From 2e9a8253187e292f731e2d44941128f7685ec808 Mon Sep 17 00:00:00 2001 From: Govind Kamtamneni Date: Sat, 10 Feb 2024 09:05:41 -0800 Subject: [PATCH] update equity analyst nb --- .../azure-openai/equity-analyst.ipynb | 274 ++++----- .../azure-openai/function-calling-ea.ipynb | 532 ++++++++++++++++++ .../azure-openai/images/assistants.png | Bin 0 -> 146301 bytes 3 files changed, 680 insertions(+), 126 deletions(-) create mode 100644 sandbox/agents/assistants-api/azure-openai/function-calling-ea.ipynb create mode 100644 sandbox/agents/assistants-api/azure-openai/images/assistants.png diff --git a/sandbox/agents/assistants-api/azure-openai/equity-analyst.ipynb b/sandbox/agents/assistants-api/azure-openai/equity-analyst.ipynb index 7b3bfabe..6164081e 100644 --- a/sandbox/agents/assistants-api/azure-openai/equity-analyst.ipynb +++ b/sandbox/agents/assistants-api/azure-openai/equity-analyst.ipynb @@ -18,45 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: openai in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (1.11.1)\n", - "Requirement already satisfied: matplotlib in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (3.8.2)\n", - "Requirement already satisfied: tenacity in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (8.2.3)\n", - "Requirement already satisfied: python-dotenv in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (1.0.1)\n", - "Requirement already satisfied: anyio<5,>=3.5.0 in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (from openai) (4.2.0)\n", - "Requirement already satisfied: distro<2,>=1.7.0 in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (from openai) (1.9.0)\n", - "Requirement already satisfied: httpx<1,>=0.23.0 in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (from openai) (0.26.0)\n", - 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"Requirement already satisfied: colorama in c:\\users\\gok\\appdata\\local\\anaconda3\\envs\\aoai\\lib\\site-packages (from tqdm>4->openai) (0.4.6)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install openai matplotlib tenacity python-dotenv" ] @@ -144,7 +108,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create the assistant with tools" + "### Create the assistant with tools and files\n", + "\n", + "![How Assistants work](./images/assistants.png)\n", + "\n", + "\n", + "The `create_assistant` function creates an assistant with tools and files. The function takes the following parameters:\n", + "- `name`: The name of the assistant.\n", + "- `instructions`: The system message (or meta prompt) that gives the assistant a persona and context.\n", + "- `tools`: A list of tools that the assistant can use to perform tasks. Currently, these are `code_intrepreter` and `retriever`.\n", + "- `functions`: Custom functions that the assistant can use to perform tasks. Similar to function calling feature.\n", + "- `model`: The name of the model to use for the assistant." ] }, { @@ -153,15 +127,7 @@ "metadata": {}, "outputs": [], "source": [ - "tools_list = [{\"type\": \"code_interpreter\"}]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ + "# Fetch the files under the datasets directory\n", "DATASETS = \"datasets/\"\n", "\n", "def upload_file(client: AzureOpenAI, path: Path) -> FileObject:\n", @@ -172,9 +138,16 @@ "file_ids = [file.id for file in assistant_files]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the assistant with tools and files" + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -182,21 +155,42 @@ " name=\"Equity Analyst\",\n", " instructions=(\"You are an equity analyst that performs analysis on the given datasets. \"\n", " \"Use the provided file only.\"),\n", - " tools=tools_list,\n", - " model=api_deployment_name,\n", + " tools=[{\"type\": \"code_interpreter\"}],\n", " file_ids=file_ids,\n", - ")\n", - "\n", + " model=api_deployment_name\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a thread, which represents a conversation. It is recommended to create one thread per user. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ "thread = client.beta.threads.create()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper function to format the response from the assistant." + ] + }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "def format_messages(messages: Iterable[MessageFile]) -> None:\n", + "def format_response(messages: Iterable[MessageFile]) -> None:\n", "\n", " message_list = []\n", "\n", @@ -226,6 +220,15 @@ " print(f\"Exception: {e}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a thread run\n", + "\n", + "![Run lifecycle](https://cdn.openai.com/API/docs/images/diagram-1.png)" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -241,8 +244,9 @@ "@retry(stop=stop_after_attempt(15), \n", " wait=wait_exponential(multiplier=1.5, min=4, max=20),\n", " retry=retry_if_exception_type(NotCompletedException))\n", - "def check_run_status(thread_id, run_id):\n", + "def get_run_lifecycle_status(thread_id, run_id):\n", " run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)\n", + " print(f\"Run status: {run.status}\")\n", " if run.status in [\"completed\", \"failed\", \"expired\", \"cancelled\"]:\n", " return run\n", " elif run.status == \"requires_action\":\n", @@ -253,7 +257,7 @@ " # This will cause a retry for statuses not explicitly handled above\n", " raise NotCompletedException(\"Run not completed yet\")\n", "\n", - "def process_message(content: str):\n", + "def analyst_assistant(content: str):\n", " client.beta.threads.messages.create(thread_id=thread.id, role=\"user\", content=content)\n", "\n", " run = client.beta.threads.runs.create(\n", @@ -263,9 +267,9 @@ " )\n", "\n", " try:\n", - " run = check_run_status(thread.id, run.id)\n", + " run = get_run_lifecycle_status(thread.id, run.id)\n", " messages = client.beta.threads.messages.list(thread_id=thread.id)\n", - " format_messages(messages)\n", + " format_response(messages)\n", " except RetryError:\n", " print(\"Operation failed or timed out after maximum retries.\")\n", " except NotCompletedException:\n", @@ -276,7 +280,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Have the model perform a DCF valuation" + "### Have the assistant perform a DCF valuation" ] }, { @@ -288,42 +292,42 @@ "name": "stdout", "output_type": "stream", "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", "user:\n", - "List out the values in CSV and visualize it\n", + "Visualize the data and provide insights on the trends.\n", "\n", "assistant:\n", - "First, I will load the CSV file that you uploaded to check its contents. Then I will list out the values for you to see, and finally, I will visualize the data using appropriate visualizations based on the type of the data present in the CSV file.\n", + "First, let's load the data from the uploaded file and take a quick look at its structure and contents to understand what kind of data we're working with. Based on this, we can decide how best to visualize it and analyze the trends. \n", "\n", - "I will start by loading the CSV file and displaying the first few rows to understand its structure and contents. Let's begin with that.\n", + "I'll start by loading the data using Python and displaying the first few rows.\n", "\n", "assistant:\n", - "The CSV file contains financial projections, including:\n", + "The dataset appears to contain financial projections for a company, including the following columns:\n", "\n", "- `Year`: The fiscal year for the projection.\n", - "- `Projected Revenue`: The expected revenue for the company in that year.\n", - "- `Projected EBIT`: Earnings Before Interest and Taxes projected for that year.\n", - "- `Projected Net Income`: The company's projected net income for that year.\n", - "- `Projected Free Cash Flow`: The projected free cash flow for the company in that year.\n", - "- `Discount Factor`: The factor used to discount future cash flows to their present value.\n", - "- `Present Value of FCF`: The present value of the projected free cash flow, after applying the discount factor.\n", + "- `Projected Revenue`: Expected revenue for the year.\n", + "- `Projected EBIT` (Earnings Before Interest and Taxes): Expected operating income for the year.\n", + "- `Projected Net Income`: Expected net income for the year.\n", + "- `Projected Free Cash Flow`: Expected free cash flow for the year.\n", + "- `Discount Factor`: Used to calculate the present value of future cash flows.\n", + "- `Present Value of FCF` (Free Cash Flow): The discounted current worth of the projected free cash flows.\n", "\n", - "Now, I will create visualizations to better illustrate this data. The visualizations might include line plots for time series data and perhaps bar plots for comparative static data. Let's create some plots for Revenue, EBIT, Net Income, and Free Cash Flow as examples.\n", + "Now that we understand the structure of the data, we can create visualizations to help identify trends and insights. We'll plot the growth of Projected Revenue, Projected EBIT, Projected Net Income, and Projected Free Cash Flow over the years. We can also look at how the Present Value of FCF changes relative to the undiscounted Free Cash Flow.\n", + "\n", + "Let's start by visualizing the financial projections over the years.\n", "\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant:\n", + "The plot above visualizes the projected financial metrics from the dataset. Here are the key trends and insights we can observe:\n", + "\n", + "- All metrics (Projected Revenue, Projected EBIT, Projected Net Income, and Projected Free Cash Flow) are on an upward trend, implying the company is expecting growth in its financial performance over the years.\n", + "- The Projected Revenue appears to be the highest among the metrics, with a steady climb each year, which indicates increasing sales or expansion of the company's business operations.\n", + "- Projected EBIT and Projected Net Income, while lower than Projected Revenue, also show a consistent increase, suggesting improving profitability.\n", + "- Projected Free Cash Flow follows a similar upward pattern, indicating the company’s ability to generate cash after accounting for capital expenditures is also expected to grow.\n", + "\n", + "Next, let’s visualize the Present Value of Free Cash Flow compared to the Projected Free Cash Flow to see the effect of discounting cash flows to their present value.\n", + "\n" + ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -356,20 +366,21 @@ "output_type": "stream", "text": [ "assistant:\n", - "Here are the visualizations for the given projections:\n", + "The chart above compares the Projected Free Cash Flow (FCF) and the Present Value of FCF over time. The following insights can be gleaned from this comparison:\n", "\n", - "1. **Projected Revenue Over Years**: This line plot shows the trend for the company's projected revenue over the years.\n", - "2. **Projected EBIT Over Years**: This line plot illustrates the trend for the company's projected Earnings Before Interest and Taxes (EBIT) over the years.\n", - "3. **Projected Net Income Over Years**: This visualization presents the trend for the company's projected net income over the years.\n", - "4. **Projected Free Cash Flow Over Years**: This plot depicts the trend of the company's projected free cash flow over the years.\n", + "- As we would expect, the Present Value of FCF is lower than the nominal Projected FCF for each year, reflecting the application of the discount factor, which accounts for the time value of money. This is a common financial analysis practice to calculate the current value of future cash flows.\n", + "- The gap between the Projected FCF and its Present Value seems to be fairly consistent, suggesting a stable discount rate is used across the projection period. However, it naturally increases with time as the cash flows are discounted back to their present values from increasingly distant future years.\n", + "- The declining Present Value of FCF, despite the increase in nominal terms, suggests that the impact of discounting becomes more significant with a further out future cash flow due to higher cumulative discounting.\n", "\n", - "Each of these projections increases over time, indicating that the company expects growth in these financial metrics year after year.\n", + "Overall, the data displays an optimistic view of the company's financial future, with growth expected in all major financial metrics. The consistency in growth rates and discount rates provides a predictable model for the company's performance. However, these projections would also need to be stress-tested against various economic scenarios to evaluate the robustness of the forecasts. Additionally, comparing these projections to industry benchmarks, past company performance, and considering macroeconomic factors would give a more comprehensive insight into the company's prospects.\n", + "\n", + "If there's a specific analysis or further exploration you would like to pursue with this dataset, please let me know!\n", "\n" ] } ], "source": [ - "process_message(\"List out the values in CSV and visualize it\")" + "analyst_assistant(\"Visualize the data and provide insights on the trends.\")" ] }, { @@ -381,63 +392,72 @@ "name": "stdout", "output_type": "stream", "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", "user:\n", - "Create and execute python code to perform a discounted cash flow valuation using the given data in csv. Make generic assumptions\n", + "Use python code to perform a discounted cash flow valuation using the given dataset. Make generic assumptions\n", "\n", "assistant:\n", - "To perform a discounted cash flow (DCF) valuation using the provided data, we need to follow these steps:\n", - "\n", - "1. **Estimate the Free Cash Flows (FCFs)**: We will use the 'Projected Free Cash Flow' column from the data.\n", - "\n", - "2. **Calculate the Present Value of Future Cash Flows**: We will discount the FCFs using the 'Discount Factor' to get the present value of each year's FCF.\n", + "To perform a discounted cash flow (DCF) valuation, we calculate the present value (PV) of projected free cash flows (FCF) for each year and then take the sum of these values. This is the value of the cash flows the company is expected to generate in the future, brought back to today's dollars.\n", "\n", - "3. **Calculate Terminal Value**: We need to estimate the value of the company's cash flows beyond the projection period. A common approach is to apply a perpetuity growth model using the last projected FCF and a perpetuity growth rate. I will assume a generic perpetual growth rate of 2.5%.\n", + "The formula for the present value of an individual year's FCF is:\n", + "\\[ PV = \\frac{FCF}{(1 + r)^n} \\]\n", + "where:\n", + "- \\( FCF \\) is the projected free cash flow for the year,\n", + "- \\( r \\) is the discount rate,\n", + "- \\( n \\) is the number of years from now.\n", "\n", - "4. **Calculate the Present Value of Terminal Value**: We need to discount the terminal value back to its present value.\n", + "Since the dataset already includes a \"Discount Factor\", we don't need to explicitly apply the discount rate \\( r \\). We can simply multiply the \"Projected Free Cash Flow\" by the \"Discount Factor\" to get the \"Present Value of FCF\", which actually has been done already in the dataset.\n", "\n", - "5. **Calculate Equity Value**: We sum the present values of the projected FCFs and the present value of the terminal value to get the total present value of the company. If there is debt or other non-equity claims on the company's cash flows, they would need to be subtracted here, but since we don't have that data, we'll assume it's an all-equity firm.\n", - "\n", - "Let's proceed with the calculation, starting by discounting the projected FCFs and then estimating and discounting the terminal value. Finally, we'll sum everything for the equity valuation. To simplify the DCF analysis, any additional assumptions such as company debt, preferred stock, or the number of shares outstanding will not be considered, as this basic model will focus solely on the provided cash flows and the standard perpetuity growth method for terminal value.\n", + "To find the total DCF valuation of the company, we would sum up these present values. Additionally, we would typically consider a terminal value that accounts for all cash flows beyond the last projected year, assuming a perpetual growth rate. To keep the analysis generic as requested, we will pick a reasonable perpetual growth rate and discount rate for the terminal value calculation. Let's proceed with these calculations using Python.\n", "\n", "assistant:\n", - "Here are the results from the discounted cash flow (DCF) valuation:\n", + "Based on the calculations using the provided dataset and generic assumptions:\n", "\n", - "- **Total Present Value of Projected Free Cash Flows**: \\$200,323,946,802.20\n", - "- **Terminal Value (at the end of the projection period)**: \\$80,590,219,910.57\n", - "- **Present Value of Terminal Value**: \\$57,459,712,997.79\n", - "- **Total Present Value of the Company (Equity Value)**: \\$257,783,659,799.99\n", + "- The total present value of the projected free cash flows (FCF) from the given data is approximately $200.32 billion.\n", + "- The terminal value (TV), calculated at the end of the projection period using a perpetual growth rate of 2% and a discount rate of 5%, amounts to approximately $1,839.15 billion.\n", + "- The present value of the terminal value is about $1,441.02 billion.\n", + "- Finally, the total discounted cash flow (DCF) valuation of the company, which combines the present value of the projected FCFs and the present value of the terminal value, is approximately $1,641.35 billion.\n", "\n", - "This valuation assumes a perpetual growth rate of 2.5% for cash flows beyond the projection period and uses the provided discount factors to discount both the cash flows and the terminal value to their present value. The last value is essentially the DCF valuation of the company based on the assumptions and projected cash flows in the CSV file.\n", + "It's essential to note that the DCF valuation is sensitive to the assumptions made about growth rates and discount rates. Different assumptions could lead to significantly different valuations. These assumptions should be tailored to match the company's sector, risk profile, and prevailing economic conditions for a more accurate valuation.\n", "\n" ] } ], "source": [ - "process_message(\"Create and execute python code to perform a discounted cash flow valuation using the given data in csv. Make generic assumptions\")" + "analyst_assistant(\"Use python code to perform a discounted cash flow valuation using the given dataset. Make generic assumptions\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", "user:\n", - "Visualize this information\n", + "Visualize this information. Seems incorrect\n", "\n", "assistant:\n", - "Certainly! A good way to visualize this discounted cash flow (DCF) valuation is by creating a bar chart that represents the present value of projected free cash flows, the present value of the terminal value, and the total present value (equity value). \n", - "\n", - "Let's create a bar chart where each bar represents one of these components:\n", + "Apologies for any confusion in the previous chart. I will rectify the visualization by distinctly presenting the present value of the free cash flows year by year and emhpasize the present value of terminal value, so the distinction and proportions are clear. Let's create a more accurate chart.\n", "\n" ] }, { "data": { - "image/png": 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NHToUl19+OQzDwMGDB/Hxxx9Hxm7U1taivLwcADBw4MBIpqqqNhuAeXojwWq14oorrsAHH3yACRMm4IMPPsCVV14Z6dn64x//CFVVUVRUhAkTJkR9jafbuXMnDh48GNnHysvLcfDgwWbLqKoaucQTreFit9sxefJkrFixAr169cL27dtx0003YfHixc1yKioqgqZpGDduHDIyMjB48OA2jZGx2+3o1asXVFVFSkoK+vXrFxlfEL4cVFpaildeeQVmszly2au+vh7p6elnvT6KvS5XDBw/fhz5+fnYs2dPZCTsRx99hEOHDiE/Px9XXnklSkpK0K9fP+zZswdAqEXYo0cPXHHFFXj55Zexbt26ZgOZSE5RFHz729+ODGQzmUzNWo0WiwWKosBsNkdGPgOhgYNNR4crigKr1Ypx48ZhxowZAEIfNGlpadi+fTs8Hk/ksZqammaj6E0mE1JSUjBnzhy4XK7I6Gqr1QpFUSKXBsItmWgmTJiA++67r9ljVVVVUFU10vVutVoxZswYzJkzJ7ItLpcL//3vfzFkyBB897vfjVw7Pn3wWO/evVFdXR3p0g8vt2/fPqiqis2bN6OoqAgPPvggrFYr5s2bBwDIzMzEI488gsOHD2Pt2rX45S9/ib/85S8A0CxnUW/WkCFDcP/99wsvE1gslma5+P1+WK1WjBo1Ck8++SRqa2sj78eaNWvg8/nQq1evZut48cUXkZycjB//+Mdwu92YO3du5G9NR7iHf2qaFikCATT7/6YcDgfuu+8+5OXlRQoAVVXhdrtbvC+DBg1qlr/T6URGRkaz7H71q1/h97//PWw2G2bPno3CwkIAwK233hoZvCq7W+H0D19FUTB16lS88cYb2LJlCw4dOoT7778fxcXFeOWVV/CnP/0J+fn5eO6559DY2Bj1+cI/w8Wt1WrF2LFjmxVspw+QHTBgAF5++WXU1NREru8bhoHdu3cjKysLLpcL06dPx6OPPoqBAwciJycH+fn5wpzCYyraOvBQUZRmuZyeYfj4vPHGG5GUlATDMKBpWqsH/lL8dY57eM5C3759MWbMmMiOuXHjRuzatQtXX3019u/fj3Xr1mHEiBHNRgXn5OTgqquugsfjwYEDByInCGo9s9kMq9UKq9UqvMwSvl66YcMG1NTU4OOPP8bw4cObtUQuuOACbNmyJTKC/K233oLH48GECROwZs0alJWVYevWrXjggQfQ0NAAVVVRXV2N1NRU5OXlYf/+/UhNTcXhw4fx6aefIjk5GZmZmVi3bh1qamqwbNmyqLdKtdbEiROxa9euyHX49957D5WVlRg7diyKioqg6zqcTieWLFmC0tLSZjnk5eXhsssuw+OPP44DBw6grq4OX331FX73u9/h8OHD8Hg8SEpKgqZp2LBhA44ePYqamhp8+umnWLhwIbKysjBjxgw4HI6oHyxtoSgKJk6ciOXLl6OiogIHDx7Enj17MGLECPTo0QNutxvbt29Hv379MGLECLz77rvo169fi9sZPR4PXC4XdF3HypUrUV1djdraWmHhNWTIEGzZsgUnTpzAvn37sHPnTuH2WSwWWK1WWCwW4Qf16NGjUVJSgkAggKSkJHz88cc4cuQIli1bhrfffhtZWVm4+OKLYbfboes6Ro4cia1btyIlJQUNDQ145513hNsKhIqX+vp6uN3uFstlZmZi5MiRmD9/PsaOHYv09PRIoet0OlFSUoJNmzahrq6u2b6XkZGBgwcPoqamBps2bcLx48cBhIrSnTt3wmQywWaz4d1330V1dXWzdQ4aNAjDhg3D//t//w+HDx9GXV0dtmzZghdffBHf/e53YbFY0LdvX1itVrzyyiu4+OKLoWkaxowZg+LiYgSDQSQlJWHp0qU4evSo8HV3lKysrGbHZ0lJCT777DNeju1CulzPwOk7V1FREQ4fPoz33ntPet94ZWUlnnnmGUydOhUDBw6M1eZ2C71792527bKp/Pz8SDdgSkoK7rvvPvznP//BwoULkZOTg7vuuqvZCf7KK69EbW0tHn30UQChE2NaWhqmTp2KY8eORQYR3nzzzUhLS8Oll16KV199FS6XC/fffz9efPFFrFy5Eg6HAzfeeCNsNht++MMf4sUXX8QXX3yBwYMHY+jQoS32gfDo79NpmhbpzgaASZMm4dixY5GW+dChQ5Gfn4/evXvjxhtvxN///nfouo7+/fu3uHVM0zTcfvvtWLRoERYsWABd15GUlIQ777wT48ePR0VFBXbs2IHf//73GDlyJObOnYtly5bhBz/4ATZs2ICHHnoIqqriqquuQmFhIXr27BlpWYW72k9v2aWmprZoxQOhllthYWGkm7uqqgqPPPIIVFXFLbfcgoEDB0JVVYwdOxbHjh2Dy+XCgAED4HQ6MWXKlBb5ffOb38QzzzyDP/7xj5gyZQpuuOEGvP3225g2bVqz9ffs2RPJyckYMWIEtm/fjkceeQQ9evTARRdd1GKsQ/g1RbsfP7z94fds8ODB+M53vhPJtbCwEIWFhejRowfWr1/fLLt+/frhjjvuwAsvvICHHnoIZrMZs2bNgtVqRV5eXuT2UlVVI+sfN24cVq5ciTfffBPf/va3W7SCr776avzxj3/E7NmzAYQaJdOnT8cjjzyC3r1747bbbsO///1vbN26NfIhPXXqVGzatAkPPfQQBg8ejGnTpsHhcGDIkCE4ceIEHn/8cQDA8OHDW9xyazabcd9992HhwoV44oknoOs6kpOTcdddd2HcuHGRnrjLLrsscglDURQMHToUN910E5566inouo5+/fqhb9++qKqqQr9+/aQfzrm5uc1uvU1LS0NeXl6L96lHjx6R99LpdKKgoAA2my1yfC5btgxOpxPf+ta3WAx0IYohK5c7IV3XcezYMTzxxBP47W9/iy+++AJbtmzB3LlzsXfvXuTn50e6nT/44APce++98Hq9ePzxxzFz5kyMGTMm0q3JHfXMwqPewxOenP43XdcjExE1fUzX9UgPQkNDA2699Vb85S9/Qc+ePSPPCaBZL0P48fDzha/BB4PByHKnb0/4sfD1VU3Tmi3fdJvC64v2+qJth2j7wiOvRftP0+XC/77pdoYHtgKIrLvpaw9vY9PXGW07AbTIWvS6RK+p6XYCoUFj0Sb+Of3fh19L+Np2tG1u+t6Fu9+bPq/oNZ3pfWmaf3hAcdPsTn+86b5y+msIryO87U33vdO3p+lEQ02fP7zO8Lqa5tH0/Qnn1fT9PP39aO2+1PT9DwaDzfZHUU6irMP/5vRjJHxMqaraLKemx3zT19d03edigiQ6t7pcMbBx40Z8+umnKC4uxpAhQ3Dttdfigw8+iNwudNNNN+HLL7/E2rVrUVZWhhEjRmD06NF44YUXItdxJ06ciPHjx3NHjQG/34+//OUvKCoqiow4JyKizqXLFQPhSjTs9BZJuDXZ9GWFK9imv7NnIDbCtyWF7yVn5kREnU+XKgZC87C3vLWqqwl321FL4VqB8UTHfUeO+Yjx2JLrDvuOogAmU9vm0elSAwj9/iBqaz3x3ox2U1UFut61d7pzhScsOe47csxHjMeWXHfYdxQFSEtzQtPOvhjocrcWdgfsKZfh5RsZZiPHfMRC2TAfiq5L9Qx0F1EmkaOTuno33bnGfOSYjxizkUv0eNgzEAeqyupchAM75ZiNHPMRY8+AXKKfl9kzEAdd/brUucTWixzzkWM+YsxGLtHPy92iGAhNmHH2/05R2lcNNp145WxaJKoKhL9lN3zLo2y+9EQS6hngiUsktJ8xGxHmIxYaLQ8wn+ianpcTUbcoBirrDHywxoPgWRYE5/U04YLhLb/hbtOmDdizZxcsFisyMjJwwQVTWnyHPBCarW3nzu0YPnykcB2NjY04frwUffoURgqGpoXLzp3bUV9fjwkTJsEwdCxa9B58Ph9UVUXv3n0waNAQrF27BtXVVUhJScGkSRdixYrPUF9fD03TkJ/fA+PHd6fvDDcS/tqdDIskOeYjxmzkEn0sV7coBgIBA8cr9bMuBnLSov+DoqKDGDBgIAYNGoxXX/03MjIyceDAfuTm5iEtLQ1bt36FpKRkTJp0ASoqKlBbW4NVq1YgEAjgwgunwe1uxJdfroPLlQpd1/H55yvx/e/fic2bN8Dr9eLCC6egsdGNL79ci7q6OuTn9wAQGsCyb98efPvbt8LpdELTNLz33tvIzc3DpZfOxJYtm1BSUoy9e/fguutuQGpqWpu/haxzY+tOhC1fOeYjw2xkQj2S8d6K+OkWxUBHMwwDu3btjHwLmd1ux7ZtX2HatIvw/PPP4aabbsbKlcuwZctmbNu2BVVVFbDZ7EhOTsbixe+jvr4OV199LY4cOQwAOO+8gfjqq01wu93Izs7GO+8sRDAYxGWXXYHNmzc0q9g9Hi+2bg19q19OTi5KSopxxRVXweFwYNKkCwEAixa9i23bQgVJTk4O+vaN/l3xXVcCH5FnwNadHPORYTYkxgvVUSiKgtzcPPTr1x/f+c4tSEtLR1paGiwWK3Q9CJcrFenpGaivrwMQ+kZEj8cDs9mM4cNHwuNxIzU1FUOGDIPD4YTJZEZ1ddXJ75QHxo4dB6/Xg7S0dGRmZjfr4jeZNOTm5iIvLx8ulwsmkxkNDfUwDAP19fWR7yXPyQkvkxqnlM6l7nLJg4ioa+gWPQMmTUGGSz3raz7JjugfOiaTCbm5uejZM/TVrD5fFUwmMywWC3r06IWlSz/E4cMluOSSy7Bnz26MGTMO27Z9BU3T4HKloX//87BkyYeoqqrE+edPRElJMSZPnoqtW7egpqYaDocDffoU4pNPlqC09CgGDRocWbfd7kBBQW84nUkwDAMXXXQJ3nnnLfTr1x8HDuzH9OkzYLPZ0KtXAdLS0tucWWeW6N11MuwGl2M+MsyGxLrUdxN4vf6o0xGHvjbz7HdzVQW0KHcT1NXVwWKxwGq1Agh9CVJdXS1crlQEAgGUl5fB4XDCMHS89NILuPPOeaiurkIwGERmZhYMwzi5jANOZxLKy8uQlpaG+vp6+Hw+ZGdnIxjUUV5eBpvNBovFCqfTCcMwUF1dBZcrtdlXAtfX16G2thYpKS4kJSWhuroaKSkp3XK8AKdMlesOU6aeS8xHjMeWXHf5boLQdMRn3+nfLYqBeDAMA1u2bILP58X48RPPajS/pikIBrtM7DEVvrWQJ/ToNE1F8GxHyiYQ5iOmqqFbC7vQKT+musN5mcVAF9MdKtBziZcJxNjylWM+Yl3puIrH+VFV1S5/Xm5PMdAtxgx0Nara9SvQc4WTDsnxmrgc8xEL9152hWOr3m3g042+mJ4n++SZMea8xP1I7BavPGgYcAcDZ30KMCsqbFGuu0c7WNo7qU/4ORUl1HJp+nvT9YXX4/f7T343tfms1xUMBuH1emC3O9q1zfGZyIiTDsl0hRN5PDEfsa6Ujc8P7DscQCCGMwJazCqLga7usKcB/7dvK/xnubNPT8/GbT37t3j8889XYs+ePXA6HbDZ7Jg580pYLKdmKjQMo9kH5em/R6PrQWzcuAHjxo2Hpmn4/PPVyMrKQf/+A+B2N2LJkg9x1VXXRKY23rBhPUwmDWPHnt/iucLrE1X669atQXFxEa6//pvQNA1vvfUGvF4PzGYL8vN7YOTI0Vi27BM0NjZAURTMmHEpvvjic1RVVcJqtSE7OxtTpkyP26yGvIwixpavHPMR43TEct1mEtc26hbFQMAwUO73InCWHyD1gUDUx0+cOIGxY8djyJChePnlF7Fz5w5s2bIR5503EHa7Ezt3boPNZsPFF8/EJ58sgdfrRf/+A5CdnY3Vq1dCUVRccsllWLbsU1gsFtTUVGPMmHF45ZUXkZmZhX79+iE3Nx8rVnyGfv36Y/v2bQgGg3jnnbfg8bgxePBQeL0e+Hwq3nnnv7jqqq/hs88+wYgRI7Fp00ZUVVWgV6/emDz5QiiKiqqqSixd+iGCQR2jR4/BqlUrInc1hF7PcVx//Rykp2dAURS8885/kZfXA+PGXYni4iKUlZXh+PFjuOyyy5GX1yPu3xzIQkCM2cgxHzFmI5fo8XDSoSgMQ8fatZ/jww8XIRgMID09HSdOnMDIkWOwePF76NOnEMePH8fmzRtw4MA+DB48BFlZ2Vi06D1kZWVD0zSsXr0SBw/uxwUXXAhNM8Hv96OwsD8KCnoDAHr1KkBjYyMqKyuwefNGjB9/PrKyslFQ0BvLln16ctSvjqKiIui6jqNHD+PAgX3YunUzBgw4DytXLkN9fQMMw8Dq1SvRv/95uPjiS7F8+afo3/88jB8/MfJ9Cl6vFytWfIalSz/C9u1bceTIYQwaNBgmkxmFhf0xcOAg+P1+rFq1AkuXfoQtWzbFL3wAnHSI6FzgcSXDngFqQVFUjBgxCoMHD4Hd7kB9fR2Sk5OhaRqCwSDsdjvOP38ievUqQM+eBdi5czs2bvwycp0+MzML6ekZKC4ugsPhgMViOdm1f+o6vKZpGDVqDJYu/QgWiwUNDQ0nv/RoBAIBP0JdeaEuT8Mw4PN54ff7YTKZYLc7cPnls2Cz2QAAPp8XdrsdVqsVgUAw8m2M4e52i8WM0aPHIi0tHRaLBRs3fonjx4/D5UpFTU0NqqurYDKZMHLkaOTm5sFsPvtxChQbiX7COhPmQ9Q23aIYsKsmjElJP+vLBL3tzqiP22x2pKamIiXFBSBUHDgcTpjNZowdOx579uyCz+dDVlY2Vq9egaSkZKSkuDBs2Ajs2rUDqqpi8uSpcDqdUBQFNpsNdrsdgUAABw8ewIABA6AoCoYPH4kPP1yEb37zJthsdvj9flRUVMAwgIaGBmRmZiEtLQMffPAeampq0KtXb+zfvw979uyGpoUKFgAYO3Y8Pv30Y9hsNowbd37kwz0sKSn55JTK6TAMAzNnXolFi97F7t07UVFRjqlTp8PpdEaWib8E76+TSPSuzDNhPjIMRybR951uMc+AYRht2s0VRB8x7/N5oapa5ANV13X4fF5YrTYYhoHGxgaYTGZYrVb4fF74fD44HE6oqorGxkYoigK73Q6PxwObzQafzweTyXTypwabzYpgMNTid7vdsNlsUBQFjY2NMJlM0PUgFEWNzDDo9XphMmmwWCwIBnV4PG7Y7Q5omhZp/Xs8Huh6EA6HE36/L7L9hmHA6/XAYrE2m9UwEAjA43HDYrHCYrHA6/XCYrFElokXTjokx0l15JiPWFeadKiiRscLixtjejfBsEIzZk2yxm6F50DCzzOgKEqHXg2zWJrvEKqqwmazR9aVlJQc+ZvVaoPVaov87nSe6m2w2+0nl7E2+z38HQqKosDhcET9t0017bZXVa1FN364+Ii2/aGeCXuL5c1mc7PnCV9yiLeucKKKJ+Yjx3zEDIO37cokejYcQBgHapTvQ6CQeN/J0NkxGznmI8ZjSy7Ro+kWPQNdDbvAxdiyk2M+csxHjNnIJXo8MSsGAoEAtmzZgp49eyInJyfy+N69e3Hw4MHI72PHjsWuXbvQ0NAAAOjfvz8KCwtjtZkxoapAMIbXwroSTkcsx0l15JiPGCcdkkv0noGYXCbQdR0LFy7EU089hR07djT7m9PpRHZ2NlwuFxYvXoyGhga88cYbSElJQXZ2drNr6t2FzvFNEgYLAQlmI8d8xELZMB+RRN91YlIMhKa8nYHJkye3+Ft+fj5GjhyJmpoaTJkyBdnZ2VBVFXV1daitrUVycnK3O8ATvQI9MwYkwn1HjtfEZZiNTKLvOjG5TKAoCjIyMoQHqsfjwapVq/DjH//45GQ8o+BwOHDw4EFs2rQJ8+bNiwx+0TQ1Mjd/eCIfIFTVnXrs9J+tWebUfPgdsYxsnaHbPvROt11NlwnXXx23Xa17jwDl5Lc66p0mi1jnJVtn6GtW9bi+R50li2jLhG6fUzrddnWG41vT1JPjlYxOtV3R3iNNC10uPLVPNv/ZfH9t+zKn7xdNJ2uLdxZteY/aMzg9bgMIm76I3bt3IysrCy6XC7quY/bs2UhNTcXAgQPx+9//HsFgMPJd06fuIY7WW2AIfrZmGeMcLCNaVo8yiLAzbNe5zKt161QUA7oePkC7ShatWaYr7DtnWrajljmX26U3+8DrPNsV/+PbMIInf3au7Yq2TGgellPbevrPaI+1ZZmmy+qGIdh3Out5p+U6DSPav2+dmBQDwWAQu3fvxpEjRxAMBtG3b18cPHgQmZmZGDZsGHbt2oV+/fpBURR4PB488cQTmDp1KoqLizFs2LBms+lR99e04qXmFIUD5GSYj0y41ch8oknwqwSxKQYMw0BNTQ3Gjx8PRVHQ0NCA3r17RybZOf/885GUlAQAcDgcuOuuu7Br1y6MHDkSQ4cOPXmAdx/8nBNr3iNAp2ORJMd8ZJiNTKLvOjEpBkwmEyZNmiT8e9NbBxVFQXZ2NrKzs2OxaXHBWwvFFIW3Fsqw5SvHfMRCPW4A84mum7U5zxr73+OAtxaKsQiQYz5yzEeM2cglejycjjgOOB2xWKhngPmIMBs55iMWyob5iCT6rsOegTjgdMQy/DIVGbbu5JiPGLORS/R42DMQB4legZ4JW3dizEaO+cgwG5lE33VYDFCnwxaMGLORYz4yzEYm0dNhMUCdUIKX6ETnBI8rmURPh8UAUReS6F2ZZ8J8iNqGxQB1QoneYSfGXnA55iPDcGQSfd9hMRAHbL3I8NZCGWYjx3zEeGuhXKLvOry1MA446ZAYB4DJMR855iNmGLxtVybRs2HPQBxw0iExTjokx2zkmI8Yjy25RI+GPQNxwEmHxNiyk2M+csxHjNnIJXo87BmIA5WpC7H1Isds5JiPGMcMyCX6rsOegThI9ApUjtc1Zdi6k2M+YsxGLtHjYRuVOqEEL9ElEr31cibMR4bhyCT6vsNigKgLSfTWy5kwH6K2YTFAnRDP6EQdj8eVTKIXkiwG4iDRu6POhIPAxJiNHPMR4wBCuUTfdTiAMA446ZBYqDpP8BJdgoPA5JiPGLORS/R42DMQB7y1UIy3FsoxGznmI8aeAblE33XYMxAH7BkQY+tFjhNWyTEfMU5HLJfo2bCNGgecjliMPQNy3HfkmI8Yjy25RI+GPQNxwNavDFsvMtx35JiPGLORS/R42DNAnVCCl+gSid56ORO2fGWYjUyi7zosBqgTSvASXSLRWy9nwtavDLORSfR0WAwQEVHCS/COARYD1Bkl+mEpxm5wOeYjw2xIjMVAHLAn80wYkAi7weWYjwyzkUn0XYfFQBxw0iEZ3v4kw2zkmI8YJx2SS/Rdh7cWxgEnHRJjy06Ok+rIcf8R46RDcomeDduoccCJUcQ4MYoc9x057jtiPLbkEj2amPUMGIaBQCAAVVWhaVrk8UAgAL/fDyC0s1qtVgCAz+eDqqowmUzdbgdm606GrRcZtnzlmI8Ys5FL9HhiUgwYhoHdu3fjmWeewZw5czBhwoTI3z777DMsW7YM+fn5SElJwfXXX481a9Zgw4YN0HUdV199NQYNGtStCgJVBYLBeG9F56UoCk9cAqHjgNmIMB+x0HEFMJ/outFHTJvErBg4cuQIevXqhcbGxmZ/q6+vx8SJEzFjxgxYrVZ4PB58+OGHeOCBB1BWVoZXX30VDzzwQLcqBvg5J8dCQIzZyDEfMWYjl+jxxKQYUBQFM2bMQElJSYu/mc1mrFixAvv27YOqqpg1axYsFguSkpKgqiqqq6vh8/lgs9mgKAo0TYWuG1DVlq1HRVEifxMvE3rTmy5rGEbk8dYuc3brbP43TVMB6KctExpYePrzxGq7ZHm1ZbtCP89+u8LXNYNB/Yzr7Njtiv58py8Tz31H18X7Tlu2q63v0dnkFct9B0Akn9OziP/7eK7fozPv0yZT6NzZ/HjrnOcdTVOgKE3X3fxn9Gzbt4yqKlBVJa7H99ksE2272jOmKGbFQDSGYWDGjBmYNm0a7HY7Hn30UezatavZ35v+W8MwEAyGToTBYLQyzmj2t+jLRF+2o5ZpzXYpii4cN3Cutr29ebVlu6JfCpGvM3QCME6+17HcrrY/X0ct05r3SNcNyZiT2LxHTXWmfQcI7TuhfDrb+9ie52vrMjhtGR2GEb2HoLOdd4K6cXJbT/6L0342e7YOW+bc7jttPwe3fv9qT+9PzC4T6Loe+S8YDKKiogJWqxWbN29GVlYW+vbtC5/Ph7y8PAQCAVRWVuLYsWPIzs6GxWKJxWbGTKJ3R8lxAKEMu3rlmI9Yl8omDpvaleI5F2JSDPh8PvznP//Bnj17UFJSgmAwCJ/Ph9zcXPTu3Rv//e9/oaoqhg4dihEjRsAwDLz00ktQVRU33nhjtxovAHCgyplwAKEYB8jJMR8xDiCUS/TzsmLE4Kwb6vI91Y/V9H7X8Ilf13Vomtbs9/By4WW9Xj9qaz3nenPPOX7YyUW7/kYh4euGFB3zEQt/2HWFY6uiRscLixsRiOFdV0P7mnHVBdbYrfAcUBQgLc15cuzM2YnZmAGTSbwqRVGgNpmjNzRQUBMu39Xx1kKxUPHXxbo0Y4gtXznmI8aeAblE7xngdMRxwOmIxVgEyDEfOeYjxumI5RI9G05HHAf8oiIxTpkqx2zkmI8Yjy25RI+GPQNxkOgVqBxbLzJs+coxHzFmI5fo8bAYoE6I131FEr31cibMR4bHlYzP0FHu88KIYUZJmgl2rXN8DHeOrSBqhicskURvvZwJ85FhODJrq8rx2o6imKZ0W49+uDwrP4ZrFGMxQERECS9g6KgPBmK6Tr/ReUaTcygbdToc5CTGbOSYjwyzITEWA3HArkw5DnQSYzZyzEeG2ZAYi4E44K2FMrz9SYbZyDEfsVA2zIei45iBOOCkQ2Js2clxql055iPGSYdIhm3UOGjPd053d5wYRY77jhzzEeOxRTLsGYgDtl5k2HqRYc+JHPMRYzYkw56BOGBxLsfWixizkWM+MhwzQGLsGaBOhy0YMWYjx3xkmA2JsWeAOiG2XoiIYonFAFEXwl5wOeYjw3BIjMUAdULszhRhL7gc85FhOCTGYiAO2HqR4e1PMsxGjvmIcdIhkuEAwjjgpENiHAAmx3zkmI8YJx0iGfYMxAEnRhHjxChyzEaO+Yjx2CIZ9gzEAScdEmPLTo75yDEfMfYMkAx7BuKAX1QkxtaLHLORYz5iPLZIhj0DccDqXIatFxm2fOWYjxizIRm2UakTYutFhA07OeYjw3BIjMUAURfCxp0c85FhOCTGYoA6IZ60iIhiicVAHLArU46DnMSYjRzzEeOkQyTDAYRxwEmH5DjQSYzZyDEfMWZDMuwZiANOOiTD259kmI0c8xFjzwDJsGcgDjjpkBhbL3LMR475iDEbkolZMdB0R2xavbdmB+1u1b6qKggGeWBGE5oYhScukdCxwGxEmI+Yoign77ZgPtRSTIoBwzBQUVGBV155BTNmzMCwYcMif6utrcXbb7+N2tpaDBw4ENOmTcMzzzwDAFBVFeeffz7Gjx8fi82MGfYMyHDSIRkWSXLMR4zZkEzMioFly5ahvLwcZWVlzf62YcMGZGdnY/bs2fjtb3+LgQMHoqSkBPfddx8cDgfMZnMsNjGmQi3feG9F5xVqwTCgaNjylWM+MsyGxGJSDCiKgmuvvRb19fUt/jZt2jQoioLq6mpomgar1YqGhga8/vrrCAQCuOqqq1BYWBgZ/BL+II32gXr632QfuvFaJvy7yLnars6aV8tlFeGy8d2uc59Xa9Yp052yaOt2xWOdnWG7Wr/t0QuCzpZXN7syLNbkM6Fj8m97cDErBjRNi/o3VVVx/PhxPPvss7j++uuRlZWFu+66C/n5+SguLsY//vEPPPTQQ9A0DYpy6sWG/v/U85z6sDUirYOmyzZdpuWyLXc+2fOJl2n5wd/0/1U1/LvSbPtPbZd428+0ztZue8vt6uh1ioue1qwz/B63LYv2vkcGVFWBrscyr7N5j8L7kNLiec7dPi3brtD2RMurfVmc+TgSrTN8XHWm4yhW79GZ9mlVDR1X4f1HfN5p/XHU/ryiny8UVWnx/N1R+MujRPv02X02tHzfzkbMLhOc/rvX64WqqqitrcVTTz2Fb3zjGxg6dCg8Hg/q6+uRkpKCrKws+P3+yL83DCNyvT1aZRR+rOnypy97apnTl23988mXaf540/8PBptnEH27xNt+pnW2Zttjs060Ypno62x6ELRtu9r7HsUjr9avU9dbjjk59/u0eLvC+3THZ4EWy7ZmneEP3c52HMX3PTKa/Gzteaf1x1Fbt0v2XuvBxBg7ZOgGDMOQ7tNnc97R2zGJTUyKAb/fj/fffx9fffUViouLoWkaysvLkZ+fj/3798PtdmP9+vXYuHEjLr74Yqxbtw7r1q1DXV0drr76amGvAnVXCXAWaKNEOEG2B/ORYTgkphgxGKml6zpqamoQPFn+WK1WmEwmaJoGv98Pt9sd2hhFQUpKChRFQV1dHcxmM5xOZ6Trw+v1o7bWc64395zTNN5aKBK+hMI7LqLTNBXBIKewFGE+YqHLBNFb/Z1NRY2OFxY3IhA887Id5URKBXbmH4jdCgHc2WsAZmf37LDnUxQgLc0JTTv7+QRj0jOgqirS0tKi/s1iscDpdLZ4XLR8d8DpiMW6wokqnpiPHPMRO/3SJFFTnI44DjgdsVh4QA1Fx2zkmI8Yjy2S4XTEccAucDG27OSYjxzzEWPPAMmwZyAOVKYuxNaLHLORYz5iPLZIhj0DccDqXIatFxm2fOWYjxizIRm2UakTYuuF2oYNXxmGQ2IsBoio22Djl6htWAxQJ8QzOlHH43FFYiwG4oBdmTIc5CTDbOSYj1j4y96IouEAwjjgpEMyHEAow0FgcsxHjNmQDHsG4oCTDsmwZ0CG2cgxHzH2DJAMewbigJMOibH1Isd85JiPGLMhGfYMxAEnHRLjxChyzEaO+YixZ4Bk2DMQBxwzIMMxAzJs3ckxHzFmQzJso8YBGy9nwoBE2PKVYz4yzIbEWAwQdSFs3ckxHxlmQ2IsBqgT4kmLiCiWWAxQJ8TuTBH2gssxHxmGQ2IsBuKAPZlnwoBEuO/IMR8ZhkNiLAbigLcWyvDWQhlmI8d8xHhrIcm0+9ZCwzCg63pk4I6maTwgz4C3FopxAJgcJ6ySYz5ihsHbdkmszcWAYRhoaGjAZ599hg0bNqCurg4mkwkFBQWYOXMmCgsLobIJHJWqKggGeVRGE5p0iEWBCPcdOeYjFm6k8diiaNpcDNTX1+Ppp59GYWEhvve978HlcsHn86G4uBhvv/02Jk6ciAsvvLAjt7XbYOtFjCcqOeYjx3zEmA3JtLkYMJvNuP7662GxWJCbm4uNGzfi6NGjmDJlCkaOHInKysqO3M5uRVWBYDDeW9E5KUqoBcMTV3Sh1h2zEWE+YqHjCmA+FE2b+/EtFgveffdd7N27F/v378fbb7+NYDCIf//739A0DTk5OR25nd0KP+fkWAiIMRs55iMWyob5UHRtLgaOHz+OrVu3ori4GE8//TRUVUVtbS02bNiAnTt3duQ2UsLhAFSijsfjisTafJkgPT0dffv2xeDBg/HVV1/he9/7HgKBAPbv34++fft25DYS0Um8UUeO+RC1TbsuE1x++eX45JNPcOWVV6JXr1747LPPcPnll8Nut0Pn/XPUZuzKFGEvuBzzkWE4JNbmnoGqqirs3bsXc+fORVpaGgDglltugWEY2LhxI2pqajBjxowO29DuhK0XGd5aKMMBcnLMR4wDCEmmzcVAamoqevTogT//+c9ISkpCeno6vF4vjh07hoKCAnzzm9/syO3sVthpIsYiQI75yDEfMWZDMm0uBlRVxeTJkzFmzBiUlJSgsrISVqsVPXv2RGZmJicckuDEKGKcdEiOLV855iPGngGSadd0xIqiwOFwYODAgR21PQmBkw6JsQiQYz5yzEeM2ZBMu5vvgUAgspMZhgG/3x91p9N1HUVFRaipqWn2uGEYOHr0KLZv3466ujoYhgGPx4Ndu3bh4MGDCHbD2XnYaSIW6hngoAoRZiPHfMT4RUUk066Ppbq6Orz++usoKSlBWVkZSktL8cILL7T4ADcMAx988AEefPBBbNq0qdnftm3bhn/84x/YtWsXnnjiCTQ0NOC5557Dl19+iXfeeQeffvppt6tou9nL6WBGt3u/OxKzkWM+Ypx0iGTadZlg69at+PDDD7Ft2zaYTKGnGjZsWNTxAiNGjMDx48ebHayGYeCTTz7BVVddhVGjRuHxxx/HunXrcOTIEdx+++2oqKjAk08+iRkzZkDTtPZsaqfCxoscpyMW4zVxOeYjxjEDJNOuYmDSpEno2bMn/H4//H4/AMBut7foqlMUBQUFBVE/0KuqqiK3JrpcLpSUlMBms0HTNDgcDng8HgQCgchXI2uaCsMwmn1ghAacnfoQafnzVGu86d903YCqypeJ9rM16zx9mabrUlUVhqG3YZ2ibT/zOqN9wJ5tXh2VhWydiqJAVRUAeszfo7Zn0Zr96+y2S/QehZ5Pidt2dUxe526dp/LpuCzamtfZn3fav07ZMpqmRsYrtfW80573Mdo+LcpC00L7+Knnbf6z+TrbvkzTZeNxBUVV1cg5r33H0anzQ1u1ewDhl19+ibVr1yI1NRUAkJubi5tvvrnVLXmHwwG32w0AaGxsRGFhIfbu3QvDMODz+WAymSLPZRgGgsHwfXnRqltD8FO8TPRR/aLnad86T61LjzKIsDXrFG17a9bZMXl1VBaiZRTFgK6HD4TYv0cdvUzH5382+04st6uzvEfhfDo2i47OS3430blZp2EET/5szfOIzzvn7hg5JRg0YBintvX0n9Eea8syRrSXGUO6rp/8XGvvcRR+j9v+ItpVDABATU0NfvKTnyAzM1O4jK7rOH78OKqrq+FwOFBRUYEDBw4gLS0NEydOxCeffIJAIIATJ07gxhtvxMaNG7F+/XoUFxdj1KhR3eoSAbUGu3pF2A0ux3xkmA2JtbsYyMzMxDPPPIOBAwdCURSkpaVh+vTpaHqpIBgMYvPmzcjIyIDf78fevXvhcDhgNpsxefJkmM1m7N27Fz/4wQ+QmZmJO++8E59//jkyMzMxefLkZs/VHbSjeEsQDEiEYynkmI8MsyGxdhcDBQUFzQ7A8EDCpsxmM6644grhc1xwwQXNfs/KysI111zT3k3rtDjpkAwnHZJhy1eO+YiFrisDzIeiaXcxUFZWhgMHDgAIXQ7Iy8vjifwMOOmQGPcdOeYjx3zEDMNgryQJtbsYOO+885CRkQEAOHLkCE6cONHtuvU7mqoC3XAupQ6hKOwZkGHLV475iIXPyzy2KJp2FwP5+fnIzc0FAAwYMADPPPMMgsEgv5tAgl9UJMYTlRzzkWM+YsyGZNpdDCxcuBDr1q0DAPj9fo7+bwX2DIiF7i3mpEMibPnKMR8xjhkgmXYXA1deeSVGjBiB8vJypKeno3///rxMcAb8nJNjISDGbOSYjxizIZl2FwPbtm3DG2+8gYyMDNTW1mLChAnd+k4AigW27oiIYqndxcBXX32Fn/zkJ8jJyYHH48GCBQsQDAaj3mJIRO3DTjc55iPDIpvE2v2JnZSUhPXr12P48OEoLS2NzJlM1HY8YYmwp1eO+cgwHBLrkDED//nPf7B+/XqkpaXhhhtu4J0EZ8BaSYa3FspwgJwc8xHjAEKSaXcxEAgEkJ2dDYfDAUVRsH37duTn57N3QIK3FoqxCJBjPnLMR4zZkEy7i4G33noLTqcTeXl5UBQFycnJHbFd3RqnIxbjpENybPnKMR8x9gyQTLuLgby8PBQWFqKgoODk9zKr7BU4A05HLMYiQI75yDEfMU5HTDLtLgasVisee+wx5OTkAAB69uyJO+64g3cTSHDSITH2DMix5SvHfMQ4HTHJtPsT+9ChQ/i///u/SDGgKApnIDwDHosybL3I8EQux3zEmA3JtLsYyMzMxCuvvBKZeTA1NRUXXXQRLxVQO7B1J8LDSo75yPC4IrF2FwN9+vSJ3EpoGAb8fj8rUKJzhIeWHPMhapt2TwgwYsQIzJw5E4WFhThw4AC2b9/OYoDaifsPUcfjcUVibe4ZMAwDVVVVWLNmDT7//HOUlJRg9uzZmDVrFscMnAG7MuX4rYViHCAnx3zEeGshybS5GAgEAvjNb36DvLw83H777fjyyy/Rp08fOByOjty+bomTDsmxEBBjNnLMR4zZkEybLxOYTCZ873vfg6ZpePbZZ7Fu3To0NjZC5yfdGakquwbEFA4+lWA2csxHLJQN86Ho2twzoCgKRo4ciREjRqC0tBSffPIJ/vvf/2Lfvn34zne+w0sFEpx0SIytFznmI8d8xDjpEMm0626C0AQxCnr06IHvfOc7uO6661BSUsLq/Aw46ZAYJx2S4zVxOeYjxkmHSKbDpglUFAVOpxODBg3qqKfstnglRYatFxmeyOWYjxizIRl+13AcsONEjj1LYsxGjvnIMBsSYzFAnQ5bMGLMRo75yDAbEmMxQJ0QWzBERLHEYoCoC2E3uBzzkWE2JMZiIA7Yk3kmDEiE3eByzEeG2ZAYi4E4UJm6BCcdkmE2csxHjJMOkUyH3VpIrcdbC8XYspNjPnLMR4zZkAzbqHHA6YjFwhNZUXTMRo75iLFngGRi1jMQDAZRV1cHi8UCu90eOWgbGhrgdrsBhHbWlJQUNDQ0IBAIAAAcDke3+/IjTkcsxtaLHPORYz5inI6YZGJSDASDQbz00kuoq6tDbW0tbrjhBvTv3x+KomDnzp3YunUrdF3H+vXrcc899+CZZ57BwIEDYTabMXr0aIwaNSoWmxkznI5YjNMRy3G6XTnmI8bpiEkmJsXAsWPHsH//fvz617/Gjh078N577+Gee+4BAIwdOxZjx47Frl274Ha70bNnT5hMJsyaNQtOpxMpKSmx2MSY4rEow9aLDE/kcsxHjNmQTEyKgfLycqSlpcFkMiEnJwdlZWXQdR2apkFRFAQCAbz77ruYM2cOTCYTFEXB+++/jxMnTmDcuHG4/PLLI9eSVVWBYbSc0jf8mGEYUBSlyc9Ty55a5vRlW7OM0eJ6pOh5ZM8HAJqmwjD0KMuIt/3UOluzzNlk0Zbn66hlWi6rKKEWTDCot/l1tv09imderVlnaN8B9CjP0/Z1tn274ncciV5n6NgKxvE96uh9p73v0allQtmcKrbFz9P69+hc5AWExlWd/vznXByGUyhq6HPtzMdR6z4b1HbcqhaTYsBkMiF4sl88GAxGioCww4cPIxgMolevXlBVFb/61a9gt9tx4sQJPProo7jkkktgNpthGGixM58u9JjR7GfT5Vr+/9ksE72yjvY8py/a9HddN5q9juZ/F297k6VbscyZs2jtMrLX2d5lTl82VOwZ7diu9r5H8curNes0DKPFmJPW7NOy52vfdsXnOBKtU1Xbs++ceZm27hdnd3y3JS/jtPW0XCa83zTdf8Tb1fr3SLZM2/OKQw9hrNeH8Ots+t61b99pT+9PTO4m6NGjByorK3HixAls3LgRgwcPxqFDh3DixAkYhoGtW7di4MCBUFUVDQ0NePnll1FZWYmSkhK4XK4m1U5rCgHxiUS2bKyWkR1o53K7OmteLU8Spx8cnWO7WrvMuV6nbPBpd8qirdsVzifRsmjNMk2Prdits33LdHsdvu+0PbiY9Ay4XC5cf/31ePPNN+FyufC1r30Na9euRXZ2NrKysuB0OtG3b18oigKHw4FBgwbhtddeg8Viwfe///12dX10RjHv/upiwt1h1BIHyMkxH7Fw9zLzoWgUI0ZnXdFqmp74RaNdw497vX7U1nrO4VbGBj/sxJpeK6OWVFXhrakSzEesKx1bFTU6XljciEAM77o6kVKBnfkHYrdCAHf2GoDZ2T077PkUBUhLc54cW3R2YjbPgGwykNP/1t0nDlFVBcFgFzgi44K3Fsqw5SvHfMTYM0AynI44DthyEWMRIMd9R475iHHSIZLpXhfju4huNgSiQ3E6YjlOZS3HfMR4bJEMewbigF9UJMPWiwx7TuSYjxizIRm2UeOAxfmZMCAR7jtyzEeG4ZAYiwGiLoSNOznmI8NwSIzFAHVCPGkREcUSiwHqhNidKcIBYHLMR4bZkBiLgThgV+aZMCARDgKTYz4yzIbEWAzEAW9/kuHtTzLMRo75iIWyYT4UHW8tjANOjCLGlp0c85FjPmLMhmTYMxAHnHRIjBOjyDEbOeYjxp4BkmHPQBxw0iEZTjokw9adHPMRYzYkwzZqHLDxIsfWnRizkWM+MuwZIDH2DFCnwxaMGLORYz4yzIbE2DNAnRBbL0Qdj8cVibEYIOpC2Asux3yI2obFAHVC7M4UYS+4HPORYTgkxmIgDth6keMgMDFmI8d8xHhrIclwAGEc8NZCsVDLji0YEQ6Qk2M+YsyGZNgzEAecjliMkw7JMRs55iPGngGSYc9AHHA6YjG2XuSYjxzzEWM2JMOegTjgdMRi7BmQYzZyzEeMPQMkw56BOOCYARlORyzD1p0c8xFjNiTDNmocsPFyJgxIhPuOHPORYTgkxmKAqAth406O+RC1DYsB6oR4RifqeDyuSIzFAHU6HAQmxmzkmI8MBxCSGAcQxgG7MsU46ZAcB4HJMR8ZZkNi7BmIA046JMZbC+WYjRzzEeOthSTDnoE44KRDYmzZyTEfOeYjxmxIhj0DccBJh8TYMyDHbOSYjxh7BkgmJj0DhmFg3759+OKLL5CamopLLrkEdrsdALB161asXr0aVqsVycnJmD17NsrKyrBixQpYrVZccsklSElJ6VYHOScdkuGkQzJs3ckxHzFmQzIxaaPW1dXhn//8JyZNmoSGhga8//77kR1z3759SE9Px+zZszFjxgwYhoFnnnkGw4YNQ3JyMl555ZVYbGJMdaO65pzoToVfR2M2csxHhtmQWEx6Bo4cOYLU1FT069cPNpsNTz/9NK677rrIgbt7927U19fjvPPOQ2FhIfx+P4YOHYrevXtj6dKl8Pv9sFgsAEJdyIZhCD5Qw3879VM8grbjlxFvV2iUfPh5Ql3hLZ8vvEzstqt1ebVlu1q/ztP+qiiRx2K7Xac/n+CpTnu+WOcFRC8mY/keRV9na5aJzXaFHo/nPh3/4yjaOlUVMIyW623Ndp2+TMfuOy3XqSTIpdTwZ2BHHUftKYZjUgx4vd6TH+aAyWSC3++PnNhGjx6NXr16IS8vD0888QQuv/xymM1mKIoCTdOg6zqCwWDkuU7/oDhV7RpN/nbqZ2g1TZdpHvrZLNO6dUZbRoksG3qeyKuJukzstqt1eUXbrtDzhZYJLd/WdZ6+TGj9inLmLMTbZZx8DrTIS7xdpz9f9GXE+44R+Xdn2q7WZxH9PQpndKYsmi7Tse9R6/OSb1f4sY7crpZ5tWXfidU6z7R/tS+L5us8tZ+2drvE+3RHblfULKAkTD+GonRAXoq8mGiNmBQD6enpqKmpga7rqKysRHp6OgzDQDAYhKIoOO+885CcnIycnBzoug6PxwO/34+6ujrY7XaYzeaTz2RERuKfuv51qkIKP3b6z45apqOeT9fD18U713a1bZ2nlu+4dYb/a18W4f/tXHm1//nasu90/HvUce91Rz9fR+w7Hf8647/vhM6dzcfkdIbtiraMrhsJMStC+HjuqEzbc6daTIqB/Px8ZGZm4l//+hdKS0txzTXXYNGiRcjLy4Pb7ca///1vFBQUoKGhAePHj0dpaSmef/551NXVYebMmdA0LRabGTNGIuzl7cKARLjvyBkMSILZkFhMigFN0zB37lyUlpYiKSkJ6enp6NGjB8xmMxwOBwYNGgS3243rr78eNpsNN954I44dOwaz2YysrCx0t0FBqgo0ufJBzZzq3qeW5NeaifmINb+0R9RcTIoBRVFgsVjQu3fvyGNpaWmR/8/JyWm+USYTevbsGYtNiwveWijGIkCO+cgxHzFmQzIJMmazc+F0xGKcdEiO2cgxHzFOOkQynI44DjgdsRhbL3LMR475iDEbkmHPQBxwOmKx0G02bL2IMBs55iPGngGSYc9AHLBAl2MLRozZyDEfMWZDMmyjUifE1osIG75yzEeG4ZAYiwEiIqIEx2KAOiF2Z4qwp1eO+cgwHBJjMRAH7MqU4yAwMWYjx3zEOICQZDiAMA446ZAYZ0iT4yAwOeYjxmxIhj0DccBJh8Q46ZAcs5FjPmLsGSAZ9gzEAScdEmPrRY75yDEfMWZDMuwZiANOOiTGngE5ZiPHfMTYM0Ay7BmIA44ZkDE4IlyCrTs55iPGbEiGbdQ4YOPlTBiQCFu+csxHhtmQGIsB6oTYghFh606O+cgwGxJjMUBERJTgWAxQp8OuXjFmI8d8ZJgNibEYiAP2ZMqxq1eM2cgxHxlmQ2IsBuKAtxbK8NZCGWYjx3zEeGshyfDWwjjgrYVibNnJccIqOeYjZhi8bZfE2EaNA05HLMZJh+S478gxHzEeWyTDnoE4YOtFhq0XGfacyDEfMWZDMuwZiAOOGZBj60WM2cgxHzFmQzL8WIoDFuhybMGIMRs55iPGbEiGxQB1QmzBiLBxJ8d8ZBgOibEYICIiSnAsBqgTYnemCHt65ZiPDMMhMRYDccCuTBne/iTDbOSYjxgnHSIZ3loYB5x0SIyDnOSYjxzzEeOkQyTDnoE44MQoYpwYRY7ZyDEfMR5bJMOegTjgpENibNnJMR855iPGbEgmJsWAYRioqqrC9u3bkZqaiiFDhkDTNABAMBjErl27UF5ejgEDBiAnJwfr1q1DQ0MDAKBfv37o27dvLDYzZlQVCAbjvRWdU6j1whOXSKhlx2xEmI+YoignLxMwH2opJpcJPB4PnnzySXi9Xnz22WdYvnx55GS/evVqfPjhhwCAP/3pTygvL8frr78Oh8OBjIwM2O32WGxiTHHMgIzBQkCC2cgxH7FQNsyHootJMXD48GGYTCbMmDEDs2fPxqpVqyJ/GzZsGG6//XZMmjQJiqLA4/FAVVW43W40NDQgJSWl2x3gvGx3JgxIhNd85ZiPDLMhsZhcJqivr4fD4YCiKHA4HGhsbISu69A0Denp6fD5fHjzzTcxZMgQ5OTkYOTIkbBarTh48CA2bNiAefPmRQa/aJoKwzBOdnkZkQ9Ww0CTx07/2ZpllEjR0RHLyNapaSoAvdNtV9NlwvVXx21X694jQIGqKggG9U6TRazzkq1TVRUYhtLh6zyb96izZBFtmVP5dK7t6gzHt6apJ8crGZ1qu6K9R5oW2sdP7ZPNfzbfX9u+TNNl41ErqZoKVY2+vwJn/x61Z3B6TIqB5ORkNDY2wjAMNDQ0wOFwQFVDH+p+vx/PP/88kpOT8Z3vfAeapuGaa66By+XCoEGD8Pvf/x7BYDCyfDAY7mOP1ltgCH62ZhnjHCwjWlaPMoiwM2zXucyrdetUFAO6Hj5Au0oWrVmmK+w7Z1q2o5Y5l9ulN/vA6zzbFf/j2zCCJ392ru2KtkwwGLoNMrytp/+M9lhbljGivYQY0oMdu78aRttfREyKgZ49eyIYDOKzzz7Dtm3bMG3aNCxfvhyZmZkoLi7G4cOHce2112LXrl3Iy8vD/PnzMX36dBQVFWHo0KEwmXjTQyJpWvFSc4rCAXIyzEeG2ZBYTD5lrVYr7r77bmzbtg0XX3wxBg8ejKKiIjidTqiqCpPJhPLycqiqioKCAtx5553YuXMnhg0bhuHDh588wLsPfs7JsRAQYzZyzEeG2ZBYTIoBRVGQnp6OqVOnRh7r168fACA3NxdDhgxp8W9ycnJisWlxwVsLZXhroQxbvnLMRyzU4wYwH4qG/e9xwFsLxVgEyDEfOeYjZhicjpjEOB1xHHA6YjFF4ZSpMsxGjvmI8dgiGfYMxAGnI5Zh60WGLV855iPGbEiGPQNxwOJcjq0XMWYjx3xkmA2JsRigToctGDFmI8d8ZJgNibEYoE6ILRiijsfjisRYDBB1IewFl2M+RG3DYoA6IXZnirAXXI75yDAcEmMxEAdsvcjw9icZZiPHfMRC2TAfio63FsYBJx0S4wAwOeYjx3zEOOkQybBnIA446ZAYJ0aRYzZyzEeMxxbJsGcgDjjpkBhbdnLMR475iDEbkmHPQByoTF2IrRc5ZiPHfMQ4ZoBk2DMQByzQZXhdU4atOznmI8ZsSIZtVOqE2HoRYcNXjvnIMBwSYzFA1IWwcSfHfIjahsUAdUI8oxN1PB5XJMZiIA7YlSnHQWBizEaO+YhxACHJcABhHHDSIbFQNy9bMCIcBCbHfMQ46RDJsGcgDnhroRhvLZRjNnLMR4zHFsmwZyAO2DMgxpadHPORYz5i7BkgGbZR44A9A2JsvcgxGznmI8Zji2TYMxAH7BmQYetFhi1fOeYjxmxIhm3UOGBxfiYMSIQtOznmI8NsSIzFAHVCbMGIsHUnx3xkmA2J8TIBdUIKeOKi7iweg/lUVWGvJAmxGCCh8AkrlucsVVG6zABLXTfQ4IntSd1h12DqIvkUlQZwsDQY03WOPs+C1KSu8Ym38isfDh2PXT4ZLg2XT7BC7RrxUIwlfDFQXa/jRFXsRvSpCtAzV4Omxu4TRFEADWc/kljXgQ++8KKqLnb5JDtUXHGBBVoMP/A0RYHahiZTg8fAv5e44fXF7r28cKQV4waaY7a+9jharmPdTn9M19knz4TUJC2m62yrylodR8tjd2wFgicn9WIxQFEkfDFwsDSIJeu8MVufoeooG1wEv9UXs3XmWGz4ad/BsChnd5I0AJRV6yirjt0Jq6Teg+W7d0BXY7fOa7N74qKM3LP+d4YBeH0GvDH8vPu8ohzrD9XFbH2KouCqrB7oaXPEbJ1tZ2BR2VFofnfM1phtseFrOb2gdYH+98ZgEJ9Xl0FRYle8ZltsOM+ZErP1UdslfDEQ60vTumGgyN0Ad9ATs3W6gwHoXeQSvF83sL+xDsEYFgNVgdgVZu21o74Gh8pKY7Y+BcBEV2aXKAYMAF9UlaHSXxOzdZ7nSMY12T27xC1Cx7xuLD6wA3oMi4FLM3JZDHQRXeTqY/fS+U8bRNQddZE2AcVBzIoBwzDg9XoRCASa3f5jGAYCgQC8Xi/0k7PxGIYBn88Hv9/fLW8V6n6viIi6BJ58SCAmlwl0Xce7776L/fv3Q9d1fOtb30J+fj4URUF5eTlefPFFKIqC/Px8XH/99fjss8+wZcsW6LqOr3/96+jXr1+3mkyk+7wSIupSePIhgZj0DJSVlWHdunW46667MHXqVCxcuBBAqAfggw8+wIgRI3D33Xdjz5492LFjB5YuXYrbb78ds2fPxhtvvNHtege616shoi6DJx8SiEnPwPHjx5GRkQGbzYaCggK8/fbbMAwDiqKgqKgIU6ZMgdlsRm5uLrZu3QqHwwGn04n8/HxUVFQgEAjAYrFA01TY7eYz3tetKGj1Mr1yVUwaHrtbkXRFR8/MnvBpsRuCnma2IMlhgUk5u9pPN4BRA4H6xtidQfwmCwoyekNXYjeAcEhaOmy20O16Z7PvKKqB84cBgUAMNvKk85KzUeG0xGx9ChT0TEmK5NPi75K8+vRQEDBiO0a5d1o+GqypMVtfjsUGh8MCtZVN7qZ5DS5UkJEau/3cazahT3pvGDEcQDjY6RLuOzKpMDBhWGy/x6XObmCwK7bzYgxKT4PNZm7Veac1FKXtU3LH5EhtunHhIqDp38Itf8MwoGlas9+bMpk0JHXwPcSDk4HB/Tr0KVshLdYrbLOZk2xxWGtGHNZ59pIBXDPNHuO1ugD0jvE622bkwNB/seWK9QrbbMroeBxbWXFY59lLTgauvSgex1Z+jNfZecTkMkFeXh4qKipQX1+PPXv2oLCwEBUVFaitrcV5552HnTt3wu1248iRIxg9ejQ8Hg+qq6tx8OBB5OXlwWzuGpOshIVm7mv9f9H+XXfVmizOJrPupi35nP5Yd9aWfSVR9p/W7Bet3Z+6qo56v3Vdh8fjadW/DQaD8Hq9XT47xYjBKzAMA59++im2bNkCi8WCG2+8EWvWrEFubi4GDBiAl19+GR6PB4MHD8bMmTOxdu1afP755zCZTLj++usjgw27Ap/Ph/feew9HjhxBeno6rrvuOuzevRsrV66E1WrFtddeC7vdjrfeegvV1dXo1asXZs+eDZPJBK/XiwULFmD27NkoLCyM90vpcIZh4NChQ1i0aBH8fj8mT56MYcOGYeHChSgvL8eAAQNw6aWXYseOHVi+fDkMw8Dll18Ok8mEl156Cenp6TCZTPjGN76BjIyu0XtwNgzDwObNm7Fq1SoAwFVXXQWXy4W33noLbrcbEydOxNixY7FixQps3boVZrMZ1113HdLS0rB48WIUFRWhoKAAs2bNgsnU/aYQcbvdePvtt1FWVoacnBx87Wtfw6ZNm7B+/Xo4HA5cd911AIC33noL9fX16N+/P6ZOnYo33ngDDQ0N8Pl8SE5Oxm233Qa1q8x53UqGYaCqqgpvvfUWGhsbMXjwYEyfPh1LlizBgQMHkJOTg2uuuQYnTpzA+++/D5/Ph0mTJmHMmDFYunQp9u/fD5vNhq9//etIS0vrMufb023duhXLly9HeXk5rFYrXC4Xrr76avTs2fOsnqeyshKrVq3C7Nmzz5jFvn37sGzZMtx2221dNjcghpcJZsyYgWnTpkFVVSiKgiuvvDLytzvuuAO6rkPTNCiKgokTJ2L8+PFQFCWyfFdRUlKCuro63HLLLXj66aexevVqLF26FD/60Y+wf/9+vP766xg3bhxSUlLwta99DQ8//DBGjhyJvn37YunSpVi/fj2mTp0a75dxzixfvhxTp05FcnIynnjiCVx88cVwu924+eab8eSTT6Jv375YsWIFrrnmGpSXl+Pll1/GNddcA5fLFTnYLJbYXTOPJb/fj1WrVuG6665DSUkJXn31VWRkZKB///4YPnw4Hn/8ceTm5uKrr77Ct7/9baxYsQLvv/8+hg0bhmPHjuHb3/42Nm/ejEAg0C2LgX379gEAbrnlFjz++OPIy8vDBx98gJ/85CfYsGED3n77bRQUFKBnz54YP348HnroIYwePRrf+ta3oOs63n//fSQnJ3ep88nZWLduXWRfeeihh2C327FlyxbcfffdeO211/DFF1/g8OHDmDx5MtLS0vDnP/8ZGRkZWL58OX7+85/jo48+wpIlSzBnzpx4v5Q2Gzx4MAYMGIDnn38ehYWFmDJlCnbt2oUvvvgCvXv3xujRo7Fu3ToEAgHk5ubi8OHDUFUVbrcbAwYMwFdffYUhQ4YgPz8fGRkZOHr0KIqKilBVVQWHw4EpU6agqqoKa9asgaIouPDCC+H3+1FbWxvvl95uMSuPFUWByWSKfLirqtrs/00mU+QgDS8bLg66ksLCQtx8882wWCxoaGiA3++HzWZDbm4uBg8ejEOHDmHMmDG4+uqrAYS6mJxOJ4qLi7Fnzx5ccMEFcX4F59ZNN92EoUOHor6+HklJSdi9ezdGjhyJlJQU9O/fHwcOHMAdd9yBnj17oqamBunp6VAUBbt27cI//vEPvPzyy3C7YzfdbCyZzWbcddddyMvLQ1VVFdLS0nDgwAEMGzYMWVlZcLlcaGhowP/8z//A5XKhqqoKmZmZ2LJlC2pqavCvf/0LR48ehaZ1jbn5z9bQoUMxZ84caJoGr9eL+vp6pKWlISMjA8OGDcO+ffswdepUXHLJJQgGg1AUBXa7HVarFfX19di9ezemT5/e5c4prXXZZZdh2rRp8Pv9MJlMOHToEAYOHIjk5GQMHz4cO3fuxDe/+U0MHz4cDQ0NSEpKQnp6OqxWKz7//HMcOHCgy/dImkwmWK1WmEwmWCwWFBUVYfHixRg2bBg+/vhjbNmyBe+//z5OnDiBpKQkvPjii7Db7di8eTMWLVqE3r174/nnn0dFRQU+/fRTFBUV4bXXXkP//v2xdOlS7Nu3D2vWrEF6ejoA4KWXXorzK+443auvrJNobGzEs88+i+HDh6N///6RLklVVSMTK1VWVmL+/PmYNWsWXC4XXnvtNcyaNQuapnXbyZbChd+2bdvw+uuvR1r64aLPZDJFTuKrVq3CypUrcdNNN6F///645557cOutt8Lv92PZsmXxfinnhKKEvkzq008/xcaNG3HDDTdEBtUCp/YfXdcjXeGXXnopGhoa0LdvX9x+++3Yu3dvpAXd3SiKgtraWjz11FOYOnUqevTo0SIbRVFw/Phx/P3vf8ecOXPgcrlgGAY+++wzTJgwAVarNc6v4twI7zuHDx/GggULcNNNN8Fms0XyMZlMCAQCUFUVO3bswCuvvILbbrsNwWAQhmFAVVXYbDbU1MRuKudY2LFjB44ePYolS5bA7XajrKwMZrMZo0ePjhRDI0eORP/+/TFw4EAMGTIEwWAwcp4GgEGDBmHgwIHo0aMHamtrYbVasWbNGmzevBmlpbGbGvxcYzHQwcLX/YcMGYKvfe1ryMjIgNvthtfrxYkTJ5CRkYH6+nrMnz8fM2fOxNSpU1FRUYGqqiq89957WL16NRYvXozGxsZ4v5QOZxgGtm/fjoULF+Kuu+5CQUEBevXqhZKSEgSDQRw+fBg9e/bEihUrsHbtWvzwhz9Eeno6ysvLAQB2ux1paWnwemP3xVKxZBgGPv74Y2zbtg3z5s1Deno6srOzcezYMfh8vkhPyX/+8x+43W7MnTsXVqsVffr0gaZpsFgscDgcCMTyXscYamhowPz583HBBRdg5syZyM7ORnV1Nfx+P0pLS5GTk4OKigosWLAA119/PcaPHw8g1Pu2efNmjBw5stv2ChiGgdLSUjz77LO46aabMGrUKPTq1QtHjhyBYRgoLi5G7969sWvXLrz55pu488470adPHxw5cgR2ux2XXnoppkyZgk2bNsX7pXSolJQUjBw5Erfffjvmzp2LSZMmRRogAJqNHRFdkg4vqygK3G43Fi1ahO9+97u4/PLLY/MiYqT7XViMs23btmHv3r1wuVwoKirCxIkTcf755+Opp56Cx+PBtddei9WrV6OqqgobN27E5s2bcdlll+EPf/gDDMPA66+/jmHDhsHh6PxfDHO2dF3Hf//7X1gsFrz//vtwOp2YNm0aXnrpJRQXF8NqtaJv37741a9+hcLCQrzxxhvIysrCqFGj8OqrryI7OxtVVVWYO3duvF/KOdHY2Ii3334bgwYNwuuvv47c3FxcffXVePPNN+FwODB69OjIYNzx48fjpZdewoABAzB16lQ8++yz+Pvf/w6LxYL+/fvH+6WcE+vXr8fRo0exY8cO7Ny5E1OnTsWQIUPw1FNPobGxETfeeCOWLl0Kt9uNzz//HGvXrsWsWbPgdDphNpuRktK9vzBn8eLFCAQCWL58OVavXo3LLrsMwWAQTz31FNxuN2677TbMnz8fJpMJixYtgsPhwKWXXgpN07BgwQJUVlZi1qxZ8X4ZHcLhcMBisWDMmDHYuHEjnnvuOdTW1uJ73/sekpOTI72R4TEk4ctJQKiA0DQNSUlJkQI7/JxJSUno06cPXnvttUjD5OjRo3A6nfF8uR0iJncTJBKfz4eGhobI7w6HA2azGQ0NDTCZTLDZbPB4PPB4Tn1rYVJSUuT2SY/HA5PJ1C0HgBmGgfr6+kjLVVVVJCcnIxAIwO12w+l0QlVV1NXVRbrpNE1DcnIyfD4fPB4P7HY7zGZzt2zh6bre7LWbTCYkJSXB4/EgEAjA6XTCMAzU1dVFLiOFT1Z+vz+SYVcca9MaXq+3WY+Z0+mEyWRCfX09LBYLrFZrpBcuLDk5OTJAzOFwdMtcgNCx1fS1hz/oDMNAQ0ND5LiJdvwFg0E0NjbCYrHAZrN1i4w8Hg80TYtcHmlsbITNZoPFYoHb7YbVao209B0OB3w+HxRFgdlsjizr9XphNpsRDAZhtVoj52YgVLg7nU54vd7I3DhdPTsWA0RERAmOYwaIiIgSHIsBIiKiBMdigIiiMgwDhw8fxoEDByLTuhYXF6O4uLhb3vpKlMhYDBCR1NNPP43a2lo0NDRgwYIFCAaDOH78OEpLSyP3qdfW1uLQoUOora2FYRiorKxERUUF6urq4r35RNQKHEBIREKGYURmbLNarZF5DI4fPw4AyMzMxMyZMzF//nz06dMHu3btwo9//GM8/fTTcDgcmD17Nvr1i/nXghLRWWLPABEJKYqCyy67DAcPHsS2bdsiX36Tn5+P/Px8LF++HIqiYPz48XA6nSgvL8eRI0fg9/sxY8aMLj+9LVGi6H43sxNRh7JYLBg7diy8Xi8sFgtMJhP69u0Lm82GoUOHYuvWrVi3bh3mzJmDtWvXRqZQ7s739RN1NywGiEgqPO+9qqqw2+0YOHAg9u7di+TkZNTU1CAvLw+KouDo0aOoq6uLDDgkoq6DYwaI6IyOHDmCYDCIgoICNDY2YsuWLQgGgxgxYgRsNhs2btwIq9Ua+S4Fq9WK3r17Izk5Od6bTkStwGKAiIgowXEAIRERUYJjMUBERJTgWAwQERElOBYDRERECY7FABERUYJjMUBERJTgWAwQERElOBYDRERECY7FABERUYJjMUBERJTgWAwQERElOBYDRERECY7FABERUYJjMUBERJTgWAwQEREluP8P2U4vamgR8BcAAAAASUVORK5CYII=", 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" ] @@ -450,19 +470,21 @@ "output_type": "stream", "text": [ "assistant:\n", - "The bar chart above visualizes the components of the discounted cash flow (DCF) valuation:\n", + "The revised bar chart shows:\n", "\n", - "1. The first bar represents the **Present Value of Projected Free Cash Flows**, which is calculated from the discounted cash flows over the projection period (Total PV of FCFs).\n", + "- The blue bars represent the projected free cash flow (FCF) for each year specified in the dataset, without discounting.\n", + "- The green bars represent the present value of those cash flows after discounting.\n", + "- The terminal value is also plotted as a projected FCF (blue) and its discounted present value (green) in the final bars.\n", "\n", - "2. The second bar shows the **Present Value of Terminal Value**, which is the discounted value at the end of the projection period, assuming a perpetual growth rate.\n", + "This visualization more accurately depicts how the present value of each year's free cash flow compares to the non-discounted projected amounts and the overall significance of the terminal value in the DCF valuation calculation.\n", "\n", - "Note: For clarity and scale, the values are represented in trillion dollars on the chart, however, the actual values are the ones provided earlier in the analysis.\n", + "I hope this chart meets your expectations. If there is anything else I can assist you with, please let me know.\n", "\n" ] } ], "source": [ - "process_message(\"Visualize this information\")" + "analyst_assistant(\"Visualize this information\")" ] }, { diff --git a/sandbox/agents/assistants-api/azure-openai/function-calling-ea.ipynb b/sandbox/agents/assistants-api/azure-openai/function-calling-ea.ipynb new file mode 100644 index 00000000..c3bdd2f7 --- /dev/null +++ b/sandbox/agents/assistants-api/azure-openai/function-calling-ea.ipynb @@ -0,0 +1,532 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Equity Analyst Agent with Assistants API and Function calling\n", + "\n", + "### This notebook showcases the capabilities of Azure OpenAI's Assistants API for an Equity Analyst Agent. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install the necessary Python packages (openai, matplotlib, tenacity, python-dotenv) for the notebook to function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install openai matplotlib tenacity python-dotenv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports the necessary Python modules and classes used in the notebook. Note the openai module is used to interact with the Assistants API." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import os\n", + "from datetime import datetime\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "from typing import Iterable\n", + "from dotenv import load_dotenv\n", + "from openai import AzureOpenAI\n", + "from openai.types import FileObject\n", + "from openai.types.beta.threads.message_content_image_file import MessageContentImageFile\n", + "from openai.types.beta.threads.message_content_text import MessageContentText\n", + "from openai.types.beta.threads.messages import MessageFile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Environment Configuration\n", + "This cell is crucial for setting up the environment configuration necessary for the notebook to interact with Azure OpenAI. \n", + "\n", + "- **Requirement**: Ensure that a `.env` file exists in the same directory as this notebook. This file should contain the necessary API credentials and configuration details, which you can obtain from https://ai.azure.com \n", + "- **Keys in .env File**: The `.env` file must include the following keys:\n", + " - `OPENAI_ENDPOINT`: The endpoint URL for the Azure OpenAI service.\n", + " - `OPENAI_API_KEY`: Your API key for accessing Azure OpenAI services.\n", + " - `OPENAI_MODEL_NAME`: The name of the specific Azure OpenAI model you intend to use.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv(\".env\")\n", + "api_endpoint = os.getenv(\"OPENAI_ENDPOINT\")\n", + "api_key = os.getenv(\"OPENAI_API_KEY\")\n", + "api_deployment_name = os.getenv(\"OPENAI_MODEL_NAME\")\n", + "api_version = \"2024-02-15-preview\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initializing Azure OpenAI Client\n", + "\n", + "Initializes `AzureOpenAI` client with necessary credentials and configurations:\n", + "- `api_key`: API key for authentication.\n", + "- `api_version`: Targeted API version, set to `\"2024-02-15-preview\"`.\n", + "- `azure_endpoint`: Endpoint URL for Azure OpenAI services.\n", + "\n", + "This step is crucial for establishing communication with Azure OpenAI services.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "client = AzureOpenAI(api_key=api_key, \n", + " api_version=api_version, \n", + " azure_endpoint=api_endpoint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the assistant with tools and files\n", + "\n", + "![How Assistants work](./images/assistants.png)\n", + "\n", + "\n", + "The `create_assistant` function creates an assistant with tools and files. The function takes the following parameters:\n", + "- `name`: The name of the assistant.\n", + "- `instructions`: The system message (or meta prompt) that gives the assistant a persona and context.\n", + "- `tools`: A list of tools that the assistant can use to perform tasks. Currently, these are `code_intrepreter` and `retriever`.\n", + "- `functions`: Custom functions that the assistant can use to perform tasks. Similar to function calling feature.\n", + "- `model`: The name of the model to use for the assistant." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch the files under the datasets directory\n", + "DATASETS = \"datasets/\"\n", + "\n", + "def upload_file(client: AzureOpenAI, path: Path) -> FileObject:\n", + " with path.open(\"rb\") as f:\n", + " return client.files.create(file=f, purpose=\"assistants\")\n", + "\n", + "assistant_files = [upload_file(client, Path(DATASETS) / file) for file in os.listdir(DATASETS)]\n", + "file_ids = [file.id for file in assistant_files]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the assistant with tools and files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "assistant = client.beta.assistants.create(\n", + " name=\"Equity Analyst\",\n", + " instructions=(\"You are an equity analyst that performs analysis on the given datasets. \"\n", + " \"Use the provided file only.\"),\n", + " tools=[{\"type\": \"code_interpreter\"}],\n", + " file_ids=file_ids,\n", + " model=api_deployment_name\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a thread, which represents a conversation. It is recommended to create one thread per user. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "thread = client.beta.threads.create()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper function to format the response from the assistant." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def format_response(messages: Iterable[MessageFile]) -> None:\n", + "\n", + " message_list = []\n", + "\n", + " # Iterate through the messages and break when a user message is encountered\n", + " for message in messages:\n", + " message_list.append(message)\n", + " if message.role == \"user\":\n", + " break\n", + "\n", + " # Reverse the list of messages\n", + " message_list = message_list[::-1]\n", + "\n", + " for message in message_list:\n", + " for item in message.content:\n", + " if isinstance(item, MessageContentText):\n", + " print(f\"{message.role}:\\n{item.text.value}\\n\")\n", + " elif isinstance(item, MessageContentImageFile):\n", + " try:\n", + " response_content = client.files.content(item.image_file.file_id)\n", + " data_in_bytes = response_content.read()\n", + " readable_buffer = io.BytesIO(data_in_bytes)\n", + " image = plt.imread(readable_buffer, format='jpeg')\n", + " plt.imshow(image)\n", + " plt.axis('off')\n", + " plt.show()\n", + " except Exception as e:\n", + " print(f\"Exception: {e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a thread run\n", + "\n", + "![Run lifecycle](https://cdn.openai.com/API/docs/images/diagram-1.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from tenacity import retry, stop_after_attempt, wait_exponential, RetryError, retry_if_exception_type\n", + "\n", + "# Custom exception for specific retry condition\n", + "class NotCompletedException(Exception):\n", + " pass\n", + "\n", + "@retry(stop=stop_after_attempt(15), \n", + " wait=wait_exponential(multiplier=1.5, min=4, max=20),\n", + " retry=retry_if_exception_type(NotCompletedException))\n", + "def get_run_lifecycle_status(thread_id, run_id):\n", + " run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)\n", + " print(f\"Run status: {run.status}\")\n", + " if run.status in [\"completed\", \"failed\", \"expired\", \"cancelled\"]:\n", + " return run\n", + " elif run.status == \"requires_action\":\n", + " # Handle cases that require action differently\n", + " # For example, you might not want to retry in this case\n", + " pass\n", + " else:\n", + " # This will cause a retry for statuses not explicitly handled above\n", + " raise NotCompletedException(\"Run not completed yet\")\n", + "\n", + "def analyst_assistant(content: str):\n", + " client.beta.threads.messages.create(thread_id=thread.id, role=\"user\", content=content)\n", + "\n", + " run = client.beta.threads.runs.create(\n", + " thread_id=thread.id,\n", + " assistant_id=assistant.id,\n", + " instructions=f\"You are a equity analyst who maps out the ask of the user to an equity analyst's task and thinks step by step to analyze, including making use of the tools.\",\n", + " )\n", + "\n", + " try:\n", + " run = get_run_lifecycle_status(thread.id, run.id)\n", + " messages = client.beta.threads.messages.list(thread_id=thread.id)\n", + " format_response(messages)\n", + " except RetryError:\n", + " print(\"Operation failed or timed out after maximum retries.\")\n", + " except NotCompletedException:\n", + " print(\"Operation did not complete in the expected status.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Have the assistant perform a DCF valuation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", + "user:\n", + "Visualize the data and provide insights on the trends.\n", + "\n", + "assistant:\n", + "First, let's load the data from the uploaded file and take a quick look at its structure and contents to understand what kind of data we're working with. Based on this, we can decide how best to visualize it and analyze the trends. \n", + "\n", + "I'll start by loading the data using Python and displaying the first few rows.\n", + "\n", + "assistant:\n", + "The dataset appears to contain financial projections for a company, including the following columns:\n", + "\n", + "- `Year`: The fiscal year for the projection.\n", + "- `Projected Revenue`: Expected revenue for the year.\n", + "- `Projected EBIT` (Earnings Before Interest and Taxes): Expected operating income for the year.\n", + "- `Projected Net Income`: Expected net income for the year.\n", + "- `Projected Free Cash Flow`: Expected free cash flow for the year.\n", + "- `Discount Factor`: Used to calculate the present value of future cash flows.\n", + "- `Present Value of FCF` (Free Cash Flow): The discounted current worth of the projected free cash flows.\n", + "\n", + "Now that we understand the structure of the data, we can create visualizations to help identify trends and insights. We'll plot the growth of Projected Revenue, Projected EBIT, Projected Net Income, and Projected Free Cash Flow over the years. We can also look at how the Present Value of FCF changes relative to the undiscounted Free Cash Flow.\n", + "\n", + "Let's start by visualizing the financial projections over the years.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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jFp2dHcyf38D+/XsJh4fIyysgEgnz8Y9/4kN614UQQkwkV30wMD+7lEJPgJH+Acd16E3GaE+EUYBleZXcWjQNhjsCTEXDr118B+26cP58G2fOnOYjH7mLQ4cOMmvWNHbt2sFtt32E3bt30tnZQSIRp7n5DHl5ebgutLef53Of+yKWleL8+TZSqRS9vT1kZ2czNDSIYegsXbqcM2dOZV5raGiIAwf2sWfPbr7whS/x05/+KwsWLMQ0PZSWlvH669vw+wOcOnWSqqoavF4f8XiC+vqZmZ6GVCqVSSHb1HSSUCgHXdcnzPIhIYSY6FzXxcLBthxMtDHP9nnVBwPF3iDF3mDm367r8nezbubxcwfINXzcUzmPkPHWzVoupGkqM2bMYvnylQAcO9aIpulUV9eyY8d2uru7mTZtGv39/VRWVhEMBgkEglhWii1bNuG6TiZbW15eHoFAkIqKSlpamtm58/XMlrAjW95ef/0qAM6cOc20afUEAkGCwSxmzZrNj3/8Q+699/7MJik+n59gMIiu6+i6hq4HOXPmNAcP7sfn81FbW5fZPEUyxgkhxPjnui7nYgP8feN6OhNhvly7lI+U1I9pQHBVZSCMRpNEIolLPu7CKl3qzdV1lUgkhuPY+Hz+4deJDKdj1ejt7cHj8XL8+FFisSiLFy9lYGCAvLx8FEWht7eHnJxcUqnkcGYsh3g8QX5+PrFYlHg8jmmaZGWld2sbGhokKyuEZVnE4zE8Hu9wb0J6n/e+vl6ys3NwXZeenh78fn9mI5yRYYZIJIKigGl60DSN/v4+cnPzhjeu0SZUEpiJltRG6jO+SdKh8e1qSDp0YdkcXCzXIW5bhK0kfckYPckIHfEwL3c2sbE73WtcHyzgsWX3EtDND/z6hqGRne2b2BkI36v3+ya8eVc3vz+Q+bmwsAjXdQkGg0yfXo/P588EDQDFxSVvOcbwLq0Eg1kEg1kXHTsUSm+XahhGppEvKSnN/D0vL/+CYxe/p/Je+HwhhBBXluu6uIA93NBH7ST9qTg9ySgd8TAdiTAd8TA9yShDVgLLsdEUlaDuIc/0UeQJUuINoikKtutS4AmgK2Pbszshg4HRpigKM2bMGutiCCGEuMJGGnrHdUk4FjE7xUAqTm8yRmci3dC3x8P0JCMMpOIkHBsFCGgm2YY3PXTtCTKtIJ9CT4Acw0dI9+DVdAxVQx2ewBazLWaFimhPhLm7dBamenlphEeLBAPv0UjXz/vtdXg/QxZCCCGujDd336ccm5htMWQl6E3G6E5EaE8M0REP05WI0JeKEbNTOK6LTzMIGR4KzQDF3iwactIT1/NNP1m6B79mYqoa2gXX+Etd7/26wb1VC1DV9L47Y90+TLhgIGlZPH14O0PJGADLK6czt6T6XZ/T1dXJ7t078Xg8lJdXMm3a9Lc8ZnBwkGg08o5d8m1trZSUlKC+KbpLpZJs3foqN9ywGtu22bRpPYqioqoKM2fOZv/+vei6TkFBIVOnTqOpqQnLStHW1oqu69TWTnnb8gghhEh7c0NvXzBO35+K0Z2IDt/RD9GZiNCbjBKxkliug6lqBHUPBaafIm+QKcF8VuRXUWAGyDY8BHQTU9XRlZF7+tG/sRvrQACucDDgui7xeJxEIkFWVha2bTM0NASA1+vF7/cTjUZxHIdgMJh5Q+LxOJFIBFVVCQQCGIbxjm9WNJXgaGcrLi6qolCRXcC/73qZ1sFeAL689FZmFVVyvLuNaCpBjjdAXV7xRcfr6GhHVVUaGhbzxBO/Ihweoqenm6lTp3HkyCG8Xj/Tpk1naGiIlpZmOjs7mDlzNrZtc/z4UUpLy3jxxXV87GOfoKOjnVgsxtKlyzl6tJFweIjm5rNcf/0qLMvixInjfPrTn0FVVWzbprn5LHff/UmefvoJCgoK2b9/L3/wB5/i3LkWioqKqap690BGCCEmA9d1cVywXIeYlSJqpTLj9J3DXfcdiSF6Eulx+pTjoCoKQd0k1/RR7AlS7M1iXnYJhZ4A2YaPLN3Eo+oYqooy3NSPh4Z5LFzRYKC9vZ3HHnuM7OxsAoEAFRUVvPbaa0ydOpVp06bh8/lYu3YtHo+HefPmsXz5chRFYe3atZw7d47i4mJaW1v52te+hs/ne9vX6I9FeOrQNizHxqsbfH7Rqov+3jbYi+XYvHRyP+cGephXUk1d3sUT81zX5dixo9i2TXV1DR0d7ZSVlbNr105WrryWPXt20dh4mFgsxsmTx2loWMTGja+gKAqf/OSn6e3toaSklFQqxdGjjZSXV7Bx4ytEo1HuvPNjtLQ0Z16rt7eHrVu3YJom8+YtoKOjg9de24yiKLiui6KAz+fD6/Xi8/nfMllQCCEmkgvv6m3XJenYxOwUg1Z6nL4rEaE9PkR7Ij0hrz8VI2FbuIBfM8gxvBR60uP0K/KqKPQEyDV9hHQvXk3HvGCcfrI29O/FFQ0GPB4P9957L6Zp8q//+q8UFBRQUFBARUUF1dXVPPnkk9x2220UFRXx4IMPsmzZMhRFIZFIMH/+fObOncu//du/kUgk8Pl8KAq8+VyWZ+fxv27/TObfsVSC8ux8dC3dXb+8ajpew+DPrrkTl0zuoWEKqqqgqipz5szl+utvRNN0Xn75hczafstK4Tg2mqahqirBYJDa2imUl1ewfftrpFJJotEoiqKgKJCbm8vMmbNwXZdt217DsqzhPATp1yosLOSmm25BUVTi8Sjl5eXcdtsdrF37PF1dHelSKRf/936ky6EA43fpzfuhqhOrPun9y5VxvTTq/Zho9VFVcN2J83lLnxtQlLGvj+26WI5N1E4xlErSl4zSlYzSER+iPR6mKxmmLxkj7lg4uHhVnZDhpcAToNgTZH5OCSW+LPIMH0HdQ0AzMFQN7YLu+6tN+vo2uufncuOdKxoM5ObmEo1GefTRR7n55pspLi4mmUwyNDTEQw89BEAwGMTj8ZBMJrFtG1VVhxvSbZw+fRpN0y6avHepxDoBj5ef/sFXM88xNf1dn6Mo6QY6EPBjmuk1njU1teTl5bFq1Wp27txBcXEpOTnZtLe3U1t7HSdPHmfmzFncdNOt7Nq1kylTpjJv3ny8Xh/V1TU0N59h8eKlzJkzl6NHjzBv3gJUVcUwdMrKytmyZSOgUF8/g7y8PLZs2UhxcTFTp07L5BKoqaklJyf3fScSGgk63hz2XK1GGpuJUp/0xVkZbnCufqqq4DjKhLnjSn/emDDnZ+Qm5cruSefiuC626xJ3UkSsFH2pGD0XjdOH0933qXT3va6oZOkmeaafYk+Q2kAuy/MrKfQOd98b6e57XVGHy58+H5qWDjydCZLaQlUZ7hUevfNzud/FK5p0KBqN8vDDD7N8+XIWLlxIa2srfn+60f2nf/onqqqqWLJkCUVFRTzyyCP8+Z//Oaqq8thjj1FXV8eSJUt46qmnKCkp4brrrnvPSYfej/eSNMV1XV555SUqKyupr585qq8/2iZaEhipz/g20eojSYcuduF6+oRjE7WTw8vsonTEI3QMz77vTkYZTCVIOTaqohDQTXINH0XeACWeLIq9QQrMADmmlyzdg1fV0VX1fXffXw1Jh96PkeDTtkevPuMy6dCRI0dob2/n6NGjtLe3M3XqVJ555hlM02TVqlVMmzaNZ599FkVRuOOOOzKFDwaDvPrqqxw7dozBwUFWr159JYv5nqxadZOk+xVCXDVc18Vy03ftmntx782bx+lT7vA4fSpBbzJK13CWvPb4EN2JKH2pGAknPU7v0wyydS9FngDF3iBL8iop8gTINXyEDA8+zcBQ3t8yOzH2rmjPgG3bpFKp9AspCqZpDo+hu5nse7Zt47ouuq5nPjCWZWFZFsBwWmAVRVHGrGfgaiL1Gd+kPuPbROgZGLmkn4r08r3jm4hYKb5Su5S6YB59mfX06Ya+KxGhN5leT2+7Dh5NJ6R7MuP0Jd4siobX04cMb2ac/sJseR9mQy89A5c2LnsGNE1D0y5edz8SBGQKoL+1COlNeT5Y0WzHRlXU9/SGRCJhzp49g6qqw/MDct7yvKGhIeLxGIWFRW95vuu6RCJh/P7AW3oPLMvi+PFjmUmFzc1nqaioxHEcIpEwubl5mcfG4zE0TcMwTBKJBENDgxQUFF7mOyCEmIhGGsILE+dErGR6nH54mV1nPMLm7tPsGzgPwMlwDzOyCgkZnsw4fbU/hyW5FZn19EHdg0fT0BXtiq2nF+PXVZ90yHVd7DfNJnFdl19v/j03zl1EeX4RqjIc7TMciSkXd5mdPn2KxsYjzJ07n6effoKbbrqVgYH0DoVNTScJBrMoLCwiFovR3HyGtrY26utn4DgOJ0+eoKysnN/+9lnuuOOjxOMxIpEIs2fPpa3tHN3d3Rw6dIAZM2ZiWSl+9rOH+OhHP05lZRX79+9lwYKFnDrVRFVVNdu3v0ZxcSkrV15LT083O3ZsZ/bsuUQi4czkxCNHDpGVFaK8vJwjRw4TDAYpKSmlufksqVSK7OwQyaSVyZGg6zqzZs1G0676Uy3EhHdhKtykYxN30l33/ak43YlIuqFPpDPk9afihK0kDi66oqbX0xvpvPdF3gB1gTz2D5zHBZbmVfC9Obfj0XRZZife1lXfQrR0tfM3v/o/mWEFgJRtse/0MXYeP0x1USl/8tFP8z+f+HdOt7eydPoc/vSuey46hutCOBymv78Xn8/P4cMHCYVCvPrqJmpqpnDw4D4KC4uIRCKcPXuGuXPn89vfPouqqqxceS3nz7ehqiqDg4Ps2vU6xcUlnD/fRmdnB9dddwOJRCLzOtOmTaep6WRm58Jnn32K2bPn8dxzz5CfX3DRRkWu67J58wZWrLiWLVs2cuZMFQUFhfT29nDkyEFqa6dw6NAB2tpa6ezsQNN0amqqOXLkCOfPtzE4OEA4HMY0zXE/8VGIiezCbm3LdUiNLLG7IBXuSN777kR6LX3MttJd96pOUDfJN/0UegJU+XNYnFtBnukjx/AR0M23TMgDuKVoGlWBHMJWknsr5uHT3jl5mxBXfTBQnJvP//OHX8IdXhfsui4/WfsEmw7tZv2BHfz7n/0tpm7wR7f9AYlUkqDX/7bHycvLo7Z2Cg0Ni9m8eQOlpWW0tbVSWlpKW9s5YrEYyWSSZDJBMBhk7tz57N69k9LScgKBIEeOHMayUiiKQnFxCYZh0N/fR0lJaWbJIoCuG6xceS0vvLCW4uISwuEwXq+XhQsX09nZ8ZbkSl6vj/LyCkzTQ39/P7NmzcHv93PqVBOlpWWZjIfFxaWAS2lpKadPn6avrwev18e0adPJzy+4Yu+/EJPZhY38G7PuLaJWigErTt9w0pyO4Tv67kSUwVScmJO+efGoGiHdS77HT5EnwPRgIdfkB8g3fcNj9OZbuu7hvd3V5xhe/mTqCgBse+xz34vx7aoPBjyGydSyysy/bcfmrqU3sHLmfBQUyvMLUVWVqsKSdzxGIOCnqqo6sx1xTk4uXq+PxYuXsmXLJjRNo6amlq6uTmbNmkNLSzO1tXXMn9/AunXPU1JSSmFhYea5ra3nWLhwMdnZOWzevJGcnNxMjoSCggLKyyuorKzCND0sW7aSc+eaKSgoory8glOnTlJdXYNpmuTl5RMIBNB1jaKiIqZNq2fHju34fD4WLVrCq69uQlU1Zs2anUnz7PF4KSgoZMqUaezatYPOzg7mzJl3Bc+AEBPXhePz6Xz3Fy6vi2W67TsTEXoSUQatBAnbQlHAq+pkG17yhyfjzQ2l0+DmmX5Cuge/blzRnPeKzOYX78MVXU0w2q7UaoJUys4k+wFwnDei6JGfN25cTygUYtGiJTiOc9FjRxIljRg5luu6wymG35ijMPJvcIczgylve7yRx8JIUgo389iR4134cybJkqmTTFrDv3MA5aLXv9pMtNnqUp/x4+KJeOk7+piboi+RvpvvSkTojIczG9sMWQlSrnPRdrWFnkBmjL7QDJBjprer9WkGHlVLT2Jm7BrjD5pnYLyR1QSXNi5XE1wt3txYXrgiYCQD4qJFiwkE0pspXbhCYuTnt3vj3/13I5nBeNvjvfn5Iz+/l8eOlF9RxnZ/bCHGwpvX0Ftuesb9yA52IzPuR7aq7U3GiNhJUo6NoWn4VINcwzfc0AeYnlVAvukn1/STpZuyjl5MSBIMvAeKopCdnTPWxRBi0nu7ZDlRO0U4laD3ghS4b8y4jxGx0mvoDVUjqJvkGemJeCXDO9jlm35yDB9B3STo8aC6ChemipeGXkwGEgwwvDXm8PLEkQRHb2ZZFo7jXDQZ8M3HgLdeOFzXJZFIZHYfdC5YBvlOr/VuZXy356RfK46mmW953oX10HX9LfkfhBgPLkx/m9m9LpWgL5Xuun9jaV2UgVScqJ3EBQxFJWR4yTN8FHmDVPtzWZxbQcFwspzg28y4f7vvkaa+sQxZiMlEggHSeQa2bNlIfn4BHo+XG25YlcmSmEwm0DSdaDRKLBalsLCQZDKVadwTiQSGYbB79w7mzl2AYejYtoPH48G2LSKRCC+8sI5PferTWJbNI4/8bHiFgYdly1ZgGMbw6yTRNA1d10mlUriug2l6UBSFZDLBhg2vkEqlsCyLm2++DdM0cRwbj8d7QcZGl3XrnueOO+7CNE00TSORiPPoo7+grKwcn89HLBZj3rwFFBYW4TjOcObH9JiiOnwhvDAbpBDvZKThfq+PhfSMe2u4oY9YyeGlddFMQ98xvKFNfyqeTn/rung1gyzdk5lxPy1YwDX5QfJMH9mGF79m4tV0NEVB5eqdHyPEWLrqg4GhVIKmSC9zs4vZ13+eWaEizseGMLX0kp3TkV7mZBezv/88M0NF2K5DQDMvumBEoxHKyytZvnwFv/71L1EUGBwcpLy8gra2VuLxGLNnz2VgYIBt215D1zVycvKwbYuhoSG8Xi87d76Ox+Pl2LGj6LrGjBmzOXhwP1lZWXR1deC66V4By7KG9znQ2LlzO+3t56mpqePs2TMkk0lWrLiG7du3oigKy5atoLq6hkOHDuL1+rjlltvp6uokHo/x4ovrSCaTzJu3gP379xIKhaiqqub06VOsX/8Spmly8823ZZYUrV59C4qi8NJLv6enp5vNmzdiGAbZ2TlkZQVpa2tj6tRpnD17hltvvSMT7AjxdlzX5dBgB4+3HqDSm81nqhrwazrW8Iz7iJ1kMBWnZ7ih7xieiNeTjDBkJUjY9kUz7guGJ+LNC5VS4PFfMOPexPM229RKgy/E6Lrqg4GoneJEuJvZoSKODXUxJZBHa3yAoO5BV1ROhLuZFSri6FAXdYE8UsPBwIVc1+Xo0SO4rsu1195IS8tZZs+ew549u7n11jvYvXsHzc1nSSQSHDvWyLx5Czh/vpXBwUE+97kvkkgkaGtrxbIs2tvbqKiooqnpBJZlccMNq+joaM+8Vk9PN5s2bcDn8xONRpgxYxZ79uzC4/EQj8fZvXsnvb3d5OcX0tHRTnV1DeHwUGZ5YkFBIeHwUCYF8rlzzfj9PqLRKB6Ph/z8Aq6//kbWrHk685rt7efZtGk9gUAQ27Y4c+Y0VVVV1NTUsWnTenp7e3Bdh9Onm7Dtdx4KEZPPhRnxUq5NfHgiXncywt8eeZnGoS5UFHb1teLXDMJWkoRjoSkqPk0nx/Bl8twvyS2nwBMgz/SRpXvwv81+9NLICzE2rvpgoMgT4JPlcwC4t3I+ANfm12T+/p/e9Le3oygK8+bN5/rrVwHQ2tqCrusUFhZy9Ohhuru7qKqqZnBwkJKSUiorq1EUaGw8zMGD+xkcHMws8ysoKKS6uoZQKJvNmzdw9GgjsVhs+HWgqKiYW265HVVV2bDhZXTdoKKiEkVR0HWd3Nx0j0NVVQ1VVdUATJ8+gxdfXIfH4+HYscbh1MhRCguL6OvrpaysHNt2OHLkEIahv2V1REVFJbfccjuKorB27W/Jycnh3LkWkskk1dW1nDhxjMrKKk6cOM6CBQtH7+SIcevCZXXW8LK6aGZ8PkpXIjq8vC5MTzLKQCpBzE7iALqi4tcMuhKRzDGq/Dl8rHQmOYaXLMODVzMwFS2d+htp5IUY7yTPgK7S3z9IKpUiNzcXgN7edPY+Xdc5c+Y0wWCQ7u4uent7mTt3Hp2dnVRWVqEo0NLSTFlZObFYDEVRSKVSRCIRampq6evrJRwewufzUVpajuu6tLefp7S0DEVR6OvrxePxYBgmZ86cxuv1UlZWTmvrOZLJJDU1Nei6geu69PX10tHRTl5ePvn5+Zw+fYqsrBDxeBxd1wmHw1RUVBKLRcjLy6erq4vi4hIcx6Gzs4OSklIURaG7u5tAIEB3dxeJRIKamlp6errx+XyEw2FycnLw+wOj+h5/EFfzOva3c6Xrc+HYvH1B2tvw8EY2b4zPR+hOROhLxQhbSSzHQVHS29OGdC8FnvSM+0LPG+vn0+PzBl5VR1NUNnaf4mdnd1Phy+Zb066jwDN+PjeXayLsWnghyTMwvo2nPAMSDLyHi7PruuzatYPq6hqKiopH9fVHmzSe49vl1ueta+cd4naKiJ2kP/VG2tuRRr5nOElO3LHABVPTyNI95A132xcNN/R5pp8cw0sgs35eRXuP29O6rguagmu7E+buX4KB8U2CgUuTpENXkKIoLFmybKyLISaoi1PeuiRsi5idzm3fn4zTnYxkMuJ1JyMMpBJErCSO66IpCgE9nQ2vwAxQ5A1Q4y8n3+Mn1xgem79CaW8VRUFXVSxn4gRrQkxWEgwIcYVc2GXvDC+nSzkO/fE4A8kY3cko3cn03XxXIkJ/MnZRylvfyJK64d3qpgbzWeGpItdId9kHdBOPqmNcYu28EEJcigQDQrxPF3ZRjkzAizsWESvJ4PC6+e5ENJMFrzeZTpCTcGwcXExVI6AZ5Jp+Ckw/RZ4gs7KKhjPheQnqHnya/paZ9iCNvRDiypBgQIhh77Qd7UgWvP5UnO5klK4LdqnrS8WI2SlSjo2uqPg0gxzTO3w3H2RBTin5ZnrdfJZuku31oToKuqKgXNDMSyMvhBhLEgyISePt1swPWYnh5DixzFK6rmSEvuEu+5HkOKaqEzLSE/AKPUFq/bksyS0nz/STrafT3Xo0HVPVGGnm366Bn2gTIoUQE4MEA2Lccl2XuG2huLxrmtm3WzMfsVIMWek95y+6mx/uso/bFs7w5jUB3SRneJe6wuF0t/medJd91vB2tOYFXfZyFy+EmGgkGBDjkuM6PNZygCdaDzIjWMi3pl+HTzOIXbBmvicRpSuZ3nO+OxmlLxkjaidJOjaaouLV0qlu8810TvvZoWIKTD+5F6yZ97xp8xqQxl4IMflIMCA+VG83Lp90RtLcJhhIxelNxWiJDvDjpq30p+I0DnZyItyDXzNQFDBUjZDuIdf0UWgGqPBnsyCnjPzhRj6oe/Bquuw5L4QQ75EEA2LUvLGUzsVy3eFGPjU8Lp+gJxnNbFzTnYzQl4wTthLp3elIb0Mb1D1kG95MIpz+VBxD1fhs1QKuLajBp+lXZM28EEJMZhIMiHf15jv5lGunU9xaKcJ2kv5kbHhcPr1WvieZ3n52KJUg5dq4rotH0wloJrmmjwIzQIHHz/SsAvIM/xu57FUdzwXb0ALcVDSVtR1HqQ8WcmfpDExVG6N3QQghJjYJBiapN6+Vt12HhD2Sxz4xnMc+RnfijUa+LxUjaqVIOBaqomCqOll6upHPN/0Ue4PMDhWTZ/rIMXwEdDOzM51+Gevl5+eUsLigXGbfCyHEFSbBwATluu5FCXFG1soPpNLpbbsT6ex3Pcko/cn4RRPvTFUjZHjJHd5nvsKXzfyc0vQyOsNDUEuPyZtXePtZBen+F0KID4MEA1eJC1PburikHJuEYxOxkgxZCfqHZ9f3WjE6YultZwdTccJ2Ett1L0pvmzfcXV/tz2VRTjl5po+QMbxWXr3yjbwQQojxRYKBMfbW3ejs4dS26Yl3fcnYG5PuEhF6U+k7+ZhjYQ3fyfs0nZDhJc/0UeQLMiWYzzKzMpPD3q8bmR3pVElvK4QQ4k0kGLgC3jzpznEdUq5D3LaI2EkGUnH6RzaqSUToHp5lP5hKELdTWK6Drqr4VIPs4Ua+wBNgRqiQfDO9G13I8BLQDLyajn7BEjrD0GSMXQghxPsiwcD79HY70cUdi7CVZDD1Rsa77uHlc73DaW1jdgoX0lvOamYmGU6BJ8DcUAl5po9cM73lbHo3Og3jgtS2IHfxQgghroxJGwy4rsvBwQ629JxmfqiUFflVqChvnwhneCe6C7vr+1JxhlJx4o6F64KuqgR1k5DupcDjp8AM0JBTNrwTnY+QkU5r631Txjtp4IUQQoy1SRsMdCYi/Pn+52mJDZCle/hk+Rws16EvGWPQSpB0LBzXxaPq+HWDHMOX2Ve+NpCbaeSzhreb9WjpRDiS1lYIIcTVZtIGA0NWgt5kDICIlURVFFbkVmXWyAd1E7+eXiNvKKpsNyuEEGLCmrTBQJU/h/sq5/P7juMszCnj63XLyTI8Y10sIYQQ4kOnuBdOfR/notEkkUhi1I5nuw5xLEx3OEPeBLjj13V1Qq0mkPqMbxOtPpqm4jgOV89V8d2pavqa5jgTo0KapuA4F6/YupopioKqgm2PXn0MQyM72/e+27NJ2zMAoCkq2bp3Ql3MhBBCiPdLHesCCCGEEGJsSTAghBBCTHISDAghhBCTnAQDQgghxCQnwYAQQggxyV3R1QS2bXPo0CF6enqYO3cuubm5HDx4kKGhIRoaGvB6vezdu5dUKsXChQvx+XwAnDhxgqamJnRdp7a2lrq6ugmx7E8IIYQYj65oz8CpU6fYt28fWVlZPPzww+zYsYMDBw6gaRqPP/4469evp7m5mVgsxpo1azJrR7dv347rulRVVfHzn/+coaGhK1lMIYQQYlK7oj0DdXV11NbWcurUKTweD4cPH+b666+npqaG9evX09fXx3333UcoFOJHP/oRjuOgaRqu69LR0YGqqvj9fkzTBNIJNHR9dOOX0T7eWJP6jG+aJvUZz9JJh5QJk9RmoiUdUtV0kp6Jcn4URUFRQFFGrz6X+528osGAqqo0Njbywgsv8JnPfIbnnnsu093vui6u61707wvl5ORQXl7O8ePHaW1tZcqUKbiuO+oJghRFmVBJhxRFwbYnTga1iXZ+gAl3fiZSfSDdcE6UxlPT0tfX0cxwN5Y0TcV1J875UVUl8x0aLZc7pH5Fg4HW1lZ+85vf8NnPfhaPx8P06dPZv38/sViMwsJCKioq2L9/Pzk5OVRXV6Oq6YhGURRyc3MpKSlB1/XMMMGVuOBMlAhzRDrIGutSjJ6Jdn7gynyOx8rE+7xNrM/cBKrKsIn3eRvNXoEP4ooGA/F4nLq6Ovbs2UN2djbXXnst27dvp6WlhXvuuQefz8err75Kf38/n/jEJzLPmzdvHo2NjfT09DB//nzmzJlzJYsphBBCTGqTeqMimHgbrUh9xjepz/gmGxWNb7JR0aVd7kZFE2v2jxBCCCHeNwkGhBBCiElOggEhhBBikpNgQAghhJjkJBgQQgghJjkJBoQQQohJToIBIYQQYpKTYEAIIYSY5CQYEEIIISY5CQaEEEKISU6CASGEEGKSk2BACCGEmOQkGBBCCCEmOQkGhBBCiElOggEhhBBikpNgQAghhJjkJBgQQgghJjkJBoQQQohJToIBIYQQYpKTYEAIIYSY5CQYEEIIISY5CQaEEEKISU6CASGEEGKSk2BACCGEmOQkGBBCCCEmOQkGhBBCiElOv9wn2rZNS0sLR48eJRaLUVJSwuzZs8nKykJRlNEsoxBCCCGuoMsKBlKpFL/5zW/o7+9n+vTpZGdn097ezvbt27nllluYM2fOaJdTCCGEEFfIZQUDjuMwd+5cSktL8Xg87Ny5k3nz5nH77bfT1dU12mUUQgghxBV0WXMGDMPg1Vdf5fz586xZs4auri6effZZ4vE4lZWVo11GIYQQQlxBlxUMDA4Ocvz4cfbu3cuGDRtIJBJ0dHSwe/fu0S6fEEIIIa6wywoGQqEQ8+fPp6+vj5tvvplbb72VgoIClixZMtrlE0IIIcQVdtkTCBctWoTP56OiooJwOMw999yDYRicOXOG2tra0S6nEEIIIa6QywoGVFXlyJEjdHR0MHXqVHw+Hx0dHZw7d4477rhjtMsohBBCiCtIcV3XvZwnOo5DW1sbJ06cIBaLUVxcTH19PYFA4IrlGYhGk0QiiVE9pq6rWJYzqsccS1Kf8U3qM75pmorjOFzeVXH8UdX0tdhxJkaFNE3BceAym61xR1EUVBVse/TqYxga2dm+990OX3bSIVVVqaiooKKi4nIPIYQQQohx4LLTEbuuS19fH67rZn5OpVKjWTYhhBBCfAguu2fg4MGDPPLIIzQ0NKAoCk1NTfzRH/0RRUVFo1k+IYQQQlxhlx0MVFdXs2LFCqqqqgBoaGggPz9/1AomhBBCiA/HZQcDoVAIXdd5/vnn0TQNj8fDl770JXJzc0ezfEIIIYS4wi47GHBdl+7ubv7iL/4C0zRRFAXDMEazbEIIIYT4EFx2MKAoCo7j8NBDD5GXl4dpmnz0ox8lKysr8xjXdXEch3g8jt/vx7IswuEwAF6vF6/XSzwex7bti5YkJhIJotEoiqIQCATQdV22RRZCCCGukMsOBgCuu+46BgYGANA0DdM0L/p7Mpnk8ccfp7m5mb/+679m586dvPrqq9TV1TF9+nQCgQC/+93vME2ThoYGli5diqIorF27lubmZoqKimhvb+erX/0qPp/vgxRVCCGEEO/gspcWAuzatYvf//73PP/88/z6178mGo1e9HfXdVmyZAmqquK6LslkksLCQmpra6mtrWXTpk3ceuut/OEf/iEbN27MJJKIx+M0NDRwxx13EI1GSSTSiYauROfAROtxmGDVmXDnBybeOZpIJtq5mWj1gYlVp3RdxkeFPlDPwKc//WkgnY1w7dq12LZ90d+9Xi9lZWWZC3plZSXhcJje3l4eeughALKysvB6vSSTSWzbzgQOr732Gk1NTWialgkSFEVB1z9Q/PIWmqbguqN7zLGkaeqEyZ4GE+/86LqKoigTJoNauj5MmM+cpk2s8zPxMhCqw8PPE6M+qqqgKMqoBjiadnnXyw8UDKxbt46WlhZc16Wrq4sbbrjhor+PfKFG/u/xeFi5ciWGYfDaa69RWVlJX18fhmHg8/nQNA1IZze86aabWLJkCU899RSHDh3iuuuuw3HcK5L6dCKlUwWpz3gn9Rm/XBdJRzyOpQMBSUd8qWNejg8UDDQ0NBAKhUgmk9x9991kZ2df9Pfe3l5+97vf0dXVxbp166irq+Ppp5/G4/Fw/fXXU19fz7PPPouqqtx+++2ZSgQCAV599VWOHz9Of38/q1at+iDFFEIIIcS7uOyNilzXZc2aNZw+fRqv10sikeCrX/0qXq838xjHcUgkEriui6qqeDweUqkUrutmJhtaloXruhiGkQkGLMsimUxmlitqmoaiKLJR0Xsg9RnfpD7jm2xUNL7JRkWX9qFvVOS6Lr29vXz961/HNE2eeeYZ+vr6KC0tzTxGVdW3rAJ484qDt8tNoOs6uv6BOi2EEEII8R59oDwDBQUF/OQnP8Hn8xGPx7njjjtGs2xCCCGE+BB8oNvvm2++maKiImKxGJWVlZKBUAghhLgKfaBgYCS3gGEYnD59mqKiIkKh0GiVTQghhBAfgg8UDOTk5HDnnXcSDAYB8Pv9o1IoIYQQQnx4PlAwMDQ0xL/8y78QCoXweDw88MAD5OTkjFLRhBBCCPFh+ECrCQzD4Fvf+hY+n092LRRCCCGuUh9oNUE0GuV73/sewWAQ0zT50pe+RG5u7miWTwghhBBX2AcaJrj//vuxLIuOjg527do1WmUSQgghxIfosoIB13VJJBIcPnyYLVu2cPz4ce67777MREIhhBBCXD0uKxhwHId//Md/xO/3c9ddd2GaJitXrkRVJ87uckIIIcRkcVmtt6qq/MEf/AFer5enn36ac+fOMTQ0NGHyRQshhBCTyWVvVATpHoKuri62bNnCyZMn+cpXvnJFJxDKRkWXJvUZ36Q+45tsVDS+yUZFl3a5GxV9oGBghOu6JJNJdF1H07QPerh3JMHApUl9xjepz/gmwcD4JsHApX3ouxZeSFEUPB7PaBxKCCGEEB8ymfEnhBBCTHISDAghhBCTnAQDQgghxCQnwYAQQggxyUkwIIQQQkxyEgwIIYQQk5wEA0IIIcQkJ8GAEEIIMclJMCCEEEJMchIMCCGEEJOcBANCCCHEJCfBgBBCCDHJSTAghBBCTHISDAghhBCTnAQDQgghxCQnwYAQQggxyUkwIIQQQkxyEgwIIYQQk5wEA0IIIcQkJ8GAEEIIMclJMCCEEEJMchIMCCGEEJOcBANCCCHEJCfBgBBCCDHJSTAghBBCTHISDAghhBCTnAQDQgghxCSnX8mDu65LV1cXLS0tLFy4EMdxOHToEOFwmPnz5+PxeNi/fz+pVIqGhga8Xi8ATU1NNDU1oes6tbW11NTUoCjKlSyqEEIIMWld0Z6BcDjMM888w7PPPovjOOzevZu9e/di2zaPP/44Gzdu5NSpUwwODrJmzRpc1wVg69atpFIpysrK+I//+A+GhoauZDGFEEKISe2K9gwEAgE+9alP8dOf/hSAgwcPcs0111BTU8OmTZvo7e3lvvvuIxQK8aMf/QjHcdA0Ddd16enpwePx4Pf7MU0TAFVVUNXR7SHQNBXHcUf1mGNJVVU0zcWdIFVSVRVVnSCVIf0Z1jRlAp2fiVUfTVMANXNjcrWbSOcG0tcDRXFxnLEuyehQVQVFUXDd0avQ5baRV7RnIN0waZl/u647fDIVXNfFcRwURcn8+0LBYJDCwkL8fj9tbW2Z54+2ifKlf8PECQTSJlRlACbY+ZlY9ZlIdYGJV5+JeD0Y7Tpd7jm/oj0DlmUxODhIIpEgHA4zbdo0Dh48SDKZpKCggIqKCg4ePEhOTg6VlZWoajo2URSFgoICKisrMU2TgYEBIF3J0b6Ldxx3QvUMSH3GN6nP+Ja+63QmXCM6Uc6RooDjTJybONcFVR3d83O5743iXsF3tbe3lw0bNtDf309lZSXXXXcd27ZtIxwOc+211+Lz+di8eTOWZXH99dcTDAZRFIW9e/fS2NiIaZoUFxezfPlyDMMgGk0SiSRGtYy6rmJZE6TPCanPeCf1Gd/Sw4YTJxgY6TKeKMGApikTKhhQFAVVBdsevfoYhkZ2tu99T7q/osHAaJNg4NKkPuOb1Gd8k2BgfJNg4NIuNxiQPANCCCHEJCfBgBBCCDHJSTAghBBCTHISDAghhBCTnAQDQgghxCQnwYAQQggxyUkwIIQQQkxyEgwIIYQQk5wEA0IIIcQkJ8GAEEIIMclJMCCEEEJMchIMCCGEEJOcBANCCCHEJCfBgBBCCDHJSTAghBBCTHL6WBdACCGEmGwc10XBBVfBcR0UFBRFGbPySDAghBBCfIhc1+UXezaw5UwjKDA9v4xvXfdxNAkGhBBCiLHluu4lH+O4buZxmqqStC1Sto3tOhiahkcz6IkOkbBTWLZNSVYusVSCM31dJG0LQ9OYX1rDkc5zbGs+BkDKsq5ovd4LCQaEEEKMe67r4roK4Ga61QFs1xn+m4uh6cStFEnbwnEdDE3Hp5ucH+ojYaWwHJvy7HwSVooT3W0kLAtd01haMZVdrU2c7u0kYaWoLyxjXkk1P9uzgf5YBMuxub/hRvpiYX65dxOWY1PgD/HtGz7BQztfZk/bKQBum76AW6ct4H9ueIpIMo6pG/z5NXfRHw/z5MFt6KpGWSiXOcVV2I4zdm/m25BgQAghJhjXdWkb7CVqJQGXgOGlNCv3A49Jv92d88hvlOGfLdvGcR1cwKsbxKwk8VQSy3EwNZ0sj49zAz3ErSRJ26IqpxDLtjnYcZakbaEqKtfXzGLnuRMc624jYaWYVlDGiqrp/N+tL9ETDWPZNg8sWkU8leTBHS+ScmzyfEH+5qY/5Jd7N7G9+TjgcmPdHD4xezn//NpviSQTmJrO15bfRsKy+O3RXXg0g6JgNovK6hhKxIimEnh1A49uoGsas4sqAfDoBgWBEPn+LL667DZ0VcNvmHgNg88vWsVnG65HVdTMc79/x+dQFAVVUdCU9Dz9ReVTIBPCwIqqenRNQwFqcosYwxECABT3vfSLjBPRaJJIJDGqx9R1FcsaXxHaByH1Gd+kPuObpqk4jsN4vipe6pLtuC6O6/IXv3s4PSYNrJ4yl3+45V58hodoMkE0lcBybDy6QY43wOm+DqLJBEnbpjavCIDdrU0krBSKonDTlHnsaj3JwfZmElaKqfkl3DRlHv/2+u/pDA+QcmweWHgjqqLyr9vWYTk22d4A/+OmP+SJg1vZcuYIqqJwTfUM7pl3Ld/d+DQD8SiGpvGfl9yMrmo8dXAruqqR58/i/oYb2HnuJM39XXh0gyl5JcwpqWLf+VPYrouhaEwrKENVFDrDA+nued2gMBAimkxiOTaaquLRdAxNx3JsQEFRyDTO7+bDmMjnui6qqqKoYA9/h0bjdQ1DIzvb976PJcHABLuYSX3GN6nP+PZeg4F3u2xeeKec6cLmjUYokoxnfp/tDTCUiNEfj5CybbyGQVlWHo1d5xhKxNJd1gXlGJrG5tNHiFtJFEXhYzOXsKu1iZ0tJ0naKabkl/DxmUv559ee5/xQH7bjsL/9DD3RIQByfUEWltXy97fcy/NHd7Oh6SCqorC4Yiqfa7iRf9yyhv54BI+mc+/868jy+Hjq0DYMVSPHF+AP517D4c4WzvalG+eqnAJmFVVysOMsjuPi0XWqcwoxNI2uyBC6qmJqOnn+IEnLwnYdVEVBVzV0VcO+YPb8OzVZb9eYaZqC47y3sf2rgaIoqCrY9ujVR4KByzTRLmZSn/FtotTHHb771A0VK+VkukTHukxv5lzwO1VRSNoWluPguA4ezUBVFPriEVK2heO6lOfk0RsZon2on6Rt4zdMpuSXsOtcE72xMEk7xdziakIeH88f3UXMSqKgcO/869jV2sTGU4dI2RZT80v5bMMN/O9Xn6O5vwvLcfhcw42UhfL4/uZnsBwbv+Hh726+hw2nDvHSif3omsa8kmo+v2gVP9n+e3qjQ5iawcdnLaUgEOK5xh0YqkaWx89HZyzidF8nzf1dmJpBSVYOMwsrON7dhuM6aKrG9zY9zc5zJwG4sW4O/+8t9xHy+tKT3RwHVVXQFQ1NVdPvnQIXdmOPGMvlbm8mwcClSTBwmSbKxXmE1Gd8myj16Y9F+M6LjxJJxXFd+PLSW1lZVf+2j3VJtzOO62K7zvD6ajA1nWgqSdJOYTkOfsPE0HTOD/aRctKNdm1uEd3RIc70dZK0LIIeDw1ldWw6fTjdYFspFldMpTiYwyP7NhFNJlAUhS8vvYV9bWf43bFdpGybKXkl/PGKO/jepmc409eJi8t9869nWkEp/3PDUziuS9D08t9v/hQ7W07ywvG96KrGjMIK7m+4gUf3baY3Fsaj6ayqm0NJVi6vNB1EV1UCppframbRFRng/FAfpqaT6wtQnVNE22Dv8Czz9O9MVSecjKfHklUVn+HJBFaKkm6KVUXJvGdweY2x67qc6DnPUDKG67rkeANMySsZVw375ZBg4NIkGLhME+XiPELqM75difq816/whV3WuqphOw4JKzXcZQtZHh99sTCxVJKUYxPy+AmYHo4PT+KyXYfZRVX0RAfZ1nycf9qyhkgq/X28bdoCpuSXkLQsVlTVMyW/hJ++/gJDyTgA37zmoxzuaOGpQ1uxHZfavCL+y3Uf5x83P8uJnvOoisKn5q5kfmkt39v4NCknfVf+revv5nhXGy83HcDUdGpyi/j0vGt4vnFXunHWdZaUT6UslMf2luPoqoZPN5lfVsNQIkZXZBBT1Ql6vJRk5dIbDeO4TqYRNzSNhGWhKukua69pYNsOjuO+7y7s8UhV0+V0nKvmMv+uJBi4NAkGLpM0NuPb1Vqfka/VyB2e67o4uOi6imOnfxtPJYfvlB0CppeUbTMQj5BybFQUSkO5tPT3DI8nWxQFsykMZLOt+Vh6Aphtc13tLLojg2w+fYSEnSLHmx7ffeLgaxzraiVpW9xYN4clFVP5/218mnAqjorCf73+bk71dvCLvRtRUKjJLeTbN/wB/7p9HUc7z6EqKnfNXMLyqmn882vPYzsOPt3kj5bewvmhPtYe28Mzh7cTt1IAfGL2MhaVT8GjGcwoLKc0lMuh9ub0JC7dYGpeCQnboj8WQVdVvIZJni9INJVIT6RSVAxNR1dVLMfODDu8c3N8ZRrkq2EC4fshwcD4JsHAZZJg4NImSn2GEjHWNx0ExcV2XK6pnkFxMOeSz7vw43xhV+vI2LHrumiqSsq2SDk2jutiqBqmptMbC5MaHlMuCIRI2Ratg70kbQtNUZlRVE5j5znOD/Wll0RlFzAlv5TnG3cymIiRcmw+NnMJvbEwzx5+naRtkesL8MfL7+CX+zaxr+00tuuwum4uN02dx9+9/DhDyRiqovLtGz5Bx1A//7F7PaqiUJGdz1/d8Af8Yu9GjnW1oasqq+rmsLy6np/v3oDtphvnT8xexlAizq7Wk3g0gzx/kGtrZnKk8xzhRAxT0ykL5VEYCNHc342iKBiaRnEgG8d1iVlJVEVFV1X8hoeUbeOSbpw1Nd0YuxdMiRt5P8PJOD/eupaYncB14JNzVrCgrHa0Tv+YkWBgfJNg4NIkGLhME6XxHPFh18fFfWP69DtwXBcXN9MQuy7DSUHSv/cbHiLJONFkAst18OkG/fEon3z0+8SHlzY9ePdXqS8s52BHM0nLQldVrqudxc5zJzPJQ6YXlLK0chr/vvMV+uJhbMfhswtuIJpK8PDuV0jZ6bXIf73qP/HLvZvY0XICx3VZNWUOH5+5lP/x8mOEk3EMTePPVn6UaCrBL/ZsRFNVSrJy+dMVd7D22B5O9rRjajoLy+pYXDGV3x3dheO6eHWDa2pmYjs2x7vbMDSdLNPLzKJKzg/1EUsl8JomWaaPLNNHfzyCgoKmKgRMLwC288ZkPE1R33h/36XLesSH2XU9ctkwDA3LSg8/XC1d5+9GgoHxTYKBS5Ng4DJdrcHAO502w9BIpeyLJm2l74rTF2tNUYlZ6QQgjuPgNzy4uPTFwlhO+qJeGsqlMzxAV2RweL2wn+qcQnacO8FgPEbStlhcPgUXeOnkfhJWClPTuWfetWw4dYjdrU2kbIs5JVXcOWMxP9j8LN2RQRzX5StLbwUFfrx1LZZjk+sN8A+33scTB17jtbNHM2uRb6ibzScf/UE6GEDh3+7+MkXBbJ46uA1D08j3Z/GZBTewo+UEZ/u7MDWdaQWlzC2u4vVz6Ube1HRmFlagKAptg70YmoZXNynNymUoESPlpO/4vYaJTzdJWClQFFRA1zRAyTRyozV2fLV+3t7JRKuPBAPjmwQDlybBwPvUGR7g5ZP7UVQFXLhtWgN5/uD7Ps6bu6XBxXXJLLMaWcbkui6mbpC0LRJWEttx0TWNoOmhMzxA3ErnsS4MZqMpKid7zpOwU7guNJTV0tTbzsnu8yRti+KsHJZVTOeZI9vpigySsi1um96ATzf5xd6NxFJJvLrBn197Fy8c38eGU4ewHYeGsloeWLiK//HyY3RFBlEUha8uvZWgx8c/v/ZbXNcl1xfk/1n9SV48sZ/tzcfRNZWG0jo+PmspP9u9nkgygUfXuaN+IT7dZOPpwxiqRsjrZ1XdHE71dtAZHsDUdIqC2VTnFnKmrwvXddFVleJgDpqqMhCPoqkqhqoR9PhI2ResRVY0Wgd7+dLT/0rcSqEqCv/4kc+zuHzKW97/q+1udKI1nhOtPhIMjG8SDFyaBAPv057WUzzw5I+wXQdd1fj+HZ9jVlEF5aF8GjvPMZCIZtYLB0wPm08fIZZKoijwkfpFHOpo5vWW4yStdDrNu2cv46evv0DrYC+WY/OpuSspDGTzT1vWkLQt/IbJ3950D680HWDt8T3gwvzSar667Ha+u/E3dIT70VSNzzXcSFEwxIM7XkJRFLI9fr62/Hb2tJ5if/tpTE1nal4pN06Zw8sn9xNPpe/KG8prCRhejvW2oroqPt1kemEZ/bEIQ4kYuqoR9HjJ9QUZjEcz48J+w0RV0mPoqqpe0D3N8F3xW1cefxgNsO3Y9MejmcYmy+PD1K7+7NkTrfGcaPWRYGB8k2Dg0iQYeJ8uDAYUFBZXTGFpxTS+tORmHtzxYnpJkqbzkfqFlIXyeebwdjRVJWh6uWvmEpr7u2nqTY8dlwRzmFNSxdHO1syuVBXZ+fgMk87wAJqqYajpru2kncrk3zY0HU8mVebwGmNVzeT4vpwx4ol2cZb6jG8TrT4SDIxvEgxcmgQD79Oh9ma++buHh9cca/z4ri8xraD0XZcywfjvlp5oF2epz/g20eojwcD4JsHApUkw8D5Zjk0kmcDQVVKWQ9D0oqmX3sBivJtoF2epz/g20eojwcD4JsHApV1uMHD1D8JeJl3VyPb6J9zFTAghhHi/rv5bYSGEEEJ8IBIMCCGEEJOcBANCCCHEJDdp5wwIIYQQY8lxbGwHVGXs78slGBBCCCHeh/e7msEFHMcBXJz0AXCB7ccO0tHXzaeuuWXMl61LMCCEEGLcc10X102nZEuneH/jb6qiYLsumqJguQ6Wld4IzXYdcBxsVSFk+ojaKYzhDcA6wwPYiSRxDZR4kqSuUJ5dgK5pdCciVPlz2N5+imhnDzGvhh2NY9k2xeVlrCip46X2E9xUNJWN7Sc5cPgAKcsi4TdwzvfgmV7FN+bcyEvdp5jiy8FQdX746nM4Z9pJTi2FYy3YlYWcOtJIVtzmlgXLyQ2Gxuy9hQ85GHBddzg6AnV4Tf9IhKUoSiYycpw3dkG78PdCCCHeG9d1Od/bg6qqFIZy3/bv7/r84X1WXFxcJ92waqqGqigkHQuvZhC2EtjJFJYCtmVh2zaKaVDkz6I10k+pL0RPMkpXXy/JZLrBtYciJAMelpXUcibaT0A1CHq8bDh9mOj5TuL5QezzPcSDHq6fOofCQIitHae5u3oe/+fwq7TuOUAiPwt7KIKVSDJl3hz+eM6N/ODwev5k+jWsaT3CS5tfwU6kcIty4NR5PPOm8sPr/pA1rUeYFswn2/Txv7c+j33yHMqMKoymdigv4AvLbqYokM32rjP8YU0D27rPMtB4Aq04HyOewlRUtPxscN+408/z+qkpKEFHRQv68OZUYhbk4tVNluRWENI9GJrGny/7CMZCG9dr4l0E25qO8N9+/yLtlsXvdm7hMzd+ZEzbug816dCmTZvYv38/oVCIJUuWkJ2dzbp161AUhTvvvJOSkhIUReHpp5+mvb0dj8dDUVERd955J6qqyq6F74HUZ3yT+oxvl5N06P1cQke27dYUlZRjo6sa9vCdrAu4CjiWhaVAluklZqUA8Gg658P9uIkkjsfAisZIKlCQlU3Q9HJ6sJuZuSXs6jrLUGc3jtcklUyybvtmCivL+W83fYq1LYe5sWQqB/rOs6vxAFYsjp2fRfxUK25NMX88fzXbu8+SbXgp82fzv19fS+LYWZQZ1SSOnsEpyuYzy25mel4JT57Zxzdn3cBf73yOlq270MuLIJbAjcaZtXwpfzX/Fr5/ZCNfn7aC9Z1NvPTaRvSUg6eiGOVMO76Ztfz5/JvZN3ieHM1DeVYuTxzejtbRj7e6FK1zAC0nyNKaeor82Rztb2d5US27e5pJ9fSj+n3oLmiuQlZONlOyi2gO91IRyCFsJYjH46goKJqG5rgohkaOJ0ByeKdSXVWx0tmLMjuVgoKqKigo7ykd/IjLacBd1+Vs13lautpRFIX8rBxmVNSMSjBwVWQg/M1vfkN+fj6zZs0iLy+Phx56iNWrV2PbNlu3buWLX/wiiqLwk5/8hJtuuony8nJ+8IMf8O1vfxu/3y/BwHsg9RnfJlJ9XNclbiXw6p5Ru6NxccFNX2Df7dI0crF2h/cKVVDSe3y4Lq6i4DpOuttYU9EVjXAqQZbppT8RJZVI4CgKrutgpSzwGJQHcjg91ENVKJfuWJiWrg6cZArbZ5LsG8DK8rG0pI622CCu61DsC/F80z5SbV3YJbkk2jpJ+UyWTpvN9NxiftdyhM/ULeInjVto3nMAOyeInUqR7B+kauE8vjV3Nf/76GY+V7OIHb0tPL5hLQxGUWpLSR06ha+hnr9f9jH2Draj2g4NhVV8Z+MTuMda0GbXoDSdxyzJ5+5F1zGvsJLfnN7H12dcy/93eCO9h09gFOTQ2d3Fui2vUDy1jifu/0t2Rzu4uXQ6Jwa7OXa2CY+jYOaGMAfjeIryWF5SR2c8jKlqhEwfJ3rO40k56EE/RspBNXTyAiG8usFQMk6eN0B/Iopqu2i6hqak91VRNRVT1Uk5NoaqXbCFuvqeGth3+yxJBsJLuyoyEObl5XHy5EmOHTtGSUkJPT09FBcXY9s2XV1dmcdZlsXzzz+PaZrk5uZiGAaQ/iDo+ujOutR1bVSPN9Z0XUVRmDDpVCfm+Xn3hm4sjAzLOa578bbcrouDi6npxKwUpqZhOQ6xVAIrZfHdp3/O566/nZKiYnK9AeK2RcqxCBpemvo7cSIxLK+BE42RUqAwN58Sfzb7e86xpLCKLe1NdDWfI2VoWK5DciBMsKqUP6idz7Mth7i2qJaW6ABrdm7B6R/CKssjcbwZZUo5f7pwNWdiA/THwlxbNpVvb3iSxOEm3OmVWKdaUXKy+NiKG7mpagY/OriRv110B9878BInt+1Czc1KX4j7w9StWMLfLr6Dp1r2c//UxRzoa+O5XZswIwm0qmKM5i6CM2qZV1LJkJvAdRxKdIWoncBQwdRUQtk5eENZlISyyfb5mFdQitc0uKF8KklfIZo33VXscVWycnMIer18afoyCr1Bbg8EuOaTlRgoKIaOeYuCZpoEDJO6/CIAdEXlF3f9EdpdCigKmqKkG1clvZvKXzbcjAJ8Z/HtKItvxwV+tWEd7WWnIQoHTh/jvmtvBqAoGOKasrq3/Rzk+P2Zn5f537pl+IiAx0wfy8h6x8cYjP53V9PU4eHm8fX9uVyqOjIUPno3CJp2eW3kh9Yz4LouAwMD+P1+wuEw//Iv/4LP5+OBBx7AsiyefPJJvvGNb2R6BlavXk1tbS0PP/wwq1atYsaMGcRiScLh0e0ZMAyVVGpi3KnBxLrzhHQAOJpR82i68KuTaeCHt5x00w/AId24Oq6LrmpoukrSstAVjaiVwLVsHFXBtiwsx8EwTLJML52xQUr8Ic5HB4kMDeEoYOGSisZwAz7m5pfRONBBuS+bmGOxr+UkdjiKm5tFvK2LZK6fVVUzGXJStEcGWFRYxcOHXiV2qgWnLJ9EezcpQ2PRrHmsKp/OQ8e28aezrucnjVto3LELx2fiGDpWezfFS+bzD4vu5EdHt3B3xRxOhXt4ePPvGDjdwpmQStbpLmbefD3fWfEx2pMROmND3Fg8hf+xbQ124xmUqRXo7b2YoSxWL1rBDaXTeOLsPj5ft4Rfn9rN+aMn0AM+DNPEE0tRNG0Kd1XOZmv3WeZkFzOQinOo5RSelIOaHcQXTeHJy2ZmfhlxxyJl2xR4g5zq78RMOeAxMB0FwzAI+vz4NINwKkHI9BJOJcBxUIcbUk1VM/85jotpaFi2g+Okt+8e2cD7apy3ZDv2cLnTY/+aevUH1pqm4LoTZ6+FdDAwPnoGPtRgYMOGDZw9ezY9RpKfT35+PkeOHMFxHBYuXMiiRYtQFIX/83/+D6FQiPz8fPbt28cXvvAFioqKZJjgPbga6vNeP3KWY/P45hf4+PJVeE1zeHhPQVdVko6NqWqkHJuUlcJyHGzXwbUdLFWhwBekPxnDpxm4CnQO9ZOKx0nqKk40TkJXqM0twlGgJx6mLlTA9o7TRNq7SAY8WEMREq5NeVk5S4pq+F3LYT5aOYeXzx/j2JEjpBSXlEcndb4bz9RKvjlvNU+c3c/ygmoGUjF+9vrLpM51oEyrJHGoCbeulD9fdgcpHQ71nOcTVXP5r1t/w+DOw2j1VditXSgeg1Urr+e+qUv4x8Mb+Os5N/HTE9vY+9pWdJ8XPSuA0t5L8aK5fHvezTzVcpDleZVEXYun92zBMxBDryrBaOvFV1XKnVPmk8SlMzpIQ0Elvzt1AL13CDUvhCdh4fF4qSgupTorj2P9HczOLaVpoItEOIKm62i6jukqeAMBSvxZ9CViBA0PtuMwEAvz3x7+ZzY37iPXF+TRv/wu00orM+dXU1Qsx043pQqopO9o38/laSwaYNmoaHyTYYJLG/fBAIBt23R0dKAoCkVFRSiKQmdnJ6qqUlBQkFlh0NPTQ39/f+b3wWAQRVEmdTDwXk+TYWhYloPjOihc3O07Mh7LcIOaytw5QCQRB8fBUgDLxtEUgqYPVVUIpxLkmn5OD3ZjR2IkNHBSFo7jEMgOURcqoHGgg/pQEUf7O2htO0fKtkjoKu5AGLcwh7tq5rK7r41Kfza26/DbIztJnu8mUZKD29xBoiCLT85dgc/jZU/POT5WMZu/372WNb98hHnXX0sgmsLGpWHRYv5o5rV87/B6/uvMG/l1y35efvlFXAWU/BDu6XayFs7kn1b+J37dvI9F2WXohsH/t/k5lJZOPLPrcI82o9aU8keLbsIwDQ50t/KJ2vn8aP96Eiea8VaVog1G0XSdmdNnsLq8nudbDvOxyjls6zxNx7lzmKYHM+BDC8fxFRWwsqSOE4NdlPpDOK7L2d5OPJaLHvSjJy1Ur4eSYA66rhFJJMg2fXTHwuiOi6rraKTvUnVdx1Q1EraFR9OHx8HfWG2jKFxym+0RV7IxtR2b5q4OXBxs26Esr5CA13fFXu/DIsHA+CbBwKVdFcHABzXawUDKSvHivu3c1rASTVW5YKQ0M6MU0j2/I42qg5uenISLoWooikLCtvDpJkPJOE4qhauq2LaFZduohk6BL4tzkX7K/CG64hF6B/pwHAfH0EgNRbADXuYXVHAm0ktQ9+BRNV5rPobTN0QqL4jV0Ysd8rG0ajohr5893S3cXFbPw0e30ne0Kf2YoQh2yqJ+wVzuqVnIT45v5fO1i/n9+WO8/OpGnFQKKz8LTp/Hv6Cef1hyF2s7jlPjzSbXG+D/3fAUnGrDrq9EO94KVUV8ZfltFGXlsL7tOF+avpz/vvN5encfRqkoRA/H0VyYu3AhX5yxkgePb+M/T13KS+0n2L9/Hxqg5WRh9gzhra3gCzNXsLu3lapADqaq81rzUZS+MFpRLmZ/FCUUYEFZLT7DQ3t0gClZBdz/4P/k1de3Mq1uKj/8/DcJen0Eg1mU+rNpjw1S4gsxlEqQSibQVQ1F01AdB3SNoOElaVvoqpqZXKYNdw2PUId/fqeZw1eiMb1ags/3aqLVR4KB8U2CgUuTYOB9cl2XHccP8bXf/Bu3zl6MlrKxCkJYp9tw68r4LwtvZVtPM9maSXUon+9t/S2Jo2dQZlWTajyLU5zDZ5ffzNScIp44vZdvzVnNt3c8y/nt+9AqCnEjcYgnmbN8Gd+adxPfPfgyfzbjOtaea2TD9i1oloNWmo92tpOc+TP4i/k38XpvC8VmgDxPgF/u34zZMYBSVYSncxBPYR43TJtLUSDE4b52ri2q5fctR0i196Bm+TEcMBWNkvISZmWXcXyokymBfDriYfr6+9BQUEwDj+2i+jxUZuURtS1MVUVTVAYTMVTHxdVUDFcBVcFrmGiKiu06GGp6gpfiDjedykgDqqAqFy/FGY1GNJqI8/TWV4il4miKxt3LV5GXlf2BjzvWJlrjOdHqI8HA+CbBwKVJMPA+pSyLr//b/+J3x/Ywp6KWb97xh/hyc1AGo+i5WTQUVtGTjGIoGiHTy+n+LsyUg+73oVk2qq6T4w/i0XSGUglyPf70MhvHRR1uYFVFQdM0jOGxbUPVsId7GEa6e5VRHkedaBdnqc/4NtHqI8HA+CbBwKVdFUsLx5OhWJQppZV8uaCIgMfHtdPmEfIHLnpMwPRmfp5TWPGOx8rX0m9jnjfwjo8xhx+jX4WzkoUQQkxsk7ZnYMREu7OR+oxvUp/xTXoGxjfpGbi0y+0ZGPt9E4UQQggxpiQYEEIIISY5CQaEEEKISU6CASGEEGKSk2BACCGEmOQkGBBCCCEmuasqGLgSS/Svxt3IJpOJeHomYp0mCjk3499EOkcjO2OOB1dVngHbTm+KMppUVZkwa3AhXR/XdSfUOumJdH4u3FJ2Iphon7eRm4Or6LL4rqQ+49uVqI+qKmiaOrHTEQshhBBi9E3IdMSu69Lb28uOHTvweDysXLmStrY2jhw5Qm1tLTNnzuTIkSOcOnWKqVOnMnPmTABOnDhBIpFg7ty5AFiWxa5du+jt7WXx4sUUFhaOybCC67p0dXWxa9cu/H4/K1as4OzZsxw7dozp06czbdo0Dhw4QHNzM/X19UyfPh2AI0eOoGkaM2bMAODcuXNs27YN0zSpra1l7ty5YzZMMjg4yLZt21AUhZUrV9Lf38++ffsoKytjwYIFnDx5kmPHjlFRUUF9fT0vvPACqqqSlZXF9ddfj67rOI7Dvn37aGtrY8GCBZSXl49ZfeLxONu3bycSibBs2TJUVeX1118nFAqxdOlSOjs72bt3L7m5uSxevJhXXnmFZDKJx+PhxhtvxOfzEY/H+f3vf4+iKASDQW644QZ0fWy+otFolG3btpFMJlm+fDmpVIpdu3aRl5fH4sWLaW1t5cCBAxQWFrJkyRJ6e3vZuXMnBQUFLFq0CE3TcF038z2bPXs2tbW1Y3Z+UqkUr7/+Ov39/SxcuJCsrCy2bt2Kx+NhxYoVDA4OsnPnzsz3K5lMsm3btszfTdMklUrxwgsvYFkWPp+PG2+8EY/HMyb1SSQSvP766wwNDbFkyRJM02T79u0EAgGWLVtGT08Pe/bsIRQKsWjRIrZu3UokEiGRSLBy5UoqKipwXZcTJ05kriPTp08fs/Nj2za7d++mo6ODuXPnUlRUxLZt23BdlxUrVpBKpdi+fTuaprFy5Ur27dtHd3c3qVSKWbNmMWvWLGzb5qWXXiIej2OaJjfeeCN+v39M6uO6LocPH+bUqVNMmTKF6dOnZ87XsmXL8Pl8bNu2jUQiwfLly9E0je3btwOwfPlyQqEQruvS39/P9u3bycrKYtmyZRiGccXKfFXNGXg/NmzYQGlpKefPn+fFF1/k8ccfZ9asWaxbt46mpia2b9/OvHnzePzxxxkaGmJwcJCf//zn7NmzJ3OMgwcPcuTIEaqrq/nVr36F44xd2tWXX36Z6upqmpqaWL9+PU8//TRz5sxhzZo1NDU1sWfPHubMmcOvfvUrYrEYvb29/OxnP+PgwYOZY5w+fRrHcVi2bBm1tbVjVhfXdXnttdcIhUIkEgmee+45Hn30Uerr69m6dSuNjY1s2rSJuXPn8vzzz3P27FmOHDnCsmXLmD9/PpqmZeqzdetW6uvr+dWvfkUqlRqz+hw8eJBYLEZ+fj6//vWvefTRRyktLeXMmTPs2LGDl156ienTp7N7926OHDnCzp07Wbx4MYsXL8Y0TQBisRhHjhxh6dKlLFiwIFPPsajP7t27AfD7/Tz55JM88sgjVFdX09jYyL59+3j55ZeZNWsWW7Zs4eTJkzz++OPU1NTQ399PIpFOGd7Z2cm6deuYM2cOTzzxBJFIZEzqA3Dy5Ek6Ojqoqqril7/8JU888QShUIjBwUHWr1/Pyy+/TFVVFadPn2bnzp08/fTT5Obm4jgOg4ODQDqg2LNnD0uWLGHhwoVX9MJ8KY2NjQwMDFBSUsIjjzzCr3/9awoKCmhvb+e1117j5ZdfZsqUKRw6dIjGxkYaGhpYsmQJzc3NmQBzYGDgoutIf3//mNWnra2NEydOUF9fz89//nOee+45HMdB0zSef/55Nm3aREFBAYODg2zYsIHZs2ezfPlyuru7M8ewbZudO3eycOFClixZMmaBGqRvdkbamCeeeIKXX36ZlpYWSktLeeyxx9i1axeO4xAIBHjqqaf47W9/i2EYqKrKc889Nzz05vLrX/+a4uJiWltbef3116/o8MiEDQY+8YlPMGfOHOLxOKlUiuzsbGpra6mqqqK/v58vfOEL+P1+TNNE0zTWrl3LihUrLroTO378OPPnz6e+vp5IJEIsFhuz+nz6059m2rRpJBIJotEoRUVF1NTUUFJSQjwe54EHHsDj8eD1elEUheeff57rrrvuogZF13WOHj3Ks88+y/79+8esLgC33norS5cuJRaLoes6iqIwdepUZsyYwblz5/jiF79Ibm4uiqLg8/no6+tj7dq1vPjii1iWBaQv8PX19UydOhVFURgYGBiz+ixcuJDbbruNRCJBMBikr6+P2bNn09DQwIkTJ/jsZz9LVVUVqVSKYDBIPB7nxRdf5LnnniMejwPp8cO+vj7WrVvHCy+8MGbBDcDKlSu58cYbicfj+Hw+IpEIM2bMYN68eTQ1NfHAAw9QWlqKbdvYts3p06fZtWsXzc3NmbvL5uZmqqqqqKmpIRQK0dXVNWb1qa+v5+6778a2bYLBIK2trcybNy9zfj71qU9RX19PPB7H7/dz5MgRGhsbOXLkSKY+iqIQjUZ54YUXeP755zNBz1iYN28ed955J6lUiqysLDo7O5k7d26mPvfeey+1tbUkk0mys7PJy8vj/PnzTJ06leLiYiDdABcXF1NTU0NxcTFtbW1jVp/y8nLuueceVFUlGAxy+vRpGhoaWLBgAWfOnOEjH/kICxYsIBaLkZubS05ODvF4HMMwMj2fiqKQSCR4+eWXefbZZ4lGo2NWn1AolGljDMPg1KlTNDQ0MGvWLHp6eli0aBGrVq0iHo+Tm5tLRUVFJnCrrKwE0sFnT08Ps2fPZsGCBRw7duyKlnnCBgOWZfHYY49RVFREQ0NDJqJKbx+s0Nrayi9/+UvuvfdeTp48SWtrKx6Ph66ursyXXFGUtzxvrCSTSR599FGmTJmSGdYAcBwHVVU5c+YMjz32GJ/97Gc5cOAAAwMDqKpKZ2cnyWQSgPnz5/PNb36Te++9lxdffHHMGpuR93XNmjW4rsv111+f+ZvrpreA7unp4eGHH+buu++moqKCv/qrv+L+++/n/PnztLS0AKCqauY5Y3l+FCW9FfWmTZs4duwYd999d+ZvjuOgKAqxWIyf/exnLFu2jLq6Ov7Lf/kv3H///Xi93kxglpWVxbe//W3uv/9+Ojs7M/Uci/pAujeqra2Nj3zkIxdNdFIUhXA4zMMPP8zq1aspLi4mEAhwzz33oGkax48fv+g4Fz5vrCiKwu7du3nttde49957L/rsKIpCKpXi0UcfzQwjaprG3XffzdSpU9mxYwcAHo+Hb33rW9x///24rktjY+OY1Qdg69at7N27l09+8pOZ79RIfRKJBL/4xS+YN28edXV1uK7Lq6++yjXXXHNRcDNiPJyfxsZGfvvb33L//fdnbspGrr+O4/Cb3/wGn8/HsmXLcF2XTZs2XVQfXdf55je/yf333092dvZFvbxjoa2tjUceeYR7770Xv9+fOT+Qru8rr7zCuXPnuPPOO2lubmb27NnMmTOHM2fOvOVYH8b5mbDBwDPPPANAQ0MDgUCAcDjM8ePHaWlpoaCggH//939n1apV+Hw+AoEAc+fOpb29nf7+fgYGBti5cyczZ85k7969HDx4kJycHLxe7yVe9cp5/PHHCQaDzJkzh9zcXLq6ujh+/DidnZ0Eg0F+9rOfccstt2AYBjk5OdTX19PR0UFfXx/9/f3s2bOHw4cPs3XrVk6dOkVWVtaYdkO/9NJLdHV1sXLlSjRNQ1VVGhsbaWxspLq6mgcffJBly5aRl5dHV1cXv/vd72hubiYWi+H1etm+fTtTp07NPEfTNLKzs8esPvv372fHjh3ceuut2LZNUVER+/btY/fu3cyaNYuf//znVFZWUltby8DAAM899xxnz56ls7OT3Nxctm/fTldXF88//zzNzc1EIhGysrLGrD6vv/46hw8fZvXq1biuSygU4sCBA+zdu5f6+noefvhh6uvrqaiowDRNsrOzOX78OL29vWRlZbFt2zYqKipobm7m+PHjRCIRioqKxqQ+AE1NTaxbt47bb78dy7Korq5m165d7Ny5k1mzZvHkk0/i9/uZM2cOlmVRU1PD0aNHOXfuHPn5+ezcuZOenh6ee+45mpub6enpIScnZ8zqc+TIETZt2sRtt92GZVmUl5ezd+/eTH0effRRCgsLmT59OvF4nEgkQjgcJi8vD9d12b59O4WFhXR2dmauI+Xl5WNWn/b2dp544gnuuOMOXNdl+vTp7Nixg507dzJ9+nTWrVuXmY8TjUaxLIuzZ89SWVmJoijs2bMnc37Onj1Le3s7+fn5Y1afSCTCQw89lJm3UF9fz65du9i3bx8lJSXs27ePgwcPctNNNxGLxYhGo2iahqZpxGIxTpw4QXd3d+axu3btYvbs2Ve0zBNyNYHjOLz66qsMDg6iqirTp0/H4/HQ2NhIXV0dBQUFbNq0CcMw0HWda665hkAgwMDAAB0dHVRWVtLY2Mj8+fM5cOBAplsnJydnTKJnx3HYuHEjsVgMRVGYNWsWAMeOHaO+vp6srCy2bNmCaZqYpsm1116L1+ulp6eHgYEBioqKOHnyJDNmzGDnzp0kEgkaGhrIy8sbswmRO3bsoLOzE03TqK6upqCggP3791NRUUF1dTXr16/PBAnLli2jpaWFtrY2pk6dSlVVFfv372fhwoUcPXqUtrY25s+fP6YTPBsbGzl58iS6rlNQUJCZH5CTk8OcOXPYuHEjlmWhKAoLFiwgGo1y8uRJysvLmTVrFnv27GHevHkcP36c1tZWpkyZkhn+GIv6HDhwgLNnz6LrOiUlJdTU1LBnzx4KCgqYMWMG69evz/TiLFq0CIB9+/ZRWlrKzJkz2bNnDwsXLuT06dOcPn2a2bNnU1ZWNmZ3n6dOneLgwYMYhkEoFGL+/Pns3r0br9dLQ0MDW7duJRqNoigKM2fOJD8/n127dpGVlUVDQwMHDhxgxowZtLS0cPr0aSorK5k5c2amh+HDdvz4cRobGzEMg7y8PGbNmpUp77x589i8eTPJZBJFUZg3bx75+fkcPXqUBQsWALBr1y7mz59PW1sbx48fZ/r06VRXV4/Z+Wlra2PHjh2YponP52PJkiXs27cP13VZsmQJe/bsobe3F1VVqaurY+rUqezdu5eFCxeiaRr79++nrq4uc5NUWlrKnDlzxuyGZ2BggI0bN2bamOXLl3Ps2DGGhoZYvHgxZ86c4cyZM+i6TnFxMdOmTWPXrl0ALFq0iO7ubgKBAH6/n127dhEKhViwYMEVnVA8IYMBIYQQQrx3E3aYQAghhBDvjQQDQgghxCQnwYAQ4l3Zts26desYGBjITC5samoa62IJIUaRBANCiHelqio5OTk888wzdHR0sGHDBmKxGL/5zW/YuHEjqVSKffv28eSTT2ayMK5du5bXXnttTBN1CSHeOwkGhBDvSlEUli5dSjwe51/+5V+47bbbeOqpp6ipqWHv3r0cOHCA8+fPU1dXx5o1a2hvb+eVV15hxowZsiuoEFcJCQaEEJekaRq33norXq+Xmpoauru7OXfuHNXV1Xi9Xvbs2cO5c+cya8ALCwszGSSFEOOfBANCiPfE4/Hg9/sJBoNUV1eTl5dHKpVC13Xi8ThFRUW4rktnZ+eYrb8XQlweyTMghHhP4vE4bW1t1NbWMjg4yPHjx8nJyaGuro6mpiaSySShUCiT5rqurk56BoS4SkgwIIQQQkxy0pcnhBBCTHISDAghhBCTnAQDQgghxCQnwYAQQggxyUkwIIQQQkxyEgwIIYQQk5wEA0IIIcQkJ8GAEEIIMclJMCCEEEJMchIMCCGEEJOcBANCCCHEJCfBgBBCCDHJSTAghBBCTHISDAghhBCT3P8fCV7AQzCXiA4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant:\n", + "The plot above visualizes the projected financial metrics from the dataset. Here are the key trends and insights we can observe:\n", + "\n", + "- All metrics (Projected Revenue, Projected EBIT, Projected Net Income, and Projected Free Cash Flow) are on an upward trend, implying the company is expecting growth in its financial performance over the years.\n", + "- The Projected Revenue appears to be the highest among the metrics, with a steady climb each year, which indicates increasing sales or expansion of the company's business operations.\n", + "- Projected EBIT and Projected Net Income, while lower than Projected Revenue, also show a consistent increase, suggesting improving profitability.\n", + "- Projected Free Cash Flow follows a similar upward pattern, indicating the company’s ability to generate cash after accounting for capital expenditures is also expected to grow.\n", + "\n", + "Next, let’s visualize the Present Value of Free Cash Flow compared to the Projected Free Cash Flow to see the effect of discounting cash flows to their present value.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant:\n", + "The chart above compares the Projected Free Cash Flow (FCF) and the Present Value of FCF over time. The following insights can be gleaned from this comparison:\n", + "\n", + "- As we would expect, the Present Value of FCF is lower than the nominal Projected FCF for each year, reflecting the application of the discount factor, which accounts for the time value of money. This is a common financial analysis practice to calculate the current value of future cash flows.\n", + "- The gap between the Projected FCF and its Present Value seems to be fairly consistent, suggesting a stable discount rate is used across the projection period. However, it naturally increases with time as the cash flows are discounted back to their present values from increasingly distant future years.\n", + "- The declining Present Value of FCF, despite the increase in nominal terms, suggests that the impact of discounting becomes more significant with a further out future cash flow due to higher cumulative discounting.\n", + "\n", + "Overall, the data displays an optimistic view of the company's financial future, with growth expected in all major financial metrics. The consistency in growth rates and discount rates provides a predictable model for the company's performance. However, these projections would also need to be stress-tested against various economic scenarios to evaluate the robustness of the forecasts. Additionally, comparing these projections to industry benchmarks, past company performance, and considering macroeconomic factors would give a more comprehensive insight into the company's prospects.\n", + "\n", + "If there's a specific analysis or further exploration you would like to pursue with this dataset, please let me know!\n", + "\n" + ] + } + ], + "source": [ + "analyst_assistant(\"Visualize the data and provide insights on the trends.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", + "user:\n", + "Use python code to perform a discounted cash flow valuation using the given dataset. Make generic assumptions\n", + "\n", + "assistant:\n", + "To perform a discounted cash flow (DCF) valuation, we calculate the present value (PV) of projected free cash flows (FCF) for each year and then take the sum of these values. This is the value of the cash flows the company is expected to generate in the future, brought back to today's dollars.\n", + "\n", + "The formula for the present value of an individual year's FCF is:\n", + "\\[ PV = \\frac{FCF}{(1 + r)^n} \\]\n", + "where:\n", + "- \\( FCF \\) is the projected free cash flow for the year,\n", + "- \\( r \\) is the discount rate,\n", + "- \\( n \\) is the number of years from now.\n", + "\n", + "Since the dataset already includes a \"Discount Factor\", we don't need to explicitly apply the discount rate \\( r \\). We can simply multiply the \"Projected Free Cash Flow\" by the \"Discount Factor\" to get the \"Present Value of FCF\", which actually has been done already in the dataset.\n", + "\n", + "To find the total DCF valuation of the company, we would sum up these present values. Additionally, we would typically consider a terminal value that accounts for all cash flows beyond the last projected year, assuming a perpetual growth rate. To keep the analysis generic as requested, we will pick a reasonable perpetual growth rate and discount rate for the terminal value calculation. Let's proceed with these calculations using Python.\n", + "\n", + "assistant:\n", + "Based on the calculations using the provided dataset and generic assumptions:\n", + "\n", + "- The total present value of the projected free cash flows (FCF) from the given data is approximately $200.32 billion.\n", + "- The terminal value (TV), calculated at the end of the projection period using a perpetual growth rate of 2% and a discount rate of 5%, amounts to approximately $1,839.15 billion.\n", + "- The present value of the terminal value is about $1,441.02 billion.\n", + "- Finally, the total discounted cash flow (DCF) valuation of the company, which combines the present value of the projected FCFs and the present value of the terminal value, is approximately $1,641.35 billion.\n", + "\n", + "It's essential to note that the DCF valuation is sensitive to the assumptions made about growth rates and discount rates. Different assumptions could lead to significantly different valuations. These assumptions should be tailored to match the company's sector, risk profile, and prevailing economic conditions for a more accurate valuation.\n", + "\n" + ] + } + ], + "source": [ + "analyst_assistant(\"Use python code to perform a discounted cash flow valuation using the given dataset. Make generic assumptions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run status: queued\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: in_progress\n", + "Run status: completed\n", + "user:\n", + "Visualize this information. Seems incorrect\n", + "\n", + "assistant:\n", + "Apologies for any confusion in the previous chart. I will rectify the visualization by distinctly presenting the present value of the free cash flows year by year and emhpasize the present value of terminal value, so the distinction and proportions are clear. Let's create a more accurate chart.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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NHToUl19+OQzDwMGDB/Hxxx9Hxm7U1taivLwcADBw4MBIpqqqNhuAeXojwWq14oorrsAHH3yACRMm4IMPPsCVV14Z6dn64x//CFVVUVRUhAkTJkR9jafbuXMnDh48GNnHysvLcfDgwWbLqKoaucQTreFit9sxefJkrFixAr169cL27dtx0003YfHixc1yKioqgqZpGDduHDIyMjB48OA2jZGx2+3o1asXVFVFSkoK+vXrFxlfEL4cVFpaildeeQVmszly2au+vh7p6elnvT6KvS5XDBw/fhz5+fnYs2dPZCTsRx99hEOHDiE/Px9XXnklSkpK0K9fP+zZswdAqEXYo0cPXHHFFXj55Zexbt26ZgOZSE5RFHz729+ODGQzmUzNWo0WiwWKosBsNkdGPgOhgYNNR4crigKr1Ypx48ZhxowZAEIfNGlpadi+fTs8Hk/ksZqammaj6E0mE1JSUjBnzhy4XK7I6Gqr1QpFUSKXBsItmWgmTJiA++67r9ljVVVVUFU10vVutVoxZswYzJkzJ7ItLpcL//3vfzFkyBB897vfjVw7Pn3wWO/evVFdXR3p0g8vt2/fPqiqis2bN6OoqAgPPvggrFYr5s2bBwDIzMzEI488gsOHD2Pt2rX45S9/ib/85S8A0CxnUW/WkCFDcP/99wsvE1gslma5+P1+WK1WjBo1Ck8++SRqa2sj78eaNWvg8/nQq1evZut48cUXkZycjB//+Mdwu92YO3du5G9NR7iHf2qaFikCATT7/6YcDgfuu+8+5OXlRQoAVVXhdrtbvC+DBg1qlr/T6URGRkaz7H71q1/h97//PWw2G2bPno3CwkIAwK233hoZvCq7W+H0D19FUTB16lS88cYb2LJlCw4dOoT7778fxcXFeOWVV/CnP/0J+fn5eO6559DY2Bj1+cI/w8Wt1WrF2LFjmxVspw+QHTBgAF5++WXU1NREru8bhoHdu3cjKysLLpcL06dPx6OPPoqBAwciJycH+fn5wpzCYyraOvBQUZRmuZyeYfj4vPHGG5GUlATDMKBpWqsH/lL8dY57eM5C3759MWbMmMiOuXHjRuzatQtXX3019u/fj3Xr1mHEiBHNRgXn5OTgqquugsfjwYEDByInCGo9s9kMq9UKq9UqvMwSvl66YcMG1NTU4OOPP8bw4cObtUQuuOACbNmyJTKC/K233oLH48GECROwZs0alJWVYevWrXjggQfQ0NAAVVVRXV2N1NRU5OXlYf/+/UhNTcXhw4fx6aefIjk5GZmZmVi3bh1qamqwbNmyqLdKtdbEiROxa9euyHX49957D5WVlRg7diyKioqg6zqcTieWLFmC0tLSZjnk5eXhsssuw+OPP44DBw6grq4OX331FX73u9/h8OHD8Hg8SEpKgqZp2LBhA44ePYqamhp8+umnWLhwIbKysjBjxgw4HI6oHyxtoSgKJk6ciOXLl6OiogIHDx7Enj17MGLECPTo0QNutxvbt29Hv379MGLECLz77rvo169fi9sZPR4PXC4XdF3HypUrUV1djdraWmHhNWTIEGzZsgUnTpzAvn37sHPnTuH2WSwWWK1WWCwW4Qf16NGjUVJSgkAggKSkJHz88cc4cuQIli1bhrfffhtZWVm4+OKLYbfboes6Ro4cia1btyIlJQUNDQ145513hNsKhIqX+vp6uN3uFstlZmZi5MiRmD9/PsaOHYv09PRIoet0OlFSUoJNmzahrq6u2b6XkZGBgwcPoqamBps2bcLx48cBhIrSnTt3wmQywWaz4d1330V1dXWzdQ4aNAjDhg3D//t//w+HDx9GXV0dtmzZghdffBHf/e53YbFY0LdvX1itVrzyyiu4+OKLoWkaxowZg+LiYgSDQSQlJWHp0qU4evSo8HV3lKysrGbHZ0lJCT777DNeju1CulzPwOk7V1FREQ4fPoz33ntPet94ZWUlnnnmGUydOhUDBw6M1eZ2C71792527bKp/Pz8SDdgSkoK7rvvPvznP//BwoULkZOTg7vuuqvZCf7KK69EbW0tHn30UQChE2NaWhqmTp2KY8eORQYR3nzzzUhLS8Oll16KV199FS6XC/fffz9efPFFrFy5Eg6HAzfeeCNsNht++MMf4sUXX8QXX3yBwYMHY+jQoS32gfDo79NpmhbpzgaASZMm4dixY5GW+dChQ5Gfn4/evXvjxhtvxN///nfouo7+/fu3uHVM0zTcfvvtWLRoERYsWABd15GUlIQ777wT48ePR0VFBXbs2IHf//73GDlyJObOnYtly5bhBz/4ATZs2ICHHnoIqqriqquuQmFhIXr27BlpWYW72k9v2aWmprZoxQOhllthYWGkm7uqqgqPPPIIVFXFLbfcgoEDB0JVVYwdOxbHjh2Dy+XCgAED4HQ6MWXKlBb5ffOb38QzzzyDP/7xj5gyZQpuuOEGvP3225g2bVqz9ffs2RPJyckYMWIEtm/fjkceeQQ9evTARRdd1GKsQ/g1RbsfP7z94fds8ODB+M53vhPJtbCwEIWFhejRowfWr1/fLLt+/frhjjvuwAsvvICHHnoIZrMZs2bNgtVqRV5eXuT2UlVVI+sfN24cVq5ciTfffBPf/va3W7SCr776avzxj3/E7NmzAYQaJdOnT8cjjzyC3r1747bbbsO///1vbN26NfIhPXXqVGzatAkPPfQQBg8ejGnTpsHhcGDIkCE4ceIEHn/8cQDA8OHDW9xyazabcd9992HhwoV44oknoOs6kpOTcdddd2HcuHGRnrjLLrsscglDURQMHToUN910E5566inouo5+/fqhb9++qKqqQr9+/aQfzrm5uc1uvU1LS0NeXl6L96lHjx6R99LpdKKgoAA2my1yfC5btgxOpxPf+ta3WAx0IYohK5c7IV3XcezYMTzxxBP47W9/iy+++AJbtmzB3LlzsXfvXuTn50e6nT/44APce++98Hq9ePzxxzFz5kyMGTMm0q3JHfXMwqPewxOenP43XdcjExE1fUzX9UgPQkNDA2699Vb85S9/Qc+ePSPPCaBZL0P48fDzha/BB4PByHKnb0/4sfD1VU3Tmi3fdJvC64v2+qJth2j7wiOvRftP0+XC/77pdoYHtgKIrLvpaw9vY9PXGW07AbTIWvS6RK+p6XYCoUFj0Sb+Of3fh19L+Np2tG1u+t6Fu9+bPq/oNZ3pfWmaf3hAcdPsTn+86b5y+msIryO87U33vdO3p+lEQ02fP7zO8Lqa5tH0/Qnn1fT9PP39aO2+1PT9DwaDzfZHUU6irMP/5vRjJHxMqaraLKemx3zT19d03edigiQ6t7pcMbBx40Z8+umnKC4uxpAhQ3Dttdfigw8+iNwudNNNN+HLL7/E2rVrUVZWhhEjRmD06NF44YUXItdxJ06ciPHjx3NHjQG/34+//OUvKCoqiow4JyKizqXLFQPhSjTs9BZJuDXZ9GWFK9imv7NnIDbCtyWF7yVn5kREnU+XKgZC87C3vLWqqwl321FL4VqB8UTHfUeO+Yjx2JLrDvuOogAmU9vm0elSAwj9/iBqaz3x3ox2U1UFut61d7pzhScsOe47csxHjMeWXHfYdxQFSEtzQtPOvhjocrcWdgfsKZfh5RsZZiPHfMRC2TAfiq5L9Qx0F1EmkaOTuno33bnGfOSYjxizkUv0eNgzEAeqyupchAM75ZiNHPMRY8+AXKKfl9kzEAdd/brUucTWixzzkWM+YsxGLtHPy92iGAhNmHH2/05R2lcNNp145WxaJKoKhL9lN3zLo2y+9EQS6hngiUsktJ8xGxHmIxYaLQ8wn+ianpcTUbcoBirrDHywxoPgWRYE5/U04YLhLb/hbtOmDdizZxcsFisyMjJwwQVTWnyHPBCarW3nzu0YPnykcB2NjY04frwUffoURgqGpoXLzp3bUV9fjwkTJsEwdCxa9B58Ph9UVUXv3n0waNAQrF27BtXVVUhJScGkSRdixYrPUF9fD03TkJ/fA+PHd6fvDDcS/tqdDIskOeYjxmzkEn0sV7coBgIBA8cr9bMuBnLSov+DoqKDGDBgIAYNGoxXX/03MjIyceDAfuTm5iEtLQ1bt36FpKRkTJp0ASoqKlBbW4NVq1YgEAjgwgunwe1uxJdfroPLlQpd1/H55yvx/e/fic2bN8Dr9eLCC6egsdGNL79ci7q6OuTn9wAQGsCyb98efPvbt8LpdELTNLz33tvIzc3DpZfOxJYtm1BSUoy9e/fguutuQGpqWpu/haxzY+tOhC1fOeYjw2xkQj2S8d6K+OkWxUBHMwwDu3btjHwLmd1ux7ZtX2HatIvw/PPP4aabbsbKlcuwZctmbNu2BVVVFbDZ7EhOTsbixe+jvr4OV199LY4cOQwAOO+8gfjqq01wu93Izs7GO+8sRDAYxGWXXYHNmzc0q9g9Hi+2bg19q19OTi5KSopxxRVXweFwYNKkCwEAixa9i23bQgVJTk4O+vaN/l3xXVcCH5FnwNadHPORYTYkxgvVUSiKgtzcPPTr1x/f+c4tSEtLR1paGiwWK3Q9CJcrFenpGaivrwMQ+kZEj8cDs9mM4cNHwuNxIzU1FUOGDIPD4YTJZEZ1ddXJ75QHxo4dB6/Xg7S0dGRmZjfr4jeZNOTm5iIvLx8ulwsmkxkNDfUwDAP19fWR7yXPyQkvkxqnlM6l7nLJg4ioa+gWPQMmTUGGSz3raz7JjugfOiaTCbm5uejZM/TVrD5fFUwmMywWC3r06IWlSz/E4cMluOSSy7Bnz26MGTMO27Z9BU3T4HKloX//87BkyYeoqqrE+edPRElJMSZPnoqtW7egpqYaDocDffoU4pNPlqC09CgGDRocWbfd7kBBQW84nUkwDAMXXXQJ3nnnLfTr1x8HDuzH9OkzYLPZ0KtXAdLS0tucWWeW6N11MuwGl2M+MsyGxLrUdxN4vf6o0xGHvjbz7HdzVQW0KHcT1NXVwWKxwGq1Agh9CVJdXS1crlQEAgGUl5fB4XDCMHS89NILuPPOeaiurkIwGERmZhYMwzi5jANOZxLKy8uQlpaG+vp6+Hw+ZGdnIxjUUV5eBpvNBovFCqfTCcMwUF1dBZcrtdlXAtfX16G2thYpKS4kJSWhuroaKSkp3XK8AKdMlesOU6aeS8xHjMeWXHf5boLQdMRn3+nfLYqBeDAMA1u2bILP58X48RPPajS/pikIBrtM7DEVvrWQJ/ToNE1F8GxHyiYQ5iOmqqFbC7vQKT+musN5mcVAF9MdKtBziZcJxNjylWM+Yl3puIrH+VFV1S5/Xm5PMdAtxgx0Nara9SvQc4WTDsnxmrgc8xEL9152hWOr3m3g042+mJ4n++SZMea8xP1I7BavPGgYcAcDZ30KMCsqbFGuu0c7WNo7qU/4ORUl1HJp+nvT9YXX4/f7T343tfms1xUMBuH1emC3O9q1zfGZyIiTDsl0hRN5PDEfsa6Ujc8P7DscQCCGMwJazCqLga7usKcB/7dvK/xnubNPT8/GbT37t3j8889XYs+ePXA6HbDZ7Jg580pYLKdmKjQMo9kH5em/R6PrQWzcuAHjxo2Hpmn4/PPVyMrKQf/+A+B2N2LJkg9x1VXXRKY23rBhPUwmDWPHnt/iucLrE1X669atQXFxEa6//pvQNA1vvfUGvF4PzGYL8vN7YOTI0Vi27BM0NjZAURTMmHEpvvjic1RVVcJqtSE7OxtTpkyP26yGvIwixpavHPMR43TEct1mEtc26hbFQMAwUO73InCWHyD1gUDUx0+cOIGxY8djyJChePnlF7Fz5w5s2bIR5503EHa7Ezt3boPNZsPFF8/EJ58sgdfrRf/+A5CdnY3Vq1dCUVRccsllWLbsU1gsFtTUVGPMmHF45ZUXkZmZhX79+iE3Nx8rVnyGfv36Y/v2bQgGg3jnnbfg8bgxePBQeL0e+Hwq3nnnv7jqqq/hs88+wYgRI7Fp00ZUVVWgV6/emDz5QiiKiqqqSixd+iGCQR2jR4/BqlUrInc1hF7PcVx//Rykp2dAURS8885/kZfXA+PGXYni4iKUlZXh+PFjuOyyy5GX1yPu3xzIQkCM2cgxHzFmI5fo8XDSoSgMQ8fatZ/jww8XIRgMID09HSdOnMDIkWOwePF76NOnEMePH8fmzRtw4MA+DB48BFlZ2Vi06D1kZWVD0zSsXr0SBw/uxwUXXAhNM8Hv96OwsD8KCnoDAHr1KkBjYyMqKyuwefNGjB9/PrKyslFQ0BvLln16ctSvjqKiIui6jqNHD+PAgX3YunUzBgw4DytXLkN9fQMMw8Dq1SvRv/95uPjiS7F8+afo3/88jB8/MfJ9Cl6vFytWfIalSz/C9u1bceTIYQwaNBgmkxmFhf0xcOAg+P1+rFq1AkuXfoQtWzbFL3wAnHSI6FzgcSXDngFqQVFUjBgxCoMHD4Hd7kB9fR2Sk5OhaRqCwSDsdjvOP38ievUqQM+eBdi5czs2bvwycp0+MzML6ekZKC4ugsPhgMViOdm1f+o6vKZpGDVqDJYu/QgWiwUNDQ0nv/RoBAIBP0JdeaEuT8Mw4PN54ff7YTKZYLc7cPnls2Cz2QAAPp8XdrsdVqsVgUAw8m2M4e52i8WM0aPHIi0tHRaLBRs3fonjx4/D5UpFTU0NqqurYDKZMHLkaOTm5sFsPvtxChQbiX7COhPmQ9Q23aIYsKsmjElJP+vLBL3tzqiP22x2pKamIiXFBSBUHDgcTpjNZowdOx579uyCz+dDVlY2Vq9egaSkZKSkuDBs2Ajs2rUDqqpi8uSpcDqdUBQFNpsNdrsdgUAABw8ewIABA6AoCoYPH4kPP1yEb37zJthsdvj9flRUVMAwgIaGBmRmZiEtLQMffPAeampq0KtXb+zfvw979uyGpoUKFgAYO3Y8Pv30Y9hsNowbd37kwz0sKSn55JTK6TAMAzNnXolFi97F7t07UVFRjqlTp8PpdEaWib8E76+TSPSuzDNhPjIMRybR951uMc+AYRht2s0VRB8x7/N5oapa5ANV13X4fF5YrTYYhoHGxgaYTGZYrVb4fF74fD44HE6oqorGxkYoigK73Q6PxwObzQafzweTyXTypwabzYpgMNTid7vdsNlsUBQFjY2NMJlM0PUgFEWNzDDo9XphMmmwWCwIBnV4PG7Y7Q5omhZp/Xs8Huh6EA6HE36/L7L9hmHA6/XAYrE2m9UwEAjA43HDYrHCYrHA6/XCYrFElokXTjokx0l15JiPWFeadKiiRscLixtjejfBsEIzZk2yxm6F50DCzzOgKEqHXg2zWJrvEKqqwmazR9aVlJQc+ZvVaoPVaov87nSe6m2w2+0nl7E2+z38HQqKosDhcET9t0017bZXVa1FN364+Ii2/aGeCXuL5c1mc7PnCV9yiLeucKKKJ+Yjx3zEDIO37cokejYcQBgHapTvQ6CQeN/J0NkxGznmI8ZjSy7Ro+kWPQNdDbvAxdiyk2M+csxHjNnIJXo8MSsGAoEAtmzZgp49eyInJyfy+N69e3Hw4MHI72PHjsWuXbvQ0NAAAOjfvz8KCwtjtZkxoapAMIbXwroSTkcsx0l15JiPGCcdkkv0noGYXCbQdR0LFy7EU089hR07djT7m9PpRHZ2NlwuFxYvXoyGhga88cYbSElJQXZ2drNr6t2FzvFNEgYLAQlmI8d8xELZMB+RRN91YlIMhKa8nYHJkye3+Ft+fj5GjhyJmpoaTJkyBdnZ2VBVFXV1daitrUVycnK3O8ATvQI9MwYkwn1HjtfEZZiNTKLvOjG5TKAoCjIyMoQHqsfjwapVq/DjH//45GQ8o+BwOHDw4EFs2rQJ8+bNiwx+0TQ1Mjd/eCIfIFTVnXrs9J+tWebUfPgdsYxsnaHbPvROt11NlwnXXx23Xa17jwDl5Lc66p0mi1jnJVtn6GtW9bi+R50li2jLhG6fUzrddnWG41vT1JPjlYxOtV3R3iNNC10uPLVPNv/ZfH9t+zKn7xdNJ2uLdxZteY/aMzg9bgMIm76I3bt3IysrCy6XC7quY/bs2UhNTcXAgQPx+9//HsFgMPJd06fuIY7WW2AIfrZmGeMcLCNaVo8yiLAzbNe5zKt161QUA7oePkC7ShatWaYr7DtnWrajljmX26U3+8DrPNsV/+PbMIInf3au7Yq2TGgellPbevrPaI+1ZZmmy+qGIdh3Out5p+U6DSPav2+dmBQDwWAQu3fvxpEjRxAMBtG3b18cPHgQmZmZGDZsGHbt2oV+/fpBURR4PB488cQTmDp1KoqLizFs2LBms+lR99e04qXmFIUD5GSYj0y41ch8oknwqwSxKQYMw0BNTQ3Gjx8PRVHQ0NCA3r17RybZOf/885GUlAQAcDgcuOuuu7Br1y6MHDkSQ4cOPXmAdx/8nBNr3iNAp2ORJMd8ZJiNTKLvOjEpBkwmEyZNmiT8e9NbBxVFQXZ2NrKzs2OxaXHBWwvFFIW3Fsqw5SvHfMRCPW4A84mum7U5zxr73+OAtxaKsQiQYz5yzEeM2cglejycjjgOOB2xWKhngPmIMBs55iMWyob5iCT6rsOegTjgdMQy/DIVGbbu5JiPGLORS/R42DMQB4legZ4JW3dizEaO+cgwG5lE33VYDFCnwxaMGLORYz4yzEYm0dNhMUCdUIKX6ETnBI8rmURPh8UAUReS6F2ZZ8J8iNqGxQB1QoneYSfGXnA55iPDcGQSfd9hMRAHbL3I8NZCGWYjx3zEeGuhXKLvOry1MA446ZAYB4DJMR855iNmGLxtVybRs2HPQBxw0iExTjokx2zkmI8Yjy25RI+GPQNxwEmHxNiyk2M+csxHjNnIJXo87BmIA5WpC7H1Isds5JiPGMcMyCX6rsOegThI9ApUjtc1Zdi6k2M+YsxGLtHjYRuVOqEEL9ElEr31cibMR4bhyCT6vsNigKgLSfTWy5kwH6K2YTFAnRDP6EQdj8eVTKIXkiwG4iDRu6POhIPAxJiNHPMR4wBCuUTfdTiAMA446ZBYqDpP8BJdgoPA5JiPGLORS/R42DMQB7y1UIy3FsoxGznmI8aeAblE33XYMxAH7BkQY+tFjhNWyTEfMU5HLJfo2bCNGgecjliMPQNy3HfkmI8Yjy25RI+GPQNxwNavDFsvMtx35JiPGLORS/R42DNAnVCCl+gSid56ORO2fGWYjUyi7zosBqgTSvASXSLRWy9nwtavDLORSfR0WAwQEVHCS/COARYD1Bkl+mEpxm5wOeYjw2xIjMVAHLAn80wYkAi7weWYjwyzkUn0XYfFQBxw0iEZ3v4kw2zkmI8YJx2SS/Rdh7cWxgEnHRJjy06Ok+rIcf8R46RDcomeDduoccCJUcQ4MYoc9x057jtiPLbkEj2amPUMGIaBQCAAVVWhaVrk8UAgAL/fDyC0s1qtVgCAz+eDqqowmUzdbgdm606GrRcZtnzlmI8Ys5FL9HhiUgwYhoHdu3fjmWeewZw5czBhwoTI3z777DMsW7YM+fn5SElJwfXXX481a9Zgw4YN0HUdV199NQYNGtStCgJVBYLBeG9F56UoCk9cAqHjgNmIMB+x0HEFMJ/outFHTJvErBg4cuQIevXqhcbGxmZ/q6+vx8SJEzFjxgxYrVZ4PB58+OGHeOCBB1BWVoZXX30VDzzwQLcqBvg5J8dCQIzZyDEfMWYjl+jxxKQYUBQFM2bMQElJSYu/mc1mrFixAvv27YOqqpg1axYsFguSkpKgqiqqq6vh8/lgs9mgKAo0TYWuG1DVlq1HRVEifxMvE3rTmy5rGEbk8dYuc3brbP43TVMB6KctExpYePrzxGq7ZHm1ZbtCP89+u8LXNYNB/Yzr7Njtiv58py8Tz31H18X7Tlu2q63v0dnkFct9B0Akn9OziP/7eK7fozPv0yZT6NzZ/HjrnOcdTVOgKE3X3fxn9Gzbt4yqKlBVJa7H99ksE2272jOmKGbFQDSGYWDGjBmYNm0a7HY7Hn30UezatavZ35v+W8MwEAyGToTBYLQyzmj2t+jLRF+2o5ZpzXYpii4cN3Cutr29ebVlu6JfCpGvM3QCME6+17HcrrY/X0ct05r3SNcNyZiT2LxHTXWmfQcI7TuhfDrb+9ie52vrMjhtGR2GEb2HoLOdd4K6cXJbT/6L0342e7YOW+bc7jttPwe3fv9qT+9PzC4T6Loe+S8YDKKiogJWqxWbN29GVlYW+vbtC5/Ph7y8PAQCAVRWVuLYsWPIzs6GxWKJxWbGTKJ3R8lxAKEMu3rlmI9Yl8omDpvaleI5F2JSDPh8PvznP//Bnj17UFJSgmAwCJ/Ph9zcXPTu3Rv//e9/oaoqhg4dihEjRsAwDLz00ktQVRU33nhjtxovAHCgyplwAKEYB8jJMR8xDiCUS/TzsmLE4Kwb6vI91Y/V9H7X8Ilf13Vomtbs9/By4WW9Xj9qaz3nenPPOX7YyUW7/kYh4euGFB3zEQt/2HWFY6uiRscLixsRiOFdV0P7mnHVBdbYrfAcUBQgLc15cuzM2YnZmAGTSbwqRVGgNpmjNzRQUBMu39Xx1kKxUPHXxbo0Y4gtXznmI8aeAblE7xngdMRxwOmIxVgEyDEfOeYjxumI5RI9G05HHAf8oiIxTpkqx2zkmI8Yjy25RI+GPQNxkOgVqBxbLzJs+coxHzFmI5fo8bAYoE6I131FEr31cibMR4bHlYzP0FHu88KIYUZJmgl2rXN8DHeOrSBqhicskURvvZwJ85FhODJrq8rx2o6imKZ0W49+uDwrP4ZrFGMxQERECS9g6KgPBmK6Tr/ReUaTcygbdToc5CTGbOSYjwyzITEWA3HArkw5DnQSYzZyzEeG2ZAYi4E44K2FMrz9SYbZyDEfsVA2zIei45iBOOCkQ2Js2clxql055iPGSYdIhm3UOGjPd053d5wYRY77jhzzEeOxRTLsGYgDtl5k2HqRYc+JHPMRYzYkw56BOGBxLsfWixizkWM+MhwzQGLsGaBOhy0YMWYjx3xkmA2JsWeAOiG2XoiIYonFAFEXwl5wOeYjw3BIjMUAdULszhRhL7gc85FhOCTGYiAO2HqR4e1PMsxGjvmIcdIhkuEAwjjgpENiHAAmx3zkmI8YJx0iGfYMxAEnRhHjxChyzEaO+Yjx2CIZ9gzEAScdEmPLTo75yDEfMfYMkAx7BuKAX1QkxtaLHLORYz5iPLZIhj0DccDqXIatFxm2fOWYjxizIRm2UakTYutFhA07OeYjw3BIjMUAURfCxp0c85FhOCTGYoA6IZ60iIhiicVAHLArU46DnMSYjRzzEeOkQyTDAYRxwEmH5DjQSYzZyDEfMWZDMuwZiANOOiTD259kmI0c8xFjzwDJsGcgDjjpkBhbL3LMR475iDEbkolZMdB0R2xavbdmB+1u1b6qKggGeWBGE5oYhScukdCxwGxEmI+Yoign77ZgPtRSTIoBwzBQUVGBV155BTNmzMCwYcMif6utrcXbb7+N2tpaDBw4ENOmTcMzzzwDAFBVFeeffz7Gjx8fi82MGfYMyHDSIRkWSXLMR4zZkEzMioFly5ahvLwcZWVlzf62YcMGZGdnY/bs2fjtb3+LgQMHoqSkBPfddx8cDgfMZnMsNjGmQi3feG9F5xVqwTCgaNjylWM+MsyGxGJSDCiKgmuvvRb19fUt/jZt2jQoioLq6mpomgar1YqGhga8/vrrCAQCuOqqq1BYWBgZ/BL+II32gXr632QfuvFaJvy7yLnars6aV8tlFeGy8d2uc59Xa9Yp052yaOt2xWOdnWG7Wr/t0QuCzpZXN7syLNbkM6Fj8m97cDErBjRNi/o3VVVx/PhxPPvss7j++uuRlZWFu+66C/n5+SguLsY//vEPPPTQQ9A0DYpy6sWG/v/U85z6sDUirYOmyzZdpuWyLXc+2fOJl2n5wd/0/1U1/LvSbPtPbZd428+0ztZue8vt6uh1ioue1qwz/B63LYv2vkcGVFWBrscyr7N5j8L7kNLiec7dPi3brtD2RMurfVmc+TgSrTN8XHWm4yhW79GZ9mlVDR1X4f1HfN5p/XHU/ryiny8UVWnx/N1R+MujRPv02X02tHzfzkbMLhOc/rvX64WqqqitrcVTTz2Fb3zjGxg6dCg8Hg/q6+uRkpKCrKws+P3+yL83DCNyvT1aZRR+rOnypy97apnTl23988mXaf540/8PBptnEH27xNt+pnW2Zttjs060Ypno62x6ELRtu9r7HsUjr9avU9dbjjk59/u0eLvC+3THZ4EWy7ZmneEP3c52HMX3PTKa/Gzteaf1x1Fbt0v2XuvBxBg7ZOgGDMOQ7tNnc97R2zGJTUyKAb/fj/fffx9fffUViouLoWkaysvLkZ+fj/3798PtdmP9+vXYuHEjLr74Yqxbtw7r1q1DXV0drr76amGvAnVXCXAWaKNEOEG2B/ORYTgkphgxGKml6zpqamoQPFn+WK1WmEwmaJoGv98Pt9sd2hhFQUpKChRFQV1dHcxmM5xOZ6Trw+v1o7bWc64395zTNN5aKBK+hMI7LqLTNBXBIKewFGE+YqHLBNFb/Z1NRY2OFxY3IhA887Id5URKBXbmH4jdCgHc2WsAZmf37LDnUxQgLc0JTTv7+QRj0jOgqirS0tKi/s1iscDpdLZ4XLR8d8DpiMW6wokqnpiPHPMRO/3SJFFTnI44DjgdsVh4QA1Fx2zkmI8Yjy2S4XTEccAucDG27OSYjxzzEWPPAMmwZyAOVKYuxNaLHLORYz5iPLZIhj0DccDqXIatFxm2fOWYjxizIRm2UakTYuuF2oYNXxmGQ2IsBoio22Djl6htWAxQJ8QzOlHH43FFYiwG4oBdmTIc5CTDbOSYj1j4y96IouEAwjjgpEMyHEAow0FgcsxHjNmQDHsG4oCTDsmwZ0CG2cgxHzH2DJAMewbigJMOibH1Isd85JiPGLMhGfYMxAEnHRLjxChyzEaO+YixZ4Bk2DMQBxwzIMMxAzJs3ckxHzFmQzJso8YBGy9nwoBE2PKVYz4yzIbEWAwQdSFs3ckxHxlmQ2IsBqgT4kmLiCiWWAxQJ8TuTBH2gssxHxmGQ2IsBuKAPZlnwoBEuO/IMR8ZhkNiLAbigLcWyvDWQhlmI8d8xHhrIcm0+9ZCwzCg63pk4I6maTwgz4C3FopxAJgcJ6ySYz5ihsHbdkmszcWAYRhoaGjAZ599hg0bNqCurg4mkwkFBQWYOXMmCgsLobIJHJWqKggGeVRGE5p0iEWBCPcdOeYjFm6k8diiaNpcDNTX1+Ppp59GYWEhvve978HlcsHn86G4uBhvv/02Jk6ciAsvvLAjt7XbYOtFjCcqOeYjx3zEmA3JtLkYMJvNuP7662GxWJCbm4uNGzfi6NGjmDJlCkaOHInKysqO3M5uRVWBYDDeW9E5KUqoBcMTV3Sh1h2zEWE+YqHjCmA+FE2b+/EtFgveffdd7N27F/v378fbb7+NYDCIf//739A0DTk5OR25nd0KP+fkWAiIMRs55iMWyob5UHRtLgaOHz+OrVu3ori4GE8//TRUVUVtbS02bNiAnTt3duQ2UsLhAFSijsfjisTafJkgPT0dffv2xeDBg/HVV1/he9/7HgKBAPbv34++fft25DYS0Um8UUeO+RC1TbsuE1x++eX45JNPcOWVV6JXr1747LPPcPnll8Nut0Pn/XPUZuzKFGEvuBzzkWE4JNbmnoGqqirs3bsXc+fORVpaGgDglltugWEY2LhxI2pqajBjxowO29DuhK0XGd5aKMMBcnLMR4wDCEmmzcVAamoqevTogT//+c9ISkpCeno6vF4vjh07hoKCAnzzm9/syO3sVthpIsYiQI75yDEfMWZDMm0uBlRVxeTJkzFmzBiUlJSgsrISVqsVPXv2RGZmJicckuDEKGKcdEiOLV855iPGngGSadd0xIqiwOFwYODAgR21PQmBkw6JsQiQYz5yzEeM2ZBMu5vvgUAgspMZhgG/3x91p9N1HUVFRaipqWn2uGEYOHr0KLZv3466ujoYhgGPx4Ndu3bh4MGDCHbD2XnYaSIW6hngoAoRZiPHfMT4RUUk066Ppbq6Orz++usoKSlBWVkZSktL8cILL7T4ADcMAx988AEefPBBbNq0qdnftm3bhn/84x/YtWsXnnjiCTQ0NOC5557Dl19+iXfeeQeffvppt6tou9nL6WBGt3u/OxKzkWM+Ypx0iGTadZlg69at+PDDD7Ft2zaYTKGnGjZsWNTxAiNGjMDx48ebHayGYeCTTz7BVVddhVGjRuHxxx/HunXrcOTIEdx+++2oqKjAk08+iRkzZkDTtPZsaqfCxoscpyMW4zVxOeYjxjEDJNOuYmDSpEno2bMn/H4//H4/AMBut7foqlMUBQUFBVE/0KuqqiK3JrpcLpSUlMBms0HTNDgcDng8HgQCgchXI2uaCsMwmn1ghAacnfoQafnzVGu86d903YCqypeJ9rM16zx9mabrUlUVhqG3YZ2ibT/zOqN9wJ5tXh2VhWydiqJAVRUAeszfo7Zn0Zr96+y2S/QehZ5Pidt2dUxe526dp/LpuCzamtfZn3fav07ZMpqmRsYrtfW80573Mdo+LcpC00L7+Knnbf6z+TrbvkzTZeNxBUVV1cg5r33H0anzQ1u1ewDhl19+ibVr1yI1NRUAkJubi5tvvrnVLXmHwwG32w0AaGxsRGFhIfbu3QvDMODz+WAymSLPZRgGgsHwfXnRqltD8FO8TPRR/aLnad86T61LjzKIsDXrFG17a9bZMXl1VBaiZRTFgK6HD4TYv0cdvUzH5382+04st6uzvEfhfDo2i47OS3430blZp2EET/5szfOIzzvn7hg5JRg0YBintvX0n9Eea8syRrSXGUO6rp/8XGvvcRR+j9v+ItpVDABATU0NfvKTnyAzM1O4jK7rOH78OKqrq+FwOFBRUYEDBw4gLS0NEydOxCeffIJAIIATJ07gxhtvxMaNG7F+/XoUFxdj1KhR3eoSAbUGu3pF2A0ux3xkmA2JtbsYyMzMxDPPPIOBAwdCURSkpaVh+vTpaHqpIBgMYvPmzcjIyIDf78fevXvhcDhgNpsxefJkmM1m7N27Fz/4wQ+QmZmJO++8E59//jkyMzMxefLkZs/VHbSjeEsQDEiEYynkmI8MsyGxdhcDBQUFzQ7A8EDCpsxmM6644grhc1xwwQXNfs/KysI111zT3k3rtDjpkAwnHZJhy1eO+YiFrisDzIeiaXcxUFZWhgMHDgAIXQ7Iy8vjifwMOOmQGPcdOeYjx3zEDMNgryQJtbsYOO+885CRkQEAOHLkCE6cONHtuvU7mqoC3XAupQ6hKOwZkGHLV475iIXPyzy2KJp2FwP5+fnIzc0FAAwYMADPPPMMgsEgv5tAgl9UJMYTlRzzkWM+YsyGZNpdDCxcuBDr1q0DAPj9fo7+bwX2DIiF7i3mpEMibPnKMR8xjhkgmXYXA1deeSVGjBiB8vJypKeno3///rxMcAb8nJNjISDGbOSYjxizIZl2FwPbtm3DG2+8gYyMDNTW1mLChAnd+k4AigW27oiIYqndxcBXX32Fn/zkJ8jJyYHH48GCBQsQDAaj3mJIRO3DTjc55iPDIpvE2v2JnZSUhPXr12P48OEoLS2NzJlM1HY8YYmwp1eO+cgwHBLrkDED//nPf7B+/XqkpaXhhhtu4J0EZ8BaSYa3FspwgJwc8xHjAEKSaXcxEAgEkJ2dDYfDAUVRsH37duTn57N3QIK3FoqxCJBjPnLMR4zZkEy7i4G33noLTqcTeXl5UBQFycnJHbFd3RqnIxbjpENybPnKMR8x9gyQTLuLgby8PBQWFqKgoODk9zKr7BU4A05HLMYiQI75yDEfMU5HTDLtLgasVisee+wx5OTkAAB69uyJO+64g3cTSHDSITH2DMix5SvHfMQ4HTHJtPsT+9ChQ/i///u/SDGgKApnIDwDHosybL3I8EQux3zEmA3JtLsYyMzMxCuvvBKZeTA1NRUXXXQRLxVQO7B1J8LDSo75yPC4IrF2FwN9+vSJ3EpoGAb8fj8rUKJzhIeWHPMhapt2TwgwYsQIzJw5E4WFhThw4AC2b9/OYoDaifsPUcfjcUVibe4ZMAwDVVVVWLNmDT7//HOUlJRg9uzZmDVrFscMnAG7MuX4rYViHCAnx3zEeGshybS5GAgEAvjNb36DvLw83H777fjyyy/Rp08fOByOjty+bomTDsmxEBBjNnLMR4zZkEybLxOYTCZ873vfg6ZpePbZZ7Fu3To0NjZC5yfdGakquwbEFA4+lWA2csxHLJQN86Ho2twzoCgKRo4ciREjRqC0tBSffPIJ/vvf/2Lfvn34zne+w0sFEpx0SIytFznmI8d8xDjpEMm0626C0AQxCnr06IHvfOc7uO6661BSUsLq/Aw46ZAYJx2S4zVxOeYjxkmHSKbDpglUFAVOpxODBg3qqKfstnglRYatFxmeyOWYjxizIRl+13AcsONEjj1LYsxGjvnIMBsSYzFAnQ5bMGLMRo75yDAbEmMxQJ0QWzBERLHEYoCoC2E3uBzzkWE2JMZiIA7Yk3kmDEiE3eByzEeG2ZAYi4E4UJm6BCcdkmE2csxHjJMOkUyH3VpIrcdbC8XYspNjPnLMR4zZkAzbqHHA6YjFwhNZUXTMRo75iLFngGRi1jMQDAZRV1cHi8UCu90eOWgbGhrgdrsBhHbWlJQUNDQ0IBAIAAAcDke3+/IjTkcsxtaLHPORYz5inI6YZGJSDASDQbz00kuoq6tDbW0tbrjhBvTv3x+KomDnzp3YunUrdF3H+vXrcc899+CZZ57BwIEDYTabMXr0aIwaNSoWmxkznI5YjNMRy3G6XTnmI8bpiEkmJsXAsWPHsH//fvz617/Gjh078N577+Gee+4BAIwdOxZjx47Frl274Ha70bNnT5hMJsyaNQtOpxMpKSmx2MSY4rEow9aLDE/kcsxHjNmQTEyKgfLycqSlpcFkMiEnJwdlZWXQdR2apkFRFAQCAbz77ruYM2cOTCYTFEXB+++/jxMnTmDcuHG4/PLLI9eSVVWBYbSc0jf8mGEYUBSlyc9Ty55a5vRlW7OM0eJ6pOh5ZM8HAJqmwjD0KMuIt/3UOluzzNlk0Zbn66hlWi6rKKEWTDCot/l1tv09imderVlnaN8B9CjP0/Z1tn274ncciV5n6NgKxvE96uh9p73v0allQtmcKrbFz9P69+hc5AWExlWd/vznXByGUyhq6HPtzMdR6z4b1HbcqhaTYsBkMiF4sl88GAxGioCww4cPIxgMolevXlBVFb/61a9gt9tx4sQJPProo7jkkktgNpthGGixM58u9JjR7GfT5Vr+/9ksE72yjvY8py/a9HddN5q9juZ/F297k6VbscyZs2jtMrLX2d5lTl82VOwZ7diu9r5H8curNes0DKPFmJPW7NOy52vfdsXnOBKtU1Xbs++ceZm27hdnd3y3JS/jtPW0XCa83zTdf8Tb1fr3SLZM2/OKQw9hrNeH8Ots+t61b99pT+9PTO4m6NGjByorK3HixAls3LgRgwcPxqFDh3DixAkYhoGtW7di4MCBUFUVDQ0NePnll1FZWYmSkhK4XK4m1U5rCgHxiUS2bKyWkR1o53K7OmteLU8Spx8cnWO7WrvMuV6nbPBpd8qirdsVzifRsmjNMk2Prdits33LdHsdvu+0PbiY9Ay4XC5cf/31ePPNN+FyufC1r30Na9euRXZ2NrKysuB0OtG3b18oigKHw4FBgwbhtddeg8Viwfe///12dX10RjHv/upiwt1h1BIHyMkxH7Fw9zLzoWgUI0ZnXdFqmp74RaNdw497vX7U1nrO4VbGBj/sxJpeK6OWVFXhrakSzEesKx1bFTU6XljciEAM77o6kVKBnfkHYrdCAHf2GoDZ2T077PkUBUhLc54cW3R2YjbPgGwykNP/1t0nDlFVBcFgFzgi44K3Fsqw5SvHfMTYM0AynI44DthyEWMRIMd9R475iHHSIZLpXhfju4huNgSiQ3E6YjlOZS3HfMR4bJEMewbigF9UJMPWiwx7TuSYjxizIRm2UeOAxfmZMCAR7jtyzEeG4ZAYiwGiLoSNOznmI8NwSIzFAHVCPGkREcUSiwHqhNidKcIBYHLMR4bZkBiLgThgV+aZMCARDgKTYz4yzIbEWAzEAW9/kuHtTzLMRo75iIWyYT4UHW8tjANOjCLGlp0c85FjPmLMhmTYMxAHnHRIjBOjyDEbOeYjxp4BkmHPQBxw0iEZTjokw9adHPMRYzYkwzZqHLDxIsfWnRizkWM+MuwZIDH2DFCnwxaMGLORYz4yzIbE2DNAnRBbL0Qdj8cVibEYIOpC2Asux3yI2obFAHVC7M4UYS+4HPORYTgkxmIgDth6keMgMDFmI8d8xHhrIclwAGEc8NZCsVDLji0YEQ6Qk2M+YsyGZNgzEAecjliMkw7JMRs55iPGngGSYc9AHHA6YjG2XuSYjxzzEWM2JMOegTjgdMRi7BmQYzZyzEeMPQMkw56BOOCYARlORyzD1p0c8xFjNiTDNmocsPFyJgxIhPuOHPORYTgkxmKAqAth406O+RC1DYsB6oR4RifqeDyuSIzFAHU6HAQmxmzkmI8MBxCSGAcQxgG7MsU46ZAcB4HJMR8ZZkNi7BmIA046JMZbC+WYjRzzEeOthSTDnoE44KRDYmzZyTEfOeYjxmxIhj0DccBJh8TYMyDHbOSYjxh7BkgmJj0DhmFg3759+OKLL5CamopLLrkEdrsdALB161asXr0aVqsVycnJmD17NsrKyrBixQpYrVZccsklSElJ6VYHOScdkuGkQzJs3ckxHzFmQzIxaaPW1dXhn//8JyZNmoSGhga8//77kR1z3759SE9Px+zZszFjxgwYhoFnnnkGw4YNQ3JyMl555ZVYbGJMdaO65pzoToVfR2M2csxHhtmQWEx6Bo4cOYLU1FT069cPNpsNTz/9NK677rrIgbt7927U19fjvPPOQ2FhIfx+P4YOHYrevXtj6dKl8Pv9sFgsAEJdyIZhCD5Qw3879VM8grbjlxFvV2iUfPh5Ql3hLZ8vvEzstqt1ebVlu1q/ztP+qiiRx2K7Xac/n+CpTnu+WOcFRC8mY/keRV9na5aJzXaFHo/nPh3/4yjaOlUVMIyW623Ndp2+TMfuOy3XqSTIpdTwZ2BHHUftKYZjUgx4vd6TH+aAyWSC3++PnNhGjx6NXr16IS8vD0888QQuv/xymM1mKIoCTdOg6zqCwWDkuU7/oDhV7RpN/nbqZ2g1TZdpHvrZLNO6dUZbRoksG3qeyKuJukzstqt1eUXbrtDzhZYJLd/WdZ6+TGj9inLmLMTbZZx8DrTIS7xdpz9f9GXE+44R+Xdn2q7WZxH9PQpndKYsmi7Tse9R6/OSb1f4sY7crpZ5tWXfidU6z7R/tS+L5us8tZ+2drvE+3RHblfULKAkTD+GonRAXoq8mGiNmBQD6enpqKmpga7rqKysRHp6OgzDQDAYhKIoOO+885CcnIycnBzoug6PxwO/34+6ujrY7XaYzeaTz2RERuKfuv51qkIKP3b6z45apqOeT9fD18U713a1bZ2nlu+4dYb/a18W4f/tXHm1//nasu90/HvUce91Rz9fR+w7Hf8647/vhM6dzcfkdIbtiraMrhsJMStC+HjuqEzbc6daTIqB/Px8ZGZm4l//+hdKS0txzTXXYNGiRcjLy4Pb7ca///1vFBQUoKGhAePHj0dpaSmef/551NXVYebMmdA0LRabGTNGIuzl7cKARLjvyBkMSILZkFhMigFN0zB37lyUlpYiKSkJ6enp6NGjB8xmMxwOBwYNGgS3243rr78eNpsNN954I44dOwaz2YysrCx0t0FBqgo0ufJBzZzq3qeW5NeaifmINb+0R9RcTIoBRVFgsVjQu3fvyGNpaWmR/8/JyWm+USYTevbsGYtNiwveWijGIkCO+cgxHzFmQzIJMmazc+F0xGKcdEiO2cgxHzFOOkQynI44DjgdsRhbL3LMR475iDEbkmHPQBxwOmKx0G02bL2IMBs55iPGngGSYc9AHLBAl2MLRozZyDEfMWZDMmyjUifE1osIG75yzEeG4ZAYiwEiIqIEx2KAOiF2Z4qwp1eO+cgwHBJjMRAH7MqU4yAwMWYjx3zEOICQZDiAMA446ZAYZ0iT4yAwOeYjxmxIhj0DccBJh8Q46ZAcs5FjPmLsGSAZ9gzEAScdEmPrRY75yDEfMWZDMuwZiANOOiTGngE5ZiPHfMTYM0Ay7BmIA44ZkDE4IlyCrTs55iPGbEiGbdQ4YOPlTBiQCFu+csxHhtmQGIsB6oTYghFh606O+cgwGxJjMUBERJTgWAxQp8OuXjFmI8d8ZJgNibEYiAP2ZMqxq1eM2cgxHxlmQ2IsBuKAtxbK8NZCGWYjx3zEeGshyfDWwjjgrYVibNnJccIqOeYjZhi8bZfE2EaNA05HLMZJh+S478gxHzEeWyTDnoE4YOtFhq0XGfacyDEfMWZDMuwZiAOOGZBj60WM2cgxHzFmQzL8WIoDFuhybMGIMRs55iPGbEiGxQB1QmzBiLBxJ8d8ZBgOibEYICIiSnAsBqgTYnemCHt65ZiPDMMhMRYDccCuTBne/iTDbOSYjxgnHSIZ3loYB5x0SIyDnOSYjxzzEeOkQyTDnoE44MQoYpwYRY7ZyDEfMR5bJMOegTjgpENibNnJMR855iPGbEgmJsWAYRioqqrC9u3bkZqaiiFDhkDTNABAMBjErl27UF5ejgEDBiAnJwfr1q1DQ0MDAKBfv37o27dvLDYzZlQVCAbjvRWdU6j1whOXSKhlx2xEmI+YoignLxMwH2opJpcJPB4PnnzySXi9Xnz22WdYvnx55GS/evVqfPjhhwCAP/3pTygvL8frr78Oh8OBjIwM2O32WGxiTHHMgIzBQkCC2cgxH7FQNsyHootJMXD48GGYTCbMmDEDs2fPxqpVqyJ/GzZsGG6//XZMmjQJiqLA4/FAVVW43W40NDQgJSWl2x3gvGx3JgxIhNd85ZiPDLMhsZhcJqivr4fD4YCiKHA4HGhsbISu69A0Denp6fD5fHjzzTcxZMgQ5OTkYOTIkbBarTh48CA2bNiAefPmRQa/aJoKwzBOdnkZkQ9Ww0CTx07/2ZpllEjR0RHLyNapaSoAvdNtV9NlwvVXx21X694jQIGqKggG9U6TRazzkq1TVRUYhtLh6zyb96izZBFtmVP5dK7t6gzHt6apJ8crGZ1qu6K9R5oW2sdP7ZPNfzbfX9u+TNNl41ErqZoKVY2+vwJn/x61Z3B6TIqB5ORkNDY2wjAMNDQ0wOFwQFVDH+p+vx/PP/88kpOT8Z3vfAeapuGaa66By+XCoEGD8Pvf/x7BYDCyfDAY7mOP1ltgCH62ZhnjHCwjWlaPMoiwM2zXucyrdetUFAO6Hj5Au0oWrVmmK+w7Z1q2o5Y5l9ulN/vA6zzbFf/j2zCCJ392ru2KtkwwGLoNMrytp/+M9lhbljGivYQY0oMdu78aRttfREyKgZ49eyIYDOKzzz7Dtm3bMG3aNCxfvhyZmZkoLi7G4cOHce2112LXrl3Iy8vD/PnzMX36dBQVFWHo0KEwmXjTQyJpWvFSc4rCAXIyzEeG2ZBYTD5lrVYr7r77bmzbtg0XX3wxBg8ejKKiIjidTqiqCpPJhPLycqiqioKCAtx5553YuXMnhg0bhuHDh588wLsPfs7JsRAQYzZyzEeG2ZBYTIoBRVGQnp6OqVOnRh7r168fACA3NxdDhgxp8W9ycnJisWlxwVsLZXhroQxbvnLMRyzU4wYwH4qG/e9xwFsLxVgEyDEfOeYjZhicjpjEOB1xHHA6YjFF4ZSpMsxGjvmI8dgiGfYMxAGnI5Zh60WGLV855iPGbEiGPQNxwOJcjq0XMWYjx3xkmA2JsRigToctGDFmI8d8ZJgNibEYoE6ILRiijsfjisRYDBB1IewFl2M+RG3DYoA6IXZnirAXXI75yDAcEmMxEAdsvcjw9icZZiPHfMRC2TAfio63FsYBJx0S4wAwOeYjx3zEOOkQybBnIA446ZAYJ0aRYzZyzEeMxxbJsGcgDjjpkBhbdnLMR475iDEbkmHPQByoTF2IrRc5ZiPHfMQ4ZoBk2DMQByzQZXhdU4atOznmI8ZsSIZtVOqE2HoRYcNXjvnIMBwSYzFA1IWwcSfHfIjahsUAdUI8oxN1PB5XJMZiIA7YlSnHQWBizEaO+YhxACHJcABhHHDSIbFQNy9bMCIcBCbHfMQ46RDJsGcgDnhroRhvLZRjNnLMR4zHFsmwZyAO2DMgxpadHPORYz5i7BkgGbZR44A9A2JsvcgxGznmI8Zji2TYMxAH7BmQYetFhi1fOeYjxmxIhm3UOGBxfiYMSIQtOznmI8NsSIzFAHVCbMGIsHUnx3xkmA2J8TIBdUIKeOKi7iweg/lUVWGvJAmxGCCh8AkrlucsVVG6zABLXTfQ4IntSd1h12DqIvkUlQZwsDQY03WOPs+C1KSu8Ym38isfDh2PXT4ZLg2XT7BC7RrxUIwlfDFQXa/jRFXsRvSpCtAzV4Omxu4TRFEADWc/kljXgQ++8KKqLnb5JDtUXHGBBVoMP/A0RYHahiZTg8fAv5e44fXF7r28cKQV4waaY7a+9jharmPdTn9M19knz4TUJC2m62yrylodR8tjd2wFgicn9WIxQFEkfDFwsDSIJeu8MVufoeooG1wEv9UXs3XmWGz4ad/BsChnd5I0AJRV6yirjt0Jq6Teg+W7d0BXY7fOa7N74qKM3LP+d4YBeH0GvDH8vPu8ohzrD9XFbH2KouCqrB7oaXPEbJ1tZ2BR2VFofnfM1phtseFrOb2gdYH+98ZgEJ9Xl0FRYle8ZltsOM+ZErP1UdslfDEQ60vTumGgyN0Ad9ATs3W6gwHoXeQSvF83sL+xDsEYFgNVgdgVZu21o74Gh8pKY7Y+BcBEV2aXKAYMAF9UlaHSXxOzdZ7nSMY12T27xC1Cx7xuLD6wA3oMi4FLM3JZDHQRXeTqY/fS+U8bRNQddZE2AcVBzIoBwzDg9XoRCASa3f5jGAYCgQC8Xi/0k7PxGIYBn88Hv9/fLW8V6n6viIi6BJ58SCAmlwl0Xce7776L/fv3Q9d1fOtb30J+fj4URUF5eTlefPFFKIqC/Px8XH/99fjss8+wZcsW6LqOr3/96+jXr1+3mkyk+7wSIupSePIhgZj0DJSVlWHdunW46667MHXqVCxcuBBAqAfggw8+wIgRI3D33Xdjz5492LFjB5YuXYrbb78ds2fPxhtvvNHtege616shoi6DJx8SiEnPwPHjx5GRkQGbzYaCggK8/fbbMAwDiqKgqKgIU6ZMgdlsRm5uLrZu3QqHwwGn04n8/HxUVFQgEAjAYrFA01TY7eYz3tetKGj1Mr1yVUwaHrtbkXRFR8/MnvBpsRuCnma2IMlhgUk5u9pPN4BRA4H6xtidQfwmCwoyekNXYjeAcEhaOmy20O16Z7PvKKqB84cBgUAMNvKk85KzUeG0xGx9ChT0TEmK5NPi75K8+vRQEDBiO0a5d1o+GqypMVtfjsUGh8MCtZVN7qZ5DS5UkJEau/3cazahT3pvGDEcQDjY6RLuOzKpMDBhWGy/x6XObmCwK7bzYgxKT4PNZm7Veac1FKXtU3LH5EhtunHhIqDp38Itf8MwoGlas9+bMpk0JHXwPcSDk4HB/Tr0KVshLdYrbLOZk2xxWGtGHNZ59pIBXDPNHuO1ugD0jvE622bkwNB/seWK9QrbbMroeBxbWXFY59lLTgauvSgex1Z+jNfZecTkMkFeXh4qKipQX1+PPXv2oLCwEBUVFaitrcV5552HnTt3wu1248iRIxg9ejQ8Hg+qq6tx8OBB5OXlwWzuGpOshIVm7mv9f9H+XXfVmizOJrPupi35nP5Yd9aWfSVR9p/W7Bet3Z+6qo56v3Vdh8fjadW/DQaD8Hq9XT47xYjBKzAMA59++im2bNkCi8WCG2+8EWvWrEFubi4GDBiAl19+GR6PB4MHD8bMmTOxdu1afP755zCZTLj++usjgw27Ap/Ph/feew9HjhxBeno6rrvuOuzevRsrV66E1WrFtddeC7vdjrfeegvV1dXo1asXZs+eDZPJBK/XiwULFmD27NkoLCyM90vpcIZh4NChQ1i0aBH8fj8mT56MYcOGYeHChSgvL8eAAQNw6aWXYseOHVi+fDkMw8Dll18Ok8mEl156Cenp6TCZTPjGN76BjIyu0XtwNgzDwObNm7Fq1SoAwFVXXQWXy4W33noLbrcbEydOxNixY7FixQps3boVZrMZ1113HdLS0rB48WIUFRWhoKAAs2bNgsnU/aYQcbvdePvtt1FWVoacnBx87Wtfw6ZNm7B+/Xo4HA5cd911AIC33noL9fX16N+/P6ZOnYo33ngDDQ0N8Pl8SE5Oxm233Qa1q8x53UqGYaCqqgpvvfUWGhsbMXjwYEyfPh1LlizBgQMHkJOTg2uuuQYnTpzA+++/D5/Ph0mTJmHMmDFYunQp9u/fD5vNhq9//etIS0vrMufb023duhXLly9HeXk5rFYrXC4Xrr76avTs2fOsnqeyshKrVq3C7Nmzz5jFvn37sGzZMtx2221dNjcghpcJZsyYgWnTpkFVVSiKgiuvvDLytzvuuAO6rkPTNCiKgokTJ2L8+PFQFCWyfFdRUlKCuro63HLLLXj66aexevVqLF26FD/60Y+wf/9+vP766xg3bhxSUlLwta99DQ8//DBGjhyJvn37YunSpVi/fj2mTp0a75dxzixfvhxTp05FcnIynnjiCVx88cVwu924+eab8eSTT6Jv375YsWIFrrnmGpSXl+Pll1/GNddcA5fLFTnYLJbYXTOPJb/fj1WrVuG6665DSUkJXn31VWRkZKB///4YPnw4Hn/8ceTm5uKrr77Ct7/9baxYsQLvv/8+hg0bhmPHjuHb3/42Nm/ejEAg0C2LgX379gEAbrnlFjz++OPIy8vDBx98gJ/85CfYsGED3n77bRQUFKBnz54YP348HnroIYwePRrf+ta3oOs63n/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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant:\n", + "The revised bar chart shows:\n", + "\n", + "- The blue bars represent the projected free cash flow (FCF) for each year specified in the dataset, without discounting.\n", + "- The green bars represent the present value of those cash flows after discounting.\n", + "- The terminal value is also plotted as a projected FCF (blue) and its discounted present value (green) in the final bars.\n", + "\n", + "This visualization more accurately depicts how the present value of each year's free cash flow compares to the non-discounted projected amounts and the overall significance of the terminal value in the DCF valuation calculation.\n", + "\n", + "I hope this chart meets your expectations. If there is anything else I can assist you with, please let me know.\n", + "\n" + ] + } + ], + "source": [ + "analyst_assistant(\"Visualize this information\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Delete the thread and assistant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for entity in [(client.beta.assistants, assistant), (client.beta.threads, thread)]:\n", + " entity[0].delete(entity[1].id)\n", + "\n", + "for file in assistant_files:\n", + " client.files.delete(file.id)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + 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