diff --git a/.gitignore b/.gitignore index 2fc99a01..1dda29d5 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,4 @@ venv.bak/ .stestr/ # project-specific ignores +results@0,[2.600974180557, 10.435233760548].png results@1,[2.600974180557, 10.435233760548].png results@2,[2.600974180557, 10.435233760548].png results@3,[2.600974180557, 10.435233760548].png results@4,[2.600974180557, 10.435233760548].png results@5,[2.600974180557, 10.435233760548].png results@6,[2.600974180557, 10.435233760548].png results@7,[2.600974180557, 10.435233760548].png results@8,[2.600974180557, 10.435233760548].png resultsK@0,[2.600974180557, 10.435233760548].png resultsK@1,[2.600974180557, 10.435233760548].png resultsK@2,[2.600974180557, 10.435233760548].png resultsK@3,[2.600974180557, 10.435233760548].png resultsK@4,[2.600974180557, 10.435233760548].png resultsK@5,[2.600974180557, 10.435233760548].png resultsK@6,[2.600974180557, 10.435233760548].png resultsK@7,[2.600974180557, 10.435233760548].png resultsK@8,[2.600974180557, 10.435233760548].png diff --git a/README.md b/README.md index ec2d0f94..b950dc59 100644 --- a/README.md +++ b/README.md @@ -1,50 +1,28 @@ -# 1. Challenge Overview: -Vanguard’s portfolio construction process lies at the heart of its investment strategy, balancing risk, return, and investor preferences across a vast landscape of asset classes and constraints. However, as portfolios grow in complexity—spanning thousands of securities, intricate guardrails, and real-time trading demands—classical optimization tools like GUROBI face growing limitations in speed, scalability, and solution diversity. This challenge explores how sampling-based quantum optimization can be harnessed to overcome these barriers. By leveraging hybrid quantum-classical algorithms and decomposition pipelines, the goal is to prototype a quantum-enhanced solution that can: +# Portfolio Optimization -1. Efficiently solve high-dimensional, constraint-heavy portfolio optimization problems. -2. Deliver near-optimal asset allocations within tight runtime windows. -3. Scale to real-world use cases like fixed income ETF creation and index tracking. -4. Preserve critical business metrics such as tracking error, excess return, and risk exposure. +Project name: +* Annealed Portfolio -The project focuses on using binary decision variables and quadratic objectives to simulate realistic trading scenarios. The challenge lies not only in achieving computational gains but also in maintaining interpretability, robustness, and alignment with investment principles. +Team Members: +* Hao Mack Yang (gst-Z2od5sCatjVtRvH) +Presentation: +* [https://youtube.com/live/ZSvtRcaG_Vc](https://youtube.com/live/ZSvtRcaG_Vc) -# 2. Challenge Duration: - * 4 weeks - * Teams start working on July 15, 2025 - * Teams submit their challenge solutions on August 10, 2025 +This **Washington Institute for STEM Entrepreneurship and Research** project demonstrates an alternative quantum computing method to optimize the portfolios based on the investor's preferences. -# 3. Team Guidelines: -* Team size - Maximum 3 participants per team. -* All team participants must be enrolled in Womanium WISER Quantum 2025. -* Everyone is eligible to participate in this challenge and win Womanium grants. -* Best participants get selected for Womanium QSL fellowships with Vanguard. +The goal is to minimize an objective function, the sum of the squared difference between the actual amount against the target amount of a target characteristic by risk bucket membership and characteristic while subject to guardrail constraints regarding the actual amounts. The minimization of this objective function allows us to maximize the expected utility of our portfolio. Our output is a set of binary decision variables indicating whether the particular bond is included in the portfolio. -# 4. Challenge Tasks/ Deliverables: -The participants are expected to complete for eligible challenge submission: +Our approach uses the D-WAVE quantum annealing method, as opposed to the variational quantum algorithm implemented in Qiskit or Xanadu. Quantum annealing is especially well-suited for scalability and handling quadratic optimization problems, even with constraints. Unfortunately, we are not able to get access to the actual quantum processor through LEAP, so we ended up using one of D-WAVE's simulators. Additionally, constrained binary quadratic optimization problem still requires a hybrid approach. -1) Review the mathematical formulation provided below, focusing on binary decision variables, linear constraints, and the quadratic objective. -2) (necessary to pass the project) Convert the binary optimization problem to a formulation that is compatible with a quantum optimization algorithm. For example, convert the constrained problem to an unconstrained problem. -3) (necessary to pass the project) Write a quantum optimization program for handling problems of the type in (2). An example of such an optimization routine which is used in portfolio optimization is the Variational Quantum Eigensolver (see resources below), however you may pursue what you judge to be the best solution. -4) (challenge) Solve the optimization problem in (1) using your quantum formulation. -5) (challenge subtask) Validate your solution in (4) using a classical optimization routine. Compare the solution quality against the benchmark classical solution in terms of the cost function, and include relevant performance metrics(e.g., convergence of the optimization routine, and scaling properties with problem size). - -Note: No formal presentation is required. Instead, we’ll host a “show-and-tell” style session where each team will walk through their approach and demonstrate their prototype live. This is your opportunity to showcase your thinking, creativity, and results in an informal, interactive format. +The structure of this submission is such that with elementary knowledge of Python or Jupyter plus the introduction of virtual environments, one can interactively see the process of data gathering, guardrail inputting, problem conversion, and simulated annealing, and presentation with one single click of the play button. -# 5. Quantum Hardware Credits / Platform: -* Participants may use any quantum SDK or platform of their choice. +With the exception of the README and presentations, all content that belongs to the team is in the `implementation` folder relative to the root working directory. Inside contains another README.md regarding the mathematical formulation, an `.ipynb` file depicting a large dataset run with randomly set bounds, a slightly modified `.ipynb` file from the `lp` bounds, a `.py` file depicting repeated runs of the small dataset runs, and the `bond_data1.json` generated for listing the selected attributes of the reduced dataset. -# 6. Judging Criteria: -Solutions will be evaluated against internal benchmark implementations at Vanguard. Evaluation will be based on: -* Speed of the solution -* Optimality (as measured by the cost function) -* Scalability (problem size handled) +Upon running the script, a `dwave_result.csv` file will be generated at the `implementation` working directory (caveat: it is relative to the working directory of VS Code if you are running on VS Code), and various results files depicting the energy value, bond selection, and constraint satisfaction of each entry ordered by the D-WAVE energy rankings (the lowest energy selection is considered the best solution.) -# 7. Resources: +We have supplied the `result` and `result2` images, depicting the energy and secondary constraint satisfaction trends ranked by D-WAVE energy rating, and the `qaoamatrix.png` images, depicting the symmetrical matrix representing the binary QAOA matrix. -* An example of how VQE can be implemented - https://eric08000800.medium.com/portfolio-optimization-with-variational-quantum-eigensolver-vqe-2-477a0ee4e988 -* A useful paper on variational Quantum Optimization https://quantum-journal.org/papers/q-2020-04-20-256/?utm_source=researcher_app&utm_medium=referral&utm_campaign=RESR_MRKT_Researcher_inbound -* Video recording for project orientation 2025 QUANTUM PROGRAM ❯ Day 7 ❯ Projects Orientation Part 2 - YouTube - - +Please bear in mind that there are some limitations present in this project, especially concerning the potential differences between the actual boundaries used by the reference method against our method. The structure of the code is designed such that there should be minimal debugging and code redundancy when correcting the code to match the reference method to evaluate against. +Word count: 460 \ No newline at end of file diff --git a/dwave_result.csv b/dwave_result.csv new file mode 100644 index 00000000..6034eca0 --- /dev/null +++ b/dwave_result.csv @@ -0,0 +1,65537 @@ +,020002BJ9,026874DS3,081437AT2,097023CJ2,13645RAD6,13645RBF0,14448CBC7,15135BAW1,21871XAS8,24422EWZ8,24422EXP9,314353AA1,36166NAK9,438516CM6,443201AC2,444859BR2,444859BV3,444859BY7,444859CA8,45687VAB2,539830CD9,540424AT5,56501RAN6,655844CR7,655844CT3,75513EAD3,759351AP4,760759BA7,760759BC3,907818FX1,91324PEJ7,slack_Sx_0,slack_Sx_1,slack_Sx_2,slack_Sx_fund_enriched.pmv_I_Capital_Goods_0,slack_Sx_fund_enriched.pmv_I_Insurance_0,slack_Sx_fund_enriched.pmv_I_Transportation_0,slack_Sx_fund_enriched.pmv_I_Transportation_1,slack_Sy_0,slack_Sy_1,slack_Sy_2,slack_Sy_3,slack_Sy_4,energy,num_occurrences 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+ +Author: Hao Mack + +We will work in parallel to consider different classical and quantum methods to implement the _simplified OneOpto optimization model_. Our goal is to match the value of each characteristic per each risk group to its target as closely as possible while respecting the many guardrails. + +The implementation is in the form of a single Jupyter notebook. + +Part 1 is detailed on the README file itself. All other parts are in the `implementation.ipynb`. + +## Mathematical formulation (Part 2) + +We are given three sets, $C$, $L$, $J$, and two global parameters, $N$, and $\vec{r}$ as the input. Our decision variables are the $\vec{y}$, indicating the particular bonds are included in the portfolio or not. + +The objective function is a quadratic optimization function in the $\vec{y}$, subject to four linear inequality constraints. + +### Securities + +$C$ is denoted as the set of securities. Each security $c \in C$ is expressed as a quintuple $(p_c, m_c, M_c, i_c, \delta_c)$, where: + +* $p_c$ is the market price +* $m_c$ and $M_c$ are the minimum and maximum trade value +* $i_c$ is the basket inventory +* $\delta_c$ is the minimum increment of a price + +### Risk buckets + +There is a set $L$ of risk buckets, and for each element of the risk bucket $\ell \in L$, there is a corresponding set of bond securities belonging to that risk bucket. Thus, there is a "set of sets" structure of the form $(\ell, \mathbb{K}_\ell)$. + +* $L$ is the set of risk buckets. +* $\mathbb{K}_l$ is the set of bond securities in risk bucket $\ell$. + * It is noted that it is not necessarily the case that the risk buckets are mutually exclusive. + +### Characteristics and Guardrails + +There is a set $J$ consisting of the characteristics of the investments. It is a strict subset of the fields that provide bond information. Optimization bonds are based on a combination of an element in $J$, and another element in either $C$ or $L$. $J$ is strictly a preference for the investor. + +There are $|J|(2|L| + |C|)$ guardrails with a two-dimensional index, which one of the indices, $j$, is from $J$. + +* $\rho_j$ represent the weight for that characteristic when determining the energy of the optimization problem to minimize. + +Each element $j \in J$ is a triple $(\vec{K}_{j}, \vec{b}_{j}, \vec{\beta}_{j})$ which the length of the vector depends on $|L|$ for $\vec{K}_j$ and $\vec{b}_j$, and on $|C|$ for $\vec{\beta}_j$. The elements of each vector element of $\vec{K}_j$ and $\vec{b}_j$ are themselves an interval containing a guardrail and a target value. + +* $K_{\ell, j} = [K^{\textrm{low}}_{\ell, j}, K^{\textrm{target}}_{\ell, j}, K^{\textrm{up}}_{\ell, j}]$ are the target and guardrails of a characteristic $j$ in risk bucket $\ell$. +* $b_{\ell, j} = [b^{\textrm{up}}_{\ell, j}, b^\textrm{low}_{\ell, j}]$ denote the guardrails for the weighed count of bonds. (unused) +* $\beta_{c, j}$ denote the contribution of a unit of bond $c$ to the target of characteristic $j$. + +### Global parameters + +Two global hyperparameters will be used to find a solution for various use cases. + +* $N$ denotes the maximum number of bonds in the portfolio. +* $R = [m_R, M_R]$ denote the range of the residual cash flow of portfolio. + * Namely, $m_R$ denote the minimum cash flow, and $M_R$ denote the maximum cash flow. Note that they are signed quantities. + +### Decision variable + +Let $\vec{y}$ denote the solution set. Each entry of $\vec{y}$ is indexed by an element $c \in C$. The objective function involves the optimzal allocation of the bonds and how many of each bond to allocate. The amount of bonds to allocate, $x_c$, is simplified as a function of $c$ and $y$: $x_c = \frac{m_c + \min{M_c, i_c}}{2\delta_c}$, the average number of bonds from what is feasible for that $c$. + +### Optimization problem + +We must match the value of each characteristic $j \in J$ in each risk group $\ell \in L$ to its target, $K^{\textrm{target}}_{\ell, j}$, subject to four constraints. + +Note that if a quantity $s \in [I^{\textrm{low}}, I^{\textrm{target}}, I^{\textrm{up}}]$ or $s \in [I^{\textrm{low}}, I^{\textrm{up}}]$, the inequality becomes $I^{\textrm{low}} \leq s \leq I^{\textrm{up}}$. + +$$ +\begin{align*} + E &= \min_{\vec{y}} \sum_{\ell \in L} \sum_{j \in J} \rho_j \left(\sum_{c\in{\mathbb{K}_{\ell}}} \beta_{c, j} x_c - K^{\textrm{target}}_{\ell, j}\right)^2 \\ + \sum_{c \in C} y_c &\leq N \\ + \sum_{c \in \mathbb{K}_{\ell}} \beta_{c,j}y_c &\in K_{\ell, j} \\ + \sum_{c \in C} \frac{p_c \delta_c}{100} x_c &\in R \\ + \sum_{c \in \mathbb{K}_{\ell}} \frac{p_c \delta_c}{100} \beta_{c,j} x_c &\in b_{\ell, j} +\end{align*} +$$ \ No newline at end of file diff --git a/implementation/bond_data1.json b/implementation/bond_data1.json new file mode 100644 index 00000000..864658ef --- /dev/null +++ b/implementation/bond_data1.json @@ -0,0 +1,277 @@ +{ + "bonds": [ + { + "id": "020002BJ9", + "min": 5.0, + "max": 30.0, + "sector": "Insurance", + "pmv": 0.02633418857, + "dxs": 11.747405182296 + }, + { + "id": "026874DS3", + "min": 5.0, + "max": 30.0, + "sector": "Insurance", + "pmv": 0.032168634542, + "dxs": 19.306390960602 + }, + { + "id": "081437AT2", + "min": 5.0, + "max": 30.0, + "sector": "Capital_Goods", + "pmv": 0.028510851146, + "dxs": 13.316192340035 + }, + { + "id": "097023CJ2", + "min": 5.0, + "max": 13.0, + "sector": "Capital_Goods", + "pmv": 0.027298851045, + "dxs": 34.461503917596 + }, + { + "id": "13645RAD6", + "min": 5.0, + "max": 60.0, + "sector": "Transportation", + "pmv": 0.0362193993, + "dxs": 0.0362193993 + }, + { + "id": "13645RBF0", + "min": 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I have yet to able to access the actual quantum computer through LEAP.\n", + "\n", + "Please ensure you are in the correct virtual environment with all the packages installed to continue. Otherwise, the notebook will terminate with a traceback." + ] + }, + { + "cell_type": "markdown", + "id": "0f44fb90", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Setup\n", + "\n", + "It is recommended to use a venv (Python virtual environment) to run this notebook to ensure compatibility.\n", + "At the time of this submission, Conda and Python 3.13 is not compatible with D-WAVE Ocean.\n", + "\n", + "We will need `dwave-ocean-sdk` for all D-WAVE procedures, and `pandas` with optional dependency `openpyxl` for reading the data file. Running the notebook also requires the `ipykernel` package, which can be automatically downloaded for you.\n", + "\n", + "If the initial check fails, a `ModuleNotFoundError` traceback will generate and execution will stop. If so, please follow the instructions on the next step." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33cc3d3d", + "metadata": {}, + "outputs": [], + "source": [ + "# Packages to import\n", + "import dimod\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Display options\n", + "np.set_printoptions(precision=3, linewidth=65536, threshold=65536)\n", + "dimod.set_printoptions(width=(1<<20))\n", + "CUTOFF = 1000\n", + "READS = 65536\n", + "\n", + "# Color format\n", + "ENDCOLOR = '\\033[0m'\n", + "HEADER = '\\033[5m\\033[1;32m'\n", + "YELLOW = '\\033[0;33m'\n", + "IN = \"\\033[0;32m\"\n", + "OUT = \"\\033[0;31m\"\n", + "OUT_LOW = '\\033[0;31m'\n", + "OUT_HIGH = '\\033[0;35m'\n", + "\n", + "# Ordinal grades for sorting by letter grade\n", + "def ordinalgrade(lettergrade):\n", + " ordinal = lettergrade\n", + " try:\n", + " ordinal = str([\"AAA\",\"AA\",\"A\",\"BBB\",\"BB\",\"B\",\"CCC\",\"CC\",\"C\",\"D\"].index(lettergrade))\n", + " except ValueError:\n", + " pass\n", + " return ordinal" + ] + }, + { + "cell_type": "markdown", + "id": "ac98ca21", + "metadata": {}, + "source": [ + "### Virtual environment\n", + "\n", + "This is for Unix users. On the _dwave subdirectory_, run this command to respectively create and activate the virtual environment. You only have to create the virtual environment once." + ] + }, + { + "cell_type": "raw", + "id": "c11745dc", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "python3 -m venv ocean\n", + "source ocean/bin/activate" + ] + }, + { + "cell_type": "markdown", + "id": "6dca8cde", + "metadata": {}, + "source": [ + "### Python packages\n", + "\n", + "Below is the installation for Unix users. Assume we also install all Contributor Ocean tools and auxiliary packages for visualization. We can simply use pip to install the remaining packages." + ] + }, + { + "cell_type": "raw", + "id": "183e676c", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "pip install dwave-ocean-sdk\n", + "dwave install --all\n", + "\n", + "pip install pandas\n", + "pip install openpyxl\n", + "pip install numpy\n", + "pip install matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "e245daab", + "metadata": {}, + "source": [ + "## Input\n", + "\n", + "We define the data structure using constants, arrays, and the formatting of data organization before converting this into a Python object." + ] + }, + { + "cell_type": "markdown", + "id": "58585a19", + "metadata": {}, + "source": [ + "### Data extraction (Part 1)\n", + "\n", + "We extract all the data from the supplied [`/data/1/data_assets_dictionary.xlsx`](/data/1/data_assets_dictionary.xlsx) file relative to this working directory. This requires the `pandas` package.\n", + "\n", + "There are 2628 securities, all listed in USD. Empty values are assigned the `NaN` value." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "127fa68d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0isinccyassetIdstrategyNamesecGroupsecTypecpnposSourcebaseCcy...Large Issuer_newissuerTicker_newsodraw.filterLevel1_newsodraw.filterLevel3_newsodraw.filterLevel4_newsecurity.elements.creditQualityBuckets_newsecurity.elements.normalizedRating_newsodraw.filterLevel2_newassetId_newmain_key
00US001055BJ00USD001055BJ0UnassignedBNDCORP3.6EODUSD...NAFLInvestment Grade CreditInsuranceLife InsuranceAAFinancial001055BJ0VCIT | VCIT (bench)
11US001084AS13USD001084AS1UnassignedBNDCORP5.8EODUSD...NAGCOInvestment Grade CreditCapital GoodsConstruction MachineryBBBBBBIndustrial001084AS1VCIT | VCIT (bench)
22US00108WAM29USD00108WAM2UnassignedBNDCORP2.1EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAM2VCIT | VCIT (bench)
33US00108WAP59USD00108WAP5UnassignedBNDCORP4.7EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAP5VCIT | VCIT (bench)
44US00108WAR16USD00108WAR1UnassignedBNDCORP5.4EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAR1VCIT | VCIT (bench)
..................................................................
26242624NaNUSDUSD_CCASHUnassignedCASHCASH0.0ORDUSD...NUSDNaNOtherOtherNRJunk / NRNaNUSD_CCASHVCIT | VCIT (bench)
26252625NaNUSDUSD_CCASHUnassignedCASHCASH0.0TRDUSD...NUSDNaNOtherOtherNRJunk / NRNaNUSD_CCASHVCIT | VCIT (bench)
26262626NaNUSDUXYZ42029UnassignedFUTUREFIN0.0EODUSD...NUXYZ4NaNOtherOtherAAA/AAAAANaNUXYZ42029VCIT | VCIT (bench)
26272627NaNUSDUXYZ4202FUnassignedCASHFUTURE0.0EODUSD...NUXYZ4NaNOtherOtherAAA/AAAAANaNUXYZ4202FVCIT | VCIT (bench)
26282628NaNUSDXUSD00000UnassignedCASHCASH-100.0CAPITALUSD...NCASH & EQUIVALENNaNOtherOtherNRJunk / NRNaNXUSD00000VCIT | VCIT (bench)
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2629 rows × 278 columns

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" + ], + "text/plain": [ + " Unnamed: 0 isin ccy assetId strategyName secGroup secType \\\n", + "0 0 US001055BJ00 USD 001055BJ0 Unassigned BND CORP \n", + "1 1 US001084AS13 USD 001084AS1 Unassigned BND CORP \n", + "2 2 US00108WAM29 USD 00108WAM2 Unassigned BND CORP \n", + "3 3 US00108WAP59 USD 00108WAP5 Unassigned BND CORP \n", + "4 4 US00108WAR16 USD 00108WAR1 Unassigned BND CORP \n", + "... ... ... ... ... ... ... ... \n", + "2624 2624 NaN USD USD_CCASH Unassigned CASH CASH \n", + "2625 2625 NaN USD USD_CCASH Unassigned CASH CASH \n", + "2626 2626 NaN USD UXYZ42029 Unassigned FUTURE FIN \n", + "2627 2627 NaN USD UXYZ4202F Unassigned CASH FUTURE \n", + "2628 2628 NaN USD XUSD00000 Unassigned CASH CASH \n", + "\n", + " cpn posSource baseCcy ... Large Issuer_new issuerTicker_new \\\n", + "0 3.6 EOD USD ... N AFL \n", + "1 5.8 EOD USD ... N AGCO \n", + "2 2.1 EOD USD ... N AEP \n", + "3 4.7 EOD USD ... N AEP \n", + "4 5.4 EOD USD ... N AEP \n", + "... ... ... ... ... ... ... \n", + "2624 0.0 ORD USD ... N USD \n", + "2625 0.0 TRD USD ... N USD \n", + "2626 0.0 EOD USD ... N UXYZ4 \n", + "2627 0.0 EOD USD ... N UXYZ4 \n", + "2628 -100.0 CAPITAL USD ... N CASH & EQUIVALEN \n", + "\n", + " sodraw.filterLevel1_new sodraw.filterLevel3_new \\\n", + "0 Investment Grade Credit Insurance \n", + "1 Investment Grade Credit Capital Goods \n", + "2 Investment Grade Credit Electric \n", + "3 Investment Grade Credit Electric \n", + "4 Investment Grade Credit Electric \n", + "... ... ... \n", + "2624 NaN Other \n", + "2625 NaN Other \n", + "2626 NaN Other \n", + "2627 NaN Other \n", + "2628 NaN Other \n", + "\n", + " sodraw.filterLevel4_new security.elements.creditQualityBuckets_new \\\n", + "0 Life Insurance A \n", + "1 Construction Machinery BBB \n", + "2 Other BBB \n", + "3 Other BBB \n", + "4 Other BBB \n", + "... ... ... \n", + "2624 Other NR \n", + "2625 Other NR \n", + "2626 Other AAA/AA \n", + "2627 Other AAA/AA \n", + "2628 Other NR \n", + "\n", + " security.elements.normalizedRating_new sodraw.filterLevel2_new \\\n", + "0 A Financial \n", + "1 BBB Industrial \n", + "2 BBB Utilities \n", + "3 BBB Utilities \n", + "4 BBB Utilities \n", + "... ... ... \n", + "2624 Junk / NR NaN \n", + "2625 Junk / NR NaN \n", + "2626 AAA NaN \n", + "2627 AAA NaN \n", + "2628 Junk / NR NaN \n", + "\n", + " assetId_new main_key \n", + "0 001055BJ0 VCIT | VCIT (bench) \n", + "1 001084AS1 VCIT | VCIT (bench) \n", + "2 00108WAM2 VCIT | VCIT (bench) \n", + "3 00108WAP5 VCIT | VCIT (bench) \n", + "4 00108WAR1 VCIT | VCIT (bench) \n", + "... ... ... \n", + "2624 USD_CCASH VCIT | VCIT (bench) \n", + "2625 USD_CCASH VCIT | VCIT (bench) \n", + "2626 UXYZ42029 VCIT | VCIT (bench) \n", + "2627 UXYZ4202F VCIT | VCIT (bench) \n", + "2628 XUSD00000 VCIT | VCIT (bench) \n", + "\n", + "[2629 rows x 278 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATA_ASSETS_DICTIONARY = '../data/1/data_assets_dictionary.xlsx'\n", + "df = pd.read_excel(DATA_ASSETS_DICTIONARY, sheet_name=0, header=0)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "12a65465", + "metadata": {}, + "source": [ + "### Summary\n", + "\n", + "From the dataframe, the sets are:\n", + "\n", + "* $|C| = 2628$\n", + " * A reduced subset containing only 31 elements is also provided.\n", + " * Another reduced subset containing only 12 elements, with none of the ${\\mathbb{K}_{\\ell}}$ empty will also be tested.\n", + "* $L = \\{\\textrm{AAA}, \\textrm{AA}, \\textrm{A}, \\textrm{BBB}, \\textrm{NR}\\}$\n", + " * $\\textrm{NR}$ encompasses both not rated bonds and speculative bonds.\n", + "* $\\sum_{\\ell \\in L} \\mathbb{K}_{\\ell} = |C|$. Using `security.elements.normalizedRating_new`, we obtain:\n", + " * $|\\mathbb{K}_{\\textrm{AAA}}| = 7$\n", + " * $|\\mathbb{K}_{\\textrm{AA}}| = 376$\n", + " * $|\\mathbb{K}_{\\textrm{A}}| = 863$\n", + " * $|\\mathbb{K}_{\\textrm{BBB}}| = 1372$\n", + " * $|\\mathbb{K}_{\\textrm{NR}}| = 11$\n", + "* $J$ depends on one of the six test cases. Note that $J \\subsetneq F$ where $F$ is the list of all fields. Also, $|F| = 278$.\n", + " * 31-bond, average only and no complex variables\n", + " * 31-bond, average only\n", + " * 31-bond, min-max average\n", + " * 31-bond, original\n", + " * full dataset, reduced\n", + " * full dataset, original\n", + "* There are a small number of duplicate entries and pending entries.\n", + "\n", + "Instead, we will use a reduced dataset; also $L$ is divided into industry-rating combinations. Furthermore, all $c$ can be mapped to a unique natural number, call this the canonical ordering.\n", + "\n", + "* $|C| = 31$\n", + "* $L = \\{\\textrm{Capital}, \\textrm{Investment}, \\textrm{Transportation}\\}$\n", + "* $J = \\{\\textrm{PMV}\\}$\n", + "* $\\forall j\\,\\forall c\\,\\beta_{j, c} = 1$\n", + "* $\\forall j\\,\\rho_{j} = 1$\n", + "* $\\mathrm{ord}: C \\rightarrow [0:|C|)$" + ] + }, + { + "cell_type": "markdown", + "id": "c9a5b51a", + "metadata": {}, + "source": [ + "### Defining the guardrails (Part 1)\n", + "\n", + "Each security is indexed with an `assetID`. According from the data assets dictionary spreadsheet, it is an \"Internal master identifier used by Vanguard systems to join to the firm‑wide security master.\"\n", + "\n", + "The reduced set is also provided. This has been obtained directly from one of the `.lp` files.\n", + "\n", + "Each element in $L$ would be augmented by two intervals $\\vec{K}, \\vec{b}$, one for each $j \\in J$. Each element in $c$ would also be augmented by a weight $\\vec{\\beta}$, one for each $j \\in J$. A sanity check will be performed to prevent any index-out-of-bounds errors. All of the bounds will be stored in the `KB` triple array." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e0150fb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0isinccyassetIdstrategyNamesecGroupsecTypecpnposSourcebaseCcy...Large Issuer_newissuerTicker_newsodraw.filterLevel1_newsodraw.filterLevel3_newsodraw.filterLevel4_newsecurity.elements.creditQualityBuckets_newsecurity.elements.normalizedRating_newsodraw.filterLevel2_newassetId_newmain_key
00US001055BJ00USD001055BJ0UnassignedBNDCORP3.60EODUSD...NAFLInvestment Grade CreditInsuranceLife InsuranceAAFinancial001055BJ0VCIT | VCIT (bench)
11US001084AS13USD001084AS1UnassignedBNDCORP5.80EODUSD...NAGCOInvestment Grade CreditCapital GoodsConstruction MachineryBBBBBBIndustrial001084AS1VCIT | VCIT (bench)
22US00108WAM29USD00108WAM2UnassignedBNDCORP2.10EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAM2VCIT | VCIT (bench)
33US00108WAP59USD00108WAP5UnassignedBNDCORP4.70EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAP5VCIT | VCIT (bench)
44US00108WAR16USD00108WAR1UnassignedBNDCORP5.40EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAR1VCIT | VCIT (bench)
..................................................................
26112611US98956PAV40USD98956PAV4UnassignedBNDCORP2.60EODUSD...NZBHInvestment Grade CreditConsumer Non CyclicalsHealth CareBBBBBBIndustrial98956PAV4VCIT | VCIT (bench)
26122612US98956PAZ53USD98956PAZ5UnassignedBNDCORP5.20EODUSD...NZBHInvestment Grade CreditConsumer Non CyclicalsHealth CareBBBBBBIndustrial98956PAZ5VCIT | VCIT (bench)
26142614US98971DAB64USD98971DAB6UnassignedBNDCORP3.25EODUSD...NZIONInvestment Grade CreditBankingOtherBBBBBBFinancial98971DAB6VCIT | VCIT (bench)
26162616US98978VAS25USD98978VAS2UnassignedBNDCORP2.00EODUSD...NZTSInvestment Grade CreditConsumer Non CyclicalsPharmaceuticalsBBBBBBIndustrial98978VAS2VCIT | VCIT (bench)
26172617US98978VAV53USD98978VAV5UnassignedBNDCORP5.60EODUSD...NZTSInvestment Grade CreditConsumer Non CyclicalsPharmaceuticalsBBBBBBIndustrial98978VAV5VCIT | VCIT (bench)
\n", + "

2319 rows × 278 columns

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" + ], + "text/plain": [ + " Unnamed: 0 isin ccy assetId strategyName secGroup secType \\\n", + "0 0 US001055BJ00 USD 001055BJ0 Unassigned BND CORP \n", + "1 1 US001084AS13 USD 001084AS1 Unassigned BND CORP \n", + "2 2 US00108WAM29 USD 00108WAM2 Unassigned BND CORP \n", + "3 3 US00108WAP59 USD 00108WAP5 Unassigned BND CORP \n", + "4 4 US00108WAR16 USD 00108WAR1 Unassigned BND CORP \n", + "... ... ... ... ... ... ... ... \n", + "2611 2611 US98956PAV40 USD 98956PAV4 Unassigned BND CORP \n", + "2612 2612 US98956PAZ53 USD 98956PAZ5 Unassigned BND CORP \n", + "2614 2614 US98971DAB64 USD 98971DAB6 Unassigned BND CORP \n", + "2616 2616 US98978VAS25 USD 98978VAS2 Unassigned BND CORP \n", + "2617 2617 US98978VAV53 USD 98978VAV5 Unassigned BND CORP \n", + "\n", + " cpn posSource baseCcy ... Large Issuer_new issuerTicker_new \\\n", + "0 3.60 EOD USD ... N AFL \n", + "1 5.80 EOD USD ... N AGCO \n", + "2 2.10 EOD USD ... N AEP \n", + "3 4.70 EOD USD ... N AEP \n", + "4 5.40 EOD USD ... N AEP \n", + "... ... ... ... ... ... ... \n", + "2611 2.60 EOD USD ... N ZBH \n", + "2612 5.20 EOD USD ... N ZBH \n", + "2614 3.25 EOD USD ... N ZION \n", + "2616 2.00 EOD USD ... N ZTS \n", + "2617 5.60 EOD USD ... N ZTS \n", + "\n", + " sodraw.filterLevel1_new sodraw.filterLevel3_new \\\n", + "0 Investment Grade Credit Insurance \n", + "1 Investment Grade Credit Capital Goods \n", + "2 Investment Grade Credit Electric \n", + "3 Investment Grade Credit Electric \n", + "4 Investment Grade Credit Electric \n", + "... ... ... \n", + "2611 Investment Grade Credit Consumer Non Cyclicals \n", + "2612 Investment Grade Credit Consumer Non Cyclicals \n", + "2614 Investment Grade Credit Banking \n", + "2616 Investment Grade Credit Consumer Non Cyclicals \n", + "2617 Investment Grade Credit Consumer Non Cyclicals \n", + "\n", + " sodraw.filterLevel4_new security.elements.creditQualityBuckets_new \\\n", + "0 Life Insurance A \n", + "1 Construction Machinery BBB \n", + "2 Other BBB \n", + "3 Other BBB \n", + "4 Other BBB \n", + "... ... ... \n", + "2611 Health Care BBB \n", + "2612 Health Care BBB \n", + "2614 Other BBB \n", + "2616 Pharmaceuticals BBB \n", + "2617 Pharmaceuticals BBB \n", + "\n", + " security.elements.normalizedRating_new sodraw.filterLevel2_new \\\n", + "0 A Financial \n", + "1 BBB Industrial \n", + "2 BBB Utilities \n", + "3 BBB Utilities \n", + "4 BBB Utilities \n", + "... ... ... \n", + "2611 BBB Industrial \n", + "2612 BBB Industrial \n", + "2614 BBB Financial \n", + "2616 BBB Industrial \n", + "2617 BBB Industrial \n", + "\n", + " assetId_new main_key \n", + "0 001055BJ0 VCIT | VCIT (bench) \n", + "1 001084AS1 VCIT | VCIT (bench) \n", + "2 00108WAM2 VCIT | VCIT (bench) \n", + "3 00108WAP5 VCIT | VCIT (bench) \n", + "4 00108WAR1 VCIT | VCIT (bench) \n", + "... ... ... \n", + "2611 98956PAV4 VCIT | VCIT (bench) \n", + "2612 98956PAZ5 VCIT | VCIT (bench) \n", + "2614 98971DAB6 VCIT | VCIT (bench) \n", + "2616 98978VAS2 VCIT | VCIT (bench) \n", + "2617 98978VAV5 VCIT | VCIT (bench) \n", + "\n", + "[2319 rows x 278 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the reduced set and constants\n", + "from types import NoneType\n", + "from typing import Union\n", + "\n", + "\n", + "REDUCED_SET = [\"020002BJ9\",\"026874DS3\",\"081437AT2\",\"097023CJ2\",\"13645RAD6\",\"13645RBF0\",\"14448CBC7\",\"15135BAW1\",\"21871XAS8\",\"24422EWZ8\",\"24422EXP9\",\"302491AU9\",\"36166NAK9\",\"438516CM6\",\"443201AC2\",\"444859BR2\",\"444859BV3\",\"444859BY7\",\"444859CA8\",\"45687VAB2\",\"539830CD9\",\"540424AT5\",\"56501RAN6\",\"655844CR7\",\"655844CT3\",\"75513EAD3\",\"759351AP4\",\"760759BA7\",\"760759BC3\",\"907818FX1\",\"91324PEJ7\"]\n", + "REDUCED_SET_L = []\n", + "OTHER_SET = [\"00130HCG8\",\"00206RMM1\",\"00287YDT3\",\"00774MAY1\",\"00774MBE4\",\"00914AAK8\",\"010392FU7\",\"015271AU3\",\"020002BJ9\",\"023135CR5\",\"026874DS3\",\"03027XBC3\",\"03027XCH1\",\"03040WBD6\",\"03073EAR6\",\"03073EAU9\",\"031162CW8\",\"031162CZ1\",\"035240AR1\",\"037833DU1\",\"038222AN5\",\"03938LBF0\",\"03938LBG8\",\"04273WAE1\",\"049560AX3\",\"053015AF0\",\"053332BF8\",\"054989AA6\",\"054989AB4\",\"05526DBX2\",\"05964HBG9\",\"06051GJB6\",\"06051GJT7\",\"06051GKA6\",\"06051GLH0\",\"06368L3L8\",\"06406RBB2\",\"06406RBH9\",\"06406RBR7\",\"06406RBU0\",\"06406RBZ9\",\"06418JAC5\",\"06738ECA1\",\"06738ECE3\",\"06738ECG8\",\"071813CB3\",\"0778FPAL3\",\"081437AT2\",\"09247XAT8\",\"09290DAB7\",\"097023CJ2\",\"10112RBE3\",\"10373QBW9\",\"11135FAS0\",\"11135FBH3\",\"12503MAC2\",\"12505BAE0\",\"126650DN7\",\"126650DR8\",\"126650DX5\",\"134429BP3\",\"13645RAD6\",\"13645RBF0\",\"14040HCY9\",\"14448CBC7\",\"15135BAW1\",\"15189WAS9\",\"15189YAH9\",\"166764BY5\",\"17275RBS0\",\"172967ML2\",\"172967NN7\",\"172967PA3\",\"17325FBG2\",\"174610BH7\",\"191216CT5\",\"194162AS2\",\"20030NCU3\",\"20825UAC8\",\"209111GE7\",\"21871XAS8\",\"22003BAM8\",\"22541LAE3\",\"22822VAW1\",\"22822VAY7\",\"22822VBE0\",\"231021AT3\",\"23338VAS5\",\"233835AQ0\",\"24422EWZ8\",\"24422EXP9\",\"25243YBD0\",\"254687FQ4\",\"254687FX9\",\"25468PBW5\",\"254709AT5\",\"25470DBJ7\",\"25746UDL0\",\"260543DC4\",\"260543DJ9\",\"26439XAC7\",\"26441CBH7\",\"26441CBL8\",\"26442UAS3\",\"28368EAE6\",\"29273VAQ3\",\"29278NAQ6\",\"29379VCF8\",\"29444UBU9\",\"29736RAT7\",\"30161NBK6\",\"30225VAK3\",\"30225VAQ0\",\"30231GBN1\",\"30303M8H8\",\"314353AA1\",\"316773DL1\",\"337738BH0\",\"337738BN7\",\"341081GG6\",\"341081GL5\",\"341081GP6\",\"345397B36\",\"36166NAK9\",\"37045XER3\",\"37045XEV4\",\"372460AA3\",\"375558BY8\",\"38141GYB4\",\"38141GYN8\",\"38143VAA7\",\"404119CK3\",\"404121AK1\",\"404280DV8\",\"404280EC9\",\"404280EG0\",\"40434LAC9\",\"431282AU6\",\"437076CF7\",\"437076CJ9\",\"438516CM6\",\"440452AF7\",\"44107TBA3\",\"44107TBB1\",\"443201AC2\",\"444859BR2\",\"444859BV3\",\"444859BY7\",\"444859CA8\",\"456837BH5\",\"45687VAB2\",\"45866FAK0\",\"459200BB6\",\"459200KN0\",\"459200KU4\",\"46590XAP1\",\"46647PBJ4\",\"46647PBL9\",\"46647PDY9\",\"48203RAP9\",\"49271VAJ9\",\"49456BAV3\",\"49456BAX9\",\"50155QAL4\",\"532457CF3\",\"539830CD9\",\"540424AT5\",\"548661DY0\",\"56501RAN6\",\"565849AR7\",\"57636QBB9\",\"58013MFV1\",\"581557BU8\",\"58507LBC2\",\"59523UAW7\",\"606822BU7\",\"606822BX1\",\"606822CX0\",\"60687YBH1\",\"60687YBL2\",\"615369AZ8\",\"6174468X0\",\"61747YEH4\",\"61747YEL5\",\"61747YEU5\",\"61747YFE0\",\"61747YFJ9\",\"637417AS5\",\"637432PA7\",\"637432PC3\",\"64110DAK0\",\"641423CD8\",\"651639AY2\",\"65339KCP3\",\"65473PAJ4\",\"65535HAS8\",\"65535HBA6\",\"65535HBV0\",\"655844CR7\",\"655844CT3\",\"665859AV6\",\"670346AS4\",\"67103HAL1\",\"682680BK8\",\"68389XAV7\",\"68389XBD6\",\"68389XCP8\",\"686330AR2\",\"693475BU8\",\"693506BS5\",\"694308KR7\",\"695114CW6\",\"695114DC9\",\"703481AD3\",\"70450YAL7\",\"713448EZ7\",\"713448FA1\",\"713448FE3\",\"713466AD2\",\"716973AE2\",\"717081EY5\",\"718172CP2\",\"723787AR8\",\"74456QCJ3\",\"74456QCQ7\",\"74456QCS3\",\"744573AX4\",\"745310AK8\",\"747525BP7\",\"74762EAL6\",\"75513EAD3\",\"756109AX2\",\"759351AP4\",\"760759BA7\",\"760759BC3\",\"78016FZQ0\",\"78016HZQ6\",\"78409VAS3\",\"797440BZ6\",\"797440CB8\",\"806854AJ4\",\"808513BS3\",\"808513CA1\",\"816851BR9\",\"824348BT2\",\"828807DW4\",\"83444MAE1\",\"842587DR5\",\"8426EPAG3\",\"8426EPAH1\",\"845011AC9\",\"855244AZ2\",\"855244BH1\",\"857477BG7\",\"862121AD2\",\"863667AY7\",\"86562MCB4\",\"86562MCS7\",\"87264ACV5\",\"87264ADF9\",\"872652AA0\",\"87612GAA9\",\"87971MBW2\",\"89157XAA9\",\"89236TGU3\",\"89788MAP7\",\"89788NAA8\",\"90265EAW0\",\"907818FX1\",\"91159HJG6\",\"91159HJL5\",\"91324PEJ7\",\"91913YAE0\",\"92343VFX7\",\"92343VGJ7\",\"92556HAB3\",\"927804GR9\",\"92940PAG9\",\"931142EY5\",\"95000U2L6\",\"95000U2U6\",\"95000U3H4\",\"958102AR6\"]\n", + "K_LOW = 0\n", + "K_TARGET = 1\n", + "K_HIGH = 2\n", + "B_LOW = 3\n", + "B_HIGH = 4\n", + "df_r = df[df['isin'].notnull()]\n", + "df_r = df_r[df_r[\"posSource\"] == 'EOD']\n", + "# df_r = df_r.sort_values(by=['security.elements.normalizedRating_new'], key=lambda x: ordinalgrade(x)) # [df['assetId'].isin(REDUCED_SET)]\n", + "\n", + "# Input variables\n", + "L = ['AAA','AA','A','BBB','Junk / NR'] #[\"I_Capital_Goods\", \"I_Insurance\", \"I_Transportation\"]\n", + "J = [\"fund_enriched.pmv\"]\n", + "NL, NU = -2000000000, 2000000000\n", + "KB_pmv = [[-4.758736462913, 57.803304406912, -1536.847872350911, 0, 1000],\n", + " [4.134392251914, 154.178960195913, 3436.223528139912, 0, 1000], \n", + " [0.240014083139, 516.284582027138, 4122.329149971137, 0, 1000],\n", + " [0.240014083139, 213.284582027138, 5552.329149971137, 0, 1000],\n", + " [0.240014083139, 112.284582027138, 3642.329149971137, 0, 1000]] # Each must be size L\n", + "KB = [KB_pmv]\n", + "\n", + "N = 31\n", + "R = [2.600974180557, 10.435233760548]\n", + "\n", + "# Unused variables\n", + "BETA = 1\n", + "RHO = 1\n", + "\n", + "# Checks for sanity\n", + "if len(KB) != len(J):\n", + " raise Exception(\"K/B matrix requires {0:d} set of guardrail sets; there are {1:d}\".format(len(J), len(KB)))\n", + "for jIdx in range(len(KB)):\n", + " if len(KB[jIdx]) != len(L):\n", + " raise Exception(\"K/B[{2:s}] by characteristic requires {0:d} set of guardrails; there are {1:d}\".format(len(L), len(KB[jIdx]), J[jIdx]))\n", + " for lIdx in range(len(KB[jIdx])):\n", + " if len(KB[jIdx][lIdx]) < 5:\n", + " raise Exception(\"Guardrail information of K/B[{0:s},{1:s}] incomplete.\".format(J[jIdx], L[lIdx]))\n", + "\n", + "df_r" + ] + }, + { + "cell_type": "markdown", + "id": "569d0057", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "We implement the D-WAVE solver provided $C, L, K, b$." + ] + }, + { + "cell_type": "markdown", + "id": "957ab001", + "metadata": {}, + "source": [ + "### Defining the data type (Part 2)\n", + "\n", + "We define the data type by assuming types match. Each security $c \\in C$ will be converted to a tuple augmented by bucket membership and the betas for each characteristic $j \\in J$. $C$ and $\\mathbb{K}$ will be implemented as a list, and an `indexmap` will convert the ID to the index in $C$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be0015ba", + "metadata": {}, + "outputs": [], + "source": [ + "CID = 0\n", + "MINTRADE = 1\n", + "MAXTRADE = 2\n", + "INVENTORY = 3\n", + "PMV = 4\n", + "DXS = 5\n", + "DELTA = 6\n", + "RATING = 7\n", + "BETAS = -1" + ] + }, + { + "cell_type": "markdown", + "id": "26e1c02e", + "metadata": {}, + "source": [ + "### Dataframe generation functions\n", + "\n", + "Functions for obtaining the $C$ in this format, and for sorting the elements of $C$ by bucket membership. The $L$ inputted will also generate the $\\mathbb{K}$ and the $\\mathrm{ord}$ function for converting the bond name into its canonical ordering. Note $\\mathbb{K}$ is represented as `KC`, and $\\mathrm{ord}$ is represented as `indexmap`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "609d8c0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00130HCG8 2000.0 4000.0 100000 85.01568 0.335171086863209 1000.0 BBB\n", + "00206RMM1 2000.0 4000.0 100000 82.90681215083707 0.8602400028097281 1000.0 BBB\n", + "00287YDT3 2000.0 4000.0 100000 102.0446820939904 0.315225438428868 1000.0 A\n", + "00774MAY1 150000.0 300000.0 100000 87.87748406386085 0.6206650452062581 1000.0 BBB\n", + "00774MBE4 150000.0 300000.0 100000 105.991305 0.188954783720116 1000.0 BBB\n", + "00914AAK8 2000.0 4000.0 100000 91.10874733508786 0.169453652451882 1000.0 BBB\n", + "010392FU7 2000.0 4000.0 100000 84.09239533333333 0.052276129314429 1000.0 A\n", + "015271AU3 2000.0 4000.0 100000 101.8348477197389 0.165660746911207 1000.0 BBB\n", + "020002BJ9 2000.0 4000.0 100000 82.51934833333333 0.137962410512948 1000.0 BBB\n", + "023135CR5 2000.0 4000.0 100000 102.8135365942059 0.215987417822014 1000.0 AA\n", + "026874DS3 2000.0 4000.0 100000 100.8018436607827 0.290454451793088 1000.0 BBB\n", + "03027XBC3 2000.0 4000.0 100000 87.22239239930316 0.11142205663273 1000.0 BBB\n", + "03027XCH1 2000.0 4000.0 100000 102.7652326955854 0.215701040110505 1000.0 BBB\n", + "03040WBD6 1000.0 2000.0 100000 102.2357444444444 0.244420589516446 1000.0 BBB\n", + "03073EAR6 2000.0 4000.0 100000 91.43213766666666 0.09055181389500801 1000.0 BBB\n", + "03073EAU9 2000.0 4000.0 100000 100.8954296666667 0.134149402154849 1000.0 BBB\n", + "031162CW8 2000.0 4000.0 100000 87.03941453749532 0.2231918578705 1000.0 BBB\n", + "031162CZ1 2000.0 4000.0 100000 83.34319194714277 0.103693448341333 1000.0 BBB\n", + "035240AR1 1000.0 2000.0 100000 102.8351431111111 0.112404234142103 1000.0 A\n", + "037833DU1 2000.0 4000.0 100000 87.45573433333334 0.127821230744503 1000.0 AA\n", + "038222AN5 2000.0 4000.0 100000 86.9588888888889 0.083214313776724 1000.0 A\n", + "03938LBF0 2000.0 4000.0 100000 111.8916658888889 0.410522258548163 1000.0 BBB\n", + "03938LBG8 2000.0 4000.0 100000 105.286905323473 0.301989767975146 1000.0 BBB\n", + "04273WAE1 2000.0 4000.0 100000 102.3973830900563 0.238083507303694 1000.0 BBB\n", + "049560AX3 2000.0 4000.0 100000 104.204836 0.06606150191809 1000.0 A\n", + "053015AF0 2000.0 4000.0 100000 83.83915778349352 0.09579914134429601 1000.0 AA\n", + "053332BF8 2000.0 4000.0 100000 101.3539402075114 0.109255524511109 1000.0 BBB\n", + "054989AA6 2000.0 4000.0 100000 107.371434356876 0.32403364431372 1000.0 BBB\n", + "054989AB4 2000.0 4000.0 100000 108.3689159114949 0.4922317398115331 1000.0 BBB\n", + "05526DBX2 2000.0 4000.0 100000 115.3765608888889 0.262202071290659 1000.0 BBB\n", + "05964HBG9 200000.0 400000.0 100000 103.6192902773156 0.489151385814992 200000.0 A\n", + "06051GJB6 2000.0 4000.0 100000 90.1948228599484 0.5863906028457461 1000.0 A\n", + "06051GJT7 2000.0 4000.0 100000 87.2002253321183 0.9919074344620641 1000.0 A\n", + "06051GKA6 2000.0 4000.0 100000 85.12908740606105 0.8183866993240271 1000.0 A\n", + "06051GLH0 2000.0 4000.0 100000 103.6559410749927 1.830759144085393 1000.0 A\n", + "06368L3L8 2000.0 4000.0 100000 105.0980558917392 0.227247565350605 1000.0 A\n", + "06406RBB2 1000.0 2000.0 100000 86.76662280923719 0.059661045421864 1000.0 A\n", + "06406RBH9 1000.0 2000.0 100000 97.34007444444444 0.244362144588289 1000.0 A\n", + "06406RBR7 1000.0 2000.0 100000 102.1956765555556 0.276948413719339 1000.0 A\n", + "06406RBU0 2000.0 4000.0 100000 113.1693406666667 0.344224566255989 1000.0 A\n", + "06406RBZ9 2000.0 4000.0 100000 102.3881718815182 0.198067973841115 1000.0 A\n", + "06418JAC5 2000.0 4000.0 100000 105.3557748888889 0.305262209322837 1000.0 A\n", + "06738ECA1 200000.0 400000.0 100000 103.6150093333333 0.525697750802907 1000.0 BBB\n", + "06738ECE3 200000.0 400000.0 100000 116.5850332173112 1.047650458687663 1000.0 BBB\n", + "06738ECG8 200000.0 400000.0 100000 108.2058296666667 0.9740663510176201 1000.0 BBB\n", + "071813CB3 2000.0 4000.0 100000 82.41581348185805 0.105483082539523 1000.0 BBB\n", + "0778FPAL3 2000.0 4000.0 100000 101.8461342942089 0.366644042269397 1000.0 BBB\n", + "081437AT2 2000.0 4000.0 100000 89.34001709356725 0.08219001532061701 1000.0 BBB\n", + "09247XAT8 2000.0 4000.0 100000 102.3790402222222 0.21109273785356 1000.0 AA\n", + "09290DAB7 2000.0 4000.0 100000 101.7265020213741 0.24945692706487 1000.0 AA\n", + "097023CJ2 2000.0 4000.0 100000 85.54216100000001 0.364259415701239 1000.0 BBB\n", + "10112RBE3 1000.0 2000.0 100000 82.04961866666666 0.341906378494889 1000.0 BBB\n", + "10373QBW9 1000.0 2000.0 100000 100.0001866666667 0.296035805591428 1000.0 A\n", + "11135FAS0 2000.0 4000.0 100000 97.5389898279218 0.5948734925071341 1000.0 BBB\n", + "11135FBH3 2000.0 4000.0 100000 87.30104235115958 0.514089379154122 1000.0 BBB\n", + "12503MAC2 2000.0 4000.0 100000 84.40593366666667 0.076368075817 1000.0 A\n", + "12505BAE0 2000.0 4000.0 100000 86.32604222222223 0.119357774452983 1000.0 BBB\n", + "126650DN7 2000.0 4000.0 100000 83.02335904883391 0.394413840532407 1000.0 BBB\n", + "126650DR8 2000.0 4000.0 100000 81.79662081833175 0.251472817830971 1000.0 BBB\n", + "126650DX5 2000.0 4000.0 100000 101.1074294179734 0.213289137438801 1000.0 BBB\n", + "134429BP3 2000.0 4000.0 100000 102.1419305140389 0.398947277546842 1000.0 BBB\n", + "13645RAD6 1000.0 2000.0 100000 113.4950947602017 0.128724734847925 1000.0 BBB\n", + "13645RBF0 2000.0 4000.0 100000 87.37712388888889 0.235125158508367 1000.0 BBB\n", + "14040HCY9 2000.0 4000.0 100000 103.1906346666667 0.6050540063696971 1000.0 BBB\n", + "14448CBC7 2000.0 4000.0 100000 106.3520144643245 0.278640165327846 1000.0 BBB\n", + "15135BAW1 2000.0 4000.0 100000 87.25496331868881 0.7831323247649891 1000.0 BBB\n", + "15189WAS9 2000.0 4000.0 100000 103.818005 0.143744258097958 1000.0 A\n", + "15189YAH9 2000.0 4000.0 100000 103.441415 0.213172262422175 1000.0 A\n", + "166764BY5 2000.0 4000.0 100000 90.0373231111111 0.142785946965934 1000.0 AA\n", + "17275RBS0 2000.0 4000.0 100000 102.817739 0.321259792190423 1000.0 A\n", + "172967ML2 1000.0 2000.0 100000 90.00018622222223 0.483199152970936 1000.0 A\n", + "172967NN7 1000.0 2000.0 100000 91.90339201006975 1.105354151882198 1000.0 A\n", + "172967PA3 1000.0 2000.0 100000 109.9318001135432 1.106452664219114 1000.0 A\n", + "17325FBG2 250000.0 500000.0 100000 106.516716600663 0.7942964376302241 1000.0 A\n", + "174610BH7 2000.0 4000.0 100000 103.3594131343383 0.558229855570034 1000.0 BBB\n", + "191216CT5 2000.0 4000.0 100000 95.88386366666667 0.086281142859623 1000.0 A\n", + "194162AS2 2000.0 4000.0 100000 101.0659907685591 0.06139906387467901 1000.0 A\n", + "20030NCU3 2000.0 4000.0 100000 97.8452913041619 0.244838297301825 1000.0 A\n", + "20825UAC8 1000.0 2000.0 100000 113.5912606918939 0.06322291198051201 1000.0 A\n", + "209111GE7 2000.0 4000.0 100000 103.1427165555556 0.148135006740153 1000.0 A\n", + "21871XAS8 2000.0 4000.0 100000 104.451479 0.374742229181628 1000.0 BBB\n", + "22003BAM8 2000.0 4000.0 100000 85.79858580849809 0.264919885900637 1000.0 BBB\n", + "22541LAE3 1000.0 2000.0 100000 114.7241343300459 0.321388441085764 1000.0 A\n", + "22822VAW1 2000.0 4000.0 100000 83.77285466666667 0.309854452110572 1000.0 BBB\n", + "22822VAY7 2000.0 4000.0 100000 85.9391482981593 0.086636595270144 1000.0 BBB\n", + "22822VBE0 2000.0 4000.0 100000 104.3030168888889 0.391706492131726 1000.0 BBB\n", + "231021AT3 2000.0 4000.0 100000 84.94778733333334 0.08851867277227 1000.0 A\n", + "23338VAS5 2000.0 4000.0 100000 102.5249244215658 0.176739514726398 1000.0 A\n", + "233835AQ0 1000.0 2000.0 100000 121.2729250375947 0.433166830214313 1000.0 A\n", + "24422EWZ8 1000.0 2000.0 100000 102.5132124444444 0.157370889240938 1000.0 A\n", + "24422EXP9 1000.0 2000.0 100000 102.2226368147291 0.292459955482375 1000.0 A\n", + "25243YBD0 200000.0 400000.0 100000 88.44114922222222 0.138763671808147 1000.0 A\n", + "254687FQ4 2000.0 4000.0 100000 96.80438377777777 0.153242456715896 1000.0 A\n", + "254687FX9 2000.0 4000.0 100000 90.02800622222222 0.359918966114411 1000.0 A\n", + "25468PBW5 1000.0 2000.0 100000 115.0530075555556 0.05455650013791701 1000.0 A\n", + "254709AT5 2000.0 4000.0 100000 119.3261832222222 0.56473972003487 1000.0 BBB\n", + "25470DBJ7 2000.0 4000.0 100000 90.41680217768402 0.343401583605413 1000.0 BBB\n", + "25746UDL0 2000.0 4000.0 100000 85.01055105620985 0.191353165731693 1000.0 BBB\n", + "260543DC4 2000.0 4000.0 100000 87.476518 0.10699278940019 1000.0 BBB\n", + "260543DJ9 2000.0 4000.0 100000 101.1518389171007 0.103383154927022 1000.0 BBB\n", + "26439XAC7 1000.0 2000.0 100000 116.5442528888889 0.071437283148008 1000.0 BBB\n", + "26441CBH7 2000.0 4000.0 100000 89.37156724513721 0.167758297236233 1000.0 BBB\n", + "26441CBL8 2000.0 4000.0 100000 87.40037157623263 0.213011044110897 1000.0 BBB\n", + "26442UAS3 2000.0 4000.0 100000 101.87403 0.170278733992379 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102.4947873084511 0.228684549242347 1000.0 BBB\n", + "637432PA7 2000.0 4000.0 100000 107.2746480557936 0.170683234775813 1000.0 A\n", + "637432PC3 2000.0 4000.0 100000 100.6889037777778 0.16208266949647 1000.0 A\n", + "64110DAK0 2000.0 4000.0 100000 90.08989700000001 0.141894049473003 1000.0 BBB\n", + "641423CD8 2000.0 4000.0 100000 89.84360133333334 0.013657846672214 1000.0 A\n", + "651639AY2 2000.0 4000.0 100000 87.922759 0.152076584529596 1000.0 BBB\n", + "65339KCP3 2000.0 4000.0 100000 100.9340004607504 0.338153518739867 1000.0 BBB\n", + "65473PAJ4 2000.0 4000.0 100000 95.84635200000001 0.197122834465364 1000.0 BBB\n", + "65535HAS8 200000.0 400000.0 100000 88.69938516304028 0.225509094747656 1000.0 BBB\n", + "65535HBA6 200000.0 400000.0 100000 87.0224021111111 0.309643612637474 1000.0 BBB\n", + "65535HBV0 200000.0 400000.0 100000 104.2419846226286 0.5125957309880891 1000.0 BBB\n", + "655844CR7 2000.0 4000.0 100000 103.0936667854891 0.100440087662771 1000.0 BBB\n", + "655844CT3 2000.0 4000.0 100000 105.01142 0.152585880973998 1000.0 BBB\n", + "665859AV6 2000.0 4000.0 100000 88.28695933333333 0.114019858844873 1000.0 A\n", + "670346AS4 2000.0 4000.0 100000 91.63343833325814 0.087269913958906 1000.0 A\n", + "67103HAL1 2000.0 4000.0 100000 100.039140865457 0.230504403325386 1000.0 BBB\n", + "682680BK8 2000.0 4000.0 100000 107.2469262355393 0.027500293156185 1000.0 BBB\n", + "68389XAV7 2000.0 4000.0 100000 95.18702011111111 0.242688173691204 1000.0 BBB\n", + "68389XBD6 2000.0 4000.0 100000 94.07446191421289 0.127238444581917 1000.0 BBB\n", + "68389XCP8 2000.0 4000.0 100000 100.2537806666667 0.4005310024250041 1000.0 BBB\n", + "686330AR2 2000.0 4000.0 100000 100.9355242222222 0.169411680925565 1000.0 A\n", + "693475BU8 2000.0 4000.0 100000 111.9149845355741 0.9456284421405441 1000.0 A\n", + "693506BS5 2000.0 4000.0 100000 90.14712038924274 0.05051895754415001 1000.0 BBB\n", + "694308KR7 2000.0 4000.0 100000 107.2500165658555 0.261456825012256 1000.0 BBB\n", + 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96.05139311111111 0.253645006292266 1000.0 A\n", + "78016HZQ6 2000.0 4000.0 100000 103.0385771618268 0.228821932609679 1000.0 A\n", + "78409VAS3 2000.0 4000.0 100000 83.69934233333333 0.041428109206041 1000.0 A\n", + "797440BZ6 1000.0 2000.0 100000 84.73706611111112 0.123398785606431 1000.0 A\n", + "797440CB8 1000.0 2000.0 100000 89.45657371940489 0.078859957162921 1000.0 A\n", + "806854AJ4 2000.0 4000.0 100000 91.2382321111111 0.206305815492969 1000.0 A\n", + "808513BS3 2000.0 4000.0 100000 87.40955311111112 0.165193314720304 1000.0 A\n", + "808513CA1 2000.0 4000.0 100000 88.37385989831917 0.224677663377784 1000.0 A\n", + "816851BR9 2000.0 4000.0 100000 103.9134795555556 0.242523093211172 1000.0 BBB\n", + "824348BT2 2000.0 4000.0 100000 100.75726 0.05373681969329101 1000.0 BBB\n", + "828807DW4 2000.0 4000.0 100000 110.16759 0.104503277924612 1000.0 A\n", + "83444MAE1 2000.0 4000.0 100000 102.0140043907214 0.277171485444744 1000.0 BBB\n", + "842587DR5 2000.0 4000.0 100000 103.0355873333333 0.275987520672377 1000.0 BBB\n", + "8426EPAG3 2000.0 4000.0 100000 105.0864976666667 0.204225104198814 1000.0 BBB\n", + "8426EPAH1 2000.0 4000.0 100000 99.074547 0.171854315858572 1000.0 BBB\n", + "845011AC9 2000.0 4000.0 100000 87.3728811468907 0.05983722207397901 1000.0 BBB\n", + "855244AZ2 2000.0 4000.0 100000 89.76867 0.212647308483578 1000.0 BBB\n", + "855244BH1 2000.0 4000.0 100000 102.0247396666667 0.03715518899609201 1000.0 BBB\n", + "857477BG7 2000.0 4000.0 100000 90.252568 0.111848947913328 1000.0 A\n", + "862121AD2 2000.0 4000.0 100000 83.90676500000001 0.157533782561281 1000.0 BBB\n", + "863667AY7 2000.0 4000.0 100000 87.21921249599215 0.161490519750256 1000.0 BBB\n", + "86562MCB4 200000.0 400000.0 100000 87.14004720212735 0.241564454526152 1000.0 A\n", + "86562MCS7 200000.0 400000.0 100000 106.5915102404903 0.56337967494911 1000.0 A\n", + "87264ACV5 2000.0 4000.0 100000 102.428961 0.4152413175628 1000.0 BBB\n", + "87264ADF9 2000.0 4000.0 100000 100.6804000029043 0.495539281604445 1000.0 BBB\n", + "872652AA0 2000.0 4000.0 100000 104.8182837777778 0.270148861956041 1000.0 BBB\n", + "87612GAA9 2000.0 4000.0 100000 93.47194533333334 0.256498086852792 1000.0 BBB\n", + "87971MBW2 2000.0 4000.0 100000 90.96007892528584 0.239194556601359 1000.0 BBB\n", + "89157XAA9 2000.0 4000.0 100000 101.6556165260566 0.346630730651793 1000.0 A\n", + "89236TGU3 2000.0 4000.0 100000 88.94535677777777 0.126478208876874 1000.0 A\n", + "89788MAP7 2000.0 4000.0 100000 105.7585562222222 0.7831820771333201 1000.0 A\n", + "89788NAA8 2000.0 4000.0 100000 97.17960077777778 0.5080699510376581 1000.0 BBB\n", + "90265EAW0 2000.0 4000.0 100000 99.41728066666667 0.068095458219151 1000.0 BBB\n", + "907818FX1 1000.0 2000.0 100000 89.18259244444445 0.161622578270101 1000.0 A\n", + "91159HJG6 1000.0 2000.0 100000 98.7270553283797 0.5589713601045451 1000.0 A\n", + "91159HJL5 1000.0 2000.0 100000 98.56572213545765 0.697040471626961 1000.0 A\n", + "91324PEJ7 2000.0 4000.0 100000 98.39763140824095 0.282276576085499 1000.0 A\n", + "91913YAE0 1000.0 2000.0 100000 114.5323717459977 0.205527420309802 1000.0 BBB\n", + "92343VFX7 2000.0 4000.0 100000 84.52960712411551 0.250348134336517 1000.0 BBB\n", + "92343VGJ7 2000.0 4000.0 100000 87.48988535009298 0.566533332445983 1000.0 BBB\n", + "92556HAB3 2000.0 4000.0 100000 94.48855903992035 0.653326937330138 1000.0 BBB\n", + "927804GR9 2000.0 4000.0 100000 100.850902808174 0.192847372061446 1000.0 A\n", + "92940PAG9 2000.0 4000.0 100000 86.528384 0.236595504088311 1000.0 BBB\n", + "931142EY5 2000.0 4000.0 100000 98.82733499999999 0.10453024746008 1000.0 AA\n", + "95000U2L6 1000.0 2000.0 100000 98.47467088822725 0.487640447488253 1000.0 A\n", + "95000U2U6 1000.0 2000.0 100000 89.78597566184982 1.082645777531214 1000.0 A\n", + "95000U3H4 1000.0 2000.0 100000 108.9153323966591 1.330436769751999 1000.0 A\n", + "958102AR6 2000.0 4000.0 100000 85.06773822222222 0.205342466445264 1000.0 BBB\n", + "023135CR5:\t2000.000\t4000.000 (=)\t 102.814\t 0.216\tAA\t 1000.0 [1]\n", + "037833DU1:\t2000.000\t4000.000 (=)\t 87.456\t 0.128\tAA\t 1000.0 [1]\n", + "053015AF0:\t2000.000\t4000.000 (=)\t 83.839\t 0.096\tAA\t 1000.0 [1]\n", + "09247XAT8:\t2000.000\t4000.000 (=)\t 102.379\t 0.211\tAA\t 1000.0 [1]\n", + "09290DAB7:\t2000.000\t4000.000 (=)\t 101.727\t 0.249\tAA\t 1000.0 [1]\n", + "166764BY5:\t2000.000\t4000.000 (=)\t 90.037\t 0.143\tAA\t 1000.0 [1]\n", + "30231GBN1:\t2000.000\t4000.000 (=)\t 90.323\t 0.189\tAA\t 1000.0 [1]\n", + "30303M8H8:\t2000.000\t4000.000 (=)\t 95.494\t 0.493\tAA\t 1000.0 [1]\n", + "314353AA1:\t2000.000\t4000.000 (=)\t 85.289\t 0.000\tAA\t 1000.0 [1]\n", + "341081GG6:\t2000.000\t4000.000 (=)\t 86.820\t 0.199\tAA\t 1000.0 [1]\n", + "341081GL5:\t2000.000\t4000.000 (=)\t 101.806\t 0.215\tAA\t 1000.0 [1]\n", + "341081GP6:\t2000.000\t4000.000 (=)\t 102.356\t 0.084\tAA\t 1000.0 [1]\n", + "723787AR8:\t1000.000\t2000.000 (=)\t 86.626\t 0.173\tAA\t 1000.0 [1]\n", + "931142EY5:\t2000.000\t4000.000 (=)\t 98.827\t 0.105\tAA\t 1000.0 [1]\n", + "00287YDT3:\t2000.000\t4000.000 (=)\t 102.045\t 0.315\tA\t 1000.0 [1]\n", + "010392FU7:\t2000.000\t4000.000 (=)\t 84.092\t 0.052\tA\t 1000.0 [1]\n", + "035240AR1:\t1000.000\t2000.000 (=)\t 102.835\t 0.112\tA\t 1000.0 [1]\n", + "038222AN5:\t2000.000\t4000.000 (=)\t 86.959\t 0.083\tA\t 1000.0 [1]\n", + "049560AX3:\t2000.000\t4000.000 (=)\t 104.205\t 0.066\tA\t 1000.0 [1]\n", + "05964HBG9:\t200000.000\t400000.000 (=)\t 103.619\t 0.489\tA\t 200000.0 [1]\n", + "06051GJB6:\t2000.000\t4000.000 (=)\t 90.195\t 0.586\tA\t 1000.0 [1]\n", + "06051GJT7:\t2000.000\t4000.000 (=)\t 87.200\t 0.992\tA\t 1000.0 [1]\n", + "06051GKA6:\t2000.000\t4000.000 (=)\t 85.129\t 0.818\tA\t 1000.0 [1]\n", + "06051GLH0:\t2000.000\t4000.000 (=)\t 103.656\t 1.831\tA\t 1000.0 [1]\n", + "06368L3L8:\t2000.000\t4000.000 (=)\t 105.098\t 0.227\tA\t 1000.0 [1]\n", + "06406RBB2:\t1000.000\t2000.000 (=)\t 86.767\t 0.060\tA\t 1000.0 [1]\n", + "06406RBH9:\t1000.000\t2000.000 (=)\t 97.340\t 0.244\tA\t 1000.0 [1]\n", + "06406RBR7:\t1000.000\t2000.000 (=)\t 102.196\t 0.277\tA\t 1000.0 [1]\n", + "06406RBU0:\t2000.000\t4000.000 (=)\t 113.169\t 0.344\tA\t 1000.0 [1]\n", + "06406RBZ9:\t2000.000\t4000.000 (=)\t 102.388\t 0.198\tA\t 1000.0 [1]\n", + "06418JAC5:\t2000.000\t4000.000 (=)\t 105.356\t 0.305\tA\t 1000.0 [1]\n", + "10373QBW9:\t1000.000\t2000.000 (=)\t 100.000\t 0.296\tA\t 1000.0 [1]\n", + "12503MAC2:\t2000.000\t4000.000 (=)\t 84.406\t 0.076\tA\t 1000.0 [1]\n", + "15189WAS9:\t2000.000\t4000.000 (=)\t 103.818\t 0.144\tA\t 1000.0 [1]\n", + "15189YAH9:\t2000.000\t4000.000 (=)\t 103.441\t 0.213\tA\t 1000.0 [1]\n", + "17275RBS0:\t2000.000\t4000.000 (=)\t 102.818\t 0.321\tA\t 1000.0 [1]\n", + "172967ML2:\t1000.000\t2000.000 (=)\t 90.000\t 0.483\tA\t 1000.0 [1]\n", + "172967NN7:\t1000.000\t2000.000 (=)\t 91.903\t 1.105\tA\t 1000.0 [1]\n", + "172967PA3:\t1000.000\t2000.000 (=)\t 109.932\t 1.106\tA\t 1000.0 [1]\n", + "17325FBG2:\t250000.000\t500000.000 (=)\t 106.517\t 0.794\tA\t 1000.0 [1]\n", + "191216CT5:\t2000.000\t4000.000 (=)\t 95.884\t 0.086\tA\t 1000.0 [1]\n", + "194162AS2:\t2000.000\t4000.000 (=)\t 101.066\t 0.061\tA\t 1000.0 [1]\n", + "20030NCU3:\t2000.000\t4000.000 (=)\t 97.845\t 0.245\tA\t 1000.0 [1]\n", + "20825UAC8:\t1000.000\t2000.000 (=)\t 113.591\t 0.063\tA\t 1000.0 [1]\n", + "209111GE7:\t2000.000\t4000.000 (=)\t 103.143\t 0.148\tA\t 1000.0 [1]\n", + "22541LAE3:\t1000.000\t2000.000 (=)\t 114.724\t 0.321\tA\t 1000.0 [1]\n", + "231021AT3:\t2000.000\t4000.000 (=)\t 84.948\t 0.089\tA\t 1000.0 [1]\n", + "23338VAS5:\t2000.000\t4000.000 (=)\t 102.525\t 0.177\tA\t 1000.0 [1]\n", + "233835AQ0:\t1000.000\t2000.000 (=)\t 121.273\t 0.433\tA\t 1000.0 [1]\n", + "24422EWZ8:\t1000.000\t2000.000 (=)\t 102.513\t 0.157\tA\t 1000.0 [1]\n", + "24422EXP9:\t1000.000\t2000.000 (=)\t 102.223\t 0.292\tA\t 1000.0 [1]\n", + "25243YBD0:\t200000.000\t400000.000 (=)\t 88.441\t 0.139\tA\t 1000.0 [1]\n", + "254687FQ4:\t2000.000\t4000.000 (=)\t 96.804\t 0.153\tA\t 1000.0 [1]\n", + "254687FX9:\t2000.000\t4000.000 (=)\t 90.028\t 0.360\tA\t 1000.0 [1]\n", + "25468PBW5:\t1000.000\t2000.000 (=)\t 115.053\t 0.055\tA\t 1000.0 [1]\n", + "26442UAS3:\t2000.000\t4000.000 (=)\t 101.874\t 0.170\tA\t 1000.0 [1]\n", + "29379VCF8:\t1000.000\t2000.000 (=)\t 99.897\t 0.291\tA\t 1000.0 [1]\n", + "29736RAT7:\t2000.000\t4000.000 (=)\t 99.686\t 0.213\tA\t 1000.0 [1]\n", + "38141GYB4:\t1000.000\t2000.000 (=)\t 86.526\t 0.886\tA\t 1000.0 [1]\n", + "38141GYN8:\t1000.000\t2000.000 (=)\t 85.767\t 0.823\tA\t 1000.0 [1]\n", + "404280DV8:\t200000.000\t400000.000 (=)\t 107.493\t 0.987\tA\t 1000.0 [1]\n", + "404280EG0:\t200000.000\t400000.000 (=)\t 105.771\t 0.437\tA\t 1000.0 [1]\n", + "437076CF7:\t2000.000\t4000.000 (=)\t 82.320\t 0.174\tA\t 1000.0 [1]\n", + "437076CJ9:\t2000.000\t4000.000 (=)\t 84.231\t 0.156\tA\t 1000.0 [1]\n", + "438516CM6:\t2000.000\t4000.000 (=)\t 99.233\t 0.221\tA\t 1000.0 [1]\n", + "440452AF7:\t2000.000\t4000.000 (=)\t 86.577\t 0.140\tA\t 1000.0 [1]\n", + "456837BH5:\t200000.000\t400000.000 (=)\t 106.704\t 0.521\tA\t 1000.0 [1]\n", + "45866FAK0:\t2000.000\t4000.000 (=)\t 88.046\t 0.176\tA\t 1000.0 [1]\n", + "459200BB6:\t1000.000\t2000.000 (=)\t 109.489\t 0.144\tA\t 1000.0 [1]\n", + "459200KN0:\t100000.000\t200000.000 (=)\t 87.976\t 0.043\tA\t 1000.0 [1]\n", + "459200KU4:\t100000.000\t200000.000 (=)\t 98.638\t 0.155\tA\t 1000.0 [1]\n", + "46647PBJ4:\t2000.000\t4000.000 (=)\t 98.871\t 0.590\tA\t 1000.0 [1]\n", + "46647PBL9:\t2000.000\t4000.000 (=)\t 89.000\t 0.424\tA\t 1000.0 [1]\n", + "46647PDY9:\t2000.000\t4000.000 (=)\t 108.069\t 1.141\tA\t 1000.0 [1]\n", + "532457CF3:\t2000.000\t4000.000 (=)\t 100.759\t 0.183\tA\t 1000.0 [1]\n", + "539830CD9:\t2000.000\t4000.000 (=)\t 100.519\t 0.172\tA\t 1000.0 [1]\n", + "540424AT5:\t2000.000\t4000.000 (=)\t 93.709\t 0.086\tA\t 1000.0 [1]\n", + "56501RAN6:\t2000.000\t4000.000 (=)\t 93.426\t 0.161\tA\t 1000.0 [1]\n", + "57636QBB9:\t2000.000\t4000.000 (=)\t 98.574\t 0.217\tA\t 1000.0 [1]\n", + "581557BU8:\t2000.000\t4000.000 (=)\t 102.621\t 0.196\tA\t 1000.0 [1]\n", + "58507LBC2:\t2000.000\t4000.000 (=)\t 98.286\t 0.218\tA\t 1000.0 [1]\n", + "59523UAW7:\t2000.000\t4000.000 (=)\t 102.865\t 0.117\tA\t 1000.0 [1]\n", + "606822BU7:\t200000.000\t400000.000 (=)\t 86.912\t 0.238\tA\t 1000.0 [1]\n", + "606822BX1:\t200000.000\t400000.000 (=)\t 85.642\t 0.429\tA\t 1000.0 [1]\n", + "606822CX0:\t200000.000\t400000.000 (=)\t 104.209\t 0.340\tA\t 1000.0 [1]\n", + "60687YBH1:\t200000.000\t400000.000 (=)\t 87.003\t 0.145\tA\t 1000.0 [1]\n", + "60687YBL2:\t200000.000\t400000.000 (=)\t 85.105\t 0.186\tA\t 1000.0 [1]\n", + "6174468X0:\t1000.000\t2000.000 (=)\t 84.047\t 0.511\tA\t 1000.0 [1]\n", + "61747YEH4:\t1000.000\t2000.000 (=)\t 85.302\t 0.568\tA\t 1000.0 [1]\n", + "61747YEL5:\t1000.000\t2000.000 (=)\t 88.186\t 0.549\tA\t 1000.0 [1]\n", + "61747YEU5:\t1000.000\t2000.000 (=)\t 100.314\t 0.663\tA\t 1000.0 [1]\n", + "61747YFE0:\t1000.000\t2000.000 (=)\t 100.778\t 1.205\tA\t 1000.0 [1]\n", + "61747YFJ9:\t1000.000\t2000.000 (=)\t 113.451\t 0.769\tA\t 1000.0 [1]\n", + "637432PA7:\t2000.000\t4000.000 (=)\t 107.275\t 0.171\tA\t 1000.0 [1]\n", + "637432PC3:\t2000.000\t4000.000 (=)\t 100.689\t 0.162\tA\t 1000.0 [1]\n", + "641423CD8:\t2000.000\t4000.000 (=)\t 89.844\t 0.014\tA\t 1000.0 [1]\n", + "665859AV6:\t2000.000\t4000.000 (=)\t 88.287\t 0.114\tA\t 1000.0 [1]\n", + "670346AS4:\t2000.000\t4000.000 (=)\t 91.633\t 0.087\tA\t 1000.0 [1]\n", + "686330AR2:\t2000.000\t4000.000 (=)\t 100.936\t 0.169\tA\t 1000.0 [1]\n", + "693475BU8:\t2000.000\t4000.000 (=)\t 111.915\t 0.946\tA\t 1000.0 [1]\n", + "695114CW6:\t2000.000\t4000.000 (=)\t 89.332\t 0.076\tA\t 1000.0 [1]\n", + "695114DC9:\t2000.000\t4000.000 (=)\t 102.917\t 0.182\tA\t 1000.0 [1]\n", + "70450YAL7:\t2000.000\t4000.000 (=)\t 99.730\t 0.146\tA\t 1000.0 [1]\n", + "713448EZ7:\t2000.000\t4000.000 (=)\t 86.849\t 0.106\tA\t 1000.0 [1]\n", + "713448FA1:\t2000.000\t4000.000 (=)\t 83.211\t 0.105\tA\t 1000.0 [1]\n", + "713448FE3:\t2000.000\t4000.000 (=)\t 84.509\t 0.164\tA\t 1000.0 [1]\n", + "713466AD2:\t2000.000\t4000.000 (=)\t 100.147\t 0.020\tA\t 1000.0 [1]\n", + "716973AE2:\t2000.000\t4000.000 (=)\t 101.030\t 1.353\tA\t 1000.0 [1]\n", + "717081EY5:\t2000.000\t4000.000 (=)\t 86.996\t 0.107\tA\t 1000.0 [1]\n", + "718172CP2:\t2000.000\t4000.000 (=)\t 88.464\t 0.137\tA\t 1000.0 [1]\n", + "74456QCJ3:\t2000.000\t4000.000 (=)\t 90.187\t 0.129\tA\t 1000.0 [1]\n", + "74456QCQ7:\t2000.000\t4000.000 (=)\t 102.756\t 0.120\tA\t 1000.0 [1]\n", + "74456QCS3:\t2000.000\t4000.000 (=)\t 100.672\t 0.157\tA\t 1000.0 [1]\n", + "747525BP7:\t2000.000\t4000.000 (=)\t 81.467\t 0.204\tA\t 1000.0 [1]\n", + "756109AX2:\t2000.000\t4000.000 (=)\t 92.555\t 0.193\tA\t 1000.0 [1]\n", + "78016FZQ0:\t2000.000\t4000.000 (=)\t 96.051\t 0.254\tA\t 1000.0 [1]\n", + "78016HZQ6:\t2000.000\t4000.000 (=)\t 103.039\t 0.229\tA\t 1000.0 [1]\n", + "78409VAS3:\t2000.000\t4000.000 (=)\t 83.699\t 0.041\tA\t 1000.0 [1]\n", + "797440BZ6:\t1000.000\t2000.000 (=)\t 84.737\t 0.123\tA\t 1000.0 [1]\n", + "797440CB8:\t1000.000\t2000.000 (=)\t 89.457\t 0.079\tA\t 1000.0 [1]\n", + "806854AJ4:\t2000.000\t4000.000 (=)\t 91.238\t 0.206\tA\t 1000.0 [1]\n", + "808513BS3:\t2000.000\t4000.000 (=)\t 87.410\t 0.165\tA\t 1000.0 [1]\n", + "808513CA1:\t2000.000\t4000.000 (=)\t 88.374\t 0.225\tA\t 1000.0 [1]\n", + "828807DW4:\t2000.000\t4000.000 (=)\t 110.168\t 0.105\tA\t 1000.0 [1]\n", + "857477BG7:\t2000.000\t4000.000 (=)\t 90.253\t 0.112\tA\t 1000.0 [1]\n", + "86562MCB4:\t200000.000\t400000.000 (=)\t 87.140\t 0.242\tA\t 1000.0 [1]\n", + "86562MCS7:\t200000.000\t400000.000 (=)\t 106.592\t 0.563\tA\t 1000.0 [1]\n", + "89157XAA9:\t2000.000\t4000.000 (=)\t 101.656\t 0.347\tA\t 1000.0 [1]\n", + "89236TGU3:\t2000.000\t4000.000 (=)\t 88.945\t 0.126\tA\t 1000.0 [1]\n", + "89788MAP7:\t2000.000\t4000.000 (=)\t 105.759\t 0.783\tA\t 1000.0 [1]\n", + "907818FX1:\t1000.000\t2000.000 (=)\t 89.183\t 0.162\tA\t 1000.0 [1]\n", + "91159HJG6:\t1000.000\t2000.000 (=)\t 98.727\t 0.559\tA\t 1000.0 [1]\n", + "91159HJL5:\t1000.000\t2000.000 (=)\t 98.566\t 0.697\tA\t 1000.0 [1]\n", + "91324PEJ7:\t2000.000\t4000.000 (=)\t 98.398\t 0.282\tA\t 1000.0 [1]\n", + "927804GR9:\t2000.000\t4000.000 (=)\t 100.851\t 0.193\tA\t 1000.0 [1]\n", + "95000U2L6:\t1000.000\t2000.000 (=)\t 98.475\t 0.488\tA\t 1000.0 [1]\n", + "95000U2U6:\t1000.000\t2000.000 (=)\t 89.786\t 1.083\tA\t 1000.0 [1]\n", + "95000U3H4:\t1000.000\t2000.000 (=)\t 108.915\t 1.330\tA\t 1000.0 [1]\n", + "00130HCG8:\t2000.000\t4000.000 (=)\t 85.016\t 0.335\tBBB\t 1000.0 [1]\n", + "00206RMM1:\t2000.000\t4000.000 (=)\t 82.907\t 0.860\tBBB\t 1000.0 [1]\n", + "00774MAY1:\t150000.000\t300000.000 (=)\t 87.877\t 0.621\tBBB\t 1000.0 [1]\n", + "00774MBE4:\t150000.000\t300000.000 (=)\t 105.991\t 0.189\tBBB\t 1000.0 [1]\n", + "00914AAK8:\t2000.000\t4000.000 (=)\t 91.109\t 0.169\tBBB\t 1000.0 [1]\n", + "015271AU3:\t2000.000\t4000.000 (=)\t 101.835\t 0.166\tBBB\t 1000.0 [1]\n", + "020002BJ9:\t2000.000\t4000.000 (=)\t 82.519\t 0.138\tBBB\t 1000.0 [1]\n", + "026874DS3:\t2000.000\t4000.000 (=)\t 100.802\t 0.290\tBBB\t 1000.0 [1]\n", + "03027XBC3:\t2000.000\t4000.000 (=)\t 87.222\t 0.111\tBBB\t 1000.0 [1]\n", + "03027XCH1:\t2000.000\t4000.000 (=)\t 102.765\t 0.216\tBBB\t 1000.0 [1]\n", + "03040WBD6:\t1000.000\t2000.000 (=)\t 102.236\t 0.244\tBBB\t 1000.0 [1]\n", + "03073EAR6:\t2000.000\t4000.000 (=)\t 91.432\t 0.091\tBBB\t 1000.0 [1]\n", + "03073EAU9:\t2000.000\t4000.000 (=)\t 100.895\t 0.134\tBBB\t 1000.0 [1]\n", + "031162CW8:\t2000.000\t4000.000 (=)\t 87.039\t 0.223\tBBB\t 1000.0 [1]\n", + "031162CZ1:\t2000.000\t4000.000 (=)\t 83.343\t 0.104\tBBB\t 1000.0 [1]\n", + "03938LBF0:\t2000.000\t4000.000 (=)\t 111.892\t 0.411\tBBB\t 1000.0 [1]\n", + "03938LBG8:\t2000.000\t4000.000 (=)\t 105.287\t 0.302\tBBB\t 1000.0 [1]\n", + "04273WAE1:\t2000.000\t4000.000 (=)\t 102.397\t 0.238\tBBB\t 1000.0 [1]\n", + "053332BF8:\t2000.000\t4000.000 (=)\t 101.354\t 0.109\tBBB\t 1000.0 [1]\n", + "054989AA6:\t2000.000\t4000.000 (=)\t 107.371\t 0.324\tBBB\t 1000.0 [1]\n", + "054989AB4:\t2000.000\t4000.000 (=)\t 108.369\t 0.492\tBBB\t 1000.0 [1]\n", + "05526DBX2:\t2000.000\t4000.000 (=)\t 115.377\t 0.262\tBBB\t 1000.0 [1]\n", + "06738ECA1:\t200000.000\t400000.000 (=)\t 103.615\t 0.526\tBBB\t 1000.0 [1]\n", + "06738ECE3:\t200000.000\t400000.000 (=)\t 116.585\t 1.048\tBBB\t 1000.0 [1]\n", + "06738ECG8:\t200000.000\t400000.000 (=)\t 108.206\t 0.974\tBBB\t 1000.0 [1]\n", + "071813CB3:\t2000.000\t4000.000 (=)\t 82.416\t 0.105\tBBB\t 1000.0 [1]\n", + "0778FPAL3:\t2000.000\t4000.000 (=)\t 101.846\t 0.367\tBBB\t 1000.0 [1]\n", + "081437AT2:\t2000.000\t4000.000 (=)\t 89.340\t 0.082\tBBB\t 1000.0 [1]\n", + "097023CJ2:\t2000.000\t4000.000 (=)\t 85.542\t 0.364\tBBB\t 1000.0 [1]\n", + "10112RBE3:\t1000.000\t2000.000 (=)\t 82.050\t 0.342\tBBB\t 1000.0 [1]\n", + "11135FAS0:\t2000.000\t4000.000 (=)\t 97.539\t 0.595\tBBB\t 1000.0 [1]\n", + "11135FBH3:\t2000.000\t4000.000 (=)\t 87.301\t 0.514\tBBB\t 1000.0 [1]\n", + "12505BAE0:\t2000.000\t4000.000 (=)\t 86.326\t 0.119\tBBB\t 1000.0 [1]\n", + "126650DN7:\t2000.000\t4000.000 (=)\t 83.023\t 0.394\tBBB\t 1000.0 [1]\n", + "126650DR8:\t2000.000\t4000.000 (=)\t 81.797\t 0.251\tBBB\t 1000.0 [1]\n", + "126650DX5:\t2000.000\t4000.000 (=)\t 101.107\t 0.213\tBBB\t 1000.0 [1]\n", + "134429BP3:\t2000.000\t4000.000 (=)\t 102.142\t 0.399\tBBB\t 1000.0 [1]\n", + "13645RAD6:\t1000.000\t2000.000 (=)\t 113.495\t 0.129\tBBB\t 1000.0 [1]\n", + "13645RBF0:\t2000.000\t4000.000 (=)\t 87.377\t 0.235\tBBB\t 1000.0 [1]\n", + "14040HCY9:\t2000.000\t4000.000 (=)\t 103.191\t 0.605\tBBB\t 1000.0 [1]\n", + "14448CBC7:\t2000.000\t4000.000 (=)\t 106.352\t 0.279\tBBB\t 1000.0 [1]\n", + "15135BAW1:\t2000.000\t4000.000 (=)\t 87.255\t 0.783\tBBB\t 1000.0 [1]\n", + "174610BH7:\t2000.000\t4000.000 (=)\t 103.359\t 0.558\tBBB\t 1000.0 [1]\n", + "21871XAS8:\t2000.000\t4000.000 (=)\t 104.451\t 0.375\tBBB\t 1000.0 [1]\n", + "22003BAM8:\t2000.000\t4000.000 (=)\t 85.799\t 0.265\tBBB\t 1000.0 [1]\n", + "22822VAW1:\t2000.000\t4000.000 (=)\t 83.773\t 0.310\tBBB\t 1000.0 [1]\n", + "22822VAY7:\t2000.000\t4000.000 (=)\t 85.939\t 0.087\tBBB\t 1000.0 [1]\n", + "22822VBE0:\t2000.000\t4000.000 (=)\t 104.303\t 0.392\tBBB\t 1000.0 [1]\n", + "254709AT5:\t2000.000\t4000.000 (=)\t 119.326\t 0.565\tBBB\t 1000.0 [1]\n", + "25470DBJ7:\t2000.000\t4000.000 (=)\t 90.417\t 0.343\tBBB\t 1000.0 [1]\n", + "25746UDL0:\t2000.000\t4000.000 (=)\t 85.011\t 0.191\tBBB\t 1000.0 [1]\n", + "260543DC4:\t2000.000\t4000.000 (=)\t 87.477\t 0.107\tBBB\t 1000.0 [1]\n", + "260543DJ9:\t2000.000\t4000.000 (=)\t 101.152\t 0.103\tBBB\t 1000.0 [1]\n", + "26439XAC7:\t1000.000\t2000.000 (=)\t 116.544\t 0.071\tBBB\t 1000.0 [1]\n", + "26441CBH7:\t2000.000\t4000.000 (=)\t 89.372\t 0.168\tBBB\t 1000.0 [1]\n", + "26441CBL8:\t2000.000\t4000.000 (=)\t 87.400\t 0.213\tBBB\t 1000.0 [1]\n", + "28368EAE6:\t1000.000\t2000.000 (=)\t 117.328\t 0.375\tBBB\t 1000.0 [1]\n", + "29273VAQ3:\t2000.000\t4000.000 (=)\t 103.836\t 0.645\tBBB\t 1000.0 [1]\n", + "29278NAQ6:\t2000.000\t4000.000 (=)\t 95.519\t 0.415\tBBB\t 1000.0 [1]\n", + "29444UBU9:\t2000.000\t4000.000 (=)\t 93.969\t 0.306\tBBB\t 1000.0 [1]\n", + "30161NBK6:\t2000.000\t4000.000 (=)\t 102.595\t 0.262\tBBB\t 1000.0 [1]\n", + "30225VAK3:\t2000.000\t4000.000 (=)\t 104.182\t 0.126\tBBB\t 1000.0 [1]\n", + "30225VAQ0:\t2000.000\t4000.000 (=)\t 84.028\t 0.210\tBBB\t 1000.0 [1]\n", + "316773DL1:\t2000.000\t4000.000 (=)\t 103.685\t 0.315\tBBB\t 1000.0 [1]\n", + "337738BH0:\t2000.000\t4000.000 (=)\t 104.389\t 0.474\tBBB\t 1000.0 [1]\n", + "337738BN7:\t2000.000\t4000.000 (=)\t 100.942\t 0.344\tBBB\t 1000.0 [1]\n", + "345397B36:\t200000.000\t400000.000 (=)\t 92.494\t 0.577\tBBB\t 1000.0 [1]\n", + "36166NAK9:\t200000.000\t400000.000 (=)\t 99.728\t 0.164\tBBB\t 1000.0 [1]\n", + "37045XER3:\t2000.000\t4000.000 (=)\t 103.305\t 0.334\tBBB\t 1000.0 [1]\n", + "37045XEV4:\t2000.000\t4000.000 (=)\t 102.493\t 0.752\tBBB\t 1000.0 [1]\n", + "372460AA3:\t2000.000\t4000.000 (=)\t 84.957\t 0.107\tBBB\t 1000.0 [1]\n", + "375558BY8:\t2000.000\t4000.000 (=)\t 84.682\t 0.160\tBBB\t 1000.0 [1]\n", + "38143VAA7:\t1000.000\t2000.000 (=)\t 106.353\t 0.423\tBBB\t 1000.0 [1]\n", + "404119CK3:\t2000.000\t4000.000 (=)\t 91.009\t 0.563\tBBB\t 1000.0 [1]\n", + "404121AK1:\t2000.000\t4000.000 (=)\t 100.887\t 0.596\tBBB\t 1000.0 [1]\n", + "404280EC9:\t200000.000\t400000.000 (=)\t 115.706\t 0.919\tBBB\t 1000.0 [1]\n", + "40434LAC9:\t2000.000\t4000.000 (=)\t 93.987\t 0.063\tBBB\t 1000.0 [1]\n", + "431282AU6:\t2000.000\t4000.000 (=)\t 114.604\t 0.278\tBBB\t 1000.0 [1]\n", + "44107TBA3:\t2000.000\t4000.000 (=)\t 86.797\t 0.199\tBBB\t 1000.0 [1]\n", + "44107TBB1:\t2000.000\t4000.000 (=)\t 103.123\t 0.328\tBBB\t 1000.0 [1]\n", + "443201AC2:\t2000.000\t4000.000 (=)\t 99.872\t 0.127\tBBB\t 1000.0 [1]\n", + "444859BR2:\t2000.000\t4000.000 (=)\t 81.741\t 0.291\tBBB\t 1000.0 [1]\n", + "444859BV3:\t2000.000\t4000.000 (=)\t 103.543\t 0.398\tBBB\t 1000.0 [1]\n", + "444859BY7:\t2000.000\t4000.000 (=)\t 103.937\t 0.413\tBBB\t 1000.0 [1]\n", + "444859CA8:\t2000.000\t4000.000 (=)\t 100.867\t 0.364\tBBB\t 1000.0 [1]\n", + "45687VAB2:\t2000.000\t4000.000 (=)\t 105.595\t 0.337\tBBB\t 1000.0 [1]\n", + "46590XAP1:\t2000.000\t4000.000 (=)\t 85.910\t 0.304\tBBB\t 1000.0 [1]\n", + "48203RAP9:\t2000.000\t4000.000 (=)\t 84.502\t 0.105\tBBB\t 1000.0 [1]\n", + "49271VAJ9:\t2000.000\t4000.000 (=)\t 94.222\t 0.127\tBBB\t 1000.0 [1]\n", + "49456BAV3:\t2000.000\t4000.000 (=)\t 97.997\t 0.279\tBBB\t 1000.0 [1]\n", + "49456BAX9:\t2000.000\t4000.000 (=)\t 101.229\t 0.608\tBBB\t 1000.0 [1]\n", + "50155QAL4:\t2000.000\t4000.000 (=)\t 86.074\t 0.280\tBBB\t 1000.0 [1]\n", + "548661DY0:\t2000.000\t4000.000 (=)\t 84.294\t 0.141\tBBB\t 1000.0 [1]\n", + "565849AR7:\t1000.000\t2000.000 (=)\t 105.063\t 0.218\tBBB\t 1000.0 [1]\n", + "58013MFV1:\t1000.000\t2000.000 (=)\t 101.984\t 0.139\tBBB\t 1000.0 [1]\n", + "615369AZ8:\t2000.000\t4000.000 (=)\t 97.139\t 0.129\tBBB\t 1000.0 [1]\n", + "637417AS5:\t2000.000\t4000.000 (=)\t 102.495\t 0.229\tBBB\t 1000.0 [1]\n", + "64110DAK0:\t2000.000\t4000.000 (=)\t 90.090\t 0.142\tBBB\t 1000.0 [1]\n", + "651639AY2:\t2000.000\t4000.000 (=)\t 87.923\t 0.152\tBBB\t 1000.0 [1]\n", + "65339KCP3:\t2000.000\t4000.000 (=)\t 100.934\t 0.338\tBBB\t 1000.0 [1]\n", + "65473PAJ4:\t2000.000\t4000.000 (=)\t 95.846\t 0.197\tBBB\t 1000.0 [1]\n", + "65535HAS8:\t200000.000\t400000.000 (=)\t 88.699\t 0.226\tBBB\t 1000.0 [1]\n", + "65535HBA6:\t200000.000\t400000.000 (=)\t 87.022\t 0.310\tBBB\t 1000.0 [1]\n", + "65535HBV0:\t200000.000\t400000.000 (=)\t 104.242\t 0.513\tBBB\t 1000.0 [1]\n", + "655844CR7:\t2000.000\t4000.000 (=)\t 103.094\t 0.100\tBBB\t 1000.0 [1]\n", + "655844CT3:\t2000.000\t4000.000 (=)\t 105.011\t 0.153\tBBB\t 1000.0 [1]\n", + "67103HAL1:\t2000.000\t4000.000 (=)\t 100.039\t 0.231\tBBB\t 1000.0 [1]\n", + "682680BK8:\t2000.000\t4000.000 (=)\t 107.247\t 0.028\tBBB\t 1000.0 [1]\n", + "68389XAV7:\t2000.000\t4000.000 (=)\t 95.187\t 0.243\tBBB\t 1000.0 [1]\n", + "68389XBD6:\t2000.000\t4000.000 (=)\t 94.074\t 0.127\tBBB\t 1000.0 [1]\n", + "68389XCP8:\t2000.000\t4000.000 (=)\t 100.254\t 0.401\tBBB\t 1000.0 [1]\n", + "693506BS5:\t2000.000\t4000.000 (=)\t 90.147\t 0.051\tBBB\t 1000.0 [1]\n", + "694308KR7:\t2000.000\t4000.000 (=)\t 107.250\t 0.261\tBBB\t 1000.0 [1]\n", + "703481AD3:\t2000.000\t4000.000 (=)\t 106.866\t 0.295\tBBB\t 1000.0 [1]\n", + "744573AX4:\t2000.000\t4000.000 (=)\t 106.592\t 0.156\tBBB\t 1000.0 [1]\n", + "745310AK8:\t2000.000\t4000.000 (=)\t 95.652\t 0.116\tBBB\t 1000.0 [1]\n", + "74762EAL6:\t2000.000\t4000.000 (=)\t 100.901\t 0.265\tBBB\t 1000.0 [1]\n", + "75513EAD3:\t2000.000\t4000.000 (=)\t 88.480\t 0.202\tBBB\t 1000.0 [1]\n", + "759351AP4:\t2000.000\t4000.000 (=)\t 92.568\t 0.119\tBBB\t 1000.0 [1]\n", + "760759BA7:\t2000.000\t4000.000 (=)\t 82.805\t 0.150\tBBB\t 1000.0 [1]\n", + "760759BC3:\t2000.000\t4000.000 (=)\t 100.430\t 0.238\tBBB\t 1000.0 [1]\n", + "816851BR9:\t2000.000\t4000.000 (=)\t 103.913\t 0.243\tBBB\t 1000.0 [1]\n", + "824348BT2:\t2000.000\t4000.000 (=)\t 100.757\t 0.054\tBBB\t 1000.0 [1]\n", + "83444MAE1:\t2000.000\t4000.000 (=)\t 102.014\t 0.277\tBBB\t 1000.0 [1]\n", + "842587DR5:\t2000.000\t4000.000 (=)\t 103.036\t 0.276\tBBB\t 1000.0 [1]\n", + "8426EPAG3:\t2000.000\t4000.000 (=)\t 105.086\t 0.204\tBBB\t 1000.0 [1]\n", + "8426EPAH1:\t2000.000\t4000.000 (=)\t 99.075\t 0.172\tBBB\t 1000.0 [1]\n", + "845011AC9:\t2000.000\t4000.000 (=)\t 87.373\t 0.060\tBBB\t 1000.0 [1]\n", + "855244AZ2:\t2000.000\t4000.000 (=)\t 89.769\t 0.213\tBBB\t 1000.0 [1]\n", + "855244BH1:\t2000.000\t4000.000 (=)\t 102.025\t 0.037\tBBB\t 1000.0 [1]\n", + "862121AD2:\t2000.000\t4000.000 (=)\t 83.907\t 0.158\tBBB\t 1000.0 [1]\n", + "863667AY7:\t2000.000\t4000.000 (=)\t 87.219\t 0.161\tBBB\t 1000.0 [1]\n", + "87264ACV5:\t2000.000\t4000.000 (=)\t 102.429\t 0.415\tBBB\t 1000.0 [1]\n", + "87264ADF9:\t2000.000\t4000.000 (=)\t 100.680\t 0.496\tBBB\t 1000.0 [1]\n", + "872652AA0:\t2000.000\t4000.000 (=)\t 104.818\t 0.270\tBBB\t 1000.0 [1]\n", + "87612GAA9:\t2000.000\t4000.000 (=)\t 93.472\t 0.256\tBBB\t 1000.0 [1]\n", + "87971MBW2:\t2000.000\t4000.000 (=)\t 90.960\t 0.239\tBBB\t 1000.0 [1]\n", + "89788NAA8:\t2000.000\t4000.000 (=)\t 97.180\t 0.508\tBBB\t 1000.0 [1]\n", + "90265EAW0:\t2000.000\t4000.000 (=)\t 99.417\t 0.068\tBBB\t 1000.0 [1]\n", + "91913YAE0:\t1000.000\t2000.000 (=)\t 114.532\t 0.206\tBBB\t 1000.0 [1]\n", + "92343VFX7:\t2000.000\t4000.000 (=)\t 84.530\t 0.250\tBBB\t 1000.0 [1]\n", + "92343VGJ7:\t2000.000\t4000.000 (=)\t 87.490\t 0.567\tBBB\t 1000.0 [1]\n", + "92556HAB3:\t2000.000\t4000.000 (=)\t 94.489\t 0.653\tBBB\t 1000.0 [1]\n", + "92940PAG9:\t2000.000\t4000.000 (=)\t 86.528\t 0.237\tBBB\t 1000.0 [1]\n", + "958102AR6:\t2000.000\t4000.000 (=)\t 85.068\t 0.205\tBBB\t 1000.0 [1]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'023135CR5': 0,\n", + " 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158,\n", + " '05526DBX2': 159,\n", + " '06738ECA1': 160,\n", + " '06738ECE3': 161,\n", + " '06738ECG8': 162,\n", + " '071813CB3': 163,\n", + " '0778FPAL3': 164,\n", + " '081437AT2': 165,\n", + " '097023CJ2': 166,\n", + " '10112RBE3': 167,\n", + " '11135FAS0': 168,\n", + " '11135FBH3': 169,\n", + " '12505BAE0': 170,\n", + " '126650DN7': 171,\n", + " '126650DR8': 172,\n", + " '126650DX5': 173,\n", + " '134429BP3': 174,\n", + " '13645RAD6': 175,\n", + " '13645RBF0': 176,\n", + " '14040HCY9': 177,\n", + " '14448CBC7': 178,\n", + " '15135BAW1': 179,\n", + " '174610BH7': 180,\n", + " '21871XAS8': 181,\n", + " '22003BAM8': 182,\n", + " '22822VAW1': 183,\n", + " '22822VAY7': 184,\n", + " '22822VBE0': 185,\n", + " '254709AT5': 186,\n", + " '25470DBJ7': 187,\n", + " '25746UDL0': 188,\n", + " '260543DC4': 189,\n", + " '260543DJ9': 190,\n", + " '26439XAC7': 191,\n", + " '26441CBH7': 192,\n", + " '26441CBL8': 193,\n", + " '28368EAE6': 194,\n", + " '29273VAQ3': 195,\n", + " '29278NAQ6': 196,\n", + " '29444UBU9': 197,\n", + " '30161NBK6': 198,\n", + " '30225VAK3': 199,\n", + " '30225VAQ0': 200,\n", + " '316773DL1': 201,\n", + " '337738BH0': 202,\n", + " '337738BN7': 203,\n", + " '345397B36': 204,\n", + " '36166NAK9': 205,\n", + " '37045XER3': 206,\n", + " '37045XEV4': 207,\n", + " '372460AA3': 208,\n", + " '375558BY8': 209,\n", + " '38143VAA7': 210,\n", + " '404119CK3': 211,\n", + " '404121AK1': 212,\n", + " '404280EC9': 213,\n", + " '40434LAC9': 214,\n", + " '431282AU6': 215,\n", + " '44107TBA3': 216,\n", + " '44107TBB1': 217,\n", + " '443201AC2': 218,\n", + " '444859BR2': 219,\n", + " '444859BV3': 220,\n", + " '444859BY7': 221,\n", + " '444859CA8': 222,\n", + " '45687VAB2': 223,\n", + " '46590XAP1': 224,\n", + " '48203RAP9': 225,\n", + " '49271VAJ9': 226,\n", + " '49456BAV3': 227,\n", + " '49456BAX9': 228,\n", + " '50155QAL4': 229,\n", + " '548661DY0': 230,\n", + " '565849AR7': 231,\n", + " '58013MFV1': 232,\n", + " '615369AZ8': 233,\n", + " '637417AS5': 234,\n", + " '64110DAK0': 235,\n", + " '651639AY2': 236,\n", + " '65339KCP3': 237,\n", + " '65473PAJ4': 238,\n", + " '65535HAS8': 239,\n", + " '65535HBA6': 240,\n", + " '65535HBV0': 241,\n", + " '655844CR7': 242,\n", + " '655844CT3': 243,\n", + " '67103HAL1': 244,\n", + " '682680BK8': 245,\n", + " '68389XAV7': 246,\n", + " '68389XBD6': 247,\n", + " '68389XCP8': 248,\n", + " '693506BS5': 249,\n", + " '694308KR7': 250,\n", + " '703481AD3': 251,\n", + " '744573AX4': 252,\n", + " '745310AK8': 253,\n", + " '74762EAL6': 254,\n", + " '75513EAD3': 255,\n", + " '759351AP4': 256,\n", + " '760759BA7': 257,\n", + " '760759BC3': 258,\n", + " '816851BR9': 259,\n", + " '824348BT2': 260,\n", + " '83444MAE1': 261,\n", + " '842587DR5': 262,\n", + " '8426EPAG3': 263,\n", + " '8426EPAH1': 264,\n", + " '845011AC9': 265,\n", + " '855244AZ2': 266,\n", + " '855244BH1': 267,\n", + " '862121AD2': 268,\n", + " '863667AY7': 269,\n", + " '87264ACV5': 270,\n", + " '87264ADF9': 271,\n", + " '872652AA0': 272,\n", + " '87612GAA9': 273,\n", + " '87971MBW2': 274,\n", + " '89788NAA8': 275,\n", + " '90265EAW0': 276,\n", + " '91913YAE0': 277,\n", + " '92343VFX7': 278,\n", + " '92343VGJ7': 279,\n", + " '92556HAB3': 280,\n", + " '92940PAG9': 281,\n", + " '958102AR6': 282}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def generate_from_C(df):\n", + " \"\"\" Generate C from the dataframe \"\"\"\n", + " C = []\n", + " KC = [[],[],[],[],[]]\n", + " indexmap = {}\n", + " count = 0\n", + " for c in df.iterrows():\n", + " _series = c[1]\n", + " _c = (_series['assetId'], \n", + " _series['minTradeSize'], 2*_series['minTradeSize'], _series['basket.unitSz'], \n", + " _series['price'], _series['fund_enriched.dxsCtr'], _series['minTradeIncrement'],\n", + " _series['security.elements.normalizedRating_new'], [BETA]*len(J))\n", + " C.append(_c)\n", + " lIdx = 4\n", + " rating = _series[\"security.elements.normalizedRating_new\"]\n", + " if rating == 'AAA':\n", + " lIdx = 0\n", + " elif rating == 'AA':\n", + " lIdx = 1\n", + " elif rating == 'A':\n", + " lIdx = 2\n", + " elif rating == 'BBB':\n", + " lIdx = 3\n", + " print(_c[0], _c[1], _c[2], _c[3], _c[4], _c[5], _c[6], _c[7])\n", + "\n", + " KC[lIdx].append(_c)\n", + "\n", + " for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " indexmap.update({c[CID]: len(indexmap)})\n", + "\n", + " return C, KC, indexmap\n", + "\n", + "def generate_from_sample():\n", + " \"\"\" Generate C from the sample data \"\"\"\n", + " # Extract JSON\n", + " _c_json = None\n", + " indexmap = {}\n", + " with open('bond_data1.json', 'r') as f:\n", + " import json\n", + " _c_json = json.loads(f.read())\n", + " del json\n", + "\n", + " C = []\n", + " KC = [[],[],[],[]]\n", + " count = 0\n", + " for _series in _c_json[\"bonds\"]:\n", + " rating = _series[\"sector\"]\n", + " c = (_series['id'], _series[\"min\"], _series[\"max\"], _series[\"max\"], _series[\"pmv\"], _series[\"dxs\"], 1, rating, [BETA]*len(J))\n", + " \n", + " C.append(c)\n", + " lIdx = 4\n", + " if rating == 'Capital_Goods':\n", + " lIdx = 0\n", + " elif rating == 'Insurance':\n", + " lIdx = 1\n", + " elif rating == 'Transportation':\n", + " lIdx = 2\n", + " else:\n", + " lIdx = 3\n", + " KC[lIdx].append(c)\n", + " count += 1\n", + "\n", + " for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " indexmap.update({c[CID]: len(indexmap)})\n", + "\n", + " return C, KC, indexmap\n", + "\n", + "df_r = df_r[df_r['assetId'].isin(OTHER_SET)]\n", + "C, KC, indexmap = generate_from_C(df_r)\n", + "for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " print(\"{0:s}:\\t{1:8.3f}\\t{2:8.3f} (=)\\t{4:8.3f}\\t{5:8.3f}\\t{6:s}\\t\".format(c[CID], c[MINTRADE], c[MAXTRADE], c[INVENTORY], c[PMV], c[DXS], c[RATING]), c[DELTA], c[BETAS])\n", + "indexmap\n" + ] + }, + { + "cell_type": "markdown", + "id": "be9cb6ba", + "metadata": {}, + "source": [ + "### Objective function (Part 2)\n", + "\n", + "We have to minimize an objective function that consists of at least $|C||J|$ terms. Duplicate cases will be merged if $c \\in C$ belongs to multiple $\\ell \\in L$. Each entry therefore consists of a block-matrix QUBO matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c14deb4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 3341.222 23771.152 266549.77 45490.313 12607.827]] 351760.2836978876\n", + "[ 3.713e+03 -2.259e+04 -2.816e+04 2.911e+03 1.712e+03 -1.846e+04 -1.800e+04 -9.350e+03 -2.596e+04 -2.358e+04 1.858e+03 2.869e+03 -2.883e+04 -3.521e+03 -2.437e+05 -2.737e+05 -1.449e+05 -2.693e+05 -2.397e+05 -7.831e+04 -2.642e+05 -2.690e+05 -2.722e+05 -2.407e+05 -2.380e+05 -1.518e+05 -1.474e+05 -1.452e+05 -2.221e+05 -2.430e+05 -2.375e+05 -1.462e+05 -2.733e+05 -2.404e+05 -2.411e+05 -2.422e+05 -1.505e+05 -1.497e+05 -1.415e+05 3.278e+08 -2.546e+05 -2.455e+05 -2.512e+05 -1.397e+05 -2.416e+05 -1.391e+05 -2.724e+05 -2.428e+05 -1.356e+05 -1.450e+05 -1.452e+05 1.591e+08 -2.530e+05 -2.644e+05 -1.389e+05 -2.440e+05 -1.462e+05 -2.480e+05 -1.518e+05 -1.521e+05 2.431e+08 2.349e+08 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def how_much_bond(c):\n", + " if not c[DELTA]:\n", + " raise Exception(\"A bond has zero interval.\")\n", + " return (c[MINTRADE] + min(c[MAXTRADE], c[INVENTORY]))/(2*c[DELTA])\n", + "\n", + "# Generate QUBO matrix\n", + "matrix = np.zeros((len(C), len(C)))\n", + "for lIdx in range(len(KC)):\n", + " for jIdx in range(len(J)):\n", + " # Obtain quadratic coefficients, including diagonal terms\n", + " KCl = KC[lIdx]\n", + " for c1 in KCl:\n", + " for c2 in KCl:\n", + " matrix[indexmap[c1[CID]]][indexmap[c2[CID]]] += RHO*BETA*c1[PMV]*how_much_bond(c1)*BETA*c2[PMV]*how_much_bond(c2)\n", + " # Obtain linear coefficients\n", + " for c in KCl:\n", + " matrix[indexmap[c[CID]]][indexmap[c[CID]]] -= RHO*2*KB[jIdx][lIdx][K_TARGET]*BETA*c1[PMV]*how_much_bond(c)\n", + "\n", + "# Consider constant term\n", + "constantterm = np.zeros((len(J), len(L)))\n", + "for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " constantterm[jIdx][lIdx] = KB[jIdx][lIdx][K_TARGET]*KB[jIdx][lIdx][K_TARGET]\n", + "\n", + "mbar = plt.matshow(np.cbrt(matrix), cmap=mpl.colormaps[\"RdGy\"], vmin=-100, vmax=100)\n", + "cbar = plt.colorbar(mbar)\n", + "cbar.set_label(\"cube root of entry\")\n", + "print(constantterm, np.sum(constantterm))\n", + "print(np.diag(matrix))\n", + "print(matrix)" + ] + }, + { + "cell_type": "markdown", + "id": "8e41aafb", + "metadata": {}, + "source": [ + "## D-WAVE Implementation\n", + "\n", + "After constructing the QUBO, we define the optimization model using the `dimod` package." + ] + }, + { + "cell_type": "markdown", + "id": "f877e9e1", + "metadata": {}, + "source": [ + "### Binary quadratic model (Part 2)\n", + "\n", + "We use the QUBO matrix to define ane name the variables (which will be the IDs), their linear entries, and their quadratic entries." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4759cdd8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BinaryQuadraticModel({'00130HCG8': 3713.0352676740877, '00206RMM1': -22586.025285355732, '00287YDT3': -28161.53509478925, '00774MAY1': 2910.6363960115705, '00774MBE4': 1711.9564261427004, '00914AAK8': -18462.098518267347, '010392FU7': -17998.770606669495, '015271AU3': -9350.33793798837, '020002BJ9': -25955.154304758544, '023135CR5': -23583.247284412704, '026874DS3': 1857.9647221802297, '03027XBC3': 2868.5401702288946, '03027XCH1': -28827.09285045145, '03040WBD6': -3520.9952065789403, '03073EAR6': -243669.78688356356, '03073EAU9': -273744.06260045705, '031162CW8': -144900.02060623176, '031162CZ1': -269331.20596542454, '035240AR1': -239660.01056445358, '037833DU1': -78307.4218029982, '038222AN5': -264171.8865398984, '03938LBF0': -268952.9274947858, '03938LBG8': -272165.1874732366, '04273WAE1': -240686.85409525444, '049560AX3': -237977.42900652744, '053015AF0': -151754.91521239682, '053332BF8': -147374.96787935536, '054989AA6': -145195.01889831043, 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'92343VGJ7'): 139279.82728839997, ('958102AR6', '92556HAB3'): 148803.27911107315, ('958102AR6', '927804GR9'): 152229.65771730948, ('958102AR6', '92940PAG9'): 87687.08835892894, ('958102AR6', '931142EY5'): 129433.36483550788, ('958102AR6', '95000U2L6'): 133966.199952971, ('958102AR6', '95000U2U6'): 144682.7040972528, ('958102AR6', '95000U3H4'): 132493.93054027058}, 0.0, 'BINARY')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dimod import BinaryQuadraticModel\n", + "\n", + "# Convert to binary quadratic model\n", + "bqm = BinaryQuadraticModel(vartype=dimod.BINARY)\n", + "for i in range(len(C)):\n", + " bqm.add_variable(C[i][CID])\n", + " bqm.add_linear(C[i][CID], matrix[i][i])\n", + "for i1 in range(len(C)):\n", + " for i2 in range(i1 + 1, len(C)):\n", + " if matrix[i1][i2]:\n", + " bqm.add_quadratic(C[i1][CID], C[i2][CID], 2*matrix[i1][i2])\n", + " \n", + "bqm" + ] + }, + { + "cell_type": "markdown", + "id": "0299850e", + "metadata": {}, + "source": [ + "### Linear inequality constraints (Part 3)\n", + "\n", + "We will implement the linear inequality constraints. There are $2 + 2|J||L|$ linear constraints to consider. The Lagrange multiplier for the global constraints is calculated to enforce the constraints. We have disabled the $b$ bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b7c77823", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0 [4.134392251914, 154.178960195913, 3436.223528139912, 0, 1000]\n", + "[('023135CR5', 308440.6097826177), ('037833DU1', 262367.20300000004), ('053015AF0', 251517.47335048055), ('09247XAT8', 307137.1206666666), ('09290DAB7', 305179.5060641223), ('166764BY5', 270111.9693333333), ('30231GBN1', 270968.27100000007), ('30303M8H8', 286482.52400000003), ('314353AA1', 255866.02000000002), ('341081GG6', 260459.838), ('341081GL5', 305418.6294540318), ('341081GP6', 307068.5829999999), ('723787AR8', 129939.195), ('931142EY5', 296482.005)]\n", + "2 0 [0.240014083139, 516.284582027138, 4122.329149971137, 0, 1000]\n", + "[('00287YDT3', 306134.0462819712), ('010392FU7', 252277.186), ('035240AR1', 154252.71466666664), ('038222AN5', 260876.6666666667), ('049560AX3', 312614.508), 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('717081EY5', 260988.18836821156), ('718172CP2', 265391.3726805829), ('74456QCJ3', 270560.04699999996), ('74456QCQ7', 308269.1662748346), ('74456QCS3', 302015.11666666646), ('747525BP7', 244401.75400000002), ('756109AX2', 277665.47099999996), ('78016FZQ0', 288154.17933333333), ('78016HZQ6', 309115.7314854804), ('78409VAS3', 251098.027), ('797440BZ6', 127105.59916666668), ('797440CB8', 134184.86057910734), ('806854AJ4', 273714.6963333333), ('808513BS3', 262228.6593333334), ('808513CA1', 265121.5796949575), ('828807DW4', 330502.77), ('857477BG7', 270757.704), ('86562MCB4', 13071007.080319101), ('86562MCS7', 15988726.536073545), ('89157XAA9', 304966.8495781698), ('89236TGU3', 266836.07033333334), ('89788MAP7', 317275.6686666666), ('907818FX1', 133773.8886666667), ('91159HJG6', 148090.58299256954), ('91159HJL5', 147848.58320318646), ('91324PEJ7', 295192.89422472287), ('927804GR9', 302552.70842452196), ('95000U2L6', 147712.00633234088), ('95000U2U6', 134678.96349277472), ('95000U3H4', 163372.99859498866)]\n", + "3 0 [0.240014083139, 213.284582027138, 5552.329149971137, 0, 1000]\n", + "[('00130HCG8', 255047.04000000004), ('00206RMM1', 248720.4364525112), ('00774MAY1', 10984685.507982606), ('00774MBE4', 13248913.125), ('00914AAK8', 273326.2420052636), ('015271AU3', 305504.5431592167), ('020002BJ9', 247558.04499999998), ('026874DS3', 302405.5309823481), ('03027XBC3', 261667.1771979095), ('03027XCH1', 308295.69808675617), ('03040WBD6', 153353.6166666666), ('03073EAR6', 274296.413), ('03073EAU9', 302686.2890000001), ('031162CW8', 261118.24361248597), ('031162CZ1', 250029.5758414283), ('03938LBF0', 335674.9976666667), ('03938LBG8', 315860.715970419), ('04273WAE1', 307192.14927016885), ('053332BF8', 304061.8206225342), ('054989AA6', 322114.30307062797), ('054989AB4', 325106.7477344847), ('05526DBX2', 346129.6826666667), ('06738ECA1', 15542251.399999997), ('06738ECE3', 17487754.98259668), ('06738ECG8', 16230874.450000005), ('071813CB3', 247247.44044557418), ('0778FPAL3', 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262201.1147286979), ('28368EAE6', 175992.2055), ('29273VAQ3', 311509.2459999999), ('29278NAQ6', 286556.4214875983), ('29444UBU9', 281907.83099999995), ('30161NBK6', 307786.48100000015), ('30225VAK3', 312545.21709328773), ('30225VAQ0', 252084.22105719603), ('316773DL1', 311056.3094422389), ('337738BH0', 313168.236), ('337738BN7', 302825.3559205623), ('345397B36', 13874125.05686982), ('36166NAK9', 14959212.51269734), ('37045XER3', 309914.3483142414), ('37045XEV4', 307477.57047652744), ('372460AA3', 254871.782), ('375558BY8', 254045.42), ('38143VAA7', 159529.1955966024), ('404119CK3', 273026.086), ('404121AK1', 302659.902), ('404280EC9', 17355938.5127973), ('40434LAC9', 281960.9479082671), ('431282AU6', 343810.677), ('44107TBA3', 260391.59000000003), ('44107TBB1', 309369.42858890584), ('443201AC2', 299614.674), ('444859BR2', 245223.89553360973), ('444859BV3', 310629.4558668819), ('444859BY7', 311810.9251339317), ('444859CA8', 302599.50899999996), ('45687VAB2', 316784.6709999999), ('46590XAP1', 257730.99599999998), ('48203RAP9', 253506.13333333333), ('49271VAJ9', 282666.1754132052), ('49456BAV3', 293992.007), ('49456BAX9', 303686.4226666668), ('50155QAL4', 258221.73), ('548661DY0', 252881.46603445557), ('565849AR7', 157594.21583059948), ('58013MFV1', 152975.727), ('615369AZ8', 291418.12486924493), ('637417AS5', 307484.3619253533), ('64110DAK0', 270269.69100000005), ('651639AY2', 263768.277), ('65339KCP3', 302802.00138225115), ('65473PAJ4', 287539.05600000004), ('65535HAS8', 13304907.774456041), ('65535HBA6', 13053360.316666666), ('65535HBV0', 15636297.69339429), ('655844CR7', 309281.0003564673), ('655844CT3', 315034.26), ('67103HAL1', 300117.422596371), ('682680BK8', 321740.7787066179), ('68389XAV7', 285561.0603333333), ('68389XBD6', 282223.38574263867), ('68389XCP8', 300761.3420000001), ('693506BS5', 270441.3611677282), ('694308KR7', 321750.04969756654), ('703481AD3', 320598.0586666668), ('744573AX4', 319776.21), ('745310AK8', 286955.55865220824), ('74762EAL6', 302702.235), ('75513EAD3', 265438.59081448667), ('759351AP4', 277703.29247113597), ('760759BA7', 248414.1338717884), ('760759BC3', 301290.93433333316), ('816851BR9', 311740.43866666674), ('824348BT2', 302271.78), ('83444MAE1', 306042.0131721642), ('842587DR5', 309106.7619999999), ('8426EPAG3', 315259.49300000013), ('8426EPAH1', 297223.64099999995), ('845011AC9', 262118.64344067214), ('855244AZ2', 269306.01), ('855244BH1', 306074.2190000001), ('862121AD2', 251720.29500000004), ('863667AY7', 261657.63748797646), ('87264ACV5', 307286.883), ('87264ADF9', 302041.20000871294), ('872652AA0', 314454.85133333335), ('87612GAA9', 280415.836), ('87971MBW2', 272880.2367758575), ('89788NAA8', 291538.80233333335), ('90265EAW0', 298251.842), ('91913YAE0', 171798.55761899654), ('92343VFX7', 253588.82137234655), ('92343VGJ7', 262469.6560502789), ('92556HAB3', 283465.67711976107), ('92940PAG9', 259585.152), ('958102AR6', 255203.21466666667)]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/samaria/github/replacement/implementation/ocean/lib/python3.13/site-packages/dimod/binary/binary_quadratic_model.py:762: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + } + ], + "source": [ + "# Add master linear inequality constraints\n", + "sy = []\n", + "sx = []\n", + "sr = []\n", + "for i in range(len(C)):\n", + " sy.append((C[i][CID], 1))\n", + " sx.append((C[i][CID], C[i][PMV]*C[i][DELTA]*how_much_bond(C[i])))\n", + "\n", + "lm = np.sum(abs(matrix), axis=1) # Hard restraint\n", + "bqm.add_linear_inequality_constraint(sy, ub = N, lagrange_multiplier=np.dot(lm,lm), label=\"Sy\") # Bonds in basket\n", + "bqm.add_linear_inequality_constraint(sx, lb = R[0], ub = R[1], lagrange_multiplier=np.dot(lm,lm), label=\"Sx\") # Residual cash flow\n", + "\n", + "# # Add bucket and characteristic based inequality constraints\n", + "for j in range(len(J)):\n", + " for l in range(len(L)):\n", + " if len(KC[l]) == 0:\n", + " continue \n", + " syl = []\n", + " sxl = []\n", + " for i in range(len(KC[l])):\n", + " # syl.append((KC[l][i][CID], BETA))\n", + " sxl.append((KC[l][i][CID], KC[l][i][PMV]*KC[l][i][DELTA]*BETA*how_much_bond(KC[l][i])))\n", + " print(l, j, KB[j][l])\n", + " # print(syl)\n", + " print(sxl)\n", + " # bqm.add_linear_inequality_constraint(syl, lb = KB[j][l][K_LOW], ub = KB[j][l][K_HIGH], lagrange_multiplier=np.sqrt(np.dot(lm,lm)), label=\"Sy_{0:s}_{1:s}\".format(J[j], L[l]))\n", + " bqm.add_linear_inequality_constraint(sxl, lb = KB[j][l][K_LOW], ub = KB[j][l][K_HIGH], lagrange_multiplier=np.dot(lm,lm), label=\"Sx_{0:s}_{1:s}\".format(J[j], L[l]))" + ] + }, + { + "cell_type": "markdown", + "id": "b43289de", + "metadata": {}, + "source": [ + "## D-WAVE Experiment (Part 4)\n", + "\n", + "We use the D-WAVE sampler to perform quantum annealing. The result will be exported as a CSV file. The process can take three minutes to complete a 65536-reads run across the 31-bonds set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47dad1ff", + "metadata": {}, + "outputs": [], + "source": [ + "from dwave.samplers import PathIntegralAnnealingSampler\n", + "sampler = PathIntegralAnnealingSampler()\n", + "sampleset = sampler.sample(bqm, num_reads=READS)\n", + "df_s = sampleset.to_pandas_dataframe().sort_values(by=\"energy\")\n", + "df_s.to_csv(\"dwave_result.csv\")\n", + "df_s" + ] + }, + { + "cell_type": "markdown", + "id": "948587ed", + "metadata": {}, + "source": [ + "## Sanity Check (Part 5)\n", + "\n", + "Determine the feasibility of each of the solutions. Consider the objective function, the constraints, and whether the feasibility has been satisfied. The results, ranked by D-WAVE energy, will be graphed, along with the shaded region indicating the satisfaction of the constraints." + ] + }, + { + "cell_type": "markdown", + "id": "41421e10", + "metadata": {}, + "source": [ + "### Feasibility summary\n", + "\n", + "The five samples with the lowest energy will be summarized to check if any constraints are violated. The structure is of the form:\n", + "\n", + "* Header with D-WAVE energy and selected bonds\n", + " * Global values\n", + " * All bonds\n", + " * Optimization function\n", + " * D-WAVE energy\n", + " * Bonds and Flow\n", + " * Additional bounds\n", + " * Applicable bonds\n", + " * Value against Target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4da574e", + "metadata": {}, + "outputs": [], + "source": [ + "answers = df_s[0:CUTOFF].to_numpy()[:,:len(C)]\n", + "totalenergies = df_s[0:CUTOFF].to_numpy()[:,-2]\n", + "energies = np.zeros(CUTOFF)\n", + "cashflow = np.zeros(CUTOFF)\n", + "characteristic = np.zeros((len(L), len(J), CUTOFF))\n", + "c_ids = np.array(list(indexmap.keys()))\n", + "\n", + "def _rangecolor_binary(lb, val, ub):\n", + " if val < lb or val > ub:\n", + " return OUT\n", + " return IN\n", + "\n", + "def _rangecolor(lb, val, ub):\n", + " if np.isnan(val):\n", + " return \"\\033[0;30m\"\n", + " if val < lb:\n", + " return OUT_LOW\n", + " if val > ub:\n", + " return OUT_HIGH\n", + " return IN\n", + "\n", + "def _printresult(a_idx, s):\n", + " if a_idx < 5:\n", + " print(s)\n", + "\n", + "for a_idx in range(min(len(answers),CUTOFF)):\n", + " answer = answers[a_idx]\n", + " totalenergy = np.trunc(totalenergies[a_idx]).astype(int)\n", + " ids = c_ids[answer.astype(bool)]\n", + " _printresult(a_idx, f\"{HEADER}Solution #1 ({totalenergy}): {ids}{ENDCOLOR}\")\n", + " energy = np.dot(answer.T, matrix @ answer) + np.sum(constantterm)\n", + " energies[a_idx] = energy\n", + " bonds = np.dot(np.ones(len(C)), answer).astype(int)\n", + "\n", + " for c_id in ids:\n", + " c = C[indexmap[c_id]]\n", + " x = how_much_bond(c)\n", + " coeff = c[PMV]*c[DELTA]*how_much_bond(c)\n", + " cashflow[a_idx] += coeff\n", + " _printresult(a_idx, \"\\t{0:.3f}\\t{1:.3f}\\t{2:.3f}\\t{3:s}\".format(x, c[PMV], coeff, str(c)))\n", + " _printresult(a_idx, YELLOW + \"\\tEnergy:\\t{0:.3f}\".format(energy) + ENDCOLOR)\n", + " _printresult(a_idx, _rangecolor_binary(0,bonds,N) + \"\\tBonds:\\t{0:d}\".format(bonds) + ENDCOLOR)\n", + " _printresult(a_idx, _rangecolor_binary(R[0],cashflow[a_idx],R[1]) + \"\\tFlow:\\t{0:.3f}\".format(cashflow[a_idx]) + ENDCOLOR)\n", + "\n", + " for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " KCl = KC[lIdx]\n", + " for c in KCl:\n", + " if not c[CID] in ids:\n", + " continue\n", + " x = how_much_bond(c)*(c[CID] in ids)\n", + " kcoeff = c[PMV]*c[DELTA]*c[-1][jIdx]*how_much_bond(c)\n", + " characteristic[lIdx, jIdx, a_idx] += kcoeff\n", + " # bcoeff = c[DXS]/100*c[DELTA]*c[-1][jIdx]*how_much_bond(c)\n", + " # bucketflow += bcoeff\n", + " _printresult(a_idx, \"\\t\\t{0:.3f}\\t{1:.3f}\\t{2:.3f}\\t{3:s}\".format(x, c[PMV], kcoeff, str(c)))\n", + " k_diff = characteristic[lIdx, jIdx, a_idx] - KB[jIdx][lIdx][K_TARGET]\n", + " _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][K_LOW], characteristic[lIdx, jIdx, a_idx], KB[jIdx][lIdx][K_HIGH]) \n", + " + \"\\t\\tBonds[{1:s}][{2:s}]: Σ={0:.3f} Δ={6:.3f} Δ²={7:.3f} ({3:.3f}, {4:.3f}, {5:.3f})\"\n", + " .format(characteristic[lIdx, jIdx, a_idx], L[lIdx], J[jIdx], \n", + " KB[jIdx][lIdx][K_LOW], KB[jIdx][lIdx][K_TARGET], KB[jIdx][lIdx][K_HIGH],\n", + " k_diff, k_diff*k_diff) + ENDCOLOR)\n", + " # _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][B_LOW], bucketflow, KB[jIdx][lIdx][B_HIGH]) \n", + " # + \"\\tFlow[{1:s}][{2:s}]: {0:.3f} ({3:.3f}, {4:.3f})\".format(\n", + " # bucketflow, L[lIdx], J[jIdx], KB[jIdx][lIdx][B_LOW], KB[jIdx][lIdx][B_HIGH]) + ENDCOLOR)\n", + " _printresult(a_idx, \"\")" + ] + }, + { + "cell_type": "markdown", + "id": "1f368cd1", + "metadata": {}, + "source": [ + "### Ranking and visualization of bond allocation\n", + "\n", + "The top graph represent the total energy of the ranked samples. The middle graph contains information about the bonds used (blue) and the cashflow (magenta). The bottom represents the selection of the securities, ordered by index, with filled indicating the security is selected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063fa33e", + "metadata": {}, + "outputs": [], + "source": [ + "T = np.linspace(0, CUTOFF, CUTOFF)\n", + "\n", + "fig = plt.subplots(3,1, figsize=(8, 9))\n", + "\n", + "plt.subplot(3,1,1)\n", + "plt.title(\"N={0:d}, R={1:s}\".format(N, str(R)))\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), totalenergies)\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), energies)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"energy\")\n", + "\n", + "plt.subplot(3,1,2)\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), cashflow)\n", + "plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), R[1], R[0], alpha=0.36)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"cashflow\")\n", + "\n", + "plt.subplot(3,1,3)\n", + "plt.pcolormesh(answers.transpose(), cmap=mpl.colormaps[\"Greys\"])\n", + "plt.gca().plot(T, N*np.ones(CUTOFF), c='r', linewidth=2)\n", + "plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), answers.sum(axis=1), alpha=0.36)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"count\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f03bb81", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.subplots(len(L), len(J), figsize=(8, 9))\n", + "for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " plt.subplot(len(L), len(J), 1 + len(J)*lIdx + jIdx)\n", + " plt.title(\"{0:s},{1:s}\".format(J[jIdx], L[lIdx]))\n", + " plt.plot(T, characteristic[lIdx, jIdx])\n", + " plt.gca().fill_between(T, KB[jIdx][lIdx][K_HIGH], KB[jIdx][lIdx][K_LOW], alpha=.36)\n", + " plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + " plt.gca().set(ylabel=\"value\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "65e2f85a", + "metadata": {}, + "source": [ + "# Full implementation\n", + "\n", + "This is an object-oriented rendition of the code demo, to allow multiple runs in succession. See `implementation.py`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ocean", + "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": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/implementation/implementation.py b/implementation/implementation.py new file mode 100644 index 00000000..64a6d6f1 --- /dev/null +++ b/implementation/implementation.py @@ -0,0 +1,338 @@ +import dimod +import pandas as pd +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt + +from dimod import BinaryQuadraticModel + +from types import NoneType +from typing import Union + +# Static variables for guardrails +K_LOW = 0 +K_TARGET = 1 +K_HIGH = 2 +B_LOW = 3 +B_HIGH = 4 + +# Static variables for security tuples +CID = 0 +MINTRADE = 1 +MAXTRADE = 2 +INVENTORY = 3 +PMV = 4 +DXS = 5 +DELTA = 6 +RATING = 7 +BETAS = -1 + +# Static variables for outputs +ENDCOLOR = '\033[0m' +HEADER = '\033[5m\033[1;32m' +YELLOW = '\033[0;33m' +IN = "\033[0;32m" +OUT = "\033[0;31m" +OUT_LOW = '\033[0;31m' +OUT_HIGH = '\033[0;35m' + +# Print methods for outputs +def _rangecolor_binary(lb, val, ub): + if val < lb or val > ub: + return OUT + return IN + +def _rangecolor(lb, val, ub): + if np.isnan(val): + return "\033[0;30m" + if val < lb: + return OUT_LOW + if val > ub: + return OUT_HIGH + return IN + +def _printresult(a_idx, s): + if a_idx < 5: + print(s) + +# Utility methods +def x(c): + """ Provided a Security, obtain the max as determined by the simplified OneOpt formula. """ + if not c[DELTA]: + raise Exception("A bond has zero interval.") + return (c[MINTRADE] + min(c[MAXTRADE], c[INVENTORY]))/(2*c[DELTA]) + +class SimplifiedOneOpto(): + """ Ad-hoc object for performing simplified OneOpto optimization, without automatic data processing. """ + def __init__(self, + C: list[tuple], + KC: list[list[tuple]], + indexmap: dict, + L: list[str], + J: list[str], + KB: list[list[list[float]]], + N: int, + R: list[float], + beta: Union[NoneType, list[list[float]]] = None, + rho: Union[NoneType, list[float]] = None): + + # Checks for sanity + if len(KB) != len(J): + raise Exception("K/B matrix requires {0:d} set of guardrail sets; there are {1:d}".format(len(J), len(KB))) + for jIdx in range(len(KB)): + if len(KB[jIdx]) != len(L): + raise Exception("K/B[{2:s}] by characteristic requires {0:d} set of guardrails; there are {1:d}".format(len(L), len(KB[jIdx]), J[jIdx])) + for lIdx in range(len(KB[jIdx])): + if len(KB[jIdx][lIdx]) < 5: + raise Exception("Guardrail information of K/B[{0:s},{1:s}] incomplete.".format(J[jIdx], L[lIdx])) + + # Set as instance variables + self.C, self.KC, self.indexmap = C, KC, indexmap + self.L = L + self.J = J + self.KB = KB + self.beta = beta + self.N = N + self.R = R + if not beta: + self.beta = np.ones((len(J), len(C))) + if not rho: + self.rho = [1]*len(J) + + # Generate QUBO matrix + matrix = np.zeros((len(C), len(C))) + for lIdx in range(len(KC)): + for jIdx in range(len(J)): + # Obtain quadratic coefficients, including diagonal terms + KCl = KC[lIdx] + for c1 in KCl: + for c2 in KCl: + i1 = indexmap[c1[CID]] + i2 = indexmap[c2[CID]] + matrix[i1][i2] += self.rho[jIdx]*self.beta[jIdx][i1]*c1[PMV]*x(c1)*self.beta[jIdx][i2]*c2[PMV]*x(c2) + # Obtain linear coefficients + for c in KCl: + i0 = indexmap[c[CID]] + matrix[indexmap[c[CID]]][indexmap[c[CID]]] -= self.rho[jIdx]*2*KB[jIdx][lIdx][K_TARGET]*self.beta[jIdx][i0]*c1[PMV]*x(c) + + # Consider constant term + constantterm = np.zeros((len(J), len(L))) + for lIdx in range(len(L)): + for jIdx in range(len(J)): + constantterm[jIdx][lIdx] = KB[jIdx][lIdx][K_TARGET]*KB[jIdx][lIdx][K_TARGET] + + self.matrix = matrix + self.constantterm = constantterm + self.results = None + + def run(self, num_reads): + # Convert to binary quadratic model + bqm = BinaryQuadraticModel(vartype=dimod.BINARY) + for i in range(len(C)): + bqm.add_variable(C[i][CID]) + bqm.add_linear(C[i][CID], self.matrix[i][i]) + for i1 in range(len(C)): + for i2 in range(i1 + 1, len(C)): + if self.matrix[i1][i2]: + bqm.add_quadratic(C[i1][CID], C[i2][CID], 2*self.matrix[i1][i2]) + # Add master linear inequality constraints + sy = [] + sx = [] + for i in range(len(self.C)): + sy.append((self.C[i][CID], 1)) + sx.append((self.C[i][CID], self.C[i][PMV]*self.C[i][DELTA]*x(self.C[i]))) + + lm = np.sum(abs(self.matrix), axis=1) # Hard restraint + bqm.add_linear_inequality_constraint(sy, ub = self.N, lagrange_multiplier=np.dot(lm,lm), label="Sy") # Bonds in basket + bqm.add_linear_inequality_constraint(sx, lb = self.R[0], ub = self.R[1], lagrange_multiplier=np.dot(lm,lm), label="Sx") # Residual cash flow + + # # Add bucket and characteristic based inequality constraints + for j in range(len(self.J)): + for l in range(len(self.L)): + if len(self.KC[l]) == 0: + continue + syl = [] + sxl = [] + for i in range(len(self.KC[l])): + c = self.KC[l][i] + i0 = self.indexmap[c[CID]] + sxl.append((c[CID], c[PMV]*c[DELTA]*self.beta[j][i0]*x(c))) + print(l, j, self.KB[j][l]) + # print(syl) + print(sxl) + # bqm.add_linear_inequality_constraint(syl, lb = KB[j][l][K_LOW], ub = KB[j][l][K_HIGH], lagrange_multiplier=np.sqrt(np.dot(lm,lm)), label="Sy_{0:s}_{1:s}".format(J[j], L[l])) + bqm.add_linear_inequality_constraint(sxl, lb = self.KB[j][l][K_LOW], ub = self.KB[j][l][K_HIGH], lagrange_multiplier=np.dot(lm,lm), label="Sx_{0:s}_{1:s}".format(self.J[j], self.L[l])) + + from dwave.samplers import PathIntegralAnnealingSampler + sampler = PathIntegralAnnealingSampler() + sampleset = sampler.sample(bqm, num_reads=num_reads) + df_s = sampleset.to_pandas_dataframe().sort_values(by="energy") + df_s.to_csv("dwave_result.csv") + self.results = df_s + return df_s + + def display_results(self, CUTOFF, prefix1="results", prefix2="resultsK", suffix="OMG"): + df_s = self.results + answers = df_s[0:CUTOFF].to_numpy()[:,:len(C)] + totalenergies = df_s[0:CUTOFF].to_numpy()[:,-2] + energies = np.zeros(CUTOFF) + cashflow = np.zeros(CUTOFF) + characteristic = np.zeros((len(L), len(J), CUTOFF)) + c_ids = np.array(list(indexmap.keys())) + + # Gather and display text data + for a_idx in range(min(len(answers),CUTOFF)): + answer = answers[a_idx] + totalenergy = np.trunc(totalenergies[a_idx]).astype(int) + ids = c_ids[answer.astype(bool)] + _printresult(a_idx, f"{HEADER}Solution #1 ({totalenergy}): {ids}{ENDCOLOR}") + energy = (answer @ self.matrix @ answer) + np.sum(self.constantterm) + + energies[a_idx] = energy + bonds = np.dot(np.ones(len(C)), answer).astype(int) + + for c_id in ids: + c = C[indexmap[c_id]] + xh = x(c) + coeff = c[PMV]*c[DELTA]*x(c) + cashflow[a_idx] += coeff + _printresult(a_idx, "\t{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:s}".format(xh, c[PMV], coeff, str(c))) + _printresult(a_idx, YELLOW + "\tEnergy:\t{0:.3f}".format(energy) + ENDCOLOR) + _printresult(a_idx, _rangecolor_binary(0,bonds,N) + "\tBonds:\t{0:d}".format(bonds) + ENDCOLOR) + _printresult(a_idx, _rangecolor_binary(R[0],cashflow[a_idx],R[1]) + "\tFlow:\t{0:.3f}".format(cashflow[a_idx]) + ENDCOLOR) + + for lIdx in range(len(L)): + for jIdx in range(len(J)): + KCl = KC[lIdx] + for c in KCl: + if not c[CID] in ids: + continue + xh = x(c)*(c[CID] in ids) + kcoeff = c[PMV]*c[DELTA]*self.beta[jIdx][self.indexmap[c[CID]]]*x(c) + characteristic[lIdx, jIdx, a_idx] += kcoeff + # bcoeff = c[DXS]/100*c[DELTA]*c[-1][jIdx]*how_much_bond(c) + # bucketflow += bcoeff + _printresult(a_idx, "\t\t{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:s}".format(xh, c[PMV], kcoeff, str(c))) + k_diff = characteristic[lIdx, jIdx, a_idx] - KB[jIdx][lIdx][K_TARGET] + _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][K_LOW], characteristic[lIdx, jIdx, a_idx], KB[jIdx][lIdx][K_HIGH]) + + "\t\tBonds[{1:s}][{2:s}]: Σ={0:.3f} Δ={6:.3f} Δ²={7:.3f} ({3:.3f}, {4:.3f}, {5:.3f})" + .format(characteristic[lIdx, jIdx, a_idx], L[lIdx], J[jIdx], + KB[jIdx][lIdx][K_LOW], KB[jIdx][lIdx][K_TARGET], KB[jIdx][lIdx][K_HIGH], + k_diff, k_diff*k_diff) + ENDCOLOR) + # _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][B_LOW], bucketflow, KB[jIdx][lIdx][B_HIGH]) + # + "\tFlow[{1:s}][{2:s}]: {0:.3f} ({3:.3f}, {4:.3f})".format( + # bucketflow, L[lIdx], J[jIdx], KB[jIdx][lIdx][B_LOW], KB[jIdx][lIdx][B_HIGH]) + ENDCOLOR) + _printresult(a_idx, "") + + # Obtain graphical charts + T = np.linspace(0, CUTOFF, CUTOFF) + fig = plt.subplots(3,1, figsize=(8, 9)) + + plt.subplot(3,1,1) + plt.title("N={0:d}, R={1:s}".format(N, str(R))) + plt.plot(np.linspace(0, CUTOFF, CUTOFF), totalenergies) + plt.plot(np.linspace(0, CUTOFF, CUTOFF), energies) + plt.gca().set(xlim=(0,CUTOFF), xlabel="rank") + plt.gca().set(ylabel="energy") + + plt.subplot(3,1,2) + plt.plot(np.linspace(0, CUTOFF, CUTOFF), cashflow) + plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), R[1], R[0], alpha=0.36) + plt.gca().set(xlim=(0,CUTOFF), xlabel="rank") + plt.gca().set(ylabel="cashflow") + + plt.subplot(3,1,3) + plt.pcolormesh(answers.transpose(), cmap=mpl.colormaps["Greys"]) + plt.gca().plot(T, N*np.ones(CUTOFF), c='r', linewidth=2) + plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), answers.sum(axis=1), alpha=0.36) + plt.gca().set(xlim=(0,CUTOFF), xlabel="rank") + plt.gca().set(ylabel="count") + + plt.savefig("{0:s}@{1:s}.png".format(prefix1, suffix)) + + fig = plt.subplots(len(L), len(J), figsize=(8, 9)) + for lIdx in range(len(L)): + for jIdx in range(len(J)): + plt.subplot(len(L), len(J), 1 + len(J)*lIdx + jIdx) + plt.title("{0:s},{1:s}".format(J[jIdx], L[lIdx])) + plt.plot(T, characteristic[lIdx, jIdx]) + plt.gca().fill_between(T, KB[jIdx][lIdx][K_HIGH], KB[jIdx][lIdx][K_LOW], alpha=.36) + plt.gca().set(xlim=(0,CUTOFF), xlabel="rank") + plt.gca().set(ylabel="value") + + plt.savefig("{0:s}@{1:s}.png".format(prefix2, suffix)) + +if __name__ == '__main__': + # Define sets + L = ["I_Capital_Goods", "I_Insurance", "I_Transportation"] + J = ["fund_enriched.pmv"] + + # Extract JSON + _c_json = None + indexmap = {} + with open('./implementation/bond_data1.json', 'r') as f: + import json + _c_json = json.loads(f.read()) + del json + + C = [] + KC = [[],[],[],[]] + count = 0 + for _series in _c_json["bonds"]: + rating = _series["sector"] + c = (_series['id'], _series["min"], _series["max"], _series["max"], _series["pmv"], _series["dxs"], 1, rating, None) + + C.append(c) + lIdx = 4 + if rating == 'Capital_Goods': + lIdx = 0 + elif rating == 'Insurance': + lIdx = 1 + elif rating == 'Transportation': + lIdx = 2 + else: + lIdx = 3 + KC[lIdx].append(c) + count += 1 + + for lIdx in range(len(L)): + for c in KC[lIdx]: + indexmap.update({c[CID]: len(indexmap)}) + + KB_pmv = [[4.758736462913, 5.803304406912, 6.847872350911, 0, 1000], + [4.134392251914, 5.178960195913, 6.223528139912, 0, 1000], + [0.240014083139, 1.284582027138, 2.329149971137, 0, 1000]] # Each must be size L + KB = [KB_pmv] + + N = 10 + R = [2.600974180557, 10.435233760548] + + for lIdx in range(len(L)): + for c in KC[lIdx]: + print("{0:s}:\t{1:8.3f}\t{2:8.3f} (=)\t{4:8.3f}\t{5:8.3f}\t{6:s}\t".format(c[CID], c[MINTRADE], c[MAXTRADE], c[INVENTORY], c[PMV], c[DXS], c[RATING]), c[DELTA], c[BETAS]) + indexmap + + # N range + for N in range(0, 32): + soo = SimplifiedOneOpto(C, KC, indexmap, L, J, KB, N, R) + dfs = soo.run(65536) + soo.display_results(1024, suffix="{0:d},{1:s}".format(N, str(R))) + + # K range + for D in range(-9, 41): + KB_pmv = [[4.758736462913, 5.803304406912, 6.847872350911, 0, 1000], + [4.134392251914, 5.178960195913, 6.223528139912, 0, 1000], + [0.240014083139, 1.284582027138, 2.329149971137, 0, 1000]] # Each must be size L + KB = [KB_pmv] + for jIdx in range(len(J)): + for lIdx in range(len(L)): + radius = (KB[jIdx][lIdx][K_HIGH] - KB[jIdx][lIdx][K_LOW])/2 + KB[jIdx][lIdx][K_HIGH] += D*radius/10 + KB[jIdx][lIdx][K_LOW] -= D*radius/10 + try: + soo = SimplifiedOneOpto(C, KC, indexmap, L, J, KB, N, R) + dfs = soo.run(65536) + soo.display_results(1024, suffix="{0:d}".format(D)) + except ValueError as e: + print(D, e) + continue \ No newline at end of file diff --git a/implementation/implementation_reduced.ipynb b/implementation/implementation_reduced.ipynb new file mode 100644 index 00000000..dd9e2241 --- /dev/null +++ b/implementation/implementation_reduced.ipynb @@ -0,0 +1,2426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "40d86095", + "metadata": {}, + "source": [ + "# D-WAVE implementation of the _simplified OneOpto_ optimization model (reduced)\n", + "\n", + "This part is written by Mack. I have yet to able to access the actual quantum computer through LEAP.\n", + "\n", + "Please ensure you are in the correct virtual environment with all the packages installed to continue. Otherwise, the notebook will terminate with a traceback." + ] + }, + { + "cell_type": "markdown", + "id": "0f44fb90", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Setup\n", + "\n", + "It is recommended to use a venv (Python virtual environment) to run this notebook to ensure compatibility.\n", + "At the time of this submission, Conda and Python 3.13 is not compatible with D-WAVE Ocean.\n", + "\n", + "We will need `dwave-ocean-sdk` for all D-WAVE procedures, and `pandas` with optional dependency `openpyxl` for reading the data file. Running the notebook also requires the `ipykernel` package, which can be automatically downloaded for you.\n", + "\n", + "If the initial check fails, a `ModuleNotFoundError` traceback will generate and execution will stop. If so, please follow the instructions on the next step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33cc3d3d", + "metadata": {}, + "outputs": [], + "source": [ + "# Packages to import\n", + "import dimod\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Display options\n", + "np.set_printoptions(precision=3, linewidth=65536, threshold=65536)\n", + "dimod.set_printoptions(width=(1<<20))\n", + "CUTOFF = 1000\n", + "READS = 65536\n", + "\n", + "# Color format\n", + "ENDCOLOR = '\\033[0m'\n", + "HEADER = '\\033[5m\\033[1;32m'\n", + "YELLOW = '\\033[0;33m'\n", + "IN = \"\\033[0;32m\"\n", + "OUT = \"\\033[0;31m\"\n", + "OUT_LOW = '\\033[0;31m'\n", + "OUT_HIGH = '\\033[0;35m'\n", + "\n", + "# Ordinal grades for sorting by letter grade\n", + "def ordinalgrade(lettergrade):\n", + " ordinal = lettergrade\n", + " try:\n", + " ordinal = str([\"AAA\",\"AA\",\"A\",\"BBB\",\"BB\",\"B\",\"CCC\",\"CC\",\"C\",\"D\"].index(lettergrade))\n", + " except ValueError:\n", + " pass\n", + " return ordinal" + ] + }, + { + "cell_type": "markdown", + "id": "ac98ca21", + "metadata": {}, + "source": [ + "### Virtual environment\n", + "\n", + "This is for Unix users. On the _dwave subdirectory_, run this command to respectively create and activate the virtual environment. You only have to create the virtual environment once." + ] + }, + { + "cell_type": "raw", + "id": "c11745dc", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "python3 -m venv ocean\n", + "source ocean/bin/activate" + ] + }, + { + "cell_type": "markdown", + "id": "6dca8cde", + "metadata": {}, + "source": [ + "### Python packages\n", + "\n", + "Below is the installation for Unix users. Assume we also install all Contributor Ocean tools and auxiliary packages for visualization. We can simply use pip to install the remaining packages." + ] + }, + { + "cell_type": "raw", + "id": "183e676c", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "pip install dwave-ocean-sdk\n", + "dwave install --all\n", + "\n", + "pip install pandas\n", + "pip install openpyxl\n", + "pip install numpy\n", + "pip install matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "e245daab", + "metadata": {}, + "source": [ + "## Input\n", + "\n", + "We define the data structure using constants, arrays, and the formatting of data organization before converting this into a Python object." + ] + }, + { + "cell_type": "markdown", + "id": "58585a19", + "metadata": {}, + "source": [ + "### Data extraction (Part 1)\n", + "\n", + "We extract all the data from the supplied [`/data/1/data_assets_dictionary.xlsx`](/data/1/data_assets_dictionary.xlsx) file relative to this working directory. This requires the `pandas` package.\n", + "\n", + "There are 2628 securities, all listed in USD. Empty values are assigned the `NaN` value." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "127fa68d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0isinccyassetIdstrategyNamesecGroupsecTypecpnposSourcebaseCcy...Large Issuer_newissuerTicker_newsodraw.filterLevel1_newsodraw.filterLevel3_newsodraw.filterLevel4_newsecurity.elements.creditQualityBuckets_newsecurity.elements.normalizedRating_newsodraw.filterLevel2_newassetId_newmain_key
00US001055BJ00USD001055BJ0UnassignedBNDCORP3.6EODUSD...NAFLInvestment Grade CreditInsuranceLife InsuranceAAFinancial001055BJ0VCIT | VCIT (bench)
11US001084AS13USD001084AS1UnassignedBNDCORP5.8EODUSD...NAGCOInvestment Grade CreditCapital GoodsConstruction MachineryBBBBBBIndustrial001084AS1VCIT | VCIT (bench)
22US00108WAM29USD00108WAM2UnassignedBNDCORP2.1EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAM2VCIT | VCIT (bench)
33US00108WAP59USD00108WAP5UnassignedBNDCORP4.7EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAP5VCIT | VCIT (bench)
44US00108WAR16USD00108WAR1UnassignedBNDCORP5.4EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAR1VCIT | VCIT (bench)
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26242624NaNUSDUSD_CCASHUnassignedCASHCASH0.0ORDUSD...NUSDNaNOtherOtherNRJunk / NRNaNUSD_CCASHVCIT | VCIT (bench)
26252625NaNUSDUSD_CCASHUnassignedCASHCASH0.0TRDUSD...NUSDNaNOtherOtherNRJunk / NRNaNUSD_CCASHVCIT | VCIT (bench)
26262626NaNUSDUXYZ42029UnassignedFUTUREFIN0.0EODUSD...NUXYZ4NaNOtherOtherAAA/AAAAANaNUXYZ42029VCIT | VCIT (bench)
26272627NaNUSDUXYZ4202FUnassignedCASHFUTURE0.0EODUSD...NUXYZ4NaNOtherOtherAAA/AAAAANaNUXYZ4202FVCIT | VCIT (bench)
26282628NaNUSDXUSD00000UnassignedCASHCASH-100.0CAPITALUSD...NCASH & EQUIVALENNaNOtherOtherNRJunk / NRNaNXUSD00000VCIT | VCIT (bench)
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2629 rows × 278 columns

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" + ], + "text/plain": [ + " Unnamed: 0 isin ccy assetId strategyName secGroup secType \\\n", + "0 0 US001055BJ00 USD 001055BJ0 Unassigned BND CORP \n", + "1 1 US001084AS13 USD 001084AS1 Unassigned BND CORP \n", + "2 2 US00108WAM29 USD 00108WAM2 Unassigned BND CORP \n", + "3 3 US00108WAP59 USD 00108WAP5 Unassigned BND CORP \n", + "4 4 US00108WAR16 USD 00108WAR1 Unassigned BND CORP \n", + "... ... ... ... ... ... ... ... \n", + "2624 2624 NaN USD USD_CCASH Unassigned CASH CASH \n", + "2625 2625 NaN USD USD_CCASH Unassigned CASH CASH \n", + "2626 2626 NaN USD UXYZ42029 Unassigned FUTURE FIN \n", + "2627 2627 NaN USD UXYZ4202F Unassigned CASH FUTURE \n", + "2628 2628 NaN USD XUSD00000 Unassigned CASH CASH \n", + "\n", + " cpn posSource baseCcy ... Large Issuer_new issuerTicker_new \\\n", + "0 3.6 EOD USD ... N AFL \n", + "1 5.8 EOD USD ... N AGCO \n", + "2 2.1 EOD USD ... N AEP \n", + "3 4.7 EOD USD ... N AEP \n", + "4 5.4 EOD USD ... N AEP \n", + "... ... ... ... ... ... ... \n", + "2624 0.0 ORD USD ... N USD \n", + "2625 0.0 TRD USD ... N USD \n", + "2626 0.0 EOD USD ... N UXYZ4 \n", + "2627 0.0 EOD USD ... N UXYZ4 \n", + "2628 -100.0 CAPITAL USD ... N CASH & EQUIVALEN \n", + "\n", + " sodraw.filterLevel1_new sodraw.filterLevel3_new \\\n", + "0 Investment Grade Credit Insurance \n", + "1 Investment Grade Credit Capital Goods \n", + "2 Investment Grade Credit Electric \n", + "3 Investment Grade Credit Electric \n", + "4 Investment Grade Credit Electric \n", + "... ... ... \n", + "2624 NaN Other \n", + "2625 NaN Other \n", + "2626 NaN Other \n", + "2627 NaN Other \n", + "2628 NaN Other \n", + "\n", + " sodraw.filterLevel4_new security.elements.creditQualityBuckets_new \\\n", + "0 Life Insurance A \n", + "1 Construction Machinery BBB \n", + "2 Other BBB \n", + "3 Other BBB \n", + "4 Other BBB \n", + "... ... ... \n", + "2624 Other NR \n", + "2625 Other NR \n", + "2626 Other AAA/AA \n", + "2627 Other AAA/AA \n", + "2628 Other NR \n", + "\n", + " security.elements.normalizedRating_new sodraw.filterLevel2_new \\\n", + "0 A Financial \n", + "1 BBB Industrial \n", + "2 BBB Utilities \n", + "3 BBB Utilities \n", + "4 BBB Utilities \n", + "... ... ... \n", + "2624 Junk / NR NaN \n", + "2625 Junk / NR NaN \n", + "2626 AAA NaN \n", + "2627 AAA NaN \n", + "2628 Junk / NR NaN \n", + "\n", + " assetId_new main_key \n", + "0 001055BJ0 VCIT | VCIT (bench) \n", + "1 001084AS1 VCIT | VCIT (bench) \n", + "2 00108WAM2 VCIT | VCIT (bench) \n", + "3 00108WAP5 VCIT | VCIT (bench) \n", + "4 00108WAR1 VCIT | VCIT (bench) \n", + "... ... ... \n", + "2624 USD_CCASH VCIT | VCIT (bench) \n", + "2625 USD_CCASH VCIT | VCIT (bench) \n", + "2626 UXYZ42029 VCIT | VCIT (bench) \n", + "2627 UXYZ4202F VCIT | VCIT (bench) \n", + "2628 XUSD00000 VCIT | VCIT (bench) \n", + "\n", + "[2629 rows x 278 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATA_ASSETS_DICTIONARY = '../data/1/data_assets_dictionary.xlsx'\n", + "df = pd.read_excel(DATA_ASSETS_DICTIONARY, sheet_name=0, header=0)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "12a65465", + "metadata": {}, + "source": [ + "### Summary\n", + "\n", + "From the dataframe, the sets are:\n", + "\n", + "* $|C| = 2628$\n", + " * A reduced subset containing only 31 elements is also provided.\n", + " * Another reduced subset containing only 12 elements, with none of the ${\\mathbb{K}_{\\ell}}$ empty will also be tested.\n", + "* $L = \\{\\textrm{AAA}, \\textrm{AA}, \\textrm{A}, \\textrm{BBB}, \\textrm{NR}\\}$\n", + " * $\\textrm{NR}$ encompasses both not rated bonds and speculative bonds.\n", + "* $\\sum_{\\ell \\in L} \\mathbb{K}_{\\ell} = |C|$. Using `security.elements.normalizedRating_new`, we obtain:\n", + " * $|\\mathbb{K}_{\\textrm{AAA}}| = 7$\n", + " * $|\\mathbb{K}_{\\textrm{AA}}| = 376$\n", + " * $|\\mathbb{K}_{\\textrm{A}}| = 863$\n", + " * $|\\mathbb{K}_{\\textrm{BBB}}| = 1372$\n", + " * $|\\mathbb{K}_{\\textrm{NR}}| = 11$\n", + "* $J$ depends on one of the six test cases. Note that $J \\subsetneq F$ where $F$ is the list of all fields. Also, $|F| = 278$.\n", + " * 31-bond, average only and no complex variables\n", + " * 31-bond, average only\n", + " * 31-bond, min-max average\n", + " * 31-bond, original\n", + " * full dataset, reduced\n", + " * full dataset, original\n", + "* There are a small number of duplicate entries and pending entries.\n", + "\n", + "Instead, we will use a reduced dataset; also $L$ is divided into industry-rating combinations. Furthermore, all $c$ can be mapped to a unique natural number, call this the canonical ordering.\n", + "\n", + "* $|C| = 31$\n", + "* $L = \\{\\textrm{Capital}, \\textrm{Investment}, \\textrm{Transportation}\\}$\n", + "* $J = \\{\\textrm{PMV}\\}$\n", + "* $\\forall j\\,\\forall c\\,\\beta_{j, c} = 1$\n", + "* $\\forall j\\,\\rho_{j} = 1$\n", + "* $\\mathrm{ord}: C \\rightarrow [0:|C|)$" + ] + }, + { + "cell_type": "markdown", + "id": "c9a5b51a", + "metadata": {}, + "source": [ + "### Defining the guardrails (Part 1)\n", + "\n", + "Each security is indexed with an `assetID`. According from the data assets dictionary spreadsheet, it is an \"Internal master identifier used by Vanguard systems to join to the firm‑wide security master.\"\n", + "\n", + "The reduced set is also provided. This has been obtained directly from one of the `.lp` files.\n", + "\n", + "Each element in $L$ would be augmented by two intervals $\\vec{K}, \\vec{b}$, one for each $j \\in J$. Each element in $c$ would also be augmented by a weight $\\vec{\\beta}$, one for each $j \\in J$. A sanity check will be performed to prevent any index-out-of-bounds errors. All of the bounds will be stored in the `KB` triple array." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0150fb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0isinccyassetIdstrategyNamesecGroupsecTypecpnposSourcebaseCcy...Large Issuer_newissuerTicker_newsodraw.filterLevel1_newsodraw.filterLevel3_newsodraw.filterLevel4_newsecurity.elements.creditQualityBuckets_newsecurity.elements.normalizedRating_newsodraw.filterLevel2_newassetId_newmain_key
00US001055BJ00USD001055BJ0UnassignedBNDCORP3.60EODUSD...NAFLInvestment Grade CreditInsuranceLife InsuranceAAFinancial001055BJ0VCIT | VCIT (bench)
11US001084AS13USD001084AS1UnassignedBNDCORP5.80EODUSD...NAGCOInvestment Grade CreditCapital GoodsConstruction MachineryBBBBBBIndustrial001084AS1VCIT | VCIT (bench)
22US00108WAM29USD00108WAM2UnassignedBNDCORP2.10EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAM2VCIT | VCIT (bench)
33US00108WAP59USD00108WAP5UnassignedBNDCORP4.70EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAP5VCIT | VCIT (bench)
44US00108WAR16USD00108WAR1UnassignedBNDCORP5.40EODUSD...NAEPInvestment Grade CreditElectricOtherBBBBBBUtilities00108WAR1VCIT | VCIT (bench)
..................................................................
26112611US98956PAV40USD98956PAV4UnassignedBNDCORP2.60EODUSD...NZBHInvestment Grade CreditConsumer Non CyclicalsHealth CareBBBBBBIndustrial98956PAV4VCIT | VCIT (bench)
26122612US98956PAZ53USD98956PAZ5UnassignedBNDCORP5.20EODUSD...NZBHInvestment Grade CreditConsumer Non CyclicalsHealth CareBBBBBBIndustrial98956PAZ5VCIT | VCIT (bench)
26142614US98971DAB64USD98971DAB6UnassignedBNDCORP3.25EODUSD...NZIONInvestment Grade CreditBankingOtherBBBBBBFinancial98971DAB6VCIT | VCIT (bench)
26162616US98978VAS25USD98978VAS2UnassignedBNDCORP2.00EODUSD...NZTSInvestment Grade CreditConsumer Non CyclicalsPharmaceuticalsBBBBBBIndustrial98978VAS2VCIT | VCIT (bench)
26172617US98978VAV53USD98978VAV5UnassignedBNDCORP5.60EODUSD...NZTSInvestment Grade CreditConsumer Non CyclicalsPharmaceuticalsBBBBBBIndustrial98978VAV5VCIT | VCIT (bench)
\n", + "

2319 rows × 278 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 isin ccy assetId strategyName secGroup secType \\\n", + "0 0 US001055BJ00 USD 001055BJ0 Unassigned BND CORP \n", + "1 1 US001084AS13 USD 001084AS1 Unassigned BND CORP \n", + "2 2 US00108WAM29 USD 00108WAM2 Unassigned BND CORP \n", + "3 3 US00108WAP59 USD 00108WAP5 Unassigned BND CORP \n", + "4 4 US00108WAR16 USD 00108WAR1 Unassigned BND CORP \n", + "... ... ... ... ... ... ... ... \n", + "2611 2611 US98956PAV40 USD 98956PAV4 Unassigned BND CORP \n", + "2612 2612 US98956PAZ53 USD 98956PAZ5 Unassigned BND CORP \n", + "2614 2614 US98971DAB64 USD 98971DAB6 Unassigned BND CORP \n", + "2616 2616 US98978VAS25 USD 98978VAS2 Unassigned BND CORP \n", + "2617 2617 US98978VAV53 USD 98978VAV5 Unassigned BND CORP \n", + "\n", + " cpn posSource baseCcy ... Large Issuer_new issuerTicker_new \\\n", + "0 3.60 EOD USD ... N AFL \n", + "1 5.80 EOD USD ... N AGCO \n", + "2 2.10 EOD USD ... N AEP \n", + "3 4.70 EOD USD ... N AEP \n", + "4 5.40 EOD USD ... N AEP \n", + "... ... ... ... ... ... ... \n", + "2611 2.60 EOD USD ... N ZBH \n", + "2612 5.20 EOD USD ... N ZBH \n", + "2614 3.25 EOD USD ... N ZION \n", + "2616 2.00 EOD USD ... N ZTS \n", + "2617 5.60 EOD USD ... N ZTS \n", + "\n", + " sodraw.filterLevel1_new sodraw.filterLevel3_new \\\n", + "0 Investment Grade Credit Insurance \n", + "1 Investment Grade Credit Capital Goods \n", + "2 Investment Grade Credit Electric \n", + "3 Investment Grade Credit Electric \n", + "4 Investment Grade Credit Electric \n", + "... ... ... \n", + "2611 Investment Grade Credit Consumer Non Cyclicals \n", + "2612 Investment Grade Credit Consumer Non Cyclicals \n", + "2614 Investment Grade Credit Banking \n", + "2616 Investment Grade Credit Consumer Non Cyclicals \n", + "2617 Investment Grade Credit Consumer Non Cyclicals \n", + "\n", + " sodraw.filterLevel4_new security.elements.creditQualityBuckets_new \\\n", + "0 Life Insurance A \n", + "1 Construction Machinery BBB \n", + "2 Other BBB \n", + "3 Other BBB \n", + "4 Other BBB \n", + "... ... ... \n", + "2611 Health Care BBB \n", + "2612 Health Care BBB \n", + "2614 Other BBB \n", + "2616 Pharmaceuticals BBB \n", + "2617 Pharmaceuticals BBB \n", + "\n", + " security.elements.normalizedRating_new sodraw.filterLevel2_new \\\n", + "0 A Financial \n", + "1 BBB Industrial \n", + "2 BBB Utilities \n", + "3 BBB Utilities \n", + "4 BBB Utilities \n", + "... ... ... \n", + "2611 BBB Industrial \n", + "2612 BBB Industrial \n", + "2614 BBB Financial \n", + "2616 BBB Industrial \n", + "2617 BBB Industrial \n", + "\n", + " assetId_new main_key \n", + "0 001055BJ0 VCIT | VCIT (bench) \n", + "1 001084AS1 VCIT | VCIT (bench) \n", + "2 00108WAM2 VCIT | VCIT (bench) \n", + "3 00108WAP5 VCIT | VCIT (bench) \n", + "4 00108WAR1 VCIT | VCIT (bench) \n", + "... ... ... \n", + "2611 98956PAV4 VCIT | VCIT (bench) \n", + "2612 98956PAZ5 VCIT | VCIT (bench) \n", + "2614 98971DAB6 VCIT | VCIT (bench) \n", + "2616 98978VAS2 VCIT | VCIT (bench) \n", + "2617 98978VAV5 VCIT | VCIT (bench) \n", + "\n", + "[2319 rows x 278 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the reduced set and constants\n", + "from types import NoneType\n", + "from typing import Union\n", + "\n", + "\n", + "REDUCED_SET = [\"020002BJ9\",\"026874DS3\",\"081437AT2\",\"097023CJ2\",\"13645RAD6\",\"13645RBF0\",\"14448CBC7\",\"15135BAW1\",\"21871XAS8\",\"24422EWZ8\",\"24422EXP9\",\"302491AU9\",\"36166NAK9\",\"438516CM6\",\"443201AC2\",\"444859BR2\",\"444859BV3\",\"444859BY7\",\"444859CA8\",\"45687VAB2\",\"539830CD9\",\"540424AT5\",\"56501RAN6\",\"655844CR7\",\"655844CT3\",\"75513EAD3\",\"759351AP4\",\"760759BA7\",\"760759BC3\",\"907818FX1\",\"91324PEJ7\"]\n", + "REDUCED_SET_L = []\n", + "OTHER_SET = [\"00130HCG8\",\"00206RMM1\",\"00287YDT3\",\"00774MAY1\",\"00774MBE4\",\"00914AAK8\",\"010392FU7\",\"015271AU3\",\"020002BJ9\",\"023135CR5\",\"026874DS3\",\"03027XBC3\",\"03027XCH1\",\"03040WBD6\",\"03073EAR6\",\"03073EAU9\",\"031162CW8\",\"031162CZ1\",\"035240AR1\",\"037833DU1\",\"038222AN5\",\"03938LBF0\",\"03938LBG8\",\"04273WAE1\",\"049560AX3\",\"053015AF0\",\"053332BF8\",\"054989AA6\",\"054989AB4\",\"05526DBX2\",\"05964HBG9\",\"06051GJB6\",\"06051GJT7\",\"06051GKA6\",\"06051GLH0\",\"06368L3L8\",\"06406RBB2\",\"06406RBH9\",\"06406RBR7\",\"06406RBU0\",\"06406RBZ9\",\"06418JAC5\",\"06738ECA1\",\"06738ECE3\",\"06738ECG8\",\"071813CB3\",\"0778FPAL3\",\"081437AT2\",\"09247XAT8\",\"09290DAB7\",\"097023CJ2\",\"10112RBE3\",\"10373QBW9\",\"11135FAS0\",\"11135FBH3\",\"12503MAC2\",\"12505BAE0\",\"126650DN7\",\"126650DR8\",\"126650DX5\",\"134429BP3\",\"13645RAD6\",\"13645RBF0\",\"14040HCY9\",\"14448CBC7\",\"15135BAW1\",\"15189WAS9\",\"15189YAH9\",\"166764BY5\",\"17275RBS0\",\"172967ML2\",\"172967NN7\",\"172967PA3\",\"17325FBG2\",\"174610BH7\",\"191216CT5\",\"194162AS2\",\"20030NCU3\",\"20825UAC8\",\"209111GE7\",\"21871XAS8\",\"22003BAM8\",\"22541LAE3\",\"22822VAW1\",\"22822VAY7\",\"22822VBE0\",\"231021AT3\",\"23338VAS5\",\"233835AQ0\",\"24422EWZ8\",\"24422EXP9\",\"25243YBD0\",\"254687FQ4\",\"254687FX9\",\"25468PBW5\",\"254709AT5\",\"25470DBJ7\",\"25746UDL0\",\"260543DC4\",\"260543DJ9\",\"26439XAC7\",\"26441CBH7\",\"26441CBL8\",\"26442UAS3\",\"28368EAE6\",\"29273VAQ3\",\"29278NAQ6\",\"29379VCF8\",\"29444UBU9\",\"29736RAT7\",\"30161NBK6\",\"30225VAK3\",\"30225VAQ0\",\"30231GBN1\",\"30303M8H8\",\"314353AA1\",\"316773DL1\",\"337738BH0\",\"337738BN7\",\"341081GG6\",\"341081GL5\",\"341081GP6\",\"345397B36\",\"36166NAK9\",\"37045XER3\",\"37045XEV4\",\"372460AA3\",\"375558BY8\",\"38141GYB4\",\"38141GYN8\",\"38143VAA7\",\"404119CK3\",\"404121AK1\",\"404280DV8\",\"404280EC9\",\"404280EG0\",\"40434LAC9\",\"431282AU6\",\"437076CF7\",\"437076CJ9\",\"438516CM6\",\"440452AF7\",\"44107TBA3\",\"44107TBB1\",\"443201AC2\",\"444859BR2\",\"444859BV3\",\"444859BY7\",\"444859CA8\",\"456837BH5\",\"45687VAB2\",\"45866FAK0\",\"459200BB6\",\"459200KN0\",\"459200KU4\",\"46590XAP1\",\"46647PBJ4\",\"46647PBL9\",\"46647PDY9\",\"48203RAP9\",\"49271VAJ9\",\"49456BAV3\",\"49456BAX9\",\"50155QAL4\",\"532457CF3\",\"539830CD9\",\"540424AT5\",\"548661DY0\",\"56501RAN6\",\"565849AR7\",\"57636QBB9\",\"58013MFV1\",\"581557BU8\",\"58507LBC2\",\"59523UAW7\",\"606822BU7\",\"606822BX1\",\"606822CX0\",\"60687YBH1\",\"60687YBL2\",\"615369AZ8\",\"6174468X0\",\"61747YEH4\",\"61747YEL5\",\"61747YEU5\",\"61747YFE0\",\"61747YFJ9\",\"637417AS5\",\"637432PA7\",\"637432PC3\",\"64110DAK0\",\"641423CD8\",\"651639AY2\",\"65339KCP3\",\"65473PAJ4\",\"65535HAS8\",\"65535HBA6\",\"65535HBV0\",\"655844CR7\",\"655844CT3\",\"665859AV6\",\"670346AS4\",\"67103HAL1\",\"682680BK8\",\"68389XAV7\",\"68389XBD6\",\"68389XCP8\",\"686330AR2\",\"693475BU8\",\"693506BS5\",\"694308KR7\",\"695114CW6\",\"695114DC9\",\"703481AD3\",\"70450YAL7\",\"713448EZ7\",\"713448FA1\",\"713448FE3\",\"713466AD2\",\"716973AE2\",\"717081EY5\",\"718172CP2\",\"723787AR8\",\"74456QCJ3\",\"74456QCQ7\",\"74456QCS3\",\"744573AX4\",\"745310AK8\",\"747525BP7\",\"74762EAL6\",\"75513EAD3\",\"756109AX2\",\"759351AP4\",\"760759BA7\",\"760759BC3\",\"78016FZQ0\",\"78016HZQ6\",\"78409VAS3\",\"797440BZ6\",\"797440CB8\",\"806854AJ4\",\"808513BS3\",\"808513CA1\",\"816851BR9\",\"824348BT2\",\"828807DW4\",\"83444MAE1\",\"842587DR5\",\"8426EPAG3\",\"8426EPAH1\",\"845011AC9\",\"855244AZ2\",\"855244BH1\",\"857477BG7\",\"862121AD2\",\"863667AY7\",\"86562MCB4\",\"86562MCS7\",\"87264ACV5\",\"87264ADF9\",\"872652AA0\",\"87612GAA9\",\"87971MBW2\",\"89157XAA9\",\"89236TGU3\",\"89788MAP7\",\"89788NAA8\",\"90265EAW0\",\"907818FX1\",\"91159HJG6\",\"91159HJL5\",\"91324PEJ7\",\"91913YAE0\",\"92343VFX7\",\"92343VGJ7\",\"92556HAB3\",\"927804GR9\",\"92940PAG9\",\"931142EY5\",\"95000U2L6\",\"95000U2U6\",\"95000U3H4\",\"958102AR6\"]\n", + "K_LOW = 0\n", + "K_TARGET = 1\n", + "K_HIGH = 2\n", + "B_LOW = 3\n", + "B_HIGH = 4\n", + "df_r = df[df['isin'].notnull()]\n", + "df_r = df_r[df_r[\"posSource\"] == 'EOD']\n", + "# df_r = df_r.sort_values(by=['security.elements.normalizedRating_new'], key=lambda x: ordinalgrade(x)) # [df['assetId'].isin(REDUCED_SET)]\n", + "\n", + "# Input variables\n", + "L = [\"I_Capital_Goods\", \"I_Insurance\", \"I_Transportation\"]\n", + "J = [\"fund_enriched.pmv\"]\n", + "NL, NU = -2000000000, 2000000000\n", + "KB_pmv = [[4.758736462913, 5.803304406912, 6.847872350911, 0, 1000],\n", + " [4.134392251914, 5.178960195913, 6.223528139912, 0, 1000], \n", + " [0.240014083139, 1.284582027138, 2.329149971137, 0, 1000]] # Each must be size L\n", + "KB = [KB_pmv]\n", + "\n", + "N = 31\n", + "R = [2.600974180557, 10.435233760548]\n", + "\n", + "# Unused variables\n", + "BETA = 1\n", + "RHO = 1\n", + "\n", + "# Checks for sanity\n", + "if len(KB) != len(J):\n", + " raise Exception(\"K/B matrix requires {0:d} set of guardrail sets; there are {1:d}\".format(len(J), len(KB)))\n", + "for jIdx in range(len(KB)):\n", + " if len(KB[jIdx]) != len(L):\n", + " raise Exception(\"K/B[{2:s}] by characteristic requires {0:d} set of guardrails; there are {1:d}\".format(len(L), len(KB[jIdx]), J[jIdx]))\n", + " for lIdx in range(len(KB[jIdx])):\n", + " if len(KB[jIdx][lIdx]) < 5:\n", + " raise Exception(\"Guardrail information of K/B[{0:s},{1:s}] incomplete.\".format(J[jIdx], L[lIdx]))\n", + "\n", + "df_r" + ] + }, + { + "cell_type": "markdown", + "id": "569d0057", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "We implement the D-WAVE solver provided $C, L, K, b$." + ] + }, + { + "cell_type": "markdown", + "id": "957ab001", + "metadata": {}, + "source": [ + "### Defining the data type (Part 2)\n", + "\n", + "We define the data type by assuming types match. Each security $c \\in C$ will be converted to a tuple augmented by bucket membership and the betas for each characteristic $j \\in J$. $C$ and $\\mathbb{K}$ will be implemented as a list, and an `indexmap` will convert the ID to the index in $C$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "be0015ba", + "metadata": {}, + "outputs": [], + "source": [ + "CID = 0\n", + "MINTRADE = 1\n", + "MAXTRADE = 2\n", + "INVENTORY = 3\n", + "PMV = 4\n", + "DXS = 5\n", + "DELTA = 6\n", + "RATING = 7\n", + "BETAS = -1" + ] + }, + { + "cell_type": "markdown", + "id": "26e1c02e", + "metadata": {}, + "source": [ + "### Dataframe generation functions\n", + "\n", + "Functions for obtaining the $C$ in this format, and for sorting the elements of $C$ by bucket membership. The $L$ inputted will also generate the $\\mathbb{K}$ and the $\\mathrm{ord}$ function for converting the bond name into its canonical ordering. Note $\\mathbb{K}$ is represented as `KC`, and $\\mathrm{ord}$ is represented as `indexmap`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "609d8c0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "081437AT2:\t 5.000\t 30.000 (=)\t 0.029\t 13.316\tCapital_Goods\t 1 [1]\n", + "097023CJ2:\t 5.000\t 13.000 (=)\t 0.027\t 34.462\tCapital_Goods\t 1 [1]\n", + "14448CBC7:\t 5.000\t 30.000 (=)\t 0.034\t 22.473\tCapital_Goods\t 1 [1]\n", + "24422EWZ8:\t 5.000\t 17.000 (=)\t 0.033\t 7.419\tCapital_Goods\t 1 [1]\n", + "24422EXP9:\t 5.000\t 30.000 (=)\t 0.033\t 16.111\tCapital_Goods\t 1 [1]\n", + "36166NAK9:\t 5.000\t 30.000 (=)\t 0.032\t 14.942\tCapital_Goods\t 1 [1]\n", + "438516CM6:\t 5.000\t 30.000 (=)\t 0.032\t 13.523\tCapital_Goods\t 1 [1]\n", + "443201AC2:\t 5.000\t 15.000 (=)\t 0.032\t 14.155\tCapital_Goods\t 1 [1]\n", + "45687VAB2:\t 5.000\t 9.000 (=)\t 0.034\t 20.093\tCapital_Goods\t 1 [1]\n", + "539830CD9:\t 5.000\t 30.000 (=)\t 0.032\t 15.855\tCapital_Goods\t 1 [1]\n", + "75513EAD3:\t 5.000\t 27.000 (=)\t 0.028\t 9.403\tCapital_Goods\t 1 [1]\n", + "760759BA7:\t 5.000\t 30.000 (=)\t 0.026\t 15.388\tCapital_Goods\t 1 [1]\n", + "760759BC3:\t 5.000\t 30.000 (=)\t 0.032\t 18.718\tCapital_Goods\t 1 [1]\n", + "020002BJ9:\t 5.000\t 30.000 (=)\t 0.026\t 11.747\tInsurance\t 1 [1]\n", + "026874DS3:\t 5.000\t 30.000 (=)\t 0.032\t 19.306\tInsurance\t 1 [1]\n", + "15135BAW1:\t 5.000\t 30.000 (=)\t 0.028\t 21.626\tInsurance\t 1 [1]\n", + "21871XAS8:\t 5.000\t 30.000 (=)\t 0.033\t 26.975\tInsurance\t 1 [1]\n", + "444859BR2:\t 5.000\t 18.000 (=)\t 0.026\t 19.714\tInsurance\t 1 [1]\n", + "444859BV3:\t 5.000\t 30.000 (=)\t 0.033\t 27.752\tInsurance\t 1 [1]\n", + "444859BY7:\t 5.000\t 30.000 (=)\t 0.033\t 30.610\tInsurance\t 1 [1]\n", + "444859CA8:\t 5.000\t 30.000 (=)\t 0.032\t 19.356\tInsurance\t 1 [1]\n", + "540424AT5:\t 5.000\t 30.000 (=)\t 0.030\t 10.363\tInsurance\t 1 [1]\n", + "56501RAN6:\t 5.000\t 30.000 (=)\t 0.030\t 13.086\tInsurance\t 1 [1]\n", + "759351AP4:\t 5.000\t 30.000 (=)\t 0.030\t 11.975\tInsurance\t 1 [1]\n", + "91324PEJ7:\t 5.000\t 21.000 (=)\t 0.031\t 11.756\tInsurance\t 1 [1]\n", + "13645RAD6:\t 5.000\t 60.000 (=)\t 0.036\t 0.036\tTransportation\t 1 [1]\n", + "13645RBF0:\t 5.000\t 41.000 (=)\t 0.028\t 0.028\tTransportation\t 1 [1]\n", + "314353AA1:\t 5.000\t 60.000 (=)\t 0.021\t 0.021\tTransportation\t 1 [1]\n", + "655844CR7:\t 5.000\t 120.000 (=)\t 0.033\t 0.033\tTransportation\t 1 [1]\n", + "655844CT3:\t 5.000\t 12.000 (=)\t 0.034\t 0.034\tTransportation\t 1 [1]\n", + "907818FX1:\t 5.000\t 60.000 (=)\t 0.028\t 0.028\tTransportation\t 1 [1]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'081437AT2': 0,\n", + " '097023CJ2': 1,\n", + " '14448CBC7': 2,\n", + " '24422EWZ8': 3,\n", + " '24422EXP9': 4,\n", + " '36166NAK9': 5,\n", + " '438516CM6': 6,\n", + " '443201AC2': 7,\n", + " '45687VAB2': 8,\n", + " '539830CD9': 9,\n", + " '75513EAD3': 10,\n", + " '760759BA7': 11,\n", + " '760759BC3': 12,\n", + " '020002BJ9': 13,\n", + " '026874DS3': 14,\n", + " '15135BAW1': 15,\n", + " '21871XAS8': 16,\n", + " '444859BR2': 17,\n", + " '444859BV3': 18,\n", + " '444859BY7': 19,\n", + " '444859CA8': 20,\n", + " '540424AT5': 21,\n", + " '56501RAN6': 22,\n", + " '759351AP4': 23,\n", + " '91324PEJ7': 24,\n", + " '13645RAD6': 25,\n", + " '13645RBF0': 26,\n", + " '314353AA1': 27,\n", + " '655844CR7': 28,\n", + " '655844CT3': 29,\n", + " '907818FX1': 30}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def generate_from_C(df):\n", + " \"\"\" Generate C from the dataframe \"\"\"\n", + " C = []\n", + " KC = [[],[],[],[],[]]\n", + " indexmap = {}\n", + " count = 0\n", + " for c in df.iterrows():\n", + " _series = c[1]\n", + " _c = (_series['assetId'], \n", + " _series['minTradeSize'], 2*_series['minTradeSize'], _series['basket.unitSz'], \n", + " _series['price'], _series['fund_enriched.dxsCtr'], _series['minTradeIncrement'],\n", + " _series['security.elements.normalizedRating_new'], [BETA]*len(J))\n", + " C.append(_c)\n", + " lIdx = 4\n", + " rating = _series[\"security.elements.normalizedRating_new\"]\n", + " if rating == 'AAA':\n", + " lIdx = 0\n", + " elif rating == 'AA':\n", + " lIdx = 1\n", + " elif rating == 'A':\n", + " lIdx = 2\n", + " elif rating == 'BBB':\n", + " lIdx = 3\n", + " print(_c[0], _c[1], _c[2], _c[3], _c[4], _c[5], _c[6], _c[7])\n", + "\n", + " KC[lIdx].append(_c)\n", + "\n", + " for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " indexmap.update({c[CID]: len(indexmap)})\n", + "\n", + " return C, KC, indexmap\n", + "\n", + "def generate_from_sample():\n", + " \"\"\" Generate C from the sample data \"\"\"\n", + " # Extract JSON\n", + " _c_json = None\n", + " indexmap = {}\n", + " with open('bond_data1.json', 'r') as f:\n", + " import json\n", + " _c_json = json.loads(f.read())\n", + " del json\n", + "\n", + " C = []\n", + " KC = [[],[],[],[]]\n", + " count = 0\n", + " for _series in _c_json[\"bonds\"]:\n", + " rating = _series[\"sector\"]\n", + " c = (_series['id'], _series[\"min\"], _series[\"max\"], _series[\"max\"], _series[\"pmv\"], _series[\"dxs\"], 1, rating, [BETA]*len(J))\n", + " \n", + " C.append(c)\n", + " lIdx = 4\n", + " if rating == 'Capital_Goods':\n", + " lIdx = 0\n", + " elif rating == 'Insurance':\n", + " lIdx = 1\n", + " elif rating == 'Transportation':\n", + " lIdx = 2\n", + " else:\n", + " lIdx = 3\n", + " KC[lIdx].append(c)\n", + " count += 1\n", + "\n", + " for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " indexmap.update({c[CID]: len(indexmap)})\n", + "\n", + " return C, KC, indexmap\n", + "\n", + "df_r = df_r[df_r['assetId'].isin(OTHER_SET)]\n", + "C, KC, indexmap = generate_from_sample()\n", + "for lIdx in range(len(L)):\n", + " for c in KC[lIdx]:\n", + " print(\"{0:s}:\\t{1:8.3f}\\t{2:8.3f} (=)\\t{4:8.3f}\\t{5:8.3f}\\t{6:s}\\t\".format(c[CID], c[MINTRADE], c[MAXTRADE], c[INVENTORY], c[PMV], c[DXS], c[RATING]), c[DELTA], c[BETAS])\n", + "indexmap\n" + ] + }, + { + "cell_type": "markdown", + "id": "be9cb6ba", + "metadata": {}, + "source": [ + "### Objective function (Part 2)\n", + "\n", + "We have to minimize an objective function that consists of at least $|C||J|$ terms. Duplicate cases will be merged if $c \\in C$ belongs to multiple $\\ell \\in L$. Each entry therefore consists of a block-matrix QUBO matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c14deb4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[33.678 26.822 1.65 ]] 62.150121734581425\n", + "[-6.261 -3.288 -6.157 -3.962 -6.184 -6.2 -6.203 -3.618 -2.548 -6.195 -5.748 -6.296 -6.195 -5.48 -5.375 -5.454 -5.352 -3.65 -5.358 -5.355 -5.375 -5.418 -5.42 -5.425 -4.062 -0.991 -1.27 -1.893 -0.342 -0.54 -1.521]\n", + "[[-6.261 0.123 0.296 0.18 0.285 0.278 0.277 0.159 0.118 0.28 0.225 0.231 0.28 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.123 -3.288 0.146 0.088 0.14 0.137 0.136 0.078 0.058 0.138 0.111 0.114 0.138 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.296 0.146 -6.157 0.214 0.339 0.331 0.329 0.189 0.14 0.333 0.268 0.275 0.333 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.18 0.088 0.214 -3.962 0.205 0.2 0.199 0.115 0.085 0.202 0.163 0.166 0.202 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.285 0.14 0.339 0.205 -6.184 0.318 0.316 0.182 0.135 0.32 0.258 0.264 0.32 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.278 0.137 0.331 0.2 0.318 -6.2 0.309 0.178 0.131 0.313 0.252 0.258 0.312 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.277 0.136 0.329 0.199 0.316 0.309 -6.203 0.177 0.131 0.311 0.25 0.256 0.311 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.159 0.078 0.189 0.115 0.182 0.178 0.177 -3.618 0.075 0.179 0.144 0.147 0.179 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.118 0.058 0.14 0.085 0.135 0.131 0.131 0.075 -2.548 0.132 0.107 0.109 0.132 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.28 0.138 0.333 0.202 0.32 0.313 0.311 0.179 0.132 -6.195 0.254 0.26 0.315 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.225 0.111 0.268 0.163 0.258 0.252 0.25 0.144 0.107 0.254 -5.748 0.209 0.253 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.231 0.114 0.275 0.166 0.264 0.258 0.256 0.147 0.109 0.26 0.209 -6.296 0.259 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0.28 0.138 0.333 0.202 0.32 0.312 0.311 0.179 0.132 0.315 0.253 0.259 -6.195 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -5.48 0.259 0.225 0.269 0.138 0.266 0.268 0.26 0.241 0.24 0.238 0.188 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.259 -5.375 0.274 0.328 0.169 0.326 0.327 0.317 0.295 0.294 0.291 0.23 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.225 0.274 -5.454 0.284 0.146 0.282 0.283 0.274 0.255 0.254 0.252 0.199 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.269 0.328 0.284 -5.352 0.175 0.337 0.339 0.329 0.305 0.304 0.302 0.238 0. 0. 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.138 0.169 0.146 0.175 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def how_much_bond(c):\n", + " if not c[DELTA]:\n", + " raise Exception(\"A bond has zero interval.\")\n", + " return (c[MINTRADE] + min(c[MAXTRADE], c[INVENTORY]))/(2*c[DELTA])\n", + "\n", + "# Generate QUBO matrix\n", + "matrix = np.zeros((len(C), len(C)))\n", + "for lIdx in range(len(KC)):\n", + " for jIdx in range(len(J)):\n", + " # Obtain quadratic coefficients, including diagonal terms\n", + " KCl = KC[lIdx]\n", + " for c1 in KCl:\n", + " for c2 in KCl:\n", + " matrix[indexmap[c1[CID]]][indexmap[c2[CID]]] += RHO*BETA*c1[PMV]*how_much_bond(c1)*BETA*c2[PMV]*how_much_bond(c2)\n", + " # Obtain linear coefficients\n", + " for c in KCl:\n", + " matrix[indexmap[c[CID]]][indexmap[c[CID]]] -= RHO*2*KB[jIdx][lIdx][K_TARGET]*BETA*c1[PMV]*how_much_bond(c)\n", + "\n", + "# Consider constant term\n", + "constantterm = np.zeros((len(J), len(L)))\n", + "for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " constantterm[jIdx][lIdx] = KB[jIdx][lIdx][K_TARGET]*KB[jIdx][lIdx][K_TARGET]\n", + "\n", + "mbar = plt.matshow(np.cbrt(matrix), cmap=mpl.colormaps[\"RdGy\"], vmin=-2, vmax=2)\n", + "cbar = plt.colorbar(mbar)\n", + "cbar.set_label(\"cube root of entry\")\n", + "print(constantterm, np.sum(constantterm))\n", + "print(np.diag(matrix))\n", + "print(matrix)" + ] + }, + { + "cell_type": "markdown", + "id": "8e41aafb", + "metadata": {}, + "source": [ + "## D-WAVE Implementation\n", + "\n", + "After constructing the QUBO, we define the optimization model using the `dimod` package." + ] + }, + { + "cell_type": "markdown", + "id": "f877e9e1", + "metadata": {}, + "source": [ + "### Binary quadratic model (Part 2)\n", + "\n", + "We use the QUBO matrix to define ane name the variables (which will be the IDs), their linear entries, and their quadratic entries." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4759cdd8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BinaryQuadraticModel({'020002BJ9': -6.260929595936642, '026874DS3': -3.2875700503087253, '081437AT2': -6.157097206317578, '097023CJ2': -3.9624176013452677, '13645RAD6': -6.18395995995356, '13645RBF0': -6.199672363030624, '14448CBC7': -6.202745000265599, '15135BAW1': -3.618345189828859, '21871XAS8': -2.5483052903963714, '24422EWZ8': -6.194731472200784, '24422EXP9': -5.747776591682852, '314353AA1': -6.296018034463562, '36166NAK9': -6.195288506138863, '438516CM6': -5.479547047581361, '443201AC2': -5.375014257130327, '444859BR2': -5.4544713649379695, '444859BV3': -5.351650425400236, '444859BY7': -3.6504173104390354, '444859CA8': -5.357542914246724, '45687VAB2': -5.354994429260476, '539830CD9': -5.374607557844501, '540424AT5': -5.4180465555766, '56501RAN6': -5.4196952039639585, '655844CR7': -5.424674366604599, '655844CT3': -4.061647588735684, '75513EAD3': -0.990763284056966, '759351AP4': -1.2704399492733585, '760759BA7': -1.8928466882348047, '760759BC3': 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quadratic model\n", + "bqm = BinaryQuadraticModel(vartype=dimod.BINARY)\n", + "for i in range(len(C)):\n", + " bqm.add_variable(C[i][CID])\n", + " bqm.add_linear(C[i][CID], matrix[i][i])\n", + "for i1 in range(len(C)):\n", + " for i2 in range(i1 + 1, len(C)):\n", + " if matrix[i1][i2]:\n", + " bqm.add_quadratic(C[i1][CID], C[i2][CID], 2*matrix[i1][i2])\n", + " \n", + "bqm" + ] + }, + { + "cell_type": "markdown", + "id": "0299850e", + "metadata": {}, + "source": [ + "### Linear inequality constraints (Part 3)\n", + "\n", + "We will implement the linear inequality constraints. There are $2 + 2|J||L|$ linear constraints to consider. The Lagrange multiplier for the global constraints is calculated to enforce the constraints. We have disabled the $b$ bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b7c77823", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0 [4.758736462913, 5.803304406912, 6.847872350911, 0, 1000]\n", + "[('081437AT2', 0.498939895055), ('097023CJ2', 0.24568965940499998), ('14448CBC7', 0.5939473112125), ('24422EWZ8', 0.359862572135), ('24422EXP9', 0.57088585099), ('36166NAK9', 0.556954443185), ('438516CM6', 0.554189150515), ('443201AC2', 0.31871754859), ('45687VAB2', 0.235887590876), ('539830CD9', 0.5613725524225), ('75513EAD3', 0.451779939008), ('760759BA7', 0.4624419751175), ('760759BC3', 0.56087619728)]\n", + "1 0 [4.134392251914, 5.178960195913, 6.223528139912, 0, 1000]\n", + "[('020002BJ9', 0.460848299975), ('026874DS3', 0.5629511044850001), ('15135BAW1', 0.48729543219), ('21871XAS8', 0.5833333333275), ('444859BR2', 0.2999877524915), ('444859BV3', 0.5782605718225), ('444859BY7', 0.580459967535), ('444859CA8', 0.5633122094599999), ('540424AT5', 0.5233370305975), ('56501RAN6', 0.5217595223925), ('759351AP4', 0.5169659916925), ('91324PEJ7', 0.408217997668)]\n", + "2 0 [0.240014083139, 1.284582027138, 2.329149971137, 0, 1000]\n", + "[('13645RAD6', 1.1771304772500002), ('13645RBF0', 0.641342075357), ('314353AA1', 0.6953795771325), ('655844CR7', 2.056251137125), ('655844CT3', 0.284852219912), ('907818FX1', 0.9249699101825001)]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/samaria/github/replacement/implementation/ocean/lib/python3.13/site-packages/dimod/binary/binary_quadratic_model.py:773: UserWarning: Did not add constraint Sy. This constraint is feasible with any value for state variables.\n", + " warnings.warn(\n", + "/home/samaria/github/replacement/implementation/ocean/lib/python3.13/site-packages/dimod/binary/binary_quadratic_model.py:762: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + } + ], + "source": [ + "# Add master linear inequality constraints\n", + "sy = []\n", + "sx = []\n", + "sr = []\n", + "for i in range(len(C)):\n", + " sy.append((C[i][CID], 1))\n", + " sx.append((C[i][CID], C[i][PMV]*C[i][DELTA]*how_much_bond(C[i])))\n", + "\n", + "lm = np.sum(abs(matrix), axis=1) # Hard restraint\n", + "bqm.add_linear_inequality_constraint(sy, ub = N, lagrange_multiplier=np.dot(lm,lm), label=\"Sy\") # Bonds in basket\n", + "bqm.add_linear_inequality_constraint(sx, lb = R[0], ub = R[1], lagrange_multiplier=np.dot(lm,lm), label=\"Sx\") # Residual cash flow\n", + "\n", + "# # Add bucket and characteristic based inequality constraints\n", + "for j in range(len(J)):\n", + " for l in range(len(L)):\n", + " if len(KC[l]) == 0:\n", + " continue \n", + " syl = []\n", + " sxl = []\n", + " for i in range(len(KC[l])):\n", + " # syl.append((KC[l][i][CID], BETA))\n", + " sxl.append((KC[l][i][CID], KC[l][i][PMV]*KC[l][i][DELTA]*BETA*how_much_bond(KC[l][i])))\n", + " print(l, j, KB[j][l])\n", + " # print(syl)\n", + " print(sxl)\n", + " # bqm.add_linear_inequality_constraint(syl, lb = KB[j][l][K_LOW], ub = KB[j][l][K_HIGH], lagrange_multiplier=np.sqrt(np.dot(lm,lm)), label=\"Sy_{0:s}_{1:s}\".format(J[j], L[l]))\n", + " bqm.add_linear_inequality_constraint(sxl, lb = KB[j][l][K_LOW], ub = KB[j][l][K_HIGH], lagrange_multiplier=np.dot(lm,lm), label=\"Sx_{0:s}_{1:s}\".format(J[j], L[l]))" + ] + }, + { + "cell_type": "markdown", + "id": "b43289de", + "metadata": {}, + "source": [ + "## D-WAVE Experiment (Part 4)\n", + "\n", + "We use the D-WAVE sampler to perform quantum annealing. The result will be exported as a CSV file. The process can take three minutes to complete a 65536-reads run across the 31-bonds set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "47dad1ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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65536 rows × 40 columns

\n", + "
" + ], + "text/plain": [ + " 020002BJ9 026874DS3 081437AT2 097023CJ2 13645RAD6 13645RBF0 \\\n", + "33317 1 1 1 1 0 0 \n", + "34287 1 1 1 1 0 0 \n", + "3268 1 1 1 1 0 0 \n", + "46530 1 1 1 1 0 0 \n", + "59865 1 1 1 1 0 0 \n", + "... ... ... ... ... ... ... \n", + "1687 1 1 0 1 0 0 \n", + "11107 0 1 1 0 0 0 \n", + "8790 1 0 0 1 0 0 \n", + "14852 1 0 1 1 0 0 \n", + "28158 1 0 0 1 0 0 \n", + "\n", + " 14448CBC7 15135BAW1 21871XAS8 24422EWZ8 ... 91324PEJ7 slack_Sx_0 \\\n", + "33317 1 1 1 1 ... 0 0 \n", + "34287 1 1 1 1 ... 0 0 \n", + "3268 1 1 1 1 ... 0 0 \n", + "46530 1 1 1 1 ... 0 0 \n", + "59865 1 1 1 1 ... 0 0 \n", + "... ... ... ... ... ... ... ... \n", + "1687 0 0 1 1 ... 0 0 \n", + "11107 1 1 1 0 ... 1 0 \n", + "8790 1 1 1 0 ... 1 0 \n", + "14852 0 1 1 1 ... 0 0 \n", + "28158 0 1 1 1 ... 1 0 \n", + "\n", + " slack_Sx_1 slack_Sx_2 slack_Sx_fund_enriched.pmv_I_Capital_Goods_0 \\\n", + "33317 0 0 1 \n", + "34287 0 0 1 \n", + "3268 0 0 1 \n", + "46530 0 0 1 \n", + "59865 0 0 1 \n", + "... ... ... ... \n", + "1687 0 1 1 \n", + "11107 0 1 1 \n", + "8790 0 1 1 \n", + "14852 0 1 1 \n", + "28158 0 1 1 \n", + "\n", + " slack_Sx_fund_enriched.pmv_I_Insurance_0 \\\n", + "33317 1 \n", + "34287 1 \n", + "3268 1 \n", + "46530 1 \n", + "59865 1 \n", + "... ... \n", + "1687 1 \n", + "11107 1 \n", + "8790 1 \n", + "14852 1 \n", + "28158 1 \n", + "\n", + " slack_Sx_fund_enriched.pmv_I_Transportation_0 \\\n", + "33317 1 \n", + "34287 1 \n", + "3268 1 \n", + "46530 1 \n", + "59865 1 \n", + "... ... \n", + "1687 1 \n", + "11107 1 \n", + "8790 1 \n", + "14852 1 \n", + "28158 1 \n", + "\n", + " slack_Sx_fund_enriched.pmv_I_Transportation_1 energy \\\n", + "33317 1 -48.657070 \n", + "34287 1 -48.657070 \n", + "3268 1 -48.448508 \n", + "46530 1 -48.071554 \n", + "59865 1 -48.071554 \n", + "... ... ... \n", + "1687 1 8254.743251 \n", + "11107 1 8261.789708 \n", + "8790 1 8281.910795 \n", + "14852 1 8297.132884 \n", + "28158 1 8299.421078 \n", + "\n", + " num_occurrences \n", + "33317 1 \n", + "34287 1 \n", + "3268 1 \n", + "46530 1 \n", + "59865 1 \n", + "... ... \n", + "1687 1 \n", + "11107 1 \n", + "8790 1 \n", + "14852 1 \n", + "28158 1 \n", + "\n", + "[65536 rows x 40 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dwave.samplers import PathIntegralAnnealingSampler\n", + "sampler = PathIntegralAnnealingSampler()\n", + "sampleset = sampler.sample(bqm, num_reads=READS)\n", + "df_s = sampleset.to_pandas_dataframe().sort_values(by=\"energy\")\n", + "df_s.to_csv(\"dwave_result.csv\")\n", + "df_s" + ] + }, + { + "cell_type": "markdown", + "id": "948587ed", + "metadata": {}, + "source": [ + "## Sanity Check (Part 5)\n", + "\n", + "Determine the feasibility of each of the solutions. Consider the objective function, the constraints, and whether the feasibility has been satisfied. The results, ranked by D-WAVE energy, will be graphed, along with the shaded region indicating the satisfaction of the constraints." + ] + }, + { + "cell_type": "markdown", + "id": "41421e10", + "metadata": {}, + "source": [ + "### Feasibility summary\n", + "\n", + "The five samples with the lowest energy will be summarized to check if any constraints are violated. The structure is of the form:\n", + "\n", + "* Header with D-WAVE energy and selected bonds\n", + " * Global values\n", + " * All bonds\n", + " * Optimization function\n", + " * D-WAVE energy\n", + " * Bonds and Flow\n", + " * Additional bounds\n", + " * Applicable bonds\n", + " * Value against Target" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e4da574e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[5m\u001b[1;32mSolution #1 (-48): ['081437AT2' '097023CJ2' '14448CBC7' '24422EWZ8' '438516CM6' '443201AC2' '45687VAB2' '539830CD9' '75513EAD3' '760759BC3' '020002BJ9' '026874DS3' '15135BAW1' '21871XAS8' '444859BR2' '444859BV3' '444859BY7' '444859CA8' '56501RAN6' '91324PEJ7' '13645RBF0' '314353AA1']\u001b[0m\n", + "\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.033\t0.571\t('24422EXP9', 5.0, 30.0, 30.0, 0.032622048628, 16.111012630144, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.557\t('36166NAK9', 5.0, 30.0, 30.0, 0.031825968182, 14.941828851305, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t8.500\t0.034\t0.285\t('655844CT3', 5.0, 12.0, 12.0, 0.033512025872, 0.033512025872, 1, 'Transportation', [1])\n", + "\t17.500\t0.030\t0.517\t('759351AP4', 5.0, 30.0, 30.0, 0.029540913811, 11.974870385817, 1, 'Insurance', [1])\n", + "\t17.500\t0.026\t0.462\t('760759BA7', 5.0, 30.0, 30.0, 0.026425255721, 15.388018208694, 1, 'Capital_Goods', [1])\n", + "\u001b[0;33m\tEnergy:\t-1.228\u001b[0m\n", + "\u001b[0;32m\tBonds:\t22\u001b[0m\n", + "\u001b[0;32m\tFlow:\t10.399\u001b[0m\n", + "\t\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t\t16.000\t0.028\t0.452\t('75513EAD3', 5.0, 27.0, 27.0, 0.028236246188, 9.403495008104, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('760759BC3', 5.0, 30.0, 30.0, 0.032050068416, 18.718284601554, 1, 'Capital_Goods', [1])\n", + "\u001b[0;31m\t\tBonds[I_Capital_Goods][fund_enriched.pmv]: Σ=4.381 Δ=-1.422 Δ²=2.022 (4.759, 5.803, 6.848)\u001b[0m\n", + "\t\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t\t13.000\t0.031\t0.408\t('91324PEJ7', 5.0, 21.0, 21.0, 0.031401384436, 11.756014097259, 1, 'Insurance', [1])\n", + "\u001b[0;32m\t\tBonds[I_Insurance][fund_enriched.pmv]: Σ=5.046 Δ=-0.133 Δ²=0.018 (4.134, 5.179, 6.224)\u001b[0m\n", + "\t\t23.000\t0.028\t0.641\t('13645RBF0', 5.0, 41.0, 41.0, 0.027884438059, 0.027884438059, 1, 'Transportation', [1])\n", + "\t\t32.500\t0.021\t0.695\t('314353AA1', 5.0, 60.0, 60.0, 0.021396294681, 0.021396294681, 1, 'Transportation', [1])\n", + "\u001b[0;32m\t\tBonds[I_Transportation][fund_enriched.pmv]: Σ=1.337 Δ=0.052 Δ²=0.003 (0.240, 1.285, 2.329)\u001b[0m\n", + "\n", + "\u001b[5m\u001b[1;32mSolution #1 (-48): ['081437AT2' '097023CJ2' '14448CBC7' '24422EWZ8' '438516CM6' '443201AC2' '45687VAB2' '539830CD9' '75513EAD3' '760759BC3' '020002BJ9' '026874DS3' '15135BAW1' '21871XAS8' '444859BR2' '444859BV3' '444859BY7' '444859CA8' '56501RAN6' '91324PEJ7' '13645RBF0' '314353AA1']\u001b[0m\n", + "\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.033\t0.571\t('24422EXP9', 5.0, 30.0, 30.0, 0.032622048628, 16.111012630144, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.557\t('36166NAK9', 5.0, 30.0, 30.0, 0.031825968182, 14.941828851305, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t8.500\t0.034\t0.285\t('655844CT3', 5.0, 12.0, 12.0, 0.033512025872, 0.033512025872, 1, 'Transportation', [1])\n", + "\t17.500\t0.030\t0.517\t('759351AP4', 5.0, 30.0, 30.0, 0.029540913811, 11.974870385817, 1, 'Insurance', [1])\n", + "\t17.500\t0.026\t0.462\t('760759BA7', 5.0, 30.0, 30.0, 0.026425255721, 15.388018208694, 1, 'Capital_Goods', [1])\n", + "\u001b[0;33m\tEnergy:\t-1.228\u001b[0m\n", + "\u001b[0;32m\tBonds:\t22\u001b[0m\n", + "\u001b[0;32m\tFlow:\t10.399\u001b[0m\n", + "\t\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t\t16.000\t0.028\t0.452\t('75513EAD3', 5.0, 27.0, 27.0, 0.028236246188, 9.403495008104, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('760759BC3', 5.0, 30.0, 30.0, 0.032050068416, 18.718284601554, 1, 'Capital_Goods', [1])\n", + "\u001b[0;31m\t\tBonds[I_Capital_Goods][fund_enriched.pmv]: Σ=4.381 Δ=-1.422 Δ²=2.022 (4.759, 5.803, 6.848)\u001b[0m\n", + "\t\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t\t13.000\t0.031\t0.408\t('91324PEJ7', 5.0, 21.0, 21.0, 0.031401384436, 11.756014097259, 1, 'Insurance', [1])\n", + "\u001b[0;32m\t\tBonds[I_Insurance][fund_enriched.pmv]: Σ=5.046 Δ=-0.133 Δ²=0.018 (4.134, 5.179, 6.224)\u001b[0m\n", + "\t\t23.000\t0.028\t0.641\t('13645RBF0', 5.0, 41.0, 41.0, 0.027884438059, 0.027884438059, 1, 'Transportation', [1])\n", + "\t\t32.500\t0.021\t0.695\t('314353AA1', 5.0, 60.0, 60.0, 0.021396294681, 0.021396294681, 1, 'Transportation', [1])\n", + "\u001b[0;32m\t\tBonds[I_Transportation][fund_enriched.pmv]: Σ=1.337 Δ=0.052 Δ²=0.003 (0.240, 1.285, 2.329)\u001b[0m\n", + "\n", + "\u001b[5m\u001b[1;32mSolution #1 (-48): ['081437AT2' '097023CJ2' '14448CBC7' '24422EWZ8' '438516CM6' '443201AC2' '45687VAB2' '539830CD9' '75513EAD3' '760759BC3' '020002BJ9' '026874DS3' '15135BAW1' '21871XAS8' '444859BR2' '444859BV3' '444859BY7' '444859CA8' '540424AT5' '91324PEJ7' '13645RBF0' '314353AA1']\u001b[0m\n", + "\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.033\t0.571\t('24422EXP9', 5.0, 30.0, 30.0, 0.032622048628, 16.111012630144, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.557\t('36166NAK9', 5.0, 30.0, 30.0, 0.031825968182, 14.941828851305, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t8.500\t0.034\t0.285\t('655844CT3', 5.0, 12.0, 12.0, 0.033512025872, 0.033512025872, 1, 'Transportation', [1])\n", + "\t17.500\t0.030\t0.517\t('759351AP4', 5.0, 30.0, 30.0, 0.029540913811, 11.974870385817, 1, 'Insurance', [1])\n", + "\t17.500\t0.026\t0.462\t('760759BA7', 5.0, 30.0, 30.0, 0.026425255721, 15.388018208694, 1, 'Capital_Goods', [1])\n", + "\u001b[0;33m\tEnergy:\t-1.212\u001b[0m\n", + "\u001b[0;32m\tBonds:\t22\u001b[0m\n", + "\u001b[0;32m\tFlow:\t10.400\u001b[0m\n", + "\t\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t\t16.000\t0.028\t0.452\t('75513EAD3', 5.0, 27.0, 27.0, 0.028236246188, 9.403495008104, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('760759BC3', 5.0, 30.0, 30.0, 0.032050068416, 18.718284601554, 1, 'Capital_Goods', [1])\n", + "\u001b[0;31m\t\tBonds[I_Capital_Goods][fund_enriched.pmv]: Σ=4.381 Δ=-1.422 Δ²=2.022 (4.759, 5.803, 6.848)\u001b[0m\n", + "\t\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t\t13.000\t0.031\t0.408\t('91324PEJ7', 5.0, 21.0, 21.0, 0.031401384436, 11.756014097259, 1, 'Insurance', [1])\n", + "\u001b[0;32m\t\tBonds[I_Insurance][fund_enriched.pmv]: Σ=5.048 Δ=-0.131 Δ²=0.017 (4.134, 5.179, 6.224)\u001b[0m\n", + "\t\t23.000\t0.028\t0.641\t('13645RBF0', 5.0, 41.0, 41.0, 0.027884438059, 0.027884438059, 1, 'Transportation', [1])\n", + "\t\t32.500\t0.021\t0.695\t('314353AA1', 5.0, 60.0, 60.0, 0.021396294681, 0.021396294681, 1, 'Transportation', [1])\n", + "\u001b[0;32m\t\tBonds[I_Transportation][fund_enriched.pmv]: Σ=1.337 Δ=0.052 Δ²=0.003 (0.240, 1.285, 2.329)\u001b[0m\n", + "\n", + "\u001b[5m\u001b[1;32mSolution #1 (-48): ['081437AT2' '097023CJ2' '14448CBC7' '24422EWZ8' '438516CM6' '443201AC2' '45687VAB2' '539830CD9' '75513EAD3' '760759BC3' '020002BJ9' '026874DS3' '15135BAW1' '21871XAS8' '444859BR2' '444859BY7' '444859CA8' '540424AT5' '56501RAN6' '91324PEJ7' '13645RBF0' '314353AA1']\u001b[0m\n", + "\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.033\t0.571\t('24422EXP9', 5.0, 30.0, 30.0, 0.032622048628, 16.111012630144, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.557\t('36166NAK9', 5.0, 30.0, 30.0, 0.031825968182, 14.941828851305, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t8.500\t0.034\t0.285\t('655844CT3', 5.0, 12.0, 12.0, 0.033512025872, 0.033512025872, 1, 'Transportation', [1])\n", + "\t17.500\t0.030\t0.517\t('759351AP4', 5.0, 30.0, 30.0, 0.029540913811, 11.974870385817, 1, 'Insurance', [1])\n", + "\t17.500\t0.026\t0.462\t('760759BA7', 5.0, 30.0, 30.0, 0.026425255721, 15.388018208694, 1, 'Capital_Goods', [1])\n", + "\u001b[0;33m\tEnergy:\t-1.780\u001b[0m\n", + "\u001b[0;32m\tBonds:\t22\u001b[0m\n", + "\u001b[0;32m\tFlow:\t10.359\u001b[0m\n", + "\t\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t\t16.000\t0.028\t0.452\t('75513EAD3', 5.0, 27.0, 27.0, 0.028236246188, 9.403495008104, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('760759BC3', 5.0, 30.0, 30.0, 0.032050068416, 18.718284601554, 1, 'Capital_Goods', [1])\n", + "\u001b[0;31m\t\tBonds[I_Capital_Goods][fund_enriched.pmv]: Σ=4.381 Δ=-1.422 Δ²=2.022 (4.759, 5.803, 6.848)\u001b[0m\n", + "\t\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t\t13.000\t0.031\t0.408\t('91324PEJ7', 5.0, 21.0, 21.0, 0.031401384436, 11.756014097259, 1, 'Insurance', [1])\n", + "\u001b[0;32m\t\tBonds[I_Insurance][fund_enriched.pmv]: Σ=4.992 Δ=-0.187 Δ²=0.035 (4.134, 5.179, 6.224)\u001b[0m\n", + "\t\t23.000\t0.028\t0.641\t('13645RBF0', 5.0, 41.0, 41.0, 0.027884438059, 0.027884438059, 1, 'Transportation', [1])\n", + "\t\t32.500\t0.021\t0.695\t('314353AA1', 5.0, 60.0, 60.0, 0.021396294681, 0.021396294681, 1, 'Transportation', [1])\n", + "\u001b[0;32m\t\tBonds[I_Transportation][fund_enriched.pmv]: Σ=1.337 Δ=0.052 Δ²=0.003 (0.240, 1.285, 2.329)\u001b[0m\n", + "\n", + "\u001b[5m\u001b[1;32mSolution #1 (-48): ['081437AT2' '097023CJ2' '14448CBC7' '24422EWZ8' '438516CM6' '443201AC2' '45687VAB2' '539830CD9' '75513EAD3' '760759BC3' '020002BJ9' '026874DS3' '15135BAW1' '21871XAS8' '444859BR2' '444859BY7' '444859CA8' '540424AT5' '56501RAN6' '91324PEJ7' '13645RBF0' '314353AA1']\u001b[0m\n", + "\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.033\t0.571\t('24422EXP9', 5.0, 30.0, 30.0, 0.032622048628, 16.111012630144, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.557\t('36166NAK9', 5.0, 30.0, 30.0, 0.031825968182, 14.941828851305, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.578\t('444859BV3', 5.0, 30.0, 30.0, 0.033043461247, 27.751655496693, 1, 'Insurance', [1])\n", + "\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t8.500\t0.034\t0.285\t('655844CT3', 5.0, 12.0, 12.0, 0.033512025872, 0.033512025872, 1, 'Transportation', [1])\n", + "\t17.500\t0.030\t0.517\t('759351AP4', 5.0, 30.0, 30.0, 0.029540913811, 11.974870385817, 1, 'Insurance', [1])\n", + "\t17.500\t0.026\t0.462\t('760759BA7', 5.0, 30.0, 30.0, 0.026425255721, 15.388018208694, 1, 'Capital_Goods', [1])\n", + "\u001b[0;33m\tEnergy:\t-1.780\u001b[0m\n", + "\u001b[0;32m\tBonds:\t22\u001b[0m\n", + "\u001b[0;32m\tFlow:\t10.359\u001b[0m\n", + "\t\t17.500\t0.029\t0.499\t('081437AT2', 5.0, 30.0, 30.0, 0.028510851146, 13.316192340035, 1, 'Capital_Goods', [1])\n", + "\t\t9.000\t0.027\t0.246\t('097023CJ2', 5.0, 13.0, 13.0, 0.027298851045, 34.461503917596, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.034\t0.594\t('14448CBC7', 5.0, 30.0, 30.0, 0.033939846355, 22.473301347308, 1, 'Capital_Goods', [1])\n", + "\t\t11.000\t0.033\t0.360\t('24422EWZ8', 5.0, 17.0, 17.0, 0.032714779285, 7.4190547577, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.554\t('438516CM6', 5.0, 30.0, 30.0, 0.031667951458, 13.522903255546, 1, 'Capital_Goods', [1])\n", + "\t\t10.000\t0.032\t0.319\t('443201AC2', 5.0, 15.0, 15.0, 0.031871754859, 14.154534858858, 1, 'Capital_Goods', [1])\n", + "\t\t7.000\t0.034\t0.236\t('45687VAB2', 5.0, 9.0, 9.0, 0.033698227268, 20.092890519524, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('539830CD9', 5.0, 30.0, 30.0, 0.032078431567, 15.855019693051, 1, 'Capital_Goods', [1])\n", + "\t\t16.000\t0.028\t0.452\t('75513EAD3', 5.0, 27.0, 27.0, 0.028236246188, 9.403495008104, 1, 'Capital_Goods', [1])\n", + "\t\t17.500\t0.032\t0.561\t('760759BC3', 5.0, 30.0, 30.0, 0.032050068416, 18.718284601554, 1, 'Capital_Goods', [1])\n", + "\u001b[0;31m\t\tBonds[I_Capital_Goods][fund_enriched.pmv]: Σ=4.381 Δ=-1.422 Δ²=2.022 (4.759, 5.803, 6.848)\u001b[0m\n", + "\t\t17.500\t0.026\t0.461\t('020002BJ9', 5.0, 30.0, 30.0, 0.02633418857, 11.747405182296, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('026874DS3', 5.0, 30.0, 30.0, 0.032168634542, 19.306390960602, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.028\t0.487\t('15135BAW1', 5.0, 30.0, 30.0, 0.027845453268, 21.626486622322, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.583\t('21871XAS8', 5.0, 30.0, 30.0, 0.033333333333, 26.975300311131, 1, 'Insurance', [1])\n", + "\t\t11.500\t0.026\t0.300\t('444859BR2', 5.0, 18.0, 18.0, 0.026085891521, 19.713755048306, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.033\t0.580\t('444859BY7', 5.0, 30.0, 30.0, 0.033169141002, 30.60969003408, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.032\t0.563\t('444859CA8', 5.0, 30.0, 30.0, 0.032189269112, 19.3556173304, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.523\t('540424AT5', 5.0, 30.0, 30.0, 0.029904973177, 10.363425862747, 1, 'Insurance', [1])\n", + "\t\t17.500\t0.030\t0.522\t('56501RAN6', 5.0, 30.0, 30.0, 0.029814829851, 13.086051978345, 1, 'Insurance', [1])\n", + "\t\t13.000\t0.031\t0.408\t('91324PEJ7', 5.0, 21.0, 21.0, 0.031401384436, 11.756014097259, 1, 'Insurance', [1])\n", + "\u001b[0;32m\t\tBonds[I_Insurance][fund_enriched.pmv]: Σ=4.992 Δ=-0.187 Δ²=0.035 (4.134, 5.179, 6.224)\u001b[0m\n", + "\t\t23.000\t0.028\t0.641\t('13645RBF0', 5.0, 41.0, 41.0, 0.027884438059, 0.027884438059, 1, 'Transportation', [1])\n", + "\t\t32.500\t0.021\t0.695\t('314353AA1', 5.0, 60.0, 60.0, 0.021396294681, 0.021396294681, 1, 'Transportation', [1])\n", + "\u001b[0;32m\t\tBonds[I_Transportation][fund_enriched.pmv]: Σ=1.337 Δ=0.052 Δ²=0.003 (0.240, 1.285, 2.329)\u001b[0m\n", + "\n" + ] + } + ], + "source": [ + "answers = df_s[0:CUTOFF].to_numpy()[:,:len(C)]\n", + "totalenergies = df_s[0:CUTOFF].to_numpy()[:,-2]\n", + "energies = np.zeros(CUTOFF)\n", + "cashflow = np.zeros(CUTOFF)\n", + "characteristic = np.zeros((len(L), len(J), CUTOFF))\n", + "c_ids = np.array(list(indexmap.keys()))\n", + "\n", + "def _rangecolor_binary(lb, val, ub):\n", + " if val < lb or val > ub:\n", + " return OUT\n", + " return IN\n", + "\n", + "def _rangecolor(lb, val, ub):\n", + " if np.isnan(val):\n", + " return \"\\033[0;30m\"\n", + " if val < lb:\n", + " return OUT_LOW\n", + " if val > ub:\n", + " return OUT_HIGH\n", + " return IN\n", + "\n", + "def _printresult(a_idx, s):\n", + " if a_idx < 5:\n", + " print(s)\n", + "\n", + "for a_idx in range(min(len(answers),CUTOFF)):\n", + " answer = answers[a_idx]\n", + " totalenergy = np.trunc(totalenergies[a_idx]).astype(int)\n", + " ids = c_ids[answer.astype(bool)]\n", + " _printresult(a_idx, f\"{HEADER}Solution #1 ({totalenergy}): {ids}{ENDCOLOR}\")\n", + " energy = np.dot(answer.T, matrix @ answer) + np.sum(constantterm)\n", + " energies[a_idx] = energy\n", + " bonds = np.dot(np.ones(len(C)), answer).astype(int)\n", + "\n", + " for c_id in ids:\n", + " c = C[indexmap[c_id]]\n", + " x = how_much_bond(c)\n", + " coeff = c[PMV]*c[DELTA]*how_much_bond(c)\n", + " cashflow[a_idx] += coeff\n", + " _printresult(a_idx, \"\\t{0:.3f}\\t{1:.3f}\\t{2:.3f}\\t{3:s}\".format(x, c[PMV], coeff, str(c)))\n", + " _printresult(a_idx, YELLOW + \"\\tEnergy:\\t{0:.3f}\".format(energy) + ENDCOLOR)\n", + " _printresult(a_idx, _rangecolor_binary(0,bonds,N) + \"\\tBonds:\\t{0:d}\".format(bonds) + ENDCOLOR)\n", + " _printresult(a_idx, _rangecolor_binary(R[0],cashflow[a_idx],R[1]) + \"\\tFlow:\\t{0:.3f}\".format(cashflow[a_idx]) + ENDCOLOR)\n", + "\n", + " for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " KCl = KC[lIdx]\n", + " for c in KCl:\n", + " if not c[CID] in ids:\n", + " continue\n", + " x = how_much_bond(c)*(c[CID] in ids)\n", + " kcoeff = c[PMV]*c[DELTA]*c[-1][jIdx]*how_much_bond(c)\n", + " characteristic[lIdx, jIdx, a_idx] += kcoeff\n", + " # bcoeff = c[DXS]/100*c[DELTA]*c[-1][jIdx]*how_much_bond(c)\n", + " # bucketflow += bcoeff\n", + " _printresult(a_idx, \"\\t\\t{0:.3f}\\t{1:.3f}\\t{2:.3f}\\t{3:s}\".format(x, c[PMV], kcoeff, str(c)))\n", + " k_diff = characteristic[lIdx, jIdx, a_idx] - KB[jIdx][lIdx][K_TARGET]\n", + " _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][K_LOW], characteristic[lIdx, jIdx, a_idx], KB[jIdx][lIdx][K_HIGH]) \n", + " + \"\\t\\tBonds[{1:s}][{2:s}]: Σ={0:.3f} Δ={6:.3f} Δ²={7:.3f} ({3:.3f}, {4:.3f}, {5:.3f})\"\n", + " .format(characteristic[lIdx, jIdx, a_idx], L[lIdx], J[jIdx], \n", + " KB[jIdx][lIdx][K_LOW], KB[jIdx][lIdx][K_TARGET], KB[jIdx][lIdx][K_HIGH],\n", + " k_diff, k_diff*k_diff) + ENDCOLOR)\n", + " # _printresult(a_idx, _rangecolor(KB[jIdx][lIdx][B_LOW], bucketflow, KB[jIdx][lIdx][B_HIGH]) \n", + " # + \"\\tFlow[{1:s}][{2:s}]: {0:.3f} ({3:.3f}, {4:.3f})\".format(\n", + " # bucketflow, L[lIdx], J[jIdx], KB[jIdx][lIdx][B_LOW], KB[jIdx][lIdx][B_HIGH]) + ENDCOLOR)\n", + " _printresult(a_idx, \"\")" + ] + }, + { + "cell_type": "markdown", + "id": "1f368cd1", + "metadata": {}, + "source": [ + "### Ranking and visualization of bond allocation\n", + "\n", + "The top graph represent the total energy of the ranked samples. The middle graph contains information about the bonds used (blue) and the cashflow (magenta). The bottom represents the selection of the securities, ordered by index, with filled indicating the security is selected." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "063fa33e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T = np.linspace(0, CUTOFF, CUTOFF)\n", + "\n", + "fig = plt.subplots(3,1, figsize=(8, 9))\n", + "\n", + "plt.subplot(3,1,1)\n", + "plt.title(\"N={0:d}, R={1:s}\".format(N, str(R)))\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), totalenergies)\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), energies)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"energy\")\n", + "\n", + "plt.subplot(3,1,2)\n", + "plt.plot(np.linspace(0, CUTOFF, CUTOFF), cashflow)\n", + "plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), R[1], R[0], alpha=0.36)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"cashflow\")\n", + "\n", + "plt.subplot(3,1,3)\n", + "plt.pcolormesh(answers.transpose(), cmap=mpl.colormaps[\"Greys\"])\n", + "plt.gca().plot(T, N*np.ones(CUTOFF), c='r', linewidth=2)\n", + "plt.gca().fill_between(np.linspace(0, CUTOFF, CUTOFF), answers.sum(axis=1), alpha=0.36)\n", + "plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + "plt.gca().set(ylabel=\"count\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5f03bb81", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eYq39VqxYgaKiItngVK+etm7dGg888AAeeOAB7N+/H927d8eLL76Ijz/+OKjyE8SFAmmICaKO0bp1a+zZs0cWHmz79u2qsGbBkp6eju7du+ODDz5Afn6+tH358uWSPjFYevXqhdatW+Pf//43ioqKVN9rhTQLJ0ajEVdddRW+/fZbzbTMoXrFR48eDbvdjjlz5sDpdKq+59cT6j08ePAgDh48qNp+6tQpWai2goICfPjhh+jevXtYp6mNRiOuueYafPvtt/joo4/gcrmqRS7BmT17NhhjuPnmmzXrxZYtW6TwYCaTCQaDQTbjceTIESxZskTz2OvWrZNJUo4fP46vv/4al1xyScDYw9HR0Zqhxkwmk6qeLFiwACdPngx0iUFx7bXXolOnTnjqqaewfv16zX2U577sssuwceNGrFu3TtpWXFyMt99+Gy1atJA06pdddhlOnz4t04a7XC68+uqriImJkUI4XnbZZXC5XJg3b560n9vtxquvvio7b0lJCcrKymTbWrdujdjYWJmMhSAaCuQhJog6xi233IKXXnoJo0ePxvTp05GdnY0333wTnTt3Vi1EC5a5c+di7NixGDRoEG655Rbk5ubi1VdfRefOnTUNGD2MRiPeffddjBkzBp07d8a0adPQpEkTnDx5Er/88gvsdrsUeqq6mDNnDn766ScMGTIEt912Gzp27IisrCwsWLAAa9euDSnJid1ux7x583DzzTejZ8+euO6665CSkoJjx47hu+++w8CBA/Haa68BCO0ejhgxAgBU8VzbtWuH6dOnY9OmTUhNTcV7772HM2fO6HpHq8KkSZPw6quvYvbs2ejSpYukNRUZOnQofv311yrLawYMGIDXX38dd911Fzp06CDLVLdq1Sp88803ePrppwEAY8eOxUsvvYRLL70UN9xwA7Kzs/H666+jTZs22LFjh+rYmZmZGD16tCzsGuCNfRyIXr16Yd68eXj66afRpk0bNGrUCMOHD8fll1+Of/3rX5g2bRoGDBiAnTt34pNPPkGrVq2qdA8A7yK3xYsXY/To0Rg0aBAmTJggxbc+efIkvvnmGxw7dkwWWu7hhx/GZ599hjFjxmDmzJlITEzEBx98gMOHD+Orr76SvMG33XYb3nrrLUydOhVbtmxBixYtsHDhQvz22294+eWXpUWN48aNw8CBA/Hwww/jyJEjUqxrcSAHeL3sI0aMkIx4s9mMxYsX48yZM2EPzUcQ9YLaCW5BEARj2mHXGGPs448/Zq1atWIWi4V1796dLVu2TDfsmlbYJChClTHG2FdffcU6duzIrFYr69SpE1u0aJHqmMGydetWNmHCBJaUlMSsVitr3rw5u/baa9mKFSukfXjYtbNnz8p+qxUiCwCbMWOG5rm0ruXo0aNs8uTJLCUlhVmtVtaqVSs2Y8YM5nA4ZOdQhmbj9/uXX35RbR89ejSLi4tjNpuNtW7dmk2dOlUW7oux4O9h8+bNNbeNHTuWLVu2jHXt2pVZrVbWoUMH1bPXK7ve/ZwyZQqLjo5W3TePx8MyMjIYAPb000+rvmeMsV69erG0tDTZtlDDrols2bKF3XDDDaxx48YsIiKCJSQksBEjRrAPPvhAFursf//7H2vbtq10D+bPny9dnwivFx9//LG0f48ePVTPT6tOnT59mo0dO5bFxsYyAFIItrKyMvbAAw+w9PR0FhkZyQYOHMjWrVunCndYmbBrnLy8PPavf/2L9ejRg8XExDCLxcIyMjLYNddcowqHxxhjBw8eZNdccw2Lj49nNpuN9e3bly1dulS135kzZ9i0adNYcnIys1gsrEuXLprly8nJYTfffDOz2+0sLi6O3XzzzWzr1q2y6zl37hybMWMG69ChA4uOjmZxcXGsX79+7Msvvwz5egniQsDAWDWsvCEIgiBktGjRApmZmVi6dGltFwWANypEYmIiXn75ZcyYMaO2i6OJwWDAjBkzJC89QRBEdUEaYoIgiAbI6tWr0aRJE9x66621XRSCIIhahzTEBEEAAHJzc1FeXq77vclkCriqn6hfjB07VpUmWUl+fj5KS0sD7tMQYtaWl5cjNzc34D5xcXHVEjqQIIiagQxigiAAABMmTJCyXWnRvHlz1SIx4sLm3nvvlaJD6NEQVHe///47hg0bFnCf+fPnY+rUqTVTIIIgwg5piAmCAOANjRUohWtkZCQGDhxYgyUiaps///wTp06dCrhPbcSjrmnOnz+PLVu2BNync+fOSE9Pr6ESEQQRbsggJgiCIAiCIBo09W5RXYsWLWAwGFT/6uoqaYIgCIIgCKJuU+80xJs2bZJlONq1axdGjRqFiRMnBvV7j8eDU6dOITY2Vje1J0EQBEEQBFF7MMZQWFiIxo0by9KVVxf1XjIxa9YsLF26FPv37w/KwD1x4gQyMjJqoGQEQRAEQRBEVTh+/DiaNm1a7eepdx5ikfLycnz88ce4//77dY1hh8Mhy8vO7f/jx4/DbrfXSDkJgiAIgiCI4CkoKEBGRoaUlry6qdcG8ZIlS5CXlxcw1M3cuXM1c97b7XYyiAmCIAiCIOowNSVvrdeSidGjR8NiseDbb7/V3UfpIeYjjvz8fDKICYIgCIIg6iAFBQWIi4urMXut3nqIjx49ip9//hmLFi0KuJ/VaoXVaq2hUhEEQRAEQRD1jXoXdo0zf/58NGrUqMLUowRBEARBEAQRiHppEHs8HsyfPx9TpkyB2VxvndwEQRAEQRBEHaBeGsQ///wzjh07hltuuaW2i0IQBEEQBEHUc+qle/WSSy5BVdcCMsaqfAyCIAiCIAgi/NS0jVYvDeJwsHTHKUTFFNZ2MQiCIAiCIAgFJUU1a6PVS8kEQRAEQRAEQYQLMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNPXSID558iRuuukmJCUlITIyEl26dMHmzZtru1gEQRAEQRBEPcRc2wUIlfPnz2PgwIEYNmwYfvjhB6SkpGD//v1ISEio7aIRBEEQBEEQ9ZB6ZxA/99xzyMjIwPz586VtLVu2rMUSEQRBEARBEPWZeieZ+Oabb9C7d29MnDgRjRo1Qo8ePfDOO+/o7u9wOFBQUCD7RxAEQRAEQRCcemcQHzp0CPPmzUPbtm2xbNky3HnnnZg5cyY++OADzf3nzp2LuLg46V9GRkYNl5ggCIIgCIKoyxgYY6y2CxEKFosFvXv3xu+//y5tmzlzJjZt2oR169ap9nc4HHA4HNLngoICZGRk4OPVfyIqJrZGykwQBEEQBEEET0lRIW66uBPy8/Nht9ur/Xz1zkOcnp6OTp06ybZ17NgRx44d09zfarXCbrfL/hEEQRAEQRAEp94ZxAMHDsTevXtl2/bt24fmzZvXUokIgiAIgiCI+ky9M4jvu+8+rF+/HnPmzMGBAwfw6aef4u2338aMGTNqu2gEQRAEQRBEPaTeGcR9+vTB4sWL8dlnnyEzMxNPPfUUXn75Zdx44421XTSCIAiCIAiiHlLv4hADwOWXX47LL7+8totBEARBEARBXADUOw8xQRAEQRAEQYQTMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNGQQEwRBEARBEA0aMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokFDBjFBEARBEATRoCGDmCAIgiAIgmjQkEFMEARBEARBNGjIICYIgiAIgiAaNDVuEB84cADLli1DaWkpAIAxVtNFIAiCIAiCIAiJGjOIc3JyMHLkSLRr1w6XXXYZsrKyAADTp0/HAw88EPRxnnjiCRgMBtm/Dh06VFexCYIgCIIgiAucGjOI77vvPpjNZhw7dgxRUVHS9kmTJuHHH38M6VidO3dGVlaW9G/t2rXhLi5BEARBEATRQDDX1Il++uknLFu2DE2bNpVtb9u2LY4ePRrSscxmM9LS0sJZPIIgCIIgCKKBUmMe4uLiYplnmJObmwur1RrSsfbv34/GjRujVatWuPHGG3Hs2DHdfR0OBwoKCmT/CIIgCIIgCIJTYwbx4MGD8eGHH0qfDQYDPB4Pnn/+eQwbNizo4/Tr1w/vv/8+fvzxR8ybNw+HDx/G4MGDUVhYqLn/3LlzERcXJ/3LyMio8rUQBEEQBEEQFw4GVkNhHnbt2oURI0agZ8+eWLlyJa644grs3r0bubm5+O2339C6detKHTcvLw/NmzfHSy+9hOnTp6u+dzgccDgc0ueCggJkZGTg49V/IiomttLXQxAEQRAEQVQPJUWFuOniTsjPz4fdbq/289WYhjgzMxP79u3Da6+9htjYWBQVFWHChAmYMWMG0tPTK33c+Ph4tGvXDgcOHND83mq1hizJIAiCIAiCIBoONWYQA0BcXBweffTRsB6zqKgIBw8exM033xzW4xIEQRAEQRANgxoziFevXh3w+4svvjio4/z973/HuHHj0Lx5c5w6dQqzZ8+GyWTC9ddfH45iEgRBEARBEA2MGjOIhw4dqtpmMBikv91ud1DHOXHiBK6//nrk5OQgJSUFgwYNwvr165GSkhKuohIEQRAEQRANiBoziM+fPy/77HQ6sXXrVjz22GN45plngj7O559/Hu6iEQRBEARBEA2YGjOI4+LiVNtGjRoFi8WC+++/H1u2bKmpohAEQRAEQRCERI3FIdYjNTUVe/fure1iEARBEARBEA2UGvMQ79ixQ/aZMYasrCw8++yz6N69e00VgyAIgiAIgiBk1JhB3L17dxgMBijzgFx00UV47733aqoYEr2aJyK2BgI9EwRBEARBEKFRWBBRo+erMYP48OHDss9GoxEpKSmw2Ww1VQQZjeMjYbdH1sq5CYIgCIIgCH0KjM4aPV+NGcTNmzevqVMRBEEQBEEQRNBUq0H83//+N+h9Z86cWY0lIQiCIAiCIAhtDEwp6g0jLVu2DK4QBgMOHTpUXcWQUVBQgLi4OOTn58NOGmKCIAiCIIg6R03ba9XqIVbqhgmCIAiCIAiirlHrcYgJgiAIgiAIojapsUV1AHDixAl88803OHbsGMrLy2XfvfTSSzVZFIIgCIIgCIIAUIMG8YoVK3DFFVegVatW2LNnDzIzM3HkyBEwxtCzZ8+aKgZBEARBEARByKgxycQjjzyCv//979i5cydsNhu++uorHD9+HEOGDMHEiRNrqhgEQRAEQRAEIaPGDOK//voLkydPBgCYzWaUlpYiJiYG//rXv/Dcc8/VVDEIgiAIgiAIQkaNGcTR0dGSbjg9PR0HDx6Uvjt37lxNFYMgCIIgCIIgZNSYhviiiy7C2rVr0bFjR1x22WV44IEHsHPnTixatAgXXXRRTRWDIAiCIAiCIGTUmEH80ksvoaioCADw5JNPoqioCF988QXatm1LESYIgiAIgiCIWqPGDOI5c+bgpptuAuCVT7z55ps1dWqCIAiCIAiC0KXGDOKzZ8/i0ksvRUpKCq677jrcdNNN6NatW02dXsWOE3mIifUAAFqnRCPWFqG7r9vD8FdWAVqlRCPK4r1lLrcHf2UVws0YTAYDOqbHwmwygjGG/dlFSI6xIjHagsPnilFQ6kSH9FhYzSYAgMPlxp6sQtgjI9AyORqMMWTllyE9zgaDwaBbDsYY9p0pQnKMBS4PQ5HDhcIyl2yfDmmxsEV4z+N0e/BXVgE8Gsm57TYzWqXEyLYVOVw4kF2ERrFWNI6PVJ17f3YRSsrdSIq2ICMxCtkFZTiVXybtExlhQrvUmIDXEAh+nyNMRtVxypxu7DldKH1Os9uQFmdTHaPY4cL+7CLps9EAdEy3IyuvDLkl5bL7U51k5Zciza7/PF1uD/acLkSbRjFVLk9OkQPHz5ciIyESSTHWCvc/nluCnGKvnr9JfCRSYiv+TUUwxnC6oAzpcZEV76zgdH4ZGsVaYTQGrjeHzxUjymJCql393AH/PW2dEoNIS+B7eq7IgVibWXonLzROnC8BADRNiJK2BXufw0F+iROHc4rROM6GRnYb3B6Gc0UO3WdXl+BtTWKUBc2SolRteihk5ZfiTIEDgH6bVVucK3IgxmqukfawMui1oYwx7DldCA9j6Jhmh9FowKm8UrjcDM2SolTHOXS2CIVlLnRMt8NiDm3ZVEm5C/vOFEl9nhZi28fb4maJUSHXlerkVF4psgsdsJqN6JAWW+k++kLHwBjTMJeqh/Pnz2PBggX49NNPsWbNGnTo0AE33ngjbrjhBrRo0aJGysBzY2fM+hJGq7eCp8fZsPah4TDpdBT/XLwTn244hjaNYvDz/UMAAH9fsB0Lt5yQ9hnXrTFevb4HPlp3BI99vRuRESY8fVUmHliwHQAwuG0yPpreDwBw8/82YM1+70LCt2/uhSM5xZjz/R7cP6odZo5oq1v2t1cfxJzv9wS8vq5N4/DN3YMAADM+/QPf7cjS3ffV63tgXLfGALwv9ZAXVuFYrrcj/XHWYHRI8+cO/3DdETz+9W7p83+v74EHvtwGp1tefR67vBOmD2oZsIx6PLJoJz7beAwA8H9jO+Jvg1tJ310973dsOXpe+mw0ACseGIqWydGyY4x4cRUOni2WbbPbzCjwDRx6NIvH4rsGVqp8wfL5xmN4eNFOTB/UEo9d3klzn1mfb8WSbadkz6sy5Jc40f/ZFSgpdyPKYsK6h0cgLkp/cLf5SC6ueXOd9NlqNuL3h4cHZUgH4pFFO/DZxuN45bruuLJ7k6B/t3LPGdzy/mZc3jUdr92gH498+Z9ncOuHm2E0AL/+Y5hm58TfyY7pdvxw72DdY504X4JBz/2C5klR+PUfw4Iua31h2/E8XPX6bwCAJTMGontGvHT/rureGC9f16Naz+9wuTHw2ZU4V1QOs9GAXx8chie+2Y3lf57Bp3/rhwFtkqv1/FXlmnm/Y7Ovrfloel8cPleMx31t+qb/G4kYa3B+pAPZRRj1n1/Be1ijAVj5wFC0ULRZtcGpvFIMeHYlmiZEYu1Dw2u7OCo+2XAUjy7ehb8Naon/U7Sh/1m+D6+s2A8AuGNIa1zdswlG/Wc1AKjq13c7sjDj0z8AAMM7NMJ7U/uEVI5LX14tOWIW3TUAPZslqPbh/dZTV3bGnO/3oNTpRrTFhHX/HAF7AEdbTbHndAHGvLJGqocV2Rl1CW6v5efnw263V/yDKlKjqZsTEhJw2223YdWqVTh69CimTp2Kjz76CG3atKnJYgAAGsfb0MTnBc3KL0NxuUt333UHcwB4GzjOXt9LEutrHPl3/P9SpxtrD/ijZxwUfit6Og+cLZKM3JeW7wtYZvF3nBirGU0TItHY53n4K6sAfIzDy5gSa0XThEjpX4yizADgcHkkYxgA9p/xfwcA+87Iz710+yk43QwRJgOaJkTCbuPHVJcxWMRz/JVVqPldmt0Gi8kID5PfU8DrYebGcOM4m+T5LBC86MrfVAfPfPcXAOB/aw/r7vPrvrMAgB0n8qt0rhN5JSgpdwMASsrdOH6+JOD+h3z3x2o2wmQ0wOHy4GReaZXKAACfbTwOAHj55/0h/e7VlQcAAEsDDNwA4M9TBQAADwOO5mhfI7+nf2UVBDzWyj3ZAPSPU985dNZfx3l9/6/PgFiy7VS1nz+3uBznirwzEC4Pw7GcEiz/8wwA4N0A70RdQZxhOnyuWLqHpU43zhY6gj7OobNFYAywmIxSm3X4XHHFP6wBVu31visnzlf93a8Onlr6JwDt+nJArN9ni2QOkIOK+7tf6I8Ong2t7WeMYa/QJx0+q/3suBPnsa93o9TpbYuLy93ILijT3L+m2XemCKLbc6+GHUF4qVGDmON0OrF582Zs2LABR44cQWpqao2X4af7hmDNg37vkNPl0d1XdKLzv3nFnzawBQDA7fH+3iXoE4odfkOsXPCklvkMGAAoD3BeJWVOt2rbtb0zsPah4fjxvou91+FmcPiOWeo7zzuTe2PtQ8OlfxN7N/WVyX9up1tejlLFuZwuuSeYG1HdM+Kx9qHhuGuYd1DjCOF6lJQK96XI4ZR9x+/Twjv7o2fzeM0yivfy5weGYL6GN0BLPhJ2gpiNihSmKZX3PhSU91t5T5Tw+jm4bQrSfYMoVxhvit4six7BXnthmb8+uIOY1HJV4Z7Wd9zC83T52iWttqO6EN9jQN5+umvkBawaYjvi9jBZfQulXvFr7ZYRh8wmXu9WeR2pl7xe1FUC1RO3W16fxH2VE94uN9P8OxgcLo/MkAym3RGpK1W9VOHsKwng/Gvo1KhB/Msvv+DWW29Famoqpk6dCrvdjqVLl+LEiRMV/7gaMBoNMPs6cOXUv4hYsfnLxxt9rj3mL5v40pUIHYPY8YtGSyjGkLKjAYAIk7f8MRYzuCyI64p5Jxip0IhxHZU4CFBev7IDVZaTG8T8+i0mo+ZxQkE8p6iNZoxJHYnFbJSuR2n8OVz+zxaTUVMbV1c6ArFsRWWVb6CUAypxEKYFb9TNQt33hLHlNodoEAc7ICwQDOJgyltWhYFZfUfst/n7WJWBaqgo30vRkPDUnEKv0ohtndfg8n8XyuDR6eHvmhERUvtYN+plVdrpmiCQQSy24S4Pk31W/s4p2ze0e6/sA0NVl9aVwV+xw3sd3FlRrGFHEF5qbFFdkyZNkJubi0svvRRvv/02xo0bB6u16ot5qorFbISr3B2woRIbcZeHwWzyvywxPqkAbyhlHmJhJMaP73R7ZPuE4iHW8v7xSm40GhBjMaPQ4UJhmRMpsVZpf5VB7GucA3qIFS8N3zcp2oKc4nLklXgNFC6ViPAZ2eWuyr9spToGsdPNpE7eajJJC6aUDRbv9M1GA8wmo+bCqpqwh4MyCYWdCstcSKjkAgyVh7iCxo4bkyajQVpcFc6GO1QPcbD1X6wPeuUVO6zScnfQWs8LDdEA5e+1owrvZago30vxcdV1g9jjYbL2mbHKe7i5N9lsMoC/8HXFIHbXEceAHoFus/h83B6P7JkofyfObIbazqkGdiHesrpS1/l1JMdYcKbAUWEf0ZCpsR7jiSeewMSJExEfH19TpwwK78jdHXAqS6zX/GXklYx3urzxE0ehJQ61J1glRQhhpF7qVJfRbPI7+WNt3CB2gTEmnctmkU8EWDS8FUrDRF1O7/eJPoPYf06vh9gaBg+x3CD2ewTFZ2ON8Ht+lS+2w3d/rD7jXDkQAEKf9qoMwazgFRtn0fsZKioPcQWNnUswiM3VYBBXl4dYZhDrPMMy4f2oSYlAXUMmmagND3G5/FyeeiSZUPYDbsYUEpQQDGLJQ2yQ2gSl9Ky2qOse4kCIz8PpZrJZWeXskdgfh3rNyv4lVAO3jtjD0qxhUrQVZwocAddLNXRqTDJx66231jljGEBQU1lMoSETjc3YoD3EDIwxmX4Y8HZUwRoRyt8CcgOEe6sLy1wy/ZPSMOTeXIcrgIdYx3BPipF7Mu2R3EPsLUcoHm8lcg2xoL8WjmkxVSyZsPq+1zSIPSzkqa9QCSaijdiIF1UgcwiE0vOn1IspkXmIfQUN5yAhZA9xkJ1URZIJ8Z0EKtZSX8iIHTefMnZoDKarC+W9F9+3Ou6YVBnEHsYUkrngL4C/4yajUZK21RkNcT02iF0KDbFL5iFWSCbclR+MKetxqAZxXfEQc+lmsm+ROXmI9amVRXV1CYupYkPOrZBMiMYmN4j5yyYuulDqOZ1upul5DdaI0OrkzSb/b7m3trDMKfOQKbW0WnpfZUOtNL6dkmRCLnPxa4hNmscJFo+HyQx0MTIEN/rMvml+fYNY7iG26sScrG4nVTBPU2yclbGkQ0Fp6JSE4CHm9S6ci+rMxtCalGAlNhV5iJ1uuSevITf64oCBeyTLalAyEWiqua4YCXoo+wGPh8klcyEYktx4jjAZ6pyGuK5LJgKh9NiL16JsG5wBZIEVoZL+hNhO1pXZEN4WJvucWRX1EQ2ZBm8Qc29poJdFOcoUXxRuEPLfiy9BsUNe8crdHk2DOFgPsaZBbBQNYp+H2OGS9hUbY46W3lc5ladnbCqDjfNzcg9IZRt8ZYdd7vJIhnC5wtCVNMSKF5s/F76f0WiALUJdxau7oQpGMiEaocqIGqGgHIBUJJngnbvJUD2L6kL3EFciyoRGeZX1tSF7iEWbjU8Z16QdqnwvZZKJOm4QK9svD6u85MPp9g8+tWRqtYmzjhhrlUG+iM6j0nzL9lUskAwFpfQnVKd6XbnFfKY62RdrnqJM6EMGMV9gFkDb5VRIC/gIy2IySsYXf9kCeV2dLo9qdFbu8gSdOUpbMiFqiLmH2CWdR0s2oKX3VZZVWU7JQ6yUTHAPsWRkV67B1xq1cq8gPyY/h61CD7H/mvVkE7WN6NWomoc4NMkEv3ajsKgurB5iU/VoiAtKAy+qU3pzAnmIxU4znIOBuoLMQ1wLU+PKDlcumajb91tZH5VhvUJ5V/jvIkxilIm6cf31OSyhUiMvflZHmZA/u1DkcoGkP8FQgznPAqL0EDvdrErSxguZhrkMW0A5lXU6vwxLd5yCwWDAVd0bIynGKjMW3R4Gh8+YsUUYpUVt/MULZGw53R5NKUKVPMSCAcIX+C3eegL9WiYCgGakBS29r9JzoRd2LUnHQ1xVDwh/aW0RRpgMBhSXu/HO6kMY0j5FZXT7JRP+eMsL/ziBrcfOA/AuvONERphwHnIPLPdSHT5XjGW7TyPaYsL4nk3x4MLt2HzkPAwGYHL/FpgxrHIJY4J5muLUa1UM4lA9xOJCn+rwEK/Zfw4D5q7A+J5N8I/RHSrcX+vUGw/nYvPRXKTH2XBV9yZY9MdJ2XW+99thJMVYMaRdirRNaQAH6yF2MwajxhMrcriw+I8TSLXbcEnntKCOVVfQijIRDIwx3P3ZVuw4kYcnxnXGiI6Viw+vXPwr6sRdHoaVe85g9je7JblPtNWM56/pij4tEit1vnCivF+MMcWi6uDvJ9dvm4yGsKyxqCovLNuDHSfyMX9qn7AOgkPh7wu2o9zlwSvXdZdm0g5kF+KuT/5AtNWMD2/pKzl29HApjNxAOmFlfgG3hwU9aFdLf0LVEIe0e7Wh9BAD3vYy1DTWDYEGbxBbFFP9z/24B4u3ngQAHM8twRNXdFbokPySiUiLSbVSP1CDqSWZcLiC0xArw7VxRA8xHwHuOlkgZeXR8pBq6X2DTczRNCEKKbFWnC10wGIyol1qrPeYVfQQizGTY20RKM4twVurD2H+70fw3hRvgg3u+eVGPjeCPtlwFE/7ssN59/PfE5vGgIA/q4e+2oGNh3MBAMdyS/D9ztPSPp+sP1p5gziYRXXVpCEOKexamBbVKT0hp/LL8PovB4MyiJWUuzyY8t5Gqf5lJETh2x3y7Gq7ThZgynsbsfWxUVK4OpVkIsB9EJ+P28Og8Yrg0w1HpQySax7UThVdV5F70IJ/H88WOaRU7ws2n6iCQawtZQK8RsLS7Vk4nitkSCt04Mddp+uEQayMxqGKMhGCh5fvW1c0xK//chCAN0udeB2MsaBkXlWlzOnGwi3enAN3DWuNDml2qTz7fJlRtxw9j6HtGwU8jtIjLM62qRJzqKJOeMOmBlVelfQnuN9plbM24W2h3RaBCJMBTjdDidOFONR+Wum6RoMfIlgUGuIzQrrF7MIyuD3KVcZMZryZBIOYMRawwQxmUZ1eg6nn8RJHuzdd1Fz6+7gvDbNWcooIjYWEFcUh5t9HW8345e9D8f3MwVj3yHA09qW/ruqUoBgz+ZnxmZjUOwMmowHlLo/0TJQeYv4c/vB5hjnBSiZyivxpWLPy5Wk280srr+sNxkcsNpZV0RDzDpw/04r0YbKwa6bwhF0Lp7cpv9Qpq+vnisp1F4GIA4nKaoj1rj232P9MsgvrRgrWYBGNglC0omKYyKz8yqf0Vc4uyQxijz/Jzh1DWuP6vhkA6o7xoGy/qqIh9s/GGOuUhrjM5VZEZqiZ84rn3H/Gn0ZZHIQEs+BLvIcuhYY40KI6ZRkqoqpRJuqKZILPGkZZTVJ/SAvrtGnwHmJJQ+xrCGWhmzQSdrg8fi+vLcKECMFD61KEgFHidHvUCS9cHpmXt8zpVi2CA7T1w4B8UV2q3Ya2jWKwP7tI6tC1JBPKQYC3HMpFdcppT7/hFWM1o1Nju+YxKxvvVJJMWEwY3DYFg9um4Oe/ziCnuBw5xV7DlXcqooZ4z+kCmWcXkHuIAxnEYuenfC7F5W643B5ZnOeqcOJ8CaIsZmlRoquKGmKn24NDZ4ulhYfxURacLXRg4+HcgB4faVGd6CGuYo+o98yP55aE7FlVhqArF94Zq9koO5fY+ektsKwIvfdV7PyKHW6UlLvwx9E8XW96m0YxaOIbHFYnDpcbJ8+XolVKjGw7YwzbT+Qj1maWRXVwujzSbBEQeNGj2Ekeyy3B+kM5cHsYejVP0BxY66F8l8Rn4XT7Eyk0SYiU2o26YhBXGGWiEok5TEZDtWmI+XO328xomRyNA9lFaJYUJXMKeM8relHVi8206oXHw7D1eB6Soi1okRxd5bKKKZd/3H0aF7dNwbYTedh2PE/aHoyhJvMQKzTEysej7L/dIdx/lUFczZKJA9mFOJlXho5psWhkt+nu9+epApwtciCzsR1JMerkZvvPFOKU4OA54/s7ymJGlMWMgjKXbPALeOvDgbNFaJ8aWyOzBXp4PAzbTuShsMyFuMgItLTXbFnqtUH87LPP4pFHHsG9996Ll19+uVLH8C+q8+tROSXlblVn73IzaZ9IiwkmwUPrjYkYQDLh8si8y6VOt6qBLHW6NTVU+h5iucHGDeBcnxGpLZnQMIh5ViWjAS5FJA1xXz3dUVWnBLWy6sXYzD6DuFx2bn6NW46ex6Uvr1EdS6Yh1spWx7hBLITI83lW4yIjJO9wQZlLFVUjGMT2hDGG8yVODHruFwDAkWfH+jpZ/z6VSd086/Nt+G5nlvQsE30G8fkSJ1btPYthHbSnHV3CyvdwhV1TLuzjDH7+F2z45wikBmjclSjvhXchqndbrM0MR5E/KYw4VRqKZEK0afUMMXF7SbkL932xDct2n9E9ZpTFhI2Pjqz27Hj/XbEfr/9yEE9flSmbEfpm+ync+/k2AMBV3RtL2w+cLcKIF3+VPtsC6AZLnf57f77EieveXg8AuLpnU7x4bbegy6h+FvLnxNs8s9EAUzXEwq4K6igT6hnCYPEvqjMIjpfweojF5/5/Yzvi6e/+Qr+Wifji9v6y/cT2nAEIFLuX8+Xm43h40U4AwMZ/jghopAWDmEb5ux1Z+GFnlsporGhRMCAvu1OhIVYl5lD0r84QNODKNiTUOhqKR/lAdiFGvrQagDeS04Z/jtB0jG0+kotr3lwHAGiWGIXVDw6Tfb//TCFG/We15jmiLCZEWbmHWH6fH1y4A4u2nsT/je2Ivw1uFXS5w81Xf5zAPxbukD4/N65yssXKUm8N4k2bNuGtt95C165dq3QcpSEnNuZlTi0PMcOa/ecAeI03s0LuEEgycSSnGC//vB+AN6FFqdONcpdH1khqdeSl5W68vfqQ5jGVC/K4J4d7TTUNYg29L198YI+MQG5xuYZkwntdFh2PqbUKGuLc4nK8/ssBVXn5gr0cnxEUKAOdvCz+77U8W7xBLdeYqouMMMHtYShyuJBf6qycQSz87XB58FdWgfTZ42GqhlXpId52PA+/HTineeyUWCuu7tkU3+30aj153RnaIQV7zxQCAA6eLdI1iKsj7FqgWYF5qw7isi7p6NsyEVuPnceGw7kY2yVd13NcqMjaJ3qIY6xmnJMZxP79+DvJWbLtJO4Z0RYA8NPu09ifXYS2jWJwSec0hcdPu+zK8In7s71e1pbJ0ar6t+d0AUrK3Vj0xwnZszQbDRjXrTHOl5Rj1d6ziIuMwDW9mmrWyfPF5Vi09SRap0QH1FByHej/LdklDdzapcZi9yl/HTvmk0sBwIHsItnv46O89Tm7oAxfb/Nqs6/s3hiN7DZd79zuU/m65VGy/XiepBPliCEVs/LLpAGS2WgAb07qSvQJdZQJddzbYPGHXTNKi+qUi7wA78B+/aEcAN72akKPJkGncj+W43/Wc3/wat43+NZFiIj92tr9Z2Xti57htl+oO1n5ZQEN4u3H85BbXK7b7gDqwYTWrQzZQ6zQEKsScyhOojWgOXKuGD/sOi39NjHagu4Z8XhlxX7N8m4+kostR89jXLfGSAtwT95afRA7T+ajaUIkrujWWOV5dbo9+GrLCeQUl8vqXW6xVyYWF6nua8X3+VhuiWo28ODZYgBe47dJfKTsGcZYzVLfqByYLfKtm3r9lwMqg/jQ2SL8uPs0IiNMGNgmGb/syYbLw3SvqyocOlcs+3z8fInOntVDvTSIi4qKcOONN+Kdd97B008/HXBfh8MBh8OvFS0oKJB9b+ENFTeIhReyVMMgdro9+Gj9UQDeSicapMqsOUoeW7IL50u8nVijWBvOFDjgdHtk59DKWrZwy3F8suGY5jGVBjHvrHmF50aliNb0HS+D3Wb2GsSqRXUe2W/1jxm6QTxv1QFsOuLVAcdH+b3j3NvGtb7ckBf3AYBh7VOQ2SQOr670GtWiZCIuUu1t552v2CjwJCpmkwF2m1kyiCuD2D6owtk53apnpkzdfNuHm5Fd6IAejWLV02RJ0RZc37cZPtt4LGDmO94hiJKJKnuIAxjE7/9+BO//fgT7nxmDSW+vR7nLgzX7z+KTv10k65z4PStUJbPxoISnSVfUZf57t4fh/d+PyL47eLYYx3NLYDQacNtHW6Ttax4cJp9i1Sm60kPMw769fkNPlVyo51PLkVtcjse/3q06zq5TBdhxIg9HfYZLhMmASX2aqfZ7a/UhvPmr19jd9vgoyXBV0jolWur0Xli211+GZvHS32Kd4wY6l5vw6/rPz/vw2cbjALwDqGev7qprjIjrKiri1g83q7YpB9d8itxsqp7Qf1VBWZe9HmLRAAu+fRMTcwTSEE//YBPySvxtQG6xI+gFqU6FcahHmeCl/3KzfMCi9ztxcFrR87ny9d8AAKv/MQzNkrQHu8H0Dco6qKXwkUWZUCw2V55COQDRKsP/LdmFtToOCBGPh8HjYZKHdtvxPLx8XXfd/X87kIPfDngHOq1TYpDZJE72/Yq/siUPvBK9xbDKvsLlYdL6EcDfj/VqnoBXruuBnk8tl76LtZlVQQSUaD3mhxftlBafK2mVHIMuTeM0v6sM6hnCmm0X6uWiuhkzZmDs2LEYOXJkhfvOnTsXcXFx0r+MjAzZ9xVJJpQPRMw+d9ewNooFcSxgo8SN4eZJUbh9iHcU5nB5ZKND0QPGOZnn75Dem9pbZuQpQ8govVd3D2+rOp6W3pe/IFEWs+wzx8E1xDpTrvyYLl+jEQo5wjXfP6q99DeXjuQWyz3EbRvFyO57hMkouyeiQTxjWBtM6d8c/57YDbFWeVZBLQ+xxWSE3XesgkoaxKKTotwlb7CLHS7VvVUasFwicmX3xriuT4b0j2tUT+erDRSr2SQNfpQZEkW0FtVVNXuYMn20FgWlTul+8xXl4qJGHlpP2SCWC7G7lXIEXm6x454/tY/0d25xOc4Xy9+nv7IKFB4/HQ8xEw1it3QOrQGmOGsSYTLguj4ZGNw2GYDXiyN6bI/kaHs88kr85RRjLitpFOv1SEVbTLiuT4YUSumPY3nSPlqdCL93/LrOC4sGeZtTKrwDN/Rrhv9e38O7b4kzKE12scMlDeSmDmiBAa2TAOjruc1GY7WE/qsK2pIJ0XEQgodYeNf0NMQut0cyhtv7IvacLwm+3QnWQA+0yFTv1suyQwb5fE7k6Xv0gjmGspxa2ma1h1hf/qF8v7XKcM7XDg1tn4KMRPk6gG5N43BFN68Eyc3k2VTPFjqCrg/nS9T9em6xepu/3HqDFHnboJyR5n1JjNWsaquiLeYK8y5o9QVHFF5bkaosvtVC2XfVdKrzemcQf/755/jjjz8wd+7coPZ/5JFHkJ+fL/07fvy47HtlQ6WUTASK89q1SRwMwtSz28OCGgX/e2I3tEqO8Z1X7iHW8sbkl3pfnPtHtcPwDqm4f1Q76TtlqlxRMxsfFYE2jeSLbwBtby5fVBjl+70yLA/fN0InhqO4PdRKzI3t2eM6ybxv3IDNkQxib9kMBgPuGOKf1rGYFQaxMChomRyNJ6/MxDW9mqq8UU4ND7FoXFfWQyxfrOhBifCSFzlcqkZZ2fHw72eP64xnr+4q/evriy2t9BIA3nsQ7RvMFDkCdH5aYdeqrCGu+HmLHm9+P84U+LcxDeMW8Bpp3JBWauv5c+QGRbTFhGEdGqG5z0Pl8nhU7+PBs8UKj5JOxyBsP1/ilDpCu8aMg6hZt9si8OzVXXHXUK/27WB2kWyApOdtFcsR6P3hg4+XJnXHs1d3RX+f0Smi1QZx7zq/LtFQ4OfjOvoh7VMwZ3wXjOuaLg0ug/ES82ccbTHhiSs6S0a4vkEcvtB/4UK1qI4x2SxCSBpiKeyaUVdDLBpYl3ROlf0uGIINAxfQIA7C+Ar2ug0BIuwEMwugNIi0DGLRe6rSECsNYqWGWON+iVFPumckyL57fFwnaUbOw5hMexthMmpKYLTQuvZA64307pXSSaOsT9wgjraaVbO5RmPF4f+0Mv2dK1LPVvJ8BAVVCBmqBS8/N+ZrOipLvZJMHD9+HPfeey+WL18Omy04gb/VaoXVqp5i5ogRF5TevBKNKBO8Mzca/AvaTL6FaOIKapFoi0lmSNttEZJ2zuHyyF7SbI2Oh3f4XCogNhJ6GmJA25sFaOt9+XVyg9opS4/pD06vpyEWF9uVuz0hrUpXZqLjxCg0xOL3PJYy3y5OMVt1vNj8vnl8sUXFRyWlujYbJIP4h11ZGNetseo4gHel7+r9ZwF4p6fEGKri83S4PDIPcLHDpZJxFDlckhZMfCbK+2Hnqbk1GiGr2Yho34KJYD3EYsjAqrDjZMUaU9GgKi53o8jhkm3jRVB6y8VBSaxVWzKRV8rfD28d4O+E082grK7P/bhHltBDr+MR74lYTq1Fc+I7YVXIepTXk12gLYUJxmsN+DtAXjdaaaz+15Kw8MESNzzF6+Yp3LmHmA+KDQYD0uNsOJJTgjX7z+HHXafBAHRrGq9piPP7xDXC3Ngt0xkwVaUOMsbw3c4sFJa5ML5Hk5Dam0CoohJ45MZ6SBpiMTGHzlS1+Kx42xtSJIsg9w20yFRvMFJRunSOaFAr5aQn80rxw84s9GuZFFQiCGU5lQ4fZVmUGmJlOZUGo9Z1iP1PhKI/NRuN/n7Dw2SSDobgHGD8t0oCeZf1JBPKtl95/mLBQ6xFhLkig1heppzics0ZhDaNYpBzOFflwKgqfFCeGG1BYZl6NrW6qVcG8ZYtW5CdnY2ePXtK29xuN1avXo3XXnsNDocDJlNoDaOo7dKKZar0GHDDVly4FWHy6/O0KnmU1SwziGNtZmnkqlzteUajw+QGMTekRG+sKsqEGKXBqh14W2uUyEe6vDNkzB+OR7wmXQ2x0HAFO2rmSA2S4tjcoOfPRWZ4RMiNkBRBV2vXyXQkdr7K5+oUvDnJvmN9v/M08kudmjrkWz/cjJN53umiKIsJWx8f5V+wIBy73OWRGahaHmK3xxufOspilhvEqvuhL+Wwmk1SIxjIIBYX1YXLIH7et5gnEEpDMLugDKcFQ5OXQakh5gax0aBOssKvhcsN/O+HT77jZppTgL/uO6s6rxJxO5eoxFjNmh4rWV30vX9KnTtH10MsTssH0M1xbzw3vHliHBEtD3OMQi4kXh83ykoUBjEANLJ7DeL/W7JL2mYxGfHH46NUna7SIOb3Ss87GWEyygapobD7VAHu/nQrAG9bdUM/tS67MijvHdeNckLRELuEaBpaoS4Bv8ffbPTrjEM7R3D7BpK86N17mYc4wPMRjXLl2/HwVzuwZv85WMxGLL5rQIXlVGqINT3ECoPYJfMQK/ZVeYjV90vsf5QSRLPJIC0a8zB5XXZ7WNCzoZoe4gC/1TOWlR7ZUA3iUDXEWvI8k9GAlsnR2HA4N6C8qzLw2c34KAuO5pSQQRyIESNGYOdOuQh92rRp6NChAx566KGQjWFASFLh9qgaDcbUxgUPCyOOdsXwVVqNWbTFhLPC51jfoi1AXfE/Wn8Us8d1khm6Sg+YSTA+VRpii/87PQ+xUu9rNBpUGmLv9x6YjCbZS69nEBt9XhCnu+JG4kB2ETYezkVSjAUjO6bqe4gVBr1oeMh1m0Z0axqHB0a1Q25JOcb3bKJ5XpMgEdArY4TRiNsGt8KnvkWM54ocmh5dbgybjAaUlLuRXeBARmIUGJMfu1zhIS4qc0mNo8VshMvtgYcBH68/ihhrhMy7rZSn2CO9z0ZLymExGxHtawSVRqWIzEOssajuyLlibDyci6HtU4IOs2QKIhWq0hD8YtNxaXEY4O9wlRpiXvejLGapvNK1+N6d/FL5DIo44DMx729ap0Tjks5pmLfqoOwYK/7KRmK0RZbWVCwPAMlwt+u9T1oe4kj5orhWKdE4dLYY+7OLkF/iRJzCYBYN1E1HcmGLMKJtAGOXn2dkp0a4c2hrZBc48NUfJ6TrVsJnD6SMmkK7U64wiCMj/NcZrRG2sNztwZurDmJCzyZolRKD3afysf14Pn476F2YlGr33kv+uMQBiEgosp3CMid+2n0GrRvFoHtGPM4KEpyjOdoax98PnsORcyVomxoTdBY8TclEJT3E/rBrfsmEcrAjDnDMRvX7WBFBe4gDSia0txfIJBOBpvf93ykjDhzyvePlrsARmDglTqWHOLCGGPBL7gCNsGtBaIjFd0rpYPIO2vy/FT3Yeg4wLbTOG+jZ6c0SqRbVqTTE3vJF63mIFXkXlPC6zhjDr/vO4pc92QD87RfgNbZ5+6X0EPP3lA+yL2qVqIqZfvhcMTYdzsWgtsnYeixPar/NRoP0Lif6jl/ZvAaVpV4ZxLGxscjMzJRti46ORlJSkmp7sIiL6sTQW7wBURofvMJpGS4uj/ZLL1ZOo8E7fRnoQS/aehLX9vYv/sv3ecASojQ8xDpRJgD9Dlyp97UZTVKjImqQXW4Gq1neweppiAGvYeB0qxciijDGcPP/NkiZ4f43pbeqk+ckKIwG0ViXyyeMMBgMUpgtPUSPqN7IM8JsQIvkaDRNiMSJ86Wa3lieBTA+KgIxVjNOnC9FdqHXIFY2kCXlLpmmt7jc5dcWGg2ItVqQU1wupQkWr0/ZuXAP8WkNL6O3MffuH9BDrLWoTmiYJ729DmcKHOjfKgmf3XaR7nFEgllwdUaR7e0tRRhB3mHwxSdcZpRf4k8wo/QW+T3EcoPYLHhBmM9nFWUx46FLO+DnP8/IQhE99+MefPD7Eaz/5wjN8gB+L4lWfHBAPlvEPcS2CHld7pGRIHUozy3bgznju+ie719L/wQAHJ57maoO+A0ok/T/Q5d6IxJkF5Zhzf5zmjM00QE8xP4Fxd56I3qIlQkeOK/9cgALthzHmgeHY9Jb62WDvnTf4k9jBeGYzCZxliLgrnjxp314//cjiDAZ8Mdjo2QLOc9qaBwPZBfhhnc2APDW9dUPDgsqcYpqip0xiK90KPpe3saIi+r0NMTWCBNMkoc4/BriQOHMgpFMBDqPzEMc4JEHkgJxSstdsml7o+KdZ0wdyUmMg670disHOFplCCyZ8A/aGJNLJrRmGvXQeqaBvJ9691spmVCtcZI8xNrvrX9gpiOZ8P2/62QBps7fJG1v2yhGar8axVqlWVilgf7fFfvxzprD0ueMxEiseXC4bJ+Jb67T1CWL8LCDoaRKDwf1blFduBG9SVK8U5tZ8vooDeKSQB5it3bYtWjBkIuxmmEUptBEuHG77mCObLvkIY7kGknBQ6xaVOc/l14HrtT7An7PRVSE3CAGhKQcJrWRJsL1SYE8xPmlTlma5Kz8Ml0P8aWZabiuTwZGdUrFVd0bS2lelfsGo00DBIOY6TdkvD74X3i1ccmjBjRLjJIWXHDtt7KRO36+VCGZcEuNsslowOPjOmFUp1SM6pQqXxio4YnnZdKaxrKYjUFJJioKu8YlOztO5OkeQ4QxpqsRFeHHbZYYhcu6pEnXzPW8vFzbj3v1yDyUT16p30BWGliShliSFHnfDy7fcQnTqdxIbqYR/1hrgCF2rHxwrKvJj1B7iJXvyd3D26BdqtdTclYjpJ5Wh6llxCg1xCK8fgeSTPDr0lpUJ0kmrKKBLz+PaCyfKXCg2OGSjOGRHVMxvkcT3NDXK19QOvduukguazAbjZLXvyLJxKq9Xk+V082QV+KU1Tmt+7nuoD+MltvDsPFwjmofLZSDecbkusrKeYgDaYj9Dpba8xBr62pFp02g5xOs0RJMO1FSLk8rrXT4aL0n4nGVxr1LeAZ6ZRX7Hy0PsbjwU0xe4wrgWFGiVe5AA59gF9WpJBPl/kV1WlS8qM573lO+6BFxkREY2zUdM4a1wX8mdcOYzDQ8eUVn3fUsvJ3naxtOni9V6ZKVxnCT+Ehc1Eo+g5Pomw0nyUSIrFq1qkq/l7RdLn9lj7KYvAkzSj0qg7hYw0NsFjpgrUoudjB8lbpSH2qLMOLtm3tj8nsbseKvM7j9o83exhj+jipOa1FdgLBreh24qPd9aOEOPH9NV0Ey4f89XxTCO4lA3mHxmh5bsksVMxYA+rVMlCIlcMoEnbZFIXmJj7Lg2au7ap5L7qEP0SAO5CHmBrFPnqBsgBhj+KcvdmRGQpTUUXBJgNLQPnyuSGagFgsaYrPJiCu7N8GV3b0Sj0lvrZOC6msZPLxMpzTDrhklb16gKBOysGsB9JvF5W78Y8F25Jc6Mb5HE4zpkq55vGCntJb/6c3yNqJjI8we11nanltcLsXKPJlXipN5pTAagL4tErH+UK5k7EZazKoFcm7f4sj//LwPgLaHmI/5eZ0PNpW0VsepFWECkL/LWou7OqXb0TI5Gn8b1AoPfrVDph08V+TAaysPYM/pQtXvcovLVR0b94ZpLRwVFxMqCaghdnrwzfZTWOBLqCEOipXnSYm1SjGVAWExqsmAd6f0lu0revceurQDMhIj8fH6Y9I2k9Efh5iXp8zpxlWv/4amCVH4K6tAeq9k2cncHrmHWGEQv7f2sORl59z3xXY4XQzX9snAwi0nsHKPtz6O7JiKCT2b4kB2IW54Z4PK26wM6yX+LR4H8MZkvXVwK7zw0x5EmIyScWIyGqU6svtUAe76xBsX22Y2oY+vPbSajSpN/7qDOfhkw1F4GEPbRrGYNbKtaqCl53VVJmxQpjZ/8NL2eH3lAW+Keg/DpLfWYcvR89L3yvCdOcXl+L8lO2ExmfDQmPbYf6YI76w5hH4tkzCykz8Zh7IpEYsbTNKN3acKZPVLOQjWMhTFuqCOkuDdYIswwelWr9/wePxOLD0NsVHQEKs8xFUwiINdVLfzRD7u+HgLHry0vcojqxzAiVEmtOB5F/QcQvz+8Vm/zo3teP0G75qtrk3jMb5HUwD+WRlVeXzlntg7A8/9uAceBjzqW3/wj0vaayacGdUpFTOGtUGfZ36WtvH9ajrsWr03iKsKN/JO5Zdiqy+WZ2SECY4IrzG8V9FR+T3E/gaDv0Ruj0czNWS0htfW4gvvxQ3uRrE2dG8WD1uEEQVlLlWa2ISoCGmVfUDJhKAh1hPWG40GpMRacbbQgR92nUZKrBWnfJpYi69hFhcr8EqpF4OYk2q3IbvQgXWHtL0xy/88gwcvbS/bJoa2C9bTC8g76mB/x29VIIPYovIQy1/4IzklUhi4jumxUme840Q+NhzKUWXa2XosT2ZcrdqbjX6+TlApARDlKlrXxOuOVmMmLqo7V+SQFkQqkRbVaRgjSriBtPtUAdqn+a+1SUIkmiZ4DctgQq6JKD20Yhn3+DL6tU6JkfTyfg2xSTV96nYzbBc82Rm+MnEvj/e+e3zbvL9tHK+vi84rKUeZ04O0OJvmIKGfYjDHseoYkO1TY7H3TCGmDmwhK4PYqf9jwXb8sldbY3u+pFxlwAfjIdZCCrvG1NPO5W4PHv/av2guLc4vLVBKJhKjLTKDhb8fNg1phWjMmIzq2awIk5Cpzne/V/yVjT2nCzUHCBynWz4rIcZuL3K48NR3fmP4zqGtJd34o0t24ppeTfHPxTuld2j1vnOY0LMp1h/KlcLGGQ1A94x4/HEsT5W6WWzf/2/JTpXXs9Tplhn9/DrFtpNnEQWAjUe8A2Cr2aTyEP/7p72CkXoaY7qkoUOaXXZsPU+i080k4wfwZkkVaZYYJb1PZwrKVNntlOsQfth5WkpecXG7ZHy8/ih+/isbX287hYFthgrl0W8PAnmpReb/5p9yVxqoWm2VOCgXvxfXc9giTN6oBQGiUHglE/I66o0y4f3bo6Eh3u5LMlMRmhriQJIJYf97P9+Kk3mlUopui9kIu82btVNpbygX1Y3r1hjfbj+Fq3t6DdmKPMT8PeQGsV5WWO5s23g4Vzb44seNj4qAweA1sPl6nE7pdlm6eY41wojkGAvMvmhdAJAgeYhrVjLR4A1i3uCv2X9OSv8aZfEvJOMpDTkl5VoeYm9lcLg8qhEqPx6HVySj0YBPb+2HP3wNXv/WybDbIvD5bf2x80QeDAYDDAZvXEeDAejZLEFqwORh1/SjTOhJJgDgo+l98cx3f2HN/nP4cN1RabvFN3UnGo2889AKgSMy76aeWLv/HLSq8Mfrj2L3qQLVoiYxkkcoBrG4r16YNSW8/B4P0/Vs8gaYewN3nczHfV9swx1DWqN9WqzM2zt9UCu852u8F209KasrTeIjcTKvFL8r5C/rD+Xizo//8JVHETLPHNgg1op2Ie4vzgi8tHyvZrYryUNsMMgWGYr/KzmZV4rhL/4qfRY1mWVBJOUQub6vfNpcrMv83Yq2mlXpxWNt6kV1bibvoK7u5fW0WzQ8xNxIFiVFIln5pRjywiqUuzz435Te0r146srOuKRzGiJMRt003lqL6gDgw+l9sf14HkZ18saXVSYBAiDzyinJUQTu9wiLeLTCHwZ6P8XBsYcpPcRuaTHTv67sjJEd/R4/5bul/Mw13kppBSCXTJiMRpmB5t0myHYU0hbO0PYpeO7qrjAAGPfaWim7p+gVPFfkwKzPt2LOhC7IKymX2uD50/pgaLsUtEqOxj8W7vDKLYQEMYA/5CE3TkZ2bIQXJ3bH19tP+g1i0UPs9r8r3Bh+ZEwHvL36EHKKyzXlG6KGGPAmZurWNB7fbD8lRV+xRogeYo90XSInckvVBrGOYeNwuaV3KL/EKWU6bdMoBn+/pD1GdmyERxd7B0FilIU1Dw1DucuDzUdzUVTmwhurDiIrv0ymJy4pd8uSh4heU6U9LPaHpeX6Ui4RMStrMB5icQ2DKJkQ6zjvF5WLA8V+wCJ46TlmoxhlgsmM+v3ZRXjyW/lMhBKL2agK5xroWqTvxEWvimc8smMj7D1d6DWIlVGwFIvqnp3QBWO7pEvStIoW1fGtvF3VC2fI+yKnm2Hx1pOY4DO4+XGtvrj44toCrXChgLffMxgMaJYYJTmU+GxosBrtcNHgDeLhHRph6Y5TUtYYk9GAyf1boMjhwge/H4GHMUSYjNifXQS3h2lqiHlHJHrLujaNQ7HDhZEdUzGgTTJ+P5gDp9uDa3wVBwA6N45D58ZxsvJ0z4hH94z4gGUWG1dlB8I9dwDQQieFJgB0SLPjval9cN8X2/CXzzMXH2XBqE5p+O+KA3AIL3GJpEsKHMWjaUIUrlMYPBwDvCkg+UvBG4rScr9WTS/GsRbWCoxHLcTEHHojT6WGmKe3/fmvM9j5xGiprM0SoxBpMWFsl3Ss3JMtyzQWH2XB89d0xdNL/5T0xudLnFId45pVdYQQ4Zo07kXzxChc2b0xdp3Ml0VoALwNUEK0BbYII8qcHhzM1l55Ly6qM5nkBnFFeq1oiwlO30KSw2eLvQZxkF4fAOjTIkHVwIpGbpkQXk95/TFWs2oA4REGbZ0b26U6wd9Hp4fBYPDNbvh+a9OpK/vOFEmN75+nCqSFVFEWsxRGTA/RGBSvL9VuwyWd06TPERoe4kAeEGWWPbFjtGp0VEoPuog4hapMMS+GhLymV1OZjlJp6CoNFD7DpbX4TuYhNmh5iI0yXT+g9kglx1il+y+GLlN6ZpdsO4WxXRtLmcaSoi0Y1t5r2F/RvTH+sXAHAHl2RE652x8Lnq+gl4wgDzSjTIjvyo0XNcfCLSeQU1yuCqMJeK87zW6D0eAdjFzbOwNNEyLxzfZT8ggHXHqniJ7SKjkah84Va2YF0xvEioZEVoH/dy9d2w1dm8YDEDTnvn0jTAbpXvOZiZV7spGVXyYzHMtd8oQ3okGs9BCL+wWKhQwAozunqmZGlTM14vXyqEZiXdDTe/N6pXzflGEulbJA5cJPPdnH8A6NsNIXkUHEYvL2c1oLFwO1t6LnV9nudW4cJy1wU16P0rMbbTXj0kyxDapIQ+w7jsvvWdeiS5N46e/DwqyoU6pL3rj4okFc6nRr6tV5G3PfqHZ4beUBjOqUGjDVeXXS4A3ijMQoLLhDOz6i6N6f9flWLNl2SlNDzF8Y0Tj4/LaLZFERfntYvtKyKgRKzJHZJA4L7uiPMqcbA1onBzxOhMmI13z6IBFpatdXGbkRq6dJDoaJvTOQarehoMyJGKsZW4/l4bVfDqDM5ZYSA1TWQxy8htj7v5sFryHm8Hsgdh4A0CI5Gl/dqV1/5k/rK/v856kCXPbfNdJnpYEgRibQuhdGowGvXNcDAPDt9lO457Ot0ne8Pj5zVRc8sGC7pF9UIi6qU4Zdq0ivdUnnNBzLLcGWo+dR5PB21sEslOFoSXjEWyDFmzYbVdcfazOrDD5Rsy++B2L9NUgeYp9BrNPAi+Heylxu2cChIqxBzlb4DR71gjYtlKldHYrOW3384A1iLUPKYFBLH5QJcJS/40abMqoGIDfQxagmsm2K1M1az50jZhXlHmLRECkpd2m2VeK94vIKPnAEfAaeh0trvPuahIVUWl5HZeQdXrZiDf2+2WhAWpwNX88YhNMFZRjSLgVr9stlMlazSaYh9niYtH6hQ3osDp0r1lw7oDegEusVN7DT42ySMQz4Byy8XmnVdR7iU+zblCFKywJEeZAluqpg8JymMfDUS8VsNPgyxbndstkCvYyPPIa5XuIOvmBce1Gd929llAnO7Re3wqC2ydoGsdkIOAC3xnseaDGiGM1E+VysZqOuYSvJG3XW+1QUh5jj9xBrt2cWsxG3XdwKb68+JGuX/BltvYu8z8A/AC0td2l6xXmbM65bYykRFl9ESxriOgp/UbgHQCvsmjh9XJG8oCrIF9WpzxNszE09JA+b76UsrCDYdzCYjAYM6+Cfij3gC31VVu7WDbsWCLGTC9azbBIkE34ZiEH2kvIGQy+5hxRxQyccVSCU90/Z0InGWkWDA+UggHvouBdfzxsjGsTKRXUVJVSx28zSNfCFe6F4iGM07qn4nvAyW4QGX/qtVS2Z8DC/p198D8TEHAbIDR09TZy4eLbM6ZFFAqkIuXxHv174o7BoT+sq+X5nFhwuDyb3b45YW4TU8RsM2h1eQA2xMLujNyCMilDrtJURNPQMYm0PsfC3QjYAaKduVnqQRNmX6DXixmy71Fh4GMOqvWdR7vJIU/viol6DwSDNSPFBRlK0VYolPv+3I9jly7bIyygaQaJN5vcQC55Ko1F6tpoeYt+z6tI0Dl3gnRFUtgXKKBOFDpekXW6fasf3O0/jQHYRTuaVysLH6Wl2xdlK3r4qByT8GsvdfGGkli7ddzxVsiH/ey/zEAdIhKFc2KckNU7DIFZcnn8AbJRJFTk7T+Tjo/VHcVO/ZrKy2Mw6BqRCrqccVCrrqFZ7ZzYZZM4vEf6eatm+WuuNOOJzVbf1fk+28nr8/ZN2/1GRh5hTkYaYlwOQh73zn9+gquOlTrdme6clt7JIbTgZxHUS/qJoZarjHZHYCAXy1oS1XEEkRQgVMa4y4I9HGUiTHCpcHuD1EFdiUV0F3lQt+K1yCauDo61mmTHk9xBrX2tlyspRSk4Cpd2uyMhXGkT8fnCNrN7UHjc8xEV1Wp28FnGREZKhUVTGPcTBG8RaMwziLeBGToRJHZYwxhqhXlQneDq1Zk3K3R4YDN7jSJIJnQZe1GuWOd1SRxych9h/TD2PiliGYBv5P47l4Y9jeYi1mTG5fwuZ1lMr/KFywCCSEuM3NvQ8xFEaA16l93tA6yRsFnTPAT3EBvkzURkbJnlaXACqRU92DQ9xuaAhtpr98ppyt8fvIVYm9fFNXecUe5+zPTIC2YVlcLoZXlq+TziHtzziglN5lAnvM+Adv9n3HvGBdLHGe6dlaCqj8FgjjDIJU4FwX1ske+ULy/88Iy1MvmtoGwD6XkbRsyZOY4soJRNadV2SAyoN4nL5VDhH6dGVSSYqaCtSY4PwELuFAb1J7b0+lV+Gx5bsQnxkBHo1T/Bdg789UXmIFe251j3i9fjrbac0Y1mbjEZdOSE/nlZik0AeYmdAD7FJNluieT16GWWltRmB23rJINZIzOMvh0bdEDK+Kut4ablHcwCntSBXuYakpiCDOEi44VniUHuIlS+m0RBYz1dVxCNXh+HtD1vl8xCHQTKhxCaECONtVCgaYpmHOMQ4xKKHONpikhnE/FnqecMlb3YIZeUoQ+GookxUwUPM7wdfwKnlqQLki+qU09UVeQ3skRGIsXAPsff4ZUJnyjubKIsJ941sh8IyJ/678oD0+1iNe2owGCRtpV8yYdKcOlclTfAwqYEVBwi80Xe5GYwGuQdZz2AVF0OVOf2av4qSSwBKo7FiD3GwsWM7N7Zj96kC5Pim+aUkDjp1Q5kxcGCbJGQ2jkOL5Gh0TPdnvRNDTYlo1XlZ0hGzCXcNa4P4KAue/u5PeFjwGmKjQcdDrNAQKw0HuWTC1y65/B5iW4S/rjicHjCm3VZZI4wodPglE7FWs5RISF4m7iHmsyfaGmJphslXJn5tJRoxwLUMTeUMlDLKhJRsJtKCgW2S0bZRDI6fL0GZ0yNFQxLLo0Q0JMSwYiL8Gst1DGbA348pvYAlgoe4TKYh1jZggYoN4jQtD7GOBMNs9Gt7tWRbf2UVoGmC13htFGuVzRqJKA1I0cEU4UvbLD4/PqsgEmE0yCJJiVgCvPOBInK4AmiIRUmZ2Ga7Pf6IKHoywmA9xPxZ6TkQAP87ryeZUN6TMh0PsdY59Az+6qb65vUvMHhDWSxM6/q/45IJj2zfmixXOIlQaB25h1hPRlAZuKZLNEYrqyEOXjLh73yluMsKI4B7enQNYt55mEMfiIixRgEtD3FwWlRA3uBFmPyGhd8g1u58JG2sSR12rSK9lt0meIgVkgkxxW+a3YZbL26F+y9pj6kDWkjb9e6pUoMvJjKQfqsVZUIIDSi+B5In1uOR6jC/13oNvGgQO1xuTc+zHjKDOICHWPJcB+H1GN6hEQa28a4BKHW68caqA7j9I2/8Wr33RFmfxnVtjEcu64jr+zaTXQeP36wkSsMbpPQQ2yJMuGVQS7TzpZUO1kOsjLTAy+uPdOLdpjRYxFkps9BJioMD3jHLPMSKtoq3EXxRXazNrLkwkdc7MRycLMqEYvDIr0nSEGt6iNV1SEsyIUaZ4Pc1LjICyTFWLL9/CF64phsAeWx0PYNY7rXTkUwo5BDaHmJ53wZ4r1FsK8TBtzLsmVi+0vLA9T4l1qrapmyS+IDJZFLPOIgwQEoAlR4f6U+epdIQy/tzMeyaNDgSzjOwTRIeGSOP3mM2GfXj/pr8Mj0lgeMQB9YQi7Ng/uMJMgudNiJYDbE44NSDt3XlitkDwK8hFil1ujXrq1ZfV1seYjKIg0SrUnKUHuJgOtFwUZ0eYl55q8dD7L1nBeEwiEPNVCdEJ4hWGAG8Y9ObAqtoSioQBoNBdr6qeIjFEFZiWbiWTU9DHCjsWsUeYlFDLJdMiI2f2PGK74lWshbAfx9KhZCGykYy1qpeVOdmfg+xfFGd33DiU/D+RXU6HuIihYdYMog1d5chPiu9KBaAoG0OIoWtxWSUOqO9pwvx/I97Jd29GHdXROnNFu8X98QDPg+xxrPW8nLpSZO4Mek3iANriLUW1XljvMpnKZSGuliv5BpivwdL7Dz98i7FQNfMDeJy6XvNhYlcMiGE2hKL5M/eKfe6Sh5ijZkZk4bTQmlAiVEmTpwvxVO+xCJiqEUu4xLDVwUKu8bRlUwoFtVpye+MijYCgCyiDgCUipniAkRQEbO8ccQqqzV1rsxyJnqIA8kFGfMbxGlxNsH7Lr9fDoVkQjymvy7492+XGovRQuQYXhatwaR4XE0PcZBxiJVOL2uEUdPjLYupHDYPsX57xs8hq2vCIkW1ZKIyHmIyiOskypdP00Ps1G9YqovqkGb4F9VxD3H4DWKuTeIGscEQmnEvTtEGMasNQN64c8NWuRiCj6z1vJmOSiQRERE7QnVDF7yGWPyt+DtucBeXu1SdCeA3PMQpR7e0qC7w9JQ9MkKqAzwqA9fNi9cldrwyg1jPQ2zgXij/4h7VojqbGcrXSpz61+rIXG7RQ8wlE6F6iCt+zlYNQ1ELvWlbLSxmo9TJntKYptVC+f4oPeri864ooyZHJpkQro0b/n7JhP50Oz+3MumByaReVKfUEGtJJrwaYtFD7O+YuZRHJZnwXQfXEMfYzJrvsFIy4fbIo0y4FBpi/kz5AFXL66fVrlkUgz5rhD/KREm5G3vPeBOTNLL7vab8msRkQcGEXfOnL1ZIJhQzFlrl1NomxiAG5FIIZUZB2X4ag3RxEBZhNqgiTbgZQ36pExsP52Lj4Vys88V19y4K1n83GRhO+8LUpdttimQ9ftSSCXHmzfu3SaGFV8UqNhl0Z/QCeYj5vRIPxw1Q0VhW2hNWs0nqp8R7LD5z3SgTGgt7tcoW1KK6CP7eaUgmzAYkx8g9/noeYi2j2yqVkzTEdRJlw6C1qG7L0VzNfcONnqctXEgrYyUPcTUsqlN4mPQWCukha4CClBmJHuK1B7wNq9IT7PcQa99jp+RRCD3KhPK4gdJuh6IhFu8FH2h4mLehUhqAkjZWaNgDhV1LjLZIK/Pttgip/H4NsTwQvPe6tA1EvQEV75ilKBMmo8rjEmM1qzoil4fB6Hv2csmEv7Pgx46QPMQVG8RlTrc/o18QdVIcVOl5igD/8w6mkRcN4myNZA9aKDXEyvvlNfKYKg4xR6vO64WU4/eRJ+bQuq/irTMZtDzEBg0Psfze8IyFgNxr5BA8xLxcr/9yUDJAKvYQR2gaMX7JhF9DLA4sVZIJs1xDrIWely3WZobDVx5xGpwTZTHhgUv8mT2l7JnCrJqeB61cU9epVR8ESYWGgamsU4DaQywLuybUK6XxqSXjirT4Y9WajUaM6pSKj9YflR3vitfWyrIjAt5ZkpbJ+v3RW78ekupCenyklORm7YFziIuMwPgeTWA0GgJGmeB1QLY41BQ4eYcS/6yQlmTC70zgTqcoixllznLZ/srzWYSFpOLzF59zReURIwopo10wIZpGUBpip9oojzAZcWO/ZnB5GA6dLcLSHVleDbHGgFFr/UEozoNwQgZxkCgbC4uG52vTEe/qaz1jKlx0SLPj7mFtNMPUhANxyrnM6cbPf3ljAoZVMuF70aQFHyF6XEUParCvjBQpxOHCt9tPAQDiIuXZx3inoTfoEONWVgaxblQl7JpMMiHsKxpnJeVutUGsFXbNt01rCq9JfKQ/VFWMRZBMyD3Eovc3Qrgusc4kRqs1grwsgN/TFGE2olVyDDKb2LHrZIH33NFWzSgTHt8mLQ+x08NglPSTgT3EopdLlEwEI9Ef0j4FE3o0AQMwtH0j3f382vzgPMTKQWOszYwypxtXdW+i+Rul8a7sE8UBIb8+MR6vUj4E6BvE1iA8xGJ59MKu8U0uqQ76783tF7eSJRcSF0uWCR5isf57I4t447GL8H3OCRpiTQ+xIuyaRxFlQhmRRakhlspqMqBfyyS0S41F65QY1XkA72DznCDhULYHj1/eCS2To6XPPDZ6kcMFj4fBKCxkVVImM4i1PcT8+SgXCGrtI5Kn8BCLMhFlSnBZmYRF51oLqS0mIx4e0wEujweZTeLw6OJdcHmYZAy3SIrCEd/fbg+r0PFU7vbAaAB6NU/A4XNeudHKPdlYuScbidEWDOvQSJUKXTwmb8eMim1qD3EAWYFOdAvAX9ejLaJBbEJusfw9UDvjdDTELu3nLKIlRVC2Ry6PP9lJUGHXZJIJv5QoKcaK+0e1w5aj57F0R5Y37JrGrKXWgDGQ1KQ6IYM4SFo3kjdqorbrrqGtEWkxodzlgQHAZV3Tq708fx/dvuKdKomot/pm2ylpu3IKpCooDZNQYhADlZOK8JG+mBL3zqGt8a2QMUryEOusGvZ7FCo3CyDGg1U2dDIPsSmwB1ommRDuncnonb5zuDwoKXep0g2LBnEwYdfuHNoa6w/loHVKDNLjIhFj807l/pVViBmf/CHpWkVPu9hhXNGtMY7llCDGZpbCIKmvhUuO/B5io9GAN27ohTdXH0Sr5Gg0S4qC6aD8fnkYA9NY/Ob3LnikDr2iTHUi4mroYDzEdlsEXprUvcL9IqRp9eA0xEo5T/eMeLx5Uy/dTkolmdCRUIge4mifRwpQy4cAuYdfHl4uGA2xfKpZ6aEUQ1op04eP7JiKRy7rKNvfouMhFo2qqQNaYMawNqoFWnyfAiksm7ZB7I9DrC0vUXqIlRpizhNXdMaN/Zqrji/bZ1xnLN56ErG2CEzo2VSVqlm5QJN7iD3MK4mKtUXoLswSIz84dTzAwUgmtNbD7PTFbOaIi+U8GvISaT/hmZVoLU43GRBtNWPuhK445jN8xen4b+4ZhKte/03K0laRNPHWwS1x+5DWSI6xIt7XX/9+IAeHzhVjz+lCr0GsiNwizwIrHxzxbVoeYj38Ydc0PMQ8Ko/QdvJ+R/TaKmVbomRCS0McyJli0WiDtGJHhyPKBIe3VyXlbs3wc9oa4pqTnYqQQRwkV3RrDKvZiN8OnIPdFiHz0rRNjcWc8V1qsXThRZyuOFPgz45U1YQfIspRYWU9rgDQPECKahHekJ33TfklRlvQplEMUmL9QfpTfEa/yWhAZIRJFSqoKovqALnRoWzoKspUJyKuIlbuG2UxweHyaGr2REOPG0iBwq61aRSDy7r4B3g8Dmd+qRPf7cwStvufgWgIxUdZ8H+Xdwp4LXx/7pXg19MsKUr2Xqk9xP4OWNSnSnG03Qwmo9xDHMibwylzuaUyhXM9gJSpzsPAGAsoEbKatWUjgWaflPVJaczz+ycaUenxNmmA2DhePeOkF0HDqvDgaGqIhdMbjeosYAaDXwfKn6O4aEoJf65Pf/eX9K5YI4yycmUkRmlGK1Aal7G2CM13WIpDLIRdE51aa/afw7trDkkOEklDrKgnwWTPHNAmGQPa+LOJKqUIykExj7lc7vagoMxrEOt5iMU4wWKyBBF+i7mHT+vdCGaBuLhY7lhOCe7/YhtuGdRS5jQC/JIJ0SBOibFKqX/FdoxXZVmqZqMRbVJiJIO4IgdNWlyktE+L5Gg8fVUX/Gf5PryyYj/2nC7AvZ9vxc4TXuNeK+yaWSGf4dvU8bTln5Nj/J5/5XsiwmfkmsRHSteUGmfD3jOFMmmBlodYSzIRKHwexx/LW9B6K4zUez7dKjk6Ai6qU0SCENd0iAatlHOgPPQoEzUNGcQhMLpzmmqF6YWIPw6xR8pSd9vFrcIaPSMx2oIoi79hbKwR8Lwilt93Mc6XONE0ITSDmE/58en896f1wbpDOUiOscqy6UVZNAziKi6qa5boL2tanLxBTxfuQRMN40REbHCUGqwoixnnS5xYtfcsTEYDWglTtrLUzYpFdVraVqU3sl1qLOZP64PjuSWyfYa0S8F7vx0GEPwiR45SMqE32FBGUXB7PFJYJlHrKC0K9TAYpUx2+oV6Znwm3B6GYocbz/24B2VOj9RIBxOHOFjEZ+byMBgCiH0sZqPKc1KRFEt525QDCK2wby9d2x0bD+ciMsIkG/iI5eCIsbdVMzyaGmJBj2nQDpOlNHykRZAaz0vs6MucXmlE4/hI7BY8llqyD0Bdp2Ks2mHXeN0Rtc3Kad453/+F130p7/kz1YsLHgrKAY2yjTEYDLBHmnGuqNy3riNSN2JJsUM0iHUkE5VcVMeJsZpR5HDJBt7vrvW2AYu2nsSKB4bI9tdaqBUXFYElMwb6ZhAEg1jjvYswGXDTRc3x059n0DI5GnMndMGKv7LhYQzHckqkc3O0DC3uPPlamPkEIMUs1lqLYFDMdKjfK+9+6x4Zjr+yCrDir2x8suGYVGZAHU8Z8Htmpw5ogfE9mqBRrA3Ldp8GIF9cqtRxezN5akgmgpDzBSOZWCGkoE6P0++XlYk5RMNadNjw513qdGvKxbTeQ4vJiOQYC7IdJarvqhMyiAkVoieLa5uqkrZZiyiLGUtmDMRfWV6N6IDWyRX8Qk3b1NiKdxLgHYCoyeTH0TqW1gCgKpnqAOCBS9qhT4sEeBhwcbsU2XdN4iPx9YyBOFfkUH2nRGz0lJ0Wv65nvv8L//5pLzb+cyTiorzeGjFTnagpBbQ9xFpxdYdp6GRFHVmgaTYteOfHO1a96TJl1Ay3xz8tK+qWxQQO/FDKCAcifGr74Nkin0Hslo4XzkGg2OH/3+Jd+GFXlu6+egsLAxGsh1h8VhkJUVJMYS3EMtjEvxX1QuuZi/dOKw6xuE9wHmL/72cOb4PRmWloEh8pW+CqlW0PUL+vFYVd47fOG3bNW6bnru6Ch77aCQ8Dcn3eXElDrDh+MB5i1bk1PIFKYm0ROFdUjite+w3dmsZJGv5XruuOCJMRX24+jlV7z8riIetKJpSL6kL0EHdKt2PjkVzdmOd6i+rENsVsNKB7RnxQ5zUZDbi4XQq+urM/WiRFIynGipsu8r67v+zJVhnEWm20cjbx+r4ZGN4hFYN8nvoILQ+xzCBWL37kv0mPi0R6XCRW7T0rfKfvIeYGZIzVjBEdUwEAK/ac8e4vxhTW0hBLi+PUUT0CyQ14eQ5kF+Hat9bh07/1k35nMHjrBO8PnroqUzNZilQOKcqE23d+f1nEdytSWC+0cMsJ1XG0vNAGgwFf3TkAv/91HDe8rFuEsEMGMaHCP+XskRZPhdsgBrzexkCdcbjhDRuXTCjTu6r2VzREjDFhUV3lokxEWcy4NFNfY95No3PQQuy8lM6Uu4a1wYe/H8GOE/lwuDw4mVfqN4i1wq4FMIiDNW6tZhNmjWyL9YdycOvgVkH9xn8tCg2xTgQPZZfiFgL/i8agFGLJ44HTE7z0gV+rw+lBlIWHRAqjZEIowxebjwfc16IjmQh4fGVUCUU/o1xEBVRs8KfZbZg2sAX2nSnE9X2aSdvFRWtGg9c4UiIeWhyAaZVJqSHWCncnGjjDOjRC58Zxqu16HmLlLEqwUSbcHial8R7cNkVahJhbpDCINRbVhYpWNAElvZsn4PC5YpS7PNIibgDo2jQeLZOj8VdWAVbtPSvLmOfUWfcgSSac+h5ivfphMPgX+emlb1eFXdPwEOtJmJSvnRg5oVdztXRP6zXVer7t0+xIirYgp7gc0RYTZo5oK/OCiuXR0hBHCKECOcq6Kn4vLarT8IxKSYU0Qr0FWkxmjfCnbv5lbzacbg/uHdk2qNnLVsnRiLV5F/FtPJyLw+eKpfNHW8wY1y0dn208jm5N43BTv2a6xwHUGmKnLOybvwziAvXvd8qdAO1SY3Q92s2TopHQtXHAMoQbMogJFWKUCR5yrbpDvdUEepIJPZomRErB3QHv/ahKprpwIjb2yoVzV3RrjCu6NcbIl37Fgewi5JX6tYn+6AnqsGtacYgDrTJWMmtku+AvQIAbRf7UzToNuqJ4biETnSx1s1B/lRpiJQNaJ0l/8wV35W6P5O0Iq4dYw8h7dkIXPLxop2q7xWyUtHecit5B5VSuuuP2eYjFGKcVXJ/BYMDscZ1V26/s3gQXtUpCscMFuy+bmtZvlefWK7MkmQjgIRa3iSEgxXdBa2EgoO0h1jKYlHGIGfN7r01GA2JtEShzOiTdNa93Kg1xJWaQtFL0Knn+mq64d2RbXPnab7LFwfy3/PplHmKdOMSSZMIdukEcIyxK1PMQKw1iPsEjDrKV3k+Osu5WlJFVq5xazzfGasbqB4fhTEEZkmOtquyrFUWZMJvUHmLlYFssurSoTlMyoW67pDZZMKCVtrTVbERKjLfNP3yuGIfPFaNZYhTapsp17VokRFuw4Z8j0Ofpn1Fc7kZWfpm0dsBsMmDO+C64e3hbpMZaKwyDqpJM+K5HOfg1GQ146qpMPLZklzQL2Co5Gh/c0hepdltI4Varm/pv5RBhR0x9yxMw2C8ggzi/JLi4yk9dlYkP1x3Fpz49WLnbU+VFdeEiwmTEmzf1xOYj53GDzkier6wW45aKi+qUYdd4x9guNQYXt01B7xaJlZr6DRUpDrEzsGRCiVcyodYISzMcHg9cbp/GU2igF981AN/vzEJcZIQsEoDYUfNQUuGMKc4HIeJCoS5N4zT39XqI5e9cqB5iVRxi36PkddhoqFpin1R7YJ27aNToedqlhZ2+W+IKoPkWr497JwHtEJhKVJkPdcKuKRfVuZlfQ2wweH93ttAhhSLU8xBXTkOsMIg1jmEwGNA0Ico7WCr2b+f3i0d7KdFYVKcnmQiUqU4vykqs1Sxds3Kdhf+82l5OcYpcb+ZGed6KZni06pdWfFvAq8VvpRMKTx5lQl4XAO3EHMqBrrg/r3eBokzopZ3nKBNnmI0GTOydAVuECYv+OIl1h3KQU1yOFjpacSVRFjN6t0jEr/vO4nRBmbQI1Wz05gJoEuR6HnFRnTh7qtV+x/reyxIhm29GYnBrf2qS+m/lEGHHv6hO1BCHLylHbcE7/0KdbFZKOqTZ8fSVmZJB7HC6VWF6apNLM9MDyi/4Km8xbqgs7JpB4SGWDOLYCiNDhBOT4I0D9O8tU7iIPYxJBpRMMiEl5mAwGdXTkj2aJaBHswTV8UWDmEe8CHcmSLNgENsi1AvnOJXTECsMYqWHWGkABRNkuQqI/bKep1Ep23FrpOLmiCvjRc+eLA63Tsp1pfEbXWHYNe9nj4dJ9dJkMEiDaC674p5gtWSiMh5ipS5b/xjKesN/y8N2FTuE7HFu7VktSUMcoD5oJeYAvLMV/Br1JBN6qYmDkUwoDdyK7qeWQVyZdR6yiBLSAkv/92afdMNo8A/i1Alw1GUIFGVCrlv2t10cpTFtMBhgizBhYu8MnMorw7pDOSgocwa1qI7DMwKezi9DxzS7qhzBILbTDpdHd/EmoI7HHM6Zt3BS+706UefgxoXLzfwa4gvAQ6zsZIPxensTCvinFqsaZaIm4brhPC0PseDpkBY0CUHVaxKVxyXI84vxdEUPsBglRavTCVQOVazcME/nifc2xhqhKaMwGLyDMavZiDa+8F5GA9BRQ6crK6viPiqnIpVxZ6u7U1J61jT3UUQ60dKEc0TDSxYOTrinevHDZdkcfRpMTcmEiRtB8sEivx7eZvCMd1IcYtWiutDvrSqaQIB1Csqy8/ur7SFWhyYEqhZlIkb0EOtIJvQyMlqDkUwoHk1FMzVadbkybbTWojp5lAmj7H/lbwB5mD5+jzRTN2vIsvwDRP+905JbcPhMSUGpM6QF3zyp194zhTjkS1oSaohJ0QPvNYj1DXLltnCGswwn9d/KIcKOOOUsaYirOfteTaD0IgSbitpiMsLpdsPh9FQ5ykRNEu/LwvfsD3vwxSbvIi5RL8gb3x0n8r0LdTSCqtcEwSwmAuQdDeAziLluTUMysftUQUDvlxY2swlOt9+YCLfRKHYEsTazyghaes8gpNpt0jTm0nsG4VhuCZKiLUiqIO6qykOsCg9VsQEUTgyhSCYUi+q0OkzRIJYZ+8Kfuh5ioU7zmSFND7FRbgSJXk6j0SD9NrciDXEYokwEamNUHmLf+bnMpkjwEOu919IAKUCoO73n5o3jLJc6KRG91CLV4yFWb6vMLJ5sUR33EBvUbYvRCMB3earF18Lffg+xenDAo0yI1ybGUOdoGdMcKZ13mSugZEFJus8g/m5HFr7b4V3oFigSjxbehY7edvnyV9fgsbGdfNv1PcQcrQFvXaBuloqoVXhDsPVYnuQhvhA0xEdzimWf26UFF+GCezS+3Hwcx8974yLWtNFYGeKj/AY/X3zBt9sjI9AiyZ8W9vudWf6wPTW8YFApS9C7t6ooE0JaXbExb5kcA4NBHk0hWJQxMcMumZB5iM0qI0g0hgGv4dMuNbZCYxjQ0hDLv/dPkft0fNXspVFGmQCAgW28ixhH+sJM8cfm8ig9xIENYhHReIjSkaCIYRU7+DztbRqpdaRKD7EsHqzRIEWmqVBDXMXpeiCwQaeUU0iSCQ0Psb5kwvs/D5ulZaToeogFyYSevZZb7NDcPrBNknRtWs/AW5YQNcTh8hAbNYzfCj3E+ufxZ6qTb1/x1xlJliVLBiLEUOeIHuK+LRNlx7EL60S0ssTpMbR9CtqlxiAx2oLEaAuSYyyY2Dujwt+JGAwGtPFpsY/nluKl5fu859foP5TPoroH45Wl/ls5RNjh6YXXHjgHwNtwButNrcuIi4BWPDAErXUWViiJsZqRW1yON1YdlG2r64gG8cA2SbjPFwWiVUoMbBEmtEiOxvgeTbB460m8+etBKR5ojXuIFW1j0B5ixqSOQ+xAWyZHY82Dw5CVX4aJb64DELy3SG8qOlxYFAZxRV7dUFBrGbWPHSiqQDiRTwV7/58/tS/2nSlEe99gVDQsPILHX2sqXc8TKXrf9DyOl2amYdXfh6KgzIkOPs3kVd2boFN6HK57ex3O+3T2/oVUvmOLHmKD37us9LqGQ0OslOcE9BCbA3uIc4rK8b+1hxEXGSF5alWSCYWGWMuzqFcfY63mCiNpHPINwC9qlYgnruiMojIX4qO82UGHtGuE3OJyXYNY6ZiuqK6GsqguEGL94Ua2eNuU9UPcxhHbKb921l+P9p4uxPQPNkufRY85j+2bLWSI5YP+yf2b45+KdObcUVVQ5pTCngUT4SQ9LhI/3Tekwv0qYvGMgXh79SH8d8V+7DldCEA7MpHaQ0wGMVFPmNS3GbILHZJ3eEDrZFUIqPrIvSPbIi4yAtMGtgwYcFzJ45d3wtfbT0mJIdLjbOgvhOuqq4ihsEZ1TEVvjdTbPZrFY/HWkygsc2HNfu8ASE+HWV0EG01BuajO7Wa6GuGmCVFomhCFBXf0x5p9Z2UZCAOh9LyFMw4xoJZMVBQZIhQqMq6VmtHq7pTkYde899ViNspiGItGoF5caU6bRjFYtvuMajuv161TolXfibRIln9vMBjQPi0WGYlROF+SD8BvNEoLTt1yDbHSMcA7+nDEIVZ6OQMZxMqEOfw+JkR5ZVJFDheeWvpnwDIpNeWai+p0prbjoywVGv3zfzviO69RGoRwuGdSj1AlE1pa/8p4iO02M5rER+JkXikyfXGujTLJhM9DHCAxkthOWST5oX/bacHYfWJcJ8RH+e/DoDbJMBiADYdz0fbR7/He1D7SGo9ezRNUUhm/h9gvmbDWoEMjxmrGrBFtUVruwp9ZBTAaDLi+rzrqkUpDTAYxUV9oEh+JZ6/uWtvFCDvpcZF4RDHCDoaRnVIxslNqNZSoehneoRFuHdwSTjfTnQ5rFOsfGHRIi0XP5gm4voKA7OHm/kvaoZHdCreHoU1KDNrqeI2UVGRAAUCfFonoozEQ0EPZ4YRdQywcL8YWXg+xKjGHclFdEFEFwolMMqEzsBCLIEpgtKbI7xraBm4PMCYzTbbdbovAridHVzryiyzro2KaXExHazQY0FJhdEsaYsU0cTgWpgYyGkQPcXKMVTJu0+Js+L+xHbHzZD7+yirAvjNF/rKalYM97//lAcKuKctwSadUX4a4Zliy9WTA8sf6ZBXjuoWeXCHUsGvBJuaoCLPJiJ/vH4JzRQ4pLJjWojctGQVHc1GdsLHUJ2fp3TwBUwe2lP22eZJ31m7RHyfhdDOs3nfWHzte4yIlg7jMGTDKQ3ViNBrw6NjAUYmUz4I8xARB1Ci2CFOFDVWq3e9F/tvgVrimV9PqLpaKns0S0FMjDJoSpWRCnGIP16pllUEcZg+x2FnFWs2qzrQq5wuUMQvwd0o8dXlNRpnQs73FMnhkAxx12aKtZjw8poPmcaoiYZIvalJEmXDLNcT9WyVp/lYZESIcRkmghAWi1v0/k7rJvvubL1Pke2sP41+Cl1g3ykQACY3Sa33P8LZS7OxA1/jx9H4Y1DZZ9/uKUEsmKvAQh0lDDACRFpMsRq54aD4AEu9VoLZHyjwn1CMpY5/OrOuLE7vBYjLi803H4XT7MyVqXSOXTJS7PPh80zHvOWs5aZQWSvlKXfUQ1/2VQQRBVBuirjpYz2xtoTRWXULYtXA1sCrJRJhbyHRBqpORGBVmDbHys/xYzZO8nfyBs74wS9WtIQ4mU50omRAHODXYYYqe0wilhlgWdg1IibWiq5BMhT/P7s3i0bt5AtLsNlzVvbFMv18diB43Hm9cCc9AxtGLQ8w9i8GEXROfY3qABA5VjcLDY/1Kx6tIMqFR9nDFitfyBmvFK9aC1yfRQ8wz++nFIDcYDFK77HR7pEV1Wh7iGKsZyb6sdcdzSwFAloq6rkAaYoIg6jxiRAO9BS51hXHd0vH1tpPYc7oQZwsdXsmElNksPJ2farFSmC3iF6/tjrUHziEywoQh7VLCrCFWdjry73l2Lj6NXv0aYrEs2ueSeYjFzIM12GGKIdN4PVJ6Rg0Gv8f2ncm9sf5QDmJtZgxumwLAa5gsvHNADZVYbkzpG8Ryw0gvU530vcY7pI4A4/88tks67DYz8kqc+HLzcfx+MEf6LhzGqNFgkAzJijPVqbeFK566zCD2lcMUpIdYiunvESUTXoNYmXhHRArX5mYBk1kYDAYsvGMAth3PA+D1Og9plxLwemoDMogJgqjzRJiMWPPgMHgYQ3Qdj5xhNZvw0fR++HDdETz+9W54ZGHXqkcyEW4PcWK0BVcE0FRW5TIq0hDzqCpnC73hsMI1iNBDtqiugjjEgC9NMu/8a1AHKXbWvB7p6a8B76zKld2b1EzhdJClsdaJAKT0FColDsHMTijrlFhnTEYDhrb3LlbNKymXGcThiNPuvefBDXi1vKeBJCehIN4XLZmIalEdE2U23v/dGgaxVjQG5TGdbo80KNC7BS2So1ULRusa6rBrdVOcUDdLRRBEjZGRGIXmSXW7QRUR0/3yRU/h8jjord6vKarSiQcy4gDvokmRhGqe1hfRlUwI293VIIEJBrFzluIQ66S9riuIWeDsOh7i5BgLuvgiesRYzejcWB7pQR3JQUNDrPQiB+HpB8JkEAuHqGjAG+5oMCJaMx1iJIlAxjr3EMsM4go0xIDf8HYKg/7qvMbqRumtJw8xQRBEGODGiSwxR7gkE9UcZaI6UU7dKsuekRiFt2/uhX1nCmE0GjAmM71ayyN6ygIlODEZDXB7GDyCh7gmDeIrujXGlqPn0T0jXhokKG2PmrJF2jSKwYHsIvRoFh9wP4cQk1mvjhoMBnw9YyByS8oRYzWrZz8UPwsmMYeePED523DIFbSkCnpU53sq8xD7rlNMPKcOuyb8VmirOCVBeIi5rt3p8gSUTNQXlIOturqort4ZxPPmzcO8efNw5MgRAEDnzp3x+OOPY8yYMbVbMIIgagRuXLkZkxYEhauzEDXERkP4pl1rAlViDo17cknnNFzSOU21vToQo4IE6gBNBgPc8HqHnWGOGhIMWmEVw7nYMRQ+vKUv1h/KqTBCA89yVhFGo0EWj1xEeU2aiTlUhoy2oau0f5UzLZXBJDOIAx+vOl/TigxztWTC/7c4m8XhGRcDaYi5R1wmmahHbZESg8EAi9lYYzHQK0u9k0w0bdoUzz77LLZs2YLNmzdj+PDhuPLKK7F79+7aLhpBEDUA7xiO5ZRInrLwhV2TayTrEx3SYtE+NRYWkxF9WyQizR588pnqJtB0L7exRE24XlzpmqKirH/VReP4SEzo2VQWH1wLnm65KigHTFr1XRUPWFcyIX9eVlPVEzmJp65IMqEs+1dhXOCoZRDLdcL6ZeP7a0km9KJMADqSiXrWHikRE4bU5IA3FOqdh3jcuHGyz8888wzmzZuH9evXo3PnzrVUKoIgagremPLUsED4FmmInVR90+xFWcxYdt/FtV0MCVFnGdBoMBoBeGpNQ6yF8tHXNVtkeMdULNl2Sgq5VRmU16SMUwwA7dNiEWM1o8jhQttGMbp6ZaUDNzwa4uA9xOK7Oqx9Cno1T6jy+bXQukfKWSSx3vNyHTpXjC1Hz6NX8wRJMqHM0CnC2zinywNuS9e3AboSi9kIeNfz1tlrqXcGsYjb7caCBQtQXFyM/v37a+7jcDjgcDikzwUFBTVVPIIgqoHeLRLRLDEKx3JLpG2NYrWnhUNFDBdVVxvt+oJs6jiQh9j3lbuWNMRaKMtb17xzl3dJR1xkBDql2yveWYcWioW0zZKiVPuk2m3Y9OhIFJQ5kRRtCRA+T6EhDoNBLD6DilJhG0OQV4SKzBvMPcQB9/f/LeqEr3t7HTb+c6QkmYi06JeTa7Cdbs8FsagOkNeJuhplol4axDt37kT//v1RVlaGmJgYLF68GJ06aWfkmjt3Lp588skaLiFBENVFk/hIrH5wGErL3fj5rzNIibWGLeyQ6CFWxnElQkOYJa5wUR0AfPD7EZw8700uUNtTqkrjo67pN41GQ5Xjzd4ysCX6tEhESbkbKbEWtGkUq7lfpMUUMCICALRLjYHR4H3mHdJiwzKYFD2voWSqC/ezEusx9xCP7pyG938/gj4tAnuiO6bH4s6hrTFv1UE43QzZhY7gFtVpSCbq+wBdNIjrqnFfLw3i9u3bY9u2bcjPz8fChQsxZcoU/Prrr5pG8SOPPIL7779f+lxQUICMjIyaLC5BENVApMWEcQFi+lYGMcPY9X2bhfXYhDbRVjPOlzjx4bqj/m0BppNrAqUBX9c8xOHAaDSgW0Z8WI7VIc2OTY+ORG5xuaanuTKIjt5QEnOE23AU9b+8HLPHdcL0QS1lmSe1MBgMeOjSDvh+ZxaO5pTgeG4Jthw9DwCIDFoyUf8X1QHyyCO1PeDVo14axBaLBW3atAEA9OrVC5s2bcIrr7yCt956S7Wv1WqF1Rqe6VSCIC5sLumUhpkj2gIAbrqIDOKqwAJOLPt5dkJX/LArS/rcOD4SvVskVlexgsJqNsJsNEia5ugKPKQEkBRjRZJORIvKYJRJJgJ7iOMiIzC0fQr+OHoel2aGN4qKmHaZS3kMBgMyErUNf61aH+NLevS3DzdL2wJmqjPxDHeCZKJuqgyCRsyoGFNHk0DVzVKFiMfjkemECYIgKkOkxYT7R7Wr0XN2aRKHnSfz63zq7FBhwdnDGNQ2ucIwYzWNLcKE/0zqjo2Hc2EwoNpjNhNqjCFoiA0GA96f1rdayiF6gYMJw6hV72NtclNrUJtkKWmKFlIcYjcTMtXVTa9qsDw+rhMW/XESkRZTnZ19q3cG8SOPPIIxY8agWbNmKCwsxKeffopVq1Zh2bJltV00giCIkHlncm+s2X8WA9rULaOwqrBgLeI6yrhujcMuySGCR/SI1uYirKQYK769e1CFOmo/6nofK6TXbpkcjY//1i/gEbgnulxMzFHPJRNdm8aja9P42i5GQOqdQZydnY3JkycjKysLcXFx6Nq1K5YtW4ZRo0bVdtEIgiBCJi3Ohom9aV0DQYiE4iGubro01ffmBoPoIU6MrjhUXoSmZKJ+G8T1gXpnEP/vf/+r7SIQBEEQFVC//cNEbTMmMx3vrjkEe2QEBtaj2RNNyYS1cgaxVzLh3VbfPcT1gXpnEBMEQRB1n3qumCBqmYfHdMDDYzrUdjFCRltD7JdMJAVlEPujTFwoYdfqA2QQEwRBEGHHQxYxQQCovGTC6fFnqiPJRPVDBjFBEARBEEQ1IXqIQ5VMcDOYJBPVTz2PbEcQBEHURTIDhJUiiAuVfq3UMbTT4/2h25rpxC8W4ZIJt4dJsbDrexzi+gB5iAmCIIiw06dFIt6+uRdapYQnrTZB1Aeu6t4EFrMR3YQQYxe3TcFL13aDhwHDOzSq8BgRZrX1Sx7i6ocMYoIgCKJauKRzeLOGEURdx2g04PKu8vjVJqMBE3o2DfoYERruYFpUV/2QE54gCIIgCKKOoBV3mRbVVT9kEBMEQRAEQdQRTEYDlAoJkkxUP2QQEwRBEARB1BEMBoNKNkGSieqHDGKCIAiCIIg6hFI2YSQPcbVDBjFBEARBEEQdoklCpPR3cowVZvIQVzsUZYIgCIIgCKIO8fH0fthy9DwAoEvTOFpUVwOQQUwQBEEQBFGHaGS3YUyX9NouRoOCJBMEQRAEQRBEg4YMYoIgCIIgCKJBQwYxQRAEQRAE0aAhg5ggCIIgCIJo0JBBTBAEQRAEQTRoyCAmCIIgCIIgGjQNLuwaYwwAUFBQUMslIQiCIAiCILTgdhq326qbBmcQ5+TkAAAyMjJquSQEQRAEQRBEIHJychAXF1ft52lwBnFiYiIA4NixYzVyg4napaCgABkZGTh+/DjsdnttF4eoZuh5NyzoeTcs6Hk3LPLz89GsWTPJbqtuGpxBbDR6ZdNxcXH0QjUg7HY7Pe8GBD3vhgU974YFPe+GBbfbqv08NXIWgiAIgiAIgqijkEFMEARBEARBNGganEFstVoxe/ZsWK3W2i4KUQPQ825Y0PNuWNDzbljQ825Y1PTzNrCaimdBEARBEARBEHWQBuchJgiCIAiCIAgRMogJgiAIgiCIBg0ZxARBEARBEESDhgxigiAIgiAIokHT4Azi119/HS1atIDNZkO/fv2wcePG2i4SESJz585Fnz59EBsbi0aNGuGqq67C3r17ZfuUlZVhxowZSEpKQkxMDK6++mqcOXNGts+xY8cwduxYREVFoVGjRvjHP/4Bl8tVk5dCVIJnn30WBoMBs2bNkrbR876wOHnyJG666SYkJSUhMjISXbp0webNm6XvGWN4/PHHkZ6ejsjISIwcORL79++XHSM3Nxc33ngj7HY74uPjMX36dBQVFdX0pRAV4Ha78dhjj6Fly5aIjIxE69at8dRTT0Fc70/Pu/6yevVqjBs3Do0bN4bBYMCSJUtk34fr2e7YsQODBw+GzWZDRkYGnn/++dALyxoQn3/+ObNYLOy9995ju3fvZrfeeiuLj49nZ86cqe2iESEwevRoNn/+fLZr1y62bds2dtlll7FmzZqxoqIiaZ877riDZWRksBUrVrDNmzeziy66iA0YMED63uVysczMTDZy5Ei2detW9v3337Pk5GT2yCOP1MYlEUGyceNG1qJFC9a1a1d27733StvpeV845ObmsubNm7OpU6eyDRs2sEOHDrFly5axAwcOSPs8++yzLC4uji1ZsoRt376dXXHFFaxly5astLRU2ufSSy9l3bp1Y+vXr2dr1qxhbdq0Yddff31tXBIRgGeeeYYlJSWxpUuXssOHD7MFCxawmJgY9sorr0j70POuv3z//ffs0UcfZYsWLWIA2OLFi2Xfh+PZ5ufns9TUVHbjjTeyXbt2sc8++4xFRkayt956K6SyNiiDuG/fvmzGjBnSZ7fbzRo3bszmzp1bi6Uiqkp2djYDwH799VfGGGN5eXksIiKCLViwQNrnr7/+YgDYunXrGGPel9RoNLLTp09L+8ybN4/Z7XbmcDhq9gKIoCgsLGRt27Zly5cvZ0OGDJEMYnreFxYPPfQQGzRokO73Ho+HpaWlsRdeeEHalpeXx6xWK/vss88YY4z9+eefDADbtGmTtM8PP/zADAYDO3nyZPUVngiZsWPHsltuuUW2bcKECezGG29kjNHzvpBQGsTherZvvPEGS0hIkLXlDz30EGvfvn1I5Wswkony8nJs2bIFI0eOlLYZjUaMHDkS69atq8WSEVUlPz8fAJCYmAgA2LJlC5xOp+xZd+jQAc2aNZOe9bp169ClSxekpqZK+4wePRoFBQXYvXt3DZaeCJYZM2Zg7NixsucK0PO+0Pjmm2/Qu3dvTJw4EY0aNUKPHj3wzjvvSN8fPnwYp0+flj3vuLg49OvXT/a84+Pj0bt3b2mfkSNHwmg0YsOGDTV3MUSFDBgwACtWrMC+ffsAANu3b8fatWsxZswYAPS8L2TC9WzXrVuHiy++GBaLRdpn9OjR2Lt3L86fPx90ecxVvaD6wrlz5+B2u2UdIgCkpqZiz549tVQqoqp4PB7MmjULAwcORGZmJgDg9OnTsFgsiI+Pl+2bmpqK06dPS/to1QX+HVG3+Pzzz/HHH39g06ZNqu/oeV9YHDp0CPPmzcP999+Pf/7zn9i0aRNmzpwJi8WCKVOmSM9L63mKz7tRo0ay781mMxITE+l51zEefvhhFBQUoEOHDjCZTHC73XjmmWdw4403AgA97wuYcD3b06dPo2XLlqpj8O8SEhKCKk+DMYiJC5MZM2Zg165dWLt2bW0Xhagmjh8/jnvvvRfLly+HzWar7eIQ1YzH40Hv3r0xZ84cAECPHj2wa9cuvPnmm5gyZUotl44IN19++SU++eQTfPrpp+jcuTO2bduGWbNmoXHjxvS8iRqlwUgmkpOTYTKZVCvPz5w5g7S0tFoqFVEV7r77bixduhS//PILmjZtKm1PS0tDeXk58vLyZPuLzzotLU2zLvDviLrDli1bkJ2djZ49e8JsNsNsNuPXX3/Ff//7X5jNZqSmptLzvoBIT09Hp06dZNs6duyIY8eOAfA/r0BteVpaGrKzs2Xfu1wu5Obm0vOuY/zjH//Aww8/jOuuuw5dunTBzTffjPvuuw9z584FQM/7QiZczzZc7XuDMYgtFgt69eqFFStWSNs8Hg9WrFiB/v3712LJiFBhjOHuu+/G4sWLsXLlStVUSa9evRARESF71nv37sWxY8ekZ92/f3/s3LlT9qItX74cdrtd1RkTtcuIESOwc+dObNu2TfrXu3dv3HjjjdLf9LwvHAYOHKgKo7hv3z40b94cANCyZUukpaXJnndBQQE2bNgge955eXnYsmWLtM/KlSvh8XjQr1+/GrgKIlhKSkpgNMpNEZPJBI/HA4Ce94VMuJ5t//79sXr1ajidTmmf5cuXo3379kHLJQA0vLBrVquVvf/+++zPP/9kt912G4uPj5etPCfqPnfeeSeLi4tjq1atYllZWdK/kpISaZ877riDNWvWjK1cuZJt3ryZ9e/fn/Xv31/6nofhuuSSS9i2bdvYjz/+yFJSUup1GK6NGzey/v37s6ioKAaAbd26tcbOPWXKFNa8efMaO58YZYKx4J43ANaqVauQnvf8+fNVK5yri5q+h3WVjRs3MrPZzJ555hm2f/9+9sknn7CoqCj28ccfS/s8++yzLD4+nn399ddsx44d7Morr9QM1dSjRw+2YcMGtnbtWta2bVsKw1UHmTJlCmvSpIkUdm3RokUsOTmZPfjgg9I+9LzrL4WFhWzr1q1s69atDAB76aWX2NatW9nRo0cZY+F5tnl5eSw1NZXdfPPNbNeuXezzzz9nUVFRFHatIl599VXWrFkzZrFYWN++fdn69etru0hEiADQ/Dd//nxpn9LSUnbXXXexhIQEFhUVxcaPH8+ysrJkxzly5AgbM2YMi4yMZMnJyeyBBx5gTqezhq8mPJSXl7PmzZuz9u3bs7feeot99NFHLDc3t8bOX9sGcTDPGwBr06ZNSM+bDOLKM2TIENa5c+eQfjN79mwGgH388ccsMzOTWa1W1qFDB/b222/L9vN4POyxxx5jqampzGq1shEjRrC9e/fK9snJyWHXX389i4mJYXa7nU2bNo0VFhZW+bqI8FJQUMDuvfde1qxZM2az2VirVq3Yo48+KguhRc+7/vLLL79o9tdTpkxhjIXv2W7fvp0NGjSIWa1W1qRJE/bss8+GXFYDY0I6GIIg6iV79uxBx44d8c477+Bvf/tbjZ9/6tSpWLVqFY4cOVLj5w6WsrIySYMcLO+//z6mTZuGTZs2ycL+VAf14R6GwtChQ3Hu3Dns2rUr6N888cQTePLJJ3H27FkkJydXY+kIgiDkNBgNMUFcyHBtrDL0WEPH4/GgrKwMAGCz2UIyhglCRKxLBEFceJBBTBD1nKlTp2LIkCEAgIkTJ8JgMGDo0KHSP639W7RoIX0+cuQIDAYD/v3vf+Ptt99G69atYbVa0adPH824v0uWLEFmZiZsNhsyMzOxePHiSpd9w4YNuPTSSxEXF4eoqCgMGTIEv/32m2yfJ554AgaDAQcOHMDUqVMRHx+PuLg4TJs2DSUlJbJ9DQYD7r77bnzyySfo3LkzrFYrfvzxR+m7J554Qrb/yZMnMX36dDRu3BhWqxUtW7bEnXfeifLyctl+DocD999/P1JSUhAdHY3x48fj7Nmzquv54YcfMHjwYERHRyM2NhZjx47VTPxRlXvYokULXH755fjpp5/QvXt32Gw2dOrUCYsWLZLt9/7778NgMGDt2rWYOXMmUlJSEB8fj9tvv12KyjF58mQkJCQgISEBDz74IPiEodPpRGJiIqZNm6Y6f0FBAWw2G/7+978HXeaqMHToUGRmZuLPP//EsGHDEBUVhSZNmuD5559X7fvqq6+ic+fOiIqKQkJCAnr37o1PP/1U+l5Z9zm8jokEqkv//ve/MWDAACQlJSEyMhK9evXCwoULVcflx+DP22q1onPnztJxRIKpi3l5eZg1axYyMjJgtVrRpk0bPPfcc9ICNIIgKg+5SwiinnP77bejSZMmmDNnDmbOnIk+ffogNTUVzzzzTEjH+fTTT1FYWIjbb78dBoMBzz//PCZMmIBDhw4hIiICAPDTTz/h6quvRqdOnTB37lzk5ORg2rRpsrB3wbJy5UqMGTMGvXr1wuzZs2E0GjF//nwMHz4ca9asQd++fWX7X3vttWjZsiXmzp2LP/74A++++y4aNWqE5557TnXcL7/8EnfffTeSk5M1DSAAOHXqFPr27Yu8vDzcdttt6NChA06ePImFCxeipKRElvXonnvuQUJCAmbPno0jR47g5Zdfxt13340vvvhC2uejjz7ClClTMHr0aDz33HMoKSnBvHnzMGjQIGzdulUqRzju4f79+zFp0iTccccdmDJlCubPn4+JEyfixx9/xKhRo2T73nPPPUhLS8OTTz6J9evX4+2330Z8fDx+//13NGvWDHPmzMH333+PF154AZmZmZg8eTIiIiIwfvx4LFq0CG+99ZbsXixZsgQOhwPXXXdd0OWtKufPn8ell16KCRMm4Nprr8XChQvx0EMPoUuXLlJGs3feeQczZ87ENddcg3vvvRdlZWXYsWMHNmzYgBtuuKFS59WrS6+88gquuOIK3HjjjSgvL8fnn3+OiRMnYunSpRg7dqzsGGvXrsWiRYtw1113ITY2Fv/9739x9dVX49ixY0hKSgIQXF0sKSnBkCFDcPLkSdx+++1o1qwZfv/9dzzyyCPIysrCyy+/XOn7SxAEGlaUCYK4UOELFxYsWCBtGzJkCBsyZIhqX+XircOHDzMALCkpSbYQ7+uvv2YA2Lfffitt6969O0tPT2d5eXnStp9++okBCGlBmMfjYW3btmWjR49mHo9H2l5SUsJatmzJRo0aJW3jC61uueUW2THGjx/PkpKSZNsAMKPRyHbv3q06JwA2e/Zs6fPkyZOZ0WjUXDDHy8QX1Y0cOVJWzvvuu4+ZTCbpPhQWFrL4+Hh26623yo5z+vRpFhcXJ9te1XvYvHlzBoB99dVX0rb8/HyWnp7OevToIW3jZVfe4/79+zODwcDuuOMOaZvL5WJNmzaV1Zdly5apnj9jjF122WWsVatWFZazKovqzp49KzsOAPbhhx9K2xwOB0tLS2NXX321tO3KK6+s8Hx6Cxf5eUUC1SUxog1j3kWtmZmZbPjw4apjWCwWduDAAWnb9u3bGQD26quvStuCqYtPPfUUi46OZvv27ZN9//DDDzOTycSOHTumc9UEQQQDSSYIggAATJo0SRazcfDgwQC8qXQBICsrC9u2bcOUKVMQFxcn7Tdq1KiQY/lu27YN+/fvxw033ICcnBycO3cO586dQ3FxMUaMGIHVq1erpoHvuOMO2efBgwcjJycHBQUFsu1DhgypsDwejwdLlizBuHHjNBfLKafPb7vtNtm2wYMHw+124+jRowC8MS/z8vJw/fXXS9dy7tw5mEwm9OvXD7/88guA8N3Dxo0bY/z48dJnu92OyZMnY+vWrapUtdOnT5eVvV+/fmCMYfr06dI2k8mE3r17S88aAIYPH47k5GSZF/z8+fNYvnw5Jk2aFHRZw0FMTAxuuukm6bPFYkHfvn1l5Y2Pj8eJEyc0ZT6VRa8uRUZGSn+fP38e+fn5GDx4MP744w/VviNHjkTr1q2lz127doXdbpfKHmxdXLBgAQYPHoyEhARZHRs5ciTcbjdWr15d5esliIYMSSYIggAANGvWTPaZG8fnz58HAMn4a9u2req37du31zQG9Ni/fz8ABEzNmp+fLzPQA5XPbrdL25WJWrQ4e/YsCgoKkJmZGVR5K7o3/HqGDx+u+XtevnDdwzZt2qiM9nbt2gHwasLF7EzKsnNDPCMjQ7WdXw8AmM1mXH311fj000/hcDhgtVqxaNEiOJ3OGjeImzZtqrrehIQE7NixQ/r80EMP4eeff0bfvn3Rpk0bXHLJJbjhhhswcODASp9Xry4tXboUTz/9NLZt2waHwyFtV5YRUN9/XnZ+r4Oti/v378eOHTuQkpKi+b0ymxdBEKFBBjFBXKAYDAZpkZSI2+3W3N9kMmlu1zpGVeHe3xdeeAHdu3fX3CcmJkb2Odjyid67cFHRufn1fPTRR5qpQmszuoVe2bW2K+/lddddh7feegs//PADrrrqKnz55Zfo0KEDunXrVi1l1SOYZ9+xY0fs3bsXS5cuxY8//oivvvoKb7zxBh5//HE8+eSTALQNVkD/ndCqS2vWrMEVV1yBiy++GG+88QbS09MRERGB+fPnyxbwhVL2YPB4PBg1ahQefPBBze/5gIggiMpBBjFBXKAkJCTIppQ53EsZKjx1LveGiihT7VYEn0K22+0YOXJkpcpTFVJSUmC320OKkRsIfj2NGjUKeD3huocHDhwAY0xm4O3btw8AdBcRTp06FXl5eboDEC0uvvhipKen44svvsCgQYOwcuVKPProo0H/vqaJjo7GpEmTMGnSJJSXl2PChAl45pln8Mgjj8BmsyEhIQF5eXmq34XyTnz11Vew2WxYtmwZrFartH3+/PmVKnOwdbF169YoKiqqlfeFIBoCpCEmiAuU1q1bY8+ePbLwYNu3b1eFNQuW9PR0dO/eHR988AHy8/Ol7cuXL8eff/4Z0rF69eqF1q1b49///jeKiopU32uFNAsnRqMRV111Fb799lts3rxZ9X2o3rvRo0fDbrdjzpw5cDqdqu/59YR6Dw8ePIiDBw+qtp86dUoWqq2goAAffvghunfvrumhrixGoxHXXHMNvv32W3z00UdwuVw1LpcIlpycHNlni8WCTp06gTEmPZPWrVsjPz9fJrXIysoKKeydyWSCwWCQeZWPHDmCJUuWVKrcwdbFa6+9FuvWrcOyZctU++Tl5cHlclXq/ARBeCEPMUFcoNxyyy146aWXMHr0aEyfPh3Z2dl488030blzZ9VCtGCZO3cuxo4di0GDBuGWW25Bbm6uFPtVy7DVw2g04t1338WYMWPQuXNnTJs2DU2aNMHJkyfxyy+/wG6349tvv61UGYNlzpw5+OmnnzBkyBDcdttt6NixI7KysrBgwQKsXbs2pCQndrsd8+bNw80334yePXviuuuuQ0pKCo4dO4bvvvsOAwcOxGuvvQYgtHs4YsQIAFBlr2vXrh2mT5+OTZs2ITU1Fe+99x7OnDlTaS9lICZNmoRXX30Vs2fPRpcuXdCxY0fVPkOHDsWvv/5aLfKaYLnkkkuQlpaGgQMHIjU1FX/99Rdee+01jB07FrGxsQC8EpCHHnoI48ePx8yZM6XQeO3atQtavz127Fi89NJLuPTSS3HDDTcgOzsbr7/+Otq0aSMztEMhmLr4j3/8A9988w0uv/xyTJ06Fb169UJxcTF27tyJhQsX4siRI5TdjyCqAHmICeICpWPHjvjwww+Rn5+P+++/H9988w0++ugj9OzZs9LHvPTSS7FgwQK43W488sgjWLRoEebPn1+ptMZDhw7FunXr0Lt3b7z22mu455578P777yMtLQ333XdfpcsYLE2aNMGGDRtwzTXX4JNPPsHMmTPx4YcfYujQoYiKigr5eDfccANWrFiBJk2a4IUXXsC9996Lzz//HN27d5cluKjoHg4dOhR33303Zs2ahePHj+P06dN46aWX0KVLF0RHR+PEiRNwOBx4//338f333+Phhx/GuXPnYLPZAHife0xMDF588cWA5d26dStSUlJUcZyVDBgwABkZGSgsLNT1DhcVFYXVM10Zbr/9dhQVFeGll17CjBkzsGTJEsycORMff/yxtE9SUhIWL16MqKgoPPjgg/jggw8wd+5cjBs3LujzDB8+HP/73/9w+vRpzJo1C5999hmee+45WdSPUAmmLkZFReHXX3/FP/7xD6xatQr33nsvnn32Wezfvx9PPvmkLGoJQRChY2C1OaQnCIIgZAwdOhRbtmzBnXfeKYVG++GHH9CtWze0bNkSF110EYqKijB58mS88cYbALxZ6W677TYMGTIEc+fOhdFoxE033YQePXrgk08+AeDXEC9ZsgQrV67EhAkT8Pzzz+O2226rUnkLCwuRmJiIl19+GTNmzKjaxRMEQdQSJJkgCIKoY7Rt21aWmrh9+/bS3zabDU2aNMGXX34pGcSAN93ym2++KS3wu/vuu/Gvf/1LdezFixdj8uTJePfdd8OiB169ejWaNGmCW2+9tcrHIgiCqC3IICYIIqzk5uaivLxc93uTyaQbS5Xw0qtXL9nnn3/+GXPnzsWePXuQlZWF48ePw+PxoKSkRDalLiaASE9PV8Wm3bBhA5YuXYqFCxfiqquuCktZx44dq0pXrCQ/Px+lpaUB96ltyQVBEA0b0hATBBFWJkyYgPT0dN1/ffr0qe0i1nmio6Olv48cOYLLL78cXbt2xVdffYXGjRtLSRzEgUdERITsGFpxqFu3bo0OHTrgvffe04yGUV3ce++9AetEenp6jZWFIAhCC/IQEwQRVl588UVZxjMl1ZE440Jmy5Yt8Hg8ePHFF2E0GnHixAk8/fTTlYpokJycjEWLFmHo0KG49tpr8eWXX6oM6ergwQcflKVeJgiCqGuQQUwQRFhRTvcTVaNNmzZwOp149dVXMW7cOPz222948803K328Ro0aYeXKlRg2bBiuv/56fP7559WeSa9Tp07o1KlTtZ6DIAiiKjQ4g9jj8eDUqVOIjY3VTeNJEARRW7hcLjgcDilWdMuWLTFnzhzMnTsXDz/8MAYOHIjHH38ct99+OwoKCmA0GlFaWgrGmCy+dElJCQBI28rLy+F0OlFQUICoqCh8/fXXuOyyy3Dttdfif//7n26KYYIgiNqAMYbCwkI0btwYRmP1K3wbXNi1EydOICMjo7aLQRAEQRAEQVTA8ePH0bRp02o/T4PzEPOMRcePH4fdbq/l0hAEQRAEQRBKCgoKkJGRIdlt1U2DM4i5TMJut5NBTBAEQRAEUYepKXkrhV0jCIIgCIIgGjRkEBMEQRAEQRANGjKICYIgCIIgiAYNGcQEQRAEQRBEg4YMYoIgCIIgCKJBQwYxQRAEQRAE0aBpcGHXOIfOFSHWQeMBgiAIgiCIukZhYVGNnq/BGsS7T+YjKsZT28UgCIIgCIIgFJQUFdbo+chFShAEQRAEQTRoyCAmCIIgCIIgGjRkEBMEQRAEQRANGjKICYIgCIIgiAYNGcQEQRAEQRBEg4YMYoIgCIIgCKJBQwYxQRAEQRAE0aAhg5ggCIIgCIJo0JBBTBAEQRAEQTRoyCAmCIIgCIIgGjRkEBMEQRAEQRANGjKICYIgCIIgiAZNrRvEJ0+exE033YSkpCRERkaiS5cu2Lx5c8DfrFq1Cj179oTVakWbNm3w/vvv10xhCYIgCIIgiAuOWjWIz58/j4EDByIiIgI//PAD/vzzT7z44otISEjQ/c3hw4cxduxYDBs2DNu2bcOsWbPwt7/9DcuWLavBkhMEQRAEQRAXCubaPPlzzz2HjIwMzJ8/X9rWsmXLgL9588038f/sfWeYHMW19tszszubc5JWq5wDQiIICYFkIxAgYwM2xrrYBGMwvtgkG9vY1xcwF8QFYxy4JtifAWObaEwySWQLCRBCgCRQzlna1eY809+Pnuquqq7qMGFndrfe59lne7qrq05VVzh16q1To0aNwl133QUAmDRpEpYtW4a7774bCxcuTKm8CgoKCgoKCgoKAw9ptRA/99xzOPbYY3HeeeehqqoKM2bMwB//+EfHd1asWIEFCxYw9xYuXIgVK1YIw3d1daG5uZn5U1BQUFBQUFBQUCBIq0K8detW3HvvvRg3bhxeeeUVfO9738NVV12Fhx9+WPrO/v37UV1dzdyrrq5Gc3MzOjo6bOGXLFmC4uJi86+uri7p+VBQUFBQUFBQUOi/SKtCHI1GMXPmTNx2222YMWMGLr/8clx22WW47777kpbGDTfcgKamJvNv165dSYtbQUFBQUFBQUGh/yOtCvGQIUMwefJk5t6kSZOwc+dO6Ts1NTU4cOAAc+/AgQMoKipCbm6uLXw4HEZRURHzp6CgoKCgoKCgoECQVoX4xBNPxIYNG5h7GzduxIgRI6TvzJ49G6+//jpzb+nSpZg9e3ZKZFRQUFBQUFBQUBjYSKtCfO211+K9997Dbbfdhs2bN+Pvf/87HnjgAVx55ZVmmBtuuAEXXnih+fuKK67A1q1b8eMf/xjr16/HH/7wBzzxxBO49tpr05EFBQUFBQUFBQWFfo60KsTHHXcc/vnPf+LRRx/F1KlTccstt+A3v/kNLrjgAjPMvn37GArFqFGj8K9//QtLly7F9OnTcdddd+FPf/qTcrmmoKCgoKCgoKAQFzRd1/V0C9GXaG5uRnFxMf76zmfIKyhMtzgKCgoKCgoKCgoc2ltb8M2TJ6OpqalP9n+l/ehmBQUFBQUFBQUFhXRCKcQKCgoKCgoKCgqDGkohVlBQUFBQUFBQGNRQCrGCgoKCgoKCgsKghlKIFRQUFBQUFBQUBjWUQqygoKCgoKCgoDCooRRiBQUFBQUFBQWFQQ2lECsoKCgoKCgoKAxqKIVYQUFBQUFBQUFhUEMpxAoKCgoKCgoKCoMaSiFWUFBQUFBQUFAY1FAKsYKCgoKCgoKCwqCGUogVFBQUFBQUFBQGNZRCrKCgoKCgoKCgMKihFGIFBQUFBQUFBYVBDaUQKygoKCgoKCgoDGoohVhBQUFBQUFBQWFQQynECgoKCgoKCgoKgxpKIVZQUFBQUFBQUBjUUAqxgoKCgoKCgoLCoIZSiBUUFBQUFBQUFAY1lEKsoKCgoKCgoKAwqKEUYgUFBQUFBQUFhUENpRArKCgoKCgoKCgMaiiFWEFBQUFBQUFBYVAjlG4B0olDLV1YvrkeIyrycHRdCQDgQHMnVmyph6ZpmDu2HOUFYQBAa1cv3t5wCJ09EeRmB/GFCVXIzQ4y8fRGowCAo4YVY1x1oWc5Wjp7sGJLA9q6e5GbFcT8CZXIyQoiEtXxxvqDaO7owfiaQkyrLTbf+WBbAwrCIUweWpSk0mDleXvjYXT1RMx7pfnZmD++EoGAhlXbj2B7fRvKC8KYN74CmqbFlQ6JBwBmDC/B6MoCX+/3RqJ4c8MhNHf0mPdys4OYN74S+WF71W7u6ME7m9h8AUBOlvFOQY7/5tDRHcFbGw+hvauXuT+hphBTqe/1/tZ67D7SAU3TcMyIUowozwMAbNjfgrV7msxwpfnZmD+hEgGPZbqroR2bD7bi5PGVCAaMd3bUt2HVjkbouo5hpXmYNbrMd74SxYb9LWho68bsMeVxx7F8cz0qCrNRkpuF5VvqMbqyAEcNK7Z994qCME52qYeRqI43NxxEU3sPxlUX4qhhxcJw3b1RvLnhIFo7ezF5aBH2NXXiSFs3QoEA5owtR2VhOO78JIL3ttRjT6NRf44dUYrhsfpDQ9d1/HvTYRxq6UJdWR6OH2X/7kw8I0sxvMweT7JA5KktzcUYh7a96UALPt1ttYGSvGx8YYLR19DoiUTx5vpDaOk0vntlYRgnjYu//yGob+3Css31qCvNxcwRpeb91TsbsfVQKwDgqGElGFftr3+KB5sPtuKTXY0AgGBAw5wxxhj01oZDaGzvRigYwIljy1GSm4W3Nx7GiPI8bDzQgo5uo08bXp6H40bav/vyLfWob+3CyeMrUZybBQBobO/BvzcdQndvFIU5WZg/oRLZIdZGFtV1vL3hEBraujG6Mh8zhpfa4gbYvpjv+0RYvrkeDe3dOHlcBYpys6DrOpZtrsfB5k4ENA2zRpehsyeK1TuPIDfbGBPzst37Z13X8e7metQU52BslfW9Gtq6sWzTYfREorZ3ggENc8ZWoMqhbbd19eLtjYfQ0W2M/+OrC7FmdxMmDinEpCFF+GRXI7YdbrPFw7dJTQN21rc79lm7GtqxcvsRZIcCmDe+AqFAAMs2H8aM4SWoKPDf/0SiOt5cfxBNHT1m/RHFs2ZPE7YcbMXsMeWoLsrxnQ6Pls4evLPxMIpzszB3XIV5f/3+Fny+txnHjypDbWluwukkE4NaIX5l3X6s2tEIbASmfq0IoWAAr6w9gNWxDqmrN4qvHzsMALByWwNe+/yg+W5RjvWRl352ACu3HzGffb6vBf/1pUme5fjzsu3YXt9u/i7Ny8Ks0eXYeKAFL67ZDwB4f1sD/ver06BpGg61dOHRD3YBAO4+f3pceXfC8i31eJ3KK8GI8jwMLc7F3z7YCV037o2uyBcOzm7o6I4w8Ww91IbrT5/gK44NB1rN8qGRnx3CvAmVtvuyfAFAOBTAgsnVvtIHgNW7GvHqugO2+yu3H8HtX50GwFDEH1u523y2s74d15w6DgDw2MpdaGzvYd4dVZGPURX5ntL/9dJNAICsYMCsj//8aC92NJD6VI/JQwtRmJPlK1+J4r63twIAhpXmoi4OpWtHfRueXGWU2bTaYqzZ04S3Nx7GXecdhfX7W2zffWxVgWPnuvlgK/71qdWWbj93mk3hAoB1e5vw8lrjey7bXM88a+3uxQWzhvvOS6JobO/B4x9a9Wf3kQ5cdcpYW7h9TZ345+q9sV/1mDy0CAXUxLCxvZuJZ09jB37wRXs8ycLn+1pMeZz6qcdW7kZDWzdzb2RFnk2J/nxfM15ay373cVUFGFKS2KD62ucH8d7WBgDAlKFFCGcF0d0bxV/f24ForH/asL8FP1vkvU+PF09+uBsHW7rM3y2dvThhdBle+HSfea+jO4LqojBzj0DbXI8pQ4sY5bGhrRtPxr57KBDAGdNqAAD/3nQIb6w/ZIarKgqbhiGCnfXteO4TI53lW+oxZWixTWkG2L6Y7vtEaGjrNtt2MKDhjKk1ONjShac/2mOGOdTahaaOHuxt7AQA5IdDmDfe3qfz2HSwFf+IxUPXuTfXH7S1ZxptXRFccIK8bX+4/QiWfmaNHeT6nU2HcefXpuGh5TsAAE0dPbhozkgz3IHmLqZN0pD1Wc9+vBdbDhmGolDAGO8/2tmItzYcwq3nTJXKKMPmg634F9Vf0noNga7r+POy7QCAQ63duOykUb7T4fHeVktnmlBTaBoT7o+NDdsOt+G608YnnE4yMagpE2RWDQC9sZ6vs9e610k97+plZ5b0786YxXFEbOCn4/ACWhkGgI4eI+52Kv2eiG52zm2cNTLZ6IqlP7I8D6dNqUZRzHLa1RNFdyRqKrGGrP7yaqbRy8bT2WufubvLaaRdlp+N06ZUoy7WucjKn+RrVIWRr9OmVJvfjP++fmUYWpyD06ZUY35MEafj4+Nm6ljs/bljy1EYK+fOOMp0X1Onec1/k3jzlgzwyr5XHKHea6Xqe0TXzfyV52cjP7ZK49bmunrZthShKx+Fzh55WXU5PEsluri8dUvyysve7dBnAbCtlCQbh1q73APBknPOmHIU55I2YC9rcq+iwPru8fY/NOjv2hPrZCNRq78F+q4NkbIYXhbry3oitrLo7I1g95EO5l5daS40ADqM+k2D/s5038LXZ76+8OGjOsxVUB50HXUrK1aGSOwel8eeKHPPa9s73NotvE/iGl9dYPb9p02pxqQhhZ5kdnpO9yX0mO32Ht+ueVnJ+5sOtgrj9gov/QddY1o7k6Nf0HkX1a2GdvG3SifSqhDfdNNN0DSN+Zs4caI0/EMPPWQLn5MTv2mfrgQRqiMkoBs/P3zq1B3yzpgqw6on6TM8oze2rNPLdWzRWMNLcIXQFab1tzIfZ0ytQWletpl+lFMkeqNixcINfDzxxWH8ryzMxhlTazAyZlWNSGQiHdfYqgKcMbUGZ0ytwajKfOaZX5D3hpXl4oypNThlYpX5TI894/MapeQjdeULE6tQmpfF5MsP6DrhlF6fI866KnstGtXN/FQXhU2ai1ub4z+vrP451ctk1Nl4wH8/mRi8fDr3m28XaawVDIicJ4+vQAnV1/Ag8tcU5aAotuwfb/9DI0qVhKzN9lVZkXGF9GVR3d43RaOwLbWPqsg3Vzz4704XEZ0vvsnw7wEANwRJ6x5fQKK4zDjp/k+3y0V+03Ek2vbI+5OHFJl9/xlTazB1qEHt0F2+sONz6hEvplM5yKou842iurzMPcIukyhQYmmI003e9+srpJ0yMWXKFLz22mvm71DIWaSioiJs2LDB/J0of4xANxumdY/ubPmKrTOdjPE/FAzEfif28Um6/Gw8GtWBIKBR6kJU1z3zTb2CyE/iJdFHdfvg3CvgZPlJg8Cp43CLg8gZoOT0Ep5+R49zEkM+EV9WgFFHNM1eZvTPiCmTFUc89YeuAXz++UGtP4AuR1bZt/ITCGgIeiwz/qlMgXbSr9KmEPMDmiScrY+yxeNNsU4WvPZKRAwNVhsQ9Qdm+w1oJl+eNxrEA1Ff3tdlxacfClj12j726ODZPqGghoAGRGCXlVao6eJyGtMsebxNDERKl2xYYhT02A/7pE/nvou3DyCdSFN1hwlPfrpE75S8zlx7n0jJDDeMQqzrrsq6G/i3RXlJxfRPl1wTaPFaS1KItCvEoVAINTU1nsNrmuYrvBPoikE6DbqToDtbp1kWqdhZwfgVGhqWQixWpBgFIaojEExuxTIHqFi0Qapz5ttw/BZiLs04oiGdKFGKiJzyjsb4zyrEsXfi/GbkWwdFCjGXLv8OQOUhoJmddTwWXTpPyZhspBuyzjKq68x3DwQ8KsQCC5QwfoeyT59C7E12N2teMtpcKmCtfGmI2RQgmmdHqO9OFMakWIgZwwf737rfN4VFkmH6XK4sIgIjSDCgxdqMXX3SBf0NnZb5TCiPt9lY1IdKJbIc2ibxUTYnXj+zXCE2/vMTCY/6sGNbiTiUqVO9kT1jLfoehHOBre8TREiHSVpVZyZfYlkyDWnnEG/atAlDhw7F6NGjccEFF2Dnzp2O4VtbWzFixAjU1dXhK1/5CtatW+cYvqurC83NzcwfQYSpBMY1S5mgnjukQRp1VoBYiB1FckUkYpeFTodVfhJLSwSdGqDo/9GonTIhUz7dYLMIxNHqI5ycAc1ZoST36U4xEass/R5RzGhFzqxTEoXGmP1bciRkIeYmSTTi/UbpBGtpZwfQCFXmVpk5x2dXDt2tM7ZnaaJie7VW2tuU2/PMqBckP5rLKomp1AQMiygARJLwUUTjgFerfLJBvkkoNpZEovb+Q9ftil0oEKBW8vj2T107LGMLKRNc8crqjB9FkJVBLAtvfPFsJJBZpTnjCR/eVSF2CBFx0BOc4pUtbvD9XaJ1zxNlgn6eYHqieEieMlwfTq9CPGvWLDz00EN4+eWXce+992Lbtm046aST0NLSIgw/YcIE/PnPf8azzz6Lv/71r4hGo5gzZw52794tDA8AS5YsQXFxsflXV1dnPtMFDZO1/EaFYY3wOhUu1okF47fw0SBUCS8c4lRYrUxLdOw3TUWwWYjjXLK0d4D+4yBRxMYOUymVWXtpSxQBeTfeT8ZbHoQWYpuCar9vKMTkvn85NIdJUqZ3QiLQ+WEG9Cg9saHLzDmTbtZSt/vGs/QUpNe66abg9DllIg4ql9OkNkJ9d6IwJpsyoZv/+7aseAGsfklghBBYiEMOq4SiFSnAW5681nmPhuSYDPb4xRxi63e8K3j8+zbKBOQUHRreLcTe640sTZZjbf/+fuFFIU5J9RZEmulc4rRSJs444wzz+qijjsKsWbMwYsQIPPHEE7j00ktt4WfPno3Zs2ebv+fMmYNJkybh/vvvxy233CJM44YbbsB1111n/m5ubjaV4oigYdINj6FMcPHSv00LcWy9T4/di5fbK+UQm5YU2goZVxKOIA01wFteqeVqS9b4LDS2DT5x5IN8K5My4WYhjt0OUp2im1XZDaZyZlqILZA8yZa86boWpHiR8XT+jpvqMrwTEoEuR3piSluIgwF4LjN+mVBeR+TxZM6mOm+yu00CMmX50pzYulBgTHpSILmUCZHV1K7g9U1ZkVSIwm9sIuXC6LpNsQsFNLMPcLLW0uXqRen3OonyM4HgObLGfy4Mtxrptaqyeyl024oDPyZ7HaKd2n6vwyTDaznI3onqiY/xdjqLPcJUdAV0uqSIEp3YpBpp5xDTKCkpwfjx47F582ZP4bOysjBjxgzH8OFwGOGw2Jm1iFvFcIg9coOIYp1FzdIT4fYSRdzGIRYs+aeigpEYSWcRYDjEvEIcpyKZhAGHthQCllXFzUIsokzEqxzwSjk7WWE7+2BAi7lzit2nBrpEN9UFGIuqHpPFqLf9UiGmvhHtRormVPqhmdiVQ+8KscHMTCdlgv0ty6ltedvNQpygXG7wvqlON8OTLlNk+KUtxMFg8hRiEX8yfZvqLKWf/KZXBnXd+M68IhcKBKR9GV2WNn6qIG3mno0yIYGPyRarEJN0rL6ZbJyVWbadIR6DzT5DsiaeyPcVec0w43VoZbIsReLKtxzeLMQpqOA6fWkf8zIRaecQ02htbcWWLVswZMgQT+EjkQjWrFnjOTwPYcOkPqKTlwlRPMRCzMfjFzLKhEjRS4WVh6dm0Ev5qaJMxJMNnq5gWYgl4TkFmn433pVXPk4RZcKk1ARY+ejvyfBh46FM0DLpXHr9Tx9m8kO3w2iUthBr1tKyb7dr4nCi+6EkbZaNF17pRW4Kr1f3bX0NerOw04oNayEmlIkkcIipKGRcx74qK5JOkGq7EVt7FlMmZBvE6LJ0XN4XyOOZv87nQxwsFqddNiuPlqcm1lLq7QPwHmn492UWYle3aw6PeYqD1/dkyi6f70TrnpMxz7opDx93ugIZMt04k1aF+Ec/+hHefvttbN++HcuXL8c555yDYDCIxYsXAwAuvPBC3HDDDWb4X/7yl3j11VexdetWfPTRR/jmN7+JHTt24Dvf+U5c6YsaDMsNpjnE7Lv0b4tDTCvE8X94kU9kOk027biTkYLET7pY2grn5tvUK5Ix8+U3tLl5HLAUaDtlIt6JBb3RB3CmTJAJE+n8SRkQRYDmDfqFaKNlVpLcAPpFMiZptKWdVnoiOjsJ8W5V55VKSR0R1MugS71KNbym66bwpmujmBus+uJMmbAsxJZymIwNo4yFOPbfPrnom9IiqZDJfSRq0dRMSp5ud7sWDGhm5+Nk+XWiIYiqmf07yJQ4H4qgwBBF3qf34fDUAb8Q7fPhN9VZHGLnuOJWiB3lk91nyycJ2+pcZfIudWJg61+m9EAW0kqZ2L17NxYvXoz6+npUVlZi7ty5eO+991BZaZz2tXPnTgSoNY4jR47gsssuw/79+1FaWopjjjkGy5cvx+TJk+NKPyqoyPQ9Jw6xqJPJonqpRDpqOYdYt8mSikpFoiTZsTbV2U/4ipdDTFt7aBpBPHEQpYgeRJzDW/fcNuL5lYGhTJBloljUIW6wj3DvJuICTsQhNhW5PjYRJyM1Oj9SCzGtEPvdVOeDQ2xYraJps7R75f66ccfp+m9wEzNrQDImhsa1qKxJV5NsDrFoaT5dFmLSeEjb1XXL24LlVtJu6QyZbtdgE5YuIqflfRG8TqJs5eVEFRCOu8ZvelWLOfzKq4WYuhblVeJkwhUiV2UEbP/EPvNKHZHd571txIP0rXbYlV+Zx5NMQVoV4scee8zx+VtvvcX8vvvuu3H33XcnLX126cZ+z/FgDuqa9zJhxJOAQkw4xLyXCWs6baWdgkrFe2OgXVvxycVLmSCvhWIKcTzZsLiksf+uFmKdCUe/G28x2igT1DPTQszVDz2mjNA+iOk44lFgaZdLOlW2xj3f0SWEZFRJhjJh4xCT7+h9EsFP2/ojZSLo0lZsfoht8cCMJxpJfCk2WaAn4AGHSS09gUwmh5iOQjfvyfv7VIIogdZmUUuWEKUk2ykTAWr5n4XIIkunZf4WVAhbnZcUBH/b2UJsj9/MI7WqxcjqsbLSoURGqyBnWpdtRLTF6/A8bsqEdJWKDuMslxfY+z57pLqgDSQK9lvY085EKl9GcYj7GuLdrtY9Jz/EouUcxg1UAh/b5BBzkZj6sESOZIFEaXGILYWAH6ji3lTHKYPxxMIruOaGHFcLsZ0yEa8V1Tw1LRYlf1KdEYYMaCzHnH83mEDdsagf1r3+TJmgY+jlvUxQdcer2zz7RiN36wxBVpos7QTmhMpsK7KB1Hkw5pWCVNMAvOzg13VLCg3OFBi6zzAtxEngjGXSwRyi1SSekhfR7aeXhQKadHLvlTIhtsp7mxjET5ngFGKKCsNa7uXxyeJmFFXB/hEA3v0QO2SI94LDvOckq/S0TFbuROuel2/D1KcUaMSiiWa6+lMnKIWYu+aXl0yagk0jZsMBsQE6wYMeALmXiYhAllQoO+bBHLHfTj4xk0GZoNP0AxvlwMUiyluU6et4y5FXykWUCZ4fR96zWYgT4KqSmOlBIG0W4mTEIZhwAjE/xNQE1PPRzfzgL5s0Ce5bioiL0CkCv2TuhXsI2NsUfzhBJoxHtAiaZrUB0eek2zv5Jsm2EPMKWqIrSH6hc/0izaXNosqGl4emTNioMowfb/uYZ23GE0xCPE4M7IZkeYGxlImYXGQiEBTn0etKqMy6Kto/AtArUS79hy28hV5Bfsz3HOT2SplItO55+TapNKzRCYjoMpmEQa4Q26/tnUlMqeHfhf3DJnr8LoG1qY5VNoVLWikY1fjOg/Z+wCcX96Y60gE6DIBu0DkF1zOHWOSHOM5iNL891dGaAwypUxK3fLzFOrGT6uzvhtJmIU5CHNK4dba9eeUQc79lwUWyJzJpSwZkO+Tt4djfsjzzPmzTCk5Iy+2aSDmzvnsyN9WJrKbkfyIrWImAnvxYdALKasyVD72pjofMKqdz9UFUvb1aPPn7zhZi6trkbBv/yWmvvKHFa9uTWojNvp9/w+P449B2ZBZ46jVXWc3wum7TSxKte6Yy39eTO8G1LW8ZphQPcoXYXpHtFlC2wRKIrLTJo0wYL9vcrsX6iHjOePcDkjcRZcJWPgm6XUtkwIlwcXjmEIsoE/FaiCmfuAQ8l8+kTHBu+eiVBSMONk7XtCmZRce2pov7moyleKeNY7Q/2ngpE7IyESliWRnEIQbkA5qrhZhblk51djQPW5ZoERivISIOMTUBtigTyVWIrY3L9r6iLwZvXhGnlV9rgmsZAwgYt2ucmFLKhJlHuTxe67wXF25ieYz/ttNeuQi8fmZZXkWGC8DeV0vjjf23Hf0M+6ZfGl4nBrLwSaGfceOeW7rJquWik4D9cKzTgcGtEAuWOnhlhHS4vJLIHsxhDVhel3CdYHmZEA9oImU8mTAd5Wucoqbb+UxJ4xDHkQ9ewXW1EIsoEwm4OmNkoFoSKTfrYA5i/bASjugCC7FPygR/9LNxz3rO+z3uKyTFQiyJg95k5Iei5KQkuKVr+UZ1TCJloL0rAA6TBTcOcRLaXLJBy0BTJoQcYmqFh+QhXsoWGy8lD3eP3oSV6uKi+dQMl5bjkNOeVmCGD1j9Du9i0CONQGghtnlNkMguyIsMTlRF2RHU3i3E1jU9DtCTaBqySYQsfdHBHhFm06/4PbGs8jpuxp2ETsecZDm4GE31fgLLy4Q3w0S6MLgVYrpziLLKCwHpcEUWAz6eANWhJ+R2LSI+mMPTLuAkgM6P8d/qMPk9LHEf3ZwEC7H9pDr/FmLNYdbsSQZRnLH/RAxLoQmYz6JRanMYT/mIo/M3OcQ6mcykz39uUhRiyX263AKa+yRIFp8suKjsad+o6QDPK5WWDT8YS56niwYgAl+HnVZJaF+yyeUQW3FYB3PY23Wqy4uOnz2pzrjHemDgFOKgt0117MEc9rR4eLcQO/9m46TDseWdJTlKzut4KqdMGP/56K3P621CLRr/RUd/e4lVXN7idBMBicKzNTxJ44VwgsXdyzTXa4NcIRbNVHmFmDRY43eQm03rup78TXWmhZgdFSJmmnQe4k5GCmtTHcttjYgsxPFSJoiS6GCdcI2DU9wtLxOy8DoTnk4/bi8T3EYlwN7x0FbkgGCgsx0sEkfnz69c+Du0IrlINWWCVuy8WtVlG8y8pGta69LUefMnlcmt584TaJ560Zf1QroZC3Qdtiy/ThziAONlIgkKsWilkOtbgNRb1OnoGQ4xbyHW7f2+sakO5nMaUsoEp/SLcudGw5Hdd1QEGctt7D+pmxILsdfuWfQtjWv7BAfwcTAH5w6PRvwn1Qnu8RbiJNQ524RaECV9LxXV3DQO2frh5KeVCJRCbF6z/wlIZednWeQ70t+X4RAn8KHlJ9WxshjpJL/2kjyZHOJYLdGj9gYa/6Y6ewfjd8DhN8l5PqkuYFde47cQkzite1YnG/uOJK8cx5xXpv3yz/m6R78bTFJdjAcptRDrHIfYY5nxj6V1RFBWhDKRLmOGbWnbo9s1mWKUyCTUFxhlUhKGu+80iWMsxCZlIgkKsUAZ0AV9RV9+fhGHmOay89/a2FRnrk0xz2Q0ApvSL8ig26qD7L5fygS5JbMQJ0qZ4OlCJjxaTZ0sxI5eJpy8bXiwyCfThaXTqay65DqxdHXbtb2PSlOnKsEgV4jpa535T6o9zyHmKxWtIAYpblsyOcSkDVsNXN4AkwGZ9wPDQiyWNd40aM6Y3yLzzSEWWAkS/V5iGobxn8QoO2rYptD7tFbTdU/j6gh9aEW/pExI4jDcrlmDm1erOh+fLLiorDLpYA5AXjZuy7XppEx4UaQCmnMboFdayDfhPfHEA9FGM9EmrFR/fp0bS0ia5oazgEWZ4JWaEEXHstd18Xihc/2P0O2ax76IT9PpLdG4KzrcikY8lAmGPiG1EHsQGPT47yybv011ojru/Dse8LKLotQF9SJZ6QK0wTGzFeK0nlSXbvA+8XRK4csOBdDVG8Wuhnb0RnWz4+UnsPzGJo1SHuNFJKpj66E2dPdGTVk6e6yjY3VBhyJCbySK1q4ISvKypGFaO3txqLULAJCXHUR1UY70YA76dLXsUADdvVG0d0ew7XAbAGBocQ7CWUHPeQQ4C7GnNy34PqmOm2AAYndl/mRgBxUjTuO/tUxkyUn7F7VbiP3VHSdOeTIoEweaO9HRHcHQklxkh7zPnf06eY9Edew+0oGorqMsPxvFuVmOlImIYILhVmZevUwIFeIkuvhyw+FYW6woCFsymW3F+C1ViF2OjbW1uRRnh9YdDFns2gQtouZi8ac3FwZjHfHexs6E5RQpjPwyM30vVaDzTKfbayrE1ioQzxQJBTX5SXVUxE0dPTjY0oWqwrCUBsjK5E3Bs596Jw7Hx8krSiGRxglxfXCL29PRzS6rLgTkqV/KhFO04jqefIXRNvERxpn8ui2Kka+3SThXJ6kY3AoxbRngfOwShfjxD3cz7/A7cul3DAtH7H6CH/r3b2y2ZAkShViuAInwm9c2Y09jB647dRzqyvJsz7t6I7j1xc/R2WMJ++25I22UCdP6qFv5zQ4aCnFDWzd+97oh65DiHPz49Ame8mdZqyxFSzZoyuNgZ/1Ox77SaTIW4gS/l3hTHdvx0JZgS4FjLV70f6+doKgzYazRHl2SibB2TxP+37LtAICxVQW48gtjPL/LWBs8dLRPfrgb729rAGAMiP991iTHTXUMJ1uz7jvKJIhHGL/gNtnMlGpjxo76dvzmtU0AgKtPGYuRFfmMTKStyMrUziEG95tV8lJ+Uh117cUir4HalCwobNrKR+gDXb1RrNndhGnDiuOWU2Q1Jbf6dlOdlQKtGJJN1kFqcyev1ND7V+yTPzadJS+uxxXzRlsbxR031fEySmSX1DURxJsYYcqiac5WbieIrM8ANamU+PJ2i160yZKAp0zoum4ZWnxTJrjfSZiE83VZlFf6VtLm/YJ2pSgTGQyGb6SzO3dPHl+BysIwCnPYOYONMkFbiJPkdq2iINv8m15XjKqiMBOn18q7p7EDALBqxxHh8+aOXnT2RKEBCMcsgIdaumyb6mg3TUSGEeV5mDSkEBUF2SiNWaAPtXR5zqPIIuC3IfKUg6CLQsmHBxKnFUQ4KzXgsKlOs6w4hoUYMblZyod3DjE9sJC0LHkSsX7T3/Kwj+8K+O9cD1Lx90Z11Ld2u1iIjeugpjl28oxMtgFWHM6JMqFLnicLdJmTVRtaJjcLsdvGJp4y0QcGb0oWcWLMXc3ev9KgeaAjy/PN+wdaErMSO7ajAGzPUgU6flrxIgoXfRQ7PWH4woRKJryMO07jUEuXVR98bKqTFYIPw6jw5Dx2n4Vd6fSsEFOZFx/MIaZMuMVOnrtZiAGefuBNVl5O67eLYB5grQTEfgsD0eGTU9HpWKx2ldkK8aC1EIvI64xCPK4SCyZVY/XORvxlxQ7zvmmRiv2m3VwZVrnEFKzjR5Zi8azhzL1739pixEkO5tDtDT0ekI42LxzElCFF+GD7Eeg6q1QBYJW4WHrZoQAunD0CANDY3oObn//MlyyiTQ5+LVYRrqFbFmJJmqZiat1LmDIhtBAbMDsBSmmmJ0w2t3EJcIh17l6ikzOZpcULRAqGyxvMr56IvCZEdJam4uSVgIbXjtiJMgHE8uN9EcMXZOVmW9qWvM8vR9ooE7yFuA8HI2lS1H0N3tyuBTQN2aEAThxTjne31CfsaUKkOIndrvVdedF1rifCGg8MK6Tx7GvH1OLEsRUA7HQAAlGd1kHnkbrJv8srexJ546VMWH2kNY4GNQ0RH/HRYF2gkXd125hGwNPbZLCVFZ0mV/+iuo6AB+8VQspEChRGfjIgqw+i60QgnmgmX+FPJuK2EG/evBmvvPIKOjoMK2QmOHn3A76hGwOt9ZtXBq377DSLX4pJ9Chg0WDLb/yio05kSYUsxdFO3ekNG+QebbnUBR2LV/+GNHjrBODfAmN1oqwl29XLBLOpjn3mFyJvGbKDOQKcmzC/J+3xYJWmWFqU0mBNZPzlCWAHOL9Fw8jl4W0+uz2RqANPVhdak9zKjH8qpUw4eJkAUssjlk1CvCqybsqLV3/GqYDUqh2TQoN3t2tkAkws9z0JEhHZ+krSMv737cEc1jXDISb9NLXhrJebTAN2qhaBTPEy+3KHzZo2yoTsO7pMxvi0CSJmH2n8NjbK2t/x2u7oZE0PUdQ96aa6JPkhBvgy81YO4neTZCGO/XekTPg2YvgDoY7wTTVdvt1l8K0Q19fXY8GCBRg/fjzOPPNM7Nu3DwBw6aWX4oc//GHSBUwV7A2dtRDzVjvzPse/oy1yRvhY/HF+aNFxpzYrJjNwxpUMAHazBj1TJlHym+r4DU0EAfpdr0tbAkXSL/id4K4cYtEgkrCFGLE4rXv8BEHkdi0Stcvj1+2aU4ca5KzRfqEzdczf+4y1wcOrfBBHhZi2rAd8uJbj4pNZlJ0oE7LnyYLsSHYvfkRFsvHh+E2cKbdheGja/GYnZ7drxn9SDoRCkIiFOKrrwvoqWvlJdXnx4w9J2qRMUJoiUZK9GCZEYxHdz5v9hMjLhM/+3IzfKSzVVvny1pJImSDv0O/a3a55mxyaZeWBMiGygAtldVFM+bjiBjdGubpdS1JFF80LUpK/JMK3QnzttdciFAph586dyMuzNmqdf/75ePnll5MqXCrBf4hIlFWI+Q1lBLTyZ8TD3ve6hCuDaNkryClKdMwJUSbIUlyQtbTxy4WmpVHwzHjudZ5tlzsRCwzP8/NsIabSdDrO0pMMQksNYnFyYQJsOfOWP7+HhNDhyBVtjaa/m1+w1md/7zLpeVGIuTA9EfumIStuboLh0arudUnXE2UiRRApwYDdO4RMBBENjP0NJh5RmGSC7spk34fcto6Jl7cBXkklE5VEfBHzYlmUCcTSosKm2KZOx04oeIBlAacnZk6GCS8cYtoAFHDgptsVXW9l4KwIipRW47ecQ+wpWdb6zPGTAbmF2K0Z8KumNJwUYqd5uqiOp+JoY/5QEa/KfxIStl0m6xyDVME3h/jVV1/FK6+8gmHDhjH3x40bhx07diRNsFSD/wy6ziq3fAdNwC878INVopu0RA3OyyDhBFkIchKeQZmIhaXKgUjCnJokGCiYQUOHJ8uQyO2a3zLz72WChLfumR40EvUyIaJMgO3sGbdrOnsEMR1HPEc3mxQeqkyclkLd47YPWvHAaZe1BTLYGLL2RKLSlQPWQuz9uGubtdRl0kSDoUyk0kLMlDl93/jP0ot0W19hc7vGxc9PwEiY+NdovENeauwTJ88ofJ9BvktvApQJ2YYoeuJP6mXKjVlU/JqmmVxa03DBWIjtCrHsS4opE3YagNiNIyeipAz4+14PpDAVYqovDArMdIl4mRBRIQlkvGseJFqRbPyEjLWAO9R84USF/Z2MQ5X47yy2TMtliBd0368LvoVMlnTCt4W4ra2NsQwTNDQ0IBwOC97ITIjI6/SyDQHfXiwXnnxDjillpEOPsyKL2ievZDMDZwINRmQh1mFxyywruZW+eBOZf4sTaQghbnD2A94HMM0HdvPRS5DoBiOetgE4WIg11s8qz2n2O5lilubIPSat+FcrEukg/b5LwmTHPqA7h9i4ZjjELu3A1t5daDU0ggH6m6ZSIabkoC1dgkmXSAzXo5tFG1n7aECSH/lr/DdX2BzqrMxC3JOQhVhcZnQfaH77uFPxKAvFpwasscQ0XDAcYrtffMuo4V7XDZ/79nrFw/OmOo+KM8Bu/iTt1pUyEcdGYz5uwMHLhEv0vFLJpMl1Pm79rbWKbH9m5yMnXut4o437SXVJqumCcSDTvUz4VohPOukk/OUvfzF/a5qGaDSKO+64A1/4wheSKlwqIdp4ILLEyDbVmRZizuqSuIXYfo+3mjCVN4EKRTrVrCC9vM5aRwDaV6/9dDVeZs9LW8LB2V9eZBZimRyiTXWJHqTi6HZNUEdoaz9PG/E7mWK5anosLcTipCZvfb6pjpbLS1oGyOEf3ZGotFM2JhJWHfTqu5l/Kqunos1UtLU9lY7kZdxDcu02eXTjEOuCvPWVgi+1LMb+k0m108mDfHvJSoKFWGYBpfsWfpNsqiDjUxMvE1mCTXUi443NyihRgPiNzaLs2d6VtRsfXiFEXpKYTXUJUCYYqpEZtzXRsMfNrua5xevN7Zpz/0e+o9hCnHyFkcRgGb1Egfz12X7Spa+9GibSBd+UiTvuuAOnnHIKPvzwQ3R3d+PHP/4x1q1bh4aGBrz77rupkDElsO121K0lbLrNyDfVxd6zbYySd+he4M1CbD1LZAm3h1qKC1A7lM1BymYhtnfatHzkfS9gB5z4liRl/G0Sf5ArTf7EL/rdeNulN8qEVa8YLxOyuuOxIIQHcwgsLYlbiP2979faYLcQ248IJ4jo7Al/nr1MeFAS6PuhgEYdg21Y9iMe0kkEMgux6DRE0SE2bsfGRqi8mWESEdgFogGRB902ALo92t/gJ5/J4BDbLHIxSek+kHz7VBuzLKs02x+I6BHkHmu8ESt3Ui8TsWvelajTu7IisOvN8sJy2vgW0Oy0Bvq5G2hjAkmH1BuRsYk3XsjAK5U0bJQJSTsmMKgvUfHhM1ESRkNvVI97XKLBr8KI8sq01RTUc9M4lAKOdDLh20I8depUbNy4EXPnzsVXvvIVtLW14dxzz8Xq1asxZoz306zSDdFMTDQLlG+qYxsybyGOf1OdvcE5KdmJ1CfGywRlhbZm1MRCbCkdohN/aJG9ikMsmQF6Odqn/LLJCCBueNYgx1r/gMQpE178EPMKnLTueOwFGUsEkYemTCRAB2F5fn7fpWV0D0++DLEQu1EmGAuxxzLze3QzrTQag3RiKz9eQMcs4lm6HXNu20xlU4wE8aRwPPIyqeJvO1FgePktt2vxZ0K28VBEn0v10M0rkeQz9cQKI0h5VbEsxFZ4epWPhmgsMgwQbHmKvpH9Xdl3dJ6M0WAVRuNdUb/FvuOt9Gl5eQuxyLorMkCJIFpdMdPhCpz+LZI6ZJa3KJ1Y/xNMXn/j5Tsz4ZNU05n2z+2nIUjQhXjSEdfBHMXFxfj5z3+ebFn6FDbLQNT6OI4WYm7ZgbdaOO3Y9QKxhTgmYyxOOmpPirckCO3fkqRrTAzYdDVqWZq36BjhWMusF7D+cg0TsV/FzealwWGDHv0zKFCI493taiq7gkmUqaRSSjOxTkeisG+q82itMNMWLHNZnT/Nx/SYGQoJWYj9Lr/FwpgW4l4nygRtIfbe3uwWYnE4k9seZOtIwv7FPUBGmRDy7QVyuFEmRHSQlFIm4L0e8DQBsYXYmggByaFM8K9ahg4iD1GK/fdPvsH1u/ymWFIPo7pu0t2YfkcWrUBuoy+Pveews8yroutNbbbS5uOMcnm0v+MQIRMXNZGMsgqxKF5wfbWbzCLrtRNlQhRxiDqCW5YOsSIno8rR9Ucikm8jhqd0Be3fzRNOuuFbIX7nnXccn5988slxC9OXsFlTdPvJYYBoUx3bSfGbqrzueqfTpSHkEPODhF+FQwLLQhxg8mW5mOHTFw+qtMh+lbmApsVvIeYmMHRn9fDyHcjNDuL0KTWoKc5hOi1WmfcnN4+IoKMkl09/tAfXLBgntGjSA1KAU+jj8TJhbfKMxamxx0T7BasQx/9uc2cP/vreToRDAZwzYyhCgm3aJLhlIZZTJvY2dqKzh2woSv7BHJY1xZKT2QyZQo2YVYKtHxGqrfBy0hApGpGojmdW70F9Wzc+39cCgPNWIZFlX2MHXll3AJGojpPHV2JcdYHf7LCyyO5zeZO50dvX2IGu3igTNpUWYvr4er/9k67reHHNfuxp7MCQ4hx86aghjkqn+V7sv8mnFhhjAgENiOpCGgVJ4/GVuzCpphDhrCAAsbWdsRA7cIg9zzW4d50UHdHGMZYywY4tOqx21xuJ4tmP92LikCJMGVokiNu6/mBbAw61dJkn+YkUYtlhJjzIU/GmOvZdWgZRrMRbiHjjaCxM0L2+eIUle+y3SKg4dYoVW+qxZk8TCnNCOGdGLXJidY5JmLq0HYhG/d58sBVvbzyESUOKMGdMuXchkgjfCvH8+fNt9+jGHolEEhKoryBaQhV6DODqpWWRMsLyPiL9coj5cELKBDdI0G8k4sdP6GVCt1MLmI1gokkDJbJfDwnBgOaZx8WD79A1TUNRTgjNnb3YeKAVAFCUk4VzZ9Yy35vx3UlxwqO6LrYieMiHJhiYdh/pwHtb6xlrE12WZt2Jc0MmE46boNGWlkQpE3osDi+DOo/VOxtxsKULADC1tgiThtgHMZIUQ5mQxLd+f4t5nZ8dsiagSaJMRMxJIltHEj2S3QtkNBVzGdWFMmGzdkLHjvp2LNtcz9z3QplYvqUen+xuAgB09EQwrnqshxxw6QvyYAsT+08k4n2uE7y7xcpDXrYx6JoHcyTgakd2yhi9Sua3f6pv68Zrnx8EAHy+rwWzRpWhqijH9T1+9Y3XiQLURlnrpDrrOXmvsyeKFz7dh68eY7hGlXnsMBUlJ+8DkvKxxWd7TxxO9IzfKEvPmYMml9Z4vmxzvfl39/nT7XJQAh5q7cah1m4U5IRicdtl8dylccYLGna3a7RyaS+IkLmpzp6MiLKVKPhJZzK9TDzz8V50xyaq46oKcezIUnGc3MoLAf37hU/3YUd9O9buae4/CvGRI0eY3z09PVi9ejV+8Ytf4NZbb02aYKmGfVOd3eIIuFMm2rqMCUB+mG10nr0tcOHEFmI2rJeBxgssP8SsUspvVmNdhYktE2Qm79lCTG10iHdJUuSc/vtfHItth9uwdk8z1uxpMpdT6XJmPQhY93Udvpyy0vQS2SRqV0MHw7umNw11dht1Jzc2wHt1IWalT8lOyQRYG8GAOCkT/G/d++BBK3akswSA+tZuSVpGeNbtmrPQx48qRW52kFJUnWXiH8vKmCSbFWTrSJ9wiCXtWkTLcRpM6TCiY41ZLjL7zrJNh7HrSDsONneZ92TfzQ265JoGv2wvozCRyfv0umLTCkWUhkROquPLUbQR1qsVkaCnly1zrxZs3SwL4z+vfNF7EER9Hx16y6E2Kl6Rokuvbhr3xJvqbD2BRHbdQ6hYnIL9HaKJPEArxEYa+xo7HGK25J1aW4S2rgi2HW5DU0evGTcP0/rv2n+wxhcajgdzCOIl9dbRQizS3uOEbeLjFt5Hc6L7l7auXmG6dJxObuXau9NvTPWtEBcXF9vunXrqqcjOzsZ1112HVatWJUWwVMM2S41621RnueAxfrd1G5UgPzumECdi5YOLl4lYa6HfSGQF17IQBxi56eVCJn16md9WLrFlOI9p06eN+V2SJLAoB9a9ysIwKgvDaOrowZo9TWYYuiHKJjyRqO7rKGn60zH+QKmveKS9m3KrRrnv0oH2HqMDyMviJ1Me6w5jiYAZL0D4tfFbiP0McLZ3qWu63BvaJAoxUUJDhrxOlAmC8VWFAJy9EojSIJBaiE0LjYQykTp9WDqQijyZiKw4tCvDSFRnlsVpyBTrju4I/vHRHlv4po4e9EaiQrqLE7xwyXn5ZJZ48ruu1PKBb3mZSMTtmn0SQf+nqUdeP72IjhcPhJQJ7h67MmXdP9JutTWRPm7UjVi8Dpu87KsOYtgmFg55Fn1bi+rF1vNQQAOZmulUnymP2/g/Y3gJdtV3YNvhNrR09gAQK7Nevy1fVjTsCrFzXGRlQ0h7IitUyaRMUHXZkE88QTKvPcfL9tP8t3Ha9A3Jb/rdeFYkE0XSpiHV1dXYsGFDsqJLOUSNkgyGouVvgqDZgIywZFaUHw7GnntbwiXg66YXyoRTPvyA8TJBDfgySwW7qU7cMXuVhz8JyrjnT36nzRL88pCl5HOUCRdeppf0+XhocRrbe6Q0hg7TQmw0w6DPMhTWh6g9rXg8niQyqNNB6XZQL1GICRgLsUsadqu6m0JsfX9Anh9yW7qpLg0cYhGVy8lCbO5ih9htkywemWKpAzjS3uMmvvA9Kx3nchPtV6AhmohnUW764oXIWglQVI44+if72OLtPRufmleIA7AZIoKSiTjh2dPxfnFiJYaX5Zky8oqSSBWyfTfZxIZ74JRl27eN8itbrIXYes/qM2Wgy5CMyS2dvWbcdngzGph7ZwRROPshtiapBKRvcXKbmQrKRNChHrMb4LyOP+xvp2/De+UiYAxVTHhPIiQdvi3En376KfNb13Xs27cPt99+O44++uhkyZVy/N+bWxAIW9YGWhEUbZAi4DfV2SkTxvNX1h3AaZOrhY3wuU/2Ytmmw8jJCmLx8XXMM7GF2JLRSNs+WDpBFoIsd2QFqaObYfckYW0oYrm/vIwRAEteXI/SvGwAQFFuCBfPGYm1e5rxzMd7MLqyAN+ZOxKPfbALa/Y0G+8F5NymhrZuPLJiB9q7I8gOBfC1Y2oxojzflnexQkzCGP9lvijpbJCwmw+24pnVezC0JBeLj6/DP1fvxXtb6xEMaCjMyUJxbhYKwyGs3dtExSNeujzY0mVaa+ijhp9aZVnicrnVhcOt3dhzpAO1pbmMrL2RKO55cwv2xpYNaUWgozuCe9/agr2NnbG0LKvmJ7uasOTF9WbYURX5OP+4YWjvjuB3r29GZ08El508CsMo6xv/LW554XN0xiwAFQVhXHXKWHYDBYB/frQHmw62Mptd6MHi091NeHntfny6u8m8n5sdNDnGhEO85VAbSvOz4QTCIyX1cG9TJ15aux+f7mrC9LpinD61BgCw9LMD+HD7EVQWhs3wvVHjtLuW2Ia/E0aXY8bwEgDi+m1QJmA+X7GlHm+sP4jvnDQK1R64oV7BDEq0P1WipDtQJg42d2JP7Ntb7pXE/UMgQFGcqDSdNlDd9uJ6k0YysjwfV8wb7XjCWSxDtsvnP9mLZZvr8YUJlTh9ao2tzyX/j7R1Y8mL6zFnTDnmTai0KYsARZnwoHEebO7E3z/YhY7uCE4YXYYvTKwCYB/UiTyJUCbinUyaSnjsP2+Q92MhBoy+ODcriOaYhXRocS6CAQ07G9rNPRNGvLH0YwIcbu3CH97cgtauXttk4543tyA/HMS3ThiBCTWFluySchTBeVOdxvTJ9KpEVHdfVqd9lJMxeV9TJ5NPGl7VTlH9I+An+r99fTOyQwGcPqXa/KZk1QawVp+cNsbKVip/+fxnONLew1C6crKC+PaJIzGyIl/4DknFaSWAsRD7nMAR8N+GfvzcJ/vw0tr9tsnD85/sw3Ejy2xxR3XdPBuhL+HbQnz00UdjxowZOProo83rM888E93d3fjTn/7kK66bbropNgO3/iZOnOj4zpNPPomJEyciJycH06ZNw4svvug3C0LQ7pxkFkT6t6kQE8pErPFVFFgD+R4J32n1zkb0RHS0dPbi091NzDMxh5i1TtFVKiHKBLXESltmzAbEWSpYVz1sXKRj7onoONjShYMtXdh8sA2f7WvGyu0N6OyJ4rO9zWho68bK7UfM92pLcqWUiVU7jmB7fTsOtnTFNqg1MM/NZTZB58FbrKWKvMBV26odR7CnsRMrtx/BgeYuLN9Sj56Ijs6eKA61dGHzwVas3tVoDhZDS3KYDoovGxKuIj+MEeX2Y8/zYoplaX6Wee+D7Q22cDsbOrCjvh09Ed02UG0+1IqNB1rRGluxqCjIZuou+SYHW7rw/rYGtHb1YmeDUbbNnb3mBMUqCzbtls5eM919TZ2mUm7lMYp3Nh3GvqZOc0OREQ8b0SvrDmBfU6cpy476dvMZPQHYdKCFeY+3mBALcRmlOL+67gD2N3filXUHzHsvrtmPgy1dWLfXyB/ti/Nfn+7HxgOt+MuKHTZ56YGY3VQHPPHhbhxu7cbrVD6TARFNgr52okys2tloXpM+iLYC0mA8kDADoXNnQr7/poOt2N/c6RjWkNECSeeN9YfQ3Rs1v5GpeHL0LB1GnX3m471sOIGy5MXt2tq9zdgR60te/eyArV/gZaaVRf+UCbvC5+094z/puyoKwuazrKCG4twsIa+YgF+1O9jShR0N7aZ1Xwuw/SKtrAHWmLb1UBuOtPdILe9tXRGs38e2T7tCLM+zcEM7RZkozLHsdLRF1ouFmB6fiIWYgC5PEx6t/3xZuaG7N4pPdjWZ8VZSaVcVhWOyihRiKx2RLkC+JWmLRI9YTbV/m+ymddtaOXKC13rOT2w6unslIQ30RAz9QQOQk2W03ZbOXuFqeiqpaU7wbSHetm0b8zsQCKCyshI5OfFZSqZMmYLXXnvNEigkF2n58uVYvHgxlixZgi996Uv4+9//jrPPPhsfffQRpk6dGlf6BDR3Vrb8DVg8T/K9CGWiINb4ThpXgRfX7EdXbxSHWrtQV2ZXgOgKcKili3mmCWZFdrdrlNwJ1JwI5WXCVLZ1ijJhpg8zfdHBHHRYAPjGccOwbm8L1uxpwuGWbmZTzqYDrab4t50zFbnZQeYkPBq7Ggyla0hxDvY1ddrKysk3JM8tlVmTRT6U6e+zbm+z+fvHp0/An5dtw+FYfgIa8PNFk1CcmyWl2Zx3zDBUF4dRlJOFysIwhpfnoTQ/Gw++u90MQ5S7wpwsnDq5Cks/OyjcyLT7iKE8jq8uwPCyPEbxJHzwocU5OP+4Ogwry8XKbdbEo6owjK8fNwz/9+YWc+MkQ2fg0hN1mqMr89Hc0YPDrd2CZU9xPZRRh06bUo09RzpMRRUAyvKyMa22CGv2NNuslfMnVGL3kQ7Ty0QepRBPGlJouhSjIVKUyIAWjepCaxMZmNmDOawJI72JhEyCkwXZyg/jolBj2ygB+X4njilHe08EOxs6oENMl5Ht1xEN0F85eigm1BQiHDLsNX9ZsQPb69ux50gHhpbk2iOR5MfVywRnIbbLRp7bLcSkLjspK3T97OyJor61G5WFYanyavWB1B4Hj12tjJfsBt7d5eLj6zB3XAWiuo6KgjDywyHh3g0C+rOeNrkaEV1nJm209dWoQ+xEixCVvIwpsomE7DcN8YZ2q46fM6MWq3Y0CtN05xBbho/coKUQzx5dhrNn1NrCy4wxNggMQTlZAXzjuDo8tHyH8BVj0mG8OKI8DxfNGQEdQH1rF17/XKz08ZQ3Gd1t9phynDqpCmv2NOGfq/dit8NmQ9t3FkRp+366O4eXF43vT0X9yc/OnGj23f/1zDpKPjatdPkn9t2jjxgxIrkChEKoqanxFPa3v/0tTj/9dFx//fUAgFtuuQVLly7FPffcg/vuuy8hOeiDOVhXNuIlqs0HW3H/21vNZUqyqU7TNEwfVowPth/B4RYxZ5JWEg63cgqxg3K3YmsDRlXkm/4lgcQ4xOQEpKxAAD2xNVpdt/OERX6I7dQD60ZdWR7auyNYs6cJe5s60Nhh8Q/Xxyx/Q4tzTEXQBGWh/WBbg2k9PGZEKV74dJ+piBJ44RBHdaC1sxd/iXVasvKl80Z3GB/vagQAjKnMx5DiHBw3sgwvrd0PwFDGygRL+3QSY6vybS6XeCsxXQ5jKguwFAfNetHdG8UTH+7GkXZrYjGywpCFRiT2LfPCIQyPxU/X4yElORhTWWB2srrOLjXXx9Jbtukw1u1txu4j9g42HApIl/uk7pgk92uKctDaabcoEBoGX69rS3NNygYA5FJtwLD82BXiBor3mh0MoDsSpXw9s2EjUR2Pr9yFbs4VHsBSJp6NWSwBoDTPsOh3dEfw9Ed7oAP46sxae72m8OnuRvx702FkBQP4ytFDGcoFa621rmnrmUV1YEG+39jqAqyJrTrRSg8N2WE4om+VFdSYuja8LA/b69ux+0gHjhslzaaRvvNjYSAZDUNkrMiirPj/86/PUZqXjayghpysIM6dWYviXGvFhZdlV0N7TCHm02H/GxZiI82/vb8ThTkhjKsuwIJJ1cx7vZEoHl+5Gw3t3bYyj9c1ZigYwChuGZzv65hJAHU5tsoYJ3iFmKj3yyk3drwfYi/i2pV+eX9woLkTz3+yD129hvcYfnWpoa0bu2L9TUAz+hkzXircA+9sY7zW8HhpzX6z39I0jWmHJ4+vNClZNKzidLOaWoo2QUDTTJduItArNJoGk7Z1JEaxEB/MQeK2aIgi1JbkoDQ/G+OqDP/gO+rb8fs3NgMAqgvD+Nqxw6wx0JQ3lpeYpf2JD3ehOdYH72poZ+LXdXevQny97nCZrABAeX42AgENXVRY4cQgky3Ev/vd7zxHeNVVV/kSYNOmTRg6dChycnIwe/ZsLFmyBMOHDxeGXbFiBa677jrm3sKFC/HMM89I4+/q6kJXl6V0Njc3C8NtONCCnbFKEeAGQxq0ZZRYqzQNKKeoEhWxiv/S2v0oCIcwrDQX+5o6AGiYMrSIqUjEJQyBqBISTi4AvPrZAZw1fYj5u6WzF/saO1BWkI1wKAhd17F+f4vQ3RKNz/Y2Y21smTwU1ED6GXazBflvXNBeAmwWYupnMKCZZbAp5g+YgPwup5aQ+CXJF9fsN9PKDgUwY3gJXvh0H5o6evB0bBd8WX425YtTrhDruo61e5vMDpceJOmwUV3HJ7saMXdsBdPZkw6WKC70dxYpw3xZiGbZhVwnyip3RpyHW7uxfEs9djW0Y9WOI0z4MZX5zDuAeDJH1+NKbrlQh87Uw+317dh6qE3oZYDOC7Eu0grlloOteG+bneLhhKBgg5Cm2d1KEYwsz8O+RmuZnqY0iDagvLn+ILZTdAz+VEN+AN9R38ZQeehvW5iTheK8bOxp7MQByh0ZiWH9/hZ8GPtGk4cWYubwUibuw61d2HSgFfnhIJZ+dtCsU8NKj+C0ydVYu7cZHd0RxkpP6uDaPU3YdNBoM4GAWJFt6ewx81qen015whFvqqMHW5Y7KFaeaZB24LZBEra4JWEgnnzzEG2qCwU1FOaE0NLZi8b2HjRSE6CcrADmj68EAJTmZ9vSJ5NrfmVEtKJERNrX1Il9TcDGA61o64xgeHkeptUWIRQMYHt9u1kHbHmMxbX5YCuaO3swrbaYUeatcPY88pBR1QB2dVHTNAwtzkF+dhBtMctdaV4WDgqoLnyZi4wsOVkBZqMescqv3dOEtu4IPuKW7J9ZvQcbYuPjpgPOFJv/e3OLeV2Sl81tpDP67KaOHoZexaOjO4JXP7OoUqV5WUxfX1UooEuA5odb93Yface+xk5MrS02lWrRCoWmiT1XzBtfgbc3Hmb2JdGhSBx7mzrx0Y4jmDasGGv2NKE4J4vjUpPprx2kf6oqykFBOITWrl5sjbna23qoDXPHVWBoSS66e6NYR/brUHSk9ftb8PGuJmHcALBq5xGU5WVjTJV1IM/GAy34fG8Ljh1ZitrSXFubau7oxXtb6jFhSCFK87KFkpPiouttsp0FJAJPCvHdd9/tKTJN03wpxLNmzcJDDz2ECRMmYN++fbj55ptx0kknYe3atSgsLLSF379/P6qr2Zl5dXU19u/fL01jyZIluPnmm233QwGN8bvY0tlr7kbNo2aWvELDd1bzJ1RixvASlFBKK21ReXLVbib8zOEljptARJSJGcNL0NTZg+c/2YdeziXVRzsb8dHORpTkZuG/vjQJe4504IF3tknjJ3jkPWuZJz8cMmfeum4fpOiZNVFUeSsY75eYlEEXN6MnyyoVhdnUuzDTBqyl7kXTajClthhl+dkozg2hqaMX/9502JYXkTcoEmdEZ/2UXjFvtC1sdiiA3u4I/rl6L8oL7MuogKV0led7UIipbyimc2jMAEMPACV52QjFNn49+aFVd+ZPqMTI8jwU5oQwqiKfGZwAajMJFRddj8n3MK0Oul3pJBYGGQxFyq5QPvBvZ8uNCLTya94D9d1ishXmhHDZSaNQkpeNISViWpZoqfy5T/Yxv+nNNoCdSsC3yRPHVmBkRT6KckIoycvCfxxfh40HWhDVgUc/2GW6NQNYaobIJ+6Dy7Zjb5NdIeiN6PhoZyMe/WCX7RlZRqb5zblZQdvy/f6mTvzvy5Z3n/KCbGaCKarLmkkEYIkxMuWZhnnsrIcBi47dzatHwBwoxXGJvNsENA3XLBiHD7Y1mJzkBZOq8NrnB/HBtiP4IEYZyssOYtZoduNORNfx8a5G/O39nUJ5aMueSKS3Nh4CAJx37DDMGVPuyGOO6oYbNKL4nT1jKObFlHVhHh02E2VznZ1s70JAMyaN15w6DnuOdKAkLwt1ZXnmgUU0+MOmiBzTaotx/KhSFOVk4fGVu5g6HNV1fLyzEX/lyo/gcGs301eHAhrOO3aYuYG7NC8Lf1mxkzGy1BTloMa28qXj6gVjsTOmDB9o7sJLa/cjnxt/6L7s2gXjzInbDWdMMNyKSmYZvDGmJxLFXa9uAgCcPK4D58ysjZWN8Zw9oVVj6uPwsjx8dWYtunqjMYVYt42lANsv//X9nTi9tcZcdVwwqcoMnxUM2MZPAnqT8LWnjjMtvE+u2o22rojZnz32wS7TcktvqiMeZYaW5GD6sBIzfYK/v2/0STeeNcnUbe59aysAYMuhVlx32ng7h7gngsc/3I3Rlfn4wRfH2nR5TaMnvtZ9NzdwfQlPCjHPG04WzjjjDPP6qKOOwqxZszBixAg88cQTuPTSS5OSxg033MBYlZubm1FXV4fLThqF7S06hpbk4sU1+9DWFUFdWS7qyvJwTGzHOWAfEPhBfFxVgenKhmDykCJ8dWYtDjR34vN9LejqjZqbnZo6eszGO7GmELuPdJjPAMmSfkDDhOpCPI99xnI39YzMDhs7etDZEzF3FDtB13VToTptSjUm1hTiva3GEpqIFlFdFMaZ02rQ0tmLnKwACsIh27GZfGdcURDGt2YPx/6mTmiahnFVBVi5vQHt3RGEQ0HMjR2nCVgDgGWdiZXj0CJTkbt4zkh8ts+Y6b63tcGcvBjp2QuNtpKRhstPXAjOPnoo/h5TSlo6e6QbkQBgWGkuxlUVoL6tG8eMKLUHtJWFuCM+Y2oN3lh/0BZHMDZwrNljzd5L87KxaFoNYxXNzTaWhYnFnFf4AGB8dSG+OrMWEV3HUcOKDdkoRYhWAsvys9FN1VMRGFdu1Lt+lWEzLpuJGLb4p9UWmzz8o4YV44sTK228fBl3lJxaKArLexijly+PHVGK4twQSvKsOp4fDmFGzPK7YX8LVm4/Ysoo2wxH0CRpkxFdl7ZXPcbXJxubFk2rwdiqAtsATlvejh9ZirzskMklFXGNATIw2eUVKs/8Er3Egi/OhPCSgdnXcPHbopJYT8vys3HalGpomsFBP2ZkKQ40d2HbYcNa1trVi/buCJo4t3HRKLt0T/pR+8EcrNKTHw6iKCfL9FxAfNw6eX6L6jrTXzW2Sb557L/TUvWZ02qwfEsDdOioK81jVg9F/U5FQZjZTCaboAN2ykRWUMPU2uJY5PY8yeo1wfEjS1EcoxWNqyrAuGrWyPWtE4bjt69bk3DRhuNIVEdpXraZz4PNhjcZvvpZLlNhUsYAeDohELDyTvdl9Dcj9YK3ENPlmZcdxPDyPGyJrejQ7Y8uvqElOfjGcXV4bOUu6DpQ32atOlku4oyJ08e7GtHQ1m16DwKMFUL6m9LUvec/MXQZ0p+tjlH+DNktGcjzktwsjKsuwEtrxeXS0tlrGzMbYl6TaJdyC6dUY/3+Fmw91IbmGEWSbxKiE2IBq7759Z6VCiR3V0iCKCkpwfjx47F5s9hSVVNTgwMHDjD3Dhw44MhBDofDCIftyyXDy/MwcYTRQImyIALff9i4s4IeJhDQMHdcBXNvzZ4m/HnZdmbG963Zw5GXHcLO+nbc/domYXp8OlHKMjWmMh9XfmEMrnvCcIWn62IrDw86zLzxFbEdrWQ5xb65UNM0nDq52hYPDdGSEL90PJZafmHe5QZ5kUeIkRX5pluZjftbXRVi+pALt6XI40aV4dM9TVi7p9mYEAgKkcQXCgbwn18YI46Iy49xLU705PGVOFlgJQKA40eV4fhRdlc0PE4aV4HDLV14Z9Nhc8ZP18egoB7SihBRaqbXFePiOSMBGMrefW9vFaZHc2kT7a8Cmv17aLDXBb5enTV9qC0umc/O0vxsDCvNxWfUhjvTRy83sSQKzbDSXFxwgpiyRctB4gA4pVIwN5ApjzKF1YiTbacnjYu1UzKh4dIeW1WAxbNicpvfSJcquZrgOzpNBM3fPk7ipIO4USZA9TUiuO0XWDjFGgO+PXekeX3D02vQ2RO1pR/VdXMifvL4CkSiOt7dbB2zTiundIp1pXn47rzR+Meq3Vi2ud783k4DON+ndEmsyV7a1MQhRZgoOP4cYOWUlaPovjlJNOUQKH/cO9Go++a7E8dV2IxFNEZW5GNGXYmptIlE5stV5u+e3ozmB3xoWY5EFmLeDR5PB6A9efBjwqzRZXjiw122dm7Wc2g4ZkQpjhlRioa2btzywudmmKPrSqT5cTqzgJbVpBsGnNYjxH2XZcAi8QKnTq7G+OpC/Oa1TeY7tkN3JIYi8u3YcnAQKoWISyHevXs3nnvuOezcuRPd3SwH69e//nXcwrS2tmLLli341re+JXw+e/ZsvP7667jmmmvMe0uXLsXs2bPjTtMNNsoE77bLY/sjAzGtEAfNQUCenhXW+G/MhK3ZMB2e9iFMw2mTB5HB8sogntW6gXVV5+NF0NZcIoN9mYlJy+afUy4P7U3BqbOkXdu5dSZu0BIoC98g9ULi/cMWnFKELL+YbCcvA23V9XvYB+2H04yLp0xo9vS9FLvMQixKI0jJL/Iw4lZ+tExRrr7y1wQyhTga1eXPOGWWlLtt8khtuDPlI7xI6jkjP3XNTgpE9Z7/LR9webjxk2kBSDJuXiY0n45CZfJGorqZVjBAe9lhJxo0hxiwvgOveDgph1FqlQoAuntlm4+c+z03eOl3hCuQtgmeKA72RT5PInjp++i+3MuxyLIJecRH+6WhcXlnT1ejrgVlommsPsDTAYyxVP5NyX4Auu5EBYMv/6pTFum0edD9JD0mOsXntBLET1LJAqbsHRH9lN3MTpd3P7EQv/766/jyl7+M0aNHY/369Zg6dSq2b98OXdcxc+ZMX3H96Ec/wllnnYURI0Zg7969uPHGGxEMBrF48WIAwIUXXoja2losWbIEAHD11Vdj3rx5uOuuu7Bo0SI89thj+PDDD/HAAw/4zYZnOG2qM557a4Bkdz7dGQa5QY6/ZtKhXKZYVcV6n1ibvLjMYRRi0sFTHYNoVusGL0q99N3Yf+cO2YLdaiWyGllxOjlV58PTvpad0vSKeN/zChK72cH50MCJVZmxejgoHAbv17j222HZFWKB8gs7jcJLXZIpxMGAvTyClCJDp2+5JnJNznaioBtlwqvSS8NY7WEnEAA7oaHTEx03z8dBPw/wEUH8Te2DmDVx9AOp5Y2TWdZevLRhEUhou3s1aslX09DLTcpNvUQTK5omtUdQB3jQfnYBOcXIbSXLDfRrsnJyOsSIQOT3mn+Nz5PXtHg4HSwC2Ln9sgmOOWb4nDDZVyftcRrP7fVPA+snmSRNy+hkXBLVTZ5CZFx71zmcKE30e5ZC7MxZd5r08FZ5fn8G/6Zock27u9WZsk+PQuz7YI4bbrgBP/rRj7BmzRrk5OTgH//4B3bt2oV58+bhvPPO8xXX7t27sXjxYkyYMAFf//rXUV5ejvfeew+VlcZS8s6dO7Fvn7U5Zs6cOfj73/+OBx54ANOnT8dTTz2FZ555JmEfxE6wu11jn3u2EMdaDmMhJgox2EYmQpAeiLhGYyqU8LbUQFdy0/JE4qAGUT+KbSJWUbvVy3nws1vpJbNvGD4v3RRsOo6oLlcivIJWLFKsD9uWr0RHi9KgJz7WRryA7bkIQUqRchsMefC0BnrZ3ronplG4xy3uxkTWDxmHOBK13nGDZSG2Wwf59ifz9EDCyikTrMJhbjqjrL902vymViOMhDIBts+QyU6na/52WJIV5YFA6oeYU3TdfCTHu/rE541eCaJPdRR6maDesw4rsuKhw4ugR9nnso1SImXID9yUS1nc/MEcXvp/WT8pitcxDDNu2MPzE69kUyZ4yChE5mSFW00TlTm9SdHJuMSvNAFWPpy+paNhx5EyYV0nx0JM0jT+m6tv5mqLs9z2SaW8H+0r+LYQf/7553j00UeNl0MhdHR0oKCgAL/85S/xla98Bd/73vc8x/XYY485Pn/rrbds98477zzfincisFmIPShjIpiUCcqzAKn0Xqyr9FI138hoV0xeBqqI2ejoDt4+q43XUhGvFUe0XCmC3UovD0OXiZP1lO5IErUQ058g5RZiicIiDR/7r8M/ZYL2DCGrZ8RDBg9Ru+HvaYL0vRSf3EKs2epKUDJgeKkjBPZNSFZc/ADiZjmUWWCiujVQBDTY+grSViKCtuK+qU4TKopeqEKkeDwcDsdAZq3mlUA3t2u+V584RZcgouvQzEkkvYfCkteQh62DNM2ClsuNMkGnL7UQJ0yZoOWUjCNCCzHbJkQGBJEV2W1jpZd8MDIL2p7MyshXJbcxQ54+m3eGJkNZPdoAAQAASURBVMFcG6ANDprG9z38WOqBMgExZUJU52S/2WfiCSDAli/DIY5bIeYns6yxwc4hFvftVj9qPUsXZcK3hTg/P9/kDQ8ZMgRbtlg+BA8ftrvE6u/glxNkS4huMGfh5De39CK6Zt6nBmG+c6c7CT8KMXvsp/GfrpTOdHs54u2UTKsXGagktZPncYk7GysuL50lbfER+mP13VLssqYE/ITNpexpRYg+upvAubNlqSWAveMKSUzUvIWYVzRiwglpFG6Qbapz4hDLLUyuydl41E5KpduAoksUS2YiJ+gr+EGErp/0QCNb7eAVazpOPiwNXxxi7trxFXNyL4krToWHQDQBosuOH5xNRYY5q05sQBDFz6flyStLgjoAXTJ+OMT82CQua/bFaDQ5HGLWQuwePkCNFXTdtVa73OOgwScptxDbJ8z8KhdjoIJ3ygRdjmaddJDZ6zjm9J5XyoRbvabjDXL9Ig+3vsTtkKC+gG8L8QknnIBly5Zh0qRJOPPMM/HDH/4Qa9aswdNPP40TTjghFTKmFbYl1zgsWAB7kADAc/7siikPesCzllVi71CutLwsZYt81loUA5265x4XAV2Z46ZMcAO4dIOhB4sm3dj8Uibc+FduoN+O19rjFXzsrhZiShEyj+72aCGmFUxrIwQbxqAvyI9MpuMSWfr55D1ZiCVKuCGvWI5olC07q464J2hOQAUeBvixwG3JUW4htixHoo075mqKK2XCHrfsOGIvFmLZhEIEXtl22vBLJJJN6Ly0YREsBcH4H1tMQyQKaOT0McHKh2WRFlteeS61s9s1tp10y7xMwEozHrDKmaxNOLwXEyAimBzyr0V1K+9ZQc10D0jDzwZVIz1/4aO6ZbH1035F4CdC9mvjP6/Ai7xM0AYqp28qWr0Q0VXsG/vl+XDa9JwsyoTpmpDb0Evzl3kvPiQtVlbjv2gs6Tdu137961+jtdXws3fzzTejtbUVjz/+OMaNG5eQh4lMhdsSrl/KBIFoE4xx7azgAdZyJRlE6Iblxcm1aCOOtRRKK7bxdS7+N9WRQZ6d2co6VFoueRjjP6NYuCh7JLyoLfoqC+r9lFuIOfjZZS2aGDlOGgKajaPG1ze5hZjtxWWW/aR6mXDZVEfDj5cJJ+sgPxC5bUqRdfy0EsWviAD0aoqzFVluIbaUZjpNHjKvOv431dnzKuJYurldi7dvIe+HAobyZrhdM0DzhHnFyO5lgv0f5cLLZKf7VRmHOFEruNuBQIC4/IJcXbDoInKN2KD7GNehQAA9EbvnDC/fiufkuoanwhjlxX5fL7xlVsZYXGD/y66ZySk04UovO5awEz4aov0YNJ2Rj9eS2X0cE9VHumzM1UHNeQ0uIqE/AHILsfEMthUP0aY6AEIefpr0Yf8K8W233YZvfvObAAz6xH333Zd0oTIJbgqw1139jgoxnZ7kfTrdXs4MLLOwEsiWcUUWYtZCKxFGADqF+Dsl1qIlt5ZTnahktky7cvPEIXaZVPjJEq1kpJpDLJt1y8Mb/3XKOsl6mZDLS1vLZNxJGX2BV5SDAbv1VoO9nL0MqrI0acsffQ9IjEPMW3ZEm2II3CzE8g133igTlrspsXxCGoQgHuPavd47cRR58MvP9om5XemXFb+uOz+Xgf9WQaIQR3WzEIy6yCoSjIWYik+2o979O1vPZZSJxDfV2eV0CmOGJZQJ01LnrpTRhoZQUAMEZ3R4oS8w3HdP4WkZqOs4N9WZoc2JjfVMSJngLMR0cvzkLhp1sRAT2QVUAVGdk/1mn1lp259Z79FeiZz6WHODHCM324fyvHrynquFmGubbqtVfQHfzMhDhw7h9NNPR11dHa6//np88sknqZArY8DXFfvSr7d4+Pf8UiZssy8qLON3VFCP+HsiDrHIQuyra/GgyMogU+i9bAyRbryjLIGeKBPUJplkbqpLsT5sg9tkhO7MiJXAD2WC8XYCe1k5bXBj5BAoq5omcLsmlcY9TRFlIkR9Zxoiy4wMNtqIYFOM+dtts5XkubGsL1CIZdY8Ea1CII/x3G4ZA7xRhfx4mWAsbIKJJu0S0VrtEi/hJr5pyvjN9AuCZWPTQgz6mb192K1bchn479zdGxVOPpyUJy9wWmYnCAgtlURO9r9sFRMwLIdkMpAl0Xw9WYg9KPGyOEVUg/j3r5D2JG7L5IpfraHLiKdVMBxikUJMJlWi/sOD0iuCE49XyiF2iM90oeagV5i+ublv43QwBx2en4jRcfc1fCvEzz77LPbt24df/OIXWLlyJWbOnIkpU6bgtttuw/bt21MgYnph31THPvdMmeAsZCFJZyNbwKArE7EQk1tWZy6hTHBzNZHPWjGH2IcSyMjqd5ZOGgZr0ZI1fMb3o4slxPumOmuA88KldAIzm06xRuy3PtKKkLgeyN81Ok9WIeI7Xln6ok11TjvevchD4EiZ4C3EUspE7LmnQZyNg+nIeXduLkvpsqV22kJM6xtEOtNCLFSarTBihZjyqiCxiPFxEcgs7CKwhxy4lI1Lm+aNAH5BZCH1MBK1lvyD1KY6IqLcQhz770IdYmTn+hQdEHJurQ8QXyZFctrCCEZ8uv8FZPQUNkJytDhgcIhF8NKWnJRuEeh80UVOijPezc9mnLJr4SRBPHljN9WxEz42HGLhrHvmJE0QjsAr9U+UHnm1l5kMyuPjD6xhnjlQJkQWYrsLW3kb6jdeJgCgtLQUl19+Od566y3s2LEDF198MR555BGMHTs22fKlHbJZjey5DDyHUkqZkHVklLXLHFhigelXRBYnmzVMtFRuNk7ddi/VYJQ0On2pomOX2xbGHPC9coiN/8mgTKQTXukqtAXSn4XYuPbCnXSSK6BptsGL7rDNe6nyMsE1Cj/8TZp2AnADmoSeJALNw7Q/o+kEYmWXTk+0AYqfYNLP6TAy2fm06XS8uF2jY2vrimDTwVb2uW7lw02Zi5dfy8tLfG7Tm8LoesIfEiC3EMOMB3BbCbBvulu5vQGf7m5k6BMJW4ipay+ra8IXYaexiGQy6q6zhdhLV0SXrZ/JKJHBvI6XMsF/R8kEUXacNS2zzrVFXU+AMuFQ9o4rnaZhR5AeNbmjN+w6lZiIMsHLKqI7eVpt4nSOTKBMxHV0M0FPTw8+/PBDvP/++9i+fTuqq6uTJVfGQHTcIPvbWwM0/A7LZ5rmtUsc0YgusBCTmZZ4qYGvW6QxsG7XLMuJSC53xF+BrY6BHaA9USakypDxPxL1Rpmg/YqK2qKfE+D6cnbr5xQjgP6musTtmrNCzFvGvPqj9WIh5gcYQ173uKU0DYHSbS0psvdNf75eeIzcsqRo6daM1we3lIZsIidzUSjanKTrYgs1PfGgn4qpQtxvH5QJOvK/vb/T9jiq60Lfu8Q/qxmNR9qTCLzSQSZ0dLkEA/JNdRrYOhgPZYL+lgRPrdoDAFgwqQqLjhrCxBHv5JtOQWpYcbjH513EXSegJxSyCamnUyYFddsJdBBRu/N9dDP3W+plIvZfRP2ywhv/A1QbFU34+HfFfojpspenycPa7Clo94hN7nSdshAnQJmIsrJqmmaeSBoRTMb5vpWmnvFeKfoNZQIA3nzzTVx22WWorq7GxRdfjKKiIrzwwgvYvXt3suVLO2RLhgR+FCW64/BrIQasCmTyHbl3pJQJySAtWoqN3+2a97A86AYe4Vy5iOCFd0YrBV52qNOdk2y5ySv6si37sR7Qzw0LseDoZof3DUWKVYjsvFDxu7zbQZG7H9npdW4Iyk6qC8gtxHybsFwIuSdo24BFtRmbou2kEEvqGsBRfQR9BZ+2yKe4Dgj9HJsDI+QKgBWXeMLljTLh/Fy0qY6/JvHE7WWCk9e0EEdZt3Z0ebR29mLt3ubY+5zllXiZIH2xF8oENzEZX12AyoJsAEBju7UbzWl53QtoCfxYiHkerRdra5SinMgsxF5WqzTJd5e9S/cRopUZ/5QJth0wVmEqlC6op/xvbuEWAL03QVTusTACSzdT53gdxKFc+c2efHqWoSgai9ujhZiZHLB1nu576NPqeNKEbHOg6DTPdFEmfFuIa2tr0dDQgNNPPx0PPPAAzjrrLITD4VTIlhEQbfxhn3uPi+xwBhw4xC4WOsC+AYie4Xtxu2Zx5+xWJboh+emWE6m+tELvZWnUD2WC5WK6dyRRQeOkn2c63DfVGYjqEsqEUxkF7JQJr0tbIsuKzRoMkdXYvdzllAl7fDIOcS81QLiBH5CZpT6u8rhaiCUWdtlmUN6ya1m27RYlXdcFHqFZJY9dprSHlQ3GXvyd21mELIxDcERpsolGmX7BPV1hWrF0yF4OesMTvfkyqgP3vr3FpDKQlT1eNut4XktGedqW8n3MiFJ884TheHvjITyzei9TP8yreLsaZnVNHERUvW30D2G9Y1+M6JyXCY9p2dO2j0EAUF0Yxt6mTuk7EZ3l3/s5el0kI4mJcUNIfxvJGMJaiO1jl9MeArovBnftlA0vRjMRhUcD6Rt0zxxiL5QJ+nVm0zVvIZZMJiK6wECRJguxb4X4pptuwnnnnYeSkpIUiJOZiK0yAIifMgGwhxaINrQBzn0h6YStTlRj3tchPk5TtozLu5Chn2lwVs6TCXpwFg3wPIKSTpSJk+rkvSxFsuHdLWWO6MPGbLcQuyjEVF3pjbBWM+N957T4ZT6v/mjtbgcFZarZ0/dmIZYoxAEHygSn0JGy8KJw8VZS9qQprq25KEoyS4iuW21Z5OvUfjCH9a41wQSiksrIKwIi2Y14eQXA+O92ShlJ3wmG6HYLml0hFvOpvYCebAP04QFWmCBlIY5EdeyLKWK1JbmYMrQYH+1stMnmz+2aNbkhxlRzc5/AJBm3Pkxdy/orp36MV+6dxiW6r5ZziN1zwqwMUD8umTsSz328F6dMqrK9I1Yk3ccNEczQ3GSAusVce8oTVRyiQ07MtAWrNMKyl1hWhWlzkxs+PWucI+XlQpkwLcT2ZyJDE2shtqfPyEqt1vHy9hsO8WWXXZYKOTIaZEYK2BucL4WYmknLrFpOvSF9EgxAWYipAVBccdnfYj/EsbBR9rdXJIMyoUOXTjxoBAQN0BaGtvh62lRnD88+l8vDoy+bMm9BjWdTnR8OMe9lgKcIyKyCQgsxv6kOoqOb3eHIIfZsIfY+oAa4tuJkZXWaMOi6/PhbKWXCHETZ9EQb76IAAhILLH0YDi0Pj4QoE27PdbFbKjtlQhcqCl7Ar6oFhRZiK/3mzh7ounHvulPH2TYdkXAml9pBYSBgviWvUDMW4vjyaL5PySBfObPfo/cVGPIKlDjuPbqflHmZ8ALZal9FQRjfnjtK/I6mAdxBLyLqkBfYLMRMe7BfO/WPojA8z5YGb4QCaIXYLqP1nkMf7cDx16h6bhpDqL5ABJNDTLVm3g8xszEywL7Hyi3+LTJC9RsL8WAE/R3j9TIByDnEzCzZoXLKjmW0rEbiisgrKVEHRSgS58CTCEQNw8ssmL8Wx6kLlQYeIr/FTHw+PnRfTm79W4it63i8TGjmpo3Y/zgtxIYSwivJ7hQlL3HT8vLxhUwvA2yr6ImZDP3sdCcKpKOF2KF8IlG5Yimrt/wALrLSeKJMmPHQstvD2igTlEIe1XXfFlsa9MYbpz6W3ujqlzJB940A62WEZJfeVHckxuktzMky2zw74GuMjDL3g6z8dmu/2ZdH7eUfLy2EhnzlzH6fLyNhvePeYSlX8fs6k1EmnEAbgCx55JZYx7ionNGUPfLbvCZ+qR0SoH1XE5jjqTBtA3S74/cHid513l9jr1d0PCS/LGVCHp+ThVgX7Pex0rf3bbK+XUSd6jcc4sEI+jvarFo+BgRmaYHpbOjpoPx9urLRQUUWVho2C7FoEOVmq8nolL2CWeL1Yc013pUoQ+ZyjLfOkraQi0/s8qEQ96mNmIWbhZhW5sReJpze5Thi8L60JdpUJ7IG835SvRS7VCEOOFslaJCy8JIePdkCuCVPnkPsUDyipULrmYQOwVl2xZ4orDhE0WtwVipo2AwA1PfRdTj2V26DGk3pEMlPyxX/wQvGf9Ln0VQF2qJH4m1s7wYAFOdmWXEI4uNXCRxXAqJANMAqVCGBQiyylvsBY8WTxCG6TfJiWUndy9rYEGpcJ2QhFihT7u+wkxGA9kMc54wJsU2oYH+b12RC5VjfY1EKjA6irIlOh7V4uewYp1HyeDEWiZoeTZmgfdA7FZloBYTUs4ignliUCftU3MntWqZYiBN0Yz044KSA+bMQW8Utc0juFJ2tsmnsO/QgSkPqZcIhX34txIkogdZJe/5cpAFyi55oU50XXrLUQtyHEwQ/sFkPXFo0bZWIy0LMKYOeFWIB1UjEF04mZYJ2E2fJYXkZoCWPmHxq9xRpd0FGXNYzujxaO3vx+b5maTz0xiQetJ9X0cEp1qBEZIIwjPD7aDRlwtlC7LQC4WT9NtJ3RlSisYs21XnZbCQGO4GgLcS0dd2q18Z/RiGmDSKE8sCt1jkVRYSyEJPvJF7tI9dxa8SUzM79Ig275xgSlg7DvkPnScYh9gLRPhb3d4z/DIfYgyFFBCa0DrmF2IPCLaRMeOgfhW7XHAR1yqITZYKmBjEn1TlRJsxNdYxGbPwT9E/0Hg1eBFlfIqZMKAtxxoL+kHaLifcGKOMQe23DFuco9tvcVGf81iGhTPAWYgFf0qaceBOJSsTvC/bEGGuuQ7kynaikL6YHOBEvSxan/GAO7yXSp22ZE8ttyZ+uKyILsdPrtMLK70h3g+jIc5FHiXgoE85HN2vCsPyGNj9eJngLlchSBQC/enUjmjp6IIMuGAgIotTgLFq6tpa3BUozpQTKJnc89YLPhxXWPpGx5He3ADuB3ojO9LHc95SVhRfwEziRH+qAptmUutI8sUJM6qNTHeBhrATE3gtwCjUlSKIWYi8uwb3E7VSnCOi6mzSF2ONY6rQZLV4OMQDGbzD4a8FKRm52kImLVhoDmlFvrc26oomI8V9EueKDa7DaStxu1yg5mI31DkUmmvDp5jPE4vCWvqwvERmhFGUig+E0i/XnZULc+Bkek8P71kYMzkJMdRBiygRnIRZRJmzOvx0EESAJ+jBHb3BSiOlrZ0sIs8PbQ5wRiZKSCFcylbAdzOE6qFizj86eCAAgJ8vq2Mmymqge0QqmqQh41IhFm+psZarFNzHTNA3nHzcMGw+0Yu2eJtO1YVCwHEgfwEJbOHscBi5bejyPWmJVclKGyXtyyoRO+Uam0uYUWdFmInrZVLhRjgrDDHQS5ZkG/R3dPr1bzTC4g0QBkPexMhd0XsDTW0LMhMh4FgwAU2uLMGdMOVq7epEdDOCk8RVmHHQbMy28pB0QyoTEIkc8ZPDfSeRlwlSI/WXRgoemSJdfTVEOTptSZaPPeNnYRfOiZW7XvMDLfhDZOyKqgV86s41DTD1jJ4tW2qSvmTe+komLrgIBTWOMK6JuxaKvUemYm9oFhgFBW7HFSa2A8CDUC4BViJ2KPSLcOByzGgvGapobz0sgWhEk8fVbt2uDEfR3jGeJh2DikEJsPdSGQEDD+OoCK36vHUEsQZPvyMmn694sxE6DKIFXbwXJgNkxeKRMiE7lssdp/KdpJF46Ehmv009x9OlJdfx382gh7o3q6Ir5Ws3jLB20VxVaOTYoCMa174M5hBZiTjaIBgLH7Jg4YXQ5Thhdjj+8ucU8IlhkhaaNWbTslpcJ97Ts1kHrmRuNgAatKImeibn+rHVMrLzQ7Uk8MFpUArt1yh6W/m1du7lec2sHjKcH6j7/zeiJsu+DOciOePPoZkthINIFNA152SGcd+wwcRyCCbitHQjMs6FAAN2RKKvQx9IXbX4S8an9wEvNo+P+yoyhmFhjjEkAXafsYXlEKMpJQhZij3Qt5h1ulYrI4ycOGWTtwbqvmX0ND1oe4gnD2u9jl4vciQrS5EMHNCBCXcsg2yNhpqmxOoSxz0IeockhZk6ONP6L9hsx7Ytr/7yxxjokyS6vokxkMGQ+Mv02vgWTqjE/NqukNxnRsTjFyLu8suQz/usQD0IyF1Oy46MBINtnJ5dI/aUtFF64YIwiLxFTxHV0ypK5fBV1UiK8oS+bsq3jdNHcyeP27oh5LzeLV4itzjeoaejVrY6PH4ycNo3R4GkZhiXarmwlYGwyZKS+sZgyYQXopYTvjcRBmRBsLPRj2aA3JvGgXY05USbMQ3YEcssoE4B4oiGzcrK/rRUEr6sDMug6hKd78WlGovQGOJ+JcNZE8v3pTXVeD7MB7AqtE2UiFNTQHSF8cCO8zUIs2FQXP9wjoMuZtDV+U51woyafki7eg+AX7OqHt3is8cKuSMZLqQGM/LutmDhHb72gcfVONLDTYw6BtxVS92cy16F8mq5eJkxrsHWPXNKGEgKeW+8kN81d92pYSTXUpjoPkHXW8cxGQ8GAbce9146AP5hDMzs0yropsFTwdcsLhzg75FMhTmhTXSwO3X+H4EaZAGgPAu5KtsFXFDzvJy3FTaEkloq2rl4AQE5WwNG3Nu+az6YMelSK6DpP4hD5IfbjhF4EfvlOdjAHYNEkANYNkXsaxn9+iZm/doOMr248A0THScsoE8KNd7pYHk2zK9b8NYGoPES7/EVwKwp2U518ck6nE++mKRtlIupdiRLROXgLL72iQkDTc3iPIUGBQmyl55otIbxUPdGBD5byxv5nugVOqIieLC8T8jHI7R1mU51ubwdeQIdmqiO4aw8TMp4yAVBjrShtYT7IM3FYNxmcFFKGMkHVfacSE654cf0eM15Q+eZF4PNkWrMFG/DSZSHuJ8N8esEowQwPOPlpOVUDvpGRqmx1+uKGwCst1gYE6x4/CCWyDOYX7BKvcc+ZMmFdyygC9G2njQ1WnKRsxVtTfFmI+7Aty07/kb9g/CMWYt46DDgoxJQbM5EyCMjrb0gw+fLmh1gSoQRs5yyiTNATJetb+zmpzrYrn/Yy4ZcyIaksjIWYqu+8dwhHKzJk9B+NaXOWPO5WHYCdPDrB9ehmXacOo5CnSVvy/VsAWVkZbzIcjUIeh102WzuIxZVFfSxL+bZ7DBH5IRbxqf3AS82jY7bKkk1PyA3l09KTRJmIY8VVyCEm39Jv0VHhDbel9u9Bc4ud1Ee6/M09KVF7/RYkbcKk9zmEdaT+OUxWNepdetXUy9HNDH3EPMAlliY9HjtyiMXGDuV2rZ+B3VThvwG7weukllS2XnMaGftHWY1ElAn+lsihOp8V3xbiBCqwyKLl1cuEF/dCEdODgFwG8qxXwgHws3u5L9syL5Urhzj2n1iI87LtrCnZKgi9ES6i2ztKJ4hcu4lk5SeZzvYLO3jZ+TZKy0ErI0Q59sKdJ4OuaLncF2VCl1MmGA6xk4VYMCjRm3X8bKoTzQVF1cl+hLwErhZisWcFEWXCSR4n2PZGUCfVifyoiiDq/3lLHClnkSchERXMaVNd3IOyh7rHTnaN/1b/yyo6TLFwRURvSuVXPP0gHg4xf1qjIY+3b2mLi3NELFoxoYvVKXr6XY3rJ4UHogju6Q7PCLxsDpe1ZV5Rd6VMEA6xwIotKnN6U5+NQ8ylQ09slJeJ/gRJZ50shdjpzHgadgsx+5ZxFKz9PX6uZlEm7HET+OUQJwKRmyjHZSGmAYrD0O97WQ4nz3olg7yfT92njZnvZFytXcbztpiFmN9Qx8cR5OoITc8BrF3IJiR5F7l2sy8LJtdCHAjYj4dmqDQCyoQX6xxvdRRRJrzUAcNKKQ5HPxNSJhwGJVppFvshtsfD54MgEcqEmxswxgoO9rvRoC35iXBEAZa7S8R36+oYZT3AykHk5w/+ACxFMarrZu5IOxBRJkR8aj/wQlsTWbvJLb5OsV6I7CDtJ2kHc3gcckT1T+Sz2wvo8uDbi9WWxeF50G0+6GHSGO93dnrPyepKUyac7tGwjm624LQ65ej2TWohtveXykKcwaA/o2jgSWb8TuBdqpD3mEYtokzo7HPiDsrphLK+pUwY/3WIlQAeXigTIg6xX4VY9t0zCbxUbhZi8p0PtXQBsPvSpMMY12wdMa2DAmXQCaJjwm3KLwSHdXiKnZKXS0d0kA651SOgTHgZ2528TJD668XbBM1j5TcmySeH7IRE5DGGObpZqA9rVjyulAn7+04bd9hMOD+O6EIKsaOF2P/+LfYFmtdLYnW3ENOyxeouxUU24iPKof3wJZHbOJGXCdqTQTzw0hJFK5zmJNdcChdNDu0ykfaTPsqEdU90qqMX0MFlHGJ6TPVKmdC4NuKVMkHA50NnnjmNjVSd49qnBrtSGtScjQAdMcMJe4y1AZFbSHoiwNdHfmxi9j7ZFGJlIe4XYD5+CjRip2pABmueMkFkeuCdbcL36Mr8+Mrd+HhXkxGfk5eJkL+8JUSZoOLw4l7JSydK3zb5oQ79tkWZsJSkUFCz/NP66GnTNLkF4MVCbPzf19QJQGIhZpZV2bLmLcSJbKrjZU2Ohdi6DgpcChHaR0TXOQuxDy8TvB9iqgyIZaTHg/sNWlEKBTVmMkZbeESbX3UqHB+Gtv6K/ZGKLcRCY7LQQkzSluWMlVH6nNJAGNWLS5OZpCZoIRZRYhLhEPM8chFlIqoDGkd/EXmZkMnsFV76YDYv7D0iiknd0MTv8en19dHN4oM5jP+JugtNjDJBT9xi31ewAmLFJY+Mf8bGLZeBbpt81YrqdvkDAWcL8b6mTmw91MaME06rU8zKB5e+LW0HWdOkDysLsV+IBp5E4TUa/lhGa1Odcwx0Zdt6qNW8HldVaMXNRZEeCzG9XCcP7+Qdw4rT4kaZ/FAPM2t68HWyoDuiDxszP4iUUSdsicGGnzK0yB5CqhDbLVu2jkySqmhTncgaLHPN4xX8ZMmWhmbVA/pb9/qY+NgmBYJBtIeaWIVDAUyssdoaAa30hrjZWm9UF24G5RXZiMBKYyo4ECutAY2qBbSVzauF2GEnOw13P8RiziTfTunvlIgFEBArTPF4meBdYIq+I02ZkG6qE3CIU7kYJeJ78vDr05evu3HL4zHfzhZivxMmKzx/QIS1qY4K7xAXHY7n8orEcsqvUzpOfZRZL6MCV2bQbd/KjUMMALuOtNvasswtJEOD4OWW9O2igznSxSFWCrEHJMsPsZf4nWLkB3LympsYok0/V58yFqMr86l02Uh8+yFOxO0as3TC3hPBS6dOh7N2+noPC4iX+b0gXRbi+eMrERZ4jaBBZ+PbJ47E1NpiWxjZKohGKZi8lcANok11og6SL2a/LcyuEHNpwCoDdlOd9wGVH5BFvEMSX1ZQw+1fnSaceNDuuERWtrbuXiY9Q/5YW+GWt1kvE1Z7kg0s5sAFe99AQ2whtlvo4gE9ENLJ8EnSeyYSmSABYp+5vvwQc6sb/C78LKGFWLcpmcyhBFwcImuiF/i3EMcMKpoli/GfyCp+j0eWzw3YrDz++1hHDrFfhZj7rQuu6bHNqf6xlmRuwiN4zUlU/hndNr1QJiJUnTPl08WHMMnyNHlIofkeX7UYSpdgL5JIyfWzqU5xiPsJ2E0AKZzKC9O2K22Au0JM10sZJYGPw6+XiURAkqYHBydrblDQqYvAKy5elposxSg+6wXQt7NbOvtZHmgudAhZ0ckmHAFNs61SxMUhJlYyodVEbEXwCr592o+MdlaAvHxnm9s1emCO1R9iISYrLbJsiDZjEbTGPIHI6BB02qJNi/SgxcvPx2Nce/yW5oTCOZxbdPTBJE4n1UUidqXZK7xQJty+uYhmQPcttP93emWNpkzwygPj7YTbuBS/ncX9+4mWt61JLrviwU6y5MhKYBxkN+16e0fj5AVo6pBPAag0dS5Oa4JgT1sEkYWYd5HKJi2PzJlOIZfBaVOdruu2PSNOZU6vcPBtWYfYKk/3K3xtlLkINdoHG3rQ+yG+/fbboWkarrnmGmmYhx56COSEK/KXk5PTd0LCeycRL5yqAenAOnuiTPpuSoOokdtna+wNv5SJhDjExEIBj8qrRyoDnydPm+qoE8t466hXpMtC7M266W6REe0aBsD4ITZ313vlEAfs7cY2KYN9kum3jTEeMjT74GFs3HO3sDiBXpYEuKXb2DVRiEm+ZYMfoUWIXFe1dvYy6dEwFWLBZiISfHt9m3Bg0cBOQnnZ3cBPimhsOdSKf326D29vOMRsWhSBSc9hkuvHeu8G+8Ye97bNuF0zlUjrnq5TExtqtk5cvNHLyyR9+n2zDSXYcXh5XaTcm3Uh9l9kNHFS3kIJcIgZC7FHjZhWpAgsP8T+ZKFD65zi55syQX0BL3shHC3EfNwCfrII5v6GqJhDbLMQO5Q5vcLBrwDTdVq06iDqG5w21dkU7jQNohmxqW7lypW4//77cdRRR7mGLSoqwoYNG8zf8Tox9wM6hVRwiL2iKJf7XGTJy+U9um7J/DWm1UJMzfh9n1Tn0KD5KLzQK2jrCP1+0jZQJhkyH9nS8IIBkQezBCbZVGftrvcmZ5A+qjwWJV+mmmaXyW+x8wq/KA0vFhYnkCDWJiS7parHdEnlbCEmyh49YQgGNESiutBCzHsEiOjsffI+YEycdx/psKVJL5O6eZkQwWnQe3j5DrTEFHk30JQOth6z4fxseOQhskqR8gW88V9FAz4tC22Now0J5JCOSJSiIcQyR1uqTV+vEpm9whNlgnbWSdKxrXgYt5nv4CBSImNFULPXbTfwk3L6OhEOMcCtmJj/dWl42bsyegCbtpNc8ridQLdNG2UCdq9CTuMa7UdbaCEW9D30t+Hf0Xgf87GwEQHfedBaiFtbW3HBBRfgj3/8I0pLS13Da5qGmpoa86+6uroPpLQQz65YP3CK8ZRJVcKwbh2oyIJlUxZtFmJ/eUuk+pJKSC+deD2Yw6vibPx2kEHSWGW/ndCXbZmWy2919HKoCb+kaXc5xlsOxGnRCp8uqYP0wR/WPan4QvCUCREFwysdR56GZdkAOF+yhENMLMRBb5NW2spWEDYGrbauSCw9Wn7E0mbTo9vLhGr7Bj4eJLSITuUGmdu13kjUszJspCemCdgoEw6uq1xl5X5rAM6YWoMRZXkYUZaHhVPdxw/RJJLuL6KUwjB5SBFGludhbFU+xlYVADBcV5GTIWkvFSRa6/CD+PMJ+LcQk3bAJ+d0+iGPrx87LMFNdWLZnGBO5qgFiAPNhhvJeGiMdFuICiaI3i3Edhmd3vRDmfA6pNBHJ4soE3lZ3ikTQSfKhA509Bh1OoeKk+aj81Zlvl0HqbB2a/YgVYivvPJKLFq0CAsWLPAUvrW1FSNGjEBdXR2+8pWvYN26dY7hu7q60NzczPz5hVNn3ZcIh4KoKgybv80Jvst7op2zrhbiNBzdHInqUoWdhlfeWTyUCdlvP+58Etlg6Be0VN6sm+6TCdmkj/baYLmb8k+ZIPDC5/TNIeasqfaJn9uqgnt6vIcARqmMDdLdHi3EBDRlgpweaFqIBYoJT5mglfz8cAinTZErerSVnF09cpaRgIjKH2JDDnvxCnqgZZbnubLy6/WAhmhie8qkKlxz6jhcc+o4fHFilfhF+h3RSXWchZjIWF6QjasXjMOVXxiLghzjO+5v7sTBmN9vUuc1TbPVIxGf2hd8KhGcgdjGmfWirB5dV+JrfwUPluLkLSK+D9pzpAN7Gjt8xcGAagssZcKoe+TbAS7t2IHWIHrP0ULskIwTaKtrTy9LWdJ1+8mkzoYn439UoNzqurWCVRAOCd7RbVq8bPVP7GXCyENje7dUvlQgrZSJxx57DB999BFWrlzpKfyECRPw5z//GUcddRSamprwq1/9CnPmzMG6deswbNgw4TtLlizBzTffnDSZRQTyvgS95EGSd5PjSHsPrn38ExSEQ+ag5coh9n10c+JK4Etr96M4RgtxPp6StmC6N2jZb1mcgLvF2BF9ObkVWK+cwG5ekkQpKV92Ux2Y/wSy5VMvJ9XxaQNxcIi55Tvbpjq4WEU8FKK1Ccn4T7vO4i3EZKXFTdGmNyblh9luWeQv3PIyQe6z8ZXnZzO/NY21zIt8uXptw6SMH1q+A786z6K5kePAvYLxtUzHz30DeqOrf/CKSWJKtYgyQVvj6PtjKvMxvroAje3GQUhl+dkYVZ5nPifUDYsyQT6QbxFj73sIwyzrs3lxWnWQCSU62McP2PHUq0LM9kF7Gy1a0KSh7qsjPDRYyjBPmbjnjc3YXt/uSUa6/G3Knyhdh7ji1S1IX7HrSAfueGWjLU77pjr3cVbI8YXV3smKFv2OyO2a00l1/DgS0XX8eulG7D7QIJUvFUibQrxr1y5cffXVWLp0qeeNcbNnz8bs2bPN33PmzMGkSZNw//3345ZbbhG+c8MNN+C6664zfzc3N6Ouri5uuZlZc9yxxI9cgVstr42nlRqwbCfWBDRMryvGZ3ubUZybhVEV+fzrKcOYqny8v82o+E0dvUL5aNQU52BEeR6aOnowaYjdnRWBXcl1aPwBcWMl8GN5SJM+7GlAYSkW4vD0XV6RJeWyr6kT97yxGYdbDetJXnYQedlBfPOE4biT64gBsYVY1BknyiEeXZGPtwIaCnNCqCwMIxTQMKoiD/uaOlFbkovygrDLIOCeBk8boQeL7t4o7l66CTsbjEHUtBDzeeIGDNpCTA8wZnhyHfv/+MrdWLGlAe0x12y8Il9ewCrEAU0zFXeNsnk+tWoPqgpzMK66wLOFeHxNIbbXtyMS1dHdGzUnQf4VYuqHw8TOz7HaPGwWYt8xCL4dF+9jH+zC4VbDkkXLnpMVxPfmj5HGGwpo6IZ9U52bX/lEwPJhSXrsM6+UifnjKxOWR0RHcX+HncyRce2YEaUozcuWvucYn67DKAF2grhLwMHn8dWZtXjh0324YNZwm4yy34DzSkC8dWBoSS7ys4O21RpjclaIzQdbmftOZW5ZcO0Kq65b7Z2ewNNWXx62TXXUpj0+fHNHD/Y2dsqFSxHSphCvWrUKBw8exMyZM817kUgE77zzDu655x50dXUhGHSefWZlZWHGjBnYvHmzNEw4HEY4HJY+9wJN8qMvNvTxYCzEJgdMLsfEmkL8x6w6PPrBLny+r8W8L2oIF88ZGbdciSiBx40sQ3VhDu5+bZN5z4nLmRUM4JoF41zjtS+Xx29NTse39gLRoQHJDM9veinMsbqMLYfazOsvTqwyOe4njavAvzcdNp/lZgWFXhTECjFv0fVX7pOHFuG2c6YiGLBcrl11CltXnDyoFOS4HWzCDhTGf2oQBUxlGACKcrNi73DKhcYq0sW5VrojyvPN0yQBoIwa5Js7e8xrkk4ooDHLlgBQXZiDgGbJWBAOmUe2axoYxeHNDQdjCrG3Vnz6lGq8s/EQOnuiqG/twpCSXABAa5eYMvHl6UMwtCQX9729lbkfjVKUCep+MjnEIsqE/zisl2jKQ2FOCC2dvVi716DhaWC/oxt4N5oiPrUfePl8+dSSudkONPZ94aoDdX38yFKcO7PW1ee5F3jdIM2+Y/wncrbEFLPCcGLqjIgyQVPCKgvEyvbccRWYM6acpX/wK4yiFz0oo35RnJuFm78yBd0UXSInK2DW4TJq5SggsBgTlOVnMxN/njIR1XWzvbOUCWu1ge9PRPQ1Iy776tShGE2lNC8Lu+TZTTrSphCfcsopWLNmDXPvkksuwcSJE/GTn/zEVRkGDAV6zZo1OPPMM1MlpgFmMHOeNacaPCkecG48WUENhTlZNiUg6VzoBM2iNcXsKkEyFFDeapYI3zgRnlxfwe83lYWnb/P+g6sKw7j85FE43NqNvKwgwlkBhEMBjK4sMMOdffRQHDeyFA1t3Wju7MXI8jzP/l8T3VQHuO96ry3NMTmHZ0ytQUE4hIiuo7Igm+Hoy8BbiIlC8915o02qRHdvFJ29URwVO/iEt8wHNCBKHe+8YFIVaktyUFkYxsiKfNSV5qKjJ4K87BBGVljL7Hsoq8mlc0cCAKqLcmw0i4KcEP7zC2Owt7ETlQXZeO3zg5ZCDGDRUTUIBIA31h8yrZteWU+apqGyIIxdRzpwuLXbVIhlFuLc7CAzkSKgB1onbntEcGKfV/CHXMRjeWMtxNavK+aNNieFAQ0YWZ6P0nzvFkr++GYRn9oPvOxdyA4F8N9fmmRsLiXKvXnYSyweFwuxpmk2ZfjHp09Ae3cv/vTvbaZbUL/w2sfy7Y+4JywQ1DGv6UZgV+IiulWi1506DiUOp4Daj6F378ecN9W5ii1FMKBJFd3KwjC+e/IoHGrtRm1Jjo1TXFGQjXNn1qKuNA9vbjgIILZ5kataHT0Rs97mCxRi8amX4jGWPpCLgPC2yySTkFQhbQpxYWEhpk6dytzLz89HeXm5ef/CCy9EbW0tlixZAgD45S9/iRNOOAFjx45FY2Mj7rzzTuzYsQPf+c53+kzueHbFJhOshZj8dxLEeGZ3P5VZGl52KICS3Cw0xgbtZBx6IuMsiSDyRkDPWv2UV1/ukGUHKg/hPdVfsXJCPokTTQUwvl1dWR7qyvIcw4lPQXMPkygqCyyld+64ctug4AaaQ/yPVbtNn8OVBdkoLxAr1DztQSO8iRjysoOYNbrc/D2myppg0KA9WohOGaQxprIAY2ITldc+P0jJYihDc8dW4I31h9DQ1o23Nx7Cp7ubZFHZUFFoKMTPf7IXy7fUo7wgW+jiDTAGftGE6ONdTWbZOS2d723qEN73guRYiK1reuVqaEkuhsYmA/GA9HGvrz+I4pws7IhZ/OPdVOe12+GVdpK/SFTHPW9sNl0GyvbLiMpwSHHi5wF4nfCQUB/tbMQxI0qFm7viBW8hJqgoCPviS3vpx5yyG+9phV4wcUgRJkqeZQcDZv/OWohZkElIdjDAGCDo1TO+PsrocOv2NmPb4TbmGZlUlcVBgUkEafcy4YSdO3di37595u8jR47gsssuw6RJk3DmmWeiubkZy5cvx+TJk1MqB71Bxauz8lSBcXES++/8EcXLjcm2eJ40vgIAcNQw50HaCbSVmHcgHg/4PIYdLIc5WQEmfCLpVxX24WExjCLh/lEDEmWXiZK6nU/xWXNCiX8TgK3DPELBAOPyLzcr+V0UOUa5JDfLtzIMGJM3ouAt21yPqG6UmVO++IkLvYmOdmfnhuFlhgI2e3SZb7np9AGgOC8LodjGrmdW77WFq3VQ9obHJjuHWruxfn8L3t1cjx3U5iM+PVH+th1uM5Voem8E/002HjB4jyLaTV+AVuaTKUN+rI/5ZFcT3tl02Cy/cJx1flxsEuVX6c/NCppKDbF4Z4cCyM2mFB2PK6NkklZd5J+m6FVu8g22HW7Dox/sTNhCzBx1Lgnjd7y0U78E6TrEyT8jK1eilZZkgqH1m8qt3QsE4SiLjoIGxBt0+UlBaa6hV7V09ppu83hUxVGPEkFGHMxB8NZbbzn+vvvuu3H33Xf3nUAxfPWYWny0oxFjq9iNZqkwsvJLnzzyGC8Tmk2OE0aXYVRFPh79wGDekM7DbdNYovjy9KGYMrQII8vj34x37sxarN7ZiGBAw6xR8Q/4BHQe544tdxzMcrKC+PbcUdjZ0A4NwLTaYjy2cheOtPdI35Hhojkj8MKn+zB/QuKbTtzgdKCBMLyDJc6K08L8CVUozMlCVVE47gGH4GvH1GLd3mbMc9iMEwxouHTuKGw93IbS3CyMTMHmziElufjRaePjzk84FMSlc0cyu8+HleY6tl3WrZiGLx01BKt3NQIwLO5eV0QuOXEk1u1txvEJtA9zIq1pqCwMY1+TQcMYXZmPUydVYVhpHrYcasXoSnnZzx1bjqrCMLp6o2jv7kVzTCnJCQVQ39qNd7fUM+nRVqTpdcUYVpprWiLDoQDT3k8eX4FwVgBNHT041NJlWsVPiGMS4GVzkxuOH1WGgy1dqCwIo9Rh2dwvvnF8HT7d3WS6n9NhUNxmUysFfjB/QiUKwiGMqxavLsiQHQrgh6eOY+g4Q0tyEJZNgB3K8KszazGqPB/ThjmvIhHQk1+vY9JJ4yrQ0d2Lz/e1YM0ey41qvBZikqzBIRarxH5XLL1QJpyswHz4i+eMwKd7mszJfF+A5QOzz3g/69Y7xn+RVZk/22DG8BIU5IRMH93BgIbhZXn4eFcjOnoiyM0KYnpVFu5LTnY8IaMU4kxFRUHY9Oup6zpGlOdhR317Uivnt2YPx77GTox36cwYfqRmu8AXJ1ahsjCMju4Ith1uwxdiSlmiu/fdEAxoGO/hQAAnVBaGHf2n+gWdx+oid6vtlKFFzDf9ytFD8c7Gw76/c1l+Ni6cPcLXO/GC56b6gayTnzSkCFsOtWF4WS6Kc0O2A2HixYljK3Di2ArXcBNqCjGhJrG65Iba0viXugGjjNyoIzQ07vqEMeU4YYx/xackL9tTGTrKQtWTs6YPwfIt9QgFNJwxbYhpiZpeV+IYRygYwGRJu3hp7X4uQWOzzxlTa7C/qROnTam27RmgkR8OefIP7AXJWBkbWpKLy08enRR5+HgToVzwCAUDcdUpAKgqykGVQx/JTKQd4snJCmLOWO8yFOZk4cvTh8RWhrxZxkeU5+Gyk0fj7+/vxMrtRwAAxbmhuKzSNEQnshH49W88uiIfn8U2W+ZmBYXjj7OFmH04pCTX5Or3FZwoEzLf4Jr5jjW5mDykEAU5IUwfVsLGH9CE/TxtSGpvbbE9TyWUQuwTmqbhqlPGoqM74mrN9YOZw0uB4e7hRG7fRPyueRMqMY+qWH4OqRgo8HqinQxjqwrM06YyFXSu/B7MIVOgT5lUhRNGlyE3K5hxXPP+CqeDJ9IJv4q9F9j2K8RqaTInu16R0ZzAfopk198vxDn5WXx8Hc6aPiR24ITYk40X0PmRUSb85pn0oZGojtzsoFDZz6R+gIA5lY/hA7Ml0ytRiK39FVb4rx4zjPFukclQCnEcCGhaUpVhv2mbiF2yy+DiVtYfvSYkCtalTxoFSSU8UCCY4B7Dp6t+D1R4aaMDBbbDUNKY3WRQJhTknjbSCcP1XeIUFoZDLPSOEF+e3fpQpz1I6RqfaVuw08EcpoWYG1eZgzkELhUzHQNVTRiwoJe5TS6gBx5pf/Grm0wEBoES4negohv8wCyRzES6y7ovD4sRnQ6YKcgkWfoTBvJ4QXOIRYfTeDm9MpF0hc8yoKYyFmLumZwyQd6hVOv0Z8UzlELczyA66tILj3QwWoidfJsOFCR2MMfALJNMRKZSJlKBVO9X8INUe9cZLGAn3mkTI+UQ+ZlIVT/px8tEOsBwiDkTMVGIbafPURvxyDuZoNx7hVKI+xncjo6WWohdHIcPRNC0rcEwEPrtuAdDHcgUsO12YJe73aKWvvza6/jALvuUgdlUN7DKkORG5DsXSKGF2NHLRPrL2LSciyzEuhtlwn5EeH+AUoj7GdyUHjmH2Pn3QARjDR2gGfZrufHKIVZIHfrTABEPMolDzA9wA73sUwVN+qP/Q6M0P5FCnBYLcUpSdAedf9ZCzIaTUSaY45hTJmXqoBTifgbZ6UGi59L3kBkz0FQjMAiUv0Q2aynKRN/BL7WlP8PuZSKNyCD6xkDBQC1DHTLKRGrScxqDM6GMGeVW5mVCsvKsU2blDMiKZyiFuJ8hwLghjlU+6rmsITGb8fpTDU0Aibpd6x+IX9EasEWSgWAs+f1qiPCPTLIQK8JEcqBxBpWBBMbtWh9SJpyUr3QZrFhdwlJuZZvq7Bxi4z+9qa4/jTNKIe5nYCoguRQsc/AYDB4XeHjxudvf4WVDJQ3RkphC6sFwiAd4sdusRhnEIR6o/UCqQZfaQOs3zGFUZ7mvBIOJMkGDUW75TXWxUybtm1YJzcK6158MAEoh7mcQzdSjjO9A8XuDw1rKgj4pMjhAs5zIQDXQl+4zCelWxGQncKUCNotaOi3EmSNK/8YALjjL7ZqEQ5wyt2uZTpmwlFu+XHqjxtHNNnpUTKOMshpxv4FSiPsZhG2TOV3GA4e4H1XQRDAYLMQ0vPTbOrP7d+CXSaaA3fw4sMs9lEF+iHmPCAO86FMGtv6mTYwUgVADxBvBUsYhdnyWpkIWeIdw3FTHFY7Qy0QKxEwVlELczxAUcIHpuurlYI7BYiGm85wqHli64Vfp91JX+hIZIEKfYDB597A7688cEnF/Wr7NJNDlNtAmdPQ4KjqpLi0Hc6RLH6auGZ/CXDjLywR7n/yORFMjX6qhFOJ+BpYyYW81XizEA31AJhgMVnGWMuHv3UyYGGWACH2CgbwpiUcmTT5tlInMEa1fQbR1ZaDAzI/ED3HqOMSZTpkw/htUEo5DTPwQSya/tIU4E8YZr1AKcT8DzYWlHWe7gfUy0X8qaCKg88lznQYK/Lpd8+KRpC8xaOoifT3A88wrxOnUjwd2SfchBOPOQINhIbbfT9XY4bypLv2FbCm3DhZiGWVCdAZ2P4BSiPsZxAR/98o3GHzy8mAnDwM/016yqHvgm/cl0i9B30AbBAoFQSZNPu0HB2SObP0JzIRugLVa2r1YVOSHOGUn1cmRrjFaeDBH1M/RzbHn6qQ6hb4Af8AG4O1EmMHoZWIwHIbAUia8ZDKzZu6DpCqyHMwBplDwsPshTl9+FWUi+RhoZUhnR+yHOEXp9hfKhNDLhJhDrCkLsUJfIsBO1Q14oUwMIgsVwWDYSMhuqnMP35fut7wgE6zUfYHBZCG2+yHOHGSSLP0JA5kD77apLmUcYsdn6S9lmg/M75GTUyZiz5mVyFRJmHwohbifgbUQx5Z6fL43UJVDHkyeB6qJmEJ//K79T+L4kP5NSX03E8qok+qUiTgpGAwceNGJbECaDuZIF2WCunY6utnyMiHhEDNu1/pPfVEKcT+DqHGKZrVO7w0C3RAAqwQP1Dz7deeVYQbiQaOfsG6r0ihIH4A/BCed2eXTHqj9QKoxkFc4TA4x+vbo5kyfWFhu1+x+iHslHGLTd7GiTCj0BQLUFyPzWU8WYuq9TG+IyYJfDwz9EX4tN14mT32JgfpdeLAKxcDOs12BSF9+bW6h0iTHQMJAK0MzPwJLKJAeykS6+kWdcZdm/BfpthZlgr0vPLq5H1WYULoFSBdOHleFoqKidIvhG21dvQDWAQBGlRfgixOq8Y9VewC0AAC+OKFa+J5x9vhOAEBhTkgabiBh9Y5GvIPDAIATx1RgWGlemiVKPgqyswBsBeAtjy+t2Q+gCYC8rvQlsoKfAYgAyAx5UoXNB1sBbAQAFOdm9XleH16+A0A7gNSXc0NbN27G5+bvY0eU4pgRZSlNU4bNB9qY3yeMrsDI8vy0yNKf8fneFgAHAQDjqgoHVFv93eubgZYuzKgrxd7GTtvzIcU5KcnvpgOtAA4In80aVY7x1YVJT9MNOVlBM69FYWNsyc0OYmwVK0tuVhCApYMQfJrfCIBdifnC+Gpkh+KzvTY358b1XrwYtApxcV4WivKy0i2Gb9AVKysYQHFeFmORKZbkqTDXuk/eG+jIDQfN69L87AGZ58JcqwmXeMhjKOheV/oSXuruQEAR9Z1CaWh/oT4s56LcEM47ZhhWbm9AXVke5oytQE5W0P3FFCAvzKZbnJs1oOtZqpCTbZVjXjg0oMqQtI3ccFBIj8jJCqYkv3nZ8jZRlKZ6GtA0M11aPwpzCi1xq5YfZsuGXNMW4pL8LGTF6apD6+3jfrJPU1NIGHSDJcR1L4vgNNcnk06SSiUGQ57pXHnx/5phjIlBw+lMtwvAvvzsmqbhzvOm92GKcvBNYrBQdJINkXOjgQKzSuhiekCIJ8UnLV15vJnQL5K2EonqzCY5AOiJnc0c5DgT5J3eqOWXIgOy4hmKQ9zPQFt6CN/HCy80GBz4yiEPOp8DNc+MouWhNWeaQjzQ+bQEjNvD9Ikx6GA/WjZNgvRzDORN2bS3JtFYGkpRhp0mZ+nqF+n8kzEzErVvquvpNW7wkwX6HYL+1McrhbifgfacEPGh3QSZDq3/VNBE4Nd62t/hJY/8TD/dGAzfBUi/28PBUcp22A8OSI8c/R3MBuUBphGbnhF0uyUUsFtBkwWnYkzbSXXUddDcIGcvF2IB5g1N5HcP5Yi4P9UWpRD3Y5BJmBcdJzQIrKU8mMadomWvdCMqmNE7IbPU4cGjoKRbIc60795X4H2gDhZjQLIxsCkTxL2YmDKRlaKxw6kuZkI9JfMAgzLBPuvuNRRi3noukjsDsuIZSiHuxyC+/rxY/ehZ/WCxytEYqHlmzp73ohBnmGaUCR1/X2Ag+3HNZCgOcXIwkN0GUhRiiYU4VRxi+bP0uV2zrkm+RRZiYgGWWYhp9Kf6kjEK8e233w5N03DNNdc4hnvyyScxceJE5OTkYNq0aXjxxRf7RsAMBOHp+LUQp2gFKKMxUK3iDOfLU8eTWRpxP+orEwJ7SMwgyXQGQHGIkwMtzSscqQQZD6OCAyiAdHGIU5KkK+iz+sh4EhGYzcmmOr5sZAd19BdkhGq0cuVK3H///TjqqKMcwy1fvhyLFy/GpZdeitWrV+Pss8/G2WefjbVr1/aRpJkFkzLhQckJDELKBI2BmueoYEbvNXwmYKANrjLQuRyME9J0QXGIkwO/J2L2J5i0Gl1mIe57DnEm1NMAtUGOP3nOPKmO9zLBFVUGZMMX0t41t7a24oILLsAf//hHlJaWOob97W9/i9NPPx3XX389Jk2ahFtuuQUzZ87EPffc00fSZhZMt2t+LcSZ0Nr6AMzyzwDNM92Be/mumXdSXbol6Bukm0M8WMHTiFTZx4eBfPQ4yc8vX/gM724+bHueMg6xQ+eXEZQJ6tQ5mSHFZiHmfvcnugSQAQrxlVdeiUWLFmHBggWuYVesWGELt3DhQqxYsUL6TldXF5qbm5m/gQJffogHuYV4oO2MJujvm+oGi4KS7uqXaROhvgJf7IOjtiUfA5lDXFtinIa27XAbmjt7AQBFOdYRDRNqUnNinLMf4vSXMe1CTbZPycYh1vgJaGpkSxXSejDHY489ho8++ggrV670FH7//v2ormaPUKyursb+/ful7yxZsgQ333xzQnJmKsxK6mGsYxTiDGhsfQEvVJL+DjqHXjqfTNOLBklVHNAczEwGr3Soso8PDOVngJXhr86bjvOPqzPH07L8MKbVFmProVbkhUOmwpxsZLrbNZMyoevSCTXvh5g3PPFeXjIdaVOId+3ahauvvhpLly5FTk5OytK54YYbcN1115m/m5ubUVdXl7L0+hIxXrsnxY/dVNe/KqmCHHRH5cVyk2H68IAbXGVgDuZIQ5YHmlXPK/j6NVjqW7LBWIjTJ0ZKkB8OYf6EKtv9cdWpsQwTZObBHNa1SZkQuF0j4PNgM7b1s8qSNoV41apVOHjwIGbOnGnei0QieOedd3DPPfegq6sLwSB71ndNTQ0OHDjA3Dtw4ABqamqk6YTDYYTD4eQKnyGI+vAyQVfcwWIhHgygTsj0hExbOh8sCkq685lp372vYJv7D47qlnTQlj61KTQ5yEQLMW0yMf0QSw4sAQR+iG0W4v6FtFXtU045BWvWrMHHH39s/h177LG44IIL8PHHH9uUYQCYPXs2Xn/9debe0qVLMXv27L4SO6Pgh0MconqxwcghHqjwe/JcpulFg0QfTrtCPFhh90OcHjn6O1gvE6oQk4GM5xBTB5bITsV180OcCfnwg7RZiAsLCzF16lTmXn5+PsrLy837F154IWpra7FkyRIAwNVXX4158+bhrrvuwqJFi/DYY4/hww8/xAMPPNDn8mcCyDKGt4M56Ov+VUnjRaYpf6mA3yxmGq+6v3WY8UJTVrW0QHGIk4PBSrlJJWbUlaAoJ4Tmzl7kZAXQ2WMt92VCPaWV20jEG4e4v/shTuumOjfs3LkTAUqTmzNnDv7+97/jv/7rv/Czn/0M48aNwzPPPGNTrAcL/LldoyzE/aySKsjhdyk80yYJg2X5VTW59EBxiJODgbypLl0YV12Ij35xKnqjOlZsqcclD1HOBdK1qU5y8mmvhEQ80PwQZ5RC/NZbbzn+BoDzzjsP5513Xt8IlOHwQ5kYjBbiwQC/B21knEI8SAbXwZLPTIM6mCM5UJSJ1CAUDCAUzBxqDz080Nbeh5ZvF4Z3P6muf9WVQWKfGZggRypeftJoAMCpk6ulYcvysjGiPA8AcHRdScplU+gbHDvCOMwmHPLWlK8/fQIA4DtzR6VMJj/obx1mvMjNCmJ8dQEA4ITR5X2efobNg/oM6ujm5IC1EKdNjAGL8dWFyMs29k2NrSpAfnbf2iq/cZzheevaBePMe7lZQUzgPG1UFGSb1zlZAYyuzGeeBwMahpVabuqGl+WlQtyUIaMsxAr+QKx9i44agmm1X0BtqdxfYigYwNJr56G5swcVBQPT6waPwaAEVBXl4P2fnYKCsLemPHN4KdbfcjpysuybVtOBwTK4BgIa/nXVSTjS3o2qwtS5mVRgYbe8DZIKl2TQq4qqCJOPoSW5WPnzBWjs6EFVYbjPV3FvO2cavjd/DEaUWwpuIKDhhavmYldDO/Y0dqAgHMLRdSXY09iBnoiO8oJsFOVkMfFomtHPrd9nHIA2pba4T/ORKJRC3I9Bb6YbXu4+E8sOBQaNMjyYUF3kT8HKFGUYGFwKSlYwoJThPga/AjF4altyQZfbYFnV6Wvkh0PI92jYSDYCAY1RhgmyggGMrizA6MoC896wUmddozg3C7PSsAqWDCjKRD9GxC+BVEEhwzBYLMQK6QFfvwbTBCypUCctKgwCKIW4H+PYkaXpFiGjkZtBllAFMU4cWwEAyFKuT1IKr5SagQbFIU4OinKs+lOYMzjrksLAh6rZ/RBv/Wg+lm+px3nHDku3KBmNS04cibc2HMSZ04akWxQFCb43fwwqC8M4eVxlukUZ0LjtnGn4z799hO+clBmbKfsKx40sw5ShRTjQ3IlTJ9eo5f44sXBKDW48azKyggEcN7Is3eIoKKQEmj7IzvRsbm5GcXExmpqaUFRUlG5xFBQUFBQUFBQUOPS1vqYoEwoKCgoKCgoKCoMaSiFWUFBQUFBQUFAY1FAKsYKCgoKCgoKCwqCGUogVFBQUFBQUFBQGNZRCrKCgoKCgoKCgMKihFGIFBQUFBQUFBYVBjUHnh5h4mWtubk6zJAoKCgoKCgoKCiIQPa2vvAMPOoW4vr4eAFBXV5dmSRQUFBQUFBQUFJxQX1+P4uLilKcz6BTisjLjlJ2dO3f2SQErpBfNzc2oq6vDrl271EEsgwDqew8uqO89uKC+9+BCU1MThg8fbuptqcagU4gDAYM2XVxcrBrUIEJRUZH63oMI6nsPLqjvPbigvvfgAtHbUp5On6SioKCgoKCgoKCgkKFQCrGCgoKCgoKCgsKgxqBTiMPhMG688UaEw+F0i6LQB1Dfe3BBfe/BBfW9BxfU9x5c6Ovvrel95c9CQUFBQUFBQUFBIQMx6CzECgoKCgoKCgoKCjSUQqygoKCgoKCgoDCooRRiBQUFBQUFBQWFQQ2lECsoKCgoKCgoKAxqDDqF+P/+7/8wcuRI5OTkYNasWfjggw/SLZKCTyxZsgTHHXccCgsLUVVVhbPPPhsbNmxgwnR2duLKK69EeXk5CgoK8NWvfhUHDhxgwuzcuROLFi1CXl4eqqqqcP3116O3t7cvs6IQB26//XZomoZrrrnGvKe+98DCnj178M1vfhPl5eXIzc3FtGnT8OGHH5rPdV3Hf//3f2PIkCHIzc3FggULsGnTJiaOhoYGXHDBBSgqKkJJSQkuvfRStLa29nVWFFwQiUTwi1/8AqNGjUJubi7GjBmDW265BfR+f/W9+y/eeecdnHXWWRg6dCg0TcMzzzzDPE/Wt/30009x0kknIScnB3V1dbjjjjv8C6sPIjz22GN6dna2/uc//1lft26dftlll+klJSX6gQMH0i2agg8sXLhQf/DBB/W1a9fqH3/8sX7mmWfqw4cP11tbW80wV1xxhV5XV6e//vrr+ocffqifcMIJ+pw5c8znvb29+tSpU/UFCxboq1ev1l988UW9oqJCv+GGG9KRJQWP+OCDD/SRI0fqRx11lH711Veb99X3HjhoaGjQR4wYoV988cX6+++/r2/dulV/5ZVX9M2bN5thbr/9dr24uFh/5pln9E8++UT/8pe/rI8aNUrv6Ogww5x++un69OnT9ffee0//97//rY8dO1ZfvHhxOrKk4IBbb71VLy8v11944QV927Zt+pNPPqkXFBTov/3tb80w6nv3X7z44ov6z3/+c/3pp5/WAej//Oc/mefJ+LZNTU16dXW1fsEFF+hr167VH330UT03N1e///77fck6qBTi448/Xr/yyivN35FIRB86dKi+ZMmSNEqlkCgOHjyoA9DffvttXdd1vbGxUc/KytKffPJJM8znn3+uA9BXrFih67rRSAOBgL5//34zzL333qsXFRXpXV1dfZsBBU9oaWnRx40bpy9dulSfN2+eqRCr7z2w8JOf/ESfO3eu9Hk0GtVramr0O++807zX2Nioh8Nh/dFHH9V1Xdc/++wzHYC+cuVKM8xLL72ka5qm79mzJ3XCK/jGokWL9G9/+9vMvXPPPVe/4IILdF1X33sggVeIk/Vt//CHP+ilpaVMX/6Tn/xEnzBhgi/5Bg1loru7G6tWrcKCBQvMe4FAAAsWLMCKFSvSKJlComhqagIAlJWVAQBWrVqFnp4e5ltPnDgRw4cPN7/1ihUrMG3aNFRXV5thFi5ciObmZqxbt64PpVfwiiuvvBKLFi1iviugvvdAw3PPPYdjjz0W5513HqqqqjBjxgz88Y9/NJ9v27YN+/fvZ753cXExZs2axXzvkpISHHvssWaYBQsWIBAI4P333++7zCi4Ys6cOXj99dexceNGAMAnn3yCZcuW4YwzzgCgvvdARrK+7YoVK3DyyScjOzvbDLNw4UJs2LABR44c8SxPKNEM9RccPnwYkUiEGRABoLq6GuvXr0+TVAqJIhqN4pprrsGJJ56IqVOnAgD279+P7OxslJSUMGGrq6uxf/9+M4yoLpBnCpmFxx57DB999BFWrlxpe6a+98DC1q1bce+99+K6667Dz372M6xcuRJXXXUVsrOzcdFFF5nfS/Q96e9dVVXFPA+FQigrK1PfO8Pw05/+FM3NzZg4cSKCwSAikQhuvfVWXHDBBQCgvvcARrK+7f79+zFq1ChbHORZaWmpJ3kGjUKsMDBx5ZVXYu3atVi2bFm6RVFIEXbt2oWrr74aS5cuRU5OTrrFUUgxotEojj32WNx2220AgBkzZmDt2rW47777cNFFF6VZOoVk44knnsDf/vY3/P3vf8eUKVPw8ccf45prrsHQoUPV91boUwwaykRFRQWCwaBt5/mBAwdQU1OTJqkUEsH3v/99vPDCC3jzzTcxbNgw835NTQ26u7vR2NjIhKe/dU1NjbAukGcKmYNVq1bh4MGDmDlzJkKhEEKhEN5++2387ne/QygUQnV1tfreAwhDhgzB5MmTmXuTJk3Czp07AVjfy6kvr6mpwcGDB5nnvb29aGhoUN87w3D99dfjpz/9Kb7xjW9g2rRp+Na3voVrr70WS5YsAaC+90BGsr5tsvr3QaMQZ2dn45hjjsHrr79u3otGo3j99dcxe/bsNEqm4Be6ruP73/8+/vnPf+KNN96wLZUcc8wxyMrKYr71hg0bsHPnTvNbz549G2vWrGEa2tKlS1FUVGQbjBXSi1NOOQVr1qzBxx9/bP4de+yxuOCCC8xr9b0HDk488USbG8WNGzdixIgRAIBRo0ahpqaG+d7Nzc14//33me/d2NiIVatWmWHeeOMNRKNRzJo1qw9yoeAV7e3tCARYVSQYDCIajQJQ33sgI1nfdvbs2XjnnXfQ09Njhlm6dCkmTJjgmS4BYPC5XQuHw/pDDz2kf/bZZ/rll1+ul5SUMDvPFTIf3/ve9/Ti4mL9rbfe0vft22f+tbe3m2GuuOIKffjw4fobb7yhf/jhh/rs2bP12bNnm8+JG67TTjtN//jjj/WXX35Zr6ys7HM3XB988IE+e/ZsPS8vTwegr169us/Svuiii/QRI0b0WXrxAIB+44032u7TXiZ0nf3eN954ow5AnzZtmvk8Vd+7P5Rhf8MHH3ygh0Ih/dZbb9U3bdqk/+1vf9Pz8vL0v/71r2aY22+/XS8pKdGfffZZ/dNPP9W/8pWvCF01zZgxQ3///ff1ZcuW6ePGjRtwbrhGjBihX3TRRekWIyFcdNFFem1trel27emnn9YrKir0H//4x2YY9b37L1paWvTVq1frq1ev1gHov/71r/XVq1frO3bs0HU9Od+2sbFRr66u1r/1rW/pa9eu1R977DE9Ly9PuV1zw+9//3t9+PDhenZ2tn788cfr7733XrpFUvAJAMK/Bx980AzT0dGh/+d//qdeWlqq5+Xl6eecc46+b98+Jp7t27frZ5xxhp6bm6tXVFToP/zhD/Wenp4+y0d3d7c+YsQIfcKECfr999+vP/LII3pDQ0Ofpd8flDmvCjH9vbOzs3UA+ksvvcS8k4rv3R/K0A/mzZunT5kyxVPYbdu2Sdsi/7dt2zZfcjz//PP61KlT9XA4rE+cOFF/4IEHmOfRaFT/xS9+oVdXV+vhcFg/5ZRT9A0bNjBh6uvr9cWLF+sFBQV6UVGRfskll+gtLS2+5EgUbW1t+o033qi/+eabccfx7rvv6jfeeKN+5MgR27OBoBA3NzfrV199tT58+HA9JydHHz16tP7zn/+ccaHVX763gh1vvvmmsE8g9TZZ3/aTTz7R586dq4fDYb22tla//fbbfcuq6Tp1HIyCgkKfYf369Zg0aRL++Mc/4jvf+U6fp3/xxRfjrbfewvbt2/s8ba/o7Ow0ecNe8dBDD+GSSy7BypUrGVc9qUB/KEM/mD9/Pg4fPoy1a9e6hm1ra8M///lP5t5dd92F3bt34+6772bun3POOcjPz0+qrP0Bhw8fRmVlJW688UbcdNNNccXxq1/9Ctdffz22bduGkSNHMs+6uroQCASQlZWVuLAKCoMcysuEgkKaQPisvLuwwY5oNIru7m7k5OQorxIZjPz8fHzzm99k7j322GM4cuSI7T4NXdfR2dmJ3NzcVIuYNpA6nGqEw+GUp6GgMFgwaDbVKShkEi6++GLMmzcPAHDeeedB0zTMnz/f/BOFp61D27dvh6Zp+NWvfoUHHngAY8aMQTgcxnHHHSf01fvMM89g6tSpyMnJwdSpU22WPT94//33cfrpp6O4uBh5eXmYN28e3n33XSbMTTfdBE3TsHnzZlx88cUoKSlBcXExLrnkErS3tzNhNU3D97//ffztb3/DlClTEA6H8fLLL5vPeMvanj17cOmll2Lo0KEIh8MYNWoUvve979kUkK6uLlx33XWorKxEfn4+zjnnHBw6dMiWn5deegknnXQS8vPzUVhYiEWLFgkP60ikDEeOHIkvfelLePXVV3H00UcjJycHkydPxtNPP82Ee+ihh6BpGpYtW4arrroKlZWVKCkpwXe/+13Tk8aFF16I0tJSlJaW4sc//jHIIl9PTw/KyspwySWX2NJvbm5GTk4OfvSjH3mWOVkgeX/llVdw7LHHIjc3F/fffz8A4MEHH8QXv/hFVFVVIRwOY/Lkybj33nulcSxbtgzHH388cnJyMHr0aPzlL39hwvX09ODmm2/GuHHjkJOTg/LycsydOxdLly41w1x88cUoKCjA1q1bsXDhQuTn52Po0KH45S9/CX7BtK2tDT/84Q9RV1eHcDiMCRMm4Fe/+pUtnKgO33fffaisrAQA3HzzzdA0janPn376KS6++GKMHj0aOTk5qKmpwbe//W3U19eb8d500024/vrrARgbkEgcZEVi5MiRuPjiixlZtm7divPOOw9lZWXIy8vDCSecgH/9619MmLfeeguapuGJJ57ArbfeimHDhiEnJwennHIKNm/e7PQ5FRQGLJSFWEEhDfjud7+L2tpa3Hbbbbjqqqtw3HHHobq6GrfeequveP7+97+jpaUF3/3ud6FpGu644w6ce+652Lp1q7mM+uqrr+KrX/0qJk+ejCVLlqC+vh6XXHIJ46rOK9544w2cccYZOOaYY3DjjTciEAiYSs2///1vHH/88Uz4r3/96xg1ahSWLFmCjz76CH/6059QVVWF//3f/7XF+8QTT+D73/8+KioqbEvDBHv37sXxxx+PxsZGXH755Zg4cSL27NmDp556Cu3t7cxJRT/4wQ9QWlqKG2+8Edu3b8dvfvMbfP/738fjjz9uhnnkkUdw0UUXYeHChfjf//1ftLe3495778XcuXOxevVqU45klOGmTZtw/vnn44orrsBFF12EBx98EOeddx5efvllnHrqqUzYH/zgB6ipqcHNN9+M9957Dw888ABKSkqwfPlyDB8+HLfddhtefPFF3HnnnZg6dSouvPBCZGVl4ZxzzsHTTz+N+++/nymLZ555Bl1dXfjGN77hWd5kYsOGDVi8eDG++93v4rLLLsOECRMAAPfeey+mTJmCL3/5ywiFQnj++efxn//5n4hGo7jyyiuZODZv3oyvfe1ruPTSS3HRRRfhz3/+My6++GIcc8wxmDJlCgBDgVyyZAm+853v4Pjjj0dzczM+/PBDfPTRR0wZRyIRnH766TjhhBNwxx134OWXX8aNN96I3t5e/PKXvwRgWLK//OUv480338Sll16Ko48+Gq+88gquv/567Nmzx0YL4evw9OnTce+99+J73/sezjnnHJx77rkAgKOOOgqAsQt+69atuOSSS1BTU4N169bhgQcewLp16/Dee+9B0zSce+652LhxIx599FHcfffdqKioAABT0eZx4MABzJkzB+3t7bjqqqtQXl6Ohx9+GF/+8pfx1FNP4ZxzzmHC33777QgEAvjRj36EpqYm3HHHHbjgggvU6W4KgxP+KdIKCgrJANls8OSTT5r35s2bp8+bN88Wlt+8RTY0lZeXMxvxnn32WR2A/vzzz5v3jj76aH3IkCF6Y2Ojee/VV1/VAfjaEBaNRvVx48bpCxcu1KPRqHm/vb1dHzVqlH7qqaea94inh29/+9tMHOecc45eXl7O3AOgBwIBfd26dbY0wW2qu/DCC/VAIMCca0/Lp+u6/uCDD+oA9AULFjByXnvttXowGDTLoaWlRS8pKdEvu+wyJp79+/frxcXFzP1Ey3DEiBE6AP0f//iHea+pqUkfMmSIPmPGDPMekZ0v49mzZ+uapulXXHGFea+3t1cfNmwYU19eeeUV2/fXdV0/88wz9dGjR7vK6WdTnQiLFi2ylQfJ+8svv2wLT3uGIVi4cKFNVhLHO++8Y947ePCgHg6H9R/+8IfmvenTp+uLFi1ylPGiiy7SAeg/+MEPzHvRaFRftGiRnp2drR86dEjXdV1/5plndAD6//zP/zDvf+1rX9M1TdM3b95s3pPV4UOHDkk3hory/uijj9ryeeedd0o3JvKb6q655hodgP7vf//bvNfS0qKPGjVKHzlypB6JRHRdt/qeSZMmMZvXfvvb3+oA9DVr1tjSUlAY6FCUCQWFfozzzz+f8bN40kknATCWTQFg3759+Pjjj3HRRRehuLjYDHfqqaf69r/78ccfY9OmTfiP//gP1NfX4/Dhwzh8+DDa2tpwyimn4J133jF9hxJcccUVzO+TTjoJ9fX1aG5uZu7PmzfPVZ5oNIpnnnkGZ511lnCznKZpzO/LL7+cuXfSSSchEolgx44dAAwLXWNjIxYvXmzm5fDhwwgGg5g1axbefPNNAMkrw6FDhzIWuqKiIlx44YVYvXq17XjZSy+9lJF91qxZ0HUdl156qXkvGAzi2GOPNb81AHzxi19ERUUFYwU/cuQIli5divPPP9+zrMnGqFGjsHDhQtt9mkfc1NSEw4cPY968edi6dSuampqYsJMnTzbrN2BYSSdMmMDkv6SkBOvWrcOmTZtcZfr+979vXhPKQ3d3N1577TUAwIsvvohgMIirrrqKee+HP/whdF3HSy+9xNz3Uodp0Hnv7OzE4cOHccIJJwAAPvroI8/x0HjxxRdx/PHHY+7cuea9goICXH755di+fTs+++wzJvwll1zCrCTw/YeCwmCCUogVFPoxhg8fzvwmyvGRI0cAwFT+xo0bZ3uXLFt7BVEyLrroIlRWVjJ/f/rTn9DV1WVTYtzkI+APVxHh0KFDaG5uxtSpUz3J65Y2yc8Xv/hFW35effVVc9Njsspw7NixNqV9/PjxAGDzUsHLThTxuro62326LEOhEL761a/i2WefRVdXFwDg6aefRk9PT9oVYhHeffddLFiwAPn5+SgpKUFlZSV+9rOfAYBrXQKMb0rn/5e//CUaGxsxfvx4TJs2Dddffz0+/fRT23uBQACjR49m7vHfYseOHRg6dCgKCwuZcJMmTTKfe8mjDA0NDbj66qtRXV2N3NxcVFZWmnHwefeKHTt2COukTGav7VNBYTBAcYgVFDIImqbZNuwABudRhGAwKLwviiNREOvvnXfeiaOPPloYpqCggPntVb5UeBxwS5vk55FHHhEe7+nH1VuyIZNddJ8vy2984xu4//778dJLL+Hss8/GE088gYkTJ2L69OkpkdULRN93y5YtOOWUUzBx4kT8+te/Rl1dHbKzs/Hiiy/i7rvvtq02eKlLJ598MrZs2YJnn30Wr776Kv70pz/h7rvvxn333Zdy14Z+6/DXv/51LF++HNdffz2OPvpoFBQUIBqN4vTTT7flPVXoy/5DQSHToRRiBYUMQmlpqXC5krfseAU57la0hMwfj+uGMWPGADCW+hcsWBCXPImgsrISRUVFnnzkegHJT1VVlWN+klWGmzdvhq7rjJV448aNACDdRBgPTj75ZAwZMgSPP/445s6dizfeeAM///nPkxZ/svD888+jq6sLzz33HGOpJFSVeEE8bVxyySVobW3FySefjJtuuolRiKPRKLZu3WpahQH7txgxYgRee+01tLS0MFbi9evXm8/dwK8IEBw5cgSvv/46br75Zvz3f/+3eV9Ux2RxiDBixAhhnfQjs4LCYIWiTCgoZBDGjBmD9evXM+7BPvnkE5tbM68YMmQIjj76aDz88MPMMuzSpUttfEI3HHPMMRgzZgx+9atfobW11fZc5NIsmQgEAjj77LPx/PPP48MPP7Q992vVWrhwIYqKinDbbbehp6fH9pzkx28ZbtmyBVu2bLHd37t3L+Oqrbm5GX/5y19w9NFHCy3U8SIQCOBrX/sann/+eTzyyCPo7e3F+eefj4svvhhnn3120tJJFMQ6SX+3pqYmPPjgg3HHSbssA4wVi7Fjx5r0ERr33HOPea3rOu655x5kZWXhlFNOAQCceeaZiEQiTDgAuPvuu6FpGs444wxXefLy8gAAjY2NzH1R3gHgN7/5jS0OcqAJH4cIZ555Jj744AOsWLHCvNfW1oYHHngAI0eO9L1vQEFhMEFZiBUUMgjf/va38etf/xoLFy7EpZdeioMHD+K+++7DlClTbBvRvGLJkiVYtGgR5s6di29/+9toaGjA73//e0yZMkWo2MoQCATwpz/9CWeccQamTJmCSy65BLW1tdizZw/efPNNFBUV4fnnn49LRq+47bbb8Oqrr2LevHm4/PLLMWnSJOzbtw9PPvkkli1b5uuQk6KiItx777341re+hZkzZ+Ib3/gGKisrsXPnTvzrX//CiSeeaCpDfsqQKFQ8L3j8+PG49NJLsXLlSlRXV+PPf/4zDhw4kJACKMP555+P3//+97jxxhsxbdo0k0NKY/78+Xj77bfTtjx+2mmnITs7G2eddRa++93vorW1FX/84x9RVVWFffv2xRXn5MmTMX/+fBxzzDEoKyvDhx9+iKeeeorZQAcAOTk5ePnll3HRRRdh1qxZeOmll/Cvf/0LP/vZz0yXZmeddRa+8IUv4Oc//zm2b9+O6dOn49VXX8Wzzz6La665xlxhcEJubi4mT56Mxx9/HOPHj0dZWRmmTp2KqVOn4uSTT8Ydd9yBnp4e1NbW4tVXX8W2bdtscRxzzDEAgJ///Of4xje+gaysLJx11lnCk/9++tOf4tFHH8UZZ5yBq666CmVlZXj44Yexbds2/OMf/0AgoGxgCgoyqNahoJBBmDRpEv7yl7+gqakJ1113HZ577jk88sgjmDlzZtxxnn766XjyyScRiURwww034Omnn8aDDz4Y17HG8+fPx4oVK3DsscfinnvuwQ9+8AM89NBDqKmpwbXXXhu3jF5RW1uL999/H1/72tfwt7/9DVdddRX+8pe/YP78+aY1zg/+4z/+A6+//jpqa2tx55134uqrr8Zjjz2Go48+mjngIhllOG7cODz++ON48cUX8dOf/hQ9PT14/PHHhd4XaMRz4tmcOXNQV1eHlpYW6Wa61tbWpFqm/WLChAl46qmnoGkafvSjH+G+++7D5ZdfjquvvjruOK+66ips374dS5YswVVXXYW3334b//M//4O77rqLCRcMBvHyyy9j//79uP7667Fy5UrceOONuOWWW8wwgUAAzz33HK655hq88MILuOaaa/DZZ5/hzjvvxK9//WvPMv3pT39CbW0trr32WixevBhPPfUUAMOH+MKFC/F///d/uOGGG5CVlWXzXAEAxx13HG655RZ88sknuPjii7F48WLpakx1dTWWL1+OU089Fb///e9xww03IDs7G88//7zNB7GCggILTVfseQUFBYWUYuTIkZg6dSpeeOEF17Dz58/H1KlTEQqF8Ne//hXTpk3DWWedhQcffBBbt25FWVkZzjrrLNxxxx3mJsaHHnoI11xzDR5//HFcc8012LVrF+bOnYsHH3wQQ4YMAWCc0NbY2IhnnnkGLS0tKC0tRU5ODn7xi1/gJz/5SUrzn0m4+OKL8dRTT/laHVFQUBj4UBZiBQUFhQzDww8/jOzsbLz77ru47777EAgE8Lvf/Q7r1q3Dww8/jDfeeAM//vGPmXfa29vxq1/9Co888gjeeecd7Ny5U3pU8+9//3tEo1Hccccdg0oZVlBQUJBBcYgVFBTQ0NDguDQfDAalx8UqJB/jxo3DHXfcYf6mfcuOHDkS//M//4MrrrgCf/jDH8z7PT09uO+++0xu6/e//33zGGIa//znP7FkyRI8+uijjr6Jm5qa0NHR4ShnOikXCgoKCsmEUogVFBRw7rnn4u2335Y+HzFihG2TmELqQDZSEbz22mtYsmQJ1q9fj+bmZvT29qKzsxPt7e0mdzovL4/Z6DVkyBDzcBGC999/Hy+88AKeeuopV48TV199NR5++GHHMIpxp6CgMFCgFGIFBQXcddddjqdTpeLgjMEEv5MJ2oPA9u3b8aUvfQnf+973cOutt6KsrAzLli3DpZdeiu7ublMhzsrKYuIQHfIyZswYlJeX489//jMWLVpke4fGj3/8Y3zzm9/0JXd/wEMPPYSHHnoo3WIoKChkGJRCrKCgYLNIKmQOVq1ahWg0irvuust0m/XEE0/EFVdFRQWefvppzJ8/H1//+tfxxBNPSJXiyZMnK7+1CgoKgwZqU52CgoJCBmPs2LHo6enB73//e2zduhWPPPII7rvvvrjjq6qqwhtvvIH169dj8eLF6O3tTaK0CgoKCv0Tg85CHI1GsXfvXhQWFvo6ElNBQUGhL9Db24uuri7zIJZRo0bhtttuw5IlS/DTn/4UJ554Iv77v/8b3/3ud9Hc3IxAIICOjg7ous4c3tLe3g4A5r3u7m709PSgubkZeXl5ePbZZ3HmmWfi61//Ov7f//t/5ulpCgoKCpkAXdfR0tKCoUOH9smhMoPOD/Hu3btRV1eXbjEUFBQUFBQUFBRcsGvXLgwbNizl6Qw6C3FhYSEAo4CLiorSLI2CgoKCgoKCggKP5uZm1NXVmXpbqjHoFGJCkygqKlIKsYKCgoKCgoJCBqOv6K1qU52CgoKCgoKCgsKghlKIFRQUFBQUFBQUBjWUQqygoKCgoKCgoDCooRRiBQUFBQUFBQWFQQ2lECsoKCgoKCgoKAxqDDovEwoKCgoKCgoKXqDrOn7w6GpUF+XgF19yP8q8sb0blz+yCl+dWYvzjxsOADjY0onL/7IKOoD7v3kMaopzXOO569UNePqjPQCAL06swi1nT00oH+nGX9/bgfvf2YLcrCDu/Np0TK8rcQx/47Nr8fLqbX0jXAzKQqygoKCgoKCgIMCGAy144dN9+H/LvClnv3t9Mz7Y1oCf/GONeW/Flnp8vKsRn+xqxLLNhz3F85cVO7CnsQN7GjvwyHs70BuJxiV/puDxlbuwq6EDGw+04pV1+x3DRqI6Hl6xA/uaOvtIOgNKIVZQUFBQUFBQEKA3Yh3m6+Vg39auHtu9HiqOrt6Ip3QjUTataD8/U5jOj1te+Lz3FZRCrKCgoKCgoKDgAg/6sBCRqGXd7e71ZumN6rxC3L81Ylp+Hc55UQqxgoKCgoKCgkIGgT4kLeJBKRUFoS3E8SrE/VwfZhViNwtxmjKrFGIFBQUFBQUFBQE0WBpxvJZLmv/rXSHmf/dvjZjOjxv1JBJRCrGCgoKCgoKCQsYgQGlJ8SqlvZQ22O1xcxyvNPZ/hVhZiBUUFBQUFBQU+iWSYSGOhzIx0DbV0TquW1YUh1hBQUFBQUFBIUMRr55GUya64qRMePFwkcnwZSGOZT4U0JwDJhlKIVZQUFBQUFBQEIDWyaLxWoh9UiZEym9/txD78jIRC6sphVhBQUFBQUFBIbMQL7fV76Y6kfLb7znEVLbdshI1LcQpFEgApRArKCgoKCgoKAhA627xWohpTqw3hVhkIe7nCrEP+ckmxKCmLMQKCgoKCgoKCmkHrch50YdFQfxuqhMpj/1cH+Y4xN4O5ggohVhBQUFBQUFBIf2gdbe4KRP0SXUeOMRRQZD+byG2rr16mVCb6hQUFBQUFBQUMgCMhbiP3K6JKRNxJZ0x0OPwMhFQCrEzbrrpJmiaxvxNnDgx3WIpKCgoKCgoDDAwFuI+OqmOVoiJThivMp4poMV3s3aT531NmQj1aWpJwpQpU/Daa6+Zv0OhfpkNBQUFBQUFhQxGcigT1ntdXigTVDKhYADdvdF+zyGmJxNuWTE31fWxhbhfapKhUAg1NTXpFkNBQUFBQUFhAMPPZjAZenxaiOl0QgEN3RgIHGL/lIm+Voj7HWUCwP9v77zj7SrKvf9ba++z9+k1p6ScVFJIhSQkhF5CF+GCBYwK2F4VFEQEkWtBLwZRuVe9CpYLWEAUpSgqiLQIhBBCAkkgIb23k+T0tst6/1h71pqZNavscs7eh/N8P5/k7LL2rFlt5plnfvM82LRpE0aNGoWJEydiyZIl2Llzp+u2fX19aG9vF/4RBEEQBEH4wRtyAZy7SuKChjjhuz3vTWVG4VA3iMXqB4syQYvqfFi4cCEeeOABPPXUU7jnnnuwbds2nHrqqejo6FBuv3TpUlRVVVn/mpubB7nGBEEQBEEMRXjTLWMNcZqZ6vjd2AZxRrsuGDLxENOiOh8uuOACfPCDH8Ts2bNx3nnn4e9//ztaW1vxxz/+Ubn9rbfeira2Nuvfrl27BrnGBEEQBEEMRQwhDnEOwq6lIZnQNXthWaZyjUIhHYOYbTvYiTmGpIaYp7q6GlOmTMHmzZuV30ejUUSj0UGuFUEQBEEQQ510oiO4EU877Jr5N6RrdpSJoW0PS3GIvQ8mTh7izOjs7MSWLVswcuTIfFeFIAgibXr6E3hlSws6++L5rkrO2XWkG11D9LgOdfThpU0teHlzCzp6Y9h0oAMvbWrBka7+fFctMC2d5jFsPtiZcRlbD3XipU0tONjR67utYRjYdKBDCDNmGAbW7GrFS5tasG5PW2BPZ1dfHC9vbsFbu1uF32xv6UJPv1OHe7CjFyu3HxH2nQvSDbumOjx+Ud3R7hi2HPK+HszwZqFlAeDlzbltIzbuN+9ndo9396dfdmt3P17a1IKN+9WSVZ504hAn85S6ech5iG+66SZcfPHFGDduHPbu3YtvfvObCIVCuPLKK/NdNYIgiLT59G9ex0ubWzBvXA3+/LmT8l2dnLH1UCfO+uGLqCktwupvnJvv6qRFPJHEBT9ahpZO0/gtCmlWcoXR1SV46ZYzLUOlUEkkDVz043/jQHsfAODvXzwV00dVplUGu4YAUFNahBVfW4xI2N2P9utXtuNbf30bH5w3Bt//4BwAwB9f34Vb/rzW2ubej87D+TP9o0R99ner8O9NLQCA//nwcbj0+NFYs6sVl/70ZUxuKMczN55ubRtLJHH2D15ER18cnz9jEm4+P3e5CZI5kUyIvzv3v5fhqetPxeTGCuX2dupiOw7xt598G399ay8e+/zJGdWBZ9WOI7j8nuXCZ6dNqcdvPrEgrXIu+9kr2NrSBQB45LOLcML4Wtdt0wm7li8N8ZAziHfv3o0rr7wShw8fRn19PU455RS8+uqrqK+vz3fVCIIoMA539mFrS5eyod55uBt723pwqKMP8WQS4+rKMHdszaDX8aXNZqe/asdRAGbn/uLGQ+joi6GyuAinT6lHOJT+ZN47+9qxYX971sf19t527DzShTOmNqC4KBT4d8vePQTA9Ih5sbe1Byu2Hbbej6oqwcKJdZlVNkd0xxKWMQyImcb2tPaguz+Bsmh23efB9l68suUwKorDOG1KPYoyuMYybd0xvLO/HQsn1KKjN2YZwwCw+2h32gbxrqM91uuj3TEc6epHU1Wx6/Y/ec6ULj6yardlEO843C2WeUR8v+lAB0K6hon15cLnO7ntmIf70Td2m7+RPN5dfXF0pLyn2w93+R+YB+8e6MDmg504fUo9yqJhZZSJ1u5+LNvUguObq9FcW+pbJvNaN1ZGcaC9D4mkgc/+bhV+tmQepjY5jWK2S13ThOQUq3e2Orbd3tKF1bvMtqOkKIwzptb7PqfvHjDPX0U0jNryCHYc7sb2FvG8rdh6GHvbelAU0nH6lHpUFBdJdTQsYxgAtrV0YcaoSryw8RD6UpE05o+rtc6PIJnwsYjjeYoyMeQM4ocffjjfVSAIYohwxg9eQEdvHPdfcwLOnNogfHfa9593bL/sK2dibJ1/BzeQPPL6bnztMduj9v0PzMYH56cXHaejN4ZLf/oy+lJ6xX/ffGagjltVzoU//jcA4PqzJ+NL50wJ/NtQQAPvEw+sxAZpyvVvXzwFM0ZVBa9ojjF8Zt3bemJZG8Sff/ANvJ4aBH3v8ln48AljsyoPAC6/9xVsPtiJH11xHI5vFgdBmfg2ZflBe2/M0yAui4ZxWJKUyN5R/n13fxzn/PcyAMDmOy4QBn68Idreaw6qOnvV0/qCzjcLxURvLIGLfvxvxBIGPnHyBHzj4unCiWOey5seeQv/eucA6iuiWHnbYt9y2YDqrg/MwdGuftzwhzXYcqgL//Gzl/H6fy5GaUS8l/hMbV7Z2gzDwAfufUUYvH3lvKm49sxjPOvT3mOez3OmN+KTp07ART9+yTJiAWD93jZ8+BevWu8/euJY/Nels4Qy+iQtdF8sgR89uwk/f3Gr9dnEEWV47qYzHDIZPw1xvhbVDXkNMUEQhBsdqQ702XcOCJ/3xtSxQA8E0EkONHtaRQ/atpb0PV6t3TGhwwqi/3Qrh7G/Lb0ygnp39reb5c4fV4PylJF5oD2/18Gvw27r8fZ6B4G/rvvSPLduME/qn1bttoxIRiZRCmRjtt3nuEsjTs8kv6DMfG/fl4ddvPCAaNiy/Xa4aGgFL24W0RgOdfRZ9WCeZtGzab55e2+btX0QWJSJsK7hgllNuHKBOcDt7k8oZ1BsDbH5z42+eNIyhsfUlAAI9pyye6OypAjRsHnNemP2CZefv+0tYpvE9i2/352aUWB1Yfe1Q3od0ENMiTkIgiByjNwgH+1WL4zKNM5oLuE7JsC9rl7EJM9epuuM+HJiabre+M7MyxhjBtMPPjgHxzSY0+Y5XheVNn63QbYGcTJpCNdVNhqzpb037jBeM7ET5XrJRrZMucJrHpfuG97I9tLkih5i0xB29RDzZWbxDPMLJtlrlbHN68eDDDR4CUA0HMLSy2ajstg8V6rBuRhlwt0o5H97+dwxqX35PzztPeZ5rCwOo7jINAN5D7H8/KkWkvZJCUb64kn0peoj10W+zn5a7CQZxARBEAOD3Gm5RQrIpjPNFT2pTmVEuRku8mhX+saX7NnLRUKBdI02frpT9v7xMKM7HNKsDjDfAxO/Djtbg7i9NyYY3fIAJls6e2OWEcnI5JTKxhUzpNxQyUjkay8srvIIxSUYxKnz7RZlgT82+d5PhyPcIIUNWFSJOXTOcgqSaIM9O7wkhOl8VREzRMmE+B3flrHBc1jXrPK8njWGm4eYlc2OM5Kqr2pQ3icN3HtjCas+FSljP5YwYBiG43kKuqhOG2QLlQxigiDe88hOk1aXhV7ZTLfmCub1GVVtajWP5MBDnOnqeL6cIJ4nnnDI7sllbxIPM2CKQrplROc7Ta3f/v2kA37I0+RBjJh06OiNOyUTGaiIc+EhTkj3DT/TIEZwEH/Hv7c0xAEkE9ncO628QazwEPOGKiNIXGEmEyninomSlLxE9WzY+4HDQ8wb/GzwXFwUssoOEnaO3b+VxUWWhxiwjXu2/7ryCAAXg1jhIWb14e+DRNJwzE4EzVRHqZsJgiByjGwMuHmI8+2ZBGzPy8jU4qXWDAxi2ZDJ2EPMlZOu0cZPK8syEIZhGFbdQrpmed7yfh0GWDIh33/pDjb86FBIJnLjIQ6uIWYDO3YPMdsmkVAbwQ4vouAhNg3hDtdFdZnPZPAc4WZj2nvjiCWSYqa61OngzTTZIFYNPGKWgcd5iBXaXcd+NM2hIeb312sZxLo1uxILcKHZeawoDlseYr4u7Pljs1S9saTDky3X2/QQpwziYtsgjicNx/Ps6yFWDDwGAzKICYJ4zyN7JApZQ8y8LCOrzIUpRwpEMpFuGbz8xM1DzJdfpNudev49xN7fZ+shlgc5ufYQ98QSDslEThbVuRikDD7cFzO6WBnsO75MIVmDZBeqPcTq855u8gw35OvS2h0Ty2aSAu7DYJIJp4eYeWa9JRNOo5CftenlPMRMjpFIUzJRFLKNbvacsnNYWRK26izPVDk8xLGkbRBHRYPYa7Cjwl5U53soOWXIhV0jiMGiL57AGztaMX1kJapKi/x/UIC8e6ADScPAtCb3+KMtnX3YsM8Me1UWDWHGqCqs3nkUxzSUo6584NOe98YSWL2zFYmkgXF1pZ7hwXYd6UZNWQR7W3twMBVjtbm2BOPqyjz3wfeR3f1xrNnVqtyupbMPB9t70VDpHlpKxbsHOmAYEGKK9sYS2Nva44ivCpjxYtfuaXN8vnF/B97abdaNSSZaOvuw6UCHaxB/FfK0aSJpoDeWwBs7jyKZNDWQc8fWWEYKq09deQTHjqxUlsN3xMmkgTd3t6KrL4GasiJMH1npSFTBby+vSLfLty+MqSE2e8CV24/g+OYa7G3rQTxhQNOA45qrXUOdedVn04EOHGjvw7EjK1BdGsHqnUct79bM0ZWoKC7CGzuPWp750mgIjT7XX+Uh3tbShT1HezClqRyxhIFth7owfkQpxtTY9zM7z69tPyKdh/Q9xP3xJN7YeRRTGytQUxZxfP+uFMpOtkGSSQOrd7UqDbLJjeVorCx2eFs3HejAGzuPYsaoSqzZ2YpJDeWWFxEQvaMvbDyI5tpSYVq/uz8heJ29PMT8++7+BJa9e8h1piEpGairdhxFT38CIyoinm0fAOG5eGefeM5efPeQtfgNsAd56aRi3nWk25LIqDTEvYrBIjuckCLsGr8/QTKRGkz6zTbsPNxtxSGuLC6CpmkoDofMQVRPHFsOHrYcBiFdR01pBAc7+vD8hoP40PxmrN55FLGEgR1HxOg3vXFeQ2z3l/FE0tE2+Jns7DyHB1lETAYxQbhwy5/ewuNr9mLCiDI8f9MZ+a5O2ryx8ygu+9krAICHP3MiTlQkO5CzWQHm1HUiaaCuLIKVty0e8GxBX3t0LR5dvQeAqRl75daz0FDhNEi2t3ThjB+84Phc14CXbjkLo6pLXPfBd9Tn/c8y7DrSo9yOZdRad/t5Sj2kilU7juLye8zzzGdr+tj/rcDK7Ufxu08uxCmTRwi/+fAvljti77K6MZiHGADO+e9l+Ot1p2DWmKpAdXJ4iA0Dt/z5LTyxZq+9rxmN+PnH5jvq8+CnFuLkY0Y4yuGNgAdf24mvP77Oev9/V83H2cc2utbBLcwdrycNhzQwB9rvXt2J3726U9h20cQ6/P4zJyrLeXDFDnz9ifXW+/uuno+zpjXird2teP//vgwAGFVVjCsWjMXdz7xrbTexvgwXzRppJZRgfOa0iWaddE25SEs2iLe3dOHM1L3JHpekYS5KevVrZ6M2ZbB+6OfLsfGA87pnshDs64+vwx9e34VRVcV45dazHd8/tX6/8F6eyv/VS1vx3b9vUJZdEQ1j5X8udmjRn994CM9vPGS9H1EexWtfO9tqI/jNv/Knt4TfFqcy3PEeXC/tr7zA9eP3vaasq/lb+/WaXa3W8wi4t32ML//xTfxt7T7ldzc98iYWc/c18wzHAhrEB9t7hTaLz/JnGcQKI5+PZuGQTPADzdRvi4t0y9j2mm3Y19aDM39o16eqxDRco0U6emIJXPfQG0K7FNKA2jLTIP7Px9fh239929UjznuIy6L2TEEsYcCRJySghniwM9WRZIIgXFi+1cyelUkc2EJg6yG73nJ2KEZ/PGkZwzUpLzhrjA539acdaisTtnGZpeJJA3tb1XE0X97SIrwvi5gLSZKGme3MC76vdTOGebZImbC82Ml5SvisXCu3HwUA/O7VHcL2yaSBdxVGkUxVSRE+d8Ykbj/qa6jCsaguaViZqNh1ZudBrg9/r7gtqpO9j/y9Zm/PSyb8PcS8ZIKHGZNbW9yviWxksrip/PXY29aLN3aa16QuVebuIz3W8dZXRK197Ujdk3J9ylIaWdkg3nXU3k/SsA20/kRSiAHNruHE+jLMaa7GBakUxplEmWBe5r1tvdYUNKu/pgHTmiqwcEItJowwZ0/kR3lb6hyNKI9gWlOF9Q8w4/229cSstuCkSXU4aVKd4A0GzNmLfmnmgDFKSuChioIgGsdi/dj7i+eMEurG6ixu625h7fDJXMfa9zE1JZjWVIFFE+vwpcV2Ahr++Wb15Z8Ft3sbMK8N+82H5o8RzoklmVCGXWMGoUoy4RxolvCL6jza7K2HuoT6NNeag26mZ5YH6SFdw6dPnWi995KHmB5idX3ka+u3wJNfVzCYkEFMEC4EWT1cyPT023o/N10d33iqsqENhqZWNgbcjAN5xfGiSXVornGmBVWRribVbTW7Ct6oU019d/WLZXX2xwMtcCouCuGW86fh5GNM71Y6C68ci+oMA/2pz1hGNHZtO/ri0tS1uhy+I26VDMJuxbR7XKF1dNtG10xvkGoRzUcXjnXdh1UfR9QGs1z5nDEDmXmA+c76c6dPshIm8B0yf98xCZGspfXy8PJVYPfhbz+5EE9cezJOSnniMzGIeR0zu73Z+fzXjafjqRtOwx/+3yKMS2VelGvI9vnJUybiqRtOs/6xUFtJw7COq7mmFA99+kRcd+YkyAg681RFbj5/Kv7zfdOF7aIpg5hvU8QQbGrJxFfOnWrV7R/XnwoAjil4r3CJ/T6aWnYevv+BOXjqhtPw+8+ciOsXT8bVJ413HJ99noNpiNkxjK0txV0fmCPUuyR1PvoUz4ZhaYg1IcQb4C6ZYAv2vDzE7DlZML5WqE+0SG0K6pqGy+eNsZ4XL3pjCWV94gmVhti7LFpURxAFBt/wZLIgJd/wBoRbh813SMVhZ3OQTUzPoMgDj5jLQERuHItCOjdV613PdC+f22p2Ffy+VSu8ZY1mm0vINxnmQQrS0cnIhmAiaaA/bntv+G3k+ggr9rly+Nds8RHzNnfHnOeLPy9uXjRr9X3KCFN5hJgeUaV1ZTCPbTR1D7N7Sj5nzFvIDNukwU3PcguY4km7Q+bDxzEPrOwh9lrIpA7bZb63dJ8ZLKrjQ6Cxcq0QdpwVpUnbMGKKhV6AnRktkTS4+Lnmh9WlTq2yEDWCC5dVJK2IYvezOOvg5SG2vaQMdn2cxrOjWhZ+jg1m0EbC4nlg+1IZ8PwxeJWfTIrXm8eWTLgn5lClbhYX1Zmvo+GQdY289OitPeZzK6+JiSrafsC+7kHs0q6+hFXvYq4+sUQyfYM4T4vqyCAmCBeEqcChZw8LBrG7h9j+POoQegVbsZwtstHS5+YhljruSFi3Ohq/AYvfUchGQVc6HmJBZ+usu+zZZF4apt9zgxmu6cQXZaiSIbDPWGgsdk/Ixh1/LmOC99t+zbyTTOesMlb538pB/O0yU0ZZ6kKqNIMsyH88abgaH+wYGiqjqX0nHXUG7Oe4jluExp5zXdcsj5mVGABiqCw2VS+fM6+BI39r8oYOYA8EgoTKEss0hPPLXskGLL8v+Rlh51I2huztxZTDgNOQAuRBkz2QiEjlRhUaYjc9sflerI/5WvzO7bc8ft53NgCPhMT2jxlj/D2UkAYegI9BzI5BcV9biTlUBjG7/zSnN7xPEXatJMJLFPw9xNVS21OsaPsB+9x7eWorUmstmLENAMUR3RoQxZOGQ64TXDIxuCYqGcQE4YJgDAyCljbX8A2tn4c4LE0N+/0ul7AOhe3fzUMsN45FId324vilAvX5XvZmdfgkIOBJCAaxwkMsdXjMmKpVRAbgYZ2UNfWYxrVQe4hTC3AiYvgrviMD5Kxf6igTrZZBbGoi/SQTbmHX2DPGPMOqe5Bfse7mJWYdfX3K88sGVXJSCEYdp0Flx6VxWcESnEHCG5d1ZWb5Dg+xl2RC4SFm9kUmgx3AeU+xcmOSAcvvS34EbA+xbBDbZcYsA9vcRjakAMnANezrKQ8ySxRh18SIE7IX0TltzozDdFIBB/UQF7l4iIU6Jg0hdrayfGEA5D7177WojhUfUmSq459DS6IQ1q320WsmiQ1kqwN6iNlz6SXlrUzdE+wZ1DRzMakVF5k8xAQxtJG9KYUQnzZdugUNsYuHjtdKKlqfwThu1iGxkFpumjzZWDI9xKyD9NmJz/eyUeAXb5WH7+BVixBlbzMzQP0MYqbrC2VgNDk9o4bVkZZIWk5Zf+uW5IC/F9hvRqZCw6kMVWFRnYuHmJXJzn9IaTjo1rVXSTPM+pjnlEUnicXZ1Lb6wvPhBNl54UNcWZ5OXRM8xLUpQ7o/nhSmur0GzOx8GoYhhNMC7ONOVzIhXzPDYMaa+Z5/ljWXZ4R5GmVPLvNmmnKSlIHtIZkQZA9ceyIbWVYcYu4+dkvjzNeXf+wtYz3pb2Cxbf08xP2Wh1h9HmSDXx6YemqIPSUT5v7UkgnbkJZ/KoQzVIRdc2vrAc5DLF1HPjkHTyiAh5jNdDGZWXE4BE3TuPo4NcR+7bVlEJOGmCDyj6x5HJoGcQAPMfMA6RoiIWfjMxipjFkDz1bwu3VgcqPMeyG8FtUAdgfjdh1lb1Y6mcj4fasMGzfJRI3CuOCxJBO6aKQFwZmYw+74HQaxR0YzwdhP2OeQ6VeZZKK732mo8kaiKtaqWaZocKmmlkO6ZqW5VXmizfqY+x9RkTJYEwlHHXh4yQQznk0NsV2m+Zk4c1JVUmRtw98jXh5Kdgr5TSzJhJVdLD0PsWoQI4ewY7BX8jS1u4fY9sLGuPYBUMt8VBIIXVNpiJ0eYm/JBPOmOz3ETuPZef7Zfe6XOKPfx1Mu65zlZ9zLA+21OKzESzLBzSTIkgkhUx17piN2Yg6vwZWlIXZIJlwW1elMQyzWISQ9EzzsWeXDwDkvj49kwtKPUxxigsg7csf7x9d3Ww/+yKpinDSpztFIFBq8185NC8wMBjcPcWdvHH9fuw/NNaVWDNzXth0RQoCVRkI4a1oDdh3pxtv72nHyMSPw9t52c1FOaoqxorgIiyapY4GyBt7yEKfe98YSeH7DQYyqLsGc5mrHgquikCZM78oIKVet+KFu3mfx2P/vpW24atF4jK1zTxLC8NMQd/bF0RtLYNeRbry5uw3Lt5jh/Gp8kr1YkokA8UUddXIk5khaHX9JxNZyHurowwMvbxO2NQz18bB7ZcfhLquDY8lD1JIJu5w/vr4L758zCj2xBF7dehgnTxqBhspi69yx86/yCIV1HaWREDp640pPNB9tgYUF8/IQl0fDgveSySt0zUVDzBmXRSEdlSVFaO2O4U+rduMzp01EUUj3NELY+eQHl7rkIWb35d7WHqzYdhgnHzNCGYuboZK5GFIIO3lf8niKPWeukomkwWmIzW34JBWMuMKoDetODTEzutxmHfj68d50pYfYMHC4sw/LNh1CIqkekJVEQujqT/hKJtg9IteX3YvyokF58OJVPjs3qr6CPd+PvrEHN5w9BXtae7CntQeRsC54ZuXZylgiif54Ei9sPIj1e9vMssK6vYgtkIdYlkyoPcRsICSPU4vDOrpSz6LDuE6dR36RX7qSCXumwXu7XEMGMUEokBvY7zz5tvD+sc+fhOPH1gxmldImnSgT4ZCu1G/e88JmPJ5K5vDmN85FS1cfPvTz5Y7tblg8Gf/73GZPL+ZTN5yqzBoVc0gmzDIeeGU77vyHmTjg9f9c7PhdJOwdZULsYM2/btEO5AV7APCx+1bgxa+c6XY4FvwUpdviqF8s24pf/XurIMWoK49C09w7B2aoMO+111SojHpRHfMQ24vUbnrkTWyRYgjznZdqUd1tj5kJOXTNzHQFqL1c/L2wbk87vvWX9XhnXwc2HujA7DFV+Mt1pzhS2rp5iEsjYQB9SsObeWrLIiGURcKpejMNsfPk1pZFoGmalYCGadZNbxyE32mShzisa6gti6C1O4bvP70RdWURXLFgrOcMEvuGP68sAZdtNJjfffT/VmDroS7MHVuNRz9/smuZcuropGEIqY8FD7HlIpYNK/O926I6PgIHOwdqWZViUZ0iygQzugTNvYuHmD+d4qI6u263/Pkt/Oudg476MEp8ZpxYXVh9ZMkEM2Jlj7Y8+HFbBAzYxrTKsOMT/5z2/eeF71h8YF13+lL74kk8+sZufPXRtdZnZdGwNQjyGpyxZ6WyWDKIfTzEsoe7uChkGcS1Ukxo1o4XcWsf5MfDb2hvS/loUR1B5B2VJ2r+uBrLQ3KgXZ08opDoSSPKREjRgQHAHi7hxaHOPisBRnk0jDOm1mN8yoO69VCX75T+PkXCjSQX/aBc8hDvb7O3P9rV7zgGflGdykPMT5ezb906R7kzBPyTfdj7cXpU5bruPtptGcOnTh6Bi2aPxBUnNLtq8/7r0pnWa3txSjqSCSmUXcLulEq4KBPsPq4uLcIJ480Bnt+iuu6U8Xvu9CZPKYPspd7b2msl0Hhrd5twTMzQUg3KwiHNml5WeQJZnOeyaNgyrJmRwupw7vRGXHLcKJw5tR5fvWAaAAiLfgAxxBUfio03AsMhDTefN816vz91/rzjEJvfqSUTooeYJTh5Y2era3kA0Nknnm/DkLL+6WojksfNQ8wvXJOvDwD88INzsPjYBuu9SgIR0jTHM1UshfvjtzePQW0c84Mk9rgkDUPIrqnCkkx4eHD59qBI9hArUiEnDcNxXwfxEKue88XHNmLBhFrl71hbGdI0xWJI+9hHVRXjkuNG4ZLjRnNhztzvRVYf2Rte5GJ4Mk+1XHt+EDWuthQ3LJ6MM6bW46xpDbjpvKkAIIRdk9tDv6hA9n3kuVnOIYOYIBSoOvill83CsSNND2c6xkm+4BcgBYoyoWh9+BXQvbGEdV4mN5bjgWsW4APzxgAIFpVBdU75Tpyl+1QlVUgYhqMR5acWVYfH24Tst26dF28U2ItxDE/vklU3hc7WmWzE3uZXV83HTz8yF+NHlDk6GgC4atE4fPTEcdZ7O8pE5h5i3oPLxyFm9bxnyTyMrzMzmvGnWYy0Yr5mBsEVC5pTnlvvRXXHj60GoF4QJ4f1UsUh1jXNChWn2g8feiqS8kIyry/z2I+pKcWPrjge91+zABfOGins01pUpzslOLKGuEjXcf7MJjtpA6erdoN9xRt59qI6tT5cNTDgkQcGhsHFDObCxwGwrBnV1DugkArodn3tRY92eZfPG4NfXXUCGlMh7niPJDvGkJdkwtVDDO41Ly8B9zqlIYZ6EMxTYs0WuG/Hzxg5FtUpNcSGYxbIO+yau2SiqrQId39ojvJ3vNRCrn0skbTkN2cf24gfXXE8mqqKA2Wqc4t6oWr7AS7KhHQ/8mHaSqNh3LB4Ch64ZgHuu/oEnDejKVWm7bGW7z2/3tO6j2hRHUHkH5XxFg7xsRULPwybGIc4QJQJhZeAD5fV3W+n5iyVFk4EyeymmlbnOxNZQyxrDeUFfpGQ7pji5kkopmDdOi++Q+CnMlV1llGFkZKlGfzCHr6RV7X3zvBy4rR6EORtVQYx03izfai87XGFZIL9LQrp1n2gXFQnef6VRjNXFqD2pIV9FtX19NuLBdm56pc8xKoOX/a8a5ozMYccdk0OD8cGdN5xiM3v+E3ssGu20cB7HmVjUkY+D0lDNOx5XD3ELok5+DjEbmUC9kBNuahO9/AQC8+1Ouyaypsu1s3wHIQAQEmRmKRFheAhls+D7lzAl0g6Zz7cQgoCvIdY/b1famLV1/3xpBDNgxEOIJlQ6bIB5yyBvX+2qE78nI9ZzxZDyxRxHnaHZMKnKSPJBEEUED0KbxbvRR0KHuKeQBpi20MndwiA6CHuiSWsMkusGLnmb4JkdlMZl/x5ZIYT66T475JJp9EbCXNRJhQtLL8YxvIQuyb9sJvCkkjI6jB6PbKj2XVzGo0ODzHXKft1grLxltGiOmkAxN8LzFMnZiLTrYxgwqI6QR+dFP6GdV7K4O79Z3pFOd6qwYWCY+dE1S+bGuLUfhT3UI/gIRYlCHKUBB72GRu8qBbVmR5iUTJh/hWND68oJ+wr/v61E3PYXmr+2HwNYmkAyqdZdkggUn/lGva7hV3jzgF/f8jYkgLxOQXMQZ8sQShWJubgjoF/rViAaL5m3/sbVWz2wivKBB9yzRFJQTE442UkchkqWPvqJo3y84DqikUGvIeYL1eWAKlw81i7zUhY0V8cGmL72pa6GMT8gNOxqM61hql6WgMrnw1zDC2qI4YNmw924sfPbsIXzz4GxzRUWJ//dvl2/G3tPowoj+I7l8xETVlE2cEXhfRAo/B88/e1+/C7V3fgUIetsXPVEHOJEVSdHh8js6dfzFUP2J1vEINYZVzySTmstLsukgm5US0K+RjEaXiIiyRPS0lq0Ui6HmLWWTrSUVvJH6QwUtAgdw+ywcw6q3QW1ckDIObFioR06zonDftc89Ps/E+FbGhGKvIAZySxzrAvnsQVv1iO8mgYX7vwWEysL7fuOctDHEtYC9lY2fKUvGpRXTikWcbNHX97Gx/j5CSsXMAcpDGvJDv//KJRmZCk39W5JAjs3tM0SIvqxIWOcete9TKIze8MwchDqhxbRsAPWrzivgIqD7Hh6g1n+2L7/9G/NmH51hYcTLUPzsVkXJnSojoe+7408Jvl27HrSLcQtUYul3kVY4kkvvWX9dh1pBsT68uEY7BfO+tjvrafd7+QkOloiFXOANU14BfhMX72whbMHlMFXdPwz7cPiAmREt4eYtX9LtdBPso+wUNsf87upSBJYuTdhlwkE3amOvHzYi4qBXs2Zfj6uCVdkdl5uBvf+dvbWLOr1dwvGcQEMTAs+dWrONDeh+VbD2PlbXbUgrue3mgZdGdObcDl88YosweZsoL0jZPB5kf/2mQtXmL4a4h1wShkyGlCeeMDsI23IBpitYfY9lIVScaMvFhHdnzwXh3V5VDFOHXzFvFetXBIs0I2BTGIxVXzzDOp1hAH0cTJxoe18MpniphHntZlxhbvVQfswP5hlxB28n0eSybB684rS4pQVVKEtp4YXt16BABwTEMFvnrBNMsYqCyxJRPRsG4Zcz2xhHVM7BjdDK/mWnPxZm8sib2tPRhVXWJ938vNWkSsQVVK3pGqv+reljXEOieZYIeta6K2nr2WvaNe7QE7n6rICXx2Rj6Bi18Sli7JIDYMcMarepGcYQBt3TH897/eFb538xAnDafGm4dfdPaNJ9YLn3llqtva0oWtLebiwde22SYIbyP5eYjZ4MyLIFEm3LzkgNpY5aO18Pzv85uxbk+743OvTHWAf3ugikLDL1Lj6xjm9OiGYSh1y/x9zeO6qE5Xbx/EQ8zPfgQNZ//nN3bjmbcPWO+bKt1DDw4EJJkghg1sZS7vOQUkoy/lSVM1ekWhoSGZOJrK2vXVC6bhkuNGAfCKQ5yGh5iXTESYh9g8H0E0xCqvex+30l2e7uaNgqRhODrAorBurUJWeYtUKZVdPcRchxjSNcsDrqqzjCoSg7wfZoj7eYTY/nnCkjcyCPL9yQZ4RSFxkViv5aFXZ/2Ty4lzCw3DITMyyRPXnoyfXHm8da8d7jSfL3ZeKrjQbHzH2htLOLyaKgMhpOv4/BmTrPeHO8UYvEy/XBwJOQZV1kDEU0NsXxuVh5gfLLH7Xc4w560hNv8KYdckD3EsmQwUJpHR41hUx+uxxWPlPb5y/GK+DgzeoyxnEuQJWc4BceBqfmca4ryXWJX8oYNrNwQPsSAv4etmv/E7R0E8xG5JOeT9MgzOa95UWYz/d9pEz314RZkA/NuDkK4pE6pYKbK5cnmj1u3cqNJhAx6L6iwNsWwQ+3uIw0LYNdlDrPwJDneZbceFs5rw208uwIWzRqk3HCCGtEF85513QtM03HDDDfmuCjGE4Rtfa3W6wvgYKovqWBaxi2aNxPRUVAz/OMTqKBP8YKGn32kQs0YviPNSlaLU00MsLaqTG9UIF3ZNNQXHG8nsegWRTIS4qAZBNMSqKBOyJ5pPD+yHvE1Gi+pkDXFM7SG2Q29pynMplxNPOLWq40eU4eI5o7Bwgpl4hQ3I5EV1gLj4rqc/4VxUp/JEauYAhUV4OdItGnU9MXtRndugSuUBkwe3bhpi/nyFJE8200kHmabmp6vZfvg4xIJB7HOtZQ8xn6lOfo5Z8l8DammTV+pmz0V1bAGj4pmyE4/Yv+MXYqngj1jlTZdf+y2qK83SQ6w65gQnTSku0nHmtAbPuvhpYeV9OCNdOOvQH7c9riGFhxhwv39YNeViXRfVWXGIxc95g5hFB5LhZUWOsGsuKuKjXWbfdcL4Wpw6ud53vUWuGbIG8cqVK/Hzn/8cs2fPzndViCGOaDi5exLDnGSiUD3E/fGk5Q2sLC7ivDjeHoyQrrlOmzF6FJIJN8+C8vceGuJISLc0xFboMmGxjiLKRFjjEnM495dUGKqumeqkSAJeaVVlxEgMasPbVUuoOH2yNzPEeVqC4hZlgtdd84S5iB1uUSYA0wC0PMRSObVlpif4aCobFrt+5Vx2M/4QTMmEWJbSQ5w6HyyzX6vDILbvSTfZjVeUCQYvmRCiTHDPBRs4yYvqvDXE5l97hT/n1eM0xF3cYMEvlbP8LCW5sGvyc8xLYeSEHnwd7O01a3s3rzNg35eqZ4SXTjCKXbKh8cegei3EIeaq6hcSkRltbsl4zDLM/ajikKskB4mkGDub11Gr8PMQy/e7nCRFLZmwPfd8Hfl73O3+cQ275raozjKI3SUTJa6SCbs9l0+P2+3NBtO1Zd5p7QeKIWkQd3Z2YsmSJfjlL3+JmpqafFeHGMLwKUIB27OnMniLuAVJhbqojtfylheHrQYtSJQJP+NWpSF28yyoSFtDzHV4CYVkIhIKCZ29jErb6x52TdQQF6dhEKsGVPL9E0tDMiF3TkHii8rwocQA2zvPx24W9qFrgSUTsleXUV1qdmJHu8xOjd1b0bCu9MD1xIJ5iNn5qEl1kke6RIOYj0McdXiIbePFrVwGv6hOSN0seIhFzye7Jt5RJgyhTMEg5oxX3ntrGN4e0C6lZMLFQ6zZ27QrtP7OTHWpenNh+VThr9h5UQ10VfFr3bKh2cfgNIjl24F/O+AeYpcoE7yu2hqQu2gAVIYrj3xa5XOkKxJz9MUDSCZ8PMTyflXPh7md2kPMU+a2qI7TmDvjEKvrx55t1pYMNkPSIL722mtx0UUXYfFiZzpXmb6+PrS3twv/CIIhN6qxeMrjo2hE+YUihSqZYNnQKqJhhHQNIZ+Vx2KmOm9jjY9DbEsm0vAQe8QhLgpp3IIotWTCmanOns5WZ6pzejrdFtXxHqKQrlvHp+rsZUTJhNrw9pp6lpGNDzubWfqSCXZclkEc0gWtrLWPkK4cXDgz3iVtg0C6X5hXh3l57JBnujWA4unpTwh6ZHNbd09urWRw8+UApkfQ6SH2WhTm9MY5M9XJi+qYZEK8JsHiEIuDFLM8+02b5L31MuRUHuKYi/HKS2Hae5ySCXcPMVyvtbkf8zOVFIoZarzxr7oHxGOwX6u86fJ7Pw8xe4a9wq7ZUSaCaYj5KCtFIS4xkMsurPbV5bF3eojFc6RrzhBlsYQ6yoSggXc5ZjcNsVvb76Yh5o/X7brykiT58XDTELemZpdqySAOxsMPP4w33ngDS5cuDbT90qVLUVVVZf1rbm4e4BoSQwl5ZG/HL3XxJGZgnAwmbEq0ssScXvb3ENtGiyoxB08Pl6lODrsWBFXH2e/hIean/cwwS+JvI2Hdofnk4Q07N0OVwXcIfHxdVZ1lVIZ30CgTqm7IEWUig0V1rB7M+8driM19SEa3oCF2lmO9TxqCoctTk+rEWntiwgCGT73M09OfEAZk/F+hbsxDXCpKMqxyuGQx8qAqSBxiRkjXLGOVn+pWLaqTr0mQTHUqI483NGU5g5eR7eUhdltU5+Yhls85H9rMTYYB2OdPtfA0pPAsFvsZxIqoMF4GMTvnKrkDYBtqzMmhoi/dKBNS7GxVemdhez/JhLQP2Vuveh7cokwA/hFp3Dzvbm2/fR3FH/D9ptuslyX1ChiH2DAMa31ATUp+NdgMKYN4165duP766/Hggw+iuDhYOI5bb70VbW1t1r9du3YNcC2JoYTcjlmGk4vBK8cfzSd/WLkTX/7jm3h7rz3rwTq8ipRuU7USnEeMQ+wjmehXJOZIQ0Os6jj7PaNM8B5i57S04KFRHJ5q9XuQxBwh3TszmmM/XD1ZRyTrFtORTLjFIU5LQ8zkCkz60S96wuR9FOl8CDvnQILBZ+WSDa/qlMFqGMD1D6+2PMVhXVOGZuqJJewoBqnOU2U4sLpakglZQ9zPa4jNbeU4xF5REhimZEI8B5ombueUTDAPsXt7IC+q48vjDc3HV+8RfqdqY17Z3IIbHl6Ng+1ipBw+YYRs6PPxpdsDxAvnUzfLAxZxu5RkQjFoZPc571mUjT0Z/vZ2W/zFv49xAy4V6XmInWWo7sVkUoydbZ0Dl3bCTzKhaZpwTLJhrmmaQ27Q7yKZAOxz4RZVyD6v6jZGRjWwMcvxb4vYOU0kk87tFT9fvuWw9dzW5MlDPKTiEK9atQoHDx7E3Llzrc8SiQSWLVuG//3f/0VfXx9CIbHhjUajiEajg11VYogge4j7fTzEquxM+aA3lsAtf14LwFy5f89H5wGANSWavodY8/X29sScqZvTWQWs6jT4RS0RLm6lXGdllAk+U53i+JTRHwJGmUhrUZ3gIVbfP2lFmXB4iNPXrbNt2eKXXmtRndoTG9L5OMT25/JAio/P7TCqQzpGVRVjb1svnnxrn1B/lXewJ+aUTHgZXszgbnPxEPOJOeSZHpXR5DQcbW1lnDNkeGPJHlCI1ySIh1hl5Om6hrqyCA539WPTwU7hd6pn9s6nNuCt3W2pOthJTpKGexIStjsD9qK6opDmOsvFzxS4eZ0BTkPsKZmwP/NrXwQNsYtnVeUhNst11sHSEHuFXYuLA0UeVXUTnDQlrNvtj9vA2S91M2Ceq7jBnlenZEImlkgimlQPbOXoJzKui+rcJBMuHuLZo6vw6Bt7VD/h6mJ7q532sPPe+9kLW6zXbrGNB5ohZRCfffbZWLt2rfDZNddcg2nTpuGWW25xGMP5oqsvjtseW4v97b04a1oDPnPaJP8fBaStO4bb/7oel80dg1Mmj3Dd7ifPbsLKHUdx/dmTMW9cjet2S//xDt5MZYWpLongGxdPxy//vRXHNJRjycJxONDei28+sR5tPTFcNnc0Pjg/M8nJgfZefOsv63G0ux/l0SLcdtGxmDCizP+HA4xDQ+wnmSiQsGv81Oc/1u3HFb9YjmlNlVZQc5YuV44ycbSrH19/Yh1aOvtQGgmjuabE2s5PD7x8qz2Cz2RR3dv72rHjcBfG1dnXvT9hT+Uz78jK7UdxxS+W4519tudblZkqooiMsK2lC3f87R00VUXxgXn2vbqntQe3/3W9a6B3eVEdO77/+dcmfPGsyZ6eXVVUhp8+v1nYhp23YBpi2Xtrvu/qj+OmR97Ehv3tQiiz6SOr8PX3HWt5fdp6Ynh2w0EAtibRXlSnHsjwYdf++tZebDzQjsvnjnFMvd74xzXcb5zX/v5rFuDGP67Bem7WIsx53HnueWGLwyBRhl1LfcaiFPBe6ntf3ILnUsdazEkmkikvNTNUvLTJDN5DbH/m4iGWpsq9DGJm6LlNn//mkwuwIpXUpCQSwq2Pmn2cagDEFt5dfdJ4nDujEdc++AaOdsdS8XHVxivbX09/Ag+8sh0A0FBRjD2tPcr6svtoa0sXth/uFo6bx4oy4SmZ4AcT3vd+0jAXBX/zL+txfHN16vfiNvx7OcuhjBVlQtGW98UT+Nqj67B611EAau+1ykP8z/UHrAQ0vMzIb9GyV+ZBXdes0ZJzgaN6UR1zoDq1wN6DZ1u2I37uuqjO0hCLnx8/tgYPXHMCxqYS5qhg1+WeF7bgxIl1ynrwsIHVF8+e7OpRH2iGlEFcUVGBmTNnCp+VlZWhrq7O8Xk+eXlzCx5fsxeA2bl/6pSJgaZKg/CDf27Eo6v34NHVe7D9zouU23T2xfHDZ8xsRJXFYVeDeH9bL37+4lbhM10H/r52PwBgycJx+Oube/HUevP9jsNdGRvE/1i7D/9Yt996P2NUJb50zpSMysolsmfRTQPKsDrCPGuI5Xiir249YjXUADCuzmyo5FTTz244KHjvWMdlxiH2Nm5bOc/c6JQhnc6iOgB4bPUe3LDYvu78NC8flok/FiDlIZauVW15xPJEMWP5sTd241/vmIMCFreWcf/L23H53DHKeslh15q5hv7dgx2Y1lSp+hkAsTOMJZPoiyewYb+ZKZB54qxYt9IpVrX7Tg2x+aO3drdZ3kGeV7cewccWjbMGmC9sPGh9N7q6BJsPdlp1ZB5UOXKCxkVYONTRh0MdfXh582EsGF8r7GvroS7XegLA1KYK/OYTC3Dy956zvMkjq4qVSRm2tdhljawyByoqDzq7h4stXbc9aP3eUxus7cbUlKA0EkZlcRjtvXE8kWqD+TKEchWGo9wR65omZMUbURZN/Tb4ojo5dbN82maMqsKMUVXW+9v/uh69saSyDWIDm8vmjsbsMdXCAjhmmMoLs9ghLd962PrsIwvH4vtPb1TWl9XvsdW7rc+aqpwDySBRJmaPqcK+tl40VkZ9B4NJw8AP//kuHn1jj+V9lPtNlWHppn+1NMSK87hq+1H8+Q37+Phr7LWvgx19VsrrUVUlvu0f27XXsfP3vByr2Uzd7HTaJFzuJXkdhoybh1iVyZGvt8pTf8bUBuVvGPw55Z0bgFpDzO6juWOrPcsdSIaUQTxUiAnaR3OqV1cun0mfHUe6fbfhg997aX2YpyUS1rFgfC1e2twihDTq7o9bDz/grqsNghxI3kvXNZjIHRmrV7/LQgy5I8wXqgD7PDedOxWAU0PcJWWUszNL6Z4enNvfP8OKIjC2ttTy8qbjIQacU4v8/pnMQ0XSsFM3N1UW4zefXIDK4iLHQrBeriOQV+3zn80dW42kAaxJzY7wC3PCuoaPLByLb/7FTEfrlenKPAb7+3jCQAuXSe2/Lp2JW/681vLeZSSZUHRWpZEQvnf5bHz9iXVo7Y4J15VlDawuLcLJx9ThxXcPWd8xw1QI6K/QezJaUlnn/vcjxwMArntotWs9GXXlUfzlulOwcX8HmmtLMbG+3NGh/uiK46zPyqIhnHJMveuxso+Y94y1Wz2xhHXdH/rUQhzfXA1N0/DEdafgzB+8IJShjpIgL2BSeyQ/dcpETGuqwOjqUlSlZBt2mtwgHmLzrxXyyuceKNJ19CKpNLKZNp0NDvgZksOptrtOiuHKe4gZnzt9Ek6fUo+GSqecUN5+wYRajKxyGowsPrRXHOLvf3AOLj2uBXOaq33biqRhYMN+0XCSz5VyAOnSbrGBAUvzzBvX7DyOrS3FbRcdi1MVs63y/T26ugRfvWAaAHOge8rkekfEExnWNnhdcn4/qjjEMrGEwUWZEDdgMzG9cbWEQxXpBHD3EFvSlwADeRm+De3slReBOu9tVme/xZcDyZA3iF944YV8V8GBbIQOtty0t9/uoL207wlummZKYwVe2twiGHotHf1Wh2iWlfmBOMIE5VmDy5CvlV8Ch0IJuyY3MDzNtSWOsGisc3XTuplJR9w7rJMm1WFyY4XzdwEX1UVCOvoTScd9wKd6ZRpRFYmkLZk4Z3ojpqTqoksGP3/d2DmaWF+GvlgSe1p70NlnGsTHj63BjsP24JLvrEMpPXVzbQl2Henx1YvLiTlY6uKmymJLusJnQ+PRFANlVQQImbryCC6eMwo/+OdGtHbHxBTbqXN8xpR6R1nWYkh+YReTKyh6ua0pL+60pgoc01AhGMRe05pTGiusawQ4O+7zZjQpOz7VTBrbT9TSQ5vXmGUR1DRg0aQ6a7sJI8rwvtkjRR2zR5QEfj+Oc6CZ4QDPmtYofFwkzbwEScxhGyPez4xXVJG+GPMC60JZhmGnpB9RoV4zw+q4cEItdF3DzNFVyu3YaWHbz3bZjp0/Xldul2F+V1lchAtmjQTgHybNMJwDffl2YIvQ+GbbLcoEH9M3ITml2LHVlUdw3owm5e/lfZ8+tR4XzxFTCasG3TzskD0lE9xXqigTcrfbH3ePMsGeb7d23m1Qlq6G2G9QB5jtytTGCmw80OGMiqLYvo/LOJkvhlSUiaGCM8TI4Bp//OjQy4blVz0zzxGfBepQZ5/g7XILPh6oTjG1IZRvnHGI/cKuFYpkwr0hruI8rSFrpa9ZX+bNUYWc8vIQu3kQ/EK1MUqj6jBmfNg3r2DsvGSCN67shWApw4S7LsxrGtY1q6Nh3lN5NbcQa9ZKz+sdw1k+BsBcQMIGkSMqIs4p34yiTLgnDSgOizICAEJ6bbksZoTy2fDYsXtVbUR5dguTVQvYVHg5EaOShriX60BlI7NKmm0IlqnOGZ/Z7ZzYMVZTHmKP9sAZZcJ101TZ7rNQvZKHmL//rftOulbMeGEzHX6DWEsXyyLQ+BhLPTHn4NwrfJ4bScOwnk+5Ll6fuXuIOYNYeoYTHvGp3faj2tJfMsE0xO7biB5ilWRCJJawozbIM05+8dNd4xD7hF1zSIkCWo7FLvVRmRKsb8inh5gM4gHAYRAPtoeYMzq8JBNWWlPdXkTE60QPd/ahhZNMZOPVladwsjGuc4n7ojo/yUR+PcRekonqEtuwlD3E7N6okaZVw7pTPyl/r8JvoQyDZTOSp1d5T0eZx8ripGEo9W9ylAnec9+R6lx1zU76wbzGsleJ7xCY0RoKOPiRo0y0dJiDyBHlUUeHlYlkQnWO2bViHY7gIeY6FtkAtyQTfCxcFvLM5RqHdc1hYKaL3AG7nQcvzxOrO5vu9ppilWcbvKIk2PtWdPwu9XGGXVMvigK4xBwBvIWAc8EeI87Fn2X74SVDtkEsPttsd5Zsx8easQxon8gonhpixW80TXOV2QBsUZ3YrqnaJDfdrAw/6JU9+F7h5Oz9+N8LftfSL3WzXAc5U53pDZdkfQE8xG7x020Psfi526DCLVNdEA8xABSnroHssVZqiK12K39mKRnEA4A8mx4kZl8u4RsoLxuWX/XMOpVWbgqopVOWTGRTJzFzVoHYw45r4xd2Te4I84UqwD6jijMG5CgTTF8uZwLyW/Di1un4LcRjlEacnkyA85opjBGeRBLKhSRyumF+IMOM35DOGcR9YoIK+zh4A1ET/vp5iOUoE4dSz0xdmXMhUSapm72uTbGUeAOwO5+SopDTg8Q8xIJkgnmB1PuoK49Y1ybTtcEqb6wKr3tAjpghx8Tm4QeFgIuXPWCUCRXyYlV2D6gSPMiSCT9jws1DzOvjbQ8xu/8NHO60B2KqY/BKUiJsr7PtvSOjWFEmFJIJP6+yiqRhOGa+VJs7Yui6SSY4b6vswednptxw3h/+28i4Ga5iufZ38kBdda/0J5JWkiI3D7G7ZEIt2/HLVBf0uZCx6+OtITYMgzzE71XyriHmGk4v3S8z3EO6Znma+M7/Zy9sFgzibAx75s1hU+e5kEw88PI23P7X9Wlrm/lnO92wa1b8UZ/6P7Z6N77yyJsD5kmWpxZ5qjlvXliqLxuYyJmA/DpJt4Y/aJSJ0lSYsD55poBbVOdFwnCTTGjW94Cou2S6tbCuWR0N0xA7PMSShpj/66cX5z3Inf1x/O7VHQDUkgm571bZRqr4vm4UKzxCfKxo+adRS0PsPF6VnhkQDaygAyCZTAYGMlHOQ8x3oLJXDRAHhar9AyoPsfMMuJ0T+d6wPcTOzlyWTPg519wGYn3cNWb3LyvrpkfetOIYywYxM36Cpg93SCZ8nv2efoVkws2z7rFvg0suIteFR/7ErUxBMiH1EX7HBqiSgji39ZvxsZ1O7tvIYf1441TX1KmbWZ/nWFTnET/dMAzXsGtu7S973P2vghom6XJ4iKWD4tM7FyueocGCDOIBwBGEeoDcoW7l8p2j157jXIzEYoVnY/fRHmX2oExgjTmbOs+FZOJbf30b97+8Hev2tPtvzME3srKR7xt2LWCmui/94U08smq3I/tUrvCUTCg9xKJkorZM9hCb15+Fa5Nx8yAENYjLXLRk9tSl9+/NDFHma13oQFLfp64jHzeXeYh1zkPMPNSyJ0+OMsH/TUdDbBjAvrZeAMCUhoqMJBNuqZt5PnnKBADcFCk3COYlE3JHV6z0ELPpd3V9+MVxXgaNFyrdtwqvtpLV3TDMDtTS0yo60OoSWTKh8hBL3jg9+Gp6Od4rm4FRSSbkxBz+HmJ1G2OlGQ7pXCY48ztmDJdGQlZIRPkYrCQlPteQGX5+kgnvTHXqsv0kE0G2D6oh5uOUy4NaPiGRG86U1optfCRjfqmb5e/MePDSYFU6L7xkQi7WSzLBP1qOc+gzC+CUEik3d8DkD3KUKXldFS+pLI7kzywd8lEmCpHB8hD3J5JKj0RQDTH7LhzSHNMUI8qj+M4lMwAA1aURXPnLV7MyYns4rxWQfZQJ/hjTDeHGP8vyT/1SN8tTpX7wixJzideiOqWGOFVfNnUlL2Bjncojn12ENTtb8dS6/XiUM+ZdF9UFlkyYTc3rO47inLtftAxhFos2yNSjaiGJnGqXNyKYhjjEaYgZXpIJp4fYT0Ns7vO7/zELNanBSGVJEU6cWIcV2w4L28qeUdVR+y2q+9qF0/CRBWMBcJnouA5HXFQnlq1Kuc3uEbmT/M+LjsW4ujIsmmQH1U8nMyFPSHOe33Thjc3eeEI4Thm3+1v4TOUhDqghlhfVsedL5a1mRr6bV89Zr5RkwpEl0OkRl+v31y+cIiRtAWwvdxCvKGDKl8ztvdONp6shBrzbC1VfpSomqIZY181kQ7GEgZc2teAXy7aiL55EfUUUp6XCrGWrIfb1EAeILCLPeIVDGpBq3jXlojqDm1mTPMQuEgVAPL9uCT2cdWPbi58H1RBb9emTJRPiduze1jT3qCGDARnEA4BsOA6Uh7g3FsQgdv+9FTRc0xw6vHOmN1rhcva1mRmNsjkO5pljU+fZSiZ4D6nKK+OFl2SCGcJu6T6tjtBjGn0wQsqxFM0qpo10hruSo0y4aYgbKopx7owmR2KMrBfVRe37S05Ry+//ygVj8fvXdmJKYznePWBvZ8Yhdurx7LBr5vuYIspESNcc94jcAYSVHuL0okxMaSzHfCmRRUYeYumcNlZGURoJobs/gUhYx2Vzx1jHrZJM8INPueNSxiGWpt8ZZ01rwMT6cqlu2UsmvIwDryaGefwMwwzR1GctqnPWaWJ9GSJhHf3xJMoiITQoQpHJ3j1dUxhBLodbJEmR7MVuCsmEtI3fLeA2C8U8xPw++PpObijHJOl6mdtAqKuvhljS5bt6D1P1VIVdy0RipXrMgixk85IUhVIG8SOv77aS5fAJYbwibgSJMuEXbSFQ6mZpxos/Hl1TL6rzjzLhvCb8+dWkersuqnPVEAdr96MBJRMsVGxx2BkxZjAhg3gAkB/sXNpHfBKA3lhCufqbb6C8jFhLMqE7PcS8gSw3kJnQa0kmQlmXBYiLytLV6fIPnDMOccBFdR4eYj7m4kCF3DvSLXqepzVV4Jcfn4/+RFLoFMPSIkC2AEYVZYJH7mNcF9UFDbsmefG+efF0vLz5sJVVjjXsd1w6E58/YxL+tnYf7vyHnYXM3UNs/rUkE4o4xOaiOnH/pofYvjZCmWl7iN0XzgTJtOX4jbRNRXERXvzKmdhxuAvNtaWCRtQyiOMKD3FRyFF3SzLB7YPd045IEAF0t0EROv0MOzxNMwc2vbEkemMJexGOwggdUR7Fv28+E7uOdGNcXRkqip3tpDoOsbRPF61kWGoHvKJMpC+ZcFlUp1iFzxflZoTKu/PT68sGiZ9xq5qhC2IQnz2tAZ87YxKuf3gN9rT2KPsq9aI68b3XoJzda7Ksg51Lr3MRRPfu1/4lAnjl+a9CmiacI9dFdT5RJlQyFk8PsU/YtUwScwB2e9PDeYANwynl7PUY3A4mZBAPAKoVlLmCN3bdQqv0BJVMpIoK6xpKJN2OqtHNZlGd7bUKZ10WALT38AZxemXxbUi6i+qs6UwPI7yd8173KbwnuaBVMoh1TUw3zLB1sGIyg9oy70VHfEehae4NutwZsQZPhl13xoIJtUIaYCu8j24eh2ysJISwa1w9uVX2gBSHuJ91eppjGk5+r8rcxoyehM+iuqSH5y2ox5FH1cnWV0RRr/ByyumMAVFDLLcRqigTtmTCu+78tukiePWzcABFwyH0xpLoi9tJXopdwvU1VhajsdKZcpjhuOcVUSbcOn55psg7yoQomfA7fjtUopuHWC2ZcDO0g+pF7e29f89Ix5i09s09d2XRMOaPr8XE+jLsae1R9glKD7FDY+9fjz5pxo89F95xiMX3QeQbMumGXdN1TfIQOyUTgH088uCyVBGGkSFqiMXvXBNzpMp3xiEO9hCz9qaLk6/FDcNhExVChAmAFtUNCPKUeS49xOJqcnVHLSyq85JMcGGA5Km+YoWH2DAyN+6ZYVieoygTvNHpl1pXRlNkLGLYiTlcNMQBwq7xxrpfiuVMOdIlhSdyXcQiTu12x1w0xA7dKudB9Oj45IbSTf8lxxiuLYsojTK39+aiOqdXRJMNYoXxyoddY5hGhSZsY78WZQVB4xCrjADnQMO/I0lHY8sGrvwgmPcQyx0x058KGmK2qC6IRyxDyURY6vTd8JtRsWMRJzwX1aVbJ4BpiOH4TAV7JgzDbMssDXHY6UhIO3WztGCPIadtBsRpfFcPsfTebyFY0EVr6SS1UP1G1q6rxp1qIzRYmwPY92ufw0PsH3HDqfd3bus3vR8ksog8qOGdDG4OBtvDLRbM7o10NcTpZ6pTbu5Ajh3u9uz3FkCWOoAM4gFBtpVy6SHuUWgFZXhD2csTm0jajUIQyQSQefxgKxRUNDdRJjqykEzwz6QzDrFz6p3H7qw8PMScQewVLzhTDMNweIj9gudbGuKUVsupIfbwmAbUCXtRKi30qSmNeBpJIamTSyQ5zbtgvGrW94B6IBPS/DXE/NtMo0x4ZZRjOD2QKqMz+PlWrSrnZ2NcM9VxdbXjEDs9po66ZeohDuDJBPzblyiXmU8lIUgH+Z7XNNX1Uf+Wv0YxIWGG3W6ye8wZds37HMoL9hjWojo3D7GrZCJND7F0Ot09xMG8nm6fy0kfVH2CWkMsvg8SKUL2mAbzEGdmBPIE8RA7okzwISA1TTlIZH28u2RCpSG2y3HITvwkEwEHijKyXcHaFPlSszYrmmeDmCQTA8BARpnoCyCZSHtRna45VmrznQzfMco54YPCbviyHEWZ4BeVydNhKgyX0bFs7DAPo3sc4gAeYs4r3C7luu/ojeG5DQcBAGdOa0ClpG1cteMIDnX04cxpDcoFOoAZPUHev1sny+rbH0/ikdd3WXF4q0qKBO+Dl4c206gAPLyHuCgV1YT3VMnGlxzei89Up9IQGx4eYj7sGkN+r8p+l66GWBkiSupncu8hTqUzTrULr28/YoV9K4norh2meH3VYddUfWSmgyPVIEaFn0HMe4hZm5KpV8nhIdZViTncBppcjNukYd13fASISEhPLYBKbRc0dbPLQEy1qE7QELucVqeGOD0PcSYL5IJEmZCTPqhmYpRxiB3eTQ8PMTOIpT6ix8XDyuM3mA1CuqmbdVlDrGsOqUPSsPt4uVwmmdh9pBuPvL4LADC6ugSl0TAmjChzPRa3WQN70BJsoCgjP5vs2AwYiCeSeObtA+jsi2P9XjN0KmmI34M4UjfncGGVKgC/1zZe3mk+dIsch5gfqfErUjPR/vJB9HOmIU7TQywYNbyHOPV5cZG5WMdPMmGtLveYRvfyEP/wn+/igVe2AwCWLByLO/5jlvXd7qPduPye5QCAb18yAx9fNF5Z/tEuZyg3t4adeZPiSQNf+dNb1uflxWGhoZXDRfENpNcqbhm3hpLXEKuNMu9Ozj/KhFNDzCguCjk1xNL9rtQQpxllQmUgqHSqPKrTlY4XlpdM7DrSjQ/cu9z6rjxahJDWK22viEPs0umpPcTZR5nwOjy/VoEZg33xpDUIyFR3KHe+uqbyhKl/yw8M4lwYrHLuPi8uCqGzL67QEPt4iF3CrvUpPOKqgZxM+hpi//vArIf6vPNxkmWEZ16amVBp9ZULVT08xJXFYcEhIXuIIyEd/Qn73vE6F/JhZ7IWlLVHnh5ioR0UwwiqEgj1xe3ZEfnalBeb99/Wli6hvQeAv33xFHufQRfVWRpiqc4BT4bcr7BjNQzg/pe3446/vyPWP5pfk3TYG8QH2nvxjSfW4WhKk6nrwKdPnYiu/gQefHUHDMN8qC6cPRIvbDhoaUKLwhquPfMYnDRphKNMuf98a3cbbvnzWmtBU3EkhJvPm4qZo6usbX7/2k6s39uGb79/pmtjsm5PGw5zxhB7KFo6+/D1x9dZqTu3ttjhqlZuP4oPcZ0ko7IkjHnjzBBRIS17ycT2li5858m3lZpZA3YWmjJLQ2x/v3Z3G+56egPiCQOfO2MS9rf34k+rdgMGcOLEWtx47lShvGTSECIQuGmI/7hyF/70hlnOrDH2uRY8xAYziM3FOt2xBD5073IhUgQP6wgPd/VZ5/W0KSNw3VmTrW14Ocfa3W340L3L0VAZxXcvm4Uth+xrs3zLYVz70Bu44ezJmNxYgf1ttvFysN3OEMgwDAPffvJtvLbtiOM7t3a9rjyKL58zBW/sPGp9Nm9cjSOb1QUzRwrv+YY202lyHv7+Yg0+P2XtjL0rvk8kDdvDJnjFzDePrNqNPa09VmilyQ3lGFNTgkhYxydPmYBl7x4SyvNaVKeKMvHUuv144JVtSCbNNqKtJ46KVOPN7hWlhjiDxSjpeKLYeX1uw0HhdzcsnoymqmJsPSSGuLMW1fHX14oy4V+PXHiIM/G0MdgA746/vYO21MBTFYc4CJcePxo/+Oe7dh01Zxxit5kX/v68+oHXsDf17J43sxHxpIFIWEdPfxyPr9lrGcLM3gsqmfj64+twTH05jmuuxlcffQtv7moFIIVd425jt3LlT/2iTMj3gZuhfcGsJry5qxVHu/uhaxp64wlEQjrOOrbRteywYlaIFa9yQqh2LcpvxPpdOGskKorDmD2mWqg7H8O5P5G0k46ksTAwk3Bg1kJgT0+0/VrXNFx/9mT87tUdKC4K4YPzx+AxLh58JJwyiF00uQsn1GHJwrHY29pjxl/e3GJ9t/NwN7cfsQ7pa4iDnQs3D/Gmg52WMXxcczVqSosQ0nV86tQJgcodKIa9QfzM2wfw9PoDwmdJw/Tysc4VAJZvPSz/FCVFYReDWHywH35tp6NDHl9XKhjEtz66FgBw9rGNOHNqg7KuD722U3jPptCefecA/rFuv/I3APDadqcBBZiZ6ACzUS0pCmFEedRK1cxHLPDS3DKeWLMXz6akAG7UlkWs8Ed8OQ+9thP/3mQ+uAYMbD7YZdXjte1H8KnTJgrSgnf2twvlunmI73p6g5UYgz8H/LPMPDu1ZRH0x5Po7k9Y25ZGQvjAvDH4zfIduGzuaABAQ0XUCvbOtlu54wg+depEyzg50m0bxF1ceRfNGmkNWgBzFL+1pQvLtxzGG18/R9CEq/R0Ww514f6Xt1vvy6NhK4WzVwP1hbMnKz+fO7Yab+xsxWOfP8kRwWBUdYnytYppTRXYsL8DJ06sRSQcctzrgOn5GFVVjL1tvZg3rgaA5C2S6j93bI3wPsGtTNZdjKtXttjP6J2Xz7IGfADwqvT8RsLi/kZW8cdrRibgo0zc88JmvLm7zXFcjOIiXcgQaNXPx0OsQg6J5wX/nLIQdhPry3DD4ikAgKYqO8pCeTRs1VF1fYOsJP/IgrF4a/dazB9X4/jOC1UyFRXzpHLHSpFTmmtL8fqOo9jMxbIeU+N9f7oxpqYUE+vLrGgn6rBrajRNw+jqEuxp7cHqna2pz4BpTZU4a5ppELI23Q67FizKBJ9p7oFXtuGKBWPxxJq91mfNtfb3gofY5bw6ojLkSDLRUFGMuz98nGdZMqpZISv9umImxi91s66JMpeQruG2i6Y79sGKLikKCU6bdOIQZ6chdt9GlhOdMbUBZ7jYAGwg75aYIxLWrVnH/ngSU/7zH9Z3zHsMqHXlsncdABoqo6n6Z3Yu5MhHI6uLBaceAPzuUwvz7hlmFEYt8ggzpuaPq8G88TX4+YtbEedGkGdPaxAMvbOmNWBcXSnuf3m7q2RBtmWY4fr+OaMAAH95c6+rESdrTnnkjEBsRM0+nzeuBp9KpXR9Y+dR/PLf2wCYqXyXclPzD6/chRffPWQFyw7r5hTXY58/Cev2tKGxqhhzXDyqbrPHzJhbfGwDLp87RrnNzNFVWJHybvKNXw/njd1xuNsyhq3jdAmZw1DFweyNJSxjmCU1YPDPMuukyqNh/O2Lp2LDPtvYntpUgbG1pbjkuNGYlRq81JVH8ffrT8WWg52IJw184ferzUQB8aRlEO9PJTI5f0YTLjluFP7nX5uw8UAHYkkDh7ucnt8jqQaCv74qjTVLkDKmpgT/edF0RIt0XHP/SgCZ6Xx/+fH5aO2JKYP5XzRrJOrKI2jvieEEKdmEzO8+tRCrdhzFiRPqAA341l/WC14NwLyHHv38yXhrd6uV+UzllWWMrSvFP790Gu59YQseXb1HjDIRYCGRPLXvDLsmei4mjCjDk184Be09MSycWJcqw/YQs/tn8bEN+Nc7dntwz5K5AIDJjRXKWLfphF27/5oT0FxTmlbncHxzNX79iQW46r7XrM94o2NifTme/MIp2HWkG9NGVlr36M3nT8XJx5jHefIxI5R1Vd1THz6hGVObKjC1qcLxnRe6whBSMWFEGZ6+4TTUlkWwr61H0D0CwHcunYkLZjZZ90JVSZF1vTKBnyo2JRPBPWF/+pyZ1ZExrq5MGGjY+nbzb9AoE19aPAU7D3fjH+v2p6Qh5r03qb4Mt110rOCE4UsK2gb4bZdpiK109y3HuFUlO1LtWpOef/7R9pOHyPHQvRcGSvvNYO1MUtFmyfjJXnjZoyxf8ypXloUlPIxzTdPwjxtOwzt727FgYi3e3NWK+oooGiqKlb8J6i0/dmQlHr/2ZOxr7UEkrKMopOPjXFt13ZnHFIwxDOTBIN68eTO2bNmC0047DSUlJTAMI6+ZSdi9Nqq6BCeMq8XPsRVJw/58SlOFYBBPbizH7NHVuP/l7a5T9bIxw0aJ00ZWmOW+mVnYMXl/8YSodx1bW2pll4uEdcsgLouErc8BU0bx4ruHbGF+6m5vri1VxrJVeVRlmIE/ubFC2JfM6ztMg5j3EPOL4tiCoGhYRyxhLkhxZJOLy++d1+FAu1lOcZGOyY0V1nQjIOoUmS2taxomjChzdMCA02s1pbECUxorYBimQSzXgR3D4lS2vwdX7MTGAx1IJg3BQywjeIhVBnGrWe6k+nKcP7NJOKZMpqHryqOoK3fGtgXMe0I1+6FiRHkU581ost6fN6NRaRA3VRWjqcreThWCiWdKYwUaUnFk3aJMBNF4As7OQRUvlp+xMfeT8sYkDGvQdezISssgLgppnvc6EGBhDvfWbWbIC03TcMJ48f6Up4Fnjq5yHFtxUQhnS1PbjqlylwVNx0ve+yDw19fvVmXGtirucnk0jHO5ey1b5NmGdAYwI6tKMHKWu3faTmgkaoj9ZgmKi0I4d0Yj/rFuP+IJw2rf6yuilveZIRuHXvVg+KZulo45F5IpBm/QsXpYGmKFZEJlG+iCASxfQ3Fb+VmQpYHeGuLceYjTSd3shdfaB9+6+OiZR1eXYHRqQHfq5Hrhu2zOxXHN1TiuuRoAsFKarR6v6G/zyaAt6Tt8+DAWL16MKVOm4MILL8S+ffsAAJ/85Cfx5S9/ebCq4YCfxmLPDr+AR45xGQ3p1k3Z5+Lllae7ec+WHCZKxmutmRxNgRmhrLPm4xfyN718/7Pt7ODe7vs0v7c3cFukxwxCvwVYcifB/5ZnZFWxtbBKXuUve4RVHmJmlI6sKsGoKjE4P29suk09BUHT7IQPfB3sfRentjM/P9rd7xmxoNdHMiGX6+VhzS/OuvjF6HUP02T+dYsy4Z4wxNsAVhnEMryHmN2jvF41yD2TSZSJdPHTXwclkyQiQQkaZWKw4auiikOcjbPG9hCb9y17poMUyScAiiXc21a+/gMVZSIbzbeMyvhjf+VFhOZ33vULaZrQHviFmJP15mlFmcjgvvXyyqrqoPQQc6/lREjpZH1k91Em1zPI+oIgyL8qoKYAwCAaxF/60pcQDoexc+dOlJbaXsgPf/jDeOqppwarGg6Y3aFzCyr4zlfO4BYJ2waxq4dYeq55HZGcWctRH4911rLhxzwHrB4RRVB4c79SI2EZxP7pK+Xfu9lz7GFTpS5VlcUbpaqwaU1Vxa7hh2QJRYzzGMcTSew60o23U2FcmiqLBR0lIHrw3XLCB0V1L7DFcWy/7Jg3cpp0FUL8aK6OXX1x7DrSbS3Ia5IMbXMfGVV/QFBPyTk/80tRCtjXJeGTmMOrbMB5X3qle7X2zd1/7PqWch6mIBEXMokykS5yPTI1ODNdSR6EoHGIBxtRfuOsWzY1tfsTCH+DHD8zfmOJpOXZ88uE6B5lQnzvN2AKmro5E/h7Vc6QqIrtro5DLN5LXh5Wue5ui7xU5OK4rZCMQSUTiu14U0FOQZ/OoDWWDD4gk8nVICndwdlgM2iSiX/+8594+umnMWaMqC+dPHkyduzYMVjVcGAZoLyxmrRvQnmKJRLWba9g3E1DLHuI7ZGZpjAIg8L2x0LHsNiX/QoPgldDybazs/V475e/id0MedtD7H2DW4scuLZPNbAYVVVixSb09xDb1+Hye14RFj+NrC7GqCpxWjOu8BBn6g2LhHWgzz6Gjt6YtdBN9uQ+vHKXZ1mqRXWHOvpw5g9esMoEYB2P4FkoUCODofQQczee2+IWK6wa7yHmrpXbcTs8xD4eYxUqDzEfPi5QTOEB1GNaZaZp8LiXk7n3yQ9x8JOzYrPGIZkImJQiUNkukokgbQ1rR+NJw9LWqj3E/gMNWfvq7yEW3+dSMqFKxuO5qE6xb8ERoIuRQeRT5GsQe3R+uRggJhWDeBm/mb5SIQxb5oZpPCsPsTRQzPiWkMspoMYAg+gh7urqEjzDjCNHjiAaVesYBwN+1M43YKwRkyUTEU4yoZqqZ7/n4bU77H52y9TmJZmwOuVU6DLmIY4pPMTekon0PEoaN5XoahCnzoVXGk2+XvzxM0/1aVPqUVEcRl1ZBBfNHsl5iNVSEft9akoyaVjGcElRCHVlEbxv9kicOa3B0kbJx8BeZxpf1R4cmXViq3QjYd0ynuTTO2NUpbIsflEdO8SthzotY7g0EsK4ulKcMtm5CKqQGhZVVZQhvNLwECeTBidv8jeuZAN73vgaTBxRhtJICBfPGeWa9ETYNxdlgsmj+CnXIIaCX5SJXFw3TXMG888Ep2Qihx7iNHSSg4kc8ioXkQXk3zqjTPgXGrY8xHZaaJVB7DUTqNoGSD/KRE4X1SnCrrHdZRx2zWNQ4MjUGMncQ5ydhth9G9F55fz+Jx85HhNHlOFnS+am3Xf/9pML7LpYdohfrZ3kavbIWU5GxQwYg+YhPvXUU/Gb3/wG3/nOdwCYjXgymcRdd92FM888c7Cq4UDQEKcujmHYn8uBpSPhkDX9GlQywe/D9pBm4CFOdcplkTBau2PWDa4yRj0lE3InHcAY1DUtFfrKpW7MQ+zjeeOnoRlMMvGZUydaxh5fL9lDLEss2L55De6qry8WvHkvf/UsHGzvxYLvPivs22okMnwy7cFRQqhDsSBfscu+5uTxWDSxDp/57SpHWaqEKmzgMLmhHM/ceLqwvV9Dmi/8pjkZQbLh8Yk3soky0VBRjOduOsO74i71iyV4D3F6GuLB8BCzurDnJHMPsVheLgkNYNnZwNdF05wddFYe4lRh6cYhBuxkKXFOQ6yaRdECnFdnVrI04xDncABTpDvbLNtDHFQywdVNF6NM+GqI5VTCHvdiLryiqjZLxitKBgDMGFVltV2/e3WHZx1lTp1cb4XF7M+hhzjTR1j+WSENjoFBNIjvuusunH322Xj99dfR39+Pm2++GevXr8eRI0fw8ssvD1Y1HDADTwMnZzAMy0MnP0DRsO5vEMtRJjijizWSrhriIB7iVKcspxl2N4jFcuRpoiCx9kOahgQM3ygTfh5i9jUvK1FpoAFuylryHMgeYvag8wal7Nk3983OPazoJqpkD+nA7gVmpDMvL+9JlBfuuDUCqigTrI9QpgUO4B3KB2oPsfMzIUi/SwtrDaAMQ3ku3CUT2Z8PNiDr46RR/CArIw/xAA1cwroGFtAvcw2x/3nNlFBIPUDMN7LkwFG3LKoqz6ql09bwC4rtgY6PZMLluqftIXbcszn0EAvJeMzXrHj1ojrvwbWmafCOMuFtEHudi1zoZuNppm72O9fpeojNfduDKyBDDXGOpES5iNwxkAyaX2nmzJl49913ccopp+CSSy5BV1cXLrvsMqxevRqTJk0arGo44HVdOteAsc/lVanBFtVJBjEXesUeDadfV1syYXbKbIqpT+Gd9dKWycZCEK9VYMmEj4eYH3QwWP3lhU8qbzLgPO/sPTMoI2F16lC+8bANzsyjTLB98XVgxlOxS6Y/06PhYhD3OzXErEH1i9JQSFEmVAaPuv5c5xhAMpFQSSZcbjcvbWBQWGfJx7AWPMQBLJtcehy9UKWeThf+Z7muphgZJLdlZ4P8bOYywoItwTPfB03dDNiDRXNRnfv6DHFBlnc9rO3SlEzkdlGdh4dYGXbNWYY8cPOSTPhHmXC/GeXDzmQgx4f1dN9P8IGoI+xagDrxGTeBzPqKXGmIyUPMUVVVhdtuuy2rMu655x7cc8892L59OwBgxowZ+MY3voELLrggo/JYY6VptqHCSyaUi+p8NcTieyu0F/fwukeZcIcZXWXMQyzFIea9s14GsexpCOTp0uxzo4JFevD1ECsGBH1uHuKQ+CBb+3JoiJmH2Pxb7GKU8w1BwjAQBpRGVjrIBrFdBz69Ku/RcG+QejlDnxnq9kIy786wkBoW1eGpOpO0JBOGOkSe23HnYiFQyMcgziTKRO4Wp4jwA4AgEigV4uKkHHuIfaaF84Uc09rZYWdeNvspe4b5vsYPljAknjDQz6JM+EgmcuYhdnhZfSqbBmFFaFBLQ5yBZIKXIcrfAc5nIZ04xI7Uza5busMvqA+yHz9jVe5fgzzqrPyYTxxiL+RfZDrLkyst8kAxaAbxsmXLPL8/7bTTApUzZswY3HnnnZg8eTIMw8Cvf/1rXHLJJVi9ejVmzJiRdr1syQQfJsewGi/HojouykQsYSCZNBw3sWzs8rEIeU1kujADnE3bMkMwpvAQiw2lWI7csAabdjH/uhnyfYpIFypUGmrmVQ3qIZYXX8QkyYTsBWDwjR9re7P2EEtxiJmXtzji4iGWPBqM/nhS8hCn/np4GITsXwXUrgSOMhHAIGbHxS+qC2Jc+d2HQWD3S4+V0VFTJhbwYiC9bW7l5kRDnGvJhK4erOcbOYTfQHiI7Ux1wRc12c4A20PsJ5lwu2bphlFz3rO5s4hVyXi8Ii+pqirLRLwWF8vNQDoa4lxM7weKQ5yGh9gRhzhApWyD2F++4YaQACaLx9e5YDPzsgaCQTOIzzjjDMdn/ElOJNQhzGQuvvhi4f0dd9yBe+65B6+++mpGBjG/8pc3+mwPsXjF+MQcgGkIFeviQybbjHFOWM8nGrC3V7+WYZ7UMhZlIikuqosKHmJwr8Wb0CGZCNDoy9N/MqpIF17l8JIJ5l2Nuoze45LnwPIop8LPyYvqZC+AvG+7zJAytm06ODzEceeiOoeGWHGKevoTggaaGerMw6COQSqWWyioaqLUEAcxiLlBkWqVvt/vssH2ENuRQ9I1PB1xiHPgdVIR5Fz6EUSPminiICanRWeFc1rc+30mZcse4iDPahHndGH9h1oyYb92u+7yp34zGw6jModtS1gxoGTVViUtUh2T4Ozh+m3V9vKxlkj9uVu4R1VZWSXm8NIqp/Hsyv1rkHuJXT9bQ5z+cQxUX1NI6wmAQdQQHz16VPh38OBBPPXUUzjhhBPwz3/+M6MyE4kEHn74YXR1dWHRokXKbfr6+tDe3i784zG4Ubtl9CVtg6QopAsPIC+ZANSyCaeG2Bazq2IuBnUWW5KJlIbYlkyoEnO4j5ozEub7eLZVsZBV2AvbzHIMwxAMXHFbXblPdrxsYPDy5sNo64lZcgXZCyDvG7A9xMwTm2mjLy+wtDzELhpi/j7jWb61BS2dfdb7hGUQp36n8rAOoAGTDcp0qz5eY7dGlo9DzBKbBAm7lgtkDXEkrAdaCMjjm7o5R+QiExxftVx7soMMYvKBXxSQbDpsfsYRSG82qojTEHtlqhuM1M259OJ5xSFWJeZQnX9Z2uMtmRA/SCdTnWOw5LqlO4FSN6fRjsv3QCBnVuonsQDeatcyciTPI8lEiqqqKsdn55xzDiKRCG688UasWuUMQ+XG2rVrsWjRIvT29qK8vByPPfYYpk+frtx26dKluP32213LYqYWP13GSyZCupmel9e58oabamGdbMAl+DjECsNS8Ba71dMwuLBrTEMsLapz1RCLZWWS2Ypt4ubBto1y77Isr0nq+Hn5gxzijk+MwMPOeXlxGEe7Y+iJJXDxT17C1983PVWOi0HMz0hk0EmpkPXkTAfMG+Wyzk+1r8/+7g3hPaufvTLdp2MooIbFb5qTEdad3iIZdlzPvH3A/p1LivJcwyIjWIs1Q+l7iAczyoTqdToM5EBDDG9WOPeq37R4LqaH5TjEQQ7fijLBxSFWLRQVPXfqstLXEKdnQKeD6vlROYnsuqjqZ7+W12T4p24Oe34v7if7wRG/fsh1P4L0zXsfjkV1Aa5N2NKj5ybsWjaPryNJTAG1BcAgeojdaGxsxMaNG9P6zdSpU7FmzRqsWLECn/vc53DVVVfh7bffVm576623oq2tzfq3a5eYLYxvpFj/zEsmdF0TbsJIWIemaY6EDGKZ4nvLqNHVi+oEj7KLRRxP2jGALQ2xZVSqEnPYv5VvukzyoftJJqzQaSHvhAe8xw8QQ1o5PcSpbV00w2dMacC0pgoAwM4j3Zbh4rWojh0q89pnvahOug/6LNmGOoKCW9i1ifVlmFhfZr234hAzyYTPdGkhtSvBo0z4G3Eqj8nxzTX295IRN6WxHF9aPCWt+rqh9BDr6lkYLwYjo2AuPMQDuUgzPAjnIBMc3lDZCMpC1CI7EdKSTDDdZ9LOSFqkuK5BBsUOr6nPggM/ozIbwgqnDdudOjGHqt0TjzmtxBwOg9IryoQ8WEr/PMQDeGUFOZGPReboIwN5iFP3UjaL6oSBVy49xBkXNSAMmof4rbfeEt4bhoF9+/bhzjvvxHHHHZdWWZFIBMcccwwAYN68eVi5ciV+9KMf4ec//7lj22g06pkJT52pztYB65o5Ld7B9p26ISNhUb8qHxsPn63GjkPM1SFACDZ+P0wqwEZ8qrTJfEPuiDKRgWRCnv6Tsab1fDzEfHpsQDwueVGdn4e4oSKKP/y/RZhzuym56UpldHNbVMf2H+di2tqRCzyr7QobhMhxiL0kE/L5vvtDc3DZXDOl+R9f34Wb//SWUzIR0MgsBPy8OgzB0+vjIWZ8eH6zcH35c/Ch+c248/LZadbWHVZ2D2cQZ3LOWQxvYOCkLbyh7qWL9CKIHjVTgoTKywcOTXcuPcS62Nbx8jw/WBttGHbb4ushdtUQp2fgOr3kOTSIFQM3SzKRUZQJ7zjE8rOQVZSJbO6FgJIJ/0V1GUSZsAYc2cQhdj/H6SDvu5Bmi4BBNIiPO+44aJrmMBZPPPFE3HfffVmVnUwm0dfX57+hAj7KhL0qWFzAw4/KmAEUCetAXzANMZ+tRq0h5iUTLhpdznC0o0y4e4j5+0y+52RPQ06iTLjogGVkyQj/O7fV0I44xNzx8g1aZyptsiopB19mnItpm+s4xL2qOMSCB0BzNCgqDyJzlnjVr9AaE4ZSQ+zjIXaNQyzfqx4xtP0WdKaL7SFOLaoL6Rl5y7y8K7m6hrnREA+chziIXjwf+HkBs9MQm3/tRXXBPXS8IccGZP5xiF0MYo/2RsWAxiFWaPBZ8XICJsA/DrGue3uIZWdEOpKDXMpnvC55NovqgkWZsOU3QKaSCf51Fs8E5OuTcVEDwqAZxNu2bRPe67qO+vp6FBcXp1XOrbfeigsuuABjx45FR0cHHnroIbzwwgt4+umnM6qXwUkj2MVJcAaTJj1ElkGcjmTCJ8qEm5HJ08+FTGGeVDai7lcYo55xiDPwEPMxmpX1U+iYVfA6bcA9KYdZT7XngN8XX/fOIB5iSYYRz9YgTklE7Gx5qTjEgoZYvBZejbYcls6rfoXqIVa1l2oNsX9HIBvSskHKv1XdQ9lgLwC1y8/IQ5wDY9UPwdDIsMMSvY3Z1kikUA1ivzjROQm7lnrPfCfpxCEGbA27OtKM/73llED4tdHi+4FLzMEMYtbOB5VMiK9Ffbr7/sIh3TGo8BrgapopsbOcZjm4F1QM9KI69pNYgKx5buRMQywPMgqsDxs0g3jcuHE5KefgwYP4+Mc/jn379qGqqgqzZ8/G008/jXPOOSej8ngNMR8P0ZZMiFMy0ZQBJE+VC2W6CG11zb6x3KJM+BmcfJQLKw4xS8zBa4g9dEnyNFI6Ydf8Ujf7GSW6NCCwQ645f+cXZUL2EDPJhBwqTyjT8sCKXptMjQjZQ9yj0BALU9Gac1GdKjC7JZnwqF+BtSUWSnmHj+TDb1Gd23b891GPmQE13gNR2XCQ77egeHmwcnUJxXOZmTWbzuKedBmMQUEmOA1g8fvspsnNv7KHOIg8q0jhIVZmXxQGMS6DyjQN3EFL3WxpiL0MYkX9pEEAf42cbau9vyJdS3tBOZPYAdk9q177SWeNQUZh11Ll50xDnMX9IP+ykAbHwAAbxD/+8Y8Db/vFL34x0Hb/93//l2l1lNiSCc3hEQKcN6ggmYCbh9jFINbVUSa8Yg8zeGkBe6gdqZu5BtNLQyzLGoJ08vL0H088kbTOmW/YNc6wvvMfG7Ds3UPKOvH1ctMQy6v+mYfYyzBiU+7X3P8aHv7MotzHIVbEQubvIU1zNo6qac8gko5Ca0wYqlOpqqqY5EJdlvy5/DzyXptcSybkc56phlicEs26WkpET1guPMQ5NoilqetCwU/TmE1d5cQc6aRu5u+zXi7Kids+gOCSibSjTOSwnSnylEwoNMQ+7R6fYVb+DhCfBdNDHDwOsV1edguvzXp67CMdyURGSbVS/Wg2GuIcyakcHuICaguAATaI//u//zvQdpqmBTaIc429qI57MLkpeocxKUkmdh3pxujqEjTXlnALz9T70jW3KBP2NvJPW7v7cbQ7hh2Hu1L7D9lSAo84xF6aH4eHOI2HSnVs/Opg38QcqX0d7Y7h3he3WJ+PqSl1bOumIeY94ma4PLNeB9p7AXhLJkZWlaC1O4bth7vxwCvbLc12tnGID3f1YVtLF4509Zt1SEMyEVIYTLJkwq9jKCRUK/P9JB9uXk3HuZLu3ZFVtuRqTE1JWvX08/nIhoNK5x6EwZAL5FpDPJAe4kK6b51xoiG9z6bzF9v6dFI3a5qGopCGWMKwJRM+kWbcLnu6muCBlEyMqrKf0VHVJan9qdt5/jsewSOseQ+2+LoXhTRFpjfv/mpkdTF2HO42X1ep5Z2nTanHsncP4eI5o/DXN/cqt/G6j0Zx5TZUuAcAAFSSCc/NAdjtWDYeYmEQkvavedydQYXAgBrEsm64EBEz1TkfTE0X9VzsAWNT/Df/2Yye8aH5Y3DXB+YIZcromnNKXN6e/+mmAx248Mf/FmP1hm0dFDOWVBpir1SLuY5DzHvJg3qIGZGwjh9fcRwWTKhzbOvnIWb7CutmxI/nN5reZq9Fdb/5xALc/tf1ePKtfbj3xS0YV2ca4pk2+swg/vva/fj72v3W56JkQjRWnI02HNvKHmKVV6eQpp55MtIQuzSMDo+OdMzjR5ThyS+cgvaeGBZOdN5D2SAb35l6oL2M1fLiMA6nBlHZIESZyPC+CKJHzZSCNYh9NMTZ6UbNv3Ic4qCnNqzriCUSVtg/dZQJ7rwGLDiYV9Qml/fCWdMa8IfPnIhIWMfM0VWp/ZnfxVQeYp+2RJY0eoWMC+sKD7HPsf3x/y3CGzuOor4iinnjapTb/OJj8/D2vnaENC0jg/jKBWMxfkQZ6iuiaKj0XlMlO72C3J+61Y9mE4c47Z8EKqeQ2gJgEDXEhY6pITZf8waormm4+uTx+P7TG3HejEbr80uPG43tLV3oiyfR3Z/Am7varO/cFBA6N73Db+O2wO7N3W2IJQyEdQ0lkRA0AJfPHW11fm/tbsNr244IURfsfYn75ckoDjEnJznc2Ycth7qs79p6Yq5lO8qR9lUaCeH8mSOV29oL4JLo6otj/d52AMDRbtOAYMcb0jWAy/x95rR61/3XV0Txvctn429r98EwYI3+M50iPnVyPcbW7rDqBAD15VGcNGmE9V724nhKJiTZjlec5AK1h9V1VdiSonZaXZaXJpDBOtZ0ueqkcfjXOwdw6uQRyu+Pb67GtKYK7GntQTSs46LZozLaz2VzR+P3K3aipiyCU44R93XPknm4/uHVuOm8qRmVzciFwcmf6lxHMElnWngwkZ97p0GcRdmyhziZnocuHNKAmC2ZUMchtl+7xyFOr713yEZyeL10XXMMXL00xKpBqCztmTDCjt/Ovza35QzikIbm2lKcNKkOa/e0YVJ9OY5pKPesb2NlMS6Ype6fGMVFIcwdW4P1e9tct/E6heGQjlMnu/dZPBPr7frKx+oGu97ZhF3LVXsgl1Ng9vDgGsS7d+/GX/7yF+zcuRP9/aJX5O677x7MqljYi+rUCRN0zRzBXblgrPD5VSeNx1Unjcfr24/gA/cut8Jt8WXKaJr9YCRcvMK8B7Y1ZWRdNHskfnTF8dbnr2xpsV5/6OfLrdduUSYco+ZMUjenyuiJJbD47hdxtDvm2IZJGDzLkdo3L28u7yH+yK9W4M1drY798dsBZmKG2WOqPetQFg3jxZvOxGnff976TNXZBGFqUwWW3Xym5zayB8M7NFCqE7XiELsvxCncsGvOz9QeYl35WtgmjVXh6XLq5Hq88tWzXKcpq0sjeOqG07Lez60XHItbLzhW+d30UZV45sbTs95HrjPV5VrrHBaegdyWnQ3yqcqlxlGeVeOznwaBeTO9PMTCWhGXcuVj8p/FE9/n8plTYWtcnf1mTWnE8Zk8+zmlsQL/vvlMxJOGw0gUIkSl1pw89OkTc1V1Aa/zmqtBxXHN1Vj2lTNxqLMXU5sqA/0mF4vqcnULyMUMWw/xs88+i/e///2YOHEiNmzYgJkzZ2L79u0wDANz584drGo44KexMlm4xBZPsVE84B6JwZwu1xzb8AY0/znzOsqNAv/g1ZQWoaYsgoUT6lBTZm8neA6k5zSbOMRHuvosY5jPrAYA7/MZSav2pYouYW9rR5nYcrATADC2thThkIbR1SWYn5rC4qe2/UIKMcbWleJbF0/H71bsRJmHlzoXyB7gtKJMWAZxAWUz8CGzKBPqsuROJtfeRaZjzIRCasuFc5nhorog3sZMGUg5RjY4NcTe79PB0hCnlADppG4GbEM0qIbYNXShzxoSx/Y+XvNco1q7w1AZxKpjbq51rkEBgHOnN+KZtw+gsy+Ojy/KTaQrNybVl+PCWU3YsL8DW7kZVCC353BsXSnG1qmPVwU7R1bq5gy6klzVXy6mkNoCYBAN4ltvvRU33XQTbr/9dlRUVODPf/4zGhoasGTJEpx//vmDVQ0HfJSJoJ4tHtsgth9mz0V1bErcJewa/5oZnlUlRUI5/Ij9C2dNxidOmeBZb784xOksquPDvz335TN8fycjd0BBPcQsOcIjn12ERklnlekK+6tPnoCrT3aeu1yTlmRCmmbNNpNePvDT/TFEz5P6usnnaqC9VUMVYTV9DjzEOY8yUaAaYqfxJ36fi9iz8qK6oMfPBoOsjypSWDKBoky4lOuGW4KkgUK1roZRW+bnIfau2+TGCjx+7clZ1jAYIV3Dz5bMAwB8/L7XrAhKQH7lbSGuHzXrkomHOEcGcYEn5hi0bvadd97Bxz/+cQBAOBxGT08PysvL8e1vfxvf+973BqsaDsQoE94jcBVs8RTvIXYLo6Zx++AlE0kXb3Gr5SEWDWK+QWuoVE/38ofipyEOFnZNMogztNDkDsjTQ5yqZ09/wrpOcupNQGywC9FgkgcnXqNk1uc5PMQFZET4oRxYqjTEAQYvshFQaB6FQkGI75ppHOIAxlWmhAvUIPbzEGdTU3lRXTqpmwFnO63MVCfEm3cxiD0WmqmQvx7otke1dodRLfV9gPf6mEKhkBaPyc6sTAZ5uaq+XE6hyf4GzSAuKyuzdMMjR47Eli12yK2Wlha3nw04YqY6lUEczEPcF09ahq17lAk+DjFfB/u1IJnoMj3ENWXukon6crVB7JVZxjENHeCmZD9hMY8zj3UqGcQeK/dZw83iCwPmIjzndpwWtQBdqXID7kw2wb2WBkxWYo4hJJlQpm729RC7y4zcf0MwcqMh5l/n9jzrgkGc06KzQjYinRri7D3EhjTbE3xRnRwzV6Eh1vzPq6P99wuNKfUdA51NzCvsmspDrAc45nwjVyufdp/TQ5x+GQNl0Bfa9Rs0ycSJJ56Il156CcceeywuvPBCfPnLX8batWvx6KOP4sQTB0bkHgTeeFVdHL/7gPdY9sWTKImEgkkmXCJLiJIJcwBRLemoeGPULUyLl4fYkf42HclEypIPqtWVcUgmFB5fa9tUvTp646l9asrpPr4qflEu8oHQaekBo0ywwdWQlEwEM4j58+AWmcUZN7Twrm8hoNKhp4t8n+aSkOIeLwRUHitNA5epNPuyHZKJgIXK7bTSQ8x9FFRDrJJe8AjXahAsOVZtVdg1tYa4MO8lnlxq0bPFkh4m8h92LZcDzoFg0Aziu+++G52d5sKo22+/HZ2dnfjDH/6AyZMn5y3CBAAhRbMqJIifS7+YG233xhIpg9gjDjE3Gv7p85vxwsaD2NZiC/D/+1/v4rkNBwAAW1Ofy5IJPu6v2wp5r1F0Jl43WTKRqeEpt8VBPMQdvaan3C3hRpBoBfnEK24m4LKozvBPzFGoqGqqeoyCXCtnRJTCu76FQK49xAMZh7iQpknVKdE1O9xhFueBT2aUSBq47+Vtqc+D/d4ZM9cnykRADbHf7J42gPeBClbvPkXWV3l2FBDrV0j3Ek8hLR5j97AdZSKDMnK2qK6wHRyDZhB/97vfxUc/+lEApnzi3nvvHaxde8JriB2r/wPcBOGQjrCuIZ40rNBrXqmb+dHwD/+50eFN7o8nsXL7Uet9JKQ7srjxGXPKoupLKHiIHdOCZpSGPa09jvLcYEVkK5lwRpnw8hCbHQCTTJS4bCtnIyo05MQbXt4Da1Fdqm9g04h+Rk4h9QuqxlPp2ec2a3S5B+XjJsmEmlxkqvNaiJstvGEzKkB7M1icfWwjfvbCFlRw7aiuCWHNM4bZr0nDEGLUNlUFi2wyuroEa/eYvwvrmnK9SCAPsfTo+T1DIxXZ5AaSJpf7oaqkCGUKJwjfXxXSvcQjH5O8EHwwccYhTv/ZzpmGeIDKzRWDZhAfOnQI559/Purr63HFFVfgox/9KObMmTNYu3fF4ELhZCqELy4KobMvbkWaUESPscpjjVZvLOEqrQCA//3I8QhpGiY1lDt0VNWlEfzrxtNRFnU3Jv06tz99bhHW7GxFQ2UUc8fWuFdEKqMvdYx+025B6gWkpyF29xAX9hSakBJXd2qIlXGIrWnWob2o7sZzpuCyuaOV10XTNLzy1bPQF0+isti5eAZwDrwK8foWArnwEA+kZ7A8GsazXz4dO4904+RJ6kQo+WDeuBr87YunYDRn+Jle18xX5IvlmLOQR7hshFee0Bzo93d9cDb+Y+5oJJMGJtaXY4RivUiQdNu8FzmsO2dCZU4+pg5/+uwiHOrow1yX7Gy55MKZIwGstt7PH1eD/3f6JEwfVams65fPnYpFk+pgGBASIBUSt104HWdMaUAskcSYmlLfBCADiZ2pLnMPMX8dPMyWAOWI74etZOKJJ57A0aNH8cgjj+Chhx7C3XffjWnTpmHJkiX4yEc+gvHjxw9WVQTYxVV57oJeK9sg9vEQa7zB413mRbNGejZcfg+YsDBC8f3IqhKMnBV89M8eov6Ee0zMYOWIv0tHQxzEQ1yYi+pE+Yo8lvCKMjEkJRNcVcfVlTpmOHj8PFAOeU8BzgAUAmKUicLzEANmnNZJ9fkzDNyYMUrMdCiuv8i8XF5DzLJ5njSpLnAbVVlchPNmNHluEyRUHn88QZ4fTdMwf3xtoDrmAl3XcML4GmtmtLw4jHOmN7puX1wUwlnT3L8vBEoiISz2OIbBhA2QY1loiHOFM+xaYbXng2o91NTU4DOf+QxeeOEF7NixA1dffTV++9vf4phjjhnMagjwmeoyHb2w0GssgHqQ1M1eFIX8R/F+5Po2k0O3+MWydMMhmQikIQ7uIc4049xAwldJ84syIS26TAaUTBQSuTSsKOxaMMIZJKeR8VqIO5zI1f3Lz/Ywg1gVRiwbRMmEehu+L8m03R5o+IxyQ2k2bChgJ+bIftYjW5yz8Pmphxt5eTpisRhef/11rFixAtu3b0djY/5GUsxTq0G1MjRYGXK2Oq/UzUE69FwsDPMKu5ZNef2WhjhTyYT43tNDHGKSidSiuveAhzikGBSpNMRyHOKh5CHOpUE8kKmb30sEyfrnx0Cmbh5KiAPYbMqxJROtVpIl5yKxbBDD2akryx9PwRrEXL0KdaHcUIXdF7aGOLvysvq5bBAXWHs+qE/H888/j09/+tNobGzE1VdfjcrKSjz55JPYvXv3YFZDgA+W7tB2BrxYzEPM9LUJF4OYT93sRS6mhTXuysrTFJnAbHS2qC5TT6ymaUIDHcRDzLTZqhjE5nZ2GYW4qE7otHTvlbauqZuHUCeh5cigAJyDQ4oyoUbU0ecgMUeBdVSDiehMyMZDbP4VDeLceoj52rlHmeBnDwrzuka5jKUFarMPWdj5zCZTXa4odMnEoGmIR48ejSNHjuD888/HL37xC1x88cWIRtUhwwaDo139SIT6LSO2N5a0MsMxtNR2frBOu6WzD0e7+hFThI8BgM6euCPDG4tQwRPStUD79aKLS2bRF09kXR4zzPhyMy1T1zRBEuBWTr90HnVNfV54j3zCo7x8wWcx7Oozr0WIC+3U2Ru36tyZkoew42AynP540vO4+mLe3w8m7anpYQDo7otnVS85NmlPf3bl5ZJ4onDuNb7NyfQc8detkI5t0OH66N5Y5m1nd3/q2U0kcbCjF4DpAMjlee0Trru6rnybreegbxlohvW9NwDI/Wi2fWTSyPz3fBvD3nuZxO2DfB8MmkH8rW99Cx/84AdRXV09WLv05KXNh1Ba3osDqYZq08EOvLjpELe+2LxxXnj3oG9Z3f1mg7NmVyvCIc3Si2maqCd+fecRVBSLpzwcchrEyYD79aKPM8L2tfVmXR7zcOxvN89Xe28s6zIBYNfRbtdytnLxmQGgrUe9z/Ze+yHLxbHmmi2HOq3X6/a2IZ5Mgr/RVmw7bIWlOtzZB8CMGfnCuwexv80831tbOj2Pa29bT8Ecdwd3Pd7e1x4oRbMbchr09XvbXGdgBptcPQO5YOfRbuv1+n3tGZ0j/rq1dPYVzLENNnzGtM0HOzI+D+v3tQEAWrv7rYHd/hw/p7u56/7O/nYUhZ3P2qYDdvsTTyQL8roe6eqzXh/uGr733kDA9z8AcKS7P6vzy/qmTGAOH8YrW1pc1wYBQHdnR0b7yZRBM4g//elPD9auMoJ57nkjNuh0GfP6Pr5mD/6xbr8VYocP8A6YNpA8ZVAU0i1JACMX05WaMO2XdXFWGcx4z6aO5jRJarGYx/yYvAs3OUShT/Vqitd8rFMx/WpKdwgx7FqhTS15Idzj2erVUhKbdLN8DTdEHXpmZfDXbTjrOHO1uJD9ctfRHqstK/UIlZnRPoS6BtmmMK9riDTEA4ZjbVSe6gHA0R8U2qUe9modg4sywf81XwcrgwXh7upLCPEmZd2nrJ8F1IsccmMQZ12EVJ64UjWbOvIB5psUweat7SrEYOZuAdx1QT9ZYE8Y1B2SaAQ7t2UG4JA0iHNcVUFjXQDn4fix1QCAs49tyG9FOPhnqk4RrzYIxUW6lQjBLQPmcIA/9voszkN9RRQaTI9zLGFA08SkF7kgyCCG/7RQNcRhXd0eEtnjTMyVWTljasx7d2pTRcZ1kXddAM25wKB5iAsVyxuces977oI+mBfMasJxzdU43NWP+1/ebn2u6xBSHmmas9GSNcXmfnNgEGddggirZTyVdSQbw/MLZx2DfW09KI+GPTvvyY3luO2iaejqiyMSDrkaz7xHrBAXn6kifrgthmH3h52Yg20zoFXMKbm+BPyMQiEYxB89cSzeP2ckqktzGzEgG2aNrsKtF05DNKxnvHArHNLxtYumobU7Fih75XuVz54+CfvaelAWDSuTYQSlsbIYX7/4WEs3WVVSlPN7Jm0PcYE2JPxC8lwsAids5EueqQf+C2cdgwPtvTnNXlho15oM4tRf21CxO9+gF0vXNIyuKXHog1UpeoPIAHItmcgFlmSCeYizKD8S1jGurizQtiPKo76dUsFLJhQeYLdIDPzKdMMwhDjZQ4VcN3K5CCmWS3RNKyhjmJELr25pJIzSyPDuFtJpn/yoKY2gZpDuFffBYmG3jwB5iAcSR8KxDMuJhHU017onWQqCM9dDVsXlnALoXvKLJZkAm8q2v0vXBolIIcRUN6L8mUpDmxODOOsSpPKYZCIHGuJcI4c1KzR4A1HVabnF7TUMTjJRgMflxsB4iJ2vCWK4EyTevJi8ozCfHz684lAa/A8F5NOZz9MrO0sK7VoPoW52YHB4iAWDOL2LJeuBZVvXlEyIn6k0XbnWEOfilrM9xNlLJnKNKrFFIaG6FnwQAP5U8seSNAxOMlF4x+VGrmvKP0dD6TwQxECT7oK5QmwfAUkyUZhVHLKo1jIVCoVTExMyiCUNsbBIIc2yQrrmuQDITA8tpohWGZaFKJnQIXmIC+ihEgzKAjLUGX6DLLeFnElDTBzjhVvSkrwwoB7i3JZNEEOZILMnfPtSSI4MnrBHv0lkh9zn5PPsFpK3WsWQM4iXLl2KE044ARUVFWhoaMCll16KjRs3ZlweC2/FbhrRW5d+efwiObdU0Pynqqx0OTc2c7FIT9IQF5LhWegeYpVB57YYRpRMGGB5Kdw6iY8saMa0pgosPjZ/6c9lBlJDXEj3HUHkm5mjKzG+rhTTR1aguVa92Il/YgrXIOYlE3msyHuQXC2qywXyngvJWw0MwUV1L774Iq699lqccMIJiMfj+NrXvoZzzz0Xb7/9NsrK0l8I4fAQK1b8p0MkrIElY3HciGBGt52pbaA8xLmGnYpYDqJM5JqhFHZNdUu5hWATPcTq4zphQi1OmFCbk3rmilxfAtIQE4SaxspiXL94suc2fjOShQDvGKJnPLeUR0UzL6+ziQV+aYecQfzUU08J7x944AE0NDRg1apVOO200xzb9/X1oa/PzoLT3t4ufO+tIU6/foKH2CX+n5CYQrFaKtdesJxoiFOlsAFEITWsoQJv8P0X1XHb8h5i2Bri4dxHhAp8wEMQQ4VCnEEDRMlEYdZw6DKhvgwfO3EsWntiCOsa5o6ryVtdCi3MmsyQM4hl2traAAC1tWov2dKlS3H77be7/t6QDA7+cmVy8Yq4SBNOMbv4F3BZVFeAjZZczUIyTApdMuG3qM5LQ+w1k1Co5Fy/ThpigsgY4fkp0AeIPMQDh67l1wgeSgw5DTFPMpnEDTfcgJNPPhkzZ85UbnPrrbeira3N+rdr1y7he8OyTMyHMNuUx54eYk4ywVBHmUh/vwONbOQUkuFZ6GHX3DzA6m1FDfGQjEOc46oWWqY6ghiqFGLfAgAh0hAPCwr92g5pD/G1116LdevW4aWXXnLdJhqNIhr1D1hvyxnszzIZTPMeYrdFdX5xIXPtDcyJZKKgPcT260KqF8NNI+yGrjHvsO1JLsDDciXXVS30KCIEUciIGuLCtIgFyQQ94u9ZCv3SDlmD+LrrrsOTTz6JZcuWYcyYMRmXI6fGzXpRnRBlQvyOlceXOyhRJnKAXKNCMjwL3YMoyHAC1E/TNMAw0NLRh/64uYixEI/LjYH0Zhfis0EQhQz/xCi6m4KAJBNEITDkDGLDMPCFL3wBjz32GF544QVMmDAh6/JMNO5/OF4Hhc9W57aozm/Vb869YDkoTjZyCsgeLvg4m+nKcEKahgQM/O/zW6zPCvCwBo1sF7oSxHCm0NtHACgiycSwoNClf0POIL722mvx0EMP4YknnkBFRQX2798PAKiqqkJJiToOYxDUUSay8xA7FtWl/ooa4oFJ3ZxrCnlRXajAG3y3OMNunDChBqt2HLXejyiPYnR15vf20Ie8RwSRKfUVUUysL8Phzn7MHF2V7+ooGVldjHG1pWjvjWH6yMp8V4cYpgw5g/iee+4BAJxxxhnC5/fffz+uvvrqtMvzjkOcfv14D7FsnLGy+c+HimRCNkRUhny+KPSwXKpZh+KiEPpScgiZD8wbgw/My1wG9F6Df0YK8foSRCET0jV84axj8l0NT4pCOm44xzueMjH0EeWDeauGK0POIDb4eFW5KC/HmermjavGtpYu1FdEUSYFwGblnTZlBF7ZfBilkRBmjq7CX9/cJ2xXiIvq5o6rxtaWLvTHk6goDmPayIoclJobZo6uxNo9bSiNhDC2tjTf1XGg0qVfNKsJz204hHnjqvNUq6HD6VPq0dkbx9TGCjKICYIghiqa8mXBMOQM4lzj8BDDabykw7i6Mnz53CkAgEff2CN8x8o7fUo9Tp9S71pGIXb6/HEVGo2VxbjBJ1tTPhHDrpl/CzHD3ICQg/HrjFGVmDGKplEJgiCGMoVn2YgUzrx3nvDMVJdl2ZkuBqLQUu8t0g27RhAEQRDvNbKN4jXQkEHMEh+k3me7qI5H573NafyuEDXERG4oxEZgQBlmh0sQBEEMTcggTv21YgQLkonsys7UuC5EyQSRG+jKEgRBEMOdQuwLySBmGuLU1cl2UZ1AhmXlfFHdcPNKFjAUNowgCIIY7hRiV0gGsVfYtSzHMOlmKGOEyUP8nqUQG4GBhI/LTRAEQRCFCkWZkMKu5TIrlq6lryGeVF+G6bSi/j3LcDGI3zd7JPa29mByY3m+q0IQBEEUGNk6HAcCMogdYddsspUaZGJcX1fgAdSJ7Bgukomzj23IdxUIgiCIQqUAu8JhP59phUm1wq5xIbJyuJ98GkIFeN8NW+haEARBEMOdQuwLh71BDCvsmjNTXfYe4txFrMiuInncNyFACxwJgiCI4U4hdoXD3iBmHmJd4SHOOuya8LoArz4x6NB6SYIgCIIoPIa9QZyUUssO2KI6MoSGLQZ3j5GHmCAIghjuFKKTcNgbxAw7MYeNnm3YtVzGNCYIgiAIgngPUIi+oWFvEFupm63EHAPj1c3naKgA7zuCIAiCIIiCYdiGXdtysAsl3Rr6E0kAfGIOe5t8hF0bCApxaoIgCIIgiOFJIVolw9Yg/tVL26BHS633zGAN8WHXsrxiYln5u/wlkVDe9k0ARaFCfPQJgiAIIj9EiwrPLhm2BnF1SRjtSfs986KeOKkOR7tj0DVgwYTarPYxc3QV1u1tR09/AidNqsuqrEy4ckEz3tzdhtOnjBj0fRM24+pKccL4Gowoj+a7KgRBEASRNy6a1YTVu1px9rTCS940bA3iD53QjF+tOGC9Zw7cGaMqMSNHqZPrK6L4Qh4zzy2YUJu1UU9kj6Zp+MjCsfmuBkEQBEHklcXTG7F4emO+q6Fk2C6qC+viodOkNkEQBEEQxPBk2BrEIUkgTPFhCYIgCIIghifD1iAOS3pusocJgiAIgiCGJ8PXIJY9xHmqBwDMHlMFADh2ZEUea0EQBEEQBDE8GbaL6sIhSUOcRxfxlQuaMXt0FabnaDEfQRAEQRAEEZzhaxAXUC7l4qIQ5o2vyXc1CIIgCIIghiVDTjKxbNkyXHzxxRg1ahQ0TcPjjz+eUTkhTV5Ul4PKEQRBEARBEEOOIWcQd3V1Yc6cOfjpT3+aVTmyZCKfmeQIgiAIgiCI/DHkJBMXXHABLrjggqzL0TVzIZ2RfZUIgiAIgiCIIcyQM4jTpa+vD319fdb79vZ2AOYiunBIQyxhpN7npXoEQRAEQRBEnhlykol0Wbp0Kaqqqqx/zc3N1nd8cg6NctURBEEQBEEMS97zBvGtt96KtrY269+uXbus7/j0zeQhJgiCIAiCGJ685yUT0WgU0WhU+V04xHuICYIgCIIgiOHIe95D7AUfi5g8xARBEARBEMOTIech7uzsxObNm63327Ztw5o1a1BbW4uxY8emVZYgmSAfMUEQBEEQxLBkyBnEr7/+Os4880zr/Y033ggAuOqqq/DAAw+kVRYfipg8xARBEARBEMOTIWcQn3HGGTCM3EQPDpFkgiAIgiAIYtgzrDXEFHaNIAiCIAiCIIM4BXmICYIgCIIghifD2iDWNQq7RhAEQRAEMdwZ1gZxTWnEes17iwmCIAiCIIjhw5BbVJdL3jd7JMbUlGBMTQnCoWE9NiAIgiAIghi2DGuDuLw4jFMmj8h3NQiCIAiCIIg8Qm5RgiAIgiAIYlhDBjFBEARBEAQxrCGDmCAIgiAIghjWkEFMEARBEARBDGvIICYIgiAIgiCGNWQQEwRBEARBEMMaMogJgiAIgiCIYQ0ZxARBEARBEMSwhgxigiAIgiAIYlhDBjFBEARBEAQxrCGDmCAIgiAIghjWkEFMEARBEARBDGvIICYIgiAIgiCGNeF8VyBfTB9ZifKKynxXgyAIgiAIgpDo7NAGdX/D1iCe1FCBysqKfFeDIAiCIAiCkGgvMQZ1fySZIAiCIAiCIIY1ZBATBEEQBEEQwxoyiAmCIAiCIIhhzZA1iH/6059i/PjxKC4uxsKFC/Haa6/lu0oEQRAEQRDEEGRIGsR/+MMfcOONN+Kb3/wm3njjDcyZMwfnnXceDh48mO+qEQRBEARBEEOMIWkQ33333fj0pz+Na665BtOnT8e9996L0tJS3HffffmuGkEQBEEQBDHEGHIGcX9/P1atWoXFixdbn+m6jsWLF2P58uWO7fv6+tDe3i78IwiCIAiCIAjGkItD3NLSgkQigcbGRuHzxsZGbNiwwbH90qVLcfvttzs+J8OYIAiCIAiiMGF2mmEMTjziIWcQp8utt96KG2+80Xq/bds2HHfccWhubs5jrQiCIAiCIAg/Dh8+jKqqqgHfz5AziEeMGIFQKIQDBw4Inx84cABNTU2O7aPRKKLRqPV+3LhxAICdO3cOygkm8kt7ezuam5uxa9cuVFZSqu73OnS9hxd0vYcXdL2HF21tbRg7dixqa2sHZX9DziCORCKYN28enn32WVx66aUAgGQyiWeffRbXXXed7+913ZRNV1VV0QM1jKisrKTrPYyg6z28oOs9vKDrPbxgdttAM+QMYgC48cYbcdVVV2H+/PlYsGAB/ud//gddXV245ppr8l01giAIgiAIYogxJA3iD3/4wzh06BC+8Y1vYP/+/TjuuOPw1FNPORbaEQRBEARBEIQfQ9IgBoDrrrsukERCJhqN4pvf/KagKybeu9D1Hl7Q9R5e0PUeXtD1Hl4M9vXWjMGKZ0EQBEEQBEEQBciQS8xBEARBEARBELmEDGKCIAiCIAhiWEMGMUEQBEEQBDGsIYOYIAiCIAiCGNYMO4P4pz/9KcaPH4/i4mIsXLgQr732Wr6rRKTJ0qVLccIJJ6CiogINDQ249NJLsXHjRmGb3t5eXHvttairq0N5eTkuv/xyR3bDnTt34qKLLkJpaSkaGhrwla98BfF4fDAPhciAO++8E5qm4YYbbrA+o+v93mLPnj346Ec/irq6OpSUlGDWrFl4/fXXre8Nw8A3vvENjBw5EiUlJVi8eDE2bdoklHHkyBEsWbIElZWVqK6uxic/+Ul0dnYO9qEQPiQSCXz961/HhAkTUFJSgkmTJuE73/kO+PX+dL2HLsuWLcPFF1+MUaNGQdM0PP7448L3ubq2b731Fk499VQUFxejubkZd911V/qVNYYRDz/8sBGJRIz77rvPWL9+vfHpT3/aqK6uNg4cOJDvqhFpcN555xn333+/sW7dOmPNmjXGhRdeaIwdO9bo7Oy0tvnsZz9rNDc3G88++6zx+uuvGyeeeKJx0kknWd/H43Fj5syZxuLFi43Vq1cbf//7340RI0YYt956az4OiQjIa6+9ZowfP96YPXu2cf3111uf0/V+73DkyBFj3LhxxtVXX22sWLHC2Lp1q/H0008bmzdvtra58847jaqqKuPxxx833nzzTeP973+/MWHCBKOnp8fa5vzzzzfmzJljvPrqq8a///1v45hjjjGuvPLKfBwS4cEdd9xh1NXVGU8++aSxbds245FHHjHKy8uNH/3oR9Y2dL2HLn//+9+N2267zXj00UcNAMZjjz0mfJ+La9vW1mY0NjYaS5YsMdatW2f8/ve/N0pKSoyf//znadUGahWYAAAKIklEQVR1WBnECxYsMK699lrrfSKRMEaNGmUsXbo0j7UisuXgwYMGAOPFF180DMMwWltbjaKiIuORRx6xtnnnnXcMAMby5csNwzAfUl3Xjf3791vb3HPPPUZlZaXR19c3uAdABKKjo8OYPHmy8cwzzxinn366ZRDT9X5vccsttxinnHKK6/fJZNJoamoyvv/971uftba2GtFo1Pj9739vGIZhvP322wYAY+XKldY2//jHPwxN04w9e/YMXOWJtLnooouMT3ziE8Jnl112mbFkyRLDMOh6v5eQDeJcXduf/exnRk1NjdCW33LLLcbUqVPTqt+wkUz09/dj1apVWLx4sfWZrutYvHgxli9fnseaEdnS1tYGAKitrQUArFq1CrFYTLjW06ZNw9ixY61rvXz5csyaNUvIbnjeeeehvb0d69evH8TaE0G59tprcdFFFwnXFaDr/V7jL3/5C+bPn48PfvCDaGhowPHHH49f/vKX1vfbtm3D/v37hetdVVWFhQsXCte7uroa8+fPt7ZZvHgxdF3HihUrBu9gCF9OOukkPPvss3j33XcBAG+++SZeeuklXHDBBQDoer+XydW1Xb58OU477TREIhFrm/POOw8bN27E0aNHA9dnyGaqS5eWlhYkEglHeufGxkZs2LAhT7UisiWZTOKGG27AySefjJkzZwIA9u/fj0gkgurqamHbxsZG7N+/39pGdS+w74jC4uGHH8Ybb7yBlStXOr6j6/3eYuvWrbjnnntw44034mtf+xpWrlyJL37xi4hEIrjqqqus66W6nvz1bmhoEL4Ph8Oora2l611gfPWrX0V7ezumTZuGUCiERCKBO+64A0uWLAEAut7vYXJ1bffv348JEyY4ymDf1dTUBKrPsDGIifcm1157LdatW4eXXnop31UhBohdu3bh+uuvxzPPPIPi4uJ8V4cYYJLJJObPn4/vfve7AIDjjz8e69atw7333ourrroqz7Ujcs0f//hHPPjgg3jooYcwY8YMrFmzBjfccANGjRpF15sYVIaNZGLEiBEIhUKOlecHDhxAU1NTnmpFZMN1112HJ598Es8//zzGjBljfd7U1IT+/n60trYK2/PXuqmpSXkvsO+IwmHVqlU4ePAg5s6di3A4jHA4jBdffBE//vGPEQ6H0djYSNf7PcTIkSMxffp04bNjjz0WO3fuBGBfL6+2vKmpCQcPHhS+j8fjOHLkCF3vAuMrX/kKvvrVr+KKK67ArFmz8LGPfQxf+tKXsHTpUgB0vd/L5Ora5qp9HzYGcSQSwbx58/Dss89anyWTSTz77LNYtGhRHmtGpIthGLjuuuvw2GOP4bnnnnNMlcybNw9FRUXCtd64cSN27txpXetFixZh7dq1woP2zDPPoLKy0tEZE/nl7LPPxtq1a7FmzRrr3/z587FkyRLrNV3v9w4nn3yyI4ziu+++i3HjxgEAJkyYgKamJuF6t7e3Y8WKFcL1bm1txapVq6xtnnvuOSSTSSxcuHAQjoIISnd3N3RdNEVCoRCSySQAut7vZXJ1bRctWoRly5YhFotZ2zzzzDOYOnVqYLkEgOEXdi0ajRoPPPCA8fbbbxuf+cxnjOrqamHlOVH4fO5znzOqqqqMF154wdi3b5/1r7u729rms5/9rDF27FjjueeeM15//XVj0aJFxqJFi6zvWRiuc88911izZo3x1FNPGfX19RSGa4jAR5kwDLre7yVee+01IxwOG3fccYexadMm48EHHzRKS0uN3/3ud9Y2d955p1FdXW088cQTxltvvWVccsklylBNxx9/vLFixQrjpZdeMiZPnkxhuAqQq666yhg9erQVdu3RRx81RowYYdx8883WNnS9hy4dHR3G6tWrjdWrVxsAjLvvvttYvXq1sWPHDsMwcnNtW1tbjcbGRuNjH/uYsW7dOuPhhx82SktLKeyaHz/5yU+MsWPHGpFIxFiwYIHx6quv5rtKRJoAUP67//77rW16enqMz3/+80ZNTY1RWlpq/Md//Iexb98+oZzt27cbF1xwgVFSUmKMGDHC+PKXv2zEYrFBPhoiE2SDmK73e4u//vWvxsyZM41oNGpMmzbN+MUvfiF8n0wmja9//etGY2OjEY1GjbPPPtvYuHGjsM3hw4eNK6+80igvLzcqKyuNa665xujo6BjMwyAC0N7eblx//fXG2LFjjeLiYmPixInGbbfdJoTQous9dHn++eeV/fVVV11lGEburu2bb75pnHLKKUY0GjVGjx5t3HnnnWnXVTMMLh0MQRAEQRAEQQwzho2GmCAIgiAIgiBUkEFMEARBEARBDGvIICYIgiAIgiCGNWQQEwRBEARBEMMaMogJgiAIgiCIYQ0ZxARBEARBEMSwhgxigiAIgiAIYlhDBjFBEARBEAQxrCGDmCAIYhhw9dVX49JLL813NQiCIAoSMogJgiAIgiCIYQ0ZxARBEAVMf39/vqtAEATxnocMYoIgiALijDPOwHXXXYcbbrgBI0aMwHnnnYe7774bs2bNQllZGZqbm/H5z38enZ2d1m8eeOABVFdX4+mnn8axxx6L8vJynH/++di3b5/rflauXIn6+np873vfG4zDIgiCKGjIICYIgigwfv3rXyMSieDll1/GvffeC13X8eMf/xjr16/Hr3/9azz33HO4+eabhd90d3fjBz/4AX77299i2bJl2LlzJ2666SZl+c899xzOOecc3HHHHbjlllsG45AIgiAKmnC+K0AQBEGITJ48GXfddZf1furUqdbr8ePH47/+67/w2c9+Fj/72c+sz2OxGO69915MmjQJAHDdddfh29/+tqPsxx57DB//+Mfxq1/9Ch/+8IcH8CgIgiCGDmQQEwRBFBjz5s0T3v/rX//C0qVLsWHDBrS3tyMej6O3txfd3d0oLS0FAJSWllrGMACMHDkSBw8eFMpZsWIFnnzySfzpT3+iiBMEQRAcJJkgCIIoMMrKyqzX27dvx/ve9z7Mnj0bf/7zn7Fq1Sr89Kc/BSAuuCsqKhLK0DQNhmEIn02aNAnTpk3Dfffdh1gsNoBHQBAEMbQgg5ggCKKAWbVqFZLJJH74wx/ixBNPxJQpU7B3796MyhoxYgSee+45bN68GR/60IfIKCYIgkhBBjFBEEQBc8wxxyAWi+EnP/kJtm7dit/+9re49957My6voaEBzz33HDZs2IArr7wS8Xg8h7UlCIIYmpBBTBAEUcDMmTMHd999N773ve9h5syZePDBB7F06dKsymxqasJzzz2HtWvXYsmSJUgkEjmqLUEQxNBEM2SRGUEQBEEQBEEMI8hDTBAEQRAEQQxryCAmCIIgCIIghjVkEBMEQRAEQRDDGjKICYIgCIIgiGENGcQEQRAEQRDEsIYMYoIgCIIgCGJYQwYxQRAEQRAEMawhg5ggCIIgCIIY1pBBTBAEQRAEQQxryCAmCIIgCIIghjVkEBMEQRAEQRDDmv8Ptrpua8KfBIAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.subplots(len(L), len(J), figsize=(8, 9))\n", + "for lIdx in range(len(L)):\n", + " for jIdx in range(len(J)):\n", + " plt.subplot(len(L), len(J), 1 + len(J)*lIdx + jIdx)\n", + " plt.title(\"{0:s},{1:s}\".format(J[jIdx], L[lIdx]))\n", + " plt.plot(T, characteristic[lIdx, jIdx])\n", + " plt.gca().fill_between(T, KB[jIdx][lIdx][K_HIGH], KB[jIdx][lIdx][K_LOW], alpha=.36)\n", + " plt.gca().set(xlim=(0,CUTOFF), xlabel=\"rank\")\n", + " plt.gca().set(ylabel=\"value\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "65e2f85a", + "metadata": {}, + "source": [ + "# Full implementation\n", + "\n", + "This is an object-oriented rendition of the code demo, to allow multiple runs in succession. See `implementation.py`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ocean", + "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": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/implementation/qaoalarge.png b/implementation/qaoalarge.png new file mode 100644 index 00000000..6ee3c8be Binary files /dev/null and b/implementation/qaoalarge.png differ diff --git a/implementation/qaoamatrix.png b/implementation/qaoamatrix.png new file mode 100644 index 00000000..1a102537 Binary files /dev/null and b/implementation/qaoamatrix.png differ diff --git a/implementation/result2_n.gif b/implementation/result2_n.gif new file mode 100644 index 00000000..f0a2c20d Binary files /dev/null and b/implementation/result2_n.gif differ diff --git a/implementation/result_n.gif b/implementation/result_n.gif new file mode 100644 index 00000000..b9349065 Binary files /dev/null and b/implementation/result_n.gif differ diff --git a/implementation/results2_k.gif b/implementation/results2_k.gif new file mode 100644 index 00000000..622e6ce7 Binary files /dev/null and b/implementation/results2_k.gif differ diff --git a/implementation/results_k.gif b/implementation/results_k.gif new file mode 100644 index 00000000..14dae10a Binary files /dev/null and b/implementation/results_k.gif differ diff --git a/presentation.pdf b/presentation.pdf new file mode 100644 index 00000000..b67ddd21 Binary files /dev/null and b/presentation.pdf differ diff --git a/presentation.pptx b/presentation.pptx new file mode 100644 index 00000000..c3b0fd08 Binary files /dev/null and b/presentation.pptx differ diff --git a/qaoa_large.png b/qaoa_large.png new file mode 100644 index 00000000..6ee3c8be Binary files /dev/null and b/qaoa_large.png differ