From 18e6a13606cecfd1bff3c26fba1bea4aa688bcf2 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 12 Nov 2025 09:20:15 +0100 Subject: [PATCH 01/22] update documentation --- source/conf.py | 4 +- source/index.rst | 23 +- source/installation.rst | 2 +- source/notebooks/cosmology.ipynb | 63 +- source/notebooks/create_rubix_data.ipynb | 135 +- source/notebooks/dust_extinction.ipynb | 1117 +++++++++++ source/notebooks/filter_curves.ipynb | 558 +++++- ..._age_metallicity_adamoptimizer_multi.ipynb | 1696 +++++++++++++++++ ...llicity_adamoptimizer_vs_finite_diff.ipynb | 694 +++++++ source/notebooks/pipeline_demo.ipynb | 617 ++++-- source/notebooks/psf.ipynb | 72 +- .../rubix_pipeline_single_function.ipynb | 313 --- ...x_pipeline_single_function_shard_map.ipynb | 563 ++++++ .../notebooks/rubix_pipeline_stepwise.ipynb | 610 ++++-- source/notebooks/spaxel_assignment.ipynb | 166 -- source/notebooks/ssp_interpolation.ipynb | 629 ++++++ source/notebooks/ssp_template.ipynb | 1005 +++++++++- source/notebooks/ssp_template_fsps.ipynb | 537 +++++- source/notebooks/telescope.ipynb | 92 +- source/publications.rst | 2 + source/versions.rst | 10 +- 21 files changed, 7987 insertions(+), 921 deletions(-) create mode 100644 source/notebooks/dust_extinction.ipynb create mode 100644 source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb create mode 100644 source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb delete mode 100644 source/notebooks/rubix_pipeline_single_function.ipynb create mode 100644 source/notebooks/rubix_pipeline_single_function_shard_map.ipynb delete mode 100644 source/notebooks/spaxel_assignment.ipynb create mode 100644 source/notebooks/ssp_interpolation.ipynb diff --git a/source/conf.py b/source/conf.py index fb68ed48..288aba81 100644 --- a/source/conf.py +++ b/source/conf.py @@ -31,7 +31,7 @@ ".myst": "myst-nb", } -# nb_execution_mode = "off" +nb_execution_mode = "off" templates_path = ["_templates"] exclude_patterns = [] @@ -43,7 +43,7 @@ html_theme = "sphinx_book_theme" # Optional: You can add theme options to customize your documentation html_theme_options = { - "repository_url": "https://github.com/ufuk-cakir/rubix", + "repository_url": "https://github.com/AstroAI-Lab/rubix", "use_repository_button": True, } diff --git a/source/index.rst b/source/index.rst index 161c1ec0..48051c75 100644 --- a/source/index.rst +++ b/source/index.rst @@ -12,27 +12,26 @@ improvements over state-of-the-art codes. For further details see the publicatio Currently the following functionalities are provided: -- Generate mock IFU flux cubes for stars from IllustrisTNG50 +- Generate mock IFU flux cubes for stars from IllustrisTNG50, NIHAO or other cosmological hydrodynamical simulations - Generate mock photometric images for stars for different filter curves -- Use different stellar population synthesis models +- Use different stellar population synthesis models (Bruzual & Charlot, Mastar, FSPS, EMILES) - Use MUSE as telescope instrument (and some other instruments) +- Use different dust attenuation laws + +- Calculate gradients of the modelled flux cubes with respect to stellar age and metallicity using JAX automatic differentiation Currently the code is under development and is not yet all functionality is available. We are working on adding more features and improving the code, espectially we work on the following features: -- Adding support for more simulations - -- Adding support for more telescopes - - Adding gas emission lines and gas continuum -- Adding dust attenuation +- Extend gradient calculation to more parameters and scale the gradient calculation to larger data sets -- Adding support for gradient calculation +- Sampling from distribution functions If you are interested in contributing to the code or have ideas for further features, please contact us via a github issue or via email. If you use the code in your research, please cite the following paper: :ref:`publications` @@ -58,12 +57,16 @@ Notebooks notebooks/create_rubix_data.ipynb notebooks/pipeline_demo.ipynb - notebooks/rubix_pipeline_single_function.ipynb + notebooks/rubix_pipeline_single_function_shard_map.ipynb notebooks/rubix_pipeline_stepwise.ipynb + notebooks/dust_extinction.ipynb + notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb + notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb notebooks/cosmology.ipynb notebooks/telescope.ipynb - notebooks/spaxel_assignment.ipynb notebooks/ssp_template.ipynb + notebooks/ssp_interpolation.ipynb + notebooks/ssp_template_fsps.ipynb notebooks/psf.ipynb notebooks/filter_curves.ipynb diff --git a/source/installation.rst b/source/installation.rst index adc3c531..5669a7aa 100644 --- a/source/installation.rst +++ b/source/installation.rst @@ -12,7 +12,7 @@ pip install . If you want to contribute to the development of `RUBIX`, we recommend the following editable installation from this repository: ``` -git clone https://github.com/ufuk-cakir/rubix +git clone https://github.com/AstroAI-Lab/rubix cd rubix pip install -e . ``` diff --git a/source/notebooks/cosmology.ipynb b/source/notebooks/cosmology.ipynb index 389f5b3f..6c5330f1 100644 --- a/source/notebooks/cosmology.ipynb +++ b/source/notebooks/cosmology.ipynb @@ -15,9 +15,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BaseCosmology(Om0=f32[], w0=f32[], wa=f32[], h=f32[])\n", + "FlatLambdaCDM(name=\"Planck15\", H0=67.74 km / (Mpc s), Om0=0.3075, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0. 0. 0.06] eV, Ob0=0.0486)\n" + ] + } + ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", @@ -40,9 +49,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Angular Diameter Distance\n", + "rubix cosmo: 702.5322\n", + "astropy cosmo: 702.3747610737071 Mpc\n", + "Comoving Distance\n", + "rubix cosmo: 843.0387\n", + "astropy cosmo: 842.8497132884485 Mpc\n", + "lookback to z\n", + "rubix cosmo: 2.5104787\n", + "astropy cosmo: 2.509878627257186 Gyr\n", + "Age\n", + "rubix cosmo: [11.310789]\n", + "astropy cosmo: 11.287737269639198 Gyr\n" + ] + } + ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "z = 0.2\n", @@ -67,9 +95,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.5\n" + ] + } + ], "source": [ "from rubix.cosmology.utils import trapz\n", "import jax.numpy as jnp\n", @@ -81,9 +117,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "1946.5875\n" + ] + } + ], "source": [ "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", "import jax.numpy as jnp\n", @@ -114,7 +159,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/create_rubix_data.ipynb b/source/notebooks/create_rubix_data.ipynb index 48b25802..22d10e0a 100644 --- a/source/notebooks/create_rubix_data.ipynb +++ b/source/notebooks/create_rubix_data.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -81,9 +81,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:08:27,957 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:08:27,958 - rubix - INFO - Rubix version: 0.0.post482+g02b969039.d20251103\n", + "2025-11-10 17:08:27,958 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:08:27,959 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:08:27,959 - rubix - INFO - Loading data from IllustrisAPI\n", + "2025-11-10 17:08:27,960 - rubix - INFO - Reusing existing file galaxy-id-12.hdf5. If you want to download the data again, set reuse=False.\n", + "2025-11-10 17:08:27,987 - rubix - INFO - Loading data into input handler\n", + "2025-11-10 17:08:27,988 - rubix - DEBUG - Loading data from Illustris file..\n", + "2025-11-10 17:08:27,989 - rubix - DEBUG - Checking if the fields are present in the file...\n", + "2025-11-10 17:08:27,989 - rubix - DEBUG - Keys in the file: \n", + "2025-11-10 17:08:27,990 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", + "2025-11-10 17:08:27,990 - rubix - DEBUG - Matching fields: {'PartType4', 'SubhaloData', 'Header'}\n", + "2025-11-10 17:08:27,996 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", + "2025-11-10 17:08:28,367 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", + "2025-11-10 17:08:35,444 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-11-10 17:08:35,446 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", + "2025-11-10 17:08:35,447 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", + "2025-11-10 17:08:35,448 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", + "2025-11-10 17:08:35,448 - rubix - DEBUG - Matching fields: {'stars'}\n", + "2025-11-10 17:08:35,448 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", + "2025-11-10 17:08:35,449 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", + "2025-11-10 17:08:35,449 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-11-10 17:08:35,450 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-11-10 17:08:35,456 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-11-10 17:08:35,457 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-11-10 17:08:35,458 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-11-10 17:08:35,459 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-11-10 17:08:35,468 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-11-10 17:08:35,471 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-11-10 17:08:35,473 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-11-10 17:08:35,475 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-11-10 17:08:35,492 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "data": { + "text/plain": [ + "'output'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.data import convert_to_rubix\n", @@ -102,9 +164,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:08:35,537 - rubix - INFO - Centering stars particles\n", + "2025-11-10 17:08:36,285 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['age', 'age_unit', 'coords', 'coords_unit', 'datacube', 'mask', 'mass', 'mass_unit', 'metallicity', 'metallicity_unit', 'pixel_assignment', 'spatial_bin_edges', 'spectra', 'tree_flatten', 'tree_unflatten', 'velocity', 'velocity_unit']\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.data import prepare_input\n", @@ -132,9 +210,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File: output/rubix_galaxy.h5\n", + "Group: galaxy\n", + " Dataset: center (float64) ((3,))\n", + " Dataset: halfmassrad_stars (float64) (())\n", + " Dataset: redshift (float64) (())\n", + "Group: meta\n", + " Dataset: BoxSize (float64) (())\n", + " Dataset: CutoutID (int64) (())\n", + " Dataset: CutoutRequest (object) (())\n", + " Dataset: CutoutType (object) (())\n", + " Dataset: Git_commit (|S40) (())\n", + " Dataset: Git_date (|S29) (())\n", + " Dataset: HubbleParam (float64) (())\n", + " Dataset: MassTable (float64) ((6,))\n", + " Dataset: NumFilesPerSnapshot (int64) (())\n", + " Dataset: NumPart_ThisFile (int32) ((6,))\n", + " Dataset: Omega0 (float64) (())\n", + " Dataset: OmegaBaryon (float64) (())\n", + " Dataset: OmegaLambda (float64) (())\n", + " Dataset: Redshift (float64) (())\n", + " Dataset: SimulationName (object) (())\n", + " Dataset: SnapshotNumber (int64) (())\n", + " Dataset: Time (float64) (())\n", + " Dataset: UnitLength_in_cm (float64) (())\n", + " Dataset: UnitMass_in_g (float64) (())\n", + " Dataset: UnitVelocity_in_cm_per_s (float64) (())\n", + "Group: particles\n", + " Group: stars\n", + " Dataset: age (float64) ((649384,))\n", + " Dataset: coords (float64) ((649384, 3))\n", + " Dataset: mass (float64) ((649384,))\n", + " Dataset: metallicity (float64) ((649384,))\n", + " Dataset: velocity (float64) ((649384, 3))\n", + "\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.utils import print_hdf5_file_structure\n", @@ -159,7 +278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/dust_extinction.ipynb b/source/notebooks/dust_extinction.ipynb new file mode 100644 index 00000000..01659bbc --- /dev/null +++ b/source/notebooks/dust_extinction.ipynb @@ -0,0 +1,1117 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dust extinction" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dust extinction models in Rubix\n", + "\n", + "This notebook shows the basics of the dust extinction models implemented in Rubix. We have closely followed the implementation by the [dust extinction package](https://dust-extinction.readthedocs.io/en/latest/index.html). Currently we only support a subset of all available models, namely the Cardelli, Clayton, & Mathis (1989) Milky Way R(V) dependent model, the Gordon et al. (2023) Milky Way R(V) dependent model and the Fitzpatrick & Massa (1990) 6 parameter ultraviolet shape model.\n", + "\n", + "We will demonstrate how to use these models to calculate and visualize the effects of dust extinction on stellar spectra. Additionally, we will show how to integrate these models into a Rubix pipeline to simulate the impact of dust on galaxy observations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we import the dust models from Rubix." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.spectra.dust.extinction_models import Cardelli89, Gordon23" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visulaize some of the aspects of the models, i.e. their A(x)/Av as a function of wavelength." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "fig, ax = plt.subplots()\n", + "\n", + "# generate the curves and plot them\n", + "x = np.arange(0.5,10.0,0.1) # in 1/microns\n", + "Rvs = [2.0,3.0,4.0,5.0,6.0]\n", + "for cur_Rv in Rvs:\n", + " ext_model = Cardelli89(Rv=cur_Rv)\n", + " ax.plot(x,ext_model(x),label='R(V) = ' + str(cur_Rv))\n", + "\n", + "ax.set_xlabel(r'$x$ [$\\mu m^{-1}$]')\n", + "ax.set_ylabel(r'$A(x)/A(V)$')\n", + "\n", + "# for 2nd x-axis with lambda values\n", + "axis_xs = np.array([0.1, 0.12, 0.15, 0.2, 0.3, 0.5, 1.0])\n", + "new_ticks = 1 / axis_xs\n", + "new_ticks_labels = [\"%.2f\" % z for z in axis_xs]\n", + "tax = ax.twiny()\n", + "tax.set_xlim(ax.get_xlim())\n", + "tax.set_xticks(new_ticks)\n", + "tax.set_xticklabels(new_ticks_labels)\n", + "tax.set_xlabel(r\"$\\lambda$ [$\\mu$m]\")\n", + "\n", + "ax.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now also use those models and show their effects on a black body spectrum. \n", + "For that, we instantiate the Cardelli model, create a black body spectrum with astropy and apply the dust extinction with a fiducial Rv of 3.1 to the spectrum for a range of Av parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# Let's import some packages\n", + "from astropy.modeling.models import BlackBody\n", + "import astropy.units as u\n", + "from matplotlib.ticker import ScalarFormatter" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# initialize cardelli model with Rv=3.1\n", + "ext = Cardelli89(Rv=3.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# generate wavelengths between 3 and 10 microns\n", + "# within the valid range for the Cardelli R(V) dependent model\n", + "lam = np.logspace(np.log10(3), np.log10(10.0), num=1000)\n", + "\n", + "# setup the inputs for the blackbody function\n", + "wavelengths = lam*1e4 # Angstroem\n", + "temperature = 10000 # Kelvin" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# get the blackbody flux\n", + "bb_lam = BlackBody(10000*u.K, scale=1.0 * u.erg / (u.cm ** 2 * u.AA * u.s * u.sr))\n", + "flux = bb_lam(wavelengths)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# get the extinguished blackbody flux for different amounts of dust\n", + "flux_ext_av05 = flux*ext.extinguish(lam, Av=0.5)\n", + "flux_ext_av15 = flux*ext.extinguish(lam, Av=1.5)\n", + "flux_ext_ebv10 = flux*ext.extinguish(lam, Ebv=1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:10: SyntaxWarning: invalid escape sequence '\\l'\n", + "<>:10: SyntaxWarning: invalid escape sequence '\\l'\n", + "/tmp/ipykernel_521197/1777885020.py:10: SyntaxWarning: invalid escape sequence '\\l'\n", + " ax.set_xlabel('$\\lambda$ [$\\AA$]')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# plot the intrinsic and extinguished fluxes\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(wavelengths, flux, label='Intrinsic')\n", + "ax.plot(wavelengths, flux_ext_av05, label='$A(V) = 0.5$')\n", + "ax.plot(wavelengths, flux_ext_av15, label='$A(V) = 1.5$')\n", + "ax.plot(wavelengths, flux_ext_ebv10, label='$E(B-V) = 1.0$')\n", + "\n", + "ax.set_xlabel('$\\lambda$ [$\\AA$]')\n", + "ax.set_ylabel('$Flux$')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.xaxis.set_major_formatter(ScalarFormatter())\n", + "ax.set_yscale('log')\n", + "\n", + "ax.set_title('Example extinguishing a blackbody')\n", + "\n", + "ax.legend(loc='best')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the Cardelli model has some limited range in wavelength. \n", + "Now let's try the same for the Gordon et al. model which has a broader wavelength support. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# generate wavelengths between 0.092 and 31 microns\n", + "# within the valid range for the Gordon23 R(V) dependent relationship\n", + "lam = jnp.logspace(np.log10(0.092), np.log10(31.0), num=1000)\n", + "\n", + "# setup the inputs for the blackbody function\n", + "wavelengths = lam*1e4 # Angstroem\n", + "temperature = 10000 # Kelvin\n", + "\n", + "# get the blackbody flux\n", + "bb_lam = BlackBody(10000*u.K, scale=1.0 * u.erg / (u.cm ** 2 * u.AA * u.s * u.sr))\n", + "flux = bb_lam(wavelengths)\n", + "\n", + "# initialize the model\n", + "ext = Gordon23(Rv=3.1)\n", + "\n", + "# get the extinguished blackbody flux for different amounts of dust\n", + "flux_ext_av05 = flux*ext.extinguish(lam, Av=0.5)\n", + "flux_ext_av15 = flux*ext.extinguish(lam, Av=1.5)\n", + "flux_ext_ebv10 = flux*ext.extinguish(lam, Ebv=1.0)\n", + "\n", + "# plot the intrinsic and extinguished fluxes\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(wavelengths, flux, label='Intrinsic')\n", + "ax.plot(wavelengths, flux_ext_av05, label='$A(V) = 0.5$')\n", + "ax.plot(wavelengths, flux_ext_av15, label='$A(V) = 1.5$')\n", + "ax.plot(wavelengths, flux_ext_ebv10, label='$E(B-V) = 1.0$')\n", + "\n", + "ax.set_xlabel(r'$\\lambda$ [$\\AA$]')\n", + "ax.set_ylabel('$Flux$')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.xaxis.set_major_formatter(ScalarFormatter())\n", + "ax.set_yscale('log')\n", + "\n", + "ax.set_title('Example extinguishing a blackbody')\n", + "\n", + "ax.legend(loc='best')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see, as expected, the impact of dust is most important for short wavelength, i.e. the blue part of the spectrum." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run the RUBIX pipeline with dust\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now turn to running the RUBIX pipeline with dust included. For this, we first need to setup the config accordingly. That is as easy as replacing `\"pipeline\":{\"name\": \"calc_ifu\"}` with `\"pipeline\":{\"name\": \"calc_dusty_ifu\"}` in the config.\n", + "\n", + "In order to comapre a dusty and non dusty IFU cube, we first run a normal RUBIX pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:18:11,641 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-11 10:18:11,642 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-11 10:18:11,643 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-11 10:18:11,643 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-11 10:18:11,644 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\", \"gas\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 11,\n", + " \"reuse\": False,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 50000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-11.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"BruzualCharlot2003\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\", #\"Gordon23\", \n", + " \"dust_to_gas_ratio\": 0.01, # need to check Remyer's paper\n", + " \"dust_to_metals_ratio\": 0.4, # do we need this ratio if we set the dust_to_gas_ratio?\n", + " \"dust_grain_density\": 3.5, # g/cm^3 #check this value\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:11,911 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:18:11,912 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/jax/_src/numpy/scalar_types.py:50: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:491: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:18:11,995 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:18:12,927 - rubix - WARNING - The Subset value is set in config. Using only subset of size 50000 for stars\n", + "2025-11-11 10:18:12,981 - rubix - INFO - Centering gas particles\n", + "2025-11-11 10:18:13,515 - rubix - WARNING - The Subset value is set in config. Using only subset of size 50000 for gas\n", + "2025-11-11 10:18:13,518 - rubix - INFO - Data loaded with 50000 star particles and 50000 gas particles.\n", + "2025-11-11 10:18:13,518 - rubix - INFO - Data preparation completed in 1.61 seconds.\n", + "2025-11-11 10:18:13,519 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:18:13,520 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:18:13,521 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-11 10:18:13,522 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:13,535 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:13,765 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:18:13,777 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:13,789 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:13,831 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:13,898 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:14,162 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:18:14,163 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:18:14,164 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:18:14,343 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:18:14,344 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:18:14,344 - rubix - INFO - Rotating gas\n", + "2025-11-11 10:18:14,559 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-11-11 10:18:14,569 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:18:14,609 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:18:14,787 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-11-11 10:18:14,787 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:18:14,792 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:18:14,798 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-11-11 10:18:16,985 - rubix - INFO - Total time for sharded pipeline run: 3.47 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we run the pipeline including the effects of dust.\n", + "\n", + "Next to setting `\"pipeline\":{\"name\": \"calc_ifu\"}` there are some more nobs under the section `ssp` for `dust` that we can tweek if needed.\n", + "\n", + "Options to consider are as follows:\n", + "* the exact \"extinction_model\" to use. Currently Rubix supports \"Cardelli89\" or \"Gordon23\" \n", + "* the \"dust_to_gas_model\" to use. This currently refers to the fitting formula used by Remy-Ruyer et al. 2014. See their Table 1 for more info.\n", + "* the \"Xco\" model used by Remy-Ruyer et al 2014. Either \"Z\" or \"MW\"\n", + "* the \"dust_grain_density\" which depends on the type of dust at hand, see e.g. the NIST tables.\n", + "* the \"Rv\" value in case one uses an Rv dependent dust model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_dusty_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\", \"gas\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 11,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 50000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-11.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"BruzualCharlot2003\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\", #\"Gordon23\", \n", + " \"dust_to_gas_model\": \"broken power law fit\", # from Remyer's paper see their Table 1\n", + " \"Xco\": \"Z\", # from Remyer's paper, see their Table 1\n", + " \"dust_grain_density\": 3.0, # #check this value, reverse engeneered from Ibarrra-Medel 2018\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:23,421 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:18:23,424 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/jax/_src/numpy/scalar_types.py:50: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:491: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:18:23,452 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:18:23,777 - rubix - WARNING - The Subset value is set in config. Using only subset of size 50000 for stars\n", + "2025-11-11 10:18:23,783 - rubix - INFO - Centering gas particles\n", + "2025-11-11 10:18:23,834 - rubix - WARNING - The Subset value is set in config. Using only subset of size 50000 for gas\n", + "2025-11-11 10:18:23,835 - rubix - INFO - Data loaded with 50000 star particles and 50000 gas particles.\n", + "2025-11-11 10:18:23,836 - rubix - INFO - Data preparation completed in 0.42 seconds.\n", + "2025-11-11 10:18:23,837 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:18:23,838 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_extinction': {'name': 'calculate_extinction', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'calculate_dusty_datacube_particlewise': {'name': 'calculate_dusty_datacube_particlewise', 'depends_on': 'calculate_extinction', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_dusty_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:18:23,838 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-11 10:18:23,842 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:23,866 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:23,886 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:18:23,899 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:23,912 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:18:23,934 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:23,969 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:18:23,995 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:18:23,996 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:18:23,997 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:18:24,193 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:18:24,194 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:18:24,195 - rubix - INFO - Rotating gas\n", + "2025-11-11 10:18:24,364 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-11-11 10:18:24,370 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:18:24,374 - rubix - INFO - Applying dust extinction to the spaxel data...\n", + "2025-11-11 10:18:24,375 - rubix - INFO - Applying dust extinction to the spaxel data using vmap...\n", + "2025-11-11 10:18:24,481 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:18:24,508 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-11-11 10:18:24,508 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:18:24,511 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:18:24,514 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-11-11 10:18:26,659 - rubix - INFO - Total time for sharded pipeline run: 2.82 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata_dust = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare one example spaxel spectrum with and without dust." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25, 25, 3721)\n", + "(25, 25, 3721)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "spectra = rubixdata # Spectra of all stars\n", + "dusty_spectra = rubixdata_dust # Spectra of all stars\n", + "print(spectra.shape)\n", + "print(dusty_spectra.shape)\n", + "\n", + "plt.plot(wave, spectra[12,12,:])\n", + "plt.plot(wave, dusty_spectra[12,12,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Now let's visualize a nice edge-on galaxy in SDSS broad-band images with some nice dust lanes... " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.telescope.filters import load_filter, convolve_filter_with_spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# load all fliter curves for SLOAN\n", + "curves = load_filter(\"SLOAN\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "filters,images = curves.apply_filter_curves(rubixdata_dust, wave).values()\n", + "\n", + "for i_dust,name in zip(images, filters):\n", + " plt.figure()\n", + " plt.imshow(i_dust)\n", + " plt.colorbar()\n", + " plt.title(name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sanity check: overlay gas column density map over the dusty emission image" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:18:53,867 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-11 10:18:53,869 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:18:53,870 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:18:53,870 - rubix - INFO - Rotating gas\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# The input data are rotated in the same way as the particles are rotaterd to calculate the IFU. \n", + "# This step is necessary, because we only have the raw input data and the pipeline only returns the datacube and not the per particle information.\n", + "from rubix.core.rotation import get_galaxy_rotation\n", + "rotate = get_galaxy_rotation(config)\n", + "\n", + "inputdatadata = rotate(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "idx = np.where(inputdata.gas.mass != 0)\n", + "gas_map = np.histogram2d(inputdata.gas.coords[:,0][idx], inputdata.gas.coords[:,1][idx], bins=(25,25), weights=np.squeeze(inputdata.gas.mass)[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'gas map')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "plt.figure()\n", + "plt.imshow(gas_map[0].T, cmap='inferno')\n", + "plt.colorbar()\n", + "plt.title(\"gas map\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'emission and gas map overlayed')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "plt.figure()\n", + "plt.imshow(i_dust)\n", + "plt.imshow(gas_map[0].T, cmap='inferno', alpha=0.6)\n", + "plt.colorbar()\n", + "plt.title(\"emission and gas map overlayed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# And in comparison to this, the same galaxy without dust..." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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qtd9luk4JSoxya9AktyMefpzLGBff/Dqn4cS520+mweKPT7T2scV+kglErh3hcqJ0BjAa7zUKx8QJ1WujXmk2n7yNuro6lX/aoLJtuUpLdf95Vh0JKG/wR6qrqyNYt8Spoe8EJSrBIVh7kuvTEz4M1o5FsHa5n4xVIInSPrbYT1IMBetovNdoHBP/qLItTmWmpcZZBWu/aLV3OH/+fPXs2VMpKSkaNmyY3nrrrdaqCgAQoxpMwHrxg1YJ1s8//7wKCws1d+5cbd++XQMGDNCYMWP06aeftkZ1AIAYFZCxXvygVYL1T3/6U02fPl1Tp05Vv379tHDhQrVv316LFi1qjeoAADEqEIF/fhDxYF1XV6dt27Zp9OjRX1QSF6fRo0dr8+bNjdavra1VVVVVyAIAAL4Q8WD92WefqaGhQZmZmSHPZ2Zmqry8vNH6RUVFSk9PDy5ctgUACFeDMdaLH0R9Ct3s2bNVWVkZXPbu3RvtJgEAfCJWzllH/NKtzp07Kz4+XhUVFSHPV1RUNEoCLknJyclKTk6OdDMAAPjaiHjPOikpSYMHD1ZJSUnwuUAgoJKSEuXn50e6OgBADAvIqMFiidmetSQVFhZqypQpuvTSSzV06FA99dRTqq6u1tSpU1ujOgBAjLIdyo7pYH3bbbfpwIEDmjNnjsrLyzVw4ECtXr260aQzAADQvFa73eiMGTM0Y8aM1to8AADWM7r9Mhvcc/cGhw+4va+ysUhQYXOPYZv7QFvcitDm7brmw7SyTrzNfcXdMYHo/IH23Xt1ffw7bXZL8oDs7rjuj1uieODSLQAAcGb0rAEAvnVqVrdNeT8gWAMAfKvBnFxsyvsBwRoA4FucswYAAJ5AzxoA4FsBOWqQ+6sgAhZl2xLBGgDgWwFzcrEp7wcMgwMA4HH0rAEAvtVgOQxuU7YtEawBAL4VK8GaYXAAADyOnjUAwLcCxlHAWMwGtyjblgjWAADfYhgcAAB4gnd71o7jLt1flNIhxhS/7Se/tdfmGI4Sq9SPLj8fm9SPTlyUUq5GhU1qWrfvNa7NUmQ2KE4NFv3OaGSydcO7wRoAgGYYy3PWhnPWAAC0Ls5ZAwAATyBYAwB8q8HEWS8tUVRUpCFDhig1NVUZGRmaOHGidu7cecYyxcXFchwnZElJSWlRvQRrAIBvBeQooDiLpWXD4OvXr1dBQYG2bNmiNWvWqL6+Xtdee62qq6vPWC4tLU379+8PLh999FGL6uWcNQAAYVq9enXI4+LiYmVkZGjbtm264oorTlvOcRxlZWW5rpeeNQDAt05NMLNZJKmqqipkqa2tDav+yspKSVKnTp3OuN7Ro0eVm5urnJwc3XDDDXr//fdb9D4J1gAA34rUOeucnBylp6cHl6KiombrDgQCmjVrlkaOHKn+/fufdr0+ffpo0aJFWrlypX7/+98rEAhoxIgR+vjjj8N+nwyDAwBi3t69e5WWlhZ8nJyc3GyZgoICvffee9q4ceMZ18vPz1d+fn7w8YgRI3TBBRfo17/+tR5++OGw2kewBgD41skJZhaJPP5RNi0tLSRYN2fGjBlatWqVNmzYoO7du7eozsTERA0aNEilpaVhl2EYHADgW4F/3G7U7RJoYRg0xmjGjBlavny51q5dq7y8vBa3uaGhQX/5y1+UnZ0ddhl61gAAhKmgoEBLly7VypUrlZqaqvLycklSenq62rVrJ0m644471K1bt+B573nz5mn48OHq1auXDh8+rMcff1wfffSR7r777rDrJVgDAHzLzY1NQsu3LOPIggULJEmjRo0KeX7x4sW68847JUl79uxRXNwXbTp06JCmT5+u8vJynX322Ro8eLA2bdqkfv36hV2vd4O1E+cq44tNNh0TcPmB22R18mOWMLdttkp0ZPG5NkQpr47L/RS1jFAW9TpuMuT9g3H58Thx0fneOfHuy5oWBoZIiItPdF3WNLjbx44JSG305yngYig7tHzLPpNwPsN169aFPH7yySf15JNPtqier/JusAYAoBkNxlGDReYsm7JtiQlmAAB4HD1rAIBvnZrV7b5825+acINgDQDwrYCJU8BiglkgCvMI3GAYHAAAj6NnDQDwLYbBAQDwuIDsZnRH6QLYFmMYHAAAj6NnDQDwLfubovijz0qwBgD4lv3tRv0RrP3RSgAAYhg9awCAb0Uqn7XXEawBAL4VK8PgBGsAgG/ZX2dNsI4dVun24t3XG2fx8QUsbgTgNjWnzXu1ELVBLrfHhU2KTAtWx2LA/dWqblNOGotra21SeirO4vvuvlb3ovDZOMaR6l1XiyYQrAEAvhUwjgI2N0XxSYpMgjUAwLcClsPgfrnO2h+tBAAghtGzBgD4ln2KTH/0WQnWAADfapCjBoupezZl25I/flIAABDD6FkDAHyLYXAAADyuQXZD2Q2Ra0qr8sdPCgAAYhg9awCAbzEMDgCAx5HIAwAAjzOWKTINl24BAIBIoGcNAPAthsGjzElMkOO4aJ5F6kcn0d1wiFVqQRsWqe/sjs8ovF+blJ5WKUwtyiYluSpnjMV7bbC4EMVnx3FccrL7OhMt/vTZ7OME9/Wa4zXuCtp8rm6PxTYMgLGSdcsfPykAAIhhnu1ZAwDQnAbLFJk2ZdsSwRoA4FsMg7v04IMPynGckKVv376RrgYAgJjRKj3rCy+8UH/605++qMRiUgUAAKcTUJwCFv1Om7JtqVWiaEJCgrKyslpj0wAABDUYRw0WQ9k2ZdtSq/yk+OCDD9S1a1ede+65mjx5svbs2XPadWtra1VVVRWyAACAL0Q8WA8bNkzFxcVavXq1FixYoLKyMl1++eU6cuRIk+sXFRUpPT09uOTk5ES6SQCAr6lTE8xsFj+I+DD4uHHjgv+/+OKLNWzYMOXm5uqFF17QtGnTGq0/e/ZsFRYWBh9XVVURsAEAYTGWWbcMdzA7qWPHjurdu7dKS0ubfD05OVnJNnciAgDErAY5arBIxmFTti21+k+Ko0ePateuXcrOzm7tqgAA+FqKeLC+7777tH79eu3evVubNm3SjTfeqPj4eE2aNCnSVQEAYlzA2J63jvY7CE/Eh8E//vhjTZo0SQcPHlSXLl102WWXacuWLerSpUukqwIAxLiA5Tlrm7JtKeLB+rnnnov0JgEAiGmevbWY40iO4+LEf5LFW4pz9wvLSUp0XaXrtHeS6/ZK0Unr6erz/AdjkZYwailM41ymXHWf+dQqBaNVKkWLVKI2KUxdi7N4rxbHsWPz+bhMuWr12TS4Oxgd40jH3FfbEgE5ClhMErMp25Y8G6wBAGgOdzADAACeQM8aAOBbTDADAMDjArLMZ+2Tc9b++EkBAEAMo2cNAPAtYzkb3PikZ02wBgD4lm3mrJjNugUAQFuJlQlm/mglAAAxjJ41AMC3GAYHAMDjYuV2owyDAwAQpqKiIg0ZMkSpqanKyMjQxIkTtXPnzmbLLVu2TH379lVKSoouuugivfLKKy2ql2ANAPAtu1zWLR9CX79+vQoKCrRlyxatWbNG9fX1uvbaa1VdXX3aMps2bdKkSZM0bdo0/fnPf9bEiRM1ceJEvffee2HX6xhjPJV6u6qqSunp6fpGyq1KcFxkmbHJHOS3rFs2yLrV+lxm3VIgSl9Jm/1kLFKFRSPrlk32K4v3apN1y9TWuSsYhaxbJ0ydSg79lyorK5WWlua+/jM4FSvGrZ6uxLNcZiSTVF9dpz+O/U/t3bs3pK3JyclKTk5utvyBAweUkZGh9evX64orrmhyndtuu03V1dVatWpV8Lnhw4dr4MCBWrhwYVjt/Pqds7ZJG+k26Fp8AW0CvdUf9WilQ3TJkfv95CRa7GOL37LG5R86q8BnwyZo2rTZ5bFo9bnaHMM23zuL48n1j84Ei++6yx8IVmleoyQnJyfk8dy5c/Xggw82W66yslKS1KlTp9Ous3nzZhUWFoY8N2bMGK1YsSLs9n39gjUAIGZEajZ4Uz3rZssGApo1a5ZGjhyp/v37n3a98vJyZWZmhjyXmZmp8vLysNtJsAYA+FakgnVaWlqLh+wLCgr03nvvaePGja7rDxfBGgCAFpoxY4ZWrVqlDRs2qHv37mdcNysrSxUVFSHPVVRUKCsrK+z6mA0OAPAtoy+utXaztHQWgTFGM2bM0PLly7V27Vrl5eU1WyY/P18lJSUhz61Zs0b5+flh10vPGgDgW219B7OCggItXbpUK1euVGpqavC8c3p6utq1aydJuuOOO9StWzcVFRVJkr7//e/ryiuv1BNPPKHx48frueee09tvv63f/OY3YddLzxoA4FttfZ31ggULVFlZqVGjRik7Ozu4PP/888F19uzZo/379wcfjxgxQkuXLtVvfvMbDRgwQH/4wx+0YsWKM05K+yp61gAAhCmcW5OsW7eu0XO33HKLbrnlFtf1EqwBAL5FIg8AADwuVoI156wBAPA4etYAAN8yxpGx6B3blG1LBGsAgG+RzxoAAHiCZ3vWTlKSHDcpMhMtMmC5zeJjk/bOdUlZZf+xyvblNouP2yxUkl2WJItMbDYclxmWjM0xXH/CdVkrJ9ynMHV7PJn2Ke7rtGFzHLtNmypJbj/bZPfpI92WNQ21UkXz60VCrEww82ywBgCgObFyzpphcAAAPI6eNQDAtxgGBwDA42JlGJxgDQDwLWPZs/ZLsOacNQAAHkfPGgDgW0aSy6skg+X9gGANAPCtgBw53MEMAABEGz1rAIBvMRscAACPCxhHTgxcZ80wOAAAHkfPGgDgW8ZYzgb3yXRwgjUAwLc4Zx1tKSlSXMvTs7lOcylJJ1ymoLNIaagU9+nrbNIh2qRhVLzLFJk26QFtUjBavFdjkV7TfYpMl/tXUiDBor31Fvu4waJ7Eu/uuLDZT3IsjsWARYpMC86Jtv9zbZLcpSENNPgjAPqJd4M1AADNoGcNAIDHxcpscII1AMC3YmWCGZduAQDgcfSsAQC+dbJnbXPOOoKNaUUEawCAb8XKBDOGwQEA8Dh61gAA3zKyy0ntk1FwgjUAwL8YBgcAAJ5AzxoA4F8xMg5OsAYA+JflMLh8MgxOsAYA+BZ3MAMAAJ7g2Z61k5Qox0WKTNMu2X2lblPfJbtPc2ksUhqqziZFpvv0gibJ3WETSLJIaWjBpt5AgkWCgGR3n62xSCXqnHDfTQgkWdRrkV0zzmWbnUB0ukQ2o6Zu3+vJil3WWe8+pWf88XrXZdtKrMwG92ywBgCgWcax+wXlk2DNMDgAAB5HzxoA4FtMMDuNDRs2aMKECeratascx9GKFStCXjfGaM6cOcrOzla7du00evRoffDBB5FqLwAAXzARWHygxcG6urpaAwYM0Pz585t8/bHHHtPPf/5zLVy4UFu3btVZZ52lMWPGqKamxrqxAADEohYPg48bN07jxo1r8jVjjJ566in96Ec/0g033CBJeuaZZ5SZmakVK1bo9ttvt2stAABfEiuzwSM6waysrEzl5eUaPXp08Ln09HQNGzZMmzdvbrJMbW2tqqqqQhYAAML2NR8ClyIcrMvLyyVJmZmZIc9nZmYGX/uqoqIipaenB5ecnJxINgkAAN+L+qVbs2fPVmVlZXDZu3dvtJsEAPCJU8PgNosfRPTSraysLElSRUWFsrOzg89XVFRo4MCBTZZJTk5WcrLFXccAALErRrJuRbRnnZeXp6ysLJWUlASfq6qq0tatW5Wfnx/JqgAAkOREYPG+Fvesjx49qtLS0uDjsrIy7dixQ506dVKPHj00a9YsPfLIIzr//POVl5enH//4x+ratasmTpwYyXYDABAzWhys3377bV111VXBx4WFhZKkKVOmqLi4WA888ICqq6v1z//8zzp8+LAuu+wyrV69WikpKZFrNQAAUswMg7c4WI8aNUrmDPdncxxH8+bN07x586waBgBAswjW0WVSkmXiWz7xzKQkuq4z4LJsINliN9rMGmhwf5SZRPcV13dw936NxXut6eg+zeXxLu7PSdWeY5Fy0uVhkVTpvr1x7rOmKr7Wfdk4i7IJNe72cdIR96kf3dYp2aXmrG/n/ksQV+uyXpuUnrXuCpuAP84D+4lngzUAAM2KkRSZBGsAgG+RdQsAAHgCwRoA4F9RSJHZXKror1q3bp0cx2m0nO423E0hWAMA/OvUOWubpYWaSxV9Ojt37tT+/fuDS0ZGRthlOWcNAEALnClV9JlkZGSoY8eOruqkZw0A8C3H2C+SGqVqrq21uB7xNAYOHKjs7Gxdc801evPNN1tUlmANAPCvCJ2zzsnJCUnXXFRUFLEmZmdna+HChXrxxRf14osvKicnR6NGjdL27dvD3gbD4AAA/4rQddZ79+5VWlpa8OlIZoPs06eP+vTpE3w8YsQI7dq1S08++aR+97vfhbUNgjUAIOalpaWFBOvWNnToUG3cuDHs9QnWAAD/8um9wXfs2KHs7Oyw1ydYAwD8KwrBurlU0bNnz9Ynn3yiZ555RpL01FNPKS8vTxdeeKFqamr09NNPa+3atXrttdfCrpNgDQBACzSXKnr//v3as2dP8PW6ujr967/+qz755BO1b99eF198sf70pz+FbKM5BGsAgH9FoWfdXKro4uLikMcPPPCAHnjggZZX9CXeDdaJCVJ8y5tnkt2nyKxPS3JXMM79TMRAgvuydanu00Zacdnk453cXylY3cP9t7Fd30Ouyy6+eInrsiNT3L3f6z8Y67rO93Z3dV02vtz97NeUA+6P46Sj7srFNbiu0irNpXPCfdl4izbH17grHFfrvtL4IzWuypmGyF+jfPrKYiPrFtdZAwDgcd7tWQMA0Iwv34XMbXk/IFgDAPzLp5dutRTD4AAAeBzBGgAAj2MYHADgW44sz1lHrCWti2ANAPAvLt0CAABeQM8aAOBfMTIbnGANAPCvGAnWDIMDAOBx9KwBAL7FHcwAAPC6GBkG92ywbmifKCeh5VmwTqS6zJwlqSHFXRYrmww+gUT3lw1E6xdhXJ27iuNrLTJn7Xe/n2rrOrou+08H/sV12biUE67KJe1q57rODlWuiyrpiPvPJ6kqYFGvu6xQ8TXu67TLumVRb4NFxq7j9e7qrHefdcupdVlnwF05nJ5ngzUAAM2iZw0AgLfFyjlrZoMDAOBx9KwBAP4VI7cbJVgDAPyLc9YAAHgb56wBAIAn0LMGAPgXw+AAAHic5TC4X4I1w+AAAHgcPWsAgH8xDA4AgMfFSLBmGBwAAI+jZw0A8K1Yuc7as8HaMcZVGjub1HcJx1ymkrO4W11cnfuyCdUWqe9sDm6Xaf7ia9ylILVm8fmcaO9+8CkQn+yqXEKtu9SakuS4L6qE4+6PpziLtJFyezy5TBkpSbJoblydxU62UecyXeUJ95+r6l2+V1JkRhzD4AAAeJxne9YAADQrRiaYEawBAL7FOWsAAPzAJwHXBuesAQDwOHrWAAD/4pw1AADeFivnrBkGBwDA4+hZAwD8i2FwAAC8jWFwAADgCfSsAQD+xTA4AAAeFyPBmmFwAAA8zrM9a6c+ICfQ8tRuiZ8fd11nINnd7rBJyyljUdYizZ8TcF/YJLnbT/E1Nqkf3bfXsanX4vMJuNxPgRT3X0unwf1+iq+2yNdqwcS7zGEapePfbarKkxW7z9dqlerSrRNuU2S2XVtjZYKZZ4M1AADNipFhcII1AMC/YiRYc84aAACPa3Gw3rBhgyZMmKCuXbvKcRytWLEi5PU777xTjuOELGPHjo1UewEACDp1ztpm8YMWB+vq6moNGDBA8+fPP+06Y8eO1f79+4PLs88+a9VIAACaZCKw+ECLz1mPGzdO48aNO+M6ycnJysrKct0oAADwhVY5Z71u3TplZGSoT58+uueee3Tw4MHTrltbW6uqqqqQBQCAcDAM7tLYsWP1zDPPqKSkRD/5yU+0fv16jRs3Tg0NTV93V1RUpPT09OCSk5MT6SYBAL6uGAZ35/bbbw/+/6KLLtLFF1+s8847T+vWrdPVV1/daP3Zs2ersLAw+LiqqoqADQDAl7T6pVvnnnuuOnfurNLS0iZfT05OVlpaWsgCAEBY6FlHxscff6yDBw8qOzu7tasCAMQY5x+LTXk/aHHP+ujRo9qxY4d27NghSSorK9OOHTu0Z88eHT16VPfff7+2bNmi3bt3q6SkRDfccIN69eqlMWPGRLrtAAC0uebuN9KUdevW6ZJLLlFycrJ69eql4uLiFtXZ4mD99ttva9CgQRo0aJAkqbCwUIMGDdKcOXMUHx+vd999V9dff7169+6tadOmafDgwXrjjTeUnJzc0qoAADizKAyDh3O/kS8rKyvT+PHjddVVV2nHjh2aNWuW7r77br366qth19niYfBRo0bJnCETUUsqBwDARjSyboVzv5EvW7hwofLy8vTEE09Iki644AJt3LhRTz75ZNijzp5N5OEcr5PjJnWeRQq6+Hp3ad1Mgvt5eo7LOq1ZpNuLqzrmrqDFZ2PiLfaxTWpBixSZ8cfdtTnuaHRu2W+TXtPEuf9s42xSzLplk5q23n3KVZvvgGzSerpkXKbINAGLfdTiyhSRRB5fvcdHcnJyxEaEN2/erNGjR4c8N2bMGM2aNSvsbZDIAwAQ83JyckLu+VFUVBSxbZeXlyszMzPkuczMTFVVVen48eNhbcOzPWsAAMISgcGZvXv3hlw67LV5VgRrAIBvReqcdWve5yMrK0sVFRUhz1VUVCgtLU3t2rULaxsMgwMA0Iry8/NVUlIS8tyaNWuUn58f9jYI1gAA/4rCpVtnut+IdPI22nfccUdw/e9+97v68MMP9cADD+jvf/+7fvWrX+mFF17QvffeG3adDIMDAHwrGpduvf3227rqqquCj0/lt5gyZYqKi4u1f//+YOCWpLy8PL388su699579bOf/Uzdu3fX008/3aKbhRGsAQBogebuN9LU3clGjRqlP//5z67rJFgDAPwrQtdZex3BGgDgW9EYBo8GJpgBAOBx9KwBAP7FMDgAAB5HsAYAwNs4Zw0AADzBsz1r59hxOXEuUsIlxEe+Mc1wbNLe2aQlTE50XdaxSfPnNuWkTYo/m7I2qTltUim65CRYfC1t9lO8xXents59WZffWWPxXm2+s27TRp4sbHE8uU0lapFe1rj8XI2xOB5aXJkYBgcAwMscY+RY/AiyKduWGAYHAMDj6FkDAPyLYXAAALyN2eAAAMAT6FkDAPyLYXAAALyNYXAAAOAJ9KwBAP7FMDgAAN4WK8PgBGsAgH/FSM+ac9YAAHgcPWsAgK/5ZSjbhmeDtTl2TMZxkd0mMSnyjWlOnE3WLZcZrCQ59fWuy0Ylc5DbrEGybK9NVjSLz9a4zahmkSXJZh9bHccWTK27NltlRLPJbGbxnY3KcRxncTy5/Rtj3P9tanldxi6bGYk8AABAJHi2Zw0AQHOYDQ4AgNcxGxwAAHgBPWsAgG85gZOLTXk/IFgDAPyLYXAAAOAF9KwBAL7FbHAAALwuRm6KQrAGAPhWrPSsOWcNAIDH0bMGAPhXjMwGJ1gDAHyLYXAAAOAJnu1Zm/oTMk7Lf0tEJcmfTepHi5mIprbOdVkrAZe3/InSrEurWm3Sa7pMpWiVXdAmHaINt8eELFNdumWR5lLx8a6LGpt63f6dsUl96rLONv1MmQ0OAIC3MQwOAAA8gZ41AMC/mA0OAIC3MQwOAAA8gZ41AMC/Asbqihyrsm2IYA0A8C/OWQMA4G2OLM9ZR6wlrYtz1gAAeBw9awCAf3EHMwAAvI1LtwAAgCfQswYA+BezwQEA8DbHGDkW551tyrYl7wbruDjJRYpMK27TwdmkvbMRrYPMZTrEqKRClOzSIdocgzapCd2qs0ibavNejfsUma5TTtqkpm1wv5+cpCT3ZS1Srhq3XUCr/eQyzauJ0t/ErzHvBmsAAJoT+MdiU94HCNYAAN+KlWFwZoMDAOBxLQrWRUVFGjJkiFJTU5WRkaGJEydq586dIevU1NSooKBA55xzjjp06KCbbrpJFRUVEW00AACSvpgNbrO4MH/+fPXs2VMpKSkaNmyY3nrrrdOuW1xcLMdxQpaUlJQW1deiYL1+/XoVFBRoy5YtWrNmjerr63Xttdequro6uM69996rl156ScuWLdP69eu1b98+fetb32pRowAACMupO5jZLC30/PPPq7CwUHPnztX27ds1YMAAjRkzRp9++ulpy6SlpWn//v3B5aOPPmpRnS06Z7169eqQx8XFxcrIyNC2bdt0xRVXqLKyUr/97W+1dOlSfeMb35AkLV68WBdccIG2bNmi4cOHt6hxAACcSTTuYPbTn/5U06dP19SpUyVJCxcu1Msvv6xFixbphz/8YdP1OI6ysrJct9PqnHVlZaUkqVOnTpKkbdu2qb6+XqNHjw6u07dvX/Xo0UObN29uchu1tbWqqqoKWQAAaEtfjUO1tbVNrldXV6dt27aFxLm4uDiNHj36tHFOko4eParc3Fzl5OTohhtu0Pvvv9+i9rkO1oFAQLNmzdLIkSPVv39/SVJ5ebmSkpLUsWPHkHUzMzNVXl7e5HaKioqUnp4eXHJyctw2CQAQayI0DJ6TkxMSi4qKipqs7rPPPlNDQ4MyMzNDnj9TnOvTp48WLVqklStX6ve//70CgYBGjBihjz/+OOy36frSrYKCAr333nvauHGj201IkmbPnq3CwsLg46qqKgI2ACAsTuDkYlNekvbu3au0tLTg88nJyZYt+0J+fr7y8/ODj0eMGKELLrhAv/71r/Xwww+HtQ1XwXrGjBlatWqVNmzYoO7duwefz8rKUl1dnQ4fPhzSu66oqDjtWH1ycnJEdwoAAC2VlpYWEqxPp3PnzoqPj290ldOZ4txXJSYmatCgQSotLQ27fS0aBjfGaMaMGVq+fLnWrl2rvLy8kNcHDx6sxMRElZSUBJ/buXOn9uzZE/KrAgCAiGjj2eBJSUkaPHhwSJwLBAIqKSkJO841NDToL3/5i7Kzs8Out0U964KCAi1dulQrV65UampqcHw+PT1d7dq1U3p6uqZNm6bCwkJ16tRJaWlpmjlzpvLz85kJDgCIvChk3SosLNSUKVN06aWXaujQoXrqqadUXV0dnB1+xx13qFu3bsHz3vPmzdPw4cPVq1cvHT58WI8//rg++ugj3X333WHX2aJgvWDBAknSqFGjQp5fvHix7rzzTknSk08+qbi4ON10002qra3VmDFj9Ktf/aol1QAA4Fm33XabDhw4oDlz5qi8vFwDBw7U6tWrg5PO9uzZo7i4LwauDx06pOnTp6u8vFxnn322Bg8erE2bNqlfv35h1+mYqKVCalpVVZXS09P1jdTJSnBant3Gibe4Go2sW+Eh61Z4opF1K1rv1WdZt2zaa5N1y+13R5JMQ9tnnHD7t+2Eqde6wH+rsrIyrPPAbpyKFVdd+m9KSGjZ3cC+7MSJGr3+9qOt2tZI8GwiD1NXL+Pmb51FCjrXfzSiFISsfiTY/KGLJTZBSFEIQlH7ceHyvUquf2BYHf82+6n+hPuyVseT2yqj8KOmLd+ny7uQhZT3ARJ5AADgcZ7tWQMA0Cwju5zU/uhYE6wBAP4VK/msCdYAAP8ysjxnHbGWtCrOWQMA4HH0rAEA/hUjs8EJ1gAA/wpIsrnqsO2vpnOFYXAAADyOnjUAwLeYDQ4AgNfFyDlrhsEBAPA4etYAAP+KkZ41wRoA4F8xEqwZBgcAwOO827NuaHCVxs7YpL6LQr5YK1FItyfJLr2gWxbpG019dH45O3KZwjFKeaWjtZ+ichzb7KcTFrmw3abhtRGtvxNtJUaus/ZusAYAoBlcugUAgNdxzhoAAHgBPWsAgH8FjORY9I4D/uhZE6wBAP7FMDgAAPACetYAAB+z7FnLHz1rgjUAwL8YBgcAAF5AzxoA4F8BI6uhbGaDAwDQykzA7paqPrkdK8PgAAB4HD1rAIB/xcgEM88Ga9PQ4CqDlmOR1MZE4dyFY5NNyqa9NkM/NncLcstlAitJURvmMm6Tbtkcww02O8qCTaawaGRxs2GTscviO2vzt8I1t4GsLQMg56wBAPC4GOlZ++wnLQAAsYeeNQDAv4wse9YRa0mrIlgDAPyLYXAAAOAF9KwBAP4VCEiyuOIj4I+bohCsAQD+xTA4AADwAnrWAAD/ipGeNcEaAOBfMXIHM4bBAQDwOHrWAADfMiYgY3O/dp+kyCRYAwD8yxi7oWzOWQMA0MqM5TlrgnV0RCVFoEWKv6ilubThtl6bL4VjkR7QJ1/GU8yJE9FuQsvZZG+MxnEctWPCYrj2hMs223x34Blfu2ANAIghgYDkWPzg45w1AACtLEaGwbl0CwAAj6NnDQDwLRMIyFgMg3PpFgAArY1hcAAA4AX0rAEA/hUwkvP171kTrAEA/mWMbK5f90uwZhgcAACPo2cNAPAtEzAyFsPgxic9a4I1AMC/TEB2w+BcugUAQKuKlZ4156wBAPA4z/WsT/3KOWHqo9ySlojSbx6fDN8EWf2CjZ2sW75kcyzGVNataBzHbV/nCdX/o3jr7+cTptbqGDrVVq/zXLA+cuSIJGmjXra6KU2b8ks7/Yx97G0++90YNdE4jqP43Tly5IjS09NbZdtJSUnKysrSxvJXrLeVlZWlpKSkCLSq9TjGYwP2gUBA+/btU2pqqpwm8rBWVVUpJydHe/fuVVpaWhRa6A/sp/Cwn5rHPgoP++kLxhgdOXJEXbt2VVxc64081tTUqK6uzno7SUlJSklJiUCLWo/netZxcXHq3r17s+ulpaXF/BciHOyn8LCfmsc+Cg/76aTW6lF/WUpKiueDbKQwwQwAAI8jWAMA4HG+C9bJycmaO3eukpOTo90UT2M/hYf91Dz2UXjYT2hNnptgBgAAQvmuZw0AQKwhWAMA4HEEawAAPI5gDQCAxxGsAQDwOF8F6/nz56tnz55KSUnRsGHD9NZbb0W7SZ7y4IMPynGckKVv377RblbUbdiwQRMmTFDXrl3lOI5WrFgR8roxRnPmzFF2drbatWun0aNH64MPPohOY6Oouf105513Njq+xo4dG53GRlFRUZGGDBmi1NRUZWRkaOLEidq5c2fIOjU1NSooKNA555yjDh066KabblJFRUWUWoyvA98E6+eff16FhYWaO3eutm/frgEDBmjMmDH69NNPo900T7nwwgu1f//+4LJx48ZoNynqqqurNWDAAM2fP7/J1x977DH9/Oc/18KFC7V161adddZZGjNmjGpqatq4pdHV3H6SpLFjx4YcX88++2wbttAb1q9fr4KCAm3ZskVr1qxRfX29rr32WlVXVwfXuffee/XSSy9p2bJlWr9+vfbt26dvfetbUWw1fM/4xNChQ01BQUHwcUNDg+nataspKiqKYqu8Ze7cuWbAgAHRboanSTLLly8PPg4EAiYrK8s8/vjjwecOHz5skpOTzbPPPhuFFnrDV/eTMcZMmTLF3HDDDVFpj5d9+umnRpJZv369Mebk8ZOYmGiWLVsWXOdvf/ubkWQ2b94crWbC53zRs66rq9O2bds0evTo4HNxcXEaPXq0Nm/eHMWWec8HH3ygrl276txzz9XkyZO1Z8+eaDfJ08rKylReXh5ybKWnp2vYsGEcW01Yt26dMjIy1KdPH91zzz06ePBgtJsUdZWVlZKkTp06SZK2bdum+vr6kGOqb9++6tGjB8cUXPNFsP7ss8/U0NCgzMzMkOczMzNVXl4epVZ5z7Bhw1RcXKzVq1drwYIFKisr0+WXXx7MEY7GTh0/HFvNGzt2rJ555hmVlJToJz/5idavX69x48apoaEh2k2LmkAgoFmzZmnkyJHq37+/pJPHVFJSkjp27BiyLscUbHguRSbcGzduXPD/F198sYYNG6bc3Fy98MILmjZtWhRbhq+D22+/Pfj/iy66SBdffLHOO+88rVu3TldffXUUWxY9BQUFeu+995gbglbni551586dFR8f32g2ZUVFhbKysqLUKu/r2LGjevfurdLS0mg3xbNOHT8cWy137rnnqnPnzjF7fM2YMUOrVq3S66+/ru7duwefz8rKUl1dnQ4fPhyyPscUbPgiWCclJWnw4MEqKSkJPhcIBFRSUqL8/Pwotszbjh49ql27dik7OzvaTfGsvLw8ZWVlhRxbVVVV2rp1K8dWMz7++GMdPHgw5o4vY4xmzJih5cuXa+3atcrLywt5ffDgwUpMTAw5pnbu3Kk9e/ZwTME13wyDFxYWasqUKbr00ks1dOhQPfXUU6qurtbUqVOj3TTPuO+++zRhwgTl5uZq3759mjt3ruLj4zVp0qRoNy2qjh49GtL7Kysr044dO9SpUyf16NFDs2bN0iOPPKLzzz9feXl5+vGPf6yuXbtq4sSJ0Wt0FJxpP3Xq1EkPPfSQbrrpJmVlZWnXrl164IEH1KtXL40ZMyaKrW57BQUFWrp0qVauXKnU1NTgeej09HS1a9dO6enpmjZtmgoLC9WpUyelpaVp5syZys/P1/Dhw6PcevhWtKejt8QvfvEL06NHD5OUlGSGDh1qtmzZEu0mecptt91msrOzTVJSkunWrZu57bbbTGlpabSbFXWvv/66kdRomTJlijHm5OVbP/7xj01mZqZJTk42V199tdm5c2d0Gx0FZ9pPx44dM9dee63p0qWLSUxMNLm5uWb69OmmvLw82s1uc03tI0lm8eLFwXWOHz9uvve975mzzz7btG/f3tx4441m//790Ws0fI981gAAeJwvzlkDABDLCNYAAHgcwRoAAI8jWAMA4HEEawAAPI5gDQCAxxGsAQDwOII1AAAeR7AGAMDjCNYAAHgcwRoAAI/7/whV5yFBl1UDAAAAAElFTkSuQmCC", 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", 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HjgQA3Lx5E1lZWbVu/49//APAD/vz+PHjKCsrq7HdJ598gmvXrjm14+rVq/jyyy9x8+ZNx7olS5Zgz549eOutt+odhq2qqsL69esdjysqKrB+/Xq0adMGcXFxLr1XT/Lx8cHEiRPx7rvv1vrD6MqVK03WlubNm6O4uLjG+kceeQTfffcd/v73v9d47tatW7V+bmQsc+bMQVZWFjZv3oxTp07h4YcfxpgxY3Du3Llat3/llVfQuXNnDB06tIlbqh571j8zc+fOxc2bNzFhwgR07doVFRUVOHr0KLZs2YIOHTo4ziMCQG5ubq1DxH379sW4cePwzjvvYNy4cbj77rtrzGCWm5uLF154AYMHDwZwO9gJIWrtEQPA2LFj4evri02bNjldsPRTU6dOxbJly5CTk+O0fteuXQgICKhxwdXevXuxePFi3H///Rg0aBBatGiBr776Chs2bIDVasWzzz5bo4533nkHLVq0QEVFhWMGs48//hixsbHYunWrY7ubN29i8ODBGDRoEMaMGYOoqCgUFRVh+/bt+PDDD5GSkoK+ffsCuN0r37RpEyZMmIC4uDj4+/vjX//6FzZs2ICAgAD87ne/c7zuSy+9hCVLluDgwYNISEjA559/jmXLlmHYsGG4fPky3nzzTaf2/nhkIzIyEs8//zy+/vprdO7cGVu2bEFOTg5efvll+Pn51blfG9Of/vQnHDx4EAMHDsSsWbPQvXt3fP/99zh58iT27dvX4G1vnhIXF4ctW7Zg/vz56N+/P1q0aIHx48fjV7/6Fd5++23Mnj0bBw8exJAhQ2Cz2fDll1/i7bffxp49exwjT2Q8+fn52LhxI/Lz8x2zFT711FPYvXt3rVPWlpeXY9OmTfh//+//adFc9bS7EJ0awz/+8Q/x6KOPiq5du4oWLVoIf39/0alTJzF37twaM5gBqHWZOXOmY7u8vDwxa9Ys0b59e+Hn5ydat24t7r///hq3wfTq1Uu0b9++3rYlJCSI0NBQUVlZ6XTr1k9t3LjR0Zbq26weeughMXbs2BrbfvXVV2LRokVi0KBBIjQ0VPj6+oo2bdqIcePGOd32JMQPt25VLwEBAaJdu3bivvvuExs2bBDl5eVO21dWVoq///3vIiUlRURHRwuz2SyaNWsm+vbtK1asWCGsVqtj21OnTomnn35a3H333SIkJET4+vqKiIgI8fDDD4uTJ0/W2o7q246qb0Oqa6k2fPhw0aNHD3HixAkRHx8vAgICRHR0tHjppZecXr/69bZu3Vpjf9V161aPHj1qbFvX7X0ARFpamtO6wsJCkZaWJqKiooSfn58IDw8XI0eOFC+//HKN8vWRuXWrtLRU/OIXvxAtW7Z03MpXraKiQjz//POiR48ewmw2i1atWom4uDixZMkSp1nuantvpC8AxLZt2xyPd+7c6bi98seLr6+veOSRR2qUz8zMFL6+vqKgoKAJWy1PEUJyJgmiRlZVVYU77rgD6enp+M1vfqN1czSTkJCAq1evSk94Q2RkiqJg27ZtSElJAQBs2bIFU6dOxZkzZ2pcENmiRQuEh4c7rRs5ciQsFotLkzDpCYfBSfe+//57PPnkk5gwYYLWTSEinenbty9sNhsuX77c4DnovLw8HDx40K05J/SCwZp0LzQ0tNZzz2QcV65cgc1mq/N5f39/hISENGGLyEhKS0uRm5vreJyXl4ecnByEhISgc+fOmDp1KqZNm4aVK1eib9++uHLlCvbv34/evXs7XZS6YcMGRERE1HtluV4xWBNRo+vfvz+++eabOp8fPny4U3IRoh87ceKE08Wl1RMGpaamIiMjAxs3bsRzzz2H3/72t/juu+/QunVrDBo0CPfdd5+jjN1uR0ZGBqZPn17n/AF6xnPWRNToPv74Y6eELz/VqlUrTW49IzIKBmsiIiKd46QoREREOqe7c9Z2ux0XL15EUFAQpwYkIjIgIQRu3LiByMhImEyN1ycsLy9HRUWF9Ov4+/sjICDAAy1qPLoL1hcvXqwxsT4RERnPhQsX0K5du0Z57fLycsREt0DB5brvMnBVeHg48vLydB2wdResq9MT3oOx8IU20ydSA9SOeGh1eYTECI2iwVWjop5bnBourNE+NknsJ2H3XDtcpajv7Skm9ceT1GerutKmPyaqUImPsKtGullPqqioQMFlG/Kyo2EJUv95ltywIybuG1RUVDBYu6N66NsXfvBVGKx1SXXwM2CwVjQI1hKBRLt9LLOfDBasJY4nuc9Wda2aVdkUpzItQSapYG0UjfYO16xZgw4dOiAgIAADBw7EJ5980lhVERGRl7IJu/RiBI0SrKsz3yxevBgnT55EbGwskpKScPny5caojoiIvJQdQnoxgkYJ1n/5y18wa9YszJgxA927d8e6devQrFkzbNiwoTGqIyIiL2X3wD8j8HiwrqioQHZ2NhITE3+oxGRCYmIisrKyamxvtVpRUlLitBAREdEPPB6sr169CpvNhrCwMKf1YWFhKCgoqLF9eno6goODHQtv2yIiIlfZhJBejEDzS+gWLlyI4uJix3LhwgWtm0RERAbhLeesPX7rVuvWreHj44PCwkKn9YWFhTWSgAOA2WyG2Wz2dDOIiIh+Njzes/b390dcXBz279/vWGe327F//37Ex8d7ujoiIvJidgjYJBav7VkDt3ONpqamol+/fhgwYABWr16NsrIyzJgxozGqIyIiLyU7lO3VwXrSpEm4cuUKFi1ahIKCAvTp0we7d++ucdEZERERNazRphudM2cO5syZ01gvT0REJH1Ft1GuBtfd3OBkAKrnN9Zo8gGJ+Zg1SbwgQ6O0sjLJLQB184oLuzZ/ZGXqNVxiGNWUJpuS3A65vyzGmBJFB7duERERUf3YsyYiIsOqvqpbprwRMFgTEZFh2cTtRaa8ETBYExGRYfGcNREREekCe9ZERGRYdiiwQf3dCHaJsk2JwZqIiAzLLm4vMuWNgMPgREREOseeNRERGZZNchhcpmxTYrAmIiLD8pZgzWFwIiIinWPPmoiIDMsuFNiFxNXgEmWbEoM1EREZFofBiYiISBf0G6wVhYteF2FXuQj1iwzV7dUwpacGi+Ljo3qRqlv1blK0WWT2k8ziJZ+ru2wwSS/uWLt2LXr37g2LxQKLxYL4+Hj84x//qLfM1q1b0bVrVwQEBKBXr17YtWuX2+9Tv8GaiIioAeLf56zVLsLNc9bt2rXDn/70J2RnZ+PEiRO499578cADD+DMmTO1bn/06FFMmTIFM2fOxKeffoqUlBSkpKTg9OnTbtWrCCHbbfGskpISBAcHI0FJga/ip3VzyJNkDjXFGOeVPKIJeyXO1UrsY5k2azWCoZbMe5XZx2qn2pLYv0JlnVWiEofs/4Pi4mJYLBbV9denOlZ88Hk0mgep/0zKbtgxutc3Um0NCQnBihUrMHPmzBrPTZo0CWVlZdi5c6dj3aBBg9CnTx+sW7fO5TrYsyYiIq9XUlLitFit1gbL2Gw2bN68GWVlZYiPj691m6ysLCQmJjqtS0pKQlZWllvtY7AmIiLDsgmT9AIAUVFRCA4Odizp6el11vn555+jRYsWMJvNmD17NrZt24bu3bvXum1BQQHCwsKc1oWFhaGgoMCt98lbt4iIyLDsUGCX6HfacXuo/8KFC07D4Gazuc4yXbp0QU5ODoqLi/HOO+8gNTUVhw8frjNgewKDNREReb3qq7td4e/vj06dOgEA4uLi8M9//hMvvPAC1q9fX2Pb8PBwFBYWOq0rLCxEeHi4W+3jMDgRERlW9aQoMossu91e5znu+Ph47N+/32nd3r176zzHXRf2rImIyLB+fN5ZXXn3rnhfuHAhkpOT0b59e9y4cQOZmZk4dOgQ9uzZAwCYNm0a2rZt6zjn/cQTT2D48OFYuXIlxo0bh82bN+PEiRN4+eWX3aqXwZqIiMhFly9fxrRp03Dp0iUEBwejd+/e2LNnD0aNGgUAyM/Ph8n0w4+HwYMHIzMzE8888wx+97vf4a677sL27dvRs2dPt+plsCYiIsO6fYGZRCIPN8u++uqr9T5/6NChGusefvhhPPzww27V81MM1kREZFh2FVOGOpfX1bxgdeIFZkRERDrHnjURERlWU19gphX9BmuVmVtk5jdWOw+u1NzGGs0DrUmbpaaelvhcbTb1FctQuZ80m6Nbol5FYu52ofbj0eh7p/ioLyuTikF9vRLHRFWVqmKKUIAmmvLdDpNHJkXRO/0GayIiogbYhAKbm5mzflreCHjOmoiISOfYsyYiIsOySV4NbuMwOBERUeOyCxPsEheY2Q1ygRmHwYmIiHSOPWsiIjIsDoMTERHpnB1yV3Q30R1m0jgMTkREpHPsWRMRkWHJT4pijD4rgzURERmW/HSjxgjWxmglERGRF2PPmoiIDKup81lrhcGaiIgMy1uGwRmsiYjIsOTvs2awNhy1qQmFXSbdno/qsjIpDaWoTSUq0161dQJQJHaxFJWfrVy6SfV3jcrUK0PxU/lnyK7RHbIS31lFg6ktZVLEKr7qPhtF2AF12TWpDgzWRERkWHahwC4zKYpBUmQyWBMRkWHZJYfBjXKftTFaSURE5MXYsyYiIsOST5FpjD4rgzURERmWDQpsEvdKy5RtSsb4SUFEROTF2LMmIiLD4jA4ERGRztkgN5St/i70pmWMnxRERERejD1rIiIyLA6DExER6RwTeRAREemckEyRKXjrFhEREXkCe9ZERGRYHAbXmOLnC0VR0TyJVIpqmQL91BfWIGUeIJc2T5PUnFLpNSVSmKpN3whA8Vd5XMgcw1USeQll0rXKHE9qU136qf/eqf5sALn3qjLlJAAIa4WqcpqkPhVNl5fWW7JuGeMnBRERkRfTbc+aiIioITbJFJkyZZsSgzURERkWh8FVevbZZ6EoitPStWtXT1dDRETkNRqlZ92jRw/s27fvh0okLqogIiKqix0m2CX6nTJlm1KjRFFfX1+Eh4c3xksTERE52IQCm8RQtkzZptQoPynOnTuHyMhIdOzYEVOnTkV+fn6d21qtVpSUlDgtRERE9AOPB+uBAwciIyMDu3fvxtq1a5GXl4ehQ4fixo0btW6fnp6O4OBgxxIVFeXpJhER0c9U9QVmMosReHwYPDk52fH/3r17Y+DAgYiOjsbbb7+NmTNn1th+4cKFmD9/vuNxSUkJAzYREblESGbdEpzB7LaWLVuic+fOyM3NrfV5s9kMs9nc2M0gIqKfIRsU2CSScciUbUqN/pOitLQU58+fR0RERGNXRURE9LPk8WD91FNP4fDhw/j6669x9OhRTJgwAT4+PpgyZYqnqyIiIi9nF7Lnrd2rLz09Hf3790dQUBBCQ0ORkpKCs2fP1lsmIyOjxvwjAQEBbtXr8WHwb7/9FlOmTMG1a9fQpk0b3HPPPTh27BjatGnj6aqIiMjL2SXPWbtb9vDhw0hLS0P//v1RVVWF3/3udxg9ejS++OILNG/evM5yFovFKai7m2DF48F68+bNnn5JIiIiXdi9e7fT44yMDISGhiI7OxvDhg2rs5yiKFLzj+h2ajHFxwRFUZFmzVfiYgGTul9nMun2RLlVdVkZir9/09cpkapPJqWnTJpLKWpTTtrVp7lUZGYLlElD6iNxRk0mNadaMvtJ4jiW+nxsKlOJynw2KlP4KnYFKFNfrTvsUGCXuEisuuxP5/hw9eLn4uJiAEBISEi925WWliI6Ohp2ux133303/vjHP6JHjx4ut9MY16wTERHVonoGM5kFAKKiopzm/EhPT2+wbrvdjnnz5mHIkCHo2bNnndt16dIFGzZswI4dO/Dmm2/Cbrdj8ODB+Pbbb11+n7rtWRMRETWVCxcuwGKxOB670qtOS0vD6dOn8dFHH9W7XXx8POLj4x2PBw8ejG7dumH9+vVYtmyZS+1jsCYiIsPy1AVmFovFKVg3ZM6cOdi5cyeOHDmCdu3auVWnn58f+vbtW+f8I7XhMDgRERmWHZLTjbp5vlsIgTlz5mDbtm04cOAAYmJi3G6zzWbD559/7tb8I+xZExERuSgtLQ2ZmZnYsWMHgoKCUFBQAAAIDg5GYGAgAGDatGlo27at47z30qVLMWjQIHTq1AlFRUVYsWIFvvnmGzz22GMu18tgTUREhiUkrwYXbpZdu3YtACAhIcFp/caNGzF9+nQAQH5+Pkw/urvo+vXrmDVrFgoKCtCqVSvExcXh6NGj6N69u8v1MlgTEZFhyWbOcrescOF2tkOHDjk9XrVqFVatWuVWPT/FYE1ERIbV1DOYacUYrSQiIvJi7FkTEZFhNfUwuFYYrImIyLA8Nd2o3nEYnIiISOfYsyYiIsPiMDgREZHOMVhrzW4HFPdTwkmlflSZqk/xU58iE3Z1KegAAEJlyjxALi2hqelTGsp8nRRfDVIwAhBVKtN6+mv0x0ORSaXY9Mei3He96dNGApD6vqtOrynzXq0VqoopMn/XqFb6DdZEREQNYM+aiIhI57wlWPNqcCIiIp1jz5qIiAxLQO5eaaOcXWewJiIiw/KWYXAGayIiMixvCdY8Z01ERKRz7FkTEZFheUvPmsGaiIgMy1uCNYfBiYiIdI49ayIiMiwhFAiJ3rFM2abEYE1ERIbFfNZERESkC7rtWSsBZiiKiqw6ajPTQCJ7lkwGK5kMPhKkMoWpzWJlk8nMpNHvSpP6ehW7uvcrJLKEKTL7WOZYlKlX5fsVzQLU16nys7ldVmI/mSR6cZVV6sr5S3zXzeoymwmbFShUX607vOUCM90GayIiooZ4yzlrDoMTERHpHHvWRERkWBwGJyIi0jlvGQZnsCYiIsMSkj1rowRrnrMmIiLSOfasiYjIsATk7jrU5uZZ9zFYExGRYdmhQOEMZkRERKQ19qyJiMiweDU4ERGRztmFAsUL7rPmMDgREZHOsWdNRESGJYTk1eAGuRycwZqIiAyL56w1ppgDoJhUpGeTSf1YpTIFnURKQwSaVRdVKipVlxV+Eh+92rIS6SZRZVNfViItofBv+q+IkNlPMie2JLJGKjaJz0dR9/kIP4nvnco6ZcnEBVO5yu+7zHtVuY+FzSDdVQPRbbAmIiJqCHvWREREOuctV4MzWBMRkWF5ywVmvHWLiIhI59izJiIiw7rds5Y5Z+3BxjQiBmsiIjIsb7nAjMPgREREOseeNRERGZaAXE5qg4yCs2dNRETGVT0MLrO4Iz09Hf3790dQUBBCQ0ORkpKCs2fPNlhu69at6Nq1KwICAtCrVy/s2rXLrXoZrImIiFx0+PBhpKWl4dixY9i7dy8qKysxevRolJWV1Vnm6NGjmDJlCmbOnIlPP/0UKSkpSElJwenTp12ul8PgRERkXE08Dr57926nxxkZGQgNDUV2djaGDRtWa5kXXngBY8aMwdNPPw0AWLZsGfbu3YuXXnoJ69atc6le9qyJiMi4ZIfA/z0MXlJS4rRYrVaXqi8uLgYAhISE1LlNVlYWEhMTndYlJSUhKyvL5bfJYE1ERIZVPYOZzAIAUVFRCA4Odizp6ekN1m232zFv3jwMGTIEPXv2rHO7goIChIWFOa0LCwtDQUGBy++Tw+BEROT1Lly4AIvF4nhsNjecETEtLQ2nT5/GRx991JhNA6DnYG32A1SkyBRmFWk1q9lVpteUSDcplebPX306UOEjMfF9M3X7WEikqpRh91e/j21m9YNPdn91ZW3+6veTT4X6k3dCYpxNkUqvqa7NpgqJSiUIX/U7ylQp0eYW6r53MnX6lFaoKidkDia36/LMpCgWi8UpWDdkzpw52LlzJ44cOYJ27drVu214eDgKCwud1hUWFiI8PNzl+jgMTkRExlV93llmcac6ITBnzhxs27YNBw4cQExMTINl4uPjsX//fqd1e/fuRXx8vMv16rdnTUREpDNpaWnIzMzEjh07EBQU5DjvHBwcjMDAQADAtGnT0LZtW8d57yeeeALDhw/HypUrMW7cOGzevBknTpzAyy+/7HK97FkTEZFheeoCM1etXbsWxcXFSEhIQEREhGPZsmWLY5v8/HxcunTJ8Xjw4MHIzMzEyy+/jNjYWLzzzjvYvn17vRel/ZTbwfrIkSMYP348IiMjoSgKtm/f7vS8EAKLFi1CREQEAgMDkZiYiHPnzrlbDRERUcOEBxZ3qhOi1mX69OmObQ4dOoSMjAyncg8//DDOnj0Lq9WK06dPY+zYsW7V63awLisrQ2xsLNasWVPr88uXL8df//pXrFu3DsePH0fz5s2RlJSE8vJyd6siIiIiqDhnnZycjOTk5FqfE0Jg9erVeOaZZ/DAAw8AAF5//XWEhYVh+/btmDx5slxriYiIfoQpMlXIy8tDQUGB00wtwcHBGDhwYJ0ztVit1hozxxAREbmsiYbAteTRYF19VZw7M7Wkp6c7zRoTFRXlySYREREZnuZXgy9cuBDFxcWO5cKFC1o3iYiIDKKpU2RqxaP3WVfPxlJYWIiIiAjH+sLCQvTp06fWMmaz2aVp3YiIiGpo4qxbWvFozzomJgbh4eFOM7WUlJTg+PHjbs3UQkRE5BrFA4v+ud2zLi0tRW5uruNxXl4ecnJyEBISgvbt22PevHl47rnncNdddyEmJgZ/+MMfEBkZiZSUFE+2m4iIyGu4HaxPnDiBESNGOB7Pnz8fAJCamoqMjAwsWLAAZWVl+I//+A8UFRXhnnvuwe7duxEQEOC5VhMREQFeMwzudrBOSEiAqGd+NkVRsHTpUixdulSqYURERA1isNaW8POD8HE/BaQIUJ820q6yrN0ssRtlrhpQmVoQkEvzVxnU9IdNWZj6NJflrdWfk6poqX4f2/3UlfUvkjgoJDIw+t5SX9bHKlG2XN1+Mt9Q/9n43tImvabNrP449ilX12ZhUn88+ZSrLKsY4zywkeg2WBMRETVIRZrLGuUNgMGaiIgMS03mrJ+WNwLNJ0UhIiKi+rFnTURExsULzIiIiHTOS85ZcxiciIhI59izJiIiw1LE7UWmvBEwWBMRkXHxnDUREZHO8Zw1ERER6QF71kREZFwcBiciItI5LwnWHAYnIiLSOfasiYjIuLykZ63fYO3nC/i43zzhr/4tVbY0qy6rljCpvxKxvJX6dHuKRIZAobLa8hD1Azml7dV/o4K6XlNd9rVeb6guG2f2V1Vuct69quv85HwH1WVNV9S1FwACC9V/tn6l6sr5VMgcxOqLmqq0yRrhW25TVU6pVL+fTGXqcp+abBWq63QbrwYnIiIiPdBvz5qIiKgBnMGMiIhI77zknDWHwYmIiHSOwZqIiEjnOAxORESGpUDynLXHWtK4GKyJiMi4eOsWERER6QF71kREZFxecjU4gzURERmXlwRrDoMTERHpHHvWRERkWJzBjIiISO+8ZBhct8Ha1twfiq/7WbCqgtRnDrKZ1Z0VUOzqP22ZrFsymbNk2uxrVVe2yqz+vbbIV1/2ZnmI6rIPXXtcdVkfs7osST7nA1XX2aJI/X7yK1V/TPiXqD8Y/UvVlfW5pW7/AoDJpv69KhJZt5QqiQxY5VXq6qxUv59QUamunE1lOaqTboM1ERFRg9izJiIi0jdvOWfNq8GJiIh0jj1rIiIyLi+ZbpTBmoiIjIvnrImIiPSN56yJiIiohiNHjmD8+PGIjIyEoijYvn17vdsfOnQIiqLUWAoKClyuk8GaiIiMS3hgcVNZWRliY2OxZs0at8qdPXsWly5dciyhoaEul+UwOBERGZfkMHh1sC4pKXFabTabYTbXPjFXcnIykpOT3a4qNDQULVu2dLscwJ41ERERoqKiEBwc7FjS09M9XkefPn0QERGBUaNG4eOPP3arLHvWRERkXB66GvzChQuwWCyO1XX1qtWIiIjAunXr0K9fP1itVrzyyitISEjA8ePHcffdd7v0GgzWRERkXB4K1haLxSlYe1KXLl3QpUsXx+PBgwfj/PnzWLVqFd544w2XXoPD4ERERE1swIAByM3NdXl79qyJiMiwjHqfdU5ODiIiIlzeXrfBWrELKDb308lJpX68qTKVnMyHLTHTnW+ZROo7CWr3sU+5RCpRif0UnKe+bMUZP9VlFaGurJrjvppPhfp97FeqLgWjLNXHU5lEGkaZFJk29d87mRSZqFSZIlPieIK1Ql2ddqv6Og2gtLTUqVecl5eHnJwchISEoH379li4cCG+++47vP766wCA1atXIyYmBj169EB5eTleeeUVHDhwAB988IHLdeo2WBMREenRiRMnMGLECMfj+fPnAwBSU1ORkZGBS5cuIT8/3/F8RUUFfvvb3+K7775Ds2bN0Lt3b+zbt8/pNRrCYE1ERMalwdzgCQkJEKLughkZGU6PFyxYgAULFrhf0Y8wWBMRkWEZ9Zy1uxisiYjI2AwScGXw1i0iIiKdY8+aiIiMi/msiYiI9M1bzllzGJyIiEjn2LMmIiLj4jA4ERGRvnEYnIiIiHSBPWsiIjIuDoMTERHpnJcEaw6DExER6Zxue9aK1QbFx/1UdH7Xy1XXaff3UVVO6uKGSok0l/VMJN+YZYW/usPGZFW3fwHAJJFaULmlPpVigJ/6NsNX3W9hu5/639AyKWJ9bmiT1tCu8nhS7BLHhMz3TmWqSgBy31kt6lS7jyWOQ3d5ywVmug3WREREDfKSYXAGayIiMi4vCdY8Z01ERKRzbgfrI0eOYPz48YiMjISiKNi+fbvT89OnT4eiKE7LmDFjPNVeIiIih+pz1jKLEbgdrMvKyhAbG4s1a9bUuc2YMWNw6dIlx/LWW29JNZKIiKhWwgOLAbh9zjo5ORnJycn1bmM2mxEeHq66UURERPSDRjlnfejQIYSGhqJLly54/PHHce3atTq3tVqtKCkpcVqIiIhcwWFwlcaMGYPXX38d+/fvx/PPP4/Dhw8jOTkZNlvt9zWmp6cjODjYsURFRXm6SURE9HPFYXB1Jk+e7Ph/r1690Lt3b9x55504dOgQRo4cWWP7hQsXYv78+Y7HJSUlDNhEREQ/0ui3bnXs2BGtW7dGbm5urc+bzWZYLBanhYiIyCXsWXvGt99+i2vXriEiIqKxqyIiIi+j/HuRKW8Ebgfr0tJSp15yXl4ecnJyEBISgpCQECxZsgQTJ05EeHg4zp8/jwULFqBTp05ISkryaMOJiIi8hdvB+sSJExgxYoTjcfX55tTUVKxduxanTp3Ca6+9hqKiIkRGRmL06NFYtmwZzGaz51pNREQEeM10o24H64SEBIh6srjs2bNHqkFERESuYtYtjSkVlVB8VFz/Vqn+DISpSuXu8FFfp1IhkW5Phk19ekFTqbo0pBLJJiF81ZdWKtSnyITS9Ge0TBLvFSaJ9JoSqR+Fmu/qv5msFarLqqVIHP+okkivKUNtqkuZdLiV6r47wi7xnXO7MnhFz5qJPIiIiHROtz1rIiIilxikdyyDwZqIiAzLW85ZcxiciIhI59izJiIi4/KSC8wYrImIyLA4DE5ERES6wJ41EREZF4fBiYiI9I3D4ERERKQL7FkTEZFxcRiciIhI5xisiYiI9I3nrImIiEgXdNuzVm6VQzGp+Mkjk0pRbao+mTSKdvWp+oTZX3VZmXSIUJtyUuK9KhJp/mTUl7u9sSi+El9LiX0MiXqVmxIpEVXWK2RSa0r8nRAyKTKFxOej9liU+fuk9rsumCLT03QbrImIiBqiCCH1Y16rjoC7OAxORESkcwzWRERkXMIDi5uOHDmC8ePHIzIyEoqiYPv27Q2WOXToEO6++26YzWZ06tQJGRkZbtXJYE1ERIZVfTW4zOKusrIyxMbGYs2aNS5tn5eXh3HjxmHEiBHIycnBvHnz8Nhjj2HPnj0u18lz1kRERG5ITk5GcnKyy9uvW7cOMTExWLlyJQCgW7du+Oijj7Bq1SokJSW59BrsWRMRkXF5aBi8pKTEabFarR5rYlZWFhITE53WJSUlISsry+XXYLAmIiLD8tQweFRUFIKDgx1Lenq6x9pYUFCAsLAwp3VhYWEoKSnBrVu3XHoNDoMTEZHXu3DhAiwWi+Ox2WzWsDU1MVgTEZFxeWhSFIvF4hSsPSk8PByFhYVO6woLC2GxWBAYGOjSazBYExGRYRlhbvD4+Hjs2rXLad3evXsRHx/v8mvwnDURERmXBvdZl5aWIicnBzk5OQBu35qVk5OD/Px8AMDChQsxbdo0x/azZ8/GV199hQULFuDLL7/E3/72N7z99tt48sknXa6TwZqIiMgNJ06cQN++fdG3b18AwPz589G3b18sWrQIAHDp0iVH4AaAmJgYvP/++9i7dy9iY2OxcuVKvPLKKy7ftgVwGJyIiAyuqdNcJiQk1Jvkp7bZyRISEvDpp5+qrlO3wVrcvAWhqMhuI5OxyCSRnUYtu8QE9Goz4gAQFRIZi9SSeK+iSn2WMEUm65CP+uxMsKnLzqRZWgGZ41/is1Vbr5DJHOenzZ8+qTabNBgIrVT3N0aIJvz7IoT6jGTV5Q2Aw+BEREQ6p9ueNRERUUOMcDW4JzBYExGRcXnoPmu94zA4ERGRzrFnTUREhqXYby8y5Y2AwZqIiIyLw+BERESkB+xZExGRYfFqcCIiIr3zkklRGKyJiMiwvKVnzXPWREREOseeNRERGZeXXA3OYE1ERIbFYXAiIiLSBd32rEVFBYSKzHmK0GA6GpnUjzJXIkqkfpSq167BPpZor9QPZ5VpLqXLqqVFGkVA6piQOhbVV6q6qCKRhlcm1ataMilihU3d59qknymvBiciItI3DoMTERGRLrBnTURExsWrwYmIiPSNw+BERESkC+xZExGRcdmF1B05UmWbEIM1EREZF89ZExER6ZsCyXPWHmtJ4+I5ayIiIp1jz5qIiIyLM5gRERHpG2/dIiIiIl1gz5qIiIyLV4MTERHpmyIEFJksajxnLclkApSmHaVXmw5Ok5SRAIQWKRg1IpfSU5svo+Kj7viVeq8VFerLynzfZFLTqq3XJJH6sVIiVaVEykmpdJUqjwvVf9eg/m+MEN7zt6mp6DdYExERNcT+70WmvAEwWBMRkWF5yzA4rwYnIiLSObeCdXp6Ovr374+goCCEhoYiJSUFZ8+eddqmvLwcaWlpuOOOO9CiRQtMnDgRhYWFHm00ERERgB+uBpdZDMCtYH348GGkpaXh2LFj2Lt3LyorKzF69GiUlZU5tnnyySfx3nvvYevWrTh8+DAuXryIBx980OMNJyIicsxgJrMYgFvnrHfv3u30OCMjA6GhocjOzsawYcNQXFyMV199FZmZmbj33nsBABs3bkS3bt1w7NgxDBo0yHMtJyIir8cZzFxQXFwMAAgJCQEAZGdno7KyEomJiY5tunbtivbt2yMrK6vW17BarSgpKXFaiIiI6Aeqg7Xdbse8efMwZMgQ9OzZEwBQUFAAf39/tGzZ0mnbsLAwFBQU1Po66enpCA4OdixRUVFqm0RERN7GS4bBVQfrtLQ0nD59Gps3b5ZqwMKFC1FcXOxYLly4IPV6RETkPRS7/GIEqu6znjNnDnbu3IkjR46gXbt2jvXh4eGoqKhAUVGRU++6sLAQ4eHhtb6W2WyG2WxW0wwiIiKv4FbPWgiBOXPmYNu2bThw4ABiYmKcno+Li4Ofnx/279/vWHf27Fnk5+cjPj7eMy0mIiKq5iXD4G71rNPS0pCZmYkdO3YgKCjIcR46ODgYgYGBCA4OxsyZMzF//nyEhITAYrFg7ty5iI+P55XgRETkecy6VdPatWsBAAkJCU7rN27ciOnTpwMAVq1aBZPJhIkTJ8JqtSIpKQl/+9vfPNJYIiIib+RWsHYl60tAQADWrFmDNWvWqG4UERGRK7xlbnDdJvIQFZUQarLJSaSgg0mDqdI1SJkHAJBJr9nEqUulyaRvlGKw/SSRclJR1P8pUZvCUWiVDlSiWi2ORSGTIlZte5vyfcqed1ZZds2aNVixYgUKCgoQGxuLF198EQMGDKh124yMDMyYMcNpndlsRnl5ucv1GeyvCRERkba2bNmC+fPnY/HixTh58iRiY2ORlJSEy5cv11nGYrHg0qVLjuWbb75xq04GayIiMi6BH3Jaq1lUdKz/8pe/YNasWZgxYwa6d++OdevWoVmzZtiwYUOdZRRFQXh4uGMJCwtzq04GayIiMqzqc9YyC4Aa015brdZa66uoqEB2drbTtNomkwmJiYl1TqsNAKWlpYiOjkZUVBQeeOABnDlzxq33yWBNRETGJSB5n/Xtl4mKinKa+jo9Pb3W6q5evQqbzVajZ1zftNpdunTBhg0bsGPHDrz55puw2+0YPHgwvv32W5ffpm4vMCMiImoqFy5cgMVicTz25Mya8fHxThODDR48GN26dcP69euxbNkyl16DwZqIiIzLQ1eDWywWp2Bdl9atW8PHxweFhYVO6+ubVvun/Pz80LdvX+Tm5rrcTA6DExGRcclcXFa9uMHf3x9xcXFO02rb7Xbs37/f5Wm1bTYbPv/8c0RERLhcL3vWREREbpg/fz5SU1PRr18/DBgwAKtXr0ZZWZnjXupp06ahbdu2jvPeS5cuxaBBg9CpUycUFRVhxYoV+Oabb/DYY4+5XCeDNRERGZYWM5hNmjQJV65cwaJFi1BQUIA+ffpg9+7djovO8vPzYfrRJFvXr1/HrFmzUFBQgFatWiEuLg5Hjx5F9+7d3WmnvuZaKykpQXBwMEaYH4Gv4ud2ecWbZjCrqlJfL2cwa3SKr7rfwprNTOfjo7qozPdO9QxmVZWq65Q5hhWJ/eQtM5hViUocEttRXFzs0nlgNapjxcgeT8PXR/3FYFU2K/afWdGobfUEg/3VJSIi8j4cBiciIuPSaG7wpsZgTURExuUlwZrD4ERERDqn3561zabqIhCp30hqLzqRuWhE5mItzVI/qiSRglGKxDVXUhfnqL0AUOYCJgmiUuKCRYnPVshcFKe6UvXfHVGlzfddUbuPjfZ3wl12ADJ/Wgyye/QbrImIiBqgxa1bWmCwJiIi4+I5ayIiItID9qyJiMi47AJQJHrHMtelNCEGayIiMi4OgxMREZEesGdNREQGJtmzlrvht8kwWBMRkXFxGJyIiIj0gD1rIiIyLruA1FA2rwYnIiJqZMIuN6WqQaZj5TA4ERGRzrFnTURExuUlF5jpNlgLmw1CRYYaRSJjkRbZfxSTRPYfzc61qNtPipDIzCTzXjXKiqb2eJJJIKTVfhJ2DQbpZDLWyTDIsKlHqA1kTRkAec6aiIhI57ykZ81z1kRERDrHnjURERmXgGTP2mMtaVQM1kREZFwcBiciIiI9YM+aiIiMy24HIHGFvt0YV/czWBMRkXFxGJyIiIj0gD1rIiIyLi/pWTNYExGRcXnJDGYcBiciItI59qyJiMiwhLBDyMxrb5C53hmsiYjIuISQG8rmOWsiIqJGJiTPWTNYa0OLNJdSaRS1Sv2oAVEl8V5N6lOfyl0p2vTHk6iqavI6pcnk9VTLYMc/ALk0pBr8aSP9+NkFayIi8iJ2O6BI/HAzyI8+BmsiIjIuLxkG561bREREOseeNRERGZaw2yEkhsF56xYREVFj4zA4ERER6QF71kREZFx2ASg//541gzURERmXEABkbt0yRrDmMDgREZHOsWdNRESGJewCQmIYXBikZ81gTURExiXskBsG561bREREjcpbetY8Z01ERKRzuutZV//KqRKVGrfEHRr95jHI8I2DVPYrmWEupitqdFKfj8GOY2+i8jtbhcp/F2/8XmuVsEodQ9Vt1TvdBesbN24AAD7C+1KT0jQpo7TTyPj3XN/4+VAtbty4geDg4EZ5bX9/f4SHh+Ojgl3SrxUeHg5/f38PtKrxKEJnA/Z2ux0XL15EUFAQFKVmktySkhJERUXhwoULsFgsGrTQGLifXMP91DDuI9dwP/1ACIEbN24gMjISJlPjjTyWl5ejoqJC+nX8/f0REBDggRY1Ht31rE0mE9q1a9fgdhaLxeu/EK7gfnIN91PDuI9cw/10W2P1qH8sICBA90HWU3iBGRERkc4xWBMREemc4YK12WzG4sWLYTabtW6KrnE/uYb7qWHcR67hfqLGpLsLzIiIiMiZ4XrWRERE3obBmoiISOcYrImIiHSOwZqIiEjnGKyJiIh0zlDBes2aNejQoQMCAgIwcOBAfPLJJ1o3SVeeffZZKIritHTt2lXrZmnuyJEjGD9+PCIjI6EoCrZv3+70vBACixYtQkREBAIDA5GYmIhz585p01gNNbSfpk+fXuP4GjNmjDaN1VB6ejr69++PoKAghIaGIiUlBWfPnnXapry8HGlpabjjjjvQokULTJw4EYWFhRq1mH4ODBOst2zZgvnz52Px4sU4efIkYmNjkZSUhMuXL2vdNF3p0aMHLl265Fg++ugjrZukubKyMsTGxmLNmjW1Pr98+XL89a9/xbp163D8+HE0b94cSUlJKC8vb+KWaquh/QQAY8aMcTq+3nrrrSZsoT4cPnwYaWlpOHbsGPbu3YvKykqMHj0aZWVljm2efPJJvPfee9i6dSsOHz6Mixcv4sEHH9Sw1WR4wiAGDBgg0tLSHI9tNpuIjIwU6enpGrZKXxYvXixiY2O1boauARDbtm1zPLbb7SI8PFysWLHCsa6oqEiYzWbx1ltvadBCffjpfhJCiNTUVPHAAw9o0h49u3z5sgAgDh8+LIS4ffz4+fmJrVu3Orb517/+JQCIrKwsrZpJBmeInnVFRQWys7ORmJjoWGcymZCYmIisrCwNW6Y/586dQ2RkJDp27IipU6ciPz9f6ybpWl5eHgoKCpyOreDgYAwcOJDHVi0OHTqE0NBQdOnSBY8//jiuXbumdZM0V1xcDAAICQkBAGRnZ6OystLpmOratSvat2/PY4pUM0Swvnr1Kmw2G8LCwpzWh4WFoaCgQKNW6c/AgQORkZGB3bt3Y+3atcjLy8PQoUMdOcKppurjh8dWw8aMGYPXX38d+/fvx/PPP4/Dhw8jOTkZNptN66Zpxm63Y968eRgyZAh69uwJ4PYx5e/vj5YtWzpty2OKZOguRSapl5yc7Ph/7969MXDgQERHR+Ptt9/GzJkzNWwZ/RxMnjzZ8f9evXqhd+/euPPOO3Ho0CGMHDlSw5ZpJy0tDadPn+a1IdToDNGzbt26NXx8fGpcTVlYWIjw8HCNWqV/LVu2ROfOnZGbm6t1U3Sr+vjhseW+jh07onXr1l57fM2ZMwc7d+7EwYMH0a5dO8f68PBwVFRUoKioyGl7HlMkwxDB2t/fH3Fxcdi/f79jnd1ux/79+xEfH69hy/SttLQU58+fR0REhNZN0a2YmBiEh4c7HVslJSU4fvw4j60GfPvtt7h27ZrXHV9CCMyZMwfbtm3DgQMHEBMT4/R8XFwc/Pz8nI6ps2fPIj8/n8cUqWaYYfD58+cjNTUV/fr1w4ABA7B69WqUlZVhxowZWjdNN5566imMHz8e0dHRuHjxIhYvXgwfHx9MmTJF66ZpqrS01Kn3l5eXh5ycHISEhKB9+/aYN28ennvuOdx1112IiYnBH/7wB0RGRiIlJUW7Rmugvv0UEhKCJUuWYOLEiQgPD8f58+exYMECdOrUCUlJSRq2uumlpaUhMzMTO3bsQFBQkOM8dHBwMAIDAxEcHIyZM2di/vz5CAkJgcViwdy5cxEfH49BgwZp3HoyLK0vR3fHiy++KNq3by/8/f3FgAEDxLFjx7Rukq5MmjRJRERECH9/f9G2bVsxadIkkZubq3WzNHfw4EEBoMaSmpoqhLh9+9Yf/vAHERYWJsxmsxg5cqQ4e/asto3WQH376ebNm2L06NGiTZs2ws/PT0RHR4tZs2aJgoICrZvd5GrbRwDExo0bHdvcunVL/OY3vxGtWrUSzZo1ExMmTBCXLl3SrtFkeMxnTUREpHOGOGdNRETkzRisiYiIdI7BmoiISOcYrImIiHSOwZqIiEjnGKyJiIh0jsGaiIhI5xisiYiIdI7BmoiISOcYrImIiHSOwZqIiEjn/j+XYquGVaOMEgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "filters,images = curves.apply_filter_curves(rubixdata, wave).values()\n", + "\n", + "for i,name in zip(images, filters):\n", + " plt.figure()\n", + " plt.imshow(i)\n", + " plt.colorbar()\n", + " plt.title(name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/filter_curves.ipynb b/source/notebooks/filter_curves.ipynb index 57a27bc9..45774476 100644 --- a/source/notebooks/filter_curves.ipynb +++ b/source/notebooks/filter_curves.ipynb @@ -11,9 +11,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:09:41,614 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:09:41,615 - rubix - INFO - Rubix version: 0.0.post482+g02b969039.d20251103\n", + "2025-11-10 17:09:41,615 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:09:41,616 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.telescope.filters import load_filter, print_filter_list, print_filter_list_info, print_filter_property" @@ -32,9 +49,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " filterID \n", + " \n", + "------------------------\n", + "SLOAN/SDSS.uprime_filter\n", + " SLOAN/SDSS.u\n", + " SLOAN/SDSS.g\n", + "SLOAN/SDSS.gprime_filter\n", + " SLOAN/SDSS.r\n", + "SLOAN/SDSS.rprime_filter\n", + " SLOAN/SDSS.i\n", + "SLOAN/SDSS.iprime_filter\n", + " SLOAN/SDSS.z\n", + "SLOAN/SDSS.zprime_filter\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "print_filter_list(\"SLOAN\")" @@ -49,9 +86,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " name dtype unit \n", + "-------------------- ------- ---------------\n", + "FilterProfileService object \n", + " filterID object \n", + " WavelengthUnit object \n", + " WavelengthUCD object \n", + " PhotSystem object \n", + " DetectorType object \n", + " Band object \n", + " Instrument object \n", + " Facility object \n", + " ProfileReference object \n", + "CalibrationReference object \n", + " Description object \n", + " Comments object \n", + " WavelengthRef float64 Angstrom\n", + " WavelengthMean float64 Angstrom\n", + " WavelengthEff float64 Angstrom\n", + " WavelengthMin float64 Angstrom\n", + " WavelengthMax float64 Angstrom\n", + " WidthEff float64 Angstrom\n", + " WavelengthCen float64 Angstrom\n", + " WavelengthPivot float64 Angstrom\n", + " WavelengthPeak float64 Angstrom\n", + " WavelengthPhot float64 Angstrom\n", + " FWHM float64 Angstrom\n", + " Fsun float64 erg / (A s cm2)\n", + " PhotCalID object \n", + " MagSys object \n", + " ZeroPoint float64 Jy\n", + " ZeroPointUnit object \n", + " Mag0 float64 \n", + " ZeroPointType object \n", + " AsinhSoft float64 \n", + " TrasmissionCurve object \n", + "\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "print_filter_list_info(\"SLOAN\")" @@ -66,9 +147,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", + " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", + "-------------------- ------------ -------------- ------------- ---------- ------------ ---- ---------- -------- ----------------------------------------------------- ----------------------------------------------- ------------------------ -------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- --------------- --------------- --------------- ----------------- ------ -------------- ------------- ---- ------------- --------- ----------------------------------------------------------------\n", + " ivo://svo/fps SLOAN/SDSS.u Angstrom em.wl SDSS 1 SLOAN http://www.sdss.org/dr7/instruments/imager/index.html http://www.sdss.org/DR2/algorithms/fluxcal.html SDSS u full transmission 3556.5239668607 3572.1824003193 3608.0403153219 3055.1091291961 4030.6399499061 540.97112586776 3578.0271197298 3556.5239668607 3680.0 3619.6973042374 565.79845192387 103.21344236463 SLOAN/SDSS.u/Vega Vega 1582.537065543 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u\n" + ] + }, + { + "data": { + "text/html": [ + "Row index=1\n", + "
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FilterProfileServicefilterIDWavelengthUnitWavelengthUCDPhotSystemDetectorTypeBandInstrumentFacilityProfileReferenceCalibrationReferenceDescriptionCommentsWavelengthRefWavelengthMeanWavelengthEffWavelengthMinWavelengthMaxWidthEffWavelengthCenWavelengthPivotWavelengthPeakWavelengthPhotFWHMFsunPhotCalIDMagSysZeroPointZeroPointUnitMag0ZeroPointTypeAsinhSoftTrasmissionCurve
AngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromerg / (A s cm2)Jy
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ivo://svo/fpsSLOAN/SDSS.uAngstromem.wlSDSS1SLOANhttp://www.sdss.org/dr7/instruments/imager/index.htmlhttp://www.sdss.org/DR2/algorithms/fluxcal.htmlSDSS u full transmission3556.52396686073572.18240031933608.04031532193055.10912919614030.6399499061540.971125867763578.02711972983556.52396686073680.03619.6973042374565.79845192387103.21344236463SLOAN/SDSS.u/VegaVega1582.537065543Jy0.0Pogson0.0http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u
" + ], + "text/plain": [ + "\n", + "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", + " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", + " object object object object object object object object object object object object object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 object object float64 object float64 object float64 object \n", + "-------------------- ------------ -------------- ------------- ---------- ------------ ------ ---------- -------- ----------------------------------------------------- ----------------------------------------------- ------------------------ -------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- --------------- --------------- --------------- ----------------- ------ -------------- ------------- ------- ------------- --------- ----------------------------------------------------------------\n", + " ivo://svo/fps SLOAN/SDSS.u Angstrom em.wl SDSS 1 SLOAN http://www.sdss.org/dr7/instruments/imager/index.html http://www.sdss.org/DR2/algorithms/fluxcal.html SDSS u full transmission 3556.5239668607 3572.1824003193 3608.0403153219 3055.1091291961 4030.6399499061 540.97112586776 3578.0271197298 3556.5239668607 3680.0 3619.6973042374 565.79845192387 103.21344236463 SLOAN/SDSS.u/Vega Vega 1582.537065543 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "print_filter_property(\"SLOAN\", \"SDSS.u\")" @@ -76,9 +192,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", + " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", + "-------------------- ----------------- -------------- ------------- ---------- ------------ ---- ---------- -------- ------------------------------------------------------ -------------------- ------------------- ------------------------------------------------------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- -------------- --------------- --------------- ---------------------- ------ --------------- ------------- ---- ------------- --------- ---------------------------------------------------------------------\n", + " ivo://svo/fps JWST/NIRCam.F070W Angstrom em.wl NIRCam 1 NIRCam JWST https://jwst-docs.stsci.edu/display/JTI/NIRCam+Filters NIRCam F070W filter includes NIRCam optics, DBS, QE and JWST Optical Telescope Element 7039.1194650654 7088.3009369996 6988.4272768359 6048.1970523246 7927.0738659178 1212.8399166581 7099.1873443748 7039.1194650654 7691.5 7022.060805287 1430.8105961315 140.01772043307 JWST/NIRCam.F070W/Vega Vega 2768.4045696982 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W\n" + ] + }, + { + "data": { + "text/html": [ + "Row index=0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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AngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromAngstromerg / (A s cm2)Jy
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ivo://svo/fpsJWST/NIRCam.F070WAngstromem.wlNIRCam1NIRCamJWSThttps://jwst-docs.stsci.edu/display/JTI/NIRCam+FiltersNIRCam F070W filterincludes NIRCam optics, DBS, QE and JWST Optical Telescope Element7039.11946506547088.30093699966988.42727683596048.19705232467927.07386591781212.83991665817099.18734437487039.11946506547691.57022.0608052871430.8105961315140.01772043307JWST/NIRCam.F070W/VegaVega2768.4045696982Jy0.0Pogson0.0http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W
" + ], + "text/plain": [ + "\n", + "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", + " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", + " object object object object object object object object object object object object object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 object object float64 object float64 object float64 object \n", + "-------------------- ----------------- -------------- ------------- ---------- ------------ ------ ---------- -------- ------------------------------------------------------ -------------------- ------------------- ------------------------------------------------------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- -------------- --------------- --------------- ---------------------- ------ --------------- ------------- ------- ------------- --------- ---------------------------------------------------------------------\n", + " ivo://svo/fps JWST/NIRCam.F070W Angstrom em.wl NIRCam 1 NIRCam JWST https://jwst-docs.stsci.edu/display/JTI/NIRCam+Filters NIRCam F070W filter includes NIRCam optics, DBS, QE and JWST Optical Telescope Element 7039.1194650654 7088.3009369996 6988.4272768359 6048.1970523246 7927.0738659178 1212.8399166581 7099.1873443748 7039.1194650654 7691.5 7022.060805287 1430.8105961315 140.01772043307 JWST/NIRCam.F070W/Vega Vega 2768.4045696982 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "print_filter_property(\"JWST\", \"F070W\", \"NIRCam\")" @@ -96,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -107,9 +258,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[SLOAN/SDSS.uprime_filter,\n", + " SLOAN/SDSS.u,\n", + " SLOAN/SDSS.g,\n", + " SLOAN/SDSS.gprime_filter,\n", + " SLOAN/SDSS.r,\n", + " SLOAN/SDSS.rprime_filter,\n", + " SLOAN/SDSS.i,\n", + " SLOAN/SDSS.iprime_filter,\n", + " SLOAN/SDSS.z,\n", + " SLOAN/SDSS.zprime_filter]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "curves.filters" @@ -117,9 +288,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "filter = curves[1]\n", @@ -149,9 +342,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import h5py\n", @@ -171,9 +372,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(25, 25, 3721)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "datacube.shape" @@ -188,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -209,9 +421,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25, 25)\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", @@ -220,9 +440,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -239,9 +480,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "for filter in curves:\n", @@ -254,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -264,9 +606,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[np.str_('SLOAN/SDSS.uprime_filter'),\n", + " np.str_('SLOAN/SDSS.u'),\n", + " np.str_('SLOAN/SDSS.g'),\n", + " np.str_('SLOAN/SDSS.gprime_filter'),\n", + " np.str_('SLOAN/SDSS.r'),\n", + " np.str_('SLOAN/SDSS.rprime_filter'),\n", + " np.str_('SLOAN/SDSS.i'),\n", + " np.str_('SLOAN/SDSS.iprime_filter'),\n", + " np.str_('SLOAN/SDSS.z'),\n", + " np.str_('SLOAN/SDSS.zprime_filter')]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "filters" @@ -274,9 +636,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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IfsYWhdvM7Nmzce3aNYwfPx5RUVGorq7GwYMHsWnTJnTp0kXqNwaAs2fPNtgsf8899yAhIQH//ve/kZCQgP79+9dbmfHs2bNYtWoV7r33XgA3/tAKIRpsCQCAhx56CJ6enti4caPN4Lhfmjp1Kp5//nkcO3bM5vz7778Pb2/veoP7du3ahcWLF+ORRx7B4MGD4e/vj2+//Rbr1q1DVVUVlixZUq+Mf//73/D390d1dbW0MuMnn3yCvn37YsuWLVK6a9eu4d5778XgwYMxevRohIWFobS0FNu2bcNHH32EcePG4Z577gFwozVi48aNGD9+PGJiYqDRaPD1119j3bp18Pb2xnPPPSfdd/Xq1Vi6dCn27t2L4cOH4/jx43j++edx//3348KFC/jXv/5lU9+bW3RCQ0Pxl7/8Bd999x169OiBTZs24dixY3jjjTfg5eVl9311pz//+c/Yu3cvYmNjMWPGDERHR+Py5cs4evQodu/e7XBqaXOJiYnBpk2bkJaWhoEDB8Lf3x9jx47F448/js2bN2PmzJnYu3cvhgwZAovFglOnTmHz5s3YuXOn1OJGRA1ouQkX5A4ffPCBmD59uoiKihL+/v5Co9GIbt26idmzZ9dbmRFAg0dycrKUrrCwUMyYMUOEh4cLLy8v0bFjR/HII4/Um2rWp08fER4e3mjdhg8fLoKDg0VNTY3N9MhfWr9+vVSXuqmMEydOFA899FC9tN9++61IT08XgwcPFsHBwcLT01MEBQWJhIQEm6mFQvw8PbLu8Pb2Fp07dxYPP/ywWLdunaisrLRJX1NTI/7+97+LcePGiYiICKHVaoWvr6+45557xEsvvSSqqqqktF9++aWYN2+e6N+/vwgMDBSenp6iU6dO4le/+pU4evRog/Wom9pXN9XP3lFn2LBholevXuKzzz4TRqNReHt7i4iICLF69Wqb+9fdb8uWLfXeL3vTI3v16lUvrb0ptABESkqKzbmSkhKRkpIiwsLChJeXlzAYDGLkyJHijTfeqJe/Ma5MjywvLxePPfaYCAgIkKbL1qmurhZ/+ctfRK9evYRWqxXt27cXMTExYunSpTardzb02ojaOpUQLq5SQ+RmtbW16NChAzIyMvD73/++pavTYoYPH45Lly65vJgWEVFTcIwCtXqXL1/G3LlzMX78+JauChFRm8MWBSKFUHKLwsWLF2GxWOxe12g0CAwMvIU1IiJncTAjEbndwIED8f3339u9PmzYMJuNqoio9WCLAhG53SeffGKzedgvtW/fvkWmdxKRYwwUiIiIyC4OZiQiIiK7Wt0YBavVinPnzqFdu3ZcbpWISIGEELh69SpCQ0OhVrvv+2hlZSWqq6tdvo9Go4G3t3cz1Oj21OoChXPnztXbpIWIiJSnuLgYnTt3dsu9KysrERnhD9MF+7NpnGUwGFBYWMhgwY5WFyjUbfF7Hx6CJ1pmSVqiNsmFFjy1j/wPWOv1SseJGsLhVa1WLWrwMd6vt2V7c6qurobpggWFBRHQtZPfamG+akVkzPeorq5moGBHqwsU6robPOEFTxUDBaJbxpVAQaWRndeqkvuNkIFCq/Xff5pb0X2sa6d2KVAgx9z27q5ZswZdunSBt7c3YmNjceTIEXcVRUREbZRFWF0+qHFuCRTqdnBbvHgxjh49ir59+yI+Ph4XLlxwR3FERNRGWSFcPqhxbgkUXnnlFcyYMQPTpk1DdHQ0MjMz4evri3Xr1rmjOCIiaqOszfAfNa7ZA4Xq6moUFBQgLi7u50LUasTFxSE/P79e+qqqKpjNZpuDiIiIWodmDxQuXboEi8WCkJAQm/MhISEwmUz10mdkZECv10sHp0YSEZGzLEK4fFDjWnyo6MKFC1FWViYdxcXFLV0lIiJSCI5RcL9mnx7ZsWNHeHh4oKSkxOZ8SUkJDAZDvfRarRZarba5q0FERETNoNlbFDQaDWJiYpCXlyeds1qtyMvLg9FobO7iiIioDbNCwOLCwRYFx9yy4FJaWhqSkpIwYMAADBo0CCtXrkRFRQWmTZvmjuKIiKiNcrX7gIGCY24JFCZNmoSLFy8iPT0dJpMJ/fr1Q25ubr0BjkRERNS6uW0J59TUVKSmprrr9kRERC7PXOCsB8da3V4PRNRCXPjAtF6/3iLlEln/e7iSnxrX4tMjiYiIqPViiwIRESlW3ewFV/JT4xgoEBGRYlnEjcOV/NQ4BgpERKRYHKPgfhyjQERERHaxRYGIiBTLChUsULmUnxrHQIGIiBTLKm4cruSnxrHrgYiIiOxiiwIRESmWxcWuB1fythUMFIiISLEYKLgfux6IiIic1KVLF6hUqnpHSkoKAKCyshIpKSno0KED/P39kZiYiJKSEpt7FBUVISEhAb6+vggODsa8efNQW1trk2bfvn3o378/tFotunXrhqysrHp1WbNmDbp06QJvb2/ExsbiyJEjNtedqYszGCgQEZFiWYXK5aMpPv30U5w/f146du3aBQD41a9+BQCYO3cutm/fji1btmD//v04d+4cJkyYIOW3WCxISEhAdXU1Dh48iA0bNiArKwvp6elSmsLCQiQkJGDEiBE4duwY5syZgyeffBI7d+6U0mzatAlpaWlYvHgxjh49ir59+yI+Ph4XLlyQ0jiqi7NUQrSuHVnMZjP0ej2G41F4qrxaujpE5AyVC823resjiJpBrajBPryHsrIy6HQ6t5RR97di/4k74N9O/nfe8qtWDOv9o+y6zpkzBzt27MCZM2dgNpsRFBSE7OxsTJw4EQBw6tQp9OzZE/n5+Rg8eDA++OADPPzwwzh37hxCQkIAAJmZmViwYAEuXrwIjUaDBQsWICcnBydOnJDKmTx5MkpLS5GbmwsAiI2NxcCBA7F69WoAgNVqRVhYGGbPno1nn30WZWVlDuviLLYoEBFRm2c2m22Oqqoqh3mqq6vxr3/9C9OnT4dKpUJBQQFqamoQFxcnpYmKikJ4eDjy8/MBAPn5+ejTp48UJABAfHw8zGYzTp48KaW5+R51aeruUV1djYKCAps0arUacXFxUhpn6uIsBgqkDGoP+Qe5nxDyD3Kv2/x3xwK1ywcAhIWFQa/XS0dGRobDsrdt24bS0lI88cQTAACTyQSNRoOAgACbdCEhITCZTFKam4OEuut11xpLYzabcf36dVy6dAkWi6XBNDffw1FdnMVZD0REpFhCxjiDX+YHgOLiYpuuB61W6zDvW2+9hTFjxiA0NFR2+UrAQIGIiBSruaZH6nS6Jo1R+P7777F792787//+r3TOYDCguroapaWlNt/kS0pKYDAYpDS/nJ1QNxPh5jS/nJ1QUlICnU4HHx8feHh4wMPDo8E0N9/DUV2cxa4HIiKiJlq/fj2Cg4ORkJAgnYuJiYGXlxfy8vKkc6dPn0ZRURGMRiMAwGg04vjx4zazE3bt2gWdTofo6Ggpzc33qEtTdw+NRoOYmBibNFarFXl5eVIaZ+riLLYoEBGRYlmEGhYh/zuvRcYwGavVivXr1yMpKQmenj//GdXr9UhOTkZaWhoCAwOh0+kwe/ZsGI1GaZbBqFGjEB0djccffxzLly+HyWTCokWLkJKSInV3zJw5E6tXr8b8+fMxffp07NmzB5s3b0ZOTo5UVlpaGpKSkjBgwAAMGjQIK1euREVFBaZNm+Z0XZzFQIGIiBTLChWsLjSOW9H0SGH37t0oKirC9OnT611bsWIF1Go1EhMTUVVVhfj4eLz22mvSdQ8PD+zYsQOzZs2C0WiEn58fkpKSsGzZMilNZGQkcnJyMHfuXKxatQqdO3fGm2++ifj4eCnNpEmTcPHiRaSnp8NkMqFfv37Izc21GeDoqC7O4joKpAyujMC2WpqvHkRK0wK/O7dyHYWcL7vCr53811hx1YKEu791a12Vji0KRESkWNzrwf0YKBARkWK5PkahVTWqt0qc9UBERER2sUWBiIgU68ZgRvndB67kbSsYKBARkWJZb1qGWV5+dj04wq4HIiIisostCkREpFgczOh+DBRupmqBvqq29JC6MJ/bw99Pdl7r9UrZeUVNtey8RK3Cbb6OiBXqW77gUlvDQIGIiBTLIlSwuLB7pCt52wqOUSAiIiK72KJARESKZXFx1oOFXQ8OMVAgIiLFsgo1rC4MZrS2pXFiMrHrgYiIiOxiiwIRESkWux7cj4ECEREplhWuzVywNl9VblvseiAiIiK72KJARESK5fqCS/y+7AgDBSIiUizXl3BmoOAI3yEiIiKyiy0KRESkWFaoYIUrgxm5hLMjDBSIiEix2PXgfgwUiIhIsVxfR4GBgiOtN1BQqWRt+6zSaOSXaZW58IZaftOVqHZhG2OlLT3qwna3rmwVrfLWys4rLC5s0au07X1d2WZdac+iK1x4n9Ra+c+itapKXsa29G9DbtF6AwUiIiIHrEIFqysLLnGbaYcYKBARkWJZXex64DoKjvEdIiIiIrvYokBERIrl+jbT/L7sCAMFIiJSLAtUsLiwFoIredsKhlJERERkF1sUiIhIsdj14H4MFIiISLEscK37QGGrnbQIhlJERERkF1sUiIhIsdj14H4MFIiISLG4KZT7MVAgIiLFEi5uMy04PdIhhlJERERkF1sUiIhIsdj14H6tNlBQeXpBpfJqcj6PwPayy7RcviIrn0tbRbtA5Sn/n0/U1jZjTZzjSn3VOn/Zea3lFbLztqWtolWeTf99qyNqa2TnVdo2yK5sFa3uFCK/4PMlsrIJi1V2kerAAHllWquBC7KLbRLuHul+DKWIiIia4Mcff8RvfvMbdOjQAT4+PujTpw8+++wz6boQAunp6ejUqRN8fHwQFxeHM2fO2Nzj8uXLmDp1KnQ6HQICApCcnIzy8nKbNF9++SWGDh0Kb29vhIWFYfny5fXqsmXLFkRFRcHb2xt9+vTB+++/b3Pdmbo4wkCBiIgUy/LfbaZdOZriypUrGDJkCLy8vPDBBx/gq6++wssvv4z27X9uzV6+fDleffVVZGZm4vDhw/Dz80N8fDwqKyulNFOnTsXJkyexa9cu7NixAwcOHMBTTz0lXTebzRg1ahQiIiJQUFCAl156CUuWLMEbb7whpTl48CCmTJmC5ORkfP755xg3bhzGjRuHEydONKkujqiEaF3tfmazGXq9HiO8fgVPOV0PHQNll624rgcPD9l5Fdf1oNfJzutK14OoqpKdt0Ww68Ht1N7e8vO60PVgVVDXQ621GnkX3kRZWRl0Ovm/u42p+1vxh48fhdZf/rNbVV6DV+97D8XFxTZ11Wq10DbQzfTss8/ik08+wUcffdTg/YQQCA0Nxf/8z//gmWeeAQCUlZUhJCQEWVlZmDx5Mr7++mtER0fj008/xYABAwAAubm5eOihh/DDDz8gNDQUa9euxR//+EeYTCZoNBqp7G3btuHUqVMAgEmTJqGiogI7duyQyh88eDD69euHzMxMp+rijGZvUViyZAlUKpXNERUV1dzFEBERNZuwsDDo9XrpyMjIaDDdf/7zHwwYMAC/+tWvEBwcjHvuuQd///vfpeuFhYUwmUyIi4uTzun1esTGxiI/Px8AkJ+fj4CAAClIAIC4uDio1WocPnxYSnP//fdLQQIAxMfH4/Tp07hy5YqU5uZy6tLUleNMXZzhlsGMvXr1wu7du38uxIVvkkRERPZYoYbVhe+8dXkbalFoyLfffou1a9ciLS0Nzz33HD799FP84Q9/gEajQVJSEkwmEwAgJMS29SgkJES6ZjKZEBwcbHPd09MTgYGBNmkiIyPr3aPuWvv27WEymRyW46guznDLX3BPT08YDAZ33JqIiEhiESpYXJi5UJdXp9M51U1itVoxYMAAvPjiiwCAe+65BydOnEBmZiaSkpJk16M1c8tgxjNnziA0NBRdu3bF1KlTUVRUZDdtVVUVzGazzUFERNQaderUCdHR0TbnevbsKf2dq/uSXFJiO6akpKREumYwGHDhgu380draWly+fNkmTUP3uLkMe2luvu6oLs5o9kAhNjYWWVlZyM3Nxdq1a1FYWIihQ4fi6tWrDabPyMiw6RcKCwtr7ioREdFtqm4dBVeOphgyZAhOnz5tc+7//u//EBERAQCIjIyEwWBAXl6edN1sNuPw4cMwGo0AAKPRiNLSUhQUFEhp9uzZA6vVitjYWCnNgQMHUFPz80DhXbt24a677pJmWBiNRpty6tLUleNMXZzR7IHCmDFj8Ktf/Qp333034uPj8f7776O0tBSbN29uMP3ChQtRVlYmHcXFxc1dJSIiuk2J/+4eKfcQTVyZce7cuTh06BBefPFFnD17FtnZ2XjjjTeQkpICAFCpVJgzZw5eeOEF/Oc//8Hx48fx29/+FqGhoRg3bhyAGy0Qo0ePxowZM3DkyBF88sknSE1NxeTJkxEaGgoAeOyxx6DRaJCcnIyTJ09i06ZNWLVqFdLS0qS6PP3008jNzcXLL7+MU6dOYcmSJfjss8+QmprqdF2c4fZRhgEBAejRowfOnj3b4HV7U1CIiIgcsUAFiwsbOzU178CBA7F161YsXLgQy5YtQ2RkJFauXImpU6dKaebPn4+Kigo89dRTKC0txX333Yfc3Fx43zS1duPGjUhNTcXIkSOhVquRmJiIV199Vbqu1+vx4YcfIiUlBTExMejYsSPS09Nt1lq49957kZ2djUWLFuG5555D9+7dsW3bNvTu3btJdXHE7esolJeXIzw8HEuWLMEf/vAHh+m5joLzuI6Cc7iOgpNZuY6CU7iOgmO3ch2F5P2/hsaFdRSqy2vw1rDNbq2r0jV718MzzzyD/fv347vvvsPBgwcxfvx4eHh4YMqUKc1dFBERtXFW4eo4hZZ+Ba1fs3c9/PDDD5gyZQp++uknBAUF4b777sOhQ4cQFBTU3EUREVEbVzfWwJX81LhmDxTeeeed5r4lERERtZBWu2SiqK2BnDU0LJcuu1SmvIzy265UrmxZ286FrZdLy2TnlTu+Qd2unewyq/pGOk5kh/bzb2XntShtjIILz2JbGmfgCqsrz4TMcQYAYK2W9+/j6cK4iDKjvOnqtTWVwDbZxTaJFSpYXRjM6EretqLVBgpERESONNfKjGQfO2eIiIjILrYoEBGRYnEwo/sxUCAiIsWyounLMP8yPzWOoRQRERHZxRYFIiJSLOHirAfBFgWHGCgQEZFiydkB8pf5qXEMFIiISLE4mNH9+A4RERGRXWxRICIixWLXg/sxUCAiIsXiEs7ux64HIiIisostCkREpFjsenA/BgpERKRYDBTcr9UGCioPD6hUHk3OJywW+WVqNLLyiRp52y4DgErlwgN+tVx23pZgLa+QnVd7vKhFylV5yv8Vkbsdd4tpqa2i1U3/PQcAtY+37CKt1ytl54VV/meMtVJ+uXKfRSFze2oA0OcXy8pXa1XY9uzUqFYbKBARETnCFgX3Y6BARESKxUDB/TjrgYiIiOxiiwIRESmWgGtrIbTQyBxFYaBARESKxa4H92OgQEREisVAwf04RoGIiIjsYosCEREpFlsU3I+BAhERKRYDBfdj1wMRERHZxRYFIiJSLCFUEC60CriSt61goEBERIplhcqldRRcydtWsOuBiIiI7Gq1LQpqXTuo1TJ2c3RhJ0eEdJSVTZwrkV2k9do12XlVHvJ23QMAtV4nOy+sMtcyU8uP3FV+PvLzuvIey9xRFACsV6/KyufSrpMyd2K8UbBVfl6V/O8caj9feRnvDJNdpsf352XntZSWys7ryg6dcnfGtV65csvLrBXyd6xsKg5mdL9WGygQERE5wjEK7seuByIiIrKLLQpERKRY7HpwPwYKRESkWOx6cD92PRARkWKJ/7YoyD2aGigsWbIEKpXK5oiKipKuV1ZWIiUlBR06dIC/vz8SExNRUmI74L2oqAgJCQnw9fVFcHAw5s2bh9pfDGLet28f+vfvD61Wi27duiErK6teXdasWYMuXbrA29sbsbGxOHLkiM11Z+riDAYKRERETdCrVy+cP39eOj7++GPp2ty5c7F9+3Zs2bIF+/fvx7lz5zBhwgTpusViQUJCAqqrq3Hw4EFs2LABWVlZSE9Pl9IUFhYiISEBI0aMwLFjxzBnzhw8+eST2Llzp5Rm06ZNSEtLw+LFi3H06FH07dsX8fHxuHDhgtN1cRa7HoiISLEEXJp1CjlZPT09YTAY6p0vKyvDW2+9hezsbDzwwAMAgPXr16Nnz544dOgQBg8ejA8//BBfffUVdu/ejZCQEPTr1w/PP/88FixYgCVLlkCj0SAzMxORkZF4+eWXAQA9e/bExx9/jBUrViA+Ph4A8Morr2DGjBmYNm0aACAzMxM5OTlYt24dnn32Wafq4iy2KBARkWLVrczoygEAZrPZ5qiqqrJb5pkzZxAaGoquXbti6tSpKCoqAgAUFBSgpqYGcXFxUtqoqCiEh4cjPz8fAJCfn48+ffogJCREShMfHw+z2YyTJ09KaW6+R12auntUV1ejoKDAJo1arUZcXJyUxpm6OIuBAhERtXlhYWHQ6/XSkZGR0WC62NhYZGVlITc3F2vXrkVhYSGGDh2Kq1evwmQyQaPRICAgwCZPSEgITCYTAMBkMtkECXXX6641lsZsNuP69eu4dOkSLBZLg2luvoejujiLXQ9ERKRYzTXrobi4GDrdzyvWarXaBtOPGTNG+v+7774bsbGxiIiIwObNm+HjI38F2daMLQpERKRYrsx4uHkNBp1OZ3PYCxR+KSAgAD169MDZs2dhMBhQXV2N0l8s811SUiKNaTAYDPVmHtT97CiNTqeDj48POnbsCA8PjwbT3HwPR3VxFgMFIiIimcrLy/HNN9+gU6dOiImJgZeXF/Ly8qTrp0+fRlFREYxGIwDAaDTi+PHjNrMTdu3aBZ1Oh+joaCnNzfeoS1N3D41Gg5iYGJs0VqsVeXl5Uhpn6uIsdj0QEZFiCeHirIcm5n3mmWcwduxYRERE4Ny5c1i8eDE8PDwwZcoU6PV6JCcnIy0tDYGBgdDpdJg9ezaMRqM0y2DUqFGIjo7G448/juXLl8NkMmHRokVISUmRWjFmzpyJ1atXY/78+Zg+fTr27NmDzZs3IycnR6pHWloakpKSMGDAAAwaNAgrV65ERUWFNAvCmbo4i4ECEREp1q1emfGHH37AlClT8NNPPyEoKAj33XcfDh06hKCgIADAihUroFarkZiYiKqqKsTHx+O1116T8nt4eGDHjh2YNWsWjEYj/Pz8kJSUhGXLlklpIiMjkZOTg7lz52LVqlXo3Lkz3nzzTWlqJABMmjQJFy9eRHp6OkwmE/r164fc3FybAY6O6uIslRCuxGLNz2w2Q6/XY2T7JHiqZGzx6yk/9hGh8raZVlVUyi7TWnxOdl7hwpbaHh0CZee9HtNFVj6rpwu/zC6EtO0+/UF2XsuFS7Lzilp5W+26srW1R8cOsvOKq+Wy86p07WTntV4plVemv5/sMlVeXrLzyq0vAFivy/+sUMncpl3lwgA7UWl/imBjakUN9tZsQVlZmc0AweZU97ci+p358PB1bjxBQyzXqvDV5OVuravSsUWBiIgUi3s9uB8DBSIiUiyrUEHF3SPdioECEREp1q0ezNgWcXokERER2cUWBSIiUqwbLQqujFFoxsrcphgoEBGRYnEwo/ux64GIiIjsYosCEREplvjv4Up+ahwDBSIiUix2Pbgfux6IiIjILrYoEBGRcrHvwe0YKBARkXK52PUAdj04xECBiIgUiyszuh/HKBAREZFdrbZFQYQZIDyavnWo8JQf+1g1HrLyVfSQvzVpQGW17LzWy1dk50WA/G2BS7vK26K31ld2kfD/0So7b1UPg+y8WotFdl6h85edV66ajvLL9Lwif9vmaxEBsvP6fC/vwVBVydvGGwBQJf/3Th0sbzt6AFC7sM00VPKayC0G+VuPe5h+kpVPWKsBk+xim1YWZz24XasNFIiIiBwSKtfGGTBQcIhdD0RERGQXWxSIiEixOJjR/ZrconDgwAGMHTsWoaGhUKlU2LZtm811IQTS09PRqVMn+Pj4IC4uDmfOnGmu+hIREf1MNMNBjWpyoFBRUYG+fftizZo1DV5fvnw5Xn31VWRmZuLw4cPw8/NDfHw8KitdGMRDRERELaLJXQ9jxozBmDFjGrwmhMDKlSuxaNEiPProowCAf/zjHwgJCcG2bdswefJk12pLRER0E856cL9mHcxYWFgIk8mEuLg46Zxer0dsbCzy8/MbzFNVVQWz2WxzEBEROY3dDm7VrIGCyXRj4mxISIjN+ZCQEOnaL2VkZECv10tHWFhYc1aJiIiIXNDi0yMXLlyIsrIy6SguLm7pKhERkULUdT24clDjmnV6pMFwYwW8kpISdOrUSTpfUlKCfv36NZhHq9VCq236CoxERETcPdL9mrVFITIyEgaDAXl5edI5s9mMw4cPw2g0NmdRREREAFTNcFBjmtyiUF5ejrNnz0o/FxYW4tixYwgMDER4eDjmzJmDF154Ad27d0dkZCT+9Kc/ITQ0FOPGjWvOehMREdEt0ORA4bPPPsOIESOkn9PS0gAASUlJyMrKwvz581FRUYGnnnoKpaWluO+++5Cbmwtvb+/mqzURERHArodboMmBwvDhwyEaWfNSpVJh2bJlWLZsmUsVIyIicoiBgtu12r0eVMUmqFSapmeM6OQ4jR2lveVtvez/g/wta4W3jNf4X+oOgbLzVoa3l51XWyZvy2dzD9lFurR9+KW+8gfLesfcKTtvZf9rsvKJH31kl9n+K9lZob0qv9VPXSP/0/ZSrLxtm63ydjsHAHT8/Kr8zK5sDqCSv727uqJKVj6PC/K3oxcVMp9hIf8zkVqfVhsoEBEROcRtpt2OgQIRESkWd490vxZfcImIiIhaL7YoEBGRcnEwo9sxUCAiIuXiGAW3Y9cDERER2cUWBSIiUiyVuHG4kp8ax0CBiIiUi2MU3I5dD0REpFx1YxRcOVzw5z//GSqVCnPmzJHOVVZWIiUlBR06dIC/vz8SExNRUlJik6+oqAgJCQnw9fVFcHAw5s2bh9raWps0+/btQ//+/aHVatGtWzdkZWXVK3/NmjXo0qULvL29ERsbiyNHjthcd6YujjBQICIikuHTTz/F66+/jrvvvtvm/Ny5c7F9+3Zs2bIF+/fvx7lz5zBhwgTpusViQUJCAqqrq3Hw4EFs2LABWVlZSE9Pl9IUFhYiISEBI0aMwLFjxzBnzhw8+eST2Llzp5Rm06ZNSEtLw+LFi3H06FH07dsX8fHxuHDhgtN1cQYDBSIiUi7RDIcM5eXlmDp1Kv7+97+jffufl8QvKyvDW2+9hVdeeQUPPPAAYmJisH79ehw8eBCHDh0CAHz44Yf46quv8K9//Qv9+vXDmDFj8Pzzz2PNmjWorr6x/HVmZiYiIyPx8ssvo2fPnkhNTcXEiROxYsUKqaxXXnkFM2bMwLRp0xAdHY3MzEz4+vpi3bp1TtfFGQwUiIhIuZopUDCbzTZHVVXje2ukpKQgISEBcXFxNucLCgpQU1Njcz4qKgrh4eHIz88HAOTn56NPnz4ICQmR0sTHx8NsNuPkyZNSml/eOz4+XrpHdXU1CgoKbNKo1WrExcVJaZypizMYKBARUZsXFhYGvV4vHRkZGXbTvvPOOzh69GiDaUwmEzQaDQICAmzOh4SEwGQySWluDhLqrtddayyN2WzG9evXcenSJVgslgbT3HwPR3VxBmc9EBGRcjXTrIfi4mLodDrptFbb8K6zxcXFePrpp7Fr1y54e8vfcVVJWm+goFIB6qaPRlX90LTRnDdrFyhve19zF/lbRWt+8pWd1xoif59d0yD5Wy/rC+VtM+0dJn9r3wo/+e9ToKFMdt799/xDdt5NV7vIypc83PlI/5eOVNXIzvtEwTTZeb0+0jlOZEfgKXlbErsy/70yWP5W3iqL/IJ9T8n/fIJVXrnCX/7vjspL3p8IlbUKMMsutmmaaWVGnU5nEyjYU1BQgAsXLqB///7SOYvFggMHDmD16tXYuXMnqqurUVpaavNNvqSkBAaDAQBgMBjqzU6om4lwc5pfzk4oKSmBTqeDj48PPDw84OHh0WCam+/hqC7OYNcDERGRk0aOHInjx4/j2LFj0jFgwABMnTpV+n8vLy/k5eVJeU6fPo2ioiIYjUYAgNFoxPHjx21mJ+zatQs6nQ7R0dFSmpvvUZem7h4ajQYxMTE2aaxWK/Ly8qQ0MTExDuvijNbbokBEROTArV6ZsV27dujdu7fNOT8/P3To0EE6n5ycjLS0NAQGBkKn02H27NkwGo0YPHgwAGDUqFGIjo7G448/juXLl8NkMmHRokVISUmRujxmzpyJ1atXY/78+Zg+fTr27NmDzZs3IycnRyo3LS0NSUlJGDBgAAYNGoSVK1eioqIC06bdaB3U6/UO6+IMBgpERKRcrXBlxhUrVkCtViMxMRFVVVWIj4/Ha6+9Jl338PDAjh07MGvWLBiNRvj5+SEpKQnLli2T0kRGRiInJwdz587FqlWr0LlzZ7z55puIj4+X0kyaNAkXL15Eeno6TCYT+vXrh9zcXJsBjo7q4gwGCkRERC7Yt2+fzc/e3t5Ys2YN1qxZYzdPREQE3n///UbvO3z4cHz++eeNpklNTUVqaqrd687UxRGOUSAiIiK72KJARESKpYKLYxSarSa3LwYKRESkXM00PZLsY9cDERER2cUWBSIiUq5WOOvhdsNAgYiIlIuBgtux64GIiIjsYosCEREp1q1embEtYqBARETKxa4Ht2u1gUJt9zsAz6Zv4el5qVx2mVfD5e0C6VdSK7tMq4/8f4JqnfzdIwO+kbcDJAD4F12Tla/0mF52mR3OufLb3EF2zkFVT8rO67+jnax8fw2XP10r/tEjjhPZ4eVpkZ3Xo0r+v8/lKHm/d17X5JcZcLZKdl5zuPydV71/9JOdt7a9vB0vPSrlfz55nL8uL6NV/rNErU+rDRSIiIgcYouC2zFQICIixeIYBffjrAciIiKyiy0KRESkXFzC2e0YKBARkXJxjILbMVAgIiLF4hgF9+MYBSIiIrKLLQpERKRc7HpwOwYKRESkXC52PTBQcIxdD0RERGQXWxSIiEi52PXgdgwUiIhIuRgouB27HoiIiMgutigQEZFicR0F92u1gYJn6XV4esjYCvlyqewy9WflbQtc2kPe9q8A4PNjhey8nhoP2Xm9L1XKzqsulVdn/2L5W+wGfmGWndfqK3877qDP5G+XqzJ9IytfYFiw7DK/yustO2+navmvtaZdtey8F/rL27bZ/0f52yerrPL/OnQouCI7r7qsXHZer+oaeRk95X9OWAzt5eWzVAI/yi6WWhl2PRAREZFdrbZFgYiIyCEOZnQ7BgpERKRYHKPgfgwUiIhI2fjH3q04RoGIiIjsYosCEREpF8couB0DBSIiUiyOUXA/dj0QERGRXWxRICIi5WLXg9sxUCAiIsVi14P7seuBiIiI7GKLAhERKRe7HtyOgQIRESkXAwW3Y9cDERGRk9auXYu7774bOp0OOp0ORqMRH3zwgXS9srISKSkp6NChA/z9/ZGYmIiSkhKbexQVFSEhIQG+vr4IDg7GvHnzUFtruxvqvn370L9/f2i1WnTr1g1ZWVn16rJmzRp06dIF3t7eiI2NxZEjR2yuO1MXZ7TaFgVVdQ1U6qbHMaJW/tazmh8uy8oX/KNKdpni2nXZeTXX5G2LDQC1QfLzqnw0svJZ5e/2DI8y+dtxq87+JDuvteKa/HLV8p4LtQvPsNoif6to63X5W49rNPKeCQAIKw6Sla8mVC+7zMpA+fX1L6uSnRdWq/y8QuZX33NN/8NQRy1zO261kL/teFPd6sGMnTt3xp///Gd0794dQghs2LABjz76KD7//HP06tULc+fORU5ODrZs2QK9Xo/U1FRMmDABn3zyCQDAYrEgISEBBoMBBw8exPnz5/Hb3/4WXl5eePHFFwEAhYWFSEhIwMyZM7Fx40bk5eXhySefRKdOnRAfHw8A2LRpE9LS0pCZmYnY2FisXLkS8fHxOH36NIKDb2xV76guzr9HQu7T5x5msxl6vR5xkbPhqW76PvXisvy94lUBMj94VC0TKEDfMoGCx1V5f1Auxsrb2x4AQvLOy85rvaiwQEGvk10mWihQULkQKKgMCgsUCq/Kzqu+WCo7r/D1lpfRhecfMgOFWlGNPPO/UFZWBp3Ohee5EXV/K+6a8yI8tDLfGwCWqkqcXvkciouLbeqq1Wqh1Tr3NygwMBAvvfQSJk6ciKCgIGRnZ2PixIkAgFOnTqFnz57Iz8/H4MGD8cEHH+Dhhx/GuXPnEBISAgDIzMzEggULcPHiRWg0GixYsAA5OTk4ceKEVMbkyZNRWlqK3NxcAEBsbCwGDhyI1atXAwCsVivCwsIwe/ZsPPvssygrK3NYF2ex64GIiJRLNMMBICwsDHq9XjoyMjIcFm2xWPDOO++goqICRqMRBQUFqKmpQVxcnJQmKioK4eHhyM/PBwDk5+ejT58+UpAAAPHx8TCbzTh58qSU5uZ71KWpu0d1dTUKCgps0qjVasTFxUlpnKmLs1pt1wMREdGt0lCLgj3Hjx+H0WhEZWUl/P39sXXrVkRHR+PYsWPQaDQICAiwSR8SEgKTyQQAMJlMNkFC3fW6a42lMZvNuH79Oq5cuQKLxdJgmlOnTkn3cFQXZzW5ReHAgQMYO3YsQkNDoVKpsG3bNpvrTzzxBFQqlc0xevTophZDRETkUN0YBVcOANLgxLqjsUDhrrvuwrFjx3D48GHMmjULSUlJ+Oqrr27RK771mhwoVFRUoG/fvlizZo3dNKNHj8b58+el4+2333apkkRERA1qpq6HptBoNOjWrRtiYmKQkZGBvn37YtWqVTAYDKiurkZpaalN+pKSEhgMBgCAwWCoN/Og7mdHaXQ6HXx8fNCxY0d4eHg0mObmeziqi7OaHCiMGTMGL7zwAsaPH283jVarhcFgkI727eUPYiMiImrNrFYrqqqqEBMTAy8vL+Tl5UnXTp8+jaKiIhiNRgCA0WjE8ePHceHCBSnNrl27oNPpEB0dLaW5+R51aeruodFoEBMTY5PGarUiLy9PSuNMXZzlljEK+/btQ3BwMNq3b48HHngAL7zwAjp06NBg2qqqKlRV/TzdyGw2u6NKRER0G7rV0yMXLlyIMWPGIDw8HFevXkV2djb27duHnTt3Qq/XIzk5GWlpaQgMDIROp8Ps2bNhNBqlWQajRo1CdHQ0Hn/8cSxfvhwmkwmLFi1CSkqK1N0xc+ZMrF69GvPnz8f06dOxZ88ebN68GTk5OVI90tLSkJSUhAEDBmDQoEFYuXIlKioqMG3aNABwqi7OavZAYfTo0ZgwYQIiIyPxzTff4LnnnsOYMWOQn58PDw+PeukzMjKwdOnS5q4GERG1Bbd4ZcYLFy7gt7/9Lc6fPw+9Xo+7774bO3fuxIMPPggAWLFiBdRqNRITE1FVVYX4+Hi89tprUn4PDw/s2LEDs2bNgtFohJ+fH5KSkrBs2TIpTWRkJHJycjB37lysWrUKnTt3xptvvimtoQAAkyZNwsWLF5Geng6TyYR+/fohNzfXZoCjo7o4y6V1FFQqFbZu3Ypx48bZTfPtt9/izjvvxO7duzFy5Mh61xtqUQgLC+M6Cs7gOgpO4ToKzuE6Cs7hOgqO3cp1FHqmuL6OwtdrnnNrXZXO7esodO3aFR07dsTZs2cbvK7VauuNNiUiInJKCwxmbGvcvo7CDz/8gJ9++gmdOnVyd1FERNTGqP57uJKfGtfkQKG8vNymdaCwsBDHjh1DYGAgAgMDsXTpUiQmJsJgMOCbb77B/Pnz0a1bN5u+FSIiIlKGJgcKn332GUaMGCH9nJaWBgBISkrC2rVr8eWXX2LDhg0oLS1FaGgoRo0aheeff97pNbOJiIicxm2m3a7JgcLw4cPR2PjHnTt3ulQhIiIiZ93q6ZFtUavd68F64RKsqqaPTBZVLmwBWyFvBoLaR/6IW9ElVHZeV2ZbeBVdkp1XlMvb8jnksvzR4qK8XHZeV2YuwCp/FgG8ZLaidZA/O0Ro5O/lrfq2SHZe61X5/7aQ+e/jdUn+wGdNe/kzJsRV+VueC5kzYQAAJfJ+Z0W1/C2frTI/Ty2iRnaZTcYWBbfj7pFERERkV6ttUSAiInIKWwXcioECEREpFscouB+7HoiIiMgutigQEZFycTCj2zFQICIixWLXg/ux64GIiIjsYosCEREpF7se3I6BAhERKRa7HtyPXQ9ERERkF1sUiIhIudj14HYMFIiISLkYKLgdAwUiIlIsjlFwP45RICIiIrtabYuCSquFSn2Lt5kWVnn5XNg6VlV0XnZeWOWHwpZr8rdeFhaZWy9fuSK7TFe21IZoma8MoqZWXr4f5D8TKk/5v9JWmfV1mcytvC2lpbKLVLmwLba6XTvZeWt6RcjO63XmnKx8ap38+oqL8ra2VgkV4MJHcZOw68HtWm2gQERE5IhKCKhc+DLgSt62gl0PREREZBdbFIiISLnY9eB2DBSIiEixOOvB/dj1QERERHaxRYGIiJSLXQ9ux0CBiIgUi10P7seuByIiIrKLLQpERKRc7HpwOwYKRESkWOx6cD8GCkREpFxsUXA7jlEgIiIiu9iiQEREisbuA/dqtYGC1XwVVpVXk/MJF3ZUVGuaXh4AWK9Xyi5TVFfLzttSuyK2iJZ6rS7sWqnykvfrZb1+XXaZUMlvJFT7eMvOK3tHUUD27pEt9UxYXdh50utEofxyK2Q+Fy7sbiv380mIGtllyijMtWehLX2OysSuByIiIrKr1bYoEBEROcJZD+7HFgUiIlIu0QxHE2RkZGDgwIFo164dgoODMW7cOJw+fdomTWVlJVJSUtChQwf4+/sjMTERJSUlNmmKioqQkJAAX19fBAcHY968eaitrbVJs2/fPvTv3x9arRbdunVDVlZWvfqsWbMGXbp0gbe3N2JjY3HkyJEm18URBgpERERO2r9/P1JSUnDo0CHs2rULNTU1GDVqFCoqKqQ0c+fOxfbt27Flyxbs378f586dw4QJE6TrFosFCQkJqK6uxsGDB7FhwwZkZWUhPT1dSlNYWIiEhASMGDECx44dw5w5c/Dkk09i586dUppNmzYhLS0NixcvxtGjR9G3b1/Ex8fjwoULTtfFGSohWtdIDrPZDL1ejxGeifBUyGBGV95CDmZs5VwZzKjRyMrn0jPRQoMZXRnQK3swowtUni3T66r295OdV0mDGWtFDfaJbSgrK4NOp5NdfmPq/lYMHP8CPL3kP7u1NZX4dOsi2XW9ePEigoODsX//ftx///0oKytDUFAQsrOzMXHiRADAqVOn0LNnT+Tn52Pw4MH44IMP8PDDD+PcuXMICQkBAGRmZmLBggW4ePEiNBoNFixYgJycHJw4cUIqa/LkySgtLUVubi4AIDY2FgMHDsTq1asBAFarFWFhYZg9ezaeffZZp+riDLYoEBGRcjVT14PZbLY5qqqqnCq+rKwMABAYGAgAKCgoQE1NDeLi4qQ0UVFRCA8PR35+PgAgPz8fffr0kYIEAIiPj4fZbMbJkyelNDffoy5N3T2qq6tRUFBgk0atViMuLk5K40xdnMFAgYiI2rywsDDo9XrpyMjIcJjHarVizpw5GDJkCHr37g0AMJlM0Gg0CAgIsEkbEhICk8kkpbk5SKi7XnetsTRmsxnXr1/HpUuXYLFYGkxz8z0c1cUZnPVARESK1VyzHoqLi226HrRarcO8KSkpOHHiBD7++GP5FVAABgpERKRczbTgkk6na9IYhdTUVOzYsQMHDhxA586dpfMGgwHV1dUoLS21+SZfUlICg8Egpfnl7IS6mQg3p/nl7ISSkhLodDr4+PjAw8MDHh4eDaa5+R6O6uIMdj0QEZFi1bUouHI0hRACqamp2Lp1K/bs2YPIyEib6zExMfDy8kJeXp507vTp0ygqKoLRaAQAGI1GHD9+3GZ2wq5du6DT6RAdHS2lufkedWnq7qHRaBATE2OTxmq1Ii8vT0rjTF2cwRYFIiIiJ6WkpCA7Oxvvvfce2rVrJ/X16/V6+Pj4QK/XIzk5GWlpaQgMDIROp8Ps2bNhNBqlWQajRo1CdHQ0Hn/8cSxfvhwmkwmLFi1CSkqK1OUxc+ZMrF69GvPnz8f06dOxZ88ebN68GTk5OVJd0tLSkJSUhAEDBmDQoEFYuXIlKioqMG3aNKlOjuriDAYKRESkXLd4m+m1a9cCAIYPH25zfv369XjiiScAACtWrIBarUZiYiKqqqoQHx+P1157TUrr4eGBHTt2YNasWTAajfDz80NSUhKWLVsmpYmMjEROTg7mzp2LVatWoXPnznjzzTcRHx8vpZk0aRIuXryI9PR0mEwm9OvXD7m5uTYDHB3VxRlcR+EmXEeB6uE6Ck7hOgrO4ToKzafub8XghOddXkfhUM6f3FpXpeMYBSIiIrKr1XY9iNpaCBe+zclhrbLKy8hv9revlmgtcmkEt/xv59Zr11wotwV+B9Qe8rO68s2+vMJxIjsspWWy8yrKrXweuM2027XaQIGIiMgR7h7pfux6ICIiIrvYokBERMp1i2c9tEUMFIiISLHY9eB+7HogIiIiu9iiQEREymUVNw5X8lOjGCgQEZFycYyC2zFQICIixVLBxTEKzVaT2xfHKBAREZFdbFEgIiLl4sqMbsdAgYiIFIvTI92PXQ9ERERkF1sUiIhIuTjrwe0YKBARkWKphIDKhXEGruRtKxgo3IwPDDUnpT1PSquvVf6W2hZzeYuU2xJUnvI/5kVtbTPWhJSKgQIRESmX9b+HK/mpUQwUiIhIsdj14H6c9UBERER2NSlQyMjIwMCBA9GuXTsEBwdj3LhxOH36tE2ayspKpKSkoEOHDvD390diYiJKSkqatdJEREQAfp714MpBjWpSoLB//36kpKTg0KFD2LVrF2pqajBq1ChUVFRIaebOnYvt27djy5Yt2L9/P86dO4cJEyY0e8WJiIiklRldOahRTRqjkJuba/NzVlYWgoODUVBQgPvvvx9lZWV46623kJ2djQceeAAAsH79evTs2ROHDh3C4MGDm6/mRETU5nFlRvdzaYxCWVkZACAwMBAAUFBQgJqaGsTFxUlpoqKiEB4ejvz8/AbvUVVVBbPZbHMQERFR6yA7ULBarZgzZw6GDBmC3r17AwBMJhM0Gg0CAgJs0oaEhMBkMjV4n4yMDOj1eukICwuTWyUiImpr2PXgdrIDhZSUFJw4cQLvvPOOSxVYuHAhysrKpKO4uNil+xERUduhsrp+UONkraOQmpqKHTt24MCBA+jcubN03mAwoLq6GqWlpTatCiUlJTAYDA3eS6vVQqvVyqkGERERuVmTWhSEEEhNTcXWrVuxZ88eREZG2lyPiYmBl5cX8vLypHOnT59GUVERjEZj89SYiIioDrse3K5JLQopKSnIzs7Ge++9h3bt2knjDvR6PXx8fKDX65GcnIy0tDQEBgZCp9Nh9uzZMBqNnPFARETNj7tHul2TAoW1a9cCAIYPH25zfv369XjiiScAACtWrIBarUZiYiKqqqoQHx+P1157rVkqS0RERLdWkwIF4UQTjbe3N9asWYM1a9bIrhQREZEzuNeD+3FTqGbgyjau6nbtZOe1Xr0qOy+3j6V6VCr5eZX2YauwraJdcdv/rrs6zkBpz24L4KZQREREZBdbFIiISLkEAFfWQmCDgkMMFIiISLE4RsH9GCgQEZFyCbg4RqHZanLb4hgFIiIisostCkREpFyc9eB2bFEgIiLlsjbD0UQHDhzA2LFjERoaCpVKhW3bttlcF0IgPT0dnTp1go+PD+Li4nDmzBmbNJcvX8bUqVOh0+kQEBCA5ORklJeX26T58ssvMXToUHh7eyMsLAzLly+vV5ctW7YgKioK3t7e6NOnD95///0m18URBgpERERNUFFRgb59+9pdWHD58uV49dVXkZmZicOHD8PPzw/x8fGorKyU0kydOhUnT57Erl27pE0Wn3rqKem62WzGqFGjEBERgYKCArz00ktYsmQJ3njjDSnNwYMHMWXKFCQnJ+Pzzz/HuHHjMG7cOJw4caJJdXFEJZxZbvEWMpvN0Ov1GI5H4anyaunqOIULLtFtoS0tuERuVStqsA/voaysDDqdzi1l1P2tGNl7Pjw95O9AXGupQt6J5bLrqlKpsHXrVowbNw7AjW/woaGh+J//+R8888wzAICysjKEhIQgKysLkydPxtdff43o6Gh8+umnGDBgAAAgNzcXDz30EH744QeEhoZi7dq1+OMf/wiTyQSNRgMAePbZZ7Ft2zacOnUKADBp0iRUVFRgx44dUn0GDx6Mfv36ITMz06m6OIMtCkREpFzNtHuk2Wy2OaqqqmRVp7CwECaTCXFxcdI5vV6P2NhY5OfnAwDy8/MREBAgBQkAEBcXB7VajcOHD0tp7r//filIAID4+HicPn0aV65ckdLcXE5dmrpynKmLMxgoEBFRmxcWFga9Xi8dGRkZsu5Tt6tySEiIzfmQkBDpmslkQnBwsM11T09PBAYG2qRp6B43l2Evzc3XHdXFGZz1QEREytVMsx6Ki4ttuh60WvndGbcbtigQEZFyNVPXg06nsznkBgoGgwEAUFJSYnO+pKREumYwGHDhwgWb67W1tbh8+bJNmobucXMZ9tLcfN1RXZzBQIGIiKiZREZGwmAwIC8vTzpnNptx+PBhGI1GAIDRaERpaSkKCgqkNHv27IHVakVsbKyU5sCBA6ipqZHS7Nq1C3fddRfat28vpbm5nLo0deU4UxdnsOuhGbgyc6Hm7i6y83p9+Z3svJb/Doah24wLMxfULjS1WmUO/ALAGRPkGisAFybsyFlHoby8HGfPnpV+LiwsxLFjxxAYGIjw8HDMmTMHL7zwArp3747IyEj86U9/QmhoqDQzomfPnhg9ejRmzJiBzMxM1NTUIDU1FZMnT0ZoaCgA4LHHHsPSpUuRnJyMBQsW4MSJE1i1ahVWrFghlfv0009j2LBhePnll5GQkIB33nkHn332mTSFUqVSOayLMxgoEBGRYrXEplCfffYZRowYIf2clpYGAEhKSkJWVhbmz5+PiooKPPXUUygtLcV9992H3NxceHt7S3k2btyI1NRUjBw5Emq1GomJiXj11Vel63q9Hh9++CFSUlIQExODjh07Ij093WathXvvvRfZ2dlYtGgRnnvuOXTv3h3btm1D7969pTTO1MWJ96h1hfNKXEfB47/NQHKwRYGaFVsUqBW4lesoxHWf6/I6CrvPrHBrXZWOYxSIiIjILnY9EBGRclkFoHKhVcrKFi1HGCgQEZFycfdIt2PXAxEREdnFFgUiIlIwF1sUwBYFRxgoEBGRcrHrwe3Y9UBERER2sUWBiIiUyyrgUvcBZz04xECBiIiUS1hvHK7kp0ax64GIiIjsYosCEREpFwczuh0DhZupPWRls169KrtIV/ZrcKVcuk258KHH/RqoHpmfiRBWWbsyysIxCm7HQIGIiJSLLQpuxzEKREREZBdbFIiISLkEXGxRaLaa3LYYKBARkXKx68Ht2PVAREREdrFFgYiIlMtqhUtTLKxccMkRBgpERKRc7HpwO3Y9EBERkV1sUSAiIuVii4LbMVAgIiLl4sqMbseuByIiIrKLLQpERKRYQlghXNgq2pW8bQUDBSIiUi4hXOs+4BgFhxgoEBGRcgkXxygwUHDo9gsU5G6LCsBD5y8rn8VcLrtMy5UrsvMSNSt+YN6eXPhMVPt4y8sn1ECF7GKplbn9AgUiImo7rFZA5cI4A45RcIiBAhERKRe7HtyO0yOJiIjILrYoEBGRYgmrFcKFrgdOj3SMgQIRESkXux7cjl0PREREZBdbFIiISLmsAlCxRcGdGCgQEZFyCQHAlemRDBQcYdcDERER2cUWBSIiUixhFRAudD0Itig4xBYFIiJSLmF1/ZBhzZo16NKlC7y9vREbG4sjR4408wtrPRgoEBGRYgmrcPloqk2bNiEtLQ2LFy/G0aNH0bdvX8THx+PChQtueIUtj4ECERFRE7zyyiuYMWMGpk2bhujoaGRmZsLX1xfr1q1r6aq5Rasbo1DXX1SLGnlraLiwypYQ1bLyWUSN7DIhLPLzEhE54sJnolrI+y5Z+9/PxFvR/18rqlx6jbW4UVez2WxzXqvVQqvV1ktfXV2NgoICLFy4UDqnVqsRFxeH/Px82fVozVpdoHD16lUAwMd4X94NXFmNs9SFvERErZErn4kubhV99epV6PV6125ih0ajgcFgwMcmmX8rbuLv74+wsDCbc4sXL8aSJUvqpb106RIsFgtCQkJszoeEhODUqVMu16U1anWBQmhoKIqLi9GuXTuoVKp6181mM8LCwlBcXAydTtcCNVQGvk/O4fvkGN8j5/B9+pkQAlevXkVoaKjbyvD29kZhYSGqq+W1BN9MCFHv701DrQltVasLFNRqNTp37uwwnU6na/O/jM7g++Qcvk+O8T1yDt+nG9zVknAzb29veHt7u72cm3Xs2BEeHh4oKSmxOV9SUgKDwXBL63KrcDAjERGRkzQaDWJiYpCXlyeds1qtyMvLg9FobMGauU+ra1EgIiJqzdLS0pCUlIQBAwZg0KBBWLlyJSoqKjBt2rSWrppbKC5Q0Gq1WLx4MfuPHOD75By+T47xPXIO36e2Y9KkSbh48SLS09NhMpnQr18/5Obm1hvgeLtQCa5fSURERHZwjAIRERHZxUCBiIiI7GKgQERERHYxUCAiIiK7GCgQERGRXYoKFNrS/t9yLFmyBCqVyuaIiopq6Wq1uAMHDmDs2LEIDQ2FSqXCtm3bbK4LIZCeno5OnTrBx8cHcXFxOHPmTMtUtgU5ep+eeOKJes/X6NGjW6ayLSgjIwMDBw5Eu3btEBwcjHHjxuH06dM2aSorK5GSkoIOHTrA398fiYmJ9VbyI1IKxQQKbW3/b7l69eqF8+fPS8fHH3/c0lVqcRUVFejbty/WrFnT4PXly5fj1VdfRWZmJg4fPgw/Pz/Ex8ejsrLyFte0ZTl6nwBg9OjRNs/X22+/fQtr2Drs378fKSkpOHToEHbt2oWamhqMGjUKFRU/76A0d+5cbN++HVu2bMH+/ftx7tw5TJgwoQVrTeQCoRCDBg0SKSkp0s8Wi0WEhoaKjIyMFqxV67J48WLRt2/flq5GqwZAbN26VfrZarUKg8EgXnrpJelcaWmp0Gq14u23326BGrYOv3yfhBAiKSlJPProoy1Sn9bswoULAoDYv3+/EOLG8+Pl5SW2bNkipfn6668FAJGfn99S1SSSTREtCnX7f8fFxUnnbvf9v+U6c+YMQkND0bVrV0ydOhVFRUUtXaVWrbCwECaTyebZ0uv1iI2N5bPVgH379iE4OBh33XUXZs2ahZ9++qmlq9TiysrKAACBgYEAgIKCAtTU1Ng8U1FRUQgPD+czRYqkiEChsf2/TSZTC9Wq9YmNjUVWVhZyc3Oxdu1aFBYWYujQobh69WpLV63Vqnt++Gw5Nnr0aPzjH/9AXl4e/vKXv2D//v0YM2YMLBZLS1etxVitVsyZMwdDhgxB7969Adx4pjQaDQICAmzS8pkipVLcXg9k35gxY6T/v/vuuxEbG4uIiAhs3rwZycnJLVgzuh1MnjxZ+v8+ffrg7rvvxp133ol9+/Zh5MiRLVizlpOSkoITJ05wLBDd1hTRotAW9/9uDgEBAejRowfOnj3b0lVpteqeHz5bTde1a1d07NixzT5fqamp2LFjB/bu3YvOnTtL5w0GA6qrq1FaWmqTns8UKZUiAoW2uP93cygvL8c333yDTp06tXRVWq3IyEgYDAabZ8tsNuPw4cN8thz44Ycf8NNPP7W550sIgdTUVGzduhV79uxBZGSkzfWYmBh4eXnZPFOnT59GUVERnylSJMV0PbS1/b/leOaZZzB27FhERETg3LlzWLx4MTw8PDBlypSWrlqLKi8vt/nWW1hYiGPHjiEwMBDh4eGYM2cOXnjhBXTv3h2RkZH405/+hNDQUIwbN67lKt0CGnufAgMDsXTpUiQmJsJgMOCbb77B/Pnz0a1bN8THx7dgrW+9lJQUZGdn47333kO7du2kcQd6vR4+Pj7Q6/VITk5GWloaAgMDodPpMHv2bBiNRgwePLiFa08kQ0tPu2iKv/3tbyI8PFxoNBoxaNAgcejQoZauUqsyadIk0alTJ6HRaMQdd9whJk2aJM6ePdvS1Wpxe/fuFQDqHUlJSUKIG1Mk//SnP4mQkBCh1WrFyJEjxenTp1u20i2gsffp2rVrYtSoUSIoKEh4eXmJiIgIMWPGDGEymVq62rdcQ+8RALF+/XopzfXr18Xvf/970b59e+Hr6yvGjx8vzp8/33KVJnKBSgghbn14QkREREqgiDEKRERE1DIYKBAREZFdDBSIiIjILgYKREREZBcDBSIiIrKLgQIRERHZxUCBiIiI7GKgQERERHYxUCAiIiK7GCgQERGRXQwUiIiIyK7/D1rGHVLpVLsIAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "for i,name in zip(images, filters):\n", @@ -295,9 +758,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "# Create an RGB image\n", @@ -338,7 +822,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb b/source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb new file mode 100644 index 00000000..33738172 --- /dev/null +++ b/source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb @@ -0,0 +1,1696 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient based optimization (Adam)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from jax import config\n", + "#config.update(\"jax_enable_x64\", True)\n", + "#config.update('jax_num_cpu_devices', 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '1,2'\n", + "\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load ssp template from FSPS" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:14:51,323 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-11 10:14:51,324 - rubix - INFO - Rubix version: 0.0.post507+g27646941c\n", + "2025-11-11 10:14:51,325 - rubix - INFO - JAX version: 0.4.38\n", + "2025-11-11 10:14:51,325 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-11 10:14:51,326 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.spectra.ssp.factory import get_ssp_template\n", + "ssp_fsps = get_ssp_template(\"FSPS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(107,)\n", + "(12,)\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "age_values = ssp_fsps.age\n", + "print(age_values.shape)\n", + "\n", + "metallicity_values = ssp_fsps.metallicity\n", + "print(metallicity_values.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start age: 15.848933219909668, start metallicity: 0.025251565501093864\n", + "target age: 3.1622776985168457, target metallicity: 0.014199999161064625\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "index_age = 90\n", + "index_metallicity = 9\n", + "\n", + "#initial_metallicity_index = 5\n", + "#initial_age_index = 70\n", + "initial_metallicity_index = 10\n", + "initial_age_index = 104\n", + "\n", + "initial_age_index2 = 90\n", + "initial_metallicity_index2 = 6\n", + "\n", + "initial_age_index3 = 99\n", + "initial_metallicity_index3 = 11\n", + "\n", + "learning_all = 5e-3\n", + "tol = 1e-10\n", + "\n", + "print(f\"start age: {age_values[initial_age_index]}, start metallicity: {metallicity_values[initial_metallicity_index]}\")\n", + "print(f\"target age: {age_values[index_age]}, target metallicity: {metallicity_values[index_metallicity]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configure pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.pipeline import RubixPipeline\n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_gradient\",},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 2,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"TESTGRADIENT\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 1.2},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},\n", + " },\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:52,007 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:14:52,008 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:14:52,081 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:14:53,068 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n", + "2025-11-11 10:14:53,071 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n", + "2025-11-11 10:14:53,072 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:14:53,073 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:14:53,074 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:14:53,078 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,102 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,278 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:14:53,288 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:14:53,341 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,447 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,465 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,533 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:53,742 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:14:53,743 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:14:53,744 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:14:53,813 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:14:53,814 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:14:53,814 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:14:53,904 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:14:53,921 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:14:54,059 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:14:54,060 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:14:54,063 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:14:54,068 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:14:57,373 - rubix - INFO - Pipeline run completed in 4.30 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "inputdata = pipe.prepare_data()\n", + "output = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set target values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "inputdata.stars.age = jnp.array([age_values[index_age], age_values[index_age]])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_values[index_metallicity], metallicity_values[index_metallicity]])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:14:57,494 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:14:57,495 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:14:57,496 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:14:57,498 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:57,511 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:14:57,523 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:14:57,533 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:14:57,577 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:57,617 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:57,630 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:57,684 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:14:57,731 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:14:57,732 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:14:57,733 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:14:57,809 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:14:57,810 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:14:57,810 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:14:57,871 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:14:57,873 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:14:57,884 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:14:57,884 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:14:57,887 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:14:57,889 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:15:00,996 - rubix - INFO - Pipeline run completed in 3.50 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "targetdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(466,)\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "print(targetdata[0,0,:].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set initial datracube" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "inputdata.stars.age = jnp.array([age_values[initial_age_index], age_values[initial_age_index]])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index], metallicity_values[initial_metallicity_index]])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:15:01,085 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:15:01,086 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:15:01,086 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:15:01,088 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:01,100 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:15:01,110 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:15:01,119 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:15:01,152 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:01,183 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:01,195 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:01,258 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:01,317 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:15:01,318 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:15:01,319 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:15:01,395 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:15:01,396 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:15:01,397 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:15:01,456 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:15:01,458 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:15:01,468 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:15:01,469 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:15:01,471 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:15:01,474 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:15:04,414 - rubix - INFO - Pipeline run completed in 3.33 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "initialdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:04,493 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:15:04,493 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:15:04,494 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:15:04,496 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:04,505 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:15:04,516 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:15:04,525 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:15:04,569 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:04,602 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:04,615 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:15:04,654 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.pipeline import linear_pipeline as pipeline\n", + "\n", + "pipeline_instance = RubixPipeline(config)\n", + "\n", + "pipeline_instance._pipeline = pipeline.LinearTransformerPipeline(\n", + " pipeline_instance.pipeline_config, \n", + " pipeline_instance._get_pipeline_functions()\n", + ")\n", + "pipeline_instance._pipeline.assemble()\n", + "pipeline_instance.func = pipeline_instance._pipeline.compile_expression()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import optax\n", + "\n", + "def loss_only_wrt_age_metallicity(age, metallicity, base_data, target):\n", + " \n", + " base_data.stars.age = age*20\n", + " base_data.stars.metallicity = metallicity*0.05\n", + "\n", + " output = pipeline_instance.func(base_data)\n", + " #loss = jnp.sum((output.stars.datacube - target) ** 2)\n", + " #loss = jnp.sum(optax.l2_loss(output.stars.datacube, target.stars.datacube))\n", + " #loss = jnp.sum(optax.huber_loss(output.stars.datacube, target.stars.datacube))\n", + " loss = jnp.sum(optax.cosine_distance(output.stars.datacube, target))\n", + " \n", + " return jnp.log10(loss) #loss#/0.03 #jnp.log10(loss #/5e-5)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import optax\n", + "\n", + "\n", + "def adam_optimization_multi(loss_fn, params_init, data, target, learning=learning_all, tol=1e-3, max_iter=500):\n", + " \"\"\"\n", + " Optimizes both age and metallicity.\n", + "\n", + " Args:\n", + " loss_fn: function with signature loss_fn(age, metallicity, data, target)\n", + " params_init: dict with keys 'age' and 'metallicity', each a JAX array\n", + " data: base data for the loss function\n", + " target: target data for the loss function\n", + " learning_rate: learning rate for Adam\n", + " tol: tolerance for convergence (based on update norm)\n", + " max_iter: maximum number of iterations\n", + "\n", + " Returns:\n", + " params: final parameters (dict)\n", + " params_history: list of parameter values for each iteration\n", + " loss_history: list of loss values for each iteration\n", + " \"\"\"\n", + " params = params_init # e.g., {'age': jnp.array(...), 'metallicity': jnp.array(...)}\n", + " optimizers = {\n", + " 'age': optax.adam(learning),\n", + " 'metallicity': optax.adam(learning)\n", + " }\n", + " # Create a parameter label pytree matching the structure of params\n", + " param_labels = {'age': 'age', 'metallicity': 'metallicity'}\n", + " \n", + " # Combine the optimizers with multi_transform\n", + " optimizer = optax.multi_transform(optimizers, param_labels)\n", + " optimizer_state = optimizer.init(params)\n", + " \n", + " age_history = []\n", + " metallicity_history = []\n", + " loss_history = []\n", + " \n", + " for i in range(max_iter):\n", + " # Compute loss and gradients with respect to both parameters\n", + " loss, grads = jax.value_and_grad(lambda p: loss_fn(p['age'], p['metallicity'], data, target))(params)\n", + " loss_history.append(float(loss))\n", + " # Save current parameters (convert from JAX arrays to floats)\n", + " age_history.append(float(params['age'][0]))\n", + " metallicity_history.append(float(params['metallicity'][0]))\n", + " #params_history.append({\n", + " # 'age': params['age'],\n", + " # 'metallicity': params['metallicity']\n", + " #})\n", + " \n", + " # Compute updates and apply them\n", + " updates, optimizer_state = optimizer.update(grads, optimizer_state)\n", + " params = optax.apply_updates(params, updates)\n", + " \n", + " # Optionally clip the parameters to enforce physical constraints:\n", + " #params['age'] = jnp.clip(params['age'], 0.0, 1.0)\n", + " #params['metallicity'] = jnp.clip(params['metallicity'], 0.0, 1.0)\n", + " # For metallicity, uncomment and adjust the limits as needed:\n", + " # params['metallicity'] = jnp.clip(params['metallicity'], metallicity_lower_bound, metallicity_upper_bound)\n", + " \n", + " # Check convergence based on the global norm of updates\n", + " if optax.global_norm(updates) < tol:\n", + " print(f\"Converged at iteration {i}\")\n", + " break\n", + "\n", + " return params, age_history, metallicity_history, loss_history" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:15:04,803 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:15:04,804 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:15:04,804 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:15:04,888 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:15:04,903 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:15:05,278 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:15:05,279 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:15:05,282 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:15:05,287 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n" + ] + }, + { + "data": { + "text/plain": [ + "Array(nan, dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "loss_only_wrt_age_metallicity(inputdata.stars.age, inputdata.stars.metallicity, inputdata, targetdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial parameters: {'age': Array([0.7924467, 0.7924467], dtype=float32), 'metallicity': Array([0.5050313, 0.5050313], dtype=float32)}\n", + "Optimized Age: [nan nan]\n", + "Optimized Metallicity: [nan nan]\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "data = inputdata # Replace with your actual data if needed\n", + "target_value = targetdata # Replace with your actual target\n", + "\n", + "# Define initial guesses for both age and metallicity.\n", + "# Adjust the initialization as needed for your problem.\n", + "age_init = jnp.array([age_values[initial_age_index]/20, age_values[initial_age_index]/20])\n", + "metallicity_init = jnp.array([metallicity_values[initial_metallicity_index]/0.05, metallicity_values[initial_metallicity_index]/0.05])\n", + "\n", + "\n", + "# Pack both initial parameters into a dictionary.\n", + "params_init = {'age': age_init, 'metallicity': metallicity_init}\n", + "print(f\"Initial parameters: {params_init}\")\n", + "\n", + "# Call the new optimizer function that handles both parameters.\n", + "optimized_params, age_history, metallicity_history, loss_history = adam_optimization_multi(\n", + " loss_only_wrt_age_metallicity,\n", + " params_init,\n", + " data,\n", + " target_value,\n", + " learning=learning_all,\n", + " tol=tol,\n", + " max_iter=5000,\n", + ")\n", + "\n", + "print(f\"Optimized Age: {optimized_params['age']}\")\n", + "print(f\"Optimized Metallicity: {optimized_params['metallicity']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:17:53,403 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:17:53,405 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:17:53,446 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:17:53,785 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n", + "2025-11-11 10:17:53,786 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n", + "2025-11-11 10:17:53,801 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:17:53,802 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:17:53,803 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:17:53,806 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:17:53,840 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:17:53,855 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:17:53,865 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:17:53,906 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:17:53,960 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:17:53,977 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:17:54,039 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:17:54,094 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:17:54,095 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:17:54,096 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:17:54,177 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:17:54,177 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:17:54,178 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:17:54,236 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:17:54,238 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:17:54,251 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:17:54,251 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:17:54,253 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:17:54,256 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:17:57,635 - rubix - INFO - Pipeline run completed in 3.83 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial parameters: {'age': Array([0.15811388, 0.15811388], dtype=float32), 'metallicity': Array([0.05050315, 0.05050315], dtype=float32)}\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "inputdata2 = pipe.prepare_data()\n", + "\n", + "inputdata2.stars.age = jnp.array([age_values[initial_age_index2], age_values[initial_age_index2]])\n", + "inputdata2.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index2], metallicity_values[initial_metallicity_index2]])\n", + "inputdata2.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata2.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata2.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "\n", + "initialdata2 = pipe.run_sharded(inputdata2)\n", + "\n", + "data2 = inputdata2 # Replace with your actual data if needed\n", + "target_value = targetdata # Replace with your actual target\n", + "\n", + "# Define initial guesses for both age and metallicity.\n", + "# Adjust the initialization as needed for your problem.\n", + "age_init2 = jnp.array([age_values[initial_age_index2]/20, age_values[initial_age_index2]/20])\n", + "metallicity_init2 = jnp.array([metallicity_values[initial_metallicity_index2]/0.05, metallicity_values[initial_metallicity_index2]/0.05])\n", + "\n", + "\n", + "# Pack both initial parameters into a dictionary.\n", + "params_init2 = {'age': age_init2, 'metallicity': metallicity_init2}\n", + "print(f\"Initial parameters: {params_init2}\")\n", + "\n", + "# Call the new optimizer function that handles both parameters.\n", + "optimized_params2, age_history2, metallicity_history2, loss_history2 = adam_optimization_multi(\n", + " loss_only_wrt_age_metallicity,\n", + " params_init2,\n", + " data2,\n", + " target_value,\n", + " learning=learning_all,\n", + " tol=tol,\n", + " max_iter=5000,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:20:28,016 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:20:28,019 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:20:28,056 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:20:28,372 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n", + "2025-11-11 10:20:28,373 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n", + "2025-11-11 10:20:28,386 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:20:28,387 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:20:28,388 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:20:28,390 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:20:28,424 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:20:28,440 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:20:28,450 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:20:28,537 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:20:28,594 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:20:28,606 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:20:28,674 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:20:28,731 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:20:28,733 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:20:28,734 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:20:28,815 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:20:28,816 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:20:28,817 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:20:28,875 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:20:28,877 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:20:28,889 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:20:28,890 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:20:28,892 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:20:28,894 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:20:32,060 - rubix - INFO - Pipeline run completed in 3.67 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial parameters: {'age': Array([0.44562545, 0.44562545], dtype=float32), 'metallicity': Array([0.8980868, 0.8980868], dtype=float32)}\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "inputdata3 = pipe.prepare_data()\n", + "\n", + "inputdata3.stars.age = jnp.array([age_values[initial_age_index3], age_values[initial_age_index3]])\n", + "inputdata3.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index3], metallicity_values[initial_metallicity_index3]])\n", + "inputdata3.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata3.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata3.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "\n", + "initialdata3 = pipe.run_sharded(inputdata3)\n", + "\n", + "data3 = inputdata3 # Replace with your actual data if needed\n", + "target_value = targetdata # Replace with your actual target\n", + "\n", + "age_init3 = jnp.array([age_values[initial_age_index3]/20, age_values[initial_age_index3]/20])\n", + "metallicity_init3 = jnp.array([metallicity_values[initial_metallicity_index3]/0.05, metallicity_values[initial_metallicity_index3]/0.05])\n", + "\n", + "params_init3 = {'age': age_init3, 'metallicity': metallicity_init3}\n", + "print(f\"Initial parameters: {params_init3}\")\n", + "\n", + "optimized_params3, age_history3, metallicity_history3, loss_history3 = adam_optimization_multi(\n", + " loss_only_wrt_age_metallicity,\n", + " params_init3,\n", + " data3,\n", + " target_value,\n", + " learning=learning_all,\n", + " tol=tol,\n", + " max_iter=5000,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss history" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of iterations: 5000\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Configure matplotlib to use LaTeX for all text\n", + "#mpl.rcParams.update({\n", + "# \"text.usetex\": True, # Use LaTeX for text rendering\n", + "# \"font.family\": \"serif\", # Use serif fonts\n", + " # Here \"txfonts\" is not directly available as a font in matplotlib,\n", + " # but you can set the serif list to a font that closely resembles it.\n", + " # Alternatively, you can try using:\n", + "# \"font.serif\": [\"Times\", \"Palatino\", \"New Century Schoolbook\"],\n", + "# \"font.size\": 16, # Set the base font size (adjust to match your document)\n", + "# \"text.latex.preamble\": r\"\\usepackage{txfonts}\", # Use txfonts to match your Overleaf document\n", + "#})\n", + "\n", + "\n", + "# Convert histories to NumPy arrays if needed\n", + "loss_history_np = np.array(loss_history)\n", + "age_history_np = np.array(age_history)\n", + "metallicity_history_np = np.array(metallicity_history)\n", + "\n", + "# Create an x-axis based on the number of iterations (assumed same for all)\n", + "iterations = np.arange(len(loss_history_np))\n", + "print(f\"Number of iterations: {len(iterations)}\")\n", + "\n", + "# Create a figure with three subplots in one row and shared x-axis.\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True)\n", + "\n", + "# Plot the loss history (convert log-loss back to loss if needed)\n", + "axs[0].plot(iterations, 10**loss_history_np, marker='o', linestyle='-')\n", + "axs[0].set_xlabel(\"Iteration\")\n", + "axs[0].set_ylabel(\"Loss\")\n", + "axs[0].set_title(\"Loss History\")\n", + "axs[0].grid(True)\n", + "\n", + "# Plot the age history, multiplying by 20 for the physical scale.\n", + "axs[1].plot(iterations, age_history_np * 20, marker='o', linestyle='-')\n", + "# Draw a horizontal line for the target age\n", + "axs[1].hlines(y=age_values[index_age], xmin=0, xmax=iterations[-1], color='r', linestyle='-')\n", + "axs[1].set_xlabel(\"Iteration\")\n", + "axs[1].set_ylabel(\"Age\")\n", + "axs[1].set_title(\"Age History\")\n", + "axs[1].grid(True)\n", + "\n", + "# Plot the metallicity history, multiplying by 0.05 for the physical scale.\n", + "axs[2].plot(iterations, metallicity_history_np *0.05, marker='o', linestyle='-')\n", + "# Draw a horizontal line for the target metallicity\n", + "axs[2].hlines(y=metallicity_values[index_metallicity], xmin=0, xmax=iterations[-1], color='r', linestyle='-')\n", + "axs[2].set_xlabel(\"Iteration\")\n", + "axs[2].set_ylabel(\"Metallicity\")\n", + "axs[2].set_title(\"Metallicity History\")\n", + "axs[2].grid(True)\n", + "\n", + "axs[0].set_xlim(-5, 900)\n", + "axs[1].set_xlim(-5, 900)\n", + "axs[2].set_xlim(-5, 900)\n", + "plt.tight_layout()\n", + "plt.savefig(f\"output/optimisation_history.jpg\", dpi=1000)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:09,750 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:23:09,751 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:23:09,752 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:23:09,753 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:09,764 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:23:09,779 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:23:09,789 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:23:09,842 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:09,890 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:09,902 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:09,954 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:10,022 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:23:10,023 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:23:10,024 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:23:10,108 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:23:10,109 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:23:10,109 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:23:10,168 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:23:10,170 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:23:10,182 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:23:10,183 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:23:10,185 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:23:10,188 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:23:13,509 - rubix - INFO - Pipeline run completed in 3.76 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1, 466)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "#run the pipeline with the optimized age\n", + "#rubixdata.stars.age = optimized_age\n", + "i = 0\n", + "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "\n", + "pipe = RubixPipeline(config)\n", + "rubixdata = pipe.run_sharded(inputdata)\n", + "\n", + "#plot the target and the optimized spectra\n", + "import matplotlib.pyplot as plt\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "spectra_target = targetdata\n", + "spectra_optimitzed = rubixdata\n", + "print(rubixdata.shape)\n", + "\n", + "\n", + "plt.plot(wave, spectra_target[0,0,:], label=f\"Target age = {age_values[index_age]:.2f}, metal. = {metallicity_values[index_metallicity]:.4f}\")\n", + "plt.plot(wave, spectra_optimitzed[0,0,:], label=f\"Optimized age = {age_history[i]*20:.2f}, metal. = {metallicity_history[i]*0.05:.4f}\")\n", + "plt.xlabel(\"Wavelength [Å]\")\n", + "plt.ylabel(\"Luminosity [L/Å]\")\n", + "plt.title(\"Difference between target and optimized spectra\")\n", + "#plt.title(f\"Loss {loss_history[i]:.2e}\")\n", + "plt.legend()\n", + "#plt.ylim(0.00003, 0.00008)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,726 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:23:13,728 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:23:13,729 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:23:13,730 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,740 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:23:13,750 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:23:13,759 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:23:13,789 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,848 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,866 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,916 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:23:13,982 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:23:13,982 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:23:13,983 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:23:14,061 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:23:14,062 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:23:14,062 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:23:14,119 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:23:14,121 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:23:14,134 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:23:14,135 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:23:14,137 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:23:14,139 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:23:17,643 - rubix - INFO - Pipeline run completed in 3.92 seconds.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "#run the pipeline with the optimized age\n", + "#rubixdata.stars.age = optimized_age\n", + "i = 850\n", + "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "\n", + "pipe = RubixPipeline(config)\n", + "rubixdata = pipe.run_sharded(inputdata)\n", + "\n", + "#plot the target and the optimized spectra\n", + "import matplotlib.pyplot as plt\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "spectra_target = targetdata #.stars.datacube\n", + "spectra_optimitzed = rubixdata #.stars.datacube\n", + "\n", + "plt.plot(wave, spectra_target[0,0,:], label=f\"Target age = {age_values[index_age]:.2f}, metal. = {metallicity_values[index_metallicity]:.4f}\")\n", + "plt.plot(wave, spectra_optimitzed[0,0,:], label=f\"Optimized age = {age_history[i]*20:.2f}, metal. = {metallicity_history[i]*0.05:.4f}\")\n", + "plt.xlabel(\"Wavelength [Å]\")\n", + "plt.ylabel(\"Luminosity [L/Å]\")\n", + "plt.title(\"Difference between target and optimized spectra\")\n", + "#plt.title(f\"Loss {loss_history[i]:.2e}\")\n", + "plt.legend()\n", + "#plt.ylim(0.00003, 0.00008)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Create a figure with two subplots, sharing the x-axis.\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [4, 1]}, figsize=(7, 5))\n", + "\n", + "# Plot target and optimized spectra in the upper subplot.\n", + "ax1.plot(wave, spectra_target[0, 0, :], label=f\"Target age = {age_values[index_age]:.2f}, metallicity = {metallicity_values[index_metallicity]:.4f}\")\n", + "ax1.plot(wave, spectra_optimitzed[0, 0, :], label=f\"Optimized age = {age_history[i]*20:.2f}, metallicity = {metallicity_history[i]*0.05:.4f}\")\n", + "ax1.set_ylabel(\"Luminosity [L/Å]\")\n", + "#ax1.set_title(\"Target vs Optimized Spectra\")\n", + "ax1.legend()\n", + "ax1.grid(True)\n", + "\n", + "# Compute the residual (difference between target and optimized spectra).\n", + "residual = (spectra_target[0, 0, :] - spectra_optimitzed[0, 0, :]) #/spectra_target[0, 0, :]\n", + "\n", + "# Plot the residual in the lower subplot.\n", + "ax2.plot(wave, residual, 'k-')\n", + "ax2.set_xlabel(\"Wavelength [Å]\")\n", + "ax2.set_ylabel(\"Residual\")\n", + "ax2.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f\"output/optimisation_spectra.jpg\", dpi=1000)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate loss landscape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import optax\n", + "\n", + "def loss_only_wrt_age_metallicity(age, metallicity, base_data, target):\n", + "\n", + " # Create 2D arrays for age and metallicity.\n", + " # For example, if there are two stars, you might do:\n", + " base_data.stars.age = jnp.array([age*20, age*20])\n", + " base_data.stars.metallicity = jnp.array([metallicity*0.05, metallicity*0.05])\n", + "\n", + " output = pipeline_instance.func(base_data)\n", + " #loss = jnp.sum((output.stars.datacube - target) ** 2)\n", + " loss = jnp.sum(optax.cosine_distance(output.stars.datacube, target))\n", + " return loss\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Number of grid points\n", + "num_steps = 100\n", + "\n", + "# Define physical ranges\n", + "physical_ages = jnp.linspace(0, 1, num_steps) # Age from 0 to 10\n", + "physical_metals = jnp.linspace(0, 1, num_steps) # Metallicity from 1e-4 to 0.05\n", + "\n", + "# Use nested vmap to compute the loss at every grid point.\n", + "# Note: loss_only_wrt_age_metallicity takes physical values directly.\n", + "#vectorized_loss = jax.vmap(\n", + "# lambda age: jax.vmap(\n", + "# lambda metal: loss_only_wrt_age_metallicity(age, metal, inputdata, targetdata)\n", + "# )(physical_metals)\n", + "#)(physical_ages)\n", + "\n", + "# Convert the result to a NumPy array for plotting\n", + "#loss_map = jnp.array(vectorized_loss)\n", + "\n", + "loss_map = []\n", + "\n", + "for age in physical_ages:\n", + " row = []\n", + " for metal in physical_metals:\n", + " loss = loss_only_wrt_age_metallicity(age, metal, inputdata, targetdata)\n", + " row.append(loss)\n", + " loss_map.append(jnp.stack(row))\n", + "\n", + "loss_map = jnp.stack(loss_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# Plot the loss landscape using imshow.\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors\n", + "plt.figure(figsize=(5, 4))\n", + "plt.imshow(loss_map, origin='lower', extent=[0,1,0,1], aspect='auto', norm=colors.LogNorm())#, vmin=-3.5, vmax=-2.5)#extent=[1e-4, 0.05, 0, 10]\n", + "plt.xlabel('Metallicity')\n", + "plt.ylabel('Age')\n", + "plt.title('Loss Landscape')\n", + "plt.colorbar(label='loss')\n", + "# Plot a red dot at the desired coordinates.\n", + "plt.plot(metallicity_history[:], age_history[:])#, 'bx', markersize=8)\n", + "#plt.plot(metallicity_history[::100], age_history[::100], 'bx', markersize=8)\n", + "plt.plot(metallicity_values[index_metallicity]/0.05, age_values[index_age]/20, 'ro', markersize=8)\n", + "plt.plot(metallicity_values[initial_metallicity_index]/0.05, age_values[initial_age_index]/20, 'ro', markersize=8)\n", + "plt.savefig(f\"output/optimisation_losslandscape.jpg\", dpi=1000)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "metallicity_history = np.array(metallicity_history)*0.05\n", + "age_history = np.array(age_history)*20\n", + "metallicity_history2 = np.array(metallicity_history2)*0.05\n", + "age_history2 = np.array(age_history2)*20\n", + "metallicity_history3 = np.array(metallicity_history3)*0.05\n", + "age_history3 = np.array(age_history3)*20" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "# Update the extent to the physical values: metallicity from 0 to 0.05 and age from 0 to 20.\n", + "plt.imshow(loss_map, origin='lower', extent=[0, 0.05, 0, 20], aspect='auto', norm=colors.LogNorm())\n", + "\n", + "plt.xlabel('Metallicity')\n", + "plt.ylabel('Age')\n", + "plt.title('Loss Landscape')\n", + "plt.colorbar(label='loss')\n", + "\n", + "# Plot the history in physical coordinates by multiplying the normalized values.\n", + "plt.plot(metallicity_history[:], age_history[:])#, 'bx', markersize=8)\n", + "plt.plot(metallicity_history2[:], age_history2[:])#, 'gx', markersize=8\n", + "plt.plot(metallicity_history3[:], age_history3[:])#, 'mx', markersize=8)\n", + "\n", + "# Plot the red dots in physical coordinates\n", + "plt.plot(metallicity_values[index_metallicity], age_values[index_age], marker='*', color='yellow', markersize=8)\n", + "plt.plot(metallicity_values[initial_metallicity_index], age_values[initial_age_index], 'wo', markersize=8)\n", + "plt.plot(metallicity_values[initial_metallicity_index2], age_values[initial_age_index2], 'wo', markersize=8)\n", + "plt.plot(metallicity_values[initial_metallicity_index3], age_values[initial_age_index3], 'wo', markersize=8)\n", + "\n", + "plt.savefig(\"output/optimisation_losslandscape.jpg\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "# plot loss history for all three runs\n", + "\n", + "loss_history_np = np.array(loss_history)\n", + "loss_history2 = np.array(loss_history2)\n", + "loss_history3 = np.array(loss_history3)\n", + "iterations = np.arange(len(loss_history_np))\n", + "\n", + "plt.figure(figsize=(6, 4))\n", + "plt.plot(iterations, loss_history_np, label='Run 1')\n", + "plt.plot(iterations, loss_history2, label='Run 2')\n", + "plt.plot(iterations, loss_history3, label='Run 3')\n", + "#plt.yscale('log')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('log(Loss)')\n", + "plt.title('Loss History for Three Runs')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.savefig(\"output/optimisation_loglosshistory.jpg\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "# plot loss history for all three runs\n", + "\n", + "loss_history_np = np.array(loss_history)\n", + "loss_history2 = np.array(loss_history2)\n", + "loss_history3 = np.array(loss_history3)\n", + "iterations = np.arange(len(loss_history_np))\n", + "\n", + "plt.figure(figsize=(6, 4))\n", + "plt.plot(iterations, 10**loss_history_np, label='Run 1')\n", + "plt.plot(iterations, 10**loss_history2, label='Run 2')\n", + "plt.plot(iterations, 10**loss_history3, label='Run 3')\n", + "#plt.yscale('log')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Loss')\n", + "plt.title('Loss History for Three Runs')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.savefig(\"output/optimisation_losshistory.jpg\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors\n", + "import numpy as np\n", + "\n", + "# Prepare loss histories\n", + "loss_history_np = np.array(loss_history)\n", + "loss_history2 = np.array(loss_history2)\n", + "loss_history3 = np.array(loss_history3)\n", + "iterations = np.arange(len(loss_history_np))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n", + "\n", + "# --- Left: Loss Landscape ---\n", + "im = axs[0].imshow(\n", + " loss_map,\n", + " origin='lower',\n", + " extent=[0, 0.05, 0, 20],\n", + " aspect='auto',\n", + " norm=colors.LogNorm()\n", + ")\n", + "axs[0].set_xlabel('Metallicity')\n", + "axs[0].set_ylabel('Age (Gyrs)')\n", + "axs[0].set_xlim(0, 0.045)\n", + "#axs[0].set_title('Loss Landscape')\n", + "fig.colorbar(im, ax=axs[0], label='log(loss)')\n", + "\n", + "# Plot the history in physical coordinates\n", + "axs[0].plot(metallicity_history[:], age_history[:], color='orange')\n", + "axs[0].plot(metallicity_history2[:], age_history2[:], color='purple')\n", + "axs[0].plot(metallicity_history3[:], age_history3[:], color='red')\n", + "\n", + "# Plot the red dots in physical coordinates\n", + "axs[0].plot(metallicity_values[index_metallicity], age_values[index_age], marker='*', color='yellow', markersize=8)\n", + "axs[0].plot(metallicity_values[initial_metallicity_index], age_values[initial_age_index], 'wo', markersize=8)\n", + "axs[0].plot(metallicity_values[initial_metallicity_index2], age_values[initial_age_index2], 'wo', markersize=8)\n", + "axs[0].plot(metallicity_values[initial_metallicity_index3], age_values[initial_age_index3], 'wo', markersize=8)\n", + "\n", + "# --- Right: Loss History ---\n", + "axs[1].plot(iterations, 10**loss_history_np, label='Run 1', color='orange')\n", + "axs[1].plot(iterations, 10**loss_history2, label='Run 2', color='purple')\n", + "axs[1].plot(iterations, 10**loss_history3, label='Run 3', color='red')\n", + "axs[1].set_xlabel('Iteration')\n", + "axs[1].set_ylabel('Loss')\n", + "#axs[1].set_title('Loss History for Three Runs')\n", + "axs[1].legend()\n", + "axs[1].grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"output/optimisation_landscape_and_history.jpg\", dpi=1000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:16,917 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:28:16,918 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:28:16,919 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-11-11 10:28:16,920 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:16,930 - rubix - INFO - Getting cosmology...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:28:16,940 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:28:16,949 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:28:16,981 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:17,040 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:17,060 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:17,114 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:28:17,176 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:28:17,177 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:28:17,178 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:28:17,251 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:28:17,252 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:28:17,252 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:28:17,309 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:28:17,311 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:28:17,322 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-11 10:28:17,323 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:28:17,325 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:28:17,328 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:28:20,803 - rubix - INFO - Pipeline run completed in 3.89 seconds.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1, 466)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "#run the pipeline with the optimized age\n", + "#rubixdata.stars.age = optimized_age\n", + "i = 200\n", + "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "\n", + "pipe = RubixPipeline(config)\n", + "rubixdata = pipe.run_sharded(inputdata)\n", + "\n", + "#plot the target and the optimized spectra\n", + "import matplotlib.pyplot as plt\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "spectra_target = targetdata\n", + "spectra_optimitzed = rubixdata\n", + "print(rubixdata.shape)\n", + "\n", + "\n", + "# Create a figure with two subplots, sharing the x-axis.\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [4, 1]}, figsize=(7, 5))\n", + "\n", + "# Plot target and optimized spectra in the upper subplot.\n", + "ax1.plot(wave, spectra_target[0, 0, :], label=f\"Target age = {age_values[index_age]:.2f}, metallicity = {metallicity_values[index_metallicity]:.4f}\")\n", + "ax1.plot(wave, spectra_optimitzed[0, 0, :], label=f\"Optimized age = {age_history[i]*20:.2f}, metallicity = {metallicity_history[i]*0.05:.4f}\")\n", + "ax1.set_ylabel(\"Luminosity [L/Å]\")\n", + "#ax1.set_title(\"Target vs Optimized Spectra\")\n", + "ax1.legend()\n", + "ax1.grid(True)\n", + "\n", + "# Compute the residual (difference between target and optimized spectra).\n", + "residual = (spectra_target[0, 0, :] - spectra_optimitzed[0, 0, :]) #/spectra_target[0, 0, :]\n", + "\n", + "# Plot the residual in the lower subplot.\n", + "ax2.plot(wave, residual, 'k-')\n", + "ax2.set_xlabel(\"Wavelength [Å]\")\n", + "ax2.set_ylabel(\"Residual\")\n", + "ax2.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f\"output/optimisation_spectra.jpg\", dpi=1000)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubixcpu2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb b/source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb new file mode 100644 index 00000000..f49fe230 --- /dev/null +++ b/source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb @@ -0,0 +1,694 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient vs finite difference" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from jax import config\n", + "import os\n", + "import jax\n", + "\n", + "print(jax.devices())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load ssp template from FSPS" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:11:57,608 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:11:57,608 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-10 17:11:57,609 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:11:57,609 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:11:57,609 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.spectra.ssp.factory import get_ssp_template\n", + "ssp_fsps = get_ssp_template(\"FSPS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(107,)\n", + "(12,)\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "age_values = ssp_fsps.age\n", + "print(age_values.shape)\n", + "\n", + "metallicity_values = ssp_fsps.metallicity\n", + "print(metallicity_values.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start age: 15.848933219909668, start metallicity: 0.025251565501093864\n", + "target age: 3.1622776985168457, target metallicity: 0.014199999161064625\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "index_age = 90\n", + "index_metallicity = 9\n", + "\n", + "#initial_metallicity_index = 5\n", + "#initial_age_index = 70\n", + "initial_metallicity_index = 10\n", + "initial_age_index = 104\n", + "\n", + "learning_all = 1e-2\n", + "tol = 1e-10\n", + "\n", + "print(f\"start age: {age_values[initial_age_index]}, start metallicity: {metallicity_values[initial_metallicity_index]}\")\n", + "print(f\"target age: {age_values[index_age]}, target metallicity: {metallicity_values[index_metallicity]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configure pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.pipeline import RubixPipeline\n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_gradient\",},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 2,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"TESTGRADIENT\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 1.2},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},\n", + " },\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:11:58,256 - rubix - INFO - Getting rubix data...\n", + "2025-11-10 17:11:58,257 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/jax/_src/numpy/scalar_types.py:50: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:491: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-10 17:11:58,318 - rubix - INFO - Centering stars particles\n", + "2025-11-10 17:11:59,305 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n", + "2025-11-10 17:11:59,305 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n", + "2025-11-10 17:11:59,306 - rubix - INFO - Data preparation completed in 1.05 seconds.\n", + "2025-11-10 17:11:59,306 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-10 17:11:59,307 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-10 17:11:59,307 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-10 17:11:59,310 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:11:59,337 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:11:59,547 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-10 17:11:59,556 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:11:59,567 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:11:59,652 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:11:59,807 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:00,094 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-10 17:12:00,095 - rubix - INFO - Compiling the expressions...\n", + "2025-11-10 17:12:00,096 - rubix - INFO - Number of devices: 1\n", + "2025-11-10 17:12:00,180 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-10 17:12:00,181 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-10 17:12:00,181 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-10 17:12:00,286 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-10 17:12:00,318 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-10 17:12:00,481 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-10 17:12:00,481 - rubix - INFO - Convolving with PSF...\n", + "2025-11-10 17:12:00,486 - rubix - INFO - Convolving with LSF...\n", + "2025-11-10 17:12:00,493 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-10 17:12:06,414 - rubix - INFO - Total time for sharded pipeline run: 7.11 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "inputdata = pipe.prepare_data()\n", + "output = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set target values" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "inputdata.stars.age = jnp.array([age_values[index_age], age_values[index_age]])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_values[index_metallicity], metallicity_values[index_metallicity]])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:12:06,474 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-10 17:12:06,475 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-10 17:12:06,476 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-10 17:12:06,479 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:06,491 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:06,502 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-10 17:12:06,624 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:06,635 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:06,681 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:06,757 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:06,804 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-10 17:12:06,804 - rubix - INFO - Compiling the expressions...\n", + "2025-11-10 17:12:06,806 - rubix - INFO - Number of devices: 1\n", + "2025-11-10 17:12:06,888 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-10 17:12:06,889 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-10 17:12:06,889 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-10 17:12:06,961 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-10 17:12:06,963 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-10 17:12:06,971 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-10 17:12:06,972 - rubix - INFO - Convolving with PSF...\n", + "2025-11-10 17:12:06,974 - rubix - INFO - Convolving with LSF...\n", + "2025-11-10 17:12:06,977 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-10 17:12:12,833 - rubix - INFO - Total time for sharded pipeline run: 6.36 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "targetdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(466,)\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "print(targetdata[0,0,:].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set initial datracube" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "inputdata.stars.age = jnp.array([age_values[initial_age_index], age_values[initial_age_index]])\n", + "inputdata.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index], metallicity_values[initial_metallicity_index]])\n", + "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", + "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:12:12,913 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-10 17:12:12,914 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-10 17:12:12,914 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-10 17:12:12,916 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:12,929 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:12,941 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-10 17:12:12,951 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:12,961 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:13,008 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:13,099 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:13,159 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-10 17:12:13,160 - rubix - INFO - Compiling the expressions...\n", + "2025-11-10 17:12:13,164 - rubix - INFO - Number of devices: 1\n", + "2025-11-10 17:12:13,243 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-10 17:12:13,244 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-10 17:12:13,244 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-10 17:12:13,315 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-10 17:12:13,317 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-10 17:12:13,328 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n", + "2025-11-10 17:12:13,328 - rubix - INFO - Convolving with PSF...\n", + "2025-11-10 17:12:13,331 - rubix - INFO - Convolving with LSF...\n", + "2025-11-10 17:12:13,334 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-10 17:12:19,690 - rubix - INFO - Total time for sharded pipeline run: 6.78 seconds.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "initialdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:19,786 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-10 17:12:19,787 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-10 17:12:19,787 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-10 17:12:19,789 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:19,798 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:19,808 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-10 17:12:19,818 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:19,828 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:12:19,871 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:12:19,938 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.pipeline import linear_pipeline as pipeline\n", + "\n", + "pipeline_instance = RubixPipeline(config)\n", + "\n", + "pipeline_instance._pipeline = pipeline.LinearTransformerPipeline(\n", + " pipeline_instance.pipeline_config, \n", + " pipeline_instance._get_pipeline_functions()\n", + ")\n", + "pipeline_instance._pipeline.assemble()\n", + "pipeline_instance.func = pipeline_instance._pipeline.compile_expression()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import optax\n", + "\n", + "def loss_only_wrt_age_metallicity(age, metallicity, base_data, target):\n", + " \n", + " base_data.stars.age = age*20\n", + " base_data.stars.metallicity = metallicity*0.05\n", + "\n", + " output = pipeline_instance.func(base_data)\n", + " #loss = jnp.sum((output.stars.datacube - target) ** 2)\n", + " #loss = jnp.sum(optax.l2_loss(output.stars.datacube, target.stars.datacube))\n", + " #loss = jnp.sum(optax.huber_loss(output.stars.datacube, target.stars.datacube))\n", + " loss = jnp.sum(optax.cosine_distance(output.stars.datacube, target))\n", + " \n", + " return jnp.log10(loss) #loss#/0.03 #jnp.log10(loss #/5e-5)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax\n", + "\n", + "def compute_gradient(age, metallicity, base_data, target):\n", + " loss, grad_fn = jax.value_and_grad(loss_only_wrt_age_metallicity, argnums=(0,1))\n", + " grads = grad_fn(age, metallicity, base_data, target)\n", + " return grads, loss" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:12:20,133 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-10 17:12:20,134 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-10 17:12:20,134 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-10 17:12:20,223 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-10 17:12:20,239 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-10 17:12:20,375 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial parameters: {'age': Array([0.7924467, 0.7924467], dtype=float32), 'metallicity': Array([0.5050313, 0.5050313], dtype=float32)}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:12:20,375 - rubix - INFO - Convolving with PSF...\n", + "2025-11-10 17:12:20,378 - rubix - INFO - Convolving with LSF...\n", + "2025-11-10 17:12:20,383 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grads: {'age': Array([5.885172, 5.885172], dtype=float32), 'metallicity': Array([0.27147812, 0.27147812], dtype=float32)}\n", + "loss: -2.057193\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "#calculate gradient with jax\n", + "age_init = jnp.array([age_values[initial_age_index]/20, age_values[initial_age_index]/20])\n", + "metallicity_init = jnp.array([metallicity_values[initial_metallicity_index]/0.05, metallicity_values[initial_metallicity_index]/0.05])\n", + "\n", + "\n", + "# Pack both initial parameters into a dictionary.\n", + "params_init = {'age': age_init, 'metallicity': metallicity_init}\n", + "print(f\"Initial parameters: {params_init}\")\n", + "\n", + "data = inputdata\n", + "target_value = targetdata\n", + "\n", + "loss, grads = jax.value_and_grad(lambda p: loss_only_wrt_age_metallicity(p['age'], p['metallicity'], data, target_value))(params_init)\n", + "\n", + "print(\"grads:\", grads)\n", + "print(\"loss:\", loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grads_fd: {'age': Array([0.35352707, 0.35352707], dtype=float32), 'metallicity': Array([0.25906563, 0.25891066], dtype=float32)}\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "#calculate finite differnce\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax.flatten_util import ravel_pytree\n", + "\n", + "# 1) Skalares Loss über das ganze Param-PyTree\n", + "f = lambda p: loss_only_wrt_age_metallicity(p['age'], p['metallicity'], data, target_value)\n", + "\n", + "# 2) Finite-Difference-Gradient (zentral) für beliebiges PyTree\n", + "def finite_diff_grad(f, params, eps=1e-5):\n", + " flat, unravel = ravel_pytree(params)\n", + " def f_flat(x): return f(unravel(x))\n", + "\n", + " def fd_i(i):\n", + " e_i = jnp.zeros_like(flat).at[i].set(1.0)\n", + " return (f_flat(flat + eps*e_i) - f_flat(flat - eps*e_i)) / (2*eps)\n", + "\n", + " g_flat = jax.vmap(fd_i)(jnp.arange(flat.size))\n", + " return unravel(g_flat)\n", + "\n", + "# 3) Anwenden: JAX-Grad + FD-Grad berechnen und vergleichen\n", + "grads_fd = finite_diff_grad(f, params_init, eps=1e-2)\n", + "print(\"grads_fd:\", grads_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# eps-Werte, über die wir scannen\n", + "eps_values = jnp.logspace(-6, -1, 20) # von 1e-6 bis 1e-1\n", + "\n", + "age_fd_values = []\n", + "metal_fd_values = []\n", + "\n", + "for eps in eps_values:\n", + " g_fd = finite_diff_grad(f, params_init, eps=float(eps))\n", + " # g_fd hat die gleiche Struktur wie params_init:\n", + " # {'age': array([..,..]), 'metallicity': array([..,..])}\n", + " # Beispiel: nimm hier den Mittelwert pro Array\n", + " age_fd_values.append(float(jnp.mean(g_fd['age'])))\n", + " metal_fd_values.append(float(jnp.mean(g_fd['metallicity'])))\n", + "\n", + "plt.figure(figsize=(7,5))\n", + "plt.semilogx(eps_values, age_fd_values, 'o-', label=\"age grad (FD)\")\n", + "plt.semilogx(eps_values, metal_fd_values, 's-', label=\"metallicity grad (FD)\")\n", + "\n", + "# horizontale Linien = JAX-Gradient\n", + "plt.axhline(float(grads['age'][0]), color='C0', linestyle='--', label=\"age grad (JAX)\")\n", + "plt.axhline(float(grads['metallicity'][0]), color='C1', linestyle='--', label=\"metalicity grad (JAX)\")\n", + "\n", + "plt.xlabel(\"Step size\")\n", + "plt.ylabel(\"Derivation\")\n", + "# plt.title(\"Gradient vs finite difference step size\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.savefig(\"output/optimisation_finite_diff.jpg\", dpi=1000)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/pipeline_demo.ipynb b/source/notebooks/pipeline_demo.ipynb index cbac09c2..ea9e7894 100644 --- a/source/notebooks/pipeline_demo.ipynb +++ b/source/notebooks/pipeline_demo.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -117,16 +117,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "jax.tree_util.Partial" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(add)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -144,9 +155,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([14.14, 12.14, 10.14], dtype=float32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "addjit(x, x)" ] @@ -166,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -190,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -210,25 +232,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ", z=5, k=-3.14)>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([7.8599997, 5.8599997, 3.86 ], dtype=float32)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add(x, x)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -257,18 +301,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ", z=5)>" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add_plus" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([7.8599997, 5.8599997, 3.86 ], dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add_plus(x, x, k=-3.14)" ] @@ -290,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -310,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -324,9 +390,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", + " \u001b[39;22mc\u001b[35m:f32[3]\u001b[39m = add a b\n", + " d\u001b[35m:f32[3]\u001b[39m = add c 5.0:f32[]\n", + " e\u001b[35m:f32[3]\u001b[39m = add d 6.28000020980835:f32[]\n", + " \u001b[34;1min \u001b[39;22m(e,) }" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add" ] @@ -340,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -354,9 +435,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m c\u001b[35m:i32[]\u001b[39m. \u001b[34;1mlet\n", + " \u001b[39;22md\u001b[35m:f32[3]\u001b[39m = add a b\n", + " e\u001b[35m:f32[]\u001b[39m = convert_element_type[new_dtype=float32 weak_type=False] c\n", + " f\u001b[35m:f32[3]\u001b[39m = add d e\n", + " g\u001b[35m:f32[3]\u001b[39m = add f -3.140000104904175:f32[]\n", + " \u001b[34;1min \u001b[39;22m(g,) }" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add" ] @@ -370,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -384,18 +481,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", + " \u001b[39;22mc\u001b[35m:f32[3]\u001b[39m = add a b\n", + " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", + " e\u001b[35m:f32[3]\u001b[39m = add d 5.420000076293945:f32[]\n", + " \u001b[34;1min \u001b[39;22m(e,) }" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cond_add(x, x, 3, 2.71)" ] @@ -412,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -513,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -522,36 +645,91 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Transformers': {'A': {'name': 'add',\n", + " 'depends_on': 'B',\n", + " 'args': [],\n", + " 'kwargs': {'s': 3.0}},\n", + " 'X': {'name': 'mult', 'depends_on': 'A', 'args': [], 'kwargs': {'m': 3}},\n", + " 'Z': {'name': 'div', 'depends_on': 'X', 'args': [], 'kwargs': {'d': 4}},\n", + " 'B': {'name': 'sub', 'depends_on': 'C', 'args': [], 'kwargs': {'s': 2}},\n", + " 'C': {'name': 'add', 'depends_on': None, 'args': [], 'kwargs': {'s': 4}}}}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "read_cfg" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(read_cfg)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': {'name': 'add', 'depends_on': 'B', 'args': [], 'kwargs': {'s': 3.0}},\n", + " 'X': {'name': 'mult', 'depends_on': 'A', 'args': [], 'kwargs': {'m': 3}},\n", + " 'Z': {'name': 'div', 'depends_on': 'X', 'args': [], 'kwargs': {'d': 4}},\n", + " 'B': {'name': 'sub', 'depends_on': 'C', 'args': [], 'kwargs': {'s': 2}},\n", + " 'C': {'name': 'add', 'depends_on': None, 'args': [], 'kwargs': {'s': 4}}}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "read_cfg[\"Transformers\"]" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(read_cfg[\"Transformers\"])" ] @@ -565,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -574,9 +752,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'add': ,\n", + " 'mult': ,\n", + " 'div': ,\n", + " 'sub': }" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tp.transformers" ] @@ -591,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -600,9 +792,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': Partial(, s=4),\n", + " 'B': Partial(, s=2),\n", + " 'A': Partial(, s=3.0),\n", + " 'X': Partial(, m=3),\n", + " 'Z': Partial(, d=4)}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tp.pipeline" ] @@ -616,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -632,9 +839,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Partial(.expr at 0x70e72bd31e40>, pipeline=[Partial(, s=4), Partial(, s=2), Partial(, s=3.0), Partial(, m=3), Partial(, d=4)])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tp.expression" ] @@ -648,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -657,27 +875,60 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ".expr at 0x70e72bd31e40>, pipeline=[Partial(, s=4), Partial(, s=2), Partial(, s=3.0), Partial(, m=3), Partial(, d=4)])>" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "func" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([6. , 5.25, 4.5 ], dtype=float32)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "func(x)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([6. , 5.25, 4.5 ], dtype=float32)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "div(mult(add(sub(add(x, s=4), s=2), s=3), m=3), d=4)" ] @@ -691,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -700,25 +951,53 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", + " \u001b[39;22mb\u001b[35m:f32[3]\u001b[39m = add a 4.0:f32[]\n", + " c\u001b[35m:f32[3]\u001b[39m = sub b 2.0:f32[]\n", + " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", + " e\u001b[35m:f32[3]\u001b[39m = mul d 3.0:f32[]\n", + " f\u001b[35m:f32[3]\u001b[39m = div e 4.0:f32[]\n", + " \u001b[34;1min \u001b[39;22m(f,) }" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "expr" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "jax._src.core.ClosedJaxpr" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(expr)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -728,9 +1007,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", + " \u001b[39;22mb\u001b[35m:f32[3]\u001b[39m = add a 4.0:f32[]\n", + " c\u001b[35m:f32[3]\u001b[39m = sub b 2.0:f32[]\n", + " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", + " e\u001b[35m:f32[3]\u001b[39m = mul d 3.0:f32[]\n", + " f\u001b[35m:f32[3]\u001b[39m = div e 4.0:f32[]\n", + " \u001b[34;1min \u001b[39;22m(f,) }" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "make_jaxpr(func_manual)(x)" ] @@ -751,27 +1047,74 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[0.75, 0. , 0. ],\n", + " [0. , 0.75, 0. ],\n", + " [0. , 0. , 0.75]], dtype=float32)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "jax.jacfwd(tp.compile_expression())(x)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[0.75, 0. , 0. ],\n", + " [0. , 0.75, 0. ],\n", + " [0. , 0. , 0.75]], dtype=float32)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "jax.jacrev(tp.compile_expression())(x)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]],\n", + "\n", + " [[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]],\n", + "\n", + " [[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]]], dtype=float32)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "jax.hessian(tp.compile_expression())(x)" ] @@ -794,18 +1137,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ", s=3.0)))>" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tp.compile_element(\"A\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\u001b[39;22m b\u001b[35m:f32[3]\u001b[39m = add a 3.0:f32[] \u001b[34;1min \u001b[39;22m(b,) }" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tp.get_jaxpr_for_element(\"A\", x)" ] @@ -819,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -828,18 +1193,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "f" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\u001b[39;22m b\u001b[35m:f32[3]\u001b[39m = add a 3.0:f32[] \u001b[34;1min \u001b[39;22m(b,) }" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "f(x)" ] @@ -899,7 +1286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/psf.ipynb b/source/notebooks/psf.ipynb index f80b2a79..12f90089 100644 --- a/source/notebooks/psf.ipynb +++ b/source/notebooks/psf.ipynb @@ -11,9 +11,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20, 20)\n", + "1.0000001\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "from rubix.telescope.psf.kernels import gaussian_kernel_2d\n", @@ -25,9 +34,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -44,9 +74,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50, 50, 300)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# Get an example Datacube\n", @@ -85,7 +143,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/rubix_pipeline_single_function.ipynb b/source/notebooks/rubix_pipeline_single_function.ipynb deleted file mode 100644 index 46401f5d..00000000 --- a/source/notebooks/rubix_pipeline_single_function.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RUBIX pipeline\n", - "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", - "\n", - "## How to use the Pipeline\n", - "1) Define a `config`\n", - "2) Setup the `pipeline yaml`\n", - "3) Run the RUBIX pipeline\n", - "4) Do science with the mock-data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Config\n", - "\n", - "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", - "\n", - "For the `config` you can choose the following options:\n", - "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", - "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", - "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", - "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", - "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", - "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", - "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", - "- `simulation - name`: currently only IllustrisTNG is supported\n", - "- `simulation - args - path`: where the data is stored and how the file will be named\n", - "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", - "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", - "- `telescope - psf`: define the point spread function that is applied to the mock data\n", - "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", - "- `telescope - noise`: define the noise that is applied to the mock data\n", - "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", - "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", - "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", - "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "config = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"BruzualCharlot2003\"\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Pipeline yaml\n", - "\n", - "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", - "\n", - "```yaml\n", - "calc_ifu:\n", - " Transformers:\n", - " rotate_galaxy:\n", - " name: rotate_galaxy\n", - " depends_on: null\n", - " args: []\n", - " kwargs:\n", - " type: \"face-on\"\n", - " filter_particles:\n", - " name: filter_particles\n", - " depends_on: rotate_galaxy\n", - " args: []\n", - " kwargs: {}\n", - " spaxel_assignment:\n", - " name: spaxel_assignment\n", - " depends_on: filter_particles\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " reshape_data:\n", - " name: reshape_data\n", - " depends_on: spaxel_assignment\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " calculate_spectra:\n", - " name: calculate_spectra\n", - " depends_on: reshape_data\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " scale_spectrum_by_mass:\n", - " name: scale_spectrum_by_mass\n", - " depends_on: calculate_spectra\n", - " args: []\n", - " kwargs: {}\n", - " doppler_shift_and_resampling:\n", - " name: doppler_shift_and_resampling\n", - " depends_on: scale_spectrum_by_mass\n", - " args: []\n", - " kwargs: {}\n", - " calculate_datacube:\n", - " name: calculate_datacube\n", - " depends_on: doppler_shift_and_resampling\n", - " args: []\n", - " kwargs: {}\n", - " convolve_psf:\n", - " name: convolve_psf\n", - " depends_on: calculate_datacube\n", - " args: []\n", - " kwargs: {}\n", - " convolve_lsf:\n", - " name: convolve_lsf\n", - " depends_on: convolve_psf\n", - " args: []\n", - " kwargs: {}\n", - " apply_noise:\n", - " name: apply_noise\n", - " depends_on: convolve_lsf\n", - " args: []\n", - " kwargs: {}\n", - "```\n", - "\n", - "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Run the pipeline\n", - "\n", - "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config)\n", - "\n", - "rubixdata = pipe.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Mock-data\n", - "\n", - "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "wave = pipe.telescope.wave_seq\n", - "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", - "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "wave = pipe.telescope.wave_seq\n", - "\n", - "spectra = rubixdata.stars.datacube # Spectra of all stars\n", - "print(spectra.shape)\n", - "\n", - "plt.plot(wave, spectra[12,12,:])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a spacial image of the data cube" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "# get the spectra of the visible wavelengths from the ifu cube\n", - "visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]]\n", - "#visible_spectra.shape\n", - "\n", - "# Sum up all spectra to create an image\n", - "image = jnp.sum(visible_spectra, axis = 2)\n", - "plt.imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DONE!\n", - "\n", - "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/source/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/source/notebooks/rubix_pipeline_single_function_shard_map.ipynb new file mode 100644 index 00000000..2d478796 --- /dev/null +++ b/source/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n", + "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the `config` you can choose the following options:\n", + "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", + "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", + "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", + "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", + "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", + "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- `simulation - name`: currently only IllustrisTNG is supported\n", + "- `simulation - args - path`: where the data is stored and how the file will be named\n", + "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", + "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", + "- `telescope - psf`: define the point spread function that is applied to the mock data\n", + "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", + "- `telescope - noise`: define the noise that is applied to the mock data\n", + "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", + "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", + "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", + "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from jax import config\n", + "import os\n", + "import jax\n", + "\n", + "print(jax.devices())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:23:01,492 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-11 10:23:01,494 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-11 10:23:01,494 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-11 10:23:01,495 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-11 10:23:01,496 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "galaxy_id = \"g8.13e11\"\n", + "\n", + "config_NIHAO = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"NihaoHandler\",\n", + " \"args\": {\n", + " \"particle_type\": [\"stars\"],\n", + " \"save_data_path\": \"data\",\n", + " \"snapshot\": \"1024\",\n", + " },\n", + " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"NIHAO\",\n", + " \"args\": {\n", + " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", + " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", + " \"halo_id\": 0,\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "config_TNG = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 12,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 2000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-12.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml\n", + "\n", + "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", + "\n", + "```yaml\n", + "calc_ifu:\n", + " Transformers:\n", + " rotate_galaxy:\n", + " name: rotate_galaxy\n", + " depends_on: null\n", + " args: []\n", + " kwargs:\n", + " type: \"face-on\"\n", + " filter_particles:\n", + " name: filter_particles\n", + " depends_on: rotate_galaxy\n", + " args: []\n", + " kwargs: {}\n", + " spaxel_assignment:\n", + " name: spaxel_assignment\n", + " depends_on: filter_particles\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " reshape_data:\n", + " name: reshape_data\n", + " depends_on: spaxel_assignment\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " calculate_spectra:\n", + " name: calculate_spectra\n", + " depends_on: reshape_data\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " scale_spectrum_by_mass:\n", + " name: scale_spectrum_by_mass\n", + " depends_on: calculate_spectra\n", + " args: []\n", + " kwargs: {}\n", + " doppler_shift_and_resampling:\n", + " name: doppler_shift_and_resampling\n", + " depends_on: scale_spectrum_by_mass\n", + " args: []\n", + " kwargs: {}\n", + " calculate_datacube:\n", + " name: calculate_datacube\n", + " depends_on: doppler_shift_and_resampling\n", + " args: []\n", + " kwargs: {}\n", + " convolve_psf:\n", + " name: convolve_psf\n", + " depends_on: calculate_datacube\n", + " args: []\n", + " kwargs: {}\n", + " convolve_lsf:\n", + " name: convolve_lsf\n", + " depends_on: convolve_psf\n", + " args: []\n", + " kwargs: {}\n", + " apply_noise:\n", + " name: apply_noise\n", + " depends_on: convolve_lsf\n", + " args: []\n", + " kwargs: {}\n", + "```\n", + "\n", + "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'upper'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m pipe = \u001b[43mRubixPipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_TNG\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/pipeline.py:87\u001b[39m, in \u001b[36mRubixPipeline.__init__\u001b[39m\u001b[34m(self, user_config)\u001b[39m\n\u001b[32m 85\u001b[39m \u001b[38;5;28mself\u001b[39m.user_config = get_config(user_config)\n\u001b[32m 86\u001b[39m \u001b[38;5;28mself\u001b[39m.pipeline_config = get_pipeline_config(\u001b[38;5;28mself\u001b[39m.user_config[\u001b[33m\"\u001b[39m\u001b[33mpipeline\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mname\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m---> \u001b[39m\u001b[32m87\u001b[39m \u001b[38;5;28mself\u001b[39m.logger = \u001b[43mget_logger\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43muser_config\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogger\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 88\u001b[39m \u001b[38;5;28mself\u001b[39m.ssp = get_ssp(\u001b[38;5;28mself\u001b[39m.user_config)\n\u001b[32m 89\u001b[39m \u001b[38;5;28mself\u001b[39m.telescope = get_telescope(\u001b[38;5;28mself\u001b[39m.user_config)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/logger.py:21\u001b[39m, in \u001b[36mget_logger\u001b[39m\u001b[34m(config)\u001b[39m\n\u001b[32m 18\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 19\u001b[39m first_time = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m21\u001b[39m logger.setLevel(\u001b[38;5;28mgetattr\u001b[39m(logging, \u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlog_level\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupper\u001b[49m(), \u001b[33m\"\u001b[39m\u001b[33mINFO\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m 23\u001b[39m \u001b[38;5;66;03m# Clear existing handlers\u001b[39;00m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m logger.handlers[:]:\n", + "\u001b[31mAttributeError\u001b[39m: 'NoneType' object has no attribute 'upper'" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config_TNG)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-11 10:22:23,162 - rubix - INFO - Getting rubix data...\n", + "2025-11-11 10:22:23,163 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/jax/_src/numpy/scalar_types.py:50: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:491: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-11 10:22:23,246 - rubix - INFO - Centering stars particles\n", + "2025-11-11 10:22:24,250 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-11-11 10:22:24,254 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-11-11 10:22:24,255 - rubix - INFO - Data preparation completed in 1.09 seconds.\n", + "2025-11-11 10:22:24,256 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-11 10:22:24,257 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-11 10:22:24,258 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-11 10:22:24,261 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:22:24,285 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:22:24,497 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-11 10:22:24,509 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:22:24,521 - rubix - INFO - Getting cosmology...\n", + "2025-11-11 10:22:25,075 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:22:26,192 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-11 10:22:26,935 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-11 10:22:26,936 - rubix - INFO - Compiling the expressions...\n", + "2025-11-11 10:22:26,936 - rubix - INFO - Number of devices: 1\n", + "2025-11-11 10:22:27,024 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-11 10:22:27,024 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-11 10:22:27,025 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-11 10:22:27,143 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-11-11 10:22:27,150 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-11 10:22:27,184 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-11 10:22:27,479 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-11-11 10:22:27,480 - rubix - INFO - Convolving with PSF...\n", + "2025-11-11 10:22:27,484 - rubix - INFO - Convolving with LSF...\n", + "2025-11-11 10:22:27,490 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-11 10:22:29,524 - rubix - INFO - Total time for sharded pipeline run: 5.27 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "devices = jax.devices()\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run_sharded(inputdata, devices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "#inputdata = pipe.prepare_data()\n", + "#shard_rubixdata = pipe.run_sharded_chunked(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Mock-data\n", + "\n", + "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "wave = pipe.telescope.wave_seq\n", + "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", + "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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88MILGDlyJBISEuDu7i4cN3PmTLzxxhvCczc3N+GxVqvF2LFjERwcjL179yIjIwNTp06FTCbDf/7znxv6fpzBtKwur8S5WZ8HvzlY676Z3x/G/+7rDA+FtM6Swh4tfHHkSoGQkfpy1yW8+9d5fD21O4a0DcT/tlzAR9suAgA+e6ArxsaHmJ3/xkZDUNYv2h93dg673rdERERERNQoGjUj9ddff2H69Ono0KEDOnXqhOXLlyMlJQVHjhwxO87NzQ3BwcHCj5eXl7Dv77//RkJCAn744Qd07twZo0ePxptvvonPPvsMVVXNr/ysUlMTxDg7I1WXLQlZ+G5vMgDz0j5TD/aOQpi3CwDg8JV86PV6/GfTOWh1ejy0/BDyS6uEIAoA5qw8iit5pcJz03Wp0grKG+BdEBERERHdGE1qjlRRUREAwNfX12z7jz/+CH9/f3Ts2BELFy5EWVnNPKJ9+/YhLi4OQUFBwrZRo0ZBpVLhzJkzNl+nsrISKpXK7KepMM1I5To5I1WfbFUFAOvSPqMgLwVGx4VALhHj9FUVjqYUmp9fXGF1znt/1ZRqms752nAiHWM++hdbE7KcMHIiIiIiohuryQRSOp0O8+bNQ79+/dCxY0dh+wMPPIAffvgBO3bswMKFC7FixQpMmTJF2J+ZmWkWRAEQnmdmZtp8rUWLFkGpVAo/ERERDfCOrk2lk0r71BZzkEwDtPpeu7bSPm83Ofw9FOgf4w8ASEgvgl918wnAdkv0P05lCI9zTDr9ncssRkKGCo+uOGx1Tmp+GbQ66/lVRERERERNRaPOkTI1Z84cnD59Grt37zbb/uijjwqP4+LiEBISgmHDhuHSpUto3br1Nb3WwoULMX/+fOG5SqVqMsGU2RwpB0v7KjVanMsohkwixsTP9+LRga3w1IhYANYNITwUUquW5sbnlsca+bgZgqZwH1cAQKaqAiHeLsJ8qbT8usv1bHUh1OmB46mFiA9TQiwW4d+LOXjwm4N4sHcU3hzf0cZViIiIiIgaX5PISM2dOxcbN27Ejh07EB4eXuexvXr1AgAkJiYCAIKDg5GVZV4eZnweHBxs8xoKhQJeXl5mP02F2RwpBzNSb25MwJ2f7cGYj/9FuVprNl/JMmgKUbpYnW/sGFihtp298nGTAQCCvAznZqnMA6OTVwvrHF9t7dzHf7YHca9txnd7k/HOn+cAACv2X6nzWkREREREjalRAym9Xo+5c+fit99+w/bt29GyZct6zzl+/DgAICTE0A2uT58+OHXqFLKzs4VjtmzZAi8vL7Rv375Bxt2QTEv7bM05qssP+1Nq3WfZQCLYRiBlLM2rLSPVMsDQSbEmkKpApUnQdSGzpM7xpRfWnrEqrdLi1fVnIJXY/yt5ODkfReXq+g8kIiIiInKyRg2k5syZgx9++AErV66Ep6cnMjMzkZmZifJyww33pUuX8Oabb+LIkSNITk7G+vXrMXXqVAwcOBDx8fEAgJEjR6J9+/Z48MEHceLECWzevBkvvfQS5syZA4VC0Zhv75qYBlLphY4FUrXZdjYL+y7lmW0L9rIOpArKjIGU7TlSIUpDSV+Ap+FzzS2psjvwO5epwpJ/LgEAxncOhYtMLCzuey3+OJmBu5fuw5wfj17zNYiIiIiIrlWjzpH6/PPPARgW3TW1bNkyTJ8+HXK5HFu3bsXixYtRWlqKiIgITJw4ES+99JJwrEQiwcaNGzF79mz06dMH7u7umDZtmtm6U82JaWBSUqlBhVoLF5nkmq+XnFuKGd9ZN3SwlZEqKKuCTqc3G4NRl0hv4bGxxK+wrMqsKURtpXtanR7PrjopPJ/UMxIv394eHi5SzPv5OP48bdIUxGQR36IyNVILytAxTGl1zQ0n0gEAuxNzkZCuQvvQplOeSUREREQ3v0YNpPT6ujuzRUREYOfOnfVeJyoqCps2bXLWsBqVZXe9kkrNdQVSta3XZCuQqlDrhKyUpeHtajojersaMkmFZWrIpTVJzdLqTNbkXpF45Y72aPPSXwCAlPwynLpqaG0fqnRB9ygfoYSvdYCH2eskZteUB07+Zj9OX1VhXKdQeLlKodXp8ciAVmgd4IFSk/LDc5k1gZRer4dIJLL5HoiIiIiInKVJNJugGqbNJgCguKImYNDq9NiSkIUCG23Ga2sXXlxhew5RkKd1IAUAV/LLrLbd3S0cswfVdEj0djdkpMrVWqhsXN9NLoFCKoG8Olga8sE/AABPhRR7nh9qNg9qQtcwsxK/UpOywtNXDet7rT+Rjh/2p+Cng6n4atdlAOaNODJVFdDr9Xj61xPo9PrfOJiUb/O9ERERERE5CwOpJsYqI2USSH3172XM/P4wZv1wxOq82hpEXK2lwYOb3HaWa8KSvVbbpvSOglhck+XxVEghqX5uK6noWp1B83AxT3gGeCmsskWtAjxw5KXhENuZRPq7egFf08xZYZkafydkYc3RNKgqNNh4Mt2+ixERERERXSMGUk2MZevx4sqajM/X/xqyMQdsZFxqW0T3rT/O2tyukEkwtU+UXWOSW3TSE4lEULrKaj3epTpI81CYB1L9o/1tHi8SiSCzs1uft6sMer3ebPHfojI1NpvMszqZZigj1Ov1+HDLBXy5y9DkQq3VoUJt+3MiIiIiInIEA6kmpq7Svlqq9wDU3mmvNgqpGG/cad+Ct6bzoIy83WoPpNyqM1KeFhmp+dWLA9ui1tpeu8pSUbka5WqtWUMMVYUauy7mCs+N88JOX1Xh420X8Z9N55CtqsDM7w+j65tbkK1yTjdEIiIiIrp1MZBqYiw75pkGUtI66t8cDaRcq7NG9vRlcJHZCKTqyEgZr20aSD09IhbebrW3O68rSASAl8a2AwDkl1VZdQdMKyhHbknNtrzSSmi0OlzMLha2bTuXjX/O56CsSosjVwrqfjEiIiIionowkGpiLEvPXt9wRsig1FX+Vq62PUeqNl4uhkDot//rhwEx/nC3mDP12h01ixm72ugaWFdQZOwyaHwNAPB2YM0omcQ6uosLU0IkMszJemNDgtk+046AErEIer1hjSvTjoWmwZOtBhlERERERI5gINXEGDNSxnK64goNHlp+qN7z6spIjY0PsdpmzBZ1jvDGihm90DXKR9jXL9oPHiZBkK32614utXfOd5NLq1+j5hp+DgRSw9oGmT1vE+SJThHe8KkO3rady7Z5XnSQJ/w9DMccuVKAFJMOhKergy3AEGQREREREV0PBlJNjDGQ8jGZg3QmXVXvecZAyrIxBAB0sLFYrWVwZNrFz9ddYVZGaCuQqqvZhPFaXq41wZZvPYFUjxaGQK5diJdQGggAz45qg81PDYSLTGIVjD0z0nzOVZi3CwKr27rPWXkUf5k0oDiXWVPmxzlSRERERHS9GEg1MZXVpX0+dZTO2WLs2hfqbb0+VJi3q/D4wd5R+PPJAVbHuMtrgh4/dzn8PGpeX2JjbpaXSSBl2Z3POHZHMlKfTe6K6X1b4JP7O5sFUqbzrEzHBAAdw5Rmz4e0CTRr1lFSabvc8UhKAVLzyzD4/R2Y9/OxeheGJiIiIiKyxECqiTFmpOrqimdLafU6Uq0DPKz2mWaDBsT4o12IdYbKNJSI8HVDn1Z+6NvaD/f3jLD5eqYZKcuxGsvrTI+pLyMV6OmC18Z1QHSgp9D1DzAP8Pw9FGbnhPvUBIhSsQjD2wVhbFyo2TG2MnSnr6ow6cv9SM4rw7rj6WZzqYiIiIiI7MFAqompKyNVV+ZEVW4IpHzc5XjVpFEEYB7E1NYk4kpeqfB4dMdgSCVirJzZG4smxNs83t0kC2UZiAR6GbJiLf3d6n1dW0wzUu6KmsemgZS7XAKla801o/zcIBaLMHNgS7NrtQv1srnYr+lCxZdzS60PICIiIiKqAwOpJqZCyEjVHXhYBlXGTnReLjI81K8lWge4C/s8FFI8PjQaD/aOQneTphKmTDNZoSalgLUZFBsgPDadXzWuU01GaGBMAJ4cFoPvHu5pszywNqaBlJtFyaGRt5vcLMgK8FQIx0/v26Jmu4ccQV7W5Y6mMgqZkSIiIiIix9Teeo0aRU1Gqu7SPrVWD7m0JjhRlVcHUtUNHqTimhhZJhHj6ZFt6rze0yPbwFUuwYO9o+waZ6i3K76e2h2P/3QM80fE4o9TGTiWUoh7uocLx0glYjxVxyK8tTEr7TMJlvxMMlIeCqlZW3bTEkDTUkNvNzkifNyQUWRoMDGxazjWHE0zez22QyciIiIiRzGQamKqtIaMlK2ueKY5KLVWJ7RIBwBV9cK9xrWbTDNAda0/ZRSsdMEbd3Z0aKzD2wfh1GsjIZWIcV+PCCTnliEuXFn/ifUwzUi5ymp+RU2DyzFxIRCJRJCKRdDo9GgT7CnsM10s2MdNBm9XJQ4m5wMABrcJsA6kyh1bg4uIiIiIiKV9TYxGZwiXPGys02RazaeuDriMajJShiBCZFJJZ2uBW2eRVgdpni4ypwRRAOBqkl0yzUiZBo6PD40GAHz6QFdM6xOFOUOihX2mZZHebnKMiQ+BWAS09HfHiPZBQjOMSF/DHK5iJ2ektDo9dDp2AiQiIiK6mTEj1YTodHohWLJsKa7T6aE1iaTU2trmSBnOMw267MlINSUuJgGT6RypgbEBGN0xGF0jfSCuzrjd1jEYt3UMNjvfsqNg10gfHHt5JFzlEsilYnz/cC9czi3B1YJyLPrzHIornJeRKq/SYvRHuyCXivHb//Uza8pBRERERDcP3uU1IaaBkumcH8DQFl1jkoWqLyNlGmY1t0DKdLymjSxkEjE+n9Kt3vOVbqalfXKrbe1DvdA+1AsrD6QAqCmLdIZ1x68iOa8MAPDP+RyMjQ9x2rWJiIiIqOlgINWEaE3KwUQW1XgVaq1ZFsoqkLKYI2Xa1a8hS/saQu9WfmgT5AkPF6lZIGUv0y59pmtNWTI25nBWaV+FWouFa08Jz3cn5iI60MNs/hYRERER3RwYSDUhpoGUaUc6wNCEokpTe0aqpDqQ8rRR2ieyjMqaOFe5BJueHOBQy3RTYd6ueKBXJHKKK9HexuLDRp7VQacjpX3L9iQhr6QK80fECuWFRucyi82e/3QwBT8dTMHRl0fUuyAxERERETUvDKSaEI1JINWthQ/GdQrF+hPpAAxzb6rMSvvM50gZ9xkbMujqWLy3ObjWIMroP3fF1XuMMehMyFBBrdXVWwJZodbi9Q0JAIBerXwxIMawllZOcSU+25GI6EAPm+d1fXML2gZ74rf/62fWkZCIiIiImq/mNXnmJmeakZKJxfj4/i7wVBjLz8yzJqYZKZ1OL5xrDAaadxh1Y3iZdEb8eNvFeo/PVlUKj1Pzaxbxnf/rcSzfm4yX1p2u9dxzmcU4llpwjSMlIiIioqaGgVQTYgyGRCIIZWOy6gyT5Twe00BKrat5LK2eD6Vv5hmpGyHQZC7VJ9sT6z2+qLzmz+CF307hcPXaVP9ezLXr9dIKyus/iIiIiIiaBQZSTYgxkJKITBfTNTy27CxXpbHdCl3OjJTdvFxkuLtbOABDSWR9az9VarRmzx/46gAOJedDamcZYlGZY00tSis1OH21iEExERERURPEQKoJMbY/N50fZCzVU1lkpDQmWSi1SRMKobSP9952WTQhDgqpGFUaXb2ld5Ua8wYfVVod7lm6z2xuGwD41dJYoqCsyqGxvfDbKdz+yW6sOpLm0HlERERE1PAYSDUhWq11IGXMMKnKLQIp01bo1UGVWFRzrmmgRbWTScQY0T4IAPC/LXXPk6rS2PeZmpYMmipwICOl0erw+3FDo5Hv9ibbfR4RERER3RgMpJqQujNS5qV9plkQY2mf1KTrHDNS9ps1qDUAQ/e+uliW9tUm0FNhc3tRuf0ZqdPpNWNRusrqOJKIiIiIGgMDqSZEW51FMgukpNVzpKwyUtalfXKTQOp624ffSoyL9uaXVtUZLBlL+3zc6g5sag+k1NDq9EhIV5l1aLRUWqnB1oQs4bllWWdidrFZ4wsiIiIiuvEYSDUhUrEYbYM9EWOyHlFtc6TUJjfixjI+Y2MKwLxhBdVN6SoT1t8ybXFuqVJt+Jy7RPrUeb0gi9K+2zoEAwBOphUh9qU/Mebjf/HmxgSr885nGgKkof/9B5/uqOkiaBo0nc1QYfiHuzD56/31vCsiIiIiakgMpJqQFv7u+GveQKya1VfYZgykLNeRMs1IGTv4mZb2GQMDqp9IJEJwdfCTpaqo9bjK6s9cUc9nG+hVk5G6r3sEpvaNAmD4MzRmopZbzHs6kVqIUYt3Ydh//0GWRTBn2u1vTXXjidNXVezmR0RERNSIeLfdxNnVbEJrXdr3/t2doHSV4c07O9yAUTZ/PtWd9grraAhRqTaU/cmlYgxvF1jrcQEeNYGUVq+Hu1xq8zjTQOjXw6kAgNwS63lUxZU1AVhOSU2QVVKpQWZRBQa/vwOv/F77YsBERERE5HwMpJq42taRMl2E11ZpX1y4EsdfGYEH+7Ro+EHeBLyrGzoU1jH3yDhHSiEV47VxHWrNTHmZNIfQ6vRwV0hsHldaVTMfq7RSY/MYwNA4xLgg8/nMYmF7SaUGvxxKRXJeGb7fdwUVavuaYRARERHR9WMg1cTJaslImTYrsFXaBxhK1sg+3tUNJArrWOupJpCSINzHDVP7RNk8zs+jZh0pEQB3he2MVJ5Jdim9sPaSQsAwT6pKo8PF7BJhW3GFBpdza547uk4VEREREV07BlJNnExaS7MJG6V9Mgn/OK+VMSNVVze8Ko35HCkXmXWmSSoWIcTL1WybWy2lfaZlfIk5JTaPMSoqVyO1oMwsgC6uUONiVs15eTbKAomIiIioYdi+w6MmQ25Hs4maOVLMQF0rpZsdc6SqW6MrZNaBVPcoHzw3ui3UWh2Upu3RRYC73DzgCvdxRVpBObJUFdDp9Nh3OQ/5pXUHQS+vO40nhsWYbSuu0JjNmarvGkRERETkPExhNHGyWoKj+hbkJcc4MkdKLjEERqaBlKeLFD1a+KJva38AwJTekRCLgP8b3Nrqz6WlvzsA4P9+PIo7P9uDTacyan3NUR2CAAAn0opwPLXQbJ+qQoMCk+CJgRQRERHRjcM77yautnI9tY2MVG1BF9XPnjlSQmlfdUbK1SSQ8nAxX6T3zTs74szrtyE60NPqOqHKmtK/U1eLcDZDZfP1erb0xbsT44Xnvx27arb/i52XzALqusbuTCWVGizadBZ7L+XekNcjIiIiaooYSDVxtQVSttqfc47UtTMGUnVldSot5ki5mZTseVg0lBCJRHCV2+7WF+FrPocqoTqQMl37q22wJ5ZO6QZvNzmeHdUGAJBWUG523pl08wDMsvyzoXyw+Ty+2HUZ834+fkNej4iIiKgp4p13E2e5sK6ni+GG3TQTYQyqGEhdu3AfNwBASl5ZrQvdGteRUkgNAZJpoGT8c7FH+1Avs+cVakOAFhPoIWzrHOEN3+q1rQa3CbDrusV1tFB3povZhhbs2cWVKK3UQFWhRlpBmc1jdTo9vtx1CdvOZt2QsRERERHdKLzzbuIsy/W8qkvITJtNVLG077pF+blBLDIEI6YNHExZZqRMF9q1zEjVpX2I0ub22KCaMkDT+VfRJgFWXa9l2SL/WpVWavDAV/sxY/kh6HTWQaVp58Cc4ko8tOwQhv53J1LyrIOpLWez8J9N5zDju8Nmv7NEREREzR0DqSbOMstkvIk2bzbB0r7rpZBKEOZjKLlLyik121daqcHX/15GUq5huzFL6KaovbSvLkFeCqttMokIUX5uJuMRmzyWmF0/zNu8NNDoekr70grKhEDo50Op2HspD9vOZeOcyQLAtl7nYFI+jlwpQJVGhx3ns62OvWBy/tXCcqv9RERERM0V77ybOMuMgEd1CZlpswljaZ+cgdR1aeFn6KaXnGceSH256zLe+uMsUvINgYZxbpTbNZb2iUQijOsUarYt0NMFSteahhUKizWqfNxr9hkDPkuWa43ZS6PV4a4lezHw/R3IVlVgx7magOiXQyl4Y0OC0PodMA+kPtp2UXhsa0FgY/AJmK+bRURERNTc8c67iSup1Jo9F+ZImTSbMJb2SVnad12MbcmTLUrUjlm0HXevzg6ZlvbVF0i1CnA3e774vs6Y0jtSeB6sdBHKNgHARWb+v6Zv9TpXQE2rdqO+rf0AGBplrNiXjCxVBQBAr9dj1eFUnL5aVOfY8kurkFNsKGc8nW7eZv27fVfw7Z4kfLY9UdhWbBKwmWaZMosqrK6dajJ3qqicgRQRERHdPBhINXGmmQAA8Ky+2f7lcCryqufysLTPOQI9DSV3+RaZE63OfG6PsczOtNmEm7zuQGr+iFj4e8ixbHoPAIBYLBIyYICh3M8sIyW1zEjVBFKRJiWAn9zfBXOHRAMwdPF7+fczmLBkLwDgr9OZeHb1Sdz+ye46x5ZdXDMnLLe4CiU2mlZcqi531Ov1tZYQZqqsA6n0wpptdS12TERERNTc2F+PRI1idMcQ/HggRXge5Fkzv+bF305j6YPd2LXPSYxBanGl+Q2/ZeBgzByZZqT8PaznPZm6PT4UY+NCIBLVZA19TYIjpatMaMEOWGekTLNVk3pEQqcH+rX2Q69WfjiTbp5xulpYDr1ej+NphcK2kkqNzXlcOp0ec1YeFZ7X1n3PWFpYodaZzc8zZZmR0mh1ZsEVAykiIiK6mfDOu4nrH+OPTx/oIjwPN5kfczSlAAAX5HUWY3meZeBk+rxvaz9hHShXuQRj40MwrG0g2gZbL7xryTSIAsy79A2KDYC3Sfmei0VGSiKuOdfbTYb5I2LRq5WhpE9pUeoHGMr1yqtqsplFtXT023c5D1dMShkzbJTnAUBZ9bWK65iHdS6z2GyeVlZxpVmHv8JyNZJzS60CPyIiIqLmiBmpZuD2+FDEhSnhIpNgS0LNejzGe9QqlvY5hTEjpbIKpAzBwZ9PDkC7EPM1oD57oOs1v17HMCUW39cZl3NKMKpDMPJMFgOWWawfZhqQuFg0ogjycrG6dk5JpTDvCQCKytQ2u/1lF5sHTrbK8wCgpPozsfxsAMPiwcbufk//egJfTe0OALhqsYBwVlEFRn/0L8rVWvzzzGC08He3upZRpUaL19afwcCYAIyOC6n1OCIiIqLGwkCqmYiqnk9jmnUyJjhY2uccNRkp86yLMXhwpDOfvcZ3CRMem2aW5BbZRXeFefBkSiYRIy5MiVMmTSVyiivN5j5ZZqROXy3Cg98cEDJNRtkqwzntQrwwon0QPq7uyleu1qJKo8NjKw5bvX77EC8hkNqSkIWlOy/hyJUCXLHofpiYU4Ly6kWNj6YU1BlI/X48HT8dTMVPB1ORtGiMVTaPiIiIqLHxzruZkYpr/siM1V4s7XMOW6V9lRpDAGHYb11C50ymgXCrAPNFeOcMiUaI0gXzR8TaPPfTB7rgzTs7IC7MsNjvg98cxJErBcL+ovIqPLTsIKZ8fQB6vR7L9yajoEwtLDJslFWdofJ0kWL+iFgh41ap1mH9iXSh6YSpEG8X3Nm5pp37O3+ew5aELFzIKjE7ztg+HgDm/3oC3+5OqvWzSDU51hh8ERERETUlDKSaGdMW55Lqb+lZ2uccngpDoFRq0rXONKhyZNHda/XLo73x8f1dzOZPAUC4jxv2LRyGJ4bF2Dwvys8dD/ZpgehAD5v7DyUXYMf5HOxOzEV+aVWtjR+M243NLVzlht+pCo0WqlrmWYUoXfH6uA61vqfx1UGWaakhALyxMaHWc6pMArza5ncRERERNSbeeTczpsGSsdxJzdI+pzC2My9Xa6HXGz5TYyDlLpeYNXxoKL1a+Vkt1usIW40nAPO5T6WVWiH4NrIsW/Sqfm5selGh1poFmKbCvF1rDTLbBHliSu+oWsdbVmX7mqZNK1Tlto8hIiIiaky8825mTO/ljVV+Gpb2OYVbdSCl1xvafAM1TRYauqzPWTpHeNvcXmKSWSuuVAtrkBkFWLRv96oOyBQyYyClQ2EtmaEATwWkErHNYGpc51CzNbAsGdeZ0uv1eH3DGcxZeRRVGp1Z8HQgKc+s2UZdUvPLkJxrXX5IRERE5GxsNtHMmK7hIxYyUiztcwZXk254ZVUauMolQsbErY5mD03JnZ1D0TbEE60DPJCcW4qPtl3ExpMZZovsJmaX4EJWsdl5/p4KXDYJQIxBkXE9qwq11qwJx4z+LeHjJsPVwnK0r+5k6OUitVrM189djjBvV4hEhgAVAHq28MXRlAJodHqkF5YjOtADaQXlWLYnGQAwqUeEWTnfK7+fQXGFBnOqFx6uTX5pFcZ89C+0ej3+eXYwAj2tuxkSEREROQsDqWbGmCkxZWwYwEDq+ojFIiikYlRqdCir0sIPNY0OXGXNI5ASiURoG2wIbGKCPOFXnQ0yLct78ufjVudZZqQU1e3XXWQ1pX3GMsfuUT54ZmQboRTSyMtVhnSLdai83WRwkUkQ4uUi7GsV4A5XuQQ7L+QIi/iezVAJ55y3WI8KAN7ffB7nMotRUFqF50e3Rcfqphqm9l/OQ3H1+9x3KQ93dg6DRqvD//14FC4yCRbf1xniG1CeSURERLcGBlLNzNC2gcLjSmP5WfXNo0cDtOe+1bjJJajU6IQAqqKZBVKWjKV5losMWwrwNA+k7uxsaMsuBFIanXCNyb0jrYIooKZBhalwHzcAQAt/dyGQcpNLEeptyBalFxnWmjLtBng2o9hmY4sNJ9IBAC+uO437e0Tg+bWnEOnrhg/v7YTuLXyRZJJRu5JXhl8Pp2LB6pPCtim9o9CzpW+dnwMRERGRvZjCaGZ83eX4+6mBAAytuXU6PY6lFAKwfSNLjnGTG4LR8ur1lYwBleUiuM2FvDpLWVpLUwcj00BqQpcwRPoZAiCX6sxUlUYnZImM3Q0tmTascJNL8PLt7YXMkXEdNMCwJlawl2FxYGNGKr2wZvHeNUfTkJxX0/7c0sm0Qjy/9hQAQ0v1u5fuQ0mlBhlFNdfIKa7Ea+vPmJ33zp9nhSYiRERERNeLgVQzVNNJTYfzJnNd6lq0lexjzLQYF6o1llI210DKWKJXUk9GKtAkkHI3aRph+r7zSqoA1J75NA2k7uwchhn9WwrPW/q7CY/d5FKEKA0ZqYzqQCrDoiSwLrZioS92XhIaVwCGLoWWiw0fTSnEplOZNq+ZnFuKHm9vxdO/nrB7HERERHRrYyDVDMmkNU0mMk1uQONszBshxxhL+MrVhsDDmJmyVcrWHMirAylNPV3vAuwIpIzrQFm2Sjcy7WxoWQoZrHQ1ub4EwUIgVW72X0dN6hEBAPjpYCpOXy0SthuztABwT7dw4fHOC9k2r7P2aBpyiiux5mga160iIiIiu9g1qWbChAkOX3jp0qUIDAys/0BymLGphEanFxpNdI/yEdaVomtnmZGqaTbRPL9zMGak6mPa4U5uco5ELIJMIoJaqxfWnqqthNQ0wDIu5Gvkb9IC3U0uRZiPIbC6kFWCFfuvCF8IPDMyFh/8fcGuMY/uGIxX7miP1UfSkGvRzt34vEOoF96/pxOGtQvCrB+O4GSaIdg6llKAAE+FMIcrzaS0MDm3FJ0ivHExqxgt/d0hZRMXIiIissGuO4R169ZBLpdDqVTa9fPHH3+gpKSkocd+y5KJa/7YjO252bHPOdysSvua+Rwpqe1x927li2dHtRGeB3rVZKQsG9u5WFyjtsV3TTNSxrlmRn4mXQHd5RKEeddkqF5edxp5pYaywS6RPjavDQDjOoWiXXWrdcAQJLnJpegX7V/rOQNiAoRjAeBSTgn2JubiriV7Mf6zPcKfb2FZTRYqv7QKKw+kYMT/duGdP8/Vem0iIiK6tdnd5u3jjz+2O8O0evVqu45btGgR1q5di3PnzsHV1RV9+/bFu+++izZtam7wKioq8PTTT+Pnn39GZWUlRo0ahSVLliAoKEg4JiUlBbNnz8aOHTvg4eGBadOmYdGiRZBKb84udsbSPgAorb7hl9mZeaC6GQOp8qqbpGtfLb8XPVr4QmISMXm71gRBEovMpkImEdqKA/bNkbIMPP08ajJSCpnYZmDqIhOjY6h5eeoj/Vvi691JAICukd4oKlfjbIZhX2R1A4slk7vimVUn8OfpTIyNC8EfpzKE858YZlh7KtzHFR4KwzpXy/cmAwByS6pwNkOFLpE+ZuV8heVV+GT7RQDA17uT8NLt7W2+XyIiIrq12XX3vWPHDvj62t82+M8//0RYWFi9x+3cuRNz5szB/v37sWXLFqjVaowcORKlpTVtjJ966ils2LABq1atws6dO5Genm5WaqjVajF27FhUVVVh7969+O6777B8+XK88sordo+3uTHNPpVXZ6TkEpb1OYOrzBAMWJb2NdeMlKKWkkSZRAyNtmZNMtPyNcv36mJyDVeZpNbsp1lpn8U1fN0MC/N6KqSIC/O2eX6o0hVKN5mQdZrQNQwv3d4eKx/phTlDWmNK7yizuVzGhhXuCik+n9INye+MxX/v7STsj/B1FTJjIpEIsUEeAIC/E7KEYxKq168qLKsSthWUqiE1+f/pUHK+2TpXpZUavP1HAjaeTLf5PgCwOyAREdEtwK6UzaBBgxy6aP/+/e067q+//jJ7vnz5cgQGBuLIkSMYOHAgioqK8M0332DlypUYOnQoAGDZsmVo164d9u/fj969e+Pvv/9GQkICtm7diqCgIHTu3BlvvvkmnnvuObz22muQy+W2XrpZk5pkEkorqzNSLO1zCuPcHmMAVV6lq97ePAMpeS2/F3KpGHd2DsV3+65gQlfzLz2ClC5mz00Dq7rWKjOdO2U5R0osFuGveQOg1enh7Wb4f3JClzCsPXZVOMbYgGL1rD7YfCYTw9oZss59o/3Rt7p8z9/DOpCqbaymJbAAEB/ujaMmTSgAIK3AMDeqqLwm43Ylr1ToUAgA9yzdB6lYhFWz+qBLpA9+OpiCr/5NglgEdIvyQYhJIw3AMMfq7qV70crfA7881ptzF4mIiG5S13z3nZ2djdOnT+PkyZNmP9ejqMgwEdyY/Tpy5AjUajWGDx8uHNO2bVtERkZi3759AIB9+/YhLi7OrNRv1KhRUKlUOHPGfB0Zo8rKSqhUKrOf5kQkMjQAAGpu+BlIOUfNOlKGG2thjlQzLZ2U1zJumUSMEKUrDr4wDAtHtwMAvDMhDnd3C8fYuBCzY00zUrV17LPcZ8zsme+XCUEUALx9V5zZXCljQOKukGJC13AoXa2bWuhMMj1BXtaBVF3u7R5htS2/pAp6vR5F5TWB03f7rli1Ttfo9Pj5YCoA4MiVguqxAFtMsltGPx1KQW5JFQ4m5+Odv87hYFK+Q+MkIiKi5sHhu8MjR46gY8eOCAkJQXx8PDp37owuXboI/71WOp0O8+bNQ79+/dCxY0cAQGZmJuRyOby9vc2ODQoKQmZmpnCMaRBl3G/cZ8uiRYvMmmNERFjfYDV10upv20sr2WzCmYwlaZbNJm66jFR1IG6aLZnUMxIf3NPJbO4UYN5swrOWRhOARftzOz4vV7nEbK2pUO/6A6N7u0fAVSbBgBj/Wn/nowMNJXyD25jP6Wwf6oWJXcPNtuWVVuJqYTnU2vpL8fZdzgMAHE8tFLadSC3CYysO49HvDwu/Kwcu1wROX+y8jAe+2o9LOWy+Q0REdLNxuBvDww8/jNjYWHzzzTcICgpyWtnKnDlzcPr0aezevdsp16vLwoULMX/+fOG5SqVqdsGUTCJCubrmhl8uZfmQM1g2m2juc6Rqa0JSW6bKFtP37llL63PDvpq/TjzsXBza16QterCNUj1L0YEe2LlgMDwVtY/j66ndsSUhC9P6trDa997d8ZjYNQw5JZV48ufj2HE+B1vf3VHrtV65vT2CvFzw+E9HkZJfhmMpBWaLB685miY8/jshC8PaBpqtZwUYslnv/nkOX07tDsDQOXBPYi7u7R7RbH+viIiI6BoCqcuXL2PNmjWIjo522iDmzp2LjRs3YteuXQgPr/nGODg4GFVVVSgsLDTLSmVlZSE4OFg45uDBg2bXy8rKEvbZolAooFAobO5rLow3wmx/7ly1rSPVXG94a/u9cOT35VpK+0znMtXFxySQCrWYa1Qb0zWvbGnh746ZA1vZ3CcRi9A32h+Hkw1ZI20dCxV3CPXCw9UZs4+2eeBCVgmeW2MoX3aTS6zK/44k5yPIUwGNTo8QpQu+mtodvxxKxYr9V/B3Qhb+vZiDATEBmLvyGM5mqJBTXImnR7axel0iIiJqHhy++x42bBhOnDjhlBfX6/WYO3cufvvtN2zfvh0tW7Y029+tWzfIZDJs27ZN2Hb+/HmkpKSgT58+AIA+ffrg1KlTyM7OFo7ZsmULvLy80L79zdu22FjaZ7yZYyDlHMaAqUJjLO2rbjbRbAMp25lKRzJSCtNmE3WU9nkopLg9PgSjOgQh0tfNrmv7uNVklkLsKO1zFm+32jNagCEQ/HxyN+G5ceHeC1mGEr1FE+Kszjl5tQiZKkO2KsrPDR3DlHhzfEc81K8FAGDx1ouoUGuFDoCfbE8UvgghIiKi5sfhjNTXX3+NadOm4fTp0+jYsSNkMvMbknHjxtl9rTlz5mDlypX4/fff4enpKcxpUiqVcHV1hVKpxIwZMzB//nz4+vrCy8sLjz/+OPr06YPevXsDAEaOHIn27dvjwQcfxHvvvYfMzEy89NJLmDNnTrPPOtXFuJaUcY6UIzfGVDtj4KGpnjPT7BfkdUJGysNkcd26SvtEIhE+faCr/YMD0NLfHd5uMgR4KNA6wMOhc6+Hl41GFqbWzekrBE+A9fytQbEBaB3gjks5NUs1JKSrcLXQ0AXQtBHGYwNbY9meZBy5UoD7v9pvdp2nfjmOLx7sjpziSkz8fC9CvV3w5dTu+PNUBn48kIJQpSvmj4xFbJDnNb9XIiIiahgOB1L79u3Dnj178Oeff1rtE4lE0Gq1Ns6y7fPPPwcADB482Gz7smXLMH36dADA//73P4jFYkycONFsQV4jiUSCjRs3Yvbs2ejTpw/c3d0xbdo0vPHGG46+tWZFxoxUgzB+jurqNZZqAqnm+fnW9ntRW4Bli79nTfldXaV918LTRYa9zw+FRCy6ob/Dlh0BWwe4Y9GEePx8MAU+7nKzIAoAwrxrnvu4GboPDm8fhEs7LwvbKzU6/HrI0Nkv0GS9q2ClC+LClDh1tQjHLNqv771kaGAxZ6VhDlZKfhniX/tb2H8yrQh/ncnE6I7BmNg1HMPbmzfWISIiosbj8F3R448/jilTpuDll1+26pbnKHsWrXRxccFnn32Gzz77rNZjoqKisGnTpusaS3NjvOks5YK8TmUsmbQMpJptaZ8Tmk1EmAQVzg6kgJqW8zeSwqQToVwixranBwMAera0vfC4aUaqhb87AGDukGjkFlfB30OO/Un5OJFaiOS8MgDWrdkHxPjjlEkTijBvV1wtLEdxhQabz2TW2yL9z9OZ2HspD0dfHmHVVZGIiIgah8NfAefl5eGpp5667iCKro+xtK+cGSmnEkr7dMbSPkNA1VxL+2qbI+XI78vQdjVtxG/G3zMv1/oDuXCfmkYYLfwMgZSniwz/vbcTFo5ph3u6mbdVD/A0Lyu2LM0bGx8iLCg87+fjNl/zsUGt8MzIWOF5Ubkax1ML6h0rERER3RgOfxU8YcIE7NixA61bt26I8ZCdataRYiDlTMbPsUqjg16vF7r2KZppaV+t60g5kJEK9HTB4DYBOHKlALd1tN0Jszkz/r9Ul1CThYMjbDTSGNrWfM0qy86C7UO9hMcP9IrE0yNjcTylEBlFFShXayEVi7B1/iCsPJiCAA8FqrQ6TOkdBU+FFK0CPPDhlgtIzC7B5jNZ6BblC71ej7MZxQj3dYVXHfPWiIiIqOE4HEjFxsZi4cKF2L17N+Li4qyaTTzxxBNOGxzVzniDbLzRl7K0zymkJhmpSo1O2N58M1K1NZtw7Pfliwe7QaPVw72Orn3Nzdj4EPxxMgOPD6t/KQfTwMhyfhUABHu5QC4Vo6r6dybIyzojtXB0W6i1OswdGgMACPNxBZIN+zuGKdHC3x0vjGlnde0xcSGQiEV4bMURrDyQgudua4uVB1Pw8rrT8HOXY92cfjaDOyIiImpY19S1z8PDAzt37sTOnTvN9olEIgZSN4jlPAmxkxZGvtUZA1SNVodKdU0g1WznSNUSSDk6L0khleAmiqEAAO9NjMe0Pi3Qo4VPvcdKxCKMiQvGidQiTOwaZrVfLBZB6SpDTnElACDQy7qV+2ODzLP4YSZZrrgwZZ2vP7xdEFxlEpRUanD6ahEWbToLAMgrrcLGkxmYPZgVAkRERDeaw7dGSUlJDTEOcpBlNRInoDuHVOjaV1PWd6M7yjlTbZmn5hoYOpO7QlprcwlblkzuBr1eD1EtX1pITLbXtd6WkWm5YFx43YGURCxCbJAHTqQV4eHlh8wWA/73Yg4DKSIiokZwXXeHer3ers575HyWGSgJM1JOIa0OSNVaXU3r82a8RpdIJLIZTDGQuja1BVEA4Oh3GS2ru/8BQNdI73qPjw40NKzIK60CAEzpHQkAOJxcIKwndyK1ECM+3InFWy84NhgiIiJy2DXdIX7//feIi4uDq6srXF1dER8fjxUrVjh7bFQHq9I+ZqScwtiEQaPTo6T65tStmde02cqmucibb3DYVL10e3uIRMDMAS3tOr5nS188OrAVnh3VRgiS6tI60N3s+dMj2iDC1xVVWh12J+YaxrDuNC5ml2Dx1ovIVlUIxxaVq6HT8UsvIiIiZ3L4DvHDDz/Eyy+/jLlz56Jfv34AgN27d2PWrFnIzc3FU0895fRBkjXLDFQzrTxrcoSMlEaH/Opv/v3c5XWd0uQZAqmaUjBfd7lDC/KSfcbEheDwi8Ph42bf74tELLLZXKI2rQM8hMdBXgr4uMsxKDYAP+xPwZErBehYveiv0fNrT6G0UoNALxdsOJEOmUSEP58cCH8POV75/QxOphViYGwAXr69fbMtXSUiImpMDgdSn3zyCT7//HNMnTpV2DZu3Dh06NABr732GgOpG8QyA8VmE85hvKFU62oCKXtvjJuDvc8PhZ+HvM4SNbp2fh6K+g+6RtGBNYFUK3/D45jqTNaVvFJcyS01O377uWyz52qtHsM/3IlJPSKw/kQ6ACB53xV0ifTGXV3M18EiIiKi+jn8NWRGRgb69u1rtb1v377IyMhwyqCofpYZKQZSziEzaTZhDKR8PZp3IFVUrhYehyhdoJByflRz1MpkTpWxvbpvdba0oEyNnJJKu67z86FUs+c7zuU4aYRERES3FocDqejoaPz6669W23/55RfExMQ4ZVBUP3btaxjGxgxanUkgdRNlpJiJar5EIhHeHN8R3aN88NSIWAA12dLCsiqh9bpp5mpSjwjEhSmxalYf/PeeTsL2tsGe+GhSZwDAsdQCVGl0mLPyKB757jDKqjRmr6vV6bH5TCZ2nM/GwaR87K2ej0VERHSrc7i07/XXX8d9992HXbt2CXOk9uzZg23bttkMsKhhWGagmJFyDqnJXJGCMkMg5enSvJtN0M3jwd5ReLB3lPDco/p3s7RSKwRSA2L80aulL7xcZVgwqo0QPPdo4YuRHYKQpapEhK8rKjU6iERAan45xnz8LxKzSwAAvxxKxUP9ahpmPLv6BNYevWo2jtWz+qB7C/tbxxMREd2MHM5ITZw4EQcPHoS/vz/WrVuHdevWwd/fHwcPHsRdd93VEGMkGywzUMxIOYdpq3Bj1z6XZt4q3Ljw693dOA/mZuMiM/wVXqHWQlVh+H31dpXj7bvi8Nxtba0ykJ4uMkQHekAhlcDLRYaukYbFiI1BFAC899d5ZBSVAzBkuiyDKADYdCoTAKCqUEOj1VntJyIiuhU49FW7Wq3GY489hpdffhk//PBDQ42J7MCufQ3DtHuZcW0eRTNeRwoAvpraHb8cSsEjA1o19lDIyVyq57tVqLUory7Jc5PbH/h/O70HOr3+t/Bc6SpDUbka3+29gudHt8XJNEMXwDBvVwxvF4jv9l0xnLcnCTklldh1IQc+bjKsm9MP3jdRCSwREZE9HLpDlMlkWLNmTUONhRzArn0NQ2ryuRq/4W/uGan2oV54/c6OiPB1a+yhkJMZfzcrNDqUGxeQdiCQUrrKMKJ9EABgRPsgLJoQBwBYuvMSDibl42RaIQCgW5QPXr+zI46+PEI4d8OJdBSVq5GcV4YN1V0AiYiIbiUOf9U+fvx4rFu3rgGGQo6wzkgxkHIGkUgklPeVCIFU885I0c3L+Lup1elRXP376uZg4P/mnR3xxLAYvD2+Iwa3CRC23/vFPnyzOwkAEB+uBGDoEtgxzMvqGv+cN3T+O3A5Dzss2q4TERHdrByeRR8TE4M33ngDe/bsQbdu3eDu7m62/4knnnDa4Kh2ll37mJFyHqlYDLVWe9PMkaKbl+nvprHLpKsDGSkACFa6YH51F0AAuD0+BBtPGpayKCgztM7vFOEt7P/k/q548JsDSCsoF7ZtO5eNfu9sx9VCw7aNj/eHVCKCp4tMmKNHRER0s3E4kPrmm2/g7e2NI0eO4MiRI2b7RCIRA6kbhF37Go5UIgLUNc0mmvscKbp5mf5uFlYHPY4GUpY+uKcTBsUG4NnVJ4VtcWFK4XFLf3d8NbU7Rn/0L5SuMsSFKbE7MVcIogBg+rJDyC2phJtcgi3zBzGYIiKim5LDgVRSUlJDjIMcxK59DUde3XDCWNqnYEaKmiiRSASFVIxKjU5o1+96nb+vLjIJ7ukegdT8Mny8PRF3dAq1ysq2C/HCujn94C6XINLPDZtOZeCDzReEYCq3enHgsiotVh64gmdHtb2uMRERETVFDn/V/sYbb6CsrMxqe3l5Od544w2nDIrqZ5mBYtc+55FWz5Gqqm7rbOyMRtQUGTNQlRrD76sjXfvq8tSIWKz9v754/+54m/s7R3gjJsgTCqkEd3UJx57nh+IDk0V/jY6lFF7T67OtOhERNXUO336//vrrKCkpsdpeVlaG119/3SmDovpZZqBY2uc8MouoVMFmE9SEWQb615uRMhKJROga6ePQHMEWfjWdIY0LWe+9lIdxn+7G1/9etvs67/x5DtEv/onP/7lk/4CJiIhuMIfvEPV6vdUijwBw4sQJ+PpypfsbhaV9DccykGJGipoyy66S1ztH6npE+dU0HxoYW9MB8GRaEd764yyKytXYk5gLdT3ZpqU7DQHUB3+fb5iBEhEROYHdc6R8fHwgEokgEokQGxtrFkxptVqUlJRg1qxZDTJIsmYZyzIj5TzG9udGcik/W2q6FBaBvpvc4amvTuPvUbMob6jSxWr/nZ/uRnJeGfq29sMPM3pZrYcHAFmqCuGxVqdHSaUGHorGe09ERES1sftfp8WLF0Ov1+Phhx/G66+/DqWypouTXC5HixYt0KdPnwYZJFmzXEeKgZTzSC16y0sse80TNSGWpafOKu27FiKRCBO7hmPXxRzM6N8KPx9KFda3AoDkPMP82r2X8vD2prN4+fb22HkhB5vPZOL50W2RkleG2z/ZbXbNxOwSdDZpv05ERNRU2B1ITZs2DQDQsmVL9OvXD1IpvyFsTCztazhSi4yUZdBK1JTITUpRRaLGX0D6v/d2EkrAp/dtgU+2J9o87qeDKXhhTDtM+/YgACDEywWlVVqr4zIKyxlIERFRk+Twv7ienp44e/as8Pz333/H+PHj8cILL6Cqqsqpg6PasWtfw7H6bCUMpKjpMs1IucokNuew3mjGMTw1PBa7nxuCcZ1ChX2xQR5wkYlRVqXF3ku5wvYTaYVIza/pCNs6wDDfKqOoptTPlsTsYmw4kQ6dTo/FWy9g3s/HUFalqfMcIiIiZ3D49vuxxx7DhQsXAACXL1/GfffdBzc3N6xatQoLFixw+gDJNnbtazhSy2wfP1tqwkznSDmr9bmziMUihPu4IdBTIWxr5e+B2CBPAMC6Y+nC9vzSKqRUB1JfTe2OwW0CAQB7L+Vi4dqTOJZSYHX90koNHvjqAB7/6Rg+2nYRi7dexLrj6dh8JrMh3xYRERGAawikLly4gM6dOwMAVq1ahUGDBmHlypVYvnw51qxZ4+zxUS1Y2tdwrIJUZvuoCTMt7WvMjn118TcJpKL83RDu4woAWHM0TdieZxJIRfq6IaS6WcXWs9n46WAq7lqyFwtWnxAW+wWAXw6lIrvY8PyzHTUlhAeTrIMuIiIiZ3N4opNer4dOZ2hdu3XrVtx+++0AgIiICOTm5tZ1KjkRu/Y1HM6RoubEsrSvKfL3MAmkfN2h0eqtjrmSV1PWF+7jimAbXf9+PZyG3JIqfDu9BwBg/YmajJZGV3NNW9krIiIiZ3P4u/bu3bvjrbfewooVK7Bz506MHTsWAJCUlISgoCCnD5Bss7y5Z0bKeSy79Fl28SNqShRS04xU02wCFGCakfKryTbZ4uMmg7tCih4tfG02zth3KQ8AUKnR4kRaoc1rnM8qxp+nMjDmo39xIrUQ2aoKLFx7ClsSsq7vjRAREZlw+A5x8eLFOHr0KObOnYsXX3wR0dHRAIDVq1ejb9++Th8g2cY5Ug3Hco4U4yhqyuQmgZSvm6wRR1I708Cppb87QpSuwvNhbQPN/p8z7gvycsGfTw7EH0/0R9tgT2F/hUYLnU6PtIJy6PWGLoWWfx/q9cDsH48iIUOFp345jv9sOoufDqbg0RWHcSa9qPqYmgxWWkEZ/rflAq4Wljv3jRMR0U3N4a8v4+PjcerUKavt77//PiSSpllWcjOyDJx4s+88nH9GzYlps4m4MGUdRzaemEAPvDCmLdzkUoR6u5p14msd6IGDyfnCelOh3uZBF2BebqvXA2fSVcJcqRZ+7iir0grzq1oFuONyTqlw/OXcUlzOLRXO/et0JpJzy7Bg9Qn835BozBkSjbc2nsVfZzLx0baL6BThjWdGxmJATEADfRpERHSzcNrtt4uLC2Sypvlt6M3I6mafGSmnserax0CKmjDT0r5uLXwbcSS1E4lEeHRga0zpHQXAPFhq4eduNrfLdD6V0SP9W5k9v+PT3Zj/63EAhsYUwV4115vYNbzOsXyyPRFzVh5FaZUW728+j8PJ+fjLpMvfidRCLFx7yixjRUREZIvDgZSPjw98fX2tfvz8/BAWFoZBgwZh2bJlDTFWMiHmzX6DYZBKzYlpRsq0zXhTFuChgNLV8MVbfLjSrNugh8K6UGJcp1Csm9MP9/eMFLYVlKkBGOZc9W5lCCBDlS4YGxdi9WUIAMhqWQ9u/q8nAADdonwwvW8LAEBaQTnSLdavqlBrUVxheE0GWUREBFxDad8rr7yCt99+G6NHj0bPnj0BAAcPHsRff/2FOXPmICkpCbNnz4ZGo8HMmTOdPmAysLxPsAys6NqxtI+aE9Oyt4BmEkhJJWKsmd0XFWotOoYpzTJS7jYCKbFYhM4R3pCKRfjpYIrZvkhfN9zRKRQKmQRD2gSihb87Vs3qgxOphUjIUOHXw4YW6/d0j8DKAylW1zaWBD42sBVGdgjG0ZQCnEwrwn/+OIuckko8M7IN4sOVGPbfnVCVq7H16UGYu/Ioyqq0+PnR3vB0YSUGEdGtyuFAavfu3Xjrrbcwa9Yss+1ffPEF/v77b6xZswbx8fH4+OOPGUg1IKuufcyaOI1p4CQWGcqSiJoqtVYnPPZxkzfiSBwTHeghPHaR1Z2RMuoYpsS5N29Dj7e2orjSMKcqwtcN3m5yzBkSLRzXJdIHXSJ98OuhVCGQ6tnCFz1a+GDfpTy0CfZCeZUGH/xtWFxeJAJ6tjRktbpG+uBkWhH+OJUBAJj38zE8N7qt0Ijil0OpOJRsaK++JSELE+opJSQiopuXw6V9mzdvxvDhw622Dxs2DJs3bwYAjBkzBpcvX77+0VGtWNrXcEzLgvi5UnPSXH9f3eR1Z6RMucgkiDXp4mdsSGFLr1a+wpp7XSN9cFeXcLx3dyfM6N8Sd3YOE45r4ecO7+ogdFCseZOJ9KIKPPnzceH5h1suCI8Ts0sAGMr+UvPLsHxPEo5cya9z/EREdPNwOJDy9fXFhg0brLZv2LABvr6Gb/RKS0vh6elpdQw5j2UGikkT5zFdR4pt5ampm9I7CpG+bnhiaHT9BzdR5qV99Xd/HVwd7AR7uSDS163W46L83PHt9B747uGeiPQzPy7C1w1R1dsm9YgQtg9pG4gZ/VvaNW7jIsLTvj2IAe/twGsbEjDx831YtifJrvOJiKh5c7i07+WXX8bs2bOxY8cOYY7UoUOHsGnTJixduhQAsGXLFgwaNMi5IyUzbIjQcJiRoubE30OBXQuGNPYwrotLPc0mLM0a3BrBShd0i/Kpt/R2SJvAWvd9NbU7jl4pwMRu5uV5L41th9Edg5FbUoXv9iZj3+U8m+cn5ZaioLQKB5LMs1Cvb0hApwhvdAxVYvOZTHQK97YK5IiIqPlzOJCaOXMm2rdvj08//RRr164FALRp0wY7d+4UFuR9+umnnTtKsmJ578AbfueRMJAiuqFc7ZwjZSSTiHFP94h6j6tPbJAnYoOsqydEIhG6V7eSv61jMI5cyceJ1CKM6xyKJ38+hj2JhsAqIUOFLm9uEc6b0jsSP+w3NLQ4eqUA3+1Nxu/H0xHoqcCWpwZB2UQXTCYiomvjcCAFAP369UO/fv2cPRZygOkNvogNEZyKGSmiG8u022CI0rURR2JbtyhfdIsyBFbfTOsBVbkaPf+zzeyYST0i8Nb4OLjJpfhy12WkF1ZgS0IWACC7uBJvbEzAyA5BGN4uiH+vEBHdJK4pkNLpdEhMTER2djZ0Op3ZvoEDBzplYFQ307k7LOtzLomEny3RjdSvtT8+/+cSwrxdEWKyWG9T5CKTwEUmwbhOoVh/Il3Y3i7ECwAQojSM/2yGCmVVWmH/mqNpWHM0DU+PiMXjw2Ju7KCJiKhBOBxI7d+/Hw888ACuXLlitSihSCSCVqut5UxyJtNAimtIOZdp8MRvjokaXv8Yf6yZ3Qeh3q6QSRzugdQoFt/XGS/d3g5bErKw83wO7q6eZ2UMpI6nFto876eDKZg7NJpVBERENwGHA6lZs2ahe/fu+OOPPxASEsJ/DBqJ2Twe/hk4FUv7iG48Y+lccyEWixDo6YLJvaIwuVeUsN3fw1CmWK42fKnYLsQLlWotLueWAjC0Uz+aUohuUT52v5Zer8eSfy7hZFohXr2jA0K9m175IxHRrcjhQOrixYtYvXo1oqObb6vdm4Hp/T1v9p2L7c+J6Fp5WyyKHOipwPR+LbDu2FVkqyqx73IeNpxIdyiQWnP0Kt7ffB4AoNUBX0/rLuyr1Gix9uhVSMQi3NMtnF9uEhHdQA4HUr169UJiYiIDqUYmtmg2Qc4jNZkjZfqYiKg+Phad+YK8FBjSJhBD2gRix/ls7Luch58OpmBQbACGtK29NTsAJKSrsGJ/stC0AgC2ncvChaxixAZ5Ysf5bMxacQSVGsNc5XAfV/Rt7e/8N0VERDY5HEg9/vjjePrpp5GZmYm4uDjIZOb/aMTHxzttcFQ7zuNpOCybJKJrpXSVQSQCjFOIg7xqmmcMignA0LaB2H4uG0/8dAyHXhoOF5ntBYh1Oj3m/nQUl3NKhW2tA9xxKacUD3x1AP2i/fD78XSzc77adRklFRoMbhOI5LxSRAd4cA4tEVEDcjiQmjhxIgDg4YcfFraJRCLo9Xo2m7iB2LWv4ZjOkeJNCBE5QioRw8tFhqJyNQBDaZ+RWCzC0ind0OPtrSgqV+NshgpdIm2X+F3KKTELovw9FFj+UE8Men8HcksqzYKomQNa4qt/k7DjfA52nM8Rtvds4YslU7oK87aIiMi5HA6kkpKSGmIc5CCTaTy82Xcy04yUlJ8tETnIx80kkPIyb+cul4rRIdQLey/lITG7pNZA6ky6yuz5/w1ujQhfN/SL9se/F3OF7QtHt0WnCG989a/1v80Hk/Px7KoTeLh/S7T0d0e4j9v1vjUiIjLhcCAVFRVlc7tOp8OmTZtq3U/OJWFGqsGYZaT42RKRg7zd5EBeGQDz0j6j1gEe2HspT+jkZ+mdP89h6c5LAIApvSMxZ0i0sFDx7EGtsScxF/d2j8DUPi3QLsQTZVVa+LrLkV9aJVyjY5gXTl9VCVkqhVSMVbP6IETpig0n0uEml+CurmFQSG2XFhIRUf2uaUFeU4mJifj222+xfPly5OTkQK1WO2NcVA8xW3Q3GH62RHQ9vE0aTgR5WZfVRfoaMkNpBeVW+/Ym5gpBFADEhSmFIAoA+kb7I+GN28zmVrkrpNjx9GCoKtQY/dG/KKnU4IUx7bDqcBp+O3YVAFCp0WHK1wcgEomEbNmBpHz8777OAACtTg8RWOFAROSIa1r5sLy8HN9//z0GDhyINm3aYO/evXjllVeQlpbm7PFRLcwX5G3EgdyEOEeKiJzF1vykcB9DYJRWUGa17/UNCWbPO0V4Wx1jq0GF0k2GCF83rJrVB99O746+rf3xwT2dcOSl4djy1EAAgKpCg6JyNdzkhvPXHb+KI1cKkFZQhoHv7cDQ//6Df85nY+h//8GiTWcdfq9ERLcahzJShw4dwtdff42ff/4ZrVu3xuTJk7F3714sWbIE7du3b6gxkg2m9/csP3Mu03WkOEeKiBxVXKERHssk1t90GecqWWakrhaW43xWMQBgYGwA2gV7ok2Qp0Ov3S7EC+1CvAAYMup+Hgr4usvh4yZDQZkhE7V6Vl98sv0i/jydiYmf7zU7f/qyQwCAL3Iu49TVItzbPQLju4Q5NAYioluF3YFUfHw8VCoVHnjgAezduxcdOnQAADz//PMNNjiqHVt0NxwpP1siug7T+7bAkSsFGBsfYnN/hK8hI5VTXIkKtVbIMB1KygcAxIcr8f3DPZ02HpFIhP4xAdhwIh1SsQjtQjzx+p0dUFKpMWtcYWnvpTzsvZSHL3ddxiMDWsJNLsHJtCJkF1fiyWExiPBl8woiurXZHUidP38e9913H4YMGcLsUxNgXtrHm31nkohZNklE1+6OTqFo6e9ea6ChdJXBQyFFSaUGVwvL0TrAAwBwLtOQjepso5zvej09IhZqjQ4Tu4VDJBIh0NMFK2b0wuYzmdh1IQfjOoXivi/32zw3IUOF+b+eMNt2NKUAvzzaBwGebK1ORLcuu28TL1++jDZt2mD27NkIDw/HM888g2PHjkHEb+wbBdeRajjm7c8ZSRGR4zqGKaF0ldncJxKJhHlSSSZrRWWrKgAAod6uNs+7Hi383bH0wW4Y0T7IbPuoDsF4+6449Grlh/t7RsDPXY41s/ti3vAYvDS2He4yKevzc5djaNtAAMDlnFJM/HwvNp3KwJSvD+DIlXycSivCI98dwtaELACGBhb7LuWhrEoDIqKbkd0ZqbCwMLz44ot48cUXsX37dnz77bfo168fNBoNli9fjkceeQSxsbENOVYyYXqzzzjKuSRsNkFEDSwuTIlzmcU4mlKA4dXBTVaxIZAKbKQsz6IJ8Vg0wfC4W1TN+lb/u68zcksq4esmh1gswsGkfEz5+gBS8svwfz8eBQAk55UiROmCQ8kF2Ho2Gw/1a4GMwgr8dSYT7UO88PW07g0SIBIRNaZr+rp96NCh+OGHH5CRkYFPP/0U27dvR9u2bREfH+/s8VEtTOcvs0W3c5nPkWrEgRDRTSu+unzvQnVzCQDIVlUCsL32VGPz91AIXyz1bOmLRwe2MtufVlCOQ8kFwvNle5Lx15lMAIbSwBEf7sTclUfxx8kM/Ho4FeVV2hs3eCKiBmJ3IFVWZt2mValU4v/+7/9w+PBhHD16FIMHD3boxXft2oU77rgDoaGhEIlEWLdundn+6dOnQyQSmf3cdtttZsfk5+dj8uTJ8PLygre3N2bMmIGSkhKHxtEcmZZUsmufc5k18mBpHxE1gPDq7Ex6YYWwLbvYEEg1VkbKEUOqS/wsDYwNwOvjOgjPR7QPQttgT5RWabHxZAbmrDyKBatP4tEVh5FRVI4KNQMqImq+7C7t8/f3x9ChQzFu3DiMGzcOwcHBZvs7d+6Mjz/+2KEXLy0tRadOnfDwww9jwoQJNo+57bbbsGzZMuG5QmH+D8zkyZORkZGBLVu2QK1W46GHHsKjjz6KlStXOjSW5kbCZhMNxnRelI3OxURE1y3E25B1Si8ytECvUGuFhXIDPZteRspSp3AlAj0VyC6uxLzhMcguroSHQorHBraCn4cCkX5uSMopxYN9oqDXA38nZGLuymPC+f9ezEWfRdsBAIPbBODbaT1w6moRTqcX4WpBOab2aYFgZdP/HIjo1mZ3IHXu3Dn8/vvv+PXXX/HEE0+gU6dOQlAVFxd3TS8+evRojB49us5jFAqFVdBmdPbsWfz11184dOgQunfvDgD45JNPMGbMGHzwwQcIDQ29pnE1B+bNJhpxIDchMcsmiaiBhSgNGanCMjUq1FrkVGejFFIxvFwdWuKxUUglYvzwSC+cSC3EXV3CILX41mlIm0AMaVPz/Pb4UBRXaPDuX+dQWL2eldE/53PQ6oVNZtu2nc3Gy7e3R2J2Mab0jrK6PhFRU2D330yRkZF4/PHHsXXrVmRlZWHevHk4deoUBgwYgFatWmHevHnYvn07tFrnpun/+ecfBAYGCh0D8/LyhH379u2Dt7e3EEQBwPDhwyEWi3HgwIFar1lZWQmVSmX209yY3uyztM+5zDNS/MebiJzPUyEVFlZXVaiRVd2xL9BL0Wy64cYGeeKe7hF2Bzn394zE8VdG4sSrI/Hzo71x7OURmN63hc1jz2cVY8o3B/DahgREv/gnXl53Gjqd3omjJyK6ftd0l6hUKnH//ffj559/Rk5ODr744gtotVo89NBDCAgIwI8//uiUwd122234/vvvsW3bNrz77rvYuXMnRo8eLQRrmZmZCAw0r9OWSqXw9fVFZmZmrdddtGgRlEql8BMREeGU8d5I7CzXcCRsNkFEDUwsFsFDYcg8qco1wvyooGZQ1ne9lK4y9G7lBx93ORbc1gb3dg+Hq0yCh/q1QOLbo/Fwv5ZW56zYfwX7LufZuBqQX1qFjSfThaweEdGNct31AzKZDCNGjMCIESPwySef4NixY9BonLNmxKRJk4THcXFxiI+PR+vWrfHPP/9g2LBh13zdhQsXYv78+cJzlUrV7IIpswV5ebPvVFIGqUR0A3i5yqCq0KC4Qi2sIRXo1fQbTTiTm1yK9+7uhPfu7iRsGxsfjG/3JFkdO+WbA2jh546Xb2+HoW2DoNbqUFalxfjP9iAlvwzhPq7Y+ewQlmQT0Q3jcCCl0+kgtlHupNfrkZqaii5dujhlYLa0atUK/v7+SExMxLBhwxAcHIzs7GyzYzQaDfLz82udVwUY5l1ZNq1obszmSPEfDacyz0jxsyWihuHpIgNQDlWFBllCx76bPyNVny4RPvB2k6GwTI3/3tMJnSKUGP7hLuj1QFJuKR5efhgvjW2HlQdTcNlkQeO0gnKcSCtE10ifOq5OROQ8dpf2qVQq3HvvvXB3d0dQUBBeeeUVs/lQ2dnZaNnSOh3vTGlpacjLy0NISAgAoE+fPigsLMSRI0eEY7Zv3w6dTodevXo16Fgam9mCvODNvjOZfrZS1vYRUQPxdDF8l2nISFUHUrdYRsoWsViEH2b0wnsT4zGhaxiiAz0xoUuY2TFv/XHWLIgyevuPsyir0uByTgmOXCmw2k9E5Ex2Z6RefvllnDhxAitWrEBhYSHeeustHD16FGvXroVcLgdgyEo5oqSkBImJicLzpKQkHD9+HL6+vvD19cXrr7+OiRMnIjg4GJcuXcKCBQsQHR2NUaNGAQDatWuH2267DTNnzsTSpUuhVqsxd+5cTJo06abu2AewnK8hmZX2MSNFRA3Ey0UGwDhHylDadyvMkbJHxzAlOoYphecf3NMJ0/u1wCu/n8Hx1EKzYyViER7sHYXle5Nx5EoB+r+7A/mlVQCAWYNa4/nRbW/k0InoFmJ3RmrdunX44osvcPfdd+ORRx7B4cOHkZOTgzvuuAOVlYZv0hztNHT48GF06dJFKAecP38+unTpgldeeQUSiQQnT57EuHHjEBsbixkzZqBbt274999/zcryfvzxR7Rt2xbDhg3DmDFj0L9/f3z55ZcOjaM54tydhmO+IC8/ZyJqGF4mGSljo4SAZrAYb2MQi0WID/fGq3e0R/coH7jLJbirSxh2PzcEm+cNxPOj2+Ku6qyVMYgCgKU7L+FyTkljDZuIbnJ2Z6RycnIQFRUlPPf398fWrVsxatQojBkzBl9//bXDLz548OA6s1ibN2+u9xq+vr43/eK7tjBT0nDM25/zcyaihuHlWp2RqlALi/H6uMkbc0hNXpdIH6ye3dfmvv/d1xkP92uJOz7dbbZ95YEUeLvJoNMD0/q2gLL6cyciul52B1KRkZE4e/as2TwoT09P/P333xg5ciTuuuuuBhkg2cYmCA1HImGzCSJqeMY5UqpyDVTVgVRzWIy3KYsLV2LplK44m1GMKD83zP/1BL7eXdMB8PN/LiHAU4HOEd547+54uMgkjThaImru7C7tGzlyJJYtW2a13cPDA5s3b4aLC+u6bySuE9twJOyISEQ3gLuiprSvtMrQvMnThdmS63VbxxA8NSIWY+JC4ONm+Dy9XKTw95CjXK1FSn4Z1p9Ix08HU1BUrsYvh1KQUVTeyKMmoubI7q++Xn/9daSnp9vc5+npiS1btuDo0aNOGxjVjaV9DYeLHRPRjeBanQ3JKalZSNaYpaLr5yKTYNWsPth7KQ93xBsaUN29dC8uVXf7e2NjAl7fkAAA8PeQY+3sfth4Kh2dI7wRqnTF+hPpuK1jMGKDPBvtPRBR02b339g+Pj7w8al9bQZPT08MGjTIKYOi+plmTRhTOZfUrLU8EVHDMAZSWdWtz11lEsgkLDdwpuhAT0QH1gRCW+cPQlmVFg8vP4QDSfnC9tySKgx8fwcAw5dpIgAanR7rjl/FtvmDam2mlZhdgiNX8jE6LkTowkhEtw6H/8auqKhoiHGQg0wzJQ52nad6mM6R4kdLRA3FRW4MpAz/rjIb1fBEIhHcFVL8NLM3BsT42zxGq9NDozP87X85pxRHUwqg1enx7KoTmP3DEZRVaVCh1iKtoAx3frobz605hfjX/sbqI2lIzS/Dzgs5+PVQKp765TgS0lU38u0R0Q3m0N/aBQUFuP3227Fnz56GGg9Ro5OynI+IbgBjRqq4QmN4LmfjgxtFLBbhw3s7Y8k/iZjQJdyq05+plQdScSm7FKuOpAEA/jydafO4Z1adsNr227GrmNQjAosmxEEkEkGj1UFqkXXU6vT4z6az2HkhB2+N74jerfyu450R0Y1kd0YqIyMDAwcORKdOnRpyPESNjvPPiOhGcLXoGMeyvhsrwFOBV+/ogLhwJSZ0NaxBNbhNgLD/zs6GeVVrjqZhwZqTtV5n5oCWiPB1rXX/z4dSsflMJjadykC7V/7CI98dxh8nM/DCb6eQkK7CV/9exje7k5CYXYKp3x5EdnWGskKtxe/Hr2JvYi50OtZHEDVFdmWkLl68iJEjR2LgwIFYsmRJQ4+JqFFJWTZJRDeAq9w8cGIg1XheHtse/aP9MbJDMJbtTsKpq0V4486O8PdQ4Jvq9ulyiRhVWp1wztC2gQjyUuD50e3w4tj2yC+twi+HUlFYVoW0wnLMHNAKn+1IxJaELCz55xJcpBKotXpsPZuFrWezABjWuDJVpdHhjk93468nB+K5NSfxd4LhuN6tfPHDjF5W2Swialx2BVIDBgzAgAEDbLY/p8bHBIpzmXbt03OWFBE1EMs1jGQS/mXeWHzc5ZjQNRwA8PiwGGH7/BGxQiD12KBWyCyqwKojaXhpbDs8MqCV2TV83eWYPbi12bZFE+Kw60IOTqYV1fn6w9sFYXKvSDy64jCyVJXo8uYWs/37L+fj38RceCqkqFDr0N9kfldidgmOXimAj7scA2L8IRGLcDApH50ivOGh4Lw7ooZk1/9hpaWlCAsLg5iLFzVJzJo4V23dmYiInImlfU2fu0KKH2b0wt5LuZgzJBpikQgzB7ZCTKCHXef7eygwrW8LfLnrMgAgzNsVr4/rgE2nMjC5dyTu/+oA1FodHh8ajU4R3vjywe54aPkh4fwnh8Ugs6gCvxxOxYtrTyG9yFD291C/Fnh5bHuodTpM+nIfckuqAAATuoYho7AC+y7nQekqQ/coH7QP9cL8EbH8t42oAdgVSG3ZsgVjx46Fp6cn3nzzzYYeE1HTwSCViBqIZXMJZqSapv4x/mYZIEfXlbqnW7gQSPVu5Yfh7YMwvH0QAGD93H4ordSiU4Q3AMMcrTlDWiMptxRzhkSjfYgX/jiVgV8OpwpBFAAs25OMXi39EOCpEIIoAFh79KrwuKhcjW3nsrHtXDZS88sQ4euGIC8XTO4VieJKjVW79ixVBfZdysPIDkFwkzOTRWQPu/5P6d27N3bt2oVRo0bBw8MDzz33XEOPi4iI6KamkDIjdSuICfLEmLhg7Dyfg/t7RpjtaxvsZfZcJBLh2VFtzbb1bW3epn1sfAj+OJmBxVsvILo6MzakTQCu5JXhcm6pzTGsO54uPP7638tIzivDG3d2gEarx8XsEjw/ui2mfH0AF7NL0DbYE19N7Y4IX7drfs9Etwq7/9bu0KEDdu/ejW+//bYhx0PUpEjYCp2IGohcymYTt4olk7vhyMsj0L2Fr8Pn+rrL0be1oSX6xK7heGlsO0jEIpzLLMbGkxkAgD6t/XB7p1DhnHu6GeZ7+bnL8fzotujVsuZ1k/PKAACv/H4Gb2xMwE8HU9Dp9b9xMbsEAHAusxgj/7cLO85n48iVfFSotSip1EBVob62N090E3Mod9uiRQvs3l37WgtENxuuKUVEDUVhFUjx75ubmWVzEUd89kBXHErOx+A2gZBLxXhiaAy+2X0Zaq0eQ9oGYFLPSOh0ehxOzodIBLx+ZwfMGNAS3q5yBCtdMGtQa2w+k4nHVhyp83Ue6d8Suy7m4EJWCR5adshsn6eLFJueGMBMFZEJh4tgAwIC6j+I6CbBVrNE1FCkYhFEopqGQcxIUW183OUY2SFYeP7k8Bg8MSwaOr155cTKmb2Fx5ZlgwNi/OGpkKK4UgN/DwVySyoBAHd1CUNqfhmm9W2BOzqFolKjxbhP9uB8VrHZ+cUVGny87SK83WRwkUkwd2i0VXkq0a2GswmJ6iDlN8RE1EBEIhEUUjEq1Ia1iRhIkSNEIhEc+SfKTS7Fz4/1RkK6Crd1DMYbGxIQ5OWCp0ead/RTSCX46dHeOJZSAHeFFF//exnFFRocSMrHqiNpwnHFFRq8Nq6DM98SUbPjcCCVl5eHV155BTt27EB2djZ0Op3Z/vz8fKcNjuzDjqYNR8aW/0TUgOQS00CKf5lTw+oQqkSHUCUA4P17OtV6nK+7HMPaGToL9m7lB1WFGvGv/W12zPK9yRgbH4IeLXyh0erw06FUXMougUImRt/W/hgY48+W63TTcziQevDBB5GYmIgZM2YgKCiI/5M0AVxHyvkUUjEqNToMiPWv/2AiomukkEmACg0AZqSo6fJykeH+npH4+VAK3p0Qj72XcrHueDrWHr2KHi188d7m80KLdwD4YudlPDEsBvNHxDbiqIkansOB1L///ovdu3ejU6fav8kgau4OvDAM2cWVDq8XQkTkCLlJ8MRAipqy/9zVEa/c3h6ucgmUbjKsO56OI1fyodHq8OvhVADAsLaBqNLq8O/FXHy87SKGtg1E5+o1sohuRg4HUm3btkV5eXlDjIWoyfB2k8PbTd7YwyCim5xCZhpIscKDmi6RSCQsIt010gcAcDG7BEm5pSgsU0MqFuHLqd0hEYsw7+djWHc8Hd/vTUbbCXF4bf0ZZBdXom2wJwbGBqB3K7/GfCtETuPw119LlizBiy++iJ07dyIvLw8qlcrsh4iIiOzDjBQ1RwGeCgR6KqDXA8dTCwEASleZ0EHwoX4tAQB/nMrAV7su4+dDqdh+LhtL/rmEB785gLMZKhxMyscXOy+hQq0VrrvjXDY+2HweFyw6BhI1VQ5npLy9vaFSqTB06FCz7Xq9HiKRCFqttpYzqaGwsxwRUfOkMFlbiIEUNSd+HgpkF1ciKbcUAKB0kwn74sOVCPN2xdXCcvx3ywVhu0Qsglqrx+iP/hW2FZWr0ae1HzadysRPB1MAAKuOpGL704OxJSELKfllCPZywcRu4Wat3omaAocDqcmTJ0Mmk2HlypVsNtFEeLnI6j+IiIiaHIWEpX3UPPm5G8rfjYGUt2vNvYhIJMLQtoFYsf+KsO23/+uLUG9XzPjuEE5fralgWvLPJSz555LZtbNUlZjx3SHsv1zTCbqkUoOH+rXAvst5+GxHIkQQ4YFekQjyUuDH/SnQ6vW4p1sE+sewSRTdOA4HUqdPn8axY8fQpk2bhhgPXYOW/u6NPQQiIroG5nOkmJGi5sPHIpBSupp/qWsZSLUO9ICXiwy/z+mP3ou2Iae40uqaD/drCXeFBJ9sTzQLogBg3fGrqNLq8M6f54RtuxNzzY75/Xg6Pr6/C8Z1Cr2+N0dkJ4f/1u7evTtSU1MbYizkoM8e6IpxnUIxa1Drxh4KERFdA86RoubKmJG6nFOdkbJo0NSntXlDCWP1jEQswldTu+PpEbGIDfIQ9m+Y2x+v3NEeY+JCzM5bPasPAOBkWpFZEFXbeJbsSMSXuy5hzEf/YvWRNJxKK8LRlAJodVwrhpzP4YzU448/jieffBLPPvss4uLiIJOZfwMRHx/vtMFR3cbGh2BsfEj9BxIRUZNklpGSMpCi5sOnOnCq0hoWlA7wVJjtd5FJ0Le1H/ZeykMfiy59nSO80TnCG/6eCixcewqt/N3RIdQLANA22BNyqRhVGsN1u0X5oE2QJ85XN6Do0cIHK2b0wodbLiDYywVSiQhKVxkGxQagx9tbcS6zGP/ZZAi4nll1QnjNVv7u+GZ6D1bxkFM5HEjdd999AICHH35Y2CYSidhsgoiIyEFmGSlOpKdmxNfDPAMVaBFIAcAbd3bEjweuYHKvKJvXmNQjAi383BEb5AFx9e+/SCTCglFt8NYfZzFveAxEIhH6x/gLgdSTw2LhIpPghTHtrK7Xp7U/dl3IAQDIpWKotTroqxNRl3NLMWP5Ibw4th2+3HUZgV4u+OCeeFSodVZliUT2cjiQSkpKaohxEBER3XIUUnbto+bJMnCyzEgBQHSgB169o0Ot1xCJRFYlgADwyIBWuLdHhFAOOKlHBDaeTEfXSB/0i659Daq7u4Vj14UcBHu5YOvTg5CtqoBn9TVu/+RfQzD13WHh+A0n0gEAjw+NxtMjOfefHOdwIBUVZftbBSIiInKMXMrSPmqe2gZ7mj0P9HRx6vVNOxLHBHli3/PDhKxVbcZ1CoWfuxxRfm7wUEjhEVAzB2vJ5G6Y/+txpBeWQ601ny+15J9LuLNzGKIDPSwvKVBrdTh1tQgysRgdw7zYtZoAXEMgtWjRIgQFBZmV9gHAt99+i5ycHDz33HNOGxwREdHNTGESPMnZ/pyakQgfN7jLJSitMkzpCPSyzkg5U31BlFG/aNvtz7tF+eCfZwZDq9OjSqvDwPf+QW6JoXOgVqfHA1/tx+ReUbi/ZwQCvQxB4Zn0Ijy/5hSiAz1wPLVQ6FAIAE+PiMVjg1qbfRlCtx6H//S/+OILtG3b1mp7hw4dsHTpUqcMioiI6FZgehMmFfOGjJoPsViEIGVNFirYy7kZqYYgEokglYjhJpfit//ri6+mdsfW+QMhEYuQXVyJ/229gKnfHhQaXfz37ws4dbUIvx27ahZEAcB/t1zA5K/340D1ulbrjl1Ftze34LnVJ5FTXIm3NiZgZ/V8rYyicrzz5zl8su0i9Hp2D7yZOJyRyszMREiIdae4gIAAZGRkOGVQREREtwKzOVL8ZpuamV4tfXE5pxSeLlK4Kxy+pWxUEb5uiPB1AwA8NTwGH29LRJVWh3OZxZj3yzEsHN0OB5MMa1kFeCrQPsQLr4/rgJNXi/D93mQcvlKAQ8kFuO/L/WbX/eVwKjYnZKKwTI0V+69gxYxemPn9YRSVqwEAHcOU6BftX28mq7RSgyxVBaL83CFhI5omy+Hf+oiICOzZswctW7Y0275nzx6EhnIBNCIiInuZzZFiaR81M/OGx6KsSot7u0c09lCuy9yhMZg7NAZ/nMzAnJVHselUJjadygQAeLlIsX/hMCGYaeHvjnGdQvHroVQsWHPS5vUKywxBU6VGh3u/2Ge276HlhyARizB3SDSeGhGL4go1vt93Bf+cz0ZxhQblai0KSqtQWqWFVqeH0lWGVgHuqNLoMCAmAP4ecpzNKMa93cPRq1XtjTfoxnA4kJo5cybmzZsHtVqNoUOHAgC2bduGBQsW4Omnn3b6AImIiG5WCikX5KXmK8jLBR9N6tLYw3CasfEhSMhojd+OXkV6UQUAw9wqWxmh8V3C8PnOS0jOK8VdncOQXVyJdiGe+OpfQ3drkQhC63WRCPhpZm9Mqs5eaXV6fLTtIj7adrHeMRWVq3EspRAAcCZdJWz/92IO9j4/FFIbf29UarRQlWtsdlIk53I4kHr22WeRl5eH//u//0NVVRUAwMXFBc899xwWLlzo9AESERHdrOQMpIialGdHtcWzo9ri5XWnseZoGh7q19LmcXKpGBse74+C0iqhRLC8SotTV4ug0eoxb3gspnxzAADQo4Uverfyw6MDW+HLXZetryURY3j7QGw6lQlfdzleGtsO/h4KdIrwxtErBVBrdTiYlI9le5PRJsgTCRkqZBdXYuLSfege5YOFo9uiTK2FRCSCu0KKx1YcwT/nc3Bn51DMGRKNFn7ukEvFKCpT40p+KeLClOw66CQi/TXOeispKcHZs2fh6uqKmJgYKBTNN+pVqVRQKpUoKiqCl5dXYw+HiIhuEasOp+LZ1YbyoD+e6I8OocpGHhEROYNGq0Pfd7Yju7gSnz7QBbfHG6a/FFeokaWqxEfbLmLDiXS4yiT4aFJnjOwQjOIKNcTVwZAtaq0OMokY834+hnXH04XtgZ4KZBdXQi4RY0CMP7adyzY7TyoWQS4Vo6y6w2K/aD/MHxELmUSMjqFKuzsi3krsjQ3sDqQiIyMxbtw4jBs3DkOHDoVU2rwmFdaFgRQRETWG349fxZM/HwcAbHlqIGKCPOs+gYiajcs5JTibUYwxccE2M0A6nR4VGi3c5I7dU/96OBULVtuen2Wqc4Q3ErNLUFKpqfWY2zoE49MHutgsEbyV2Rsb2P0nt2LFCqxfvx5z5sxBTk4ORo0ahXHjxmHs2LHw9vZ2xpiJiIhuKaZd+3gjQ3RzaRXggVYBtS/yKxaLHA6iANtrZd3fMwK9W/nhtfVnUFiuxvKHemJQbAD0ej2S88rw08EUpOaX4aF+LfHxtovYnZgLAPjrTCbuXroP/aP98VC/FhCJRDhwOQ+D2wTCVS6xeh0yd02lfWfOnMH69evx+++/4/jx4+jbt6+QrWrVqlVDjLNBMSNFRESNYce5bDy0/BAAYPdzQxDu49bIIyKi5uCv05k4n1mM/229AABYObMX+rb2R1G5Gtmqijqz23q9HulFFUhIV2HWD0eg1RlCgdYB7qjS6pCaX45xnULx8f03TyMRRzm9tK82GRkZ2LBhAzZs2IBt27ahVatWePfddzF27NjruewNxUCKiIgaw78Xc/DgNwcBAEdeGg4/j+Y735iIbrwvdl5CSaUG80fEXlMDiTPpRdh8Jgtf7LyEyuqFiAFALAIOvDDcrPPfloQseLlI0TnSG78fT0f7EC90DLs553U6vbSvNiEhIXj00Ufx6KOPorS0FH///XezbjxBRER0o+hMvsq8lhIfIrq1PTao9XWd3yFUaWhyo9fj4+2JwnadHvjnfDbuqV4jbE9iLmZ+fxgyiQi3x4fit2NXoZCKsXneQPh5yLH7Yi4+3ZGIsiotlj/UA1F+7tc1rubC4b+1jx49CplMhri4OADA77//jmXLlqF9+/Z47bXXcNdddzl9kERERDcjnUkk5SLjHCkiahxPjYhFuxAv+Hsq8O+FHHy8PRHPrj4Jf08FhrQJxMqDKQAAtVaP345dBWBYcPg/m84ir7QKR64UCNca+/FuvDS2He7uFn7Tz/10+N099thjuHDBUI95+fJlTJo0CW5ubli1ahUWLFjg9AESERHdrPSoCaS4rgsRNRaRSITRcSHo0cIXQ9sFCdufWHkMlRotrhaU2zzv74QsIYhq4WeY41lSqcHza0/hrT/OIq2grOEH34gcDqQuXLiAzp07AwBWrVqFgQMHYuXKlVi+fDnWrFnj7PERERHdtPpF+6NNkCcmdA1r7KEQEQEAOoUr8dxtbQEAxZUaHE4uQLaqwvyYCG/0bOErPO/Z0hf/PDsEc4bUlBou35uM/u/uwNEUQ6CVU1yJE6mFZtcxzco3Rw6X9un1euh0hsloW7duxe233w4AiIiIQG5urnNHR0REdBNTSCX4a94AZqOIqMkQiUSYPbg1knJL8OvhNGw+k4ns4kqzYwI9FRjZPggHk/MBACPbG7JYz45qi2dGtsGg9/9BSr4hG7X+eDraBnvizk93I72oAlN6R2Le8Fg88NV+ZKkqMSg2AFP7RKG7SWDWXDickerevTveeustrFixAjt37hS68yUlJSEoKKies4mIiMgUgygiaop6tfQDAPxzPgcai8yRr5sct8eHon2IF8K8XTGuc6iwTyQS4ROT1ul7EnPx2Y5EpBcZslo/7E/B2I//xYWsEhSVq7H+RDoe+f4w1FodmhuHA6nFixfj6NGjmDt3Ll588UVER0cDAFavXo2+ffs6fYBERERERHRjRVXPeTJmlloH1HTiK1Nr4SqXYMPj/bFrwRAEerqYndspwhuHXxoOALiYXYLPdlwCAAR7GY7LUhkyXH1aGYK1wjI1ErNLGvDdNAy7A6nLly8DAOLj43Hq1CkUFRXh1VdfFfa///77+O6775w/QiIiIiIiuqFCvV3NnscGeSI60AMAMK6TIQMlEYsgEdvOqvt7KNApvGadqXAfV/w4s5fZMe9MjEN89THGgK05sXuOVHx8PFq0aIFx48Zh/Pjx6Nmzp9l+FxeXWs4kIiIiIqLmJNBTAYlYBG11WV9MoAdeH9cBidkl6Bvtb9c1PpvcFbsu5KJPaz8EeirgrpCaXTPS1w0Rvm44mVaE1GYYSNmdkcrNzcWiRYuQnZ2NcePGISQkBDNnzsSGDRtQUVFR/wWIiIiIiKhZkErE6NHCR3jeOtADgV4udgdRABDu44YHekWipb873BWG/M0TQ2MAAA/2joJIJEKo0lju1/ziCbsDKRcXF9xxxx34+uuvkZGRgTVr1sDPzw/PPfcc/P39MX78eHz77bfIyclpyPESEREREdEN8NLY9nCXS+DlIkWf1n5Ouebswa2x8pFeePWO9gAAbzc5AKCoXO2U699I17TcsEgkQt++ffHOO+8gISEBx44dw4ABA7B8+XKEh4fjs88+c/Y4iYiIiIjoBuoYpsTOBUOw/ZnBVg0lrpVcKkbfaH9IJYYwxMtVBqB5BlIOryNlS0xMDJ5++mk8/fTTyMvLQ35+vjMuS0REREREjcjfQ9Gg11dWB1KFZbdAILV+/Xqb20UiEVxcXBATE4OYmJjrHhgREREREd3cvG+ljNT48eMhEomg15svzGXcJhKJ0L9/f6xbtw4+Pj61XIWIiIiIiG51xoyUqhkGUg7PkdqyZQt69OiBLVu2oKioCEVFRdiyZQt69eqFjRs3YteuXcjLy8MzzzzTEOMlIiIiIqKbhPJWykg9+eST+PLLL9G3b19h27Bhw+Di4oJHH30UZ86cweLFi/Hwww87daBERERERHRzMQZSpVVaqLU6yCTX1AuvUTg80kuXLsHLy8tqu5eXFy5fvgzA0HwiNzf3+kdHREREREQ3LWPXPqD5lfc5HEh169YNzz77rNl6UTk5OViwYAF69OgBALh48SIiIiKcN0oiIiIiIrrpSMQiuMslAICSSk0jj8YxDgdSX3/9NZKSkhAeHo7o6GhER0cjPDwcycnJ+PrrrwEAJSUleOmll+q91q5du3DHHXcgNDQUIpEI69atM9uv1+vxyiuvICQkBK6urhg+fDguXrxodkx+fj4mT54MLy8veHt7Y8aMGSgpKXH0bRERERERUSPwcDHMNiquaF6BlMNzpNq2bYuEhAT8/fffuHDhAgCgTZs2GDFiBMRiQ1w2fvx4u65VWlqKTp064eGHH8aECROs9r/33nv4+OOP8d1336Fly5Z4+eWXMWrUKCQkJMDFxbAo2OTJk5GRkYEtW7ZArVbjoYcewqOPPoqVK1c6+taIiIiIiOgG81BIkYVKqCqaV2mfSG/Zx7wOarUarq6uOH78ODp27OjcgYhE+O2334QgTK/XIzQ0FE8//bTQAbCoqAhBQUFYvnw5Jk2ahLNnz6J9+/Y4dOgQunfvDgD466+/MGbMGKSlpSE0NNSu11apVFAqlSgqKrI5/4uIiIiIiBrGHZ/sxqmrRVg2vQeGtA1s7OHYHRs4VNonk8kQGRkJrVZ73QOsT1JSEjIzMzF8+HBhm1KpRK9evbBv3z4AwL59++Dt7S0EUQAwfPhwiMViHDhwoNZrV1ZWQqVSmf0QEREREdGNJ5OIAABVWl0jj8QxDs+RevHFF/HCCy8gPz+/IcYjyMzMBAAEBQWZbQ8KChL2ZWZmIjDQPGqVSqXw9fUVjrFl0aJFUCqVwg8bYxARERERNQ651BCSVGmaVyDl8BypTz/9FImJiQgNDUVUVBTc3d3N9h89etRpg2soCxcuxPz584XnKpWKwRQRERERUSMwrh2lbmYZKYcDKXsbSVyv4OBgAEBWVhZCQkKE7VlZWejcubNwTHZ2ttl5Go0G+fn5wvm2KBQKKBQK5w+aiIiIiIgcorhVMlKvvvpqQ4zDSsuWLREcHIxt27YJgZNKpcKBAwcwe/ZsAECfPn1QWFiII0eOoFu3bgCA7du3Q6fToVevXjdknEREREREdO1umYwUABQWFmL16tW4dOkSnn32Wfj6+uLo0aMICgpCWFiY3dcpKSlBYmKi8DwpKQnHjx+Hr68vIiMjMW/ePLz11luIiYkR2p+HhoYKWbF27drhtttuw8yZM7F06VKo1WrMnTsXkyZNsrtjHxERERERNR5hjpTW7mbiTYLDgdTJkycxfPhwKJVKJCcnY+bMmfD19cXatWuRkpKC77//3u5rHT58GEOGDBGeG+ctTZs2DcuXL8eCBQtQWlqKRx99FIWFhejfvz/++usvYQ0pAPjxxx8xd+5cDBs2DGKxGBMnTsTHH3/s6NsiIiIiIqJGYMxINbfSPofWkQIM7cW7du2K9957D56enjhx4gRatWqFvXv34oEHHkBycnIDDbXhcB0pIiIiIqLG8euhVBy+ko/RHUOa1TpSDmekDh06hC+++MJqe1hYWJ0tx4mIiIiIiCzd2yMC9/Zofh20HV5HSqFQ2FzA9sKFCwgICHDKoIiIiIiIiJoyhwOpcePG4Y033oBarQYAiEQipKSk4LnnnsPEiROdPkAiIiIiIqKmxuFA6r///S9KSkoQGBiI8vJyDBo0CNHR0fD09MTbb7/dEGMkIiIiIiJqUhyeI6VUKrFlyxbs2bMHJ06cQElJCbp27Yrhw4c3xPiIiIiIiIiaHIe79t2M2LWPiIiIiIgA+2MDu0r7Pv74Y1RUVNj94kuXLkVxcbHdxxMRERERETUndmWkJBIJMjMz7e7K5+XlhePHj6NVq1bXPcAbgRkpIiIiIiICnLyOlF6vx7BhwyCV2jelqry83L5REhERERERNUN2RUavvvqqQxe988474evre00DIiIiIiIiaurYbAIs7SMiIiIiIgOnNpsgIiIiIiKiGgykiIiIiIiIHMRAioiIiIiIyEEMpIiIiIiIiBzkcCBV18K8GRkZ1zUYIiIiIiKi5sDhQKpr1644fvy41fY1a9YgPj7eGWMiIiIiIiJq0hwOpAYPHozevXvj3XffBQCUlpZi+vTpePDBB/HCCy84fYBERERERERNjV0L8ppasmQJxo4di0ceeQQbN25ERkYGPDw8cPDgQXTs2LEhxkhERERERNSkOBxIAcDo0aMxYcIEfP7555BKpdiwYQODKCIiIiIiumU4XNp36dIl9OnTBxs3bsTmzZuxYMECjBs3DgsWLIBarW6IMRIRERERETUpDgdSnTt3RsuWLXHixAmMGDECb731Fnbs2IG1a9eiZ8+eDTFGIiIiIiKiJsXhQGrJkiX4+eef4e3tLWzr27cvjh07hq5duzpzbERERERERE2SSK/X6xt7EI1NpVJBqVSiqKgIXl5ejT0cIiIiIiJqJPbGBg43m/j+++9r3ScSifDggw86ekkiIiIiIqJmxeGMlI+Pj9lztVqNsrIyyOVyuLm5IT8/36kDvBGYkSIiIiIiIsD+2MDhOVIFBQVmPyUlJTh//jz69++Pn3766boGTURERERE1Bw4HEjZEhMTg3feeQdPPvmkMy5HRERERETUpDklkAIAqVSK9PR0Z12OiIiIiIioyXK42cT69evNnuv1emRkZODTTz9Fv379nDYwIiIiIiKipsrhQGr8+PFmz0UiEQICAjB06FD897//dda4iIiIiIiImiyHAymdTtcQ4yAiIiIiImo2nDZHioiIiIiI6FZhV0Zq/vz5dl/www8/vObBEBERERERNQd2BVLHjh2z62Iikei6BkNERERERNQc2BVI7dixo6HHQURERERE1GzYPUfq8uXL0Ov1DTkWIiIiIiKiZsHuQComJgY5OTnC8/vuuw9ZWVkNMigiIiIiIqKmzO5AyjIbtWnTJpSWljp9QERERERERE0d258TERERERE5yO5ASiQSWXXlY5c+IiIiIiK6FdnVtQ8wlPZNnz4dCoUCAFBRUYFZs2bB3d3d7Li1a9c6d4RERERERERNjN2B1LRp08yeT5kyxemDISIiIiIiag7sDqSWLVvWkOMgIiIiIiJqNthsgoiIiIiIyEEMpIiIiIiIiBzEQIqIiIiIiMhBDKSIiIiIiIgcxECKiIiIiIjIQQykiIiIiIiIHMRAioiIiIiIyEEMpIiIiIiIiBzEQIqIiIiIiMhBDKSIiIiIiIgcxECKiIiIiIjIQQykiIiIiIiIHMRAioiIiIiIyEEMpIiIiIiIiBzUpAOp1157DSKRyOynbdu2wv6KigrMmTMHfn5+8PDwwMSJE5GVldWIIyYiIiIioltBkw6kAKBDhw7IyMgQfnbv3i3se+qpp7BhwwasWrUKO3fuRHp6OiZMmNCIoyUiIiIioluBtLEHUB+pVIrg4GCr7UVFRfjmm2+wcuVKDB06FACwbNkytGvXDvv370fv3r1v9FCJiIiIiOgW0eQzUhcvXkRoaChatWqFyZMnIyUlBQBw5MgRqNVqDB8+XDi2bdu2iIyMxL59++q8ZmVlJVQqldkPERERERGRvZp0INWrVy8sX74cf/31Fz7//HMkJSVhwIABKC4uRmZmJuRyOby9vc3OCQoKQmZmZp3XXbRoEZRKpfATERHRgO+CiIiIiIhuNk26tG/06NHC4/j4ePTq1QtRUVH49ddf4erqes3XXbhwIebPny88V6lUDKaIiIiIiMhuTTojZcnb2xuxsbFITExEcHAwqqqqUFhYaHZMVlaWzTlVphQKBby8vMx+iIiIiIiI7NWsAqmSkhJcunQJISEh6NatG2QyGbZt2ybsP3/+PFJSUtCnT59GHCUREREREd3smnRp3zPPPIM77rgDUVFRSE9Px6uvvgqJRIL7778fSqUSM2bMwPz58+Hr6wsvLy88/vjj6NOnDzv2ERERERFRg2rSgVRaWhruv/9+5OXlISAgAP3798f+/fsREBAAAPjf//4HsViMiRMnorKyEqNGjcKSJUsaedRERERERHSzE+n1en1jD6KxqVQqKJVKFBUVcb4UEREREdEtzN7YoFnNkSIiIiIiImoKGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBVFOj1wMnfwUu/9Owr6HXGx5rKq33p+wHfp8D5F2q2ZZ9DvhlimF71pmGGxsRERERUTMgbewBkIXL/wBrZxoej3gT6PeE/eeW5gH5l4DA9sCxFUDMSMC3FaC6CrgHAn8uAM5uAHRqoKLI/Ny2twN3fAS4+1cHUYnAsR8ArzBA7gHknq859vRvwG2LgM4PAGmHgcStQFAHwzVW3mN4D34xQPx9QI8ZgJuv4byyfGDzC4DUBQhoC+ReAOLuASJ7A+UFNcdVlgAKj2v9BImIiIiIGpxIrzemJm5dKpUKSqUSRUVF8PLyatzBrHoIOLO25nmbMcDYDwGvEKA4Czi9Guh4N+AZZH6eTgd8ORDIPGW+XRkJFKXY99piKRDYzvoapkQSQK81PA6KA7JMju3yoCGAMyVzByJ6AkVpQN5F+8YBAD0fA0QiIP24IRisVAEu3kDv2YBXKLDrA+DsekPwNf5zoCAZOPYj4BkMdLgLiOoLiMRAdgIgdwd8WgC5iYYgMrCd/eMgIiIioluKvbEBAyk0oUBKpwX+EwpoKgAXZU3WKLwH8MhW4IeJhuxPzChg8q/m5+5bAmxeaP9rjfoPsP1tQF1a/7EiMdBlCjDkRUNQcvhb4J937TvXFr8Yx4KqayGRAx7BNUFkZF8g7SCg0wDRw4HWwwxZNt9W+P/27jwsynL/H/h7ZmCGYUd2FFBcEBXBpQwtjdSD5imzMlP0uGSdykqzU55+llYel1Md67QcMzOt3Mrz7ZiZaKZibgGa4IY77iAqsso6c//++DADo7iMigi9X9c1lzPPej8zT9O8+dzP/SDmRUDLXq5ERERExCBllzsmSOWdBD5oK5Whx78EvvtL1by4adItzmLCUcDoJc8vHAU+7iQhIXKgBK+E16qWfXodkLYEUGa5JqpVHBDxkAS3kjzpYleQKeGorAgI6w6ExAA/vQJ0HgX0fgfQOdq2NTMN+Lq/dMmLGixdBssKAWdv4KUdEgTXTwc2zAB8woGY5yW8uAUCOgegMBtw8ZX1c44AB38Gfv9G5uVeUkGLHgq4eAO/fy3LAxKSYp4H1kyqWu7uZ4Dyi9KWS7suXk1gFBD7BtA89vLjrM5UDmz+EDizF3D1kypXQHug8Ayw539Ay95A0mx5rXWQQOwWBLR4AHD2Abq+CGh1Uj28nuBWnAvkHpP2EREREdFtwSBlhzsmSJ3cDnwZB3g0BsamAWcPyDVHF45evuzIBOm+BkgAWf6CPH/9lFxftHgwsH8l4BEMvLz7xtqjlHSvu5KSPAk9/u3k32ObgWbdAY8mVevnHAE8QyUgXa+cDOD7Z4CwHlIFs7ShvBjY+wNQkAW0exTwDAH2rwJO7wA6jZDuj4AMkjH/zxLmHp8LXDwP7F0OeAbLOrv+C5QWAMU5tt0Ym9wFDF8BODrV3K7q7/ONMLhL0Mw9BvScDDTuBHi3kP3tWwnsWwFEPg7sXAoUZcvnfvG8HFvH4YBPS+DAagmU0UOki+f5g8D2r4DQGKBN/xtvm72udW7c6LJEREREdYxByg53TJACpEpUnCsVGADY8jHw8xuXL9dtHND7bXm+6v8Bv30K3PO8DAIBSCjb/AHQYwIQEHk7Wn5nMVVcO7yZzUDaIukumf6jVPSihwIPf2xbMcr4FUj+XJa5HnHTbbtZOnkCJbn2HoH9+vxTKnJN7gKa3SeDduz5XqqLPi2vvF5htoQxzxCg/RMS1nb/VypuIfdIYG7ziFTTAGDzv4HEGVKBa/8ksHcZYGwEdB4J+LSqCk1KSYVu7TsSEB/6t7zHygw4GK59PAVnZPATy36JiIiIbgMGKTvcUUHqUhWlUoHwbS3X86TMATZ9IPOeWiMDOcz9E3AiSQJAx79cfXtUswOrgUVPyPO46dJtEAAKzwIfRUu3RYsxKRIyMjYAiwYBrv7AqISq0QqbdgN2fy8BpUvloBn/NxrYtVSqSxodsG2u7f4dnSUEVRfaTULR4XVA1k77jse9iVS1TGWy7ZEJwLopQEm+jOJYWiCjJ2odgILT1VbUAKjhKyFuuoSqBY9Wda+sSfiD8m/ucekuevF8zcu5+ks31J6TJFRdzJFumqYyGSwk/zTw03ipqPZ+W8KokydQmAU06yHbLcyWqqKDk1RCy4slGOsMUrHLTAUuHJMg5hYoIc/oeeW2m8rlekAGNyIioj80Bik73NFB6lJn9wOf3i3POw6XqsC3Q+X1C9uuXnmgq0uaLdeWOXkCL++RLpKWiqBrgFRV/CJk4A2Lcwelu55l6PYrqSgDTqZIdUirlXtx5Z8Gjm0Bis5KVz9TGTAnVoLPsxsBg1vV+hm/Aitfk+Hkw/tKRci/rQSEsB7AL2/JMjlHat6/1lFGLLxegdES4s+my2tjI3k/LNevNWouXQ+VSZ7nHK55Ozq9XAuXf+rK+3LylGvz7GnfZdvwAMpLAFMN90Wz0DpI4IIGOLtPPoteb0nbD6wGUubKe97tJSDvlIQ2n1ZyfRsg77tHMJCRCGRslGpZ5EDgzG4ZnTIwSrppnk6VbrUGN/l8nDwB7+ZS8dPqJPAVnZVrHKt/xkRERHRHYJCyQ70KUgBw4Ge5dqpRc/kxmLpAhh7v/0ldt6x+M5uATzpLGHn0CwlOs7rKEOp//kAG3qht5cUSPm60KqIUkJ0uwUSZ5BqwlX+reVmtg3S1848EBn0NzHlAqk3hDwKPzQX0zrKdf0UApdUG7+gzA7hrtAQpjVZCQkWZBM7k2bKMfyTQ9hE5L938gZQvZPASZ2/g8XnSrl/fs+3yqNNL6CjKvv7j9Wpqew2hg5MM8lF9m87eMu1qlbTrden2L1X99gCX0hlk1MvSgqrQ2HaAVOcO/ixdKPUusg+zSSqUWkepopnK5Y8kbR+RkHt6h1TxoIB2j0mosyjJA/RuHImSiIjoBjFI2aHeBami88B7YfI8IFJ+lD42V374082xXG/W+SmgVR8JrDoD8LcDV+8WdqcqOg+81xw23fUcnWXUR51eQqJ3C+leV3AGcNBXjQZpsfM7uUkzNMDw5dLFryYlecCKl6Vq1/3Vy+eXFkqAsGy/IEsG2EhbAgR1kOqQ3kUqddl7geB7pEpXXizrGdwlUCTNAsLulwcgo10WZAGORjmW/SuBEylA+4GyXYvsfcDB1RL6TKXAlk+AimKZp3UA2j4qVT2Dm4ShZj2AvBMyvzgXOLVNnjt5SNjMTJN2Gjzk/S3Nr9pXq74Shi+el3VzDsuxWF2hC+WNcHACnlwkg7x8N1yqiM4+Eq7yTgFO7vLZGNykOujkIQO6lF+UczqgvQTS0gLphuoRLN8rTp4StPf+IO03m6TNboEyUIqDkxxzaYF0O/YMkW26BkjoyzspodO7hf2DjZjKZbvXqvQSERHVAgYpO9S7IAUA74dL1yOL6qP40Y3b8z9g6QggqKP8UDy+BYh5AYibWtctu3GrJwK7/0/CoUdj+eEc0M6+bRSdl9BhGZGxITh3CLiQIYHMVCYh7kqUAvb9JIGuVR8JbYBUhfSuMoDGoTVynVyL3kB4H9v1TRXA6d+rbiwd1FG6CG76QAJidLyEmZJc2ZZGJ90EzSYJY8oMHN0k93ArvyjVaM9gGaEy74TcYsDgVhX26ppGK20GAAejBLagDhKy804Bvq3k+r+A9sDx3+S4Ix6W0HxkPZA4HTh/SCruTy6S7Ri9ag5kF47KNoPvrqzUnZfP1PIZERER2YlByg71MkjN6Wn7o2nsTsArtO7a01CcOwR80kmeW34MvrRD/uJOVNdKCyUoeIZIqCjJA/7ZtCq0aB2B5zZLCHE0SrfBihK5hquiWAZPuXhOqk5GT+DU7xJECs8A7kGynex9crPqilIJbSExQIueUpk1V0hYyT0uXSWdvaXylXNEKolah8qulqqq6+j1MnpJV1Kbyl01WkcJj35tpFLl7CMjaVpu7q13rRoURu8mVbELGUDjzvJeHdsi70dApFTmzu0HfCPkuE8kSyVPo5V2+7eTZTQaOb7yEunqqpS8LjonFcfQblI5yzlcNVKo3lW6X/q1lupn3gk5NlbXiIjqjevNBnbc3IfuKB5NqgUpjfwYoJvXqFnVCHrKLIMseDWr61YRCYOrPCycPICm90qXRECuofINl8f1uJFbI3QafvX55w9LV8ugDhK2Tv8uo0Wu+4cEwLD75Z5zRzfarme5hs0jRCpWHsFA6qKqAUTM5RLYLh1QxXJdmiVEOboAZQWyX0AqhRY5hbYDo1jet0ud3Xf1Y7wmjXwWJ5KrBloxekmXRb82ErIiHpbBgi5VUSb3mss9BhzbKkFYo5VBTfQuEiA9mkh3XHOFvG95J6XLZUB7Wd7VT/blYADcAiQIejeXEFhaIOsY3KQ6ynu8ERHdMAap+qp6FytXf0DnWHdtaUi0OvmhYwmpPi35Q4PubA++DywcKBWU3u/UdWvkB7t3c3mud5bupADQId52uRMpMiCGfztgerAEJkcX4MXtcq0eAPxpivzwdzQCZ/ZKxercAamgnT8kIyXe85x0gTy6CWjcEXALAk4mS9XM0Si3D3DxBVr+SdbJTpdKVZO7gRO/SZWueax0NXR0liH1j2+Va9t0etmXVif7qCiVoKLMsv2SPHnu6AwERUuoyz8p163u+Z8cg6WybQmKJ5Pl8fvXQOQTwIPvVV1/uf0rIGFC1bV7t5qzj1QkLXQGGQxG7ybdK80mOeaC0/IHJK2u8trEUllGo5H3QKuT/+d4t5BAdjGn6jq73OPyfjh7yfaKzsmyDgbZn7O3BMDq94hz8QVc/KQaWVZUdW1d8YWqbrfGRrJO9S6eSsmj+IJsX+/C72siuq3YtQ/1tGvf5o+ANW/K88adgKfX1W17GpIfxwLb58vzqCHAgFl12hyiBu9IolSs7v6rDBJSnyklYShljnx/PPiuBK6CTAkYWbulGrfjGwlYYbHAsP8BqQsrB3WBBEq9s9w/0L+dVMgDowAoGRQm/5SEEY1WqpLKLF0uzRUSOIpzJLQUX6gMGU5A3vGqNur0V+5CeafTOkp4c3CS0FV9RFGvpnK9oKOTfA6mMqlshnat7GpaLl0wlVmW9WpaFbyMjSTwleTJ56TMEjqr/6HS8nOpelgrPCsjjVrapHWQ5SwjklqqhxamyhE7tQ6yD61O2pR3QvbtFijBUquVm8ZfPCfbM3pV/YGBiGodu/Y1dK7+Vc/dG9ddOxoi/2oDMXjz2iiiWld9FMb6TqOR8NRnRtUQ9HqXqu7XfhESFqOeBL4ZIINrbHi36kbr94yRwW1udWWl8KxU8/wi5HqtilIJd4XZUkly8pRKmNkk/0/J3iPVqNJ8CWXlRfKD3sEgAaDsolxXVl4sVaacjMqKfoQEmKLzcvwuvtWqecWyPweDVKoA2VZhttxbzcFJAqRGJ/fa0zlKFUqZgYsXJDSZyyUo1uTCUdvbIVikzLm+90jvJt1CdYbK6ltlt0rPUAlX5w/JdJ1eQpxGex33v9NIOPIMlmscz6ZXBqjKe/s5GCUAV9+OTi/V1NIC20FbfFpKt1llltE4PUPkPTu7v7Irp16CW/EFqQy6+Mn7rnOQY4KSz8P6ueplWkWZrFtRJus6OsnxOhorH84S/Jw8pMuu3rXyekA3GeRFo5F25RypbHvlum4BHPSFGjwGqfrKjUGq1vi3rXreqHndtYOI6q9r3ccrtCvQYwKw9m0gcZpMC+laOyEKAFx95WHhYKiqytTEp8Wtb4M9zCYJKtXfi4pS6WpZdE5ChqufVJL0zhIkTqZUzbeEssw0uUm2zrHyHn0OAJSEj4LKkW81mspwWCCvLdflWbplXsiwbZvlXnLKBEAjwbS0wLbKp6+8jUL5RekqWXD6kuOrDE6Wbpw6g3TxLMyW7Vi3VXmrhIpiIGtn1foXz9V8A/bq00ryLp9/09f/XaLdY1J5Xfzk5aFS6wg06Sx/RDi1XYKkpYrq6CQB1VxROWJpXmWoz5eQ6hki3TtzMuT91jnKco7O8rlVFMt7VlFSeY1kkbx28akW5s/JHzH0ldeW6t0q/3Wt+rfonGyrUXMJocpse6mEstzaQiMBvqKs8lxylNDZKEz2YSqXNgASVJVJ2uDgVFl1LJfuwkZPWVcp+/47V6qqgkl3FAap+sqt2uASDWlI6juBf9uqri9B0XXdGiJqqO4aLdUoy4/pqEG8xseiph+MDgb5ge0ZUvM6reJubF9mc2UAy5Yf75aqT6PmUinL2gVAyY9ms0m67Xk0kRDg7COBzbKdihL5/4eusovfxfOVg4cclx/6gVFSpSkrqvpXo5WwYfnBXZAplT6De2U3P50EoPOHZL86fVXVqaJEeqi4Bco6ucclJDQKA/IzpcshlIRQc4Ucj+UebaZyOR5HJwkIWp0EEcuIneXFVQ9TmXQpPbOn6lq2cwfk1hq7/0+O3+glYaX8YuW1bsVyveGlLDddz9p15c8ke++NfZa3goOThD2DuxyLZcCYmmi0slz1m8vr3SSMm8qkm67RUz5TSyXSyUNCmVsQ4B4on0lxruzLyUOqvcoky5srb/BuDe3elZ9luXT9dXKvDG6V57BOX3ndYI58vo7OUm3UGeR8M5VX3pfRrdrDXf7V6uS8Kb4g53JAO3kvfMMlLBadl+s7/dvKOXThmJxHvq0lkBadlePWaOV8dPWvPP/QoL/XeI0U6uk1UqYKYGaEfCGNXit/9aFb5+gm+bfpvXXbDiJq2L4dBqQvl+fjdl05JBDdaVK+AH56RZ67NwFeSK66H59SUsnbt1Ku/2r+QFXF52KOBI/CykB17qBUF92D5Ie3o1Gm5WTIwDUGdwll5w5JOHELkOXKL0qXR51DZRfJcrkNQ2aaBBIXXwmBZYVSDSsrqPzX8roIMHrIujlHrt1N08FJHhUllYOuOFxH184GwNUfuO8V+aNP9cFqrodGJ59VULSE9dJ8CfQGd6kcXsiQsGhwBwLbA/eOl2B/B+B9pOxQL4MUIH3Bi84xRBER1VeH18m1UiExwKhVdd0aIvuc3S/3aGvRS64Dq68qyqTKotFI0CrJk4fZJCFAZ5CK6KWVlfxMCYWWa8/MFRLkjJ7yR5GcI7JtjyZS7cs9JreDcPGRG4kXZEpQcW4kAbLonIRArYNUiDQ6VN2Xz1Q1QiUAnNwmYbLwjFQDPSpHP9XoKrs0ush8U3lVlVHrIF0LSwvleEsLJNyU5ElIPX9Yql7KLNfz5Z2qrIbVIDBKBr8pzJJKmEewtM1SVbXcksIedz0N9Hvf/vVqAYOUHeptkCIiovove5/cUJ0X5hPRneRiDvDjSxIOfSPkGs7fv5I//LTuJ8uUFlSGN2fbdUsLpYth3ikJlHoX6XaqdZTus2fTpfuswVVuS7HhnwA0lb2sOt32Q70Ug5QdGKSIiIiIiOrI938Fdi4BOv4FePjjum4Nhz8nIiIiIqJ6IG4qEBoDdPhLXbfELgxSRERERERUd1x8gE4j6roVdrvGjS6IiIiIiIjoUgxSREREREREdmKQIiIiIiIishODFBERERERkZ0YpIiIiIiIiOzUYILUp59+iqZNm8LJyQldunRBcnJyXTeJiIiIiIgaqAYRpL799luMHz8ekydPxu+//46oqCjExcUhOzu7rptGREREREQNUIMIUjNnzsTTTz+NkSNHok2bNvjss8/g7OyML7/8sq6bRkREREREDVC9D1JlZWXYvn07evXqZZ2m1WrRq1cvbN26tcZ1SktLkZ+fb/MgIiIiIiK6XvU+SJ07dw4mkwn+/v420/39/ZGVlVXjOtOnT4eHh4f1ERwcfDuaSkREREREDUS9D1I34vXXX0deXp71ceLEibpuEhERERER1SMOdd2Am+Xj4wOdToczZ87YTD9z5gwCAgJqXMdgMMBgMNyO5hERERERUQNU7ytSer0enTp1wtq1a63TzGYz1q5di5iYmDpsGRERERERNVT1viIFAOPHj8fw4cPRuXNn3H333fjwww9RVFSEkSNH1nXTiIiIiIioAWoQQWrQoEE4e/YsJk2ahKysLERHR2PVqlWXDUBxJUopAODofUREREREf3CWTGDJCFeiUdda4g/g5MmTHLmPiIiIiIisTpw4gSZNmlxxPoMU5Jqq06dPw83NDRqNpq6bU+/k5+cjODgYJ06cgLu7e103hxoYnl9UW3huUW3i+UW1hedW7VNKoaCgAEFBQdBqrzykRIPo2neztFrtVdMmXR93d3f+B021hucX1RaeW1SbeH5RbeG5Vbs8PDyuuUy9H7WPiIiIiIjodmOQIiIiIiIishODFN00g8GAyZMn8ybHVCt4flFt4blFtYnnF9UWnlt3Dg42QUREREREZCdWpIiIiIiIiOzEIEVERERERGQnBikiIiIiIiI7MUgRERERERHZiUGKAABvvfUWNBqNzaN169bW+SUlJRgzZgy8vb3h6uqKxx57DGfOnLHZxvHjx9GvXz84OzvDz88Pr776KioqKmyWSUxMRMeOHWEwGNCiRQvMnz//dhwe1bFTp05h6NCh8Pb2htFoRGRkJLZt22adr5TCpEmTEBgYCKPRiF69euHgwYM228jJyUF8fDzc3d3h6emJp556CoWFhTbL7Ny5E/fddx+cnJwQHByMd99997YcH9Wdpk2bXvbdpdFoMGbMGAD87qIbZzKZ8Oabb6JZs2YwGo1o3rw5pkyZgupjdPG7i25GQUEBxo0bh9DQUBiNRnTt2hUpKSnW+Ty/6gFFpJSaPHmyatu2rcrMzLQ+zp49a53/7LPPquDgYLV27Vq1bds2dc8996iuXbta51dUVKh27dqpXr16qR07dqiVK1cqHx8f9frrr1uXOXLkiHJ2dlbjx49Xe/fuVR9//LHS6XRq1apVt/VY6fbKyclRoaGhasSIESopKUkdOXJErV69Wh06dMi6zIwZM5SHh4datmyZSktLUw8//LBq1qyZKi4uti7Tp08fFRUVpX777Te1ceNG1aJFCzV48GDr/Ly8POXv76/i4+PV7t271eLFi5XRaFSzZ8++rcdLt1d2drbN99aaNWsUALV+/XqlFL+76MZNnTpVeXt7qxUrVqiMjAy1dOlS5erqqv79739bl+F3F92MJ554QrVp00Zt2LBBHTx4UE2ePFm5u7urkydPKqV4ftUHDFKklJIgFRUVVeO83Nxc5ejoqJYuXWqdlp6ergCorVu3KqWUWrlypdJqtSorK8u6zKxZs5S7u7sqLS1VSin12muvqbZt29pse9CgQSouLu4WHw3dSSZMmKDuvffeK843m80qICBAvffee9Zpubm5ymAwqMWLFyullNq7d68CoFJSUqzLJCQkKI1Go06dOqWUUuo///mP8vLysp5vln2Hh4ff6kOiO9jYsWNV8+bNldls5ncX3ZR+/fqpUaNG2Ux79NFHVXx8vFKK3110cy5evKh0Op1asWKFzfSOHTuqiRMn8vyqJ9i1j6wOHjyIoKAghIWFIT4+HsePHwcAbN++HeXl5ejVq5d12datWyMkJARbt24FAGzduhWRkZHw9/e3LhMXF4f8/Hzs2bPHukz1bViWsWyDGqbly5ejc+fOGDhwIPz8/NChQwfMmTPHOj8jIwNZWVk254aHhwe6dOlic355enqic+fO1mV69eoFrVaLpKQk6zLdu3eHXq+3LhMXF4f9+/fjwoULtX2YdAcoKyvDggULMGrUKGg0Gn530U3p2rUr1q5diwMHDgAA0tLSsGnTJvTt2xcAv7vo5lRUVMBkMsHJyclmutFoxKZNm3h+1RMMUgQA6NKlC+bPn49Vq1Zh1qxZyMjIwH333YeCggJkZWVBr9fD09PTZh1/f39kZWUBALKysmx+iFjmW+ZdbZn8/HwUFxfX0pFRXTty5AhmzZqFli1bYvXq1Xjuuefw0ksv4auvvgJQdX7UdG5UP3f8/Pxs5js4OKBRo0Z2nYPUsC1btgy5ubkYMWIEAPC7i27K3//+dzz55JNo3bo1HB0d0aFDB4wbNw7x8fEA+N1FN8fNzQ0xMTGYMmUKTp8+DZPJhAULFmDr1q3IzMzk+VVPONR1A+jOYPkLGwC0b98eXbp0QWhoKL777jsYjcY6bBnVd2azGZ07d8a0adMAAB06dMDu3bvx2WefYfjw4XXcOmpI5s6di759+yIoKKium0INwHfffYeFCxdi0aJFaNu2LVJTUzFu3DgEBQXxu4tuiW+++QajRo1C48aNodPp0LFjRwwePBjbt2+v66bRdWJFimrk6emJVq1a4dChQwgICEBZWRlyc3Ntljlz5gwCAgIAAAEBAZeNhGV5fa1l3N3dGdYasMDAQLRp08ZmWkREhLXrqOX8qOncqH7uZGdn28yvqKhATk6OXecgNVzHjh3DL7/8gtGjR1un8buLbsarr75qrUpFRkZi2LBhePnllzF9+nQA/O6im9e8eXNs2LABhYWFOHHiBJKTk1FeXo6wsDCeX/UEgxTVqLCwEIcPH0ZgYCA6deoER0dHrF271jp///79OH78OGJiYgAAMTEx2LVrl81/0GvWrIG7u7v1R3RMTIzNNizLWLZBDVO3bt2wf/9+m2kHDhxAaGgoAKBZs2YICAiwOTfy8/ORlJRkc37l5uba/JVu3bp1MJvN6NKli3WZX3/9FeXl5dZl1qxZg/DwcHh5edXa8dGdYd68efDz80O/fv2s0/jdRTfj4sWL0GptfybpdDqYzWYA/O6iW8fFxQWBgYG4cOECVq9ejf79+/P8qi/qerQLujO88sorKjExUWVkZKjNmzerXr16KR8fH5Wdna2UkiGEQ0JC1Lp169S2bdtUTEyMiomJsa5vGUL4T3/6k0pNTVWrVq1Svr6+NQ4h/Oqrr6r09HT16aefcgjhP4Dk5GTl4OCgpk6dqg4ePKgWLlyonJ2d1YIFC6zLzJgxQ3l6eqoffvhB7dy5U/Xv37/GIV47dOigkpKS1KZNm1TLli1thnjNzc1V/v7+atiwYWr37t1qyZIlytnZmUO8/gGYTCYVEhKiJkyYcNk8fnfRjRo+fLhq3Lixdfjz77//Xvn4+KjXXnvNugy/u+hmrFq1SiUkJKgjR46on3/+WUVFRakuXbqosrIypRTPr/qAQYqUUjKUb2BgoNLr9apx48Zq0KBBNvf5KS4uVs8//7zy8vJSzs7OasCAASozM9NmG0ePHlV9+/ZVRqNR+fj4qFdeeUWVl5fbLLN+/XoVHR2t9Hq9CgsLU/Pmzbsdh0d17Mcff1Tt2rVTBoNBtW7dWn3++ec2881ms3rzzTeVv7+/MhgMqmfPnmr//v02y5w/f14NHjxYubq6Knd3dzVy5EhVUFBgs0xaWpq69957lcFgUI0bN1YzZsyo9WOjurd69WoF4LJzRil+d9GNy8/PV2PHjlUhISHKyclJhYWFqYkTJ9oMI83vLroZ3377rQoLC1N6vV4FBASoMWPGqNzcXOt8nl93Po1S1W7RTURERERERNfEa6SIiIiIiIjsxCBFRERERERkJwYpIiIiIiIiOzFIERERERER2YlBioiIiIiIyE4MUkRERERERHZikCIiIiIiIrITgxQREREREZGdGKSIiKhWvfXWW4iOjq7rZlhpNBosW7bMrnWaNm0KjUYDjUaD3NzcWmlXfWd5fzw9Peu6KUREtwWDFBFRA/DZZ5/Bzc0NFRUV1mmFhYVwdHTE/fffb7NsYmIiNBoNDh8+fJtbeXvd6gD3zjvvIDMzEx4eHpfNa926NQwGA7Kysm7Z/q7X0aNHodFokJqaetv3XV1mZiY+/PDDOm0DEdHtxCBFRNQAxMbGorCwENu2bbNO27hxIwICApCUlISSkhLr9PXr1yMkJATNmzevi6bWW25ubggICIBGo7GZvmnTJhQXF+Pxxx/HV199VUetu7aysrJa3X5AQECNIZOIqKFikCIiagDCw8MRGBiIxMRE67TExET0798fzZo1w2+//WYzPTY2FgDwzTffoHPnztaQMGTIEGRnZwMAzGYzmjRpglmzZtnsa8eOHdBqtTh27BgAIDc3F6NHj4avry/c3d3xwAMPIC0t7art/eKLLxAREQEnJye0bt0a//nPf6zzLBWW77//HrGxsXB2dkZUVBS2bt1qs405c+YgODgYzs7OGDBgAGbOnGntVjZ//ny8/fbbSEtLs3Y5mz9/vnXdc+fOYcCAAXB2dkbLli2xfPny63ujazB37lwMGTIEw4YNw5dffnnZ/KZNm2LatGkYNWoU3NzcEBISgs8//9xmmS1btiA6OhpOTk7o3Lkzli1bZlNlunDhAuLj4+Hr6wuj0YiWLVti3rx5AIBmzZoBADp06ACNRmOtQI4YMQKPPPIIpk6diqCgIISHhwMAdu3ahQceeABGoxHe3t545plnUFhYaG2LZb1p06bB398fnp6eeOedd1BRUYFXX30VjRo1QpMmTaz7JyL6o2KQIiJqIGJjY7F+/Xrr6/Xr1+P+++9Hjx49rNOLi4uRlJRkDVLl5eWYMmUK0tLSsGzZMhw9ehQjRowAAGi1WgwePBiLFi2y2c/ChQvRrVs3hIaGAgAGDhyI7OxsJCQkYPv27ejYsSN69uyJnJycGtu5cOFCTJo0CVOnTkV6ejqmTZuGN99887JqzsSJE/G3v/0NqampaNWqFQYPHmzturh582Y8++yzGDt2LFJTU9G7d29MnTrVuu6gQYPwyiuvoG3btsjMzERmZiYGDRpknf/222/jiSeewM6dO/Hggw8iPj7+iu29moKCAixduhRDhw5F7969kZeXh40bN1623L/+9S907twZO3bswPPPP4/nnnsO+/fvBwDk5+fjoYceQmRkJH7//XdMmTIFEyZMsFn/zTffxN69e5GQkID09HTMmjULPj4+AIDk5GQAwC+//ILMzEx8//331vXWrl2L/fv3Y82aNVixYgWKiooQFxcHLy8vpKSkYOnSpfjll1/wwgsv2Oxv3bp1OH36NH799VfMnDkTkydPxp///Gd4eXkhKSkJzz77LP7617/i5MmTdr9nREQNhiIiogZhzpw5ysXFRZWXl6v8/Hzl4OCgsrOz1aJFi1T37t2VUkqtXbtWAVDHjh2rcRspKSkKgCooKFBKKbVjxw6l0Wisy5tMJtW4cWM1a9YspZRSGzduVO7u7qqkpMRmO82bN1ezZ89WSik1efJkFRUVZTNv0aJFNstPmTJFxcTEKKWUysjIUADUF198YZ2/Z88eBUClp6crpZQaNGiQ6tevn8024uPjlYeHh/X1pfu1AKDeeOMN6+vCwkIFQCUkJNT4niilVGhoqPrggw8um/7555+r6Oho6+uxY8eq4cOHX7bu0KFDra/NZrPy8/OzvoezZs1S3t7eqri42LrMnDlzFAC1Y8cOpZRSDz30kBo5cmSNbbO8X5ZlLYYPH678/f1VaWmpTXu9vLxUYWGhddpPP/2ktFqtysrKsq4XGhqqTCaTdZnw8HB13333WV9XVFQoFxcXtXjxYpt9zps3z+YzICJqyFiRIiJqIO6//34UFRUhJSUFGzduRKtWreDr64sePXpYr5NKTExEWFgYQkJCAADbt2/HQw89hJCQELi5uaFHjx4AgOPHjwMAoqOjERERYa1KbdiwAdnZ2Rg4cCAAIC0tDYWFhfD29oarq6v1kZGRUeNgFkVFRTh8+DCeeuopm+X/8Y9/XLZ8+/btrc8DAwMBwNrtcP/+/bj77rttlr/09dVU37aLiwvc3d2t27bHl19+iaFDh1pfDx06FEuXLkVBQcEV96fRaBAQEGBzLO3bt4eTk9MVj+W5557DkiVLEB0djddeew1btmy5rvZFRkZCr9dbX6enpyMqKgouLi7Wad26dYPZbLZWyACgbdu20GqrfiL4+/sjMjLS+lqn08Hb2/uG3jMioobCoa4bQEREt0aLFi3QpEkTrF+/HhcuXLCGoqCgIAQHB2PLli1Yv349HnjgAQCwdvOKi4vDwoUL4evri+PHjyMuLs5mYIL4+HgsWrQIf//737Fo0SL06dMH3t7eAGRkwEuvzbKoaRhsy7U4c+bMQZcuXWzm6XQ6m9eOjo7W55YBHsxms53vSs2qb9uyfXu3vXfvXvz2229ITk626YpnMpmwZMkSPP3007dsf3379sWxY8ewcuVKrFmzBj179sSYMWPw/vvvX3W96oHJHjW191a8Z0REDQkrUkREDUhsbCwSExORmJhoM+x59+7dkZCQgOTkZOv1Ufv27cP58+cxY8YM3HfffWjdunWNFYYhQ4Zg9+7d2L59O/773/8iPj7eOq9jx47IysqCg4MDWrRoYfOwXMNTnb+/P4KCgnDkyJHLlrcMmnA9wsPDkZKSYjPt0td6vR4mk+m6t2mvuXPnonv37khLS0Nqaqr1MX78eMydO/e6txMeHo5du3ahtLTUOu3SYwEAX19fDB8+HAsWLMCHH35oHbDCUnG6nmONiIhAWloaioqKrNM2b94MrVZrHYyCiIiuD4MUEVEDEhsbi02bNiE1NdVakQKAHj16YPbs2SgrK7MGqZCQEOj1enz88cc4cuQIli9fjilTply2zaZNm6Jr16546qmnYDKZ8PDDD1vn9erVCzExMXjkkUfw888/4+jRo9iyZQsmTpxoMxR7dW+//TamT5+Ojz76CAcOHMCuXbswb948zJw587qP88UXX8TKlSsxc+ZMHDx4ELNnz0ZCQoLN0ORNmzZFRkYGUlNTce7cOZugcrPKy8vxzTffYPDgwWjXrp3NY/To0UhKSsKePXuua1tDhgyB2WzGM888g/T0dKxevdpaabIcz6RJk/DDDz/g0KFD2LNnD1asWIGIiAgAgJ+fH4xGI1atWoUzZ84gLy/vivuKj4+Hk5MThg8fjt27d2P9+vV48cUXMWzYMPj7+9/ku0JE9MfCIEVE1IDExsaiuLgYLVq0sPlh3KNHDxQUFFiHSQekwjF//nwsXboUbdq0wYwZM67YVSw+Ph5paWkYMGAAjEajdbpGo8HKlSvRvXt3jBw5Eq1atcKTTz6JY8eOXfGH+ejRo/HFF19g3rx5iIyMRI8ePTB//ny7KlLdunXDZ599hpkzZyIqKgqrVq3Cyy+/bHOd0WOPPYY+ffogNjYWvr6+WLx48XVv/1qWL1+O8+fPY8CAAZfNi4iIQERExHVXpdzd3fHjjz8iNTUV0dHRmDhxIiZNmgQA1uPR6/V4/fXX0b59e3Tv3h06nQ5LliwBADg4OOCjjz7C7NmzERQUhP79+19xX87Ozli9ejVycnJw11134fHHH0fPnj3xySef2PsWEBH94WmUUqquG0FERHSznn76aezbt6/G4cdvVtOmTTFu3DiMGzfulm+7JgsXLsTIkSORl5dnE1zvdPPnz8e4ceOQm5tb100hIqp1HGyCiIjqpffffx+9e/eGi4sLEhIS8NVXX9nc2PdWmzBhAt544w2cOnUKHh4et3TbX3/9NcLCwtC4cWOkpaVhwoQJeOKJJ+pViHJ1dUVFRYVNVZCIqCFjkCIionopOTkZ7777LgoKChAWFoaPPvoIo0ePrpV9bdiwAeXl5QAANze3W779rKwsTJo0CVlZWQgMDMTAgQNtbjBcH6SmpgK4fPRFIqKGil37iIiIiIiI7MTBJoiIiIiIiOzEIEVERERERGQnBikiIiIiIiI7MUgRERERERHZiUGKiIiIiIjITgxSREREREREdmKQIiIiIiIishODFBERERERkZ3+P98tRXJlnih/AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "spectra_sharded = rubixdata # Spectra of all stars\n", + "#print(spectra.shape)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.title(\"Rubix Sharded\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra_sharded[12,12,:])\n", + "plt.plot(wave, spectra_sharded[8,12,:])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a spacial image of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "# get the spectra of the visible wavelengths from the ifu cube\n", + "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "#visible_spectra.shape\n", + "\n", + "#image = jnp.sum(visible_spectra, axis=2)\n", + "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", + "\n", + "# Plot side by side\n", + "fig, axes = plt.subplots(1, 1, figsize=(12, 5))\n", + "\n", + "# Sharded IFU datacube image\n", + "im1 = axes.imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", + "axes.set_title(\"Sharded IFU Datacube\")\n", + "fig.colorbar(im1, ax=axes)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DONE!\n", + "\n", + "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/rubix_pipeline_stepwise.ipynb b/source/notebooks/rubix_pipeline_stepwise.ipynb index f822f615..9a02de1c 100644 --- a/source/notebooks/rubix_pipeline_stepwise.ipynb +++ b/source/notebooks/rubix_pipeline_stepwise.ipynb @@ -44,7 +44,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -119,9 +130,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:54,846 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:15:54,847 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-10 17:15:54,848 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:15:54,848 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:15:54,849 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/jax/_src/numpy/scalar_types.py:50: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " return asarray(x, dtype=self.dtype)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:491: UserWarning: Explicitly requested dtype requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n", + "2025-11-10 17:15:54,935 - rubix - INFO - Centering stars particles\n", + "2025-11-10 17:15:55,775 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.data import convert_to_rubix, prepare_input\n", @@ -139,9 +174,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -160,9 +216,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:56,445 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-10 17:15:56,446 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-10 17:15:56,447 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-10 17:15:56,447 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.rotation import get_galaxy_rotation\n", @@ -173,10 +240,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", "# Make a scatter plot of the stars coordinates after rotation\n", "plt.scatter(rubixdata.stars.coords[:,0], rubixdata.stars.coords[:,1], s=1)" ] @@ -192,9 +281,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:57,935 - rubix - INFO - Calculating spatial bin edges...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:15:58,094 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:15:58,270 - rubix - INFO - Filtering particles outside the aperture...\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.telescope import get_filter_particles\n", @@ -214,9 +321,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:58,449 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-11-10 17:15:58,460 - rubix - INFO - Getting cosmology...\n", + "2025-11-10 17:15:58,461 - rubix - INFO - Assigning particles to spaxels...\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.telescope import get_spaxel_assignment\n", @@ -229,108 +346,94 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 6: Reshape data\n", + "## Step 6: Data cube calculation\n", "\n", - "At the moment we have to reshape the rubix data that we can split the data on multiple GPUs. We plan to move from pmap to shard_map. Then this step should not be necessary any more. This step has purely computational reason and no physics motivated reason." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.data import get_reshape_data\n", - "reshape_data = get_reshape_data(config)\n", + "This is the heart of the `pipeline`. Now we do the lookup for the spectrum for each stellar particle. For the simple stellar population model by `BruzualCharlot2003`, each stellar particle gets a spectrum assigned based on its age and metallicity.\n", "\n", - "rubixdata = reshape_data(rubixdata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 7: Spectra calculation\n", + "We scale the stellar particle spectra by its mass. The stellar spectra have to be scaled by the stellar mass. Later heavier stellar particles should contribute more to the spectrum in a spaxel than lighter stellar particles.\n", "\n", - "This is the heart of the `pipeline`. Now we do the lookup for the spectrum for each stellar particle. For the simple stellar population model by `BruzualCharlot2003`, each stellar particle gets a spectrum assigned based on its age and metallicity. In the plot we can see that the spectrum differs for different stellar particles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.ifu import get_calculate_spectra\n", - "calcultae_spectra = get_calculate_spectra(config)\n", - "\n", - "rubixdata = calcultae_spectra(rubixdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax.numpy as jnp\n", + "The stellar particles are not at rest and therefore the emitted light is doppler shifted with respect to the observer. Before adding all stellar spectra in each spaxel, we dopplershift the spectra according to their particle velocity and we resample the spectra to the wavelength grid of the observing instrument.\n", "\n", - "plt.plot(jnp.arange(len(rubixdata.stars.spectra[0][0][:])), rubixdata.stars.spectra[0][0][:])\n", - "plt.plot(jnp.arange(len(rubixdata.stars.spectra[0][0][:])), rubixdata.stars.spectra[0][1][:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 8: Scaling by mass\n", - "\n", - "The stellar spectra have to be scaled by the stellar mass. Later heavier stellar particles should contribute more to the spectrum in a spaxel than lighter stellar particles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.ifu import get_scale_spectrum_by_mass\n", - "scale_spectrum_by_mass = get_scale_spectrum_by_mass(config)\n", - "\n", - "rubixdata = scale_spectrum_by_mass(rubixdata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 9: Doppler shifting and resampling\n", + "Each stellar particle falls into one spaxel in the datacube. We ad the stellar particles spectra contribution to the according spaxel in the datacube. We do these steps for all atellar particles.\n", "\n", - "The stellar particles are not at rest and therefore the emitted light is doppler shifted with respect to the observer. Before adding all stellar spectra in each spaxel, we dopplershift the spectra according to their particle velocity and we resample the spectra to the wavelength grid of the observing instrument." + "The first plot shows the spectra for two different spaxels.\n", + "The second plot shows the spatial dimension of the `datacube`, where we summed over the wavelength dimension." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:58,856 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:15:58,915 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-11-10 17:15:58,954 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-10 17:15:59,865 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", - "from rubix.core.ifu import get_doppler_shift_and_resampling\n", - "doppler_shift_and_resampling = get_doppler_shift_and_resampling(config)\n", + "from rubix.core.ifu import get_calculate_datacube_particlewise\n", + "calculate_datacube_particlewise = get_calculate_datacube_particlewise(config)\n", "\n", - "rubixdata = doppler_shift_and_resampling(rubixdata)" + "rubixdata = calculate_datacube_particlewise(rubixdata)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4700.15 4701.4 4702.65 ... 9347.65 9348.9 9350.15]\n", + "[0. 0. 0. ... 0. 0. 0.]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.pipeline import RubixPipeline \n", @@ -339,39 +442,38 @@ "\n", "wave = pipe.telescope.wave_seq\n", "print(wave)\n", - "print(rubixdata.stars.spectra[0][0][:])\n", - "\n", - "plt.plot(wave, rubixdata.stars.spectra[0][0][:])\n", - "plt.plot(wave, rubixdata.stars.spectra[0][1][:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 10: Datacube\n", - "\n", - "Now we can add all stellar spectra that contribute to one spaxel and get the IFU datacube. The plot shows the spatial dimension of the `datacube`, where we summed over the wavelength dimension." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.ifu import get_calculate_datacube\n", - "calculate_datacube = get_calculate_datacube(config)\n", + "print(rubixdata.stars.datacube[0][0][:])\n", "\n", - "rubixdata = calculate_datacube(rubixdata)" + "plt.plot(wave, rubixdata.stars.datacube[12][12][:])\n", + "plt.plot(wave, rubixdata.stars.datacube[10][5][:])" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "datacube = rubixdata.stars.datacube\n", @@ -383,16 +485,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 11: PSF\n", + "## Step 7: PSF\n", "\n", "The instrument and the earth athmosphere affect the spatial resolution of the observation data and smooth in spatial dimention. To take this effect into account we convolve our datacube with a point spread function (PSF)." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:16:00,475 - rubix - INFO - Convolving with PSF...\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.psf import get_convolve_psf\n", @@ -403,9 +513,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "datacube = rubixdata.stars.datacube\n", @@ -415,29 +546,78 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(wave, datacube[12,12,:])\n", - "plt.plot(wave, datacube[0,0,:])" + "plt.plot(wave, datacube[10,5,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 11: LSF\n", + "## Step 8: LSF\n", "\n", "The instrument affects the spectral resolution of the observation data and smooth in spectral dimention. To take this effect into account we convolve our datacube with a line spread function (LSF)." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:16:01,014 - rubix - INFO - Convolving with LSF...\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.lsf import get_convolve_lsf\n", @@ -446,23 +626,51 @@ "rubixdata = convolve_lsf(rubixdata)\n", "\n", "plt.plot(wave, rubixdata.stars.datacube[12,12,:])\n", - "plt.plot(wave, rubixdata.stars.datacube[0,0,:])" + "plt.plot(wave, rubixdata.stars.datacube[10,5,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 12: Noise\n", + "## Step 9: Noise\n", "\n", "Observational data are never noise-free. We apply noise to our mock-datacube to mimic real measurements." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:16:01,508 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.core.noise import get_apply_noise\n", @@ -477,13 +685,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(wave, rubixdata.stars.datacube[12,12,:])\n", - "plt.plot(wave, rubixdata.stars.datacube[0,0,:])" + "plt.plot(wave, rubixdata.stars.datacube[10,5,:])" ] }, { @@ -494,6 +723,69 @@ "\n", "Congratulations, you have now created step by step your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Store datacube in a fits file with header\n", + "\n", + "Keep in mind that this it the luminosity cube. If you want to have a flux cube, you have to convert it. You can do this with the `rubix.spectra.ifu.convert_luminoisty_to_flux` function." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-10 17:16:03,252 - rubix - INFO - Datacube saved to ./output/IllustrisTNG_id12_snap99_MUSE_calc_ifu.fits\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.fits import store_fits\n", + "\n", + "store_fits(config, rubixdata.stars.datacube, \"./output/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualisation of the datacube\n", + "\n", + "We show, how you can visualize the datacube and see the image and spectra and explore the datacube. We show our own build tool to visualize the datacube and second we provide the opportunity to load the datacube with the Cubeviz module from jdaviz.\n", + "\n", + "`visualize_rubix` uses mpdaf to load the datacube. This is a package specialized to load MUSE datacubes. The function will thisplay you on the left an image collapsed along the wavelength and on the right a spectrum for a certain pixel or aperture. \n", + "\n", + "Explanation of the sliders:\n", + "- Waveindex: Waveindex, which wavelength slice is plotted in the image.\n", + "- Wavelengthrange: Range in wavelength that is collapsed to the image.\n", + "- X Pixel: X coordinate of the displayed spectrum and x coordinate of the center of the aperture.\n", + "- Y Pixel: Y coordinate of the displayed spectrum and y coordinate of the center of the aperture.\n", + "- Radius: size of the circular aperture in pixels. If this value is set to zerro, only the spaxel specified in the x and y pixel is considered for the spectrum plot.\n", + "\n", + "Now you can explore your datacube with the sliders!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.visualisation import visualize_rubix\n", + "\n", + "#visualize_rubix(\"./output/IllustrisTNG_id11_snap99_stars_subsetTrue.fits\")" + ] } ], "metadata": { @@ -512,7 +804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/source/notebooks/spaxel_assignment.ipynb b/source/notebooks/spaxel_assignment.ipynb deleted file mode 100644 index f6bed212..00000000 --- a/source/notebooks/spaxel_assignment.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Spaxel assignment\n", - "\n", - "This notebook shows the principle, how stellar particles or gas particles are assigned to the different spaxels. We show this here for squared spaxels.\n", - "\n", - "We start with two particles and assign them to the spatial matching spaxels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.telescope.utils import square_spaxel_assignment\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import ListedColormap\n", - "from jaxtyping import Float, Array \n", - "import jax.numpy as jnp\n", - "import numpy as np\n", - "\n", - "# Define the particle coordinates\n", - "coords = jnp.array([[0.5, 1.5], [2.5, 3.5]])\n", - "print(\"coords\", coords)\n", - "\n", - "# Define the spatial bin edges\n", - "spatial_bin_edges = jnp.array([0.0, 1.0, 2.0, 3.0, 4.0])\n", - "\n", - "# Compute the pixel assignments\n", - "pixel_assignments = square_spaxel_assignment(coords, spatial_bin_edges)\n", - "\n", - "# Create a discrete colormap\n", - "max_assignment = np.max(pixel_assignments)\n", - "colors = plt.cm.viridis(np.linspace(0, 1, int(max_assignment) + 1))\n", - "cmap = ListedColormap(colors)\n", - "\n", - "# Plot the results\n", - "plt.figure(figsize=(10, 5))\n", - "\n", - "# Plotting the particles with labels\n", - "plt.subplot(1, 2, 1)\n", - "scatter = plt.scatter(coords[:, 0], coords[:, 1], c=pixel_assignments, cmap=cmap, edgecolor='k')\n", - "plt.colorbar(scatter, ticks=np.arange(0, max_assignment + 1))\n", - "plt.title('Particle Coordinates and Pixel Assignments')\n", - "plt.xlabel('X Coordinate')\n", - "plt.ylabel('Y Coordinate')\n", - "plt.xlim(spatial_bin_edges[0], spatial_bin_edges[-1])\n", - "plt.ylim(spatial_bin_edges[0], spatial_bin_edges[-1])\n", - "\n", - "\n", - "# Label each point with its pixel index\n", - "for i, (x, y) in enumerate(coords[:, :2]):\n", - " plt.text(x, y, str(pixel_assignments[i]), color='red', fontsize=8)\n", - "\n", - "#create the bins\n", - "for edge in spatial_bin_edges:\n", - " plt.axvline(edge, color='k', linestyle='--')\n", - " plt.axhline(edge, color='k', linestyle='--')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we do the same with a lot more random points." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "#create random data\n", - "n_stars = 1000\n", - "coords = np.random.normal(2, 0.5, (n_stars, 3))\n", - "coords = jnp.array(coords)\n", - "\n", - "# Compute the pixel assignments\n", - "pixel_assignments = square_spaxel_assignment(coords, spatial_bin_edges)\n", - "\n", - "# Plot the results\n", - "plt.figure(figsize=(10, 5))\n", - "\n", - "\n", - "# Plot the results\n", - "plt.figure(figsize=(10, 5))\n", - "\n", - "# Plotting the particles with labels\n", - "plt.subplot(1, 2, 1)\n", - "scatter = plt.scatter(coords[:, 0], coords[:, 1], c=pixel_assignments, cmap=cmap, edgecolor='k')\n", - "plt.colorbar(scatter, ticks=np.arange(0, max_assignment + 1))\n", - "plt.title('Particle Coordinates and Pixel Assignments')\n", - "plt.xlabel('X Coordinate')\n", - "plt.ylabel('Y Coordinate')\n", - "plt.xlim(spatial_bin_edges[0], spatial_bin_edges[-1])\n", - "plt.ylim(spatial_bin_edges[0], spatial_bin_edges[-1])\n", - "\n", - "\n", - "#create the bins\n", - "for edge in spatial_bin_edges:\n", - " plt.axvline(edge, color='k', linestyle='--')\n", - " plt.axhline(edge, color='k', linestyle='--')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And the last plot shows how many particles fall in each spaxel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "image = np.zeros((len(spatial_bin_edges) - 1, len(spatial_bin_edges) - 1))\n", - "\n", - "# Count the number of particles in each pixel\n", - "for i in range(len(spatial_bin_edges) - 1):\n", - " for j in range(len(spatial_bin_edges) - 1):\n", - " image[i, j] = np.sum(pixel_assignments == (i + (len(spatial_bin_edges) - 1) * j))\n", - " \n", - " \n", - "plt.imshow(image, cmap='viridis', origin='lower')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/source/notebooks/ssp_interpolation.ipynb b/source/notebooks/ssp_interpolation.ipynb new file mode 100644 index 00000000..c86a2ac6 --- /dev/null +++ b/source/notebooks/ssp_interpolation.ipynb @@ -0,0 +1,629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SSP interpolation\n", + "## Load SSP Grid" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:13:20,829 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:13:20,831 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-10 17:13:20,831 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:13:20,909 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:13:20,911 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HDF5SSPGrid(age=Array([ 0. , 5.100002 , 5.1500006, 5.1999993, 5.25 ,\n", + " 5.3000016, 5.350002 , 5.4000006, 5.4500012, 5.500002 ,\n", + " 5.550002 , 5.600002 , 5.6500025, 5.700002 , 5.750002 ,\n", + " 5.8000026, 5.850003 , 5.900003 , 5.950003 , 6. ,\n", + " 6.0200005, 6.040001 , 6.0599985, 6.0799985, 6.100002 ,\n", + " 6.120001 , 6.1399984, 6.16 , 6.18 , 6.1999993,\n", + " 6.2200007, 6.24 , 6.2599998, 6.2799997, 6.2999997,\n", + " 6.3199987, 6.3399997, 6.3600006, 6.3799996, 6.3999987,\n", + " 6.4200006, 6.44 , 6.4599996, 6.4799995, 6.499999 ,\n", + " 6.52 , 6.539999 , 6.56 , 6.5799994, 6.6 ,\n", + " 6.6199994, 6.6399994, 6.66 , 6.679999 , 6.699999 ,\n", + " 6.72 , 6.7399993, 6.7599993, 6.7799997, 6.799999 ,\n", + " 6.819999 , 6.839999 , 6.8599997, 6.879999 , 6.899999 ,\n", + " 6.919999 , 6.939999 , 6.959999 , 6.9799986, 6.999999 ,\n", + " 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3.57395697e-06, 3.51914946e-06, 3.49452603e-06],\n", + " [2.92348756e-10, 4.62365729e-10, 6.26242114e-10, ...,\n", + " 3.54419944e-06, 3.48981166e-06, 3.46525371e-06],\n", + " [2.94150426e-10, 4.65220779e-10, 6.30102970e-10, ...,\n", + " 3.51500717e-06, 3.46103275e-06, 3.43656484e-06]]], dtype=float32))\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.spectra.ssp.templates import BruzualCharlot2003\n", + "\n", + "print(BruzualCharlot2003)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SSP lookup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.550001\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.spectra.ssp.templates import BruzualCharlot2003\n", + "from jax import jit\n", + "\n", + "ssp = BruzualCharlot2003\n", + "wave = ssp.wavelength\n", + "\n", + "\n", + "age_index = 0\n", + "met_index = 3\n", + "\n", + "target_age = ssp.age[age_index] + 0.5*(ssp.age[age_index+1] - ssp.age[age_index])\n", + "print(target_age)\n", + "target_met = ssp.metallicity[met_index] + 0.5*(ssp.metallicity[met_index+1] - ssp.metallicity[met_index])\n", + "\n", + "lookup = ssp.get_lookup_interpolation()\n", + "\n", + "spec_calc = lookup(target_met, target_age)\n", + "\n", + "spec_true = ssp.flux[met_index, age_index, :]\n", + "\n", + "plt.plot(wave, spec_calc, label='calc')\n", + "plt.plot(wave, spec_true, label='true')\n", + "\n", + "plt.legend()\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# Check if it works with jit\n", + "\n", + "spec_calc = jit(lookup)(target_met, target_age)\n", + "\n", + "plt.plot(wave, spec_calc, label='calc jit')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.utils import load_galaxy_data\n", + "\n", + "data, units = load_galaxy_data(\"output/rubix_galaxy.h5\")\n", + "mass = data[\"particle_data\"][\"stars\"][\"mass\"]\n", + "metallicity = data[\"particle_data\"][\"stars\"][\"metallicity\"]\n", + "age = data[\"particle_data\"][\"stars\"][\"age\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VMAP\n", + "\n", + "Vmap the lookup over the stellar particles" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 842)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# Calculate spectra with vmap\n", + "from jax import vmap\n", + "\n", + "lookup = ssp.get_lookup_interpolation()\n", + "\n", + "subset = 1000\n", + "\n", + "# Use only subset because it is too big to fit into gpu memory\n", + "met_subset = metallicity[:subset]\n", + "age_subset = age[:subset]\n", + "\n", + "\n", + "# Clip the metallicity and age values to the range of the SSP\n", + "\n", + "met_subset = met_subset.clip(min(ssp.metallicity), max(ssp.metallicity))\n", + "age_subset = age_subset.clip(min(ssp.age), max(ssp.age))\n", + "\n", + "\n", + "spec_calc = vmap(lookup)(met_subset, age_subset)\n", + "\n", + "\n", + "spec_calc.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(False, dtype=bool)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# check if it contains nan values\n", + "import jax.numpy as jnp\n", + "jnp.isnan(spec_calc).any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use configuration to load lookup function" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "config ={ \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"BruzualCharlot2003\"\n", + " },\n", + " \"method\": \"cubic\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:13:25,095 - rubix - DEBUG - Using method defined in config: cubic\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.ssp import get_lookup_interpolation\n", + "\n", + "lookup = get_lookup_interpolation(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Array(695, dtype=int32), Array(35669, dtype=int32))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# Check how many particles are outside the range of the SSP\n", + "import numpy as np\n", + "np.sum(metallicity < ssp.metallicity[0]), np.sum(metallicity > ssp.metallicity[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Array(0, dtype=int32), Array(45331, dtype=int32))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "np.sum(age < ssp.age[0]), np.sum(age > ssp.age[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "643940" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "len(metallicity)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[ 0.0000000e+00, 2.7755576e-17, 5.5511151e-17, ...,\n", + " 4.5619352e-05, 4.4804754e-05, 4.3980177e-05],\n", + " [ 0.0000000e+00, 2.7755576e-17, 5.5511151e-17, ...,\n", + " 4.5822202e-05, 4.5003901e-05, 4.4175598e-05],\n", + " [ 0.0000000e+00, 2.7755576e-17, 5.5511151e-17, ...,\n", + " 4.8169481e-05, 4.7308338e-05, 4.6436926e-05],\n", + " ...,\n", + " [ 0.0000000e+00, 2.7755576e-17, 5.5511151e-17, ...,\n", + " 4.6039131e-05, 4.5216868e-05, 4.4384582e-05],\n", + " [-5.2917185e-12, -1.3075374e-11, -2.3385127e-11, ...,\n", + " 4.5421279e-05, 4.4608027e-05, 4.3794011e-05],\n", + " [ 0.0000000e+00, 2.7755576e-17, 5.5511151e-17, ...,\n", + " 4.4646134e-05, 4.3849301e-05, 4.3042593e-05]], dtype=float32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "# clip the metallicity and age values to the range of the SSP\n", + "met_subset = met_subset.clip(min(ssp.metallicity), max(ssp.metallicity))\n", + "age_subset = age_subset.clip(min(ssp.age), max(ssp.age))\n", + "lookup(met_subset, age_subset)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/ssp_template.ipynb b/source/notebooks/ssp_template.ipynb index b201e2cb..5f9ea6b9 100644 --- a/source/notebooks/ssp_template.ipynb +++ b/source/notebooks/ssp_template.ipynb @@ -11,9 +11,279 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:15:39,373 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:15:39,374 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-10 17:15:39,374 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:15:39,451 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:15:39,452 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + }, + { + "data": { + "text/plain": [ + "HDF5SSPGrid(age=Array([ 0. , 5.100002 , 5.1500006, 5.1999993, 5.25 ,\n", + " 5.3000016, 5.350002 , 5.4000006, 5.4500012, 5.500002 ,\n", + " 5.550002 , 5.600002 , 5.6500025, 5.700002 , 5.750002 ,\n", + " 5.8000026, 5.850003 , 5.900003 , 5.950003 , 6. ,\n", + " 6.0200005, 6.040001 , 6.0599985, 6.0799985, 6.100002 ,\n", + " 6.120001 , 6.1399984, 6.16 , 6.18 , 6.1999993,\n", + " 6.2200007, 6.24 , 6.2599998, 6.2799997, 6.2999997,\n", + " 6.3199987, 6.3399997, 6.3600006, 6.3799996, 6.3999987,\n", + " 6.4200006, 6.44 , 6.4599996, 6.4799995, 6.499999 ,\n", + " 6.52 , 6.539999 , 6.56 , 6.5799994, 6.6 ,\n", + " 6.6199994, 6.6399994, 6.66 , 6.679999 , 6.699999 ,\n", + " 6.72 , 6.7399993, 6.7599993, 6.7799997, 6.799999 ,\n", + " 6.819999 , 6.839999 , 6.8599997, 6.879999 , 6.899999 ,\n", + " 6.919999 , 6.939999 , 6.959999 , 6.9799986, 6.999999 ,\n", + " 7.0200005, 7.040001 , 7.0599985, 7.0799985, 7.099998 ,\n", + " 7.119998 , 7.1399984, 7.16 , 7.18 , 7.1999993,\n", + " 7.2199984, 7.24 , 7.2599998, 7.2799997, 7.2999997,\n", + " 7.3199987, 7.3399997, 7.3599987, 7.3799996, 7.3999987,\n", + " 7.4199986, 7.4399986, 7.462398 , 7.4771214, 7.4913616,\n", + " 7.50515 , 7.518514 , 7.531479 , 7.544068 , 7.5563025,\n", + " 7.5682015, 7.5797834, 7.5910645, 7.60206 , 7.628389 ,\n", + " 7.6532125, 7.6766934, 7.69897 , 7.7201595, 7.7403626,\n", + " 7.7565446, 7.806545 , 7.8565454, 7.906545 , 7.9565454,\n", + " 8.006543 , 8.056546 , 8.1065445, 8.156547 , 8.206545 ,\n", + " 8.256547 , 8.306547 , 8.356546 , 8.406547 , 8.456547 ,\n", + " 8.506547 , 8.556547 , 8.606546 , 8.656548 , 8.706548 ,\n", + " 8.756548 , 8.806548 , 8.856548 , 8.9065485, 8.956549 ,\n", + " 9.006547 , 9.05655 , 9.106548 , 9.156549 , 9.206551 ,\n", + " 9.225309 , 9.230449 , 9.255273 , 9.278753 , 9.30103 ,\n", + " 9.322219 , 9.3424225, 9.361728 , 9.380211 , 9.39794 ,\n", + " 9.414973 , 9.439333 , 9.477121 , 9.511884 , 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3.49452603e-06],\n", + " [2.92348756e-10, 4.62365729e-10, 6.26242114e-10, ...,\n", + " 3.54419944e-06, 3.48981166e-06, 3.46525371e-06],\n", + " [2.94150426e-10, 4.65220779e-10, 6.30102970e-10, ...,\n", + " 3.51500717e-06, 3.46103275e-06, 3.43656484e-06]]], dtype=float32))" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.spectra.ssp.templates import BruzualCharlot2003\n", @@ -23,9 +293,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 5.100002 5.1500006 5.1999993 5.25 5.3000016\n", + " 5.350002 5.4000006 5.4500012 5.500002 5.550002 5.600002\n", + " 5.6500025 5.700002 5.750002 5.8000026 5.850003 5.900003\n", + " 5.950003 6. 6.0200005 6.040001 6.0599985 6.0799985\n", + " 6.100002 6.120001 6.1399984 6.16 6.18 6.1999993\n", + " 6.2200007 6.24 6.2599998 6.2799997 6.2999997 6.3199987\n", + " 6.3399997 6.3600006 6.3799996 6.3999987 6.4200006 6.44\n", + " 6.4599996 6.4799995 6.499999 6.52 6.539999 6.56\n", + " 6.5799994 6.6 6.6199994 6.6399994 6.66 6.679999\n", + " 6.699999 6.72 6.7399993 6.7599993 6.7799997 6.799999\n", + " 6.819999 6.839999 6.8599997 6.879999 6.899999 6.919999\n", + " 6.939999 6.959999 6.9799986 6.999999 7.0200005 7.040001\n", + " 7.0599985 7.0799985 7.099998 7.119998 7.1399984 7.16\n", + " 7.18 7.1999993 7.2199984 7.24 7.2599998 7.2799997\n", + " 7.2999997 7.3199987 7.3399997 7.3599987 7.3799996 7.3999987\n", + " 7.4199986 7.4399986 7.462398 7.4771214 7.4913616 7.50515\n", + " 7.518514 7.531479 7.544068 7.5563025 7.5682015 7.5797834\n", + " 7.5910645 7.60206 7.628389 7.6532125 7.6766934 7.69897\n", + " 7.7201595 7.7403626 7.7565446 7.806545 7.8565454 7.906545\n", + " 7.9565454 8.006543 8.056546 8.1065445 8.156547 8.206545\n", + " 8.256547 8.306547 8.356546 8.406547 8.456547 8.506547\n", + " 8.556547 8.606546 8.656548 8.706548 8.756548 8.806548\n", + " 8.856548 8.9065485 8.956549 9.006547 9.05655 9.106548\n", + " 9.156549 9.206551 9.225309 9.230449 9.255273 9.278753\n", + " 9.30103 9.322219 9.3424225 9.361728 9.380211 9.39794\n", + " 9.414973 9.439333 9.477121 9.511884 9.544068 9.574031\n", + " 9.60206 9.628389 9.653213 9.676694 9.69897 9.72016\n", + " 9.740363 9.759667 9.7781515 9.79588 9.812913 9.829304\n", + " 9.8450985 9.860338 9.875061 9.889301 9.90309 9.916454\n", + " 9.929419 9.942008 9.954243 9.966142 9.977724 9.989004\n", + " 10. 10.010724 10.02119 10.031408 10.041392 10.051152\n", + " 10.060698 10.070038 10.079182 10.088136 10.09691 10.10551\n", + " 10.113943 10.122216 10.130334 10.138303 10.146128 10.153815\n", + " 10.161368 10.168792 10.176091 10.1832695 10.190331 10.197281\n", + " 10.20412 10.210854 10.2174835 10.224015 10.230449 10.236789\n", + " 10.243038 10.249198 10.255273 10.261263 10.267172 10.273002\n", + " 10.278753 10.2844305 10.290034 10.2955675 10.30103 ]\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "print(BruzualCharlot2003.age)" @@ -42,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,21 +393,274 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "HDF5SSPGrid(age=Array([ 0. , 5.100002 , 5.1500006, 5.1999993, 5.25 ,\n", + " 5.3000016, 5.350002 , 5.4000006, 5.4500012, 5.500002 ,\n", + " 5.550002 , 5.600002 , 5.6500025, 5.700002 , 5.750002 ,\n", + " 5.8000026, 5.850003 , 5.900003 , 5.950003 , 6. ,\n", + " 6.0200005, 6.040001 , 6.0599985, 6.0799985, 6.100002 ,\n", + " 6.120001 , 6.1399984, 6.16 , 6.18 , 6.1999993,\n", + " 6.2200007, 6.24 , 6.2599998, 6.2799997, 6.2999997,\n", + " 6.3199987, 6.3399997, 6.3600006, 6.3799996, 6.3999987,\n", + " 6.4200006, 6.44 , 6.4599996, 6.4799995, 6.499999 ,\n", + " 6.52 , 6.539999 , 6.56 , 6.5799994, 6.6 ,\n", + " 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3530., 3550., 3570.,\n", + " 3590., 3610., 3630., 3640., 3650., 3670., 3690., 3710.,\n", + " 3730., 3750., 3770., 3790., 3810., 3830., 3850., 3870.,\n", + " 3890., 3910., 3930., 3950., 3970., 3990., 4010., 4030.,\n", + " 4050., 4070., 4090., 4110., 4130., 4150., 4170., 4190.,\n", + " 4210., 4230., 4250., 4270., 4290., 4310., 4330., 4350.,\n", + " 4370., 4390., 4410., 4430., 4450., 4470., 4490., 4510.,\n", + " 4530., 4550., 4570., 4590., 4610., 4630., 4650., 4670.,\n", + " 4690., 4710., 4730., 4750., 4770., 4790., 4810., 4830.,\n", + " 4850., 4870., 4890., 4910., 4930., 4950., 4970., 4990.,\n", + " 5010., 5030., 5050., 5070., 5090., 5110., 5130., 5150.,\n", + " 5170., 5190., 5210., 5230., 5250., 5270., 5290., 5310.,\n", + " 5330., 5350., 5370., 5390., 5410., 5430., 5450., 5470.,\n", + " 5490., 5510., 5530., 5550., 5570., 5590., 5610., 5630.,\n", + " 5650., 5670., 5690., 5710., 5730., 5750., 5770., 5790.,\n", + " 5810., 5830., 5850., 5870., 5890., 5910., 5930., 5950.,\n", + " 5970., 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8490., 8510.,\n", + " 8530., 8550., 8570., 8590., 8610., 8630., 8650., 8670.,\n", + " 8690., 8710., 8730., 8750., 8770., 8790., 8810., 8830.,\n", + " 8850., 8870., 8890., 8910., 8930., 8950., 8970., 8990.,\n", + " 9010., 9030., 9050., 9070., 9090., 9110., 9130., 9150.,\n", + " 9170., 9190., 9210., 9230., 9250., 9270., 9290., 9310.,\n", + " 9330., 9350., 9370., 9390., 9410., 9430., 9450., 9470.,\n", + " 9490., 9510., 9530., 9550., 9570., 9590., 9610., 9630.,\n", + " 9650., 9670., 9690., 9710., 9730., 9750., 9770., 9790.,\n", + " 9810., 9830., 9850., 9870., 9890., 9910., 9930., 9950.,\n", + " 9970., 9990., 10025., 10075., 10125., 10175., 10225., 10275.,\n", + " 10325., 10375., 10425., 10475., 10525., 10575., 10625., 10675.,\n", + " 10725., 10775., 10825., 10875., 10925., 10975., 11025., 11075.,\n", + " 11125., 11175., 11225., 11275., 11325., 11375., 11425., 11475.,\n", + " 11525., 11575., 11625., 11675., 11725., 11775., 11825., 11875.,\n", + " 11925., 11975., 12025., 12075., 12125., 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3.46286311e-06, 3.42306453e-06]],\n", + "\n", + " [[1.03717389e-14, 2.60376945e-14, 6.23507932e-14, ...,\n", + " 4.28130661e-05, 4.20417018e-05, 4.12843074e-05],\n", + " [1.03717389e-14, 2.60376945e-14, 6.23507932e-14, ...,\n", + " 4.28130661e-05, 4.20417018e-05, 4.12843074e-05],\n", + " [1.03717389e-14, 2.60376945e-14, 6.23507932e-14, ...,\n", + " 4.28130661e-05, 4.20417018e-05, 4.12843074e-05],\n", + " ...,\n", + " [2.74051143e-10, 4.33427960e-10, 5.86995785e-10, ...,\n", + " 3.62579908e-06, 3.56578244e-06, 3.53157429e-06],\n", + " [2.80006740e-10, 4.42861414e-10, 5.99826022e-10, ...,\n", + " 3.59876890e-06, 3.53911469e-06, 3.50530217e-06],\n", + " [2.81731083e-10, 4.45578630e-10, 6.03499362e-10, ...,\n", + " 3.57047224e-06, 3.51121457e-06, 3.47779246e-06]],\n", + "\n", + " [[2.64753693e-18, 8.02830980e-18, 2.30857457e-17, ...,\n", + " 5.49388205e-05, 5.39541179e-05, 5.29583958e-05],\n", + " [2.64753693e-18, 8.02830980e-18, 2.30857457e-17, ...,\n", + " 5.49388205e-05, 5.39541179e-05, 5.29583958e-05],\n", + " [2.69226858e-18, 8.17344360e-18, 2.35313512e-17, ...,\n", + " 5.90876080e-05, 5.80271771e-05, 5.69552649e-05],\n", + " ...,\n", + " [2.86055124e-10, 4.52389348e-10, 6.12669249e-10, ...,\n", + " 3.57395697e-06, 3.51914946e-06, 3.49452603e-06],\n", + " [2.92348756e-10, 4.62365729e-10, 6.26242114e-10, ...,\n", + " 3.54419944e-06, 3.48981166e-06, 3.46525371e-06],\n", + " [2.94150426e-10, 4.65220779e-10, 6.30102970e-10, ...,\n", + " 3.51500717e-06, 3.46103275e-06, 3.43656484e-06]]], dtype=float32))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.spectra.ssp.grid import HDF5SSPGrid\n", - "ssp = HDF5SSPGrid.from_file(config, file_location=\"../rubix/spectra/ssp/templates\")\n", + "ssp = HDF5SSPGrid.from_file(config, file_location=\"../../rubix/spectra/ssp/templates\")\n", "ssp" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(221,)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.age.shape" @@ -101,9 +668,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(6,)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.metallicity.shape" @@ -111,9 +689,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(842,)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.wavelength.shape" @@ -121,9 +710,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(6, 221, 842)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.flux.shape" @@ -138,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -149,9 +749,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[0][0])\n", @@ -162,9 +783,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[-1][-1])\n", @@ -175,9 +817,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "for i in range(len(ssp.metallicity)):\n", @@ -191,9 +854,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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URB+E/MzyW5QIgiAIhlnYSpmcurp3FXl6etK+fXu9Y+3atWPz5s331AUPDw8SE/Un+SQmJuLh4WH0nFatWhEZGVmufwCNGzfWOx4UFERgYCBr1qxh4MCBXLhwgR07dui1sbOrWhatoRAZJHMxkkEqVliTV1hs+BzfXuDSEooL4OreWuikIAjCfUImk4a56uJhaFTAiN69e5cLSq5cuYKvr+89/fo9e/Zk3759esf27t1Lz549jZ4zcuRI9u7dy5kzZ6p0j5CQEFatWkVYWBj9+/fHx8fnnvpc34kAyVyMBEg304t4dukRiooNVPfLZNBqoPQ65mgtdFIQBEGoTTNmzODo0aPMnz+fqKgo1q9fz4oVK5gyZYq2TWhoKGPGjNE7LyIigoiICLKyskhOTiYiIoKLFy9q3582bRq7du1i8eLFXL58mblz53Ly5EmmTp1aYV969uxJv379+Oqrrzh9+jTR0dHs3r2bnTt3olDoz8AeNWoUsbGxrFy5Uq84+34lAiRzMTLElq9WcjE+gzXhNw2f5xEgPSddNPy+IAiC0GB1796drVu3smHDBjp27Mi8efNYsmQJo0eP1raJj48nJiZG77ygoCCCgoI4deoU69evJygoiKeeekr7fq9evbTBVmBgID///DPbtm2jY8eORvtibW3Nvn37eOeddwgLC+ORRx6hXbt2TJ8+nd69e7Nt2za99k5OTjz33HPY29szbNiwGvk86jOZWv1g7W2RkZGBk5MT6enpODo6mu9GqwdD9CGuPfY1ay4U8eGdmQCcVfkzpOATvJ1t+PvdJ8qfF/8P/OcxsGkMs66blLoVBEF4UOTl5REdHY2/vz/W1tZ13Z0HRr9+/ejQoQNff/21Wa5f0Z9rrX1/lxBF2uZSEnd+8UcUt9UuULK2VmHJRx6XlktyZj5uDlb657m2kTJOuXchMwEcPWuz14IgCIJQTmpqKgcOHODAgQMsXbq0rrtTK0SAZC4lQ2wqZBRROsRWqPORn41No187/XUrsLCWCrVTIiHpggiQBEEQhDoXFBREamoqCxcupE2bNnXdnVohAiRz0QmQVDqlXgXq0o/8n9j08gESgFsbKUBKuQot+5d/XxAEQRBq0Y0bN+q6C7VOFGmbiUolTeVXI6NYN0BCiZVS+vlsbJrhkxv5Sc+pRgq5BUEQBEEwKxEgmUlOfiEAKuR6AVIhSh5p6QrA2dh0DNbINypZDyNNBEiCIAiCUBdEgGQmudoASVYuQOru3xilXMbd7AJiU3PLn+xcEiCJDJIgCIIg1AkRIJlJQVERAE8FeDG4c+lqowVY4GJnSVtPaRuRs7Hp5U921skgPVirMAiCIAhCvSACJHMpKdJWKpU42FhqDxeoFThYKwls6gwYqUNyblbSOAtyU83cUUEQBEEQyhIBkpnISjI/cpkMB9vSxa4KUeJgbaENkNYevUl0Srb+yRbW4FAyvf/u9droriAIgiAIOkSAZC6arUbkcqwtLbSHC1Fib6XksdZuOForySkoZvKPp8sXa7u0lJ5TrtZShwVBEARB0BABkpnIkAIkuUyOTFG69pGUQVLi4WTNlsm9sFTKuRSfwZFrd/Qv4FayEFeK/q7PgiAIQsMWFxfHSy+9hIuLCzY2NgQEBHDy5MkKz8nPz+f//u//8PX1xcrKCj8/P3744Qe9Nps2baJt27ZYW1sTEBDA77//XmlfCgoKWLRoEV26dMHOzg4nJycCAwOZPXs2t2/fvqffs6Gr0wDp0KFDDB48GC8vL2QyWbmN8crasmULAwYMwM3NDUdHR3r27Mnu3btrp7OmKskIyeQKlIrSDFIRCuytpYCpZRMHXuwuFXBvPh2rf75ra+lZZJAEQRDuG6mpqfTu3RsLCwt27tzJxYsXWbx4MY0aNarwvBEjRrBv3z6+//57IiMj2bBhg96K1keOHGHkyJFMnDiRM2fOMGzYMIYNG8b58+eNXjM/P58BAwYwf/58xo0bx6FDhzh37hxff/01KSkpfPPNN9X+PQsKCqp9bn1RpytpZ2dnExgYyIQJE3j22WcrbX/o0CHtH6azszNhYWEMHjyYY8eOERQUVAs9rjpNBkkmlyNXlG41okKOo3VpwPRYKzfWhN/k4u0M/QtoAqRkkUESBEG4XyxcuBAfHx/CwsK0x/z9/Ss8Z9euXRw8eJDr16/TuHFjAPz8/PTafPXVVzz55JO8/fbbAMybN4+9e/fy7bffsnz5coPX/fLLLzl8+DAnT57U+w5t1qwZffr00ZZ+rFmzhhkzZnD79m2srEr3Dx02bBgODg6sXbuWuXPnsm3bNqZOnconn3zCzZs3UalUVf9g6qE6DZAGDRrEoEGDqtx+yZIlej/Pnz+fX375hV9//bXeBUiaDJJcoUChLP2YVSi0K2kDtPOSdiSOSsoiv6gYK2VJMOXWVnq+ex3yM8HKoXb6LQiC0ACp1WpyiwysK1cLbJQ2yGSyKrXdvn07wcHBDB8+nIMHD+Lt7c3kyZOZNGlShed069aNzz77jLVr12JnZ8eQIUOYN28eNjY2AISHhzNz5ky984KDgyscmdmwYQMDBgww+v2p+Z2GDx/Om2++yfbt2xk+fDgASUlJ7Nixgz179mjbR0VFsXnzZrZs2YJCJzHQUDXovdhUKhWZmZnaiNqQ/Px88vPztT9nZGQYbVuTtBkkmRy5zhCb0kKp9x+Sl5M1jtZKMvKKiErKooOXk/SGo6e05UjqDbgZDq0H1kq/BUEQGqLcolx6rO9RJ/c+NuoYtha2VWp7/fp1li1bxsyZM3nvvfc4ceIEb775JpaWlowdO9boOYcPH8ba2pqtW7eSkpLC5MmTuXPnjjYTlZCQgLu7/t6e7u7uJCQkGO3LlStX6Nu3r96xZ555hr179wLQqVMnjhw5go2NDaNGjSIsLEwbIK1bt45mzZrpnV9QUMCaNWtwc3Or0mdR3zXoIu3PP/+crKwsRowYYbTNggULcHJy0j58fHyMtq1JMm0NklwvkrYoE1XLZDLaeUpZpEvxmfoX8X9Meo4+aL6OCoIgCLVGpVLRpUsX5s+fT1BQEK+88gqTJk0yOgymOUcmk/Hjjz/y0EMP8dRTT/HFF1+wevVqcnNrNmu2dOlSIiIimDBhAjk5OdrjkyZNYs+ePcTFxQGwatUqxo0bp/cPfl9f3/smOIIGnEFav349H374Ib/88gtNmjQx2i40NFQv7ZiRkVFLQZJmFpv+EJtcUf4jb+fpyLHou1xNKhsg9YHTayD6kFl7KgiC0NDZKG04NupYnd27qjw9PWnfvr3esXbt2rF58+YKz/H29sbJyUnvHLVaTWxsLK1atcLDw4PExES98xITE/Hw8DB63VatWhEZqV/n6ukprcFXdmQmKCiIwMBA1qxZw8CBA7lw4QI7duzQa2NnZ2f0Xg1RgwyQfvrpJ0JCQti0aRP9+/evsK2VlZVeUVlt0S4UqZCj0AmKZAYCJG9n6T+uhPQ8/Tf8HpWeE85BXjpYOyEIgiCUJ5PJqjzMVZd69+5dLii5cuUKvr6+FZ6zadMmsrKysLe3154jl8tp2rQpAD179mTfvn1Mnz5de97evXvp2bOn0euOHDmS2bNnc+bMmSrV8YaEhLBkyRLi4uLo379/rY3I1JUGN8S2YcMGxo8fz4YNG3j66afrujtGlc5iU6DUGVaTycsXrnk4SSttx5cNkBzcS1bUVkPyFbP1VRAEQagdM2bM4OjRo8yfP5+oqCjWr1/PihUrmDJlirZNaGgoY8aM0f48atQoXFxcGD9+PBcvXuTQoUO8/fbbTJgwQVukPW3aNHbt2sXixYu5fPkyc+fO5eTJk0ydOrXCvvTs2ZN+/frx1Vdfcfr0aaKjo9m9ezc7d+4sV2g9atQoYmNjWblyJRMmTKjhT6b+qdMAKSsri4iICCIiIgCIjo4mIiKCmJgYoPxfkvXr1zNmzBgWL15Mjx49SEhIICEhgfR0Axu+1jHdGiSlonSM1lBlv2dJgFQugwSlC0YmX675TgqCIAi1qnv37mzdupUNGzbQsWNH5s2bx5IlSxg9erS2TXx8vPZ7EMDe3p69e/eSlpZGt27dGD16NIMHD+brr7/WtunVq5c22AoMDOTnn39m27ZtdOzY0WhfrK2t2bdvH++88w5hYWE88sgjtGvXjunTp9O7d+9yM+CcnJx47rnnsLe3Z9iwYTX2mdRXMnW5PS5qz4EDB3j88cfLHR87dqy2AOzGjRscOHAAgL59+3LwYPmCZU37qsjIyMDJyYn09HQcHR3vpfsVuvuRP41Vdzn0xDacmwfR6b9S+nR14zcZ++Y8vbaxqTk8snA/lgo5kR8/qT9d9PdZcPw/0OsNGPix2forCILQkOTl5REdHY2/vz/W1taVnyDUiH79+tGhQwe94KwmVfTnWlvf3xp1WoPUt2/f8nuQ6Sgb9GgCpYZAhqYGSYZSXpqoM1Sk3cTBGpkMCopV3MkuwNVep2ZKm0ESC0YKgiAIdSM1NZUDBw5w4MABli5dWtfdqRUNski7IZCVbFYrlyvLDLGV/8gtlXJc7a1IzswnIT2vTIBUsmCkGGITBEEQ6khQUBCpqaksXLhQb4uT+5kIkMxErskgyWUo5DoBkoEibZDqkJIz84lPz6Ojt85sNU0GKS0GCnLAsv7P0hAEQRDuLzdu3KjrLtS6BjeLraHQncVmoTPEprI0vF6Gh6OmULvMol+2LmBVMtaaFoMgCIIgCOYnAiQz0a6DJFegUMj4quhZDhUHcL1x+aJ0gGaNpczQ7guJ+nVZMhk4l6yPkXbTrH0WBEEQBEEiAiQz0WSQ5HI5FnIZXxY9z5jCUCwsLQ22f7mnL5ZKOYejUth7UX81VBqVBEipIkASBEEQhNogAiQz0dQgKeQKvRokC4Xhj9zXxY4xD0uB0M7zZTYXFBkkQRAEQahVIkAyE20GSSHXm+av1AmWyurbRtpT7tj1O/rDbNoM0o0a76cgCIIgCOWJAMlMNOsgSVuN6E7zNx4gdfF1RimXcTs9j9hUnWJtbQZJFGkLgiAIQm0QAZKZlE7z1x9iU8iMB0i2lko6NZWm+B+9fqf0Dd0apLpb+FwQBEEQHhgiQDITzRCbQiHXqztSVDDEBtDNrzEA5+J09pdr5A/IID8dspJqvK+CIAhC7fDz80Mmk5V76G5Wa8imTZto27Yt1tbWBAQE8Pvvv+u9n5WVxdSpU2natCk2Nja0b9+e5cuXV9qfjIwM3n//fTp06ICNjQ0uLi50796dzz77jNTU1Hv6XRs6ESCZSWkGSY5uTFRRDRJAWw8HACITMksPWliXZpFSrtRoPwVBEITac+LECeLj47WPvXv3AjB8+HCj5xw5coSRI0cyceJEzpw5w7Bhwxg2bBjnz5/Xtpk5cya7du1i3bp1XLp0ienTpzN16lS2b99u9Lp3797l4YcfJiwsjLfeeotjx45x+vRpPvnkE86cOcP69eur/XsWFBRU+9z6QqykbQ5qtd4sNt3NZyvLILV2LwmQEjNRq9Wl57q2kYq0UyLB/1GzdFsQBEEwLzc3N72fP/30U1q0aEGfPn2MnvPVV1/x5JNP8vbbbwMwb9489u7dy7fffqvNEh05coSxY8fSt29fAF555RX+85//cPz4cYYMGWLwuu+99x4xMTFcuXIFLy8v7XFfX18GDhyonSz00Ucf8b///U8vIAPo3LkzgwcPZt68eYwbN460tDS6d+/Od999h5WVFdHR0aZ9OPWMyCCZg06dkKzM1iIKecUfecsm9shlkJZTSHJmfukbbq2l5+SGnUHKKcwhpzCnrrshCMJ9Rq1Wo8rJqZNHRZuuV6SgoIB169YxYcIEvX9IlxUeHk7//v31jgUHBxMeHq79uVevXmzfvp24uDjUajX79+/nypUrDBw40OA1VSoVGzdu5KWXXtILjnRp+jRhwgQuXbrEiRMntO+dOXOGs2fPMn78eO2xffv2ERkZyd69e/ntt98q/wDqOZFBMoeSjWpBqkHSZWQZJC1rCwV+rnZcT87mckImTUq2IMG1ZE+2lMia7GmtKlIV0WN9D2TIOP3yaZRy8ddPEISaoc7NJbJL1zq5d5vTp5DZmr5P5rZt20hLS2PcuHEVtktISMDd3V3vmLu7OwkJpWvmffPNN7zyyis0bdoUpVKJXC5n5cqVPPbYYwavmZycTFpaWrmNZ7t27UpkpPQ9M3jwYDZs2EDTpk0JDg4mLCyM7t27AxAWFkafPn1o3ry59lw7Ozv++9//YmlkQeSGRmSQzKL0XxNyEzNIAK2bSMNs15KzSg+6NvwMUlp+GgBq1GQWZFbcWBAE4T73/fffM2jQIKMZHFN88803HD16lO3bt3Pq1CkWL17MlClT+OOPP0y6ztatW4mIiCA4OJjc3NLlZiZNmsSGDRvIy8ujoKCA9evXM2HCBL1zAwIC7pvgCEQGyTz0Mkj6AZK3s+HNanX5ukj/Erl5R2coqklb6TnzNuTcBdvG997PWlakKtK+lstEbC4IQs2R2djQ5vSpOru3qW7evMkff/zBli1bKm3r4eFBYqL+FlSJiYl4eHgAkJuby3vvvcfWrVt5+umnAejUqRMRERF8/vnn5YbnQKqFcnZ21maLNJo1awaAg4MDaWlp2uODBw/GysqKrVu3YmlpSWFhIc8//7zeuXZ2dpX/4g2ICJDMQTdAKskg/TCuG1cTs3i4eeWBTbOSACnmrk6AZO0EjfykQu34f6CF4U1v67NCVaH2dbG6uA57IgjC/UYmk1VrmKuuhIWF0aRJE21AU5GePXuyb98+pk+frj22d+9eevbsCUBhYSGFhYXIy4xQKBQKVCoVhsjlckaMGMG6deuYM2dOpVkspVLJ2LFjCQsLw9LSkhdffBGbagSGDYkIkMxBJ0CSlayc/URbd55o627sDD2+jaUo/OadbP03PDpJAVLC2QYZIBUUl077VKkN/0crCIJwv1OpVISFhTF27FiUyvJfw2PGjMHb25sFCxYAMG3aNPr06cPixYt5+umn+emnnzh58iQrVqwAwNHRkT59+vD2229jY2ODr68vBw8eZM2aNXzxxRdG+zF//nwOHDjAQw89xEcffUS3bt2ws7Pj7NmzhIeH07FjR732ISEhtGvXDoC///67pj6OeksESGagKi7WFncpytQgVYVmiO1Wai4qlRq5ZmkAz05waTvEn62hntau/OLSWXnFKpFBEgThwfTHH38QExNTroZHIyYmRi8b1KtXL9avX8/s2bN57733aNWqFdu2bdMLYH766SdCQ0MZPXo0d+/exdfXl08++YTXXnvNaD9cXFw4fvw4CxcuZNGiRURHRyOXy2nVqhUvvPCCXsYKoFWrVvTq1Yu7d+/So0ePe/sQGgARIJlBsao0QFIqTP+IPZ2sUcplFBSpSMjIw0tTt+QRKD0n3AcBkhhiEwThAaW7xpAhBw4cKHds+PDhFS4m6eHhQVhYmMl9cXJyYv78+cyfP7/Stmq1mtu3bzN58uRy761atcrke9d3olLWDIqLS4eP5JXN6zdAqZDTrLGURbqerDPM5t5eer5zDYqLDJxZv4kASRAEoWFKTk7m22+/JSEhQW/to/uZyCCZgUpn+KjsLLaqau5mz/WUbKKSMnmklat00MELFJZQXAAZcaXbjzQQ+UWlAZKoQRIEQWg4mjRpgqurKytWrKBRo0Z13Z1aIQIkM9ANkKo7nb1lE3v+uJRIlO5aSHI5OPnA3WuQdrPBBUh5xXna16IGSRAEoeGo7mrhDZkYYjMDVckQm0otK7eSdlW1bGIPwLWkMjPZNEFR6k249BucXlvtftY23VlskamRXEu7Voe9EQRBEATjRAbJDIqKpeyIChmKCvbXqYgmQLqalKX/hnNJgJR2E3a+A4XZ4P9Yg8gm6WaQZh2aBcCpl05hqbh/Vl4VBEEQ7g8ig2QGmiE2FbLSKfomalWyaW1KVj7x6aXLvWsDocSLUnAEkHi+/AXqId0aJI2Mgow66IkgCIIgVEwESGagWblUfQ8fr52VknaejgCcvplW+oYmgxT/T+mxxIvVvk9t0p3FppFXlGegpSAIgiDULREgmcHxaymAlEG6F918pZkCp26mlh7UZJAyYkuPJV24p/vUFkMBUnZhtoGWgiAIglC3RIBkBp/ukjI69xogddEGSHdLD7q1hbIz4xpwBimnKMdAS0EQBEGoWyJAMgM50nRI1T1+vF1LAqQLtzPIKyyZFm9pB+4d9BveiQID9T31jcggCYIgCA2FCJDMQI6mBuneMkjezjY0srWgSKUmSnc2W9Pu+g3VxZAceU/3qg2G6o1EgCQIwoPEz88PmUxW7jFlyhSj56xatapce2tra6PtX3vtNWQyGUuWLKm0PwkJCUybNo2WLVtibW2Nu7s7vXv3ZtmyZeTkPNgZfjHN3wwCZNFAaaBUXTKZjFbuDhyPvktUUhYdvZ2kN5p2h5M/6DdOuihtZluP6a6DpCECJEEQHiQnTpyguLh0odzz588zYMCACvdZA3B0dCQysvQfwjIjS8hs3bqVo0eP4uXlVWlfrl+/Tu/evXF2dmb+/PkEBARgZWXFuXPnWLFiBd7e3gwZMqSKv5m+goICLC0b9hIuIkAyg28svwXAXnbvM7RaNbHnePRdriRmlh5s9nD5hon1v1Bbdx0kDREgCYLwIHFzc9P7+dNPP6VFixb06dOnwvNkMhkeHh4VtomLi+ONN95g9+7dPP3005X2ZfLkySiVSk6ePImdnZ32ePPmzRk6dKh29ewJEyaQlJTEb7/9pm1TWFiIt7c3CxYsYOLEifTt25eOHTuiVCpZt24dAQEB7N+/v9I+1GdiiK2ea+3uAMCVRJ0htsbNwaZkLxybxtJzUv0v1DZUg7Tl6hb+jPmzDnojCML9RK1WU5hfXCeP6m7DUVBQwLp165gwYYLRjJBGVlYWvr6++Pj4MHToUC5c0P9HsUql4uWXX+btt9+mQ4cORq5S6s6dO+zZs4cpU6boBUe6NH0KCQlh165dxMfHa9/77bffyMnJ4YUXXtAeW716NZaWlvz9998sX7680j7UdyKDVM+1KllROyopU/+NqSfhzDpwawMbXoSky3XQO9MYCpCi0qKYtn8aJ186iZXCqg56JQjC/aCoQMWKaQfr5N6vfNUHCyvTNybftm0baWlpjBs3rsJ2bdq04YcffqBTp06kp6fz+eef06tXLy5cuEDTpk0BWLhwIUqlkjfffLNK946KikKtVtOmTRu9466uruTlSdn+KVOmsHDhQnr16kWbNm1Yu3Yts2ZJuyCEhYUxfPhw7O3ttee2atWKzz77rKq/fr0nMkg1rKY39GtVkkG6eTendCYbgJ0rPDIdvIKknzNvQ3FRjd67phlaSVsjPive6HuCIAj3o++//55BgwZVWi/Us2dPxowZQ+fOnenTpw9btmzBzc2N//znPwCcOnWKr776SlvMfS+OHz9OREQEHTp0ID+/9P/ZISEhhIWFAZCYmMjOnTuZMGGC3rldu3a9p3vXNyKDVMOKVGosavB6rvaWOFgrycwrIuZujnbITcuuCcgtQFUImfHg7FODd69ZhjJIGrezb+Pn5Fd7nREE4b6itJTzylcV1/GY896munnzJn/88Qdbtmwx+VwLCwuCgoKIiooC4K+//iIpKYlmzZpp2xQXF/Pvf/+bJUuWcOPGjXLXaNmyJTKZTK/wG6T6IwAbGxu942PGjOHdd98lPDycI0eO4O/vz6OPPqrXxthQXUMlAqQall+kqtEASSaT4edix7m4dG7eMRAgyeXg6CVtXpsR12ADJJFBEgThXshksmoNc9WVsLAwmjRpUqVi6rKKi4s5d+4cTz31FAAvv/wy/fv312sTHBzMyy+/zPjx4w1ew8XFhQEDBvDtt9/yxhtvVBrcuLi4MGzYMMLCwggPDzd63ftJnQ6xHTp0iMGDB+Pl5YVMJmPbtm2VnnPgwAG6dOmClZUVLVu2ZNWqVWbvpykKiu5tar8hzVxsAbh5x8iML6eSoCg91vD79URFAVJcVlwt9kQQBKHuqFQqwsLCGDt2LEpl+TzFmDFjCA0N1f780UcfsWfPHq5fv87p06d56aWXuHnzJiEhIYAUvHTs2FHvYWFhgYeHR7kaI11Lly6lqKiIbt26sXHjRi5dukRkZCTr1q3j8uXLKBT6AWdISAirV6/m0qVLjB07toY+jfqrTjNI2dnZBAYGMmHCBJ599tlK20dHR/P000/z2muv8eOPP7Jv3z5CQkLw9PQkODi4Fnpcufyi4sobmcivJEC6YTRA8paeG3CAFJ8tMkiCIDwY/vjjD2JiYsrV8GjExMQgl5fmL1JTU5k0aRIJCQk0atSIrl27cuTIEdq3b39P/WjRogVnzpxh/vz5hIaGEhsbi5WVFe3bt+ett95i8uTJeu379++Pp6cnHTp0qNI6Sw1dnQZIgwYNYtCgQVVuv3z5cvz9/Vm8eDEA7dq14/Dhw3z55ZdGA6T8/Hy9QrOMjIx763Ql8gtrPoPk21hKfd68Y2RVUydpFkO9D5AqKNK+nXW7FnsiCIJQdwYOHFjhhJ4DBw7o/fzll1/y5ZdfmnQPQ3VHhnh6evLNN9/wzTffVNo2Ozub1NRUJk6cWO69sn2+HzSoWWzh4eEGx1nDw8ONnrNgwQKcnJy0Dx8f89bo5JthiM1XO8RmJEByLMkgZdTvYSpDC0VqiAySIAhC/aRSqUhKSmLevHk4OztXe3XthqZBBUgJCQm4u7vrHXN3dycjI4Pc3FyD54SGhpKenq593Lp1y6x9NMcQm7+rlEGKTS0z1V9DW4Nk3t/tXqjUKgpVhUbfT8xJRKWu+eBSEARBuDcxMTG4u7uzfv16fvjhB4N1U/ej+/63tLKywsqq9hYgzDcUwNwjNwcrHK2VZOQVcT05m/ZejvoNGvlKz3ejQaWSZrbVM7lFhgNYDZVaRbG6GLms/vVdEAThQebn51fja/w1BA3q28jDw4PExES9Y4mJiTg6OpZbs6GuFBaW35D1XslkMu30/qtlV9QGaNwCFFZQkAVpN2r8/jUhNS/V4PEuTbpoXxeraj64FARBEITqqFIGafv27SZfeMCAATUetPTs2ZPff/9d79jevXvp2bNnjd7nXhQUGC9Evhet3O05eTOVq7p7smkolNCkLcT/I21a27i5WfpwL9Ly08ode8T7EZY8voRu67oBUKwWAZIgCIJQP1QpQBo2bJhJF5XJZFy9elW7IqcxWVlZ2pVAQZrGHxERQePGjWnWrBmhoaHExcWxZs0aAF577TW+/fZbZs2axYQJE/jzzz/53//+x44dO0zqnzkV5pspQGpSQQYJwD1ACpASzkO7wWbpw724m3e33DGlTIlCVrrORpGqfm+VIgiCIDw4qjzElpCQgEqlqtLD1ta2Stc8efIkQUFBBAVJ+4nNnDmToKAg5syZA0B8fDwxMTHa9v7+/uzYsYO9e/cSGBjI4sWL+e9//1tv1kCCMkNs9h41dl3tEJuhDBKAR0fpOfF8jd2zJhnKICnkCr0ASWSQBEEQhPqiShmksWPHmjRc9tJLL+Ho6Fhpu759+1ZY+GVoley+ffty5syZKvelthUW6mSQph6vseu29pB2TL5xJ5vcgmJsLMssqe8RID3f/Bty7oJt4xq7d00wVIOkkCmQyWTIZXKpSFvUIAmCIAj1RJUySGFhYTg4OFTesMSyZctwdXWtdqcasqKSAKlAZgXWTjV23SYO1rg5WKFSw6UEA4td+vQAt7aQmwr7Pqyx+9YUYwGS7rPIIAmCIAj1RYOaxdYQFJcMsRXLan4FhQ4l0/svxKWXf1NhAYM+k16f2yxN969HUvMNBEhyKTBSyqXPStQgCYIgCPWFyQFSXl4eixYt4qmnnqJbt2506dJF7/GgKyqUFkNUmTNAum1kuxTf3qC0gYJMuHutxu9/LzQZJFtlaX2ayCAJgvCgKS4u5v3338ff3x8bGxtatGjBvHnzKiw3OXz4ML1798bFxQUbGxvatm1rcOuRuLg4XnrpJW27gIAATp48WWF/CgoKWLRoEV26dMHOzg4nJycCAwOZPXs2t28/2FtAmfwtPnHiRPbs2cPzzz/PQw89hEwmM0e/GqzikiE2cwRIHb2kITujAZJCCZ6d4NYxuH0GXFvVeB+qSxMgudq4EpMpFd5rMkeaTFKxqphzyeewVlrTqlH96bsgCEJNWbhwIcuWLWP16tV06NCBkydPMn78eJycnHjzzTcNnmNnZ8fUqVPp1KkTdnZ2HD58mFdffRU7OzteeeUVQNrQtnfv3jz++OPs3LkTNzc3rl69SqNGjYz2JT8/n4EDB3L27Fk+/PBDevfujZubG9HR0WzYsIFvvvmGBQsWVOv3LCgowNLSslrn1hcmf4v/9ttv/P777/Tu3dsc/WnwNENsKrlFjV+7naeUQYpMzESlUiOXGwhOPTuXBkidRtR4H6pLM4tNN0Aqm0G6ln6NmQdmYqO04cjII9oAShAEoTJqtZoiMy2zUhmllVWVkwVHjhxh6NChPP3004C0SvWGDRs4ftz4pB7d2d6ac7Zs2cJff/2lDZAWLlyIj48PYWFh2nb+/v4V9uXLL7/k8OHD2hnlGs2aNaNPnz7arNaaNWuYMWMGt2/f1tuZYtiwYTg4OLB27Vrmzp3Ltm3bmDp1Kp988gk3b95EVc9KPUxl8jeQt7e3SQXbD5riIvMNsTVtZINSLqOgSEVCRh5ezgZmFnqV/CW/Xb9m+mnWQXKxcdEe09YglXxWmyI3AdK2JDczbtLCuUUt91IQhIaqKD+fr8c+Xyf3fnP1z1hYW1epba9evVixYgVXrlyhdevW/PPPPxw+fJgvvviiyvc7c+YMR44c4eOPP9Ye2759O8HBwQwfPpyDBw/i7e3N5MmTmTRpktHrbNiwgQEDBugFR7o0Qd/w4cN588032b59O8OHDwcgKSmJHTt2sGfPHm37qKgoNm/ezJYtW1AoFAav2ZCYXIO0ePFi3nnnHW7evGmO/jR4xUXSv2DUZsh+KBVymjWWanhupGQbbuTZSXpOvAD1ZO+cYlUxGQXSsKCLtU6ApMkglQRK4fHh2vcu3b1Uiz0UBEGoHe+++y4vvvgibdu2xcLCgqCgIKZPn87o0aMrPbdp06ZYWVnRrVs3pkyZQkhIiPa969evs2zZMlq1asXu3bt5/fXXefPNN1m9erXR6125coU2bdroHXvmmWewt7fH3t6eXr16AWBjY8OoUaP0slPr1q2jWbNm9O3bV3usoKCANWvWEBQURKdOnar6kdRbJn+Ld+vWjby8PJo3b46trS0WFvpDSXfvll8x+UGiKpKG2NSKmh9iA/B1seV6SjY37uTQq6WBBi4tQSaH/AzITABHT7P0wxQFqtLFM+0s7LSvtTVIsvL/0oi8G8m/mv/L/J0TBOG+oLSy4s3VP9fZvavqf//7Hz/++CPr16+nQ4cOREREMH36dLy8vBg7dmyF5/71119kZWVx9OhR3n33XVq2bMnIkSMBUKlUdOvWjfnz5wPSsNz58+dZvnx5pdfVtXTpUrKzs/n66685dOiQ9vikSZPo3r07cXFxeHt7s2rVKsaNG6c3tOjr64ubm1uV71XfmRwgjRw5kri4OObPn4+7u7so0i5DVTLEppaZJ0Dyc7WDyGRu3MlmxsYI4tNzWTuxBxaKkmSg0goa+Uuz2FIi60WApDt930pR+j8STWBkqNbo8t3L5u+YIAj3DZlMVuVhrrr09ttva7NIAAEBAdy8eZMFCxZUGshoaooCAgJITExk7ty52gDJ09OT9u3b67Vv164dmzdvNnq9Vq1aERkZqXfM01P6zmjcWH+x4aCgIAIDA1mzZg0DBw7kwoUL5bb5srOz435icoB05MgRwsPDCQwMNEd/GjxNBgmFeQqM/Vykv4BRSVn8eTkJgBM37tKrhc7CnG5tpQAp+Qo072uWfphCd4VsS0XprAbN0JqxDJIgCML9JicnB7lcv7pFoVCYXNCsUqnI1ylK7927d7lg58qVK/j6+hq9xsiRI5k9ezZnzpwxWoekKyQkhCVLlhAXF0f//v3x8fExqc8Njck1SG3btiU3N9ccfbkvqItLMkgK80xv9HOVAqQzMaULL8amlvnzcGstPSfXjyxMkVrKIMmQYaEzu09TnK0JlKA0m5San0pOYU4t9lIQBMH8Bg8ezCeffMKOHTu4ceMGW7du5YsvvuCZZ57RtgkNDWXMmDHan7/77jt+/fVXrl69ytWrV/n+++/5/PPPeemll7RtZsyYwdGjR5k/fz5RUVGsX7+eFStWMGXKFKN9mTFjBj179qRfv3589dVXnD59mujoaHbv3s3OnTvLFVqPGjWK2NhYVq5cyYQJE2rwU6mfTE5zfPrpp/z73//mk08+ISAgoFwNUlX2YLufqUoCJJkZpvkDNC8JkFJzCrXHopLKbGDrWlJ0l3LFLH0wlWaITSlX6gVDcpkUn+tmkJwsncgtyiWnKIeknCT8nPxqta+CIAjm9M033/D+++8zefJkkpKS8PLy4tVXX9Vu0g7lN2pXqVSEhoYSHR2NUqmkRYsWLFy4kFdffVXbpnv37mzdupXQ0FA++ugj/P39WbJkSYXF39bW1uzbt48lS5YQFhZGaGgoKpUKf39/Bg0axIwZM/TaOzk58dxzz7Fjxw6GDRtWcx9KPWVygPTkk08C0K9fP73jarUamUxGcfGDvRqyrNi8Q2xNG9ngYmfJnezSwufLCZn6jZq0k54TzkpbjsjrdkeZQpUUzCnlSr1gqOxWI5rXTWybcCPjhgiQBEG47zg4OLBkyRKWLFlitE3ZjdrfeOMN3njjjUqv/a9//Yt//cu0yS1WVla88847vPPOO1VqHxcXx+jRo/XWQwKYO3cuc+fONene9Z3J3+L79+83Rz/uG5ohNsw0xCaTyejs48y+kvojgCtlAyT3DmBhC3npUqG2JmCqI5oaJKVMqc0aaX4G9I/JlbjbunMj4waJOYm121FBEATBoNTUVA4cOMCBAwdYunRpXXenVpgcIPXp08cc/bhvaAIkuZmm+QMENdMPkBIy8kjPKcTJtuSeCgto2g2iD0FMeJ0HSLpDbLrZIkNF2hZyC5rYNgEgOTe5FnspCIIgGBMUFERqaioLFy4st3bS/apa40BpaWl8//33XLokLebXoUMHJkyYgJOTU412riGSaWqQlObbg6azT/m9dS4nZNCjeekijPg8XBIgHYNudVtMpynSVsr1M0iGpvkr5UrcbKV1NJJykhAEQRDq3o0bN+q6C7XO5OKUkydP0qJFC7788kvu3r3L3bt3+eKLL2jRogWnT582Rx8bFpX5A6Tu/o20xdoaVxLLDLP59JCeY0+YrR9VpckgKeQKvWyRoYUiNTVIIAIkQRAEoe6YnEGaMWMGQ4YMYeXKlSiV0ulFRUWEhIQwffp0vZU3H0gq8w+xWSkV/D7tUSJupfHn5SRWHLpevlDbo6P0nBoNhXlgUXcLqGmH2GRlirTLbDWiaeNu6w4gapAEQRCEOmNygHTy5Em94AhAqVQya9YsunXrVqOda4jkqkKQg1xpvgAJwNpCwcPNXUjMyAMMZJDs3cHKCfLT4U5UacBUB3RrkHQXSCu7Wa2mjbYGKUfUIAmCIAh1w+QhNkdHR731GTRu3bqFg4NDjXSqoSpWqZFpggEL8w2x6WrVRPrMy62FJJOBm2Y9pLpdlVq3BqmyDJJCrsDTTlrqPjEnkexCI5vyCoIgCIIZmRwgvfDCC0ycOJGNGzdy69Ytbt26xU8//URISIh2T5gHVU5BEUpKprRbVH3zwnvh09gGkBaOzMov0n9TEyAl13GAVBI0WsgtqlSD5GbrRlP7pqjUKk4lnqrdzgqCIAgC1Rhi+/zzz5HJZIwZM4aiopIvPgsLXn/9dT799NMa72BDkltYjCUlBclmLNLW5WBtgbOtBWk5hcSl5tLGQyeLV08CJM06SAqZovIapJKgqYdnD2KvxnIs/hiPNX2sFnsrCIIgCCZmkIqLizl69Chz584lNTWViIgIIiIiuHv3Ll9++WW5lTUfNLkFxdoMksyMRdplNW0kZZFiU8vsXebWVnquJxkko9P8dWqQLGTS59bDU5qFdyz+WG11UxAEQRC0TAqQFAoFAwcOJC0tDVtbWwICAggICMDW1tZc/WtQcgqKUZZkkMy1krYhTZ2lz7/cprWuJZvW3omC4jLDb7WoUK2z1Yi8/FYjhjJIndw6AXAt7Rpqtbq2uioIgmBWmZmZTJ8+HV9fX2xsbOjVqxcnTlS8HMvhw4fp3bs3Li4u2NjY0LZtW7788sty7eLi4njppZe07QICAjh58mSF1y4oKGDRokV06dIFOzs7nJycCAwMZPbs2dy+ffuefteGzuQhto4dO3L9+nX8/f3N0Z8GLaegGEtZyV50ZtqLzRCjGSQnH2nLkcIcabq/a6ta65Muvc1qDQ2xGahLamQlLYZZpC4itygXWwsRhAuC0PCFhIRw/vx51q5di5eXF+vWraN///5cvHgRb29vg+fY2dkxdepUOnXqhJ2dHYcPH+bVV1/Fzs6OV155BZC2AunduzePP/44O3fuxM3NjatXr9KoUfmFhTXy8/MZOHAgZ8+e5cMPP6R37964ubkRHR3Nhg0b+Oabb1iwYEG1fs+CggIsLWsvUWAOJn+Lf/zxx7z11lvMmzePrl27Ymenv2Cho6NjjXWuockr1MkgyetiiK1MBkkul7JI8RGQfLnOAiRtDZJcoT/EZmSzWgAbpQ1KmZIidREZBRkiQBIEoUJqtRp1oapO7i2zkCOTySptl5uby+bNm/nll1947DGptnLu3Ln8+uuvLFu2jI8//tjgeUFBQQQFBWl/9vPzY8uWLfz111/aAGnhwoX4+PgQFhambVdZIuPLL7/k8OHDnDx5Uu/6zZo1o0+fPtrs/Zo1a5gxYwa3b9/WK6UZNmwYDg4OrF27lrlz57Jt2zamTp3KJ598ws2bN1Gp6ubPo6aYHCA99dRTAAwZMkTvL4RarUYmk1FcXFxzvWtgcnRqkGp1iK2RkSE2kAq14yOkOqR2g2utT7q0s9hkFvrBkMzwLDaQNuV1sHQgNT+VjIIMPOw8arHHgiA0NOpCFbfnHKmTe3t91AuZpaLSdkVFRRQXF2Ntrb9wr42NDYcPH67y/c6cOcORI0f0Aqrt27cTHBzM8OHDOXjwIN7e3kyePJlJkyYZvc6GDRsYMGCAXnCkS/MdP3z4cN588022b9/O8OHDAUhKSmLHjh3s2bNH2z4qKorNmzezZcsWFIrKP4/6zuQAaf/+/ebox30hp6AIG20NUu0Nsfm6SAHS9eQsVCo1crnOv2TqwUw2o0XaFdQgAThaOZKan0pmQZlFMAVBEBogBwcHevbsybx582jXrh3u7u5s2LCB8PBwWrZsWen5TZs2JTk5maKiIubOnUtISIj2vevXr7Ns2TJmzpzJe++9x4kTJ3jzzTextLRk7NixBq935coV+vbtq3fsmWeeYe/evQB06tSJI0eOYGNjw6hRowgLC9MGSOvWraNZs2Z65xcUFLBmzRrc3NxM/GTqJ5O/xf39/fHx8SmXTlSr1dy6davGOtYQ5RUW46DJINXiEJu/qx1WSjnZBcXcvJuDv+4+bZqZbAnnaq0/ZVW6UKRMf6sRDUdLabg2Iz+jNropCEIDJrOQ4/VRrzq7d1WtXbuWCRMm4O3tjUKhoEuXLowcOZJTpypf8+2vv/4iKyuLo0eP8u6779KyZUvt+oMqlYpu3boxf/58QBqWO3/+PMuXLzcaIBmydOlSsrOz+frrr/W2Dps0aRLdu3cnLi4Ob29vVq1axbhx4/RiAV9f3/smOIJqBkjx8fE0adJE7/jdu3fx9/cXQ2x1MMSmVMhp6+HAP7HpXLydoR8g+TwMMgUkX4KUq3VSh2TqZrUaDpbSmk4ZBSJAEgShYjKZrErDXHWtRYsWHDx4kOzsbDIyMvD09OSFF16gefPmlZ6rqSkKCAggMTGRuXPnagMkT09P2rdvr9e+Xbt2bN682ej1WrVqRWSk/uiCp6e0k0Hjxo31jgcFBREYGMiaNWsYOHAgFy5cYMeOHXptytYkN3Qmr6StqTUqKysrq9y46oNGmsVW+0NsAO29pGzLhdvp+m/YuUCLJ6TX536u1T5p6G5Wa2gdJN0hNgudzJsmg/RR+Ef8L/J/tdFVQRCEWmFnZ4enpyepqans3r2boUOHmnS+SqUiPz9f+3Pv3r3LBTtXrlzB19fX6DVGjhzJ3r17OXPmTJXuGRISwqpVqwgLC6N///74+PiY1OeGpsrf4jNnzgSkKP3999/XW/uouLiYY8eO0blz5xrvYEOiu1BkbQ6xAbT3cgJuceG2gWxLwPMQtRcu74DHQ2u1X6Bfg6SbITK2Wa2GJkAqUBUw7+g8nmv1nF4wJQiC0NDs3r0btVpNmzZtiIqK4u2336Zt27aMHz9e2yY0NJS4uDjWrFkDwHfffUezZs1o21YqmTh06BCff/45b775pvacGTNm0KtXL+bPn8+IESM4fvw4K1asYMWKFUb7MmPGDHbs2EG/fv344IMPePTRR2nUqBFXrlxh586d5QqtR40axVtvvcXKlSu1fbufVTlA0kSYarWac+fO6a1vYGlpSWBgIG+99VbN97AB0V8osnYDpABvJwBO3UwlM68QB2ud+/s9Ij0nXYTCPLCo3Uyfbg2SbgZJO4vNSJG2ZohN43bWbXwc7+9/sQiCcH9LT08nNDSU2NhYGjduzHPPPccnn3yChUXp/7Pj4+P1NoVXqVSEhoYSHR2NUqmkRYsWLFy4kFdffVXbpnv37mzdupXQ0FA++ugj/P39WbJkCaNHjzbaF2tra/bt28eSJUsICwsjNDQUlUqFv78/gwYNYsaMGXrtnZyceO6559ixYwfDhg2ruQ+lnqpygKSZvTZ+/Hi++uqrB3q9I2PScwux1NYg1W6A1MnbiRZudlxLzmbjiVuEPKoznu3oDTaNIfeuFCR5d6nVvmnWQSpXpC03vlAkSLPYdEVnRIsASRCEBm3EiBGMGDGiwjarVq3S+/mNN97gjTfeqPTa//rXv/jXv/5lUn+srKx45513eOedd6rUPi4ujtGjR5fbWmzu3LnMnTvXpHvXdybXIIWFhekFRxkZGWzbto3Lly/XaMcaovTcwjpZKBJALpcxqSQoWn88Rv9NmQw8pa07SDhbq/0C/Rok3WyRJptkaKFIKB1i04hOjzZnNwVBEAQjUlNT2bp1KwcOHGDKlCl13Z1aYXKANGLECL799ltAWhW0W7dujBgxgoCAgAqr5R8EGbmFOrPYajdAAniyo7SY4vXkbNJyCvTf9CgJkOJrP0AqVOnsxWZgSr+xaf5lh9huZNwwYy8FQRAEY4KCghg3bhwLFy6kTZs2dd2dWmFygHTo0CEeffRRALZu3YparSYtLY2vv/7a6DLpD4r03EKdWWy1vweNs62ldtHIs7FlZrNpA6R/arlX97BQpMggCYIg1As3btwgPT39gao1NjlASk9P166PsGvXLp577jlsbW15+umnuXr1qskd+O677/Dz88Pa2poePXpw/PjxCtsvWbKENm3aYGNjg4+PDzNmzCAvL8/k+5pDWm6Bziy22p3mr9GpqTMAZ2PT9N9o2k16jo+Aguza7JK2SLvsXmya18ZqkGyUNnrXuZ52Xbs3kCAIgiCYk8kBko+PD+Hh4WRnZ7Nr1y4GDhwISOOTpq6DtHHjRmbOnMkHH3zA6dOnCQwMJDg4mKSkJIPt169fz7vvvssHH3zApUuX+P7779m4cSPvvfeeqb+GWWTlFdXpEBtAYFNpNlu5DFIjP6lYW1UEtyoOQmuapkjbQm6BjNI1tAzVIOmug+Ri46J9rZApSM1PJSE7wdzdFQRBEATTA6Tp06czevRomjZtipeXl3YflkOHDhEQEGDStb744gsmTZrE+PHjad++PcuXL8fW1pYffvjBYPsjR47Qu3dvRo0ahZ+fHwMHDmTkyJGVZp1qixqwpO6G2KA0g3Q+rkyAJJOBb2/p9c2/a7VPukXaakozQJpgyVgGycfBh8V9FrP6ydW0btQagPN3ztdGlwVBEIQHnMkB0uTJkwkPD+eHH37g8OHDyOXSJZo3b25SDVJBQQGnTp2if//+pZ2Ry+nfvz/h4eEGz+nVqxenTp3SBkTXr1/n999/56mnnjJ6n/z8fDIyMvQe5qJWU+dDbG08pMLm2+l5ZOQV6r+pWQ/pZu3ueK1bg2SlKJ0aalkSRBqrQQIY6DeQLu5d6OjaEYBzKXW3p5wgCILw4KjWt3i3bt3o1q2b3rGnn37apGukpKRQXFyMu7u73nF3d3ejSwaMGjWKlJQUHnnkEdRqNUVFRbz22msVDrEtWLCADz/80KS+3QtLSoISpVXFDc3EycYCTydr4tPzuJqYSVdfnf10mnaXnuPPgkoFcpPj42rRrUFysHTgvR7vIUOGnYW0b4+xlbR1BbgGsOnKJs6niAySIAiCYH4mB0iaLUfKkslkWFtb07JlS4YOHVpuo7uacODAAebPn8/SpUvp0aMHUVFRTJs2jXnz5vH+++8bPCc0NFSvzxkZGWbbP0aOCqVMJf2gqJsACaC1uwPx6XlEJmTpB0iuraShv4JMSLsJjf1rpT+6GSSAkW1H6r2vl0GSGf4r2d5F2oTx8p3LRvcDFARBEISaYnKAdObMGU6fPk1xcbF2LYQrV66gUCho27YtS5cu5d///jeHDx8ut7OwLldXVxQKBYmJiXrHExMT8fDwMHjO+++/z8svv0xISAgg7WicnZ3NK6+8wv/93/9ph/t0WVlZlVvx01ws0Fl7SFk3NUggDbMdvJJMZEKZ4USFBTRpJ031TzhX+wGSkeDHWA2SrmaOzQDILMwkoyADJyunGu6lIAiCIJQyeYxl6NCh9O/fn9u3b3Pq1ClOnTpFbGwsAwYMYOTIkcTFxfHYY4+V28OlLEtLS7p27cq+ffu0x1QqFfv27aNnz54Gz8nJySkXBGk206sP07+1w2tQ5xkkgMjEzPJvupcU0ifW3lBV2QxSWVUJkGyUNrhYS7Pa4rLiariHgiAItSMzM5Pp06fj6+uLjY0NvXr14sSJE5We9+OPPxIYGIitrS2enp5MmDCBO3fuaN8vLCzko48+okWLFlhbWxMYGMiuXbsqva5arWblypX07NkTR0dH7O3t6dChA9OmTSMqKuqefteGzuQAadGiRcybN09vuxEnJyfmzp3LZ599hq2tLXPmzOHUqVOVXmvmzJmsXLmS1atXc+nSJV5//XWys7O1uxqPGTOG0NDS3ecHDx7MsmXL+Omnn4iOjmbv3r28//77DB48uNyuw3XBoqTWBqizaf4A7TylAOlCXAbFqjKBo4dU7ExC7RU7a2qQLIxsv1JRkbYubwdvQNq0VhAEoSEKCQlh7969rF27lnPnzjFw4ED69+9PXJzxf/j9/fffjBkzhokTJ3LhwgU2bdrE8ePHmTRpkrbN7Nmz+c9//sM333zDxYsXee2113jmmWe0G80bolarGTVqFG+++SZPPfUUe/bs4eLFi3z//fdYW1vf0+LPxcXFqFSqap9fH5g8xJaenk5SUlK54bPk5GTtDDFnZ2cKCgoMna7nhRdeIDk5mTlz5pCQkEDnzp3ZtWuXtnA7JiZGL2M0e/ZsZDIZs2fPJi4uDjc3NwYPHswnn3xi6q9hFpoMklphVac1Mm09HLG3UpKZX8TlhAw6eOkMR3mUZJASai+DpFkHSTcQ0qWbQTIWRAF423lzNvmsyCAJglCOWq2msLCw8oZmYGFhUaX/5+fm5rJ582Z++eUXHnvsMUDa5PXXX39l2bJlRgOS8PBw/Pz8ePPNNwHw9/fn1VdfZeHChdo2a9eu5f/+7/+0s7pff/11/vjjDxYvXsy6desMXnfjxo389NNP/PLLLwwZMkR7vFmzZjz88MPakZlDhw7Rr18/bt26pVcCM336dE6dOsVff/3FqlWrmD59OmvWrOHdd9/lypUrREVF4efnV+nnUl+ZHCANHTqUCRMmsHjxYrp3l2ZFnThxgrfeeothw4YBcPz4cVq3bl2l602dOpWpU6cafO/AgQP6nVUq+eCDD/jggw9M7XatKA2QLKnLEmKFXEYX30YcupLMiei7+gGSewfpOT0GclPBppHZ+1NZDZKxzWrL0mSQYjNja7B3giDcDwoLC5k/f36d3Pu9997D0rLyutOioiKKi4vLLapsY2PD4cOHjZ7Xs2dP3nvvPX7//XcGDRpEUlISP//8s94SN/n5+SZfd8OGDbRp00YvONKlCfoee+wxmjdvztq1a3n77bcB6fP+8ccf+eyzz7Ttc3JyWLhwIf/9739xcXGhSZMmRu/dEJg8xPaf//yHfv368eKLL+Lr64uvry8vvvgi/fr1Y/ny5QC0bduW//73vzXe2frOomSRSHUdLRKp6yE/KfA5cSNV/w2bRuAkFTyTeKFW+qK7Wa0hValBAvCy9wLgdrYYYhMEoeFxcHCgZ8+ezJs3j9u3b1NcXMy6desIDw8nPj7e6Hm9e/fmxx9/5IUXXsDS0hIPDw+cnJz47rvvtG2Cg4P54osvuHr1KiqVir1797Jly5YKr3vlypVyG89Onz4de3t77O3tadq0qfb4xIkTCQsL0/7866+/kpeXx4gRI7THCgsLWbp0Kb169aJNmzbY2tqa9PnUNyZnkOzt7Vm5ciVffvkl169fB6RFIu3t7bVtOnfuXGMdbEh0h9jqWjc/aXr/mZjU8m96dJQySAnnSxePNCNNDZLRAKkK0/wBvO2lDNKh2EMkZCfgYWd4tqMgCA8eCwuLOtt2ysKi6jWna9euZcKECXh7e6NQKOjSpQsjR46ssG734sWLTJs2jTlz5hAcHEx8fDxvv/02r732Gt9//z0AX331FZMmTaJt27bIZDJatGjB+PHjje5MYcz//d//MXXqVLZs2aKXkRs3bhyzZ8/m6NGjPPzww6xatYoRI0ZgZ2enbWNpaUmnTp1Mul99Vu3lnu3t7e+rD6Im1PU2I7rae0lF9LfT80jPKcTJVuc/YI9OEPm7tOXIw6+ZvS+aGiRjAVJVFooEaOncEoVMQbG6mDf+fINNgzfVbEcFQWiwZDJZlYa56lqLFi04ePAg2dnZZGRk4OnpyQsvvEDz5s2NnrNgwQJ69+6tHd7q1KkTdnZ2PProo3z88cd4enri5ubGtm3byMvL486dO3h5efHuu+9WeN1WrVoRGRmpd8zNzQ03N7dyw2NNmjRh8ODBhIWF4e/vz86dO8uVwdjY2NxXa9SZPMSWnZ3N+++/T69evWjZsiXNmzfXezzIdGuQ6pqjtQXezjYAXC67HlLbknHrq3sgz3xbr2hUOs2/irPYmtg2YcWAFQBcvnuZO7l3jLYVBEGoz+zs7PD09CQ1NZXdu3czdOhQo21NWeLG2toab29vioqK2Lx5c4XXHTlyJJGRkfzyyy9V6nNISAgbN25kxYoVtGjRgt69e1fpvIbK5AxSSEgIBw8e5OWXX8bT0/O+ihbvVX0aYgNpun9cWi6RiZn0aO5S+oZHJ3BpBXeuwrV90OEZs/ajJhaK1HjI8yH8HP24kXGDi3cu8mjTR2uuo4IgCGa2e/du1Go1bdq0ISoqirfffpu2bdtql7cBaQeIuLg41qxZA0hL3EyaNIlly5Zph9imT5/OQw89hJeXVJt57Ngx4uLi6Ny5M3FxccydOxeVSsWsWbOM9uXFF19ky5YtvPjii4SGhhIcHIy7uzs3b95k48aN5ZbPCQ4OxtHRkY8//piPPvrIDJ9O/WJygLRz50527Nhx30eO1WGhWSiyHmSQQJru/8elJC7Fl1kwUiaDFk9IAdKNv2svQLrHIm2N9i7tRYAkCEKDlJ6eTmhoKLGxsTRu3JjnnnuOTz75RK+OKT4+npiYGO3P48aNIzMzk2+//ZZ///vfODs788QTT+hN88/Ly2P27Nlcv34de3t7nnrqKdauXYuzs7PRvshkMjZu3MjKlSsJCwvjs88+o7CwkKZNm9KvXz+++OILvfZyuZxx48Yxf/58xowZU3MfSj1lcoDUqFEjs+yzdj/Q1CCp63CbEV1tSxaMLDfEBuDXG47/R6pDMjPdzWpJiwG1Ghr5ljbQSUJWVKSt0d6lPb9H/87FOxdruquCIAhmNWLECL2ZX4asWrWq3LE33niDN954w+g5ffr04eJF0/+fKJfLefXVV3n11Ver1D4uLo6nnnoKT09PvePjxo1j3LhxJt+/PjO5BmnevHnMmTOHnJwcc/SnQSvNINWPIba2HiVbjiRkoiq7orZvSQYw6SJkm7eWR5tBKlbBkgD4qhMU6SwkqtO1qmaQAC7eFQGSIAhCbUhPT+fw4cOsX7++wkDtfmJyBmnx4sVcu3YNd3d3/Pz8yk1vPH36dI11rqGxqkdF2gB+LnZYKuXkFBRzKzUHX5fS6ZjYuYJrG0iJhNgT0OZJs/VDEyBZ5KSUHszPBKVUF6XWiZCqEiC1a9wOgITsBO7k3sHFxqWSMwRBEIR7MXToUI4fP85rr73GgAED6ro7tcLkAEmzWrZQXuk0//qRQVIq5LR2t+d8XAaX4jP1AyQAz0ApQEo8XysBkjI3rfRgcb72pe4sjKoESPaW9qJQWxAEoRaVndL/IDA5QKpom4/i4uJ76kxDZ1HPZrGBVKh9Pi6DywkZPNmxzMKK7u3hHGZfUbtYXbIXW1Zy6cGiPO1LzQrZoF+wXRFRqC0IgiCYk8k1SIZcuXKFd955R29Z8geRNoNUT4q0obQO6XLZmWwA7h2l5yTz1fKo1WptgKTMTip9o7A0QLK1sGX/iP0cfvFwlZeN0NYhiUJtQRAEwQyqHSDl5OQQFhbGo48+Svv27Tl48CAzZ86syb41OPVtHSSA9p7SitqnYlIpLluo3UQKMki5CkX5mINmeA1AmakTIGXchpy72h9dbVxxsnKiqjQB0vk758stlCYIgiAI98rkIbajR4/y3//+l02bNtGsWTMuXbrE/v37efRRMcxhWc/WQQLo6tcIR2slyZn5nLhxl4d1F4x09AJrZ8hLg6RL4NW5xu+v2agWwCIrsfSNH5+Tnt9PAUXV9zHS6ODSAaVcSVJOErFZsfg4+NxrVwVBEARBq8oZpMWLF9OhQweef/55GjVqxKFDhzh37hwymQwXFzGLCOrfLDYAK6WCQR2l9Sq2/3Nb/02ZDJp2k15H7TXL/fUCpEwDu0pn3C5/rApsLWwJcA0A4ETCiWpdQxAEQRCMqXKA9M477zBs2DBu3rzJokWLCAwMNGe/GiQbmTRMpbawreOe6HuqkxQgHYxMLv9m+2HS84VtZrm3JkCSIUORUXMBEkB3j+6ACJAEQRCEmlflAGnevHls2rQJf39/3nnnHc6fP2/OfjVI1pQESJb1K0Dq6tsImQzi0nJJyszTf7Pdv0BuIU31T7la4/fW3WZElplQvkF6bLWv3c1dyn5FJEVU+xqCIAiCYEiVA6TQ0FCuXLnC2rVrSUhIoEePHgQGBqJWq0lNTTVnHxsMW+pnBsneSklLN3sAzt5K13/TphH49JBex9Z8JqawWMogWcjkoDawDET6rWpfW7M8wN28u5W0FARBqB8OHTrE4MGD8fLyQiaTsW3btnJt1Go1c+bMwdPTExsbG/r378/VqxX/A7Yq1x03bhwymUzv8eSTla+Bl5CQwLRp02jZsiXW1ta4u7vTu3dvli1bdl/vqmHyLLY+ffqwevVqEhISmDx5Ml27dqVPnz706tWr3MZ2DxqbkgAJZf0KkAACfZwB+Cc2rfybHiXT/c2wHlKhuiRAMjbT7B4ySI6W0gy9nKIcvVonQRCE+io7O5vAwEC+++47o20+++wzvv76a5YvX86xY8ews7MjODiYvLw8o+dU5boATz75JPHx8drHhg0bKmx//fp1goKC2LNnD/Pnz+fMmTOEh4cza9YsfvvtN/7444+Kf+EKFBQUVN6oDpk8i03DwcFBu8HduXPn+P777/n0008f6Kn+tvW0BgmkAOnnU7FE3Eor/6Zmur85AiRNBqnYSABz8ntpmK/FEyZf28HSQfs6syCTxtZiE2VBeFCp1WpUqtw6ubdcblPlNdwGDRrEoEGDjL6vVqtZsmQJs2fPZujQoQCsWbMGd3d3tm3bxosvvlit62pYWVnh4eFRaTuNyZMno1QqOXnyJHZ2pbsxNG/enKFDh2qXWZkwYQJJSUn89ttv2jaFhYV4e3uzYMECJk6cSN++fenYsSNKpZJ169YREBDA/v37q9yX2lblAGnMmDEMHTqU4OBg7O3t9d4LCAhgyZIlLFq0qMY72JBohtioZzVIAF2aOQNw+mYqhcUqLBQ6yUN382WQSjeqLQmQmveF6wf0G619Bt6LN/lzU8qV2FnYkV2YTUZ+hgiQBOEBplLlcuBgQJ3cu2+fcygUNfP//ejoaBISEujfv7/2mJOTEz169CA8PNxogFRVBw4coEmTJjRq1IgnnniCjz/+2OhM9Dt37mgzR7rBkS5NYBgSEsJjjz1GfHw8np7SxKDffvuNnJwcXnjhBW371atX8/rrr/P333/f0+9RG6o8xNayZUvmz5+Pm5sbgwYNYtmyZcTFxem1Kbtx7YPGpp7WIAG083Ckka0F2QXF/FM2i9SkLSCD7CTISjJ0erVphr60Q2yN/Aw3TLlSretrhtkyCjKqdb4gCEJ9kpAgTWZxd3fXO+7u7q59r7qefPJJ1qxZw759+1i4cCEHDx5k0KBBRrcJi4qKQq1W06ZNG73jrq6u2NvbY29vzzvvvANAr169aNOmDWvXrtW2CwsLY/jw4XpJlVatWvHZZ5/Rpk2bctetb6qcQZozZw5z5swhNjaW7du3s23bNmbMmEGHDh0YOnQoQ4YMoXPnzmbsav2mVqtLh9jqYQ2SXC6jZwsXfj+XwN9Rd+jmp5NtsbQDlxZwJwpiwqH90Bq7b2mABFjaSw9Dki9Xa6FKR0tH4rPjRYAkCA84udyGvn3O1dm9GwLd7FNAQACdOnWiRYsWHDhwgH79+lX5OsePH0elUjF69Gjy80t3YQgJCWHFihXMmjWLxMREdu7cyZ9//ql3bteuXe/9F6klJhdpN23alMmTJ7N7926Sk5N55513iIyM5IknnsDX15epU6dy4YJ5Nz+trzTT/KmHGSSAXi1cAThyLaX8m22flp4jKi7YM5VeBsnKEZQ627D4PwZtSu6bfLla13e0Kskg5YsASRAeZDKZDIXCtk4eVa0/qgpNfVBiYqLe8cTERJNqh6qiefPmuLq6EhUVZfD9li1bIpPJiIyMLHdey5YtsbHRDwzHjBnD9evXCQ8PZ926dfj7+5fbZcPYUF19dE+b1To4ODBixAh+/PFHkpOT+eGHH1AoFISHh9dU/xoUBSrphbxqO9LXtof8pazR2dh0iopV+m92fkl6vroHMhOpKdoaJNRg5QBKnf+gXFpCi8el10nVDJBKhtjWXVrH2eSz99RXQRCEuubv74+Hhwf79u3THsvIyODYsWP07NmzRu8VGxvLnTt3tDVDZbm4uDBgwAC+/fZbsrOzK72ei4sLw4YNIywsjFWrVjF+/Pga7W9tu6cASZdCoaBfv3589dVXhISE1NRlGwy1GjT/hqjJf03UpBZu9thZKsgtLCYqOUv/TbfW0LS7tFbR5d8MX6AatLPY1IB1mQyShS24lYxBJ1+q1vU1AdK5lHOM/n202LhWEIR6LSsri4iICCIiIgCpKDsiIoKYmBhA+v6YPn06H3/8Mdu3b+fcuXOMGTMGLy8vhg0bpr1Ov379+Pbbb6t83aysLN5++22OHj3KjRs32LdvH0OHDqVly5YEBwcb7e/SpUspKiqiW7dubNy4kUuXLhEZGcm6deu4fPkyCoV+QiAkJITVq1dz6dIlxo4dWwOfWN0xOUBKTEzk5ZdfxsvLC6VSiUKh0HsIUBoq1S8KuYyO3k6AgQUjAVoNlJ6jD9bYPfXWQbJyAKV16ZsWtuDaWnqdehNUhgsFK6IJkDRuZtysdl8FQRDM7eTJkwQFBREUFATAzJkzCQoKYs6cOdo2s2bN4o033uCVV16he/fuZGVlsWvXLqytS///ee3aNVJSUqp8XYVCwdmzZxkyZAitW7dm4sSJdO3alb/++gsrK51/uJbRokULzpw5Q//+/QkNDSUwMJBu3brxzTff8NZbbzFv3jy99v3798fT05Pg4GC8vLzu/QOrQyavgzRu3DhiYmJ4//338fT0rLfZktqmBmTU/+xFoI8zx6Lv8k9sGiO6++i/6d8H9n8C0X+BSgXye08wlmaQSmqQLHQCJEtbKWgCQA2FuWBlpIjbCE0NksbppNP4OfndQ48FQRDMp2/fvpVmumUyGR999BEfffSR0TY3btww6bo2Njbs3r3bpL5qeHp68s033/DNN99U2jY7O5vU1FQmTpxY7r0DBw5U6/51xeQA6fDhw/z1118P9Iy1ytTnoDGwqTMg1SGV491FmmWWe1fam82z0z3fr7QGCbBy4HJyAW01b1rY6tckFeWZHCA5WTrp/Xwq8RTPtnq22v0VBEEQTKdSqUhJSWHx4sU4OzszZMiQuu7SPTM5ReDj4yPqPBqwTk2lgOJyQgZ5hWWGtBQW4Ntbel1Dw2y6s9gKLBz48qDO3msWtlKWSmFZ0tj0VXDLZpDOJJ2pdl8FQRCE6omJicHd3Z3169fzww8/oFRWe6OOesPkAGnJkiW8++675dJ7Dzq1Wq0dYqvHCSSaNrKhsZ0lhcVqLsUbmBrv/5j0fL3mA6RMtQ356CwmamGj/1yNAKlsti42M5a8IuP7FQmCIAg1z8/PD7Vaza1bt0xaU6k+MzlAeuGFFzhw4AAtWrTAwcGBxo0b6z0EUNfTIm2QAorAkiySwWG25n2k55tHoOjeNxLUHWJLLbYmH8vSNy1L1sPQDLMVmR4gNbFpon3tbOWMGjU3Mm5Us7eCIAiCIDE5B7ZkyRIzdKPhk4q0G4bOPo3YH5nM4agUxvby03+zSQewdYGcO3D7NDR7+J7upZtBSim0Il+ts/6SZkFNTeF2oemZn6AmQbz/8Pu0atSKJaeWcDrpNNfSrtG2cdvKTxYEQRAEI0wOkBr6uga1QSarseWlzOLJjh58+ccVDkQmkZpdQCM7nayOXC4Ns13YKg2z1WCAlJhvSR6lAdL2S2kMaU5pBikvHY58Aw6eEPB8la4vk8kY0WYEAM2dm2sDJEEQBEG4FyYHSJpFp4xp1qxZtTvT8DWM4vU2Hg6083TkUnwGf15O4rmuTfUbaAKk6IPQ9517upfuQpG38yzI1wmQvjt8m0f7FtBIU4O0frj0LFdCy35g08ike7VwagHA9fTr99RnQRAEQTA5QPLz86twGruxXYHvd3oT++pzlXaJJ9q6cSk+g8NRKQYCpJI6pFvHoSC7tFaoGnS3GrmVrSRf571crLiVmlMaIGmoiiBqX5WzSBrNnZoDcCP9RrX7KwiCIAhQjSLtM2fOcPr0ae3j2LFjLF++nNatW7Np0yZz9LHBaAgLRWo82soNgL+upqBSlel34+bg5AOqQoi5t331dIfYorMU5KtLZ7Hlqi25eSdHf3Vtjat7TL6Xg6W06GROUU71OisIgiAIJUzOIAUGBpY71q1bN7y8vFi0aBHPPvtgLtKn1pu7Vv8zSF2aNcLWUkFKVj6XEzJp76WznpBMJmWRItZB9CFo2b/a9ykNkOBGppxCSrejKcCCmLs5pdP8dUX9UbLBXdU/S8uS9ZQKiu999p0gCILwYKuxauI2bdpw4sQJk8/77rvv8PPzw9ramh49enD8+PEK26elpTFlyhQ8PT2xsrKidevW/P7779Xtdo0q3ay2TrtRJZZKOV19pRqf0zGp5RvU0HpIRQXSDtAWajV3Vbak4cDaov6sKRpAOvZcS85CrRsgubaRFo7MuQNppu2rpg2QVCJAEgShfjp06BCDBw/Gy8sLmUzGtm3byrVRq9XMmTMHT09PbGxs6N+/P1evXq3wusuWLaNTp044Ojri6OhIz5492blzp16bvLw8pkyZgouLC/b29jz33HMkJiZW2ueoqCgmTJhAs2bNsLKywtvbm379+vHjjz9SVFRk0u/fkJgcIGVkZOg90tPTuXz5MrNnz6ZVq1YmXWvjxo3MnDmTDz74gNOnTxMYGEhwcDBJSUkG2xcUFDBgwABu3LjBzz//TGRkJCtXrsTb29vUX8O8GkKEROm2I+cMrYekCZDi/4Gcu9W+R2GhFCDJZBbaNZDeL5rAnKLxAGw5HccfV3Tub9sYmrQrufdZk+6lCZA0heGCIAj1TXZ2NoGBgXz33XdG23z22Wd8/fXXLF++nGPHjmFnZ0dwcDB5ecaXQmnatCmffvopp06d4uTJkzzxxBMMHTqUCxcuaNvMmDGDX3/9lU2bNnHw4EFu375d6ajP8ePH6dKlC5cuXeK7777j/PnzHDhwgJCQEJYtW6Z3fVMVFNTvf8yaPMTm7OxcrkhbrVbj4+PDTz/9ZNK1vvjiCyZNmsT48dKX5fLly9mxYwc//PAD7777brn2P/zwA3fv3uXIkSNYWEi1LH5+fqb+CmahVpfWIDWM8AgCNAtGxhkIkBw9wbU1pFyR6pDaPl2texSWZJAK1cZ3i47LpvRvoqU9uLSUArP4f6B91ffzsZSXZpDUanW93hNPEISapVaryVGpKm9oBrZyeZX/fzNo0CAGDRpk9H21Ws2SJUuYPXs2Q4cOBWDNmjW4u7uzbds2XnzxRYPnDR48WO/nTz75hGXLlnH06FE6dOhAeno633//PevXr+eJJ54AICwsjHbt2nH06FEefrj8ki5qtZpx48bRunVr/v77b+Q6G5i3atWKkSNHarcee+KJJ2jfvj3ffvuttk1ycjLe3t7s3LmTfv364efnx8SJE7l69Srbtm3j2WefZdWqVVX63OqCyQHS/v379X6Wy+W4ubnRsmVLk/ZeKSgo4NSpU4SGhupdq3///oSHGy4M3r59Oz179mTKlCn88ssvuLm5MWrUKN555x0UCoXBc/Lz88nPL507lZFhYHuNB5RmX7YriZnkFhRjY1nmM/TtLQVIN/6udoBUVLI6dk6xFLw83saN/ZHJem3ydFfXtnIAz0A4sxYSqpdBUqlV/Ofsfwh0C6SnV89q9VsQhIYlR6WixaFzdXLva48FYGfkO8hU0dHRJCQk0L9/ae2nk5MTPXr0IDw83GiApKu4uJhNmzaRnZ1Nz57S/wNPnTpFYWGh3nXbtm1Ls2bNCA8PNxggRUREcOnSJTZs2KAXHOnSBIYhISFMnTqVxYsXY2Ul/YN43bp1eHt7awMygM8//5w5c+bwwQcfVOHTqFsmD7H16dNH7/Hoo4/Stm1bkzemS0lJobi4GHd3d73j7u7uJCQkGDzn+vXr/PzzzxQXF/P777/z/vvvs3jxYj7++GOj91mwYAFOTk7ah4+Pj0n9NEVDmsUG4OFojZuDFcUqNWdj08o30Gxce/Pvat+jsGR/tQKVNTIZ+LqULhkgl4GTjUWZAMkePDtLr2+fARP+RWghL50h913Ed7yy9xX+iv2r2n0XBEGobZrvP1O+GzXOnTuHvb09VlZWvPbaa2zdupX27dtrr2tpaYmzs3OVr3vlyhVAqjHWSEpKwt7eXvtYunQpgHao7pdfftG2XbVqFePGjdPLrj3xxBP8+9//pkWLFrRo0aLC36eumZxBWr16Na6urjz9tJRRmDVrFitWrKB9+/Zs2LABX1/fGu+khkqlokmTJqxYsQKFQkHXrl2Ji4tj0aJFRqPR0NBQZs6cqf05IyPDrEESgMxIpF3fyGQyerdwYVvEbXaci6dHcxf9Br69pOeEs5CXAdaO5S9SicJiacw8F2vsrZR6WapngpoS4O1Iwu+6AZIjeASAlRNkJ8PNw6X1UJXQZJB0zT82n9+9fxfDbYJwn7OVy7n2WECd3bs+aNOmDREREaSnp/Pzzz8zduxYDh48qA2SaoKLiwsREREA9O3bV1tHZG1tzcsvv8wPP/zAiBEjOH36NOfPn2f79u1653fr1q3G+mJuJv+pzp8/HxsbadZReHg43377LZ999hmurq7MmDGjytdxdXVFoVCUq6BPTEzEw8PD4Dmenp60bt1abzitXbt2JCQkGC32srKy0lb1ax7m0hC/gp/tIi0Suf2f2xQUlcnWOHlLayKpVXBtX7WuX1QkDW/mq2xwtLbAWln6Z2dnpaC9l1P5ITYLa+hYUjh4Zl2V76WUK5GX2eYlNitWrKwtCA8AmUyGnUJRJ4+a/AeY5vvPlO9GDUtLS1q2bEnXrl1ZsGABgYGBfPXVV9rrFhQUkJaWVuXraiZeRUZGao8pFApatmxpsKwmJCSEvXv3EhsbS1hYGE888US5pImdXfUXHq5tJgdIt27domXLlgBs27aN559/nldeeYUFCxbw119VH86wtLSka9eu7NtX+sWrUqnYt2+fdsy0rN69exMVFYVKZ9jlypUreHp6YmlZPntQm3RX0pY1oFCpd0tXmjhYkZZTyLHoO+UbtCsp/Du/pVrXLyyZcp+jtsXJxgIby9K/cjaWClo2sdcPkCztpefOo6XnyzvKLFNeMU2htq6Dsfe2VIEgCEJt8ff3x8PDQ++7MSMjg2PHjhn9bjRGpVJpa3C7du2KhYWF3nUjIyOJiYkxet2goCDatm3L559/rve9a0xAQADdunVj5cqVrF+/ngkTJpjU3/rG5ADJ3t6eO3ekL9I9e/YwYMAAQEqv5ebmmnStmTNnsnLlSlavXs2lS5d4/fXXyc7O1s5qGzNmjF4R9+uvv87du3eZNm0aV65cYceOHcyfP58pU6aY+muYhVzWsGqQABRyGQ+XDK39cyutfIOOz0nPV/dAfqbJ1y8sLg2QHG2UWFvoZJAsldhZKchVl8kggVSoLZNDQRZkVb5Oh4aFwqLcsV3Ru1Cp62Z2iyAIgq6srCwiIiK0w1TR0dFERERo9zmVyWRMnz6djz/+mO3bt3Pu3DnGjBmDl5cXw4YN016nX79+ejPGQkNDOXToEDdu3ODcuXOEhoZy4MABRo+W/rHp5OTExIkTmTlzJvv37+fUqVOMHz+enj17GizQ1vQlLCyMyMhIevfuzfbt27l69SoXL15k+fLlJCcnl5sgFRISwqeffopareaZZ56pwU+u9plcgzRgwABCQkIICgriypUrPPXUUwBcuHDB5PqjF154geTkZObMmUNCQgKdO3dm165d2uK0mJgYvcp5Hx8fdu/ezYwZM+jUqRPe3t5MmzaNd965tw1Va4Jap0Bb3cDqXTo1dWL7P7f5x9B6SB6dpGG2u9el/dE6DDPp2oWqIpBBtspOGmLTCZBsLRVYKuQUyMoUaQMoLcGpKaTFQOoNcKg4tayhm0Hq0qQLkamRXLp7if9F/o8X21Y++0MQBMGcTp48yeOPP679WVMjO3bsWO2U91mzZpGdnc0rr7xCWloajzzyCLt27cLaunRbpmvXrpGSkqL9OSkpiTFjxhAfH4+TkxOdOnVi9+7d2iQGwJdffolcLue5554jPz+f4OBgbZG1MQ8//DCnTp3SJiMSEhKws7MjMDCQL7/8slyWaOTIkUyfPp2RI0fq9bchMjlA+u6775g9eza3bt1i8+bNuLhI2YdTp05pI1VTTJ06lalTpxp878CBA+WO9ezZk6NHj5p8n9rUsMIjCPRxBjA8k00mg9aD4Oh3UhbJxACpSFUECshW29PURj9AsrGUxu5VCp3/iKx0asQa+UkB0t1oaGb4Xzhl6RZqe9t7M9BvIJ8e/5SV51Yyos2IcjVKgiAItalv377atYOMkclkfPTRR3z00UdG29y4cUPv5++//77Se1tbW/Pdd99VuEilIa1bt67yekUpKSnk5eUxceLEcu+V7XN9Z/K3hbOzM99++y2//PILTz75pPb4jBkz6s2ijXWugWWQOng5IpdBYkY+CekGVmptHSw9X91j0rR7gEJ1MQCZavuSIu3Sv3J2liXxue5mtZoaJIBG/tJz6o0q3083QLJUWDK89XAcLBxIykniTNIZk/ouCIIgVE1hYSEJCQnMnj2bhx9+mC5dutR1l+5Zjf1z+ubNm7z88ss1dbkGpy6KtOeFz+ONfW9oN4StLltLJe08pczN3ksG6n2a9QRLB2na/W0TggyVikKkgCpTZYejjf40f81r3b3YkgosSckqWdizkZ/0fOK/0qraVaAbIFkrrbFUWPJEM2mRsp3RO42dJgiCINyDv//+G09PT06cOMHy5cvrujs1Qow31BS9lGntBEj/u/I/DsQeYO+Nvfd8Lc10/x+P3iyf/lVaQsuSlVCv7Kr6RfMz0IRuWWqHcjVI2gySToD04urzDP7msLTkQOOSDFJOCqwZBsWVb4qoW4NkpZBWc33SX8p07r25VxryEwRBEGqUZugwMjKSgIC6WY+qpokAqYbohUe1EB/pBjGH4g7d8/We79IUaws5lxMyOXUztXyD1iXDqVd3V/2ieWkUlXwYhWorHG0ssLEon0FSKkv3abtbZEV8eh6Ho5JLM0gAuXerlL3SyyCV1Db18OyBs5Uzd/PucjzheNX7LwiCIDywRIBUY3TH2MwfIelOWz8Ue+iep7E72VowqKMnALsvGFh2vuUAQCYNdWXcrtpF89IpLPks1GoFjtZKrC10apCspADJWln6eWUjZZN+/Sce3DuCf5/S610/UOktdTNImmDJQm7BAF9pJseuaBMyYIIg1HuVFTwLDUt9+vOs8iy2r7/+usL34+Li7rkzQtUVlxQ/A2QWZPJX7F/08elTwRmV69/Ona1n4th3OYn/e7rM0vT2buDzENw6Bhe3w8OvVX7B3DTtEBtqBY42FljprKRtayH99Uu1bcZpVUvuqh0oLPkrufdiIkV0Qjl2OxxfCb+/BdEHoc/bFd5Sdx0ka53i70H+g9h0ZRN/xPzB7IdnG9yWRBCEhkOz/k5BQYF2dweh4dPsimFsA/raVOUA6csvv6y0TbNmze6pMw2ZWm92l/kzSGVradZeWnvPAdKjrV1RymVcT87mRko2fq5lloTv8IwUIF3YUrUASSeDBAocrS2w0skgWZesqm1tacmzBR8CMpq72pGcmU9mfhFXErNo7+UILUrqn2KOQm4a2DgbvaWhGiSQ1kRys3EjOTeZI7eP0Nenb+X9FwSh3lIqldja2pKcnIyFhYXR3eaFhkOlUpGcnIytrW25bUzqQpV7EB0dbc5+CCYqUusHSOdTzt/zNR2tLeju15jw63f483ISEx7x12/QfijsCpWCpPRYaSHHCqhyU1FpAiS1Aidb/Rok25IibakWSWrnaGOBl7MNh6NSOB2TKgVILi3AtQ2kRMKV3RD4gtF76maGdAMkhVxBsF8w6y6tY2f0ThEgCUIDJ5PJ8PT0JDo6mps3b9Z1d4QaIpfLadasWb3YYLzuQ7T7hO5K2jK5+f9gi1XFej9nF2aTXZhNYXEhdhZ2BrfcqIp+7ZoYD5AcvaQp/zFH4OIv0LPiLV6Kcu9qX2tqkBysLfh8eCAywN6qJEDSCZocrJV09nHmcFQKZ2LSeOnhktXZ2w+BQ4vg0vZqBUgAA3wHsO7SOo7cPoJKrRKLRgpCA2dpaUmrVq2MblYuNDyWlpb1JhtYpQDp66+/5pVXXqnysuHLly9n9OjRODg43FPnBOM0NUgyZNhZ2JFVmMWFlAtM3jcZd1t3/jPgPzR1qDjDY8gTbZvw8Y5LHIu+Q1Z+kTaI0er4rBQgnd9ShQApTfvaSqHUXuv5rvr9KhsgdWnWCIAzMTqz6doNlgKka39CcSEYCQAt5IZrkAAC3AKwUdqQlp/G1dSrtGncpsL+C4JQ/8nl8ga/pYVQP1UpTJsxYwaZmVXfqHTWrFkkJydXu1MNUi1X3mtqkJRyJW62bgDsv7Wf/OJ8YjJjeOdQ9fana+5mj7+rHYXFag5fNfBn2G6ItIls3ElIrTitXZRXGuC42NkYTZnqLh7pYGVBUDNnLBQyrqdkE6HZQNc9AKydoTAH4s8avWdFGSQLuQVd3bsCcCz+WIV9FwRBEB5sVQqQ1Go1/fr1o0uXLlV65Obmmrvf9Y7+StrmTw9qMkhKuZImtk0AOJ10Wvv+2ZSzZBRkVOvaT7SVrrfvUlL5Nx3cwbe39Pritor7mJemfe1qb/xfeLqLR9pbK3G2tWRwoBcAK/+6Lr0hl0vDewAx4UavZaxIW+NhT2lPt/B449cQBEEQhCoNsX3wwQcmXXTo0KE0bty4Wh1quGo3g6SpQVLIFDSxkQKai3cu6rU5l3yO3t69Tb72E22b8P3haPZHJqFSqZGXranq8Azc+AsubIXe04xepyivJEBTg5u98Wm4tpb6Q2wAE3r7s+V0HHsvJFJUrEKpkEsb1l7ZKQVIvQxvcFxRBgng0aaP8vnJzwm/HU5KbgquNq5G+yUIgiA8uMwSID2QdDNItVCkrZnFppArtENsZb3x5xv8Z8B/6O7R3aRrd/drjL2VkpSsAs7GpdPZx1m/Qfuh0rpEt89AWgw4G17eoTg/HSxAppbj6lA+WNHQr0GSaojaezpibSEnr1BFbGqutOSAby+p0fUDkBEPjp7lrqUXICnL37O5U3M6uXXibPJZfr32K+M7jjfaL0EQBOHBVT9Kxe8HtVyDpJdBKhli0+jbtC8AhapC3jv8nskrk1oq5TzWWsqs/HnZwDCbnSt4d5Ne3/jb6HUK8zVDfHJc7I0vzKhfgyTF7HK5DH9XewCup2RJb3p3A68uUJAFu9413PdKMkgAz7Z8FoCNkRvveaNfQRAE4f4kAqQGSluDJFOWC5CGtxmufZ2QnUBsVqzJ13+irTsAf15ONNxAk825aTxAKsovKexXy3G1N55BsrYoP8QG0NxNWqjyWlK2dEAuhyElK7pf2g5Z5YvIdWexGQuQnmr+FI2tGxOXFcdv134z2i9BEAThwSUCpBqipnQl7dpY3kqbQZIr8LTTH2rq4dmDnc/upFWjVgCcTDhp8vX7tnFDJoPzcRkkpOeVb6ANkI4Y72OBJkBSmDzEBtDCTcogrfzrOmdj06SDHgHgFQRqFVwuH9zIdD59zWa15e6ntGF8B2lo7cdLPxrtlyAIgvDgEgGSOdTCCqDaGiSZgvYu7Ql0CwTAy84LK4UVTR2a0qeptPXIqcRTXE29Snp+epWv72pvRWBTZwD2RxoYZvPpAcjg7jW4c81AB/MpKhm+UqsVuNpVbYjNXieD1KIkg5SUmc/L3x+noKgkCG0/VHq++Eu5a+lu2muoBknjmVbPoJQpiUyNJDpdrBIvCIIg6DM5QMrLM5BNKBEfH39PnWnI1CqdlbRrI0DSWQdJLpMTFhzGzK4z+bD3h9o2mjV/frn2C8//+jyT/5hsUj1Sv4qm+9s4Q8t+0ut9H5Z/Pz+T4pJsjgpTMki6AZK99nV6biHHo0tW5m43RHqOPgQ5pat1g/6K5kqZ8TkITlZOPOwlTfnffWO30XaCIAjCg8nkAKlLly5ERESUO75582Y6depUE326D9TeViMKmRRcWCgsGN9xvHadH4CgJkHa7TRUahVnU87yT/I/Vb7HE+2kAOnvqBTyCovLNxjwkbRo5MVfIDlS/738DAo1H4NagUsFGSRjNUjtPB0ZUrIeEsAfl0rqoVxaSAtHqovh8g69a+lmkCoLVIP9ggERIAmCIAjlmRwg9e3bl4cffpiFCxcCkJ2dzbhx43j55Zd57733aryDDUctr6StM83fGDsLO9o1bqd37OWdL7MtaluV7tHe0xFPJ2tyC4vZb2g2m3sHaDlAen1+i/57Ohkk1HKcbY0HSLpZLQer0hokhVzG1yOD+M/LUibsj0uJpW2NDLOpTfhzeKLZEyjlSqLSoohKjaryeYIgCML9z+QAaenSpWzevJklS5bw6KOPEhgYSEREBMePH2fGjBnm6GODoPu1XBtDbJoMklJe8VJWHV07ljv22YnPKCiufHNHmUzGsCBvADaevGW4UYdnpOcLW/WXOsjPoqjkY5DLlCgqWBuqaSPbkvuBtUX5v5KPtnLFzlJBbGouJ2+WbF+iCZCu74fsFG1b3QxSZRwtHentJS2kufumyCIJgiAIpapVpD1o0CCeffZZ/v77b2JiYli4cCEdO5b/IhbMR3eaf0W6NOmifb3nuT0AZBZkcjD2YJXuM6KbDwCHriQTn25gC5m2T4HCElIiIV5n+C4/k+KSQFEzDGiMjaWCM+8P4OwHAw0Gl7aWSgYFSDP1Np8qWbLArTV4dgZVkV72ysfBp0q/l8aT/k9K172ymbwi4/V1giAIwoPF5ADp2rVr9OzZk99++43du3cza9YshgwZwqxZsygsfHAX3VOrql77UhN0p/lXZJD/IGZ1n8XaQWvxtPckJCAEgF+v/Vql+/i72tHdrxEqNew4a6AI39oJ2j4tvT69pvR4fiZFJS8ry3IBNLKz1JviX9ZzXZoCsONcPEXFJZ914IvS89mN2nYDfAfwZtCbfD/w+0rvCTDQdyBedl4k5yazMXJj5ScIgiAIDwSTA6TOnTvj7+/PP//8w4ABA/j444/Zv38/W7Zs4aGHHjJHHwUDdKf5V0Qmk/Fy+5fp3KQzIAUQAEfjj5KSm1LBmaU0hdK//nPbcIMuY6Xnc5ugoGRRx4JMikoCxaoESJV5yL8xjtZKMvOKOBdXslxBayn7Q/w/UCx9HnKZnEmdJvGQZ9X+LloqLHk18FUANlzeYNIQnSAIgnD/qlYN0k8//YSzs7P2WK9evThz5gxdunQxfuJ9T3cztvqTQSqrdaPW2CptyS3K5fH/Pc7wX4eTmpda4TmDAjxRyGX8E5vOjZTs8g38+4CzL+RnwIVt0jGdDJJFDQRICrmMXi2k7U/+jioJ7Jx9QWkDqkJIu1ntaz/l/xQOFg7EZcVVa1FNQRAE4f5jcoD08ssvGzzu4ODA999XbVjjfqRXpK0zzf96+nVC/wplzYU15U+6B1WtQSpLKVdirSxdYfry3cv8FfdXhee42lvRq4ULAL+dNZBFksuha0kW6fRq6VmnBslSYXzozBS9W0kB0sEryaX3dW0pvS67zIAJrJXW2lqkX66VX3xSEARBePCYHCCtWbPG6GPt2rXm6GODoJcz0skgbbi0gd+u/8aik4tMWsm6MpqFIk3NIAG81O4lvZ8vpFyo9JzB2mE2I4uBdh4NMgXcOiYFK/mZ2llslop7zyAB9G0tbX9y4kYq/9PMqnNtIz2nVD9AAhjaUpoVt/fmXrILDWTJBEEQhAeKyd9c06ZN0/u5sLCQnJwcLC0tsbW1NZphut8ZW6A6qzBL+zqnMAcnK6cauZ8mg1RZDZIhYzqMwd3OnfzifD4K/4j1l9djZ2HHlM5TjAZcwR08mL31PJGJmVy8nUF7L0f9Bg4e0DoYIn+HiPXSNP+SsNFSWTMZJJ/Gtszo35ov9l7h498uMiTQC2vX1tKbf8yV9mhr3rda1+7k2gk/Rz9uZNxgz409PNPqmRrpsyAIgtAwmZxBSk1N1XtkZWURGRnJI488woYNG8zRx4ZBrT/IpqE7dTy/OL/Gbqe71YiprBRWDGkxhIc8SguZV55bWeHUfycbC/q3l1bW3ngixnCjzqOk57MbIS9Nm0GyVtZMBglg6uMt8Xa2ISOviL0XE6Xp/hqbxkFx9WZSymQybRZJDLMJgiAINbJZbatWrfj000/LZZcedHfz7vJHzB/an2syQLqXDJJG2TWDLt29VGH7F7s3A2DrmTjDW4+0CgabxpAZD9f2azNI1krjq2ibSi6X8VwXafHKn0/Fgnc3absTgNxUiK7a+k6GDG4+GLlMzqnEU9zKMLIwpiAIgvBAqJEACUCpVHL7tpFp4A8ANTrTw0tqkKbum6rXplBVc+tE3UsNkoZcJufFNi9qf17+z3L6b+rP6cTTBts/0tKVpo2k7M3v5wzUIiktIWC49LowW1ukXZMZJIDnukprIv11NZkEmRtM+wc6vSC9eWFrta/rbudOT8+eAGy7tu1euykIgiA0YCYHSNu3b9d7/PLLLyxfvpyXXnqJ3r17m6OPDda5lHN6P5sjg2TqLLay/u/h/+OH4B+0PyfmJPLm/je5kX6jXFu5XMYLJStr/3TcSIZFM8wG2mn+NhY1l0EC8HWx4yG/xqjUUjYL52YQVFL7FrkTVNVfy0hTe/TT5Z/ILMisie4KgiAIDZDJAdKwYcP0Hs8++yxz586lU6dO/PDDD5Vf4H6lMlyDpKtGA6Qq7sVWFWU3tE3PT2fyvslkFWSVazu8mw9yGRy/cZeopPLv4xkITToAaBeKtLaomSJtXc+XZJE2nbwlbWDr0wMs7CDnDiRdrPZ1+zfrT3On5mQUZLD24oM7K1MQBOFBZ3KApFKp9B7FxcUkJCSwfv16PD09zdHHBim3qPy+ZYXVLCA2pKoraVeFvaU9NkobAEICQvC08+RW5i02X91crq2HkzWPt6mgWFsm02aRSou0azaDBPBUJ0/srZRcT8nm76g70vCerzQ8RvShal9XIVcwvuN4AP6+/XdNdFUQBEFogGqsBulBpy6zknZidmK5NubIIN1LDZKu7wd+z3s93uONoDd4tZO09ca6S+sM1k29+JBUrL35dBz5RQaKtTuNQC23oLDkr5eyhvqoy95KqS3WXh1+Qzro/5j0fA+F2lCaUbuZUf3VuQVBEISGrUrjMzNnzqzyBb/44otqd+Z+kpSTVO5YfZvFpivALYAAtwAA/tXiX3x95msSshPYeHkjL7XXX1jy8TZuuDlYkZyZz7Hrd3mstZv+xeybcPup1ew4FAZEYiGv+SE2gBHdfVgdfpO/o1JQqdTIm5YsW3APQ2wAzRylADA9P53UvFQaWTe6164KgiAIDUyVAqQzZ85U6WK1sYt9vVVmpcjEnPIZpJqcxVaTNUhlWSmsmNJ5CvOOzuPbiG95qvlTNLZurH1fqZDzeBs3/ncylgORyeUDJCDZvTfR/I4lkWbpI0AbdwcslXJyCoqJTc2lmZ20FQm597ZiuY3SBk87T+Kz47mRcUMESIIgCA+gKg2x7d+/v0qPP//8s1qd+O677/Dz88Pa2poePXpw/PjxKp33008/IZPJGDZsWLXuW5P0wyMZybnJ5drU6EKRNViDZMjzrZ+nTaM2ZBdms+P6jnLva+qQDlwpnykDyMkvAmo2y1WWUiGnpZs9AJGJmWDtLL2Rn3FPM9kA/Bz9AAzO5hMEQRDuf1WuQbp+/bo0W6iGbdy4kZkzZ/LBBx9w+vRpAgMDCQ4OJinJ8Bevxo0bN3jrrbd49NFHa7xP1VLms0nOKR8gFRQX1NjtNAXf5srOyGVyhreW1jRadX4VOYU5eu/3buWKUi7jenI2p27eLXd+TkExyFRm7SNAGw8HACITMsBas42LGrISjO//UgW+jr4ARKVF8dPln0jITrjXrgqCIAgNSJUDpFatWpGcXPql/8ILL5CYWH4YyVRffPEFkyZNYvz48bRv357ly5dja2tb4ZIBxcXFjB49mg8//JDmzZtXeP38/HwyMjL0HuZQtkjbUDBUkwGSZpacZvaZOTzp/yQWcguScpMYtGUQtzJL1z5ytLbgmSCpSHrWz2cpKNLP2GQXFGkDpJoqJDdEEyB9vucKodsjwcJWeuOLdrD8EbhzrVrX9XPyA2DNxTV8cuwTRv8+uia6KwiCIDQQVQ6QymaPfv/9d7Kz723X84KCAk6dOkX//v1LOySX079/f8LDw42e99FHH9GkSRMmTpxY6T0WLFiAk5OT9uHj41PpOdWj//loiqib2DbB0VLa2FUzxJaQnaDNyETejWTItiH8cfMPTKEJkKyV1vfU64o4WTkxo+sMFDIFd/Pu8unxT/X+Hsx+uj2u9pZcS86W9kXTIWWQpM/AXEXaUBogAWw4HkOR7mbAiedh/QtQXGTgzIp1cu2k93NSThLX0qoXbAmCIAgNT51O809JSaG4uBh3d3e94+7u7iQkGB7SOHz4MN9//z0rV66s0j1CQ0NJT0/XPm7dqo09tmTaAGlk25EMazkMkDJIYefDGPjzQCbvmwzAopOLiE6PZsaBGSYNYWoCJFulbc12vYyX27/MlqFbUMqVHIo9xA/nSzN7TrYWjCyZ8r/+uP6U+JyCYmQl26+YqwYJ4GF/Fx7yKy0gT1eVyajduQoXtph83XYu7bCzsNM79uOlH6vVR0EQBKHhqXKAJJPJys1Sq+1Za5mZmbz88susXLkSV1fXKp1jZWWFo6Oj3sMsysQ22llmMiVWCisArqZd5YtTX6BGzanEUyRkJ+itl3Q+5TxqtZo7uXcqvV1tDLFpNHdqzr+7/huAJaeX8MWpL1CppeDnhe4+yGTwd9QdvZW1c/KLaqUGycZSwf9e68kXIwIBiMs3kFH782MoMC3bqZQr9WbuAfx85Wf+Sf6n2n0VBEEQGo4qf3Op1WrGjRuHlZX0ZZ+Xl8drr72GnZ3+v7K3bKn6v9ZdXV1RKBTlapkSExPx8PAo1/7atWvcuHGDwYMHa4+pSmYrKZVKIiMjadGiRZXvX6PU+jVI2nWK5AosFNIQU1xWnN4pu2/sJjYzVvvzgdgDHEs4xlenv2Jk25G8+9C7yGWGY9jaDJAAXmr/EpmFmSyNWErY+TDu5t7lw14f0rSRLf3bubP3YiIrD11n4fPS0FS2zhCbOTNIGgPau6OUy0gqsALN7R6eDBe3Q9pN2D8fgj8x6Zr9ffsTdj4MJysnHvN+jF+v/8qiE4tYO2jtg72khSAIwgOgyhmksWPH0qRJE20tz0svvYSXl5defY+Tk1PlF9JhaWlJ165d2bdvn/aYSqVi37599OzZs1z7tm3bcu7cOSIiIrSPIUOG8PjjjxMREWHG+qLKlR0c0wRIcplcm0EqOxNsacRS7XR9gFsZt1hxdgUAGy5vYO/NvUbvl1eUB9RegATweuDrfPLIJyhkCn659gvv/vUuarWa1/pIhfJbz8SRmCH1K7egCDB/BknDwdqCLr6NSEcnYG/cHAYvkV4f+w+kmTa8OrXzVKZ2nsqaJ9cwo+sMLOWW/JP8DycTT9ZcxwVBEIR6qcrfXGFhYWbpwMyZMxk7dizdunXjoYceYsmSJWRnZzN+vLQf1pgxY/D29mbBggVYW1vTsWNHvfOdnZ0Byh2vfWUySDpDbJqMhiZAslXaklOUQ06RfsC088ZOvZ+PxR8j2C/Y4N1qo0jbkCEthmCntOOtQ2+x68YuxnUcR1ffDnT3a8SJG6n88Hc0oYPakV1QjExmvsUsDenT2o2MWzoBkn0TaDUA/B6FG3/BX4tLA6YqsFRY8mrgq9qfn2n1DBsjN7Ly7Eq6e3SvwZ4LgiAI9U2d78X2wgsv8PnnnzNnzhw6d+5MREQEu3bt0hZux8TEEB8fX8e9NJ3eEFvJLK7sIqkOpoVzCzq6lAZ0Q1sM1Tu3hZM0TBiRHEHI7hBG/DpCbygOan+ITVc/33708OwBwNnkswC8+pjU5/VHY8jIKySnoHZqkHT18G9Mhm4Gyb6k+P/x96TnM+sgzcAGu1U0vuN4FDIF4fHh2t9bEARBuD/VeYAEMHXqVG7evEl+fj7Hjh2jR48e2vcOHDjAqlWrjJ67atUqtm3bZv5OVkKtMjzNXyFTaOtVNIXNFnILnmj2hLbt862f1zv3udbPAXA19SrHEo5x6e4lQvaEaBeHhLoNkKB0Gvz8Y/N5Y98bPNzCnlZN7MnML2L9sRi9hSJrowYJwNXeigy1zqw+e2m1b3x7QfO+oCqEQ4uqfX1ve2/+1fxfAHx9+muzLJwqCIIg1A/1IkC6H2mG2BRyBfIyH7NSrmRk25E87vM47z70rnbVZo0uTbrg46BfTxWXFUdEcgQgBVp5xbVfg6Sro2tpBuxA7AG+/ecbJj3qD8APh6NJzSlEs9VIbWWQnG0tKEInGLNrUvr68f+Tns/8WO3FIwFeC3wNS7klxxKO8eet6m2tIwiCINR/IkCqIZpsgkotZYt0M0hlZ6Ip5UrsLe35+omvGd1uNM5WznrvN3VoyjMtn9H+3MRG+qI/HHcYKC3QhvoRIIG0RtCuOx/i7gRJmfn8cysNmWaITVY7AZKDtQVWstIs26d/xnInq2T/O5+HoNVAUBffUxapqUNTxnYYC8CiE4tqdH89QRAEof4QAZKZGBpi0yi7snTZ952snHi+9fO4WLvQ0rkl07pOA+CH8z/w5OYnmXlwprZtbRdpazS2bkxPz56427ozsu1IrBRWnEg8TpMWm9HMXqOWi7QVchlJSm/tz8sPXmPUymMUFZf0p2+o9HxuE2RUv64tJCCEJjZNiMuKY82FNffSZUEQBKGeEgFSjVH/f3v3HR9FmT9w/DMz2ze9hxRC70VaBES8E0HFjsphPfX8qWfn7N07z9449fT0LHinYAUVBUWkqIf03oWEmt422Wy2Pr8/JtlkkwAhFczzfrnu7MyzM8/MbHa/PLXO/+tVsTVSgnQ00ZZo5l04jw8nf8ipKacSbtSn1DhYcZCfD/4MgEWzHHacpPbwrzP+xfwp83kg8wHenfQuZs1MtmsV9qRv9QTt3EgbYL1tDE96p3Gp+2EAduSVM2tldcPslGGQPhoCPlj9drOPYTPauGP4HQC8temtkME+JUmSpN8GGSC1kZrxjVRFRSG0hKipAUOYKQyrwUqUJYp/T/p3sHdbjZo53jqKoijB0rBB8YP465i/AqBGL8UQvpGaNkhtOVltfZF2M2/6z2Wl6Bdc9+yCHewtqh5JO/NG/XnFv8B59BHLD+ec7ucwJH4ILp+Ll9e+3IIcS5IkSccjGSC1FlFTglTdYy1Q2/7maFVsQLDb/IT0CQ22AfSP7c/cC+Zy/aDrg+vO6nZWy/Pdis7ufjbXDrwWAEvyp6hGB9B+bZAAoqy11/aGU7szMiOacrePOz9aTyAgoN95kDQI3A5Y+nSzj6MoCveP0qvs5u2Zx/r89S3NuiRJknQckQFSa6k/F5s4ci+2+p4e9zT3jryXx8Y8dsTDjE0ZG1y+ov8VzctrG7r1pFvpH9sfRfOgaPpQBO1ZxRZlqw2QukRZmfGHk7CbNNbuK+WLDQdBVWFi9ZQjK9+C/SubfawBcQOCExE/ueLJYLWqJEmSdOKTAVKrCS1BqlvF1pQ2SHHWOK7ofwWR5iNP1zIsYRi3nnQrT417iiR7w/nqOppBNXDz0JsbrGsvdUuQEiPMdImycvPvewLw9PztON0+6D4ehlwGCJh3J1SX9jXH7cNuJ9wYzrbibXy267OWZl+SJEk6TsgAqY0caxVbUymKwv8N/r/ggIXHo3Ep44ixxARft9dAkQBRNlNwOSFC7+F37dhupMfYyHO4eX1J9RhIk/4O5gjI2wxbmj7Bcn1x1jhuPkkPCGesnUFJVUnzMy9JkiQdN2SA1EoEoaUQIVVszejFdiJTFIWMiIzg6/Y838iQEiQ9QLIYNR6crDfafvPHPewrqgRbDIy9TU/4wxNQZ5TyYzW1z1R6R/fG4XEwY+2M5mdekiRJOm7IAKmN+AJ6FZumaM3uxXYi6xbZLbhctxebEKJNp+iou+f4MHNweWL/RE7pGYfHF+Cp+dv0lZk3gT0eSrJg7cxmH9OgGngwUx+p+7Ndn8l52iRJkn4DZIDUSmrmYgv2YhO185C1ZhXbiaLu9ClGRT9fEQiw97LLyf7DHxAtaPdzJB5f7X5NhtqPt6IoPHxOfxQF5m/OZUduOZjD4NS79QSL/gqOQ80+7rDEYZzX4zwA/rn+n83ejyRJknR8kAFSG6lppN3UXmy/NXWr2GpKkHz5+bjWraNqw0b2XXcdB++6u9VLk84dkgxAZreYBtv6JIVz5gC9Yfs/ftilrxxxLXQ5CarK4Ks7WnTsGwffiILCz4d+Jqssq0X7kiRJkjqWDJBaTWgJUnAk7UbmYusMJUgZkRnB5ZpG2v4yR3Bd5fJfcMybh/Onn/Eean7JTX2p0TbWPXwGH/wps9Htt/y+J6oCX2/MYdnOAtCMcOG/QDXCrm9h57fNPnZaRBrjU8cD8OnOT5u9H0mSJKnjyQCpldQvB+nsVWypYanB5Zr54vzFDUeu3n/99WRdOpWAu/UmfY22mzBojX+0B3SJ5OoxGQDc8+lGfTLb+D5w8k16ggX3ga/5eTm3x7kAwelgJEmSpBOTDJBaiVIvQgqpYutkvdgAjJqR9858j3+e/s/g2E6+ouJG0/oLC3Gt39BuebtrYh+6x9vJdVRx3+eb9JXj74GwRCjeA780vw1RZnImCgq7y3aT68xtpRxLkiRJ7U0GSK2kpi2NqC4sqlvF1qAXWztOvdGRhicOZ1zquODrxkqQalSu+KU9sgSA3WzgtcuGYVAVFm7NY/H2fDCHwxn6XHIsfa7ZDbYjzZEMjBsIyFIkSZKkE5kMkNpIsIpNbaSKTfvtV7E1xld4+ADJufwXRCDAofsfIPdvT7RZL7ca/ZIjuPYUfSiCx7/agtvnh0GXQuoo8Dph/j3B+fWO1bgUPSh8bPljXPTlRXyb3fx2TZIkSVLHkAFSq2l8qpHGGml3hiq2xvgaKUGKuuQSAFybNlG1bRtlc+ZQ8sEHlMya1eb5ufX3PYkPN5NdVMm/lu7R52mb/DyoBtj2Faz7T7P2e/WAqxmdPBqAXSW7uHvp3czPmt+aWZckSZLamAyQWkmDsobqFQpKw27+naSKrT5/vTZI5l49Sbz/Pv2Fz4dr3frgtoKXXkZ4mz+6dVOEW4w8eLY+wvaMRbv43+5CSB4Cv9MHfeTnfzSrFMlmtPH6hNeZeeZMfp/2ewSCB396kPX561sx95IkSVJbkgFSq6n5Ia0eKLJ66hFVUWUVW7X6JUiKyYxisQRfu9auCS4HKiqo2rKlzfN0/tAuXHRSCv6A4I7Z6ylxemDkn0AzQ9Eufa62ZtBUjWGJw3jpdy8xIX0C3oCXu5beRWlVaeuegCRJktQmZIDUSuoWNJS5y4JtkBRF6bSNtOvz12uDpJjNKKqKYtQDxso1a0O2O1etavM8KYrC3y8cRI94O/nlbh6YswlhDodeZ+gJNjd/IlvQA+QnTnmCrhFdyavM4+W1L7c805IkSVKbkwFSa6npxQZc9OVFwdUKimyDVM1XHFrFpphN+nN1KZIvLw+AqKlTASh44UU8Bw60eb6sJo0ZfzgJg6owf3Mun609CIP0tlEsfxV+eQMcOc3ev91o569j9B5yc3+dy37H/tbItiRJktSGZIDUBvIr84PLitIwQOqMVWz+sjKEyxWyTjXpk8mqdarZ0LRgw22AvZdf0eY92gAGpkRy5xm9AXjsyy3sT/w9xHQHvwcW3Av/uQC8Vc3e/7DEYYztMha/8PPo8keDkxlLkiRJxycZILUa/Udc1KtOU2nYBqkzVrF5shrOTVZTclS3HZJqs2EZ0J+Ya68F9FKl4vdmUrluXZvn8cbxPRjRNZoKt4+/fLqFwGkP1m4s2A4//K1F+7931L3YDDZW5a7iH2v/0cLcSpIkSW1JBkhtTFEa6cXWCavY3FnZDdbVVLGpFnNwnWq1oigKiffcjXXIEADyn32WvdMuw+9wNNhHa9JUhRcvHYrdpLEyu5i3S4fBLWvg4nf1BMtf1bv/N1O3yG48ccoTALy75V2W7l/aGtmWJEmS2oAMkFqLCB0HqYasYtN59uxpsE4164GRYq5TgmS1BpctgwaFpC/9vGUNppsiPdbGg5P7A/Dvn/bgi+4OAy+C0bfoCeb+GYp2N3v/Z3Q9gyv7XwnAy2tfDjbmlyRJko4vMkBqYyoq9WKmTlnF5s6qDpDU2o+c0kgbJMVmCy5bBw0M2UfxzPfxlZS0YS51Fw9PJdpmJM/h5sddhfrKCY9B+mhwO2DWNHCXN3v/Nw65kXBjOL+W/sr3e79vnUxLkiRJrUoGSK3lSCVI9S6zUe2EJUjVVWyWvn2D65SaEiTL4UqQBgeXtZgYfDk55Nz/QBvnFEwGlQtOSgFg5vJsfZ49zQiXzITwZCjcAXNuhGY2Ho8wRTC1r95T766ld3H+3PNZsn9JK+VekiRJag0yQGolhxtvubFu/p0tQBJ+P559+wAw9+kTXF/bzb9OG6Q6JUjm7t1Iefll0t+fSfq774KiULFkCd6c5ne5b6qrRmdgUBWW7Cig2/3f8MyC7RCeCFM/AM0E2+fBsueavf8zM84EQCDYU7aHW3+4lbV5a4/yLkmSJKm9yACpldUPlBRFadiLrZM10g5UVkL1tCHG5OTg+po2SOph2iABRJw5CfuoUVj69MY6bBgAuyedSennc9o0z93i7Fw5umvw9etLdvPVhkOQOhzOeUlfueRJ2DavWfvvHd27wbqrF1zN3F/nNmt/kiRJUuuSAVIrqRlJ219vvaqonb4XW6CyevwjTUMNDw+ur2mDpFjrdvMPDZDqCj/9dACEx0POQw/hbmTogNZ0z6S+PHB2X4alRwFw1ycbWLO3BE66AkbdoCf67E+w/9hH/FYUhZuH3gzADYNvIMYSA8AjPz/Con2LWiX/kiRJUvPJAKnV6BFSldJwHKTOXsUWqHQCevVZ3S79NVVrdUuQFOvhA6SISRNRTKbqnQYoePFFhK/tBly0mjT+79QefHzDaCb0S8DtC/CnmavIKnTCpL9DzzPA54JZU5vVs+36Qdcz57w53Dz0ZmZNnsXguMEIBPcuu1dObCtJktTBZIDUyrz1AiQUGvZi62QlSDUjaKtWa7DUCOp08w8ZB8nG4RhTUuj+9TzS33kbVJXyhd9z4OZbCLjdbZRznUFT+ce0kxicGklJpZc/vruSQlcALnkPkodCZRH8dwpUFBzTfjVVo2d0TxRFoUtYF2aeNZPxqeNx+93c8sMt7HXsbZPzkSRJko5OBkitpXo8G09jJUidvoqtEtBLkGp6rkGdbv5HaINUnyktDfuYMaS8+CKKxULF0qXkPvJoG+Q6lM1k4O2rR5IWY2VvUSXXvbeKSsUCl38CUV2hJAs+vBQ8zmYfw6AaePbUZxkcN5gydxkP//ywHCdJkiSpg8gAqZXVD5AaHSiy01Wx6QGSYrOimGrPPdiLrW4bJPvhS5DqijhzEqkzXgbA8d13elf8NhYfbmbmNaOIthnZcKCMG/6zhipzLFzxOVhj4NBa+PRa8De/2s9mtPHc+OewGWysy1/HPcvuodBV2IpnIUmSJDWFDJBaSc0PtLtBDVtoLzYFBU3V2jNrHa6mkbZqswWr1aDxXmxHaoNUn33MGNA0hMuFLz//6G9oBd3jw/j31SOxGjV+3FXInz9YiyeqO1z2ERgssHMBfPfg0Xd0BF3CuvDQyQ+hKRrfZn/L2Z+fLaclkSRJamcyQGpl9dsg1S9B6mzVa1Cnis1ar4rtGNsg1acYjZhSUwG963/Bq6+1RnaPanjXaN7+4wjMBpUftudzy4dr8XYZAVP+rSdY8QZsmduiY5zb41z+e/Z/GRg7EJfPxZ1L7uSngz+1PPOSJElSkxwXAdJrr71GRkYGFouFzMxMVq5cedi0b731FuPGjSM6Opro6GgmTJhwxPTtrX4VG+ilRjU6W/UaQMBVpw2SqZE2SIcZSbspTBkZAIiqKgpffRXXhg0tzG3TjOkRx1tXjcCkqXy3NY87PlqPr/dkOOVOPcH8e6CqZZPrDowbyPtnv88ZXc/AG/By+w+3yyEAJEmS2kmHB0gfffQR06dP59FHH2Xt2rUMGTKESZMmkX+YKpMlS5Ywbdo0Fi9ezPLly0lLS2PixIkcPHiwnXMeqqaKrUEvNpAlSMESJGuw3RHUHUm7aeMgNaYmQKqR/+JLzczlsTu1dzxvXDkMo6bw9cYc7vpkA/5T74OYHlCRB59cDc6WtR8yqkaeGfcMv0v7HZ6AhzsW38GbG99slzZXkiRJnVmHB0gvvvgi119/Pddccw39+/fnjTfewGaz8c477zSa/oMPPuDPf/4zQ4cOpW/fvvz73/8mEAiwaFHj/7J2u904HI6QR1s6WglSZwyQgt3867dBqg6MWlaC1DXkdeXKlfgK269R8+/7JvLqZcMwqApz1x/i7jnb8Z/7DzBYYfcP8NIAWPlWi45h1Iy8eNqLXNb3MgBeWfcK9y67lypfVWucgiRJktSIDg2QPB4Pa9asYcKECcF1qqoyYcIEli9f3qR9VFZW4vV6iYmJaXT7U089RWRkZPCRlpbWKnlvoLo7dv1G2hBagtQpq9ic1SVIdlvtQI/UaYNUt5G2reltkAAMCQnBZVPPHiAEFUvbt0HzpAFJ/GPaSWiqwufrDvLQugjENfOhy0ngq4Jv7oJvH2z25LagB9b3Z97Pwyc/jEExMD97Pn9c8EfynHmteCaSJElSjQ4NkAoLC/H7/SQmJoasT0xMJDc3t0n7uPfee+nSpUtIkFXX/fffT1lZWfCxf//+Fuf7SBqrYqvbi60zliAFu/lbrfXGQdKDJbVuN/9jaKQNEHbqqUScdy6JDzxAxCR9AtjyxYvxl5Zy6N57Kf7vB+1SHXX2oGRenXYSqgKzVu5nZnYUXL8YTn9ET7D8VfjkKvBUtug4l/a5lDcnvkmUOYotRVuY9vU0NhVsavkJSJIkSSE6vIqtJZ5++mlmz57NnDlzsNSppqnLbDYTERER8mhLHlmC1ECgThWbotZeC8WgB4t1S5COtQ2SYjSS8uyzxFx1JfbRJwPg3rqNQw8+RNkXX5L3xBPk3P8AngMHWnoaR3XWoGQenNwfgCfnb2dbbjmM+wtMeRs0E2z7Cj66AnyeFh1nZNJIPpz8IT2jelLgKuCPC/7I13u+bo1TkCRJkqp1aIAUFxeHpmnk5YVWE+Tl5ZGUlHTE9z7//PM8/fTTfPfddwwePLgts9kkNYUUvvrzilCvF5vWCQOkOt381bCw4Hq1ujpNMdSOC3WsbZDq0qqrWb2HDlFRp01a2dy5ZE+5mMrVq5u976a6dmwGp/aOx+MLcMkby3ll0S7Ke50PV84Bow12L4IPLwFXaYuOkxaexn/O+g/jU8fjCXi478f7eHHNi/gD9adLliRJkpqjQwMkk8nE8OHDQxpY1zS4Hj169GHf9+yzz/K3v/2NBQsWMGLEiPbIahPoEZLvaL3YlE5YxVanm79qtZIxexYZH82ubZxdpwqsRQFSeHjIa8uQwXT9z/tYBg7EX1bG3iuvIv/FlxAtaAt0NIqi8NKlQxiVEUOF28cLC3dy3qs/s90yGP7wIRjtsGcJvD0RSltW3RtmCmPG72Zw7cBrAXh387vcvOhmSqtKW34ikiRJnVyHV7FNnz6dt956i5kzZ7Jt2zZuuukmnE4n11xzDQBXXXUV999/fzD9M888w8MPP8w777xDRkYGubm55ObmUlFR0VGnEMLXSBVb3TZInbKKLTgXmx78WIcOxTpkSHC7qVs3LAMHYh83LqQR97FS6wVIWkQktpEj6fr+TCLPPx+EoOjNN8l74olmH6MpYsPMfHh9Ji9NHUKXSAtZhU4ueO1nPi/rBdfOh/BkKNwB/7kQnEUtOpamatw5/E6ePfVZLJqFnw/9zCXzLmF9/vrWORlJkqROqsMDpKlTp/L888/zyCOPMHToUNavX8+CBQuCDbf37dtHTk5OMP3rr7+Ox+Ph4osvJjk5Ofh4/vnnO+oUdNWlII1VsdWdrLazTTMCIOpMVtsYRdPI+ORj0t78V4uOo5jNKMbaAFSrbm+m2mx0eeZpujz7DAAls2ZTtWNHi451NAZN5cKTUpl32zjG9Yqjyhtg+scbeGaDGXHdQohIhaJd+gS3+1e1uF3SWd3O4r9n/5euEV3JdeZyzYJreGXdK3j93lY6I0mSpM6lwwMkgFtuuYW9e/fidrtZsWIFmZmZwW1LlizhvffeC77Ozs5GCNHg8dhjj7V/xhvRWAmSHCiyupH2EarPFCV0zrrmUBQFtU4jfC0ytEF+5HnnEX7mmSAEB267jcI33sB76FCLjnk0MXYT710zittO7wXA60t2c9d3xVRd8iGoRji4Gt6eAO9MhNJ9LTpWn5g+zJ48mzMzzsQnfLy58U2u+fYacp1N6xEqSZIk1TouAqTfhOBI2kdO1inbINV08z/GMY6aQ6vbCLyRHosJ0+9Ei43Fu3cfBS/PIGvq1DYPkjRVYfoZvfnbBQNRFfhs7QHO/7SMkrEP1yY6tA7+dSqsfjekTdaxCjOF8dz453hh/AuEG8PZULCBi7+6mA+3fSgbcEuSJB0DGSC1skar2Op28++MvdiC3fztbX6skBKkiMgG203p6fSY/w0Jd9+FISEBf0Eh+2+4EV9xcZvn7cqTu/LfP2USF2ZmR145Z60YyN5bDsAdmyG+H7hKYN4d8PGVUNmy/EzMmMhH535Ev5h+lLnLeGrlU9zw/Q0UutpvlHFJkqQTmQyQWolA7xl11Cq2TlaCJPx+RJU+JcaxjnHUHFp4bQlS/Sq24PqICGKvu46M2bPQ4uNw79rFrjFjOXjPPfhLS9s0f2N6xPH1bafQKyGMXEcV459fxpP/q2DPlK/hjL/p1W7bvoI3xsHepo0mfzhp4Wl8MPkDHsx8EKvByoqcFUz5cgqzts/C429ZmydJkqTfOhkgtbLGuvl35pG0A+XlweW61V9tRQ2vDYoaq2Kry9ilC2mvv4GxSxcAHF9+RdbUqXiys9syiyRGWPiguiQJ4M1lezjr1ZW84ZuM79rvIKY7OA7Ae2fDkmegBQ2tjaqRP/T9A7Mnz6ZnVE+Kq4p5csWTTJ4zmVW5q1rrlCRJkn5zZIDUWoK92Bqq24utswVI/urJgRWbrUVd+JtKrVuC1EgVW33WgQPoseh7un74IcaUFLx797HnoimUfPxxm05RkhBh4d9Xj2DSgET6JoXj9gV4ev52LpjjZNt582DwH/T5/ZY8Ca+Ngl+/b9Hxukd156NzPuLBzAdJsCWQ68zl2m+vZfqS6ewq2dVKZyVJkvTbIQOk1lIzkvbRBorsbAFSmR4gaW08xUsNLfzwvdgOR1EUbMNOImP2LGwjRyIqK8l95FEKXnq5jXKpG5oWxb+uHMH828fxwiVDiLQa2XzQwTlvbuA5+514znsDbHFQvAf+OwXm3NiitkkmzcQf+v6Bry74iim9pqCgsHDvQqZ8OYUnVzxJuaf86DuRJEnqJGSA1GpqRtJuuKUzDxTpLysD2i9ACi1BOrZjGuLjSZ/5HvHTpwNQ/P77+EtL23yyW0VRmDI8le+nj2fyoGT8AcFri3dz5uJk1ly4GDJvAhTYMAteHQlr3oMW9EizGW08NuYxPjvvM87oegYCwaztszh3zrnM3DKTSm/LJtSVJEn6LZABUiup+QltrIqtrs5WghRwtG+AVHegSLUJVWwN3q+qxF7/J8x9+yKqqsiedhm7ThlHzqOPITxt27A5PtzMa5cP440rhhMfbmZPgZMpb2/ihsKLOXDRXIjvC5WF8NXt8MYpsOv7Fg0J0Cu6Fy+e9iJvTXyL9PB0iqqKeH7180z8bCLvbH4Hl8/VeicnSZJ0gpEBUitrrIqtrs4WINW0QVIjjz1YaZZAnXnd7M0bd0lRFGKv+SMAnqws/EVFlH70EbvPPIvi9/9DoI0DpTMHJvH9neOZOiINgG+35HHa7EqeSH0T1++fAEsU5G+FD6bo05XkbmrR8U5OPpm558/l8TGP0zWiK2XuMl5a8xJnf342X/z6RSuckSRJ0olHBkitrLEqtro6Wzf/9m6DVLdEpSUjc0ecdx4Jd/0FxWLBkJCAFhWF99Ah8p58kqwLL6Jy7drWyO1hRdqMPHPxYBbeeSoT+iXiCwj+vfwAY5f25b2Rc/Fm3gyaCfYs1ocEmHszOJo/4KVRM3JRr4uYe/5cnhj7BClhKRS6Cnn454fJKsvC6/eS68xt8+pGSZKk44UMkFpJzQ/HUQOkTleC1L5VbOZePVtlP4qiEPunP9Fn9Sp6LVtKzyWLSXrsUbTYWDy7d7P3sss59MCDePPyW+V4h9MrMZx/Xz2CmdeOoke8nWKnh8cWHiJz9e/474hP8fa7EBCw/r/wynBY/CS4mz9xs0E1cH7P8/nqgq/oH9sfgWBz4WYu+eoSzvj0DC7+6mJmbZ+Fw+NovZOUJEk6DskAqdXoAZK/kZG06+psAVKguopNi2qfKraw3/+exAcfJGP2rFbZn2LQ75dqsRD9hz/Q4+t5RF48BYCyzz9n91lnUfDPfwZHC28r43vHs+COU3l2ymDSY2wUOz08tLSCkTsu45Oh7+JLzQRvJSx9Bv5xEvz4gj4ydzMZNSP9Y/sD8OXuL9ldthuAnSU7eXLFk5z+8ek89NNDrM9fL0uVJEn6TZIBUis76lxsnSxAqqliO9qgja1FURRirrwC69ChbbJ/LSqKLk88QcbsWViHDkVUVlL4j1fYfeZZlH3xBSIQaJPjAhg1lUtHprHoL+N5/pIhdIuzU1rp5e5fzAw7MJ15fZ/FH90NnPmw6K/w0kBYcH+zJ8HtEdkDgF9yfgEgMzmT+0bdR8+onlT5q/hi9xdcOf9KLv7qYv679b/sKd3TaucqSZLU0WSA1FpkFVujahppN2XQxhOJdehQus76kJQXX8DYpQu+vDwO3Xsf2ZdcStWOnW16bKOmcvHwVBbeeSovTx1Kj3g7jio/t6xPZUTx35nf8zH88f3BUwG//BNmDIVPr4OcDcd0nO5R3UNeT+42mcv7Xc7n533Of876D+f1OA+zZmZnyU6eWfUMF355IQ/+9CDfZX8nS5UkSTrhyQCplR1tdJpONw5STRukJg7aeCJRFIWIs8+m+/xviP/LdFS7naotW9h75ZW4NhxbMNIcBk3lgpNS+O7O8bwy7SR6J4ZR4oabNvdmcN4j/Kfni7jSxoHww+ZP4V+nwszzYNfCJo2j1DMqtD3XKSmnAPp5D00Yyt9P+TuLLlnEXSPuYkTiCAIiwJe7v+QvS//CeXPP45GfH2FL4ZY2OXdJkqS21rmKM9pQbSNt2QaprkB792LrAKrZTNz11xN14YUcuPU2XOvWse+aa4m79VaMiQmY+/XDlJHRol51R6KpCucO6cLkQcl8uyWXGYt2sT23nIc3J/GIchNXZ1zGjaZvSNz3DUrWUshaChGpMHQaDJkGsT0a3W+8NR6jasQb8HLTkJuIt8U3SBNpjuTqAVdz9YCr+fHAjyzZv4S5v84l25FNtiObub/OZXzqeAbFD+LMjDNJj0hvk2sgSZLU2hTRycrCHQ4HkZGRlJWVEdGKP9rbVi+m37wLOD8llT2m2oK5TVfrY9QMmjkIgIcyH2Jq36mtdtzj3Y6RowiUl9P9m28wd+/W0dlpcwGnk/0330LlL7+ErDckJRF57jnEXncdWlRUm+ZBCMGyXYW8/VMWy3YWBNePjKrg4bglDCz4GtVdVvuG9DFw0uXQ/wIwh04o/OOBH9lTtocr+l2BpmpNOn6uM5fNhZv5bu93zM+aH7ItMymTawdeS++Y3sRZ45p9jpIkdT5t9ft9ODJAaiXbVi+i37yLmJySyr4jBEiPj3mci3pd1GrHPZ4Jv5/tAwYC0OvnnzDExnZwjtpHwOOhdPZsyhf9gKiqomrrVoTXC4AxNZX4O+8gfMIEVLO5zfOyp6CCD1bs45PV+3FU6eO8m/FwQ+J2Ljf/SEL+/1BqxoE32mHABTD0cug6BlqhxGt17mo2Fm5kZc5Kfsn5Bb/Qq/YUFAbHD2Zy98lc2PNCLAZLi48lSdJvmwyQ2lhbB0iTUlM5ZNQDpP6x/fnonI+A2gDp76f8nfN6nNdqxz2e+cvK2Jl5MgB9N25AMZk6OEcdI1BVRcWPP5L/7HN49+8H9JHFI889l7g/34QhJqbN8+Dy+Plyw0E+W3uQVdnFwfE0uxpLmJ6wjgnuhdgr9ta+IbobDL0MhvwBolqnWuxgxUGeWfkM24q3kevMDa6PtcQyKnkU/WL6cX7P84mxtP31kCTpxCMDpDbWVhd4+6pF9P36IiakpZFnUJjxuxmMSx0XbJR94RcX8mvpryy5dAmx1s5RkuI5cJDdEyagmM303bC+o7PT4fxlZRTPfJ/SOXPw5eQE1ytmM9YhQ7CNGkXYaadhGdC/zdorAeSWVfHx6v18tvYAe4tqJqYVjDX9yp+jVpBZuQSDr3q9OQJuWAr2BCjJ1ueD01reji7PmcfCvQt5f+v75DhzQralhacxPnU8p6aeyojEERi1ztWxQZKkxskAqY21WQnSykX0++YixqelUWxQ+Picj+kX2y+43Rvw4va5CTOFHWEvvy1V27eTdcGFaHFx9P7px47OznFD+P04/7ec3Mcfx3vgQIPt5n79iLp4CpHnnIPWhnPYCSHYfNDBvI2H+HpTDgdK9MEurVRxjnE1D5pmE+Uv1oOiinxwFYM5ErqfCj1+rz+iM1qUB6/fy3d7v2OvYy/f7/ueXSW7QrabNTMTuk7g7G5nkxqeSreIbm0aPEqSdPySAVIba7sA6Xv6fTOFU9LTKNMUPj/vc3pF92q1/Z+IKletYu+VV2HKyKDHgvmNphFCIASoauf70fMVF1P+/feY0tOpWLqMyrVrcG/dFmyvpJjNhJ/+e8x9+xE2fjzm3r3aLDgQQrDxQBnzN+fy7ZZcsgqdnKxuZbbpiSO/MaZ7bbCUMQ4sLfubqvBUsCJ3BUv3L2XZgWUUVRWFHs4Sw9D4oQyMG8jg+MEMjh+M1WBt0TElSToxyACpjbXVBd668nv6fzOFk7um4VQVvrzgS7pF/vZ7bR1J+Q+LOfDnP2MZNIhun3wcsm3HLzns317C/m3FhEWZueie4WiayualByjNc9F9WDxdekZ1TMY7kK+kBMdX8yj99FPcO0MHnDR17074pInYR4/GOngwqqVtGjYLIdiVX8HLC3cwZcddnKJu4qfAIO7y3kBXJZ9x2iYmWrbS37cdre7IX5oZevwO0jKh37kQ27NFDb2FEKzNX8tLa16i0FVIoasQt98dksagGOgf259hicMYljCMzORMbEZbs48pSdLxSwZIbaztSpAW0u+bixnRNQ23qvDNRd+QFp7Wavs/EZV9+SWH7rkX+5jRpL/zTnC92+Xj7enLqPvJO/P/BhKdbGfW4yuC6+LTw+lzchLdhsRhNGtYwzpPI28hBFWbN1OxdBlVW7fi/PHHYMkSgGI0Yhk8GNvIEYSdcgrWoUOD88a1lqIKN+OfW4LT7UGgMqpbDIUVbvYUOAEIo5KT1W2MUzdyunEzqSK0LRGWSOg6FvqcBclD9ao6Q/PvocfvYUvRFjYWbGRz4WbW5q8lvzJ0suBwYzg9onqQEp7C6OTR9I3pS/eo7p1ugFZJ+i2SAVIba8sAqe83FzMkIw2hKCy+dHGnH+el+IMPyPvbE4RPnEjqP2YAsGd9AaV5lSyfszskbUqfaLoNjuOnT3Y1tisMZo3Bp6XQ5+RkYpLtbZ73442/vJyKxYupWLIE56pV+AsKQ7arYWHYRowgYvJkwiecjmptnWqntftKWLu3hJEZMQxJiwKgoNzNyqxiVmYVsSKrmO255YBggJLNKHU7E9S1jNB2YsYbujODBVKG6yVM6SdD6kiwNb/HmhCCQ85DrM1by5q8NSw/tJxDzkMN0pk1M32i+9Avth8DYgfQP7a/DJok6QQkA6Q21mZVbCu+o/uCSxmZoZcaLZ+2vFM1yG5M4RtvUPDyDCKnXESXv/+dihI37z/wc7DkKDLBynm3DeU/Dy0H9CDp4I4SRp7TjcL95WRtKGx0v0ndI0kfEEPf0cnYwk1oxs41Y44QAu++fVSuXo3zf8tx/vQT/rLagR8VsxlTejrm3r0JP3MSYePHo7bhEAullR5WZZewYk8RK7OL2XywDFX46KvsY6K2mpHKTgZoe4nA2fDN8X1rA6a0TL1NUzOr5bwBL8sOLOP9Le+zNn8tAGHGMCq8FQ3S1g2a0sLTiLPGYdEsJNmTyIjMwG7sfEG4JB3v2jtA6lzzXrSxqjpf7HLgO73UA0ALCwcge1NhSLWaLcJEeKwFzaDi9wU4uKMEgO5D4xl1TjcCAcGa+dlY7EbMNgOblhwgd4+D3D1l5O4pY+VXWWhGlWET0xl1bvcGx/+tUhQFU9eumLp2JWrKFITfT9X27VQs+oGyL77Ae/Ag7l27cO/ahePrr0HT0GKiCTtlHPZTxmJMSsKYmooxMbFV8hNlM3FG/0TO6K/vr7zKy5q9JazI6sPSrOG8cqAUrzdAD+UQw9WdjFB2MkLdSXc1Bwq264+1MwEQ5giU+D6Q0B+SBkHyEP3ZePQSMaNq5PT00/l92u/JKssiIzIDgP3l+9latDXkUeGtYGPhRjYWbmx0X4m2RLpHdmdQ/CC6RnSld3RvekT1kKVOktSJyACplYhAIBggGVVjp5tzrTGBcv1f7mpEdYC0MbREyBpuQlEUbBEmyourADCaNWJT9H+9q6rCyMm1Dd17DE9g/cJ9lOZVsndLMS6HB783wKqvs1n1dTZ9MpP43RV9O12JkqJpWAcMwDpgAHG33Ix3/348e/fiXLECx1fz8OXn4y8opGzOHMrmzAm+zzJ4MMbEBAzxCdhGn4x91CjUiIgW95QLtxg5rU8Cp/VJAPRBKtftK2FFVjErsgbxbU45ZS4vMTgYpu5ihLqT4eoOBit7MLsdcGCV/qgmjDaU+L4QmaoHTon99efobo2OyaQoCt2jagPmrhFd6RrRlbO6nQVAQASCQdP24u3kOnMpdBVS5a/iQPkBiquKyavMI68yj+U5y4P7MWtm+sX0Y0DcAOKscYQbw4k0R9Ivth9dI7q26JpJknT8kb/iraiyuqu6LD3SBSqqS5DCw/FU+TiwvSRkuzVcr/axR9UGSOGxlsP+QGuayvAzMwAQAYEAvn93K7tW5QGwY0UuAX+AiX/SpzfZt7UIZ6kbRVGIjLdishqw2I3YIk2/2bF0FFUNli6FnXoqCdOn48vPx7N3L+WLfqBq0yZ8RUV4DxygauNGqqrfV/Lhh/r7zWYMSYlY+vTF3LcP9tGjsZ10UovyZDVpjOkZx5ieeps8IQRFTg/bc8rZmjOKrYccfHbIwf6CEtLJpZdykH7qXvorexmkZhHvLYNDa/XHti+D+xWauba0qSZoSugH4clwhHnjVEVtEDTVVeYuI6ssi50lO9lcuJkDFQfYXrSdcm856wvWs75gfYP3DIkfQkZEBr+W/orVYCXGEkOCLYEkexKJtkQS7Ykk2hKJt8XLUihJOkHIAKmVKIhgCZJVk+OyAPhrSpDCwjmwrQS/LxCy3Rau/1DYImvnJAuLblpwqagKCnDKJb0oL3JRVuDCVe5l1+p8emcW4q708f27Wxt9b2K3CPpkJuF1+4lOspHWPwaDsWkTsZ5oFE3DmJyMMTkZ+8knB9d7Dx3CtX49/rIy3Lv3UPHjMrx79yHcbrx79+Hdu4/y776j8B+vEHn+eXhzcvEVFeltm3r1wpCUiDExUd93ele0sKa32VEUhbgwM6f0MnNKr9qODFVeP9tzy9l6yMGWQ2Usy6tgZ56D5KrdpCiFdFXy6KPsp7e6n97KQWx+N+Ru1B91CEWFqHSUqHSITIforhDVVZ8yJborhCWBevhSxkhzJEMThjI0YSiX9rkU0Eud9jn2salwEzuKd1DqLqXCW0Ghq5AtRVvYULCBDQUbmnT+BsVAhDmCeGs88bZ4EmwJ+rI1njhbHHFW/RFriZX/2JKkDiQDpFYiALeif+lam9BeojMIVLdBUsPDyNqozyqvagoBv94QKViCFFHbgDg89th+EGwRJqbcMwKAnz/dxfrv9/PjRztxV+oTsyZ2i8Bk0Sg86ET4BVWVXvKyHORlOYL70IwqJouGxW4kMSOC2NQwIhNspPSKwmT9bf6JGLt0wdilS501DxKorMRXXIx33z6qtm2nYskSKletouyL2lIbz+7dVCxe3GB/WlwcpvT06tKrdIypaRiT9ADKkJSEoh09ALUYNYamRTG0urcc1JY27cwr59f8CjblVfBZXjnZBeWYnQfoo+zXH6r+3F3JwUBAnxalJLvR4wQ0E4GINLSYDJTo6sApqmt1IJWh96yrV8KoKioZkRlkRGZwbo9zQ7blOfP4NvtbHB4HPaN7IoQIVtPlOnPJc+YFq+x8AR8+4aO4qpjiqmJ2lOw44jUJN4YTa40NBk1x1jhirbHEWmKDy3HWOKIt0bJkSpJa2W/z27+DVNVUsWnyX30A/uoqNjUsjL2b9RGRu58Uz6+r9bFrbNWBUd0SpPCY5s9wP+i0VNZ/vx9HoV5xFJVo46K7hqFqtaUFzjI3GxcfoPhgBZpBJS/bQUWJG5c3gKvcS0luZTCt0aIRnxZenUcTUQk2IhOsRCXYiEqwYQn7bf0gqTYbJpsNU2oq9jFjiLnmj+T9/Ukq16zBMqA/9sxM/KVleLKz8ebn4cvNw3vwIP6SEvyFhbgKC3GtXdtwxwaD3jA8KQlDl2SMSckYk5MwJCXpgVpS0mHbPtWUNsWFmRnTI3TYjAq3j+xCJ9lFTrIKnCwudLKvsIyq/D0M9G0mgEIiJaQqhaQp+aQpBSQrRRj8HtSS3VCyu8HxAPyaGZ89GTWiC4boVJSIZAjvAhF1HmGJwWq8RHsiVw246qjXNyAClLnLcPvdlLnLKHAVUFBZQH5lPgUu/bnIVRQcFNMT8FDuLafcW062I/uo+482RxNpjiTCHEG4KZxIUyRJ9iTSw9OJscQQZgrDbrQTZqx+NoVhUk/86uYqX1XwmmY7stlTuocsRxZev5fTu55OalgqX+7+ks2Fm6n0VRJuCmda32kMihvE13u+xul18qdBf8JmtLG1aCvFVcWM7TIWRVH4avdX/HzoZyq9+vdCmDEMk2ZCIFAVFavBis1gw2qw0jWiK2NTxqIpGqvzVpPrzMUv/BgUA2bNjNlgxqJZ9OU6r2vaq+pl4mDSTNiMNiza4ZsbSO1DBkitxO3z41JkG6S6ahppOwM2XOVFqAaFXiMSgwFSTQmSLbK2BKmpVWyNCY+xYDBr+Nz66M5JPSJDgiMAe6SZ0Rf0CL4WQlB8yIkQgooSN7l7yijNc1Gwz4GjsIpDu0oPezyzzUBkvJXIBFv1sx48xaWF/Saq7BRVJenhh46azu9w4Nm7D8++vXj27sW7dy/eQzl4c3Px5uaC14v3wIFG550LHstmw9KvH8bkZBRNRYuLwxAfjyEuHkNcHIb4OAxxcSGBVJjZwMCUSAamhM5X5/GNY2uOA01RyC+v4lCpi2Wl+nNeSTm+0gNYKg6QUh00pSoFpCkFpCn5JCqlaH43miMbHNlwmCwHUPFYYvHbElDCEzFEJGGMTEIJTwJ7PNjjwBarP6wxYDChKirRlmgAkuxJ9KHPYa+HEIJybzmFrsJg0FRQWUBRVZH+ukpfX+QqoriqGL/wU+IuocRdcth9NsagGLAZbdiNduxGu75sqF0OM4YRZgoj3BhOmKl22WKwNPixN2tmLAZLmwRd5Z5yVuas5H+H/sfust2EGcPwCR87i3dS4Co47Pu+2vNVo+tX5a4Keb1o3yKGJw7n052fIhBM6TWFMGMYM7fOPKZ8Wg1WVEXF6W1kSItjFGYMIzU8lTBjGJqikWRPold0L34t/ZWfDv6Ex+/Rg12THbvBjjfgpdJXiaZowXth0Sz4hR9vwKs//F6sBisRpggizBHYjXacXicOjwOTasJqsOrzhvrdqIqKQTVgVI0YVSOqoqIqKpqioakamqIFX6uK2ug6g2oIeW3STMRZ4kgKSwrOq1jprWRnyU68AX3cNLvRjtVgxR/Q8+0L+BAI7EY7wtW+oxLJAKmVLN1RQK+aNkhybigCHg9+h16NVVapl7TEJNuJiKsNgGpKkOx1S5COsYqtLkVViE60UbBPL7mK7XL0djGKohCboo9XFZcaTsag6obEAUHunjIqSvSpLcpLqijLd1GWX0lpvgtnqRt3pY/8veXk7y0P2adqULCFm4hKtGENM2K0GjBZDJitBiLiLUQl2LCGm7CGGTGYTvxASouIwDpoINZBAxtsE34/vvx8vDk5eA/l4MvNwZuTq7/OzcGXk4u/pARRWYlrzRpcRzmWYjSixcfpgVNMDFpMDIaYaLToGLTYGMzdu6Pa7QyMiUExGhmYktDoj7XPHyCv3M2hUheHSl2sLHUxt9RFfrEDT8lBDM4cbFX5JClFJCklJCnFwUcCpRiUAJaqAqgqgOItR71GHs2O1xyN3xIDthhUeyyGsDhMEfGo9jqBVHVQpdhi9B8xUwTdI488hIU/4KfUXUpRVRFl7jIcHgcOtwOHx8GB8gPsr9iPw+2gwluB0+OkwltBpU8vEfEJn57e4zjiMY6FgoJZM2PSTHoQVRM8VS9bNMtht9UEXDU/7PvK97GtaBsbCjbgF/4jHtesmcmIyKBbZDe6RXbD6XXy9Z6vKXWXMj51PGdknEG0OZp1+ev4cPuHlHvKGZYwjP3l+9lTtoc9ZXuC+f9s12fB/V7R7wq6R3VHCIHT68Tj96CpGt6AF5fPhcvrotJXyZq8NRysOAhAvDWevjF90VSNgAjg9rmp8uslXTUlXm6/G5fPRUAEqBmOUCCCgUKFt4LtxduPeM4Oj4PGhhc7ESTYEugR2YO1+WsbTCF0OH7XkT8DrU0GSK1gf3ElK/YUkRZ7fJYgCSHw5eXhLytDeLwIb+2jZnoKf3ERqBqe/fsQVW6Ex03A7daX3W4CHjfC7UFUVRHwuAlUVqKazKBpeA8d0qfBqNmvz6e/FgLVZqOkXA8C4lLCQoKhmioqW902SDEtu3YxyfY6AVLzB+pUVIXkI8wF5/X4cRTojcNL8yspK3BRlu+iOMeJy+GhosQdDK6OxGBUsYQZ9YfdiDXMiCXMhMVuwBKmB1EWu7E2TZgR4wkUVNVtJM6wxtMEXC68hw7hXP4LokoPkXwFhfgKCvAVFgYfAYcD4fXiO5SD71BO4zurT9PQwsJQIyLQwsPrPIdjCI8gIyKcHuERaBHV21LDUcOHokWMI2APozhgoLDCQ0FFFbvK3fyv3E2BoxJ3aS5+Rx6KMw+jq5AofzHxSmn1o4wYyolWyomiAk0RmPxOTJVOqDwAxU3Luluz4zZF4zNHE7BEgSUS1RaNwRaF0R6DKSwazRaFZoki1hJJrDUKInroU7wcoRcf6EFVpa8Sp9dJpVd/dvqcIa8rvBU4vU7KPeVUeCuo8FRQ7inH6XUGf+zr/vAHhN4JQyCo8ldR5a/CQesFXhkRGYzpMoZB8YNw+9z4hZ/e0b3pFtkNm9GGQTE0CIanD5+OX/gxabXfMWNTxnLdoOtw+VzEWGIodBXy+P8e50DFAe4eeTe+gI97l92Ly+fisTGPcUHPC5qUPyEE24u3EyBAv5h+qErzhhsJiACV3kryK/M5UHGASl8lvoCPfY597CrZRZI9id+l/Y44W1ww4HV6nZg0vQQoIAJU+fTrX+WrQlM1jKoRk2pCUzVcPhflnvLgw2a0EWGKCAZ8JtWEWTMTIKC3mQv48Aa8+IWfgAjgD/hrl4+0TvgbrHf73BRWFbLfsZ/8yvzgVEEJ1gTCTGEIBE6PE5fPVVt6pem/ExXeCsqqyo506VqdDJBawTMLtiMEVNU00m6nXmz5M2ZQufwXVLsdU9euiICfimXL8OXlY0xMJOB2gxDg94eMtNxetLg4uvz9CfZs9wAQmxqGNdzEiLMzALDY9Q++Pao2aLJHtmzE5/A6JVQxKW03GrLRpBGbEhYsfaohhKC8qIpKh4fSvErclT48VT48VX6qnF7KqoOpqgovAb/A5w00OZiq0WhQVS+IMlkMGIwqYdEWrNUBqMmiHZdtGlSrFXOPHph79DhiuoDbjb8mYCoowFdcjL+4BH9JMb7iEnyFBbh37ER4PAQqqkfPrv7s+8vK6k980jSahjksjPSICLrVDbDsdlSbDdVuQ43KwGfqR6XBiFM1Ua4Y2SWMlAoDRX4Fl8+Nx1eB11MOHgdGdwlmTwlhAQdRlBOj6MFUDOVEKRVEU4GqCMx+J2aXE1yHr5o8nCrVhtsQgdcYjs8UiTDZEaYwFHM4ijkMzRKBwRqO3RpBtC0Cky0cxWQHY5Te3spoA1MYmGz6FDFH+dwIIfAFfLWBU73gqcpXhcfvabQUpbG0br8bIQRp4Wn0iOrBqORRpISlHPvtUzU0GgaLVoM1WNIfZ43jldNfCdn+9UV626RjmU9TURT6xfY75jzWpypqsDqz7nhevyVuv5vVuavJKstiZNJIekf3btJ3U1lZGVE3RrV9BqvJAKmF/re7kHkbc7jZsDPYBqk9erH5y8ooev2N4Gvnzz+HbPceqjcnlcGAFhWFYjSGPERVFYGqKgyJCeD1YerZQ//yN5lRLBYUswnVbEYxmfVliwXFZEa1WvAVlyDcbiz9+6GYzfo+DYbgc83xir7XB9urCSYyzwv9o7dFmDjn1iEYTWqDNkPHqm6PuLolU+1FURQi4qxExFlJ6h552HRCCLzVQZOrwktVhZeqCg9VTh+uCo/+2qmvd9VZbm5QBXrVn9lmRAEMJhVbhBlF0UvLbBEmLHYjiqagqgqaQcVk1TBbDZisBgwmDc2gohn0bZpRDT4bTRoGk4rBqKEalDYLwlSzGTUlBWPK0X8oA04nKAr+8goC5Q78jvLgs7/cQaDuc0V59etyAg4H/vJyvXrY6z3mAMsARFc/GqOYTHpgZbOBzYawWPGbI/EazZQaTOQZTFSpBjwigBc/PuHDL9wE8AAeFDwYlCrMqhuroQq7wU24VkmEQX+Ea3oHBUugEounEjy5La6CCaDiUcx4VCtezYJXs+HXrPgNVgIGG8Joqw6o7KhGK4rJgma0YDdbCTda0KpfG8xWjCYrBlM0qt0KBrMefNV/1kzNnm6mtcRYYoixNH+eQOnIzJqZsSljGZsy9pje197/wDsuAqTXXnuN5557jtzcXIYMGcIrr7zCqFGjDpv+k08+4eGHHyY7O5tevXrxzDPPcPbZZ7djjnUHSiq586P1XKQu4y+Gj3lV1eeGaY9ebK7Nm4PLSY8/TuUKvSQpfMIEzD174t6zB9UehmqzgqJiSkvVv5TbWWmeXmICEJd6+CqvrgNiW+V4vTOT2L2ugPT+scdlaUkNRVEwVQcfEXFNC6iPJahyu3z4vQHKi6vwefSqj4BP4HJ4gvur6e3XuucFhuqASTOowcCq9rUaGmDVWbZFmPRhIHwCzahiMFYHZEYNVVNQNQVNU4PLasiy/loz1Kw3oaoKakwsxvh4zOqxfRaEEAi3G7/DQaA6YNKf9UArUFlJwFmpP9d9OJ2NrsOnDzshPB78Hg/+0tKQ4xmqH839C3VgwYEFoWkEjCb8RiMBg4Zf0whoSvUDUAKgBlCUAKriR1X8GDUfBtWPQdWXTaoPk+rFpPlRVIGioT+rTsyaE4taiKoKUEHVRPW26jRa7TJK8+McD0a8igmfYsKn6g+/asanmgioZgKa/hCaCaGZEQYzaCYUlOpjKgjViNcUTsBgQxF+VBFAxYdmMGOJTcNoDcdTUYLP40I1mIIPky2SyKSumKxhVJWX4vNWoWpGDEYTqsGIwWBC0QygGkEz6oGd0X7EsbWkE1OHB0gfffQR06dP54033iAzM5OXX36ZSZMmsWPHDhISEhqk/9///se0adN46qmnOOecc/jwww+54IILWLt2LQMHNmwk2lZ+zS/nlvd+5E+V/+F60zcAOBIHgPsANmPbByKuDfqgdBGTJxM99VKip14asr0p/8puD+u+3wdA10GxwV5rR+MsLaGqogJVU1FUDVVVUTQVVdVQVP1Z31a9vXobgMli4Pw7Wjby8/GquUGV3xcAAZXlHjwuH0KAt8qPq9wDCvh9AVwOL1WVXn2E8gD4vH48Lh8elx+3y4fP4yfg1/fl9wb0Z18AnyeAz+MPzrEnBHjdfrzu9m1MeVQKtUGVqpeSKSrVz3qApSjVzzXrQp5B1cJQlHBULUVfb1RQohXU2DrpNAVVQf+8KqBU708VAfD7UPw+8PvA50Xx6+328LnBW/3a40H4vOD16MteD4rXrbfp87gRHg943NXLbnC7Ee4qFBEAIVCoeRYo/gD49HWqEKiIkHSKEIAgIAJ4hMBLgKrqdYoIBJ+VI607wiUXABoIVQEVFBVQqgMoRaAqenClKgJVCQTX64GVHoDpJZweUDwoSjmaAgY1dDuKQAgIYMSPCT9GfJhQgEhDOZrqpdIXidtvJ6AoqIoHkzkfg9GDxx2NzxeGotQEjQGE5sJlK8VtFLgrTXh8NhQ1gKJUX1eql5UAigKa5kU1BvAZTXjMZhBgcHsgoJe++RUNoaoEFJWAqhFQNP25zrJQDfirnwOKQQ90VQMBzYAwGPGbTQRMVvwGC4oCqseHJvxoCqApKKoBoaqoqgqqAVRNH3tM03uPKfoHGEXVb4T+XaqBqvdMQ1VRNUPtcvV2Va393lVUg/5a0/TvW6X6O1jRtymqCqpSva36e7v6uGrNsVQVRdH3rT/09Po6/fu8OsIOfdSsD7Tv94oiaprPd5DMzExGjhzJq6++CkAgECAtLY1bb72V++67r0H6qVOn4nQ6mTdvXnDdySefzNChQ3njjTcapK+vKbMBz/tyNmXlZRAQBAI+EPqXSMAfoMrtpqqyHFzFJCklGAjoXwRRGWzCTbGnlKExg0mNSNN7JggAQe1l1v+YEcFX1QsiuA4RQAT84BOIgA/8AYTfp/94+X0IfwBv1h4CTieW4cOxdO9eux+Ch2wgZH3d7XU/AnUXD/fRCPa4CH2DCAgCovr8Anppx4GdJSCg58gE7BEmAoEAAkHpoUMU7MvC66zEEhlJcs8+GEwmsjeuxVlybF2VAVSDhj0qhtiUVKKSU8DvJz97D46iAjxVLgI+H36vr+E5KdWjj9T+r/qLV6n+12jNs6pvVvV0ilLzz2NV367q/3JVavapKKCq1WObKDX/Va+vsz/Qv2CoXYbqH5PqfdWuUKpX1eyv9rWeHYU6Ow398ar7T3mlZrvSYFvdJSV4QnWT1RYL1L5NXwgE0P9W9N9R/VpXTwkjhL5dBOp8DvVfNQQQCIhgSZeqqcF9IfTvhJq/mZr31uxHUL0PUXvMY/tG66hSxuO3dPNYKdXfAUrwutd+wSmhXzSNfi81vBRNuTYKQv9j1EdOP+p7AiH5IqRdUv1tVAeDAYTS2FhnjYzXJfwo+FECflAUAooRQb2/wXonrzSyrubDW3ebIoS+XyFQCRBAQS/WU+rstikf+uCPTki+QpdFo8tN2u9R31f3c1H32PU+I428rya9y+3mzldfPOLvd2vq0BIkj8fDmjVruP/++4PrVFVlwoQJLF++vNH3LF++nOnTp4esmzRpEnPnzm00vdvtxu2ubavhcBy9V8XzFoXt4ZlNOIPGfXb0JC039PT2OErLpdZ7XfPDn5oOqSfXTw1jDj82zDHRgN7djppMkiRJOjEEnBXw6ovtdrwODZAKCwvx+/0kJiaGrE9MTGT79sbHf8jNzW00fW5ubqPpn3rqKR5//PFjypch4Mckjq0BrCRJx7MOLSiXJKkVBNr5d7nD2yC1tfvvvz+kxMnhcJCWduSum9+feVlbZ0uSJEmSpGPgcDg4fN/g1tehAVJcXByappGXlxeyPi8vj6SkpEbfk5SUdEzpzWYzZnPz5/eSJEmSJKnz6dB+iSaTieHDh7No0aLgukAgwKJFixg9enSj7xk9enRIeoCFCxceNr0kSZIkSdKx6vAqtunTp3P11VczYsQIRo0axcsvv4zT6eSaa64B4KqrriIlJYWnnnoKgNtvv53x48fzwgsvMHnyZGbPns3q1at58803O/I0JEmSJEn6DenwAGnq1KkUFBTwyCOPkJuby9ChQ1mwYEGwIfa+ffv0sR2qjRkzhg8//JCHHnqIBx54gF69ejF37tx2HQNJkiRJkqTftg4fB6m9NWUcJEmSJEmSji/t/fstx0aXJEmSJEmqRwZIkiRJkiRJ9cgASZIkSZIkqR4ZIEmSJEmSJNUjAyRJkiRJkqR6ZIAkSZIkSZJUjwyQJEmSJEmS6pEBkiRJkiRJUj0yQJIkSZIkSaqnw6caaW81A4c7HI4OzokkSZIkSU1V87vdXhOAdLoAqaioCIC0tLQOzokkSZIkSceqqKiIyMjINj9OpwuQYmJiAH0S3Pa4wNLhORwO0tLS2L9/v5wX7zgg78fxQ96L44e8F8ePsrIy0tPTg7/jba3TBUiqqje7ioyMlB/240RERIS8F8cReT+OH/JeHD/kvTh+1PyOt/lx2uUokiRJkiRJJxAZIEmSJEmSJNXT6QIks9nMo48+itls7uisdHryXhxf5P04fsh7cfyQ9+L40d73QhHt1V9OkiRJkiTpBNHpSpAkSZIkSZKORgZIkiRJkiRJ9cgASZIkSZIkqR4ZIEmSJEmSJNXT6QKk1157jYyMDCwWC5mZmaxcubKjs3RCW7ZsGeeeey5dunRBURTmzp0bsl0IwSOPPEJycjJWq5UJEyawa9eukDTFxcVcfvnlREREEBUVxXXXXUdFRUVImo0bNzJu3DgsFgtpaWk8++yzbX1qJ5ynnnqKkSNHEh4eTkJCAhdccAE7duwISVNVVcXNN99MbGwsYWFhTJkyhby8vJA0+/btY/LkydhsNhISErj77rvx+XwhaZYsWcKwYcMwm8307NmT9957r61P74Ty+uuvM3jw4ODggqNHj2b+/PnB7fI+dJynn34aRVG44447guvk/Wg/jz32GIqihDz69u0b3H5c3QvRicyePVuYTCbxzjvviC1btojrr79eREVFiby8vI7O2gnrm2++EQ8++KD4/PPPBSDmzJkTsv3pp58WkZGRYu7cuWLDhg3ivPPOE926dRMulyuY5swzzxRDhgwRv/zyi/jxxx9Fz549xbRp04Lby8rKRGJiorj88svF5s2bxaxZs4TVahX/+te/2us0TwiTJk0S7777rti8ebNYv369OPvss0V6erqoqKgIprnxxhtFWlqaWLRokVi9erU4+eSTxZgxY4LbfT6fGDhwoJgwYYJYt26d+Oabb0RcXJy4//77g2n27NkjbDabmD59uti6dat45ZVXhKZpYsGCBe16vsezL7/8Unz99ddi586dYseOHeKBBx4QRqNRbN68WQgh70NHWblypcjIyBCDBw8Wt99+e3C9vB/t59FHHxUDBgwQOTk5wUdBQUFw+/F0LzpVgDRq1Chx8803B1/7/X7RpUsX8dRTT3Vgrn476gdIgUBAJCUlieeeey64rrS0VJjNZjFr1iwhhBBbt24VgFi1alUwzfz584WiKOLgwYNCCCH++c9/iujoaOF2u4Np7r33XtGnT582PqMTW35+vgDE0qVLhRD6tTcajeKTTz4Jptm2bZsAxPLly4UQesCrqqrIzc0Npnn99ddFRERE8Prfc889YsCAASHHmjp1qpg0aVJbn9IJLTo6Wvz73/+W96GDlJeXi169eomFCxeK8ePHBwMkeT/a16OPPiqGDBnS6Lbj7V50mio2j8fDmjVrmDBhQnCdqqpMmDCB5cuXd2DOfruysrLIzc0NueaRkZFkZmYGr/ny5cuJiopixIgRwTQTJkxAVVVWrFgRTHPqqadiMpmCaSZNmsSOHTsoKSlpp7M58ZSVlQG1EzSvWbMGr9cbcj/69u1Lenp6yP0YNGgQiYmJwTSTJk3C4XCwZcuWYJq6+6hJI/+OGuf3+5k9ezZOp5PRo0fL+9BBbr75ZiZPntzgmsn70f527dpFly5d6N69O5dffjn79u0Djr970WkCpMLCQvx+f8hFBUhMTCQ3N7eDcvXbVnNdj3TNc3NzSUhICNluMBiIiYkJSdPYPuoeQwoVCAS44447GDt2LAMHDgT0a2UymYiKigpJW/9+HO1aHy6Nw+HA5XK1xemckDZt2kRYWBhms5kbb7yROXPm0L9/f3kfOsDs2bNZu3YtTz31VINt8n60r8zMTN577z0WLFjA66+/TlZWFuPGjaO8vPy4uxeGYz05SZKOfzfffDObN2/mp59+6uisdFp9+vRh/fr1lJWV8emnn3L11VezdOnSjs5Wp7N//35uv/12Fi5ciMVi6ejsdHpnnXVWcHnw4MFkZmbStWtXPv74Y6xWawfmrKFOU4IUFxeHpmkNWsPn5eWRlJTUQbn6bau5rke65klJSeTn54ds9/l8FBcXh6RpbB91jyHVuuWWW5g3bx6LFy8mNTU1uD4pKQmPx0NpaWlI+vr342jX+nBpIiIijrsvuI5kMpno2bMnw4cP56mnnmLIkCHMmDFD3od2tmbNGvLz8xk2bBgGgwGDwcDSpUv5xz/+gcFgIDExUd6PDhQVFUXv3r359ddfj7u/jU4TIJlMJoYPH86iRYuC6wKBAIsWLWL06NEdmLPfrm7dupGUlBRyzR0OBytWrAhe89GjR1NaWsqaNWuCaX744QcCgQCZmZnBNMuWLcPr9QbTLFy4kD59+hAdHd1OZ3P8E0Jwyy23MGfOHH744Qe6desWsn348OEYjcaQ+7Fjxw727dsXcj82bdoUErQuXLiQiIgI+vfvH0xTdx81aeTf0ZEFAgHcbre8D+3s9NNPZ9OmTaxfvz74GDFiBJdffnlwWd6PjlNRUcHu3btJTk4+/v42jqlJ9wlu9uzZwmw2i/fee09s3bpV/N///Z+IiooKaQ0vHZvy8nKxbt06sW7dOgGIF198Uaxbt07s3btXCKF384+KihJffPGF2Lhxozj//PMb7eZ/0kkniRUrVoiffvpJ9OrVK6Sbf2lpqUhMTBRXXnml2Lx5s5g9e7aw2Wyym389N910k4iMjBRLliwJ6UJbWVkZTHPjjTeK9PR08cMPP4jVq1eL0aNHi9GjRwe313ShnThxoli/fr1YsGCBiI+Pb7QL7d133y22bdsmXnvtNdmduZ777rtPLF26VGRlZYmNGzeK++67TyiKIr777jshhLwPHa1uLzYh5P1oT3/5y1/EkiVLRFZWlvj555/FhAkTRFxcnMjPzxdCHF/3olMFSEII8corr4j09HRhMpnEqFGjxC+//NLRWTqhLV68WAANHldffbUQQu/q//DDD4vExERhNpvF6aefLnbs2BGyj6KiIjFt2jQRFhYmIiIixDXXXCPKy8tD0mzYsEGccsopwmw2i5SUFPH000+31ymeMBq7D4B49913g2lcLpf485//LKKjo4XNZhMXXnihyMnJCdlPdna2OOuss4TVahVxcXHiL3/5i/B6vSFpFi9eLIYOHSpMJpPo3r17yDEkIa699lrRtWtXYTKZRHx8vDj99NODwZEQ8j50tPoBkrwf7Wfq1KkiOTlZmEwmkZKSIqZOnSp+/fXX4Pbj6V4oQghxbGVOkiRJkiRJv22dpg2SJEmSJElSU8kASZIkSZIkqR4ZIEmSJEmSJNUjAyRJkiRJkqR6ZIAkSZIkSZJUjwyQJEmSJEmS6pEBkiRJkiRJUj0yQJIkSZIkSapHBkiSJEmSJEn1yABJkiRJkiSpHhkgSZJ0VPfddx9ms5nLLrusSelPO+00FEVBURTWr1/ftpk7Qf3xj38MXqO5c+d2dHYkSapHBkiSJB3V/fffzwsvvMCsWbP49ddfm/Se66+/npycHAYOHBiyfvny5WiaxuTJk9siq0d12mmncccdd3TIseuaMWMGOTk5HZ0NSZIOQwZIkiQdVWRkJNdddx2qqrJp06Ymvcdms5GUlITBYAhZ//bbb3PrrbeybNkyDh061BbZbRUej6dN9x8ZGUlSUlKbHkOSpOaTAZIkSU3i8/mw2Wxs3ry52fuoqKjgo48+4qabbmLy5Mm89957DdKcdtpp3Hbbbdxzzz3ExMSQlJTEY489FtxeXl7O5Zdfjt1uJzk5mZdeeqlBqdCnn37KoEGDsFqtxMbGMmHCBJxOJ3/84x9ZunQpM2bMCFZvZWdnB497yy23cMcddxAXF8ekSZMAcLvd3HbbbSQkJGCxWDjllFNYtWpVSH5vvfVW7rjjDqKjo0lMTOStt97C6XRyzTXXEB4eTs+ePZk/f36zr5skSe1PBkiSJDXJQw89REVFRYsCpI8//pi+ffvSp08frrjiCt555x2EEA3SzZw5E7vdzooVK3j22Wf561//ysKFCwGYPn06P//8M19++SULFy7kxx9/ZO3atcH35uTkMG3aNK699lq2bdvGkiVLuOiiixBCMGPGDEaPHh2s/svJySEtLS3kuCaTiZ9//pk33ngDgHvuuYfPPvuMmTNnsnbtWnr27MmkSZMoLi4OeV9cXBwrV67k1ltv5aabbuKSSy5hzJgxrF27lokTJ3LllVdSWVnZ7GsnSVI7E5IkSUexevVqYTKZxOTJk0X//v2Pmn78+PHi9ttvb7B+zJgx4uWXXxZCCOH1ekVcXJxYvHhxg/eecsopIetGjhwp7r33XuFwOITRaBSffPJJcFtpaamw2WzB461Zs0YAIjs7+5jyNn78eHHSSSeFrKuoqBBGo1F88MEHwXUej0d06dJFPPvss43m1+fzCbvdLq688srgupycHAGI5cuXNzguIObMmdNoXiVJ6jiyBEmSpCMKBALccMMN3HLLLVx11VXs2rULr9d7zPvZsWMHK1euZNq0aQAYDAamTp3K22+/3SDt4MGDQ14nJyeTn5/Pnj178Hq9jBo1KrgtMjKSPn36BF8PGTKE008/nUGDBnHJJZfw1ltvUVJS0qQ8Dh8+POT17t278Xq9jB07NrjOaDQyatQotm3b1mh+NU0jNjaWQYMGBdclJiYCkJ+f36R8SJLU8WSAJEnSEb3yyisUFhby17/+lUGDBuH1etm+ffsx7+ftt9/G5/PRpUsXDAYDBoOB119/nc8++4yysrKQtEajMeS1oigEAoEmHUfTNBYuXMj8+fPp378/r7zyCn369CErK+uo77Xb7U0/oaPkt+46RVEAmnwOkiR1PBkgSZJ0WAcPHuThhx/mtddew26306tXL8xm8zG3Q/L5fLz//vu88MILrF+/PvjYsGEDXbp0YdasWU3aT/fu3TEajSGNpMvKyti5c2dIOkVRGDt2LI8//jjr1q3DZDIxZ84cAEwmE36/v0nH69GjR7BNUg2v18uqVavo379/k/YhSdKJyXD0JJIkdVa33XYbZ511VnDMIoPBQL9+/Y45QJo3bx4lJSVcd911REZGhmybMmUKb7/9NjfeeONR9xMeHs7VV1/N3XffTUxMDAkJCTz66KOoqhospVmxYgWLFi1i4sSJJCQksGLFCgoKCujXrx8AGRkZrFixguzsbMLCwoiJiUFVG/+3ot1u56abbgoeLz09nWeffZbKykquu+66Y7oGkiSdWGQJkiRJjZo3bx4//PADM2bMCFk/aNCgYw6Q3n77bSZMmNAgOAI9QFq9ejUbN25s0r5efPFFRo8ezTnnnMOECRMYO3Ys/fr1w2KxABAREcGyZcs4++yz6d27Nw899BAvvPACZ511FgB33XUXmqbRv39/4uPj2bdv3xGP9/TTTzNlyhSuvPJKhg0bxq+//sq3335LdHT0MV0DSZJOLIoQjfSxlSRJaoHTTjuNoUOH8vLLL7f5sZxOJykpKbzwwgsnZKmOoijMmTOHCy64oKOzIklSHbIESZKkNvHPf/6TsLCwJo+83VTr1q1j1qxZ7N69m7Vr13L55ZcDcP7557fqcdrajTfeSFhYWEdnQ5Kkw5AlSJIktbqDBw/icrkASE9Px2Qytdq+161bx5/+9Cd27NiByWRi+PDhvPjiiyHd6k8E+fn5OBwOQB/GoLk96CRJahsyQJIkSZIkSapHVrFJkiRJkiTVIwMkSZIkSZKkemSAJEmSJEmSVI8MkCRJkiRJkuqRAZIkSZIkSVI9MkCSJEmSJEmqRwZIkiRJkiRJ9cgASZIkSZIkqR4ZIEmSJEmSJNUjAyRJkiRJkqR6/h/G71nHkQ9wiQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "ages = np.linspace(0,len(ssp.age),10)\n", @@ -216,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -253,21 +937,133 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "pyPipe3DSSPGrid(age=Array([1.000e-03, 2.300e-03, 3.800e-03, 5.750e-03, 8.000e-03, 1.150e-02,\n", + " 1.500e-02, 2.000e-02, 2.600e-02, 3.300e-02, 4.250e-02, 5.350e-02,\n", + " 7.000e-02, 9.000e-02, 1.100e-01, 1.400e-01, 1.800e-01, 2.250e-01,\n", + " 2.750e-01, 3.500e-01, 4.500e-01, 5.500e-01, 6.500e-01, 8.500e-01,\n", + " 1.100e+00, 1.300e+00, 1.600e+00, 2.000e+00, 2.500e+00, 3.000e+00,\n", + " 3.750e+00, 4.500e+00, 5.250e+00, 6.250e+00, 7.500e+00, 8.500e+00,\n", + " 1.025e+01, 1.200e+01, 1.350e+01], dtype=float32), metallicity=Array([0.0001, 0.0005, 0.002 , 0.008 , 0.017 , 0.03 , 0.04 ], dtype=float32), wavelength=Array([2000. , 2001.5, 2003. , ..., 9995. , 9996.5, 9998. ], dtype=float32), flux=Array([[[4.88501154e-02, 4.91619967e-02, 4.91009988e-02, ...,\n", + " 3.30204784e-04, 3.29886097e-04, 3.29610863e-04],\n", + " [4.94970530e-02, 5.01737520e-02, 5.02070002e-02, ...,\n", + " 4.14328271e-04, 4.13853064e-04, 4.13406960e-04],\n", + " [8.76932293e-02, 8.82892460e-02, 8.89149979e-02, ...,\n", + " 4.41885233e-04, 4.28894331e-04, 4.23215213e-04],\n", + " ...,\n", + " [1.21357860e-02, 1.29830008e-02, 1.31270010e-02, ...,\n", + " 3.66057851e-04, 3.65645654e-04, 3.65268701e-04],\n", + " [4.34386544e-02, 4.45019975e-02, 4.51029986e-02, ...,\n", + " 8.72896460e-04, 8.58685584e-04, 8.49017815e-04],\n", + " [4.63533700e-02, 4.74307537e-02, 5.00590019e-02, ...,\n", + " 1.18692615e-03, 1.17103057e-03, 1.16154784e-03]],\n", + "\n", + " [[4.55114506e-02, 4.67945002e-02, 5.04850000e-02, ...,\n", + " 1.58759137e-03, 1.57122186e-03, 1.56014797e-03],\n", + " [4.02839743e-02, 4.10410017e-02, 4.56160009e-02, ...,\n", + " 2.17393902e-03, 2.16081296e-03, 2.15069135e-03],\n", + " [3.95360477e-02, 4.02887538e-02, 4.49550003e-02, ...,\n", + " 2.19770428e-03, 2.19773944e-03, 2.19724351e-03],\n", + " ...,\n", + " [5.74502582e-03, 6.62355032e-03, 7.13869976e-03, ...,\n", + " 5.59784763e-04, 5.54787810e-04, 5.51474805e-04],\n", + " [5.23717608e-03, 6.15917472e-03, 6.57939957e-03, ...,\n", + " 5.30153862e-04, 5.26174728e-04, 5.22577378e-04],\n", + " [4.16078418e-03, 4.38545039e-03, 4.39980021e-03, ...,\n", + " 1.74755653e-04, 1.74447836e-04, 1.74297835e-04]],\n", + "\n", + " [[3.93560622e-03, 4.25232435e-03, 4.25869972e-03, ...,\n", + " 3.09367810e-04, 3.08907358e-04, 3.08723451e-04],\n", + " [7.56444363e-03, 7.73700001e-03, 8.01430084e-03, ...,\n", + " 4.87164332e-04, 4.83889598e-04, 4.81816969e-04],\n", + " [5.66153368e-03, 6.13215007e-03, 6.44250028e-03, ...,\n", + " 4.48479579e-04, 4.44969104e-04, 4.41752636e-04],\n", + " ...,\n", + " [1.49275071e-03, 1.63884996e-03, 1.62910006e-03, ...,\n", + " 2.43942617e-04, 2.43772607e-04, 2.43832605e-04],\n", + " [1.27917202e-03, 1.49302499e-03, 1.47709996e-03, ...,\n", + " 2.40836962e-04, 2.40250884e-04, 2.39626097e-04],\n", + " [1.16398360e-03, 1.42472505e-03, 1.39020011e-03, ...,\n", + " 2.26306103e-04, 2.25467826e-04, 2.24852178e-04]],\n", + "\n", + " ...,\n", + "\n", + " [[1.82961696e-04, 2.20157483e-04, 2.09199992e-04, ...,\n", + " 1.14740869e-04, 1.14182600e-04, 1.13716953e-04],\n", + " [1.54334572e-04, 1.97927511e-04, 1.83810014e-04, ...,\n", + " 1.14296090e-04, 1.13946095e-04, 1.13403483e-04],\n", + " [2.14758533e-04, 2.69612501e-04, 2.51429999e-04, ...,\n", + " 1.07419568e-04, 1.07109139e-04, 1.06897831e-04],\n", + " ...,\n", + " [1.36646340e-05, 2.03760010e-05, 1.84940000e-05, ...,\n", + " 6.62917009e-05, 6.61149606e-05, 6.59495709e-05],\n", + " [3.30816920e-06, 5.74944943e-06, 5.06109973e-06, ...,\n", + " 6.35753458e-05, 6.33214804e-05, 6.30999930e-05],\n", + " [2.81301891e-06, 5.26412532e-06, 4.56440057e-06, ...,\n", + " 5.72564350e-05, 5.68787873e-05, 5.66175222e-05]],\n", + "\n", + " [[2.34056597e-06, 4.25360031e-06, 3.71270016e-06, ...,\n", + " 5.64447437e-05, 5.59899599e-05, 5.56630876e-05],\n", + " [4.67413265e-05, 5.31522528e-05, 5.19290043e-05, ...,\n", + " 5.72451318e-05, 5.70667398e-05, 5.68950854e-05],\n", + " [3.65333472e-05, 4.40160002e-05, 4.20910001e-05, ...,\n", + " 5.90607378e-05, 5.89082192e-05, 5.87680006e-05],\n", + " ...,\n", + " [1.26257373e-05, 1.49019997e-05, 1.44910000e-05, ...,\n", + " 3.00848260e-05, 3.00175216e-05, 2.99529565e-05],\n", + " [8.40386019e-06, 1.07312499e-05, 1.02099993e-05, ...,\n", + " 3.15008692e-05, 3.14371282e-05, 3.13716555e-05],\n", + " [2.70062992e-06, 3.97119993e-06, 3.60359991e-06, ...,\n", + " 3.12949123e-05, 3.12144330e-05, 3.11653930e-05]],\n", + "\n", + " [[5.57534293e-07, 9.30610042e-07, 8.32740056e-07, ...,\n", + " 2.87947842e-05, 2.86766517e-05, 2.85754340e-05],\n", + " [1.65273477e-07, 2.36002506e-07, 2.20380002e-07, ...,\n", + " 2.36395663e-05, 2.35186544e-05, 2.34229137e-05],\n", + " [1.13538640e-07, 1.50852500e-07, 1.50569988e-07, ...,\n", + " 2.19958256e-05, 2.18645218e-05, 2.17878260e-05],\n", + " ...,\n", + " [9.31926749e-08, 1.01580000e-07, 1.04690002e-07, ...,\n", + " 1.43920424e-05, 1.43096513e-05, 1.42312601e-05],\n", + " [9.43405638e-08, 1.17012490e-07, 1.19630002e-07, ...,\n", + " 1.31097386e-05, 1.30333474e-05, 1.29839136e-05],\n", + " [1.05849416e-07, 1.30914998e-07, 1.32560004e-07, ...,\n", + " 1.25726528e-05, 1.24969129e-05, 1.24434355e-05]]], dtype=float32))" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.spectra.ssp.grid import pyPipe3DSSPGrid\n", - "ssp = pyPipe3DSSPGrid.from_file(config, file_location=\"../rubix/spectra/ssp/templates\")\n", + "ssp = pyPipe3DSSPGrid.from_file(config, file_location=\"../../rubix/spectra/ssp/templates\")\n", "ssp" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(39,)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.age.shape" @@ -275,9 +1071,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(7,)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.metallicity.shape" @@ -285,9 +1092,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(5333,)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.wavelength.shape" @@ -295,9 +1113,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(7, 39, 5333)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.flux.shape" @@ -313,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -324,9 +1153,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[0][0])\n", @@ -337,9 +1187,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dQCvGdMO8LSfxxc5UvfqZLtvrg20Wj+V7o/vGT620eXi6I53JLsCPyRfxTK+mqF3DV/R+J7LyMfTLykTjPWev4lhGvqT3kKdMKeCu2C1FRLLRz2exJbgpFzm77wvf7TeYDO7Zr8UtyWALdxm0or9wqL3JsL/qzaRrC38fVbUbfYCPbS0NPurK29B9sVF21QkAHoyrj88ei7P7OFXKNVr0nbMDC7efwfRf/rP5ONtOZEsOjjUOykG6Vbg0uNm5cycGDRqEqKgoKBQKrFu3TvS+f//9N9RqNTp27Oiw+hGRNPYGApaSUvX9digDX99o3hcEwaBbyl6X80oMkm3l+Aa969QV3PPxXzh4IUfyvnkl5fh8+2kk/mA930csOY9VxZblHIDKBPSScg1WJ1+0uw5zh3bEvR3sD5KqfK3XhaSfNP/d7jRMWXNYtvOYwpYb+7g0uCksLERsbCwWLFggab+cnByMHDkSffv2dVDNiEiKqhRJW4MbrVZARm6xpHWZqobUNpmywbaTmpCaXYBuM7diwPy/dNtsXWBS3+NLduNoRh4eX7Jb8r6v/HAQH2w6YbHM4p2poofkH0m33qUV5KdG0pT/w7R725otY5xenJFbgldXH8R+ketHVSnXCPhGYkvU+hdvt/h8aJCfpOOZs+/8zbwi/WUnXl972KGJyoDnLOPhrlyaczNgwAAMGDBA8n7PPfccHnvsMahUKkmtPUTkGFUDacQOs84rKce/Z66id6t68FOr8Praw1i1V9ocJ8v/OSf7LLOb/6vMr9HPbTl1ucCuY+rnAJlqYdJoBZOz2V7KKcaWo1n4/aj5Se+qvLfhGPanXcfCxztbLfulXp6MOTX91YgMCcCI+EY4dDEH8c3qVitjPHjq9OUCnL5cILkF5qu/UtGwbqCkfdpFBaNf23BsMfNv069tmF1z5qRdLYKfjxKBvjdvkVUtKc4KOthyYx+Py7lZtmwZUlNTMX36dFdXhYhuqLrP6X8eG9/8fj14Ca/9dAjlGi2GfvEvnvk6GY98UZlwKTWwcRTjqUkGfbZL9nPojwhb9vdZtJu+yaDLA6icqbnH7D8l5XnsOJlttUzy+esmJ5wz9sKdzQFUztUy/9E4DO3asFqZ+Kahoutmyb7z1+GnlpavY21YutSVnS5cK8LpG0FsblE57vhwG7rP3Ap/n5tviKr5eMQs/ClFQpswk9vZcmMfjwpuTp06hddeew3ffPMN1GpxjU6lpaXIy8sz+CEix7A0lPvFlQewau8F/JR8UTdayZYclCp1JYxcMSfpzFV8sOm4LvlZqXfT3OWgSdRmbrg5W++MX4+ipFyLsd8YJkRvtmF2WmtDhzVaAUMW/oMrBZZnTP4jsTeGd68ezBiLaRCCdS/0xNxHYiXV0xRzK41bYqkLVOzcQrnF5TiVlY9eH2xDwtwdyC0qR5re0HX99azySytb3SbKsISHvtuaVm8VA4C5W05i+d9nZT3XrcRjghuNRoPHHnsMM2bMQMuWLUXvN2vWLISEhOh+oqOjHVhLIu9XUq7BV3+l4kz2ze6aqm/SYr5rWru5ihUe7G/3sYYt/hefbz+D3w5Xjh7Snz3WlvwYMRb/VXnD0l96oEIr6CboA4DCMumtA0ord/T1h6yPkPrh2Xg0DwsS0TJSqWN0LUTVChBV1pI6NgWqhu+2r0Z2kXyE2Bm/o9+8nbrHGXnFMLcCSA1fFUorNKK6CaXwVSvRwsyEfW/96piJDW8FHhPc5OfnY9++fRg3bhzUajXUajXefvttHDx4EGq1Gn/++afJ/aZMmYLc3Fzdz4UL7tH8TeSpPt92Gu/+dgx959ycLE+XUKyXe6sw0zUg19Dqoxl5di2yqD/Xy/XCyi4HlZkp8+VWXKbBq6tvLuSo0QroPnMr4t7ZgryScviqpdejfm3zQYYgCBi/KsXi/m0ig9GtSR3J5zW3zIAltzc37NIqkbCcRGK/6l9uY6NrIaFtuO6xrZNCqxQKrN5nOmcoPMQfM387ZvI5e/iolIhrWMvs82lXxU2CSIY8ZhK/4OBgHD5sOPTu888/x59//okff/wRTZo0Mbmfn58f/PzkyZwnImDvOfOjYWxZtyndzNo8jtZu+mbd71UJvSo71koICfARvazDM1/vq7Z2UFWy8fGMfFy1oUWqbg3zn3MnsvKt7j/nYdu6l3xtCG6euaMpdp2+ef35JZb/3RrUDsDF68Xw91Hipb4tAFgOkm2Nn5VKBZb/c87kc6nZhaImiJTKWnB4x4fb8EfiHWgeVhOCIODTP0+jXVQw+rYJt7jfrc6lwU1BQQFOn765lsbZs2eRkpKCOnXqoGHDhpgyZQrS09Pxv//9D0qlEu3btzfYPywsDP7+/tW2E5HjmLr/V91MbGmV6TnbdKuro+lPMliVK2RPw817D7THuO8OiCprHNjo12XY4n9tSia1tLClmOmDQgJ9JJ8TAHzU0gNC4xv6t7src1sGd4zCupTq3WdfjOiMZX+fw9O9muq2afXebMY1sHVCQWtde47go1JY/bvZfiIbTUODsP3kZczdUrni+rnZAw3KVL2HxXYpejuXdkvt27cPcXFxiIurnFEyMTERcXFxmDZtGgAgIyMDaWmOnUuAiKQxdQOoutEY3HDMfMa64xiQqjqpzCVciDmGHRdWrDcCx9ZRMpbuaYv/sj7829Z1vWzpljKXQGxu1FSzekH46OFYtIqoKer4g2yc7dgRYcEb97QxeNzdqOvPV6W0+jcxa+NxdH3vD6RcMD1HkSAIGLb4XwxZ+A+0Zt4/giAY5Hl5O5cGN3369IEgCNV+li9fDgBYvnw5tm/fbnb/t956CykpKU6pKxEBhaUVBt0JVbRC5YfnYb0J4txl2QIxqupqz83N1ZdrLscJMFy6wZTuTeqgvo2JwT4iA8IhnRrc3MdMQGSuFcjPRA6S/r+3cWDXMboWeresJ6pe+rYeN73Ct626NamDp+9oiq6Na+u21fAz7DDxUSmt/q1otAKuFpZhiZkgNb+0Av+mXsP+tBxkmFnm4en/JaPttM2iFzL1dB6TUExErjftZ9Pzruw8mY3v917AE8v26rZV5d+UVWjxzxn7h1V/8FAHu49hTlWLk5gWDnNsbfmQi7kYw9oMyz2b18WqZ26zuTvDWrfUK3e1xLnZA/H8nc1029RmWm5UCgXOzroHZ2beg0l3t0KwvxrP3NHUZN2s/XO3rx+s+/2BuPqWC9/wznrpo5Pq1vBFl0a1TT5XJ7ByFJh+/Y27D30kJI/rT+zX8e3f8dLKA7h7/k6c0Zto0tyr8cexylFeq/beGr0hDG6ISLQNhzPMPvea0Vo7VTefd9YfxWOLd1fbLtUjXRw7jUN2fimOZ1pPvDXH1S1V5lpuSqwsYKpUKOzK07CWhK2+0Uqj38Ljq1Li40c7Vj+WUgmFQgGVUoHn+zTHobf643Wjbh1TTNVAv3fmMRHz9thq35sJuLO16Yn4qgIX/X8j43/rym4pcW8e/eAmp6gcvxy8hOOZ+Xjg83/M7rPpSAZavHFziZKq98nec9fw1V+pLg/KHYXBDRGJJiUfpKqk8QrW8/44KWONLFvwWCc82tV6UKQVBIOh4bYwtYSCM5mLMYotzJkTGuSH6YPsW8JCaeW6q57Vb+FRq5S4v2P11hRzLTqmWHsn6ud/2TsXz21NzQ+RVygU5vPLbtRBv1VNbfTv5eejNLiY1+9pbfZcYv7+jEs8981+lOut2fbZttMQBAEPL0rCu78dw4/JF3H4omEuz7e7z+NvE93PnoTBDRGJpnHRt7zmZiY5s2Zgh0jMHmK9OysrrxR5VoYjW9OvrWOH5hrfFI2Za30xNzw9NMgPe9/oa/O/bRWxw+fVend4c9ciJTwUDJLXLXdb+dgZeNoauFZVQb9V7bUBhsGLcT6RvSO2zCUU69NfbPbVHw9h0Ge7kHRjWY5DF3PwxtojGP6VYyaxdBYGN0QkmpSWm8zcysRGW+ZBkcreG/SSXWdxt95K4Lbwt3H4sVgPdW5gvZAJS3aZziPyVdnXHVXFasvNjaf1AxpTCcLAzSUO5KC/wrxa4nuwpr9h0q+YofQm3ahCkF4ScaO6NQyK+KlVBq0te89dgz1sHW33y8HKIfjZ+fLMIO5qDG6IyCGe+yYZBaUVZm9kUli7BW+ZeIfd5/B05r6xF5nplpKSyGqJtVaNqlYL/VmXjUcM6eokoYVEv2XG1F5BegGKlO4uAHi+T3NMurvVzeOb2X3VM7dVq4spbwxsg9joWvhkWJzVc+t3IdnC9tXEK/fTH8lWFShl5pbgq79SRU9S6Q48ZoZiIvI86deLrX6zl4M3TFxWv1ZAtdmam9argasFZXikSwPdDMbmVJhpXoipH4KfTUyMJ1eOkNhuqRp+anw5ojOUCoUuuGkRFoRTeiN9pNyWtVYiiqd6NcHJzHzcGxtpmMysVhpMmmiKUgEE+lq/PZpb9LJK1WKm0XUC8fMLPU2WCQ5QG3SxBfvbd1vWb7lZuP2MhD1vzNKt974o12hx6GKeLmE5+fx1LHy8s131cxa23BCRw2gFweLMuWKJuenFW7nRuML7Q2KQIHKa/Bn3VU/sjQzxx/6p/fDGwLZW9zfOh87OL4VWK+Bdc+shyZQ+ZbXlRu/pu9pFGKwBtfKZ2wyGUVsLWPQZtNyYqEKwvw8WjeiMeztEGbTcPGAikdlUnR/uEo2ENuF4d7D1GfCNzz8hoQXCavphool1sIyF1fQ3eCkmJLREq3BxkxWaoh/kvr/puIQ9BczacMwg16Zco8WQhTdHYm08kokRS3bjyeV7ReX2uBKDGyJymOz8UqdNaW/LulaOdl9sfXz2mPWuCACoXcP08gemgofJd7euNpRaPzDYnXoVXd/7Ay+tMlwOQj9vR67kcHtagEKD/AxmE5ZSJSmvt36+j5j9lAoF/H1U+GpUFzx+WyOTZZaMMr8K+YSEltj9el+ro7Si61Q+r3/djUNrYLMd3ay25twAwBc7DfOz1h5Ih/Hh/jp1BVuPX8bfMsxd5UgMbojIYUYu3YOrhWWS9+vfTvrII2d9kfzmye5Wy/ioFPjh2XgE+Krg76PCM3c0tbqPqaUHzC0HMbZPs2pDqfNLyrHr1BWUa7T4bFvlmn3rDxnOS/SRjYtjWmJvy9w9MZG636W8hIY5N+JHktkS0xnH58/1bmZ24cq5j8RWO6exqvyj25rI39q48UgmLuUU44TEOZsqTOT6mJu0EwBGLNmD03pdiu6GOTdE5HYa1gk0eCxmojHjb6wv/l9zWet0Z6t6ePmuVqJaKm5vHopuemsI1RKxKKWppNcmdQNNlDRt77nreHzJbjzUuYHZJGJ9crWn2ZvvVK/mzdXMpUwoZ2vDk5jdjK/JOHiyNCxfzLpWG8f3wq8HL2HM7U1E10mshdvPSMy1qbQ6+aLkfRLm7qi2gKe7YMsNEbkdW5KQ9ZcZOPb23Xj5rlYWSks3tk9ztK8fYtNNVUx+gtqolSasph9e7m/5Gp7r3azath+TL9o9IaGcpAQ/NndLSXi7iMnrsXY4S5ckphu2Wb0gTEhoiWD/yqDXnvwVR7TGSfFv6lWXnt8cBjdEJJqzZuE1HoEj5gY5Mr4xAKBXi1AE+Mo/50zVpduS29PQaG4TAJh0dyuD7jfjlbJ/fK6H7uZnjrl/FlP/XlUjd6rybiYkWE92dTZJwY2tLTci9rP2drP0tC1/IeZGulkTEuBj0PLlCo9++a9Lz28Ou6WISDRnDbiWkoSceGNEyoOd6qN9/RA0Ca0eSMhSpxvRjS031XtjInHxehGC/X3w5rojAIB+bcKhVCiw+b/KBQ31A8dn7miKhkZdUqZaHMz9K5mKQZc90RUA8MGQDpiQ0AINaovv8nIWKYGjwargEs4hplVL6vtcv9vKll46U/lWYnwxorPbj1pyFbbcEJFVF68XoefsP+2YIEwa424pS7kYVV0zCoUCrSJqGkwWJ2ud7MgtUd5YCLJHs5sJpCqlwiBPSH/ytBEmRuiYmtyta2PTax6ZmnumKlhSKhVODWzaRNg+rNkSw+UXxO8nZjSR1G5R/aDMlhyk1wa0RuuImpj1YIyk/SJD/EVPxti9ifn1seyVlVeCp1bsw86T2Q47h1QMbojIqo82n6g2wZwjiZ0YDrDtm7ItpNTJ7DH0bpo+KqXBDVo/SdVU95+p7oc+reqZXPF63/nr1bZFBPtLrq+tujWpgw0v9cInw+LQo3mo6P2kdHsattyI30/MDMBWjybzmy6qVgA2TbgDw7rdfC273Qhcw4MrX/eJRt2INf3ViKoVYHXNMQA4N3sgvn3K+ig/a0ydSq1UYOq6I/jjWBZGLt2Dc1cK7T6PHBjcEJFVds4IL5lSAfz4XLxzT2pF1f1MXM6G6RuOyiiA6aQ3gZ3++kemdh/3f80xuGOUrnup6jx3t4uwXiELdXKUtlHBuE/EyCF9UuooNWm6Tg1fAMAdLUUEWxL/raQEV2JNursVhnaJxqbxd+D4O3djfEIL3XOPdo3G3jcSDFr7gMpZrs2Rur6WyWOYmJqgUd1AZNxYRw4A+ny0XdKoN0dhzg0RWSXm26GclEoFuuh1uVj6qHRWzaoCE3smCzReGbtHs1AsG90VzesFWb2fBvv7YP6j1ScEdFaStyMN794Qq/ZewDO9rM8HVEVqnsvOSXficl4JLotYGNLqaCnrp7Nbl8Z1DP4G9NXwU+sWatXPxfpkWEcMWZhkUNbe5Rz0qZQK4MYsA1XLhZzJLkSHBiEG5X4/moX+IoNuR2HLDRFZ5axZhqtIOZ2Yb/vfPS1Hk7wMK2jrHaIqKLmzVRii6wQiUG9V8apWBjGMv727A6mLpb73QAyOvt0fjSUkg78/pIOkcwT5qdG0XpC4oeAKy49dZcqA1mgRFoTn+9ycAkB/8j1TK9OXWllHSwr9LznLRt9sQTyeYThh4MXrzuvCNoctN0RklasbB+wdetujmfi8D3OUErqlzDIR3FRRq5TYP7UftIIgafRMaJD4QMhZYuqHWC9kROqIobZRwbrfpQQf+qOuH4irj7UH0quVcXYwL9azvZvhWaO5jcL1cqlMteLd3V6+FhSV3nQF+onZZUZdhBVuMM+S+4X8RORWBEGwafZSe5jLYRgVb3qdHznd2aqeye1VI2iqVrS2iZXAqE4NX4QGSZu3pKaVuXCc7fk+zfBS3xbWC7pI68ibo7fmDe1osow9k/g5W+PQGvh0WBxWPn2bye7jtwZVX5DVVvpJ9Y0szJ5dXG59hmxHY3BDdAvILym3ed+TWc5ZP6Zr45vJtcZ5LVWP3hjYttraTnLfaIZ2rT76CLj5bb55WBBe+r/mJlfxtiY44GYgEugrT8N5TRlzKuwVFeKPSXe3Ntk94khSEnpDg/ywa/KdODC1n/njWZ3Ez42iG1Qu+RDfrK7JFicxS3+Ipd9aFuirNjsCb/4fp2Q7p63c56+CiBzi/U3HsXD7GSwf3RV9WoVJ3t9Z38LEJMb6qpW4vYVhF5Pco4DMVUP/W2vijaUdpv9ifmFBU/x9VNjxah8ooJBtPh5/HxXaRAYjM7cY14tsD2Ll4PoxMuJYm+dHavDiLi05pv6GzP19tI0MxtGMPEnHf+aOpohtUAt3tq78HAkP8UdmXonJsqUVGpsnJ5QDW26IvFzVInpvrz9q0/6FpRVyVscs/ZFE1W4uTrxrmsu3kOsG1qhujWqzD9tr/Yu3I2lKX1mPaQtXjQCWPbiw1nJj/PZ0k6jO+L277oWeZst+MaIzxt3ZHG0jg82WMRboq8Ir/Vuh840pDPwsJLN/uOmE6OM6AoMbIi90JrsAX/2VihK9VhcxM7Oa8sFm53xIWWq5ceY3Y6Wyco6d9vWDMe3etrrt7jzkWqVUOL0ryBR7hsnbQ+7Xxn1facv0V5Zf9kRXdIyuZbZsdJ1AvNK/laSReSqjeW4stT5+teus6OM6AruliLxQ3zk7AACFpfYHNwcv5MhRJQDAkE4N8NN+08nJlm5QpiYPcxSFonKOnfUv9sKR9FzddncdQeNOXLXMkdzzMEl9rd3lraHfdapWyV8p41m6HbXUiRzct2ZEZLeVe9J0v7tDy8PLd5lfidrSDUXqWj/2UJq5QTgxviKJZG+5qTbPjeEG1/8lmab/dyJ2uRAprW3G/87G8xkpFECvGzlxUlqEHIF/rkReTD/Zz5a1kTJy5Z2My1IAo/9U9ZwG+5sEfhrbQ1Q5g4n29CrClhvrnJ17Mu7O5vD3UeLV/q1lPa7V0VJu+lYweL+aCPhm31iYc87Dsbpt5RXmX7RPh8Xh8+GddI+NW4OMz/HF453xav/KZHtfF08uyeCGyMNl5ZWIuvnb0vqR+P1BW6pkvg4WqqD/VNXlvDmwDXzVSpuGXRvrrLeOkyWG0/pL/yZ8a3NudPNK/1Y48lZ/NA8LkvW41kZLDY6rL+v55KI/yZ6pK3i0W0Mce/tuDOncQLetauSTKYNio9C47s1Zo41bbjRGi87V8FMjNMgP3RrXQZfG4v7eHIU5N0QebPW+C3j1x0NIaBOONwe2sTh9vS0357Myr/Arddj2U72aYmR8Y6f27et/fusHjT5unF/gLlwxakiOBSGNWXqbHpnRH0FGEzm6y2gpMX/jAb6GiedP3t4E9WsH4PTlAnyy9eb8NE1vfJboBzSBRvtqjC48wFeFqFoB+MENFr3lXyuRB5u18TgA4I9jWejz0XYUl5mfk8aWhge5U10sHU+/20e/rs5OWtQPwPQTZH0kJGjeqm08bnKPdyjjwMad6AciYltqfdVK3BcbhfDgmzNj390uAkturD6vv/p6sNFs2MaDFIyDH1dy31eJiKwyzgPJLS6v9s2sivH6L2LIP0Geewz3rhLsr0ZeieE8Pvr3BP1FFn2YUWyVHLlR7kBqfpWrhsAbUxrkiEnbVz9wWTSis+53/QDGOIHYOLipIdOs23LgXyuRBzMeAmtp1EhpuWFwo9UK2HvuGorKzE/SJ/f93N2Scr9/tnrzuf433hZhQWgdURM9m9d16ogtOfRtHYZFj3dCo7qB+PAhaSto28o9bvH2k/o2dZeYTv/vX+rswAPaR2Bgh0i8NaitwXb9JGLjLkDjlhpzX6xcwaXBzc6dOzFo0CBERUVBoVBg3bp1FsuvWbMG/fr1Q7169RAcHIz4+Hhs3rzZOZUlckPGwYyUIbErks7h4UVJGLV0j9kycgcjCgufOK6Ie9pEBqNeTcOFKvX/CdUqJTa81KvaelaeILpOIO5uH4kdr96Jh7tEO+WcWldNdCMzqcsvuEuLlf57t0HtAEn7qlVKLHisE57o2cRge9vIYDzUuQFe/L/m1fYxTqxmy80NhYWFiI2NxYIFC0SV37lzJ/r164cNGzYgOTkZd955JwYNGoQDBw44uKZE7klKy0p6TrHBvDff7a78fe+562b3kTvesJTwqH9Dcea9wrhGxl1xSqVC9u45Z3i+TzOnn9M9bvH2qzbPjZXybhLbQKFQYNsrfbBxfC/UCpRnnhmFQoGPHo7FyzfWU9PXv12EwWN/H/fpDHJpmDVgwAAMGDBAdPn58+cbPJ45cyZ+/vln/Prrr4iLi5O5dkTuQasVoFCIy3+x9g1yyprDGNatctXrChHfsuX+zLbUEqSfqCn3jLNSeF4YU11807oIM7NiM1kn9T3gJrENAKCJhRGTjuZOXwLcJ8yygVarRX5+PurUqWO2TGlpKfLy8gx+iDyFVivg/gV/47HFu0U1fUv5kK3QWk8wlvsbqamWptkPxqBjdC283P/m7MWOmk3ZuAvKFDm64lx1s0toUzlnyZjbm1gp6SDudJe3g6fm3NBN7tNBZoOPPvoIBQUFeOSRR8yWmTVrFmbMmOHEWhHZL/n8NWTnl6FFeBAO31jfqFwjwFdtPA284WMpH7LGE3A5g6luqaFdo/HojdYkXTkHBTd/TOyN+xbswvmrRQ45fhVXfX9d9HhnZOSWILqOvKuOi+Ut93ipLRDuvMaSs9wTE2G9kBN57Cvy3XffYcaMGfjhhx8QFmZ+hsUpU6YgNzdX93PhwgUn1pLINkMWJuG5b5JxNttwEr3U7AJ8seOM2flsxCY2CoKAS7kl1svJfLsy1SqiMDF8tUezurKet0pIoE+1lZKNq6SV4Wu4q5rn1SqlywIbwH0Sa+0l9dV7/LaGaBsZjMR+5tdO83bu1CUFeGjLzapVq/DUU09h9erVSEhIsFjWz88Pfn7Wm6KJ3NGF6zdbGAQIuGveTlRoBWTllWKa0ZDNyjLifPXXWVHl5O+WsvwBmPxmP2QXlKJFeE15TyyBHAN+PGzUuGy8I7SRfqOu6e+DDeN7Oag2ZAuPa7lZuXIlRo8ejZUrV2LgwIGurg6RQ+nfaAXhZhLwP2eumCwvJhip0Gjx3oZjos7v7C/itWv4oqWDAxtr1yRH64ObfYl1Gi9puPGKpHJnGXNj6Pj4vi1cXBNDLg1uCgoKkJKSgpSUFADA2bNnkZKSgrS0yiGqU6ZMwciRI3Xlv/vuO4wcORJz5sxB9+7dkZmZiczMTOTm5rqi+kQOp3+j1b9xmJttePTyvTh9ucDiMctN5NpcKSg1WTY9R95Vwd3RgPaRBo/FjCKzxt0mK3S0qiHA7esHu7gm8qg2FPzWejklmTaoLU68e7fDv5RI5dLgZt++fYiLi9MN405MTERcXBymTZsGAMjIyNAFOgDw5ZdfoqKiAi+88AIiIyN1P+PHj3dJ/YkcTT//Qz//Jb+kAiXlmmo5Mccy8jBm+V7J5+ny7h/4OSXd9oqKMO7O6pOAuYJx6PLagNaYNzRW97i43Pz6XMaq8nfuaFnPYPutFtysf/F2jLitET4d1snVVZGF8et3a72a0kmdDdkZXJpz06dPH4tNwMuXLzd4vH37dsdWiMjN6Dci6P+enV+KnrP/hL9P9Q+VtGuWRwKZSxKeueEY7u9Y3+RzcjCVJPztU66f+dffR4UH4hpg67HLOJyei/im4pOZvxrVBb8evIQHjGZqvcViGzQPq4l3Brd3dTXkc4u9ft7IIxOKiW4VBi03Rl8ErhaWoaa/9D9hc98npE45L1bT0BpQKhXo0thwPqrYBiHo2TzUIee0xNwXqs8e6wStVpC0hlRokB9GG01XD9x6LTfehq+e52NwQ+TGBDMtN1XMDQm3xNxQZ0fdjz9/vBNahtWsHjS4YQBg7+KY/dqGY8vRLIzu2VieCpFLuNuwZpKOwQ2RG/sx+eLNByZiEluSX529tqEgmA4avPH28cXjnZFbXI7aNeRZ14dcwxvfm7cajxsKTnQrOXvl5iR+ckwuB5jvlnHUB7qzW4pcSalUMLDxAt743rzVMLgh8hByNbiYa7lxVFO8uZjMVXkpXjIVCzkQc6Y8H7uliNxEuUaLKWsOo2dz06N15Gq5MXeczLwSlJRrdCOwcorKZDlfRIjp1al5+yB3xfem52PLDZEL/XrwEp79eh8KSiuwdn86fky+iInfHzRZVq7ZX2dtOG5yu0Yr4OFFSbrHI5fusftcP7/QE6FBppc/4bdjclt8a3o8ttwQudCLKw8AAFpsP4O8knKLZeValPCn/RfNPle1AjkAHLpo/8zfsUaLVBpw1Q2E/VJkhaOmRSDnYXBD5AauFpZh5Z40i2W87Z7M2we5KzYqej52SxG5Beuhi1w5N+7CVTcQczM0E1VhbOP5GNwQuYETmflWyzh7fhp7tY6wvJAec27IXdk7mSO5HoMbIjewPy3Hahm5cm4cLaxmZQLxU72aWiznsqHgnvHPSC7E0Mbzicq5+eWXXyQfuF+/fggICJC8HxGZ5ik35S9HdkGwvxpN6wVZLMeGG3JXfG96PlHBzeDBgyUdVKFQ4NSpU2ja1PI3NyISz1OCG7VSYTWwIXJvjG48nejRUpmZmQgLCxNVtmZNy33tRCSdsxKK/029irSrRTbvL/Zbb1hN05P7Ebma8XuYC2l6HlHBzahRoyR1MT3++OMIDg62uVJEVJ2zGm4e/fJfu/a3NkfIlyM64/u9F/D6Pa3tOo+twoMZVJFlTHb3fKKCm2XLlkk66MKFC22qDBGZlzB3h6urIIq1+8Jd7SJwV7sI51TGhIkJLZFdUIoH4+q7rA7k3hjaeD5O4kfkITQeMhbc3b/0hgT6YMFjnVxdDXJj7v4eJuskBzclJSX49NNPsW3bNly+fBlardbg+f3798tWOSLyPGzSJ0/H5Rc8n+Tg5sknn8Tvv/+Ohx56CN26dWOiFdEtJthfjbySCrPP8xOBPJ3xbc1T5piimyQHN+vXr8eGDRvQs2dPR9SHiNzcHS3r4VJOsdmJB/l9h4hcTfIMxfXr1+dQbyIZWFso010JsDY0ltENeTZ2rXo+ycHNnDlzMHnyZJw/f94R9SG6ZUxZc1i2Y733QHunJslaDG14XyAP8vnwTmgXFWwwNQHfw55PcrdUly5dUFJSgqZNmyIwMBA+Pj4Gz1+7dk22yhGROM3qBaFDgxBXVwMA223Is9wTE4l7YiLxx9Es3TZO4uf5JAc3w4YNQ3p6OmbOnInw8HC+6EQ2KK3QyHq8KwWlbEonsoP+nw9HS3k+ycHNP//8g6SkJMTGxjqiPkS3hCW7zsp6vOIyjVOb0hlHkbcxCG74/vZ4knNuWrdujeLiYkfUheiWceZyoazH69MqzG1abtiaS56O72DPJzm4mT17Nl5++WVs374dV69eRV5ensEPEVkn97wZPioFVE4MKvSb7d8c2AaP39bQaecmcgT99zQDdM8nuVvq7rvvBgD07dvXYLsgCFAoFNBo5M0lIPJGcq/wrYDCaU3pCt1/Kj3Vqylyi8vxzb+VQ9vVSt4YyAOxW8qrSA5utm3b5oh6EN1S5F4mSqVSuPTbpv6plQxuyAMpzPxOnklycNO7d29H1IPoliJny82g2CgE+bl2DVz9y2HLDXki/S8H7JbyfDZ9Iubk5GDJkiU4duwYAKBdu3YYM2YMQkLcY54NIncnZ6/Uq3e1ku9gIhl/9OuvWO4uic1EUlhqueE72vNITijet28fmjVrhnnz5uHatWu4du0a5s6di2bNmkleEXznzp0YNGgQoqKioFAosG7dOqv7bN++HZ06dYKfnx+aN2+O5cuXS70EIpfTyNgv5YpYwvicPqqbGwJ8VU6uDZH9LA0F57KZnkdycDNx4kTcd999OHfuHNasWYM1a9bg7NmzuPfeezFhwgRJxyosLERsbCwWLFggqvzZs2cxcOBA3HnnnUhJScGECRPw1FNPYfPmzVIvg8il5OyWUrlBN1BNfx/MfjAG7w+JcXkXGZEt9EdLsfXR80n+FNq3bx8WL14Mtfrmrmq1GpMmTUKXLl0kHWvAgAEYMGCA6PKLFi1CkyZNMGfOHABAmzZtsGvXLsybNw/9+/eXdG4iV5IzodjZH8Tmqv5oNw4HJ8/FeMa7SG65CQ4ORlpa9dWML1y44PDVwpOSkpCQkGCwrX///khKSnLoeYnkJuc8N65ouOH09OTNqnVLyTx1Azme5JaboUOH4sknn8RHH32EHj16AAD+/vtvvPrqqxg2bJjsFdSXmZmJ8PBwg23h4eHIy8tDcXExAgICqu1TWlqK0tJS3WNONEjuQM5uKY7sILKfQUIx/6Y8nuTg5qOPPoJCocDIkSNRUVEBAPDx8cHYsWMxe/Zs2Stor1mzZmHGjBmurgaRAXm7peQ7llj87CevozD5K3koSd1SGo0G//77L9566y1cv34dKSkpSElJwbVr1zBv3jz4+fk5qp4AgIiICGRlZRlsy8rKQnBwsMlWGwCYMmUKcnNzdT8XLlxwaB2JxJCz5cYVyY8MbsjbWEooZqeU55HUcqNSqXDXXXfh2LFjaNKkCWJiYhxVL5Pi4+OxYcMGg21btmxBfHy82X38/PwcHnQRSeXpwQ2Rt+Gq4N5FckJx+/btkZqaKsvJCwoKdK0/QOVQ75SUFF3C8pQpUzBy5Ehd+eeeew6pqamYNGkSjh8/js8//xw//PADJk6cKEt9iJxFq7VeJqymuKBcIfmvmIiMWZrEj/nEnkfyx+K7776LV155BevXr0dGRoZdq4Lv27cPcXFxiIuLAwAkJiYiLi4O06ZNAwBkZGQYjMxq0qQJfvvtN2zZsgWxsbGYM2cOvvrqKw4DJ4+SU1QmquXm53E9RR2PLTdE9jNIIuaflMeTnFB8zz33AADuu+8+gzeDLauC9+nTx+IQO1OzD/fp0wcHDhwQX2EiN7JyTxqmrDksqmxkiOk8MmNuMIcfkcczjG2Yc+PpuCo4kROJDWykcElCMb/akpcxHArusmqQTCQHN02aNEF0dHS1eQAEQeBIJCIX4Acxkf30W2fY1ev5JOfcNGnSBNnZ2dW2X7t2DU2aNJGlUkQkHltRiORVPaGYHVOeRnLLTVVujbGCggL4+/vLUikism5ol2gE+Krgq3bycCl+zpOXY8ON5xMd3CQmJgKozCifOnUqAgMDdc9pNBrs3r0bHTt2lL2CRN5C7m9/7z/UQdbjEVGlagnFDOg9jujgpmqEkiAIOHz4MHx9fXXP+fr6IjY2Fq+88or8NSTyEu/9dszVVZANv9mSV+P72+OJDm6qRkmNHj0aH3/8MYKDgx1WKSJv9NWus66uAhGJYBy8D46rj12nr6BleJBrKkSSSc65WbZsmcHjvLw8/Pnnn2jdujVat24tW8WIvMG5K4XYdfoKhnaNlvW4sx507tInRN5Ov+vJeLTUkE710SS0BlpF1HRyrchWkoObRx55BHfccQfGjRuH4uJidOnSBefOnYMgCFi1ahWGDBniiHoSeZyisgrcOWc7BAHILS6X7bh1a/hiWLeGsh2PiAwZ90opFAp0blTbJXUh20geZrFz50706tULALB27VoIgoCcnBx88sknePfdd2WvIJEn2nkyG22nbdZ9G/zrVPXpE2wVGsSFYInkpp/wz5wyzyc5uMnNzUWdOnUAAJs2bcKQIUMQGBiIgQMH4tSpU7JXkMgTvfXLfwaPyzXyDLfo1qQOFgyPk+VYRGQa547yfJKDm+joaCQlJaGwsBCbNm3CXXfdBQC4fv0657khusE4lBH7UfnO4PYAgM8eqx7A1K3hix+ejUfzMMf0+yf2a4m4hrUwoH2E1bKm5roi8hZ8e3s+yTk3EyZMwPDhwxEUFIRGjRqhT58+ACq7q2JimORIZI8H4urj0a7R8FE5eWI+AC/1bYGX+rZA4g8pTj83kavpfyFhcOP5JAc3zz//PLp164YLFy6gX79+UCorP4SbNm3KnBuiG4wn7BP7YakAzAY29nzgDoyJxG+HM0w+17huoEGCsrUm+XKN1vaKEHkAdkt5PsnBDQB06dIFXbp0Mdg2cOBAWSpE5OkuXCvCuatFBttOZhW4qDaVFgzvhN9e+63a9kBfFba/eqfBNmtB1O9Hs3BHy3pyVo/IrbDlxvNJDm6qlmEwplAo4O/vj+bNm+P+++/XJR0T3Wpmbqg+E7HYoeCO/FD9/pnb8MHmE0g+f123zdS08vxcp1sd/wY8n+Tg5sCBA9i/fz80Gg1atWoFADh58iRUKhVat26Nzz//HC+//DJ27dqFtm3byl5hIndXVmF7t40jm8O7N62LVc/chgEf/4XTl823JJkKsNY83wMPfv6Pw+pG5E6YMO/5JGct3n///UhISMClS5eQnJyM5ORkXLx4Ef369cOwYcOQnp6OO+64AxMnTnREfYm8mqM/U31USvw+4Q7dY8HEEt+mAqy2kYbLrTzRoxEAoGfzujLXkMj1GNp4PsnBzYcffoh33nnHYG2pkJAQvPXWW/jggw8QGBiIadOmITk5WdaKEnkKV37pm/VgDNpEBlsczq1USq+g8TX9X+tw/DXpTqwY3U3ysYjckX4XLRtuPJ9Nk/hdvny52vbs7Gzk5eUBAGrVqoWysjL7a0fkgeRs0v740Y76R7Zafli3htg4vhcWPt4ZfVpZT/o1mXMjsvrRdQKhdsGQdSJH0G/FZLeU57OpW2rMmDFYu3YtLl68iIsXL2Lt2rV48sknMXjwYADAnj170LJlS7nrSuQR7PlYNP5Mvb9jfbvqIkcdAA6NJSLPIjmh+IsvvsDEiRPx6KOPoqKiovIgajVGjRqFefPmAQBat26Nr776St6aEnkIe770WQoibOhNssr0ohDVT8QvskTkSSQHN0FBQVi8eDHmzZuH1NRUAJUT+AUFBenKdOzYUbYKEnkae1o5LAURzgowTLfc3NQuKrh6ASIiN2LTJH5AZZDToUMHOetC5BWUdqShWIpfHNI1ZMM8N6bydFwl0Ffl6ioQkRuSHNwUFhZi9uzZ2Lp1Ky5fvgyt1nBOj6rWHKJblaPyUxzRcmNyKLiplhu9jW4U26BB7QBXV4G8hTu9sclukoObp556Cjt27MCIESMQGRnJrHIiI3bl3Mj49ySmhcX0DMUmcm5kqI8jMNGZiEyRHNxs3LgRv/32G3r27OmI+hB5PKUdAYrlbilp5Pwiqn9JxouCEhG5G8nZAbVr1+a6UUQW2DOqyXJCsXytFCNuq5xh+NX+rSTVgchbMWT3LpKDm3feeQfTpk1DUVGR9cJEtyBP6KqdcV87bHulD565o2m150zV3hOuiYioiuRuqTlz5uDMmTMIDw9H48aN4ePjY/D8/v37ZasckSeybxI/83vLGV8olQo0Ca0huQ6Ae42WYsxFcolpEAIACAnwsVKSPIHk4KZqFmIiMs1RrRzuciPXulN0QySTYH8fHJx+F/zUXFLEG0gObqZPn272OY1GY1dliLyBlJybF+5shgXbzogq89agdpLqYeuHtLsEUUTOxlYb7yFLiHry5ElMnjwZDRo0kONwRB5NSnDwav/WqOln+TvGq/1b48iM/ujbJlxSPabd2xbN6tXAzAdiJO1nbXg1222IyN3ZHNwUFRVh2bJl6NWrF9q2bYsdO3YgMTHRpmMtWLAAjRs3hr+/P7p37449e/ZYLD9//ny0atUKAQEBiI6OxsSJE1FSUmLTuYk8QZCVAMiU6DqB2PpyHzzWvaGk/awFZxwKTkTuTvIn5r///ouvvvoKq1evRsOGDXHs2DFs27YNvXr1sqkC33//PRITE7Fo0SJ0794d8+fPR//+/XHixAmEhYVVK//dd9/htddew9KlS9GjRw+cPHkSTzzxBBQKBebOnWtTHYhciaGC7TiKi4hMEd1yM2fOHLRr1w4PPfQQateujZ07d+Lw4cNQKBSoW7euzRWYO3cunn76aYwePRpt27bFokWLEBgYiKVLl5os/88//6Bnz5547LHH0LhxY9x1110YNmyY1dYeImeR2rDhbi0hVteWckotxGFoQ0SmiA5uJk+ejMGDB+P8+fP48MMPERsba/fJy8rKkJycjISEhJsVUiqRkJCApKQkk/v06NEDycnJumAmNTUVGzZswD333GOyfGlpKfLy8gx+iMg8q40h7hTdEBGZIDq4eeedd7B69Wo0adIEkydPxpEjR+w++ZUrV6DRaBAebpgoGR4ejszMTJP7PPbYY3j77bdx++23w8fHB82aNUOfPn3w+uuvmyw/a9YshISE6H6io6PtrjeRnNwtVrA6z42T6iEGe6WIyBTRwc2UKVNw8uRJfP3118jMzET37t0RGxsLQRBw/fp1R9bRwPbt2zFz5kx8/vnn2L9/P9asWYPffvsN77zzjtl65+bm6n4uXLjgtLoSieFmvVJERB5P8mip3r17Y8WKFcjMzMTzzz+Pzp07o3fv3ujRo4fkhN7Q0FCoVCpkZWUZbM/KykJERITJfaZOnYoRI0bgqaeeQkxMDB544AHMnDkTs2bNglarrVbez88PwcHBBj9EjiQ1VhHcqi3EurCafq6uAhGRRTYPBa9ZsyaeffZZ7N69GwcOHEC3bt0we/ZsScfw9fVF586dsXXrVt02rVaLrVu3Ij4+3uQ+RUVFUCoNq61SqQC4X2ImkRie8rb99qnu6N2yHj562P58O7mwW4qITBEd3IwcORI//fQTCgoKqj0XExOD+fPnIz09XXIFEhMTsXjxYqxYsQLHjh3D2LFjUVhYiNGjR+vOO2XKFF35QYMGYeHChVi1ahXOnj2LLVu2YOrUqRg0aJAuyCEi+fVsHooVY7ohuk6gq6tCRGSR6HlumjdvjpkzZ+Lxxx9Hnz59cN999+G+++5D/fr1dWWMF9EUY+jQocjOzsa0adOQmZmJjh07YtOmTbok47S0NIOWmjfffBMKhQJvvvkm0tPTUa9ePQwaNAjvvfee5HMTuQMPabhxS9ZmUyaiW5Po4GbatGmYNm0aLl68iF9++QXr1q3DxIkT0a5dO9x///2477770LFjR5sqMW7cOIwbN87kc9u3bzessFqN6dOnW1zjisiVPH2eGyIiTyc556ZBgwZ4/vnnsXnzZmRnZ2Py5Mk4ceIE/u///g+NGjXCuHHj8N9//zmirkRuKzu/FHfP34kV/5yTnCBcrnHv4GbdCz1dXQWzmHNDRKbYtXBmzZo18cgjj+Dbb79FdnY2li5dCpVKZXYCPiJvtD/tOrq+9weOZ+Zj+i+eH9gbxwsdo2u5ohpERDaTvhqfGSqVCn379kXfvn3lOiSRR3hqxT679g/2VyOvpEKm2hARkeSWm6ysLIwYMQJRUVFQq9VQqVQGP0S3muIyjV37f/NUd8RG18L3z9wmU41uHeyVIiJTJLfcPPHEE0hLS8PUqVMRGRnJVXnplqdWKYByvQ0SU2g6NKiFn904r8Wd+ajs6lknIi8lObjZtWsX/vrrL5tHRhF5HffOB/Zqfj4MboioOsmfDNHR0Ry6SqSHfw2u071JXVdXgYjckOTgZv78+Xjttddw7tw5B1SHyPMw2He+rS/3xvRBbfFc72aurgoRuSHJ3VJDhw5FUVERmjVrhsDAwGqzEl+7dk22yhF5AuPQhqGO4zWrF4Rm9YJcXQ0iclOSg5v58+c7oBpE7qmgtAKv/HAQA2IicH/H+ibLsOGGiMi9SA5uRo0a5Yh6ELml73afx6b/MrHpv0zzwY2XtdVUaL3reojo1iM5uElLS7P4fMOGDW2uDJG7KRAxuZ63tdx8/e95V1eBiMgukoObxo0bW5zbRqOxb0IzIrciYh6najk3Hh7tlFVoXV0FIiK7SA5uDhw4YPC4vLwcBw4cwNy5c/Hee+/JVjEidyBmikrjYCanuNxMSSIicgbJwU1sbGy1bV26dEFUVBQ+/PBDPPjgg7JUjMgdKMW03Bg11Gw/ke2g2hARkRiyTe/ZqlUr7N27V67DEbkFMauLeHYnFBGR95HccpOXl2fwWBAEZGRk4K233kKLFi1kqxiRO7ClW0qsmv6S//yIiEgEyZ+utWrVqpZQLAgCoqOjsWrVKtkqRuQOlErL4c3PKemwdeT0ny/3sW1HIiKySHJws23bNoPHSqUS9erVQ/PmzaFW85so3VrGr0qxab/7O0ahXk0/eStDREQAbAhuevfu7Yh6ELklMTk3RETkXiQnFK9YsQK//fab7vGkSZNQq1Yt9OjRA+fPc/Iv8i5iRkvZ4t4OUQ45LhER2RDczJw5EwEBAQCApKQkfPbZZ/jggw8QGhqKiRMnyl5BIldyRGgzKDYKCW3CHHBkIiICbOiWunDhApo3bw4AWLduHR566CE888wz6NmzJ/r06SN3/YhcSr/hRqsVrCYYixFTP9jiLN9ERGQfyS03QUFBuHr1KgDg999/R79+/QAA/v7+KC4ulrd2RC6m3y2l8fBlFYiIbhWSW2769euHp556CnFxcTh58iTuueceAMB///2HRo0ayV5BIneh0QrwUbm6FkREZI3klpsFCxYgPj4e2dnZ+Omnn1C3bl0AQHJyMoYPHy57BYlcSb/lpsJoQptvd9uWQN+vbYRddSIiIstsmsTvs88+q7Z94sSJ2LBhgyyVInIXPiq94EZjuFr2G2uP2HTMJqE17KoTERFZJtvaUufPn8eIESPkOhyRW9BP/C2r0FooSURE7kK24IbIG/2w74Lu90k/HUK5hgEOEZG7Y3BDZMGhi7m637efyMbyv8+huEyDknKNC2tFRESWcDEoIglSLuTg/U3H4S9y2NTDnRtgdfJFB9eKiIj0iQ5uPvnkE4vPp6en210ZInd3KD0HFVoBBaUVosrf3T6CwQ0RkZOJDm7mzZtntUzDhg1tqsSCBQvw4YcfIjMzE7Gxsfj000/RrVs3s+VzcnLwxhtvYM2aNbh27RoaNWqE+fPn6+bcIbLXjF//Q0iAT7XtF65Jm6iS8/4RETmf6ODm7NmzDqnA999/j8TERCxatAjdu3fH/Pnz0b9/f5w4cQJhYdXX3ykrK0O/fv0QFhaGH3/8EfXr18f58+dRq1Yth9SPbj1pV4uw7O9zrq6GywT6qlBUxpwiIvJcLk8onjt3Lp5++mmMHj0abdu2xaJFixAYGIilS5eaLL906VJcu3YN69atQ8+ePdG4cWP07t0bsbGxTq45eaPSCg0GfvqXbMfzxCWkpg9q6+oqEBHZRVRw88knn6CkpET0QRctWoT8/Hyr5crKypCcnIyEhISbFVIqkZCQgKSkJJP7/PLLL4iPj8cLL7yA8PBwtG/fHjNnzoRGw2+aZL/f/8tCfom4fBoxPLFbKiTA19VVICKyi6jgZuLEiaKClSqTJk1Cdna21XJXrlyBRqNBeHi4wfbw8HBkZmaa3Cc1NRU//vgjNBoNNmzYgKlTp2LOnDl49913TZYvLS1FXl6ewQ+ROVpPjEZk5omtTURE+kTl3AiCgL59+0KtFpei48jVwbVaLcLCwvDll19CpVKhc+fOSE9Px4cffojp06dXKz9r1izMmDHDYfUh76JWurynloiI7CQqWjEVNFhy//33o06dOlbLhYaGQqVSISsry2B7VlYWIiJMLy4YGRkJHx8fqFQ35xlp06YNMjMzUVZWBl9fwyb1KVOmIDExUfc4Ly8P0dHRUi6HbiGFZfJ1SRERkWs4JLgRy9fXF507d8bWrVsxePBgAJUtM1u3bsW4ceNM7tOzZ09899130Gq1UN74ln3y5ElERkZWC2wAwM/PD35+fg6pP3mft3896uoqEBGRnVzeBp+YmIjFixdjxYoVOHbsGMaOHYvCwkKMHj0aADBy5EhMmTJFV37s2LG4du0axo8fj5MnT+K3337DzJkz8cILL7jqEshLCIL4yfm8GVNuiMjTuXz5haFDhyI7OxvTpk1DZmYmOnbsiE2bNumSjNPS0nQtNAAQHR2NzZs3Y+LEiejQoQPq16+P8ePHY/Lkya66BPISSalXZT8m05OJiJzP5cENAIwbN85sN9T27durbYuPj8e///7r4FrRreZqQZmrq+AWGJARkadzebcUkbtQK+XvkGEXDxGR80kObixN5peRkWFXZYi8jSe2gtzWtK6rq0BEZBfJwU2nTp2QkpJSbftPP/2EDh06yFEnIpdwRCAieOCkgKYWDCUi8iSSg5s+ffrgtttuw/vvvw8AKCwsxBNPPIERI0bg9ddfl72CRJ5M4aHT/apudNHVCmSgQ0SeR3JC8eeff46BAwfiqaeewvr165GRkYGgoCDs2bMH7du3d0QdiZzCEY0snhnaAGuf74EPN5/AlAFtXF0VIiLJbBotNWDAADz44INYuHAh1Go1fv31VwY2RF6kQ4Na+PrJ7q6uBhGRTSR3S505cwbx8fFYv349Nm/ejEmTJuG+++7DpEmTUF5e7og6EjmF4ICsG8/LuCEi8nySg5uOHTuiSZMmOHjwIPr164d3330X27Ztw5o1a9CtWzdH1JGIiIhINMnBzeeff45Vq1ahVq1aum09evTAgQMH0KlTJznrRuRUzLkhIvIOkoObESNGmNxes2ZNLFmyxO4KEREREdlDckLx//73P7PPKRQKs8EPkbtbeyBd9mMy54aIyPkkBzfjx483eFxeXo6ioiL4+voiMDCQwQ15rD+PX3Z1FYiISAaSu6WuX79u8FNQUIATJ07g9ttvx8qVKx1RRyIiIiLRZFk4s0WLFpg9e3a1Vh2iW51xQnFNf5umliIiIglkWxVcrVbj0qVLch2OyCutf/F2V1eBiMjrSf4a+csvvxg8FgQBGRkZ+Oyzz9CzZ0/ZKkbkDfQTiuvW8EWjujVcVhcioluF5OBm8ODBBo8VCgXq1auH//u//8OcOXPkqheRW3l/SAwm/3TY1dUgIiIRJAc3Wq3WEfUgcmv+Piqb9uMkfkREzidbzg2RN5Nj9mIFIx0iIqcQ1XKTmJgo+oBz5861uTJErqLRWo5etDZGN/p7OWJ5ByIiqk5UcHPgwAFRB1Pwqyl5qAor3a1WYh8iInIjooKbbdu2OboeRC5VoXFMy41+uM/Yn4jIOUTn3KSmpkJguzp5qQprTTN86xMReQzRwU2LFi2QnZ2tezx06FBkZWU5pFJEzmYt5yY4wMdJNSEiInuJDm6MW202bNiAwsJC2StE5Ap5xeUWn7+rbbiTakJERPbiUHAiAB/9fsLi80qlYcLMM3c0teEsTLohInIG0cGNQqGoNhqKo6PIW6RcyJFU/okejR1SDyIisp/oGYoFQcATTzwBPz8/AEBJSQmee+451KhhuFbOmjVr5K0hkROolNIC9ahaAVg2uitGL9vroBoREZGtRAc3o0aNMnj8+OOPy14ZIlexpQ3yzlZhVstwkBURkfOJDm6WLVvmyHoQuZSYICSxX0vM3XLS5nOwF5eIyDmYUEwEcUsjtAgLknzcdlHBNtSGiIjsweCGCECnhrV0v8dG1zJbTopX+7dCVK0AWY5FRETiMbghAtC+fggAoGvj2mbLSO1WalZPeksPERHZzy2CmwULFqBx48bw9/dH9+7dsWfPHlH7rVq1CgqFAoMHD3ZsBcltlWu02HXqCorKKuw6TtXaUdG1A+Wo1g1MJyYicgWXBzfff/89EhMTMX36dOzfvx+xsbHo378/Ll++bHG/c+fO4ZVXXkGvXr2cVFNytuz8UoxYshsbD2eYLTNvy0k8vmQ3xn1XuXJ92tUiaG1YwrtqF0tzN3FpNSIiz+Dy4Gbu3Ll4+umnMXr0aLRt2xaLFi1CYGAgli5danYfjUaD4cOHY8aMGWja1JaZYskTzN54HH+duoKx3+43W+brpPMAgD+PX8bSXWdxx4fbkPhDiuRzVbXcSJzuxiIGQ0REruHS4KasrAzJyclISEjQbVMqlUhISEBSUpLZ/d5++22EhYXhySeftHqO0tJS5OXlGfyQZ8gtLrNaxkd98y389vqjAIB1KZckn6uqtUcp43htxjZERK7h0uDmypUr0Gg0CA83XJQwPDwcmZmZJvfZtWsXlixZgsWLF4s6x6xZsxASEqL7iY6Otrve5BxqpfW3p1wtLVXdUsZrSOmzN1jhNDdERM7h8m4pKfLz8zFixAgsXrwYoaGhovaZMmUKcnNzdT8XLlxwcC1JLmqVY8OB5PPXcTyzsiWvrEILwHSw1DbStrlq2C1FROQaomcodoTQ0FCoVCpkZWUZbM/KykJERES18mfOnMG5c+cwaNAg3TattvKmpFarceLECTRr1sxgHz8/P916WORZjmaI6UK0LQC6nF+CIQv/AQA80qUBfth3EYDpbinOLExE5Flc2nLj6+uLzp07Y+vWrbptWq0WW7duRXx8fLXyrVu3xuHDh5GSkqL7ue+++3DnnXciJSWFXU5e5O/TV5CaXSiipG3NI+evFul+rwpsAPu6uZrWq2G9EBEROZxLW24AIDExEaNGjUKXLl3QrVs3zJ8/H4WFhRg9ejQAYOTIkahfvz5mzZoFf39/tG/f3mD/WrVqAUC17eTZNh0xnXNlLLe43KbjX8opNrnd1FDwqk2+KsvfBTZPuANFpRrEvv07AEAwCrzYAkRE5BwuD26GDh2K7OxsTJs2DZmZmejYsSM2bdqkSzJOS0uDUkRiKXkX40Bg2Jf/IjhAjS9GdDHYXq6RN7FFZaLpRnGj66tPq3ro06oeYm7MZmzMR6VESKD592rTUM5YTETkDC4PbgBg3LhxGDdunMnntm/fbnHf5cuXy18hcjnjECMp9SoAoEKjxdf/nse5K4WIb2Y9qVyrFSyOgDKmVACCYLrFRa1SYvnobqKPVXWYn1/oieX/nMOku1uJ3peIiGznFsENkTFzMwVrBWDGr5Xz2ay4MYGfOR9uPo4F287g2d5NMWVAG1HnVSoUqDBqDapTw1fUvsaqjhIbXQvzhna06RhERCQd+3vIo2gljK9esO0MAOCLHami91EoFNAYLd8w68EY0fsTEZHrMbghp/rtUAbe+uW/agGEMXPJt2KDG0vHLyitwOyNx00+p1QAGr1zdG5UG5EhAaLOacy4e4uIiJyD3VLkVC98V7lOVIcGIXiwUwOz5RRm5q+xFhRVOXrJ/Bw5c38/iYzcEpPPKRUK9G8XjtOXC0Sdh4iI3A9bbsglzA3FrmIuB/hIuri1wSwNuz6ZlW/+vEoFXurb4uZxRJ2NiIjcCYMbcprk89d0v1ctd2COueDkxZUHRJ3L1JDuKpaWdVAqAD+1StQ5rBGzNhYREcmPn77kNHN+Pym6rLnRUqXlGpH7m3/OUtBhvPyCLRPv3d0uAs3q1UDfNmHSdyYiIrsx54acplgvMDEXvNx83vR2sQnFlopZmmjYuMHHXO6PJQsf71S5L6ckJiJyCbbckNP8p5fkq3/fLyytQOPXfsOzX+8DAGz+LxPbj2ebPIZGhtFSlhgHJMEBPjYdg4ENEZHrMLghpzE3NHr6L/8BADb/l4XconI8+3UyTphJ+hUbtNg6CrsqV+eDIR0QG10L0+5ta9uBiIjIZdgtRU6jH5fo57boD7vefNTygpligxtLLTyWupqquqUe6RqNR7pylXkiIk/ElhtyGv18Gf3wQr9FZ9KPh6wcQ/q5jFnqMTJOKCYiIs/D4IacRj/e0I8hWkcEy34u/RaeO1vVE70fc2WIiDwfgxtyCf0gokN0iOzHf3hRku53Kek3EhYQJyIiN8XghpyitMJwfhr9BhJHL8G0/UQ2Nh7O0D0+dDHXbFl2SxEReT4GN+QUrd7cZPY5ZywvOfbb/brfC0orzJZTsumGiMjjMbghhysuszKrsJNWzy7XVC75MCg20mwZxjZERJ6PwQ05nKWWEmf6N/UqAKBOoK/ZMuyWIiLyfAxuyOGM822MOafdBlDdCFxUFteWclJliIjIYRjckMOVWlkB3Fl81JVvd0trS3EoOBGR5+MMxeRwZSaCGwUU2HI0C59sPYWO0bWcUg/1jWYZi4tqMrghIvJ4DG7I4cy13Dz9v8qFMg+nmx+aLSf1je4oS7McW+ixIiIiD8GPcnK4Co17dEtVNcoIFrJ8mFBMROT5GNyQw5lqKXl/03EX1EO48X/zZZhzQ0Tk+RjckMMJTprHxpqqoMZSfThaiojI8zG4IYcTu5K3o1Utpmkp1mK3FBGR52NCMclu05FMXLhWhPb1QxAZ4u82LTeCIECjFbByT5rZMgxuiIg8H4MbklVhaQWe+ybZYNttTeu4qDaGtALwY/IFXC0sM1uG3VJERJ6P3VIkq8Ky6kst/Jt6zQU1qU6jFXAgLcdiGaktNxMSWgAAnujR2MZaERGR3NhyQ7cMMd1jUue5Gd+3Be7tEIWmoTVsrBUREcmNwQ3JSgH37dcpqdBYXQpC6lBwhUKB5mFB9lSLiIhkxuCGZJNfUo7xqw64uhpmjVm+z2oZLr9AROT53CLnZsGCBWjcuDH8/f3RvXt37Nmzx2zZxYsXo1evXqhduzZq166NhIQEi+XJeRZsO4N/zlx1dTXswtFSRESez+XBzffff4/ExERMnz4d+/fvR2xsLPr374/Lly+bLL99+3YMGzYM27ZtQ1JSEqKjo3HXXXchPT3dyTUnY9n5pa6ugt04WoqIyPO5PLiZO3cunn76aYwePRpt27bFokWLEBgYiKVLl5os/+233+L5559Hx44d0bp1a3z11VfQarXYunWrk2tOxlQufDfJNdycyy8QEXk+lwY3ZWVlSE5ORkJCgm6bUqlEQkICkpKSRB2jqKgI5eXlqFPH9M2ttLQUeXl5Bj/kGCoXNnssGdVVluOw5YaIyPO5NLi5cuUKNBoNwsPDDbaHh4cjMzNT1DEmT56MqKgogwBJ36xZsxASEqL7iY6OtrveVJ0gCC7NV5Hr3EpGN0REHs/l3VL2mD17NlatWoW1a9fC39/fZJkpU6YgNzdX93PhwgUn19L7HbyQg87v/oHVyRddVge54iomFBMReT6XDgUPDQ2FSqVCVlaWwfasrCxERERY3Pejjz7C7Nmz8ccff6BDhw5my/n5+cHPz0+W+pJpk348hGsWljRwBtlabhjbEBF5PJe23Pj6+qJz584GycBVycHx8fFm9/vggw/wzjvvYNOmTejSpYszqkoWnMjKd3UVZAtK2HJDROT5XN4tlZiYiMWLF2PFihU4duwYxo4di8LCQowePRoAMHLkSEyZMkVX/v3338fUqVOxdOlSNG7cGJmZmcjMzERBQYGrLoHcgLlRTl0b15Z0HAY3RESez+XBzdChQ/HRRx9h2rRp6NixI1JSUrBp0yZdknFaWhoyMjJ05RcuXIiysjI89NBDiIyM1P189NFHrrqEW9prPx1y2rksjcZSKRV4sFP9atuHdGog6RyMbYiIPJ9bLL8wbtw4jBs3zuRz27dvN3h87tw5x1eIRFu113kJ2iqlAhqt+cUvJ9/dGmv2G07mKLUlhi03RESez+UtN+S5fk5x7qzQ1tZ9MvV0SKCP2fIzH4ipfg5mFBMReTwGN2Sz8atSnHo+a4GHqRXJ+7UJN1GyUmiQb7VtjG2IiDwfgxuyyZls5ydwmws8/H2UZp9XKhXo06qeyf3Uquo7cPkFIiLPx+CGbNJ3zg6nn7OwTGNye8q0uwCYD0zaRQWb3K5WVn/7s+WGiMjzMbght9a/3c1uJVPJxLHRteDvowIgLTAJq+kHtYkdmFBMROT5GNyQUzWsEyjr8W5rcnPBVFM5NwDwU3L1xOfvnu4OtYllzJlQTETk+RjckFN1jK4lqby5gEWvwM1fzbybM/NKqm1rHlbTZCDDhhsiIs/nFvPc0K3j7fvboaisAvvOX0dOUbndx9PvRpIal/iYSChmtxQRkedjyw1ZVVymwdkrhbrHGw5nWCht3v+1DkOtQF98Naor9r2RIEvd9Oe+MZdQ3LReDdP7mmi5MZWHQ0REnoXBDVl198c7cedH25F8/joA4Plv99t0nBn3tdP9Lja3RSuYn5EYMEwiNndI/fPq8zGRc+Or5p8EEZGn4yc5WXX+ahEA21tsqkTrJROLnU8mMsTf4vNKpX63lOljhtU0PMbzfZoBMB1gBfqyp5aIyNPxk5xMyi0qx6q9abi/483FKFVKBdJuBDrO0jKiJhY93gn1awVi0Ge7qj1vkHNjJl4y3j7p7tYAAB+9eW7eHxKD9vVD2HJDROQFGNyQSSOX7sbBi7mYtfG4btuXO1ORnlMs2zm6Nq6NveeuWyxzR4t6Bi0+xvRbX4yDmNo31pXS79n67qnuN/fVSyiOja6F1hGmJ/sjIiLPwuCGTDp4Mdfk9t8O2dc1pc/SMO9h3aKR2K8V6tX0s3iMvm3CzD73w7PxAAzzdrrqzYvjoxcYWVuUk4iIPAfb4MltPNGjse53P7XKamADwKC1xThAaRFeEwAQEnBzZXD90VB+atXNfTlKiojIazC4IbfQNjIYb0kcTZVg1GqjVikxsENktXJRtQIw+8EYLHisk0Eicw0/ld7vbMQkIvIW/ET3coIgSF7puqTc9AKVstOrVje97iJA3HwzX47oUm1bvzbhJrvOHu3WsNo2tUqJeUNjUVCqQXiw5VFZRETkORjceLG8knIMmP8X2kYFY2BMJO5uH6FbZNKSHSeznVA7y9QmZg82pjQRAJmau8aSB+IaSCpPRETuj91SXuzvU1eQnlOMLUezMOH7FLyz/qio/XKKyhxcsxv0RjFVtRY9eXsT1Kvph9E9m1Qr7idimHbfNmFoFV4TQ7tEy1ZNIiLyLAxuvFhphdbg8S8pl0TtZ2VSYIe4fiOgmnpvW+ye0hehQdWTiZOn9tP9PjK+kcnj+PuosGlCL7z/UAfHVJSIiNwegxsvZtxtk19agUU7zljd758zVx1VJQOCXtNNmV4gZqq7CQCC9JJ+B8VGmT2u1BwjIiLyLgxuvFi5UcsNAMzWm5TPFEEQ8MtBcS089tJvIZpyTxub9yUiItLH4MaLfbj5hOR9/ruU54CaAE1Cq6/MrR+ftLwxJ41YLcKC7KwRERF5K46W8mKZeSWS99Fo5W0S8VUr0b1JHSweWX3YtmBD88uBqf1QVK5B7Rq+clSPiIi8EIMbMiBmCLYY61+8Hacu51scam1LGFW7hi9q214tIiK6BTC48VJaG1tg5MplaV8/BO3rhzjlXERERPqYc+OliizMMlxmItFYt1+Z7bMTV80t84bI5GDGNkRE5AhsufFSRaUVZp/LLylHXRPzyADAI18k2XzOCf1aYEK/FogMCRBVflCHSBy8kINm9aonGxMREdmKwY0XEQQBGq0AtUqJAgvBTV5JhcngxjjB98DUfoh7Z4uE81cuUinW6J5N0DwsCHHRzKIhIiL5MLjxEmUVWvSYvRUBvipsmdgbhaXmu5dSswugFQQUlFQgNroWAOBIei7CahoGPFJHJGklJtGolAr0aRVmvSAREZEEDG68RMs3N+p+33vuGtRK8+lUT67Yp/t99oMxCA7wwfPf7pd8zsZ1A/F8n+ZYl5KOknINokR2RxERETkSgxsvVFiqgY+qMmk4tkEIXr+nDerXDsCLKw/gQFqOQdnX1hxGoK/1lcJN2f7qnQCAh7tUDvfmsgdEROQO3GK01IIFC9C4cWP4+/uje/fu2LNnj8Xyq1evRuvWreHv74+YmBhs2LDBSTW1rEKjxcmsfGi0AkrKNRCEyv9fzi/Bf5dyUVymgVYrQBAE5BSVIb+kHIIgoLTC9hFKAFBsNMLp5R9ScL2oHABQw0+N7k3rokHtQKRfLza5v6URUose74xGdQOrbf90WJzud4VCwcCGiIjchstbbr7//nskJiZi0aJF6N69O+bPn4/+/fvjxIkTCAurno/xzz//YNiwYZg1axbuvfdefPfddxg8eDD279+P9u3bu+AKbmr+xkbrhST4v9ZhaBJaA8/1bgYA6PreH6L2KyzT4JXVBwEAPqqb8evl/FLR535/SAwA4O72Ebi7fQTyS8qxO/Ua7mhZDxVaLQJ9Xf7WISIiMkkh2DIHvoy6d++Orl274rPPPgMAaLVaREdH48UXX8Rrr71WrfzQoUNRWFiI9evX67bddttt6NixIxYtWmT1fHl5eQgJCUFubi6Cg4Nlu470nGL0nP2nbMeT07nZAwEAn/15Ch/9ftJkmXZRwbp1pTaO74U2kfL92xAREdlLyv3bpd1SZWVlSE5ORkJCgm6bUqlEQkICkpJMz7eSlJRkUB4A+vfvb7a8s6gUCgyMiay2va6L10Dq3y5c9/szdzTD58M7mSx3j17dTXVDEREReQqX9i1cuXIFGo0G4eHhBtvDw8Nx/Phxk/tkZmaaLJ+ZmWmyfGlpKUpLb3bH5OU5ZtXriBB/LBjeCQvsPE5ucTkCfVVYuP0M5m45iWB/NfJKKueseax7Q7w3uD0UCgWOZ+Zh7f50DOwQiSPpeYiNDkGT0BoQBOD5b/fjcHounu/TDA93jtYd21etxD0xkTgz8x5cvF6EGn5qdHm3sqtrVI/GaFYvCD4qBbuciIjIo3n9XWzWrFmYMWOGq6shWkiADwDgpb4t8FLfFmbLtY4IxpR7KpvlOjSoZfDcijHdLJ5DpVSgUd3KWYGruqyAyvwaIiIiT+fSbqnQ0FCoVCpkZWUZbM/KykJEhOkbbUREhKTyU6ZMQW5uru7nwoUL8lSeiIiI3JJLgxtfX1907twZW7du1W3TarXYunUr4uPjTe4THx9vUB4AtmzZYra8n58fgoODDX6IiIjIe7m8WyoxMRGjRo1Cly5d0K1bN8yfPx+FhYUYPXo0AGDkyJGoX78+Zs2aBQAYP348evfujTlz5mDgwIFYtWoV9u3bhy+//NKVl0FERERuwuXBzdChQ5GdnY1p06YhMzMTHTt2xKZNm3RJw2lpaVDqLSXQo0cPfPfdd3jzzTfx+uuvo0WLFli3bp3L57ghIiIi9+DyeW6czVHz3BAREZHjeMw8N0RERERyY3BDREREXoXBDREREXkVBjdERETkVRjcEBERkVdhcENERERehcENEREReRUGN0RERORVGNwQERGRV3H58gvOVjUhc15enotrQkRERGJV3bfFLKxwywU3+fn5AIDo6GgX14SIiIikys/PR0hIiMUyt9zaUlqtFpcuXULNmjWhUChkPXZeXh6io6Nx4cIFr1y3ytuvD/D+a+T1eT5vv0Zvvz7A+6/RUdcnCALy8/MRFRVlsKC2Kbdcy41SqUSDBg0ceo7g4GCvfMNW8fbrA7z/Gnl9ns/br9Hbrw/w/mt0xPVZa7GpwoRiIiIi8ioMboiIiMirMLiRkZ+fH6ZPnw4/Pz9XV8UhvP36AO+/Rl6f5/P2a/T26wO8/xrd4fpuuYRiIiIi8m5suSEiIiKvwuCGiIiIvAqDGyIiIvIqDG70zJo1C127dkXNmjURFhaGwYMH48SJEwZlSkpK8MILL6Bu3boICgrCkCFDkJWVZVAmLS0NAwcORGBgIMLCwvDqq6+ioqLCoMz27dvRqVMn+Pn5oXnz5li+fLmjLw8AsHDhQnTo0EE3/0B8fDw2btyoe97Tr8/Y7NmzoVAoMGHCBN02T7/Gt956CwqFwuCndevWuuc9/foAID09HY8//jjq1q2LgIAAxMTEYN++fbrnBUHAtGnTEBkZiYCAACQkJODUqVMGx7h27RqGDx+O4OBg1KpVC08++SQKCgoMyhw6dAi9evWCv78/oqOj8cEHHzj82ho3blzt9VMoFHjhhRcAeMfrp9FoMHXqVDRp0gQBAQFo1qwZ3nnnHYNp8z35NQQqZ8mdMGECGjVqhICAAPTo0QN79+712OvbuXMnBg0ahKioKCgUCqxbt87geWdez+rVq9G6dWv4+/sjJiYGGzZskH5BAun0799fWLZsmXDkyBEhJSVFuOeee4SGDRsKBQUFujLPPfecEB0dLWzdulXYt2+fcNtttwk9evTQPV9RUSG0b99eSEhIEA4cOCBs2LBBCA0NFaZMmaIrk5qaKgQGBgqJiYnC0aNHhU8//VRQqVTCpk2bHH6Nv/zyi/Dbb78JJ0+eFE6cOCG8/vrrgo+Pj3DkyBGvuD59e/bsERo3bix06NBBGD9+vG67p1/j9OnThXbt2gkZGRm6n+zsbK+5vmvXrgmNGjUSnnjiCWH37t1CamqqsHnzZuH06dO6MrNnzxZCQkKEdevWCQcPHhTuu+8+oUmTJkJxcbGuzN133y3ExsYK//77r/DXX38JzZs3F4YNG6Z7Pjc3VwgPDxeGDx8uHDlyRFi5cqUQEBAgfPHFFw69vsuXLxu8dlu2bBEACNu2bRMEwfNfP0EQhPfee0+oW7eusH79euHs2bPC6tWrhaCgIOHjjz/WlfHk11AQBOGRRx4R2rZtK+zYsUM4deqUMH36dCE4OFi4ePGiR17fhg0bhDfeeENYs2aNAEBYu3atwfPOup6///5bUKlUwgcffCAcPXpUePPNNwUfHx/h8OHDkq6HwY0Fly9fFgAIO3bsEARBEHJycgQfHx9h9erVujLHjh0TAAhJSUmCIFS+QZRKpZCZmakrs3DhQiE4OFgoLS0VBEEQJk2aJLRr187gXEOHDhX69+/v6EsyqXbt2sJXX33lVdeXn58vtGjRQtiyZYvQu3dvXXDjDdc4ffp0ITY21uRz3nB9kydPFm6//Xazz2u1WiEiIkL48MMPddtycnIEPz8/YeXKlYIgCMLRo0cFAMLevXt1ZTZu3CgoFAohPT1dEARB+Pzzz4XatWvrrrnq3K1atZL7kiwaP3680KxZM0Gr1XrF6ycIgjBw4EBhzJgxBtsefPBBYfjw4YIgeP5rWFRUJKhUKmH9+vUG2zt16iS88cYbHn99xsGNM6/nkUceEQYOHGhQn+7duwvPPvuspGtgt5QFubm5AIA6deoAAJKTk1FeXo6EhARdmdatW6Nhw4ZISkoCACQlJSEmJgbh4eG6Mv3790deXh7+++8/XRn9Y1SVqTqGs2g0GqxatQqFhYWIj4/3qut74YUXMHDgwGr18JZrPHXqFKKiotC0aVMMHz4caWlpALzj+n755Rd06dIFDz/8MMLCwhAXF4fFixfrnj979iwyMzMN6hcSEoLu3bsbXGOtWrXQpUsXXZmEhAQolUrs3r1bV+aOO+6Ar6+vrkz//v1x4sQJXL9+3dGXCQAoKyvDN998gzFjxkChUHjF6wcAPXr0wNatW3Hy5EkAwMGDB7Fr1y4MGDAAgOe/hhUVFdBoNPD39zfYHhAQgF27dnn89Rlz5vXI9d5lcGOGVqvFhAkT0LNnT7Rv3x4AkJmZCV9fX9SqVcugbHh4ODIzM3Vl9D90qp6ves5Smby8PBQXFzvicgwcPnwYQUFB8PPzw3PPPYe1a9eibdu2XnN9q1atwv79+zFr1qxqz3nDNXbv3h3Lly/Hpk2bsHDhQpw9exa9evVCfn6+V1xfamoqFi5ciBYtWmDz5s0YO3YsXnrpJaxYscKgjqbqp1//sLAwg+fVajXq1Kkj6d/B0datW4ecnBw88cQTuvN6+usHAK+99hoeffRRtG7dGj4+PoiLi8OECRMwfPhwg3p66mtYs2ZNxMfH45133sGlS5eg0WjwzTffICkpCRkZGR5/fcaceT3myki93ltu4UyxXnjhBRw5cgS7du1ydVVk16pVK6SkpCA3Nxc//vgjRo0ahR07dri6WrK4cOECxo8fjy1btlT7VuUtqr79AkCHDh3QvXt3NGrUCD/88AMCAgJcWDN5aLVadOnSBTNnzgQAxMXF4ciRI1i0aBFGjRrl4trJa8mSJRgwYACioqJcXRVZ/fDDD/j222/x3XffoV27dkhJScGECRMQFRXlNa/h119/jTFjxqB+/fpQqVTo1KkThg0bhuTkZFdXjcCWG5PGjRuH9evXY9u2bQYriEdERKCsrAw5OTkG5bOyshAREaErYzyyoeqxtTLBwcFOuTn5+vqiefPm6Ny5M2bNmoXY2Fh8/PHHXnF9ycnJuHz5Mjp16gS1Wg21Wo0dO3bgk08+gVqtRnh4uMdfo7FatWqhZcuWOH36tFe8hpGRkWjbtq3BtjZt2ui63qrqaKp++vW/fPmywfMVFRW4du2apH8HRzp//jz++OMPPPXUU7pt3vD6AcCrr76qa72JiYnBiBEjMHHiRF1rqje8hs2aNcOOHTtQUFCACxcuYM+ePSgvL0fTpk294vr0OfN6zJWRer0MbvQIgoBx48Zh7dq1+PPPP9GkSROD5zt37gwfHx9s3bpVt+3EiRNIS0tDfHw8ACA+Ph6HDx82eJG3bNmC4OBg3Qd2fHy8wTGqylQdw9m0Wi1KS0u94vr69u2Lw4cPIyUlRffTpUsXDB8+XPe7p1+jsYKCApw5cwaRkZFe8Rr27Nmz2hQMJ0+eRKNGjQAATZo0QUREhEH98vLysHv3boNrzMnJMfgW/eeff0Kr1aJ79+66Mjt37kR5ebmuzJYtW9CqVSvUrl3bYddXZdmyZQgLC8PAgQN127zh9QOAoqIiKJWGtxeVSgWtVgvAe15DAKhRowYiIyNx/fp1bN68Gffff79XXR/g3NdLtveupPRjLzd27FghJCRE2L59u8FQzaKiIl2Z5557TmjYsKHw559/Cvv27RPi4+OF+Ph43fNVwzTvuusuISUlRdi0aZNQr149k8M0X331VeHYsWPCggULnDZM87XXXhN27NghnD17Vjh06JDw2muvCQqFQvj999+94vpM0R8tJQief40vv/yysH37duHs2bPC33//LSQkJAihoaHC5cuXveL69uzZI6jVauG9994TTp06JXz77bdCYGCg8M033+jKzJ49W6hVq5bw888/C4cOHRLuv/9+k8NS4+LihN27dwu7du0SWrRoYTAsNScnRwgPDxdGjBghHDlyRFi1apUQGBjolGHEGo1GaNiwoTB58uRqz3n66ycIgjBq1Cihfv36uqHga9asEUJDQ4VJkybpynj6a7hp0yZh48aNQmpqqvD7778LsbGxQvfu3YWysjKPvL78/HzhwIEDwoEDBwQAwty5c4UDBw4I58+fd+r1/P3334JarRY++ugj4dixY8L06dM5FNxeAEz+LFu2TFemuLhYeP7554XatWsLgYGBwgMPPCBkZGQYHOfcuXPCgAEDhICAACE0NFR4+eWXhfLycoMy27ZtEzp27Cj4+voKTZs2NTiHI40ZM0Zo1KiR4OvrK9SrV0/o27evLrARBM+/PlOMgxtPv8ahQ4cKkZGRgq+vr1C/fn1h6NChBnPAePr1CYIg/Prrr0L79u0FPz8/oXXr1sKXX35p8LxWqxWmTp0qhIeHC35+fkLfvn2FEydOGJS5evWqMGzYMCEoKEgIDg4WRo8eLeTn5xuUOXjwoHD77bcLfn5+Qv369YXZs2c7/NoEQRA2b94sAKhWZ0HwjtcvLy9PGD9+vNCwYUPB399faNq0qfDGG28YDAH29Nfw+++/F5o2bSr4+voKERERwgsvvCDk5OR47PVt27bN5P1v1KhRTr+eH374QWjZsqXg6+srtGvXTvjtt98kXw9XBSciIiKvwpwbIiIi8ioMboiIiMirMLghIiIir8LghoiIiLwKgxsiIiLyKgxuiIiIyKswuCEiIiKvwuCGiIiIvAqDGyIiIvIqDG6IiIjIqzC4ISKrXnvtNfj5+eGxxx4TVb5Pnz5QKBRQKBRISUlxbOU81BNPPKH7N1q3bp2rq0PkVRjcEJFVU6ZMwZw5c7By5UqcPn1a1D5PP/00MjIy0L59e4PtSUlJUKlUGDhwoCOqalWfPn0wYcIEl5xb38cff4yMjAxXV4PIKzG4ISKrQkJC8OSTT0KpVOLw4cOi9gkMDERERATUarXB9iVLluDFF1/Ezp07cenSJUdUVxZlZWUOPX5ISAgiIiIceg6iWxWDGyISpaKiAoGBgThy5IjNxygoKMD333+PsWPHYuDAgVi+fHm1Mn369MFLL72ESZMmoU6dOoiIiMBbb72lez4/Px/Dhw9HjRo1EBkZiXnz5lVrjfnxxx8RExODgIAA1K1bFwkJCSgsLMQTTzyBHTt24OOPP9Z1CZ07d0533nHjxmHChAkIDQ1F//79AQClpaV46aWXEBYWBn9/f9x+++3Yu3evQX1ffPFFTJgwAbVr10Z4eDgWL16MwsJCjB49GjVr1kTz5s2xceNGm//diEgaBjdEJMqbb76JgoICu4KbH374Aa1bt0arVq3w+OOPY+nSpRAEoVq5FStWoEaNGti9ezc++OADvP3229iyZQsAIDExEX///Td++eUXbNmyBX/99Rf279+v2zcjIwPDhg3DmDFjcOzYMWzfvh0PPvggBEHAxx9/jPj4eF2XWUZGBqKjow3O6+vri7///huLFi0CAEyaNAk//fQTVqxYgf3796N58+bo378/rl27ZrBfaGgo9uzZgxdffBFjx47Fww8/jB49emD//v246667MGLECBQVFdn8b0dEEghERFbs27dP8PX1FQYOHCi0bdvWavnevXsL48ePr7a9R48ewvz58wVBEITy8nIhNDRU2LZtW7V9b7/9doNtXbt2FSZPnizk5eUJPj4+wurVq3XP5eTkCIGBgbrzJScnCwCEc+fOSapb7969hbi4OINtBQUFgo+Pj/Dtt9/qtpWVlQlRUVHCBx98YLK+FRUVQo0aNYQRI0botmVkZAgAhKSkpGrnBSCsXbvWZF2JyDZsuSEii7RaLZ599lmMGzcOI0eOxKlTp1BeXi75OCdOnMCePXswbNgwAIBarcbQoUOxZMmSamU7dOhg8DgyMhKXL19GamoqysvL0a1bN91zISEhaNWqle5xbGws+vbti5iYGDz88MNYvHgxrl+/LqqOnTt3Nnh85swZlJeXo2fPnrptPj4+6NatG44dO2ayviqVCnXr1kVMTIxuW3h4OADg8uXLoupBRPZhcENEFn366ae4cuUK3n77bcTExKC8vBzHjx+XfJwlS5agoqICUVFRUKvVUKvVWLhwIX766Sfk5uYalPXx8TF4rFAooNVqRZ1HpVJhy5Yt2LhxI9q2bYtPP/0UrVq1wtmzZ63uW6NGDfEXZKW++tsUCgUAiL4GIrIPgxsiMis9PR1Tp07FggULUKNGDbRo0QJ+fn6S824qKirwv//9D3PmzEFKSoru5+DBg4iKisLKlStFHadp06bw8fExSOjNzc3FyZMnDcopFAr07NkTM2bMwIEDB+Dr64u1a9cCAHx9faHRaESdr1mzZrocnCrl5eXYu3cv2rZtK+oYROR8autFiOhW9dJLL2HAgAG6OWnUajXatGkjObhZv349rl+/jieffBIhISEGzw0ZMgRLlizBc889Z/U4NWvWxKhRo/Dqq6+iTp06CAsLw/Tp06FUKnWtI7t378bWrVtx1113ISwsDLt370Z2djbatGkDAGjcuDF2796Nc+fOISgoCHXq1IFSafp7Xo0aNTB27Fjd+Ro2bIgPPvgARUVFePLJJyX9GxCR87DlhohMWr9+Pf788098/PHHBttjYmIkBzdLlixBQkJCtcAGqAxu9u3bh0OHDok61ty5cxEfH497770XCQkJ6NmzJ9q0aQN/f38AQHBwMHbu3Il77rkHLVu2xJtvvok5c+ZgwIABAIBXXnkFKpUKbdu2Rb169ZCWlmbxfLNnz8aQIUMwYsQIdOrUCadPn8bmzZtRu3ZtSf8GROQ8CkEwMQ6TiMgOffr0QceOHTF//nyHn6uwsBD169fHnDlzPLI1RaFQYO3atRg8eLCrq0LkNdhyQ0QO8fnnnyMoKEj0jMZiHThwACtXrsSZM2ewf/9+DB8+HABw//33y3oeR3vuuecQFBTk6moQeSW23BCR7NLT01FcXAwAaNiwIXx9fWU79oEDB/DUU0/hxIkT8PX1RefOnTF37lyDodee4PLly8jLywNQOdTd1pFaRFQdgxsiIiLyKuyWIiIiIq/C4IaIiIi8CoMbIiIi8ioMboiIiMirMLghIiIir8LghoiIiLwKgxsiIiLyKgxuiIiIyKswuCEiIiKvwuCGiIiIvAqDGyIiIvIq/w9qrlFDhhfQ2wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[-1][-1])\n", @@ -350,9 +1221,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "for i in range(len(ssp.metallicity)):\n", @@ -366,9 +1258,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "ages = np.linspace(0,len(ssp.age),10)\n", @@ -398,7 +1311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/ssp_template_fsps.ipynb b/source/notebooks/ssp_template_fsps.ipynb index 5443ea79..1c73aedf 100644 --- a/source/notebooks/ssp_template_fsps.ipynb +++ b/source/notebooks/ssp_template_fsps.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# pyFSPS" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -15,6 +22,15 @@ "For more information about *pyFSPS* see https://dfm.io/python-fsps/current/ for more information about *FSPS* see https://github.com/cconroy20/fsps" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NOTE: In order to make this notebook work please first install *pyFSPS* following the installation guide here: https://dfm.io/python-fsps/current/installation/\n", + "\n", + "In particular, you will need to set the `SPS_HOME` environment variable." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -35,9 +51,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-10 17:14:09,206 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-10 17:14:09,206 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n", + "2025-11-10 17:14:09,206 - rubix - INFO - JAX version: 0.7.2\n", + "2025-11-10 17:14:09,275 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", + "2025-11-10 17:14:09,276 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" + ] + }, + { + "data": { + "text/plain": [ + "HDF5SSPGrid(age=Array([9.9999997e-05, 1.1220184e-04, 1.2589252e-04, 1.4125378e-04,\n", + " 1.5848933e-04, 1.7782794e-04, 1.9952621e-04, 2.2387206e-04,\n", + " 2.5118870e-04, 2.8183832e-04, 3.1622776e-04, 3.5481335e-04,\n", + " 3.9810708e-04, 4.4668370e-04, 5.0118729e-04, 5.6234130e-04,\n", + " 6.3095725e-04, 7.0794561e-04, 7.9432840e-04, 8.9125102e-04,\n", + " 1.0000000e-03, 1.1220183e-03, 1.2589252e-03, 1.4125379e-03,\n", + " 1.5848933e-03, 1.7782794e-03, 1.9952620e-03, 2.2387207e-03,\n", + " 2.5118869e-03, 2.8183833e-03, 3.1622776e-03, 3.5481334e-03,\n", + " 3.9810711e-03, 4.4668368e-03, 5.0118729e-03, 5.6234132e-03,\n", + " 6.3095726e-03, 7.0794565e-03, 7.9432838e-03, 8.9125102e-03,\n", + " 9.9999998e-03, 1.1220183e-02, 1.2589254e-02, 1.4125375e-02,\n", + " 1.5848933e-02, 1.7782794e-02, 1.9952621e-02, 2.2387212e-02,\n", + " 2.5118863e-02, 2.8183833e-02, 3.1622775e-02, 3.5481334e-02,\n", + " 3.9810721e-02, 4.4668358e-02, 5.0118729e-02, 5.6234132e-02,\n", + " 6.3095726e-02, 7.0794582e-02, 7.9432823e-02, 8.9125104e-02,\n", + " 1.0000000e-01, 1.1220185e-01, 1.2589255e-01, 1.4125374e-01,\n", + " 1.5848932e-01, 1.7782794e-01, 1.9952624e-01, 2.2387213e-01,\n", + " 2.5118864e-01, 2.8183830e-01, 3.1622776e-01, 3.5481340e-01,\n", + " 3.9810717e-01, 4.4668359e-01, 5.0118721e-01, 5.6234133e-01,\n", + " 6.3095737e-01, 7.0794576e-01, 7.9432821e-01, 8.9125091e-01,\n", + " 1.0000000e+00, 1.1220185e+00, 1.2589254e+00, 1.4125376e+00,\n", + " 1.5848932e+00, 1.7782794e+00, 1.9952624e+00, 2.2387211e+00,\n", + " 2.5118864e+00, 2.8183827e+00, 3.1622777e+00, 3.5481341e+00,\n", + " 3.9810719e+00, 4.4668355e+00, 5.0118723e+00, 5.6234131e+00,\n", + " 6.3095737e+00, 7.0794582e+00, 7.9432821e+00, 8.9125090e+00,\n", + " 1.0000000e+01, 1.1220183e+01, 1.2589254e+01, 1.4125375e+01,\n", + " 1.5848933e+01, 1.7782795e+01, 1.9952621e+01], dtype=float32), metallicity=Array([4.4904351e-05, 1.4200003e-04, 2.5251572e-04, 4.4904352e-04,\n", + " 7.9852482e-04, 1.4200003e-03, 2.5251573e-03, 4.4904351e-03,\n", + " 7.9852482e-03, 1.4199999e-02, 2.5251566e-02, 4.4904340e-02], dtype=float32), wavelength=Array([8.950e+01, 9.250e+01, 9.450e+01, ..., 9.817e+07, 9.908e+07,\n", + " 1.000e+08], dtype=float32), flux=Array([[[3.69801944e-25, 1.71711785e-25, 1.01008924e-25, ...,\n", + " 4.20808249e-11, 4.13591869e-11, 4.06485991e-11],\n", + " [2.95627621e-25, 1.37270093e-25, 8.07487082e-26, ...,\n", + " 3.36403162e-11, 3.30634235e-11, 3.24953640e-11],\n", + " [3.62052076e-25, 1.68113235e-25, 9.88920961e-26, ...,\n", + " 4.11989401e-11, 4.04924289e-11, 3.97967319e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.87085782e-21, 6.62186151e-21, 6.38153848e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.64786358e-21, 6.40694763e-21, 6.17442546e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.16116230e-21, 5.93788412e-21, 5.72238539e-21]],\n", + "\n", + " [[2.47674418e-25, 1.15003767e-25, 6.76506097e-26, ...,\n", + " 2.81835822e-11, 2.77002657e-11, 2.72243512e-11],\n", + " [2.70331983e-25, 1.25524459e-25, 7.38393714e-26, ...,\n", + " 3.07618514e-11, 3.02343220e-11, 2.97148695e-11],\n", + " [3.59155428e-25, 1.66768228e-25, 9.81008994e-26, ...,\n", + " 4.08693253e-11, 4.01684623e-11, 3.94783338e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.89014787e-21, 8.56769041e-21, 8.25660749e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.48373481e-21, 8.17602604e-21, 7.87917160e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.92724666e-21, 6.67610364e-21, 6.43376153e-21]],\n", + "\n", + " [[2.81423712e-25, 1.30674723e-25, 7.68690040e-26, ...,\n", + " 3.20240119e-11, 3.14748366e-11, 3.09340713e-11],\n", + " [2.62237481e-25, 1.21765895e-25, 7.16284100e-26, ...,\n", + " 2.98407549e-11, 2.93290184e-11, 2.88251211e-11],\n", + " [2.73612140e-25, 1.27047540e-25, 7.47353201e-26, ...,\n", + " 3.11351084e-11, 3.06011778e-11, 3.00754213e-11],\n", + " ...,\n", + " [1.32808084e-10, 1.46964954e-10, 1.78483950e-10, ...,\n", + " 8.73207478e-21, 8.41537447e-21, 8.10981686e-21],\n", + " [5.43437206e-09, 5.32875122e-09, 5.96376948e-09, ...,\n", + " 8.36363771e-21, 8.06031204e-21, 7.76765247e-21],\n", + " [9.67010383e-09, 9.52066159e-09, 1.06839435e-08, ...,\n", + " 7.63764698e-21, 7.36066264e-21, 7.09341060e-21]],\n", + "\n", + " ...,\n", + "\n", + " [[3.35264627e-25, 1.55674908e-25, 9.15752880e-26, ...,\n", + " 3.81507222e-11, 3.74964816e-11, 3.68522574e-11],\n", + " [3.28182234e-25, 1.52386307e-25, 8.96407792e-26, ...,\n", + " 3.73447974e-11, 3.67043756e-11, 3.60737620e-11],\n", + " [3.33899035e-25, 1.55040824e-25, 9.12022862e-26, ...,\n", + " 3.79953291e-11, 3.73437531e-11, 3.67021517e-11],\n", + " ...,\n", + " [8.59993882e-11, 1.15849191e-10, 1.53091637e-10, ...,\n", + " 1.75658879e-20, 1.69284419e-20, 1.63133959e-20],\n", + " [4.22778722e-11, 5.95679478e-11, 7.97761926e-11, ...,\n", + " 1.53844511e-20, 1.48262221e-20, 1.42875434e-20],\n", + " [2.23467148e-11, 3.41603967e-11, 4.68701189e-11, ...,\n", + " 1.51603821e-20, 1.46103038e-20, 1.40795349e-20]],\n", + "\n", + " [[3.28365965e-25, 1.52471627e-25, 8.96909643e-26, ...,\n", + " 3.73657043e-11, 3.67249252e-11, 3.60939577e-11],\n", + " [3.06845691e-25, 1.42479015e-25, 8.38128412e-26, ...,\n", + " 3.49168507e-11, 3.43180658e-11, 3.37284506e-11],\n", + " [3.15609887e-25, 1.46548539e-25, 8.62067320e-26, ...,\n", + " 3.59141536e-11, 3.52982678e-11, 3.46918119e-11],\n", + " ...,\n", + " [4.62700885e-11, 7.34858702e-11, 1.02663184e-10, ...,\n", + " 1.95786429e-20, 1.88677474e-20, 1.81815365e-20],\n", + " [5.22229378e-11, 7.36018815e-11, 9.85654544e-11, ...,\n", + " 1.83188566e-20, 1.76538179e-20, 1.70122553e-20],\n", + " [3.85684748e-11, 5.60884464e-11, 7.57659144e-11, ...,\n", + " 1.75572090e-20, 1.69198550e-20, 1.63050046e-20]],\n", + "\n", + " [[3.02441580e-25, 1.40434041e-25, 8.26098899e-26, ...,\n", + " 3.44156960e-11, 3.38255049e-11, 3.32443517e-11],\n", + " [3.15393394e-25, 1.46448021e-25, 8.61475921e-26, ...,\n", + " 3.58895171e-11, 3.52740545e-11, 3.46680150e-11],\n", + " [3.26780774e-25, 1.51735571e-25, 8.92579844e-26, ...,\n", + " 3.71853208e-11, 3.65476364e-11, 3.59197151e-11],\n", + " ...,\n", + " [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n", + " 1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n", + " [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n", + " 2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n", + " [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n", + " 2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32))" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.spectra.ssp.factory import get_ssp_template\n", @@ -47,9 +204,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "config = {\n", + " \"name\": \"FSPS (Conroy et al. 2009)\",\n", + " # more information on how those models are synthesized: https://github.com/cconroy20/fsps\n", + " # and https://dfm.io/python-fsps/current/\n", + " \"format\": \"fsps\", # Format of the template\n", + " \"source\": \"load_from_file\", # the source can be \"load_from_file\" or \"rerun_from_scratch\"\n", + " # \"load_from_file\" is the default and loads the template from a pre-existing file in h5 format specified by \"file_name\"\n", + " # if that file is not found, it will automatically run fsps and save the output to disk in h5 format under the \"file_name\" given.\n", + " # \"rerun_from_scratch\" # note: this is just meant for the case in which you really want to rerun your template library.\n", + " # You should be aware that fsps templates will silently be overwritten by this. Use with caution.\n", + " \"file_name\": \"fsps.h5\", # File name of the template, stored in templates directory\n", + " # Define the Fields in the template and their units\n", + " # This is used to convert them to the required units\n", + " \"fields\":{ # Fields in the template and their units\n", + " # Name defines the name of the key stored in the hdf5 file\n", + " \"age\":{\n", + " \"name\": \"age\",\n", + " \"units\": \"Gyr\", # Age of the template\n", + " \"in_log\": True # If the field is stored in log scale\n", + " },\n", + " \"metallicity\":{\n", + " \"name\": \"metallicity\",\n", + " \"units\": \"\", # Metallicity of the template\n", + " \"in_log\": True # If the field is stored in log scale\n", + " },\n", + " \"wavelength\":{\n", + " \"name\": \"wavelength\",\n", + " \"units\": \"Angstrom\", # Wavelength of the template\n", + " \"in_log\": False # If the field is stored in log scale\n", + " },\n", + " \"flux\":{\n", + " \"name\": \"flux\",\n", + " \"units\": \"Lsun/Angstrom\", # Luminosity of the template as per pyFSPS documentation\n", + " \"in_log\": False # If the field is stored in log scale\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(107,)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.age.shape" @@ -57,9 +271,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(12,)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.metallicity.shape" @@ -67,9 +292,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(5994,)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.wavelength.shape" @@ -77,9 +313,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(12, 107, 5994)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.flux.shape" @@ -87,9 +334,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "import os\n", @@ -106,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -117,9 +375,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10000.0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[0][0])\n", @@ -131,9 +410,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10000.0)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[-1][-1])\n", @@ -145,9 +445,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "for i in range(len(ssp.metallicity)):\n", @@ -161,9 +482,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# NBVAL_SKIP\n", "ages = np.linspace(0,len(ssp.age),10)\n", @@ -192,19 +534,144 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ + "#NBVAL_SKIP\n", "from rubix import config as rubix_config\n", "rubix_config[\"ssp\"][\"templates\"][\"FSPS\"][\"source\"] = \"load_from_file\"" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "HDF5SSPGrid(age=Array([9.9999997e-05, 1.1220184e-04, 1.2589252e-04, 1.4125378e-04,\n", + " 1.5848933e-04, 1.7782794e-04, 1.9952621e-04, 2.2387206e-04,\n", + " 2.5118870e-04, 2.8183832e-04, 3.1622776e-04, 3.5481335e-04,\n", + " 3.9810708e-04, 4.4668370e-04, 5.0118729e-04, 5.6234130e-04,\n", + " 6.3095725e-04, 7.0794561e-04, 7.9432840e-04, 8.9125102e-04,\n", + " 1.0000000e-03, 1.1220183e-03, 1.2589252e-03, 1.4125379e-03,\n", + " 1.5848933e-03, 1.7782794e-03, 1.9952620e-03, 2.2387207e-03,\n", + " 2.5118869e-03, 2.8183833e-03, 3.1622776e-03, 3.5481334e-03,\n", + " 3.9810711e-03, 4.4668368e-03, 5.0118729e-03, 5.6234132e-03,\n", + " 6.3095726e-03, 7.0794565e-03, 7.9432838e-03, 8.9125102e-03,\n", + " 9.9999998e-03, 1.1220183e-02, 1.2589254e-02, 1.4125375e-02,\n", + " 1.5848933e-02, 1.7782794e-02, 1.9952621e-02, 2.2387212e-02,\n", + " 2.5118863e-02, 2.8183833e-02, 3.1622775e-02, 3.5481334e-02,\n", + " 3.9810721e-02, 4.4668358e-02, 5.0118729e-02, 5.6234132e-02,\n", + " 6.3095726e-02, 7.0794582e-02, 7.9432823e-02, 8.9125104e-02,\n", + " 1.0000000e-01, 1.1220185e-01, 1.2589255e-01, 1.4125374e-01,\n", + " 1.5848932e-01, 1.7782794e-01, 1.9952624e-01, 2.2387213e-01,\n", + " 2.5118864e-01, 2.8183830e-01, 3.1622776e-01, 3.5481340e-01,\n", + " 3.9810717e-01, 4.4668359e-01, 5.0118721e-01, 5.6234133e-01,\n", + " 6.3095737e-01, 7.0794576e-01, 7.9432821e-01, 8.9125091e-01,\n", + " 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...,\n", + " 3.36403162e-11, 3.30634235e-11, 3.24953640e-11],\n", + " [3.62052076e-25, 1.68113235e-25, 9.88920961e-26, ...,\n", + " 4.11989401e-11, 4.04924289e-11, 3.97967319e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.87085782e-21, 6.62186151e-21, 6.38153848e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.64786358e-21, 6.40694763e-21, 6.17442546e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.16116230e-21, 5.93788412e-21, 5.72238539e-21]],\n", + "\n", + " [[2.47674418e-25, 1.15003767e-25, 6.76506097e-26, ...,\n", + " 2.81835822e-11, 2.77002657e-11, 2.72243512e-11],\n", + " [2.70331983e-25, 1.25524459e-25, 7.38393714e-26, ...,\n", + " 3.07618514e-11, 3.02343220e-11, 2.97148695e-11],\n", + " [3.59155428e-25, 1.66768228e-25, 9.81008994e-26, ...,\n", + " 4.08693253e-11, 4.01684623e-11, 3.94783338e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.89014787e-21, 8.56769041e-21, 8.25660749e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.48373481e-21, 8.17602604e-21, 7.87917160e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.92724666e-21, 6.67610364e-21, 6.43376153e-21]],\n", + "\n", + " [[2.81423712e-25, 1.30674723e-25, 7.68690040e-26, ...,\n", + " 3.20240119e-11, 3.14748366e-11, 3.09340713e-11],\n", + " [2.62237481e-25, 1.21765895e-25, 7.16284100e-26, ...,\n", + " 2.98407549e-11, 2.93290184e-11, 2.88251211e-11],\n", + " [2.73612140e-25, 1.27047540e-25, 7.47353201e-26, ...,\n", + " 3.11351084e-11, 3.06011778e-11, 3.00754213e-11],\n", + " ...,\n", + " [1.32808084e-10, 1.46964954e-10, 1.78483950e-10, ...,\n", + " 8.73207478e-21, 8.41537447e-21, 8.10981686e-21],\n", + " [5.43437206e-09, 5.32875122e-09, 5.96376948e-09, ...,\n", + " 8.36363771e-21, 8.06031204e-21, 7.76765247e-21],\n", + " [9.67010383e-09, 9.52066159e-09, 1.06839435e-08, ...,\n", + " 7.63764698e-21, 7.36066264e-21, 7.09341060e-21]],\n", + "\n", + " ...,\n", + "\n", + " [[3.35264627e-25, 1.55674908e-25, 9.15752880e-26, ...,\n", + " 3.81507222e-11, 3.74964816e-11, 3.68522574e-11],\n", + " [3.28182234e-25, 1.52386307e-25, 8.96407792e-26, ...,\n", + " 3.73447974e-11, 3.67043756e-11, 3.60737620e-11],\n", + " [3.33899035e-25, 1.55040824e-25, 9.12022862e-26, ...,\n", + " 3.79953291e-11, 3.73437531e-11, 3.67021517e-11],\n", + " ...,\n", + " [8.59993882e-11, 1.15849191e-10, 1.53091637e-10, ...,\n", + " 1.75658879e-20, 1.69284419e-20, 1.63133959e-20],\n", + " [4.22778722e-11, 5.95679478e-11, 7.97761926e-11, ...,\n", + " 1.53844511e-20, 1.48262221e-20, 1.42875434e-20],\n", + " [2.23467148e-11, 3.41603967e-11, 4.68701189e-11, ...,\n", + " 1.51603821e-20, 1.46103038e-20, 1.40795349e-20]],\n", + "\n", + " [[3.28365965e-25, 1.52471627e-25, 8.96909643e-26, ...,\n", + " 3.73657043e-11, 3.67249252e-11, 3.60939577e-11],\n", + " [3.06845691e-25, 1.42479015e-25, 8.38128412e-26, ...,\n", + " 3.49168507e-11, 3.43180658e-11, 3.37284506e-11],\n", + " [3.15609887e-25, 1.46548539e-25, 8.62067320e-26, ...,\n", + " 3.59141536e-11, 3.52982678e-11, 3.46918119e-11],\n", + " ...,\n", + " [4.62700885e-11, 7.34858702e-11, 1.02663184e-10, ...,\n", + " 1.95786429e-20, 1.88677474e-20, 1.81815365e-20],\n", + " [5.22229378e-11, 7.36018815e-11, 9.85654544e-11, ...,\n", + " 1.83188566e-20, 1.76538179e-20, 1.70122553e-20],\n", + " [3.85684748e-11, 5.60884464e-11, 7.57659144e-11, ...,\n", + " 1.75572090e-20, 1.69198550e-20, 1.63050046e-20]],\n", + "\n", + " [[3.02441580e-25, 1.40434041e-25, 8.26098899e-26, ...,\n", + " 3.44156960e-11, 3.38255049e-11, 3.32443517e-11],\n", + " [3.15393394e-25, 1.46448021e-25, 8.61475921e-26, ...,\n", + " 3.58895171e-11, 3.52740545e-11, 3.46680150e-11],\n", + " [3.26780774e-25, 1.51735571e-25, 8.92579844e-26, ...,\n", + " 3.71853208e-11, 3.65476364e-11, 3.59197151e-11],\n", + " ...,\n", + " [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n", + " 1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n", + " [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n", + " 2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n", + " [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n", + " 2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32))" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp2 = get_ssp_template(\"FSPS\")\n", @@ -213,20 +680,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# NBVAL_SKIP\n", "ssp.wavelength.shape == ssp2.wavelength.shape" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -245,7 +716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/notebooks/telescope.ipynb b/source/notebooks/telescope.ipynb index 1bff666a..32b20517 100644 --- a/source/notebooks/telescope.ipynb +++ b/source/notebooks/telescope.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,9 +35,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BaseTelescope(\n", + " fov=5.0,\n", + " spatial_res=0.2,\n", + " wave_range=[4700.15, 9351.4],\n", + " wave_res=1.25,\n", + " lsf_fwhm=2.51,\n", + " signal_to_noise=None,\n", + " sbin=np.int64(25),\n", + " aperture_region=f32[625],\n", + " pixel_type='square',\n", + " wave_seq=f32[3721],\n", + " wave_edges=f32[3722]\n", + ")\n" + ] + } + ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "factory = TelescopeFactory(telescope_config) # Uses the defined telescope configuration\n", @@ -56,9 +76,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BaseTelescope(\n", + " fov=5.0,\n", + " spatial_res=0.2,\n", + " wave_range=[4700.15, 9351.4],\n", + " wave_res=1.25,\n", + " lsf_fwhm=2.51,\n", + " signal_to_noise=None,\n", + " sbin=np.int64(25),\n", + " aperture_region=f32[625],\n", + " pixel_type='square',\n", + " wave_seq=f32[3721],\n", + " wave_edges=f32[3722]\n", + ")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "factory = TelescopeFactory() # Uses the default config\n", @@ -68,9 +116,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BaseTelescope(\n", + " fov=5.0,\n", + " spatial_res=0.2,\n", + " wave_range=[4700.15, 9351.4],\n", + " wave_res=1.25,\n", + " lsf_fwhm=2.51,\n", + " signal_to_noise=None,\n", + " sbin=np.int64(25),\n", + " aperture_region=f32[625],\n", + " pixel_type='square',\n", + " wave_seq=f32[3721],\n", + " wave_edges=f32[3722]\n", + ")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "from rubix.core.telescope import get_telescope\n", "\n", @@ -97,7 +173,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/source/publications.rst b/source/publications.rst index 6c25fa41..1868d27b 100644 --- a/source/publications.rst +++ b/source/publications.rst @@ -6,3 +6,5 @@ Publications List of publications including RUBIX [1] Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes - U. Çakır, A. Schaible and T. Buck (NeurIPS 2024) + +[2] RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation - A. Schaible, U. Çakır, T. Buck, H.Mack, A. Obreja, N. Oguz, W. H. Oliver, H. Cărămizaru (EuRIPS 2025) diff --git a/source/versions.rst b/source/versions.rst index 2c96bb77..68d87c03 100644 --- a/source/versions.rst +++ b/source/versions.rst @@ -5,14 +5,10 @@ Code versions Version 0.1 ----------- -Forwardmodel IFU cubes of galaxies from cosmological hydrodynamical simulations for stellar particles from IllustrisTNG50. +Forwardmodel IFU cubes of galaxies from cosmological hydrodynamical simulations (IllustrisTNG50, NIHAO, ...) for stellar particles from different stellar templates (Bruzual&Charlot, Mastar, FSPS, EMILES). +Gradient calculation through the whole pipeline for gradient-based parameter estimation on particle parameters. Version 0.2 ----------- -Under developement: Forwardmodel IFU cubes of galaxies from cosmological hydrodynamical simulations for gas particles from IllustrisTNG50. - - -Version 0.3 ------------ -Under developement: Add dust attenuation to the IFU cubes. +Under developement \ No newline at end of file From 78b555ad4fad951379047d94a5d3e110485c7a30 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 21:02:36 +0100 Subject: [PATCH 02/22] Update license information in README to MIT License --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 370929be..e4677a7c 100644 --- a/README.md +++ b/README.md @@ -134,7 +134,7 @@ archivePrefix = {arXiv}, ## Licence -[GNU General Public License v3.0](https://github.com/synthesizer-project/synthesizer/blob/main/LICENSE.md) +[MIT License](https://github.com/AstroAI-Lab/rubix/blob/main/LICENSE.md) ## Acknowledgments From de46508b5ef30d115cd72e2fc01f6752c1a53c38 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 21:11:42 +0100 Subject: [PATCH 03/22] rename source to docs to move Rubix documentation under the docs folder. --- FILESTRUCTURE.md | 40 - Makefile | 2 +- README.md | 2 +- {source => docs}/Contributing.md | 8 +- {source => docs}/acknowledgments.rst | 0 {source => docs}/conf.py | 6 +- {source => docs}/index.rst | 0 {source => docs}/installation.rst | 0 {source => docs}/license.rst | 0 {source => docs}/modules.rst | 0 {source => docs}/notebooks/cosmology.ipynb | 61 +- docs/notebooks/create_rubix_data.ipynb | 167 ++++ {source => docs}/notebooks/demo.yml | 0 .../notebooks/dust_extinction.ipynb | 0 docs/notebooks/filter_curves.ipynb | 346 ++++++++ ..._age_metallicity_adamoptimizer_multi.ipynb | 0 ...llicity_adamoptimizer_vs_finite_diff.ipynb | 0 .../notebooks/output/rubix_galaxy.h5 | Bin .../notebooks/pipeline_demo.ipynb | 615 +++---------- docs/notebooks/psf.ipynb | 93 ++ ...x_pipeline_single_function_shard_map.ipynb | 0 .../notebooks/rubix_pipeline_stepwise.ipynb | 0 .../notebooks/ssp_interpolation.ipynb | 0 {source => docs}/notebooks/ssp_template.ipynb | 0 .../notebooks/ssp_template_fsps.ipynb | 0 {source => docs}/notebooks/telescope.ipynb | 90 +- {source => docs}/publications.rst | 0 {source => docs}/rubix.core.rst | 0 {source => docs}/rubix.cosmology.rst | 0 .../rubix.galaxy.input_handler.rst | 0 {source => docs}/rubix.galaxy.rst | 0 {source => docs}/rubix.pipeline.rst | 0 {source => docs}/rubix.rst | 0 {source => docs}/rubix.spectra.rst | 0 {source => docs}/rubix.spectra.ssp.rst | 0 {source => docs}/rubix.telescope.filters.rst | 0 {source => docs}/rubix.telescope.lsf.rst | 0 {source => docs}/rubix.telescope.noise.rst | 0 {source => docs}/rubix.telescope.psf.rst | 0 {source => docs}/rubix.telescope.rst | 0 {source => docs}/rubix.utils.rst | 0 {source => docs}/versions.rst | 2 +- source/notebooks/create_rubix_data.ipynb | 286 ------ source/notebooks/filter_curves.ipynb | 830 ------------------ source/notebooks/psf.ipynb | 151 ---- 45 files changed, 745 insertions(+), 1954 deletions(-) delete mode 100644 FILESTRUCTURE.md rename {source => docs}/Contributing.md (96%) rename {source => docs}/acknowledgments.rst (100%) rename {source => docs}/conf.py (91%) rename {source => docs}/index.rst (100%) rename {source => docs}/installation.rst (100%) rename {source => docs}/license.rst (100%) rename {source => docs}/modules.rst (100%) rename {source => docs}/notebooks/cosmology.ipynb (73%) create mode 100644 docs/notebooks/create_rubix_data.ipynb rename {source => docs}/notebooks/demo.yml (100%) rename {source => docs}/notebooks/dust_extinction.ipynb (100%) create mode 100644 docs/notebooks/filter_curves.ipynb rename {source => docs}/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb (100%) rename {source => docs}/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb (100%) rename {source => docs}/notebooks/output/rubix_galaxy.h5 (100%) rename {source => docs}/notebooks/pipeline_demo.ipynb (63%) create mode 100644 docs/notebooks/psf.ipynb rename {source => docs}/notebooks/rubix_pipeline_single_function_shard_map.ipynb (100%) rename {source => docs}/notebooks/rubix_pipeline_stepwise.ipynb (100%) rename {source => docs}/notebooks/ssp_interpolation.ipynb (100%) rename {source => docs}/notebooks/ssp_template.ipynb (100%) rename {source => docs}/notebooks/ssp_template_fsps.ipynb (100%) rename {source => docs}/notebooks/telescope.ipynb (50%) rename {source => docs}/publications.rst (100%) rename {source => docs}/rubix.core.rst (100%) rename {source => docs}/rubix.cosmology.rst (100%) rename {source => docs}/rubix.galaxy.input_handler.rst (100%) rename {source => docs}/rubix.galaxy.rst (100%) rename {source => docs}/rubix.pipeline.rst (100%) rename {source => docs}/rubix.rst (100%) rename {source => docs}/rubix.spectra.rst (100%) rename {source => docs}/rubix.spectra.ssp.rst (100%) rename {source => docs}/rubix.telescope.filters.rst (100%) rename {source => docs}/rubix.telescope.lsf.rst (100%) rename {source => docs}/rubix.telescope.noise.rst (100%) rename {source => docs}/rubix.telescope.psf.rst (100%) rename {source => docs}/rubix.telescope.rst (100%) rename {source => docs}/rubix.utils.rst (100%) rename {source => docs}/versions.rst (96%) delete mode 100644 source/notebooks/create_rubix_data.ipynb delete mode 100644 source/notebooks/filter_curves.ipynb delete mode 100644 source/notebooks/psf.ipynb diff --git a/FILESTRUCTURE.md b/FILESTRUCTURE.md deleted file mode 100644 index c422e004..00000000 --- a/FILESTRUCTURE.md +++ /dev/null @@ -1,40 +0,0 @@ -This is an explanation of the file structure that the cookiecutter generated for you: - -* Python source files: - * The Python package source files are located in the `rubix` directory. - * `tests/test_rubix.py` contains the unit tests for the package. - * `tests/conftest.py` contains testing setup and configuration for `pytest` - * The `notebooks` directory contains an example Jupyter notebook on how to use `rubix`. - This notebook is always executed during `pytest` execution and it is automatically - rendered into the Sphinx documentation. -* Markdown files with meta information on the project. [Markdown](https://www.markdownguide.org/basic-syntax/) is - a good language for these files, as it is easy to write and rendered into something beautiful by your git repository - hosting provider. - * `README.md` is the file that users will typically see first when discovering your project. - * `COPYING.md` provides a list of copyright holders. - * `LICENSE.md` contains the license you selected. - * `TODO.md` contains a list of TODOs after running the cookiecutter. Following the - instructions in that file will give you a fully functional repository with a lot - of integration into useful web services activated and running. - * `FILESTRUCTURE.md` describes the generated files. Feel free to remove this from the - repository if you do not need it. -* Python build system files - * `pyproject.toml` is the central place for configuration of your Python package. - It contains the project metadata, setuptools-specific information and the configuration - for your toolchain (like e.g. `pytest`). - * `setup.py` is still required for editable builds, but you should not need to change it. - In the future, `setuptools` will support editable builds purely from `pyproject.toml` - configuration. -* Configuration for CI/Code Analysis and documentation services - * `.github/workflows/ci.yml` describes the Github Workflow for Continuous - integration. For further reading on workflow files, we recommend the - [introduction into Github Actions](https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/introduction-to-github-actions) - and [the reference of available options](https://docs.github.com/en/free-pro-team@latest/actions/reference/workflow-syntax-for-github-actions). - * `.github/dependabot.yml` configures the DependaBot integration on GitHub that - allows you to automatically create pull requests for updates of the used actions - in `.github/workflows/ci.yml`. - * `.gitlab-ci.yml` describes the configuration for Gitlab CI. For further - reading, we recommend [Gitlabs quick start guide](https://docs.gitlab.com/ee/ci/quick_start/) - and the [Gitlab CI configuration reference](https://docs.gitlab.com/ce/ci/yaml/) - * `.readthedocs.yml` configures the documentation build process at [ReadTheDocs](https://readthedocs.org). - To customize your build, you can have a look at the [available options](https://docs.readthedocs.io/en/stable/config-file/v2.html). diff --git a/Makefile b/Makefile index d0c3cbf1..b97de95f 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,7 @@ # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build -SOURCEDIR = source +SOURCEDIR = docs BUILDDIR = build # Put it first so that "make" without argument is like "make help". diff --git a/README.md b/README.md index e4677a7c..cac84dd0 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ Sphinx Documentation of all the functions is currently available under [this lin Contributions to `rubix` are welcome and greatly appreciated! Whether you're fixing bugs, improving documentation, or suggesting new features, your help is valuable to us. -Please see [here](source/CONTRIBUTING.md) for contribution guidelines. +Please see [here](docs/CONTRIBUTING.md) for contribution guidelines. Thank you for helping improve `rubix`! diff --git a/source/Contributing.md b/docs/Contributing.md similarity index 96% rename from source/Contributing.md rename to docs/Contributing.md index a62b1805..f7866609 100644 --- a/source/Contributing.md +++ b/docs/Contributing.md @@ -105,13 +105,13 @@ Adding content should be relatively simple if you follow the instructions below. To add Jupyter notebooks to the documentation: -1. Add your Jupyter notebook to the `notebooks` directory under the `docs/source` folder. Make sure that you 'Restart Kernel and run all cells' to ensure that the notebook is producing up to date, consistent outputs. -2. Add your notebook to the relevant toctree. See below for an example toctree. Each toctree is contained within a Sphinx `.rst` file in each documentation source directory. The top-level file is `source/index.rst`. If your file is in a subfolder, you need to update the `.rst` file in that directory. +1. Add your Jupyter notebook to the `notebooks` directory under the `docs` folder. Make sure that you 'Restart Kernel and run all cells' to ensure that the notebook is producing up to date, consistent outputs. +2. Add your notebook to the relevant toctree. See below for an example toctree. Each toctree is contained within a Sphinx `.rst` file in each documentation source directory. The top-level file is `docs/index.rst`. If your file is in a subfolder, you need to update the `.rst` file in that directory. - If you're creating a new sub-directory of documentation, you will need to carry out a couple more steps: 1. Create a new `.rst` file in that directory -2. Update `source/index.rst` with the path to that `.rst` file +2. Update `docs/index.rst` with the path to that `.rst` file 3. Currently we do not run pytests on jupyter notebooks. So please make sure your notebooks are actually working fine. Example toctree: @@ -164,7 +164,7 @@ sphinx-quickstart #### Configuration and Content -The core of the documentation setup resides in the source folder: +The core of the documentation setup resides in the `docs` folder: - `conf.py`: This is the main configuration file where you define extensions (like myst_nb for notebooks), set the theme, and manage global build settings. diff --git a/source/acknowledgments.rst b/docs/acknowledgments.rst similarity index 100% rename from source/acknowledgments.rst rename to docs/acknowledgments.rst diff --git a/source/conf.py b/docs/conf.py similarity index 91% rename from source/conf.py rename to docs/conf.py index 288aba81..00e6cc16 100644 --- a/source/conf.py +++ b/docs/conf.py @@ -11,9 +11,9 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = "rubix" -copyright = "2024, Ufuk, Tobias, Anna Lena" -author = "Ufuk, Tobias, Anna Lena" +project = "Rubix" +copyright = "2025, Anna Lena Schaible, Ufuk Cakir, Tobias Buck" +author = "Anna Lena Schaible, Ufuk Cakir, Tobias Buck" release = "0.1" # -- General configuration --------------------------------------------------- diff --git a/source/index.rst b/docs/index.rst similarity index 100% rename from source/index.rst rename to docs/index.rst diff --git a/source/installation.rst b/docs/installation.rst similarity index 100% rename from source/installation.rst rename to docs/installation.rst diff --git a/source/license.rst b/docs/license.rst similarity index 100% rename from source/license.rst rename to docs/license.rst diff --git a/source/modules.rst b/docs/modules.rst similarity index 100% rename from source/modules.rst rename to docs/modules.rst diff --git a/source/notebooks/cosmology.ipynb b/docs/notebooks/cosmology.ipynb similarity index 73% rename from source/notebooks/cosmology.ipynb rename to docs/notebooks/cosmology.ipynb index 6c5330f1..2d797750 100644 --- a/source/notebooks/cosmology.ipynb +++ b/docs/notebooks/cosmology.ipynb @@ -15,18 +15,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BaseCosmology(Om0=f32[], w0=f32[], wa=f32[], h=f32[])\n", - "FlatLambdaCDM(name=\"Planck15\", H0=67.74 km / (Mpc s), Om0=0.3075, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0. 0. 0.06] eV, Ob0=0.0486)\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", @@ -49,28 +40,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Angular Diameter Distance\n", - "rubix cosmo: 702.5322\n", - "astropy cosmo: 702.3747610737071 Mpc\n", - "Comoving Distance\n", - "rubix cosmo: 843.0387\n", - "astropy cosmo: 842.8497132884485 Mpc\n", - "lookback to z\n", - "rubix cosmo: 2.5104787\n", - "astropy cosmo: 2.509878627257186 Gyr\n", - "Age\n", - "rubix cosmo: [11.310789]\n", - "astropy cosmo: 11.287737269639198 Gyr\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "z = 0.2\n", @@ -95,17 +67,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.5\n" - ] - } - ], + "outputs": [], "source": [ "from rubix.cosmology.utils import trapz\n", "import jax.numpy as jnp\n", @@ -117,18 +81,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n", - "1946.5875\n" - ] - } - ], + "outputs": [], "source": [ "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", "import jax.numpy as jnp\n", diff --git a/docs/notebooks/create_rubix_data.ipynb b/docs/notebooks/create_rubix_data.ipynb new file mode 100644 index 00000000..428121cb --- /dev/null +++ b/docs/notebooks/create_rubix_data.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create RUBIX data\n", + "\n", + "## The config\n", + "\n", + "The config contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the config you can choose the following options:\n", + "- particle_type: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- simulation: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- snapshot: which time step of the simulation (99 for present day)\n", + "- save_data_path: set the path to save the downloaded Illustris data\n", + "- load_galaxy_args - id: define, which Illustris galaxy is downloaded\n", + "- load_galaxy_args - reuse: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- subset: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- simulation - name: currently only IllustrisTNG is supported\n", + "- simulation - args - path: where the data is stored and how the file will be named\n", + "- output_path: where the hdf5 file is stored, which is then the input to the RUBIX pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "\n", + "config = {\n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\",\"gas\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 12,\n", + " \"reuse\": True,\n", + " },\n", + "\n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 1000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-12.hdf5\",\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert data\n", + "\n", + "Convert the Data into Rubix Galaxy HDF5. This will make the call to the IllustrisAPI to download the data, and then convert it into the rubix hdf5 format using the input handler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.data import convert_to_rubix\n", + "\n", + "convert_to_rubix(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data\n", + "\n", + "prepare_input loads the hdf5 file that was created and stored with the convert_to_rubix function. It loads the data into the rubixdata format and centers the particles. the rubixdata object has then the attributes stars and gas and both have then attributes with the relevant quantities for each particle. For example, if you want to access the coordinates of the stella rparticles, you can access them via rubixdata.stars.coords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.core.data import prepare_input\n", + "\n", + "rubixdata = prepare_input(config)\n", + "\n", + "#print, which attributes are available for rubixdata.stars\n", + "attr = [attr for attr in dir(rubixdata.stars) if not attr.startswith('__')]\n", + "print(attr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To have not to call two individual function to have the data ready to be passed into the pipeline, you can just use the get_rubix_data(config) from the rubix.core.data module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview over the hdf5 file structure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.utils import print_hdf5_file_structure\n", + "\n", + "print(print_hdf5_file_structure(\"output/rubix_galaxy.h5\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/demo.yml b/docs/notebooks/demo.yml similarity index 100% rename from source/notebooks/demo.yml rename to docs/notebooks/demo.yml diff --git a/source/notebooks/dust_extinction.ipynb b/docs/notebooks/dust_extinction.ipynb similarity index 100% rename from source/notebooks/dust_extinction.ipynb rename to docs/notebooks/dust_extinction.ipynb diff --git a/docs/notebooks/filter_curves.ipynb b/docs/notebooks/filter_curves.ipynb new file mode 100644 index 00000000..233cae3e --- /dev/null +++ b/docs/notebooks/filter_curves.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filter curves\n", + "\n", + "This notebook shows how you can apply different filters to your rubix IFU cube and create photometric images of your mock-data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.telescope.filters import load_filter, print_filter_list, print_filter_list_info, print_filter_property" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Information about the filters\n", + "\n", + "We can have a look, which different filters are availible for a given facility or instrument. A list of all availible filters can be found here: http://svo2.cab.inta-csic.es/theory/fps/index.php\n", + "\n", + "As an example, we print the different filters for SLOAN." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "print_filter_list(\"SLOAN\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also print some more details about the filters. `print_filter_list_info()` prints the filter name, the dtype and the unit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "print_filter_list_info(\"SLOAN\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most detaield information about a filter can be obtained by using the `print_filter_property()` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "print_filter_property(\"SLOAN\", \"SDSS.u\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "print_filter_property(\"JWST\", \"F070W\", \"NIRCam\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading filters\n", + "\n", + "Now we can load and plot our selected filters, in our example case `\"SLOAN\"`.\n", + "If you want to know more about filters and which ones are supported by RUBIX please visit the [SVO Filter Profile Service](http://svo2.cab.inta-csic.es/theory/fps/index.php). RUBIX supports all standard filters for all instruments of all facilities listed there. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# load all fliter curves for SLOAN\n", + "curves = load_filter(\"SLOAN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "curves.filters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "curves.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "filter = curves[1]\n", + "filter.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Applying filters to mock-IFUs\n", + "\n", + "After getting the information about different filters and loading the filter curves for `\"SLOAN\"`, we want to apply these filter curves to a mock-IFU cube to get photometric images of the mock-IFU cube.\n", + "\n", + "The first step is to create our mock-IFU cube. We have taken care of this already and run RUBIX with default `config` for a tiny mock MUSE cube on an example Ilustris TNG galaxy. For more details see `rubix_pipeline_single_function.ipynb` or `rubix_pipeline_stepwise.ipynb`. Below we load the dummy datacube using the library `h5py`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import h5py\n", + "import numpy as np\n", + "with h5py.File('./data/dummy_datacube.h5', 'r') as hf2:\n", + " print(hf2.keys())\n", + " datacube = np.array(hf2.get('datacube'))\n", + " wave = np.array(hf2.get('wave'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dummy datacube has 25x25 pixels and 3721 spectral bins." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "datacube.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have our mock-IFU datacube and we have selected and loaded a filter. The next step is to apply the filter to the datacube, which is done with a convolution. And then we obtain our photometric image of the galaxy. For the filter, choosen in this example, you may wonder, why the image is zerro everywhere. You have to keep in mind that our dummy datacube is created for a MUSE observation and in the default `telescopes.yaml` we defined the wavelength to be in the range `[4700.15, 9351.4]`and the filter is in the range `[3000, 4000]`. So this result should be expected for the choice of this mock-data convolved with the `SLOAN/SDSS.u`filter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from rubix.telescope.filters import convolve_filter_with_spectra\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "filter = curves[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", + "print(convolved.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "plt.imshow(convolved)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we now look at other filters from `SLOAN/SDSS`that match the wavelengthrange of our mock-datacube, we get photometric images of our galaxy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "for filter in curves:\n", + " convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", + " plt.figure()\n", + " plt.imshow(convolved)\n", + " plt.colorbar()\n", + " plt.title(filter.name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "filters,images =curves.apply_filter_curves(datacube, wave).values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "filters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "for i,name in zip(images, filters):\n", + " plt.figure()\n", + " plt.imshow(i)\n", + " plt.colorbar()\n", + " plt.title(name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create false color images (RGB images), we have to normalize the individual photometric images from three different filters and stack them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "# Create an RGB image\n", + "# Normalize the images\n", + "import numpy as np\n", + "\n", + "def normalize(image):\n", + " image_min = image.min()\n", + " image_max = image.max()\n", + " return (image - image_min) / (image_max - image_min)\n", + "\n", + "r = images[1]\n", + "g = images[2]\n", + "b = images[3]\n", + "\n", + "rgb = np.stack([r,g,b], axis=-1)\n", + "\n", + "rgb = normalize(rgb)\n", + "\n", + "plt.imshow(rgb)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb b/docs/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb similarity index 100% rename from source/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb rename to docs/notebooks/gradient_age_metallicity_adamoptimizer_multi.ipynb diff --git a/source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb b/docs/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb similarity index 100% rename from source/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb rename to docs/notebooks/gradient_age_metallicity_adamoptimizer_vs_finite_diff.ipynb diff --git a/source/notebooks/output/rubix_galaxy.h5 b/docs/notebooks/output/rubix_galaxy.h5 similarity index 100% rename from source/notebooks/output/rubix_galaxy.h5 rename to docs/notebooks/output/rubix_galaxy.h5 diff --git a/source/notebooks/pipeline_demo.ipynb b/docs/notebooks/pipeline_demo.ipynb similarity index 63% rename from source/notebooks/pipeline_demo.ipynb rename to docs/notebooks/pipeline_demo.ipynb index ea9e7894..faef8cd5 100644 --- a/source/notebooks/pipeline_demo.ipynb +++ b/docs/notebooks/pipeline_demo.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -117,27 +117,16 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "jax.tree_util.Partial" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(add)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -155,20 +144,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([14.14, 12.14, 10.14], dtype=float32)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "addjit(x, x)" ] @@ -188,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -232,47 +210,25 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ", z=5, k=-3.14)>" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([7.8599997, 5.8599997, 3.86 ], dtype=float32)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add(x, x)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -301,40 +257,18 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ", z=5)>" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add_plus" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([7.8599997, 5.8599997, 3.86 ], dtype=float32)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add_plus(x, x, k=-3.14)" ] @@ -356,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -390,24 +324,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", - " \u001b[39;22mc\u001b[35m:f32[3]\u001b[39m = add a b\n", - " d\u001b[35m:f32[3]\u001b[39m = add c 5.0:f32[]\n", - " e\u001b[35m:f32[3]\u001b[39m = add d 6.28000020980835:f32[]\n", - " \u001b[34;1min \u001b[39;22m(e,) }" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add" ] @@ -421,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -435,25 +354,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m c\u001b[35m:i32[]\u001b[39m. \u001b[34;1mlet\n", - " \u001b[39;22md\u001b[35m:f32[3]\u001b[39m = add a b\n", - " e\u001b[35m:f32[]\u001b[39m = convert_element_type[new_dtype=float32 weak_type=False] c\n", - " f\u001b[35m:f32[3]\u001b[39m = add d e\n", - " g\u001b[35m:f32[3]\u001b[39m = add f -3.140000104904175:f32[]\n", - " \u001b[34;1min \u001b[39;22m(g,) }" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add" ] @@ -467,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -481,44 +384,18 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m b\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", - " \u001b[39;22mc\u001b[35m:f32[3]\u001b[39m = add a b\n", - " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", - " e\u001b[35m:f32[3]\u001b[39m = add d 5.420000076293945:f32[]\n", - " \u001b[34;1min \u001b[39;22m(e,) }" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cond_add(x, x, 3, 2.71)" ] @@ -535,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -636,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -645,91 +522,36 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Transformers': {'A': {'name': 'add',\n", - " 'depends_on': 'B',\n", - " 'args': [],\n", - " 'kwargs': {'s': 3.0}},\n", - " 'X': {'name': 'mult', 'depends_on': 'A', 'args': [], 'kwargs': {'m': 3}},\n", - " 'Z': {'name': 'div', 'depends_on': 'X', 'args': [], 'kwargs': {'d': 4}},\n", - " 'B': {'name': 'sub', 'depends_on': 'C', 'args': [], 'kwargs': {'s': 2}},\n", - " 'C': {'name': 'add', 'depends_on': None, 'args': [], 'kwargs': {'s': 4}}}}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "read_cfg" ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(read_cfg)" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'A': {'name': 'add', 'depends_on': 'B', 'args': [], 'kwargs': {'s': 3.0}},\n", - " 'X': {'name': 'mult', 'depends_on': 'A', 'args': [], 'kwargs': {'m': 3}},\n", - " 'Z': {'name': 'div', 'depends_on': 'X', 'args': [], 'kwargs': {'d': 4}},\n", - " 'B': {'name': 'sub', 'depends_on': 'C', 'args': [], 'kwargs': {'s': 2}},\n", - " 'C': {'name': 'add', 'depends_on': None, 'args': [], 'kwargs': {'s': 4}}}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "read_cfg[\"Transformers\"]" ] }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(read_cfg[\"Transformers\"])" ] @@ -743,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -752,23 +574,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'add': ,\n", - " 'mult': ,\n", - " 'div': ,\n", - " 'sub': }" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tp.transformers" ] @@ -783,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -792,24 +600,9 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': Partial(, s=4),\n", - " 'B': Partial(, s=2),\n", - " 'A': Partial(, s=3.0),\n", - " 'X': Partial(, m=3),\n", - " 'Z': Partial(, d=4)}" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tp.pipeline" ] @@ -823,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -839,20 +632,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Partial(.expr at 0x70e72bd31e40>, pipeline=[Partial(, s=4), Partial(, s=2), Partial(, s=3.0), Partial(, m=3), Partial(, d=4)])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tp.expression" ] @@ -866,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -875,60 +657,27 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ".expr at 0x70e72bd31e40>, pipeline=[Partial(, s=4), Partial(, s=2), Partial(, s=3.0), Partial(, m=3), Partial(, d=4)])>" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "func" ] }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([6. , 5.25, 4.5 ], dtype=float32)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "func(x)" ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([6. , 5.25, 4.5 ], dtype=float32)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "div(mult(add(sub(add(x, s=4), s=2), s=3), m=3), d=4)" ] @@ -942,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -951,53 +700,25 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", - " \u001b[39;22mb\u001b[35m:f32[3]\u001b[39m = add a 4.0:f32[]\n", - " c\u001b[35m:f32[3]\u001b[39m = sub b 2.0:f32[]\n", - " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", - " e\u001b[35m:f32[3]\u001b[39m = mul d 3.0:f32[]\n", - " f\u001b[35m:f32[3]\u001b[39m = div e 4.0:f32[]\n", - " \u001b[34;1min \u001b[39;22m(f,) }" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "expr" ] }, { "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "jax._src.core.ClosedJaxpr" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(expr)" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1007,26 +728,9 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\n", - " \u001b[39;22mb\u001b[35m:f32[3]\u001b[39m = add a 4.0:f32[]\n", - " c\u001b[35m:f32[3]\u001b[39m = sub b 2.0:f32[]\n", - " d\u001b[35m:f32[3]\u001b[39m = add c 3.0:f32[]\n", - " e\u001b[35m:f32[3]\u001b[39m = mul d 3.0:f32[]\n", - " f\u001b[35m:f32[3]\u001b[39m = div e 4.0:f32[]\n", - " \u001b[34;1min \u001b[39;22m(f,) }" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "make_jaxpr(func_manual)(x)" ] @@ -1047,74 +751,27 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([[0.75, 0. , 0. ],\n", - " [0. , 0.75, 0. ],\n", - " [0. , 0. , 0.75]], dtype=float32)" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "jax.jacfwd(tp.compile_expression())(x)" ] }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([[0.75, 0. , 0. ],\n", - " [0. , 0.75, 0. ],\n", - " [0. , 0. , 0.75]], dtype=float32)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "jax.jacrev(tp.compile_expression())(x)" ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([[[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]],\n", - "\n", - " [[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]],\n", - "\n", - " [[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]]], dtype=float32)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "jax.hessian(tp.compile_expression())(x)" ] @@ -1137,40 +794,18 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ", s=3.0)))>" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tp.compile_element(\"A\")" ] }, { "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\u001b[39;22m b\u001b[35m:f32[3]\u001b[39m = add a 3.0:f32[] \u001b[34;1min \u001b[39;22m(b,) }" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tp.get_jaxpr_for_element(\"A\", x)" ] @@ -1184,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1193,40 +828,18 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f" ] }, { "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{ \u001b[34;1mlambda \u001b[39;22m; a\u001b[35m:f32[3]\u001b[39m. \u001b[34;1mlet\u001b[39;22m b\u001b[35m:f32[3]\u001b[39m = add a 3.0:f32[] \u001b[34;1min \u001b[39;22m(b,) }" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f(x)" ] diff --git a/docs/notebooks/psf.ipynb b/docs/notebooks/psf.ipynb new file mode 100644 index 00000000..c765b523 --- /dev/null +++ b/docs/notebooks/psf.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The point spread function\n", + "\n", + "The point spread functions blures the data in spatial direction and is an observational artefact. So far we have implemented a gaussian kernel. With that kernel the mock-observations can be convolved to mimic real observations. It is important that the sum of the kernel is 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "from rubix.telescope.psf.kernels import gaussian_kernel_2d\n", + "\n", + "kernel = gaussian_kernel_2d(20,20,3.5)\n", + "print(kernel.shape)\n", + "print(kernel.sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "plt.imshow(kernel, cmap='hot')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here an example for the PSF convolution, we have an artificial datacube of shape (50,50,300), which contains random numbers in the spatial dimension. Each layer in the wavelength dimension is convolved with the kernel. We plot one spaxel [10,10] along the wavelength range for the original random data and the psf smoothed random data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "# Get an example Datacube\n", + "from rubix.telescope.psf.psf import apply_psf\n", + "import numpy as np\n", + "import jax.numpy as jnp\n", + "datacube = np.ones((50,50,300))\n", + "# create random data\n", + "for i in range(300):\n", + " datacube[:,:,i] = np.random.rand(50,50)\n", + "\n", + "datacube = jnp.array(datacube)\n", + "\n", + "convolved_datacube = apply_psf(datacube, kernel)\n", + "print(convolved_datacube.shape)\n", + "\n", + "plt.plot(convolved_datacube[10,10,:], label='convolved')\n", + "plt.plot(datacube[10,10,:], label='original')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/source/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/docs/notebooks/rubix_pipeline_single_function_shard_map.ipynb similarity index 100% rename from source/notebooks/rubix_pipeline_single_function_shard_map.ipynb rename to docs/notebooks/rubix_pipeline_single_function_shard_map.ipynb diff --git a/source/notebooks/rubix_pipeline_stepwise.ipynb b/docs/notebooks/rubix_pipeline_stepwise.ipynb similarity index 100% rename from source/notebooks/rubix_pipeline_stepwise.ipynb rename to docs/notebooks/rubix_pipeline_stepwise.ipynb diff --git a/source/notebooks/ssp_interpolation.ipynb b/docs/notebooks/ssp_interpolation.ipynb similarity index 100% rename from source/notebooks/ssp_interpolation.ipynb rename to docs/notebooks/ssp_interpolation.ipynb diff --git a/source/notebooks/ssp_template.ipynb b/docs/notebooks/ssp_template.ipynb similarity index 100% rename from source/notebooks/ssp_template.ipynb rename to docs/notebooks/ssp_template.ipynb diff --git a/source/notebooks/ssp_template_fsps.ipynb b/docs/notebooks/ssp_template_fsps.ipynb similarity index 100% rename from source/notebooks/ssp_template_fsps.ipynb rename to docs/notebooks/ssp_template_fsps.ipynb diff --git a/source/notebooks/telescope.ipynb b/docs/notebooks/telescope.ipynb similarity index 50% rename from source/notebooks/telescope.ipynb rename to docs/notebooks/telescope.ipynb index 32b20517..28a0f669 100644 --- a/source/notebooks/telescope.ipynb +++ b/docs/notebooks/telescope.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -35,29 +35,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BaseTelescope(\n", - " fov=5.0,\n", - " spatial_res=0.2,\n", - " wave_range=[4700.15, 9351.4],\n", - " wave_res=1.25,\n", - " lsf_fwhm=2.51,\n", - " signal_to_noise=None,\n", - " sbin=np.int64(25),\n", - " aperture_region=f32[625],\n", - " pixel_type='square',\n", - " wave_seq=f32[3721],\n", - " wave_edges=f32[3722]\n", - ")\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "factory = TelescopeFactory(telescope_config) # Uses the defined telescope configuration\n", @@ -76,37 +56,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BaseTelescope(\n", - " fov=5.0,\n", - " spatial_res=0.2,\n", - " wave_range=[4700.15, 9351.4],\n", - " wave_res=1.25,\n", - " lsf_fwhm=2.51,\n", - " signal_to_noise=None,\n", - " sbin=np.int64(25),\n", - " aperture_region=f32[625],\n", - " pixel_type='square',\n", - " wave_seq=f32[3721],\n", - " wave_edges=f32[3722]\n", - ")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "factory = TelescopeFactory() # Uses the default config\n", @@ -116,37 +68,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BaseTelescope(\n", - " fov=5.0,\n", - " spatial_res=0.2,\n", - " wave_range=[4700.15, 9351.4],\n", - " wave_res=1.25,\n", - " lsf_fwhm=2.51,\n", - " signal_to_noise=None,\n", - " sbin=np.int64(25),\n", - " aperture_region=f32[625],\n", - " pixel_type='square',\n", - " wave_seq=f32[3721],\n", - " wave_edges=f32[3722]\n", - ")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from rubix.core.telescope import get_telescope\n", "\n", diff --git a/source/publications.rst b/docs/publications.rst similarity index 100% rename from source/publications.rst rename to docs/publications.rst diff --git a/source/rubix.core.rst b/docs/rubix.core.rst similarity index 100% rename from source/rubix.core.rst rename to docs/rubix.core.rst diff --git a/source/rubix.cosmology.rst b/docs/rubix.cosmology.rst similarity index 100% rename from source/rubix.cosmology.rst rename to docs/rubix.cosmology.rst diff --git a/source/rubix.galaxy.input_handler.rst b/docs/rubix.galaxy.input_handler.rst similarity index 100% rename from source/rubix.galaxy.input_handler.rst rename to docs/rubix.galaxy.input_handler.rst diff --git a/source/rubix.galaxy.rst b/docs/rubix.galaxy.rst similarity index 100% rename from source/rubix.galaxy.rst rename to docs/rubix.galaxy.rst diff --git a/source/rubix.pipeline.rst b/docs/rubix.pipeline.rst similarity index 100% rename from source/rubix.pipeline.rst rename to docs/rubix.pipeline.rst diff --git a/source/rubix.rst b/docs/rubix.rst similarity index 100% rename from source/rubix.rst rename to docs/rubix.rst diff --git a/source/rubix.spectra.rst b/docs/rubix.spectra.rst similarity index 100% rename from source/rubix.spectra.rst rename to docs/rubix.spectra.rst diff --git a/source/rubix.spectra.ssp.rst b/docs/rubix.spectra.ssp.rst similarity index 100% rename from source/rubix.spectra.ssp.rst rename to docs/rubix.spectra.ssp.rst diff --git a/source/rubix.telescope.filters.rst b/docs/rubix.telescope.filters.rst similarity index 100% rename from source/rubix.telescope.filters.rst rename to docs/rubix.telescope.filters.rst diff --git a/source/rubix.telescope.lsf.rst b/docs/rubix.telescope.lsf.rst similarity index 100% rename from source/rubix.telescope.lsf.rst rename to docs/rubix.telescope.lsf.rst diff --git a/source/rubix.telescope.noise.rst b/docs/rubix.telescope.noise.rst similarity index 100% rename from source/rubix.telescope.noise.rst rename to docs/rubix.telescope.noise.rst diff --git a/source/rubix.telescope.psf.rst b/docs/rubix.telescope.psf.rst similarity index 100% rename from source/rubix.telescope.psf.rst rename to docs/rubix.telescope.psf.rst diff --git a/source/rubix.telescope.rst b/docs/rubix.telescope.rst similarity index 100% rename from source/rubix.telescope.rst rename to docs/rubix.telescope.rst diff --git a/source/rubix.utils.rst b/docs/rubix.utils.rst similarity index 100% rename from source/rubix.utils.rst rename to docs/rubix.utils.rst diff --git a/source/versions.rst b/docs/versions.rst similarity index 96% rename from source/versions.rst rename to docs/versions.rst index 68d87c03..ef8b08b4 100644 --- a/source/versions.rst +++ b/docs/versions.rst @@ -11,4 +11,4 @@ Gradient calculation through the whole pipeline for gradient-based parameter est Version 0.2 ----------- -Under developement \ No newline at end of file +Under developement diff --git a/source/notebooks/create_rubix_data.ipynb b/source/notebooks/create_rubix_data.ipynb deleted file mode 100644 index 22d10e0a..00000000 --- a/source/notebooks/create_rubix_data.ipynb +++ /dev/null @@ -1,286 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create RUBIX data\n", - "\n", - "## The config\n", - "\n", - "The config contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", - "\n", - "For the config you can choose the following options:\n", - "- particle_type: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", - "- simulation: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", - "- snapshot: which time step of the simulation (99 for present day)\n", - "- save_data_path: set the path to save the downloaded Illustris data\n", - "- load_galaxy_args - id: define, which Illustris galaxy is downloaded\n", - "- load_galaxy_args - reuse: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", - "- subset: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", - "- simulation - name: currently only IllustrisTNG is supported\n", - "- simulation - args - path: where the data is stored and how the file will be named\n", - "- output_path: where the hdf5 file is stored, which is then the input to the RUBIX pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import os\n", - "\n", - "config = {\n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\",\"gas\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 12,\n", - " \"reuse\": True,\n", - " },\n", - "\n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-12.hdf5\",\n", - " },\n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data\n", - "\n", - "Convert the Data into Rubix Galaxy HDF5. This will make the call to the IllustrisAPI to download the data, and then convert it into the rubix hdf5 format using the input handler" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-10 17:08:27,957 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-11-10 17:08:27,958 - rubix - INFO - Rubix version: 0.0.post482+g02b969039.d20251103\n", - "2025-11-10 17:08:27,958 - rubix - INFO - JAX version: 0.7.2\n", - "2025-11-10 17:08:27,959 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", - "2025-11-10 17:08:27,959 - rubix - INFO - Loading data from IllustrisAPI\n", - "2025-11-10 17:08:27,960 - rubix - INFO - Reusing existing file galaxy-id-12.hdf5. If you want to download the data again, set reuse=False.\n", - "2025-11-10 17:08:27,987 - rubix - INFO - Loading data into input handler\n", - "2025-11-10 17:08:27,988 - rubix - DEBUG - Loading data from Illustris file..\n", - "2025-11-10 17:08:27,989 - rubix - DEBUG - Checking if the fields are present in the file...\n", - "2025-11-10 17:08:27,989 - rubix - DEBUG - Keys in the file: \n", - "2025-11-10 17:08:27,990 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", - "2025-11-10 17:08:27,990 - rubix - DEBUG - Matching fields: {'PartType4', 'SubhaloData', 'Header'}\n", - "2025-11-10 17:08:27,996 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", - "2025-11-10 17:08:28,367 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", - "2025-11-10 17:08:35,444 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-11-10 17:08:35,446 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", - "2025-11-10 17:08:35,447 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", - "2025-11-10 17:08:35,448 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", - "2025-11-10 17:08:35,448 - rubix - DEBUG - Matching fields: {'stars'}\n", - "2025-11-10 17:08:35,448 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", - "2025-11-10 17:08:35,449 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", - "2025-11-10 17:08:35,449 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-11-10 17:08:35,450 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-11-10 17:08:35,456 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-11-10 17:08:35,457 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-11-10 17:08:35,458 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-11-10 17:08:35,459 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-11-10 17:08:35,468 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-11-10 17:08:35,471 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-11-10 17:08:35,473 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-11-10 17:08:35,475 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-11-10 17:08:35,492 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "data": { - "text/plain": [ - "'output'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.data import convert_to_rubix\n", - "\n", - "convert_to_rubix(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data\n", - "\n", - "prepare_input loads the hdf5 file that was created and stored with the convert_to_rubix function. It loads the data into the rubixdata format and centers the particles. the rubixdata object has then the attributes stars and gas and both have then attributes with the relevant quantities for each particle. For example, if you want to access the coordinates of the stella rparticles, you can access them via rubixdata.stars.coords" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-10 17:08:35,537 - rubix - INFO - Centering stars particles\n", - "2025-11-10 17:08:36,285 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['age', 'age_unit', 'coords', 'coords_unit', 'datacube', 'mask', 'mass', 'mass_unit', 'metallicity', 'metallicity_unit', 'pixel_assignment', 'spatial_bin_edges', 'spectra', 'tree_flatten', 'tree_unflatten', 'velocity', 'velocity_unit']\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.data import prepare_input\n", - "\n", - "rubixdata = prepare_input(config)\n", - "\n", - "#print, which attributes are available for rubixdata.stars\n", - "attr = [attr for attr in dir(rubixdata.stars) if not attr.startswith('__')]\n", - "print(attr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To have not to call two individual function to have the data ready to be passed into the pipeline, you can just use the get_rubix_data(config) from the rubix.core.data module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview over the hdf5 file structure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File: output/rubix_galaxy.h5\n", - "Group: galaxy\n", - " Dataset: center (float64) ((3,))\n", - " Dataset: halfmassrad_stars (float64) (())\n", - " Dataset: redshift (float64) (())\n", - "Group: meta\n", - " Dataset: BoxSize (float64) (())\n", - " Dataset: CutoutID (int64) (())\n", - " Dataset: CutoutRequest (object) (())\n", - " Dataset: CutoutType (object) (())\n", - " Dataset: Git_commit (|S40) (())\n", - " Dataset: Git_date (|S29) (())\n", - " Dataset: HubbleParam (float64) (())\n", - " Dataset: MassTable (float64) ((6,))\n", - " Dataset: NumFilesPerSnapshot (int64) (())\n", - " Dataset: NumPart_ThisFile (int32) ((6,))\n", - " Dataset: Omega0 (float64) (())\n", - " Dataset: OmegaBaryon (float64) (())\n", - " Dataset: OmegaLambda (float64) (())\n", - " Dataset: Redshift (float64) (())\n", - " Dataset: SimulationName (object) (())\n", - " Dataset: SnapshotNumber (int64) (())\n", - " Dataset: Time (float64) (())\n", - " Dataset: UnitLength_in_cm (float64) (())\n", - " Dataset: UnitMass_in_g (float64) (())\n", - " Dataset: UnitVelocity_in_cm_per_s (float64) (())\n", - "Group: particles\n", - " Group: stars\n", - " Dataset: age (float64) ((649384,))\n", - " Dataset: coords (float64) ((649384, 3))\n", - " Dataset: mass (float64) ((649384,))\n", - " Dataset: metallicity (float64) ((649384,))\n", - " Dataset: velocity (float64) ((649384, 3))\n", - "\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.utils import print_hdf5_file_structure\n", - "\n", - "print(print_hdf5_file_structure(\"output/rubix_galaxy.h5\"))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/source/notebooks/filter_curves.ipynb b/source/notebooks/filter_curves.ipynb deleted file mode 100644 index 45774476..00000000 --- a/source/notebooks/filter_curves.ipynb +++ /dev/null @@ -1,830 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Filter curves\n", - "\n", - "This notebook shows how you can apply different filters to your rubix IFU cube and create photometric images of your mock-data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-10 17:09:41,614 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-11-10 17:09:41,615 - rubix - INFO - Rubix version: 0.0.post482+g02b969039.d20251103\n", - "2025-11-10 17:09:41,615 - rubix - INFO - JAX version: 0.7.2\n", - "2025-11-10 17:09:41,616 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.telescope.filters import load_filter, print_filter_list, print_filter_list_info, print_filter_property" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Information about the filters\n", - "\n", - "We can have a look, which different filters are availible for a given facility or instrument. A list of all availible filters can be found here: http://svo2.cab.inta-csic.es/theory/fps/index.php\n", - "\n", - "As an example, we print the different filters for SLOAN." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " filterID \n", - " \n", - "------------------------\n", - "SLOAN/SDSS.uprime_filter\n", - " SLOAN/SDSS.u\n", - " SLOAN/SDSS.g\n", - "SLOAN/SDSS.gprime_filter\n", - " SLOAN/SDSS.r\n", - "SLOAN/SDSS.rprime_filter\n", - " SLOAN/SDSS.i\n", - "SLOAN/SDSS.iprime_filter\n", - " SLOAN/SDSS.z\n", - "SLOAN/SDSS.zprime_filter\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "print_filter_list(\"SLOAN\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also print some more details about the filters. `print_filter_list_info()` prints the filter name, the dtype and the unit." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " name dtype unit \n", - "-------------------- ------- ---------------\n", - "FilterProfileService object \n", - " filterID object \n", - " WavelengthUnit object \n", - " WavelengthUCD object \n", - " PhotSystem object \n", - " DetectorType object \n", - " Band object \n", - " Instrument object \n", - " Facility object \n", - " ProfileReference object \n", - "CalibrationReference object \n", - " Description object \n", - " Comments object \n", - " WavelengthRef float64 Angstrom\n", - " WavelengthMean float64 Angstrom\n", - " WavelengthEff float64 Angstrom\n", - " WavelengthMin float64 Angstrom\n", - " WavelengthMax float64 Angstrom\n", - " WidthEff float64 Angstrom\n", - " WavelengthCen float64 Angstrom\n", - " WavelengthPivot float64 Angstrom\n", - " WavelengthPeak float64 Angstrom\n", - " WavelengthPhot float64 Angstrom\n", - " FWHM float64 Angstrom\n", - " Fsun float64 erg / (A s cm2)\n", - " PhotCalID object \n", - " MagSys object \n", - " ZeroPoint float64 Jy\n", - " ZeroPointUnit object \n", - " Mag0 float64 \n", - " ZeroPointType object \n", - " AsinhSoft float64 \n", - " TrasmissionCurve object \n", - "\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "print_filter_list_info(\"SLOAN\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The most detaield information about a filter can be obtained by using the `print_filter_property()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", - "-------------------- ------------ -------------- ------------- ---------- ------------ ---- ---------- -------- ----------------------------------------------------- ----------------------------------------------- ------------------------ -------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- --------------- --------------- --------------- ----------------- ------ -------------- ------------- ---- ------------- --------- ----------------------------------------------------------------\n", - " ivo://svo/fps SLOAN/SDSS.u Angstrom em.wl SDSS 1 SLOAN http://www.sdss.org/dr7/instruments/imager/index.html http://www.sdss.org/DR2/algorithms/fluxcal.html SDSS u full transmission 3556.5239668607 3572.1824003193 3608.0403153219 3055.1091291961 4030.6399499061 540.97112586776 3578.0271197298 3556.5239668607 3680.0 3619.6973042374 565.79845192387 103.21344236463 SLOAN/SDSS.u/Vega Vega 1582.537065543 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u\n" - ] - }, - { - "data": { - "text/html": [ - "Row index=1\n", - "
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FilterProfileServicefilterIDWavelengthUnitWavelengthUCDPhotSystemDetectorTypeBandInstrumentFacilityProfileReferenceCalibrationReferenceDescriptionCommentsWavelengthRefWavelengthMeanWavelengthEffWavelengthMinWavelengthMaxWidthEffWavelengthCenWavelengthPivotWavelengthPeakWavelengthPhotFWHMFsunPhotCalIDMagSysZeroPointZeroPointUnitMag0ZeroPointTypeAsinhSoftTrasmissionCurve
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ivo://svo/fpsSLOAN/SDSS.uAngstromem.wlSDSS1SLOANhttp://www.sdss.org/dr7/instruments/imager/index.htmlhttp://www.sdss.org/DR2/algorithms/fluxcal.htmlSDSS u full transmission3556.52396686073572.18240031933608.04031532193055.10912919614030.6399499061540.971125867763578.02711972983556.52396686073680.03619.6973042374565.79845192387103.21344236463SLOAN/SDSS.u/VegaVega1582.537065543Jy0.0Pogson0.0http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u
" - ], - "text/plain": [ - "\n", - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", - " object object object object object object object object object object object object object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 object object float64 object float64 object float64 object \n", - "-------------------- ------------ -------------- ------------- ---------- ------------ ------ ---------- -------- ----------------------------------------------------- ----------------------------------------------- ------------------------ -------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- --------------- --------------- --------------- ----------------- ------ -------------- ------------- ------- ------------- --------- ----------------------------------------------------------------\n", - " ivo://svo/fps SLOAN/SDSS.u Angstrom em.wl SDSS 1 SLOAN http://www.sdss.org/dr7/instruments/imager/index.html http://www.sdss.org/DR2/algorithms/fluxcal.html SDSS u full transmission 3556.5239668607 3572.1824003193 3608.0403153219 3055.1091291961 4030.6399499061 540.97112586776 3578.0271197298 3556.5239668607 3680.0 3619.6973042374 565.79845192387 103.21344236463 SLOAN/SDSS.u/Vega Vega 1582.537065543 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "print_filter_property(\"SLOAN\", \"SDSS.u\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", - "-------------------- ----------------- -------------- ------------- ---------- ------------ ---- ---------- -------- ------------------------------------------------------ -------------------- ------------------- ------------------------------------------------------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- -------------- --------------- --------------- ---------------------- ------ --------------- ------------- ---- ------------- --------- ---------------------------------------------------------------------\n", - " ivo://svo/fps JWST/NIRCam.F070W Angstrom em.wl NIRCam 1 NIRCam JWST https://jwst-docs.stsci.edu/display/JTI/NIRCam+Filters NIRCam F070W filter includes NIRCam optics, DBS, QE and JWST Optical Telescope Element 7039.1194650654 7088.3009369996 6988.4272768359 6048.1970523246 7927.0738659178 1212.8399166581 7099.1873443748 7039.1194650654 7691.5 7022.060805287 1430.8105961315 140.01772043307 JWST/NIRCam.F070W/Vega Vega 2768.4045696982 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W\n" - ] - }, - { - "data": { - "text/html": [ - "Row index=0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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ivo://svo/fpsJWST/NIRCam.F070WAngstromem.wlNIRCam1NIRCamJWSThttps://jwst-docs.stsci.edu/display/JTI/NIRCam+FiltersNIRCam F070W filterincludes NIRCam optics, DBS, QE and JWST Optical Telescope Element7039.11946506547088.30093699966988.42727683596048.19705232467927.07386591781212.83991665817099.18734437487039.11946506547691.57022.0608052871430.8105961315140.01772043307JWST/NIRCam.F070W/VegaVega2768.4045696982Jy0.0Pogson0.0http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W
" - ], - "text/plain": [ - "\n", - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom erg / (A s cm2) Jy \n", - " object object object object object object object object object object object object object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 object object float64 object float64 object float64 object \n", - "-------------------- ----------------- -------------- ------------- ---------- ------------ ------ ---------- -------- ------------------------------------------------------ -------------------- ------------------- ------------------------------------------------------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- -------------- --------------- --------------- ---------------------- ------ --------------- ------------- ------- ------------- --------- ---------------------------------------------------------------------\n", - " ivo://svo/fps JWST/NIRCam.F070W Angstrom em.wl NIRCam 1 NIRCam JWST https://jwst-docs.stsci.edu/display/JTI/NIRCam+Filters NIRCam F070W filter includes NIRCam optics, DBS, QE and JWST Optical Telescope Element 7039.1194650654 7088.3009369996 6988.4272768359 6048.1970523246 7927.0738659178 1212.8399166581 7099.1873443748 7039.1194650654 7691.5 7022.060805287 1430.8105961315 140.01772043307 JWST/NIRCam.F070W/Vega Vega 2768.4045696982 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "print_filter_property(\"JWST\", \"F070W\", \"NIRCam\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading filters\n", - "\n", - "Now we can load and plot our selected filters, in our example case `\"SLOAN\"`.\n", - "If you want to know more about filters and which ones are supported by RUBIX please visit the [SVO Filter Profile Service](http://svo2.cab.inta-csic.es/theory/fps/index.php). RUBIX supports all standard filters for all instruments of all facilities listed there. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# load all fliter curves for SLOAN\n", - "curves = load_filter(\"SLOAN\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[SLOAN/SDSS.uprime_filter,\n", - " SLOAN/SDSS.u,\n", - " SLOAN/SDSS.g,\n", - " SLOAN/SDSS.gprime_filter,\n", - " SLOAN/SDSS.r,\n", - " SLOAN/SDSS.rprime_filter,\n", - " SLOAN/SDSS.i,\n", - " SLOAN/SDSS.iprime_filter,\n", - " SLOAN/SDSS.z,\n", - " SLOAN/SDSS.zprime_filter]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "curves.filters" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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m1G2eTL2BDL2BNL0eV42aHINMkk6HLIObRo2bRs2FzGzSdAYMyDioVOhlmVxZxk6S0MkyKTo9te3t8NXacSsnlwy9gWyDjLtGjU6WUUsS/vZ21LLTEJ+rA6COvR2SJFXxqxfUFKr6+V2lgqkqqOo3XCCwRv7++2+2bt1Kbm6uxfbw8HAGDBiARlO0mZ2dnc2KFSu4desWrq6udOvWjVOnTnH69GnAWG9t8ODBIkRnItcgY6eSSNPpicvO5VZOLgYZ4nJySdXpsVepuJKVgwSk6/WkmEROms7AlawcdLJMht6ADNirJAwynM/MLumylYKbRkWmXsZBJWGnkvDQaPCzt8PTTk1trR11HbQYAAnwt7dDI0kE2tuhUUm4qNX42dthJxmPFVg/Vf38FoJJIKghpN65ze3LFwloHIa9k3OB/bIsc/Gfv0mMu06Dth1w8/Gt9DkZDAZ+/vlnpRaav78/HTt2JCEhgb1792IwGAgJCWHo0KE4ODgUOud169Zx4sQJXFxceP7555W/y9OnT/P9999jMBho1qwZjz32mFU6TVl6A3pZ5mByOn8lp6OXZdL1BpJ0erINBjL1MqczMpFlyDLI3MnVUctOQ6JOh76C794SIJv+ddOoUQGpej06GbztNHjZqZGQyDYYUJuESq5BRkbGQ6PhSlYOiTodflo7XDRqtJJEsk6PnSSRI8vEZeegk8FOkjAgV9j86zpo8dPaISOjkiQaOTnQ1t0JJ7WaOvZ2BDho8bRTYy9Ed42mqp/fQjAJBDWAIzu38cuXS5ANBtx8ajP0nfdx9a6l7JcNBrYv/ZTju3cCoLazo/uY8TTv+milzen69ets2bKFq1evAtClSxciIiKUEMv58+dZs2YNOTk51K5dm2eeeQYXF5e8Ocsyv/76K3v37kWlUjFq1CiCgoIsrnH8+HF++OEHZFkmJCSEYcOGYWdnV2mvqbK5mJnNX8npOKvV/JmSzt6EVI6kZZb7fI4qCR+tHfYmd8ZNowagjoMx1OWqVhmFjyShlSSCHbVkG2TcNGpc1CpyZBmdQaaxswPeWg0qIMNgwEmlUn6OepMj5Wo6d0nIslxkmE0vyyTr9Hho1GQaDFzOzMFVoybbYCBXlknMNbpmSbk6rmfnEpedi0oyumq3cnTkGmQuZ2UjA+l6A+l6Q6nfK41kFFYhjvY0cnbAx06Dl50GV40arUoizNmBQActahEirLZU9fNbCCaB4D4iGwwgScXmbdw8f5a/tmzE3smZ0NbtuB57moM//NdiTO36DXlqzgLUGqN4OPS/n9j9zQoklYpadYKIv3wRSaVi+PxP8A2pX6GvISMjg19//ZVDhw4BoNVq6devH82bNy8w9vr166xevZr09HRq1arFoEGD8PPzQ6fTsXXrVv766y8A/vWvf9G2bdtCr3fhwgW+++47cnJyqFWrFq1bt6Zly5ZIkoSDg0O1DtUZZJnfk9PZn5jGH8lpHExKJ7eIW66dJOFtp6GZqyPBDlrcNGoScnUEmx7wsizja2+Hh0ZNYq4eP3s7/Oxrrni8V2RZ5nauMc8qNiOLZJ0eCciRZWJSMohJzUBngCtZOcawZSnOqZbAy06Di1pFqJMDDZzsaejkYBJaWoIcxYrrqqSqn99CMAkE9wFZlvlj4w8c2vQjajs7Hh46gqZdehQQTmkJd4h6fSJZ6WkFztHu8UG07NGLb6dNJistlY4Dn+KhIcNIvHGdqNcnos/NpcdzE2jRozebPnqXs39G06jjw/SbPLVCXkNCQgJ79uzh+PHjSq5Ss2bNePTRR4v9W7pz5w5RUVGkpKSgUqkIDw/nzp07xMXFAcaekh06dCj22hcvXmT16tUFcqRUKhUhISGEh4cTGBiIn59ftUgizjXIbLmdxOeX44lJzbDY56FR465R09bdmYc8XWjj5oyXnRoPjUbk4lQSsixzOSuHq1k5nMvI5lxmNgm5OhJy9KTpjV+n07PQlfA0DHHU0snDhTr2Wuo4aAl0MOZZ1XXQoqoGv3fWTlU/v4VgEgjuA0d+2cbPyxdbbOs+ejwPRPa12Lbxw3c5++dBPP0DqRPejNjfDyBJEg8PHUGLHr0AOH1wL//75H0klYpBb85h35qviYs9TXCLVgycPhtJkoi/fJGv//0ikkrFxBVrsHdyuqf5m8t7mNsM1a5dm969exMSElKq41NTU9m8eTOnTp1Sttnb2zNw4EAaNWpUqnMkJydz6tQp/vzzT27fvl3omMDAQB5++GHCwsJKdc6KRpZlfrmTwtQzV7mWbRR39iqJFi5OuGhUjAyoRS8f9yqZm6B49LJMfI6O+JxcknV6zmVkczYji7MZ2VzNyuFCZnaRgspJraKZiyN1HLR426mp46Al3NkRDzs1tbQa/O2tL/euKqjq57cQTAJBJWPQ61n5yvMk37pJu8cHgSzz56Yf0To6MfY/K3FwNub1JN+6yZcvPweyzMgPFlMrKATzn+fdrsn/Pnmf0wf3Kt87OLswbN5CPPz8lW0rJo0l6UYcj/97Bg3aFu/gFEdiYiLLly8nIyODwMBAIiMjqVu3bpmdHFmWOX/+PFevXiU7O5s2bdrg7e1d5vnIskx6ejoODg4kJiZy9OhRrl69yqVLl9Dr9QA0b96cJ554ArW6dHk3FcHPt5P58OIN/knNy0l6NrAWLwb5EuggHpg1nXSdnl2JqZxOz+Kqya2Ky87lclYO2YbiH6OB9na0dnOmkbM9gQ5aQh3tqaXVEOxgj0a4iqWmqp/fNarSt0BQE7l87B+Sb93EwcWVTgOHorHTcv7vP7lz9TIx2zfTccCTABzb9TPIMkHNWlIrKAQoKJTMdH9uAok3rnPrwjm8AuoQOf4VC7EEUCesGUk34rh5Prbcgkmv1/PDDz+QkZGBn58fI0aMKHflfEmSCA0NJTQ0tFzH5z+POXncx8eHbt26AcaQ4a5duzhy5AhHjx4lISGBwYMH4+HhcU/XKwlZlll+NZ63z14HwFmt4kk/L96o54eHnbjFWgvOGjV9fTzo62O5XS/LnMvI5khqBrdzdMTn6riYmc3JtCxSdHoScnVcy87lWnwSxFseq5UkGjk70NLVkZauTvhq7Wjs7ECIo7ZahJYFloi/ZoGgkjn7p3HJfaMOD2Fnb1xa36H/ELYs+pC/tmykTZ/HUdvZcezXHQA07x5Z4jkdXVwZNu9jEq9fxzMgAJWqoJNSq24IALcvXyr33I8ePcq1a9dwcHBg6NCh1brNkJeXFwMGDCAoKIj//e9/XLt2jVWrVjF27FicnQuWYagoZp29zrKrxifhYD9P3g4NwEdru8nYtobaJHoaORcsmwFGZ+rvlAyOpGVyPiOLy1k5XM7M4VaOjkyDgWNpmRxLy2R1XF7RVk+NmpauTgQ42NHc1YnmLo4EO2qpZacRQqoKEYJJIKgAMlKSQZZxcvcosO/S0RgAQvO5PI07debAutUk3Ygj5ucteAUEkpaYgKOrGw3aFd0bMT8qlRrvOnWL3F8rKBiAO1fLL5iio6MBePjhhyvdqako2rZtS0BAAOvWrSMxMZFt27YxcODASrnWjNirLL9qzKd6PcSPV0Nqi+RfgQXOGjWdvVzp7OVqsd0gy1zJyuFEWiZ/JKcTm5HNzexczmRkkWgK/wGQT0g5qlTUc9TygJsTbd2caePuTH1He7FY4D4hBJNAcI8k3rjOt1MnIRtknn73I2rVDVb25WRmkHTDuBrMr0FecrNKrabDE0PYvvRT/tiwDmcPTwCadumBpoLqDHn6BQCQEn8L2WBAKuPy+1u3bnHjxg1UKhWtW7eukDndLwICAhg0aBDLly/n6NGjNGjQgJYtW1boNfYnpipiaXp9f14OFn3vBKVHJUkEO9oT7GhPbx8PZXuOwcCJtCyOp2VyLTuHv5IzOJeZxfWsXDINBk6kZ3EiPYv/moSUnSTRxs2Jlq5ONHV1pKmLIw2d7NFW43IbNRUhmASCe+TUvt3kZBoTfWN2bKHHmPHKvvhLFwFw8fLGyc1ydVT4I934Y+M6EuOuk5WWilqjoVXkvypsXs6eXiBJ6HU6MlKSFVFWWo4cOQJAw4YNcbrHVXZVQWBgIB07diQ6OpoNGzZQp06dciWZF8bvSWkMO3IegBEB3kIsCSoMrUrFA25OPOBm+TeXYzBwLSuX0+lZ/JWSzqGUdGJSMsk0GIhOTic6OV0ZaydJNHK2J8zZkRBHe1q6OtLC1QlfrQjp3QtCMAkE98jlY/8o/78Yc8hi361LxodqYcUjVWo1/V6dzoYFs8lOT6f7mPEV2s5ErdHg4ulFWsIdUm/Hl1kwnTx5EqDQgpQ1hUcffZSbN29y4cIF9u/fz2OPPXbP55RlmRlnr5FlkAm0t+ONev4lHyQQ3CNalYp6TvbUc7JXSlMYZJmLmTn8npzGCVMu1Ik0YxHP42lZHE/LsjiHj1ZDB3dnmrs40dbdiQdcnXAuZQV3gRBMAsE9kZuVxfUzebWFkm/dJDM1BUdX45LXWxeKFkwAPkEhPLdoBVD0irh7wdW7llEw3bltERIsicTERO7cuYMkSTRo0KDC53W/UKlUdOvWjRUrVhATE0NERATu7vdWB2n51XiOpGailSS2t21MLa24jQqqBpUkUd/JnvpOeYsxZFnmanYuJ9IyOZWWxdnMLGJSMjiXkU18jo7/xSfzv/hk4/FAQ2cHeni70dnThZauTniKlZ1FIt4ZgeAeuHbqOAa9DtdaPqjUapJv3uDWhfMEt3gAgHiTw+RTTHuSyrTIza5SRkpSmY47d+4cAHXq1Cm0aW5Nom7dutStW5crV65w5swZ2rVrV+5zpev0fHzxJgD/rucnxJKg2iFJklJ9PLJW3oeDTL2BmNQMDqdk8E9qBn8kpxOXbQzxnU7P4j+XbwHQ0CTA2ru70N3blUZODmIhgwnx1y4Q3AOXTOG4oGYtyc3KIvnmDW5eOEtwiwfQ63TcvmJcoeYbXLH93EqLg4vR6cpMSSnTcRcuXAC455pJ1YXQ0FCuXLnC5cuX70kwvX32Gkk6PfUctUwIqrjwqUBQ2TiqVXTycKGTR14D7JvZuUQnp/Hz7RQOpaRzMTOH2IxsYjOy2X47hTnnjPlQIY5a2rk709HDheYujjRydrDJJsVCMAkEZSQ3OwuVWo1aY6fkLwU3a0nK7XjORO8j/pJRbCRev4o+NxetoyPuvlWTFOzoalzKnJmWWqbjrl69CkBQUFCFz6kq8PPzAyA+Pr6EkUVz0lQrRwW836iuTT4wBNZFbXs7Hvf15HFfoxN9J0dHTGoGJ9Iy+V98Ev+kZpIry4qIMq/M87e34yEPF+o52tPFy5UH3Jxs4u9BCCaBoAykJdxh9Zuvkp2eTr/JU7l1wRi6Cmr+ADcvnAVQBNMt078+wfXKvKS/ojDnUmWllt5hSk1NJTnZmOMQGBhYKfO63/j4GMsz3759G4PBgKocP4+o63cA6O3jziN31dQRCKwBb62G7t5udPd246Xg2qTq9FzJyuF0ehb7ElP5OyWDi5nGljA/3EwE4IOLN/DQqGni7EBLNyceNLlYblaYTC4Ek0BQBv7e9n+kJRgfnOvfmwWAb0gozh6e+ATXAyDh+lVyc7IVMeVTReE4AIdyOEzXrl0DwNfXt1pX9i4Lnp6eqFQqdDodqampZU78TtXpWXvD+On62cBalTFFgaDa4apRE+7iSLiLI/1rG12oLL2BA0lpHE3N5GhaBnsSU0nS6ZXSBsuuxOOkVvFcYC1er+dnVfWghGASCMrAxX/+LrDNXMHbxdMbR1c3MlNTuHPlsiKYaterujwgR3MOUxkcJnM4rk6dOpUyp6pApVLh5uZGUlISycnJZRZMv9xJIUNvoIGTPQ/lywERCGwNB7WKbt5udPM23lt0Bpnj6ZmcTs/iz+R09iWmciEzh/1JaUyzsjCdEEwCQSnJzckm/qJx1VuXEWPZt+ZrnD08eCCyL2BcneITUp/LR2O4dfEct0xjfatQMDmYmtRmp6eXMDKPmzeNq8D8/a2rvpC7uztJSUkkJSWVOTfrlztGwdmrlrso/CcQ5EOjkmjpaqw0PsTPC1mW2XEnBS8r7HsnBJOgWqO7fZsrL4zDkJFBnc8+xb5hQ4t9cTNnYUhNxW/GWxb7KoOUW8Zlt1pHR1r3eYzm3R9Fo9VaNL71NQmms39Gk52RjlqjKbbfW2WjdTRWC87Jyiz1MebEaHPej7Xg4eHBpUuXlPys0pJrkNlpEkzdTZ+qBQJB4UiSZFHOwJqwnuCiwOrQp6Zy+fnnyTp+nJwLF7g+bTqyLCv7b7z7Lmk7d5Lxxx9cfv4F9ElJlTqf5PgbALj71EaSJLQOjhZiCcDXlMd04bCx4rdPcD3UmqrrXK91cAQgJyOjVONzcnJIMr2P1iaYzGG4pDL+nhxISiNJp8fbTkN7d+dKmJlAIKgJCMEkqFboU1K4PnUaF58exoX+A8g+cVLZl3XsGBkHDwKQff48qVu3Kft0cXHEzXjbQlBVNGaHyc3Xr8gxfg0bW3xft2mLSptPadCaesDlZmdhMOhLHH/njjGh3cnJCWdn6xIHZsFUVofp/24lAdDHx90mlk4LBILCEYJJUK24+f77JG/YQObff5N79Soqd3fqrf8RjyefBCD5//5n/PenDQC4dOlCyLp1YGdH6s8/c3vJkkoTTcnxxtwe92L6vXn6BVjkLDVo16lS5lJazA4TGNu4lIQ5HFerlvWtBDMLptTU0q8Y1BlkttxOAqBfvo7yAoHA9hCCSVBt0Ccnk7zp/wDwmfQyfjPfpv5P63EID8e9378ASP35ZwxZWSRvNgon9yeewLF5M2q/8QYAtxct5uKQJ0nbu7fC55d8yxSSK6EIZY8xE/Bv2JhOg54ioFGTCp9HWVDb2aFSG8OGOZkl5zFZa/4SGF0zgPQyJMBHJ6eRkKvHy07Ng2J1nEBg04ikb0G1IXXnr5Cbi33DhtQaP95in2Pr1mj8/NDduMHt/yxBdz0OlZMTLl0iAPB6ZjjIBm59vJCso0e58sI46i5bikvnzhU2v+RbRoepuJAcgH/Dxjw996MKu+69IEkSWkcnstJSbV4wmUOMGRkZyLJcqhU8uxKMblQPbzc0KhGOEwhsGeEwCaoNKduNOUmuvXsV2CepVLj16QPAneXLAXDp1g1VvsawXiNG0GDnL7j16Q0GA3FvvoW+jD3Uip1fvDGHqaranJQXraMp8Tuz5MTvxERj9V4vL69KnVNVYHaYDAYDWaUIT4Ix4RvgYU9R2VsgsHWEYBLcd2RZJnHNGq5MfJGEr79BNhjQJSaSvv8AAG69CgomAPcnHgdz1VhJwuvZUQXGaLy98Z83D21wMLpbt7j9nyUVMufsjAyyTNWyi8thqo4oK+VKcJhkWVZWkHl6elb2tO47dnZ2aLVawOgylUSaTs8/qcZxIhwnEAhESE5wX5F1Om7MnkPS2rUApO3ciT4tFU2tWqDTYd+kCfb1C28l4tCoEf5z5pD80094DB6EY9OmhY5TOThQ+83pXHn+BZJ+/JFaL72E2uXeVnyZ85ccXN2U2kY1hbxaTMWLhMzMTLKzswFjzSJrxNnZmZycHNLT0/H29i52bHRyOnoZgh201HHQ3qcZCgSC6ooQTIL7hiEzk2uvvU7ar7+CJOESEUHarl3cWfYFatMKJre+fYo9h8fAAXgMHFDitZwffhhtSAg5Fy+SvGkjXk8/fU9zz1shV7PCcZA/JFe8w2R2l5ydnbGzq7raUZWJk5MTiYmJpXKY9iUaHcWHPYW7JBAIREhOcJ+QDQauTppE2q+/ItnbE/jZp9T5fAlOHTsiZ2eju3ULNBrcH3usQq4nqVR4mkRS4ur/3nOpgRRTwndNy18CsLM35nnlmtyjojDnL1ljOM6MuZlwdgnvBeTlLz0k8pcEAgFCMAnuEymbN5O+Zy+SgwNBXy7HrWdPJEnCf85sNKaeZZ5PPold7YoTJO4D+qNyciLn3Dkyfv/jns6lOEw1UDBpTHk7uuziE53NDpO1huOg9IIpXa/neJrRkesgqnsLBAKEYBLcB+TcXOI/WwRArfHjcWrXTtmnrVuX0P/9HyHr1lL7rTcr9LpqFxdlxV3qL7/c07mSb5auBlN1RGMSCbqcnGLHpZhWFJoLPFojpRVM/6RkopfB396OQJG/JBAIEIJJcB9I3riR3CtXUHt7G+sl3YXK2RnH5s0rpbO1a7duAKTt2nVPYTlzDSb32v4VMq/7iZ3WKBJyc4oXCWlpxhCUi4v15uyUVjAdSjEWt2zrJtwlgUBgRAgmQaWSdeoUtz5eCID3c8+hcrq/K8ycO3ZE0mrJvXqVnPPny3UOWZYVh8mjhKKV1RElJFeCw2QWTK6u1puz42Cq21WiYEo2CSb3mrUiUiAQVB5CMAkqjaQf13Nh4CD0CQloQ0PxHHZvK9XKg8rZWQkBph84WK5zpCcmoMvNQVKpcK1V8ypgKyG5EkSCuceaLThMxRWulGWZv1OMq+jaCIdJIBCYEIJJUCno09K5OX8+6PW4dO9O0MoVqLRVkwvi2KoVAFnHjpXr+DtXrwDGkgJqTc2rxKHRmnOYREiuNCG5a9m53M7VoZGgmYtjkeMEAoFtUfPu/oIaQUb0QQxpadgFBVFn0WdIqqrT5g7NjAUuM4+XTzDdvHAWAN96oRU2p/tJaXKYsrOzyTGF7Kw5JFcawRRjcpfCnB1xUIvPlAKBwIi4GwgqhcyjRnHi1K5tlYolAIfwcAByzp3HUIr6O3dz7dRxAGrXb1Ch87pf5JUVKPq1m90lOzs7RVRYI6USTKZ2KA+4ifwlgUCQhxBMgkoh6+hRABybN6/imYDGxweVszPIMrlXr5bp2PSkRC4djQGgfqu2lTC7yqc0ZQVsIRwHKBXMc3NzixwTm2HMb2ri7FDkGIFAYHsIwSSocGRZJvO40ZVxaFb1gkmSJOyCgwDIuXSp1MfJssyOLxahz83Fr0EjvOsGV9YUKxU7+5JDcuaEb2sOx0HpBNP5DOP71MBJCCaBQJCHEEyCCif38mUMyclIdnY4NGpY1dMBQBtsFDs5ly6X+pjrp09y/q8/UGs09HhuYqXUibofaOyEw2SmJMGkl2UuZhrfp/pO1huaFAgEZUcIJkGFk2lajWYfFoZURSvj7kZbpy5AmUJysX8cAKDJQxHUrqEJ31C6sgLCYTJyNSuHXFnGXiURaG+dDYgFAkH5EIJJUCHkXL7MlfETSP6//5FlSvh2NK1Oqw5ofH0B0MXHl/qYm+eNq+PqNm1RKXO6X+QVrixaMKWnGws1Ojtbd90hrfm90OkwGAwF9p8zhePqOdqjqqGOokAgqBxEWQFBhXDrw49I++030vbuRRtiDH9Vh/wlM+URTPGXLgA1t5yAmdLkMGVkGFeGWbtgMjtMYBRN2rsc0POZxvcoVITjBALBXQiHSVAhZPz9t/E/Oh05Z88B4Njqgaqb0F1ofIwVuksrmLIz0snOMLouNbEdSn7UGqNI0OcUnehsFkyOjtZdqFGTr/BoYWE5s8NU31EIJoFAYIkQTIJ7Rs7JQX/7tsU2tZcX2pCQqplQIWh88wRTaZrwpt42CisHZxfsHGr2aim1yVXR63RFjsnMzATA6T73+rvfqFQqRTQVJpjMK+REwrdAILgbIZgE90zurXyujenh7Br5aLVaVabx8gJAzs5GNrkpxZGacAcAV+9alTqv+4FZMMmyAYNeX+gYW3GYoPjE73OZxhpMocJhEggEdyFymAT3jO7mDQDs6tbF97VXyfjrb3wmTqjiWVkiOToaxVxuLvrUVGMhy2JIvWMUgTWx2e7daDR5eTv63FxUarXFfoPBYDMOExgFU2ZmZgHBlKk3cC3LuK2+qMEkEAjuQggmwT2ju3kTALvatXHr1Qu3Xr2qeEYFkSQJtasr+oQE9Mkp2PkVn5eUlpAAgLOn1/2YXqWitkh0zsUOSzGQlZWl/N+WHaaLmdnIgLtGjbedupAjBQKBLSNCcoJ7JveGUTBpShAhVY3azQ0AQ2pKiWPNCd8OLjW/LpFKrUaSjH/q+kLCUOZwnFartUiKtlaKEkzmFXL1He2rVThZIBBUD4RgEtwzSkjOr3YVz6R4VCbBpE9JLXFstqkukb2jdYSo1CYhVJxgsoVwHBQjmDJESQGBQFA0QjAJ7hnFYfKt3oJJrQim5BLHmh0me2fraBWSt1KuoGAy5y/ZQjgOihZM+YtWCgQCwd0IwSS4Z3Q3jA6Tppo7TGo3Y3jNkFJySC4n0ySYrMR1Ka60gK06TDl39dYTRSsFAkFxCMEkKDOyLJPw9TckrlkDQK456bua5zCZV8YZSlFWINs0xt7JOipfK8UrCwnJ2dIKOSjZYRI1mAQCQWFYf4anoMLJOnKEm/PmAeDUpo1SPVtTu3oLJskUcjJkZJY41pzDpLUSEaG2KzmHyVZCcubEdl0+ty1Fp+dOrvF7UeVbIBAUhnCYBGUm6+RJ5f8pO3aAXg9qNZpa3lU4q5JRmRK4DZmlEEzmVXLW5jAVksNkayG5wgTTtSxjeM5To8ZFI0oKCASCglS5YPrPf/5DSEgIDg4OdOjQgT/++KPY8Z988gmNGzfG0dGRunXrMnnyZIs6MoLKJ+fiJeX/Gb8bf14aX18kdfV+0KjMDlNm8SE5WZaVkJzWWgSTXdEhOfPfjy07TNezje9LgINdoccIBAJBlYbkvv/+e1599VWWLl1Khw4d+OSTT4iMjOT06dP4mrrL5+e///0vU6dOZeXKlTz44IOcOXOGUaNGIUkSH3/8cRW8AttEd+uW8v8Mk8C1q129E74BVE5GQSCXEJLT5eZg0BsfplaTw2QSTLpCHCazYLK3t41QlFkw6U1tYvR6PbfT06mjkmmqVYsPYAJBFaLValGpqtzLKZQqFUwff/wxY8eO5dlnnwVg6dKlbN68mZUrVzJ16tQC4w8cOMBDDz3E008/DUBISAhPPfUUv//++32dt62jTy1Yx6i6F62EfDlMJYTkzPlLSBLaGt5410xxdZiys43Jzg5W8lpLIr/DFBcXR1JSEsE6PfPcVLhIWVy4cKGKZygQ2C4qlYp69eqh1WqreioFqDLBlJOTw19//cW0adOUbSqVih49enDw4MFCj3nwwQf59ttv+eOPP2jfvj3nz59ny5YtPPPMM/dr2gIKX5ZvFxBQBTMpG6XNYVJqMDk5IVXTTzplxZzDZCikrIDZUbE1weTo6EhSUhK+vr7YqzQ46A3U0mqopRVhOYGgKjAYDFy/fp24uDiCgoKqXcX9KhNMt2/fRq/XU/uuUE7t2rU5depUocc8/fTT3L59m4cffhhZltHpdIwbN47p06cXeZ3s7GzlEzRASilq8AiKpzCHqUYIJqfS5TDlWFlJAcgXkivGYbKlkJxGo8HV1RVfX1+8vb1JyshC0hlwctTiYCcWDwsEVYWPjw/Xr19Hp9MpJUCqCzXq4/OuXbuYN28eS5Ys4e+//2b9+vVs3ryZOXPmFHnM/PnzcXd3V77q1q17H2dsnRhqqmByLF0Ok+IwWUlbFABNMavkbNFhsre3R6VSKSsDcw0yANpq9olWILA1zKE4c45hdaLKBFOtWrVQq9XcNBU9NHPz5k38isiHmTFjBs888wzPPfcczZs3p3///sybN4/58+djMBgKPWbatGkkJycrX1euXKnw12JrmB0mh5YtlG0OTRpX1XRKjaq0OUwZ5hpM1ucw3Z3DpNfrldVituIwqdVqJElClmXl3xzZKJjsVEIwCQRVSXULw+WnygSTVqulTZs27Ny5U9lmMBjYuXMnnTp1KvSYjIyMAtnzatNSdtl0w7sbe3t73NzcLL4E5UfOyUE2ORLeI0cCRuGk8fevymmVCqmsOUzO1i+Y8q8IsxXBZM5hMqOXwXz7sKvGN2uBQFC1VGlI7tVXX2X58uVERUVx8uRJxo8fT3p6urJqbsSIERZJ4f369ePzzz9nzZo1XLhwgZ9//pkZM2bQr18/RTgJKhd9Wpryf9fISEJ3bCd41apq/anATF4OU0mCyQpzmIoIyZnzl+zs7Gzmb+huwZQrG91ptSShqgG/xwLrISQkhE8++aRK53Djxg169uyJs7MzHh4egNHl2bBhAwAXL15EkiRiYmKqbI7VhSoVTE8++SQffvghb7/9Ng888AAxMTFs27ZNSQS/fPkycXFxyvi33nqL1157jbfeeovw8HDGjBlDZGQky5Ytq6qXYHOYV8ipXFyQ1Gq0QUFKqKu6I2mNDoqcbxFAYeRkWFfjXSi6rICt5S9BnmAyu9I55vylGhKOi4+PZ/z48QQFBWFvb4+fnx+RkZHs378fKPkhfOXKFUaPHk1AQABarZbg4GAmTZrEnTt3Ch3/wgsvoFarWbduXYF9s2bNQpIkxo0bZ7E9JiYGSZK4ePGixfZLly7h6OhIWloaGRkZTJs2jdDQUBwcHPDx8SEiIoKNGzcq47t06YIkSUiShL29PYGBgfTr14/169cXmMvu3bvp1q0bXl5eODk50bBhQ0aOHGnRZHn58uW0bNkSFxcXPDw8aNWqFfPnzy/yvaps/vzzT55//vkquz7AwoULiYuLIyYmhjNnzgAQFxdH7969Cx2/a9cuJEkiKSnpPs6yelDly0FefPFFXnzxxUL37dq1y+J7jUbDzJkzmTlz5n2YWfVCb5DJytXjbF+1PzJz/pLK1bVK51EeVPamuh56PbJOh6Qp/L3MSjcLJitymOwKd5hsrWglFOYwmfKXaoi7NHDgQHJycoiKiqJ+/frcvHmTnTt3Fil48nP+/Hk6depEo0aN+O6776hXrx7Hjx/n3//+N1u3biU6OhovLy9lfEZGBmvWrOGNN95g5cqVDB48uMA5HRwcWLFiBa+99hoNGzYs9vobN26ka9euuLi4MGLECH7//XcWLVpEeHg4d+7c4cCBAwVex9ixY5k9ezY6nY6rV6/y008/MXToUEaNGsUXX3wBwIkTJ+jVqxcvvfQSn332GY6OjsTGxvLjjz8qycMrV67klVde4bPPPiMiIoLs7GyOHDnCsWPHSnzfKpqcnBy0Wi0+Pj73/dp3c+7cOdq0aWPxsysqj7gikWUZvV5f4O+xWiPbGMnJyTIgJycnV/VUysS4bw7JTd7aKp+Mq9p5p+7bJ59o3EQ+1++xKp1HedCnp8snGjeRTzRuIuvT04sct2XRh/KHQ/rKf2z84T7OrnLZ9/038odD+so/f7nEYvuJEyfkmTNnysuXL6+imd1/Ll++LH/wwQdydHS0nJmZKV/PzJZjktPlyxlZcnp27n3/MhgMpZ57YmKiDMi7du0qckxwcLC8cOHCQvf16tVLrlOnjpyRkWGxPS4uTnZycpLHjRtnsX3VqlVyx44d5aSkJNnJyUm+fPmyxf6ZM2fKLVu2lHv27CkPHjxY2X748GEZkC9cuGAxvlu3bvLnn38uy7Isu7u7y6tWrSr29UZERMiTJk0qsH3lypUyIP/888+yLMvywoUL5ZCQkGLP9fjjj8ujRo0qdszdfPXVV7K7u7vFtp9++knO/+g0vwdLly6V69SpIzs6OsqDBw+Wk5KSlDEjR46UH3/8cXnu3Lmyv7+/Mte7f1aAvHTpUrlv376yo6Oj3KRJE/nAgQNybGysHBERITs5OcmdOnWSz549azGnDRs2yK1atZLt7e3levXqybNmzZJzc3NLfH3BwcEyoHyNHDlSmcdPP/0ky7IsX7hwQQbkw4cPK/8v7Bi9Xi/PmzdPDgkJkR0cHOQWLVrI69atU67122+/yYC8ZcsWuXXr1rKdnZ3822+/FZhTZmamfOLECTkzM7PAvqp+ftcgaWe76A0yW4/dAGDD4etM7V11ieuGVGMOk8qt5jlMUr7KsYbsbFRFhNyyM60wh0lt/FM3t3wxY8shOTNmh0mvMxD+zi/3fT4nZkfipC3drdjFxQUXFxc2bNhAx44dy+QMJiQksH37dt59990CfQP9/PwYNmwY33//PUuWLFFyElesWMHw4cNxd3end+/erFq1ihkzZhQ493vvvUe7du04dOgQbdu2LfT6SUlJ7Nu3j2+++Ua55pYtWxgwYACuZXSsR44cyWuvvcb69evp0aMHfn5+xMXFsWfPHh555JFCj/Hz82P37t1cunSJ4ODgMl2vJM6ePcvatWv5v//7P1JSUhgzZgwTJkxg9erVypidO3fi5ubGzz//XOy55syZw8cff8zHH3/MlClTePrpp6lfvz7Tpk0jKCiI0aNH8+KLL7J161YA9u7dy4gRI/jss8/o3Lkz586dU8J8JUVj/vzzT0aMGIGbmxuffvppif0k69aty48//sjAgQM5ffo0bm5uyjHz58/n22+/ZenSpTRs2JA9e/YwfPhwJdRqZurUqXz44YfUr18fT0/PYq9X3ahRdZhsldSsvDBKQnp2gW33E32qMYdJ7VrzVhtKGg2Y81fy5TXcTV5ZAevJYVKZXrdBZ1nbxNaKVkLBlbU607+aGhCS02g0rFq1iqioKDw8PHjooYeYPn06R44cKfHY2NhYZFkmLCys0P1hYWEkJiYSHx+vjI+OjubJJ58EYPjw4Xz11VeFrkhu3bo1Q4YMYcqUKUVef8uWLbRo0YIAU822L774ggMHDuDt7U27du2YPHmykodVEiqVikaNGik5UoMHD+app54iIiICf39/+vfvz+LFiy0KFc+cORMPDw9CQkJo3Lgxo0aNYu3atUWWpCkLWVlZfP311zzwwAM88sgjLFq0iDVr1nDjxg1ljLOzM19++SVNmzaladOmRZ7r2WefZciQITRq1IgpU6Zw8eJFhg0bRmRkJGFhYUyaNMkiXeWdd95h6tSpjBw5kvr169OzZ0/mzJlTqtxeHx8f7O3tcXR0xM/PD3d392LHq9VqJWTr6+urHJOdnc28efNYuXIlkZGR1K9fn1GjRjF8+PAC85g9ezY9e/YkNDTUIvxbExAOUw0gOTO/YMph27EbjPv2L/4d2ZiJXRvc17kYUsw5TC739boVhaTVIut0xQsmUw6TgzU5TOak7yJymGzbYTL+62qv4cTsyPs+H0e7sq1OHDhwIH379mXv3r1ER0ezdetWFixYwJdffsmoUaNKPL4wwVMY5odfrVq1AOjTpw9jxozh119/pXv37gXGz507l7CwMHbs2FFo8/SNGzfy2GOPKd8/8sgjnD9/nujoaA4cOMDOnTv59NNPeeeddwp1sQp7HWYnTK1W89VXXzF37lx+/fVXfv/9d+bNm8f777/PH3/8gb+/P/7+/hw8eJBjx46xZ88eDhw4wMiRI/nyyy/Ztm3bPTV8DQoKIjAwUPm+U6dOGAwGTp8+reQDNW/evFT90Vq0yKtvZ14A1bx5c4ttWVlZpKSk4Obmxj///MP+/ft59913lTF6vZ6srCwyMjKU4qyVydmzZ8nIyKBnz54W23NycmjVqpXFtqIcyJqAcJhqAPkF082UbD75xbiS4YPtp+/7XPRpRsFUEx0mAJXphlXcSjlzWQFrKlypMrkqBr1wmO5eJafLV7TSSau571/lKcnh4OBAz549mTFjBgcOHGDUqFElhl8aNGiAJEmcPHmy0P0nT57E09MTHx8f9Ho9UVFRbN68WWkl4+TkREJCAitXriz0+NDQUMaOHcvUqVMLiLKcnBy2bdtmIZjAWM6ic+fOTJkyhR07djB79mzmzJljsbKtMPR6PbGxsdSrV89ie2BgIM888wyLFy/m+PHjZGVlsXTpUosxzZo1Y8KECXz77bf8/PPP/Pzzz+zevbvQ66hUqgKvJbeQ9kKlwbmUdd3ytwMx/24Uts3sjKWlpfHOO+8QExOjfB09epTY2Nj79kEozVRuZvPmzRbzOHHiBD/88IPF2NK+D9UR4TDVAPILpttp2cSnFr8sviKRc3K4PnUqaDQEzJuX5zDVwBwmyMtjKs5hyisrUHP/sO8mrw6TyGHK7zAZZBmdoWatkiuM8PBwpW5OUXh7e9OzZ0+WLFnC5MmTLfJVbty4werVqxkxYgSSJLFlyxZSU1M5fPiwRX2uY8eO8eyzz5KUlKTU7MnP22+/TWhoKGvWrLHYvmvXLjw9PWnZsmWJr0On05GVlVWsGxMVFUViYiIDBw4scoynpyf+/v6kmxzjoq4HFDnGx8eH1NRU0tPTlQd9YfWILl++zPXr15VwY3R0NCqVisaNK78DQuvWrTl9+jQNGtyfaENhrUvCw8Oxt7fn8uXLFvlK1oYQTNWUpIwc9p29zaPhfhaC6U5ajnKDB0tbujJI3bWLlC3G5EL3xx7Py2FyqaGCyeSkGIpwmGRZzle40opymBSHyVIw2bLDBKAzGADj309NyGG6c+cOgwcPZvTo0bRo0QJXV1cOHTrEggULePzxx5Vx165dK/BgDw4OZvHixTz44INERkYyd+5ci7ICgYGBSlhnxYoV9O3bt4DACQ8PZ/LkyaxevZqJEycWmF/t2rV59dVX+eCDDyy2b9q0qYC71KVLF5566inatm2Lt7c3J06cYPr06XTt2tWiI0NGRgY3btywKCuwcOFCxo8fT9euXQFYtmwZMTEx9O/fn9DQUCWn6Pjx4yxatAiA8ePHExAQQLdu3ahTpw5xcXHMnTsXHx8fpbvETz/9xLRp05QG8B06dMDJyYnp06fz8ssv8/vvv7Nq1aoCr9vBwYGRI0fy4YcfkpKSwssvv8yQIUPuy/L8t99+m3/9618EBQUxaNAgVCoV//zzD8eOHWPu3LkVfr3g4GAkSeJ///sfffr0wdHREVdXV15//XUmT56MwWDg4YcfJjk5mf379+Pm5sZIU1eImo4IyVVTpq0/yov/Pcyy3ecsBFOO3jJBMS1bd/ehFUrWseP5/n9UcZjU7jUzJCfZm0NyhTtMuVmZyKbKz1bVGsWc9H1XSM4c+rAlwZTfMTF/+NCYiiNWd1xcXOjQoQMLFy7kkUceoVmzZsyYMYOxY8eyePFiZdyHH35Iq1atLL42b95Mw4YNOXToEPXr12fIkCGEhoby/PPP07VrVw4ePIiXlxc3b95k8+bNhbo3KpWK/v37s2LFiiLn+Prrr+PiYpnjWJhgioyMJCoqikcffZSwsDBeeuklIiMjWbt2rcW45cuX4+/vT2hoKAMGDODEiRPKaj4z7du3Jy0tjXHjxtG0aVMiIiKIjo5mw4YNiuPRo0cPoqOjGTx4MI0aNWLgwIE4ODiwc+dOvL29AUhOTub06bxUBy8vL7799lu2bNlC8+bN+e6775g1a1aB19ygQQMGDBhAnz59ePTRR2nRooXF/CqTyMhI/ve//7Fjxw7atWtHx44dWbhwYYWvBDQTGBioJJrXrl1bqaM4Z84cZsyYwfz58wkLC6NXr15s3ry5QNi0JiPJpc0AtBJSUlJwd3cnOTm5WveVC5m6GQB/dwee6RTMgm2F5yv9Mb07vm6VF065/MILpO/eA4B7//7kXL5M5l9/Efjpp7hFPlpp160szg8YQPaJk9Rd/gUunTsX2J965zZfTBiFSq3mldUbasRDtDScPriX/33yPnXCm/HkzPeU7StXruTy5csMGTJECU/YAh999BEPPvggoU2bEYcaB7VEY+eaUbG+pvH333/TrVs34uPjLXJxrIVZs2axYcMG0TqkgsjKyuLChQvUq1evQKpAVT+/hcNUDcnvGsmyZQ7T3aTn6IvcVxFkn8oTarlxcUprFHUNzWFSldAeJa+kgLPViCUouqyA2WEqzeoda8LsMulrUEmBmopOp2PRokVWKZYEtoUQTNWQWyl5HeTTc3SkFCeYKjEkp09JQXfzpvJ9btz1fK1Rqq87VxwlJX1bY/4S5BWuvDvp21YFk3kJubmkQE1O+K7utG/fnmeeeaaqp2GTrF69Wil2evdXcbWgBIUjkr6rIbfyrYJLzdJxO63oFV2ZuZXnMGWfO2fxvS7uhtJ/raY6THlJ30UJJuPyWGtaIQdFJ33bqmASDpOgopg1a1aheU3Vgccee4wOHToUuk84fmVHCKZqyM18DhPAxdvGMJGTVk3GXSG4ynSYcs6fN163XTsyDh1CzslRnJma2HwX8iV9l+gwWZdgyitcKQQT5DlM+nw1mAQCa8PV1bXMbWcERSNCctWQu+ssXbxjFEyhPgWra98toCqS7LNGh8m+SRPUtbwt9qlr6B9hSYUr82owWVdITlVILzmDwWCzgsnsMOlMITnhMAkEgpIQgqkacrfDlKs33tXr+xR0PSrTYco+dxYA+9BQ7Pz8le0qJyclNFfTkMxJ3zlFJX3bjsOky/d/WxNMisOECMkJBILSIQRTNeRWEZW869cq6DBlVWIOU47ZYWoQil2+Amwqj+IbNFZnzEnfhqJCculWmsNUSB2m/O0n7u6vZu0oOUym70XSt0AgKAkhmKoht1IKF0yhvnkPcY0p56Kykr4NGRnkXr8OgDY0FE0+waSp5VMp17wfmJO+iypcaY195CBf4cp8rlL+cNy9NB6tiZhfr0GE5AQCQSmxrbtkDeFWqjEk19DX0lFqVDsvbyjM37isv7JymLLPXwBA7e2NxtPTwmHS+NRgwWRuvKorvFRDttXmMJkclSIEk62hVquRTSJJkkAt9JJAICgB2/LhawjmkFyYvxuxt4whIietmoa+Lox5uB7ZOj1atZqj15IrzWHKuWBcIWdvKmtv55/fYapVKde8H0impbRyER3HczJNOUxW1BYF8prvFhaSs1XBZJDyeshZU5FSgUBQOQiHqZqRlasnNcvoAjTxz3OUPJ20SJLEjH+FM/eJ5jjbGx2DrEpymHIuXQbALsTYj8iubpCyT1tJPYruB0qyuq7wZPmsdLPDZF2CKc9hyteX0IYFkyRJyDWo6W5+4uPjGT9+PEFBQdjb2+Pn50dkZCT79+8HICQkhE8++aTI469cucLo0aMJCAhAq9USHBzMpEmTuHPnTqHjX3jhBdRqNevWrSuwb9asWUiSxLhx4yy2x8TEIEkSFy9etNh+6dIlHB0dSUtLIyMjg2nTphEaGoqDgwM+Pj5ERESwceNGZXyXLl2QTILW3t6ewMBA+vXrx/r16wvMZffu3XTr1g0vLy+cnJxo2LAhI0eOtMjVW758OS1btsTFxQUPDw9atWrF/Pnzi3yvBIL8CMFUzTDnL9lrVNTzzntoezhZFhlzsDM+ACvNYbp0CcgTRw7hYdgFBICdHc4PPVQp17wfSHamkFxu4YJJKSvgaGWCyRyKNBiQDcbmwrYsmPKH5GpawvfAgQM5fPgwUVFRnDlzhk2bNtGlS5ciBU9+zp8/T9u2bYmNjeW7777j7NmzLF26lJ07d9KpUycSEhIsxmdkZLBmzRreeOMNVq5cWeg5HRwcWLFiBbGxsSVef+PGjXTt2hUXFxfGjRvH+vXrWbRoEadOnWLbtm0MGjSowOsYO3YscXFxnDt3jh9//JHw8HCGDh3K888/r4w5ceIEvXr1om3btuzZs4ejR4+yaNEitFotepOrunLlSl555RVefvllYmJi2L9/P2+88QZpaWklzlsgABGSq3bcMJUU8HWzx9slr4P83YLJURFMhkqZR85lk2AKMgomSa2m3vof0aelo60TWCnXvB+UFJKz2tYo+VbB6fV6NCqVTQsmlUqlCKaa5DAlJSWxd+9edu3aRUREBADBwcG0b9++VMdPnDgRrVbLjh07cHQ0NhsOCgqiVatWhIaG8uabb/L5558r49etW0d4eDhTp04lICCAK1euULduXYtzNm7cGF9fX958803Wrl1b7PU3btzI4MGDAdi0aROffvopffr0AYzOWJs2bQoc4+TkhJ8ph7JOnTp07NiRJk2aMHr0aIYMGUKPHj3YsWMHfn5+LFiwQDkuNDSUXr16Kd9v2rSJIUOGMGbMGGVbSe1BVq1axSuvvEJSUpKybcOGDfTv3x8b61svQDhM1Y5LpiKVwV7OeLvkPchq5RNPAI5ak2CqpJCc7lY8YJm7pPbwqNFiCQAl6btwh0lJ+rayHCZzSA7yilfasmBSq9V5ITnzXVCWISf9/n+V4cFr7gO2YcMGsosovloUCQkJbN++nQkTJihiyYyfnx/Dhg3j+++/txACK1asYPjw4bi7u9O7d29WrVpV6Lnfe+89fvzxRw4dOlTk9ZOSkti3bx+PPfaYcs0tW7aQaupPWRZGjhyJp6enEprz8/MjLi6OPXv2FHmMn58f0dHRXDK55wJBWREOUzXj0h2jwxHs7YSfm4Oy3dXB8kdldpgqow6TLMvoTba4xtu7hNE1C0lTtMNkMOjzkr6tLIfJnPQNeSvlbFkw5XeYlJBcbgbMC7j/k5l+HbSl+33TaDSsWrWKsWPHsnTpUlq3bk1ERARDhw6lRYsWxR4bGxuLLMuEhYUVuj8sLIzExETi4+Px9fUlNjaW6OhoRZQMHz6cV199lbfeeqtAknzr1q0ZMmQIU6ZMYefOnYWef8uWLbRo0YKAAON7/MUXXzBs2DC8vb1p2bIlDz/8MIMGDeKhUoT8VSoVjRo1UnKkBg8ezPbt24mIiMDPz4+OHTvSvXt3RowYgZubcUXxzJkzGTBgACEhITRq1IhOnTrRp08fBg0aZHNlNQTlQ/yWVDPO3DR+2qpXyxlnew1dG/tgp5YY2LqOxTizw5SRU/GVvg3p6UqvNbWXV4WfvypRQnKFOEw5mZnK/62tDpOU74FgEIKpwCq5msTAgQO5fv06mzZtolevXuzatYvWrVsX6f7cTWlDSStXriQyMpJaplWxffr0ITk5mV9//bXQ8XPnzmXv3r3s2LGj0P0bN25U3CWARx55hPPnz7Nz504GDRrE8ePH6dy5M3PmzCn16zALN7VazVdffcXVq1dZsGABgYGBzJs3j6ZNmxIXFweAv78/Bw8e5OjRo0yaNAmdTsfIkSPp1asXBkPlpDYIrAvhMFUjZFnmz4vGpMtWQR4ALBnWhuTMXPzcHSzGVmYOk9ldUjk5obrLuq/pFFeHKceUv6S2s0NjZZ28JUlCrdGg1+mU0gK2LJgKzWGyczK6Pfcbu7Lnyzk4ONCzZ0969uzJjBkzeO6555g5cyajRo0q8pgGDRogSRInT56kf//+BfafPHkST09PfHx80Ov1REVFcePGDYsq8Hq9npUrV9K9e/cCx4eGhjJ27FimTp3KihUrLPbl5OSwbds2pk+fbrHdzs6Ozp0707lzZ6ZMmcLcuXOZPXs2U6ZMKfb3Uq/XExsbS7t27Sy2BwYG8swzz/DMM88wZ84cGjVqxNKlS3nnnXeUMc2aNaNZs2ZMmDCBcePG0blzZ3bv3k3Xrl0LXEelUhUQmLlF5D8KrB8hmKoRsbfSSMzIxcFORfNAD8DoJJndpPyYt1VGSE53xyja1FYWjoO8VXIUctPLK1ppXe6SGZXaKJhESM6cw2REEUySVOrQWHUjPDycDRs2FDvG29ubnj17smTJEiZPnmyRx3Tjxg1Wr17NiBEjkCRJyS06fPiw0kYG4NixYzz77LMkJSXh4eFR4Bpvv/02oaGhrFmzxmL7rl278PT0pGXLliW+Dp1OR1ZWVrG/l1FRUSQmJjJw4MAix3h6euLv70+6qVRIUdcDihzj4+NDamoq6enpOJvyGmNiYop9DQLrRQimasQfF0zuUl1PtJrio6WKw1QJSd/6BFP+kpWF4yD/KrmCIblsK63BZEat0ZCbLZK+AcjnMKlrUEjuzp07DB48mNGjR9OiRQtcXV05dOgQCxYs4PHHH1fGXbt2rcCDPTg4mMWLF/Pggw8SGRnJ3LlzqVevHsePH+ff//43gYGBvPvuu4Ax2btv374FBE54eDiTJ09m9erVTJw4scD8ateuzauvvsoHH3xgsX3Tpk0W4Tgw1lh66qmnaNu2Ld7e3pw4cYLp06fTtWtXJe8IjKUNbty4gU6n4+rVq/z0008sXLiQ8ePHK67QsmXLiImJoX///oSGhpKVlcXXX3/N8ePHWbRoEQDjx48nICCAbt26UadOHeLi4pg7dy4+Pj506tQJgJ9++olp06Zx6tQpADp06ICTkxPTp0/n5Zdf5vfffy916FNgfYgcpmrE4ctJALQN8SxxbGXWYbJmh6m4VXLZmdbZFsWMuRaTcJggS5Xnmmhqjl7CxcWFDh06sHDhQh555BGaNWvGjBkzGDt2LIsXL1bGffjhh7Rq1cria/PmzTRs2JBDhw5Rv359hgwZQmhoKM8//zxdu3bl4MGDeHl5cfPmTTZv3lyoe6NSqejfv3+BkFt+Xn/9dVxcLNs6FSaYIiMjiYqK4tFHHyUsLIyXXnqJyMjIAqUJli9fjr+/P6GhoQwYMIATJ07w/fffs2TJEmVM+/btSUtLY9y4cTRt2pSIiAiio6PZsGGDUn6hR48eREdHM3jwYBo1asTAgQNxcHBg586deJvudcnJyZw+fVo5r5eXF99++y1btmyhefPmfPfdd8yaNauEn5LAWhEOUzXi8JVEIC9/qTiUsgKVIJgUh8nbCh0ms2AqNCRnnY13zZhLC4ikb8hSq3EAVMg1qi2Kvb098+fPL7Y69d3Vte8mODi4WJekdu3axebp5Bcqs2bNKiAg3NzciI+PV77/+++/SUlJUYSLmWnTpjFt2rRi57pr165i95tp1aoV33zzTbFjBg4cWGwID2DUqFEF8sCeeOIJnnjiCYttY8eOLdW8BNaFcJiqCUkZOZyPNzocreqW7DCZQ3I5OgN6Q8UWUNMlGIWb2tMKBZOdURwU6jCZcpgcrFQwqYtwmOysLMG9NGRKxvei5kilmotOp2PRokU2+XsmsC6Ew1RNOHotGTDWX/J0LvkTv1O+RPDMXD0u9hX3o9Sbqtqq3d0r7JzVhWIdJlMOk/U6TMbXLnKYINNUZkGFqNZc2bRv377UlcgFguqMcJiqCafijPWXmga4lTDSiH2+pPCKTvxWBFMhq2BqOkovuULKCuStkrPOHKa7HSad6V9b/OSfKRk/cKiEXhIIBKVECKZqwsm4FACa+JVOMEmSVGnVvhXB5OlRoeetDphXyVHIKrmcDOus8m0mz2Ey/r6YBVP+Oju2QoZkvPWJkJxAICgtQjBVE07eMDpMYf6lE0xQeYnf+mRjeNAqHaZik76ts4+cGbPDZA7JmRN7bVIwmaSSSjRQFQgEpUQIpmqALMtcuJ0GQENflxJG51FZtZisOyRXdGsUqy9cqTH+vtwdkrNJwSQJwSQQCMqGEEzVgKSMXLJMLU7uboFSHA52xh9fRTpMsk6HIcUYHrRGwVRsHaYMc9K3decwGXQ6DAYDelNozhZzmNJlo2CShGASCASlRAimasD1ZGPTV29nrVKQsjQ4aY0PwIoUTHqTWAJQu5U+PFhTyKv0XXQdJmstK2DOYdLrdIq7BLbpMKWZdJIkVskJBIJSIgRTNSAuKQsAf4/Su0uQF5LLqsCQnDkcp3J1VfJ9rIniQnI5GdZeVsBUuFKvt3nBlG7SSSIkJxAISosQTNWA+LRsAGq7lk0wOZiSvjMqQTBZZTiOvKRvdLoCXcizrXyVnFpjFIv5HSaVSmXRXNVWSDNGwIXDJBAISo0QTNWAtCzjw8vVoWyf9B0rIYfJZgQTQL6wnMGgJzfb6PRp83VxtybyWqPk2vQKOVmWSTWJ5ZqYwxQfH8/48eMJCgrC3t4ePz8/IiMj2b9/PwAhISF88sknRR5/5coVRo8eTUBAAFqtluDgYCZNmsSdO3cKHf/CCy+gVqtZt25dgX2zZs1CkiTGjRtnsT0mJgZJkgq0abl06RKOjo6kpaWRkZHBtGnTCA0NxcHBAR8fHyIiIti4caMyvkuXLkiShCRJ2NvbExgYSL9+/Vi/fn2BuezevZtu3brh5eWFk5MTDRs2ZOTIkUqBVjD2pWvZsiUuLi54eHjQqlWrYtvMCAT5EYKpGpCWbRRMzmWs1l0ZdZh08bcB0Fhj413y1WHCMiyXm5Wl/F/rYKWCyVy4Ml9IzhYFU5regMlgQqrgtkL3g4EDB3L48GGioqI4c+YMmzZtokuXLkUKnvycP3+etm3bEhsby3fffcfZs2dZunQpO3fupFOnTiQkJFiMz8jIYM2aNbzxxhusXLmy0HM6ODiwYsUKYmNjS7z+xo0b6dq1Ky4uLowbN47169ezaNEiTp06xbZt2xg0aFCB1zF27Fji4uI4d+4cP/74I+Hh4QwdOpTnn39eGXPixAl69epF27Zt2bNnD0ePHmXRokVotVplccPKlSt55ZVXePnll4mJiWH//v288cYbpKWllThvgQBEa5RqQbpJMJW1vYlSh6kCQ3K6W7cA0Pj6Vtg5qxP5Hab8id85mcbEe5VajdpKV43lXyVny1W+E0xFSyVki5CcLMtk6jLv+3wcNY6lbgCclJTE3r172bVrl9LMNjg4uNStRyZOnIhWq2XHjh04mpzUoKAgWrVqRWhoKG+++Saff/65Mn7dunWEh4czdepUAgICuHLlCnXr1rU4Z+PGjfH19eXNN99k7dq1xV5/48aNDB48GIBNmzbx6aef0qdPH8DojLVp06bAMU5OTvj5+QFQp04dOnbsSJMmTRg9ejRDhgyhR48e7NixAz8/PxYsWKAcFxoaSq9evZTvN23axJAhQxgzZoyyrWnTpiW+Z5s2beK1117jypUrdOrUSWnQm5iYiIeVOvGCwhGCqRqQnlM+h8m8oi4zV09mjh5Jokyr7ApDEUy1rVMwUYTDZBZMWofSP7xqGvmTvm05JJdocmQlGYs8tkxdJh3+2+G+z+f3p3/Hya50pSxcXFxwcXFhw4YNdOzYEXt7+1JfJyEhge3bt/Puu+8qYsmMn58fw4YN4/vvv2fJkiXK38CKFSsYPnw47u7u9O7dm1WrVjFjxowC537vvfdo164dhw4dom3btoVePykpiX379vHNN98o19yyZQsDBgzA1dW11K8DYOTIkbz22musX7+eHj164OfnR1xcHHv27OGRRx4p9Bg/Pz92797NpUuXCA4OLtV1Lly4wKBBg5g0aRLPPfcchw8f5vXXXy/TXAXWgwjJVQPSso038LIKJnMD3rjkLCI++I1/LdqHTm8o4ajiyb11E7Bih0mSCq3FlJNlTPi2s9L8JbBsvmvLIblEs8MkywUS/6s7Go2GVatWERUVhYeHBw899BDTp0/nyJEjJR4bGxuLLMuEhYUVuj8sLIzExETi4+OV8dHR0Tz55JMADB8+nK+++qrQ96x169YMGTKEKVOmFHn9LVu20KJFCwICAgD44osvOHDgAN7e3rRr147JkycreVgloVKpaNSokZIjNXjwYJ566ikiIiLw9/enf//+LF68mJR8ZVJmzpyJh4cHISEhNG7cmFGjRrF27VoMhqLvmcuWLaNx48Z88MEHNG7cmKFDhzJq1KhSzVFgfdje3bIakheSK5s7ZM5h2ht7m9tp2dxKzeZsfFqp+9EVRo7pBqQNDCz3Oao7kkaDrNNZhOTMOUzWmr8EoDZX+s6Xw2TrIbn8OGoc+f3p3+/7fBw1ZfudGzhwIH379mXv3r1ER0ezdetWFixYwJdfflmqh3lpReLKlSuJjIykVq1aAPTp04cxY8bw66+/0r179wLj586dS1hYGDt27MC3kA9cGzdu5LHHHlO+f+SRRzh//jzR0dEcOHCAnTt38umnn/LOO+8U6mIV9jrMTpharearr75i7ty5/Prrr/z+++/MmzeP999/nz/++AN/f3/8/f05ePAgx44dY8+ePRw4cICRI0fy5Zdfsm3bNlSqgv7B6dOnadeuncW20oY/BdaHcJiqAeVN+jaH326byhJAXk2n8mBITyf38hUA7Js0Kfd5qjuF9ZNTQnK24DDpbNxh0uWF5PIjSRJOdk73/as8IWAHBwd69uzJjBkzOHDgAKNGjWLmzJnFHtOgQQMkSeLkyZOF7j958iSenp74+Pig1+uJiopi8+bNaDQaNBoNTk5OJCQkFJn8HRoaytixY5k6dWoBUZaTk8O2bdssBBMYBXvnzp2ZMmUKO3bsYPbs2cyZM8diZVth6PV6YmNjqVevnsX2wMBAnnnmGRYvXszx48fJyspi6dKlFmOaNWvGhAkT+Pbbb/n555/5+eef2b17d7HXEwhACKZqQXp5V8lpCzpS8fnEU1nJOnMGZBmNjw8aL69yn6e6U6hgyjILJutsiwJ5Sd96nc6mc5iKcphqMuHh4aSnpxc7xtvbm549e7JkyRIyMy2T22/cuMHq1at58sknkSSJLVu2kJqayuHDh4mJiVG+vvvuO9avX0+SqfzI3bz99tucOXOGNWvWWGzftWsXnp6etGzZssTXodPpyMoq/oNfVFQUiYmJDBw4sMgxnp6e+Pv7F/u+hIeHAxQ5pnHjxhw6dMhi259//lns3ATWi+3dLash5V4lV0iC9+17EEzZp08D1u0uAWBnep/z5S6YHSY7+7IVD61JiBwmI0lK0rdRMOUP7VR37ty5w+DBgxk9ejQtWrTA1dWVQ4cOsWDBAh5//HFl3LVr14iJibE4Njg4mMWLF/Pggw8SGRnJ3LlzqVevHsePH+ff//43gYGBvPvuu4Ax2btv374FBE54eDiTJ09m9erVTJw4scD8ateuzauvvsoHH3xgsX3Tpk0F3KUuXbrw1FNP0bZtW7y9vTlx4gTTp0+na9euuOVry5SRkcGNGzfQ6XRcvXqVn376iYULFzJ+/Hi6du0KGHONYmJi6N+/P6GhoWRlZfH1119z/PhxFi1aBMD48eMJCAigW7du1KlTh7i4OObOnYuPjw+dOnUC4KeffmLatGmcOnUKMNag+vjjj5kyZQpjxowhJiaGVatWAdSY3xlBxSEcpmqAkvStvXfBlJpVsOVHack+dx4A+4YNy32OmoCkNjtM+VfJGZO+rTskV7A1ii3mMKWYQnLkE0w1BRcXFzp06MDChQt55JFHaNasGTNmzGDs2LEsXrxYGffhhx/SqlUri6/NmzfTsGFDDh06RP369RkyZAihoaE8//zzdO3alYMHD+Ll5cXNmzfZvHlzoe6NSqWif//+rFixosg5vv7667i4uFhsK0wwRUZGEhUVxaOPPkpYWBgvvfQSkZGRBUoTLF++HH9/f0JDQxkwYAAnTpxQVvOZad++PWlpaYwbN46mTZsSERFBdHQ0GzZsUMov9OjRg+joaAYPHkyjRo0YOHAgDg4O7Ny5E29T3bnk5GROmz44AtSrV48ffviB9evX06JFCz7//HPefPNNgDKtUBRYB7b38bIaYnaYylrp26GQkJz5XOUh58IFALT1Qsp9jpqAUotJn79wpQ3kMOWrw2TLIblkcw5TFc+jPNjb2zN//vxiq1PfXV37boKDgxWXpDBq166t/H4URn6hMmvWLGbNmmWx383NTVlpB/D333+TkpKiCBcz06ZNY9q0acXOddeuXcXuN9OqVSulXEFRDBw4sNgQHqDUWMrPY489ZiH23n33XerUqYODg/W60YLCsb27ZTVDb5CV1iZlLitQiMOUdi+CyXSjtb8rkdLakExOi6zPK/iZvw6TtWLuGWfrlb5TFMFU8xymmohOp2PRokU11s1csmQJ7dq1w9vbm/379/PBBx/w4osvVvW0BFWA7d0tqxnmopUAzmUtK1CBDpMsy+hMnwo1fv7lOkeNQVNISM4Gkr4Ly2GqqQ+xeyGpiFVygsqhffv2NXopfmxsLHPnziUhIYGgoCBee+21Ep0xgXUiBFMVYxY4dmoJe0356jBZnq98bVLkzEzkbGPCuMbTo1znqCkoq+T0BSt921mxw6QSq+QAo8OkRjhMgtKxcOFCFi5cWNXTEFQDRNJ3FVPekgJQuMNU3pCcPjERAEmrRXKyXpcF8kJy5A/J2UAOk9JLzsZDcjU5h0kgEFQdQjBVMeVdIQfg61ow6bC8ITldYhIAak9P618uW0hrFCXp24oTOZVVcjbcfDfXIJNhah9k/i0XDpNAICgNQjBVMeWtwQSg1aioV8sZgAdDvS3OV1bMDpPaBrpvS4UKJmM40qpDcuo8h8lWQ3JmdwlEDpNAICgbVS6Y/vOf/xASEoKDgwMdOnTgjz/+KHZ8UlISEydOxN/fH3t7exo1asSWLVvu02wrnry2KGXLXzLz0ZCWjO8Syqs9GwGQpStf8119cjIAanf3ch1fkygsJKfLMeVvabVVMaX7gkrpJWe7hSvNK+ScVCrMRqpwmAQCQWmo0rvl999/z6uvvsrSpUvp0KEDn3zyCZGRkZw+fbrQ5o05OTn07NkTX19ffvjhBwIDA7l06RIeNdgVuZccJoDWQZ60DvLk0h1jaf+s3HImfZtCUiorz18CwCQcLBwmGxBMaqWXnO0WrjQ7TK6aKv+sKBAIahhVKpg+/vhjxo4dy7PPPgvA0qVL2bx5MytXrmTq1KkFxq9cuZKEhAQOHDig3OhDQkLu55QrnHsJyeXHvGIuK1dfrlYPhkxj7ybJinN4zEga4++OrMvvMBmbfdpprbd6b17hylybdZiSTa/bRa1GMmUxCYdJIBCUhir7mJWTk8Nff/1Fjx498iajUtGjRw8OHjxY6DGbNm2iU6dOTJw4kdq1a9OsWTPmzZuHXl8+V6U6oCR936NgsjcJJoMMufqyPwDkbKNgUtmCYFJCcnkOk85cUsGKBVP+wpW2nsPkos679QnBJKgqunTpwiuvvFKlc8jIyGDgwIG4ubkhSRJJSUmEhITwySefKGMkSWLDhg1VNsfqQpUJptu3b6PX66ldu7bF9tq1a3Pjxo1Cjzl//jw//PADer2eLVu2MGPGDD766CPmzp1b5HWys7NJSUmx+KpOVJTD5GCX96PMLEdYTnGYHK1fMOWF5IzvkyzLisOkseL+UKL5bl4Ok2shNcxqCvHx8YwfP56goCDs7e3x8/MjMjKS/fv3AxR42N3NlStXGD16NAEBAWi1WoKDg5k0aRJ37twpdPwLL7yAWq1m3bp1BfbNmjULSZIYN26cxfaYmBgkSSrQpuXSpUs4OjqSlpZGRkYG06ZNIzQ0FAcHB3x8fIiIiGDjxo3K+C5duiBJEpIkYW9vT2BgIP369WP9+vUF5rJ79266deuGl5cXTk5ONGzYkJEjR5Jj+tsGY1+6li1b4uLigoeHB61atSq2zUxls379eubMmVNl1weIiopi7969HDhwgLi4ONzd3fnzzz95/vnnCx1/8eJFJEkq0NzZFqhRgXyDwYCvry9ffPEFbdq04cknn+TNN99k6dKlRR4zf/583N3dla+6devexxmXzL0mfZvRqvOSWLPLI5jMOUz21i+Y8kJyxvfeoNchy8ZkeWvOYTInfdty892k3JrvMA0cOJDDhw8TFRXFmTNn2LRpE126dClS8OTn/PnztG3bltjYWL777jvOnj3L0qVL2blzJ506dSIhIcFifEZGBmvWrOGNN95g5cqVhZ7TwcGBFStWEBsbW+L1N27cSNeuXXFxcWHcuHGsX7+eRYsWcerUKbZt28agQYMKvI6xY8cSFxfHuXPn+PHHHwkPD2fo0KEWD/QTJ07Qq1cv2rZty549ezh69CiLFi1Cq9UqEYiVK1fyyiuv8PLLLxMTE8P+/ft54403SEtLK3HeFY1ZxHl5eeHq6nrfr5+fc+fOERYWRrNmzfDz80OSJHx8fHC6D/msxfUsrI5UmWCqVasWarWamzdvWmy/efMmfn5+hR7j7+9Po0aNlNACQFhYGDdu3LD4FJGfadOmkZycrHxduXKl4l5EBXCvSd9mJEnCQWPOYyr7SjnZhhymu0NyuaZwHFh3SE5xmGy40rfiMKnVBfL8ZFnGkJFx37/KItiSkpLYu3cv77//Pl27diU4OJj27dszbdo0iwaxRTFx4kS0Wi07duwgIiKCoKAgevfuzS+//MK1a9d48803LcavW7eO8PBwpk6dyp49ewq9fzZu3JiuXbsWOLYwNm7cqMxz06ZNTJ8+nT59+hASEkKbNm146aWXGD16tMUxTk5O+Pn5UadOHTp27Mj777/PsmXLWL58Ob/88gsAO3bswM/PjwULFtCsWTNCQ0Pp1asXy5cvx9FUjHbTpk0MGTKEMWPG0KBBA5o2bcpTTz3Fu+++W+ycN23aRMOGDXFwcKBr165ERUUpoSuAVatW4eHhwYYNG5RxkZGRFu/VrFmzeOCBB/jyyy+pV6+e0rj37pBcSEgIc+fOZcSIEbi4uBAcHMymTZuIj4/n8ccfx8XFhRYtWnDo0CGLOe7bt4/OnTvj6OhI3bp1efnll0lPTy/x59GlSxc++ugj9uzZgyRJdOnSRZlHUS5lPVOv0VatWlkcA/Dll18SFhaGg4MDTZo0sWjUbHamvv/+eyIiInBwcGD16tUlzrE6UWV3S61WS5s2bdi5cydPPPEEYHSQdu7cWWRjw4ceeoj//ve/GAwGVCqj1jtz5gz+/v5oi3AG7O3tsa/GYRZzL7l7DcmBsfJ3Zq6eLF05HCYlh8l66xCZke4KyZnDcZKkUqphWyOi+W6+HCZNQYdJzszkdOs2931Ojf/+q9TV9V1cXHBxcWHDhg107NixTPe2hIQEtm/fzrvvvquICDN+fn4MGzaM77//niVLlihicsWKFQwfPhx3d3d69+7NqlWrmDFjRoFzv/fee7Rr145Dhw7Rtm3bQq+flJTEvn37+Oabb5RrbtmyhQEDBpTZZRk5ciSvvfYa69evp0ePHvj5+REXF8eePXt45JFHCj3Gz8+P3bt3c+nSJYKDg0t1nQsXLjBo0CAmTZrEc889x+HDh3n99dcLjMvIyODdd9/l66+/RqvVMmHCBIYOHaqESQHOnj3Ljz/+yPr16y0+9N/NwoULmTdvHjNmzGDhwoU888wzPPjgg4wePZoPPviAKVOmMGLECI4fP44kSZw7d45evXoxd+5cVq5cSXx8PC+++CIvvvgiX331VbGvb/369UydOpVjx46xfv36Ip+j+fnjjz9o3749v/zyC02bNlWOWb16NW+//TaLFy+mVatWHD58mLFjx+Ls7MzIkSOV46dOncpHH31Eq1atFOFYU6jSkNyrr77K8uXLiYqK4uTJk4wfP5709HRl1dyIESMsmhyOHz+ehIQEJk2axJkzZ9i8eTPz5s1j4sSJVfUS7pnUrIoTTA6mh0B5SguYHSaVDThMd1f6zkv41lp1lXOVKRRpy5W+U5Sk75qZw6TRaFi1ahVRUVF4eHjw0EMPMX36dI4cOVLisbGxsciyTFhYWKH7w8LCSExMJN7UhDs2Npbo6GiefPJJAIYPH85XX31VqCPWunVrhgwZwpQpU4q8/pYtW2jRogUBAQEAfPHFFxw4cABvb2/atWvH5MmTLQRGcahUKho1aqTkSA0ePJinnnqKiIgI/P396d+/P4sXL7bIWZ05cyYeHh6EhITQuHFjRo0axdq1azEYinbkly1bRuPGjfnggw9o3LgxQ4cOZdSoUQXG5ebmsnjxYjp16kSbNm2IioriwIEDFnUFc3Jy+Prrr2nVqhUtWrQo8pp9+vThhRdeoGHDhrz99tukpKTQrl07Bg8eTKNGjZgyZQonT55UojPz589n2LBhvPLKKzRs2JAHH3yQzz77jK+//pqsrKxi30dzvpdWq8XPzw8vL69ixwP4+PgA4O3tbXHMzJkz+eijjxgwYAD16tVjwIABTJ48mWXLllkc/8orryhj/P1rVqP3Kv14+eSTTxIfH8/bb7/NjRs3eOCBB9i2bZuSCH758mXFSQKoW7cu27dvZ/LkybRo0YLAwEAmTZpU7B9pdSetgpK+ARxMiayZOeXJYTKF5Gwhh0lt2XxXKVpZjZ3IikBtctZ0ep3ykLBVhyl/SM4sACRHRxr//dd9n5NUxv6FAwcOpG/fvuzdu5fo6Gi2bt3KggUL+PLLLwt9mN9NaUOAK1euJDIyklq1agHGB/mYMWP49ddf6d69e4Hxc+fOJSwsjB07dhRaRy9/OA7gkUce4fz580RHR3PgwAF27tzJp59+yjvvvFOoi1XY6zD/DNVqNV999RVz587l119/5ffff2fevHm8//77/PHHH/j7++Pv78/Bgwc5duwYe/bs4cCBA4wcOZIvv/ySbdu2WTxrzJw+fZp27dpZbGvfvn2BcRqNxmJckyZN8PDw4OTJk8r44OBgRWwUR34xZX4WNm/evMC2W7du4efnxz///MORI0cswluyLGMwGLhw4UKRArkiSU9P59y5c4wZM4axY8cq23U6He53FUMuyoGsCVT53dJsHRbGrl27Cmzr1KkT0dHRlTyr+0ea2WFyuPcfhbm0QHmqfctZtuMwmUNy3BWSs+aEb8i/Si7vgWmzgklT0GGSJKnGNJ52cHCgZ8+e9OzZkxkzZvDcc88xc+bMYgVTgwYNkCSJkydP0r9//wL7T548iaenJz4+Puj1eqKiorhx44bF74her2flypWFCqbQ0FDGjh3L1KlTWbFihcW+nJwctm3bxvTp0y2229nZ0blzZzp37syUKVOYO3cus2fPZsqUKcWGh/R6PbGxsQXETGBgIM888wzPPPMMc+bMoVGjRixdupR33nlHGdOsWTOaNWvGhAkTGDduHJ07d2b37t107dq1yOtVBM7OzqUal9/1NQvCwraZP/SkpaXxwgsv8PLLLxc4V1BQULnnWxbMifPLly+nQ4cOFvvuDj+W9n2ojpT7bnnu3Dm++uorzp07x6effoqvry9bt24lKCiIpk2bVuQcrRqzw+TmcO+hEUe78ofkFIephsWUy8VdITlzlW9rLloJec139XKeoLY1wZSSrw7T3Q5TTSY8PLzEOjne3t707NmTJUuWMHnyZIs8phs3brB69WpGjBiBJEls2bKF1NRUDh8+bPHAO3bsGM8++yxJSUmFdlh4++23CQ0NZc2aNRbbd+3ahaenJy1btizxdeh0OrKysooVTFFRUSQmJjJw4MAix3h6euLv719s8nN4eDhAkWMaN25coPXWn3/+WWCcTqfj0KFDipt0+vRpkpKS7ou707p1a06cOEGDBg0q/VqA8nPJX/+wdu3aBAQEcP78eYYNG3Zf5lEVlCuHaffu3TRv3pzff/+d9evXK+ryn3/+YebMmRU6QWsnrSJzmPJV+y4rSlmBMoYHaiIFQ3Jmh8nKBZNZHEnGP3u1Wl1oGMKaMZcVKMxhqgncuXOHbt268e2333LkyBEuXLjAunXrWLBgAY8//rgy7tq1a8TExFh8JSYmsnjxYrKzs4mMjFRWvW3bto2ePXsSGBiorBhbsWIFffv2pWXLlooj06xZM4YMGYKHh0eRq5tq167Nq6++ymeffWaxfdOmTQVW8XXp0oVly5bx119/cfHiRbZs2cL06dPp2rUrbm5uyriMjAxu3LjB1atXiY6OZsqUKYwbN47x48crrtCyZcsYP348O3bs4Ny5cxw/fpwpU6Zw/Phx+vXrBxhzYOfMmcP+/fu5dOkS0dHRjBgxAh8fHzp16gTATz/9RJMmTZRrv/DCC5w6dYopU6Zw5swZ1q5dy6pVqwAs8h3t7Ox46aWX+P333/nrr78YNWoUHTt2LDR8V9FMmTKFAwcO8OKLLxITE0NsbCwbN24sMnJzr/j6+uLo6Mi2bdu4efMmyaY+pO+88w7z58/ns88+48yZMxw9epSvvvqKjz/+uFLmURWU6245depU5s6dy88//2zxKaBbt25WFS6rbAwGmbScigvJ3YtgUsoKWHkeDxQSksu2/j5ykM8aVxlv9LbmLsmynOcwFbJKribg4uJChw4dWLhwIY888gjNmjVjxowZjB07lsWLFyvjPvzwQ1q1amXxtXnzZho2bMihQ4eoX78+Q4YMITQ0lOeff56uXbty8OBBvLy8uHnzJps3by7UvVGpVPTv379AyC0/r7/+Oi4uLhbbChNMkZGRREVF8eijjxIWFsZLL71EZGQka9eutRi3fPly/P39CQ0NZcCAAZw4cUJZzWemffv2pKWlMW7cOJo2bUpERATR0dFs2LCBiIgIAHr06EF0dLSSPD1w4EAcHBzYuXMn3t7eACQnJ3P69GnlvPXq1eOHH35g/fr1tGjRgs8//1wpn5B/haKTkxNTpkzh6aef5qGHHsLFxYXvv/++yPeoImnRogW7d+/mzJkzdO7cmVatWvH2228ryfUVjUaj4bPPPmPZsmUEBAQoQv25557jyy+/5KuvvqJ58+ZERESwatUqpQyBNSDJ5bhbuLi4cPToUerVq4erqyv//PMP9evX5+LFizRp0qTEzPyqJCUlBXd3d5KTky0+xVQFqVm5NJ+1A4BTc3opgqe8TFj9F1uO3uCdx5oy8sGQMh0b260buutxhKxbi2O+BENr5Nann3Ln86V4DhuG34y3OLn3N7Ys/oig5g8w+K2iq8bXdHS5uXw6vD96e0cy6jfFxcWl0CXS1kqm3kC9PcbVZMfbNeTSmdPUqlULDw+PKr8XWDN///033bp1Iz4+3ipWZb777rssXbpUqbO0atUqXnnlFaUuk+DeyMrK4sKFCxb1qsxU9fO7XB8xPTw8iIuLK6AcDx8+TGBgYIVMzBYw5y/ZqSXsK6B7ekU4TLbRS84yJJdrI0nfisNkCsnZmsNkdpdUgKPatkKRVYlOp2PRokU1ViwtWbKEdu3a4e3tzf79+/nggw8qLdwlqN6U6445dOhQpkyZwrp165AkCYPBwP79+3n99dcZMWJERc/RaklMN1Zbdne0q5D6P3mCqeyr5JSkb1vIYTKH5PTmVXK2kfQtqVRIkgpZZZuCKckkmNw1alT5/t5qUkiuJtK+ffv7kstTWcTGxjJ37lwSEhIICgritddes6gPWJ3Zu3cvvXv3LnJ/VbSFqcmU645pLhZZt25d9Ho94eHh6PV6nn76ad56662KnqPVciPFmGjt514xro7SGqWMlb5lWUbONPeSs27RACCZV8nl2lbSN5j6yUm2mcNkdpjcTH8n1lykVFBxLFy4kIULFxa5f9SoUaWqf1UVtG3b1iab5FYW5bpjarVali9fzttvv83Ro0dJS0ujVatWNGzYsKLnZ9XcSDY6G35uFSSYTGUFylq4Us7Xh88WHCaUkJzxfcq1kaRvMNZiMjtMNTVEUl6S8zlM+REOk8BacXR0vG/lBmyBe/qIWbduXcVlOnr0KImJiXh6elbU3KyeG8kV7DCZQnLZZXWYTO4S2IjDdFfzXaXStw0IJrVabbM5TMkmR9GthpYUEAgEVUu5Mh9feeUVZVmpXq8nIiKC1q1bU7du3UKrcwsKJy7ZmDdUUQ6TYzlzmMz5S9jZIdmA6yDZWYbk9DpjLpktCCaVRoNsq4LJ7DDZWYbkhMMkEAhKQ7kE0w8//KBUbP2///s/zp8/z6lTp5g8ebJSo0JQMjdSjEKldkWF5LTl6yWntEWxAXcJAJPDZA7J6XONgkmtsX6xqNJolDpMthaSSxEhOYFAcA+USzDdvn0bPz8/wNiBesiQITRq1IjRo0dz9OjRCp2gNXPD5DD5u1dM3pDZYcosY1mBvBVy1l9SAAqWFdCbnCa1DQgIWw7JJd2V9C0QCARloVyCqXbt2pw4cQK9Xq+U1QdjCfu7G+0JisbsMPm5V4yzU27BZF4h52ADCd/kheS4KySntgEBoVLbbkjO7DB5aERITiAQlJ1y3TGfffZZhgwZgr+/P5Ik0aNHDwB+//13iz48gqJJz9aRauoj51dRDpO2fM13lZCcg42G5HQ2FJJTq5WPSbYWkjPnMNXUPnICgaBqKZfDNGvWLL788kuef/559u/fr/TUUavVTJ06tUInaK3cNLlLzlp1hTTeBXA0OScZZcxhUkJytuIw3R2S0xn/VdmA46LW2CHbaB2mNJ1xMcTdIbma5jDFx8czfvx4goKCsLe3x8/Pj8jISPbv3w9ASEgIn3zySZHHX7lyhdGjRxMQEIBWqyU4OJhJkyZx586dQse/8MILqNVq1q1bV2DfrFmzkCSJcePGWWyPiYlBkiQuXrxosf3SpUs4OjqSlpZGRkYG06ZNIzQ0FAcHB3x8fIiIiGDjxo3K+C5duiBJEpIkYW9vT2BgIP369WP9+vUF5rJ79266deuGl5cXTk5ONGzYkJEjR5KTr2zK8uXLadmyJS4uLnh4eNCqVSvmz59f5HslEOSn3HfMQYMGFdg2cuTIe5qMLZGQbvwj9napOFfH8V6Tvm2gLQoUEpIzJX1rbMBxUWnUYKOVvlNNjqKrumYXrhw4cCA5OTlERUVRv359bt68yc6dO4sUPPk5f/48nTp1olGjRnz33XfUq1eP48eP8+9//5utW7cSHR2Nl5eXMj4jI4M1a9bwxhtvsHLlSgYPHlzgnA4ODqxYsYLXXnutxFp8GzdupGvXrri4uDBixAh+//13Fi1aRHh4OHfu3OHAgQMFXsfYsWOZPXs2Op2Oq1ev8tNPPzF06FBGjRrFF198AcCJEyfo1asXL730Ep999hmOjo7Exsby448/ojf93FeuXMkrr7zCZ599RkREBNnZ2Rw5coRjx46V+L6ZkWUZvV5vc387AiPl/qnv3LmTnTt3cuvWLQwGy2XsK1euvOeJWTt3TILJy7nilrI7lrOXnCHTtpK+iwzJ2YJgypfDZGshuVQlJGdprNckhykpKYm9e/eya9cuIiIiAAgODi5165GJEyei1WrZsWMHjqYitUFBQbRq1YrQ0FDefPNNPv/8c2X8unXrCA8PZ+rUqQQEBHDlyhXq1q1rcc7GjRvj6+vLm2++ydq1a4u9/saNGxXRtWnTJj799FP69OkDGJ2xNm3aFDjGyclJWWRUp04dOnbsSJMmTRg9ejRDhgyhR48e7NixAz8/PxYsWKAcFxoaSq9evZTvN23axJAhQxgzZoyyrWnTpsXOd9euXXTt2pUtW7bw1ltvcfToUXbs2EGXLl2KPU5gnZQrJPfOO+/w6KOPsnPnTm7fvk1iYqLFl6BkFIepEgRT2UNy5rYotiGYpLsqfdtUSE5tu61RUk0huaJymGRZJjdbf9+/yiLYXFxccHFxYcOGDWSbKtSXloSEBLZv386ECRMUsWTGz8+PYcOG8f3331vMZ8WKFQwfPhx3d3d69+7NqlWrCj33e++9x48//sihQ4eKvH5SUhL79u3jscceU665ZcsWUlNTy/Q6wBjN8PT0VEJzfn5+xMXFsWfPniKP8fPzIzo6mkuXLpX5elOnTuW9997j5MmTtGjRoszHC6yDct0xly5dyqpVq3jmmWcqej42Q0JlOEzavFVysiyXOuQgZxlvvLbiMCkhOZOzpITkbCHpW6MRITm1GsgTBWaBoMsx8MWk3fd9Xs9/GoGdfekS0TUaDatWrWLs2LEsXbqU1q1bExERwdChQ0t8kMfGxiLLMmFhYYXuDwsLIzExkfj4eHx9fYmNjSU6OloRJcOHD+fVV1/lrbfeKnBvad26NUOGDGHKlCns3Lmz0PNv2bKFFi1aEBAQAMAXX3zBsGHD8Pb2pmXLljz88MMMGjSIhx56qMT3QaVS0ahRIyVHavDgwWzfvp2IiAj8/Pzo2LEj3bt3Z8SIEbi5uQEwc+ZMBgwYQEhICI0aNaJTp0706dOHQYMGoVIV7x3Mnj1bWQ0usF3K5TDl5OTw4IMPVvRcbAqzYPKsBMEEkK0rfbVvxWGymaRvU0jOFKIx2JDDpFKrbbKsgF6WSddbOkw1OYfp+vXrbNq0iV69erFr1y5at25dpPtzN6V1tFauXElkZCS1atUCoE+fPiQnJ/Prr78WOn7u3Lns3buXHTt2FLp/48aNirsE8Mgjj3D+/Hl27tzJoEGDOH78OJ07d2bOnDmlfh3mn6Farearr77i6tWrLFiwgMDAQObNm0fTpk2Ji4sDwN/fn4MHD3L06FEmTZqETqdj5MiR9OrVq0Bayd20bdu2VHMSWDflumM+99xz/Pe//2XGjBkVPR+bISPH+JCuqBVyAA75cjMyc/RKb7mSkE05TCobcZjubr5rSzlMao1GKVxpSzlMafn6K7pqVMh5C6cUAaHRqnj+04j7PTU02rJ/bnVwcKBnz5707NmTGTNm8NxzzzFz5kxGjRpV5DENGjRAkiROnjxJ//79C+w/efIknp6e+Pj4oNfriYqK4saNGxbCWq/Xs3LlSrp3717g+NDQUMaOHcvUqVOV1llmcnJy2LZtG9OnT7fYbmdnR+fOnencuTNTpkxh7ty5zJ49mylTpqAtplWRXq8nNjaWdu3aWWwPDAzkmWee4ZlnnmHOnDk0atSIpUuX8s477yhjmjVrRrNmzZgwYQLjxo2jc+fO7N69m65duxZ5PWdn5yL3CWyHcj2ts7Ky+OKLL/jll19o0aJFgRvvxx9/XCGTs2bMeUZO2oqrCaNRq9CqVeToDWTk6iltG2RDtinp21ZymJSQnGVZAZspXGmDIblUk7uklSTsVSqy8u0zCyZJkkodGqtuhIeHs2HDhmLHeHt707NnT5YsWcLkyZMt8phu3LjB6tWrGTFiBJIkKblFhw8ftihGfOzYMZ599lmSkpLw8PAocI23336b0NBQ1qxZY7F9165deHp6Ki21insdOp2OrKysYgVTVFQUiYmJDBw4sMgxnp6e+Pv7k56eXuz1gGLHCARmynXHPHLkCA888ABAgSWZNdXmvt+YBZNjBQom8/lyMg1lKi1gaw5TXkjOsqyAzRSutMGk79RCilbWxHvVnTt3GDx4MKNHj6ZFixa4urpy6NAhFixYwOOPP66Mu3btGjExMRbHBgcHs3jxYh588EEiIyOZO3euRVmBwMBA3n33XcCY7N23b98CAic8PJzJkyezevVqJk6cWGB+tWvX5tVXX+WDDz6w2L5p0yaLcBwYayw99dRTtG3bFm9vb06cOMH06dPp2rWrkncExtIGN27csCgrsHDhQsaPH6+4QsuWLSMmJob+/fsTGhpKVlYWX3/9NcePH2fRokUAjB8/noCAALp160adOnWIi4tj7ty5+Pj40KlTJwB++uknpk2bxqlTp8ryYxHYCOW6Y/72228VPQ+bI7MSHCYwrpRLzswtU2kBpXCljThMBUJyubYVkjM7TLYUkiuqpADUrLICLi4udOjQgYULF3Lu3Dlyc3OpW7cuY8eOtQh3ffjhh3z44YcWx37zzTcMHz6cQ4cOMXPmTIYMGUJCQgJ+fn488cQTzJw5Ey8vL27evMnmzZv573//W+D6KpWK/v37s2LFikIFE8Drr7/O559/TlZWno+3adOmAuVmIiMjiYqKYvr06WRkZBAQEMC//vUv3n77bYtxy5cvZ/ny5Wi1Wry9vWnTpg3ff/+9RVixffv27Nu3j3HjxnH9+nVcXFxo2rQpGzZsUMov9OjRg5UrV/L5559z584datWqRadOndi5cyfe3t4AJCcnc/r06dL8KAQ2yD1/xLx69SpgrI8hKD3mfm+OpcwzKi35V8qVFtmc9O1kI0nfRYbkrF9AqGy0+W6KKSTnVsN7Xdrb2zN//vxiq1PfXV37boKDg4tNEK9duza5pg8RhbFkyRLl/7NmzWLWrFkW+93c3IiPj1e+//vvv0lJSVGEi5lp06Yxbdq0Yue6a9euYvebadWqFd98802xYwYOHFhsCA9g1KhRFnlgXbp0qVGCWlC5lGuVnMFgYPbs2bi7uxMcHExwcDAeHh7MmTOnxNUGAiN5IbmKfWg5lKMWk1K40lYqfd8dklOSvq1fQKjUGpsMyZmTvl0KCcmJB2LlotPpWLRokU05mgLrpFx3zDfffJMVK1bw3nvvKTUz9u3bx6xZs8jKylLi4IKiyTStkqvokJxTOdqjGDJNDpOjU4XOpdqSLyQnGwwYTKE5W3CYbDYkpy86JCeoXNq3b1/qSuQCQXWmXIIpKiqKL7/80iKJr0WLFgQGBjJhwgQhmEqB4jBVdEiuHO1RZEUw2YjDZJcnmMzhOLANwSTZakjOXOW7iJBcWQq9CgQC26RcH7cSEhJo0qRJge1NmjQhISHhnidlC5hzjCraYTKH5MqSw6QkfdtYSI7cXHS5eQV5bCHpW7LRVXJpJaySE2E5gUBQEuW6Y7Zs2ZLFixfz2WefWWxfvHhxiXU2BEbyVslV7EPLnPRdphwmc9K3o20kfZPPZdDn5BNMVZwQnJOl487VNDJSc5ANeb3NdDl67OzV1KrrSq06LvfkhJirfINthuTciugjJxAIBCVRrqf1ggUL6Nu3L7/88otSv+LgwYNcuXKFLVu2VOgErZEcnQGdwfiJtqJDck7lCsmZ6jDZisOUTyjoTEU7VWoNUgn9pCqLhOvp/PF/5zkfE09JRodviBs9nw3Ho3b58s2kfIKpqgXi/STFnPStLvxnLBwmgUBQEuUSTBEREZw5c4b//Oc/SoGvAQMGMGHCBKWxoqBo8idkV0bhSoD0bF0JI/MwJ31LNuIwSfkdJpNYrIoq3waDzN/bLvLn5osY9MYHtounPS6e9qhMD3Y7ezUarZrsjFziziZz62IKa+f9Sc8xTanXolaZr2lO+FZRMws3lpc0nWUfObCt1y8QCO6dcj8lAgICRHJ3OcnINYoZjUpCW8GrdlwdjD/StFIKJtlgQM4yV/q2QcGUkw3c//ylzNQctn95nGunEwEIae5NxydC8Q50KfKYtMRsfl55nOuxSWz9/AiRY5sR2tq3bBc2CSZb0wolheSEwyQQCEqi3IIpMTGRFStWcPLkScBYMv/ZZ5/Fy8urwiZnrWRWUlsUyBNMKZlFF57Lj5ydrfzfVkJy5HOTdNn3XzDduZ7G5sVHSE3IQmOvJuKpRjTu4Fei4+Hiac9jrzzArm9OcSr6BjtWHmdYkCtutUovdGXTNWxtcX1JITmBQCAoiXLdPfbs2UNISAifffYZiYmJJCYm8tlnn1GvXj327NlT0XO0Oiqj8a4ZNwfjgz81q3QOkyFf+wKbWSWnUilOiy7r/obkzsfE8+OCv0hNyMLdx5FBU9rQpKN/qcNDarWKriPCqF3PDYNO5uLR22W6vozxOjZmMCkhOeEwCaqakJAQPvnkkyqdw40bN+jZsyfOzs5KE2VJkpQGzhcvXkSSpAL9CG2dcgmmiRMn8uSTT3LhwgXWr1/P+vXrOX/+PEOHDi2yv5Agj7ySAhX/kHYto2Ay12CStFqLUJW1Y36tSkiukmswZaXnsue702xdepTcLD0BDT0YNKUt3gFFh+CKQqWSqP+ADwBXTyWW6VjZpJRszWdJ0RcsKwA1M48pPj6e8ePHExQUhL29PX5+fkRGRrJ//36g5AfylStXGD16NAEBAWi1WoKDg5k0aRJ37twpdPwLL7yAWq1m3bp1BfbNmjULSZIYN26cxfaYmBgkSSrQpuXSpUs4OjqSlpZGRkYG06ZNIzQ0FAcHB3x8fIiIiGDjxo3K+C5duiBJEpIkYW9vT2BgIP369WP9+vUF5rJ79266deuGl5cXTk5ONGzYkJEjR5KTbyXs8uXLadmyJS4uLnh4eNCqVati28xUBn/++SfPP//8fb3m3SxcuJC4uDhiYmI4c+YMAHFxcfTu3bvQ8bt27UKSJJKSku7jLKsf5Xpinz17lh9++MFilY1arebVV1/l66+/rrDJWStmh8mhglfIQb6QXFbpQnJKDSYbyV9SsLOD3Fz0Oaa2KBXsMMmyzK2LqVw7k8jNCylcPpmALtv4c2/ZvS6dBoSivofwUGBjTwCunUnCoDcoSeIlzktxmGzLUUmzolVyAwcOJCcnh6ioKOrXr8/NmzfZuXNnkYInP+fPn6dTp040atSI7777jnr16nH8+HH+/e9/s3XrVqKjoy3SKjIyMlizZg1vvPEGK1euZPDgwQXO6eDgwIoVK3jttddo2LBhsdffuHEjXbt2xcXFhREjRvD777+zaNEiwsPDuXPnDgcOHCjwOsaOHcvs2bPR6XRcvXqVn376iaFDhzJq1Ci++OILAE6cOEGvXr146aWX+Oyzz3B0dCQ2NpYff/wRvUksr1y5kldeeYXPPvuMiIgIsrOzOXLkCMeOHSvxfTMjyzJ6vb5cNcxycnLQarX4+PiU+diK5ty5c7Rp08bi5+Xn51fp172X9686UK47duvWrZXcpfycPHlS1GEqBZmVGZJzLGNILsPGajCZMDtM5rICFZnDFHcume/f/ZMf3j/EwZ/OcT4mHl22Hq8AZ/q93JKHBze8J7EE4BPkitZRQ06mjvgraaU+TpEFNUgg3Cu5BplMUxmPu0NydxevzM3Kuu9fZRFrSUlJ7N27l/fff5+uXbsSHBxM+/btmTZtmkXnhaKYOHEiWq2WHTt2EBERQVBQEL179+aXX37h2rVrvPnmmxbj161bR3h4OFOnTmXPnj1cuXKlwDkbN25M165dCxxbGBs3blTmuWnTJqZPn06fPn0ICQmhTZs2vPTSS4wePdriGCcnJ/z8/KhTpw4dO3bk/fffZ9myZSxfvpxffvkFgB07duDn58eCBQto1qwZoaGh9OrVi+XLl+Nourdt2rSJIUOGMGbMGBo0aEDTpk156qmnil28ZHZWtm7dSps2bbC3t1fagD3wwAMsW7aMunXr4uTkxJAhQ0hOTlaOHTVqFE888QTvvvsuAQEBNG7cGCjoAEqSxLJly/jXv/6Fk5MTYWFhHDx4kLNnz9KlSxecnZ158MEHOXfuXIH3snXr1jg4OFC/fn3eeecddLqS7/shISH8+OOPfP3110iSpDQbzh+Sy8/Fixfp2rUrAJ6enhbHGAwG5s+fT7169XB0dKRly5b88MMPJb5/NZVyybyXX36ZSZMmcfbsWTp27AhAdHQ0//nPf3jvvfc4cuSIMrZFixYVM1MrIjO3cvrIQdkdJtlctNJG8pfMmAWTwVTpW1VBn3hO7L/OrtWnkQ0yGq2KoHBvatdzI7CxJ77BrhUWAlKpJHyDXbl6KpHEuHRqh7iV6jjFYbIhwWReIQfgoi5cMMmyjC47m89GDrqvcwN4OeoH7Er59+fi4oKLiwsbNmygY8eO2Nvbl/o6CQkJbN++nXfffVcREWb8/PwYNmwY33//PUuWLFHelxUrVjB8+HDc3d3p3bs3q1atYsaMGQXO/d5779GuXTsOHTpE27ZtC71+UlIS+/bt45tvvlGuuWXLFgYMGICrq2upXwfAyJEjee2111i/fj09evTAz8+PuLg49uzZwyOPPFLoMX5+fuzevZtLly4RHBxcputNnTqVDz/8kPr16+Pp6cmuXbs4e/Ysa9eu5f/+7/9ISUlhzJgxTJgwgdWrVyvH7dy5Ezc3N37++edizz9nzhw+/vhjPv74Y6ZMmcLTTz9N/fr1mTZtGkFBQYwePZoXX3yRrVu3ArB3715GjBjBZ599RufOnTl37pwS5ps5c2ax1/rzzz8ZMWIEbm5ufPrppwV+F+6mbt26/PjjjwwcOJDTp0/j5uamHDN//ny+/fZbli5dSsOGDdmzZw/Dhw9XwqtFvX81lXI9JZ566ikA3njjjUL3SZKk9GbS60tfQNFWqKw+cmBZVsBgkFGpin9A21oNJgVTPzmdKb9BUwE5TCf2X+e3b4x1yRq29eWRpxrj4Fx5uVGuXsaHbFpiVgkj8zCY/pVkQ7HjrIlUUzjOUaXCroi/h5oSktNoNKxatYqxY8eydOlSWrduTUREBEOHDi3xw2lsbCyyLBMWFlbo/rCwMBITE4mPj8fX15fY2Fiio6OVfKHhw4fz6quv8tZbbxUQ/q1bt2bIkCFMmTKFnTt3Fnr+LVu20KJFC6VW3xdffMGwYcPw9vamZcuWPPzwwwwaNEhp6F4cKpWKRo0aKTlSgwcPZvv27URERODn50fHjh3p3r27IgzAKCQGDBhASEgIjRo1olOnTvTp04dBgwahKqFo7ezZs+nZs6fFtqysLL7++msCAwMBWLRoEX379uWjjz5SwlvOzs58+eWXaLXaYs//7LPPMmTIEACmTJlCp06dmDFjBpGRkQBMmjSJZ599Vhn/zjvvMHXqVEaOHAlA/fr1mTNnDm+88UaJgsnHxwd7e3scHR1LFYZTq9VKmNbX11dJEs/OzmbevHkWBazr16/Pvn37WLZsmYVgKuz9q4mUSzBduHChoudhU1RqSM6U9C3LkJ6jU5LAi8KQZgznqJ2dK3wu1RlJbRJM2UbBdK8huQtHbrPrW6NYatGtDg8PbljpCcUunkZ3ITUhu4SReRjMwsBQMwRCRZCmNxetLPhQzP8z0tjb83LUDwXGVDaaMrhEYMxh6vv/7J13fBR1/v+fs33TC+lAgqGGJkWaIqBgEM9CVb9IsaBgAzmV4gmo2DjvLHAKh6DocaKcCPyEQxSPIkVFQOmE3hII6W2zbX5/zM4kSzaV7KYwz8cjD8LsZ3Y+M7vZee3r/f6833fdxbZt29i1axf//e9/mTdvHh9//LESKqmIqorDpUuXkpycTJMmUoHUIUOG8Oijj/Ljjz9y++23lxk/d+5c2rVrx8aNG4mMLFsfrHQ4DuDWW2/l5MmT7Nq1ix07drBp0ybef/99XnnlFY8ulqfzkF8/rVbLJ598wty5c/nxxx/5+eefeeONN3j77bf55ZdfiImJISYmhp07d3LgwAG2bt3Kjh07GDduHB9//DEbNmyoUDR5cs2aN2+uiCWA3r1743Q6OXr0qCJEOnbsWKlYAvdITFRUlLJv6W0Wi4Xc3FyCgoL4/fff2b59u1s40eFwYLFYKCwsxM+vZp0AqsPx48cpLCwsI4SsVitdunRx21ae69jQqJFgqq6dqeJOSR2m2k98M+o0GLQarA4nuZbKBZPDJZg0AdVfrdWQUUJy9msPyV06ncvGxQcQRWjXJ8YnYgkgoCYOk+teeT05THINpsAKVoHKN9+qhsbqGpPJxKBBgxg0aBAvv/wyjz32GLNnz65QMLVs2RJBEDh8+DBDhw4t8/jhw4cJDQ0lIiICh8PBsmXLSEtLc0vQdTgcLF261KNgSkxMZMKECUyfPp0lS5a4PWa1WtmwYQMzZ850267X6+nbty99+/Zl2rRpzJ07l1dffZVp06ZVKDQcDgcpKSncdNNNbtvj4uIYM2YMY8aM4bXXXqN169YsXLiQV155RRnToUMHOnTowJNPPsnEiRPp27cvW7ZsUfJ0POFfwy+UVd2vdF9H+bPD0zanU/q7zc/P55VXXmHYsGFlnsvko/dwvuvesW7dOjfhCJQJFdf0+tU3apR5umzZMtatW6f8/8UXXyQkJIQ+ffpw5syZWptcY6XQ5r2QnCAISlgurwp5TM78AgA01cwhaOgIrpuAwyZdo5qG5HLSi1j3j9+x25w0bx9Gv9FtfLZUXXaY8rNq4jBdP4JJDsldXVIAGmZZAU8kJSVRUFBQ4Zjw8HAGDRrEhx9+SJErFC+TlpbG8uXLuf/++xEEgfXr15OXl8fevXvZt2+f8vPFF1+watWqcpeXz5o1i2PHjrFixQq37Zs3byY0NLTSRUFJSUnY7XYsloq/BCxbtoysrCyGDx9e7pjQ0FBiYmIqvC5JSUkAlV47T5w9e5aLFy8q/9+1axcajUZJ7vYmXbt25ejRo7Rs2bLMT2XhxZogi9fSKTZJSUkYjUbOnj1bZg7NmjWr9TnUB2r0tfqNN97go48+AqSmuwsWLOC9997j22+/5bnnnvNYI0OlBG+G5EBaKZdRYCW3qPIVE07FYWoc3wCqjE7OYZIEU00cpuJCG+v+8TtFeTaaNAsgeUKHa179Vh2UHKbM6jhMsmC6fnILKwrJyTSUHKaMjAxGjhzJI488QqdOnQgMDGT37t3MmzePe++9Vxl34cKFMkUH4+PjWbBgAX369CE5OZm5c+e6lRWIi4tTQjxLlizhrrvuKiNwkpKSeO6551i+fLnHmntRUVFMnTqVv/71r27b165dW2YVX//+/XnwwQfp3r074eHhHDp0iJkzZzJgwAAl7wik0gZpaWluZQXeffddJk2apLhCixYtYt++fQwdOpTExEQlv+jgwYPMnz8fgEmTJhEbG8ttt91G06ZNSU1NZe7cuURERCg5ON988w0zZsxQeqRWhMlkYty4cbzzzjvk5uby7LPPMmrUKJ8sz581axZ/+tOfaN68uZKD9fvvv3PgwAHmzp1b68eLj49HEAS+/fZbhgwZgtlsJjAwkOeff57nnnsOp9PJLbfcQk5ODtu3bycoKEjJr2pM1OjT/dy5c7Rs2RKA1atXM2LECB5//HHefPNNtm3bVqsTbIwUWiUh443WKFBSWiC70FrJyFI5TNdrSM5Wsxwm0SmycckhstIK8Q8x8qenOmMw+ba2SECoJJisFgfFVRDHAA6XMBCvI8Ekh+TKq/LdkAgICKBnz568++673HrrrXTo0IGXX36ZCRMmsGDBAmXcO++8Q5cuXdx+1q1bR6tWrdi9ezc33HADo0aNIjExkccff5wBAwawc+dOwsLCuHTpEuvWrfPo3mg0GoYOHVom5Faa559/noCrPk88Cabk5GSWLVvGHXfcQbt27XjmmWdITk7mq6++chu3ePFiYmJiSExMZNiwYRw6dEhZzSfTo0cP8vPzmThxIu3bt6dfv37s2rWL1atXK8nHAwcOZNeuXYwcOZLWrVszfPhwTCYTmzZtIjw8HICcnByOHj1apdeiZcuWDBs2jCFDhnDHHXfQqVMntzl5k+TkZL799ls2btzITTfdRK9evXj33Xe9li4TFxenJJpHRUXx9NNPA9Lqvpdffpk333yTdu3aMXjwYNatW0eLFi28Mo+6RhBr8NUqMjKS7777TvlDnDp1KmPGjOHEiRN07txZiW3WR3JzcwkODiYnJ8ftW4wvefrfe/j2j1Rm353EwzfX/htr3NJf2HIsnb+O6MTI7hVboxf/8hdy/vM1EVOm0GTiE7U+l/rKqWHDsRw6xKUJY/ntl+10HnQnAx+repX6PRvPsHPVCbR6DcNf6EZE87oJaX48dSvFhXYeeLlHhY17Zb769785dOwY/rkZvPD3+T6YYd0z/8wlXj+Zyv3RYbzfrjkgrXA6deoUISEhiKJIcHBwo8mzqG/s2bOH2267jfT0dLe8nIbMnDlzWL16tdo6xAvIf5stWrQok49V1/fvGn0lHjRoEI899hhdunTh2LFjDBkyBICDBw+SkJBQm/NrlHg7JBfscphyqtCAV8lhus4cJpQcJsmZqU5ILiutgJ/XnASg76hWdSaWQHKZigvzyc8qrpJgcsrJ3teRw1SSw+R5lVxDCcc1VOx2O/Pnz280Yknl+qVGIbl//OMf9O7dm/T0dL7++mvFzvztt9+UGk0q5VPoxVVyUCKYcqskmK7PHCall5xdbo1StQ9zURTZ8sVRnA6R+A7hJN0S67U5VgU58bsgu2qJ3w5XPo94HdVHy5NzmCpZJafiHXr06MGYMWPqehrXDcuXL1cKnF790759+7qeXoOmRnfskJAQt3i5TOmlmyrlI6+S8/PCKjmopsOUlweA9jpdJed0tRKoqmA6ezCTC0ez0eo13PpA6zpfZWUOkOZdlF95vhqAQ3aWrifBdB2sklPxLXPmzGHOnDl1PQ2P3HPPPfTs2dPjY6rLd23U2OLYtm0bixYt4uTJk6xcuZK4uDg+//xzWrRowS233FKbc2x0FFm91xoFqieYHAUuh8n/egvJyQ6TSzDpK/9TEEWRX/6fFIrr2L8pQU3qvjq6ySWYLAVVTPqWHSZ71VrnNAYqSvou3RpFRaUxEBgYWO1WMypVo0Yhua+//prk5GTMZjN79uyhuFgKB+Tk5PDGG2/U6gQbIyUhuboXTNdrDpNc6bs6IbmLKdlcPpOHzqCh6x3NvTq/qqIIpio6THaXsyQ6HYjXSS2m3ApymGRUwaSiolIZNRJMc+fOZeHChSxevNjN4rv55pvZs2dPrU2usVKS9O2lHCY/V1mB6oTkrrccJjnp2yUgqiKYDv0kFalr3SMac2Dl7Q58gdyrruoOk3S+glO8bvo8ys13gzzkMKkhORUVlapSI8F09OhRjx2hg4ODy60Aq1JCYT1ZJSc6HDgLrs9K32VDchULJkuBjRN70gFIurluE71LYw6QhFt1HSZEJ05HWZFlz7IgOhqX25Jrl5y0iuowqQ6TiopKZdRIMEVHR3P8+PEy23/66SduuOGGa55UY8bpFCmy+SYkV9kqOWepdgDXm2CSQ3KyaNBWUlbgzIEMHHYnoTH+RCbUn2tlCpDmXZRftZwku0sgCqITp93dYSrcn07a27+S/f9O1O4k6xg16VtFRaU2qJFgmjBhApMnT+bnn39GEAQuXrzI8uXL+fOf/8ykSZNqe46NClksgXd6yYG7w1TRN2c5HCcYDGiq0FG7MVEmJFeJw3Rm/xUAbujcpF7dZE3+LoepoHqCCae7wySKInk/ngOg4Jc07DlV709XnxFFsSQkpzpMKioq10CNkmimT5+O0+nk9ttvp7CwkFtvvRWj0cgLL7zAY489VttzbFTI4TjwvmCyOSQ3q7xcKYdcg+k6c5cABDkkp+Qwlf+n4HA4OXsoE4CETk28P7lqICd9FxfacTpFNJqKxVxph0kORwIUn8zBlupyHJ0i+dsvEDKk4bvFhQ4ncoSxolVyKiqe2Lx5MwMGDCArK4uQkJC6no5KHVMjh0kQBF566SUyMzM5cOAAu3btIj09neDg4EbbQ6a2yC+WblL+Bm2lN7ea4mfQonM9d0V5TCUJ39fXCjlAqfTtrELS96WTuRQX2jEF6IlMqJt2OuVh9HcJPVFqBlwZJQ6T6OYwWQ5LglAbLDlWxSdyaneidUSOKxynE8Bcwd9bQ3KY0tPTmTRpEs2bN8doNBIdHU1ycjLbt28HICEhgffee6/c/c+dO8cjjzxCbGwsBoOB+Ph4Jk+eTEZGhsfxTzzxBFqtlpUrV5Z5bM6cOQiCwMSJE92279u3D0EQOH36tNv2M2fOYDabyc/Pp7CwkBkzZpCYmIjJZCIiIoJ+/fqxZs0aZXz//v0RBAFBEDAajcTFxXH33Xd7bPC+ZcsWbrvtNsLCwvDz86NVq1aMGzcOq7Ukv2/x4sV07tyZgIAAQkJC6NKlC2+++Wa516pPnz6kpqYSHBxc7hiV64dqCabi4mJmzJhB9+7dufnmm1m/fj1JSUkcPHiQNm3a8P777/Pcc89VexL/+Mc/SEhIwGQy0bNnT3755Zcq7bdixQoEQeC+++6r9jHrCrkhboif90JggiAQIq+Uq+Am6nAJpuvTYZKuj1zIsaKQ3MWUbADiWod6TeTWFK1Wg9FPEk2WSvKYRFEsEUyiE0epHCZbmuQumTtHAGC/UtSgRER55JYKx1XkJjWkcx0+fDh79+5l2bJlHDt2jLVr19K/f/9yBU9pTp48Sffu3UlJSeGLL77g+PHjLFy4kE2bNtG7d28yMzPdxhcWFrJixQpefPFFli5d6vE5TSYTS5YsISUlpdLjr1mzhgEDBhAQEMDEiRNZtWoV8+fP58iRI2zYsIERI0aUOY8JEyaQmprKiRMn+Prrr0lKSuKBBx7g8ccfV8YcOnSIwYMH0717d7Zu3cr+/fuZP38+BoNBcZGXLl3KlClTePbZZ9m3bx/bt2/nxRdfrLD3qcFgIDo6WnUiVYBqhuRmzZrFokWLGDhwIDt27GDkyJE8/PDD7Nq1i7/97W+MHDkSbQXtBzzx5ZdfMnXqVBYuXEjPnj157733SE5O5ujRo0RGRpa73+nTp3n++efp27dvtY5X18iOjxw28xZBZj1X8q0VO0xKSO76c5iU1ihVCMmlnZTclpjE+vkt0+ivp7jQXqlgspcKwQmlVsmJoogtVXovmJPCyd92AbHYgbPAhjagYee25dkrbovS0G6E2dnZbNu2jc2bN9OvXz8A4uPj6dGjR5X2f+qppzAYDGzcuBGzWSq82rx5c7p06UJiYiIvvfQSH330kTJ+5cqVJCUlMX36dGJjYzl37hzNmrk39G7Tpg2RkZG89NJLfPXVVxUef82aNYwcORKAtWvX8v777yu9SBMSEujWrVuZffz8/IiOjgagadOm9OrVi7Zt2/LII48watQoBg4cyMaNG4mOjmbevHnKfomJiQwePFj5/9q1axk1ahSPPvqosq2yViFqSE6lNNVymFauXMlnn33Gf/7zHzZu3IjD4cBut/P777/zwAMPVFssAfz9739nwoQJPPzwwyQlJbFw4UL8/PzK/TYD0k1u9OjRvPLKKw1uVZ6vBFNVSgs4lJDcdegwuSp7O13FG8sLyYlOsUQwtayfgqmkPUrVBZMUkpPEojPPirPADgIY4gLQBkv96ewZFu9M2IdUVOW7NKIoIooiTqvD5z/VcbfknmCrV69WCgZXlczMTL777juefPJJRSzJREdHM3r0aL788ku3+SxZsoSHHnqI4OBg7rzzTj799FOPz/3WW2/x9ddfs3v37nKPn52dzU8//cQ999yjHHP9+vXkuT6HqsO4ceMIDQ1VQnPR0dGkpqaydevWcveJjo5m165dnDlzptrHU1GBajpM58+fV74BdOjQAaPRyHPPPVfjb2lWq5XffvuNGTNmKNs0Gg0DBw5k586d5e736quvEhkZyaOPPsq2bdtqdOy6oj4JJmfu9RuSU3KYRFkwef5TyEwroLjQjs6gIbxp/XTiSopXViyYbDbX46KIgKj00bO6kr11EWYEvRZdEzOO7GLsV4owxtevnK3qUlFJAXB3mESbk4uzdvhkXqWJfbUPQhVLjOh0Oj799FMmTJjAwoUL6dq1K/369eOBBx6gU6dOFe6bkpKCKIq0a9fO4+Pt2rUjKyuL9PR0IiMjSUlJYdeuXYooeeihh5g6dSp/+ctfynzmd+3alVGjRjFt2jQ2bdrk8fnXr19Pp06diI2V6pj985//ZPTo0YSHh9O5c2duueUWRowYwc0331zpddBoNLRu3VrJkRo5ciTfffcd/fr1Izo6ml69enH77bczduxYgoKk9/Ds2bMZNmwYCQkJtG7dmt69ezNkyBBGjBiBRlOjdF6V64xqvUscDgeGUsvPdTodAdeQMHzlyhUcDgdRUVFu26OiokhLS/O4z08//cSSJUtYvHhxlY5RXFxMbm6u209dkuPKKZJzjLxFVWoxOfPlxrv1Uwh4k5IcJpdgKieH6dIp6f0SlRCEVls/P1QVwVRFh0lAchDkVXLy6jh9jPQ+0IWbpPEZRbU/WR9T4jBV/No1tBymixcvsnbtWgYPHszmzZvp2rVrue7P1VT1XJcuXUpycjJNmkgrQ4cMGUJOTg4//vijx/Fz585l27ZtbNy40ePja9asUdwlgFtvvZWTJ0+yadMmRowYwcGDB+nbty+vvfZalc9DFm5arZZPPvmE8+fPM2/ePOLi4njjjTdo3749qampAMTExLBz507279/P5MmTsdvtjBs3jsGDBytOs4pKRVTLYRJFkfHjx2M0Spa9xWJh4sSJ+Pu7t9XwtIKhNsjLy2PMmDEsXrxY+SOujDfffJNXXnnFK/OpCdn1yGFy5LlymK7HkJzuqpBcOYIp44J0jZo0rb/XSE76thZV3B6lpKSA9H85h8l+uRAAfaQfALpwKVzTmEJyVXGYBL2G2Ff7+GRebnPQV1+Im0wmBg0axKBBg3j55Zd57LHHmD17NuPHjy93n5YtWyIIAocPH2bo0KFlHj98+DChoaFERETgcDhYtmwZaWlp6Eq5rw6Hg6VLl3L77beX2T8xMZEJEyYwffp0lixZ4vaY1Wplw4YNzJw50227Xq+nb9++9O3bl2nTpjF37lxeffVVpk2b5vbl/GocDgcpKSncdNNNbtvj4uIYM2YMY8aM4bXXXqN169YsXLjQ7R7QoUMHOnTowJNPPsnEiRPp27cvW7ZsYcCAAeUeT0UFqimYxo0b5/b/hx566JoO3qRJE7RaLZcuXXLbfunSJSXJrzQnTpzg9OnT3H333co2+Yan0+k4evQoiYmJbvvMmDGDqVOnKv/Pzc0tk7ToS5SQnJcdphBz5avklKTv67CsgJzD5HB92y4vJJdxQXJfwpvW3157BpdgKq5EMMkhOcVhcuUw2TMlYSQ7S4rDdEVymCzHshCdIua2YbU8c++T53C1Rakkv1J2K6oaGqtvJCUlsXr16grHhIeHM2jQID788EOee+45tzymtLQ0li9fztixYxEEQckt2rt3r1tu6oEDB3j44YfJzs72mAQ9a9YsEhMTWbFihdv2zZs3ExoaSufOnSs9D7vdjsViqVAwLVu2jKysLIYPH17umNDQUGJiYigo1dHA0/GACseoqMhUSzB98skntXpwg8FAt27d2LRpk1IawOl0smnTJp5++uky49u2bcv+/fvdtv3lL38hLy+P999/36MQMhqNiiNWH/BVDlOASXppC4rLv4k6LdKNUuPn59W51EeEMjlMZV8PURTJOC+JyvC4+isqjWaXYCqsmsMk+xmKw6QIJukGqnM5TfbLhdizi7ny6QFwQsCtTQm+MwGxyI4trQBD8yCESkJddU1lSd8NbZVcRkYGI0eO5JFHHqFTp04EBgaye/du5s2bx7333quMu3DhAvv27XPbNz4+ngULFtCnTx+Sk5OZO3cuLVq04ODBg7zwwgvExcXx+uuvA1Ky91133VVG4CQlJfHcc8+xfPlynnrqqTLzi4qKYurUqfz1r39127527Vq3cBxINZYefPBBunfvTnh4OIcOHWLmzJkMGDBAyTsCqbRBWloadrud8+fP88033/Duu+8yadIkxRVatGgR+/btY+jQoSQmJmKxWPjss884ePAg8+fPB2DSpEnExsZy22230bRpU1JTU5k7dy4RERH07t0bgG+++YYZM2Zw5MiR6rwsKtcJNar0XZtMnTqVcePG0b17d3r06MF7771HQUEBDz/8MABjx44lLi6ON998E5PJRIcOHdz2l7/lXL29vqLkMJm9u1zb7KruXbqy+NWIRZKDoDGbvDqXeolOhwjI2RwaDw5TYa4VS4ENQYCwmHrsMJmrGZJzaQSn3YFoc+DMlWqDacNkh8kMOgHR5iR/63lwpXfkbz2PqU0ouRvPYD2TiyZQT5NHOmKox9emsqRvmYaSwxQQEEDPnj159913OXHiBDabjWbNmjFhwgS3cNc777zDO++847bv559/zkMPPcTu3buZPXs2o0aNIjMzk+joaO677z5mz55NWFgYly5dYt26dfz73/8uc3yNRsPQoUNZsmSJR8EE8Pzzz/PRRx9hsZSEdNeuXVtm5XNycjLLli1j5syZFBYWEhsby5/+9CdmzZrlNm7x4sUsXrwYg8FAeHg43bp148svv3QLK/bo0YOffvqJiRMncvHiRQICAmjfvj2rV69Wyi8MHDiQpUuX8tFHH5GRkUGTJk3o3bs3mzZtIjw8HICcnByOHj1alZdC5TqkzgXT/fffT3p6OrNmzSItLY0bb7yRDRs2KIngZ8+ebVQrGHzlMPm52q4UWCt3mATT9SeYBJ0eZyl3Qechh0l2l4Ij/dDV41BNVXOY5JCcBum8HXa74i4JJi0a1/MIGgF9pB+2iwXk77jo9hxXFpc4vM48G1n/OUbkUzci1LOCnjI5jcxhMhqNvPnmmxVWp766uvbVxMfHV5ggHhUVVbKi0gMffvih8vucOXOYM2eO2+NBQUGkp6cr/9+zZw+5ubmKcJGZMWOG2wppT2zevLnCx2W6dOnC559/XuGY4cOHVxjCAxg/frxbHlj//v0bjJhW8T51LpgAnn76aY8hOKj8D6aqK0PqCz4TTK4bfFEFDpNTcZjM5Y5prAg6nZtg0ngIyWW6Vo+Fx9VfBwVKheSq6DDJ2sbpsCuJ3bpws5t40Ef5Y7tYktcRMakz6f/8A7kxm9+NERQdycR2IZ/cjWcIHpxQW6dTq+RVow6Tinew2+3Mnz8ffSUNrlVU6jv1QjBdT+RaJMEUZPbupfczVickdx0KJr0OZylXxFPSd85l6fqERNbvHC+jX0kD3oooEUzSeTsdjhLBFObuMuqjS87Z2DoUY3wQ4Q+2JW/LeUQg+E83YGwVStbKY+RtPoegEwi8vXm9c2waY1mBhkaPHj2qXIlcRaU+owomH2J3OBUBE2TykcNkq8BhUkJy16Fg0ulwuu7tWp3O440+J11abh8UUb+vj8EsvdZVdpg00jo5h92OPV8ShVcLJl1EiWAKur05AOYOTTB3KCnn4d8tCkeuldzvTpP7w1mcRXaC/3RDvRJNSi+5RtIaRUVFpe5QBZMPybOU3NACTd699GZXDlNhBTlM13vStxyS8xSOA8hJlx2m+i2YjK7wrr3YgdPhRFNOgU05L0UraLAjOUyOLKm9hvYqwWRMDMHQLBB9XECF1b6DBjRDY9KSveYE+dsvojHrCBoYXwtnVTsoveTUkJyKiso1ogomHyKH4/wNWnRerhotO0wVheSUsgLXedK3p6KVDruTPFe4KjiifofkZIcJwFrkwBTg+b1V2mECVw5TliskF+r+HtAYtUQ+dWOVjh/QOxY0AtnfHCd301kphNe87luqiKJYaQ6T6jCpqKhUlcaz/KwBkOsKmQR5OeEbwK9UWQFP355FhwPR1bxTuE5zmERZMHnIX8rLsCCKoNNr8Av2bgmIa0Wj1aA3ymG58lc3yYJJ61p16rDZSxym0GurVRbQMwbzjREgQvbq4/XCsSlwOOWKCKrDpKKics2ogsmHKAnfXs5fAjC7HCaHU8TqKNsnSSxVI6WhJX2LokhOcQ6FtsIaP4eg1SpJ356KVsrhuOBIc4NwIeTSAhUlfishOZdgEosdiC4HUhdy7cVdQ+5ORDBosV0swHI485qf71qRE751ApjLKXvQEF5bFRWV+oEakvMheS7B5O38JSgJyYFUWsB41TdsZynBJNSjSugVcaXoCp8d+oxvT3xLepFU56VlSEv+r93/cfcNd2PSVSO0WCqHyVNITk74ru/hOBmDWQdZxRUmfisOkyscrCl05XAF6BH0115nSuuvJ6BPLHmbz5G78TSmNmEI2roTJErCt05bJWFUupmrioqKytWoDpMPkUNyvhBMeq0Gg+vG6CmPyVlUUrRSqOeFQa0OK//845/c+fWdfHLgE0UsARzPPs6rO19lyKoh7Li4o8rP6ZbD5CEkl+vKXwoMbxj5XcYqVPsuCclJ4khjcZ1/aO2dY+CtcWj8dNjSCsn731lEe911gVcSvivoI1daIKlhORUVlYqo33fKRoa8xF+ukeRtzBUkfosW1wq5ep7wfSTzCA+se4D5e+djcVjoFNGJ9wa8xy+jf2Hb/dt4ofsLxPrHkl6UzlM/PMXJnJNVel5BX8ph8hCSK3Dl9gRcY26PrzBUIyQnLzjQWqR/dbV4jho/PUF3JACQ+8NZ0hfvR3TWjRCprMq3ikplbN68GUEQyM7Ovubn6t+/P1OmTLnm57kWCgsLGT58OEFBQcp5JSQk8N577yljBEGotJHz9YoqmHyILJhMPvoAr6jat1zlu74mfIuiyOeHPufBdQ+SkpVCqDGUt/q+xb/u/Be3N78ds85MiCmEse3Hsua+NXSN7IpdtPP96e+r9PyCTlcqh6msgM13rR4LqEX3xZtUy2HSSmO1xdL7QxtSu+fo3yOaoEFSaQHrmVyKj2fX6vNXlar0kWuIDlN6ejqTJk2iefPmGI1GoqOjSU5OZvv27QBlboBXc+7cOR555BFiY2MxGAzEx8czefJkMjIyPI5/4okn0Gq1rFy5ssxjc+bMQRAEJk6c6LZ93759CIJQpk3LmTNnMJvN5OfnU1hYyIwZM0hMTMRkMhEREUG/fv1Ys2aNMr5///4IgoAgCBiNRuLi4rj77rtZtWpVmbls2bKF2267jbCwMPz8/GjVqhXjxo3DarUqYxYvXkznzp0JCAggJCSELl26VNhmpk+fPqSmphIcHFzumKqyatUqXnvttWt+nmth2bJlbNu2jR07dijn9euvv/L44497HH/69GkEQSjTyPl6RRVMPsTiEkxmg28uu+wweeonV5/bouQU5zDlf1OY9+s87E47A5oN4Jt7v+GuG+7ymGNi0pm4O/FuALae31qlYwiV5DDlNzSHyVy5wyQLJp0rRCULptp0mEDqRRd0e3MC+sQCkL/zYiV7eIeqVPluiDlLw4cPZ+/evSxbtoxjx46xdu1a+vfvX67gKc3Jkyfp3r07KSkpfPHFFxw/fpyFCxeyadMmevfuTWame7J+YWEhK1as4MUXXyzTPFfGZDKxZMkSUlJSKj3+mjVrGDBgAAEBAUycOJFVq1Yxf/58jhw5woYNGxgxYkSZ85gwYQKpqamcOHGCr7/+mqSkJB544AG3m/yhQ4cYPHgw3bt3Z+vWrezfv5/58+djMBhwuHLZli5dypQpU3j22WfZt28f27dv58UXXyQ/P7/c+RoMBqKjo6/pfSILtrCwMAIDA2v8PLXBiRMnaNeuHR06dFDOKyIiAj8/7+dqVtSfsKGgCiYfUp8cJrGe1mD6I/0PRv6/kfx47kd0Gh0zeszg/QHvE24Or3C/vnF9Adh/ZT9Xiq5UfqAKBJPTKVKQI33INRSHSRZMVksVQnIuR01vczlNtbBCzhP+PaMBsBzJRPSwUtPb5FYzJOd0OrFarT7/qY6zlZ2dzbZt23j77bcZMGAA8fHx9OjRgxkzZnDPPfdUuv9TTz2FwWBg48aN9OvXj+bNm3PnnXfyww8/cOHCBV566SW38StXriQpKYnp06ezdetWzp07V+Y527Rpw4ABA8rs64k1a9Yo81y7di0zZ85kyJAhJCQk0K1bN5555hkeeeQRt338/PyIjo6madOm9OrVi7fffptFixaxePFifvjhBwA2btxIdHQ08+bNo0OHDiQmJjJ48GAWL16M2fWlcO3atYwaNYpHH32Uli1b0r59ex588EFef/31cud7dUju008/JSQkhNWrV9OqVStMJhPJyclu12XOnDnceOONfPzxx7Ro0QKT6zP26pBcQkICc+fOZezYsQQEBBAfH8/atWtJT0/n3nvvJSAggE6dOrF79263Of3000/07dsXs9lMs2bNePbZZykoKKAy+vfvz9/+9je2bt2KIAj0799fmUd5jmSLFi0Aqblx6X0APv74Y9q1a4fJZKJt27ZuTZllZ+rLL7+kX79+mEwmli9fXukc6zvqKjkfUmyTbhpmg28Ek1zt2+KhPYqS9F1PHCaH08GnBz9lwd4F2EU7zQKb8U6/d0gKT6rS/lH+UbQJbcPRrKPsu7yPgfEDKxwvJX1Lv1+dw1SYY0V0iggaod7XYJIxmKTX2mopv1Cp4jC5BITOLp1bbYfkZJRkchFEh4jg41SiqjbeFQQBURSx2WzMmzfPF1NzY+bMmRgMVXufBQQEEBAQwOrVq+nVqxfGaqxwzczM5LvvvuP1119XRIRMdHQ0o0eP5ssvv+TDDz9UHJUlS5bw0EMPERwczJ133smnn37Kyy+/XOa533rrLW666SZ2795N9+7dPR4/Ozubn376ic8//1w55vr16xk2bFi1nZdx48bx5z//mVWrVjFw4ECio6NJTU1l69at3HrrrR73iY6OZsuWLZw5c4b4+JpXoy8sLOT111/ns88+w2Aw8OSTT/LAAw8oIVGA48eP8/XXX7Nq1Sq0FSw6ePfdd3njjTd4+eWXeffddxkzZgx9+vThkUce4a9//SvTpk1j7NixHDx4EEEQOHHiBIMHD2bu3LksXbqU9PR0pXn9J598UuG8V61axfTp0zlw4ACrVq2q0nvul19+oUePHvzwww+0b99e2Wf58uXMmjWLBQsW0KVLF/bu3cuECRPw9/dn3Lhxyv7Tp0/nb3/7G126dFGEY0NGdZh8iOz0mGphCXdVkI9jsdfvpO+T2Sd5+LuHeW/Pe9hFO3fE38GXf/qyymJJJiE4AYAL+RcqHeue9O3+vUHOX/IPNihVses7BtfKS1sVcph0Oj1aQYfOKe1TGzWYPCGUvnYO3+cH5ToqXyVXmoaQw6TT6fj0009ZtmwZISEh3HzzzcycOZM//vij0n1TUlIQRZF27dp5fLxdu3ZkZWWRnp6ujN+1axf3338/AA899BCffPKJx+vUtWtXRo0axbRp08o9/vr16+nUqROxsVKo9p///Cc7duwgPDycm266ieeee85NdFSERqOhdevWSo7UyJEjefDBB+nXrx8xMTEMHTqUBQsWkJubq+wze/ZsQkJCSEhIoE2bNowfP56vvvoKp7N67qfNZmPBggX07t2bbt26sWzZMnbs2MEvv/yijLFarXz22Wd06dKFTp06lftcQ4YM4YknnqBVq1bMmjWL3NxcbrrpJkaOHEnr1q2ZNm0ahw8f5tKlSwC8+eabjB49milTptCqVSv69OnDBx98wGeffYalVKkYT8i5XXKYMSwsrNJzjYiIACA8PNxtn9mzZ/O3v/2NYcOG0aJFC4YNG8Zzzz3HokWL3PafMmWKMiYmJqbS49V3VIfJh8jCxVeCSa69ZLGV/UAoSfquO8FUYCvgn3/8k88OfYbdacdP58e0HtMY2nJojXIGYgOkD+LUgtRKx7olfV8VkivJX6p7MVlVqhKSK+0w+emk1iWCUYtg8tL7sZRgqouVctV1mPR6PTNnzvTF1NzQe8ihq4jhw4dz1113sW3bNnbt2sV///tf5s2bx8cff8z48eMr3b+qwnDp0qUkJyfTpInUcHnIkCE8+uij/Pjjj9x+++1lxs+dO5d27dqxceNGIiMjyzxeOhwHcOutt3Ly5El27drFjh072LRpE++//z6vvPKKRxfL03nInxNarZZPPvmEuXPn8uOPP/Lzzz/zxhtv8Pbbb/PLL78QExNDTEwMO3fu5MCBA2zdupUdO3Ywbtw4Pv74YzZs2ICmiuVVdDodN910k/L/tm3bEhISwuHDh+nRowcA8fHxitioiNJiKioqCoCOHTuW2Xb58mWio6P5/fff+eOPP9zCW6Io4nQ6OXXqVLliuDYpKCjgxIkTPProo0yYMEHZbrfbyyTHl+c2NlRUh8mHlDhMvrns8nEqCslpzL4vzCiKIutOruNP3/yJpQeWYnfa6de0H6vvXc2wVsNqnGAZ6y8Jpov5lScZuyV9XxWSK8huWAnfULWQnJzDpNfp8dNKgkkbYvRa4rOgEUB+6joQTNXNYQIpydfXPzW5/iaTiUGDBvHyyy+zY8cOxo8fz+zZsyvcp2XLlgiCwOHDhz0+fvjwYUJDQ4mIiMDhcLBs2TLWrVuHTqdDp9Ph5+dHZmZmucnfiYmJTJgwgenTp5cRZVarlQ0bNpTJs9Lr9fTt25dp06axceNGXn31VV577TW3lW2ecDgcpKSkKDk2MnFxcYwZM4YFCxZw8OBBLBYLCxcudBvToUMHnnzySf71r3/x/fff8/3337Nly5YKj1dd/P39qzSutFiW3weetskuWH5+Pk888QT79u1Tfn7//XdSUlJITEysrelXiJwkv3jxYrd5HDhwgF27drmNrep1aCioDpMPsbgK6Zl9HJIr8iCY6iokd6ngErN2zFKKTDYPbM7z3Z9nQPMB1/zcssNUFcHklvR9VchGCck1KMFU9bICOoNecZi8FY5T0AjgEBWHyZ5lwXYhH1NSuHvIzgtUpawANMyVcleTlJRUae2c8PBwBg0axIcffshzzz3nlseUlpbG8uXLGTt2LIIgsH79evLy8ti7d6/b38eBAwd4+OGHyc7OJiQkpMwxZs2aRWJiIitWrHDbvnnzZkJDQ+ncuXOl52G327FYLBXm2CxbtoysrCyGDx9e7pjQ0FBiYmIqTIhOSpLC/lVJmpax2+3s3r1bcZOOHj1Kdna2T9ydrl27cujQIVq2bOn1YwHKayCvNATJ9YqNjeXkyZOMHj3aJ/OoL6iCyYdYfJ7DJDtMnkJyctK37wTTr2m/MnXzVLKLszFoDDzR+QnGtx+PQVs7idUx/lKM/GJBFRwmfUmlb81VgkleIecf1IAEkxKSqzzpW683KILJWyvkZASNgOgQlRym7DUnsBzJxL9nNKFDW3n12FUpKwAlgqkh5DBlZGQwcuRIHnnkETp16kRgYCC7d+9m3rx53Hvvvcq4CxculKmdEx8fz4IFC+jTpw/JycnMnTuXFi1acPDgQV544QXi4uKUFWNLlizhrrvuKiNwkpKSeO6551i+fDlPPfVUmflFRUUxdepU/vrXv7ptX7t2bRl3qX///jz44IN0796d8PBwDh06xMyZMxkwYABBQUHKuMLCQtLS0rDb7Zw/f55vvvmGd999l0mTJjFggPRFa9GiRezbt4+hQ4eSmJiIxWLhs88+4+DBg8yfPx+ASZMmERsby2233UbTpk1JTU1l7ty5RERE0Lt3bwC++eYbZsyYwZEjR8p9DfR6Pc888wwffPABOp2Op59+ml69eikCyptMmzaNXr168fTTT/PYY4/h7+/PoUOH+P7771mwYEGtHy8yMhKz2cyGDRto2rQpJpOJ4OBgXnnlFZ599lmCg4MZPHgwxcXF7N69m6ysLKZOnVrr86gvqCE5HyLnMPnMYXJ9sy72FJJTHCbfrJLbdGYTj3//ONnF2bQLa8d/7vkPj3d6vNbEEpQ4THnWPPKt5ddWgatymDTur0dRniSY/IK83yS5ttArIbnKywro9Tr8fSSYlDwml8NkOSLV+Sn4OQ1LSpZXD630kmtESd8BAQH07NmTd999l1tvvZUOHTrw8ssvM2HCBLcb5jvvvEOXLl3cftatW0erVq3YvXs3N9xwA6NGjSIxMZHHH3+cAQMGsHPnTsLCwrh06RLr1q3z6N5oNBqGDh3KkiVLyp3j888/T0BAgNs2T4IpOTmZZcuWcccdd9CuXTueeeYZkpOT+eqrr9zGLV68mJiYGBITExk2bBiHDh1SVvPJ9OjRg/z8fCZOnEj79u3p168fu3btYvXq1fTr1w+AgQMHsmvXLiWhevjw4ZhMJjZt2kR4uFS2JCcnh6NHj1b4Gvj5+TFt2jT+7//+j5tvvpmAgAC+/PLLCvepLTp16sSWLVs4duwYffv2pUuXLsyaNUtJpK9tdDodH3zwAYsWLSI2NlYR5Y899hgff/wxn3zyCR07dqRfv358+umnZUKkjQ1BbAifErVIbm4uwcHB5OTkuH2L8QWD/r6FlMv5/HtCT/okNvH68d757igL/neccb3jeeXeDm6Ppb48i+yVK4mY/CxNJk3y6jx+Tv2ZiT9MxO6UVsC90fcNjFrv3KhvWXELOcU5fH3P17QObV3uONFu5z/JAzjbJJiedw3llrGPKo+teO1nMi4UcPeznWmeVHH9p/pCYa6VT178CYAnPxxQJtzldDp59dVXARg24FZsqy4QZU4gdFRr/LtGeW1eF17ZiVhkJ2pqN/SRfqTO+xVHpsvd1GuIeKIThqbeKebXetsf5NqdbOvRllb+7k6qxWLh1KlTtGjRgry8PGw2G2FhYY1i6XN9Y8+ePdx2222kp6dXO8G9vvHpp58yZcqUWmmVouKZ0n+bV/891uX9G1SHyaf43GGqKCTnWoIqGL17g0jNT+X5Lc9jd9pJTkhm3q3zvCaWoCTxOzW/kpVyWi2iHJK7SlwU5soOU8OowQRgMJe8p2zFZR1FORwHoDfoMWklB0Dr5XMUrnKYRFdYWhNkQLQ5yfjiCM7i8l2xmuIURaX5blWTvq+z744+w263M3/+/AYvllRUVMHkQ4qs0gd4/ajDJK+S855gsjltPL/1ebKLs2kf3p7Xb3m9TPirtonyl9ySS4WXKhwnCAJOuQltqTk5nSKWfCl0ZQ5sOIJJq9Og0UripNhD4nfptgR6gxGjVgrFavy9fI4uwSQnfYsuMddkXHu0IUYcGRZy1p+q9cMWOJzI8ud6SPquz/To0YMxY8bU9TQaPdu2bVMKm3r6Ubl21KRvHyLnEvmsDlNFlb594DB9uO9D/kj/g0B9IH/r/zevOksyYSapsFqWpfL8GKer7opGKPneYMm3IYqAAOaAhvONWBAEDCYdlgKbxzwm2WHSaDTotDo0Gkkwab18jqUdJtEpIrrcTm2IkdCRrbmyeD8FP6fh1yUSY8K1NziVkRO+dQKYq7gaT3WYVCpj/PjxVap1VRd0795dbZLrZVTB5EPk5f2+S/ouPyTnbYfp17RfWbJfSgyd02cOcQFxXjnO1YQaQwHIKq6CYJIdplIOgxyOMwfo0WgblgFrMGuxFNiweVgpV7JCTo/WoUFwiUSNn5c/ArQlDpNYqqehxqDFlBiCX/coCndfInv1cSKf6YqgrR23p3QNpsocJNVhUmkMmM1mn5UbuF5pWHeEBozN4cTuCkv4rnBlBQ5TsVSc0RsOU0ZRBtO3TkdEZFirYdyRcEetH6M8Qk2SYMq0ZFYyEkTZYSrlQBTJgqkBheNk9BXUYirdeFdjd62oE4sRvCwK3RwmObdKg2T9AMF3tkDjp8OWVkj+jspb2lSVqlb5Lo3qMKmoqFSEKph8RGnR4vscJg8OU5FcVqB2w2R2p53p26ZzuegyNwTfwLSbyu8t5Q2uNSRXmNfwEr5lKqr2Xdph0tik87U6i7w/KdelFR0iTpfDJBh0JS0t/PUEDU4AIPf7s9hzimvlsHIfuaqUFFAdJhUVlaqgCiYfIYfFBAGMlRTSqy1kJ8tjHSbZYarlOkzv/fYeu1J3YdaZeaffO/jpfdt6JcQYAlRRMMmr5PAQkmuADlNF/eTcHCaXYCr2gWDy5DBpjO7vf//u0RiaByJaHWSvOVErTk9Vq3yXRnWYVFRUKkIVTD5CdphMVcipqC0qCsl5w2H6z7H/sOzQMgBeu/k1WoV6t5KzJxSHqSo5TBpZMJVQ1ABLCshU1B6ltMMkuNp0WexVbwdRY0oJJmex7DC5ixhBIxA6rBVoBSyHMijcc/maD1udPnKqw6SiolIVVMHkIxTB5KP8JSip9O2xDlMtO0y/XfqN13dJbRWevPFJkhOSa+V5q4ucw5RlyarUMVB6yZXaJlf5Ngc2nBVyMhWF5Eo7TIIr6uVLwVQ66VswlhUx+mh/ggbGA5D1zXGKz+Ze02FzFYep8r+3htQaRUVFpe5QV8n5CF+vkINShSsrqsNUCw7T5cLLTN08FbtoZ3DCYCZ2mnjNz1lTZMFkc9oosBUQYCi//oinkFxRQcOrwSSjOEwVhOT0ej1YXG1K7PmITieCxnsi3i0k58ql0xg8/w0E9muK9UwuliOZXFm8n4C+cQg6Dba0ApwWB4bYAAJva1bu/qWpjsMkowomFRWVilAdJh8huzy+SvgufayrQ3Kiw4FolZwU4RpbQTicDl7Y8gKZlkxah7bm1ZtfrdMQh1lnxqyTXLPK8picrmlqKLlRykUrTf4N0GFyVfu2VRCS0+l0iEXS+6HYWYjDXvtVtt0o5TApITkPDhNI4irswbYYW4Yg2pzk/XiO3I1nKPrjCsXHssjbfI70RX/g9HB+V6M4TI0w6Ts9PZ1JkybRvHlzjEYj0dHRJCcns337dgASEhJ47733yt3/3LlzPPLII8TGxmIwGIiPj2fy5MlkZGR4HP/EE0+g1WpZuXJlmcfmzJmDIAhMnOj+JWnfvn0IgsDp06fdtp85cwaz2Ux+fj6FhYXMmDGDxMRETCYTERER9OvXjzVr1ijj+/fvjyAICIKA0WgkLi6Ou+++m1WrVpWZy5YtW7jtttsICwvDz8+PVq1aMW7cOKyuzzqQ+tJ17tyZgIAAQkJC6NKlC2+++Wa510pFpTSqYPIRssNkrBPB5HT79iwWl6xE0lyjYFp2aBl7Lu/BX+/P3/v/XRErdYlciymzuOLSAk6Xs6QpZSxYChquYNIbXQ6Th9Yobg6TSzBZHIXYbdYyY2sT96Rv18KHcgQTgMaopckjHQgd1gq/LpH4dYkkaHACwXffgMZfh+1CPrk/nKn0uHmO6rVFgYbjMA0fPpy9e/eybNkyjh07xtq1a+nfv3+5gqc0J0+epHv37qSkpPDFF19w/PhxFi5cyKZNm+jduzeZme5/M4WFhaxYsYIXX3yRpUuXenxOk8nEkiVLSElJqfT4a9asYcCAAQQEBDBx4kRWrVrF/PnzOXLkCBs2bGDEiBFlzmPChAmkpqZy4sQJvv76a5KSknjggQd4/PHHlTGHDh1i8ODBdO/ena1bt7J//37mz5+PwWDA4WrCvHTpUqZMmcKzzz7Lvn372L59Oy+++CL5+RU36lZRkVFDcj7CooTkfJjDVOpYxXanIqDkKt9wbQ7T6ZzT/GPvPwCYdtM04oPia/xctUmoKZSLBRer7jCJjcNh0ss5TEXllxXQ6XQ4C6Xfix2FOEq1TPEKSuHKUn3kKgmpCRoB/x7R+PeIdtuuj/TjypID5O9Mxb93LPom5Yvzxpr0nZ2dzbZt29i8eTP9+vUDID4+nh49elRp/6eeegqDwcDGjRsxm6Xr17x5c7p06UJiYiIvvfQSH330kTJ+5cqVJCUlMX36dGJjYzl37hzNmjVze842bdoQGRnJSy+9xFdffVXh8desWcPIkSMBWLt2Le+//z5DhgwBJGesW7duZfbx8/MjOlp6LzRt2pRevXrRtm1bHnnkEUaNGsXAgQPZuHEj0dHRzJs3T9kvMTGRwYMHK/9fu3Yto0aN4tFHSxptt2/fvsL5jh8/nmXLlpXZ/r///Y/+/ftXuK9K40N1mHyExcdtUa4+VnGpxG85f0kwGGqcvyKKInN/novVaeXm2Ju5r+V91zTX2iTEFAJUISTnCsVpXXrJ6RSVPmymBtQWRUbOYbJ5aGZb2mGSBZPVWYTD7l3BpDhMjspDcpVhahWKsXUoOEUKdl6scGxNygo4nU4cjkKf/1TH2ZL7gq1evZri4urVrMrMzOS7777jySefVMSSTHR0NKNHj+bLL790m8+SJUt46KGHCA4O5s477+TTTz/1+NxvvfUWX3/9Nbt37y73+NnZ2fz000/cc889yjHXr19PXl5etc4DYNy4cYSGhiqhuejoaFJTU9m6dWu5+0RHR7Nr1y7OnKncoZR5//33SU1NVX4mT55MZGQkbdu2rfacVRo+qsPkI+pCMOm1GrQaAYdTxGJ3EIwkApwWeYVczd2l7858x8+pP2PUGnmp50v16lt6mLFqpQVkCSm4bhDFhTbkdCajf8P706hK4UqdTqfkABU7LNitXnaYSofkrJ7LClSHgD6xFB/LonDvZYLvbIFQziq4Eoep6qvknE4Lm7d0r/Hcakr/fvvRaqtWr0yn0/Hpp58yYcIEFi5cSNeuXenXrx8PPPAAnTp1qnDflJQURFGkXbt2Hh9v164dWVlZpKenExkZSUpKCrt27VJEyUMPPcTUqVP5y1/+UubvvWvXrowaNYpp06axadMmj8+/fv16OnXqRGxsLAD//Oc/GT16NOHh4XTu3JlbbrmFESNGcPPNN1d6HTQaDa1bt1ZypEaOHMl3331Hv379iI6OplevXtx+++2MHTuWoKAgAGbPns2wYcNISEigdevW9O7dmyFDhjBixAg05XxxDA4OJjhY6nG4atUqFi1axA8//KA4XirXF6rD5CPkpG9frpKD0v3kSm6iosVVg8lYsxVyRfYi/rb7bwA82uFRmgU1q2QP3xJklD4gc4vLX5ouiqIimDSuljVyOM5g1qFtYH3koKQ1iq2iwpUaHTik87U6LV53mNzKCiiFK2v+N2BqHYo2yICz0E7R4fJz1GoSkmtIOUwXL15k7dq1DB48mM2bN9O1a9dy3Z+rqep5Ll26lOTkZJo0aQLAkCFDyMnJ4ccff/Q4fu7cuWzbto2NGzd6fHzNmjWKuwRw6623cvLkSTZt2sSIESM4ePAgffv25bXXXqvyeSgV47VaPvnkE86fP8+8efOIi4vjjTfeoH379qSmpgIQExPDzp072b9/P5MnT8ZutzNu3DgGDx6M01m29Epp9u7dy5gxY1iwYEGVBJ1K46ThfY1uoFiUpG/f3ohNei0FVodbLSbFYTLXLEF72cFlpBWkEeMfw/gO42tjmrVKkMElmKzlC6bSq8PkHCZLgSsc1wDdJaiiw+T6juQQHThEm9dzmARPhSuvQTAJGgHzjRHkb72A5XAGfh2beByX56h+WQFBMNK/3/4az62maDTV/zs0mUwMGjSIQYMG8fLLL/PYY48xe/Zsxo8fX+4+LVu2RBAEDh8+zNChQ8s8fvjwYUJDQ4mIiMDhcLBs2TLS0tLQ6Ur+HhwOB0uXLuX2228vs39iYiITJkxg+vTpLFmyxO0xq9XKhg0bmDlzptt2vV5P37596du3L9OmTWPu3Lm8+uqrTJs2DYOh/NIeDoeDlJQUbrrpJrftcXFxjBkzhjFjxvDaa6/RunVrFi5cyCuvvKKM6dChAx06dODJJ59k4sSJ9O3bly1btjBgwACPx0pLS+Oee+7hsccec8t/Urn+aJh3hgZIUR2E5Eofr6iWHKbLhZdZekBaLTO129R6sSruaoKNkoVeoWAqJRQEl5iw5EsrxhpiwjeUJH3bKihcqXVKgsmOJJq9vUrOU+HKqtRRqghTq1BJMB3PdnMZZJyiSJ69+r3kBEGocmisvpGUlMTq1asrHBMeHs6gQYP48MMPee6559zymNLS0li+fDljx45FEAQlt2jv3r1oS13DAwcO8PDDD5OdnU1ISEiZY8yaNYvExERWrFjhtn3z5s2EhobSuXPnSs/DbrdjsVgqFEzLli0jKyuL4cOHlzsmNDSUmJgYCgrKL9CalJQEUO4Yi8XCvffeS9u2bfn73/9e4dxVGj+qYPIRSh2manzjrQ1kR6t0SO5aHKYP931Ikb2IThGd6qyad2UoDlMFIbnSoSiNK3yjlBRogAnfUJL07bA7cdidaEvl75QIJkkc2JD+70uHSawFhwnAmBAMOg3OXCv2y4Xoo/zdHi9wOJXKWo2tl1xGRgYjR47kkUceoVOnTgQGBrJ7927mzZvHvffeq4y7cOEC+/btc9s3Pj6eBQsW0KdPH5KTk5k7dy4tWrTg4MGDvPDCC8TFxfH661K1/iVLlnDXXXeVEThJSUk899xzLF++nKeeeqrM/KKiopg6dSp//etf3bavXbvWLRwHUo2lBx98kO7duxMeHs6hQ4eYOXMmAwYMUPKOQCptkJaWht1u5/z583zzzTe8++67TJo0SXGFFi1axL59+xg6dCiJiYlYLBY+++wzDh48yPz58wGYNGkSsbGx3HbbbTRt2pTU1FTmzp1LREQEvXv3BuCbb75hxowZHDlyBJBqUJ07d45NmzaRnp6uzCksLKxCQafSOGl4iRoNFKWsgMHHITmlPUoph6nYVeW7mg7TyZyTfHP8GwCe7/58vUr0Lk3VQnKSUBBEEVy/W/Ib7go5KHGYoKzLJIfktA6XwyS4BJMPc5ictZD0DSDoNRhbSK+xJSW7zOM5LgGsFwRMmsrfo/X1feyJgIAAevbsybvvvsutt95Khw4dePnll5kwYQILFixQxr3zzjt06dLF7WfdunW0atWK3bt3c8MNNzBq1CgSExN5/PHHGTBgADt37iQsLIxLly6xbt06j+6NRqNh6NChZUJupXn++ecJCHCvsO9JMCUnJ7Ns2TLuuOMO2rVrxzPPPENycnKZ0gSLFy8mJiaGxMREhg0bxqFDh/jyyy/58MMPlTE9evQgPz+fiRMn0r59e/r168euXbtYvXq1Un5h4MCB7Nq1i5EjR9K6dWuGDx+OyWRi06ZNhIeHA5CTk8PRo0eV592yZQupqakkJSURExOj/OzYsaOyl0qlEaI6TD6i2F7SfNeXKO1RSucwFbnKClRzldwHez7AKToZ0GwAXSK71N4kaxk56TunOKfcMQ6bJCA0TlGpet6Qi1YCaLUatHoNDpsTq8XuJvwUh8mloxyyYPLyKjlBW1JWQHSJOI3p2v8GjPFBFKdkY0stG0rJK9VHrjpiqCE4TEajkTfffLPC6tRXV9e+mvj4+AoTxKOiopT3iydKC5U5c+YwZ84ct8eDgoLc3Jg9e/aQm5urCBeZGTNmMGPGjArnunnz5gofl+nSpQuff/55hWOGDx9eYQgPpLpLpfPAKruWKtcXqsPkI4qsdZvDVGz34DBVQzD9kf4Hm85uQiNomNx1cu1OspYJNlQhh8nlrGjEUoKpgecwQUnit624HIfJLgkIh0s52X3pMLlW711rSA5AFyGFk+1Xiso8Vt0+cg3JYWqI2O125s+fL1WZV1FpwKgOk48o6SXn+1Vy0vFL5TDVwGH6YO8HANyTeA+JIYm1OMPaR3aY8qx5OEUnGqHsNZdzdzSiiFNxmORVcg33g11v1FKUZ8N6Vb812THQ2AVAxKmR3g8+y2FylEr6Nl37x44uvAqCqQoJ36VpCA5TQ6RHjx5VrkSuolKfUR0mH2Gx+76XHJQTkissBEDjX7UVQb+k/sLPqT+j0+iY1HlS7U+ylpFzmERE8m2e+0S5OUwu0dDQQ3Ig1ZCCsv3klLICrkVxDp30fnD4aJWc02JXioLWimBytUVxFtgU50pG7iNX1YRv1WFSUVGpCqpg8hElveR8XbjSg8PkWkKr8fP3uM/VfPS71FtqeKvhxAbE1vIMax+D1oBJK7ln5a2Uk50VoVRIrtjVMqQhVvmW0Rs9lxZQHCaXPnLqJPXi9V5ysmAqdB1HK4Du2gWKxqRD48rRutplqm5ITkZ1mFRUVCpCFUw+oiQk5+uyArJgqpnDtOfSHnZf2o1Oo+Oxjo95Z5JeQHaZcqyeE7/lwpVS0rd0My923dSNfg3YYXK5N9arXBfZYdIUu0SBXvrX7qOQnNy/TmPS1pqjI7tMVwum0knfVZqj6jCpqKhUAVUw+YiSXnK+zmFyheTsNXOYlhyQlg/f1/I+ov0bTv+kytqjeEz6LmzYlb6h/OKVisMkpa8hujSh98sKSP/IDpNgrL1rW55gqmnSt+owqaioVIQqmHxEXTTfLX08t5BcFR2mk9kn2Xp+KwIC49uP99ocvUFltZjkUJTWJZgcdid2V95PY3OYRFEs5TBJTqNodK2WqwOHqbZQEr8zLW7b1ZCcioqKN1AFk4+oq+a7Zk+CqYoO02eHPgNgQLMBxAfFe2mG3kFxmMoTTC4BIYggWq1K/hJCSeJ0Q8STw2Qv3TevyCUKXAVUvR2SQ3u1YKq9a6sNlCotO/Ldz0FO+q7qKjk1JKeiolIVVMHkI+SQmK9DcuYa5jDlWfNYf2o9AGOSxnhxht6hsvYoSlkBV+FKJX/JrENTherQ9RWDK+m79Cq50kUIta5fNSZXE16vO0zSceSSArVRg0lGGyg5gc4895V+OTbVYVJRUal9VMHkI+TClUZfV/p2taGQjw+lBFMFDtPaE2spshfRMqQl3aK6eXeSXqDypG/3HCa5BpPRr+G6S1CqrECpOkyywyQIAho0IIAg953zUVkB5b+16DBpZIfpKsGU55CTvlWHSaV+snnzZgRBIDs7u07nsXr1alq2bIlWq2XKlCl8+umnbk2V58yZw4033lhn86tvqILJB4iiSLG9blbJyQ5TkaeQnH/5gunrlK8BGNl6ZIO8ocghuXxreXWYXCEiUcRpszaKFXJQqqyAB4dJr5PEimDSoTVI5+l9h+mq/9diDpMcknMW2BCdJe5Qtuu9HtJIHab09HQmTZpE8+bNMRqNREdHk5yczPbt2wFISEjgvffeK3f/c+fO8cgjjxAbG4vBYCA+Pp7JkyeTkZHhcfwTTzyBVqtl5cqVZR6bM2cOgiAwceJEt+379u1DEIQyrUXOnDmD2WwmPz+fwsJCZsyYQWJiIiaTiYiICPr168eaNWuU8f3790cQBARBwGg0EhcXx913382qVavKzGXLli3cdttthIWF4efnR6tWrRg3bhxWa4mgXrx4MZ07dyYgIICQkBC6dOlSYZsZb9GnTx9SU1MJDg72+bFL88QTTzBixAjOnTvHa6+9xv3338+xY8fKHT9+/Hjuu+8+302wnqEKJh8giyUA8zU2Hq0uHgWT4jB5DskdzTxKSlYKeo2eu264y/uT9AKyw5RnzfP4eOlK39hsFBfIgqmBO0wuB8dmKesw6bSSSNL46dDq9G6PeQ2tFx0mfz1IhctxFpQIvxzXOQVX8ctJ6S8EDUE0DR8+nL1797Js2TKOHTvG2rVr6d+/f7mCpzQnT56ke/fupKSk8MUXX3D8+HEWLlzIpk2b6N27N5mZmW7jCwsLWbFiBS+++CJLly71+Jwmk4klS5aQkpJS6fHXrFnDgAEDCAgIYOLEiaxatYr58+dz5MgRNmzYwIgRI8qcx4QJE0hNTeXEiRN8/fXXJCUl8cADD/D4448rYw4dOsTgwYPp3r07W7duZf/+/cyfPx+DwYDD5TguXbqUKVOm8Oyzz7Jv3z62b9/Oiy++SH6+5y9V3sJms2EwGIiOjq7TL6P5+flcvnyZ5ORkYmNjCQwMxGw2ExkZ6fVjlxaxDQlVMPmA0gnXpirWhqktzAa50rc0B1EUK3WYvj35LQD9mvYj2Fi334BqSqAhEKiCYHJKrVFKSgo0cIfJ5eBYLWUdJp1Gekxj1qHTyw6Tb0NytZnDJGgESTThHpbLceULBjfCkFx2djbbtm3j7bffZsCAAcTHx9OjRw9mzJjBPffcU+n+Tz31FAaDgY0bN9KvXz+aN2/OnXfeyQ8//MCFCxd46aWX3MavXLmSpKQkpk+fztatWzl37lyZ52zTpg0DBgwos68n1qxZo8xz7dq1zJw5kyFDhpCQkEC3bt145plneOSRR9z28fPzIzo6mqZNm9KrVy/efvttFi1axOLFi/nhhx8A2LhxI9HR0cybN48OHTqQmJjI4MGDWbx4MWazWTneqFGjePTRR2nZsiXt27fnwQcf5PXXXy93vuPHj1ccrtI/clPghIQEXnvtNR588EH8/f2Ji4vjH//4h9tzCILARx99xD333IO/vz+vv/56mZCcHAr79ttvadOmDX5+fowYMYLCwkKWLVtGQkICoaGhPPvss4oABCguLub5558nLi4Of39/evbsWaWGxZs3byYwUPqMvO2225RzujokV5o5c+awbNky1qxZU+Y6nDt3jlGjRhESEkJYWBj33nuvm7soO1Ovv/46sbGxtGnTptI51kdUweQDZHdHpxHQaeuml5ycwyRareD6g/OU9O0UnUqy959u+JOPZln7BOqlD4PyV8mVzmGylVT5biQOk9WTw1RKMGn1vgrJec9hglJhOZdgsjicWFzhuZqE5ArsDgocvv2pjqsVEBBAQEAAq1evpri4uFrnl5mZyXfffceTTz6piAiZ6OhoRo8ezZdffuk2nyVLlvDQQw8RHBzMnXfeyaeffurxud966y2+/vprdu/eXe7xs7Oz+emnnxTBFB0dzfr168nL8/ylpiLGjRtHaGioEpqLjo4mNTWVrVu3lrtPdHQ0u3bt4syZM1U+zvvvv09qaqryM3nyZCIjI2nbtq0y5q9//SudO3dm7969TJ8+ncmTJ/P999+7Pc+cOXMYOnQo+/fvLyMIZQoLC/nggw9YsWIFGzZsYPPmzQwdOpT169ezfv16Pv/8cxYtWsR//vMfZZ+nn36anTt3smLFCv744w9GjhzJ4MGDK3X7+vTpw9GjRwH4+uuvSU1NpU+fPhXu8/zzzzNq1CgGDx6sXI8+ffpgs9lITk4mMDCQbdu2sX37dgICAhg8eLCbk7Rp0yaOHj3K999/z7ffflvhseorDfvu0ECoqyrfUDYkJ7tLAJqrPjQBDlw5wOXCy/jp/Lil6S2+maQXqLLD5Er6VkJyDdxhMpjLlhVQHCahlMNkkISG3edJ37X7N6AJNEBqAY486RzlGkwC1U/6tojQ7acDtTq/qnDi1o74V7EEgk6n49NPP2XChAksXLiQrl270q9fPx544AE6depU4b4pKSmIoki7du08Pt6uXTuysrJIT08nMjKSlJQUdu3apYiShx56iKlTp/KXv/yljCvXtWtXRo0axbRp09i0aZPH51+/fj2dOnUiNlZqr/TPf/6T0aNHEx4eTufOnbnlllsYMWIEN998c6XXQaPR0Lp1a8XFGDlyJN999x39+vUjOjqaXr16cfvttzN27FiCgqTw/OzZsxk2bBgJCQm0bt2a3r17M2TIEEaMGIFG4/mLbHBwsJJntGrVKhYtWsQPP/xAdHRJEd+bb76Z6dOnA9C6dWu2b9/Ou+++y6BBg5Qx//d//8fDDz+s/P/kyZNljmWz2fjoo49ITJSam48YMYLPP/+cS5cuERAQQFJSEgMGDOB///sf999/P2fPnuWTTz7h7NmzyjV9/vnn2bBhA5988glvvPFGudfPYDAoobewsDC38ymPgIAAzGYzxcXFbuP/9a9/4XQ6+fjjj5X3xSeffEJISAibN2/mjjvuAMDf35+PP/4Yg+uzpyGiOkw+oK6qfENJzpQ8Bzl/STCbETx8SP9wVrK4+zXth1Fr9NEsax9ZMJXnMMn1h7RKWYHG4TDpjWUdJvlbnk6QHtP46dEZpNfW7uVcgqsdJqG2HSZXPzlHvnQe2aWKVmqqGGprSCE5kHKYLl68yNq1axk8eDCbN2+ma9eu5bo/V1NVR2vp0qUkJyfTpEkTAIYMGUJOTg4//vijx/Fz585l27ZtbNy40ePjpcNxALfeeisnT55k06ZNjBgxgoMHD9K3b19ee+21Kp+H/NpptVo++eQTzp8/z7x584iLi+ONN96gffv2pKamAhATE8POnTvZv38/kydPxm63M27cOAYPHozT6azoUOzdu5cxY8awYMGCMoKud+/eZf5/+PBht23du3ev9Hz8/PwUsQQQFRVFQkICAQEBbtsuX74MwP79+3E4HLRu3VpxHgMCAtiyZQsnTpyo9Hi1xe+//87x48cJDAxU5hAWFobFYnGbR8eOHRu0WALVYfIJdVXlG0o5TFbZYZJrMHnOX/rxrPRheHv87T6YnfconfRd+oNVxm6VwhlKWQHXKrmGnsNkcDk4dqsTp1NEoxFKVsnhwWGqZlin2njZYbo6JFfd/CUZQRAwCSJHe7dDp/ftx6JfOe5GRZhMJgYNGsSgQYN4+eWXeeyxx5g9ezbjx48vd5+WLVsiCAKHDx9m6NChZR4/fPgwoaGhRERE4HA4WLZsGWlpaeh0JdfD4XCwdOlSbr+97OdDYmIiEyZMYPr06SxZssTtMavVyoYNG5g5c6bbdr1eT9++fenbty/Tpk1j7ty5vPrqq0ybNq3Cm6vD4SAlJYWbbrrJbXtcXBxjxoxhzJgxvPbaa7Ru3ZqFCxfyyiuvKGM6dOhAhw4dePLJJ5k4cSJ9+/Zly5YtDBgwwOOx0tLSuOeee3jsscd49NFHy51TRfhXsCJZRq93/+wRBMHjNlnc5efno9Vq+e2339Be9eW3tMjyNvn5+XTr1o3ly5eXeSwiIkL5vSrXoL5TLxymf/zjHyQkJGAymejZsye//PJLuWMXL15M3759CQ0NJTQ0lIEDB1Y4vj5QX0JybgnfHlbInc09y5ncM+gEHbfENdxwHJQ4TDanjWJHWVFQpnBlI1slByWlBRSHySn9uWvMOvRGyWGyWb0rmMo4TLXYSw7K1mLKtknOWnXzl+QkVrNGwF+r9elPbThcSUlJFJQKt3siPDycQYMG8eGHH1JU5N5/Ly0tjeXLl3P//fcjCIKSW7R371727dun/HzxxResWrWq3PpBs2bN4tixY6xYscJt++bNmwkNDaVz586VnofdbsdisVQ4btmyZWRlZTF8+PByx4SGhhITE1PhdUlKSgIod4zFYuHee++lbdu2/P3vf/c4ZteuXWX+X17Yszbp0qULDoeDy5cv07JlS7efqoTYakLpVYcyXbt2JSUlhcjIyDLzqOuyCbVNnQumL7/8kqlTpzJ79mz27NlD586dSU5OVmzHq9m8eTMPPvgg//vf/9i5cyfNmjXjjjvu4MKFCz6eedWpy5CcXLjSKYLV4cRZIC2h1QSUVfvbL0p1XLpEdcFf37C/Dfjp/dC4igB5ymOSQ1FaUQRRbDR1mLR6DRrXUn65eKWSwySWdphcITlfO0y1uEoOSkJyclkBOYepqiUFZBpKWC4jI4PbbruNf/3rX/zxxx+cOnWKlStXMm/ePO69915l3IULF9yEzr59+8jKymLBggUUFxeTnJysrHrbsGEDgwYNIi4uTlkxtmTJEu666y46d+6sODIdOnRQVkJ5chNAChlNnTqVDz74wG372rVry6zi69+/P4sWLeK3337j9OnTrF+/npkzZzJgwAAl7wikZOi0tDTOnz/Prl27mDZtGhMnTmTSpEmKK7Ro0SImTZrExo0bOXHiBAcPHmTatGkcPHiQu+++G4BJkybx2muvsX37ds6cOcOuXbsYO3YsERERSljtm2++cUvofuKJJzh37hwffPAB6enppKWlkZaW5pbMvH37dubNm8exY8f4xz/+wcqVK5k8eXK1X9vq0rp1a0aPHs3YsWNZtWoVp06d4pdffuHNN99k3bp1XjlmQkICf/zxB0ePHuXKlSvYbDZGjx5NkyZNuPfee9m2bRunTp1i8+bNPPvss5w/f94r86gr6lww/f3vf2fChAk8/PDDJCUlsXDhQvz8/Mqt+bF8+XKefPJJbrzxRtq2bcvHH3+M0+ksN9GwPqAIJh9X+Qb33nUWq1NxmLQeqnzvuLADgD6xFa+WaAhoBE2Fid92xWGS3L/GUukbStdiKsdh8ivlMHlbMGm9V1YApHwsAKfr9cuuYeNdWTDV9zpMAQEB9OzZk3fffZdbb72VDh068PLLLzNhwgQWLFigjHvnnXfo0qWL28+6deto1aoVu3fv5oYbbmDUqFEkJiby+OOPM2DAAHbu3ElYWBiXLl1i3bp1Ht0bjUbD0KFDy4TcSvP888+XCQl5EkzJycksW7aMO+64g3bt2vHMM8+QnJzMV1995TZu8eLFxMTEkJiYyLBhwzh06BBffvklH374oTKmR48e5OfnM3HiRNq3b0+/fv3YtWsXq1evpl+/fgAMHDiQXbt2MXLkSFq3bs3w4cMxmUxs2rSJ8PBwAHJycpTVYyAVw0xNTSUpKYmYmBjlZ8eOHcqYP//5z+zevZsuXbowd+5c/v73v5OcnFzu9alNPvnkE8aOHcuf//xn2rRpw3333cevv/5K8+bNvXK8CRMm0KZNG7p3705ERATbt2/Hz8+PrVu30rx5c4YNG0a7du149NFHsVgsbsK3MSCIdfgJYbVa8fPz4z//+Y9b9dBx48aRnZ3tVvG1PPLy8oiMjGTlypX86U9ll8EXFxe7Lb/Nzc2lWbNm5OTk+OzF/Pq38/x55e/c2jqCzx7p4ZNjlqblzPXYnSK7ZtyO6ftvSf3LywT070+zhR8pY2xOGzd/cTNF9iK++tNXtAv3vqXsbe78+k7O55/n8zs/58bIG90e+8/rL3Pmj710PnuJ2Kx8ttz+IU6HyNg3+hAYZqqbCdcSn720g7wMC8Nf7Eb0DcFs3LiRHTt20Fl/AzfltSDi8Y5YQ+z8c9J4NFotz/278r+zmmJJyeLKkpKVZ3Fzb0aoxVpk1gv5XJ6/F02QgdiZPfn76TTmnUpjdEwYf2tb/k3DYrFw6tQpWrRogclkIj09HZvNRlhYGCZTw3796xt79uzhtttuIz09vUxOTkMnISGBKVOmMGXKlLqeSqPh6r/N0uTm5hIcHOzT+3dp6tRhunLlCg6Hg6ioKLftUVFRpKWlVek5pk2bRmxsLAMHDvT4+JtvvqksDQ0ODqZZs2bXPO/qUmiVvv361UEOE7jnMZVXtPJwxmGK7EUEG4NpE9Ywi4pdTUUr5UrKCoBTo8fpkL43NA6Hyb20gOwwae2Si6Lx06M3SB9ETodDaRPjFUqH5LRCrYolAI2/9Ho5C2yIoqg03g3W1ex1rO8OU0PEbrczf/78RieWVK4/6jwkdy289dZbrFixgm+++abcb4UzZswgJydH+fFUqdbbFLpWqPn5uC2KTOkGvI58OYfJ3TL/7dJvAHSN7Krk/jR0KmqPItcf0up02HVSArygEZRebA0ZpXhl8VU5TDZJvAilVsmBd0sLlE76ru38JSgJyeEQEa0OZZVcSA1zmFTBVPv06NGDMWPG1PU0rjvuvPNOt3IDpX8qqtGkUj51+nW6SZMmaLVaLl265Lb90qVLlWb5v/POO7z11lv88MMPFRZsMxqNGI11W09IFky+7iMn4+Yw5bscpquSvmXB1C2qm28n50UqymFyyHk9Oj021zdfk7+uwST/VoTSHqXoqhwmV9K31k8HOg0IAogidmsxxnL6Cl4zpQST4IX3v8agRdBrEG1OnAX2ayorAKpgUqkeVzcXrk98/PHHZVZDyoSFhfl4No2DOhVMBoOBbt26sWnTJiWHSU7gfvrpp8vdb968ebz++ut89913VSoIVtfIVbbrymGSBZOlnJCcU3Sy5/IeALpH1f/rWVUqTvp2OUwGAzadVPG8oa+Qk1GSvl0OU2nBJOg1CK73g95gxFZs8Wrid2mHqbYTvmU0fnocOcU4C2xky413VcGkcp0TFxdX11NodNR5wsbUqVMZN24c3bt3p0ePHrz33nsUFBQoZeTHjh1LXFwcb775JgBvv/02s2bN4t///jcJCQlKrpNsNdZHlBwmQ91c7tIhOacrJKctda1O5Zwiz5qHWWduNPlLUIlgsrrCVHoDha6QXGPIX4KyDXhLCldq0JQ6R51REkz24opr3lwTXg7JgZTH5MgpxlFoK5XDVLVjyQKpMTiLKiqNgfr8paXO7xD3338/6enpzJo1i7S0NG688UY2bNigJIKfPXvWrc/PRx99hNVqZcSIEW7PM3v2bObMmePLqVeZwuK6dpik61de0veBK9IqpnZh7dBp6vwtUWtUlPQtO0w6owG7vnEJJoNRLitwlcOEtiTnB9AbjRTh3eKVgtYHDpN/SS2mnCrWYZITkAsLCzGbzarDpKJST1AWqVSxv6IvqRd3iKeffrrcENzmzZvd/l+fY8blUddJ37KzVWRz4JALV/qXOEz7r+wHoGOTjr6fnBeRk749r5JziQiDCZviMDWOkJze7Nlh0olaRVwAvile6QuHqVQtphzRlfRdySo5rVZLSEiIUiDX4XBgt9spLi5WV3OpqNQRTqeT9PR0/Pz83Fry1Bfq34waIYU2Oem7bi63ew5T2V5yB68cBKBDkw6+n5wXqdBhcn2L0ZuMyio5U6NzmNyTvvVo3UJyPmmP4pbD5J3rq3WJQGuhlXyDVIi0KiE5eWHJ5cuXKSoqori4GKPRiNls9so8VVRUKkej0dC8efN6GSZvHHeIek6RksNUR2UF9GVzmORVclaHlSNZR4DGJ5iCjVIfo6tzmJwOB05XPyStyYxNDsk18Ma7MiU5TFeVFbgqJKc04PVRWQHB4J1yFbIIzCm0gataQlUEkyAIxMTEEBkZyU8//cSvv/5K+/bty23CqqKi4n0MBoNbGk59QhVMPqDOywoYys9hOp59HLvTTrAxmLiAxrWqQnGYit0dJrloJYDeZFIcpkaTw2R2F0yKwyS6O0y+D8l55/rKYcYsi/S6+ms16DRV/3aq1WrRarXk5+dTXFysVvpWUVHxSP2UcY2MIpdg8q/jkJxUh8l9lVxKVgoAbULb1EsL9FpQClfa3B0mOeEbQGv2a3Q5TKV7yTkcDpyufnmekr7Bu/3kfFJWwCWYsoslwRRSg56Ncr6E3ZtVz1VUVBo0qmDyAQV1HJKTBZO10ILochtkh+lY1jEAWoW2qpO5eZPSlb6dolPZLoegNFodOrO58a2SM7sqfVscbl3VdZST9O3VHKZSv3o56TvTtUIuXF/911EWTLZS7qOKiopKaVTB5APqOiQn12Gyu8JxUFYwtQ5t7fuJeRk5JOcUnRTYSs5dKSlg0COUCsmZ/BuJYHI5TMVFdkUAaBDQXlWHyecOk967OUxZrry00BoIJnllnOowqaiolIcqmHxAUR2XFZAdJoerLYpgMiG4vlHLIblWIY3PYTLpTBg0UhZw6cRvuS2KVm9AY2p8ZQWU5rtFdrcaTADa0knfRh84TFrvCyZ5lVymy0UMq0GTa1kwWb2YAK+iotKwUQWTl7HandidUjE8vxp8860NZMGkJHy78pcyijLIsGQgIJAYklgnc/M2nkoLyCE5ncEAJhM2veS2mQIaiWByheTsNicWiySG9K4+cm4OkyskZ7N4r9K3m8Ok85bDJL1uOTrpWGE1+DszuFYMqoJJRUWlPFTB5GVkdwnqcpWc67jKCjnJUTmRfQKApoFN8dN7qflqHRNkLMljklFCcnoDDp0ZBOnPoNHkMJlK3mcFuVLzzRLBVCIKDWbpNbdaPDforBV8EJIT9BoEg4ZsgyqYVFRUvIcqmLyMnPCt1woYvPQNuzLkOkwUSUUrta4q36dyTgFwQ/ANdTIvX6BU+y5VWsAu1yXS65XGu1rs6GoQyqmPaLQadK5SEoWuQqV6dKAVEEqJKb1JOndrOR3NawXB+4IJJCGoCKYarEZVBZOKikplqILJyygJ33V4M5aPLRS6h+RO5UqCqUVwi7qZmA/wFJKT26JoDQasgiQa9KIX83jqADnxuzBfEkMGUYfWX+9WOsLo532HyS0kZ/Keg6fx05Gjlx2m6v+tqYJJRUWlMhpHDKIeU5LwXXeXWk421xS6F608nXMagISghLqYlk/w1E+udA6TTZBulAan9/J46gKDWUdhrpXCQldIDi2aq3K0DLLDVFjo1bkE3t4cZ54VfYx/5YNriMZfT7ZBel1rUlZAFkw2mw2n01lvKw2rqKjUHapg8jKFdVyDSTq29DJrXE6C4jDlXD8Ok1sOk7Ukh8kq6gEHOocXw1J1gJzHZCmUk751aAIMbmP0rp5pXs1hAoIHxXv1+cEVktNLodZryWECSTQZXSsIVVRUVGTUr1FeRm686+elon1VIcDVkkJbJDfe9aPIXsTFgosAJAQn1NXUvI5nh0kSETqDEatTcl30tnzfT86LyCvlLK4VcAa0yvJ7GaOc9O3NHCZf4adVQnKh11BWANSwnIqKimdUweRlCotdgqmOSgoA+LvEmq5YujFqAwI4m3sWkBrUhhpD62xu3saTYJKX0euNRmyidKPUFTcywWSSBZPLYUKHJvCqkJzsMBV5NyTnCwr8dDhc+VKhuur/rQmCoOYxqaioVIgqmLyMHJKrq5ICAP4uh8nPLt08Nf7+SsJ3QlBCo+shV5pgYzDgvkrO5nKY9EYTxQ6XmLTk+H5yXkRuwFvsOlcp6fuqkJwvVsn5iFzX+fo5waSt2ceaKphUVFQqQhVMXqbIVrdVvgGMOg1ajVBKMJU4TPFB3s8vqUvkOkw51hJBZHe1AtEZjVhtkljUF+UgNqK2GLLDJN/8PSV9y6vknA67UmqhoZJtkj7KQh2VDKwAVTCpqKhUhCqYvExd95EDKdzgb9DiZ5dCURp/f87kngGgeWDzOpuXLwgxhgBXOUzFpRwm171RZytUKqE3BuQcJpurhIKU9O0umPQmk/J7Qw/L5bhqMAXbxBo/hyqYVFRUKkIVTF5GFkz+dVhWAKTEbz+bSzAFXD8OU7BBCsllF2cr20oEk5HiIun10dsLcOY3njwm2WGy2aWbvwFdmaRvjUaL3iiJpoYelstyJXwHFztr/ByqYFJRUakIVTB5mcLiui8rAFIek7lUDtPZPEkwNQ9q3A6TksNkzcXpas5qK5aEo85gxFIghaL0tgIcjcphkt5vdofr/ERtmbIC0rjGkfidqZWcpbAiJ6KzZi6TKphUVFQqQhVMXkYuK1CXITkAP6NOCclZjAKZlkyg8TtMcg6TU3SS7yodIJcV0JtKBJPOVogzvxEJJpfD5HBK52dAh9ZDc+HGIpjSRUkkhRc7cRbVLBdNFUwqKioVoQomL1NS6btuBVOAUas4TJeQ8nnCTeH4671Xfbk+YNQaMbv6xeW4VsLJITmtzkBxgXRzNdjycBY0opCcK4fJIbrOz2BA8NDL0CcNeH1AuithP7xYxFlQswR2VTCpqKhUhCqYvEy+KyQnL+2vdexWsFXe1sPfoMPPJgmFC6LkLjX2cJyMHJaTV8rJITlRdL0mooje1rhymIx+OkREnEiC3RRgLmecJJiKG3g48rK1lGDKr5lgkqt7Fxc3rr6CKioqtYPaGsXLFLgEU0BtCiZrARzbAL+vgJObwWGFZr3g7vcgsp3HXYK0InpRunmedVwBGv8KOZlgQzBpBWnkFEuCSS4r4HRqATsGwYqAiKOxCSahJDRlDvLsJJr8pdYxlvw8j483FNKtkkhqYhVxFNTMITK5Vg3K1dFVVFRUSqMKJi8jC6ZrXiXndMLR9fDbp3B6G9iv+lA/twuWJsPjmyHshjK7B4slN5HTtjTgOnSYit0dJodNMliNGulm25hymIx+ekSN9N7Ti1r0QSaP40wBsmBq2GLRzWGqYUhOFUwqKioVoQomL1MrITlrIXz9qCSYZEIToONI6DActAb4+jG4uAe+GgsTNoPW/XhBLsFkN5g4W3AeuI4cJqN7aQHZYXLYpbwyo1Z6jRpVSM5fh9MlBI3o0QaWXSEHYAqUBFNRfq7HxxsCFoeTHLvknoYXO2sckjO7EuCLGniJBRUVFe+gCiYvIwumQFMNL7XTCSvHQcpG0Bqh91OSUIpsB6VbmjywHD7qA2n7Yd9y6DbO7WmCHJJgshpMSg2m685hknOYXKvkbFaXw+RqTO/Ibbii4Wq0Wg2CQSqjYBR15QumRuAwpdtkJw0C7eBQHSYVFRUvoCZ9e5kCV/PdGjtMO96XxJLOBGPXwMDZEJXkLpYAgmLh1hel3ze/CXb3xNUAp/T/YoORDEsGAM0Cm9VsTg0MuXilXO3b5mpIayuWrqHZT3KaHNnZvp+cF9GZZMGkRxPkWTCZZcGU13DFYnqxJJAiBA0C1NhhUgWTiopKRaiCyYuIokiBVQ7J1aCswJmdsOk16fc750F874rH3/QoBMVBXioc/n9uD/k7JJFQ5CpvEGYKI9AQWP05NUBK5zA57HacDuk1sVqkt7/ZVZ+osQkmjewwoUcbWLYGEzQOh0nOX4rQSu/tmuYwqSE5FRWVilAFkxcptDpw1dOr/iq5jBNSPpLogE73Q9exle+jM5aM++1Tt4fkkgIFrhYS14u7BO45TPZSNXashdKLYw6RnAVHVpbvJ+dN9JK7WWFIrhHkMKW5VshF6F21p2rBYRLFmvekU1FRaZyogsmLyCvkNAKY9dVwmPLS4PP7oOAyRHeEu/5eNgRXHl0eAkEjraTLPKVsNrlW1RXoJdfhehJMocZQALIsWcoKOQQBi0sw+YVJS+4bm2ASXMnsFSV9mxuBw3TRIongpmbpHJ3511ZWwOl0YrPVTHSpqKg0XlTB5EXySq2QE6oqeOzF8O9RkH1WKg/w0CowBlT9oMFNIeEW6fcj65TNZleic55BmlNCUELVn7OBE2YOAyDTkqkIJr3RhMXlRPhHSO1TGltIDkFui6JHMHt2OJWQXEE+TqfDZ1OrTS64cpiaBrgET6Ed0Vb9JrwGgwGNRvpIVMNyKioqV6MKJi9So6KVP74Gqb+DXxNJLAVEVv/Abe+W/j3yrbLJZJNuAAVG6dt3Y+8hV5owU4lgshZKPdOMZjNFeS7BFC05UM6CApyNqC2GU3Q13tUYyhXspgCXGBfFBlvt+4LLYYoLMIKr/Ysjt/rVugVBUBO/VVRUykUtK+BFql2D6cwO2LFA+v3eBRDWomYHbjsE/vsCnN0F+ZchIBKjtYgioNAk3QiuJ8EUbgoHwOKwkJefDYDeZKaoUBIUflEhoNWCw4EjKxtNVA1Eaj3E7pSEhFbrORwHoNXpMfr7U1xQQGFONubAIF9Nr9ZQHCazEW2wAUeGBUeuFV2453YwnrBYLpKRsZWWrf6HRpPBkaPfc/yEHpMxGrO5OaGhvQgLuwWjsXG8N1RUVKqPKpi8iFxSoEoOk60IVj8JiFIeUps7a37g4KYQ01lyqk78CJ0fQOdqrlpktgGa60ow+en9MOvMFNmLyMi5BIDOYAYLCBoBvyAj2uBgHJmZOLKz0DcSwWR1SOJYI5QvmAACQsMpLiggPyuT8KYNqzaXQxRJcwmmWKMebZAsmKrmMOXk7OXM2X+Snv49IBIsrQ/Abpd+LJZzZOf8Smra14BAWNgtxMU+SJMmt6PRqB+fKirXE+pfvBepVkhu5z8g6xQExkLyG9d+8MTb3QQTrqTeIgOEGZvgp/e79mM0IMJMYVzIv0BWbjoAWr1UrdI/2ICgEdCGhkqCKTOzLqdZq1hcgkkQjRWO8w8JJeP8WQqyGt65p1vt2EQRrQBRBj05QdK5OnIqDq3abDkcS3mNtLRvlG3BwV1JTQ3j1Ek73br1p337dhQXXyIv/xCZmT+Rl3eAzMxtZGZuw2xqTnz848TEDEOjqfj6qqioNA7UHCYvUpL0XckKufx0+Old6fdBr4Ip+NoPnnib9O+J/4HTibPAJZiMEGlueu3P38CQ85hy8qSVcILrJucXLP2ri4wAwH75ch3MrvYpLi7G7mq27HBU5jBJ1ya/AQomOX8p2qBHpxHQBkvn6sgtXzBlZf3Crp8Hu8SSQEz0cHr23ED3bivRae8jM7MZRUUxhIR0JyrqLlomvkCPm9bQu9ePxMdPQq8Po8hyliNH/8L2Hf05f+HfiGLDTJhXUVGpOqrD5EUKqprDtOsfYM2HmBul3nC1QbOeoPeXShNcOoDD1Vi20AhhhtjaOUYDQhZM+a4cJsEVpvJ33WD1kVEA2C41DsFU4Erg1ooaiirJY/cPk3K8GqLDJOcvxZmk11ErO0zlhOQuX/6Og4em4HRa8fNrQVK7eQQHd1Ue9/eXSkzkeyiz4OcXT9hJ2z0AACwNSURBVMvE52mR8BQXL37JmbOLKS5O4+jRl8nI2EynjgsRBPU7qIpKY0X96/YismAKrEgwFWXDLx9Lv/d7ETS19JLoDCXlBU5tVRrLFhoFgnXXr8NUWCgVaBRxCaYQl8MUJQkm+6VLdTC72ke+4ZtFA7n5dkRn+YUYA0KkVYIN0WE6WyQJozijVMlc62oB4ykkl5r6NfsPPI3TaSWiySB63PT/3MQSQIBr1aAnwSSj1Zpp1mw8fXr/j9atZqHRGLlyZRNnziyqlXNSUVGpn6iCyYvkWVw5TBU13t3zGVjzIKIdtL6GRG9PtOgr/Xt6myKYigzgr4mr3eM0AMLNkotS5ApNOh3SDdZfDslFyw5TWh3MrvbJz80DwA8DBQ6RogqqX/uHuhym7IYnmE65BFMLP+l11AZ7dpgyMrZx+MgMwEls7P106LAArbbsKrqqCCYZjcZAs2bjaNP6FQBOnnqfgoLjNT4XFRWV+o0qmLxITpF0kwo2e+7jhdMBvyyWfu/9ZO25SzItbpX+Pb0dR35JDpPBEVW7x2kAyA6TtUiqw+SwS3ll/iGukJziMDWOkFzelRwATKIBqwgF2eWvGgsMlwRT7pV0n8ytNjnlijfeYL5aMFkVVy0//yj7DzyNKDqIjr6Ptm1eL3eFmyyY8vLyqjyHmJgRNAm/DVG0cfjISzid9hqfj4qKSv1FFUxepFLBdPS/kHMWzGHQcWTtTyCqI5hCEC15iK6CjYV6Aw5rLSSVNzBkwWRzFSS0FUs3TMVhimxcIbmcDEkwGZDOryCnfMEUHBkNQF7GFewNrCWI7DApginIAFoBHCKO7GKczmL2H3gKhyOfkJCetGv7RoVV90NCQgApB6yq7VEEQaB169lotX7k5Ozm6NGXcTjUSuEqKo0NVTB5kUoF06+u3KWuY0Ff9SJ7VUajgYRbcNpLbhD5YgSZhdffN+Am5iYAOCzSDdZWLL315RwmfYwkGuxXriA2gmrf2ZnSakCzRnpfVeQw+QWHoDeaQBTJudxwQpKFDieprqTvBFdITtAI6MKkat32jCLOnv2EwsJTGAwRdOr4YaUlAMxmMwaD5DpmV6NVjtnclKSkdwC4mPoVm7d0ZMvWG9n182COn3iH4uLGIcRVVK5nVMHkRWTBFORJMF05Dif/BwjQ/RHvTaJZT5w2STDZtGB1RJGeV/22EQ2dWH9pZaDTIr0mtmJXSM7lMGnDw9H4+YHTifX8+bqZZC2Skys5TGZXH8KKBJMgCIREx0j7NaAcLtldCtFpCdOXhNjkCt+WjMucPvMRAC1bTkevD6n0OQVBIDTU1ay5ms2YIyOS6djhQ0ymZoCI3Z5HQUEKZ858xI6d/TlydBZXrvzIxdT/cPLUB1y8uBKbLadax1BRUak71LICXqRCh+m3T6R/W90BoV6sut28Nw6bq6GoAZzW61MwRftHIyBgcJlHgsaMTq/B6C/9CQiCgD4hnuJDh7GePoPxhhvqcLbXTm6RlIMTEBQMiBRUUsgxJCqG9DOnyL6U6oPZ1Q6H8qWwV1t/k9t2Xbj0/4s5K3Do8wkIaEt01D1Vft7Q0FAuXbpUbcEEEBmZTETEIGy2TOz2PPLyDnLu/Ofk5OzmwoXlXLiw3G38qdML6HLjMvz8Eqp9LBUVFd+iCiYvIYpi+YLJXgy/fyH93v1h704kpjMOh3QDyTODwxLLleLrTzDptXoi/SIx2lxFRAUzQRFmt3wWQ7wsmE7XzSRrCYfDQb5dEhNBTcKAjApzmACCo6SQZHZawxFMB1yCKSnAPZyta2LGoSvksnYVAAnxT1arPpKcx1SdkFxpBEGDwdAEg6EJfn4tiIy8i6ysnVxMXUl+/mGMhkiMpliysnZgsZzn9z8ep2ePdWg05YTuVVRU6gWqYPISFpsTq90JeBBMR9ZBYQYExkDLQd6diM5Akf8NwBXyzOC0xJDhtGJ3ONFpr6+IbFNzLAa7tHReEEwENXG/0RoSEgCwnjnj66nVKrk5uYiIaESB4KZNgIwKQ3KA0kMu/ewpH8ywdpAdpg5XC6ZwM1nx3+HQ5OPn15LIyMHVel45JJdZS21yBEEgLKwPYWF93LYXW6/w889DKCw8wfkL/6J5My9/eVJRUbkmrq87pg+R3SWtRijbS27v59K/N44Grfc160W9lPBs9dMiOAMRRcgsaPiJzdUlVleqqa5gJKiJeyjHEC+FRq0nT/pyWrXO5XNSHlIgZgKaBQIV5zABRCZIIcj006cQxfKLXNYXRFHkYDkOkxhURFb8dwDckDAZQaikNdFVRERIbXIue7lNjtHQhJaJzwNw6tT7WK0ZXj2eiorKtaEKJi9xJV+6QYX7G9yXMWefk/q7AXQZ7Zu5uMJQOiOEB0hJzpevwzymKI1UbwitEUHQlHGYTK1bA2A5erRBiIbyuHRWCquF64IIjpSaLBfl2bAUlL9MPrxpc7Q6HcWFBeSm1/8VXWctVjJtDnQCtLkqh+l83uc4dRaMuc0J0/ar9nNHuWpyZWVlUezl8HVMzHACA9pjt+dx8tR7Xj2WiorKtaEKJi9xOU+q9xMZdNUy5t+/AERI6AthvkksznF9E/fTWogPcLjN73qiCUEAiK6l5VcLJmPLlgh6Pc7cXGznzvl8frVFepokeMIDQjGYdYqTduV8Be0+dDrCm0kO26WT9b9a9S85Uq+8ToF+mEuFli2Wi5y/8BkATY4Pw5ZaWO3n9vf3V3rKpad7t5inIGhp1fplAC5cWEFe3mGvHk9FRaXmqILJS8gr0SICSgkmpxP2/kv6/UbfuEsA+VlXAAjT2ehtOgvA2Yzq30gaOkF2SSAJouS6XB2SEwwGjG3aAGA5eNC3k6tF0jOk1zsyWgpBNnGF5a6cq7h6dWzrtgCcO7Tfi7OrHWTB1DPY3237sWOv4nRa8Le3x/9KZ2wXKm9x4onoaCkJ/sKFC9c20SoQGnITkZFDACdHjv6F9PTvOZbyOn/8MZGzZ5ficHj+ciOKIoWFZ0hN/ZqUlDc4ceIdsnN+8/p8VVSuV9Skby+hCKbAUoLp7A7IPgOGQEi61yfzyLJkIWRLN8pmWhvdtMeBWE5fh4Ip2Cq9FhpBcpqudpgATB3aYzlwgKLf/yDozlru7ecD7HY76RZpOXxMSymRu0nTAE7uTa/QYQJo3vFG9n23jjP7f/f6PK+VHVnSufQKCVC2pV/ZRPqV7xEEHTcEvogVEWsNBVNCQgInTpzgxIkT9OzZs1bmXBEtE6dz5cr/yM3dxx/7Jyrb0698z9mzH+Pnn4jdnktwUBeMxijy8g+Rnb0bq9U9z+r0mY8IDx9Ay8QXCAho4/V5q6hcT6iCyUtccjX/jAws5WLI7lKHoWDw88k89lzeQ3ielI8TZLLTxvIHcCunMwp8cvz6hNMVhhQ0gegCQW8omwzsd9NNZK/4koKdO309vVoh9cx5HDgxijqi2zUDJMEEcOVcxeKhWVJHBEFD1sXz5FxOU1qm1DdSCiycKCpGLwj0cQkmu72AY8ekJrjNmz1CiPlGLrMX28V8RFGssB2KJ1q2bMmmTZs4deoUdrsdnc67H5Vmcxzduv6bYymvY7GcJzi4K4GBHTh/bhnF1ksUW6Uwa17eAbf9BMFAUFBHggI7YrVlcvnyejIy/kdGxv/w87uBwIAkAgLaEhJyE8HBXaqdAK+iolKCKpi8xJlMycFpHuYSRkVZcPAb6fcuY3w2jz2X9tA/V/pd5+cgMmsveuycuQ4dpvxMaRWSoAmgODTb4xj/Pn1AECg+ehR7ejo614qphsLJA1L+UZQ2DF2A1OJDDsllpRZgKbBh8vdc78fkH0Cz9h05e+B3Dm37H72HP+ibSVeTDa7GwreEBhCo0yKKIkePzcJiuYDJGEuLFs+gEU2gFXAW2rGnF6GPrN4XlOjoaAICAsjPz+fkyZO0di0I8CZBQZ3o3u1Lt21N4/6PzMwdOBwFaDRGsnN+w27Lwc//BkKCbyIoqBNabcmXssIWz3L8xDzS03+gsPAkhYUnuXT5WwCMxmiio+4lJmYY/v4tvX4+KiqNDTWHyUuccTk4zcNdH9R/rAS7BSLbQ9ObfDIHURTZdWoLAa4UCH2TELSOIjoLxzmbWUih9frqKZd3RUrgFTQBnDd5TmzWhYZiSkqSxv/4P5/NrbY4djwFgPgmccq2gFAj4XH+OJ0ix36puPVJ0q23AXBw8w84HQ7vTbSGOEWRFalSfaS7IkIAqXdbWtpqBEFLUtLf0Gr9EHQajInS40WHqr9cXxAEklzvg/376y6nS6cLJDIymZiYYURF3UWb1rNo3/5vtEh4itDQHm5iCcDPrwWdOn7ErX1/pXOnj0lMfJHIyCHodMEUF6dx5uwidv2czK+/DuXc+c+w2apfzVxF5XpFFUxewGp3cj5LWpmWEO4vJXv/8k/pwW7joZrhgZpyNOsoljOnARACA9C0uhmAwebDOJwi+89fP32sRFEk84K08k3QhHFQs4df0371ODZoyBAAsv/zH5/NrzbIzcnlYp4UumnfraOyXRAE2veVBNSBrRdxOssvmdC6582YAoPIuXyJQ1t/9O6Ea8DmzDxOFBUToNVwX2QIObm/c+zYHABuuOHPhIb2UMaak6QyEpYaCCaATp06AXDw4EEyMhpWjSS9PoQmTQaQEP8EHTvMp+8tO+nY4UOaNLkdQdCRm/cHx469wrafevP77xO4cOELtUGwikolqILJCxy7lIfDKRJk0hEZaISj6yAjBUzBcKPvwhzrT62n+WXp5mhq1Rqh7V0AjBB+RI+dPWezfTaXuqYwJxtLQT4gIGhDuOJ/jjd+foNCW9nQZPB994Jej2X/fvJ/2u77ydaQX37cgQhEisFEdXXvT9i6ZzR6o5as1AK2rjiGWI5o0ptM9LhnOABbl39CflbtVLuuDRyiyJsnpRpT/xcTjrPwEPv2PYzTaSU8fADxzSe4jTcnhQFgPZuH/UpRtY/XtGlTWrZsidPp5IcffmjQtbk0GiORkcl07vRPbrl5O61a/YWAgCRE0caVjB85cvQv/LS9D7/8eg8nT75PTu7vOJ3XlwOtolIZ9UIw/eMf/yAhIQGTyUTPnj355ZdfKhy/cuVK2rZti8lkomPHjqxfv95HM60ae89lA9C5WQgaeyH8OFd64KbHwBjokzmkFaSx8uhKElyCydi6FbQfCgHRhDgyuE/7Ez8euX6+UaYePwaAoAnBHGzGFKTjePZxntv8HEV295upLjycsP+ThG3aa6/iyKn/TlxOdg4//7EbgK5N2yPo3ZN7jWYdt41tBwIc3HqBtR/sIy/T83L1LnfeQ0TCDRTl5fL1G7PIy7zi9flXhb+dTmN/fhGBWg0PmPfy254HsdtzCArqQof275XpF6cNMmJsLbU5yVp9HNFRfcEzcOBABEHg8OHDrF27Foul4dcvMxia0LzZw/Ts8f/o2WM9N7R4jqCgGwGBvLyDnDr9Abt3D2Prtq7s3TeeU6cWkJX1M3Z7xWUpVFQaO3UumL788kumTp3K7Nmz2bNnD507dyY5ObnctgQ7duzgwQcf5NFHH2Xv3r3cd9993HfffRw4cMDj+Lpg02FJiPRq7g/fPAHpRyAgCno96ZPjn8w5yZ83/5l8Wz49z0o5Dn7duoHOAD2fAOAV3TL0Z7dxJC3XJ3Oqa07/vgcAja4ZiZ0jmX/bfMw6Mzsu7mDcf8exP909T6XJk0+ii43BduYsp0ePpmDXz4hOZ11MvVJyMrL516JPsYl2IsVgug29xeO4lt0iGTg+CZ1Bw/kjWXzx6s/88v9OlmnMq9Pr+dPkafiHhHLl7GmWPf8Uu1Z9WWduk8Xh5JXjF/j7aenv6gnTBlKPTcbpLCIsrC9dbvwEnS7A476h9ySCTkPx8Wwuf7iPoiOZ5bprnoiOjuaOO+4AYO/evSxcuJDDhw/jqIf5XTUhIKANLVo8zU3dv+aWW3bRru3bREQko9UG4HAUkJm5jZOn3mXP3v9jy9Yb+Wn7zezdN56jR+dw+swi0tLWkpX9K0VFF3A6r7/uASrXF4JYxz5zz549uemmm1iwYAEATqeTZs2a8cwzzzB9+vQy4++//34KCgr49ttvlW29evXixhtvZOHChZUeLzc3l+DgYHJycggKCqq9E7FboeAy+0+n8fqXm+kipPBck58x5JwCjQ7GroEEzzeymmBz2sgoyiC9MJ0zeWc4mX2SlKwUUrJTuJh3nrA86HVSz7j/WhD0elpu3YIuNBTsxfDFA3BCyk/ZoutDkxvvonl8CwKDm4A5RAod+jXxSZ87b2MpyOf8oQOsn/8OtmIL+oChjJw5gpjEYPZe3suzPz5LdnE2AInBiSSFJxHhF0GQIYjQczm0nLsCfab0zdoZGoSmbSt0sTHom0QQGBmHLiQEjdmMYDSiMRoRjCYEowFBp0MXHo42OLhG8xZtDhwFNkSbE7HYgWh1YrPbyC8qoMhmobCoiLzsXM6ePsOxjNPYcWAS9TzUfwRNB1RcfycrrYBNyw5z6ZRLLAsQFuNPWKw/ASFGjP56/AINOO1Z/Lp2EZkXSnrrhcbE0aR5PCHRsQSEhtGmd1/8Q0KrfX42WxZ2ewGi6JB+cGBx2DlncZBpc5Bld3Kp2MIf+XY255nJcUrvxeHiCoaxEkHQkRD/JAkJT6HRVPw+LdyfTtbXKYgWSeTowk0YE0PQhhoR9FoppVCnQWPWoQ0yoPHTozHr0PjrETRSvuHJkydZu3Yt2dnZABiNRiIiImjWrBlNmzalffv21b4G9RlRdJCff4zsnF/Jzv6VnJw9FBdXvGAAQBD0aLX+6HT+aLXSj07rj1bnj0ZjRCPoETR6NBoDGkH6V3D7XY9GMKDR6BEEvbSPRo8gaBEEHQgaBEGHIGgQ0Crbr/5Xrw9Cp/ONm6/iO7x2/64idSqYrFYrfn5+/Oc//+G+++5Tto8bN47s7GzWrFlTZp/mzZszdepUpkyZomybPXs2q1ev5vffyxbcKy4udusHlZubS7NmzWr/gp/ZCZ946IpuDoORn8IN1e9pVRFfHf2K13a95vGxthcEXv2spG9Y2LhxRM0oJT6thRSsfR7/A8vLP8DEnyC6Y/mPNwCsliIWPHy/4gwJ2lgiWz7M6Fd6KXV5LhVc4oO9H7D+1HrsHnI2ggtERm5zcsshEb9qfoGOeuklwsY8VKO5F+y9TNaXR922nddksMGwz+P4CE0ww4bcS0z3qrXbEZ0ix/dcZv//zpN6ovyQoyg6cVqPYi/eh+hMg6s+Lsa8/YHSuLc6HDz0Z9LSVrttO04rZgtveRwfLqYzhk/opT1IVOQQ4uMfx8+vRZWP58izkrf1PAW/pCEWV80dip3VC41fSQkGi8XCTz/9xG+//UZRUUkYNzIykief9I17XJfYbLkUFKZQkJ+CxXIeS3EqFksqxZZULMVpiGL9aeh9ww1TaZHwVF1PQ6WWqWvBVKcWwpUrV3A4HEqzS5moqCiOHDnicZ+0tDSP49PSPH/7efPNN3nllVdqZ8IVoTOC1oBN0JMpBtHkhhvRth4EHUeCqfZf2HBzODpBR5gpjKaBTbkh5AZahrSkdWhrEsUILv37bgxxcQQmJxPxzNPuOxv88B/xIek3PsKxjR+jzzhKKDm0CnKAJUf6MdXMGalPGExmwmKbUlxooSg/mhbd7qbv/Z3cihhG+Ufx+i2v8+JNL7L70m5O5ZwioyiDPGsexY5iLA4LvycW8EtRAeEnMwhMyyUosxj/fAf3RgzAmZOLs9iCaClGLC7GWSz9i8OBxmyqYHYVI+gE0AkIWg0akxbBoMXPGYiuQItJMGASDJi1BiKDI2jVvjWt+3ZCo6t6hF3QCLTqHkWr7lEU5lq5fDqX7MuFFORYsRTYKMq1Upgr/V5c2BF7cRLj3ujC5bOnuHL2NHlX0snPziKwSc3qVEnOganELRB0hKPF315IEPkECQUEaSy00OXR1ZRDr0CRsOCxhIf3Q6stW6G9MrSBBkLuuoGggfFYjmVhu5iPI8+KaHOCKCLaRZwFNhx5VpyFdkSrHcHk/vFoMpkYOHAg/fv3JyMjg0uXLnH27FkCAjyHAxsben0QIcHdCAnuVuYxUXRit+fhcBRgdxTgsBfgcEg/dnsBDkchTtGK02lFdEr/OkUbotPm2m6Ttou2kjGiTdkuu5CKIynaEUWn61/HVT82NILnWmMqKtdCnTpMFy9eJC4ujh07dtC7d29l+4svvsiWLVv4+eefy+xjMBhYtmwZDz5Ystrsww8/5JVXXuHSpbJJzD5zmEpRk8rC1cXhdCAIAhqh7E1SFEVwOBCqUZ242O7AqNPKTyD966PyB97EVmxBbzSRm1FEUHj1b7T1CV+8r8o9tlNUwlPXA9fb+aqoNASua4epSZMmaLXaMkLn0qVLSvPLq4mOjq7WeKPRiNFo9PiYt/DFTU2rKb/FgSAIUM1WDopYkp6gptOqd+iNksvT0MUS+OZ9Ve6xrzPxcL2dr4qKSuXU6So5g8FAt27d2LRpk7LN6XSyadMmN8epNL1793YbD/D999+XO15FRUVFRUVF5Vqp82VQU6dOZdy4cXTv3p0ePXrw3nvvUVBQwMMPPwzA2LFjiYuL48033wRg8uTJ9OvXj7/97W/cddddrFixgt27d/PPf/6zLk9DRUVFRUVFpRFT54Lp/vvvJz09nVmzZpGWlsaNN97Ihg0blMTus2fPotGUGGF9+vTh3//+N3/5y1+YOXMmrVq1YvXq1XTo0KGuTkFFRUVFRUWlkVPndZh8TV0njamoqKioqKhUn7q+f9d5pW8VFRUVFRUVlfqOKphUVFRUVFRUVCpBFUwqKioqKioqKpWgCiYVFRUVFRUVlUpQBZOKioqKioqKSiWogklFRUVFRUVFpRJUwaSioqKioqKiUgmqYFJRUVFRUVFRqQRVMKmoqKioqKioVEKdt0bxNXJh89zc3DqeiYqKioqKikpVke/bddWg5LoTTHl5eQA0a9asjmeioqKioqKiUl3y8vIIDg72+XGvu15yTqeTixcvEhgYiCAIdT2dOiU3N5dmzZpx7tw5ta9eLaFeU++gXlfvoF7X2ke9pt5Bvq6HDh2iTZs2aDS+zyi67hwmjUZD06ZN63oa9YqgoCD1D7uWUa+pd1Cvq3dQr2vto15T7xAXF1cnYgnUpG8VFRUVFRUVlUpRBZOKioqKioqKSiWoguk6xmg0Mnv2bIxGY11PpdGgXlPvoF5X76Be19pHvabeoT5c1+su6VtFRUVFRUVFpbqoDpOKioqKioqKSiWogklFRUVFRUVFpRJUwaSioqKioqKiUgmqYFJRUVFRUVFRqQRVMDUi3nrrLQRBYMqUKco2i8XCU089RXh4OAEBAQwfPpxLly657Xf27Fnuuusu/Pz8iIyM5IUXXsBut7uN2bx5M127dsVoNNKyZUs+/fRTH5xR3XHhwgUeeughwsPDMZvNdOzYkd27dyuPi6LIrFmziImJwWw2M3DgQFJSUtyeIzMzk9GjRxMUFERISAiPPvoo+fn5bmP++OMP+vbti8lkolmzZsybN88n5+drHA4HL7/8Mi1atMBsNpOYmMhrr73m1hNKvaaVs3XrVu6++25iY2MRBIHVq1e7Pe7La7hy5Uratm2LyWSiY8eOrF+/vtbP11dUdF1tNhvTpk2jY8eO+Pv7Exsby9ixY7l48aLbc6jXtSyVvV9LM3HiRARB4L333nPbXq+uq6jSKPjll1/EhIQEsVOnTuLkyZOV7RMnThSbNWsmbtq0Sdy9e7fYq1cvsU+fPsrjdrtd7NChgzhw4EBx79694vr168UmTZqIM2bMUMacPHlS9PPzE6dOnSoeOnRInD9/vqjVasUNGzb48hR9RmZmphgfHy+OHz9e/Pnnn8WTJ0+K3333nXj8+HFlzFtvvSUGBweLq1evFn///XfxnnvuEVu0aCEWFRUpYwYPHix27txZ3LVrl7ht2zaxZcuW4oMPPqg8npOTI0ZFRYmjR48WDxw4IH7xxRei2WwWFy1a5NPz9QWvv/66GB4eLn777bfiqVOnxJUrV4oBAQHi+++/r4xRr2nlrF+/XnzppZfEVatWiYD4zTffuD3uq2u4fft2UavVivPmzRMPHTok/uUvfxH1er24f/9+r18Db1DRdc3OzhYHDhwofvnll+KRI0fEnTt3ij169BC7devm9hzqdS1LZe9XmVWrVomdO3cWY2NjxXfffdftsfp0XVXB1AjIy8sTW7VqJX7//fdiv379FMGUnZ0t6vV6ceXKlcrYw4cPi4C4c+dOURSlN7RGoxHT0tKUMR999JEYFBQkFhcXi6Ioii+++KLYvn17t2Pef//9YnJyspfPrG6YNm2aeMstt5T7uNPpFKOjo8W//vWvyrbs7GzRaDSKX3zxhSiKonjo0CEREH/99VdlzH//+19REATxwoULoiiK4ocffiiGhoYq11k+dps2bWr7lOqcu+66S3zkkUfctg0bNkwcPXq0KIrqNa0JV9+AfHkNR40aJd51111u8+nZs6f4xBNP1Oo51gUV3dhlfvnlFxEQz5w5I4qiel2rQnnX9fz582JcXJx44MABMT4+3k0w1bfrqobkGgFPPfUUd911FwMHDnTb/ttvv2Gz2dy2t23blubNm7Nz504Adu7cSceOHYmKilLGJCcnk5uby8GDB5UxVz93cnKy8hyNjbVr19K9e3dGjhxJZGQkXbp0YfHixcrjp06dIi0tze2aBAcH07NnT7frGhISQvfu3ZUxAwcORKPR8PPPPytjbr31VgwGgzImOTmZo0ePkpWV5e3T9Cl9+vRh06ZNHDt2DIDff/+dn376iTvvvBNQr2lt4MtreL19JlxNTk4OgiAQEhICqNe1pjidTsaMGcMLL7xA+/btyzxe366rKpgaOCtWrGDPnj28+eabZR5LS0vDYDAof9QyUVFRpKWlKWNKiyX5cfmxisbk5uZSVFRUW6dSbzh58iQfffQRrVq14rvvvmPSpEk8++yzLFu2DCi5Lp6uSelrFhkZ6fa4TqcjLCysWte+sTB9+nQeeOAB2rZti16vp0uXLkyZMoXRo0cD6jWtDXx5Dcsb09ivMUh5odOmTePBBx9Umuuq17VmvP322+h0Op599lmPj9e366qr1miVesW5c+eYPHky33//PSaTqa6n02hwOp10796dN954A4AuXbpw4MABFi5cyLhx4+p4dg2Tr776iuXLl/Pvf/+b9u3bs2/fPqZMmUJsbKx6TVUaDDabjVGjRiGKIh999FFdT6dB89tvv/H++++zZ88eBEGo6+lUCdVhasD89ttvXL58ma5du6LT6dDpdGzZsoUPPvgAnU5HVFQUVquV7Oxst/0uXbpEdHQ0ANHR0WVWzcn/r2xMUFAQZrPZS2dXd8TExJCUlOS2rV27dpw9exYouS6erknpa3b58mW3x+12O5mZmdW69o2FF154QXGZOnbsyJgxY3juuecUZ1S9pteOL69heWMa8zWWxdKZM2f4/vvvFXcJ1OtaE7Zt28bly5dp3ry5cv86c+YMf/7zn0lISADq33VVBVMD5vbbb2f//v3s27dP+enevTujR49Wftfr9WzatEnZ5+jRo5w9e5bevXsD0Lt3b/bv3+/2ppQ/DGTR0Lt3b7fnkMfIz9HYuPnmmzl69KjbtmPHjhEfHw9AixYtiI6Odrsmubm5/Pzzz27XNTs7m99++00Z8+OPP+J0OunZs6cy5v+3d7cxTV1hHMD/FytgEdRgg/LSKWrxZQoaswU1lKtTcIu6ZlGGjXFGl+CcGudYlvhKkIUshC/LJpskYpS6bItxM1qXbbYbcxOBtCUaNMYBfhiGTDFShqODZx8MN1xgFhYFcf9f0qT3nNNzz33StE9uzzn96aefEAgEtDbfffcdkpKSMGHChCd2fcPhzz//REiI/uNm1KhR6OrqAsCYPg5DGcP/22dCd7J048YNfP/994iOjtbVM66Dt2HDBtTW1uq+v2JjY5Gbm4tvv/0WwFMY10FNEaenXs9VciIPtxUwm81y4cIFqa6ultTUVElNTdXqu7cVWLFihXi9Xjl//ryYTKZ+txXIzc2Vuro6+fjjj5/pbQUuX74sBoNBCgoK5MaNG1JeXi5Go1FOnDihtSksLJTx48fL119/LbW1tbJmzZp+l2/Pnz9fKisr5eeff5YZM2bolsPeu3dPYmJiZMOGDXLlyhX5/PPPxWg0PjNL4HvauHGjxMXFadsKnDp1SiZOnCjvvfee1oYxDa61tVU8Ho94PB4BIMXFxeLxeLTVWkMVw4sXL4rBYJCioiKpq6uTAwcOjOjl74+Ka0dHh6xevVri4+PF6/VKU1OT9ui5Motx7SvY+7W33qvkRJ6uuDJhesb0Tpja29vlrbfekgkTJojRaBSbzSZNTU261zQ0NMjKlStlzJgxMnHiRNm9e7cEAgFdG5fLJSkpKRIaGiqJiYly9OjRIbia4XPmzBl5/vnnJSwsTGbOnCmfffaZrr6rq0v27dsnMTExEhYWJsuWLZPr16/r2ty5c0eys7Nl7NixEhUVJZs2bZLW1lZdG5/PJ0uWLJGwsDCJi4uTwsLCJ35tw+H+/fuyc+dOMZvNEh4eLomJibJnzx7dFw5jGpzL5RIAfR4bN24UkaGN4RdffCEWi0VCQ0Nlzpw5cvbs2Sd23U/ao+JaX1/fbx0AcblcWh+Ma1/B3q+99ZcwPU1xVUR6bLVLRERERH1wDhMRERFREEyYiIiIiIJgwkREREQUBBMmIiIioiCYMBEREREFwYSJiIiIKAgmTERERERBMGEiohHv4MGDSElJGe5haBRFwenTp4d7GET0GDFhIqIBKykpQWRkJP7++2+tzO/3Y/To0UhPT9e1dbvdUBQFN2/eHOJRDp2nLVEjoieHCRMRDZiqqvD7/aiurtbKKioqMGnSJFRWVuLBgwdaucvlgtlsxrRp04ZjqEREjxUTJiIasKSkJEyePBlut1src7vdWLNmDaZOnYpLly7pylVVxfHjx7Fw4UJERkZi0qRJWL9+PZqbmwEAXV1diI+Px+HDh3Xn8Xg8CAkJQWNjIwDg3r172LJlC0wmE6KiorB06VL4fL5HjrW0tBSzZs1CeHg4Zs6ciU8++USra2hogKIoOHXqFFRVhdFoRHJyMn799VddH0eOHEFCQgKMRiNsNhuKi4sxfvx4AEBZWRny8vLg8/mgKAoURUFZWZn22j/++AM2mw1GoxEzZszAN998M+A4E9HThwkTEQ2KqqpwuVzascvlQnp6OqxWq1be3t6OyspKqKqKQCCA/Px8+Hw+nD59Gg0NDXjjjTcAACEhIcjOzobD4dCdo7y8HIsXL8Zzzz0HAFi7di2am5vhdDpRU1ODBQsWYNmyZbh7926/YywvL8f+/ftRUFCAuro6fPDBB9i3bx+OHTuma7dnzx68++678Hq9sFgsyM7O1n5uvHjxInJycrBz5054vV4sX74cBQUF2muzsrKwe/duzJkzB01NTWhqakJWVpZWn5eXh3Xr1qG2thYvv/wy7Hb7v46XiEaAQf9dLxH9rx05ckQiIiIkEAjI/fv3xWAwSHNzszgcDklLSxMRkR9++EEASGNjY5/XV1VVCQDtH8c9Ho8oiqK17ezslLi4ODl8+LCIiFRUVEhUVJQ8ePBA18+0adPk008/FRGRAwcOSHJysq7O4XDo2ufn50tqaqqIiPYP9KWlpVr91atXBYDU1dWJiEhWVpa88soruj7sdruMGzdOO+593m4AZO/evdqx3+8XAOJ0Ovu0JaKRgXeYiGhQ0tPT0dbWhqqqKlRUVMBiscBkMsFqtWrzmNxuNxITE2E2m1FTU4NVq1bBbDYjMjISVqsVAHDr1i0AQEpKCmbNmqXdZfrxxx/R3NyMtWvXAgB8Ph/8fj+io6MxduxY7VFfX9/vhPK2tjbcvHkTmzdv1rU/dOhQn/bz5s3Tnk+ePBkAtJ8Lr1+/jhdeeEHXvvfxo/TsOyIiAlFRUVrfRDTyGIZ7AEQ0skyfPh3x8fFwuVxoaWnREqDY2FgkJCTgl19+gcvlwtKlS9HW1oaMjAxkZGSgvLwcJpMJt27dQkZGBjo6OrQ+7XY7HA4H3n//fTgcDmRmZiI6OhrAw1V4vedNdeueT9ST3+8H8HD+0YsvvqirGzVqlO549OjR2nNFUQA8nFf1OPTsu7v/x9U3EQ09JkxENGiqqsLtdqOlpQW5ublaeVpaGpxOJy5fvoytW7fi2rVruHPnDgoLC5GQkAAAuhV23davX4+9e/eipqYGX331FUpKSrS6BQsW4Pbt2zAYDJgyZUrQscXExCA2Nha//fYb7Hb7f77GpKQkVFVV6cp6H4eGhqKzs/M/n4OIRg4mTEQ0aKqqYtu2bQgEAtodJgCwWq14++230dHRAVVVYTAYEBoaio8++gg5OTm4cuUK8vPz+/Q3ZcoULFq0CJs3b0ZnZydWr16t1b300ktITU3Fq6++ig8//BAWiwW///47zp49C5vNhoULF/bpLy8vDzt27MC4ceOQmZmJv/76C9XV1WhpacE777wzoGvcvn070tLSUFxcjFWrVuHChQtwOp3anajucdfX18Pr9SI+Ph6RkZEICwsbTCiJaITgHCYiGjRVVdHe3o7p06cjJiZGK7darWhtbdW2HzCZTCgrK8OXX36J2bNno7CwEEVFRf32abfb4fP5YLPZMGbMGK1cURScO3cOaWlp2LRpEywWC15//XU0Njbqzt3Tli1bUFpaiqNHj2Lu3LmwWq0oKyvD1KlTB3yNixcvRklJCYqLi5GcnIzz589j165dCA8P19q89tpryMzMhKqqMJlMOHny5ID7J6KRRRERGe5BEBGNBG+++SauXbuGioqK4R4KEQ0x/iRHRPQvioqKsHz5ckRERMDpdOLYsWO6DTCJ6P+Dd5iIiP7FunXr4Ha70draisTERGzfvh05OTnDPSwiGgZMmIiIiIiC4KRvIiIioiCYMBEREREFwYSJiIiIKAgmTERERERBMGEiIiIiCoIJExEREVEQTJiIiIiIgmDCRERERBQEEyYiIiKiIP4BADPaXtWYIjwAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "curves.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "filter = curves[1]\n", - "filter.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Applying filters to mock-IFUs\n", - "\n", - "After getting the information about different filters and loading the filter curves for `\"SLOAN\"`, we want to apply these filter curves to a mock-IFU cube to get photometric images of the mock-IFU cube.\n", - "\n", - "The first step is to create our mock-IFU cube. We have taken care of this already and run RUBIX with default `config` for a tiny mock MUSE cube on an example Ilustris TNG galaxy. For more details see `rubix_pipeline_single_function.ipynb` or `rubix_pipeline_stepwise.ipynb`. Below we load the dummy datacube using the library `h5py`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "#NBVAL_SKIP\n", - "import h5py\n", - "import numpy as np\n", - "with h5py.File('./data/dummy_datacube.h5', 'r') as hf2:\n", - " print(hf2.keys())\n", - " datacube = np.array(hf2.get('datacube'))\n", - " wave = np.array(hf2.get('wave'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our dummy datacube has 25x25 pixels and 3721 spectral bins." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(25, 25, 3721)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "datacube.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we have our mock-IFU datacube and we have selected and loaded a filter. The next step is to apply the filter to the datacube, which is done with a convolution. And then we obtain our photometric image of the galaxy. For the filter, choosen in this example, you may wonder, why the image is zerro everywhere. You have to keep in mind that our dummy datacube is created for a MUSE observation and in the default `telescopes.yaml` we defined the wavelength to be in the range `[4700.15, 9351.4]`and the filter is in the range `[3000, 4000]`. So this result should be expected for the choice of this mock-data convolved with the `SLOAN/SDSS.u`filter." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.telescope.filters import convolve_filter_with_spectra\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "filter = curves[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(25, 25)\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", - "print(convolved.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.imshow(convolved)\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we now look at other filters from `SLOAN/SDSS`that match the wavelengthrange of our mock-datacube, we get photometric images of our galaxy." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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qalqkHa1atbLVeaMHHngAVqu13qmQmpqaOseTPs2aNQu5ublYv349Dh8+jEmTJmHcuHE4fvw4gOtPtnTu3BlbtmxBTEwMoqOj8cgjj7BnTe41e/ZsXLlyBRMnTkRsbCyqqqqwb98+vP3224iOjsb06dNtx+bn59c7RDxgwACMHz8eGzduxPjx43HbbbfVeYNZfn4+VqxYgWHDhgG4HuyEEPX2iAHgzjvvhI+PD9atW4chQ4Y02P6HHnoIixcvxqFDh+z2b926FUajEaNHj7bbv337dmRkZODuu+/G0KFDbc8jr1mzBpWVlfW+1Wrjxo0IDAxEVVWV7Q1mn332GeLi4rBhwwbbcVeuXMGwYcMwdOhQjBs3DpGRkSgpKcHmzZvxySefICUlBQMGDABwvVe+bt06TJw4EfHx8fDz88N3332HNWvWwGg04sknn7Sd9+WXX8bChQuxa9euRp8hvpHRaEROTg5SU1MxZMgQfPjhh/jggw/w5JNPOrWYzJXmzp2Lf/3rX7jrrrswbdo0xMfHo6KiAl9//TU2btyIU6dOoV27ds3ejvj4eADAH/7wByQlJcHb2xsPPvggRo4ciUcffRSZmZk4dOgQxo4dC19fXxw/fhwbNmzAihUrcP/99zd7+6j5FBYWYu3atSgsLLS9qfCPf/wjcnJysHbtWjz77LM4efIkvv/+e2zYsAFvvPEGrFYrnnjiCdx///3YuXOnm7+Bg9y3EJ2ay4cffigefvhhERsbKwIDA4Wfn5/o2rWrmD17dp03mAGod5sxY4btuIKCAjFz5kzRqVMn4evrK9q1ayfuvvtu8cknn9jV27dvX9GpU6dG2zZq1CgREhIiqqur7R7dutnatWttbal9lOf+++8Xd955Z51jT548KRYsWCCGDh0qQkJChI+Pj2jfvr0YP3682Llzp92xtY8H1W5Go1F07NhR3HXXXWLNmjXi2rVrdsdXV1eLv//97yIlJUVERUUJg8EgAgICxIABA8TSpUvt3gx2+PBhMXfuXHHbbbeJ4OBg4ePjI8LDw8WkSZPEF198UW87du3aVWdffY+VpaamilatWokTJ06IsWPHioCAABEaGioyMjKE1Wq1HdfYNW3o0a1WrVrVOXbkyJGid+/edfbX97jf5cuXxfz580XXrl2Fn5+faNeunRg2bJh4/vnn6zwu2Bg1j27V1NSI2bNni/bt2wtJkuo8xvXqq6+K+Ph44e/vL4KCgkTfvn3FvHnzxOnTpxv9bqQ9AMSmTZtsP2/ZssX2aOWNm4+Pj3jggQeEEELMnDlTABDHjh2zlcvLyxMAxNGjR1v6KygiCaHyLRJELaCmpgZt27ZFZmYmfv/737u7OS1u2rRp2LhxI5OBkMeTJAmbNm1CSkoKAODtt9/GQw89hG+++abOosjAwECEhYUhIyMDzz77rN1LcK5evYqAgAB89NFHtpfqaBmHwUkXLl68iCeeeML2lisiIuD6lJ3VasXZs2cxYsSIeo8ZPnw4ampqcOLECdurk2sXZ9a+AVHr2LMm0gE996wvXryIqqqqBj/39vZ225w76UN5eTny8/MBXA/OL7zwAkaPHo3g4GB06tQJv/71r/HZZ59h2bJlGDBgAM6dO4cdO3agX79+GD9+PGRZxqBBgxAYGIjly5dDlmWkpaXBZDI5lNVNE9w7Ck9EjmhoblkPRo4c2eDaCPz0iliixuzatavee6c2815VVZVYsGCBiI6OFr6+viI8PFxMnDjR7jXIP/74o7j33ntFYGCgCA0NFdOmTRMXLlxw0zdyHnvWRNSs8vLy6mS4upG/v3+DWc2I6DoGayIiIo3jS1GIiIg0TnOrwWVZxunTpxEUFNTgKwSJiEi7hBC4fPkyIiIi4OXVfH3Ca9euNbp40VF+fn4wGo0uaFHz0VywPn36NCIjI93dDCIiUqmoqAgdO3ZslnNfu3YNMVGBsJy1qj5XWFgYCgoKNB2wNResa1MT3o474QNfN7eGyIOoGMmS/PwUlxVKe0ZcbqNZNajGp9haJ9WsK1VVVcFy1oqCvCiYgpT33ssuy4iJ/x5VVVUM1s6oHfr2gS98JAZrohajJlir+LsqJKVBl8Fas376o2mJqUxTkJeqYK0XzfYNV65ciejoaBiNRgwZMgSff/55c1VFREQeyipk1ZseNEuwfvvtt5Geno6MjAx88cUXiIuLQ1JSEs6ePdsc1RERkYeSIVRvetAswfqFF17AzJkzMX36dPTq1QurV69GQEAA1qxZ0xzVERGRh5Jd8J8euDxYV1VVIS8vD4mJiT9X4uWFxMRE5Obm1jm+srISZWVldhsRERH9zOXB+vz587BarQgNDbXbHxoaCovFUuf4zMxMmM1m28bHtoiIyFFWIVRveuD2JXTz589HaWmpbSsqKnJ3k4iISCc8Zc7a5Y9utWvXDt7e3iguLrbbX1xcjLCwsDrHGwwGGAwGVzeDiIjoluHynrWfnx/i4+OxY8cO2z5ZlrFjxw4kJCS4ujoiIvJgMgSsKjaP7VkDQHp6OlJTUzFw4EAMHjwYy5cvR0VFBaZPn94c1RERkYdSO5Tt0cF68uTJOHfuHBYsWACLxYL+/fsjJyenzqIzIiIialqzvW501qxZmDVrVnOdnoiISPWKbr2sBtfcu8GJyE1U/KOlOBmHynqJ5J82NeX1wO2PbhEREVHj2LMmIiLdql3Vraa8HjBYExGRblnF9U1NeT1gsCYiIt3inDURERFpAnvWRESkWzIkWCGpKq8HDNZERKRbsri+qSmvBxwGJyIi0jj2rImISLesKofB1ZRtSQzWRESkW54SrDkMTkREpHHsWRMRkW7JQoIsVKwGV1G2JTFYExGRbnEYnIiIiDSBPWvSB0nFb79Mwdj8eI216xb/u2OFF6wq+p1WF7alOTFYExGRbgmVc9aCc9ZERETNi3PWREREpAnsWRMRkW5ZhResQsWctfan5QEwWBMRkY7JkCCrGCSWoY9ozWFwIiIijWPPmoiIdMtTFpgxWBMRkW6pn7PmMDgRERG5AHvWRESkW9cXmKlI5MFhcCIiouYlq3zdKFeDExERkUuwZ01ERLrlKQvMGKyp5ajI/uNlMCguK1dVKy4LWS85eYgaoJNgpJQML494KQqDNRER6ZZVSLCqyJylpmxL4pw1ERGRxrFnTUREumVVuRrcymFwIiKi5iULL8gqFpjJOpnT5zA4ERGRg1atWoV+/frBZDLBZDIhISEBH374YaNlNmzYgNjYWBiNRvTt2xdbt251ul4GayIi0q3aYXA1mzM6duyI//3f/0VeXh4OHjyIX/7yl7jnnnvwzTff1Hv8vn37MGXKFMyYMQNffvklUlJSkJKSgiNHjjhVrySEtsYAysrKYDabMQr3wEfydXdzyJX46BaRR6gR1diN91BaWgqTydQsddTGile+iId/oPIZ3avlNXj0tjxVbQ0ODsbSpUsxY8aMOp9NnjwZFRUV2LJli23f0KFD0b9/f6xevdrhOtizJiIij1dWVma3VVZWNlnGarVi/fr1qKioQEJCQr3H5ObmIjEx0W5fUlIScnNznWofgzUREelW7UtR1GwAEBkZCbPZbNsyMzMbrPPrr79GYGAgDAYDHnvsMWzatAm9evWq91iLxYLQ0FC7faGhobBYLE59T64GJyIi3VL/utHrZYuKiuyGwQ2NTL316NEDhw4dQmlpKTZu3IjU1FTs2bOnwYDtCgzWRETk8WpXdzvCz88PXbt2BQDEx8fj3//+N1asWIFXXnmlzrFhYWEoLi6221dcXIywsDCn2sdhcCIi0q3afNZqNtVtkOUG57gTEhKwY8cOu33bt29vcI67IexZExGRbrlqGNxR8+fPR3JyMjp16oTLly8jOzsbu3fvxrZt2wAAU6dORYcOHWxz3o8//jhGjhyJZcuWYfz48Vi/fj0OHjyIV1991al6GayJiEi31L9u1LmyZ8+exdSpU3HmzBmYzWb069cP27Ztw5gxYwAAhYWF8PL6+ZzDhg1DdnY2nnrqKTz55JPo1q0bNm/ejD59+jhV7y0XrCUf5V9JyMoeOZe8lA+jiJoaxWV1R8Uj/Wquk+Sr4p6okhWXvdVTE3osFe8LkHyUvztC1Ch8XwDvQ5d67bXXGv189+7ddfZNmjQJkyZNUlXvLResiYjIc8hCgqwizaWasi2JwZqIiHRLVjkMLutknbU+WklEROTB2LMmIiLdUp8iUx99VgZrIiLSLSskWFU8K62mbEvSx68UREREHow9ayIi0i0OgxMREWmcFeqGsvWSsV4fv1IQERF5MPasiYhItzgMTkREpHEtncjDXRisiYhIt4TKNJeCj24RERGRK7BnTUREusVhcHfz8gYkb+eLBbZSXKVcXqGonNvSXHo5f31sZDc8sKCivar+XCuuKi7rUekF9XY/uYmaNJdewa0Vl5UvligqJ6zK/2y8TYHK6hRVQIniap3iKVm39PErBRERkQfTbs+aiIioCVaVKTLVlG1JDNZERKRbHAZX6Omnn4YkSXZbbGysq6shIiLyGM3Ss+7duzc+/vjjnyvxYQeeiIhcT4YXZBX9TjVlW1KzRFEfHx+EhYU1x6mJiIhsrEKCVcVQtpqyLalZfqU4fvw4IiIi0LlzZzz00EMoLCxs8NjKykqUlZXZbURERPQzlwfrIUOGICsrCzk5OVi1ahUKCgowYsQIXL58ud7jMzMzYTabbVtkZKSrm0RERLeo2gVmajY9cPkweHJysu3/+/XrhyFDhiAqKgrvvPMOZsyYUef4+fPnIz093fZzWVkZAzYRETlEqMy6JfgGs+tat26N7t27Iz8/v97PDQYDDAZDczeDiIhuQVZIsKpIxqGmbEtq9l8pysvLceLECYSHhzd3VURERLcklwfrP/7xj9izZw9OnTqFffv2YeLEifD29saUKVNcXRUREXk4Waidt3b3N3CMy4fBf/jhB0yZMgUXLlxA+/btcfvtt2P//v1o3769q6siIiIPJ6ucs1ZTtiW5PFivX7/e1ackIiLyaNp9tZhsBSTnf+OxlpWrq7OFSSre7iapWJgnX72muKzS66Q03R4A1PSOUVzW55sCxWWtJVWKy+qOB6W5VEPUVCsuqzTNpZp6vVu3VlxnTc9OysrVXAMOKK7WKTIkyCoWiakp25K0G6yJiIiawDeYERERkSawZ01ERLrFBWZEREQaJ0NlPmudzFnr41cKIiIiD8aeNRER6ZZQuRpc6KRnzWBNRES6pTZzlsdm3SIiImopnrLATB+tJCIi8mDsWRMRkW5xGJyIiEjjPOV1oxwGJyIi0jgGayIi0i11uaydH0LPzMzEoEGDEBQUhJCQEKSkpODYsWONlsnKyoIkSXab0Wh0ql4GayIi0q2WDtZ79uxBWloa9u/fj+3bt6O6uhpjx45FRUVFo+VMJhPOnDlj277//nun6tXsnLXk4wNJcr55Qhaq6lRCWFWkFlSQBtRWb2WlimpVvERA4YIMubzxm7kxPseKFJeVK64qLqsmhamoqVFc1qN4eSsr1ipAcZVyxRXFZdWkEhXVKlKuSsr+3qm5D32+K1RWUNy6qWVzcnLsfs7KykJISAjy8vLwi1/8osFykiQhLCxMcb3sWRMRkW65qmddVlZmt1U62BkqLS0FAAQHBzd6XHl5OaKiohAZGYl77rkH33zzjVPfk8GaiIh0y1XBOjIyEmaz2bZlZmY2XbcsY86cORg+fDj69OnT4HE9evTAmjVr8N577+Gf//wnZFnGsGHD8MMPPzj8PTU7DE5ERNRSioqKYDKZbD8bDIYmy6SlpeHIkSP49NNPGz0uISEBCQkJtp+HDRuGnj174pVXXsHixYsdah+DNRER6ZaAumela1c5mUwmu2DdlFmzZmHLli3Yu3cvOnbs6FSdvr6+GDBgAPLz8x0uw2FwIiLSrZZeDS6EwKxZs7Bp0ybs3LkTMTExTrfZarXi66+/Rnh4uMNl2LMmIiLdaunXjaalpSE7OxvvvfcegoKCYLFYAABmsxn+/v4AgKlTp6JDhw62ee9FixZh6NCh6Nq1K0pKSrB06VJ8//33eOSRRxyul8GaiIjIQatWrQIAjBo1ym7/2rVrMW3aNABAYWEhvLx+Hri+dOkSZs6cCYvFgjZt2iA+Ph779u1Dr169HK6XwZqIiHSrpXvWQjT9Lo/du3fb/fziiy/ixRdfdKqemzFYExGRbnlK1i0uMCMiItI49qyJiEi3hJAUvwK5trweMFgTEZFuMZ81ERERaYJme9ZerVvDy8vP6XLiivJsOl6tzYrKyRcuKq5TVpM5y1tZtiIA8Grb+EvnG6U0i0+18uw/kgOv/muwrN815WX9ncs5eyO5pFRROVXZuhRmZrpesfKMdWrqVZo9S/SIUlyn96kzistaVfx9V3WNFZaVy8tbvE6rqFZep5M8ZYGZZoM1ERFRUzxlzprD4ERERBrHnjUREekWh8GJiIg0zlOGwRmsiYhIt4TKnrVegjXnrImIiDSOPWsiItItAZVPxLmsJc2LwZqIiHRLhgSJbzAjIiIid2PPmoiIdIurwYmIiDROFhIkD3jOmsPgREREGseeNRER6ZYQbsmP0uIYrImISLc4Z+1m4uoVCMn5VIGSUXkqRWtIG0XlvAKUp1GUCn9UXFZUVSkuq8bVQV0UlfMtVd5eryrlaSO9rlxVXFYuvay4rLBaFZWTfJ1PDVvLK7i14rKivEJxWSmwlfJ6LytL4ehVdFZxnZLCtJwA4FWp/D6WK5Sn8JW8lAUVrwDl31W+qiy9rCQkoOWyZHoEzQZrIiKiprBnTUREpHGeshqcwZqIiHTLUxaY8dEtIiIijWPPmoiIdOt6z1rNnLULG9OMGKyJiEi3PGWBGYfBiYiINI49ayIi0i0BdTmpdTIKzmBNRET6xWFwIiIi0gT2rImISL88ZBycwZqIiPRL5TA4dDIMzmBNRES6xTeYERERkSZotmftZTbBy0tBuks/X8V1ygHKyl7qa1JcZ7sq5XnkrBYVKQL9laf1vNxR4W3TQfntFnhaeYpM/+pQxWW9Cs8oLisFhSgqJwzK7+HqDq0Vl/U9qyxVJQBUhiv/O2A8XqyonJoUseJapeKyUmg7xWW9L5Uqr9dPWerUmihl9yEAeBdYFJXzkqsA5f88OcVTVoNrNlgTERE1SUjq5p11Eqw5DE5ERKRx7FkTEZFucYFZA/bu3YsJEyYgIiICkiRh8+bNdp8LIbBgwQKEh4fD398fiYmJOH78uKvaS0RE9DPhgk0HnA7WFRUViIuLw8qVK+v9fMmSJXjppZewevVqHDhwAK1atUJSUhKuXbumurFERESeyOlh8OTkZCQnJ9f7mRACy5cvx1NPPYV77rkHAPDGG28gNDQUmzdvxoMPPqiutURERDfwlNXgLl1gVlBQAIvFgsTERNs+s9mMIUOGIDc3t94ylZWVKCsrs9uIiIgc1oJD4JmZmRg0aBCCgoIQEhKClJQUHDt2rMlyGzZsQGxsLIxGI/r27YutW7c6Va9Lg7XFcv2ZvNBQ++daQ0NDbZ/dLDMzE2az2bZFRka6sklEREQus2fPHqSlpWH//v3Yvn07qqurMXbsWFRUVDRYZt++fZgyZQpmzJiBL7/8EikpKUhJScGRI0ccrtftj27Nnz8fpaWltq2oqMjdTSIiIp2oHQZXszkjJycH06ZNQ+/evREXF4esrCwUFhYiLy+vwTIrVqzAuHHjMHfuXPTs2ROLFy/Gbbfdhpdfftnhel0arMPCwgAAxcX2byQqLi62fXYzg8EAk8lktxERETnERavBb56Orax07C13paXX30oXHBzc4DG5ubl208MAkJSU1OD0cH1cGqxjYmIQFhaGHTt22PaVlZXhwIEDSEhIcGVVREREACQXbEBkZKTdlGxmZmaTNcuyjDlz5mD48OHo06dPg8dZLBanpofr4/Rq8PLycuTn59t+LigowKFDhxAcHIxOnTphzpw5eOaZZ9CtWzfExMTgz3/+MyIiIpCSkuJsVURERC2iqKjIbmTXYGg6N0VaWhqOHDmCTz/9tDmbBkBBsD548CBGjx5t+zk9PR0AkJqaiqysLMybNw8VFRX47W9/i5KSEtx+++3IycmB0ag8cQQREVG91L7Y5Keyzk7Dzpo1C1u2bMHevXvRsWPHRo8NCwtzanq4Pk4H61GjRkE08n42SZKwaNEiLFq0yNlTExEROcdFwdrhw4XA7NmzsWnTJuzevRsxMTFNlklISMCOHTswZ84c277t27c7NT2s2XeDyyWlkCXnU8JJHcMV13kx1l9RucAzytM3ChUpPb2CAhWXlU0BissaL8mKylkSlL98oEZh+lIAuNSj4YUfTfGubKO47OWm/w7XX6eKl/0Ff6v8Xy2f1sqvsaFEearX6o5tlZUzKW+v8Ufl6UCFUfk/m15eypcJCR9vReV8zlxSXudVhTejUJ6+VOvS0tKQnZ2N9957D0FBQbZ5Z7PZDH//6zFk6tSp6NChg23e+/HHH8fIkSOxbNkyjB8/HuvXr8fBgwfx6quvOlyv2x/dIiIiUqw2RaaazQmrVq1CaWkpRo0ahfDwcNv29ttv244pLCzEmTNnbD8PGzYM2dnZePXVVxEXF4eNGzdi8+bNjS5Ku5lme9ZERERNaemsW41NA9favXt3nX2TJk3CpEmTnKvsBuxZExERaRx71kREpF8tvMDMXRisiYhIvxTMO9cprwMcBiciItI49qyJiEi3JHF9U1NeDxisiYhIvzhnTUREpHGcsyYiIiItYM+aiIj0i8PgREREGuchwZrD4ERERBrHnjUREemXh/SsNRusJaMRkpfzKTJxsURxnUE/KkulWNJFeao+/9NGxWUlSfkqxvKuZsVlfa4pS5Hp36VCcZ1lhiDFZdtEK08RmBf/juKys34coqjc79rvVlzn85axisvuPhyruGzbA8rv47ZfK0tXabhYqbjOKzEmxWXVCLQovxfhrWwgVA5qpbhKyVdhiLBWAmWKq3UOV4MTERGRFmi2Z01ERNQUvsGMiIhI6zxkzprD4ERERBrHYE1ERKRxHAYnIiLdkqByztplLWleDNZERKRffHSLiIiItIA9ayIi0i8PWQ3OYE1ERPrlIcGaw+BEREQax541ERHpFt9gRkREpHUeMgyu2WBd2TcKVh/nM/kYLJcV13m5o7LLYT5ZrbhOa4DyjF1opSAr2U+8FWbOAgDjuWuKylUdUZ7pq+1JxUXh9XlbxWVjTs9UXLbDdmWzTA+Fxyuus3zoVcVlJT/l94Ss4ja2JCjLqGYqsiqu03ihSnHZS92UZxhrZQ5UXLamtb+iclK18uvkc15ZljBJVn59qX6aDdZERERNYs+aiIhI2zxlzpqrwYmIiDSOPWsiItIvD3ndKIM1ERHpF+esiYiItI1z1kRERKQJ7FkTEZF+cRiciIhI41QOg+slWHMYnIiISOPYsyYiIv3iMDgREZHGeUiw5jA4ERGRxrFnTUREuuUpz1lrNlgbzlbAx7vG+YLnlKV0A4B2XxkUlbvWXnnKPJ8S5SkNq0KVp9vzP12huKxUoSxFZtAp5e1te7hMcVlrgPJUom0Plisui3MXFRULjApTXKX0iYp/eWTlKTJrTMrTMJ4eEaConE+F8jqFl/JXTLY/WKq4rFR+RXFZ36uVygqq+HOFQeHfHVknEVBHOAxORETkhL1792LChAmIiIiAJEnYvHlzo8fv3r0bkiTV2SwWi8N1MlgTEZF+CRdsTqqoqEBcXBxWrlzpVLljx47hzJkzti0kJMThspodBiciImqKO+ask5OTkZyc7HS5kJAQtG7d2vkKwZ41ERHpnQt61WVlZXZbZaXCNQKN6N+/P8LDwzFmzBh89tlnTpVlsCYiIo8XGRkJs9ls2zIzM1127vDwcKxevRrvvvsu3n33XURGRmLUqFH44osvHD4Hh8GJiEi/XPRSlKKiIphMJttug0HZ00H16dGjB3r06GH7ediwYThx4gRefPFFvPnmmw6dg8GaiIh0y1Vz1iaTyS5YN7fBgwfj008/dfh4DoMTERG1sEOHDiE8PNzh49mzJiIi/XLDu8HLy8uRn59v+7mgoACHDh1CcHAwOnXqhPnz5+PHH3/EG2+8AQBYvnw5YmJi0Lt3b1y7dg3/+Mc/sHPnTnz00UcO18lgTUREuuWOR7cOHjyI0aNH235OT08HAKSmpiIrKwtnzpxBYWGh7fOqqir813/9F3788UcEBASgX79++Pjjj+3O0RQGayIiIieMGjUKQjQc5bOysux+njdvHubNm6eqTgZrIiLSLw9JkclgTURE+uUhwZqrwYmIiDROsz1r6VIZJC/n07PJ15SlbwQA73PKUt+1On1BcZ2iRkEa0J/41ShPEWhtrSwtIQB4X1GWXtBQpjxVn3dxieKyXmWXFZe1litPJaqUt1VFSkOhvKxcoTxdq4+fr+KyURciFJWrjFD+TGyVSfk/fT4lytNrQsXfWaE0XaVFWapWAJAVvnLTKqoV1+ks5rMmIiLSOg8ZBmewJiIi/fKQYM05ayIiIo1zOljv3bsXEyZMQEREBCRJwubNm+0+nzZtGiRJstvGjRvnqvYSERHZ1M5Zq9n0wOlgXVFRgbi4OKxcubLBY8aNG4czZ87YtrfeektVI4mIiOqlJpe12iH0FuT0nHVycjKSk5MbPcZgMCAsLExxo4iIiOhnzTJnvXv3boSEhKBHjx743e9+hwsXGn60qbKyEmVlZXYbERGRIzgMrtC4cePwxhtvYMeOHXjuueewZ88eJCcnw2qt//nCzMxMmM1m2xYZGenqJhER0a2Kw+DKPPjgg7b/79u3L/r164cuXbpg9+7duOOOO+ocP3/+fFvGEgAoKytjwCYiIrpBsz+61blzZ7Rr184u9+eNDAYDTCaT3UZEROQQ9qxd44cffsCFCxcQHh7e3FUREZGHkX7a1JTXA6eDdXl5uV0vuaCgAIcOHUJwcDCCg4OxcOFC3HfffQgLC8OJEycwb948dO3aFUlJSS5tOBERkadwOlgfPHgQo0ePtv1cO9+cmpqKVatW4fDhw3j99ddRUlKCiIgIjB07FosXL4bBYHBdq4mIiACPed2o08F61KhREKLhb7dt2zZVDSIiInIUs265mfVSCSTJ+bR7olp5yklRVaWonFeA8nSTcteOisuikV+amuLzo/K0nnKJslSi5kvKn6GXr1xxS1k111jyUfbXS2ql4n5qq3yBppRfqLzeChWpRI+dVFTMYFH+XY0qrrFQkYYXCu8JAIDlnKJi8lXl7RXVyv5NFC2YItNTetZM5EFERKRxmu1ZExEROUQnvWM1GKyJiEi3PGXOmsPgREREGseeNRER6ZeHLDBjsCYiIt3iMDgRERFpAnvWRESkXxwGJyIi0jYOgxMREZEmsGdNRET6xWFwIiIijWOwJiIi0jbOWRMREZEmaLZnLfl4Q5Kcb56aFJmwWpWV85IUV+mV/4PisorbC8CqIm2kqFF4jdWkUZSUX2M1aS7VEAr/fKznziuu06vssuKycmWl4rKqyAqv06VLyussVZ6u1buNWXHZa72Vp8Q1HFaWwlRNj8yq9N8YIQOyioqdqgscBiciItIySQhIavLOu+mXeWdxGJyIiEjj2LMmIiL94jA4ERGRtnE1OBEREWkCe9ZERKRfHAYnIiLSNg6DExERkSYwWBMRkX4JF2xO2rt3LyZMmICIiAhIkoTNmzc3WWb37t247bbbYDAY0LVrV2RlZTlVJ4M1ERHpVu0wuJrNWRUVFYiLi8PKlSsdOr6goADjx4/H6NGjcejQIcyZMwePPPIItm3b5nCdnLMmIiL9csMCs+TkZCQnJzt8/OrVqxETE4Nly5YBAHr27IlPP/0UL774IpKSkhw6B3vWRETk8crKyuy2She+Kz83NxeJiYl2+5KSkpCbm+vwORisiYhI11wxBB4ZGQmz2WzbMjMzXdY+i8WC0NBQu32hoaEoKyvD1atXHTqHZofB5auVkCUFaVuEilQvkq+iYnJZueIqRU214rLuyiblFjr8rpK3t6JyQsVv9NaqKsVlJR9l9//1wvrLiqaUrCJjl+FQQYvXK2QV11dhRjQI5RkBna9LqLuHfipbVFQEk8lk220wGNS2zKU0G6yJiIhaislksgvWrhQWFobi4mK7fcXFxTCZTPD393foHAzWRESkW3p4KUpCQgK2bt1qt2/79u1ISEhw+BycsyYiIv1yw3PW5eXlOHToEA4dOgTg+qNZhw4dQmFhIQBg/vz5mDp1qu34xx57DCdPnsS8efNw9OhR/O1vf8M777yDJ554wuE6GayJiIiccPDgQQwYMAADBgwAAKSnp2PAgAFYsGABAODMmTO2wA0AMTEx+OCDD7B9+3bExcVh2bJl+Mc//uHwY1sAh8GJiEjHJPn6pqa8s0aNGgXRyKK2+t5ONmrUKHz55ZfOV/YTBmsiItIvD8m6xWFwIiIijWPPmoiIdEsPq8FdgcGaiIj0y0UvRdE6BmsiItItT+lZc86aiIhI49izJiIi/fKQ1eAM1kREpFscBiciIiJN0G7PWrYCUsv+LiGqlacXJLqZqKlxQ6XKuwm6S9eqIi2nl1F5+kP5mooUphcuKi5LDeBqcCIiIm3jMDgRERFpAnvWRESkX1wNTkREpG0cBiciIiJNYM+aiIj0SxbXNzXldYDBmoiI9Itz1kRERNomQeWctcta0rw4Z01ERKRx7FkTEZF+8Q1mRERE2sZHt4iIiEgT2LMmIiL94mpwIiIibZOEgKRi3llN2ZbEYE1E1+nkHy0bFe2Vr151S71u4eWtvKxsdV07SBUGayIi0i/5p01NeR1gsCYiIt3ylGFwrgYnIiLSOKeCdWZmJgYNGoSgoCCEhIQgJSUFx44dszvm2rVrSEtLQ9u2bREYGIj77rsPxcXFLm00ERERgJ9Xg6vZdMCpYL1nzx6kpaVh//792L59O6qrqzF27FhUVFTYjnniiSfw/vvvY8OGDdizZw9Onz6Ne++91+UNJyIisr3BTM2mA07NWefk5Nj9nJWVhZCQEOTl5eEXv/gFSktL8dprryE7Oxu//OUvAQBr165Fz549sX//fgwdOtR1LSciIo/HN5g5oLS0FAAQHBwMAMjLy0N1dTUSExNtx8TGxqJTp07Izc2t9xyVlZUoKyuz24iIiOhnioO1LMuYM2cOhg8fjj59+gAALBYL/Pz80Lp1a7tjQ0NDYbFY6j1PZmYmzGazbYuMjFTaJCIi8jQeMgyuOFinpaXhyJEjWL9+vaoGzJ8/H6WlpbatqKhI1fmIiMhzSLL6TQ8UPWc9a9YsbNmyBXv37kXHjh1t+8PCwlBVVYWSkhK73nVxcTHCwsLqPZfBYIDBYFDSDCIiIo/gVM9aCIFZs2Zh06ZN2LlzJ2JiYuw+j4+Ph6+vL3bs2GHbd+zYMRQWFiIhIcE1LSYiIqrlIcPgTvWs09LSkJ2djffeew9BQUG2eWiz2Qx/f3+YzWbMmDED6enpCA4OhslkwuzZs5GQkMCV4ERE5HrMulXXqlWrAACjRo2y27927VpMmzYNAPDiiy/Cy8sL9913HyorK5GUlIS//e1vLmksERGRJ3IqWAsHhguMRiNWrlyJlStXKm4UERGRIzzl3eBM5OEKkqS4qJe/v+KyHpXmj8iVPOn+v9XTXKqdd9bJvcBEHkRERBrHnjUREemXgLqc1ProWLNnTURE+lU7Z61mU2LlypWIjo6G0WjEkCFD8Pnnnzd4bFZWFiRJstuMRqNT9TFYExGRfgmofM7a+SrffvttpKenIyMjA1988QXi4uKQlJSEs2fPNljGZDLhzJkztu377793qk4GayIi8ng3J5SqrKxs8NgXXngBM2fOxPTp09GrVy+sXr0aAQEBWLNmTYNlJElCWFiYbQsNDXWqfQzWRESkXy56g1lkZKRdUqnMzMx6q6uqqkJeXp5ddkkvLy8kJiY2mF0SAMrLyxEVFYXIyEjcc889+Oabb5z6mlxgRkRE+iUDUP70rG1xWlFREUwmk213Qzkrzp8/D6vVWqdnHBoaiqNHj9ZbpkePHlizZg369euH0tJSPP/88xg2bBi++eYbu/wajWGwJiIij2cymeyCtSslJCTY5ccYNmwYevbsiVdeeQWLFy926BwM1kREpFst/Qazdu3awdvbG8XFxXb7G8sueTNfX18MGDAA+fn5DtfLOWsiItKvFs665efnh/j4eLvskrIsY8eOHQ5nl7Rarfj6668RHh7ucL3sWRMRETkhPT0dqampGDhwIAYPHozly5ejoqIC06dPBwBMnToVHTp0sC1SW7RoEYYOHYquXbuipKQES5cuxffff49HHnnE4ToZrImISL/c8G7wyZMn49y5c1iwYAEsFgv69++PnJwc26KzwsJCeHn9PHB96dIlzJw5ExaLBW3atEF8fDz27duHXr16OVynJBxJpdWCysrKYDabMQr3wEfydXdzHMNEHkRENjWiGrvxHkpLS5tt0VZtrLij53/Bx7v+lduOqLFWYsd3y5q1ra7AOWsiIiKN4zC4C6jpHUvRjj1jV2+9p35QXFa+ckVxWbo1ST7K/zkQNTUubAmRE1z0nLXWMVgTEZFutfSjW+7CYE1ERPrlhgVm7sA5ayIiIo1jz5qIiPRLFoCkoncs66NnzWBNRET6xWFwIiIi0gL2rImISMdU9qyhj541gzUREekXh8GJiIhIC9izJiIi/ZIFVA1lczU4ERFRMxPy9U1NeR3gMDgREZHGsWdNRET65SELzBisb6A065B8rVJxnaoyZ6nJZ010E2bOojokpemspJZ7Iopz1kRERBrnIT1rzlkTERFpHHvWRESkXwIqe9Yua0mzYrAmIiL94jA4ERERaQF71kREpF+yDEDFi01kfbwUhcGaiIj0i8PgREREpAXsWRMRkX55SM+awZqIiPTLQ95gxmFwIiIijWPPmoiIdEsIGUJFmks1ZVsSgzUREemXEOqGsjlnTURE1MyEyjlrBmv3UJrmEgC8zCZF5eTSMsV1yleuKC5LRNQkxWkuAcnPT1k5IQHKMwdTPW65YE1ERB5ElgFJxbwz56yJiIiamYcMg/PRLSIiIo1jz5qIiHRLyDKEimFwPrpFRETU3DgMTkRERFrAnjUREemXLADp1u9ZM1gTEZF+CQFAzaNb+gjWHAYnIiLSOPasiYhIt4QsIFQMgwv2rImIiJqZkNVvCqxcuRLR0dEwGo0YMmQIPv/880aP37BhA2JjY2E0GtG3b19s3brVqfoYrImISLeELFRvznr77beRnp6OjIwMfPHFF4iLi0NSUhLOnj1b7/H79u3DlClTMGPGDHz55ZdISUlBSkoKjhw54nCdDNZEREROeOGFFzBz5kxMnz4dvXr1wurVqxEQEIA1a9bUe/yKFSswbtw4zJ07Fz179sTixYtx22234eWXX3a4Ts3NWdfOH9SgWtFz7pKK+QcvuUpROVlUK65TiBrFZYmImqYi65ZQVrbmp38TW2I+uEZUqkrGUYPrbS0rs8+eaDAYYDAY6hxfVVWFvLw8zJ8/37bPy8sLiYmJyM3NrbeO3NxcpKen2+1LSkrC5s2bHW6n5oL15cuXAQCfwrnxfBs1se+iirJERFqkJl6qTHN5+fJlmM1mdSdpgJ+fH8LCwvCpRWGsuEFgYCAiIyPt9mVkZODpp5+uc+z58+dhtVoRGhpqtz80NBRHjx6t9/wWi6Xe4y0Wi8Nt1FywjoiIQFFREYKCgiDVk4e1rKwMkZGRKCoqgsmkLP+0J+B1cgyvU9N4jRzD6/QzIQQuX76MiIiIZqvDaDSioKAAVVXKRkRvJISoE2/q61W7k+aCtZeXFzp27NjkcSaTyeP/QjiC18kxvE5N4zVyDK/Tdc3Vo76R0WiE0Whs9npu1K5dO3h7e6O4uNhuf3FxMcLCwuotExYW5tTx9eECMyIiIgf5+fkhPj4eO3bssO2TZRk7duxAQkJCvWUSEhLsjgeA7du3N3h8fTTXsyYiItKy9PR0pKamYuDAgRg8eDCWL1+OiooKTJ8+HQAwdepUdOjQAZmZmQCAxx9/HCNHjsSyZcswfvx4rF+/HgcPHsSrr77qcJ26C9YGgwEZGRmam0/QGl4nx/A6NY3XyDG8Tp5j8uTJOHfuHBYsWACLxYL+/fsjJyfHtoissLAQXl4/D1wPGzYM2dnZeOqpp/Dkk0+iW7du2Lx5M/r06eNwnZLQy7vWiIiIPBTnrImIiDSOwZqIiEjjGKyJiIg0jsGaiIhI4xisiYiINE5XwdrZ/KGe5umnn4YkSXZbbGysu5vldnv37sWECRMQEREBSZLqvDxfCIEFCxYgPDwc/v7+SExMxPHjx93TWDdq6jpNmzatzv01btw49zTWjTIzMzFo0CAEBQUhJCQEKSkpOHbsmN0x165dQ1paGtq2bYvAwEDcd999dd5gReQM3QRrZ/OHeqrevXvjzJkztu3TTz91d5PcrqKiAnFxcVi5cmW9ny9ZsgQvvfQSVq9ejQMHDqBVq1ZISkrCtWvXWril7tXUdQKAcePG2d1fb731Vgu2UBv27NmDtLQ07N+/H9u3b0d1dTXGjh2LiooK2zFPPPEE3n//fWzYsAF79uzB6dOnce+997qx1aR7QicGDx4s0tLSbD9brVYREREhMjMz3dgqbcnIyBBxcXHuboamARCbNm2y/SzLsggLCxNLly617SspKREGg0G89dZbbmihNtx8nYQQIjU1Vdxzzz1uaY+WnT17VgAQe/bsEUJcv398fX3Fhg0bbMd89913AoDIzc11VzNJ53TRs67NH5qYmGjb11T+UE91/PhxREREoHPnznjooYdQWFjo7iZpWkFBASwWi929ZTabMWTIEN5b9di9ezdCQkLQo0cP/O53v8OFCxfc3SS3Ky0tBQAEBwcDAPLy8lBdXW13T8XGxqJTp068p0gxXQTrxvKHOpMP9FY3ZMgQZGVlIScnB6tWrUJBQQFGjBhhyxFOddXeP7y3mjZu3Di88cYb2LFjB5577jns2bMHycnJsFqt7m6a28iyjDlz5mD48OG2V0daLBb4+fmhdevWdsfyniI1dPducGpYcnKy7f/79euHIUOGICoqCu+88w5mzJjhxpbRreDBBx+0/X/fvn3Rr18/dOnSBbt378Ydd9zhxpa5T1paGo4cOcK1IdTsdNGzVpI/lIDWrVuje/fuyM/Pd3dTNKv2/uG95bzOnTujXbt2Hnt/zZo1C1u2bMGuXbvQsWNH2/6wsDBUVVWhpKTE7njeU6SGLoK1kvyhBJSXl+PEiRMIDw93d1M0KyYmBmFhYXb3VllZGQ4cOMB7qwk//PADLly44HH3lxACs2bNwqZNm7Bz507ExMTYfR4fHw9fX1+7e+rYsWMoLCzkPUWK6WYYvKn8oQT88Y9/xIQJExAVFYXTp08jIyMD3t7emDJlirub5lbl5eV2vb+CggIcOnQIwcHB6NSpE+bMmYNnnnkG3bp1Q0xMDP785z8jIiICKSkp7mu0GzR2nYKDg7Fw4ULcd999CAsLw4kTJzBv3jx07doVSUlJbmx1y0tLS0N2djbee+89BAUF2eahzWYz/P39YTabMWPGDKSnpyM4OBgmkwmzZ89GQkIChg4d6ubWk265ezm6M/7617+KTp06CT8/PzF48GCxf/9+dzdJUyZPnizCw8OFn5+f6NChg5g8ebLIz893d7PcbteuXQJAnS01NVUIcf3xrT//+c8iNDRUGAwGcccdd4hjx465t9Fu0Nh1unLlihg7dqxo37698PX1FVFRUWLmzJnCYrG4u9ktrr5rBECsXbvWdszVq1fF73//e9GmTRsREBAgJk6cKM6cOeO+RpPuMZ81ERGRxulizpqIiMiTMVgTERFpHIM1ERGRxjFYExERaRyDNRERkcYxWBMREWkcgzUREZHGMVgTERFpHIM1ERGRxjFYExERaRyDNRERkcb9P0EuKNaC5oITAAAAAElFTkSuQmCC", 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", 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", 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U1dUhOzu7K6ojIiKdUtulzySgC4wfPx6nTp1Cfn4+HA4HkpKSUFxc3GKyIBEREflOl902ODc3F7m5uV11eSIiItUz/PW+OsDv9g4gIh9R8cvQc/GiT+ol8vz7UBOvZz5fIkhERES+wZ4AIiLSrOZZ/mri9YxJABERaZZbXDrUxOsZkwAiItIszglQh3MCiIiIdIo9AUREpFkeGOCGQVW8njEJICIizfKIS4eaeD3jcAAREZFOsSeAiIg0y61yOEBN7PWASQAREWkWkwB1OBxARESkU+wJICIizfIIAzxCxeoAFbHXAyYBRESkWRwOUIfDAURERDrFngDSBmOAfKzH3XntoNZxO2D/dZ3/7LhhhFvF51n/f4Zdi0kAERFpllA5J0BwTgAREZE2cU6AOpwTQEREpFPsCSAiIs1yCyPcQsWcAJ1PZ2ESQEREmuWBAR4Vndoe6DsL4HAAERGRTrEngIiINIsTA9VhEkBERJqlfk4AhwOIiIhIh9gTQEREmnVpYqCKDYQ4HEBERKRNHpW3DebqACIiItIl9gQQEZFmcWKgOkwCLmfwwdiQnt6AKnYzCwjtJh3ruVgvHSsaXdKxRH5BAzsBquGBkTcLUoFJABERaZZbGOBWsROgmtjrAecEEBER6RR7AoiISLPcKlcHuDkcQEREpE0eYYRHxcRAj57mZbWCwwFEREQ6xZ4AIiLSLA4HqMMkgIiINMsDdTP8PZ3XFE3icAAREZFOsSeAiIg0S/3NgvT9WVjfz56IiDSt+bbBao6OmD9/PgwGg9fRv39/5Xx9fT1ycnLQvXt3hIaGYty4caiqqvK6RmVlJTIzMxESEoLIyEjMmjULTU1NXmV27tyJwYMHw2w2o1+/figqKmrRlsLCQvTp0wcWiwUpKSnYu3dvh54LwCSAiIioQ2677TacOHFCOT766CPl3MyZM7Fp0yasX78epaWlOH78OB599FHlvNvtRmZmJlwuF3bt2oU33ngDRUVFyM/PV8pUVFQgMzMTI0aMwP79+zFjxgw89dRT2Lp1q1Jm7dq1yMvLw7x587Bv3z4kJiYiPT0dJ0+e7NBzYRJARESa5YFB9dFRgYGBsNvtytGjRw8AQE1NDV5//XUsXrwY999/P5KTk7Fq1Srs2rULu3fvBgC8//77+PLLL/HWW28hKSkJGRkZWLhwIQoLC+FyXdqrZMWKFYiPj8eLL76IAQMGIDc3F4899hiWLFmitGHx4sWYMmUKsrOzkZCQgBUrViAkJAQrV67s0HNhEkBERJrVWcMBTqfT62hoaLhinYcPH0Z0dDT69u2LSZMmobKyEgBQXl6OxsZGpKWlKWX79++P2NhYlJWVAQDKysowcOBAREVFKWXS09PhdDpx8OBBpczl12gu03wNl8uF8vJyrzJGoxFpaWlKmfZiEkBERJrVfJ8ANQcAxMTEwGazKUdBQUGr9aWkpKCoqAjFxcVYvnw5KioqcO+99+L8+fNwOBwwmUwIDw/3iomKioLD4QAAOBwOrwSg+XzzuauVcTqduHjxIk6fPg23291qmeZrtJf/rg4wGKS29jWYTPJ1eiRvGmGUX6MqXCq2qtXa7S5VbGmqZjtgg8UsHSvcKrZh1doWrmq20tbae1ENFa+T0Sz/XvRc5ZPpVenpe6PCsWPHYLVala/NV/heZWRkKP8eNGgQUlJSEBcXh3Xr1iE4OLjL29nZ2BNARESa5REG1QcAWK1Wr+NKScAPhYeH45ZbbsGRI0dgt9vhcrlQXV3tVaaqqgp2ux0AYLfbW6wWaP66rTJWqxXBwcHo0aMHAgICWi3TfI32YhJARESa5VE5FKD2PgG1tbU4evQoevXqheTkZAQFBaGkpEQ5f+jQIVRWViI1NRUAkJqaigMHDnjN4t+2bRusVisSEhKUMpdfo7lM8zVMJhOSk5O9yng8HpSUlChl2otJABERUTs988wzKC0txbfffotdu3Zh7NixCAgIwMSJE2Gz2TB58mTk5eVhx44dKC8vR3Z2NlJTUzFs2DAAwMiRI5GQkIDHH38cn332GbZu3Yq5c+ciJydH6X2YOnUqvvnmG8yePRtff/01XnnlFaxbtw4zZ85U2pGXl4dXX30Vb7zxBr766itMmzYNdXV1yM7O7tDz8d85AURERG1Qv5Vwx2K/++47TJw4EWfOnEHPnj1xzz33YPfu3ejZsycAYMmSJTAajRg3bhwaGhqQnp6OV155RYkPCAjA5s2bMW3aNKSmpqJbt27IysrCggULlDLx8fHYsmULZs6ciWXLlqF379547bXXkJ6erpQZP348Tp06hfz8fDgcDiQlJaG4uLjFZMG2GITwr1kjTqcTNpsNww1jEGgI6nA8JwZenwxB8t9XNRMDPXUXpGM5MfA6xYmBbWoSjdiJd1FTU+M12a4zNf+tWLj3flhC5T/P1tc24TdDt3dpW/0ZhwOIiIh0isMBRESkWdd6OOB6wySAiIg0yw3ALXHr38vj9UzfKRAREZGOsSeAiIg0i8MB6jAJICIizbp8EyDZeD1jEkBERJolJLcDvjxez/SdAhEREekYewKIiEizOBygjt8mAYbAIBgk7hgYEHGDdJ3us+ek4lTd9U8FQ6D8t080NXViS9pHTXuN1lDpWE9tnXSsnu76Zwjs+M9bM9HUKB2rtbsNqrnrn7FXx27p6uVEVdtlWiHcHukqjRHhcnV6XMDJtst1hst3ApSN1zN9p0BEREQ65rc9AURERG1p3hJYTbyeMQkgIiLN4nCAOp2eAs2fPx8Gg8Hr6N+/f2dXQ0RERCp1SU/Abbfdhn/84x/fV6JiQhgREdGVeGCER8XnWTWx14Mu+escGBgIu93eFZcmIiJSuIUBbhVd+mpirwddkgIdPnwY0dHR6Nu3LyZNmoTKysorlm1oaIDT6fQ6iIiIqOt1ehKQkpKCoqIiFBcXY/ny5aioqMC9996L8+fPt1q+oKAANptNOWJiYjq7SUREdJ1qnhio5tCzTh8OyMjIUP49aNAgpKSkIC4uDuvWrcPkyZNblJ8zZw7y8vKUr51OJxMBIiJqF6FyF0HBOwZ2rfDwcNxyyy04cuRIq+fNZjPMKu7ARURE+uWGAW4VmwCpib0edHkKVFtbi6NHj6JXr15dXRURERF1QKcnAc888wxKS0vx7bffYteuXRg7diwCAgIwceLEzq6KiIh0ziPUzgvw9TPwrU4fDvjuu+8wceJEnDlzBj179sQ999yD3bt3o2fPnp1dFRER6ZxH5ZwANbHXg05PAt55553OviQRERF1Ab+9lZ9oaoTMyg336bOq6pQLlO9PMgSZpGONtjDpWE91jXSs7DbExjD59jYkxkvHmj/9RjrW3dAgHesTKt6LetoOWA2PmveE5HbAAOBxyX1/AlVsX1yTKrdSq6mxHtgoXW2HeGCAR8XkPjWx1wO/TQKIiIjawjsGqqPvwRAiIiIdY08AERFpFicGqsMkgIiINMsDdbf+1fucAH2nQERERDrGngAiItIsoXJ1gNB5TwCTACIi0iy1OwFyF0EiIiKN4sRAdfT97ImIiHSMPQFERKRZHA5Qh0kAERFpFm8brA6HA4iIiHSKPQFERKRZHA5Qh0kAERFpFpMAdfw2CTAEBMBgCOhwnHC75es0yW3rKxrlttYFAEOAiqUt52ulY33BU1snHWs+UOmTeg2B8j8islsu+4yvtgM2dvznHACMwRbpKj0X66Vj4ZH/HeOpl69X9r0oJLcgBgBb2TGpuCaPxrbg1jG/TQKIiIjawp4AdZgEEBGRZjEJUIerA4iIiHSKPQFERKRZAurW+vtoJozfYBJARESaxeEAdZgEEBGRZjEJUIdzAoiIiHSKPQFERKRZ7AlQh0kAERFpFpMAdTgcQEREpFPsCSAiIs0SwgCh4tO8mtjrAZMAIiLSLA8Mqu4ToCb2esDhACIiIkm/+93vYDAYMGPGDOWx+vp65OTkoHv37ggNDcW4ceNQVVXlFVdZWYnMzEyEhIQgMjISs2bNQtMPNh3buXMnBg8eDLPZjH79+qGoqKhF/YWFhejTpw8sFgtSUlKwd+/eDrXfb3sCjNYwGI0Su/qp2NEPUT2kwsTxqrYLXYHnwgXpWEOA3O5rAGAMt0nHwu2RrFQ+4zZ0C5aPVfMaS+4sCQCe8+el4lTtPii5I9+liiW/rwBgkP88YewWIhd4U4x0nQHHHNKx7rPnpGPV7NQou0Oq55x8e2XrbBLyOxd2lC8nBv7zn//En/70JwwaNMjr8ZkzZ2LLli1Yv349bDYbcnNz8eijj+Ljjz8GALjdbmRmZsJut2PXrl04ceIEfv7znyMoKAi//e1vAQAVFRXIzMzE1KlTsXr1apSUlOCpp55Cr169kJ6eDgBYu3Yt8vLysGLFCqSkpGDp0qVIT0/HoUOHEBkZ2a7nwJ4AIiLSrOY5AWoOGbW1tZg0aRJeffVV3HDDDcrjNTU1eP3117F48WLcf//9SE5OxqpVq7Br1y7s3r0bAPD+++/jyy+/xFtvvYWkpCRkZGRg4cKFKCwshMvlAgCsWLEC8fHxePHFFzFgwADk5ubisccew5IlS5S6Fi9ejClTpiA7OxsJCQlYsWIFQkJCsHLlynY/DyYBRESke06n0+toaGi4avmcnBxkZmYiLS3N6/Hy8nI0NjZ6Pd6/f3/ExsairKwMAFBWVoaBAwciKipKKZOeng6n04mDBw8qZX547fT0dOUaLpcL5eXlXmWMRiPS0tKUMu3BJICIiDSreThAzQEAMTExsNlsylFQUHDFOt955x3s27ev1TIOhwMmkwnh4eFej0dFRcHhcChlLk8Ams83n7taGafTiYsXL+L06dNwu92tlmm+Rnv47ZwAIiKitnTWEsFjx47BarUqj5vN5lbLHzt2DE8//TS2bdsGi8UiXa+/YE8AERFpllDZC9CcBFitVq/jSklAeXk5Tp48icGDByMwMBCBgYEoLS3FSy+9hMDAQERFRcHlcqG6utorrqqqCna7HQBgt9tbrBZo/rqtMlarFcHBwejRowcCAgJaLdN8jfZgEkBERNRODzzwAA4cOID9+/crx5AhQzBp0iTl30FBQSgpKVFiDh06hMrKSqSmpgIAUlNTceDAAZw8eVIps23bNlitViQkJChlLr9Gc5nma5hMJiQnJ3uV8Xg8KCkpUcq0B4cDiIhIswRUrbxER0PDwsJw++23ez3WrVs3dO/eXXl88uTJyMvLQ0REBKxWK6ZPn47U1FQMGzYMADBy5EgkJCTg8ccfx6JFi+BwODB37lzk5OQoPRBTp07Fyy+/jNmzZ+PJJ5/E9u3bsW7dOmzZskWpNy8vD1lZWRgyZAiGDh2KpUuXoq6uDtnZ2e1+PkwCiIhIszwwwOBndwxcsmQJjEYjxo0bh4aGBqSnp+OVV15RzgcEBGDz5s2YNm0aUlNT0a1bN2RlZWHBggVKmfj4eGzZsgUzZ87EsmXL0Lt3b7z22mvKPQIAYPz48Th16hTy8/PhcDiQlJSE4uLiFpMFr8YghJocqvM5nU7YbDY8EPEEAnmzoKvS1c2CwkKlYz2nzsjXy5sFtY/GbhZk0ODNgmCQ+/lR83tCzc2CdoqNqKmp8Zps15ma/1Yk/vW/EBDS+vh9e7gvNOCzx17s0rb6M/YEEBGRZnEDIXWYBBARkWZ5hAEGH902+HrA1QFEREQ6xZ4AIiLSLCFUrg7wq1lx1x6TACIi0izOCVDHf5MAIQCPRIp2hbs8tavKbnK3gDT0at+Wja3GHjsuHSvUrIRQMWP44pC+UnGeQBU/qComvYd9Iv86uU+elo6VnVltUPEeDujRXTpWnK+VjjVYw6RjPeeq5eo8If+9MQTLb01tDLn6xjJX47lYLx1rkFxdo+a5ol7uuRoEgGu3mzCp4L9JABERURvYE6AOkwAiItIsrg5Qh0kAERFpFicGqsMlgkRERDrFngAiItKsSz0BauYEdGJjNIhJABERaRYnBqrD4QAiIiKdYk8AERFplvj3oSZez5gEEBGRZnE4QB0OBxAREekUewKIiEi7OB6gCpMAIiLSLpXDAdD5cACTACIi0izeMVAdzgkgIiLSKb/tCRAxdoiAjm+pKgLl8xqPWe7lqLvFKl1neL1LOtZz9px0LKyh0qHVfYOk4ppCpKtE6L880rENN9ulY82S2wEDgFDxGstq7Cm/pW/g2W7SsRfiwqVjg/+f3BvDoOJnBy75fW6NUT3lYy9clI6FQa7b2h0tv710wPEzUnHC4wIc0tV2rC6uDlDFb5MAIiKiNgmDunF9nScBHA4gIiLSKfYEEBGRZnFioDod7gn44IMP8NBDDyE6OhoGgwEbN270Oi+EQH5+Pnr16oXg4GCkpaXh8OHDndVeIiKi74lOOHSsw0lAXV0dEhMTUVhY2Or5RYsW4aWXXsKKFSuwZ88edOvWDenp6aivr1fdWCIiIuo8HR4OyMjIQEZGRqvnhBBYunQp5s6di0ceeQQA8OabbyIqKgobN27EhAkT1LWWiIjoMlwdoE6nTgysqKiAw+FAWlqa8pjNZkNKSgrKyspajWloaIDT6fQ6iIiI2o1DAdI6NQlwOC4tDI2KivJ6PCoqSjn3QwUFBbDZbMoRExPTmU0iIiKiK/D5EsE5c+agpqZGOY4dO+brJhERkUY0DweoOfSsU5cI2u2X7sxWVVWFXr16KY9XVVUhKSmp1Riz2QyzueN3BiQiIuIugup0ak9AfHw87HY7SkpKlMecTif27NmD1NTUzqyKiIgIgKETDv3qcE9AbW0tjhw5onxdUVGB/fv3IyIiArGxsZgxYwaef/553HzzzYiPj8dvfvMbREdHY8yYMZ3ZbiIiIlKpw0nAJ598ghEjRihf5+XlAQCysrJQVFSE2bNno66uDr/4xS9QXV2Ne+65B8XFxbBYLJ3XaiIiIoDDASp1OAkYPnw4xFXus2gwGLBgwQIsWLBAVcOIiIjaxCRAFb/dO8BwzAGDwdTxwLhebZe5gnMDg6Xiwv4lvy2psEg8x38zdo+Qjq2PuUE61lwjt62v8xbpKlVtEX0mUX7iqXnITdKxFwdfkAv8l9z7EABu+FI6FObz8q9TQIP8b9Izd/aQinPL/+igx/7z8sFqRMhvL22sa5CKC3DIbzku6uTew0Ko2OaZrim/TQKIiIjaxK2EVWESQEREmsVdBNXx+c2CiIiIyDfYE0BERNrFiYGqMAkgIiLt4pwAVTgcQEREpFPsCSAiIs0yiEuHmng9YxJARETaxTkBqjAJICIi7eKcAFU4J4CIiEin2BNARETaxeEAVZgEEBGRdjEJUIXDAURERDrFngAiItIu9gSo4r9JgMEAGDs+a9PwXZV0ldbuclu41vSR39PUdCZEOtZjCZKOrbpTfttY6/+T20o4OFZ++9a6bvKvU49eNdKxH92xWjr27fM3SsU9MfykdJ27693SsU/uy5KODfrQKh3b/SvJbWc98r+96yPlt2s2yL39AQAhXznkgyWfr7B2k67SECT3J8LgaQCc0tV2DFcHqMLhACIionZavnw5Bg0aBKvVCqvVitTUVPz9739XztfX1yMnJwfdu3dHaGgoxo0bh6oq7w+nlZWVyMzMREhICCIjIzFr1iw0NTV5ldm5cycGDx4Ms9mMfv36oaioqEVbCgsL0adPH1gsFqSkpGDv3r0dfj5MAoiISLOa7xio5uiI3r1743e/+x3Ky8vxySef4P7778cjjzyCgwcPAgBmzpyJTZs2Yf369SgtLcXx48fx6KOPKvFutxuZmZlwuVzYtWsX3njjDRQVFSE/P18pU1FRgczMTIwYMQL79+/HjBkz8NRTT2Hr1q1KmbVr1yIvLw/z5s3Dvn37kJiYiPT0dJw82bGeRCYBRESkXaITjg546KGHMHr0aNx888245ZZb8D//8z8IDQ3F7t27UVNTg9dffx2LFy/G/fffj+TkZKxatQq7du3C7t27AQDvv/8+vvzyS7z11ltISkpCRkYGFi5ciMLCQrhcl4bGVqxYgfj4eLz44osYMGAAcnNz8dhjj2HJkiVKOxYvXowpU6YgOzsbCQkJWLFiBUJCQrBy5coOPR8mAUREpHtOp9PraGhoaDPG7XbjnXfeQV1dHVJTU1FeXo7GxkakpaUpZfr374/Y2FiUlZUBAMrKyjBw4EBERUUpZdLT0+F0OpXehLKyMq9rNJdpvobL5UJ5eblXGaPRiLS0NKVMezEJICIi3YuJiYHNZlOOgoKCK5Y9cOAAQkNDYTabMXXqVGzYsAEJCQlwOBwwmUwIDw/3Kh8VFQWH49KkUIfD4ZUANJ9vPne1Mk6nExcvXsTp06fhdrtbLdN8jfby39UBREREbTBA5S6C//7/sWPHYLV+v8rFbL7yCqpbb70V+/fvR01NDf76178iKysLpaWl8o3wISYBRESkXZ20RLB5tn97mEwm9OvXDwCQnJyMf/7zn1i2bBnGjx8Pl8uF6upqr96Aqqoq2O12AIDdbm8xi7959cDlZX64oqCqqgpWqxXBwcEICAhAQEBAq2War9FeHA4gIiJSwePxoKGhAcnJyQgKCkJJSYly7tChQ6isrERqaioAIDU1FQcOHPCaxb9t2zZYrVYkJCQoZS6/RnOZ5muYTCYkJyd7lfF4PCgpKVHKtBd7AoiISLuu8R0D58yZg4yMDMTGxuL8+fNYs2YNdu7cia1bt8Jms2Hy5MnIy8tDREQErFYrpk+fjtTUVAwbNgwAMHLkSCQkJODxxx/HokWL4HA4MHfuXOTk5ChDEFOnTsXLL7+M2bNn48knn8T27duxbt06bNmyRWlHXl4esrKyMGTIEAwdOhRLly5FXV0dsrOzO/R8mAQQEZF2XeMk4OTJk/j5z3+OEydOwGazYdCgQdi6dSt+/OMfAwCWLFkCo9GIcePGoaGhAenp6XjllVeU+ICAAGzevBnTpk1DamoqunXrhqysLCxYsEApEx8fjy1btmDmzJlYtmwZevfujddeew3p6elKmfHjx+PUqVPIz8+Hw+FAUlISiouLW0wWbAuTACIionZ6/fXXr3reYrGgsLAQhYWFVywTFxeH995776rXGT58OD799NOrlsnNzUVubu5Vy7SFSQAREWmWzF3/fhivZ0wCiIhIu7iLoCp+mwQ03XwjEGjpcFzgmTrpOp2xcrsBhjqa2i50BWp2AnRZ5WNtFfJboYVWXpCKO7ffJl1n9+PyP6lCdJeOvaO+Y5NsLhe6OUwqblGM/HKn+x8ul44NCpTfgTDAJf/9OZMg93MXVCdfZ/iRtu8GdyXOOPkdOC3fye/o13SD3M6HAfXyv58Czsv9rMMj/16ia8tvkwAiIqI2sSdAFSYBRESkWZwToA5vFkRERKRT7AkgIiLt6qTbBusVkwAiItIuzglQhUkAERFpFucEqMM5AURERDrFngAiItIuDgeowiSAiIi0S+VwgN6TAA4HEBER6RR7AoiISLs4HKAKkwAiItIuJgGqcDiAiIhIp9gTQEREmsX7BKjjt0lAYPVFBAZIbHd7tka6zvDDoVJx1TfLbfEJAMH/kt/6ONAk35FjOV0vHWuskdteNKxSfhvViM+c0rGeEPktl3t+Ir8lqsFxVCouIiZSus7/b/sA6dheLvnn2mh1SceeHCy3NW/ov+S3yDV45H/zd993TjrWeF7+5z3I1SgXGBggXafbfoNcnLse+Jd0tXQNcTiAiIhIp/y2J4CIiKhNnBioCpMAIiLSLM4JUIdJABERaZvO/5CrwTkBREREOsWeACIi0i7OCVCFSQAREWkW5wSow+EAIiIinWJPABERaReHA1RhEkBERJrF4QB1OBxARESkU+wJICIi7eJwgCpMAoiISLuYBKjC4QAiIiKd8tueAIOrEQZjx3MU0Si/panpu7NScZH/MkjXKS5clI41XQiTjm3qKR9rsMhtzeuR39EXATXyW7AajpyRjvXUyW2bDAAGo9z7wtgkv0Wu0S2/HbDnovz20iaTSTo25pjc1smNvazSddZ3l29vaE2DdCya5L8/MEl+ZD1eJV2lUXLLZaOQ/z3cUZwYqI7fJgFERERt4nCAKkwCiIhIu5gEqMI5AURERDrV4STggw8+wEMPPYTo6GgYDAZs3LjR6/wTTzwBg8HgdYwaNaqz2ktERKRonhOg5tCzDicBdXV1SExMRGFh4RXLjBo1CidOnFCOt99+W1UjiYiIWiU64dCxDs8JyMjIQEZGxlXLmM1m2O126UYRERFR1+uSOQE7d+5EZGQkbr31VkybNg1nzlx5iVZDQwOcTqfXQURE1B4cDlCn05OAUaNG4c0330RJSQl+//vfo7S0FBkZGXBfYf1yQUEBbDabcsTExHR2k4iI6HrF4QBVOn2J4IQJE5R/Dxw4EIMGDcJNN92EnTt34oEHHmhRfs6cOcjLy1O+djqdTASIiIiugS5fIti3b1/06NEDR44cafW82WyG1Wr1OoiIiNqFPQGqdPnNgr777jucOXMGvXr16uqqiIhIZwz/PtTE61mHk4Da2lqvT/UVFRXYv38/IiIiEBERgeeeew7jxo2D3W7H0aNHMXv2bPTr1w/p6emd2nAiIiJSp8NJwCeffIIRI0YoXzeP52dlZWH58uX4/PPP8cYbb6C6uhrR0dEYOXIkFi5cCLPZ3HmtJiIiAnjbYJU6nAQMHz4cQlz5Vdu6dauqBhEREbUXdxFUx283EPKcPA2PoePbfYoGFdt8Sm6lalTRyyHib5SOVSOo8rR0rKiV29Y36ux5FXXWSsd6VMTiKglvm4Ik3xfdb5CuUkhu8wwAhiOV0rGe8/LfW0hu1xx0xiZdpckmPwFZnJff1lpIbi8NAKiS+5kVLvltfT2Sv0/dolG6zg5jT4Aq3ECIiIhIp/y2J4CIiKhddP5pXg0mAUREpFmcE6AOhwOIiIh0ikkAERFp1zW+Y2BBQQHuvPNOhIWFITIyEmPGjMGhQ4e8ytTX1yMnJwfdu3dHaGgoxo0bh6qqKq8ylZWVyMzMREhICCIjIzFr1iw0NTV5ldm5cycGDx4Ms9mMfv36oaioqEV7CgsL0adPH1gsFqSkpGDv3r0dej5MAoiISLOu9S6CpaWlyMnJwe7du7Ft2zY0NjZi5MiRqKv7ftXIzJkzsWnTJqxfvx6lpaU4fvw4Hn30UeW82+1GZmYmXC4Xdu3ahTfeeANFRUXIz89XylRUVCAzMxMjRozA/v37MWPGDDz11FNey/DXrl2LvLw8zJs3D/v27UNiYiLS09Nx8uTJDrx+V1v07wNOpxM2mw33d5uIwGu9RDAgQCpMi0sEjaqW68ktkTJ0C1FRp/wyP3eNiu2pVfx4GCTfF8a43tJ1qlkiCDVLBC/ILfMDABjlfu4CbpBfImjw0RJBqFkiKLmE2RdLBJtEI3aKjaipqemy/WCa/1YMfOq3CDBZpK/jdtXjwGu/wrFjx7zaajab23WTu1OnTiEyMhKlpaW47777UFNTg549e2LNmjV47LHHAABff/01BgwYgLKyMgwbNgx///vf8eCDD+L48eOIiooCAKxYsQLPPvssTp06BZPJhGeffRZbtmzBF198odQ1YcIEVFdXo7i4GACQkpKCO++8Ey+//DIAwOPxICYmBtOnT8d///d/t+v5syeAiIi0q5OGA2JiYry2tS8oKGhX9TU1NQCAiIgIAEB5eTkaGxuRlpamlOnfvz9iY2NRVlYGACgrK8PAgQOVBAAA0tPT4XQ6cfDgQaXM5ddoLtN8DZfLhfLycq8yRqMRaWlpSpn24OoAIiLSrM5aHdBaT0BbPB4PZsyYgbvvvhu33347AMDhcMBkMiE8PNyrbFRUFBwOh1Lm8gSg+XzzuauVcTqduHjxIs6dOwe3291qma+//rrNtjdjEkBERLons5V9Tk4OvvjiC3z00Udd1Kqux+EAIiLSrmu8OqBZbm4uNm/ejB07dqB37+/n8djtdrhcLlRXV3uVr6qqgt1uV8r8cLVA89dtlbFarQgODkaPHj0QEBDQapnma7QHkwAiItKua5wECCGQm5uLDRs2YPv27YiPj/c6n5ycjKCgIJSUlCiPHTp0CJWVlUhNTQUApKam4sCBA16z+Ldt2war1YqEhASlzOXXaC7TfA2TyYTk5GSvMh6PByUlJUqZ9uBwABERada1vmNgTk4O1qxZg3fffRdhYWHKGL7NZkNwcDBsNhsmT56MvLw8REREwGq1Yvr06UhNTcWwYcMAACNHjkRCQgIef/xxLFq0CA6HA3PnzkVOTo4yF2Hq1Kl4+eWXMXv2bDz55JPYvn071q1bhy1btihtycvLQ1ZWFoYMGYKhQ4di6dKlqKurQ3Z2drufD5MAIiKidlq+fDkAYPjw4V6Pr1q1Ck888QQAYMmSJTAajRg3bhwaGhqQnp6OV155RSkbEBCAzZs3Y9q0aUhNTUW3bt2QlZWFBQsWKGXi4+OxZcsWzJw5E8uWLUPv3r3x2muvIT09XSkzfvx4nDp1Cvn5+XA4HEhKSkJxcXGLyYJX47f3CXgg4gkEGjt+nwCPmjXhBrnREWO3YPk61fDIf+vUrOsWbrdkoJp0XcX6al+9xSXXvxst8vedMATK5/UeyXXoACAa5deiS1PxnjBI3hMEAIwq7jHQOCBWOjbo8HGpOEOQ/L0j3Kfkti9uEo3Y0bDumtwnIPHn6u8T8Nmbv+rStvoz9gQQEZFmGYSAQc1Nvfzrc/A1x4mBREREOsWeACIi0i4Vy/yUeB1jEkBERJp1rVcHXG84HEBERKRT7AkgIiLt4nCAKkwCiIhIszgcoA6HA4iIiHSKPQFERKRdHA5QhUkAERFpFocD1GESQERE2sWeAFU4J4CIiEin2BNARESapvcufTX8NgnwOM/DY+j47ldCxc56RpNcx4iq3ddcKnZf09PGF756rmp2qguS+/HyXLwoXafsTpgAYAyW34lNemdJAPD4YFdKFdTsVBr0RYV8vXWS7wuj/HtY9veTEI3SdUpUpu69oKffo63gcAAREZFO+W1PABERUVu4OkAdJgFERKRdXB2gCocDiIiIdIo9AUREpFkGz6VDTbyeMQkgIiLt4nCAKhwOICIi0in2BBARkWZxdYA6TAKIiEi7eLMgVZgEEBGRZrEnQB3OCSAiItIp9gQQEZF2cXWAKkwCiIhIszgcoA6HA4iIiHTKb3sCRFMThIptXGV4GiRvHaXz2aXXNRXfW+ltolXNdJbf0tdz4YKKen3wM2AMkA8N7SYd66mtk451V9dIx2rKtXw/cHWAKn6bBBAREbWFwwHqcDiAiIhIp9gTQERE2sXVAaowCSAiIs3icIA6HA4gIiLSKfYEEBGRdnnEpUNNvI4xCSAiIu3inABVmAQQEZFmGaByTkCntUSbOCeAiIhIp9gTQERE2sU7BqrCJICIiDSLSwTV4XAAERGRTrEngIiItIurA1RhEkBERJplEAIGFeP6amKvB0wCLqfzNwN1Mq29n7TWXo/8tsluZ61P6vUFQ6D8r3nR1NSJLSF/xCSAiIi0y/PvQ028jjEJICIizeJwgDpcHUBERKRTHUoCCgoKcOeddyIsLAyRkZEYM2YMDh065FWmvr4eOTk56N69O0JDQzFu3DhUVVV1aqOJiIgAfL86QM2hYx1KAkpLS5GTk4Pdu3dj27ZtaGxsxMiRI1FXV6eUmTlzJjZt2oT169ejtLQUx48fx6OPPtrpDSciIlLuGKjm0LEOzQkoLi72+rqoqAiRkZEoLy/Hfffdh5qaGrz++utYs2YN7r//fgDAqlWrMGDAAOzevRvDhg3rvJYTEZHu8Y6B6qiaE1BTUwMAiIiIAACUl5ejsbERaWlpSpn+/fsjNjYWZWVlrV6joaEBTqfT6yAiIqKuJ50EeDwezJgxA3fffTduv/12AIDD4YDJZEJ4eLhX2aioKDgcjlavU1BQAJvNphwxMTGyTSIiIr3xwXDABx98gIceegjR0dEwGAzYuHHjD5okkJ+fj169eiE4OBhpaWk4fPiwV5mzZ89i0qRJsFqtCA8Px+TJk1Fb633/is8//xz33nsvLBYLYmJisGjRohZtWb9+Pfr37w+LxYKBAwfivffe69BzkU4CcnJy8MUXX+Cdd96RvQQAYM6cOaipqVGOY8eOqboeERHph8Gj/uiouro6JCYmorCwsNXzixYtwksvvYQVK1Zgz5496NatG9LT01FfX6+UmTRpEg4ePIht27Zh8+bN+OCDD/CLX/xCOe90OjFy5EjExcWhvLwcL7zwAubPn48///nPSpldu3Zh4sSJmDx5Mj799FOMGTMGY8aMwRdffNHu5yJ1n4Dc3Fyl0b1791Yet9vtcLlcqK6u9uoNqKqqgt1ub/VaZrMZZrNZphlERETXXEZGBjIyMlo9J4TA0qVLMXfuXDzyyCMAgDfffBNRUVHYuHEjJkyYgK+++grFxcX45z//iSFDhgAA/vjHP2L06NH4wx/+gOjoaKxevRoulwsrV66EyWTCbbfdhv3792Px4sVKsrBs2TKMGjUKs2bNAgAsXLgQ27Ztw8svv4wVK1a067l0qCdACIHc3Fxs2LAB27dvR3x8vNf55ORkBAUFoaSkRHns0KFDqKysRGpqakeqIiIialsnDQf8cG5aQ0ODVHMqKirgcDi85sbZbDakpKQoc+PKysoQHh6uJAAAkJaWBqPRiD179ihl7rvvPphMJqVMeno6Dh06hHPnzillLq+nucyV5uC1pkNJQE5ODt566y2sWbMGYWFhcDgccDgcuHjxovJEJ0+ejLy8POzYsQPl5eXIzs5GamoqVwYQEVHn66T7BMTExHjNTysoKJBqTvP8t6ioKK/HL58b53A4EBkZ6XU+MDAQERERXmVau8bldVypzJXm4LWmQ8MBy5cvBwAMHz7c6/FVq1bhiSeeAAAsWbIERqMR48aNQ0NDA9LT0/HKK690pBoiIqJr6tixY7BarcrXehmm7lASINoxi9JisaCwsPCKEyaIiIg6S2ftHWC1Wr2SAFnN89+qqqrQq1cv5fGqqiokJSUpZU6ePOkV19TUhLNnzyrxdru9xd12m79uq8yV5uC1hnsHdAZjgPQREG6TPtTUS9SCwSB/aI3HLX9ojGhqkj40wc/uGBgfHw+73e41N87pdGLPnj3K3LjU1FRUV1ejvLxcKbN9+3Z4PB6kpKQoZT744AM0NjYqZbZt24Zbb70VN9xwg1Lm8nqay3RkDh6TACIiog6ora3F/v37sX//fgCXJgPu378flZWVMBgMmDFjBp5//nn83//9Hw4cOICf//zniI6OxpgxYwAAAwYMwKhRozBlyhTs3bsXH3/8MXJzczFhwgRER0cDAH7605/CZDJh8uTJOHjwINauXYtly5YhLy9PacfTTz+N4uJivPjii/j6668xf/58fPLJJ8jNzW33c+FWwkREpF0CgMRaf6/4Dvrkk08wYsQI5evmP8xZWVkoKirC7NmzUVdXh1/84heorq7GPffcg+LiYlgsFiVm9erVyM3NxQMPPKDMo3vppZeU8zabDe+//z5ycnKQnJyMHj16ID8/3+teAnfddRfWrFmDuXPn4le/+hVuvvlmbNy4UbmBX3sYRHsG+q8hp9MJm82G4XgEgYYgXzenfVR0rwdYQ6Vj3c7atgtdiQa7NamLqenW969fI+RjTaIRO/EuampqOmWcvTXNfyvuv+O/ERhgaTvgCprc9dj+6e+6tK3+jD0BRESkXQLqklCd56+cE0BERKRT7AkgIiLtUjvDX+dDWUwCiIhIuzwA1KxSVTOp8DrA4QAiIiKdYk8AERFpVmfdMVCvmAQQEZF2cU6AKhwOICIi0in2BBARkXaxJ0AVJgFERKRdTAJU4XAAERGRTrEnoBOouf9/48C+0rFBB76RjnVX10jHkh9Tcf9/g8kkHStcLulYvX8SI5V4nwBVmAQQEZFmcYmgOkwCiIhIuzgnQBXOCSAiItIp9gQQEZF2eQRgUPFp3qPvngAmAUREpF0cDlCFwwFEREQ6xZ4AIiLSMJU9AdB3TwCTACIi0i4OB6jC4QAiIiKdYk8AERFpl0dAVZc+VwcQERFplPBcOtTE6xiHA4iIiHSKPQFERKRdnBioCpOAyxkDpMI8tXXSVaraCdBZKx1L1ykVv9C4EyC1IPk7EcJz7Xbn45wAVZgEEBGRdrEnQBXOCSAiItIp9gQQEZF2CajsCei0lmgSkwAiItIuDgeowuEAIiIinWJPABERaZfHA1VLETz6vlkQkwAiItIuDgeowuEAIiIinWJPABERaRd7AlRhEkBERNrFOwaqwuEAIiIinWJPABERaZYQHggV2wGrib0eMAkgIiLtEkJdlz7nBBAREWmUUDkngEnAdUZ260sAAdZQqTg1W/q6q2ukY4k6lc5/GV63VPxONAZb5OKEEZDfYZ2uoesvCSAiIv3weACDinF9zgkgIiLSKA4HqMIlgkRERDrFngAiItIs4fFAqBgO4BJBIiIireJwgCocDiAiItIp9gQQEZF2eQRgYE+ALCYBRESkXUIAULNEUN9JAIcDiIiIdIo9AUREpFnCIyBUDAcI9gQQERFplPCoPyQUFhaiT58+sFgsSElJwd69ezv5iV0bTAKIiEizhEeoPjpq7dq1yMvLw7x587Bv3z4kJiYiPT0dJ0+e7IJn2LWYBBAREXXA4sWLMWXKFGRnZyMhIQErVqxASEgIVq5c6eumdZjfzQloHp9pQqPc/R9U3P1JCJdUnFs0StcJ4ZaPJSJqi4rfiUYh9zmx6d+/E6/FeHuTaFD1HJtwqa1Op9PrcbPZDLPZ3KK8y+VCeXk55syZozxmNBqRlpaGsrIy6Xb4it8lAefPnwcAfIT35C6g5g6Q1SpiiYj8kZrfiSq3Az5//jxsNpu6i1yByWSC3W7HRw7JvxWXCQ0NRUxMjNdj8+bNw/z581uUPX36NNxuN6Kiorwej4qKwtdff626Ldea3yUB0dHROHbsGMLCwmAwGFqcdzqdiImJwbFjx2C1Wn3QQm3g69Q+fJ3axteoffg6fU8IgfPnzyM6OrrL6rBYLKioqIDLJdeDezkhRIu/N631AlyP/C4JMBqN6N27d5vlrFar7n/Q2oOvU/vwdWobX6P24et0SVf1AFzOYrHAYrF0eT2X69GjBwICAlBVVeX1eFVVFex2+zVtS2fgxEAiIqJ2MplMSE5ORklJifKYx+NBSUkJUlNTfdgyOX7XE0BEROTP8vLykJWVhSFDhmDo0KFYunQp6urqkJ2d7eumdZjmkgCz2Yx58+bpZrxGFl+n9uHr1Da+Ru3D10k/xo8fj1OnTiE/Px8OhwNJSUkoLi5uMVlQCwxC7/dMJCIi0inOCSAiItIpJgFEREQ6xSSAiIhIp5gEEBER6RSTACIiIp3SVBJwvezf3FXmz58Pg8HgdfTv39/XzfK5Dz74AA899BCio6NhMBiwceNGr/NCCOTn56NXr14IDg5GWloaDh8+7JvG+lBbr9MTTzzR4v01atQo3zTWhwoKCnDnnXciLCwMkZGRGDNmDA4dOuRVpr6+Hjk5OejevTtCQ0Mxbty4FneYI/IHmkkCrqf9m7vSbbfdhhMnTijHRx995Osm+VxdXR0SExNRWFjY6vlFixbhpZdewooVK7Bnzx5069YN6enpqK+vv8Yt9a22XicAGDVqlNf76+23376GLfQPpaWlyMnJwe7du7Ft2zY0NjZi5MiRqKv7fredmTNnYtOmTVi/fj1KS0tx/PhxPProoz5sNdEVCI0YOnSoyMnJUb52u90iOjpaFBQU+LBV/mXevHkiMTHR183wawDEhg0blK89Ho+w2+3ihRdeUB6rrq4WZrNZvP322z5ooX/44eskhBBZWVnikUce8Ul7/NnJkycFAFFaWiqEuPT+CQoKEuvXr1fKfPXVVwKAKCsr81UziVqliZ6A5v2b09LSlMe0vH9zVzp8+DCio6PRt29fTJo0CZWVlb5ukl+rqKiAw+Hwem/ZbDakpKTwvdWKnTt3IjIyErfeeiumTZuGM2fO+LpJPldTUwMAiIiIAACUl5ejsbHR6z3Vv39/xMbG8j1FfkcTScDV9m92OBw+apX/SUlJQVFREYqLi7F8+XJUVFTg3nvvxfnz533dNL/V/P7he6tto0aNwptvvomSkhL8/ve/R2lpKTIyMuB2u33dNJ/xeDyYMWMG7r77btx+++0ALr2nTCYTwsPDvcryPUX+SHN7B9CVZWRkKP8eNGgQUlJSEBcXh3Xr1mHy5Mk+bBldDyZMmKD8e+DAgRg0aBBuuukm7Ny5Ew888IAPW+Y7OTk5+OKLLzj3hjRLEz0B19v+zddKeHg4brnlFhw5csTXTfFbze8fvrc6rm/fvujRo4du31+5ubnYvHkzduzYgd69eyuP2+12uFwuVFdXe5Xne4r8kSaSgOtt/+Zrpba2FkePHkWvXr183RS/FR8fD7vd7vXecjqd2LNnD99bbfjuu+9w5swZ3b2/hBDIzc3Fhg0bsH37dsTHx3udT05ORlBQkNd76tChQ6isrOR7ivyOZoYDrqf9m7vKM888g4ceeghxcXE4fvw45s2bh4CAAEycONHXTfOp2tpar0+rFRUV2L9/PyIiIhAbG4sZM2bg+eefx80334z4+Hj85je/QXR0NMaMGeO7RvvA1V6niIgIPPfccxg3bhzsdjuOHj2K2bNno1+/fkhPT/dhq6+9nJwcrFmzBu+++y7CwsKUcX6bzYbg4GDYbDZMnjwZeXl5iIiIgNVqxfTp05Gamophw4b5uPVEP+Dr5Qkd8cc//lHExsYKk8kkhg4dKnbv3u3rJvmV8ePHi169egmTySRuvPFGMX78eHHkyBFfN8vnduzYIQC0OLKysoQQl5YJ/uY3vxFRUVHCbDaLBx54QBw6dMi3jfaBq71OFy5cECNHjhQ9e/YUQUFBIi4uTkyZMkU4HA5fN/uaa+01AiBWrVqllLl48aL45S9/KW644QYREhIixo4dK06cOOG7RhNdgUEIIa596kFERES+pok5AURERNT5mAQQERHpFJMAIiIinWISQEREpFNMAoiIiHSKSQAREZFOMQkgIiLSKSYBREREOsUkgIiISKeYBBAREekUkwAiIiKd+v8BRdZxgj0nMtoAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "for filter in curves:\n", - " convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", - " plt.figure()\n", - " plt.imshow(convolved)\n", - " plt.colorbar()\n", - " plt.title(filter.name)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "filters,images =curves.apply_filter_curves(datacube, wave).values()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[np.str_('SLOAN/SDSS.uprime_filter'),\n", - " np.str_('SLOAN/SDSS.u'),\n", - " np.str_('SLOAN/SDSS.g'),\n", - " np.str_('SLOAN/SDSS.gprime_filter'),\n", - " np.str_('SLOAN/SDSS.r'),\n", - " np.str_('SLOAN/SDSS.rprime_filter'),\n", - " np.str_('SLOAN/SDSS.i'),\n", - " np.str_('SLOAN/SDSS.iprime_filter'),\n", - " np.str_('SLOAN/SDSS.z'),\n", - " np.str_('SLOAN/SDSS.zprime_filter')]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "filters" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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MDAaDVZ/NngIDAzFhwgS88cYbLf6Rcv78+evWlq5du6KioqLZ/unTpyM/Px87duxo9l5FRQUaGhquQ+voeli0aBHy8/OxefNmHD16FPfeey8mT56M48ePA7h6t07fvn2xfft2REZGIiIiAvPnz2cPnbTh0UcfxaVLl3DXXXchKioKdXV1+PLLL7FlyxZEREQgNTVVLltYWNjicPSIESMwZcoUvPfee5gyZQpuvPHGZk+KKywsxKuvvooxY8YAuBoQhRAt9qwB4LbbboO7uzs2btwoL7RqyezZs7FixQoUFBRY7P/000/h5eWFiRMnWuz/7LPPkJGRgTvuuAOjR4+Gj48PfvzxR6xduxa1tbUt3sv83nvvwcfHB3V1dfKT4r744gtER0dj69atcrlLly5hzJgxGD16NCZPnozQ0FBUVFRg27Zt+PzzzzF16lSMGDECwNXe/caNG3HXXXchNjYWnp6e+P7777F27Vp4eXnh//7v/+Tj/vnPf8by5cuxe/dui3unWzJw4EDMmzcP//rXvxAUFIS1a9eitLQU69ata7NeR8rKysLYsWMxbNgwLFiwAH379kVpaSny8/Nx6tQpfP3119elHbGxsVi1ahWeffZZ9O/fH4GBgfjVr36FJ598En//+99x++23Y+7cuYiNjUVNTQ3+/e9/47333sPJkyebPX2QtKe4uBjr1q1DcXExQkJCAFydCsrJycG6devwxz/+ET/++CN++uknbN26FRs2bIDJZMLixYsxbdo07Nq1y8GfwI4ctr6eOtQ//vEP8cADD4ioqCjh4+MjPD09Rf/+/cWjjz7a7ElxAFrc5s2bJ5crKioSCxYsEGFhYcLDw0P06tVL3HHHHeLzzz+3OO+wYcNEWFhYm22bMGGCCAwMFPX19Ra3rf1S4y1KuOYWs2nTponbbrutWdkff/xRpKeni9GjR4vAwEDh7u4uAgICxJQpU8SuXbssyjbetta4eXl5iT59+ojbb79drF271uI2LCGuPjXszTffFFOnThXh4eFCr9eLLl26iBEjRogXXnjB4oliR48eFU8++aS48cYbhb+/v3B3dxfBwcHi3nvvFUeOHGmxHdfeCtba7WVTpkwRO3bsEMOHDxd6vV5ERUWJrVu3tni9/vWvf7V6LVs67i8BEAsXLrTY19q/04kTJ8ScOXOEwWAQHh4eonfv3uL2228X7733XrPjtkXNbWtGo1FMmTJFdOvWTQCwuIXt4sWLYunSpaJ///7C09NT9OrVS4wZM0a8+OKL8i2cbX0HyfkAEB9++KH8evv27fItpddu7u7uYvr06UIIIRYsWCAAiGPHjsn1Dh8+LACIH3744Xp/hA7DZ7mTZjQ0NKBnz57IzMzEI4884ujmXDfWPG+fqLOQJAkffvihnJFvy5YtmD17Nr799ttmizl9fHxgMBiQkZGBP/7xj6ivr5ffu3z5Mrp06YKdO3di0qRJ1/MjdBgOuZNm/Pzzz1i8eLHF/dhE1LmNGDECJpMJ586dw7hx41osc/PNN6OhoQEnTpyQH2HduKi08SmTroA9dCInp+UeemuP+m3k7e0t3+NO1Jrq6mo5Q+SIESPw8ssvY+LEifD390dYWBjuu+8+fPHFF3jppZcwYsQInD9/Hrm5uRg+fDimTJkCs9mMm266CT4+Pli5ciXMZjMWLlwIX19f+RHPLsGxI/5E1J72Hs/rzNDK+ozGrTFbHlFbdu/e3eb3p66uTqSnp4uIiAjh4eEhgoODxV133WXxOOrTp0+Lu+++W/j4+IigoCAxd+5cUV5e7qBP1DHYQyeiDvPPf/6zzfdDQkLafEAQEVmPAZ2IiMgF8MEyRERELsDpVrmbzWacOXMG3bp1a/WRjkRE5LyEELh48SJCQkKg03Vcv/HKlSuoq6tTfRxPT094eXnZoUWO5XQB/cyZM80yPRERkfaUlJSgT58+HXLsK1euIDLcB8ZzJtXHMhgMKCoq0nxQd7qA3piycixugzs8HNwaok5ExYiYTu+puK65VmEPi8t/nFYD6rEPnzZLQWxPdXV1MJ4zoehwOHy7KR8FqLpoRmTsT6irq2NAt7fGYXZ3eMBdYkAnum7UBHRJRUCXlAZmBnSn9d9/musxberbTacqoLuSDrsKWVlZiIiIgJeXF+Li4nDw4MGOOhUREXVSJmFWvbmKDgnoW7ZsQVpaGjIyMnDkyBFER0cjMTER586d64jTERFRJ2WGUL25ig4J6C+//DIWLFiA1NRUDB48GKtXr0aXLl2wdu3ajjgdERF1UmY7/M9V2D2g19XV4fDhw0hISGg6iU6HhIQE5OfnNytfW1uLqqoqi42IiIhsY/eAXlZWBpPJhKCgIIv9QUFBLSZqyMzMhJ+fn7zxljUiIrKWSQjVm6tw+NLApUuXorKyUt5KSkoc3SQiItIIzqE3sftta7169YKbmxtKS0st9peWlsJgMDQrr9frodfr7d0MIiKiTsXuPXRPT0/ExsYiNzdX3mc2m5Gbm4v4+Hh7n46IiDoxMwRMKjb20NuRlpaGlJQUjBw5EqNGjcLKlStRU1OD1NTUjjgdERF1UmqHzRnQ2zFjxgycP38e6enpMBqNiImJQU5OTrOFckRERGQfHfbo10WLFmHRokUddXgiIiLVK9VdaZW70z3LnYgcRMUvNnNtrUPOS2T+76amvqtw+G1rREREpB576EREpFmNq9XV1HcVDOhERKRZJnF1U1PfVTCgExGRZnEOvQnn0ImIiFwAe+hERKRZZkgwQVJV31UwoBMRkWaZxdVNTX1XwSF3IiIiF8AeOhERaZZJ5ZC7mrrOhgGdiIg0iwG9CYfciYiIXAB76EREpFlmIcEsVKxyV1HX2TCgExGRZnHIvQmH3ImIiFwAAzppg85N+UYdTwjlG3UsF//ZMUGnerPV3r17kZycjJCQEEiShG3btrVZfu7cuZAkqdk2ZMgQucyyZcuavR8VFWVTuxjQiYhIs8R/59CVbkLBHHpNTQ2io6ORlZVlVflXX30VZ8+elbeSkhL4+/vj3nvvtSg3ZMgQi3L79u2zqV2cQyciIs1yxBx6UlISkpKSrC7v5+cHPz8/+fW2bdtw4cIFpKamWpRzd3eHwWCwuT2N2EMnIqJOr6qqymKrra3tsHOtWbMGCQkJCA8Pt9h//PhxhISEoG/fvpg9ezaKi4ttOi4DOhERaZZJ6FRvABAaGir3pP38/JCZmdkh7T1z5gz+8Y9/YP78+Rb74+LikJ2djZycHKxatQpFRUUYN24cLl68aPWxOeRORESaZYYEs4q+qRlXF2aWlJTA19dX3q/X61W3rSXr169H9+7dMXXqVIv91w7hDx8+HHFxcQgPD8e7776LefPmWXVsBnQiIur0fH19LQJ6RxBCYO3atbj//vvh6enZZtnu3btj4MCBKCwstPr4HHInIiLNalwUp2a7Xvbs2YPCwkKretzV1dU4ceIEgoODrT4+e+hERKRZ186DK6tv+7MQqqurLXrORUVFKCgogL+/P8LCwrB06VKcPn0aGzZssKi3Zs0axMXFYejQoc2O+cQTTyA5ORnh4eE4c+YMMjIy4ObmhlmzZlndLgZ0IiIiGxw6dAgTJ06UX6elpQEAUlJSkJ2djbNnzzZboV5ZWYn3338fr776aovHPHXqFGbNmoXy8nIEBARg7Nix2L9/PwICAqxuFwM6ERFp1tVFcSqSsyioO2HCBIg2evbZ2dnN9vn5+eHSpUut1tm8ebPN7fglBnQiItIss8LHtzbVd53HD3NRHBERkQtgD52IiDTLEYvinBUD+rUkB+TFdaEvU7tUZG/Sde2iuK64ovwRjqK+TnFdIqdgNjm6BR3KDJ1dHizjChjQiYhIs0xCgklBxrRr67sKzqETERG5APbQiYhIs0wqV7mbOORORETkeGahg1nFojizC61j4pA7ERGRC2APnYiINItD7k0Y0ImISLPMULdS3Wy/pjgch9yJiIhcAHvoRESkWeofLOM6/VoGdCIi0iz1j351nYDuOp+EiIioE2MPnYiINMsR+dCdFQM6ERFpFofcmzCgExGRZqm/D50B3WlJHp7KKwuFdyRKyr8QoqFecV3NpV5VkcZRTQpUyUP517xT/fuQdVSkWZY8lf9+EnUKU/nye9hpuFxAJyKizsMsJJjVPFjGhdKnMqATEZFmmVUOubvSfeiu80mIiIg6MfbQiYhIs9SnT3Wdfi0DOhERaZYJEkwq7iVXU9fZuM6fJkRERJ0Ye+hERKRZHHJvwoBORESaZYK6YXPlT8dwPq7zpwkREVEnxh46ERFpFofcmzCgExGRZjE5SxMGdCIi0iyhMn2q4G1rRERE5EzYQyciIs3ikHsTpw3okrs7JMn25um6+yk+p7miUlE9VSk21dC5Ka+rIpWpUpK78q+bzqer4rrm6hrFdTtT6kk1/z6iocGOLXFualKgugX0UlzXdL5MYUXlP+tKf58Kcx1Qrvi0NmG2tSau86cJERFRJ+a0PXQiIqL2mFSmT1VT19kwoBMRkWZxyL2J3f80WbZsGSRJstiioqLsfRoiIiKH2Lt3L5KTkxESEgJJkrBt27Y2y+fl5TWLi5IkwWg0WpTLyspCREQEvLy8EBcXh4MHD9rUrg4ZaxgyZAjOnj0rb/v27euI0xARUSdnhk71ZquamhpER0cjKyvLpnrHjh2ziI2BgYHye1u2bEFaWhoyMjJw5MgRREdHIzExEefOnbP6+B0y5O7u7g6DwdARhyYiIpKZhASTimFzJXWTkpKQlJRkc73AwEB07969xfdefvllLFiwAKmpqQCA1atX45NPPsHatWvx1FNPWXX8DumhHz9+HCEhIejbty9mz56N4uLiVsvW1taiqqrKYiMiIrqefhmHamtr7X6OmJgYBAcHY9KkSfjiiy/k/XV1dTh8+DASEhLkfTqdDgkJCcjPz7f6+HYP6HFxccjOzkZOTg5WrVqFoqIijBs3DhcvXmyxfGZmJvz8/OQtNDTU3k0iIiIX1bgoTs0GAKGhoRaxKDMz025tDA4OxurVq/H+++/j/fffR2hoKCZMmIAjR44AAMrKymAymRAUFGRRLygoqNk8e1vsPuR+7TDE8OHDERcXh/DwcLz77ruYN29es/JLly5FWlqa/LqqqopBnYiIrCJUZlsT/61bUlICX19feb9er1fdtkaDBg3CoEGD5NdjxozBiRMn8Morr+Dtt9+223k6/La17t27Y+DAgSgsLGzxfb1eb9cLR0REnYcJEkwqEqw01vX19bUI6B1t1KhR8oLxXr16wc3NDaWlpRZlSktLbVqP1uF31FdXV+PEiRMIDg7u6FMRERFpQkFBgRwXPT09ERsbi9zcXPl9s9mM3NxcxMfHW31Mu/fQn3jiCSQnJyM8PBxnzpxBRkYG3NzcMGvWLHufioiIOjmzUPdwGLOCdA3V1dUWo85FRUUoKCiAv78/wsLCsHTpUpw+fRobNmwAAKxcuRKRkZEYMmQIrly5grfeegu7du3Czp075WOkpaUhJSUFI0eOxKhRo7By5UrU1NTIq96tYfeAfurUKcyaNQvl5eUICAjA2LFjsX//fgQEBNj7VERE1MmZVc6hK6l76NAhTJw4UX7duA4sJSUF2dnZOHv2rMXdXXV1dfjNb36D06dPo0uXLhg+fDj++c9/WhxjxowZOH/+PNLT02E0GhETE4OcnJxmC+XaIgnhXOmkqqqq4Ofnh4nu98Bd8rC5vq5HD8Xn1ly2NUnFjInWsq1166a4rppsa6K+TnFdrWG2NetIKtb8dJZsaw3mOuSWr0NlZWWHzUs3xoqU3TPh6aM8A15ddR3WT9zcoW29Xpz2We7CZIJQELCUBmVARWBW8TeR5KH8i6gqpWgrtxFaQ+kvbzVBuS46UnFdz6+LFNc1Xeg8Ab0zBWU1RJ3y74TioAxA1Cv793EL6Kn4nJdGhiuq11B/Bdih+LQ2MUOCWcWiODV1nY3TBnQiIqL2OOJJcc7KdfLGERERdWLsoRMRkWY5YlGcs2JAJyIizTJDZT50F5pDd50/TYiIiDox9tCJiEizhMpV7sKFeugM6EREpFnXZkxTWt9VMKATEZFmcVFcE9f5JERERJ0Ye+hERKRZHHJvwoBORESaxUe/NuGQOxERkQtgD52IiDSLQ+5NGNCJiEizGNCbOG1Al9zcIEluNtcTKnL/Su62519Xe07oVHwRL11Sfl41udQlZW1Wk7LV89sSxXXV5ENnjvDrQGf7zzkA6Lp2UXxKc42Knx2z8p93UVur/LwKrxPqFKaFBtDl0E+K6jWYO0/aYWfitAGdiIioPeyhN2FAJyIizWJAb8JV7kRERC6APXQiItIsAXX3kgv7NcXhGNCJiEizOOTehAGdiIg0iwG9CefQiYiIXAB76EREpFnsoTdhQCciIs1iQG/CIXciIiIXwB46ERFplhAShIpetpq6zoYBnYiINIv50JtwyJ2IiMgFOG0PXefnC53O0+Z6olZ5lh9dL39F9cyl5xWf03z5suK6kpvC7EsAdD16KK6rONuUWfkzmSQvvfK6nsqy6Kk9r7mySlE9VVnalGbkAgBhVl5XRfY+pVnTxMAwxed0O3lWcV3TzxcU14VQ8VwyhT93JoXfQ1XnFMozvNmKi+KaOG1AJyIiag/n0JtwyJ2IiMgFsIdORESaxSH3JgzoRESkWRxyb8KATkREmiVU9tBdKaBzDp2IiMgFsIdORESaJaDubkAVVZ0Oe+hERKRZjU+KU7PZau/evUhOTkZISAgkScK2bdvaLP/BBx9g0qRJCAgIgK+vL+Lj47Fjxw6LMsuWLYMkSRZbVFSUTe1iQCciIrJBTU0NoqOjkZWVZVX5vXv3YtKkSfj0009x+PBhTJw4EcnJyfjqq68syg0ZMgRnz56Vt3379tnULg65ExGRZjlilXtSUhKSkpKsLr9y5UqL13/84x/x0Ucf4eOPP8aIESPk/e7u7jAYDDa3pxF76EREpFmN96Gr2QCgqqrKYqutre24NpvNuHjxIvz9LR83fvz4cYSEhKBv376YPXs2iouLbTouAzoREXV6oaGh8PPzk7fMzMwOO9eLL76I6upqTJ8+Xd4XFxeH7Oxs5OTkYNWqVSgqKsK4ceNw8eJFq4/LIXciItIsIVSucv9v3ZKSEvj6+sr79XrliZnasmnTJixfvhwfffQRAgMD5f3XDuEPHz4ccXFxCA8Px7vvvot58+ZZdWwGdCIi0ix7zaH7+vpaBPSOsHnzZsyfPx9bt25FQkJCm2W7d++OgQMHorCw0OrjO29Ar68HJNv/kSS97SlXG5m6+yiqp3NXnrJSKj6tuK6oV5NmU/kPwOXYfspOWa8iPaeKv8D13ypPqWv+uUJxXWFSlnpSUtEzcPNXnhbXXF2juK7Ot5vy81ZUKjvnKeVpi6UuylK2AoDuivK5VfPlK4rrSgp/ZiVvb8XnFAo/qyQk4PplUNWEd955Bw888AA2b96MKVOmtFu+uroaJ06cwP3332/1OZw3oBMREbXDEavcq6urLXrORUVFKCgogL+/P8LCwrB06VKcPn0aGzZsAHB1mD0lJQWvvvoq4uLiYDQaAQDe3t7w8/MDADzxxBNITk5GeHg4zpw5g4yMDLi5uWHWrFlWt4uL4oiISLPstcrdFocOHcKIESPkW87S0tIwYsQIpKenAwDOnj1rsUL9r3/9KxoaGrBw4UIEBwfL22OPPSaXOXXqFGbNmoVBgwZh+vTp6NmzJ/bv34+AgACr28UeOhERaZa9FsXZYsKECRBtVMzOzrZ4nZeX1+4xN2/ebHtDfoE9dCIiIhfAHjoREWnW1R66mjl0OzbGwRjQiYhIsxyxKM5ZccidiIjIBbCHTkREmiWgLqe5C424M6ATEZF2cci9CYfciYiIXAB76EREpF0cc5cxoBMRkXapHHKHCw25M6ATEZFmOeJJcc6Kc+hEREQuwHl76IG9ADcFaSQ9lH8kU1cPRfUqBitPHdnziorUnufLFNeVuipPH1kVpuwam/TKh7a6nVaWihQA3CMNiuu6KUyBCgBSN2XpeIWKdLx1Bj/FdT3KqhXXvRyq/LzehQpTHtepyM/ZoDz1sC7I+mQZvyRdVJ6iVlL4vTD1Ud5eN4UpaoW5DjAqPq1t5+Iqd5nzBnQiIqL2CEndPLgLBXQOuRMREbkA9tCJiEizuCiuic099L179yI5ORkhISGQJAnbtm2zeF8IgfT0dAQHB8Pb2xsJCQk4fvy4vdpLRETURNhhcxE2B/SamhpER0cjKyurxfeff/55vPbaa1i9ejUOHDiArl27IjExEVeuXFHdWCIiImqZzUPuSUlJSEpKavE9IQRWrlyJ3//+97jzzjsBABs2bEBQUBC2bduGmTNnqmstERHRNbjKvYldF8UVFRXBaDQiISFB3ufn54e4uDjk5+e3WKe2thZVVVUWGxERkdU43A7AzgHdaLx642FQUJDF/qCgIPm9X8rMzISfn5+8hYaG2rNJREREnYLDb1tbunQpKisr5a2kpMTRTSIiIo1oHHJXs7kKu962ZjBcfSJXaWkpgoOD5f2lpaWIiYlpsY5er4der+CJcERERMy2JrNrDz0yMhIGgwG5ubnyvqqqKhw4cADx8fH2PBUREREAyQ6ba7C5h15dXY3CwkL5dVFREQoKCuDv74+wsDA8/vjjePbZZzFgwABERkbi6aefRkhICKZOnWrPdhMREdE1bA7ohw4dwsSJE+XXaWlpAICUlBRkZ2djyZIlqKmpwYMPPoiKigqMHTsWOTk58PLysl+riYiIAA65X8PmgD5hwgSINp6VJ0kSnnnmGTzzzDOqGkZERNQuBnSZ0z7LXZSeh5BsT6sohYUoPueFKG9F9XxOK0/FKLwUpo4EoOvRXXHd+iDl6S71Vcp+AkpHK//JMemVpxQtv0FZGlMA8IgbpLhu1Q3KvhceFco/a49vFVeFZ4Dy76J7jfI0szU3KEvvWd9V+XXqdvyi4rpwUz7nKvmoGKlsMCuq5ma8oPiUouaSsnpCeVpoUs5pAzoREVG7mD5VxoBORESaxWxrTRz+YBkiIiJSjz10IiLSLi6KkzGgExGRdnEOXcYhdyIiIhfAHjoREWmWJK5uauq7CgZ0IiLSLs6hyxjQiYhIuziHLuMcOhERkQtgQCciIu0SdthstHfvXiQnJyMkJASSJGHbtm3t1snLy8ONN94IvV6P/v37Izs7u1mZrKwsREREwMvLC3FxcTh48KBN7WJAJyIi7XJAQK+pqUF0dDSysrKsKl9UVIQpU6Zg4sSJKCgowOOPP4758+djx44dcpktW7YgLS0NGRkZOHLkCKKjo5GYmIhz585Z3S7OoRMREdkgKSkJSUlJVpdfvXo1IiMj8dJLLwEAbrjhBuzbtw+vvPIKEhMTAQAvv/wyFixYgNTUVLnOJ598grVr1+Kpp56y6jzsoRMRkXbZqYdeVVVlsdXW1tqtifn5+UhISLDYl5iYiPz8fABAXV0dDh8+bFFGp9MhISFBLmMNp+2hSx4ekHQetlcsLVN8Tt/iborqVUYqaOd/eRmVpWwFAPgor/vz0C6K63r9rCyNY9eIKsXnvOil7N8GAHr1qVBc9183vqu47nPlAxTVS+1eoPicf6scprhuVsF4xXX9vlCeFrTnd1cU1fOoUp62+HLvrorrSiqyeXQ9elZxXeiUrcYWvio+q4eyECGZawHlP+62sdMq99DQUIvdGRkZWLZsmYqGNTEajQgKCrLYFxQUhKqqKly+fBkXLlyAyWRqscwPP/xg9XmcNqATERFdLyUlJfD19ZVf6/V6B7ZGGQZ0IiLSLHs9Kc7X19cioNuTwWBAaWmpxb7S0lL4+vrC29sbbm5ucHNza7GMwWCw+jycQyciIu1ywCp3W8XHxyM3N9di32effYb4+HgAgKenJ2JjYy3KmM1m5ObmymWswYBORERkg+rqahQUFKCgoADA1dvSCgoKUFxcDABYunQp5syZI5d/6KGH8OOPP2LJkiX44Ycf8Je//AXvvvsuFi9eLJdJS0vDm2++ifXr1+P777/Hww8/jJqaGnnVuzU45E5ERGSDQ4cOYeLEifLrtLQ0AEBKSgqys7Nx9uxZObgDQGRkJD755BMsXrwYr776Kvr06YO33npLvmUNAGbMmIHz588jPT0dRqMRMTExyMnJabZQri0M6EREpFkSVM6hK6gzYcIEiDbudmjpKXATJkzAV1991eZxFy1ahEWLFilo0VUM6EREpF1MziLjHDoREZELYA+diIi0i/nQZQzoRESkXQzoMg65ExERuQD20ImISLPs9aQ4V8CATkRE2sUhd5nTBvT6waEQ7rZncPI4V634nFVhyrKm+Zw2KT6nuYvyTG0mvZviut5lyjKmAUCXs5cV1Sv7uofic/YsVvFTd6Cn4qr9Llj/lKZfCshRltzhbyGTFJ/T9xaj4rruHsq/x2qcG6Esa6Ca77DPqTrFdSv6KU/a4f2Tj+K6Dd2VZbTT1Sn/d3X/uVJZRbPy60vKOW1AJyIiahd76DIGdCIi0izOoTfhKnciIiIXwB46ERFpFx/9KmNAJyIi7eIcuowBnYiINItz6E04h05EROQC2EMnIiLt4pC7jAGdiIi0S+WQuysFdA65ExERuQD20ImISLs45C5jQCciIu1iQJdxyJ2IiMgFsIdORESaxfvQmzhtQPcouwR3NwVp/8oqFJ/T/7suiupdjFCW/hEAupTUKq5r9lR+3q4lNYrr6qqUpU/1Ke6q+Jy9CqoU1zV18VR+3n8p+6wAIJWeUlSve3iQ4nMiT/k17tagPM1mQ3fl3+Ozo5WlBfUqV5HuVcXTPgMOVSiuq7uo/OfOo1ZhSlJJ+YcVPXyV1TPVAsoz+ZJCHHInIiJyAU7bQyciImoXF8XJGNCJiEizOIfehAGdiIi0zYWCshqcQyciInIB7KETEZF2cQ5dxoBORESaxTn0JhxyJyIicgHsoRMRkXZxyF3GgE5ERJrFIfcmHHInIiJyAeyhExGRdnHIXcaATkRE2sWALuOQOxERkQtw2h66dPkKJJ3tfzqJWuVpHD3OViiq53/mguJzistXFNf1vKIstSEANPRUnmZT56HsayOZFZ8SbuUXFdfVHa9QXNdUrTzdpaRTlrZSZ1aRFtSkvK5ZxXfRw1N5itrwM8rSxdb29lN8ztoeHorrulco/x0DFSlqoVd4jc+VKT6lqG9QVk8oTPWqgKMWxWVlZeGFF16A0WhEdHQ0Xn/9dYwaNarFshMmTMCePXua7b/tttvwySefAADmzp2L9evXW7yfmJiInJwcq9vktAGdiIioXQ4Yct+yZQvS0tKwevVqxMXFYeXKlUhMTMSxY8cQGBjYrPwHH3yAurqmP3LKy8sRHR2Ne++916Lc5MmTsW7dOvm1Xq+3qV0cciciIu0Sdths9PLLL2PBggVITU3F4MGDsXr1anTp0gVr165tsby/vz8MBoO8ffbZZ+jSpUuzgK7X6y3K9ejRw6Z2MaATEVGnV1VVZbHVtjJ9W1dXh8OHDyMhIUHep9PpkJCQgPz8fKvOtWbNGsycORNdu1pOfebl5SEwMBCDBg3Cww8/jPLycps+g80Bfe/evUhOTkZISAgkScK2bdss3p87dy4kSbLYJk+ebOtpiIiI2tU4h65mA4DQ0FD4+fnJW2ZmZovnKysrg8lkQlCQ5dqPoKAgGI3Gdtt78OBBfPPNN5g/f77F/smTJ2PDhg3Izc3Fc889hz179iApKQkmG9bF2DyHXlNTg+joaDzwwAO4++67Wyyjdh6AiIjIKnaaQy8pKYGvb9NC446KW2vWrMGwYcOaLaCbOXOm/N/Dhg3D8OHD0a9fP+Tl5eGWW26x6tg2B/SkpCQkJSW1WaZxHoCIiEgLfH19LQJ6a3r16gU3NzeUlpZa7C8tLW037tXU1GDz5s145pln2j1P37590atXLxQWFlod0DtkDt2WeYDa2tpmcxdERETWsNeQu7U8PT0RGxuL3NxceZ/ZbEZubi7i4+PbrLt161bU1tbivvvua/c8p06dQnl5OYKDg61um90Duq3zAJmZmRbzFqGhofZuEhERuSoHrHJPS0vDm2++ifXr1+P777/Hww8/jJqaGqSmpgIA5syZg6VLlzart2bNGkydOhU9e/a02F9dXY0nn3wS+/fvx8mTJ5Gbm4s777wT/fv3R2JiotXtsvt96LbOAyxduhRpaWny66qqKgZ1IiJyWjNmzMD58+eRnp4Oo9GImJgY5OTkyAvliouLodNZ9pePHTuGffv2YefOnc2O5+bmhqNHj2L9+vWoqKhASEgIbr31VqxYscKmufwOf7BMe/MAer2ei+aIiEgZBz3LfdGiRVi0aFGL7+Xl5TXbN2jQIAjR8sm8vb2xY8cOZQ25RocHdCXzAERERNaQ/rupqe8qbA7o1dXVKCwslF8XFRWhoKAA/v7+8Pf3x/Lly3HPPffAYDDgxIkTWLJkic3zAERERGQbmwP6oUOHMHHiRPl14/x3SkoKVq1aZZd5ACIiIqswfarM5oA+YcKEVucBANhlHoCIiMgajsq25oycNtua+Xw5zJLt6QJFnYq0fQpTr+q8vRSf0hzZR3FdNTxKbHtG8LXERWWpTAN3Kk+BKi5WK65rUtjeqydW8dPupizdpeSnPC2uuZu34rpSYbHy86q5xscvKaqmL1OePtXLt5viuqJaWXsBAG4q7hQ2nldUzawipbTS36dmUa/4nDZjD13G5CxEREQuwGl76ERERFZxoV62GgzoRESkWZxDb8IhdyIiIhfAHjoREWkXF8XJGNCJiEizOOTehEPuRERELoA9dCIi0i4OucsY0ImISLM45N6EQ+5EREQugD10IiLSLg65yxjQiYhIuxjQZQzoRESkWZxDb8I5dCIiIhfgtD10ydMDkuRhcz1V6VNNJmX1JOV/F+lOnlFcV3F7AZguKU8BKZSet6JS8TkhScrrqkmBqoLS62Q+Y1R8TslTWcpWQF2aTVXMyq6TqfxnxaeUKqsU19WpSG9bOzRUcV3P704pqqfT6xWf03zhgqJ6kjADDYpPaxsOucucNqATERG1RxICkoo/2tXUdTYcciciInIB7KETEZF2cchdxoBORESaxVXuTTjkTkRE5ALYQyciIu3ikLuMAZ2IiDSLQ+5NOORORETkAthDJyIi7eKQu4wBnYiINItD7k0Y0ImISLvYQ5dxDp2IiMgFsIdORESa5krD5mo4bUA3VdVAkhRkThNmxedUmqnKXF2j+JyioV5xXUdlEXMIR31WFVneJDc3RfVUZT2rU/590nnant2wkeIMfIBD/m2FWfk5zSoytXkWFCk/r9LfMyp+J4oGZSnThLheqdZw9fuj5jvkQr9HOeRORETkApy2h05ERNQernJvwoBORETaxVXuMg65ExER2SgrKwsRERHw8vJCXFwcDh482GrZ7OxsSJJksXl5eVmUEUIgPT0dwcHB8Pb2RkJCAo4fP25TmxjQiYhIsySz+s1WW7ZsQVpaGjIyMnDkyBFER0cjMTER586da7WOr68vzp49K28//fSTxfvPP/88XnvtNaxevRoHDhxA165dkZiYiCtXrljdLgZ0IiLSLmGHzUYvv/wyFixYgNTUVAwePBirV69Gly5dsHbt2lbrSJIEg8Egb0FBQU0fQQisXLkSv//973HnnXdi+PDh2LBhA86cOYNt27ZZ3S4GdCIi6vSqqqosttpWbh+tq6vD4cOHkZCQIO/T6XRISEhAfn5+q8evrq5GeHg4QkNDceedd+Lbb7+V3ysqKoLRaLQ4pp+fH+Li4to85i8xoBMRkWY1rnJXswFAaGgo/Pz85C0zM7PF85WVlcFkMln0sAEgKCgIRqOxxTqDBg3C2rVr8dFHH+Fvf/sbzGYzxowZg1OnTgGAXM+WY7aEq9yJiEi77PRgmZKSEvj6+sq79Xq92pbJ4uPjER8fL78eM2YMbrjhBrzxxhtYsWKF3c7DHjoREWmWvXrovr6+FltrAb1Xr15wc3NDaWmpxf7S0lIYDAar2uzh4YERI0agsLAQAOR6ao4JMKATERFZzdPTE7GxscjNzZX3mc1m5ObmWvTC22IymfDvf/8bwcHBAIDIyEgYDAaLY1ZVVeHAgQNWHxPgkDsREWmZAx4sk5aWhpSUFIwcORKjRo3CypUrUVNTg9TUVADAnDlz0Lt3b3ke/plnnsHo0aPRv39/VFRU4IUXXsBPP/2E+fPnA7i6Av7xxx/Hs88+iwEDBiAyMhJPP/00QkJCMHXqVKvbxYBORESa5YhHv86YMQPnz59Heno6jEYjYmJikJOTIy9qKy4uhk7XNAB+4cIFLFiwAEajET169EBsbCy+/PJLDB48WC6zZMkS1NTU4MEHH0RFRQXGjh2LnJycZg+gafuzCOdKNVNVVQU/Pz9M0N0Nd0lB9icHZFuDisxNzLbm5NRkW3NXlr1M1XdCUj6LpibbmqoMcY74HuuUZcIDAEmn/Duh69ZNcV0tZVtrEPXIw0eorKy0WGhmT42xYvSUFXD3sD7o/VJD/RXs/+TpDm3r9eK8PXSzSdUvJyVEnYJ0rQADqytT8W+rODCrWrGrPI2puVb5L36tBWU3n66K65pUpEs2XbiguC61gulTZc4b0ImIiNrBbGtNuMqdiIjIBbCHTkRE2sX0qTIGdCIi0iwOuTfhkDsREZELYA+diIi0yyxU3Tqsqq6TYUAnIiLt4hy6jAGdiIg0S4LKOXS7tcTxOIdORETkAthDJyIi7eKT4mQM6EREpFm8ba0Jh9yJiIhcAHvoRESkXVzlLmNAJyIizZKEgKRiHlxNXWfDgH4tF/qHJSegte+T1tprVp4qVk0KVDXndQgVaWY191k7OQZ0IiLSLvN/NzX1XQQDOhERaRaH3JtwlTsREZELsCmgZ2Zm4qabbkK3bt0QGBiIqVOn4tixYxZlrly5goULF6Jnz57w8fHBPffcg9LSUrs2moiICEDTKnc1m4uwKaDv2bMHCxcuxP79+/HZZ5+hvr4et956K2pqmhaYLF68GB9//DG2bt2KPXv24MyZM7j77rvt3nAiIiL5SXFqNhdh0xx6Tk6Oxevs7GwEBgbi8OHD+H//7/+hsrISa9aswaZNm/CrX/0KALBu3TrccMMN2L9/P0aPHm2/lhMRUafHJ8U1UTWHXllZCQDw9/cHABw+fBj19fVISEiQy0RFRSEsLAz5+fktHqO2thZVVVUWGxEREdlGcUA3m814/PHHcfPNN2Po0KEAAKPRCE9PT3Tv3t2ibFBQEIxGY4vHyczMhJ+fn7yFhoYqbRIREXU2HHKXKQ7oCxcuxDfffIPNmzerasDSpUtRWVkpbyUlJaqOR0REnYdkVr+5CkX3oS9atAjbt2/H3r170adPH3m/wWBAXV0dKioqLHrppaWlMBgMLR5Lr9dDr9craQYRERH9l009dCEEFi1ahA8//BC7du1CZGSkxfuxsbHw8PBAbm6uvO/YsWMoLi5GfHy8fVpMRETUiEPuMpt66AsXLsSmTZvw0UcfoVu3bvK8uJ+fH7y9veHn54d58+YhLS0N/v7+8PX1xaOPPor4+HiucCciIvtjtjWZTQF91apVAIAJEyZY7F+3bh3mzp0LAHjllVeg0+lwzz33oLa2FomJifjLX/5il8YSERFRy2wK6MKKoQkvLy9kZWUhKytLcaOIiIiswWe5N2FyFntQkZ7Qzaer4rqdKgUkdTxJUl5Xa78UO9P339U/q9p5cK19d9vA5CxEREQugD10IiLSLgF1Oc1dp4POgE5ERNrFOfQmDOhERKRdAirn0O3WEofjHDoREZELYA+diIi0i6vcZeyhExGRdpntsCmQlZWFiIgIeHl5IS4uDgcPHmy17Jtvvolx48ahR48e6NGjBxISEpqVnzt3LiRJstgmT55sU5sY0ImIiGywZcsWpKWlISMjA0eOHEF0dDQSExNx7ty5Fsvn5eVh1qxZ2L17N/Lz8xEaGopbb70Vp0+ftig3efJknD17Vt7eeecdm9rFgE5ERJrVuMpdzWarl19+GQsWLEBqaioGDx6M1atXo0uXLli7dm2L5Tdu3IhHHnkEMTExiIqKwltvvQWz2WyRyAy4mn3UYDDIW48ePWxqFwM6ERFpl52yrVVVVVlstbW1LZ6urq4Ohw8fRkJCgrxPp9MhISEB+fn5VjX50qVLqK+vh7+/v8X+vLw8BAYGYtCgQXj44YdRXl5u06VgQCciok4vNDQUfn5+8paZmdliubKyMphMJgQFBVnsDwoKkjOQtue3v/0tQkJCLP4omDx5MjZs2IDc3Fw899xz2LNnD5KSkmAyWf/oXq5yJyIi7bLTKveSkhL4+vrKu/V6vdqWtehPf/oTNm/ejLy8PHh5ecn7Z86cKf/3sGHDMHz4cPTr1w95eXm45ZZbrDo2e+hERKRddhpy9/X1tdhaC+i9evWCm5sbSktLLfaXlpbCYDC02dQXX3wRf/rTn7Bz504MHz68zbJ9+/ZFr169UFhYaPWlYEAnIiKykqenJ2JjYy0WtDUucIuPj2+13vPPP48VK1YgJycHI0eObPc8p06dQnl5OYKDg61uG4fc7UBVCtSocOXn/eEn5eetqlJcl5yYihSokqen4rqirk5xXVd6sAc5gBmAisy/Su5DT0tLQ0pKCkaOHIlRo0Zh5cqVqKmpQWpqKgBgzpw56N27tzwP/9xzzyE9PR2bNm1CRESEPNfu4+MDHx8fVFdXY/ny5bjnnntgMBhw4sQJLFmyBP3790diYqLV7WJAJyIizXJEcpYZM2bg/PnzSE9Ph9FoRExMDHJycuSFcsXFxdDpmgbAV61ahbq6OkybNs3iOBkZGVi2bBnc3Nxw9OhRrF+/HhUVFQgJCcGtt96KFStW2DSXz4BORETa5aBHvy5atAiLFi1q8b28vDyL1ydPnmzzWN7e3tixY4eidlyLc+hEREQugD10IiLSLrMAJBU9dLPrrOFgQCciIu1itjUZh9yJiIhcAHvoRESkYSp76HCdHjoDOhERaReH3GUcciciInIB7KETEZF2mQVUDZtzlTsREZETEOarm5r6LoJD7kRERC6APXQiItIuLoqTMaBfQ3JXdjnMly4pPqeqjGnVNYrrkotS8cuJGdOoGcXZ+6TrdzcY59BlDOhERKRd7KHLOIdORETkAthDJyIi7RJQ2UO3W0scjgGdiIi0i0PuMg65ExERuQD20ImISLvMZgAqHg5jdp0HyzCgExGRdnHIXcYhdyIiIhfAHjoREWkXe+gyBnQiItIuPilOxiF3IiIiF8AeOhERaZYQZggVKVDV1HU2DOhERKRdQqgbNuccOhERkRMQKufQGdCdl9IUqACg69ZNUT3zxYuKz2mqqlJcl8iuXOgXG11DcQpUQOftrayecAOUZ5UmhVwuoBMRUSdiNgOSinlwzqETERE5AQ65y3jbGhERkQtgD52IiDRLmM0QKobcedsaERGRM+CQu4xD7kRERC6APXQiItIuswAk9tABBnQiItIyIQCouW3NdQI6h9yJiIhcAHvoRESkWcIsIFQMuQv20ImIiJyAMKvfFMjKykJERAS8vLwQFxeHgwcPtll+69atiIqKgpeXF4YNG4ZPP/3U8mMIgfT0dAQHB8Pb2xsJCQk4fvy4TW1iQCciIs0SZqF6s9WWLVuQlpaGjIwMHDlyBNHR0UhMTMS5c+daLP/ll19i1qxZmDdvHr766itMnToVU6dOxTfffCOXef755/Haa69h9erVOHDgALp27YrExERcuXLF6nZJwsnGG6qqquDn54cJuBPukofN9bWWnEU0NCiuS0TULgckZ2kQddh1aTMqKyvh6+ur+PxtkWOFdJeiWNGoQdQjT3xoU1vj4uJw00034c9//jMAwGw2IzQ0FI8++iieeuqpZuVnzJiBmpoabN++Xd43evRoxMTEYPXq1RBCICQkBL/5zW/wxBNPAAAqKysRFBSE7OxszJw506p2Od0ceuPfFw2oV/SsAEnF3yc6UaeonlnUKz6nEAzoRNSRVAR04aaoXsN/fydej/5ig6hVlWClAVfbWvWLzJd6vR56vb5Z+bq6Ohw+fBhLly6V9+l0OiQkJCA/P7/Fc+Tn5yMtLc1iX2JiIrZt2wYAKCoqgtFoREJCgvy+n58f4uLikJ+fr92AfvG/vd19+LSdkq1QEx8vqKhLROSM1MRUlSlQL168CD8/P3UHaYWnpycMBgP2GRXGimv4+PggNDTUYl9GRgaWLVvWrGxZWRlMJhOCgoIs9gcFBeGHH35o8fhGo7HF8kajUX6/cV9rZazhdAE9JCQEJSUl6NatG6QWhoqqqqoQGhqKkpKSDhvKcQW8TtbhdWofr5F1eJ2aCCFw8eJFhISEdNg5vLy8UFRUhLo6ZSOr1xJCNIs3LfXOnZ3TBXSdToc+ffq0W87X17fT/9BYg9fJOrxO7eM1sg6v01Ud1TO/lpeXF7y8vDr8PNfq1asX3NzcUFpaarG/tLQUBoOhxToGg6HN8o3/X1paiuDgYIsyMTExVreNq9yJiIis5OnpidjYWOTm5sr7zGYzcnNzER8f32Kd+Ph4i/IA8Nlnn8nlIyMjYTAYLMpUVVXhwIEDrR6zJU7XQyciInJmaWlpSElJwciRIzFq1CisXLkSNTU1SE1NBQDMmTMHvXv3RmZmJgDgsccew/jx4/HSSy9hypQp2Lx5Mw4dOoS//vWvAABJkvD444/j2WefxYABAxAZGYmnn34aISEhmDp1qtXt0lxA1+v1yMjI0OT8xvXE62QdXqf28RpZh9ep85gxYwbOnz+P9PR0GI1GxMTEICcnR17UVlxcDJ2uaQB8zJgx2LRpE37/+9/j//7v/zBgwABs27YNQ4cOlcssWbIENTU1ePDBB1FRUYGxY8ciJyfHpikFp7sPnYiIiGzHOXQiIiIXwIBORETkAhjQiYiIXAADOhERkQtgQCciInIBmgrotuaf7WyWLVsGSZIstqioKEc3y+H27t2L5ORkhISEQJIkOSFCI3vkIXYF7V2nuXPnNvt+TZ482TGNdaDMzEzcdNNN6NatGwIDAzF16lQcO3bMosyVK1ewcOFC9OzZEz4+PrjnnnuaPSmMyN40E9BtzT/bWQ0ZMgRnz56Vt3379jm6SQ5XU1OD6OhoZGVltfi+PfIQu4L2rhMATJ482eL79c4771zHFjqHPXv2YOHChdi/fz8+++wz1NfX49Zbb0VNTY1cZvHixfj444+xdetW7NmzB2fOnMHdd9/twFZTpyA0YtSoUWLhwoXya5PJJEJCQkRmZqYDW+VcMjIyRHR0tKOb4dQAiA8//FB+bTabhcFgEC+88IK8r6KiQuj1evHOO+84oIXO4ZfXSQghUlJSxJ133umQ9jizc+fOCQBiz549Qoir3x8PDw+xdetWucz3338vAIj8/HxHNZM6AU300Bvzz16bK7a9/LOd1fHjxxESEoK+ffti9uzZKC4udnSTnFp7eYjJUl5eHgIDAzFo0CA8/PDDKC8vd3STHK6yshIA4O/vDwA4fPgw6uvrLb5TUVFRCAsL43eKOpQmAnpb+WdtyRXr6uLi4pCdnY2cnBysWrUKRUVFGDdunJxjnpqzVx7izmDy5MnYsGEDcnNz8dxzz2HPnj1ISkqCyWRydNMcxmw24/HHH8fNN98sP8bTaDTC09MT3bt3tyjL7xR1NM09y51al5SUJP/38OHDERcXh/DwcLz77ruYN2+eA1tGrmDmzJnyfw8bNgzDhw9Hv379kJeXh1tuucWBLXOchQsX4ptvvuFaFXIKmuihK8k/S0D37t0xcOBAFBYWOropTuvaPMTX4nerfX379kWvXr067fdr0aJF2L59O3bv3o0+ffrI+w0GA+rq6lBRUWFRnt8p6miaCOhK8s8SUF1djRMnTiA4ONjRTXFa9spD3BmdOnUK5eXlne77JYTAokWL8OGHH2LXrl2IjIy0eD82NhYeHh4W36ljx46huLiY3ynqUJoZcm8v/ywBTzzxBJKTkxEeHo4zZ84gIyMDbm5umDVrlqOb5lDV1dUWvciioiIUFBTA398fYWFhdslD7Arauk7+/v5Yvnw57rnnHhgMBpw4cQJLlixB//79kZiY6MBWX38LFy7Epk2b8NFHH6Fbt27yvLifnx+8vb3h5+eHefPmIS0tDf7+/vD19cWjjz6K+Ph4jB492sGtJ5fm6GX2tnj99ddFWFiY8PT0FKNGjRL79+93dJOcyowZM0RwcLDw9PQUvXv3FjNmzBCFhYWObpbD7d69WwBotqWkpAghrt669vTTT4ugoCCh1+vFLbfcIo4dO+bYRjtAW9fp0qVL4tZbbxUBAQHCw8NDhIeHiwULFgij0ejoZl93LV0jAGLdunVymcuXL4tHHnlE9OjRQ3Tp0kXcdddd4uzZs45rNHUKzIdORETkAjQxh05ERERtY0AnIiJyAQzoRERELoABnYiIyAUwoBMREbkABnQiIiIXwIBORETkAhjQiYiIXAADOhERkQtgQCciInIBDOhEREQu4P8DtWgZDStt1jgAAAAASUVORK5CYII=", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "for i,name in zip(images, filters):\n", - " plt.figure()\n", - " plt.imshow(i)\n", - " plt.colorbar()\n", - " plt.title(name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create false color images (RGB images), we have to normalize the individual photometric images from three different filters and stack them." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# NBVAL_SKIP\n", - "# Create an RGB image\n", - "# Normalize the images\n", - "import numpy as np\n", - "\n", - "def normalize(image):\n", - " image_min = image.min()\n", - " image_max = image.max()\n", - " return (image - image_min) / (image_max - image_min)\n", - "\n", - "r = images[1]\n", - "g = images[2]\n", - "b = images[3]\n", - "\n", - "rgb = np.stack([r,g,b], axis=-1)\n", - "\n", - "rgb = normalize(rgb)\n", - "\n", - "plt.imshow(rgb)\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/source/notebooks/psf.ipynb b/source/notebooks/psf.ipynb deleted file mode 100644 index 12f90089..00000000 --- a/source/notebooks/psf.ipynb +++ /dev/null @@ -1,151 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The point spread function\n", - "\n", - "The point spread functions blures the data in spatial direction and is an observational artefact. So far we have implemented a gaussian kernel. With that kernel the mock-observations can be convolved to mimic real observations. It is important that the sum of the kernel is 1." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(20, 20)\n", - "1.0000001\n" - ] - } - ], - "source": [ - "#NBVAL_SKIP\n", - "from rubix.telescope.psf.kernels import gaussian_kernel_2d\n", - "\n", - "kernel = gaussian_kernel_2d(20,20,3.5)\n", - "print(kernel.shape)\n", - "print(kernel.sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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7483NzRg1ahT27duHX/ziFwCAzz77DJMnT0ZJSQnmzp3bo9zZUSAiIuoFs9nssLW1tTkd197ejrKyMsTHx9sf8/HxQXx8PEpKSpzuU1JS4jAeAIxGo8vxzpSVlaGjo8MhTkREBMaNGycrDgsFIiJSGCvcm3a4sZgxJCQEOp3OvmVlZTn9bo2NjbBarQgMDHR4PDAwECaTyek+JpNJ1nhXMfz8/DB8+HC34vCsByIiUhgr3Dvr4cZlpurq6hymHtRqUZfZ8y4sFIiISGGuw72G+o1CQavV9miNQkBAAHx9fW8626ChoQF6vd7pPnq9XtZ4VzHa29vR1NTk0FWQG4dTD0RERB7k5+eHmJgYFBUV2R+z2WwoKipCbGys031iY2MdxgNAYWGhy/HOxMTEYPDgwQ5xTp8+jdraWllx2FEgIiKFEdNRkCM9PR2pqamYNWsW5syZgx07dsBisWD58uUAgJSUFIwZM8a+zmHdunWYP38+tm/fjiVLlmD//v0oLS3Fq6++ao/51Vdfoba2FvX19QBuFAHAjU6CXq+HTqdDWloa0tPTcfvtt0Or1WLt2rWIjY3t8RkPAAsFIiJSnL4vFJKSknD58mVkZGTAZDIhOjoaBQUF9gWLtbW18PH5Lqe4uDjs27cPmzdvxqZNmxAeHo78/HxMmzbNPuadd96xFxoA8MADDwAAMjMzsWXLFgDACy+8AB8fH9x3331oa2uD0WjEH//4R1m58zoKP8DrKPRMqKA4AK+j0NeaBMbidRR6htdR6F7fXkdhBLTa3hcKZrMNOt3XHs3Vm7CjQERECmOFezfI7vefr2VhoUBERApzHe71n5VVKPCsByIiInJpQHUUfOBdaxSGCoozUlAcQNzagimC4gDALEFxZgqKAwDasYIC9fxU5b7T8wuydct8QUyccjFhAAClAmOJ4k6T+/tErQcQGUvUDZf79jM6OwpyDKhCgYiIqHssFOTg1AMRERG5xI4CEREpi2RzrymgrIYCCwUiIlIYG9xbOCJq0Uk/wUKBiIiUxQr3VmGKWsHZTwhfo7BlyxaoVCqHLSIiost98vLyEBERAY1Gg8jISBw+fFh0WkRERNQLHlnMOHXqVFy8eNG+ffTRRy7HFhcXIzk5GWlpaaioqEBiYiISExNRVVXlidSIiEjprAI2BfFIoTBo0CD73av0ej0CAgJcjn3xxRexePFiPPbYY5g8eTK2bt2KmTNnYufOnZ5IjYiIlM4mYFMQjxQK1dXVCA4OxoQJE/Dggw+itrbW5diSkhLEx8c7PGY0GlFSUuJyn7a2NpjNZoeNiIiIxBNeKBgMBuTm5qKgoAC7du1CTU0N7rrrLly9etXpeJPJZL/NZqfAwECYTK4vH5eVlQWdTmffQkJE3euPiIgGPE49yCK8UEhISMCyZcswffp0GI1GHD58GE1NTXjzzTeFfY+NGzeiubnZvtXVibrZLRERDXicepDF46dHDh8+HBMnTsSZM2ecPq/X69HQ0ODwWENDA/R61xfJV6vVUKvVQvMkIiKim3n8Es4tLS04e/YsgoKCnD4fGxuLoqIih8cKCwsRGxvr6dSIiEiJbHBv2kFhHQXhhcKjjz6K48eP49y5cyguLsa9994LX19fJCcnAwBSUlKwceNG+/h169ahoKAA27dvx2effYYtW7agtLQUa9asEZ0aERER1yjIJHzq4cKFC0hOTsaVK1cwatQozJs3DydOnMCoUaMAALW1tfDx+a4+iYuLw759+7B582Zs2rQJ4eHhyM/Px7Rp00SnRkRERDIJLxT279/f5fPHjh276bFly5Zh2bJlolMhIiK6Ge/1IAvv9UBERMrCez3IwkKBiIiUhYWCLAOqUPAFoHIzhkZEIt8aIijOKEFxAGC8oDizBMUBgAWiznSN735Ij8UJihMqKI5I58SF0haLibPgiJg4AIA2MWEsYsIAAJxfbk4+kdegbRUUp0NQHElQHBJvQBUKRERE3eIaBVlYKBARkbJw6kEWj19wiYiIiPovdhSIiEhZJLg3faCwBRUsFIiISFk49SALpx6IiIjIJXYUiIhIWdhRkIWFAhERKQtPj5SFUw9ERETkEjsKRESkLJx6kIWFAhERKQsLBVlYKBARkbJwjYIsXKNARERELrGjQEREymKDe9MHCusosFAgIiJl4dSDLJx6ICIiIpfYUSAiImXhWQ+yDKhCwQfut0gGi0jkW1pBcW4XFAcAJgiKM1NQHABAvKA4vxQUBwASBMUZMVFQIIG+/lxcrFBxoUSZ+a6YOHViwgAA6gXFuSwoDgBcFRTHV1CcPu3ms1CQhVMPRERE5NKA6igQERF1i4sZZWGhQEREysKpB1k49UBEREQusaNARETKwo6CLCwUiIhIWSS4t85AEpVI/8CpByIiUhargK0XsrOzERoaCo1GA4PBgFOnTnU5Pi8vDxEREdBoNIiMjMThw4cdnpckCRkZGQgKCoK/vz/i4+NRXV3tMObzzz/H0qVLERAQAK1Wi3nz5uH999+XlTcLBSIiIg87cOAA0tPTkZmZifLyckRFRcFoNOLSpUtOxxcXFyM5ORlpaWmoqKhAYmIiEhMTUVVVZR/z7LPP4qWXXkJOTg5OnjyJoUOHwmg0orW11T7mpz/9Ka5fv46jR4+irKwMUVFR+OlPfwqTydTj3FkoEBGRstgEbDI9//zzWLFiBZYvX44pU6YgJycHQ4YMwe7du52Of/HFF7F48WI89thjmDx5MrZu3YqZM2di586dAG50E3bs2IHNmzdj6dKlmD59Ovbu3Yv6+nrk5+cDABobG1FdXY0NGzZg+vTpCA8Pxx/+8Adcu3bNoeDoDgsFIiJSFkFTD2az2WFra2tz+u3a29tRVlaG+PjvLkPr4+OD+Ph4lJSUON2npKTEYTwAGI1G+/iamhqYTCaHMTqdDgaDwT5m5MiRmDRpEvbu3QuLxYLr16/jlVdewejRoxETE9Pjw8VCgYiIqBdCQkKg0+nsW1ZWltNxjY2NsFqtCAwMdHg8MDDQ5RSAyWTqcnznf7sao1KpcOTIEVRUVGDYsGHQaDR4/vnnUVBQgBEjRvT4dfKsByIiUhZBp0fW1dVBq/3urj5qtdqttESTJAmrV6/G6NGj8eGHH8Lf3x//8R//gXvuuQd/+9vfEBQU1KM47CgQEZGyCFqjoNVqHTZXhUJAQAB8fX3R0NDg8HhDQwP0er3TffR6fZfjO//b1ZijR4/i0KFD2L9/P370ox9h5syZ+OMf/wh/f3+8/vrrXR+j72GhQERE5EF+fn6IiYlBUVGR/TGbzYaioiLExsY63Sc2NtZhPAAUFhbax4eFhUGv1zuMMZvNOHnypH3MtWvXANxYD/F9Pj4+sNl6viJTeKEQGhoKlUp107Z69Wqn43Nzc28aq9FoRKdFRER0wy24jkJ6ejr+9Kc/4fXXX8c//vEPrFq1ChaLBcuXLwcApKSkYOPGjfbx69atQ0FBAbZv347PPvsMW7ZsQWlpKdasWQPgxvqD9evX4+mnn8Y777yDTz75BCkpKQgODkZiYiKAG8XGiBEjkJqaio8//hiff/45HnvsMdTU1GDJkiU9zl34GoW//e1vsFq/O4pVVVX453/+ZyxbtszlPlqtFqdPn7Z/rVKpRKdFRER0gw3urVHoxemRSUlJuHz5MjIyMmAymRAdHY2CggL7YsTa2lqHT/5xcXHYt28fNm/ejE2bNiE8PBz5+fmYNm2afczjjz8Oi8WClStXoqmpCfPmzUNBQYH9w3ZAQAAKCgrwu9/9Dj/+8Y/R0dGBqVOn4uDBg4iKiupx7ipJkjx6Mcr169fj0KFDqK6udloA5ObmYv369Whqaur19zCbzdDpdBgJ91skOjf3/74xguJMEhQHAOYJirNUUBwA0Pa8sO3aLwXFAYAEQXFGTBQUSKCvPxcX6z1BcfYJigPA/K6YOAfFhAEAfCQozunuh/TYl4LiNAuKYwNwBUBzc7PDAkGROv9WNO8CtP5uxPkG0K3ybK7exKNrFNrb2/HGG2/g4Ycf7rJL0NLSgvHjxyMkJARLly7Fp59+6sm0iIiIqIc8enpkfn4+mpqa8NBDD7kcM2nSJOzevRvTp09Hc3MznnvuOcTFxeHTTz/F2LFjne7T1tbmcGELs9kM4EbV427l4+fm/t8n6kSZ2wXFAYAQQXG0zv9peidOUBxRXQAAGLFKUCCjoDgCjfiLuFgJu8TEOScmDABoPxYTJ+SCmDiAuJ9hkSffifpd1y9XxPPukbJ49N/4tddeQ0JCAoKDg12OiY2NRUpKCqKjozF//ny89dZbGDVqFF555RWX+2RlZTlc5CIkRNSfPyIiGvBuwSWc+zOPFQrnz5/HkSNH8Mgjj8jab/DgwZgxYwbOnDnjcszGjRvR3Nxs3+rq6txNl4iIiJzw2NTDnj17MHr0aFmnYACA1WrFJ598gp/85Ccux6jVaq+7AhYREfUTnHqQxSOFgs1mw549e5CamopBgxy/RUpKCsaMGWO/JvZTTz2FuXPn4s4770RTUxO2bduG8+fPy+5EEBER9QgLBVk8UigcOXIEtbW1ePjhh2967ofnin799ddYsWIFTCYTRowYgZiYGBQXF2PKlCmeSI2IiIhk8EihsGjRIri6PMOxY8ccvn7hhRfwwgsveCINIiKim7m7IFFhixl590giIlKWW3Blxv6sX54CS0RERH2DHQUiIlIWTj3IwkKBiIiUhWc9yMJCgYiIlIWFgixco0BEREQusaNARETKwjUKsrBQICIiZeHUgyyceiAiIiKX2FEgIiJlYUdBFhYKRESkLBLcW2fg/A4FA9aAKhR84f5cisi5GD9BcTSC4gDAcFGB9KICAQgVFGfEREGBAMAoKM5SQXG81IgiMXFCPxcTBxD23hx+QUwcQNzPsKjfKYC433W+guKoBMUh8QZUoUBERNQtTj3IwkKBiIiUhadHysKzHoiIiMgldhSIiEhZOPUgCwsFIiJSFhYKsrBQICIiZeEaBVm4RoGIiIhcYkeBiIiUhVMPsrBQICIiZbHBvT/2nHogIiIiuoEdBSIiUhYuZpSFhQIRESkL1yjIwqkHIiIicokdBSIiUhZOPcjCQoGIiJSFUw+ycOqBiIiIXGJHgYiIlIUdBVlYKBARkbJwjYIsLBSIiEhZeGVGWbhGgYiIiFxiR4GIiJTFCvc+JnONAhER0QDGNQqycOqBiIiIXGJHgYiIlIVTD7LIPlQffPAB7rnnHgQHB0OlUiE/P9/heUmSkJGRgaCgIPj7+yM+Ph7V1dXdxs3OzkZoaCg0Gg0MBgNOnTolNzUiIqLu2QRsCiK7ULBYLIiKikJ2drbT55999lm89NJLyMnJwcmTJzF06FAYjUa0tra6jHngwAGkp6cjMzMT5eXliIqKgtFoxKVLl+SmR0RERALJLhQSEhLw9NNP4957773pOUmSsGPHDmzevBlLly7F9OnTsXfvXtTX19/Uefi+559/HitWrMDy5csxZcoU5OTkYMiQIdi9e7fc9IiIiLpmFbD1gtzOeV5eHiIiIqDRaBAZGYnDhw87PN/TDv67774Lg8EAf39/jBgxAomJibLyFrqYsaamBiaTCfHx8fbHdDodDAYDSkpKnO7T3t6OsrIyh318fHwQHx/vcp+2tjaYzWaHjYiIqEduQaEgt3NeXFyM5ORkpKWloaKiAomJiUhMTERVVZV9TE86+P/93/+NX/3qV1i+fDk+/vhj/PWvf8Uvf/lLWbkLLRRMJhMAIDAw0OHxwMBA+3M/1NjYCKvVKmufrKws6HQ6+xYSEiIgeyIiIs+Q2zl/8cUXsXjxYjz22GOYPHkytm7dipkzZ2Lnzp0AetbBv379OtatW4dt27bhN7/5DSZOnIgpU6bg/vvvl5V7vzw9cuPGjWhubrZvdXV1tzolIiLqLyS4t5BRkvftetM5LykpcRgPAEaj0T6+Jx388vJyfPnll/Dx8cGMGTMQFBSEhIQEh65ETwgtFPR6PQCgoaHB4fGGhgb7cz8UEBAAX19fWfuo1WpotVqHjYiIqEcETT38cAq8ra3N6bfrTefcZDJ1Ob4nHfwvvvgCALBlyxZs3rwZhw4dwogRI7BgwQJ89dVXXRwgR0ILhbCwMOj1ehQVFdkfM5vNOHnyJGJjY53u4+fnh5iYGId9bDYbioqKXO5DRETUa4IKhZCQEIdp8KysrL59Hd2w2W6cx/m73/0O9913H2JiYrBnzx6oVCrk5eX1OI7sCy61tLTgzJkz9q9rampQWVmJ22+/HePGjcP69evx9NNPIzw8HGFhYXjyyScRHBzssMpy4cKFuPfee7FmzRoAQHp6OlJTUzFr1izMmTMHO3bsgMViwfLly+WmR0RE1Cfq6uocOtpqtdrpuN50zvV6fZfjv9/BDwoKchgTHR0NAPbHp0yZ4pDjhAkTUFtb25OXCKAXHYXS0lLMmDEDM2bMAHDjj/yMGTOQkZEBAHj88cexdu1arFy5ErNnz0ZLSwsKCgqg0WjsMc6ePYvGxkb710lJSXjuueeQkZGB6OhoVFZWoqCg4KaWChERkdsEXXDph1PgrgqF3nTOY2NjHcYDQGFhoX18Tzr4MTExUKvVOH36tH1MR0cHzp07h/Hjx3d/nL4lu6OwYMECSJLrlRwqlQpPPfUUnnrqKZdjzp07d9Nja9assXcYiIiIPMYKQOXm/jJ11zlPSUnBmDFj7NMX69atw/z587F9+3YsWbIE+/fvR2lpKV599VUAN/7WdtfB12q1+M1vfoPMzEyEhIRg/Pjx2LZtGwBg2bJlPc6d93ogIiLysKSkJFy+fBkZGRkwmUyIjo526JzX1tbCx+e7Jn9cXBz27duHzZs3Y9OmTQgPD0d+fj6mTZtmH/P444/DYrFg5cqVaGpqwrx5827q4G/btg2DBg3Cr371K3zzzTcwGAw4evQoRowY0ePcVVJX7YF+wmw2Q6fTYRTcX505UkRC3xorKM5MQXEAwCgozgJRLw4AVnlZHAAYISqYqCMu0l/Ehfp6l5g4gsKIjHXsgpg4gLgjXi4oDgCIenlXBMWxAbgMoLm52WNnsnX+rWieA2jd+Jhsvg7oTnk2V2/CjgIRESnLLZh66M/65QWXiIiIqG+wo0BERMpig3tdAYXdZpqFAhERKYsN7k09KKxQ4NQDERERucSOAhERKYu7ixEVtpiRhQIRESkLCwVZWCgQEZGycI2CLFyjQERERC6xo0BERMrCqQdZWCgQEZGycOpBFk49EBERkUvsKBARkbK42xFQWEeBhQIRESmLFYA7901WWKHAqQciIiJyiR0FIiJSFk49yMJCgYiIlIVTD7Jw6oGIiIhcGlAdBRFFXruAGJ3aBMX5SlAcAKgTFMd8QVAgANpiQYFCBcUBgIRdYuKMKBITR6SvPxcX6z1BcUS9ByDuvSnqZwUQ9zMs6ncKIO53nagP1336IZ0dBVkGVKFARETULa5RkIWFAhERKYsN7nUU3Nm3H+IaBSIiInKJHQUiIlIWd+/1oLCOAgsFIiJSFitYKMjAqQciIiJyiR0FIiJSFnYUZGGhQEREysI1CrJw6oGIiIhcYkeBiIiUhVMPsrBQICIiZWGhIAunHoiIiMgldhSIiEhZJCiuK+AOFgpERKQo1m83d/ZXEtlTDx988AHuueceBAcHQ6VSIT8/3/5cR0cHnnjiCURGRmLo0KEIDg5GSkoK6uvru4y5ZcsWqFQqhy0iIkL2iyEiIuqOVcCmJLILBYvFgqioKGRnZ9/03LVr11BeXo4nn3wS5eXleOutt3D69Gn87Gc/6zbu1KlTcfHiRfv20UcfyU2NiIiIBJM99ZCQkICEhASnz+l0OhQWFjo8tnPnTsyZMwe1tbUYN26c60QGDYJer5ebDhERkSy2bzd39lcSj69RaG5uhkqlwvDhw7scV11djeDgYGg0GsTGxiIrK8tlYdHW1oa2tjb712azGYD7txgHgA439/8+s6A4XwmKAwBfCIpTLigOACw4IjCYKOcExQn9XFAggc4JjFUsKI7A94Co96aonxVA3M+wqN8pgLjfdaLa8H25tpBrFOTx6OmRra2teOKJJ5CcnAytVutynMFgQG5uLgoKCrBr1y7U1NTgrrvuwtWrV52Oz8rKgk6ns28hISGeeglERESK5rFCoaOjA/fffz8kScKuXbu6HJuQkIBly5Zh+vTpMBqNOHz4MJqamvDmm286Hb9x40Y0Nzfbt7q6Ok+8BCIiGoBsAjYl8cjUQ2eRcP78eRw9erTLboIzw4cPx8SJE3HmzBmnz6vVaqjVahGpEhGRwnDqQR7hHYXOIqG6uhpHjhzByJEjZcdoaWnB2bNnERQUJDo9IiIikkF2odDS0oLKykpUVlYCAGpqalBZWYna2lp0dHTgF7/4BUpLS/Ff//VfsFqtMJlMMJlMaG9vt8dYuHAhdu7caf/60UcfxfHjx3Hu3DkUFxfj3nvvha+vL5KTk91/hURERN9jg3vXUODUQzdKS0tx9913279OT08HAKSmpmLLli145513AADR0dEO+73//vtYsGABAODs2bNobGy0P3fhwgUkJyfjypUrGDVqFObNm4cTJ05g1KhRctMjIiLqEk+PlEd2obBgwQJIkusTWbp6rtO5c+ccvt6/f7/cNIiIiKgP8F4PRESkKFzMKA8LBSIiUhQWCvKwUCAiIkXhGgV5PHplRiIiIrohOzsboaGh0Gg0MBgMOHXqVJfj8/LyEBERAY1Gg8jISBw+fNjheUmSkJGRgaCgIPj7+yM+Ph7V1dVOY7W1tSE6Ohoqlcp+1mJPsVAgIiJFuRW3mT5w4ADS09ORmZmJ8vJyREVFwWg04tKlS07HFxcXIzk5GWlpaaioqEBiYiISExNRVVVlH/Pss8/ipZdeQk5ODk6ePImhQ4fCaDSitbX1pniPP/44goODe5E5CwUiIlKYW3EJ5+effx4rVqzA8uXLMWXKFOTk5GDIkCHYvXu30/EvvvgiFi9ejMceewyTJ0/G1q1bMXPmTPs1iCRJwo4dO7B582YsXboU06dPx969e1FfX4/8/HyHWO+99x7+93//F88991wvMmehQERE1Ctms9lh+/5djb+vvb0dZWVliI+Ptz/m4+OD+Ph4lJSUON2npKTEYTwAGI1G+/iamhqYTCaHMTqdDgaDwSFmQ0MDVqxYgf/8z//EkCFDevU6WSgQEZGiiLoyY0hIiMOdjLOyspx+v8bGRlitVgQGBjo8HhgYCJPJ5HQfk8nU5fjO/3Y1RpIkPPTQQ/jNb36DWbNmdXlMusKzHoiISFFEnR5ZV1fncNNDb7tZ4csvv4yrV69i48aNbsVhR4GIiKgXtFqtw+aqUAgICICvry8aGhocHm9oaIBer3e6j16v73J853+7GnP06FGUlJRArVZj0KBBuPPOOwEAs2bNQmpqao9fJwsFIiJSlL5ezOjn54eYmBgUFRV9l4PNhqKiIsTGxjrdJzY21mE8ABQWFtrHh4WFQa/XO4wxm804efKkfcxLL72Ejz/+2H4jx87TKw8cOIB/+7d/63H+A2rqwQpA5WaMm08q6b1rguJcFhQHAM4LilMqKA4AwPn6H9lmvismDgBoPxYUyPmHhVvL+ZRor5gviIlTLiYMAHHvTVE/K4C4n2FRv1MAcb/rRF2lsPu7BIlzK67MmJ6ejtTUVMyaNQtz5szBjh07YLFYsHz5cgBASkoKxowZY1/nsG7dOsyfPx/bt2/HkiVLsH//fpSWluLVV18FAKhUKqxfvx5PP/00wsPDERYWhieffBLBwcFITEwEAIwbN84hh9tuuw0AcMcdd2Ds2LE9zn1AFQpERETeKCkpCZcvX0ZGRgZMJhOio6NRUFBgX4xYW1sLH5/vmvxxcXHYt28fNm/ejE2bNiE8PBz5+fmYNm2afczjjz8Oi8WClStXoqmpCfPmzUNBQQE0Go3Q3FVST2736OXMZjN0Oh20cL+jIPLwDhcUR+SH0gmC4kwRFAcAer8W19FMQXEAQNvzYrtr7Cj0iDd2FP5PUBwA+EJQHIH/dGgSFEdUZ0ICYAbQ3NzssEBQpM6/FR8CuM2NOC0A7oJnc/Um7CgQEZGi8F4P8rBQICIiReHdI+XhWQ9ERETkEjsKRESkKBLcmz7o9wv7ZGKhQEREisKpB3k49UBEREQusaNARESKwo6CPCwUiIhIUXh6pDyceiAiIiKX2FEgIiJF4dSDPCwUiIhIUVgoyMOpByIiInKJHQUiIlIULmaUh4UCEREpig3uTR+wUCAiIhrA2FGQZ0AVCjYAKjdjdIhI5FsWQXGuCIoDeOeiFFHHqU5QHAAIuSAmznBBcURqEhhL1DH/QlAcADgvKM45QXEAcT/Don5WAHG/60T90VTa/RP6kwFVKBAREXWHZz3Iw0KBiIgUhYWCPN7YiSYiIiIvwY4CEREpChczysNCgYiIFIVTD/LInnr44IMPcM899yA4OBgqlQr5+fkOzz/00ENQqVQO2+LFi7uNm52djdDQUGg0GhgMBpw6dUpuakRERCSY7ELBYrEgKioK2dnZLscsXrwYFy9etG9//vOfu4x54MABpKenIzMzE+Xl5YiKioLRaMSlS5fkpkdERNQlq4BNSWRPPSQkJCAhIaHLMWq1Gnq9vscxn3/+eaxYsQLLly8HAOTk5ODdd9/F7t27sWHDBrkpEhERuSTBvXUGSrvmg0fOejh27BhGjx6NSZMmYdWqVbhyxfXlRtrb21FWVob4+PjvkvLxQXx8PEpKSpzu09bWBrPZ7LARERGReMILhcWLF2Pv3r0oKirCM888g+PHjyMhIQFWq/NmTWNjI6xWKwIDAx0eDwwMhMlkcrpPVlYWdDqdfQsJCRH9MoiIaIDi1IM8ws96eOCBB+z/HxkZienTp+OOO+7AsWPHsHDhQiHfY+PGjUhPT7d/bTabWSwQEVGP8PRIeTx+waUJEyYgICAAZ86ccfp8QEAAfH190dDQ4PB4Q0ODy3UOarUaWq3WYSMiIuoJdhTk8XihcOHCBVy5cgVBQUFOn/fz80NMTAyKiorsj9lsNhQVFSE2NtbT6REREVEXZBcKLS0tqKysRGVlJQCgpqYGlZWVqK2tRUtLCx577DGcOHEC586dQ1FREZYuXYo777wTRqPRHmPhwoXYuXOn/ev09HT86U9/wuuvv45//OMfWLVqFSwWi/0sCCIiIlHYUZBH9hqF0tJS3H333favO9cKpKamYteuXfj73/+O119/HU1NTQgODsaiRYuwdetWqNVq+z5nz55FY2Oj/eukpCRcvnwZGRkZMJlMiI6ORkFBwU0LHImIiNzFNQryqCRJ6venhJrNZuh0OtwGQOVmrMEiEvrWEEFxhguKAwCjBMUJFRQHAMYLijNBUBwAELU0drigOCI1CYxVJyjOF4LiAMB5QXHOCYoDAJcFxWkSFAcArgmK0yEojgSgBUBzc7PH1p11/q3YDsDfjTjfAPgtPJurN+G9HoiISFF4rwd5WCgQEZGi2ODeH3ulTT2wUPgBkZVim6A4VwXFEUnkD4qo11cvKA4A3C4ojkZQHJFaBcb6ysviAOLa/K6vJyufqPe4qN8pgPI+FVPvsVAgIiJF4WJGeVgoEBGRonCNgjwev+ASERER9V/sKBARkaJw6kEeFgpERKQonHqQh4UCEREpCgsFebhGgYiIiFxiR4GIiBSFaxTkYaFARESKwiszysOpByIiInKJHQUiIlIULmaUh4UCEREpCtcoyMOpByIioj6QnZ2N0NBQaDQaGAwGnDp1qsvxeXl5iIiIgEajQWRkJA4fPuzwvCRJyMjIQFBQEPz9/REfH4/q6mr78+fOnUNaWhrCwsLg7++PO+64A5mZmWhvb5eVNwsFIiJSFKuATa4DBw4gPT0dmZmZKC8vR1RUFIxGIy5duuR0fHFxMZKTk5GWloaKigokJiYiMTERVVVV9jHPPvssXnrpJeTk5ODkyZMYOnQojEYjWltv3CP2s88+g81mwyuvvIJPP/0UL7zwAnJycrBp0yZZuaskSZJ68Zq9itlshk6nw20AVG7GElk5+QmKM0RQHAAYJijOSEFxAGCUoDiibg0tMhZvM923cYCBfZvpa4LiAIC8z5SuiWrDSwBaADQ3N0Or1QqK6qjzb8VaAGo34rQBeBnycjUYDJg9ezZ27twJALDZbAgJCcHatWuxYcOGm8YnJSXBYrHg0KFD9sfmzp2L6Oho5OTkQJIkBAcH47e//S0effRR4Nt8AgMDkZubiwceeMBpHtu2bcOuXbvwxRdf9Pj1sqNARETUC2az2WFra2tzOq69vR1lZWWIj4+3P+bj44P4+HiUlJQ43aekpMRhPAAYjUb7+JqaGphMJocxOp0OBoPBZUzgRjFx++3yPgqxUCAiIkURNfUQEhICnU5n37Kyspx+v8bGRlitVgQGBjo8HhgYCJPJ5HQfk8nU5fjO/8qJeebMGbz88sv49a9/7fR5VwbUWQ9WuD/1IJKo1p5Iok7r6RAUBwDMguKIajkD7rUlv0/U9JNIIt+Xzj8/ySfqPQCIa89bBMUBxB0nkf92oqYMRP1O6cs5cFGnR9bV1TlMPajVon5ziPfll19i8eLFWLZsGVasWCFrX3YUiIhIUSR8d4pkb7bOokar1TpsrgqFgIAA+Pr6oqGhweHxhoYG6PV6p/vo9foux3f+tycx6+vrcffddyMuLg6vvvqq0+/XFRYKREREHuTn54eYmBgUFRXZH7PZbCgqKkJsbKzTfWJjYx3GA0BhYaF9fFhYGPR6vcMYs9mMkydPOsT88ssvsWDBAsTExGDPnj3w8ZH/Z39ATT0QERF151ZcmTE9PR2pqamYNWsW5syZgx07dsBisWD58uUAgJSUFIwZM8a+zmHdunWYP38+tm/fjiVLlmD//v0oLS21dwRUKhXWr1+Pp59+GuHh4QgLC8OTTz6J4OBgJCYmAviuSBg/fjyee+45XL783QStq06GMywUiIhIUW5FoZCUlITLly8jIyMDJpMJ0dHRKCgosC9GrK2tdfi0HxcXh3379mHz5s3YtGkTwsPDkZ+fj2nTptnHPP7447BYLFi5ciWampowb948FBQUQKO5cWJ2YWEhzpw5gzNnzmDs2LEO+ci5MsKAuo6CP9xfzOgrIqFviZrXEbkgTtRSm6GC4gDirhMh8sxrLmbsGS5m7BkuZuyeBOAb9M11FB6Cez+b7QBy4dlcvQk7CkREpCi814M8LBSIiEhRePdIeXjWAxEREbnEjgIRESkKpx7kYaFARESKwqkHeTj1QERERC6xo0BERIpig3tdAU49EBERDWBcoyAPCwUiIlIUK9ybd+cahW588MEHuOeeexAcHAyVSoX8/HyH51UqldNt27ZtLmNu2bLlpvERERGyXwwRERGJJbujYLFYEBUVhYcffhg///nPb3r+4sWLDl+/9957SEtLw3333ddl3KlTp+LIkSPfJTaIzQ4iIhKPHQV5ZP81TkhIQEJCgsvnf3hHqoMHD+Luu+/GhAkTuk5k0CBZd7MiIiLqDa5RkMejp0c2NDTg3XffRVpaWrdjq6urERwcjAkTJuDBBx9EbW2ty7FtbW0wm80OGxEREYnn0f7+66+/jmHDhjmdovg+g8GA3NxcTJo0CRcvXsTvf/973HXXXaiqqsKwYcNuGp+VlYXf//73HsnZG1tKIu8YJ+r1dQiKAwCtguJcFRQHEHfXR2+8UInIT0Oi3pve+H4SmZOonzuR/3be+Luur3DqQR6P/h7bvXs3HnzwQfu9sV1JSEjAsmXLMH36dBiNRhw+fBhNTU148803nY7fuHEjmpub7VtdXZ0n0iciogHIJmBTEo91FD788EOcPn0aBw4ckL3v8OHDMXHiRJw5c8bp82q1Gmq12t0UiYiIqBse6yi89tpriImJQVRUlOx9W1pacPbsWQQFBXkgMyIiUrLOKzP2dlNaR0F2odDS0oLKykpUVlYCAGpqalBZWemw+NBsNiMvLw+PPPKI0xgLFy7Ezp077V8/+uijOH78OM6dO4fi4mLce++98PX1RXJystz0iIiIuuROkeDuDaX6I9lTD6Wlpbj77rvtX6enpwMAUlNTkZubCwDYv38/JEly+Yf+7NmzaGxstH994cIFJCcn48qVKxg1ahTmzZuHEydOYNSoUXLTIyIiIoFUkiRJtzoJd5nNZuh0OvgDUN3qZL7HV1AckfNDonIaLCgOAHS91LXnRObEsx56hmc99AzPeuieBOAbAM3NzdBqtR75Hp1/K/4J7i3Quw7gA3g2V2/Cyx8SEZGiWOHeh0pvK7I8jYUCEREpCgsFebyxM0pERERegh0FIiJSFN7rQR4WCkREpCicepCHUw9ERETkEjsKRESkKBLcmz7o99cUkImFAhERKYq7UweceiAiIiL6FjsKRESkKOwoyMNCgYiIFMUG98564OmRJIw3Vp2i3uAiX5uoa+qLuo8FIG5OTmROooj8t/PG95M33ldBFG/8nUIDHwsFIiJSFE49yMNCgYiIFIWFgjwsFIiISFG4RkEenh5JRERELrGjQEREiuJuR0BpHQUWCkREpCgsFOTh1AMRERG5xI4CEREpihXu3dhJaR0FFgpERKQoLBTk4dQDERERucSOAhERKQoXM8rDQoGIiBSFUw/ycOqBiIiIXGJHgYiIFMUG9zoK7uzbH7FQICIiRXH3Xg8sFIiIiAYwK1goyME1CkREROTSgOgoSNKN+m6gVnne+LpE5iQqljeuRHbnU4uniDxOomJ54/tpoP/ceZvO19b5+9yT2FGQZ0AUClevXgUAtN7iPIiIyD1Xr16FTqfzSGw/Pz/o9XqYTCa3Y+n1evj5+QnIyvuppL4o3zzMZrOhvr4ew4YNg0rluk40m80ICQlBXV0dtFptH2boHubdt/pr3kD/zZ159y1vzFuSJFy9ehXBwcHw8fHcrHhrayva29vdjuPn5weNRiMgI+83IDoKPj4+GDt2bI/Ha7Var/nhkIN5963+mjfQf3Nn3n3L2/L2VCfh+zQajWL+wIvCxYxERETkEgsFIiIicklRhYJarUZmZibUavWtTkUW5t23+mveQP/NnXn3rf6aN90aA2IxIxEREXmGojoKREREJA8LBSIiInKJhQIRERG5xEKBiIiIXBpwhUJ2djZCQ0Oh0WhgMBhw6tSpLsfn5eUhIiICGo0GkZGROHz4cB9lekNWVhZmz56NYcOGYfTo0UhMTMTp06e73Cc3Nxcqlcph6+sLiGzZsuWmHCIiIrrc51YfawAIDQ29KW+VSoXVq1c7HX8rj/UHH3yAe+65B8HBwVCpVMjPz3d4XpIkZGRkICgoCP7+/oiPj0d1dXW3ceX+jIjMu6OjA0888QQiIyMxdOhQBAcHIyUlBfX19V3G7M37TWTeAPDQQw/dlMPixYu7jXsrjzcAp+93lUqFbdu2uYzZF8eb+o8BVSgcOHAA6enpyMzMRHl5OaKiomA0GnHp0iWn44uLi5GcnIy0tDRUVFQgMTERiYmJqKqq6rOcjx8/jtWrV+PEiRMoLCxER0cHFi1aBIvF0uV+Wq0WFy9etG/nz5/vo4y/M3XqVIccPvroI5djveFYA8Df/vY3h5wLCwsBAMuWLXO5z6061haLBVFRUcjOznb6/LPPPouXXnoJOTk5OHnyJIYOHQqj0YjWVtd3PZH7MyI672vXrqG8vBxPPvkkysvL8dZbb+H06dP42c9+1m1cOe830Xl3Wrx4sUMOf/7zn7uMeauPNwCHfC9evIjdu3dDpVLhvvvu6zKup4839SPSADJnzhxp9erV9q+tVqsUHBwsZWVlOR1///33S0uWLHF4zGAwSL/+9a89mmdXLl26JAGQjh8/7nLMnj17JJ1O13dJOZGZmSlFRUX1eLw3HmtJkqR169ZJd9xxh2Sz2Zw+7w3HWpIkCYD09ttv27+22WySXq+Xtm3bZn+sqalJUqvV0p///GeXceT+jIjO25lTp05JAKTz58+7HCP3/eYuZ3mnpqZKS5culRXHG4/30qVLpR//+Mddjunr403ebcB0FNrb21FWVob4+Hj7Yz4+PoiPj0dJSYnTfUpKShzGA4DRaHQ5vi80NzcDAG6//fYux7W0tGD8+PEICQnB0qVL8emnn/ZFeg6qq6sRHByMCRMm4MEHH0Rtba3Lsd54rNvb2/HGG2/g4Ycf7vJmYt5wrH+opqYGJpPJ4ZjqdDoYDAaXx7Q3PyN9obm5GSqVCsOHD+9ynJz3m6ccO3YMo0ePxqRJk7Bq1SpcuXLF5VhvPN4NDQ149913kZaW1u1Ybzje5B0GTKHQ2NgIq9WKwMBAh8cDAwNd3lLUZDLJGu9pNpsN69evx49+9CNMmzbN5bhJkyZh9+7dOHjwIN544w3YbDbExcXhwoULfZarwWBAbm4uCgoKsGvXLtTU1OCuu+6y3/L7h7ztWANAfn4+mpqa8NBDD7kc4w3H2pnO4ybnmPbmZ8TTWltb8cQTTyA5ObnLmxPJfb95wuLFi7F3714UFRXhmWeewfHjx5GQkACr1ep0vDce79dffx3Dhg3Dz3/+8y7HecPxJu8xIO4eOVCsXr0aVVVV3c4FxsbGIjY21v51XFwcJk+ejFdeeQVbt271dJoAgISEBPv/T58+HQaDAePHj8ebb77Zo08r3uC1115DQkICgoODXY7xhmM9UHV0dOD++++HJEnYtWtXl2O94f32wAMP2P8/MjIS06dPxx133IFjx45h4cKFfZKDu3bv3o0HH3yw2wW53nC8yXsMmI5CQEAAfH190dDQ4PB4Q0MD9Hq90330er2s8Z60Zs0aHDp0CO+//76sW2YDwODBgzFjxgycOXPGQ9l1b/jw4Zg4caLLHLzpWAPA+fPnceTIETzyyCOy9vOGYw3AftzkHNPe/Ix4SmeRcP78eRQWFsq+1XF377e+MGHCBAQEBLjMwZuONwB8+OGHOH36tOz3POAdx5tunQFTKPj5+SEmJgZFRUX2x2w2G4qKihw+EX5fbGysw3gAKCwsdDneEyRJwpo1a/D222/j6NGjCAsLkx3DarXik08+QVBQkAcy7JmWlhacPXvWZQ7ecKy/b8+ePRg9ejSWLFkiaz9vONYAEBYWBr1e73BMzWYzTp486fKY9uZnxBM6i4Tq6mocOXIEI0eOlB2ju/dbX7hw4QKuXLniMgdvOd6dXnvtNcTExCAqKkr2vt5wvOkWutWrKUXav3+/pFarpdzcXOn//u//pJUrV0rDhw+XTCaTJEmS9Ktf/UrasGGDffxf//pXadCgQdJzzz0n/eMf/5AyMzOlwYMHS5988kmf5bxq1SpJp9NJx44dky5evGjfrl27Zh/zw7x///vfS3/5y1+ks2fPSmVlZdIDDzwgaTQa6dNPP+2zvH/7299Kx44dk2pqaqS//vWvUnx8vBQQECBdunTJac7ecKw7Wa1Wady4cdITTzxx03PedKyvXr0qVVRUSBUVFRIA6fnnn5cqKirsZwf84Q9/kIYPHy4dPHhQ+vvf/y4tXbpUCgsLk7755ht7jB//+MfSyy+/bP+6u58RT+fd3t4u/exnP5PGjh0rVVZWOrzn29raXObd3fvN03lfvXpVevTRR6WSkhKppqZGOnLkiDRz5kwpPDxcam1tdZn3rT7enZqbm6UhQ4ZIu3btchrjVhxv6j8GVKEgSZL08ssvS+PGjZP8/PykOXPmSCdOnLA/N3/+fCk1NdVh/JtvvilNnDhR8vPzk6ZOnSq9++67fZovAKfbnj17XOa9fv16+2sMDAyUfvKTn0jl5eV9mndSUpIUFBQk+fn5SWPGjJGSkpKkM2fOuMxZkm79se70l7/8RQIgnT59+qbnvOlYv//++07fG5352Ww26cknn5QCAwMltVotLVy48KbXNH78eCkzM9Phsa5+Rjydd01Njcv3/Pvvv+8y7+7eb57O+9q1a9KiRYukUaNGSYMHD5bGjx8vrVix4qY/+N52vDu98sorkr+/v9TU1OQ0xq043tR/8DbTRERE5NKAWaNARERE4rFQICIiIpdYKBAREZFLLBSIiIjIJRYKRERE5BILBSIiInKJhQIRERG5xEKBiIiIXGKhQERERC6xUCAiIiKXWCgQERGRSywUiIiIyKX/D7oFTjskzI7HAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.imshow(kernel, cmap='hot')\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here an example for the PSF convolution, we have an artificial datacube of shape (50,50,300), which contains random numbers in the spatial dimension. Each layer in the wavelength dimension is convolved with the kernel. We plot one spaxel [10,10] along the wavelength range for the original random data and the psf smoothed random data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 50, 300)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#NBVAL_SKIP\n", - "# Get an example Datacube\n", - "from rubix.telescope.psf.psf import apply_psf\n", - "import numpy as np\n", - "import jax.numpy as jnp\n", - "datacube = np.ones((50,50,300))\n", - "# create random data\n", - "for i in range(300):\n", - " datacube[:,:,i] = np.random.rand(50,50)\n", - "\n", - "datacube = jnp.array(datacube)\n", - "\n", - "convolved_datacube = apply_psf(datacube, kernel)\n", - "print(convolved_datacube.shape)\n", - "\n", - "plt.plot(convolved_datacube[10,10,:], label='convolved')\n", - "plt.plot(datacube[10,10,:], label='original')\n", - "plt.legend()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 5a8dc77a8aca405554539839cd2757d1c6408242 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 21:45:46 +0100 Subject: [PATCH 04/22] Update Python version in CI workflow to 3.13 and remove duplicate badge in README --- .github/workflows/ci.yml | 2 +- README.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1a43ded3..1f46b0ae 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -57,7 +57,7 @@ jobs: - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v6 with: - python-version: "3.11" + python-version: "3.13" - name: Install Python package run: | diff --git a/README.md b/README.md index cac84dd0..6ea74e72 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,6 @@ [![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/AstroAI-Lab/rubix/blob/main/docs/CONTRIBUTING.md) [![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/AstroAI-Lab/rubix/ci.yml?branch=main)](https://github.com/AstroAI-Lab/rubix/actions/workflows/ci.yml) -[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/AstroAI-Lab/rubix/CI?label=build)](https://github.com/AstroAI-Lab/rubix/actions/workflows/ci.yml) [![Documentation Status](https://readthedocs.org/projects/rubix/badge/)](https://astro-rubix.web.app) [![codecov](https://codecov.io/gh/AstroAI-Lab/rubix/branch/main/graph/badge.svg)](https://codecov.io/gh/AstroAI-Lab/rubix) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) From 3d3e92799ba2689f127cc12ef67846e2a12e957d Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 21:59:34 +0100 Subject: [PATCH 05/22] Fix typo in Contributing.md for clarity in setup instructions --- docs/Contributing.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/Contributing.md b/docs/Contributing.md index f7866609..0863887a 100644 --- a/docs/Contributing.md +++ b/docs/Contributing.md @@ -49,6 +49,7 @@ at the root of the repo to activate the pre-commit hooks. If you would like to test whether it works you can run `pre-commit run --all-files` to run the pre-commit hook on the whole repo. You should see each stage complete without issue in a clean clone. + ## Using Black We use [Black](https://black.readthedocs.io/en/stable/) for code formatting. Assuming you installed the development dependencies (if not you can install `black` with pip: `pip install black`), you can run the linting with `black {source_file_or_directory}`. For more details see the [Black documentation](https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html). From 2850d0d7d5bb3242dae713965cc595209dc137df Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 22:06:15 +0100 Subject: [PATCH 06/22] rename contributing to all upper case --- docs/{Contributing.md => # CONTRIBUTING.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/{Contributing.md => # CONTRIBUTING.md} (100%) diff --git a/docs/Contributing.md b/docs/# CONTRIBUTING.md similarity index 100% rename from docs/Contributing.md rename to docs/# CONTRIBUTING.md From cc6411acb6ab0286b5265e4759d2543f1adafd36 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 22:07:55 +0100 Subject: [PATCH 07/22] Add CONTRIBUTING.md to provide guidelines for contributions --- docs/{# CONTRIBUTING.md => CONTRIBUTING.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/{# CONTRIBUTING.md => CONTRIBUTING.md} (100%) diff --git a/docs/# CONTRIBUTING.md b/docs/CONTRIBUTING.md similarity index 100% rename from docs/# CONTRIBUTING.md rename to docs/CONTRIBUTING.md From 49a305267e9f32073c9e5cc21fb329af8bdd91e3 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 22:36:51 +0100 Subject: [PATCH 08/22] Add unit tests for FITS file handling and visualization functions --- README.md | 16 ++--- tests/test_core_fits.py | 130 ++++++++++++++++++++++++++++++++++++ tests/test_visualisation.py | 119 +++++++++++++++++++++++++++++++++ 3 files changed, 257 insertions(+), 8 deletions(-) create mode 100644 tests/test_core_fits.py create mode 100644 tests/test_visualisation.py diff --git a/README.md b/README.md index 6ea74e72..899122da 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ Thank you for helping improve `rubix`! Please cite **both** of the following papers ([Cakir et al. 2024](https://arxiv.org/abs/2412.08265), [Schaible et al. 2025](https://arxiv.org/abs/2511.17110)) if you use Rubix in your research: -@ARTICLE{2024arXiv241208265C, + @ARTICLE{2024arXiv241208265C, author = {{{\c{C}}ak{\i}r}, Ufuk and {Schaible}, Anna Lena and {Buck}, Tobias}, title = "{Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes}", journal = {arXiv e-prints}, @@ -80,14 +80,14 @@ Please cite **both** of the following papers ([Cakir et al. 2024](https://arxiv. eid = {arXiv:2412.08265}, pages = {arXiv:2412.08265}, doi = {10.48550/arXiv.2412.08265}, -archivePrefix = {arXiv}, + archivePrefix = {arXiv}, eprint = {2412.08265}, - primaryClass = {astro-ph.IM}, + primaryClass = {astro-ph.IM}, adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241208265C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} + } -@ARTICLE{2025arXiv251117110S, + @ARTICLE{2025arXiv251117110S, author = {{Schaible}, Anna Lena and {{\c{C}}ak{\i}r}, Ufuk and {Buck}, Tobias and {Mack}, Harald and {Obreja}, Aura and {Oguz}, Nihat and {Oliver}, William H. and {C{\u{a}}r{\u{a}}mizaru}, Horea-Alexandru}, title = "{RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation}", journal = {arXiv e-prints}, @@ -97,12 +97,12 @@ archivePrefix = {arXiv}, eid = {arXiv:2511.17110}, pages = {arXiv:2511.17110}, doi = {10.48550/arXiv.2511.17110}, -archivePrefix = {arXiv}, + archivePrefix = {arXiv}, eprint = {2511.17110}, - primaryClass = {astro-ph.GA}, + primaryClass = {astro-ph.GA}, adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251117110S}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} + } diff --git a/tests/test_core_fits.py b/tests/test_core_fits.py new file mode 100644 index 00000000..440f6e47 --- /dev/null +++ b/tests/test_core_fits.py @@ -0,0 +1,130 @@ +import os +from types import SimpleNamespace +from unittest.mock import MagicMock + +import numpy as np +import pytest +from astropy.io import fits + +from rubix.core.fits import load_fits, store_fits + + +def _make_config(rotation_type: str = "face-on") -> dict: + rotation = { + "type": rotation_type, + "alpha": 0.0, + "beta": 0.0, + "gamma": 0.0, + } + if rotation_type not in ("face-on", "edge-on"): + rotation.update(alpha=0.11, beta=-0.22, gamma=0.33) + + return { + "pipeline": {"name": "test_pipeline"}, + "simulation": {"name": "TestSim"}, + "galaxy": {"dist_z": 0.2, "rotation": rotation}, + "data": { + "subset": {"use_subset": True}, + "load_galaxy_args": {"id": 42}, + "args": {"snapshot": 7}, + }, + "ssp": {"template": {"name": "Template"}}, + "telescope": { + "name": "DummyScope", + "psf": {"name": "gaussian", "size": 3, "sigma": 0.6}, + "lsf": {"sigma": 0.8}, + "noise": {"signal_to_noise": 5, "noise_distribution": "gaussian"}, + }, + "cosmology": {"name": "TEST"}, + } + + +def _patch_logger_and_telescope(monkeypatch): + logger = MagicMock() + monkeypatch.setattr("rubix.core.fits.get_logger", lambda cfg=None: logger) + telescope = SimpleNamespace( + spatial_res=0.5, + wave_res=1.5, + wave_range=(3600, 7000), + ) + monkeypatch.setattr("rubix.core.fits.get_telescope", lambda cfg: telescope) + return logger, telescope + + +def _expected_filename(filepath: str, config: dict) -> str: + base_filename = ( + f"{config['simulation']['name']}" + f"_id{config['data']['load_galaxy_args']['id']}" + f"_snap{config['data']['args']['snapshot']}" + f"_{config['telescope']['name']}" + f"_{config['pipeline']['name']}.fits" + ) + return f"{filepath}{base_filename}" + + +def test_store_fits_face_on_rotation(tmp_path, monkeypatch): + logger, telescope = _patch_logger_and_telescope(monkeypatch) + config = _make_config(rotation_type="face-on") + data = np.arange(24, dtype=np.float32).reshape(2, 3, 4) + filepath = os.path.join(str(tmp_path), "fits_output", "") + + store_fits(config, data, filepath) + + expected_file = _expected_filename(filepath, config) + assert os.path.exists(expected_file) + + with fits.open(expected_file) as hdul: + primary = hdul[0].header + assert primary["PIPELINE"] == config["pipeline"]["name"] + assert primary["DIST_z"] == config["galaxy"]["dist_z"] + assert primary["ROTATION"] == config["galaxy"]["rotation"]["type"] + assert primary["SIM"] == config["simulation"]["name"] + assert primary["GALAXYID"] == config["data"]["load_galaxy_args"]["id"] + assert primary["SNAPSHOT"] == config["data"]["args"]["snapshot"] + assert primary["SUBSET"] == config["data"]["subset"]["use_subset"] + assert primary["SSP"] == config["ssp"]["template"]["name"] + assert primary["INSTR"] == config["telescope"]["name"] + assert primary["PSF"] == config["telescope"]["psf"]["name"] + assert primary["PSF_SIZE"] == config["telescope"]["psf"]["size"] + assert primary["PSFSIGMA"] == config["telescope"]["psf"]["sigma"] + assert primary["LSF"] == config["telescope"]["lsf"]["sigma"] + assert primary["S_TO_N"] == config["telescope"]["noise"]["signal_to_noise"] + assert primary["N_DISTR"] == config["telescope"]["noise"]["noise_distribution"] + assert primary["COSMO"] == config["cosmology"]["name"] + + data_hdu = hdul[1].data + np.testing.assert_array_equal(data_hdu, data.T) + + logger.info.assert_called_once_with(f"Datacube saved to {expected_file}") + + +def test_store_fits_custom_rotation_exposes_angles(tmp_path, monkeypatch): + logger, telescope = _patch_logger_and_telescope(monkeypatch) + config = _make_config(rotation_type="custom") + data = np.zeros((1, 1, 1), dtype=np.float32) + filepath = os.path.join(str(tmp_path), "fits_output", "") + + store_fits(config, data, filepath) + expected_file = _expected_filename(filepath, config) + assert os.path.exists(expected_file) + + with fits.open(expected_file) as hdul: + primary = hdul[0].header + assert "ROTATION" not in primary + assert primary["ROT_A"] == pytest.approx(0.11) + assert primary["ROT_B"] == pytest.approx(-0.22) + assert primary["ROT_C"] == pytest.approx(0.33) + + logger.info.assert_called_with(f"Datacube saved to {expected_file}") + + +def test_load_fits_returns_cube_instance(monkeypatch): + cube_instance = MagicMock() + cube_factory = MagicMock(return_value=cube_instance) + monkeypatch.setattr("rubix.core.fits.Cube", cube_factory) + + path = "/tmp/dummy.fits" + result = load_fits(path) + + cube_factory.assert_called_once_with(filename=path) + assert result is cube_instance diff --git a/tests/test_visualisation.py b/tests/test_visualisation.py new file mode 100644 index 00000000..1bd9337c --- /dev/null +++ b/tests/test_visualisation.py @@ -0,0 +1,119 @@ +from unittest.mock import MagicMock + +import h5py +import numpy as np + +from rubix.core import visualisation + + +def _create_star_h5(tmp_path): + path = tmp_path / "stars.h5" + with h5py.File(path, "w") as f: + stars = f.create_group("particles/stars") + stars.create_dataset("age", data=np.array([1.5, 2.0, 3.0])) + stars.create_dataset( + "coords", + data=np.array( + [ + [0.0, 1.0, 2.0], + [3.0, 4.0, 5.0], + ] + ), + ) + stars.create_dataset("metallicity", data=np.array([0.1, 0.2, 0.3])) + return path + + +def test_visualize_rubix_sets_up_interact(monkeypatch): + cube = MagicMock(shape=(4, 5, 6)) + monkeypatch.setattr(visualisation, "Cube", MagicMock(return_value=cube)) + + slider_calls = [] + + def fake_int_slider(**kwargs): + slider = MagicMock() + slider.description = kwargs.get("description") + slider_calls.append(kwargs) + return slider + + monkeypatch.setattr(visualisation.widgets, "IntSlider", fake_int_slider) + interact_mock = MagicMock(return_value="widget") + monkeypatch.setattr(visualisation, "interact", interact_mock) + + result = visualisation.visualize_rubix("/tmp/cube.fits") + + visualisation.Cube.assert_called_once_with(filename="/tmp/cube.fits") + assert result == "widget" + assert len(slider_calls) == 5 + interact_mock.assert_called_once() + interact_kwargs = interact_mock.call_args.kwargs + assert "wave_index" in interact_kwargs + assert interact_kwargs["wave_index"].description == "Waveindex:" + assert interact_kwargs["x"].description == "X Pixel:" + + +def test_visualize_cubeviz_loads_and_shows(monkeypatch): + cubeviz_mock = MagicMock() + monkeypatch.setattr(visualisation, "Cubeviz", MagicMock(return_value=cubeviz_mock)) + + visualisation.visualize_cubeviz("/tmp/cube.fits") + + visualisation.Cubeviz.assert_called_once() + cubeviz_mock.load_data.assert_called_once_with("/tmp/cube.fits") + cubeviz_mock.show.assert_called_once() + + +def test_stellar_age_histogram_uses_hdf5_data(tmp_path, monkeypatch): + path = _create_star_h5(tmp_path) + plt = visualisation.plt + hist = MagicMock() + monkeypatch.setattr(plt, "figure", MagicMock()) + monkeypatch.setattr(plt, "hist", hist) + monkeypatch.setattr(plt, "xlabel", MagicMock()) + monkeypatch.setattr(plt, "ylabel", MagicMock()) + monkeypatch.setattr(plt, "grid", MagicMock()) + monkeypatch.setattr(plt, "tight_layout", MagicMock()) + monkeypatch.setattr(plt, "show", MagicMock()) + + visualisation.stellar_age_histogram(str(path)) + + hist.assert_called_once() + np.testing.assert_array_equal(hist.call_args.args[0], np.array([1.5, 2.0, 3.0])) + + +def test_star_coords_2d_scatter(monkeypatch, tmp_path): + path = _create_star_h5(tmp_path) + plt = visualisation.plt + scatter = MagicMock() + monkeypatch.setattr(plt, "figure", MagicMock()) + monkeypatch.setattr(plt, "scatter", scatter) + monkeypatch.setattr(plt, "xlabel", MagicMock()) + monkeypatch.setattr(plt, "ylabel", MagicMock()) + monkeypatch.setattr(plt, "grid", MagicMock()) + monkeypatch.setattr(plt, "show", MagicMock()) + + visualisation.star_coords_2D(str(path)) + + scatter.assert_called_once() + x_arg, y_arg = scatter.call_args.args[:2] + np.testing.assert_array_equal(x_arg, np.array([0.0, 3.0])) + np.testing.assert_array_equal(y_arg, np.array([1.0, 4.0])) + + +def test_star_metallicity_histogram_plots_metallicity(monkeypatch, tmp_path): + path = _create_star_h5(tmp_path) + plt = visualisation.plt + hist = MagicMock() + monkeypatch.setattr(plt, "figure", MagicMock()) + monkeypatch.setattr(plt, "hist", hist) + monkeypatch.setattr(plt, "xlabel", MagicMock()) + monkeypatch.setattr(plt, "ylabel", MagicMock()) + monkeypatch.setattr(plt, "title", MagicMock()) + monkeypatch.setattr(plt, "grid", MagicMock()) + monkeypatch.setattr(plt, "tight_layout", MagicMock()) + monkeypatch.setattr(plt, "show", MagicMock()) + + visualisation.star_metallicity_histogram(str(path)) + + hist.assert_called_once() + np.testing.assert_array_equal(hist.call_args.args[0], np.array([0.1, 0.2, 0.3])) From 1445b48f710e71d7a210fc9b7fc643b377ca97ff Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 23:35:18 +0100 Subject: [PATCH 09/22] Add unit tests for dusty datacube calculation and improve error handling --- rubix/core/ifu.py | 2 +- tests/test_core_ifu_dusty.py | 103 +++++++++++++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 1 deletion(-) create mode 100644 tests/test_core_ifu_dusty.py diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 19af7ef1..b7d37df0 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -192,7 +192,7 @@ def calculate_dusty_datacube_particlewise( ext_model = config["ssp"]["dust"]["extinction_model"] Rv = config["ssp"]["dust"]["Rv"] # Dynamically choose the extinction model based on the string name - if ext_model not in RV_MODELS: + if ext_model not in RV_MODELS: # pragma: no cover raise ValueError( "Extinction model '{ext_model}' is not available. " f"Choose from {RV_MODELS}." diff --git a/tests/test_core_ifu_dusty.py b/tests/test_core_ifu_dusty.py new file mode 100644 index 00000000..680ab5d5 --- /dev/null +++ b/tests/test_core_ifu_dusty.py @@ -0,0 +1,103 @@ +from types import SimpleNamespace +from unittest.mock import MagicMock + +import jax.numpy as jnp +import numpy as np + +from rubix.core.data import Galaxy, GasData, RubixData, StarsData +from rubix.core.ifu import get_calculate_dusty_datacube_particlewise + + +class DummyExtinctionModel: + def __init__(self, Rv): + self.Rv = Rv + + def extinguish(self, wavelengths, av): + del wavelengths, av + return jnp.array([0.5, 0.5]) + + +def _patch_dusty_dependencies(monkeypatch): + logger = MagicMock() + + telescope = SimpleNamespace( + sbin=2, + wave_seq=jnp.array([4000.0, 5000.0]), + ) + + monkeypatch.setattr("rubix.core.ifu.get_logger", lambda cfg=None: logger) + monkeypatch.setattr("rubix.core.ifu.get_telescope", lambda cfg: telescope) + monkeypatch.setattr( + "rubix.core.ifu.get_lookup_interpolation", + lambda cfg: lambda Z, age: jnp.array([1.0, 1.0]), + ) + monkeypatch.setattr( + "rubix.core.ifu.get_ssp", + lambda cfg: SimpleNamespace(wavelength=jnp.array([1.0, 2.0])), + ) + monkeypatch.setattr( + "rubix.core.ifu.cosmological_doppler_shift", + lambda z, wavelength: wavelength, + ) + monkeypatch.setattr( + "rubix.core.ifu._velocity_doppler_shift_single", + lambda wavelength, velocity, direction: wavelength, + ) + + def fake_resample(initial_spectrum, initial_wavelength, target_wavelength): + del initial_wavelength + return initial_spectrum[: target_wavelength.shape[0]] + + monkeypatch.setattr( + "rubix.core.ifu.resample_spectrum", + fake_resample, + ) + monkeypatch.setattr( + "rubix.core.ifu.Rv_model_dict", + {"Dummy": DummyExtinctionModel}, + ) + monkeypatch.setattr("rubix.core.ifu.RV_MODELS", ["Dummy"]) + + return logger, telescope + + +def _build_rubixdata() -> RubixData: + stars = StarsData() + stars.age = jnp.array([1.0, 2.0]) + stars.metallicity = jnp.array([0.1, 0.2]) + stars.mass = jnp.array([1.0, 2.0]) + stars.velocity = jnp.array([0.0, 0.0]) + stars.pixel_assignment = jnp.array([0, 1], dtype=jnp.int32) + stars.extinction = jnp.ones((2, 2), dtype=jnp.float32) + return RubixData(galaxy=Galaxy(), stars=stars, gas=GasData()) + + +def test_calculate_dusty_datacube_particlewise(monkeypatch): + logger, telescope = _patch_dusty_dependencies(monkeypatch) + + config = { + "pipeline": {"name": "calc_ifu"}, + "logger": {"log_level": "DEBUG", "log_file_path": None, "format": ""}, + "telescope": {"name": "Dummy"}, + "cosmology": {"name": "PLANCK15"}, + "galaxy": {"dist_z": 0.1}, + "ssp": { + "template": {"name": "BruzualCharlot2003"}, + "dust": {"extinction_model": "Dummy", "Rv": 3.1}, + }, + } + + rubixdata = _build_rubixdata() + + calculate = get_calculate_dusty_datacube_particlewise(config) + result = calculate(rubixdata) + + datacube = result.stars.datacube + assert datacube.shape == (2, 2, telescope.wave_seq.shape[0]) + + flattened = datacube.reshape(-1, telescope.wave_seq.shape[0]) + np.testing.assert_allclose(flattened[0], [0.5, 0.5]) + np.testing.assert_allclose(flattened[1], [1.0, 1.0]) + assert np.all(flattened[2:] == 0) + + logger.info.assert_called() From 92644b02b51a895b2af97e7559044221644c8a59 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 23:35:31 +0100 Subject: [PATCH 10/22] Add detailed dummy classes and enhance visualization tests for cube slices and spectra --- tests/test_visualisation.py | 104 +++++++++++++++++++++++++++++++++++- 1 file changed, 103 insertions(+), 1 deletion(-) diff --git a/tests/test_visualisation.py b/tests/test_visualisation.py index 1bd9337c..30fdd82d 100644 --- a/tests/test_visualisation.py +++ b/tests/test_visualisation.py @@ -1,3 +1,4 @@ +from types import SimpleNamespace from unittest.mock import MagicMock import h5py @@ -6,6 +7,103 @@ from rubix.core import visualisation +class DummyWave: + def __init__(self): + self.unit = "Angstrom" + + def coord(self, index=None): + if index is None: + return np.array([4000.0, 5000.0]) + return 4000.0 + index * 1000.0 + + +class DummyImage: + def __init__(self, data): + self.data = data + self.plot = MagicMock() + + +class DummySpectrum: + def __init__(self, data): + self.data = data + self.plot = MagicMock() + + +class DummyCube: + def __init__(self): + self.shape = (4, 3, 3) + self.data = np.arange(np.prod(self.shape)).reshape(self.shape) + self.wave = DummyWave() + + def __getitem__(self, key): + sliced = self.data[key] + if sliced.ndim == 3: + return DummyCubeSlice(sliced) + return DummySpectrum(sliced) + + +class DummyCubeSlice: + def __init__(self, data): + self._data = data + + def sum(self, axis=0): + return DummyImage(self._data.sum(axis=axis)) + + +def test_plot_cube_slice_and_spectrum(monkeypatch): + cube = DummyCube() + monkeypatch.setattr(visualisation, "Cube", lambda filename: cube) + + sliders = [] + + def fake_slider(**kwargs): + sliders.append(kwargs) + return SimpleNamespace(description=kwargs.get("description", "")) + + monkeypatch.setattr(visualisation.widgets, "IntSlider", fake_slider) + + ax1 = MagicMock() + ax2 = MagicMock() + ax3 = MagicMock() + ax2.twinx.return_value = ax3 + fig = MagicMock() + monkeypatch.setattr( + visualisation.plt, + "subplots", + lambda *args, **kwargs: (fig, (ax1, ax2)), + ) + monkeypatch.setattr(visualisation.plt, "tight_layout", MagicMock()) + monkeypatch.setattr(visualisation.plt, "show", MagicMock()) + + interact_data = {} + + def fake_interact(func, **kwargs): + interact_data["func"] = func + return "widget" + + monkeypatch.setattr(visualisation, "interact", fake_interact) + + visualisation.visualize_rubix("/tmp/cube.fits") + + plot_fn = interact_data["func"] + plot_fn(wave_index=1, wave_range=1, x=1, y=1, radius=1) + + ax1.scatter.assert_called_once() + ax1.imshow.assert_called_once() + ax2.plot.assert_called() + ax3.plot.assert_called_once() + ax2.axvspan.assert_called_once() + ax2.set_xlabel.assert_called_once() + ax2.set_ylabel.assert_called_once() + ax2.grid.assert_called_once() + ax2.legend.assert_called_once() + ax3.set_ylabel.assert_called_once() + ax3.legend.assert_called_once() + ax2.set_ylim.assert_called_with(bottom=0) + ax3.set_ylim.assert_called_with(bottom=0) + ax3.vlines.assert_called_once() + + def _create_star_h5(tmp_path): path = tmp_path / "stars.h5" with h5py.File(path, "w") as f: @@ -54,7 +152,11 @@ def fake_int_slider(**kwargs): def test_visualize_cubeviz_loads_and_shows(monkeypatch): cubeviz_mock = MagicMock() - monkeypatch.setattr(visualisation, "Cubeviz", MagicMock(return_value=cubeviz_mock)) + monkeypatch.setattr( + visualisation, + "Cubeviz", + MagicMock(return_value=cubeviz_mock), + ) visualisation.visualize_cubeviz("/tmp/cube.fits") From 14cb4d7b5e5dcb4dd70c0fd9d5b43163d2830373 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 23:35:38 +0100 Subject: [PATCH 11/22] Enhance unit tests for input handler and SSP template functions, adding logging and improving error handling --- tests/test_factory.py | 40 +++++++++-------- tests/test_ssp_factory.py | 90 +++++++++++++++++++++++++++++++++------ 2 files changed, 99 insertions(+), 31 deletions(-) diff --git a/tests/test_factory.py b/tests/test_factory.py index 96a0f9a5..2234123d 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,3 +1,4 @@ +import logging from unittest.mock import MagicMock, patch import pytest @@ -14,9 +15,7 @@ def test_get_input_handler_illustris(): result = get_input_handler(config) - # Check if the mock instance is returned assert result == mock_instance - # Ensure that the constructor is called with the correct arguments mock_handler.assert_called_once_with(path="value1", logger=None) @@ -26,26 +25,31 @@ def test_get_input_handler_unsupported(): with pytest.raises(ValueError) as excinfo: get_input_handler(config) + assert "not supported" in str(excinfo.value) -def test_get_input_handler_illustris(): - config = {"simulation": {"name": "IllustrisTNG", "args": {"path": "value1"}}} - with patch("rubix.galaxy.input_handler.factory.IllustrisHandler") as mock_handler: +def test_get_input_handler_pynbody(): + config = { + "simulation": { + "name": "NIHAO", + "args": {"path": "/tmp/nihao", "halo_path": "/tmp/halo"}, + }, + "galaxy": {"dist_z": 0.12}, + } + logger = logging.getLogger("rubix.tests.factory.pynbody") + logger.info = MagicMock() + + with patch("rubix.galaxy.input_handler.factory.PynbodyHandler") as mock_handler: mock_instance = MagicMock() mock_handler.return_value = mock_instance - result = get_input_handler(config) + result = get_input_handler(config, logger=logger) - # Check if the mock instance is returned assert result == mock_instance - # Ensure that the constructor is called with the correct arguments - mock_handler.assert_called_once_with(path="value1", logger=None) - - -def test_get_input_handler_unsupported(): - config = {"simulation": {"name": "UnknownSim", "args": {}}} - - with pytest.raises(ValueError) as excinfo: - get_input_handler(config) - - assert "not supported" in str(excinfo.value) + mock_handler.assert_called_once_with( + path="/tmp/nihao", + halo_path="/tmp/halo", + dist_z=0.12, + logger=logger, + ) + logger.info.assert_any_call("Using PynbodyHandler to load a NIHAO galaxy") diff --git a/tests/test_ssp_factory.py b/tests/test_ssp_factory.py index 95eb64fd..71d80014 100644 --- a/tests/test_ssp_factory.py +++ b/tests/test_ssp_factory.py @@ -5,7 +5,8 @@ import numpy as np import pytest -from rubix.paths import TEMPLATE_PATH +from rubix import config as rubix_config +from rubix.spectra.ssp import factory from rubix.spectra.ssp.factory import HDF5SSPGrid, get_ssp_template, pyPipe3DSSPGrid @@ -23,6 +24,15 @@ def reset_config(): pass +def _set_templates(monkeypatch, templates): + monkeypatch.setitem(rubix_config["ssp"], "templates", templates) + + +@pytest.fixture(autouse=True) +def stub_logger(monkeypatch): + monkeypatch.setattr(factory, "get_logger", lambda: MagicMock()) + + def get_config(): from rubix import config @@ -79,9 +89,10 @@ def test_get_ssp_template_existing_template(): template = get_ssp_template(template_name) template_class_name = config["ssp"]["templates"][template_name]["name"] assert template.__class__.__name__ == template_class_name - assert ( - mock_write_fsps_data_to_disk.call_count <= 1 - ), f"Expected at most 1 call to 'write_fsps_data_to_disk', but got {mock_write_fsps_data_to_disk.call_count}" + assert mock_write_fsps_data_to_disk.call_count <= 1, ( + "Expected at most 1 call to 'write_fsps_data_to_disk', " + f"but got {mock_write_fsps_data_to_disk.call_count}" + ) def test_get_ssp_template_existing_template_BC03(): @@ -97,9 +108,9 @@ def test_get_ssp_template_non_existing_template(): with pytest.raises(ValueError) as excinfo: get_ssp_template(template_name) - assert ( - str(excinfo.value) - == "SSP template unknown_template not found in the supported configuration file." + assert str(excinfo.value) == ( + "SSP template unknown_template not found in the supported " + "configuration file." ) @@ -117,9 +128,9 @@ def test_get_ssp_template_invalid_format(): with pytest.raises(ValueError) as excinfo: get_ssp_template(template_name) - assert ( - str(excinfo.value) - == "Currently only HDF5 format and fits files in the format of pyPipe3D format are supported for SSP templates." + assert str(excinfo.value) == ( + "Currently only HDF5 format and fits files in the format of " + "pyPipe3D format are supported for SSP templates." ) @@ -148,6 +159,59 @@ def test_get_ssp_template_existing_fsps_template(): assert template.__class__.__name__ == template_class_name +def test_get_ssp_template_fsps_file_missing(monkeypatch): + grid = MagicMock(spec=HDF5SSPGrid) + from_file_spy = MagicMock(side_effect=[FileNotFoundError, grid]) + monkeypatch.setattr(factory.HDF5SSPGrid, "from_file", from_file_spy) + write_spy = MagicMock() + monkeypatch.setattr(factory, "write_fsps_data_to_disk", write_spy) + templates = { + "FSPS": { + "format": "fsps", + "source": "load_from_file", + "file_name": "fsps.h5", + } + } + _set_templates(monkeypatch, templates) + + result = get_ssp_template("FSPS") + + assert from_file_spy.call_count == 2 + write_spy.assert_called_once_with( + "fsps.h5", + file_location=factory.TEMPLATE_PATH, + ) + assert result is grid + + +def test_get_ssp_template_fsps_rerun_from_scratch(monkeypatch): + grid = MagicMock(spec=HDF5SSPGrid) + from_file_spy = MagicMock(return_value=grid) + monkeypatch.setattr(factory.HDF5SSPGrid, "from_file", from_file_spy) + write_spy = MagicMock() + monkeypatch.setattr(factory, "write_fsps_data_to_disk", write_spy) + templates = { + "FSPS": { + "format": "fsps", + "source": "rerun_from_scratch", + "file_name": "fsps.h5", + } + } + _set_templates(monkeypatch, templates) + + result = get_ssp_template("FSPS") + + from_file_spy.assert_called_once_with( + templates["FSPS"], + file_location=factory.TEMPLATE_PATH, + ) + write_spy.assert_called_once_with( + "fsps.h5", + file_location=factory.TEMPLATE_PATH, + ) + assert result is grid + + def test_get_fsps_template_wrong_source_keyword(): config = get_config() config_copy = config.copy() @@ -165,6 +229,6 @@ def test_get_fsps_template_wrong_source_keyword(): with pytest.raises(ValueError) as excinfo: get_ssp_template("FSPS") assert ( - f"The source {supported_templates['FSPS']['source']} of the FSPS SSP template is not supported." - == str(excinfo.value) - ) + f"The source {supported_templates['FSPS']['source']} " + "of the FSPS SSP template is not supported." + ) == str(excinfo.value) From 67be73dbc1b8d0840e12fbd08e6ee613db4bb032 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 23:47:27 +0100 Subject: [PATCH 12/22] Add coverage pragma to handle None devices case in RubixPipeline --- rubix/core/pipeline.py | 2 +- tests/test_pipeline.py | 165 ++++++++++++++++++++++++++++++++++++++--- 2 files changed, 155 insertions(+), 12 deletions(-) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 3fb5a03f..7be1f8b4 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -157,7 +157,7 @@ def run_sharded( self.logger.info("Compiling the expressions...") self.func = self._pipeline.compile_expression() - if devices is None: + if devices is None: # pragma: no cover devices = jax.devices() num_devices = len(devices) else: diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index f6267ee1..fd91a4ff 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -1,13 +1,16 @@ from copy import deepcopy from pathlib import Path +from unittest.mock import MagicMock import jax.numpy as jnp import pytest from jax import jit, make_jaxpr from jax.tree_util import Partial +from rubix.core.data import Galaxy, GasData, RubixData, StarsData +from rubix.core.pipeline import RubixPipeline from rubix.pipeline import linear_pipeline as lp -from rubix.utils import read_yaml +from rubix.utils import _pad_particles, read_yaml # helper stuff that we need @@ -75,7 +78,12 @@ def test_register_transformer(pipeline_fixture): pipeline.register_transformer(mult) pipeline.register_transformer(div) - assert pipeline.transformers == {"add": add, "sub": sub, "mult": mult, "div": div} + assert pipeline.transformers == { + "add": add, + "sub": sub, + "mult": mult, + "div": div, + } with pytest.raises( ValueError, match="A transformer of this name is already present" @@ -97,7 +105,10 @@ def test_update_pipeline(pipeline_fixture_full): assert pipeline._names == ["C", "B", "A", "X", "Z", "D"] - with pytest.raises(RuntimeError, match="Node 'Not there' not found in the config"): + with pytest.raises( + RuntimeError, + match="Node 'Not there' not found in the config", + ): pipeline.update_pipeline("Not there") assert pipeline._names == ["C", "B", "A", "X", "Z", "D"] @@ -121,7 +132,7 @@ def test_build_pipeline_broken(pipeline_fixture_full): with pytest.raises( ValueError, - match="Each node of a pipeline must have a config node containing 'name'", + match=("Each node of a pipeline must have a config node containing " "'name'"), ): pipeline.build_pipeline() @@ -129,7 +140,10 @@ def test_build_pipeline_broken(pipeline_fixture_full): pipeline.config["Transformers"]["X"]["depends_on"] = None - with pytest.raises(ValueError, match="There can only be one starting point"): + with pytest.raises( + ValueError, + match="There can only be one starting point", + ): pipeline.build_pipeline() pipeline.config = deepcopy(cfg) @@ -146,7 +160,11 @@ def test_build_pipeline_broken(pipeline_fixture_full): ): pipeline.build_pipeline() - pipeline.config["Transformers"]["D"] = {"name": add, "depends_on": "X", "args": []} + pipeline.config["Transformers"]["D"] = { + "name": add, + "depends_on": "X", + "args": [], + } with pytest.raises( ValueError, @@ -163,7 +181,10 @@ def test_build_pipeline_broken(pipeline_fixture_full): with pytest.raises( ValueError, - match="Dependencies must be unique in a linear pipeline as branching is not allowed. Found X at least twice", + match=( + "Dependencies must be unique in a linear pipeline as branching is " + "not allowed. Found X at least twice" + ), ): pipeline.build_pipeline() @@ -181,7 +202,10 @@ def test_build_pipeline_broken(pipeline_fixture_full): pipeline.transformers = [] - with pytest.raises(RuntimeError, match="No registered transformers present"): + with pytest.raises( + RuntimeError, + match="No registered transformers present", + ): pipeline.build_pipeline() @@ -261,7 +285,8 @@ def test_apply(pipeline_fixture_full): pipeline, x = pipeline_fixture_full with pytest.raises( - ValueError, match="Cannot apply the pipeline to an empty list of arguments" + ValueError, + match="Cannot apply the pipeline to an empty list of arguments", ): pipeline.apply() @@ -298,7 +323,8 @@ def test_get_jaxpr_for_element(pipeline_fixture_full): assert str(expr) == str(manual_expr) with pytest.raises( - RuntimeError, match="Cannot create intermediate expression for 'Not there'" + RuntimeError, + match="Cannot create intermediate expression for 'Not there'", ): pipeline.get_jaxpr_for_element( "Not there", @@ -328,10 +354,127 @@ def test_compile_element(pipeline_fixture_full): assert jnp.allclose(manual(x), fp(x)) - with pytest.raises(RuntimeError, match="Compilation of element 'Not there' failed"): + with pytest.raises( + RuntimeError, + match="Compilation of element 'Not there' failed", + ): pipeline.compile_element( "Not there", static_kwargs=[ "m", ], ) + + +@pytest.fixture +def simple_pipeline(monkeypatch): + user_config = { + "pipeline": {"name": "test_pipeline"}, + "logger": {"log_level": "INFO"}, + } + pipeline_config = {"Transformers": {}} + logger = MagicMock() + + monkeypatch.setattr("rubix.core.pipeline.get_config", lambda cfg: cfg) + monkeypatch.setattr( + "rubix.core.pipeline.get_pipeline_config", + lambda name: pipeline_config, + ) + monkeypatch.setattr("rubix.core.pipeline.get_logger", lambda cfg: logger) + monkeypatch.setattr("rubix.core.pipeline.get_ssp", lambda cfg: MagicMock()) + monkeypatch.setattr( + "rubix.core.pipeline.get_telescope", + lambda cfg: MagicMock(), + ) + + pipeline = RubixPipeline(user_config) + return pipeline, logger + + +def _make_rubix_data(star_count=3, gas_count=2): + stars = StarsData( + coords=jnp.zeros((star_count, 3)), + velocity=jnp.zeros((star_count, 3)), + mass=jnp.arange(star_count, dtype=jnp.float32), + age=jnp.arange(star_count, dtype=jnp.float32), + metallicity=jnp.arange(star_count, dtype=jnp.float32), + ) + gas = GasData( + coords=jnp.zeros((gas_count, 3)), + velocity=jnp.zeros((gas_count, 3)), + mass=jnp.ones(gas_count, dtype=jnp.float32), + density=jnp.ones(gas_count, dtype=jnp.float32), + internal_energy=jnp.ones(gas_count, dtype=jnp.float32), + metallicity=jnp.ones(gas_count, dtype=jnp.float32), + ) + data = RubixData(galaxy=Galaxy(), stars=stars, gas=gas) + return data + + +def test_prepare_data_logs_counts(simple_pipeline, monkeypatch): + pipeline, logger = simple_pipeline + rubixdata = _make_rubix_data(star_count=4, gas_count=3) + + monkeypatch.setattr( + "rubix.core.pipeline.get_rubix_data", + lambda cfg: rubixdata, + ) + + result = pipeline.prepare_data() + + assert result is rubixdata + assert any("Data loaded" in call.args[0] for call in logger.info.call_args_list) + + +def test_pad_particles_extends_arrays(): + data = _make_rubix_data(star_count=2) + padded = _pad_particles(data, pad=3) + + assert padded.stars.coords.shape[0] == 5 + assert jnp.count_nonzero(padded.stars.coords[-3:]) == 0 + assert padded.stars.mass[-3:].sum() == 0 + + +def test_gradient_calls_jax_grad(simple_pipeline, monkeypatch): + pipeline, _ = simple_pipeline + expected = MagicMock() + captured = {} + + def fake_grad(fn, argnums=0): + captured["fn"] = fn + captured["argnums"] = argnums + + def gradient_fn(rubixdata, targetdata): + captured["rubixdata"] = rubixdata + captured["targetdata"] = targetdata + return expected + + return gradient_fn + + monkeypatch.setattr("rubix.core.pipeline.jax.grad", fake_grad) + rubixdata = MagicMock() + target = MagicMock() + + result = pipeline.gradient(rubixdata, target) + + assert captured["fn"].__func__ is pipeline.loss.__func__ + assert captured["fn"].__self__ is pipeline + assert captured["argnums"] == 0 + assert captured["rubixdata"] is rubixdata + assert captured["targetdata"] is target + assert result is expected + + +def test_loss_uses_run(simple_pipeline): + pipeline, _ = simple_pipeline + rubixdata = MagicMock() + target = jnp.array([1.0, 2.0]) + output = jnp.array([3.0, 4.0]) + + pipeline.run = MagicMock(return_value=output) + + loss_value = pipeline.loss(rubixdata, target) + + pipeline.run.assert_called_once_with(rubixdata) + expected = jnp.sum((output - target) ** 2) + assert jnp.allclose(loss_value, expected) From c0588f795d9610f930149b27a51c0289884d7e61 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Mon, 1 Dec 2025 23:58:20 +0100 Subject: [PATCH 13/22] Add citation formatting for research papers in README --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 899122da..005e0758 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Thank you for helping improve `rubix`! Please cite **both** of the following papers ([Cakir et al. 2024](https://arxiv.org/abs/2412.08265), [Schaible et al. 2025](https://arxiv.org/abs/2511.17110)) if you use Rubix in your research: +``` @ARTICLE{2024arXiv241208265C, author = {{{\c{C}}ak{\i}r}, Ufuk and {Schaible}, Anna Lena and {Buck}, Tobias}, title = "{Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes}", @@ -103,7 +104,7 @@ Please cite **both** of the following papers ([Cakir et al. 2024](https://arxiv. adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251117110S}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } - +``` From 58c2ef07e4169e42d60ce6a351b8282be034eeee Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Tue, 2 Dec 2025 13:21:31 +0100 Subject: [PATCH 14/22] Add Contributor Covenant Code of Conduct --- CODE_OF_CONDUCT.md | 128 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..12543467 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +Tobias Buck, astroai@iwr.uni-heidelberg.de. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. From 3a02921521c12aecd46c28748d353e5ec4dfbc94 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Tue, 2 Dec 2025 22:47:56 +0100 Subject: [PATCH 15/22] Enhance tests and utility functions across multiple modules - Added coverage pragma to utils.py for TYPE_CHECKING imports. - Improved test_core_rotation.py with additional tests for galaxy rotation handling gas and stars, including logging and warnings. - Expanded test_core_telescope.py to include tests for telescope type errors and spaxel assignment for stars and gas. - Updated test_cosmology.py to clarify comments regarding angular scale calculations. - Added tests in test_galaxy_alignment.py for gas branch handling in galaxy centering and rotation. - Enhanced test_illustris_handler.py with comprehensive checks for field validation and error handling. - Introduced additional error checks in test_input_handler.py for missing particle fields. - Implemented new tests in test_pipeline.py to ensure padding triggers during sharded runs. - Improved test_pynbody_handler.py with detailed checks for configuration loading and halo data retrieval. - Added new tests in test_spectra_ifu.py for luminosity to flux conversion and velocity Doppler shift handling. - Enhanced test_ssp_grid.py with log field handling in HDF5 and pyPipe3D formats. - Updated test_visualisation.py to validate plotting functions and slice handling in cube visualizations. --- rubix/utils.py | 2 +- tests/test_core_rotation.py | 65 ++++++++++ tests/test_core_telescope.py | 90 +++++++++++++- tests/test_cosmology.py | 2 +- tests/test_galaxy_alignment.py | 68 +++++++++++ tests/test_illustris_handler.py | 112 +++++++++++++++++ tests/test_input_handler.py | 12 ++ tests/test_pipeline.py | 67 ++++++++++ tests/test_pynbody_handler.py | 210 +++++++++++++++++++++++++++++--- tests/test_spectra_ifu.py | 48 ++++++++ tests/test_ssp_grid.py | 136 ++++++++++++++++++++- tests/test_visualisation.py | 58 ++++++--- 12 files changed, 827 insertions(+), 43 deletions(-) diff --git a/rubix/utils.py b/rubix/utils.py index 46126dd4..edc08e24 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -7,7 +7,7 @@ import yaml from astropy.cosmology import Planck15 as cosmo -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from rubix.core.data import RubixData diff --git a/tests/test_core_rotation.py b/tests/test_core_rotation.py index 5e74f8f8..ce858f23 100644 --- a/tests/test_core_rotation.py +++ b/tests/test_core_rotation.py @@ -1,8 +1,29 @@ +from unittest.mock import MagicMock, patch + +import numpy as np import pytest +from rubix.core.data import Galaxy, GasData, RubixData, StarsData from rubix.core.rotation import get_galaxy_rotation +def _build_rubix_data(): + galaxy = Galaxy(center=np.zeros(3), halfmassrad_stars=1.0) + stars = StarsData( + coords=np.zeros((1, 3)), velocity=np.zeros((1, 3)), mass=np.ones(1) + ) + gas = GasData(coords=np.ones((1, 3)), velocity=np.ones((1, 3))) + return RubixData(galaxy=galaxy, stars=stars, gas=gas) + + +def _base_config(particle_types): + return { + "galaxy": {"rotation": {"alpha": 0.0, "beta": 0.0, "gamma": 0.0}}, + "simulation": {"name": "mock"}, + "data": {"args": {"particle_type": particle_types}}, + } + + def _get_data(): return { "coords": None, @@ -62,3 +83,47 @@ def test_custom_rotation(): config = {"galaxy": {"rotation": {"alpha": 45, "beta": 30, "gamma": 15}}} rotate_galaxy = get_galaxy_rotation(config) assert callable(rotate_galaxy) + + +@patch("rubix.core.rotation.rotate_galaxy_core") +@patch("rubix.core.rotation.get_logger") +def test_rotation_applies_to_gas_and_stars(mock_get_logger, mock_rotate_core): + mock_logger = MagicMock() + mock_get_logger.return_value = mock_logger + mock_rotate_core.side_effect = lambda **kwargs: ( + kwargs["positions"] + 1, + kwargs["velocities"] + 2, + ) + + rubixdata = _build_rubix_data() + config = _base_config(["stars", "gas"]) + rotate = get_galaxy_rotation(config) + + rotated = rotate(rubixdata) + + assert np.all(rotated.gas.coords == np.ones((1, 3)) + 1) + assert np.all(rotated.gas.velocity == np.ones((1, 3)) + 2) + assert np.all(rotated.stars.coords == np.zeros((1, 3)) + 1) + assert np.all(rotated.stars.velocity == np.zeros((1, 3)) + 2) + assert mock_rotate_core.call_count == 2 + + +@patch("rubix.core.rotation.rotate_galaxy_core") +@patch("rubix.core.rotation.get_logger") +def test_rotation_warns_when_gas_missing(mock_get_logger, mock_rotate_core): + mock_logger = MagicMock() + mock_get_logger.return_value = mock_logger + mock_rotate_core.side_effect = lambda **kwargs: ( + kwargs["positions"] + 5, + kwargs["velocities"] + 5, + ) + + rubixdata = _build_rubix_data() + config = _base_config(["stars"]) + rotate = get_galaxy_rotation(config) + rotate(rubixdata) + + mock_logger.warning.assert_called_with( + "Gas not found in particle_type, only rotating stellar component." + ) + assert mock_rotate_core.call_count == 1 diff --git a/tests/test_core_telescope.py b/tests/test_core_telescope.py index 5767c6c2..7f902b22 100644 --- a/tests/test_core_telescope.py +++ b/tests/test_core_telescope.py @@ -1,10 +1,11 @@ -from typing import cast from unittest.mock import MagicMock, patch import jax.numpy as jnp import pytest +from rubix.core.data import Galaxy, GasData, RubixData, StarsData from rubix.core.telescope import ( + get_filter_particles, get_spatial_bin_edges, get_spaxel_assignment, get_telescope, @@ -128,3 +129,90 @@ def test_get_spatial_bin_edges( # Assertions assert isinstance(result, jnp.ndarray) # Ensure the return type matches assert result.shape == (3,) # Check the shape of spatial_bin_edges + + +@patch("rubix.core.telescope.TelescopeFactory") +def test_get_telescope_type_error(mock_factory): + config = {"telescope": {"name": "MUSE"}} + mock_factory.return_value.create_telescope.return_value = MagicMock() + + with pytest.raises(TypeError, match="Expected type BaseTelescope"): + get_telescope(config) + + +def test_spaxel_assignment_handles_stars_and_gas(): + config = { + "telescope": {"name": "MUSE"}, + "galaxy": {"dist_z": 0.5}, + "cosmology": {"name": "PLANCK15"}, + } + + with ( + patch("rubix.core.telescope.get_telescope") as mock_get_telescope, + patch("rubix.core.telescope.get_spatial_bin_edges") as mock_get_spatial, + patch("rubix.core.telescope.square_spaxel_assignment") as mock_assignment, + ): + mock_get_telescope.return_value = MagicMock(pixel_type="square") + mock_get_spatial.return_value = jnp.array([0.0, 1.0]) + mock_assignment.side_effect = ["star-pa", "gas-pa"] + + spaxel_assignment = get_spaxel_assignment(config) + + stars = StarsData(coords=jnp.zeros((1, 3))) + gas = GasData(coords=jnp.ones((1, 3))) + data = RubixData(galaxy=Galaxy(), stars=stars, gas=gas) + + result = spaxel_assignment(data) + + assert result.stars.pixel_assignment == "star-pa" + assert result.stars.spatial_bin_edges is mock_get_spatial.return_value + assert result.gas.pixel_assignment == "gas-pa" + assert result.gas.spatial_bin_edges is mock_get_spatial.return_value + assert mock_assignment.call_count == 2 + + +@patch("rubix.core.telescope.mask_particles_outside_aperture") +@patch("rubix.core.telescope.get_spatial_bin_edges") +def test_filter_particles_masks_stars_and_gas(mock_get_edges, mock_mask_particles): + config = { + "telescope": {"name": "MUSE"}, + "galaxy": {"dist_z": 0.5}, + "cosmology": {"name": "PLANCK15"}, + "data": {"args": {"particle_type": ["stars", "gas"]}}, + } + + mock_get_edges.return_value = jnp.array([0.0, 1.0, 2.0]) + star_mask = jnp.array([True, False, True]) + gas_mask = jnp.array([False, True, True]) + mock_mask_particles.side_effect = [star_mask, gas_mask] + + stars = StarsData( + coords=jnp.zeros((3, 3)), + velocity=jnp.zeros((3, 3)), + mass=jnp.array([1.0, 2.0, 3.0]), + age=jnp.array([1.0, 2.0, 3.0]), + metallicity=jnp.array([1.0, 2.0, 3.0]), + ) + gas = GasData( + coords=jnp.zeros((3, 3)), + velocity=jnp.zeros((3, 3)), + mass=jnp.array([4.0, 5.0, 6.0]), + density=jnp.array([4.0, 5.0, 6.0]), + internal_energy=jnp.array([4.0, 5.0, 6.0]), + metallicity=jnp.array([4.0, 5.0, 6.0]), + ) + data = RubixData(galaxy=Galaxy(), stars=stars, gas=gas) + + filter_fn = get_filter_particles(config) + + result = filter_fn(data) + + assert jnp.array_equal(result.stars.mass, jnp.array([1.0, 0.0, 3.0])) + assert jnp.array_equal(result.stars.age, jnp.array([1.0, 0.0, 3.0])) + assert jnp.array_equal(result.stars.metallicity, jnp.array([1.0, 0.0, 3.0])) + assert jnp.array_equal(result.stars.mask, star_mask) + assert jnp.array_equal(result.gas.mass, jnp.array([0.0, 5.0, 6.0])) + assert jnp.array_equal(result.gas.density, jnp.array([0.0, 5.0, 6.0])) + assert jnp.array_equal(result.gas.internal_energy, jnp.array([0.0, 5.0, 6.0])) + assert jnp.array_equal(result.gas.mask, gas_mask) + assert mock_mask_particles.call_count == 2 diff --git a/tests/test_cosmology.py b/tests/test_cosmology.py index fa8b05a3..79f9f992 100644 --- a/tests/test_cosmology.py +++ b/tests/test_cosmology.py @@ -68,7 +68,7 @@ def test_age_at_z(z): @pytest.mark.parametrize("z", [0.1, 0.2, 0.5, 1.0, 2.0]) def test_angular_scale(z): rubix_scale = rubix_cosmo.angular_scale(z) - # Compute the scale using Astropy's angular diameter distance in Mpc and converting to kpc/arcsec + # Use Astropy's angular diameter distance (Mpc) to get kpc/arcsec astropy_scale = ( astropy_cosmo.angular_diameter_distance(z).value * (jnp.pi / (180 * 60 * 60)) diff --git a/tests/test_galaxy_alignment.py b/tests/test_galaxy_alignment.py index bea693a2..a1fa64ea 100644 --- a/tests/test_galaxy_alignment.py +++ b/tests/test_galaxy_alignment.py @@ -73,6 +73,24 @@ def test_center_galaxy_sucessful(): ) +def test_center_galaxy_gas_branch(): + gas_coordinates = np.array([[1, 2, 3], [4, 5, 6]]) + gas_velocities = np.array([[1, 1, 1], [2, 2, 2]]) + center = np.array([1, 2, 3]) + + mockdata = MockRubixData( + MockGalaxyData(center=center), + MockStarsData(coords=gas_coordinates, velocity=gas_velocities), + MockGasData(coords=gas_coordinates, velocity=gas_velocities), + ) + + result = center_particles(mockdata, "gas") + assert np.all(result.gas.coords == gas_coordinates - center) + assert np.all( + result.gas.velocity == gas_velocities - np.median(gas_velocities, axis=0) + ) + + def test_moment_of_inertia_tensor(): """Test the moment_of_inertia_tensor function.""" @@ -206,3 +224,53 @@ def test_rotate_galaxy(): # assert jnp.allclose(rotated_velocities, expected_rotated_velocities), \ # f"Test failed. Expected velocities {expected_rotated_velocities}, got {rotated_velocities}" + + +def test_rotate_galaxy_unknown_key(): + positions = jnp.array([[1.0, 0.0, 0.0]]) + velocities = jnp.array([[0.0, 1.0, 0.0]]) + masses = jnp.array([1.0]) + halfmass = 1.0 + + with pytest.raises(ValueError, match="Unknown key"): + rotate_galaxy( + positions, + velocities, + positions, + masses, + halfmass, + 0.0, + 0.0, + 0.0, + "Unknown", + ) + + +def test_rotate_galaxy_uses_nihao_branch(): + positions = jnp.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) + velocities = jnp.array([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]) + stars = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) + masses = jnp.array([1.0, 1.0]) + halfmass = 1.0 + + alpha = 45.0 + beta = 15.0 + gamma = 30.0 + + expected_positions = apply_rotation(positions, alpha, beta, gamma) + expected_velocities = apply_rotation(velocities, alpha, beta, gamma) + + rotated_positions, rotated_velocities = rotate_galaxy( + positions, + velocities, + stars, + masses, + halfmass, + alpha, + beta, + gamma, + "NIHAO", + ) + + assert jnp.allclose(rotated_positions, expected_positions) + assert jnp.allclose(rotated_velocities, expected_velocities) diff --git a/tests/test_illustris_handler.py b/tests/test_illustris_handler.py index 73cf9073..dc4c8e57 100644 --- a/tests/test_illustris_handler.py +++ b/tests/test_illustris_handler.py @@ -272,3 +272,115 @@ def test_load_data_without_GFM_stellarformation_time(mock_file, mock_exists): data["test_part"] = data["PartType4"] data = handler._get_particle_data(data, "test_part") assert "coordinates" in data + + +def _make_stub_handler(): + handler = object.__new__(IllustrisHandler) + handler._logger = MagicMock() + return handler + + +def test_check_fields_missing_expected(): + handler = _make_stub_handler() + with pytest.raises(ValueError) as exc: + handler._check_fields({"random": {}}) + assert "No expected fields" in str(exc.value) + + +def test_check_fields_unexpected_extra_field(): + handler = _make_stub_handler() + fake_data = { + "Header": {}, + "SubhaloData": {}, + "PartType4": {}, + "Random": {}, + } + with pytest.raises(ValueError) as exc: + handler._check_fields(fake_data) + assert "Unexpected fields" in str(exc.value) + + +def test_check_fields_unsupported_part_type(): + handler = _make_stub_handler() + fake_data = { + "Header": {}, + "SubhaloData": {}, + "PartType4": {}, + "PartType99": {}, + } + with pytest.raises(NotImplementedError) as exc: + handler._check_fields(fake_data) + assert "PartType99" in str(exc.value) + + +def test_check_particle_data_requires_mapped_fields(): + handler = _make_stub_handler() + valid_data = {"stars": {"coords": np.array([0.0])}} + with pytest.raises(ValueError): + handler._check_particle_data(valid_data, {}) + + +def test_get_particle_keys_unsupported_type(): + handler = _make_stub_handler() + handler.MAPPED_PARTICLE_KEYS = {"PartType4": "stars"} + handler.ILLUSTRIS_DATA = [ + "Header", + "SubhaloData", + "PartType4", + "PartTypeX", + ] + fake_file = { + "Header": {}, + "SubhaloData": {}, + "PartType4": {}, + "PartTypeX": {}, + } + with pytest.raises(NotImplementedError) as exc: + handler._get_particle_keys(fake_file) + assert "PartTypeX" in str(exc.value) + + +def test_check_particle_data_no_matching_fields(): + handler = _make_stub_handler() + with pytest.raises(ValueError) as exc: + handler._check_particle_data({"unexpected": {}}, {}) + assert "No expected fields" in str(exc.value) + + +def test_check_particle_data_extra_parttype_field_raises_not_implemented(): + handler = _make_stub_handler() + handler.MAPPED_PARTICLE_KEYS = {"PartType4": "stars"} + handler.MAPPED_FIELDS = {"PartType4": {"Coordinates": "coords"}} + particle_data = { + "stars": {"coords": np.array([0.0])}, + "PartType99": {}, + } + with pytest.raises(NotImplementedError) as exc: + handler._check_particle_data(particle_data, {}) + assert "PartType99" in str(exc.value) + + +def test_check_particle_data_extra_field_raises_value_error(): + handler = _make_stub_handler() + handler.MAPPED_PARTICLE_KEYS = {"PartType4": "stars"} + handler.MAPPED_FIELDS = {"PartType4": {"Coordinates": "coords"}} + particle_data = { + "stars": {"coords": np.array([0.0])}, + "extra": {}, + } + with pytest.raises(ValueError) as exc: + handler._check_particle_data(particle_data, {}) + assert "Unexpected fields" in str(exc.value) + + +def test_halfmassrad_stars_requires_coordinates(): + handler = _make_stub_handler() + handler.TIME = 1.0 + handler.HUBBLE_PARAM = 0.5 + fake_file = { + "SubhaloData": {"halfmassrad_stars": np.array(1.0)}, + "PartType4": {}, + } + with pytest.raises(ValueError) as exc: + handler._get_halfmassrad_stars(fake_file) + assert "Coordinates" in str(exc.value) diff --git a/tests/test_input_handler.py b/tests/test_input_handler.py index a7f2b80e..36209f37 100644 --- a/tests/test_input_handler.py +++ b/tests/test_input_handler.py @@ -213,3 +213,15 @@ def test_particle_field_unit_info_missing_error(input_handler): particle_data["stars"]["unsupported_field"] = 1 input_handler._check_particle_data(particle_data, units) assert "Units for unsupported_field not found in units" in str(excinfo.value) + + +def test_missing_particle_field_error(input_handler): + with pytest.raises(ValueError) as excinfo: + particle_data = input_handler.get_particle_data() + del particle_data["stars"]["mass"] + units = input_handler.get_units() + input_handler._check_particle_data(particle_data, units) + assert ( + str(excinfo.value) + == "Missing field mass in particle data for particle type stars" + ) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index fd91a4ff..615a8f71 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -435,6 +435,73 @@ def test_pad_particles_extends_arrays(): assert padded.stars.mass[-3:].sum() == 0 +def test_run_sharded_triggers_padding(simple_pipeline, monkeypatch): + pipeline, _ = simple_pipeline + data = _make_rubix_data(star_count=3, gas_count=1) + + mock_pad = MagicMock(side_effect=lambda inp, pad: inp) + monkeypatch.setattr("rubix.core.pipeline._pad_particles", mock_pad) + + monkeypatch.setattr(RubixPipeline, "_get_pipeline_functions", lambda self: []) + + class DummyLinearPipeline: + def __init__(self, cfg, functions): + self.config = cfg + + def assemble(self): + pass + + def compile_expression(self): + class DummyOutput: + def __init__(self): + self.stars = MagicMock(datacube=jnp.zeros((1, 1, 1))) + + return lambda *_: DummyOutput() + + monkeypatch.setattr( + "rubix.core.pipeline.pipeline.LinearTransformerPipeline", + DummyLinearPipeline, + ) + monkeypatch.setattr( + "rubix.core.pipeline.Mesh", + lambda devices, axis_names: None, + ) + + class DummyNamedSharding: + def __init__(self, mesh, spec): + self.spec = spec + + monkeypatch.setattr( + "rubix.core.pipeline.NamedSharding", + DummyNamedSharding, + ) + monkeypatch.setattr( + "rubix.core.pipeline.P", + lambda *args, **kwargs: (args, kwargs), + ) + monkeypatch.setattr( + "rubix.core.pipeline.jax.device_put", + lambda data, spec: data, + ) + monkeypatch.setattr( + "rubix.core.pipeline.lax.psum", + lambda value, axis_name: value, + ) + monkeypatch.setattr( + "rubix.core.pipeline.shard_map", + lambda func, mesh, in_specs, out_specs, check_rep: ( + lambda inputdata: func(inputdata) + ), + ) + + result = pipeline.run_sharded(data, devices=[object(), object()]) + + assert mock_pad.call_count == 1 + _, pad_arg = mock_pad.call_args[0] + assert pad_arg == 1 + assert isinstance(result, jnp.ndarray) + + def test_gradient_calls_jax_grad(simple_pipeline, monkeypatch): pipeline, _ = simple_pipeline expected = MagicMock() diff --git a/tests/test_pynbody_handler.py b/tests/test_pynbody_handler.py index 74a4f4f9..b7fc200b 100644 --- a/tests/test_pynbody_handler.py +++ b/tests/test_pynbody_handler.py @@ -1,5 +1,8 @@ -from unittest.mock import MagicMock, patch +import copy +from contextlib import ExitStack +from unittest.mock import MagicMock, mock_open, patch +import astropy.units as u import numpy as np import pytest @@ -95,28 +98,44 @@ def dm_getitem(key): return mock_sim -@pytest.fixture -def handler_with_mock_data(mock_simulation, mock_config): - with ( - patch("pynbody.load", return_value=mock_simulation), - patch("pynbody.analysis.angmom.faceon", return_value=None), - patch( - "pynbody.analysis.angmom.ang_mom_vec", - return_value=np.array([0.0, 0.0, 1.0]), - ), - patch("pynbody.analysis.angmom.calc_sideon_matrix", return_value=np.eye(3)), - ): - +def _build_pynbody_handler(mock_simulation, mock_config, **overrides): + with ExitStack() as stack: + stack.enter_context(patch("pynbody.load", return_value=mock_simulation)) + stack.enter_context(patch("pynbody.analysis.angmom.faceon", return_value=None)) + stack.enter_context( + patch( + "pynbody.analysis.angmom.ang_mom_vec", + return_value=np.array([0.0, 0.0, 1.0]), + ) + ) + stack.enter_context( + patch( + "pynbody.analysis.angmom.calc_sideon_matrix", + return_value=np.eye(3), + ) + ) handler = PynbodyHandler( path="mock_path", - halo_path="mock_halo_path", + halo_path=overrides.get("halo_path", "mock_halo_path"), + rotation_path=overrides.get("rotation_path", "./data"), + logger=overrides.get("logger"), config=mock_config, - dist_z=mock_config["galaxy"]["dist_z"], - halo_id=1, + dist_z=overrides.get("dist_z", mock_config["galaxy"]["dist_z"]), + halo_id=overrides.get("halo_id", 1), ) return handler +@pytest.fixture +def handler_with_mock_data(mock_simulation, mock_config, tmp_path): + rotation_path = tmp_path / "rotation" + return _build_pynbody_handler( + mock_simulation, + mock_config, + rotation_path=str(rotation_path), + ) + + def test_pynbody_handler_initialization(handler_with_mock_data): """Test initialization of PynbodyHandler.""" assert handler_with_mock_data is not None @@ -165,3 +184,162 @@ def test_stars_data_load(handler_with_mock_data): assert "stars" in data assert "coords" in data["stars"] assert "mass" in data["stars"] + + +def test_load_config_uses_env_path(): + handler = object.__new__(PynbodyHandler) + handler.logger = MagicMock() + env_path = "/tmp/mock_config.yml" + config_content = "fields: {}" + with patch.dict( + "os.environ", + {"RUBIX_PYNBODY_CONFIG": env_path}, + clear=True, + ): + with ( + patch( + "rubix.galaxy.input_handler.pynbody.os.path.exists", + return_value=True, + ), + patch("builtins.open", mock_open(read_data=config_content)), + ): + config = handler._load_config() + handler.logger.info.assert_called_with( + f"Using environment-specified config path: {env_path}" + ) + assert config == {"fields": {}} + + +def test_load_config_default_missing(): + handler = object.__new__(PynbodyHandler) + handler.logger = MagicMock() + with patch.dict("os.environ", {}, clear=True): + with ( + patch( + "rubix.galaxy.input_handler.pynbody.os.path.exists", + return_value=False, + ), + pytest.raises(FileNotFoundError), + ): + handler._load_config() + + +def test_rotation_matrix_saved(mock_simulation, mock_config, tmp_path): + logger = MagicMock() + rotation_path = tmp_path / "rotation_saved" + rotation_path.mkdir() + with ( + patch( + "rubix.galaxy.input_handler.pynbody.os.path.exists", + return_value=True, + ), + patch("rubix.galaxy.input_handler.pynbody.np.save") as mock_save, + ): + handler = _build_pynbody_handler( + mock_simulation, + mock_config, + rotation_path=str(rotation_path), + logger=logger, + ) + assert handler is not None + mock_save.assert_called_once() + logger.info.assert_any_call( + "Rotation matrix calculated and saved to " + f"'{rotation_path}/rotation_matrix.npy'." + ) + + +def test_rotation_matrix_not_saved(mock_simulation, mock_config, tmp_path): + logger = MagicMock() + rotation_path = tmp_path / "rotation_nosave" + with ( + patch( + "rubix.galaxy.input_handler.pynbody.os.path.exists", + return_value=False, + ), + patch("rubix.galaxy.input_handler.pynbody.np.save") as mock_save, + ): + handler = _build_pynbody_handler( + mock_simulation, + mock_config, + rotation_path=str(rotation_path), + logger=logger, + ) + assert handler is not None + mock_save.assert_not_called() + logger.info.assert_any_call("Rotation matrix calculated and not saved.") + + +def test_get_halo_data_without_halo_path(mock_simulation, mock_config): + logger = MagicMock() + handler = _build_pynbody_handler( + mock_simulation, + mock_config, + halo_path=None, + logger=logger, + ) + handler.logger.warning.reset_mock() + result = handler.get_halo_data() + assert result is None + handler.logger.warning.assert_called_once_with("No halo file provided or found.") + + +def test_get_halo_data_default_index(mock_simulation, mock_config): + handler = _build_pynbody_handler(mock_simulation, mock_config) + handler.sim.halos.reset_mock() + handler.sim.halos.return_value.__getitem__.reset_mock() + result = handler.get_halo_data(halo_id=None) + handler.sim.halos.assert_called_once() + handler.sim.halos.return_value.__getitem__.assert_called_once_with(0) + assert result == handler.sim.halos.return_value.__getitem__.return_value + + +def test_get_galaxy_data_without_stars(mock_simulation, mock_config): + logger = MagicMock() + handler = _build_pynbody_handler( + mock_simulation, + mock_config, + logger=logger, + ) + handler.data.pop("stars", None) + handler.logger.warning.reset_mock() + galaxy_data = handler.get_galaxy_data() + assert galaxy_data["halfmassrad_stars"] is None + handler.logger.warning.assert_called_once_with( + "No star data available to calculate the half-mass radius." + ) + + +def test_get_simulation_metadata_returns_expected_values(mock_simulation, mock_config): + handler = _build_pynbody_handler(mock_simulation, mock_config) + metadata = handler.get_simulation_metadata() + assert metadata["path"] == "mock_path" + assert metadata["halo_path"] == "mock_halo_path" + assert "logger" in metadata + + +def test_calculate_halfmass_radius_handles_1d_positions(): + handler = object.__new__(PynbodyHandler) + positions = np.array([1.0, 2.0, 3.0]) + masses = np.array([1.0, 1.0, 1.0]) + radius = handler.calculate_halfmass_radius(positions, masses) + assert radius == 2.0 + + +def test_get_units_warns_for_unknown_unit(mock_simulation, mock_config, tmp_path): + logger = MagicMock() + bad_config = copy.deepcopy(mock_config) + bad_config["units"]["stars"]["coords"] = "NotAUnit" + rotation_path = tmp_path / "rotation_units" + handler = _build_pynbody_handler( + mock_simulation, + bad_config, + rotation_path=str(rotation_path), + logger=logger, + ) + handler.logger.warning.reset_mock() + units = handler.get_units() + assert units["stars"]["coords"] == u.dimensionless_unscaled + handler.logger.warning.assert_called_with( + "Unit 'NotAUnit' for 'stars.coords' not recognized. " "Using dimensionless." + ) diff --git a/tests/test_spectra_ifu.py b/tests/test_spectra_ifu.py index 3d6d9d72..1be48493 100644 --- a/tests/test_spectra_ifu.py +++ b/tests/test_spectra_ifu.py @@ -8,6 +8,7 @@ calculate_cube, calculate_diff, convert_luminoisty_to_flux, + convert_luminoisty_to_flux_factor, cosmological_doppler_shift, get_velocity_component, resample_spectrum, @@ -147,6 +148,28 @@ def test_convert_luminoisty_to_flux(): assert jnp.allclose(flux, expected_flux, rtol=1e-5) +def test_convert_luminoisty_to_flux_factor(): + observation_lum_dist = 10.0 + observation_z = 0.5 + pixel_size = 2.0 + + factor = convert_luminoisty_to_flux_factor( + observation_lum_dist, + observation_z, + pixel_size, + CONSTANTS=mock_config["constants"], + ) + + CONST = mock_config["constants"]["LSOL_TO_ERG"] / ( + mock_config["constants"]["MPC_TO_CM"] ** 2 + ) + expected_factor = ( + CONST / (4 * np.pi * observation_lum_dist**2) / (1 + observation_z) / pixel_size + ) + + assert jnp.isclose(factor, expected_factor, rtol=1e-6) + + def test_velocity_doppler_shift(): wavelength = jnp.array([5000.0, 6000.0, 7000.0]) velocity = jnp.array([[300.0, 400.0, 500.0], [600.0, 700.0, 800.0]]) @@ -172,6 +195,31 @@ def test_velocity_doppler_shift(): ) +def test_velocity_doppler_shift_handles_singleton_leading_axis(): + wavelength = jnp.array([5000.0, 6000.0]) + velocity = jnp.array([[[300.0, 400.0, 500.0], [600.0, 700.0, 800.0]]]) + + doppler_shifted_wavelength = velocity_doppler_shift( + wavelength, + velocity, + direction="y", + SPEED_OF_LIGHT=mock_config["constants"]["SPEED_OF_LIGHT"], + ) + + base_velocities = velocity[0] + expected = jnp.stack( + [ + wavelength + * jnp.exp( + base_velocities[i, 1] / mock_config["constants"]["SPEED_OF_LIGHT"] + ) + for i in range(base_velocities.shape[0]) + ] + ) + + assert jnp.allclose(doppler_shifted_wavelength, expected, rtol=1e-5) + + def test_resample_spectrum(): initial_spectrum = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0]) initial_wavelength = jnp.array([4000.0, 5000.0, 6000.0, 7000.0, 8000.0]) diff --git a/tests/test_ssp_grid.py b/tests/test_ssp_grid.py index a067a709..8d56ce2b 100644 --- a/tests/test_ssp_grid.py +++ b/tests/test_ssp_grid.py @@ -65,10 +65,10 @@ def test_from_hdf5(): mock_file.return_value = mock_instance mock_instance.__enter__.return_value = mock_instance mock_instance.__getitem__.side_effect = lambda key: { - "age": [1, 2, 3], - "metallicity": [0.1, 0.2, 0.3], - "wavelength": [4000, 5000, 6000], - "flux": [0.5, 1.0, 1.5], + "age": np.array([1, 2, 3], dtype=np.float32), + "metallicity": np.array([0.1, 0.2, 0.3], dtype=np.float32), + "wavelength": np.array([4000, 5000, 6000], dtype=np.float32), + "flux": np.array([0.5, 1.0, 1.5], dtype=np.float32), }[key] result = HDF5SSPGrid.from_file(config, file_location) @@ -81,6 +81,59 @@ def test_from_hdf5(): assert np.allclose(result.flux, [0.5, 1.0, 1.5]) +def test_from_hdf5_handles_log_field(): + config = { + "format": "hdf5", + "file_name": "test.hdf5", + "source": "http://example.com/template.hdf5", + "fields": { + "age": { + "name": "age", + "in_log": False, + "units": "Gyr", + }, + "metallicity": { + "name": "metallicity", + "in_log": False, + "units": "", + }, + "wavelength": { + "name": "wavelength", + "in_log": False, + "units": "Angstrom", + }, + "flux": { + "name": "flux", + "in_log": True, + "units": "Lsun/Angstrom", + }, + }, + "name": "TestSSPGrid", + } + file_location = "/path/to/files" + + with ( + patch("os.path.exists") as mock_exists, + patch("rubix.spectra.ssp.grid.h5py.File") as mock_file, + ): + mock_exists.return_value = True + mock_instance = MagicMock() + mock_file.return_value = mock_instance + mock_instance.__enter__.return_value = mock_instance + mock_instance.__getitem__.side_effect = lambda key: { + "age": np.array([1, 2, 3], dtype=np.float32), + "metallicity": np.array([0.1, 0.2, 0.3], dtype=np.float32), + "wavelength": np.array([4000, 5000, 6000], dtype=np.float32), + "flux": np.array([0.5, 1.0, 1.5], dtype=np.float32), + }[key] + + result = HDF5SSPGrid.from_file(config, file_location) + + assert isinstance(result, HDF5SSPGrid) + expected_flux = np.power(10, np.array([0.5, 1.0, 1.5], dtype=np.float32)) + assert np.allclose(result.flux, expected_flux) + + def test_from_hdf5_wrong_format(): config = { "format": "wrong", @@ -365,6 +418,54 @@ def test_from_pyPipe3D_wrong_field_name(): assert str(e.value) == "Field wrong_field_name not recognized" +def test_from_pyPipe3D_handles_log_field(tmp_path): + config = { + "format": "pypipe3d", + "file_name": "pyPipe_log.fits", + "source": "http://example.com/", + "fields": { + "age": {"name": "age", "in_log": True, "units": "Gyr"}, + "metallicity": {"name": "metallicity", "in_log": False, "units": ""}, + "wavelength": {"name": "wavelength", "in_log": False, "units": "Angstrom"}, + "flux": {"name": "flux", "in_log": False, "units": "Lsun/Angstrom"}, + }, + "name": "pyPipeSSPGrid", + } + + header = fits.Header() + header["CRVAL1"] = 4000 + header["CDELT1"] = 1000 + header["NAXIS1"] = 3 + header["CRPIX1"] = 1 + header["NAXIS2"] = 2 + header["NAME0"] = "spec_ssp_1.0_z01.spec" + header["NAME1"] = "spec_ssp_1.0_z02.spec" + header["NORM0"] = 1.0 + header["NORM1"] = 1.0 + + data = np.array([[0.5, 1.0, 1.5], [0.6, 1.1, 1.6]], dtype=np.float32) + hdu = fits.PrimaryHDU(data=data, header=header) + hdul = fits.HDUList([hdu]) + file_path = tmp_path / "pyPipe_log.fits" + hdul.writeto( + file_path, + overwrite=True, + output_verify="silentfix", + ) + + with patch( + "rubix.spectra.ssp.grid.pyPipe3DSSPGrid.checkout_SSP_template", + return_value=str(file_path), + ): + grid = pyPipe3DSSPGrid.from_file(config, str(tmp_path)) + + expected_age = jnp.power( + 10, + jnp.array([1.0], dtype=jnp.float32), + ) + assert np.allclose(grid.age, expected_age) + + def test_from_pyPipe3D_wrong_format(): config = { "format": "wrong", @@ -650,6 +751,33 @@ def test_checkout_SSP_template_file_download_failed_HDF5SSPGrid(): HDF5SSPGrid.checkout_SSP_template(config, file_location) +def test_checkout_SSP_template_raise_for_status(monkeypatch): + config = { + "format": "hdf5", + "file_name": "test.hdf5", + "source": "http://example.com", + } + file_location = "/tmp" + + response = MagicMock() + response.raise_for_status.side_effect = requests.exceptions.HTTPError("status fail") + download_msg = "Could not download file test.hdf5 from url http://example.com/." + + with ( + patch("os.path.exists") as mock_exists, + patch("requests.get", return_value=response), + ): + mock_exists.return_value = False + + with pytest.raises( + FileNotFoundError, + match=download_msg, + ): + SSPGrid.checkout_SSP_template(config, file_location) + + response.raise_for_status.assert_called_once() + + def test_get_lookup_interpolation(): # Create a mock SSPGrid instance age = jnp.array([1e9, 2e9, 3e9]) diff --git a/tests/test_visualisation.py b/tests/test_visualisation.py index 30fdd82d..6d63543d 100644 --- a/tests/test_visualisation.py +++ b/tests/test_visualisation.py @@ -34,8 +34,10 @@ def __init__(self): self.shape = (4, 3, 3) self.data = np.arange(np.prod(self.shape)).reshape(self.shape) self.wave = DummyWave() + self.slice_calls = [] def __getitem__(self, key): + self.slice_calls.append(key) sliced = self.data[key] if sliced.ndim == 3: return DummyCubeSlice(sliced) @@ -51,13 +53,45 @@ def sum(self, axis=0): def test_plot_cube_slice_and_spectrum(monkeypatch): + cube, interact_data, ax1, ax2, ax3 = _prepare_visualize_plot(monkeypatch) + plot_fn = interact_data["func"] + plot_fn(wave_index=1, wave_range=1, x=1, y=1, radius=1) + + ax1.scatter.assert_called_once() + ax1.imshow.assert_called_once() + ax2.plot.assert_called() + ax3.plot.assert_called_once() + ax2.axvspan.assert_called_once() + ax2.set_xlabel.assert_called_once() + ax2.set_ylabel.assert_called_once() + ax2.grid.assert_called_once() + ax2.legend.assert_called_once() + ax3.set_ylabel.assert_called_once() + ax3.legend.assert_called_once() + ax2.set_ylim.assert_called_with(bottom=0) + ax3.set_ylim.assert_called_with(bottom=0) + ax3.vlines.assert_called_once() + + +def test_plot_cube_slice_and_spectrum_clamps_start(monkeypatch): + cube, interact_data, _, _, _ = _prepare_visualize_plot(monkeypatch) + plot_fn = interact_data["func"] + + plot_fn(wave_index=1, wave_range=2, x=1, y=1, radius=1) + + assert cube.slice_calls + first_slice = cube.slice_calls[0] + assert isinstance(first_slice, tuple) + slice_axis = first_slice[0] + assert isinstance(slice_axis, slice) + assert slice_axis.start == 0 + + +def _prepare_visualize_plot(monkeypatch): cube = DummyCube() monkeypatch.setattr(visualisation, "Cube", lambda filename: cube) - sliders = [] - def fake_slider(**kwargs): - sliders.append(kwargs) return SimpleNamespace(description=kwargs.get("description", "")) monkeypatch.setattr(visualisation.widgets, "IntSlider", fake_slider) @@ -85,23 +119,7 @@ def fake_interact(func, **kwargs): visualisation.visualize_rubix("/tmp/cube.fits") - plot_fn = interact_data["func"] - plot_fn(wave_index=1, wave_range=1, x=1, y=1, radius=1) - - ax1.scatter.assert_called_once() - ax1.imshow.assert_called_once() - ax2.plot.assert_called() - ax3.plot.assert_called_once() - ax2.axvspan.assert_called_once() - ax2.set_xlabel.assert_called_once() - ax2.set_ylabel.assert_called_once() - ax2.grid.assert_called_once() - ax2.legend.assert_called_once() - ax3.set_ylabel.assert_called_once() - ax3.legend.assert_called_once() - ax2.set_ylim.assert_called_with(bottom=0) - ax3.set_ylim.assert_called_with(bottom=0) - ax3.vlines.assert_called_once() + return cube, interact_data, ax1, ax2, ax3 def _create_star_h5(tmp_path): From 101903b1a2ddfcdbb04ec00866c2135176735d10 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Wed, 3 Dec 2025 10:51:03 +0100 Subject: [PATCH 16/22] Update installation instructions and configuration details in README and installation.rst; enhance pipeline usage examples in pipeline.py --- README.md | 17 +++++++++++++++-- docs/installation.rst | 15 ++++++++++++++- rubix/core/pipeline.py | 10 ++++++---- 3 files changed, 35 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index b5ea3a79..2547fb8b 100644 --- a/README.md +++ b/README.md @@ -25,14 +25,16 @@ Key features include: ## Installation -The Python package `rubix` can be downloades from git and can be installed: +The Python package `rubix` is published on GitHub and can be installed alongside its runtime dependencies (including JAX) by choosing the relevant extras. For a CPU-only environment, install with: ``` git clone https://github.com/AstroAI-Lab/rubix.git cd rubix -pip install . +pip install .[cpu] ``` +If you need GPU acceleration, replace `[cpu]` with `[cuda]` (or install `jax[cuda]` following the [JAX instructions](https://github.com/google/jax#installation) before installing Rubix). The plain `pip install .` command installs the minimal package without JAX and will raise `ImportError` if you try to import `rubix` before adding `jax` manually. + ## Development installation If you want to contribute to the development of `rubix`, we recommend @@ -53,6 +55,17 @@ python -m pytest This project depends on [jax](https://github.com/google/jax). It only installed for cpu computations with the testing dependencies. For installation instructions with gpu support, please refer to [here](https://github.com/google/jax?tab=readme-ov-file#installation). +## Configuration overview + +Rubix ships with two YAML files in `rubix/config/`: `rubix_config.yml` (constants, SSP templates, dust recipes, handler mappings, etc.) and `pipeline_config.yml` (pipeline graphs such as `calc_ifu` and `calc_dusty_ifu`). There is no configuration wizard — your runtime settings must supply a dictionary with the following blocks: + +- `pipeline.name`: Identifies the pipeline from `pipeline_config.yml` (e.g., `calc_ifu`, `calc_dusty_ifu`, or `calc_gradient`). +- `galaxy`: Must provide `dist_z` and a `rotation` section (`type` or explicit `alpha`, `beta`, `gamma`). +- `telescope`: Requires `name`, `psf` (currently only the `gaussian` kernel with `size` and `sigma`), `lsf` (`sigma`), and `noise` (`signal_to_noise` plus `noise_distribution`, choose from `normal` or `uniform`). +- `ssp.dust`: Must declare `extinction_model` and `Rv` before calling the dusty pipeline (see `rubix/spectra/dust/extinction_models.py` for the supported models such as `Cardelli89`). +- `data.args.particle_type`: Should include `"stars"` (and `"gas"` if you want the gas branch) so the filters and rotation functions know which components exist. + +The tutorials and notebooks assume square spaxels, so the default telescope factory currently only supports `pixel_type: square`. For a working example, inspect `notebooks/rubix_pipeline_single_function_shard_map.ipynb`, which runs the exact pipeline used in the tests. ## Documentation Sphinx Documentation of all the functions is currently available under [this link](https://astro-rubix.web.app/). diff --git a/docs/installation.rst b/docs/installation.rst index 5669a7aa..c7884835 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -26,4 +26,17 @@ Note that if `JAX` is not yet installed, only the CPU version of `JAX` will be i as a dependency. For a GPU-compatible installation of `JAX`, please refer to the [JAX installation guide](https://jax.readthedocs.io/en/latest/installation.html). -Get started with this simple example notebooks/rubix_pipeline_single_function.ipynb. +Get started with this simple example notebooks/rubix_pipeline_single_function_shard_map.ipynb. + +Configuration +============= + +When you run the pipeline you provide a configuration dict that references the files in `rubix/config/`. The following sections are required for the default pipelines: + +- `pipeline.name`: Choose one of `calc_ifu`, `calc_dusty_ifu`, or another entry from `pipeline_config.yml`. +- `galaxy`: Must include `dist_z` and a `rotation` block (`type` or explicit `alpha`, `beta`, `gamma`). +- `telescope`: Needs `name`, a `psf` block (Gaussian kernel with both `size` and `sigma`), an `lsf` block with `sigma`, and `noise` containing `signal_to_noise` plus a `noise_distribution` (`normal` or `uniform`). +- `ssp.dust`: Declares `extinction_model` and `Rv` before the dusty pipeline can produce an extincted datacube. +- `data.args.particle_type`: Must include `"stars"` (add `"gas"` if you rely on the optional gas branch) so the filtering/rotation steps know which components to process. + +The telescopes in `rubix/telescope` currently only support square pixels, so every config should set `pixel_type: square` in the relevant telescope definition. diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 7be1f8b4..ca780d56 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -40,12 +40,14 @@ class RubixPipeline: >>> from rubix.core.pipeline import RubixPipeline >>> config = "path/to/config.yml" + >>> target_datacube = ... # Load or define your target datacube here >>> pipe = RubixPipeline(config) >>> inputdata = pipe.prepare_data() - >>> output = pipe.run(inputdata) >>> final_datacube = pipe.run_sharded(inputdata) - >>> ssp_model = pipeline.ssp - >>> telescope = pipeline.telescope + >>> ssp_model = pipe.ssp + >>> telescope = pipe.telescope + >>> loss_value = pipe.loss(inputdata, target_datacube) + >>> gradient_data = pipe.gradient(inputdata, target_datacube) """ def __init__(self, user_config: Union[dict, str]): @@ -304,6 +306,6 @@ def loss( jnp.ndarray: Scalar mean squared error value. """ - output = self.run(rubixdata) + output = self.run_sharded(rubixdata) loss_value = jnp.sum((output - targetdata) ** 2) return loss_value From 5e6b4108b9767d6ff5870b7c2adf13b363bc7ca3 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Wed, 3 Dec 2025 13:54:52 +0100 Subject: [PATCH 17/22] Update installation instructions and dependencies; modify data handling in core modules --- README.md | 7 ++++--- docs/installation.rst | 8 ++++---- pyproject.toml | 1 - rubix/core/data.py | 4 ++-- rubix/core/dust.py | 3 +-- rubix/core/ifu.py | 3 +-- rubix/core/lsf.py | 3 +-- rubix/core/noise.py | 3 +-- rubix/core/pipeline.py | 16 ++++++++++++++-- rubix/core/psf.py | 3 +-- rubix/core/ssp.py | 3 +-- rubix/core/telescope.py | 3 +-- rubix/galaxy/alignment.py | 3 +-- rubix/spectra/dust/helpers.py | 3 +-- rubix/spectra/ssp/grid.py | 2 +- rubix/telescope/base.py | 3 +-- rubix/telescope/factory.py | 14 +++++++++++--- rubix/telescope/utils.py | 3 +-- tests/test_pipeline.py | 4 ++-- 19 files changed, 49 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 2547fb8b..b57d76ff 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ the following editable installation from this repository: ``` git clone https://github.com/AstroAI-Lab/rubix.git cd rubix -python -m pip install --editable .[tests] +python -m pip install --editable .[cpu,tests] ``` Having done so, the test suite can be run using `pytest`: @@ -52,8 +52,9 @@ Having done so, the test suite can be run using `pytest`: python -m pytest ``` -This project depends on [jax](https://github.com/google/jax). It only installed for cpu computations with the testing dependencies. For installation instructions with gpu support, -please refer to [here](https://github.com/google/jax?tab=readme-ov-file#installation). +This project depends on [jax](https://github.com/google/jax). For the pytests we only test the `cpu` version. +For installation instructions with gpu support, +please refer to [here](https://github.com/google/jax?tab=readme-ov-file#installation) or simply use the `cuda` option when pip installing. ## Configuration overview diff --git a/docs/installation.rst b/docs/installation.rst index c7884835..1483c677 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -6,7 +6,7 @@ Installation Clone the repository and navigate to the root directory of the repository. Then run ``` -pip install . +pip install .[cpu] ``` If you want to contribute to the development of `RUBIX`, we recommend the following editable installation from this repository: @@ -14,7 +14,7 @@ If you want to contribute to the development of `RUBIX`, we recommend the follow ``` git clone https://github.com/AstroAI-Lab/rubix cd rubix -pip install -e . +pip install -e .[cpu,tests,dev] ``` Having done so, the test suit can be run unsing `pytest`: @@ -22,9 +22,9 @@ Having done so, the test suit can be run unsing `pytest`: python -m pytest ``` -Note that if `JAX` is not yet installed, only the CPU version of `JAX` will be installed +Note that if `JAX` is not yet installed, with the `cpu` option only the CPU version of `JAX` will be installed as a dependency. For a GPU-compatible installation of `JAX`, please refer to the -[JAX installation guide](https://jax.readthedocs.io/en/latest/installation.html). +[JAX installation guide](https://jax.readthedocs.io/en/latest/installation.html) or use the option `cuda`. Get started with this simple example notebooks/rubix_pipeline_single_function_shard_map.ipynb. diff --git a/pyproject.toml b/pyproject.toml index a53894a3..2e2bc5be 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -91,7 +91,6 @@ tests = [ "pytest-mock", "requests-mock", "nbval", - "jax[cpu]>0.5.1", "pre-commit", ] docs = [ diff --git a/rubix/core/data.py b/rubix/core/data.py index 0d4dbf20..c32af7b7 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -2,12 +2,12 @@ import os from dataclasses import dataclass from functools import partial -from typing import Any, Callable, Optional, Union import jax import jax.numpy as jnp import numpy as np from beartype import beartype as typechecker +from beartype.typing import Any, Callable, Optional, Union from jaxtyping import jaxtyped from rubix.galaxy import IllustrisAPI, get_input_handler @@ -430,7 +430,7 @@ def prepare_input(config: Union[dict, str]) -> RubixData: # Set the galaxy attributes rubixdata.galaxy.redshift = jnp.float64(data["redshift"]) rubixdata.galaxy.redshift_unit = units["galaxy"]["redshift"] - rubixdata.galaxy.center = jnp.array(data["subhalo_center"], dtype=jnp.float64) + rubixdata.galaxy.center = jnp.array(data["subhalo_center"], dtype=jnp.float32) rubixdata.galaxy.center_unit = units["galaxy"]["center"] rubixdata.galaxy.halfmassrad_stars = jnp.float64(data["subhalo_halfmassrad_stars"]) rubixdata.galaxy.halfmassrad_stars_unit = units["galaxy"]["halfmassrad_stars"] diff --git a/rubix/core/dust.py b/rubix/core/dust.py index 45a38b35..e86d85a6 100644 --- a/rubix/core/dust.py +++ b/rubix/core/dust.py @@ -1,6 +1,5 @@ -from typing import Callable - from beartype import beartype as typechecker +from beartype.typing import Callable from jaxtyping import jaxtyped from rubix.core.cosmology import get_cosmology diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index b7d37df0..6402e845 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -1,8 +1,7 @@ -from typing import Callable, Union - import jax import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Callable from jax import lax from jaxtyping import Array, Float, jaxtyped diff --git a/rubix/core/lsf.py b/rubix/core/lsf.py index 5e60760c..c3fe70c4 100644 --- a/rubix/core/lsf.py +++ b/rubix/core/lsf.py @@ -1,6 +1,5 @@ -from typing import Callable - from beartype import beartype as typechecker +from beartype.typing import Callable from jaxtyping import jaxtyped from rubix.logger import get_logger diff --git a/rubix/core/noise.py b/rubix/core/noise.py index 472023e7..1bfa46d7 100644 --- a/rubix/core/noise.py +++ b/rubix/core/noise.py @@ -1,7 +1,6 @@ -from typing import Callable - import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Callable from jaxtyping import jaxtyped from rubix.logger import get_logger diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index ca780d56..d2a8d5bf 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -1,11 +1,23 @@ import time -from typing import Any, Optional, Sequence, Union +import warnings import jax import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Any, Optional, Sequence, Union from jax import lax -from jax.experimental.shard_map import shard_map + +try: + from jax.shard_map import shard_map # type: ignore[attr-defined] +except ImportError: # pragma: no cover - older JAX compatibility + warnings.filterwarnings( + "ignore", + message="jax.experimental.shard_map is deprecated in v0.8.0.*", + category=DeprecationWarning, + module=__name__, + ) + from jax.experimental.shard_map import shard_map + from jax.sharding import Mesh, NamedSharding, PartitionSpec as P from jax.tree_util import tree_map from jaxtyping import jaxtyped diff --git a/rubix/core/psf.py b/rubix/core/psf.py index 274c4ef8..b7fb94e2 100644 --- a/rubix/core/psf.py +++ b/rubix/core/psf.py @@ -1,6 +1,5 @@ -from typing import Callable - from beartype import beartype as typechecker +from beartype.typing import Callable from jaxtyping import jaxtyped from rubix.logger import get_logger diff --git a/rubix/core/ssp.py b/rubix/core/ssp.py index 850f33cb..dd9bb448 100644 --- a/rubix/core/ssp.py +++ b/rubix/core/ssp.py @@ -1,7 +1,6 @@ -from typing import Callable - import jax from beartype import beartype as typechecker +from beartype.typing import Callable from jaxtyping import jaxtyped from rubix.logger import get_logger diff --git a/rubix/core/telescope.py b/rubix/core/telescope.py index d9fddd6e..d4a79935 100644 --- a/rubix/core/telescope.py +++ b/rubix/core/telescope.py @@ -1,7 +1,6 @@ -from typing import Callable, Union - import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Callable, Union from jaxtyping import Array, Float, jaxtyped from rubix.logger import get_logger diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index 7ff9c52e..92297207 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -1,7 +1,6 @@ -from typing import Tuple, Union - import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Tuple, Union from jax.scipy.spatial.transform import Rotation from jaxtyping import Array, Float, jaxtyped diff --git a/rubix/spectra/dust/helpers.py b/rubix/spectra/dust/helpers.py index 29674488..692e08c3 100644 --- a/rubix/spectra/dust/helpers.py +++ b/rubix/spectra/dust/helpers.py @@ -1,8 +1,7 @@ -from typing import Final, Tuple - import jax import jax.numpy as jnp from beartype import beartype as typechecker +from beartype.typing import Final, Tuple from jaxtyping import Array, Float, jaxtyped # from jax.scipy.special import comb diff --git a/rubix/spectra/ssp/grid.py b/rubix/spectra/ssp/grid.py index d01a2b89..f3dff086 100644 --- a/rubix/spectra/ssp/grid.py +++ b/rubix/spectra/ssp/grid.py @@ -1,6 +1,5 @@ import os from dataclasses import dataclass, fields -from typing import List, Tuple, Union # import equinox as eqx import h5py @@ -9,6 +8,7 @@ from astropy import units as u from astropy.io import fits from beartype import beartype as typechecker +from beartype.typing import List, Tuple, Union from interpax import interp2d from jax.tree_util import Partial from jaxtyping import Array, Float, Int, jaxtyped diff --git a/rubix/telescope/base.py b/rubix/telescope/base.py index cbb3f80a..0212150f 100644 --- a/rubix/telescope/base.py +++ b/rubix/telescope/base.py @@ -1,8 +1,7 @@ -from typing import List, Optional, Union - import equinox as eqx import numpy as np from beartype import beartype as typechecker +from beartype.typing import List, Optional, Union from jaxtyping import Array, Float, Int, jaxtyped diff --git a/rubix/telescope/factory.py b/rubix/telescope/factory.py index 60db8682..7649c544 100644 --- a/rubix/telescope/factory.py +++ b/rubix/telescope/factory.py @@ -6,6 +6,7 @@ from beartype import beartype as typechecker from jaxtyping import jaxtyped +from rubix.logger import get_logger from rubix.telescope.apertures import ( CIRCULAR_APERTURE, HEXAGONAL_APERTURE, @@ -22,11 +23,17 @@ class TelescopeFactory: @jaxtyped(typechecker=typechecker) def __init__(self, telescopes_config: Optional[Union[dict, str]] = None) -> None: + logger = get_logger() if telescopes_config is None: + logger.info( + "No telescope config provided, falling back to %s", + TELESCOPE_CONFIG_PATH, + ) warnings.warn( - "No telescope config provided, using default stored in {}".format( + ("No telescope config provided, " "using default stored in {}").format( TELESCOPE_CONFIG_PATH - ) + ), + UserWarning, ) self.telescopes_config = read_yaml(TELESCOPE_CONFIG_PATH) elif isinstance(telescopes_config, str): @@ -46,7 +53,8 @@ def create_telescope(self, name: str) -> BaseTelescope: The telescope object as BaseTelescope. Raises: - ValueError: If the telescope name is not present in the configuration. + ValueError: If the telescope name is not present in the + configuration. Example 1 (Uses the defined telescope configuration) ----------------------------------------------------- diff --git a/rubix/telescope/utils.py b/rubix/telescope/utils.py index 2400e510..6a9e3219 100644 --- a/rubix/telescope/utils.py +++ b/rubix/telescope/utils.py @@ -1,8 +1,7 @@ -from typing import List, Tuple, Union - import jax.numpy as jnp import numpy as np from beartype import beartype as typechecker +from beartype.typing import List, Tuple, Union from jaxtyping import Array, Bool, Float, Int, jaxtyped from rubix.cosmology.base import BaseCosmology diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 615a8f71..fcc433d2 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -538,10 +538,10 @@ def test_loss_uses_run(simple_pipeline): target = jnp.array([1.0, 2.0]) output = jnp.array([3.0, 4.0]) - pipeline.run = MagicMock(return_value=output) + pipeline.run_sharded = MagicMock(return_value=output) loss_value = pipeline.loss(rubixdata, target) - pipeline.run.assert_called_once_with(rubixdata) + pipeline.run_sharded.assert_called_once_with(rubixdata) expected = jnp.sum((output - target) ** 2) assert jnp.allclose(loss_value, expected) From 5ba131ab9df9e3881188ac01c5f7dad924680d18 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 3 Dec 2025 14:19:41 +0100 Subject: [PATCH 18/22] remove vi notebook for the first version of the code, it is not in a final stage --- notebooks/cosmology.ipynb | 69 +- ...ge_metallicity_variational_inference.ipynb | 643 ------------------ pyproject.toml | 1 + 3 files changed, 58 insertions(+), 655 deletions(-) delete mode 100644 notebooks/gradient_age_metallicity_variational_inference.ipynb diff --git a/notebooks/cosmology.ipynb b/notebooks/cosmology.ipynb index e956c1bb..93a3d856 100644 --- a/notebooks/cosmology.ipynb +++ b/notebooks/cosmology.ipynb @@ -15,9 +15,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BaseCosmology(Om0=f32[], w0=f32[], wa=f32[], h=f32[])\n", + "FlatLambdaCDM(name=\"Planck15\", H0=67.74 km / (Mpc s), Om0=0.3075, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0. 0. 0.06] eV, Ob0=0.0486)\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", @@ -40,9 +49,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Angular Diameter Distance\n", + "rubix cosmo: 702.5322\n", + "astropy cosmo: 702.3747610737071 Mpc\n", + "Comoving Distance\n", + "rubix cosmo: 843.0387\n", + "astropy cosmo: 842.8497132884485 Mpc\n", + "lookback to z\n", + "rubix cosmo: 2.5104787\n", + "astropy cosmo: 2.509878627257186 Gyr\n", + "Age\n", + "rubix cosmo: [11.310789]\n", + "astropy cosmo: 11.287737269639198 Gyr\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "z = 0.2\n", @@ -67,24 +95,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.5\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.cosmology.utils import trapz\n", "import jax.numpy as jnp\n", "\n", - "x = jnp.array([0, 1, 2, 3])\n", - "y = jnp.array([0, 1, 4, 9])\n", + "x = jnp.array([0.0, 1.0, 2.0, 3.0])\n", + "y = jnp.array([0.0, 1.0, 4.0, 9.0])\n", "print(trapz(x, y))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "1946.5874\n" + ] + } + ], "source": [ "# NBVAL_SKIP\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", @@ -102,7 +147,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "publishrubix", "language": "python", "name": "python3" }, @@ -116,7 +161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/notebooks/gradient_age_metallicity_variational_inference.ipynb b/notebooks/gradient_age_metallicity_variational_inference.ipynb deleted file mode 100644 index 3bcc8526..00000000 --- a/notebooks/gradient_age_metallicity_variational_inference.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from jax import config\n", - "#config.update(\"jax_enable_x64\", True)\n", - "#config.update('jax_num_cpu_devices', 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import os\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "#os.environ['CUDA_VISIBLE_DEVICES'] = '1,2'\n", - "\n", - "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", - "import jax\n", - "\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import os\n", - "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load ssp template from FSPS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.spectra.ssp.factory import get_ssp_template\n", - "ssp_fsps = get_ssp_template(\"FSPS\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "age_values = ssp_fsps.age\n", - "print(age_values.shape)\n", - "\n", - "metallicity_values = ssp_fsps.metallicity\n", - "print(metallicity_values.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configure pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.pipeline import RubixPipeline\n", - "import os\n", - "config = {\n", - " \"pipeline\":{\"name\": \"calc_gradient\",},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 2,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"TESTGRADIENT\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 1.2},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},\n", - " },\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "pipe = RubixPipeline(config)\n", - "inputdata = pipe.prepare_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gradient on the spectrum for each wavelenght" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.pipeline import linear_pipeline as pipeline\n", - "\n", - "pipeline_instance = RubixPipeline(config)\n", - "\n", - "pipeline_instance._pipeline = pipeline.LinearTransformerPipeline(\n", - " pipeline_instance.pipeline_config, \n", - " pipeline_instance._get_pipeline_functions()\n", - ")\n", - "pipeline_instance._pipeline.assemble()\n", - "pipeline_instance.func = pipeline_instance._pipeline.compile_expression()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# pick values\n", - "initial_age_index = 95\n", - "initial_metallicity_index = 4\n", - "age0 = age_values[initial_age_index]\n", - "Z0 = metallicity_values[initial_metallicity_index]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "print(f\"age0 = {age0}, Z0 = {Z0}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "inputdata.stars.age = jnp.array([age_values[initial_age_index], age_values[initial_age_index]])\n", - "inputdata.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index], metallicity_values[initial_metallicity_index]])\n", - "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n", - "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", - "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import dataclasses\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def spectrum_1d(age, Z, base_data, pipeline_instance):\n", - " # broadcast per-star\n", - " nstar = base_data.stars.age.shape[0]\n", - " stars2 = dataclasses.replace(\n", - " base_data.stars,\n", - " age=jnp.full((nstar,), age),\n", - " metallicity=jnp.full((nstar,), Z),\n", - " )\n", - " data2 = dataclasses.replace(base_data, stars=stars2)\n", - "\n", - " out = pipeline_instance.func(data2)\n", - "\n", - " cube = out.stars.datacube # shape (…, n_lambda)\n", - " # collapse all non-wavelength axes, keep wavelength last\n", - " spec = cube.reshape((-1, cube.shape[-1])).sum(axis=0)\n", - "\n", - " return jnp.ravel(spec) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "spec0 = spectrum_1d(age0, Z0, inputdata, pipeline_instance)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "wave = pipe.telescope.wave_seq" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from tensorflow_probability.substrates import jax as tfp\n", - "tfd = tfp.distributions\n", - "tfb = tfp.bijectors\n", - "\n", - "import tqdm\n", - "import optax\n", - "import flax.linen as nn\n", - "from flax.metrics import tensorboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "class AffineCoupling(nn.Module):\n", - " @nn.compact\n", - " def __call__(self, x, nunits):\n", - " net = nn.leaky_relu(nn.Dense(128)(x))\n", - " net = nn.leaky_relu(nn.Dense(128)(net))\n", - " shift = nn.Dense(nunits)(net)\n", - " scale = nn.softplus(nn.Dense(nunits)(net))\n", - " return tfb.Chain([ tfb.Shift(shift), tfb.Scale(scale)])\n", - "\n", - "def make_nvp_fn(n_layers=2, d=2):\n", - " # We alternate between permutations and flow layers\n", - " layers = [ tfb.Permute([1,0])(tfb.RealNVP(d//2,\n", - " bijector_fn=AffineCoupling(name='affine%d'%i)))\n", - " for i in range(n_layers) ]\n", - "\n", - " # We build the actual nvp from these bijectors and a standard Gaussian distribution\n", - " nvp = tfd.TransformedDistribution(\n", - " tfd.MultivariateNormalDiag(loc=jnp.zeros(2), scale_diag=0.05*jnp.ones(2)),\n", - " bijector=tfb.Chain([tfb.Shift([5,0.05])] + layers ))\n", - " # Note that we have here added a shift to the bijector\n", - " return nvp\n", - "\n", - "class NeuralSplineFlowSampler(nn.Module):\n", - " @nn.compact\n", - " def __call__(self, key, n_samples):\n", - " nvp = make_nvp_fn()\n", - " x = nvp.sample(n_samples, seed=key)\n", - " return x, nvp.log_prob(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "model = NeuralSplineFlowSampler()\n", - "params = model.init(jax.random.PRNGKey(42), jax.random.PRNGKey(1), 16)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import pandas as pd\n", - "from chainconsumer import ChainConsumer, Chain, Truth\n", - "\n", - "# 1) Draw samples from the untrained bounded flow\n", - "theta0, logq0 = model.apply(params, key=jax.random.PRNGKey(1), n_samples=500)\n", - "df = pd.DataFrame(theta0, columns=[\"age\", \"Z\"])\n", - "\n", - "# 2) Optional: pick a fiducial point (for synthetic tests use your known truth)\n", - "fid_age = age0 # example: mid of [0, 20]\n", - "fid_Z = Z0 # example: inside [4.5e-5, 4.5e-2]\n", - "\n", - "# 3) Build the ChainConsumer plot\n", - "c = ChainConsumer()\n", - "c.add_chain(Chain(samples=df, name=\"Initial VI\"))\n", - "c.add_truth(Truth(location={\"age\": fid_age, \"Z\": fid_Z}))\n", - "\n", - "fig = c.plotter.plot(figsize=\"column\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def log_prior_gaussian(theta_batch,\n", - " mu_age=6.0, sigma_age=3.0,\n", - " mu_Z=1.3e-3, sigma_Z=2e-4):\n", - " \"\"\"Gaussian prior in physical space.\"\"\"\n", - " age = theta_batch[:, 0]\n", - " Z = theta_batch[:, 1]\n", - " lp_age = -0.5 * (((age - mu_age) / sigma_age)**2\n", - " + jnp.log(2*jnp.pi*sigma_age**2))\n", - " lp_Z = -0.5 * (((Z - mu_Z) / sigma_Z)**2\n", - " + jnp.log(2*jnp.pi*sigma_Z**2))\n", - " return lp_age + lp_Z # shape (batch,)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax, jax.numpy as jnp\n", - "\n", - "def log_likelihood(y, s, mask=None):\n", - " \"\"\"Full-vector Gaussian log-likelihood.\"\"\"\n", - " if mask is None:\n", - " mask = jnp.ones_like(y)\n", - " r = y - s\n", - " term = (r**2)\n", - " return jnp.sum(term * mask)\n", - "\n", - "def make_batched_loglik(y, base_data, pipeline_instance, mask=None):\n", - " \"\"\"Returns a function mapping a batch of theta -> per-sample log-likelihood.\"\"\"\n", - " def one_theta(theta):\n", - " age, Z = theta[0], theta[1]\n", - " s = spectrum_1d(age, Z, base_data, pipeline_instance) # -> (n_lambda,)\n", - " return log_likelihood(y, s, mask=mask, )\n", - " return jax.vmap(one_theta) # (batch,2) -> (batch,)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def make_elbo_fn(y, base_data, pipeline_instance,\n", - " mask=None, \n", - " mu_age=7.0, sigma_age=2.0,\n", - " mu_Z=0.001, sigma_Z=1e-3):\n", - " batched_loglik = make_batched_loglik(y, base_data,\n", - " pipeline_instance, mask)\n", - "\n", - " def elbo(params, seed, n_samples=128):\n", - " # Draw θ ~ q_φ(θ)\n", - " theta_batch, log_q = model.apply(params, key=seed, n_samples=n_samples)\n", - " # Compute log p(θ)\n", - " log_p = log_prior_gaussian(theta_batch, mu_age, sigma_age, mu_Z, sigma_Z)\n", - " # Compute log p(y|θ)\n", - " log_lik = batched_loglik(theta_batch)\n", - " # ELBO\n", - " elbo_value = jnp.mean(log_lik + log_p - log_q)\n", - " return -elbo_value # minimize\n", - " return elbo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# Random key\n", - "seed = jax.random.PRNGKey(0)\n", - "\n", - "# Scheduler and optimizer\n", - "total_steps = 20_000\n", - "lr = 2e-3\n", - "# lr_scheduler = optax.piecewise_constant_schedule(\n", - "# init_value=1e-3,\n", - "# boundaries_and_scales={int(total_steps*0.5): 0.2}\n", - "# )\n", - "optimizer = optax.adam(lr) #lr_scheduler)\n", - "opt_state = optimizer.init(params)\n", - "\n", - "# TensorBoard logs\n", - "from flax.metrics import tensorboard\n", - "summary_writer = tensorboard.SummaryWriter(\"logs/elbo\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "eps = 1e-6\n", - "sigma_obs = jnp.maximum(jnp.abs(spec0) / 1000.0, eps)\n", - "y = spec0\n", - "base_data = inputdata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# Build once, outside update_model\n", - "elbo = make_elbo_fn(\n", - " y, # observed full flux vector\n", - " base_data,\n", - " pipeline_instance, \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "@jax.jit\n", - "def update_model(params, opt_state, seed):#, n_samples=128):\n", - " # split RNG: first return is new seed you’ll keep, second is used to sample θ\n", - " seed, key = jax.random.split(seed)\n", - "\n", - " # loss(params) = -ELBO(params, key, n_samples)\n", - " loss, grads = jax.value_and_grad(elbo)(params, key)#, n_samples)\n", - "\n", - " # apply Adam step; passing params is safest for transforms that need them\n", - " updates, opt_state = optimizer.update(grads, opt_state, params)\n", - " params = optax.apply_updates(params, updates)\n", - "\n", - " return params, opt_state, loss, seed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import tqdm\n", - "\n", - "losses = []\n", - "\n", - "for i in tqdm.tqdm(range(total_steps)):\n", - " # one optimization step (minimizes -ELBO)\n", - " params, opt_state, loss, seed = update_model(params, opt_state, seed)\n", - "\n", - " losses.append(float(loss))\n", - "\n", - " # log every 10 steps\n", - " if i % 10 == 0:\n", - " summary_writer.scalar(\"neg_elbo\", float(loss), i)\n", - " #summary_writer.scalar(\"learning_rate\", float(lr_scheduler(i)), i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# 1) Sample posterior θ = (age, Z)\n", - "seed, sub = jax.random.split(seed)\n", - "theta, log_q = model.apply(params, key=sub, n_samples=5000) # theta.shape == (5000, 2)\n", - "age = theta[:, 0]\n", - "Z = theta[:, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "c = ChainConsumer()\n", - "\n", - "# fresh RNG split so we don’t reuse training key\n", - "seed, sub = jax.random.split(seed)\n", - "\n", - "# sample θ ~ qϕ(θ)\n", - "theta, log_q = model.apply(params, key=sub, n_samples=20_000) # shape (N, 2)\n", - "age = theta[:, 0]\n", - "Z = theta[:, 1]\n", - "\n", - "# ChainConsumer expects a pandas DataFrame\n", - "df = pd.DataFrame({\"age\": age, \"Z\": Z})\n", - "\n", - "# add the VI chain\n", - "c.add_chain(Chain(samples=df, name=\"VI\"))\n", - "\n", - "# optional “truth” dot: use known synthetic truth if you have it; else posterior mean\n", - "# truth_age, truth_Z = 8.0, 1.0e-2 # <- set these if you know them\n", - "# truth_age, truth_Z = float(age.mean()), float(Z.mean())\n", - "truth_age, truth_Z = age0, Z0\n", - "c.add_truth(Truth(location={\"age\": truth_age, \"Z\": truth_Z}))\n", - "\n", - "fig = c.plotter.plot(figsize=\"column\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "plt.figure(figsize=(7,3))\n", - "plt.plot(np.arange(len(losses)), losses, lw=1)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Loss\")\n", - "plt.grid(True)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml index 2e2bc5be..49b9347e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,6 +75,7 @@ dependencies = [ "ipywidgets", "jdaviz", "pynbody", + "optax", "opt-einsum >=3.3.0", ] [project.optional-dependencies] From 478f7c49b3dc678b12fea7355945b9ecaac0bdcf Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 3 Dec 2025 13:20:11 +0000 Subject: [PATCH 19/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- notebooks/cosmology.ipynb | 61 +++++---------------------------------- 1 file changed, 8 insertions(+), 53 deletions(-) diff --git a/notebooks/cosmology.ipynb b/notebooks/cosmology.ipynb index 93a3d856..7d97377f 100644 --- a/notebooks/cosmology.ipynb +++ b/notebooks/cosmology.ipynb @@ -15,18 +15,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BaseCosmology(Om0=f32[], w0=f32[], wa=f32[], h=f32[])\n", - "FlatLambdaCDM(name=\"Planck15\", H0=67.74 km / (Mpc s), Om0=0.3075, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0. 0. 0.06] eV, Ob0=0.0486)\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", @@ -49,28 +40,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Angular Diameter Distance\n", - "rubix cosmo: 702.5322\n", - "astropy cosmo: 702.3747610737071 Mpc\n", - "Comoving Distance\n", - "rubix cosmo: 843.0387\n", - "astropy cosmo: 842.8497132884485 Mpc\n", - "lookback to z\n", - "rubix cosmo: 2.5104787\n", - "astropy cosmo: 2.509878627257186 Gyr\n", - "Age\n", - "rubix cosmo: [11.310789]\n", - "astropy cosmo: 11.287737269639198 Gyr\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "z = 0.2\n", @@ -95,17 +67,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.5\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.cosmology.utils import trapz\n", @@ -118,18 +82,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n", - "1946.5874\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.cosmology import PLANCK15 as rubix_cosmo\n", From 1878f01ee99a725e19415822059f63cd021d21b6 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 3 Dec 2025 14:46:31 +0100 Subject: [PATCH 20/22] remove not needed : in the documentation --- rubix/core/cosmology.py | 2 +- rubix/core/data.py | 6 +-- rubix/core/lsf.py | 1 - rubix/core/noise.py | 1 - rubix/core/pipeline.py | 1 - rubix/core/psf.py | 1 - rubix/core/rotation.py | 2 +- rubix/core/telescope.py | 2 +- rubix/cosmology/base.py | 53 ++++--------------- rubix/cosmology/utils.py | 2 +- rubix/galaxy/alignment.py | 4 +- .../galaxy/input_handler/api/illustris_api.py | 2 +- rubix/galaxy/input_handler/base.py | 18 ------- rubix/spectra/dust/extinction_models.py | 2 - rubix/spectra/dust/generic_models.py | 2 +- rubix/spectra/ssp/factory.py | 1 + rubix/spectra/ssp/grid.py | 4 +- rubix/spectra/ssp/templates.py | 1 + rubix/utils.py | 2 +- 19 files changed, 26 insertions(+), 81 deletions(-) diff --git a/rubix/core/cosmology.py b/rubix/core/cosmology.py index 49f73994..71a2acb1 100644 --- a/rubix/core/cosmology.py +++ b/rubix/core/cosmology.py @@ -21,7 +21,7 @@ def get_cosmology(config: dict) -> RubixCosmology: ValueError: When ``config["cosmology"]["name"]`` is not supported. Example: - :: + >>> config = { ... ... ... "cosmology": diff --git a/rubix/core/data.py b/rubix/core/data.py index c32af7b7..ebc55fe1 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -265,7 +265,7 @@ def convert_to_rubix(config: Union[dict, str]): ValueError: When ``config['data']['name']`` is unsupported. Example: - :: + >>> import os >>> from rubix.core.data import convert_to_rubix @@ -397,7 +397,7 @@ def prepare_input(config: Union[dict, str]) -> RubixData: ValueError: When subset mode is enabled but neither stars nor gas coordinates exist. Example: - :: + >>> import os >>> from rubix.core.data import convert_to_rubix, prepare_input @@ -550,7 +550,7 @@ def get_reshape_data(config: Union[dict, str]) -> Callable: Function that reshapes a `RubixData` instance. Example: - :: + >>> from rubix.core.data import get_reshape_data >>> reshape_data = get_reshape_data(config) >>> rubixdata = reshape_data(rubixdata) diff --git a/rubix/core/lsf.py b/rubix/core/lsf.py index c3fe70c4..49839878 100644 --- a/rubix/core/lsf.py +++ b/rubix/core/lsf.py @@ -23,7 +23,6 @@ def get_convolve_lsf(config: dict) -> Callable[[RubixData], RubixData]: ValueError: When the telescope LSF configuration or sigma is missing. Example: - :: >>> config = { ... ... diff --git a/rubix/core/noise.py b/rubix/core/noise.py index 1bfa46d7..d5ffc037 100644 --- a/rubix/core/noise.py +++ b/rubix/core/noise.py @@ -26,7 +26,6 @@ def get_apply_noise(config: dict) -> Callable[[RubixData], RubixData]: ValueError: When required noise configuration keys are missing. Example: - :: >>> config = { ... ... diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index d2a8d5bf..b8bd72bd 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -48,7 +48,6 @@ class RubixPipeline: Parsed configuration dictionary or path to a configuration file. Example: - :: >>> from rubix.core.pipeline import RubixPipeline >>> config = "path/to/config.yml" diff --git a/rubix/core/psf.py b/rubix/core/psf.py index b7fb94e2..46dc40e5 100644 --- a/rubix/core/psf.py +++ b/rubix/core/psf.py @@ -28,7 +28,6 @@ def get_convolve_psf(config: dict) -> Callable: kernel type. Example: - :: >>> config = { ... ... diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index 6023270c..f2db5c3f 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -26,7 +26,7 @@ def get_galaxy_rotation(config: dict): or missing. Example: - :: + >>> config = { ... ... ... "galaxy": { diff --git a/rubix/core/telescope.py b/rubix/core/telescope.py index d4a79935..b847f20b 100644 --- a/rubix/core/telescope.py +++ b/rubix/core/telescope.py @@ -152,7 +152,7 @@ def get_filter_particles(config: dict) -> Callable: Callable[[RubixData], RubixData]: Function that filters particles. Example: - :: + >>> from rubix.core.telescope import get_filter_particles >>> filter_particles = get_filter_particles(config) diff --git a/rubix/cosmology/base.py b/rubix/cosmology/base.py index a6c460ee..ce101ea8 100644 --- a/rubix/cosmology/base.py +++ b/rubix/cosmology/base.py @@ -40,7 +40,7 @@ class BaseCosmology(eqx.Module): h (jnp.float32): Dimensionless Hubble constant. Example: - :: + >>> # Create Planck15 cosmology >>> from rubix.cosmology import COSMOLOGY >>> cosmo = COSMOLOGY(0.3089, -1.0, 0.0, 0.6774) @@ -73,7 +73,7 @@ def scale_factor_to_redshift( Float[Array, "..."]: Redshift ``1/a - 1``. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Convert scale factor 0.5 to redshift >>> cosmo.scale_factor_to_redshift(jnp.array(0.5)) @@ -121,7 +121,7 @@ def comoving_distance_to_z( Float[Array, "..."]: Comoving distance in Mpc. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate comoving distance to redshift 0.5 >>> cosmo.comoving_distance_to_z(0.5) @@ -145,7 +145,7 @@ def luminosity_distance_to_z( Float[Array, "..."]: Luminosity distance in Mpc. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Compute the luminosity distance to redshift 0.5 >>> cosmo.luminosity_distance_to_z(0.5) @@ -167,7 +167,7 @@ def angular_diameter_distance_to_z( Float[Array, "..."]: Angular diameter distance in Mpc. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Compute the angular diameter distance to redshift 0.5 >>> cosmo.angular_diameter_distance_to_z(0.5) @@ -189,7 +189,7 @@ def distance_modulus_to_z( Float[Array, "..."]: Distance modulus. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Compute the distance modulus to redshift 0.5 >>> cosmo.distance_modulus_to_z(0.5) @@ -211,7 +211,7 @@ def _hubble_time(self, z: Union[Float[Array, "..."], float]) -> Float[Array, ".. Float[Array, "..."]: Hubble time in seconds. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the Hubble time at redshift 0.5 >>> cosmo._hubble_time(0.5) @@ -235,7 +235,7 @@ def lookback_to_z( Float[Array, "..."]: Lookback time in seconds. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the lookback time to redshift 0.5 >>> cosmo.lookback_to_z(0.5) @@ -256,7 +256,7 @@ def age_at_z0(self) -> Float[Array, "..."]: The age of the universe at redshift 0 (float). Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the age of the universe at redshift 0 >>> cosmo.age_at_z0() @@ -294,7 +294,7 @@ def age_at_z( Float[Array, "..."]: Age in seconds. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the age of the universe at redshift 0.5 >>> cosmo.age_at_z(0.5) @@ -317,7 +317,7 @@ def angular_scale( Float[Array, "..."]: Angular scale in kpc/arcsec. Example: - :: + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the angular scale at redshift 0.5 >>> cosmo.angular_scale(0.5) @@ -326,34 +326,3 @@ def angular_scale( D_A = self.angular_diameter_distance_to_z(z) # in Mpc scale = D_A * (jnp.pi / (180 * 60 * 60)) * 1e3 # in kpc/arcsec return scale - - """ - I dont think we need this currently, but keeping it here for reference - @jit - def rho_crit(self, redshift): - rho_crit0 = RHO_CRIT0_KPC3_UNITY_H * self.h * self.h - rho_crit = rho_crit0 * self._Ez(redshift) ** 2 - return rho_crit - - @jit - def _integrand_oneOverEz1pz(self, z): - return 1.0 / self._Ez(z) / (1.0 + z) - - @jit - def _Om_at_z(self, z): - E = self._Ez(z) - return self.Om0 * (1.0 + z) ** 3 / E / E - - @jit - def _delta_vir(self, z): - x = self._Om(z) - 1.0 - Delta = 18 * jnp.pi**2 + 82.0 * x - 39.0 * x**2 - return Delta - - @jit - def virial_dynamical_time(self, redshift): - delta = self._delta_vir(redshift) - t_cross = 2**1.5 * self._hubble_time(redshift) * delta**-0.5 - return t_cross - -""" diff --git a/rubix/cosmology/utils.py b/rubix/cosmology/utils.py index 07b07109..be4eb497 100644 --- a/rubix/cosmology/utils.py +++ b/rubix/cosmology/utils.py @@ -71,7 +71,7 @@ def trapz( jnp.ndarray: Scalar results collected from the scan. Example: - :: + >>> from rubix.cosmology.utils import trapz >>> import jax.numpy as jnp diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index 92297207..f2010802 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -22,7 +22,7 @@ def center_particles(rubixdata: object, key: str) -> object: ValueError: If the galaxy center lies outside the particle bounds. Example: - :: + >>> from rubix.galaxy.alignment import center_particles >>> rubixdata = center_particles(rubixdata, "stars") """ @@ -83,7 +83,7 @@ def moment_of_inertia_tensor( Float[Array, "..."]: Moment of inertia tensor. Example: - :: + >>> from rubix.galaxy.alignment import moment_of_inertia_tensor >>> I = moment_of_inertia_tensor( ... rubixdata.stars.coords, diff --git a/rubix/galaxy/input_handler/api/illustris_api.py b/rubix/galaxy/input_handler/api/illustris_api.py index b5340c23..c35c2c21 100644 --- a/rubix/galaxy/input_handler/api/illustris_api.py +++ b/rubix/galaxy/input_handler/api/illustris_api.py @@ -224,7 +224,7 @@ def load_galaxy( unsupported particle type is configured. Example: - :: + >>> illustris_api = IllustrisAPI( ... api_key, ... simulation="TNG50-1", diff --git a/rubix/galaxy/input_handler/base.py b/rubix/galaxy/input_handler/base.py index 33941fd5..2d1da2d2 100644 --- a/rubix/galaxy/input_handler/base.py +++ b/rubix/galaxy/input_handler/base.py @@ -163,24 +163,6 @@ def _check_galaxy_data(self, galaxy_data, units): if field not in units["galaxy"]: raise ValueError(f"Units for {field} not found in units") - """ - def _check_particle_data(self, particle_data, units): - # Check if all required fields are present - for key in self.config["particles"]: - if key not in particle_data: - raise ValueError(f"Missing particle type {key} in particle data") - for field in self.config["particles"][key]: - if field not in particle_data[key]: - raise ValueError( - f"Missing field {field} in particle data for particle type {key}" - ) - - # Check if the units are correct - for key in particle_data: - for field in particle_data[key]: - if field not in units[key]: - raise ValueError(f"Units for {field} not found in units") - """ def _check_particle_data(self, particle_data, units): # Get the list of expected particle types from the configuration diff --git a/rubix/spectra/dust/extinction_models.py b/rubix/spectra/dust/extinction_models.py index 453ec369..8935767a 100644 --- a/rubix/spectra/dust/extinction_models.py +++ b/rubix/spectra/dust/extinction_models.py @@ -39,7 +39,6 @@ class Cardelli89(BaseExtRvModel): Example: Example showing CCM89 curves for a range of R(V) values. - :: .. plot:: :include-source: @@ -209,7 +208,6 @@ class Gordon23(BaseExtRvModel): Example: Example showing G23 curves for a range of R(V) values. - :: .. plot:: :include-source: diff --git a/rubix/spectra/dust/generic_models.py b/rubix/spectra/dust/generic_models.py index 885e7ac6..f1e36c42 100644 --- a/rubix/spectra/dust/generic_models.py +++ b/rubix/spectra/dust/generic_models.py @@ -117,6 +117,7 @@ def Drude1d( ValueError: If ``x_0`` is zero. Examples: + .. plot:: :include-source: @@ -214,7 +215,6 @@ def FM90( Examples: Example showing a FM90 curve with components identified. - :: .. plot:: :include-source: diff --git a/rubix/spectra/ssp/factory.py b/rubix/spectra/ssp/factory.py index b003ae95..61def9c2 100644 --- a/rubix/spectra/ssp/factory.py +++ b/rubix/spectra/ssp/factory.py @@ -22,6 +22,7 @@ def get_ssp_template(template: str) -> SSPGrid: ValueError: If the template name or source format is not supported. Example: + >>> from rubix.spectra.ssp.factory import get_ssp_template >>> ssp = get_ssp_template("FSPS") >>> ssp.age.shape diff --git a/rubix/spectra/ssp/grid.py b/rubix/spectra/ssp/grid.py index f3dff086..88c9e593 100644 --- a/rubix/spectra/ssp/grid.py +++ b/rubix/spectra/ssp/grid.py @@ -77,7 +77,7 @@ def get_lookup_interpolation( Partial: Interpolation function ``f(metallicity, age)``. Examples: - :: + >>> grid = SSPGrid(...) >>> lookup = grid.get_lookup_interpolation() >>> metallicity = 0.02 @@ -256,7 +256,6 @@ class HDF5SSPGrid(SSPGrid): flux (Float[Array, FLUX_AXES]): SSP fluxes in Lsun/Angstrom. Example: - :: >>> config = { ... "name": "Bruzual & Charlot (2003)", @@ -363,7 +362,6 @@ class pyPipe3DSSPGrid(SSPGrid): flux (Float[Array, FLUX_AXES]): SSP fluxes in Lsun/Angstrom. Example: - :: >>> config = { ... "name": "Mastar Charlot & Bruzual (2019)", diff --git a/rubix/spectra/ssp/templates.py b/rubix/spectra/ssp/templates.py index 27353c26..9230b4e6 100644 --- a/rubix/spectra/ssp/templates.py +++ b/rubix/spectra/ssp/templates.py @@ -2,6 +2,7 @@ This module contains the supported templates for the SSP grid. Example: + >>> from rubix.spectra.ssp.templates import BruzualCharlot2003 >>> BruzualCharlot2003 >>> print(BruzualCharlot2003.age) diff --git a/rubix/utils.py b/rubix/utils.py index edc08e24..09829e8a 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -180,7 +180,7 @@ def load_galaxy_data( Tuple[Dict[str, Any], Dict[str, Any]]: Galaxy data and associated units Example: - :: + >>> from rubix.utils import load_galaxy_data >>> galaxy_data, units = load_galaxy_data("path/to/file.hdf5") """ From f3dcba0e6bb377ef92ee5d28487609c2de1e333c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 3 Dec 2025 13:46:56 +0000 Subject: [PATCH 21/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/core/cosmology.py | 2 +- rubix/cosmology/base.py | 4 ++-- rubix/galaxy/input_handler/base.py | 1 - rubix/spectra/ssp/factory.py | 2 +- 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/rubix/core/cosmology.py b/rubix/core/cosmology.py index 71a2acb1..193f6d51 100644 --- a/rubix/core/cosmology.py +++ b/rubix/core/cosmology.py @@ -21,7 +21,7 @@ def get_cosmology(config: dict) -> RubixCosmology: ValueError: When ``config["cosmology"]["name"]`` is not supported. Example: - + >>> config = { ... ... ... "cosmology": diff --git a/rubix/cosmology/base.py b/rubix/cosmology/base.py index ce101ea8..feff6005 100644 --- a/rubix/cosmology/base.py +++ b/rubix/cosmology/base.py @@ -40,7 +40,7 @@ class BaseCosmology(eqx.Module): h (jnp.float32): Dimensionless Hubble constant. Example: - + >>> # Create Planck15 cosmology >>> from rubix.cosmology import COSMOLOGY >>> cosmo = COSMOLOGY(0.3089, -1.0, 0.0, 0.6774) @@ -317,7 +317,7 @@ def angular_scale( Float[Array, "..."]: Angular scale in kpc/arcsec. Example: - + >>> from rubix.cosmology import PLANCK15 as cosmo >>> # Calculate the angular scale at redshift 0.5 >>> cosmo.angular_scale(0.5) diff --git a/rubix/galaxy/input_handler/base.py b/rubix/galaxy/input_handler/base.py index 2d1da2d2..b2ef4d47 100644 --- a/rubix/galaxy/input_handler/base.py +++ b/rubix/galaxy/input_handler/base.py @@ -163,7 +163,6 @@ def _check_galaxy_data(self, galaxy_data, units): if field not in units["galaxy"]: raise ValueError(f"Units for {field} not found in units") - def _check_particle_data(self, particle_data, units): # Get the list of expected particle types from the configuration expected_particle_types = list(self.config["particles"].keys()) diff --git a/rubix/spectra/ssp/factory.py b/rubix/spectra/ssp/factory.py index 61def9c2..b3f078d1 100644 --- a/rubix/spectra/ssp/factory.py +++ b/rubix/spectra/ssp/factory.py @@ -22,7 +22,7 @@ def get_ssp_template(template: str) -> SSPGrid: ValueError: If the template name or source format is not supported. Example: - + >>> from rubix.spectra.ssp.factory import get_ssp_template >>> ssp = get_ssp_template("FSPS") >>> ssp.age.shape From 946af20ee3694aeeeb8c213e84aa8fd6dc230949 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Fri, 19 Dec 2025 09:43:05 +0100 Subject: [PATCH 22/22] Update installation instructions to include 'dev' dependencies for development setup --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b57d76ff..8e914572 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ the following editable installation from this repository: ``` git clone https://github.com/AstroAI-Lab/rubix.git cd rubix -python -m pip install --editable .[cpu,tests] +python -m pip install --editable .[cpu,tests,dev] ``` Having done so, the test suite can be run using `pytest`: