mdb = mock_mdbExomol(crit=1.e-37) + Eopt = optelower(mdb, nu_grid, Tmax, Pmin, accuracy=0.0) + print("optimal elower_max=",Eopt,"cm-1") + assert Eopt == pytest.approx(11615.5075) diff --git a/tests/unittests/spec/presolar/presolar_xsection_test.py b/tests/unittests/spec/presolar/presolar_xsection_test.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tests/unittests/spec/twostream/toon_test.py b/tests/unittests/spec/twostream/toon_test.py new file mode 100644 index 000000000..a18746fbc --- /dev/null +++ b/tests/unittests/spec/twostream/toon_test.py @@ -0,0 +1,173 @@ +from exojax.spec.toon import zetalambda_coeffs +from exojax.spec.toon import reduced_source_function_isothermal_layer +from exojax.spec.toon import reduced_source_function +from exojax.spec.toon import params_eddington +from exojax.spec.toon import params_quadrature +from exojax.spec.toon import params_hemispheric_mean + +import jax.numpy as jnp + + +def test_zetalambda_coeffs(): + gamma_1 = 2.0 + gamma_2 = 1.0 + zeta_plus, zeta_minus, lambdan = zetalambda_coeffs(gamma_1, gamma_2) + zeta_plus_ref = 0.7886751 + zeta_minus_ref = 0.21132487 + lambdan_ref = 1.7320508 + assert jnp.isclose(zeta_plus, zeta_plus_ref), f"Expected 0.75, got {zeta_plus}" + assert jnp.isclose(zeta_minus, zeta_minus_ref), f"Expected 0.25, got {zeta_minus}" + assert jnp.isclose(lambdan, lambdan_ref), f"Expected {jnp.sqrt(3.0)}, got {lambdan}" + + +def test_reduced_source_function_isothermal_layer(): + single_scattering_albedo = 0.5 + gamma_1 = 2.0 + gamma_2 = 1.0 + source_function = 3.0 + result = reduced_source_function_isothermal_layer( + single_scattering_albedo, gamma_1, gamma_2, source_function + ) + expected_result = ( + 2.0 * (1.0 - single_scattering_albedo) / (gamma_1 - gamma_2) * source_function + ) + + assert jnp.isclose( + result, expected_result + ), f"Expected {expected_result}, got {result}" + + +def test_reduced_source_function(): + single_scattering_albedo = 0.5 + gamma_1 = 2.0 + gamma_2 = 1.0 + source_function = 3.0 + source_function_derivative = 0.5 + + result_plus = reduced_source_function( + single_scattering_albedo, + gamma_1, + gamma_2, + source_function, + source_function_derivative, + sign=1.0, + ) + expected_result_plus = ( + 2.0 + * (1.0 - single_scattering_albedo) + / (gamma_1 - gamma_2) + * (source_function + source_function_derivative / (gamma_1 + gamma_2)) + ) + + result_minus = reduced_source_function( + single_scattering_albedo, + gamma_1, + gamma_2, + source_function, + source_function_derivative, + sign=-1.0, + ) + expected_result_minus = ( + 2.0 + * (1.0 - single_scattering_albedo) + / (gamma_1 - gamma_2) + * (source_function - source_function_derivative / (gamma_1 + gamma_2)) + ) + + assert jnp.isclose( + result_plus, expected_result_plus + ), f"Expected {expected_result_plus}, got {result_plus}" + assert jnp.isclose( + result_minus, expected_result_minus + ), f"Expected {expected_result_minus}, got {result_minus}" + + +def test_params_eddington(): + single_scattering_albedo = 0.5 + asymmetric_parameter = 0.3 + mu0 = 0.8 + + gamma_1, gamma_2, gamma_3, mu1 = params_eddington( + single_scattering_albedo, asymmetric_parameter, mu0 + ) + + expected_gamma_1 = ( + 7.0 - single_scattering_albedo * (4.0 + 3.0 * asymmetric_parameter) + ) / 4.0 + expected_gamma_2 = ( + -(1.0 - single_scattering_albedo * (4.0 - 3.0 * asymmetric_parameter)) / 4.0 + ) + expected_gamma_3 = (2.0 - 3.0 * asymmetric_parameter * mu0) / 4.0 + expected_mu1 = 0.5 + + assert jnp.isclose( + gamma_1, expected_gamma_1 + ), f"Expected {expected_gamma_1}, got {gamma_1}" + assert jnp.isclose( + gamma_2, expected_gamma_2 + ), f"Expected {expected_gamma_2}, got {gamma_2}" + assert jnp.isclose( + gamma_3, expected_gamma_3 + ), f"Expected {expected_gamma_3}, got {gamma_3}" + assert jnp.isclose(mu1, expected_mu1), f"Expected {expected_mu1}, got {mu1}" + + +def test_params_quadrature(): + single_scattering_albedo = 0.5 + asymmetric_parameter = 0.3 + mu0 = 0.8 + + gamma_1, gamma_2, gamma_3, mu1 = params_quadrature( + single_scattering_albedo, asymmetric_parameter, mu0 + ) + + s3 = jnp.sqrt(3.0) + expected_gamma_1 = ( + s3 * (2.0 - single_scattering_albedo * (1.0 + asymmetric_parameter)) / 2.0 + ) + expected_gamma_2 = ( + single_scattering_albedo * s3 * (1.0 - asymmetric_parameter) / 2.0 + ) + expected_gamma_3 = (1.0 - s3 * asymmetric_parameter * mu0) / 2.0 + expected_mu1 = 1.0 / s3 + + assert jnp.isclose( + gamma_1, expected_gamma_1 + ), f"Expected {expected_gamma_1}, got {gamma_1}" + assert jnp.isclose( + gamma_2, expected_gamma_2 + ), f"Expected {expected_gamma_2}, got {gamma_2}" + assert jnp.isclose( + gamma_3, expected_gamma_3 + ), f"Expected {expected_gamma_3}, got {gamma_3}" + assert jnp.isclose(mu1, expected_mu1), f"Expected {expected_mu1}, got {mu1}" + + +def test_params_hemispheric_mean(): + single_scattering_albedo = 0.5 + asymmetric_parameter = 0.3 + + gamma_1, gamma_2, mu1 = params_hemispheric_mean( + single_scattering_albedo, asymmetric_parameter + ) + + expected_gamma_1 = 2.0 - single_scattering_albedo * (1.0 + asymmetric_parameter) + expected_gamma_2 = single_scattering_albedo * (1.0 - asymmetric_parameter) + expected_mu1 = 0.5 + + assert jnp.isclose( + gamma_1, expected_gamma_1 + ), f"Expected {expected_gamma_1}, got {gamma_1}" + assert jnp.isclose( + gamma_2, expected_gamma_2 + ), f"Expected {expected_gamma_2}, got {gamma_2}" + assert jnp.isclose(mu1, expected_mu1), f"Expected {expected_mu1}, got {mu1}" + + +if __name__ == "__main__": + test_reduced_source_function_isothermal_layer() + test_zetalambda_coeffs() + test_reduced_source_function() + test_params_eddington() + test_params_quadrature() + test_params_hemispheric_mean() diff --git a/tests/unittests/utils/grids_test.py b/tests/unittests/utils/grids_test.py index 49f488d4f..01ef65a9e 100644 --- a/tests/unittests/utils/grids_test.py +++ b/tests/unittests/utils/grids_test.py @@ -4,7 +4,7 @@ from exojax.utils.grids import velocity_grid from exojax.utils.grids import delta_velocity_from_resolution from exojax.utils.grids import check_eslog_wavenumber_grid -from exojax.utils.grids import check_scale_xsmode +from exojax.utils.grids import check_grid_mode_in_xsmode from exojax.utils.checkarray import is_sorted from exojax.utils.checkarray import is_outside_range @@ -70,16 +70,16 @@ def test_check_eslog_wavenumber_grid(): def test_check_scale_xsmode(): - assert check_scale_xsmode("lpf") == "ESLOG" - assert check_scale_xsmode("modit") == "ESLOG" - assert check_scale_xsmode("premodit") == "ESLOG" - assert check_scale_xsmode("presolar") == "ESLOG" - assert check_scale_xsmode("dit") == "ESLIN" - assert check_scale_xsmode("LPF") == "ESLOG" - assert check_scale_xsmode("MODIT") == "ESLOG" - assert check_scale_xsmode("PREMODIT") == "ESLOG" - assert check_scale_xsmode("PRESOLAR") == "ESLOG" - assert check_scale_xsmode("DIT") == "ESLIN" + assert check_grid_mode_in_xsmode("lpf") == "ESLOG" + assert check_grid_mode_in_xsmode("modit") == "ESLOG" + assert check_grid_mode_in_xsmode("premodit") == "ESLOG" + assert check_grid_mode_in_xsmode("presolar") == "ESLOG" + assert check_grid_mode_in_xsmode("dit") == "ESLIN" + assert check_grid_mode_in_xsmode("LPF") == "ESLOG" + assert check_grid_mode_in_xsmode("MODIT") == "ESLOG" + assert check_grid_mode_in_xsmode("PREMODIT") == "ESLOG" + assert check_grid_mode_in_xsmode("PRESOLAR") == "ESLOG" + assert check_grid_mode_in_xsmode("DIT") == "ESLIN" def test_is_sorted(): diff --git a/tests/unittests/utils/indexing_test.py b/tests/unittests/utils/indexing_test.py index 73d88deb2..221598343 100644 --- a/tests/unittests/utils/indexing_test.py +++ b/tests/unittests/utils/indexing_test.py @@ -1,4 +1,9 @@ -from exojax.utils.indexing import uniqidx, find_or_add_index, uniqidx_neibouring, unique_rows +from exojax.utils.indexing import uniqidx +from exojax.utils.indexing import find_or_add_index +from exojax.utils.indexing import uniqidx_neibouring +from exojax.utils.indexing import unique_rows +from exojax.utils.indexing import get_smooth_index +from exojax.utils.indexing import get_value_at_smooth_index import numpy as np @@ -12,8 +17,7 @@ def test_uniqidx_2D(): def test_uniqidx_3D(): - a = np.array([[4, 1, 2], [7, 1, 2], [7, 2, 2], [7, 1, 2], [8, 0, 2], - [4, 1, 1]]) + a = np.array([[4, 1, 2], [7, 1, 2], [7, 2, 2], [7, 1, 2], [8, 0, 2], [4, 1, 1]]) uidx, val = uniqidx(a) ref = np.array([1, 2, 3, 2, 4, 0]) assert np.all(uidx - ref == 0.0) @@ -42,12 +46,15 @@ def test_uniqidx_neibouring(): index_of_uidx = 3 # can be 0,1 ... , np.max(udix) assert np.all(multi_index_update[index_of_uidx] == [8, 0]) i, j, k = neighbor_indices[index_of_uidx, :] - assert np.all(multi_index_update[i] == multi_index_update[index_of_uidx] + - np.array([1, 0])) - assert np.all(multi_index_update[j] == multi_index_update[index_of_uidx] + - np.array([0, 1])) - assert np.all(multi_index_update[k] == multi_index_update[index_of_uidx] + - np.array([1, 1])) + assert np.all( + multi_index_update[i] == multi_index_update[index_of_uidx] + np.array([1, 0]) + ) + assert np.all( + multi_index_update[j] == multi_index_update[index_of_uidx] + np.array([0, 1]) + ) + assert np.all( + multi_index_update[k] == multi_index_update[index_of_uidx] + np.array([1, 1]) + ) def test_unique_rows(): @@ -56,8 +63,26 @@ def test_unique_rows(): assert np.all(np.unique(a, axis=0) == u) +def test_get_value_at_smooth_index(): + array = np.array([10, 20, 30, 40, 50]) + smooth_index = np.array([0.5, 1.7, 2.5, 3.5]) + result = get_value_at_smooth_index(array, smooth_index) + expected = np.array([15, 27, 35, 45]) + assert np.allclose(result, expected) + + +def test_get_smooth_index(): + xp = np.array([10, 20, 30, 40, 50]) + x = np.array([15, 27, 35, 45]) + result = get_smooth_index(xp, x) + expected = np.array([0.5, 1.7, 2.5, 3.5]) + assert np.allclose(result, expected) + + if __name__ == "__main__": - #test_uniqidx_2D() - #test_uniqidx_3D() - #test_find_or_add_index() - test_uniqidx_neibouring() + # test_uniqidx_2D() + # test_uniqidx_3D() + # test_find_or_add_index() + # test_uniqidx_neibouring() + test_get_value_at_smooth_index() + test_get_smooth_index() diff --git a/tests/unittests/utils/isotopes_test.py b/tests/unittests/utils/isotopes_test.py index d79d3cd54..4a8de964e 100644 --- a/tests/unittests/utils/isotopes_test.py +++ b/tests/unittests/utils/isotopes_test.py @@ -1,6 +1,6 @@ from exojax.utils.isotopes import get_isotope from exojax.utils.isotopes import get_stable_isotope -from exojax.utils.isotopes import isodata +from exojax.utils.isodata import read_mnlist from exojax.utils.isotopes import molmass_hitran import numpy as np import pytest @@ -9,27 +9,29 @@ def test_molarmass_hitran(): molmass_isotope, abundance_isotope = molmass_hitran() assert molmass_isotope["CO"][1] == 27.994915 - assert molmass_isotope["CO"][0] == pytest.approx(28.01044518292034) #mean - assert abundance_isotope["CO"][1] == pytest.approx(9.86544E-01) + assert molmass_isotope["CO"][0] == pytest.approx(28.01044518292034) # mean + assert abundance_isotope["CO"][1] == pytest.approx(9.86544e-01) def test_get_isotope(): - isolist = isodata.read_mnlist() - ref = (['1H', '2H', '3H'], [1.007825, 2.014102, - 3.016049], [99.9885, 0.0115, np.nan]) - assert np.all(get_isotope('H', isolist)[0:2] == ref[0:2]) - assert (get_isotope('H', isolist)[2][2] != get_isotope('H', isolist)[2][2]) - assert np.all(np.array(get_isotope('H', isolist)[2][0:2]) == ref[2][0:2]) + isolist = read_mnlist() + ref = ( + ["1H", "2H", "3H"], + [1.007825, 2.014102, 3.016049], + [99.9885, 0.0115, np.nan], + ) + assert np.all(get_isotope("H", isolist)[0:2] == ref[0:2]) + assert get_isotope("H", isolist)[2][2] != get_isotope("H", isolist)[2][2] + assert np.all(np.array(get_isotope("H", isolist)[2][0:2]) == ref[2][0:2]) def test_get_stable_isotope(): - isolist = isodata.read_mnlist() - ref = ('1H', 1.007825, 99.9885) - assert np.all(get_stable_isotope('H', isolist) == ref) + isolist = read_mnlist() + ref = ("1H", 1.007825, 99.9885) + assert np.all(get_stable_isotope("H", isolist) == ref) if __name__ == "__main__": test_get_isotope() test_get_stable_isotope() test_molarmass_hitran() - \ No newline at end of file diff --git a/tests/unittests/utils/recexomol_test.py b/tests/unittests/utils/recexomol_test.py deleted file mode 100644 index 15e6a1d1e..000000000 --- a/tests/unittests/utils/recexomol_test.py +++ /dev/null @@ -1,10 +0,0 @@ -import pytest -from exojax.utils.recexomol import get_exomol_database_list - -# when the exomol server is down it fails -def get_recexomol(): - db, db0 = get_exomol_database_list('CO', '12C-16O') - assert db0 == 'Li2015' - -if __name__ == "__main__": - get_recexomol() \ No newline at end of file diff --git a/tests/unittests/utils/zsol_test.py b/tests/unittests/utils/zsol_test.py new file mode 100644 index 000000000..e2823559e --- /dev/null +++ b/tests/unittests/utils/zsol_test.py @@ -0,0 +1,48 @@ +from exojax.utils.zsol import nsol +from exojax.utils.zsol import mass_fraction +from exojax.utils.zsol import mass_fraction_XYZ +import numpy as np +import pytest + + +def test_check_sum_nsol(): + n = nsol() + sum_n = sum([n[atom] for atom in n]) + assert sum_n == pytest.approx(1.0) + + +def test_mass_fraction_AAG21(): + n = nsol("AAG21") + X = mass_fraction("H", n) + Y = mass_fraction("He", n) + C = mass_fraction("C", n) + + ref = np.array([0.7438051457070488, 0.24230752749452047, 0.0025561881514610443]) + assert np.allclose([X, Y, C], ref) + + +def test_solar_abundance_AAG21(): + n = nsol("AAG21") + X, Y, Z = mass_fraction_XYZ(n) + ref = np.array([0.7438051457070488, 0.24230752749452047, 0.013887326798430723]) + + assert np.allclose([X, Y, Z], ref) + + +def test_nsol_from_others_AG89(): + n = nsol("AG89") + X, Y, Z = mass_fraction_XYZ(n) + ref = np.array([0.7065223726926153, 0.2741121020257724, 0.019365525281612284]) + + assert np.allclose([X, Y, Z], ref) + + +def test_nsol_no_existence_database(): + with pytest.raises(ValueError): + n = nsol("no_existence_database") + + +if __name__ == "__main__": + test_nsol_from_others_AG89() + #n = nsol("no_existence_database") + \ No newline at end of file