From 41f196a946af6f2f7a38313aaf17b5a7cfe61b91 Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 06:13:05 -0700 Subject: [PATCH 01/10] adding ror, george, switching to jupyter:true --- myst.yml | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/myst.yml b/myst.yml index bdbc465..27f7b9a 100644 --- a/myst.yml +++ b/myst.yml @@ -8,12 +8,12 @@ project: thumbnail: thumbnail.png banner: banner.png github: https://github.com/msa-em/guide-tem-simulation - # jupyter: true - jupyter: - binder: - url: https://y2j74k9mn0bz.curvenote.dev/services/binder/ - repo: msa-em/guide-tem-simulation - ref: main + jupyter: true + # jupyter: + # binder: + # url: https://y2j74k9mn0bz.curvenote.dev/services/binder/ + # repo: msa-em/guide-tem-simulation + # ref: main # jupyter: # server: # url: 'http://localhost:8888' @@ -38,6 +38,16 @@ project: orcid: 0000-0002-7117-066X email: sribet@lbl.gov affiliations: [ncem] + - name: Georgios Varnavides + corresponding: true + email: gvarnavides@berkeley.edu + orcid: 0000-0001-8338-3323 + affiliations: + - id: miller + department: Miller Institute for Basic Research in Science + institution: University of California, Berkeley + address: 468 Donner Lab, Berkeley, CA, USA, 94720 + ror: 01an7q238 - name: Alexander Rakowski corresponding: true orcid: 0000-0003-4565-8642 @@ -52,6 +62,7 @@ project: department: Faculty of Physics institution: University of Vienna address: Boltzmanngasse 5, Vienna, 1090, Austria + ror: 03prydq77 - name: Toma Susi corresponding: true orcid: 0000-0003-2513-573X @@ -66,6 +77,7 @@ project: department: Ian Holmes Imaging Centre, Bio21 Institute institution: The University of Melbourne address: 30 Flemington Rd, Parkville VIC 3052, Australia + ror: 01ej9dk98 open_access: true requirements: - 'environment.yml' From c855a3f2d3588884f64228ea66e7377991bc3863 Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 06:28:31 -0700 Subject: [PATCH 02/10] fixed complex numbers widget --- 03_math.md | 4 +- {static => figures}/complex_numbers.png | Bin myst.yml | 10 +- ...numbers.ipynb => 03_complex_numbers.ipynb} | 89 ++++++++++-------- notebooks/concepts_Fourier_transform_1D.ipynb | 6 +- 5 files changed, 60 insertions(+), 49 deletions(-) rename {static => figures}/complex_numbers.png (100%) rename notebooks/{complex_numbers.ipynb => 03_complex_numbers.ipynb} (98%) diff --git a/03_math.md b/03_math.md index aa5efad..bac48f5 100644 --- a/03_math.md +++ b/03_math.md @@ -26,9 +26,9 @@ i^2 = -1. Examples of complex numbers include $3+5i$, $4/5-(9/7)i$, $-0.23+4.33i$, and $1+\sqrt{5}i$. [](#fig_complex) shows examples of complex numbers. Complex numbers are enormously powerful, and essential when performing calclations using [quantum mechanics](wiki:Quantum_mechanics). -```{figure} +```{figure} #app:complex_numbers :name: fig_complex -:placeholder: ./static/complex_numbers.png +:placeholder: ./figures/complex_numbers.png **Complex numbers.** Every complex value consists of a real and an imaginary component, which can be used to compute the magnitude and phase of each value. ``` diff --git a/static/complex_numbers.png b/figures/complex_numbers.png similarity index 100% rename from static/complex_numbers.png rename to figures/complex_numbers.png diff --git a/myst.yml b/myst.yml index 27f7b9a..de730f1 100644 --- a/myst.yml +++ b/myst.yml @@ -8,16 +8,16 @@ project: thumbnail: thumbnail.png banner: banner.png github: https://github.com/msa-em/guide-tem-simulation - jupyter: true + # jupyter: true # jupyter: # binder: # url: https://y2j74k9mn0bz.curvenote.dev/services/binder/ # repo: msa-em/guide-tem-simulation # ref: main - # jupyter: - # server: - # url: 'http://localhost:8888' - # token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c' + jupyter: + server: + url: 'http://localhost:8888' + token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c' keywords: - Transmission electron microscopy - Scanning transmission electron microscopy diff --git a/notebooks/complex_numbers.ipynb b/notebooks/03_complex_numbers.ipynb similarity index 98% rename from notebooks/complex_numbers.ipynb rename to notebooks/03_complex_numbers.ipynb index 70648c0..06a6f67 100644 --- a/notebooks/complex_numbers.ipynb +++ b/notebooks/03_complex_numbers.ipynb @@ -5,14 +5,16 @@ "id": "4a78d716-1345-4afa-aaf7-d9224ae3d6e3", "metadata": {}, "source": [ - "# complex number example\n", - "\n", - "Colin Ophus - 2024 July" + "---\n", + "title: Complex Numbers Example\n", + "authors: [Colin Ophus]\n", + "date: 2024/07/25\n", + "---" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "bb1eb591-4b8f-4326-b19d-f528a6b84ac0", "metadata": {}, "outputs": [], @@ -25,46 +27,19 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 4, "id": "5bd98dec-51be-4283-9951-071af49373ac", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8584cc5a40444ca09618293b40f7e9de", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", - "text/html": [ - "\n", - "
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\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(footer_visible=False, header_visible=False, layout=Layout(height='360px', width='400px'), resizable=Fal…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# static figure\n", "bound = 8\n", "lw = 4\n", "x,y = 3.0, 5.0\n", "\n", - "# with plt.ioff():\n", - "dpi = 72\n", - "fig, ax = plt.subplots(figsize=(350/dpi, 350/dpi), dpi=dpi)\n", - "\n", + "with plt.ioff():\n", + " dpi = 72\n", + " fig, ax = plt.subplots(figsize=(350/dpi, 350/dpi), dpi=dpi)\n", "\n", "ax.plot(\n", " (-bound,bound),\n", @@ -177,9 +152,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "1d6845c7-06cc-4d55-8ec3-f83830b249af", "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "698a3befcb10454e9350efd49ba6ceb3", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", - "\n" + "display(widget); \n" ] }, { From 1380df55b4a80900af6e1b1645c792ac5f4a2c9c Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 06:33:01 -0700 Subject: [PATCH 04/10] oops, commited myst.yml --- myst.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/myst.yml b/myst.yml index de730f1..2b5ab70 100644 --- a/myst.yml +++ b/myst.yml @@ -8,16 +8,16 @@ project: thumbnail: thumbnail.png banner: banner.png github: https://github.com/msa-em/guide-tem-simulation - # jupyter: true + jupyter: true # jupyter: # binder: # url: https://y2j74k9mn0bz.curvenote.dev/services/binder/ # repo: msa-em/guide-tem-simulation # ref: main - jupyter: - server: - url: 'http://localhost:8888' - token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c' + # jupyter: + # server: + # url: 'http://localhost:8888' + # token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c' keywords: - Transmission electron microscopy - Scanning transmission electron microscopy From 345685e9af6b73ba2533ff6a1e793c789edafa46 Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 08:04:16 -0700 Subject: [PATCH 05/10] fixing potentials widget --- 04_physics.md | 16 ++- environment.yml | 1 + figures/dft_iam_diff.png | Bin 0 -> 43704 bytes .../03.2_concepts_Fourier_transform_1D.ipynb | 2 +- ...tials.ipynb => 04_atomic_potentials.ipynb} | 103 ++++++++++-------- notebooks/data/H2_DFT_dists.zarr/.zattrs | 5 +- .../data/H2_DFT_dists.zarr/array0/0.0.0.0 | Bin 844860 -> 838471 bytes .../data/H2_DFT_dists.zarr/array0/1.0.0.0 | Bin 847199 -> 847248 bytes .../data/H2_DFT_dists.zarr/array0/2.0.0.0 | Bin 839960 -> 843103 bytes .../data/H2_DFT_dists.zarr/array0/3.0.0.0 | Bin 851641 -> 847522 bytes .../data/H2_DFT_dists.zarr/array0/4.0.0.0 | Bin 845390 -> 853769 bytes .../data/H2_DFT_dists.zarr/array0/5.0.0.0 | Bin 844982 -> 846364 bytes .../data/H2_DFT_dists.zarr/array0/6.0.0.0 | Bin 847716 -> 851953 bytes .../data/H2_DFT_dists.zarr/array0/7.0.0.0 | Bin 846844 -> 851761 bytes .../data/H2_DFT_dists.zarr/array0/8.0.0.0 | Bin 850125 -> 847353 bytes .../data/H2_DFT_dists.zarr/array0/9.0.0.0 | Bin 842647 -> 843650 bytes notebooks/data/H2_IAM_dists.zarr/.zattrs | 5 +- 17 files changed, 78 insertions(+), 54 deletions(-) create mode 100644 figures/dft_iam_diff.png rename notebooks/{Atomic_potentials.ipynb => 04_atomic_potentials.ipynb} (81%) diff --git a/04_physics.md b/04_physics.md index 5b0d383..4dbdecb 100644 --- a/04_physics.md +++ b/04_physics.md @@ -41,7 +41,7 @@ where $\bm{r}^{\prime}$ now denotes the location of a scattering center, and the To describe the propagation of these waves in free space, the imaginary term in the exponential describes periodic variation. Thus for any fixed sum of the spatial and temporal terms in the exponent, the wavefunction has the same value; these represent the planes (or shells) of constant amplitude. To calculate the wave in an arbitrary position, we can simply substitute the new spatial location and time arguments to calculate the resulting amplitude. (bound-systems-ts)= -## Bound Systems: The Schrödinger Equation (TS) +## Bound Systems: The Schrödinger Equation To derive the wavefunction of a bound system, we need to solve the quantum "equation of motion" for the electron(s) in the corresponding confining potential: this is the Schrödinger equation. The Schrödinger equation gives the fundamental mathematical description of quantum systems, describing the time-evolution of their wavefunction. The equation for a single non-relativistic particle can in the position representation be written as @@ -62,7 +62,7 @@ The equation can only be solved analytically for a handful of simple cases, of w where wavefunctions $\psi(\bm{r})$ are the eigenvectors and energies $E$ the eigenvalues of the system. (electrostatic-potentials-ts)= -## Electrostatic Potentials (TS) +## Electrostatic Potentials The electrostatic potential of a specimen determines not only how the electrons of the system are bound, but also how transmitting electrons scatter via the Lorentz force. It therefore connects the properties of the material to the resulting images or diffraction patterns. The electrostatic potential is fundamentally speaking derived from the electron density of the atoms in a specimen, which is described by their quantum mechanical many-body wavefunction. However, this is only rarely analytically solvable, and various approximations may be needed. @@ -145,10 +145,10 @@ By further assuming that the detector is far from the scatterer, which allows us By substituting this to [](#eq:scattering_schrodinger) we get ```{math} -\begin{align} +\begin{aligned} \psi(\bm{r}) &\simeq \mathrm{e}^{\mathrm{i} k_{0} \cdot \bm{r}}-\frac{m}{2 \pi \hbar^{2}} \int V\left(\bm{r}^{\prime}\right) \mathrm{e}^{\mathrm{i} k_{0} \cdot \bm{r}^{\prime}} \frac{\mathrm{e}^{\mathrm{i} k \cdot\left(\bm{r}-\bm{r}^{\prime}\right)}}{|\bm{r}|} \mathrm{d}^{3} \bm{r}^{\prime}\\ &=\mathrm{e}^{\mathrm{i} k_{0} \cdot \bm{r}}-\frac{m}{2 \pi \hbar^{2}} \frac{\mathrm{e}^{\mathrm{i} \bm{k} \cdot \bm{r}}}{|\bm{r}|} \int V\left(\bm{r}^{\prime}\right) \mathrm{e}^{\mathrm{i}\left(\bm{k}_{0}-\bm{k}\right) \cdot \bm{r}^{\prime}} \mathrm{d}^{3} \bm{r}^{\prime}. -\end{align} +\end{aligned} ``` By further defining $\Delta \bm{k} \equiv \bm{k}-\bm{k}_{0}$, we can write the scattered part of the wave as @@ -167,7 +167,7 @@ f(\Delta \bm{k}) \equiv-\frac{m}{2 \pi \hbar^{2}} \int V\left(\bm{r}^{\prime}\ri is called the atomic form factor (or electron scattering factor {cite:p}`kirkland_advanced_2010` and it describes the angular distribution of scattered intensity. In the first Born approximation it corresponds to the Fourier transform of the scattering potential ($f(\Delta \bm{k})=\mathcal{F}_k[V(r)]$). (atomic-form-factors-ts)= -### Atomic form factors (TS) +### Atomic form factors The atomic form factors thus describe the angular amplitude for scattering of a single electron of by a single atom. While the first Born approximation is inadequate for describing real specimens (as electrons typically scatter multiple times when passing through a crystal), this description is quite useful since it relates the three-dimensional Fourier transform of the atomic potential to the scattering amplitude. @@ -180,7 +180,10 @@ V(\bm{r})=-\frac{Z e^{2}}{|\bm{r}|}+\int_{-\infty}^{+\infty} \frac{e^{2} \rho\le By substituting this into [](#eq:formfactor) and defining a new variable $\bm{R} \equiv \bm{r} - \bm{r}^{\prime}$, so that $\bm{r} = \bm{R} - \bm{r}^{\prime}$ and rearranging, we get ```{math} -f(\Delta \bm{k})=\frac{m Z e^{2}}{2 \pi \hbar^{2}} \int_{-\infty}^{+\infty} \frac{1}{|\bm{r}|} \mathrm{e}^{-\mathrm{i} \Delta k \cdot \bm{r}} \mathrm{d}^{3} \bm{r} -\frac{m e^{2}}{2 \pi \hbar^{2}} \int_{-\infty}^{+\infty} \frac{1}{|\bm{R}|} \mathrm{e}^{-\mathrm{i} \Delta \bm{k} \cdot \bm{R}} \mathrm{d}^{3} \bm{R} \int_{-\infty}^{+\infty} \rho\left(\bm{r}^{\prime}\right) \mathrm{e}^{-\mathrm{i} \Delta \bm{k} \cdot \bm{r}^{\prime}} \mathrm{d}^{3} \bm{r}^{\prime}. +\begin{aligned} +f(\Delta \bm{k})=&\frac{m Z e^{2}}{2 \pi \hbar^{2}} \int_{-\infty}^{+\infty} \frac{1}{|\bm{r}|} \mathrm{e}^{-\mathrm{i} \Delta k \cdot \bm{r}} \mathrm{d}^{3} \bm{r} \\ +-&\frac{m e^{2}}{2 \pi \hbar^{2}} \int_{-\infty}^{+\infty} \frac{1}{|\bm{R}|} \mathrm{e}^{-\mathrm{i} \Delta \bm{k} \cdot \bm{R}} \mathrm{d}^{3} \bm{R} \int_{-\infty}^{+\infty} \rho\left(\bm{r}^{\prime}\right) \mathrm{e}^{-\mathrm{i} \Delta \bm{k} \cdot \bm{r}^{\prime}} \mathrm{d}^{3} \bm{r}^{\prime}. +\end{aligned} ``` The two first integrals are simply Fourier transforms of $1/r$ that each yield $4 \pi / \Delta k^2$, giving the general expression for the electron scattering factor of an atom as @@ -267,5 +270,6 @@ Although IAM potentials are useful for many purposes, they do neglect chemical b ```{figure} #H2_potential :name: fig:H2_potential +:placeholder: ./figures/dft_iam_diff.png The difference between the indepdendent atom model potential to the DFT potential for the hydrogen molecule as a function of the distance between the H atoms. ``` diff --git a/environment.yml b/environment.yml index 4e36120..6a5d498 100644 --- a/environment.yml +++ b/environment.yml @@ -2,6 +2,7 @@ name: guide-tem-simulation channels: - conda-forge dependencies: + - 'python<3.12' - 'numpy<2' - scipy - h5py diff --git a/figures/dft_iam_diff.png b/figures/dft_iam_diff.png new file mode 100644 index 0000000000000000000000000000000000000000..ae08815b7f9d44a52c4908173268b508190242f6 GIT binary patch literal 43704 zcmeEuXIoQ2v@X3@=>h>8AWBzSXo`qR6RAq?(i15GLaz!4R-_A}6zPHx2_>OLI!cEG zf=Gvi9w3yv@t$+fIrkUb5BC$#6S8N|teLgeyWaIqUOY6^Wo0_gL_tBpdjH;?M-&uP z1PTgD7y}*f$zgUs7WhNy_el3PMMb~RG6jVw#r-?COoMFKa_G~!bq-Hwa{BZ*cl*kKS=wTze!T44vTJUvr>Dof(aKasRYq|Q^8CjF0@0bdto=eCCo z#N7QO`R)H&5HM-_|3B&f+T;I+Ed4x=N=HZM6G%!*s$%!Y%GXl-N@-<0bgq-~MDwdl zWz$}C#3^}ZW5`Go9Z5<3NtK1D6iGGA2MYenseJJFE%n;fLj#ep9pn8rkrR&KmM;ou 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b/notebooks/03.2_concepts_Fourier_transform_1D.ipynb index f92b4ff..7787410 100644 --- a/notebooks/03.2_concepts_Fourier_transform_1D.ipynb +++ b/notebooks/03.2_concepts_Fourier_transform_1D.ipynb @@ -456,7 +456,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/Atomic_potentials.ipynb b/notebooks/04_atomic_potentials.ipynb similarity index 81% rename from notebooks/Atomic_potentials.ipynb rename to notebooks/04_atomic_potentials.ipynb index a54cff3..eefff1d 100644 --- a/notebooks/Atomic_potentials.ipynb +++ b/notebooks/04_atomic_potentials.ipynb @@ -9,9 +9,7 @@ "title: Atomic potentials\n", "authors: [Toma Susi]\n", "date: 2023/08/23\n", - "---\n", - "\n", - "Updated by Georgios Varnavides, 2024/07/15" + "---" ] }, { @@ -40,14 +38,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "42e02f5e-d032-4d35-9525-01ab4b1e6fde", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d2f35460c564129ac142465e54cd76d", + "model_id": "f596cc2f93c3416db9f0e5ef9f22b06d", "version_major": 2, "version_minor": 0 }, @@ -204,7 +202,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_77981/4172123578.py:4: RuntimeWarning: divide by zero encountered in divide\n", + "/tmp/ipykernel_32953/4172123578.py:4: RuntimeWarning: divide by zero encountered in divide\n", " y = q / (4 * pi * eps0) * (exp(-2 * r / Bohr) / r + exp(-2 * r / Bohr) / Bohr) * 1e10;\n" ] } @@ -238,7 +236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fdd48f6569404fb4b58969770dd4865c", + "model_id": "7b051b9210b44053b2e72a4e3a773b9c", "version_major": 2, "version_minor": 0 }, @@ -319,45 +317,45 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "0003efdf-d29c-449c-ba5e-8de0750e5c76", "metadata": {}, "outputs": [], "source": [ - "# Pre-calculates the DFT step for different H-H distances. Requires GPAW!\n", + "# #Pre-calculates the DFT step for different H-H distances. Requires GPAW!\n", "\n", - "#from ase import Atoms\n", - "#from gpaw import GPAW, PW, FermiDirac\n", - "#from gpaw import restart\n", - "#import numpy as np\n", - "#from abtem import Potential, GPAWPotential\n", + "# from ase import Atoms\n", + "# from gpaw import GPAW, PW, FermiDirac\n", + "# from gpaw import restart\n", + "# import numpy as np\n", + "# from abtem import Potential, GPAWPotential\n", "\n", - "#a = 12. # Size of unit cell (Angstrom)\n", - "#c = a / 2 # Center\n", + "# a = 12. # Size of unit cell (Angstrom)\n", + "# c = a / 2 # Center\n", "\n", - "# Hydrogen molecule equilibrium bond length\n", - "#d = 0.75 \n", + "# #Hydrogen molecule equilibrium bond length\n", + "# d = 0.75 \n", "\n", "\n", - "# Initialize empty lists to store the potentials.\n", - "#iampotentials = []\n", - "#dftpotentials = []\n", - "#ats = []\n", + "# #Initialize empty lists to store the potentials.\n", + "# iampotentials = []\n", + "# dftpotentials = []\n", + "# ats = []\n", "\n", - "# List of ten H-H separations to calculate.\n", - "#distances = np.arange(0.75,3.25,0.25)\n", + "# #List of ten H-H separations to calculate.\n", + "# distances = np.arange(0.75,3.25,0.25)\n", "\n", - "#for i in np.arange(len(distances)):\n", + "# for i in np.arange(len(distances)):\n", " \n", "# d = distances[i]\n", " \n", - " # Set up an two H atoms at a distance d.\n", + "# # Set up an two H atoms at a distance d.\n", "# atoms = Atoms('H2',\n", "# positions=([c - d / 2, c/2, c],\n", "# [c + d / 2, c/2, c]),\n", "# cell=(a, a/2, a))\n", "# ats.append(atoms)\n", - " # Defines a GPAW LCAO calculator.\n", + "# # Defines a GPAW LCAO calculator.\n", "# calc = GPAW(#mode=PW(800),\n", "# h=0.15,\n", "# mode='lcao',\n", @@ -370,28 +368,28 @@ "# atoms.calc = calc\n", "# atoms.get_potential_energy()\n", "\n", - " # Prebuild and save the IAM and DFT potentials.\n", + "# # Prebuild and save the IAM and DFT potentials.\n", "# iampotentials.append(Potential(atoms, projection='finite', sampling = 0.05).build())\n", "# dftpotentials.append(GPAWPotential(calc, sampling = 0.05).build())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "2ba3071a-33e1-4c32-a4df-d7df061d3f32", "metadata": {}, "outputs": [], "source": [ - "#iamstack = abtem.stack(iampotentials, list(distances.astype(str)))\n", - "#dftstack = abtem.stack(dftpotentials, list(distances.astype(str)))\n", + "# iamstack = abtem.stack(iampotentials, list(distances.astype(str)))\n", + "# dftstack = abtem.stack(dftpotentials, list(distances.astype(str)))\n", "\n", - "#iamstack.to_zarr('data/H2_IAM_dists.zarr');\n", - "#dftstack.to_zarr('data/H2_DFT_dists.zarr');" + "# iamstack.to_zarr('data/H2_IAM_dists.zarr');\n", + "# dftstack.to_zarr('data/H2_DFT_dists.zarr');" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "69dde3c9-da60-4c36-b5e8-99a397a50b26", "metadata": {}, "outputs": [], @@ -404,35 +402,54 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "f44471ab-8b79-4b1c-b3fc-4cf2c5475133", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 103.01 ms\n" + ] + }, { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "191a89a2e8af441ab56d9ffb87ee9418", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "ImageGUI(children=(VBox(children=(SelectionSlider(continuous_update=False, options=('0.75', '1.0', '1.25', '1.…" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "" ] }, + "execution_count": 15, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "#| label: H2_potential\n", - "diffstack.show(cbar=True, title=\"Hydrogen molecule (IAM-DFT)\")" + "diffstack.show(cbar=True, title=\"Hydrogen molecule (IAM-DFT)\",figsize=(6,3),interact=True)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb57d096-3270-4a26-9690-7414e31437b2", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -451,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/data/H2_DFT_dists.zarr/.zattrs b/notebooks/data/H2_DFT_dists.zarr/.zattrs index b22e448..5718158 100644 --- a/notebooks/data/H2_DFT_dists.zarr/.zattrs +++ b/notebooks/data/H2_DFT_dists.zarr/.zattrs @@ -3,11 +3,12 @@ "ensemble_axes_metadata": [ { "_concatenate": true, - "_default_type": null, + 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29 Jul 2024 08:58:36 -0700 Subject: [PATCH 06/10] adding ctf, psf widget --- 05_algorithms.md | 29 --- 07_CTF.md | 10 +- environment.yml | 1 + figures/ctf_psf.png | Bin 0 -> 28988 bytes notebooks/07_ctf_psf_widget.ipynb | 404 ++++++++++++++++++++++++++++++ 5 files changed, 413 insertions(+), 31 deletions(-) create mode 100644 figures/ctf_psf.png create mode 100644 notebooks/07_ctf_psf_widget.ipynb diff --git a/05_algorithms.md b/05_algorithms.md index a1253e5..878c280 100644 --- a/05_algorithms.md +++ b/05_algorithms.md @@ -267,32 +267,3 @@ I(\bm{R},n) ``` where $\theta$ is the annular coordinate and $k'$ is the radial coordinate for $\bm{k}$-space. - - -(bloch-wave-method)= -### The Bloch Wave Method - -text - - -(prism-method)= -### PRISM - -text - - -(other-method)= -### Other Methods - -text - - - - \ No newline at end of file diff --git a/07_CTF.md b/07_CTF.md index 967d279..93fe7bf 100644 --- a/07_CTF.md +++ b/07_CTF.md @@ -16,7 +16,7 @@ This deviation is typically expressed as a phase error or the aberration functio Another, possibly more intuitive way of framing the phase error is the point spread function ```{math} - \mathrm{PSF}(\bm{r}) = F \mathrm{e} ^ {-i \chi(\bm{k})} , + \mathrm{PSF}(\bm{r}) = \mathscr{F}^{-1}_{\bm{k} \rightarrow \bm{r}}\left\{ \mathrm{e} ^ {-i \chi(\bm{k})} \right\} , ``` the point spread function describes how an imaging system responds to a point source. In STEM, the PSF would be the image of the probe, given an infinite objective aperture, in HRTEM the PSF would be how a perfect point source is imaged by an objective lens with an infinite collection angle. @@ -30,4 +30,10 @@ For an uncorrected microscope the dominant aberration is ther third order spheri ```{math} \chi(k) \approx \frac{2\pi}{\lambda}\left( \frac{\lambda^2 k^2}{2} \Delta f + \frac{\lambda^4 k^4}{4} C_s \right) \quad . ``` -Here we used the common aliases of the aberration coefficients, so $C10 = -\Delta f$ is the negative defocus and $C_{30} = C_s$ is the third order spherical aberration. \ No newline at end of file +Here we used the common aliases of the aberration coefficients, so $C10 = -\Delta f$ is the negative defocus and $C_{30} = C_s$ is the third order spherical aberration. + +```{figure} #app:ctf_psf +:name: fig_ctf_psf +:placeholder: ./figures/ctf_psf.png +**Interactive widget showing the influence of common aberrations on the point spread function (PSF) and contact transfer function (CTF) **: +``` diff --git a/environment.yml b/environment.yml index 6a5d498..f8e9257 100644 --- a/environment.yml +++ b/environment.yml @@ -7,6 +7,7 @@ dependencies: - scipy - h5py - matplotlib + - colorspacious - abtem - ipywidgets - jupyterlab diff --git a/figures/ctf_psf.png b/figures/ctf_psf.png new file mode 100644 index 0000000000000000000000000000000000000000..7036643d657c21477626d3827b1dc785ee69ecd4 GIT binary patch literal 28988 zcmbrm2{@E(`#&BkR8mPoNRplGWmh4_lHJ$~W2fxe~M#ygL`xyI@Y-KkX8C%G{ z?+nKOo}Typ{-5Xl_3Ql}$L~1mNcVBOuIt>-&v~BL9sELBj_eZ6rE}-bk;y-QrgrWe z5$N1G0#}lYz!N8v{$SuA0!KBuC+CWL?<}1=clVt9GiePs!_{QsPjqHSm8s%-h@?qY z#%oXf(|n)Dynr9j-(=gSq9sWsVSWEDpY?r+v2>gN1(_F1C_+NNbAQm1_g6jzX_0qrIm!dlV5Hx=4uV4J1HGoG%|MAG{{73Nin|qZ1 z*y;l>;YWDbg-`2G{=N6FB_s$xGBdEKN&n*^HIpw`w~L_Zc)f1zXd$(V2YS@yjgF2t z^!Qd>w_cmuoBlMYxN@=yw^veWGs<_|bavb%VVzo);U!^zwsCeUS+~_rYrDTX>Vi93 z7`s5F|D$JJ`QXcCp6?5iXYQWMc{%rPl{DdM!)b+klc7o!61c-zmkv7D`V#x)6&0Ut 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    vmax, vmax, amp)\n", + " amp = ((amp - vmin) / vmax).clip(1e-16, 1)\n", + "\n", + " J = amp * 61.5 # Note we restrict luminance to the monotonic chroma cutoff\n", + " C = np.minimum(chroma_boost * 98 * J / 123, 110)\n", + " h = np.rad2deg(phase) + 180\n", + "\n", + " JCh = np.stack((J, C, h), axis=-1)\n", + " rgb = cspace_convert(JCh, \"JCh\", \"sRGB1\").clip(0, 1)\n", + "\n", + " return rgb\n", + "\n", + "\n", + "def add_colorbar_arg(ax, chroma_boost=1, c=49, j=61.5):\n", + " \"\"\"\n", + " cax : axis to add cbar to\n", + " chroma_boost (float): boosts chroma for higher-contrast (~1-2.25)\n", + " c (float) : constant chroma value\n", + " j (float) : constant luminance value\n", + " \"\"\"\n", + "\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes(\"right\", size=\"5%\", pad=\"2.5%\")\n", + " \n", + " h = np.linspace(0, 360, 256, endpoint=False)\n", + " J = np.full_like(h, j)\n", + " C = np.full_like(h, np.minimum(c * chroma_boost, 110))\n", + " JCh = np.stack((J, C, h), axis=-1)\n", + " rgb_vals = cspace_convert(JCh, \"JCh\", \"sRGB1\").clip(0, 1)\n", + " newcmp = mcolors.ListedColormap(rgb_vals)\n", + " norm = mcolors.Normalize(vmin=-np.pi, vmax=np.pi)\n", + "\n", + " cb = plt.colorbar(cm.ScalarMappable(norm=norm, cmap=newcmp), cax=cax)\n", + "\n", + " cb.set_label(\"arg\", rotation=0, ha=\"center\", va=\"bottom\")\n", + " cb.ax.yaxis.set_label_coords(0.5, 1.01)\n", + " cb.set_ticks(np.array([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]))\n", + " cb.set_ticklabels(\n", + " [r\"$-\\pi$\", r\"$-\\dfrac{\\pi}{2}$\", \"$0$\", r\"$\\dfrac{\\pi}{2}$\", r\"$\\pi$\"]\n", + " )\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "33a64997-5076-4526-b701-71e85e04bbb1", + "metadata": {}, + "outputs": [], + "source": [ + "probe_init = abtem.Probe(\n", + " energy=300*1e3,\n", + " semiangle_cutoff=25,\n", + " gpts=(256,256),\n", + " sampling=0.4,\n", + ").build()\n", + "\n", + "ctf = abtem.CTF(\n", + " energy=300*1e3,\n", + " semiangle_cutoff=25,\n", + " defocus=100,\n", + ")\n", + "\n", + "probe = probe_init.apply_ctf(ctf)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "f83e7c71-e8e1-4a64-a746-522eedeee990", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_33501/13827917.py:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n", + " fig, (ax_ctf,ax_psf) = plt.subplots(1,2,figsize=(675/dpi, 300/dpi), dpi=dpi)\n" + ] + } + ], + "source": [ + "array = probe.array.compute()\n", + "array_fourier = np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(array)))\n", + "rgb_psf = Complex2RGB(array,vmin=0,vmax=1)\n", + "rgb_ctf = Complex2RGB(array_fourier,vmin=0,vmax=1)\n", + "\n", + "# widget figure generation\n", + "with plt.ioff():\n", + " dpi = 72\n", + " fig, (ax_ctf,ax_psf) = plt.subplots(1,2,figsize=(675/dpi, 300/dpi), dpi=dpi)\n", + "\n", + "im_ctf = ax_ctf.imshow(rgb_ctf)\n", + "im_psf = ax_psf.imshow(rgb_psf)\n", + "\n", + "for ax, title in zip((ax_ctf,ax_psf),['contrast transfer function (CTF)','point spread function (PSF)']):\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " add_colorbar_arg(ax)\n", + " ax.set_title(title)\n", + "\n", + "fig.tight_layout()\n", + "\n", + "fig.canvas.resizable = False\n", + "fig.canvas.header_visible = False\n", + "fig.canvas.footer_visible = False\n", + "fig.canvas.toolbar_visible = True\n", + "fig.canvas.layout.width = '675px'\n", + "fig.canvas.layout.height = '330px'\n", + "fig.canvas.toolbar_position = 'bottom'" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "4db48b85-be46-4743-8e10-5b9b0ea83ee0", + "metadata": {}, + "outputs": [], + "source": [ + "def update_aberrations(energy, defocus, C30, semiangle):\n", + "\n", + " ctf = abtem.CTF(\n", + " energy=energy*1e3, \n", + " semiangle_cutoff = semiangle,\n", + " defocus = defocus,\n", + " C30 = C30*1e7,\n", + " )\n", + "\n", + " probe_init = abtem.Probe(\n", + " energy=energy*1e3,\n", + " semiangle_cutoff=semiangle,\n", + " gpts=(256,256),\n", + " sampling=0.4,\n", + " ).build()\n", + " \n", + " probe = probe_init.apply_ctf(ctf)\n", + " array = probe.array.compute()\n", + " array_fourier = np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(array)))\n", + " rgb_psf = Complex2RGB(array,vmin=0,vmax=1)\n", + " rgb_ctf = Complex2RGB(array_fourier,vmin=0,vmax=1)\n", + " \n", + " im_ctf.set_data(rgb_ctf)\n", + " im_psf.set_data(rgb_psf)\n", + " fig.canvas.draw_idle()\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7ad86424-7685-456e-b02e-6a4543213849", + "metadata": {}, + "outputs": [], + "source": [ + "# List of options\n", + "option_list = (\n", + " 'select an option',\n", + " 'uncorrected STEM',\n", + " 'uncorrected STEM at Scherzer',\n", + " 'aberration corrected STEM',\n", + " 'SEM',\n", + " 'boundary artifacts', \n", + ")\n", + "\n", + "# update the plots with a pre-selected function\n", + "def select_preset_eventhandler(change):\n", + " if change.new == option_list[1]: #uncorrected STEM\n", + " energy.value = 300\n", + " defocus.value = 0\n", + " C30.value = 1.3\n", + " semiangle.value = 10\n", + " \n", + " if change.new == option_list[2]: #uncorrected STEM @ Scherzer\n", + " #calculate scherzer \n", + " lambda_300kv = abtem.core.energy.energy2wavelength(300e3)\n", + " C1_Scherzer = 0.87 * (1.3e7*lambda_300kv) ** 0.5 \n", + " \n", + " energy.value = 300\n", + " defocus.value = C1_Scherzer\n", + " C30.value = 1.3\n", + " semiangle.value = 10\n", + " \n", + " if change.new == option_list[3]: #corrected STEM\n", + " energy.value = 300\n", + " defocus.value = 0\n", + " C30.value = 0.001\n", + " semiangle.value = 10\n", + " \n", + " if change.new == option_list[4]: #SEM\n", + " energy.value = 20\n", + " defocus.vaue = 0\n", + " C30.value = 5\n", + " semiangle.value = 2.5\n", + " \n", + " if change.new == option_list[5]: #artifacts\n", + " energy.value = 10\n", + " defocus.value = 2000\n", + " C30.value = 0\n", + " semiangle.value = 20 \n", + " \n", + "# Widgets\n", + "dropdown = ipywidgets.Dropdown(\n", + " options = option_list,\n", + " layout = ipywidgets.Layout(width='200px',height='30px'),\n", + ")\n", + "dropdown.observe(select_preset_eventhandler, names='value')\n", + "\n", + "style = {\n", + " 'description_width': 'initial',\n", + "}\n", + "\n", + "energy = ipywidgets.IntSlider(\n", + " value=60, min=10, max=300, \n", + " step = 2,\n", + " description = \"energy (kV)\",\n", + " style = style,\n", + ")\n", + "\n", + "defocus = ipywidgets.IntSlider(\n", + " value = 0, min = -2000, max = 2000, \n", + " step = 20,\n", + " description = \"defocus / -1*C1 (A)\",\n", + " style = style\n", + ")\n", + "\n", + "C30 = ipywidgets.FloatSlider(\n", + " value = 0, min = 0, max =5, \n", + " step = 0.1,\n", + " description = \"C3 (mm)\",\n", + " style = style\n", + ")\n", + "\n", + "semiangle = ipywidgets.FloatLogSlider(\n", + " value=20,\n", + " base=10,\n", + " min=0, # min exponent of base\n", + " max=1.6021, # max exponent of base\n", + " step=0.05, # exponent step\n", + " description = \"semiangle (mrad)\",\n", + " style = style,\n", + ")\n", + "\n", + "ipywidgets.interactive_output(\n", + " update_aberrations, \n", + " {\n", + " 'energy':energy,\n", + " 'defocus':defocus,\n", + " 'semiangle':semiangle,\n", + " 'C30':C30,\n", + " },\n", + ")\n", + "None" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "37ec6eba-1870-48c0-82c3-882c310c553b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0a02f03f4d75477e9ccf81621b12aab7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='330px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:ctf_psf\n", + "\n", + "widget = ipywidgets.VBox(\n", + " [\n", + " fig.canvas,\n", + " ipywidgets.HBox([\n", + " ipywidgets.VBox([\n", + " energy,\n", + " defocus,\n", + " semiangle,\n", + " C30, \n", + " ]),\n", + " ipywidgets.VBox([\n", + " ipywidgets.Label('Preset probes',layout=ipywidgets.Layout(width='100px',height='30px')), \n", + " dropdown,\n", + " ])\n", + " ]),\n", + " ],\n", + ")\n", + "\n", + "display(widget);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44634d55-85d4-40f8-a32b-da5817fb1d2d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ac54d4947ba361aa5249fcc73c478f65e7545dba Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 11:07:21 -0700 Subject: [PATCH 07/10] code text, excluding incomplete sections --- 02_code.md | 13 +++++++++---- myst.yml | 2 ++ 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/02_code.md b/02_code.md index d859f1d..795967e 100644 --- a/02_code.md +++ b/02_code.md @@ -4,7 +4,10 @@ numbering: enumerator: 1.%s --- -Python has inarguably become the leading programming language in the scientific community, continually gaining popularity for the past two decades. This is due to Python being friendly for beginners, owing to its less strict language structure (dynamic typing, automatic memory management, non-compiled language) and the plethora of tutorials and examples. It also benefits from being free (this text would be less accessible if it required a licence), with numerous high quality libraries covering a broad range of applications (numeric calculations, image analysis, machine learning, ...), including a number of packages dedicated to S/TEM. The diverse and broad scope of the python ecosystem allows for interoperability between different domains, and file formats as well as easily re-purposing existing implementations algorithms to new domains. +Python has inarguably become the leading programming language in the scientific community, continually gaining popularity for the past two decades. +This is due to Python being friendly for beginners, owing to its less strict language structure (dynamic typing, automatic memory management, non-compiled language) and the plethora of tutorials and examples. +It also benefits from being open-source, with numerous high quality libraries covering a broad range of applications (numeric calculations, image analysis, machine learning, etc.), including a number of packages dedicated to S/TEM. +The diverse and broad scope of the python ecosystem allows for interoperability between different domains, and file formats as well as easily re-purposing existing implementations algorithms to new domains. There are a number of codes dedicated to S/TEM: - *ab*TEM - all-Python S/TEM image simulation @@ -20,9 +23,11 @@ The main libraries used in the code examples of this text are: - NumPy (np) - fast numerical calculations - CuPy (cp) - drop-in replacement for NumPy to run on GPUs - Numba - just-in-time Python compiler for scientific and array-oriented computing -- matplotlib - all things plotting +- matplotlib - plotting and visualization - ipywidgets - making interactive figures -- ASE (Atomic Simulation Environment) - creating and visualizing atomic structures +- Atomic Simulation Environment (ASE) - creating and visualizing atomic structures - ... -Dynamically interpreted languages including Python are particularly attractive for domain experts and scientists trying out new ideas, but the performance of the interpreter can be a barrier to high performance. However, by making appropriate use of Python open-source numerical libraries including NumPy, CuPy, and Numba, it is possible to write a purely Python-based code that performs as well or even better than prior codes based on traditional languages such C++ or Fortran. That is indeed what *ab*TEM has been able to achieve, which has made it one of the fastest-growing and popular tools for S/TEM simulations, and our choice for this text. \ No newline at end of file +Dynamically interpreted languages including Python are particularly attractive for domain experts and scientists trying out new ideas, but the performance of the interpreter can be a barrier to high performance. +However, by making appropriate use of Python open-source numerical libraries including NumPy, CuPy, and Numba, it is possible to write a purely Python-based code that performs as well or even better than prior codes based on traditional languages such C++ or Fortran. +That is indeed what *ab*TEM has been able to achieve, which has made it one of the fastest-growing and popular tools for S/TEM simulations, and our choice for this text. diff --git a/myst.yml b/myst.yml index 2b5ab70..bd541fd 100644 --- a/myst.yml +++ b/myst.yml @@ -87,6 +87,8 @@ project: - format: meca exclude: - README.md + - 06_sim_inputs.md + - 11_errors.md - main.md - original/* abbreviations: From 423bdb9ed7e0364ef11635ef00760ed157d3aef1 Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 11:58:56 -0700 Subject: [PATCH 08/10] fix TEM widgets --- 08_TEM.md | 10 +- ...M_imaging.ipynb => 08.1_TEM_imaging.ipynb} | 113 +++--- notebooks/08.2_TEM_diffraction.ipynb | 182 +++++++++ notebooks/TEM_diffraction.ipynb | 355 ------------------ 4 files changed, 233 insertions(+), 427 deletions(-) rename notebooks/{TEM_imaging.ipynb => 08.1_TEM_imaging.ipynb} (93%) create mode 100644 notebooks/08.2_TEM_diffraction.ipynb delete mode 100644 notebooks/TEM_diffraction.ipynb diff --git a/08_TEM.md b/08_TEM.md index 26a8a50..fbe76fe 100644 --- a/08_TEM.md +++ b/08_TEM.md @@ -18,14 +18,14 @@ After building an atomic potential as described in the [](#algorithms_page), the ```{figure} #app:tem_imaging :name: fig_tem_phase :placeholder: ./static/tem_imaging.png -**TEM imaging of SrTiO$_$ grains**: +**TEM imaging of SrTiO$_3$ grains**: ``` ### Diffraction -```{figure} +```{figure} #app:tem_diffraction :name: fig_tem_diffraction :placeholder: ./static/tem_diffraction.png **TEM diffraction of STO as a function of thickness**: @@ -41,9 +41,7 @@ In TEM, aberrations modify the exit wave ({math}`\Psi_{exit}`) after the multisl ```{math} :label: eq:TEM_aberrations -\begin{align} - \Psi_{image}(\bm{k}) = \Psi_{exit}(\bm{k}) \mathrm{e}^{-i\chi(\bm{k})}, -\end{align} +\Psi_{image}(\bm{k}) = \Psi_{exit}(\bm{k}) \mathrm{e}^{-i\chi(\bm{k})}, ``` One example of the importance of aberrations and wavfunction modification is shown in [](#tem_contrast). Although the wavfunction at the imaging plane ({math}`\Psi_{image}(\bm{k}`)) may be complex, only the intensity of the exit wave is measured by detectors. Weakly scattering samples, impart very little ampltidue contrast on the incident beam, so most of the structural information is encoded in the phase of the exit wave. This is especially well known in the case of imaging of biological materials, as these structures are beam-sensitive and composed of low atomic number (weakly scattering elements). This effect is evident in the left pannel of [](#tem_contrast), where as the dose decreases, it is very challenging to observe the simulated covid spike protein. @@ -51,7 +49,7 @@ One example of the importance of aberrations and wavfunction modification is sho ```{figure} #app:tem_contrast :name: fig_tem_phase :placeholder: ./static/tem_contrast.png -**TEM imaging of covid spike protein **: (left) In focus, (middle) defocued, and (right) Zernike phase plate TEM images. [Covid structure](https://www.rcsb.org/structure/3jcl) from {cite}`walls2016cryo` +**TEM imaging of covid spike protein**: (left) In focus, (middle) defocued, and (right) Zernike phase plate TEM images. [Covid structure](https://www.rcsb.org/structure/3jcl) from {cite}`walls2016cryo` ``` For the same, dose the image contrast can be increased by modfying the wavefunctions through the introduction of aberrations. This is most often done with defocus, which is illustrated in the middle pannel. The resulting image has improved contrast, making it easier to visualize the covid spike protein. The middle pannel shows the defocused image, but the aberrations can often be corrected for later in post-processing. diff --git a/notebooks/TEM_imaging.ipynb b/notebooks/08.1_TEM_imaging.ipynb similarity index 93% rename from notebooks/TEM_imaging.ipynb rename to notebooks/08.1_TEM_imaging.ipynb index af8e20a..7b576c5 100644 --- a/notebooks/TEM_imaging.ipynb +++ b/notebooks/08.1_TEM_imaging.ipynb @@ -5,10 +5,11 @@ "id": "43989778-1e53-41ff-8eb2-0a8eda0396e0", "metadata": {}, "source": [ - "# STO BF/DF TEM\n", - "> Stephanie Ribet | 26 July 2024\n", - "\n", - "> Structure from George Varnavides" + "---\n", + "title: TEM Imaging\n", + "authors: [Stephanie Ribet, Georgios Varnavides]\n", + "date: 2024/07/26\n", + "---" ] }, { @@ -26,11 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| label: app:tem_imaging\n", - "\n", "%matplotlib widget\n", - "\n", - "\n", "import ase\n", "import abtem\n", "\n", @@ -39,7 +36,7 @@ "from scipy.spatial import Voronoi, voronoi_plot_2d, cKDTree\n", "\n", "from matplotlib.patches import Circle\n", - "from ipywidgets import HBox, VBox, widgets, interact, Dropdown, Label, Layout\n" + "from ipywidgets import HBox, VBox, widgets, interact, Dropdown, Label, Layout" ] }, { @@ -63,28 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "7dd8a0b0-0ebc-4268-83a5-b577237344d9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Cell([178.53620324239898, 189.36624, 44.634050810599746])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "STO_atoms.cell" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "b949df4c-c2cf-40a3-b770-822ade89b3b2", "metadata": { "scrolled": true @@ -142,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "73834767-d40d-4c14-bf35-8fb66c88b862", "metadata": {}, "outputs": [], @@ -176,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "4aceff97-684d-4288-b2d7-b051679b72b5", "metadata": {}, "outputs": [], @@ -186,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "30ecb602-7b65-4ab9-9c08-d9e0ccbb46ec", "metadata": {}, "outputs": [ @@ -194,16 +170,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[########################################] | 100% Completed | 2.30 ss\n" + "[########################################] | 100% Completed | 3.81 ss\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "560d0d60-361c-4a71-a7f2-0a73ad0c8ba1", "metadata": {}, "outputs": [], @@ -277,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "56a07873-35e9-4519-9db4-edd7db32cc82", "metadata": {}, "outputs": [], @@ -287,30 +263,15 @@ "x = np.fft.fftfreq(s[0], 1/s[0])\n", "y = np.fft.fftfreq(s[1], 1/s[1])\n", "\n", - "yy, xx = np.meshgrid(x, y)\n" + "yy, xx = np.meshgrid(x, y)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "fd12f232-8f93-4cbf-b1c6-b983e0210dbf", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b48f69644adc45828bde303cad98dcda", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='400px', width='675px')…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with plt.ioff():\n", " dpi = 72\n", @@ -446,8 +407,6 @@ ")\n", "\n", "\n", - "\n", - "\n", "fig.canvas.resizable = False\n", "fig.canvas.header_visible = False\n", "fig.canvas.footer_visible = False\n", @@ -465,18 +424,40 @@ " )\n", " # ),\n", " ],\n", - ")\n", - "\n", - "display(widget);\n", - "#\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e23e1caa-d087-492c-b58c-2c0cd05cbf53", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a2c3b236f157477eb53a95171e36caf6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='400px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:tem_imaging\n", "\n", - "# plt.show()" + "display(widget);" ] }, { "cell_type": "code", "execution_count": null, - "id": "e23e1caa-d087-492c-b58c-2c0cd05cbf53", + "id": "f2f7f1cd-20dd-4081-93d3-bad9ef653fca", "metadata": {}, "outputs": [], "source": [] @@ -498,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/08.2_TEM_diffraction.ipynb b/notebooks/08.2_TEM_diffraction.ipynb new file mode 100644 index 0000000..8fb93e1 --- /dev/null +++ b/notebooks/08.2_TEM_diffraction.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "72e9df98-529f-4fac-88fc-84bedd29cee0", + "metadata": {}, + "source": [ + "---\n", + "title: TEM Diffraction\n", + "authors: [Stephanie Ribet, Georgios Varnavides]\n", + "date: 2024/07/26\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fc7da6a7-aded-43e1-9ca0-2b703f786cdb", + "metadata": {}, + "outputs": [], + "source": [ + "# %matplotlib ipympl\n", + "import ase\n", + "import abtem\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c7f9ca08-e464-4a84-ac6e-58e5b8a563f5", + "metadata": {}, + "outputs": [], + "source": [ + "STO_unit_cell = ase.io.read('data/SrTiO3.cif')\n", + "# STO_atoms = ase.build.surface(STO_unit_cell, (1,1,0), 4, periodic=True)*(8,12,40)\n", + "STO_atoms = STO_unit_cell*(8,12,41)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bbd6a624-a8d7-4305-81b1-1be3d2be78da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "abtem.show_atoms(STO_atoms, \"yz\");" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6767d2d2-5dfb-4587-88a6-fbd7db54e7da", + "metadata": {}, + "outputs": [], + "source": [ + "potential = abtem.Potential(\n", + " STO_atoms,\n", + " gpts=(512,512),\n", + " device='cpu',\n", + " projection='infinite',\n", + " parametrization='kirkland',\n", + " exit_planes=40,\n", + " # exit_planes = ((-1, 40, 80, 120))\n", + ").build()\n", + "\n", + "wave = abtem.PlaneWave(energy=300e3)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "01d7f7ed-67cd-4f34-9916-a5c82af6c599", + "metadata": {}, + "outputs": [], + "source": [ + "exit_waves = wave.multislice(potential)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8d36172b-31fc-4631-b441-789e7c4dc2a6", + "metadata": {}, + "outputs": [], + "source": [ + "measurement_ensemble = exit_waves.diffraction_patterns(max_angle=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eee44fc1-c7ef-4a61-8e26-ec764811900d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 3.65 ss\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:tem_diffraction\n", + "\n", + "measurement_ensemble[1:-1].block_direct().show(\n", + " explode=True,\n", + " # interact = True,\n", + " # ax = ax,\n", + " figsize=(18, 5),\n", + " cbar=True,\n", + " common_color_scale=True,\n", + " vmax = 0.03,\n", + " cmap = \"turbo\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc4d42b7-5795-4027-8d04-0d4e0bf4df3c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/TEM_diffraction.ipynb b/notebooks/TEM_diffraction.ipynb deleted file mode 100644 index 18316bf..0000000 --- a/notebooks/TEM_diffraction.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "72e9df98-529f-4fac-88fc-84bedd29cee0", - "metadata": {}, - "source": [ - "---\n", - "title: TEM Diffraction\n", - "authors: [Stephanie Ribet]\n", - "date: 2024/07/26\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fc7da6a7-aded-43e1-9ca0-2b703f786cdb", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib ipympl\n", - "import ase\n", - "import abtem\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c7f9ca08-e464-4a84-ac6e-58e5b8a563f5", - "metadata": {}, - "outputs": [], - "source": [ - "STO_unit_cell = ase.io.read('data/SrTiO3.cif')\n", - "# STO_atoms = ase.build.surface(STO_unit_cell, (1,1,0), 4, periodic=True)*(8,12,40)\n", - "STO_atoms = STO_unit_cell*(8,12,41)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "21844332-816f-42ef-9789-eb1aec23a20f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Cell([31.56104, 47.34156, 161.75033])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "STO_atoms.cell" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bbd6a624-a8d7-4305-81b1-1be3d2be78da", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2e0ccc61963c44dfbdf77d8cfba77e63", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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QGxVfVDqmdiR52ZyO3whaULpFwF9ex0OXCFrQS/X0eYdzeK/ojTo7Bs8NF9cmgqb9pdRxajHIUqGWMPjon+dhXoleCfocJQfUQBj9W1Cix4RR1Ui5zjkef/jhf3dJi2EYVznPdAb6n+H++++fVd029fOWt7xl+jWf+cxndMWKFZrP57W7u1vvueeeWccYGxvTd7zjHVpXV6dlZWV600036dGjR/+f+mH/gzCMS7nmudeoe7lTBkr+fd4/Z/Srfc7o1wAqJaNzrnbO6NcY6prczPEilGMleoK6bqfiSs752OwROnm1zIzwgfL5OSN470QlLhkB/JM5+kfnjAC+Y47+xZJrzIjKTTJb/0FJ3xzqupySlOhzRkFdo1MulugX0lHBqWNkCKOspTFYOxOD22691UYADeMK2P37J8AG5pnEfoEM41I+//nPh6TjTSUJz+KQtNXU1Gg2m1UE5XUoD6F8HZVNokTB6qS8fJ7iUHmxKP+G8h1UbhTFoWVlZVpVVaVEqHSKch/Kwyi3h/NkMhmtrasL+mJRPpcmXu9IkyrntKGhMRy/VpS/QdmM8nszyV1jak9DDuXPULagfCx9XqoLygfT938GlbpwDQ0Njepcmqj9GsrjKJ9DpS3ojY2Nmk1tYbg17f9XUekOenV1TbDAcai8TJTvoHwblRtCDCoqKrS6uiZc4wZRvpbG8Q2hf9lsNsQA9M4777QE0DAug92/LQF8StgvkGFcnr/4i7/QTD4zkwAK2tnVPe2R19TUrC43M5olGdFVq9s1dk5zmYy2LVmqLjuju6zThYsWa1k2p05E16xZO2s0z2Wd1tc3aHVFhQLa3b1h9nq6jNPy8nJtaWhM9fXTCR+gEom6yOmy1IR6zer2mZHAkr/Xtq9RQJcsWqxRFKmUrjl06XFBWxoatbx8nrrM7FHLrlSvmleh9Q0Nl8Sgfc1ajZzTfDarixe3XRKDtrYlmstkNHJOV7evuSQGjY1NWpX6J1573XPMB9AwroDdv5+lawANw/ivzZ133klVbmabNRTO9fdznXMUxsZY3NaGTHiWE4pxtaBEIiwFalOPPD/paQLmA37S09jQSHmxwBoRipMTaEHJA6uBqKAsXbacCxcu8DznOHm0F5Jw6vUiuIKnvX0t/WdO85IooqdnK7lM2BqySwRNlBUrVnHoaPD52713D/Pnt4Jnek1gU3MLu/bs5sVRRO+xo7S3r0GTdM0gUJYrY9u2Hl4aRfSfOU37mjVE6qZ1Ehg4doznOsfYxQssXbIMCrCSsGRRC4ovFFgFVBSL1NXXT8egIY1BTW0tNUnCCgDvp2OwAnCTnrYlS5i4eJHrnOPE4UM/wk/YMIxnO5YAGobxtPPNb36TYqSz2hpTH8CCCGfPnEFyMQeBUQFxQjaXZ7/39HtPsVDExY5TQB8QZSLGxi5y1nt2ek95ZRUIjAvsBTQbcWqgD5zjEe+Zv6gtnNTBVlXIx/QeOUx5Ps/9SUL7qtVMTIxDDNtUiXKOo0d6qa+pmfb56+/vQzJCT+pTeGpggMULgk9gfXU1hw8fIso5elSDJ+HYGO2rVvOdJKE8l+PokV40JujpN23LokU86j0qwunTp4iyEfuBSQEEyioq2O09Z7xnfGycKBNxCjgDuIyjMDnJgPfs9Z5cWRnihHGBAwC5mDOnT1MU4XHvmb+47cfyWRuG8exEVFX/45cZl2N4eJjq6mqGhobME9AwUh588EFe8tKX4K/z8P20sQK4CKtWtjM0NMTAqT5cp8P/uocRkD8V9LSyrG0FSbHIkeO9uMUO/5uhUtb9ucMf9Cycv4hcLs/Bw/uRekF/S6Ee5JOCblGaGpuprall7/49UAb8FrAM5O8F/bZSXV1D64IF7Nm7CxWF3wA2EPwGvwBl5eUsXbKM/fv3UigU4BeBlwAPAJ+GOM6watVqensPcfHiRXgDcAuwFfhzkERYu6aDEyeOMTg0iLxU0DcrHE6v8aLSvmotg4Pn6D/Vj1vv8L/q4Xyqn1FWLl/NxOQ4R48fwS1z+N/woOA+7vC9nraFS8hkMxw4tB9pFPQ9CtUgfyXoNqWlqYXqmlr279vLq171Kr761a8CMDAwEHwYDcOw+zeWAD4l7BfIMC7lp175U9x/+n6S+5IZm5LPA7cGV5TIOXwz+P0lNid7gTXgEEQEn1X0kIb5X4AzwSLFXQzbxCmEpKs71cfBrXG4Y1BM0p03vgK8OtU9uBc43JNCcTIJHfkL4B0lHb8donscyYRHYkHfpfAnJfp7Qf5M0GIYMUxu9uG6pvgk8OuAQpSN0A2Kf7jE6uXrwM+EU7soQhcofo8PiSrAdqALREIMtEzRwwqNqT4Q7G9kUlANhb/sDHED4CK4VQ7XD0niufW22/joRz/KwoULw9stATSMaez+bVPAhmE8zTzx5BMktySzff5eBFTAAhGSyONfNcfjbjVIu1CpSuw9+ryS5A+gAfQGxamnjtQHsLtEz4O/yVN0nqUiwUjvZ0p0B/51nmIxodO5UBbx+jkdfz0kE55uCUne5XQthjV/yYS/VH8doNDpHEmS4F83x+fvp4EsLBYhcQn+Z0qSP4BOkGVCrSqR9+iLSpI/gGbQFyoZ76lWRVbJTPIHwSvxVZ7Eea5zjicffZRMJoNhGMblsATQMIynlQULFiDb5hgQHwVG4bgqWgTXkyZhU4yE1wwBE4Db5aBQoifgtjkKCmchLAw8N/sUbquDBA6rhvfum9OxHiCC7VN7826bo28Leo+mBs6X00n16Mr6du/DN+tcfT8wCUdUp69nFueBk+GyCoDb4aYLWSA0uh2OCWAQ4BgwXKJriKsW4THv6dywAcMwjCthCaBhGE8rd/7qnegXFT5e0vh2IArTmyj4Jzz8JnAc2AVyu6BjMxmhH/DwFkJ1wyHgDvBH08SNsBOH3Cph2vQE8F7wD/kw1etcONcbBR4DTgN/CvxfQhI1pf+ywL8RMq7/Q5juTSCOw9ClvE/gy4TE7G6Q9wpIqifpMT+Tvv/bIG8XiFK9APwT8MfAKeCxtD8RRFEEnjA9/Ntp/3eA3CbopIb+Af6Yh19Ir/8g8Dbw/R5EcM6h44rcJmEa+DjwHvCPh/WCcZzhnnvv5W/+5m+els/UMIyfPGwN4FPA1hAYxqWoKr/127/Fn330z2YaHaxeuYZjvYeorallZOwiIxdG0CR8/UgsLF6wmPNnzpLJxMT5PKfODISp2FRvqG1AEs/Y+Bj1zS30HjsExZnjV5RXUFtVxekzZ1i6YiW79+2c0dPEbemiNnqPHGFtRwc9O7bOHmFz0NG+jj17drOus4uebVtnj1JG0N2xnp3bt7F69Rp27t0Ztowr0bs61rN7xw7aFi/myPFjFIqFmWPE0L5qLb0HDtBY38D5kREujI1Ox4AYlixcxtlT/eRzeSQTc/rc6VkxaKxvxk9MMFmYpLahkaMnjszokVA5r5Kq8nLOnj9Pe0cnPVs249MRT1sDaBgz2P3bRgANw3iaERE++qcfpb66eqbRw4WhQbqKRc6dOsWq1e24RGlzjgrn0KJSXlZOy9hFciMjtLTMR4tKg3O0pPrCRYvxg+dpGx8nG0VQhLwIy50jo0L7mrX09fVxTZJwbqB/OvnrcI4YobOzm/2HDvJilB3bt5FxYX3cutSnb+WK1ezYtZMXitDTs5Xm5pZZelNDMz09W3mRCDt372RNe8csPRfn2Ll9Gy9GOXD4EB2d3WTiDOum9h4uwuCpU2xKEvr7+2hfswZRYZlz5ESgCLlMzKLxCWR4iAULFoUYiFCfrkucP7+VeGSY1rExyvN5tKjkRVjqHHhYtbqd0wMDrC8WOXX82HTyZxiGMRdLAA3DeNrZunUrLju7AKGusZFHk4Qx9YyOjiJxxBHnGXUhScmVlbHPe04mYVhORDjjPP3O45yjWCxyxnt2eE95RQUA45Fy0Hk0jhgaGqKoykNJQmOavBHDDueRTMRA/wCxc/xbkrBowYJg8xLDzkiJsxED/X3TPoH1NTWcO3sWlxF2RorLCOfOnqWhtnba5+/kyeNE2YidUfABnJiYoG3hIv4tSYic49SpAXCw0/npgpjG5hYeThIKqgwPDxNFEYecZyL1TCybV8Eun3AqSUiSBOccZyLlbJRODaunP0nY4z258vLpGBx2niiOGRkZYUKV7ycJ9U3NP+qP2TCMZzE2BfwUsCFkw7iUnp4ern/+9Yy3jKMH068XF8yeuzs3cOHCKPsP7sM1O/zbPAyDfEbQCaVr3XqSJGHnnu1IpaB3hIIL+Yyg55U17evIZrP0bN8SKn1/AagH93cOf8KzfMkKqqqr6dm2Ba8efh5YBu6fHX6vZ0HrIpqbm9m5YzsTExOhkncjuHsd/nFPfX0DS5YuZd/evYyMDCM3CvpSRe4X9F+ViopKVre309vby9mzp3HXOPwtHtki6BeVXC7Huo4uTg30cfzkcdwqh3+jh8PAP4ATR3fXBkaGhzlweD9uvsO/NfUB/GxYA9jVsZ5CocDuvTuR6jQGmsZgWFnb3kEcx2zbsRXJCfoLwQfQ/a3D93tWLFtFRUUFe/fspquri8ceewywKWDDKMXu35YAPiXsF8gwLuX1b3g9926/l+SbCSxNG/838MuQcQ5xjmKlx+/1MzYnjwLXQyaKEGBSEthB2OcNgpFyu5DxEXjPpPfwHYJJM8B5cO2O+JxDvafgfSjseGuqj4O7xhEdcDCZUBCFDwG/l+oe5JVC9L0Ixov4rMP/nA9FHkJYx/dL4P7B4SY95GOS5yfoN3XG6uV/huNlogw4SJYn+Cc95FP9H4A3QybOgEBSk+B3e6hP9QeBFzNt3VKkiO5RWJbq+4G1kJGgFwoFeBh4XqqfAbfaEY9EiHO88pWv5KMf/SgrVqwALAE0jFLs/m1TwIZhPM1893vfJbk9gZKtgLkZKIcmVQoU8Tf72R53zwVZJZR5jyQJPJ+Z5A9gKehLFU2KVKgiC2Um+QOoDT5/k1JkQbo1G28q0fPg3+QpFIusTvf25RdKdAf6NqU4XmSNCH4yrcCdcrMRQiXypGetCMXxIvo2ne3z9wuEdXhJQqFYCAlkvkR/I5CF+UmRAgX8a0uSP4AXgbQJFcUiUiigN5QkfxA2DX4huEKB8mIRWS4zyR9AA/jXeAoU6CwW2btjB5WVlXM/HsMwDMASQMMwnmZqamuC718pZ4AJOKGKepAjc3wCJ4BTMKwafACPuNkVtgquN/gAnlMN1isXZh9CekNi16saCkBOzunDEUBgx1RhxNw+HgEcbJ+aFLmcTtg7GHdlfaf3IWGcq/cDk3BUNYw49s6JwUXgLJxXZXIqBqXzMxraJoAh1WBvM35pDDSBJ5KEpcuXYxiGcSUsATQM42nll976S8g/CnyppPFdzCQzHvS7Ch+DaVfjXwcdKvEB7PXwXmCUkBh9iLBtWoqOKfwKwRV6Evgr0G/ojK1LBPKLEjz2PPAF4LPM2MLEIL8uwSxage+AfERmkk6X+gA+mT7fnPoATo34+fT1/5a+fz/IO2Rm95Nier7PEfp0EuSOkvcnoN9S+ETa/3PAr4JeKInBPg+/S0h0LwDvB3+oJAYjCr9G8CmcBP4c9H6dvoYHHnqI++67b+7HYxiGAdgawKeErSEwjEuZnJzkjT/3Rr78xS/PNAosXrwEnZykorKSQ0ePMDE+DllCgqTQWN9ERT5PFMecPneOoeHBkDAJUIDKykrmNzUzMTHORJLQP9AXjh0Dk5DJZVm5dDnDQ4Nkyso5fORgSIZywHjwyetc28nZgQGqGhrYvXdnOHc5Icl00N21gVPHj9O4YAHbpnwCp/TU5+/0iRM0L1zI1m1bwvFL9DWr1jJ09iz1TU3s2L0jePzlSbc3gSWLl1EcH6OqqpoDvYeYnJgMMUgT0+bGFsoyGbLZLH2nTzMyMhyKXTTEqaqymqaGeoqFAhcnJjl1ZiDdYDnEIJfLsXzJUkaGh6luaODA/n2Mj4dhQlsDaBgz2P3bRgANw3iayWazfOkLX6KhtnamUUGKBWpODXD4wH46OrqIgMaCkPdh5K2psRF/8gRDR4+wZOkyUKhMhJpi6tO3cjWnew+T6eujuqISPGRUaC4IGXF0d65n3949NJ46xfjIyHRiuXhCiJ2jq3M927ZvY/GZ0xzct5dYwnDdorFw/OXLVtCzdQuLzp1lW89WGmobZ+n1NfVs69nKonNn2bp1C6tWts/SMy7DoQP7aTt7hu07ttPVuZ5MHLNoQqYTuMLFCzScOsX+fXvp7OwmiiKaC0KsIQa1NTVEfSc5c6SXFStWAlBVFCqScIxly5czeOQIcvIkDXV1IQZeaCqE3UE6Ors5uG8vtacGGDpzZjr5MwzDmIslgIZhPO0cO3YMF8ez2uZVVrHPe8a9p1CYRKKIM5Ey7sIkRCaX44T3nEkSnAtJ1YhTBp0iIijK+SThiPfky8oAKDjlVKQQOcbGxyiqsst7qqZMqB0ci4OH3shw2Dh3syq1NTUUi0VwcDxWIucYGhwkjiJ6VKkoK+PChVHECcdjRRyMjo5SUVZOjyqxcwyeP49zjuNx2Du4UChQV1vL5nSN4ejIKCLh/VPftJVV1ez2noIqkxMTOOc4FSnFNAa5fJ6j3nMuSUDCtnXDThlNYyAinE0SjnlPJp+fjsHp1CdwcnKCCVX2ek/lVTqqYRjGD4dNAT8FbAjZMC7l8OHDXHv9tZybOIcOzny9ZHNZOju7KUwW2LYz9bB7rcIw8LWQ7Kzv3oh6Zcu2J0PSdDNhevMeIIH1XRuJooitPZtJigny04LWK3J32Eu4Y20X+VyenTu3MzY2hrxY0OWK+4rDn/esWt5OZVUVB/bvZWhoCLfJ4Td53L8EH8HFC5fQ1NzE0aNHOHVqIPj43eBxDwQfwabGJha3LeHUqVMcPdaLa3X4V3ncZod/wlNVVcXKVe2Mjg6zd/8eXI3Dv8YjhwT9rpLP51nX0cnkxCTbd/UgZWkMzgH/EvYJXt+1kcQnbN22OVz7awhTzV8Jf2/o2oQ4x9aeJ8NOH68CaggxGFc613WTzWbpPXyIhoYG9u7dC9gUsGGUYvdvQI3/NENDQwro0NDQM90Vw/gvw1vf+laNF8TKThTSnz8Kf8ciGkeRSrko+9HpP19Jdec0dk4RlEdK9CdRolQXCce8q0TvRaUqHDueOudHSvSzqGtzGmdijUElFuXXUHyqj6FyrWicC3qUi5SbUYqpXkS5JbTHoHEuVtkkysVU9yh3ohKJiohmshl1i51ypqQPfxr65ZzTTJxRqRTlcIn+hRk9jmPFofygRH8URdA4jjWKonCNd5foB1CZJ9P6DTfcoFu3bp3+DAYGBp7pXw3D+C+D3b9VbQrYMIynla9+46sUf74IDSWNbwbyUAskkqCvUVhRot8EslTIqeJUkecIXF+ibwReDKinHJAWgVtL9DbQNyhFl9AgabXtnSV6Hfhf8BR9kcXpvrq8mxmfvzzoO5TiRJElQDKRwG8wU7UbAe8K7W1AcaKI/rpCWaoL8G7QRFkqQsEXwi4npT5/vw7E0KhKgQL6eoUlJfrrQVqFeapQLMKLgGtK9OcAzwMpFoNf4mIJo4NTLAd9rZJQZCVwdmCA+fPnYxiGcTksATQM42klX5aHoTmNY0AxzPaqBxmc44GXAKMwrkpBNbx/zuIUGRQShYuq4XiFOecYBHz4Cx+ON4shQGCg9PlcHej7D/R+wnGupJ+cWlUzVx8N/ToP4doG5+gFYCxcXxGQ83NipCEGRWBMNVjDJHOOMQiqwWuwtr4ewzCMK2EJoGEYTys/f/vPE/19FLZ3m+JDgIeJ1ARZv6nwZUIiVAT+J+hpnSrcRXenHnk+/fkb0M06/XIdUvggIWlS4GvAvUASksipETkupud/HOTTAkW4oIpkBPltSbMxYF/q6+dCXiUZwf2emzGT7gP5oCAZCf7TAvInAntTfRDkt4M+nhpRy1/LTAzGgPeEh5Oa+hV+Bbgv7X8B+B+g53U6p9MehU+XxOCToDt1qqAYPavw+1MBIayT/HoQL3rPgUOHePTR0g/BMAxjBisCeQrYIlLDuJSRkRFufMWNPP79x2e1NzU10VDfQDaXY9uOHnwxnca8CHpGqayspG1xGy6K2Ld/L+Nj42Gq14GeVLK5LGtWryFJihw7cZKhwfNIvUAlaK8isdDVsZ7C+DjnBgfpH+hDKgSaQQ+EbdvWd21k/OJFJiYnOHzkMJIVWAK6N+hd69YzOTYGUcSefbtCordCwvsV2lethSQhV1ZGz86tkIQt7DgKOqG0LV5KWS5Hvrw8FHEk4f0MgI4qLc0t1NXWzY5Bm8BoSOiqq2tYtHAhLorYs28Pk+MTyPxgAaP9oYhk1crVeO85crSXkZERpEGgHPSo4mJHV0c3k+MTTCYFzpw+w+BgyHKtCMQwZrD7N1YE8lSwRaSGcXkmJye1sb5+pggEdNniNl0SRZoR0Y2brtFIRCtEpos2Ojo6tSmKtNpFun7DRgU0J6L5tOhj46ZNWuGctkaRrl2zVgGNIBzDOd24cZM60OVRpIvmt06ft0ZEM85pR2eXAtrunJbn89OFFDWgArpk6TIFdJVz6kS0pqZmWge0qqpanYiudi5cz/IVKiLTehTFWlFWpu2p3tnZrZk4ozVTRSugixcs0GVRpJGIbtp0rcZRrBUimm76puvWrNWWKNJK53TjpmtCDNIfQDds3KRVzmlzFGnHuo5ZMYhcFI4pokujSJcsWjQr/lYEYhgz2P3bikAMw/gRMDY2hrjZXy9xJsMoUFRFVUGECZ2Z8nRRxJgqEwTPO9LXFqePIEyqckGD5x2EmdEJVQTwqnjCOsM4k5npi4bjJUk40iBCNpMJFiqE2VknwuTERKpD5CKKxWRaB/A+IY6i6aV7hclJRGRaV/VkM1kGp/qehLnZsZJJlkycYQRIVElSr7+JtN8zMQhtU28rMrODnYgwMXVNc2KAgFdPosoIEMczMTAMw5iLTQE/BWwI2TAu5fTp01z/wus5uP/gzN66QGVVFcuXr0BE2NLzZNCuBxkWdIeSy+dYu7YDgG3bt5IUEmRTOgX8gzC9ub5rI4qyZ/eukGSuEbRB4WFAwhQvwJHew5w/fx63xOGXeeR7giZKV0c34iIG+vvoH+jDNTt8p0ceFnRCaV+5llw+x/DQEIePHMJVO/y1HvcDhx/yLFm8lOqaGiYmJtmzb2fwMny+4rY7/ICnpXk+zS3zQT0927eGVdYvAnfY4Q97ampqWbJ0KYIEr0MPPB/knKC7lHxZGWvWrEWArdu34AseuSadAn5SiTIRXZ3rAdi9ayfj4+NIh6BVCt8HHGzo3oSqcub0aYrFAv39/YBNARtGKXb/xqaAnwo2hGwYl3LnnXdqVBcFH7+pKch3zjwWESWe4/P3KWZNVwLKl0r0r19G/7MSH78nUMkFDz4Ifnn8JkqS6gdRaRAVF3TJiPIGlMlUP4261U5dJkzfuqxTeb4oI6k+isoLRV021TNO3UqnnEr1SZTbUn/B1MtP6kU5kOoJynvSfk3FIIvyeImP4J9f5hrvK4nBPZfR/3KOT2CG6Rh0dHToAw88YFPAhnEZ7P5tU8CGYTzNfP7uz5O8JYHlJY2/CWShSgR1Cq9mts/f20EWCDkRIhFkg8DrSvSfBnm+4IAykVD88U5mfPw2BR9AjZQakdD+P5jxOVgG+nZFndIoghbSCtqpWdIG8O/2+IKnCfCTHv2AQkWqzwP9XcVPpnrB49/toTHVM8DvgxaVehG88+gv6UwMHPB7ob+VImik8Hrg2lQX4NdBGoVc+nJ5rsBNJTF4DcgmIQJypF6Iv1KiPwe4GdQpTc5Rns3S3t7+739YhmFctTwrE8AHH3yQV7/61bS2tiIi3HPPPVd87S//8i8jInz84x+f1T4xMcGdd95JQ0MD8+bN4+abb+b48eM/2o4bxlWATtmwXAa55MEVXnM5Xa7weE7bFQ8tgJZ86c19Yfo8+g/0/+j9V9TTtn/3/fLvhuaHjkFM+jkYhmFcgWdlAnjhwgW6u7v5xCc+8e++7p577uGxxx6jtbX1Eu1d73oXd999N3fddRcPPfQQo6Oj3HTTTWFhtmEY/2lufe2tRH8XweGSxj8HJmFIFfES/O9KXWI+A3pCmVANRSKbNfjkTfEt0IdCscSYKnpG4a9K9K0gXxQkEc5rsGzhD5kxkz4SfACdOgY0WMbwB8xUV5wF9zGHyzj6AJd1yP9PZnwEL4L8keCyjn7AZRzuYw7OpnoR+H2QWDitivMu+AD2proC/xPwIQbOO+RLAptLruFToKeUccCLoI8qfKNE/yroE6FoZgLQPoW/KdGfAO4Fp46T3jPpPfv27bvi52QYxtXNs74IRES4++67ueWWW2a1nzhxguc85zl885vf5Gd+5md417vexbve9S4AhoaGaGxs5B/+4R+47bbbADh58iSLFi3i61//Oq94xSt+qHPbIlLDuJSBgQGuf+H1HD58mJISXiorq1ixciVAKAJR4IUgQ4JuDT5/69Z1IiL0bNsSikCek04Zfx8kdmzo2ggCu3btCD6BnWkRyIPhHOu7NiIiHDp4kKGhQdyKtAjku2H7t8513cRxzMmTJxg41Y9rdfj1HnlA0DFl9Yp2ysrLOX/+PEeOHsbVOvxzPe4xhz/naVu8hNraOsbGxti7fzdSJugLFbfN4U96mhqbaV2wEO+LbNveg8SC3qChCGS/p6qqmuUrVoAqW7ZtDjF4MchZQbcpuXyedetCIczWns3BJ/D6tAjk0bQQpjsUuuzYuT34BK4XtCaNgYQiEIBTAwOoek6ePDn9uVgRiGEE7P7Ns78IBNC77757VluSJPqSl7xEP/7xj6uqaltbm37sYx+b1r/97W8roOfOnZv1vq6uLv3Qhz50xXONj4/r0NDQ9M+xY8eu+kWkhnE5BgcHtamxcVbBwuoVK7UuilRAN2zYpJFzGqcefIB2d6/XCuc055xu2LgpFFOkPneAbtx0jWZEtMo57ezonD5uDJqJIu1eH7wD66NIl7Utmdazqb66fY0C2uic1lZVTRdLZEAjEW1tXaAiog3OaTaOtaKyUiXVAa2oqNBcJqMNqc/fwkWL1IlM6yKiddXV2pjq7WvWaiaT0WxJDFYsWar1qf/g+vUbNI7jaR9EQLs6u7TSueCVuPEaldQj0JXEICuiFc5pV1f3rBjEcazrN2xUAa2LIl21bLn5ABrGFbAikJ/QIpCPfOQjxHHMO9/5zsvq/f39ZLNZamtrZ7U3NzdPWyZcjg9/+MNUV1dP/yxatOhp7bdh/KQwb9481PtZbYXJSWqAjEjwsFOlTGR6zV2SJMwToUxmFrdlRcimz1WVnAiVIiTFMLQYAeUioEoUOSIR6mDa0w+gItUzqTdgIzA+MTHtJVhB8BDM5XKoKo1AIUmI4xhlpg4kimIKxeJ03Ucul5ulOxcxMTHJ1BhbJpMBVeaVXM/k5CS1QCRCFEVoGoOpL+JisUilCPm0kEVVyTJTqxJe76gQmV6uEhEKY7z3RFFELEJtei7DMIwrET/THXi6efLJJ/nzP/9zNm/ePG0m+8Oiqv/ue97//vfz7ne/e/r58PCwJYGGMYfR0VFe/tMv5/TZs7PaL0xOULdiJWvzeXq2bSZRZbRVYEzgvHL06BEWrW7HOcee3bsAmGggFDacCtO+q9d1UCwU6RvoA8DXCCMVoMc9W3o2093dzfjFMYojI6Ggolw43wja69mxaxvrN2xibHSE+d5z6PABJCcMLgQ9pBw+eoiu9euZGL3AmlyOXXt2QAyDSwR6laHRQdasWYefnKR7XgU9O7aAwuByQY5DUijS1LqYYhSzft48tm7fAkl4v5wW9KJSQClfvoLO8nK29GzGJ57RBQIXBAaVk/19tK5aTUMcs2vXjhCDpnSM9LSye/cuVq5di0+S6aI1XyuMloOe8Gzt2UxHVzcT4+NEGraWGxoa/DF98oZhPKt4Jocfnw6YMwX8sY99TEVEoyia/iH15Wpra1PV//wU8FxsCNkwLuUDH/iARuWRcneJX91tKK7kuaB8LvW/m0T5wBV8/oqph94nL6O/h7BtiE898kqP71DeGPz7UJSHUZk3syWbZETlBlHOpvouVObLjE9fRlTWinI01Y+hrtMF/8C0/9Iiys5UP4vKS2VGB5VyUb434yPIz12mj19O+z+B8ttX8PlL0jhczifw/Wn8PMoXZh+/ublZ7777bpsCNozLYPfvsCXTs5q5CeCZM2d0+/bts35aW1v1ve99r+7Zs0dVw/qkTCajn/vc56bfd/LkSXXO6Te+8Y0f+tz2C2QYl7Jw6ULlV1AGShKVfSgxmhVRHCqvlBkDY0UpoNIoGqVrAl27mzF5Tv/IBple7ybVoozP1nktShT2D0bS85f++c3Qh6m9hXlijv7xkNiVTfX5S3P0e0J72VQC+2dz9C1MrzkkRvmNOfrpkKBlRJQI5TVz9AlUamb2BZbuOTHyhKQ0XRMo9RKGFEv/vCrEIO+c3vCCF+jAwIAlgIZxGez+rfqsnAIeHR3lwIED088PHz7M1q1bqaurY/HixdTX1896fSaToaWlhdWrVwNQXV3NHXfcwXve8x7q6+upq6vjt37rt+js7OTGG2/8sV6LYfykMXZxDGrnNJYDMdQkcNqB1s0xH4iBSsifEQrAZK2/xOdO65RYYB7CcDmQnXOOOsBBnYc+B8wt7KsFFBaIcFD10j7WBX0hsH/q9XPfT6rrlfXFznFA/aV6JRBBfRH6JT1fKRlgHswbEsZUKc6NkYQYZICcCBcqKDEtTKkHEViKcObMGQzDMK7Es7II5IknnmDDhg1s2LABgHe/+91s2LCBD33oQz/0MT72sY9xyy23cOutt/L85z+f8vJy7rvvPqJo7jeqYRj/L7zqFa8i/ocYzpU0/iMwHpoijZB7BA6V6F8P6/AmRPDOwaPAYyX6VuC7gDguigQPvC+V6MeCD2BGY84AJMBfluiD4P6PIxPFHPUeiQQ+ThgbA5gA+Ushzsf0AlEugr9gZi9jD/w5uJyjF4hzMfJXAuOprsDHgw/gISATZXD/x8H5kj58EijAaefISCb4AB4p0e8BPaFcdA7iGB5gtk/gD4CHQeOYcRehRxS+WqIfBvmyEEuG/SI0LVjAwMAAhmEYl+NZ7wP4TGI+QoZxKQcPHuS666/jvD+Pnp35eslks3R1dTM5Ocn2nT3BQ+8NCkPAV4Kn54buTaj3wSMvImwH50C+FHz81nduxMURW7c+ifc+bJVWn+qjSsfaTvL5Mrbv2MbE+Dhyo6DLFXe3w5/1rFy2iuqaWvbt3c3w8DDuOQ6/yeO+7vBHPQsXLKalpYUjR45w+vQAbq3D3+BxDzj8Dk9DQyNLli7l1EA/R48fxS12+Fd53GaHf9RTWVXF6tVrGBoeZP+Bvbh6h7/FI4cE/bdQadzRGYo0duzahlQI+noNmfF94JxjQ/dGEu/Zum1z8BF8fWps/SUggfWdG4miiM09T4ZK65uBmjQGF5XOtV3kcnkOHjzA/Pkt7NoVCmrMB9AwZrD7N8/+IpBnEltDYBiXp7e39xIfwHVr1ob1b6Cdnd0aR1FYKxeR+uJt1Egk+ARu3BR8+qKwnk5EdP2G4PMXi2h31/pw3FTPxLGuS70BMyK6euWqmUKLDJqNY12a+uJlRHR+U9MsPXJO6xsaNRPFmhHRivJyzefzKqkuDs1mc1o1b55mRDSOIm1sbFIXOSUzU3yxoLl5+hqXLV+hmUxmlr5m1eppvaOjK+izYrBB41TfsGGjOueCnsZgw4bgjxiJ6Pr1G+bEIKOdnd3T17g29T2c+rE1gIYxg92/f0J9AA3DeGZpa2vDF4uz2kaGhljrHHnnyOayaJLQlAhlPiz2m5wYZ5FzNEYR6hVVpcoLtYlMWzTVRRFLnWN8POzRlvXC/EQgScjn88QidDnH0Pl07tXDkmLwyKuurgbgOuc4c+4ccRyDh7aikHhPbU0NhaTIJhFGL16ksrIKTXX1UFlZwfCFC1wjQjFJqKuvwyeetqKAh2w2y5lz57gu9ResqqoC1WkdYGhwkA7nyIiQy+fx3tOSCJk0BuMXL7LEOeqjCFJvv+pEqExjoCiNUUSbc0yMj0/HoDkREp+Qy+fIibDOOYbN/sUwjH8HmwJ+CtgQsmFcSqFQ4E1vfhOfv+vzM40CbW1LScbGqKiq4sjxY4yNjYXijzQ5ampsoSyOieKY88NDnB88P7NK2UNlRRXNDQ1MjI9TFOjrD1ucEQGFkIAtX7KUocFB8hWVHDpyIKwFzAKTIJHQsaaD0/191DW1sGvvjqDnCJvrOljftYG+o0dpWbyYntTHb1qPoLtzA/1Hj9KyKNX9bH3N6nWcPzVAY3MLO/bsRBOdPj8RLG1bwcToCNU1NRw62svE+EQo/ki3IJ/f0koGIZvNcurcOYZHhmbFoKa6lvrqagrFApNJQv+p1LjeAUXI5/MsWbSYkeEh6ppb2Ld3DxOpKbZNARvGDHb/xqaAnwo2hGwYl/KRj3xEJRbl/yuxgXnJzDTn9M8fo4wRvPjeyrQH3/TPb6IMoYygvG+OJgSfv1MEO5i5HnlR8OXjKMFD75/DVO60HqPSKcquYK/CN1GpmvHww6GyUJRHU3uVx1BZJLN89qRSlG+k79+d2rbEs8/B/03PfxSVG+XSGHws7f9pgk/g3Bj8Tnr9wwTfw7kxeDPKmTSOfzpbz+fL9NOf/rRNARvGZbD7t6qNAD4F7H8QhnEpK9as4OB1B+FPgOa0cTvQTRgxcyAvEPSBkq+ecZAWQYdCm2tz+EN+ZvRLwa1x+L1huFDKBR3QmX3YAHllKLSYGk2jF2gr6divAJ8BpmamHwReWKJ/GPhdZip//wF4U4n+T8DPpY8d8AfAB0r0h4EXpI9j4G3Ap0v0Y8Di9HEE8lJBv1USgwsgzYJeSGOwMr3eKTscD26Fwx9OY1CZxqCsJAYvEfRBBQ8/88pX8tm/+zuam8OHYCOAhjGD3b+fpTYwhmH81+Xc2XOwdE5jA1AGC0UQB7pszv8780AzVImQB/wSP/vbScAv9WQE6kSgnlnJH6THdLBEJEyrLpjTh6WAQme6Ru+SPi4FPHRPbQd5OZ1U91fWO11q5TxXbwWy0CYCDnTpnBjMAxqgVoQs4XpneSG60JYDakRCcl02+xC6TJEIrosiDuzfj2EYxpWwBNAwjKeVF73gRUR3RTBW0vhV4AIMiJAhxn3FQalP8Q9A9yljzuGjKIymleYvR0DuFySKGRFBj2kYwZtiENyXHVmNOS4CBeCfS/QJcP/XkYljdmtIFPnbEt2H53E+ZocqLutm6wTdZR07VInzMfK3M8UdUzoO9kQRmTiD+yc34xMIcBcwCSejiAwZ3D1zfAIfAj2ijEYRmskg35UwijnFQeBB8HHMhShCD2jwS5ziLLh7HRky9EQRazo7GR0dxTAM43LYFPBTwIaQDeNStmzZwvXPv57JhZPo/vTrJQZRoatzPaOjoxw8vB833+Hv8DAM8teCjimd67pJikV27d2JVAv6SyFZk78R9KzSvnotmUwm+AhmU70O3N86/DHPsrZlVNXUsq1nC148vBVYBu6fHH6Xp7VlAS0t89mxYxuTk5PIbYJuVNy9Dv+Ip66ujiVLl7N3724ujI6GaeWXKnK/oP+izJs3j9Xta+k9fIhz587irk99/rYIepeSyWbp7OhiYKCPE30nwrT1z3o4DPxtiEF31waGh4c51HsAt9Dh3+bhfBqDiRCDQqHAnr27kLr0GjXVB5U1q9cRRzHbd/Ug+VSvDkbX/oRn+ZKVVFZVsmf3LjZu3MgjjzwC2BSwYZRi92+sCOSpYItIDePyPPHEE9rQWD+rKKGrs2v6cXv7Go3jaNrjDpj2+QO0q3v9LA8859y0x92s16YeeplMrCumvP9AO9Z1zNZzsc5vbQ3eg6DLFreFQo5M0ONspJWVVVpRVqaANtTWahzH6jKhcMNlRKMo1qa6cE3z8nmtrq7WKBuFwo60wGRZ25LgVeictrYu0EwuM+3TB2jnVL9AV65arZnsbH3a2w+0q7tboyiaFYOu7vVXjEGcjXV1e/u03t3VPSv+VgRiGDPY/dt8AA3D+BGwadMmtJDMajt3+jTPjSLyIlRUVKLFhKXeMS8JC90mxsZY5RytUYSkHngN3tGShMdxJqbBOTqcY2xkBIB8IqxQB8WEmpoaYhFeGEWc6kstYorQqQ6dTGhpmU8xSbgxiug9dpRMJoMWoEMdxcmE+a3zGR0b46VRxJnz56lvaMAXlA51+ILS0NDAqXNneWkUcWF8nNYFC0kmEzrUQQFyuRxHjh3lp6KIovc0t8wHH84/VXhyeqCf50cRGRGqq2tIigkr1JFN9bELF1jnHE1RRORiktQrsT6NgYijJYpod47xixenY7BMHUkhoaKiipwI10cRZ/r7f/QftGEYz1osATQM42lFVXnf+97H2VIjYgcVNTVscY7ahkb279+Lj4TD3nNBFckIF8fHOJnPMzZvHgP9/UhGOOM9/apILJw4fgytquJwNkchfc+4Kge8p+hg757dNDc185gIDS2toRIX2O49RZQdO7axfMlS7lelo6ubgi8AsMOHhXz79u9l3Zq13O893V3rGRjon6UPnOqnq2s993vP2vY17N6zc5Y+UZygo7Ob76iyrG0JO3Zso1AssD3ViaG2qZknnKOxqYm9e3ajEvo/CUhGmEyK9GZzFCsqOHHiOBILp1Q5m8ZgoL+PyXkVHM/lGJuYmI7BIR+KZvbv20NdYyObnaNl0WKcs694wzCuwDM9BPlsxoaQDeNSpr3nfrvEl64jTFNG6RQsoPw6ymGUbai8PHjsOefCFnCC8obgr8c+lDel3nsiYWrYoXKDKJtRjqC8e+ZcURQHH8BOUR5COYny4RmPvThO9UWpj98AyqdRskHPZrOKoFItyudSr8EvoFIT+pXNZsO5sij/O33/N1FZHKaLc7ncjE/fH6XnfxiVrqBPvx+Ud6H0omxJfQtd6P90DH4WZS/KHpTbwjFFwnQ0LvUW3JrG8c6ZGEyd4w//8A9tCtgwLoPdv80H8Clhi0gN41I6N3Syc9lO9JM64wP4GPCc9LGA2+DwT5TYnAyBtAp6MfXAa3b4oz7sogFQBLcsFHoAof0kwQ4mxT3f4R/1M5W5O4G1JR37OeALhAphgK8DP12ifwD4CMFHUIC/IngHTvFp4JfTxxHw2wTvwCm+CbwyfZwB3kDwDpxiD7BmqrPgnuPwD5fE4Fwag4k0BgtTz790JJMCuMUO35/6AJYJ2hcKQIDglXidwz/pQeH1r30tf/WpT5kPoGFcBrt/2xSwYRhPM8ePH0fXz/l/ZRtQkfoAxuA3zPG4qwaWhL9ygO8oSf4AYvDrgw9gPSALZFbyB+A3+rDdmkh475rZOhuABDqmpkXXX17vFgljZpfTgS6RkCReQe9wLiShc/XVzPgARpeJQR2wEGoJ+aPvLkn+CI2+M/UBhGAqXV2iS4iBxPAc5+jZvBnDMIwrYQmgYRhPK5s2biL6SjSzIwcEX79ROK6KSxzuGw4uluj7QfcoIyIURZBHBAZK9HMg3xU8wjlADyvsKNEnwH3NEXnHYdWw9+6/lOge3D2OOI7Cmj0B7pnT8bshyjl60vV2l9MlFrapEuXcZXUkrAmMoij4/JX6BH4TmIQjqjjvcF+f4xO4C/SgMihCIoI8IHC2RD8N8pBQcI4hEXSfwt4SfQzc1x2SOB5XZdNzn0uxWMQwDONyxP/xSwzDMH54fvf9v8t3XvYduK2k8W2AgxXLVzE8NMSpvgHkhYK+Q4MP4J8IKkpb21IKxQLHTx4LU7rvCcUN7uMOf9HT3LqAfD7PocMHkRsF/Z3gAyifEvwRT0NdA7W19ew/uBe5PdWXgfyd4B/2VFRVMX9ZK3v37YHfIBgtbwTuBf4ZMvksK9uXceDAPop/XIQh4CXAd4FPQRRFrGhfRW/vIZK7xsNI4WuBLcDHAIG1a9dx4vhxhr4/FHwE36JwGOSPBXXK6lXtnDt7jtNHTyEvEvTXNPgA/rGgkbJ86QrGJ8ZnYvCbYUrX/ZnDT3oWLVxEnMlw6MjBsJ3cb4dpYPlLwZ/0NDU0U1tXx+c//3nGx0szTMMwjBKe4TWIz2psEalhXJ6vfe1rWlVXNcuHbsrjLnZOlyxZqnEunimWcMzyuFuzZp262E0/dxmnq1a3h+II0O7uDeF9U4UduVgXLVqsmbTIpHvqWC7V87HW1dVP+/ytWZ365UXp8bNO8/m8NtTWKaCLFyxQEVHJSOoXKAqibQsXTvsElpWVqcu5WcdZ275m2iewrq5O43w8qx/d3WkMoij0N5+ZFYMpHdDV7Ws0ykQlMYi0fc26GZ+/7vWKk5kY5GNta1uisQt92rTpWvMBNIwrYPdv8wE0DONHwKte9Sqiwux1gKdPnuRa54hVaWhsRCeKLAfmKeBhcnyclc7R4hyZbAZf9DQB8wFf8JSXz6NOhLXOcXF0GBTyCqsAmUxobmlBved65zh59Eg4abq3r44XWbJkKaNjY7wkiti9dw+5XB6SsKbPT3ra2pZw5vw5XhRFHD1xgpaW+WhB6RJBC0pzSzNHjh/nxalP4LJly/ETfnpNYFlZGbv37pn2CVyydBkU0jWD6VRw37EjPNc5xHuaW1pIJhNWANk0BmOjI6xxjgbnKCsrIykkNBG2UvaFhFwuS7NzrHaOibEx8EpeYQWQTCQ0NjURqXKdc/Qd7f1xfNSGYTxLsQTQMIynnU9+8pOMFkumHwXqW+bzA++J83mOHj2C5hwHgQsEDzwV4RBwzjlGhodxWccpoA9wOce5s2cYjSJ2qxLnypBYGAf2AUlW6D18iPLycr7vPa1tS6a/3XpU0axjz55dNNXXc3+S0N3VzcRk6N+2dM3fgQP7WbJoMQ8mCe2rVtPXdxJc0HEw0N/PmtXtPJAktC1cyN69e6bXBAKMjY/R1dnNd5KEprp69u7ZjXd+WsdBy6LFPOo9+bIyeg8fRjLCAZj2AYyyWfaqMhJFnD9/DpcLMThD2Id4aGiQ886xXxVcFHwAgQOpfvRIL5l8GY97T+uSZT/aD9kwjGc3z/QQ5LMZG0I2jEv54he/GKYdby/xAVwYpknr6+s1n8+HacubUb6L8hVU1gePvKrqaq2oqAged89Pffr+FZWXBI+8efPmaXV1TfDxWyvK3SgPpp6BoNlsTuvq64O+UJT/i/J9lF+Z8giMtLm5ecbn73+jPI7ygZnp5Kbmlhmfvz9G+QHKn8z4BAY9nXp9f/r+T8/4BC5YsDB4DYLydpRHUP4p9R2M0ebmlhADUF6H8gDKPah0hBjU1ddrRWVliMGLRflW6jP4whCDqqoqraurC9fYLcq9aRxvCddQVlamzek1/Oqv/qpNARvGZbD7t/kAPiXMR8gwLuU5z38OT5Q/gf9HDy1p43eAl4aHIiArHX5XsG0BoJ9ga5J69Lma1POvItXHwLU5/Ol0LjUCDqXvgVDlu96hOz06VXn7feC5M/2SmwT5Fvipqem7mF2o8g6QT4fpXhzB4+93SvQ/Bd4bziUZQX9Rg1fgFF8Abk3PlRG4EfTrJV+vjzPjhejArXX4Hj8zD3McWMr0tnGuweGPeChP9QvgFjn8+ZmdRThKmCMHSMB1OHRfiMHtt9/Oxz/+cVpawodgPoCGMYPdv20K2DCMp5l9+/bhXzzH424dUBl8AInBv6gk+QNoAVklMz6A15QkfwBl4K8PPoB1gLTJTPIH4MDf4NEIlkz5AD6HWehLFJ/ojA/gi+d0/Aam1/zhL6/jmV4TyA2X0Qk+gOoVfcmc/1tfFy5u2gfwxX72N/BCkCUy4wP43JLkD2Ae+GtnfABlhcwkfxCOmcb1Oud4/OGHESn9EAzDMGawBNAwjKeV1atX4+53YeJxiu3ASPABpAjuATc90gVAP+g+ZQiYAOQHAiMl+hi47zuKSvABPKJwpET34L7rkAR6p3wAH53dL/mO4GKZ3ruX++d0/P4wcrdNNSSnl9Gn1gRKRi6vE3wAnRPkO3OSr8fCxR1RRZLQ31leiceCv+F5wkCo+/4cr8RRcI87JoBBQA9o2A1liiSNawKPe891z38+NsFjGMaVMB9AwzCeVt77nvfyute9Dt5Z0vhmIIL62gYuXrzI2IGLcAvwboIP4O8J6pWamhqSJGFkZAR5haC/G6Zj5X8J/qynorKSTCbD+aFzwWPvfwYfQD4Bfrsnn89TWVnF6XOnkDcI+r+CDyB/F6ZjozimqaWB/oF+5FcEPa8zPoCfDF2dP781FID8Xtr3KR/ADwE+6P39ffC/gSqmfQDl/cHLcOGCRfT391H8RhF+EXgrwQfw/cHnb37TfAYHBxnbNRa2i3snwQfwg4KiNDQ0MDk5yfD54XCN/11BQf5I8MOequpqMnGGs4NncK90+N/3YUeQj4Hf5ynLl1NTU81dd91FdXXpViGGYRglPMNrEJ/V2CJSw7g8n/rUpzRbnp3lQzfl8zcvn9eWlvka5WY87iQj2t6+Rp2IZqJYly5bri5b4gOYddq2ZInmMhkVEV23rkMllmk9ykXa0NColeXlwSNv/YZp772p98+bN0+b6upDXzq7Z/kISiwaRZG2LVykgLavXDXLv2/q7/ZVq6d9AuM4nulDeqzurnCNjXV1Oq+iYtY14NJ+gVaUlWtjY9MlMViztkNFRLNxrG1Lll4Sg6VLl2kmitWJaPuatdM+hVMxaGpunvY6vPa655gPoGFcAbt/mw+gYRg/An75l3+ZeVFuVtvZvj6udY5kYoKFixbBRMJywlI/LSioslyEevVUVlbiJz2NpD6Ak57a2noqkoS1IkyOj6FFJU/wAYwmPUuWLmVsbIznOcfJI73T3nvrRZBJz+rVazh17iwviSK2be8hl82Dpj6BRWXFipUcOX6MF0cRe/bvY/781mkfQTw0t7SwZ99eXpz6BK5e3Y4WdXrv4LJ8Gdu29/DSKOL0uXOsXt2OSyToAB76jgYfwInxMdqWLEELykrCkkUtKMnEOO0iVHlPbW0tfjJ4IdanMaiqrqZOPStFIEnQQojB8lRfvLiNwvg41znHicOHfvQftGEYz1osATQM42nnvvvuoxjPXn/W2NrKD7yn6BynT51CcjEHgVEAB9lcjv3e0+89k5OTuNhxmuADGGUiLl68wDlVdnpPeWUVCIxL8AH0uYj+vj7ERTziPa2Ll4STOtiqiuQjensPUVlezv1Jwtr2NUxMjEMUfAJd1tHbe5jGurrU528RfX19SEbC3sAZYaC/nyWLFvNAktBQW8fBgwdx2bB3MBGMjY2xtn0N30kSKsrK6D18CNL3T33Tti5u41HvwUX09/Xjso79wKSEvpZVVrLbe86qMnbxIlE24hRhS2CXcUxMTDDgPXu9J1tWhkTBB/AgEOUiTg0MkDjH494zv23Jj/xzNgzj2YvZwDwFrIzcMC7l/vvv52U3vgx9ocIDaWM1MALLl61keGiQ02dPIxvTvYBHQD4iaJ/StmgJhUKRk/3HccvSvYAjcB9z+L2e+c2t5HM5Dh89jDQK+l6FepBPCvqYUl/bQE1tLQcP7UcqUn0Z8PfAv0BFRQWtCxawb//esAL6PYQ1gPcA/xdyuRxLlizlwIH9JD6BXyXY13wX+Etw4li5chW9vb0hgfxZZvYC/lOgCGva13H82DFGRobhFcysAfyIoCPKqhXtnDt/ljNnTyPXCvrrGtYAfkTQAWVZ23ImJsY50X8Ct2rOXsAHPAvmLyKXy4a9gJvT/Y5rQD4h6JNKQ10jdfX17Nu7h9e+7nXc/eUvA2YDYxil2P3bEsCnhP0CGcalvOzlL+OBwQdIvpLM2JR8GXhdeBg5h7aGggXKUv0AsBpEBQf4PGivwlS+cj5Yv7hR8KqhwHgHwV4GYCL46skRSJJ07vfrwE+nugf3Yoc8LiSTSbCo+SvgV0o6/iZwX3D4SY/Egv6WBi/AKT4A8sdhuthlHf71Hv6pRP9r4O3pNWYj9BrFf6/E6uVbhIQQcM7BYvB7fPC9AdgNrA0PRQTmpdXOdal+OtjEyFiIAQLsIcyBA4yDW+XgBHjvuf322/noRz/KggULAEsADaMUu3/bFLBhGE8zm7dsJnl1Mvvb5XlARfAB9JHHv6Ik+QNYAdIuVKkSq6LPK0n+AGpBbwi1GHWALJWZ5A8gB/5nPInzLBUJRnqvLNEd+Fs8STEJPoAKvGZOx28J6+im1gReTteiTu8dfImePu9wjiRJ8K+Z4/P3U0A2+AD6yONfVZL8AawBWR58AGNV9MUlyR9AI+gLlIxq8AFcJTPJH0Ae/E97NPI8xzme+P73iWMzejAM4/I8KxPABx98kFe/+tW0trYiItxzzz3TWqFQ4L3vfS+dnZ3MmzeP1tZW3vzmN3Py5MlZx5iYmODOO++koaGBefPmcfPNN3P8+PEf85UYxk8eixYtQrbM8cDrBUaDD6AWwW2e4xM4FF4z5QPotrvg5TdFEdwWR0HDejg9oWGD3BLcE8ED77BqMNLbNadjTwIRMz6AW+bom5leE4hcQRdmfAIvp5Me33FpDPYAk8EHkOQyMTgLeqzEB3DrHK/ESXA9Mz6AHJ16kKLgnnRoAo95T/c112AYhnElnpUJ4IULF+ju7uYTn/jEJdrFixfZvHkzH/zgB9m8eTNf/vKX2bdvHzfffPOs173rXe/i7rvv5q677uKhhx5idHSUm266iSRJLjmmYRg/PL/x67+B3q3wkZLGXwQiwoiUgt/i4dcI1QtbCJ5940oURYhI2PLtjcBOQuL0ZvAnPCJCFEWQgLxe4AngMPAu8N/34CGTyUAEcpvAg4Qt1v6IsPVbATLZbNDfLvA1wjZ0nwI+CiSQy+UBkPcJ/HOq3wXy3pDQ5XL5YOD8Z4Rp5D7g6+nxIsiXlUEB9C6FP0zP/720PxFksznw4B/18BuELe2eDDGgGPrvnMOf9PDzhKnhXcDPgT/liaKIOI7RCQ3v2ZzG8R3gn/SICrlcni996Ut88pOf/BF8woZh/ETwjJrQPA0Aevfdd/+7r3n88ccV0CNHjqiq6uDgoGYyGb3rrrumX3PixAl1zuk3vvGNH/rc5iNkGJfivdf//t//+ywfPhy6Zs1azcaxNtc3aE1NzSwfP8mIti1ZqvPyZVpTWanz57fO8riTWLSpqVnrqqq1PJfTZctXKJmZ40skWllZpfObmjQTx7puXYcSzz5/JpvVZW1t6kSCH19Uokt4zdr2NcFHsKt72iOw9DVTPn9rVreH6yt9TRS8Dp2ILlvcptlsdnYMYnTtug7NxLHOb2zSqqqqS2KwbPkKLcvltLaqSpubWy7RW1rma01lpZbn87p06dJLYlRdXa3NDQ2ajWPddM216tyMj6D5ABrGDHb/vkp8AIeGhhARampqAHjyyScpFAq8/OUvn35Na2srHR0dPPLII1c8zsTEBMPDw7N+DMOYjYjwR3/0R9RX1cw0ehg5f46NqgydO8uKFatwRWWpc8xL99Ytz+dZMDlB2YULNDU1oQWlwTla0jV5CxYuREZHWFYohC2MCpAXYaUIsYfV7e2cPn2a56hyuu/k9PRpp3PEKnR0dHLoyBFe4hzbe7aSibIA02sCV61cza49u3lJFNGzrYfm5pYZHWhuaqZn21ZeEkXs3ruHNe3rQGf0XJxj5/ZtvNQ5Dh09wrqOLjJRhs6pvYeLcG6gn2u958yZ06xa3Y54Yblz0z6AGedYWigQj44yv3UBWlQaRahPY9TcMp/8hYssnpwkl81N+wAuE4EEVqxczeDZs2zynv6jR/FT092GYRhz+IlPAMfHx3nf+97Hz/7sz05X+vT395PNZqmtrZ312ubmZvr7+694rA9/+MNUV1dP/yxatOhH2nfDeLbyxBNPQCaa1Vbf2MyjScK4KiMjw0gm5rB4LkRhIVyurIx93tPnPQqIE844T79TnHMUC0XOes8O7ymrrARgPFL2O4U4YvD8eYqqPJQkNM9vDSeNYbt4JBvR33eSOIr4dpKwZPFiCpOTkIEdeKJsxMkTJ6Z9Ahvr6jhz+gwuK+wQj8sIZ86coam+nvtTn78Tx48TZSN24CET/oO4ZHEb/5YkxFHEQH8fuHD+qU03G1vm84j3FFQZGhokiiMOimcy1csqKtjlPae9JykWcZHjtFPORiEGqp5+n7DHe/Ll5SEGMRxySpyJGRkeZkKV73tPY8t8DMMwrsRPdIlYoVDg9ttvx3vPX/3VX/2Hr1fVYL9wBd7//vfz7ne/e/r58PCwJYGGMYfNmzfzghe9gMm2STidNsawbdc2utdvYGRkhL0H9uAWOvgFgg/g/xa2bttMR2cXSbHIth1bkVoJtioR8GnYvquH1e1ryGaz9GzfgswT9JeCD2DyfzwHDu9nyZKl1NTU0rN9S6gEfhuwDJJ/8vTt6GN+83xa5reyY8f2UOjx34CNoPcoow+NUltbx8b2tezdu5skKSKvFHgp6P3gv5owOjbGxo3X0Nt7iHPnzuGe72Z8AP8Jjp48zoaNm+jv7+Nk/wncOgc/R1in+FnYsXsH3es3MDw8zP6D+3CL0xicnx2DQqHA9p09SH0aAwX9tLJt+1ba16wljmO2btscvA7fHnwAk88k7Du4h2XLllNZVcXuPbt5wQtewEMPPfRj/g0wDONZwTM9B/1U4QprACcnJ/WWW27Rrq4uPXPmzCzt29/+tgJ67ty5We1dXV36oQ996Ic+t60hMIxLec1rX6NRe6QcKVn/9pnwd+ycZuNYXb1TzqDTf34Q9IxzmomisH7vQIl+FJWcaDaKNDO1ru17Jfog6lrCsWNJ18X9Y4k+gboup5lcqjuUPyrRE5SfRuN8rC7dd5e3l+iK8iuh3RFeJ6+Q8L6pPx9O1xrGsWZyGXWdThkv0f8pjUEcayaTUdfslPMl+sOzdcmK0luiHwzrCDNxrJk4Dtf4WIl+FnUNTjOZjOayWX3ta1+rBw8etDWAhnEZ7P79E7oGsFAocOutt7J//37+7d/+jfr6+ln6pk2byGQy/Ou//ut0W19fHzt27OB5z3vej7u7hvETxYMPPUhyWwL5ksZXAfOgRZUCRfyrfdjgdoprQFYL5apESQIvIGxwO8Ui0Jcq6hMqVZFFEl4zRTX413ompcgiCKN/t5XoWfA/6ykUi6xJ9/blzSW6C8+L40U6p3z+SnWAtwSfwE4RiuNF9M06exHNWwAP7d5TKBbwb5zj83dr6MeCJKFAAX+Lh5oS/XnB6LkqSZBCAb1Boa1EXwa8CFyxyLwkQVYIXFei14G/2VOkQHeSsKunh4qKCgzDMC7Hs3IKeHR0lAMHDkw/P3z4MFu3bqWuro7W1lbe8IY3sHnzZr761a+SJMn0ur66ujqy2SzV1dXccccdvOc976G+vp66ujp+67d+i87OTm688cZn6rIM4yeC+oZ6zh86P7vxNDAWfADxIAcFLTXBGwf6YCjdmMgdcqGAYSrBUnAHHQX1nEORMxI2ES7Jb+SgoF5nfACPA0tK+nAIENg+tVPIIWDhHN1Bj9eZ58+fo8PM3r6H5lx4+ny7T9f8zdVPEHwACT6Al8TgAnAKzpXGQH2Yqoawm8khxwSeCVVkQGCMWYbaclDQRHncJ/zMqlKXaMMwjDk800OQ/xnuv//+2fYM6c9b3vIWPXz48GU1QO+///7pY4yNjek73vEOraur07KyMr3pppv06NGj/0/9sCFkw7iUP/mTP1GJRPl4yb+/FzPbdoV0yvQiymmUN3Op7co7CVOkQyi/M0cTlNtQ+lHGUP5sjh6hcoMoh1EKKP/AbFuYGJV1omxH8ShfR6VqxlIFh0qrhGlZj/IIKgtklq2LVIry1VTfgUqnXHIO/i49fy8qL5VLY/Cnaf8HUN54mRj8Vnr9gyjvukwM3oRyKo3jR2br+XxeP/WpT9kUsGFcBrt/q9pewE8B20vQMC6lWCzy5re+mX/+v/880yiwZMkyJi+MUllZxbG+k1y8eDEUePigtzS1kHGOTJzh/Mgw54dKRhEVqqqqaaypZWJyAu8cJ/tOhNTGkRo451i6uI3B8+eZV13Dwd79wbA5BoogkdDRvo6Bvj4aW1vZuXt70DOkW29Ad+cGTh7tpbVtSSgkKdWjVD/SS+viNnq2bw19L9HXtndwpu8kTfNb2bVnFz7x0+cngmVtKxgbGaaqpobeY0eYGJ+YHYPmVjIEs+qzg4MMDQ/OjAAq1NbUUltZRbFYYMIrA6f6ZsWgrKyMxQsWMjw0ROP8Vvbu3c3ExARgewEbRil2/74KbGAMw/jxEscx//SP/0RDqc2Sgp+YoOnsWY4cOsiateuIgGYvlBHW5NXV1REPDHDhxHEWty0BD9Uq1KqAwvLlKzh//Bjlp05RWT4PPGQRWr2QEaGjs4sDB/az8Pw5Lg4NhuQNWJoIsQjdnRvYvnMHq4YG2bd7N7GEFTBLiyHDWr5sJT09W1gxNETP1i001jXN0utrG+jZuoWVQ0P09Gxl9cr2WXo2ynJg7x5WDQ2xY+cOurrWE8cxS5I0g0tgfHSE1nNnOXRgP50d3URRxHwvZNIY1FRWkj81wNDxYyxbvhw0xKAqjUHbkqWMnDhOZmCA+pqa6Ri0eME5x7p1nfQePEDLubOcOz0wnfwZhmHMxRJAwzCedg4fPoyLZy8xrqyuZof3jKsyOTmBRBEDkTLmwiRENpfnSJIwkCQ45xARhiLlfBTsmVSVc0nCwRIPvEmnnIwVooixsTGKqmz1nuqp5DOCw7HioojBwTCi+Kj3NNTXUSwWwQU9ihyD58+TjWN+oEpleXnwKoyEw7EiDkaGR6iaN4/HVclEEWfPnsVFjsNxWBM4OTlJY30Dj6bmy8OpAX1vrGGUD6iuqaEn9QEcGxvDOUdfrBTSGOTLyzjkPWeTBNXg/TcUKcPRjEXV6SThcJKQyeenY9AfhW30xifGmVBlu/dUV8/2OTUMwyjFpoCfAjaEbBiXcuDAAa67/joGGUTPzHy9ZLIZuro2MDExzo5d24KP3xsUhoG7ww4i67s2oeqDx10s6OtD8iRfFHRS6e7cQBTHbN36ZCgSuRloSPVhZe2aDsrKytixfRsTExPIywVdrri7Hf60Z8XSldTU1rJ3zx5GRoZxz3P4jR73dYfv9SxoXcT8+a30Hj7EmTOncZ0Of4PHPeDw2zwNDQ0sWbqc/r6THD95DNfm8D/jcVsc/mFPZWUlq9vXMDg4yIFD+3ANDv86jxwS9JtKNpuls6ub8fFxdu7ajlSmMTgH3AviHBu6N5IkSfA6zKS6ptdYVNZ3bsA5x5Ztm1FVeA1Qk+oXlHVrOsnn8xw8eIDW+fPZtWsXYFPAhlGK3b95dhaB/FfBFpEaxqW86effpPGiWNlTUpTwx6nHnTjNxLHKPFEOlXjY/QvTPoFxFIUCh8dL9J5Q2JEp9QH8Uol+DJVqCR55Uz6Af1aiD6JumdNMJugSifIbJfo4KteLxvmgR7lIeR0zPn8JyhvQKBdpRkTjXKzyHJnt8/fusCdxFEWayWbULZnj8/fn6TVGUfD5qxLlaIl+D7N8AIlQtpToT4TilDiONZ7yAfxqid6LSoVoJpPRTCajN954o27fvt2KQAzjMtj9+yfUB9AwjGeOf/nWv1D8+SKUzkD+LFAGdShFiugtCktL9FeCLBPyqjjv4Xrg2hK9C7gB1CeUqyLzJezAMcVC0P+mFKRII4Qp118t0avBv81T8EXaAE0UfqNEz4H+qlIcL7JElWQigTuZWSTjgDshmUhYqkpxooj+qs72+XtnOO5SVQpJAf+2OT5/vwxkoNF7CloII3ulGwndDLJQKE8StFCAFwHrS/RNwPODD2A+SZA2Cf6KU7SBvlYpaoGV3tN//LiN+BmGcUUsATQM42mlfF45nJ3TeAGYhPOAeoKPXylFYBguqlJQRc6GoodS5KxQVBhVnT7eLM4APvyFB4bm6GcBCfaA088vox+7kn4u/HWU8Lor6Ue8v7w+FK7zNIRrOzNHnwRGwvUVuUyMNLRNEuLEaDheKXJGUA8HvafRkj/DMP4dLAE0DONp5S0/+xbc3zv4Xknj7wIKhdQIWr+l8M+hjUngQ6BnFJ826V6FPyNU8ibAX4JuDRlhAuiwwvuAifQNXwa+EsRJ1ZCAvRMYSc//EMinBYowrgoZkPfITBK2E+QjIeEaByQW5IMCR1L9KMgHBImF8bRJ/lhge/rkDMi7BckIBQi2M38t8GCqjwLvAgSKGoyg+SrwxbT/E8AHQIdCDAB0u8L/VxKDj4PuDjHwgJ7VENfJ9BifA/1GiO+EKjt277Z9gA3DuCJWBPIUsEWkhnEpFy5c4BWvegUPP/jwrPbm5hZqq6vJ5vPs3L2DpJAgLQLjoINKdXUNC+a34iLHwUMHGRsbQ+oEXEgOc/k8q1asJCkW6RsY4Pz5c0iVQAXoScXFjs51XYxfvMjIxQucPHkCyQs0gB4LlbrdXRsYGxmhCBzqPRAKTBYIelinff4ujgyTzZexc88OAGSxoEfD1+Sa1R0UJ8Yor6gKPoE+bN+mJxWKsGzpCmKgvKqKrds2hx0/FgmcBR1T5s9vpaqigny+nO27tuGLPkxnXwzJX21tLfObW4jimP0H9zM+No40hNFQPauUlZWxfNlyvPec6DvJ0OAgUi1QBtqvRHFEx9oOxsfHUHH09/cxPBSGQq0IxDBmsPu3jQAahvE0M2/ePB74zgM0ztmDe14uS+HgAfZs38b6ro1EIlQMQDwYkqtFixZxbv9eTuzZTXv7WgDy56EsnUpdt66D3l07Gd6/j/nNzQBEI1DVBxnnWL9+Ezu29aCHDoZ1hBqSrrrjQe/s6KZn6xayhw/Rd/wYERFMQt1hRYCli5fSs3ULmcOH2bVrJ7XVdZAEnQSqK2rYu3sn2cOHg1/gspWICHW9CpMQSczAiRNkDx9i65bNdK7rJhPH1B4HvRgqeTMI/uBBdm7vYf36TURRRFU/yFCIQWvLfIb27+PIrp2sXdsRYnBGyZ0NevuatRzbvYvBfXtZtCDsY+eGlMoBcM6xfv1Gdm3fRnLwIJMXLkwnf4ZhGHOxBNAwjKed4eFhxM3+eslmc5wjTAMn3iMiXESnl7FFUcyIKhem1tARpjLHpyYpFMZUGVIlSj0Gi6pcQEGEpFgkUeUMkMvOVGcMp/rkZFg02AeU5fLBRoawNM+JMD4xgYjQD2TjmEKhgBBcagQoFgtk4ph+gmXN2NhY0NO+ep+Qz+foT887OVkAEUZKFjNmcznOaOi3LyaICBeYmfaN4pghVcY0tX4RYYIwQwwgyHQMXBTMBRPgAsEzMEkSCqqcTc9lGIZxJWwK+ClgQ8iGcSn9/f089wXP5cjRI2GLtJSKigpWrWpHUbb0PBkaXwwyLOgTSjaXpaOjCxS2bt+ML3jkeYJGCt8L6/I2dG1CUXbv2sn4+DiyQdB6he8CCuu7NiIiHDx4gOGhIdwqh1/uke8Imiida7qJMzEnjh/n1OkB3EKHX++RB4KH3qoV7cwrn8fZc2c5eqwXV+/w13vcow5/xrN4YRv19Q1cuHiBfQf2IOWC3qC4Hoc/6mlqamLBwkUUC0W27+oJXoYvVdwhh9/rqaysYsXKlahqmCIGuAHknKBblFw+x9q1HYgIW3o2o0UPLwBRQR8O09zruzcCyo4d25mcmEQ2CVqt8EA43IbuTQhCf38fInDixAnApoANoxS7f2M+gE8F8xEyjEv5tV/7NY3qI+WxEh/A98w8FhElg/KDEg+7z5a8durnvhL9Xy+jf2K2T6DkJRwbgo/g76D4VD+KuianLgoeghKL8rMohVQ/h7q1TqNMpIC6nFN5sSgXU/0iKi8Rddnwfpdx6tqdcjbViyhvSo8L6iKnrtEpvanuUd6X9gvUOaeSF2VryTV8ck6MQPlmif61y8Tgr0v0J1Gy4diAbtiwQR9++GHzATSMy2D3b/MBNAzjaeaL936R5K0JLClpvBPIQrUI6jTs4HFNif7W4IGXl7Bvr2wSuKlEvxF4QVizUi4SCiN+pUTvCj6AGim1ImHO9neZnkpmEfi3ezye+RJ21OCDwNRudbXgf9OTFBJaAT/h0fcrlKV6Gej7FT/pg17w+N/0UJfqEfBB0KLSJIIXj/8lD22pLsAHAAc1Injnwy4n3SXX8HaQJiEPONXghfjyEv1VINcKEZAHpFXgjhJ9I3ALePEscI5IlRUrVlz+QzIM46rHEkDDMJ5WRITpRW1TpAtNppsvp/t0qGrKJmXucROZOdTlFq4k/8E5EkCgIHJ5PX1emPP8adW1xLpv7jWWxoAw7XsJpdc45ZlzGX2SUBRiGIZxJewbwjCMp5XbXncb0d9GsL+k8aPAJIxMJXf3AVMuMQp8Kli5TKgGn7+tCl8oef9XQb8fiiXGVIMH3seZSYB+APIlQRJhaGpZ84eYScIOBh9A8cIZ75GMhBHAKTPpU+D+1OFix2nAZR3yP2XGR3AU5A8Fl0312OH+1MFAqk8CHwTJCOdVcd4F38GpGHjgf4T+jqqGZPbLwGMlMfiLYHczkb5cH9XgbTjFl0E3h/hMEmxf+KuSGHyfsJ+wF057z4WJiel9gA3DMOZiRSBPAVtEahiXcubMGZ73ouexf+/+WaNgVVXVLFu2HOccm3ueCB55zxUYBt2l5PN51qxdB8D27T0UC0VkfeoDuHmmAEK9smfPrlCFu1rQOoVHARcKIFSVI72HOX/+PG6xwy/z8BCg0NWxHhHh1KkB+vpO4hodvtMjjwg6qaxZtZZsNsvwyAiHew/iKh3+Wo97wuGHPUvbllFVVUWhUGTX3h1IVtDnKW67w5/2tLS00tzcjKqybcfWMPX7fHC9Dn/EU1tbS1vbUsRJKITxIQZyXvB7PGVlZbSvWYuI0LNtS/BK3BhGVHWrEmUiujrXoxpiMD42jqwRqE4TxiiNgVfOnjvL5MQ4AwMhS7UiEMOYwe7fWBHIU8EWkRrG5RkeHtamxsZZBQurli3XxihSB7ph4yaNnNOsiKabvmlXV7dWOadlzunGTdcooBFonBZEbNx0jWZFtMZF2rGuIxRLgGZFNI4iXb9hkwLaHEW6dNHi6fPmQTNRpGvXrlNA5zunNZWV04UWOdBIRFsXLFRAW5zTTBRrZWXltA5oRUWFZuNYW9Iii0WLFqsTmdadc1pbVaWtUSgkWbt2nWYyGc2XxGBZ2xJtSvUNGzZqHMeanSr4AO3s6NSaKNLcnBhEqb5x0ybNO6fVLtLOzq5ZMYiiSDdu3KQC2hhFumLpslnxtyIQw5jB7t9WBGIYxo+AfD6P+tmL4IpJQjkQieDEgSoZQv0EhLV/OREy6WOAWGS6TgMgI0JemD62A7LhzYhIKBIBisnMJrk5ESjxzasECoXi9Bq5PGGgMpOJp/XEJ0Tp6/PpcVwUkXhP5VRfMhm0RBdxFIpFKtO+R3EMmp4/JSkWmZfGYMonMcvMWhzvPXkgU3LN8awYBS1XEgMpfb0IkQjl6bkMwzCuhE0BPwVsCNkwLmV4eJiXvfxlPPHYE7PaGxqbaG5sJJPLsW371rAN2lKBC6CnlMrKSpa0LUGcY9++vcHnrzWdAj4efALXtK8lSYocO3acoaFBpFGgCvSgIpHQ3bmByYlxzp0/R/9Af9gqroWwt3AUfAInxsa4OD7OkaOHw1ZxS0D3KAh0rltPcXICRdizfxc4kFWC7lPwsHrlGhyQyebYtnNrMGtuF+gFHVfaFi+hPF9GrqxsZiu41QL9oMNKU1MLDfX1ZLM5enZsQYuKLAtrDfW0UlVVxeLFi4mimN17dgWfv4XpVnAngk/g6lVr8D7h6NEjwXC7Md0O73CYJu/q6KYwOcnE5CRnz57l/PlzgE0BG0Ypdv/GpoCfCjaEbBiX8t73vlejikj5aolf3c/NeOABikO5J/XHK6L8/mU87v4y1f0VfALfX+Lj9y8o0ZzjvxllLNV/gErlzFSrxKJyoyiDqb4flYUS3gcqGVHpEqUv1ftRWS8qGZk+viwQZV+qD6Hy8hIdVCokeCFq2o+3Mn18SPv7tVQvoHzgCj5/UzH45GX030vj51G+Mvv4CxYs0Pvuu8+mgA3jMtj926aADcN4mvnHz/0jyVsSuLak8X8AEeQljOjx08BrCPOXEfCB4IEXp02yVuBXp54AbwPZJNPTnVIj4ZhT88OvBG4Jz8skXVX4UWbmZ68BfbtCDPNSH0D9E4XqVF8B+jthlK8C0IKif6DQkurNoH+gaEGpgDAi9zsKK1O9CvRPgp4XgTg933Wpngf+NDzMiYRrvhl4VarHwO+B1AafPwFkg8AvlsTgV0A6ZDpk0iDB63DqDa9OjxfBPOdY3tbGdddNdcAwDGM2lgAahvG0MjE+wfRCuSnyQBySK3HA3BkXB8wLyVFGwrTutIlzilYrTtIkcu5COZhO5qpKjjeLKkBhehJ0bh/TPjX/MLpeWZ8/teZvrj4v9KsawrVVz9FjoCwYXUeAVl26OkerlJg0BuXMLA6cohpEYIEIg4ODl7zfMAxjCksADcN4Wrnpp28i+vsITpc0/h0wDudViTRC7hHYV6J/JaxhGwcSkWBp8nCJ/gTw3VBocRGCB97nSvRekC8IsUacmfIa/IsS/Sy4zzpiF3NUFYkljBBO5VhjIJ8Q4lxMLxDlIvgYM67NCfCx0N4LxLkY+UuBi6muwJ+G/YoPe0/sYtxnHJwp6cP/F453GoiJkS8KHCrRvwB6UrkggkYRfA94vET/fvjxUcSYCHpU4Z4S/QDI3UJEzAFVahoaOXnyJIZhGJfDikCeAraI1DAupbe3l2uvv5azY2fRoZmvl1wuR0dnN4VCgW07tgQPvVsUhoBvhJ0rNqzfhPfKlm1PhKTq1YT/pt4HeNjQtQnnHFu3bSEpFpGXC1qvyL2Cjisd67rIZ/Ps2rWDixcvIi8UdLnivuLwQ57VK9qprKriwP59DA4O4jY4/EaP+4bD93naFi+hsaGJ48eP0T/Qh1vh8C/2uAcdfr+nuamFRYsWc+bMaXqPHsbNd/hXetxmh9/iqa6uZuWq1YyOjLBn/25ctcO/2iOHBP2eUlZWxtp1HUxOTrJ9Zw+SCzGQc4J+U4niiPVdG/Hes2Xbk+HaXx2una8CGnz+RIStPZvx3iOvELRGkXuCl2FXx3oymSxHeg9TV1fLvn0h07YiEMOYwe7fWBHIU8EWkRrG5Tl+/PglPoBr29doPvXQ6+reoHEUqcQzhQvrN2zUWCT1CbxmplAiQkVEN6Qed1kR7erqninGiNFMHGtH6ouXF6erl68IugQ9G8e6LG3Li2hzfcOMnkEj57SxsVFj5zQvovPyZVpWXq6S6iJoPp/XirIyzYto7Jw2NTerc04lM1PgMr+xSfOpr9/yFSs1k8nMusb2laumY9DZ1a1xHM/Su7vWayb1RtywcVPwKrxMDDIiun79hjkxyGhX1/pwjc7p2tXt5gNoGFfA7t9WBGIYxo+A1tZW/Bwfugsjw6wQIe8c2UwGTRLqEyGf7nlbmJhggXPUR9G0x12lF6q9TPsC1kQRi5xjcnwcgIwKjYlA4snlcsQitDthZGQ4nFRhYSJ476msDIvy1jvH+aFB4tSnb0FRSLynqrqGovd0inBhfIzy8nI01VVh3rwKRsfG6BKh6D01NTV471lQDEUnmUyGc4PnWZ/6+1VUVIAqC5KZvZFHhodYna5zzGazqCqNiRCnMZiYGGeRc9SmHoSqSpUXKtIYqCr1UcQC55icmJiOQUMiJD4hk82QE2GlCKPDwz+Kj9YwjJ8QbAr4KWBDyIZxKZOTk9x6+63ce/e9s9oXt7VBsUhFZRUHDx9iYmIiFHMUgQQaG5qoLC8njiL6z5xheHgodXkGJkNCtaC5hcnJScYKk/Sf6g9TpBlgDDLZDKuWr2RkaAiX+/+z99/xkV31/T/+fJ97Z0a9r7pWq5W0RXV33Q24AYYY0xKqcegEQkL7BEJMQgudVIKTUBJMMyYQSmgGjBvggo29vfciabtWXZqZe96/P87VaEa7/obPD9t8bM5zH+PV3Nct575nPfetU17vFAcO7XN9X8XAFEgg9Pf0c/rEccqra9i+a5ub21cGTAIGBgfWcnJ4mNqmJlfKLcIt5pgAAhjoXcOpoyPUNTezcdN6l9jNHx/Aqu7VTJ45Q82SJWzethmN1C3+mAEE2ts60HSa8spKdu3bQ2YuvRADC/VLGilLpQgTCUaOH3eJbF4MyisqaFpSTzaTYXJ2huMnj7sYhMAsJFNJujo6mZoYp7y6hr179zAzMwP4IWCPJx///MYPAf82+C5kj+dsPvrRj6pJGOWzeX51z1zkgQfKp1DSOC++PzmHx927UaZQpmO/u3xNYp+/0/E5PrNID5wvH8MoEcq3UJJ5eojKWlH2xB56d6FSveDhh0FlqSjrY5++Dai0S4HXoFSKcmd8/F5U1okS5l0jifLN+PojqDy78HhA+be4/aOxT6As0t8b3/8UzvdwcQxeH8cvjfLpQr20tFS/8IUv+CFgj+cc+Oe3qu8B/C3wv0F4PGfTuaqTfZfsg0+w4KmyGVgDEoEakMsEvTPvq2cOpFHQM26b6TDYPXbBp0DB9Brs9rj8Wamgx7TA6kWuEfSn6q4BcAhoy2vYm4HPs7Cy95fAU/L0TwDvITdcy83AdXn6fwEvi382wIeBG/L0+4FL4p9D4HXAZ/L0obg9CgQgzxD0x3kxmAZpEHQyjsHK+H7n7XAUTLfB7o1jUCluNXTRwink6YLepYiFZz3zmXzpq1+locF9CL4H0ONZwD+/vQ2Mx+N5lDkzegaWLtpYC6ScR54Y0PZFv3emgCVQIUKRCLbNFn47CdilloRAjQhUc5bPn7YrGFgaGzHnTJznWQoo9MZz9M5q41LAQv+8j99iPU4m+yWe0/cIeq8xLslbrDcACWiLzbB16aIYlAA1UBXXQ7ZLbaEXYhyDFFApAnUUJH/zMZAA1gUBB/bvx+PxeB6JJ2QC+POf/5znPve5NDc3IyJ897vfLdBVlQ984AM0NzdTXFzMFVdcwdatWwv2mZub4y1veQt1dXWUlpbyvOc9jyNHjjyOd+HxPDm58rIrCW4JYCpv4/eAaTgpQoIQ8z8Gjufp94HuVmaMwQYB3AvsyNP3AXeABCGTIugRhTvy9NNgvmVIkmBYxPXyfTVPnwXzFUMiTLBbXaLIf+bpEcgXhLAoZLsqJmmcPp+jqdvfJA3bVQmLQuQmcXME5/lPwMCuICARJjBfNW7u3zw3A2k4GoQkSGC+s8gn8C7QQ8pUGCKJBHKXwJ48fRfwC7CJBNNhiO5V14s5zwkw3zUkSLAlDOlevZpxvxDE4/E8Ak/IBHBqaorBwUFuvPHGc+qf/OQn+cd//EduvPFGHnzwQRobG3nmM5/JxMREbp+3v/3tfOc73+HrX/86v/zlL5mcnOTaa68liqJzntPj8fxmvO+97yM5kkSentd99WYwoaFncA3LOruwExYzaNwQ6p+5oUsCWN3XT/eqHlCQiwXeAfwFyPmu161r5SpWDwy6ChjXAG8C3gNm0GBPW9qWLqNvzVpMGLgyaq8EPgBmjcHusDQsaaT/vPMpShbBB0FeIPAhMBcb9HaluryawfMuoKyoDL4I5ukGPuyGa/kClKZKGTzvAqrLq9E7FHORgQ+BvFDg/ZBKFDEwMEhDfRN2p8WsMfD+uB2vAxMG9A0MsGxZB3Y0jsF7gD8F+QMXg57ePrpWrEStIhcI/AXwf0AudKuNV6xcxeqeXjeMfLW4oe0b4hiMW9o7ltPT28ftt9/OK17xisfzo/d4PE8kfsdzEH9rAP3Od76Te2+t1cbGRv34xz+e2zY7O6uVlZX6mc98RlVVz5w5o4lEQr/+9a/n9hkaGlJjjP74xz/+ja/tJ5F6POdm48aNumRJXcGihP6+/tzPq1f3ahgGbtFEuOADOK8PrlnrPPBi3Rij/YNrcnrOAy/WE4lQV+T53vX29BbqyVBbWlo1iD34lrW2FehhwmhlZaWWpFIKaG1lpSYSCTUJ1waTEA3DUOuqqhXQ4mRKq6qqNEiYgnvoaFuq4HwFW9vaNJFKFOh98+0CXblytSYShfqaNQsxGBhco8aYghgMzt836Jq15y26h1BXrepZOL5vwPsAejyPgH9+Pwl9APfv38/Ro0e5+uqrc9tSqRSXX3459957LwAPPfQQmUymYJ/m5mb6+vpy+3g8nv//GRgYQDOFPoBnTp3koiCg2BhKy0rRbES7NZRa9zWUnp1lhTE0BwGom8pRaw0N1mCtJQwC6kxAnwmYnpwEoCgSllsD2YiKigpCEZ4aBJw8dtRdNAt9kaCZiPr6BiJreUYQcHh4iEQiAVnoiYRsxlLf0Mj03BxXBgGnxsaoqanFZpSeSLAZpbqmhpNnRrkqCJhJz9HU1EKUsfRGbsg5mUxxaOgIzwgCImtZsqQerDv//MKTU8ePc2kQkBChvKKcKIpYbg2pyPWWzkxN0hME1AcBgQmw1lIXCbWxl6GI0BgErAoC5qancjFYZg1RNqKsrIyUCBcHAadOHHscPmmPx/NE5UmXAB496r7451e+zdPQ0JDTjh49SjKZpLq6+hH3ORdzc3OMj48XvDweTyHWWt7xf97ByTNnFjYaKK6oZEsiSW1DA7t27sAGwkFrmbIWQpiYnOREaRmZikpGjo4goXDKWo5Zi4TCoUMHCaqrOVxcTDqbhRBmVdlnLRlRtm/bSnNzMw+HIdUNDW4hCLBFlSzK5i0bWdHZxS+Mobd/gIzNALAtNkLYvWcnfb19/BJhcHANx04cLdBPnDrOmsE1/AKhr6eX7TvdvOKtsZ6O5ujp6+cXxrCis5PNmzeSyWZyxxNC5ZI6NiQSNDU1s2P7NqxY9lnLnCqEMJdJM1RcDFVVHDlyCAmFk6qciusXDw0NYSurOFpSwvTsDBIKs6ocsBYVZefOHdQ3NrEpkaC+pZUgNpT2eDyexTzpEsB5RKTgvaqetW0x/9s+H/vYx6isrMy92traHnFfj+f3lc997nP88z/9M/xN3sZB2LVnB9NzcxwZGWF8fBx9q8IwsAPkWcLhoUOcmZzgxOhpjh8/ir5U3eKPg6B/rJw6eZJjp04yPj3FgUP7kSsFtgJHgXe7ucGHhoaYSafZsXMbMiDwIHAK+AfIZrPs2ruHrLVs2rIBaRO4EzgDfAlIwJatW7AoGzdtQKoEvourVfw/znZlw+YNWJQt27a6BPOm+Pi7QJYKm7duJFJl1969ZLNZ+DvcQo9fgwwKO3ZtZy6T4dDwEJOTk25+3wiwzc2DPHDoABPT0xw/dYoTJ06g1yscAPaDvlw5ceIYJ0ZPMzY5xcHDB+FqYDsuju+AiYlxDo8Mk85m2bD+YT784Q8/pp+1x+N54vKkSwAbG533w+KevOPHj+d6BRsbXTWB0dHRR9znXNxwww2MjY3lXocPH36UW+/xPPG58TM3Ii8WeEvexs8BEag6axNzgYF/AJqAlaC3KFK8UPLNNBiXXHXg7FQ+D6bdfV2pKpIQ9L8UenD2Kh8F8zTj7FVU3bW+pnA+UINbTHI9kMAt9IpAP6twBVCJW6TxLiCIdQX9uMLzgQrgeaCfVLCxHsT7vzo+/nLQz7nrZrNZV53kFbgErxY4L25PFB9vwDzFOO/BRmA16NcVSbqhXgDTZuA/gHZgGfAFMI0mLglnkSJBb1FYFcfx78Fc6GKQzWZ5wfOfz2tf+9pH86P1eDxPIp50CWBHRweNjY3cdtttuW3pdJq7776bSy+9FIDzzjuPRCJRsM/IyAhbtmzJ7XMuUqkUFRUVBS+Px1PI0NAQ2rfI464NKIVWESQE27/I464cWOpyqSLA9liXRM0TuGMS4vIpmnBegHnYQQsBdIi4Y7sXNWwAiKB/3gew79z64PwowGK93/01KLH9yyPo/cY4n8DFeheQhHZxq33tgC3Uq4Bml68mANvn7idH6Lal4l1pxSWnedhBiwRwsTFsXr8ej8fjeSSekAng5OQkGzZsYMOGDYBb+LFhwwYOHTqEiPD2t7+dj370o3znO99hy5YtvPrVr6akpITrrnO2/pWVlbzuda/jL/7iL7j99ttZv349119/Pf39/TzjGc/4Hd6Zx/PE54LzLyD4brBQcQPgbmAKhlQJbIC51bj6ufPsAN2hTIqQMQa5R9yw5jwnQO4UrBhGRdADChvy9Bkw3zeE1nBAFTLAD/J0C+a/DWEYsDnuYeNbixr+3xCkDBvj+Xbn0iUUNqoSpIzTtVBH3JzDMAgw3zaFPoE/AtKuQElgA8wPDEzn6ZtA9ymjIkTGIHdLoVfiUZBfCFljGBdBdytsy9OnwPzQYGzAA8B5F19MOp3G4/F4zsnvcAXy/9/ceeedhTUx49erXvUqVXVWMO9///u1sbFRU6mUXnbZZbp58+aCc8zMzOif//mfa01NjRYXF+u1116rhw4d+r9qh19G7vGczc9//nM1gVEuyvv/s9TV112xcrU2NTUrBjX9Rvl3lE+g0iCKQbu6u7Wjo0MJUNNulH9C+RRquowSoEuXtmtX9wpXq7dOlI+6OsCy1h3f0NCgq1atdnWHS1D+BuULqFwpiqCVVVXa29unMm+v8jaUL6H8oWtnSUmp9vb2aTKZdO1+NcqXUV7j9EQiob29fVpaWur0F8bHvz2uLxyI9vX1a3V1tSKoXC7Kf8btiGOwuqdXGxubXAzWGFfH+GOoLHH30L1ipba3L3Mx6DSuZvI/o2aZi0FHR4d2r4hjUC/Kx1H+HTUDRjFoU3Ozru7pVWOMXnPNNd4GxuM5B/75rfqETAD/X8H/A/J4zs1Pf/pTraytLPgFbd67LzBGOzqWa5gKnSbuNZDncdfb268mNLn3JjS6evWCx13OD0/c+zAV6tL2dg1jn7/BgUGnB7FeFGptXV3O529V94oFDz3QIGm0uLhYaytdm1ubmlREVBKigEpCVES0rblZAa2pqNSS0lINkqbgPKtXrMz5BNbVLdGwKL5HE7d7wHkZhsZoe/syTRQlCmKQ7/O3uqdHg0SQex+Egfb09hXGQBZikEgldFnH8pzX4bp153sfQI/nEfDP7yehD6DH4/nd88xnPpOw0AaQE8PDXGAMCVVq6+rQuSydQFmcomRmZ+k2hkZjCBMhNmtZgpvuZ7OWoqJiao2h1xhm4qo+Reqm+kna+fyhyqXGMHz4kLtoPKdPZ7MsbV+W8/nbsXsXyVQKsjAgQpR2VUROjY1xeRBwZGSExsYmNKMMiKAZZUl9A4eHh7k8CDg9PkbHsg6itGUgLj1XXFzMzt27cj6BS9vbkay4OYPxqPPRI4e52BhElSX19UTpiG4g7m9kZnKS1cZQZwypVBFRJqIeV/Y3ykYkEgkajGGlMcxOT4O6GHQC2XSWuro6QlUuNIaRwwcf88/Z4/E8cfEJoMfjedT51Kc+xdhsXjFggeqGBh60lmRJCQcPHECThr24qYCSECJr2S/CaBAwOjqKSRpO4FxSTNJw4sRxZhIJdgAmkUQSwiywG4gSwt69eygvK+dea2laujS3gGKjKjYh7Ny+jaYl9dwZRQwOriGdmQNgkyoEsGf3Tpa3L+PuKKJn1WpGRoZBYl3g+LGj9K7u4e4oYvnSdrbv2AZBrAMzczMMDK7hjiiisW4JO7ZvIytZNs77AAbQ0NbG/dZSWlbG/n37IHTtT8cxMImQXcBUmODkyZOYpOE4zknGJA2jo6OMhSF7ARXJxWBvfPyB/ftIlZTygLW0dix/TD9jj8fzBOd33QX5RMZ3IXs8Z/P1r3/d9We9Km8OYLsbjq2IS6wByotQ7kX5CSoXiBKgZWVlWlxc7Oa3XSHKHSh3o3K1mx9XXFys5eXlSoDKoCg/RLkf5bp4qDcMtbKyyuntonwT5aF4rh+oiGhtbZ07f40oN6FsRPngwjBtbW1cwi7l5t6xCeVfUIoW6YLygfj4L6JS6+6hoaHRlXAD5S3x9f8blWVOr62tW5hj+LK4/bfG8xgDXEm6khLXxmeIchfKnahc5WJQWlqqlZWV7h7PE+VWlPtQXuLal0wmtbbOtfGtb32rHwL2eM6Bf36riur8r6ee/1vGx8eprKxkbGzMW8J4PDEXXHIBD1c+jP2ydR59AHfhPPcAEZBVBrvFLoxBnABpFTQd+wBWG+xhC6WxPud8AO2xeCw1xBkkt8S6glln0E0WnXdXeQC4YKFd8nyBW0Ez8VfeN4EX5TX8bSD/JmhWXbs+ifPxm+ef4vfqetv0TQr/kqd/G/ij+FoJgWeDfi/v6/Vh4Lz4ZwOmz2DX58VgBOd5GA+dmyUGe8g6XxyAaTBLDfZUfIMJ4DALMbZg+g12uwWFl77kJXzqX/4l54167Ngx6uvr8Xg8/vkNfgjY4/E8yuzZswf71EUed6uBcucDSAj2Kbbw22cJ0O18AFOAPS8v+cNttBct+ADKUllI/gAE7NMsOu8DmMSZQOehlykaKX3zPoBPXdTwp4FmdWHO3jl0lNycwHPqQJ8xqFX0aYt+t17r7iPnA/i0RTFoAlkmVBP7AF6Ul/wBlLi4zPsASqcsJH8ABuxTLRLChcbw6/vv/1+rH3k8nt9ffALo8XgeVVavXo35mSn0yNsATMARVcgK5k7jvPrmGQLdqYwBc4A8IK4E2zxTYO4xZNVVdtMD6srEzROBud1gImG/qptU98s8XUF+KpjQsGXeB/Bnixr+M9dztzGeE3iWfhu5OX+SkHPrwBZrMUaQ26QwBve6mzuoikSCuX2RT+CB2AcQFxpzzyKvxHGQ+4U5XPU53aOuB3CeLJg7DETCA9Zy4VOegh/g8Xg8j0T4u26Ax+N5cnHDu27gec97Hvxp3sZX4hZBLGlkcnKSqX2TyHMEfYfCOJgPGqxa6uqWkM1mODNxBnmGoO9xyZh8XLCjlsrKKpLJJCdOH0eeJegH1XUJfhrsdktJcQmVlVWMHB9GXizohxWWA18C/akSJhM0tdYzNHTEte8Eblj2u8BnQYzQ0trKkSOH4QO44dgrcUPYHwYiaG1tY3h4CP28QgnwQmA98D5AYFl7B8PDw6Rvm4NX4crF7Qf5G0EDpbV5KWdGTzO5cxKeC7wVGAX5gKCiNDQ2kp6bY3RsFHmmoH+lLoH9mKBTSk1tLYkwwbGTRzFXG+z7LVSCfEqwey1lZeVUVVVzyy23UFSU34Xo8Xg8efxupyA+sfGTSD2ec3PTTTdpUVlRgQ/dvMddcTKlTc3NGqQWPO4kFO3p6VUR0dAY7ezqVpPM8wFMOu/AROB89fr6+nPee4AGqUDrGxq0tKho4VqGguPLy8u1tqpKAR3o7y/wEZSEaBiG2tbkfP5WdHYV+AjO/72iqzvnE5hIJHI+gTkvw37nP1hbWanl5RUF94hB16xdp4CWFBVpQ0OjBkWFMejtde0Kg0A7lncWHG+SRpd3duW8Dnt6+hauH8egsbEp53V4wYUXeR9Aj+cR8M9v7wPo8XgeA1796ldTLImCbSdjH0DNpGlpaUXnIjpxZYA1q9gookuEOqCstBSbdj6AzYBNWyqrqqlQS68xzM1MQ9ZNkVsByFzE0qXtzM3NcYkxDB88kPPeWyOCpC3d3Ss5deYMVwYBmzZvJpUqAo19AjNKZ2cXh0ecz9+uvXtoam6GyM35I4KGxkZ27dmd8wlcsXIVmonnDCoUFxWzecsmrgoCTo2N0b1y5YIPIICF4YMHuNgYMnNztC1dip2zdOPmPWpWyczOsNoYqlSprKwkmotowPkA2rSlvLycWmClMdhsBs0oRbgyw9FcRGtbG1E6zYXGMLQ/f4zc4/F4CvEJoMfjedT51re+RdoULgRZ0tLCg9YSmYBjR49iUs7PbgKQQAiTKXZbyzFVZudmMQnnAziM88CbnJxgVJWt1lJcVg4GZoFdAEUhw8NDBGHIfdbS0r7MXVRggyqkAvbu3UNFaSl3RhG9q3uYm5uFwPkEmqRh//59NNYt4e4oomNpOyPDw0gobs5fKBw7epTO2CewobaOPbt3YZKudjAGZmZm6O3p4Y4ooqK0lH179qChOj3OAZuWtnO/tZgwZGR4mCAVsBs37xEDRaVlbLeW06pMTU4RJAOO4XwAg2TAzMwMx1XZaS2JoiIkcD6Ae4CwKOTo0RE0CHjAWprbOx7rj9nj8TyB8TYwvwV+GbnHcza33XYbz3rWs9CrFG6PN9YAZ6B9aQfj42OMnjmNXCToWxUmQD4q6GGlpamNbDbDsRNHMSsN9l3WzQH8B0G3KPW1DaRSKQ6PHEIa4zmCtSD/KugvlKqqaqqqqjhwcD9SKehfx3MAvwx8F0pKS2lsbGLf/j1uqe0NuNW5/wN8ARKJBO3t7ezbtw+Lhbfh5gDeDfwTiAqdnZ0cPHiITCYNrwFegFvk8jEgDSu6VzF05DBTU1PwfNw8wP0gHxZ0TFne0cXo6VNujt9TBP0zdXMAPyrosNLeuoy5uTmOnhhBegX9i3gO4N8Lul1pqm8imUxy8MhBZ51zg7o5gDcKep9SU1VLdXU1e/fu4SUveQnf+MY3AG8D4/Hk45/fPgH8rfD/gDyes7ni6Vfwy+lfEn0ncnXcwCVYz3c/BkbQVsHuzLM52Q90g0QgImgJ6MF4gQfAmPMBZFxRVVSAbcCqWM+A6THIfoiiuOfxp8AzY11BrhTMfa7sGwCfB16f1/BXg7nFYNMWCeLE6kN5+vtBPiJo5HoM7UutSyzn+QLwuvgekwZ7saJ3LfT+cTvwDPejMQIdgt1mnWUNuK7MVXFbRaA8jkFVrJ+O7W+m1C0uNrgyIvMFP+bArDJwSLFWednLXsY//uM/0tzcDPgE0OPJxz+//RCwx+N5lNm4aSPRc6LCb5eLgDLnA2gDxV69yOOuA2SlUAEkVNGL85I/gEqwl1kClGqcX14u+QNIgP0DS2Qsy+Z9AJ+Rpwvoc5Uoaxd8AK9d1PBr3Ty7AXFJ3rl0jVxtYJu259TB+QBGkUWvzUv+AK4i5wNoA8U+Ky/5A1gBstz5AIYa+whW5ek1oE9RksQ+gCtkIfnDndtebdHA1QJ+8N57CYIAj8fjORc+AfR4PI8q7e3tyIOLDIj3AZPOB1CzYB5Y5BM46vaZ9wE0G008MS4mA+YhQ0bhNKBHFI7n6QrmV85X78C8D+DmwibIA4IELPgAPrio4Q8A4ULt37P0B1moDRw+go47vxjOjsFWcj6ARGAeXBSDE6CH8nwAH17klTgHZr3J+QCyHxeM/Bg8YNAsPGAtay/IK4Pi8Xg8i/AJoMfjeVR5x1ve4UqgfThv42uBAFKpFIJgN1k3/LoNuB/kha4MXCKRIAxDV+7sRbjyaRuBl4MdsQRBQDKZBAvyAoF7gB3Am8E+YBEV530XgLxEnDnzXuD9oN9QNAPFxSVO/xOB7wAHgU/hSr1loayszLX5BuCLsf5l4K/c5rKyMucP+M/x6wDwXZA3uAof5eUVaAb0mwrvja//M5AXO720tBRRwT5onRfhDuBeFwMsFBUVEQQh9qiFl8b3vx54CdiTljAMSaWK0Ky6Y+4HtgN/AnaDRRBKS8v47299i09/+tOP6mfr8XieRPwuPWie6HgfIY/nbKy1+sEPflAlXPCow6A9vX0aGKN1VdVaXV1d4GEnCdHly7u0KJnUspISbWlpPUtvbGrSitIyTSUS2tW9olAPRSsqKrW+tlYDY7Svf6DAJ1AC0VQqpe2trc4ncHDNgsffvI+fQVd2r1jwCZQ8nXmfvwEFdGVXt4qRwn0CdHDQeR0ubWnRVFGRSpAXg9D5FwbGaH1NrVZWVp11j11d3ZpKJLSitFSbmpvP0ptbWrS8pESLkkld3tl1ll5dXa111dUaGKPnn3+hiizo3gfQ41nAP7+9D6DH43mUERHe9773UVOaN7HawvjJk5wPTI6dYXlnNyajLBehNPbhSyUTtGWzlM/OUrdkCZpR6kRoivWmpmYSM9N0RxHGWueBJ0K3CGEEK1as5PTp01wswvEjR1wvHTBgDIGFnt4+Dh45wlVBwKZNG0mGKQD6jRuKXdG1ip27d3FF7BPY2NC4oAMN9Q1s2ryJK4KAnXt2s3pVD+iCXpQoYsvmjTw9CDg0NERPTy+hCRmYn3OYhZMjI1wIjI6epnvFCiQSukRIAppRDNAZRSRnZmhsaEIzSj1QF8dgyZJ6SmZnac9mSQZBzgewUwSy0NnVzcSZM5yP8xxUv8bP4/E8Aj4B9Hg8jzr33XcfmihcgFDb0MCvrGVWlfHxM0giZJ8oU8YlKamiYnZby4i1WGsRI5wUZUQUExjS6TSnrGWLtRSXu+Ry1ii7RSERcHr0NBFwTxTR0NLiLhrCJrVIMmB4aIhkGHJHFNG5rIP03BwkYLNagqRhaOgwFaWl3BVFNNbVceLECUxS2KwWkxBOnDhB05J67ooiyktKOHT4EEEyYLNaSMDs7CzLl3VwexSRDENGhofAuOvPF92sb27mPmvJAqOnRwkSAXtEScehKikvZ5u1nLSWTCaDCQzHDZyMY2BtxDFr2WktRaWlLgYB7BUlSASMnTnDnCq/spaG5pbH+mP2eDxPYLwNzG+BX0bu8ZzNgw8+yFOe9hSy3Vl0S/z1kgAs9PcOMjE+wYHD+zDLDPYN1vkA/qugE0rPyj6y2Sy79u7A1BnsnzofQPPvBnvM0rV8JalUgq3btyAlgr7ZrRY2/2mwuy1LW9upqq5m85aNaKDwRmA5mJsN9iFL/ZIGmptb2LJlE9ko6zz61oH8j6C3u+oby5d3sXPndqanp5E/EvQKRe4W9L+V4uJiVq3qYd++PYyNjSFXCfoCdXP0vghhENLXN8DwyBDHjx/DrDPY661bsPEZXGWR3jWMjZ3h4JEDmE6DfZ11PoD/5mr99qzqI5NJs3vvLkx9HAONY3DCsqJrJWGYYNuOLUhZHINKMP9hsPst7a3LqKysYseObVxyySXcfffdgLeB8Xjy8c9vnwD+Vvh/QB7P2Tz3+c/l1r23Et0awdJ445eAV0EgQhAEZKstdpddsDnZAKyF0BgQITIW3aWwLNaHQTqFMGNQVbLWwn3AxbE+4TzwguOCjSyRKnwdt4gC3CriiwzBNkM0l8UK8HHgL2NdgRdA+JOA7FyEScTJ6b/m3dhbwHzWYDOWsCgg+/QIvs+C1cs/AO+CwASYUIhWW+wDrncQgP8GXgxBEGBEiJZY7A4L818dDwAXOV1EiIII3aPQGuuHQLqFwAagSjaK4CFgXayPgVlhCEZdd+Lznvc8/v7v/56ODlcRxCeAHs8C/vn9GA4Bj4+P/1+/PB7PE5977ruH6MWRK3A7z7OAUlfXN0MWe60t9LhbA7JKKFUljCL0KXnJH0AzrrKIjahUdYbIF+fp5WBfYMlIRDu4pOtFeXoC7MssmWzW+QAq8Io8XYDrIDsXsUYEm7GFOsD1YDOWNSJkZyOn5zu9vAJQ6FUlk806o+j8csgvBJLQZi0ZyWKfn5f8AVwI0iFUWovJZtEr8pI/gKWgT1OCbJYya5FuWUj+wHklPteSJcN51rL54YcpKSnB4/F4zkX4WJ24qqrKudn/hogIu3btYvny5f/7zh6P5/9ZltQvYXT3aOHGo8AMHFYFC2aXcaXW5pkGhmAsHpAweww2csO/gBsC3WXIqOUUipwQGKcggZKdglpln6rzzztIoVHyLpyP33ylkN1AyyLdwAarC/qli3Ti2sLzVTg4W99k4zl/uxbph4E0HMD5AMpOQfONACeAY3B6Pga7DdbahV/TI7dtDsucKjIiMAWULopBpNxvI57b24vH4/E8Eo9ZAgjw3//939TU1Pyv+6kq11xzzWPZFI/H8zjxpte9ib9451+gA3nJzZ+x0Ftmwd5j4W+B/wNMAu8AnVzY3x6x8Oc4L0EDfBDsnoWEUefUzd+7EagGPg16e971ApBXCnqTQjvwVZynX7Sgmz812K9ZGAS+B3zCtQ3cNeXdgrYrXA78HOQvBTUugcUCnwT6gecBm0HeJGiobvVxFucdeCnwStzw7WvFzUuM3PF6p7r6wW/FOWC/BXQ2Lwb7LLzd3TsWeB/YQ3kxmFJXi/hTQDnwT6C/XDj+xz/5Cd/85jcf8XPyeDy/3zxmcwA7Ojr49a9/TW1t7f++M9DX18ett95KW1vbY9GcxwQ/h8DjOZsoinj9G17PF2/64sJGA50dXYyPnqaiopKR48eZnplaqIQRQFN9MxJFhGHI1Nwcp0ZPFiRkVZXVVJWWMjs7R1hUxJGRwwsJnUBRqpilLc2cPnWaqvol7Nm3yyVigutBDAyrV6xiZGiI5vZ2tmzb5I6PdQwM9g1yaN8+lnZ1sXHz+kI9gDX9azmwdw9LO5azactG1748vXd1PyOHDtHU0sz23TuxWbugh9DVsYKxkyeoqq7hyMgIMzPTCzEKoLmxDU3PkUwmGZ+eYnRstCAGNVW1lBcXk8mkIUwwfGyoIAbFRcW0NDYxPnaGpqXtbN+2lXQ6Dfg5gB5PPv75/RjOAdy/f/9vnPwBbNmy5QmV/Hk8nnMTBAE3feEm6qqrFzZayMxM0zY2xvChg/T09BIoNItQIgIRVFdVUXzqJOljR2ltawMLVSLUiquQ0dGxnImREapGT1NaVAQRpERoEyGB0NvXx759++icGGfy9OmcD2AXQijCwMAatm7fRv/UJDu2bSVhEjkdoGt5Nxs3baR3ZpqNG9ZTX+uSpc5Yr62qY8OG9fRPT7Np00ZWrlhdcHwySLJrx3b6pybZun07g/1rCcMwp5OF6bEzLBsf5+D+ffT09mKMoUWERByDitISKk6fYuroCO3LloOFahEq4xgsXdrOzNERSk+dorKiIheDFhEEoae3n8MH9tM2NsapoyO55M/j8XgW8zv1ATx27Bh/93d/97tsgsfjeQzYs2cPEhbOMKmoqmZ9FDFjLTOzM0gQMBwq04HrBkwWFbE3ijgaRZjYPPlMoJwKFBFBreVUFLEziiiOy7XNBcrhUCEImJ6ZJqvKA1FEdW2du2gAe0LFBAGnT59GRPh5FNG4ZAmZTCanB6Hh1OlTpBIJ7okiKkvLGBsbwwTC3lAxgTA5OUFVeTm/jH3+Tp44QRAa9oQKAaTTaRqW1PPzKEJEGB1115vXAapqavl1FJFWZWZmhiAIGAqVTByDopISdkURJ6MIVDHGMBooY3EMRITjUcTuKCJVXJyLwVCohEHI7OwMc6o8HEVUVf/mv4B7PJ7fPx53G5hsNsv3v/99brrpJm699Vaqqqo4ceLE49mERw3fhezxnM3OnTu58JILmUhOoMcWvl7CRMjg4FpmZmbYtn0LUiHoy9UtfvgvwAprB9YR2YhNWzYgSUFf5pInuUXQWWWgd5AwDFm/6WG3gOJFOB/ArxvsGcuqFT2UlpayadMGMpkMcq2gnYr5lsEOW5a3d1JTW8v2bduYmprEXGmw6yzmhwa709LU1ExLSyv79u3l9KlTmPMM9kqLuctgf22prq6hs6uL4eEhhoeHMCsM9lqLrBf0DqWkpJSenl5Oj55i34G9mCaDfZFF9gn6fVfreGBgDdPT02zftRVTYbAvs3Aa+G8QhDUD5xFFWReDVBwjBfm6oHPKQN8agiBg/caHwSi8GKiM9TGlZ3UfxcXF7Nm9m6VL29i8eTPgh4A9nnz885vHrxbwxo0b9e1vf7vW1dWpMUavv/56ve222zSbzT5eTXjU8bUEPZ6zue4V12m4LFR25dXJ/Sf3d8IYTYahSrkoh9Dcn585PWmMJoNAMSgP5+lbXS3dZBBowhh3zu/l6SOoqXbnzumfztPHUdNlNJFMaFLE1eh9Z56eRuWpomFRqEkRDVKB8hIUG+sW5WWoSRlNittPLhVlLu8c73Y1h8Mw1EQyoabTKGN5+r+5ewzDUJOJhJoqowzl6T9c0BOJhKtlvDlP3+DqFScSCQ3D0N3jT/L0w6hUiCaTSU0mk/rsZz9bt27d6msBezznwD+/H4NawL/61a+4+OKLec5znsP69eu58cYbWbduHRdeeCEHDx7k3//93zHGcMMNN/CMZzyDIAj+95N6PJ4nDD/52U/IviILlXkbXwIUQ60qGbLo8xXyp/w+3Rk9p1QRa+ESYG2e3gNcCdZGlKoizQLX5umNYF9kSUuWelXnb/CGPL0c7KstmShDhzirFP4sT0+AvlHJzmZZrko0F8GbWVi5LMCfgZ2zdKrbT9+okMw7x5+BRspy665jX7XI5+917jr1UUSaDPaPrDNGnOcPQFqF0iiCTAYuA/ry9EHgqSCZDMVRhCwTuDpPbwV9gZKxaVZGEYf27aOuru6sz8fj8XjgMbCBed3rXsd73vMeOjs7ueCCC7j00kt54xvfyEte8hKq40nhL3/5yx/ty3o8nv9HKC0r5dSJU4Ubx4E0nATUghxf5IGXAc7ApLqt5rjBqi0wWpZjQlaVCRQmBOaAorxrHAMsHNfYimUUaMzTjwMC++Z9AE9QaDY9r883a/HMlOPur724/R5Rn/cBXKyPAlk4FnshcmyRngYmYDyOgRwXVHUhBupikEbJqMK4uGPyklA55o7ZYS2XNTfj8Xg8j8Sj3gOoqoRhSBiGqLqJy8aY3KTux4tsNsvf/M3f0NHRQXFxMcuXL+dv//ZvnbFqXls/8IEP0NzcTHFxMVdccQVbt259XNvp8TzZeM31r8F82cDteRvfAyhk4+RHf6quPJwFZoEbQE8tpIR2t3UeeRlcMvePoJucGgE6ofBOnIG0xZV9+4ETM+AWXbwZOOOuy+0gnxfIxnoI8nZxBtUA60E+4TKt9Lz+HoE9sb43fh/Gusb7PxzrR+PzhbErSxb4PHBbfP0xXI+jifUI+CHwtbj9M8C7QMcWYqBb1JWXy8Zx+AToTqcqoKcV3h0fa4Evx3GNIKPK+k2buOuuu36zD83j8fze8agvArn//vt5+9vfTlVVFR/+8Ie59957uemmm9ixYwfXXHMN119/PS996UvZsGEDPT09j+alC/jIRz7CP/3TP/GlL32J3t5efv3rX/Oa17yGD3/4w7ztbW8D4BOf+AQf+chH+OIXv8iKFSv48Ic/zM9//nN27txJeXn5/3oNP4nU4zmbmZkZnvO853Dnz+4s2N7U3ExZUTHFpaVs27GNbCaL1AjMKToF1dXVNC6pR4KAg4cOMjU1hVQA4hY3FJcU07lsOZlMhpOnT3Pq9EmkWKBY0VMQJAJ6V/UyMzXFTDrNkeHDkACpAj1G7OO3jskzo5BIOJ9AQBpAhwEDawbXMX76FMXlFWzdvhksSPOC3ru6n5mJcSpqatmw8eEFPe7N61q+AjIZSiur2LjF+QhKPegYkIGW5jZKkkmKS0vZumMrUSZCaoEZQaeV2tpa6mpqCRMJ9u3fz8zMNFIpoKDjSklpKR1L24myWY6dPOlWGpcKFLkYhImQnlU9zExNEaSSDB05wsTEBOAXgXg8+fjnN4/fIpCHH35Y3/rWt2pdXZ2KiF533XX605/+9DFbBPKc5zxHX/va1xZs+8M//EO9/vrrVVXVWquNjY368Y9/PKfPzs5qZWWlfuYzn/mNruEnkXo85yaKIl1SW7uwCAS0o61NVwSBpozR886/QAMRrRLRpIgC2tfbp81BoDVBoGvWrlNAS0S0NNbXnXe+VhijS4NAV69c5RZMiGi1iCaM0fPOO18DEV0dBNrS0Ji7br2IhsbowOAaBXSNMVqUTGoQBAroElAB7VjeqYAOxItIauP2L4nPU11ToyKS05d3dqnk6UEQaEkqpWtifWBwjYZhqEvi9gPa1tSsq4JAQxFdd975GgSBVotoEOs9q1ZrWxBopTG6dt15uRgUx+dYu+48rQ4CbQ0C7e3pzcWgSkSDINDzzrtAkyK6Mgi0vaW1IP5+EYjHs4B/fj8Gi0AeibVr1/KpT32K4eFhvvGNbzA2NsY111xDQ0PDY3K9pz71qdx+++3s2uV+y9+4cSO//OUvcyXn9u/fz9GjR7n66oVZ1KlUissvv5x77733MWmTx/P7wunTp5FF0z6SRcUcVWXOWqJsFkSYECUdD3oGYcioKhPWYuI64jMoM2iurviUKqdUCZNu4ltWlXFRECGbzRKpMqxKcUnJQlvEHT87MwPAAaCspDQ3HeS0AWMMM9PTGBEOAkXJJLOzs0isCzA3O0dRIskhwIgwMzONMYbT8W1aayktKeVAfN252VnnBygLgyxFxcWMqJJVJRv7BY6L5op5hMkkp1SZUkVwvn8zKLMs+ABOWMtpVcJEIheDifgeoyhLWpURVYpin0CPx+M5F49pLeBzkUgkeNGLXsSLXvQiRkZG+MpXvvKYXOfd7343Y2NjrFq1iiAIiKKIj3zkI7kFKEePusk/ixPQhoYGDh48eM5zzs3NMTc3l3s/Pj7+mLTd43kiMzw8zIWXXsjxicJVEIeHjrB67TqsKus3PuRmID8TZELQXyrbtm+lf+06UFi/6WEQ0CtBDXCHO2btuvMA2LJlEwBysRDVKtFPIzZu2cCaNWsxQcCuXTsBMP2GbJdFfmrZtXcnvX0DFKVSHDh4AFXFdBqitRZzBxw9MUJXZzcVlZWcOH6Mw0OHMc2G6CkWc49h+ugULc1tNDQ0MDE+we59OzGVBvt0MOsNdq/FGsPyteuYm5tj6/bNzsfvWjB7DXaTZeT4cVasXUcUWTZuftjF4Nkgo4Lep2zfud3FAFi/8SFUFK7E9eHdWRiDTZs3uhhcKkSVCrdl2bB5PWvWrMMEhuHhIZYuXcqhQ4ce64/c4/E8Efldd0E+Vtxyyy3a2tqqt9xyi27atEm//OUva01NjX7xi19UVdV77rlHAR0eHi447vWvf70+61nPOuc53//+9xcMqcy/fp+7kD2exbzxjW/UsD5UHsr7/+SvFn42xijJ2Ndu/s9XF3SZPybf4+4uFEElbziVz+Xp21ApEXdu3L78dZ6P3xBqGo2awOkSivJKlCjWx1DTbzRIumFhkzIqTxfX9aauC06eIWpS7vggGajpNcqZWI9QXh2fNx4ONg1GOZLnI/i+whhIsShb8u7hP/NiMH+fd+Tpty3SQPlSnr4RldRCDC688EK97777/BCwx3MO/BCw6mOWAFZXV+uJEyd+4/3b2tr0wIEDj9r1W1tb9cYbbyzY9qEPfUhXrlypqqp79+5VQB9++OGCfZ73vOfpK1/5ynOec3Z2VsfGxnKvw4cP/97/A/J4FrOkeYkzWT6WlwAeREmh1SLO4PjFeYlLnCDJUjfXLRRROV+UxX8uQwNBS0VUlshC8jb/55UoIVon4oykJxbp70UJ0Jb5BGrHIj1OwFrPlYDmmVW3nSsBVZTdbnvT/D3+9SJ90l2/Zl6/fpEeodIgWgJuTuBTz4qAyoWiIWixiEqLLCS4839e5q6x1BhdNzCgx44d8wmgx3MOfAKo+pgNAZ85c4Zbb72VysrK/31n4NSpU0RR9L/v+BsyPT19lvVMEAS5eT8dHR00NjZy2223sXatc5xNp9PcfffdfOITnzjnOVOpFKlU6lFro8fzZCQIgthrJY8soAsWKu6HPBTIOHcUqwppYTGSFqwqWSHekUIjq/icuUkai9uQBgRmRED13DrOWeYRj/8N9KlH0uMY5No3t0hXt0+8GzK3yCsxvoYlttPJxn2l+aGKzzkJNCeTeDwezyPyWGWWIvJ//dq7d++jdv1XvepV2tLSoj/4wQ90//79+u1vf1vr6ur0L//yL3P7fPzjH9fKykr99re/rZs3b9aXv/zl2tTUpOPj47/RNfxvEB7P2bzjHe/QoDJQ7szrAXwTblh2/n3AwvCmJVcqruD11byerW+dQ/8oCz1g9+CGlec1g/KnKJlY345KteSGTyUU5Xm4FSbzQ8TLjZrQDZ+apFG5UJTRWD+DysWiJhnroVHpkIUh3hmUFywMAYuISpUo22I9i/LnCzEQESWB8ou8GHziHPf4zbwY3HIO/R/yYnBXHNdYW7Fihf7sZz/zPYAezznwz2/VR90H8P8VJiYmeO9738t3vvMdjh8/TnNzMy9/+ct53/veRzL+zVhV+eAHP8hnP/tZRkdHueiii/jXf/1X+vr6/pezO7yPkMdzNqdPn+apVzyV7Zu3F2yvrq6mbWk7QRCwYePDaKTIWoFx0L1KcXExK1etRkTYsmUTmXQGWSVgQLcpQSJgoH8N1lr27N7N1NQkpsOg1Yo+rBDA2oHziGzE0NARTp08iWkyaLuiD7qvucH+dai1nDp1kqHhI5gqg+2xyIOuPFzPqn4CY5iZnWbPvt1IkaBrFdkg6IzSuaybkpISrFq3yCMQ9ALFbDfYUUtLcwu1tUsQI2zcvB4U5AJBDgl2xFJTU0trWxtBELiFMBEuBmdA9yslJSWsWLEKMcLmzRudV2KPOPPsHUqYDOnvG0StsnPndmZmZpBOgQrQ9QqBsHZwHVEUMTY2xvTUJCdOuMU43gfQ41nAP7958i4CeTzwv0F4POdmampK6+vqCnqrujuWa2MQaDDvgSeiKRE1sT7QP6hVxmhJngdeKKKJnA/gBVpkjNYGgfbFHngCWiSiiSDQtWvXqYA2B4Eua23LXbck1nviY9qM0YrSslxvYDGoEdG2tqVuDqAxGgaBVlRU5HRAKyoqNAwCbY0XWSxdukyNSE4XEa0qL9e2WO/t7ddEGOY8/ABdvnSpNgWBCuiatedpGIZaJJJb+NLX26c1QaBFxui68853MYhfLgbna4kxWh0E2t8/kItBKvYBXHfe+WpAG4NAO5d1FMTf9wB6PAv45/fj6APo8Xh+fwiCYPHsNawqIQtT1kQEk/deUQIRgrxjDPnT/BSDq/I2f26Z11VBBMF5W1ldKPk4fz4Rd6ZkrM97C4aL9ATEZSxNwfHE10nm9qfgeHBegLmZd6bw+k5XEvPtNpK/W3x6JVi0rSBGmheDePBGFl1DRNw95JW99Hg8nsU8aYeAHw98F7LHczZjY2NcdtVlbHp4U8H22to6mpqbSCSSbNj0MJpVN8Q7ATqklJaW0tnVjYhh+/YtpOfSyDKBwA0RJ5IJenv6iWzEwQP7GR8fR5oFqtwQMYGwdmAdmUya48ePcfz4caRWoBVXR9i4UnCZTJqJyQkOHT7oyqh1k6sz3Ld6AGst2WyWXXt2IAmBXmAraEZZ0bmSMJHAmIAt2zaCgAwI7AadUtpal1JRXkEi6e4RC9IvMAx6UlmypJ6GhoacrllFVguMgQ4rZWXlLF/eiTHC1u1byMxlkOVxKbj9SjKVpKenDxtF7N27l6mpSaRFoNwNEUsorIljMDU1zcT4GCdPngT8ELDHk49/fuOHgH8bfBeyx3M273rXuzQoD5Sf5i1WeM2iRSAG5VYWPPQ+do4FDv8RL3CwKDefQ38fC1Ywd+CsVfLP/1qUORY88irzFoEkROXZ4qxZFOWAs6GRIE9fJ8qJWD+Jynmikoj1QFTaRNlPzuJFrsnTRVQqRFkf62mUN8Ttyl8Ic1teDD54jnv8cl4MvnAO/SN5MfhJ4fmXLVumt956qx8C9njOgX9++yFgj8fzKHPzN24melUEg3kb/xoIoVjcog6uAZ4dawZ4F0iDEMZvpU/gdbjxTQGuAznfDfEmRZBqgb9hYaz0SuCF7hol82Ozn4TceOwA6J8oGigVxqAZRT+uUBrr7aB/qWikVOJ6+/T9CnWxXgv6AUUzsR4p+i6FZbFeCvpxp5eKoIGib1BYE+sJIHaXKhbXq8kLgGfkxeA9INWSGyKWdQJ/nBeD17jexvlhblki8O68GFwNXAsEUGEMrY2NrFu37jf4xDwez+8jj0sCeNVVV/HBD37wrO2jo6NcddVVj0cTPB7P40Q6nV5IrOZJAgZKADFA2SLdAEUuuQtE3I6L0DLFCKTmz7fYxTQ+Zym4hGlxKdxSQKFqftbL4jbG76sXvf+/1WsfSS8CTF77FusBuRgY3P0uRkvdHMFk3vkKKHPnrgMmJyfPOt7j8XjmeVwSwLvuuosbb7yRF7zgBUxN5WxSc8bLHo/nycPzn/N8gi8FcCxv438CczCqirEG+a7Atjz9W6AHlVlVIkAfULgrT78f+DmAMKWKHlPILyO+B+SbQmANJ1WdUfQ/5OnHwfyHISDgsLq5cnyChdUkUyD/IoSpkENAkAqQv5cFM+cMyN8JJmU4BISpEPkXcY7LxOf5BEgoHFIlJMT8hymMwT8BWTipSqAB8i2BXXn610BHlGlAjYFfAPfm6T8H7gNrDDMi6GGFb+TpO0C+IwQasF+VkooKXwfY4/E8Io/LIhBjDOvXr+eNb3wjU1NTfP/732fZsmUcO3aM5ubmR7UCyOOJn0Tq8ZzN4cOHueCSCzh25lheWQwoLi6mp7ePbDZi4+aHnYfecxQZF/R25/O3ZnAdqsr6DQ+hqBvWNMCPAYF1A+eDwObNG51P4BWC1ijyI0EzykDfGhKJBNu3b2N6agpzocF2WuSHgk4pq1f2UFJSyv59ezl9+jSmx2DXWcxPDfakpWNZJzXVNYyMDDE8MoxZarCXWcwvDPagpampheamZkbPnGbf/r2YOoO92mLWG+xWS3V1Ncs7u5ienmb7zq1IqaDXKLJP0Aecz9/qnl6ymSwbt6xHwjgGo4Le6Xz+BgfWoqo8vPHXLrF8Nq78x0/dCt+1A+chImzYtJ4ok0WuErRS3T1GymD/WsIw5NChg5SXlbFv3z7ALwLxePLxz28en0UgIqLHjh3T2dlZve6667Surk7vvPNOPXr0qBpjHo8mPCb4SaQez7kZGRnR+iVLChYsrF6xSktij7zBNWs0DAKVYGFxyJo1azUpooFIzgcQg0rgFlWsXXeeGkSLjNGBgUGnC2oCNBGG2h9vKzVGu5d3LughmgxD7epekdPra2pzC0JMiAbGaH1DowbGaKmIlqRSWlJSqhLrAlpcXKwlRUVaKkYDY7SxsUkDY9TkLT5pqK3T0vgeu1es0EQi4fT4Hld2def0gYFBTSQSBTEYHFijKTFqQNeuO9+10cRxENG162L/RGN0cHBt7h4lQMMw1MHBNbl7XBXf7/zLLwLxeBbwz+/HaRHIvN9WKpXi5ptv5m1vexvPfvaz+bd/+7fH4/Iej+dxpqGhAV3Usz8zPckyEVIihGESjSKqrZCKXe4ymQwNxlBtTM7jrlSFMiuxL59QERiaREjPzgKQQKixAtaSTCYJRVguwtT8/DeFhkhQaykpcRMLV4swNjGOMc49ryESImspKy0lspaVIkzPzVFcXITGuuJ6MKdnZ1kpuP3LyoispSFy7Q+CgLGJcXri77uSklJQdXo8zjI1OUGHCAkREskk1lpqrBDGejo9R5MRKgPndqiqlKlQqi4GqkqlMTSIkEm7wr9hHANrLWEiQVKEdhGm86bbeDwez2IetyHgo0ePFgw/fOtb3+JVr3oVMzMzfgjY43kSMTc3xwv/6IXc+sNbC7a3traRNIbisjJ279ntFouU4ebZpaG2ppbayipMEDB09CgTE+NuIYcA01BSWkp7axuZdJqxyUlOnDzuVkMkgQkIEyGrV6xicnycyBgOHT7gji0HzgABDPat4czJkxSVlbNzz3aXmFUBpwEDgwNrOXP8OBV1dWzeutHNJawFTrnj+3oGGD91ipqGBjZsdD5/1MTnF1jRuYr09BSVtbVs3LLBHV+FmytoYWlbO6FCSXk5O3bvIJvOFsSgrq6eqvIyEmGCw8NDbiFHCa6dM1BWVk5bczPZbJbTY+OcOn3C3X/CXSORTLKyq5upyUmKy8o5eHB/bt61HwL2eBbwz28enyHgAwcOqLX2rO1btmzRL37xi49HEx4TfBeyx3M2H/rQh9QkTaFv3TWLPPBA+XeUDMoEyp+dw+PuvSgzKLOx312+JiivRxlDyaLcRKHPYIDKc0Q5Hnvo/QAllaeHqFwgysHYQ+9eVGql8PgOUbbE+lZUOsV598X7SI0o98T6IVQukkIvwhTK9+Lrn0DluYXHIzivwyzKOMqfLLoHUD4U3/8MyvvPEaM/jeOXQflsoVZRUaFf/epX/RCwx3MO/PNb1VcC+S3wv0F4PGfTsaKDA087AB8DGuKNW4EBCCKIDMgVbuFHjjRIk6Cn3TbTabC7bX6dOKRf0K3qNpWJWwmcbxdzLfDj+BoAR4CWPP3Pgc9CkI31e4FL8vS/A94NRl3HHrcAL8vTvwm8xK1JsYK7v3fn6Q8AF7kmawi8Acif5TLi2iMKGoBcLeiP8mIw47wQmVAUMKvdwpL8GJiVcVwAqRL0qMa+OA55pqB3KMbCM57+dL7yta/R0OA+BN8D6PEs4J/fj5MNjMfj+f1hfGwcmhdtrAZS0CCCGNCWRb93JoFaKI/nCNqmvMQHQNwxCYEqEajkbJ+/FsBAi4jzCFyySG8GFFYYs/B+8fEKq/P3X3w8sa6PrK8y5tx6HRDG7TtXDIqBKqiMa/nalrNjYJtdreFKkVxM89FWRQIYCAIOewsYj8fz/4FPAD0ez6PKM658BsHXggWPPIBvA9NwUoQEIfI/4nrE5vk56G5lzhg0COA+YEuevhu4AyQImRJBhxR+mqefAPMtQ5IER0UgC3wxT58G82VDIkiwVxUJBD6Xp2dB/kMIi0J2ASZlnD6foynwWTBJw25x+8l/xNeZ53MggbAnCEiECcxXTIENDl8GMnAsCElKEvNtA8fz9J+BHlamwhBJJJwP4s48fRvwS9BEgukwRPcv8ko8Cua7hgQJtochy1esYHR0FI/H4zkXfgj4t8B3IXs8Z7N161YuuvQipium0SMLXy8mDFgzuJap6Sl27tqOqTbYP7YwDtwMZGBN/1oia9m8dSNSLOgfKxiQrzgfv97V/SQSCTZsXu/Gaq8DasDc7Hz8VnStpKysgk2b1pONsvBHQCeYbxjsAUt72zKWLKln69YtzMxMI88W9DzFfN9gN1sa6htobWtn755dnDlzBnOpwV5lMXca7D2WyspKurpXcuTwIY4dP4rpM9jnWeRhQW/V2Ouwn5MnjnPw8AFMu8G+xMI+4FsQBiEDA2uZnJxg154dmFqDvd7CaByDCNYMrCObzbJl2yakRNBXqhsC/7KgM0p/7yDGBGzc/LDr6XwFUAnmqwZ72rKyezWlpaXs2rmD7u5u1q9fD/ghYI8nH//85vFZBPJkxU8i9XjOzdatW3XJkrqCRQn9vX25n3t6+zQMQyVBbuHEmrXrcvrgmrXOAy9ESaAm3/cOFn6O9UQY6spVqxfOP//zvJ4Ita2tTU3s/dfW3FKghwmjVVVVWpRMKqDV5RWaTCbVhOKuH4omEgmtqaxUQFOJhFZVVWmYMAX30N7S6rwFRbRtabsmkokCvXd1T66Nq1b3aCJRqK9ZsxCDgTVr1Zj4/HEM8vU1axbFIJHQnp6+c8YbvwjE4ynAP78fJx9Aj8fz+0VPTw82ky3YNnr6FBcEAcXGUFxcgmaztGaFUuu+huZmZug2hqbA+fOpKjWRsCTrPO5MYKgLAnqCgJkpN75cFAntWYEoorysnFCES4OA0ydOuItmYVVWIBtRu6Qeq8pVQcDw0RESiUROz2YsS5Y0MJtOc3kQMDoxTlVVNTarrMwKNqtUVlZxemyMK4KAuUyGhoZGshkbnx/CMGTo6AhXBQFWlbq6OlBYmV0YKj594gQXBwEJEcpKy4iiiPaskIy9BGemJlkdBCwJAgJjsNZSmxWq4xiA0hAErAgC5mZmcjFYmhWy2SwlJSWkRLgwCBg9efIx/pQ9Hs8TGZ8AejyeRxVrLW/+szdz6syZhY0GisvL2V1SQkNzCzt3bCcycESVKWshgInJSc5UVkJNLcNDQxDAaVVOqEIABw8cIFW3hGPl5cymMxDCrCoH1bmgbN22haVtS9laVExlXZ0bHgV2qJJRZfOmDaxesZIHkkl6+vrJ2ExOB9i9dycD/QP8OpFgcHANx0+5Qr47Y/3k6AkG16zlwUSC/r5+du7eUXB8VrOs7unlgWSSVStWsmnTBjKZTO54QiivrWV7UTGtbW1s27YFq5aDqqRVIYTpuTmOl5eTrKvj0MGDEMApVUbjeYtHjhzB1NZyuryCianpXAwOqaKibN+xjebWNnYWF1PX3EwYho/xp+3xeJ6w/K67IJ/I+C5kj+dsPv3pTzs/u7/N86W7kEIPPFBuQDmJsg/lhZztE/hqlCMoIyhvWqQZVK4RZTfK6XN45AWoXCjKxtgn718XnT+Iff3uiT32bkEpXnT+OlF+FPvw/RiVJVJ4jiKUr8XH34tKt/P5my8xh0H5dHz9TahcLGfH4L1x+/egcq2cHYM/QRlGGUJ57dkx4Pkoe1FOobxnQRMRDYJA/+Ef/sEPAXs858A/v70P4G+Fn0Tq8ZxNz0APO3p2oP+iCz6ADwHn41IRAXOhwd5vFw6ajH0AJ2MfwGaDPWhzvXhYMF0Guz/2wCsSdERdlY0Yc6XB/tzGJn7ALqA7r2GvAb7Kwsrd24Bn5OkfAD7EwvH/AbwuT78JeG38cwC8B/jbPP0O4OnxzyFugcqX8vS9QNd8Y0GeKujdeV+/Y3EMZuIYtBvsPrswThOBWWawR+IYlMYxKM+LwaUG+ysXgxc873l89vOf9z6AHs858M9vPwTs8XgeZUZGRtCeRb9XtgAl0CaCBGB7baFeBrQ6f7siwK7MS/4ADNgeS0JcZTYaKEj+ID5nAMtFXGm0zkUN6wEUBuZ9AHvOoVtYE9fyPacOrBVxTtKPoA/M+wAu1juAJHSIQADauyhGlUAj1Mz7AK62hd/QAdhVlhSxF2ILBcnffAwkgEuMYeumTXg8Hs8j4RNAj8fzqHLJRZcQfCtw9W3nuQOYgiFVQg0wPzTO/mWeLaA7lEkgawxyj7hKHvMcA7lTUDGcEUEPKvw6T58G8z+G0AbsV3XX/m6eHjkrmEQQssnGBsv/laerex+kAjaoIgkp1HG6JIT1qgSpwOlaqCPOvjAMQsw3TaFP4PeANBwSIbQB5nuLfALXg+5XxkTQIEDuXuSVOATyCyEbBEyIQXcp5Od4E2B+YAg05AER1lxwAbOzs3g8Hs+58DOEPR7Po8p7//q93Hb5bfDcvI2vBQysWtXH+PgYR0YOYy4y2Dc5H0D5F0EDpatrJdlsxN4DuzEXG+xbXK+eudFg5yxt7R2kUil27N7uSqm9VaEWzOcMdtjS0NhMTXUtW7dvdv54bwGWOx9B+5ClpraGppYWtm3ZQvTOyA0TnwfyHVeWrbishOWrOtm5Ywdzn5qFE8CVOMPlmyGZSrJycDX79+1l4rsTyHMEfaHCeuBzYIyhr2+A4aEjnHz4JHJZ7OO3H/gXF4PVPX2Mjp5maOSIi8GfOB9A+ZSgRuleuYr03Bz7Du51Mfhz68rAfdpgI0tHRwdhmGTH7m3IVXEMKsF81mBPWJqbWqiqqubb3/4OZ/IX4ng8Hk8+v9spiE9s/CRSj+fc3HnnnVpdV12waGFwcE1ugUJnZ7eGyXBhMYM477/5ffv6B9QExi0mEdSEpsDjLueHFy+aSKRCXdbRoYExzkOvfyC32GNeX1Jfn/P5W9HZteChBxqkjJaUlGh1eYUC2lzfoMYYNUm3oEOSoiKiLQ2NCmhVebmWlpZqkAoKzrOyqzvnE1hf36BhUVjQjoGBwZxPYEdHpyZSiYL7GMzz+evp7dMgDBZiEBjt6+sv9AGU/BgkdHlnV24Ryto8X0X8IhCPpwD//PY+gB6P5zHgiiuuQDKF8/xOHh3hQmNIAdU11Wg6y3Kg1Nnb5XwAG43BmAAbWZYoNCrYrCWVSlFrDL3GMD3hxo+LLKwASEfU1S1BVLnUGI4eicePIxgUwc5laW1bymw6zZVBwO59e0mliiALAyJEc5bWtnZGJ8a5PAgYPn6M+voGbFoZEEHTypIl9QwdO8rlQcCZiQla25YSzUUMxKXnEokEu/fu4arYJ7C1tQ3JitMj15xjR45wsTEEQG1dLVEmohtIxqGamZxgtTHUGUMymSTKRixRqFWwkSUIQxqMYaUxzE5Pg0LKuumO2XSWmpoaksBFxnBsKH8M3ePxeArxCaDH43lUUVU++clPcmZ6YmGjgeol9fxaleKycg7s34cmDftw0+AkIWRtxEFjGE8kODN6CpM0nACO4mrwHjt+lLlUip0iEIZIQpjFjeJGobB3zx4qKyu5T5WGtja3UhfYqIoNhe1bt9LS2MidUcTg4BrmMm5+3CZVMLB79w66l3dydxTR29PL0aMjCzpw/Pgx+nv7uDuK6OpYzu5dO8Es6JkoQ//AGu6IIloaGtm+fStZsjmdAJa0tvIrVcorKti7Zw8ErsxxOo4BxrBHDDPJFCdOHM/F4FQcg1OnTjKRTLLfGLLxXMU53AJjCYX9+/ZSUl7Og6o0ty97zD5jj8fzJOB33QX5RMZ3IXs8Z/PVr351wcNufgiy0w2DlpWVaRDEw6avQHkI5S5ULnUeecXFxZpKpZwP39WxT9/9KNe6YdBEIqElJSXOb+98UX6Gsh7lNfFQbhBoWVnZgs/f91C2oPzlQlsqKiqcvkScj992lI8vDNNWVVW5fYtR/h1lB8pnUUoW6QblY/Hxt6BS7+6hprZWIfYCfGd8/e+j0uVK21VWVbkyeKC8CuVhlNtj38IALS0t1VSqSJHY6/C+2Gfw2c4nMJUq0tLSUncPF4tyRxzH6137wjDMtfGd73ynHwL2eM6Bf357H8DfCu8j5PGczXkXnceGug3Ym+yCD+AvgKfFPwuYHoPdlGdzcgqkRdC52AOvJva7K471dOyBNxKPlYbAIaAp1hXMBQa7Ps8H8CFg3UK75EUC3wPNxF953wZemNfwvwD5F9CsayP/CLw9T/808DZ3LQlB/xz4pzz9e8Dz42slgGsF/Xbe1+tGYE38swEzaLAPxSuSAY4BreRWDpuG2AsxFeuzYNoM9mTsA5gUdEihLi8Ggwa7xS0aecmLX8ynb7zR+wB6POfAP7/9ELDH43mU2bdvH/aSRT5/K4ByaBVBQpye/+1TC3Q5K7wUYNflJX8ASbAXOB/AGkDaZCH5AxCwl7oVw8vmfQDXFjZBL1E0UvrmfQAvWdTwS1zyNyDi+szOoaNO1+wj6ECfMah11ytgwN1ce+wDaC/NS/4AGkCWCdW45tvz85I/gCIXlxSxBeJyFpK/+RhcYpEQLjSGh371Kzwej+eR8Amgx+N5VOnv68f8xBR65P0amHC1fyUSzM+Mm/g2zyHQncoY8Xy4+wVG8/QJML8wZBVOA3pA3eS5ebJgfmowVjgw7wN4V56uILcKJjRssXFy+uNFDf8xmISwSRUJzqHfCgRuzp9JyjmPB9hiLSYwyK1SGINfAHNwcD4Gt5lCr8S9oPuUUdxmc88ir8QzIPe5OX9nAN2tcDBPz+DiGgkPWMuFT3kK1i5KxD0ejyfmSZ0ADg0Ncf3111NbW0tJSQlr1qzhoYceyumqygc+8AGam5spLi7miiuuYOvWrb/DFns8T3xu+MsbsL+0C2XTAF4NGGhpbaWsrAJ7yCLPEmfW/GUwz3AJY0NjE7V1deiMIlcJfB34JsjTBTtmqa6ppbGxyZVSu1pcebbvgVwj2F2WkuIyWlvbnP4SgX/FJW7Xgd6uJE2Spe3LXM/bm4FP4ErC/RnwnyDWsGzZMjTClYV7T6z/DfBBIIJly5ZhbOCu/Wbgp8AngTcBAp1d3SSDFHqnwsvi6/9bPARt3PFlZeXY3Rb5AzcszRfBXG1AoKWlldq6OuyEdTH4Js6E+ipBp5W6uiU0NzcDIM8Q+DLwPyDPEux+S3lZBcuWdXDLLbfwzne+8zH6lD0ezxOe3/EcxMeM06dPa3t7u7761a/WX/3qV7p//3792c9+pnv27Mnt8/GPf1zLy8v1W9/6lm7evFlf+tKXalNTk46Pj/9G1/CTSD2ec3PzzTdrcUVxoQ9g7HGXSiS0paVVg6Igp0ko2ht73BkR7V6xUk3S5HSTNLq8s1PD2Oevf2Awt2gD0KAo0MamJi1OpuJrrc355wEapAKtqKjI+fz19/YV+O+ZhGgYJrSloUEB7epYnmuX+9vt17W8UwFtqq/XRCKx0Mb4PP3xPVSVl2tlZeWCT2DcljWx12FxMqVNTU0LPoHxtfpi/8LAGO3s6lo4Po5BV/eKnNdhX19/rn2AhkWhNje3aCrhvAUvuPAi7wPo8TwC/vn9JPYB/MQnPkFbWxs33XQTF154IcuWLePpT386nZ2uQKiq8s///M/89V//NX/4h39IX18fX/rSl5ienuZrX/va77j1Hs8Tm+uuu44iLSw0dHJ4iAuMQbJZmpqa0dmITlwZYM0qUSZDtzHUi1BSXIxNW+qBZsCmLRUVVVQCPcYwOzUJkZsmuAKQ2YjW1jYymTSXGMPQgf251GeNCDoX0dW1gtGJca4MAjZv3eJ8AK2b02czSmdnJ0PHjnFZELBn/z6ampvRrDIYz/lraGxkz769XB4EjBw/TmdnNzZt3ZxBC8lkki1bt3BV7BPY2bUCyQqD83MKFYYPHeRiY8hm0rS0thHNOR/AVByD9PQ0q42hGigvryCai2jATfWzaUtpaSl1wEpjyKTn0KxSDHQB2dkszS0tkM1yoTEc2bfv8fmwPR7PE5InbQL4ve99j/PPP58Xv/jF1NfXs3btWj7/+c/n9P3793P06FGuvvrq3LZUKsXll1/Ovffe+7tossfzpOHrX/86c5It2FbX3MKD1qJhyMjRYSQVsBeYxHnYBYkEu63luCrTM9OYpOE4MIzzwBsfH2MM2GYtRSVlEMAMzgdQiwKGjhwmkUhyn7W0tHe4YV7B1fZNBezZu5uq8nLujCL6e/uYm5vN+fiZhGHfvr001zfw8yiic1kHI8PDSChsjOcEHjt6lK6O5dwdRTTV17Nv3x5MwuR8BNPpNH29fdwRRVSVl7N3zy40VDaq5trStLSd+60lSCQYGjpCkArYDcwBEgip0lK2W8soMDkxQZAMOAacjGMwPTXFSWCntSRSKSQUZoA9QFgUMjw0BGHIA9bS0rH88fmwPR7PE5PfdRfkY0UqldJUKqU33HCDPvzww/qZz3xGi4qK9Etf+pKqqt5zzz0K6NDQUMFxb3jDG/Tqq68+5zlnZ2d1bGws9zp8+PDvfReyx7OYH/3oR66/6w/yfACXuGHS1ralWllZ6Xz+Lhfl2yhfij3yArSpqVmXLKl3HneDsU/ff8Wef8Z57DU1Nzt9mSj/ifJdVJ4hiqDl5eXa1rbUnb9WlE+j/BDlpa4dRcXF2t6+zA3JFsc+fj9G+dMFD71lHR2uzQHKX6H8BOU9Cz6Byzo6Fnz83hQf//HYJ1DQrq4uLS4ucfqL4+vfiEqdu4dlHcu1oqLS+fw9XZTvoHwBlQ4Xg5bWNq2rW+LucZ0oX499Btc4fUl9g7a0tC54Hd6E8m1UrnAxqKys0mXxEPZ1113nh4A9nnPgh4BVn7QJYCKR0EsuuaRg21ve8ha9+OKLVXUhARweHi7Y5/Wvf70+61nPOuc53//+9xfMqZl//T7/A/J4FvO0K56m5qlGOZr3/8kPF342RtQsM8ocmvtzECVEhfhVJsrpPH0cNdVGTVznFkHZladnULPSaBAszInjjjzdumQrf14hN+XpivJaFmr/BigfWKT/7UISaJLizKfz/3wp7x6TRuVKUWyefleeHoiabqOk8/Q9C3MJBdRUGWUsTx9FpTzv/gKU/Xn6HGqWGxXj9nn5y1+uIyMjPgH0eM6BTwCfxHMAm5qa6OnpKdi2evVqDh06BEBjYyMAR48eLdjn+PHjOePUxdxwww2MjY3lXocPH34MWu7xPLHZvGUz9lmLPO7OJ+cDqIFin2EhmacvBVkpVOA268UK1Xl6OdinWQKUWkA6BLrz9BDs1ZbIKB3zPoBX5OkC+geKzdoFH8BnL2r4H4BNx3P+onPrRPGcwbSeW8f5ANrIon+ghTG4jJwPoDWKvdq6ds7TCbLc+QCGgH2qhXx/2irnLTjvAyjdAsvy9CQuroFyoTE8cM89GPOk/Yr3eDy/JU/ab4enPOUp7Ny5s2Dbrl27aG9vB6Cjo4PGxkZuu+22nJ5Op7n77ru59NJLz3nOVCpFRUVFwcvj8RTS0dHhfPzy2UXOB1CzYO4zCxU7wBW73QNjuPlwZr1xE/zmSYN50JBRt6seUhjJ0xXMvQYi2D/vA7ihsAlynyCBLPgA3r+o4fe5Ch+5OXvn0BE3Z1DC+P1iHecDKMZdr4BN5HwAieL25vsEHnP+hjkfwAeNC8Y8s2AeNjkfQPbhJgfmx+A+g2bhAWtZd9FFeDwezyPyu+6CfKx44IEHNAxD/chHPqK7d+/Wm2++WUtKSvSrX/1qbp+Pf/zjWllZqd/+9rd18+bN+vKXv9zbwHg8vyVf+cpX3LDjG/OGgLvI1bnN1QK+HlfH9+6FWsBFRUWaTCbdHL5nxXVwf4XyXHK1gIuLi938twviOrgb3PAtoMYYVyc3jOfH/QBlG8q7F9pSUAv4FpSdKJ9YGN6trKx0+5bgagDvQvkcuTl+Od3g5v7tRPl6Xi3gmhrN1QJ+F8pWlB+g0u30ysrKhTmEr47bf2dhLeBkKuXmCD5H3P3fj8ofLNQCLi6O6yFfIm5oeT3KH7v2B2GYa+O73vUuPwTs8ZwD//x+Es8BVFX9/ve/r319fZpKpXTVqlX6uc99rkC31ur73/9+bWxs1FQqpZdddplu3rz5Nz6//wfk8ZyNtVY/9rGPqUnkzbczaG/fgBoRra6o0NraugJdEqJdXd2aDEMtKSrStralKsmF+W4mabS5uUXLios1EYS6YuUqlUSenjBaVVWttVVVKiLa31/oEyihaFFRsbY2NjmfwMG1ufl28+0TI7qi0/n89fX0nnO+77x/YNfyThVjzjrHYOzz19LYqMXFxQU+fYRo34CLQW1VlVZXVxfMSZSEaPeKFZoIQi0rLtaWlpazvBBb29q0pKhIk2GoXd3dhTFIGq2pqdGaiko1Inr+BRd6H0CP5xHwz+8n8RxAgGuvvZbNmzczOzvL9u3becMb3lCgiwgf+MAHGBkZYXZ2lrvvvpu+vr7fUWs9nicHIsJf/dVfUVVSvrDRwpkTxzhfhJmJCTqWL0cyluVAKaAZJRGGtFtLRTpNdU0tmlbqcCV/bdrS0NBIam6OFWohm0Uzbj5cNxBkla7ubsbGxrhEhGNHDkPkLj0ogskqq3t7OXJ0hKuCgE2bNpBMuEK78z5+3d2r2LXX+fxt2bbVVRwB+sUN5dY3NLJ56xauCAL27NvLihUrcz6CAMkwyeaNG7gqCBg6epTVq3sJCXM6WThxZIiLRBgfG6Ozqxuyrv3JOAZilS61FM3NsaS+IeeFWBvHoLa2jvJ0mg5rCcXkYtAZH7+8s4upiXEuEGH44IHH7DP2eDxPfJ7UCaDH4/nd8POf/xxNFH691DU28YC1bg7b6CiSDNknMBXvliwqYre1HLUWayNMYDgpMCJgAsNceo5T1rLVWkrK3fzbOeNKAmsy4NTJE1jgXmtpbG11Jw3cnD5TFHLk8CFSiQR3RBHdyztJz81B6Ob0BSnD0JFDVJWX53z+jh8/hkkKm1WRpHDi+DGaGxq4K4qoLCvjyOFDBKnA+QCGbg5xV2cXd0QRqUSCI0cOQ6hOD1xzGlpauc9aIuD0qZMECecDmI5jUFJeznZrOWkt6XSaIAw4LnBKwISGKBtxzFp2WkuqpATExWCvQJgMGT19mjTwK2tpaGl9bD9kj8fzhEZUVf/33TznYnx8nMrKSsbGxvyCEI8n5r777uOyyy8j25tdWIhRBKShZ3UfE+PjHB4+hOk22DdamAD5lKBnlJVdq8hms+w9uAfTYLB/biEAc6PBDlk6li4nVVTEjl3bkHJB36pQC+bzBrvN0tzYQnVNNVu3b3ErbP8cWA7yVUHvU2pr6mhpaWXr1s1EGsGfAGtBvivorUp5eTkdy7vYtXM7s7OzcB1uNfHdwM1uIdjKVT3s37+XifFx5NmCvlDdfX4WjBj6+gYYHjrCyVMnkYsEfaXCfuDTLgZ9Pf2cOTPKkeEjmFUG+ycWRkH+RdBxZdWKHtLpNPsO7ME0xzHQOAYjls6ObhJhgh27tyGVcQwqwXzOYHdZWpuWUlVdxfZt27jyyiv42c9+BsCxY8eor69/HP8leDz/7+Kf3z4B/K3w/4A8nrO55tpr+OnhnxL9KIL5TqivAde5IYcgCIjqFLsrz+ZkC9APgQgiQhQqukehLdaPOYuUYM6g1hKpwoM4exmAKTCrDGZEsFHkFhj/N/BHsZ4Fc4kh2GzIzGXdKt+/B/5PrCvwIgh+GBDNRUhC0D9V+FTejf0fkBsFzShBKiB6dgTfYcHq5VPAO1wSaEKD7bfY+63zdAH4LvBCd/8iYBsUu8O6WngADwPnxToQJS26V90YOMAQSKdgsq67MIoi2AgMxPoEmBUGc9LpL3zhC/n7v//7nPOBTwA9ngX889sPAXs8nkeZ+x+4n+iPokKPu6uAUmgRISsR9jmLPO76QFYJZUBoLXppXvIH0AB6pSI2ch547bKQ/OHObZ9vyZqIZfM+gC/M00OwL7FkslkGTWy/8rI8XYCXQjQXudrBGS3UcbpmlLUiRHOR06VQR6EPyEZZ7Ivzkj+A5wFJWGqti8Fz85I/gHXO37DSWkwUoZfnJX8ALaBPU8IootxaZIUsJH/gvBKvtUSS5QJVNjz4IEVFRXg8Hs+58Amgx+N5VGloaEC2L/LAGwam4bAqGoHZtuirZwo4AmOqzAJmt4H8UsIWzPbYB1AVjhGb4S0gWwUi2DfvA7h3UcO2AQY2zvsAbl+kb3f6hvlBkXPpwPr5OX2PoG+ysQn2Yn0/kI59CiOQbYtiNA4chdOqpAGzY5FXYuS2zQFnVGEIV0g5D7PNoBHcF0Ws7u/H4/F4HgmfAHo8nkeVN//Jm9H/Uvhc3sY/ZeHbRnFDo38NjAIHgVeCTi3MRrFD1s3PG8Ele38Odt9CNqRpRa4XZ4Z8Bvhb0Lt0IWEKQP5Y3NDyNPDvwJdxieG8/iZx5s1zwDeAT7JwvAH5K4GfAGngpyDvloV7iIC/A74eH39/fL75Hr8s8BXgX3HJ7Za4PcH8DYL+XOEDcfv3g1wv6FxeDA5YeHN8/yPAm8AeyYvBjMIfAwfiOL4X7L02dw8//NGPuPnmm/F4PJ5z4ecA/hb4OQQez9lYa3nzn72Zz37mswsbDXR1ruDU0aNUVVVxYvQ0k9OTBQlbc2Mb2ZlpwjAkbS0nR0/krFwIoLqyhtJUitmZWUoqKzg0dHChl1CgpLiEpiX1nDp9ivrmFnbt3VHQixiEASuWdzN05BDtnd1s3rZx4fxxGwd6B9i7exddK1ezccv6Qj2Awf617Nmxnc7uFWzauqmwhy6A/t4BDuzZQ2trG7v27SHK5p0ghBWdqzg+PERNTS1HTxxnemZ6oRpICG1NS5mdnCCVKmImk+bUmVMFMaitrqMoCEinM6RKSzkysmB3g4HS4lIaausYHR2lvauLLZs3kc26IPg5gB7PAv757XsAPR7Po4wxhs/8+2eoq6pa2GhhbnKSrukpjg4dYeWqHgLragOXiBu6raoop2LsDHrqJC2trRBBjQhLYn3Zsg5mjx+nfmKcokQSslAkQrsICYSe3j4OHjrI6ulpxk6ezCV/K0VIiNA/sIbtu3Zw3twc27ZuJmESOR2gu3MFmzZv4vxMho0b11Nf21Cg11UvYeOG9ZyfybBp8yZWdq8q0EMJ2b51KxfMzbF9104GBtaQSCRyOlmYOH2aVdPTHDl0kNU9vRgxtIuQFIEslBYXUzs+ztyJ47Qtbc/FoDqOQVvbUrInT1I9PkZ5aSlELgZtIogKq1f3MnT4EN3TU5wcHs4lfx6Px7MYnwB6PJ5Hne3btyOJRMG2qpoaHowiZqxlZmYaCUOOhMp04LrAkkVF7IkiRqIIiVcDnw6VE6FijMFay8koYlsUUVzqVk/MBsrBUCEImJyYJKvKfVFE7ZIl7qIh7AwVQucTaES4M4pobmgkk8nk9CA0nDhxnKJkkrujiOryCs6cGcWEws5QMaEwNnaGmopK7o59/o4dP0aYCOLzQzabpaWxiTuiCCPCqZMnQcjpADV1ddwfRaRVmZqaJggCDoZKOo5BUUkpO6KIE1GEqrvv06EyGscA4FgUsSuKSBWX5GJwOFTCMGR6epo5VR6IIqrr6h7rj9nj8TyB8UPAvwW+C9njOZtt27Zx4SUXMl0+jQ4tfL0EYcDg4Fqmp2fYsWsrptpgr3c+gHwVyMJg/zpsFLF520akSNA/dgsu5MuCTit9qwdIJBKs3/QwBAovx/kA3mywJy0rOldRVl7Gpk0byEZZ+EOgE8w3DPagpb1tGXX19WzfuoXp6Wnn47dOMd832C2WhvpG2tqWsmfPbpcAXmqwV1rMXQZ7j6WyspLu7pUcPnKYY8dGML0G+zyLrHc+gsXFJfT09nLq1EkOHNyPaTfYl1g3V/FbEAYhAwNrmJqaZOfuHZiaOAajOKucCNYMrCPKZtm8bRNSEsdAQb4i6IzS1ztAaAI2bF7vEsvrcD6AXzXYM5aV3aspLSlj964ddHV1sX79esAPAXs8+fjnNzypawE/1vhagh7P2bz0ZS/VYHmg7Mmrk/tp93fSGE2GoUqFKENo7s9dsR4EmgxDV2N3U56+09XSTYahJk1cH/fWPP0YampNof6ZPH0SNSuNJpKhJkVUjCg35OkZVK4QDYtCTYAGqUC5DsXGukW53tXbTYrbT54mSibvHH+NSiCaSCQ0mUqoWWGUiTz9c+4eE2GoyWRSTbVRjubpP4n1REKTiYQSomzP07egBE5PJBLuHu/I04dRqRRNJhKaSqX0Oc95ju7YscPXAvZ4zoF/fj/JawF7PJ7Hn9vuuI3ougjySgHzR0AJ1KqSIYs+X6E5T78cpFtIWYtEEVwK5LuYrACuAo2ylKoiLQLPytPrwf6RJS1ZGuLSbLwmTy8F+0pLJsrSKYJahTfm6SHoG5TsbJYVOD9A3siCz5/gVuGmLd3q9tM3aKHP35tAI6UrikhnM9g/XuTz92ogAQ1RRFrT2D+y0JCnXw3SJpRks2gm4yqQrMrTe4GngmQyFGezSIfAlXl6E+gLlQwZVmez7Nu1i+rqajwej+dc+ATQ4/E8qlRUVjjbknzOAHNwTBW1IMOLPPDSwCmYVGVOFTNiFlbHghsCHRIyGnvgjQGzi64xDFgYVnULQE4s0kcAgV3zPoCL2zjs9O35+y/WgR24/R5J3znvA7hYPwlkYUidXc1ZMZgFzsC4Khnc/S6OgRk2pHH7MIqzoMlDhgSNYHMU0drWhsfj8TwSPgH0eDyPKq/949divmLgR3kb34Xz/wPngXd77BMY4Xzy3gl6WnP5jt1r4YO4pCgNfAJ0q1OV2DPwbbj5gxHwJeCH7ucIFnz+TsYH/Aj4PJCN9RDkbQKH4wv+CuQTstDGAOQ9edngjvh9EB+v8f73xfqR+HxhfHwW+E/gB/H1TwFvAubz2gj0VoWb4vZPAm8HnVyIgW5X+Fh8/3PAh8DuzvMBPKPwF/GxEfAfoLe5G4iABx56KFcH2OPxeBbjF4H8FvhJpB7P2czOzvLCP3ohP/7Rjwu2t7a1kTQBJWVl7Ny9i0w67YaJ0+5VW1NLbVU1QRBwZGSYiYkJKMH1pk1BSUkJy5a2k56bY2xqkhMnjkMSSAHjECZCVq9YzdTEOFmBQ4cPul9xy3G9ZQGs6VvDmVOnSJaVsmv3DpeNVeMSNOMWYIweP0ZFXR2bt8Y+gXW4RDKA/p5Bxk+fpLq+gQ0bH3bZXm18foEVnStJT09TWVvLxi0b3PHVuETVQlvrUhIIpRUVbN+5nWwmWxCDurp6qsvLCRMJDh05zNTUFJTi2jkNZWVltDW3kM1mOT02xqnTJ10Mku4aiWSCld0rmJ6coqi0lIMHD7hz4BeBeDz5+Oe37wH0eDyPMkVFRfzoBz9iSU1NwfZQldSRw+zetpWBgUECoGZSSKVdz1tjYxPTB/ZzfO8eOru6ASidEcqn3VDpylWrGdq1k+yhg9RV1YBCIi3UTggJYxgcXMv2bVsoGTpCdmbWJWdZaDzj9IG+NWzYuIHakWEO7t1LEHfXNZ5y7evs6GTDhoepOTrC5k0bqa2qBaDxpNNrqmrZvHkjNSMjbFj/MJ3LuxDi4y0EBBw+eIC6kWE2btzAYN9aEmFI4xnn8YcFTWcoHjrCjq1bWDO4jiAIqJ0UwjlAYUlNDemDBxjZvZuVK1cDUDYtlM64GHR1r+DY3j3MHTxAQ309KIRpoWZSMMYwOLCWXdu2UXTkMJNnzuSSP4/H41mMTwA9Hs+jzvHjx5EgKNhWXFLKoXiOXzabBWM4Y5S5eNAzkUhwQpUz1iKxefK0KJOiufcT1nJMlURRCoAMyqhRECGTSZNV5YAqpWXx6guB44EixjAzPQ3ATlUqysqx1pXQOB5CYAwTk1MExrAbKE6mmJmZRWJdgJnpaUqKitgNGBGmJqcwxnA8XggSRRHlZWXsjAdVpmemQYTjRnOLSUpKSzmoSkaVdHoOI8KoUbKxnkilOKbKuI1QQESYEmUqjoExwpi1HLNKIulikEU5Y5xPYCabJq3KQVVKSksfvQ/U4/E86fBDwL8FvgvZ4zmbw4cPc+GlF3J09Kib3xdTXFJCT08v2WyWjZvXI4Gg1ygyLugdSpAIWTO4FlVl/caH3Erdq3H1c+PR5LWD5yEibNq8kWw6g1wuaK0iPxQ0qwz0rSEME+zYsY3pqSnMBQbbaZEfCTqlrF7ZS0lxCfv37+P06VOY1Qa7zmJucz6Cyzs6qa6qYWRkmOGRIcxSg32axfzCYA9ZmpuaaWpqYXR0lH0H9zgfv6stZr3BbrNU19SwfHknM9MzbNu5xfn4XaOY/Qb7gKWkpITV+TEI4xiMCnqXEiZDBvvXYlVZv/HX7qafhRsC/qlLCNcOngcIGzetJ8pkkSsFrVR3j5Ey2L+WIAg5cvgQZWWl7Nu3D/BDwB5PPv75jfcB/G3wPkIez9m8/g2v16AhUDbm+QC+z/0toIExKkXifO3m//yX0w2oiJztcXcPiqBGRM38Ob+Yp+9CpVTUGKMyr39wkU9gi9EgCFRAJRTldXk+fxOorBENUk4PUoHK1aKkYz2NyrMLdTOQ5/NnUd4Qnxc0DEM1TUYZyWvDhxbiEQSBSokoO/L0L8cxEncfCMov8vTYK9EYsxCjW/L0bagUiQZBoIBeeuml+uCDD3ofQI/nHPjnt/cB9Hg8jzL/84P/IXpVBI15G18PpKBaBGus8wHszdNfDNIuFIkQAnLhIo+7S3FegSglIki9wB/n6d2gL1assdSJuMktf5Gn14N9vSUios0YNKvwbhZ8/spA36pEcxFLcT6A+i6F+Wp2CdC/dHp7rNu35vn8CfBXoFmlTYQsWezrbWEM/g8QQp0IkUToixRW5unXgzQKJaqItfBU3Guey4GLwVhLsSrSKvDSPH218wGMJGK5MUyPj7N06dJH+JQ8Hs/vOz4B9Hg8jyphIoSZRRszgHWbNV7RWoAFZiGtSqR6tg7IlBCp24cMsR9LHvFwc+7QRR55TAMS+wjC2W2M359Z9L7g+N9AH30kfQ6wcTOVguFxwN1P2i0ItoBML/IJjLfFu+XOd1Yb1C1qLvVzAD0ez/8HPgH0eDyPKq94ySsIvhjApryNHwGyMKPqEp0fAj+JNQv8A+gxnV8si25R+ALkBk1vAX3QJW5pQEcVPspCAnQX8B13jan5BO/d8c4Am0E+J0gkjKsiCUFukIUk7BDIJwUJhDFw+gfFZVIAp0A+IEhCOANIIMjfCRyM9SmQG5w+qYpEgnxOYGOsZ4C/cj/mYvA/wB15MfiY80LMzN/2Qwo358XgS6CbnE9gFtATCp/Mi8FtON/BCMasZfj48VwdYI/H41mMXwTyW+AnkXo8Z3PmzBkuu+oyNq/fXLC9traWpuYWwjDBxs0Po1lFVgtMgB5RSkpK6eruxhjDtm1bSM+lXbmzAHSPWyDR3ztINspyYP8+JiYmkGaBKtBtCoGwdmAdmWyGY0ePcuLEcaROoBV0o+Z8/jKZDOPjYxw+cggpEzd8vFFBoa+nH2uVbDbDrj07kYRAH7AFNKN0L19BIpnEmIAt2zaCgAwK7HYmzm0tS6morCQRJtiw2fkEyoDAEOhJpa5uCY1NTSQSCTZsfBiNFOkROAM6rJSVlbN8+XJMELB162Yy6QzS6WY16j4lkUzS29uLjZS9e/cwNTXphoLLnXG0hMJg/zqibIbJqUkmxsc5edL52PhFIB7PAv75jV8E8tvgJ5F6POdmZmZG6+vqFhaBgHYt69CWINBQRNedd74Gxmhx3qKOgf4BrTGBlhmja9edp4AmRDQZL3hYd975WmyM1geB9vb05haNlIhowhhdu+58FdC2IND2ltbcdctENBEE2tvbr4B2GKNlJSVuoQVoGW5xydKlyxTQZcZoYIxWVlbldEArKio0NEaXxce1ty9TI5LTRUQrSkt1eaz39vdrIgy1bH7BBmhHW5u2BYGa+H7CMNRikdzClb7ePl0SBFpijK4773wFNAmaiPV1552vpcZobRBof/9ALgbF4hZ/rDvvfA1EtDUIdHn7soL4+0UgHs8C/vntF4F4PJ7HAGttQRlbAETc8C7xf1QLprDllgkvOmxROVwiFtZuKPEIqAiq7mzufV5bcvu6M0U4Hz+NBz9yenz8/Pnn9y+YZieSm3o4P3ZiC2QhO6/bcxyPO14BG+uL79dy9j3nysOpLmR0eYM3BfurFsTI4/F4zoUfAv4t8F3IHs/ZnD59mqdd+TS2bdpWsL26upq2pe0EQcCGTfEQ8EA8BLxfKS4uZuXK1YgIW7ZtIjOXQVa6Fb26XQkSAQP9a7DW8v9r77zj67jK/P28Z+beq95lNTdZli3barbTQxolLEsSAoSlhoQFFpaWAj/6brILJMCytA1lgSV0kg2QQFhKgBQSICS4yr13yZItWb3cO/P+/jijq6sSmpOYWOfJ536kO9+ZuWfece68Oue837Nr104GBwaQBQIloOvULvXWvJowSHH48GGOHz+GqTToPLXz6QTrsReG9PR0c+jwQaRA0GWK/EHQUFnR0IQYYWRkmF17diJZgrYqsl7QEaWutp7s7GxQZdPWNsQIeoZithnCEyE11XMpKS3FGI8NbWtBQVYLclAIO0JKS0upqZmH53us27AGApCVAj2g+5Tc3Fzq65eACJs2byQ1lkIabCqn25RYPEZjYzMaKtt3bGV4aNgOkxfYYWzxhdbmVaRSAf19vQwODtDV1QW4IWCHIxP3/MYNAZ8MrgvZ4ZjOjTfeqF6hZ338xjuw/sn6+KXfeyi/yvDQ+88Mbfz1jQyfvrtm0D+coT+MEsvQDMqbUZIZHnnFkvbPk5gol6MMRfoh1NQaNb4dvpW4qJwpSnek96BytqiJW934Rs1CoxyM9GGUF0XnBRUxKkUZXodJlLdMiYGP8lBGDG6d4RrvzNC/PYP+HxkxeCCKa6TV19frL37xCzcE7HDMgHt+uyFgh8PxJPPd733X+gBm+vy9B/AhV2xRBy8Enh1pAlxvPfBiIhiiwomrmRjHvArkHEGAhAhSLJN9/J4FvNR+Rn60bBy32PeA9cj7J0U9pdgYNKm2Mjk70msg/H8hYSqkBNAxRf9VoTjSi0BvUsIxq4epkPBdIcyN9Cz7eZpUCkVQL0TfqBMx8LGfJ5AjYt+/GLgwIwbvAikV4lh7BjlD4B8y9FeBtNr4xMB6Id6QEYOLgcsBD0qMoayoiObm5j9xtxwOx2zFJYAOh+NJJZVKQWLKRh8wEAebsGRN0QWI2908kWjHKSTARLkTMaZ/eyUm/Zgwcc7UFXKn7D/1+KdMjwOScelT9Siz84nmIGbNMDsnYXd7whhEJ88DRkenGiE6HA7HBC4BdDgcTyovufwl+F/34XDGxv8GRqFHFRMa5Icy2Sfwu6AHlBG1XoD6B7W+duM8DPprJUQYVEU7FW7P0LeB3CWY0HBMraULH2eiOqIdzJcNBsOhMER8mewj2AfmMwYv7nEQMAmDfFwmzKTHQD4qmIThIODFPeQzAr2RHgIfAfGFI6p46mG+YuBIpI+3J4Du8Rh8XyBzmuTXQTuUISAUgd9g/Q3H+RXo75VAxBpqH458AsdpA7lb8PA4EIbEsrPZu3fvH7tVDodjFuOKQE4CN4nU4ZjO4cOHOfv8szl89DCMTGzPycmhYdlywiC0HnkCXArSJ+gj1uevuakVAdZtWEsYhHY5OA/4FYgRVrasBmDTpo3WJ/BcQUvVmkqHtsjD8zx27NjOQH8/ptkQLg6Rn9sijhXLmshKZLFv/z6OH+vCLDaEq0LMLw1hb0hd7WIKC4ro7DrKocMHMZWG8Fkh5jeGsD2kpmouFRWV9PX3sWvPDkyhIXxOiFlvCHeGlJaWsWDhQkZHR9m8tQ1JCPp3itltCDeE5OXlsWRpA2EQsL5tnf0T/FKQHkF/a33+mprssO26DWtsJfGzsQnk/daAejwGbW0brE/g+YIWKtxn92ttXoVnDIePHCYRj7N/v3WrdkUgDscE7vnN7CgCueWWWxTQ6667Lr0tDEO96aabtKqqSrOysvSiiy7STZs2/UXndZNIHY6Z6erq0jnl5ZMKFhrql2h+5JHX2rpSPWPUmInCiJaWlZpljPoiaR9AMaiI9dgb97jLMSbtgQeoZ9CY52lzS6v16zNGF9cumtA9NO55Wr9kqQJaaIyWFRWnC0I8D/WM0YqKSjUiWmCMZsXjmpubqxLpApqdk6PZ8YQWGqNGRKuqq9UzRr2MwovykhItjK5xydIGjcVik/Qli+q0INKbW1rV930bg0hvbm7RHGPUE9FVq1ariETXb2OwctUZ6ototjHaEl0voMagvu9rS+tKBTTfGF26uN75ADocT4B7fs+CIpDHH3+cL33pS9MmQ3/84x/nk5/8JLfddhuPP/44lZWVPO95z6O/v/8UtdThOH0oKSlBg8mL9Y4MD1MtQkIMxvNAlQIV4lEVQyqVpFSEAmPSHnfZKuQy4dmXK0K5CKkxu8abL0KhCqji+z6eCHNFGB6aWGi3NBRCtTYzAIuAgaFBjLFff6WB9S3Myc0lVKUWGBkbIysrC410BbKyshhJjrEQCFXJysomDENKo8v0PI/BwUEWRZ+blZWFqlIaTjjyDQ0NUSOCL0LMj6GqFKp9D5AcG6NMhDwRiK47ByF7PAaq5BtDqQjJjBgUqBCGIb7nExehWoTh4RkWVHY4HI6I03oIeGBggFWrVvH5z3+eD3/4w7S2tvLpT38aVaW6uprrr7+e97znPYCdMF1RUcHHPvYx3vSmN/1Z53ddyA7HdIaHh7nsRZdx/y/un7S9uqaG3EQWWTk5bN2xldRYylbZjgJDUFRcTEVZOZ7nsf/gQQYHByAfRED7IDs7m0ULa0klkxzrOcHx7i7IAskG7QYv5rGiYQVDAwOMpFIcOnwAfJBC0C7A2CHigRM9mESCnbu3AyDloB2R3ryS/u5ucgoL2bRlo53bVwlE+oplTQz39VFQUsL6jevsUm+V0fmBxYuWoGNj5BcX2yHeIDp/L5CCudVzyYrFyc7NY8v2zQTJwMZgxL5KS8ooKy7Gj8XYs2+fTeLGv1r6IDc3lwXz5hMEAV3Hj9Hd3Y3kgCaAHvBjPsuWLmN4cBA/K5tDB/czMDAAuCFghyMT9/zm9B4Cfu1rX6vXX3+9qqpedNFF6SHg3bt3K6Br166dtP8VV1yhr33ta5/wfCMjI9rb25t+HTx4cNZ3ITscU7n55pvVJMxk37orI2++TA+7r6IEkRffDU/g8zeKMobyiRn0t6AMRuf4zpTze9Fndkc+eb9AyZrswSfniXIk8tBbg0q5TPj0+aipN8qOSN+JyhKx3n3ROaRMlMcj/Qgq58tkL8IslJ9Hn9+D8pLJPn0Iyjej9g+ivG2Ga/x4dP1jKLfMoF8XxS9A+dpkrbi4WO+44w43BOxwzIAbAj6Nh4DvuOMO1q5dy6233jpN6+joAKCiomLS9oqKirQ2E7feeiuFhYXp17x5857cRjscpwG3f+t2wteG8NyMjbcC4xYuBuR5Aq+zv5MNfMx64I0Plpp6A+/HWqfEgBtBmqxqAMkX+E8gJ9rwSqy3oBd9RgB8Htu7Jti2vBHwI3eYFOinFKqiD1wF+l47GDKuhx8JoT7SF4PeopCKdAF9j8IZkV4F+mmFpK1ZwQfeAFwafX5R1J4w0j3g74HXRO3PAf4DpGAiBrJC4F1Rg2LAe7Ero0SnlGKxlcXZ0TmuAXm+9VmMibC6tZVLLrnkz7hjDodjNnJaJoAHDx7kuuuu41vf+hZZWVMNxyYQkUnvVXXatkze97730dvbm34dPHjwSWuzw3G6MNA/AFNHGguAOJSJIAa0YsrMkxhQBHkiJEQI54STF7MV0ErFFygQgXym++hVAgYqx42WS6foFYBC3fj/41PbGOmLx9/PpBPp+kd0Y2bWiwE/ap+J2ptJAiiw1xfDXu+0GFQocSKz60Km+SVqpSICy4zhyOHDOBwOxxNxWiaAa9asobOzk9WrV+P7Pr7v89BDD/HZz34W3/fTPX9Te/s6Ozun9QpmkkgkKCgomPRyOByTufQ5l+J924O+jI13AcPQLUIM3/oAZv79dD/obmXUGEJj4HfA+gx9K/AAGOMxKIIeUfi/DL0DzPcMcfXpFIEU8OUMfQDM1w0xL8YerJ0Kn8/QkyBfFPwsn90ieAkPvsCEj6ACn7f+gLvF7idfEkhmnOPz9ry7jSHmxzDfMJBZU/ZV+zlHPY84MesD2J6h/wz0kDLoeRCLwUPA5gx9I/AIaCzGsOej+6Z4JR62PoAxE2eb5zF/0SKOHz+Ow+FwzMRpWQTS39+f9r4a53Wvex0NDQ285z3vYcWKFVRXV3PDDTfw7ne/G4CxsTHmzJnjikAcjpNk+/btnHnOmQwkBtCjE18vfsynpXklwyPDbNm6CSkU9JVqE8X/BUKhtWklqsqGtnXWQ+8VCh7Id6yPX1NjCzEvxrqNa1AUXgaUgrnDEPaELFuynJzcXDZuXE8ymUQuF7ROMd8zhEdCahcsorS0jK1btjA4OIC5xBCeEWLuNYTbQ6qqqqmpmcvu3bvp6T6OOcMQXhJiHjSEj4eUlJSwqG4xRw4f5kj7YcwSQ3h5iKwV9H4lJyeH5Ssa6T5+nD37d2OqDOHLQmSPoD9SYrEYzc2tDA4Nsm37FkyRIXxFCD02BqJCa8sqglTAxs3rkawoRgryXUFHlZbGVjzPY+2GtWDULhdXCOa71stwWcMKcnJy2LljBwsWzKetrQ1wRSAORybu+c3pXQSSSWYRiKrqRz/6US0sLNQf/OAH2tbWpq985Su1qqpK+/r6/uxzukmkDsfM7NixQ8un+AA2rmhUiX5f0dikvufZoomosKJ15er0vq0rV1mfPh8lhhpj0h53EnkGEhVrEENjvq/Llq9I68uWNEwUg8TQWMzXBQsWpr3/aiqrJumeb7S4pEQTsZj1CszL00QioeLZwg7xROPxuBbl5yugiVhMS0pK1fONvYaouGNeVbVtg4guWFirsVhs0jUub1iWjsHy5Sum6a2tK9N6S+sqNcZYzbcxaF25aiJGrVNiEIvpihVN6Rg0RvEYf7kiEIdjAvf8Po2LQP4U7373u7n++ut5y1vewhlnnMHhw4e57777yM/PP9VNczie8dTX16PJ5KRtvd3drPI8so0hOysbDQKqU0JOYCe6jQ0PU+d5VHoeqoqqUhwIZSnrcWeModTzWOp5jAxaa5NEIMxLCQQBOTm5+CKc5Xn0dB+zHxpAfUrQVEBJSSmqykWex9GuTmKxGASwOCUEqZDSklJGk0ku8Dx6BwYoKCxEA2VxStBAyc8v4ER/Pxd4HqPJJKVlZQSpkMUpgQA8z6ejq5OLo/YXFxcD9vykbHN6jh/nTM8jLkJ2Tg5BEDA3JcSiGAwPDbHE8yjzPETsdRcHQmFgf0dhjudR73mMjoykY1CTElKpFNk52SREWO15nOjufqpvs8PheAZzWg4BP124LmSHYzpBEPBPb/onvvo/X53YaKCutp6+7mMUFBbR3tnJ0PDgxBw7D6oqaiCZxPd9hsbGON5zbGKtXgPFhcUU5OQyOjqCn53DoSMHbLVvRHZ2NnOrqunpPk7xnAp27tmeTrwAjG9YVr+UI4cOU7NwIZu2bpykY6C5sYUDe3azcHF92scvjWd9BPfv2sn8RXVs3LRhon2R3ri8mcP79lFVU8P2XTsIUhkn8KG+dgk9XZ0UF5dw+GgHQ8NDk2Iwt3oeqeFh4okE/UND9PT2TIpBSVEpuVkJkmNJTCLOkY7DE20UyMnOoXpOBSdOnKBm4UK2bN5EMkrE3RCwwzGBe34ze4aAnwpcF7LDMZ1PfepTKkaUj2b40p03xQMPlJtQ+lAOo7ycCQ++8debULpQjqO8Y4pmUF6EcgBlYAaPPC/y5duG9RL8ypTP91BZKsofUFIoP0DJnXx+qRDlV5HH3gOoVMpkr8EclO9Hx69BZZlM/gyD8iWUEZTtqDxLpsfgwyj9KAdRXsx0r8S3RdffhfLPUzRBuQrlUBTHf5usx2Ix/exnP+uGgB2OGXDPb1XXA3gSuL8gHI7pNDQ2sL1lO3yKtDUK64DV2N4sA+YcQ/ibjO6zIZBKQfvt15GZawj3jZvm2ePMEkO42x4j2YJ26MQqGYA8R9CHdKJHbBdQl9GwfwS+yUSv3/1Apk3eh4Cbmehxux24NkP/BnBN9LsB/hW4KUN/CLg4+t3HevzdnqHvhfQ6cQbkQkEfyPj67Y9iMBTFoNYQ7gonvBoCMIsM4YEoBnlRDHInTmEuMIS/DSGEKy67jC//z/+knQ1cD6DDMYF7fp+mNjAOh+PU0dnZCUumbKwCsmFe5AMYLg0n6zlADRSKkAWEizOSP4DomJhAqQiUMyn5A9AGBQOLRKyv4MIpbWgAFFqiNYCntXEJEELruE/gVH2p/bFSxCaJT6C3jPsALp2izwfiUCvWrFkbpvztnQ9UQIkIcSBcEk7+hvYgrA9JAEUiNqa5k08RLg0RD871PLZt3ozD4XA8ES4BdDgcTyrnnXMe3l0ejGVsvA8YhCOArx7mxwZOZOgbQLcpgyKkjEF+I7AvQz8Ccr+A8egVQQ8oPJqhD4C5xxBTj/1qV+Tg+xl60lrFxHyfjWFkMv3tDF2B74CX8FiviomZyTp2fxMT1qniZXnwHSbm70U6AptE8H0fc6eZ7BN4NzAGB4zBVx/zQzPZK/Fx0L1KrzGEvo88KJDp5XwA5GEh8Hz6jUF3KqzN0HvB3GvP/QcxNK1axfDwMA6HwzET/qlugMPhOL341w/+K/ddeB/BCzIKIN4AeLB8RTP9fb3sO7gPc6Yh/KcQ+kA+J6inLGlYRioVsGPXNuQcQd9ifQDNFwxhMmTBojoS8QSbt7Uhz4/0UjD/Ywg7QmrmzqeoqJi2TRvgauC3QC2YbxvC9SFl5WVUV89l06aNpN6bgm3AapB7BP2lkleYR+3yOrZv28rw54ehE7gY5CFB71IS2VksbVzG3j276f1xL/I8Qa9UO8T9NVsJ3NTYzJEjh+nccBRzniF8TWiHf784HoNGTpw4wcHD+zFnGcI3WB/A8RgsbVjG2FiSXXu2W/0tdkhXPm+rkRfV1eH7MbZs32SHvd+q1gfwy4bweMi8ufMpLCriRz/6Ed2uEtjhcDwRp3gO4jMaN4nU4ZiZRx55REvKSyYVJbQ0t6R/X7xkifpx3xY9eBPef+N6U1OzLSQxtjDCeEZXNDZN98DzbDFELO7rokV1aZ+/pvF9/UhP+DpnTkXa529x7SLrlxezupfwNDc3Vwtz8xTQyrJy9TxPTVwUQU1c1BijVXPmKKAFubmam5enXsKzxRgx264ldXUKaNz3taKyUmNZMatHPn/NTc1pn8BFdYs1loj0GWKworFJjWcmxaCpaSKGLZkxMGgsEdO6xfVpfeW47opAHI5puOf3LPYBdDgcTx3nn38+MhZM2nb86FHOMoYsEYoKi9CxFItUyAvtnLvR4WHqjaHSGIznoaFSrkKVCmEQEo/HKTWGFcYw1G/XWMsKhXoEktbnzwPOM4aj4+vgpqBFDDoWUDN3LqPJJM/2PHbv20s8kUCT0CyGYDSgpmYevYMDXOR5dBzrorx8DuGY0iSGcEwpLy+nvdP6/PUNDjJ37jyC0YBmsUO9sViMXXv28GzPYyyVoqZmLgTQJCZdeNJ55AjnGoMPlJSUECQDFiPEo1AN9fez3BjKjCEeixMGIXNCKAshDEKMZ6gwhqXGMDo0ZGMQQJ0KqbEUxcXFJEQ4OzMGDofDMQMuAXQ4HE8qqsqHPvQhugczJrgZKCgrYw2QW1jI7t27CGPCHlUGVJGYMDqW5KDvM5DI4vixY0hM6FKlPdI72o+QzM5hl+ehnofEhBFVdqqS8mHnzh2UFJfwe2BOzdx0EcmGMCQwsGXzJhbUzOX+IKC5uYWx1ChAek7gzl3bWVpfz0NBQFNjEx1H7UK9baEtWDnaeZTmpmYeDAKWLK5nx85tINHxQDJM0tzSyv1BwLzqGjZv3kQqTKWPx4fS6moeA4qKi9m5cwfqKbtUGQMkJoQCuz2Psexsjh5tx8QNncCxSD/W1clgVhb7PI+xILAxAHargg+7d+0kv7CIPwBVCxci4wUtDofDMZVT3QX5TMZ1ITsc0/na17424WE3PgS51A5VZmVlpYdpeR3KJpTfoXKhHe6Nx+Pq+74iqLww8ulbh/KSaKjW8zSRSFgfv3NFeRhlS+QZGA2tZmdnT/j8/RxlJ8q/TLQlJyfH6pViffz2oHxqYpg2Ly/PDsvmoXwVZS/K16L3Eunjw8ufjI7/ASpV1uevoKBg4ro/EH3+fag0WD0vL28iBv+EshnlEVTOs3p2dnY6BlyJstb6DHJ5NJwdi2lWVpa9hgtE+U0Ux9fbzzTGpNv4/ve/3w0BOxwz4J7fzgfwpHA+Qg7HdFrPaGVj9Ub0KzrhA/gb4PzodwHTZIsyGO+g6gGpFnQk8sArM4QHQ8iK9CSYhYbwyERvGoeYOL9G3oJ/CCd8/NYDLRPtkpcL3K3oeGXuD4ErMhr+bqx3Ycq2kc8Ab8/QPxe91+jzrwM+kaH/GLg8+qwY6IuAuzL0NqA5+t2AWWUIH8uIQRdQQ7py2FRFXojxSB8FM88QdkU+gAlBjyiUZMRglSHcYJeMu+qqq/jc5z7nfAAdjhlwz283BOxwOJ5k9u/fj5415e/KxUA+zBVBfAjPykh8AIqBOigEEkDYkpH8AcQgPCPyAQRknkwkfwAC4dnWO7BWxCZNzUxCz1I0gMZxH8CzpjT8LKI5g2KTvKn62YBCs0Rr+86kY8+v4Qx6o724BZEP4LQYlIMsEIrt5RKuykj+sMeGKyMfQIBaJpK/8RicFSI+nGUM6x57DIfD4XgiXALocDieVFqaWzA/MZPXyf090A+HVDEpwfzCwGiGvs/6APYBSRHkUYHjGXovmF8bAoRuQPcpbM/Qk2B+ZvBCw15V60H4qwxdQX4seL5h0/icvP+b0vD/AxM3bFBFfJmu/xjEEzaqYuJmRh1gUxjieQb5sUz2CXwAGIX9qphAMD83k70Sd4LuVnqAlAjysEz2SuwG+a0wht2sOxT2ZOijYO4zSCA8Foaced55BMHkQhyHw+EYx/kAOhyOJ5X3v+f9/N3f/Z314RvnWsDAgnkL6e3t5cTBHuTZgl6n1gfwFkFRqmvmkkqlOHqsA7lI0Hfb1T3kP4WwP6S8rJxEVhaH2g9aD74P2CFQ+ZwQ7gopKiyiqKiYfQf2Iv8g6L+oXX7t66APKomcbCrnVbNn7y54K9ZoeRVwD/A1MDGPhXWL2LtnN3wEGMIuF/cg8ElAYVHdYg4eOED4jTH7DfpirA/gLYDAkiUNHD50iMFfD8BLsMvH7QX5sKBGqauto7unh5693fYa3qZ2CPwjVl8wdyEjIyMcPdaBucgQ/j87pGv+wxAOh1RUVhGPxzl4eL/1AXy/9QGU/xLCAyHFhSWUlJZwxx134Gb4OByOJ+TUTkF8ZuMmkTocM3PXXXdpbmHuJB+6ce++uO/rvHnz1c/y05p4oo0ZHnlLlzaoiZm07sU9XVxfryYqnmhubrH+eJHuZ/laVV2T9vlLe+RJdHyWp4WFRWmfvxXLlk946IGauNF4PK6V5eUK6KL5C2xbfJn0s25hbdonMJFIqIlHbYza0riiMe0TWFRUpF6WN9EOmWhXIhbT6pqaaTFobrE+f0ZEF9cvsT6DGTFYsqRhwuuwqVnFk0kxmDt3nsZ9e84zzjjT+QA6HE+Ae347H0CHw/EUcNVVVxEPJ3+9dB05wlnGYIKAORWVhCMp6oA8QAMlNTZGvTFUiJCVlUWYDJmDXfI2GAvIyyugWIQVxjA8OADRNMGlACMpampqCFIpzjOGw/v22Q9Vu7avjgTU1S2md3CASzyPzVu3kEhkQWDn/IVjIbW1i+jo6uIiz2PPgf1UVVWjKaVFBE0pFZWV7N63N+0TWFu7iHAstHMGQ4jH42zesplnRz6Bi+rqkaRMzClUaD+wn3OMIUylqK6uIRgNWIKd96iBMjIwyDJjKBEhLy+PYDRgDlAWxSA7J5s5Iiw1hrHRETRQsrBTLFMjKSqrqpAg4KzMGDgcDscMuATQ4XA86Xz9619nWJOTtpVVV/NYGCKxOO3th5GEB4tEHAAARVxJREFUx25gABBfML7PLlWOAYNDg2kPvHbAJAwnTvTQK8LmMCSenQs+jGCnAmqWx4H9+4knEvw2DKlZuDBdYLFeFUl47Ny5ndLCIh4IApqbmhgdHQGDnfMXE3bv3sXcqioeCgLq6+pobz8CntXx4GhHB0vqFvNQEDC3sopdu3ZhYmJ1gbGxMZoam7g/CCgpLGTnzu2or1YHMFA9fwGPhiGxRIKDBw5gEoYd2OmQ4gux7Gy2hiEnROjt7cNLeGkfQBM39Pf3cxzYHoZ4sXjaB3AX4GV5HDp0CBOL81gYMnfRoqf6NjscjmcwzgbmJHBl5A7HdO69916uuOIKuBI7tw5gDnAcquZU0d8/wMBgv52/doNCP8i/CbpDKS+ZQyqVpKe3B1kl6Pts8iUfE/T3SlFBEbF4nK7jnUitoDerLQv+L+CnkJuTS0FBIe2dR5BSQT88MQeQb9peujlzKjh0+CDkAB8CVmItYT4Nxhiqa+Zy6OABO7/vA9g5gA9F+6Zg7rz5HDl8iDAMrRXMlVjLmX8BBqF2YS1HjhxhdHQUXoOd/7gX5IOCHlPmVc+n50QPAwP98HzgHUAPmJsN4Z6QyvIqxsZG6T7RjZwp6HvVFrHcKuhapbSoFD8W42hXB7JY0JuiOYCfEfQXSn5ePkXFJRw8sJ9rr72Wr33ta4CzgXE4MnHPb5cAnhTuH5DDMZ3zLzyfR82jhHeGUBlt/Dk22QGMCCwSwq2h9TsBW4yxEEhFHXf5gh60iQ0Ag2AWGPR4aAtrDbATm9wBBGCaDWxTwjD6Svs1cEGkK8jfCfKgHe4F4BtMLlR5E8jtgiZt0snNwAcz9FuxSV4AJiaE1yh8OUP/DvDq6Bpjgl4M+nOdsHr5DfAs+6sYQZYIYVs4UYq3DzuWGxXummJDeCC0Y+QAfWDmG8LeDC/EPcC8SE+BWWbQPYqGyite8Qo+/elPU1lpb4JLAB2OCdzz2w0BOxyOJ5ktW7cQPneKx10raR9A9ZXwkozkD6AGZKlQiLW+07Mzkj+AXAjPD/HHfQAXykTyB9ZX7zkhoafWBzBGOtkCQEAvVcJUOOED+LwpDX8eaNLO+SOYWSewPoBhUmfWsT6AYajo83RyDM4j7QOonhI+J5zsw7AQpDbDB/D8jOQPoMB6HY77AMpimUj+AHwInx2Cp5xlDI/95jduKTiHw/GEuATQ4XA8qdTV1WF+M+WrZStpH0BSWD3TJ7AL2Am92PlwZo2xFizjjIL5vSGp1h5QD6jtNRxHwTxikADrA5gE1kxugjwiiCcTPoC/mdLwR0D8aM6fmVlHYKMq4kfvp+pYH0AxkY9fJutI+wAS2PZO8glst/6GPdjmm8eMneQ4zrCNyyiRD+Buhc4MPbRx1RQ8Foaccc45OBwOxxPhEkCHw/Gk8q7r30X4sxDek7HxWsCDgoICfD9mh39fiTWI/gXIZYIGSm5urq0A7g2Ry8XOvXsY5EVC2BWSlZVFXn6+HdK9TOBnwGPAayFcF+IZn4LCQjtv8GUCP8DOz7sB9B6bfJaUlFr9TWLnBrYBHwb+CzQFpWVlNjn9gN3GJuA24H2AWl1T2KXh/j06/hsg/2RX+KioqEACQe9VO0dwHfADkKusXlJaSsyL2SXbXhO1/+fR9aiNUXZ2NuGxEHmRwMPAr0EuF8KekJycHDtkFYK8UOC+KI6vgnBzSDwWp7S0lP+96y5uueWWp/BOOxyOZzSn1ITmGY7zEXI4ZuaTn/yk+okJjzsEbWpqURHRgtw8LS8vn/DQA5WYaP2Spep7nmbF47pgwcJJuokbrZk7T3MSCfWN0YaGZSoxmaQXl5RocUGB9QlsaU17/I2fPycnR6vmzLE+gS0rJ/kIiocaY9I+f2mfQGHSzxXLV1g/wAUL1XhGJeMzMBNeh5XlczQnJ2dSG/HQ5uZWBbQ4v0BLS8umxWDp0mXqG6M5iYTOnTt/WgzmzV+g2fGE+l7kCTglBmVlZVqYl6ciomeeebbzAXQ4ngD3/HY+gA6H4ynghhtuoDArd2KDQndnB2eIMDY0yIKFtchYSB2Qi5175xtDrSpFqRRFxcWEYyHlWB/AcCxkTvkcspNJlgBhMokmrQfeEsBLKnV1i+nv7+dcY2g/sD9dTNEigkkqS5ctp72zk0s8jw0b1pGI2cWGm0XQABbXL2X3vr1cGPkEVlVVT6z9qzCnopLNWzZzkeexe/8+ltQvRaM5gQBxP87GDet5tufR0dVJw7Ll+OqndQI4evgg5xjDwEA/tYsWQcrWfcSjGGiQoh7ISSYpKy+z142d9xiOhZSUlFCQSrJIbU46HoO66PiFtYsYGRzkTBEO7ctcJ87hcDgm4xJAh8PxpPPLX/6S1JSFJssqq3k8DBkDjh8/jiR8dgODAgjEE1nsDEM6wpBUKoXxDV0S+QDGDCOjwxwPQ7aEIdn5+QCMGNgBaNyjq7MTRPhdGFI1b7790MjHT7J8Du7fR3Y8wQNBwNL6eusD6Ns5fV7CcPDAPkoKCvl1EFBTWUVHRzsSs2v/Skzo6jzKvKpqHgoCigsK2H9gP17CsFEVfOsDuLR+CfcHAdlx6/Onvlrds82pqpnHo2FIKEJXVxde3GMXMGZsDLLz8tgahhwLQ0ZHR/Fi1gfwuIDne6SSKY6GITvCkKycbMQIIwK7AT/hc/z4MZLYOYBVc+c/5ffZ4XA8c3E2MCeBKyN3OKbzyCOPcPElFxOsCuz8NrCeeyPQsGQZfX19HDl6GLPcEP5zaH0APylol1K/eAnJZIp9B/Zgagzh9SF4YD5rCPeFzK9ZQCKRxc4925EiQW+0PoDy34JuUCrKKykuLmHbji12mZAbgEUg3xD010pRYRE1c+exZctmVEJ4GxM+gHdDTk4OtXV17Ny+nbGxMTt3cXwt4Nutj2D90qXs3b2boaEh6wF4JXae4W0galixrJHDhw/Rc6IbuUDQaxT2YtcSHoHlDSvo6emm/Wg7pskQvjm0awF/UtBuZWl9A6Ojo+w7uNfavlwXrQX8GUN4MKR2/iL8WIydu7YjZZGXYiGYLxjCzSHVlTUUFRexfds2Lr30Un76058CzgbG4cjEPb9dAnhSuH9ADsd0nv/3z+dXHb8i+HEANdHGO4GXW1cUz/MI5yjhjgybk63ACvAQECGMKbpHoTrSu6xFihkWVJVQFdZikzewFbINBnMYgiDyCrwHeFGkB2DON5h1QmosGhv+DNaEGewsuZeD9yNDMBoiMUHfrvCfGRf2/yKz5aTtMQwuC+EuJqxePge8HQTBixvCZiX8XZju/ePHwOUgIhhj0Gol3Bba5BhgI9BifRJFhDBL0b1qTbQB2kEWCWYsigFqC1BWRPogmCUGOQqq8LKXvYxPfOITzJtnvWJcAuhwTOCe324I2OFwPMk89vhjBFcGkz3uLgJyrQ9gYALCF0zxuFsG0iDkqRILQ/T8jOQPoBz0EsVoSLGq9QFcmaFnQ3hFSMqELBz3Abw8Q/cgvCoklQpoGvcBvCpDF+BlEIzatX01qZN1rD7uExiMhlbPdHq5ClBoFCGVCghfmpH8Afw9EIcFYGNwWUbyB9BsE7wiVUwYohdlJH8AVaAXKH4YUqCK1MtE8oeNb/jCkNCEnAWsefRR4vE4DofDMRMuAXQ4HE8q1dXVyKYpHngHgUE4qIqmwLRN+erpBw5YH8ARwGw11gxvnMAeM+4DSDvQM/kUZoOBTB/AnVMathHwoG3cB3DTzHp67d6pepv9Mb428BPpbWFkgj1V3wWMwb5xH8CNU2JwAjgC3UQ+gJtNupAFsP6Jmyd8ADkE9E0+hdlg0AAeDUOaWltxOByOJ8IlgA6H40nlrW96K3qXWu+8cd6E9d6LKmrDx0N4F9AB7AB5taDDml65IuwI4R+xy6MdAP4Jwv02cZOoh05eIXbo+CjwAQgfDq03nli/PXmVWDPoHuDTwLeBZIb+ZrFz+/qAbwKfwCZmxoCAvFfgR9jk9F6Q9wgYO4RNEO3/9ej4hyJfQS/SU9HnfRKb0a2x14gXnT+E8Deh9RY8CmwDeaWgY4qI/VoOD4Twhuj69wOvh7B9PAYGHVF7jdujOL4bwsdsDDzP454f/Si9DrDD4XBMxc0BPAncHAKHYzphGHLDjTfw2c98dmKjgSX1DRw5sJ/i4mJODAzQP9A3sRqID/Oq5jPQe4KY74Mfo6u7E03ZryfxhJKiUmIiDI8MU1Q2h/2H9thEKzp/bnYuZcXFHDt2jPm1i9i6c8uELuD7PnULatl/YD9Lli1n4+b1E3p0jqZljWzfvo3ljU2sb1s3uQfOg9amlWzZ1MbSpQ20bd00eTUTD5obW9mxdQvz581nz4F9pFKpidU+fGioX8bBvXspKy3j2IkeBocHJ8VgQc1CeruPk5VIkEI4fuLYRAx8obykHIKA5FiSvOJiDh05kNYxkJ+bT3F+Ad093dQvW87GDesJAnsRbg6gwzGBe36fxj2At956K2eeeSb5+fnMmTOHK6+8ku3bt0/aR1W5+eabqa6uJjs7m4svvpjNmzefohY7HKcHxhg+8+nPUFZUNLExhOHeXlaMjXGso4OlSxvwQrsubq4xkIL83DzKBwfxe3upqq5GU0qpMcwxBg2U+QsWkOruZu7QMFm+7WXLEmGRCDEVli1fweHDh2lNJunuPJpO7paL4CM0NbawfddOzg8CNrdtJCaxtA6wuG4pbZs3cb4q69evY05pxSS9rGQO69ev43xV2jZvYsnihkm6Lz6b2zbyrCBgx+5dNDW1EPNjaZ0U9HYdoyWZ5MiRwyxbtgKDYaEIcRF7PfE41UNDhD09zJs/H00pZSKUiKAppbpmLubECeYMDpCbnY2mlCwRFoggKixduoyj7UdoHBuj8/ChdPLncDgcUzltE8CHHnqIt771rTz66KP84he/IJVKcemllzI4OJje5+Mf/zif/OQnue2223j88ceprKzkec97Hv39/aew5Q7HM5+2tjYkNtkIsKisjN8HAcNhyODgIOJ77PeUQWO7wOLZ2ewIAo4EAWArYY+bkE4vxBhDEAQcCwM2hwHZebaCZMRT9ngKvkdfXx8pVX4TBJTNsckbPmzxFIl5dHYexTOGXwUBc6urSSaTad2PeXR1dpCTsD6BJYWF9PR0Y3xhi6eYmHCip5vSoiIeiHz+Ojs78GMeWzzrA5hKpZhXU8MvgwDPmMiXkLQOUDpnDr8NApKq9A/04Xke+zxlzLO9eNm5eWwJAjqDgDAI8DyPY57S7amtHFalIwjYHoZk5eSkY7DfUzzfY2BogFFVHg0CSstdb5/D4XhiZs0QcFdXF3PmzOGhhx7iwgsvRFWprq7m+uuv5z3vsYuWjo6OUlFRwcc+9jHe9KY3/clzui5kh2M6bW1tnH3e2YyUjaD7oq8XAeMZWppWMjg0yI5d2zBlhvCayAfwa3b+W0vjSoIwYNOWjUiuoNfaggu5XdB+ZXlDI/FYnPVta21S9VqgFMw3DWFHyOJFS8gvKGDjhnUEYWDXG14E5k5DuCtkbs18KuZUsHlzGyMjI8gVgq5WzA8N4bqQ8rJy5i9YyM4dO+jr68VcbAifHWIeMIQPhBQUFFC/ZCkHDuynq6sTs9IQvihE1gr6QyUrK4sVK5ro7DrKwUMHMHWG8BWh9QH8Dhjj0dq8kv6BPnbu3oGpMISvtT6AfB1IQkvTSpKpJFu2bkLyoxhoFKMBZcWyJnzfZ0PbOmtXc41CEZivG8KukCV1DeTm5bJj+zaWL1/O448/DrghYIcjE/f8ZvasBbxz504FtK2tTVVVd+/erYCuXbt20n5XXHGFvva1r53xHCMjI9rb25t+HTx4cNavJehwTOWql12l3mJP2ZOxTu4X7M+4MRr3fTVFRulA0//9xuoxz9O479t1fLdk6LvsWrlx39e4idbH/WWGfgw15UbjsZjGxvX/ydCHULPcaCwR07iIXQf4XzP0FCrPEfWzfPWjdXW5Bmu2p9HP19ntPqif5as8W5RUxjlutusBx2IxjSViahqMMpih3x5dYyym8XhcTalRujL0+zP0WMyu87szQ9+G4kfnj8XsNf46Qz+KmmIbg0QioZdffrnu2LHDrQXscMyAWwt4lqwFrKrceOONPOtZz6KxsRGAjo4OACoqKibtW1FRkdamcuutt1JYWJh+jRusOhyOCe5/6H6CVwZ2kd9xXgTkQJkqSVKEV4SQ+b/eeSD1QnYYYoIAzgeWZeh1oM+2a+XmqSJzBZ6ToZdC+NKQMZJUR0uzcXWGng3h1SHJVJJ6EVt48foM3QN9vZIaSbFMhHAstPq4m40Ab7Dr8S4TITWSQl+nk33+3gCEsCQISKaShK+Z4vP3GiAOlakUYzpG+JIQyjL0S0DmC7mpFJpMoherXSh4nKXABWCSSXJSKWSRwAUZ+hwIrwxJkqQxlWLn1q0UFhZOuz8Oh8MBp/EcwEze9ra3sXHjRr773e9O08ZtJ8ZR1Wnbxnnf+95Hb29v+nXw4MGnpL0OxzOZoqIi61GXSTcwCu2qaAhycMr/Y6PAMehTZUQVc9BMrrBVMAesD2CPqh0yHZx8CtlvE7sDqrYAZOrfcQewc/LGfQCntvEgYKBtfFbMTDqRbp5Y3zzuAzhVPwokrRciIciBKTEYArrhhKr1ATxgJiqIM2IwCvRqZIg4MiUGBwQNYG0QsLC2FofD4XgiTvsE8O1vfzs/+tGPeOCBB5g7d256e2VlJcC03r7Ozs5pvYLjJBIJCgoKJr0cDsdk3nDtGzDfMnYptnFuBDTKZ0LQBxT+C+t43AtcB9ozke2Ee0P4ADYpGgb+HcKtNnFTQIfUruN7ApvsfQn0p9ZgWcHOG3yj2CQwBO4G/sfuqwA+yNsF9kQn/DXIR2Ui6fRA3iewIXq/MfIFHO/xC0E+FvkIKrAnOt943UsK+Crw/ejzO6L2jH/jBqA/V/hiFIMTwNtABzNisD2Em6PrHwI+COHuiaxY+9QuZdcbneNzoL+yyaUCj/zud/zkJz/5Y7fK4XDMYk7bIhBV5e1vfzt33303Dz74IPX19dP06upqbrjhBt797ncDMDY2xpw5c1wRiMNxEoyNjfGyl7+MH93zo0nb582fjwlCcvPz2b13D6Ojo5CNTZZSUFZaTmFuLp7v09HVSV9fH8SxvWmjkJeXR01VNWOjowyNjXL0aIdNyGLAMMTiMZbU1TPQ14vE4+zbv9d+cDYwaL0Em1c0093VSW5RMdt2bLE+f/lYs2cDLc0rOdZ+hNLKKjZuWm/1AqzZswdNK1roPtpBWVU1Gzaus8nd+PEeLF2yjKGeExSXl9O2pQ0N1A6FRz11C+YthGSSvIJCduzeRXJszLYvCYQwp7yCvEQWsXicI0c7rCPB+GpuY5CfX0DVnDmkUin6h4fo6uq0SadvYxBPxKmvXcxAfx95xcXs2b2L4eFhwBWBOByZuOf3adwD+Na3vpVvfetbfOc73yE/P5+Ojg46OjrSX4YiwvXXX88tt9zC3XffzaZNm7j22mvJycnhVa961SluvcPxzCUej3PPD+6hvKRk0nYThOR3tLNnx3aamlrwgLIRIStlVwepmDOH5KGD9Ozby6JFdvJbflIoHLNDpUuWNHB09y7M4UMUFxSAQiwQ5owIMTE0N69k+7atFHV0MDYYGSyHMHdIiBlDc1MrGzZuoLqzk727duKL7a6rGbDnX1S7mA3r11Hd1cXGDespLbYT9Gr6rV5SWErbxg1Ud3WxYf066hbVTzrew2Pfrl1Ud3WysW0jzU2txHyfmiGxiWQAwfAIhR0d7Ni2lebmFjzPo3xE8APb+1haVIwePkTX3j0sXrwkHYO8pP2MusWL6d63l/DQQcpLy2wMUkL5iGCMoamphV3bt5Lf0U5/9/H0953D4XBMxf/Tuzwz+cIXvgDAxRdfPGn77bffzrXXXgvAu9/9boaHh3nLW95CT08PZ599Nvfddx/5+flPc2sdjtOL9vZ2xPMmbcvNy2OXKiNhyFhyDPE8jkuAhthEJh6nPQwJVJlr7N+m/SZaBSMUFKU3DBkSYVm2ra5IitJlwFfD6OgoKVV2qDIvvwA6O8HAYaPE8BgYsP6eG8OQoqJijh4/BgJHfMULhP6+Pnxj2KRKTlYWQ0NDSKRLCoaGBsnLzmbTyAi+MfT39eEZwxEvhBQEQUB5cQkbjx8DYGBgAEQ44mk6Gc3Nz2dnVydJVZJjYxgRjnmajkE8kcXuMCSpykKxf6hmxkBE6A4CekVYkUikY3DMgI9PMplkVJVdqtTmue8xh8PxxJy2Q8BPB64L2eGYzr59+zjrvLM4NnjMzlOLSGRl0djYRDKZZOOm9Uhc0CsU6RX054rxDK0tq1ENWbdhjT3ohdhh3nsBhZXNqzGeYf2GdQSpFPI8QUsV+aGgo0rTihbi8ThbNm9ieHgYOV/QOsXcawj7QpYuXkZefj67d+3kxIkeTIshXB1ifmZ9BBfOr6W0rJzDhw7QcbTD+vhdFGJ+bX0EKyoqmTt3PsePHWPfgT2YSkP4dyFmrSFcH1JYWMTi+iUMDg6wbccWTIEhvDxE9gj6iJKdnc3y5Y2MJcdo27wBSQj6IkW6Bb3Pmjm3Nq8iHI+BAJfZa+fHNiQrW1ZjxLB+41qCVIA8X9CiKAZjSnNjK7FYjP379lFcXMTOnTsBNwTscGTint/MHh/ApwLnI+RwTOd1//g69at9pS3DB/BD9qcH6nueSrZYX7vx/+6OdBH1ROwxD2foj1mPPc+YCf3bGfoeVPJFfc9Tb/wzb5niEzjfqO9bXXxR3pzh8zeIympRP+GrB+olPOWFKMlIT6Jcbrd7oH7CV1klEz5/IcpbovOC+rGYmrlTfP4+NhEP3/dV8kTZlaHfYTVjjHqeZ70KH83Qf4MiqOd5asa9Dr+Xoe9AJUfU9301xuiFF16oa9eudT6ADscMuOf3LPEBdDgcTx/3/uReUq9NQWZn07VAFhSLEEiAXqnW126cF4EsFLKwHX5ytsCzMvQzgYsADckBpFLsKh/j1IK+TElJQJlE1brXZ+ilEL4+JKUB86N1dXkXEz5/OaBvV1KjKRYAwWhgK5fHJ8n4wI12+wIgNZpC36YTPn8CvAs0pSwQIRUmCV8/xefvHfY85SKkSKEvVajL0P8BpFrICUMkCKzH39kZ+nnAuWCCgOwwROYLvCRDrwd9sZIiRR1w4tgxampqcDgcjplwCaDD4XhSSWQlbFVsJiNACgbAznfrm6KHwBCMqpJUtcdPmZwifUKoMKJqz5ecco5e+6Nv/HxTfALpAwS6xt9PbWP0vvNP6F3Y8/zR42fSB227+sBe29QYpIBhe2kB9nqnIn1CiqioeCjaMZM+e+52Ij9Gh8PheAJcAuhwOJ5UXvPy1+B93YM/ZGz8dyCIkrcQ+CnwI2wiFAC3gHYqqWiTbok88sYHML8GukZRbN6nJxT+DZs0Afwc6zuYgmHVdI9c2ih5DciXxCahqkhMkHfLRBK2O/L1MzZJlZggN8mEmfRRkJsEiYnN6wTk4wK7Ir0P5N1WH46MqOVLMhGDkag92CSXILr+n0Z6ysZIezJisE6tl+B4DL4EuknTIdNjCh8Zf4OdI/h/9v1AGLL34MH0OsAOh8MxFVcEchK4SaQOx3T6+vp49vOezZrH1kzaXlZWTkVFBbFYjI2bNhCmQruc2SDoUSUvL5/a2lqMMWzbvo3RkRGkxiZlelCJxeOsWLacIAjYf+AAfX29SLlAIeguRXyhpWklydFRjnUf5+jRDqRAoBp0q122rbV5FWMjIwwND7HvwD4kS2BRlHAaaFreQmpsjBDYvnMrGJClgm63ievS+gYMQiweZ+OWDdYQepnAPtBhZf68heTl5JDIymbdxjUQRHo7aK8yp7yC8rJyYok4G9rWoSlF6gT6bQKcX1DAwgULMcZj67bNjI2OIfOsTY4eUuJZCRqWLkPDkH379tLf34/MEcgD3aOIb2hpbCU5NsrI2Bg93cfp7u4GXBGIw5GJe37jikBOBjeJ1OGYmdHRUS0vK5soAgGtW7BQ53uexkR09RlnqieiOSLqR3pTU7OWe54WGKMrV61WQOMimhUVfaxafYbmGqOVnqfLly23BROgeSLqG6OrVp2hBrTW83R+dU36cwtFNGaMNjY1K6BLjNG87GxbaAFaACoiumDBAgV0sTFqRLSoqCitA1pQWKieMbo4KsBYuLBWjUhaFxHNz8nRJZHe2NSssVhMC8aLVkAXzp2rCz1PPRFdueoM9X1fc0U0WvRNG5ev0ArP07yMGCRA45G+ctVqzTdG53ieNq5oTMcgV0Q9z9NVq89QX0QXeJ4umj9/UvxdEYjDMYF7frsiEIfD8RQwNjY2bU1tz/Ps/DZVwtAOPKSYmMYmYuwcwIxjAiZGecf3HwVM5BOowJg9mFB1fCohXoYH4WikByn7SQORHkZrAo9hR4xTgdUHAc94BNH7seg8YRDgGZOeWpgKUkiGDuB7PgPjbZ9yvI2BzzAQ6nhell4ExMbAGEbVbpOoQmVyjKw2qjopBuMxU1UCVYYB4522Nq8Oh+NJwA0BnwSuC9nhmM6xY8c478Lz2Llt56RCjsLCQmpr6xAj1uMuADnLzsPTbUpWdhYNDcsREdraNpBKppBmW9Gr6xQv5tHS3EoYwvbtWxkeGsLUG7RU0d/bIdyVLasIQ+XA/v309HRj5hnChSH8DlBobmwFhK7OTto7DmPKDOGKEHlU0KSybOkKYrEYA/397Nm3G8kTdLVan7/+kNoFdeQX5JNKptiyfRMSE/QcxWw2hMdCqqqqKS+vQAQ2tK2zmeW5YPYbwgMhxcXFzF+wECPGDhGHUQx6QHdYn8ClDcsQETa2rSdIBkhrNAS8wcaguakVVWXbtq2MDA9jGgxaoOhjdph7ZctqwjCkp7ubkZFhOjttWYsbAnY4JnDPb9wQ8MngupAdjulcd9116hV71sdvfAjyrUwajsTP8PkLUW6booNyZ4bH3Y9m0P8jw8fv9yhxOwwL1i+Pd6AEkb4TlVJJ6xIT5cUoI5HegZp6o8a3w7cmblTOFaUv0vtQOV/UxCPdN2rqjNIe6aMoV034ABpjVEpE2R7pAcr1Ubui4WLiKL/LiMF/znCN92TE4Hsz6J/NiMFvbFzHr3HZsmV6//33uyFgh2MG3PPbDQE7HI4nmTt/cCfBNQEsydj4TiAO+eMefZcx4fMnwD9bD7y4iPUBbBX4h4zjLwc5xw6KZokgJQI3MOHjdxZwFainFIrY7f/OhM/BYtB/UtQoZcagSYUPAYlIr4DwnSFhKqQMCMdC9IMK46up5YN+UAnHIj0VEr4zhMpIj9vP05RSLEJoQvSNOhEDg61aFsgTQY1aD79zMmJwHUiZkIh2l7MFXpQRg5eCrBJM9HFSKfDWjBicB1xB+hoLcnJYsWLFn75hDodjVuISQIfD8aQShqFN8jKJvmnSm6fqYvcx2PVvp+mAxnR8t2jHKTt4U0499dstEmLjs16mfkb0Pv5EbYym1CX+hP6Ex0dt9rE//2gMsIncNDz+eAwy2jg+B9HhcDhmwiWADofjSeWqF11lfQD3Z2y8DRiDE6pIKNYDL9Ml5uvW5mRElZQqukatp904vwIejvyiVa0H3n9n6G0g3xMkELpV7aDnR5iYg3jQ+vIZDO1qLWP4EBPVFT1gPmXwYh5HAJMwyEcFhiN9BOQWwSQMhwEv5mE+baA70gPgQyC+cFQVTz3kywIHIl2BW4DQxsCEBvm+wIaMa/iytYIZBkIRO2/xFxn6T0EfV0IRRgA9onB7hr4OuAeMGg6HIaEx7Nq1C4fD4ZgJVwRyErhJpA7HdDo6Ojj7/LM5cPDApNU68vLyWLK0AVW1RSAAF4P0CvoH6/PX1NQMwPqNawmTIXK+oJ7CwyCesLJ5NQhs3rzJ+gSuFLRM4QFArc+fEcPOXTvo7+vDNBjCRSFyvy3yaFzWTCwe49Chg3R1ddoikZUh8oCgg0p93VLy8vI4fuwYBw7tt0Ui54aY39kij/lzF1BaVsbg4CA7dm1DcgW9WDEbDOH+kPI5c5g7dx6pZIq2zRtskchzFLPHEG4Lyc8voL5+CaGGrN+41vbgXQJyXNC1SiIrwYoVTaCwbuNaNBXChdjM9xEwvqG1ZRUAmzZttD6BZwhaFMUAaG1ZhcHQ0dGOMcKhQ4cAVwTicGTint+4IpCTwU0idThmpru7W+eUl08qWFi6uF6LjKcC2rpylfrGqCcTektLq+Yao3GRtAeeRD53gK5atVp9Ec03RpsiTz9AfUFjnqctrSsV0GLjad3C2rQei/SlDQ0KaKkxWlJYqCImfbwnopVV1SoiWmKMJmIxzcvPV4l0Ac3NzdVELKYlxqiIaHV1tXoi6mcUdpQVFWtp5AO4tGGZxmIxjWVc4+LaRVoc+Q+2tq7UmO+njyfyQswzRmMiumr1GSqRR6Bk+ADGRTTXGG1ubkkf5wnq+762rlxlY+B5uqSuzvkAOhxPgHt+uyIQh8PxFFBQUICG4aRtY6OjlAnERPA8D1UlFyEW6UEQUCBCrpn4WsoSIXvcTzD6vVCEIGm7Fn0RchFQxfd8PBHKBUZHhtPnKIj0eNzO3qsGhkdGMMaet1CtL19WIoGqUgWMpVLEYjE00hWIxWKMpVJUYf32srNzCFUp1PHmCUMjw1RHnxuPx0HVfn46BiOUR+02vk8YxWB8OmCQSlGYcc2qSkJkYt4hkGOMjUHKOiR6QC5CGIZ4nkdMhFJgdHT0L7hjDodjtuGGgE8C14XscExncHCQF1z2Ah5+8OFJ2ysqKynKLyArO5tNWzcTJFNIhcAIaK9SWFRETWUVxhh279vL8NAQUhwtBXdcycrKon5xPalkkqNdnXR3dyP5ArmgHYrxDU3LmxkZGmRgeIjD7YeRhECpnV+IB61Nqxjq7yMQYffenXapt2pB91sfwZbmlQz19RHLyWbL1k2gIPMFPWi/JpcvbSQ5MkxuQQHrN66zPn4LxM7HC2HRwjp8hJz8fNa3rbNeh3MFjoOOKNXVNeRn55CVk0vblo12ObxKgSHQPqWouJiqORX4MZ+du3cxMjxiK54B7bY+gXWL6giCgPajHZzo6UEKBbJAj1qfwMZljYwMD4NnaD9yhL4+u+CxGwJ2OCZwz2/cEPDJ4LqQHY7p/Mu//IuabKPcleFXdxWKyXgvKN+OPOxGUd47g8fdx1GSKCmU/5pBvy7y8Qsjj7zM83soL2PCx+8hVHImlmSTmKhcKEpnpLehUilpnz581DQYZW+k70NluaR9/hBUKkTZEOldqFws1l9w/DOyRXlwwkeQV0xpo0H536j9Iyg3znCNn4muP/UEPoH/LyMG32Wi/aDl5eX6ve99zw0BOxwz4J7fqq4H8CRwf0E4HNOZXzefg887aH34KqKN24HlEAuFpCjyPEF/lvHVkwKpEuSY2sLdpYJu1Uk2J9IqdjUMEcJ8WzE7aWz0xcC9EA+FMRTaMz4frG/gbZAIhFFVeBw4I0P/NHAjJDRaPu57wEsz9LuBl9iPHBXgE3b/NGuB1RADkj7wFuAzGXqXbY+vkPKAv8dWQ48zar395IRd0k6aBN2YESMFWWHjIgClgrYr6TF0QP5e0J8rCRXOv/BCvvu//0tFhQ2C6wF0OCZwz29nA+NwOJ5khgaHoHTKxjwgDsWAGGzlbiY+UAg5IsRE0FKd5nGn5Yov9lTj55tEGWDsDwxQNEUvBRTmZ76fQV/wRHqZ/bEQu98T6iIz6wX2OsvHjarLp+hxe115IvjY652E2LjFsfuQz6TkD6wuBhYbw9GODhwOh+OJcAmgw+F4UnnBpS/A/5YPPRkbvw0M200+PnKPwL4M/eegu5VREdQY64H3hwy9DXgQxHgMSTTn7p4M/bD1AYypTxdYX74vZOi9YG43xDyffVhLGT6boY+BfEHws3z2ieAlPOtdOF7HEgL/BV7CY4/Y/eQLEnUVRnzWnnePMcS8GOZrBk5k6F8CktDlecQkhnxP4GCGfq+dqzjkeUgsBg8x2SdwLfAbUN9n2PPQfQo/ydD3g9wt+BJjhzFUz5+fXgfY4XA4puKGgE8C14XscExn586dnHXuWfSaXrRr4uslFovR3NLKyMgIm7e2WQ+9f1DoBe4GQWhtXo1qwPqN6xBf0JfZ4gy5S9AxpaVpJZ7nsX7DWrviyJVAaaT3KcuXrSAnO5eNG9czNjaG/J2gdYr5gSHsDKmrrae4uJjt27bS39+POd8Qrg4x/2cI94bUVM2lqrqafXv3cuxYF6bZEF4SYh40hBtCSkvLqF20iI6Odg4dPoipNYQvDDFrDeEjIXl5eTQsW86JEz3s2rMTU24IXxIie+yQdzwep6m5lZGRITZv2YQURNfYjTVxFsPKllWkgoANbeuQeKRrdI1JpaVpFcYI6zeuRVXt0HdRpPcrK5Y3kZWVxa5dO5lbU8PmzZsBNwTscGTint+4IpCTwU0idThmZs+ePdN8ABuXrVBfbJFEU3OL+p6nxKKCjcgbcNzzb+XK1Soiim8LMkREW1eujjzvRFtaWieKPWJozPd1+fLGyNdPtGHJkkl63Pd1YeQN6IFWz6mYKMSIoZ5ntLS0TOO+rx5ofk6OJhIJFU+UGCoemkgktDA3Tz3QmOdrWVmZ+p6x1xAVd8ytrFQvuoba2kUaj8UmXeOypQ3pGKxobNTYFL21dWU6Bq0rV6kxZkoMrM+fgbTvIX4Ug1hMG5ua0jFYsWy58wF0OJ4A9/x2PoAOh+MpoLa2ljCZnLStr7eHRmPIEiERT6BBQGVKyA7tZL+xkREWeh4VnodqiKpSGAglgaBqCx9KPI86YxgZGgJswUd1SiAIyM7Jxheh1Rh6u6Px5wAWpYQwCCgqKgbgXM+j6/hxYrEYhFCbEoIgpKi4mLFUirONoX9oiILCQjRQalOCBpCXn0/v4ABnG0MySFFcXEoqCKlNCYTgeT6dx45xnmdd/QqLiwnVHj++5FxvTzctxhATITsrhzAMqU4JsSgGI0ND1HkeZZ6HiPX2KwqEgigGKMzxPGo9j+TIiI1BIFSlhCAIyEpkkxChyRj6TmSOwTscDsdk3BDwSeC6kB2O6aRSKa553TV851vfmdgosHDhIkYHBsgvKOBQ+xGGhobsLGS1esWcKuIieL5H78AgPSe6JwpBFAryCykrLmZ0ZAR8j8Mdh21iZYAQElkJFs5bQG9PN7lFxdbnL8A6JQdgPMPyhuV0HjlCeU0Nm7e2Wd0HUvY8LU2tHNm/n+qFC9kQ+fildQ9amlZyZN8+qhcsYEPbejs3MENfvrSRY+1HmFNVxZYdWwlTYfrz8aBu4WIGe09QWFTMvkMH7bVE7ceDqooavDAgHk9wvK+X3t4Tk4phiguLKcrLJ5lMkhKho/OIPVbsObKzs5lfXUNvby9zamrYvm1r2hDaDQE7HBO45zduCPhkcF3IDsd0PvGJT9ih009l+NVdNDHMmX7dgjKE9eK7erKHHaC8HaUHpTfyu8vUJPL5a0cZnsEjz0PlIlH2YL0EvxkNlWbqK0Rpizz0/g+V/AkPPwwq1aI8Eum/RaVGJvn4SZ4oP0YJUDah0iiTP8NH+Xr0+XtRuUSmx+A/ohh0RD6BU2Pwzuj6T2B9D6fG4NUoR6NzfHSynkgk9Atf+IIbAnY4ZsA9v50P4Enh/oJwOKZTv7yeXat3wX8y4cO3AViJ7a0yIOcJ+nDGV8+w9cDTPrvNzLdFGWmfAgXTYAh32LJcyRG0Q60VSoRcKuj9mh5uZS+RZ0vEPwG3Y3vrAB4ELsrQPwL8KxOVv18HXpuhfxt4TfS7AW4G/iVDfwS4IPrdB64BvpKh789ojwG5RNBfZsRgAKRC0KEoBouj6x3vAQzB1BnCfVEM8qMY5GTE4CJBH1EI4bIXvID/+drXnA+gwzED7vntbGAcDseTzPFjx6FuysY5QDbME7E+gIun/N2ZDVRBoQhZQLgonPztJBDWhcQESkWs517+5FPoYlsxXCti/fHmTmlDHaDQNL7W8NQ2LgZCaB1fe3iqHr1vETvn74n0JhOVcUzV5wLxyCfQmyEGecAcKBEhjr3eSV6IxsYlARSJ2OQ6Z/IptF4RD872PHZs347D4XA8ES4BdDgcTyoXnH8B3v96MJKx8afAILSLEMPH3Gus9ck4a0C3K0MiBJ5ne9P2ZOgHQe4XMB69IugBhd9k6H1g7jbE1bfWekngfzP0MTDfNcR8ny1qE0W+maEr8A3wszw2qmLiZrKO1U3c0KZqfQC/KRO9hZGOwFZjiPkxzHcNjGXod9l2HDIeMWKYe4y1wBnnd6D7lD7PQ30feVDgQIa+F+RhIfB9BjwP3RWtZjJOD5gfGnxirPM8VrS0MDg4iMPhcMyEGwI+CVwXssMxnTVr1nDes84jWZu0y7kB+CAqNDW2MNDfz579uzHzDOHrQ+gD+W9BB5XGZc2kgoBtOzYjxYK+ySZr5kuG8FjI0voGYrE4m7ZsRLIivcSaPIf7QhbOq6WouJgNG9ehRuH1wCIw3zaEbSGVcyqpqq5h06aNJJNJeBWwCuQeOyRdVFxM7aI6tm/dytDQIHKZoM9W5AFB71VycnJYumw5+/bsoaenG/MsQ/jiENYB34aYH6OxqZmO9nbaO45gmgzhq0M7HP0/IKHQ0rSSE70n2HdgD2aBIfzHEHqiGAwrjcubSSbH2L5zG6bUEL4ptD6A/y3ocaVh6XI8z2fz1o12KPxNCoVg/scQHgxZtKCO/IICtm3dwllnncXDDz8MuCFghyMT9/zGFYGcDG4SqcMxM7///e+1rLx0UlFCS3NL+vclSxvUj/m2KMKf8L0b15tbWlRMVDThocYYbWxqTuutmR54HhqL+Vq3uD6tN61onNANGkv4WllZqTHPU0AXLVho9cjDz4t7mpeXp3k5OQronJJS9TxfTdwWfpi4qOd5WlFapoDmZWdrfn6B+gnPFozE7Ocurl1kffg8T6uqqjWWiNnCkegamxub0m1cXL9EY/HYE8agsalFPc+bFIPm5taJeLaumhyDeEzrlyydiFFL66T4uyIQh2MC9/x2PoB8/vOfp7a2lqysLFavXp3+a9nhcPz1nHXWWehYatK240ePco7nkSVCQUEBmkyxSA25gZ3oNjo8zBJjqDIGEYOGSrkaqkLrhxeLxSg1hkZjGOrvByArEOpDgVRAcVExvgjP8jyOth+xH5qCZgw6FlBdPZdkEPAcz2Pfgf3E4nFIQhOGYCyguqaGgaEhLvE8OruPU15eRjimNGEIx5SysjKOHj/GJZ7HwPAw1TXVpEYDmjBoEnzfZ+/+fTzX80gFAVVV1RDa848XnnS2t3Ne5ANYVFREkApYrIZ4pA/197PcGMqNwfd9giCgPBTKohiIESqNR4MxjA5bL8SsFCwKhVQyRWFhIQkRzjWGrvb2p/5GOxyOZyyzOgG88847uf766/nABz7AunXruOCCC3jBC17AgQMH/vTBDod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    \n", - " " - ], - "text/plain": [ - "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "measurement_ensemble[1:-1].block_direct().show(\n", - " explode=True,\n", - " # interact = True,\n", - " # ax = ax,\n", - " figsize=(18, 5),\n", - " cbar=True,\n", - " common_color_scale=True,\n", - " vmax = 0.03,\n", - " cmap = \"turbo\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc4d42b7-5795-4027-8d04-0d4e0bf4df3c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 2100aa04be6aac58e052fe2ee94637e7643bdbee Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 11:59:26 -0700 Subject: [PATCH 09/10] fix post processing widget --- notebooks/10_STEM_post_processing.ipynb | 96 +++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 notebooks/10_STEM_post_processing.ipynb diff --git a/notebooks/10_STEM_post_processing.ipynb b/notebooks/10_STEM_post_processing.ipynb new file mode 100644 index 0000000..de1cb0c --- /dev/null +++ b/notebooks/10_STEM_post_processing.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e05c48d-a101-4670-bd06-aa261a12532f", + "metadata": {}, + "source": [ + "---\n", + "title: STEM post processing\n", + "authors: [Toma Susi]\n", + "date: 2024/07/24\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "744695c6-083f-486e-a6fb-7cb959c49637", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib ipympl\n", + "import abtem\n", + "\n", + "\n", + "abtem.config.set({\"visualize.cmap\": \"viridis\"})\n", + "abtem.config.set({\"visualize.continuous_update\": True})\n", + "abtem.config.set({\"visualize.autoscale\": True})\n", + "\n", + "all_measurements = abtem.from_zarr(\"data/processed_STEM.zarr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3e3d8994-459e-49d8-ab1e-c8ca2efab8f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 103.55 ms\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "aebe6b1c6e0c4bf2afe222d85edb8b16", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ImageGUI(children=(VBox(children=(SelectionSlider(options=('Nyquist-sampled', 'Interpolated', 'Blurred', 'Nois…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:stem_processing\n", + "viz = all_measurements.show(interact=True, explode=(1,), figsize=(5,5))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44381286-90b4-4570-9144-01ca47d653b8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b11ed24c37a7f93dfcbfddd3ddef1deea22f52a3 Mon Sep 17 00:00:00 2001 From: gvarnavi Date: Mon, 29 Jul 2024 12:26:44 -0700 Subject: [PATCH 10/10] cleaning up STEM widgets --- 09_STEM.md | 35 ++----- ...plate.ipynb => 08.3_TEM_phase_plate.ipynb} | 55 ++++++----- ...ize.ipynb => 09.0_inputs_probe_size.ipynb} | 42 ++++---- ...ectors.ipynb => 09.1_STEM_detectors.ipynb} | 58 +++++------ ...ence.ipynb => 09.2_STEM_convergence.ipynb} | 67 ++++++++----- ....2_STEM_post_processing_calculation.ipynb} | 0 notebooks/STEM_post_processing.ipynb | 98 ------------------- 7 files changed, 130 insertions(+), 225 deletions(-) rename notebooks/{TEM_phase_plate.ipynb => 08.3_TEM_phase_plate.ipynb} (97%) rename notebooks/{inputs_probe_size.ipynb => 09.0_inputs_probe_size.ipynb} (90%) rename notebooks/{STEM_detectors.ipynb => 09.1_STEM_detectors.ipynb} (96%) rename notebooks/{STEM_convergence.ipynb => 09.2_STEM_convergence.ipynb} (89%) rename notebooks/{STEM_post_processing_calculation.ipynb => 10.2_STEM_post_processing_calculation.ipynb} (100%) delete mode 100644 notebooks/STEM_post_processing.ipynb diff --git a/09_STEM.md b/09_STEM.md index 043982b..f581078 100644 --- a/09_STEM.md +++ b/09_STEM.md @@ -17,21 +17,19 @@ The incident probe wavefunction is easiest to define in Fourier space ```{math} :label: eq:fourier_probe -\begin{align} - \Psi_0(\bm{k}) = A(\bm{k}) \mathrm{e}^{-i\chi(\bm{k})}, -\end{align} +\Psi_0(\bm{k}) = A(\bm{k}) \mathrm{e}^{-i\chi(\bm{k})}, ``` where the amplitude, {math}`A(\bm{k})`, is the aperture function and the phase, {math}`\chi(\bm{k})`, is the aberration function introduced in section [%s](). The aperture is usually a disk with a radius given by the cutoff semiangle ```{math} -\begin{align} +\begin{aligned} A(\bm{k}) = \begin{cases} 1, & \lambda k = \alpha < \alpha_{cutoff} \\ 0, & \text{otherwise} . \end{cases} -\end{align} +\end{aligned} ``` While the above definition is typical for most STEM experiments, the initial wavefunction can be defined using any other complex function and other initial conditions may be required for simulating more exotic experiments such phase plate STEM. @@ -41,37 +39,20 @@ The probe is transferred to the specimen using an inverse Fourier transform and, ```{math} :label: eq:realspace_probe -\begin{align} \psi_0(\bm{r}, \bm{r}_p) = \mathcal{F}_{\bm{q}}^{-1} \left[\mathrm{e^{-i\chi(\bm{k}) - 2 \pi i \bm{k}\cdot \bm{r}_p }} A(\bm{k}) \right] , -\end{align} ``` where, {math}`\bm{r}_p`, is a specified probe position in relative to the atomic coordinates. -In [](#fig_probe) , we present an interactive visualization for exploring the relationship between size and shape of the incident probe and some parameters of the aperture and aberration functions. - - - -```{figure} #app:probe_size -:name: fig_probe -:placeholder: ./static/probe_size.png -**Left** The Fourier space initial wavefunction in equation [](#eq:fourier_probe). **Middle** The real space probe at the specimen in equation [](#eq:realspace_probe). **Right** The real space intensity. Start by considering the effect of changing just the aperture and energy. In real space, the probe forms the diffraction-limited Airy disk pattern; increasing the aperture or energy decreases the radius of the pattern. Consider the differences between the STEM and SEM probes. Next, add defocus. In Fourier space, the phase starts oscillating radially with a linearly decreasing period, the resulting probe grows and its radial intensity profile may have multiple peaks and valleys. Now try to decrease the aperture to include only the inner slowly varying part of the phase; the result is a smaller, more well-behaved probe. Add some spherical aberration and try to compensate by adding some defocus to flatten the phase inside the aperture resulting in a better probe (uncorrected STEM at Scherzer). Lastly, make a large probe and observe the diffraction fringes from self-interaction(boundary artifacts). This is the issue that should be avoided by increasing the size of the unit cell, as described in section. -``` - - Because STEM simulations can require long computation times, we often try to reduce the size of the simulation cell as much as possible. However, we must be careful to use a simulation cell large enough to hold the STEM probe. In particular, we need to consider the size of the probe throughout the full simulation cell volume. For an empty cell, the probe will have a maximum size at either the entrance or exit surface, depending on the probe defocus. -Use the drop down menu in [](#fig_probe), to see what the probe looks like when it has wrap-around artifacts. Try modifying the aberrations and notice as the probe gets bigger the error gets worse. Note that this example only considers an empty cell volume. Atoms inside the simulation volume will scatter the electron beam, with heavier elements scattering electrons to higher angles. We recommend positioning individual STEM probes at the positions where the highest scattering is expected (for example directly on or adjacent to the thickest atomic columns), and carefully checking the dimensions of the probe at the exit surface. This is especially important for PRISM simulations, as the cropping box around the STEM probe can be significantly smaller than the full cell dimensions for high PRISM interpolation factors {cite:t}`ophus_fast_2017`. - (imaging)= ### Imaging The initial wavefunction is passed through the specimen potential according to the multislice algorithm. The exit wavefunction, {math}`\psi_t(\bm{r}, \bm{r}_p)`, is then diffracted to the detector plane using another Fourier transform ```{math} -\begin{align*} - \Psi_t(\bm{k}, \bm{r}_p) = \mathcal{F}_{\bm{r}} [\psi_t(\bm{r}, \bm{r}_p)] . -\end{align*} +\Psi_t(\bm{k}, \bm{r}_p) = \mathcal{F}_{\bm{r}} [\psi_t(\bm{r}, \bm{r}_p)] . ``` The square modulus, {math}`|\Psi_t(\bm{k}, \bm{r}_p)|^2` is the diffraction pattern for the probe position {math}`\bm{r}_p`. @@ -81,22 +62,20 @@ Every STEM mode requires calculating a diffraction pattern for each shifted init In bright-field (BF) and annular-dark-field (ADF) STEM the detector integrates the CBED pattern on a region in the diffraction plane. This is equivalent to multiplying with a detector function ```{math} -\begin{align*} - g(\bm{r}_p) = \int\int D(\bm{k})|\Psi_t(\bm{k}, \bm{r}_p)|^2 ~d^2 \bm{k} -\end{align*} +g(\bm{r}_p) = \int\int D(\bm{k})|\Psi_t(\bm{k}, \bm{r}_p)|^2 ~d^2 \bm{k} ``` where ```{math} -\begin{align*} +\begin{aligned} D(\bm{k}) = \begin{cases} 1, & \alpha_a < \lambda k < \alpha_b \\ 0, & \text{otherwise} \end{cases} -\end{align*} +\end{aligned} ``` diff --git a/notebooks/TEM_phase_plate.ipynb b/notebooks/08.3_TEM_phase_plate.ipynb similarity index 97% rename from notebooks/TEM_phase_plate.ipynb rename to notebooks/08.3_TEM_phase_plate.ipynb index 492f942..4735e71 100644 --- a/notebooks/TEM_phase_plate.ipynb +++ b/notebooks/08.3_TEM_phase_plate.ipynb @@ -7,19 +7,16 @@ "source": [ "---\n", "title: Interactive plot for contrast in TEM\n", - "authors: [Stephanie Ribet]\n", + "authors: [Stephanie Ribet, Georgios Varnavides]\n", "date: 2023/08/14\n", "---\n", "\n", - "\n", - "Updated by Georgios Varnavides, 2024/07/15\n", - "\n", "[cif file](https://drive.google.com/file/d/161gO_iC7cHCBtCUlAAPrg9Q3SOPWARK0/view?usp=sharing)\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "190f4206-c55e-4de4-af84-ccf696b59fcf", "metadata": {}, "outputs": [ @@ -27,27 +24,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[########################################] | 100% Completed | 404.91 ms\n" + "[########################################] | 100% Completed | 506.21 ms\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "53394bc217004a3e867bb409bf557125", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='255px', width='675px')…" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "#| label: app:tem_contrast\n", - "\n", "%matplotlib widget\n", "\n", "import numpy as np\n", @@ -377,15 +358,39 @@ " VBox([phase_shift,zernike_radius])\n", " ]),\n", " ],\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f75e937c-034c-45bc-944b-f5d1517a6410", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dc09b49ee6a148b79aedc4e890c3434b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='255px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:tem_contrast\n", "display(widget);" ] }, { "cell_type": "code", "execution_count": null, - "id": "f75e937c-034c-45bc-944b-f5d1517a6410", + "id": "767dcd0c-217d-49a6-a7da-20ec3dc9241f", "metadata": {}, "outputs": [], "source": [] diff --git a/notebooks/inputs_probe_size.ipynb b/notebooks/09.0_inputs_probe_size.ipynb similarity index 90% rename from notebooks/inputs_probe_size.ipynb rename to notebooks/09.0_inputs_probe_size.ipynb index 360c977..da2b020 100644 --- a/notebooks/inputs_probe_size.ipynb +++ b/notebooks/09.0_inputs_probe_size.ipynb @@ -9,9 +9,7 @@ "title: Interactive plot for probe size\n", "authors: [Stephanie Ribet, Colin Ophus]\n", "date: 2023/08/14\n", - "---\n", - "\n", - "Updated by Georgios Varnavides, 2024/07/15" + "---" ] }, { @@ -23,7 +21,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "332dd9f076e44e71bb5fd83a28326407", + "model_id": "11111dc902f8499d9fae19a0aea1f3c7", "version_major": 2, "version_minor": 0 }, @@ -41,21 +39,27 @@ "%matplotlib widget\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "from colorspacious import cspace_convert\n", "from IPython.display import display\n", "from ipywidgets import HBox, VBox, widgets, Dropdown, Label, Layout, IntSlider, FloatSlider,FloatLogSlider\n", "import abtem\n", "\n", "# Copied from py4DSTEM directly\n", - "def Complex2RGB(complex_data, vmin=None, vmax = None, hue_start = 0, invert=False):\n", + "def Complex2RGB(complex_data, vmin=None, vmax=None, power=None, chroma_boost=1):\n", " \"\"\"\n", " complex_data (array): complex array to plot\n", - " vmin (float) : minimum absolute value \n", - " vmax (float) : maximum absolute value \n", - " hue_start (float) : rotational offset for colormap (degrees)\n", - " inverse (bool) : if True, uses light color scheme\n", + " vmin (float) : minimum absolute value\n", + " vmax (float) : maximum absolute value\n", + " power (float) : power to raise amplitude to\n", + " chroma_boost (float): boosts chroma for higher-contrast (~1-2.5)\n", " \"\"\"\n", " amp = np.abs(complex_data)\n", - " if np.isclose(np.max(amp),np.min(amp)):\n", + " phase = np.angle(complex_data)\n", + "\n", + " if power is not None:\n", + " amp = amp**power\n", + "\n", + " if np.isclose(np.max(amp), np.min(amp)):\n", " if vmin is None:\n", " vmin = 0\n", " if vmax is None:\n", @@ -75,15 +79,16 @@ "\n", " amp = np.where(amp < vmin, vmin, amp)\n", " amp = np.where(amp > vmax, vmax, amp)\n", + " amp = ((amp - vmin) / vmax).clip(1e-16, 1)\n", "\n", - " phase = np.angle(complex_data) + np.deg2rad(hue_start)\n", - " amp /= np.max(amp)\n", - " rgb = np.zeros(phase.shape +(3,))\n", - " rgb[...,0] = 0.5*(np.sin(phase)+1)*amp\n", - " rgb[...,1] = 0.5*(np.sin(phase+np.pi/2)+1)*amp\n", - " rgb[...,2] = 0.5*(-np.sin(phase)+1)*amp\n", - " \n", - " return 1-rgb if invert else rgb\n", + " J = amp * 61.5 # Note we restrict luminance to the monotonic chroma cutoff\n", + " C = np.minimum(chroma_boost * 98 * J / 123, 110)\n", + " h = np.rad2deg(phase) + 180\n", + "\n", + " JCh = np.stack((J, C, h), axis=-1)\n", + " rgb = cspace_convert(JCh, \"JCh\", \"sRGB1\").clip(0, 1)\n", + "\n", + " return rgb\n", "\n", "\n", "# widget figure generation\n", @@ -165,7 +170,6 @@ " probe_R = Complex2RGB(probe_R_vals, vmax = 1, vmin = 0)\n", " probe_R_int = np.abs(probe_R_vals)**2\n", " probe_R_int /= np.max(probe_R_int)\n", - " probe_R_int **= 0.25\n", " \n", " im0.set_data(probe_Q)\n", " im1.set_data(probe_R)\n", diff --git a/notebooks/STEM_detectors.ipynb b/notebooks/09.1_STEM_detectors.ipynb similarity index 96% rename from notebooks/STEM_detectors.ipynb rename to notebooks/09.1_STEM_detectors.ipynb index 993293a..0171519 100644 --- a/notebooks/STEM_detectors.ipynb +++ b/notebooks/09.1_STEM_detectors.ipynb @@ -7,15 +7,10 @@ "source": [ "---\n", "title: Interactive STEM detector angles\n", - "authors: [Stephanie Ribet]\n", + "authors: [Stephanie Ribet, Georgios Varnavides]\n", "date: 2023/08/14\n", "---\n", "\n", - "\n", - "Updated by Georgios Varnavides, 2024/07/15\n", - "\n", - "TODO: re run simulations with frozen phonons \n", - "\n", "Data [here](https://drive.google.com/file/d/1O0sAsL0oX7n3VsejrLr9LNLZNAysWI-v/view?usp=sharing)" ] }, @@ -24,25 +19,8 @@ "execution_count": 1, "id": "8efdd632-53df-4a08-ab7a-ae959a8bfbd2", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0fa7b6d1fd794520a0f1b11567f100c4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='255px', width='675px')…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "#| label: app:stem_detectors\n", - "\n", "%matplotlib widget\n", "\n", "import numpy as np\n", @@ -239,15 +217,39 @@ " dropdown\n", " ]),\n", " ],\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1a15d388-5640-4c28-ae40-4b2cfbacfee7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3b3876696b0e4a3b85723cbbe04c3210", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='255px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: app:stem_detectors\n", "display(widget);" ] }, { "cell_type": "code", "execution_count": null, - "id": "1a15d388-5640-4c28-ae40-4b2cfbacfee7", + "id": "351a792d-3339-4312-832f-f98055674c5c", "metadata": {}, "outputs": [], "source": [] @@ -269,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/STEM_convergence.ipynb b/notebooks/09.2_STEM_convergence.ipynb similarity index 89% rename from notebooks/STEM_convergence.ipynb rename to notebooks/09.2_STEM_convergence.ipynb index 3b50b32..79578b3 100644 --- a/notebooks/STEM_convergence.ipynb +++ b/notebooks/09.2_STEM_convergence.ipynb @@ -7,11 +7,9 @@ "source": [ "---\n", "title: Interactive STEM convergence angles\n", - "authors: [Stephanie Ribet]\n", + "authors: [Stephanie Ribet, Georgios Varnavides]\n", "date: 2023/08/27\n", - "---\n", - "\n", - "Updated by Georgios Varnavides, 2024/07/15" + "---" ] }, { @@ -24,27 +22,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[########################################] | 100% Completed | 854.22 ms\n" + "[########################################] | 100% Completed | 1.58 sms\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0955cf25ea6049d0aec5be3fa7d83f8c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='375px', width='675px')…" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "#| label: app:stem_convergence\n", - "\n", "%matplotlib widget\n", "\n", "import numpy as np\n", @@ -157,15 +139,46 @@ " fig.canvas,\n", " convergence_angle\n", " ],\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cfd1079e-9491-46f0-9844-49791ea6fc4c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c478c6e11ca4dff961276c1308499c3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Canvas(footer_visible=False, header_visible=False, layout=Layout(height='375px', width='675px')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################## ] | 66% Completed | 604.76 ms" + ] + } + ], + "source": [ + "#| label: app:stem_convergence\n", "display(widget);" ] }, { "cell_type": "code", "execution_count": null, - "id": "cfd1079e-9491-46f0-9844-49791ea6fc4c", + "id": "6a970758-fad3-4e71-8814-04a0982b10be", "metadata": {}, "outputs": [], "source": [] @@ -173,9 +186,9 @@ ], "metadata": { "kernelspec": { - "display_name": "guide-tem-simulation", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "guide-tem-simulation" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -187,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/STEM_post_processing_calculation.ipynb b/notebooks/10.2_STEM_post_processing_calculation.ipynb similarity index 100% rename from notebooks/STEM_post_processing_calculation.ipynb rename to notebooks/10.2_STEM_post_processing_calculation.ipynb diff --git a/notebooks/STEM_post_processing.ipynb b/notebooks/STEM_post_processing.ipynb deleted file mode 100644 index 515784a..0000000 --- a/notebooks/STEM_post_processing.ipynb +++ /dev/null @@ -1,98 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4e05c48d-a101-4670-bd06-aa261a12532f", - "metadata": {}, - "source": [ - "---\n", - "title: STEM post processing\n", - "authors: [Toma Susi]\n", - "date: 2024/07/24\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 549, - "id": "744695c6-083f-486e-a6fb-7cb959c49637", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[########################################] | 100% Completed | 106.18 ms\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5fda696e7e374bdc857be1744791e0fb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "ImageGUI(children=(VBox(children=(SelectionSlider(options=('Nyquist-sampled', 'Interpolated', 'Blurred', 'Nois…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 549, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| label: app:stem_processing\n", - "\n", - "%matplotlib ipympl\n", - "abtem.config.set({\"visualize.cmap\": \"viridis\"})\n", - "abtem.config.set({\"visualize.continuous_update\": True})\n", - "abtem.config.set({\"visualize.autoscale\": True})\n", - "\n", - "import abtem\n", - "\n", - "all_measurements = abtem.from_zarr(\"data/processed_STEM.zarr\")\n", - "\n", - "all_measurements.show(interact=True, explode=(1,))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e3d8994-459e-49d8-ab1e-c8ca2efab8f2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "guide-tem-simulation", - "language": "python", - "name": "guide-tem-simulation" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}

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