76 | 77 | 2. Once WinPython is installed, go to one of the notebooks above and download the **.ipynb** file to your computer (the "download" icon is at the top-right of the screen).
78 | 79 | 3. Open the folder containing your WinPython installation and run the **WinPython Command Prompt** (not the normal Windows Command Prompt).
80 | 81 | 4. **Change directories** to wherever you saved the **.ipynb** file and then type `ipython notebook` at the command prompt. Your browser should open to display the IPython dashboard and you'll see a link to the notebook you just downloaded.
82 | 83 | 5. Click to open the notebook then choose `Cell > Run All` from the menu bar. Python will import all the necessary modules and run the notebook cells, which might take a few moments.
84 | 85 | You can now work through the notebook **interactively**, modifying the code etc. as you go. 86 | -------------------------------------------------------------------------------- /notebooks/09_Summary.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Summary\n", 8 | "\n", 9 | "This notebook attempts to provide a practical summary of everything covered previously, particularly with regard to choosing an appropriate workflow. The full range of methods available for model calibration, uncertainty estimation and comparison is huge, so this is by no means a comprehensive summary - it's simply an attempt to place the methods discussed earlier into context.\n", 10 | "\n", 11 | "## 1. Choosing an approach\n", 12 | "\n", 13 | "The flow chart below may be helpful when selecting an approach to take for your modelling assessment.\n", 14 | "\n", 15 | "
![\"Bayesian](\"https://github.com/JamesSample/enviro_mod_notes/blob/master/images/Bayesian_Workflow.png?raw=true\")
\n",
16 | "\n",
17 | "Regardless of whether you're performing single- or multi-model analysis, arguably the most difficult stages in either of the above workflows are:\n",
18 | "\n",
19 | " 1. Choosing an appropriate **likelihood function** and \n", 20 | " \n", 21 | " 2. Choosing an **efficient** method for estimating the **marginal posteriors** (or some summary statistics to represent them).\n", 22 | "\n", 23 | "## 2. Choosing a likelihood function\n", 24 | "\n", 25 | "Throughout this set of notes we have focused on **formal** statistical likelihoods, based on careful consideration of the expected error structure between the model and the observed data. If you don't know for certain what the error structure should be (and, in reality, you almost never do), it's usually best to start off with something simple and then progress in stages to more complex functions. At each stage, simple **diagnostic plots of the residuals** will show whether the assumptions have been satisfied, and may also suggest what modifications to try next.\n", 26 | "\n", 27 | "### 2.1.Simple independent Gaussian errors\n", 28 | "\n", 29 | "The simplest likelihood functions use independent and identically distributed (iid) Gaussian errors, such as those presented in notebooks [2](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/02_Calibration_Likelihood.ipynb#2.1.-The-likelihood-function) and [4](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/04_MCMC.ipynb#7.3.-Define-likelihood). These are rarely adequate for real-world environmental modelling, but it's a good place to start.\n", 30 | "\n", 31 | "When using these likelihoods with real data, residual plots will often show **[heteroscedasticity](https://en.wikipedia.org/wiki/Heteroscedasticity)**, which imples that the errors are *not* identically distributed (e.g. in hydrology it's common to find bigger errors at high flows). Autocorrelation plots may also show **serial dependence**, in which case the errors are not independent either. This latter problem is very common for models that simulate **time series** at fine-grained temporal resolution.\n", 32 | "\n", 33 | "### 2.2. Weighted errors\n", 34 | "\n", 35 | "One simple approach to deal with problems of heteroscedasticity is to use **weighted errors**. For example, the error variance may be made proportional to a parameter such as flow, exactly as we did with the hydrological model in [notebook 6](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/06_Beyond_Metropolis.ipynb#2.-Choosing-a-likelihood-function). An alternative way of achieving essentially the same thing is to consider **transforming** the data - for example by taking the **logarithm** or **square root** of the simulated and observed data series and performing the calibration using the transformed datasets instead.\n", 36 | "\n", 37 | "Simple weighted errors schemes often perform surprisingly well and are relatively easy to implement. \n", 38 | "\n", 39 | "### 2.3. Autoregressive errors\n", 40 | "\n", 41 | "If the residuals show autocorrelation, it may be worth considering an [autoregressive model](https://en.wikipedia.org/wiki/Autoregressive_model). An **AR(1) scheme** is often sufficient. We have not considered autoregressive models in these notebooks and there are some subtleties to be considered - see [Evin *et al.* (2013)](http://onlinelibrary.wiley.com/doi/10.1002/wrcr.20284/full) and [Evin *et al.* (2014)](http://onlinelibrary.wiley.com/doi/10.1002/2013WR014185/abstract) for further details. Such schemes are nevertheless well-established and not too difficult to code.\n", 42 | "\n", 43 | "## 3. Estimating marginal posteriors for the parameters\n", 44 | "\n", 45 | "Perhaps the most efficient approach for obtaining parameter estimates and confidence intervals for your model is to use the **Gaussian approximation** from [notebook 8](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/08_Gaussian_Approx.ipynb). This requires running an optimiser to find the [MAP](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) and then estimating the **covariance structure** in this vicinity using the **Hessian matrix**. This method has the additional advantage that it can be used to approximate the \"**evidence**\" in Bayes' equation, so it can also be used in a **model comparison** context. However, the technique is only valid *if* the posterior can be reasonably approximated by a multi-dimensional Gaussian, which is not always the case.\n", 46 | "\n", 47 | "* **Add something on how to test for this??**\n", 48 | "\n", 49 | "For more complicated posteriors, it may be necessary to use strategies based on **random sampling** or **random walks**. Of these **stochastic** approaches, the simplest are **Monte Carlo** techniques such as **Importance** or **Rejection** sampling (see [notebook 3](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/03_Monte_Carlo.ipynb#2.2.-Monte-Carlo-(MC)). Monte Carlo techniques are expected to be very inefficient in high dimensional parameter spaces, however, so for complex models **Markov chain Monte Carlo (MCMC)** is likely to be a much better option. The simplest MCMC approach uses the **Metropolis algorithm**, introduced in [notebook 4](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/04_MCMC.ipynb#3.-The-Metropolis-algorithm). More sophisticated MCMC methods include: **adaptive** algorithms (e.g. [DREAM](https://www.researchgate.net/profile/James_Hyman/publication/41035177_Accelerating_Markov_chain_Monte_Carlo_simulation_by_differential_evolution_with_self-adaptive_randomized_subspace_sampling/links/09e4150c0a96b6d034000000.pdf), which is very popular in hydrology); **affine invariant** algorithms, such as the one provided by [emcee](http://dan.iel.fm/emcee/current/) and used in [notebook 6](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/06_Beyond_Metropolis.ipynb); and algorithms which make use of gradient information, such as e.g. [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hybrid_Monte_Carlo).\n", 50 | "\n", 51 | "All these approaches may be useful in different circumstances, but it is worth noting many of them - especially standard MC technqiues - may struggle to evaluate the evidence in complex models and are therefore **not necessarily well-suited to model comparison**. This is because the evidence, $P(D|M_i)$, is often a very computationally intensive integral, which is why we ignored it for the *parameter-inference-only* examples in previous notebooks\n", 52 | "\n", 53 | "$$P(D|M_i) = \\int_\\theta{P(D|\\theta, M_i)P(\\theta|M_i) d\\theta}$$\n", 54 | "\n", 55 | "A variety of methods exist for estimating this integral, but one of the most promising makes use of an analogy to statistical physics and is called **[thermodynamic integration](http://users.wpi.edu/~balnan/MarLik-BF-0.pdf)**. The details of this approach are beyond the scope of these notes, but it's worth pointing out that the emcee **[parallel-tempered ensemble sampler](http://dan.iel.fm/emcee/current/user/pt/)** includes a `thermodynamic_integration_log_evidence` method. In brief, parallel-tempering involves initialising multiple versions of the standrad affine invariant sampler, but with each one running at a different \"temperature\". Higher temperatures correspond to larger, more energetic steps in the Markov chains, in exactly the same way that molecules in a gas become more energetic at higher temperatures. Bigger steps mean chains running at high temperatures are more likely to move \"downhill\" into lower probability regions, whereas chains at lower temperatures focus their attemtion on exploring the \"peaks\" of the posterior distribution. At very low temperatures, the chains essentially become optimisers, in the sense that they will only ever move uphill.\n", 56 | "\n", 57 | "Running an ensemble of MCMC analyses at a range of temperatures provides a very robust exploration of the parameter space and can be very effective if the posterior is **multi-modal**. In addition, by computing the average of the log-likelihoods at various temperatures, it is possible to estimate the value of the **evidence integral** - emcee uses the method of [Goggans & Chi (2004)](http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.1751356).\n", 58 | "\n", 59 | "If your work involves comparing a number of complex models whose posteriors cannot be represented using the Gaussian approximation ([notebook 8](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/08_Gaussian_Approx.ipynb)), using the **parallel-tempered ensemble sampler** in emcee could be an excellent option." 60 | ] 61 | } 62 | ], 63 | "metadata": { 64 | "kernelspec": { 65 | "display_name": "Python 2", 66 | "language": "python", 67 | "name": "python2" 68 | }, 69 | "language_info": { 70 | "codemirror_mode": { 71 | "name": "ipython", 72 | "version": 2 73 | }, 74 | "file_extension": ".py", 75 | "mimetype": "text/x-python", 76 | "name": "python", 77 | "nbconvert_exporter": "python", 78 | "pygments_lexer": "ipython2", 79 | "version": "2.7.10" 80 | } 81 | }, 82 | "nbformat": 4, 83 | "nbformat_minor": 0 84 | } 85 | -------------------------------------------------------------------------------- /notebooks/08_Gaussian_Approx.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Gaussian approximations and model comparison\n", 8 | "\n", 9 | "The previous notebooks have introduced a variety of methods for performing **parameter inference** using complex models. All these methods depend on **Bayes' Theorem** to estimate something proportional to the **posterior probability density**.\n", 10 | "\n", 11 | "We've also covered a variety of ways for searching complex posteriors, ranging from simple Monte Carlo approaches like importance sampling and GLUE, to basic MCMC (e.g. Metropolis) and some more sophisticated MCMC algorithms such as the affine invariant sampler in [emcee](http://dan.iel.fm/emcee/current/).\n", 12 | "\n", 13 | "So far, we've only focussed on inferring parameter distributions for a **single model**, but what if we have several different models that we'd like to compare? This notebook introduces some of the basic ideas behind **Bayesian model comparison**. At the same time, we'll take a step back from the earlier considerations of sampling schemes (MC, MCMC etc.) to take a look at the **Gaussian approximation** approach to model calibration.\n", 14 | "\n", 15 | "The Gaussian approximation method is both simpler and more computationally efficient than the methods discussed in earlier notebooks. It also provides a very complete approach to the inference process, allowing both parameter & uncertainty estimation *and* model comparison (as we will see, some of the more complex methods introduced earlier can struggle when it comes to model comparison). \n", 16 | "\n", 17 | "The Gaussian approximation method is only valid in particular circumstances, but you may be surprised by just how often you can get away with using it. Because of it's power and simplicity, **this approach is well worth considering** for an initial attempt at the inference process - especially if you're planning to compare multiple model structures.\n", 18 | "\n", 19 | "\n", 20 | "## Work in progress\n", 21 | "\n", 22 | "### Plan\n", 23 | "\n", 24 | "* Ability to approximate the posterior in the vicinity of the MAP by a multi-dimensional Gaussian
\n", 25 | "* Justification for why this often works OK for large $N$ (link to **Central Limit Theorem**?)
\n", 26 | "* Estimates of uncertainty from parameter covariance matrix (from **Hessian**?)
\n", 27 | "* Introduce model comparison and the idea that the **normalising constant** from a parameter inference step becomes the **model likelihood** in the model comparison step
\n", 28 | "* Describe how the Bayesian method automatically penalises \"**over-fitting**\"
\n", 29 | "* Estimate the \"**evidence**\" for a model from **determinant** of **Hessian**
\n", 30 | "* Remember to stress that for model comparison we need **proper** priors - see page 354 of [Information Theory, Inference and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/book.html)
\n", 31 | "* As long as the Gaussian approximation is appropriate, provides very efficient and comprehensive method of finding:\n", 32 | " 1. The optimum parameter set for each model under consideration
\n", 33 | " 2. The uncertainty associated with the parameter optimum for each model
\n", 34 | " 3. The relative likelihoods of each model
\n", 35 | "* Only requires a single optimisation run; no very intensive numerical sampling (unless required for optimisation)
\n", 36 | "* Good place to start!
\n", 37 | "* How do we know if the Gaussian approximation is valid/appropriate?\n", 38 | "\n", 39 | "Notes so far are based on Chapter 28 of David MacKay's superb book **[Information Theory, Inference and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/book.html)**. \n", 40 | "\n", 41 | "Still need to produce some working code\n", 42 | "\n", 43 | "## 1. A simple Bayesian workflow\n", 44 | "\n", 45 | "Suppose we have several different models for a particular process or system of interest. We have a single observed dataset that we will use to calibrate each of the models. Some of the models are more complex (e.g. have more poorly-constrained parameters) than others. We are interested in:\n", 46 | "\n", 47 | " 1. **Calibrating each model**. This involves identifying plausible parameter sets and associated **credible intervals**.
\n", 48 | " \n", 49 | " 2. **Comparing the models**. Are the more complex models *significantly* better at simulating the data than the simple ones? Is the additional complexity \"worth it\"?
\n", 50 | " \n", 51 | " 3. **Making predictions**. What is our best estimate for the next observation? Should we select the \"best\" model and use it, or can we integrate output from all the models, weighted according to their relative probability?\n", 52 | "\n", 53 | "We have already considered the problem of **model calibration** (i.e. inferring posterior parameter distributions) in some detail. However, in the discussions so far, we have generally ignored the normalising constant in Bayes' equation by writing \n", 54 | "\n", 55 | "$$posterior \\propto likelihood \\times prior$$\n", 56 | "\n", 57 | "instead of the full version\n", 58 | "\n", 59 | "$$posterior = \\frac{likelihood \\times prior}{normalising \\; constant}$$\n", 60 | "\n", 61 | "Recall from [notebook 3](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/03_Monte_Carlo.ipynb#1.1.-A-Bayesian-model-calibration-framework) that the normalising constant is sometimes referred to as the \"**probability of the data**\" or the \"**evidence**\".\n", 62 | "\n", 63 | "If we're just interested in parameter inference for a single model, it is OK to ignore the normalising constant. However, if we want to compare several competing models, this constant becomes important. Below, we'll consider a slightly more general case of model calibration where more than one model is available.\n", 64 | "\n", 65 | "### 1.1. Parameter inference\n", 66 | "\n", 67 | "If we assume that a particular model, $M_i$, is correct, we can write Bayes' equation as\n", 68 | "\n", 69 | "$$P(\\theta|D, M_i) = \\frac{P(D|\\theta, M_i)P(\\theta|M_i)}{P(D|M_i)}$$\n", 70 | "\n", 71 | "where $\\theta$ is the vector of parameters for model $M_i$ and $D$ is the data (i.e. the observations) used for calibration.\n", 72 | "\n", 73 | "If all of the model parameters, $\\theta$, are continuous variables, we can re-write the denominator in this equation as\n", 74 | "\n", 75 | "$$P(D|M_i) = \\int_\\theta{P(D|\\theta, M_i)P(\\theta|M_i) d\\theta}$$\n", 76 | "\n", 77 | "(See the end of [notebook 1](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/01_Distributions.ipynb#1.4.-Marginal-and-conditional-distributions) for an explanation of where these equations come from).\n", 78 | "\n", 79 | "To calibrate a single model to a dataset, we are primarily interested in finding two things:\n", 80 | "\n", 81 | " 1. Some kind of **central estimate** for an appropriate parameter set (e.g. the median, mean or \"best\" parameter set), and
\n", 82 | " \n", 83 | " 2. An indication of the **uncertainty** associated with the estimate for each parameter.\n", 84 | " \n", 85 | "There are many ways of achieving these goals, depending on the complexity of the problem. In the previous notebooks we have taken a very comprehensive approach, by using MC or MCMC techniques to estimate **marginal posterior distributions** for each parameter. From these distributions we can calculate a whole range of different statistics for the central estimate (e.g. the MAP) and the uncertainty bounds (e.g. a 95% credible interval). The truly Bayesian approach, however, is simply to report the distribution (i.e. a histogram) in its entirety.\n", 86 | "\n", 87 | "As we have seen, though, estimating marginal posteriors is not always easy and can be computationally expensive. In some cases, simpler approaches may provide adequate answers much more quickly.\n", 88 | "\n", 89 | "#### 1.1.1. Gaussian approximation\n", 90 | "\n", 91 | "For the simplest possible example of a one parameter model with an approximately Gaussian posterior distribution, we could simply report the **mean** and the **variance** to achieve the two aims listed above. Most real problems are more complex, but for some multi-parameter models it may still be reasonable to assume an approximately Gaussian posterior in the region around the most promising parameter set. In this case, the two aims listed above can be achieved by:\n", 92 | "\n", 93 | " 1. Finding the **[Maximum a posteriori (MAP)](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation)** estimate. This is just the maximum of the posterior distribution, so finding it is an optimisation problem rather than a sampling problem, so it is likely to be faster (see examples in notebooks 2 and 6).
\n", 94 | " \n", 95 | " 2. Obtain **confidence intervals** for each parameter by considering the **local curvature** of the posterior distribution in the region surrounding the MAP. This can be done by evaluating the **[Hessian](https://en.wikipedia.org/wiki/Hessian_matrix)**, which can provide an estimate of the parameter **covariance matrix**.\n", 96 | " \n", 97 | "As long as the posterior can be reasonably approximated by a multi-dimensional Gaussian in the vicinity of the MAP, reporting the **location of the MAP** and the **parameter covariance matrix** is simply the multi-dimensional equivalent of reporting the mean and variance in the 1D example mentioned above. This approach therefore provides a very efficient (though less generally applicable) method of model auto-calibration and uncertainty estimation.\n", 98 | " \n", 99 | "Note also that, as the amount of data collected increases, the Gaussian approximation can often be surprisingly effective. **(Link to the Central Limit Theorem?)**\n", 100 | "\n", 101 | "### 1.2. Model comparison\n", 102 | "\n", 103 | "For a discrete set of models, $M_i$, we can write another version of Bayes' equation\n", 104 | "\n", 105 | "$$P(M_i|D) = \\frac{P(D|M_i)P(M_i)}{P(D)}$$\n", 106 | "\n", 107 | "In this case, because the set of models is discrete, we can re-write the denominator as a sum, rather than as an integral like we did above\n", 108 | "\n", 109 | "$$P(D) = \\sum_iP(D|M_i)P(M_i)$$\n", 110 | "\n", 111 | "As we have done previosuly, in many cases it is usual to ignore the \"evidence\" in this equation and just write\n", 112 | "\n", 113 | "$$P(M_i|D) \\propto P(D|M_i)P(M_i)$$\n", 114 | "\n", 115 | "The prior probability for each model, $P(M_i)$, represents our initial belief about how likely model $M_i$ is to be correct. If we have no reason to prefer some models over others, $P(M_i)$ will be the same for all models (i.e. a uniform prior). \n", 116 | "\n", 117 | "The likelihood term, $P(D|M_i)$, encapsulates what the data can tell us about the probability of $M_i$ being correct. Note from section 1.1 above that this term is exactly the same as the **evidence** (i.e. the denominator) in the form of Bayes' equation used for parameter inference. This is generally the case: **the \"evidence\" from the parameter inference stage becomes a crucial component of Bayes' equation at the model comparison stage**. \n", 118 | "\n", 119 | "In previous notebooks, we have generally ignored the normalising constant in Bayes' equation when performing **parameter inference**. This is fine as long as you're only interested in evaluating parameter-related uncertainty for a **single model**. However, as you can see, it is necessary to include it if you're interested in comparing multiple models.\n", 120 | "\n", 121 | "#### 1.2.1 Occam's razor\n", 122 | "\n", 123 | "**[Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor)** embodies the intuitive principle that, if two models provide equally good explanations of the data, the simplest model should be preferred. This principle is automatically incorporated into Bayesian model comparison in a quantitative way, which is extremely useful. This is because simple models have less flexibility and therefore make a narrower range of predictions than complex models. Because the total volume under the probabiltiy distribution $P(D|M_i)$ must equal $1$, the probabiltiy density for more complex models tends to be more \"spread out\" (and therefore lower) than for simple models. ***If*** a simple model manages to match the data just as well, it is therefore likely to have a higher probability density in the region of credible parameter sets than a more complex model with a wider range of output. The simpler model will therefore \"win\" during model comparison.\n", 124 | "\n", 125 | "#### 1.2.2. Gaussian approximation\n", 126 | "\n", 127 | "If the posterior can be well approximated by a Gaussian in the vicinity of the MAP (which, perhaps surprisingly, is often the case given enough data), then the **evidence** for model $M_i$, $P(D|M_i)$, can be estimated using the **best-fit likelihood** and **prior** and the **determinant** of the corresponding covariance matrix (which is obtained from the **Hessian**). See [equation 28.10](http://www.inference.phy.cam.ac.uk/itprnn/book.pdf) in David MacKay's book for further details.\n", 128 | "\n", 129 | "For the case where the Gaussian approximation is valid, a full Bayesian model evaluation can therefore be performed in a surprisingly efficient way, without requiring any complicated and computationally intensive numerical sampling (e.g. MCMC). First, for each model:\n", 130 | "\n", 131 | " 1. Find the MAP.
\n", 132 | " 2. Use the Hessian evaluated at the MAP to find the parameter covariance matrix.\n", 133 | " \n", 134 | "These two steps provide, for each model, an estimate of the \"best\" parameter set and an indication of the parameter-related uncertainty. Then, for model comparison:\n", 135 | "\n", 136 | " 1. Estimate the **evidence** for each model using the best-fit likelihood, $P(D|\\theta_{MAP}, M_i)$, the best-fit prior, $P(\\theta_{MAP}, M_i)$ and the **determinant** of the Hessian (from above).
\n", 137 | " 2. Multiply the evidence by the prior for each model and use the result to rank the models according their probability. Predictions can be made by calculating a weighted sum of model predictions, with the weights based on the posetrior probabiltiy of each model.\n", 138 | " \n", 139 | "Note that this workflow uses only a single **optimisation** step - no complex sampling algorithms are required. As long as the Gaussian approximation is valid, it is therefore an extremely efficient way to perform model calibration, uncertainty analysis and comparison." 140 | ] 141 | } 142 | ], 143 | "metadata": { 144 | "kernelspec": { 145 | "display_name": "Python 2", 146 | "language": "python", 147 | "name": "python2" 148 | }, 149 | "language_info": { 150 | "codemirror_mode": { 151 | "name": "ipython", 152 | "version": 2 153 | }, 154 | "file_extension": ".py", 155 | "mimetype": "text/x-python", 156 | "name": "python", 157 | "nbconvert_exporter": "python", 158 | "pygments_lexer": "ipython2", 159 | "version": "2.7.10" 160 | } 161 | }, 162 | "nbformat": 4, 163 | "nbformat_minor": 0 164 | } 165 | -------------------------------------------------------------------------------- /notebooks/02_Calibration_Likelihood.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "%matplotlib inline\n", 12 | "import matplotlib.pyplot as plt, seaborn as sn, numpy as np\n", 13 | "sn.set_context('notebook')" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "# Model calibration\n", 21 | "\n", 22 | "In contrast to the fairly abstract consideration of distributions in the previous notebook, this one takes a more practical approach to the problem of model **calibration**. It also introduces the very important concept of the **likelihood function**.\n", 23 | "\n", 24 | "## 1.1. Types of model\n", 25 | "\n", 26 | "There are many different types of model. At one end of the spectrum are **process-** or **physically-based** models, which use coupled (often differential) equations underpinned by the laws of physics to represent key processes of interest. In principle, if we know all the equations governing the behaviour of a system (and assuming we can measure the **parameter values** associated with those equations) we should be able to construct an accurate model to predict the state of the system at some point in the future.\n", 27 | "\n", 28 | "At the other end of the spectrum are **empirical** models. Just like their process-based counterparts, empirical models use equations to represent relationships between variables, but these equations need not have any physical basis and the parameter values are usually chosen so as to maximise “goodness-of-fit\", rather than being independently measured.\n", 29 | "\n", 30 | "Empirical models are often simpler to setup and run much faster than process-based equivalents, but their predictions are only as good as the data used to train them. They may therefore perform poorly if used to make predictions under conditions that differ significantly from those encountered in the training dataset (for example by trying to predict river flows under some future climate). \n", 31 | "\n", 32 | "In theory, a well-designed physically-based model will make better \"out-of-sample\" predictions than an empirical model, because the process knowledge incorporated into the model's structure will constrain a physically reasonable response, even under conditions different to those experienced during calibration. In reality, natural systems are often extraordinarily complex and outside of the lab it is rare to have enough process understanding to build genuinely physically-based models. Instead, we are forced to create **conceptual** models, which still use physical relationships and differential equations, but which also make dramatic simplifications by abstracting the complexity of the real system into some idealised conceptual framework. As an example, hydrological models often use a conceptual framework consisting of systems of connected \"bucket reservoirs\", where one bucket represents e.g. the soil water store, another the groundwater store and so on. These abstract conceptualisations are useful – especially if we can write down the physically-based equations that would control our conceptual system (e.g. the water flows between our idealised system of buckets). However, it is important not to confuse a physically-based model of a conceptual system with a physically-based model of the *real world*.\n", 33 | "\n", 34 | "One of the difficulties associated with conceptual models is that, although the equations appear to be physically-based, the parameters in the equations will often have no concrete meaning in the real world, making them impossible to measure directly. For example, in equations commonly used in hydrological modelling, the time constant (or residence time), $\\tau$, of a groundwater reservoir is the length of time, on average, that a molecule of water will spend in that reservoir between flowing in and flowing out. In reality, the true groundwater aquifer is much more complicated than the model's representation of a bucket with a couple of holes in it. This means that values of $\\tau$ measured in the field (using e.g. isotopic tracer techniques) will not necessarily be compatible with the $\\tau$ parameter as represented in the model.\n", 35 | "\n", 36 | "The upshot of all this is that, in practice, **virtually all** environmental models - whether empirical or supposedly physically based - will have parameter values that are not physically meaningful, are too difficult/expensive to measure, or cannot be measured at a temporal/spatial scale which is compatible with the model conceptualisation. In order to get our models to give meaningful output, we therefore need to **calibrate** them by adjusting the poorly constrained parameter values until the output looks sensible.\n", 37 | "\n", 38 | "## 1.2. Over-parameterisation\n", 39 | "\n", 40 | "Attempts to make physically-based models of complex environmental systems have led to increasingly complex conceptual frameworks. Within the field of hydrology and water quality, some of the most popular models applied today (e.g. [SWAT](http://swat.tamu.edu/ \"SWAT\") and [HYPE](http://www.smhi.se/en/research/research-departments/hydrology/hype-1.7994 \"HYPE\")) include tens or even hundreds of parameters, many of which have no direct physical meaning and are therefore usually poorly constrained. Although technically impressive, it can be very difficult to apply such models effectively even in data-rich environments. This is because a process-based model with a very large number of unconstrained parameters will behave very much like an **overly complex** empirical model, simply because the freedom afforded by the unknown parameters will completely swamp any limitations on the model's behaviour imposed by the process knowledge. \n", 41 | "\n", 42 | "In empirical modelling it is usual to choose the simplest possible model that still explains the data. However, in the case of many conceptual and physically-based environmental models, it is often neither possible nor meaningful to \"turn off\" parameters to test whether a simpler model would suffice. Furthermore, in many cases the amount of calibration data available is limited: in hydrological applications, for example, a model will typically be calibrated against a single streamflow dataset measured at the catchment outflow. There is simply not enough information contained in such a dataset to meaningfully constrain dozens or even hundreds of model parameters, some of which might represent e.g. soil properties or transport coefficients in the upper part of catchment (which have only a limited influence on streamflow).\n", 43 | "\n", 44 | "These issues mean that highly parameterised conceptual and process-based models can produce output which may appear to reproduce observations well, but which have little predictive value. With so much parameter-related flexibility (i.e. many \"degrees of freedom\"), models can generally do a good job of matching the calibration data, regardless of whether the process representation is correct. In a worst case scenario such models exhibit the \"worst of both worlds\", in the sense that they have the long runtimes and complexity of process-based models, but with the same limitations and poor out-of-sample predictive power as empirical models.\n", 45 | "\n", 46 | "This does not necessarily mean that complex conceptual models cannot be used effectively, but it does mean they must be used with caution.\n", 47 | "\n", 48 | "## 1.3. Manual calibration\n", 49 | "\n", 50 | "If we have an environmental model of a real-world system, it is more than likely it will include some poorly constrained parameters that will need calibrating before the model can be used effectively. Calibration usually requires having observed input datasets (e.g. rainfall and evapotranspiration for a hydrological model) together with observed data from the same time period for the variable you're trying to simulate (e.g. streamflow). The observed input data is used to drive the model, and the parameters are adjusted until the simulated output matches the observed data as closely as possible.\n", 51 | "\n", 52 | "As an example, suppose we have a **deterministic** \"black box\" model such as the one shown below. Deterministic means that if we run the model with the same inputs and parameter values we will always get the same output, because the model has no **stochastic** components. The model could represent anything at all, but we'll stick with a hydrological theme for the moment.\n", 53 | "\n", 54 | "We have no knowledge about how the model works internally - all we can do is set values for the two parameters, $\\alpha$ and $\\beta$, and then press the **Run** button. The model produces an output time series, $S_i = \\{S_1, .., S_n\\}$ for time points $t_i = \\{t_1, .., t_n\\}$. We also have a measured dataset, $M_i = \\{M_1, .., M_n\\}$, which we'd like to reproduce.\n", 55 | "\n", 56 | "![Black box model](https://github.com/JamesSample/enviro_mod_notes/blob/master/images/Black_Box_Model2.png?raw=true \"Black box model\")\n", 57 | "\n", 58 | "For **manual calibration**, we start off by choosing some sensible values for $\\alpha$ and $\\beta$, then we run the model and compare $S_i$ to $M_i$, then we change $\\alpha$ and $\\beta$ and repeat until the $S_i$ and $M_i$ are as similar as possible.\n", 59 | "\n", 60 | "Manual calibration is clearly a laborious process, but because humans are remarkably good at picking up patterns it's often surprising how quickly experienced modellers can achieve reasonable results. If you're just starting out with a new model (especially one you didn't create yourself), I'd **strongly recommend** putting some time aside for manual calibration: you'll learn a lot about which parameters the model is sensitive to as well as which ones control different aspects of the output. It also forces you to think about which parameter values are sensible versus which ones give the best calibration (not necessarily the same!). If nothing else, manual calibration gives you an initial benchmark that you can refer to later, once you start applying more sophisticated \"auto-calibration\" techniques.\n", 61 | "\n", 62 | "## 1.4. Which parameters to calibrate?\n", 63 | "\n", 64 | "In an ideal world, any parameter whose value is uncertain would be included in the calibration process. You might even decide to include the uncertainty in your input data (because our measurements are never perfect). However, in practice, if you try to do this with a complex conceptual model you might end up with far too many parameters (hundreds?) to stand any chance of achieving a successful calibration. Instead, it is necessary to choose a **subset of parameters** that (i) are poorly constrained (i.e. you don't already know what the value should be) and (ii) actually have an effect on the model's behaviour/output. After a bit of experimenting with manual calibration (or, more formally, using **sensitivity analysis**, which I won't cover here), you should be able to get a reasonable idea of which parameters might be suitable.\n", 65 | "\n", 66 | "You will also need to choose fixed values for any parameters you choose not to calibrate. This is best done using system knowledge (e.g. literature values) where possible, although this is often difficult. Beware of studies presenting complex conceptual models where only a few calibrated parameters have been reported. In such cases it is likely that large numbers of other parameters have been fixed arbitrarily in order to avoid over-parameterisation. This may be acceptable, but it should be done transparently and with some discussion of the implications.\n", 67 | "\n", 68 | "## 1.5. Auto-calibration\n", 69 | "\n", 70 | "Computers are ideally suited to performing laborious, repetitative tasks like the steps involved in model calibration. Based on the \"black box\" model illustrated above, we need an algorithm that can:\n", 71 | "\n", 72 | "1. **Choose values for $\\alpha$ and $\\beta$**. Based on the image above, it is obvious that whoever created the black box model is pretty certain that $\\alpha$ and $\\beta$ must lie between 1 and 12. In general, if we can narrow the parameter choices to within a particular range, the calibration process is likely to be more efficient than if the algorithm has to search the entire real number line.\n", 73 | "\n", 74 | " It is also important to consider *how* we sample from the possible parameter values: are all the numbers (including fractions) between 1 and 12 equally likely? Are $\\alpha$ and $\\beta$ integers? Do we have reason to believe that e.g. numbers towards the middle of the range are more likely than those at the extremes? In the former case, we might sample randomly from a uniform distribution between 1 and 12, whereas in the latter we might use something Gaussian-like to assign greater weight to the values around 6.\n", 75 | " \n", 76 | " This kind of reasoning leads to the concept of a **prior distribution** for each of our parameters. Defining priors is a fundamental part of Bayesian inference and it's something we'll return to later.
\n", 77 | " \n", 78 | "2. **Run the model with the chosen parameter values**. This step is usually pretty straightforward - it's just a question of telling your computer how to feed input data to your model and press the \"Run\" button. If your model is written in Python it's just a matter of linking your calibration code to your model code. Alternatively, if your model is available as a command line executable you should be able to call it from your Python calibration code using e.g. `subprocess.call()`.
\n", 79 | "\n", 80 | "3. **Evaluate \"goodness-of-fit\"**. The simplest form of manual calibration involves **visually comparing** the model output to the observed data to determine the performance of each parameter set. In most cases it is also useful to calculate some simple summary statistics, such as **simple least squares (SLS)** or the [**Nash-Sutcliffe efficiency (NS)**](https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient \"Wikipedia: Nash-Sutcliffe\") (the latter being especially common in hydrology).\n", 81 | "\n", 82 | " The range of different summary statistics (sometimes called **skill scores**) used by the modelling community is huge. Some have useful properties in specific cirumstances (this is a whole topic in itself), but it is important to understand that **all** skill scores involve making assumptions about your data (e.g. many assume independent, identically distributed Gaussian errors). Often the assumptions are transparent, but in some cases authors seem unaware of the implicit assumptions made by their chosen metric.\n", 83 | " \n", 84 | " Rather than discussing the pros and cons of a whole range of different skill scores, we will initially take a more formal statistical approach by explicitly setting out our assumptions and formulating an appropriate \"goodness-of-fit\" metric. This is called a **likelihood function**.\n", 85 | " \n", 86 | "## 2.1. The likelihood function\n", 87 | "\n", 88 | "Suppose we run the model illustrated above with a particular set of parameters and generate the data shown in red on the image below. The blue curve shows the observations we're trying to simulate.\n", 89 | "\n", 90 | "
![\"Simulated](\"https://github.com/JamesSample/enviro_mod_notes/blob/master/images/Output_And_Obs_Time_Series.png?raw=true\")
\n",
91 | "\n",
92 | "We want to define a metric that awards higher scores when the simulated (red) points are closer to the observed (blue) ones. However, we know that our model will never be perfect and we also know our observations have error associated with them too, so we don't expect the two curves to coincide exactly. How close we can reasonably expect them to be depends on the quality of our model and the accuracy of our measurements. If we expect both to be very good, we might decide to heavily penalise even small discrepancies between the model results and the observations; on the other hand, we might decide to be more lenient by penalising only very large errors.\n",
93 | "\n",
94 | "The simplest and most common way to formulate an error structure is to assume our model results should differ from the observed series by errors that are normally distributed with a mean, $\\mu_\\epsilon$, of 0 and some (unknown) standard deviation, $\\sigma_\\epsilon$. We can write this error structure as a **stochastic component** added to our **deterministic** black box model:\n",
95 | "\n",
96 | "$$y = f(x, \\theta) + \\mathcal{N}(0, \\sigma_\\epsilon)$$\n",
97 | "\n",
98 | "where $y$ is the observed data, $f$ is a (possibly very complex) function representing the **deterministic** part of our model, run using input data, $x$, and parameters, $\\theta$, and $\\mathcal{N}(0, \\sigma_\\epsilon)$ is the **stochastic** error term drawn from a normal distribution.\n",
99 | "\n",
100 | "Note that by setting the mean of the error distribution to zero we are assuming our model is **unbiased**. This is a sensible choice, because if you suspect your model to be biased you'd be better off working out why and fixing the problem (or using a different model), rather than building the bias into the error term by changing $\\mu_\\epsilon$.\n",
101 | "\n",
102 | "We can visualise this error structure by plotting a small Gaussian, $\\mathcal{N}(f(x, \\theta), \\sigma_\\epsilon)$ at each simulated point, as on the image below.\n",
103 | "\n",
104 | "![\"Gaussian](\"https://github.com/JamesSample/enviro_mod_notes/blob/master/images/Output_And_Obs_Time_Series_With_Errors.png?raw=true\")
\n",
105 | "\n",
106 | "For each pair of points, $S_i$, $M_i$, we can evaluate the probability density of the measured data, $M_i$, being drawn from a Gaussian centred on the simulated data, $S_i$, with standard deviation $\\sigma_\\epsilon$.\n",
107 | "\n",
108 | "![\"Gaussian](\"https://github.com/JamesSample/enviro_mod_notes/blob/master/images/Gauss_Error.png?raw=true\")
\n",
109 | "\n",
110 | "Looking at the above images, you can hopefully see that if $\\sigma_\\epsilon$ is small, we heavily penalise small differences between simulated and observed values. This is because the Gaussian error distribution is narrow and pointed, meaning that the probability density falls away quickly and so very low likelihood values are assigned when the $S_i$ and $M_i$ are far apart. A larger value of $\\sigma_\\epsilon$ gives a broader error distribution which penalises errors less severely.\n",
111 | "\n",
112 | "So far we have assumed that our model differs from the observed data by errors that are described by a Gaussian distribution with mean 0 and standard deviation $\\sigma_\\epsilon$. If we assume that this distribution stays the same for every time point, $t_i$, where $i = \\{1, .., n\\}$, we can calculate a probability density, $P(M_i)$, for each time step. This is simply the density associated with drawing $M_i$ from a Gaussian with mean $S_i$ and standard deviation $\\sigma_\\epsilon$, as illustrated on the plot above.\n",
113 | "\n",
114 | "If we further assume that each point in the time series is independent of the others, we can calculate the overall likelihood for the full dataset as the product of the densities for each individual point:\n",
115 | "\n",
116 | "$$L(M|\\theta) = \\prod_{i=1}^{n} P(M_i)$$\n",
117 | "\n",
118 | "where $L(M|\\theta)$ is the **likelihood** of the observations **given** the model parameters i.e. the probability that the model, run with a particular set of parameters, will simulate the observed dataset.\n",
119 | "\n",
120 | "If the parameters produce output that is similar to the observed data, the $S_i$ will be similar to the $M_i$ and so the probability densities, $P(M_i)$, will be large and the likelihood will be high. On the other hand, if the parameters produce poor output, the $P(M_i)$ will be small and the likelihood will be low. Higher values of the likelihood therefore correspond to \"better\" (more likely) parameter sets, *as long as the assumptions for the error structure are met*. As a recap, these assumptions are:\n",
121 | "\n",
122 | "1. The errors, $\\epsilon_i = (S_i - M_i)$, are normally distributed with mean zero and standard deviation $\\sigma_\\epsilon$.\n", 123 | "\n", 124 | "2. The errors are **independent** i.e. successive values of $\\epsilon_i$ are **not autocorrelated** and do not show **[heteroscedasticity](https://en.wikipedia.org/wiki/Heteroscedasticity \"Wikipedia: Heteroscedasticity\")**.\n", 125 | "\n", 126 | "In a later notebook we will look at some simple diagnostic plots to test these assumptions, and we'll also consider how to generalise the likelihood function to make it more widely applicable.\n", 127 | "\n", 128 | "As an aside, it's worth noting that the assumptions described above are identical to those for the **simple least squares (SLS)** skill score, so using SLS to assess goodness-of-fit is functionally identical to using the simple **independent and identically distributed (iid)** Gaussian likelihood function described above.\n", 129 | "\n", 130 | "## 2.2. Log likelihood\n", 131 | "\n", 132 | "Probability densities are always numbers less than 1, and the formula given above for the likelihood involves multiplying lots of them together. Likelihoods therefore become very tiny and it is possible for computers to run into numerical problems (\"[arithmetic underflow](https://en.wikipedia.org/wiki/Arithmetic_underflow \"Wikipedia: Arithmetic underflow\")\") when calculating them. For this reason, it is usually better to work with the **log likelihood**, which converts the product in the formula above into a sum of logs:\n", 133 | "\n", 134 | "$$LL(M|\\theta) = \\sum_{i=1}^{n} ln(P(M_i))$$\n", 135 | "\n", 136 | "where $LL$ is the log likelihood.\n", 137 | "\n", 138 | "Recall from the previous notebook that the equation for a Gaussian is:\n", 139 | "\n", 140 | "$$P(x)=\\frac{1}{{\\sigma \\sqrt {2\\pi } }}e^{{{ - ( {x - \\mu } )^2 /{2\\sigma ^2 }}}}$$\n", 141 | "\n", 142 | "We can re-write this for our error distribution at a single time point as:\n", 143 | "\n", 144 | "$$P(M_i)=\\frac{1}{{\\sigma_\\epsilon \\sqrt {2\\pi } }}e^{{{ - ( {M_i - S_i } )^2 /{2\\sigma_\\epsilon ^2 }}}}$$\n", 145 | "\n", 146 | "Taking natural logs and re-arranging, this can be written:\n", 147 | "\n", 148 | "$$P(M_i)= \\frac{-ln(2\\pi{\\sigma_\\epsilon}^2)}{2} - \\frac{(M_i - S_i)^2}{2{\\sigma_\\epsilon}^2}$$\n", 149 | "\n", 150 | "which we can sum over $n$ time points to give the overall log likelihood (assuming iid Gaussian errors):\n", 151 | "\n", 152 | "$$LL(D|\\theta) = \\frac{-nln(2\\pi{\\sigma_\\epsilon}^2)}{2} - \\sum_{i=1}^{n} \\frac{(M_i - S_i)^2}{2{\\sigma_\\epsilon}^2}$$\n", 153 | "\n", 154 | "## 2.3. Maximum likelihood estimation\n", 155 | "\n", 156 | "Before going any further, I think it's worth stressing that likelihoods are **not** an exclusively Bayesian concept - they are relevant in both Bayesian and Frequentist statistics. In many cases, Bayesians and Frequentists will use the same likelihood functions and get the same answers. If you're interested in the differences between Bayesian and Frequentist paradigms, I thoroughly recommend reading [this blog post](http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/) (and the follow-ups) by Jake Vanderplas, as well as his excellent [article on arXiv](http://arxiv.org/abs/1411.5018).\n", 157 | "\n", 158 | "Now that we have a likelihood function, we can develop an automated calibration procedure to identify the **\"best\"** parameter set, just like we were trying to do with the manual calibration procedure described in section 1.3. Note that as well as wanting to calibrate our black-box model parameters, $\\alpha$ and $\\beta$, constructing our error model has introduced one additional parameter: $\\sigma_\\epsilon$. Because we don't know the value of this, we'll simply include it as an additional variable in our optimisation routine. \n", 159 | "\n", 160 | "We want to find values for $\\alpha$, $\\beta$ and $\\sigma_\\epsilon$ that **maximise** the likelihood function. As an illustrative example, we'll assume a particular form for the \"true\" model, generate some synthetic data from it, and then use maximum likelihood estimation to try to identify the true parameters. If the method works here, perhaps it will also work in the real world where we never get to know the \"true\" parameter values. Let's suppose our black box model from above is actually just a simple linear model\n", 161 | "\n", 162 | "$$y = \\alpha x + \\beta$$" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": 2, 168 | "metadata": { 169 | "collapsed": true 170 | }, 171 | "outputs": [], 172 | "source": [ 173 | "# Generate some fake data, incorporating Gaussian noise\n", 174 | "alpha_true = 3\n", 175 | "beta_true = 7\n", 176 | "sigma_true = 2\n", 177 | "\n", 178 | "x = np.arange(0, 10, 0.1)\n", 179 | "y = alpha_true*x + beta_true + np.random.normal(loc=0, scale=sigma_true, size=len(x)) # The observed data" 180 | ] 181 | }, 182 | { 183 | "cell_type": "markdown", 184 | "metadata": {}, 185 | "source": [ 186 | "Next we'll define our log likelihood function. This function takes a vector of estimated values for $\\alpha$, $\\beta$ and $\\sigma_\\epsilon$ and estimates the likelihood of the data *given the parameters*, assuming that:\n", 187 | "\n", 188 | "$$y = \\alpha x + \\beta + \\mathcal{N}(0, \\sigma_\\epsilon)$$\n", 189 | "\n", 190 | "We want to **maximise** this function, but Scipy includes optimisation tools for **minimising**. Thereore we'll also define a function for the *negative log likelihood*. Minimising this is the same as maximising the log likelihood." 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 3, 196 | "metadata": { 197 | "collapsed": true 198 | }, 199 | "outputs": [], 200 | "source": [ 201 | "def log_likelihood(params, obs):\n", 202 | " \"\"\" Returns log likelihood assuming iid Gaussian errors.\n", 203 | " params is a vector of parameter estimates [alpha, beta, sigma]\n", 204 | " obs is the observed dataset we're trying to match\n", 205 | " \"\"\"\n", 206 | " # Get number of value pairs\n", 207 | " n = len(obs)\n", 208 | " \n", 209 | " # Extract parameter values\n", 210 | " alpha, beta, sigma = params\n", 211 | " \n", 212 | " # Calculate model results with these parameters\n", 213 | " sim = alpha*x + beta\n", 214 | " \n", 215 | " # Calculate log likelihood (see equations above)\n", 216 | " ll = -n*np.log(2*np.pi*sigma**2)/2 - np.sum(((obs - sim)**2)/(2*sigma**2))\n", 217 | " \n", 218 | " return ll\n", 219 | "\n", 220 | "def neg_log_likelihood(params, obs):\n", 221 | " \"\"\" Maximising the log likelihood is the same as minimising the negative log\n", 222 | " likelihood.\n", 223 | " \"\"\"\n", 224 | " return -log_likelihood(params, obs)" 225 | ] 226 | }, 227 | { 228 | "cell_type": "markdown", 229 | "metadata": {}, 230 | "source": [ 231 | "Finally, we import the optimiser from Scipy and make some starting guesses for $\\alpha$, $\\beta$ and $\\sigma_\\epsilon$. The optimiser does a pretty good job of recovering the \"true\" values for $\\alpha$ and $\\beta$, which are what we wanted to find." 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": 4, 237 | "metadata": { 238 | "collapsed": false 239 | }, 240 | "outputs": [ 241 | { 242 | "name": "stdout", 243 | "output_type": "stream", 244 | "text": [ 245 | "Optimization terminated successfully.\n", 246 | " Current function value: 219.518861\n", 247 | " Iterations: 130\n", 248 | " Function evaluations: 227\n", 249 | "\n", 250 | "\n", 251 | "Estimated alpha: 2.98. True value 3.00\n", 252 | "Estimated beta: 7.36. True value 7.00\n", 253 | "Estimated sigma: 2.17. True value 2.00\n" 254 | ] 255 | } 256 | ], 257 | "source": [ 258 | "from scipy import optimize\n", 259 | "\n", 260 | "# Guess some starting values for [alpha, beta, sigma]\n", 261 | "param_guess = [6., 6., 1.]\n", 262 | "\n", 263 | "# Run optimiser\n", 264 | "param_est = optimize.fmin(neg_log_likelihood, param_guess, args=(y,))\n", 265 | "\n", 266 | "# Print results\n", 267 | "print '\\n'\n", 268 | "print 'Estimated alpha: %.2f. True value %.2f' % (param_est[0], alpha_true) \n", 269 | "print 'Estimated beta: %.2f. True value %.2f' % (param_est[1], beta_true) \n", 270 | "print 'Estimated sigma: %.2f. True value %.2f' % (param_est[2], sigma_true) " 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": {}, 276 | "source": [ 277 | "So far so good, but although we've estimated the \"best\" parameter set by maximising the likelihood, we have no indication of how much **confidence** we should have in this result. If the likelihood function consists of a sharp, well-defined peak, the values for $\\alpha$, $\\beta$ and $\\sigma_\\epsilon$ may be tightly constrained (i.e. have narrow **confidence intervals**). On the other hand, the likelihood function may describe a broad, flat plateau with no clear maximum, or a complex hilly landscape with several widely separated maxima. In such cases a single \"point estimate\" for each parameter value may obscure the fact that a range of different parameter sets could produce essentially the same answer. The \"best\" parameter set is therefore not much use without some additional information describing the confidence interval (or **credible interval** to the Bayesians) around each estimated value.\n", 278 | "\n", 279 | "### Aside: A simpler way to calculate the log likelihood\n", 280 | "\n", 281 | "The `log_likelihood` function above explicitly calculates the result using the formula for a Gaussian. However, `scipy` has some convenience functions to make coding this kind of calculation easier. The following code does exactly the same thing, and is much less prone to typos." 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 5, 287 | "metadata": { 288 | "collapsed": false 289 | }, 290 | "outputs": [ 291 | { 292 | "name": "stdout", 293 | "output_type": "stream", 294 | "text": [ 295 | "-3496.39607138\n", 296 | "-3496.39607138\n" 297 | ] 298 | } 299 | ], 300 | "source": [ 301 | "from scipy.stats import norm\n", 302 | "\n", 303 | "def log_likelihood2(params, obs):\n", 304 | " \"\"\" An alternative way of coding the log likelihood.\n", 305 | " Returns log likelihood assuming iid Gaussian errors.\n", 306 | " params is a vector of parameter estimates [alpha, beta, sigma]\n", 307 | " obs is the observed dataset we're trying to match.\n", 308 | " \"\"\"\n", 309 | " # Get number of value pairs\n", 310 | " n = len(obs)\n", 311 | " \n", 312 | " # Extract parameter values\n", 313 | " alpha, beta, sigma = params\n", 314 | " \n", 315 | " # Calculate model results with these parameters\n", 316 | " sim = alpha*x + beta\n", 317 | " \n", 318 | " # Calculate log likelihood\n", 319 | " ll = np.sum(norm(sim, sigma).logpdf(obs))\n", 320 | " \n", 321 | " return ll\n", 322 | "\n", 323 | "# Quick check that results from both functions are the same\n", 324 | "# Generate fake obs assuming alpha=6 and beta=3\n", 325 | "x = np.arange(0, 10, 0.1)\n", 326 | "obs = 6*x+3\n", 327 | "\n", 328 | "# Get log likelihood for alpha=3 and beta=4, if sigma=2\n", 329 | "print log_likelihood([3, 4, 2], obs) \n", 330 | "print log_likelihood2([3, 4, 2], obs)" 331 | ] 332 | }, 333 | { 334 | "cell_type": "markdown", 335 | "metadata": {}, 336 | "source": [ 337 | "## 3. Summary\n", 338 | "\n", 339 | "* Most models of real world environmental systems are complex enough to need **calibrating**, because we rarely have sufficiently detailed information to constrain all the parameters.
\n", 340 | "\n", 341 | "* Calibration can be performed **manually**, but this is time consuming (although useful!) and may be impossible for models with lots of parameters.
\n", 342 | "\n", 343 | "* **Auto-calibration** procedures require us to:\n", 344 | " 1. Define rules for how to pick parameter values (based on **prior knowledge**?), and
\n", 345 | " 2. Devise a method for **evaluating model performance**.
\n", 346 | " \n", 347 | "* A variety of summary statistics and **skill scores** are commonly used, but the underlying assumptions for these may not be obvious.
\n", 348 | "\n", 349 | "* Formal **likelihoods** involve describing the difference between simulated and observed model output in terms of probabilities. To do this, we need to devise an appropriate **error structure** which is used as the basis for assessing model performance. This forces us to think about the assumptions being made, but we need to remember to actually *go back and check them* (more on this in a later notebook).
\n", 350 | "\n", 351 | "* **Log** likelihoods are used to avoid numeric errors.
\n", 352 | "\n", 353 | "* Once we have a likelihood function, we can use an **optimiser** to identify the most likely parameter set (although this can be difficult in high-dimensional parameter spaces). Note also that this method *only* finds the **\"best\"** parameter set - it gives no indication of how much **confidence** we should have in the values identified. This is a major limitation and one of the main motivations for everything that follows." 354 | ] 355 | } 356 | ], 357 | "metadata": { 358 | "kernelspec": { 359 | "display_name": "Python 2", 360 | "language": "python", 361 | "name": "python2" 362 | }, 363 | "language_info": { 364 | "codemirror_mode": { 365 | "name": "ipython", 366 | "version": 2 367 | }, 368 | "file_extension": ".py", 369 | "mimetype": "text/x-python", 370 | "name": "python", 371 | "nbconvert_exporter": "python", 372 | "pygments_lexer": "ipython2", 373 | "version": "2.7.10" 374 | } 375 | }, 376 | "nbformat": 4, 377 | "nbformat_minor": 0 378 | } 379 | -------------------------------------------------------------------------------- /data/Tarland_Flow_And_Met_Data.csv: -------------------------------------------------------------------------------- 1 | Date,Q_Cumecs,Rainfall_mm,PET_mm 2 | 2000-01-01,0.7764,0.1,0.72 3 | 2000-01-02,0.7808,1.0,0.72 4 | 2000-01-03,0.7302,1.1,0.72 5 | 2000-01-04,0.6917,1.38,0.72 6 | 2000-01-05,0.6984,3.62,0.72 7 | 2000-01-06,0.664,0.08,0.72 8 | 2000-01-07,0.6341,2.98,0.72 9 | 2000-01-08,0.6042,0.8,0.72 10 | 2000-01-09,0.5464,0.12,0.72 11 | 2000-01-10,0.5289,0.08,0.72 12 | 2000-01-11,0.5136,0.02,0.72 13 | 2000-01-12,0.4872,0.22,0.72 14 | 2000-01-13,0.4924,6.98,0.72 15 | 2000-01-14,0.5135,0.04,0.72 16 | 2000-01-15,0.5077,0.02,0.72 17 | 2000-01-16,0.5662,0.3,0.72 18 | 2000-01-17,0.516,0.08,0.72 19 | 2000-01-18,0.4764,0.56,0.72 20 | 2000-01-19,0.4524,0.14,0.72 21 | 2000-01-20,0.4295,0.18,0.72 22 | 2000-01-21,0.4406,5.76,0.72 23 | 2000-01-22,0.6156,2.78,0.72 24 | 2000-01-23,0.6079,1.58,0.72 25 | 2000-01-24,0.5751,0.0,0.72 26 | 2000-01-25,0.4825,0.0,0.72 27 | 2000-01-26,0.4317,0.08,0.72 28 | 2000-01-27,0.4239,0.2,0.72 29 | 2000-01-28,0.4446,2.82,0.72 30 | 2000-01-29,0.4276,1.72,0.72 31 | 2000-01-30,0.5051,4.0,0.72 32 | 2000-01-31,0.702,8.24,0.72 33 | 2000-02-01,0.6511,0.32,0.88 34 | 2000-02-02,0.5093,0.52,0.88 35 | 2000-02-03,0.4673,0.06,0.88 36 | 2000-02-04,0.4528,0.02,0.88 37 | 2000-02-05,0.4326,0.0,0.88 38 | 2000-02-06,0.4241,1.72,0.88 39 | 2000-02-07,0.405,1.28,0.88 40 | 2000-02-08,0.4133,1.76,0.88 41 | 2000-02-09,0.3693,0.9,0.88 42 | 2000-02-10,0.3369,0.3,0.88 43 | 2000-02-11,0.3316,1.12,0.88 44 | 2000-02-12,0.3178,0.74,0.88 45 | 2000-02-13,0.2983,0.28,0.88 46 | 2000-02-14,0.3054,0.14,0.88 47 | 2000-02-15,0.2972,0.58,0.88 48 | 2000-02-16,0.2685,0.68,0.88 49 | 2000-02-17,0.2663,1.46,0.88 50 | 2000-02-18,0.2918,3.06,0.88 51 | 2000-02-19,0.3092,0.54,0.88 52 | 2000-02-20,0.2897,0.96,0.88 53 | 2000-02-21,0.2901,0.0,0.88 54 | 2000-02-22,0.2697,0.0,0.88 55 | 2000-02-23,0.2631,0.12,0.88 56 | 2000-02-24,0.2411,0.16,0.88 57 | 2000-02-25,0.2191,0.7,0.88 58 | 2000-02-26,0.2207,0.64,0.88 59 | 2000-02-27,0.2315,1.78,0.88 60 | 2000-02-28,0.2585,0.72,0.88 61 | 2000-02-29,0.2277,0.62,0.88 62 | 2000-03-01,0.2103,1.7,1.29 63 | 2000-03-02,0.2278,1.78,1.29 64 | 2000-03-03,0.2234,1.92,1.29 65 | 2000-03-04,0.2545,0.66,1.29 66 | 2000-03-05,0.2629,0.06,1.29 67 | 2000-03-06,0.2461,0.14,1.29 68 | 2000-03-07,0.212,0.16,1.29 69 | 2000-03-08,0.1854,0.06,1.29 70 | 2000-03-09,0.1901,3.32,1.29 71 | 2000-03-10,0.2192,0.08,1.29 72 | 2000-03-11,0.1847,0.0,1.29 73 | 2000-03-12,0.1802,0.32,1.29 74 | 2000-03-13,0.178,1.48,1.29 75 | 2000-03-14,0.1774,1.28,1.29 76 | 2000-03-15,0.1664,0.0,1.29 77 | 2000-03-16,0.1537,0.0,1.29 78 | 2000-03-17,0.1473,0.06,1.29 79 | 2000-03-18,0.1374,0.0,1.29 80 | 2000-03-19,0.1354,0.0,1.29 81 | 2000-03-20,0.1352,0.0,1.29 82 | 2000-03-21,0.131,0.32,1.29 83 | 2000-03-22,0.1242,0.0,1.29 84 | 2000-03-23,0.2965,22.46,1.29 85 | 2000-03-24,0.4277,0.96,1.29 86 | 2000-03-25,0.2668,1.66,1.29 87 | 2000-03-26,0.2246,1.36,1.29 88 | 2000-03-27,0.2144,0.14,1.29 89 | 2000-03-28,0.1911,0.28,1.29 90 | 2000-03-29,0.182,0.38,1.29 91 | 2000-03-30,0.1772,0.0,1.29 92 | 2000-03-31,0.1882,5.72,1.29 93 | 2000-04-01,0.3744,18.08,1.46 94 | 2000-04-02,0.6638,14.1,1.46 95 | 2000-04-03,0.591,4.0,1.46 96 | 2000-04-04,0.5589,0.22,1.46 97 | 2000-04-05,0.7535,0.0,1.46 98 | 2000-04-06,0.6907,0.0,1.46 99 | 2000-04-07,0.586,0.36,1.46 100 | 2000-04-08,0.5419,0.02,1.46 101 | 2000-04-09,0.4657,0.0,1.46 102 | 2000-04-10,0.4321,1.76,1.46 103 | 2000-04-11,0.6058,16.76,1.46 104 | 2000-04-12,2.513,16.6,1.46 105 | 2000-04-13,2.3055,6.68,1.46 106 | 2000-04-14,1.4014,1.28,1.46 107 | 2000-04-15,1.0424,2.86,1.46 108 | 2000-04-16,0.9193,0.26,1.46 109 | 2000-04-17,0.8909,5.82,1.46 110 | 2000-04-18,0.8251,0.64,1.46 111 | 2000-04-19,0.7414,3.14,1.46 112 | 2000-04-20,0.6894,2.1,1.46 113 | 2000-04-21,0.6304,1.78,1.46 114 | 2000-04-22,0.685,4.98,1.46 115 | 2000-04-23,0.9115,12.56,1.46 116 | 2000-04-24,1.7768,19.98,1.46 117 | 2000-04-25,4.3537,26.16,1.46 118 | 2000-04-26,9.9592,36.32,1.46 119 | 2000-04-27,3.8934,0.2,1.46 120 | 2000-04-28,2.6645,0.14,1.46 121 | 2000-04-29,1.9162,0.0,1.46 122 | 2000-04-30,1.5635,0.0,1.46 123 | 2000-05-01,1.3543,0.0,2.58 124 | 2000-05-02,1.1097,0.1,2.58 125 | 2000-05-03,0.9983,0.0,2.58 126 | 2000-05-04,0.8895,0.0,2.58 127 | 2000-05-05,0.8063,0.0,2.58 128 | 2000-05-06,0.7389,0.0,2.58 129 | 2000-05-07,0.6778,0.0,2.58 130 | 2000-05-08,0.6239,0.0,2.58 131 | 2000-05-09,0.5755,0.0,2.58 132 | 2000-05-10,0.5418,0.0,2.58 133 | 2000-05-11,0.5055,0.0,2.58 134 | 2000-05-12,0.4929,0.0,2.58 135 | 2000-05-13,,0.0,2.58 136 | 2000-05-14,,0.0,2.58 137 | 2000-05-15,,0.88,2.58 138 | 2000-05-16,,6.56,2.58 139 | 2000-05-17,,2.32,2.58 140 | 2000-05-18,,7.0,2.58 141 | 2000-05-19,,1.04,2.58 142 | 2000-05-20,,0.92,2.58 143 | 2000-05-21,,4.06,2.58 144 | 2000-05-22,,1.44,2.58 145 | 2000-05-23,,1.38,2.58 146 | 2000-05-24,,1.5,2.58 147 | 2000-05-25,,4.52,2.58 148 | 2000-05-26,,9.4,2.58 149 | 2000-05-27,,8.12,2.58 150 | 2000-05-28,,2.26,2.58 151 | 2000-05-29,,1.22,2.58 152 | 2000-05-30,,7.72,2.58 153 | 2000-05-31,0.3542,4.46,2.58 154 | 2000-06-01,0.3081,0.6,2.73 155 | 2000-06-02,0.2622,0.0,2.73 156 | 2000-06-03,0.2443,0.74,2.73 157 | 2000-06-04,0.2461,2.1,2.73 158 | 2000-06-05,0.2354,0.28,2.73 159 | 2000-06-06,0.2518,6.5,2.73 160 | 2000-06-07,0.2233,1.2,2.73 161 | 2000-06-08,0.2349,0.96,2.73 162 | 2000-06-09,0.229,5.48,2.73 163 | 2000-06-10,0.209,1.2,2.73 164 | 2000-06-11,0.1898,0.12,2.73 165 | 2000-06-12,0.1802,0.92,2.73 166 | 2000-06-13,0.1567,0.0,2.73 167 | 2000-06-14,0.1501,0.0,2.73 168 | 2000-06-15,0.1395,0.04,2.73 169 | 2000-06-16,0.1357,0.0,2.73 170 | 2000-06-17,0.1373,0.0,2.73 171 | 2000-06-18,0.1317,0.18,2.73 172 | 2000-06-19,0.1281,0.06,2.73 173 | 2000-06-20,0.123,0.02,2.73 174 | 2000-06-21,0.1238,2.26,2.73 175 | 2000-06-22,0.339,13.52,2.73 176 | 2000-06-23,0.1827,0.26,2.73 177 | 2000-06-24,0.1481,1.18,2.73 178 | 2000-06-25,0.127,0.0,2.73 179 | 2000-06-26,0.1146,0.08,2.73 180 | 2000-06-27,0.1101,0.0,2.73 181 | 2000-06-28,0.1001,0.0,2.73 182 | 2000-06-29,0.0959,0.64,2.73 183 | 2000-06-30,0.0966,0.1,2.73 184 | 2000-07-01,0.0966,0.18,2.53 185 | 2000-07-02,0.0844,0.0,2.53 186 | 2000-07-03,0.0781,0.0,2.53 187 | 2000-07-04,0.075,0.64,2.53 188 | 2000-07-05,0.0675,0.02,2.53 189 | 2000-07-06,0.0639,0.0,2.53 190 | 2000-07-07,0.074,0.48,2.53 191 | 2000-07-08,0.0934,2.38,2.53 192 | 2000-07-09,0.3352,15.3,2.53 193 | 2000-07-10,0.2186,0.54,2.53 194 | 2000-07-11,0.1432,0.34,2.53 195 | 2000-07-12,0.1188,0.0,2.53 196 | 2000-07-13,0.1045,0.76,2.53 197 | 2000-07-14,0.0938,0.56,2.53 198 | 2000-07-15,0.0915,0.0,2.53 199 | 2000-07-16,0.0904,0.0,2.53 200 | 2000-07-17,0.0896,0.0,2.53 201 | 2000-07-18,0.0893,0.0,2.53 202 | 2000-07-19,0.0883,0.0,2.53 203 | 2000-07-20,0.0891,0.0,2.53 204 | 2000-07-21,0.0919,0.12,2.53 205 | 2000-07-22,0.0896,0.0,2.53 206 | 2000-07-23,0.0891,0.02,2.53 207 | 2000-07-24,0.0873,0.12,2.53 208 | 2000-07-25,0.089,1.56,2.53 209 | 2000-07-26,0.0885,0.0,2.53 210 | 2000-07-27,0.0912,1.02,2.53 211 | 2000-07-28,0.1175,8.78,2.53 212 | 2000-07-29,0.0951,2.42,2.53 213 | 2000-07-30,0.179,8.82,2.53 214 | 2000-07-31,0.1079,1.2,2.53 215 | 2000-08-01,0.1005,3.12,2.15 216 | 2000-08-02,0.0932,0.16,2.15 217 | 2000-08-03,0.0927,0.18,2.15 218 | 2000-08-04,0.0912,0.0,2.15 219 | 2000-08-05,0.0911,0.16,2.15 220 | 2000-08-06,0.085,0.0,2.15 221 | 2000-08-07,0.0848,4.62,2.15 222 | 2000-08-08,0.143,7.84,2.15 223 | 2000-08-09,0.1836,4.9,2.15 224 | 2000-08-10,0.0989,0.0,2.15 225 | 2000-08-11,0.0949,0.0,2.15 226 | 2000-08-12,0.0951,0.02,2.15 227 | 2000-08-13,0.138,16.82,2.15 228 | 2000-08-14,0.2817,0.64,2.15 229 | 2000-08-15,0.1616,4.48,2.15 230 | 2000-08-16,0.1304,3.0,2.15 231 | 2000-08-17,0.1438,5.98,2.15 232 | 2000-08-18,0.1347,5.72,2.15 233 | 2000-08-19,0.9449,18.62,2.15 234 | 2000-08-20,0.3876,1.18,2.15 235 | 2000-08-21,0.5615,6.62,2.15 236 | 2000-08-22,0.3668,0.56,2.15 237 | 2000-08-23,0.2452,0.0,2.15 238 | 2000-08-24,0.1859,0.0,2.15 239 | 2000-08-25,0.1613,0.32,2.15 240 | 2000-08-26,0.1688,6.88,2.15 241 | 2000-08-27,0.2529,4.38,2.15 242 | 2000-08-28,0.2392,0.4,2.15 243 | 2000-08-29,0.1777,0.0,2.15 244 | 2000-08-30,0.1527,0.54,2.15 245 | 2000-08-31,0.1722,7.5,2.15 246 | 2000-09-01,0.4533,10.7,1.58 247 | 2000-09-02,0.3761,1.04,1.58 248 | 2000-09-03,0.258,0.0,1.58 249 | 2000-09-04,0.2214,2.6,1.58 250 | 2000-09-05,0.2209,7.64,1.58 251 | 2000-09-06,0.5447,4.74,1.58 252 | 2000-09-07,0.5199,1.1,1.58 253 | 2000-09-08,0.3061,0.0,1.58 254 | 2000-09-09,0.235,0.0,1.58 255 | 2000-09-10,0.2183,4.34,1.58 256 | 2000-09-11,0.6677,19.02,1.58 257 | 2000-09-12,0.5688,0.84,1.58 258 | 2000-09-13,0.4213,0.18,1.58 259 | 2000-09-14,0.3632,0.96,1.58 260 | 2000-09-15,0.3177,0.0,1.58 261 | 2000-09-16,0.2849,0.02,1.58 262 | 2000-09-17,0.2938,7.34,1.58 263 | 2000-09-18,0.3509,0.2,1.58 264 | 2000-09-19,0.8133,26.32,1.58 265 | 2000-09-20,1.461,3.22,1.58 266 | 2000-09-21,0.7572,7.16,1.58 267 | 2000-09-22,0.7596,1.74,1.58 268 | 2000-09-23,0.5909,0.5,1.58 269 | 2000-09-24,0.5281,4.02,1.58 270 | 2000-09-25,0.6647,0.96,1.58 271 | 2000-09-26,0.7447,10.44,1.58 272 | 2000-09-27,0.8001,2.76,1.58 273 | 2000-09-28,0.6086,0.1,1.58 274 | 2000-09-29,0.5194,1.52,1.58 275 | 2000-09-30,0.4917,0.08,1.58 276 | 2000-10-01,0.4666,0.0,0.99 277 | 2000-10-02,0.4111,0.18,0.99 278 | 2000-10-03,0.5888,13.58,0.99 279 | 2000-10-04,0.7438,0.0,0.99 280 | 2000-10-05,0.508,0.62,0.99 281 | 2000-10-06,0.4422,0.02,0.99 282 | 2000-10-07,0.5171,4.32,0.99 283 | 2000-10-08,0.4728,0.06,0.99 284 | 2000-10-09,2.8402,42.52,0.99 285 | 2000-10-10,6.5368,34.32,0.99 286 | 2000-10-11,5.3674,23.54,0.99 287 | 2000-10-12,4.679,5.9,0.99 288 | 2000-10-13,2.787,2.48,0.99 289 | 2000-10-14,2.0051,0.06,0.99 290 | 2000-10-15,1.6062,0.0,0.99 291 | 2000-10-16,1.3456,1.98,0.99 292 | 2000-10-17,1.1056,1.34,0.99 293 | 2000-10-18,0.9747,0.02,0.99 294 | 2000-10-19,0.8573,0.0,0.99 295 | 2000-10-20,0.8093,2.7,0.99 296 | 2000-10-21,0.7292,0.32,0.99 297 | 2000-10-22,0.6924,0.88,0.99 298 | 2000-10-23,0.6561,0.1,0.99 299 | 2000-10-24,0.6279,4.7,0.99 300 | 2000-10-25,0.6172,0.0,0.99 301 | 2000-10-26,0.5643,0.82,0.99 302 | 2000-10-27,0.5456,2.04,0.99 303 | 2000-10-28,1.1096,16.38,0.99 304 | 2000-10-29,0.8315,0.38,0.99 305 | 2000-10-30,0.6637,1.54,0.99 306 | 2000-10-31,0.5892,0.08,0.99 307 | 2000-11-01,0.5388,0.32,0.45 308 | 2000-11-02,0.5143,1.12,0.45 309 | 2000-11-03,0.4966,1.16,0.45 310 | 2000-11-04,0.4631,0.36,0.45 311 | 2000-11-05,0.4543,4.92,0.45 312 | 2000-11-06,3.9427,42.12,0.45 313 | 2000-11-07,6.8497,24.18,0.45 314 | 2000-11-08,7.29,12.58,0.45 315 | 2000-11-09,3.6255,0.74,0.45 316 | 2000-11-10,2.5058,1.14,0.45 317 | 2000-11-11,2.0032,7.62,0.45 318 | 2000-11-12,1.8523,0.26,0.45 319 | 2000-11-13,1.4433,0.42,0.45 320 | 2000-11-14,1.1778,0.18,0.45 321 | 2000-11-15,1.0897,5.9,0.45 322 | 2000-11-16,1.1593,0.38,0.45 323 | 2000-11-17,0.9637,1.16,0.45 324 | 2000-11-18,0.9478,1.8,0.45 325 | 2000-11-19,2.6163,24.7,0.45 326 | 2000-11-20,2.2421,3.44,0.45 327 | 2000-11-21,2.4034,18.32,0.45 328 | 2000-11-22,3.2072,5.06,0.45 329 | 2000-11-23,1.7122,0.14,0.45 330 | 2000-11-24,1.4029,3.4,0.45 331 | 2000-11-25,1.3505,6.26,0.45 332 | 2000-11-26,1.3756,1.06,0.45 333 | 2000-11-27,1.1297,0.54,0.45 334 | 2000-11-28,1.0907,3.32,0.45 335 | 2000-11-29,1.0474,1.92,0.45 336 | 2000-11-30,0.9397,6.62,0.45 337 | 2000-12-01,1.0417,3.2,0.21 338 | 2000-12-02,0.9579,0.4,0.21 339 | 2000-12-03,0.8396,1.84,0.21 340 | 2000-12-04,0.8321,4.68,0.21 341 | 2000-12-05,0.837,3.34,0.21 342 | 2000-12-06,0.7671,0.3,0.21 343 | 2000-12-07,0.7318,13.06,0.21 344 | 2000-12-08,2.571,17.12,0.21 345 | 2000-12-09,1.3195,1.24,0.21 346 | 2000-12-10,1.0506,2.04,0.21 347 | 2000-12-11,0.9704,0.26,0.21 348 | 2000-12-12,1.0924,6.92,0.21 349 | 2000-12-13,1.0073,2.2,0.21 350 | 2000-12-14,1.4693,11.44,0.21 351 | 2000-12-15,1.1234,1.94,0.21 352 | 2000-12-16,0.9379,0.08,0.21 353 | 2000-12-17,0.8424,0.2,0.21 354 | 2000-12-18,0.7949,0.24,0.21 355 | 2000-12-19,0.9744,7.16,0.21 356 | 2000-12-20,1.1195,0.84,0.21 357 | 2000-12-21,0.899,0.74,0.21 358 | 2000-12-22,0.8247,1.98,0.21 359 | 2000-12-23,0.9786,1.38,0.21 360 | 2000-12-24,0.8477,2.0,0.21 361 | 2000-12-25,0.7555,1.12,0.21 362 | 2000-12-26,0.7062,0.22,0.21 363 | 2000-12-27,0.6447,0.3,0.21 364 | 2000-12-28,0.6197,3.98,0.21 365 | 2000-12-29,0.6125,4.4,0.21 366 | 2000-12-30,0.5929,0.1,0.21 367 | 2000-12-31,0.6009,7.9,0.21 368 | 2001-01-01,0.9346,4.6,0.24 369 | 2001-01-02,1.3312,0.24,0.24 370 | 2001-01-03,0.9122,3.2,0.24 371 | 2001-01-04,0.9771,0.18,0.24 372 | 2001-01-05,0.7888,1.84,0.24 373 | 2001-01-06,0.6909,0.02,0.24 374 | 2001-01-07,0.6381,0.34,0.24 375 | 2001-01-08,0.6441,0.18,0.24 376 | 2001-01-09,0.5797,0.84,0.24 377 | 2001-01-10,0.5337,0.6,0.24 378 | 2001-01-11,0.5118,0.0,0.24 379 | 2001-01-12,0.5119,0.0,0.24 380 | 2001-01-13,0.4831,0.0,0.24 381 | 2001-01-14,0.4578,0.0,0.24 382 | 2001-01-15,0.4458,0.0,0.24 383 | 2001-01-16,0.422,0.0,0.24 384 | 2001-01-17,0.407,0.06,0.24 385 | 2001-01-18,0.4009,1.04,0.24 386 | 2001-01-19,0.3922,1.02,0.24 387 | 2001-01-20,0.3906,0.46,0.24 388 | 2001-01-21,0.403,3.8,0.24 389 | 2001-01-22,2.5604,30.54,0.24 390 | 2001-01-23,2.0949,10.86,0.24 391 | 2001-01-24,1.2949,0.42,0.24 392 | 2001-01-25,0.9125,0.88,0.24 393 | 2001-01-26,0.8051,0.24,0.24 394 | 2001-01-27,0.7146,0.0,0.24 395 | 2001-01-28,0.6353,0.0,0.24 396 | 2001-01-29,0.5845,0.0,0.24 397 | 2001-01-30,0.5506,0.82,0.24 398 | 2001-01-31,0.5242,0.04,0.24 399 | 2001-02-01,0.5648,7.6,0.53 400 | 2001-02-02,1.0314,10.5,0.53 401 | 2001-02-03,1.1823,6.06,0.53 402 | 2001-02-04,0.796,6.06,0.53 403 | 2001-02-05,0.7224,6.9,0.53 404 | 2001-02-06,0.7868,8.5,0.53 405 | 2001-02-07,0.9111,0.12,0.53 406 | 2001-02-08,0.7435,0.0,0.53 407 | 2001-02-09,0.6587,0.0,0.53 408 | 2001-02-10,0.7698,3.86,0.53 409 | 2001-02-11,2.2023,0.72,0.53 410 | 2001-02-12,2.1178,0.0,0.53 411 | 2001-02-13,1.4672,0.0,0.53 412 | 2001-02-14,1.2269,0.0,0.53 413 | 2001-02-15,1.2365,0.0,0.53 414 | 2001-02-16,1.0617,0.04,0.53 415 | 2001-02-17,1.0961,0.0,0.53 416 | 2001-02-18,0.9824,0.08,0.53 417 | 2001-02-19,1.073,0.0,0.53 418 | 2001-02-20,1.2734,0.04,0.53 419 | 2001-02-21,1.1817,0.22,0.53 420 | 2001-02-22,1.0273,1.64,0.53 421 | 2001-02-23,0.9242,3.18,0.53 422 | 2001-02-24,0.815,2.18,0.53 423 | 2001-02-25,0.7614,1.32,0.53 424 | 2001-02-26,0.6961,8.26,0.53 425 | 2001-02-27,0.7199,1.02,0.53 426 | 2001-02-28,0.6487,0.48,0.53 427 | 2001-03-01,0.6034,0.08,0.78 428 | 2001-03-02,0.5752,0.04,0.78 429 | 2001-03-03,0.5453,0.1,0.78 430 | 2001-03-04,0.5259,0.12,0.78 431 | 2001-03-05,0.622,0.0,0.78 432 | 2001-03-06,0.6804,4.26,0.78 433 | 2001-03-07,1.1225,0.54,0.78 434 | 2001-03-08,1.0529,0.32,0.78 435 | 2001-03-09,0.8631,1.46,0.78 436 | 2001-03-10,0.8072,0.08,0.78 437 | 2001-03-11,0.9221,19.3,0.78 438 | 2001-03-12,2.3791,16.92,0.78 439 | 2001-03-13,1.6517,2.42,0.78 440 | 2001-03-14,1.2091,0.46,0.78 441 | 2001-03-15,0.9894,0.72,0.78 442 | 2001-03-16,0.8887,1.48,0.78 443 | 2001-03-17,0.833,1.12,0.78 444 | 2001-03-18,0.7977,1.08,0.78 445 | 2001-03-19,0.7402,0.14,0.78 446 | 2001-03-20,0.7,1.44,0.78 447 | 2001-03-21,0.7142,3.9,0.78 448 | 2001-03-22,0.7342,3.2,0.78 449 | 2001-03-23,0.8753,10.0,0.78 450 | 2001-03-24,1.0436,5.36,0.78 451 | 2001-03-25,0.9067,1.88,0.78 452 | 2001-03-26,0.7652,0.28,0.78 453 | 2001-03-27,0.8532,9.24,0.78 454 | 2001-03-28,1.9129,17.64,0.78 455 | 2001-03-29,1.5808,2.5,0.78 456 | 2001-03-30,1.1812,0.96,0.78 457 | 2001-03-31,1.0585,0.0,0.78 458 | 2001-04-01,0.9393,0.02,1.76 459 | 2001-04-02,0.9161,2.94,1.76 460 | 2001-04-03,0.8268,0.38,1.76 461 | 2001-04-04,0.7637,0.1,1.76 462 | 2001-04-05,0.7213,0.0,1.76 463 | 2001-04-06,0.6967,2.98,1.76 464 | 2001-04-07,0.6562,0.06,1.76 465 | 2001-04-08,0.6084,0.0,1.76 466 | 2001-04-09,0.5866,1.04,1.76 467 | 2001-04-10,0.5522,1.2,1.76 468 | 2001-04-11,0.5307,0.1,1.76 469 | 2001-04-12,0.4971,0.0,1.76 470 | 2001-04-13,0.4815,0.18,1.76 471 | 2001-04-14,0.4668,1.68,1.76 472 | 2001-04-15,0.4477,1.72,1.76 473 | 2001-04-16,0.4234,0.12,1.76 474 | 2001-04-17,0.4239,4.04,1.76 475 | 2001-04-18,,8.26,1.76 476 | 2001-04-19,,4.18,1.76 477 | 2001-04-20,,0.66,1.76 478 | 2001-04-21,,1.24,1.76 479 | 2001-04-22,,2.1,1.76 480 | 2001-04-23,,7.38,1.76 481 | 2001-04-24,,1.9,1.76 482 | 2001-04-25,,2.62,1.76 483 | 2001-04-26,,0.1,1.76 484 | 2001-04-27,,0.34,1.76 485 | 2001-04-28,,4.92,1.76 486 | 2001-04-29,,7.04,1.76 487 | 2001-04-30,,0.5,1.76 488 | 2001-05-01,,0.0,2.54 489 | 2001-05-02,,0.0,2.54 490 | 2001-05-03,,5.26,2.54 491 | 2001-05-04,,0.16,2.54 492 | 2001-05-05,,0.0,2.54 493 | 2001-05-06,,0.0,2.54 494 | 2001-05-07,,0.0,2.54 495 | 2001-05-08,,0.0,2.54 496 | 2001-05-09,,0.0,2.54 497 | 2001-05-10,,0.0,2.54 498 | 2001-05-11,,0.0,2.54 499 | 2001-05-12,,0.0,2.54 500 | 2001-05-13,,0.0,2.54 501 | 2001-05-14,,0.0,2.54 502 | 2001-05-15,,4.94,2.54 503 | 2001-05-16,0.3341,6.36,2.54 504 | 2001-05-17,0.3032,2.56,2.54 505 | 2001-05-18,0.2613,1.14,2.54 506 | 2001-05-19,0.2409,0.1,2.54 507 | 2001-05-20,0.2256,0.0,2.54 508 | 2001-05-21,0.2238,0.0,2.54 509 | 2001-05-22,0.2081,0.0,2.54 510 | 2001-05-23,0.1968,0.0,2.54 511 | 2001-05-24,0.1888,0.0,2.54 512 | 2001-05-25,0.1875,0.0,2.54 513 | 2001-05-26,0.1746,0.0,2.54 514 | 2001-05-27,0.1724,0.08,2.54 515 | 2001-05-28,0.1622,0.16,2.54 516 | 2001-05-29,0.1487,0.04,2.54 517 | 2001-05-30,0.1564,2.56,2.54 518 | 2001-05-31,0.1534,0.92,2.54 519 | 2001-06-01,0.1623,4.98,2.49 520 | 2001-06-02,0.1618,2.0,2.49 521 | 2001-06-03,0.1475,0.0,2.49 522 | 2001-06-04,0.1342,0.36,2.49 523 | 2001-06-05,0.1363,0.26,2.49 524 | 2001-06-06,0.1478,3.3,2.49 525 | 2001-06-07,0.1589,3.22,2.49 526 | 2001-06-08,0.1491,4.62,2.49 527 | 2001-06-09,0.1476,2.34,2.49 528 | 2001-06-10,0.1319,0.04,2.49 529 | 2001-06-11,0.124,0.0,2.49 530 | 2001-06-12,0.1179,0.0,2.49 531 | 2001-06-13,0.117,0.96,2.49 532 | 2001-06-14,0.1146,2.38,2.49 533 | 2001-06-15,0.1367,5.26,2.49 534 | 2001-06-16,0.1183,0.38,2.49 535 | 2001-06-17,0.1097,0.2,2.49 536 | 2001-06-18,0.1007,1.94,2.49 537 | 2001-06-19,0.1056,0.6,2.49 538 | 2001-06-20,0.0938,0.02,2.49 539 | 2001-06-21,0.0923,0.0,2.49 540 | 2001-06-22,0.0922,0.0,2.49 541 | 2001-06-23,0.0921,0.0,2.49 542 | 2001-06-24,0.0914,0.0,2.49 543 | 2001-06-25,0.0906,0.3,2.49 544 | 2001-06-26,0.0966,7.52,2.49 545 | 2001-06-27,0.0925,0.44,2.49 546 | 2001-06-28,0.0921,1.0,2.49 547 | 2001-06-29,0.0907,0.06,2.49 548 | 2001-06-30,0.0864,0.0,2.49 549 | 2001-07-01,0.0852,0.0,2.44 550 | 2001-07-02,0.0825,2.8,2.44 551 | 2001-07-03,0.0805,0.26,2.44 552 | 2001-07-04,0.0792,0.06,2.44 553 | 2001-07-05,0.0779,0.0,2.44 554 | 2001-07-06,0.0773,0.86,2.44 555 | 2001-07-07,0.0753,2.28,2.44 556 | 2001-07-08,0.0759,0.04,2.44 557 | 2001-07-09,0.0777,0.24,2.44 558 | 2001-07-10,0.0909,8.7,2.44 559 | 2001-07-11,0.1528,9.34,2.44 560 | 2001-07-12,0.132,6.02,2.44 561 | 2001-07-13,0.1026,4.98,2.44 562 | 2001-07-14,0.0962,0.4,2.44 563 | 2001-07-15,0.1024,2.66,2.44 564 | 2001-07-16,0.1217,8.02,2.44 565 | 2001-07-17,0.1013,2.4,2.44 566 | 2001-07-18,0.0967,1.78,2.44 567 | 2001-07-19,0.0954,0.12,2.44 568 | 2001-07-20,0.0955,2.76,2.44 569 | 2001-07-21,0.0954,1.26,2.44 570 | 2001-07-22,0.0944,0.26,2.44 571 | 2001-07-23,0.093,0.12,2.44 572 | 2001-07-24,0.0913,0.88,2.44 573 | 2001-07-25,0.0911,0.38,2.44 574 | 2001-07-26,0.0906,0.38,2.44 575 | 2001-07-27,0.1394,5.92,2.44 576 | 2001-07-28,0.0947,0.06,2.44 577 | 2001-07-29,0.0932,0.0,2.44 578 | 2001-07-30,0.0902,0.46,2.44 579 | 2001-07-31,0.0891,0.1,2.44 580 | 2001-08-01,0.0887,3.52,2.23 581 | 2001-08-02,0.0912,6.88,2.23 582 | 2001-08-03,0.0907,0.66,2.23 583 | 2001-08-04,0.1064,12.0,2.23 584 | 2001-08-05,0.1087,3.92,2.23 585 | 2001-08-06,0.0957,0.8,2.23 586 | 2001-08-07,0.0958,0.52,2.23 587 | 2001-08-08,0.0953,0.2,2.23 588 | 2001-08-09,0.0936,0.74,2.23 589 | 2001-08-10,0.0932,2.32,2.23 590 | 2001-08-11,0.0933,1.06,2.23 591 | 2001-08-12,0.0923,1.54,2.23 592 | 2001-08-13,0.0912,0.28,2.23 593 | 2001-08-14,0.0917,0.16,2.23 594 | 2001-08-15,0.1176,10.86,2.23 595 | 2001-08-16,0.2359,9.52,2.23 596 | 2001-08-17,0.1259,0.12,2.23 597 | 2001-08-18,0.105,13.18,2.23 598 | 2001-08-19,1.4194,34.04,2.23 599 | 2001-08-20,0.6114,0.0,2.23 600 | 2001-08-21,0.2991,0.34,2.23 601 | 2001-08-22,0.2133,0.0,2.23 602 | 2001-08-23,0.1693,0.0,2.23 603 | 2001-08-24,0.1412,0.0,2.23 604 | 2001-08-25,0.124,0.14,2.23 605 | 2001-08-26,0.1043,0.02,2.23 606 | 2001-08-27,0.095,0.12,2.23 607 | 2001-08-28,0.0993,0.02,2.23 608 | 2001-08-29,0.0963,0.64,2.23 609 | 2001-08-30,0.4511,14.32,2.23 610 | 2001-08-31,0.3176,0.04,2.23 611 | 2001-09-01,0.2056,0.94,1.3 612 | 2001-09-02,0.1599,0.02,1.3 613 | 2001-09-03,0.1414,2.86,1.3 614 | 2001-09-04,0.1233,0.34,1.3 615 | 2001-09-05,0.1126,0.0,1.3 616 | 2001-09-06,0.1051,1.64,1.3 617 | 2001-09-07,0.2942,14.7,1.3 618 | 2001-09-08,0.2378,3.3,1.3 619 | 2001-09-09,0.1908,0.7,1.3 620 | 2001-09-10,0.162,0.98,1.3 621 | 2001-09-11,0.1391,0.12,1.3 622 | 2001-09-12,0.1377,5.14,1.3 623 | 2001-09-13,0.1646,1.48,1.3 624 | 2001-09-14,0.1616,2.68,1.3 625 | 2001-09-15,0.1429,1.34,1.3 626 | 2001-09-16,0.1381,1.86,1.3 627 | 2001-09-17,0.132,0.28,1.3 628 | 2001-09-18,0.1297,0.12,1.3 629 | 2001-09-19,0.1303,1.74,1.3 630 | 2001-09-20,0.1807,7.24,1.3 631 | 2001-09-21,0.1739,0.42,1.3 632 | 2001-09-22,0.1523,0.34,1.3 633 | 2001-09-23,0.1411,0.8,1.3 634 | 2001-09-24,0.1641,2.72,1.3 635 | 2001-09-25,0.1455,0.22,1.3 636 | 2001-09-26,0.1346,0.0,1.3 637 | 2001-09-27,0.1333,1.44,1.3 638 | 2001-09-28,0.1491,1.6,1.3 639 | 2001-09-29,0.1672,2.64,1.3 640 | 2001-09-30,0.2662,10.44,1.3 641 | 2001-10-01,0.3286,0.56,0.93 642 | 2001-10-02,0.1985,0.16,0.93 643 | 2001-10-03,0.1524,0.0,0.93 644 | 2001-10-04,0.1349,0.38,0.93 645 | 2001-10-05,0.1559,6.44,0.93 646 | 2001-10-06,0.1819,0.86,0.93 647 | 2001-10-07,0.3977,21.62,0.93 648 | 2001-10-08,1.4909,15.86,0.93 649 | 2001-10-09,0.8889,1.94,0.93 650 | 2001-10-10,0.5649,1.22,0.93 651 | 2001-10-11,0.4619,0.04,0.93 652 | 2001-10-12,0.3876,0.86,0.93 653 | 2001-10-13,0.3489,0.02,0.93 654 | 2001-10-14,0.3594,4.68,0.93 655 | 2001-10-15,0.4018,2.48,0.93 656 | 2001-10-16,0.3489,1.06,0.93 657 | 2001-10-17,0.31,4.58,0.93 658 | 2001-10-18,0.4989,6.78,0.93 659 | 2001-10-19,0.4994,4.64,0.93 660 | 2001-10-20,3.198,31.4,0.93 661 | 2001-10-21,2.452,10.96,0.93 662 | 2001-10-22,1.5217,4.24,0.93 663 | 2001-10-23,2.0135,7.54,0.93 664 | 2001-10-24,1.2639,0.06,0.93 665 | 2001-10-25,1.0033,0.78,0.93 666 | 2001-10-26,0.8636,0.0,0.93 667 | 2001-10-27,0.748,0.0,0.93 668 | 2001-10-28,0.6685,0.0,0.93 669 | 2001-10-29,0.6261,3.16,0.93 670 | 2001-10-30,0.7419,7.92,0.93 671 | 2001-10-31,0.7508,1.28,0.93 672 | 2001-11-01,0.6312,0.02,0.48 673 | 2001-11-02,0.5546,0.18,0.48 674 | 2001-11-03,0.5073,0.02,0.48 675 | 2001-11-04,0.484,1.02,0.48 676 | 2001-11-05,0.5059,5.8,0.48 677 | 2001-11-06,0.6307,4.02,0.48 678 | 2001-11-07,0.5838,6.6,0.48 679 | 2001-11-08,0.6198,8.62,0.48 680 | 2001-11-09,0.6085,1.56,0.48 681 | 2001-11-10,1.3696,0.18,0.48 682 | 2001-11-11,0.8258,2.14,0.48 683 | 2001-11-12,0.7612,0.18,0.48 684 | 2001-11-13,0.6623,0.34,0.48 685 | 2001-11-14,0.6221,0.26,0.48 686 | 2001-11-15,0.5785,0.02,0.48 687 | 2001-11-16,0.5529,0.0,0.48 688 | 2001-11-17,0.5355,0.48,0.48 689 | 2001-11-18,0.5359,1.02,0.48 690 | 2001-11-19,0.5162,0.44,0.48 691 | 2001-11-20,0.5014,0.6,0.48 692 | 2001-11-21,0.5016,1.78,0.48 693 | 2001-11-22,0.5009,1.14,0.48 694 | 2001-11-23,0.5034,1.06,0.48 695 | 2001-11-24,0.4928,0.04,0.48 696 | 2001-11-25,0.4666,0.0,0.48 697 | 2001-11-26,0.4521,1.86,0.48 698 | 2001-11-27,0.4609,0.62,0.48 699 | 2001-11-28,0.4391,0.82,0.48 700 | 2001-11-29,0.434,5.02,0.48 701 | 2001-11-30,0.8324,16.34,0.48 702 | 2001-12-01,1.1291,0.12,0.29 703 | 2001-12-02,0.6608,0.12,0.29 704 | 2001-12-03,0.7945,15.8,0.29 705 | 2001-12-04,0.7438,0.0,0.29 706 | 2001-12-05,0.6057,0.02,0.29 707 | 2001-12-06,0.5482,0.52,0.29 708 | 2001-12-07,0.548,0.0,0.29 709 | 2001-12-08,0.5294,1.18,0.29 710 | 2001-12-09,0.5145,0.0,0.29 711 | 2001-12-10,0.4783,0.0,0.29 712 | 2001-12-11,0.4501,0.0,0.29 713 | 2001-12-12,0.4303,0.0,0.29 714 | 2001-12-13,0.417,0.12,0.29 715 | 2001-12-14,0.4341,2.94,0.29 716 | 2001-12-15,0.4629,1.46,0.29 717 | 2001-12-16,0.4556,0.34,0.29 718 | 2001-12-17,0.4423,0.28,0.29 719 | 2001-12-18,0.4301,0.1,0.29 720 | 2001-12-19,0.4499,2.04,0.29 721 | 2001-12-20,0.5676,12.5,0.29 722 | 2001-12-21,1.0387,6.3,0.29 723 | 2001-12-22,0.6697,8.6,0.29 724 | 2001-12-23,0.9601,3.7,0.29 725 | 2001-12-24,1.6075,5.04,0.29 726 | 2001-12-25,0.9682,3.32,0.29 727 | 2001-12-26,0.8018,7.24,0.29 728 | 2001-12-27,0.784,11.58,0.29 729 | 2001-12-28,0.9509,4.88,0.29 730 | 2001-12-29,0.8166,1.08,0.29 731 | 2001-12-30,0.7005,0.7,0.29 732 | 2001-12-31,0.6414,1.44,0.29 733 | 2002-01-01,0.6048,0.04,0.28 734 | 2002-01-02,0.5994,0.0,0.28 735 | 2002-01-03,0.5554,0.1,0.28 736 | 2002-01-04,0.5414,0.0,0.28 737 | 2002-01-05,0.5379,0.0,0.28 738 | 2002-01-06,0.574,0.0,0.28 739 | 2002-01-07,0.6908,0.0,0.28 740 | 2002-01-08,0.7481,0.22,0.28 741 | 2002-01-09,0.9064,0.0,0.28 742 | 2002-01-10,0.7211,0.1,0.28 743 | 2002-01-11,0.8008,0.9,0.28 744 | 2002-01-12,1.7306,0.28,0.28 745 | 2002-01-13,1.2072,0.0,0.28 746 | 2002-01-14,0.9599,0.08,0.28 747 | 2002-01-15,0.8311,0.0,0.28 748 | 2002-01-16,0.7693,0.18,0.28 749 | 2002-01-17,0.7141,0.08,0.28 750 | 2002-01-18,0.6874,0.74,0.28 751 | 2002-01-19,0.648,1.58,0.28 752 | 2002-01-20,0.6968,8.38,0.28 753 | 2002-01-21,1.0078,1.34,0.28 754 | 2002-01-22,0.7685,1.76,0.28 755 | 2002-01-23,1.7844,20.7,0.28 756 | 2002-01-24,1.4606,0.88,0.28 757 | 2002-01-25,0.9696,7.54,0.28 758 | 2002-01-26,1.3694,7.14,0.28 759 | 2002-01-27,1.3132,5.68,0.28 760 | 2002-01-28,2.2113,3.26,0.28 761 | 2002-01-29,1.5383,9.8,0.28 762 | 2002-01-30,1.759,0.32,0.28 763 | 2002-01-31,1.3264,5.44,0.28 764 | 2002-02-01,1.646,9.94,0.6 765 | 2002-02-02,1.4682,1.14,0.6 766 | 2002-02-03,1.9459,8.9,0.6 767 | 2002-02-04,1.4223,1.3,0.6 768 | 2002-02-05,1.2207,2.88,0.6 769 | 2002-02-06,1.0968,1.28,0.6 770 | 2002-02-07,1.0318,2.2,0.6 771 | 2002-02-08,0.9796,1.68,0.6 772 | 2002-02-09,0.9129,0.08,0.6 773 | 2002-02-10,0.8601,3.84,0.6 774 | 2002-02-11,0.9552,3.02,0.6 775 | 2002-02-12,0.8517,2.36,0.6 776 | 2002-02-13,0.8229,0.98,0.6 777 | 2002-02-14,0.7665,0.08,0.6 778 | 2002-02-15,0.7252,0.0,0.6 779 | 2002-02-16,0.7014,3.74,0.6 780 | 2002-02-17,0.7193,0.18,0.6 781 | 2002-02-18,0.7098,5.74,0.6 782 | 2002-02-19,0.8513,7.32,0.6 783 | 2002-02-20,0.9771,3.38,0.6 784 | 2002-02-21,1.1186,4.2,0.6 785 | 2002-02-22,1.2531,0.5,0.6 786 | 2002-02-23,0.7781,1.2,0.6 787 | 2002-02-24,0.7341,0.94,0.6 788 | 2002-02-25,0.8228,5.16,0.6 789 | 2002-02-26,0.8604,3.22,0.6 790 | 2002-02-27,0.7855,2.56,0.6 791 | 2002-02-28,0.7822,3.12,0.6 792 | 2002-03-01,0.8258,3.04,1.23 793 | 2002-03-02,0.875,0.42,1.23 794 | 2002-03-03,0.8814,0.46,1.23 795 | 2002-03-04,0.7633,0.72,1.23 796 | 2002-03-05,0.7267,3.28,1.23 797 | 2002-03-06,0.7458,3.62,1.23 798 | 2002-03-07,0.6979,1.4,1.23 799 | 2002-03-08,0.6845,2.0,1.23 800 | 2002-03-09,0.6589,1.72,1.23 801 | 2002-03-10,0.7066,4.68,1.23 802 | 2002-03-11,0.6562,0.22,1.23 803 | 2002-03-12,0.593,0.06,1.23 804 | 2002-03-13,0.5776,0.28,1.23 805 | 2002-03-14,0.5681,0.58,1.23 806 | 2002-03-15,0.5567,1.96,1.23 807 | 2002-03-16,0.5609,0.38,1.23 808 | 2002-03-17,0.537,0.22,1.23 809 | 2002-03-18,0.5239,1.12,1.23 810 | 2002-03-19,0.4993,0.1,1.23 811 | 2002-03-20,0.4893,3.64,1.23 812 | 2002-03-21,0.6157,8.14,1.23 813 | 2002-03-22,0.671,0.28,1.23 814 | 2002-03-23,0.5661,0.0,1.23 815 | 2002-03-24,0.5239,0.0,1.23 816 | 2002-03-25,0.491,0.0,1.23 817 | 2002-03-26,0.4631,0.0,1.23 818 | 2002-03-27,0.4447,0.0,1.23 819 | 2002-03-28,0.4281,0.08,1.23 820 | 2002-03-29,0.4157,0.0,1.23 821 | 2002-03-30,0.4078,0.04,1.23 822 | 2002-03-31,0.4016,0.0,1.23 823 | 2002-04-01,0.3925,1.56,1.8 824 | 2002-04-02,0.3927,1.32,1.8 825 | 2002-04-03,0.3737,0.0,1.8 826 | 2002-04-04,0.3605,0.0,1.8 827 | 2002-04-05,0.3471,0.0,1.8 828 | 2002-04-06,0.3374,0.0,1.8 829 | 2002-04-07,0.3284,0.0,1.8 830 | 2002-04-08,0.3245,0.88,1.8 831 | 2002-04-09,0.3129,0.58,1.8 832 | 2002-04-10,0.3097,0.0,1.8 833 | 2002-04-11,0.3385,4.98,1.8 834 | 2002-04-12,0.3202,0.28,1.8 835 | 2002-04-13,0.3049,0.24,1.8 836 | 2002-04-14,0.3118,2.78,1.8 837 | 2002-04-15,0.2978,0.0,1.8 838 | 2002-04-16,0.2807,0.04,1.8 839 | 2002-04-17,0.2725,0.0,1.8 840 | 2002-04-18,0.2696,0.0,1.8 841 | 2002-04-19,0.258,0.0,1.8 842 | 2002-04-20,0.2506,0.3,1.8 843 | 2002-04-21,0.2449,0.52,1.8 844 | 2002-04-22,0.2329,0.02,1.8 845 | 2002-04-23,0.2277,0.0,1.8 846 | 2002-04-24,0.2179,0.0,1.8 847 | 2002-04-25,0.214,0.72,1.8 848 | 2002-04-26,0.2156,0.88,1.8 849 | 2002-04-27,0.2121,0.12,1.8 850 | 2002-04-28,0.2847,7.5,1.8 851 | 2002-04-29,0.2431,0.66,1.8 852 | 2002-04-30,0.2449,3.62,1.8 853 | 2002-05-01,0.2251,1.74,2.4 854 | 2002-05-02,0.3095,10.08,2.4 855 | 2002-05-03,0.2857,1.4,2.4 856 | 2002-05-04,,0.26,2.4 857 | 2002-05-05,,0.0,2.4 858 | 2002-05-06,,0.0,2.4 859 | 2002-05-07,,1.6,2.4 860 | 2002-05-08,,0.02,2.4 861 | 2002-05-09,,0.12,2.4 862 | 2002-05-10,,0.08,2.4 863 | 2002-05-11,,0.0,2.4 864 | 2002-05-12,,1.3,2.4 865 | 2002-05-13,,4.12,2.4 866 | 2002-05-14,,0.18,2.4 867 | 2002-05-15,,2.12,2.4 868 | 2002-05-16,,0.2,2.4 869 | 2002-05-17,,0.0,2.4 870 | 2002-05-18,,3.34,2.4 871 | 2002-05-19,,0.26,2.4 872 | 2002-05-20,,4.14,2.4 873 | 2002-05-21,,12.36,2.4 874 | 2002-05-22,,3.5,2.4 875 | 2002-05-23,,1.28,2.4 876 | 2002-05-24,,6.44,2.4 877 | 2002-05-25,0.1781,0.44,2.4 878 | 2002-05-26,0.1838,8.92,2.4 879 | 2002-05-27,0.328,4.52,2.4 880 | 2002-05-28,0.2966,5.98,2.4 881 | 2002-05-29,0.3314,0.78,2.4 882 | 2002-05-30,0.2197,0.16,2.4 883 | 2002-05-31,0.1777,0.28,2.4 884 | 2002-06-01,0.1662,0.08,2.83 885 | 2002-06-02,0.1639,2.74,2.83 886 | 2002-06-03,0.1626,1.12,2.83 887 | 2002-06-04,0.1479,0.54,2.83 888 | 2002-06-05,0.1578,4.28,2.83 889 | 2002-06-06,0.1412,0.04,2.83 890 | 2002-06-07,0.1574,7.76,2.83 891 | 2002-06-08,0.1914,1.04,2.83 892 | 2002-06-09,0.1913,6.48,2.83 893 | 2002-06-10,0.182,2.16,2.83 894 | 2002-06-11,0.1563,9.22,2.83 895 | 2002-06-12,0.1496,4.14,2.83 896 | 2002-06-13,0.2175,7.76,2.83 897 | 2002-06-14,0.3198,2.84,2.83 898 | 2002-06-15,0.269,0.52,2.83 899 | 2002-06-16,0.2106,1.4,2.83 900 | 2002-06-17,0.1741,0.08,2.83 901 | 2002-06-18,0.1657,0.02,2.83 902 | 2002-06-19,0.1452,0.0,2.83 903 | 2002-06-20,0.1597,3.68,2.83 904 | 2002-06-21,0.1518,0.14,2.83 905 | 2002-06-22,0.1489,1.46,2.83 906 | 2002-06-23,0.1366,0.84,2.83 907 | 2002-06-24,0.1309,0.22,2.83 908 | 2002-06-25,0.1203,0.04,2.83 909 | 2002-06-26,0.1216,2.36,2.83 910 | 2002-06-27,0.1283,1.72,2.83 911 | 2002-06-28,0.1206,0.16,2.83 912 | 2002-06-29,0.1316,2.94,2.83 913 | 2002-06-30,0.2141,8.06,2.83 914 | 2002-07-01,0.2763,10.6,2.38 915 | 2002-07-02,0.2702,5.28,2.38 916 | 2002-07-03,0.2073,0.14,2.38 917 | 2002-07-04,0.1792,5.4,2.38 918 | 2002-07-05,0.1648,0.7,2.38 919 | 2002-07-06,0.1652,0.18,2.38 920 | 2002-07-07,0.1651,3.52,2.38 921 | 2002-07-08,0.1576,0.22,2.38 922 | 2002-07-09,0.1329,0.02,2.38 923 | 2002-07-10,0.1206,0.04,2.38 924 | 2002-07-11,0.1645,8.62,2.38 925 | 2002-07-12,0.1371,0.62,2.38 926 | 2002-07-13,0.1208,0.0,2.38 927 | 2002-07-14,0.1172,0.54,2.38 928 | 2002-07-15,0.1058,0.08,2.38 929 | 2002-07-16,0.1087,3.72,2.38 930 | 2002-07-17,0.1569,5.42,2.38 931 | 2002-07-18,0.1356,7.94,2.38 932 | 2002-07-19,0.9909,33.26,2.38 933 | 2002-07-20,2.7895,16.48,2.38 934 | 2002-07-21,0.7127,0.42,2.38 935 | 2002-07-22,0.5397,12.44,2.38 936 | 2002-07-23,1.206,1.2,2.38 937 | 2002-07-24,0.6003,0.32,2.38 938 | 2002-07-25,0.5071,1.14,2.38 939 | 2002-07-26,0.4546,0.4,2.38 940 | 2002-07-27,0.4139,0.0,2.38 941 | 2002-07-28,0.4185,14.2,2.38 942 | 2002-07-29,0.6112,3.86,2.38 943 | 2002-07-30,0.7386,10.18,2.38 944 | 2002-07-31,0.6171,0.16,2.38 945 | 2002-08-01,0.4719,0.02,2.21 946 | 2002-08-02,0.4728,12.42,2.21 947 | 2002-08-03,1.1672,16.52,2.21 948 | 2002-08-04,0.7783,0.24,2.21 949 | 2002-08-05,0.5894,0.1,2.21 950 | 2002-08-06,0.5746,8.08,2.21 951 | 2002-08-07,0.8381,12.52,2.21 952 | 2002-08-08,0.7577,1.86,2.21 953 | 2002-08-09,0.6495,2.84,2.21 954 | 2002-08-10,0.5984,2.02,2.21 955 | 2002-08-11,0.6758,8.08,2.21 956 | 2002-08-12,0.6187,0.0,2.21 957 | 2002-08-13,0.5259,0.0,2.21 958 | 2002-08-14,0.4955,1.66,2.21 959 | 2002-08-15,0.4609,0.04,2.21 960 | 2002-08-16,0.4364,0.0,2.21 961 | 2002-08-17,0.4297,0.32,2.21 962 | 2002-08-18,0.4272,2.88,2.21 963 | 2002-08-19,0.4413,5.56,2.21 964 | 2002-08-20,0.4293,4.0,2.21 965 | 2002-08-21,0.4027,0.0,2.21 966 | 2002-08-22,0.4066,1.32,2.21 967 | 2002-08-23,0.6012,12.9,2.21 968 | 2002-08-24,0.6396,0.62,2.21 969 | 2002-08-25,0.4607,0.0,2.21 970 | 2002-08-26,0.4207,0.0,2.21 971 | 2002-08-27,0.473,2.68,2.21 972 | 2002-08-28,0.4485,0.0,2.21 973 | 2002-08-29,0.4092,4.36,2.21 974 | 2002-08-30,0.8273,13.08,2.21 975 | 2002-08-31,0.6098,0.1,2.21 976 | 2002-09-01,0.4766,0.26,1.57 977 | 2002-09-02,0.4224,0.0,1.57 978 | 2002-09-03,0.4047,0.0,1.57 979 | 2002-09-04,0.4003,1.48,1.57 980 | 2002-09-05,0.3941,0.36,1.57 981 | 2002-09-06,0.3674,0.28,1.57 982 | 2002-09-07,0.9371,21.5,1.57 983 | 2002-09-08,0.9837,11.08,1.57 984 | 2002-09-09,1.0828,1.76,1.57 985 | 2002-09-10,0.7031,0.14,1.57 986 | 2002-09-11,0.5793,0.04,1.57 987 | 2002-09-12,0.5273,0.0,1.57 988 | 2002-09-13,0.4921,0.1,1.57 989 | 2002-09-14,0.4684,0.12,1.57 990 | 2002-09-15,0.4561,0.1,1.57 991 | 2002-09-16,0.4386,0.26,1.57 992 | 2002-09-17,0.4233,0.0,1.57 993 | 2002-09-18,0.4046,0.0,1.57 994 | 2002-09-19,0.3883,0.38,1.57 995 | 2002-09-20,0.3687,0.0,1.57 996 | 2002-09-21,0.3565,1.08,1.57 997 | 2002-09-22,0.3479,0.04,1.57 998 | 2002-09-23,0.3407,0.0,1.57 999 | 2002-09-24,0.3322,0.08,1.57 1000 | 2002-09-25,0.3333,1.46,1.57 1001 | 2002-09-26,0.3223,0.0,1.57 1002 | 2002-09-27,0.3124,0.0,1.57 1003 | 2002-09-28,0.3083,0.0,1.57 1004 | 2002-09-29,0.3059,2.26,1.57 1005 | 2002-09-30,0.3158,1.14,1.57 1006 | 2002-10-01,0.3056,0.0,0.71 1007 | 2002-10-02,0.3041,1.32,0.71 1008 | 2002-10-03,0.3059,0.18,0.71 1009 | 2002-10-04,0.3037,0.9,0.71 1010 | 2002-10-05,0.2905,0.42,0.71 1011 | 2002-10-06,0.2874,0.14,0.71 1012 | 2002-10-07,0.2864,0.8,0.71 1013 | 2002-10-08,0.2986,2.04,0.71 1014 | 2002-10-09,0.2864,1.9,0.71 1015 | 2002-10-10,0.2991,4.28,0.71 1016 | 2002-10-11,0.3197,11.54,0.71 1017 | 2002-10-12,1.152,40.0,0.71 1018 | 2002-10-13,4.7355,19.78,0.71 1019 | 2002-10-14,3.1367,8.32,0.71 1020 | 2002-10-15,1.7573,3.02,0.71 1021 | 2002-10-16,1.2579,0.86,0.71 1022 | 2002-10-17,1.3839,12.78,0.71 1023 | 2002-10-18,3.4709,7.16,0.71 1024 | 2002-10-19,1.591,0.4,0.71 1025 | 2002-10-20,1.2001,5.22,0.71 1026 | 2002-10-21,4.6005,35.72,0.71 1027 | 2002-10-22,14.1041,48.32,0.71 1028 | 2002-10-23,9.3187,0.5,0.71 1029 | 2002-10-24,4.1497,3.58,0.71 1030 | 2002-10-25,5.4975,15.62,0.71 1031 | 2002-10-26,3.5181,14.5,0.71 1032 | 2002-10-27,5.0836,3.94,0.71 1033 | 2002-10-28,2.6645,0.0,0.71 1034 | 2002-10-29,2.2095,0.0,0.71 1035 | 2002-10-30,1.9119,0.0,0.71 1036 | 2002-10-31,1.6693,1.56,0.71 1037 | 2002-11-01,1.5457,5.28,0.36 1038 | 2002-11-02,2.8603,21.36,0.36 1039 | 2002-11-03,2.3888,0.18,0.36 1040 | 2002-11-04,1.6441,0.0,0.36 1041 | 2002-11-05,1.4269,0.26,0.36 1042 | 2002-11-06,1.2817,0.0,0.36 1043 | 2002-11-07,1.1536,0.02,0.36 1044 | 2002-11-08,1.0924,1.44,0.36 1045 | 2002-11-09,1.0231,1.46,0.36 1046 | 2002-11-10,1.0086,0.78,0.36 1047 | 2002-11-11,0.9323,1.32,0.36 1048 | 2002-11-12,0.9237,3.48,0.36 1049 | 2002-11-13,1.7435,6.38,0.36 1050 | 2002-11-14,6.5717,45.96,0.36 1051 | 2002-11-15,6.1345,9.1,0.36 1052 | 2002-11-16,3.2178,2.28,0.36 1053 | 2002-11-17,2.3052,0.0,0.36 1054 | 2002-11-18,1.9408,0.0,0.36 1055 | 2002-11-19,1.7457,1.78,0.36 1056 | 2002-11-20,3.9168,37.26,0.36 1057 | 2002-11-21,9.6173,40.18,0.36 1058 | 2002-11-22,8.8135,21.76,0.36 1059 | 2002-11-23,7.3824,10.16,0.36 1060 | 2002-11-24,4.2353,0.96,0.36 1061 | 2002-11-25,3.201,0.2,0.36 1062 | 2002-11-26,2.5255,4.78,0.36 1063 | 2002-11-27,2.8428,10.64,0.36 1064 | 2002-11-28,2.322,0.68,0.36 1065 | 2002-11-29,1.9057,0.2,0.36 1066 | 2002-11-30,1.7493,5.44,0.36 1067 | 2002-12-01,2.4706,15.96,0.16 1068 | 2002-12-02,5.3574,23.2,0.16 1069 | 2002-12-03,3.3085,8.12,0.16 1070 | 2002-12-04,3.1007,2.82,0.16 1071 | 2002-12-05,2.4316,4.54,0.16 1072 | 2002-12-06,2.2253,3.08,0.16 1073 | 2002-12-07,2.0226,2.52,0.16 1074 | 2002-12-08,1.789,0.58,0.16 1075 | 2002-12-09,1.5884,0.48,0.16 1076 | 2002-12-10,1.4382,1.06,0.16 1077 | 2002-12-11,1.361,2.24,0.16 1078 | 2002-12-12,1.3107,0.44,0.16 1079 | 2002-12-13,1.5087,8.98,0.16 1080 | 2002-12-14,1.4753,0.6,0.16 1081 | 2002-12-15,1.2869,3.32,0.16 1082 | 2002-12-16,1.1895,0.42,0.16 1083 | 2002-12-17,1.0739,0.0,0.16 1084 | 2002-12-18,0.9868,0.04,0.16 1085 | 2002-12-19,0.905,0.0,0.16 1086 | 2002-12-20,0.861,0.08,0.16 1087 | 2002-12-21,0.8527,1.28,0.16 1088 | 2002-12-22,0.8714,0.6,0.16 1089 | 2002-12-23,1.2216,17.5,0.16 1090 | 2002-12-24,1.2353,2.7,0.16 1091 | 2002-12-25,0.9817,1.26,0.16 1092 | 2002-12-26,1.1209,7.84,0.16 1093 | 2002-12-27,1.1575,2.02,0.16 1094 | 2002-12-28,0.9651,0.66,0.16 1095 | 2002-12-29,0.9407,3.06,0.16 1096 | 2002-12-30,0.8798,0.0,0.16 1097 | 2002-12-31,0.8318,0.28,0.16 1098 | 2003-01-01,1.1703,12.02,0.39 1099 | 2003-01-02,1.135,5.46,0.39 1100 | 2003-01-03,0.9165,3.28,0.39 1101 | 2003-01-04,0.8436,0.68,0.39 1102 | 2003-01-05,0.8039,0.4,0.39 1103 | 2003-01-06,0.7694,0.3,0.39 1104 | 2003-01-07,0.7124,0.12,0.39 1105 | 2003-01-08,0.6936,0.34,0.39 1106 | 2003-01-09,0.7064,0.0,0.39 1107 | 2003-01-10,0.6758,0.04,0.39 1108 | 2003-01-11,0.6434,0.04,0.39 1109 | 2003-01-12,0.8918,0.98,0.39 1110 | 2003-01-13,0.9685,0.0,0.39 1111 | 2003-01-14,0.799,0.34,0.39 1112 | 2003-01-15,0.7774,2.66,0.39 1113 | 2003-01-16,0.731,1.58,0.39 1114 | 2003-01-17,0.6962,0.92,0.39 1115 | 2003-01-18,0.6591,0.78,0.39 1116 | 2003-01-19,0.6564,0.08,0.39 1117 | 2003-01-20,0.8683,7.18,0.39 1118 | 2003-01-21,0.758,4.6,0.39 1119 | 2003-01-22,2.1603,11.88,0.39 1120 | 2003-01-23,1.1581,0.08,0.39 1121 | 2003-01-24,0.9693,0.26,0.39 1122 | 2003-01-25,0.8749,1.98,0.39 1123 | 2003-01-26,0.8346,0.0,0.39 1124 | 2003-01-27,0.8006,1.44,0.39 1125 | 2003-01-28,0.9078,8.6,0.39 1126 | 2003-01-29,1.0142,6.82,0.39 1127 | 2003-01-30,0.8581,2.68,0.39 1128 | 2003-01-31,0.7957,0.96,0.39 1129 | 2003-02-01,0.768,1.78,0.37 1130 | 2003-02-02,0.7365,0.44,0.37 1131 | 2003-02-03,0.691,4.9,0.37 1132 | 2003-02-04,0.6637,0.12,0.37 1133 | 2003-02-05,0.6321,0.04,0.37 1134 | 2003-02-06,0.7411,0.04,0.37 1135 | 2003-02-07,1.106,0.0,0.37 1136 | 2003-02-08,1.2042,0.88,0.37 1137 | 2003-02-09,0.8797,0.08,0.37 1138 | 2003-02-10,0.7799,0.7,0.37 1139 | 2003-02-11,0.7314,0.0,0.37 1140 | 2003-02-12,0.701,0.0,0.37 1141 | 2003-02-13,0.6773,0.22,0.37 1142 | 2003-02-14,0.6361,0.06,0.37 1143 | 2003-02-15,0.6037,0.0,0.37 1144 | 2003-02-16,0.5888,0.0,0.37 1145 | 2003-02-17,0.5697,0.0,0.37 1146 | 2003-02-18,0.5527,0.0,0.37 1147 | 2003-02-19,0.5476,0.0,0.37 1148 | 2003-02-20,0.5423,0.0,0.37 1149 | 2003-02-21,0.5186,0.0,0.37 1150 | 2003-02-22,0.5144,0.08,0.37 1151 | 2003-02-23,0.5215,1.0,0.37 1152 | 2003-02-24,0.4966,0.08,0.37 1153 | 2003-02-25,0.4915,0.0,0.37 1154 | 2003-02-26,0.4774,0.0,0.37 1155 | 2003-02-27,0.4646,0.16,0.37 1156 | 2003-02-28,0.4784,3.58,0.37 1157 | 2003-03-01,0.5735,15.04,1.45 1158 | 2003-03-02,0.9333,0.08,1.45 1159 | 2003-03-03,0.6247,0.32,1.45 1160 | 2003-03-04,0.5779,1.0,1.45 1161 | 2003-03-05,0.5274,0.2,1.45 1162 | 2003-03-06,0.4906,0.06,1.45 1163 | 2003-03-07,0.4922,3.08,1.45 1164 | 2003-03-08,0.482,0.04,1.45 1165 | 2003-03-09,0.4612,0.44,1.45 1166 | 2003-03-10,0.4427,0.38,1.45 1167 | 2003-03-11,0.4642,4.38,1.45 1168 | 2003-03-12,0.442,0.0,1.45 1169 | 2003-03-13,0.4249,0.0,1.45 1170 | 2003-03-14,0.4113,0.0,1.45 1171 | 2003-03-15,0.3955,0.04,1.45 1172 | 2003-03-16,0.3849,0.06,1.45 1173 | 2003-03-17,0.3763,0.0,1.45 1174 | 2003-03-18,0.3699,0.0,1.45 1175 | 2003-03-19,0.3631,0.04,1.45 1176 | 2003-03-20,0.3529,0.0,1.45 1177 | 2003-03-21,0.3462,0.0,1.45 1178 | 2003-03-22,0.3369,0.0,1.45 1179 | 2003-03-23,0.3291,0.0,1.45 1180 | 2003-03-24,0.3205,0.0,1.45 1181 | 2003-03-25,0.3121,0.04,1.45 1182 | 2003-03-26,0.3081,0.0,1.45 1183 | 2003-03-27,0.3052,0.06,1.45 1184 | 2003-03-28,0.2985,0.0,1.45 1185 | 2003-03-29,0.2848,0.1,1.45 1186 | 2003-03-30,0.2744,0.0,1.45 1187 | 2003-03-31,0.2825,2.24,1.45 1188 | 2003-04-01,0.3001,4.36,1.93 1189 | 2003-04-02,0.2938,1.1,1.93 1190 | 2003-04-03,0.2681,0.1,1.93 1191 | 2003-04-04,0.2548,0.0,1.93 1192 | 2003-04-05,0.2512,0.0,1.93 1193 | 2003-04-06,0.2514,0.0,1.93 1194 | 2003-04-07,0.2456,0.0,1.93 1195 | 2003-04-08,0.2423,0.0,1.93 1196 | 2003-04-09,0.2407,0.04,1.93 1197 | 2003-04-10,0.2329,0.0,1.93 1198 | 2003-04-11,0.2246,0.0,1.93 1199 | 2003-04-12,0.2218,0.14,1.93 1200 | 2003-04-13,0.2183,0.0,1.93 1201 | 2003-04-14,0.2153,0.04,1.93 1202 | 2003-04-15,0.2058,0.0,1.93 1203 | 2003-04-16,0.2151,0.04,1.93 1204 | 2003-04-17,0.2105,0.0,1.93 1205 | 2003-04-18,0.2177,0.0,1.93 1206 | 2003-04-19,0.2191,0.0,1.93 1207 | 2003-04-20,0.2086,0.0,1.93 1208 | 2003-04-21,0.37,24.06,1.93 1209 | 2003-04-22,0.4705,0.38,1.93 1210 | 2003-04-23,0.2914,0.12,1.93 1211 | 2003-04-24,0.2633,0.12,1.93 1212 | 2003-04-25,0.2713,5.24,1.93 1213 | 2003-04-26,0.2598,0.38,1.93 1214 | 2003-04-27,0.2346,0.12,1.93 1215 | 2003-04-28,0.3281,11.78,1.93 1216 | 2003-04-29,0.2799,1.08,1.93 1217 | 2003-04-30,0.4671,12.24,1.93 1218 | 2003-05-01,0.9018,22.1,2.29 1219 | 2003-05-02,2.0913,9.82,2.29 1220 | 2003-05-03,0.9826,2.78,2.29 1221 | 2003-05-04,0.7242,1.98,2.29 1222 | 2003-05-05,0.5999,0.7,2.29 1223 | 2003-05-06,0.5077,0.06,2.29 1224 | 2003-05-07,0.4931,5.12,2.29 1225 | 2003-05-08,0.4382,0.2,2.29 1226 | 2003-05-09,0.4112,0.28,2.29 1227 | 2003-05-10,0.3847,0.2,2.29 1228 | 2003-05-11,0.3661,1.5,2.29 1229 | 2003-05-12,0.3474,0.36,2.29 1230 | 2003-05-13,0.3816,8.66,2.29 1231 | 2003-05-14,0.3673,0.94,2.29 1232 | 2003-05-15,0.334,0.0,2.29 1233 | 2003-05-16,0.4264,11.46,2.29 1234 | 2003-05-17,1.0099,11.24,2.29 1235 | 2003-05-18,0.7913,3.58,2.29 1236 | 2003-05-19,1.2455,7.74,2.29 1237 | 2003-05-20,0.987,6.54,2.29 1238 | 2003-05-21,0.9603,5.04,2.29 1239 | 2003-05-22,0.7668,0.44,2.29 1240 | 2003-05-23,0.691,5.2,2.29 1241 | 2003-05-24,1.0384,7.44,2.29 1242 | 2003-05-25,0.8552,5.36,2.29 1243 | 2003-05-26,0.6888,0.0,2.29 1244 | 2003-05-27,0.6043,0.0,2.29 1245 | 2003-05-28,0.5636,0.78,2.29 1246 | 2003-05-29,0.5139,0.06,2.29 1247 | 2003-05-30,0.4867,0.14,2.29 1248 | 2003-05-31,0.456,0.0,2.29 1249 | 2003-06-01,0.4265,0.0,3.14 1250 | 2003-06-02,0.3975,0.0,3.14 1251 | 2003-06-03,0.3742,0.06,3.14 1252 | 2003-06-04,0.3719,3.88,3.14 1253 | 2003-06-05,0.3492,0.1,3.14 1254 | 2003-06-06,0.323,0.0,3.14 1255 | 2003-06-07,0.3077,0.24,3.14 1256 | 2003-06-08,0.3121,2.46,3.14 1257 | 2003-06-09,0.2895,1.98,3.14 1258 | 2003-06-10,0.2895,1.44,3.14 1259 | 2003-06-11,0.26,0.98,3.14 1260 | 2003-06-12,0.2622,0.52,3.14 1261 | 2003-06-13,0.2457,0.04,3.14 1262 | 2003-06-14,0.2385,0.08,3.14 1263 | 2003-06-15,0.2259,0.0,3.14 1264 | 2003-06-16,0.2181,0.0,3.14 1265 | 2003-06-17,0.2843,8.34,3.14 1266 | 2003-06-18,0.24,0.02,3.14 1267 | 2003-06-19,0.2114,0.02,3.14 1268 | 2003-06-20,0.1958,0.0,3.14 1269 | 2003-06-21,0.1938,1.48,3.14 1270 | 2003-06-22,0.208,2.0,3.14 1271 | 2003-06-23,0.2045,0.92,3.14 1272 | 2003-06-24,0.1807,0.0,3.14 1273 | 2003-06-25,0.1789,0.0,3.14 1274 | 2003-06-26,0.1711,0.0,3.14 1275 | 2003-06-27,0.1755,1.1,3.14 1276 | 2003-06-28,0.168,0.04,3.14 1277 | 2003-06-29,0.1564,0.0,3.14 1278 | 2003-06-30,0.1591,6.6,3.14 1279 | 2003-07-01,0.1559,0.14,3.15 1280 | 2003-07-02,0.1479,0.0,3.15 1281 | 2003-07-03,0.1425,0.24,3.15 1282 | 2003-07-04,0.131,0.0,3.15 1283 | 2003-07-05,0.142,1.08,3.15 1284 | 2003-07-06,0.1402,0.14,3.15 1285 | 2003-07-07,0.1468,3.66,3.15 1286 | 2003-07-08,0.1364,0.04,3.15 1287 | 2003-07-09,0.1276,0.0,3.15 1288 | 2003-07-10,0.1242,0.06,3.15 1289 | 2003-07-11,0.1236,0.0,3.15 1290 | 2003-07-12,0.1167,0.0,3.15 1291 | 2003-07-13,0.11,0.0,3.15 1292 | 2003-07-14,0.1062,0.0,3.15 1293 | 2003-07-15,0.1031,0.0,3.15 1294 | 2003-07-16,0.0925,0.0,3.15 1295 | 2003-07-17,0.0936,1.4,3.15 1296 | 2003-07-18,0.1322,5.02,3.15 1297 | 2003-07-19,0.1255,3.66,3.15 1298 | 2003-07-20,0.1193,1.72,3.15 1299 | 2003-07-21,0.1197,1.68,3.15 1300 | 2003-07-22,0.1175,0.24,3.15 1301 | 2003-07-23,0.1171,0.16,3.15 1302 | 2003-07-24,0.0965,0.1,3.15 1303 | 2003-07-25,0.1172,5.34,3.15 1304 | 2003-07-26,0.1141,0.16,3.15 1305 | 2003-07-27,0.0988,0.58,3.15 1306 | 2003-07-28,0.1137,3.62,3.15 1307 | 2003-07-29,0.1212,1.92,3.15 1308 | 2003-07-30,0.1218,1.28,3.15 1309 | 2003-07-31,0.129,2.64,3.15 1310 | 2003-08-01,0.1082,0.0,3.04 1311 | 2003-08-02,0.1024,0.0,3.04 1312 | 2003-08-03,0.0982,0.0,3.04 1313 | 2003-08-04,0.0901,0.0,3.04 1314 | 2003-08-05,0.0941,0.16,3.04 1315 | 2003-08-06,0.0889,0.04,3.04 1316 | 2003-08-07,0.0811,0.0,3.04 1317 | 2003-08-08,0.0741,0.0,3.04 1318 | 2003-08-09,0.0727,0.52,3.04 1319 | 2003-08-10,0.092,1.2,3.04 1320 | 2003-08-11,0.0852,0.0,3.04 1321 | 2003-08-12,0.086,0.0,3.04 1322 | 2003-08-13,0.0805,0.24,3.04 1323 | 2003-08-14,0.0768,0.0,3.04 1324 | 2003-08-15,0.0826,0.0,3.04 1325 | 2003-08-16,0.0823,0.0,3.04 1326 | 2003-08-17,0.0838,1.3,3.04 1327 | 2003-08-18,0.0793,0.12,3.04 1328 | 2003-08-19,0.0738,0.02,3.04 1329 | 2003-08-20,0.0825,2.92,3.04 1330 | 2003-08-21,0.0922,0.14,3.04 1331 | 2003-08-22,0.0782,0.02,3.04 1332 | 2003-08-23,0.074,0.0,3.04 1333 | 2003-08-24,0.0807,0.0,3.04 1334 | 2003-08-25,0.082,0.04,3.04 1335 | 2003-08-26,0.0802,0.0,3.04 1336 | 2003-08-27,0.0854,1.72,3.04 1337 | 2003-08-28,0.1144,4.54,3.04 1338 | 2003-08-29,0.1191,3.18,3.04 1339 | 2003-08-30,0.1088,1.56,3.04 1340 | 2003-08-31,0.1069,0.58,3.04 1341 | 2003-09-01,0.0982,0.0,1.95 1342 | 2003-09-02,0.0952,0.0,1.95 1343 | 2003-09-03,0.0902,0.0,1.95 1344 | 2003-09-04,0.0867,0.0,1.95 1345 | 2003-09-05,0.0851,0.16,1.95 1346 | 2003-09-06,0.1049,3.04,1.95 1347 | 2003-09-07,0.1021,2.2,1.95 1348 | 2003-09-08,0.0969,0.02,1.95 1349 | 2003-09-09,0.1111,2.76,1.95 1350 | 2003-09-10,0.1031,0.06,1.95 1351 | 2003-09-11,0.1061,0.4,1.95 1352 | 2003-09-12,0.0956,0.32,1.95 1353 | 2003-09-13,0.0953,0.0,1.95 1354 | 2003-09-14,0.0924,0.1,1.95 1355 | 2003-09-15,0.0861,0.0,1.95 1356 | 2003-09-16,0.0872,0.0,1.95 1357 | 2003-09-17,0.0814,0.0,1.95 1358 | 2003-09-18,0.1237,0.4,1.95 1359 | 2003-09-19,0.1246,1.74,1.95 1360 | 2003-09-20,0.1578,1.36,1.95 1361 | 2003-09-21,0.1502,3.64,1.95 1362 | 2003-09-22,0.1476,1.8,1.95 1363 | 2003-09-23,0.1403,0.36,1.95 1364 | 2003-09-24,0.1473,0.04,1.95 1365 | 2003-09-25,0.1446,2.44,1.95 1366 | 2003-09-26,0.1422,0.12,1.95 1367 | 2003-09-27,0.17,1.92,1.95 1368 | 2003-09-28,0.1598,0.76,1.95 1369 | 2003-09-29,0.1641,1.44,1.95 1370 | 2003-09-30,0.1537,0.04,1.95 1371 | 2003-10-01,0.1468,0.02,1.1 1372 | 2003-10-02,0.1438,0.1,1.1 1373 | 2003-10-03,0.1399,1.76,1.1 1374 | 2003-10-04,0.1567,3.68,1.1 1375 | 2003-10-05,0.1559,1.26,1.1 1376 | 2003-10-06,0.1552,2.36,1.1 1377 | 2003-10-07,0.1467,0.24,1.1 1378 | 2003-10-08,0.1421,1.8,1.1 1379 | 2003-10-09,0.1473,0.0,1.1 1380 | 2003-10-10,0.1354,0.18,1.1 1381 | 2003-10-11,0.14,0.0,1.1 1382 | 2003-10-12,0.1437,0.08,1.1 1383 | 2003-10-13,0.1446,0.0,1.1 1384 | 2003-10-14,0.1438,0.0,1.1 1385 | 2003-10-15,0.1407,0.0,1.1 1386 | 2003-10-16,0.1528,0.18,1.1 1387 | 2003-10-17,0.1486,0.08,1.1 1388 | 2003-10-18,0.1523,2.02,1.1 1389 | 2003-10-19,0.1668,2.88,1.1 1390 | 2003-10-20,0.1883,2.32,1.1 1391 | 2003-10-21,0.1915,1.28,1.1 1392 | 2003-10-22,0.2468,8.2,1.1 1393 | 2003-10-23,0.2178,1.78,1.1 1394 | 2003-10-24,0.2096,1.74,1.1 1395 | 2003-10-25,0.2897,6.9,1.1 1396 | 2003-10-26,0.2864,0.04,1.1 1397 | 2003-10-27,0.2147,0.16,1.1 1398 | 2003-10-28,0.1959,0.0,1.1 1399 | 2003-10-29,0.189,0.14,1.1 1400 | 2003-10-30,0.1907,2.12,1.1 1401 | 2003-10-31,0.2565,8.08,1.1 1402 | 2003-11-01,0.2663,1.5,0.44 1403 | 2003-11-02,0.2302,0.38,0.44 1404 | 2003-11-03,0.204,0.1,0.44 1405 | 2003-11-04,0.1984,0.9,0.44 1406 | 2003-11-05,0.1904,0.02,0.44 1407 | 2003-11-06,0.1813,0.0,0.44 1408 | 2003-11-07,0.1777,0.0,0.44 1409 | 2003-11-08,0.19,8.1,0.44 1410 | 2003-11-09,0.27,1.62,0.44 1411 | 2003-11-10,0.2273,0.02,0.44 1412 | 2003-11-11,0.2375,9.84,0.44 1413 | 2003-11-12,0.2948,0.32,0.44 1414 | 2003-11-13,0.2301,4.02,0.44 1415 | 2003-11-14,0.3582,7.5,0.44 1416 | 2003-11-15,0.2768,0.12,0.44 1417 | 2003-11-16,0.2246,0.26,0.44 1418 | 2003-11-17,0.2109,0.2,0.44 1419 | 2003-11-18,0.2038,3.14,0.44 1420 | 2003-11-19,0.2207,0.28,0.44 1421 | 2003-11-20,0.2272,1.0,0.44 1422 | 2003-11-21,0.2126,0.14,0.44 1423 | 2003-11-22,0.2055,0.06,0.44 1424 | 2003-11-23,0.1942,0.06,0.44 1425 | 2003-11-24,0.2072,4.34,0.44 1426 | 2003-11-25,0.2416,5.58,0.44 1427 | 2003-11-26,0.3035,0.26,0.44 1428 | 2003-11-27,0.2428,0.0,0.44 1429 | 2003-11-28,0.226,2.74,0.44 1430 | 2003-11-29,0.5167,15.24,0.44 1431 | 2003-11-30,0.3791,0.32,0.44 1432 | 2003-12-01,0.2751,1.78,0.24 1433 | 2003-12-02,0.2622,1.82,0.24 1434 | 2003-12-03,0.2384,0.12,0.24 1435 | 2003-12-04,0.2264,0.1,0.24 1436 | 2003-12-05,0.2133,0.2,0.24 1437 | 2003-12-06,0.2068,0.0,0.24 1438 | 2003-12-07,0.2038,0.04,0.24 1439 | 2003-12-08,0.2038,0.04,0.24 1440 | 2003-12-09,0.2012,0.08,0.24 1441 | 2003-12-10,0.2122,1.36,0.24 1442 | 2003-12-11,0.2159,0.0,0.24 1443 | 2003-12-12,0.2179,1.02,0.24 1444 | 2003-12-13,0.2118,0.94,0.24 1445 | 2003-12-14,0.2207,2.7,0.24 1446 | 2003-12-15,0.2182,0.68,0.24 1447 | 2003-12-16,0.2392,1.08,0.24 1448 | 2003-12-17,0.2235,0.04,0.24 1449 | 2003-12-18,0.212,0.14,0.24 1450 | 2003-12-19,0.2083,0.32,0.24 1451 | 2003-12-20,0.7467,27.56,0.24 1452 | 2003-12-21,0.8092,8.16,0.24 1453 | 2003-12-22,0.4746,1.04,0.24 1454 | 2003-12-23,0.7805,1.92,0.24 1455 | 2003-12-24,0.7906,1.04,0.24 1456 | 2003-12-25,0.5429,0.68,0.24 1457 | 2003-12-26,0.4878,7.28,0.24 1458 | 2003-12-27,0.7594,4.16,0.24 1459 | 2003-12-28,0.9665,10.28,0.24 1460 | 2003-12-29,0.6039,0.0,0.24 1461 | 2003-12-30,0.4741,0.0,0.24 1462 | 2003-12-31,0.4574,12.76,0.24 1463 | 2004-01-01,0.5074,1.48,0.22 1464 | 2004-01-02,0.4469,0.78,0.22 1465 | 2004-01-03,0.5948,0.3,0.22 1466 | 2004-01-04,0.5885,0.98,0.22 1467 | 2004-01-05,0.7501,0.16,0.22 1468 | 2004-01-06,0.7239,0.66,0.22 1469 | 2004-01-07,0.6567,4.46,0.22 1470 | 2004-01-08,1.6137,16.74,0.22 1471 | 2004-01-09,1.1491,0.1,0.22 1472 | 2004-01-10,0.8381,2.92,0.22 1473 | 2004-01-11,0.7908,0.14,0.22 1474 | 2004-01-12,0.6885,0.1,0.22 1475 | 2004-01-13,0.6183,0.08,0.22 1476 | 2004-01-14,0.5503,0.08,0.22 1477 | 2004-01-15,0.5177,1.52,0.22 1478 | 2004-01-16,0.6133,6.16,0.22 1479 | 2004-01-17,0.615,0.48,0.22 1480 | 2004-01-18,0.617,5.28,0.22 1481 | 2004-01-19,0.8792,4.66,0.22 1482 | 2004-01-20,0.8172,3.94,0.22 1483 | 2004-01-21,,1.94,0.22 1484 | 2004-01-22,0.8657,0.08,0.22 1485 | 2004-01-23,0.7959,1.64,0.22 1486 | 2004-01-24,0.7407,0.0,0.22 1487 | 2004-01-25,0.682,0.42,0.22 1488 | 2004-01-26,0.6737,4.28,0.22 1489 | 2004-01-27,0.6594,5.96,0.22 1490 | 2004-01-28,0.6414,5.46,0.22 1491 | 2004-01-29,0.6627,2.32,0.22 1492 | 2004-01-30,0.6342,1.64,0.22 1493 | 2004-01-31,0.6143,0.56,0.22 1494 | 2004-02-01,0.6295,0.68,0.55 1495 | 2004-02-02,1.3535,2.1,0.55 1496 | 2004-02-03,1.4565,0.44,0.55 1497 | 2004-02-04,1.0063,2.2,0.55 1498 | 2004-02-05,0.9035,1.92,0.55 1499 | 2004-02-06,0.8829,1.06,0.55 1500 | 2004-02-07,0.8215,9.32,0.55 1501 | 2004-02-08,0.8419,2.06,0.55 1502 | 2004-02-09,0.8017,0.58,0.55 1503 | 2004-02-10,1.21,0.0,0.55 1504 | 2004-02-11,0.9312,0.0,0.55 1505 | 2004-02-12,0.8158,0.04,0.55 1506 | 2004-02-13,0.7686,0.76,0.55 1507 | 2004-02-14,0.7353,0.18,0.55 1508 | 2004-02-15,0.7165,0.0,0.55 1509 | 2004-02-16,0.6904,0.2,0.55 1510 | 2004-02-17,0.6661,0.06,0.55 1511 | 2004-02-18,0.6386,0.0,0.55 1512 | 2004-02-19,0.6172,0.0,0.55 1513 | 2004-02-20,0.6055,0.18,0.55 1514 | 2004-02-21,0.5973,0.98,0.55 1515 | 2004-02-22,,1.76,0.55 1516 | 2004-02-23,,1.68,0.55 1517 | 2004-02-24,,3.0,0.55 1518 | 2004-02-25,,9.08,0.55 1519 | 2004-02-26,0.6083,14.44,0.55 1520 | 2004-02-27,0.893,14.04,0.55 1521 | 2004-02-28,0.7525,5.1,0.55 1522 | 2004-02-29,0.7395,0.0,0.55 1523 | 2004-03-01,0.7319,0.08,1.08 1524 | 2004-03-02,0.6265,0.0,1.08 1525 | 2004-03-03,0.7451,2.88,1.08 1526 | 2004-03-04,0.9093,0.42,1.08 1527 | 2004-03-05,0.9469,0.48,1.08 1528 | 2004-03-06,1.3191,5.74,1.08 1529 | 2004-03-07,2.4875,0.24,1.08 1530 | 2004-03-08,1.5098,0.0,1.08 1531 | 2004-03-09,1.2252,0.8,1.08 1532 | 2004-03-10,1.1044,1.94,1.08 1533 | 2004-03-11,1.1211,2.38,1.08 1534 | 2004-03-12,1.1383,0.88,1.08 1535 | 2004-03-13,1.1263,0.1,1.08 1536 | 2004-03-14,1.1926,1.82,1.08 1537 | 2004-03-15,1.1208,0.06,1.08 1538 | 2004-03-16,0.9832,0.12,1.08 1539 | 2004-03-17,0.8868,0.14,1.08 1540 | 2004-03-18,0.8656,1.46,1.08 1541 | 2004-03-19,0.9321,5.06,1.08 1542 | 2004-03-20,1.0357,4.24,1.08 1543 | 2004-03-21,0.9267,0.72,1.08 1544 | 2004-03-22,0.8493,2.08,1.08 1545 | 2004-03-23,0.9298,6.08,1.08 1546 | 2004-03-24,1.0589,2.78,1.08 1547 | 2004-03-25,1.009,3.8,1.08 1548 | 2004-03-26,1.0012,0.06,1.08 1549 | 2004-03-27,0.9036,1.68,1.08 1550 | 2004-03-28,0.8383,0.2,1.08 1551 | 2004-03-29,0.7853,0.0,1.08 1552 | 2004-03-30,0.7581,0.0,1.08 1553 | 2004-03-31,0.7278,0.0,1.08 1554 | 2004-04-01,0.795,12.06,1.61 1555 | 2004-04-02,1.7615,13.82,1.61 1556 | 2004-04-03,1.3619,4.3,1.61 1557 | 2004-04-04,1.0949,0.16,1.61 1558 | 2004-04-05,1.0047,1.66,1.61 1559 | 2004-04-06,1.0544,5.62,1.61 1560 | 2004-04-07,1.1165,2.28,1.61 1561 | 2004-04-08,0.9942,1.88,1.61 1562 | 2004-04-09,0.931,0.9,1.61 1563 | 2004-04-10,0.8634,0.0,1.61 1564 | 2004-04-11,0.8225,2.6,1.61 1565 | 2004-04-12,0.8478,2.92,1.61 1566 | 2004-04-13,0.8281,0.14,1.61 1567 | 2004-04-14,0.7757,0.36,1.61 1568 | 2004-04-15,0.7432,1.46,1.61 1569 | 2004-04-16,0.7086,1.36,1.61 1570 | 2004-04-17,0.7055,2.62,1.61 1571 | 2004-04-18,1.0794,23.04,1.61 1572 | 2004-04-19,1.3948,0.88,1.61 1573 | 2004-04-20,0.9371,4.48,1.61 1574 | 2004-04-21,0.9037,0.66,1.61 1575 | 2004-04-22,0.7942,0.32,1.61 1576 | 2004-04-23,0.754,1.38,1.61 1577 | 2004-04-24,0.7163,0.0,1.61 1578 | 2004-04-25,0.6856,0.0,1.61 1579 | 2004-04-26,0.6786,7.9,1.61 1580 | 2004-04-27,0.7557,0.22,1.61 1581 | 2004-04-28,0.7165,6.04,1.61 1582 | 2004-04-29,1.137,14.18,1.61 1583 | 2004-04-30,1.4359,2.12,1.61 1584 | 2004-05-01,0.9914,0.0,2.59 1585 | 2004-05-02,0.863,0.0,2.59 1586 | 2004-05-03,0.8026,3.08,2.59 1587 | 2004-05-04,0.8751,7.36,2.59 1588 | 2004-05-05,0.9558,7.42,2.59 1589 | 2004-05-06,0.9173,0.06,2.59 1590 | 2004-05-07,0.8118,2.14,2.59 1591 | 2004-05-08,0.8011,1.76,2.59 1592 | 2004-05-09,0.7497,0.12,2.59 1593 | 2004-05-10,0.6921,0.02,2.59 1594 | 2004-05-11,0.6523,0.0,2.59 1595 | 2004-05-12,0.6321,0.0,2.59 1596 | 2004-05-13,0.6193,0.64,2.59 1597 | 2004-05-14,0.5852,0.0,2.59 1598 | 2004-05-15,0.5538,0.0,2.59 1599 | 2004-05-16,0.5332,0.0,2.59 1600 | 2004-05-17,0.5255,0.08,2.59 1601 | 2004-05-18,0.5197,0.0,2.59 1602 | 2004-05-19,0.4939,0.1,2.59 1603 | 2004-05-20,0.5882,14.3,2.59 1604 | 2004-05-21,0.5397,3.14,2.59 1605 | 2004-05-22,0.4916,0.0,2.59 1606 | 2004-05-23,0.4664,0.46,2.59 1607 | 2004-05-24,0.4762,1.04,2.59 1608 | 2004-05-25,0.47,0.0,2.59 1609 | 2004-05-26,0.4482,0.04,2.59 1610 | 2004-05-27,0.4325,0.0,2.59 1611 | 2004-05-28,0.423,0.72,2.59 1612 | 2004-05-29,0.4277,0.54,2.59 1613 | 2004-05-30,0.4128,0.18,2.59 1614 | 2004-05-31,0.4017,0.1,2.59 1615 | 2004-06-01,0.3994,2.74,2.87 1616 | 2004-06-02,0.3813,2.28,2.87 1617 | 2004-06-03,0.3882,0.44,2.87 1618 | 2004-06-04,0.3699,0.02,2.87 1619 | 2004-06-05,0.3562,0.56,2.87 1620 | 2004-06-06,0.3447,0.0,2.87 1621 | 2004-06-07,0.3482,0.22,2.87 1622 | 2004-06-08,0.368,1.0,2.87 1623 | 2004-06-09,0.3515,0.36,2.87 1624 | 2004-06-10,0.3473,1.8,2.87 1625 | 2004-06-11,0.3506,4.34,2.87 1626 | 2004-06-12,0.3447,0.0,2.87 1627 | 2004-06-13,,0.0,2.87 1628 | 2004-06-14,0.3013,1.24,2.87 1629 | 2004-06-15,0.3146,0.76,2.87 1630 | 2004-06-16,0.3868,5.96,2.87 1631 | 2004-06-17,0.3619,1.52,2.87 1632 | 2004-06-18,0.3616,3.62,2.87 1633 | 2004-06-19,0.3711,1.96,2.87 1634 | 2004-06-20,0.3625,0.9,2.87 1635 | 2004-06-21,0.3647,3.24,2.87 1636 | 2004-06-22,0.404,1.58,2.87 1637 | 2004-06-23,0.6072,32.08,2.87 1638 | 2004-06-24,0.8127,4.84,2.87 1639 | 2004-06-25,0.42,0.06,2.87 1640 | 2004-06-26,0.3654,1.56,2.87 1641 | 2004-06-27,0.3342,3.74,2.87 1642 | 2004-06-28,0.3155,0.0,2.87 1643 | 2004-06-29,0.2964,1.68,2.87 1644 | 2004-06-30,0.2992,1.36,2.87 1645 | 2004-07-01,0.282,0.2,2.64 1646 | 2004-07-02,0.2775,0.4,2.64 1647 | 2004-07-03,0.3156,8.14,2.64 1648 | 2004-07-04,0.292,2.56,2.64 1649 | 2004-07-05,0.361,10.42,2.64 1650 | 2004-07-06,0.3951,7.24,2.64 1651 | 2004-07-07,0.4759,5.84,2.64 1652 | 2004-07-08,0.3649,0.0,2.64 1653 | 2004-07-09,0.3417,4.82,2.64 1654 | 2004-07-10,0.3893,2.68,2.64 1655 | 2004-07-11,0.3493,0.04,2.64 1656 | 2004-07-12,0.316,0.02,2.64 1657 | 2004-07-13,0.2961,0.02,2.64 1658 | 2004-07-14,0.2821,0.0,2.64 1659 | 2004-07-15,0.2763,1.76,2.64 1660 | 2004-07-16,0.2796,0.62,2.64 1661 | 2004-07-17,0.2735,0.62,2.64 1662 | 2004-07-18,0.2642,0.0,2.64 1663 | 2004-07-19,0.2814,4.64,2.64 1664 | 2004-07-20,0.2736,0.24,2.64 1665 | 2004-07-21,0.265,0.0,2.64 1666 | 2004-07-22,0.2636,0.14,2.64 1667 | 2004-07-23,0.255,0.14,2.64 1668 | 2004-07-24,0.2567,1.24,2.64 1669 | 2004-07-25,0.247,0.0,2.64 1670 | 2004-07-26,0.2437,0.0,2.64 1671 | 2004-07-27,0.2478,3.76,2.64 1672 | 2004-07-28,0.2452,0.12,2.64 1673 | 2004-07-29,0.2436,2.46,2.64 1674 | 2004-07-30,0.2458,0.0,2.64 1675 | 2004-07-31,0.2554,0.68,2.64 1676 | 2004-08-01,0.2435,0.0,2.29 1677 | 2004-08-02,0.2383,0.0,2.29 1678 | 2004-08-03,0.2661,15.06,2.29 1679 | 2004-08-04,0.3481,1.06,2.29 1680 | 2004-08-05,0.2786,6.3,2.29 1681 | 2004-08-06,0.3226,0.04,2.29 1682 | 2004-08-07,0.2571,0.0,2.29 1683 | 2004-08-08,0.2555,0.0,2.29 1684 | 2004-08-09,0.2627,6.2,2.29 1685 | 2004-08-10,0.2605,3.1,2.29 1686 | 2004-08-11,0.4327,24.48,2.29 1687 | 2004-08-12,0.5009,0.18,2.29 1688 | 2004-08-13,0.3324,0.0,2.29 1689 | 2004-08-14,0.2848,0.04,2.29 1690 | 2004-08-15,0.2738,3.7,2.29 1691 | 2004-08-16,0.3341,3.68,2.29 1692 | 2004-08-17,0.306,17.92,2.29 1693 | 2004-08-18,3.7594,45.28,2.29 1694 | 2004-08-19,2.122,2.84,2.29 1695 | 2004-08-20,0.925,0.74,2.29 1696 | 2004-08-21,0.7133,0.5,2.29 1697 | 2004-08-22,0.6149,2.34,2.29 1698 | 2004-08-23,0.6928,10.56,2.29 1699 | 2004-08-24,0.9101,10.38,2.29 1700 | 2004-08-25,0.8456,2.2,2.29 1701 | 2004-08-26,0.6973,1.9,2.29 1702 | 2004-08-27,0.6333,0.1,2.29 1703 | 2004-08-28,0.5849,0.28,2.29 1704 | 2004-08-29,0.554,5.88,2.29 1705 | 2004-08-30,0.5861,2.0,2.29 1706 | 2004-08-31,0.5264,0.04,2.29 1707 | 2004-09-01,0.5011,0.0,1.83 1708 | 2004-09-02,0.4769,0.0,1.83 1709 | 2004-09-03,0.4628,0.06,1.83 1710 | 2004-09-04,0.4378,0.0,1.83 1711 | 2004-09-05,0.4332,2.04,1.83 1712 | 2004-09-06,0.4219,0.0,1.83 1713 | 2004-09-07,0.4073,0.06,1.83 1714 | 2004-09-08,0.3984,0.04,1.83 1715 | 2004-09-09,0.3935,0.0,1.83 1716 | 2004-09-10,0.3975,0.28,1.83 1717 | 2004-09-11,0.3942,1.02,1.83 1718 | 2004-09-12,0.3849,1.88,1.83 1719 | 2004-09-13,0.387,1.32,1.83 1720 | 2004-09-14,0.379,2.66,1.83 1721 | 2004-09-15,0.3703,0.98,1.83 1722 | 2004-09-16,0.3786,0.76,1.83 1723 | 2004-09-17,0.3669,1.08,1.83 1724 | 2004-09-18,0.3577,0.0,1.83 1725 | 2004-09-19,0.3556,0.46,1.83 1726 | 2004-09-20,0.3538,0.82,1.83 1727 | 2004-09-21,0.3406,0.08,1.83 1728 | 2004-09-22,0.3368,0.32,1.83 1729 | 2004-09-23,0.3475,3.76,1.83 1730 | 2004-09-24,0.3354,1.18,1.83 1731 | 2004-09-25,0.3371,0.0,1.83 1732 | 2004-09-26,0.3275,0.0,1.83 1733 | 2004-09-27,0.346,4.62,1.83 1734 | 2004-09-28,0.3485,2.34,1.83 1735 | 2004-09-29,0.3376,2.48,1.83 1736 | 2004-09-30,0.3555,1.88,1.83 1737 | 2004-10-01,0.3496,1.94,0.8 1738 | 2004-10-02,0.3369,0.24,0.8 1739 | 2004-10-03,0.3436,12.66,0.8 1740 | 2004-10-04,0.4851,3.16,0.8 1741 | 2004-10-05,0.373,0.44,0.8 1742 | 2004-10-06,0.3486,2.78,0.8 1743 | 2004-10-07,0.3572,3.06,0.8 1744 | 2004-10-08,0.3379,0.22,0.8 1745 | 2004-10-09,0.3294,0.22,0.8 1746 | 2004-10-10,0.3256,0.56,0.8 1747 | 2004-10-11,0.3241,0.42,0.8 1748 | 2004-10-12,0.3371,10.38,0.8 1749 | 2004-10-13,0.5642,8.04,0.8 1750 | 2004-10-14,0.9253,17.68,0.8 1751 | 2004-10-15,1.788,22.3,0.8 1752 | 2004-10-16,2.539,10.42,0.8 1753 | 2004-10-17,1.8499,0.34,0.8 1754 | 2004-10-18,0.993,0.12,0.8 1755 | 2004-10-19,0.9552,6.64,0.8 1756 | 2004-10-20,0.9146,14.64,0.8 1757 | 2004-10-21,1.7516,1.74,0.8 1758 | 2004-10-22,0.9868,0.06,0.8 1759 | 2004-10-23,0.8472,0.18,0.8 1760 | 2004-10-24,1.5594,17.82,0.8 1761 | 2004-10-25,1.5825,0.7,0.8 1762 | 2004-10-26,1.0493,0.08,0.8 1763 | 2004-10-27,0.9692,10.02,0.8 1764 | 2004-10-28,2.0379,10.78,0.8 1765 | 2004-10-29,2.5468,13.94,0.8 1766 | 2004-10-30,1.8204,0.14,0.8 1767 | 2004-10-31,1.3572,0.0,0.8 1768 | 2004-11-01,1.1702,0.04,0.41 1769 | 2004-11-02,1.0811,2.6,0.41 1770 | 2004-11-03,1.0497,0.18,0.41 1771 | 2004-11-04,0.9345,2.14,0.41 1772 | 2004-11-05,0.8609,0.3,0.41 1773 | 2004-11-06,0.8743,4.04,0.41 1774 | 2004-11-07,0.8719,0.22,0.41 1775 | 2004-11-08,0.8069,0.56,0.41 1776 | 2004-11-09,0.7716,2.74,0.41 1777 | 2004-11-10,0.7491,1.32,0.41 1778 | 2004-11-11,0.7353,2.6,0.41 1779 | 2004-11-12,0.7767,2.04,0.41 1780 | 2004-11-13,0.7257,0.08,0.41 1781 | 2004-11-14,0.6918,0.04,0.41 1782 | 2004-11-15,0.6667,1.64,0.41 1783 | 2004-11-16,0.6652,2.98,0.41 1784 | 2004-11-17,0.7006,2.66,0.41 1785 | 2004-11-18,0.6691,0.5,0.41 1786 | 2004-11-19,0.6073,0.12,0.41 1787 | 2004-11-20,0.5829,0.08,0.41 1788 | 2004-11-21,0.7471,7.48,0.41 1789 | 2004-11-22,0.9083,5.74,0.41 1790 | 2004-11-23,0.8465,0.9,0.41 1791 | 2004-11-24,0.7576,0.06,0.41 1792 | 2004-11-25,0.7048,0.04,0.41 1793 | 2004-11-26,0.6555,0.14,0.41 1794 | 2004-11-27,0.9011,14.68,0.41 1795 | 2004-11-28,1.2077,0.86,0.41 1796 | 2004-11-29,0.8918,0.12,0.41 1797 | 2004-11-30,0.81,0.0,0.41 1798 | 2004-12-01,0.7318,0.06,0.45 1799 | 2004-12-02,0.6868,0.38,0.45 1800 | 2004-12-03,0.6779,0.16,0.45 1801 | 2004-12-04,0.6607,0.1,0.45 1802 | 2004-12-05,0.6311,0.0,0.45 1803 | 2004-12-06,0.6163,0.02,0.45 1804 | 2004-12-07,0.5865,0.04,0.45 1805 | 2004-12-08,0.5779,0.0,0.45 1806 | 2004-12-09,0.57,0.0,0.45 1807 | 2004-12-10,0.5488,0.0,0.45 1808 | 2004-12-11,0.5312,0.04,0.45 1809 | 2004-12-12,0.5202,0.04,0.45 1810 | 2004-12-13,0.519,0.0,0.45 1811 | 2004-12-14,0.6482,12.76,0.45 1812 | 2004-12-15,0.7397,0.12,0.45 1813 | 2004-12-16,0.6713,2.38,0.45 1814 | 2004-12-17,0.6162,0.86,0.45 1815 | 2004-12-18,0.5581,0.6,0.45 1816 | 2004-12-19,0.514,0.0,0.45 1817 | 2004-12-20,0.4996,0.0,0.45 1818 | 2004-12-21,0.5061,0.2,0.45 1819 | 2004-12-22,0.5435,4.56,0.45 1820 | 2004-12-23,0.631,5.76,0.45 1821 | 2004-12-24,0.6507,2.42,0.45 1822 | 2004-12-25,0.575,2.88,0.45 1823 | 2004-12-26,0.5385,0.76,0.45 1824 | 2004-12-27,0.8574,0.3,0.45 1825 | 2004-12-28,0.7022,0.74,0.45 1826 | 2004-12-29,0.6377,0.14,0.45 1827 | 2004-12-30,0.6204,0.66,0.45 1828 | 2004-12-31,0.5849,1.0,0.45 1829 | 2005-01-01,0.6189,2.56,0.55 1830 | 2005-01-02,0.5774,1.18,0.55 1831 | 2005-01-03,0.6143,0.1,0.55 1832 | 2005-01-04,0.596,0.26,0.55 1833 | 2005-01-05,0.6341,2.6,0.55 1834 | 2005-01-06,0.6205,3.24,0.55 1835 | 2005-01-07,0.7373,12.66,0.55 1836 | 2005-01-08,1.5044,4.02,0.55 1837 | 2005-01-09,0.9653,1.02,0.55 1838 | 2005-01-10,0.8297,0.02,0.55 1839 | 2005-01-11,0.8048,5.86,0.55 1840 | 2005-01-12,0.7831,1.54,0.55 1841 | 2005-01-13,0.7119,0.02,0.55 1842 | 2005-01-14,0.688,0.02,0.55 1843 | 2005-01-15,0.6807,0.28,0.55 1844 | 2005-01-16,0.6572,0.04,0.55 1845 | 2005-01-17,0.6635,0.3,0.55 1846 | 2005-01-18,0.6141,0.86,0.55 1847 | 2005-01-19,0.6201,3.78,0.55 1848 | 2005-01-20,0.6995,5.18,0.55 1849 | 2005-01-21,0.7209,0.26,0.55 1850 | 2005-01-22,0.6398,1.22,0.55 1851 | 2005-01-23,0.6093,0.3,0.55 1852 | 2005-01-24,0.6042,2.1,0.55 1853 | 2005-01-25,0.7579,4.18,0.55 1854 | 2005-01-26,0.7154,0.8,0.55 1855 | 2005-01-27,0.6687,0.8,0.55 1856 | 2005-01-28,0.6387,1.18,0.55 1857 | 2005-01-29,0.6054,0.0,0.55 1858 | 2005-01-30,0.5797,0.48,0.55 1859 | 2005-01-31,0.5732,2.74,0.55 1860 | 2005-02-01,0.5591,0.06,0.44 1861 | 2005-02-02,0.5436,0.04,0.44 1862 | 2005-02-03,0.5346,0.04,0.44 1863 | 2005-02-04,0.5313,1.38,0.44 1864 | 2005-02-05,0.524,1.06,0.44 1865 | 2005-02-06,0.5101,0.24,0.44 1866 | 2005-02-07,0.5065,0.5,0.44 1867 | 2005-02-08,0.4937,0.02,0.44 1868 | 2005-02-09,0.52,3.14,0.44 1869 | 2005-02-10,0.5156,0.42,0.44 1870 | 2005-02-11,0.5316,8.3,0.44 1871 | 2005-02-12,0.8464,8.34,0.44 1872 | 2005-02-13,1.2462,5.5,0.44 1873 | 2005-02-14,0.8919,1.7,0.44 1874 | 2005-02-15,0.7497,0.0,0.44 1875 | 2005-02-16,0.6919,0.08,0.44 1876 | 2005-02-17,0.6581,1.08,0.44 1877 | 2005-02-18,0.6374,2.44,0.44 1878 | 2005-02-19,0.6139,4.66,0.44 1879 | 2005-02-20,0.6166,8.3,0.44 1880 | 2005-02-21,0.605,3.38,0.44 1881 | 2005-02-22,0.6006,5.56,0.44 1882 | 2005-02-23,0.5918,3.16,0.44 1883 | 2005-02-24,0.5909,1.64,0.44 1884 | 2005-02-25,0.6175,2.06,0.44 1885 | 2005-02-26,0.9777,0.2,0.44 1886 | 2005-02-27,0.9217,0.12,0.44 1887 | 2005-02-28,0.9359,11.76,0.44 1888 | 2005-03-01,1.0504,9.74,0.81 1889 | 2005-03-02,0.9112,4.58,0.81 1890 | 2005-03-03,0.8586,2.52,0.81 1891 | 2005-03-04,1.0628,1.92,0.81 1892 | 2005-03-05,1.3696,0.94,0.81 1893 | 2005-03-06,1.0485,0.36,0.81 1894 | 2005-03-07,1.3556,0.0,0.81 1895 | 2005-03-08,1.2557,0.2,0.81 1896 | 2005-03-09,1.1138,3.68,0.81 1897 | 2005-03-10,1.0418,3.4,0.81 1898 | 2005-03-11,1.0527,1.74,0.81 1899 | 2005-03-12,0.9562,0.26,0.81 1900 | 2005-03-13,0.8814,0.22,0.81 1901 | 2005-03-14,0.8494,5.12,0.81 1902 | 2005-03-15,0.9372,0.7,0.81 1903 | 2005-03-16,0.8674,0.42,0.81 1904 | 2005-03-17,0.7978,0.06,0.81 1905 | 2005-03-18,0.7686,0.58,0.81 1906 | 2005-03-19,0.7505,0.0,0.81 1907 | 2005-03-20,0.7303,0.0,0.81 1908 | 2005-03-21,0.7158,1.38,0.81 1909 | 2005-03-22,0.7183,1.22,0.81 1910 | 2005-03-23,0.6686,0.0,0.81 1911 | 2005-03-24,0.6458,0.08,0.81 1912 | 2005-03-25,0.6184,0.16,0.81 1913 | 2005-03-26,0.6106,4.38,0.81 1914 | 2005-03-27,0.648,3.28,0.81 1915 | 2005-03-28,0.6471,3.3,0.81 1916 | 2005-03-29,0.7037,4.3,0.81 1917 | 2005-03-30,0.7093,0.14,0.81 1918 | 2005-03-31,0.6502,0.0,0.81 1919 | 2005-04-01,0.6155,0.1,1.86 1920 | 2005-04-02,0.58,0.12,1.86 1921 | 2005-04-03,0.5562,0.16,1.86 1922 | 2005-04-04,0.5372,0.32,1.86 1923 | 2005-04-05,0.5173,1.38,1.86 1924 | 2005-04-06,0.5091,6.2,1.86 1925 | 2005-04-07,0.8888,19.72,1.86 1926 | 2005-04-08,0.8331,5.06,1.86 1927 | 2005-04-09,0.9622,0.44,1.86 1928 | 2005-04-10,0.7691,0.0,1.86 1929 | 2005-04-11,0.6684,0.04,1.86 1930 | 2005-04-12,0.6144,0.0,1.86 1931 | 2005-04-13,0.5935,0.06,1.86 1932 | 2005-04-14,0.6243,7.06,1.86 1933 | 2005-04-15,0.6449,5.98,1.86 1934 | 2005-04-16,0.7012,0.94,1.86 1935 | 2005-04-17,0.6494,10.5,1.86 1936 | 2005-04-18,2.0065,15.06,1.86 1937 | 2005-04-19,1.178,0.3,1.86 1938 | 2005-04-20,0.908,0.0,1.86 1939 | 2005-04-21,0.8143,0.0,1.86 1940 | 2005-04-22,0.7573,0.0,1.86 1941 | 2005-04-23,0.7166,0.0,1.86 1942 | 2005-04-24,0.6781,0.0,1.86 1943 | 2005-04-25,0.6681,0.04,1.86 1944 | 2005-04-26,0.6425,2.4,1.86 1945 | 2005-04-27,0.7608,7.78,1.86 1946 | 2005-04-28,0.7617,4.52,1.86 1947 | 2005-04-29,0.6669,0.0,1.86 1948 | 2005-04-30,0.6236,0.88,1.86 1949 | 2005-05-01,0.7007,4.94,2.35 1950 | 2005-05-02,0.6399,0.14,2.35 1951 | 2005-05-03,0.8174,6.64,2.35 1952 | 2005-05-04,0.6889,0.08,2.35 1953 | 2005-05-05,0.6464,3.6,2.35 1954 | 2005-05-06,0.6169,2.04,2.35 1955 | 2005-05-07,0.6035,2.12,2.35 1956 | 2005-05-08,0.6046,3.5,2.35 1957 | 2005-05-09,0.5967,3.06,2.35 1958 | 2005-05-10,0.5627,0.14,2.35 1959 | 2005-05-11,0.5353,0.82,2.35 1960 | 2005-05-12,0.5106,0.1,2.35 1961 | 2005-05-13,0.4906,0.04,2.35 1962 | 2005-05-14,0.4747,0.0,2.35 1963 | 2005-05-15,0.4639,1.4,2.35 1964 | 2005-05-16,0.461,0.44,2.35 1965 | 2005-05-17,0.4597,0.88,2.35 1966 | 2005-05-18,0.4457,3.4,2.35 1967 | 2005-05-19,0.5059,5.6,2.35 1968 | 2005-05-20,0.4962,7.1,2.35 1969 | 2005-05-21,0.4607,0.16,2.35 1970 | 2005-05-22,0.4409,4.38,2.35 1971 | 2005-05-23,0.5482,2.14,2.35 1972 | 2005-05-24,0.4643,0.5,2.35 1973 | 2005-05-25,0.4602,1.2,2.35 1974 | 2005-05-26,0.4272,0.02,2.35 1975 | 2005-05-27,0.4443,9.14,2.35 1976 | 2005-05-28,0.5292,2.34,2.35 1977 | 2005-05-29,0.4436,0.32,2.35 1978 | 2005-05-30,0.4756,3.02,2.35 1979 | 2005-05-31,0.457,0.68,2.35 1980 | 2005-06-01,0.6304,8.4,2.54 1981 | 2005-06-02,0.5946,2.3,2.54 1982 | 2005-06-03,0.6928,7.46,2.54 1983 | 2005-06-04,0.672,1.82,2.54 1984 | 2005-06-05,0.5717,0.8,2.54 1985 | 2005-06-06,0.4992,0.04,2.54 1986 | 2005-06-07,0.4561,0.0,2.54 1987 | 2005-06-08,0.4354,0.0,2.54 1988 | 2005-06-09,0.4145,0.0,2.54 1989 | 2005-06-10,0.3983,0.0,2.54 1990 | 2005-06-11,0.3976,1.64,2.54 1991 | 2005-06-12,0.4163,7.72,2.54 1992 | 2005-06-13,0.4642,7.62,2.54 1993 | 2005-06-14,0.5843,7.12,2.54 1994 | 2005-06-15,0.5035,1.06,2.54 1995 | 2005-06-16,0.45,0.18,2.54 1996 | 2005-06-17,0.4162,0.0,2.54 1997 | 2005-06-18,0.4059,3.76,2.54 1998 | 2005-06-19,0.4219,0.76,2.54 1999 | 2005-06-20,0.3876,0.96,2.54 2000 | 2005-06-21,0.3727,1.58,2.54 2001 | 2005-06-22,0.375,0.0,2.54 2002 | 2005-06-23,0.354,0.0,2.54 2003 | 2005-06-24,0.3498,1.16,2.54 2004 | 2005-06-25,0.3441,0.0,2.54 2005 | 2005-06-26,0.33,0.0,2.54 2006 | 2005-06-27,0.3204,0.0,2.54 2007 | 2005-06-28,0.319,0.0,2.54 2008 | 2005-06-29,0.3187,0.82,2.54 2009 | 2005-06-30,0.3239,0.08,2.54 2010 | 2005-07-01,0.3046,0.0,2.87 2011 | 2005-07-02,0.303,0.34,2.87 2012 | 2005-07-03,0.2914,0.0,2.87 2013 | 2005-07-04,0.2966,6.02,2.87 2014 | 2005-07-05,0.3193,7.16,2.87 2015 | 2005-07-06,0.3947,8.28,2.87 2016 | 2005-07-07,0.3188,0.0,2.87 2017 | 2005-07-08,0.2944,0.0,2.87 2018 | 2005-07-09,0.2845,0.14,2.87 2019 | 2005-07-10,0.278,0.0,2.87 2020 | 2005-07-11,0.2728,0.0,2.87 2021 | 2005-07-12,0.2712,0.0,2.87 2022 | 2005-07-13,0.2693,0.0,2.87 2023 | 2005-07-14,0.2696,1.26,2.87 2024 | 2005-07-15,0.2661,0.1,2.87 2025 | 2005-07-16,0.2688,0.22,2.87 2026 | 2005-07-17,0.2714,0.94,2.87 2027 | 2005-07-18,0.2745,1.92,2.87 2028 | 2005-07-19,0.2754,2.12,2.87 2029 | 2005-07-20,0.2683,0.26,2.87 2030 | 2005-07-21,0.2583,0.1,2.87 2031 | 2005-07-22,0.2557,0.08,2.87 2032 | 2005-07-23,0.2565,0.0,2.87 2033 | 2005-07-24,0.2546,0.32,2.87 2034 | 2005-07-25,0.2548,0.0,2.87 2035 | 2005-07-26,0.2485,0.0,2.87 2036 | 2005-07-27,0.2494,0.02,2.87 2037 | 2005-07-28,0.2521,4.34,2.87 2038 | 2005-07-29,0.2708,2.54,2.87 2039 | 2005-07-30,0.2671,0.64,2.87 2040 | 2005-07-31,0.2574,0.08,2.87 2041 | 2005-08-01,0.246,0.0,2.49 2042 | 2005-08-02,0.2435,0.0,2.49 2043 | 2005-08-03,0.2366,0.0,2.49 2044 | 2005-08-04,0.2347,0.0,2.49 2045 | 2005-08-05,0.2609,6.12,2.49 2046 | 2005-08-06,0.2619,0.98,2.49 2047 | 2005-08-07,0.2432,0.16,2.49 2048 | 2005-08-08,0.2335,0.02,2.49 2049 | 2005-08-09,0.3317,11.02,2.49 2050 | 2005-08-10,0.2764,0.18,2.49 2051 | 2005-08-11,0.2538,1.58,2.49 2052 | 2005-08-12,0.2524,1.56,2.49 2053 | 2005-08-13,0.4957,18.76,2.49 2054 | 2005-08-14,0.3572,0.14,2.49 2055 | 2005-08-15,0.3136,2.0,2.49 2056 | 2005-08-16,,0.0,2.49 2057 | 2005-08-17,,2.76,2.49 2058 | 2005-08-18,,2.12,2.49 2059 | 2005-08-19,,2.68,2.49 2060 | 2005-08-20,,0.2,2.49 2061 | 2005-08-21,,6.78,2.49 2062 | 2005-08-22,,0.3,2.49 2063 | 2005-08-23,,1.32,2.49 2064 | 2005-08-24,,0.08,2.49 2065 | 2005-08-25,0.2562,1.76,2.49 2066 | 2005-08-26,0.2504,0.34,2.49 2067 | 2005-08-27,0.2577,9.74,2.49 2068 | 2005-08-28,0.3561,2.26,2.49 2069 | 2005-08-29,0.2713,0.0,2.49 2070 | 2005-08-30,0.2642,0.0,2.49 2071 | 2005-08-31,0.2755,3.3,2.49 2072 | 2005-09-01,0.2675,0.22,1.85 2073 | 2005-09-02,0.2612,0.04,1.85 2074 | 2005-09-03,0.2637,0.0,1.85 2075 | 2005-09-04,0.2578,0.0,1.85 2076 | 2005-09-05,0.263,0.0,1.85 2077 | 2005-09-06,0.2535,0.0,1.85 2078 | 2005-09-07,0.2564,2.82,1.85 2079 | 2005-09-08,0.2558,1.48,1.85 2080 | 2005-09-09,0.2488,0.46,1.85 2081 | 2005-09-10,0.2612,0.0,1.85 2082 | 2005-09-11,0.2462,0.0,1.85 2083 | 2005-09-12,0.2465,5.66,1.85 2084 | 2005-09-13,0.271,0.36,1.85 2085 | 2005-09-14,0.242,0.04,1.85 2086 | 2005-09-15,0.2476,1.26,1.85 2087 | 2005-09-16,0.2437,1.3,1.85 2088 | 2005-09-17,0.2458,0.18,1.85 2089 | 2005-09-18,0.2394,0.0,1.85 2090 | 2005-09-19,0.239,0.16,1.85 2091 | 2005-09-20,0.2337,1.0,1.85 2092 | 2005-09-21,0.2405,0.02,1.85 2093 | 2005-09-22,0.238,0.6,1.85 2094 | 2005-09-23,0.2313,0.46,1.85 2095 | 2005-09-24,0.2294,0.38,1.85 2096 | 2005-09-25,0.2288,0.24,1.85 2097 | 2005-09-26,0.2409,2.82,1.85 2098 | 2005-09-27,0.226,0.0,1.85 2099 | 2005-09-28,0.2296,0.44,1.85 2100 | 2005-09-29,0.2325,3.58,1.85 2101 | 2005-09-30,0.2372,0.74,1.85 2102 | 2005-10-01,0.2466,1.58,0.97 2103 | 2005-10-02,0.2275,0.0,0.97 2104 | 2005-10-03,0.2284,0.0,0.97 2105 | 2005-10-04,0.2309,0.0,0.97 2106 | 2005-10-05,0.2262,0.0,0.97 2107 | 2005-10-06,0.2276,0.0,0.97 2108 | 2005-10-07,0.2322,4.7,0.97 2109 | 2005-10-08,0.2409,0.72,0.97 2110 | 2005-10-09,0.2326,0.36,0.97 2111 | 2005-10-10,0.2618,24.88,0.97 2112 | 2005-10-11,1.1775,42.72,0.97 2113 | 2005-10-12,1.1086,5.44,0.97 2114 | 2005-10-13,0.4865,0.1,0.97 2115 | 2005-10-14,0.3663,0.0,0.97 2116 | 2005-10-15,0.326,0.0,0.97 2117 | 2005-10-16,0.3033,0.0,0.97 2118 | 2005-10-17,0.2903,0.18,0.97 2119 | 2005-10-18,0.2803,0.0,0.97 2120 | 2005-10-19,0.2883,2.14,0.97 2121 | 2005-10-20,0.2947,0.34,0.97 2122 | 2005-10-21,0.2789,1.5,0.97 2123 | 2005-10-22,0.2774,0.72,0.97 2124 | 2005-10-23,0.2716,4.2,0.97 2125 | 2005-10-24,0.9197,23.28,0.97 2126 | 2005-10-25,0.761,0.24,0.97 2127 | 2005-10-26,0.5827,3.36,0.97 2128 | 2005-10-27,0.5222,2.18,0.97 2129 | 2005-10-28,0.4799,2.64,0.97 2130 | 2005-10-29,0.5635,8.38,0.97 2131 | 2005-10-30,0.7257,5.82,0.97 2132 | 2005-10-31,0.5651,0.1,0.97 2133 | 2005-11-01,0.4639,1.74,0.46 2134 | 2005-11-02,0.5096,6.44,0.46 2135 | 2005-11-03,0.597,1.66,0.46 2136 | 2005-11-04,0.5196,0.32,0.46 2137 | 2005-11-05,0.4617,0.1,0.46 2138 | 2005-11-06,0.4319,0.56,0.46 2139 | 2005-11-07,0.4517,8.22,0.46 2140 | 2005-11-08,0.4789,0.0,0.46 2141 | 2005-11-09,0.4306,0.36,0.46 2142 | 2005-11-10,0.415,5.58,0.46 2143 | 2005-11-11,0.5754,4.42,0.46 2144 | 2005-11-12,0.6196,4.06,0.46 2145 | 2005-11-13,0.5839,0.12,0.46 2146 | 2005-11-14,0.5091,0.38,0.46 2147 | 2005-11-15,0.47,4.04,0.46 2148 | 2005-11-16,0.5972,5.84,0.46 2149 | 2005-11-17,0.5754,0.18,0.46 2150 | 2005-11-18,0.486,0.0,0.46 2151 | 2005-11-19,0.4472,0.14,0.46 2152 | 2005-11-20,0.4624,0.0,0.46 2153 | 2005-11-21,0.4361,0.12,0.46 2154 | 2005-11-22,0.4071,0.14,0.46 2155 | 2005-11-23,0.4051,2.74,0.46 2156 | 2005-11-24,0.5102,19.54,0.46 2157 | 2005-11-25,2.7753,24.5,0.46 2158 | 2005-11-26,7.2957,23.96,0.46 2159 | 2005-11-27,3.5606,8.64,0.46 2160 | 2005-11-28,2.3036,7.38,0.46 2161 | 2005-11-29,1.7655,0.62,0.46 2162 | 2005-11-30,1.4873,1.0,0.46 2163 | 2005-12-01,2.8313,8.14,0.16 2164 | 2005-12-02,4.2463,19.56,0.16 2165 | 2005-12-03,4.6876,16.44,0.16 2166 | 2005-12-04,3.537,4.38,0.16 2167 | 2005-12-05,2.914,8.44,0.16 2168 | 2005-12-06,2.5281,0.3,0.16 2169 | 2005-12-07,1.9647,0.12,0.16 2170 | 2005-12-08,1.582,0.5,0.16 2171 | 2005-12-09,1.4945,0.68,0.16 2172 | 2005-12-10,1.334,0.0,0.16 2173 | 2005-12-11,1.1911,0.7,0.16 2174 | 2005-12-12,1.0846,0.14,0.16 2175 | 2005-12-13,1.0224,0.18,0.16 2176 | 2005-12-14,0.9621,0.0,0.16 2177 | 2005-12-15,1.0031,6.06,0.16 2178 | 2005-12-16,1.038,2.5,0.16 2179 | 2005-12-17,0.9257,1.42,0.16 2180 | 2005-12-18,0.9308,1.44,0.16 2181 | 2005-12-19,0.9413,0.0,0.16 2182 | 2005-12-20,0.8964,0.54,0.16 2183 | 2005-12-21,0.9253,1.34,0.16 2184 | 2005-12-22,0.9762,2.26,0.16 2185 | 2005-12-23,0.8909,0.0,0.16 2186 | 2005-12-24,0.8261,1.48,0.16 2187 | 2005-12-25,0.8089,0.28,0.16 2188 | 2005-12-26,0.7926,0.8,0.16 2189 | 2005-12-27,0.7514,1.3,0.16 2190 | 2005-12-28,0.7222,0.46,0.16 2191 | 2005-12-29,0.7013,0.34,0.16 2192 | 2005-12-30,0.7152,3.94,0.16 2193 | 2005-12-31,0.6872,1.72,0.16 2194 | 2006-01-01,0.7452,0.68,0.44 2195 | 2006-01-02,0.7388,1.62,0.44 2196 | 2006-01-03,0.7734,0.04,0.44 2197 | 2006-01-04,0.683,0.58,0.44 2198 | 2006-01-05,0.7199,4.36,0.44 2199 | 2006-01-06,0.7484,1.8,0.44 2200 | 2006-01-07,0.7562,2.3,0.44 2201 | 2006-01-08,0.7062,0.04,0.44 2202 | 2006-01-09,0.6795,0.24,0.44 2203 | 2006-01-10,0.6932,1.24,0.44 2204 | 2006-01-11,0.6569,0.08,0.44 2205 | 2006-01-12,0.6409,0.46,0.44 2206 | 2006-01-13,0.639,4.7,0.44 2207 | 2006-01-14,0.6263,0.06,0.44 2208 | 2006-01-15,0.6139,0.66,0.44 2209 | 2006-01-16,0.6049,0.08,0.44 2210 | 2006-01-17,0.5779,0.24,0.44 2211 | 2006-01-18,0.5744,0.5,0.44 2212 | 2006-01-19,0.5767,1.92,0.44 2213 | 2006-01-20,0.5805,2.02,0.44 2214 | 2006-01-21,0.5632,0.0,0.44 2215 | 2006-01-22,0.5499,0.0,0.44 2216 | 2006-01-23,0.5376,0.0,0.44 2217 | 2006-01-24,0.5175,0.0,0.44 2218 | 2006-01-25,0.505,0.08,0.44 2219 | 2006-01-26,0.4981,0.0,0.44 2220 | 2006-01-27,0.4927,0.96,0.44 2221 | 2006-01-28,0.4917,0.56,0.44 2222 | 2006-01-29,0.4798,0.0,0.44 2223 | 2006-01-30,0.4547,0.08,0.44 2224 | 2006-01-31,0.4482,0.0,0.44 2225 | 2006-02-01,0.4384,0.0,0.5 2226 | 2006-02-02,0.4317,0.0,0.5 2227 | 2006-02-03,0.4313,0.0,0.5 2228 | 2006-02-04,0.4237,0.0,0.5 2229 | 2006-02-05,0.4281,0.0,0.5 2230 | 2006-02-06,0.4415,0.0,0.5 2231 | 2006-02-07,0.4508,1.9,0.5 2232 | 2006-02-08,0.4376,2.24,0.5 2233 | 2006-02-09,0.4242,0.42,0.5 2234 | 2006-02-10,0.4175,0.0,0.5 2235 | 2006-02-11,0.4271,0.08,0.5 2236 | 2006-02-12,0.4261,0.04,0.5 2237 | 2006-02-13,0.427,1.08,0.5 2238 | 2006-02-14,0.5049,3.94,0.5 2239 | 2006-02-15,0.4949,0.04,0.5 2240 | 2006-02-16,0.4809,3.78,0.5 2241 | 2006-02-17,0.4814,0.78,0.5 2242 | 2006-02-18,0.53,7.96,0.5 2243 | 2006-02-19,0.7676,2.48,0.5 2244 | 2006-02-20,0.6449,1.58,0.5 2245 | 2006-02-21,0.6366,2.72,0.5 2246 | 2006-02-22,0.5798,0.4,0.5 2247 | 2006-02-23,0.5967,4.2,0.5 2248 | 2006-02-24,0.6774,0.76,0.5 2249 | 2006-02-25,0.6052,0.56,0.5 2250 | 2006-02-26,0.5662,1.22,0.5 2251 | 2006-02-27,0.5889,5.54,0.5 2252 | 2006-02-28,0.5672,12.12,0.5 2253 | 2006-03-01,0.5692,5.18,0.45 2254 | 2006-03-02,0.546,3.8,0.45 2255 | 2006-03-03,0.5316,2.76,0.45 2256 | 2006-03-04,0.5158,1.86,0.45 2257 | 2006-03-05,0.5059,0.86,0.45 2258 | 2006-03-06,0.487,0.32,0.45 2259 | 2006-03-07,0.6022,3.14,0.45 2260 | 2006-03-08,1.1163,0.5,0.45 2261 | 2006-03-09,1.5815,9.58,0.45 2262 | 2006-03-10,1.5423,3.76,0.45 2263 | 2006-03-11,1.1733,2.78,0.45 2264 | 2006-03-12,0.9793,2.5,0.45 2265 | 2006-03-13,1.0182,5.24,0.45 2266 | 2006-03-14,1.0397,0.98,0.45 2267 | 2006-03-15,0.9745,0.86,0.45 2268 | 2006-03-16,0.865,1.48,0.45 2269 | 2006-03-17,0.8421,0.34,0.45 2270 | 2006-03-18,0.8193,0.04,0.45 2271 | 2006-03-19,0.8202,1.14,0.45 2272 | 2006-03-20,0.782,0.54,0.45 2273 | 2006-03-21,0.7323,1.0,0.45 2274 | 2006-03-22,0.7123,2.42,0.45 2275 | 2006-03-23,0.7853,0.28,0.45 2276 | 2006-03-24,0.8918,8.46,0.45 2277 | 2006-03-25,1.2703,7.3,0.45 2278 | 2006-03-26,1.3863,3.42,0.45 2279 | 2006-03-27,1.2991,2.68,0.45 2280 | 2006-03-28,1.195,1.36,0.45 2281 | 2006-03-29,1.0066,1.5,0.45 2282 | 2006-03-30,1.0926,6.58,0.45 2283 | 2006-03-31,1.3987,8.34,0.45 2284 | 2006-04-01,1.2588,1.02,1.51 2285 | 2006-04-02,1.0326,0.22,1.51 2286 | 2006-04-03,0.9762,4.98,1.51 2287 | 2006-04-04,0.954,0.18,1.51 2288 | 2006-04-05,0.8946,0.36,1.51 2289 | 2006-04-06,0.8337,0.56,1.51 2290 | 2006-04-07,0.805,2.66,1.51 2291 | 2006-04-08,0.8096,11.46,1.51 2292 | 2006-04-09,0.9475,1.62,1.51 2293 | 2006-04-10,0.8266,0.12,1.51 2294 | 2006-04-11,0.7662,0.3,1.51 2295 | 2006-04-12,0.7209,0.06,1.51 2296 | 2006-04-13,0.6743,0.66,1.51 2297 | 2006-04-14,0.6447,0.0,1.51 2298 | 2006-04-15,0.6391,0.08,1.51 2299 | 2006-04-16,0.6102,0.06,1.51 2300 | 2006-04-17,0.5951,0.08,1.51 2301 | 2006-04-18,0.5714,0.94,1.51 2302 | 2006-04-19,0.5606,0.24,1.51 2303 | 2006-04-20,0.5544,0.22,1.51 2304 | 2006-04-21,0.5349,0.0,1.51 2305 | 2006-04-22,0.531,0.0,1.51 2306 | 2006-04-23,0.5027,0.0,1.51 2307 | 2006-04-24,0.5012,0.16,1.51 2308 | 2006-04-25,0.4815,0.18,1.51 2309 | 2006-04-26,0.4636,0.0,1.51 2310 | 2006-04-27,0.4551,0.2,1.51 2311 | 2006-04-28,0.4508,0.0,1.51 2312 | 2006-04-29,0.4382,0.12,1.51 2313 | 2006-04-30,0.4426,2.42,1.51 2314 | 2006-05-01,0.4654,2.68,2.14 2315 | 2006-05-02,0.4531,4.48,2.14 2316 | 2006-05-03,0.4224,0.0,2.14 2317 | 2006-05-04,0.413,0.66,2.14 2318 | 2006-05-05,0.3963,0.04,2.14 2319 | 2006-05-06,0.398,3.32,2.14 2320 | 2006-05-07,0.3908,0.0,2.14 2321 | 2006-05-08,0.3854,0.58,2.14 2322 | 2006-05-09,0.3596,0.0,2.14 2323 | 2006-05-10,0.3494,0.0,2.14 2324 | 2006-05-11,0.3463,1.12,2.14 2325 | 2006-05-12,0.3504,1.02,2.14 2326 | 2006-05-13,0.3365,1.52,2.14 2327 | 2006-05-14,0.3436,3.88,2.14 2328 | 2006-05-15,0.3784,2.1,2.14 2329 | 2006-05-16,0.3674,2.06,2.14 2330 | 2006-05-17,0.3912,6.44,2.14 2331 | 2006-05-18,0.3716,1.08,2.14 2332 | 2006-05-19,0.3618,3.02,2.14 2333 | 2006-05-20,0.4134,9.62,2.14 2334 | 2006-05-21,0.4485,2.06,2.14 2335 | 2006-05-22,0.3713,0.26,2.14 2336 | 2006-05-23,0.3431,0.38,2.14 2337 | 2006-05-24,0.3394,1.0,2.14 2338 | 2006-05-25,0.3228,2.72,2.14 2339 | 2006-05-26,0.4065,6.24,2.14 2340 | 2006-05-27,0.3762,2.2,2.14 2341 | 2006-05-28,0.3302,2.04,2.14 2342 | 2006-05-29,0.3307,2.52,2.14 2343 | 2006-05-30,0.3252,1.7,2.14 2344 | 2006-05-31,0.3149,1.12,2.14 2345 | 2006-06-01,0.3034,0.02,2.92 2346 | 2006-06-02,0.2866,0.0,2.92 2347 | 2006-06-03,0.2917,1.38,2.92 2348 | 2006-06-04,0.2921,0.0,2.92 2349 | 2006-06-05,0.291,0.0,2.92 2350 | 2006-06-06,0.2766,0.16,2.92 2351 | 2006-06-07,0.2767,0.0,2.92 2352 | 2006-06-08,0.2674,0.0,2.92 2353 | 2006-06-09,0.2645,0.0,2.92 2354 | 2006-06-10,0.2537,0.0,2.92 2355 | 2006-06-11,0.2497,2.9,2.92 2356 | 2006-06-12,0.2584,0.68,2.92 2357 | 2006-06-13,0.2386,0.0,2.92 2358 | 2006-06-14,0.2403,0.0,2.92 2359 | 2006-06-15,0.2363,0.06,2.92 2360 | 2006-06-16,0.2483,0.06,2.92 2361 | 2006-06-17,0.2673,4.86,2.92 2362 | 2006-06-18,0.2826,5.82,2.92 2363 | 2006-06-19,0.296,4.6,2.92 2364 | 2006-06-20,0.2649,1.98,2.92 2365 | 2006-06-21,0.2549,3.24,2.92 2366 | 2006-06-22,0.2602,1.4,2.92 2367 | 2006-06-23,0.2562,2.2,2.92 2368 | 2006-06-24,0.2607,3.78,2.92 2369 | 2006-06-25,0.2422,0.0,2.92 2370 | 2006-06-26,0.2307,0.0,2.92 2371 | 2006-06-27,0.225,0.0,2.92 2372 | 2006-06-28,0.2217,0.0,2.92 2373 | 2006-06-29,0.2232,0.1,2.92 2374 | 2006-06-30,0.2474,3.6,2.92 2375 | 2006-07-01,0.2276,0.1,3.13 2376 | 2006-07-02,0.3086,15.4,3.13 2377 | 2006-07-03,0.2577,0.0,3.13 2378 | 2006-07-04,0.2356,0.0,3.13 2379 | 2006-07-05,0.2247,0.0,3.13 2380 | 2006-07-06,0.2156,0.2,3.13 2381 | 2006-07-07,0.207,0.0,3.13 2382 | 2006-07-08,0.2098,2.6,3.13 2383 | 2006-07-09,0.2391,7.8,3.13 2384 | 2006-07-10,0.2251,0.7,3.13 2385 | 2006-07-11,0.2079,0.0,3.13 2386 | 2006-07-12,0.2042,0.0,3.13 2387 | 2006-07-13,0.1974,0.0,3.13 2388 | 2006-07-14,0.1938,0.0,3.13 2389 | 2006-07-15,0.186,0.0,3.13 2390 | 2006-07-16,0.1816,0.0,3.13 2391 | 2006-07-17,0.1849,0.0,3.13 2392 | 2006-07-18,0.187,0.0,3.13 2393 | 2006-07-19,0.1808,0.0,3.13 2394 | 2006-07-20,0.1867,0.0,3.13 2395 | 2006-07-21,0.1967,4.56,3.13 2396 | 2006-07-22,0.1928,0.08,3.13 2397 | 2006-07-23,0.183,0.0,3.13 2398 | 2006-07-24,0.1785,0.0,3.13 2399 | 2006-07-25,0.1735,4.0,3.13 2400 | 2006-07-26,0.2235,4.32,3.13 2401 | 2006-07-27,0.2675,9.88,3.13 2402 | 2006-07-28,0.2171,2.76,3.13 2403 | 2006-07-29,0.2074,0.22,3.13 2404 | 2006-07-30,0.1904,0.72,3.13 2405 | 2006-07-31,0.2237,6.64,3.13 2406 | 2006-08-01,0.2234,4.82,2.32 2407 | 2006-08-02,0.227,1.64,2.32 2408 | 2006-08-03,0.2068,0.06,2.32 2409 | 2006-08-04,0.2007,0.0,2.32 2410 | 2006-08-05,0.1926,0.04,2.32 2411 | 2006-08-06,0.1886,0.1,2.32 2412 | 2006-08-07,0.1833,0.0,2.32 2413 | 2006-08-08,0.1858,0.58,2.32 2414 | 2006-08-09,0.1887,0.7,2.32 2415 | 2006-08-10,0.2056,5.26,2.32 2416 | 2006-08-11,0.2058,0.22,2.32 2417 | 2006-08-12,0.2014,0.86,2.32 2418 | 2006-08-13,0.2028,0.22,2.32 2419 | 2006-08-14,0.2011,0.1,2.32 2420 | 2006-08-15,0.2015,0.08,2.32 2421 | 2006-08-16,0.2171,1.2,2.32 2422 | 2006-08-17,0.221,13.52,2.32 2423 | 2006-08-18,0.3048,18.9,2.32 2424 | 2006-08-19,0.4266,1.84,2.32 2425 | 2006-08-20,0.2704,2.46,2.32 2426 | 2006-08-21,0.2387,0.04,2.32 2427 | 2006-08-22,0.2282,1.32,2.32 2428 | 2006-08-23,0.2412,1.62,2.32 2429 | 2006-08-24,0.22,0.0,2.32 2430 | 2006-08-25,0.2196,0.04,2.32 2431 | 2006-08-26,0.217,0.32,2.32 2432 | 2006-08-27,0.2148,1.44,2.32 2433 | 2006-08-28,0.2132,0.04,2.32 2434 | 2006-08-29,0.21,0.14,2.32 2435 | 2006-08-30,0.201,0.0,2.32 2436 | 2006-08-31,0.2152,5.8,2.32 2437 | 2006-09-01,0.2793,3.62,1.84 2438 | 2006-09-02,0.2305,0.12,1.84 2439 | 2006-09-03,0.2139,0.0,1.84 2440 | 2006-09-04,0.2002,0.0,1.84 2441 | 2006-09-05,0.2829,14.4,1.84 2442 | 2006-09-06,0.2566,0.14,1.84 2443 | 2006-09-07,0.2217,0.22,1.84 2444 | 2006-09-08,0.2115,0.04,1.84 2445 | 2006-09-09,0.2107,0.0,1.84 2446 | 2006-09-10,0.2109,0.0,1.84 2447 | 2006-09-11,0.2095,0.08,1.84 2448 | 2006-09-12,0.2095,0.04,1.84 2449 | 2006-09-13,0.2076,1.78,1.84 2450 | 2006-09-14,0.305,11.16,1.84 2451 | 2006-09-15,0.2623,0.0,1.84 2452 | 2006-09-16,0.2415,0.04,1.84 2453 | 2006-09-17,0.2308,0.0,1.84 2454 | 2006-09-18,0.2284,1.6,1.84 2455 | 2006-09-19,0.2174,1.54,1.84 2456 | 2006-09-20,0.2232,0.3,1.84 2457 | 2006-09-21,0.2177,0.0,1.84 2458 | 2006-09-22,0.2204,3.18,1.84 2459 | 2006-09-23,0.2258,0.08,1.84 2460 | 2006-09-24,0.3038,8.02,1.84 2461 | 2006-09-25,0.2698,0.6,1.84 2462 | 2006-09-26,0.2472,0.32,1.84 2463 | 2006-09-27,0.2445,1.42,1.84 2464 | 2006-09-28,0.2332,0.0,1.84 2465 | 2006-09-29,0.2309,0.4,1.84 2466 | 2006-09-30,0.2235,4.96,1.84 2467 | 2006-10-01,0.592,17.28,1.02 2468 | 2006-10-02,0.5181,10.92,1.02 2469 | 2006-10-03,0.4208,2.2,1.02 2470 | 2006-10-04,0.3327,0.38,1.02 2471 | 2006-10-05,0.3126,2.3,1.02 2472 | 2006-10-06,0.2923,0.94,1.02 2473 | 2006-10-07,0.2649,0.04,1.02 2474 | 2006-10-08,0.26,0.62,1.02 2475 | 2006-10-09,0.2474,0.04,1.02 2476 | 2006-10-10,0.2503,3.94,1.02 2477 | 2006-10-11,0.5158,16.78,1.02 2478 | 2006-10-12,0.4884,0.18,1.02 2479 | 2006-10-13,0.3431,0.04,1.02 2480 | 2006-10-14,0.307,0.0,1.02 2481 | 2006-10-15,0.2881,0.04,1.02 2482 | 2006-10-16,0.2792,0.1,1.02 2483 | 2006-10-17,0.2926,6.9,1.02 2484 | 2006-10-18,0.4021,7.38,1.02 2485 | 2006-10-19,0.9829,18.9,1.02 2486 | 2006-10-20,1.0695,15.24,1.02 2487 | 2006-10-21,0.9037,2.02,1.02 2488 | 2006-10-22,0.6343,0.3,1.02 2489 | 2006-10-23,0.5156,0.08,1.02 2490 | 2006-10-24,0.5183,11.7,1.02 2491 | 2006-10-25,1.3749,22.74,1.02 2492 | 2006-10-26,2.0747,4.54,1.02 2493 | 2006-10-27,1.1096,3.2,1.02 2494 | 2006-10-28,0.8947,1.76,1.02 2495 | 2006-10-29,0.7681,3.64,1.02 2496 | 2006-10-30,0.8002,2.52,1.02 2497 | 2006-10-31,1.0217,9.38,1.02 2498 | 2006-11-01,0.937,1.66,0.56 2499 | 2006-11-02,0.7615,0.08,0.56 2500 | 2006-11-03,0.6749,0.04,0.56 2501 | 2006-11-04,0.6175,0.0,0.56 2502 | 2006-11-05,0.5707,0.0,0.56 2503 | 2006-11-06,0.5338,0.0,0.56 2504 | 2006-11-07,0.5058,2.28,0.56 2505 | 2006-11-08,0.5017,0.56,0.56 2506 | 2006-11-09,0.4639,0.14,0.56 2507 | 2006-11-10,0.4686,3.5,0.56 2508 | 2006-11-11,0.4718,1.28,0.56 2509 | 2006-11-12,0.4679,1.06,0.56 2510 | 2006-11-13,0.4723,0.16,0.56 2511 | 2006-11-14,0.4236,0.02,0.56 2512 | 2006-11-15,0.5874,13.54,0.56 2513 | 2006-11-16,0.7439,0.3,0.56 2514 | 2006-11-17,0.6803,5.66,0.56 2515 | 2006-11-18,0.6183,0.02,0.56 2516 | 2006-11-19,0.5864,5.84,0.56 2517 | 2006-11-20,0.584,0.72,0.56 2518 | 2006-11-21,0.5353,0.58,0.56 2519 | 2006-11-22,0.5063,1.5,0.56 2520 | 2006-11-23,0.7122,6.14,0.56 2521 | 2006-11-24,0.6872,6.82,0.56 2522 | 2006-11-25,0.73,0.28,0.56 2523 | 2006-11-26,0.6167,6.04,0.56 2524 | 2006-11-27,0.7009,1.52,0.56 2525 | 2006-11-28,0.6213,0.12,0.56 2526 | 2006-11-29,0.585,0.5,0.56 2527 | 2006-11-30,0.5805,5.96,0.56 2528 | 2006-12-01,0.6176,2.06,0.33 2529 | 2006-12-02,0.6504,10.66,0.33 2530 | 2006-12-03,1.0521,2.96,0.33 2531 | 2006-12-04,0.8342,0.78,0.33 2532 | 2006-12-05,0.7122,0.32,0.33 2533 | 2006-12-06,0.6593,2.7,0.33 2534 | 2006-12-07,0.674,1.12,0.33 2535 | 2006-12-08,0.6085,0.02,0.33 2536 | 2006-12-09,0.5575,0.12,0.33 2537 | 2006-12-10,0.5603,0.32,0.33 2538 | 2006-12-11,0.5257,0.3,0.33 2539 | 2006-12-12,0.6841,6.64,0.33 2540 | 2006-12-13,0.923,4.26,0.33 2541 | 2006-12-14,0.8178,0.48,0.33 2542 | 2006-12-15,0.689,0.0,0.33 2543 | 2006-12-16,0.6186,0.12,0.33 2544 | 2006-12-17,0.571,0.0,0.33 2545 | 2006-12-18,0.5459,0.04,0.33 2546 | 2006-12-19,0.5008,0.0,0.33 2547 | 2006-12-20,0.4725,0.08,0.33 2548 | 2006-12-21,0.4728,0.12,0.33 2549 | 2006-12-22,0.4813,0.04,0.33 2550 | 2006-12-23,0.4697,0.0,0.33 2551 | 2006-12-24,0.4469,0.04,0.33 2552 | 2006-12-25,0.4231,0.0,0.33 2553 | 2006-12-26,0.4167,0.06,0.33 2554 | 2006-12-27,0.4159,0.0,0.33 2555 | 2006-12-28,0.4141,1.52,0.33 2556 | 2006-12-29,0.5739,8.86,0.33 2557 | 2006-12-30,0.6247,0.2,0.33 2558 | 2006-12-31,0.8344,13.34,0.33 2559 | 2007-01-01,0.8266,0.3,0.64 2560 | 2007-01-02,0.6553,1.14,0.64 2561 | 2007-01-03,0.7412,0.5,0.64 2562 | 2007-01-04,0.6739,0.54,0.64 2563 | 2007-01-05,0.628,0.26,0.64 2564 | 2007-01-06,0.5936,0.16,0.64 2565 | 2007-01-07,0.657,5.88,0.64 2566 | 2007-01-08,0.7238,4.4,0.64 2567 | 2007-01-09,0.8337,0.54,0.64 2568 | 2007-01-10,0.7403,10.72,0.64 2569 | 2007-01-11,1.2702,6.1,0.64 2570 | 2007-01-12,0.9458,0.54,0.64 2571 | 2007-01-13,0.7932,2.96,0.64 2572 | 2007-01-14,0.7285,0.14,0.64 2573 | 2007-01-15,0.6806,0.66,0.64 2574 | 2007-01-16,0.6359,1.16,0.64 2575 | 2007-01-17,0.649,2.32,0.64 2576 | 2007-01-18,0.6637,3.3,0.64 2577 | 2007-01-19,0.7493,5.48,0.64 2578 | 2007-01-20,0.9242,0.38,0.64 2579 | 2007-01-21,0.8094,4.84,0.64 2580 | 2007-01-22,0.7781,2.3,0.64 2581 | 2007-01-23,0.7275,3.14,0.64 2582 | 2007-01-24,0.8026,1.0,0.64 2583 | 2007-01-25,0.7792,0.8,0.64 2584 | 2007-01-26,1.0342,2.84,0.64 2585 | 2007-01-27,0.9117,0.28,0.64 2586 | 2007-01-28,0.789,0.46,0.64 2587 | 2007-01-29,0.7247,0.08,0.64 2588 | 2007-01-30,0.6999,0.4,0.64 2589 | 2007-01-31,0.6624,0.4,0.64 2590 | 2007-02-01,0.6314,0.62,0.84 2591 | 2007-02-02,0.6024,0.0,0.84 2592 | 2007-02-03,0.5811,0.06,0.84 2593 | 2007-02-04,0.5839,0.68,0.84 2594 | 2007-02-05,0.578,0.38,0.84 2595 | 2007-02-06,0.5735,3.3,0.84 2596 | 2007-02-07,0.5588,2.42,0.84 2597 | 2007-02-08,0.5604,4.8,0.84 2598 | 2007-02-09,0.5448,3.44,0.84 2599 | 2007-02-10,0.66,21.98,0.84 2600 | 2007-02-11,3.7109,10.68,0.84 2601 | 2007-02-12,3.1312,4.22,0.84 2602 | 2007-02-13,1.8834,0.3,0.84 2603 | 2007-02-14,1.5191,0.0,0.84 2604 | 2007-02-15,1.4623,3.48,0.84 2605 | 2007-02-16,1.2553,0.0,0.84 2606 | 2007-02-17,1.0911,0.0,0.84 2607 | 2007-02-18,1.0007,0.1,0.84 2608 | 2007-02-19,0.9913,2.62,0.84 2609 | 2007-02-20,0.9368,2.08,0.84 2610 | 2007-02-21,1.0369,4.1,0.84 2611 | 2007-02-22,1.0731,4.08,0.84 2612 | 2007-02-23,0.999,0.8,0.84 2613 | 2007-02-24,0.9692,2.0,0.84 2614 | 2007-02-25,0.9396,1.48,0.84 2615 | 2007-02-26,0.8742,1.42,0.84 2616 | 2007-02-27,1.0511,5.42,0.84 2617 | 2007-02-28,0.9788,3.0,0.84 2618 | 2007-03-01,1.1097,3.98,1.33 2619 | 2007-03-02,0.9747,2.2,1.33 2620 | 2007-03-03,0.9277,0.0,1.33 2621 | 2007-03-04,1.1163,16.3,1.33 2622 | 2007-03-05,1.3113,13.28,1.33 2623 | 2007-03-06,1.3277,0.84,1.33 2624 | 2007-03-07,1.027,0.06,1.33 2625 | 2007-03-08,0.9323,0.1,1.33 2626 | 2007-03-09,0.8637,0.06,1.33 2627 | 2007-03-10,0.8268,0.0,1.33 2628 | 2007-03-11,0.8308,3.66,1.33 2629 | 2007-03-12,0.8157,0.08,1.33 2630 | 2007-03-13,0.7518,0.0,1.33 2631 | 2007-03-14,0.7257,0.0,1.33 2632 | 2007-03-15,0.7031,0.38,1.33 2633 | 2007-03-16,0.6952,1.1,1.33 2634 | 2007-03-17,0.6937,2.38,1.33 2635 | 2007-03-18,0.6909,3.02,1.33 2636 | 2007-03-19,0.8054,9.3,1.33 2637 | 2007-03-20,0.8096,2.64,1.33 2638 | 2007-03-21,0.8576,2.14,1.33 2639 | 2007-03-22,0.9094,0.0,1.33 2640 | 2007-03-23,0.7697,0.02,1.33 2641 | 2007-03-24,0.7113,0.0,1.33 2642 | 2007-03-25,0.6755,0.08,1.33 2643 | 2007-03-26,0.6535,0.06,1.33 2644 | 2007-03-27,0.6337,0.0,1.33 2645 | 2007-03-28,0.9236,12.6,1.33 2646 | 2007-03-29,1.1242,0.08,1.33 2647 | 2007-03-30,0.8105,0.0,1.33 2648 | 2007-03-31,0.7216,0.0,1.33 2649 | 2007-04-01,0.6746,0.14,2.44 2650 | 2007-04-02,0.6516,0.14,2.44 2651 | 2007-04-03,0.6295,0.0,2.44 2652 | 2007-04-04,0.6087,0.0,2.44 2653 | 2007-04-05,0.5887,0.0,2.44 2654 | 2007-04-06,0.5846,0.0,2.44 2655 | 2007-04-07,0.5581,0.0,2.44 2656 | 2007-04-08,0.55,0.0,2.44 2657 | 2007-04-09,0.5382,0.02,2.44 2658 | 2007-04-10,0.532,0.0,2.44 2659 | 2007-04-11,0.5187,0.0,2.44 2660 | 2007-04-12,0.4962,0.0,2.44 2661 | 2007-04-13,0.4865,0.0,2.44 2662 | 2007-04-14,0.4702,0.0,2.44 2663 | 2007-04-15,0.4608,0.0,2.44 2664 | 2007-04-16,0.4441,0.26,2.44 2665 | 2007-04-17,0.4464,0.42,2.44 2666 | 2007-04-18,0.4401,1.58,2.44 2667 | 2007-04-19,0.4477,0.62,2.44 2668 | 2007-04-20,0.444,1.56,2.44 2669 | 2007-04-21,0.4669,4.06,2.44 2670 | 2007-04-22,0.4967,0.78,2.44 2671 | 2007-04-23,0.4536,0.22,2.44 2672 | 2007-04-24,0.4459,8.06,2.44 2673 | 2007-04-25,0.4756,0.44,2.44 2674 | 2007-04-26,0.4207,0.0,2.44 2675 | 2007-04-27,0.4028,0.12,2.44 2676 | 2007-04-28,0.3924,0.0,2.44 2677 | 2007-04-29,0.3875,0.0,2.44 2678 | 2007-04-30,0.3764,0.0,2.44 2679 | 2007-05-01,0.3674,0.0,2.24 2680 | 2007-05-02,0.3629,0.08,2.24 2681 | 2007-05-03,0.358,0.0,2.24 2682 | 2007-05-04,0.3616,0.0,2.24 2683 | 2007-05-05,0.3525,0.08,2.24 2684 | 2007-05-06,0.3377,0.08,2.24 2685 | 2007-05-07,0.3379,1.34,2.24 2686 | 2007-05-08,0.3451,0.9,2.24 2687 | 2007-05-09,0.447,20.1,2.24 2688 | 2007-05-10,0.6258,2.56,2.24 2689 | 2007-05-11,0.4475,8.64,2.24 2690 | 2007-05-12,0.5339,1.38,2.24 2691 | 2007-05-13,0.463,2.7,2.24 2692 | 2007-05-14,0.4597,4.12,2.24 2693 | 2007-05-15,0.428,0.0,2.24 2694 | 2007-05-16,0.4037,0.26,2.24 2695 | 2007-05-17,0.3899,0.24,2.24 2696 | 2007-05-18,0.3801,2.7,2.24 2697 | 2007-05-19,0.3565,0.08,2.24 2698 | 2007-05-20,0.3449,2.16,2.24 2699 | 2007-05-21,0.3379,1.06,2.24 2700 | 2007-05-22,0.3283,0.0,2.24 2701 | 2007-05-23,0.3216,0.0,2.24 2702 | 2007-05-24,0.3228,0.46,2.24 2703 | 2007-05-25,0.3188,0.24,2.24 2704 | 2007-05-26,0.3134,1.24,2.24 2705 | 2007-05-27,0.3105,2.32,2.24 2706 | 2007-05-28,0.3193,5.4,2.24 2707 | 2007-05-29,0.4785,26.74,2.24 2708 | 2007-05-30,1.2114,9.12,2.24 2709 | 2007-05-31,0.6894,0.04,2.24 2710 | 2007-06-01,0.5375,0.0,2.63 2711 | 2007-06-02,0.4872,0.34,2.63 2712 | 2007-06-03,0.4731,1.72,2.63 2713 | 2007-06-04,0.4494,0.14,2.63 2714 | 2007-06-05,0.4355,0.02,2.63 2715 | 2007-06-06,0.4237,0.0,2.63 2716 | 2007-06-07,0.44,5.58,2.63 2717 | 2007-06-08,0.4391,0.0,2.63 2718 | 2007-06-09,0.4218,0.1,2.63 2719 | 2007-06-10,0.418,0.0,2.63 2720 | 2007-06-11,0.4032,0.0,2.63 2721 | 2007-06-12,0.4353,10.86,2.63 2722 | 2007-06-13,0.4599,0.2,2.63 2723 | 2007-06-14,0.4072,0.0,2.63 2724 | 2007-06-15,0.4074,3.64,2.63 2725 | 2007-06-16,0.4634,3.78,2.63 2726 | 2007-06-17,0.4478,1.64,2.63 2727 | 2007-06-18,0.4266,0.0,2.63 2728 | 2007-06-19,0.4111,1.64,2.63 2729 | 2007-06-20,0.502,8.7,2.63 2730 | 2007-06-21,0.4986,4.78,2.63 2731 | 2007-06-22,1.1471,15.56,2.63 2732 | 2007-06-23,1.8782,37.4,2.63 2733 | 2007-06-24,3.5014,9.04,2.63 2734 | 2007-06-25,1.712,2.08,2.63 2735 | 2007-06-26,1.1701,0.1,2.63 2736 | 2007-06-27,1.045,4.48,2.63 2737 | 2007-06-28,1.0104,15.06,2.63 2738 | 2007-06-29,2.2113,2.34,2.63 2739 | 2007-06-30,1.2937,12.94,2.63 2740 | 2007-07-01,1.5051,1.76,2.61 2741 | 2007-07-02,1.3022,1.38,2.61 2742 | 2007-07-03,1.1894,18.52,2.61 2743 | 2007-07-04,1.2057,3.3,2.61 2744 | 2007-07-05,1.1195,7.1,2.61 2745 | 2007-07-06,1.3164,3.34,2.61 2746 | 2007-07-07,1.0708,1.62,2.61 2747 | 2007-07-08,0.9253,0.0,2.61 2748 | 2007-07-09,0.841,0.0,2.61 2749 | 2007-07-10,0.8016,0.16,2.61 2750 | 2007-07-11,0.7605,0.0,2.61 2751 | 2007-07-12,0.7376,0.58,2.61 2752 | 2007-07-13,0.8644,20.82,2.61 2753 | 2007-07-14,1.1761,0.36,2.61 2754 | 2007-07-15,0.8276,4.24,2.61 2755 | 2007-07-16,1.0294,7.62,2.61 2756 | 2007-07-17,0.9898,6.94,2.61 2757 | 2007-07-18,0.8978,2.6,2.61 2758 | 2007-07-19,0.7931,0.08,2.61 2759 | 2007-07-20,0.7241,0.0,2.61 2760 | 2007-07-21,0.774,14.98,2.61 2761 | 2007-07-22,1.0259,4.98,2.61 2762 | 2007-07-23,1.037,9.48,2.61 2763 | 2007-07-24,0.8996,0.6,2.61 2764 | 2007-07-25,0.818,5.08,2.61 2765 | 2007-07-26,0.9112,3.84,2.61 2766 | 2007-07-27,0.8288,0.06,2.61 2767 | 2007-07-28,0.7537,1.94,2.61 2768 | 2007-07-29,0.7596,3.32,2.61 2769 | 2007-07-30,0.7141,0.06,2.61 2770 | 2007-07-31,0.6899,2.26,2.61 2771 | 2007-08-01,0.6794,0.08,2.22 2772 | 2007-08-02,0.6527,0.76,2.22 2773 | 2007-08-03,0.6596,2.64,2.22 2774 | 2007-08-04,0.6305,0.14,2.22 2775 | 2007-08-05,1.4967,46.32,2.22 2776 | 2007-08-06,3.0605,2.68,2.22 2777 | 2007-08-07,1.3967,0.28,2.22 2778 | 2007-08-08,1.0392,0.0,2.22 2779 | 2007-08-09,0.9298,0.12,2.22 2780 | 2007-08-10,0.8717,0.36,2.22 2781 | 2007-08-11,0.9318,13.18,2.22 2782 | 2007-08-12,1.5225,4.22,2.22 2783 | 2007-08-13,1.1451,0.14,2.22 2784 | 2007-08-14,0.9796,1.94,2.22 2785 | 2007-08-15,0.9095,0.34,2.22 2786 | 2007-08-16,0.8303,0.08,2.22 2787 | 2007-08-17,0.788,0.62,2.22 2788 | 2007-08-18,1.3955,26.96,2.22 2789 | 2007-08-19,1.7401,0.24,2.22 2790 | 2007-08-20,1.1119,0.14,2.22 2791 | 2007-08-21,0.9765,0.12,2.22 2792 | 2007-08-22,0.9066,0.12,2.22 2793 | 2007-08-23,0.8634,0.08,2.22 2794 | 2007-08-24,0.8238,0.02,2.22 2795 | 2007-08-25,0.7986,0.2,2.22 2796 | 2007-08-26,0.7671,0.0,2.22 2797 | 2007-08-27,0.768,0.58,2.22 2798 | 2007-08-28,0.7605,1.88,2.22 2799 | 2007-08-29,0.7593,1.48,2.22 2800 | 2007-08-30,0.7341,0.06,2.22 2801 | 2007-08-31,0.7131,0.06,2.22 2802 | 2007-09-01,0.6979,0.46,1.54 2803 | 2007-09-02,0.6797,0.54,1.54 2804 | 2007-09-03,0.6614,0.28,1.54 2805 | 2007-09-04,0.6559,0.38,1.54 2806 | 2007-09-05,0.6472,0.0,1.54 2807 | 2007-09-06,0.6348,0.0,1.54 2808 | 2007-09-07,0.6286,0.0,1.54 2809 | 2007-09-08,0.624,0.0,1.54 2810 | 2007-09-09,0.6288,0.56,1.54 2811 | 2007-09-10,0.6107,0.0,1.54 2812 | 2007-09-11,0.608,0.62,1.54 2813 | 2007-09-12,0.6076,0.16,1.54 2814 | 2007-09-13,0.6099,0.38,1.54 2815 | 2007-09-14,0.5996,1.34,1.54 2816 | 2007-09-15,0.6308,4.5,1.54 2817 | 2007-09-16,0.8145,12.66,1.54 2818 | 2007-09-17,0.8205,7.8,1.54 2819 | 2007-09-18,0.7541,0.62,1.54 2820 | 2007-09-19,0.6896,0.06,1.54 2821 | 2007-09-20,0.654,0.02,1.54 2822 | 2007-09-21,0.6299,0.0,1.54 2823 | 2007-09-22,0.6181,0.18,1.54 2824 | 2007-09-23,0.6116,3.16,1.54 2825 | 2007-09-24,0.7356,7.12,1.54 2826 | 2007-09-25,0.724,6.48,1.54 2827 | 2007-09-26,0.7727,1.56,1.54 2828 | 2007-09-27,0.7132,0.0,1.54 2829 | 2007-09-28,0.6778,0.76,1.54 2830 | 2007-09-29,0.6584,0.0,1.54 2831 | 2007-09-30,0.6358,0.14,1.54 2832 | 2007-10-01,0.6197,0.04,0.99 2833 | 2007-10-02,0.6119,0.0,0.99 2834 | 2007-10-03,0.6165,2.52,0.99 2835 | 2007-10-04,0.6113,0.0,0.99 2836 | 2007-10-05,0.5955,0.0,0.99 2837 | 2007-10-06,0.615,0.36,0.99 2838 | 2007-10-07,0.5869,0.0,0.99 2839 | 2007-10-08,0.5819,2.64,0.99 2840 | 2007-10-09,0.6015,0.4,0.99 2841 | 2007-10-10,0.5743,0.14,0.99 2842 | 2007-10-11,0.558,0.06,0.99 2843 | 2007-10-12,0.5513,0.0,0.99 2844 | 2007-10-13,0.5537,0.0,0.99 2845 | 2007-10-14,0.5511,0.0,0.99 2846 | 2007-10-15,0.5512,0.0,0.99 2847 | 2007-10-16,0.5427,0.0,0.99 2848 | 2007-10-17,0.5279,0.26,0.99 2849 | 2007-10-18,0.5285,0.0,0.99 2850 | 2007-10-19,0.5276,0.04,0.99 2851 | 2007-10-20,0.5262,0.0,0.99 2852 | 2007-10-21,0.5298,0.0,0.99 2853 | 2007-10-22,0.5234,0.04,0.99 2854 | 2007-10-23,0.5149,0.0,0.99 2855 | 2007-10-24,0.5114,0.06,0.99 2856 | 2007-10-25,0.5126,0.0,0.99 2857 | 2007-10-26,0.5118,0.24,0.99 2858 | 2007-10-27,0.5755,13.3,0.99 2859 | 2007-10-28,0.665,0.16,0.99 2860 | 2007-10-29,0.5584,0.18,0.99 2861 | 2007-10-30,0.5416,0.28,0.99 2862 | 2007-10-31,0.5329,0.0,0.99 2863 | 2007-11-01,0.5277,0.98,0.58 2864 | 2007-11-02,0.5225,0.0,0.58 2865 | 2007-11-03,0.5147,0.0,0.58 2866 | 2007-11-04,0.5163,2.54,0.58 2867 | 2007-11-05,0.545,1.02,0.58 2868 | 2007-11-06,0.524,0.0,0.58 2869 | 2007-11-07,0.5132,0.98,0.58 2870 | 2007-11-08,0.5186,2.7,0.58 2871 | 2007-11-09,0.5237,5.9,0.58 2872 | 2007-11-10,0.7168,8.0,0.58 2873 | 2007-11-11,0.7158,1.78,0.58 2874 | 2007-11-12,0.6765,3.88,0.58 2875 | 2007-11-13,0.7079,1.18,0.58 2876 | 2007-11-14,0.6418,0.02,0.58 2877 | 2007-11-15,0.6036,0.0,0.58 2878 | 2007-11-16,0.583,0.14,0.58 2879 | 2007-11-17,0.585,5.7,0.58 2880 | 2007-11-18,0.8815,7.94,0.58 2881 | 2007-11-19,1.5989,16.36,0.58 2882 | 2007-11-20,1.4281,12.82,0.58 2883 | 2007-11-21,4.0891,34.62,0.58 2884 | 2007-11-22,3.0095,2.6,0.58 2885 | 2007-11-23,1.7572,0.48,0.58 2886 | 2007-11-24,1.4156,2.54,0.58 2887 | 2007-11-25,1.2362,0.0,0.58 2888 | 2007-11-26,1.1077,0.84,0.58 2889 | 2007-11-27,1.2004,4.38,0.58 2890 | 2007-11-28,2.7516,17.26,0.58 2891 | 2007-11-29,1.7636,1.58,0.58 2892 | 2007-11-30,2.5832,13.9,0.58 2893 | 2007-12-01,2.2567,0.6,0.23 2894 | 2007-12-02,1.6503,1.5,0.23 2895 | 2007-12-03,1.6205,2.16,0.23 2896 | 2007-12-04,1.4922,1.88,0.23 2897 | 2007-12-05,1.4324,1.6,0.23 2898 | 2007-12-06,1.3136,1.1,0.23 2899 | 2007-12-07,1.2857,2.56,0.23 2900 | 2007-12-08,1.6755,15.08,0.23 2901 | 2007-12-09,2.9696,8.84,0.23 2902 | 2007-12-10,1.9606,0.32,0.23 2903 | 2007-12-11,1.5306,0.08,0.23 2904 | 2007-12-12,1.4318,0.0,0.23 2905 | 2007-12-13,1.3343,0.0,0.23 2906 | 2007-12-14,1.2475,0.0,0.23 2907 | 2007-12-15,1.1707,0.0,0.23 2908 | 2007-12-16,1.0929,0.0,0.23 2909 | 2007-12-17,1.0131,0.0,0.23 2910 | 2007-12-18,0.9629,0.0,0.23 2911 | 2007-12-19,0.9095,0.04,0.23 2912 | 2007-12-20,0.8827,0.04,0.23 2913 | 2007-12-21,0.849,0.0,0.23 2914 | 2007-12-22,0.8366,0.02,0.23 2915 | 2007-12-23,0.8449,0.32,0.23 2916 | 2007-12-24,0.8296,0.0,0.23 2917 | 2007-12-25,0.8064,0.18,0.23 2918 | 2007-12-26,0.8121,1.96,0.23 2919 | 2007-12-27,0.8408,9.3,0.23 2920 | 2007-12-28,1.2544,0.58,0.23 2921 | 2007-12-29,0.9563,0.84,0.23 2922 | 2007-12-30,0.8632,0.06,0.23 2923 | 2007-12-31,1.0237,4.26,0.23 2924 | 2008-01-01,1.1674,8.68,0.48 2925 | 2008-01-02,1.4918,3.42,0.48 2926 | 2008-01-03,1.2029,19.78,0.48 2927 | 2008-01-04,2.547,22.04,0.48 2928 | 2008-01-05,2.9896,0.66,0.48 2929 | 2008-01-06,1.9508,0.54,0.48 2930 | 2008-01-07,1.5474,0.48,0.48 2931 | 2008-01-08,2.1602,12.66,0.48 2932 | 2008-01-09,2.7795,1.26,0.48 2933 | 2008-01-10,1.9119,4.02,0.48 2934 | 2008-01-11,1.5888,0.0,0.48 2935 | 2008-01-12,1.4031,3.4,0.48 2936 | 2008-01-13,2.0859,6.32,0.48 2937 | 2008-01-14,2.1284,0.06,0.48 2938 | 2008-01-15,1.7238,3.0,0.48 2939 | 2008-01-16,1.6589,0.2,0.48 2940 | 2008-01-17,1.5824,4.04,0.48 2941 | 2008-01-18,1.5966,1.22,0.48 2942 | 2008-01-19,1.4288,0.0,0.48 2943 | 2008-01-20,1.318,0.14,0.48 2944 | 2008-01-21,1.2388,0.0,0.48 2945 | 2008-01-22,1.1859,1.06,0.48 2946 | 2008-01-23,1.2297,1.12,0.48 2947 | 2008-01-24,1.2613,4.08,0.48 2948 | 2008-01-25,1.5077,11.0,0.48 2949 | 2008-01-26,1.5948,2.02,0.48 2950 | 2008-01-27,1.3073,0.0,0.48 2951 | 2008-01-28,1.1948,0.68,0.48 2952 | 2008-01-29,1.3468,6.0,0.48 2953 | 2008-01-30,1.279,4.6,0.48 2954 | 2008-01-31,1.3906,8.32,0.48 2955 | 2008-02-01,2.3493,14.88,0.9 2956 | 2008-02-02,1.6284,1.68,0.9 2957 | 2008-02-03,2.1304,4.64,0.9 2958 | 2008-02-04,1.6338,0.16,0.9 2959 | 2008-02-05,1.4648,1.74,0.9 2960 | 2008-02-06,1.3103,0.6,0.9 2961 | 2008-02-07,1.2728,0.2,0.9 2962 | 2008-02-08,1.2054,0.0,0.9 2963 | 2008-02-09,1.1392,0.14,0.9 2964 | 2008-02-10,1.0958,0.04,0.9 2965 | 2008-02-11,1.0647,0.0,0.9 2966 | 2008-02-12,1.0249,0.04,0.9 2967 | 2008-02-13,0.9885,0.04,0.9 2968 | 2008-02-14,0.9735,0.0,0.9 2969 | 2008-02-15,0.9423,0.0,0.9 2970 | 2008-02-16,0.905,0.04,0.9 2971 | 2008-02-17,0.8907,0.0,0.9 2972 | 2008-02-18,0.8847,0.0,0.9 2973 | 2008-02-19,0.8706,0.0,0.9 2974 | 2008-02-20,0.8595,0.0,0.9 2975 | 2008-02-21,0.8448,2.26,0.9 2976 | 2008-02-22,0.8912,3.16,0.9 2977 | 2008-02-23,0.8628,0.02,0.9 2978 | 2008-02-24,0.8195,0.0,0.9 2979 | 2008-02-25,0.8179,0.96,0.9 2980 | 2008-02-26,0.7929,1.04,0.9 2981 | 2008-02-27,0.7521,0.0,0.9 2982 | 2008-02-28,0.7468,0.38,0.9 2983 | 2008-02-29,0.8681,8.22,0.9 2984 | 2008-03-01,0.899,3.58,0.91 2985 | 2008-03-02,0.9092,0.18,0.91 2986 | 2008-03-03,0.8221,2.06,0.91 2987 | 2008-03-04,0.7927,0.4,0.91 2988 | 2008-03-05,0.7819,2.12,0.91 2989 | 2008-03-06,0.7858,0.1,0.91 2990 | 2008-03-07,0.7535,0.5,0.91 2991 | 2008-03-08,0.7617,2.96,0.91 2992 | 2008-03-09,0.7323,2.86,0.91 2993 | 2008-03-10,1.3282,12.94,0.91 2994 | 2008-03-11,1.0586,0.72,0.91 2995 | 2008-03-12,0.8859,0.0,0.91 2996 | 2008-03-13,0.8198,0.0,0.91 2997 | 2008-03-14,0.7774,0.0,0.91 2998 | 2008-03-15,0.7593,1.28,0.91 2999 | 2008-03-16,0.7619,1.4,0.91 3000 | 2008-03-17,0.7417,0.96,0.91 3001 | 2008-03-18,0.7297,1.66,0.91 3002 | 2008-03-19,0.7188,0.92,0.91 3003 | 2008-03-20,0.7651,6.8,0.91 3004 | 2008-03-21,0.8877,7.46,0.91 3005 | 2008-03-22,0.8718,2.94,0.91 3006 | 2008-03-23,0.9174,5.34,0.91 3007 | 2008-03-24,0.8873,4.22,0.91 3008 | 2008-03-25,0.8444,0.28,0.91 3009 | 2008-03-26,0.8404,1.78,0.91 3010 | 2008-03-27,0.9391,5.52,0.91 3011 | 2008-03-28,1.2259,2.0,0.91 3012 | 2008-03-29,1.1479,3.96,0.91 3013 | 2008-03-30,1.0038,0.32,0.91 3014 | 2008-03-31,0.9089,2.84,0.91 3015 | 2008-04-01,0.9195,2.06,1.56 3016 | 2008-04-02,0.861,0.04,1.56 3017 | 2008-04-03,0.825,2.38,1.56 3018 | 2008-04-04,0.8365,3.42,1.56 3019 | 2008-04-05,0.8861,7.86,1.56 3020 | 2008-04-06,1.1423,18.34,1.56 3021 | 2008-04-07,2.4918,8.0,1.56 3022 | 2008-04-08,2.2214,8.22,1.56 3023 | 2008-04-09,1.7398,1.1,1.56 3024 | 2008-04-10,1.5287,8.94,1.56 3025 | 2008-04-11,2.1401,6.74,1.56 3026 | 2008-04-12,1.841,2.96,1.56 3027 | 2008-04-13,1.6053,4.26,1.56 3028 | 2008-04-14,1.5359,2.5,1.56 3029 | 2008-04-15,1.3659,0.58,1.56 3030 | 2008-04-16,1.2663,0.3,1.56 3031 | 2008-04-17,1.1961,0.0,1.56 3032 | 2008-04-18,1.1291,0.0,1.56 3033 | 2008-04-19,1.069,0.0,1.56 3034 | 2008-04-20,1.0188,0.0,1.56 3035 | 2008-04-21,0.9788,0.0,1.56 3036 | 2008-04-22,0.9494,0.0,1.56 3037 | 2008-04-23,0.9253,0.68,1.56 3038 | 2008-04-24,0.9012,0.64,1.56 3039 | 2008-04-25,0.873,0.4,1.56 3040 | 2008-04-26,0.8461,0.24,1.56 3041 | 2008-04-27,0.8291,9.12,1.56 3042 | 2008-04-28,1.0978,5.04,1.56 3043 | 2008-04-29,1.0502,6.8,1.56 3044 | 2008-04-30,1.4046,12.72,1.56 3045 | 2008-05-01,1.532,4.68,2.58 3046 | 2008-05-02,1.2158,0.48,2.58 3047 | 2008-05-03,1.0465,0.0,2.58 3048 | 2008-05-04,0.9791,0.1,2.58 3049 | 2008-05-05,0.9223,0.04,2.58 3050 | 2008-05-06,0.8924,0.0,2.58 3051 | 2008-05-07,0.8649,0.0,2.58 3052 | 2008-05-08,0.838,0.0,2.58 3053 | 2008-05-09,0.8152,0.0,2.58 3054 | 2008-05-10,1.1413,6.9,2.58 3055 | 2008-05-11,0.9044,3.12,2.58 3056 | 2008-05-12,0.9726,0.06,2.58 3057 | 2008-05-13,0.8739,0.0,2.58 3058 | 2008-05-14,0.8315,0.0,2.58 3059 | 2008-05-15,0.8245,0.0,2.58 3060 | 2008-05-16,0.8125,0.62,2.58 3061 | 2008-05-17,0.7784,0.1,2.58 3062 | 2008-05-18,0.7562,0.02,2.58 3063 | 2008-05-19,0.74,0.0,2.58 3064 | 2008-05-20,0.7291,0.06,2.58 3065 | 2008-05-21,0.7141,0.0,2.58 3066 | 2008-05-22,0.6985,0.36,2.58 3067 | 2008-05-23,0.6874,0.32,2.58 3068 | 2008-05-24,0.664,0.0,2.58 3069 | 2008-05-25,0.6439,0.0,2.58 3070 | 2008-05-26,0.6339,0.0,2.58 3071 | 2008-05-27,0.626,0.32,2.58 3072 | 2008-05-28,0.7032,11.58,2.58 3073 | 2008-05-29,0.6606,1.54,2.58 3074 | 2008-05-30,0.6517,0.46,2.58 3075 | 2008-05-31,0.6188,0.0,2.58 3076 | 2008-06-01,0.6548,8.32,2.69 3077 | 2008-06-02,0.6445,0.8,2.69 3078 | 2008-06-03,0.6805,4.56,2.69 3079 | 2008-06-04,0.6177,0.0,2.69 3080 | 2008-06-05,0.586,0.0,2.69 3081 | 2008-06-06,0.5732,0.0,2.69 3082 | 2008-06-07,0.5516,0.0,2.69 3083 | 2008-06-08,0.5373,0.0,2.69 3084 | 2008-06-09,0.53,0.0,2.69 3085 | 2008-06-10,0.5234,0.42,2.69 3086 | 2008-06-11,0.5185,1.5,2.69 3087 | 2008-06-12,0.5122,1.4,2.69 3088 | 2008-06-13,0.5122,2.62,2.69 3089 | 2008-06-14,0.515,4.26,2.69 3090 | 2008-06-15,0.5035,0.46,2.69 3091 | 2008-06-16,0.4957,2.86,2.69 3092 | 2008-06-17,0.502,0.36,2.69 3093 | 2008-06-18,0.4972,1.38,2.69 3094 | 2008-06-19,0.4925,1.82,2.69 3095 | 2008-06-20,0.4786,0.8,2.69 3096 | 2008-06-21,0.5246,18.2,2.69 3097 | 2008-06-22,0.6633,5.72,2.69 3098 | 2008-06-23,0.5245,1.12,2.69 3099 | 2008-06-24,0.5322,5.94,2.69 3100 | 2008-06-25,0.6109,3.74,2.69 3101 | 2008-06-26,0.5349,0.06,2.69 3102 | 2008-06-27,0.5017,0.22,2.69 3103 | 2008-06-28,0.4847,0.02,2.69 3104 | 2008-06-29,0.471,0.16,2.69 3105 | 2008-06-30,0.4636,0.18,2.69 3106 | 2008-07-01,0.49,8.56,2.9 3107 | 2008-07-02,0.4795,0.14,2.9 3108 | 2008-07-03,0.4627,0.0,2.9 3109 | 2008-07-04,0.4559,0.0,2.9 3110 | 2008-07-05,0.4534,0.14,2.9 3111 | 2008-07-06,0.4604,1.42,2.9 3112 | 2008-07-07,0.4736,4.74,2.9 3113 | 2008-07-08,0.4824,4.94,2.9 3114 | 2008-07-09,0.4725,4.36,2.9 3115 | 2008-07-10,0.6435,18.7,2.9 3116 | 2008-07-11,0.5922,1.14,2.9 3117 | 2008-07-12,0.5056,0.72,2.9 3118 | 2008-07-13,0.4684,0.26,2.9 3119 | 2008-07-14,0.4466,0.06,2.9 3120 | 2008-07-15,0.4311,0.0,2.9 3121 | 2008-07-16,0.4255,0.9,2.9 3122 | 2008-07-17,0.4575,9.44,2.9 3123 | 2008-07-18,0.4597,2.32,2.9 3124 | 2008-07-19,0.4382,1.92,2.9 3125 | 2008-07-20,0.4247,0.4,2.9 3126 | 2008-07-21,0.4062,0.0,2.9 3127 | 2008-07-22,0.3995,0.0,2.9 3128 | 2008-07-23,0.3948,0.0,2.9 3129 | 2008-07-24,0.3934,0.04,2.9 3130 | 2008-07-25,0.3973,0.0,2.9 3131 | 2008-07-26,0.3947,0.0,2.9 3132 | 2008-07-27,0.397,1.22,2.9 3133 | 2008-07-28,0.3853,2.24,2.9 3134 | 2008-07-29,0.3953,0.28,2.9 3135 | 2008-07-30,0.3897,0.0,2.9 3136 | 2008-07-31,0.3916,8.08,2.9 3137 | 2008-08-01,0.452,5.72,2.4 3138 | 2008-08-02,0.4283,2.66,2.4 3139 | 2008-08-03,0.4272,7.46,2.4 3140 | 2008-08-04,0.4085,0.26,2.4 3141 | 2008-08-05,0.4018,1.66,2.4 3142 | 2008-08-06,0.4717,15.72,2.4 3143 | 2008-08-07,0.586,1.06,2.4 3144 | 2008-08-08,0.4493,0.32,2.4 3145 | 2008-08-09,0.4854,9.8,2.4 3146 | 2008-08-10,0.6982,4.88,2.4 3147 | 2008-08-11,0.5098,0.14,2.4 3148 | 2008-08-12,0.4783,3.52,2.4 3149 | 2008-08-13,0.4617,0.2,2.4 3150 | 2008-08-14,0.4258,0.02,2.4 3151 | 2008-08-15,0.4196,0.56,2.4 3152 | 2008-08-16,0.4119,0.3,2.4 3153 | 2008-08-17,0.4114,1.34,2.4 3154 | 2008-08-18,0.4478,18.84,2.4 3155 | 2008-08-19,0.8824,5.58,2.4 3156 | 2008-08-20,0.6044,8.5,2.4 3157 | 2008-08-21,1.2177,14.98,2.4 3158 | 2008-08-22,0.7992,2.2,2.4 3159 | 2008-08-23,0.6689,0.96,2.4 3160 | 2008-08-24,0.5788,0.22,2.4 3161 | 2008-08-25,0.5343,1.78,2.4 3162 | 2008-08-26,0.5127,0.24,2.4 3163 | 2008-08-27,0.4932,0.12,2.4 3164 | 2008-08-28,0.4744,0.0,2.4 3165 | 2008-08-29,0.463,0.0,2.4 3166 | 2008-08-30,0.4596,0.0,2.4 3167 | 2008-08-31,0.4814,3.38,2.4 3168 | 2008-09-01,0.4637,0.28,1.63 3169 | 2008-09-02,0.4503,0.24,1.63 3170 | 2008-09-03,0.4516,5.1,1.63 3171 | 2008-09-04,0.5545,4.02,1.63 3172 | 2008-09-05,0.5038,0.0,1.63 3173 | 2008-09-06,0.4637,1.18,1.63 3174 | 2008-09-07,0.4576,0.16,1.63 3175 | 2008-09-08,0.4468,0.18,1.63 3176 | 2008-09-09,0.5064,9.44,1.63 3177 | 2008-09-10,0.5008,4.4,1.63 3178 | 2008-09-11,0.5018,0.02,1.63 3179 | 2008-09-12,0.4892,3.24,1.63 3180 | 2008-09-13,0.5448,1.88,1.63 3181 | 2008-09-14,0.5305,2.06,1.63 3182 | 2008-09-15,0.6428,13.92,1.63 3183 | 2008-09-16,1.225,5.52,1.63 3184 | 2008-09-17,0.7949,0.06,1.63 3185 | 2008-09-18,0.8369,7.14,1.63 3186 | 2008-09-19,0.8068,0.02,1.63 3187 | 2008-09-20,0.6654,0.06,1.63 3188 | 2008-09-21,0.6,0.06,1.63 3189 | 2008-09-22,0.5645,0.04,1.63 3190 | 2008-09-23,0.5509,0.0,1.63 3191 | 2008-09-24,0.5364,0.0,1.63 3192 | 2008-09-25,0.5209,0.0,1.63 3193 | 2008-09-26,0.5127,0.12,1.63 3194 | 2008-09-27,0.5122,1.18,1.63 3195 | 2008-09-28,0.5064,0.02,1.63 3196 | 2008-09-29,0.5037,0.48,1.63 3197 | 2008-09-30,0.4964,0.06,1.63 3198 | 2008-10-01,0.5103,3.6,0.8 3199 | 2008-10-02,0.6381,16.1,0.8 3200 | 2008-10-03,0.9653,1.1,0.8 3201 | 2008-10-04,0.6713,1.68,0.8 3202 | 2008-10-05,0.589,0.02,0.8 3203 | 2008-10-06,0.5603,1.78,0.8 3204 | 2008-10-07,0.8449,13.14,0.8 3205 | 2008-10-08,1.1223,0.1,0.8 3206 | 2008-10-09,0.7514,3.72,0.8 3207 | 2008-10-10,0.6854,0.34,0.8 3208 | 2008-10-11,0.6224,0.02,0.8 3209 | 2008-10-12,0.5996,0.0,0.8 3210 | 2008-10-13,0.5767,0.8,0.8 3211 | 2008-10-14,0.5612,0.08,0.8 3212 | 2008-10-15,0.5519,0.0,0.8 3213 | 2008-10-16,0.5311,0.48,0.8 3214 | 2008-10-17,0.5275,0.3,0.8 3215 | 2008-10-18,0.5178,1.84,0.8 3216 | 2008-10-19,0.5347,2.86,0.8 3217 | 2008-10-20,0.5404,1.6,0.8 3218 | 2008-10-21,0.5154,0.5,0.8 3219 | 2008-10-22,0.5067,0.16,0.8 3220 | 2008-10-23,0.5096,0.8,0.8 3221 | 2008-10-24,0.4915,0.34,0.8 3222 | 2008-10-25,0.4943,0.68,0.8 3223 | 2008-10-26,0.4879,0.54,0.8 3224 | 2008-10-27,0.5222,6.24,0.8 3225 | 2008-10-28,0.5238,1.74,0.8 3226 | 2008-10-29,0.5688,0.12,0.8 3227 | 2008-10-30,0.6331,10.14,0.8 3228 | 2008-10-31,1.1179,4.36,0.8 3229 | 2008-11-01,0.797,0.06,0.45 3230 | 2008-11-02,0.6681,0.0,0.45 3231 | 2008-11-03,0.6154,0.64,0.45 3232 | 2008-11-04,0.5982,0.04,0.45 3233 | 2008-11-05,0.5886,2.48,0.45 3234 | 2008-11-06,0.7014,4.98,0.45 3235 | 2008-11-07,0.8112,3.46,0.45 3236 | 2008-11-08,0.7439,5.7,0.45 3237 | 2008-11-09,0.7249,0.44,0.45 3238 | 2008-11-10,0.6594,0.38,0.45 3239 | 2008-11-11,0.7021,6.44,0.45 3240 | 2008-11-12,0.7592,0.1,0.45 3241 | 2008-11-13,0.7005,1.54,0.45 3242 | 2008-11-14,0.6701,0.26,0.45 3243 | 2008-11-15,0.6241,0.24,0.45 3244 | 2008-11-16,0.5948,0.0,0.45 3245 | 2008-11-17,0.611,1.48,0.45 3246 | 2008-11-18,0.597,0.0,0.45 3247 | 2008-11-19,0.5861,1.18,0.45 3248 | 2008-11-20,0.7403,7.32,0.45 3249 | 2008-11-21,0.8779,6.0,0.45 3250 | 2008-11-22,0.794,3.62,0.45 3251 | 2008-11-23,0.9925,9.6,0.45 3252 | 2008-11-24,1.693,3.34,0.45 3253 | 2008-11-25,1.5383,0.28,0.45 3254 | 2008-11-26,1.3211,0.48,0.45 3255 | 2008-11-27,1.0274,0.0,0.45 3256 | 2008-11-28,0.8892,0.0,0.45 3257 | 2008-11-29,0.7963,0.1,0.45 3258 | 2008-11-30,0.7616,4.26,0.45 3259 | 2008-12-01,0.8342,0.7,0.21 3260 | 2008-12-02,0.7751,0.94,0.21 3261 | 2008-12-03,0.7446,4.46,0.21 3262 | 2008-12-04,0.8246,18.1,0.21 3263 | 2008-12-05,3.463,8.72,0.21 3264 | 2008-12-06,1.5585,0.1,0.21 3265 | 2008-12-07,1.2441,0.04,0.21 3266 | 2008-12-08,1.1588,0.04,0.21 3267 | 2008-12-09,1.0051,1.64,0.21 3268 | 2008-12-10,1.0486,2.94,0.21 3269 | 2008-12-11,1.0044,0.2,0.21 3270 | 2008-12-12,1.1585,8.44,0.21 3271 | 2008-12-13,3.2375,26.16,0.21 3272 | 2008-12-14,3.2423,2.76,0.21 3273 | 2008-12-15,1.9887,1.2,0.21 3274 | 2008-12-16,1.6609,0.2,0.21 3275 | 2008-12-17,1.4147,0.56,0.21 3276 | 2008-12-18,1.2831,0.82,0.21 3277 | 2008-12-19,1.1913,0.72,0.21 3278 | 2008-12-20,1.0901,1.14,0.21 3279 | 2008-12-21,1.032,0.0,0.21 3280 | 2008-12-22,0.9643,0.0,0.21 3281 | 2008-12-23,0.9241,0.48,0.21 3282 | 2008-12-24,0.8927,0.0,0.21 3283 | 2008-12-25,0.8609,0.04,0.21 3284 | 2008-12-26,0.833,0.0,0.21 3285 | 2008-12-27,0.8284,0.04,0.21 3286 | 2008-12-28,0.8072,2.74,0.21 3287 | 2008-12-29,0.7964,0.06,0.21 3288 | 2008-12-30,0.7589,0.0,0.21 3289 | 2008-12-31,0.7232,0.22,0.21 3290 | 2009-01-01,0.7181,0.78,0.25 3291 | 2009-01-02,0.6944,0.0,0.25 3292 | 2009-01-03,0.6792,0.36,0.25 3293 | 2009-01-04,0.6894,1.74,0.25 3294 | 2009-01-05,0.6534,0.06,0.25 3295 | 2009-01-06,0.6505,0.32,0.25 3296 | 2009-01-07,0.6451,0.0,0.25 3297 | 2009-01-08,0.6335,0.1,0.25 3298 | 2009-01-09,0.6183,0.04,0.25 3299 | 2009-01-10,0.658,2.16,0.25 3300 | 2009-01-11,0.7169,1.52,0.25 3301 | 2009-01-12,0.7246,0.02,0.25 3302 | 2009-01-13,0.6678,0.0,0.25 3303 | 2009-01-14,0.6537,3.84,0.25 3304 | 2009-01-15,0.7736,5.5,0.25 3305 | 2009-01-16,0.7815,1.92,0.25 3306 | 2009-01-17,0.8095,3.82,0.25 3307 | 2009-01-18,0.7753,2.5,0.25 3308 | 2009-01-19,0.8281,2.62,0.25 3309 | 2009-01-20,0.7264,0.56,0.25 3310 | 2009-01-21,0.712,12.1,0.25 3311 | 2009-01-22,1.0793,0.0,0.25 3312 | 2009-01-23,0.8307,0.38,0.25 3313 | 2009-01-24,0.7435,5.06,0.25 3314 | 2009-01-25,1.0789,2.6,0.25 3315 | 2009-01-26,0.8304,0.4,0.25 3316 | 2009-01-27,0.7699,0.0,0.25 3317 | 2009-01-28,0.7302,0.04,0.25 3318 | 2009-01-29,0.7372,0.56,0.25 3319 | 2009-01-30,0.9356,6.56,0.25 3320 | 2009-01-31,0.8944,0.0,0.25 3321 | 2009-02-01,0.8075,0.94,0.6 3322 | 2009-02-02,0.8027,10.28,0.6 3323 | 2009-02-03,2.0177,11.98,0.6 3324 | 2009-02-04,1.5837,9.24,0.6 3325 | 2009-02-05,1.222,2.72,0.6 3326 | 2009-02-06,1.0734,9.54,0.6 3327 | 2009-02-07,1.0178,5.1,0.6 3328 | 2009-02-08,0.9374,0.2,0.6 3329 | 2009-02-09,0.8892,0.0,0.6 3330 | 2009-02-10,0.8379,0.7,0.6 3331 | 2009-02-11,0.803,1.58,0.6 3332 | 2009-02-12,0.8006,7.66,0.6 3333 | 2009-02-13,0.7797,0.22,0.6 3334 | 2009-02-14,0.8379,1.78,0.6 3335 | 2009-02-15,1.2582,2.52,0.6 3336 | 2009-02-16,4.6157,0.26,0.6 3337 | 2009-02-17,3.2521,0.88,0.6 3338 | 2009-02-18,2.2371,0.18,0.6 3339 | 2009-02-19,1.8259,0.18,0.6 3340 | 2009-02-20,1.6177,2.26,0.6 3341 | 2009-02-21,1.4692,0.04,0.6 3342 | 2009-02-22,1.325,1.36,0.6 3343 | 2009-02-23,1.2505,0.1,0.6 3344 | 2009-02-24,1.1725,0.42,0.6 3345 | 2009-02-25,1.1051,0.22,0.6 3346 | 2009-02-26,1.0531,2.56,0.6 3347 | 2009-02-27,1.0427,0.1,0.6 3348 | 2009-02-28,0.9903,0.04,0.6 3349 | 2009-03-01,0.9417,0.0,1.29 3350 | 2009-03-02,0.9118,0.2,1.29 3351 | 2009-03-03,1.0028,6.66,1.29 3352 | 2009-03-04,1.0263,1.08,1.29 3353 | 2009-03-05,0.9037,0.26,1.29 3354 | 2009-03-06,0.8649,0.2,1.29 3355 | 2009-03-07,0.8755,4.48,1.29 3356 | 2009-03-08,0.862,0.26,1.29 3357 | 2009-03-09,0.815,0.26,1.29 3358 | 2009-03-10,0.776,0.22,1.29 3359 | 2009-03-11,0.7788,0.48,1.29 3360 | 2009-03-12,0.7501,0.0,1.29 3361 | 2009-03-13,0.7356,0.28,1.29 3362 | 2009-03-14,0.7175,0.0,1.29 3363 | 2009-03-15,0.699,0.2,1.29 3364 | 2009-03-16,0.6971,1.44,1.29 3365 | 2009-03-17,0.6792,0.24,1.29 3366 | 2009-03-18,0.6668,0.0,1.29 3367 | 2009-03-19,0.6588,0.0,1.29 3368 | 2009-03-20,0.653,0.04,1.29 3369 | 2009-03-21,0.6443,0.0,1.29 3370 | 2009-03-22,0.6576,7.0,1.29 3371 | 2009-03-23,0.794,6.2,1.29 3372 | 2009-03-24,0.7413,0.54,1.29 3373 | 2009-03-25,0.6975,2.02,1.29 3374 | 2009-03-26,0.6838,2.32,1.29 3375 | 2009-03-27,0.8845,17.78,1.29 3376 | 2009-03-28,1.4762,0.92,1.29 3377 | 2009-03-29,0.9157,0.0,1.29 3378 | 2009-03-30,0.8028,0.0,1.29 3379 | 2009-03-31,0.7447,0.0,1.29 3380 | 2009-04-01,0.7178,0.12,2.0 3381 | 2009-04-02,0.6963,0.16,2.0 3382 | 2009-04-03,0.676,0.0,2.0 3383 | 2009-04-04,0.6506,1.34,2.0 3384 | 2009-04-05,0.6663,0.0,2.0 3385 | 2009-04-06,0.6912,4.9,2.0 3386 | 2009-04-07,0.8128,14.0,2.0 3387 | 2009-04-08,0.7185,0.76,2.0 3388 | 2009-04-09,0.7175,0.2,2.0 3389 | 2009-04-10,0.693,2.44,2.0 3390 | 2009-04-11,0.6499,0.12,2.0 3391 | 2009-04-12,0.6267,0.0,2.0 3392 | 2009-04-13,0.6129,0.0,2.0 3393 | 2009-04-14,0.6041,0.16,2.0 3394 | 2009-04-15,0.5985,0.0,2.0 3395 | 2009-04-16,0.5901,0.0,2.0 3396 | 2009-04-17,0.5711,0.0,2.0 3397 | 2009-04-18,0.5665,0.0,2.0 3398 | 2009-04-19,0.5526,0.12,2.0 3399 | 2009-04-20,0.5397,0.0,2.0 3400 | 2009-04-21,0.5378,1.14,2.0 3401 | 2009-04-22,0.5357,0.02,2.0 3402 | 2009-04-23,0.5317,0.8,2.0 3403 | 2009-04-24,0.5281,0.0,2.0 3404 | 2009-04-25,0.5219,0.28,2.0 3405 | 2009-04-26,0.5197,0.5,2.0 3406 | 2009-04-27,0.5194,1.82,2.0 3407 | 2009-04-28,0.5165,1.32,2.0 3408 | 2009-04-29,0.5072,0.02,2.0 3409 | 2009-04-30,0.4997,0.5,2.0 3410 | 2009-05-01,0.4884,0.1,2.33 3411 | 2009-05-02,0.4752,0.32,2.33 3412 | 2009-05-03,0.4705,0.46,2.33 3413 | 2009-05-04,0.4739,0.12,2.33 3414 | 2009-05-05,0.4984,6.34,2.33 3415 | 2009-05-06,0.4834,0.52,2.33 3416 | 2009-05-07,0.4738,3.58,2.33 3417 | 2009-05-08,0.474,0.5,2.33 3418 | 2009-05-09,0.4609,0.7,2.33 3419 | 2009-05-10,0.4486,0.06,2.33 3420 | 2009-05-11,0.4425,0.04,2.33 3421 | 2009-05-12,0.4387,0.0,2.33 3422 | 2009-05-13,0.4349,0.0,2.33 3423 | 2009-05-14,0.4324,0.3,2.33 3424 | 2009-05-15,0.6219,27.46,2.33 3425 | 2009-05-16,0.7749,8.3,2.33 3426 | 2009-05-17,0.6675,2.84,2.33 3427 | 2009-05-18,0.6058,4.4,2.33 3428 | 2009-05-19,0.6336,3.52,2.33 3429 | 2009-05-20,0.5777,0.18,2.33 3430 | 2009-05-21,0.7141,7.74,2.33 3431 | 2009-05-22,0.6288,0.34,2.33 3432 | 2009-05-23,0.5693,0.2,2.33 3433 | 2009-05-24,0.5348,0.06,2.33 3434 | 2009-05-25,0.628,12.0,2.33 3435 | 2009-05-26,0.6688,0.06,2.33 3436 | 2009-05-27,0.5653,2.24,2.33 3437 | 2009-05-28,0.5288,0.0,2.33 3438 | 2009-05-29,0.5056,0.0,2.33 3439 | 2009-05-30,0.4874,0.0,2.33 3440 | 2009-05-31,0.4715,0.0,2.33 3441 | 2009-06-01,0.4576,0.0,2.68 3442 | 2009-06-02,0.4572,0.0,2.68 3443 | 2009-06-03,0.4515,0.0,2.68 3444 | 2009-06-04,0.4495,1.86,2.68 3445 | 2009-06-05,0.5219,11.64,2.68 3446 | 2009-06-06,0.527,1.14,2.68 3447 | 2009-06-07,0.4755,2.18,2.68 3448 | 2009-06-08,0.4559,0.26,2.68 3449 | 2009-06-09,0.4515,3.16,2.68 3450 | 2009-06-10,0.554,13.28,2.68 3451 | 2009-06-11,0.5289,2.18,2.68 3452 | 2009-06-12,0.4716,0.4,2.68 3453 | 2009-06-13,0.5741,14.34,2.68 3454 | 2009-06-14,0.6377,3.5,2.68 3455 | 2009-06-15,0.5576,0.18,2.68 3456 | 2009-06-16,0.5049,2.32,2.68 3457 | 2009-06-17,0.5176,8.36,2.68 3458 | 2009-06-18,0.4814,0.72,2.68 3459 | 2009-06-19,0.4696,2.82,2.68 3460 | 2009-06-20,0.4497,0.02,2.68 3461 | 2009-06-21,0.4384,0.68,2.68 3462 | 2009-06-22,0.457,6.82,2.68 3463 | 2009-06-23,0.437,0.1,2.68 3464 | 2009-06-24,0.4284,0.0,2.68 3465 | 2009-06-25,0.4133,0.0,2.68 3466 | 2009-06-26,0.4295,4.66,2.68 3467 | 2009-06-27,0.4247,0.06,2.68 3468 | 2009-06-28,0.4218,0.5,2.68 3469 | 2009-06-29,0.4088,0.04,2.68 3470 | 2009-06-30,0.4028,0.0,2.68 3471 | 2009-07-01,0.3957,0.04,2.8 3472 | 2009-07-02,0.3848,0.06,2.8 3473 | 2009-07-03,0.3932,0.92,2.8 3474 | 2009-07-04,0.3802,0.0,2.8 3475 | 2009-07-05,0.3783,0.62,2.8 3476 | 2009-07-06,1.1785,23.78,2.8 3477 | 2009-07-07,0.688,1.56,2.8 3478 | 2009-07-08,0.541,2.54,2.8 3479 | 2009-07-09,0.5014,0.8,2.8 3480 | 2009-07-10,0.474,0.06,2.8 3481 | 2009-07-11,0.4521,3.32,2.8 3482 | 2009-07-12,0.8006,13.32,2.8 3483 | 2009-07-13,0.6388,6.2,2.8 3484 | 2009-07-14,0.6163,8.52,2.8 3485 | 2009-07-15,0.6335,4.0,2.8 3486 | 2009-07-16,0.582,5.36,2.8 3487 | 2009-07-17,1.093,23.0,2.8 3488 | 2009-07-18,1.7571,1.98,2.8 3489 | 2009-07-19,0.9271,2.16,2.8 3490 | 2009-07-20,0.7327,0.0,2.8 3491 | 2009-07-21,1.3089,22.8,2.8 3492 | 2009-07-22,1.2776,2.96,2.8 3493 | 2009-07-23,0.9426,0.98,2.8 3494 | 2009-07-24,0.982,6.2,2.8 3495 | 2009-07-25,0.8394,2.28,2.8 3496 | 2009-07-26,0.8681,3.96,2.8 3497 | 2009-07-27,0.7957,0.72,2.8 3498 | 2009-07-28,0.7722,4.88,2.8 3499 | 2009-07-29,0.7473,0.14,2.8 3500 | 2009-07-30,0.6719,0.56,2.8 3501 | 2009-07-31,0.6405,2.66,2.8 3502 | 2009-08-01,0.6451,0.8,2.41 3503 | 2009-08-02,0.6008,0.06,2.41 3504 | 2009-08-03,0.5798,0.08,2.41 3505 | 2009-08-04,0.5853,1.14,2.41 3506 | 2009-08-05,0.5536,0.0,2.41 3507 | 2009-08-06,0.5382,0.0,2.41 3508 | 2009-08-07,0.5612,1.72,2.41 3509 | 2009-08-08,0.5458,0.0,2.41 3510 | 2009-08-09,0.5562,7.96,2.41 3511 | 2009-08-10,0.5776,1.28,2.41 3512 | 2009-08-11,0.5552,1.56,2.41 3513 | 2009-08-12,0.5347,1.58,2.41 3514 | 2009-08-13,0.5386,1.16,2.41 3515 | 2009-08-14,0.6383,13.88,2.41 3516 | 2009-08-15,0.6059,0.34,2.41 3517 | 2009-08-16,0.544,0.08,2.41 3518 | 2009-08-17,0.5316,0.66,2.41 3519 | 2009-08-18,0.5362,0.62,2.41 3520 | 2009-08-19,0.5414,3.2,2.41 3521 | 2009-08-20,0.5666,3.92,2.41 3522 | 2009-08-21,0.5281,0.1,2.41 3523 | 2009-08-22,0.5218,3.86,2.41 3524 | 2009-08-23,0.5486,0.36,2.41 3525 | 2009-08-24,0.5198,0.12,2.41 3526 | 2009-08-25,0.5086,0.06,2.41 3527 | 2009-08-26,0.5173,1.28,2.41 3528 | 2009-08-27,0.522,7.4,2.41 3529 | 2009-08-28,0.5243,1.3,2.41 3530 | 2009-08-29,0.4997,0.04,2.41 3531 | 2009-08-30,0.4979,2.56,2.41 3532 | 2009-08-31,0.5439,3.96,2.41 3533 | 2009-09-01,0.5276,0.0,1.7 3534 | 2009-09-02,0.5241,20.36,1.7 3535 | 2009-09-03,3.3191,33.26,1.7 3536 | 2009-09-04,3.3672,0.98,1.7 3537 | 2009-09-05,1.3375,0.46,1.7 3538 | 2009-09-06,0.9831,0.88,1.7 3539 | 2009-09-07,0.8501,0.34,1.7 3540 | 2009-09-08,0.7805,0.16,1.7 3541 | 2009-09-09,0.7119,0.0,1.7 3542 | 2009-09-10,0.6823,0.0,1.7 3543 | 2009-09-11,0.6664,0.0,1.7 3544 | 2009-09-12,0.6436,0.0,1.7 3545 | 2009-09-13,0.6336,0.18,1.7 3546 | 2009-09-14,0.6193,0.0,1.7 3547 | 2009-09-15,0.6112,0.06,1.7 3548 | 2009-09-16,0.5944,0.0,1.7 3549 | 2009-09-17,0.5823,0.0,1.7 3550 | 2009-09-18,0.5788,0.0,1.7 3551 | 2009-09-19,0.5648,0.04,1.7 3552 | 2009-09-20,0.5564,0.0,1.7 3553 | 2009-09-21,0.5594,0.46,1.7 3554 | 2009-09-22,0.5406,0.0,1.7 3555 | 2009-09-23,0.5372,0.12,1.7 3556 | 2009-09-24,0.5333,0.0,1.7 3557 | 2009-09-25,0.5255,0.0,1.7 3558 | 2009-09-26,0.5229,0.04,1.7 3559 | 2009-09-27,0.5247,0.06,1.7 3560 | 2009-09-28,0.5282,4.96,1.7 3561 | 2009-09-29,0.5618,3.5,1.7 3562 | 2009-09-30,0.5412,0.56,1.7 3563 | 2009-10-01,0.532,0.72,0.96 3564 | 2009-10-02,0.5402,1.2,0.96 3565 | 2009-10-03,0.5352,2.32,0.96 3566 | 2009-10-04,0.529,0.0,0.96 3567 | 2009-10-05,0.5281,1.42,0.96 3568 | 2009-10-06,0.53,0.04,0.96 3569 | 2009-10-07,0.5174,0.2,0.96 3570 | 2009-10-08,0.5116,0.22,0.96 3571 | 2009-10-09,0.5428,3.66,0.96 3572 | 2009-10-10,0.56,0.14,0.96 3573 | 2009-10-11,0.5322,0.36,0.96 3574 | 2009-10-12,0.536,4.6,0.96 3575 | 2009-10-13,0.7826,7.88,0.96 3576 | 2009-10-14,0.7204,0.1,0.96 3577 | 2009-10-15,0.6197,0.34,0.96 3578 | 2009-10-16,0.5778,0.0,0.96 3579 | 2009-10-17,0.557,0.12,0.96 3580 | 2009-10-18,0.557,4.3,0.96 3581 | 2009-10-19,0.6135,3.3,0.96 3582 | 2009-10-20,0.6393,11.68,0.96 3583 | 2009-10-21,4.3944,69.7,0.96 3584 | 2009-10-22,6.453,18.86,0.96 3585 | 2009-10-23,3.9615,6.34,0.96 3586 | 2009-10-24,4.5396,25.16,0.96 3587 | 2009-10-25,3.1503,4.72,0.96 3588 | 2009-10-26,2.3539,4.28,0.96 3589 | 2009-10-27,2.8375,8.56,0.96 3590 | 2009-10-28,2.3832,0.0,0.96 3591 | 2009-10-29,1.8494,1.0,0.96 3592 | 2009-10-30,1.7383,13.84,0.96 3593 | 2009-10-31,1.6552,2.0,0.96 3594 | 2009-11-01,6.3794,36.58,0.53 3595 | 2009-11-02,5.1814,1.28,0.53 3596 | 2009-11-03,3.6528,2.98,0.53 3597 | 2009-11-04,2.6015,0.98,0.53 3598 | 2009-11-05,2.1882,0.04,0.53 3599 | 2009-11-06,1.9352,0.58,0.53 3600 | 2009-11-07,1.6092,0.16,0.53 3601 | 2009-11-08,1.4007,0.12,0.53 3602 | 2009-11-09,1.3054,2.2,0.53 3603 | 2009-11-10,1.3465,1.46,0.53 3604 | 2009-11-11,1.2873,3.8,0.53 3605 | 2009-11-12,1.6235,7.7,0.53 3606 | 2009-11-13,1.6469,10.68,0.53 3607 | 2009-11-14,2.1928,0.66,0.53 3608 | 2009-11-15,1.4716,0.0,0.53 3609 | 2009-11-16,1.7152,8.5,0.53 3610 | 2009-11-17,1.5444,0.02,0.53 3611 | 2009-11-18,1.3317,2.78,0.53 3612 | 2009-11-19,1.295,4.24,0.53 3613 | 2009-11-20,1.247,0.0,0.53 3614 | 2009-11-21,1.6483,13.8,0.53 3615 | 2009-11-22,1.7398,5.58,0.53 3616 | 2009-11-23,1.6192,0.16,0.53 3617 | 2009-11-24,1.7079,5.16,0.53 3618 | 2009-11-25,1.719,6.96,0.53 3619 | 2009-11-26,1.5154,0.0,0.53 3620 | 2009-11-27,1.3339,1.0,0.53 3621 | 2009-11-28,1.2808,1.88,0.53 3622 | 2009-11-29,1.2524,2.06,0.53 3623 | 2009-11-30,1.2001,0.48,0.53 3624 | 2009-12-01,1.1577,3.62,0.01 3625 | 2009-12-02,1.2414,0.22,0.01 3626 | 2009-12-03,1.4589,5.0,0.01 3627 | 2009-12-04,1.3012,0.72,0.01 3628 | 2009-12-05,1.2502,6.04,0.01 3629 | 2009-12-06,1.2921,0.16,0.01 3630 | 2009-12-07,1.1383,0.34,0.01 3631 | 2009-12-08,1.0705,0.78,0.01 3632 | 2009-12-09,1.0275,0.0,0.01 3633 | 2009-12-10,0.9694,0.04,0.01 3634 | 2009-12-11,0.9278,0.04,0.01 3635 | 2009-12-12,0.8923,0.0,0.01 3636 | 2009-12-13,0.8779,1.32,0.01 3637 | 2009-12-14,1.0072,5.04,0.01 3638 | 2009-12-15,1.159,3.84,0.01 3639 | 2009-12-16,1.3977,4.66,0.01 3640 | 2009-12-17,1.2038,1.28,0.01 3641 | 2009-12-18,1.0588,4.24,0.01 3642 | 2009-12-19,1.02,1.78,0.01 3643 | 2009-12-20,0.9755,2.04,0.01 3644 | 2009-12-21,0.9354,0.76,0.01 3645 | 2009-12-22,0.9145,8.8,0.01 3646 | 2009-12-23,1.0182,3.66,0.01 3647 | 2009-12-24,0.981,5.92,0.01 3648 | 2009-12-25,1.0045,0.32,0.01 3649 | 2009-12-26,0.9162,0.0,0.01 3650 | 2009-12-27,0.8692,0.36,0.01 3651 | 2009-12-28,0.8204,0.0,0.01 3652 | 2009-12-29,0.7995,8.5,0.01 3653 | 2009-12-30,0.8041,8.08,0.01 3654 | 2009-12-31,0.8056,7.3,0.01 3655 | 2010-01-01,0.8053,16.82,0.0 3656 | 2010-01-02,0.8012,1.78,0.0 3657 | 2010-01-03,0.7892,0.12,0.0 3658 | 2010-01-04,0.7985,8.92,0.0 3659 | 2010-01-05,0.7913,3.4,0.0 3660 | 2010-01-06,0.7774,3.5,0.0 3661 | 2010-01-07,0.7625,1.3,0.0 3662 | 2010-01-08,0.8389,1.32,0.0 3663 | 2010-01-09,,0.48,0.0 3664 | 2010-01-10,,0.14,0.0 3665 | 2010-01-11,0.7603,1.98,0.0 3666 | 2010-01-12,1.0043,4.98,0.0 3667 | 2010-01-13,1.3369,5.06,0.0 3668 | 2010-01-14,1.6071,1.96,0.0 3669 | 2010-01-15,2.495,19.38,0.0 3670 | 2010-01-16,7.2431,6.76,0.0 3671 | 2010-01-17,2.9635,0.04,0.0 3672 | 2010-01-18,2.5727,0.0,0.0 3673 | 2010-01-19,,1.04,0.0 3674 | 2010-01-20,2.2406,1.32,0.0 3675 | 2010-01-21,2.1809,7.84,0.0 3676 | 2010-01-22,2.8536,3.2,0.0 3677 | 2010-01-23,3.1903,12.8,0.0 3678 | 2010-01-24,3.4,3.3,0.0 3679 | 2010-01-25,2.4286,0.1,0.0 3680 | 2010-01-26,2.1661,0.66,0.0 3681 | 2010-01-27,2.9987,2.04,0.0 3682 | 2010-01-28,2.7432,8.58,0.0 3683 | 2010-01-29,2.3507,1.24,0.0 3684 | 2010-01-30,1.9449,0.74,0.0 3685 | 2010-01-31,1.7379,0.86,0.0 3686 | 2010-02-01,1.6221,1.52,0.01 3687 | 2010-02-02,1.536,3.12,0.01 3688 | 2010-02-03,1.4134,0.18,0.01 3689 | 2010-02-04,1.4134,3.46,0.01 3690 | 2010-02-05,2.5158,6.94,0.01 3691 | 2010-02-06,2.2472,2.78,0.01 3692 | 2010-02-07,1.8913,1.54,0.01 3693 | 2010-02-08,1.7165,1.8,0.01 3694 | 2010-02-09,1.6265,3.22,0.01 3695 | 2010-02-10,1.537,0.46,0.01 3696 | 2010-02-11,1.5259,0.78,0.01 3697 | 2010-02-12,1.4517,0.0,0.01 3698 | 2010-02-13,1.3756,0.54,0.01 3699 | 2010-02-14,1.3651,1.58,0.01 3700 | 2010-02-15,1.3601,3.48,0.01 3701 | 2010-02-16,1.3492,9.8,0.01 3702 | 2010-02-17,1.3193,1.64,0.01 3703 | 2010-02-18,1.209,0.5,0.01 3704 | 2010-02-19,1.1412,0.3,0.01 3705 | 2010-02-20,1.0775,0.16,0.01 3706 | 2010-02-21,1.0344,0.64,0.01 3707 | 2010-02-22,0.9994,0.08,0.01 3708 | 2010-02-23,0.9814,2.6,0.01 3709 | 2010-02-24,0.9949,44.84,0.01 3710 | 2010-02-25,1.0708,30.48,0.01 3711 | 2010-02-26,1.2006,12.86,0.01 3712 | 2010-02-27,1.095,1.34,0.01 3713 | 2010-02-28,1.0423,1.54,0.01 3714 | 2010-03-01,0.9929,0.16,1.02 3715 | 2010-03-02,0.9562,0.04,1.02 3716 | 2010-03-03,0.9309,0.0,1.02 3717 | 2010-03-04,0.9045,0.36,1.02 3718 | 2010-03-05,1.2422,0.86,1.02 3719 | 2010-03-06,1.7155,0.24,1.02 3720 | 2010-03-07,1.5281,0.0,1.02 3721 | 2010-03-08,1.4934,0.0,1.02 3722 | 2010-03-09,1.3565,0.0,1.02 3723 | 2010-03-10,1.3024,0.06,1.02 3724 | 2010-03-11,2.3516,2.64,1.02 3725 | 2010-03-12,3.3121,0.24,1.02 3726 | 2010-03-13,3.3542,2.1,1.02 3727 | 2010-03-14,3.2723,4.72,1.02 3728 | 2010-03-15,2.9038,0.24,1.02 3729 | 2010-03-16,2.3078,0.02,1.02 3730 | 2010-03-17,2.1345,0.0,1.02 3731 | 2010-03-18,2.2022,0.16,1.02 3732 | 2010-03-19,2.0356,0.0,1.02 3733 | 2010-03-20,1.8308,0.0,1.02 3734 | 2010-03-21,1.678,0.16,1.02 3735 | 2010-03-22,1.5721,0.16,1.02 3736 | 2010-03-23,1.4763,0.0,1.02 3737 | 2010-03-24,1.4196,0.52,1.02 3738 | 2010-03-25,1.3963,6.34,1.02 3739 | 2010-03-26,1.4053,0.28,1.02 3740 | 2010-03-27,1.2789,0.34,1.02 3741 | 2010-03-28,1.2247,2.1,1.02 3742 | 2010-03-29,1.1791,6.76,1.02 3743 | 2010-03-30,1.5577,16.94,1.02 3744 | 2010-03-31,1.8663,14.98,1.02 3745 | 2010-04-01,1.8335,1.2,1.97 3746 | 2010-04-02,1.7851,4.14,1.97 3747 | 2010-04-03,2.9528,0.96,1.97 3748 | 2010-04-04,2.5326,1.24,1.97 3749 | 2010-04-05,2.5275,0.08,1.97 3750 | 2010-04-06,2.1986,1.62,1.97 3751 | 2010-04-07,1.8292,0.04,1.97 3752 | 2010-04-08,1.6829,0.34,1.97 3753 | 2010-04-09,1.5708,0.0,1.97 3754 | 2010-04-10,1.4781,0.0,1.97 3755 | 2010-04-11,1.4003,0.0,1.97 3756 | 2010-04-12,1.3256,0.0,1.97 3757 | 2010-04-13,1.2699,0.0,1.97 3758 | 2010-04-14,1.2169,0.0,1.97 3759 | 2010-04-15,1.1664,0.0,1.97 3760 | 2010-04-16,1.1162,0.0,1.97 3761 | 2010-04-17,1.0796,3.0,1.97 3762 | 2010-04-18,1.056,0.56,1.97 3763 | 2010-04-19,1.0226,4.24,1.97 3764 | 2010-04-20,1.0134,0.36,1.97 3765 | 2010-04-21,0.9539,0.2,1.97 3766 | 2010-04-22,0.9354,1.2,1.97 3767 | 2010-04-23,1.0166,9.14,1.97 3768 | 2010-04-24,0.9955,0.0,1.97 3769 | 2010-04-25,0.9221,0.56,1.97 3770 | 2010-04-26,0.867,0.0,1.97 3771 | 2010-04-27,0.8838,4.04,1.97 3772 | 2010-04-28,0.8645,0.04,1.97 3773 | 2010-04-29,0.8331,0.08,1.97 3774 | 2010-04-30,0.8314,7.32,1.97 3775 | 2010-05-01,0.8607,0.34,2.28 3776 | 2010-05-02,0.7956,0.7,2.28 3777 | 2010-05-03,0.7688,0.22,2.28 3778 | 2010-05-04,0.7578,0.66,2.28 3779 | 2010-05-05,0.7502,0.8,2.28 3780 | 2010-05-06,0.7368,1.64,2.28 3781 | 2010-05-07,0.7209,0.0,2.28 3782 | 2010-05-08,0.7045,0.82,2.28 3783 | 2010-05-09,0.6826,0.52,2.28 3784 | 2010-05-10,0.6682,2.46,2.28 3785 | 2010-05-11,0.6652,3.22,2.28 3786 | 2010-05-12,0.6476,0.02,2.28 3787 | 2010-05-13,0.6393,1.0,2.28 3788 | 2010-05-14,0.6412,0.38,2.28 3789 | 2010-05-15,0.6211,0.0,2.28 3790 | 2010-05-16,0.6138,2.58,2.28 3791 | 2010-05-17,0.5958,0.2,2.28 3792 | 2010-05-18,0.5827,0.06,2.28 3793 | 2010-05-19,0.5744,0.0,2.28 3794 | 2010-05-20,0.5654,0.0,2.28 3795 | 2010-05-21,3.2848,35.62,2.28 3796 | 2010-05-22,1.6886,0.8,2.28 3797 | 2010-05-23,1.1903,7.9,2.28 3798 | 2010-05-24,1.069,0.9,2.28 3799 | 2010-05-25,0.9234,1.56,2.28 3800 | 2010-05-26,0.8791,2.9,2.28 3801 | 2010-05-27,0.9752,5.58,2.28 3802 | 2010-05-28,0.9477,2.9,2.28 3803 | 2010-05-29,0.928,12.18,2.28 3804 | 2010-05-30,1.496,2.8,2.28 3805 | 2010-05-31,1.0155,0.3,2.28 3806 | 2010-06-01,0.9157,0.0,2.93 3807 | 2010-06-02,0.8453,0.0,2.93 3808 | 2010-06-03,0.8033,0.0,2.93 3809 | 2010-06-04,0.7764,0.0,2.93 3810 | 2010-06-05,0.9366,8.38,2.93 3811 | 2010-06-06,0.8896,6.14,2.93 3812 | 2010-06-07,0.9413,4.36,2.93 3813 | 2010-06-08,1.2583,10.82,2.93 3814 | 2010-06-09,1.177,0.44,2.93 3815 | 2010-06-10,0.9536,0.0,2.93 3816 | 2010-06-11,0.8734,0.2,2.93 3817 | 2010-06-12,0.8226,1.74,2.93 3818 | 2010-06-13,0.848,3.6,2.93 3819 | 2010-06-14,0.805,0.0,2.93 3820 | 2010-06-15,0.7687,0.0,2.93 3821 | 2010-06-16,0.7587,2.26,2.93 3822 | 2010-06-17,0.7414,0.0,2.93 3823 | 2010-06-18,0.7052,0.0,2.93 3824 | 2010-06-19,0.6859,0.0,2.93 3825 | 2010-06-20,0.6707,0.0,2.93 3826 | 2010-06-21,0.6583,0.0,2.93 3827 | 2010-06-22,0.6423,0.0,2.93 3828 | 2010-06-23,0.6282,0.04,2.93 3829 | 2010-06-24,0.6144,0.0,2.93 3830 | 2010-06-25,0.6013,0.0,2.93 3831 | 2010-06-26,0.5963,0.0,2.93 3832 | 2010-06-27,0.5886,0.44,2.93 3833 | 2010-06-28,0.5744,0.0,2.93 3834 | 2010-06-29,0.5636,0.0,2.93 3835 | 2010-06-30,0.5582,0.28,2.93 3836 | 2010-07-01,0.6474,14.08,2.8 3837 | 2010-07-02,0.6282,0.24,2.8 3838 | 2010-07-03,0.5629,1.3,2.8 3839 | 2010-07-04,0.5612,2.44,2.8 3840 | 2010-07-05,0.5365,0.06,2.8 3841 | 2010-07-06,0.5364,1.6,2.8 3842 | 2010-07-07,0.5207,0.0,2.8 3843 | 2010-07-08,0.5137,0.0,2.8 3844 | 2010-07-09,0.5161,0.76,2.8 3845 | 2010-07-10,0.5245,5.22,2.8 3846 | 2010-07-11,0.5171,0.06,2.8 3847 | 2010-07-12,0.679,2.54,2.8 3848 | 2010-07-13,0.563,0.0,2.8 3849 | 2010-07-14,0.7507,29.66,2.8 3850 | 2010-07-15,0.7805,5.08,2.8 3851 | 2010-07-16,0.6412,0.82,2.8 3852 | 2010-07-17,0.5677,0.08,2.8 3853 | 2010-07-18,0.5441,0.0,2.8 3854 | 2010-07-19,0.5842,5.6,2.8 3855 | 2010-07-20,0.5739,18.68,2.8 3856 | 2010-07-21,1.5696,16.76,2.8 3857 | 2010-07-22,0.822,0.0,2.8 3858 | 2010-07-23,0.6643,0.0,2.8 3859 | 2010-07-24,0.6114,0.0,2.8 3860 | 2010-07-25,0.5858,0.28,2.8 3861 | 2010-07-26,0.5901,4.2,2.8 3862 | 2010-07-27,0.57,1.12,2.8 3863 | 2010-07-28,0.5755,3.34,2.8 3864 | 2010-07-29,0.5662,0.08,2.8 3865 | 2010-07-30,0.5496,0.06,2.8 3866 | 2010-07-31,0.5634,6.9,2.8 3867 | 2010-08-01,0.5535,1.08,2.3 3868 | 2010-08-02,0.5386,0.18,2.3 3869 | 2010-08-03,0.5255,0.06,2.3 3870 | 2010-08-04,0.517,1.2,2.3 3871 | 2010-08-05,0.5177,2.22,2.3 3872 | 2010-08-06,0.5208,2.44,2.3 3873 | 2010-08-07,0.5218,0.12,2.3 3874 | 2010-08-08,0.5034,2.72,2.3 3875 | 2010-08-09,0.6541,6.36,2.3 3876 | 2010-08-10,0.6829,4.64,2.3 3877 | 2010-08-11,0.9615,13.1,2.3 3878 | 2010-08-12,1.0773,1.4,2.3 3879 | 2010-08-13,0.7008,0.0,2.3 3880 | 2010-08-14,0.6164,0.0,2.3 3881 | 2010-08-15,0.5811,0.0,2.3 3882 | 2010-08-16,0.5832,6.08,2.3 3883 | 2010-08-17,0.6126,1.1,2.3 3884 | 2010-08-18,0.5659,0.22,2.3 3885 | 2010-08-19,0.5417,0.6,2.3 3886 | 2010-08-20,0.5408,0.62,2.3 3887 | 2010-08-21,0.5228,0.2,2.3 3888 | 2010-08-22,0.5274,1.46,2.3 3889 | 2010-08-23,0.6138,16.64,2.3 3890 | 2010-08-24,0.7527,2.3,2.3 3891 | 2010-08-25,0.6,1.4,2.3 3892 | 2010-08-26,0.5802,1.9,2.3 3893 | 2010-08-27,0.5803,4.88,2.3 3894 | 2010-08-28,0.5883,9.0,2.3 3895 | 2010-08-29,1.135,5.06,2.3 3896 | 2010-08-30,0.7106,0.0,2.3 3897 | 2010-08-31,0.626,0.0,2.3 3898 | 2010-09-01,0.6042,0.96,1.63 3899 | 2010-09-02,0.5796,0.0,1.63 3900 | 2010-09-03,0.5783,0.0,1.63 3901 | 2010-09-04,0.5579,0.04,1.63 3902 | 2010-09-05,0.5494,0.04,1.63 3903 | 2010-09-06,0.5473,0.0,1.63 3904 | 2010-09-07,1.0433,29.26,1.63 3905 | 2010-09-08,1.3069,1.3,1.63 3906 | 2010-09-09,0.8397,0.28,1.63 3907 | 2010-09-10,0.7497,4.44,1.63 3908 | 2010-09-11,0.8369,1.22,1.63 3909 | 2010-09-12,0.7347,2.4,1.63 3910 | 2010-09-13,0.8145,3.92,1.63 3911 | 2010-09-14,0.7511,5.78,1.63 3912 | 2010-09-15,0.8254,3.0,1.63 3913 | 2010-09-16,0.7665,1.04,1.63 3914 | 2010-09-17,0.7059,1.64,1.63 3915 | 2010-09-18,0.8453,9.68,1.63 3916 | 2010-09-19,1.1469,6.28,1.63 3917 | 2010-09-20,1.1081,0.36,1.63 3918 | 2010-09-21,0.8949,0.56,1.63 3919 | 2010-09-22,1.1101,17.42,1.63 3920 | 2010-09-23,2.4333,6.56,1.63 3921 | 2010-09-24,1.4652,1.16,1.63 3922 | 2010-09-25,1.149,0.0,1.63 3923 | 2010-09-26,1.0205,0.04,1.63 3924 | 2010-09-27,0.9598,1.1,1.63 3925 | 2010-09-28,0.9642,4.3,1.63 3926 | 2010-09-29,2.3328,18.3,1.63 3927 | 2010-09-30,1.4815,0.4,1.63 3928 | 2010-10-01,2.2123,14.6,0.91 3929 | 2010-10-02,1.6614,3.66,0.91 3930 | 2010-10-03,1.4237,0.36,0.91 3931 | 2010-10-04,1.2555,0.28,0.91 3932 | 2010-10-05,1.1337,0.28,0.91 3933 | 2010-10-06,1.0475,0.0,0.91 3934 | 2010-10-07,0.9809,0.04,0.91 3935 | 2010-10-08,0.9654,1.68,0.91 3936 | 2010-10-09,0.9443,0.22,0.91 3937 | 2010-10-10,0.9055,0.02,0.91 3938 | 2010-10-11,0.8715,0.18,0.91 3939 | 2010-10-12,0.8503,0.0,0.91 3940 | 2010-10-13,0.8307,0.18,0.91 3941 | 2010-10-14,0.8064,0.3,0.91 3942 | 2010-10-15,0.8388,7.76,0.91 3943 | 2010-10-16,0.9039,0.4,0.91 3944 | 2010-10-17,0.8393,0.0,0.91 3945 | 2010-10-18,0.8062,4.04,0.91 3946 | 2010-10-19,0.9991,4.68,0.91 3947 | 2010-10-20,0.9945,3.7,0.91 3948 | 2010-10-21,1.5089,10.18,0.91 3949 | 2010-10-22,1.9025,8.5,0.91 3950 | 2010-10-23,1.8096,6.7,0.91 3951 | 2010-10-24,1.6951,1.58,0.91 3952 | 2010-10-25,1.341,5.2,0.91 3953 | 2010-10-26,1.5448,0.96,0.91 3954 | 2010-10-27,1.2374,0.06,0.91 3955 | 2010-10-28,1.1416,0.94,0.91 3956 | 2010-10-29,1.1259,3.36,0.91 3957 | 2010-10-30,1.0561,0.0,0.91 3958 | 2010-10-31,0.9813,0.0,0.91 3959 | 2010-11-01,0.9844,4.34,0.27 3960 | 2010-11-02,0.9882,2.18,0.27 3961 | 2010-11-03,0.9247,1.1,0.27 3962 | 2010-11-04,0.933,2.0,0.27 3963 | 2010-11-05,0.9145,0.86,0.27 3964 | 2010-11-06,0.8943,0.78,0.27 3965 | 2010-11-07,0.9104,11.48,0.27 3966 | 2010-11-08,2.5885,19.28,0.27 3967 | 2010-11-09,2.5908,1.0,0.27 3968 | 2010-11-10,1.4899,6.6,0.27 3969 | 2010-11-11,1.6264,0.9,0.27 3970 | 2010-11-12,1.3098,0.78,0.27 3971 | 2010-11-13,1.189,0.28,0.27 3972 | 2010-11-14,1.1291,0.64,0.27 3973 | 2010-11-15,1.0617,0.12,0.27 3974 | 2010-11-16,1.0291,0.78,0.27 3975 | 2010-11-17,1.0996,6.18,0.27 3976 | 2010-11-18,1.7218,17.48,0.27 3977 | 2010-11-19,3.1823,10.94,0.27 3978 | 2010-11-20,2.4419,2.3,0.27 3979 | 2010-11-21,1.8105,7.64,0.27 3980 | 2010-11-22,1.923,6.3,0.27 3981 | 2010-11-23,2.8594,9.16,0.27 3982 | 2010-11-24,2.7435,8.46,0.27 3983 | 2010-11-25,2.076,10.38,0.27 3984 | 2010-11-26,1.8436,11.3,0.27 3985 | 2010-11-27,1.7187,13.78,0.27 3986 | 2010-11-28,1.6237,6.4,0.27 3987 | 2010-11-29,,4.52,0.27 3988 | 2010-11-30,,7.38,0.27 3989 | 2010-12-01,,7.58,0.0 3990 | 2010-12-02,,2.3,0.0 3991 | 2010-12-03,1.3577,0.94,0.0 3992 | 2010-12-04,1.3066,4.28,0.0 3993 | 2010-12-05,1.2717,0.76,0.0 3994 | 2010-12-06,1.2287,0.0,0.0 3995 | 2010-12-07,1.1728,0.0,0.0 3996 | 2010-12-08,1.1296,0.72,0.0 3997 | 2010-12-09,1.2424,0.72,0.0 3998 | 2010-12-10,4.996,0.18,0.0 3999 | 2010-12-11,5.8135,0.1,0.0 4000 | 2010-12-12,3.4731,0.02,0.0 4001 | 2010-12-13,2.4688,0.98,0.0 4002 | 2010-12-14,2.6677,1.22,0.0 4003 | 2010-12-15,2.9015,4.16,0.0 4004 | 2010-12-16,2.6152,5.58,0.0 4005 | 2010-12-17,2.0781,0.78,0.0 4006 | 2010-12-18,1.7965,3.62,0.0 4007 | 2010-12-19,1.667,3.24,0.0 4008 | 2010-12-20,1.55,0.42,0.0 4009 | 2010-12-21,1.4272,0.0,0.0 4010 | 2010-12-22,1.3492,1.06,0.0 4011 | 2010-12-23,1.2949,0.0,0.0 4012 | 2010-12-24,1.2465,0.0,0.0 4013 | 2010-12-25,1.2155,0.04,0.0 4014 | 2010-12-26,1.2053,0.78,0.0 4015 | 2010-12-27,1.2038,2.18,0.0 4016 | 2010-12-28,1.5912,2.56,0.0 4017 | 2010-12-29,1.506,0.0,0.0 4018 | 2010-12-30,1.3816,0.32,0.0 4019 | 2010-12-31,1.8038,0.56,0.0 4020 | --------------------------------------------------------------------------------