From c43ccca89352b2956490c009ed226f2b2fb2261a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:18:01 +0200 Subject: [PATCH 1/9] Fix delim_whitespace warning --- src/elli/dispersions/table_spectraray.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/elli/dispersions/table_spectraray.py b/src/elli/dispersions/table_spectraray.py index d1a69131..87fc9b83 100644 --- a/src/elli/dispersions/table_spectraray.py +++ b/src/elli/dispersions/table_spectraray.py @@ -48,7 +48,7 @@ def load_dispersion_table(self, fname: str) -> TableEpsilon: df = pd.read_csv( self.spectraray_path + fname, - delim_whitespace=True, + sep=r"\s+", skiprows=start, nrows=stop - start, index_col=0, From d42c2f736f0cebafa529a16c2897ef8b31b84ad9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:23:58 +0200 Subject: [PATCH 2/9] Avoid evaluating Sellmeier dispersion at resonance --- tests/test_dispersion_adding.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_dispersion_adding.py b/tests/test_dispersion_adding.py index 946a1ab9..7a29022e 100644 --- a/tests/test_dispersion_adding.py +++ b/tests/test_dispersion_adding.py @@ -1,11 +1,11 @@ """Test adding of dispersions""" -import pytest import numpy as np -from numpy.testing import assert_array_almost_equal +import pytest from elli import Cauchy, Sellmeier from elli.dispersions.base_dispersion import DispersionSum, IndexDispersionSum from elli.dispersions.table_epsilon import TableEpsilon +from numpy.testing import assert_array_almost_equal def test_adding_index_dispersion(): @@ -191,7 +191,7 @@ def test_adding_of_dispersion_function_and_table(): """Dispersion function and table can be added""" table = TableEpsilon(lbda=np.linspace(200, 1000, 801), epsilon=np.ones(801)) - sellmeier = Sellmeier().add(1, 1) + sellmeier = Sellmeier().add(1, 0.0001) assert_array_almost_equal( (table + sellmeier).get_dielectric(), From ea567ffa6b99a8cf3d743102d8289c845be5913b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:26:40 +0200 Subject: [PATCH 3/9] Handle pandas future_stack warning --- src/elli/importer/spectraray.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/elli/importer/spectraray.py b/src/elli/importer/spectraray.py index 6e66dc64..ac1c98b2 100644 --- a/src/elli/importer/spectraray.py +++ b/src/elli/importer/spectraray.py @@ -6,6 +6,7 @@ import re import pandas as pd +from packaging.version import Version, parse from ..utils import calc_rho @@ -49,7 +50,10 @@ def read_spectraray_psi_delta( psi_delta_df.columns = index # reorder dataframe - psi_delta_df = psi_delta_df.stack(0) + if Version("2.2") <= parse(pd.__version__) < Version("3.0"): + psi_delta_df = psi_delta_df.stack(0, future_stack=True) + else: + psi_delta_df = psi_delta_df.stack(0) psi_delta_df = psi_delta_df.reorder_levels(["Angle of Incidence", "Wavelength"]) psi_delta_df.sort_index(axis=0, inplace=True) psi_delta_df.sort_index(axis=1, ascending=False, inplace=True) From ebbdd75bf0f245284d54163e4020557aff93d11b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:27:15 +0200 Subject: [PATCH 4/9] Woollam importer: use iloc indexing --- src/elli/importer/woollam.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/elli/importer/woollam.py b/src/elli/importer/woollam.py index e52c1a59..c96da134 100644 --- a/src/elli/importer/woollam.py +++ b/src/elli/importer/woollam.py @@ -3,12 +3,12 @@ It supports loading of standard psi/delta values. """ -from typing import TextIO -import re import logging +import re +from typing import TextIO -from pint import UndefinedUnitError, DimensionalityError import pandas as pd +from pint import DimensionalityError, UndefinedUnitError from ..units import ureg from ..utils import calc_rho @@ -125,8 +125,9 @@ def _read_wvase_dataframe(file_object: TextIO) -> pd.DataFrame: header=None, names=["Wavelength", "Angle of Incidence", "Ψ", "Δ", "Ψ_err", "Δ_err"], ) + print(dframe) dframe = ( - dframe[dframe.apply(lambda x: is_float(x[0]), axis=1)] + dframe[dframe.apply(lambda x: is_float(x.iloc[0]), axis=1)] .set_index(["Wavelength", "Angle of Incidence"]) .swaplevel(0, 1) ) From 0e7ae8f0cdf1cddfdc756e5e716d0f6a6e445aa8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:29:16 +0200 Subject: [PATCH 5/9] Consistently use loc indexing --- examples/Basic Usage/Basic Usage.ipynb | 803 ++++++++++++++------- examples/TiO2 Fit/TiO2 Multilayerfit.ipynb | 683 +++++++++++++----- tests/test_TiO2.py | 8 +- 3 files changed, 1068 insertions(+), 426 deletions(-) diff --git a/examples/Basic Usage/Basic Usage.ipynb b/examples/Basic Usage/Basic Usage.ipynb index 3ed65880..125c4bf3 100644 --- a/examples/Basic Usage/Basic Usage.ipynb +++ b/examples/Basic Usage/Basic Usage.ipynb @@ -1,277 +1,562 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:24:42.779064Z", + "iopub.status.busy": "2024-06-28T08:24:42.778755Z", + "iopub.status.idle": "2024-06-28T08:24:43.074754Z", + "shell.execute_reply": "2024-06-28T08:24:43.074256Z", + "shell.execute_reply.started": "2024-06-28T08:24:42.779045Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Basic usage\n\nBasic usage of building a model and fitting it to measurement data of SiO2 on Si.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import elli\nfrom elli.fitting import ParamsHist, fit\n\n# sphinx_gallery_thumbnail_path = '_static/basic_usage.png'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading data\n\nWe load the data from the generated\n[NeXus file](https://manual.nexusformat.org/classes/contributed_definitions/NXellipsometry.html#nxellipsometry)\nand select the angle we want to analyse.\nYou may set the ANGLE constant to 50 or 60 to select\nother angles of incidence from the example file.\nAdditionally, we're cutting the wavelength axis to be in between 210 nm and 800 nm.\nThis is because we're using literature values for Si,\nwhich are only defined in this wavelength range.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ANGLE = 70\npsi_delta = elli.read_nexus_psi_delta(\"SiO2onSi.ellips.nxs\").loc[ANGLE][210:800]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting parameters\n\nAs an example we analyse an oxidation layer of SiO2 on Si.\nPrior to defining our model, we have to set the parameters we want to use.\nWe're going to use a `Cauchy model ` for SiO2 and load the Si values from\n[literature values](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes).\nThe parameter names can be choosen freely,\nbut you have to use the exact same name in the later model definition.\nThe package uses lmfit as fitting tool and you may refer to their\n[documentation](https://lmfit.github.io/lmfit-py/parameters.html#lmfit.parameter.Parameters.add)\nfor details on parameter definition.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "params = ParamsHist()\nparams.add(\"SiO2_n0\", value=1.452, min=-100, max=100, vary=False)\nparams.add(\"SiO2_n1\", value=36.0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_n2\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_k0\", value=0, min=-100, max=100, vary=False)\nparams.add(\"SiO2_k1\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_k2\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_d\", value=20, min=0, max=40000, vary=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load silicon dispersion from the refractiveindexinfo database\nYou can load any material from the index\n[refractiveindex.info](https://refractiveindex.info)_, which is\nembedded into the software (so you may use it offline, too). Here, we\nare interested in the literature values for the silicon substrate.\nFirst we need to load the database with ``rii_db = elli.db.RII()`` and\nthen we can query it with ``rii_db.get_mat(\"Si\", \"Aspnes\")`` to load\nthis\n[entry](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes)_.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "rii_db = elli.db.RII()\nSi = rii_db.get_mat(\"Si\", \"Aspnes\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building the model\n\nFor simple parameter estimation,\nthe fit decorator (**@fit**) in conjuction with the model definition is used.\nThe fitting decorator takes a pandas dataframe containing\nthe psi/delta measurement data (**psi_delta**) and the model parameters (**params**) as an input.\nIt then passes the wavelength from measurement dataframe (**lbda**)\nand the parameters to the actual model function.\n\nInside the model function the optical model is built,\ni.e. the Si literature values are loaded\nand the fitting parameters are filled into the Cauchy dispersion.\nFor details on how to insert data into the Cauchy model or other optical dispersion models,\nyou may refer to the documentation of pyElli.\nPlease keep in mind that the parameters you use here\nhave to be defined in the parameter object **param**.\n\nFrom the dispersion model isotropic materials are generated\n(could also be an anisotropic material, refer to the docs for an overview).\nThis is done by calling the :code:`elli.IsotropicMaterial(...)` function\nwith a dispersion model as a parameter\nor simply calling :code:`.get_mat()` on a dispersion model.\nThese two approaches are equivalent.From these materials the layer is build,\nwhich only consists of the SiO2 layer in this example.\nThe final structure consists of an incoming half-space,\nthe layers and an outgoing half space. Specifically,\ntypically the light is coming from air and finally gets absorbed by the bulk material,\nin our example this is Si, i.e. we call :code:`elli.Structure(elli.AIR, Layer, Si)`.\n\nTo provide simulated data, we have to evaluate the structure\nby calling the :code:`evaluate(...)` function,\nwhich takes the experimental wavelength array **lbda**, **ANGLE** under which the experiment\nwas performed and the solver to be used to solve the transfer-matrix problem.\nHere, we use a simple 2x2 matrix approach,\nwhich splits the interaction in s and p-parts and therefore cannot account for anisotropy.\nThere exist 4x4 matrix solvers as well.\nYou may refer to the `solver documentation ` for further details.\n\nExecuting the cell below in a jupyter notebook displays a comparison of the simulated \u03a8 / \u0394 values\nat the current parameter values with their measured counterparts.\nAdditionally, input fields for each model parameter are shown.\nYou may change the parameters and the calcualted data will change accordingly.\nFor clarification the modeled data is shown with `_calc` postfix in the legend.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "@fit(psi_delta, params)\ndef model(lbda, params):\n # Generate the cauchy model from the current lmfit parameters\n SiO2 = elli.Cauchy(\n params[\"SiO2_n0\"],\n params[\"SiO2_n1\"],\n params[\"SiO2_n2\"],\n params[\"SiO2_k0\"],\n params[\"SiO2_k1\"],\n params[\"SiO2_k2\"],\n ).get_mat()\n # get_mat() generates an IsotropicMaterial from the dispersion relation\n\n # Construct the layers you expect in your sample\n # Here, it only consists of one layer SiO2 in between air and Si.\n # We build the structure coming from air, through the layers,\n # represented as an array, and having Si as bulk material.\n structure = elli.Structure(\n elli.AIR, # Input medium\n [elli.Layer(SiO2, params[\"SiO2_d\"])], # Overlayer structure\n Si,\n ) # Output medium / Substrate\n\n # The model should return the evaluation of the structure at the experimental wavelengths lbda,\n # the experimental angle ANGLE and it should define a solver to calculate the transfer matrix.\n return structure.evaluate(lbda, ANGLE, solver=elli.Solver2x2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fit and plot fit results\n\nThe fit of the data can be executed by calling the fit() function on the model function,\nwhich automatically gets attached by the @fit decorator in the cell above.\nThe following cell basically executes the fit and plots\na comparison between the measurement and fitted data.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fit_stats = model.fit()\nmodel.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extracting the optical properties from the fit\n\nSince we want to extract the dispersion relation of a layer in our measured stack,\nwe can use our fitted parameters.\nThe fit parameters are contained in the fits output :code:`params` attribute,\ni.e. :code:`fit_stats.params` for our example.\nWe can use it to call our dispersion relation we used in our model\n(here it is a Cauchy dispersion relation)\nand fill in our fitted value.\nBy calling :code:`get_dielectric_df()` we can get the dielectric function of the material,\nwhich is plotted here as an example. :code:`get_dielectric_df` uses a default wavelength range\nwhich can also be changed by inputting a wavelength array as a parameter.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fitted_model = elli.Cauchy(\n fit_stats.params[\"SiO2_n0\"],\n fit_stats.params[\"SiO2_n1\"],\n fit_stats.params[\"SiO2_n2\"],\n fit_stats.params[\"SiO2_k0\"],\n fit_stats.params[\"SiO2_k1\"],\n fit_stats.params[\"SiO2_k2\"],\n)\n\nfitted_model.get_dielectric_df().plot(backend=\"plotly\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also call :code:`get_refractive_index_df()`\nto get the refractive index of the material as dataframe.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Basic usage\n", + "\n", + "Basic usage of building a model and fitting it to measurement data of SiO2 on Si.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:24:43.075322Z", + "iopub.status.busy": "2024-06-28T08:24:43.075163Z", + "iopub.status.idle": "2024-06-28T08:24:44.359396Z", + "shell.execute_reply": "2024-06-28T08:24:44.359104Z", + "shell.execute_reply.started": "2024-06-28T08:24:43.075311Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fitted_model.get_refractive_index_df().plot(backend=\"plotly\")" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import elli\n", + "from elli.fitting import ParamsHist, fit\n", + "\n", + "# sphinx_gallery_thumbnail_path = '_static/basic_usage.png'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading data\n", + "\n", + "We load the data from the generated\n", + "[NeXus file](https://manual.nexusformat.org/classes/contributed_definitions/NXellipsometry.html#nxellipsometry)\n", + "and select the angle we want to analyse.\n", + "You may set the ANGLE constant to 50 or 60 to select\n", + "other angles of incidence from the example file.\n", + "Additionally, we're cutting the wavelength axis to be in between 210 nm and 800 nm.\n", + "This is because we're using literature values for Si,\n", + "which are only defined in this wavelength range.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:25:12.248765Z", + "iopub.status.busy": "2024-06-28T08:25:12.248477Z", + "iopub.status.idle": "2024-06-28T08:25:12.255453Z", + "shell.execute_reply": "2024-06-28T08:25:12.254917Z", + "shell.execute_reply.started": "2024-06-28T08:25:12.248748Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to write your data to a file you simply call pandas :code:`to_csv(...)`\nfunction to write a csv file, i.e. for the dielectric function this writes as\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "ANGLE = 70\n", + "psi_delta = elli.read_nexus_psi_delta(\"SiO2onSi.ellips.nxs\").loc[ANGLE].loc[210:800]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting parameters\n", + "\n", + "As an example we analyse an oxidation layer of SiO2 on Si.\n", + "Prior to defining our model, we have to set the parameters we want to use.\n", + "We're going to use a `Cauchy model ` for SiO2 and load the Si values from\n", + "[literature values](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes).\n", + "The parameter names can be choosen freely,\n", + "but you have to use the exact same name in the later model definition.\n", + "The package uses lmfit as fitting tool and you may refer to their\n", + "[documentation](https://lmfit.github.io/lmfit-py/parameters.html#lmfit.parameter.Parameters.add)\n", + "for details on parameter definition.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.363402Z", + "iopub.status.idle": "2024-06-28T08:24:44.363553Z", + "shell.execute_reply": "2024-06-28T08:24:44.363484Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.363474Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fitted_model.get_dielectric_df().to_csv(\"SiO2_diel_func.csv\")" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "params = ParamsHist()\n", + "params.add(\"SiO2_n0\", value=1.452, min=-100, max=100, vary=False)\n", + "params.add(\"SiO2_n1\", value=36.0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_n2\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_k0\", value=0, min=-100, max=100, vary=False)\n", + "params.add(\"SiO2_k1\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_k2\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_d\", value=20, min=0, max=40000, vary=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load silicon dispersion from the refractiveindexinfo database\n", + "You can load any material from the index\n", + "[refractiveindex.info](https://refractiveindex.info)_, which is\n", + "embedded into the software (so you may use it offline, too). Here, we\n", + "are interested in the literature values for the silicon substrate.\n", + "First we need to load the database with ``rii_db = elli.db.RII()`` and\n", + "then we can query it with ``rii_db.get_mat(\"Si\", \"Aspnes\")`` to load\n", + "this\n", + "[entry](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes)_.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.364189Z", + "iopub.status.idle": "2024-06-28T08:24:44.364328Z", + "shell.execute_reply": "2024-06-28T08:24:44.364274Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.364267Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may also access a single value of your optical model\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "rii_db = elli.db.RII()\n", + "Si = rii_db.get_mat(\"Si\", \"Aspnes\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building the model\n", + "\n", + "For simple parameter estimation,\n", + "the fit decorator (**@fit**) in conjuction with the model definition is used.\n", + "The fitting decorator takes a pandas dataframe containing\n", + "the psi/delta measurement data (**psi_delta**) and the model parameters (**params**) as an input.\n", + "It then passes the wavelength from measurement dataframe (**lbda**)\n", + "and the parameters to the actual model function.\n", + "\n", + "Inside the model function the optical model is built,\n", + "i.e. the Si literature values are loaded\n", + "and the fitting parameters are filled into the Cauchy dispersion.\n", + "For details on how to insert data into the Cauchy model or other optical dispersion models,\n", + "you may refer to the documentation of pyElli.\n", + "Please keep in mind that the parameters you use here\n", + "have to be defined in the parameter object **param**.\n", + "\n", + "From the dispersion model isotropic materials are generated\n", + "(could also be an anisotropic material, refer to the docs for an overview).\n", + "This is done by calling the :code:`elli.IsotropicMaterial(...)` function\n", + "with a dispersion model as a parameter\n", + "or simply calling :code:`.get_mat()` on a dispersion model.\n", + "These two approaches are equivalent.From these materials the layer is build,\n", + "which only consists of the SiO2 layer in this example.\n", + "The final structure consists of an incoming half-space,\n", + "the layers and an outgoing half space. Specifically,\n", + "typically the light is coming from air and finally gets absorbed by the bulk material,\n", + "in our example this is Si, i.e. we call :code:`elli.Structure(elli.AIR, Layer, Si)`.\n", + "\n", + "To provide simulated data, we have to evaluate the structure\n", + "by calling the :code:`evaluate(...)` function,\n", + "which takes the experimental wavelength array **lbda**, **ANGLE** under which the experiment\n", + "was performed and the solver to be used to solve the transfer-matrix problem.\n", + "Here, we use a simple 2x2 matrix approach,\n", + "which splits the interaction in s and p-parts and therefore cannot account for anisotropy.\n", + "There exist 4x4 matrix solvers as well.\n", + "You may refer to the `solver documentation ` for further details.\n", + "\n", + "Executing the cell below in a jupyter notebook displays a comparison of the simulated Ψ / Δ values\n", + "at the current parameter values with their measured counterparts.\n", + "Additionally, input fields for each model parameter are shown.\n", + "You may change the parameters and the calcualted data will change accordingly.\n", + "For clarification the modeled data is shown with `_calc` postfix in the legend.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.364786Z", + "iopub.status.idle": "2024-06-28T08:24:44.364902Z", + "shell.execute_reply": "2024-06-28T08:24:44.364851Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.364846Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fit_stats.params[\"SiO2_n0\"].value" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "@fit(psi_delta, params)\n", + "def model(lbda, params):\n", + " # Generate the cauchy model from the current lmfit parameters\n", + " SiO2 = elli.Cauchy(\n", + " params[\"SiO2_n0\"],\n", + " params[\"SiO2_n1\"],\n", + " params[\"SiO2_n2\"],\n", + " params[\"SiO2_k0\"],\n", + " params[\"SiO2_k1\"],\n", + " params[\"SiO2_k2\"],\n", + " ).get_mat()\n", + " # get_mat() generates an IsotropicMaterial from the dispersion relation\n", + "\n", + " # Construct the layers you expect in your sample\n", + " # Here, it only consists of one layer SiO2 in between air and Si.\n", + " # We build the structure coming from air, through the layers,\n", + " # represented as an array, and having Si as bulk material.\n", + " structure = elli.Structure(\n", + " elli.AIR, # Input medium\n", + " [elli.Layer(SiO2, params[\"SiO2_d\"])], # Overlayer structure\n", + " Si,\n", + " ) # Output medium / Substrate\n", + "\n", + " # The model should return the evaluation of the structure at the experimental wavelengths lbda,\n", + " # the experimental angle ANGLE and it should define a solver to calculate the transfer matrix.\n", + " return structure.evaluate(lbda, ANGLE, solver=elli.Solver4x4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit and plot fit results\n", + "\n", + "The fit of the data can be executed by calling the fit() function on the model function,\n", + "which automatically gets attached by the @fit decorator in the cell above.\n", + "The following cell basically executes the fit and plots\n", + "a comparison between the measurement and fitted data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.365416Z", + "iopub.status.idle": "2024-06-28T08:24:44.365556Z", + "shell.execute_reply": "2024-06-28T08:24:44.365504Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.365499Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our simply print the fitted values in a list together with their fitting errors\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fit_stats = model.fit()\n", + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting the optical properties from the fit\n", + "\n", + "Since we want to extract the dispersion relation of a layer in our measured stack,\n", + "we can use our fitted parameters.\n", + "The fit parameters are contained in the fits output :code:`params` attribute,\n", + "i.e. :code:`fit_stats.params` for our example.\n", + "We can use it to call our dispersion relation we used in our model\n", + "(here it is a Cauchy dispersion relation)\n", + "and fill in our fitted value.\n", + "By calling :code:`get_dielectric_df()` we can get the dielectric function of the material,\n", + "which is plotted here as an example. :code:`get_dielectric_df` uses a default wavelength range\n", + "which can also be changed by inputting a wavelength array as a parameter.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.365940Z", + "iopub.status.idle": "2024-06-28T08:24:44.366040Z", + "shell.execute_reply": "2024-06-28T08:24:44.365997Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.365992Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fit_stats.params" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fitted_model = elli.Cauchy(\n", + " fit_stats.params[\"SiO2_n0\"],\n", + " fit_stats.params[\"SiO2_n1\"],\n", + " fit_stats.params[\"SiO2_n2\"],\n", + " fit_stats.params[\"SiO2_k0\"],\n", + " fit_stats.params[\"SiO2_k1\"],\n", + " fit_stats.params[\"SiO2_k2\"],\n", + ")\n", + "\n", + "fitted_model.get_dielectric_df().plot(backend=\"plotly\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also call :code:`get_refractive_index_df()`\n", + "to get the refractive index of the material as dataframe.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.366465Z", + "iopub.status.idle": "2024-06-28T08:24:44.366575Z", + "shell.execute_reply": "2024-06-28T08:24:44.366526Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.366522Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show fit statistics\nNow, we may also print out the fit statictics from the model fit in the cell above.\nThe fit statistics are simple [lmfit fit statistics](https://lmfit.github.io/lmfit-py/fitting.html#), too.\nTypically, one uses chi square values as a figure of merit for the fit quality.\nIt is stored in the chisqr attribute of the fit_stats variable we defined above.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fitted_model.get_refractive_index_df().plot(backend=\"plotly\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to write your data to a file you simply call pandas :code:`to_csv(...)`\n", + "function to write a csv file, i.e. for the dielectric function this writes as\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.366919Z", + "iopub.status.idle": "2024-06-28T08:24:44.367022Z", + "shell.execute_reply": "2024-06-28T08:24:44.366975Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.366971Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fit_stats.chisqr" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fitted_model.get_dielectric_df().to_csv(\"SiO2_diel_func.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also access a single value of your optical model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.367318Z", + "iopub.status.idle": "2024-06-28T08:24:44.367420Z", + "shell.execute_reply": "2024-06-28T08:24:44.367373Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.367368Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can print the full fit statistics, too.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fit_stats.params[\"SiO2_n0\"].value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simply print the fitted values in a list together with their fitting errors\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.367818Z", + "iopub.status.idle": "2024-06-28T08:24:44.367941Z", + "shell.execute_reply": "2024-06-28T08:24:44.367888Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.367883Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fit_stats" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "fit_stats.params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show fit statistics\n", + "Now, we may also print out the fit statictics from the model fit in the cell above.\n", + "The fit statistics are simple [lmfit fit statistics](https://lmfit.github.io/lmfit-py/fitting.html#), too.\n", + "Typically, one uses chi square values as a figure of merit for the fit quality.\n", + "It is stored in the chisqr attribute of the fit_stats variable we defined above.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.368659Z", + "iopub.status.idle": "2024-06-28T08:24:44.368775Z", + "shell.execute_reply": "2024-06-28T08:24:44.368725Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.368720Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n[Here](https://github.com/PyEllips/pyElli/tree/master/examples/Basic%20Usage)\nyou can find the latest jupyter notebook and data files of this example.\n\n" - ] + "jupyter": { + "outputs_hidden": false } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + }, + "outputs": [], + "source": [ + "fit_stats.chisqr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print the full fit statistics, too.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "execution": { + "iopub.status.busy": "2024-06-28T08:24:44.369199Z", + "iopub.status.idle": "2024-06-28T08:24:44.369318Z", + "shell.execute_reply": "2024-06-28T08:24:44.369265Z", + "shell.execute_reply.started": "2024-06-28T08:24:44.369260Z" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" + "jupyter": { + "outputs_hidden": false } + }, + "outputs": [], + "source": [ + "fit_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "[Here](https://github.com/PyEllips/pyElli/tree/master/examples/Basic%20Usage)\n", + "you can find the latest jupyter notebook and data files of this example.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/TiO2 Fit/TiO2 Multilayerfit.ipynb b/examples/TiO2 Fit/TiO2 Multilayerfit.ipynb index 7317d34a..bda5d800 100644 --- a/examples/TiO2 Fit/TiO2 Multilayerfit.ipynb +++ b/examples/TiO2 Fit/TiO2 Multilayerfit.ipynb @@ -1,187 +1,542 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Multilayer fit\n\nFits a multilayer model to an ALD grown TiO2 sample on SiO2 / Si.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import elli\nfrom elli.fitting import ParamsHist, fit\n\n# sphinx_gallery_thumbnail_path = '_static/multilayer.png'" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:44.843806Z", + "iopub.status.busy": "2024-06-28T09:13:44.843591Z", + "iopub.status.idle": "2024-06-28T09:13:45.917983Z", + "shell.execute_reply": "2024-06-28T09:13:45.917335Z", + "shell.execute_reply.started": "2024-06-28T09:13:44.843784Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data\n\nLoad data collected with Sentech Ellipsometer and cut the spectral range (to use Si Aspnes file)\n\nThe sample is an ALD grown TiO2 sample (with 400 cycles)\non commercially available SiO2 / Si substrate.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "tss = elli.read_spectraray_psi_delta(\"TiO2_400cycles.txt\").loc[70.06][400:800]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set start parameters\nHere we set the start parameters for the TiO2 and SiO2 layer.\nWe set the SiO2 layer parameters to a fixed value from another\nfit of the substrate. See the `Basic usage` example for details\non how to perform such a fit.\nIn general it is a good idea to fit your data layer-wise if possible\nto yield a better fit quality.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "params = ParamsHist()\nparams.add(\"SiO2_n0\", value=1.452, min=-100, max=100, vary=False)\nparams.add(\"SiO2_n1\", value=36.0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_n2\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_k0\", value=0, min=-100, max=100, vary=False)\nparams.add(\"SiO2_k1\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_k2\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"SiO2_d\", value=276.36, min=0, max=40000, vary=False)\n\nparams.add(\"TiO2_n0\", value=2.236, min=-100, max=100, vary=True)\nparams.add(\"TiO2_n1\", value=451, min=-40000, max=40000, vary=True)\nparams.add(\"TiO2_n2\", value=251, min=-40000, max=40000, vary=True)\nparams.add(\"TiO2_k0\", value=0, min=-100, max=100, vary=False)\nparams.add(\"TiO2_k1\", value=0, min=-40000, max=40000, vary=False)\nparams.add(\"TiO2_k2\", value=0, min=-40000, max=40000, vary=False)\n\nparams.add(\"TiO2_d\", value=20, min=0, max=40000, vary=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load silicon dispersion from the refractiveindexinfo database\nYou can load any material from the index\n[refractiveindex.info](https://refractiveindex.info)_, which is\nembedded into the software (so you may use it offline, too). Here, we\nare interested in the literature values for the silicon substrate.\nFirst we need to load the database with ``rii_db = elli.db.RII()`` and\nthen we can query it with ``rii_db.get_mat(\"Si\", \"Aspnes\")`` to load\nthis\n[entry](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes)_.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Multilayer fit\n", + "\n", + "Fits a multilayer model to an ALD grown TiO2 sample on SiO2 / Si.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:45.919042Z", + "iopub.status.busy": "2024-06-28T09:13:45.918777Z", + "iopub.status.idle": "2024-06-28T09:13:48.809389Z", + "shell.execute_reply": "2024-06-28T09:13:48.808795Z", + "shell.execute_reply.started": "2024-06-28T09:13:45.919022Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "rii_db = elli.db.RII()\nSi = rii_db.get_mat(\"Si\", \"Aspnes\")" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import elli\n", + "from elli.fitting import ParamsHist, fit\n", + "\n", + "# sphinx_gallery_thumbnail_path = '_static/multilayer.png'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data\n", + "\n", + "Load data collected with Sentech Ellipsometer and cut the spectral range (to use Si Aspnes file)\n", + "\n", + "The sample is an ALD grown TiO2 sample (with 400 cycles)\n", + "on commercially available SiO2 / Si substrate.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:48.813956Z", + "iopub.status.busy": "2024-06-28T09:13:48.810424Z", + "iopub.status.idle": "2024-06-28T09:13:48.846659Z", + "shell.execute_reply": "2024-06-28T09:13:48.845069Z", + "shell.execute_reply.started": "2024-06-28T09:13:48.813929Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building the model\nHere the model is build and the experimental structure is returned.\nFor details on this process please refer to the `Basic usage` example.\nWhen executed in an jupyter notebook this displays an interactive graph\nwith which you can select the start parameters before fitting the data.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tss = elli.read_spectraray_psi_delta(\"TiO2_400cycles.txt\").loc[70.06].loc[400:800]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set start parameters\n", + "Here we set the start parameters for the TiO2 and SiO2 layer.\n", + "We set the SiO2 layer parameters to a fixed value from another\n", + "fit of the substrate. See the `Basic usage` example for details\n", + "on how to perform such a fit.\n", + "In general it is a good idea to fit your data layer-wise if possible\n", + "to yield a better fit quality.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:48.847906Z", + "iopub.status.busy": "2024-06-28T09:13:48.847650Z", + "iopub.status.idle": "2024-06-28T09:13:48.857282Z", + "shell.execute_reply": "2024-06-28T09:13:48.854987Z", + "shell.execute_reply.started": "2024-06-28T09:13:48.847883Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "@fit(tss, params)\ndef model(lbda, params):\n SiO2 = elli.Cauchy(\n params[\"SiO2_n0\"],\n params[\"SiO2_n1\"],\n params[\"SiO2_n2\"],\n params[\"SiO2_k0\"],\n params[\"SiO2_k1\"],\n params[\"SiO2_k2\"],\n ).get_mat()\n TiO2 = elli.Cauchy(\n params[\"TiO2_n0\"],\n params[\"TiO2_n1\"],\n params[\"TiO2_n2\"],\n params[\"TiO2_k0\"],\n params[\"TiO2_k1\"],\n params[\"TiO2_k2\"],\n ).get_mat()\n\n Layer = [elli.Layer(TiO2, params[\"TiO2_d\"]), elli.Layer(SiO2, params[\"SiO2_d\"])]\n\n return elli.Structure(elli.AIR, Layer, Si).evaluate(lbda, 70, solver=elli.Solver2x2)\n # Alternative: Use 4x4 Solver with scipy propagator\n # return elli.Structure(elli.AIR, Layer, Si).evaluate(lbda, 70, solver=elli.Solver4x4, propagator=elli.PropagatorExpm())" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "params = ParamsHist()\n", + "params.add(\"SiO2_n0\", value=1.452, min=-100, max=100, vary=False)\n", + "params.add(\"SiO2_n1\", value=36.0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_n2\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_k0\", value=0, min=-100, max=100, vary=False)\n", + "params.add(\"SiO2_k1\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_k2\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"SiO2_d\", value=276.36, min=0, max=40000, vary=False)\n", + "\n", + "params.add(\"TiO2_n0\", value=2.236, min=-100, max=100, vary=True)\n", + "params.add(\"TiO2_n1\", value=451, min=-40000, max=40000, vary=True)\n", + "params.add(\"TiO2_n2\", value=251, min=-40000, max=40000, vary=True)\n", + "params.add(\"TiO2_k0\", value=0, min=-100, max=100, vary=False)\n", + "params.add(\"TiO2_k1\", value=0, min=-40000, max=40000, vary=False)\n", + "params.add(\"TiO2_k2\", value=0, min=-40000, max=40000, vary=False)\n", + "\n", + "params.add(\"TiO2_d\", value=20, min=0, max=40000, vary=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load silicon dispersion from the refractiveindexinfo database\n", + "You can load any material from the index\n", + "[refractiveindex.info](https://refractiveindex.info)_, which is\n", + "embedded into the software (so you may use it offline, too). Here, we\n", + "are interested in the literature values for the silicon substrate.\n", + "First we need to load the database with ``rii_db = elli.db.RII()`` and\n", + "then we can query it with ``rii_db.get_mat(\"Si\", \"Aspnes\")`` to load\n", + "this\n", + "[entry](https://refractiveindex.info/?shelf=main&book=Si&page=Aspnes)_.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:48.858129Z", + "iopub.status.busy": "2024-06-28T09:13:48.857916Z", + "iopub.status.idle": "2024-06-28T09:13:50.073971Z", + "shell.execute_reply": "2024-06-28T09:13:50.070507Z", + "shell.execute_reply.started": "2024-06-28T09:13:48.858108Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot & Fit model\nWe plot the model to see the deviation with the initial parameters.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "rii_db = elli.db.RII()\n", + "Si = rii_db.get_mat(\"Si\", \"Aspnes\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building the model\n", + "Here the model is build and the experimental structure is returned.\n", + "For details on this process please refer to the `Basic usage` example.\n", + "When executed in an jupyter notebook this displays an interactive graph\n", + "with which you can select the start parameters before fitting the data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:50.078086Z", + "iopub.status.busy": "2024-06-28T09:13:50.075461Z", + "iopub.status.idle": "2024-06-28T09:13:50.709374Z", + "shell.execute_reply": "2024-06-28T09:13:50.708627Z", + "shell.execute_reply.started": "2024-06-28T09:13:50.078060Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "874d3823dee24df48c0cd31b156c65a1", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "model.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets perform the fit and plot the comparison of\ncalculation and experimental data afterwards.\n\n" + "text/plain": [ + "VBox(children=(HBox(children=(HBox(children=(BoundedFloatText(value=1.452, description='SiO2_n0', min=-100.0),…" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "@fit(tss, params)\n", + "def model(lbda, params):\n", + " SiO2 = elli.Cauchy(\n", + " params[\"SiO2_n0\"],\n", + " params[\"SiO2_n1\"],\n", + " params[\"SiO2_n2\"],\n", + " params[\"SiO2_k0\"],\n", + " params[\"SiO2_k1\"],\n", + " params[\"SiO2_k2\"],\n", + " ).get_mat()\n", + " TiO2 = elli.Cauchy(\n", + " params[\"TiO2_n0\"],\n", + " params[\"TiO2_n1\"],\n", + " params[\"TiO2_n2\"],\n", + " params[\"TiO2_k0\"],\n", + " params[\"TiO2_k1\"],\n", + " params[\"TiO2_k2\"],\n", + " ).get_mat()\n", + "\n", + " Layer = [elli.Layer(TiO2, params[\"TiO2_d\"]), elli.Layer(SiO2, params[\"SiO2_d\"])]\n", + "\n", + " return elli.Structure(elli.AIR, Layer, Si).evaluate(lbda, 70, solver=elli.Solver2x2)\n", + " # Alternative: Use 4x4 Solver with scipy propagator\n", + " # return elli.Structure(elli.AIR, Layer, Si).evaluate(lbda, 70, solver=elli.Solver4x4, propagator=elli.PropagatorExpm())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot & Fit model\n", + "We plot the model to see the deviation with the initial parameters.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:50.711150Z", + "iopub.status.busy": "2024-06-28T09:13:50.710861Z", + "iopub.status.idle": "2024-06-28T09:13:50.824346Z", + "shell.execute_reply": "2024-06-28T09:13:50.822566Z", + "shell.execute_reply.started": "2024-06-28T09:13:50.711126Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "351d155eabeb4d8bb2a47b84b1b1fcf5", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "fit_stats = model.fit()\nmodel.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also have a look at the fit statistics.\n\n" + "text/plain": [ + "FigureWidget({\n", + " 'data': [{'hovertemplate': 'variable=Ψ
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Ψ',\n", + " 'line': {'color': '#636efa', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Ψ',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': 'f0bcf5c1-4723-4ebc-b873-1f657593b0c8',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([32.75947, 32.84076, 32.84675, ..., nan, nan, nan]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Δ
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Δ',\n", + " 'line': {'color': '#EF553B', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Δ',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': '2efd25e6-6489-4320-a23a-991d48f6217f',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([-132.57268, -132.11725, -131.5141 , ..., nan, nan,\n", + " nan]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Ψ_fit
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Ψ_fit',\n", + " 'line': {'color': '#00cc96', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Ψ_fit',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': 'df405615-ed32-40c3-9f27-126daacea7f8',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([ nan, nan, nan, ..., 21.13502205, 21.16231399,\n", + " 21.18955407]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Δ_fit
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Δ_fit',\n", + " 'line': {'color': '#ab63fa', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Δ_fit',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': '29509ac8-da20-4fef-b02f-10376fe9cb9b',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([ nan, nan, nan, ..., -111.09599625,\n", + " -110.98086027, -110.86609469]),\n", + " 'yaxis': 'y'}],\n", + " 'layout': {'legend': {'title': {'text': 'variable'}, 'tracegroupgap': 0},\n", + " 'margin': {'t': 60},\n", + " 'template': '...',\n", + " 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Wavelength'}},\n", + " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'value'}}}\n", + "})" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets perform the fit and plot the comparison of\n", + "calculation and experimental data afterwards.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:50.825319Z", + "iopub.status.busy": "2024-06-28T09:13:50.825083Z", + "iopub.status.idle": "2024-06-28T09:13:51.143279Z", + "shell.execute_reply": "2024-06-28T09:13:51.141989Z", + "shell.execute_reply.started": "2024-06-28T09:13:50.825298Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b63385c609c94a66baf0eedc7f9ba25e", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "fit_stats" + "text/plain": [ + "FigureWidget({\n", + " 'data': [{'hovertemplate': 'variable=Ψ
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Ψ',\n", + " 'line': {'color': '#636efa', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Ψ',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': 'ed335efa-f785-4766-9a5b-8c48f9ad4c8d',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([32.75947, 32.84076, 32.84675, ..., nan, nan, nan]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Δ
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Δ',\n", + " 'line': {'color': '#EF553B', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Δ',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': 'b25fd3e4-c40a-4ef7-8065-baa5ca83fe15',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([-132.57268, -132.11725, -131.5141 , ..., nan, nan,\n", + " nan]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Ψ_fit
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Ψ_fit',\n", + " 'line': {'color': '#00cc96', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Ψ_fit',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': 'a15841f2-0835-4d85-8e9a-e63717422f31',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([ nan, nan, nan, ..., 20.20488814, 20.23141868,\n", + " 20.25790068]),\n", + " 'yaxis': 'y'},\n", + " {'hovertemplate': 'variable=Δ_fit
Wavelength=%{x}
value=%{y}',\n", + " 'legendgroup': 'Δ_fit',\n", + " 'line': {'color': '#ab63fa', 'dash': 'solid'},\n", + " 'marker': {'symbol': 'circle'},\n", + " 'mode': 'lines',\n", + " 'name': 'Δ_fit',\n", + " 'showlegend': True,\n", + " 'type': 'scattergl',\n", + " 'uid': '541a415a-946f-488a-84c5-4d2c0b6b1ecb',\n", + " 'x': array([400.07646, 400.51975, 400.96301, ..., 798.88197, 799.30046, 799.71891]),\n", + " 'xaxis': 'x',\n", + " 'y': array([ nan, nan, nan, ..., -118.64650333,\n", + " -118.5165033 , -118.38691231]),\n", + " 'yaxis': 'y'}],\n", + " 'layout': {'legend': {'title': {'text': 'variable'}, 'tracegroupgap': 0},\n", + " 'margin': {'t': 60},\n", + " 'template': '...',\n", + " 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Wavelength'}},\n", + " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'value'}}}\n", + "})" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_stats = model.fit()\n", + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also have a look at the fit statistics.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T09:13:51.145117Z", + "iopub.status.busy": "2024-06-28T09:13:51.144864Z", + "iopub.status.idle": "2024-06-28T09:13:51.156820Z", + "shell.execute_reply": "2024-06-28T09:13:51.155249Z", + "shell.execute_reply.started": "2024-06-28T09:13:51.145098Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n[Here](https://github.com/PyEllips/pyElli/tree/master/examples/TiO2%20Fit)\nyou can find the latest jupyter notebook and data files of this example.\n\n" + "data": { + "text/html": [ + "

Fit Result

Fit Statistics
fitting methodleastsq
# function evals32
# data points1852
# variables4
chi-square 0.04160577
reduced chi-square 2.2514e-05
Akaike info crit.-19814.9521
Bayesian info crit.-19792.8560
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
SiO2_n0 1.45200000 0.00000000(0.00%)1.452-100.000000 100.000000False
SiO2_n1 36.0000000 0.00000000(0.00%)36.0-40000.0000 40000.0000False
SiO2_n2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k0 0.00000000 0.000000000-100.000000 100.000000False
SiO2_k1 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_d 276.360000 0.00000000(0.00%)276.36 0.00000000 40000.0000False
TiO2_n0 2.22973557 0.00472756(0.21%)2.236-100.000000 100.000000True
TiO2_n1 461.689341 22.1724775(4.80%)451-40000.0000 40000.0000True
TiO2_n2 189.779573 25.7876508(13.59%)251-40000.0000 40000.0000True
TiO2_k0 0.00000000 0.000000000-100.000000 100.000000False
TiO2_k1 0.00000000 0.000000000-40000.0000 40000.0000False
TiO2_k2 0.00000000 0.000000000-40000.0000 40000.0000False
TiO2_d 24.8446396 0.01013894(0.04%)20 0.00000000 40000.0000True
Correlations (unreported values are < 0.100)
Parameter1Parameter 2Correlation
TiO2_n0TiO2_n1-0.9910
TiO2_n1TiO2_n2-0.9879
TiO2_n0TiO2_n2+0.9640
" + ], + "text/plain": [ + "" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } + ], + "source": [ + "fit_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "[Here](https://github.com/PyEllips/pyElli/tree/master/examples/TiO2%20Fit)\n", + "you can find the latest jupyter notebook and data files of this example.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tests/test_TiO2.py b/tests/test_TiO2.py index 20afda8c..19fef823 100644 --- a/tests/test_TiO2.py +++ b/tests/test_TiO2.py @@ -32,9 +32,11 @@ def datadir(tmpdir, request): @fixture def meas_data(datadir): """Fixture for getting the reference measurement data from the file.""" - return elli.read_spectraray_rho(datadir.join("TiO2_400cycles.txt")).loc[70.06][ - 400:800 - ] + return ( + elli.read_spectraray_rho(datadir.join("TiO2_400cycles.txt")) + .loc[70.06] + .loc[400:800] + ) @fixture From 1622c78d522dc3890e8cc609683273474f27d61a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:30:13 +0200 Subject: [PATCH 6/9] Minor example file fixes --- examples/Bragg-mirror/validation-Bragg.ipynb | 92 +++++++++++++++---- .../FrustratedTIR-thickness.ipynb | 64 ++++++++++--- 2 files changed, 123 insertions(+), 33 deletions(-) diff --git a/examples/Bragg-mirror/validation-Bragg.ipynb b/examples/Bragg-mirror/validation-Bragg.ipynb index 102273bc..e54eb464 100644 --- a/examples/Bragg-mirror/validation-Bragg.ipynb +++ b/examples/Bragg-mirror/validation-Bragg.ipynb @@ -14,7 +14,15 @@ "cell_type": "code", "execution_count": 1, "id": "1813cc69", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:40.789029Z", + "iopub.status.busy": "2024-06-28T08:27:40.788906Z", + "iopub.status.idle": "2024-06-28T08:27:42.202751Z", + "shell.execute_reply": "2024-06-28T08:27:42.202220Z", + "shell.execute_reply.started": "2024-06-28T08:27:40.789018Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -38,7 +46,15 @@ "cell_type": "code", "execution_count": 2, "id": "c46da69e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.203348Z", + "iopub.status.busy": "2024-06-28T08:27:42.203170Z", + "iopub.status.idle": "2024-06-28T08:27:42.206205Z", + "shell.execute_reply": "2024-06-28T08:27:42.205745Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.203339Z" + } + }, "outputs": [], "source": [ "n_a = 1.0\n", @@ -59,7 +75,15 @@ "cell_type": "code", "execution_count": 3, "id": "88d0dce7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.206788Z", + "iopub.status.busy": "2024-06-28T08:27:42.206672Z", + "iopub.status.idle": "2024-06-28T08:27:42.210747Z", + "shell.execute_reply": "2024-06-28T08:27:42.210353Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.206778Z" + } + }, "outputs": [], "source": [ "lbda0 = 1550 # nm\n", @@ -83,7 +107,15 @@ "cell_type": "code", "execution_count": 4, "id": "d00ef39c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.211427Z", + "iopub.status.busy": "2024-06-28T08:27:42.211265Z", + "iopub.status.idle": "2024-06-28T08:27:42.215951Z", + "shell.execute_reply": "2024-06-28T08:27:42.215550Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.211413Z" + } + }, "outputs": [], "source": [ "d_SiO2 = elli.get_qwp_thickness(SiO2, lbda0)\n", @@ -119,7 +151,15 @@ "cell_type": "code", "execution_count": 5, "id": "908c30b3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.216515Z", + "iopub.status.busy": "2024-06-28T08:27:42.216409Z", + "iopub.status.idle": "2024-06-28T08:27:42.231412Z", + "shell.execute_reply": "2024-06-28T08:27:42.230879Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.216506Z" + } + }, "outputs": [], "source": [ "n = np.ones(N + 1, dtype=complex)\n", @@ -195,7 +235,15 @@ "cell_type": "code", "execution_count": 6, "id": "ead81627", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.232401Z", + "iopub.status.busy": "2024-06-28T08:27:42.232285Z", + "iopub.status.idle": "2024-06-28T08:27:42.246823Z", + "shell.execute_reply": "2024-06-28T08:27:42.246497Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.232391Z" + } + }, "outputs": [], "source": [ "# Incidence angle Phi_i = 0, 's' polarization\n", @@ -224,30 +272,34 @@ "cell_type": "code", "execution_count": 7, "id": "b0e5b5ec", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:27:42.247527Z", + "iopub.status.busy": "2024-06-28T08:27:42.247357Z", + "iopub.status.idle": "2024-06-28T08:27:42.501253Z", + "shell.execute_reply": "2024-06-28T08:27:42.500833Z", + "shell.execute_reply.started": "2024-06-28T08:27:42.247516Z" + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -258,11 +310,11 @@ "\n", "d = np.vstack((R_ss_0, R_ss, R_pp)).T\n", "lines1 = ax.plot(lbda_list, d)\n", - "legend1 = (\"R_ss (0$^\\circ$)\", \"R_ss (45$^\\circ$)\", \"R_pp (45$^\\circ$)\")\n", + "legend1 = (\"R_ss (0°)\", \"R_ss (45°)\", \"R_pp (45°)\")\n", "\n", "d = np.vstack((R_th_ss_0, R_th_ss, R_th_pp)).T\n", "lines2 = ax.plot(lbda_list, d, \"x\")\n", - "legend2 = (\"R_th_ss (0$^\\circ$)\", \"R_th_ss (45$^\\circ$)\", \"R_th_pp (45$^\\circ$)\")\n", + "legend2 = (\"R_th_ss (0°)\", \"R_th_ss (45°)\", \"R_th_pp (45°)\")\n", "\n", "ax.legend(\n", " lines1 + lines2,\n", @@ -317,7 +369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/Total internal reflection/FrustratedTIR-thickness.ipynb b/examples/Total internal reflection/FrustratedTIR-thickness.ipynb index 3f58fb59..85c1307a 100644 --- a/examples/Total internal reflection/FrustratedTIR-thickness.ipynb +++ b/examples/Total internal reflection/FrustratedTIR-thickness.ipynb @@ -14,7 +14,15 @@ "cell_type": "code", "execution_count": 1, "id": "2c23f5a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:33:51.210932Z", + "iopub.status.busy": "2024-06-28T08:33:51.210682Z", + "iopub.status.idle": "2024-06-28T08:33:52.564176Z", + "shell.execute_reply": "2024-06-28T08:33:52.563715Z", + "shell.execute_reply.started": "2024-06-28T08:33:51.210912Z" + } + }, "outputs": [], "source": [ "import elli\n", @@ -35,7 +43,15 @@ "cell_type": "code", "execution_count": 2, "id": "b80da4d7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:33:52.565109Z", + "iopub.status.busy": "2024-06-28T08:33:52.564819Z", + "iopub.status.idle": "2024-06-28T08:33:52.568524Z", + "shell.execute_reply": "2024-06-28T08:33:52.568027Z", + "shell.execute_reply.started": "2024-06-28T08:33:52.565096Z" + } + }, "outputs": [], "source": [ "# Refractive indices\n", @@ -75,7 +91,15 @@ "cell_type": "code", "execution_count": 3, "id": "ee00445c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:33:52.569364Z", + "iopub.status.busy": "2024-06-28T08:33:52.569141Z", + "iopub.status.idle": "2024-06-28T08:33:52.578484Z", + "shell.execute_reply": "2024-06-28T08:33:52.578049Z", + "shell.execute_reply.started": "2024-06-28T08:33:52.569347Z" + } + }, "outputs": [], "source": [ "# Reduced wavenumber\n", @@ -150,7 +174,15 @@ "cell_type": "code", "execution_count": 4, "id": "9d600576", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:33:52.579372Z", + "iopub.status.busy": "2024-06-28T08:33:52.579139Z", + "iopub.status.idle": "2024-06-28T08:33:52.609375Z", + "shell.execute_reply": "2024-06-28T08:33:52.608915Z", + "shell.execute_reply.started": "2024-06-28T08:33:52.579355Z" + } + }, "outputs": [], "source": [ "data = elli.ResultList()\n", @@ -180,20 +212,26 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "id": "b528881f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-28T08:36:27.036582Z", + "iopub.status.busy": "2024-06-28T08:36:27.036360Z", + "iopub.status.idle": "2024-06-28T08:36:27.136751Z", + "shell.execute_reply": "2024-06-28T08:36:27.136183Z", + "shell.execute_reply.started": "2024-06-28T08:36:27.036570Z" + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -220,7 +258,7 @@ "\n", "ax.set_title(\n", " \"FTIR: Glass1 / Air (d) / Glass2, for incidence angle \"\n", - " + \"$\\Phi_i$ = {:.0f}$^\\circ$\".format(Phi_i * 180 / pi)\n", + " + \"$Φ_i$ = {:.0f}°\".format(Phi_i * 180 / pi)\n", ")\n", "ax.set_xlabel(r\"Air layer thickness, $d$ (nm)\")\n", "ax.set_ylabel(r\"Reflexion and transmission coefficients $R$, $T$\")\n", @@ -260,7 +298,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.3" }, "vscode": { "interpreter": { From 3de870c02dd90a62090a43af06bbfccd46040a53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:30:20 +0200 Subject: [PATCH 7/9] removal of non notebook examples --- examples/Bragg-mirror/Bragg-Mirror.ipynb | 434 ++++++++++++------ examples/Bragg-mirror/validation-Bragg.py | 169 ------- examples/Interfaces/Interferences.py | 55 --- examples/Liquid crystals/twisted-nematic.py | 106 ----- .../Liquid crystals/validation-cholesteric.py | 108 ----- .../FrustratedTIR-angle.py | 149 ------ .../FrustratedTIR-thickness.py | 154 ------- examples/Total internal reflection/TIR.py | 57 --- 8 files changed, 301 insertions(+), 931 deletions(-) delete mode 100755 examples/Bragg-mirror/validation-Bragg.py delete mode 100644 examples/Interfaces/Interferences.py delete mode 100644 examples/Liquid crystals/twisted-nematic.py delete mode 100644 examples/Liquid crystals/validation-cholesteric.py delete mode 100644 examples/Total internal reflection/FrustratedTIR-angle.py delete mode 100644 examples/Total internal reflection/FrustratedTIR-thickness.py delete mode 100644 examples/Total internal reflection/TIR.py diff --git a/examples/Bragg-mirror/Bragg-Mirror.ipynb b/examples/Bragg-mirror/Bragg-Mirror.ipynb index 5514fe50..b37ff140 100644 --- a/examples/Bragg-mirror/Bragg-Mirror.ipynb +++ b/examples/Bragg-mirror/Bragg-Mirror.ipynb @@ -1,151 +1,319 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:11.132616Z", + "iopub.status.busy": "2024-06-28T08:26:11.132372Z", + "iopub.status.idle": "2024-06-28T08:26:11.412976Z", + "shell.execute_reply": "2024-06-28T08:26:11.412487Z", + "shell.execute_reply.started": "2024-06-28T08:26:11.132598Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# TiO2/SiO2 Bragg mirror\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example of a TiO2/SiO2 Bragg mirror with 8.5 periods\n\nAuthors: O. Castany, M.M\u00fcller\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import elli\nimport elli.plot as elliplot\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnp.set_printoptions(suppress=True, precision=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Material definition\nWe define air as incidence material and glass as exit material.\nSiO2 and TiO2 are defined by simplified dispersion relations.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "air = elli.AIR\nglass = elli.ConstantRefractiveIndex(1.5).get_mat()\n\nn_SiO2 = 1.47\nn_TiO2 = 2.23 + 1j * 5.2e-4\n\nSiO2 = elli.ConstantRefractiveIndex(n_SiO2).get_mat()\nTiO2 = elli.ConstantRefractiveIndex(n_TiO2).get_mat()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create layers and structure\nThe SiO2 and TiO2 layers are set to the thickness of an\nquarterwaveplate of the respective material at 1550 nm.\n\nThe layers are then stacked alternatingly and put into the\ncomplete structure with air and the glass substrate.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# TiO2/SiO2 Bragg mirror\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of a TiO2/SiO2 Bragg mirror with 8.5 periods\n", + "\n", + "Authors: O. Castany, M.Müller\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:11.413924Z", + "iopub.status.busy": "2024-06-28T08:26:11.413595Z", + "iopub.status.idle": "2024-06-28T08:26:12.488892Z", + "shell.execute_reply": "2024-06-28T08:26:12.488381Z", + "shell.execute_reply.started": "2024-06-28T08:26:11.413906Z" }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lbda0 = 1550\n\nd_SiO2 = elli.get_qwp_thickness(SiO2, lbda0)\nd_TiO2 = elli.get_qwp_thickness(TiO2, lbda0)\n\nprint(\"Thickness of the SiO2 QWP: {} nm\".format(d_SiO2))\nprint(\"Thickness of the TiO2 QWP: {} nm\".format(d_TiO2))\n\nL_SiO2 = elli.Layer(SiO2, d_SiO2)\nL_TiO2 = elli.Layer(TiO2, d_TiO2)\n\n# Repeated layers: 8.5 periods\nlayerstack = elli.RepeatedLayers([L_TiO2, L_SiO2], 8, 0, 1)\n\ns = elli.Structure(air, [layerstack], glass)" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import elli\n", + "import elli.plot as elliplot\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "np.set_printoptions(suppress=True, precision=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Material definition\n", + "We define air as incidence material and glass as exit material.\n", + "SiO2 and TiO2 are defined by simplified dispersion relations.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:12.489479Z", + "iopub.status.busy": "2024-06-28T08:26:12.489283Z", + "iopub.status.idle": "2024-06-28T08:26:12.492266Z", + "shell.execute_reply": "2024-06-28T08:26:12.491917Z", + "shell.execute_reply.started": "2024-06-28T08:26:12.489469Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculation\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "air = elli.AIR\n", + "glass = elli.ConstantRefractiveIndex(1.5).get_mat()\n", + "\n", + "n_SiO2 = 1.47\n", + "n_TiO2 = 2.23 + 1j * 5.2e-4\n", + "\n", + "SiO2 = elli.ConstantRefractiveIndex(n_SiO2).get_mat()\n", + "TiO2 = elli.ConstantRefractiveIndex(n_TiO2).get_mat()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create layers and structure\n", + "The SiO2 and TiO2 layers are set to the thickness of an\n", + "quarterwaveplate of the respective material at 1550 nm.\n", + "\n", + "The layers are then stacked alternatingly and put into the\n", + "complete structure with air and the glass substrate.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:12.492806Z", + "iopub.status.busy": "2024-06-28T08:26:12.492695Z", + "iopub.status.idle": "2024-06-28T08:26:12.498792Z", + "shell.execute_reply": "2024-06-28T08:26:12.498451Z", + "shell.execute_reply.started": "2024-06-28T08:26:12.492795Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "(lbda1, lbda2) = (1100, 2500)\nlbda_list = np.linspace(lbda1, lbda2, 200)\n\ndata = s.evaluate(lbda_list, 0)\n\nR = data.R\nT = data.T" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Thickness of the SiO2 QWP: 263.6054421768708 nm\n", + "Thickness of the TiO2 QWP: 173.76681614349775 nm\n" + ] + } + ], + "source": [ + "lbda0 = 1550\n", + "\n", + "d_SiO2 = elli.get_qwp_thickness(SiO2, lbda0)\n", + "d_TiO2 = elli.get_qwp_thickness(TiO2, lbda0)\n", + "\n", + "print(\"Thickness of the SiO2 QWP: {} nm\".format(d_SiO2))\n", + "print(\"Thickness of the TiO2 QWP: {} nm\".format(d_TiO2))\n", + "\n", + "L_SiO2 = elli.Layer(SiO2, d_SiO2)\n", + "L_TiO2 = elli.Layer(TiO2, d_TiO2)\n", + "\n", + "# Repeated layers: 8.5 periods\n", + "layerstack = elli.RepeatedLayers([L_TiO2, L_SiO2], 8, 0, 1)\n", + "\n", + "s = elli.Structure(air, [layerstack], glass)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:12.499340Z", + "iopub.status.busy": "2024-06-28T08:26:12.499203Z", + "iopub.status.idle": "2024-06-28T08:26:12.515044Z", + "shell.execute_reply": "2024-06-28T08:26:12.514734Z", + "shell.execute_reply.started": "2024-06-28T08:26:12.499328Z" }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Structure Graph\nSchema of the variation of the refractive index in z-direction.\n\n" - ] + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "(lbda1, lbda2) = (1100, 2500)\n", + "lbda_list = np.linspace(lbda1, lbda2, 200)\n", + "\n", + "data = s.evaluate(lbda_list, 0)\n", + "\n", + "R = data.R\n", + "T = data.T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Structure Graph\n", + "Schema of the variation of the refractive index in z-direction.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:12.516271Z", + "iopub.status.busy": "2024-06-28T08:26:12.516081Z", + "iopub.status.idle": "2024-06-28T08:26:12.572254Z", + "shell.execute_reply": "2024-06-28T08:26:12.571599Z", + "shell.execute_reply.started": "2024-06-28T08:26:12.516256Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "elliplot.draw_structure(s)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reflection and Transmission Graph\n\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "elliplot.draw_structure(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reflection and Transmission Graph\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-06-28T08:26:12.573011Z", + "iopub.status.busy": "2024-06-28T08:26:12.572839Z", + "iopub.status.idle": "2024-06-28T08:26:12.775363Z", + "shell.execute_reply": "2024-06-28T08:26:12.774854Z", + "shell.execute_reply.started": "2024-06-28T08:26:12.572992Z" }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\nax.plot(lbda_list, T, label=\"$T$\")\nax.plot(lbda_list, R, label=\"$R$\")\nax.legend(loc=\"center right\")\nax.set_xlabel(r\"Wavelength $\\lambda$ (nm)\")\nax.set_ylabel(r\"Power reflection $R$ or transmission $T$\")\nax.set_title(r\"Bragg mirror: Air/{TiO$_2$/SiO$_2$}x8/TiO$_2$/Glass\")\nplt.show()" + "data": { + "image/png": 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f//nnn1FYWNih905UVBQeeeQR9O3bF2q1Gn/+85+h1+s7/C1c/TzOmDBhAgDg9OnTfl+nI5s3b0ZbWxveffdd/OlPf+qQ+gQvDp544gksWrQIZ5xxBhYvXtwh0gAA5eXlTlOVFosFcPjfh4e/Iyl4+jvy5u940UUXIT8/H7t27ZK9XinvbQLp6em4/fbb8fnnn6OgoACJiYl4+umnPd6H8BwSOIRkLBYLNm/eDL1ej2HDhnW7v0aj6XLV/q9//avDm6hGo8GcOXPw+eefd8hB5+fnd7iCkbpfKKDRaHDeeefhf//7X4ey0vLycnz00Uc466yzxCv57pBSJt7a2ooNGzbg4osvxpVXXtnla+nSpWhsbHTrJXHHN9984zZVIaQEnb35e8Pf/vY3RERE4C9/+UuH9An4Muhbb70VkZGR+Nvf/ubyGHl5eaipqemQ5nT28/zwww9OI1DffPMNADhNN/p6nY4I3hJnwthiseDGG29E79698eqrr2LNmjUoLy/Hvffe22G/wYMHY9++fTh27FiH+//9739DrVZj9OjRHX5uJdJTAt7+jqT+HQFg+fLliIyMxE033YTy8vIuj0uJNEp5b7PZbF1EZEpKCnr37g2TySR5H0I+VCZOuOTbb7/FkSNHAN4D89FHHyEvLw8PPPCApA/giy++GB988AHi4uIwfPhw7NixA1u2bEFiYmKH/R577DFs3rwZ06ZNw2233QabzYbXXnsNI0eOxP79+z3eLxR46qmn8N133+Gss87C7bffDq1Wi7feegsmk8mjXh9SysS/+OILNDY2Yt68eU4fP/PMM5GcnIx169Zh/vz5Hv0cBQUF+OOPP/Dmm2+63EcoW1a6afqgQYPw/vvv49prr8WoUaO6dL+tqqrCv//97w4ddh1pbW3FddddhwcffBBxcXFuf54777wTLS0tuPzyyzF06FCYzWb8+uuv+Pjjj5GVlYVFixb5dZ2dEX63zkrxn3rqKezfvx9bt25FTEwMRo8ejUceeQT/+Mc/cOWVV4opnL/97W/49ttvMX36dCxduhSJiYn46quv8O2332Lx4sWiuV/K39xTvPkdefJ3FM710UcfYcGCBRgyZIjYyZjjOBQUFOCjjz6CWq3ukqpzRMp7W2NjI/r06YMrr7wSY8aMQXR0NLZs2YLdu3eLPbak7EN4QaDLuIjgw1mZuNFo5MaOHcu9+eabnN1uF/cVyocrKyu7HKe2tpZbtGgRl5SUxEVHR3Nz587ljhw5wvXr169L+fXWrVu5M844g9Pr9dzAgQO5d955h/vrX//KGY1GWfs5w9VaFy5cyEVFRXXZf+bMmdyIESOc/m46l4k7+/ndPcZxHJeTk8PNnTuXi46O5iIjI7lZs2Zxv/76q0fHkFImfskll3BGo5Frbm52uc+NN97I6XQ6rqqqym2ZeOd1vPbaa1xcXBxnsVhcHnvt2rUcAO7nn392u07Ow/JigQMHDnALFizg0tPTOZ1Ox6WlpXELFizgDh486PI8ZrOZu+iii7hrrrmmw+vZ1c/z7bffcjfddBM3dOhQLjo6mtPr9Vx2djZ35513dulk7I91dmb58uWcRqPpcv/evXs5rVbL3XnnnR3ut1qt3MSJE7nevXt36M68c+dO7oILLuDS0tI4nU7HDR48mHv66ac7/D6k/M3l/B05Gb8jT/+OjuTn53O33XYbl52dzRmNRi4iIoIbOnQod+utt3L79+93u2Yp720mk4n729/+xo0ZM4aLiYnhoqKiuDFjxnBvvPGGeFwp+xDyoVlURNBy2WWX4dChQ04NkXL2I5TnwgsvRHR0NP7zn/+43Of777/H7Nmzccstt+Dvf/+76EEKFHa7Hddccw2am5vx3//+t0Nnbik/j79wt06BmpoanDp1Ctdddx3q6upQVFTk83UFy+8oVP6OROCgFBURFLS2tnao0MnLy8M333yDhQsXytqP8A9nn322y+6zAtOnT8e0adOwatUqrFq1Co8++igee+wxv62xM3/5y19w+vRpbNq0qYtokPLz+At36xQYN24cioqKoNFo8Morr/hlXcHyOwqVvyMROCiCQwQF6enpuPHGGzFgwAAUFRXhzTffhMlkwr59+zBo0CCP9yOCj/z8fJw6dQqZmZldqnz8RVFREbKysmA0Gju0BhC8J8GC1HX++uuvUKlUGDp0KHr16hWg1fqfUPk7EoGFBA4RFCxatAg//PADysrKYDAYMGXKFDzzzDNdZrFI3Y8gCILo2ZDAIQiCIAgi7KA+OARBEARBhB0kcAiCIAiCCDt6ZBWV3W5HaWkpYmJiFB0ASBAEQRCE7+A4Do2Njejdu7fTxpaO9EiBU1paqvisH4IgCIIg/ENJSYnbbtPoqQInJiYG4H9BUmf+EARBEAQRWBoaGpCZmSl+jrujRwocIS0VGxtLAocgCIIgQgwp9hIyGRMEQRAEEXaQwCEIgiAIIuwggUMQBEEQRNhBAocgCIIgiLCDBA5BEARBEGEHCRyCIAiCIMIOEjgEQRAEQYQdJHAIgiAIggg7SOAQBEEQBBF2BFzg/PTTT7jkkkvQu3dvqFQqfP75590+Z9u2bRg3bhwMBgOys7OxZs0av6yVIAiCIIjQIOACp7m5GWPGjMHrr78uaf+CggJcdNFFmDVrFvbv34977rkHixcvxqZNm3y+VoIgCIIgQoOAz6K64IILcMEFF0jef+XKlejfvz9eeuklAMCwYcOwfft2vPzyy5g7d64PV0oQBEEQRKgQcIHjKTt27MCcOXM63Dd37lzcc889Lp9jMplgMpnE7xsaGny6RqIHwnGA3QZwNoCzO/ni+C8nj4EDoAJUakDFb7t8DzePawCNjt0mCIIggFAUOGVlZUhNTe1wX2pqKhoaGtDa2oqIiIguz3n22Wfx+OOP+3GVRMhgagIaT/NfZUBLDWBqBEz1QFsDYGrgt42AtQ2wmQGrCbBZAJsJsJr5rYkXKgFEpQbUOiZ21Fp+qwM0Wof7Hb7XGgBdJKCLcPiKBLTG9tuGaMAY1/HLEAcYY9nxCIIggpSQEzhyePDBB7Fs2TLx+4aGBmRmZgZ0TYSfaakBynOB8kPtX9X5TMD4HSH6om6PzoCP8IBrj/h4Kpg4OxNbNpOEnRUgohcQnQbEpPJbh6+EgUDiQEAf5Z+1EARBdCLkBE5aWhrKy8s73FdeXo7Y2Fin0RsAMBgMMBgMflohETSU5QK5nwHHtwKnf3e9nz4aiElnH8yRiSw6YYjloxWxgCGG3aeNYFELrQHQ6Nu3jrfVmo7iRRQxjiklD1JJnIPgEdJZHW7bWWrMbmVRJbsVsFsAm7C1ODzW6XtrG2BpZV9WfmtpASxt/LaFRbja6vlIVj37MjextbXWsq/KP1yvP6Y3Ezopw4E+E4CM8UDCAEqnEQThc0JO4EyZMgXffPNNh/u+++47TJkyJWBrIoIIuw048B9g55tdRU2vLCB1JPuwTR0OJA8D4jKYgAlWBI9NMGGzMqHTXMHSek3lfIqvHGgqA+pPATXHgZZqoLGUfRX+DOx6iz0/IgHoMxEYOAsYdB4TQARBEAoTcIHT1NSE/Px88fuCggLs378fCQkJ6Nu3Lx588EGcOnUKa9euBQDceuuteO2117B8+XLcdNNN+P777/Gf//wHX3/9dQB/CiIoyN8CbH4YqDjMvlfrgCHnA0MvBgaczSI0hPdotEBUIvtKGeZ6v5YaoOYEUJXHxObJ3UDZAaC1BsjbxL42PgAkDwVGXw2MuhqIp9QxQRDKoOI4LqDOyG3btmHWrFld7l+4cCHWrFmDG2+8EYWFhdi2bVuH59x77704fPgw+vTpg4cffhg33nij5HM2NDQgLi4O9fX1iI2NVexnIQKEzQp8/wTwy6vse2McMO0eYNxC9iFMBA9WE0sdFv3CBGnRryx1Bt4kPewSYMpSIHNSoFdKEEQQ4snnd8AFTiAggRM4dp6oxoacU/j7hcMQF6lAFU5bA/DxtUDBT+z7SbcAs/7ODLBE8NNaB/zxJfD7eqBoe/v9gy8Azn0cSB4SyNURBBFkePL5HfBOxkTgsdk52O3+0bkrfzyOj/eUYMsf5RL27gabFfjkRiZudFHAVWuAC/+PxE0oEREPjLseWPQ1cNuvwNjrmOfo2LfAG1OALY+xUnwiKCiubsHbP51ASU1LoJdCEN1CAqeHw3EcFq3ZjTOf3Yr6VovPz1fHn6OhTYFzbXqQVUjpIoEbvwJGXO79MYnAkToCuOx14I6dwJCLWNPE7S8D785hPh4iYNS3WvDkV4cxe8U2PP3NH5j7yk94/9dCv10YEV3hOA4FVc34Jb8Kh0rrUdHYFuglBR0BNxkT7Zitdqz5tQCzhqRgUKp/Knu+zS3DT8cqAQC5p+oxLTvJp+drarMCAFrMNu8OtO9DYNcqdvuKVUDGOAVWRwQFSYOABR8Bh78AvryLGZTfng1csx7oNzXQq+txcByHm9fsxp6iWgBAepwRp+vb8OgXh5BTXItX/3xGoJfYoyipacE/t+bhh6OVqGrq2PNqTJ84XDkhE5eN7Y0YIzXipAhOEPHu9gI8880R/O3TA345n9Vmx0ubj4rfF1X7PuzcZGICp5nfysLUxFIXADDrH8yYSoQfw+cBt+0AMs9knaU/uBw4EphqyYY2C65euQOvfd/zIknf5pZhT1EtIvUarFk0Eb/cfw6evHQEtGoV/re/FD/yF0iEb2mz2PD8xiOYveJHfLL3JKqaTNBr1RiYHIXkGAPUKuD3k/V4+PNczFnxI7YqYQMIcUjgBAk2O4cPfysCAOwvqUNZve/DjRv2ncLxymbx+2I/5NWFCI5XAmfH60BzJWsYd5brGWREGBCbDtzwOTDkQtaY8OPrgePf+30ZPxypwK7CGryzvQA9qS7DYrPj/zaxi6DF0wfg7CEpUKtVuH5KFhZOzQIAPP7lIZit9gCvNLypb7Hghnd34c1tx2G22jF1YCL+veRMHHzsPGz969nY/dAc7HpoDh6+eDj6JUaivMGEm9/fg7/+53e0WbyMlocwJHCChC1/lONUXav4/Xc+Vt9mqx2vbmFXo1mJkQAf+vQldjuHJjMvcOSmqJoqgV//yW6f8zDNQ+oJ6CKAqz8ARl7JfDn/uRGoPCrhicqxr7gOAFDXYkFVU88xPa/fVYyCqmYkRetxy4wBHR67e84gJEXrcaKyGWt3FAZsjeFOaV0rrlz5K3YV1iDGoMXK68Zj3eLJmDIwEQZtexPQpGgDbj6rPzbdMwN/mTkAahXwWc5J3PjeLmU8jyEICZwgQXiDSIpmIyW+O+xbgXO0rBGn6loRF6HDX89jpbhFNc3dPs8bWiw2CBe/LWaZEZyfX2SjAtLHAsMvU3R9RBCj0QKXvQH0ncLSVeuuApqr/Xb6/SV14u28ika/nTeQmKw2vLqVNWG9a/YgRBs6WjZjjTosnzsUAPDKlrwe+yHqSxrbLFi4ehfyKpqQFmvEf26dgvNHpkHlZtSJUafBgxcMw4c3T0a0QYvfTtTg6pU7UN3kpxl1QQQJnCAgv6IRv+RXQ60CVlw9BgCw43iVT98whGhR/6QoDEljhuZiH3twhPQUADSZZERwrCZg3zp2e/YjgJpevj0KrQGYv46N3KgrAjbe75fTmqw2HC5tH8qaX9Hkl/MGml+PV6OqyYTkGAMWTOrrdJ8rx/dBdko0mkxW/G9/qd/XGM7Y7Bzu+vc+5FU0ITXWgM9un4ph6dL7tk3NTsL6W85EUrQBR8oacdP7e+RfWIYo9AkRBHy8uwQAMGdYKmYMTsbA5ChYbBy2HfWdee90PRM4veONyOzFUlQNbVbUt/hOVDWZ2o/dIseDc/wHwNzIBjgO6Nr9mugBRCUCV77Huh4f/AQ4tsnnpzxc2gCzrd1jcqy8Z0RwNh4sAwCcPyINOo3zjwq1WiWKn493F/t1feHOCxuP4IejlTDq1Hj7hgnIiHc+TNodIzPisP6WMxEfqcPvJXW486N9sNp6jl+KBE4QkHuKXR2eNyKtw9aXaarTvIk5LTYCEXoNkmNYasyXRuNGhwiOLA/O4f+x7fB5FL3pyWSMA868nd3+ahlg8q3gENJTWjVLC+SVh38Ex2qziz7A80e6n+F2+RkZ0GvUyD3VgNxT9X5aYXjz6/EqvPXTCQDAi1eNweg+8bKPlZ0SjXcXToBBq8bWIxV46us/FFxpcEOfEkHAiSr2hjkwOQoAcO7wVADAtiMVPmukVVrXHsEBgL4JLIrjSx9Ok0PUxuMqKpsFOMpPkR82T+GVESHHrIdYqqrhJLDtOZ+eShA4Zw9JAXpIimp3YS1qms2Ij9Rhcv8Et/smROkxlxdB6ymK4zXNJiuW861CrpncFxeP7u31Mcf3SxD7Fa35tRBf/t4z0okkcAJMY5sF5Q3M/DUgORoAMCojDmoV0GiydmnkpBRCBCc9joU9+/ECx5cRHEcPjse54IKfgLY6ICoZ6Hum8osjQgt9JHDhi+z27neB5iqfnUoQOFeO7wMAqG42h71hc2PuaQDAucNSoXWRnnLkzxPZFPj/7SvtcT4PpXl+4xGcrG1FRnwEHrxgqGLHPX9kGm47eyAA4IHPDvQIoU4CJ8AUVLGISVK0AXERrORZp1FDEB4ltb4RHEKfnXQ+gpOZ4PtS8cYOERwPU1RCemrYJYBa093eRE8gew7QexxgbQV+e8Mnp6hpNosNMKcMTERmAvu/zAvjDwe7ncOmQ9LSUwJTBiSib0IkGk1WbD5EDebksq+4Fmt3sH5oz/9ptOLdiP967mCcOSABzWYbln6UA5M1vHvkkMAJMMcrO6anBPr04gVOTavT53mDzc6hrIEJnN68kBJTVD6spHKM4LRabLBJTb/Zbe0dbCk9RQioVMD0v7Lbu95mk8kV5nc+ejMgOQpxEToMSmEVh+EscH4/WYeyhjZE6TWSR7eo1SrMG8NSKRtzy3y8wvCE4zg8+dVhAMCfxvXBWYOUH5uj1ajxzwVnIClajyNljVix+Zji5wgmSOAEmBN8J2EhPSUgRFRO+iCCU9logs3OQatWiebifol+SFF18t1IDmVX/AG0VAGGWCDrLN8sjghNhlwIJA8DTA3A7rcVP7xgmh3DmzwHpbD/0/wwrqT69TjrL3TWoCQYddKjpXP54ogfj1X26O65cvnywGnkFNchUq/B8vOH+Ow8KTFGPHvFaADAqp9PYOcJ//WT8jckcAKMqwiOULrtiwhOKV8inhprhIavDBEiOKV1rbD4qIywq8CR+CZYdpBt00ZT52KiI2p1exRn51vMjK4gNS2sa3FqLEvlZvMC51gYV1LtKawBAEzqn+jR80ZmxCIjPgKtFps4wJeQRpvFhue/PQIAuHXmQPH15ivOHZ6Kqyf0AccBf/3kd+9G5wQxJHACjBDBGdgpgiOmqHwQwTldJxiM2/+JkmMMMGjVsHPAqVrlRRU6lYnDk0oqUeCM8sGqiJBnxOVAZBKbT3biR0UPLbxmY4ysi+/g1PBOUdntnDg1fGJWL4+eq1KpcN4IVgG6iXw4HrF2RyFO1bUiLdaIJdMHSHiG9zx88XBkxEfgZG0rXtkSnqkqEjgBxGbncKJKSFF1iuAIpl9fCBw+gpPu0DhKpVKJURxfpak6R3AkG43L+OnqaSN9sCoi5NFomcgBgIP/UfTQDa0sIhTLFwAIEZyqJhPqWsJvJtWxikY0tlkRqddguAddcwWENNXWI+U+iwSHGy1mK1bxPW/uPXcQIvT+KaKIMerw1OXsPXX1L4Vh2cOIBE4AKa1rhdlqh16jRh8+JSUgVGucrmtTvPNkaZ1gMO4YBvW1D6ep0+iJZikeHI6jCA7RPaOvZts/vgLMyvVyEiI4sXwEJ8qgRa9IJnaE9g7hxO5CFr0Z17eXpPLwzkzMSkBClB51LRbsKqjxwQrDj3W/FaOqyYzMhAhcMa6PX889a0gKLhqdDpudw0P/PSi98CNEIIETQAT/TVZSpOiFEUiNMUKnUcHqUPGkFEIEJ62TwEmOYd9X+2hasiyTcf1J1v9GrQWSlesJQYQZfSYC8f0ASzNw9FvFDtvIjxcRUlRwGIjrqx5VgWQ3L0omeJieEtCoVZgzjDVE3HyIqqm6o9Vsw1s/HQcALJ2V7XIkhi959OLhiDFo8fvJenz4W5Hfz+9LSOAEkOMu/Dfgyy6F2SNKG407N/kTiOevTOtafSNwOntwJA3cLM9l2+ShbNgiQThDpQJGXcVuH/xEscO2e3Daze2J0XogTAWOYDCemOW+e7E75gzjO7GT0bhb1u0sQlWTGX16+T96I5ASa8RyvqHg/206KvZICwdI4ASQE3wEp7P/RsBXPhzHQZuOxPM+A18N3BQiOEn8B4SkgZuUniKkIqSp8rcALcqkRzqbjAEgUYzghJcH51RdK0rr26BRq3BGX/mzj6ZmJ0GnUaGougWFVb4b/RLqmK12vPNzAQDgjgBFbwSundQXYzPj0WSy4vEvDwVsHUpDAieAtJeId43gABB9OScVrGqy2OyoaGRXnq4jOL4VOEIJpKSBm6LBmAQO0Q3JQ1hPHLuVjfbwEo7j0NgmpKjaIzjJvMAJt3ENQvRmZO9YROq13e7vimiDFhP6sQjQtqMViq0v3Pj6YCnKGtqQHGPAFeMyAroWtVqFZ68YBY1ahW9zy7DFh4Oe/QkJnAAijGnon+QqgsMEyEkFTb/lDW3gOECvUSMxSt/hsbgI9r0vqkM4jhM7GYsCx5MITipVUBESGHA22yogcExWOyw2ZrqMdYzg8P83vvKqBYo9vMF4fD/56SmBmUOSAb7pH9EVjuOw6icWvblxahYM2sCPnxmWHovF0/sDAB778lBYNGskgRMg2iw2sQqjX6JzgSNEcJRMUQn+m7Q4I9SdjM3CLKx6H0RwTFY7rLxDPzWWXQF3W0XV1gDUFrLbFMEhpNB/OtsqIHCEEnGVCojSO0tRhVcEJ7eU79qcGef1sc7mBc6OE9Vh8UGpNL/kV+OP0w2I1Gtw7eS+gV6OyN2zByE9zoiTta14Y9vxQC/Ha0jgBIjSOpZ2itRrxLLTzmT6YB6VcN70uK6dMoUUlS8EjuBlUKnaQ/wt3ZmMy/lccGwfINL7q0qiB9BvGqBSA9V5QMNprw7VwL9mow3aDhcDgoesqjl8Ijg2O4c/TjcAAEZmeC9whqTGIC3WiDaLncrFnbDqZ9b35uoJmYiP1He7v7+I1Gvx8MXDAQArfzyOourQ9lCRwAkQgq8ms1ckVCqV030Ek3F5Y5tiU1/LG9ojOJ0RPTgtFnCcsv0QBP9NtF6LaD7c322KqpK1LkfqcEXXQoQxEfFspAcAFP7s1aEE/01sp4nOiWHowTlR2YQ2ix2Reg36u4goe4JKpcLMwSyKs+0opakcya9oxE/HKqFWATef1T/Qy+nCBSPTcFZ2EsxWOx7/8nCgl+MVJHAChJB2EkYyOCMxSo8InQYc196cz1uEyg8hiuJIPO/Bsdo5aQZgDxD8N9FGrWhg7DZF1cj30YgLTPkkEaL0n8G2Bd6NbXBWQQXHCE6TSfELgUBxqJRFb4alx3ZJXcul3YdDRmNHPtjBes3MHpYqXsQGEyqVCo/NGwGdRoXvj1SEtOGYBE6AECI47gSOSqUSH1dqqngVX0GVFNNV4Bh1aui17CWhdJpKaJgWbdAiysAMdd0O22zkUwzRaYquhQhz+s9k2wJvIziuBA7732mz2KUPjA1yDvH+mxG9PR/P4Ipp2UlQq1i/LyE13tNpbLPg070nAQALp2QFejkuyU6Jxs1nsZlYj38VuoZjEjgBQkxRdaPghVSSUm3hK/mwurMIjkqlEo3GSldSOUZwBMNmtykqIYITQwKH8IC+Z7LO13VF7SZ1GbhKUUXqNTDq2FtnuFRS5Z7i/Te9vfffCMRF6DAmk/XT2Z5XpdhxQ5kNOafQbLZhYHIUpmV7Nq3d39x5TjbS44woqWnFmyFqOCaBEyBKarpPUQFASowgcJRJUVW6ieDAh83+RA+OQYsogyBwJEZwYtIVXQsR5hiigYzx7HbhdtmHcRXBUalUYhSnMgx8OBzHiRGc4QpGcABgenYSAODnfBI4HMfh/R1McC+cmuXSexksRBm0eOiiYQCAN388juJq38wo9CUkcAJEe4rKfQRHKKmuUEjgCB4cwUfQGV81+xMEToxRi0h+Wq5kDw5FcAhPyZzEtqX7ZR+iwUmTP4FwMhqfrG1FQ5sVOo0Kg1NjFD32dN5o/Et+FexhNsjRU3Ycr8aJymZEG7QBG8vgKReNSse07ESYrXY88VXodTgmgRMA2iw2sYdGZjcCR8kUlc3OoabZdYoKHZr9KezBaesawXHrX7BZgGa++oIiOISnpPJ9k4RZZjJwFcEBgCSh2V8YlIoLBuPBqTGiB08pxmbGI9qgRU2zGYf5MvSeyr93lwAA5o3tjWiD/E7R/kSlUuHxeSOgVauw5Y8KbP0jtAzHJHACgGAYjjFoERvh/oUupqgavY/g1LaYYedYL5qEKOcRHF81+2tPUelEgdN5unjHJ1QA4JiXIjK4c9VEEJLGd74uPwTIrHRyF8ERJ4o3hn4ExxcGYwGdRo0zB7AeVj/l9dxy8ZpmMzblsoj0gonB09hPCtkpMWI5++NfHg4pwzEJnABQIqSnElz3wBEQUlTlCkx4FaJGvSL10LoY7OarieIdTcYsRWW22mGx2Z0/QUhPRacBanqZEh6SNBjQ6AFTAzMby8BdBEeYKB5OEZwRChqMHZk+iKWperLR+L/7TsFss2NE71iM6uOb37MvuXP2IKTFGlFc04K3fjwR6OVIhj45AsBJiQZjOMxtqmg0eZ3Drmp077+BH0zGMQZth0F+LtNUosGY/DeEDDQ6NnwTAMrkpanEKqoI1x6ccBjXIHQw9kUEBwDOGsSMxnsKa9EaJmX1nsBxHNbvKgYA/HlSaEVvBKIdDMdvbMsXi2SCHRI4AUBKDxyB5BgDVCrWfK/Gy9Jt4c04yYX/Bp26GStJo0MER69VQ89HkFyWipPAIbxF9OHIM0e69eA4NPsLZRrbLOJ8ukEKG4wFBiRFoXecEWabHTsLqn1yjmAmp7gWeRVNMOrUuHRs70AvRzYXj07H1IGJMIVQh2OPBM6WLVvCpnNnIHEc09AdOo0aiVF8msrLSiopAieOn4uivAenvdEfAESKzf5cCRyqoCK8JHUE25YflPV0QeDEOhU4QhVVaKeojleyWUMpMQbRf6c0KpWqR6ep/r2LmYsvGtW7S0+lUKKj4bgcPxwJ/g7VHgmcuXPnorKy5xrFlELKmAZH2kvFvbtarJQicCJ8WyYuzKFqb/bnImTdRAKH8BLBaCwzReW+TDw8Ijh55Y0A37nWlwhpqp97mMBpaLPg6wMsGr1gUmagl+M1g1JjcBNvOH7sy+DvcOyRwKHojTJI7WIsIPhwyryN4AgenBgpHhzfmIxj+AiOMK7BdYpKEDhUIk7IREhR1RYApkaPnspxXDcpKnaRUNtigdWVUT4EyK9sAgAM8rHAmZadBJUKOFreqFhPr1Dgi/2laLXYkJ0SjfH9egV6OYpw1+xBSI01oKi6Bat+Cm7DMXlw/EyzyYoavvIiw8MIjj9SVL5u9CdEcNoHbroyGVMEh/CSqMR2gVzumWeg1WKDjTf1O4vg9IrUQyiA9NYbF0jyy5nA8XUEJyFKL46B2N6Duhqv382biydmBn3nYqkww/FwAMDrPwS34dhjgfPmm29i69atqK2t9c2KwhwhPRUXoZOcj20f1+BliqrRfZM/OEwUbzHbYLYqd2UqXA0Lqamobj04NKaBUIBUoR+OZz4c4fWqVkFsa+CIRq1CAu9XEyKjoYgQwclO8Y3B2JGelqbKPVWP3FMN0GvUIdO5WCqXjE7HlAHMcPzEV8FrOPZY4Lz22ms499xzkZSUhKysLFxxxRV46qmn8M0336CsrMw3qwwjCquYqS8rKUryc8RScT9EcGKMWvHKVCmjsc3OwcSLJaHJX6Q7D47VBLTw1RYkcAhvkOnDaXTw37i68haNxs2h6cNps9hQzF99D0r1bQQHAKY7CJyeYHcQojfnjUh12Vg1VFGpVHj8UmY4/u5wOb46UBroJTnFY4Fz6NAhnDx5El988QVuvvlmcByHt99+GxdffDEyMjKQkZHhm5WGCQVV7A2lf6I0/w0ApMXxKSovuhnb7ZzYlMydB0etVomRpXqFmv21OhjRhDlU0QY3E8Wb+HbgGj0QER55ayJApPCVVFXHPHpagxv/jUCoG42PVzaB41haOtEPH8Dj+/VChE6DqiYTjpR55okKNVrMVvxvH/vQXxCivW+6Y3BqDG6flQ0AeOR/h4Ly/8CjgRjClUzv3r3Ru3dvXHTRReJj1dXV2Lt3L/bvlz/cricgJ4IjpKjK6uW/gOpaLaKnQCg7d0V8pA71rRbFeuEIaSiVCjDws27cDtx09N+ESd6aCBC9sti21rNuxu0GY9dpZOGqvKZZWb+av8ivaDcY+8MfYtBqMKl/An48VonteVUYlu6bxoLBwNcHTqPRZEXfhEhMGRC+o2aWzsrG5kNlOFLWiEf/dwivXzsu0EvqgGJVVImJiTjvvPOwfPlyJdYVthRUM4HTX0aKqrrZ5Hq0QTcI6jo+UtftQD2hkkopgdNmZmuO0GnEN1K3AzfJf0MoRa9+bNtwiqU+JdLQKqSo3ERwRIETfFeuUhAEjq8Nxo6IaaowNxqv5wdrzp+YCbU6fC/S9Fo1XrxqDDRqFb4+eFosiQ8WPBI4GzduRFxc6M3RCCbECE6idIGTGKWHRq0Cx8kPhwtDAd35bwSUbvbXYmFXwxG6drOmYDZ2OnCTKqgIpYhKBnSRbHBr/UnJT3PX5E8ggY+E1oToPKp2geN7g7GA0PBv54nqoO+hIpe88kbsLaqFRq3CVePDy1zsjJEZcbjj7IEAgEf+l4vqIEpVeSRwzjvvPBgM3X9AEs5pMllRwQsNT1JUarUKKTFCqbi8F097k7/uc+1KN/sT5s9EOFSjCCkqp7NphAhONAkcwktUKiCe90DUFkp+WqObJn8CCVHssVDtZpwXgAjO4NRopMQYYLLasbcoPCtxhejNOUNTkMJH38OdpecMwtC0GFQ3m/HIF/JGo/gC6oPjR4ToTUKU3uO26EKaSm4vnKomYdBm9wJV6WZ/osBxiODoNCxsa3aWcmvkTcYxqYqcn+jhxPNpKg+minsSwakNwT44FptdfD/ydZM/R1QqlVgu/lNe+HXFN1lt2JDDIoXh0LlYKh1SVQdOB01VFQkcP1Iow38j4G2zPykl4gJKN/sTqqgiHSI4Wn7YptMusG11bBuRoMj5iR6ODKOxtAgOi4ZWh2CKqqi6GVY7hyi9Bulx/o0yCD6ccJxLtelQOWpbLEiLNWIGn47rKTimqv6+4SBO1ga+ASAJHD8ix38jII5rqJcpcIQmfzESPDhCBEcpDw4fwTE6RHCEaeIWmxPjutBW3+A/bwARxvSSH8FxZzJur6IKPYGT59DB2N8ddqdlM4FzqLQhqPwaSvAx3/vm6gl9xIu4nsSdswfhjL7xaGiz4u71+wM+xqTn/QUCiNgDJ0l6DxyBtDjv5lHJ8eDUKlRF5TyCw95UnVaFkcAhlERIUXkQwXE3aFNAEDj1raE3jyoQBmOBlBgjhqax84bT2Iai6mb8kl8NlQq4emLPSU85otOo8c8/n4EYgxZ7i2rxypa8gK7Hoz44jmzduhVbt25FRUUF7PaO/9yrV69WYm1hh5Ci8sRgLCCEkeVGcARzshTTWywvcIQwvbcI1RKOJmOdGMFx8sFgZm++0PvPG0CEMTIiOFIa/fXiU7kcx9K5UtK/wUIgDMaOTB+UhCNljfg5rwqXjg2P5rAf8+bi6YOS0aeX5xex4UJmQiSeuWIU7vz3Pnx1oBR3zMru8N7vT2QJnMcffxxPPPEEJkyYgPT09LAZIuZrvElRpcWywZxyBY4w5iE1RoLA4a9aG3yYohJMxlanKSpe4FAEh1ACIYLTUs2igxJeV0JzSqHjtjO0GjXiIlhTzJpmc0gJHMcmf4Fg5uAUvP1zAbYdrYTdzoV8rxiLzY5P9jJz8Z97aPTGkUvG9EaL2YoLRqUHTNxArsBZuXIl1qxZg+uvv175FYUpDW0W0YzoTQTndH0bOI7zSFSarXbx3GkSDIWxEexlIfgQvEWoouqQolLzERy7Ow8ORXAIBTDGspEfrbUsTSXMp3KDs9YGzkiM0osCJ1Sw2TkcrwxsBGdS/wRE6dnYhtzSeozuEx+QdSjF90cqUNloQmKUHnOGUfUnAMyfGPgRFbI8OGazGVOnTlV+NWGMEL1JjjG4vSp0hSBMWi02NLR6JjwE/41OoxLD6u4QIzgKpagED06HMnG+m7Kl88Ryuw2wsN8VDOHbyp3wMx6WijtrbeCMUDQan6pthclqh16rRmZCYFIpeq1aLBf//khFQNagJOt2MnPxlRP6dNspnvAfsv4SixcvxkcffaTYIl5//XVkZWXBaDRi8uTJ2LVrl9v9X3nlFQwZMgQRERHIzMzEvffei7Y27yZt+5oCXuD0l5GeAp/eEcTJ6YZWj54rlJanxBglRX4ED06bxQ6T1ftuo+1Xw+3CTseHpK2d/Fui/wbkwSEUpJdnRmNnxninhw3BUvG8ChYhHZAUBU0AU0PnDE0BAPwQ4gKnuLoFPx1jPX2undQv0MshHJCVompra8OqVauwZcsWjB49Gjpdx6jAihUrJB/r448/xrJly7By5UpMnjwZr7zyCubOnYujR48iJSWly/4fffQRHnjgAaxevRpTp07FsWPHcOONN0KlUnl0Xn9zqLQBADAoVf6HdlpcBGpbLDhd34ahadKjG+W8b0fopdMdMQYtVCpmnmxss8IQ7V0OtcVZoz+tizJxwX+j1gHa0PE0EEGOhxEcZ74xZ4jzqEKom7FgMB6UGliP26wh7P3995P1qGw0SWphEYx8tItFb2YMTkbfxJ5rLg5GZEVwDhw4gLFjx0KtViM3Nxf79u0TvzydJr5ixQosWbIEixYtwvDhw7Fy5UpERka6rMT69ddfMW3aNFxzzTXIysrCeeedhwULFnQb9Qk0QlvycX17yT6G3EoqIYIjxX8DfjRENB9tUcJo3OasTFztokzc0X9D5nVCKTxo9me3czDxqdPuIjhCiiqUuhkH2mAskBJrxMgMdqG27WhoRnFMVhs+2cOqp66dHHjPCdERWRGcH374QZGTm81m7N27Fw8++KB4n1qtxpw5c7Bjxw6nz5k6dSo+/PBD7Nq1C5MmTcKJEyfwzTffBLXh2WS14eCpegDA+H7yBU6ag9HYE8r5Jn8pEiqoBGIjdGg0WcVyWW8QKlI6jmpwUSZupgoqwgd4UCre6jAEsjuTcSh2Mw50ibgj5wxNRe6pBvxwtAJXTQi96qNNh8pR3WxGaqwBs4d2zTgQgUV2HxwlqKqqgs1mQ2pqR9d5amoqjhw54vQ511xzDaqqqnDWWWeB4zhYrVbceuut+Pvf/+7yPCaTCSZTe8fMhoYGBX+K7jlU2gCz1Y7EKD36eRHCTBe7Gcvz4KR6MPhN6P+hRC+cVjd9cLqUiQsRHD0JHEJB4h0iOBznNjrY4jAA1qiVajIOjY68HMfheJBEcMD7cP65NQ8/HauCyWqDoZvfd7Cx7jcmmP88sW+P7Fwc7Mj+i9TV1eGll17C4sWLsXjxYqxYsQL19fXKrs4J27ZtwzPPPIM33ngDOTk52LBhA77++ms8+eSTLp/z7LPPIi4uTvzKzPTvlUKOkJ7q18urnkFyIzgVfJM/qR4cOBiNPa3YcoazihSXnYypRJzwBbHpbGtp7mhkd4KQUjXq1N32Z2kXOMpUHPqasoY2NJms0KhV6Cez4EFJRmfEISXGgCaTFb8erw70cjwiv6IROwtqoFGrsGASpaeCEVkCZ8+ePRg4cCBefvll1NTUoKamBi+//DIGDhyInJwcycdJSkqCRqNBeXl5h/vLy8uRlpbm9DkPP/wwrr/+eixevBijRo3C5ZdfjmeeeQbPPvtsl47KAg8++CDq6+vFr5KSEg9/Yu8Q/DfepKcAID1OXrO/MhkRHCVLxZ1VpLicRUUpKsIX6KPaq/Ka3Ps9WsS+Td0HuBP5ieKhEsER/DdZiZFBUc6sVqswdwR7r9+UWxbo5XiEUBo+e2iKZH8j4V9kvcLvvfdezJs3D4WFhdiwYQM2bNiAgoICXHzxxbjnnnskH0ev12P8+PHYunWreJ/dbsfWrVsxZcoUp89paWmBWt1x2RoN++DkOCdN4wAYDAbExsZ2+PIXHMdhjwIGYzjOo5JpMvZM4ChnMhYEjtHJLKouZeJiiooiOITCRPHTnbsROM76NrmiVxS7EKhpNrt8/wkmHIdsBgvnj2QCZ/PhcticNf4MQlrNNnzGdy6+9kwqDQ9WZEdw7r//fmi17Vc4Wq0Wy5cvx549ezw61rJly/D222/j/fffxx9//IHbbrsNzc3NWLRoEQDghhtu6GBCvuSSS/Dmm29i/fr1KCgowHfffYeHH34Yl1xyiSh0AoXdzuGrA6XYmHtavO9kbSsqG03QqlUY3SfOq+MLAqfRZEWTSVrqqMVsFTsSy0pRKRHBcVYm7hDB6fDBQIM2CV8RzZtAm7uL4PCmeAkt5oUIjsXGSf6fDCRiiXgAhmy6YlL/BMRH6lDTbMbuwppAL0cSXx4oRUObFZkJEZjOT0cngg9ZJuPY2FgUFxdj6NChHe4vKSlBTIxn/zjz589HZWUlHnnkEZSVlWHs2LHYuHGjaDwuLi7uELH5xz/+AZVKhX/84x84deoUkpOTcckll+Dpp5+W86Moyqc5J7H80wPoHWfErKEpMGg1yClm0ZsRGXHd9tTojmiDFjFGLRrbrCirb5N0FSb4byL1Go86KMcalRvX4GxUg87hb2q1c+JsKhI4hM8QBE43ERxnbQ1cEaHXIEKnQavFhppms9vp48GAaDD2oh+X0ug0aswZlopP957ExtwynDkgMdBLcgvHcfiQNxdfM6lfyM/RCmdkRXDmz5+Pm2++GR9//DFKSkpQUlKC9evXY/HixViwYIHHx1u6dCmKiopgMpmwc+dOTJ48WXxs27ZtWLNmjfi9VqvFo48+ivz8fLS2tqK4uBivv/464uMDP8tk3pjeSIs1orS+TZwsu4M3zo33Mj0l4GkvHEf/jScG53aTsXcRHI7j0OJ0VEP7WjoYjcmDQ/iKKGkCR2qTP4FQKhUXuhgPTA4egQNA9OFsPlQW9Km+nOJaHDhZD71Wjasn9An0cgg3yIrgvPjii1CpVLjhhhtgtVrBcRz0ej1uu+02PPfcc8qvMkQw6jS445xsPPx5Lv71fT56x0XgY74J1NlDkhU5R1pcBI6VN+G0xFLxdv+NZ11C203G3kVwTFY7hPerCCfDNtHZaCx0MiYPDqE0ElNUziKO7kiI0uNUXStqg1zgVDeZUNtigUoVfAJn+qAkROo1KK1vw/6SOpyh0AWhL1j9SyEA4LKxvZEYQhPkeyKyIjh6vR6vvvoqamtrsX//fvz+++9iJZXB0LP/4PMnZCIjPgKVjSbc8sEecBzw54mZmDFYGYHT3gtHWgSnvUTcM5d/jEIm41aHniIdPTguIjiUoiJ8hcQUlScmY4RQBEfw3/TpFSHJX+RPjDqNOIX7f/tLA70cl5yqa8VGvtpr0bT+gV4O0Q2SIzjLli3Dk08+iaioKCxbtsztvsE8E8rX6LVq3D17EJZ/dgB2DhieHovH5o1Q7PhiL5wGaQJHTgUVFDQZCx8Weo26QyMslUoFrVoFq53r2OzPTAKH8BEepqikioBQmSieH4QGY0cuO6M3vvi9FF8dKMU/LhoWlI3z1u4ohM3OYerARAxL9181LiEPyQJn3759sFgs4m1XeNPILly4YlwG1v5WiLL6Nrxx7TivzcWOeOrBaR/TIC9F5a3JuN3P0PXNSqthAociOIRfkBrBkZGiQggJnGAqEXdk+qBkJETpUdVkxi/HqzFToai3UjSbrPg33/vmJorehASSBY7j/CmlZlGFK1qNGp/fPg1WO6eouAGA9HjW7O9kbYuk/dsniXsawVEmRdVekdL1pabTqNFmsXcSOOTBIXyEowfHzbgG2SmqIJ8oLhiMg1Xg6DRqXDQqHR/8VoT/7TsVdAJn/e4SNLRZ0T8pCufQ3KmQIPhigGGCVqNWXNwAQH++vXpRdQvsEppilTd6NklcQIjgNJttsHYep+AB7sL94jwqO/XBIfyAkKKytrW/zpwg9m2S0MkYAJKiBQ9OcHczDpYp4u647IzeAIBNh8o6+PcCjdlqxzs/nwAA3DJjAJWGhwiyBE5raytaWtojCEVFRXjllVewadMmJddGOKF3vBE6jQomq71bH47dzomprFQPJokDQLSx/c3dmzSVu6thwWhstjorEw/eN2EiRNFHtg9xdZOmanHSmNIdSXwlTVVT8AqchjYLyvmCg4FBLHDG9e2FzIQINJtt2Hw4eEY3fPF7KU7XtyElxoArxmUEejmERGQJnEsvvRRr164F+KGbkydPxksvvYTLLrsMb775ptJrJBzQatTITGATyQsqm93uW97YBpPVDq1ahfR4zwSOTqMWPQjeGI1b3URwhFJxMYJjt7cLHJomTviCaD7t4aZU3JNGf3AUOI3Bm6ISojdpsUYxOhuMqFQqXD6WCQihl1igsds5rPzxOADgprP6h9zE856MLIGTk5OD6dOnAwA+/fRTpKamoqioCGvXrsU///lPpddIdGJAEktTFVS7FziCAOqbECmmgzxBCaNxq4Vve+8mgiN6cBynPFOKivAFEiqpxFENEiM4ybyBv7rZJCltHAjyg3AGlSvmT+oLtQr49Xg1jle6n/zuDzYfLkd+RRNijFpcO5mmhocSsgROS0uLOJJh8+bNuOKKK6BWq3HmmWeiqKhI6TUSncjifTjdRXBOVLHH+/OCyFOUMBq3mpl4cefB6SJw1FpA27P7KRE+QkIlladl4om8B8di41CvwHBaX5BfGToCJyM+AmcPYX8noWopUNjtHF7ZcgwAsHBKVtCP4iA6IkvgZGdn4/PPP0dJSQk2bdqE8847DwBQUVHh10ndPZX+yUywFHYXwfFW4Bi974Xj7mpY6HMh9sFxNBhTuwHCF0joZtzmYRWVQasRZ7cFqw8nrzy4K6g6I0RKPs05Kf49AsG3uWU4UtaIGIMWi6dTaXioIUvgPPLII7jvvvuQlZWFyZMnY8qUKQAfzTnjjDOUXiPRCaGSqrBKosBJlhvBEeZRyU9RufMz6DunqEzkvyF8jJiiKne5S4uHfXAAIIlPU1UGq8AJgQoqR84ekoLecUbUtVjwbe7pgKzBZufwMh+9uXl6f8RH6gOyDkI+sgTOlVdeieLiYuzZswcbN24U7589ezZefvllJddHOCGLj8gU17S4LeH2NoIjjmvwKoLjenChVkxRCRGcBrYl/w3hK8QUVaXLXcTKPw8ETrJYSRV8RuMWsxWn6tjsukGpofG/pVGr8OdJLIqz5pfCgAzg/PL3UuRXNCEuQoebzqLoTSgiu0w8NjYWZ5xxBtRqtVgmXltbi6FDhyq/SqIDabFGGHVqWO0cTtY6H7ppsdlRXMNK+QckybtqE1NU3nhw3ERwtGoXJmMqESd8hYQUlbvKP1cIEZyqxuCL4JyobAbHsYaEQlPCUGDBpL4waNX4/WQ9dhyv9uu52yw2/N+mowDf9yaYK88I11CZeAiiVqvajcYufDglNS2w2TlE6DQeTxIXEE3G3lRRuekpotcKZeJCioqa/BE+RkIVlSjKdZIbvYsRnGBMUQX7iAZXJMcYMH9iJgDgjW3H/Xrud7cX4FRdK9LjjDSWIYShMvEQRUg7uaqkckxPyZ0PpoTJ2F24X4zgWIUUFY1pIHyMYxWVk7QHx3Hia9aol/72KHQzDsYITrCPaHDHkukDoFGrsD2/CgdO1vnlnBWNbXjjh3wAwPLzhwTd5HVCOlQmHqIIPhxXlVTeGoyhkMlYyqgGixDBoUnihK8RBI7NBLTVd3nYZLWLusfZ/DRXCL1wgrGKKhRGNLgiMyESl45h4xve+ME/UZyXNh1Ds9mGMX3icOkY6locylCZeIgiVFIVuKikEnrgDJBpMIZCJmN3VVQ6d2XiBOELdBGAgX+Pau5qNG5xmH8ktUwcHcY1BJ/JOC9EU1QCt509EACw8VAZfi/xbRRn54lqfLyHdVB++OLhNHMqxKEy8RBFiMy4EjhC6kpuBRWUMhm78eB06WQspKhI4BC+JIof1+DEhyP0bdJr1dB48OEWrPOozFY7iqpZscGglND8vxqUGiPOf3riq8M+q6hqs9jwwIaDAIAFkzIxISvBJ+ch/AeViYcogsn4VF2r06m7gvAZkCz/qk1IUXkzqsGzMnE+gkMeHMKXRPRi27au0QBPm/wJJDmkqAJR0uyKwupm2OwcYgxa2cUGwcDyuUMRodNgb1Etvjrgm744r27NQ0FVM1JiDHjggmE+OQfhX2QJHABIS0sTy8QFJk2aRGXifiIpWo/0OCM4DthbVNvhsWaTFWX8pHEhlSWHWEVTVF39DC5nUVGZOOFLIuLZtrWrwJHT5A8AEqOCc1xDHj+DamBKtOxig2AgLc4opqqe+/aI4t2N9xTWYNVPJwAAT102EnERVBYeDkh20S1btgxPPvkkoqKisGzZMrf7rlixQom1EW5QqVSYlp2ET/eexM/5lThrUJL42Ak+PZUYpUdcpPx/VMcIjs3OeRSyF2hxm6ISPDhCikpo9Ec+LsKHGOPY1kkER04PHPARylijFg1tVlQ1mYKm620oG4w7s2T6AKzfVYxTda149ps/8PilIxU5blWTCUs/2gebncNlY3vjvBFpihyXCDySBc6+fftgsVjE264I5auEUOMsXuD8kl/V4f6f8ph5ckxmvFfHd7yKaWi1oJeMJmHuy8SZwDHbqEyc8CNG/v/CSRVVi8wUFfg0VUObFZWNZmSneL9MJQjlEvHOROg1ePZPo7Fw9S68v6MIM4ck45yhqV4d02bncM/6/ShraEN2SjSevnyUYuslAo9kgfPDDz84vU0EjqnZiQCAQ6UNqGk2i11KNx8qAwCcN9y7f36dRo0YgxaNJivq5Aocd2XiWiaG2yM4VEVF+AE3Kao2mSkq8EbjE5XNQWU0FiM4qaEvcABg5uBk3DStP1b/UoC/fXIA394zHSkxRlnH4jgOj395CNvzqxCh0+DNa8chyiC9NQAR/Mj+a7a1teHAgQOoqKiA3d4+D0mlUuGSSy5Ran2EG1JijBiSGoOj5Y3YcbwaF41Ox+n6Vvx+sh4qFTB7mHcCBwDiInVoNFlR22JGf3jm57Ha7DDz4iXSWYpKLXQy5iM45MEh/IGbFJU7U3x3JAdZJZXVZhfbRWQnh89Fw/Lzh+DX41U4UtaIm9bsxkdLzpQ1SuH/Nh3F2h1FUKmA/7tqdMjM6SKkI0vgbNy4Eddffz2qq7vOB1GpVLDZAjfevqcxLTsJR8sbsT2/CheNTseWw2xK8ri+vcTmY94QH6nDydpW1Ld4bpxsdTACumv0J4ggWFr4ByJlr5cgusXoOoLjbnZadwRbs7+imhaYrXZE6DTo0ysi0MtRDKNOg9evHYerVu5A7qkG3PTebqy9eZLkxox2O4eXvjsqjn946rKRuHh0bx+vmggEsqqo7rzzTlx99dU4ffo07HZ7hy8SN/7lrEEsTSX4cDbzAsfb9JRAfARLS9W1et7ATPiwUKkAg7brS02r6ZSisvLn0ASHQZMIUyJce3Dc9W3qDmFcQ2WQjGs4VsZSvoNSo8OuYd3A5GisvWkSYoxa7CmqxbXv7EQJP1zYHfWtFixZuwev812RH7hgKK6d3M8PKyYCgSyBU15ejmXLliE1VZkPUUI+k/onQqtWobimBR/tLBan7ipVCRDPV2HVyYngOHxYODOft5eJ8ykqG//BoJWXUycISYgmY9cpqggPxjQIBFs342N8ifjgME29jMyIw5pFTOTsK67Dha/+jPW7ip2WkFtsdqzbWYS5L/+ErUcqYNCqseLqMbh15sCArJ3wD7JSVFdeeSW2bduGgQPpxRFoog1aTMxKwI4T1fj7f1kXzkEp0V51MHbEK4HTTUWKOIvKZgdsVoDjIzlaiuAQPkTw4Cicogq2bsbHylkEZ3CYGIydMb5fL3xz13Tc+/F+7CmqxQMbDuLZb4/ggpFp6B0fAaNOjUOlDfjtRDXKG9jfJTMhAm9eOx4jM+ICvXzCx8gSOK+99hquuuoq/Pzzzxg1ahR0uo4Gr7vuukup9RESeHn+WLy/oxBf/l6Kk7WtmD8xU7FjiymqFhkpqm56imgdBY61rf0BTeh2XCVCALcpKta1W06KSvDgBE2KShQ44RnBEchMiMT6W87Eu9sL8P6vhSitb8P63SVd9kuK1uOOWdlYMKmvLBM5EXrIEjj//ve/sXnzZhiNRmzbtq1D+kGlUpHA8TNpcUbcf/5QLJ87BFVNZtELoARiBEdGd9Zmk/urYb3oweEAm4OA0pLAIXyIkKKytgJWU4fXm7u+Td2RGstSq5WNJtmNMZXCbLWL41rCXeCAv1j6y8yBWDx9AH7Oq8TOghrUNpvRaLJiUEo0JvRLwISsXiRsehiyBM5DDz2Exx9/HA888ECHUQ1EYFGpVIpUTjkiNPuTk6Jq5Ec8uCrhbG/0Z2cfNACg1gJqehMifIghFoAKAMfSVDHtXkJ3nbe7IznGAI1aBaudQ1WTSRQ8gaCgqhlWfgZVelzP8bRp1CqcPSQFZw8Jkk6LRECRpU7MZjPmz59P4qYH0CtSqKKSI3BYuD/G6FxH67TCqAau3WBM6SnC16jVgJEfB9LJaNzmhQdHo1Yhlb/AKK1rVWKlsjla3l5BRd3liZ6KLIWycOFCfPzxx8qvhgg62k3GnntwhCGdsS4G1+n4EL7V7hDBIYMx4Q9cjGtokTmLSiCNj5aU1bd1u68vyesh/huCcIesFJXNZsMLL7yATZs2YfTo0V1MxjRsM3zwpoqqoZsIjlbjMIvKShEcwo9ExAN1RV0qqbxJUQFAenwEUFyH0gALnKNlJHAIQpbAOXjwIM444wwAQG5ubofHKBwaXsTxVVQNbRaPjZMNre49ODrHRn+CyZgMxoQ/cDGuoc0LkzEApMcKEZzApqjyKsK7Bw5BSEGWwKFhmz0HIYLDccw0HB8pPYXU7sFxJXAcy8SFFBUJHMIPuBjX0OLFsE0IERwgoBGcNosNhdV8BVVa+PbAIYjukOXBaW1tRUtLe1vsoqIivPLKK9i8ebOSayOCAJ1GjWh+wq6naap2D44Lk7HGwWQs9MGhFBXhD1z0wmlvTilvDnF6EHhw8iuawHHs4kQYAEoQPRFZAufSSy/F2rVrAQB1dXWYNGkSXnrpJVx66aV48803lV4jEWCEUvFaD43GQpm4qwiOMIvK3CFFRSZjwg+4SFF115yyOwSBczqAVVSHTzcAAIamxZBlgOjRyBI4OTk5mD59OgDg008/RVpaGoqKirB27Vr885//VHqNRICR2+yvoZWlqGJdmIz1HSI4NIeK8CNOUlQcx3k1qgEA0uNYiqqcb/YXCP7gBc6w9NiAnJ8gggVZAqelpQUxMcy8tnnzZlxxxRVQq9U488wzUVRUpPQaiQAjCJx6D1NUjSZpERyr3SGCQ5PECX8Q0XXgptlmF0WJ3I63QrM/m50L2MgGEjgEwZAlcLKzs/H555+jpKQEmzZtwnnnnQcAqKioQGws/VOFG4Kx2NNeOEIEJ86FB0fsZGx1mEVFJmPCHzjpg9Nmtou35UZwHJv9nQ5AJRXHcThcygTOcBI4RA9HlsB55JFHcN999yErKwuTJ0/GlClTAD6aI5SPE+FDfITnKSqO47r14IgpKrtjHxyK4BB+wEmKqsXCBLlWrRIN8HIQmv2dDoDRuLS+DQ1tVmjVKgwK4yniBCEFWaUCV155Jc466yycPn0aY8aMEe+fPXs2Lr/8ciXXRwQBcpr9NZttECwILmdR8SkqSweTMXlwCD/gJEXlbRdjAaHZXyAEzh989GZgcjQMWprpRvRs5NVCAkhLS0NaWlqH+yZNmqTEmoggIz7C8xSVEL3RqlUw6pxfDbf3weHAWdqgAlVREX7CSYqq1csuxgJCs79AVFK1+2+owR9ByBY4W7duxdatW1FRUQG73d7hsdWrVyuxNiJIkFNFJVZQRehclqoKnYwBwG41QQPqg0P4CaFM3NQA2G2AWuN1BZWA0OzvdEMAIjhlZDAmCAFZAufxxx/HE088gQkTJiA9PZ16LYQ57SZj6QKn3X/j+iXm6HOwW3iBQyZjwh8IKSrwUZzIBIceOLKv+4AA98L54zSbQTW8NwkcgpD1n7xy5UqsWbMG119/vfIrIoIOsUzcgwiOMKbBlf8GDh4cALBTFRXhTzQ6QBcFWJqB1logMsFh0KZ8gzEC2M242WQVRzRQBIcgZFZRmc1mTJ06VfnVEEFJvIxOxg1SIjjq9pcfZ6ZRDYSf6TSuoU1MUXkbwQlMs78jZY3gONaLJ4lGNBCEPIGzePFifPTRR8qvhghK4hwiOHaJb9gNEiI4arVKnE7OiZ2MyWRM+IlO4xqECI7cJn8CgWr2Rw3+CKIjsi5V2trasGrVKmzZsgWjR4+GTtfxQ2zFihVKrY8IAoQqKjZR3CoKHnc0tHYfwQFfZWWzc7CLfXDoypPwE5164ShlMtaoVUiLNeJUXStKalvEvji+JvcUi0SNIP8NQQByBc6BAwcwduxYAEBubm6Hx8hwHH7otWpE6TVoNttQ12qWJHBED06E+331GjVMVjtgFfrgkMAh/ESnXjitZvaa9bZMHACykiJxqq4VBVXNmJiV4PXxpPD7SSZwxvSJ88v5CCLYkSVwfvjhB+VXQgQ18ZF6NJtbUdtiQb/E7veX4sGBg9GYI5Mx4W869cJRqtEfAGQlRuGX/GoUVjV7fSwptJptOFbOKqhG94nvdn+C6Al4Vy5A9BgSoliaqrpJmqdAShUVAGiFUnFKURH+xsincniBo1SKCgD6J0UBgFjV5GsOn26Azc4hKdogVnERRE/Hq3KBw4cPo7i4GGZzx+qaefPmebsuIshI5gcIVkg0TUr14AjzqNpHNZDJmPATen5Wk5mJEKU6GYOP4ABAYVWL18eSwoGTLM02uk8c2QQIgkeWwDlx4gQuv/xyHDx4ECqVChzHKmuEfyybzabsKomAkyIInAapERwmcLrz4Ii9cCiCQ/gbAy9wTE2AQwRHkRSVQwSH4zifi44DvP9mNPlvCEJEVorq7rvvRv/+/VFRUYHIyEgcOnQIP/30EyZMmIBt27Ypv0oi4IgCp1Fa8zKhTLy7CI7QzVhlE8rESeAQfkKM4DCBo6QHp29CJNQqdkx/lIoLEZwx5L8hCBFZAmfHjh144oknkJSUBLVaDbVajbPOOgvPPvss7rrrLo+P9/rrryMrKwtGoxGTJ0/Grl273O5fV1eHO+64A+np6TAYDBg8eDC++eYbOT8KIZFkfoCg1BSVGMHpzoPD98FR2aiKivAznQROm4IeHL1WjYxerOFfgY+Nxo1tFpzgzzGKIjgEISJL4NhsNsTEsGm1SUlJKC0tBQD069cPR48e9ehYH3/8MZYtW4ZHH30UOTk5GDNmDObOnYuKigqn+5vNZpx77rkoLCzEp59+iqNHj+Ltt99GRkaGnB+FkEiKxx4caSZjvbZTBIdSVIS/0LM0kuDBaVHQgwNHH46Pjca5pxrAcUBGfAR1MCYIB2R5cEaOHInff/8d/fv3x+TJk/HCCy9Ar9dj1apVGDBggEfHWrFiBZYsWYJFixYB/Jyrr7/+GqtXr8YDDzzQZf/Vq1ejpqYGv/76q9hgMCsrS86PQXiAIHAqJUxIttjsop8hNqL7Rn/okKIikzHhJzp7cBQatimQlRiFn/OqUOBjo7GjwZggiHZkRXD+8Y9/iMbiJ554AgUFBZg+fTq++eYb/POf/5R8HLPZjL1792LOnDntC1KrMWfOHOzYscPpc7744gtMmTIFd9xxB1JTUzFy5Eg888wzbo3NJpMJDQ0NHb4Iz0jhU1SVTSbxb+8KoUQcAKIN3fXBYS9BtV1IUVGJK+EnOqWoRJOxUhEc3mhc5OMIjmAwpvQUQXTEY4FjsVjwwgsvYOTIkQCA7OxsHDlyBFVVVaioqMA555wj+VhVVVWw2WxITU3tcH9qairKysqcPufEiRP49NNPYbPZ8M033+Dhhx/GSy+9hKeeesrleZ599lnExcWJX5mZmZLXSDCS+dC3xcahtsX9VHHBfxOl17T3uXGBUCauFjw4GorgEH6ii8mYCXMlPDgA0D8pEvCxB4fjOOwpqgEAnJHZy2fnIYhQxGOBo9PpcODAgS73JyQk+KX/gt1uR0pKClatWoXx48dj/vz5eOihh7By5UqXz3nwwQdRX18vfpWUlPh8neGGXqtGL35EQ3eVVI1iBVX3Ix2EMnG1nRdNZDIm/EUnD06rglVUcPDgFFW3dBv1lMvJ2laUN5ig06gwNpMqqAjCEVkpquuuuw7vvvuu1ydPSkqCRqNBeXl5h/vLy8uRlpbm9Dnp6ekYPHgwNJr2N6Fhw4ahrKysS8NBAYPBgNjY2A5fhOekxPCVVN30whGa/HXnvwFfJq6GHWqOT2uRyZjwFwZWKAFrG2CzKp6iykyIhEatQqvFhnKJ/aM8ZVcBi96MzIhTTJgRRLggy01ntVqxevVqbNmyBePHj0dUVFSHx6VOE9fr9Rg/fjy2bt2Kyy67DOAjNFu3bsXSpUudPmfatGn46KOPYLfboVYzfXbs2DGkp6dDr6f0hi9JiTXgaHljt5VUDR5EcHQaFfRwSHlRBIfwF/r29y1LWyMsNhZlUSpFpdOo0adXBIqqW1BQ1eyTqeJCespfAz0JIpSQJXByc3Mxbtw4gBcXjniaplq2bBkWLlyICRMmYNKkSXjllVfQ3NwsVlXdcMMNyMjIwLPPPgsAuO222/Daa6/h7rvvxp133om8vDw888wzsvrvEJ6RLLHZX4PYA0daBIcEDhEQtAZArQPsFrQ114t3GxWK4IBPUxVVt6CwuhlTBkqYUushuwtrARI4BOGUgE8Tnz9/PiorK/HII4+grKwMY8eOxcaNG0XjcXFxsRipAYDMzExs2rQJ9957L0aPHo2MjAzcfffduP/++xVbE+EcqSkqjzw4ajUMEKquVIBamRJdgpCEPgpoq4O5mVVWqlWAQavcDOLBqdH48VgljpxWvnKzptmM/ApmkJ7QjwzGBNEZWZ8mxcXFyMzMdBqtKS4uRt++fT063tKlS12mpJyNfpgyZQp+++03j85BeI/YC6ebFFW9xEGb4FNUBpWDwZgGBRL+xBADtNXB1MIESIROo2ixxIjerHQ7t1R5gbOnkKWnBqVEo1cUpecJojOyLlX69++PysrKLvdXV1ejf//+SqyLCEJSYqWlqCr4ZoCpsd17DjqkqCg9Rfgb3odjbmkEFGzyJzAygxU0HC5tgM2ubCXVbl7gTKD0FEE4RZbAcTUdt6mpCUYjNWoLV8QUVTcRnFN1rQCAdAmmSq1GBT2ogooIEHwvHGsrH8HRK5eeAoD+SdGI0GnQarEp3g+n3X9D6SmCcIZHlyvLli0DeCPxww8/jMjISPExm82GnTt3YuzYscqvkggKxHlUDSaXIhcATtezCE7v+Ihuj6mnCA4RSPgIjq2tCUA0InXKRnA0ahWGpccgp7gOh0rrkZ0Srchxm0xW5J5ixmgyGBOEczz6b963bx/AR3AOHjzYoSxbr9djzJgxuO+++5RfJREUCCmqVosNTSarUxMxx3E47WEExyAIHOpiTPgbvheOva0RQJpPesmMzIhDTnEdck/V49KxygwF/jW/ClY7h6zESGQmREp4BhEO2Gw2WCzuO8mHA3q9vkNxkVw8EjhC9dSiRYvw6quvUsO8HkakXotogxZNJisqGk1OBU5DqxXNfEdYKREcnUYNvWgypvQm4WeECI6J9+AoWCIuMFIwGp9Szmj84zHmgZwxOFmxYxLBC8dxKCsrQ11dXaCX4hfUajX69+/vdW87WfHY9957z6uTEqFLSoyBCZwGEwYmdw23l9az6E1ClF5SPxFmMuY9ODRJnPA3neZRKdXkz5ERvNH4UGm929SuVDiOEwXOTBI4PQJB3KSkpCAyMtIvY5EChd1uR2lpKU6fPo2+fft69bNS0xHCI5JjDDhR1YzKJudG49P10tNTAKBVO6aoyIND+Bmhm7GJGYCNPhA4g1JioNOo0NBmxcnaVq9TSgVVzThZ2wq9Ro0zByjfPJAILmw2myhuEhN7xt87OTkZpaWlsFqt0Om676fmCmVLBoiwJyVWaPbnvFT8VJ10gzG6lIlTBIfwM7wHR2XhIzg+SFHptWoMSWPnEYzB3iBEbyb274UoA12jhjuC58axqCfcEVJTNpvNq+OQwCE8QojMnKxtdfq4YDDuLTGC07HRH3lwCD/DR3DUFhbB8dXAStGHU6qcwJkxiNJTPYlwTkt1Rqmf1WOBY7FYMHv2bOTl5SmyACK0yOZ9N0KL+M4IJeLpHkVwhD44FMEh/AzvwdH4WOCMyGACZ3+JdybRNosNv52oBgDMHEIChyDc4bHA0el0OHDggG9WQwQ92ansAyGvotHp46UelIgDgFajbvfgUB8cwt/wERyttQUAFO+DIzBlAOtVs7uwFq1m+WH3Hcer0WaxIzXWgCGpMQqukCDCD1kpquuuuw7vvvuu8qshgh6hUVl5g0mcOeWIUEWVITmCo2r34JDJmPA3vAdHZ2MCR+lOxgIDk6PRO84Is9WOnQXVso/z5e+lAIDzR6T1qJQFEXps2rQJKpXK7dfmzZt9ugZZlytWqxWrV6/Gli1bMH78eERFRXV4fMWKFUqtjwgyYo06pMcZcbq+DfkVTRjvMMXYbudQ5k2KiiI4hL/hIzjtAsc3ERyVSoXpg5Lx8Z4S/HSsCmcPSfH4GG0WGzYdKgMAzBvb2werJAjlmDFjBk6fPi1+P3LkSNx+++24/fbbxfuSk32bZpX135ybm4tx48YBAI4dO9bhMbqqCH+yU6Jxur4NeeWNHQROVbMJFhsHtQpIjZEmVrRqlUOjPxI4hJ/hPTh6Oy9wfFBFJTBjMBM4P+d1HVQshe+PVKDZbENGfATG9aX5U0RwExERgYgIdqF76tQpVFdXY/r06UhLS/PbGmQJHKGjMdEzGZQSg5/zqpDXyWhcWtc+RVyrkRbq12nVNKqBCBy8wDHaWWrVF43+BM7KToJaBeRVNKG0rlVyKwWB/+0/BQC4ZExvupDs4XAch1aLdyXUcojQaWS99oQxT0JgxF/IjsfW1dXh3XffxR9//AEAGDFiBG666SbExcUpuT4iCBkkGo07ChxPZlAJ6NQ0bJMIIAb2WjZwbQA4n0Zw4iJ1GJMZj33Fdfg5rxLzJ/aV/NyGNgt+OMoiP/PGUHqqp9NqsWH4I5v8ft7DT8xFpIw0bk5ODjIzM/3eqFCWo27Pnj0YOHAgXn75ZdTU1KCmpgYrVqzAwIEDkZOTo/wqiaBiEG80zi/vWElV6qH/BqLJWCgTJ4FD+Bneg6OBHUaYfVYmLjCd713zU16VR8/bmFsGs9WO7JRoDEun6ikitMjJyfF79AZyIzj33nsv5s2bh7fffhtaLTuE1WrF4sWLcc899+Cnn35Sep1EEDEohb3Blta3obHNIg7d9LTJH/gycfLgEAFD114gEY02n0ZwAGDm4CT8c2sefj5WiTaLTdK8No7j8N4vhQCAy8/IoPQUgQidBoefmBuQ88ohJycHixcvVnw93SFL4OzZs6eDuAEArVaL5cuXY8KECUqujwhC4iJ1SIkxoKLRhPyKJpzBGx6FEnFPvAV66oNDBBK1mokcSzMiVW0+9eAAwNjMXsiIj8CpulZ88Xsprp6Q2e1zfjxWiT9ONyBSr8G1k6WntYjwRaVSyUoVBYKqqiqUlJQEJIIjK0UVGxuL4uLiLveXlJQgJobCpz0BZz6co2UsZSW1Bw4AaDUqGKiTMRFIeB9ONFp9nqLSqFW4fko/AMCaXwrBcVy3z1n543EAwIJJfREfSf8jRGgh2FZCRuDMnz8fN998Mz7++GOUlJSgpKQE69evx+LFi7FgwQLlV0kEHUKaShjZcKSsAccrm6HXqDHZgwnHBi2ZjInAwvE+nEg/pKgA4M8TM2HUqXH4dAP2FNW63XdfcS1+O1EDrVqFm8/q7/O1EYTS7Nu3D6mpqejd2//meFkxrhdffBEqlQo33HADrFZ29a3T6XDbbbfhueeeU3qNRBAidDQ+xA8P/GI/67A6c0gy4iKkj7fXa6nRHxFY7LpoaABEq9r8EvaPj9TjsrEZWL+7BGt+KcTErASn+3Ech5e3sJl/l52R4XFZOUEEA/fffz/uv//+gJxbVgRHr9fj1VdfRW1tLfbv34/9+/ejpqYGL7/8MgwG+pDqCZw5IAEqFfBLfjVyimvx5QEmcDwtYdVr1TCozAAAjlJURACwaSMBPoJj0PpmVENnFk7NAgBsPFSGfBdz3T78rQg/HauEXqPGbWcP9Mu6CCKc8Oq/OTIyEqNGjcKoUaMQGRmp3KqIoCc7JQZ/GtcHAHDnR/tQUtOKSL0Gc4alenQcg1YjRnCsKumRH4JQChtfSdVLa4Za7Z8KpWHpsZg5OBk2O4dbPtiLhraOc92OlTfiqa9Zj7H7LxiKgcnRflkXQYQT/rlcIcKSv543GEadGqf48vBzh6d6bNJ09OBYQAKH8D8WDbs4i9OY/HreF68ag/Q4I05UNuOe9fthtdkB3ndz6wd7YbLaMXNwMhbx0R6CIDyDBA4hm/S4CCyZPkD8Xk6HVccycYuKUlSE/zEHSOAkxxiw6voJMGjV+P5IBc548jtcvXIHLn/jV5yoakZKjAEvXjXGb1Elggg3SOAQXvGXmQMxIDkKA5OjxC6tnqBWq2BQsRSVmVJURAAwq5l5N1btX4EDAKP6xOHVP49FfKQOjW1W7CqsAQD8aVwffHXXWUiWOLSWIIiuhEanICJoiTZosemuadCoNVBLHLDZGSFFZeZI4BD+x6RmEZxACBwAOH9kOs4dnoaDp+rxe0kdxmTGY2xmfEDWQhDhBAkcwjvMzdC9PRtQqYBbfgS0nqeZ9EIEh16ORABoVbHRItGqtsAsgOOgObUHY/M2YWzJTqBlApCyDDBQ01SC8AaPP1GKiopw9OhRjB49GmlpaV0eLy0tDUhDHyJA/PovoJJVe+DENmDweR4fQvDgmEEeHML/NIOlqAImcDb/A9jxWvv3BT8B+z8CLngeGHFZYNZEEGGARzmFf//738jOzsb555+PAQMG4IMPPgAAFBcX47nnnsPkyZPRty/NSukxNJYBv/yz/ftDGzw/ht0OHV8m3sb5vossQXSmmWM+l0hVAFJUv3/cLm6GXwac9zTQqz/QVAZ8ciNQ8LP/10QQYYJHAufJJ5/EnXfeiYMHD+Lcc8/FbbfdhocffhgDBw7EmjVrMGHCBHzyySe+Wy0RXPzwDGBpBqL5SN4fXwEWD6+CbWbxZht5cIgA0GRnEZxIrtW/Jy47CHx5N7s9Yzlw9fvA1KXAHTuBUVcB4ID//gVoqfHvuggiTPBI4Bw/fhx33303RowYgddffx0tLS345ZdfcODAARw5cgSvv/46Lr/8ct+tlggeqo8D+1gED1etAWIzAHMjkL/Fs+PY2q+aTRx5cAj/08ix1KiR82MEh+OA/94KWFuB7DnA2Q+0P6Y1ABe/AiQMABpOAV/dw/YniBBk5syZUKlUUKlU0Ov1GDZsGD766CO/nNsjgWOxWBARwa52+vTpA6PRiBdffBHDhg3z1fqIYKXgJ4CzA1nTgX5TgBG8sM39zLPjWNs/VFrtJHAI/9NgZa87PedHD07hdqA8F9BFAVe8Dag7pWcN0cCf3gHUWuDw/4BjG/23NoJQCI7jsG/fPrz44os4ffo0jh49ivPPPx833HADCgoKfH5+j+t6P/roIxw5cgQAoNFo0KtXL1+siwh2qo6xbfoYth15Bdse2wiYm6Ufhxc4Jk4Ls42uUgn/U29jERy/Cpzdb7Pt6KuBSOfDNpExHjjzNnZ7+yv+WxtBKEReXh4aGxtx/vnnIy0tDf3798fNN98Mm82Go0eP+vz8Hgmc6dOn49FHH8WIESOQlJSEtrY2vPrqq/jPf/6Dw4cPi5PFiR5AJRO5SB7Ctr3HAfH9AEsLuzqVCu/BMUMHs9Xui5UShFvqLSyCo7P5SeA0lDK/GgBMWuJ+3zPvADR6oOQ3oPg3vyyPCAE4jl1I+vvLw1Tp3r170atXLwwfPhwAcPLkSTz00EMwGAwYPXq0j3457XiUE/jxxx8BXpXt3bsXOTk5yMnJwdq1a1FXVwe9Xo/BgwfjwIEDvlovESxU8uo7eSjbqlRA2iigrgioLZJ+HD6CY4YWJhI4RACoszJzu9bmJ5Px3jUAZwP6TgVSR7jfNzYdGD2f+d1++SfQ90z/rJEIbiwtwDMBaMfy91JAHyV595ycHNTX1yMmJgY2mw1tbW2IiIjAypUr/dJORpbpYdCgQRg0aBD+/Oc/i/cVFBRgz5492Ldvn5LrI4KRtgZmfgSApMHt98ex6eJoOCn9WB0iODZFl0kQUqjlIzhqzgrYLIDGh9V8NgsTOAAwabG050y9iwmco18DlceA5MESnkQQgScnJwd33HEH7rrrLtTV1eG+++7DtGnTcOONN/rl/Iq5Ovv374/+/fvjqquuUuqQRLAi+G+i04AIh5bysRls21Aq/Vg21uTPymlgtlEEh/A/1WYHg6+lBdDE+e5kJbuApnIgMhEYeom05yQPBoZcxATOrreAi17y3fqI0EAXyaIpgTivB+Tk5GDJkiXIzs4GALzxxhsYPXo0lixZgqysLB8tsh0atkl4Tmf/jUAcL3DqT0k/lp2fJA4NTBYSOIT/qTOpYOP4id0WH6epTvzAtgNmeTbWZMJNbHvoc8BGXscej0rFUkX+/lJJn2x/4sQJ1NXVYeTIkeJ9w4cPx8CBA4OzTJwgAEeBM7Tj/bHyU1QWaCmCQwSEFrMdreCndltafHuy47zAGTjLs+cNmMmiPi1VQOFPPlkaQSjJ3r17odPpMHhwx5Tq7Nmz8d///tcvayCBQ3hOJZ+ichXBaTgN2CWKFf5q1AoNVVERfofjODSbrWgV5qCZfShwWmuB0hx2e4CHAkejA4Zfym4f9LDXFEEEgJycHAwaNAh6fcdI5Zw5c7B3716cPOnBhbBMSOAQnuMqghOdBqjULO3UXCHtWI4pKhI4hJ9ptdhg54BWTojg+DBFVfAza46ZNLj9YsATRl7Jtn982aFBJkEEI88++ywOHTrU5f4rrrgCdrsdffr08fkaZJuMt27diq1bt6KiogL2Tlfrq1evVmJtRDBibgbqitntzgJHo2Uip7GU+XBiuk6b74JDiooEDuFvmkwsgtieovKgSaWnOPpv5NB3ChCTDjSeBvK3AkMvVHR5BBFuyIrgPP744zjvvPOwdetWVFVVoba2tsMXEcZU5bEhgJFJQFRi18fFNJXE8KNYRaWlFBXhd5pNrDWBSWVkd/gygiPXfyOgVgMj+I7huZ8qty6CCFNkRXBWrlyJNWvW4Prrr1d+RURwIzb4G+L88dgMALulV1LZ2RU0S1FRHxzCvzTzERyz2gBwPjQZ1xQAtQVstlTWWfKPM+Jy4LfXgbwtzL+mofltBOEKWREcs9mMqVOnKr8aIvip6kbgiM3+JAocxyoqiuAQfkYQOFY1H8HxlclYGF/SZyJgiJF/nIxxQEQvwFQPnNqj2PIIIhyRJXAWL17stzp2Isio51NPvVw0aRKa/dV7mKICNfoj/E+zmRc4mgh2h69SVKd/Z9uM8d4dR61p9/Dkb/V+XUTIwHk4ByqUUepnlRXfbGtrw6pVq7BlyxaMHj0aOl3H1uYrVqxQZHFEENJYxrbRLgzEcR52M7YJVVRaavRH+J0m3oNj0wgeHB+ZjMsOsm36GO+PlT0bOLQByN8CnPOQ98cjghrh87WlpQURERGBXo5fMJtZZF+j0XS7rztkCZwDBw5g7NixAIDc3NwOj6k86HRIhCBN5WwbneL88VgPU1QOZeIUwSH8jZCismn5FvS+iODY7UA5/z6ZpsAE5YHnsG3pPqC52rnZnwgbNBoN4uPjUVHBWm9ERkaG9ees3W5HZWUlIiMjodV65zGT9ewffvjBq5MSIYwQwXFVAi5EcBpPSzNBOs6iIg8O4WcEgcNphRSVDzw4NScAcxOgNQKJ2d4fL7Y3kDIcqDjMSs9HXanEKokgJi2Nvd8KIifcUavV6Nu3r9dCTrY8qqurw7vvvos//vgDADBixAjcdNNNiIvz4aA6IrBYTUBbHbsdnep8n6gUQK1jkZmmsnbTsSscU1RURUX4GaFMXBwi6AuTcdkBtk0doVzV08BzmMA5/j0JnB6ASqVCeno6UlJSYLFYAr0cn6PX66FWe9+HWNZ/2549ezB37lxERERg0qRJAO+7efrpp7F582aMGzfO64URQYiQntLoWSWHM9RqIDadNQOsP9W9wHFMUVEEh/AzgskYeh+mqASBo0R6SiB7NrDjNSZwOM6jIYhE6KLRaLz2pfQkZEmke++9F/PmzUNhYSE2bNiADRs2oKCgABdffDHuuece5VdJBAeNgv8m1f0bqidDNx0iOCRwCH8jdDJWiQLHByZjwWCcNkq5Y/adylJejaeBqmPKHZcgwghZAmfPnj24//77OxiAtFotli9fjj17qDdD2NIkVFC5SE8JCD4cKc3+xDJxGtVA+J8WXuBo9FHsDl9EcE7zERwlKqgEdEYgYwK7XfybcscliDBClsCJjY1FcXFxl/tLSkoQE+NFEysiuBFSVN3NmIr1oFScUlREABHKxDVGHwmcxjI2eFalZsZgJel7JtuSwCEIp8gSOPPnz8fNN9+Mjz/+GCUlJSgpKcH69euxePFiLFiwQNZCXn/9dWRlZcFoNGLy5MnYtWuXpOetX78eKpUKl112mazzEh7gmKJyh+DPEQzJ7hA6GXNamKhMnPAzQhWV1iCYjBVOUQnpqcRB7T4fpeg7hW2Ldyh7XIIIE2SZjF988UWoVCrccMMNsFrZG4ROp8Ntt92G5557zuPjffzxx1i2bBlWrlyJyZMn45VXXsHcuXNx9OhRpKS46LcCoLCwEPfddx+mT58u58cgPKWpmxJxAWMs27Y1dH9MG99Jlo/gcBwX1j0eiOBCMBnrI6LZHUpHcIQOxukKGowFMicCULEZV43lQEw3Fx4E0cOQFcHR6/V49dVXUVtbi/3792P//v2oqanByy+/DIPB4PHxVqxYgSVLlmDRokUYPnw4Vq5cicjISKxevdrlc2w2G6699lo8/vjjGDBggJwfg/CUxm6a/AkY+VYBJgkCh09RmXmtTc3+CH8imIx1RkHgKBzBKT/EtqkjlT0u+P+z1BHsdgmlqQiiM14VmkdGRmLUqFEYNWoUIiPlhV/NZjP27t2LOXPmtC9KrcacOXOwY4fr0OsTTzyBlJQU3Hzzzd2ew2QyoaGhocMXIQOxi3E3ERwDL3Da6rs/Jp+isoKVPpIPh/AnLbwHxxDJeweVjuDUnGDbpMHKHleAfDgE4RLJKaply5bhySefRFRUFJYtW+Z2X09mUVVVVcFmsyE1tWN4NTU1FUeOHHH6nO3bt+Pdd9/F/v37JZ3j2WefxeOPPy55TYQLRJNxN6FwMUUlReC0D9sEAJPVDrKpE/5C8OAYI31gMuY4oKaA3U7wUZS57xRg9zvkwyEIJ0gWOPv27RM7KO7bt8/lfr72TzQ2NuL666/H22+/jaSkJEnPefDBBzuIsoaGBmRmZvpwlWGI3QY08W3Cu43g8AJHUoqKfcDYVWygHEVwCH/BcZzowYkUIjjmZuUa57XUAKZ65pPpleX98ZwhRHBOHwBMTYAh2jfnIYgQRLLAcZw/9f7776NPnz5dWilzHIeSkhKPFpCUlASNRoPy8vIO95eXl4vzNxw5fvw4CgsLcckll4j32e3sQ1Gr1eLo0aMYOHBgh+cYDAZZ3iDCgZZqgLOxN+uoZPf7Ch6ctobuPyz4FBU0OsBKAofwH60WG+wcu22MFuKGHBtJojN6fwIhPRWboczxnBHXB4jLBOpLgFN7gAFn++Y8BBGCyPLg9O/fH1VVVV3ur6mpQf/+/T06ll6vx/jx47F161bxPrvdjq1bt2LKlCld9h86dCgOHjwompv379+PefPmYdasWdi/fz9FZnyFkJ6KSup+no6QouJs3Zfd8ikqqFkEh5r9Ef5CnEMFIDLCIfKh1MBNQeAkePae6DGZbFwOTu317XkIIsSQVSbOcZzT+5uammA0en6lsmzZMixcuBATJkzApEmT8Morr6C5uRmLFi0CANxwww3IyMjAs88+C6PRiJEjO1YkxMfHA0CX+wkFaZRoMAY/uFClYQLH1OA+bM4LHE5DKSrCvwj+myi9Bmqdvn1IrKUFQIL3JxAFjo+rPHufAeR+BpzK8e15CCLE8EjgCD4WlUqFRx55pEPllM1mw86dOzF27FiPFzF//nxUVlbikUceQVlZGcaOHYuNGzeKxuPi4mJFJosSXiD2wJHQa0OlYmmq1hqWport7XpfvkwcgsCx0URxwj8IJeJRBv5tUB/JjPFKGY39KXAAoFRa0QVB9BQ8EjiCuZjjOBw8eBB6vV58TK/XY8yYMbjvvvtkLWTp0qVYunSp08e2bdvm9rlr1qyRdU7CAxqFOVQSIjjg01StNd1XUvERHBUvcEwWiuAQ/kGI4EQLAkfHCxyluhn7S+Ckj2HeuIaTrBCguz5VBNFD8EjgCEbjRYsW4dVXX0VsbKyv1kUEG2IFlcQ3T6mVVILA0fIChxr9EX5CrKAysBYF0AkTxUMsgmOIYX12qo6yKM7g83x7PoIIEWTlfd577z0SNz0NqWMaBIwSm/3xKSqVmjw4hH8RTMZReocIDhQyGbfWsggm4LsScUfENJXrFh4E0dOQJXCeffZZp2MUVq9ejeeff16JdRHBhtRBmwJSBY4YwWFl/FRFRfiLrimqCLZVQuAIDf6iU/3Tm0YUOGQ0JggBWQLnrbfewtChQ7vcP2LECKxcuVKJdRHBRrNvU1RqLUVwCP/i1GQMhVJU/kpPCThGcFxUuRJET0OWwCkrK0N6enqX+5OTk3H69Gkl1kUEG621bBshsXxW6kRxIUWlZYZ1EjiEvxBTVJ09OEqYjP0tcNJGASo161fVSO/BBAG5AiczMxO//PJLl/t/+eUX9O7tpiSYCE3sdqC1jt2O6CXtOVInivMRHA0vcExWKhMn/INgMu7qwVEyguPjJn8C+kggeRi7TT4cggDkNvpbsmQJ7rnnHlgsFpxzzjkAgK1bt2L58uX461//qvQaiUBjqmct7OGBwDFIHLhJKSoiQDS28R4coy88OH6O4ABAxhlAxSHW8G/oRf47L0EEKbIEzt/+9jdUV1fj9ttvh9nMZgkZjUbcf//9ePDBB5VeIxFohPSUPhrQ6rvbm+FhiopFcMwkcAi/0dDKXntxEUxcK1pFJZiMe/kpggMA6WOBfR8CZQf8d06CCGJkCRyVSoXnn38eDz/8MP744w9ERERg0KBBNNAyXBH9NxKjN5BoMuY4cdimVmcAYKYqKsJv1PMCJz6SFzhKmYyt5nZTfnxf747lCWmj2LYs13/nJIggRpbAEYiOjsbEiROVWw0RnIgCJ176c6SUidvb/TaCB8dMjf4IP1HXysR1lwiOtyZjweSr0QORid4dyxNShvPnLwVaaoBIBeZpEUQII3vA088//4zrrrsOU6dOxalTpwAAH3zwAbZv367k+ohgwFODMSSmqIQ5VAAbdkgeHMKP1LtMUXkZwWkoZdvY3mwum78wxgLx/djtcoriEIQsgfPZZ59h7ty5iIiIQE5ODkwmEwCgvr4ezzzzjNJrJAKNrBSVhCoqPj0FADqdUEVFAofwD/UtnQWOQibjRl7gxASgopTSVAQhIkvgPPXUU1i5ciXefvtt6HQ68f5p06YhJ4c6aYYdcgSOY5m43UXpt80q3tTqqEyc8B92O4dGvtFfrNImY8cIjr9JHcG2FMEhCHkC5+jRo5gxY0aX++Pi4lBXV6fEuohgQpbAcZhVZmp0vo+QolKpRaFMKSrCHzS2WcWGv2IERymTcUAFzki2LTvo/3MTRJAhS+CkpaUhPz+/y/3bt2/HgAF+7PtA+Ac5AkdrADR8VZ2rNJWQolLrYNCybrIkcAh/IPhvInQa8bWnmMm4gXkSEZvh3XHkkMYLnMojHSKkBNETkSVwlixZgrvvvhs7d+6ESqVCaWkp1q1bh/vuuw+33Xab8qskAoscgQMJRmO+yR80eui17KVIHhzCH3QxGENJkzFfRRWICE58FutXZTMD1Xn+Pz9BBBGyysQfeOAB2O12zJ49Gy0tLZgxYwYMBgPuu+8+3HnnncqvkggssgVOHNBc6bpUXBQ4Whh4gUMRHMIfOBc4gsk4hFNUajUrFz+5ixmNU4b5fw0EESTIiuCoVCo89NBDqKmpQW5uLn777TdUVlbiySefVH6FROBpqWFbTwVOd83+BA+OWidGcKgPDuEPuvTAgWMEx4sUld3W3gcnEAIHDmmqcvLhED0brxr96fV6DB8+XLnVEMGJX1NUVEVF+B4hghPrKHCUMBk3VQCcDVBpgOhUb5cpD8FoXH4oMOcniCBBssBZtmyZ5IOuWLFC7nqIYIPjvEtRwU03Y0pREQHCrQfH2sYiMWqN5wcWe+CkyXu+ElAvHIIAPBE47733HkaOHAmtVguVSgVOqLHshMqfnTsJ32NqZFek8CZF5ULg2NsjOCRwCH/SZQ4VHAQO+CiOIdrzAwfSfyOQPJRtm8rYxYmn/7cEESZIFjj19fX47LPPkJKSggEDBmD37t1ITPTjnBUiMAjRG62x3YQpFTGC002KSq2DXsOudqmKivAHXSaJg3+NC4SywDHGArF9gIaTQMURoN+UwK2FIAKIZJNxr169UFBQAAAoLCyE3U4fRD0CuekpOERwpKSodBTBIfyH0xSVWg1ohUoqmUZjoQdOIMY0OJLCR3Eq/wjsOggigEiO4PzpT3/CjBkz0Lt3b6hUKkyYMAEajfMc84kTJ5RcIxFIRIEjYzKxsZt5VA4pKr2GBA7hP5wKHPBGY2urfKNxIHvgOJI8FMjfwiI4BNFDkSxwVq1ahSuuuAL5+fm46667sGTJEsTExPh2dUTg8SaC020VVXsnY2r0R/gTlwJHFwmgWv48qmBIUQHt/W8ogkP0YDwqEz///PMBAHv37sXdd99NAqcnIAqceM+f222Kim8lr9G1m4xtdnAcR2Z1wqfUtTgpE4dDsz+zXIETwDENjiTzAqcnRnAsrWwWV3MVK5JIGsQqyzQ6CU8mwglZfXDee+895VdCeEdLDfDHF8DJ3cC0e9g/tRIoEcHpNkXVHsEBL3LE+UAE4QPcR3Bk9sLhuOCJ4CQPYdvmCvbeECkjxRxqVB4Fdr0NHPhP18pNXSQw5ALg7AeVe28kgh5ZnYwB4Oeff8Z1112HKVOm4NQpdtXywQcfYPv27Uquj5DCjjeAFwcDX94N7PsQ2PKYcsf2RuDoo9jW1YeFkxQVKE1F+BibnUNjG4sedigTh5fdjFtrAZuJ3Y5J93qdXmGIBuL6stsVYZ6mstuBX/8FvDkN2P02EzdRKUDGeCBrOvMCWlqA3M+A1ycBX9zl/UBVIiSQJXA+++wzzJ07FxEREdi3bx9MJvZPXV9fj2eeeUbpNRLu4Djg13+yaEjCQHbf8e+9n6cj0FrHtnIEjvhh4SLcb3OI4GgcIjgkcAgf0thmEW87NRlDZgRHiN5EJgFavVdrVISe4MNpawA+ugrY/A/2Hpg9B7huA/DXo8CS74EbvwKWF7LbQy4EODuQ8z7wzrlATUGgV0/4GFkC56mnnsLKlSvx9ttvQ6drf4OYNm0acnJylFwf0R1VeWz2jdYI3PYLEJfJBMWJbcoc35sITnd+Bnu7B0elUrXPoyKBQ/gQIT0VqddAp+n0FigO3JThwWmuYNtAjWjojFAqHq4+HEsr8O8FrFpMawQufhm49lMgezYr+RdQq1k0Z8G/gRu/Zn+fikPAqrOBkt2B/AkIHyNL4Bw9ehQzZszocn9cXBzq6uqUWBchFUHIZE5mb85DLmTfH/lameN7JXD4FJW1lYWRO+OQogIAg4YqqQjf49J/A4eooxyTcVMl20Yne7U+xRCNxmEYwbFZgP8sBIq2A/oYYNE3wISbgO6KE7LOAm75EegzEWirA9b9iUZahDGyBE5aWhry8/O73L99+3YMGDBAiXURUin4kW0HnM22Q3mBc2wjm6fjLa0yJ4kDHTsfW9u6Pu6QogIAg44Zi9ssNHCT8B2SBI6cFJUQwYkKEoETzs3+Nv8DyNvEIjfXfMwiNFKJTQdu+B+7KGyrBz64HKg+7svVEgFClsBZsmQJ7r77buzcuRMqlQqlpaVYt24d7rvvPtx2223Kr5Jwjs0KFPzMbg+Yybb9pjFTXXMlq6jyFiVSVHAR8u8kcKINTOA0m6xyVkoQknA6SVzAG5NxMx/BiUrxan2KkTQEgApoqW6PLoUDeVuAnSvZ7SvfA7KmeX4MfRRwzX9Y+XhzBUt1mZoUXyoRWGQJnAceeADXXHMNZs+ejaamJsyYMQOLFy/GX/7yF9x5553Kr5JwzunfWcWAMQ5IH8vu0+iAQXPZbW/TVN5MEgfYNGWNgd12JnAcOhkDQLSRdS1oIoFD+BChB068M4HjjclYEBFRSV6tTzH0kUCvfux21dFAr0YZmquAz/mL6El/aY9YyyEinhmSY9LZ7+ere9l7HhE2yBI4KpUKDz30EGpqapCbm4vffvsNlZWVePLJJ5VfIeGaAt5/kzWdiQkB4Z8+f4t3x7e0tPtk5E4kdveBIQ7bZMImSk8Ch/A97lNU3piMBQ9OkERwIERxAFSGidH462Us4pI8DDj3ce+PF50C/OldQKUBDv4H2LtGiVUSQYLHAsdisWD27NnIy8uDXq/H8OHDMWnSJERHy5i8S3iHYDAW/DcCQj66Kq+9W7AchBJxtba9p42nuCsV75SiiuEjOJSiInyJ00niAt6YjIPNgwMAyYPZtvJYoFfiPYXbgcP/Y2LkilUdU+DekDUNmP0wu73xQaCGZimGCx4LHJ1OhwMHDvhmNYR0rGageCe73X9mx8di+7DUkN0C1BfLP4cwYsEY3311givclYp3SlFFGZjAEZqwEYQv8JnJWExRBZPA4Y3GoZ6istuBTX9nt8ffCKSPVvb4U+9mkXBrK2uYSqmqsEBWiuq6667Du+++q/xqCOnUFbGuqbqorq3H1WogkW/6V+3F1YgocOLkH8PdB4aYohJMxpSiInyPKHA6dzGGFyZjjgvyFFWIC5wD65nn0BDLxi0ojVoNXPIqq8oq+AnYv075cxB+R9YsKqvVitWrV2PLli0YP348oqI6pi9WrFih1PoIVwhljQkDnEdXEgYAFYeB6nxg0Bx55xAFTqz8dUpKUbGXYTSlqAg/4DaCI9dk3FbXHpGMDBKTMRxSVI2n2f+zNxcrgcLSBmx9gt2e/lff9RlKHAjM+jvw3SMsWjRobvD0NCJkIUvg5ObmYty4cQCAY8c65nZpCrSfqOEFTqKLvkOJ2R33k4MwJNOrCI4b02bnKioyGRN+wH2ZuPB69VDgNFexrSEO0Bm9XqNiGOOA6DSgqYx58vpMCPSKPGf/OibQYvsAk2/17bnOvAM4+ClQdgDY9gzrjkyELLIEzg8//KD8SgjPECI4gpDpjHB/ddeGjJJRIkUlDtx0FsHp2MlYiOCQB4fwJTXN7HWXEOlkXpRoMvYwRdUkGIyDKHojkDyECZzKo6EncGxWNmsPAKbe6XvxqNEC5z8HrLmQVVRNuqV9phcRcsieJk4EGCEyIwzY7IzowfFG4PBVVAZvUlRuroht7bOo4ODBoRQV4Svsdg5VTWw4cEqsoesOck3G4hyqIPLfCCSHcKn4H/8DaguBiARg3PX+OWfWNGDYJWww5+Z/+OechE8ggROqCObhRFcCh4/g1JUAVpO8c7QpmKJyW0VFJmPCP9S3WmCxsQqZxCh3AsfDMnEhRRWMEZwk3odTFWKl4hwHbOdTRJNvld+qQg7nPsEiy/lbvO8nRgQMEjihiKUNqC9ht11FcKKS+cgLB9QUyDuPY5m4XHSUoiKCh0o+etMrUidOr++AXqbAEVNUwRzBCbFKqhPbgLKD7D1k0hL/njthADD5L+z2909R2XiIQgInFKktZMLFEOv6ilGlYv+k8CJNpUgVlfQUldAHp9lMAofwDRUNTOAkxziJ3sDh9Wq3tlf5SaE5CHvgCAi9cOqK5PX3CRR7VrPt2GuAyAT/n/+se5m4Kt0HHNvk//MTXuNVJ2MiQNR0UyIu4G0llSJVVG76inRKUcUIKSqK4BA+orKJTbV3LXAi2297YjQWe+AEocCJSmZRWM7unSfPnzSWA0e/Ybcn3BSYNUQltUeOfniaojghCHUyDkXECioX6SkBbyupFKmictfoj09RdRq22WyyyT8fQbihspGP4ES7EDgaPRsFAA+NxsGcolKpQi9Ntf9DFkXLnAykDg/cOqbeBeijWdm4t8OLCb9DnYxDke4qqATESiqZERxB4Pi6ikoYtslHcMw2O0xWEjmE8ogCx1UER6WSZzQO5hQVQsxobLcDe99nt8ffGNi1RCW2e3F+fJ6iOCEGdTIORSRHcLwVOAqmqJyF+8UIDu/B0be/HJvarDBEa7o+hyC8QBA4KTFu+qnoIgBzozyBE4xl4nDw4YRCBOfED8wvZIwDRlwe6NUAU5YCv73JojgnfgAGnhPoFRESoU7GoYgw7ba7CI7weFMZYGoEDDGencfXs6g6dTLWqFWI1GvQYrah2WRDIg2oJxRGqKJyGcEBn1Zt9iBFZW4BzE3sdjCWiSPEKqn2fci2o/+s3MRwb4hMAMbdAOxcCWx/hQROCEGdjEMNcwvQcIrd7i6CExEPRPQCWmuB+pOedeS0tLFhnvDlLCohRdXeMj/aoEWL2YZGkwcVLAQhkW5TVJDRzViI3mgM3qVzfYmQoqrOZ/93Gllv/b7H1Agc/ZbdHrsg0KtpZ8odwK63gYIfgVM5QMa4QK+IkIDsMvGff/4Z1113HaZOnYpTp9gH7gcffIDt27cruT6iM0L0xhgvrXQyJp1tG097dh4hegMVoPcw8uOIu1lUYoqq/c1WMBpTJRXhCzwSOFIjOI7+m2CNYMdlsp/LbuHbTAQpR74BrK2sQCJ9bKBX0058X2DUlez2L68EejWERGQJnM8++wxz585FREQEcnJyYDKxN436+no888wzSq8xNGmqBGqLlD+u8OaU4GLIZmdi0ti2scyz84gl4rGA2ot2Se5Mxp1SVHAc10C9cAiFMVvtqG1hrzmXVVToRpQ7I5hLxAXUaiBpELtdFcRpqoOfsO2oq4JPLE67m20PfxHcIpEQkfXJ9dRTT2HlypV4++23odO1pxemTZuGnJwcJdcXmphbgFVnA69NAEp2K3vshlK2jcuQtr+3ERyDF/4bdDds03mKCtTNmPAB1c3sQkynUSHO2SRxAU+rqMQS8SAWOACQFOQzqZqrgOPfs9sjrwz0arqSOgIYMIs1Wd31dqBXQ0hAlsA5evQoZsyY0eX+uLg41NXVKbGu0Gb3O0DDSZaC+eRGoKVGuWML/ptYqQJHZgRHGLTpjcEY3ZWJd6yigkOpOM2jIpRGSE8lRRugVruJDrjr3eSMFmEOVZALnGTeh1MZpKXih/4LcDag9xlAUnagV+OcM29n25wPAFNToFdDdIMsgZOWlob8/K7N47Zv344BAySmTsIVU1N7jlYXxYTOhltYbwclECIxsb2l7S87gqNAiTgcroatbYC9U2+bTp2M4dDNmCaKE0ojyX8DGSZj4QImMtGr9fkcIYITrCmqg5+y7airAr0S12TPYf4gUz3w+78DvRqiG2QJnCVLluDuu+/Gzp07oVKpUFpainXr1uG+++7DbbfdpvwqQ4ldq4CWauaRWfQNoDUC+d8Bhz9X5vhCiipGqsCRG8FRYA4VOrW+d7wi5jjWqRSdUlRkMiZ8RLddjAU8NRm3VLNtsAscsRfOseBrWNdYBpT8xm4HQ+8bV6jVwCS+8d/OlcpduBI+QZbAeeCBB3DNNddg9uzZaGpqwowZM7B48WL85S9/wZ133ilrIa+//jqysrJgNBoxefJk7Nq1y+W+b7/9NqZPn45evXqhV69emDNnjtv9/UZbA/DrP9ntmQ8Avce2z1Ep/FmZc4gpKk8jOHIFjpcRHK1DQzXHDwzHQYZOUlSNFMEhFKZCcgTHQ5NxqAichP6sa7ilmbWNCCaEMQgZE6S/twWKsQtYO4DqfODE94FeDeEGWQJHpVLhoYceQk1NDXJzc/Hbb7+hsrISTz75pKxFfPzxx1i2bBkeffRR5OTkYMyYMZg7dy4qKiqc7r9t2zYsWLAAP/zwA3bs2IHMzEycd955Yrl6wDj6Des5kziovaSw75lsW6KAAOO49giOZIHjEMHx5GpDiUGb4K94tMIHhkPI3+5c4ERTiorwER6nqMJN4Gh07c0/gy1NJQicYRcHeiXdY4hhE84BYM97gV4N4QYv6n8BvV6P4cOHY9KkSYiOlt92dsWKFViyZAkWLVqE4cOHY+XKlYiMjMTq1aud7r9u3TrcfvvtGDt2LIYOHYp33nkHdrsdW7du9eKnUYAxfwYWbQQueglQ82MG+kxi24rDrImVN7TWMi8LHCIz3RGdyrZ2C9DqgdlZiTlUAs5Mm4LBGB3LxGOMZDImfINkgeOxyThEBA6C1GjcVg8U/MRuDw0BgQMA4xex7dFv2y86iaBDlsC54YYbsHr1ahw/LnPGkQNmsxl79+7FnDlz2helVmPOnDnYsWOHpGO0tLTAYrEgIcF54zuTyYSGhoYOXz6j3xRgwMz272PTgbi+AGcHTu317tjCP1JkIqBzM0vHEY2uvbrDE6OxUikquLgitjkIGHV7oz9hHlUTTRQnFEYc0yDVgyO5k3EoCRzehxNMEZxjm9kFWNKQ9l49wU7KUKDvVFb1lfNBoFdDuECWwNHr9XjuuecwaNAgZGZm4rrrrsM777yDvLw8j49VVVUFm82G1NTUDvenpqairEyab+T+++9H7969O4gkR5599lnExcWJX5mZmR6v0ysyJ7Ktt2kqT9NTAnKMxkpVUcHB02B2EDhCikqt7dDQq91kTKMaCGXxPEUlIYJjNbHBnIC0zuKBJikIZ1Id+YptQyE95cgEPoqT837HCzYiaJAlcN555x0cO3YMJSUleOGFFxAdHY2XXnoJQ4cORZ8+fZRfpRuee+45rF+/Hv/9739hNDqPajz44IOor68Xv0pKSvy6RmROZluvBY6HPXAE5JSK+ySC4yRF5ZCegoMHh1JUhJJwHOeBwHHTu6kzQom4Ss3GpwQ7YooqSASOpQ3I38Juh0p6SmDYPCAigb0v538X6NUQTvDKg9OrVy8kJiaiV69eiI+Ph1arRXKyZ82ukpKSoNFoUF5e3uH+8vJypKWluX3uiy++iOeeew6bN2/G6NGjXe5nMBgQGxvb4cuv9OEjOCd3e1dW6GkPHAFZERyFysTRTYpK3bGjbLvJmFJUhHI0m21otbDXVJLkMnEJKSrBfxOR4N1IE3+ROIjNl2utYZ2DA03RdjaJPaY3a/AXSuiMwBnXsttkNg5KZP1H/v3vf8fUqVORmJiIBx54AG1tbXjggQdQVlaGffv2eXQsvV6P8ePHdzAIC4bhKVOmuHzeCy+8gCeffBIbN27EhAkT5PwY/iNtFKskaqsDqj1P44kIERypPXAE5ERwlKqigouyWyeDNuGQomqkFBWhIKfrWDQmxqgVWxG4xBOTcSgZjMH/bPF92e1giOIc28y2g88LvtlTUhDMxnmbgbriQK+G6EQ3/+nOee6555CcnIxHH30UV1xxBQYPHuzVIpYtW4aFCxdiwoQJmDRpEl555RU0Nzdj0SL24rnhhhuQkZGBZ599FgDw/PPP45FHHsFHH32ErKws0asTHR3tVTWXz9DogIxxQNEvLE2VPETecfzqwVEwRaV3EsFxMmgTnVJUHMdBFYpvekTQUVTNXnt9EyK73bfdZCyhTDzUBA7A3n/qithMqqxpgVsHxwF5m9jtQecFbh3ekDgQ6D8TKPgRyFkLnPOPQK+IcEBWBGffvn146KGHsGvXLkybNg0ZGRm45pprsGrVKhw75nn54fz58/Hiiy/ikUcewdixY7F//35s3LhRNB4XFxfj9On26MObb74Js9mMK6+8Eunp6eLXiy++KOfH8Q9imsoLH45sgeNhBMdmZWFjKDBsE648OO5TVHYOaLNQl1BCGYprZAgcKX1wRIETAgZjgST+grQqwKXi1cfZVG61jomEUEU0G6/t2MCUCDiyIjhjxozBmDFjcNdddwEAfv/9d7z88su44447YLfbYbN57p9YunQpli5d6vSxbdu2dfi+sDAER9WnjWJbb/pP+CuCY3Ioo1fEg+OkisrJoE0AiNRroFKxi7tGkwUReo335yd6PKLASZQicGSYjEMtgoMgSFHl8emprGmAIQgj71IZchEQlQI0lbO+OMPnBXpFBI8sgcNxHPbt24dt27Zh27Zt2L59OxoaGjB69GjMnBnCStyXJPIdRGtk9g4yNbYLD7kRnKZyNvBS3Y1oENJTuqguAkQWuii2dZqi6nh8lUqFaL0WjSYrmtqsSInx/vQEUSI3gsNx7r0hQgQnKkmRdfoFsRdOgCM4oZ6eEtDqgTOuA7avAPasJoETRMgSOAkJCWhqasKYMWMwc+ZMLFmyBNOnT0d8fAiUSQYKoUV6cyXrMeNpZKSBTy8ZYlmrcE+ISmZlrJydnT/GfXWaohVUcHFFLIRy1V0FVLSRCRyqpCKUwqMUleAZA8c6hwuvX2eEogdHSFE1nJL3XqQEpiag8Bd2O9QFDgCMX8gEzokfWNqtV1agVxR4TmwD+p3VpZDEn8jy4Hz44Yeorq7Gnj178NJLL+GSSy4hcdMdxtj2jsI1Jzx/vqdDNh1Ra9pHNkjx4ShZQQVHgeNQdmtzHsFBh4GblM8mvIfjOHkeHEgwGoeiwImIb38/qPKiqtMbCn5kUdxe/YHE7MCsQUl6ZQEDzma3960L9GoCT+F2YO1lwJoLpZn1fYQsgXPRRRfBbrfjpZdewuLFi7F48WKsWLEC9fX1yq8wnEjwIk0l+G+kzqDqjCc+HCUrqODCZOwiRQXqhUMoTEWjCSarHWoV0DveTTRGQK0BNHyvnO6MxqEocOBoNA6QD+c4P4U7e05oloc7Y9xCtt33IbMC9FRaaoANt7AIaOIgh4io/5ElcPbs2YOBAwfi5ZdfRk1NDWpqavDyyy9j4MCByMnJUX6V4ULCALatlhHBaRQMxh52MRaIFgSOhAiOkoM20c2wTScpqvaBmxTBIbxHiN70jo+ATiPxLU+q0Vg0GYdQFRUcfDiBMhof/4FtB84KzPl9wdCLWMPHxtL27sw9DY4DvriTZRwSs4ELng/ocmQJnHvvvRfz5s1DYWEhNmzYgA0bNqCgoAAXX3wx7rnnHuVXGS4k8gJHVgRH6GIsM4ITzafHpHQvba1l24he8s7VGXedjJ2lqPRCsz8a10B4TzHfA6eflAoqAandjEM1ghPISqq6YvYeqNIAWWf5//y+QmsAxixgt3PWBno1gSHnfTZbTK0D/vRuwKvjZEdw7r//fmi17eYhrVaL5cuXY8+ePUquL7wQU1RyIjh8akluiioqhW2bKrrft7WObRUTOG6GbToROL2iWPO/mmazMucnejQe+W8EpHQzNrcAVv7xUBM4gUxRneDbfmSMVy4NHiyMu4Ftj34LNJZ3t3d4UVcCbOIbHc5+BOg9NtArkidwYmNjUVzctS11SUkJYmKortclQql4tYwIjpBakitwonmB0yxF4AgRHIWM4x4M2wSA1FjmfyhvaFPm/ESPRigRz/RE4DgT5Z0RojcaPaAPsT4uQoqqtpANvPQn4ZieEkgZyoYrczbg948CvRr/wXHAl3cD5kb280+5I9ArAuQKnPnz5+Pmm2/Gxx9/jJKSEpSUlGD9+vVYvHgxFixYoPwqwwXBg9NS1e5zkYoYwemmxNsVQgVXU2X3+/ozRaXuWkKYGsumwpc3mJQ5P9GjkRXBEXo3CR29ndHCp3sjE0PPKBudwqInnF1+by452O2sggoABoShwIFDFCdnLfvg7wnsXwcc3wpojcClr3ffa81PyCpQf/HFF6FSqXDDDTfAamUfVDqdDrfddhuee+45pdcYPhhiWHlmUzmL4mSMk/Y8u409B96kqAQPjoQIThufojIqFcFxMmzTTYoqTRQ4FMEhvKdIjsAResOYGl3vE6r+G4AJsqQhbHRM5REgdYR/zlt+kP3e9NFAnyAfkiyX4ZcB3z7ArAiF24H+0wO9It/SUAps/Du7PevvQNKgQK9IRFYER6/X49VXX0VtbS3279+P/fv3i5VUBoNB+VWGE3J8OM1VLOSpUrcLFU8RUlSBiODonXQyFkL/uq4fOiliiooiOIR3tJptqGxkryOPBI5QQeg4tqQzoVpBJZDM+3C8GR/jKUJ6KussZbqkByOGaGDUn9jtcDcbcxzw1b2AqR7oPQ44MzhSUwIeCRy73Y7nn38e06ZNw8SJE/HEE08gOzsbo0aNQmRk4GrdQwqxVNyDsLDgv4lKkd8VUhBGpnrA2o1wUDxF5aTkVgj9O/EuCCmq6mYTLDYauEnIp6SWCekYoxZxER58oAoRHHep5FCO4MBxZIMfjcYneIETrukpAaEnzuH/tb+fhiMH/gMc28h8aJe9EdCuxc7wSOA8/fTT+Pvf/47o6GhkZGTg1VdfxR13BJdiC3rEUnEPIjje+m/AixWh50xzN1EcsYpKYZOxzdzuvTHz5bdCdMeBhEg9dBoVOA7i1TdByMGxRFzliU9GiOC0uYvghLjASfJzqbilFSjawW6Ho8HYkd5nAKmjAJsJOPBJoFfjG5qrgI33s9szlwMpwwK9oi54JHDWrl2LN954A5s2bcLnn3+OL7/8EuvWrYPdTlfZkpHTzVisoPJC4KhUDkZjNz4cu739qlVpkzEc0lRiBKerwFGrVUiJIR8O4T3HKpiHJiux6+vMLUYpKaoQFzhCiqo6v/3Cw5cU/8Y+8GN6t5ephysqlYPZ+P3wNBtvfYJFp1JHAdOCs/+dRwKnuLgYF154ofj9nDlzoFKpUFpa6ou1hSdySsWViODAsdmfmwiOqZ612IaCJmOtAQB/9dxZ4LhoBJVCpeKEAhwqZQJlRG8P+60YekCKKq4voI1gkdW6It+f74RDeXioVZ3JYfRVrKqoPBcoDbMO/6f2tvuLLnoxaP1UHgkcq9UKo9HY4T6dTgeLhVrqSya+H9u21riv0HDE2x44AlKa/Qn5Yl0UoO3ao0YWKlX7BHQhNSWmqJwLnNQYKhUnvOcPUeB4OHZEaEDnLoLTHOICR61ur3jxR5pKMBgLQynDnYhewPBL2e1wMhvb7cA3f2MXwqP/DPQ9M9ArcolHjiCO43DjjTd2qJRqa2vDrbfeiqio9hDwhg0blF1lOGGMZS/81lrW+TF1ePfPUSyCI6HZn9IGYwF9NPuwED4w3HhwACAtjlJUhHc0m6woqGavs+FyBY5bDw7fBycqSfYaA07yEKDsACsVH3qhhCfIpLmanQc9SOCA74lz4GPg4KfAeU8HfHSBIuxfxyI4+hjg3McDvRq3eCRwFi5c2OW+6667Tsn19Azi+/ICp1iiwFEqgiOh2Z/SYxoEDDFAo0NfEZPrKipQqTihAEfKGsBxrDN2UrSH7SuklIkLqV65rRuCAcFoXOXjUvECfjxD6sj2C62eQL9prHK25gRw6L/AuOsDvSLvaK0DtjzGbp/9gPcX3T7GI4Hz3nvv+W4lPYn4vsDp35nAkYJSEZwoCR4cpcc0CAgpKkHYuCkTR4cUFUVwCHkI/pvh6R5Gb+BYJu5C4NhtDn1wQjmCI/TC8XGKqqelpwQEs/GWx1iaKtQFzrZnWeQyaQgw+S+BXk23yGr0R3iJ4MORYuyzWdoFibcRHI9SVL4SOHwEh1JUhI85LNdgjE4RHGcVMC017Wb8UPXgwLEXzjHfVfpwXPuAzXDvf+OMMdewkTQndwEVfwR6NfIpywV2rWK3L3g+aI3FjpDACQTxfdlWSgSnqYK9kao03l8pSklRKT2mQUDIPUv04NDATcJbxAiOp/4bOERwbGbA6uQ1KPhvInoFXXMzj0gYwD58zU1AwynfnKP6OFBfwprB9Zvqm3MEMzGpwODz2e2cDwK9GnlwHPDtcja7bPilIdPHiAROIPBE4Dimp9Re/rkkRXB85cFxmO1jswJWvquxwfn0+RS+m3FDmxWtZpuyayHCHovNjqPlLFrocQUVwAyUQmsDZ2mqcPDfgJ8FJ/Tm8lV0QSgP73smoO+hHe+Fzsa//7v7TvLBSO5nQNEvrK3AeU8HejWSIYETCESBIyFFpUSTPwGhTLylxnVjL19VUYll4k2Apbn9fhcRnBiDFpF6NpGWojiEpxyvbILZakeMQYvMXjI+VNVqh7SqM4EjTBIPYf+NQNootj39u2+Of/x7th14jm+OHwpkz2YNDltrgCNfBXo1nmFqBDb/g92e8VcgPjPQK5IMCZxAIAic1lr3ZahQsIIK/FBAlZqlvIQQe2eUHtMg4OjBEdJTai0LWztBpVKJM6lI4BCeIvhvhqXHQq2W2VTO3biG5jAoERdIH822Qhm3ktgsQMHP7HZP9N8IqDXAGXzF8d73A70az/jp/9jnUK/+wJQ7A70ajyCBEwgMMUAEP4G4vsT9vk3lbKtEBEft4ONx1ezPl31wwAscxxJxNx1NU2J4Hw7NoyI8xCv/jYA4rsFJN+Nw6IEjkMYLnNM+EDgn9wDmRmbEFs7TUxl3PUt7FvwIVOUFejXSqMoDdrzBbp//HKAzdveMoIIETqCQ6sNRMkUFRx+OC6Nxmw/74ECI4LgvERcQIzj1FMEhPGNXASvhHpvpRSTSbQQnTDw4AJA+hm1rC9yPppCDkJ4acLb3HsJQJ74vMOQCdnv3O4FeTfcIxmK7BRg0FxhyfqBX5DE9/BUXQCQLHMFkrECKChJ64QgRHMWrqBxMxt1UUAlQqTghh9pmM3JL2Qf11GwvSrjdDdwUU1RhIHAiE4DYPux2Wa6yxxbnT/Vg/40jExez7f6PpI/qCRRHvmYCVaMHzn820KuRBQmcQCFV4DTwEZxohSI43U0U97XJ+P/bu+/4qKq8f+CfO5mSQnpIT2iJgZDQCQSRBB6kSBN8FBGBRVaFpShoBBQfXXz8Ie7SZJGmi/qsirIKspQohAQDhNBCCSUkUkJJAdL7TOb8/rgzNzOpkzD15vt+vfKayZ0z955zXynfOed7zmlFgKMdosqlAIe0QsqNR2AMCPNxFnalb5PmtmsQkoxteA0cXabIw6ks5Jf0RzvPv9HVdTjgGcIHzRd/sHRtmqasBOKX88+HLKrbJNrGUIBjKYYs9sdYXQBkrMz15qaKKyvr1vwwepKxTg5OCzuJawV78LNf/nhQ3mw5QnQdy+KDjydDHjM/prntGsQ0RAUT5eHc/J1fN8UrDHANMN55bZlEUteLc+oL0y2u+LiOrQeKs/mevaeWWLo2bUYBjqUY0oNTWcgn6OmWf1zN9eBoZ1BxdnV/3I2lDTk42gTRzLxSVKtoLRximOOaAGdo6GP2rgjbNYg8yRgm6sHRbs9gI4vCmU2flwCZE/Dgat0Kz9ak8BZwbB3/fPTHLfa0WzMKcCzFkABH+5qTNyBzMM51Xfz5x5L7DV/T3aahmdlNbaK7Do6BQ1QBbg5wdZBBpWbIzCszbn2IKN0pqMDtRxWQSjhEdXnMAKepJONaZd3vith6cB5cA5RGGhKm9W8aZ+9aN2U85R+Wrk1Dv74H1FYDXWL4VYttGAU4lqIdcmpuLRzt8JV7J+Nd10XTVdxYgGOqbRqg88+ipqyuvS0EOBzHCRslatc1IaQ52t6bvsFu6KB4zC0Umkoy1m6yCc74uWqW4hrIt0WtAvKvPP75Cm7wf78kMn5HbaJv8Dx+TbKsw0CeEe63sWQe5hcilEiBsZ8a/4OumVGAYym6a+E0lYcj5N8YaXgKuj049xqO/5oqwRj1tmQo08wMa2GICjrL7F/JoQCHtMxo+TcAoNAmGdcbotLm3zh68mtLiQHH1fXiGGOYStt7EzSoxVy7dsmjC9BjAv88ZZOla8NTVgIH3uKfD5oLeHe3dI0eGwU4lqTNTH+U1fjrhZrAx82YPTiaAEdVpfNJVMOUAY5UwX+ag87Ud0MCnAA+wLl838jrcxDRqVbVIjlTk39jjACnyR4ckeXfaPn35R/vnnn8c1H+Tcu0qwJf/KHub6Il/f53Pv/GJQCIXWbp2hgFBTiW5BnKPz5sIsAxRQ+OVFGXN1B/92BTbdOgpe3FEQKclpPXwv34T9FX7pdArbbSGQfEKiRey0dxpRK+LvboG2yEIL2pHBwxrYGjKziaf8w++XjnqVXxM6hAAU6zggYCQYP5hfQsnYvzIAM4voF/PnZ1k5sg2xoKcCzJK4R/fNTEst2myMGBbh5O/QDHhD040A1wNGv7GNB13a2jE+RSCcprapFdUGGaehFR+PdZ/ud5cr8A2LV1/yldTfXgiG0NHK2gKP7xUWZdG9vi3ln+njm4A359jFY9UXpKMyR06gugrInFV02NMWDfYj7QemIs0H28ZephAhTgWJLQg9NIgKO3Bo6RAxxXzaql9QMcUyYZQ+cTsXaKugFDVFI7Cbr78oHRZUo0Jk14VFaNpAz+52pKXyOtuaLbg6Obrya2NXC0HD2Ajj3454/Ti5N1mH/sEiOeHCVTCX2aHxpUVQInPrNMHc5/B9w+DsgcgWdsP7FYFwU4luSlE+DUT/iteAQoK/iZGtqAxFi0eTjF9QIc7caepvpkKvTYaNpq4PoK2kRjysMhTfnPhftQqRl6Bboi1MdI3evalYzVyroFMCHiHBwACB7MP2antP0c1/bzj9p9l0jTOA6I0eS7nP7i8XrO2qKiAPhtBf88dplx0yGsAAU4luTRlZ8qWFNaF1xoaROMnf34vBljamqISntN987GvZ5W/XFdAwOccH9NHg7NpCJN+Okc/7NstN4baHsYNZ9mdWdSlYs5wHnMPJyCm0D+ZX6x0NBRRq2aaD0xmh/KU1bU5cGYy6H3gcoCwLsnMPgv5r22GVCAY0lSRd3wU/1hKm3+jSkiamGISmctHMb4DHqYM8Ax7JO2di2c9HslYNa6tDmxmEt3i3HpXjGkEg4T+xgxwJFIGk80FnJwxBjgaHpwcs4DNW3Iecs4wD92fpIf8iIt4zhg+Lv889StdR80TS3zMJD2L/75+HWAncw81zUjCnAsTTtMVT/R2FQJxtAdorpbd6yysC6Z0lTdlG3swenp7wJHuR0ellXj4l0apiL6NiRcBwBM6O0PDye5cU/eWKKxWHNwoPndd/bnF/zTbpTZGsLw1DijV03UQkcBXYbxKwgf/tD016ssBPYu4J8PmgsEDzL9NS2AAhxLa2qquCmmiGvprmas7RHR9t508AXkjsa/JhpJKjYwwLGX2WF4d36T0AOXckxRM2KjLt4twuGr+ZBwwMIRIca/gKKR/ajEnIPDcTp5OK0cpip/WJe70/0Z49dNzDgOGPUxPyR6+WfgzinTXu/AO/xsVs8Q4L8+MO21LIgCHEtraqq4KRb503L243+Raqv5ZGbA9MNTQMMNPFuxwum4SD8AwP5LOTRMRQTrD/O/N8/2DUDXjiZYMbd+D05lUV2w4+xn/OtZA20ezu1jrXvf9Xh+93DfXqJLVjULv151e1QdXAqoTbTB8MVdwKUf+fzPyVtN94HWClCAY2lNTRU3ZQ+OVA504HtEhGEqswQ49YaoZIbvUjs8zBsOMjvcLazEpXs0TEWAtOxCHLmWDzsJh4UjQk1zkfo5ONpVx5396oIfsdEuznfrWN3in4a4spd/FNE6KmY3YgX/M3f/nGm2cHiQAfznDf75sDggcIDxr2FFKMCxNG0OTtFtQFXNP2cMKL7DPzdFDg4amUll7gDHTs4HWgZykNthhGaYaj8NU7V71apaLPvpEgBgct8AdPEyPFhulfo9ONoPIp4mGA6zFl6hQMfufB7O9V8Ne09pbt36Nz0nm7R6oubsC4z+mH9+5H+BB9eNd+6acuDHmYCynF+jKGap8c5tpSjAsbQOPvxsIqbmd+CFZideVRW/o6uLEWeF6BI23dTMpDJLgKMzhGBg/o2usZG+AICDl3JpmKqd23A4Exl5pfB0kmP5WBNuCli/B+eh5h+O1xOmu6Y16DGRf7y617DyF74HWC2/uWZHkd8bU+s7A+g2gk8h+GU+v/XF41LXAj+/Bjy4xudZPvdFu1iEkQIcS+M4/QX/oBnLhmYs3FRT97RTxS01RGXgFHFdI7p7w14mQXZBBU7fKjRu3YjNSMsuxJajfwAAPp4cAc8ORl4nSpd2sb9Kzca02lw5LxMNiVkL7U7XWQn8J//mMFY33bjvDNPXTew4DpjwGf838u4p4Lf3Hu98jAHxy4Fr+/ie8+e/qktREDkKcKxBxzD+UTtr4ZpmLYnuJpxqqTuTqlZZF+iYK8m4DT04jnIpJmsWcvvk4FXqxWmH7hdVYt6/zkHNgEl9/DEmwsSJvh01vUM5F/jHh+0kwPGN5Cc4qCrrhp6akp3C5ybJnICez5qrhuLmFgQ8+zn/PHULcGZH2891bC1waiv/fPJWoFO0cepoAyjAsQbhmj8K57/lt0/IPsF/H2bCqZbCENU9PrhhtYDUnh8yMxW9Hpy25Uy8OfIJ2MskOJddhF8v5xqvbsTqFVcoMeufp5BbUoUQ7w5YOTHC9BfVbkB5/zxQXVY3jOwp8gCH4+p6ca7+p/my5/6Pf4yYLJpdqK1C+ERguGYbhQNv133wNRRjwKEPgISV/Pej/heImGL8eloxCnCsQejT/KelqiJg9+t8Po5PhOkSjKEzRFV4Cyi8yT9378yv3moquuvgtGKKuC4fF3u8+lRXAMDq+Awoa9XGqh2xYoXlNZj91Slk5pfBx0WBr1+JgqujGVZede8MOHnz+1Fd3QvU1vAfBFyDTH9tSwufxD9mxOuvA6Sr5D5weTf/nIanjG/Y20Dk83zC9w/TgTP/NOx91WXAnr8Ax9fz34/8EIheYNKqWiMKcKyBxA4YOId/fiuZfzRl7w0AePfgu5RL7gEpmq5QUw5PoX4PTtvXLHltWFd4Oslx82E5Nif9YZy6Eat140EZJn9+HOeyi+BsL8VXs6MQ4OZgnotzXF0vjjbPxDPUtB8ErEXAAMArjN8r79j6xsskrOSHsYIG8V/EuDgOeHYzvz4OUwP7FgN7FwFlD5p+z81kYPMQ4MJ3/Fo3EzcCQxeLapdwQ7WD31Ib0XcG/8lQy9Q78dq7AoPn8s+zDvGPZg1w2j6t19lehqWamTNrD13Hfy7cb/E9xPYwxvDL+XuY/PkJ3HpUgQA3B/w0bwh6+Jl5/Rntyr63j/OPXiKeIq5LIuE/+QPAyc/54XNdd8/ys6cAYMyqdvkP1CzsZMDEfwCxy/nvz30NfNYX+PU9IOMgnxeWncr37mwfAXw9nl92xDUImLEb6DfT0i2wGKmlK0A0HD2AyP/mPyU6+wP+fU1/zSELgVNfANWa7mdTBzgSO77XSFn+WAEOALwwIAjXckrxz+M38dauC/BxsUdUF9rcTyzuFFRg5b4rOHQlDwDQJ8gN22cOQEdnE86Yakr9ngmxTxHXFTaWn82ZnQIk/T9gkmbxObUaiF/GP+89DQjob9Fqih7HAbHLgM5P8bOq7qcBKf/gv+qTyPgen6dXincxSgNRgGNNnnoLyLkIDHjFPJ+GHNyBJxfyC0rBDAEONLk3yvLHGqLSem9cD9wprMChK3l4+YtUfDAxHC9FBYOjT5I2635RJTYn/YGdp7OhrGWQ2fGrFM+L7QaZnYU6nP1689Nra2v478WeYKyL44CnPwK+HAmkfcuvoRIxhd9K4O4pQOYo6r2MrE7nJ4E/H+GnfGf+BtxJ5fOgHD351bV7jAd6vQh0EOFGsG3AsXY417akpASurq4oLi6Gi0v7jnBRXQp81o/fk2pRmmkTmwFgY39+Smnsu0Ds46+kWVGjwhs7zwuf9MdF+mHZ2O4I8hDv/ipiU62qxbHMh/j+1B0cuZYHteYv0tAQL7w3rof5h6Qa8+Uo/p8JALx2FPDvY+kamdcvC4C0/9M/JnPk8zsi/9tStSLtUGv+f1MPTnuncAb+fAgoyzd9cAOdPJzHHKLScpRLsW1Gf2xPvoHV8RnYfykHv13JxQsDgjB9UCeE+1vBP0fSQG5xFY5nPURy5gMkXMtHaVXdaq3RXT2x6L9CEd3N06J11BM0qC7AEfM2DU2ZuBF4YgyfVPwwAwiMAiZvATy7WbpmhDTJagKcTZs24W9/+xtyc3PRu3dvbNy4EVFRUU2W37VrF95//33cunULoaGhWL16NZ55xsQzj8TKvbN5hqegE+C0cZp4YziOw2vDumFINy+sjr+G5MyH+DY1G9+mZqO7rzOeDvfBkyFe6B3oBge5+JcntyaqWjVyiquQkVuKS/eKcfl+MdLvlSC3pEqvnLezAuN6+WH6oGCEeFvhWiraPByXAKP+7NoMjuOHP8LG8sv9d+zeLpb6J7bNKoaofvjhB8ycORNbtmzBoEGDsH79euzatQsZGRnw9m64pPSJEycwbNgwrFq1CuPHj8d3332H1atX49y5c4iIaHnxLxqisqDjG4DktcDsg4BPuEkucfLGI3yTcguHr+SjRmedHI4DOnk4IszXGWE+zgjxcYafqz18nO3h7aKAvYz+YBuqVs1QVqVCSZUSj8pr8KC0Gg9Kq/GwrBq5JVW4U1CB7IIK3CushErd8E8MxwG9AlwxJMQLI7p7o3+wOyQSK86dqlUCv63gE25ptV5CLKY1/7+tIsAZNGgQBg4ciH/8g88IV6vVCAoKwsKFC7Fs2bIG5adOnYry8nLs27dPODZ48GD06dMHW7ZsafF6FOBYmFptlnVEiiuU+PVKLo5nPcTxrEd4WFbdbHlXBxncHWVwtpehg0IKZ3spOthL4aL53l4mgVwqgdxOArnUDnKpBDI7Dgqp9rgd7CQc7CQcJBzfs6R9LuE4SLimX2MMYGDQ/jYyzTRpplmQlD8CTTkI5YVjmu9r1QwqNYOqlkGlVvPf1/LHatVqndfqvq9RqVGprEWVUo1qZS2qNM+rVHXPK2pUKK1SoaRSiZIqFcqqDd8AUG4nQRcvJ0QEuCIiwAURAa4I93OBk8JqOpAJITbCpnJwampqcPbsWSxfvlw4JpFIMHLkSKSkpDT6npSUFCxZskTv2OjRo7Fnz55Gy1dXV6O6uu6fW0lJidHqT9rATIukuTrK8MKAILwwIAiMMTwsq8H1vFJk5PJfNx+WI6+0CrnFVahWqVFcqURxpdIsdRMLe5kEnk4KeHWQo6Ozgv/qoECghyOCNV8+Lvaws+beGUKIKFk8wHn48CFqa2vh46O/B5KPjw+uXbvW6Htyc3MbLZ+b2/jeRKtWrcJf//pXI9aa2BqO44R/wE+GeOm9xhhDSZUKeSVVKK5UorRKidIqvseirFqF0iolyqpUqFapUaNSo7qWfxS+dL6vZQxqxqBWM6gZ9J7XMgbGNM/V+uU4DuA09RRCAd1jmufQKcPPhuc0x/hnMjsJ7CQcpBIOUjsOdhIJpJpeJZkdp3lNove9zE4Ce5kd7GUS2Evt6p7L7KCQ2cFeKoGjXApXBxlcHPgeLWd7KZztZZBLaa1QQoh1sniAYw7Lly/X6/EpKSlBUFA72EuGGITjOLg6yODqYIa9jQghhJiFxQMcLy8v2NnZIS8vT+94Xl4efH19G32Pr69vq8orFAooFBZYAZUQQgghFmHx/mW5XI7+/fsjISFBOKZWq5GQkIDo6OhG3xMdHa1XHgAOHTrUZHlCCCGEtC8W78EBgCVLlmDWrFkYMGAAoqKisH79epSXl2P27NkAgJkzZyIgIACrVq0CALzxxhuIiYnBmjVrMG7cOOzcuRNnzpzBtm3bLNwSQgghhFgDqwhwpk6digcPHuB//ud/kJubiz59+iA+Pl5IJM7OzoZEZ+bNkCFD8N1332HFihV49913ERoaij179hi0Bg4hhBBCxM8q1sExN1oHhxBCCLE9rfn/bfEcHEIIIYQQY6MAhxBCCCGiQwEOIYQQQkSHAhxCCCGEiA4FOIQQQggRHQpwCCGEECI6FOAQQgghRHQowCGEEEKI6FCAQwghhBDRsYqtGsxNu3hzSUmJpatCCCGEEANp/28bsglDuwxwSktLAQBBQUGWrgohhBBCWqm0tBSurq7NlmmXe1Gp1Wrcv38fzs7O4DjO0tUxqpKSEgQFBeHOnTvtcp+t9t5+0D1o9+0H3YN2336I+B4wxlBaWgp/f3+9Tbgb0y57cCQSCQIDAy1dDZNycXER1Q91a7X39oPuQbtvP+getPv2Q6T3oKWeGy1KMiaEEEKI6FCAQwghhBDRoQBHZBQKBT744AMoFApLV8Ui2nv7Qfeg3bcfdA/afftB9wBor0nGhBBCCBE36sEhhBBCiOhQgEMIIYQQ0aEAhxBCCCGiQwEOIYQQQkSHAhwr9Pvvv2PChAnw9/cHx3HYs2eP8JpSqcTSpUsRGRkJJycn+Pv7Y+bMmbh//77eOQoKCjB9+nS4uLjAzc0Nc+bMQVlZmV6Zixcv4qmnnoK9vT2CgoLw6aefmq2NzWmu/fXNnTsXHMdh/fr1esdtuf0w8B5cvXoVEydOhKurK5ycnDBw4EBkZ2cLr1dVVWH+/Pnw9PREhw4d8NxzzyEvL0/vHNnZ2Rg3bhwcHR3h7e2NuLg4qFQqs7SxOS21v6ysDAsWLEBgYCAcHBwQHh6OLVu26JWx5favWrUKAwcOhLOzM7y9vfHss88iIyNDr4yx2peUlIR+/fpBoVAgJCQEX331lVna2JKW7kFBQQEWLlyIsLAwODg4IDg4GIsWLUJxcbHeeWz1HhjyM6DFGMPYsWMb/V2x1fYbBSNW58CBA+y9995jP//8MwPAdu/eLbxWVFTERo4cyX744Qd27do1lpKSwqKiolj//v31zjFmzBjWu3dvdvLkSZacnMxCQkLYtGnThNeLi4uZj48Pmz59OktPT2fff/89c3BwYFu3bjVrWxvTXPt1/fzzz6x3797M39+frVu3Tu81W24/M+AeZGVlMQ8PDxYXF8fOnTvHsrKy2C+//MLy8vKEMnPnzmVBQUEsISGBnTlzhg0ePJgNGTJEeF2lUrGIiAg2cuRIlpaWxg4cOMC8vLzY8uXLzdrWxrTU/ldffZV169aNJSYmsps3b7KtW7cyOzs79ssvvwhlbLn9o0ePZjt27GDp6ens/Pnz7JlnnmHBwcGsrKxMKGOM9t24cYM5OjqyJUuWsCtXrrCNGzcyOzs7Fh8fb/Y219fSPbh06RKbMmUK27t3L8vKymIJCQksNDSUPffcc8I5bPkeGPIzoLV27Vo2duzYBr8rttx+Y6AAx8o19w9e69SpUwwAu337NmOMsStXrjAA7PTp00KZgwcPMo7j2L179xhjjH3++efM3d2dVVdXC2WWLl3KwsLCTNaWtmiq/Xfv3mUBAQEsPT2dderUSS/AEVP7WRP3YOrUqezll19u8j1FRUVMJpOxXbt2CceuXr3KALCUlBTGNEGERCJhubm5QpnNmzczFxcXvftiaY21v2fPnmzlypV6x/r168fee+89xkTWfsYYy8/PZwDY0aNHGTNi+9555x3Ws2dPvWtNnTqVjR492kwtM1z9e9CYH3/8kcnlcqZUKhkT2T1oqv1paWksICCA5eTkNPhdEVP724KGqESguLgYHMfBzc0NAJCSkgI3NzcMGDBAKDNy5EhIJBKkpqYKZYYNGwa5XC6UGT16NDIyMlBYWGiBVhhOrVZjxowZiIuLQ8+ePRu83h7av3//fjzxxBMYPXo0vL29MWjQIL2u6bNnz0KpVGLkyJHCse7duyM4OBgpKSmA5h5ERkbCx8dHKDN69GiUlJTg8uXLZm5V6wwZMgR79+7FvXv3wBhDYmIirl+/jlGjRgEibL922MXDwwMwYvtSUlL0zqEtoz2HNal/D5oq4+LiAqmU32ZRTPegsfZXVFTgpZdewqZNm+Dr69vgPWJqf1tQgGPjqqqqsHTpUkybNk3YUC03Nxfe3t565aRSKTw8PJCbmyuU0f2hByB8ry1jrVavXg2pVIpFixY1+rrY25+fn4+ysjJ88sknGDNmDH777TdMnjwZU6ZMwdGjRwFNG+RyuRD0avn4+IjiHmzcuBHh4eEIDAyEXC7HmDFjsGnTJgwbNgwQWfvVajXefPNNPPnkk4iIiACM2L6mypSUlKCystKk7WqNxu5BfQ8fPsRHH32E1157TTgmlnvQVPsXL16MIUOGYNKkSY2+Tyztb6t2uZu4WCiVSrzwwgtgjGHz5s2Wro5ZnD17Fhs2bMC5c+fAcZylq2MRarUaADBp0iQsXrwYANCnTx+cOHECW7ZsQUxMjIVraHobN27EyZMnsXfvXnTq1Am///475s+fD39//wafRm3d/PnzkZ6ejmPHjlm6KhbT0j0oKSnBuHHjEB4ejg8//NDs9TO1xtq/d+9eHDlyBGlpaRatmzWjHhwbpQ1ubt++jUOHDgm9NwDg6+uL/Px8vfIqlQoFBQVCN6avr2+DGRfa7xvr6rQWycnJyM/PR3BwMKRSKaRSKW7fvo233noLnTt3BkTefgDw8vKCVCpFeHi43vEePXoIs6h8fX1RU1ODoqIivTJ5eXk2fw8qKyvx7rvvYu3atZgwYQJ69eqFBQsWYOrUqfj73/8OiKj9CxYswL59+5CYmIjAwEDhuLHa11QZFxcXODg4mKxdrdHUPdAqLS3FmDFj4OzsjN27d0MmkwmvieEeNNX+I0eO4I8//oCbm5vwtxAAnnvuOcTGxgIiaf/joADHBmmDm8zMTBw+fBienp56r0dHR6OoqAhnz54Vjh05cgRqtRqDBg0Syvz+++9QKpVCmUOHDiEsLAzu7u5mbE3rzJgxAxcvXsT58+eFL39/f8TFxeHXX38FRN5+AJDL5Rg4cGCDKaPXr19Hp06dAAD9+/eHTCZDQkKC8HpGRgays7MRHR0NaO7BpUuX9IJBbbBcP3iyJkqlEkqlEhKJ/p8vOzs7oXfL1tvPGMOCBQuwe/duHDlyBF26dNF73Vjti46O1juHtoz2HJbU0j2Apudm1KhRkMvl2Lt3L+zt7fVet+V70FL7ly1b1uBvIQCsW7cOO3bsAGy8/UZh6Sxn0lBpaSlLS0tjaWlpDABbu3YtS0tLY7dv32Y1NTVs4sSJLDAwkJ0/f57l5OQIX7ozP8aMGcP69u3LUlNT2bFjx1hoaKjeNOmioiLm4+PDZsyYwdLT09nOnTuZo6OjVUyTbq79jak/i4rZePuZAffg559/ZjKZjG3bto1lZmYKUzuTk5OFc8ydO5cFBwezI0eOsDNnzrDo6GgWHR0tvK6dQjpq1Ch2/vx5Fh8fzzp27GgV06Rban9MTAzr2bMnS0xMZDdu3GA7duxg9vb27PPPPxfOYcvtnzdvHnN1dWVJSUl6v+MVFRVCGWO0TztFOC4ujl29epVt2rTJaqYIt3QPiouL2aBBg1hkZCTLysrSK6NSqRiz8XtgyM9AfU1NE7fF9hsDBThWKDExkQFo8DVr1ix28+bNRl8DwBITE4VzPHr0iE2bNo116NCBubi4sNmzZ7PS0lK961y4cIENHTqUKRQKFhAQwD755BMLtLah5trfmMYCHFtuPzPwHnz55ZcsJCSE2dvbs969e7M9e/bonaOyspL95S9/Ye7u7szR0ZFNnjyZ5eTk6JW5desWGzt2LHNwcGBeXl7srbfeEqbYWlJL7c/JyWF/+tOfmL+/P7O3t2dhYWFszZo1TK1WC+ew5fY39Tu+Y8cOoYyx2peYmMj69OnD5HI569q1q941LKmle9DUzwgAdvPmTeE8tnoPDPkZaOw99ZdUsNX2GwPH+JtCCCGEECIalINDCCGEENGhAIcQQgghokMBDiGEEEJEhwIcQgghhIgOBTiEEEIIER0KcAghhBAiOhTgEEIIIUR0KMAhhBBCiOhQgEMIIYQQ0aEAhxBiFrGxsXjzzTctXQ1BW+uzbNkyKBQKvPTSSwa/59GjR/D29satW7dafb3WePHFF7FmzRqTXoMQW0EBDiEismXLFjg7O0OlUgnHysrKIJPJEBsbq1c2KSkJHMfhjz/+sEBNzcfYgdXy5cuxZs0afP/998jKyjLoPR9//DEmTZqEzp07G60ejVmxYgU+/vhjFBcXm/Q6hNgCCnAIEZHhw4ejrKwMZ86cEY4lJyfD19cXqampqKqqEo4nJiYiODgY3bp1s1BtbZOrqyvmzJkDiUSCS5cutVi+oqICX375JebMmWPyukVERKBbt27417/+ZfJrEWLtKMAhRETCwsLg5+eHpKQk4VhSUhImTZqELl264OTJk3rHhw8fDgCIj4/H0KFD4ebmBk9PT4wfP16vZ2fbtm3w9/eHWq3Wu96kSZPwyiuvAADUajVWrVqFLl26wMHBAb1798a///3vJutqSPnY2FgsWrQI77zzDjw8PODr64sPP/xQr0xpaSmmT58OJycn+Pn5Yd26dUKvzZ/+9CccPXoUGzZsAMdx4DhOb5hIrVY3e+6mqFQqODo6Ij09vcWyBw4cgEKhwODBg1vVrtjYWCxcuBBvvvkm3N3d4ePjg+3bt6O8vByzZ8+Gs7MzQkJCcPDgQb33TZgwATt37jSoHYSIGQU4hIjM8OHDkZiYKHyfmJiI2NhYxMTECMcrKyuRmpoqBDjl5eVYsmQJzpw5g4SEBEgkEkyePFkIaJ5//nk8evRI77wFBQWIj4/H9OnTAQCrVq3CN998gy1btuDy5ctYvHgxXn75ZRw9erTRehpa/uuvv4aTkxNSU1Px6aefYuXKlTh06JDw+pIlS3D8+HHs3bsXhw4dQnJyMs6dOwcA2LBhA6Kjo/Hqq68iJycHOTk5CAoKMvjcTVmxYgXKysoMCnCSk5PRv3//BscNufbXX38NLy8vnDp1CgsXLsS8efPw/PPPY8iQITh37hxGjRqFGTNmoKKiQnhPVFQUTp06herq6hbrRoioMUKIqGzfvp05OTkxpVLJSkpKmFQqZfn5+ey7775jw4YNY4wxlpCQwACw27dvN3qOBw8eMADs0qVLwrFJkyaxV155Rfh+69atzN/fn9XW1rKqqirm6OjITpw4oXeeOXPmsGnTpjHGGIuJiWFvvPEGY4wZVF77nqFDh+qVGThwIFu6dCljjLGSkhImk8nYrl27hNeLioqYo6OjcC3d6+pq6dxNOXPmDJPL5WzcuHEsPDy82bKskftm6LXrl1GpVMzJyYnNmDFDOJaTk8MAsJSUFOHYhQsXGAB269atFutGiJhRDw4hIhMbG4vy8nKcPn0aycnJeOKJJ9CxY0fExMQIeThJSUno2rUrgoODAQCZmZmYNm0aunbtChcXFyEZNjs7Wzjv9OnT8dNPPwk9A99++y1efPFFSCQSZGVloaKiAk8//TQ6dOggfH3zzTeNJjG3pnyvXr30vvfz80N+fj4A4MaNG1AqlYiKihJed3V1RVhYmEH3qrlzN0atVuP111/HggULMHPmTGRmZkKpVDZ7jcrKStjb27fp2rpl7Ozs4OnpicjISOGYj48PAOi9z8HBAdDk/hDSnkktXQFCiHGFhIQgMDAQiYmJKCwsRExMDADA398fQUFBOHHiBBITEzFixAjhPRMmTECnTp2wfft2IdcmIiICNTU1emUYY9i/fz8GDhyI5ORkrFu3DtDM1AKA/fv3IyAgQK8+CoWiQR1bU14mk+l9z3Fcg1ygtmrtuTdu3IiHDx9i5cqVyM7OhlKpxLVr1/SCjvq8vLxQWFjYpms3Vkb3GMdxgCbw0iooKAAAdOzYsck6EdIeUIBDiAgNHz4cSUlJKCwsRFxcnHB82LBhOHjwIE6dOoV58+YBmjVaMjIysH37djz11FMAgGPHjjU4p729PaZMmYJvv/0WWVlZCAsLQ79+/QAA4eHhUCgUyM7OFgKq5rS2fFO6du0KmUyG06dPC71RxcXFuH79OoYNGwYAkMvlqK2tbfM1tO7du4f3338f33//PZycnBAaGgqFQoH09PRmA5y+ffuadVZTeno6AgMD4eXlZbZrEmKNKMAhRISGDx+O+fPnQ6lU6gUQMTExWLBgAWpqaoQEY3d3d3h6emLbtm3w8/NDdnY2li1b1uh5p0+fjvHjx+Py5ct4+eWXhePOzs54++23sXjxYqjVagwdOhTFxcU4fvw4XFxcMGvWLL3ztLZ8U5ydnTFr1izExcXBw8MD3t7e+OCDDyCRSITejc6dOyM1NRW3bt1Chw4d4OHhAYmk9aPzixYtwtixYzFu3DgAgFQqRY8ePVpMNB49ejSWL1+OwsJCuLu7t/q6rZWcnIxRo0aZ/DqEWDsKcAgRoeHDh6OyshLdu3cX8jSgCXBKS0uF6eQAIJFIsHPnTixatAgREREICwvDZ5991mBhQAAYMWIEPDw8kJGR0WAl348++ggdO3bEqlWrcOPGDbi5uaFfv3549913G61ja8s3Ze3atZg7dy7Gjx8PFxcXvPPOO7hz546Q9/L2229j1qxZCA8PR2VlJW7evNnqBff27duHI0eO4OrVq3rHIyMjWwxwIiMj0a9fP/z44494/fXXW3Xd1qqqqsKePXsQHx9v0usQYgs4xhizdCUIIcRYysvLERAQgDVr1phlcT1D7N+/H3FxcUhPT29T75GhNm/ejN27d+O3334z2TU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" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "ax.plot(lbda_list, T, label=\"$T$\")\n", + "ax.plot(lbda_list, R, label=\"$R$\")\n", + "ax.legend(loc=\"center right\")\n", + "ax.set_xlabel(r\"Wavelength $\\lambda$ (nm)\")\n", + "ax.set_ylabel(r\"Power reflection $R$ or transmission $T$\")\n", + "ax.set_title(r\"Bragg mirror: Air/{TiO$_2$/SiO$_2$}x8/TiO$_2$/Glass\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Bragg-mirror/validation-Bragg.py b/examples/Bragg-mirror/validation-Bragg.py deleted file mode 100755 index b5e8a621..00000000 --- a/examples/Bragg-mirror/validation-Bragg.py +++ /dev/null @@ -1,169 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Validation for a TiO2/SiO2 Bragg mirror -# -# Author: O. Castany, C. Molinaro, M. Müller - -# %% -import numpy as np -import elli -import elli.plot as elliplot -import matplotlib.pyplot as plt -from scipy.constants import pi - -# %% [markdown] -# ## Structure definition -# -# ### Front and back materials - -# %% -n_a = 1.0 -n_g = 1.5 -air = elli.ConstantRefractiveIndex(n_a).get_mat() -glass = elli.ConstantRefractiveIndex(n_g).get_mat() - -# %% [markdown] -# ### Materials for a SiO2/TiO2 Bragg mirror - -# %% -lbda0 = 1550 # nm -k0 = 2 * pi / lbda0 -n_SiO2 = 1.47 -n_TiO2 = 2.23 + 1j * 5.2e-4 - -SiO2 = elli.ConstantRefractiveIndex(n_SiO2).get_mat() -TiO2 = elli.ConstantRefractiveIndex(n_TiO2).get_mat() - -# %% [markdown] -# ### Layers and Structure - -# %% -d_SiO2 = elli.get_qwp_thickness(SiO2, lbda0) -d_TiO2 = elli.get_qwp_thickness(TiO2, lbda0) - -L_SiO2 = elli.Layer(SiO2, d_SiO2) -L_TiO2 = elli.Layer(TiO2, d_TiO2) - -# print("Thickness of the SiO2 QWP: {:.1f} nm".format(L_SiO2.h*1e9)) -# print("Thickness of the TiO2 QWP: {:.1f} nm".format(L_TiO2.h*1e9)) - -# Repeated layers: n periods -Layerstack = elli.RepeatedLayers([L_TiO2, L_SiO2], 4, 0, 0) - -# Number of interfaces -N = 2 * Layerstack.repetitions + 1 - -# Structure -s = elli.Structure(air, [Layerstack], glass) - -# %% [markdown] -# ## Analytical calculation -# %% -n = np.ones(N + 1, dtype=complex) -n[0] = n_a -n[1::2] = n_TiO2 -n[2::2] = n_SiO2 -n[-1] = n_g - -n.shape = (-1, 1) - -d = np.ones(N + 1) -d[1::2] = L_TiO2.thickness #  d[0] is not used -d[2::2] = L_SiO2.thickness - -(lbda1, lbda2) = (1100, 2500) -lbda_list = np.linspace(lbda1, lbda2, 200) - - -def ReflectionCoeff(incidence_angle=0.0, polarisation="s"): - """Returns the reflection coefficient in amplitude""" - Kx = n[0] * np.sin(incidence_angle) - sinPhi = Kx / n - kz = 2 * pi / lbda_list * np.sqrt(n**2 - (Kx) ** 2) - - # Reflexion coefficient r_{k,k+1} for a single interface - # polarisation s: - # r_ab(p) = r_{p,p+1} = (kz(p)-kz(p+1))/(kz(p)+kz(p+1)) - # polarisation p: - # r_ab(p) = r_{p,p+1} = (kz(p)*n[p+1]**2-kz(p+1)*n[p]**2) \ - # /(kz(p)*n[p]**2+kz(p+1)*n[p+1]**2) - if polarisation == "s": - r_ab = (-np.diff(kz, axis=0)) / (kz[:-1] + kz[1:]) - elif polarisation == "p": - r_ab = (kz[:-1] * (n[1:]) ** 2 - kz[1:] * (n[:-1]) ** 2) / ( - kz[:-1] * (n[1:]) ** 2 + kz[1:] * (n[:-1]) ** 2 - ) - - # Local function definition for recursive calculation - def U(k): - """Returns reflection coefficient U(k) = r_{k, {k+1,...,N}} - - Used recursively. - """ - p = k + 1 - if p == N: - res = r_ab[N - 1] - else: - res = (r_ab[p - 1] + U(p) * np.exp(2j * kz[p] * d[p])) / ( - 1 + r_ab[p - 1] * U(p) * np.exp(2j * kz[p] * d[p]) - ) - return res - - return U(0) - - -# Power reflexion coefficient for different incidence angles and polarisations -R_th_ss_0 = (np.abs(ReflectionCoeff(0, "s"))) ** 2 # Phi_i = 0 -R_th_ss = (np.abs(ReflectionCoeff(pi / 4, "s"))) ** 2 #  Phi_i = pi/4 -R_th_pp = (np.abs(ReflectionCoeff(pi / 4, "p"))) ** 2 - -# %% [markdown] -# ## Calculation with pyElli -# %% -# Incidence angle Phi_i = 0, 's' polarization -data = s.evaluate(lbda_list, 0) - -R_ss_0 = data.R_ss - -# Incidence angle Phi_i = pi/4, 's' and 'p' polarizations -data2 = s.evaluate(lbda_list, np.rad2deg(pi / 4)) - -R_ss = data2.R_ss -R_pp = data2.R_pp - -# %% [markdown] -# ## Plotting -# %% -fig = plt.figure(figsize=(12.0, 6.0)) -plt.rcParams["axes.prop_cycle"] = plt.cycler("color", "bgr") -ax = fig.add_axes([0.1, 0.1, 0.7, 0.8]) - -d = np.vstack((R_ss_0, R_ss, R_pp)).T -lines1 = ax.plot(lbda_list, d) -legend1 = ("R_ss (0$^\circ$)", "R_ss (45$^\circ$)", "R_pp (45$^\circ$)") - -d = np.vstack((R_th_ss_0, R_th_ss, R_th_pp)).T -lines2 = ax.plot(lbda_list, d, "x") -legend2 = ("R_th_ss (0$^\circ$)", "R_th_ss (45$^\circ$)", "R_th_pp (45$^\circ$)") - -ax.legend( - lines1 + lines2, - legend1 + legend2, - loc="upper left", - bbox_to_anchor=(1.05, 1), - borderaxespad=0.0, -) - -ax.set_title( - r"Bragg mirror: Air/{TiO$_2$/SiO$_2$}x" + str(Layerstack.repetitions) + "/Glass" -) -ax.set_xlabel(r"Wavelength $\lambda$ (m)") -ax.set_ylabel(r"$R$") -fmt = ax.xaxis.get_major_formatter() -fmt.set_powerlimits((-3, 3)) - -elliplot.draw_structure(s) -plt.show() - -# %% diff --git a/examples/Interfaces/Interferences.py b/examples/Interfaces/Interferences.py deleted file mode 100644 index baa19b6a..00000000 --- a/examples/Interfaces/Interferences.py +++ /dev/null @@ -1,55 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # The simplest example: a homogeneous glass layer in air -# -# Berreman4x4 example -# Author: O. Castany, M. Müller -# %% -import numpy as np -import elli -import matplotlib.pyplot as plt - -# %% -# Materials: -air = elli.AIR -glass = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(1.5)) - -# Layer and half-spaces: -layer = elli.Layer(glass, 1) -front = back = air - -# Structure: -s = elli.Structure(front, [layer], back) - -# %% -# Wavelength and wavenumber: -lbda = 1000 # nm - -# Incidence angle: -angle = 30 - -# %% -# Variation of the reflexion and transmission coefficients with the -# thickness of the glass layer: -h_list = np.linspace(0, 1000, 1000) - -data = elli.ResultList() - -for h in h_list: - layer.set_thickness(h) - data.append(s.evaluate(lbda, angle)) - -# %% -plt.figure(figsize=(12.0, 6.0)) -plt.plot(h_list, data.R_pp, label="R_pp") -plt.plot(h_list, data.R_ss, label="R_ss") -plt.plot(h_list, data.T_pp, label="T_pp") -plt.plot(h_list, data.T_ss, label="T_ss") -plt.title("Glass layer at 30 degree incidence angle") -plt.xlabel("Layer thickness (nm)") -plt.ylabel(r"Reflexion coefficients $R$") -plt.legend() -plt.show() - -# %% diff --git a/examples/Liquid crystals/twisted-nematic.py b/examples/Liquid crystals/twisted-nematic.py deleted file mode 100644 index 2fa855e9..00000000 --- a/examples/Liquid crystals/twisted-nematic.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Example of a 90° twisted nematic liquid crystal -# -# Author: O. Castany, M. Müller -# %% [markdown] -# Consider the following situation: -# - twisted liquid crystal with 90° twist between z=0 and z=d -# - liquid crystal aligned along x at z=0. -# - input and output polarizers aligned parallel to x -# -# Gooch-Tarry law gives: T_pp = sin²(pi/2·√(1+u²)) / (1+u²), -# with u = 2dΔn/λ. -# The transmission minima are given by u = ((2m)²-1)^{-1/2} = √(3),√(15),√(35),… -# -# We consider a birefringence Δn = 0.10 and a thickness d = 4.33 µm. The first -# minimum should be at λ = 500 nm, or k0 = 1.257e7 m⁻¹. -# -# Note: Gooch-Tarry law does not take into account interferences between the two -# glass substrates. A glass with n = 1.55 minimizes the interferences. - -# %% -import elli -import elli.plot as elliplot -import matplotlib.pyplot as plt -import numpy as np -from numpy.lib.scimath import sqrt -from scipy.constants import c, pi - -# %% [markdown] -# ## Set parameters - -# %% -# Materials -glass = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(1.55)) -front = back = glass - -# Liquid crystal oriented along the x direction -(no, ne) = (1.5, 1.6) -Dn = ne - no -LC = elli.UniaxialMaterial( - elli.ConstantRefractiveIndex(no), elli.ConstantRefractiveIndex(ne) -) -R = elli.rotation_v_theta(elli.E_Y, 90) -LC.set_rotation(R) -d = 4330 -TN = elli.TwistedLayer(LC, d, 7, 90) - -# Structure -s = elli.Structure(front, [TN], back) - -# Calculation parameters -(lbda_min, lbda_max) = (200e-9, 1) #  (m) -k0_list = np.linspace(2 * pi / lbda_max, 2 * pi / lbda_min) -lbda_list = (2 * pi) / k0_list * 1e9 - -# %% [markdown] -# ## Calculate theoretical curve with Gooch-Tarry law - -# %% -u = 2 * d * Dn / lbda_list -GT = np.sin(pi / 2 * sqrt(1 + u**2)) ** 2 / (1 + u**2) - -# %% [markdown] -# ## Simulate with pyElli - -# %% -TN.set_divisions(7) -data = s.evaluate(lbda_list, 0) -T7 = np.real(data.T_pp) - -TN.set_divisions(18) -data2 = s.evaluate(lbda_list, 0) -T18 = np.real(data2.T_pp) - -# %% [markdown] -# ## Plot - -# %% -fig = plt.figure() -ax = fig.add_subplot(1, 1, 1) - -# Plot Gooch-Tarry law, for comparison -ax.plot(k0_list, GT, label="Gooch-Tarry law") - -# Two plots are made, with 7 or 18 divisions in the TwistedMaterial -ax.plot(k0_list, T7, "x", label="Berreman4x4, 7 Divisions") -ax.plot(k0_list, T18, "x", label="Berreman4x4, 18 Divisions") - -ax.set_title("90° Twisted Nematic Liquid Crystal, " + "d = {:.2f} µm".format(d * 1e-3)) -ax.set_xlabel(r"Wavenumber $k_0$ (m$^{-1}$)") -ax.set_ylabel(r"Power transmission $T$") -ax.legend() -plt.show() - -# %% [markdown] -# ## Plot Structure - -# %% -TN.set_divisions(7) -elliplot.draw_structure(s) -TN.set_divisions(18) -elliplot.draw_structure(s) - -# %% diff --git a/examples/Liquid crystals/validation-cholesteric.py b/examples/Liquid crystals/validation-cholesteric.py deleted file mode 100644 index e9598253..00000000 --- a/examples/Liquid crystals/validation-cholesteric.py +++ /dev/null @@ -1,108 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Example of a cholesteric liquid crystal -# -# Author: O. Castany, C. Molinaro, M. Müller - -# %% -import elli -import elli.plot as elliplot -import matplotlib.pyplot as plt -import numpy as np -from numpy.lib.scimath import sqrt -from scipy.constants import c, pi - -# %% [markdown] -# ## Set parameters - -# %% -# Materials -front = back = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(1.6)) - -# Liquid crystal oriented along the x direction -(no, ne) = (1.5, 1.7) -Dn = ne - no -n_med = (ne + no) / 2 -LC = elli.UniaxialMaterial( - elli.ConstantRefractiveIndex(no), elli.ConstantRefractiveIndex(ne) -) #  ne along z -R = elli.rotation_v_theta(elli.E_Y, 90) #  rotation round y -LC.set_rotation(R) #  apply rotation from z to x -# Cholesteric pitch: -p = 650 -# One half turn of a right-handed helix: -TN = elli.TwistedLayer(LC, p / 2, 25, 180) - -# Inhomogeneous layer, repeated layer, and structure -N = 5 #  number half pitch repetitions -h = N * p / 2 -L = elli.RepeatedLayers([TN], N) -s = elli.Structure(front, [L], back) - -# Normal incidence: -Kx = 0.0 - -# Calculation parameters -lbda_min, lbda_max = 600, 1500 #  (nm) -lbda = np.linspace(lbda_min, lbda_max, 100) -k0 = 2 * pi / (lbda * 1e-9) - -# %% [markdown] -# ## Analytical calculation for the power reflection coefficient -# %% -q = 2 * pi / p / 1e-9 -alpha = q / k0 -epsilon = (no**2 + ne**2) / 2 -delta = (no**2 - ne**2) / 2 -n2 = sqrt((alpha**2 + epsilon - sqrt(4 * epsilon * alpha**2 + delta**2))) -w = 1j * (ne**2 - n2**2 - alpha**2) / (2 * alpha * n2) # not k0/c -A = -2j * k0 * n2 * h * 1e-9 - -R_th = ( - np.abs( - (w**2 + 1) - * (1 - np.exp(-2j * k0 * n2 * h * 1e-9)) - / ( - 2 * w * (1 + np.exp(-2j * k0 * n2 * h * 1e-9)) - - 1j * (w**2 - 1) * (1 - np.exp(-2j * k0 * n2 * h * 1e-9)) - ) - ) - ** 2 -) - -# %% [markdown] -# ## Calculation with pyElli -# %% -data = s.evaluate(lbda, 0) - -# Jones matrices for the circular wave basis -# Right-circular wave is reflected in the stop-band -# R_LR, T_LR close to zero -R_RR = data.Rc_RR - -# %% [markdown] -# ## Plotting -# %% -fig = plt.figure() -ax = fig.add_subplot(1, 1, 1) - -ax.plot(lbda, R_RR, label="R_RR") -ax.plot(lbda, R_th, "r", label="R_th") - -ax.legend(loc="center right", bbox_to_anchor=(1.00, 0.50)) - -ax.set_title( - "Right-handed Cholesteric Liquid Crystal, " + "{:.1f} helix pitches".format(N / 2.0) -) -ax.set_xlabel(r"Wavelength $\lambda_0$ (m)") -ax.set_ylabel(r"Power reflexion $R$") -fmt = ax.xaxis.get_major_formatter() -fmt.set_powerlimits((-3, 3)) - -plt.show() - -# %% -elliplot.draw_structure(s) - -# %% diff --git a/examples/Total internal reflection/FrustratedTIR-angle.py b/examples/Total internal reflection/FrustratedTIR-angle.py deleted file mode 100644 index 8c328124..00000000 --- a/examples/Total internal reflection/FrustratedTIR-angle.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Frustrated Total Internal Reflection: Glass1 / Air / Glass2 -# -# Author: O. Castany, C. Molinaro, M. Müller - -# %% -import elli -import matplotlib.pyplot as plt -import numpy as np -from scipy.constants import pi - -# %% [markdown] -# ## Structure definition - -# %% -# Refractive indices -n_f = 1.5 -n_s = 1.0 -n_b = 1.7 - -# Materials: -glass1 = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_f)) -air = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_s)) -glass2 = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_b)) - -# Layer: -layer = elli.Layer(air, 0) - -# Structure: -s = elli.Structure(glass1, [layer], glass2) - -# Wavelength and wavenumber: -lbda = 1000 -k0 = 2 * pi / lbda - -# Layer thickness -d = lbda * 0.347 -layer.set_thickness(d) - -# Variation of incidence angle -Phi_list = np.deg2rad(np.linspace(0, 89, 90)) - -# %% [markdown] -# Analytical calculation -# Incidence angle -# %% -# Reduced wavenumber -Kx = n_f * np.sin(Phi_list) - -Phi_s = np.arcsin((Kx / n_s).astype(complex)) -Phi_b = np.arcsin((Kx / n_b).astype(complex)) - -# Wave vector: -kz_f = n_f * k0 * np.cos((Phi_list.astype(complex))) -kz_s = k0 * np.sqrt((-(Kx**2 - n_s**2)).astype(complex)) -kz_b = n_b * k0 * np.cos(Phi_b) - -# Amplitude coefficient for 's' polarisation: -r_sf_s = (kz_f - kz_s) / (kz_s + kz_f) -r_bs_s = (kz_s - kz_b) / (kz_s + kz_b) -t_sf_s = 1 + r_sf_s -t_bs_s = 1 + r_bs_s - -# Amplitude coefficient for 'p' polarisation: -r_sf_p = (kz_f * n_s**2 - kz_s * n_f**2) / (kz_s * n_f**2 + kz_f * n_s**2) -r_bs_p = (kz_s * n_b**2 - kz_b * n_s**2) / (kz_s * n_b**2 + kz_b * n_s**2) -t_sf_p = np.cos((Phi_list.astype(complex))) * (1 - r_sf_p) / np.cos(Phi_s) -t_bs_p = np.cos(Phi_s) * (1 - r_bs_p) / np.cos(Phi_b) - -# Power coefficients: -R_th_s = ( - np.abs( - (r_sf_s + r_bs_s * np.exp(2j * kz_s * d)) - / (1 + r_bs_s * r_sf_s * np.exp(2j * kz_s * d)) - ) -) ** 2 - -t2_th_s = ( - np.abs( - (t_bs_s * t_sf_s * np.exp(1j * kz_s * d)) - / (1 + r_bs_s * r_sf_s * np.exp(2j * kz_s * d)) - ) -) ** 2 - -R_th_p = ( - np.abs( - (r_sf_p + r_bs_p * np.exp(2j * kz_s * d)) - / (1 + r_bs_p * r_sf_p * np.exp(2j * kz_s * d)) - ) -) ** 2 - -t2_th_p = ( - np.abs( - (t_bs_p * t_sf_p * np.exp(1j * kz_s * d)) - / (1 + r_bs_p * r_sf_p * np.exp(2j * kz_s * d)) - ) -) ** 2 - -correction = np.real(n_b * np.cos(Phi_b) / (n_f * np.cos(Phi_list.astype(complex)))) -# This is a correction term used in R +T*correction = 1 - -T_th_s = t2_th_s * correction -T_th_p = t2_th_p * correction - -# %% [markdown] -# ## Calculation with pyElli -# %% -data = elli.ResultList([s.evaluate(lbda, np.rad2deg(Phi_i)) for Phi_i in Phi_list]) - -# Extraction of the transmission and reflexion coefficients -R_p = data.R_pp -R_s = data.R_ss -T_p = data.T_pp -T_s = data.T_ss -t2_p = np.abs(data.t_pp) ** 2 # Before power correction -t2_s = np.abs(data.t_ss) ** 2 - -# %% [markdown] -# ## Plotting -# %% -fig = plt.figure(figsize=(12.0, 6.0)) -plt.rcParams["axes.prop_cycle"] = plt.cycler("color", "bgrcmk") -ax = fig.add_axes([0.1, 0.1, 0.7, 0.8]) - -y = np.vstack((R_s, R_p, t2_s, t2_p, T_s, T_p)).T -legend1 = ("R_s", "R_p", "t2_s", "t2_p", "T_s", "T_p") -lines1 = ax.plot(np.rad2deg(Phi_list), y) - -y_th = np.vstack((R_th_s, R_th_p, t2_th_s, t2_th_p, T_th_s, T_th_p)).T -legend2 = ("R_th_s", "R_th_p", "t2_th_s", "t2_th_p", "T_th_s", "T_th_p") -lines2 = ax.plot(np.rad2deg(Phi_list), y_th, "x") - -ax.legend( - lines1 + lines2, - legend1 + legend2, - loc="upper left", - bbox_to_anchor=(1.05, 1), - borderaxespad=0.0, -) - -ax.set_title("FTIR: Glass1 / Air ($d$ = {:.3g} nm) / Glass2".format(d)) -ax.set_xlabel(r"Reduced wave number, $Kx$") -ax.set_ylabel(r"Reflexion and transmission coefficients $R$, $T$") - -plt.show() - -# %% diff --git a/examples/Total internal reflection/FrustratedTIR-thickness.py b/examples/Total internal reflection/FrustratedTIR-thickness.py deleted file mode 100644 index 43347dba..00000000 --- a/examples/Total internal reflection/FrustratedTIR-thickness.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Frustrated Total Internal Reflection: Glass1 / Air / Glass2 -# -# Author: O. Castany, C. Molinaro, M. Müller - -# %% -import elli -import matplotlib.pyplot as plt -import numpy as np -from scipy.constants import pi - -# %% [markdown] -# ## Structure definition - -# %% -# Refractive indices -n_f = 1.5 -n_s = 1.0 -n_b = 1.7 - -# Materials: -glass1 = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_f)) -air = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_s)) -glass2 = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_b)) - -# Layer: -layer = elli.Layer(air, 0) - -# Structure: -s = elli.Structure(glass1, [layer], glass2) - -# Wavelength and wavenumber: -lbda = 1000 -k0 = 2 * pi / lbda -Phi_i = pi / 2 * 0.6 #  Incidence angle (higher than the limit angle) - -# Air thickness variation range -d = np.linspace(0, 1000) - -# %% [markdown] -# ## Analytical calculation - -# %% -# Reduced wavenumber -Kx = n_f * np.sin(Phi_i) - -# Incidence angle -Phi_s = np.arcsin((complex(Kx / n_s))) -Phi_b = np.arcsin(Kx / n_b) - -# Wave vector: -kz_f = n_f * k0 * np.cos(Phi_i) -kz_s = k0 * np.sqrt(complex(n_s**2 - Kx**2)) -kz_b = n_b * k0 * np.cos(Phi_b) - -# Amplitude coefficient polarisation s: -r_sf_s = (kz_f - kz_s) / (kz_s + kz_f) -r_bs_s = (kz_s - kz_b) / (kz_s + kz_b) -t_sf_s = 1 + r_sf_s -t_bs_s = 1 + r_bs_s - -# Amplitude coefficient polarisation p: -r_sf_p = (kz_f * n_s**2 - kz_s * n_f**2) / (kz_s * n_f**2 + kz_f * n_s**2) -r_bs_p = (kz_s * n_b**2 - kz_b * n_s**2) / (kz_s * n_b**2 + kz_b * n_s**2) -t_sf_p = np.cos(Phi_i) * (1 - r_sf_p) / np.cos(Phi_s) -t_bs_p = np.cos(Phi_s) * (1 - r_bs_p) / np.cos(Phi_b) - -# Power coefficients: -R_th_s = ( - np.abs( - (r_sf_s + r_bs_s * np.exp(2j * kz_s * d)) - / (1 + r_bs_s * r_sf_s * np.exp(2j * kz_s * d)) - ) -) ** 2 - -t2_th_s = ( - np.abs( - (t_bs_s * t_sf_s * np.exp(1j * kz_s * d)) - / (1 + r_bs_s * r_sf_s * np.exp(2j * kz_s * d)) - ) -) ** 2 - -R_th_p = ( - np.abs( - (r_sf_p + r_bs_p * np.exp(2j * kz_s * d)) - / (1 + r_bs_p * r_sf_p * np.exp(2j * kz_s * d)) - ) -) ** 2 - -t2_th_p = ( - np.abs( - (t_bs_p * t_sf_p * np.exp(1j * kz_s * d)) - / (1 + r_bs_p * r_sf_p * np.exp(2j * kz_s * d)) - ) -) ** 2 - -correction = np.real(n_b * np.cos(Phi_b) / (n_f * np.cos(Phi_i))) -# This is a correction term used in R +T*correction = 1 - -T_th_s = t2_th_s * correction -T_th_p = t2_th_p * correction - -# %% [markdown] -# ## Calculation with pyElli - -# %% -data = elli.ResultList() - -for dd in d: - layer.set_thickness(dd) - data.append(s.evaluate(lbda, np.rad2deg(Phi_i))) - -# Extraction of the transmission and reflexion coefficients -R_p = data.R_pp -R_s = data.R_ss -T_p = data.T_pp -T_s = data.T_ss -t2_p = np.abs(data.t_pp) ** 2 # Before power correction -t2_s = np.abs(data.t_ss) ** 2 - -# %% [markdown] -# ## Plotting -# %% -fig = plt.figure(figsize=(12.0, 6.0)) -plt.rcParams["axes.prop_cycle"] = plt.cycler("color", "bgrcmk") -ax = fig.add_axes([0.1, 0.1, 0.7, 0.8]) - -y = np.vstack((R_s, R_p, t2_s, t2_p, T_s, T_p)).T -legend1 = ("R_s", "R_p", "t2_s", "t2_p", "T_s", "T_p") -lines1 = ax.plot(d, y) - -y_th = np.vstack((R_th_s, R_th_p, t2_th_s, t2_th_p, T_th_s, T_th_p)).T -legend2 = ("R_th_s", "R_th_p", "t2_th_s", "t2_th_p", "T_th_s", "T_th_p") -lines2 = ax.plot(d, y_th, "x") - -ax.legend( - lines1 + lines2, - legend1 + legend2, - loc="upper left", - bbox_to_anchor=(1.05, 1), - borderaxespad=0.0, -) - -ax.set_title( - "FTIR: Glass1 / Air (d) / Glass2, for incidence angle " - + "$\Phi_i$ = {:.0f}$^\circ$".format(Phi_i * 180 / pi) -) -ax.set_xlabel(r"Air layer thickness, $d$ (nm)") -ax.set_ylabel(r"Reflexion and transmission coefficients $R$, $T$") -plt.show() - -# %% diff --git a/examples/Total internal reflection/TIR.py b/examples/Total internal reflection/TIR.py deleted file mode 100644 index 1ff96d03..00000000 --- a/examples/Total internal reflection/TIR.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/python -# encoding: utf-8 -# %% [markdown] -# # Example of Total Internal Reflection on the Glass / Air interface -# -# Author: O. Castany, M.Müller - -# %% -import elli -import matplotlib.pyplot as plt -import numpy as np - - -# %% [markdown] -# ## Structure definition - -# %% -# Refractive indices -n_glass = 1.5 -n_air = 1.0 - -# Materials: -glass = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_glass)) -air = elli.IsotropicMaterial(elli.ConstantRefractiveIndex(n_air)) - -# Structure: -s = elli.Structure(glass, [], air) - -# Wavelength -lbda = 1000 # nm - -# Variation of incidence angle -Phi_list = np.linspace(0, 89, 90) - - -# %% [markdown] -# ## Calculation - -# %% -data = elli.ResultList([s.evaluate(lbda, Phi_i) for Phi_i in Phi_list]) - - -# %% [markdown] -# ## Plotting - -# %% -plt.figure() -plt.plot(Phi_list, data.R_pp, label="R_pp") -plt.plot(Phi_list, data.R_ss, label="R_ss") -plt.title("FTIR: Glass / Air") -plt.xlabel("Angle of incidence (°)") -plt.ylabel(r"Reflexion coefficients $R$") -plt.legend() -plt.show() - - -# %% From cec432f083876c1ac343e31e490e13f96709843f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:34:41 +0200 Subject: [PATCH 8/9] Update GitHub actions to newest versions --- .github/workflows/benchmark.yml | 4 ++-- .github/workflows/build-docker.yml | 2 +- .github/workflows/linting.yml | 4 ++-- .github/workflows/pytest.yml | 4 ++-- .github/workflows/python-publish.yml | 4 ++-- .github/workflows/requirements.yml | 4 ++-- 6 files changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index fcef18fb..4707c701 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -14,10 +14,10 @@ jobs: name: Run pytest-benchmark benchmark example runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: submodules: recursive - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install deps diff --git a/.github/workflows/build-docker.yml b/.github/workflows/build-docker.yml index b3e8f638..7041d014 100644 --- a/.github/workflows/build-docker.yml +++ b/.github/workflows/build-docker.yml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out the repo - uses: actions/checkout@v2 + uses: actions/checkout@v4 with: submodules: recursive - name: Log in to Docker Hub diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml index 1c26d539..464969f4 100644 --- a/.github/workflows/linting.yml +++ b/.github/workflows/linting.yml @@ -10,8 +10,8 @@ jobs: linting: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install deps diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 396e8cb4..e796c2dc 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -18,11 +18,11 @@ jobs: python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: submodules: recursive - name: Set up Python ${{ matrix.python_version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python_version }} - name: Install module diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index d1e2f6f3..4caa4fad 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -18,11 +18,11 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: submodules: recursive - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies diff --git a/.github/workflows/requirements.yml b/.github/workflows/requirements.yml index 20e9fd9f..6e340b51 100644 --- a/.github/workflows/requirements.yml +++ b/.github/workflows/requirements.yml @@ -10,11 +10,11 @@ jobs: test_python: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: submodules: recursive - name: Set up Python 3.10 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install package (no deps) From c0a39fa03541df10ad762fdf1cfab893e018bb24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marius=20M=C3=BCller?= <49639740+MarJMue@users.noreply.github.com> Date: Fri, 28 Jun 2024 11:43:01 +0200 Subject: [PATCH 9/9] Revert accidental solver change in basic example --- examples/Basic Usage/Basic Usage.ipynb | 8637 +++++++++++++++++++++++- 1 file changed, 8557 insertions(+), 80 deletions(-) diff --git a/examples/Basic Usage/Basic Usage.ipynb b/examples/Basic Usage/Basic Usage.ipynb index 125c4bf3..5488005d 100644 --- a/examples/Basic Usage/Basic Usage.ipynb +++ b/examples/Basic Usage/Basic Usage.ipynb @@ -6,11 +6,11 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-06-28T08:24:42.779064Z", - "iopub.status.busy": "2024-06-28T08:24:42.778755Z", - "iopub.status.idle": "2024-06-28T08:24:43.074754Z", - "shell.execute_reply": "2024-06-28T08:24:43.074256Z", - "shell.execute_reply.started": "2024-06-28T08:24:42.779045Z" + "iopub.execute_input": "2024-06-28T09:40:55.239337Z", + "iopub.status.busy": "2024-06-28T09:40:55.239204Z", + "iopub.status.idle": "2024-06-28T09:40:55.532451Z", + "shell.execute_reply": "2024-06-28T09:40:55.531753Z", + "shell.execute_reply.started": "2024-06-28T09:40:55.239324Z" }, "jupyter": { "outputs_hidden": false @@ -37,11 +37,11 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-06-28T08:24:43.075322Z", - "iopub.status.busy": "2024-06-28T08:24:43.075163Z", - "iopub.status.idle": "2024-06-28T08:24:44.359396Z", - "shell.execute_reply": "2024-06-28T08:24:44.359104Z", - "shell.execute_reply.started": "2024-06-28T08:24:43.075311Z" + "iopub.execute_input": "2024-06-28T09:40:55.533120Z", + "iopub.status.busy": "2024-06-28T09:40:55.532970Z", + "iopub.status.idle": "2024-06-28T09:40:56.212872Z", + "shell.execute_reply": "2024-06-28T09:40:56.212325Z", + "shell.execute_reply.started": "2024-06-28T09:40:55.533105Z" }, "jupyter": { "outputs_hidden": false @@ -74,15 +74,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-06-28T08:25:12.248765Z", - "iopub.status.busy": "2024-06-28T08:25:12.248477Z", - "iopub.status.idle": "2024-06-28T08:25:12.255453Z", - "shell.execute_reply": "2024-06-28T08:25:12.254917Z", - "shell.execute_reply.started": "2024-06-28T08:25:12.248748Z" + "iopub.execute_input": "2024-06-28T09:40:56.213562Z", + "iopub.status.busy": "2024-06-28T09:40:56.213372Z", + "iopub.status.idle": "2024-06-28T09:40:56.222229Z", + "shell.execute_reply": "2024-06-28T09:40:56.221782Z", + "shell.execute_reply.started": "2024-06-28T09:40:56.213551Z" }, "jupyter": { "outputs_hidden": false @@ -114,14 +114,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.363402Z", - "iopub.status.idle": "2024-06-28T08:24:44.363553Z", - "shell.execute_reply": "2024-06-28T08:24:44.363484Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.363474Z" + "iopub.execute_input": "2024-06-28T09:40:56.222867Z", + "iopub.status.busy": "2024-06-28T09:40:56.222729Z", + "iopub.status.idle": "2024-06-28T09:40:56.226619Z", + "shell.execute_reply": "2024-06-28T09:40:56.226318Z", + "shell.execute_reply.started": "2024-06-28T09:40:56.222856Z" }, "jupyter": { "outputs_hidden": false @@ -157,14 +158,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.364189Z", - "iopub.status.idle": "2024-06-28T08:24:44.364328Z", - "shell.execute_reply": "2024-06-28T08:24:44.364274Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.364267Z" + "iopub.execute_input": "2024-06-28T09:40:56.227173Z", + "iopub.status.busy": "2024-06-28T09:40:56.227044Z", + "iopub.status.idle": "2024-06-28T09:40:56.804251Z", + "shell.execute_reply": "2024-06-28T09:40:56.803638Z", + "shell.execute_reply.started": "2024-06-28T09:40:56.227162Z" }, "jupyter": { "outputs_hidden": false @@ -228,20 +230,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.364786Z", - "iopub.status.idle": "2024-06-28T08:24:44.364902Z", - "shell.execute_reply": "2024-06-28T08:24:44.364851Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.364846Z" + "iopub.execute_input": "2024-06-28T09:40:56.806192Z", + "iopub.status.busy": "2024-06-28T09:40:56.805780Z", + "iopub.status.idle": "2024-06-28T09:40:57.028076Z", + "shell.execute_reply": "2024-06-28T09:40:57.027451Z", + "shell.execute_reply.started": "2024-06-28T09:40:56.806174Z" }, "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "96dcd81d9bfd47bb8a71c50eb1ce3721", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HBox(children=(HBox(children=(BoundedFloatText(value=1.452, description='SiO2_n0', min=-100.0),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "@fit(psi_delta, params)\n", "def model(lbda, params):\n", @@ -268,7 +286,7 @@ "\n", " # The model should return the evaluation of the structure at the experimental wavelengths lbda,\n", " # the experimental angle ANGLE and it should define a solver to calculate the transfer matrix.\n", - " return structure.evaluate(lbda, ANGLE, solver=elli.Solver4x4)" + " return structure.evaluate(lbda, ANGLE, solver=elli.Solver2x2)" ] }, { @@ -286,20 +304,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.365416Z", - "iopub.status.idle": "2024-06-28T08:24:44.365556Z", - "shell.execute_reply": "2024-06-28T08:24:44.365504Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.365499Z" + "iopub.execute_input": "2024-06-28T09:40:57.028949Z", + "iopub.status.busy": "2024-06-28T09:40:57.028764Z", + "iopub.status.idle": "2024-06-28T09:40:57.090217Z", + "shell.execute_reply": "2024-06-28T09:40:57.089849Z", + "shell.execute_reply.started": "2024-06-28T09:40:57.028931Z" }, "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7adb2c9cb86948cfbffde83449431635", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FigureWidget({\n", + " 'data': [{'hovertemplate': 'variable=Ψ
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Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
SiO2_n0 1.45200000 0.00000000(0.00%)1.452-100.000000 100.000000False
SiO2_n1 36.0000000 0.00000000(0.00%)36.0-40000.0000 40000.0000False
SiO2_n2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k0 0.00000000 0.000000000-100.000000 100.000000False
SiO2_k1 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_d 2.06578929 0.00753074(0.36%)20 0.00000000 40000.0000True
" + ], + "text/plain": [ + "ParamsHist([('SiO2_n0', ), ('SiO2_n1', ), ('SiO2_n2', ), ('SiO2_k0', ), ('SiO2_k1', ), ('SiO2_k2', ), ('SiO2_d', )])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_stats.params" ] @@ -481,20 +8931,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.368659Z", - "iopub.status.idle": "2024-06-28T08:24:44.368775Z", - "shell.execute_reply": "2024-06-28T08:24:44.368725Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.368720Z" + "iopub.execute_input": "2024-06-28T09:40:57.294832Z", + "iopub.status.busy": "2024-06-28T09:40:57.294602Z", + "iopub.status.idle": "2024-06-28T09:40:57.304022Z", + "shell.execute_reply": "2024-06-28T09:40:57.303564Z", + "shell.execute_reply.started": "2024-06-28T09:40:57.294815Z" }, "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.016923374970708012)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_stats.chisqr" ] @@ -509,20 +8971,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false, "execution": { - "iopub.status.busy": "2024-06-28T08:24:44.369199Z", - "iopub.status.idle": "2024-06-28T08:24:44.369318Z", - "shell.execute_reply": "2024-06-28T08:24:44.369265Z", - "shell.execute_reply.started": "2024-06-28T08:24:44.369260Z" + "iopub.execute_input": "2024-06-28T09:40:57.304890Z", + "iopub.status.busy": "2024-06-28T09:40:57.304673Z", + "iopub.status.idle": "2024-06-28T09:40:57.311057Z", + "shell.execute_reply": "2024-06-28T09:40:57.310485Z", + "shell.execute_reply.started": "2024-06-28T09:40:57.304874Z" }, "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

Fit Result

Fit Statistics
fitting methodleastsq
# function evals13
# data points1182
# variables1
chi-square 0.01692337
reduced chi-square 1.4330e-05
Akaike info crit.-13182.0548
Bayesian info crit.-13176.9798
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
SiO2_n0 1.45200000 0.00000000(0.00%)1.452-100.000000 100.000000False
SiO2_n1 36.0000000 0.00000000(0.00%)36.0-40000.0000 40000.0000False
SiO2_n2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k0 0.00000000 0.000000000-100.000000 100.000000False
SiO2_k1 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_k2 0.00000000 0.000000000-40000.0000 40000.0000False
SiO2_d 2.06578929 0.00753074(0.36%)20 0.00000000 40000.0000True
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_stats" ]