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generatief modelleren, veel generaties om mee te trainen +% - Achtergrond electrititetismarkt +% - Achtergrond Generatief modelleren (van NRV) +\subsection{Electricity market} +The electricity market consists of many different parties who all work together and want to make a profit in the end. An overview of the most important parties can be found in Table \ref{tab:parties}. + +% table +\begin{table}[h] + \centering + \begin{tabularx}{\textwidth}{|C|C|} + \hline + \textbf{Party} & \textbf{Description} \\ + \hline + Producers & Generates electricty. The electricity can be generated using coal, nuclear energy, wind parks etc. \\ + \hline + Consumers & Uses electricity. This can be normal households, companies but also industry. \\ + \hline + Transmission system operator (TSO) & Party responsible for reliable transmission of electricity from generation plants to local distribution networks. This is done over the high-voltage grid. In Belgium, this party is Elia.\\ + \hline + Distribution system operator (DSO) & Party responsible for the distribution of electricity to the end users. Here, the electricity is transported over the low-voltage grid. \\ + \hline + Balancing responsible party (BRP) & These parties forecast the electricity consumption and generation of their clients. They make balanced nominations to Elia. + \\ + \hline + Balancing Service Provider (BSP) & Parties that provide the TSO (Elia) with balancing services. They submit Balancing Energy Bids to Elia. If needed, they will provide balancing energy at a set price. \\ + \hline + \end{tabularx} + \caption{Overview of the most important parties in the electricity market} + \label{tab:parties} +\end{table} + +Elia, the Transmission system operator (TSO) in Belgium is responsible for keeping the grid stable. They do this by balancing the electricity consumption and generation. If there is an imbalance, Elia will use reserves to balance the grid. These reserves are expensive and are paid by the market participants. The prices paid for the activations of these reserves is called the imbalance price. Keeping the grid balanced is a very important but also a very difficult task. If the grid is not balanced, it can lead to blackouts but also other problems like damage to equipment and so on. +\\\\ +Balance responsible parties (BRPs) forecast the electricity consumption and generation of their portfolio to effectively manage the balance between supply and demand within the grid they operate in. They submit a daily balance schedule for their portfolio the day before to the transmission system operator. This consists of the expected physical injections and offtakes from the grid and the commercial power trades. The power trades can be purchases and sales between BRPs or they can even be trades with other countries. BRPs must provide and deploy all reasonable resources to be balanced on a quarter-hourly basis. They can exchange electricity with other BRPs for the following day or the same day. There is one exception where a BRP can deviate from the balance schedule. This is when the grid is not balanced and they can help Elia to stabilize the grid. In this case, they will receive a compensation for their help. When a BRP deviates from the balance schedule in a way that destabilizes the grid, it will need to pay the imbalance price for the deviation. +\\\\ +The imbalance price is determined based on which reserves Elia needs to activate to stabilize the grid. The imbalance of a BRP is the quarter-hourly difference between total injections and offtakes from the grid. The Net Regulation Volume (NRV) is the net control volume of energy that Elia applies to maintain balance in the Elia control area. The Area Control Error is the current difference between the scheduled values and the actual values of power exchanged in the Belgian control area. The imbalance of the system (SI) is the Area Control Error minus the NRV. Using the System Imbalance, the imbalance price is calculated. +\\\\ +Elia, the Transmission System Operator (TSO) in Belgium, maintains grid stability by activating three types of reserves, each designed to address specific conditions of imbalance. These reserves are crucial for ensuring that the electricity supply continuously meets the demand, thereby maintaining the frequency within the required operational limits. The reserves include: + +1) \textbf{Frequency Containment Reserve (FCR)} \\ +FCR is a reserve that responds automatically to frequency deviations in the grid. The reserve responds automatically in seconds and provides a proportional response to the frequency deviation. Elia must provide a minimal share of this volume within the Belgian control area. This type of volume can also be offered by the BSPs. +\\\\ +2) \textbf{Automatic Frequency Restoration Process (aFRR)} \\ +aFRR is the second reserve that Elia can activate to restore the frequency to 50Hz. The aFRR is activated when the FCR is not sufficient to restore the frequency. Every 4 seconds, Elia sends a set-point to the BSPs. The BSPs use this set-point to adjust their production or consumption. The BSPs have a 7.5-minute window to activate the full requested energy volume. +\\\\ +3) \textbf{Manual Frequency Restoration (mFRR)} \\ +Sometimes the FCR and aFRR are not enough to restore the imbalance between generation and consumption. Elia activates the mFRR manually and the requested energy volume is to be activated in 15 minutes. + +The order in which the reserves are activated is as follows: FCR, aFRR and mFRR. BSPs provide bids for the aFRR and mFRR volumes. The provided bids consist of the type (aFRR or mFRR), bid volume (MW), bid price (per MWh) and start price (per MWh). +The start price is used to cover the costs of starting a unit. +\\\\ +Elia selects the bids based on the order of activation and then the price. The highest marginal price paid for upward or downward activation determines the imbalance price. This means that the last bid that is activated determines the imbalance price. This price is paid by the BRPs that are not balanced. The imbalance price calculation is shown in Table \ref{tab:imbalance_price}. + +\begin{table}[h] + \centering + \begin{tabular}{|c|c|c|} + \hline + & \multicolumn{2}{c|}{\textbf{System Imbalance}} \\ + \cline{2-3} + \textbf{Imbalance of the balance responsible party} & \textbf{Positive} & \textbf{Negative or zero} \\ + \hline + \textbf{Positive} & MDP - \(\alpha\) & MIP + \(\alpha\) \\ + \hline + \textbf{Negative} & MDP - \(\alpha\) & MIP + \(\alpha\) \\ + \hline + \end{tabular} + \caption{Prices paid by the BRPs} + \label{tab:imbalance_price} +\end{table} + +The imbalance price calculation includes the following variables: \\ +- MDP: Marginal price of downward activation \\ +- MIP: Marginal price of upward activation \\ +- \(\alpha\): Extra parameter dependent on System Imbalance \\ +\\ + +TODO: Add more information about the imbalance price calculation, alpha? + +The imbalance price can be reconstructed given the bids of a certain quarter/day and the System Imbalance. During this thesis, the system imbalance is assumed to be almost the same as the Net Regulation Volume. This is a simplification but it is a good approximation. The goal of this thesis is to model the Net Regulation Volume which can then be used to reconstruct the imbalance price and to make decisions on when to buy or sell electricity. + +\subsection{Generative modeling} +Simple forecasting of the NRV is often not accurate and defining a policy using this forecast will lead to wrong decisions. A better method would be to try to model the NRV and sample multiple generations of the NRV. This should give better predictions and confidence intervals can be calculated from these. +\\\\ +Generative modeling is a type of machine learning that is used to generate new data samples that look like the training data. The goal of generative modeling is to learn the true data distribution and use this distribution to generate new samples. Generative modeling is used in many different fields including image generation, text generation etc. +\\\\ +In this thesis, generative modeling can be used to model the NRV of the Belgian electricity market using different conditional input features like the weather, the load forecast etc. The model can then be used to generate new samples of the NRV. +\\\\ +There exist many different types of generative models. Some of the most popular ones are: +\begin{itemize} + \item Generative Adversarial Networks (GANs) + \item Variational Autoencoders (VAEs) + \item Normalizing Flows + \item Diffusion models +\end{itemize} + +In this thesis, autoregressive models will be used to model the NRV. Autoregressive models are models that predict the next value in a sequence based on the previous values. The model can be trained to predict the next value in the NRV sequence based on the previous values of the NRV, the weather, the load forecast etc. Using this method, the model will always generate the same sequence of values given the same input features. Instead of using the autoregressive model to predict the next value in the sequence, the model can also be trained to predict the distribution of the next value. This way, the model can generate multiple generations of the NRV given the same input features. For example, Quantile Regression can be used to predict the distribution of the next value in the sequence. +\\\\ +In this thesis, the utilization of diffusion models is also explored. Diffusion models are a type of generative model that can be used to generate new data samples that follow the distribution of the input data set. Using a structured training process, diffusion models learn to reverse a diffusion process. Starting from a random noise distribution, the model learns to transform the noise into a sample from the data distribution using multiple denoising steps. + +\subsection{Diffusion models} +TODO: reference the paper +The "Denoising Diffusion Probabilistic Models" (DDPM) +\subsubsection{Overview} +Diffusion models are a type of probabilistic model designed to generate high-quality, diverse samples from complex data distributions. The way this type of model is trained is unique. The model is trained to reverse an iterative noise process that is applied to the data. This process is called the diffusion process. The model denoises the data in each iteration. During the training, the model learns to reverse the diffusion process. A training sample is transformed into a noise sample by applying the diffusion process. The model is then trained to recover the original sample from the noise sample. The model is trained to maximize the likelihood of the data given the noise. By doing this, the model learns to generate samples from the data distribution. Starting from the noise, the model can generate samples that look like the data. The model can also be conditioned on additional information to generate samples that follow other distributions. + +\subsubsection{Applications} +Diffusion models gained popularity in the field of computer vision. They are used for inpainting, super-resolution, image generation, image editing etc. The paper introducing "Denoising Diffusion Probabilistic Models" (DDPM) showed that diffusion models can achieve state-of-the-art results in image generation. This type of model was then applied to other fields like text generation, audio generation etc. The most popular application of diffusion models is still image generation. Many different models and products exist that make use of diffusion models to generate images. Some examples are DALL·E, Stable Diffusion, Midjourney, etc. These models can generate or edit images based on a given text description. +\\\\ +This method can also be applied to other fields like audio generation, text generation etc. In this thesis, diffusion models are explored to model time series data conditioned on additional information. + +\subsubsection{Generation process} +The generation process is quite different in comparison to other models. For example, GANs and VAE generate samples by sampling from a noise distribution and then transforming the noise into a sample that looks like the training data in one step using a generator network. Diffusion models generate samples by starting from a noise distribution and then applying a series of denoising steps to the noise. The diffusion process consists of 3 main components: the forward process, the reverse process and the sampling process. + +\begin{itemize} + \item \textbf{Forward process} \\ + During this process, Gaussian noise is added to the data in each of the T time steps according to a variance schedule $\beta_1, ..., \beta_T$. \\\\ + $q(\mathbf{x}_{1:T}|\mathbf{x}_0) \coloneqq \prod_{t=1}^{T} q(\mathbf{x}_t|\mathbf{x}_{t-1}) \quad$ with $\quad q(\mathbf{x}_t|\mathbf{x}_{t-1}) \coloneqq \mathcal{N}(\mathbf{x}_t; \sqrt{1-\beta_t}\mathbf{x}_{t-1}, \beta_t\mathbf{I})$ + \\\\ + This formula shows that the noisy data distribution after T diffusion steps is the product of the transition probabilities at each step t. The noise added in each time step is a Gaussian distribution with mean $\sqrt{1-\beta_t}\mathbf{x}_{t-1}$ and variance $\beta_t\mathbf{I}$. The variance schedule $\beta_1, ..., \beta_T$ is a hyperparameter that needs to be chosen or optimized during training. + + \item \textbf{Reverse process} \\ + The diffusion process must then be reversed. The model is trained to model the noise distribution given the data and timestep. \\\\ + $p_{\theta}(\mathbf{x}_{0:T}) \coloneqq p(\mathbf{x}_T) \prod_{t=1}^{T} p_{\theta}(\mathbf{x}_{t-1}|\mathbf{x}_t) \quad$ with $\quad p_{\theta}(\mathbf{x}_{t-1}|\mathbf{x}_t) \coloneqq \mathcal{N}(\mathbf{x}_{t-1}; \mu_{\theta}(\mathbf{x}_t, t), \Sigma_{\theta}(\mathbf{x}_t, t))$ + \\\\ + In the reverse process, each step aims to undo the diffusion by estimating what the previous, less noisy state might have been. This is done using a series of conditional Gaussian distributions $p_{\theta}(\mathbf{x}_{t-1}|\mathbf{x}_t)$. For each of these Gaussians, a neural network with parameters $\theta$ is used to estimate the mean $\mu_{\theta}(\mathbf{x}_t, t)$ and the covariance $\Sigma_{\theta}(\mathbf{x}_t, t)$ of the distribution. The joint distribution $p_{\theta}(\mathbf{x}_{0:T})$ is then the product the marginal distribution of the last timestep $p(\mathbf{x}_T)$ and the conditional distributions $p_{\theta}(\mathbf{x}_{t-1}|\mathbf{x}_t)$ for each timestep. + + \item \textbf{Training} \\ + The model training is done by optimizing the variational bound of the negative log-likelihood. This is also called the evidence lower bound (ELBO) in the context of generative models. \\\\ + TODO: add formula and explain? + + \item \textbf{Conditioning} \\ + The model can be conditioned on additional information. This can be used to guide the generation process. In the context of image generation, this can be used to generate images of a certain class or with certain attributes. This requires some changes in the model architecture and training process. + TODO: add more information about conditioning +\end{itemize} + +The diffusion process can be seen in Figure \ref{fig:diffusion_process}. The model is trained to reverse this process. Starting from the noise, the model learns to generate samples that look like the data. + +\begin{figure}[h] + \centering + \includegraphics[width=0.8\textwidth]{images/diffusion/diffusion_graphical_model.png} + TODO: fix citation + \caption[Diffusion process]{Diffusion process (adapted from \cite{ho2020denoising}).} + \label{fig:diffusion_process} +\end{figure} + + diff --git a/Reports/Thesis/sections/introduction.tex b/Reports/Thesis/sections/introduction.tex new file mode 100644 index 0000000..f055a71 --- /dev/null +++ b/Reports/Thesis/sections/introduction.tex @@ -0,0 +1,10 @@ +\section{Introduction} +The electricity market is a complex system influenced by numerous factors. The rise of renewable energy sources adds to this complexity, introducing greater volatility compared to traditional energy sources. Renewables, with their unpredictable nature, exacerbate the challenge of maintaining a stable balance between supply and demand. This critical balance is managed by the transmission system operator, Elia, which utilizes reserves to mitigate any potential shortages or surpluses, directly influencing electricity prices. + +(TODO: Market participants met flexible assets (Groot genoeg), zij willen grote winst maken. Elia moet minder eigen reserves gebruiken -> goedkoper voor iedereen) + +Forecasting the imbalance price is vital for market participants engaged in buying or selling electricity. It enables them to make informed decisions on the optimal times to buy or sell, aiming to maximize their profits. However, current industry practices often rely on simplistic policies, such as adhering to a fixed price for transactions. This approach is not optimal and overlooks the potential benefits of adaptive policies that consider the forecasted imbalance prices. + +The goal of this thesis is to generatively model the Belgian electricity market. This allows to reconstruct the imbalance price for a given day which can then be used by other simple policies to make decisions on when to buy or sell electricity. These policies can then be compared to the current industry practices to assess their performance. + +Forecasting the system imbalance will become increasingly important as the share of renewable energy sources continues to grow. \ No newline at end of file diff --git a/Reports/Thesis/sections/nrv_prediction.tex b/Reports/Thesis/sections/nrv_prediction.tex new file mode 100644 index 0000000..cb6316c --- /dev/null +++ b/Reports/Thesis/sections/nrv_prediction.tex @@ -0,0 +1,169 @@ +\section{NRV Prediction} +As discussed in the background information, the imbalance prices are based on the Net Regulation Volume (NRV). This means that the imbalance prices can be reconstructed from the sampled NRV. Multiple baselines and models will be compared that forecast and model the NRV using different metrics. The data utilized in this thesis is provided by Elia. Elia makes a lot of data public and provides them in quarterly hour or minute intervals. The data used in this thesis is on a quarterly hourly basis. This makes the number of input features and output features way more manageable and makes the training more computationally efficient. A full-day sample of the NRV exists of 96 values. One value for every quarter. Further research could be done using smaller data intervals to see if this improves the models. + +\subsection{Data} +Elia offers a lot of different data on their website (TODO: open data citation). They provide data for the following categories: +(TODO: Relevant? or too much information?) +\begin{itemize} + \item Balancing + \item Transmission + \item Power generations + \item Congestion management + \item Load + \item Studies +\end{itemize} + +The data useful to model the NRV is scattered over multiple categories. The data used in this thesis is the following: + +TODO: ask Jonas: add urls to the correct data? via citation? +\begin{itemize} + \item \textbf{Imbalance prices per quarter-hour (Historical data) } \\ + % https://opendata.elia.be/explore/dataset/ods047/information/?sort=datetime + This dataset contains the NRV and system imbalance in a quarter-hour interval. The data is available from 01-01-2015 to the present day. The NRV is used as the target variable that needs to be modeled but can also be used as input features. The next day NRV modeling can be conditioned on the real NRV of the previous day. + + \item \textbf{Measured and forecasted total load on the Belgian grid (Historical data)} \\ + % https://opendata.elia.be/explore/dataset/ods001/table/?sort=datetime + Elia publishes what the total load on the Belgian grid is. This data is also provided in a quarter-hour interval. This data consists of the real load for a certain quarter but also the different forecasted loads. There are day-ahead and week-ahead forecasts available. The total load on the Belgian grid can be used as input features for the NRV modeling. The data is also available from 01-01-2015 to the present day. + + \item \textbf{Photovoltaic power production estimation and forecast on Belgian grid (Historical)} \\ + % https://opendata.elia.be/explore/dataset/ods032/table/?sort=datetime + The photovoltanic power production is also available in a quarter-hour interval. The production is also forecasted day-ahead and week-ahead. The data is provided for each of the provinces in Belgium. Forecasts are also available for the 3 Belgian regions (Flanders, Wallonia, Brussels) and the total Belgian production. The photovoltanic data has been provided since 01-04-2018 and is available to the present day. + + \item \textbf{Wind power production estimation and forecast on Belgian grid (Historical)} \\ + % https://opendata.elia.be/explore/dataset/ods031/information/ + Just as the photovoltanic power production data, wind power production is available in a quarterly-hour interval for each of the provinces and regions in Belgium. This data also includes the real production and the forecasts. An additional column is available that shows if the power is generated offshore or onshore. During this thesis, the offshore and onshore data will be combined. The wind power production data has been provided since 01-01-2015 and is available to the present day. + + \item \textbf{Day-ahead implicit net position (Belgium's balance)} \\ + % https://opendata.elia.be/explore/dataset/ods022/information/?sort=datetime + The day-ahead implicit net position shows the total amount of electricity that will be imported or exported to neighboring countries. The trades are done on the day-ahead market and are thus known in advance. This data is available in a quarter-hour interval and has been provided since 01-11-2020 and is available to the present day. The data before 01-11-2020 is also available but only in hourly intervals. +\end{itemize} + +A lot of data is available but only the most relevant data needs to be used. Experiments will be done to identify which data and features improve the NRV modeling. The data will be split into a training and test set. The training dataset starts depending on which data features are used but ends on 31-12-2022. The test set starts on 01-01-2023 and ends on (TODO: check the end date). This makes sure enough data is available to train the models and the test set is large enough to evaluate the models. The year 2023 is chosen as the test set because it is the most recent data available when the thesis experiments were conducted. Using data from 2022 in the test set also does not make a lot of sense because the trained models would be used to predict the future. Data from 2022 is not relevant anymore to evaluate the models. + +\subsection{Quantile Regression} +Forecasting the NRV is very difficult and most of the time not accurate. It is a very volatile time series and is hard to predict. Instead of just forecasting the NRV, a generative model can be trained and used to sample multiple full NRV samples for the next day. Sampling multiple times can give a better understanding of the uncertainty of the NRV. To be able to sample multiple times, a distribution of the NRV is needed. Only one value for the NRV for each quarter is available in the training and test data. There is no information on the distribution of this value. +\\\\ +Quantile regression can be used to model the distribution of the NRV. This is a technique that estimates the conditional quantiles of the target variable. A quantile is a statistical value of a random variable below which a certain proportion of observations fall. Figure \ref{fig:quantile_example} shows the cumulative distribution function of a normal distribution. The figure shows the 25th, 50th and 75th quantiles. The 25th quantile is the value below which 25\% of the observations fall. In the example, this value is -0.67. The other quantiles work in the same way. + +\begin{figure}[H] + \centering + \includegraphics[width=0.8\textwidth]{images/quantile_regression/cdf_quantiles_example.png} + \caption{Example of quantiles} + \label{fig:quantile_example} +\end{figure} + +Instead of training the model to output the NRV forecast, the model is trained to output the values for different quantiles. The amount and which quantiles can be chosen. Experiments are done to identify what quantiles are the most useful. Using the outputted quantiles, the cumulative distribution function can be reconstructed and used to sample the NRV value for the quarter to predict. An example of the output of a quantile regression model is shown in figure \ref{fig:quantile_regression_example}. The values of the different quantiles are plotted and these are interpolated to get the cumulative distribution function. + +TODO: figure goes under 0, maybe use other values or other interpolation? +\begin{figure}[H] + \centering + \includegraphics[width=0.8\textwidth]{images/quantile_regression/reconstructed_cdf.png} + \caption{Example of quantile regression output for one-quarter of the NRV, showing interpolated values for quantiles at 1\%, 5\%, 10\%, 15\%, 30\%, 40\%, 50\%, 60\%, 70\%, 85\%, 90\%, 95\%, and 99\%. These quantiles are used to reconstruct the cumulative distribution function.} + \label{fig:quantile_regression_example} +\end{figure} + + +TODO: reduce the use of the world NRV and cumulative distribution function +The NRV value for a quarter can be sampled from the reconstructed cumulative distribution function. A full-day prediction for the NRV exists of 96 values. The cumulative distribution function which is sampled is only used for a certain quarter. +\\\\ +TODO: Explain non autoregressive and autoregressive models\\\\ +Two methods exist to sample full-day NRV values. The first method is a non-autoregressive model. This model outputs the quantiles for every quarter. For each quarter, the cumulative distribution function is reconstructed and sampled. The model is conditioned on the NRV timeline of the previous day. This consists of 96 values. The second method is an autoregressive model. This model outputs the quantiles for the next quarter for which the NRV distribution is wanted. This model is conditioned on the 96 previous NRV values. When a full-day sample of the NRV is wanted, the model is used recursively. The model predicts the quantiles for the next quarter, the cumulative distribution function is reconstructed and the NRV value is sampled. This value can then be used as input for the next quarter. This process is repeated until a full-day sample is obtained. +\\\\ +\subsubsection{Training} +The quantile regression model is trained using the pinball loss function, also known as the quantile loss. The model outputs the quantile values for the NRV. The quantile values themselves are not available in the training data. Only the real NRV values are known. The loss function is defined as: +\begin{equation} + L_\tau(y, \hat{y}) = \begin{cases} + \tau(y - \hat{y}) & \text{if } y \geq \hat{y} \\ + (1 - \tau)(\hat{y} - y) & \text{if } y < \hat{y} + \end{cases} +\end{equation} +\begin{align*} + \textbf{Where:} \\ + \tau & = \text{Quantile of interest} \\ + y & = \text{Actual observed value of NRV} \\ + \hat{y} & = \text{Predicted quantile value of NRV} \\ +\end{align*} +The loss function works by penalizing underestimation and overestimation differently. When a quantile is predicted that is lower than or equal to the actual value, the loss is calculated as the difference between the actual value and the predicted quantile value multiplied by the quantile of interest. This means that underestimations for high quantiles are penalized higher than for lower quantiles. +\\\\ +When the quantile value prediction is higher than the real NRV value, the loss is calculated as the difference between the predicted quantile value and the real NRV multiplied by $(1-\tau)$. This means that overestimations are penalized less for high quantiles of interest. + +\begin{equation} + L = \frac{1}{N} \sum_{i=1}^{N} \sum_{\tau \in T} L_\tau(y_i, \hat{y}_i) +\end{equation} + +\begin{align*} + \textbf{Where:} \\ + N & = \text{Number of samples} \\ + T & = \text{Quantiles of interest} \\ + y_i & = \text{Actual observed value of NRV for sample i} \\ + \hat{y}_i & = \text{Predicted quantile value of NRV for sample i} \\ +\end{align*} + +To calculate the pinball loss, the mean over the quantiles of interest and samples need to be taken. This gives a scalar loss value which can be used to do backpropagation. The lower this value, the better the NRV distribution is modeled. + +\subsubsection{Evaluation} +To evaluate the performance of the quantile regression models, multiple metrics can be used. The pinball loss itself can be used to compare models on the test set. Other metrics that can be used are the mean absolute error (MAE) and the mean squared error (MSE). This can be done by generating multiple full-day NRV samples for each day of the test set and calculating the error metrics for each of the samples. The mean can then be taken over the different samples to get a single value for the error metrics. +\\\\ +MAE does not consider the direction of the error. It is the average of the absolute differences between the predicted and actual values. The formula in our case with full-day NRV samples is: +\begin{equation} + MAE = \frac{1}{N} \sum_{i=1}^{N} \frac{1}{96} \sum_{j=1}^{96} |y_{ij} - \hat{y}_{ij}| +\end{equation} + +\begin{align*} + \textbf{Where:} \\ + N & = \text{Number of samples} \\ + y_{ij} & = \text{Actual observed value of NRV for sample i and quarter j} \\ + \hat{y}_{ij} & = \text{Sampled value of NRV for sample i and quarter j} \\ +\end{align*} + +MSE is more sensitive to outliers than MAE because it squares the error between the predicted and actual values. The formula in our case with full-day NRV samples is: +\begin{equation} + MSE = \frac{1}{N} \sum_{i=1}^{N} \frac{1}{96} \sum_{j=1}^{96} (y_{ij} - \hat{y}_{ij})^2 +\end{equation} + +The MAE and MSE metrics do not compare the distribution of the NRV to the real NRV value but only take into account the sampled values. Evaluating the outputted distribution for the NRV must be done differently. The Continuous Ranked Probability Score (CRPS) can be used to evaluate the distribution to the real NRV value. The CRPS metric is used to evaluate the accuracy of the predicted cumulative distribution function. The CRPS can be seen as a generalization of the MAE for probabilistic forecasts. The formula for the CRPS is: + +\begin{equation} + CRPS(F, x) = \int_{-\infty}^{\infty} (F(y) - \mathbbm{1}(y - x))^2 \, dy +\end{equation} + +\begin{align*} + \textbf{Where:} \\ + F & = \text{Predicted cumulative distribution function} \\ + x & = \text{Real NRV value} \\ + \mathbbm{1}(x) & = \text{Heavyside function} = \begin{cases} + 1 & \text{if } x \geq 0 \\ + 0 & \text{if } x < 0 + \end{cases} \\ +\end{align*} + +The mean CRPS can be calculated over the different days to get a single value. The lower this value, the better the NRV is modeled. The CRPS metric can be visualized as shown in figure \ref{fig:crps_visualization}. The CRPS is the area between the predicted cumulative distribution function and the Heavyside function. The lower the area between the curves, the better the NRV is modeled. + +TODO: improve visualisation? +\begin{figure}[H] + \centering + \includegraphics[width=0.8\textwidth]{images/quantile_regression/crps_visualization.png} + \caption{Visualization of the CRPS metric} + \label{fig:crps_visualization} +\end{figure} + +\subsubsection{Models} +A linear quantile regression model is used as a baseline. This model is very simple and can be used to compare the more complex models. Starting with a simple autoregressive model, the model is conditioned on the 96 previous NRV values. The model outputs the quantiles for the next quarter. The model is trained using the pinball loss function and the MAE, MSE, CRPS metrics are calculated on the test set. The linear model for the quantile regression is defined as: + +TODO: better equation for linear quantile regression model +\begin{equation} + Q_{\tau}(Y | \mathbf{X}) = \beta_{0,\tau} + \beta_{1,\tau}x_1 + \beta_{2,\tau}x_2 + \ldots + \beta_{n,\tau}x_n + \end{equation} + +where: +\begin{itemize} + \item \( Q_{\tau}(y | \mathbf{x}) \) is the \( \tau \)-th quantile of the conditional distribution of the NRV + \item \( \mathbf{X} \) is the input features (e.g. the 96 previous NRV values) + \item \(\beta_{0,\tau}, \beta_{1,\tau}, \beta_{2,\tau}, \ldots, \beta_{n,\tau} \) are the coefficients +\end{itemize} + +The linear model outputs the values for the chosen quantiles. The total amount of parameters depends on the input features and the number of chosen quantiles. 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diff --git a/Reports/Thesis/verslag.out b/Reports/Thesis/verslag.out deleted file mode 100644 index 9319bd5..0000000 --- a/Reports/Thesis/verslag.out +++ /dev/null @@ -1,6 +0,0 @@ -\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1 -\BOOKMARK [1][-]{section.2}{\376\377\000B\000a\000c\000k\000g\000r\000o\000u\000n\000d}{}% 2 -\BOOKMARK [2][-]{subsection.2.1}{\376\377\000E\000l\000e\000c\000t\000r\000i\000c\000i\000t\000y\000\040\000m\000a\000r\000k\000e\000t}{section.2}% 3 -\BOOKMARK [2][-]{subsection.2.2}{\376\377\000G\000e\000n\000e\000r\000a\000t\000i\000v\000e\000\040\000m\000o\000d\000e\000l\000i\000n\000g}{section.2}% 4 -\BOOKMARK [1][-]{section.3}{\376\377\000L\000i\000t\000e\000r\000a\000t\000u\000r\000e\000\040\000S\000t\000u\000d\000y}{}% 5 -\BOOKMARK [1][-]{section.4}{\376\377\000M\000o\000d\000e\000l\000l\000e\000n\000\040\000+\000\040\000r\000e\000s\000u\000l\000t\000a\000t\000e\000n}{}% 6 diff --git a/Reports/Thesis/verslag.pdf b/Reports/Thesis/verslag.pdf index 816cf72..70546f9 100644 Binary files a/Reports/Thesis/verslag.pdf and b/Reports/Thesis/verslag.pdf differ diff --git a/Reports/Thesis/verslag.run.xml b/Reports/Thesis/verslag.run.xml index 7fb8345..12fa594 100644 --- a/Reports/Thesis/verslag.run.xml +++ b/Reports/Thesis/verslag.run.xml @@ -41,7 +41,7 @@ > ]> - + latex verslag.bcf @@ -64,7 +64,7 @@ english-apa.lbx - + biber biber diff --git a/Reports/Thesis/verslag.synctex.gz b/Reports/Thesis/verslag.synctex.gz index 82ddfe3..cac3cd3 100644 Binary files a/Reports/Thesis/verslag.synctex.gz and b/Reports/Thesis/verslag.synctex.gz differ diff --git a/Reports/Thesis/verslag.tex b/Reports/Thesis/verslag.tex index c3a4d67..15b2c63 100644 --- a/Reports/Thesis/verslag.tex +++ b/Reports/Thesis/verslag.tex @@ -18,7 +18,10 @@ \usepackage{tabularx} \usepackage{array} \usepackage{amsmath} +\usepackage{mathtools} \usepackage{multirow} +\usepackage{float} +\usepackage{bbm} \newcolumntype{C}{>{\centering\arraybackslash}X} @@ -58,9 +61,6 @@ % Input for title page %---------------------- -% The title -\thesubtitle{February Intermediate Report} - %% Note: a stricter UGent style could be achieved with, e.g.: \usepackage{ulem} % for colored underline \renewcommand{\ULthickness}{2pt} % adjust thickness of underline @@ -133,126 +133,23 @@ % \vspace{1cm} % {\large Mentor: dr. ir. Femke De Backere\par} -% {\large TechWolf supervisor: ir. Jens-Joris Decorte} % \end{titlepage} \newpage -\section{Introduction} -The electricity market is a complex system influenced by numerous factors. The rise of renewable energy sources adds to this complexity, introducing greater volatility compared to traditional energy sources. Renewables, with their unpredictable nature, exacerbate the challenge of maintaining a stable balance between supply and demand. This critical balance is managed by the transmission system operator, Elia, which utilizes reserves to mitigate any potential shortages or surpluses, directly influencing electricity prices. - -(TODO: Market participants met flexible assets (Groot genoeg), zij willen grote winst maken. Elia moet minder eigen reserves gebruiken -> goedkoper voor iedereen) - -Forecasting the imbalance price is vital for market participants engaged in buying or selling electricity. It enables them to make informed decisions on the optimal times to buy or sell, aiming to maximize their profits. However, current industry practices often rely on simplistic policies, such as adhering to a fixed price for transactions. This approach is not optimal and overlooks the potential benefits of adaptive policies that consider the forecasted imbalance prices. - -The goal of this thesis is to generatively model the Belgian electricity market. This allows to reconstruct the imbalance price for a given day which can then be used by other simple policies to make decisions on when to buy or sell electricity. These policies can then be compared to the current industry practices to assess their performance. - -Forecasting the system imbalance will become increasingly important as the share of renewable energy sources continues to grow. - -\section{Background} -% Achtergrond informatie -% Generatief modelleren -% -> enkel forecast is vaak brak -> reinforcement learning is lastig -> generatief modelleren, veel generaties om mee te trainen -% - Achtergrond electrititetismarkt -% - Achtergrond Generatief modelleren (van NRV) -\subsection{Electricity market} -The electricity market consists of many different parties who all work together and want to make a profit in the end. An overview of the most important parties can be found in Table \ref{tab:parties}. - -% table -\begin{table}[h] - \centering - \begin{tabularx}{\textwidth}{|C|C|} - \hline - \textbf{Party} & \textbf{Description} \\ - \hline - Producers & Generates electricty. The electricity can be generated using coal, nuclear energy, wind parks etc. \\ - \hline - Consumers & Uses electricity. This can be normal households, companies but also industry. \\ - \hline - Transmission system operator (TSO) & Party responsible for reliable transmission of electricity from generation plants to local distribution networks. This is done over the high-voltage grid. In Belgium, this party is Elia.\\ - \hline - Distribution system operator (DSO) & Party responsible for the distribution of electricity to the end users. Here, the electricity is transported over the low-voltage grid. \\ - \hline - Balancing responsible party (BRP) & These parties forecast the electricity consumption and generation of their clients. They make balanced nominations to Elia. - \\ - \hline - Balancing Service Provider (BSP) & Parties that provide the TSO (Elia) with balancing services. They submit Balancing Energy Bids to Elia. If needed, they will provide balancing energy at a set price. \\ - \hline - \end{tabularx} - \caption{Overview of the most important parties in the electricity market} - \label{tab:parties} -\end{table} - -Elia, the Transmission system operator (TSO) in Belgium is responsible for keeping the grid stable. They do this by balancing the electricity consumption and generation. If there is an imbalance, Elia will use reserves to balance the grid. These reserves are expensive and are paid by the market participants. The price of these reserves is called the imbalance price. Keeping the grid balanced is a very important but also a very difficult task. If the grid is not balanced, it can lead to blackouts but also other problems like damage to equipment. -\\\\ -Balance responsible parties (BRPs) forecast the electricity consumption and generation of their portfolio to effectively manage the balance between supply and demand within the grid they operate in. They submit a daily balance schedule for their portfolio the day before. This consists of the expected physical injections and offtakes from the grid and the commercial power trades. The power trades can be purchases and sales between BRPs or they can even be trades with other countries. BRPs must provide and deploy all reasonable resources to be balanced on a quarter-hourly basis. They can exchange electricity with other BRPs for the following day or the same day. There is one exception where a BRP can deviate from the balance schedule. This is when the grid is not balanced and they can help Elia to stabilize the grid. In this case, they will receive a compensation for their help. When a BRP deviates from the balance schedule in a way that destabilizes the grid, it will need to pay the imbalance price for the deviation. -\\\\ -The imbalance price is determined based on which reserves Elia needs to activate to stabilize the grid. The imbalance of a BRP is the quarter-hourly difference between total injections and offtakes from the grid. The Net Regulation Volume (NRV) is the net control volume of energy that Elia applies to maintain balance in the Elia control area. The Area Control Error is the current difference between the scheduled values and the actual values of power exchanged in the Belgian control area. The imbalance of the system (SI) is the Area Control Error minus the NRV. Using the System Imbalance, the imbalance price is calculated. -\\\\ -Elia, the Transmission System Operator (TSO) in Belgium, maintains grid stability by activating three types of reserves, each designed to address specific conditions of imbalance. These reserves are crucial for ensuring that the electricity supply continuously meets the demand, thereby maintaining the frequency within the required operational limits. The reserves include: - -1) \textbf{Frequency Containment Reserve (FCR)} \\ -FCR is a reserve that responds automatically to frequency deviations in the grid. The reserve responds automatically in seconds and provides a proportional response to the frequency deviation. Elia must provide a minimal share of this volume within the Belgian control area. This type of volume can also be offered by the BSPs. -\\\\ -2) \textbf{Automatic Frequency Restoration Process (aFRR)} \\ -aFRR is the second reserve that Elia can activate to restore the frequency to 50Hz. The aFRR is activated when the FCR is not sufficient to restore the frequency. Every 4 seconds, Elia sends a set-point to the BSPs. The BSPs use this set-point to adjust their production or consumption. The BSPs have a 7.5-minute window to activate the full requested energy volume. -\\\\ -3) \textbf{Manual Frequency Restoration (mFRR)} \\ -Sometimes the FCR and aFRR are not enough to restore the imbalance between generation and consumption. Elia activates the mFRR manually and the requested energy volume is to be activated in 15 minutes. - -The order in which the reserves are activated is as follows: FCR, aFRR and mFRR. BSPs provide bids for the aFRR and mFRR volumes. The provided bids consist of the type (aFRR or mFRR), bid volume (MW), bid price (per MWh) and start price (per MWh). -The start price is used to cover the costs of starting a unit. -\\\\ -Elia selects the bids based on the order of activation and then the price. The highest marginal price paid for upward or downward activation determines the imbalance price. This means that the last bid that is activated determines the imbalance price. This price is paid by the BRPs that are not balanced. The imbalance price calculation is shown in Table \ref{tab:imbalance_price}. - -\begin{table}[h] - \centering - \begin{tabular}{|c|c|c|} - \hline - & \multicolumn{2}{c|}{\textbf{System Imbalance}} \\ - \cline{2-3} - \textbf{Imbalance of the balance responsible party} & \textbf{Positive} & \textbf{Negative or zero} \\ - \hline - \textbf{Positive} & MDP - \(\alpha\) & MIP + \(\alpha\) \\ - \hline - \textbf{Negative} & MDP - \(\alpha\) & MIP + \(\alpha\) \\ - \hline - \end{tabular} - \caption{Prices paid by the BRPs} - \label{tab:imbalance_price} -\end{table} - -The imbalance price calculation includes the following variables: \\ -- MDP: Marginal price of downward activation \\ -- MIP: Marginal price of upward activation \\ -- \(\alpha\): Extra parameter dependent on System Imbalance \\ -\\ -% TODO: Add more information about the imbalance price calculation, alpha? -TODO: Add more information about the imbalance price calculation, alpha? - -The imbalance price can be reconstructed given the bids of a certain quarter/day and the System Imbalance. During this thesis, the system imbalance is assumed to be almost the same as the Net Regulation Volume. This is a simplification but it is a good approximation. The goal of this thesis is to model the Net Regulation Volume which can then be used to reconstruct the imbalance price and to make decisions on when to buy or sell electricity. - -\subsection{Generative modeling} -Simple forecasting of the NRV is often not accurate and defining a policy using this forecast will lead to wrong decisions. A better method would be to try to model the NRV and sample multiple generations of the NRV. This gives a better prediction and confidence intervals can be calculated from this. -\\\\ -Generative modeling is a type of machine learning that is used to generate new data samples. The goal of generative modeling is to learn the true data distribution of the training data. From this learned distribution, new samples can be generated. Generative modeling is used in many different fields including image generation, text generation etc. -\\\\ -TODO: Formulas of generative modeling -\\\\ -In this thesis, generative modeling can be used to model the NRV of the Belgian electricity market using different input features like the weather, the electricity price etc. The model can then be used to generate new samples of the NRV. -\\\\ -Multiple methods can be used to generatively model the NRV. - - - +\include{sections/introduction} +\include{sections/background} \section{Literature Study} - Literatuur forecasting imbalance price - Literatuur policies adhv forecasts -\section{Modellen + resultaten} +\section{TODO: Better title for this section} +This thesis can be divided into two main parts. The first part focuses on modeling the Net Regulation Volume (NRV) of the Belgian electricity market for the next day. This modeling is conditioned on multiple inputs that can be obtained from Elia (TODO: add citation to the open data of Elia). The second part of the thesis focuses on optimizing a simple policy using the NRV generations for the next day. The policy tries to maximize profit by charging and discharging a battery and thereby buying and selling electricity on the market. Multiple models are trained and tested to model the NRV and compared to each other based on their profit optimization. + +\input{sections/nrv_prediction} \end{document} diff --git a/Result-Reports/Policies.md b/Result-Reports/Policies.md index 9d3f499..e333f75 100644 --- a/Result-Reports/Policies.md +++ b/Result-Reports/Policies.md @@ -216,4 +216,32 @@ Baseline -> thresholds bepalen training data, penalty aanpassen na evaluatie op # profit evaluation done day by day. Start with fresh battery. Maybe electritiy bought but not sold -> negative profits? What to do with this? 2 solutions: - work further with the state of charge of the battery - - don't count the electricity bought but not sold in the profit calculation \ No newline at end of file + - don't count the electricity bought but not sold in the profit calculation + + + + + + + + +# Global baseline: +uitleggen + +-> penalty bepalen aan de hand van de test set + +# Perfect baseline: +uitleggen + niet absoluut maximum, normaal integer linear programming nodig + +Simple linear model + +# forecast inputs + + +Plotten van thresholds over de test set (baseline, GRU, diffusion) +Plotten van state of charge + +State of charge waarde van batterij wordt niet meegenomen in threshold bepaling, enkel de verkochte elektriciteit. -> simpele policy maar zelfde policy voor baselines en complexere modellen + + +# Finer thresholds for the models \ No newline at end of file diff --git a/Result-Reports/Profit.md b/Result-Reports/Profit.md index 8d3fc66..228d2cf 100644 --- a/Result-Reports/Profit.md +++ b/Result-Reports/Profit.md @@ -1,3 +1,21 @@ -| Experiment | Model Type | Input Parameters | Profit | Charge Cycles | -|------------|------------|------------------|--------|----------------| -| [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/432396923e354d579494048656228c32/info-output/metrics/scalar) | Diffusion Model |  \ No newline at end of file +# Results +## Global Baseline +2 static thresholds determined to buy and sell electricity + +| Tuned on | Charge Threshold | Discharge Threshold | Profit | Charge Cycles (target 283) | +|----------|------------------|---------------------|--------|---------------| +| Training data | 100 | 200 | 266294.15 | 492.0 | +| Test data | 200 | 250 | 143004.34 | 287.125 | + +## Yesterday NRV Baseline +Thresholds are determiend on the reconstructed prices using the NRV of yesterday. + +| Penalty | Profit | Charge Cycles (target 283) | +|---|---|---| +| 807.25 | 200771.44 | 282.5 |  + +## Trained models +| Model | Experiment | Profit | Charge Cycles (target 283) | +|---|---|---|---| +| Diffusion Model | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/8487afb705bf47b78f3f013c19394da7/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=m.290612199861c31d1036b185b4e69b75.0fa0d9b8ebcf2a3d9c95c22cd6e72912.value.Summary.OptimalProfit&columns=m.290612199861c31d1036b185b4e69b75.6d53291d99dd32fc27ecee29939046ec.value.Summary.OptimalChargeCycles&columns=m.0fa0d9b8ebcf2a3d9c95c22cd6e72912.098f6bcd4621d373cade4e832627b4f6.max_value.Optimal%20Profit.test&columns=m.1a899a19b54957e02a21c3a1d82577ad.0970ca62a85af2722008c5220e9d8a9e.value.Summary%2Ftest_CRPSLoss.lastreported&columns=m.293da6b015ca6a65992dcf7a53fa0237.098f6bcd4621d373cade4e832627b4f6.min_value.PinballLoss.test&order=-started&filter=) | 219848.9 | 283.06 | +|  \ No newline at end of file diff --git a/src/notebooks/loss_test.ipynb b/src/notebooks/loss_test.ipynb index dc81861..b9f8c80 100644 --- a/src/notebooks/loss_test.ipynb +++ b/src/notebooks/loss_test.ipynb @@ -8,7 +8,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 2, @@ -17,7 +17,7 @@ }, { "data": { - "image/png": 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", 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hBK58+iucr23Cq3ckYhabnTlFdz+/GQWJiKjfOXJej/O1TfD39cIvRw2Supx+j2GEiIj6nfTctlM0M2LDoPTh/EWpMYwQEVG/IoTAziNtl/TOZqMzl8AwQkRE/UrueQPOVjVC6SPHzNFhUpdDYBghIqJ+5pMjJQCAmaM1GKCw+6JScgKGESIi6jeEENj5Y9spmuvGs9GZq2AYISKifiOnuBbnatquorkqlqdoXAXDCBER9RufXDgqkjpGAz9fXkXjKhhGiIioX7BYBHYd4SkaV8QwQkRE/cLBohqU6psRqPBmozMXwzBCRET9wsVTNNfEadjozMUwjBARkcczWy41OrtuAk/RuBqGESIi8nhZhdWoqDNCpfTGlSN4isbVMIwQEZHH23mh0dnssVr4evOjz9XwO0JERB6t1WzBp0fabox33YTBEldDHWEYISIij7avoApVDSYM9PfBtJgQqcuhDjCMEBGRR9uRcx4AMG98OHy8+LHnivhdISIij9VkMmN3btspmgUJERJXQ51hGCEiIo/1xfEyNJjMiBzoh8ShA6UuhzrBMEJERB5rx6G2UzQLEiIgk8kkroY6wzBCREQeqbrBhK9PVgAAFkzkVTSujGGEiIg80s4jpWi1CIwdrMKIsECpy6EuMIwQEZFH+ukpGnJtDCNERORxiqsbkX22BjIZcH0CT9G4OoYRIiLyOB9e6C0yLSYEGpVS4mrochhGiIjIowgh8MGFUzQ38BSNW2AYISIij3K0xICCigYovOWYM04rdTnUDQwjRETkUS5OXE0do4FK6SNxNdQdDCNEROQxWswW7MgpAQAsmMhTNO6CYYSIiDzGNycrUFlvRMgAX1wVO0jqcqibGEaIiMhjvJd9DkDbURHeodd98DtFREQeoabBhC+OlwEAbkmMlLgasgfDCBEReYSPDpegxdzW/n1MuErqcsgODCNEROQRLp6i+T8eFXE7DCNEROT28nQGHDmvh4+XDNez0ZnbYRghIiK3978LR0VmjtYgeICvxNWQvRhGiIjIrbWYLdb275y46p4YRoiIyK19faIClfUmhAb4Yjp7i7glhhEiInJrFyeu3sjeIm6L3zUiInJb1Q0mZOS19Ra5mado3BbDCBERua33D55Di1kgPkKN0Vr2FnFXDCNEROSWhBB4O6sIAPCrpCiJq6HeYBghIiK3dOBsDQoqGuDn44XrJwyWuhzqBYYRIiJyS29/33ZU5PoJgxGo9JG4GuoNhhEiInI7+sYW7DxSCoCnaDwBwwgREbmdHTnnYWy1YLQ2EAlRQVKXQ73EMEJERG7lpxNXFyUNgUwmk7gi6i2GESIicis5xbXI09VB4S3HAt4UzyMwjBARkVvZllUMAJgXHw61PyeuegKGESIicht1zS346HAJAGBR8hCJqyFHYRghIiK38dHhEjS1mDEiLACThw6UuhxyEIYRIiJyC0II/Hf/hY6rU6I4cdWDMIwQEZFbOFhUg2OlBii85biFN8XzKAwjRETkFt7MPAugreNqkL+vxNWQIzGMEBGRy6uoM2LXhY6ri1OipS2GHI5hhIiIXN72H4rQYhZIiApCfKRa6nLIwRhGiIjIpbWaLfjvhZviLU4ZKnE15AwMI0RE5NK+OF6OUn0zggf44tr4cKnLISdgGCEiIpf2n/1nAAALp0RB6eMlbTHkFAwjRETksvLL6/FdfhXkMuA2dlz1WD0KIxs3bkR0dDSUSiWSk5ORlZXV5fja2lqsWLEC4eHhUCgUGDVqFHbt2tWjgomIqP/Yur/tct6rR2sQOdBf4mrIWbztXWH79u1YuXIlNm3ahOTkZGzYsAGzZ8/GiRMnEBYW1m68yWTCNddcg7CwMLz33nuIiIjA2bNnERQU5Ij6iYjIQzUYW/G/7HMAOHHV09kdRl544QUsW7YMS5cuBQBs2rQJO3fuxJYtW7Bq1ap247ds2YLq6mrs27cPPj5td1eMjo7uXdVEROTx/nfwHOqMrRgWOgBXjgiVuhxyIrtO05hMJmRnZyM1NfXSC8jlSE1NRWZmZofrfPTRR0hJScGKFSug0Wgwbtw4PPnkkzCbzZ2+j9FohMFgsHkQEVH/YbEIvP7dGQDAndOiIZfzPjSezK4wUllZCbPZDI1GY7Nco9FAp9N1uM7p06fx3nvvwWw2Y9euXVizZg2ef/55PPHEE52+T1paGtRqtfURFRVlT5lEROTmvswrR2FlA1RKb96Hph9w+tU0FosFYWFhePXVV5GYmIiFCxfiL3/5CzZt2tTpOqtXr4Zer7c+iouLnV0mERG5kH/tPQ0AWJQ8BAMUds8oIDdj13c4NDQUXl5eKCsrs1leVlYGrVbb4Trh4eHw8fGBl9ela8PHjBkDnU4Hk8kEX9/2NztSKBRQKBT2lEZERB7iaIke+09Xw0suwxLeh6ZfsOvIiK+vLxITE5GRkWFdZrFYkJGRgZSUlA7XueKKK5Cfnw+LxWJddvLkSYSHh3cYRIiIqH97bW8hAODa+HAMDvKTuBrqC3afplm5ciU2b96Mf//73zh+/DiWL1+OhoYG69U1ixcvxurVq63jly9fjurqajz44IM4efIkdu7ciSeffBIrVqxw3FYQEZFHKDc04+PDJQCAu64cJnE11FfsPhG3cOFCVFRUYO3atdDpdEhISEB6erp1UmtRURHk8ksZJyoqCrt378ZDDz2E8ePHIyIiAg8++CAefvhhx20FERF5hDczz6LFLJA4dCASooKkLof6iEwIIaQu4nIMBgPUajX0ej1UKpXU5RARkRM0t5iRkpaBmsYWvHLbJMzlTfHcXnc/v3lvGiIicgnvHzyPmsYWRA70w6yxHV8UQZ6JYYSIiCRnsQjr5bx3TouGF5uc9SsMI0REJLnPjpXhdEVbk7OFU9josr9hGCEiIkkJIfDK1wUAgMUp0QhU+khcEfU1hhEiIpJU5ukqHC6uhcJbjjuviJa6HJIAwwgREUnqlT1tR0VunRyF0AB23+6PGEaIiEgyuef1+PZUJbzkMtz9y+FSl0MSYRghIiLJbLowV+S68eGICvaXuBqSCsMIERFJ4kxlA3YdKQUA3Ds9RuJqSEoMI0REJIlXvz0NiwCuih2EMeHsrt2fMYwQEVGfK69rxnvZ5wAAy3lUpN9jGCEioj736tenYWq1YNKQICQNC5a6HJIYwwgREfWpijojtn5/FgDwwMyRkMnY+r2/YxghIqI+tfnb02husWBCpBrTRw2SuhxyAQwjRETUZyrrjfhPZttRkQdTeVSE2jCMEBFRn9n87Wk0tZgxPlKNGbFhUpdDLoJhhIiI+kR1g8l6VOSBq3lUhC5hGCEioj7xr29Po9FkxrgIFWaO4VERuoRhhIiInK6mwYR/7zsDgEdFqD2GESIicrrX9haiwWRGXLgK18RppC6HXAzDCBEROVVlvRFbvisEwL4i1DGGESIicqqNX+Wj0dR2Bc3ssTwqQu0xjBARkdOcq2nEf/cXAQD+NDuWR0WoQwwjRETkNH//4hRMZgtShofgyhGhUpdDLophhIiInCK/vB7/O9h2Z94/zeFREeocwwgRETnFC5+fgEUAqWM0mDRkoNTlkAtjGCEiIoc7ck6PXUd0kMna5ooQdYVhhIiIHO6Z3XkAgAUJEYjVBkpcDbk6hhEiInKoffmV+PZUJbzlMjyUOkrqcsgNMIwQEZHDWCwCf9t1HADw6+QhGBLiL3FF5A4YRoiIyGHeP3QeR0sMCFR448GZI6Uuh9wEwwgRETlEk8mM53afAACsuHoEQgIUEldE7oJhhIiIHGLzt6ehMzQjIsgPd06LlrocciMMI0RE1Gvldc3Y9HUBAODhuaOh9PGSuCJyJwwjRETUay9+fhKNJjMSooIwf3y41OWQm2EYISKiXsnTGbD9h2IAwJrrxrDtO9mNYYSIiHpMCIG/fnQUFgFcG69F4tBgqUsiN8QwQkREPfbJj6XYf7oaSh85Hrl2jNTlkJtiGCEioh5pNLXiyQsNzpZPH4HIgWxwRj3DMEJERD2y8at8lOqbERXsh3umD5e6HHJjDCNERGS3M5UN2PxNIQBgzbw4XspLvcIwQkREdnvsk2MwmS345ahBuCZOI3U55OYYRoiIyC5fHCvDl3nl8PGSYd38OF7KS73GMEJERN3WYGzFuo+OAgB+c+UwxAwKkLgi8gQMI0RE1G0vfn4S52ubEDnQj3flJYdhGCEiom7JPa/Hlu/aJq0+vmAc/H29Ja6IPAXDCBERXZbZIvDIB0dgEcB148MxIzZM6pLIgzCMEBHRZb2ZeQY/ntMjUOmNtdfFSV0OeRiGESIi6lKpvgnP7T4BAHh4zmiEqZQSV0SehmGEiIg6JYTAXz7IRYPJjElDgvDrpCFSl0QeiGGEiIg69f7B8/gyrxy+XnI8ffN4yOXsKUKOxzBCREQdKjM0Y/3HbT1Ffn/NSIzUBEpcEXkqhhEiImpHCIFH3j8CQ3MrxkeqcfcveCM8ch6GESIiaueDQ+eRceH0zLO3TIC3Fz8uyHn400VERDbKDc3464WW7w+mjkSslqdnyLkYRoiIyEoIgT+99yMMza2Ij1Djnl/y9Aw5H8MIERFZvZl5Fl+frIDCW47nb+XpGeob/CkjIiIAwKmyOjy56zgAYPXc0RjFq2eojzCMEBERjK1mPLgtB8ZWC345ahCWTIuWuiTqRxhGiIgIL3x2EsdKDQge4IvnbhkPmYzNzajv9CiMbNy4EdHR0VAqlUhOTkZWVla31tu2bRtkMhkWLFjQk7clIiIn2FdQiVe/PQ0AeOqmeN57hvqc3WFk+/btWLlyJdatW4eDBw9iwoQJmD17NsrLy7tc78yZM/jjH/+IX/ziFz0uloiIHKuizogHt+VACGBRUhRmjdVKXRL1Q3aHkRdeeAHLli3D0qVLERcXh02bNsHf3x9btmzpdB2z2YzbbrsN69evx/DhvEyMiMgVmC0CD23PQUWdESPDArDmujipS6J+yq4wYjKZkJ2djdTU1EsvIJcjNTUVmZmZna732GOPISwsDHfddVe33sdoNMJgMNg8iIjIsV76Mh978yvh5+OFl2+bBH9fb6lLon7KrjBSWVkJs9kMjUZjs1yj0UCn03W4zt69e/Haa69h8+bN3X6ftLQ0qNVq6yMqKsqeMomI6DL25VdiQ8ZJAMATC8bxJngkKadeTVNXV4c77rgDmzdvRmhoaLfXW716NfR6vfVRXFzsxCqJiPqX8rpmPHBhnsitkyNxc2Kk1CVRP2fXMbnQ0FB4eXmhrKzMZnlZWRm02vaTngoKCnDmzBnMnz/fusxisbS9sbc3Tpw4gZiYmHbrKRQKKBQKe0ojIqJuaDFbcP9bh1BZb0SsJhDrrx8ndUlE9h0Z8fX1RWJiIjIyMqzLLBYLMjIykJKS0m786NGjceTIEeTk5Fgf119/PWbMmIGcnByefiEi6mN/23kc3xdWI0DhjY23TYKfr5fUJRHZd2QEAFauXIklS5Zg8uTJSEpKwoYNG9DQ0IClS5cCABYvXoyIiAikpaVBqVRi3Djb1B0UFAQA7ZYTEZFzvXugGG/sOwMAeOHWCRgRFiBtQUQX2B1GFi5ciIqKCqxduxY6nQ4JCQlIT0+3TmotKiqCXM7GrkREruRwcS3+siMXAPDgzJHsJ0IuRSaEEFIXcTkGgwFqtRp6vR4qlUrqcoiI3EpFnRHXv7QXpfpmpI7R4NU7EiGXs907OV93P795CIOIyIM1t5hx79ZslOqbMXzQALy4cAKDCLkchhEiIg8lhMCf3/sR2WdrEKj0xqt3TEag0kfqsojaYRghIvJQL35+Eh8dLoG3XIZ/3p7ICavkshhGiIg80P+yz+H/fZkPAHjyxnhMG9H9xpNEfY1hhIjIw+w/XYVV7/8IAFh+VQxuncKeTuTaGEaIiDzI8VIDlr15AC1mgXnx4fjTrFipSyK6LIYRIiIPUVzdiMVbslDX3Iop0QPx/K28cobcA8MIEZEHqKgz4vbXvkdFnRGjtYH415IpUPqw1Tu5B4YRIiI3Z2huwZItWThb1YjIgX548zdJUPvxEl5yHwwjRERurNHUit/++wCOlRoQGuCLrXclI0yllLosIrswjBARuakmkxm//fcBZBVWI1DhjTeWJiE6dIDUZRHZjWGEiMgNNbeYcfd/DmBfQRUG+Hrhjd8kYVyEWuqyiHqEYYSIyM0YW9vuN/PtqUr4XwgiiUMHSl0WUY8xjBARuZHmFjPu23oQe05UQOkjx5Y7p2BKdLDUZRH1irfUBRARUfc0GFtx938O4Lv8Kii85XhtyRRMHR4idVlEvcYwQkTkBvRNLfjNGz8g+2wNBvh64V9LpiAlhkGEPAPDCBGRi6uqN2LxliwcLTFApfTGv3+ThIlDOEeEPAfDCBGRCyuubsSdr2ehoKIBIQN88Z+7khE3WCV1WUQOxTBCROSics/rsfSNH1BRZ0S4Womtv01GzKAAqcsicjiGESIiF7TnRDnu++9BNJrMGK0NxOtLpyBc7Sd1WUROwTBCRORitv9QhEc+yIXZInDFiBC8cnsiVErea4Y8F8MIEZGLMFsEnvvsBF7ZUwAAuGliBJ66eTx8vdkSijwbwwgRkQvQN7XgwW2HsOdEBQDgdzNG4A+zRkEmk0lcGZHzMYwQEUksv7wed795AKcrG6DwluOZW8bjhoQIqcsi6jMMI0REEso4Xobfb8tBnbEVg9VKvLp4Mm94R/0OwwgRkQRazBY899kJ/PPr0wCApOhgvHz7JIQGKCSujKjvMYwQEfWx87VNuP+tgzhYVAsAWJwyFI/Oi+NEVeq3GEaIiPrQ58fK8Md3D0Pf1IJApTeeuXk85saHS10WkaQYRoiI+kCjqRVPfZqHNzPPAgAmRKrxj0WTMCTEX+LKiKTHMEJE5GTZZ6vxh3cO40xVIwDgN1cMw6q5o3lahugChhEiIicxtpqx4YtT+OfXBbAIQKtS4tn/G49fjBwkdWlELoVhhIjICbLP1uCR94/gRFkdgLZuquuuHwu1H9u6E/0cwwgRkQPpm1rw7O48/Pf7IggBBA/wxZM3jsOccZykStQZhhEiIgcQQmDXER3++vFRVNQZAQC3JEbikWvHIHiAr8TVEbk2hhEiol7K0xnwxCfHsTe/EgAwPHQAnrhxHKbFhEpcGZF7YBghIuqhqnojXvziJN76vggWAfh6yXHvVTG476oYKH28pC6PyG0wjBAR2cnYasZ/Ms/i7xmnUNfcCgC4Nl6L1XPHICqYfUOI7MUwQkTUTS1mC/6XfQ7/L+MUSvTNAIBxESqsmReH5OEhEldH5L4YRoiILsNsEfj4cAk2fHHS2rhMq1Ji5TWjcEtiJORymcQVErk3hhEiok60mi3YeaQUL32Zj1Pl9QCAkAG+uG/GCNyWPITzQogchGGEiOhnmkxmvJtdjFe/OY1zNU0AAJXSG/dMj8Gd06IxQMFfnUSOxH9RREQXVDeY8Nb3Z/H6d2dQ1WAC0HYkZOkV0bgjJZrdU4mchGGEiPq9I+f0+HfmGXx0uASmVgsAIHKgH+755XD83+Qono4hcjKGESLql4ytZnx6RId/Z57BoaJa6/L4CDXuunIYrhsfDm8v3lWXqC8wjBBRvyGEwNESA97LPocdOedR29gCAPDxkuG68YOxOGUoEqKCIJPx6hiivsQwQkQer6reiB05JXj3QDHydHXW5VqVErdPHYKFU4ZgUKBCwgqJ+jeGESLySHXNLfj8WBl2/liKr09WoNUiAAC+3nLMitPglsRI/GLkIHixRwiR5BhGiMhj1BtbkXG8DJ9cCCAXJ6MCwPhINf4vMRLXT4iA2p9XxRC5EoYRInJrFXVGfJVXji+Ol+HrkxUw/iSAxAwagOvGD8Z148MxUhMoYZVE1BWGESJyK0IInCirQ8bxcnx+rAyHz9VCiEvPDwsdgOvGh2Pe+HDEagI5GZXIDTCMEJHLq200Yf/pauwrqETG8XKcr22yeT4+Qo2ZY8JwTZwGceEqBhAiN8MwQkQux9Dcgh8Kq5FZUIV9BVU4rjPYHP1QeMtx5YhQzByjwdWjw6BVK6Urloh6jWGEiCTXaGrFD2dqkFlQhczTVThyrhYWYTtmRFgApsWE4BcjB+HKEaHw82VXVCJPwTBCRH2uucWMg0UXwkdBFQ6fq0WL2TZ9RIf4IyUmFCkxIZg6PBhhgTz6QeSpGEaIyOlMrRYcPld74bRLJQ4W1dpcdgsAEUF+SIkJQcrwEKTEhGBwkJ9E1RJRX2MYISKHazVbkFtiwL6CSmQWVOHAmRo0tZhtxoQFKjAtJuRCAAlFVLAfJ54S9VMMI0TUaxaLwLFSA/afbptwmlVYjXpjq82YkAG+mHrhqEdKTAiGhw5g+CAiAAwjRNQDQgicKq/HvvxKZJ6uwv7T1dA3tdiMUfv5IHlYMFJiQjAtJhSjNAEMH0TUIYYRIuqWczWN2Jdfhe8KKrGvoAoVdUab5wMU3kgaFmyd8zEmXMX7vhBRt/QojGzcuBHPPvssdDodJkyYgH/84x9ISkrqcOzmzZvx5ptvIjc3FwCQmJiIJ598stPxROQaqhtM2FdQie/y2yadnq1qtHle6SPHlOhg66TT+Ag1vL3kElVLRO7M7jCyfft2rFy5Eps2bUJycjI2bNiA2bNn48SJEwgLC2s3fs+ePVi0aBGmTZsGpVKJp59+GrNmzcLRo0cRERHhkI0got4zWwQOn6vFnrxy7DlZgR/P6W2e95LLMCFSjStGhGJaTCgmDQ2Cwpu9Poio92RCCHH5YZckJydjypQpeOmllwAAFosFUVFRuP/++7Fq1arLrm82mzFw4EC89NJLWLx4cbfe02AwQK1WQ6/XQ6VS2VMuEXWhpsGEb05V4Ku8cnx9sgI1jbbzPmI1gZg2IgRXxIQieXgwApW82y0RdV93P7/tOjJiMpmQnZ2N1atXW5fJ5XKkpqYiMzOzW6/R2NiIlpYWBAcH2/PWROQgxdWN2H1Uh91Hdcg+W2PT6TRQ4Y1fjhqEq2IHYfqoQQhTsdEYETmfXWGksrISZrMZGo3GZrlGo0FeXl63XuPhhx/G4MGDkZqa2ukYo9EIo/HS5DiDwWBPmUT0M/nldUjP1SH9qA65523/PY3WBuKq2DDMiB2ESUMHwofzPoioj/Xp1TRPPfUUtm3bhj179kCp7Px/XGlpaVi/fn0fVkbkeYqrG/FhznnsyClBfnm9dblcBkyJDsbccVpcM1aLCHY6JSKJ2RVGQkND4eXlhbKyMpvlZWVl0Gq1Xa773HPP4amnnsIXX3yB8ePHdzl29erVWLlypfXvBoMBUVFR9pRK1C/VNJiw80gpdhw6jwNna6zLfbxkuGJEKOaM1SI1ToPQAIWEVRIR2bIrjPj6+iIxMREZGRlYsGABgLYJrBkZGfjd737X6XrPPPMM/va3v2H37t2YPHnyZd9HoVBAoeAvS6LuMFsEvjlZgbezivDViXLrDedkMmBaTAhuSIjA7LFaqP04+ZSIXJPdp2lWrlyJJUuWYPLkyUhKSsKGDRvQ0NCApUuXAgAWL16MiIgIpKWlAQCefvpprF27Fm+99Raio6Oh0+kAAAEBAQgICHDgphD1L6X6Jrzzwzls/6EIJfpm6/K4cBVunBiB+RMGQ6vmBFQicn12h5GFCxeioqICa9euhU6nQ0JCAtLT062TWouKiiCXX5oA98orr8BkMuGWW26xeZ1169bhr3/9a++qJ+pnhBDYm1+Jf+87gy/zyq1XwgT5++DmSZG4dXIUYrWB0hZJRGQnu/uMSIF9Rqi/a24x46PDJdiytxB5ujrr8qRhwbgteQhmj9VC6cMGZETkWpzSZ4SI+lZVvRFb9xfhP/vPoLLeBADw9/XCrZOjcPvUoRgRxlOdROT+GEaIXFB5XTM2f3MaW/cXoanFDAAIVytx57Ro/CppCCejEpFHYRghciFlhmZs+roAb31fBGOrBQAQH6HGsl8Ox9xxWjYkIyKPxDBC5AIq64146ct8vJVVBNOFEDJxSBAenDkS00cNgkwmk7hCIiLnYRghklCjqRWvfVuIf35zGvXGVgDAlOiBeHDmKFwxIoQhhIj6BYYRIgm0mi14N/scXvz8JMrr2u7DFB+hxsNzRjOEEFG/wzBC5ARmi0BWYTXK65oRFqhE0rBgeMnbAkZmQRXWfZSLk2Vt94uJCvbDH2fFYv74wZDLGUKIqP9hGCFysPTcUqz/+BhKf9IVNVytxAMzR2BfQTU+PlwCoK1R2QNXj8RtU4dA4c0eIUTUfzGMEDlQem4plm89iJ93EizVN2P1+7kA2u4Zc1vyEPxxViyC/H37vkgiIhfDMELkIGaLwPqPj7ULIj/l4yXDe/dOw4SooL4qi4jI5bFpAZGDZBVW25ya6UiLWaDRZO6jioiI3APDCJGDlNd1HUTsHUdE1F8wjBA5SMgARbfGhQUqnVwJEZF74ZwRIgcorm7EC5+f7HKMDIBW3XaZLxERXcIwQtRLH+acx6Mf5KLO2AqljxzNLRbIAJuJrBe7h6ybH2ftN0JERG0YRoh6qK65BWs/PIoPDp0HAEweOhAvLkzA0RJ9uz4jWrUS6+bHYc64cKnKJSJyWQwjRD2QfbYGv99+CMXVTZDLgAdmjsTvZoyAt5ccUcH+uCZO22kHViIissUwQmSHVrMFG78qwP/78hTMFoHIgX74+68SkDjUdh6Il1yGlJgQiaokInIvDCNE3VRc3YiHtufgwNkaAMCChMF4bME4qJQ+EldGROTeGEaIuuGnk1QDFN54YsE4LJgYIXVZREQegWGEqAt1zS1Y9+FRvH9hkuqkIUH4+68mIirYX+LKiIg8B8MIUScOnKnG77fn4FxN2yTV+68eifuvbpukSkREjsMwQvQzLWYL/pFxCi99lQ+LAKKC/bBhYftJqkRE5BgMI0Q/caayAb/fnoOc4loAwE2TIrD++rEI5CRVIiKnYRihfsdsEe16gMhlwDsHirH+42NoNJmhUnrjbzfGY/6EwVKXS0Tk8RhGqF9Jzy1t1x01LFCB0AAFjpUaAADJw4Lx4sIEDA7yk6pMIqJ+hWGE+o303FIs33rQ5p4xAFBeZ0R5nRHechn+NDsWv/3FcHZLJSLqQ7wsgPoFs0Vg/cfH2gWRnxro78MgQkQkAYYR6heyCqttTs10pKLehKzC6j6qiIiILmIYoX6hzNDUrXHldV0HFiIicjyGEfJ4OcW1ePWbwm6NDQtUOrkaIiL6OU5gJY91uqIez312AruO6C47VgZAq267zJeIiPoWwwh5nNMV9XhlTwHeP3QeZouATAbcNDESk4ao8eiOowBgM5H14nTVdfPjOHmViEgCDCPkMfJ0Bmz8qgA7fyyB5ULamDk6DH+aE4vRWhUAICRA0a7PiFatxLr5cZgzLlyKsomI+j2GEXJrFovA1ycr8Ma+M/j6ZIV1+czRYVhx9QhMGjLQZvycceG4Jk7brgMrj4gQEUmHYYTckr6pBe8eKMZ/9p/F2apGAIBMBlwbH44VV41A3GBVp+t6yWVIiQnpq1KJiOgyGEbIbZgtAt/lV+J/B89h91EdmlssAIBApTdunRyFxSlDMTRkgMRVEhGRvRhGyKUJIXCs1ICPDpdgx6HzKDMYrc+N0gRgybRo3DgxAv6+/FEmInJX/A1OLsdiEThUXIP0XB3Sj+pQXH2pYZnazwfXTxiMmxMjMSFSDZmMcz2IiNwdwwi5hDJDM/aeqsS3pyqwN78KlfWXjoAofeSYPmoQbpwYgRmjw6Dw9pKwUiIicjSGEZJEqb4JB8/W4sDZanyXX4mTZfU2zwcqvZE6RoPZY7WYPmoQ/HwZQIiIPBXDCDldbaMJx0oNOFZiQE5xLQ6erUHJz25aJ5MB8RFq/GJkKK4cMQiJQwfC15t3KyAi6g8YRjyE2SIk751R02BCYVUDzlQ24HRFA46XGnCs1NDh3XLlMmBMuAqJQwciaVgwrogJxcABvn1aLxERuQaGEQ+QnlvarqtouIO7igohUN1gQqm+GWWGZpTqm6HTN+NcTSMKqxpxprIB+qaWTtePCvbDGK0K8RFqJEYPxITIIAxQ8MePiIgYRtxeem4plm89aHOvFQDQ6ZuxfOtBvHL7JJtAYrYI1DW3QN/UAkNTK/RNLTYPQ/NP/nzha02jCWUGI0ytlsvWE65WIjpkAKJDB2C0NhBjwlUYHR4IldLHwVtORESegmHEDZktAvqmFlQ3GPGXD3LbBRHg0o3gHng7ByM1+daAUW9shehohW4KDVAgXK2EVq2EVqXE4CA/DAv1R3ToAAwNHsCJpkREZDeGERdgtghU1RtRZjCivK7Z+rW2se2oRG1jC2obTahtakFNgwmG5tZuv7bJbMHREkO75f6+XlApfaD2a3uorF+9rcsuPoL8faBRKREWqOSkUiIicjiGkT7QaragpLYZRdWNOFvdgKLqRhRVNeJcTRPKDM2orDda7zJrD4W3HMZunDq555fDMXuc9lLwUPowVBARkctgGHGgVrMFZ6oacbKsDid0dW1fy+pwtqoR5sukDbms7RRImEoBTaASYSoFggf4IsjPF0H+Pgjy98VAfx/rn9V+PjhwpgaLNu+/bF1XxYa1u3stERGRq2AY6SEhBIqrm3CouAaHi/XIKa5Bbomh00mevt5yDAn2t3lEBftDq1JCo1IgJEBh96W4ScOCEa5WQqdv7nDeiAyAVt12mS8REZGr6rdhxN6+HEII5JfXY19BFfYVVOKHMzWobjC1G+fv64WRmkDEagIwShOIWG0gRoQFQBOohNzBfT+85DKsmx+H5VsPQgbYBJKL77Ruflyf9xshIiKyR78MI93ty1HbaMKXeeX4+mQF9hVUoaLOaPM6Pl4yxA1WIyFSjYQhQZgQGYTokAEODx1dmTMuHK/cPqnd9mgd3GeEiIjIWWRC9OZCz75hMBigVquh1+uhUql69Vqd9eW4GB8eu2EsjK0WfH6sDAfO1tjM9VB4yzElOhgpMSGYOjwE4yJULnPTNlfowEpERPRT3f387ldHRswWgfUfH+uyL8eaD4/aLB+tDcTMMWG4csQgTBwSBKWPa4SPn/OSy5ASEyJ1GURERHbrV2Ekq7C6w/uk/FxcuAq3JEbimjgNooL9+6AyIiKi/qtfhZHyussHEQC4Z/pw3JAQ4eRqiIiICAD6VeersEClQ8cRERFR7/WrMHKxL0dn0zplaLuqhn05iIiI+k6/CiMX+3IAaBdI2JeDiIhIGv0qjACX+nJo1banYrRqJV65fRL7chAREfWxHoWRjRs3Ijo6GkqlEsnJycjKyupy/LvvvovRo0dDqVQiPj4eu3bt6lGxjjJnXDj2Pnw13l42FX//VQLeXjYVex++mkGEiIhIAnaHke3bt2PlypVYt24dDh48iAkTJmD27NkoLy/vcPy+ffuwaNEi3HXXXTh06BAWLFiABQsWIDc3t9fF98bFvhw3JEQgJSaEp2aIiIgkYncH1uTkZEyZMgUvvfQSAMBisSAqKgr3338/Vq1a1W78woUL0dDQgE8++cS6bOrUqUhISMCmTZu69Z6O7MBKREREfaO7n992HRkxmUzIzs5GamrqpReQy5GamorMzMwO18nMzLQZDwCzZ8/udDwAGI1GGAwGmwcRERF5JrvCSGVlJcxmMzQajc1yjUYDnU7X4To6nc6u8QCQlpYGtVptfURFRdlTJhEREbkRl7yaZvXq1dDr9dZHcXGx1CURERGRk9jVDj40NBReXl4oKyuzWV5WVgatVtvhOlqt1q7xAKBQKKBQKOwpjYiIiNyUXUdGfH19kZiYiIyMDOsyi8WCjIwMpKSkdLhOSkqKzXgA+PzzzzsdT0RERP2L3TfKW7lyJZYsWYLJkycjKSkJGzZsQENDA5YuXQoAWLx4MSIiIpCWlgYAePDBBzF9+nQ8//zzmDdvHrZt24YDBw7g1VdfdeyWEBERkVuyO4wsXLgQFRUVWLt2LXQ6HRISEpCenm6dpFpUVAS5/NIBl2nTpuGtt97Co48+ikceeQQjR47Ejh07MG7cOMdtBREREbktu/uMSIF9RoiIiNxPdz+/7T4yIoWLeYn9RoiIiNzHxc/tyx33cIswUldXBwDsN0JEROSG6urqoFarO33eLU7TWCwWlJSUIDAwEDIZ7yHzUwaDAVFRUSguLuYprB7iPuw97kPH4H7sPe7D3nPkPhRCoK6uDoMHD7aZT/pzbnFkRC6XIzIyUuoyXJpKpeI/vF7iPuw97kPH4H7sPe7D3nPUPuzqiMhFLtmBlYiIiPoPhhEiIiKSFMOIm1MoFFi3bh3b5/cC92HvcR86Bvdj73Ef9p4U+9AtJrASERGR5+KRESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGHED1dXVuO2226BSqRAUFIS77roL9fX1XY6///77ERsbCz8/PwwZMgQPPPAA9Hq9zbiioiLMmzcP/v7+CAsLw5/+9Ce0trY6e3MkYe8+BIBXX30VV111FVQqFWQyGWpra9uNiY6Ohkwms3k89dRTTtoKaTlrH/bkdd1VT7a1ubkZK1asQEhICAICAnDzzTejrKzMZszPfwZlMhm2bdvmzE3pMxs3bkR0dDSUSiWSk5ORlZXV5fh3330Xo0ePhlKpRHx8PHbt2mXzvBACa9euRXh4OPz8/JCamopTp045cxMk5+h9eOedd7b7eZszZ07vihTk8ubMmSMmTJgg9u/fL7799lsxYsQIsWjRok7HHzlyRNx0003io48+Evn5+SIjI0OMHDlS3HzzzdYxra2tYty4cSI1NVUcOnRI7Nq1S4SGhorVq1f3xSb1OXv3oRBCvPjiiyItLU2kpaUJAKKmpqbdmKFDh4rHHntMlJaWWh/19fVO2gppOWsf9uR13VVPtvXee+8VUVFRIiMjQxw4cEBMnTpVTJs2zWYMAPH666/b/Bw2NTU5c1P6xLZt24Svr6/YsmWLOHr0qFi2bJkICgoSZWVlHY7/7rvvhJeXl3jmmWfEsWPHxKOPPip8fHzEkSNHrGOeeuopoVarxY4dO8Thw4fF9ddfL4YNG+YR+6sjztiHS5YsEXPmzLH5eauuru5VnQwjLu7YsWMCgPjhhx+syz799FMhk8nE+fPnu/0677zzjvD19RUtLS1CCCF27dol5HK50Ol01jGvvPKKUKlUwmg0Om4DXEBv9+FXX33VZRh58cUXHVita3LWPnTUz7c76Mm21tbWCh8fH/Huu+9alx0/flwAEJmZmdZlAMQHH3zgtNqlkpSUJFasWGH9u9lsFoMHDxZpaWkdjr/11lvFvHnzbJYlJyeLe+65RwghhMViEVqtVjz77LPW52tra4VCoRBvv/22E7ZAeo7eh0K0hZEbbrjBoXXyNI2Ly8zMRFBQECZPnmxdlpqaCrlcju+//77br6PX66FSqeDt7W193fj4eGg0GuuY2bNnw2Aw4OjRo47bABfgqH3YmaeeegohISGYOHEinn32WY881eWsfejs740r6cm2Zmdno6WlBampqdZlo0ePxpAhQ5CZmWkzdsWKFQgNDUVSUhK2bNly2Vu2uzqTyYTs7GybbZfL5UhNTW237RdlZmbajAfafq9dHF9YWAidTmczRq1WIzk5udPXdGfO2IcX7dmzB2FhYYiNjcXy5ctRVVXVq1rd4kZ5/ZlOp0NYWJjNMm9vbwQHB0On03XrNSorK/H444/j7rvvtnndnwYRANa/d/d13YUj9mFnHnjgAUyaNAnBwcHYt28fVq9ejdLSUrzwwgu9el1X46x96MzvjavpybbqdDr4+voiKCjIZrlGo7FZ57HHHsPVV18Nf39/fPbZZ7jvvvtQX1+PBx54wOHb0VcqKythNps7/D2Vl5fX4Tqd/V67uK8ufu1qjCdxxj4EgDlz5uCmm27CsGHDUFBQgEceeQRz585FZmYmvLy8elQrw4hEVq1ahaeffrrLMcePH+/1+xgMBsybNw9xcXH461//2uvXcyV9tQ+7snLlSuufx48fD19fX9xzzz1IS0tzi3bUrrAP3Z0r7MM1a9ZY/zxx4kQ0NDTg2WefdeswQq7rV7/6lfXP8fHxGD9+PGJiYrBnzx7MnDmzR6/JMCKRP/zhD7jzzju7HDN8+HBotVqUl5fbLG9tbUV1dTW0Wm2X69fV1WHOnDkIDAzEBx98AB8fH+tzWq223YzqizP0L/e6rqIv9qG9kpOT0draijNnziA2Ntahr+0MUu/DvvzeOIsz96FWq4XJZEJtba3N0ZGysrIu909ycjIef/xxGI1GtwjFHQkNDYWXl1e7K4e62natVtvl+Itfy8rKEB4ebjMmISHBgdW7Bmfsw44MHz4coaGhyM/P73EY4QRWF3dx0tuBAwesy3bv3n3ZCX56vV5MnTpVTJ8+XTQ0NLR7/uIE1p/OqP7nP/8pVCqVaG5uduxGSKyn+/Ciriaw/tzWrVuFXC7v9cxyV+Osfdjb13UnPdnWixNY33vvPeuyvLy8dhNYf+6JJ54QAwcOdFzxEklKShK/+93vrH83m80iIiKiy8mX1113nc2ylJSUdhNYn3vuOevzer3e4yewOnIfdqS4uFjIZDLx4Ycf9rhOhhE3MGfOHDFx4kTx/fffi71794qRI0faXA547tw5ERsbK77//nshRNs/ruTkZBEfHy/y8/NtLr9qbW0VQly6tHfWrFkiJydHpKeni0GDBnn0pb327EMhhCgtLRWHDh0SmzdvFgDEN998Iw4dOiSqqqqEEELs27dPvPjiiyInJ0cUFBSIrVu3ikGDBonFixf3+fb1BWfsw+68rifpyT689957xZAhQ8SXX34pDhw4IFJSUkRKSor1+Y8++khs3rxZHDlyRJw6dUq8/PLLwt/fX6xdu7ZPt80Ztm3bJhQKhXjjjTfEsWPHxN133y2CgoKsVwHecccdYtWqVdbx3333nfD29hbPPfecOH78uFi3bl2Hl/YGBQWJDz/8UPz444/ihhtu8PhLex25D+vq6sQf//hHkZmZKQoLC8UXX3whJk2aJEaOHNmr/8gyjLiBqqoqsWjRIhEQECBUKpVYunSpqKursz5fWFgoAIivvvpKCHHpf6EdPQoLC63rnTlzRsydO1f4+fmJ0NBQ8Yc//MF66a+nsXcfCiHEunXrOtyHr7/+uhBCiOzsbJGcnCzUarVQKpVizJgx4sknn/S4I0sXOWMfdud1PUlP9mFTU5O47777xMCBA4W/v7+48cYbRWlpqfX5Tz/9VCQkJIiAgAAxYMAAMWHCBLFp0yZhNpv7ctOc5h//+IcYMmSI8PX1FUlJSWL//v3W56ZPny6WLFliM/6dd94Ro0aNEr6+vmLs2LFi586dNs9bLBaxZs0aodFohEKhEDNnzhQnTpzoi02RjCP3YWNjo5g1a5YYNGiQ8PHxEUOHDhXLli2zaRPREzIh3Pz6LyIiInJr7DNCREREkmIYISIiIkkxjBAREZGkGEaIiIhIUgwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFL/HwU1UigK542WAAAAAElFTkSuQmCC", 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" ] @@ -51,12 +51,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -94,14 +94,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.04024377 -0.04024377]\n" + "ename": "TypeError", + "evalue": "_PPolyBase.__call__() missing 1 required positional argument: 'x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterpolate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CubicSpline\n\u001b[1;32m 5\u001b[0m spline \u001b[38;5;241m=\u001b[39m CubicSpline(probs, [quantiles, quantiles], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mspline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mTypeError\u001b[0m: _PPolyBase.__call__() missing 1 required positional argument: 'x'" ] } ], @@ -117,22 +121,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (1000,) and (2, 1000)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# plot the interpolation (approximated CDF)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mInterpolated CDF\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/pyplot.py:3590\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3582\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3583\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3584\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3588\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3591\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3592\u001b[0m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3593\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3594\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3595\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3596\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1482\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1483\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1721\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1722\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1723\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1724\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1725\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1726\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 301\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 302\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 496\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (1000,) and (2, 1000)" + ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -148,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -196,14 +205,20 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "CRPS over all values 0.24\n" + "ename": "ValueError", + "evalue": "The length of `y` along `axis`=1 doesn't match the length of `x`", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m probs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, n_x)\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# Create a cubic spline with the new values and probs\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m spline \u001b[38;5;241m=\u001b[39m \u001b[43mCubicSpline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Evaluate the spline at the probability values\u001b[39;00m\n\u001b[1;32m 17\u001b[0m imbalances \u001b[38;5;241m=\u001b[39m spline(probs)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/scipy/interpolate/_cubic.py:635\u001b[0m, in \u001b[0;36mCubicSpline.__init__\u001b[0;34m(self, x, y, axis, bc_type, extrapolate)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, bc_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnot-a-knot\u001b[39m\u001b[38;5;124m'\u001b[39m, extrapolate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 635\u001b[0m x, dx, y, axis, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 636\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(x)\n\u001b[1;32m 638\u001b[0m bc, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_bc(bc_type, y, y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m:], axis)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/scipy/interpolate/_cubic.py:48\u001b[0m, in \u001b[0;36mprepare_input\u001b[0;34m(x, y, axis, dydx)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`x` must contain at least 2 elements.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[axis]:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe length of `y` along `axis`=\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatch the length of `x`\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(axis))\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39misfinite(x)):\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`x` must contain only finite values.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: The length of `y` along `axis`=1 doesn't match the length of `x`" ] } ], @@ -256,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -265,15 +280,15 @@ "Text(0.5, 1.0, 'Interpolated CDF')" ] }, - "execution_count": 2, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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iMs8xX9OQlOF0KTU9T31aNqqdD1KLLIZh1M+q6yjy8/PlcDjkdDoVFhZmdhwAAAAAAHAEhmEo/2CZsgtcyikoVnZB8R/f//m4WM6DpTWW4z9XdNawznE19vrV6UQ6D6/aVBoAAAAAANQP+a5S7dl/UBn7XdrjPPiPX7uUme9SyXFm8fydn9Wihg0CFBEcoIYN/BXRIEDhwX8+DlBEA381DA5QRIMANQwO0MasAl333pLjvm5UqP1kPmadRSEEAAAAAACqnbOoVNvzCrUjr0g78oq0M69Iu/e7lLH/oDKcLh0oLqvU6ziC/BUZGqio0MAK3w/92l5+zRHkL4vFUul8TcKDFOuwK9PpOuI+QhZJMQ67eiZGVPo1vQmFEAAAAAAAOGFlbo8ynK7ywmf73kOlz/a8Qu3YW6R81/ELn4bB/op1BKlJuP2PguavX8eEHSp77P62Gslvs1o0YWiSxkxfJotUoRT6s1aaMDRJNmvlSyZvQiEEAAAAAACOan9RibbkFGpLzgFtLf9+QNv3FqnsOCdwRYUGqllEsJpFBCs+Ilhx4UGHip9wu5o4ghQUUDNlT2UNTo7Va1d31cRZayscPR/jsGvC0CQNTo41MV3NohACAAAAAMDHuT2GduYVaXP2AW3NPaAt2YWHvucUKq+w5Kj3BfhZFd8wqLz0adaogZpFBKt5o2DFNww2vfCpjMHJsTorKUap6XnKLnApKvTQMrH6OjPoTxRCAAAAAAD4CMMwlFNQrPWZBdqQWaANWYe+b8oukKv06Bs4xzrsahHZQC0jQ9SicQO1jApRi8gQxYbZZa0HxYnNaqmXR8sfC4UQAAAAAAD1UFFJmdZlFGh9Zv6h8uePAmh/0ZGPaA/0s6pFZEh58dPyj++JjRuoQSD1QX3Dv1EAAAAAALyc82Cp1uxxas3ufK3Z41TannxtzTmgI23xY7VICY0bqG10qNrGhJZ/b96oQb1fJoW/UAgBAAAAAOBFcg8Ua/Vup9bsdmrNnnyl7XFqZ97BI46NDA1U+9gwtftb8dMqKqTGTu6C96AQAgAAAACgjnKVupW226kVO/eXf+3ad+TyJz4iSB1iHUqOC1OHJg51aBKmqDB7LSeGt6AQAgAAAACgDvB4DG3NPaDlO/4qfzZkFhx2tLvFIrVo3EDJcQ4l/1H8dGjikCPY36Tk8EYUQgAAAAAAmKCopEwrduzXku37tHhbnlbs2K+C4rLDxkWGBqpzfHj5V8emDoXZKX9wciiEAAAAAACoBTkFxVq6PU+Lt+3Tkm15StuTL/c/Zv8E+dvUsamjQgEU67DLYmGzZ1QvCiEAAAAAAKqZYRjakVekRVvztHhbnpZs36f03MLDxsU67OqREKEeCQ3VtXlDtY0OlZ/NakJi+BoKIQAAAAAAqsHu/Qe1cMveP75ytcfpqvC8xSK1jQ5V94SG6pEQoe4JEYoLDzIpLXwdhRAAAAAAAFWQXeD6qwDaulfb9xZVeN7fZlHn+PA/ZgBFqGuzhmz8jDqDQggAAAAA4JPcHkOp6XnKLnApKtSunokRslmPvldPgatUv23Zq1825Wrh1r3anH2gwvM2q0Ud4xw6pWUj9WnZSN2aN1RwAH/tRt3Ef5kAAAAAAJ8zOy1DE2etVcbflnXFOuyaMDRJg5NjJR06Bj5tj1M/b8zRzxtztWzHvgpHwFssUocmYerT4lAB1CMhQqGc/gUvQSEEAAAAAPAps9MyNGb6Mhn/uJ7pdOmm6cs0ok9z7Ssq1S+bcrSvqLTCmMTGDdSvdWOd2qqxeiVGKDw4oPaCA9WIQggAAAAA4DPcHkMTZ609rAySVH7t/YXby6+FBPrplJaNdFqbSPVvE6n4iOBayQnUNAohAAAAAIDPSE3Pq7BM7Ggu6Bynq3o1U5dm4fLnGHjUQxRCAAAAAIB6b0vOAc1dl6VPF++s1PgB7SLVMzGihlMB5qEQAgAAAADUO2VujxZv26cf12Vp3vpspecWntD9UaH2GkoG1A0UQgAAAACAeqG4zK3fNu/Vd2kZ+mFtVoUNof1tFvVu0UgD2kbptZ82K7eg5Ij7CFkkxTjszA5CvUchBAAAAADwWkUlZfppQ46+S8vUvPXZOlBcVv5cw2B/ndEuWgPbR6lfm0iFBB76K3CTcLvGTF8mi1ShFLL88X3C0CTZrBYB9RmFEAAAAADAqzgPlmre+izNTsvUTxtz5Cr1lD8XHRaoQR1iNLhDjHomRsjvCBtCD06O1WtXd9XEWWsrbDAd47BrwtAkDU6OrZXPAZiJQggAAAAAUOflu0r1/Zos/d+qPfp1c65K3X/N7YmPCNI5ybEa1CFGXeLDZa3E7J7BybE6KylGqel5yi5wKSr00DIxZgbBV1AIAQAAAADqpIMlbs1dn6VZK/do/oYclZT9NROoTXSIBneI0aDkGCXFhsliOfEix2a1qE/LRtUZGfAaFEIAAAAAgDqjpMyjnzfmaNaqPfphbZaKStzlz7WMbKDzU+J0bqdYtYoKMTEl4P0ohAAAAAAApipze/T71jzNWrlH36VlKN/118bQTRsGaWhKE52f0kTtYkKrNBMIwOEohAAAAAAApliXka/Pl+3Slyv2KKeguPx6VGigzu0Uq/NTmqhzfDglEFADKIQAAAAAALUmp6BYX63Yrc+X7dbajPzy6+HB/jon+VAJxObOQM2jEAIAAAAA1ChXqVs/rsvS58t266eNOXJ7Dp0Q5m+z6Mx20bqoa5xObxulAL/Dj4gHUDMohAAAAAAA1c4wDC3dvk//XbZb/7dqjwr+ti9Q5/hwXdw1Tud1aqKGDQJMTAn4LgohAAAAAEC1ySko1n+X7dKMxTuVnltYfr2Jw64Lu8bpwi5NOSEMqAMohAAAAAAAJ8XtMfTzphx9krpDc9dlq+yPJWHBATadkxyri7vGqXeLRrKyLxBQZ1AIAQAAAACqZNe+In26ZJc+W7JTGU5X+fUuzcJ1RY94ndupiUIC+WsnUBfxv0wAAAAAQKWVlHn047osfbJ4p/63KUfGoclACg/214Vd4nRFj2ZqGxNqbkgAx0UhBAAAAAA4rj37D+rj1B36OHWncg8Ul18/pWUjXdGzmc5Oipbd32ZiQgAngkIIAAAAAHBEHo+hX7fk6oOF2/Xjuiz9sTWQIkMDdVn3prqse7yaN2pgbkgAVUIhBAAAAACowFlUqpnLdunD37dr699OCuvTopGu6dNcZyVFy99mNTEhgJNFIQQAAAAAkCSl7Xbqg4Xb9dXK3XKVeiRJIYF+urhrnK7u3Vyto9kbCKgvKIQAAAAAwIeVuT2asyZL7/6arqXb95VfbxcTqmv6NNcFnePUgJPCgHqH/1UDAAAAgA9yFpXqk8U79N5v27TnjyPj/W0WDekYq2t6N1e35g1lsVhMTgmgplAIAQAAAEA94vYYSk3PU3aBS1GhdvVMjJDN+lexsyXngKb9uk0zl+7SwVK3JKlRgwAN791cV/dupqhQu1nRAdQiCiEAAAAAqCdmp2Vo4qy1yvhjxo8kxTrsGn9eezUI9NfUX9M1f0NO+XPtYkJ1bd9EnZ/ShCPjAR9DIQQAAAAA9cDstAyNmb5Mxj+uZzhdGvPh8vLHFot0ZrtoXds3QX1aNGJZGOCjKIQAAAAAwMu5PYYmzlp7WBn0dxZJI09J0OhTE9S8UYPaigagjrKaHQAAAAAAcHJS0/MqLBM7EkPSoA4xlEEAJFEIAQAAAIDXW7lr3/EHScouOHZpBMB3sGQMAAAAALxUanqe3vhpi+auz67UeE4QA/AnCiEAAAAA8CKGYejnTbl6Zd4mLd52aGaQxSIF2qxylXmOeI9FUozj0BH0ACBRCAEAAACAV/B4DP24LkuvzN+sVbuckqQAm1UXd2uqG09roQ2Z+RozfZkkVdhc+s8zxCYMTZLNyoliAA6hEAIAAACAOsztMfTN6gxNmbdZG7IKJEl2f6uG92quG09roeiwQ8vAEhs30GtXd9XEWWsrbDAd47BrwtAkDU6ONSU/gLqJQggAAAAA6qBSt0dfLt+t1xZs0dbcQklSSKCfRvRpruv6JqpRSOBh9wxOjtVZSTFKTc9TdoFLUaGHlokxMwjAP1EIAQAAAEAdUlzm1mdLdum1BVu0e/9BSVJ4sL+uPTVRI/skyBHsf8z7bVaL+rRsVBtRAXgxCiEAAAAAqANK3R7NXLpLr8zbXF4ENQ4J0A39Wmh47+YKCeSvbwCqD7+jAAAAAICJytwefb58t16et0k78w4VQVGhgRpzektd2bOZ7P42kxMCqI8ohAAAAADABGVuj75euUf/mbtJ2/cWSZIahwTq5tNb6qpeFEEAahaFEAAAAADUIrfH0P+t2qP//LipfLPoRg0CdFP/lrq6d3MFBVAEAah5FEIAAAAAUAs8HkPfpWXqxR83anP2AUlSw2B//at/S43o01zBAfz1DEDt4XccAAAAAKhBhmHo5025enbOeqXtzpckOYL8deNpLTTylAQ2iwZgCn7nAQAAAIAasmzHPj0ze71+35onSQoJ9NP1/RJ1bd9EhdmPfXw8ANQkCiEAAAAAqGabsgr07JwN+n5tliQpwM+qEb2b6+YBrRTRIMDkdABAIQQAAAAA1WbXviJN/nGTPl+2Sx5DslqkS7o11diBbRQXHmR2PAAoRyEEAAAAACdp74FiTZm/RdN/364St0eSNLhDjO4e1EatokJNTgcAh6MQAgAAAIAqcpW69c4v6XptwRYdKC6TJPVp0Uj3ndNOnePDzQ0HAMdAIQQAAAAAJ8jjMfTVyt16dvYG7XG6JEnJcWG6b3A79W3VWBaLxeSEAHBsFEIAAAAAcAIWbtmrJ75dW36EfBOHXfcObqfzU5rIaqUIAuAdKIQAAAAAoBI2Zx/QU9+t04/rsiUdOkL+5gEtde2pibL720xOBwAnhkIIAAAAAI4h90Cx/vPjJn2UukNujyGb1aLhvZpp7Jmt1Sgk0Ox4AFAlFEIAAAAAcARH2jD6rKRo3X9OO7WMDDE5HQCcHAohAAAAAD7J7TGUmp6n7AKXokLt6pkYIZvVIsMw9O3qTD357Trt3n9QktSpqUMPDmmv3i0amZwaAKoHhRAAAAAAnzM7LUMTZ61Vxh8nhElSrMOu6/om6Ie12VqUnifp0IbR953TTkM7sWE0gPqFQggAAACAT5mdlqEx05fJ+Mf1DKdLj3+zXpJk97fqpv4t9a/TWioogA2jAdQ/FEIAAAAAfIbbY2jirLWHlUF/Z/e36oc7+ys+IrjWcgFAbbOaHQAAAAAAaktqel6FZWJH4ir1aNe+g7WUCADMQSEEAAAAwGdkFxy7DDrRcQDgrSiEAAAAAPiEgyVuzV+fU6mxUaH2Gk4DAOZiDyEAAAAA9d6Pa7P06Kw1x10KZpEU4zh0BD0A1GcUQgAAAADqrZ15RZo4a41+XJct6dAx8kNTYvXGz+mySBU2l/7zUPkJQ5Nk44h5APUchRAAAACAeqe4zK23ft6ql+dtVnGZR35Wi67v10K3n9lKwQF+6tKsoSbOWlthg+kYh10ThiZpcHKsickBoHZQCAEAAACoV/63KUcTvlqjrbmFkqTeLSL02LBktY4OLR8zODlWZyXFKDU9T9kFLkWFHlomxswgAL6CQggAAABAvZDpdOmxb9bqm1UZkqTGIYF65Lz2Oj+liSyWw4sem9WiPi0b1XZMAKgTKIQAAAAAeLVSt0fv/bZNL/6wUYUlblkt0og+CRp3dhuF2f3NjgcAdRKFEAAAAACvtWLnft3/31Van1kgSeraLFyPXZCsDk0cJicDgLqNQggAAACA1zlQXKbn5mzQewu3yTCk8GB/PXBOO13aLV5W9gECgOOiEAIAAADgVX5Ym6XxX6WVnxB2UZc4PXRuezUKCTQ5GQB4DwohAAAAAF4h0+nSo1+v0ew1mZKkZhHBeuLCZPVrHWlyMgDwPhRCAAAAAOo0j8fQh4u26+nZG3SguEw2q0U3ntZCt5/RWkEBNrPjAYBXohACAAAAUGetz8zXA5+v1vId+yVJnePDNemijmofG2ZuMADwchRCAAAAAOocV6lbL8/bpDd+2qoyj6GQQD/dM6itru7dXDY2jQaAk0YhBAAAAKBOWbItT/fOXKWtuYWSpLOTojVxWAfFOoJMTgYA9QeFEAAAAIA6obC4TM/+7Sj5yNBAPTasgwYnx5odDQDqHQohAAAAAKb7dXOu7vvvKu3ad1CSdGm3pnr43CQ5gv1NTgYA9ROFEAAAAADT5LtKNenbdfo4dackKS48SE9e1FH923CUPADUJAohAAAAAKaYtz5LD36epsx8lyTpmt7Ndd857RQSyF9TAKCm8TstAAAAgFq1r7BE//6/tfpi+W5JUkKjYD19cSf1atHI5GQA4DsohAAAAADUmu9WZ+iRr9KUe6BEVot0fb8WunNgGwUF2MyOBgA+hUIIAAAAQI3bV1iiR75K0/+typAktYkO0TOXpKhzfLi5wQDAR1EIAQAAAKhRP67N0gNfrFZOQbFsVotuPr2lbj2jlQL9mBUEAGaxmh1gypQpSkhIkN1uV69evZSamnrM8ZMnT1bbtm0VFBSk+Ph43XnnnXK5XLWUFgAAAEBl5btKdc9nK3X9+0uUU1CsVlEh+uLmU3TX2W0pgwDAZKbOEJoxY4bGjRun119/Xb169dLkyZM1aNAgbdiwQVFRUYeN/+ijj3T//ffr3Xff1SmnnKKNGzdq1KhRslgseuGFF0z4BAAAAACO5JdNubp35krtcbpksUg39GuhcWe1kd2fIggA6gKLYRiGWW/eq1cv9ejRQ6+88ookyePxKD4+Xrfddpvuv//+w8bfeuutWrdunebOnVt+7a677tKiRYv0yy+/VOo98/Pz5XA45HQ6FRYWVj0fBAAAAIAkqbC4TE99t14f/L5dktS8UbCeuzRFPRIiTE4GAPXfiXQepi0ZKykp0dKlSzVw4MC/wlitGjhwoBYuXHjEe0455RQtXbq0fFnZ1q1b9e2332rIkCFHfZ/i4mLl5+dX+AIAAABQ/RZvy9OQl/5XXgaN6NNc343tRxkEAHWQaUvGcnNz5Xa7FR0dXeF6dHS01q9ff8R7rrrqKuXm5qpv374yDENlZWW66aab9OCDDx71fSZNmqSJEydWa3YAAAAAf3GVuvX89xv09i/pMgypicOuZy5JUd/Wjc2OBgA4CtM3lT4RCxYs0JNPPqlXX31Vy5Yt0+eff65vvvlGjz322FHveeCBB+R0Osu/du7cWYuJAQAAgPpt5c79Ou/lX/TW/w6VQZd2a6rZd55GGQQAdZxpM4QaN24sm82mrKysCtezsrIUExNzxHseeeQRXXPNNbr++uslSR07dlRhYaFuvPFGPfTQQ7JaD++3AgMDFRgYWP0fAAAAAPBhZW6PpszfopfmbZLbYygyNFBPXdRRZ7aPPv7NAADTmTZDKCAgQN26dauwQbTH49HcuXPVp0+fI95TVFR0WOljsx06pcDEvbEBAAAAn7J9b6EufWOhXvxxo9weQ+d2itX3d5xGGQQAXsTUY+fHjRunkSNHqnv37urZs6cmT56swsJCjR49WpI0YsQIxcXFadKkSZKkoUOH6oUXXlCXLl3Uq1cvbd68WY888oiGDh1aXgwBAAAAqBmGYeizJbv06Kw1KipxKzTQT49dkKxhnZvIYrGYHQ8AcAJMLYQuv/xy5eTkaPz48crMzFTnzp01e/bs8o2md+zYUWFG0MMPPyyLxaKHH35Yu3fvVmRkpIYOHaonnnjCrI8AAAAA+IS8whI98PkqzVlzaMuHnokReuGyFDVtGGxyMgBAVVgMH1trlZ+fL4fDIafTqbCwMLPjAAAAAHXegg3ZumfmKuUUFMvfZtFdZ7fVDf1ayGZlVhAA1CUn0nmYOkMIAAAAQN3lKnVr0rfr9N7C7ZKkVlEhmnx5ZyXHOUxOBgA4WRRCAAAAAA6TttupO2as0ObsA5KkUack6P5z2snuz96dAFAfUAgBAAAAKOf2GHrz56164YcNKnUfOk7+uUtT1L9NpNnRAADViEIIAAAAgCRpz/6DunPGCi1Kz5MkDeoQrUkXdVJEgwCTkwEAqhuFEAAAAADNTsvUff9dJefBUjUIsGnC+R10abemHCcPAPUUhRAAAADgw1ylbj3+zVpN/32HJKlTU4deuqKLEho3MDkZAKAmUQgBAAAAPmpDZoFu+3iZNmYd2jj6X/1b6K6z2irAz2pyMgBATaMQAgAAAHyMYRiavmiHHv+/tSou86hxSKBeuCxFp7FxNAD4DAohAAAAwIfsKyzRff9dpe/XZkmS+reJ1POXpahxSKDJyQAAtYlCCAAAAPARv2/dqztnrFCG0yV/m0X3DW6na09NlNXKxtEA4GsohAAAAIB6rszt0UtzN+mV+ZvlMaQWjRvopSu7KDnOYXY0AIBJKIQAAACAemzXviKN/WSFlm7fJ0m6tFtTPXp+BzUI5K8CAODL+FMAAAAAqKe+X5Opuz9bqXxXmUID/fT4hcka1jnO7FgAgDqAQggAAACoZ0rKPHrqu/V699d0SVJKfLhevqKLmjUKNjkZAKCuoBACAAAA6pGdeUW69aNlWrnLKUm6vm+i7h3cTgF+VpOTAQDqEgohAAAAoJ6YnZape2auVIGrTI4gfz13aYrOSoo2OxYAoA6iEAIAAAC8XEmZR5O+W6epv26TJHVpFq6Xr+yipg1ZIgYAODIKIQAAAMCL7dhbpFs/XqZVfywRu/G0FrpnUFv521giBgA4OgohAAAAwEvNTsvQPTNXqcBVpvBgfz1/aYrObM8SMQDA8VEIAQAAAF6muMytJ79Zp/cWbpckdW0Wrpev6qq48CCTkwEAvAWFEAAAAOBFtu8t1K0fLdfq3YeWiP2rfwvdfTZLxAAAJ4ZCCAAAAPAS363O0L0zV6mg+NASsRcuS9EZ7VgiBgA4cRRCAAAAQB1X6vbo6e/W6+1f0iVJ3Zo31MtXdlETlogBAKqIQggAAACow7LyXbr1o2VavG2fJE4RAwBUDwohAAAAoI5auGWvbvt4mXIPlCg00E/PXpqiwckxZscCANQDFEIAAABAHePxGHr95y16bs4GeQypXUyoXru6mxIbNzA7GgCgnqAQAgAAAOoQ58FS3fXpSv24LkuSdFHXOD1xQUcFBdhMTgYAqE8ohAAAAIA6Ys0ep8ZMX6YdeUUK8LNq4vkddEWPeFksFrOjAQDqGQohAAAAoA74dPFOPfJVmorLPGraMEivDe+mjk0dZscCANRTFEIAAACAiVylbo3/Kk2fLtklSTqjXZReuCxF4cEBJicDANRnFEIAAACASXbsLdJN05dqbUa+rBbprrPbakz/lrJaWSIGAKhZFEIAAACACeauy9IdM1aowFWmRg0C9NKVXXRqq8ZmxwIA+AgKIQAAAKAWeTyGXpq3SZN/3CRJ6ta8oV65qotiHUEmJwMA+BIKIQAAAKCW5LtKNW7GCv24LluSNLJPcz10bpIC/KwmJwMA+BoKIQAAAKAWbMwq0L8+WKr03EIF+ln1xIUddUm3pmbHAgD4KAohAAAAoIZ9uzpDd3+2UkUlbsWFB+n1qzlSHgBgLgohAAAAoIa4PYaenbNBr/+0RZJ0SstGevnKLmoUEmhyMgCAr6MQAgAAAGrAvsIS3fbxcv2yOVeSdONpLXTvoLbys7FfEADAfBRCAAAAQDVL2+3UTdOXate+gwryt+mZSzppaEoTs2MBAFCOQggAAACoRl8s36X7/7taxWUeNW8UrDev6a62MaFmxwIAoAIKIQAAAKAalLo9euKbdZr22zZJ0oC2kZp8RRc5gvzNDQYAwBFQCAEAAAAnKaegWLd8tEyp6XmSpNvPbK07zmwtq9VicjIAAI6MQggAAAA4CSt37te/PliqzHyXQgL99MJlKTq7Q4zZsQAAOCYKIQAAAKCKvli+S/f9d7VKyjxqFRWiN67pppaRIWbHAgDguCiEAAAAgGNwewylpucpu8ClqFC7eiZGSJKenr1eb/68VZI0sH20Xrw8RaF29gsCAHgHCiEAAADgKGanZWjirLXKcLrKr0WHBapRgwCtzSiQJN12RivdObAN+wUBALwKhRAAAABwBLPTMjRm+jIZ/7ielV+srPxiBdisevHyzjq3U6wp+QAAOBlWswMAAAAAdY3bY2jirLWHlUF/Fxbkp8HJbB4NAPBOFEIAAADAP6Sm51VYJnYkuQdKyo+ZBwDA21AIAQAAAP+QXXDsMuhExwEAUNdQCAEAAAD/EBVqr9ZxAADUNRRCAAAAwD9YJB3r0DCLpFjHX0fQAwDgbSiEAAAAgL/5aNEOXfPuInmOsqP0nz3RhKFJsnHUPADAS1EIAQAAAJJK3R498mWaHvxitUrdhs7rFKuXruisWEfFZWExDrteu7qrBidz3DwAwHv5mR0AAAAAMJuzqFQ3f7RUv27eK4tFuvvstrr59JayWCw6t1MTpabnKbvApajQQ8vEmBkEAPB2FEIAAADwaVtzDuj695Zoa26hGgTYNPmKLjorKbr8eZvVoj4tG5mYEACA6kchBAAAAJ/16+ZcjZm+VPmuMsWFB+ntkd3VPjbM7FgAANQ4CiEAAAD4pA8Xbdf4r9bI7THUtVm43rimuyJDA82OBQBAraAQAgAAgE8pc3v0xLfrNPXXbZKkCzo30VMXd5Ld32ZuMAAAahGFEAAAAHxGvqtUt320XD9tzJEk3TPor82jAQDwJRRCAAAA8Anb9xbquveWaHP2AQX52/Ti5SkcHQ8A8FkUQgAAAKj3Fm3dq5umL9W+olLFhNn19sjuSo5zmB0LAADTUAgBAACgXvt0yU499MVqlboNdWrq0Fsjuis6zG52LAAATEUhBAAAgHrJ4zH09Oz1euPnrZKkczvF6rlLUhQUwObRAABQCAEAAKDeOVji1p0zVmj2mkxJ0tgzW+uOga3ZPBoAgD9QCAEAAKBeySko1vXvL9HKnfsVYLPq2Us7aVjnOLNjAQBQp1AIAQAAoN7YlFWgUVMXa/f+gwoP9tdbI7qrR0KE2bEAAKhzKIQAAABQL/y6OVc3TV+qAleZEhoFa+ronkps3MDsWAAA1EkUQgAAAPB6ny7ZqQc/X60yj6HuzRvqzRHdFdEgwOxYAADUWRRCAAAA8FqGYej57zfqlfmbJUnnpzTRM5d0kt2fk8QAADgWCiEAAAB4JVepW/fOXKWvV+6RJN06oJXGndVGVisniQEAcDwUQgAAAPA6eYUluvH9JVqyfZ/8rBY9eVFHXdY93uxYAAB4DQohAAAAeJX03EKNnpqqbXuLFGr30+tXd9OprRqbHQsAAK9CIQQAAACvsWzHPl03bbH2FZUqLjxI00b3UOvoULNjAQDgdSiEAAAA4BV+WJul2z5eJlepR52aOvT2yO6KCrWbHQsAAK9EIQQAAIA678NF2/XIl2nyGNKAtpF65aquahDIj7IAAFQVf4oCAACgzjIMQy/8sFEvzzt0rPzl3eP1xIXJ8rNZTU4GAIB3oxACAABAnVTq9uiBz1dr5tJdkqSxZ7bWHQNby2LhWHkAAE4WhRAAAADqnMLiMo35cJl+3pgjm9WiJy5I1hU9m5kdCwCAeoNCCAAAAHVKdoFL105brLTd+Qryt2nK8C46o1202bEAAKhXKIQAAABQZ2zNOaCRU1O1M++gIhoE6N1RPdQ5PtzsWAAA1DsUQgAAAKgTlu3Yp+umLda+olI1bxSs90b3VELjBmbHAgCgXqIQAgAAgOkWbMjWmOnLdLDUrZSmDr0zqocahwSaHQsAgHqLQggAAACm+mrFbt316UqVeQz1bxOp167uquAAfkwFAKAm8SctAAAATDPt13Q9OmutJOn8lCZ67tIUBfhZTU4FAED9RyEEAACAWmcYhl78YaNemrdZkjTqlASNPy9JVqvF5GQAAPgGCiEAAADUKrfH0Piv0vThoh2SpHFntdFtZ7SSxUIZBABAbaEQAgAAQK0pLnNr3Kcr9c2qDFks0r+HJeua3s3NjgUAgM+hEAIAAECtKCwu078+WKpfNufK32bRi5d31nmdmpgdCwAAn0QhBAAAgBqXV1ii0VNTtXKXU8EBNr1xTTf1ax1pdiwAAHwWhRAAAABq1J79B3XNO4u0JadQDYP9NXV0T3WODzc7FgAAPo1CCAAAADVmW26hhr+9SLv3H1Ssw64PruupVlGhZscCAMDnUQgBAACgRmzILNDV7yxSTkGxWjRuoA+u76W48CCzYwEAAFEIAQAAoAas2rVfI95N1f6iUrWLCdUH1/VSZGig2bEAAMAfKIQAAABQrVLT83TttMU6UFymzvHhmja6h8KDA8yOBQAA/oZCCAAAANXm5405uvGDJXKVetS7RYTeHtlDIYH8yAkAQF3Dn84AAACoFrPTMnX7x8tV4vZoQNtIvXZ1N9n9bWbHAgAAR0AhBAAAgJP2xfJduvuzVXJ7DJ3bMVYvXt5ZAX5Ws2MBAICjoBACAADASflw0XY9/GWaDEO6pFtTPXVRR/nZKIMAAKjLKIQAAABQZe/8kq7H/m+tJGlkn+aaMLSDrFaLyakAAMDxUAgBAADguNweQ6npecoucCkq1K6eiRF6639b9dR36yVJN5/eUvcMaiuLhTIIAABvYPpc3ilTpighIUF2u129evVSamrqMcfv379ft9xyi2JjYxUYGKg2bdro22+/raW0AAAAvmd2Wob6Pj1PV771u8Z+skJXvvW7UiZ+X14G3Tmwje4d3I4yCAAAL2LqDKEZM2Zo3Lhxev3119WrVy9NnjxZgwYN0oYNGxQVFXXY+JKSEp111lmKiorSzJkzFRcXp+3btys8PLz2wwMAAPiA2WkZGjN9mYx/XD9QXCZJGta5icYObF37wQAAwEkxtRB64YUXdMMNN2j06NGSpNdff13ffPON3n33Xd1///2HjX/33XeVl5en3377Tf7+/pKkhISE2owMAADgM9weQxNnrT2sDPq71PQ8uT2GbOwbBACAVzFtyVhJSYmWLl2qgQMH/hXGatXAgQO1cOHCI97z9ddfq0+fPrrlllsUHR2t5ORkPfnkk3K73Ud9n+LiYuXn51f4AgAAwPGlpucpw+k65pgMp0up6Xm1lAgAAFQX0wqh3Nxcud1uRUdHV7geHR2tzMzMI96zdetWzZw5U263W99++60eeeQRPf/883r88ceP+j6TJk2Sw+Eo/4qPj6/WzwEAAFBfZRccuww60XEAAKDuMH1T6RPh8XgUFRWlN998U926ddPll1+uhx56SK+//vpR73nggQfkdDrLv3bu3FmLiQEAALxXVKi9WscBAIC6w7Q9hBo3biybzaasrKwK17OyshQTE3PEe2JjY+Xv7y+bzVZ+rX379srMzFRJSYkCAgIOuycwMFCBgYHVGx4AAMAHdG/eUMEBNhWVHHl5vkVSjOPQEfQAAMC7mDZDKCAgQN26ddPcuXPLr3k8Hs2dO1d9+vQ54j2nnnqqNm/eLI/HU35t48aNio2NPWIZBAAAgKoxDEOPzlpzzDJIkiYMTWJDaQAAvJCpS8bGjRunt956S++9957WrVunMWPGqLCwsPzUsREjRuiBBx4oHz9mzBjl5eVp7Nix2rhxo7755hs9+eSTuuWWW8z6CAAAAPWOYRia8PUafbhohywWafQpzRXrqLgsLMZh12tXd9Xg5FiTUgIAgJNh6rHzl19+uXJycjR+/HhlZmaqc+fOmj17dvlG0zt27JDV+ldnFR8frzlz5ujOO+9Up06dFBcXp7Fjx+q+++4z6yMAAADUK4Zx6Kj59xdul8UiPXdJii7u1lQPn9dBqel5yi5wKSr00DIxZgYBAOC9LIZhGGaHqE35+flyOBxyOp0KCwszOw4AAECdYRiGHv9mnd75JV2S9MwlnXRZd05oBQDAW5xI5+FVp4wBAACgZhiGoae+W19eBk26qCNlEAAA9RiFEAAAgI8zDEPPzNmgN37eKkl6/IJkXdmzmcmpAABATaIQAgAA8GGGYeiFHzbqtQVbJEn/HtZBV/dubnIqAABQ0yiEAAAAfNh/5m7Sy/M2S5LGn5ekEX0SzA0EAABqBYUQAACAj3p57iZN/nGTJOnhc9vr2r6JJicCAAC1hUIIAADAB02Zv1nP/7BRknT/Oe10fb8WJicCAAC1iUIIAADAx7zx0xY9O2eDJOmeQW11U/+WJicCAAC1jUIIAADAh0z9NV2TvlsvSRp3VhvdMqCVyYkAAIAZKIQAAAB8xIzFOzRx1lpJ0m1ntNLtZ7Y2OREAADALhRAAAIAP+GrFbt3/+WpJ0vV9EzXurDYmJwIAAGaiEAIAAKjnvl+TqXGfrpRhSFf1aqaHzm0vi8VidiwAAGAiCiEAAIB67OeNObr1o+Vyewxd1CVOjw9LpgwCAAAUQgAAAPVVanqebvxgiUrcHg3uEKNnLukkq5UyCAAAUAgBAADUSyt37te10xbLVerR6W0j9dKVXeRn40c/AABwCD8VAAAA1DPrMvI14t1UHSguU+8WEXr96m4K8OPHPgAA8Bd+MgAAAKhHtuYc0DXvLJLzYKm6NAvX2yN7yO5vMzsWAACoY6pUCB08eFBFRUXlj7dv367Jkyfr+++/r7ZgAAAAODE784o0/O1Fyj1QoqTYME0b1VMhgX5mxwIAAHVQlQqhYcOG6f3335ck7d+/X7169dLzzz+vYcOG6bXXXqvWgAAAADi+TKdLw99epAynS62iQvTBdT3lCPY3OxYAAKijqlQILVu2TP369ZMkzZw5U9HR0dq+fbvef/99vfTSS9UaEAAAAMe290Cxhr/9u3bkFalZRLCmX9dLjUICzY4FAADqsCoVQkVFRQoNDZUkff/997roootktVrVu3dvbd++vVoDAgAA4OicRaW65p1UbckpVKzDrg+v76UYh93sWAAAoI6rUiHUqlUrffnll9q5c6fmzJmjs88+W5KUnZ2tsLCwag0IAACAIztQXKZR01K1NiNfjUMC9eH1vRQfEWx2LAAA4AWqVAiNHz9ed999txISEtSzZ0/16dNH0qHZQl26dKnWgAAAADicq9St699brOU79ssR5K/p1/dUi8gQs2MBAAAvYTEMw6jKjZmZmcrIyFBKSoqs1kO9UmpqqsLCwtSuXbtqDVmd8vPz5XA45HQ6mc0EAAC8UnGZWze+v1Q/bcxRSKCfPry+l1Liw82OBQAATHYinUeVZghJUkxMjEJDQ/XDDz/o4MGDkqQePXrU6TIIAADA25W5PRr78Qr9tDFHdn+r3h3VgzIIAACcsCoVQnv37tWZZ56pNm3aaMiQIcrIyJAkXXfddbrrrruqNSAAAAAO8XgM3TNzlWavyVSAzaq3RnRXz8QIs2MBAAAvVKVC6M4775S/v7927Nih4OC/Ni68/PLLNXv27GoLBwAAgEMMw9DDX6Xpi+W7ZbNaNGV4V/VrHWl2LAAA4KX8qnLT999/rzlz5qhp06YVrrdu3Zpj5wEAAKqZYRh64pt1+mjRDlks0ouXd9ZZSdFmxwIAAF6sSjOECgsLK8wM+lNeXp4CAwNPOhQAAAD+8uKPm/T2L+mSpKcv6qTzU5qYnAgAAHi7KhVC/fr10/vvv1/+2GKxyOPx6JlnntGAAQOqLRwAAICve/2nLXpp7iZJ0qNDk3RZj3iTEwEAgPqgSkvGnnnmGZ155plasmSJSkpKdO+992rNmjXKy8vTr7/+Wt0ZAQAAfNIHC7fpqe/WS5LuHdxWo05NNDkRAACoL6o0Qyg5OVkbN25U3759NWzYMBUWFuqiiy7S8uXL1bJly+rOCAAA4HNmLt2lR75aI0m6dUAr3Xx6K5MTAQCA+sRiGIZhdojalJ+fL4fDIafTqbCwMLPjAAAAHOb/Vu3R7R8vl8eQRp+aoPHnJclisZgdCwAA1HEn0nlUesnYqlWrlJycLKvVqlWrVh1zbKdOnSr7sgAAAPibueuydMcnK+QxpCt6xFMGAQCAGlHpQqhz587KzMxUVFSUOnfuLIvFoiNNLrJYLHK73dUaEgAAwBf8ujlXYz5cpjKPoWGdm+iJCztSBgEAgBpR6UIoPT1dkZGR5b8GAABA9VmyLU/Xv7dEJWUenZ0UrecuTZHNShkEAABqRqULoebNm5f/evv27TrllFPk51fx9rKyMv32228VxgIAAODYVu9yavTUxTpY6la/1o318lVd5G+r0tkfAAAAlVKlnzQGDBigvLy8w647nU4NGDDgpEMBAAD4ig2ZBbrm3UUqKC5Tz8QIvXlNdwX62cyOBQAA6rkqFUKGYRxxPfvevXvVoEGDkw4FAADgC7bmHNDV7yzS/qJSpcSH691RPRQUQBkEAABqXqWXjEnSRRddJOnQxtGjRo1SYGBg+XNut1urVq3SKaecUr0JAQAA6qGdeUUa/vYi5RQUq11MqN4b3UMhgSf0oxkAAECVndBPHQ6HQ9KhGUKhoaEKCgoqfy4gIEC9e/fWDTfcUL0JAQAA6plMp0vD316kDKdLLSMbaPr1vRQeHGB2LAAA4ENOqBCaOnWqJCkhIUF33303y8MAAABOUE5Bsa56+3ftyCtS80bB+uiG3mocEnj8GwEAAKqRxTAMw+wQtSk/P18Oh0NOp1NhYWFmxwEAAD5kf1GJrnjzd63PLFATh12f3tRHTRsGmx0LAADUEyfSeVR6hlDXrl01d+5cNWzYUF26dDniptJ/WrZsWeXTAgAA+IB8V6lGvJuq9ZkFigwN1Ec39KYMAgAApql0ITRs2LDyTaQvuOCCmsoDAABQ7xQWl2n01MVatcupiAYB+uj6XkpozNJ7AABgHpaMAQAAVCO3x1Bqep6yC1yKCrWrU1OHbnh/iX7bsldhdj99dENvJcc5zI4JAADqoRpZMgYAAIBjm52WoYmz1irD6Sq/FuhnVXGZRw0CbHrv2p6UQQAAoE6odCHUsGHDY+4b9Hd5eXlVDgQAAOCNZqdlaMz0Zfrn1OviMo8k6ab+LdSlWcPaDwYAAHAElS6EJk+eXIMxAAAAvJfbY2jirLWHlUF/91HqTt08oLVs1sr9H2wAAAA1qdKF0MiRI2syBwAAgNdKTc+rsEzsSDKcLqWm56lPy0a1lAoAAODoKl0I5efnl29IlJ+ff8yxbNYMAAB8SXbBscugEx0HAABQ005oD6GMjAxFRUUpPDz8iPsJGYYhi8Uit9tdrSEBAADqsqhQe7WOAwAAqGmVLoTmzZuniIgISdL8+fNrLBAAAIC36ZkYoZBAPx0oLjvi8xZJMQ67eiZG1G4wAACAo6h0IdS/f/8j/hoAAMDXvfPL1mOWQZI0YWgSG0oDAIA6o9KF0D/t27dP77zzjtatWydJSkpK0ujRo8tnEQEAAPiCDxZu05PfrpckDevc5LANpmMcdk0YmqTBybFmRQQAADiMxTCMY52QekQ///yzhg4dKofDoe7du0uSli5dqv3792vWrFk67bTTqj1odcnPz5fD4ZDT6WTzawAAcFI+XbJT985cJUm6dUAr3T2ordweQ6npecoucCkq9NAyMWYGAQCA2nAinUeVCqGOHTuqT58+eu2112Sz2SRJbrdbN998s3777TetXr26aslrAYUQAACoDl+t2K07ZqyQYUjX9U3Uw+e2P+KhGwAAALXlRDoPa1XeYPPmzbrrrrvKyyBJstlsGjdunDZv3lyVlwQAAPAas9MyNe7TlTIM6apezSiDAACA16lSIdS1a9fyvYP+bt26dUpJSTnpUAAAAHXVgg3Zuu3jZXJ7DF3UJU6PD0umDAIAAF6n0ptKr1q1qvzXt99+u8aOHavNmzerd+/ekqTff/9dU6ZM0VNPPVX9KQEAAOqAhVv26l8fLFWp29C5HWP1zCWdZGV/IAAA4IUqvYeQ1WqVxWLR8YZbLBa53e5qCVcT2EMIAABUxdLt+3TNO4tUVOLWwPZRenV4NwX4VWmyNQAAQI04kc6j0jOE0tPTTzoYAACAN0rb7dSoqakqKnGrb6vGeuWqrpRBAADAq1W6EGrevHlN5gAAAKiTNmYV6Jp3FqnAVaYeCQ315ohusvvbjn8jAABAHVbpQuhI1q5dqx07dqikpKTC9fPPP/+kQgEAANQF6bmFGv72Iu0rKlVKU4feHdVDwQEn9eMTAABAnVCln2i2bt2qCy+8UKtXr66wr9CfJ2zU5T2EAAAAKmPXviINf+t35RQUq11MqN67tqdC7f5mxwIAAKgWVVr8PnbsWCUmJio7O1vBwcFas2aNfv75Z3Xv3l0LFiyo5ogAAAC1KyvfpeFvL9Iep0stIhto+vW9FB4cYHYsAACAalOlGUILFy7UvHnz1LhxY1mtVlmtVvXt21eTJk3S7bffruXLl1d3TgAAgFqx90Cxhr+9SNv3Fik+IkgfXd9bjUMCzY4FAABQrao0Q8jtdis0NFSS1LhxY+3Zs0fSoY2nN2zYUH3pAAAAapGzqFTXvJOqzdkHFOuw66PreyvGYTc7FgAAQLWr0gyh5ORkrVy5UomJierVq5eeeeYZBQQE6M0331SLFi2qOyMAAECNO1BcppFTU7U2I1+NQwI0/fpeio8INjsWAABAjahSIfTwww+rsLBQkvTvf/9b5513nvr166dGjRppxowZ1RoQAACgph0sceu6aYu1Yud+hQf7a/r1vdQyMsTsWAAAADXGYvx5RNhJysvLU8OGDctPGqur8vPz5XA45HQ6FRYWZnYcAABgsuIyt254f6l+3pij0EA/fXhDL3VqGm52LAAAgBN2Ip1HlWYI/d3OnTslSfHx8Sf7UgAAALXK7TF0xycr9PPGHAX52zR1dA/KIAAA4BOqtKl0WVmZHnnkETkcDiUkJCghIUEOh0MPP/ywSktLqzsjAABAtTMMQw9+vlrfpWUqwGbVWyO6q3tChNmxAAAAakWVZgjddttt+vzzz/XMM8+oT58+kg4dRf/oo49q7969eu2116o1JAAAQHV76rv1mrFkp6wW6aUrO6tv68ZmRwIAAKg1VdpDyOFw6JNPPtE555xT4fq3336rK6+8Uk6ns9oCVjf2EAIAAK8t2KKnZ6+XJD1zcSdd1oOl7wAAwPudSOdRpSVjgYGBSkhIOOx6YmKiAgICqvKSAAAAteLj1B3lZdBDQ9pTBgEAAJ9UpULo1ltv1WOPPabi4uLya8XFxXriiSd06623Vls4AACA6vTNqgw9+MVqSdLNp7fUDae1MDkRAACAOSq9h9BFF11U4fGPP/6opk2bKiUlRZK0cuVKlZSU6Mwzz6zehAAAANXg5405umPGchmGdFWvZrpnUFuzIwEAAJim0oWQw+Go8Pjiiy+u8Jhj5wEAQF21dPs+/euDpSp1Gzq3U6weG5Ysi8VidiwAAADTVLoQmjp1ak3mAAAAqBEbswp07bTFOljq1mltIvXiZZ1ls1IGAQAA31alY+f/lJOTow0bNkiS2rZtq8jIyGoJBQAAUB0ynAc18t1UOQ+WqmuzcL1+dVcF+FVpC0UAAIB6pUo/ERUWFuraa69VbGysTjvtNJ122mlq0qSJrrvuOhUVFVV3RgAAgBPmPFiqUe8uVobTpZaRDfTuqB4KDjip/y8MAACg3qhSITRu3Dj99NNPmjVrlvbv36/9+/frq6++0k8//aS77rqrujMCAACckOIyt258f4k2ZBUoKjRQ713bU+HBAWbHAgAAqDMshmEYJ3pT48aNNXPmTJ1++ukVrs+fP1+XXXaZcnJyqitftcvPz5fD4ZDT6VRYWJjZcQAAQDXzeAzd9slyfbMqQyGBfvr0X32U1IQ/8wEAQP13Ip1HlWYIFRUVKTo6+rDrUVFRLBkDAACmeuLbdfpmVYb8bRa9cU03yiAAAIAjqFIh1KdPH02YMEEul6v82sGDBzVx4kT16dOn2sIBAACciLf/t1Xv/JIuSXru0hSd2qqxyYkAAADqpirtrDh58mQNHjxYTZs2VUpKiiRp5cqVstvtmjNnTrUGBAAAqIyvV+7R49+skyQ9OKSdhnWOMzkRAABA3VWlQqhjx47atGmTPvzwQ61fv16SdOWVV2r48OEKCgqq1oAAAADH8/vWvbrr0xWSpNGnJuiGfi3MDQQAAFDHnXAhVFpaqnbt2un//u//dMMNN9REJgAAgErbmnNA//pgqUrdhoZ0jNEj5ybJYrGYHQsAAKBOO+E9hPz9/SvsHQQAAGCWfYUlunbaYjkPlqpzfLheuKyzrFbKIAAAgOOp0qbSt9xyi55++mmVlZVVdx4AAIBKKS5z618fLNW2vUVq2jBIb43oLru/zexYAAAAXqFKewgtXrxYc+fO1ffff6+OHTuqQYMGFZ7//PPPqyUcAADAkRiGoQf+u1qp2/IUGuind0f1UGRooNmxAAAAvEaVCqHw8HBdfPHF1Z0FAACgUl6et1mfL98tm9WiV6/uqjbRoWZHAgAA8ConVAh5PB49++yz2rhxo0pKSnTGGWfo0Ucf5WQxAABQa75asVsv/LBRkvTYsGT1ax1pciIAAADvc0J7CD3xxBN68MEHFRISori4OL300ku65ZZbaiobAABABct37NM9M1dJkm7ol6irejUzOREAAIB3OqFC6P3339err76qOXPm6Msvv9SsWbP04YcfyuPx1FQ+AAAASVJWvkv/+mCpSso8Gtg+Wvef097sSAAAAF7rhAqhHTt2aMiQIeWPBw4cKIvFoj179lR7MAAAgD+5Sg+dKJZdUKw20SGafEVn2TheHgAAoMpOqBAqKyuT3W6vcM3f31+lpaXVGgoAAOBPhmHooS/StGLnfjmC/PXWiO4KCazSuRgAAAD4wwn9NGUYhkaNGqXAwL+OdXW5XLrpppsqHD3PsfMAAKC6vPvrNv132S7ZrBZNuaqrmjdqcPybAAAAcEwnVAiNHDnysGtXX311tYUBAAD4u1825eqJb9ZKkh4a0l59Wzc2OREAAED9cEKF0NSpU2sqBwAAQAXbcgt1y0fL5DGkS7o11ehTE8yOBAAAUG+wAB8AANQJbo+h1PQ8ZRe4FGb315PfrpPzYKk6x4fr8QuSZbGwiTQAAEB1OaFNpWvKlClTlJCQILvdrl69eik1NbVS933yySeyWCy64IILajYgAACoUbPTMtT36Xm68q3fNfaTFRo9bbE2ZR+QI8hfb1zTTXZ/m9kRAQAA6hXTC6EZM2Zo3LhxmjBhgpYtW6aUlBQNGjRI2dnZx7xv27Ztuvvuu9WvX79aSgoAAGrC7LQMjZm+TBlO12HPOQ+WavmOfSakAgAAqN9ML4ReeOEF3XDDDRo9erSSkpL0+uuvKzg4WO++++5R73G73Ro+fLgmTpyoFi1a1GJaAABQndweQxNnrZVxlOctkibOWiu352gjAAAAUBWmFkIlJSVaunSpBg4cWH7NarVq4MCBWrhw4VHv+/e//62oqChdd911x32P4uJi5efnV/gCAAB1Q2p63hFnBv3JkJThdCk1Pa/2QgEAAPgAUwuh3Nxcud1uRUdHV7geHR2tzMzMI97zyy+/6J133tFbb71VqfeYNGmSHA5H+Vd8fPxJ5wYAANUju+DoZVBVxgEAAKByTF8ydiIKCgp0zTXX6K233lLjxo0rdc8DDzwgp9NZ/rVz584aTgkAACorKtRereMAAABQOaYeO9+4cWPZbDZlZWVVuJ6VlaWYmJjDxm/ZskXbtm3T0KFDy695PB5Jkp+fnzZs2KCWLVtWuCcwMFCBgYE1kB4AAJysnokRahjsr31FpUd83iIpxmFXz8SI2g0GAABQz5k6QyggIEDdunXT3Llzy695PB7NnTtXffr0OWx8u3bttHr1aq1YsaL86/zzz9eAAQO0YsUKloMBAOBlMvNdKi7zHPE5yx/fJwxNks1qOeIYAAAAVI2pM4Qkady4cRo5cqS6d++unj17avLkySosLNTo0aMlSSNGjFBcXJwmTZoku92u5OTkCveHh4dL0mHXAQBA3Vbm9uiOT5arqMSthEbBcpW6lZlfXP58jMOuCUOTNDg51sSUAAAA9ZPphdDll1+unJwcjR8/XpmZmercubNmz55dvtH0jh07ZLV61VZHAACgEqbM36LF2/YpJNBP71/bS3ENg5SanqfsApeiQg8tE2NmEAAAQM2wGIZhmB2iNuXn58vhcMjpdCosLMzsOAAA+KQl2/J02RsL5TGkFy9P0YVdmpodCQAAwOudSOfB1BsAAFCr8l2lGvvJCnkM6cIucZRBAAAAJqAQAgAAtcYwDD30RZp27z+o+Igg/XtYB7MjAQAA+CQKIQAAUGv+u2y3Zq3cI5vVov9c0UWhdn+zIwEAAPgkCiEAAFArtuUWavxXaZKkcWe1UddmDU1OBAAA4LsohAAAQI0rKfPo9j+OmO+VGKGb+rc0OxIAAIBPoxACAAA17oUfNmrVLqccQf568fLOHCcPAABgMgohAABQo37dnKs3ft4iSXr64o5qEh5kciIAAABQCAEAgBqTV1iiO2eskGFIV/ZspsHJsWZHAgAAgCiEAABADTEMQ/fOXKXsgmK1jGygR85rb3YkAAAA/IFCCAAA1Ijpi3box3VZCrBZ9dKVXRQc4Gd2JAAAAPyBQggAAFS7DZkFevz/1kqS7junnTo0cZicCAAAAH9HIQQAAKqVq9St2z9eruIyj/q3idToUxLMjgQAAIB/oBACAADV6qnv1mtDVoEahwTouUtTZOWIeQAAgDqHQggAAFSbueuyNO23bZKk5y5NUWRooLmBAAAAcEQUQgAAoFpk57t0z8xVkqRrT03U6W2jTE4EAACAo6EQAgAAJ83jMXTXZyuVV1ii9rFhuu+ctmZHAgAAwDFQCAEAgJP29i9b9b9NubL7W/XylZ0V6GczOxIAAACOgUIIAACclNW7nHp2zgZJ0vjzOqhVVKjJiQAAAHA8FEIAAKDKCovLdPsny1XqNjS4Q4yu7BlvdiQAAABUAoUQAACosn/PWqv03ELFOux66uKOslg4Yh4AAMAbUAgBAIAq+WZVhmYs2SmLRXrx8s4KDw4wOxIAAAAqiUIIAACcsF37inT/54eOmL/l9Fbq3aKRyYkAAABwIiiEAADACXF7DN05Y4UKXGXqHB+usQNbmx0JAAAAJ4hCCAAAnJBX5m3W4m37FBLop5eu6CJ/Gz9OAAAAeBt+ggMAAJW2ZFue/jN3oyTp8QuS1axRsMmJAAAAUBUUQgAAoFLyXaUa+8kKeQzpwi5xuqBLnNmRAAAAUEUUQgAA4LgMw9BDX6Rp9/6DahYRrH8P62B2JAAAAJwECiEAAHBc/122W7NW7pHNatF/ruisULu/2ZEAAABwEiiEAADAMW3LLdT4r9IkSePOaqMuzRqanAgAAAAni0IIAAAcVUmZR7d/slxFJW71bhGhm/q3NDsSAAAAqgGFEAAAOKrnvt+gVbucCg/214uXd5bNajE7EgAAAKoBhRAAADiiBRuy9ebPWyVJz1zcSbGOIJMTAQAAoLpQCAEAgMNkF7h092crJUkj+zTX2R1iTE4EAACA6kQhBAAAKvB4DN316UrlHihRu5hQPTCkvdmRAAAAUM0ohAAAQAVv/m+r/rcpV0H+Nr1yVRfZ/W1mRwIAAEA1oxACAADlVuzcr+fmbJAkPXp+klpFhZqcCAAAADWBQggAAEiSClyluv3j5SrzGDq3U6wu6x5vdiQAAADUEAohAAAgwzD00Bdp2pFXpKYNgzTpoo6yWDhiHgAAoL6iEAIAAPps6S59vXKPbFaLXrqyi8Ls/mZHAgAAQA2iEAIAwMetz8zX+K/SJEnjzmqjrs0ampwIAAAANY1CCAAAH3aguEw3f7hMrlKP+reJ1Jj+Lc2OBAAAgFpAIQQAgI8yDEMPfL5aW3MKFeuw68XLO8tqZd8gAAAAX0AhBACAj5q+aIdmrdwjP6tFr1zVVRENAsyOBAAAgFpCIQQAgA9atWu/Hpu1VpJ0/znt1K05+wYBAAD4Ej+zAwAAgJrn9hhKTc9TdoFLwQF+mjhrjUrcHp2dFK3r+iaaHQ8AAAC1jEIIAIB6bnZahibOWqsMp6vC9cYhAXr20hRZLOwbBAAA4GtYMgYAQD02Oy1DY6YvO6wMkqTcAyVauCXXhFQAAAAwG4UQAAD1lNtjaOKstTKO8rxF0sRZa+X2HG0EAAAA6isKIQAA6qnU9Lwjzgz6kyEpw+lSanpe7YUCAABAnUAhBABAPZVdcPQyqCrjAAAAUH9QCAEAUE9FhdqrdRwAAADqDwohAADqqZ6JEQq1H/1AUYukWIddPRMjai8UAAAA6gQKIQAA6qlfNufqQHHZEZ/786D5CUOTZLNy7DwAAICvoRACAKAe2pRVoFs/XCbDkE5p2UgxYYEVno9x2PXa1V01ODnWpIQAAAAw09HnkQMAAK+090Cxrn1vsQqKy9QzIUJTR/eQn9Wq1PQ8ZRe4FBV6aJkYM4MAAAB8F4UQAAD1SHGZW//6YKl25h1Us4hgvX5NNwX62SRJfVo2MjkdAAAA6gqWjAEAUE8YhqEH/rtaS7bvU6jdT++O6q6IBgFmxwIAAEAdRCEEAEA98eqCLfp8+W7ZrBa9OryrWkWFmh0JAAAAdRSFEAAA9cD/rdqjZ+dskCRNPL+D+rWONDkRAAAA6jIKIQAAvNzCLXs1bsZKSdLoUxN0de/mJicCAABAXUchBACAF9uQWaAbP1iiErdH5yTH6OFzk8yOBAAAAC9AIQQAgJfas/+gRr6bqgLXoePlX7y8M0fJAwAAoFIohAAA8ELOg6UaNTVVmfkutY4K0VsjusvubzM7FgAAALwEhRAAAF7GVerWje8v0casA4oOC9S0a3vKEexvdiwAAAB4EQohAAC8SJnbo9s+Xq5F6XkKDfTTtNE9FRceZHYsAAAAeBkKIQAAvITHY+jemav0w9osBfhZ9eaI7mofG2Z2LAAAAHghCiEAALyAYRia8PUafb58t/ysFr16VVf1adnI7FgAAADwUhRCAAB4gWfnbNAHv2+XxSI9f1mKBiZFmx0JAAAAXoxCCACAOu61BVv06oItkqTHL0jWsM5xJicCAACAt6MQAgCgDpv2a7qenr1eknT/Oe00vFdzkxMBAACgPqAQAgCgjnp/4TY9OmutJOmWAS11U/+WJicCAABAfUEhBABAHfTB79s1/qs1kqSb+rfU3We3NTkRAAAA6hMKIQAA6pgPF23XI1+mSZL+dVoL3Te4rSwWi8mpAAAAUJ9QCAEAUId8nLpDD31xqAy6oV+i7j+nHWUQAAAAqp2f2QEAAMAhHyzcpkf+WCZ2Xd9EPTikPWUQAAAAagSFEAAAdcDrP23RU98dOk3s2lMT9fC5lEEAAACoORRCAACYyDAMvfjDRr00b7Mk6dYBrXTX2W0ogwAAAFCjKIQAADCJYRh6/Jt1eueXdEnSvYPb6ubTW5mcCgAAAL6AQggAABO4PYYe/nK1Pk7dKUmaeH4HjTwlwdxQAAAA8BkUQgAA1LJSt0d3f7ZSX63YI6tFeuriTrqse7zZsQAAAOBDKIQAAKhFB4rLdPOHy/Tzxhz5WS36zxVddG6nWLNjAQAAwMdQCAEAUEtyCop17bTFWr3bqSB/m14d3lUD2kWZHQsAAAA+iEIIAIBakJ5bqBHvLtLOvINq1CBA747qoZT4cLNjAQAAwEdRCAEAUMNW7Nyva6ctVl5hiZpFBOv9a3sqoXEDs2MBAADAh1EIAQBQg+atz9ItHy7XwVK3OjV16J2RPRQZGmh2LAAAAPg4CiEAAGqAYRh677dt+vf/rZXHkPq3idSrw7uqQSB/9AIAAMB8/FQKAEA1K3V7NHHWGk3/fYck6bLuTfXEhR3lb7OanAwAAAA4hEIIAIBq5Cwq1S0fLdMvm3NlsUj3D26nG09rIYvFYnY0AAAAoByFEAAA1SQ9t1DXvbdYW3MKFRxg03+u6KKzkqLNjgUAAAAchkIIAIBqsHDLXt00famcB0vVxGHX2yN7KKlJmNmxAAAAgCOiEAIA4CQYhqFpv23TE9+sU5nHUOf4cL05opuiQu1mRwMAAACOikIIAIAqOlji1v2fr9JXK/ZIkoZ1bqKnL+4ku7/N5GQAAADAsVEIAQBQBTv2FunGD5ZofWaBbFaLHhrSXqNPTWDzaAAAAHgFCiEAAE7Q/A3ZGvvxcuW7ytQ4JECvXNVVvVs0MjsWAAAAUGkUQgAAVJLbY2jK/M168ceNMgypS7NwvTa8m2Ic7BcEAAAA70IhBABAJeQUFOvOGSv0y+ZcSdLwXs00fmiSAv3YLwgAAADeh0IIAIDj+G1zrsbOWKGcgmIF+dv072EddGn3eLNjAQAAAFVGIQQAwFG4PYb+M3eTXp63SYYhtYkO0ZSruqp1dKjZ0QAAAICTQiEEAMARZOW7NPaT5fp9a54k6Yoe8ZowtIOCAlgiBgAAAO9HIQQAwD/MTsvUA5+v0r6iUjUIsOnJizpqWOc4s2MBAAAA1YZCCACAPxwoLtNjs9ZqxpKdkqSk2DC9clUXtYgMMTkZAAAAUL2sZgeQpClTpighIUF2u129evVSamrqUce+9dZb6tevnxo2bKiGDRtq4MCBxxwPAEBlLN2+T0P+8z/NWLJTFot0U/+W+vKWUymDAAAAUC+ZXgjNmDFD48aN04QJE7Rs2TKlpKRo0KBBys7OPuL4BQsW6Morr9T8+fO1cOFCxcfH6+yzz9bu3btrOTkAoD4odXv0wvcbdOnrv2lHXpHiwoP0yQ29df857RTgZ/ofkwAAAECNsBiGYZgZoFevXurRo4deeeUVSZLH41F8fLxuu+023X///ce93+12q2HDhnrllVc0YsSI447Pz8+Xw+GQ0+lUWFjYSecHAHivrTkHdOeMFVq5yylJurBLnCYO66Awu7/JyQAAAIATdyKdh6l7CJWUlGjp0qV64IEHyq9ZrVYNHDhQCxcurNRrFBUVqbS0VBEREUd8vri4WMXFxeWP8/PzTy40AMDruT2G3v0lXc99v0HFZR6F2f30xIUdNTSlidnRAAAAgFphaiGUm5srt9ut6OjoCtejo6O1fv36Sr3GfffdpyZNmmjgwIFHfH7SpEmaOHHiSWcFANQPm7MLdM/MVVq+Y78kqW+rxnr20k6KdQSZGwwAAACoRV59ythTTz2lTz75RAsWLJDdbj/imAceeEDjxo0rf5yfn6/4+PjaiggAqCPK3B69+b+tmvzjJpWUeRQa6KeHzm2vy3vEy2KxmB0PAAAAqFWmFkKNGzeWzWZTVlZWhetZWVmKiYk55r3PPfecnnrqKf3444/q1KnTUccFBgYqMDCwWvICALzThswC3TNzpVb9sVfQ6W0j9eSFHdUknFlBAAAA8E2mHp8SEBCgbt26ae7cueXXPB6P5s6dqz59+hz1vmeeeUaPPfaYZs+ere7du9dGVACAFyouc2vyjxt13sv/06pdToXZ/fTcpSmaOqoHZRAAAAB8mulLxsaNG6eRI0eqe/fu6tmzpyZPnqzCwkKNHj1akjRixAjFxcVp0qRJkqSnn35a48eP10cffaSEhARlZmZKkkJCQhQSEmLa5wAA1C0Lt+zVQ1+u1tacQknSwPbReuLCZEWHHXmJMQAAAOBLTC+ELr/8cuXk5Gj8+PHKzMxU586dNXv27PKNpnfs2CGr9a+JTK+99ppKSkp0ySWXVHidCRMm6NFHH63N6ACAOiivsERPfLNO/122S5IUGRqo8ecl6bxOsewVBAAAAPzBYhiGYXaI2pSfny+HwyGn06mwsDCz4wAAqolhGJq5dJee/Had9hWVymKRhvdqpnsGtZMjyN/seAAAAECNO5HOw/QZQgAAnKzN2Qf00BertSg9T5LULiZUT17UUV2bNTQ5GQAAAFA3UQgBALxWUUmZXp2/RW/+vFUlbo/s/lbdObCNru2bKH+bqecmAAAAAHUahRAAwOsYhqFvVmfoiW/WKcPpkiQNaBupfw9LVnxEsMnpAAAAgLqPQggA4FU2ZBbo0a/XaOHWvZKkuPAgPXJekgZ1iGbTaAAAAKCSKIQAAF7BebBUk3/cqPcXbpfbYyjQz6oxp7fUTf1byu5vMzseAAAA4FUohAAAdZrHY2jmsl16ZvZ65R4okSQN6hCth89NYnkYAAAAUEUUQgCAOmvp9n167P/WasXO/ZKklpEN9Oj5HdSvdaS5wQAAAAAvRyEEAKhztuUW6pk56/Xt6kxJUoMAm8YObK1RpyQqwI/TwwAAAICTRSEEAKgz8gpL9NLcTfpw0XaVug1ZLNKl3Zrq7rPbKirMbnY8AAAAoN6gEAIAmM5V6ta037ZpyvzNKnCVSZL6t4nUA0PaqV1MmMnpAAAAgPqHQggAYBqPx9BXK3fruTkbtXv/QUlSUmyYHhzSXn1bNzY5HQAAAFB/UQgBAEzx2+ZcPfndOqXtzpckxTrsuvvstrqwS5ysVovJ6QAAAID6jUIIAFCrNmYVaNK36zR/Q44kKSTQT2NOb6nr+ibK7m8zOR0AAADgGyiEAAC1IjvfpRd/3KgZi3fKY0h+VouG92qm289srUYhgWbHAwAAAHwKhRAAoEYVFpfpzZ+36q3/bVVRiVuSNKhDtO4b3E4tIkNMTgcAAAD4JgohAECNKHN79OmSXXrxx43KKSiWJHVpFq4Hh7RXj4QIk9MBAAAAvo1CCABQJW6PodT0PGUXuBQValfPxAjZrBYZhqH5G7I16dv12pR9QJLULCJY9w1upyEdY2SxsGE0AAAAYDYKIQDACZudlqGJs9Yqw+kqvxbrsGv0KQmavyFHC7fulSSFB/vr9jNa6+rezRXgZzUrLgAAAIB/oBACAJyQ2WkZGjN9mYx/XM9wuvTkd+slSQF+Vo0+JUE3D2glR5B/7YcEAAAAcEwUQgCASnN7DE2ctfawMujv7P5WfX9HfzVrFFxruQAAAACcGObvAwAqLTU9r8IysSNxlXq0e//BWkoEAAAAoCoohAAAlZaVX7miJ7vg2KURAAAAAHNRCAEAKmXJtjxNmb+lUmOjQu01nAYAAADAyWAPIQDAMW3NOaCnZ6/XnDVZkiSLdNQ9hCySYhyHjqAHAAAAUHdRCAEAjmjvgWK9NHeTPly0Q2UeQ1aLdHmPeHVuGq77P18tqWIxZPnj+4ShSbJZLYe9HgAAAIC6g0IIAFCBq9Std35J1+sLtqiguEySNKBtpB4Y0l5tokMlSY5gf02ctbbCBtMxDrsmDE3S4ORYU3IDAAAAqDwKIQCAJMnjMfT58t16/vsN5UVPhyZhemhIe53SqnGFsYOTY3VWUoxS0/OUXeBSVOihZWLMDAIAAAC8A4UQAEC/bMrVk9+u09qMfElSE4dddw9qqws6x8l6lJLHZrWoT8tGtRkTAAAAQDWhEAIAH7Y+M1+Tvl2vnzbmSJJCA/1084BWGn1qguz+NpPTAQAAAKgpFEIA4IOy8l164fuN+mzpTnkMyc9q0dW9m+v2M1srokGA2fEAAAAA1DAKIQDwIQeKy/TmT1v01v/SdbDULUka0jFG9w5qp4TGDUxOBwAAAKC2UAgBgA8oc3v0yeKdmvzjRuUeKJEkdW0WrofOba9uzSNMTgcAAACgtlEIAUA9ZhiGflyXrae+W6ctOYWSpIRGwbpvcDsNTo6RxcKpYAAAAIAvohACgHpq1a79euKbdVqUnidJahjsr7FnttZVvZorwM9qcjoAAAAAZqIQAoB6ZmdekZ6ds0Ffr9wjSQrws+raUxN184CWCrP7m5wOAAAAQF1AIQQA9cS+whK9PG+zPvh9m0rdhiTpoi5xumtQW8WFB5mcDgAAAEBdQiEEAF7OVerW1F+36dUFm1XgKpMk9W3VWPef007JcQ6T0wEAAACoiyiEAMBLuT2GPl+2Sy/8sFEZTpckqX1smB44p51OaxNpcjoAAAAAdRmFEAB4GcMw9NPGHD313XqtzyyQJDVx2HXX2W11QZc42aycHAYAAADg2CiEAMCLpO12atJ36/Tr5r2SpDC7n24Z0EojT0mQ3d9mcjoAAAAA3oJCCAC8wM68Ij33/QZ9teKPk8NsVo08pbluGdBK4cEBJqcDAAAA4G0ohACgDttXWKIp8zfr/YXbVeL2SJIu7BKncWe1UXxEsMnpAAAAAHgrCiEAqINcpW5N+22bpsz/6+SwU1s10gPntOfkMAAAAAAnjUIIAOoQt8fQF8t364XvN2jPHyeHtYsJ1QND2uu01o1lsbBhNAAAAICTRyEEALXI7TGUmp6n7AKXokLt6pkYIZvVwslhAAAAAGoVhRAA1JLZaRmaOGutMv6Y+SNJsQ67Rp+SoJ825ZSfHBZq99OtnBwGAAAAoAZRCAFALZidlqEx05fJ+Mf1DKdLT363XhInhwEAAACoPRRCAFDD3B5DE2etPawM+ju7v1Vz7jhNzRs1qLVcAAAAAHyX1ewAAFDfpabnVVgmdiSuUo/27D/2GAAAAACoLhRCAFDDMpwHKzUuu4BCCAAAAEDtYMkYANQQwzD0XVqmnp2zoVLjo0LtNZwIAAAAAA6hEAKAGvDb5lw9PXu9Vu5ySpKsFslzlE2ELJJiHIeOoAcAAACA2kAhBADVKG23U0/PXq//bcqVJAUH2HR9vxZKbBSscZ+ulKQKm0tb/vg+YWiSbFaLAAAAAKA2UAgBQDXYlluo53/YqFkr90iS/G0WXdWzmW49o7UiQwMlSUEBNk2ctbbCBtMxDrsmDE3S4ORYU3IDAAAA8E0UQgBwErILXHp57mZ9nLpDZX+sCRvWuYnuOqutmjUKrjB2cHKszkqKUWp6nrILXIoKPbRMjJlBAAAAAGobhRAAVEG+q1Rv/rRV7/ySroOlbknS6W0jdc+gturQxHHU+2xWi/q0bFRbMQEAAADgiCiEAOAEuErdmv77dk2Zv1n7ikolSZ3jw3X/Oe3UuwVFDwAAAADvQCEEAJVQ5vbov8t26T8/btKeP/YAahnZQPcMaqdBHaJlsbDsCwAAAID3oBACgGPweAzNWrVHL/6wUdv2FkmSYsLsuvOs1rq4a1P52awmJwQAAACAE0chBABHYBiGflibpRd+2Kj1mQWSpIgGAbr59Ja6undz2f1tJicEAAAAgKqjEAKAvzEMQ79sztVz32/Uyp37JUmhdj/d2K+FRvdNVEggv20CAAAA8H78zQYA/rBkW56enbNBi9LzJElB/jaNPjVBN57WQuHBASanAwAAAIDqQyEEwOel7Xbque83aMGGHElSgM2q4b2b6ebTWykyNNDkdAAAAABQ/SiEAPisTVkFeuGHjfouLVOSZLNadFn3prrtjNZqEh5kcjoAAAAAqDkUQgB8TnpuoV6eu0lfrtgtjyFZLNKwlCa6Y2AbJTRuYHY8AAAAAKhxFEIAfEZ6bqFenrdJXy4/VARJ0qAO0Rp3Vlu1jQk1NxwAAAAA1CIKIQD13rbcQr08b7O+XLFb7j+aoDPbRWnswNbq1DTc3HAAAAAAYAIKIQD11rbcQr0yf7O+WE4RBAAAAAB/RyEEoN7ZvvfQjKC/F0FntIvS2DNbKyU+3NxwAAAAAFAHUAgBqDe27y3UK/M26/O/FUED2kbqjoFtKIIAAAAA4G8ohAB4vR17i/TyvE2HFUFjB7ZRZ4ogAAAAADgMhRAAr7Upq0CvLtiir1fuKS+CTm8bqbFntlaXZg1NTgcAAAAAdReFEIA6x+0xlJqep+wCl6JC7eqZGCGb1VL+/OpdTk2Zv1mz12SWX6MIAgAAAIDKoxACUKfMTsvQxFlrleF0lV+Lddg1YWiSIhoEasr8zfppY44kyWKRBneI0S0DWik5zmFWZAAAAADwOhRCAOqM2WkZGjN9mYx/XM9wunTT9GXlj21Wi4alNNGY01uqdXRo7YYEAAAAgHqAQghAneD2GJo4a+1hZdA/XdkzXmP6t1KzRsG1kgsAAAAA6iOr2QEAQJJS0/MqLBM7mvNT4iiDAAAAAOAkUQgBqBO27T1QqXHZBccvjQAAAAAAx8aSMQCmynAe1Lu/pGv67zsqNT4q1F7DiQAAAACg/qMQAmCK9Zn5evOnrfp65R6VeQ7tHORntZT/+p8skmIch46gBwAAAACcHAohALXGMAwt3LpXb/y0tfzoeEnq3SJC/+rfUq6SMt384fJDY/92n+WP7xOGJslmtQgAAAAAcHIohADUOFepW7NW7tHUX7dpbUa+JMlqkc5JjtWNp7VQSnx4+djXrrZo4qy1FTaYjnHYNWFokgYnx9Z2dAAAAAColyiEANSY7HyXpv++XR8u2qG9hSWSJLu/VZd1j9d1fRPVvFGDw+4ZnByrs5JilJqep+wCl6JCDy0TY2YQAAAAAFQfCiEA1W7Vrv2a+us2/d+qPSp1H1r81cRh1zV9EnRlz3iFBwcc836b1aI+LRvVRlQAAAAA8EkUQgCqRZnbo9lrMjX1121aun1f+fXuzRtq9KmJGtQhWn42q4kJAQAAAAB/ohACcFKyC1z6dPFOfbhoR/m+P/42i4Z2aqLRpyaqY1OHyQkBAAAAAP9EIQTghP15WtiHv+/QnDWZ5UfFNw4J0PBezTW8dzNFhdpNTgkAAAAAOBoKIQCV5iwq1cxlu/Thou3amlNYfr1b84Ya3quZzu0Uq0A/m4kJAQAAAACVQSEE4JgMw9CqXU5N/327Zq3aI1epR5LUIMCmC7rEaXiv5kpqEmZySgAAAADAiaAQAnBE+wpL9OWK3ZqxeKfWZxaUX28XE6rhvZvrwi5xCgnktxAAAAAA8Eb8bQ5AObfH0P825eizJbv0w9oslbgPzQYK8LPq3I6xGt6rmbo1byiLxWJyUgAAAADAyaAQAqAde4v02dKdmrl0V/lJYZKUHBemy7rHa1hKnBzB/iYmBAAAAABUJwohwEflu0o1e3WmPl++S79vzSu/Hh7srws6x+nS7k3VoQlHxgMAAABAfUQhBPiQ4jK3FmzI0ZfLd2vu+myVlB1aEmaxSP1aR+qy7k01sH207P6cFAYAAAAA9RmFkBdyewylpucpu8ClqFC7eiZGyGZlTxccmcdjKHVbnr5asVvfrMpQvqus/LnWUSG6oEucLugSp7jwIBNTAgAAAABqE4WQl5mdlqGJs9ZW2Ocl1mHXhKFJGpwca2Iy1CWGYWj1bqe+WZ2hWSv2aM/f/nuJCbPr/M5NNKxzEyXFhrFBNAAAAAD4IAohLzI7LUNjpi+T8Y/rmU6Xxkxfpteu7kop5MM8HkMrdu3Xd6sz9O3qTO3ef7D8udBAP53TMUYXdIlTr8RGzCgDAAAAAB9HIeQl3B5DE2etPawMkiRDkkXSxFlrdVZSDH/Z9yEej6GlO/bp29UZmp2WWWHmWJC/TWe0j9J5HWM1oF0U+wIBAAAAAMpRCHmJ1PS8Cn/Z/ydDUobTpdT0PPVp2aj2gqHWFZWU6X+bcjVvXbbmrs9W7oHi8udCAv10ZvsonZMcq/5tIhUUQAkEAAAAADgchZCXyC44ehlUlXHwLnv2H9Tc9dmauy5Lv23ZW346mCSF2v10VlK0hiTHqm/rxswEAgAAAAAcF4WQl4gKtVdq3C+bctUxzqEWkSE1nAg1qaTMo2U79umXTbmatz5bazPyKzwfHxGkM9tF68z2UeqV2EgBflaTkgIAAAAAvFGdKISmTJmiZ599VpmZmUpJSdHLL7+snj17HnX8Z599pkceeUTbtm1T69at9fTTT2vIkCG1mLj29UyMUKzDrkyn64j7CP3ps6W79Nn/t3fn4VFV9x/HP5NlspDMZCELmIQESFiKyB4CKhSpwINWFBWVR0Sx/lxBUDaVTUWpiojlwYW2xFopahVKUZBFxQqRJQpFCBSQgEAWSMi+J+f3B2XqmBASSIhh3q/nmWfIveeee871m8v1yznnJh9ThzB/De0SrmFXhqtDmD9vkvqFM8boQGaB/nXglL4+cFJbD2erqKzSsd9ikXpEBeq6TqEa3ClMsaF+/DcFAAAAAFwwizGmtvxCo3v//fc1ZswYvfnmm4qPj9drr72mDz/8UPv371doaGi18lu2bNG1116rF198UTfccIOWLVum3//+9/r222/VpUuX854vLy9Pdrtdubm5stlsjdGlRnP2LWOSnJJClv/+fE9CG/1wqlBJh7JUUfW/EjEtW2hol3AN6hiq7pEB8nBnNElTM8bo2OlibTucrS2HsvT1wZPKyCt1KtPSz6r+7VvqmtgQ/bpDiIL9vJqotQAAAACA5qA+OY8mTwjFx8erd+/eWrRokSSpqqpKkZGReuyxxzRt2rRq5UeNGqXCwkKtXr3asa1v377q1q2b3nzzzfOerzknhKQzSaE5/9zrtMB0K7u3Zt3Y2fHK+dyicm1IydCa79P11YGT1dabuSa2pQbEhWhAXKjC7XWbioaLU1Vl9J/MfG0/nK1tqae1/XC20vOc13vy8nBTn5ggXRPbUle3D1HHcH+58cY4AAAAAEAd1Sfn0aRTxsrKypScnKzp06c7trm5uWnw4MFKSkqq8ZikpCRNmjTJaduQIUO0cuXKGsuXlpaqtPR/Iy/y8vJqLNdcDO3SSr/pHK5th7OVmV+iUH9v9YkJcnrVvN3XUyN7RmhkzwgVlFboi32ZWr83Q18dOKmconJ9ujtdn+5OlyR1CPNX37ZBim8brN7RQQrxZxRKQ8gtKte/j+fo38dy9e2R09px5LRyi8udyni4WXRlhP1MEqh9iHpFB7IgNAAAAADgkmjShNCpU6dUWVmpsLAwp+1hYWHat29fjcekp6fXWD49Pb3G8i+++KLmzJnTMA3+hXB3s9T51fJ+Xh668arWuvGq1qqsMvr3sRxt+s9Jfbn/pHYdy9H+jHztz8jXO0lHJEltQ1ooPiZYfWICdVVEgKKDWzBK5TwKSiuUkpanXT+eSQD9+1iOUrOKqpXztbqrR1SgekcHqXdMoLpHBvJaeAAAAABAk/hFLCrdmKZPn+40oigvL0+RkZFN2KKm4+5mUfeoQHWPCtTjg+N0urBMWw5ladvhLG09nK39Gfn64WShfjhZqL9tOypJ8vfyUJcr7OoaaVfXKwLU5QqbIgN9XTJJVFFZpcOnCrUvPV/70/O1Lz1f+9LzdOx0cY3l2wT7qmtEgK6KsKt3dJA6t7bJk/WbAAAAAAC/AE2aEGr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Xp7/97W+KiYnRnj17NHnyZK1Zs0bffPONgoKCmrqZslqtCg8Pb/B6f/WrX2nDhg2qqKjQ5s2bdd9996moqEhvvfVWtbJlZWWyWq0Nct6GSGw1ZHIMAAD8sjFCCAAANKhHHnlEVqtV69at04ABAxQVFaVhw4Zpw4YNOn78uJ5++mlHWYvFopUrVzodHxAQ4DRKZerUqYqLi5Ovr6/atm2rGTNmqLy83LH/7NSpd999V9HR0bLb7brjjjuUn58v6cw0qE2bNmnhwoWO0TupqanVpozV5B//+Id69Oghb29vtW3bVnPmzFFFRUWt/ffw8FB4eLgiIiI0atQojR49WqtWrXJq6x//+EfFxMTI29tbkpSTk6P7779fISEhstlsGjRokHbt2uVU77x58xQWFiZ/f3+NGzdOJSUlTvt/Pt2rqqpKL730ktq3by8vLy9FRUVp7ty5kqSYmBhJUvfu3WWxWDRw4MAa6ygtLdX48eMVGhoqb29vXX311dq+fbtj/9lruHHjRvXq1Uu+vr7q16+f9u/f7yiza9cu/frXv5a/v79sNpt69uypHTt21HoNAQBA4yMhBAAAGkx2drY+++wzPfzww/Lx8XHaFx4ertGjR+v999+XMabOdfr7+ysxMVF79+7VwoULtWTJEi1YsMCpzKFDh7Ry5UqtXr1aq1ev1qZNmzRv3jxJZ6ZBJSQk6He/+53S0tKUlpamyMjI8573X//6l8aMGaMJEyZo7969euutt5SYmOhIqtSVj4+PysrKHD8fPHhQH330kT7++GPHlK3bbrtNmZmZWrNmjZKTk9WjRw9dd911ys7OliR98MEHmj17tl544QXt2LFDrVq10uLFi2s97/Tp0zVv3jzNmDFDe/fu1bJlyxQWFiZJ2rZtmyRpw4YNSktL08cff1xjHVOmTNFHH32kd955R99++63at2+vIUOGONp11tNPP6358+drx44d8vDw0H333efYN3r0aEVERGj79u1KTk7WtGnT5OnpWa9rCAAAGh5TxgAAQIM5cOCAjDHq1KlTjfs7deqk06dP6+TJkwoNDa1Tnc8884zjz9HR0XryySe1fPlyTZkyxbG9qqpKiYmJ8vf3lyTdfffd2rhxo+bOnSu73S6r1SpfX996TRGbM2eOpk2bpnvuuUeS1LZtWz333HOaMmWKZs2aVac6kpOTtWzZMg0aNMixraysTH/5y18UEhIiSfr666+1bds2ZWZmysvLS5L0yiuvaOXKlfr73/+uBx54QK+99prGjRuncePGSZKef/55bdiwodooobPy8/O1cOFCLVq0yNH+du3a6eqrr5Ykx7mDg4PPeU0KCwv1xhtvKDExUcOGDZMkLVmyROvXr9ef/vQnTZ482VF27ty5GjBggCRp2rRpGj58uEpKSuTt7a2jR49q8uTJ6tixoyQpNja2TtcOAAA0LhJCAACgwZ1vBFB91s15//339frrr+vQoUMqKChQRUWFbDabU5no6GhHMkiSWrVqpczMzPo1+md27dqlzZs3O40IqqysVElJiYqKiuTr61vjcbt375afn58qKytVVlam4cOHa9GiRY79bdq0cSRkzp6noKBAwcHBTvUUFxfr0KFDkqSUlBQ9+OCDTvsTEhL0xRdf1NiGlJQUlZaW6rrrrqtfp3/i0KFDKi8vV//+/R3bPD091adPH6WkpDiV7dq1q+PPrVq1kiRlZmYqKipKkyZN0v333693331XgwcP1m233aZ27dpdcLsAAEDDICEEAAAaTPv27WWxWJSSkqKbb7652v6UlBSFhIQ4Xu9usViqJY9+uj5QUlKSRo8erTlz5mjIkCGy2+1avny55s+f73TMz6cgWSwWVVVVXVRfCgoKNGfOHN1yyy3V9p1d+6cmHTp00KpVq+Th4eF4y9pPtWjRotp5WrVqpS+//LJaXWevU339fLpeY/vp9bdYLJLkuP6zZ8/WXXfdpU8++URr1qzRrFmztHz58hrjAwAAXDqsIQQAABpMcHCwfvOb32jx4sUqLi522peenq733ntPY8eOdWwLCQlRWlqa4+cDBw6oqKjI8fOWLVvUpk0bPf300+rVq5diY2N15MiRerfLarWqsrKyXsf06NFD+/fvV/v27at93NzO/QhltVrVvn17RUdH12kkVI8ePZSeni4PD49q52nZsqWkM1Pttm7d6nTcN998c846Y2Nj5ePjo40bN56zjZJqvSbt2rWT1WrV5s2bHdvKy8u1fft2de7c+bz9+qm4uDhNnDhR69at0y233KKlS5fW63gAANDwGCEEAAAa1KJFi9SvXz8NGTJEzz//vNNr5+Pi4jRz5kxH2UGDBmnRokVKSEhQZWWlpk6d6jTaJDY2VkePHtXy5cvVu3dvffLJJ1qxYkW92xQdHa2tW7cqNTVVfn5+dXrt/cyZM3XDDTcoKipKt956q9zc3LRr1y59//33ev755+vdhnMZPHiwEhISNGLECL300kuKi4vTiRMn9Mknn+jmm29Wr169NGHCBI0dO1a9evVS//799d5772nPnj1q27ZtjXV6e3tr6tSpmjJliqxWq/r376+TJ09qz549GjdunEJDQ+Xj46O1a9cqIiJC3t7e1V4536JFCz300EOaPHmygoKCFBUVpZdeeklFRUWOtYzOp7i4WJMnT9att96qmJgYHTt2TNu3b9fIkSMv+roBAICLwwghAADQoGJjY7V9+3a1bdtWt99+u9q0aaNhw4YpLi5Omzdvlp+fn6Ps/PnzFRkZqWuuuUZ33XWXnnzySae1eX77299q4sSJevTRR9WtWzdt2bJFM2bMqHebnnzySbm7u6tz584KCQnR0aNHz3vMkCFDtHr1aq1bt069e/dW3759tWDBArVp06be56+NxWLRp59+qmuvvVb33nuv4uLidMcdd+jIkSOOt4KNGjVKM2bM0JQpU9SzZ08dOXJEDz30UK31zpgxQ0888YRmzpypTp06adSoUY51lTw8PPT666/rrbfeUuvWrXXTTTfVWMe8efM0cuRI3X333erRo4cOHjyozz77TIGBgXXqm7u7u7KysjRmzBjFxcXp9ttv17BhwzRnzpx6XCEAANAYLKY+730FAAC4ALNmzdKrr76q9evXq2/fvk3dHAAAAJdHQggAAFwSS5cuVW5ursaPH1/rGjwAAABofCSEAAAAAAAAXAz/PAcAAAAAAOBiSAgBAAAAAAC4GBJCAAAAAAAALoaEEAAAAAAAgIshIQQAAAAAAOBiSAgBAAAAAAC4GBJCAAAAAAAALoaEEAAAAAAAgIshIQQAAAAAAOBi/h99Q7LTzAQKUQAAAABJRU5ErkJggg==", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -286,7 +301,8 @@ "from scipy.interpolate import interp1d\n", "\n", "# set figure\n", - "fig = plt.figure(figsize=(14, 8))\n", + "fig = plt.figure(figsize=(10, 6))\n", + "plt.grid()\n", "\n", "# Given lists of quantiles and their corresponding probabilities\n", "quantiles = np.array([-0.3059, -0.2530, -0.2374, -0.2228, -0.1920, -0.1803, -0.1659, -0.1505,\n", @@ -310,12 +326,12 @@ "plt.ylabel('Probabilities')\n", "\n", "# set title\n", - "plt.title('Interpolated CDF')\n" + "plt.title('Interpolated CDF')" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -324,15 +340,15 @@ "Text(0.5, 1.0, 'CRPS visualization')" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -341,7 +357,7 @@ ], "source": [ "target_value = -0.2161144\n", - "fig = plt.figure(figsize=(14, 8))\n", + "fig = plt.figure(figsize=(10, 6))\n", "plt.grid()\n", "\n", "heavyside = np.heaviside(x - target_value, 0.5)\n", @@ -384,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/src/notebooks/thesis-visualizations.ipynb b/src/notebooks/thesis-visualizations.ipynb new file mode 100644 index 0000000..c812f82 --- /dev/null +++ b/src/notebooks/thesis-visualizations.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quantiles explanation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentile: -0.6744897501960817, CDF Value: 0.25\n", + "Percentile: 0.0, CDF Value: 0.5\n", + "Percentile: 0.6744897501960817, CDF Value: 0.75\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "\n", + "# Define the range of values for our theoretical normal distribution\n", + "x = np.linspace(-3, 3, 1000)\n", + "\n", + "# Calculate the CDF values for these points\n", + "cdf_values = norm.cdf(x, 0, 1) # mean=0, std=1 for standard normal distribution\n", + "\n", + "# Plotting the CDF\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.plot(x, cdf_values, label=\"CDF\", color=\"blue\")\n", + "\n", + "# Calculate and plot the 25th, 50th, and 75th percentiles\n", + "percentiles = norm.ppf([0.25, 0.5, 0.75], 0, 1) # Inverse CDF (percent point function)\n", + "colors = [\"orange\", \"red\", \"green\"]\n", + "labels = [\"Quantile 25%\", \"Quantile 50%\", \"Quantile 75%\"]\n", + "\n", + "for percentile, color, label in zip(percentiles, colors, labels):\n", + " cdf_value = norm.cdf(percentile, 0, 1)\n", + " print(f\"Percentile: {percentile}, CDF Value: {cdf_value}\")\n", + " plt.scatter(percentile, cdf_value, color=color)\n", + " plt.axhline(y=cdf_value, color=color, linestyle=\"--\", label=label)\n", + "\n", + "# set figure size\n", + "\n", + "plt.legend()\n", + "plt.title(\"CDF of Standard Normal Distribution with Quantiles\")\n", + "plt.xlabel(\"Value\")\n", + "plt.ylabel(\"CDF - Probability\")\n", + "plt.grid(True, which=\"both\", linestyle=\"--\", linewidth=0.5)\n", + "plt.savefig(\"cdf_quantiles_example.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quantile CDF reconstruction" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import interp1d\n", + "\n", + "# set figure\n", + "fig = plt.figure(figsize=(10, 6))\n", + "plt.grid()\n", + "\n", + "# Given lists of quantiles and their corresponding probabilities\n", + "quantiles = np.array(\n", + " [\n", + " -0.3059,\n", + " -0.2530,\n", + " -0.2374,\n", + " -0.2228,\n", + " -0.1920,\n", + " -0.1803,\n", + " -0.1659,\n", + " -0.1505,\n", + " -0.1325,\n", + " -0.0987,\n", + " -0.0834,\n", + " -0.0620,\n", + " -0.0245,\n", + " ]\n", + ")\n", + "probs = np.array(\n", + " [0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]\n", + ")\n", + "\n", + "\n", + "# cubic spline interpolation of the CDF\n", + "f = interp1d(\n", + " quantiles, probs, kind=\"cubic\", bounds_error=False, fill_value=\"extrapolate\"\n", + ")\n", + "\n", + "# define a range for the x values\n", + "x = np.linspace(min(quantiles), max(quantiles), 1000)\n", + "\n", + "# plot the interpolation (approximated CDF)\n", + "plt.plot(x, f(x), label=\"Interpolated CDF\", color=\"red\", linestyle=\"--\")\n", + "\n", + "# scatter plot of the quantiles vs probabilities\n", + "plt.scatter(quantiles, probs)\n", + "\n", + "# set labels\n", + "plt.xlabel(\"Quantile Predictions\")\n", + "plt.ylabel(\"Probabilities\")\n", + "\n", + "# set title\n", + "plt.title(\"Reconstructed CDF\")\n", + "plt.savefig(\"reconstructed_cdf.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/training_scripts/autoregressive_quantiles.py b/src/training_scripts/autoregressive_quantiles.py index 86ed0e7..949898c 100644 --- a/src/training_scripts/autoregressive_quantiles.py +++ b/src/training_scripts/autoregressive_quantiles.py @@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper #### ClearML #### clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast") -task = clearml_helper.get_task(task_name="AQR: Non Linear") +task = clearml_helper.get_task(task_name="AQR: Linear Baseline") task.execute_remotely(queue_name="default", exit_process=True) from src.policies.PolicyEvaluator import PolicyEvaluator @@ -27,21 +27,21 @@ from src.models.time_embedding_layer import TimeEmbedding data_config = DataConfig() data_config.NRV_HISTORY = True -data_config.LOAD_HISTORY = True -data_config.LOAD_FORECAST = True +data_config.LOAD_HISTORY = False +data_config.LOAD_FORECAST = False -data_config.WIND_FORECAST = True -data_config.WIND_HISTORY = True +data_config.WIND_FORECAST = False +data_config.WIND_HISTORY = False -data_config.QUARTER = True -data_config.DAY_OF_WEEK = True +data_config.QUARTER = False +data_config.DAY_OF_WEEK = False -data_config.NOMINAL_NET_POSITION = True +data_config.NOMINAL_NET_POSITION = False data_config = task.connect(data_config, name="data_features") -data_processor = DataProcessor(data_config, path="", lstm=False) +data_processor = DataProcessor(data_config, path="", lstm=True) data_processor.set_batch_size(512) data_processor.set_full_day_skip(False) @@ -64,17 +64,18 @@ else: model_parameters = { "learning_rate": 0.0001, - "hidden_size": 512, - "num_layers": 5, + "hidden_size": 256, + "num_layers": 2, "dropout": 0.2, "time_feature_embedding": 8, } model_parameters = task.connect(model_parameters, name="model_parameters") -time_embedding = TimeEmbedding( - data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"] -) +# time_embedding = TimeEmbedding( +# data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"] +# ) + # lstm_model = GRUModel( # time_embedding.output_dim(inputDim), # len(quantiles), @@ -83,17 +84,19 @@ time_embedding = TimeEmbedding( # dropout=model_parameters["dropout"], # ) -non_linear_model = NonLinearRegression( - time_embedding.output_dim(inputDim), - len(quantiles), - hiddenSize=model_parameters["hidden_size"], - numLayers=model_parameters["num_layers"], - dropout=model_parameters["dropout"], -) +# non_linear_model = NonLinearRegression( +# time_embedding.output_dim(inputDim), +# len(quantiles), +# hiddenSize=model_parameters["hidden_size"], +# numLayers=model_parameters["num_layers"], +# dropout=model_parameters["dropout"], +# ) # linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles)) +linear_model = LinearRegression(inputDim, len(quantiles)) -model = nn.Sequential(time_embedding, non_linear_model) +# model = nn.Sequential(time_embedding, lstm_model) +model = linear_model model.output_size = 1 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"]) @@ -117,8 +120,8 @@ trainer = AutoRegressiveQuantileTrainer( trainer.add_metrics_to_track( [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)] ) -trainer.early_stopping(patience=10) -trainer.plot_every(5) +trainer.early_stopping(patience=5) +trainer.plot_every(2) trainer.train(task=task, epochs=epochs, remotely=True) ### Policy Evaluation ### @@ -137,7 +140,7 @@ optimal_penalty, profit, charge_cycles = ( test_loader=test_loader, initial_penalty=1000, target_charge_cycles=283, - learning_rate=15, + initial_learning_rate=3, max_iterations=150, tolerance=1, )