From 9b1b6fa4a779cb3ad861f27e3820c8168f1e9384 Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Sat, 11 Jul 2026 16:19:44 +0100 Subject: [PATCH 1/2] Add markdown_examples.yaml + README browse link (HowToFit chapter 1) Batch 2b of the executed-markdown rollout: registers chapter_1_introduction tutorials for generate_markdown.py and links the (to-be-generated) markdown/ index from the README. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_01NyqcbXuZvGVTHHEWhoEWkC --- README.md | 1 + config/build/markdown_examples.yaml | 16 ++++++++++++++++ 2 files changed, 17 insertions(+) create mode 100644 config/build/markdown_examples.yaml diff --git a/README.md b/README.md index 6e55d6a..d0c437a 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,7 @@ [Installation Guide](https://pyautofit.readthedocs.io/en/latest/installation/overview.html) | [PyAutoFit readthedocs](https://pyautofit.readthedocs.io/en/latest/index.html) | +[Browse Chapter 1 With Images](markdown/README.md) | [autofit_workspace](https://github.com/PyAutoLabs/autofit_workspace) diff --git a/config/build/markdown_examples.yaml b/config/build/markdown_examples.yaml new file mode 100644 index 0000000..8b4783a --- /dev/null +++ b/config/build/markdown_examples.yaml @@ -0,0 +1,16 @@ +# Curated examples rendered to executed markdown pages (markdown/) with real +# output images, for GitHub browsing. Built by PyAutoBuild's generate_markdown.py +# (never TEST_MODE; features/ never rendered; order = execution + index order). +# Batch 2b: chapter_1_introduction (the intro lecture, in reading order). +- script: scripts/chapter_1_introduction/start_here.py + max_minutes: 30 +- script: scripts/chapter_1_introduction/tutorial_1_models.py + max_minutes: 30 +- script: scripts/chapter_1_introduction/tutorial_2_fitting_data.py + max_minutes: 30 +- script: scripts/chapter_1_introduction/tutorial_3_non_linear_search.py + max_minutes: 90 +- script: scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py + max_minutes: 120 +- script: scripts/chapter_1_introduction/tutorial_5_results_and_samples.py + max_minutes: 90 From 7fd5337396435b84d88977c377e698c5b742a11c Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Sat, 11 Jul 2026 16:31:24 +0100 Subject: [PATCH 2/2] Add executed markdown pages for HowToFit Chapter 1 (browse with images) The full chapter_1_introduction (6 tutorials) executed with output images, index linked from the README. Batch 2b of the rollout (HowToLens#24). 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a/markdown/README.md b/markdown/README.md new file mode 100644 index 0000000..d5a67a2 --- /dev/null +++ b/markdown/README.md @@ -0,0 +1,12 @@ +# HowToFit examples, executed — browse with output images + +Every page below is the corresponding example script **fully executed**, rendered to markdown with its real output images, so you can read the examples on GitHub exactly as they run. Each page links back to the `.py` script and Jupyter notebook it was generated from. + +- [HowToFit Lectures](chapter_1_introduction/start_here.md) — from `scripts/chapter_1_introduction/start_here.py` +- [Tutorial 1: Models](chapter_1_introduction/tutorial_1_models.md) — from `scripts/chapter_1_introduction/tutorial_1_models.py` +- [Tutorial 2: Fitting Data](chapter_1_introduction/tutorial_2_fitting_data.md) — from `scripts/chapter_1_introduction/tutorial_2_fitting_data.py` +- [Tutorial 3: Non Linear Search](chapter_1_introduction/tutorial_3_non_linear_search.md) — from `scripts/chapter_1_introduction/tutorial_3_non_linear_search.py` +- [Tutorial 4: Why Modeling Is Hard](chapter_1_introduction/tutorial_4_why_modeling_is_hard.md) — from `scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py` +- [Tutorial 5: Results And Samples](chapter_1_introduction/tutorial_5_results_and_samples.md) — from `scripts/chapter_1_introduction/tutorial_5_results_and_samples.py` + +These pages are regenerated manually by PyAutoBuild's `generate_markdown.py` when a curated script changes. diff --git a/markdown/chapter_1_introduction/start_here.md b/markdown/chapter_1_introduction/start_here.md new file mode 100644 index 0000000..6a532fa --- /dev/null +++ b/markdown/chapter_1_introduction/start_here.md @@ -0,0 +1,62 @@ +> ✏️ **This page is auto-generated from [`scripts/chapter_1_introduction/start_here.py`](../../scripts/chapter_1_introduction/start_here.py) — do not edit it directly.** +> It shows the example fully executed, with its real output images. +> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/start_here.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/start_here.ipynb). + +HowToFit Lectures +================= + +Welcome to the HowToFit Jupyter Notebook lectures! + +At the core of data science is fitting a model to data. This process extracts meaningful patterns, relationships, +and insights, enabling accurate predictions, decision-making, and understanding of underlying processes. + +However, data science can be quite challenging. With a vast array of statistical methods to choose from, it can be +difficult to determine the right one for your problem. Interpreting large volumes of results is complex, and managing big datasets requires significant computational power and sophisticated statistical methods. + +The HowToFit lectures teach you how to perform effective data science analysis. Designed at an undergraduate level, +these lectures assume no prior knowledge of model-fitting, Bayesian statistics, or scientific analysis. They +introduce core concepts without formal statistical equations, aiming to provide an understanding of the +phenomenological methods used in data science. By the end of the lectures, you'll be equipped to perform your own +data analysis. + +The lectures use the probabilistic programming language PyAutoFit, an open-source library for model-fitting, +scientific analysis, and big data analysis (https://github.com/PyAutoLabs/PyAutoFit). + +The HowToFit lectures are composed of 3 chapters: + +**Chapter 1: Introduction**: How to fit a model to data, perform statistical inference, and interpret the results +for scientific analysis. + +**Chapter 2: Scientific Workflow**: Scaling model-fitting to big datasets while ensuring detailed scientific analysis +of the results. + +**Chapter 3: Graphical Models**: Simultaneous model fitting of large datasets, scaling up to models with tens of +thousands of parameters. + +After each chapter, it is advised that you apply what you've learned to your own model-fitting analysis based on +your scientific problem to build confidence in the techniques. Once confident, proceed to the next chapter. + +__Chapter 1: Introduction__ + +The first chapter of the HowToFit lectures covers the basics of model-fitting, statistical inference, and scientific +interpretation. The chapter includes: + +`tutorial_1_models.py`: What probabilistic models are and how to compose them using PyAutoFit. + +`tutorial_2_fitting_data.py`: Fitting a model with an input set of parameters to data and quantifying the goodness of fit. + +`tutorial_3_non_linear_search.py`: Searching non-linear parameter spaces to find the best-fit model. + +`tutorial_4_why_modeling_is_hard.py`: Why modeling becomes difficult and how to over model-fitting problems. + +`tutorial_5_results_and_samples.py`: Interpreting model-fit results and using the samples for scientific analysis. + +An applied astronomy-focused example (previously tutorial 8) now lives in +`autofit_workspace_developer/scripts/howtofit/chapter_1_introduction/tutorial_8_astronomy_example.py`, +alongside the larger `projects/cosmology/` example. These depend on astronomy-specific datasets +that only live in the developer workspace. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_1_models.md b/markdown/chapter_1_introduction/tutorial_1_models.md new file mode 100644 index 0000000..4f5b485 --- /dev/null +++ b/markdown/chapter_1_introduction/tutorial_1_models.md @@ -0,0 +1,736 @@ +> ✏️ **This page is auto-generated from [`scripts/chapter_1_introduction/tutorial_1_models.py`](../../scripts/chapter_1_introduction/tutorial_1_models.py) — do not edit it directly.** +> It shows the example fully executed, with its real output images. +> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/tutorial_1_models.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/tutorial_1_models.ipynb). + +Tutorial 1: Models +================== + +At the heart of model-fitting is the model: a set of equations, numerical processes, and assumptions describing a +physical system of interest. The goal of model-fitting is to better understand this physical system and develop +predictive models that describe it more accurately. + +In astronomy, a model might describe the distribution of stars within a galaxy. In biology, it might represent the +interaction of proteins within a cell. In finance, it could describe the evolution of stock prices in a market. +Regardless of the field, the model acts as a mathematical description of the physical system, aiming to enhance +understanding and enable new predictions. + +Whatever your model, its equations are defined by "free parameters." Changing these parameters alters the +behavior and predictions of the model. + +Once the model is defined and parameter values are chosen, the model creates "model data"—a realization of how the +physical system appears given those parameters. This process, often referred to as "forward modeling," describes the +physical system from its starting point and predicts the data we observe. + +By varying the model parameters, we can generate numerous model datasets. The ultimate goal of model-fitting, which +you will learn by the end of this chapter, is to determine the model parameters and corresponding dataset that best +fit the observed data. + +__Astronomy Example__ + +For instance, in astronomy, we might model the distribution of stars, including: + +- A parameter describing the brightness of the stars. + +- Multiple parameters defining their distribution. + +- Several parameters describing their colors. + +If our model pertains to the distribution of stars within a galaxy, the forward model will produce an image of what +that galaxy looks like when observed with a telescope. This forward model might account for physical effects such as +the blurring of light due to diffraction in the telescope optics. + +By altering the parameters describing the stars, we can generate many different model images via this forward model. + +At the end of this chapter, we will use a real-world astronomy example to illustrate everything you have learned, +including fitting a real galaxy observed with the Hubble Space Telescope. + +__Overview__ + +In tutorial 1, we will cover the basics of defining a model, specifically: + +- Defining a simple model described by a few simple equations. + +- Showing that this model is characterized by three or more free parameters. + +- Using the model, with different sets of parameters, to generate model data. + +__Contents__ + +This tutorial is split into the following sections: + +- **Paths**: Setting up the working directory path so the tutorial runs correctly on your computer. +- **Model Parameterization**: An example of how a model is parameterized and is made up of free parameters. +- **Model Composition**: Composing a model using PyAutoFit's model composition API. +- **Model Creation**: Creating an instance of the model using PyAutoFit's `Model` python object. +- **Model Mapping**: Mapping an input vector of parameters to the model to create an instance of the model. +- **Complex Models**: Composing a more complex model with multiple model components and more free parameters. +- **Tuple Parameters**: Defining a model component with tuple parameters. +- **Extensibility**: Discussing how PyAutoFit's model composition API is scalable and extensible. +- **Wrap Up**: Concluding the tutorial and considering how to apply the concepts to your own scientific problem. + +This tutorial introduces the PyAutoFit API for model composition, which forms the foundation of all model-fitting +performed by PyAutoFit. + + +```python + +import numpy as np +import matplotlib.pyplot as plt + +import autofit as af +``` + + .../PyAutoConf/autoconf/workspace.py:206: UserWarning: Cannot verify the workspace at HowToFit/scripts/chapter_1_introduction is compatible with the installed library version (2026.7.6.649): no `version.minimum_library_version` or `version.workspace_version` key in config/general.yaml and no version.txt at the workspace root. + + If you cloned the workspace from `main` rather than a release tag, set `version.workspace_version_check: False` in config/general.yaml to silence this warning. The `main` branch updates more frequently than library releases, so version mismatches are expected and not actionable for `main`-branch users. + + You can also set the environment variable PYAUTO_SKIP_WORKSPACE_VERSION_CHECK=1 to disable temporarily. + warnings.warn(_missing_version_warning(root, library_version)) + + +__Paths__ + +PyAutoFit assumes the current working directory is /path/to/HowToFit/ on your hard-disk (or in Colab). +This setup allows PyAutoFit to: + +- Load configuration settings from config files in the HowToFit/config folder. + +- Load example data from the HowToFit/dataset folder. + +- Output the results of model fits to your hard disk in the autofit/output folder. + +If you don't have a HowToFit clone, you can download it here: + + https://github.com/PyAutoLabs/autofit_workspace + +__Model Parameterization__ + +A model is a set of equations, numerical processes, and assumptions that describe a physical system and dataset. + +In this example, our model is one or more 1-dimensional Gaussians, defined by the following equation: + +\begin{equation*} +g(x, I, \sigma) = \frac{N}{\sigma\sqrt{2\pi}} \exp{(-0.5 (x / \sigma)^2)} +\end{equation*} + +Where: + +- `x`: The x-axis coordinate where the Gaussian is evaluated. + +- `N`: The overall normalization of the Gaussian. + + +- `\sigma`: The size of the Gaussian (Full Width Half Maximum, $\mathrm{FWHM}$, is $2{\sqrt{2\ln 2}}\;\sigma$). + +While a 1D Gaussian might seem like a rudimentary model, it has many real-world applications in signal processing. +For example, 1D Gaussians are fitted to datasets to measure the size of an observed signal. Thus, this model has +practical real-world applications. + +We now have a model, expressed as a simple 1D Gaussian. The model has three parameters, $(x, N, \sigma)$. Using +different combinations of these parameters creates different realizations of the model, which we illustrate below. + +__Model Composition__ + +We now define the 1D Gaussian as a "model component" in PyAutoFit. We use the term "model component" because the model +can be extended to include multiple components, each related to different equations and numerical processes. + +We first illustrate a model composed of a single model component, the 1D Gaussian. We then show a model made of +multiple model components. + +To define a "model component" in PyAutoFit, we simply write it as a Python class using the format shown below: + + +```python + + +class Gaussian: + def __init__( + self, + centre: float = 30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization: float = 1.0, # <- are the Gaussian`s model parameters. + sigma: float = 5.0, + ): + """ + Represents a 1D Gaussian profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to perform model-fitting + of example datasets. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + sigma + The sigma value controlling the size of the Gaussian. + """ + self.centre = centre + self.normalization = normalization + self.sigma = sigma + + def model_data_from(self, xvalues: np.ndarray) -> np.ndarray: + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + + Returns + ------- + np.array + The Gaussian values at the input x coordinates. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return np.multiply( + np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)), + np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))), + ) + +``` + +The format of this Python class defines how PyAutoFit composes the Gaussian as a model component, where: + +- The name of the class is the name of the model component, in this case, "Gaussian". + +- The input arguments of the constructor (the `__init__` method) are the parameters of the model, in the example +above `centre`, `normalization`, and `sigma`. + +- The default values and typing of the input arguments define whether a parameter is a single-valued float or a +multi-valued tuple. For the `Gaussian` class above, no input parameters are tuples, but later examples use tuples. + +- It includes functions associated with that model component, specifically the model_data function. When we create +instances of a `Gaussian` below, this function is used to generate a 1D representation of it as a NumPy array. + +__Model Creation__ + +The `Gaussian` class above is a standard Python class. It does not yet act as a model component that can be used +for model fitting with PyAutoFit. + +To transform the Gaussian class into a model component that can be used for model fitting with PyAutoFit, we use +the `af.Model` object. This tells PyAutoFit to treat the input Python class as a model component. + + +```python +model = af.Model(Gaussian) +print("Model `Gaussian` object: \n") +print(model) +``` + + Model `Gaussian` object: + + Gaussian (centre, UniformPrior [0], lower_limit = 0.0, upper_limit = 100.0), (normalization, LogUniformPrior [1], lower_limit = 1e-06, upper_limit = 1000000.0), (sigma, UniformPrior [2], lower_limit = 0.0, upper_limit = 25.0) + + +In PyAutoFit, a Model object encapsulates a model component that can be used for model fitting. It provides several +attributes that describe the model component, such as the `total_free_parameters` attribute, which indicates the +number of free parameters in the model: + + +```python +print(model.total_free_parameters) +``` + + 3 + + +In PyAutoFit, you can retrieve comprehensive information about a model by accessing its `info` attribute. + +When you print the model info, it displays detailed information about each parameter in the model, including its name, +type, and associated prior distribution. Priors define the expected range or distribution of values for each +parameter during the model fitting process. If you're unfamiliar with priors, they are covered in tutorial 3 of +this chapter, which explains their role in model fitting. + +[The `info` below may not display optimally on your computer screen, for example the whitespace between parameter +names on the left and parameter priors on the right may lead them to appear across multiple lines. This is a +common issue in Jupyter notebooks. + +The`info_whitespace_length` parameter in the file `config/general.yaml` in the "output" section can be changed to +increase or decrease the amount of whitespace (The Jupyter notebook kernel will need to be reset for this change to +appear in a notebook).] + + +```python +print(model.info) +``` + + Total Free Parameters = 3 + + model Gaussian (N=3) + + centre UniformPrior [0], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [1], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [2], lower_limit = 0.0, upper_limit = 25.0 + + +__Model Mapping__ + +In PyAutoFit, instances of model components created via the af.Model object can be instantiated by mapping an input +vector of parameters to the Python class that the model object represents. The order of parameters in the model is +crucial for correctly defining the input vector. + +To determine the order of parameters in the model, PyAutoFit provides the paths attribute of the model object. +This attribute contains information about the parameter paths within the model. + +Here's how you can access the paths attribute to understand the order of parameters in the model: + + +```python +print(model.paths) +``` + + [('centre',), ('normalization',), ('sigma',)] + + +To create an instance of the Gaussian model component using PyAutoFit, following the order of parameters defined by +the paths attribute (`centre`, `normalization`, and `sigma`), you can initialize the instance as follows: + + +```python +instance = model.instance_from_vector(vector=[30.0, 2.0, 3.0]) +``` + +This is an instance of the `Gaussian` class. + + +```python +print("Model Instance: \n") +print(instance) +``` + + Model Instance: + + <__main__.Gaussian object at 0x7fe0b75dbd40> + + +It has the parameters of the `Gaussian` with the values input above. + + +```python +print("Instance Parameters \n") +print("x = ", instance.centre) +print("normalization = ", instance.normalization) +print("sigma = ", instance.sigma) +``` + + Instance Parameters + + x = 30.0 + normalization = 2.0 + sigma = 3.0 + + +We can use all class functions, such as the `model_data_from` function, to generate an instance of the +1D `Gaussian` and visualize it through plotting. + +The code below generates the 1D Gaussian model data, which requires an input list of x values where the Gaussian is +evaluated. The output is a NumPy array of the Gaussian's y values at the input x coordinates. + +Although simple, the code below is essentially the process of forward modeling, where we use the model to generate +the data we would observe in an experiment for a given set of parameters. + + +```python +xvalues = np.arange(0.0, 100.0, 1.0) + +model_data = instance.model_data_from(xvalues=xvalues) + +plt.plot(xvalues, model_data, color="r") +plt.title("1D Gaussian Model Data.") +plt.xlabel("x values of profile") +plt.ylabel("Gaussian Value") +plt.show() +plt.clf() +``` + + + +![png](tutorial_1_models_files/tutorial_1_models_19_0.png) + + + + +
+ + +__Complex Models__ + +The code above may seem like a lot of steps just to create an instance of the `Gaussian` class. Couldn't we have +simply done this instead? + +```python +instance = Gaussian(centre=30.0, normalization=2.0, sigma=3.0) +``` + +Yes, we could have. + +However, the model composition API used above is designed to simplify the process of composing complex models that +consist of multiple components with many free parameters. It provides a scalable approach for defining and +manipulating models. + +To demonstrate this capability, let's conclude the tutorial by composing a model composed of a Gaussian +component and another 1D profile, an `Exponential`, defined by the equation: + +\begin{equation*} +g(x, I, \lambda) = N \lambda \exp{- \lambda x } +\end{equation*} + +where: + +- `x`: Represents the x-axis coordinate where the Exponential profile is evaluated. + +- `N`: Describes the overall normalization of the Exponential profile. + +- $\lambda$: Represents the rate of decay of the exponential. + +We'll start by defining the `Exponential` profile using a format similar to the Gaussian definition above. + + +```python + + +class Exponential: + def __init__( + self, + centre: float = 30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization: float = 1.0, # <- are the Exponential`s model parameters. + rate: float = 0.01, + ): + """ + Represents a 1D Exponential profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to fit example datasets + via a non-linear search. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + ratw + The decay rate controlling has fast the Exponential declines. + """ + self.centre = centre + self.normalization = normalization + self.rate = rate + + def model_data_from(self, xvalues: np.ndarray): + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the `Exponential`, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return self.normalization * np.multiply( + self.rate, np.exp(-1.0 * self.rate * abs(transformed_xvalues)) + ) + +``` + +We can construct a model comprising one `Gaussian` object and one `Exponential` object using the `af.Collection` object: + + +```python +model = af.Collection(gaussian=af.Model(Gaussian), exponential=af.Model(Exponential)) +``` + +You can retrieve all the information about the model created via the `af.Collection` by printing its `info` attribute +in one go: + + +```python +print(model.info) +``` + + Total Free Parameters = 6 + + model Collection (N=6) + gaussian Gaussian (N=3) + exponential Exponential (N=3) + + gaussian + centre UniformPrior [3], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [4], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [5], lower_limit = 0.0, upper_limit = 25.0 + exponential + centre UniformPrior [6], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [7], lower_limit = 1e-06, upper_limit = 1000000.0 + rate UniformPrior [8], lower_limit = 0.0, upper_limit = 1.0 + + +When `Gaussian` and `Exponential` are added to a `Collection`, they are automatically assigned as `Model` objects. + +Therefore, there's no need to use the `af.Model` method when passing classes to a `Collection`, which makes the Python +code more concise and readable. + + +```python +model = af.Collection(gaussian=Gaussian, exponential=Exponential) +``` + +The `model.info` is identical to the previous example. + + +```python +print(model.info) +``` + + Total Free Parameters = 6 + + model Collection (N=6) + gaussian Gaussian (N=3) + exponential Exponential (N=3) + + gaussian + centre UniformPrior [9], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [10], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [11], lower_limit = 0.0, upper_limit = 25.0 + exponential + centre UniformPrior [12], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [13], lower_limit = 1e-06, upper_limit = 1000000.0 + rate UniformPrior [14], lower_limit = 0.0, upper_limit = 1.0 + + +A `Collection` functions analogously to a `Model`, but it includes multiple model components. + +This can be observed by examining its `paths` attribute, which displays paths to all 6 free parameters across both model components. + +The paths contain entries such as `.gaussian.` and `.exponential.`, corresponding to the names we provided when +defining the `af.Collection` earlier. Modifying the names of the model components supplied to the `Collection` +would adjust the paths accordingly. + + +```python +print(model.paths) +``` + + [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')] + + +A model instance can again be created by mapping an input `vector`, which now has 6 entries. + + +```python +instance = model.instance_from_vector(vector=[0.1, 0.2, 0.3, 0.4, 0.5, 0.01]) +``` + +This `instance` contains each of the model components we defined above. + +The argument names input into the `Collection` define the attribute names of the `instance`: + + +```python +print("Instance Parameters \n") +print("x (Gaussian) = ", instance.gaussian.centre) +print("normalization (Gaussian) = ", instance.gaussian.normalization) +print("sigma (Gaussian) = ", instance.gaussian.sigma) +print("x (Exponential) = ", instance.exponential.centre) +print("normalization (Exponential) = ", instance.exponential.normalization) +print("sigma (Exponential) = ", instance.exponential.rate) +``` + + Instance Parameters + + x (Gaussian) = 0.1 + normalization (Gaussian) = 0.2 + sigma (Gaussian) = 0.3 + x (Exponential) = 0.4 + normalization (Exponential) = 0.5 + sigma (Exponential) = 0.01 + + +In the context of the model's equations, it is simply the sum of the equations defining the `Gaussian` +and `Exponential` components. + +To generate the `model_data`, we sum the `model_data` of each individual model component, as demonstrated and +visualized below. + + +```python +xvalues = np.arange(0.0, 100.0, 1.0) + +model_data_0 = instance.gaussian.model_data_from(xvalues=xvalues) +model_data_1 = instance.exponential.model_data_from(xvalues=xvalues) + +model_data = model_data_0 + model_data_1 + +plt.plot(xvalues, model_data, color="r") +plt.plot(xvalues, model_data_0, "b", "--") +plt.plot(xvalues, model_data_1, "k", "--") +plt.title("1D Gaussian + Exponential Model Data.") +plt.xlabel("x values of profile") +plt.ylabel("Value") +plt.show() +plt.clf() +``` + + + +![png](tutorial_1_models_files/tutorial_1_models_37_0.png) + + + + +
+ + +__Tuple Parameters__ + +The `Gaussian` and `Exponential` model components above only has parameters that are single-valued floats. + +Parameters can also be tuples, which is useful for defining model components where certain parameters are naturally +grouped together. + +For example, we can define a 2D Gaussian with a center that has two coordinates and therefore free parameters, (x, y), +using a tuple. + + +```python +from typing import Tuple + + +class Gaussian2D: + def __init__( + self, + centre: Tuple[float, float] = (0.0, 0.0), + normalization: float = 0.1, + sigma: float = 1.0, + ): + self.centre = centre + self.normalization = normalization + self.sigma = sigma + +``` + +The model's `total_free_parameters` attribute now includes 4 free parameters, as the tuple `centre` parameter accounts +for 2 free parameters. + + +```python +model = af.Model(Gaussian2D) + +print("Total Free Parameters:", model.total_free_parameters) +``` + + Total Free Parameters: 4 + + +This information is again displayed in the `info` attribute: + + +```python +print("\nInfo:") +print(model.info) +``` + + + Info: + Total Free Parameters = 4 + + model Gaussian2D (N=4) + + centre + centre_0 UniformPrior [15], lower_limit = 0.0, upper_limit = 100.0 + centre_1 UniformPrior [16], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [17], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [18], lower_limit = 0.0, upper_limit = 25.0 + + +The `paths` attribute provides information on the order of parameters in the model, illustrating how the +`centre` tuple is split into two parameters. + + +```python +print("\nPaths:") +print(model.paths) +``` + + + Paths: + [('centre', 'centre_0'), ('centre', 'centre_1'), ('normalization',), ('sigma',)] + + +This ordering is used to create an instance of the `Gaussian2D` model component: + + +```python +instance = model.instance_from_vector(vector=[40.0, 60.0, 2.0, 3.0]) + +print("\nInstance Parameters:") +print("centre (x) = ", instance.centre[0]) +print("centre (y) = ", instance.centre[1]) +print("normalization = ", instance.normalization) +print("sigma = ", instance.sigma) +``` + + + Instance Parameters: + centre (x) = 40.0 + centre (y) = 60.0 + normalization = 2.0 + sigma = 3.0 + + +__Extensibility__ + +It should now be clear why we use `Model` and `Collection` objects to construct our model. + +These objects facilitate the straightforward extension of our models to include multiple components and parameters. +For instance, we can add more `Gaussian` and `Exponential` components to the `Collection`, or define new Python +classes to represent entirely new model components with additional parameters. + +These objects serve numerous other essential purposes that we will explore in subsequent tutorials. + +**PyAutoFit** offers a comprehensive API for building models, which includes models constructed using NumPy arrays, +hierarchies of Python classes, and graphical models where parameters are interconnected. These advanced modeling +techniques are gradually introduced throughout the HowToFit lectures. + +For a detailed understanding of PyAutoFit's model composition API and a quick reference guide on how to construct +models, you may want to take a quick look at the model cookbook in the PyAutoFit documentation. It provides an +extensive overview and can serve as a helpful resource as you progress: + +[PyAutoFit Model Cookbook](https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html) + +Don't worry if it seems a bit overwhelming at this stage; the concepts will become clearer as you continue exploring +and working with PyAutoFit. + +__Wrap Up__ + +In this tutorial, we've learned how to define and compose a model that can generate model data. + +Now, think about your specific field of study and the problem you want to address through model-fitting. Consider +the following questions: + +- What type of model would best describe your data? + +- Which Python class, following the format introduced here, would you need to compose this model? + +- What are the free parameters of your model that need to be determined through fitting? + +If you decide to incorporate a new model component into your autofit_workspace tailored to your specific model-fitting +task, refer to the following script: + +autofit_workspace/scripts/overview/new_model_component/new_model_component.py + +This script provides guidance on setting up the PyAutoFit configuration files associated with your custom model. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_1_models_files/tutorial_1_models_19_0.png b/markdown/chapter_1_introduction/tutorial_1_models_files/tutorial_1_models_19_0.png new file mode 100644 index 0000000000000000000000000000000000000000..b758e2db9c2ec84a8b76a83b41e7998addd357a5 GIT binary patch literal 21878 zcmbWf2{@N;*EV{ikRepY%uPr{l$oTc6hb6JLXl($$*hnDNhPTanTHIOIU<#W(m)9r zDkU?SGVb&8f8O`|zP+Dk@8jJa&;Ri7yMOn6-Pf?zxz2U2by0u6HY*b!6Gc(1yLGe< zP!z2OMbW%sq{m-OUj3@W4<+}VrtXH$$K20YyV+6utleEsI=i2AI4bOE=XTn``P3FE znaxs)lEU`x?k=a5q@|ty`wA&%x8u_5cYQpByDW3jIdqz$*sRHaH0kPT4ishdZMW8T zBd>b{U(cEw84J-5A8!uzTWM>s%Jn&?uKBxF9iq=93nX2|8*6LjJ5wIx2Lj`Bp6IG?xW zvLeA02M0$a@55`Qlqmk~uvz@^_&KG8f5$G+gyCQPayoWg6(c||j7wydSv2sOttU?) zKC9{9ym*H%*7#!57TX&3shOF*7NOfc`aZFKE%nIyaAt09P6mIE{j|gAqYTCId;9+V z%fEg5#?H&T?97=nHMX*nR!__73vuyz-Sy;uAt6ie#m2tRcQ7oQ?#*g`>i6VRYkRrR zw(0S{3rZfnmDSbsQc_Yq@9xv3>c*TqTy)C#bi;|lx@hUF1`?}G;qItK@@7CN;a9qV~N^(#Igv#hAn{T@BNS2x8C^>Zy%mn>bXI>EgIL*ozguDtjTwxLjFs0!cCfRv2hPs=@~ipsoc>w=>=NfjmyvHfE&3BJHytiy zVG7ZFG|<}8a&(}jU~1}ng#4{vKYxC^AmksIot>=}W!$l)LFIf;k3FNvwV0TT-(N-8 zyu8F|TIMBltp0Y;?b|%rpJSq-r+-XeS86a6CA2oP@|=z8|i=Cu*XOsvTJx1Po!W?w;M)f;wW(I)~(y>0cv-) zIbX#zif-BRxN73z!-vDe!efy^TDZ6LqmfF8hT-rY{(Dn0Y=(TI?4-P%vHFwt2b8Gb#Ub=vQ0DP}8 z$6|GZ${&`i{ECUE3_tNCe0h942{%eN&f@Ct?|)b5bogXTeumLL*%+2J+E-spb_7J+ z+H_b`DmrQBWiA~Z9kE|EQCxg{m*`pf>>F9BER)=W@t-8?zP-HsK5-{Ao-j2%ee^{b zM@>zQSkquu?o-X@p{&-&k6)M=XtkLf{A!nzn~*@cy1LporgQvoQ=FZh-F&J|Vb!Wt zM&4EuNe8pdOp8tlU~3NlPK(cs=H}uO-MW>_skJcV@?~aze*SkUd%63YAMW`2bF{l| zcx-G5r5mSm$nVm=@yDLuUM%x)PR`EWR90R-(&Cug^)^;n=_`Y%6V4c~%-+3wS$H;6 zXV0FU>dURDbviTHrg%^J%yPf!vG*rV*5YlI@It=yNnrK=f)d?{sR<$G#= zrBxmQdl za~}jYAE7>FA7&6)LUlX5=?GX5pkr7r`FJjQ_l?a*UoesxetS=`r?p7>>FnrU_n~%y zojZ5-dRhg{$+vs=ZNkp7`B@*+(^j&@d%QOUOLXFa-hKT9lU&Q&sy z?`PPcSJTte_Y7&_DG)Uv^P&d3kwFLxYKQmQ}@59u+T_n1Tx-AsHRm zV^6C5I5Zi#xw&Pq6Pa3B1XWBMbFHjwz9bcJadBP3e!i3z=e@^9k+Qb2StDg$oM7oW zJLO8FfoVyo8c0n}W~7dOdT{95kJmS71Dnfzir6*7u>F^5Yimc|y}OZ~O;8eBv9SYN zY9(w$qR-@DVsWu)xo=lh2yNih&%4fz@~}3CmoP9~K6j4R<4Nw*={?jtgY1FmjNZAGLfVm!9~3o`Ti<|T~oztSR-k_NO19)p}67E(G|Jn z6L-A7473(4r8u~`O-{tx&0Wd1cyf?pf8sgdee#Xgbd{j6=$f?N_pjf+l`0DF4d{=U zn72K4tm@-O!|(T}hRTO*-vb?^aTP82hYpS6)&-!_1fB%EQ6eaf__75LE z3{SK=JKjs>m$hXyH#e92RO0%5mz?7#mY|>@9X&l0^*HZdam*TP%EpNK2ZzkeGW(3g zRu%h9N(riX1zx*$O*X>k(4P5{?q9#iD+wrjE(zR|dtOHfZup8hTq;%h`gKOz*31SY zCu4pAfroxGo+mq=3fR25x@o)|Mz1S`+cFSl1@F@J`*((*@Jz9cwjHm!zY4B?|J2V% z?o=Dknq5~a<5Yd*TI%m9uH2v(7aSii)KcUevHQk)V~N`*TMJq7;9wY?wy$5C(kyE; 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a/markdown/chapter_1_introduction/tutorial_2_fitting_data.md b/markdown/chapter_1_introduction/tutorial_2_fitting_data.md new file mode 100644 index 0000000..71b2a15 --- /dev/null +++ b/markdown/chapter_1_introduction/tutorial_2_fitting_data.md @@ -0,0 +1,830 @@ +> ✏️ **This page is auto-generated from [`scripts/chapter_1_introduction/tutorial_2_fitting_data.py`](../../scripts/chapter_1_introduction/tutorial_2_fitting_data.py) — do not edit it directly.** +> It shows the example fully executed, with its real output images. +> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/tutorial_2_fitting_data.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/tutorial_2_fitting_data.ipynb). + +Tutorial 2: Fitting Data +======================== + +We've learned that a model consists of equations, numerical processes, and assumptions that describe a physical system. +Using **PyAutoFit**, we defined simple 1D models like the Gaussian, composed them into models using `Model` and +`Collection` objects, and generated model data by varying their parameters. + +To apply our model to real-world situations, we must fit it to data. Fitting involves assessing how well the model +matches observed data. A good fit indicates that the model's parameter values accurately describe the physical system. +Conversely, a poor fit suggests that adjustments are needed to better reflect reality. + +Model-fitting is a cyclical process: define the model, fit it to data, and refine the model based on insights gained. +Iteratively improving the model's complexity enhances its ability to accurately represent the system under study. +This iterative process lies at the core of model-fitting in scientific analysis. + +__Astronomy Example__ + +In Astronomy, this process has been crucial for understanding the distributions of stars within galaxies. By +fitting high-quality images of galaxies with increasingly sophisticated models, astronomers have determined that +stars within galaxies are organized into structures such as disks, bars, and bulges. This approach has also revealed +that stars appear differently in red and blue images due to variations in their age and composition. + +__Overview__ + +In this tutorial, we will explore how to fit the `model_data` generated by a model to actual data. Specifically, we will: + +- Load data representing a 1D Gaussian signal, which serves as our target dataset for fitting. + +- Compute quantities such as residuals by subtracting the model data from the observed data. + +- Quantitatively assess the goodness-of-fit using a critical measure in model-fitting known as the `log_likelihood`. + +All these steps will utilize the **PyAutoFit** API for model composition, introduced in the previous tutorial. + +__Contents__ + +This tutorial is split into the following sections: + +- **Data**: Load and plot the 1D Gaussian dataset we will fit. +- **Model Data**: Generate model data of the `Gaussian` model using a forward model. +- **Residuals**: Compute and visualize residuals between the model data and observed data. +- **Normalized Residuals**: Compute and visualize normalized residuals, which account for the noise properties of the data. +- **Chi Squared**: Compute and visualize the chi-squared map, a measure of the overall goodness-of-fit. +- **Noise Normalization**: Compute the noise normalization term which describes the noise properties of the data. +- **Likelihood**: Compute the log likelihood, a key measure of the goodness-of-fit of the model to the data. +- **Recap**: Summarize the standard metrics for quantifying model fit quality. +- **Fitting Models**: Fit the `Gaussian` model to the 1D data and compute the log likelihood, by guessing parameters. +- **Guess 1**: A first parameter guess with an explanation of the resulting log likelihood. +- **Guess 2**: An improved parameter guess with a better log likelihood. +- **Guess 3**: The optimal parameter guess providing the best fit to the data. +- **Extensibility**: Use the `Collection` object for fitting models with multiple components. +- **Wrap Up**: Summarize the key concepts of this tutorial. + + +```python + +from autoconf import setup_notebook; setup_notebook() + +from os import path +import matplotlib.pyplot as plt +import numpy as np + +import autofit as af +``` + + Working Directory has been set to `HowToFit` + + +__Data__ + +Our dataset consists of noisy 1D data containing a signal, where the underlying signal can be modeled using +equations such as a 1D Gaussian, a 1D Exponential, or a combination of multiple 1D profiles. + +We load this dataset from .json files, where: + +- `data` is a 1D NumPy array containing values representing the observed signal. + +- `noise_map` is a 1D NumPy array containing values representing the estimated root mean squared (RMS) noise level at + each data point. + +These datasets are generated using scripts located in `HowToFit/scripts/simulators`. Feel free to explore +these scripts for more details! + + +```python +dataset_path = path.join("dataset", "example_1d", "gaussian_x1") +``` + +__Dataset Auto-Simulation__ + +If the dataset does not already exist on your system, it will be created by running the corresponding +simulator script. This ensures that all example scripts can be run without manually simulating data first. + + +```python +if not path.exists(dataset_path): + import subprocess + import sys + + subprocess.run( + [sys.executable, "scripts/simulators/simulators.py"], + check=True, + ) + +data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json")) + +noise_map = af.util.numpy_array_from_json( + file_path=path.join(dataset_path, "noise_map.json") +) +``` + +Next, we visualize the 1D signal using `matplotlib`. + +The signal is observed over uniformly spaced `xvalues`, computed using the `arange` function and `data.shape[0]` method. + +We will reuse these `xvalues` shortly when generating model data from the model. + + +```python +xvalues = np.arange(data.shape[0]) +plt.plot(xvalues, data, color="k") +plt.title("1D Dataset Containing a Gaussian.") +plt.xlabel("x values of profile") +plt.ylabel("Signal Value") +plt.show() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_7_0.png) + + + +The earlier plot depicted only the signal without indicating the estimated noise at each data point. + +To visualize both the signal and its `noise_map`, we can use `matplotlib`'s `errorbar` function. + + +```python +plt.errorbar( + xvalues, + data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.title("1D Gaussian dataset with errors from the noise-map.") +plt.xlabel("x values of profile") +plt.ylabel("Signal Value") +plt.show() + +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_9_0.png) + + + +__Model Data__ + +To fit our `Gaussian` model to this data, we start by generating `model_data` from the 1D `Gaussian` model, +following the same steps as outlined in the previous tutorial. + +We begin by again defining the `Gaussian` class, following the **PyAutoFit** format for model components. + + +```python + + +class Gaussian: + def __init__( + self, + centre: float = 30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization: float = 1.0, # <- are the Gaussian`s model parameters. + sigma: float = 5.0, + ): + """ + Represents a 1D Gaussian profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to perform model-fitting + of example datasets. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + sigma + The sigma value controlling the size of the Gaussian. + """ + self.centre = centre + self.normalization = normalization + self.sigma = sigma + + def model_data_from(self, xvalues: np.ndarray) -> np.ndarray: + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + + Returns + ------- + np.array + The Gaussian values at the input x coordinates. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return np.multiply( + np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)), + np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))), + ) + +``` + +To create `model_data` for the `Gaussian`, we use the model by providing it with `xvalues` corresponding to the +observed data, as demonstrated in the previous tutorial. + +The following code essentially utilizes a forward model to generate the model data based on a specified set of +parameters. + + +```python +model = af.Model(Gaussian) + +gaussian = model.instance_from_vector(vector=[60.0, 20.0, 15.0]) + +model_data = gaussian.model_data_from(xvalues=xvalues) + +plt.plot(xvalues, model_data, color="r") +plt.title("1D Gaussian model.") +plt.xlabel("x values of profile") +plt.ylabel("Profile Normalization") +plt.show() +plt.clf() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_13_0.png) + + + + +
    + + +For comparison purposes, it is more informative to plot both the `data` and `model_data` on the same plot. + + +```python +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.plot(xvalues, model_data, color="r") +plt.title("Model-data fit to 1D Gaussian data.") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_15_0.png) + + + +Changing the values of `centre`, `normalization`, and `sigma` alters the appearance of the `Gaussian`. + +You can modify the parameters passed into `instance_from_vector()` above. After recomputing the `model_data`, plot +it again to observe how these changes affect the Gaussian's appearance. + +__Residuals__ + +While it's informative to compare the `data` and `model_data` above, gaining insights from the residuals can be even +more useful. + +Residuals are calculated as `data - model_data` in 1D: + + +```python +residual_map = data - model_data +plt.plot(xvalues, residual_map, color="k") +plt.title("Residuals of model-data fit to 1D Gaussian data.") +plt.xlabel("x values of profile") +plt.ylabel("Residuals") +plt.show() +plt.clf() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_17_0.png) + + + + +
    + + +Are these residuals indicative of a good fit to the data? Without considering the noise in the data, it's difficult +to ascertain. + +We can plot the residuals with error bars based on the noise map. The plot below reveals that the model is a poor fit, +as many residuals deviate significantly from zero even after accounting for the noise in each data point. + +A blue line through zero is included on the plot, to make it clear where residuals are not constent with zero +above the noise level. + + +```python +residual_map = data - model_data +plt.plot(range(data.shape[0]), np.zeros(data.shape[0]), "--", color="b") +plt.errorbar( + x=xvalues, + y=residual_map, + yerr=noise_map, + color="k", + ecolor="k", + elinewidth=1, + capsize=2, + linestyle="", +) +plt.title("Residuals of model-data fit to 1D Gaussian data.") +plt.xlabel("x values of profile") +plt.ylabel("Residuals") +plt.show() +plt.clf() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_19_0.png) + + + + +
    + + +__Normalized Residuals__ + +Another method to quantify and visualize the quality of the fit is using the normalized residual map, also known as +standardized residuals. + +The normalized residual map is computed as the residual map divided by the noise map: + +\[ \text{normalized\_residual} = \frac{\text{residual\_map}}{\text{noise\_map}} = \frac{\text{data} - \text{model\_data}}{\text{noise\_map}} \] + +If you're familiar with the concept of standard deviations (sigma) in statistics, the normalized residual map represents +how many standard deviations the residual is from zero. For instance, a normalized residual of 2.0 (corresponding +to a 95% confidence interval) means that the probability of the model underestimating the data by that amount is only 5%. + +Both the residual map with error bars and the normalized residual map convey the same information. However, +the normalized residual map is particularly useful for visualization in multidimensional problems, as plotting +error bars in 2D or higher dimensions is not straightforward. + + +```python +normalized_residual_map = residual_map / noise_map +plt.plot(xvalues, normalized_residual_map, color="k") +plt.title("Normalized residuals of model-data fit to 1D Gaussian data.") +plt.xlabel("x values of profile") +plt.ylabel("Normalized Residuals") +plt.show() +plt.clf() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_21_0.png) + + + + +
    + + +__Chi Squared__ + +Next, we define the `chi_squared_map`, which is obtained by squaring the `normalized_residual_map` and serves as a +measure of goodness of fit. + +The chi-squared map is calculated as: + +\[ \chi^2 = \left(\frac{\text{data} - \text{model\_data}}{\text{noise\_map}}\right)^2 \] + +The purpose of squaring the normalized residual map is to ensure all values are positive. For instance, both a +normalized residual of -0.2 and 0.2 would square to 0.04, indicating the same level of fit in terms of `chi_squared`. + +As seen from the normalized residual map, it's evident that the model does not provide a good fit to the data. + + +```python +chi_squared_map = (normalized_residual_map) ** 2 +plt.plot(xvalues, chi_squared_map, color="k") +plt.title("Chi-Squared Map of model-data fit to 1D Gaussian data.") +plt.xlabel("x values of profile") +plt.ylabel("Chi-Squareds") +plt.show() +plt.clf() +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_23_0.png) + + + + +
    + + +Now, we consolidate all the information in our `chi_squared_map` into a single measure of goodness-of-fit +called `chi_squared`. + +It is defined as the sum of all values in the `chi_squared_map` and is computed as: + +\[ \chi^2 = \sum \left(\frac{\text{data} - \text{model\_data}}{\text{noise\_map}}\right)^2 \] + +This summing process highlights why ensuring all values in the chi-squared map are positive is crucial. If we +didn't square the values (making them positive), positive and negative residuals would cancel each other out, +leading to an inaccurate assessment of the model's fit to the data. + + +```python +chi_squared = np.sum(chi_squared_map) +print("Chi-squared = ", chi_squared) +``` + + Chi-squared = 3894.173830959268 + + +The lower the `chi_squared`, the fewer residuals exist between the model's fit and the data, indicating a better +overall fit! + +__Noise Normalization__ + +Next, we introduce another quantity that contributes to our final assessment of the goodness-of-fit: +the `noise_normalization`. + +The `noise_normalization` is computed as the logarithm of the sum of squared noise values in our data: + +\[ +\text{{noise\_normalization}} = \sum \log(2 \pi \text{{noise\_map}}^2) +\] + +This quantity is fixed because the noise-map remains constant throughout the fitting process. Despite this, +including the `noise_normalization` is considered good practice due to its statistical significance. + +Understanding the exact meaning of `noise_normalization` isn't critical for our primary goal of successfully +fitting a model to a dataset. Essentially, it provides a measure of how well the noise properties of our data align +with a Gaussian distribution. + + +```python +noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0)) +``` + +__Likelihood__ + +From the `chi_squared` and `noise_normalization`, we can define a final goodness-of-fit measure known as +the `log_likelihood`. + +This measure is calculated by taking the sum of the `chi_squared` and `noise_normalization`, and then multiplying the +result by -0.5: + +\[ \text{log\_likelihood} = -0.5 \times \left( \chi^2 + \text{noise\_normalization} \right) \] + +Why multiply by -0.5? The exact rationale behind this factor isn't critical for our current understanding. + + +```python +log_likelihood = -0.5 * (chi_squared + noise_normalization) +print("Log Likelihood = ", log_likelihood) +``` + + Log Likelihood = -1717.0931863132812 + + +Above, we mentioned that a lower `chi_squared` indicates a better fit of the model to the data. + +When calculating the `log_likelihood`, we multiply the `chi_squared` by -0.5. Therefore, a higher log likelihood +corresponds to a better model fit. This is what we aim for when fitting models to data, we want to maximize the +log likelihood! + +__Recap__ + +If you're familiar with model-fitting, you've likely encountered terms like 'residuals', 'chi-squared', +and 'log_likelihood' before. + +These metrics are standard ways to quantify the quality of a model fit. They are applicable not only to 1D data but +also to more complex data structures like 2D images, 3D data cubes, or any other multidimensional datasets. + +If these terms are new to you, it's important to understand their meanings as they form the basis of all +model-fitting operations in **PyAutoFit** (and in statistical inference more broadly). + +Let's recap what we've learned so far: + +- We can define models, such as a 1D `Gaussian`, using Python classes that follow a specific format. + +- Models can be organized using `Collection` and `Model` objects, with parameters mapped to instances of their + respective model classes (e.g., `Gaussian`). + +- Using these model instances, we can generate model data, compare it to observed data, and quantify the + goodness-of-fit using the log likelihood. + +__Fitting Models__ + +Now, armed with this knowledge, we are ready to fit our model to our data! + +But how do we find the best-fit model, which maximizes the log likelihood? + +The simplest approach is to guess parameters. Starting with initial parameter values that yield a good +fit (i.e., a higher log likelihood), we iteratively adjust these values to refine our model until we achieve an +optimal fit. + +For a 1D `Gaussian`, this iterative process works effectively. Below, we fit three different `Gaussian` models and +identify the best-fit model—the one that matches the original dataset most closely. + +To streamline this process, I've developed functions that compute the `log_likelihood` of a model fit and visualize +the data alongside the model predictions, complete with error bars. + + +```python + + +def log_likelihood_from( + data: np.ndarray, noise_map: np.ndarray, model_data: np.ndarray +) -> float: + """ + Compute the log likelihood of a model fit to data given the noise map. + + Parameters + ---------- + data + The observed data. + noise_map + The root mean square noise (or uncertainty) associated with each data point. + model_data + The model's predicted data for the given data x points. + + Returns + ------- + float + The log likelihood of the model fit to the data. + """ + # Calculate residuals and normalized residuals + residual_map = data - model_data + normalized_residual_map = residual_map / noise_map + + # Compute chi-squared and noise normalization + chi_squared_map = normalized_residual_map**2 + chi_squared = np.sum(chi_squared_map) + noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0)) + + # Compute log likelihood + log_likelihood = -0.5 * (chi_squared + noise_normalization) + + return log_likelihood + + +def plot_model_fit( + xvalues: np.ndarray, + data: np.ndarray, + noise_map: np.ndarray, + model_data: np.ndarray, + color: str = "k", +): + """ + Plot the observed data, model predictions, and error bars. + + Parameters + ---------- + xvalues + The x-axis values where the data is observed and model is predicted. + data + The observed data points. + noise_map + The root mean squared noise (or uncertainty) associated with each data point. + model_data + The model's predicted data for the given data x points. + color + The color for plotting (default is "k" for black). + """ + plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color=color, + ecolor="k", + elinewidth=1, + capsize=2, + ) + plt.plot(xvalues, model_data, color="r") + plt.title("Fit of model-data to data") + plt.xlabel("x values of profile") + plt.ylabel("Profile Value") + plt.show() + plt.clf() # Clear figure to prevent overlapping plots + +``` + +__Guess 1__ + +The first guess correctly pinpoints that the Gaussian's peak is at 50.0, but the width and normalization are off. + +The `log_likelihood` is computed and printed, however because we don't have a value to compare it to yet, its hard +to assess if it is a large or small value. + + +```python + +gaussian = model.instance_from_vector(vector=[50.0, 10.0, 5.0]) +model_data = gaussian.model_data_from(xvalues=xvalues) +plot_model_fit( + xvalues=xvalues, + data=data, + noise_map=noise_map, + model_data=model_data, + color="r", +) + +log_likelihood = log_likelihood_from( + data=data, noise_map=noise_map, model_data=model_data +) +print(f"Log Likelihood: {log_likelihood}") +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_33_0.png) + + + + Log Likelihood: -1529.6103506638979 + + + +
    + + +__Guess 2__ + +The second guess refines the width and normalization, but the size of the Gaussian is still off. + +The `log_likelihood` is computed and printed, and increases a lot compared to the previous guess, indicating that +the fit is better. + + +```python + +gaussian = model.instance_from_vector(vector=[50.0, 25.0, 5.0]) +model_data = gaussian.model_data_from(xvalues=xvalues) +plot_model_fit( + xvalues=xvalues, + data=data, + noise_map=noise_map, + model_data=model_data, + color="r", +) + +log_likelihood = log_likelihood_from( + data=data, noise_map=noise_map, model_data=model_data +) +print(f"Log Likelihood: {log_likelihood}") +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_35_0.png) + + + + Log Likelihood: -2391.6438609542743 + + + +
    + + +__Guess 3__ + +The third guess provides a good fit to the data, with the Gaussian's peak, width, and normalization all accurately +representing the observed signal. + +The `log_likelihood` is computed and printed, and is the highest value yet, indicating that this model provides the +best fit to the data. + + +```python + +gaussian = model.instance_from_vector(vector=[50.0, 25.0, 10.0]) +model_data = gaussian.model_data_from(xvalues=xvalues) +plot_model_fit( + xvalues=xvalues, + data=data, + noise_map=noise_map, + model_data=model_data, + color="r", +) + +log_likelihood = log_likelihood_from( + data=data, noise_map=noise_map, model_data=model_data +) +print(f"Log Likelihood: {log_likelihood}") +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_37_0.png) + + + + Log Likelihood: 177.645544225071 + + + +
    + + +__Extensibility__ + +Fitting models composed of multiple components is straightforward with PyAutoFit. Using the `Collection` object, +we can define complex models consisting of several components. Once defined, we generate `model_data` +from this collection and fit it to the observed data to compute the log likelihood. + + +```python +model = af.Collection(gaussian_0=Gaussian, gaussian_1=Gaussian) + +instance = model.instance_from_vector(vector=[40.0, 0.2, 0.3, 60.0, 0.5, 1.0]) + +model_data_0 = instance.gaussian_0.model_data_from(xvalues=xvalues) +model_data_1 = instance.gaussian_1.model_data_from(xvalues=xvalues) + +model_data = model_data_0 + model_data_1 +``` + +We plot the data and model data below, showing that we get a bad fit (a low log likelihood) for this model. + +We could attempt to improve the model-fit and find a higher log likelihood solution by varying the parameters of +the two Gaussians. However, with 6 parameters, this would be a challenging and cumbersome task to perform by eye. + + +```python +plot_model_fit( + xvalues=xvalues, + data=data, + noise_map=noise_map, + model_data=model_data, + color="r", +) + +log_likelihood = log_likelihood_from( + data=data, noise_map=noise_map, model_data=model_data +) +print(f"Log Likelihood: {log_likelihood}") + +``` + + + +![png](tutorial_2_fitting_data_files/tutorial_2_fitting_data_41_0.png) + + + + Log Likelihood: -5119.60036353835 + + + +
    + + +When our model consisted of only 3 parameters, it was manageable to visually guess their values and achieve a good +fit to the data. However, as we expanded our model to include six parameters, this approach quickly became +inefficient. Attempting to manually optimize models with even more parameters would effectively become impossible, +and a more systematic approach is required. + +In the next tutorial, we will introduce an automated approach for fitting models to data. This method will enable +us to systematically determine the optimal values of model parameters that best describe the observed data, without +relying on manual guesswork. + +__Wrap Up__ + +To conclude, take a moment to reflect on the model you ultimately aim to fit using **PyAutoFit**. What does your +data look like? Is it one-dimensional, like a spectrum or a time series? Or is it two-dimensional, such as an image +or a map? Visualize the nature of your data and consider whether you can define a mathematical model that +accurately generates similar data. + +Can you imagine what a residual map would look like if you were to compare your model's predictions against this +data? A residual map shows the differences between observed data and the model's predictions, often revealing +patterns or areas where the model fits well or poorly. + +Furthermore, can you foresee how you would calculate a log likelihood from this residual map? The log likelihood q +uantifies how well your model fits the data, incorporating both the residual values and the noise characteristics of +your observations. + +If you find it challenging to visualize these aspects right now, that's perfectly fine. The first step is to +grasp the fundamentals of fitting a model to data using **PyAutoFit**, which will provide you with the tools +and understanding needed to address these questions effectively in the future. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_2_fitting_data_files/tutorial_2_fitting_data_13_0.png b/markdown/chapter_1_introduction/tutorial_2_fitting_data_files/tutorial_2_fitting_data_13_0.png new file mode 100644 index 0000000000000000000000000000000000000000..e4cf568a24afcbf6442dd9261104930c425dec04 GIT binary patch literal 25728 zcmb5W1z6Pk_bxgE0t!+hAf&J(lLlMibyCDA}LBEB_N@MbVy1| zN=k#oSzmwWocn+7J@=e@_p|qd?wR?{C)aw{`@V}Xb=B*n#Pq}{6pHlb4Xg$Vg(rqW zp_@+=!v9g!@Z^FYlCDa6u9}WkuI~4oEm11>T%GJ4UF~h|v$|P2yVy872=NJA<&)rH zwRUxNa*^ccfA~M&!RP4wfS+BtPamFg#_5K>3kpSk5BU%MUM|}Pg)(2hiM^)fk+d@A zZlt|^LbN_;nSMv9K47|)_ceZx(M^fl9Rb|B`g3GLr5cPE4j#|92x@BH)$HkYPP3w) z&iLU(ZiFLI*OFhI-))|E=QkmkZLLX?GU;mmCA&H`>aynhNXTO|fs5@aijtBtI;ilj 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[`scripts/chapter_1_introduction/tutorial_3_non_linear_search.py`](../../scripts/chapter_1_introduction/tutorial_3_non_linear_search.py) — do not edit it directly.** +> It shows the example fully executed, with its real output images. +> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/tutorial_3_non_linear_search.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb). + +Tutorial 3: Non Linear Search +============================= + +In the previous tutorials, we laid the groundwork by defining a model and manually fitting it to data using fitting +functions. We quantified the goodness of fit using the log likelihood and demonstrated that for models with only a few +free parameters, we could achieve satisfactory fits by manually guessing parameter values. However, as the complexity +of our models increased, this approach quickly became impractical. + +In this tutorial, we will delve into a more systematic approach for fitting models to data. This technique is designed +to handle models with a larger number of parameters—ranging from tens to hundreds. By adopting this approach, we aim +to achieve more efficient and reliable model fits, ensuring that our models accurately capture the underlying +structure of the data. + +This approach not only improves the accuracy of our fits but also allows us to explore more complex models that better +represent the systems we are studying. + +__Overview__ + +In this tutorial, we will use a non-linear search to fit a 1D Gaussian profile to noisy data. Specifically, we will: + +- Introduce concept like a "parameter space", "likelihood surface" and "priors", and relate them to how a non-linear + search works. + +- Introduce the `Analysis` class, which defines the `log_likelihood_function` that quantifies the goodness of fit of a + model instance to the data. + +- Fit a 1D Gaussian model to 1D data with different non-linear searches, including a maximum likelihood estimator (MLE), + Markok Chain Monte Carlo (MCMC) and nested sampling. + +All these steps utilize **PyAutoFit**'s API for model-fitting. + +__Contents__ + +This tutorial is split into the following sections: + +- **Parameter Space**: Introduce the concept of a "parameter space" and how it relates to model-fitting. +- **Non-Linear Search**: Introduce the concept of a "non-linear search" and how it fits models to data. +- **Search Types**: Introduce the maximum likelihood estimator (MLE), Markov Chain Monte Carlo (MCMC) and nested sampling search algorithms used in this tutorial. +- **Deeper Background**: Provide links to resources that more thoroughly describe the statistical principles that underpin non-linear searches. +- **Data**: Load and plot the 1D Gaussian dataset we'll fit. +- **Model**: Introduce the 1D `Gaussian` model we'll fit to the data. +- **Priors**: Introduce priors and how they are used to define the parameter space and guide the non-linear search. +- **Analysis**: Introduce the `Analysis` class, which contains the `log_likelihood_function` used to fit the model to the data. +- **Searches**: An overview of the searches used in this tutorial. +- **Maximum Likelihood Estimation (MLE)**: Perform a model-fit using the MLE search. +- **Markov Chain Monte Carlo (MCMC)**: Perform a model-fit using the MCMC search. +- **Nested Sampling**: Perform a model-fit using the nested sampling search. +- **What is The Best Search To Use?**: Compare the strengths and weaknesses of each search method. +- **Wrap Up**: A summary of the concepts introduced in this tutorial. + +__Parameter Space__ + +In mathematics, a function is defined by its parameters, which relate inputs to outputs. + +For example, consider a simple function: + +\[ f(x) = x^2 \] + +Here, \( x \) is the parameter input into the function \( f \), and \( f(x) \) returns \( x^2 \). This +mapping between \( x \) and \( f(x) \) defines the "parameter space" of the function, which in this case is a parabola. + +Functions can have multiple parameters, such as \( x \), \( y \), and \( z \): + +\[ f(x, y, z) = x + y^2 - z^3 \] + +Here, the mapping between \( x \), \( y \), \( z \), and \( f(x, y, z) \) defines a parameter space with three +dimensions. + +This concept of a parameter space relates closely to how we define and use instances of models in model-fitting. +For instance, in our previous tutorial, we used instances of a 1D Gaussian profile with +parameters \( (x, I, \sigma) \) to fit data and compute a log likelihood. + +This process can be thought of as complete analogous to a function \( f(x, y, z) \), where the output value is the +log likelihood. This key function, which maps parameter values to a log likelihood, is called the "likelihood function" +in statistical inference, albeit we will refer to it hereafter as the `log_likelihood_function` to be explicit +that it is the log of the likelihood function. + +By expressing the likelihood in this manner, we can consider our model as having a parameter space -— a +multidimensional surface that spans all possible values of the model parameters \( x, I, \sigma \). + +This surface is often referred to as the "likelihood surface", and our objective during model-fitting is to find +its peak. + +This parameter space is "non-linear", meaning the relationship between the input parameters and the log likelihood +does not behave linearly. This non-linearity implies that we cannot predict the log likelihood from a set of model +parameters without actually performing a fit to the data by performing the forward model calculation. + +__Non-Linear Search__ + +Now that we understand our problem in terms of a non-linear parameter space with a likelihood surface, we can +introduce the method used to fit the model to the data—the "non-linear search". + +Previously, our approach involved manually guessing models until finding one with a good fit and high log likelihood. +Surprisingly, this random guessing forms the basis of how model-fitting using a non-linear search actually works! + +A non-linear search involves systematically guessing many models while tracking their log likelihoods. As the +algorithm progresses, it tends to favor models with parameter combinations that have previously yielded higher +log likelihoods. This iterative refinement helps to efficiently explore the vast parameter space. + +There are two key differences between guessing random models and using a non-linear search: + +- **Computational Efficiency**: The non-linear search can evaluate the log likelihood of a model parameter + combinations in milliseconds and therefore many thousands of models in minutes. This computational speed enables + it to thoroughly sample potential solutions, which would be impractical for a human. + +- **Effective Sampling**: The search algorithm maintains a robust memory of previously guessed models and their log + likelihoods. This allows it to sample potential solutions more thoroughly and converge on the highest + likelihood solutions more efficiently, which is again impractical for a human. + +Think of the non-linear search as systematically exploring parameter space to pinpoint regions with the highest log +likelihood values. Its primary goal is to identify and converge on the parameter values that best describe the data. + +__Search Types__ + +There are different types of non-linear searches, each of which explores parameter space in a unique way. +In this example, we will use three types of searches, which broadly represent the various approaches to non-linear +searches used in statistical inference. + +These are: + +- **Maximum Likelihood Estimation (MLE)**: This method aims to find the model that maximizes the likelihood function. + It does so by testing nearby models and adjusting parameters in the direction that increases the likelihood. + +- **Markov Chain Monte Carlo (MCMC)**: This approach uses a group of "walkers" that explore parameter space randomly. + The likelihood at each walker's position influences the probability of the walker moving to a new position. + +- **Nested Sampling**: This technique samples points from the parameter space iteratively. Lower likelihood points + are replaced by higher likelihood ones, gradually concentrating the samples in regions of high likelihood. + +We will provide more details on each of these searches below. + +__Deeper Background__ + +**The descriptions of how searches work in this example are simplfied and phoenomenological and do not give a full +description of how they work at a deep statistical level. The goal is to provide you with an intuition for how to use +them and when different searches are appropriate for different problems. Later tutorials will provide a more formal +description of how these searches work.** + +If you're interested in learning more about these principles, you can explore resources such as: + +- [Markov Chain Monte Carlo (MCMC)](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) +- [Introduction to MCMC Sampling](https://twiecki.io/blog/2015/11/10/mcmc-sampling/) +- [Nested Sampling](https://www.imperial.ac.uk/media/imperial-college/research-centres-and-groups/astrophysics/public/icic/data-analysis-workshop/2016/NestedSampling_JRP.pdf) +- [A Zero-Math Introduction to MCMC Methods](https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50) + + +```python + +import numpy as np +import matplotlib.pyplot as plt +from os import path + +import autofit as af + +from autoconf import setup_notebook; setup_notebook() +``` + + .../PyAutoConf/autoconf/workspace.py:206: UserWarning: Cannot verify the workspace at HowToFit/scripts/chapter_1_introduction is compatible with the installed library version (2026.7.6.649): no `version.minimum_library_version` or `version.workspace_version` key in config/general.yaml and no version.txt at the workspace root. + + If you cloned the workspace from `main` rather than a release tag, set `version.workspace_version_check: False` in config/general.yaml to silence this warning. The `main` branch updates more frequently than library releases, so version mismatches are expected and not actionable for `main`-branch users. + + You can also set the environment variable PYAUTO_SKIP_WORKSPACE_VERSION_CHECK=1 to disable temporarily. + warnings.warn(_missing_version_warning(root, library_version)) + + + Working Directory has been set to `HowToFit` + + +__Data__ + +Load and plot the dataset from the `HowToFit/dataset` folder. + + +```python +dataset_path = path.join("dataset", "example_1d", "gaussian_x1") +``` + +__Dataset Auto-Simulation__ + +If the dataset does not already exist on your system, it will be created by running the corresponding +simulator script. This ensures that all example scripts can be run without manually simulating data first. + + +```python +if not path.exists(dataset_path): + import subprocess + import sys + + subprocess.run( + [sys.executable, "scripts/simulators/simulators.py"], + check=True, + ) + +data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json")) +noise_map = af.util.numpy_array_from_json( + file_path=path.join(dataset_path, "noise_map.json") +) + +xvalues = np.arange(data.shape[0]) + +plt.errorbar( + xvalues, + data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.title("1D Gaussian dataset.") +plt.xlabel("x values of profile") +plt.ylabel("Profile Normalization") +plt.show() +plt.clf() +``` + + + +![png](tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_5_0.png) + + + + +

    + + +__Model__ + +Create the `Gaussian` class from which we will compose model components using the standard format. + + +```python + + +class Gaussian: + def __init__( + self, + centre: float = 30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization: float = 1.0, # <- are the Gaussian`s model parameters. + sigma: float = 5.0, + ): + """ + Represents a 1D Gaussian profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to perform model-fitting + of example datasets. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + sigma + The sigma value controlling the size of the Gaussian. + """ + self.centre = centre + self.normalization = normalization + self.sigma = sigma + + def model_data_from(self, xvalues: np.ndarray) -> np.ndarray: + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + + Returns + ------- + np.array + The Gaussian values at the input x coordinates. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return np.multiply( + np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)), + np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))), + ) + +``` + +We now compose our model, a single 1D Gaussian, which we will fit to the data via the non-linear search. + + +```python +model = af.Model(Gaussian) + +print(model.info) +``` + + Total Free Parameters = 3 + + model Gaussian (N=3) + + centre UniformPrior [0], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [1], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [2], lower_limit = 0.0, upper_limit = 25.0 + + +__Priors__ + +When we examine the `.info` of our model, we notice that each parameter (like `centre`, `normalization`, +and `sigma` in our Gaussian model) is associated with priors, such as `UniformPrior`. These priors define the +range of permissible values that each parameter can assume during the model fitting process. + +The priors displayed above use default values defined in the `config/priors` directory. These default values have +been chosen to be broad, and contain all plausible solutions contained in the simulated 1D Gaussian datasets. + +For instance, consider the `centre` parameter of our Gaussian. In theory, it could take on any value from +negative to positive infinity. However, upon inspecting our dataset, we observe that valid values for `centre` +fall strictly between 0.0 and 100.0. By using a `UniformPrior` with `lower_limit=0.0` and `upper_limit=100.0`, +we restrict our parameter space to include only physically plausible values. + +Priors serve two primary purposes: + +**Defining Valid Parameter Space:** Priors specify the range of parameter values that constitute valid solutions. +This ensures that our model explores only those solutions that are consistent with our observed data and physical +constraints. + +**Incorporating Prior Knowledge:** Priors also encapsulate our prior beliefs or expectations about the model +parameters. For instance, if we have previously fitted a similar model to another dataset and obtained certain +parameter values, we can incorporate this knowledge into our priors for a new dataset. This approach guides the +model fitting process towards parameter values that are more probable based on our prior understanding. + +While we are using `UniformPriors` in this tutorial due to their simplicity, **PyAutoFit** offers various other +priors like `TruncatedGaussianPrior` and `LogUniformPrior`. These priors are useful for encoding different forms of prior +information, such as normally distributed values around a mean (`TruncatedGaussianPrior`) or parameters spanning multiple +orders of magnitude (`LogUniformPrior`). + + +```python +model.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0) +model.normalization = af.UniformPrior(lower_limit=0.0, upper_limit=10.0) +model.sigma = af.UniformPrior(lower_limit=0.0, upper_limit=10.0) +``` + +__Analysis__ + +In **PyAutoFit**, the `Analysis` class plays a crucial role in interfacing between the data being fitted and the +model under consideration. Its primary responsibilities include: + +**Receiving Data:** The `Analysis` class is initialized with the data (`data`) and noise map (`noise_map`) that + the model aims to fit. + +**Defining the Log Likelihood Function:** The `Analysis` class defines the `log_likelihood_function`, which + computes the log likelihood of a model instance given the data. It evaluates how well the model, for a given set of + parameters, fits the observed data. + +**Interface with Non-linear Search:** The `log_likelihood_function` is repeatedly called by the non-linear search + algorithm to assess the goodness of fit of different parameter combinations. The search algorithm call this function + many times and maps out regions of parameter space that yield high likelihood solutions. + +Below is a suitable `Analysis` class for fitting a 1D gaussian to the data loaded above. + + +```python + + +class Analysis(af.Analysis): + def __init__(self, data: np.ndarray, noise_map: np.ndarray): + """ + The `Analysis` class acts as an interface between the data and model in **PyAutoFit**. + + Its `log_likelihood_function` defines how the model is fitted to the data and it is called many times by + the non-linear search fitting algorithm. + + In this example the `Analysis` `__init__` constructor only contains the `data` and `noise-map`, but it can be + easily extended to include other quantities. + + Parameters + ---------- + data + A 1D numpy array containing the data (e.g. a noisy 1D signal) fitted in the workspace examples. + noise_map + A 1D numpy array containing the noise values of the data, used for computing the goodness of fit + metric, the log likelihood. + """ + super().__init__() + + self.data = data + self.noise_map = noise_map + + def log_likelihood_function(self, instance) -> float: + """ + Returns the log likelihood of a fit of a 1D Gaussian to the dataset. + + The `instance` that comes into this method is an instance of the `Gaussian` model above. The parameter values + are chosen by the non-linear search, based on where it thinks the high likelihood regions of parameter + space are. + + The lines of Python code are commented out below to prevent excessive print statements when we run the + non-linear search, but feel free to uncomment them and run the search to see the parameters of every instance + that it fits. + + print("Gaussian Instance:") + print("Centre = ", instance.centre) + print("Normalization = ", instance.normalization) + print("Sigma = ", instance.sigma) + + The data is fitted using an `instance` of the `Gaussian` class where its `model_data_from` + is called in order to create a model data representation of the Gaussian that is fitted to the data. + """ + xvalues = np.arange(self.data.shape[0]) + + model_data = instance.model_data_from(xvalues=xvalues) + residual_map = self.data - model_data + chi_squared_map = (residual_map / self.noise_map) ** 2.0 + chi_squared = sum(chi_squared_map) + noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0)) + log_likelihood = -0.5 * (chi_squared + noise_normalization) + + return log_likelihood + +``` + +We create an instance of the `Analysis` class by simply passing it the `data` and `noise_map`: + + +```python +analysis = Analysis(data=data, noise_map=noise_map) +``` + +__Searches__ + +To perform a non-linear search, we create an instance of a `NonLinearSearch` object. **PyAutoFit** offers many options +for this. A detailed description of each search method and guidance on when to use them can be found in +the [search cookbook](https://pyautofit.readthedocs.io/en/latest/cookbooks/search.html). + +In this tutorial, we’ll focus on three searches that represent different approaches to model fitting: + +1. **Maximum Likelihood Estimation (MLE)** using the `LBFGS` non-linear search. +2. **Markov Chain Monte Carlo (MCMC)** using the `Emcee` non-linear search. +3. **Nested Sampling** using the `Dynesty` non-linear search. + +In this example, non-linear search results are stored in memory rather and not written to hard disk because the fits +are fast and can therefore be easily regenerated. The next tutorial will perform fits which write results to the +hard-disk. + +__Maximum Likelihood Estimation (MLE)__ + +Maximum likelihood estimation (MLE) is the most straightforward type of non-linear search. Here’s a simplified +overview of how it works: + +1. Starts at a point in parameter space with a set of initial values for the model parameters. +2. Calculates the likelihood of the model at this starting point. +3. Evaluates the likelihood at nearby points to estimate the gradient, determining the direction in which to move "up" in parameter space. +4. Moves to a new point where, based on the gradient, the likelihood is higher. + +This process repeats until the search finds a point where the likelihood can no longer be improved, indicating that +the maximum likelihood has been reached. + +The `LBFGS` search is an example of an MLE algorithm that follows this iterative procedure. Let’s see how it +performs on our 1D Gaussian model. + +In the example below, we don’t specify a starting point for the MLE, so it begins at the center of the prior +range for each parameter. + + +```python +search = af.LBFGS() +``` + +To begin the model-fit via the non-linear search, we pass it our model and analysis and begin the fit. + +The fit will take a minute or so to run. + + +```python +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +model = af.Model(Gaussian) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:22:50,877 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:22:50,890 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:22:51,022 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:22:51,023 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + 2026-07-11 16:22:51,024 - root - INFO - Starting new L-BFGS-B non-linear search (no previous samples found). + + + 2026-07-11 16:22:51,025 - root - INFO - Visualizing Starting Point Model in image_start folder. + + + 2026-07-11 16:22:51,043 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:22:51,044 - autofit.non_linear.search.updater - INFO - Drawing via PDF not available for this search, using all samples above the samples weight threshold instead. + + + 2026-07-11 16:22:51,053 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:22:51,102 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +Upon completion the non-linear search returns a `Result` object, which contains information about the model-fit. + +The `info` attribute shows the result in a readable format. + +[Above, we discussed that the `info_whitespace_length` parameter in the config files could b changed to make +the `model.info` attribute display optimally on your computer. This attribute also controls the whitespace of the +`result.info` attribute.] + + +```python +print(result.info) +``` + + Maximum Log Likelihood 178.11809146 + + model Gaussian (N=3) + + Maximum Log Likelihood Model: + + centre 50.119 + normalization 25.114 + sigma 10.036 + + + + +The result has a "maximum log likelihood instance", which refers to the specific set of model parameters (e.g., +for a `Gaussian`) that yielded the highest log likelihood among all models tested by the non-linear search. + + +```python +print("Maximum Likelihood Model:\n") +max_log_likelihood_instance = result.samples.max_log_likelihood() +print("Centre = ", max_log_likelihood_instance.centre) +print("Normalization = ", max_log_likelihood_instance.normalization) +print("Sigma = ", max_log_likelihood_instance.sigma) +``` + + Maximum Likelihood Model: + + Centre = 50.11929554760558 + Normalization = 25.113855320837537 + Sigma = 10.036449187081434 + + +We can use this to plot the maximum log likelihood fit over the data and determine the quality of fit was inferred: + + +```python +model_data = result.max_log_likelihood_instance.model_data_from( + xvalues=np.arange(data.shape[0]) +) +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.plot(xvalues, model_data, color="r") +plt.title("Dynesty model fit to 1D Gaussian dataset.") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + + +![png](tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_25_0.png) + + + +The fit quality was poor, and the MLE failed to identify the correct model. + +This happened because the starting point of the search was a poor match to the data, placing it far from the true +solution in parameter space. As a result, after moving "up" the likelihood gradient several times, the search +settled into a "local maximum," where it couldn't find a better solution. + +To achieve a better fit with MLE, the search needs to begin in a region of parameter space where the log likelihood +is higher. This process is known as "initialization," and it involves providing the search with an +appropriate "starting point" in parameter space. + + +```python +initializer = af.InitializerParamStartPoints( + { + model.centre: 55.0, + model.normalization: 20.0, + model.sigma: 8.0, + } +) + +search = af.LBFGS(initializer=initializer) + +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +model = af.Model(Gaussian) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:22:51,221 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:22:51,231 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:22:51,232 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:22:51,233 - autofit.non_linear.initializer - WARNING - Range for centre not set in the InitializerParamBounds. Using defaults. + + + 2026-07-11 16:22:51,546 - autofit.non_linear.initializer - WARNING - Range for normalization not set in the InitializerParamBounds. Using defaults. + + + 2026-07-11 16:22:51,547 - autofit.non_linear.initializer - WARNING - Range for sigma not set in the InitializerParamBounds. Using defaults. + + + 2026-07-11 16:22:51,548 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + 2026-07-11 16:22:51,549 - root - INFO - Starting new L-BFGS-B non-linear search (no previous samples found). + + + 2026-07-11 16:22:51,549 - root - INFO - Visualizing Starting Point Model in image_start folder. + + + 2026-07-11 16:22:51,652 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:22:51,653 - autofit.non_linear.search.updater - INFO - Drawing via PDF not available for this search, using all samples above the samples weight threshold instead. + + + 2026-07-11 16:22:51,662 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:22:51,719 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +By printing `result.info` and looking at the maximum log likelihood model, we can confirm the search provided a +good model fit with a much higher likelihood than the incorrect model above. + + +```python +print(result.info) + +model_data = result.max_log_likelihood_instance.model_data_from( + xvalues=np.arange(data.shape[0]) +) +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.plot(xvalues, model_data, color="r") +plt.title("Dynesty model fit to 1D Gaussian dataset.") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + Maximum Log Likelihood -3328.20371089 + + model Gaussian (N=3) + + Maximum Log Likelihood Model: + + centre -17956.265 + normalization 63480.416 + sigma 8029.315 + + + + + + +![png](tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_29_1.png) + + + +MLE is a great starting point for model-fitting because it’s fast, conceptually simple, and often yields +accurate results. It is especially effective if you can provide a good initialization, allowing it to find the +best-fit solution quickly. + +However, MLE has its limitations. As seen above, it can get "stuck" in a local maximum, particularly if the +starting point is poorly chosen. In complex model-fitting problems, providing a suitable starting point can be +challenging. While MLE performed well in the example with just three parameters, it struggles with models that have +many parameters, as the complexity of the likelihood surface makes simply moving "up" the gradient less effective. + +The MLE also does not provide any information on the errors on the parameters, which is a significant limitation. +The next two types of searches "map out" the likelihood surface, such that they not only infer the maximum likelihood +solution but also quantify the errors on the parameters. + +__Markov Chain Monte Carlo (MCMC)__ + +Markov Chain Monte Carlo (MCMC) is a more powerful method for model-fitting, though it is also more computationally +intensive and conceptually complex. Here’s a simplified overview: + +1. Place a set of "walkers" in parameter space, each with random parameter values. +2. Calculate the likelihood of each walker's position. +3. Move the walkers to new positions, guided by the likelihood of their current positions. Walkers in high-likelihood +regions encourage those in lower regions to move closer to them. + +This process repeats, with the walkers converging on the highest-likelihood regions of parameter space. + +Unlike MLE, MCMC thoroughly explores parameter space. While MLE moves a single point up the likelihood gradient, +MCMC uses many walkers to explore high-likelihood regions, making it more effective at finding the global maximum, +though slower. + +In the example below, we use the `Emcee` MCMC search to fit the 1D Gaussian model. The search starts with walkers +initialized in a "ball" around the center of the model’s priors, similar to the MLE search that failed earlier. + + +```python +search = af.Emcee( + nwalkers=10, # The number of walkers we'll use to sample parameter space. + nsteps=200, # The number of steps each walker takes, after which 10 * 200 = 2000 steps the non-linear search ends. +) + +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +model = af.Model(Gaussian) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") + +print(result.info) + +model_data = result.max_log_likelihood_instance.model_data_from( + xvalues=np.arange(data.shape[0]) +) +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.plot(xvalues, model_data, color="r") +plt.title("Dynesty model fit to 1D Gaussian dataset.") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:22:51,829 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:22:51,837 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:22:51,863 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:22:51,866 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + 2026-07-11 16:22:51,867 - root - INFO - Visualizing Starting Point Model in image_start folder. + + + 2026-07-11 16:22:51,868 - root - INFO - Starting new Emcee non-linear search (no previous samples found). + + + 0%| | 0/200 [00:00 0.059] + + 91it [00:00, 260.29it/s, bound: 0 | nc: 2 | ncall: 248 | eff(%): 36.694 | loglstar: -inf < -5358.318 < inf | logz: -5362.807 +/- 0.191 | dlogz: 4643.330 > 0.059] + + 121it [00:00, 167.23it/s, bound: 0 | nc: 18 | ncall: 414 | eff(%): 29.227 | loglstar: -inf < -5307.724 < inf | logz: -5314.539 +/- 0.357 | dlogz: 4596.699 > 0.059] + + 142it [00:01, 103.18it/s, bound: 0 | nc: 13 | ncall: 675 | eff(%): 21.037 | loglstar: -inf < -5189.045 < inf | logz: -5196.460 +/- 0.383 | dlogz: 4482.471 > 0.059] + + 157it [00:01, 81.41it/s, bound: 0 | nc: 5 | ncall: 868 | eff(%): 18.088 | loglstar: -inf < -4990.432 < inf | logz: -4997.101 +/- 0.362 | dlogz: 4277.207 > 0.059] + + 169it [00:01, 60.41it/s, bound: 0 | nc: 62 | ncall: 1115 | eff(%): 15.157 | loglstar: -inf < -4879.085 < inf | logz: -4887.029 +/- 0.396 | dlogz: 4171.314 > 0.059] + + 178it [00:02, 43.80it/s, bound: 0 | nc: 33 | ncall: 1393 | eff(%): 12.778 | loglstar: -inf < -4635.657 < inf | logz: -4643.787 +/- 0.401 | dlogz: 3947.173 > 0.059] + + 185it [00:02, 36.48it/s, bound: 0 | nc: 1 | ncall: 1603 | eff(%): 11.541 | loglstar: -inf < -4446.264 < inf | logz: -4454.532 +/- 0.405 | dlogz: 3777.640 > 0.059] + + 190it [00:02, 33.49it/s, bound: 0 | nc: 30 | ncall: 1746 | eff(%): 10.882 | loglstar: -inf < -4369.135 < inf | logz: -4377.503 +/- 0.407 | dlogz: 3686.870 > 0.059] + + 195it [00:03, 25.26it/s, bound: 0 | nc: 110 | ncall: 1992 | eff(%): 9.789 | loglstar: -inf < -4159.827 < inf | logz: -4168.294 +/- 0.409 | dlogz: 3484.804 > 0.059] + + 227it [00:03, 56.83it/s, bound: 5 | nc: 5 | ncall: 2153 | eff(%): 10.543 | loglstar: -inf < -3197.218 < inf | logz: -3206.320 +/- 0.423 | dlogz: 2547.137 > 0.059] + + 260it [00:03, 93.07it/s, bound: 9 | nc: 5 | ncall: 2318 | eff(%): 11.217 | loglstar: -inf < -2364.645 < inf | logz: -2373.284 +/- 0.408 | dlogz: 2009.988 > 0.059] + + 289it [00:03, 124.41it/s, bound: 12 | nc: 5 | ncall: 2463 | eff(%): 11.734 | loglstar: -inf < -1725.724 < inf | logz: -1734.941 +/- 0.418 | dlogz: 1608.689 > 0.059] + + 323it [00:03, 162.53it/s, bound: 17 | nc: 5 | ncall: 2633 | eff(%): 12.267 | loglstar: -inf < -1175.677 < inf | logz: -1186.693 +/- 0.453 | dlogz: 1089.470 > 0.059] + + 348it [00:03, 180.21it/s, bound: 20 | nc: 5 | ncall: 2758 | eff(%): 12.618 | loglstar: -inf < -913.460 < inf | logz: -923.229 +/- 0.416 | dlogz: 1027.920 > 0.059] + + 377it [00:03, 204.86it/s, bound: 23 | nc: 5 | ncall: 2903 | eff(%): 12.987 | loglstar: -inf < -579.293 < inf | logz: -591.397 +/- 0.469 | dlogz: 707.096 > 0.059] + + 409it [00:03, 232.43it/s, bound: 27 | nc: 5 | ncall: 3063 | eff(%): 13.353 | loglstar: -inf < -307.357 < inf | logz: -320.097 +/- 0.479 | dlogz: 490.797 > 0.059] + + 440it [00:04, 252.43it/s, bound: 31 | nc: 5 | ncall: 3218 | eff(%): 13.673 | loglstar: -inf < -178.602 < inf | logz: -188.971 +/- 0.420 | dlogz: 317.799 > 0.059] + + 471it [00:04, 266.73it/s, bound: 35 | nc: 5 | ncall: 3373 | eff(%): 13.964 | loglstar: -inf < -45.073 < inf | logz: -58.981 +/- 0.485 | dlogz: 213.996 > 0.059] + + 501it [00:04, 268.57it/s, bound: 39 | nc: 5 | ncall: 3523 | eff(%): 14.221 | loglstar: -inf < 40.637 < inf | logz: 26.376 +/- 0.484 | dlogz: 136.735 > 0.059] + + 531it [00:04, 276.83it/s, bound: 43 | nc: 5 | ncall: 3673 | eff(%): 14.457 | loglstar: -inf < 94.520 < inf | logz: 80.831 +/- 0.460 | dlogz: 80.335 > 0.059] + + 563it [00:04, 286.04it/s, bound: 47 | nc: 5 | ncall: 3833 | eff(%): 14.688 | loglstar: -inf < 111.136 < inf | logz: 98.837 +/- 0.443 | dlogz: 61.333 > 0.059] + + 593it [00:04, 274.02it/s, bound: 50 | nc: 5 | ncall: 3983 | eff(%): 14.888 | loglstar: -inf < 123.610 < inf | logz: 107.766 +/- 0.496 | dlogz: 55.390 > 0.059] + + 622it [00:04, 277.21it/s, bound: 54 | nc: 5 | ncall: 4128 | eff(%): 15.068 | loglstar: -inf < 140.977 < inf | logz: 125.969 +/- 0.477 | dlogz: 35.745 > 0.059] + + 653it [00:04, 282.41it/s, bound: 58 | nc: 5 | ncall: 4283 | eff(%): 15.246 | loglstar: -inf < 154.748 < inf | logz: 138.700 +/- 0.498 | dlogz: 22.602 > 0.059] + + 683it [00:04, 284.45it/s, bound: 62 | nc: 5 | ncall: 4433 | eff(%): 15.407 | loglstar: -inf < 161.739 < inf | logz: 146.497 +/- 0.487 | dlogz: 17.739 > 0.059] + + 718it [00:05, 302.50it/s, bound: 66 | nc: 5 | ncall: 4608 | eff(%): 15.582 | loglstar: -inf < 168.350 < inf | logz: 151.956 +/- 0.497 | dlogz: 11.617 > 0.059] + + 752it [00:05, 308.04it/s, bound: 71 | nc: 5 | ncall: 4778 | eff(%): 15.739 | loglstar: -inf < 171.360 < inf | logz: 155.949 +/- 0.493 | dlogz: 6.800 > 0.059] + + 785it [00:05, 312.46it/s, bound: 75 | nc: 5 | ncall: 4943 | eff(%): 15.881 | loglstar: -inf < 173.652 < inf | logz: 157.073 +/- 0.493 | dlogz: 5.008 > 0.059] + + 817it [00:05, 310.19it/s, bound: 79 | nc: 5 | ncall: 5103 | eff(%): 16.010 | loglstar: -inf < 175.084 < inf | logz: 158.020 +/- 0.499 | dlogz: 3.432 > 0.059] + + 849it [00:05, 307.12it/s, bound: 83 | nc: 5 | ncall: 5263 | eff(%): 16.131 | loglstar: -inf < 176.159 < inf | logz: 158.808 +/- 0.506 | dlogz: 2.094 > 0.059] + + 880it [00:05, 296.37it/s, bound: 87 | nc: 5 | ncall: 5418 | eff(%): 16.242 | loglstar: -inf < 177.212 < inf | logz: 159.480 +/- 0.514 | dlogz: 1.173 > 0.059] + + 910it [00:05, 263.21it/s, bound: 90 | nc: 5 | ncall: 5568 | eff(%): 16.343 | loglstar: -inf < 177.585 < inf | logz: 159.872 +/- 0.517 | dlogz: 0.599 > 0.059] + + 940it [00:05, 271.66it/s, bound: 94 | nc: 5 | ncall: 5718 | eff(%): 16.439 | loglstar: -inf < 177.856 < inf | logz: 160.101 +/- 0.518 | dlogz: 0.307 > 0.059] + + 970it [00:05, 279.05it/s, bound: 98 | nc: 5 | ncall: 5868 | eff(%): 16.530 | loglstar: -inf < 177.943 < inf | logz: 160.229 +/- 0.519 | dlogz: 0.157 > 0.059] + + 999it [00:06, 274.78it/s, bound: 101 | nc: 5 | ncall: 6013 | eff(%): 16.614 | loglstar: -inf < 177.970 < inf | logz: 160.295 +/- 0.519 | dlogz: 0.079 > 0.059] + + 1013it [00:06, 165.99it/s, +50 | bound: 103 | nc: 1 | ncall: 6133 | eff(%): 17.475 | loglstar: -inf < 178.098 < inf | logz: 160.366 +/- 0.520 | dlogz: 0.001 > 0.059] + + + + + 2026-07-11 16:22:59,192 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:22:59,295 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + Bayesian Evidence 160.36570347 + Maximum Log Likelihood 178.09800271 + + model Gaussian (N=3) + + Maximum Log Likelihood Model: + + centre 50.119 + normalization 25.176 + sigma 10.052 + + + Summary (3.0 sigma limits): + + centre 50.11 (49.71, 50.54) + normalization 25.11 (24.29, 26.00) + sigma 10.06 (9.64, 10.42) + + + Summary (1.0 sigma limits): + + centre 50.11 (50.01, 50.24) + normalization 25.11 (24.94, 25.34) + sigma 10.06 (9.95, 10.19) + + instances + + + + + + +![png](tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_37_48.png) + + + +The **Nested Sampling** search was successful, identifying the same high-likelihood model as the MLE and MCMC searches. +One of the main benefits of Nested Sampling is its ability to provide accurate parameter estimates and uncertainties, +similar to MCMC. Additionally, it features a built-in stopping criterion, which eliminates the need for users to +specify the number of steps the search should take. + +This method also excels in handling complex parameter spaces, particularly those with multiple peaks. This is because +the live points will identify each peak and converge around them, but then begin to be discard from a peak if higher +likelihood points are found elsewhere in parameter space. In MCMC, the walkers can get stuck indefinitely around a +peak, causing the method to stall. + +Another significant advantage is that Nested Sampling estimates an important statistical quantity +known as "evidence." This value quantifies how well the model fits the data while considering the model's complexity, +making it essential for Bayesian model comparison, which will be covered in later tutorials. + +Nested sampling cannot use a starting point, as it always samples parameter space from scratch by drawing live points +from the priors. This is both good and bad, depending on if you have access to a good starting point or not. If you do +not, your MCMC / MLE fit will likely struggle with initialization compared to Nested Sampling. Conversely, if you do +possess a robust starting point, it can significantly enhance the performance of MCMC, allowing it to begin closer to +the highest likelihood regions of parameter space. This proximity can lead to faster convergence and more reliable results. + +However, Nested Sampling does have limitations; it often scales poorly with increased model complexity. For example, +once a model has around 50 or more parameters, Nested Sampling can become very slow, whereas MCMC remains efficient +even in such complex parameter spaces. + +__What is The Best Search To Use?__ + +The choice of the best search method depends on several factors specific to the problem at hand. Here are key +considerations that influence which search may be optimal: + +Firstly, consider the speed of the fit regardless of the search method. If the fitting process runs efficiently, +nested sampling could be advantageous for low-dimensional parameter spaces due to its ability to handle complex +parameter spaces and its built-in stopping criterion. However, in high-dimensional scenarios, MCMC may be more +suitable, as it scales better with the number of parameters. + +Secondly, evaluate whether you have access to a robust starting point for your model fit. A strong initialization can +make MCMC more appealing, allowing the algorithm to bypass the initial sampling stage and leading to quicker convergence. + +Additionally, think about the importance of error estimation in your analysis. If error estimation is not a priority, +MLE might suffice, but this approach heavily relies on having a solid starting point and may struggle with more complex models. + +Ultimately, every model-fitting problem is unique, making it impossible to provide a one-size-fits-all answer regarding +the best search method. This variability is why **PyAutoFit** offers a diverse array of search options, all +standardized with a consistent interface. This standardization allows users to experiment with different searches on the +same model-fitting problem and determine which yields the best results. + +Finally, it’s important to note that MLE, MCMC, and nested sampling represent only three categories of non-linear +searches, each containing various algorithms. Each algorithm has its strengths and weaknesses, so experimenting with +them can reveal the most effective approach for your specific model-fitting challenge. For further guidance, a detailed +description of each search method can be found in the [search cookbook](https://pyautofit.readthedocs.io/en/latest/cookbooks/search.html). + +__Wrap Up__ + +This tutorial has laid the foundation with several fundamental concepts in model fitting and statistical inference: + +1. **Parameter Space**: This refers to the range of possible values that each parameter in a model can take. It +defines the dimensions over which the likelihood of different parameter values is evaluated. + +2. **Likelihood Surface**: This surface represents how the likelihood of the model varies across the parameter space. +It helps in identifying the best-fit parameters that maximize the likelihood of the model given the data. + +3. **Non-linear Search**: This is an optimization technique used to explore the parameter space and find the +combination of parameter values that best describe the data. It iteratively adjusts the parameters to maximize the +likelihood. Many different search algorithms exist, each with their own strengths and weaknesses, and this tutorial +used the MLE, MCMC, and nested sampling searches. + +4. **Priors**: Priors are probabilities assigned to different values of parameters before considering the data. +They encapsulate our prior knowledge or assumptions about the parameter values. Priors can constrain the parameter +space, making the search more efficient and realistic. + +5. **Model Fitting**: The process of adjusting model parameters to minimize the difference between model predictions +and observed data, quantified by the likelihood function. + +Understanding these concepts is crucial as they form the backbone of model fitting and parameter estimation in +scientific research and data analysis. In the next tutorials, these concepts will be further expanded upon to +deepen your understanding and provide more advanced techniques for model fitting and analysis. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_25_0.png b/markdown/chapter_1_introduction/tutorial_3_non_linear_search_files/tutorial_3_non_linear_search_25_0.png new file mode 100644 index 0000000000000000000000000000000000000000..5513e0e562eb39703cb0752f06a93fc6938f0aae GIT binary patch literal 33397 zcmbTeWmr{V*EI@a5E3FFjdTczbOHQnNL6ay4`?K~XStva_;vvNC^h*Tuxa(cIRCi~Ruyy8!FmmrhQ0 zj)M2^TmK)gV7GNJyU!pws0~-au#?tyL_xtdM1D|nMRUwiP+rZ+NIX_|OWvAt*HSkj zYnq$!vntJh`;lpTo;^M!E>0zJIZE3is*+hF+f=(USkA&EaVhF$7X5)q)_8{Ya#XBP zZWhh99A*gFU3T>P87j2fXlOksEwAQkiY(dV9j6oAdkp#ay6V0L+(Q1M@P1Jec>(W}|2Hq_C{ak_O&?qvD>sjBjnpqS z#t=-2xXq|IS-JVE!qR-ImZztuXVpotP_L%>w$j7L&!2y6ExOlHS$g2g*5j$Ct`{|l 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b/markdown/chapter_1_introduction/tutorial_4_why_modeling_is_hard.md new file mode 100644 index 0000000..9e6e740 --- /dev/null +++ b/markdown/chapter_1_introduction/tutorial_4_why_modeling_is_hard.md @@ -0,0 +1,7757 @@ +> ✏️ **This page is auto-generated from [`scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py`](../../scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py) — do not edit it directly.** +> It shows the example fully executed, with its real output images. +> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb). + +Tutorial 4: Why Modeling Is Hard +================================ + +We have successfully fitted a simple 1D Gaussian profile to a dataset using a non-linear search. While achieving an +accurate model fit has been straightforward, the reality is that model fitting is a challenging problem where many things can go wrong. + +This tutorial will illustrate why modeling is challenging, highlight common problems that occur when fitting complex +models, and show how a good scientific approach can help us overcome these challenges. + +We will build on concepts introduced in previous tutorials, such as the non-linear parameter space, likelihood surface, +and the role of priors. + +__Overview__ + +In this tutorial, we will fit complex models with up to 15 free parameters and consider the following: + +- Why more complex models are more difficult to fit and may lead the non-linear search to infer an incorrect solution. + +- Strategies for ensuring the non-linear search estimates the correct solution. + +- What drives the run-times of a model fit and how to carefully balance run-times with model complexity. + +__Contents__ + +- **Data**: Load and plot the 1D Gaussian dataset we'll fit, which is more complex than the previous tutorial. +- **Model**: The `Gaussian` model component that we will fit to the data. +- **Analysis**: The log likelihood function used to fit the model to the data. +- **Alternative Syntax**: An alternative loop-based approach for creating a summed profile from multiple model components. +- **Collection**: The `Collection` model used to compose the model-fit. +- **Search**: Set up the nested sampling search (Dynesty) for the model-fit. +- **Model Fit**: Perform the model-fit and examine the results. +- **Result**: Determine if the model-fit was successful and what can be done to ensure a good model-fit. +- **Why Modeling is Hard**: Introduce the concept of randomness and local maxima and why they make model-fitting challenging. +- **Prior Tuning**: Adjust the priors of the model to help the non-linear search find the global maxima solution. +- **Reducing Complexity**: Simplify the model to reduce the dimensionality of the parameter space. +- **Search More Thoroughly**: Adjust the non-linear search settings to search parameter space more thoroughly. +- **Summary**: Summarize the three strategies for ensuring successful model-fitting. +- **Run Times**: Discuss how the likelihood function and complexity of a model impacts the run-time of a model-fit. +- **Model Mismatch**: Introduce the concept of model mismatches and how it makes inferring the correct model challenging. +- **Astronomy Example**: How the concepts of this tutorial are applied to real astronomical problems. +- **Wrap Up**: A summary of the key takeaways of this tutorial. + + +```python + +from autoconf import setup_notebook; setup_notebook() + +from os import path +import numpy as np +import matplotlib.pyplot as plt + +import autofit as af +``` + + Working Directory has been set to `HowToFit` + + +__Data__ + +Load the dataset we fit. + +This is a new `dataset` where the underlying signal is a sum of five `Gaussian` profiles. + + +```python +dataset_path = path.join("dataset", "example_1d", "gaussian_x5") +``` + +__Dataset Auto-Simulation__ + +If the dataset does not already exist on your system, it will be created by running the corresponding +simulator script. This ensures that all example scripts can be run without manually simulating data first. + + +```python +if not path.exists(dataset_path): + import subprocess + import sys + + subprocess.run( + [sys.executable, "scripts/simulators/simulators.py"], + check=True, + ) + +data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json")) +noise_map = af.util.numpy_array_from_json( + file_path=path.join(dataset_path, "noise_map.json") +) +``` + +Plotting the data reveals that the signal is more complex than a simple 1D Gaussian, as the wings to the left and +right are more extended than what a single Gaussian profile can account for. + + +```python +xvalues = np.arange(data.shape[0]) +plt.errorbar( + xvalues, + data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.title("1D Gaussian dataset with errors from the noise-map.") +plt.xlabel("x values of profile") +plt.ylabel("Signal Value") +plt.show() +plt.clf() +plt.close() +``` + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_7_0.png) + + + +__Model__ + +Create the `Gaussian` class from which we will compose model components using the standard format. + + +```python + + +class Gaussian: + def __init__( + self, + centre: float = 30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization: float = 1.0, # <- are the Gaussian`s model parameters. + sigma: float = 5.0, + ): + """ + Represents a 1D Gaussian profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to perform model-fitting + of example datasets. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + sigma + The sigma value controlling the size of the Gaussian. + """ + self.centre = centre + self.normalization = normalization + self.sigma = sigma + + def model_data_from(self, xvalues: np.ndarray) -> np.ndarray: + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + + Returns + ------- + np.array + The Gaussian values at the input x coordinates. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return np.multiply( + np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)), + np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))), + ) + +``` + +__Analysis__ + +To define the Analysis class for this model-fit, we need to ensure that the `log_likelihood_function` can handle an +instance containing multiple 1D profiles. Below is an expanded explanation and the corresponding class definition: + +The log_likelihood_function will now assume that the instance it receives consists of multiple Gaussian profiles. +For each Gaussian in the instance, it will compute the model_data and then sum these to create the overall `model_data` +that is compared to the observed data. + + +```python + + +class Analysis(af.Analysis): + def __init__(self, data: np.ndarray, noise_map: np.ndarray): + """ + The `Analysis` class acts as an interface between the data and model in **PyAutoFit**. + + Its `log_likelihood_function` defines how the model is fitted to the data and it is called many times by + the non-linear search fitting algorithm. + + In this example, the `log_likelihood_function` receives an instance containing multiple instances of + the `Gaussian` class and sums the `model_data` of each to create the overall model fit to the data. + + In this example the `Analysis` `__init__` constructor only contains the `data` and `noise-map`, but it can be + easily extended to include other quantities. + + Parameters + ---------- + data + A 1D numpy array containing the data (e.g. a noisy 1D signal) fitted in the workspace examples. + noise_map + A 1D numpy array containing the noise values of the data, used for computing the goodness of fit + metric, the log likelihood. + """ + super().__init__() + + self.data = data + self.noise_map = noise_map + + def log_likelihood_function(self, instance) -> float: + """ + Returns the log likelihood of a fit of a 1D Gaussian to the dataset. + + In the previous tutorial, the instance was a single `Gaussian` profile, however this function now assumes + the instance contains multiple `Gaussian` profiles. + + The `model_data` is therefore the summed `model_data` of all individual Gaussians in the model. + + The docstring below describes this in more detail. + + Parameters + ---------- + instance + A list of 1D profiles with parameters set via the non-linear search. + + Returns + ------- + float + The log likelihood value indicating how well this model fit the `MaskedDataset`. + """ + + """ + In the previous tutorial the instance was a single `Gaussian` profile, meaning we could create the model data + using the line: + + model_data = instance.gaussian.model_data_from(xvalues=self.data.xvalues) + + In this tutorial our instance is comprised of three 1D Gaussians, because we will use a `Collection` to + compose the model: + + model = Collection(gaussian_0=Gaussian, gaussian_1=Gaussian, gaussian_2=Gaussian). + + By using a Collection, this means the instance parameter input into the fit function is a + dictionary where individual profiles (and their parameters) can be accessed as followed: + + print(instance.gaussian_0) + print(instance.gaussian_1) + print(instance.gaussian_2) + + print(instance.gaussian_0.centre) + print(instance.gaussian_1.centre) + print(instance.gaussian_2.centre) + + The `model_data` is therefore the summed `model_data` of all individual Gaussians in the model. + + The function `model_data_from_instance` performs this summation. + """ + model_data = self.model_data_from_instance(instance=instance) + + residual_map = self.data - model_data + chi_squared_map = (residual_map / self.noise_map) ** 2.0 + chi_squared = sum(chi_squared_map) + noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0)) + log_likelihood = -0.5 * (chi_squared + noise_normalization) + + return log_likelihood + + def model_data_from_instance(self, instance): + """ + To create the summed profile of all individual profiles, we use a list comprehension to iterate over + all profiles in the instance. + + The `instance` has the properties of a Python `iterator` and therefore can be looped over using the standard + Python for syntax (e.g. `for profile in instance`). + + __Alternative Syntax__ + + For those not familiar with Python list comprehensions, the code below shows how to use the instance to + create the summed profile using a for loop and numpy array: + + model_data = np.zeros(shape=self.data.xvalues.shape[0]) + + for profile in instance: + model_data += profile.model_data_from(xvalues=self.data.xvalues) + + return model_data + """ + xvalues = np.arange(self.data.shape[0]) + + return sum([profile.model_data_from(xvalues=xvalues) for profile in instance]) + +``` + +__Collection__ + +In the previous tutorial, we fitted a single `Gaussian` profile to the dataset by turning it into a model +component using the `Model` class. + +In this tutorial, we will fit a model composed of five `Gaussian` profiles. To do this, we need to combine +five `Gaussian` model components into a single model. + +This can be achieved using a `Collection` object, which was introduced in tutorial 1. The `Collection` object allows +us to group together multiple model components—in this case, five `Gaussian` profiles—into one model that can be +passed to the non-linear search. + + +```python +model = af.Collection( + gaussian_0=Gaussian, + gaussian_1=Gaussian, + gaussian_2=Gaussian, + gaussian_3=Gaussian, + gaussian_4=Gaussian, +) +``` + +The `model.info` confirms the model is composed of 5 `Gaussian` profiles. + + +```python +print(model.info) +``` + + Total Free Parameters = 15 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + gaussian_0 + centre UniformPrior [0], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [1], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [2], lower_limit = 0.0, upper_limit = 25.0 + gaussian_1 + centre UniformPrior [3], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [4], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [5], lower_limit = 0.0, upper_limit = 25.0 + gaussian_2 + centre UniformPrior [6], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [7], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [8], lower_limit = 0.0, upper_limit = 25.0 + gaussian_3 + centre UniformPrior [9], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [10], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [11], lower_limit = 0.0, upper_limit = 25.0 + gaussian_4 + centre UniformPrior [12], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [13], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [14], lower_limit = 0.0, upper_limit = 25.0 + + +__Search__ + +We again use the nested sampling algorithm Dynesty to fit the model to the data. + + +```python +search = af.DynestyStatic( + sample="rwalk", # This makes dynesty run faster, don't worry about what it means for now! +) +``` + +__Model Fit__ + +Perform the fit using our five `Gaussian` model, which has 15 free parameters. + +This means the non-linear parameter space has a dimensionality of N=15, making it significantly more complex +than the simpler model we fitted in the previous tutorial. + +Consequently, the non-linear search takes slightly longer to run but still completes in under a minute. + + +```python +analysis = Analysis(data=data, noise_map=noise_map) + +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:23:05,069 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:23:05,079 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:23:05,080 - root - INFO - Starting new Dynesty non-linear search (no previous samples found). + + + 2026-07-11 16:23:05,289 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:23:05,324 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + ~/venv/PyAuto/lib/python3.12/site-packages/dynesty/dynesty.py:194: UserWarning: Specifying slice option while using rwalk sampler does not make sense + warnings.warn('Specifying slice option while using rwalk sampler' + + + 0it [00:00, ?it/s] + + 36it [00:00, 349.06it/s, bound: 0 | nc: 3 | ncall: 100 | eff(%): 36.000 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.059] + + 71it [00:00, 234.58it/s, bound: 0 | nc: 1 | ncall: 204 | eff(%): 34.804 | loglstar: -inf < -inf < inf | logz: -inf +/- 0.347 | dlogz: inf > 0.059] + + 97it [00:00, 155.48it/s, bound: 0 | nc: 7 | ncall: 332 | eff(%): 29.217 | loglstar: -inf < -676962.382 < inf | logz: -676968.908 +/- 0.359 | dlogz: 541835.334 > 0.059] + + 116it [00:01, 86.30it/s, bound: 0 | nc: 48 | ncall: 584 | eff(%): 19.863 | loglstar: -inf < -655809.826 < inf | logz: -655816.728 +/- 0.370 | dlogz: 520222.316 > 0.059] + + 129it [00:01, 76.05it/s, bound: 0 | nc: 1 | ncall: 723 | eff(%): 17.842 | loglstar: -inf < -647634.124 < inf | logz: -647641.283 +/- 0.377 | dlogz: 511816.618 > 0.059] + + 140it [00:01, 68.17it/s, bound: 0 | nc: 23 | ncall: 840 | eff(%): 16.667 | loglstar: -inf < -632562.848 < inf | logz: -632570.225 +/- 0.382 | dlogz: 496673.227 > 0.059] + + 149it [00:01, 58.54it/s, bound: 0 | nc: 11 | ncall: 967 | eff(%): 15.408 | loglstar: -inf < -620370.724 < inf | logz: -620378.279 +/- 0.387 | dlogz: 485513.004 > 0.059] + + 156it [00:02, 46.49it/s, bound: 0 | nc: 2 | ncall: 1111 | eff(%): 14.041 | loglstar: -inf < -606573.503 < inf | logz: -606581.197 +/- 0.390 | dlogz: 471472.051 > 0.059] + + 162it [00:02, 37.74it/s, bound: 0 | nc: 59 | ncall: 1268 | eff(%): 12.776 | loglstar: -inf < -599639.808 < inf | logz: -599647.622 +/- 0.393 | dlogz: 464375.439 > 0.059] + + 167it [00:02, 31.86it/s, bound: 0 | nc: 25 | ncall: 1371 | eff(%): 12.181 | loglstar: -inf < -583199.863 < inf | logz: -583207.776 +/- 0.396 | dlogz: 452150.423 > 0.059] + + 171it [00:02, 31.63it/s, bound: 0 | nc: 5 | ncall: 1447 | eff(%): 11.818 | loglstar: -inf < -569894.638 < inf | logz: -569902.630 +/- 0.398 | dlogz: 436253.538 > 0.059] + + 175it [00:02, 27.67it/s, bound: 0 | nc: 42 | ncall: 1559 | eff(%): 11.225 | loglstar: -inf < -564236.979 < inf | logz: -564245.050 +/- 0.400 | dlogz: 428434.317 > 0.059] + + 178it [00:03, 25.65it/s, bound: 0 | nc: 17 | ncall: 1638 | eff(%): 10.867 | loglstar: -inf < -562490.713 < inf | logz: -562498.843 +/- 0.401 | dlogz: 426853.159 > 0.059] + + 181it [00:03, 16.51it/s, bound: 1 | nc: 6 | ncall: 1845 | eff(%): 9.810 | loglstar: -inf < -557232.231 < inf | logz: -557240.420 +/- 0.403 | dlogz: 425013.706 > 0.059] + + 206it [00:03, 47.74it/s, bound: 4 | nc: 5 | ncall: 1970 | eff(%): 10.457 | loglstar: -inf < -471575.571 < inf | logz: -471584.257 +/- 0.413 | dlogz: 348098.046 > 0.059] + + 232it [00:03, 81.14it/s, bound: 7 | nc: 5 | ncall: 2100 | eff(%): 11.048 | loglstar: -inf < -376179.075 < inf | logz: -376188.281 +/- 0.421 | dlogz: 256926.185 > 0.059] + + 258it [00:03, 114.25it/s, bound: 10 | nc: 5 | ncall: 2230 | eff(%): 11.570 | loglstar: -inf < -261154.544 < inf | logz: -261164.268 +/- 0.430 | dlogz: 137801.135 > 0.059] + + 280it [00:03, 136.17it/s, bound: 13 | nc: 5 | ncall: 2340 | eff(%): 11.966 | loglstar: -inf < -213400.730 < inf | logz: -213410.902 +/- 0.435 | dlogz: 182118.092 > 0.059] + + 302it [00:04, 155.31it/s, bound: 16 | nc: 5 | ncall: 2450 | eff(%): 12.327 | loglstar: -inf < -184912.417 < inf | logz: -184920.530 +/- 0.380 | dlogz: 140640.287 > 0.059] + + 324it [00:04, 168.77it/s, bound: 19 | nc: 5 | ncall: 2560 | eff(%): 12.656 | loglstar: -inf < -175034.588 < inf | logz: -175045.673 +/- 0.445 | dlogz: 130836.599 > 0.059] + + 344it [00:04, 176.54it/s, bound: 21 | nc: 5 | ncall: 2660 | eff(%): 12.932 | loglstar: -inf < -159297.074 < inf | logz: -159306.914 +/- 0.411 | dlogz: 115026.106 > 0.059] + + 364it [00:04, 181.46it/s, bound: 24 | nc: 5 | ncall: 2760 | eff(%): 13.188 | loglstar: -inf < -151485.314 < inf | logz: -151495.568 +/- 0.414 | dlogz: 107214.346 > 0.059] + + 384it [00:04, 184.35it/s, bound: 26 | nc: 5 | ncall: 2860 | eff(%): 13.427 | loglstar: -inf < -136064.884 < inf | logz: -136077.188 +/- 0.457 | dlogz: 91900.187 > 0.059] + + 404it [00:04, 187.96it/s, bound: 29 | nc: 5 | ncall: 2960 | eff(%): 13.649 | loglstar: -inf < -124853.150 < inf | logz: -124864.207 +/- 0.425 | dlogz: 80582.182 > 0.059] + + 426it [00:04, 195.99it/s, bound: 31 | nc: 5 | ncall: 3070 | eff(%): 13.876 | loglstar: -inf < -111504.314 < inf | logz: -111516.346 +/- 0.442 | dlogz: 67234.464 > 0.059] + + 448it [00:04, 202.74it/s, bound: 34 | nc: 5 | ncall: 3180 | eff(%): 14.088 | loglstar: -inf < -98643.791 < inf | logz: -98657.386 +/- 0.472 | dlogz: 57842.330 > 0.059] + + 471it [00:04, 209.73it/s, bound: 37 | nc: 5 | ncall: 3295 | eff(%): 14.294 | loglstar: -inf < -93382.628 < inf | logz: -93395.042 +/- 0.441 | dlogz: 49111.660 > 0.059] + + 494it [00:04, 213.22it/s, bound: 40 | nc: 5 | ncall: 3410 | eff(%): 14.487 | loglstar: -inf < -86782.656 < inf | logz: -86796.065 +/- 0.457 | dlogz: 42512.806 > 0.059] + + 516it [00:05, 213.80it/s, bound: 43 | nc: 5 | ncall: 3520 | eff(%): 14.659 | loglstar: -inf < -76030.319 < inf | logz: -76044.167 +/- 0.464 | dlogz: 33874.773 > 0.059] + + 539it [00:05, 218.21it/s, bound: 45 | nc: 5 | ncall: 3635 | eff(%): 14.828 | loglstar: -inf < -66467.299 < inf | logz: -66481.077 +/- 0.458 | dlogz: 24310.634 > 0.059] + + 561it [00:05, 210.52it/s, bound: 48 | nc: 5 | ncall: 3745 | eff(%): 14.980 | loglstar: -inf < -58401.375 < inf | logz: -58417.241 +/- 0.499 | dlogz: 16653.533 > 0.059] + + 583it [00:05, 202.79it/s, bound: 51 | nc: 5 | ncall: 3855 | eff(%): 15.123 | loglstar: -inf < -54018.533 < inf | logz: -54034.842 +/- 0.504 | dlogz: 12091.627 > 0.059] + + 604it [00:05, 195.08it/s, bound: 54 | nc: 5 | ncall: 3960 | eff(%): 15.253 | loglstar: -inf < -52277.995 < inf | logz: -52293.606 +/- 0.486 | dlogz: 16571.889 > 0.059] + + 626it [00:05, 199.56it/s, bound: 56 | nc: 5 | ncall: 4070 | eff(%): 15.381 | loglstar: -inf < -50131.770 < inf | logz: -50148.941 +/- 0.514 | dlogz: 14503.677 > 0.059] + + 647it [00:05, 202.09it/s, bound: 59 | nc: 5 | ncall: 4175 | eff(%): 15.497 | loglstar: -inf < -46247.663 < inf | logz: -46265.252 +/- 0.520 | dlogz: 10553.178 > 0.059] + + 669it [00:05, 206.46it/s, bound: 62 | nc: 5 | ncall: 4285 | eff(%): 15.613 | loglstar: -inf < -44656.592 < inf | logz: -44674.626 +/- 0.524 | dlogz: 9017.177 > 0.059] + + 690it [00:05, 207.29it/s, bound: 64 | nc: 5 | ncall: 4390 | eff(%): 15.718 | loglstar: -inf < -44274.455 < inf | logz: -44290.637 +/- 0.482 | dlogz: 11785.137 > 0.059] + + 713it [00:06, 211.76it/s, bound: 67 | nc: 5 | ncall: 4505 | eff(%): 15.827 | loglstar: -inf < -42622.954 < inf | logz: -42641.878 +/- 0.533 | dlogz: 10171.492 > 0.059] + + 735it [00:06, 213.43it/s, bound: 70 | nc: 5 | ncall: 4615 | eff(%): 15.926 | loglstar: -inf < -42160.151 < inf | logz: -42179.525 +/- 0.536 | dlogz: 9835.917 > 0.059] + + 760it [00:06, 221.66it/s, bound: 73 | nc: 5 | ncall: 4740 | eff(%): 16.034 | loglstar: -inf < -39924.884 < inf | logz: -39943.638 +/- 0.521 | dlogz: 7437.574 > 0.059] + + 786it [00:06, 231.37it/s, bound: 76 | nc: 5 | ncall: 4870 | eff(%): 16.140 | loglstar: -inf < -39022.002 < inf | logz: -39042.409 +/- 0.547 | dlogz: 6549.020 > 0.059] + + 810it [00:06, 229.17it/s, bound: 79 | nc: 5 | ncall: 4990 | eff(%): 16.232 | loglstar: -inf < -37544.420 < inf | logz: -37565.307 +/- 0.553 | dlogz: 5186.148 > 0.059] + + 838it [00:06, 242.02it/s, bound: 83 | nc: 5 | ncall: 5130 | eff(%): 16.335 | loglstar: -inf < -36800.267 < inf | logz: -36821.710 +/- 0.557 | dlogz: 4679.464 > 0.059] + + 865it [00:06, 248.71it/s, bound: 86 | nc: 5 | ncall: 5265 | eff(%): 16.429 | loglstar: -inf < -35519.025 < inf | logz: -35539.371 +/- 0.534 | dlogz: 4912.010 > 0.059] + + 891it [00:06, 250.69it/s, bound: 90 | nc: 5 | ncall: 5395 | eff(%): 16.515 | loglstar: -inf < -34188.087 < inf | logz: -34210.598 +/- 0.572 | dlogz: 3591.748 > 0.059] + + 917it [00:06, 250.16it/s, bound: 93 | nc: 5 | ncall: 5525 | eff(%): 16.597 | loglstar: -inf < -33434.010 < inf | logz: -33457.042 +/- 0.578 | dlogz: 2864.468 > 0.059] + + 943it [00:06, 249.55it/s, bound: 97 | nc: 5 | ncall: 5655 | eff(%): 16.676 | loglstar: -inf < -32749.017 < inf | logz: -32771.448 +/- 0.565 | dlogz: 5273.469 > 0.059] + + 972it [00:07, 260.68it/s, bound: 101 | nc: 5 | ncall: 5800 | eff(%): 16.759 | loglstar: -inf < -32285.709 < inf | logz: -32308.681 +/- 0.569 | dlogz: 7339.013 > 0.059] + + 1001it [00:07, 267.34it/s, bound: 105 | nc: 5 | ncall: 5945 | eff(%): 16.838 | loglstar: -inf < -31683.093 < inf | logz: -31707.823 +/- 0.595 | dlogz: 11152.980 > 0.059] + + 1028it [00:07, 267.13it/s, bound: 108 | nc: 5 | ncall: 6080 | eff(%): 16.908 | loglstar: -inf < -30068.373 < inf | logz: -30093.644 +/- 0.600 | dlogz: 10372.474 > 0.059] + + 1055it [00:07, 266.03it/s, bound: 113 | nc: 5 | ncall: 6215 | eff(%): 16.975 | loglstar: -inf < -28601.815 < inf | logz: -28627.624 +/- 0.607 | dlogz: 8640.504 > 0.059] + + 1082it [00:07, 239.30it/s, bound: 116 | nc: 5 | ncall: 6350 | eff(%): 17.039 | loglstar: -inf < -27057.195 < inf | logz: -27081.536 +/- 0.579 | dlogz: 7978.674 > 0.059] + + 1107it [00:07, 224.32it/s, bound: 120 | nc: 5 | ncall: 6475 | eff(%): 17.097 | loglstar: -inf < -25983.378 < inf | logz: -26010.239 +/- 0.616 | dlogz: 10992.740 > 0.059] + + 1131it [00:07, 225.97it/s, bound: 123 | nc: 5 | ncall: 6595 | eff(%): 17.149 | loglstar: -inf < -23547.514 < inf | logz: -23574.853 +/- 0.623 | dlogz: 10684.604 > 0.059] + + 1154it [00:07, 226.90it/s, bound: 127 | nc: 5 | ncall: 6710 | eff(%): 17.198 | loglstar: -inf < -21859.080 < inf | logz: -21885.764 +/- 0.609 | dlogz: 8721.452 > 0.059] + + 1178it [00:07, 230.43it/s, bound: 130 | nc: 5 | ncall: 6830 | eff(%): 17.247 | loglstar: -inf < -20281.639 < inf | logz: -20309.921 +/- 0.633 | dlogz: 7203.562 > 0.059] + + 1202it [00:08, 231.94it/s, bound: 134 | nc: 5 | ncall: 6950 | eff(%): 17.295 | loglstar: -inf < -19180.535 < inf | logz: -19208.178 +/- 0.620 | dlogz: 6042.911 > 0.059] + + 1229it [00:08, 241.47it/s, bound: 139 | nc: 5 | ncall: 7085 | eff(%): 17.347 | loglstar: -inf < -18161.991 < inf | logz: -18188.795 +/- 0.601 | dlogz: 8424.477 > 0.059] + + 1255it [00:08, 245.08it/s, bound: 143 | nc: 5 | ncall: 7215 | eff(%): 17.394 | loglstar: -inf < -16677.263 < inf | logz: -16707.097 +/- 0.646 | dlogz: 6995.471 > 0.059] + + 1285it [00:08, 258.34it/s, bound: 146 | nc: 5 | ncall: 7365 | eff(%): 17.447 | loglstar: -inf < -15471.839 < inf | logz: -15502.272 +/- 0.652 | dlogz: 6935.616 > 0.059] + + 1312it [00:08, 260.26it/s, bound: 151 | nc: 5 | ncall: 7500 | eff(%): 17.493 | loglstar: -inf < -14107.515 < inf | logz: -14138.489 +/- 0.657 | dlogz: 5514.862 > 0.059] + + 1341it [00:08, 268.54it/s, bound: 155 | nc: 5 | ncall: 7645 | eff(%): 17.541 | loglstar: -inf < -13124.280 < inf | logz: -13155.826 +/- 0.664 | dlogz: 4525.702 > 0.059] + + 1370it [00:08, 274.65it/s, bound: 159 | nc: 5 | ncall: 7790 | eff(%): 17.587 | loglstar: -inf < -12290.026 < inf | logz: -12320.509 +/- 0.645 | dlogz: 3684.529 > 0.059] + + 1400it [00:08, 278.36it/s, bound: 163 | nc: 5 | ncall: 7940 | eff(%): 17.632 | loglstar: -inf < -11090.478 < inf | logz: -11123.217 +/- 0.676 | dlogz: 2529.903 > 0.059] + + 1428it [00:08, 271.40it/s, bound: 166 | nc: 5 | ncall: 8080 | eff(%): 17.673 | loglstar: -inf < -10385.434 < inf | logz: -10418.733 +/- 0.680 | dlogz: 1786.569 > 0.059] + + 1457it [00:08, 276.67it/s, bound: 170 | nc: 5 | ncall: 8225 | eff(%): 17.714 | loglstar: -inf < -9723.039 < inf | logz: -9755.279 +/- 0.661 | dlogz: 2378.173 > 0.059] + + 1488it [00:09, 285.27it/s, bound: 174 | nc: 5 | ncall: 8380 | eff(%): 17.757 | loglstar: -inf < -9244.603 < inf | logz: -9277.466 +/- 0.667 | dlogz: 2645.872 > 0.059] + + 1517it [00:09, 282.34it/s, bound: 178 | nc: 5 | ncall: 8525 | eff(%): 17.795 | loglstar: -inf < -8750.492 < inf | logz: -8785.585 +/- 0.696 | dlogz: 2199.812 > 0.059] + + 1547it [00:09, 286.39it/s, bound: 183 | nc: 5 | ncall: 8675 | eff(%): 17.833 | loglstar: -inf < -8184.322 < inf | logz: -8220.020 +/- 0.703 | dlogz: 1595.906 > 0.059] + + 1578it [00:09, 292.45it/s, bound: 188 | nc: 5 | ncall: 8830 | eff(%): 17.871 | loglstar: -inf < -7524.929 < inf | logz: -7561.244 +/- 0.709 | dlogz: 1145.003 > 0.059] + + 1608it [00:09, 293.74it/s, bound: 192 | nc: 5 | ncall: 8980 | eff(%): 17.906 | loglstar: -inf < -7296.280 < inf | logz: -7333.222 +/- 0.713 | dlogz: 1105.129 > 0.059] + + 1638it [00:09, 289.38it/s, bound: 196 | nc: 5 | ncall: 9130 | eff(%): 17.941 | loglstar: -inf < -7031.596 < inf | logz: -7067.947 +/- 0.697 | dlogz: 1091.543 > 0.059] + + 1667it [00:09, 280.05it/s, bound: 201 | nc: 5 | ncall: 9275 | eff(%): 17.973 | loglstar: -inf < -6714.885 < inf | logz: -6752.933 +/- 0.719 | dlogz: 778.239 > 0.059] + + 1699it [00:09, 289.84it/s, bound: 205 | nc: 5 | ncall: 9435 | eff(%): 18.007 | loglstar: -inf < -6556.201 < inf | logz: -6593.854 +/- 0.713 | dlogz: 616.372 > 0.059] + + 1729it [00:09, 284.76it/s, bound: 209 | nc: 5 | ncall: 9585 | eff(%): 18.039 | loglstar: -inf < -6405.988 < inf | logz: -6445.038 +/- 0.723 | dlogz: 856.664 > 0.059] + + 1758it [00:10, 280.72it/s, bound: 214 | nc: 5 | ncall: 9730 | eff(%): 18.068 | loglstar: -inf < -6217.292 < inf | logz: -6257.243 +/- 0.740 | dlogz: 771.000 > 0.059] + + 1788it [00:10, 285.50it/s, bound: 218 | nc: 5 | ncall: 9880 | eff(%): 18.097 | loglstar: -inf < -6081.272 < inf | logz: -6121.680 +/- 0.738 | dlogz: 625.590 > 0.059] + + 1818it [00:10, 288.43it/s, bound: 222 | nc: 5 | ncall: 10030 | eff(%): 18.126 | loglstar: -inf < -5980.286 < inf | logz: -6020.269 +/- 0.734 | dlogz: 574.204 > 0.059] + + 1848it [00:10, 290.36it/s, bound: 226 | nc: 5 | ncall: 10180 | eff(%): 18.153 | loglstar: -inf < -5791.723 < inf | logz: -5832.353 +/- 0.743 | dlogz: 385.784 > 0.059] + + 1878it [00:10, 288.56it/s, bound: 230 | nc: 5 | ncall: 10330 | eff(%): 18.180 | loglstar: -inf < -5694.234 < inf | logz: -5735.901 +/- 0.746 | dlogz: 313.606 > 0.059] + + 1907it [00:10, 279.89it/s, bound: 234 | nc: 5 | ncall: 10475 | eff(%): 18.205 | loglstar: -inf < -5555.597 < inf | logz: -5596.907 +/- 0.740 | dlogz: 409.483 > 0.059] + + 1936it [00:10, 281.48it/s, bound: 237 | nc: 5 | ncall: 10620 | eff(%): 18.230 | loglstar: -inf < -5520.492 < inf | logz: -5563.596 +/- 0.762 | dlogz: 376.924 > 0.059] + + 1966it [00:10, 285.15it/s, bound: 242 | nc: 5 | ncall: 10770 | eff(%): 18.254 | loglstar: -inf < -5439.713 < inf | logz: -5483.816 +/- 0.780 | dlogz: 392.344 > 0.059] + + 1997it [00:10, 291.25it/s, bound: 247 | nc: 5 | ncall: 10925 | eff(%): 18.279 | loglstar: -inf < -5371.853 < inf | logz: -5416.531 +/- 0.783 | dlogz: 488.928 > 0.059] + + 2028it [00:10, 292.20it/s, bound: 252 | nc: 5 | ncall: 11080 | eff(%): 18.303 | loglstar: -inf < -5279.399 < inf | logz: -5323.611 +/- 0.766 | dlogz: 549.241 > 0.059] + + 2058it [00:11, 294.38it/s, bound: 255 | nc: 5 | ncall: 11230 | eff(%): 18.326 | loglstar: -inf < -5197.653 < inf | logz: -5243.262 +/- 0.782 | dlogz: 545.679 > 0.059] + + 2088it [00:11, 291.54it/s, bound: 259 | nc: 5 | ncall: 11380 | eff(%): 18.348 | loglstar: -inf < -5085.850 < inf | logz: -5130.117 +/- 0.773 | dlogz: 430.834 > 0.059] + + 2119it [00:11, 294.72it/s, bound: 264 | nc: 5 | ncall: 11535 | eff(%): 18.370 | loglstar: -inf < -4989.770 < inf | logz: -5035.643 +/- 0.780 | dlogz: 436.647 > 0.059] + + 2150it [00:11, 297.19it/s, bound: 268 | nc: 5 | ncall: 11690 | eff(%): 18.392 | loglstar: -inf < -4899.780 < inf | logz: -4946.368 +/- 0.797 | dlogz: 347.254 > 0.059] + + 2180it [00:11, 293.89it/s, bound: 271 | nc: 5 | ncall: 11840 | eff(%): 18.412 | loglstar: -inf < -4800.515 < inf | logz: -4847.357 +/- 0.796 | dlogz: 366.033 > 0.059] + + 2210it [00:11, 283.57it/s, bound: 276 | nc: 5 | ncall: 11990 | eff(%): 18.432 | loglstar: -inf < -4764.792 < inf | logz: -4810.659 +/- 0.785 | dlogz: 328.269 > 0.059] + + 2239it [00:11, 266.87it/s, bound: 279 | nc: 5 | ncall: 12135 | eff(%): 18.451 | loglstar: -inf < -4711.500 < inf | logz: -4761.102 +/- 0.829 | dlogz: 288.766 > 0.059] + + 2268it [00:11, 271.81it/s, bound: 283 | nc: 5 | ncall: 12280 | eff(%): 18.469 | loglstar: -inf < -4657.597 < inf | logz: -4706.908 +/- 0.818 | dlogz: 224.333 > 0.059] + + 2298it [00:11, 279.43it/s, bound: 287 | nc: 5 | ncall: 12430 | eff(%): 18.488 | loglstar: -inf < -4610.278 < inf | logz: -4661.015 +/- 0.838 | dlogz: 265.700 > 0.059] + + 2327it [00:12, 277.68it/s, bound: 292 | nc: 5 | ncall: 12575 | eff(%): 18.505 | loglstar: -inf < -4556.876 < inf | logz: -4606.788 +/- 0.826 | dlogz: 444.007 > 0.059] + + 2356it [00:12, 277.40it/s, bound: 296 | nc: 5 | ncall: 12720 | eff(%): 18.522 | loglstar: -inf < -4505.287 < inf | logz: -4557.231 +/- 0.851 | dlogz: 450.880 > 0.059] + + 2385it [00:12, 280.16it/s, bound: 299 | nc: 5 | ncall: 12865 | eff(%): 18.539 | loglstar: -inf < -4459.479 < inf | logz: -4510.327 +/- 0.834 | dlogz: 712.685 > 0.059] + + 2415it [00:12, 283.98it/s, bound: 303 | nc: 5 | ncall: 13015 | eff(%): 18.556 | loglstar: -inf < -4409.904 < inf | logz: -4461.642 +/- 0.840 | dlogz: 663.608 > 0.059] + + 2445it [00:12, 286.60it/s, bound: 307 | nc: 5 | ncall: 13165 | eff(%): 18.572 | loglstar: -inf < -4316.912 < inf | logz: -4370.394 +/- 0.857 | dlogz: 573.184 > 0.059] + + 2474it [00:12, 284.42it/s, bound: 310 | nc: 5 | ncall: 13310 | eff(%): 18.588 | loglstar: -inf < -4235.231 < inf | logz: -4288.364 +/- 0.856 | dlogz: 502.345 > 0.059] + + 2503it [00:12, 247.76it/s, bound: 314 | nc: 5 | ncall: 13455 | eff(%): 18.603 | loglstar: -inf < -4116.690 < inf | logz: -4169.590 +/- 0.851 | dlogz: 382.330 > 0.059] + + 2529it [00:12, 244.70it/s, bound: 317 | nc: 5 | ncall: 13585 | eff(%): 18.616 | loglstar: -inf < -4058.960 < inf | logz: -4112.301 +/- 0.851 | dlogz: 457.538 > 0.059] + + 2555it [00:12, 240.26it/s, bound: 321 | nc: 5 | ncall: 13715 | eff(%): 18.629 | loglstar: -inf < -3974.989 < inf | logz: -4030.954 +/- 0.882 | dlogz: 386.044 > 0.059] + + 2580it [00:13, 228.71it/s, bound: 324 | nc: 5 | ncall: 13840 | eff(%): 18.642 | loglstar: -inf < -3933.400 < inf | logz: -3988.059 +/- 0.859 | dlogz: 332.297 > 0.059] + + 2604it [00:13, 228.95it/s, bound: 327 | nc: 5 | ncall: 13960 | eff(%): 18.653 | loglstar: -inf < -3903.190 < inf | logz: -3960.114 +/- 0.888 | dlogz: 307.461 > 0.059] + + 2629it [00:13, 230.14it/s, bound: 330 | nc: 5 | ncall: 14085 | eff(%): 18.665 | loglstar: -inf < -3884.056 < inf | logz: -3939.796 +/- 0.874 | dlogz: 288.997 > 0.059] + + 2653it [00:13, 224.40it/s, bound: 333 | nc: 5 | ncall: 14205 | eff(%): 18.677 | loglstar: -inf < -3843.682 < inf | logz: -3899.117 +/- 0.870 | dlogz: 365.637 > 0.059] + + 2677it [00:13, 228.08it/s, bound: 336 | nc: 5 | ncall: 14325 | eff(%): 18.688 | loglstar: -inf < -3798.061 < inf | logz: -3855.180 +/- 0.882 | dlogz: 321.799 > 0.059] + + 2703it [00:13, 234.95it/s, bound: 339 | nc: 5 | ncall: 14455 | eff(%): 18.699 | loglstar: -inf < -3761.573 < inf | logz: -3820.350 +/- 0.898 | dlogz: 287.779 > 0.059] + + 2727it [00:13, 231.11it/s, bound: 342 | nc: 5 | ncall: 14575 | eff(%): 18.710 | loglstar: -inf < -3729.105 < inf | logz: -3788.540 +/- 0.909 | dlogz: 258.064 > 0.059] + + 2751it [00:13, 208.03it/s, bound: 345 | nc: 5 | ncall: 14695 | eff(%): 18.721 | loglstar: -inf < -3691.375 < inf | logz: -3750.877 +/- 0.902 | dlogz: 216.550 > 0.059] + + 2776it [00:13, 216.42it/s, bound: 348 | nc: 5 | ncall: 14820 | eff(%): 18.731 | loglstar: -inf < -3644.681 < inf | logz: -3703.636 +/- 0.902 | dlogz: 311.575 > 0.059] + + 2801it [00:14, 224.84it/s, bound: 352 | nc: 5 | ncall: 14945 | eff(%): 18.742 | loglstar: -inf < -3591.772 < inf | logz: -3651.564 +/- 0.912 | dlogz: 259.446 > 0.059] + + 2827it [00:14, 233.60it/s, bound: 355 | nc: 5 | ncall: 15075 | eff(%): 18.753 | loglstar: -inf < -3548.807 < inf | logz: -3608.553 +/- 0.909 | dlogz: 215.304 > 0.059] + + 2851it [00:14, 233.54it/s, bound: 359 | nc: 5 | ncall: 15195 | eff(%): 18.763 | loglstar: -inf < -3504.303 < inf | logz: -3565.368 +/- 0.921 | dlogz: 259.892 > 0.059] + + 2875it [00:14, 235.29it/s, bound: 362 | nc: 5 | ncall: 15315 | eff(%): 18.772 | loglstar: -inf < -3480.360 < inf | logz: -3540.181 +/- 0.907 | dlogz: 233.170 > 0.059] + + 2899it [00:14, 236.65it/s, bound: 365 | nc: 5 | ncall: 15435 | eff(%): 18.782 | loglstar: -inf < -3440.971 < inf | logz: -3501.378 +/- 0.906 | dlogz: 215.082 > 0.059] + + 2923it [00:14, 224.19it/s, bound: 368 | nc: 5 | ncall: 15555 | eff(%): 18.791 | loglstar: -inf < -3388.419 < inf | logz: -3451.788 +/- 0.943 | dlogz: 169.525 > 0.059] + + 2946it [00:14, 216.71it/s, bound: 372 | nc: 5 | ncall: 15670 | eff(%): 18.800 | loglstar: -inf < -3366.886 < inf | logz: -3429.279 +/- 0.929 | dlogz: 142.514 > 0.059] + + 2969it [00:14, 217.91it/s, bound: 375 | nc: 5 | ncall: 15785 | eff(%): 18.809 | loglstar: -inf < -3340.426 < inf | logz: -3404.417 +/- 0.942 | dlogz: 118.237 > 0.059] + + 2992it [00:14, 220.07it/s, bound: 378 | nc: 5 | ncall: 15900 | eff(%): 18.818 | loglstar: -inf < -3322.582 < inf | logz: -3386.217 +/- 0.944 | dlogz: 98.952 > 0.059] + + 3015it [00:14, 218.97it/s, bound: 381 | nc: 5 | ncall: 16015 | eff(%): 18.826 | loglstar: -inf < -3303.484 < inf | logz: -3367.789 +/- 0.945 | dlogz: 80.005 > 0.059] + + 3037it [00:15, 216.95it/s, bound: 383 | nc: 5 | ncall: 16125 | eff(%): 18.834 | loglstar: -inf < -3291.004 < inf | logz: -3356.147 +/- 0.946 | dlogz: 74.722 > 0.059] + + 3062it [00:15, 223.54it/s, bound: 387 | nc: 5 | ncall: 16250 | eff(%): 18.843 | loglstar: -inf < -3278.511 < inf | logz: -3342.283 +/- 0.942 | dlogz: 61.537 > 0.059] + + 3086it [00:15, 227.30it/s, bound: 390 | nc: 5 | ncall: 16370 | eff(%): 18.852 | loglstar: -inf < -3267.117 < inf | logz: -3333.471 +/- 0.960 | dlogz: 53.455 > 0.059] + + 3111it [00:15, 232.99it/s, bound: 393 | nc: 5 | ncall: 16495 | eff(%): 18.860 | loglstar: -inf < -3252.052 < inf | logz: -3318.751 +/- 0.963 | dlogz: 37.967 > 0.059] + + 3137it [00:15, 238.64it/s, bound: 396 | nc: 5 | ncall: 16625 | eff(%): 18.869 | loglstar: -inf < -3241.167 < inf | logz: -3307.366 +/- 0.954 | dlogz: 60.780 > 0.059] + + 3161it [00:15, 225.76it/s, bound: 399 | nc: 5 | ncall: 16745 | eff(%): 18.877 | loglstar: -inf < -3228.201 < inf | logz: -3293.740 +/- 0.959 | dlogz: 47.071 > 0.059] + + 3185it [00:15, 227.66it/s, bound: 402 | nc: 5 | ncall: 16865 | eff(%): 18.885 | loglstar: -inf < -3221.507 < inf | logz: -3288.603 +/- 0.959 | dlogz: 41.640 > 0.059] + + 3208it [00:15, 225.99it/s, bound: 405 | nc: 5 | ncall: 16980 | eff(%): 18.893 | loglstar: -inf < -3218.473 < inf | logz: -3285.662 +/- 0.958 | dlogz: 38.082 > 0.059] + + 3234it [00:15, 235.10it/s, bound: 408 | nc: 5 | ncall: 17110 | eff(%): 18.901 | loglstar: -inf < -3208.836 < inf | logz: -3276.342 +/- 0.974 | dlogz: 53.717 > 0.059] + + 3258it [00:16, 221.87it/s, bound: 411 | nc: 5 | ncall: 17230 | eff(%): 18.909 | loglstar: -inf < -3198.968 < inf | logz: -3267.567 +/- 0.978 | dlogz: 47.690 > 0.059] + + 3281it [00:16, 216.10it/s, bound: 415 | nc: 5 | ncall: 17345 | eff(%): 18.916 | loglstar: -inf < -3191.602 < inf | logz: -3259.916 +/- 0.982 | dlogz: 43.418 > 0.059] + + 3305it [00:16, 221.80it/s, bound: 418 | nc: 5 | ncall: 17465 | eff(%): 18.924 | loglstar: -inf < -3186.406 < inf | logz: -3254.757 +/- 0.982 | dlogz: 45.126 > 0.059] + + 3328it [00:16, 220.68it/s, bound: 421 | nc: 5 | ncall: 17580 | eff(%): 18.931 | loglstar: -inf < -3180.130 < inf | logz: -3250.980 +/- 0.995 | dlogz: 52.236 > 0.059] + + 3352it [00:16, 222.46it/s, bound: 425 | nc: 5 | ncall: 17700 | eff(%): 18.938 | loglstar: -inf < -3171.766 < inf | logz: -3242.285 +/- 0.995 | dlogz: 42.641 > 0.059] + + 3375it [00:16, 218.59it/s, bound: 428 | nc: 5 | ncall: 17815 | eff(%): 18.945 | loglstar: -inf < -3161.316 < inf | logz: -3232.514 +/- 1.008 | dlogz: 33.032 > 0.059] + + 3398it [00:16, 220.35it/s, bound: 431 | nc: 5 | ncall: 17930 | eff(%): 18.951 | loglstar: -inf < -3155.385 < inf | logz: -3226.563 +/- 1.002 | dlogz: 34.306 > 0.059] + + 3422it [00:16, 225.38it/s, bound: 435 | nc: 5 | ncall: 18050 | eff(%): 18.958 | loglstar: -inf < -3151.227 < inf | logz: -3222.548 +/- 1.001 | dlogz: 29.723 > 0.059] + + 3446it [00:16, 229.25it/s, bound: 438 | nc: 5 | ncall: 18170 | eff(%): 18.965 | loglstar: -inf < -3144.038 < inf | logz: -3216.278 +/- 1.012 | dlogz: 29.213 > 0.059] + + 3469it [00:16, 228.92it/s, bound: 441 | nc: 5 | ncall: 18285 | eff(%): 18.972 | loglstar: -inf < -3140.698 < inf | logz: -3211.973 +/- 1.009 | dlogz: 24.218 > 0.059] + + 3493it [00:17, 230.28it/s, bound: 445 | nc: 5 | ncall: 18405 | eff(%): 18.979 | loglstar: -inf < -3135.273 < inf | logz: -3207.607 +/- 1.018 | dlogz: 20.942 > 0.059] + + 3517it [00:17, 232.18it/s, bound: 448 | nc: 5 | ncall: 18525 | eff(%): 18.985 | loglstar: -inf < -3131.892 < inf | logz: -3203.824 +/- 1.016 | dlogz: 16.593 > 0.059] + + 3542it [00:17, 235.95it/s, bound: 451 | nc: 5 | ncall: 18650 | eff(%): 18.992 | loglstar: -inf < -3125.801 < inf | logz: -3198.731 +/- 1.026 | dlogz: 16.917 > 0.059] + + 3567it [00:17, 237.62it/s, bound: 455 | nc: 5 | ncall: 18775 | eff(%): 18.999 | loglstar: -inf < -3123.639 < inf | logz: -3196.680 +/- 1.023 | dlogz: 14.322 > 0.059] + + 3592it [00:17, 239.88it/s, bound: 458 | nc: 5 | ncall: 18900 | eff(%): 19.005 | loglstar: -inf < -3121.695 < inf | logz: -3194.972 +/- 1.025 | dlogz: 12.083 > 0.059] + + 3616it [00:17, 229.01it/s, bound: 461 | nc: 5 | ncall: 19020 | eff(%): 19.012 | loglstar: -inf < -3119.236 < inf | logz: -3193.290 +/- 1.031 | dlogz: 9.948 > 0.059] + + 3639it [00:17, 224.41it/s, bound: 464 | nc: 5 | ncall: 19135 | eff(%): 19.018 | loglstar: -inf < -3117.744 < inf | logz: -3191.885 +/- 1.033 | dlogz: 8.049 > 0.059] + + 3662it [00:17, 219.18it/s, bound: 467 | nc: 5 | ncall: 19250 | eff(%): 19.023 | loglstar: -inf < -3116.298 < inf | logz: -3190.845 +/- 1.034 | dlogz: 9.367 > 0.059] + + 3684it [00:17, 213.58it/s, bound: 470 | nc: 5 | ncall: 19360 | eff(%): 19.029 | loglstar: -inf < -3115.586 < inf | logz: -3189.995 +/- 1.036 | dlogz: 8.062 > 0.059] + + 3706it [00:18, 212.63it/s, bound: 473 | nc: 5 | ncall: 19470 | eff(%): 19.034 | loglstar: -inf < -3114.786 < inf | logz: -3189.469 +/- 1.037 | dlogz: 7.085 > 0.059] + + 3729it [00:18, 215.48it/s, bound: 476 | nc: 5 | ncall: 19585 | eff(%): 19.040 | loglstar: -inf < -3113.585 < inf | logz: -3188.709 +/- 1.041 | dlogz: 5.863 > 0.059] + + 3752it [00:18, 217.67it/s, bound: 479 | nc: 5 | ncall: 19700 | eff(%): 19.046 | loglstar: -inf < -3112.587 < inf | logz: -3188.192 +/- 1.043 | dlogz: 4.893 > 0.059] + + 3776it [00:18, 222.67it/s, bound: 482 | nc: 5 | ncall: 19820 | eff(%): 19.051 | loglstar: -inf < -3112.001 < inf | logz: -3187.720 +/- 1.045 | dlogz: 3.946 > 0.059] + + 3801it [00:18, 229.20it/s, bound: 486 | nc: 5 | ncall: 19945 | eff(%): 19.057 | loglstar: -inf < -3111.140 < inf | logz: -3187.310 +/- 1.047 | dlogz: 3.850 > 0.059] + + 3826it [00:18, 233.50it/s, bound: 489 | nc: 5 | ncall: 20070 | eff(%): 19.063 | loglstar: -inf < -3110.382 < inf | logz: -3186.971 +/- 1.050 | dlogz: 3.691 > 0.059] + + 3851it [00:18, 236.02it/s, bound: 493 | nc: 5 | ncall: 20195 | eff(%): 19.069 | loglstar: -inf < -3109.558 < inf | logz: -3186.610 +/- 1.053 | dlogz: 137.273 > 0.059] + + 3877it [00:18, 242.74it/s, bound: 496 | nc: 5 | ncall: 20325 | eff(%): 19.075 | loglstar: -inf < -3108.505 < inf | logz: -3186.212 +/- 1.057 | dlogz: 180.588 > 0.059] + + 3902it [00:18, 243.41it/s, bound: 499 | nc: 5 | ncall: 20450 | eff(%): 19.081 | loglstar: -inf < -3107.894 < inf | logz: -3185.828 +/- 1.061 | dlogz: 214.871 > 0.059] + + 3927it [00:18, 240.42it/s, bound: 502 | nc: 5 | ncall: 20575 | eff(%): 19.086 | loglstar: -inf < -3107.409 < inf | logz: -3185.574 +/- 1.063 | dlogz: 262.167 > 0.059] + + 3954it [00:19, 249.04it/s, bound: 506 | nc: 5 | ncall: 20710 | eff(%): 19.092 | loglstar: -inf < -3106.449 < inf | logz: -3185.313 +/- 1.066 | dlogz: 401.968 > 0.059] + + 3979it [00:19, 242.51it/s, bound: 509 | nc: 5 | ncall: 20835 | eff(%): 19.098 | loglstar: -inf < -3089.190 < inf | logz: -3171.230 +/- 1.116 | dlogz: 400.672 > 0.059] + + 4006it [00:19, 249.49it/s, bound: 513 | nc: 5 | ncall: 20970 | eff(%): 19.103 | loglstar: -inf < -3028.774 < inf | logz: -3113.872 +/- 1.142 | dlogz: 511.433 > 0.059] + + 4037it [00:19, 266.87it/s, bound: 518 | nc: 5 | ncall: 21125 | eff(%): 19.110 | loglstar: -inf < -2935.952 < inf | logz: -3021.403 +/- 1.139 | dlogz: 413.664 > 0.059] + + 4066it [00:19, 270.70it/s, bound: 522 | nc: 5 | ncall: 21270 | eff(%): 19.116 | loglstar: -inf < -2804.742 < inf | logz: -2890.069 +/- 1.135 | dlogz: 280.633 > 0.059] + + 4094it [00:19, 259.11it/s, bound: 526 | nc: 5 | ncall: 21410 | eff(%): 19.122 | loglstar: -inf < -2703.924 < inf | logz: -2789.659 +/- 1.144 | dlogz: 179.833 > 0.059] + + 4121it [00:19, 253.93it/s, bound: 530 | nc: 5 | ncall: 21545 | eff(%): 19.127 | loglstar: -inf < -2676.905 < inf | logz: -2763.583 +/- 1.140 | dlogz: 287.628 > 0.059] + + 4147it [00:19, 251.85it/s, bound: 534 | nc: 5 | ncall: 21675 | eff(%): 19.133 | loglstar: -inf < -2654.489 < inf | logz: -2741.413 +/- 1.145 | dlogz: 264.911 > 0.059] + + 4173it [00:19, 247.55it/s, bound: 538 | nc: 5 | ncall: 21805 | eff(%): 19.138 | loglstar: -inf < -2595.726 < inf | logz: -2683.058 +/- 1.153 | dlogz: 206.388 > 0.059] + + 4198it [00:20, 244.69it/s, bound: 541 | nc: 5 | ncall: 21930 | eff(%): 19.143 | loglstar: -inf < -2564.045 < inf | logz: -2652.720 +/- 1.158 | dlogz: 189.504 > 0.059] + + 4223it [00:20, 207.94it/s, bound: 545 | nc: 5 | ncall: 22055 | eff(%): 19.148 | loglstar: -inf < -2528.320 < inf | logz: -2614.630 +/- 1.140 | dlogz: 176.314 > 0.059] + + 4245it [00:20, 187.73it/s, bound: 549 | nc: 5 | ncall: 22165 | eff(%): 19.152 | loglstar: -inf < -2490.456 < inf | logz: -2579.553 +/- 1.161 | dlogz: 189.182 > 0.059] + + 4265it [00:20, 185.33it/s, bound: 551 | nc: 5 | ncall: 22265 | eff(%): 19.156 | loglstar: -inf < -2464.797 < inf | logz: -2554.431 +/- 1.163 | dlogz: 224.042 > 0.059] + + 4285it [00:20, 186.16it/s, bound: 554 | nc: 5 | ncall: 22365 | eff(%): 19.159 | loglstar: -inf < -2446.211 < inf | logz: -2536.298 +/- 1.167 | dlogz: 272.742 > 0.059] + + 4307it [00:20, 192.66it/s, bound: 557 | nc: 5 | ncall: 22475 | eff(%): 19.164 | loglstar: -inf < -2417.344 < inf | logz: -2507.510 +/- 1.169 | dlogz: 243.208 > 0.059] + + 4327it [00:20, 190.78it/s, bound: 560 | nc: 5 | ncall: 22575 | eff(%): 19.167 | loglstar: -inf < -2397.637 < inf | logz: -2488.421 +/- 1.169 | dlogz: 373.970 > 0.059] + + 4347it [00:20, 182.05it/s, bound: 562 | nc: 5 | ncall: 22675 | eff(%): 19.171 | loglstar: -inf < -2358.398 < inf | logz: -2448.698 +/- 1.172 | dlogz: 408.663 > 0.059] + + 4366it [00:21, 174.86it/s, bound: 565 | nc: 5 | ncall: 22770 | eff(%): 19.174 | loglstar: -inf < -2328.265 < inf | logz: -2420.536 +/- 1.186 | dlogz: 382.616 > 0.059] + + 4384it [00:21, 173.72it/s, bound: 567 | nc: 5 | ncall: 22860 | eff(%): 19.178 | loglstar: -inf < -2301.027 < inf | logz: -2392.674 +/- 1.180 | dlogz: 352.430 > 0.059] + + 4404it [00:21, 176.69it/s, bound: 570 | nc: 5 | ncall: 22960 | eff(%): 19.181 | loglstar: -inf < -2245.881 < inf | logz: -2336.707 +/- 1.174 | dlogz: 497.115 > 0.059] + + 4422it [00:21, 177.17it/s, bound: 572 | nc: 5 | ncall: 23050 | eff(%): 19.184 | loglstar: -inf < -2199.901 < inf | logz: -2292.184 +/- 1.185 | dlogz: 452.960 > 0.059] + + 4443it [00:21, 184.65it/s, bound: 575 | nc: 5 | ncall: 23155 | eff(%): 19.188 | loglstar: -inf < -2165.182 < inf | logz: -2257.935 +/- 1.189 | dlogz: 541.031 > 0.059] + + 4462it [00:21, 180.09it/s, bound: 577 | nc: 5 | ncall: 23250 | eff(%): 19.191 | loglstar: -inf < -2084.769 < inf | logz: -2179.031 +/- 1.201 | dlogz: 505.635 > 0.059] + + 4481it [00:21, 182.82it/s, bound: 580 | nc: 5 | ncall: 23345 | eff(%): 19.195 | loglstar: -inf < -2009.045 < inf | logz: -2103.452 +/- 1.196 | dlogz: 456.785 > 0.059] + + 4502it [00:21, 188.70it/s, bound: 583 | nc: 5 | ncall: 23450 | eff(%): 19.198 | loglstar: -inf < -1930.708 < inf | logz: -2024.381 +/- 1.194 | dlogz: 376.102 > 0.059] + + 4521it [00:21, 188.81it/s, bound: 585 | nc: 5 | ncall: 23545 | eff(%): 19.202 | loglstar: -inf < -1862.088 < inf | logz: -1955.832 +/- 1.194 | dlogz: 306.935 > 0.059] + + 4543it [00:21, 195.88it/s, bound: 588 | nc: 5 | ncall: 23655 | eff(%): 19.205 | loglstar: -inf < -1794.314 < inf | logz: -1889.577 +/- 1.200 | dlogz: 240.798 > 0.059] + + 4563it [00:22, 192.88it/s, bound: 591 | nc: 5 | ncall: 23755 | eff(%): 19.209 | loglstar: -inf < -1717.877 < inf | logz: -1814.146 +/- 1.213 | dlogz: 379.909 > 0.059] + + 4584it [00:22, 196.54it/s, bound: 593 | nc: 5 | ncall: 23860 | eff(%): 19.212 | loglstar: -inf < -1669.403 < inf | logz: -1765.933 +/- 1.210 | dlogz: 349.557 > 0.059] + + 4604it [00:22, 196.63it/s, bound: 596 | nc: 5 | ncall: 23960 | eff(%): 19.215 | loglstar: -inf < -1629.887 < inf | logz: -1724.199 +/- 1.192 | dlogz: 407.633 > 0.059] + + 4624it [00:22, 195.67it/s, bound: 599 | nc: 5 | ncall: 24060 | eff(%): 19.219 | loglstar: -inf < -1590.358 < inf | logz: -1686.571 +/- 1.208 | dlogz: 370.273 > 0.059] + + 4644it [00:22, 189.72it/s, bound: 602 | nc: 5 | ncall: 24160 | eff(%): 19.222 | loglstar: -inf < -1553.892 < inf | logz: -1651.558 +/- 1.218 | dlogz: 336.168 > 0.059] + + 4664it [00:22, 192.35it/s, bound: 604 | nc: 5 | ncall: 24260 | eff(%): 19.225 | loglstar: -inf < -1504.288 < inf | logz: -1601.485 +/- 1.217 | dlogz: 284.677 > 0.059] + + 4684it [00:22, 192.45it/s, bound: 607 | nc: 5 | ncall: 24360 | eff(%): 19.228 | loglstar: -inf < -1462.571 < inf | logz: -1560.022 +/- 1.214 | dlogz: 242.562 > 0.059] + + 4705it [00:22, 196.34it/s, bound: 609 | nc: 5 | ncall: 24465 | eff(%): 19.232 | loglstar: -inf < -1427.355 < inf | logz: -1526.389 +/- 1.226 | dlogz: 338.098 > 0.059] + + 4725it [00:22, 186.75it/s, bound: 612 | nc: 5 | ncall: 24565 | eff(%): 19.235 | loglstar: -inf < -1380.576 < inf | logz: -1479.819 +/- 1.226 | dlogz: 290.639 > 0.059] + + 4745it [00:23, 189.26it/s, bound: 614 | nc: 5 | ncall: 24665 | eff(%): 19.238 | loglstar: -inf < -1348.555 < inf | logz: -1447.005 +/- 1.219 | dlogz: 256.020 > 0.059] + + 4765it [00:23, 188.60it/s, bound: 617 | nc: 5 | ncall: 24765 | eff(%): 19.241 | loglstar: -inf < -1321.917 < inf | logz: -1420.266 +/- 1.221 | dlogz: 228.608 > 0.059] + + 4784it [00:23, 187.02it/s, bound: 620 | nc: 5 | ncall: 24860 | eff(%): 19.244 | loglstar: -inf < -1294.259 < inf | logz: -1393.464 +/- 1.226 | dlogz: 247.838 > 0.059] + + 4803it [00:23, 177.15it/s, bound: 622 | nc: 5 | ncall: 24955 | eff(%): 19.247 | loglstar: -inf < -1266.353 < inf | logz: -1366.339 +/- 1.233 | dlogz: 220.836 > 0.059] + + 4821it [00:23, 170.98it/s, bound: 624 | nc: 5 | ncall: 25045 | eff(%): 19.249 | loglstar: -inf < -1234.459 < inf | logz: -1335.287 +/- 1.234 | dlogz: 189.475 > 0.059] + + 4839it [00:23, 172.44it/s, bound: 626 | nc: 5 | ncall: 25135 | eff(%): 19.252 | loglstar: -inf < -1223.327 < inf | logz: -1322.906 +/- 1.220 | dlogz: 175.745 > 0.059] + + 4858it [00:23, 176.89it/s, bound: 629 | nc: 5 | ncall: 25230 | eff(%): 19.255 | loglstar: -inf < -1195.042 < inf | logz: -1295.463 +/- 1.233 | dlogz: 274.986 > 0.059] + + 4879it [00:23, 183.43it/s, bound: 632 | nc: 5 | ncall: 25335 | eff(%): 19.258 | loglstar: -inf < -1158.544 < inf | logz: -1261.169 +/- 1.251 | dlogz: 332.831 > 0.059] + + 4898it [00:23, 185.13it/s, bound: 634 | nc: 5 | ncall: 25430 | eff(%): 19.261 | loglstar: -inf < -1139.580 < inf | logz: -1239.890 +/- 1.231 | dlogz: 306.826 > 0.059] + + 4917it [00:23, 169.07it/s, bound: 637 | nc: 5 | ncall: 25525 | eff(%): 19.263 | loglstar: -inf < -1111.420 < inf | logz: -1214.704 +/- 1.254 | dlogz: 313.410 > 0.059] + + 4935it [00:24, 166.44it/s, bound: 639 | nc: 5 | ncall: 25615 | eff(%): 19.266 | loglstar: -inf < -1087.309 < inf | logz: -1190.842 +/- 1.252 | dlogz: 288.249 > 0.059] + + 4954it [00:24, 170.86it/s, bound: 642 | nc: 5 | ncall: 25710 | eff(%): 19.269 | loglstar: -inf < -1077.323 < inf | logz: -1179.547 +/- 1.243 | dlogz: 275.037 > 0.059] + + 4972it [00:24, 169.31it/s, bound: 644 | nc: 5 | ncall: 25800 | eff(%): 19.271 | loglstar: -inf < -1047.803 < inf | logz: -1151.825 +/- 1.253 | dlogz: 247.711 > 0.059] + + 4992it [00:24, 175.23it/s, bound: 647 | nc: 5 | ncall: 25900 | eff(%): 19.274 | loglstar: -inf < -1015.339 < inf | logz: -1119.214 +/- 1.258 | dlogz: 214.694 > 0.059] + + 5010it [00:24, 175.57it/s, bound: 649 | nc: 5 | ncall: 25990 | eff(%): 19.277 | loglstar: -inf < -982.561 < inf | logz: -1087.229 +/- 1.260 | dlogz: 182.691 > 0.059] + + 5029it [00:24, 177.84it/s, bound: 653 | nc: 5 | ncall: 26085 | eff(%): 19.279 | loglstar: -inf < -942.331 < inf | logz: -1047.945 +/- 1.269 | dlogz: 240.876 > 0.059] + + 5051it [00:24, 188.59it/s, bound: 655 | nc: 5 | ncall: 26195 | eff(%): 19.282 | loglstar: -inf < -922.753 < inf | logz: -1027.475 +/- 1.257 | dlogz: 216.980 > 0.059] + + 5074it [00:24, 199.89it/s, bound: 658 | nc: 5 | ncall: 26310 | eff(%): 19.285 | loglstar: -inf < -890.187 < inf | logz: -996.023 +/- 1.265 | dlogz: 185.567 > 0.059] + + 5095it [00:24, 198.38it/s, bound: 661 | nc: 5 | ncall: 26415 | eff(%): 19.288 | loglstar: -inf < -860.389 < inf | logz: -964.609 +/- 1.255 | dlogz: 152.874 > 0.059] + + 5119it [00:25, 208.93it/s, bound: 664 | nc: 5 | ncall: 26535 | eff(%): 19.292 | loglstar: -inf < -834.812 < inf | logz: -940.638 +/- 1.263 | dlogz: 128.693 > 0.059] + + 5141it [00:25, 210.00it/s, bound: 667 | nc: 5 | ncall: 26645 | eff(%): 19.294 | loglstar: -inf < -816.368 < inf | logz: -922.785 +/- 1.272 | dlogz: 117.135 > 0.059] + + 5163it [00:25, 210.22it/s, bound: 670 | nc: 5 | ncall: 26755 | eff(%): 19.297 | loglstar: -inf < -793.441 < inf | logz: -901.470 +/- 1.280 | dlogz: 152.407 > 0.059] + + 5185it [00:25, 205.98it/s, bound: 673 | nc: 5 | ncall: 26865 | eff(%): 19.300 | loglstar: -inf < -778.968 < inf | logz: -886.112 +/- 1.273 | dlogz: 151.699 > 0.059] + + 5206it [00:25, 207.08it/s, bound: 677 | nc: 5 | ncall: 26970 | eff(%): 19.303 | loglstar: -inf < -757.569 < inf | logz: -866.255 +/- 1.285 | dlogz: 132.610 > 0.059] + + 5227it [00:25, 204.77it/s, bound: 680 | nc: 5 | ncall: 27075 | eff(%): 19.306 | loglstar: -inf < -735.428 < inf | logz: -842.552 +/- 1.275 | dlogz: 192.294 > 0.059] + + 5248it [00:25, 200.99it/s, bound: 682 | nc: 5 | ncall: 27180 | eff(%): 19.308 | loglstar: -inf < -709.536 < inf | logz: -819.026 +/- 1.288 | dlogz: 169.466 > 0.059] + + 5269it [00:25, 200.85it/s, bound: 685 | nc: 5 | ncall: 27285 | eff(%): 19.311 | loglstar: -inf < -688.247 < inf | logz: -797.605 +/- 1.292 | dlogz: 147.363 > 0.059] + + 5290it [00:25, 198.89it/s, bound: 688 | nc: 5 | ncall: 27390 | eff(%): 19.314 | loglstar: -inf < -671.449 < inf | logz: -781.591 +/- 1.288 | dlogz: 130.684 > 0.059] + + 5311it [00:25, 199.61it/s, bound: 690 | nc: 5 | ncall: 27495 | eff(%): 19.316 | loglstar: -inf < -656.615 < inf | logz: -766.339 +/- 1.288 | dlogz: 114.740 > 0.059] + + 5331it [00:26, 192.01it/s, bound: 693 | nc: 5 | ncall: 27595 | eff(%): 19.319 | loglstar: -inf < -642.213 < inf | logz: -752.912 +/- 1.295 | dlogz: 101.231 > 0.059] + + 5352it [00:26, 194.77it/s, bound: 696 | nc: 5 | ncall: 27700 | eff(%): 19.321 | loglstar: -inf < -627.427 < inf | logz: -738.732 +/- 1.295 | dlogz: 133.584 > 0.059] + + 5372it [00:26, 189.59it/s, bound: 698 | nc: 5 | ncall: 27800 | eff(%): 19.324 | loglstar: -inf < -609.015 < inf | logz: -720.284 +/- 1.302 | dlogz: 114.804 > 0.059] + + 5392it [00:26, 190.97it/s, bound: 701 | nc: 5 | ncall: 27900 | eff(%): 19.326 | loglstar: -inf < -601.285 < inf | logz: -712.229 +/- 1.292 | dlogz: 151.299 > 0.059] + + 5412it [00:26, 188.95it/s, bound: 704 | nc: 5 | ncall: 28000 | eff(%): 19.329 | loglstar: -inf < -593.134 < inf | logz: -704.499 +/- 1.302 | dlogz: 143.205 > 0.059] + + 5433it [00:26, 193.67it/s, bound: 707 | nc: 5 | ncall: 28105 | eff(%): 19.331 | loglstar: -inf < -582.559 < inf | logz: -693.887 +/- 1.302 | dlogz: 134.669 > 0.059] + + 5455it [00:26, 199.87it/s, bound: 710 | nc: 5 | ncall: 28215 | eff(%): 19.334 | loglstar: -inf < -574.027 < inf | logz: -685.884 +/- 1.306 | dlogz: 126.871 > 0.059] + + 5477it [00:26, 203.79it/s, bound: 713 | nc: 5 | ncall: 28325 | eff(%): 19.336 | loglstar: -inf < -560.920 < inf | logz: -673.836 +/- 1.309 | dlogz: 159.789 > 0.059] + + 5498it [00:26, 201.14it/s, bound: 717 | nc: 5 | ncall: 28430 | eff(%): 19.339 | loglstar: -inf < -532.500 < inf | logz: -645.145 +/- 1.312 | dlogz: 130.443 > 0.059] + + 5519it [00:27, 202.86it/s, bound: 719 | nc: 5 | ncall: 28535 | eff(%): 19.341 | loglstar: -inf < -511.752 < inf | logz: -625.603 +/- 1.321 | dlogz: 157.373 > 0.059] + + 5541it [00:27, 205.72it/s, bound: 722 | nc: 5 | ncall: 28645 | eff(%): 19.344 | loglstar: -inf < -491.757 < inf | logz: -606.488 +/- 1.324 | dlogz: 147.342 > 0.059] + + 5562it [00:27, 199.37it/s, bound: 725 | nc: 5 | ncall: 28750 | eff(%): 19.346 | loglstar: -inf < -469.098 < inf | logz: -584.013 +/- 1.327 | dlogz: 147.191 > 0.059] + + 5582it [00:27, 195.81it/s, bound: 727 | nc: 5 | ncall: 28850 | eff(%): 19.348 | loglstar: -inf < -450.258 < inf | logz: -566.810 +/- 1.336 | dlogz: 155.611 > 0.059] + + 5602it [00:27, 193.33it/s, bound: 730 | nc: 5 | ncall: 28950 | eff(%): 19.351 | loglstar: -inf < -434.797 < inf | logz: -551.605 +/- 1.336 | dlogz: 139.631 > 0.059] + + 5622it [00:27, 193.89it/s, bound: 732 | nc: 5 | ncall: 29050 | eff(%): 19.353 | loglstar: -inf < -419.318 < inf | logz: -536.363 +/- 1.338 | dlogz: 132.572 > 0.059] + + 5645it [00:27, 204.10it/s, bound: 735 | nc: 5 | ncall: 29165 | eff(%): 19.355 | loglstar: -inf < -397.858 < inf | logz: -514.214 +/- 1.336 | dlogz: 108.934 > 0.059] + + 5667it [00:27, 208.54it/s, bound: 738 | nc: 5 | ncall: 29275 | eff(%): 19.358 | loglstar: -inf < -382.623 < inf | logz: -500.059 +/- 1.337 | dlogz: 137.147 > 0.059] + + 5688it [00:27, 207.18it/s, bound: 741 | nc: 5 | ncall: 29380 | eff(%): 19.360 | loglstar: -inf < -364.064 < inf | logz: -482.779 +/- 1.350 | dlogz: 129.827 > 0.059] + + 5709it [00:28, 188.87it/s, bound: 744 | nc: 5 | ncall: 29485 | eff(%): 19.362 | loglstar: -inf < -345.731 < inf | logz: -463.445 +/- 1.343 | dlogz: 108.256 > 0.059] + + 5729it [00:28, 186.35it/s, bound: 746 | nc: 5 | ncall: 29585 | eff(%): 19.365 | loglstar: -inf < -326.340 < inf | logz: -444.575 +/- 1.347 | dlogz: 89.175 > 0.059] + + 5749it [00:28, 188.66it/s, bound: 749 | nc: 5 | ncall: 29685 | eff(%): 19.367 | loglstar: -inf < -316.235 < inf | logz: -434.091 +/- 1.342 | dlogz: 107.706 > 0.059] + + 5769it [00:28, 185.96it/s, bound: 752 | nc: 5 | ncall: 29785 | eff(%): 19.369 | loglstar: -inf < -296.988 < inf | logz: -416.301 +/- 1.351 | dlogz: 133.948 > 0.059] + + 5790it [00:28, 191.54it/s, bound: 754 | nc: 5 | ncall: 29890 | eff(%): 19.371 | loglstar: -inf < -280.015 < inf | logz: -398.863 +/- 1.348 | dlogz: 115.741 > 0.059] + + 5810it [00:28, 191.80it/s, bound: 757 | nc: 5 | ncall: 29990 | eff(%): 19.373 | loglstar: -inf < -254.450 < inf | logz: -375.261 +/- 1.359 | dlogz: 113.409 > 0.059] + + 5831it [00:28, 195.95it/s, bound: 759 | nc: 5 | ncall: 30095 | eff(%): 19.375 | loglstar: -inf < -240.922 < inf | logz: -360.935 +/- 1.356 | dlogz: 97.927 > 0.059] + + 5851it [00:28, 195.62it/s, bound: 762 | nc: 5 | ncall: 30195 | eff(%): 19.377 | loglstar: -inf < -226.959 < inf | logz: -347.459 +/- 1.360 | dlogz: 115.360 > 0.059] + + 5872it [00:28, 197.87it/s, bound: 765 | nc: 5 | ncall: 30300 | eff(%): 19.380 | loglstar: -inf < -211.126 < inf | logz: -331.402 +/- 1.360 | dlogz: 98.639 > 0.059] + + 5892it [00:28, 191.35it/s, bound: 767 | nc: 5 | ncall: 30400 | eff(%): 19.382 | loglstar: -inf < -208.113 < inf | logz: -328.995 +/- 1.353 | dlogz: 95.850 > 0.059] + + 5913it [00:29, 195.36it/s, bound: 770 | nc: 5 | ncall: 30505 | eff(%): 19.384 | loglstar: -inf < -188.645 < inf | logz: -310.444 +/- 1.366 | dlogz: 83.967 > 0.059] + + 5936it [00:29, 202.71it/s, bound: 773 | nc: 5 | ncall: 30620 | eff(%): 19.386 | loglstar: -inf < -181.932 < inf | logz: -303.491 +/- 1.364 | dlogz: 76.442 > 0.059] + + 5957it [00:29, 198.91it/s, bound: 776 | nc: 5 | ncall: 30725 | eff(%): 19.388 | loglstar: -inf < -168.216 < inf | logz: -291.719 +/- 1.374 | dlogz: 65.578 > 0.059] + + 5979it [00:29, 204.09it/s, bound: 779 | nc: 5 | ncall: 30835 | eff(%): 19.390 | loglstar: -inf < -157.946 < inf | logz: -282.255 +/- 1.380 | dlogz: 98.802 > 0.059] + + 6001it [00:29, 206.32it/s, bound: 782 | nc: 5 | ncall: 30945 | eff(%): 19.392 | loglstar: -inf < -149.472 < inf | logz: -272.369 +/- 1.374 | dlogz: 87.494 > 0.059] + + 6022it [00:29, 202.16it/s, bound: 785 | nc: 5 | ncall: 31050 | eff(%): 19.395 | loglstar: -inf < -142.910 < inf | logz: -266.626 +/- 1.375 | dlogz: 107.377 > 0.059] + + 6044it [00:29, 206.25it/s, bound: 788 | nc: 5 | ncall: 31160 | eff(%): 19.397 | loglstar: -inf < -134.279 < inf | logz: -257.319 +/- 1.375 | dlogz: 97.464 > 0.059] + + 6065it [00:29, 205.61it/s, bound: 791 | nc: 5 | ncall: 31265 | eff(%): 19.399 | loglstar: -inf < -120.829 < inf | logz: -246.435 +/- 1.391 | dlogz: 87.072 > 0.059] + + 6086it [00:29, 204.88it/s, bound: 794 | nc: 5 | ncall: 31370 | eff(%): 19.401 | loglstar: -inf < -104.454 < inf | logz: -230.058 +/- 1.388 | dlogz: 69.687 > 0.059] + + 6107it [00:30, 203.61it/s, bound: 796 | nc: 5 | ncall: 31475 | eff(%): 19.403 | loglstar: -inf < -95.193 < inf | logz: -220.601 +/- 1.389 | dlogz: 59.713 > 0.059] + + 6128it [00:30, 200.83it/s, bound: 800 | nc: 5 | ncall: 31580 | eff(%): 19.405 | loglstar: -inf < -87.232 < inf | logz: -212.385 +/- 1.390 | dlogz: 91.878 > 0.059] + + 6149it [00:30, 190.13it/s, bound: 803 | nc: 5 | ncall: 31685 | eff(%): 19.407 | loglstar: -inf < -75.430 < inf | logz: -203.226 +/- 1.404 | dlogz: 83.560 > 0.059] + + 6169it [00:30, 188.15it/s, bound: 805 | nc: 5 | ncall: 31785 | eff(%): 19.409 | loglstar: -inf < -66.947 < inf | logz: -193.211 +/- 1.395 | dlogz: 71.932 > 0.059] + + 6189it [00:30, 190.21it/s, bound: 808 | nc: 5 | ncall: 31885 | eff(%): 19.410 | loglstar: -inf < -59.118 < inf | logz: -186.989 +/- 1.400 | dlogz: 73.215 > 0.059] + + 6209it [00:30, 176.71it/s, bound: 812 | nc: 5 | ncall: 31985 | eff(%): 19.412 | loglstar: -inf < -51.009 < inf | logz: -177.749 +/- 1.399 | dlogz: 63.242 > 0.059] + + 6231it [00:30, 186.39it/s, bound: 815 | nc: 5 | ncall: 32095 | eff(%): 19.414 | loglstar: -inf < -42.764 < inf | logz: -169.966 +/- 1.404 | dlogz: 66.641 > 0.059] + + 6253it [00:30, 194.78it/s, bound: 817 | nc: 5 | ncall: 32205 | eff(%): 19.416 | loglstar: -inf < -32.884 < inf | logz: -161.935 +/- 1.412 | dlogz: 58.689 > 0.059] + + 6274it [00:30, 197.98it/s, bound: 820 | nc: 5 | ncall: 32310 | eff(%): 19.418 | loglstar: -inf < -23.739 < inf | logz: -153.127 +/- 1.413 | dlogz: 49.270 > 0.059] + + 6296it [00:31, 203.59it/s, bound: 823 | nc: 5 | ncall: 32420 | eff(%): 19.420 | loglstar: -inf < -17.428 < inf | logz: -147.226 +/- 1.412 | dlogz: 42.878 > 0.059] + + 6317it [00:31, 203.09it/s, bound: 826 | nc: 5 | ncall: 32525 | eff(%): 19.422 | loglstar: -inf < -7.229 < inf | logz: -136.509 +/- 1.417 | dlogz: 32.571 > 0.059] + + 6338it [00:31, 201.78it/s, bound: 828 | nc: 5 | ncall: 32630 | eff(%): 19.424 | loglstar: -inf < -2.005 < inf | logz: -131.988 +/- 1.417 | dlogz: 29.397 > 0.059] + + 6359it [00:31, 201.75it/s, bound: 831 | nc: 5 | ncall: 32735 | eff(%): 19.426 | loglstar: -inf < 1.861 < inf | logz: -128.496 +/- 1.418 | dlogz: 35.712 > 0.059] + + 6380it [00:31, 203.51it/s, bound: 833 | nc: 5 | ncall: 32840 | eff(%): 19.428 | loglstar: -inf < 8.200 < inf | logz: -122.959 +/- 1.424 | dlogz: 31.874 > 0.059] + + 6402it [00:31, 205.76it/s, bound: 836 | nc: 5 | ncall: 32950 | eff(%): 19.429 | loglstar: -inf < 11.893 < inf | logz: -119.006 +/- 1.419 | dlogz: 37.513 > 0.059] + + 6423it [00:31, 199.62it/s, bound: 839 | nc: 5 | ncall: 33055 | eff(%): 19.431 | loglstar: -inf < 15.607 < inf | logz: -115.545 +/- 1.424 | dlogz: 43.839 > 0.059] + + 6444it [00:31, 201.83it/s, bound: 842 | nc: 5 | ncall: 33160 | eff(%): 19.433 | loglstar: -inf < 21.194 < inf | logz: -111.096 +/- 1.431 | dlogz: 39.186 > 0.059] + + 6466it [00:31, 206.94it/s, bound: 844 | nc: 5 | ncall: 33270 | eff(%): 19.435 | loglstar: -inf < 26.036 < inf | logz: -105.937 +/- 1.431 | dlogz: 33.380 > 0.059] + + 6487it [00:31, 206.96it/s, bound: 847 | nc: 5 | ncall: 33375 | eff(%): 19.437 | loglstar: -inf < 33.854 < inf | logz: -99.073 +/- 1.438 | dlogz: 34.651 > 0.059] + + 6509it [00:32, 209.51it/s, bound: 850 | nc: 5 | ncall: 33485 | eff(%): 19.439 | loglstar: -inf < 39.978 < inf | logz: -92.981 +/- 1.440 | dlogz: 35.337 > 0.059] + + 6532it [00:32, 215.23it/s, bound: 853 | nc: 5 | ncall: 33600 | eff(%): 19.440 | loglstar: -inf < 45.423 < inf | logz: -88.198 +/- 1.443 | dlogz: 31.148 > 0.059] + + 6554it [00:32, 208.95it/s, bound: 856 | nc: 5 | ncall: 33710 | eff(%): 19.442 | loglstar: -inf < 48.905 < inf | logz: -84.626 +/- 1.440 | dlogz: 29.968 > 0.059] + + 6577it [00:32, 214.90it/s, bound: 858 | nc: 5 | ncall: 33825 | eff(%): 19.444 | loglstar: -inf < 54.140 < inf | logz: -80.586 +/- 1.448 | dlogz: 35.359 > 0.059] + + 6599it [00:32, 211.08it/s, bound: 861 | nc: 5 | ncall: 33935 | eff(%): 19.446 | loglstar: -inf < 58.880 < inf | logz: -75.889 +/- 1.450 | dlogz: 30.152 > 0.059] + + 6621it [00:32, 209.28it/s, bound: 864 | nc: 5 | ncall: 34045 | eff(%): 19.448 | loglstar: -inf < 62.936 < inf | logz: -72.279 +/- 1.452 | dlogz: 26.090 > 0.059] + + 6642it [00:32, 202.11it/s, bound: 867 | nc: 5 | ncall: 34150 | eff(%): 19.449 | loglstar: -inf < 67.089 < inf | logz: -68.622 +/- 1.455 | dlogz: 31.986 > 0.059] + + 6664it [00:32, 205.87it/s, bound: 870 | nc: 5 | ncall: 34260 | eff(%): 19.451 | loglstar: -inf < 72.067 < inf | logz: -64.768 +/- 1.459 | dlogz: 27.857 > 0.059] + + 6686it [00:32, 207.65it/s, bound: 873 | nc: 5 | ncall: 34370 | eff(%): 19.453 | loglstar: -inf < 74.062 < inf | logz: -61.687 +/- 1.458 | dlogz: 24.074 > 0.059] + + 6707it [00:32, 206.02it/s, bound: 876 | nc: 5 | ncall: 34475 | eff(%): 19.455 | loglstar: -inf < 77.417 < inf | logz: -59.009 +/- 1.461 | dlogz: 21.008 > 0.059] + + 6728it [00:33, 190.66it/s, bound: 879 | nc: 5 | ncall: 34580 | eff(%): 19.456 | loglstar: -inf < 80.386 < inf | logz: -56.140 +/- 1.463 | dlogz: 17.682 > 0.059] + + 6748it [00:33, 191.22it/s, bound: 881 | nc: 5 | ncall: 34680 | eff(%): 19.458 | loglstar: -inf < 82.976 < inf | logz: -54.602 +/- 1.464 | dlogz: 20.369 > 0.059] + + 6768it [00:33, 190.37it/s, bound: 885 | nc: 5 | ncall: 34780 | eff(%): 19.459 | loglstar: -inf < 87.917 < inf | logz: -50.536 +/- 1.473 | dlogz: 20.620 > 0.059] + + 6790it [00:33, 197.11it/s, bound: 888 | nc: 5 | ncall: 34890 | eff(%): 19.461 | loglstar: -inf < 90.094 < inf | logz: -47.914 +/- 1.471 | dlogz: 20.682 > 0.059] + + 6812it [00:33, 201.59it/s, bound: 891 | nc: 5 | ncall: 35000 | eff(%): 19.463 | loglstar: -inf < 93.103 < inf | logz: -45.626 +/- 1.475 | dlogz: 17.999 > 0.059] + + 6833it [00:33, 197.58it/s, bound: 894 | nc: 5 | ncall: 35105 | eff(%): 19.464 | loglstar: -inf < 96.306 < inf | logz: -42.407 +/- 1.479 | dlogz: 14.301 > 0.059] + + 6854it [00:33, 198.90it/s, bound: 897 | nc: 5 | ncall: 35210 | eff(%): 19.466 | loglstar: -inf < 101.004 < inf | logz: -39.057 +/- 1.484 | dlogz: 14.613 > 0.059] + + 6874it [00:33, 198.72it/s, bound: 899 | nc: 5 | ncall: 35310 | eff(%): 19.468 | loglstar: -inf < 102.224 < inf | logz: -36.830 +/- 1.482 | dlogz: 13.025 > 0.059] + + 6895it [00:33, 196.53it/s, bound: 902 | nc: 5 | ncall: 35415 | eff(%): 19.469 | loglstar: -inf < 105.375 < inf | logz: -34.822 +/- 1.484 | dlogz: 10.648 > 0.059] + + 6915it [00:34, 190.06it/s, bound: 905 | nc: 5 | ncall: 35515 | eff(%): 19.471 | loglstar: -inf < 106.720 < inf | logz: -33.407 +/- 1.484 | dlogz: 10.684 > 0.059] + + 6935it [00:34, 175.97it/s, bound: 907 | nc: 5 | ncall: 35615 | eff(%): 19.472 | loglstar: -inf < 108.175 < inf | logz: -32.152 +/- 1.486 | dlogz: 9.009 > 0.059] + + 6954it [00:34, 177.83it/s, bound: 909 | nc: 5 | ncall: 35710 | eff(%): 19.474 | loglstar: -inf < 108.880 < inf | logz: -31.331 +/- 1.486 | dlogz: 10.241 > 0.059] + + 6974it [00:34, 182.37it/s, bound: 912 | nc: 5 | ncall: 35810 | eff(%): 19.475 | loglstar: -inf < 110.361 < inf | logz: -30.617 +/- 1.487 | dlogz: 9.145 > 0.059] + + 6994it [00:34, 185.79it/s, bound: 915 | nc: 5 | ncall: 35910 | eff(%): 19.476 | loglstar: -inf < 112.202 < inf | logz: -29.377 +/- 1.493 | dlogz: 7.514 > 0.059] + + 7015it [00:34, 191.58it/s, bound: 917 | nc: 5 | ncall: 36015 | eff(%): 19.478 | loglstar: -inf < 113.573 < inf | logz: -28.276 +/- 1.495 | dlogz: 5.987 > 0.059] + + 7036it [00:34, 195.20it/s, bound: 920 | nc: 5 | ncall: 36120 | eff(%): 19.480 | loglstar: -inf < 114.236 < inf | logz: -27.523 +/- 1.495 | dlogz: 5.312 > 0.059] + + 7056it [00:34, 195.37it/s, bound: 923 | nc: 5 | ncall: 36220 | eff(%): 19.481 | loglstar: -inf < 114.818 < inf | logz: -27.045 +/- 1.496 | dlogz: 4.750 > 0.059] + + 7078it [00:34, 202.54it/s, bound: 925 | nc: 5 | ncall: 36330 | eff(%): 19.483 | loglstar: -inf < 115.740 < inf | logz: -26.594 +/- 1.497 | dlogz: 5.688 > 0.059] + + 7099it [00:35, 204.72it/s, bound: 928 | nc: 5 | ncall: 36435 | eff(%): 19.484 | loglstar: -inf < 116.330 < inf | logz: -26.170 +/- 1.498 | dlogz: 5.619 > 0.059] + + 7120it [00:35, 204.46it/s, bound: 931 | nc: 5 | ncall: 36540 | eff(%): 19.485 | loglstar: -inf < 117.045 < inf | logz: -25.799 +/- 1.500 | dlogz: 4.824 > 0.059] + + 7142it [00:35, 206.87it/s, bound: 933 | nc: 5 | ncall: 36650 | eff(%): 19.487 | loglstar: -inf < 117.506 < inf | logz: -25.487 +/- 1.501 | dlogz: 4.117 > 0.059] + + 7163it [00:35, 204.60it/s, bound: 936 | nc: 5 | ncall: 36755 | eff(%): 19.489 | loglstar: -inf < 118.094 < inf | logz: -25.230 +/- 1.502 | dlogz: 3.455 > 0.059] + + 7185it [00:35, 207.08it/s, bound: 939 | nc: 5 | ncall: 36865 | eff(%): 19.490 | loglstar: -inf < 118.597 < inf | logz: -25.010 +/- 1.503 | dlogz: 3.167 > 0.059] + + 7206it [00:35, 207.52it/s, bound: 942 | nc: 5 | ncall: 36970 | eff(%): 19.491 | loglstar: -inf < 119.511 < inf | logz: -24.777 +/- 1.505 | dlogz: 2.555 > 0.059] + + 7227it [00:35, 203.84it/s, bound: 944 | nc: 5 | ncall: 37075 | eff(%): 19.493 | loglstar: -inf < 120.003 < inf | logz: -24.550 +/- 1.507 | dlogz: 2.373 > 0.059] + + 7249it [00:35, 207.05it/s, bound: 947 | nc: 5 | ncall: 37185 | eff(%): 19.494 | loglstar: -inf < 120.543 < inf | logz: -24.336 +/- 1.508 | dlogz: 3.048 > 0.059] + + 7270it [00:35, 203.42it/s, bound: 950 | nc: 5 | ncall: 37290 | eff(%): 19.496 | loglstar: -inf < 121.164 < inf | logz: -24.127 +/- 1.510 | dlogz: 2.462 > 0.059] + + 7292it [00:35, 207.87it/s, bound: 952 | nc: 5 | ncall: 37400 | eff(%): 19.497 | loglstar: -inf < 121.489 < inf | logz: -23.951 +/- 1.512 | dlogz: 2.408 > 0.059] + + 7315it [00:36, 213.78it/s, bound: 955 | nc: 5 | ncall: 37515 | eff(%): 19.499 | loglstar: -inf < 121.775 < inf | logz: -23.810 +/- 1.513 | dlogz: 1.873 > 0.059] + + 7338it [00:36, 216.61it/s, bound: 958 | nc: 5 | ncall: 37630 | eff(%): 19.500 | loglstar: -inf < 122.137 < inf | logz: -23.706 +/- 1.514 | dlogz: 1.951 > 0.059] + + 7360it [00:36, 211.50it/s, bound: 961 | nc: 5 | ncall: 37740 | eff(%): 19.502 | loglstar: -inf < 122.558 < inf | logz: -23.619 +/- 1.515 | dlogz: 5.117 > 0.059] + + 7382it [00:36, 186.66it/s, bound: 964 | nc: 5 | ncall: 37850 | eff(%): 19.503 | loglstar: -inf < 122.884 < inf | logz: -23.540 +/- 1.516 | dlogz: 7.240 > 0.059] + + 7402it [00:36, 187.55it/s, bound: 967 | nc: 5 | ncall: 37950 | eff(%): 19.505 | loglstar: -inf < 123.581 < inf | logz: -23.473 +/- 1.517 | dlogz: 9.735 > 0.059] + + 7422it [00:36, 190.37it/s, bound: 970 | nc: 5 | ncall: 38050 | eff(%): 19.506 | loglstar: -inf < 124.263 < inf | logz: -23.388 +/- 1.518 | dlogz: 9.252 > 0.059] + + 7446it [00:36, 202.35it/s, bound: 973 | nc: 5 | ncall: 38170 | eff(%): 19.507 | loglstar: -inf < 125.337 < inf | logz: -23.249 +/- 1.521 | dlogz: 16.149 > 0.059] + + 7467it [00:36, 198.60it/s, bound: 976 | nc: 5 | ncall: 38275 | eff(%): 19.509 | loglstar: -inf < 127.708 < inf | logz: -22.804 +/- 1.532 | dlogz: 15.310 > 0.059] + + 7488it [00:36, 200.45it/s, bound: 980 | nc: 5 | ncall: 38380 | eff(%): 19.510 | loglstar: -inf < 130.188 < inf | logz: -21.826 +/- 1.548 | dlogz: 13.967 > 0.059] + + 7509it [00:37, 201.13it/s, bound: 984 | nc: 5 | ncall: 38485 | eff(%): 19.511 | loglstar: -inf < 132.042 < inf | logz: -20.064 +/- 1.562 | dlogz: 11.773 > 0.059] + + 7530it [00:37, 203.12it/s, bound: 987 | nc: 5 | ncall: 38590 | eff(%): 19.513 | loglstar: -inf < 133.550 < inf | logz: -18.624 +/- 1.565 | dlogz: 12.998 > 0.059] + + 7552it [00:37, 205.13it/s, bound: 989 | nc: 5 | ncall: 38700 | eff(%): 19.514 | loglstar: -inf < 135.517 < inf | logz: -17.458 +/- 1.566 | dlogz: 11.397 > 0.059] + + 7575it [00:37, 211.89it/s, bound: 992 | nc: 5 | ncall: 38815 | eff(%): 19.516 | loglstar: -inf < 137.749 < inf | logz: -15.521 +/- 1.571 | dlogz: 10.384 > 0.059] + + 7598it [00:37, 214.86it/s, bound: 995 | nc: 5 | ncall: 38930 | eff(%): 19.517 | loglstar: -inf < 139.500 < inf | logz: -14.102 +/- 1.573 | dlogz: 8.499 > 0.059] + + 7623it [00:37, 223.02it/s, bound: 998 | nc: 5 | ncall: 39055 | eff(%): 19.519 | loglstar: -inf < 141.088 < inf | logz: -12.828 +/- 1.574 | dlogz: 9.913 > 0.059] + + 7646it [00:37, 222.73it/s, bound: 1001 | nc: 5 | ncall: 39170 | eff(%): 19.520 | loglstar: -inf < 142.941 < inf | logz: -11.730 +/- 1.577 | dlogz: 8.372 > 0.059] + + 7671it [00:37, 228.74it/s, bound: 1004 | nc: 5 | ncall: 39295 | eff(%): 19.522 | loglstar: -inf < 143.947 < inf | logz: -10.633 +/- 1.579 | dlogz: 6.752 > 0.059] + + 7695it [00:37, 231.26it/s, bound: 1008 | nc: 5 | ncall: 39415 | eff(%): 19.523 | loglstar: -inf < 145.572 < inf | logz: -9.609 +/- 1.582 | dlogz: 5.261 > 0.059] + + 7719it [00:37, 227.73it/s, bound: 1011 | nc: 5 | ncall: 39535 | eff(%): 19.524 | loglstar: -inf < 146.408 < inf | logz: -8.914 +/- 1.583 | dlogz: 5.130 > 0.059] + + 7742it [00:38, 225.64it/s, bound: 1014 | nc: 5 | ncall: 39650 | eff(%): 19.526 | loglstar: -inf < 146.968 < inf | logz: -8.398 +/- 1.584 | dlogz: 4.155 > 0.059] + + 7765it [00:38, 210.86it/s, bound: 1017 | nc: 5 | ncall: 39765 | eff(%): 19.527 | loglstar: -inf < 147.337 < inf | logz: -8.060 +/- 1.584 | dlogz: 3.365 > 0.059] + + 7787it [00:38, 208.79it/s, bound: 1020 | nc: 5 | ncall: 39875 | eff(%): 19.529 | loglstar: -inf < 148.322 < inf | logz: -7.788 +/- 1.585 | dlogz: 3.161 > 0.059] + + 7809it [00:38, 210.96it/s, bound: 1023 | nc: 5 | ncall: 39985 | eff(%): 19.530 | loglstar: -inf < 148.824 < inf | logz: -7.478 +/- 1.586 | dlogz: 5.166 > 0.059] + + 7831it [00:38, 210.81it/s, bound: 1026 | nc: 5 | ncall: 40095 | eff(%): 19.531 | loglstar: -inf < 149.500 < inf | logz: -7.219 +/- 1.588 | dlogz: 4.476 > 0.059] + + 7853it [00:38, 212.26it/s, bound: 1029 | nc: 5 | ncall: 40205 | eff(%): 19.532 | loglstar: -inf < 150.158 < inf | logz: -6.977 +/- 1.589 | dlogz: 3.899 > 0.059] + + 7875it [00:38, 210.53it/s, bound: 1032 | nc: 5 | ncall: 40315 | eff(%): 19.534 | loglstar: -inf < 150.906 < inf | logz: -6.736 +/- 1.591 | dlogz: 3.239 > 0.059] + + 7897it [00:38, 206.26it/s, bound: 1035 | nc: 5 | ncall: 40425 | eff(%): 19.535 | loglstar: -inf < 151.216 < inf | logz: -6.523 +/- 1.593 | dlogz: 3.947 > 0.059] + + 7920it [00:38, 211.02it/s, bound: 1039 | nc: 5 | ncall: 40540 | eff(%): 19.536 | loglstar: -inf < 151.732 < inf | logz: -6.350 +/- 1.594 | dlogz: 3.487 > 0.059] + + 7943it [00:39, 215.26it/s, bound: 1042 | nc: 5 | ncall: 40655 | eff(%): 19.538 | loglstar: -inf < 152.567 < inf | logz: -6.165 +/- 1.596 | dlogz: 2.869 > 0.059] + + 7965it [00:39, 214.83it/s, bound: 1045 | nc: 5 | ncall: 40765 | eff(%): 19.539 | loglstar: -inf < 153.643 < inf | logz: -5.921 +/- 1.599 | dlogz: 2.245 > 0.059] + + 7987it [00:39, 209.51it/s, bound: 1047 | nc: 5 | ncall: 40875 | eff(%): 19.540 | loglstar: -inf < 154.536 < inf | logz: -5.603 +/- 1.603 | dlogz: 5.520 > 0.059] + + 8009it [00:39, 211.50it/s, bound: 1050 | nc: 5 | ncall: 40985 | eff(%): 19.541 | loglstar: -inf < 155.198 < inf | logz: -5.300 +/- 1.606 | dlogz: 5.040 > 0.059] + + 8031it [00:39, 202.57it/s, bound: 1053 | nc: 5 | ncall: 41095 | eff(%): 19.543 | loglstar: -inf < 155.547 < inf | logz: -5.024 +/- 1.609 | dlogz: 4.322 > 0.059] + + 8052it [00:39, 200.41it/s, bound: 1057 | nc: 5 | ncall: 41200 | eff(%): 19.544 | loglstar: -inf < 156.025 < inf | logz: -4.841 +/- 1.610 | dlogz: 4.209 > 0.059] + + 8073it [00:39, 201.59it/s, bound: 1061 | nc: 5 | ncall: 41305 | eff(%): 19.545 | loglstar: -inf < 156.992 < inf | logz: -4.638 +/- 1.612 | dlogz: 4.322 > 0.059] + + 8094it [00:39, 199.78it/s, bound: 1063 | nc: 5 | ncall: 41410 | eff(%): 19.546 | loglstar: -inf < 157.935 < inf | logz: -4.339 +/- 1.616 | dlogz: 4.227 > 0.059] + + 8117it [00:39, 208.30it/s, bound: 1066 | nc: 5 | ncall: 41525 | eff(%): 19.547 | loglstar: -inf < 159.200 < inf | logz: -3.880 +/- 1.622 | dlogz: 5.612 > 0.059] + + 8140it [00:40, 214.47it/s, bound: 1069 | nc: 5 | ncall: 41640 | eff(%): 19.549 | loglstar: -inf < 159.763 < inf | logz: -3.464 +/- 1.626 | dlogz: 4.736 > 0.059] + + 8164it [00:40, 221.50it/s, bound: 1073 | nc: 5 | ncall: 41760 | eff(%): 19.550 | loglstar: -inf < 160.551 < inf | logz: -3.143 +/- 1.628 | dlogz: 3.940 > 0.059] + + 8187it [00:40, 222.55it/s, bound: 1076 | nc: 5 | ncall: 41875 | eff(%): 19.551 | loglstar: -inf < 161.081 < inf | logz: -2.836 +/- 1.631 | dlogz: 6.148 > 0.059] + + 8210it [00:40, 216.62it/s, bound: 1079 | nc: 5 | ncall: 41990 | eff(%): 19.552 | loglstar: -inf < 161.622 < inf | logz: -2.598 +/- 1.632 | dlogz: 5.439 > 0.059] + + 8233it [00:40, 217.85it/s, bound: 1082 | nc: 5 | ncall: 42105 | eff(%): 19.553 | loglstar: -inf < 162.034 < inf | logz: -2.404 +/- 1.634 | dlogz: 4.785 > 0.059] + + 8255it [00:40, 216.83it/s, bound: 1085 | nc: 5 | ncall: 42215 | eff(%): 19.555 | loglstar: -inf < 162.941 < inf | logz: -2.222 +/- 1.635 | dlogz: 4.171 > 0.059] + + 8277it [00:40, 216.18it/s, bound: 1088 | nc: 5 | ncall: 42325 | eff(%): 19.556 | loglstar: -inf < 163.790 < inf | logz: -1.972 +/- 1.638 | dlogz: 5.531 > 0.059] + + 8300it [00:40, 218.86it/s, bound: 1091 | nc: 5 | ncall: 42440 | eff(%): 19.557 | loglstar: -inf < 165.113 < inf | logz: -1.530 +/- 1.644 | dlogz: 5.437 > 0.059] + + 8323it [00:40, 221.25it/s, bound: 1094 | nc: 5 | ncall: 42555 | eff(%): 19.558 | loglstar: -inf < 165.776 < inf | logz: -1.150 +/- 1.647 | dlogz: 4.600 > 0.059] + + 8346it [00:40, 223.23it/s, bound: 1097 | nc: 5 | ncall: 42670 | eff(%): 19.559 | loglstar: -inf < 166.286 < inf | logz: -0.851 +/- 1.650 | dlogz: 3.848 > 0.059] + + 8369it [00:41, 220.13it/s, bound: 1100 | nc: 5 | ncall: 42785 | eff(%): 19.561 | loglstar: -inf < 166.891 < inf | logz: -0.590 +/- 1.652 | dlogz: 3.323 > 0.059] + + 8392it [00:41, 220.00it/s, bound: 1103 | nc: 5 | ncall: 42900 | eff(%): 19.562 | loglstar: -inf < 167.684 < inf | logz: -0.318 +/- 1.654 | dlogz: 3.061 > 0.059] + + 8415it [00:41, 218.89it/s, bound: 1106 | nc: 5 | ncall: 43015 | eff(%): 19.563 | loglstar: -inf < 167.931 < inf | logz: -0.107 +/- 1.656 | dlogz: 5.397 > 0.059] + + 8437it [00:41, 215.25it/s, bound: 1108 | nc: 5 | ncall: 43125 | eff(%): 19.564 | loglstar: -inf < 168.492 < inf | logz: 0.064 +/- 1.657 | dlogz: 4.790 > 0.059] + + 8459it [00:41, 210.09it/s, bound: 1111 | nc: 5 | ncall: 43235 | eff(%): 19.565 | loglstar: -inf < 169.331 < inf | logz: 0.230 +/- 1.659 | dlogz: 4.252 > 0.059] + + 8481it [00:41, 212.42it/s, bound: 1114 | nc: 5 | ncall: 43345 | eff(%): 19.566 | loglstar: -inf < 170.287 < inf | logz: 0.452 +/- 1.661 | dlogz: 3.606 > 0.059] + + 8503it [00:41, 197.05it/s, bound: 1117 | nc: 5 | ncall: 43455 | eff(%): 19.567 | loglstar: -inf < 171.000 < inf | logz: 0.712 +/- 1.664 | dlogz: 2.936 > 0.059] + + 8523it [00:41, 176.36it/s, bound: 1121 | nc: 5 | ncall: 43555 | eff(%): 19.568 | loglstar: -inf < 171.223 < inf | logz: 0.911 +/- 1.667 | dlogz: 2.550 > 0.059] + + 8542it [00:41, 168.25it/s, bound: 1123 | nc: 5 | ncall: 43650 | eff(%): 19.569 | loglstar: -inf < 171.765 < inf | logz: 1.060 +/- 1.668 | dlogz: 2.075 > 0.059] + + 8560it [00:42, 160.79it/s, bound: 1125 | nc: 5 | ncall: 43740 | eff(%): 19.570 | loglstar: -inf < 172.030 < inf | logz: 1.191 +/- 1.669 | dlogz: 1.654 > 0.059] + + 8577it [00:42, 152.19it/s, bound: 1127 | nc: 5 | ncall: 43825 | eff(%): 19.571 | loglstar: -inf < 172.623 < inf | logz: 1.310 +/- 1.671 | dlogz: 1.537 > 0.059] + + 8593it [00:42, 138.09it/s, bound: 1129 | nc: 5 | ncall: 43905 | eff(%): 19.572 | loglstar: -inf < 173.279 < inf | logz: 1.454 +/- 1.673 | dlogz: 2.248 > 0.059] + + 8608it [00:42, 135.57it/s, bound: 1131 | nc: 5 | ncall: 43980 | eff(%): 19.573 | loglstar: -inf < 173.624 < inf | logz: 1.584 +/- 1.674 | dlogz: 1.873 > 0.059] + + 8624it [00:42, 141.06it/s, bound: 1134 | nc: 5 | ncall: 44060 | eff(%): 19.573 | loglstar: -inf < 173.829 < inf | logz: 1.704 +/- 1.676 | dlogz: 1.512 > 0.059] + + 8639it [00:42, 139.40it/s, bound: 1135 | nc: 5 | ncall: 44135 | eff(%): 19.574 | loglstar: -inf < 174.123 < inf | logz: 1.796 +/- 1.677 | dlogz: 1.222 > 0.059] + + 8655it [00:42, 144.65it/s, bound: 1138 | nc: 5 | ncall: 44215 | eff(%): 19.575 | loglstar: -inf < 174.300 < inf | logz: 1.885 +/- 1.678 | dlogz: 1.735 > 0.059] + + 8673it [00:42, 154.16it/s, bound: 1140 | nc: 5 | ncall: 44305 | eff(%): 19.576 | loglstar: -inf < 174.391 < inf | logz: 1.958 +/- 1.679 | dlogz: 1.389 > 0.059] + + 8689it [00:43, 155.15it/s, bound: 1142 | nc: 5 | ncall: 44385 | eff(%): 19.576 | loglstar: -inf < 174.587 < inf | logz: 2.006 +/- 1.679 | dlogz: 1.188 > 0.059] + + 8708it [00:43, 163.38it/s, bound: 1144 | nc: 5 | ncall: 44480 | eff(%): 19.577 | loglstar: -inf < 174.810 < inf | logz: 2.056 +/- 1.680 | dlogz: 0.906 > 0.059] + + 8728it [00:43, 172.50it/s, bound: 1147 | nc: 5 | ncall: 44580 | eff(%): 19.578 | loglstar: -inf < 175.026 < inf | logz: 2.096 +/- 1.680 | dlogz: 1.178 > 0.059] + + 8746it [00:43, 170.07it/s, bound: 1149 | nc: 5 | ncall: 44670 | eff(%): 19.579 | loglstar: -inf < 175.322 < inf | logz: 2.128 +/- 1.681 | dlogz: 0.924 > 0.059] + + 8766it [00:43, 178.00it/s, bound: 1151 | nc: 5 | ncall: 44770 | eff(%): 19.580 | loglstar: -inf < 175.493 < inf | logz: 2.157 +/- 1.681 | dlogz: 0.804 > 0.059] + + 8790it [00:43, 194.74it/s, bound: 1154 | nc: 5 | ncall: 44890 | eff(%): 19.581 | loglstar: -inf < 175.901 < inf | logz: 2.187 +/- 1.681 | dlogz: 0.697 > 0.059] + + 8813it [00:43, 203.91it/s, bound: 1157 | nc: 5 | ncall: 45005 | eff(%): 19.582 | loglstar: -inf < 176.377 < inf | logz: 2.214 +/- 1.682 | dlogz: 0.482 > 0.059] + + 8834it [00:43, 200.92it/s, bound: 1160 | nc: 5 | ncall: 45110 | eff(%): 19.583 | loglstar: -inf < 176.705 < inf | logz: 2.238 +/- 1.682 | dlogz: 0.335 > 0.059] + + 8857it [00:43, 208.42it/s, bound: 1163 | nc: 5 | ncall: 45225 | eff(%): 19.584 | loglstar: -inf < 177.199 < inf | logz: 2.262 +/- 1.683 | dlogz: 0.400 > 0.059] + + 8880it [00:43, 211.64it/s, bound: 1167 | nc: 5 | ncall: 45340 | eff(%): 19.585 | loglstar: -inf < 177.430 < inf | logz: 2.283 +/- 1.683 | dlogz: 0.264 > 0.059] + + 8902it [00:44, 208.82it/s, bound: 1170 | nc: 5 | ncall: 45450 | eff(%): 19.586 | loglstar: -inf < 177.766 < inf | logz: 2.299 +/- 1.684 | dlogz: 0.175 > 0.059] + + 8926it [00:44, 215.99it/s, bound: 1173 | nc: 5 | ncall: 45570 | eff(%): 19.587 | loglstar: -inf < 178.046 < inf | logz: 2.314 +/- 1.684 | dlogz: 0.134 > 0.059] + + 8948it [00:44, 190.86it/s, bound: 1176 | nc: 5 | ncall: 45680 | eff(%): 19.588 | loglstar: -inf < 178.219 < inf | logz: 2.325 +/- 1.684 | dlogz: 0.175 > 0.059] + + 8968it [00:44, 177.14it/s, bound: 1179 | nc: 5 | ncall: 45780 | eff(%): 19.589 | loglstar: -inf < 178.551 < inf | logz: 2.333 +/- 1.685 | dlogz: 0.140 > 0.059] + + 8987it [00:44, 177.14it/s, bound: 1181 | nc: 5 | ncall: 45875 | eff(%): 19.590 | loglstar: -inf < 178.927 < inf | logz: 2.341 +/- 1.685 | dlogz: 0.097 > 0.059] + + 9008it [00:44, 184.19it/s, bound: 1184 | nc: 5 | ncall: 45980 | eff(%): 19.591 | loglstar: -inf < 179.131 < inf | logz: 2.348 +/- 1.685 | dlogz: 0.110 > 0.059] + + 9028it [00:44, 188.16it/s, bound: 1187 | nc: 5 | ncall: 46080 | eff(%): 19.592 | loglstar: -inf < 179.333 < inf | logz: 2.354 +/- 1.685 | dlogz: 0.074 > 0.059] + + 9039it [00:44, 201.70it/s, +50 | bound: 1188 | nc: 1 | ncall: 46185 | eff(%): 19.701 | loglstar: -inf < 181.115 < inf | logz: 2.376 +/- 1.690 | dlogz: 0.001 > 0.059] + + + + + 2026-07-11 16:23:51,837 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:23:52,602 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:23:52,655 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +__Result__ + +The `info` attribute shows the result in a readable format, which contains information on the full collection +of all 5 model components. + + +```python +print(result.info) +``` + + Bayesian Evidence 2.37555245 + Maximum Log Likelihood 181.11496469 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + Maximum Log Likelihood Model: + + gaussian_0 + centre 49.879 + ... [51 lines of output truncated] ... + centre 51.71 (41.37, 60.03) + normalization 0.00 (0.00, 0.00) + sigma 18.81 (16.40, 21.77) + gaussian_2 + centre 50.00 (49.99, 50.00) + normalization 20.50 (20.32, 20.65) + sigma 1.01 (1.01, 1.02) + gaussian_3 + centre 50.21 (50.11, 50.29) + normalization 125.71 (121.73, 129.59) + sigma 13.44 (13.28, 13.61) + gaussian_4 + centre 49.96 (49.93, 50.00) + normalization 55.27 (54.46, 56.10) + sigma 5.58 (5.53, 5.62) + + instances + + + + +From the result info, it is hard to assess if the model fit was good or not. + +A good way to evaluate the fit is through a visual inspection of the model data plotted over the actual data. + +If the model data (red line) consistently aligns with the data points (black error bars), the fit is good. +However, if the model misses certain features of the data, such as peaks or regions of high intensity, +the fit was not successful. + + +```python +instance = result.max_log_likelihood_instance + +model_data_0 = instance.gaussian_0.model_data_from(xvalues=np.arange(data.shape[0])) +model_data_1 = instance.gaussian_1.model_data_from(xvalues=np.arange(data.shape[0])) +model_data_2 = instance.gaussian_2.model_data_from(xvalues=np.arange(data.shape[0])) +model_data_3 = instance.gaussian_3.model_data_from(xvalues=np.arange(data.shape[0])) +model_data_4 = instance.gaussian_4.model_data_from(xvalues=np.arange(data.shape[0])) + +model_data_list = [model_data_0, model_data_1, model_data_2, model_data_3, model_data_4] + +model_data = sum(model_data_list) + +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + linestyle="", + color="k", + ecolor="k", + elinewidth=1, + capsize=2, +) +plt.plot(range(data.shape[0]), model_data, color="r") +for model_data_1d_individual in model_data_list: + plt.plot(range(data.shape[0]), model_data_1d_individual, "--") +plt.title(f"Fit (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_23_0.png) + + + +It's challenging to determine from the plot whether the data and model data perfectly overlap across the entire dataset. + +To clarify this, the residual map introduced in tutorial 2 is useful. It provides a clear representation of where +the differences between the model and data exceed the noise level. + +Regions where the black error bars do not align with the zero line in the residual map indicate areas where the model +did not fit the data well and is inconsistent with the data above the noise level. Furthermore, regions where +larger values of residuals are next to one another indicate that the model failed to accurate fit that +region of the data. + + +```python +residual_map = data - model_data +plt.plot(range(data.shape[0]), np.zeros(data.shape[0]), "--", color="b") +plt.errorbar( + x=xvalues, + y=residual_map, + yerr=noise_map, + color="k", + ecolor="k", + elinewidth=1, + capsize=2, + linestyle="", +) +plt.title(f"Residuals (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Residuals") +plt.show() +plt.clf() +plt.close() +``` + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_25_0.png) + + + +The normalized residual map, as discussed in tutorial 2, provides an alternative visualization of the fit quality. + +Normalized residuals indicate the standard deviation (σ) level at which the residuals could have been drawn from the +noise. For instance, a normalized residual of 2.0 suggests that a residual value is 2.0σ away from the noise, +implying there is a 5% chance such a residual would occur due to noise. + +Values of normalized residuals above 3.0 are particularly improbable (occurring only 0.3% of the time), which is +generally considered a threshold where issues with the model-fit are likely the cause of the residual as opposed +to it being a noise fluctuation. + + +```python +residual_map = data - model_data +normalized_residual_map = residual_map / noise_map +plt.plot(xvalues, normalized_residual_map, color="k") +plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Normalized Residuals ($\sigma$)") +plt.show() +plt.clf() +plt.close() +``` + + <>:6: SyntaxWarning: invalid escape sequence '\s' + <>:6: SyntaxWarning: invalid escape sequence '\s' + /tmp/ipykernel_20726/582017931.py:6: SyntaxWarning: invalid escape sequence '\s' + plt.ylabel("Normalized Residuals ($\sigma$)") + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_27_1.png) + + + +So, did you achieve a good fit? Maybe a bad one? Or just an okay one? + +The truth is, I don't know, and I can't tell you for sure. Modeling is inherently random. It's not uncommon to +fit the same model to the same dataset using the same non-linear search and get a different result each time. + +When I ran the model fit above, that's exactly what happened. It produced a range of fits: some bad, some okay, and +some good, as shown in the images below: + + + + + +
    + +Distinguishing between the good and okay fit is difficult, however the normalized residuals make this easier. They show +that for the okay fit there are residuals above 3.0 sigma, indicating that the model did not perfectly fit the data. + + + + + +
    + +You should quickly rerun the code above a couple of times to see this variability for yourself. + +__Why Modeling is Hard__ + +This variability is at the heart of why modeling is challenging. The process of model-fitting is stochastic, +meaning it's hard as the scientist to determine if a better fit is possible or not. + +Why does modeling produce different results each time, and why might it sometimes infer solutions that are not good fits? + +In the previous tutorial, the non-linear search consistently found models that visually matched the data well, +minimizing residuals and yielding high log likelihood values. These optimal solutions are called 'global maxima', +they are where the model parameters correspond to the highest likelihood regions across the entire parameter space. +This ideal scenario is illustrated in the `good_fit.png` image above. + +However, non-linear searches do not always find these global maxima. Instead, they might settle on 'local maxima' +solutions, which have high log likelihood values relative to nearby models in parameter space but are significantly +lower than the true global maxima found elsewhere. + +This is what happened for the okay and bad fits above. The non-linear search converged on solutions that were locally +peaks on the likelihood surface but were not the global maximum solution. This is why the residuals were higher and +the normalized residuals above 3.0 sigma. + +Why does a non-linear search end up at local maxima? As discussed, the search iterates through many models, +focusing more on regions where previous guesses yielded higher likelihoods. It gradually converges around +solutions with higher likelihoods compared to surrounding models. If the search isn't exhaustive enough, it might +converge on a local maxima that appears good compared to nearby models but isn't the global maximum. + +Modeling is challenging because the parameter spaces of complex models are typically filled with local maxima, +making it hard for a non-linear search to locate the global maximum. + +Fortunately, there are strategies to help non-linear searches find the global maxima, and we'll now explore three of +them. + +__Prior Tuning__ + +First, let's assist our non-linear search by tuning our priors. Priors provide guidance to the search on where to +explore in the parameter space. By setting more accurate priors ('tuning' them), we can help the search find the +global solution instead of settling for a local maximum. + +For instance, from the data itself, it's evident that all `Gaussian` profiles are centered around pixel 50. In our +previous fit, the `centre` parameter of each `Gaussian` had a `UniformPrior` spanning from 0.0 to 100.0, which is +much broader than necessary given the data's range. + +Additionally, the peak value of the data's `normalization` parameter was around 17.5. This indicates that +the `normalization` values of our `Gaussians` do not exceed 500.0, allowing us to refine our prior accordingly. + +The following code snippet adjusts these priors for the `centre` and `normalization` parameters of +each `Gaussian` using **PyAutoFit**'s API for model and prior customization: + + +```python +gaussian_0 = af.Model(Gaussian) + +gaussian_0.centre = af.UniformPrior(lower_limit=45.0, upper_limit=55.0) +gaussian_0.normalization = af.LogUniformPrior(lower_limit=0.1, upper_limit=500.0) + +gaussian_1 = af.Model(Gaussian) + +gaussian_1.centre = af.UniformPrior(lower_limit=45.0, upper_limit=55.0) +gaussian_1.normalization = af.LogUniformPrior(lower_limit=0.1, upper_limit=500.0) + +gaussian_2 = af.Model(Gaussian) + +gaussian_2.centre = af.UniformPrior(lower_limit=45.0, upper_limit=55.0) +gaussian_2.normalization = af.LogUniformPrior(lower_limit=0.1, upper_limit=500.0) + +gaussian_3 = af.Model(Gaussian) + +gaussian_3.centre = af.UniformPrior(lower_limit=45.0, upper_limit=55.0) +gaussian_3.normalization = af.LogUniformPrior(lower_limit=0.1, upper_limit=500.0) + +gaussian_4 = af.Model(Gaussian) + +gaussian_4.centre = af.UniformPrior(lower_limit=45.0, upper_limit=55.0) +gaussian_4.normalization = af.LogUniformPrior(lower_limit=0.1, upper_limit=500.0) + +model = af.Collection( + gaussian_0=gaussian_0, + gaussian_1=gaussian_1, + gaussian_2=gaussian_2, + gaussian_3=gaussian_3, + gaussian_4=gaussian_4, +) +``` + +The `info` attribute shows the model is now using the priors specified above. + + +```python +print(model.info) +``` + + Total Free Parameters = 15 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + gaussian_0 + centre UniformPrior [18], lower_limit = 45.0, upper_limit = 55.0 + normalization LogUniformPrior [19], lower_limit = 0.1, upper_limit = 500.0 + sigma UniformPrior [17], lower_limit = 0.0, upper_limit = 25.0 + gaussian_1 + centre UniformPrior [23], lower_limit = 45.0, upper_limit = 55.0 + normalization LogUniformPrior [24], lower_limit = 0.1, upper_limit = 500.0 + sigma UniformPrior [22], lower_limit = 0.0, upper_limit = 25.0 + gaussian_2 + centre UniformPrior [28], lower_limit = 45.0, upper_limit = 55.0 + normalization LogUniformPrior [29], lower_limit = 0.1, upper_limit = 500.0 + sigma UniformPrior [27], lower_limit = 0.0, upper_limit = 25.0 + gaussian_3 + centre UniformPrior [33], lower_limit = 45.0, upper_limit = 55.0 + normalization LogUniformPrior [34], lower_limit = 0.1, upper_limit = 500.0 + sigma UniformPrior [32], lower_limit = 0.0, upper_limit = 25.0 + gaussian_4 + centre UniformPrior [38], lower_limit = 45.0, upper_limit = 55.0 + normalization LogUniformPrior [39], lower_limit = 0.1, upper_limit = 500.0 + sigma UniformPrior [37], lower_limit = 0.0, upper_limit = 25.0 + + +We now repeat the model-fit using these updated priors. + +First, you should note that the run time of the fit is significantly faster than the previous fit. This is because +the prior is telling the non-linear search where to look, meaning it converges on solutions more quickly +and spends less time searching regions of parameter space that do not contain solutions. + +Second, the model-fit consistently produces a good model-fit more often, because our tuned priors are centred +on the global maxima solution ensuring the non-linear search is less likely to converge on a local maxima. + + +```python +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:23:53,402 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:23:53,412 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:23:53,412 - root - INFO - Starting new Dynesty non-linear search (no previous samples found). + + + 2026-07-11 16:23:53,418 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:23:53,453 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + ~/venv/PyAuto/lib/python3.12/site-packages/dynesty/dynesty.py:194: UserWarning: Specifying slice option while using rwalk sampler does not make sense + warnings.warn('Specifying slice option while using rwalk sampler' + + + 0it [00:00, ?it/s] + + 41it [00:00, 399.92it/s, bound: 0 | nc: 3 | ncall: 111 | eff(%): 36.937 | loglstar: -inf < -400171.406 < inf | logz: -400176.823 +/- 0.328 | dlogz: 361498.731 > 0.059] + + 81it [00:00, 191.86it/s, bound: 0 | nc: 5 | ncall: 233 | eff(%): 34.764 | loglstar: -inf < -171719.837 < inf | logz: -171726.046 +/- 0.351 | dlogz: 122393.273 > 0.059] + + 106it [00:00, 142.80it/s, bound: 0 | nc: 8 | ncall: 359 | eff(%): 29.526 | loglstar: -inf < -122968.119 < inf | logz: -122974.823 +/- 0.364 | dlogz: 90810.063 > 0.059] + + 124it [00:01, 96.75it/s, bound: 0 | nc: 10 | ncall: 527 | eff(%): 23.529 | loglstar: -inf < -98148.709 < inf | logz: -98155.769 +/- 0.374 | dlogz: 65951.495 > 0.059] + + 137it [00:01, 73.41it/s, bound: 0 | nc: 8 | ncall: 692 | eff(%): 19.798 | loglstar: -inf < -86398.862 < inf | logz: -86406.180 +/- 0.381 | dlogz: 55750.237 > 0.059] + + 147it [00:01, 64.32it/s, bound: 0 | nc: 17 | ncall: 811 | eff(%): 18.126 | loglstar: -inf < -80449.473 < inf | logz: -80456.989 +/- 0.386 | dlogz: 48307.329 > 0.059] + + 155it [00:01, 50.04it/s, bound: 0 | nc: 2 | ncall: 971 | eff(%): 15.963 | loglstar: -inf < -75766.995 < inf | logz: -75774.670 +/- 0.390 | dlogz: 43565.352 > 0.059] + + 162it [00:02, 45.67it/s, bound: 0 | nc: 5 | ncall: 1081 | eff(%): 14.986 | loglstar: -inf < -71902.617 < inf | logz: -71910.430 +/- 0.393 | dlogz: 39768.980 > 0.059] + + 168it [00:02, 31.07it/s, bound: 0 | nc: 43 | ncall: 1321 | eff(%): 12.718 | loglstar: -inf < -69039.241 < inf | logz: -69047.173 +/- 0.396 | dlogz: 36999.448 > 0.059] + + 172it [00:02, 28.95it/s, bound: 0 | nc: 9 | ncall: 1433 | eff(%): 12.003 | loglstar: -inf < -67424.806 < inf | logz: -67432.817 +/- 0.398 | dlogz: 50124.627 > 0.059] + + 176it [00:03, 26.09it/s, bound: 0 | nc: 13 | ncall: 1547 | eff(%): 11.377 | loglstar: -inf < -66929.174 < inf | logz: -66937.265 +/- 0.400 | dlogz: 49486.890 > 0.059] + + 179it [00:03, 21.55it/s, bound: 0 | nc: 75 | ncall: 1689 | eff(%): 10.598 | loglstar: -inf < -65922.218 < inf | logz: -65930.368 +/- 0.402 | dlogz: 49052.141 > 0.059] + + 182it [00:03, 22.29it/s, bound: 0 | nc: 33 | ncall: 1753 | eff(%): 10.382 | loglstar: -inf < -65485.241 < inf | logz: -65493.450 +/- 0.403 | dlogz: 48577.668 > 0.059] + + 185it [00:03, 20.43it/s, bound: 0 | nc: 28 | ncall: 1844 | eff(%): 10.033 | loglstar: -inf < -63221.079 < inf | logz: -63229.348 +/- 0.405 | dlogz: 47182.924 > 0.059] + + 188it [00:03, 21.24it/s, bound: 1 | nc: 5 | ncall: 1910 | eff(%): 9.843 | loglstar: -inf < -62665.543 < inf | logz: -62673.871 +/- 0.406 | dlogz: 45862.578 > 0.059] + + 220it [00:03, 77.22it/s, bound: 5 | nc: 5 | ncall: 2070 | eff(%): 10.628 | loglstar: -inf < -50558.309 < inf | logz: -50567.274 +/- 0.418 | dlogz: 34284.096 > 0.059] + + 245it [00:03, 112.87it/s, bound: 8 | nc: 5 | ncall: 2195 | eff(%): 11.162 | loglstar: -inf < -45528.438 < inf | logz: -45536.262 +/- 0.382 | dlogz: 28437.321 > 0.059] + + 269it [00:04, 141.74it/s, bound: 11 | nc: 5 | ncall: 2315 | eff(%): 11.620 | loglstar: -inf < -39794.131 < inf | logz: -39804.089 +/- 0.430 | dlogz: 31410.159 > 0.059] + + 294it [00:04, 168.40it/s, bound: 14 | nc: 5 | ncall: 2440 | eff(%): 12.049 | loglstar: -inf < -33611.185 < inf | logz: -33619.992 +/- 0.399 | dlogz: 23945.788 > 0.059] + + 321it [00:04, 193.51it/s, bound: 17 | nc: 5 | ncall: 2575 | eff(%): 12.466 | loglstar: -inf < -31645.622 < inf | logz: -31655.518 +/- 0.417 | dlogz: 21981.344 > 0.059] + + 347it [00:04, 210.28it/s, bound: 21 | nc: 5 | ncall: 2705 | eff(%): 12.828 | loglstar: -inf < -25260.596 < inf | logz: -25272.129 +/- 0.452 | dlogz: 16112.031 > 0.059] + + 373it [00:04, 222.42it/s, bound: 24 | nc: 5 | ncall: 2835 | eff(%): 13.157 | loglstar: -inf < -20323.644 < inf | logz: -20335.697 +/- 0.459 | dlogz: 10841.977 > 0.059] + + 398it [00:04, 228.48it/s, bound: 27 | nc: 5 | ncall: 2960 | eff(%): 13.446 | loglstar: -inf < -17644.223 < inf | logz: -17656.779 +/- 0.466 | dlogz: 10718.196 > 0.059] + + 422it [00:04, 224.09it/s, bound: 30 | nc: 5 | ncall: 3080 | eff(%): 13.701 | loglstar: -inf < -16012.203 < inf | logz: -16025.243 +/- 0.471 | dlogz: 8953.956 > 0.059] + + 446it [00:04, 225.40it/s, bound: 33 | nc: 5 | ncall: 3200 | eff(%): 13.938 | loglstar: -inf < -14844.944 < inf | logz: -14858.465 +/- 0.477 | dlogz: 8818.940 > 0.059] + + 471it [00:04, 231.54it/s, bound: 36 | nc: 5 | ncall: 3325 | eff(%): 14.165 | loglstar: -inf < -13237.662 < inf | logz: -13251.680 +/- 0.485 | dlogz: 7033.616 > 0.059] + + 497it [00:04, 238.96it/s, bound: 39 | nc: 5 | ncall: 3455 | eff(%): 14.385 | loglstar: -inf < -11648.091 < inf | logz: -11660.356 +/- 0.441 | dlogz: 5367.508 > 0.059] + + 522it [00:05, 239.66it/s, bound: 43 | nc: 5 | ncall: 3580 | eff(%): 14.581 | loglstar: -inf < -10103.510 < inf | logz: -10118.560 +/- 0.497 | dlogz: 4182.646 > 0.059] + + 547it [00:05, 241.95it/s, bound: 46 | nc: 5 | ncall: 3705 | eff(%): 14.764 | loglstar: -inf < -9668.884 < inf | logz: -9683.370 +/- 0.477 | dlogz: 4633.770 > 0.059] + + 572it [00:05, 242.94it/s, bound: 49 | nc: 5 | ncall: 3830 | eff(%): 14.935 | loglstar: -inf < -8910.047 < inf | logz: -8926.151 +/- 0.507 | dlogz: 3910.858 > 0.059] + + 597it [00:05, 222.87it/s, bound: 52 | nc: 5 | ncall: 3955 | eff(%): 15.095 | loglstar: -inf < -7732.972 < inf | logz: -7748.406 +/- 0.487 | dlogz: 2697.716 > 0.059] + + 620it [00:05, 221.58it/s, bound: 55 | nc: 5 | ncall: 4070 | eff(%): 15.233 | loglstar: -inf < -7300.102 < inf | logz: -7317.172 +/- 0.516 | dlogz: 3024.185 > 0.059] + + 643it [00:05, 221.61it/s, bound: 58 | nc: 5 | ncall: 4185 | eff(%): 15.364 | loglstar: -inf < -6859.649 < inf | logz: -6877.188 +/- 0.525 | dlogz: 2618.412 > 0.059] + + 668it [00:05, 228.65it/s, bound: 61 | nc: 5 | ncall: 4310 | eff(%): 15.499 | loglstar: -inf < -6386.942 < inf | logz: -6404.980 +/- 0.531 | dlogz: 2240.373 > 0.059] + + 692it [00:05, 223.90it/s, bound: 64 | nc: 5 | ncall: 4430 | eff(%): 15.621 | loglstar: -inf < -6040.997 < inf | logz: -6059.386 +/- 0.527 | dlogz: 2219.338 > 0.059] + + 716it [00:05, 226.94it/s, bound: 67 | nc: 5 | ncall: 4550 | eff(%): 15.736 | loglstar: -inf < -5570.984 < inf | logz: -5589.982 +/- 0.543 | dlogz: 1825.647 > 0.059] + + 741it [00:06, 231.17it/s, bound: 71 | nc: 5 | ncall: 4675 | eff(%): 15.850 | loglstar: -inf < -5158.786 < inf | logz: -5177.717 +/- 0.533 | dlogz: 1560.985 > 0.059] + + 765it [00:06, 231.19it/s, bound: 75 | nc: 5 | ncall: 4795 | eff(%): 15.954 | loglstar: -inf < -4916.839 < inf | logz: -4936.817 +/- 0.556 | dlogz: 1452.023 > 0.059] + + 790it [00:06, 233.77it/s, bound: 79 | nc: 5 | ncall: 4920 | eff(%): 16.057 | loglstar: -inf < -4570.053 < inf | logz: -4590.523 +/- 0.561 | dlogz: 1084.535 > 0.059] + + 814it [00:06, 230.64it/s, bound: 83 | nc: 5 | ncall: 5040 | eff(%): 16.151 | loglstar: -inf < -4285.316 < inf | logz: -4306.276 +/- 0.568 | dlogz: 1302.550 > 0.059] + + 838it [00:06, 208.95it/s, bound: 86 | nc: 5 | ncall: 5160 | eff(%): 16.240 | loglstar: -inf < -4201.688 < inf | logz: -4220.729 +/- 0.527 | dlogz: 1559.350 > 0.059] + + 860it [00:06, 195.94it/s, bound: 89 | nc: 5 | ncall: 5270 | eff(%): 16.319 | loglstar: -inf < -3998.734 < inf | logz: -4020.632 +/- 0.577 | dlogz: 1750.804 > 0.059] + + 880it [00:06, 194.51it/s, bound: 92 | nc: 5 | ncall: 5370 | eff(%): 16.387 | loglstar: -inf < -3842.021 < inf | logz: -3862.025 +/- 0.539 | dlogz: 1564.707 > 0.059] + + 902it [00:06, 199.27it/s, bound: 96 | nc: 5 | ncall: 5480 | eff(%): 16.460 | loglstar: -inf < -3582.729 < inf | logz: -3605.244 +/- 0.575 | dlogz: 1309.522 > 0.059] + + 924it [00:06, 203.42it/s, bound: 100 | nc: 5 | ncall: 5590 | eff(%): 16.530 | loglstar: -inf < -3371.740 < inf | logz: -3394.920 +/- 0.591 | dlogz: 1131.829 > 0.059] + + 948it [00:07, 212.82it/s, bound: 103 | nc: 5 | ncall: 5710 | eff(%): 16.602 | loglstar: -inf < -3234.813 < inf | logz: -3256.822 +/- 0.569 | dlogz: 1315.372 > 0.059] + + 972it [00:07, 219.69it/s, bound: 107 | nc: 5 | ncall: 5830 | eff(%): 16.672 | loglstar: -inf < -3025.641 < inf | logz: -3049.783 +/- 0.601 | dlogz: 1115.809 > 0.059] + + 999it [00:07, 231.68it/s, bound: 110 | nc: 5 | ncall: 5965 | eff(%): 16.748 | loglstar: -inf < -2828.227 < inf | logz: -2852.908 +/- 0.608 | dlogz: 930.053 > 0.059] + + 1023it [00:07, 215.87it/s, bound: 114 | nc: 5 | ncall: 6085 | eff(%): 16.812 | loglstar: -inf < -2685.033 < inf | logz: -2709.189 +/- 0.579 | dlogz: 766.329 > 0.059] + + 1046it [00:07, 219.64it/s, bound: 117 | nc: 5 | ncall: 6200 | eff(%): 16.871 | loglstar: -inf < -2628.968 < inf | logz: -2652.033 +/- 0.571 | dlogz: 770.775 > 0.059] + + 1070it [00:07, 223.66it/s, bound: 121 | nc: 5 | ncall: 6320 | eff(%): 16.930 | loglstar: -inf < -2453.210 < inf | logz: -2479.324 +/- 0.622 | dlogz: 802.736 > 0.059] + + 1093it [00:07, 220.28it/s, bound: 124 | nc: 5 | ncall: 6435 | eff(%): 16.985 | loglstar: -inf < -2337.381 < inf | logz: -2361.948 +/- 0.594 | dlogz: 724.438 > 0.059] + + 1118it [00:07, 226.69it/s, bound: 129 | nc: 5 | ncall: 6560 | eff(%): 17.043 | loglstar: -inf < -2244.253 < inf | logz: -2269.682 +/- 0.605 | dlogz: 696.651 > 0.059] + + 1144it [00:07, 234.43it/s, bound: 132 | nc: 5 | ncall: 6690 | eff(%): 17.100 | loglstar: -inf < -2114.202 < inf | logz: -2141.746 +/- 0.633 | dlogz: 570.828 > 0.059] + + 1170it [00:07, 239.81it/s, bound: 136 | nc: 5 | ncall: 6820 | eff(%): 17.155 | loglstar: -inf < -1988.523 < inf | logz: -2015.528 +/- 0.624 | dlogz: 714.957 > 0.059] + + 1198it [00:08, 250.92it/s, bound: 139 | nc: 5 | ncall: 6960 | eff(%): 17.213 | loglstar: -inf < -1840.172 < inf | logz: -1867.742 +/- 0.628 | dlogz: 792.772 > 0.059] + + 1225it [00:08, 253.15it/s, bound: 143 | nc: 5 | ncall: 7095 | eff(%): 17.266 | loglstar: -inf < -1689.754 < inf | logz: -1718.559 +/- 0.639 | dlogz: 843.108 > 0.059] + + 1251it [00:08, 252.00it/s, bound: 147 | nc: 5 | ncall: 7225 | eff(%): 17.315 | loglstar: -inf < -1561.233 < inf | logz: -1589.859 +/- 0.640 | dlogz: 993.155 > 0.059] + + 1277it [00:08, 249.73it/s, bound: 152 | nc: 5 | ncall: 7355 | eff(%): 17.362 | loglstar: -inf < -1377.445 < inf | logz: -1407.716 +/- 0.663 | dlogz: 844.570 > 0.059] + + 1303it [00:08, 249.85it/s, bound: 156 | nc: 5 | ncall: 7485 | eff(%): 17.408 | loglstar: -inf < -1196.682 < inf | logz: -1225.465 +/- 0.637 | dlogz: 761.658 > 0.059] + + 1329it [00:08, 249.60it/s, bound: 159 | nc: 5 | ncall: 7615 | eff(%): 17.452 | loglstar: -inf < -1024.194 < inf | logz: -1055.072 +/- 0.651 | dlogz: 622.676 > 0.059] + + 1355it [00:08, 251.57it/s, bound: 162 | nc: 5 | ncall: 7745 | eff(%): 17.495 | loglstar: -inf < -877.206 < inf | logz: -909.041 +/- 0.678 | dlogz: 701.457 > 0.059] + + 1381it [00:08, 248.54it/s, bound: 165 | nc: 5 | ncall: 7875 | eff(%): 17.537 | loglstar: -inf < -783.338 < inf | logz: -815.681 +/- 0.682 | dlogz: 577.656 > 0.059] + + 1406it [00:08, 245.51it/s, bound: 169 | nc: 5 | ncall: 8000 | eff(%): 17.575 | loglstar: -inf < -685.095 < inf | logz: -716.291 +/- 0.663 | dlogz: 473.268 > 0.059] + + 1431it [00:09, 244.22it/s, bound: 172 | nc: 5 | ncall: 8125 | eff(%): 17.612 | loglstar: -inf < -586.887 < inf | logz: -620.226 +/- 0.691 | dlogz: 380.328 > 0.059] + + 1456it [00:09, 241.35it/s, bound: 175 | nc: 5 | ncall: 8250 | eff(%): 17.648 | loglstar: -inf < -489.720 < inf | logz: -523.580 +/- 0.698 | dlogz: 352.233 > 0.059] + + 1481it [00:09, 240.42it/s, bound: 179 | nc: 5 | ncall: 8375 | eff(%): 17.684 | loglstar: -inf < -423.863 < inf | logz: -457.773 +/- 0.690 | dlogz: 280.541 > 0.059] + + 1506it [00:09, 233.88it/s, bound: 182 | nc: 5 | ncall: 8500 | eff(%): 17.718 | loglstar: -inf < -334.261 < inf | logz: -368.762 +/- 0.697 | dlogz: 377.826 > 0.059] + + 1530it [00:09, 230.09it/s, bound: 186 | nc: 5 | ncall: 8620 | eff(%): 17.749 | loglstar: -inf < -267.518 < inf | logz: -302.864 +/- 0.714 | dlogz: 330.437 > 0.059] + + 1555it [00:09, 233.38it/s, bound: 189 | nc: 5 | ncall: 8745 | eff(%): 17.782 | loglstar: -inf < -211.245 < inf | logz: -246.627 +/- 0.701 | dlogz: 254.186 > 0.059] + + 1579it [00:09, 231.64it/s, bound: 193 | nc: 5 | ncall: 8865 | eff(%): 17.812 | loglstar: -inf < -171.154 < inf | logz: -205.829 +/- 0.702 | dlogz: 212.081 > 0.059] + + 1603it [00:09, 233.49it/s, bound: 196 | nc: 5 | ncall: 8985 | eff(%): 17.841 | loglstar: -inf < -133.804 < inf | logz: -169.844 +/- 0.715 | dlogz: 176.398 > 0.059] + + 1627it [00:09, 233.99it/s, bound: 199 | nc: 5 | ncall: 9105 | eff(%): 17.869 | loglstar: -inf < -98.617 < inf | logz: -135.827 +/- 0.731 | dlogz: 143.797 > 0.059] + + 1651it [00:09, 232.75it/s, bound: 202 | nc: 5 | ncall: 9225 | eff(%): 17.897 | loglstar: -inf < -77.248 < inf | logz: -114.608 +/- 0.727 | dlogz: 137.772 > 0.059] + + 1675it [00:10, 233.45it/s, bound: 205 | nc: 5 | ncall: 9345 | eff(%): 17.924 | loglstar: -inf < -42.908 < inf | logz: -80.923 +/- 0.736 | dlogz: 106.638 > 0.059] + + 1699it [00:10, 234.89it/s, bound: 208 | nc: 5 | ncall: 9465 | eff(%): 17.950 | loglstar: -inf < -35.240 < inf | logz: -71.714 +/- 0.712 | dlogz: 107.013 > 0.059] + + 1723it [00:10, 236.36it/s, bound: 211 | nc: 5 | ncall: 9585 | eff(%): 17.976 | loglstar: -inf < -15.039 < inf | logz: -52.862 +/- 0.727 | dlogz: 90.894 > 0.059] + + 1747it [00:10, 226.69it/s, bound: 215 | nc: 5 | ncall: 9705 | eff(%): 18.001 | loglstar: -inf < 3.716 < inf | logz: -35.032 +/- 0.727 | dlogz: 72.663 > 0.059] + + 1770it [00:10, 212.22it/s, bound: 218 | nc: 5 | ncall: 9820 | eff(%): 18.024 | loglstar: -inf < 18.161 < inf | logz: -21.471 +/- 0.751 | dlogz: 66.012 > 0.059] + + 1792it [00:10, 205.41it/s, bound: 220 | nc: 5 | ncall: 9930 | eff(%): 18.046 | loglstar: -inf < 38.243 < inf | logz: -1.423 +/- 0.749 | dlogz: 46.821 > 0.059] + + 1814it [00:10, 208.38it/s, bound: 223 | nc: 5 | ncall: 10040 | eff(%): 18.068 | loglstar: -inf < 43.973 < inf | logz: 5.787 +/- 0.733 | dlogz: 52.139 > 0.059] + + 1836it [00:10, 210.88it/s, bound: 227 | nc: 5 | ncall: 10150 | eff(%): 18.089 | loglstar: -inf < 49.619 < inf | logz: 10.549 +/- 0.740 | dlogz: 47.018 > 0.059] + + 1858it [00:10, 213.10it/s, bound: 230 | nc: 5 | ncall: 10260 | eff(%): 18.109 | loglstar: -inf < 58.851 < inf | logz: 19.202 +/- 0.750 | dlogz: 37.958 > 0.059] + + 1881it [00:11, 217.01it/s, bound: 233 | nc: 5 | ncall: 10375 | eff(%): 18.130 | loglstar: -inf < 64.400 < inf | logz: 23.698 +/- 0.752 | dlogz: 40.691 > 0.059] + + 1905it [00:11, 222.42it/s, bound: 236 | nc: 5 | ncall: 10495 | eff(%): 18.152 | loglstar: -inf < 69.396 < inf | logz: 29.470 +/- 0.751 | dlogz: 43.342 > 0.059] + + 1929it [00:11, 225.17it/s, bound: 239 | nc: 5 | ncall: 10615 | eff(%): 18.172 | loglstar: -inf < 75.363 < inf | logz: 34.720 +/- 0.763 | dlogz: 37.631 > 0.059] + + 1952it [00:11, 222.19it/s, bound: 243 | nc: 5 | ncall: 10730 | eff(%): 18.192 | loglstar: -inf < 81.450 < inf | logz: 39.809 +/- 0.769 | dlogz: 32.163 > 0.059] + + 1976it [00:11, 225.66it/s, bound: 246 | nc: 5 | ncall: 10850 | eff(%): 18.212 | loglstar: -inf < 85.381 < inf | logz: 44.017 +/- 0.769 | dlogz: 29.722 > 0.059] + + 2000it [00:11, 227.52it/s, bound: 249 | nc: 5 | ncall: 10970 | eff(%): 18.232 | loglstar: -inf < 91.202 < inf | logz: 49.099 +/- 0.777 | dlogz: 24.173 > 0.059] + + 2023it [00:11, 228.04it/s, bound: 253 | nc: 5 | ncall: 11085 | eff(%): 18.250 | loglstar: -inf < 94.878 < inf | logz: 52.875 +/- 0.779 | dlogz: 19.892 > 0.059] + + 2046it [00:11, 227.15it/s, bound: 257 | nc: 5 | ncall: 11200 | eff(%): 18.268 | loglstar: -inf < 96.868 < inf | logz: 54.667 +/- 0.778 | dlogz: 17.615 > 0.059] + + 2069it [00:11, 224.39it/s, bound: 260 | nc: 5 | ncall: 11315 | eff(%): 18.285 | loglstar: -inf < 101.870 < inf | logz: 58.413 +/- 0.796 | dlogz: 18.765 > 0.059] + + 2092it [00:11, 220.89it/s, bound: 264 | nc: 5 | ncall: 11430 | eff(%): 18.303 | loglstar: -inf < 103.680 < inf | logz: 60.256 +/- 0.791 | dlogz: 16.403 > 0.059] + + 2115it [00:12, 205.00it/s, bound: 266 | nc: 5 | ncall: 11545 | eff(%): 18.320 | loglstar: -inf < 105.509 < inf | logz: 62.197 +/- 0.794 | dlogz: 13.949 > 0.059] + + 2136it [00:12, 194.15it/s, bound: 269 | nc: 5 | ncall: 11650 | eff(%): 18.335 | loglstar: -inf < 107.356 < inf | logz: 63.412 +/- 0.797 | dlogz: 12.317 > 0.059] + + 2156it [00:12, 176.41it/s, bound: 272 | nc: 5 | ncall: 11750 | eff(%): 18.349 | loglstar: -inf < 109.092 < inf | logz: 64.527 +/- 0.801 | dlogz: 10.820 > 0.059] + + 2175it [00:12, 166.98it/s, bound: 274 | nc: 5 | ncall: 11845 | eff(%): 18.362 | loglstar: -inf < 110.081 < inf | logz: 65.514 +/- 0.804 | dlogz: 9.434 > 0.059] + + 2192it [00:12, 162.27it/s, bound: 277 | nc: 5 | ncall: 11930 | eff(%): 18.374 | loglstar: -inf < 111.073 < inf | logz: 66.382 +/- 0.808 | dlogz: 11.454 > 0.059] + + 2209it [00:12, 146.17it/s, bound: 280 | nc: 5 | ncall: 12015 | eff(%): 18.385 | loglstar: -inf < 112.034 < inf | logz: 66.983 +/- 0.808 | dlogz: 10.494 > 0.059] + + 2224it [00:12, 144.74it/s, bound: 282 | nc: 5 | ncall: 12090 | eff(%): 18.395 | loglstar: -inf < 112.600 < inf | logz: 67.454 +/- 0.810 | dlogz: 9.720 > 0.059] + + 2240it [00:12, 148.15it/s, bound: 284 | nc: 5 | ncall: 12170 | eff(%): 18.406 | loglstar: -inf < 113.989 < inf | logz: 68.086 +/- 0.814 | dlogz: 8.787 > 0.059] + + 2259it [00:13, 159.17it/s, bound: 287 | nc: 5 | ncall: 12265 | eff(%): 18.418 | loglstar: -inf < 115.762 < inf | logz: 69.172 +/- 0.824 | dlogz: 8.064 > 0.059] + + 2281it [00:13, 174.64it/s, bound: 289 | nc: 5 | ncall: 12375 | eff(%): 18.432 | loglstar: -inf < 116.288 < inf | logz: 70.076 +/- 0.827 | dlogz: 6.684 > 0.059] + + 2304it [00:13, 186.74it/s, bound: 292 | nc: 5 | ncall: 12490 | eff(%): 18.447 | loglstar: -inf < 117.558 < inf | logz: 70.723 +/- 0.828 | dlogz: 5.585 > 0.059] + + 2327it [00:13, 198.66it/s, bound: 296 | nc: 5 | ncall: 12605 | eff(%): 18.461 | loglstar: -inf < 118.490 < inf | logz: 71.356 +/- 0.831 | dlogz: 4.483 > 0.059] + + 2351it [00:13, 208.46it/s, bound: 299 | nc: 5 | ncall: 12725 | eff(%): 18.475 | loglstar: -inf < 118.951 < inf | logz: 71.851 +/- 0.833 | dlogz: 3.547 > 0.059] + + 2373it [00:13, 205.31it/s, bound: 302 | nc: 5 | ncall: 12835 | eff(%): 18.489 | loglstar: -inf < 119.447 < inf | logz: 72.185 +/- 0.835 | dlogz: 2.800 > 0.059] + + 2394it [00:13, 204.92it/s, bound: 305 | nc: 5 | ncall: 12940 | eff(%): 18.501 | loglstar: -inf < 120.122 < inf | logz: 72.458 +/- 0.837 | dlogz: 2.167 > 0.059] + + 2416it [00:13, 206.34it/s, bound: 308 | nc: 5 | ncall: 13050 | eff(%): 18.513 | loglstar: -inf < 120.620 < inf | logz: 72.701 +/- 0.839 | dlogz: 3.108 > 0.059] + + 2438it [00:13, 208.37it/s, bound: 310 | nc: 5 | ncall: 13160 | eff(%): 18.526 | loglstar: -inf < 121.044 < inf | logz: 72.909 +/- 0.841 | dlogz: 2.853 > 0.059] + + 2461it [00:13, 212.33it/s, bound: 313 | nc: 5 | ncall: 13275 | eff(%): 18.539 | loglstar: -inf < 121.433 < inf | logz: 73.085 +/- 0.842 | dlogz: 2.260 > 0.059] + + 2484it [00:14, 214.12it/s, bound: 316 | nc: 5 | ncall: 13390 | eff(%): 18.551 | loglstar: -inf < 121.816 < inf | logz: 73.237 +/- 0.844 | dlogz: 1.711 > 0.059] + + 2506it [00:14, 213.09it/s, bound: 319 | nc: 5 | ncall: 13500 | eff(%): 18.563 | loglstar: -inf < 122.077 < inf | logz: 73.336 +/- 0.845 | dlogz: 1.710 > 0.059] + + 2528it [00:14, 202.82it/s, bound: 322 | nc: 5 | ncall: 13610 | eff(%): 18.575 | loglstar: -inf < 122.619 < inf | logz: 73.419 +/- 0.847 | dlogz: 1.307 > 0.059] + + 2549it [00:14, 198.35it/s, bound: 325 | nc: 5 | ncall: 13715 | eff(%): 18.585 | loglstar: -inf < 123.021 < inf | logz: 73.494 +/- 0.848 | dlogz: 0.972 > 0.059] + + 2569it [00:14, 197.56it/s, bound: 328 | nc: 5 | ncall: 13815 | eff(%): 18.596 | loglstar: -inf < 123.438 < inf | logz: 73.560 +/- 0.849 | dlogz: 0.710 > 0.059] + + 2590it [00:14, 200.06it/s, bound: 331 | nc: 5 | ncall: 13920 | eff(%): 18.606 | loglstar: -inf < 123.737 < inf | logz: 73.621 +/- 0.850 | dlogz: 0.493 > 0.059] + + 2612it [00:14, 205.10it/s, bound: 334 | nc: 5 | ncall: 14030 | eff(%): 18.617 | loglstar: -inf < 123.979 < inf | logz: 73.675 +/- 0.852 | dlogz: 0.445 > 0.059] + + 2634it [00:14, 207.27it/s, bound: 337 | nc: 5 | ncall: 14140 | eff(%): 18.628 | loglstar: -inf < 124.242 < inf | logz: 73.718 +/- 0.853 | dlogz: 0.297 > 0.059] + + 2656it [00:14, 210.98it/s, bound: 340 | nc: 5 | ncall: 14250 | eff(%): 18.639 | loglstar: -inf < 124.392 < inf | logz: 73.751 +/- 0.854 | dlogz: 0.195 > 0.059] + + 2678it [00:15, 212.58it/s, bound: 343 | nc: 5 | ncall: 14360 | eff(%): 18.649 | loglstar: -inf < 124.665 < inf | logz: 73.777 +/- 0.854 | dlogz: 0.125 > 0.059] + + 2700it [00:15, 214.06it/s, bound: 346 | nc: 5 | ncall: 14470 | eff(%): 18.659 | loglstar: -inf < 124.749 < inf | logz: 73.795 +/- 0.855 | dlogz: 0.096 > 0.059] + + 2722it [00:15, 215.70it/s, bound: 349 | nc: 5 | ncall: 14580 | eff(%): 18.669 | loglstar: -inf < 124.891 < inf | logz: 73.808 +/- 0.855 | dlogz: 0.062 > 0.059] + + 2731it [00:15, 178.52it/s, +50 | bound: 350 | nc: 1 | ncall: 14675 | eff(%): 19.015 | loglstar: -inf < 125.941 < inf | logz: 73.841 +/- 0.861 | dlogz: 0.001 > 0.059] + + + + + 2026-07-11 16:24:09,309 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:24:09,531 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:24:09,590 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +Lets print the result info and plot the fit to the dataset to confirm the priors have provided a better model-fit. + + +```python +print(result.info) + +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + color="k", + ecolor="k", + elinewidth=1, + capsize=2, + linestyle="", +) +plt.plot(range(data.shape[0]), model_data, color="r") +for model_data_1d_individual in model_data_list: + plt.plot(range(data.shape[0]), model_data_1d_individual, "--") +plt.title(f"Fit (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.clf() +plt.close() + +residual_map = data - model_data +normalized_residual_map = residual_map / noise_map +plt.plot(xvalues, normalized_residual_map, color="k") +plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Normalized Residuals ($\sigma$)") +plt.show() +plt.clf() +plt.close() +``` + + Bayesian Evidence 73.84079536 + Maximum Log Likelihood 125.94072461 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + Maximum Log Likelihood Model: + + gaussian_0 + sigma 6.053 + ... [51 lines of output truncated] ... + sigma 1.03 (1.03, 1.04) + centre 50.00 (49.99, 50.00) + normalization 20.93 (20.81, 21.08) + gaussian_2 + sigma 10.35 (9.05, 19.85) + centre 52.24 (51.07, 52.39) + normalization 2.46 (0.46, 3.09) + gaussian_3 + sigma 16.61 (16.57, 16.64) + centre 49.98 (49.94, 50.01) + normalization 206.37 (205.66, 206.90) + gaussian_4 + sigma 4.02 (2.66, 5.25) + centre 48.04 (47.53, 48.52) + normalization 0.45 (0.35, 0.60) + + instances + + + + + <>:28: SyntaxWarning: invalid escape sequence '\s' + <>:28: SyntaxWarning: invalid escape sequence '\s' + /tmp/ipykernel_20726/2299532750.py:28: SyntaxWarning: invalid escape sequence '\s' + plt.ylabel("Normalized Residuals ($\sigma$)") + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_35_2.png) + + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_35_3.png) + + + +Lets consider the advantages and disadvantages of prior tuning: + +**Advantages:** + +- Higher likelihood of finding the global maximum log likelihood solutions in parameter space. + +- Faster search times, as the non-linear search explores less of the parameter space. + +**Disadvantages:** + +- Incorrectly specified priors could lead the non-linear search to an incorrect solution. + +- It is not always clear how the priors should be tuned, especially for complex models. + +- Priors tuning must be applied to each dataset fitted. For large datasets, this process would be very time-consuming. + +__Reducing Complexity__ + +The non-linear search may fail because the model is too complex, making its parameter space too difficult to +sample accurately consistent. To address this, we may be able to simplify the model while ensuring it remains +realistic enough for our scientific study. By making certain assumptions, we can reduce the number of model +parameters, thereby lowering the dimensionality of the parameter space and improving the search's performance. + +For example, we may know that the `Gaussian`'s in our model are aligned at the same `centre`. We can therefore +compose a model that assumes that the `centre` of each `Gaussian` is the same, reducing the dimensionality of the +model from N=15 to N=11. + +The code below shows how we can customize the model components to ensure the `centre` of each `Gaussian` is the same: + + +```python +gaussian_0 = af.Model(Gaussian) +gaussian_1 = af.Model(Gaussian) +gaussian_2 = af.Model(Gaussian) +gaussian_3 = af.Model(Gaussian) +gaussian_4 = af.Model(Gaussian) + +gaussian_1.centre = gaussian_0.centre +gaussian_2.centre = gaussian_0.centre +gaussian_3.centre = gaussian_0.centre +gaussian_4.centre = gaussian_0.centre + +model = af.Collection( + gaussian_0=gaussian_0, + gaussian_1=gaussian_1, + gaussian_2=gaussian_2, + gaussian_3=gaussian_3, + gaussian_4=gaussian_4, +) +``` + +The `info` attribute shows the model is now using the same `centre` for all `Gaussian`'s and has 11 free parameters. + + +```python +print(model.info) +``` + + Total Free Parameters = 11 + + model Collection (N=11) + gaussian_0 - gaussian_4 Gaussian (N=3) + + gaussian_0 - gaussian_4 + centre UniformPrior [40], lower_limit = 0.0, upper_limit = 100.0 + gaussian_0 + normalization LogUniformPrior [41], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [42], lower_limit = 0.0, upper_limit = 25.0 + gaussian_1 + normalization LogUniformPrior [44], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [45], lower_limit = 0.0, upper_limit = 25.0 + gaussian_2 + normalization LogUniformPrior [47], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [48], lower_limit = 0.0, upper_limit = 25.0 + gaussian_3 + normalization LogUniformPrior [50], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [51], lower_limit = 0.0, upper_limit = 25.0 + gaussian_4 + normalization LogUniformPrior [53], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [54], lower_limit = 0.0, upper_limit = 25.0 + + +We now repeat the model-fit using this updated model where the `centre` of each `Gaussian` is the same. + +You should again note that the run time of the fit is significantly faster than the previous fits +and that it consistently produces a good model-fit more often. This is because the model is less complex, +non-linear parameter space is less difficult to sample and the search is less likely to converge on a local maxima. + + +```python +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:24:09,915 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:24:09,924 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:24:09,924 - root - INFO - Starting new Dynesty non-linear search (no previous samples found). + + + 2026-07-11 16:24:09,930 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:24:09,961 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + ~/venv/PyAuto/lib/python3.12/site-packages/dynesty/dynesty.py:194: UserWarning: Specifying slice option while using rwalk sampler does not make sense + warnings.warn('Specifying slice option while using rwalk sampler' + + + 0it [00:00, ?it/s] + + 29it [00:00, 279.69it/s, bound: 0 | nc: 2 | ncall: 86 | eff(%): 33.721 | loglstar: -inf < -inf < inf | logz: -inf +/- 48.943 | dlogz: inf > 0.059] + + 57it [00:00, 207.86it/s, bound: 0 | nc: 7 | ncall: 141 | eff(%): 40.426 | loglstar: -inf < -inf < inf | logz: -inf +/- 0.295 | dlogz: inf > 0.059] + + 79it [00:00, 182.30it/s, bound: 0 | nc: 10 | ncall: 216 | eff(%): 36.574 | loglstar: -inf < -inf < inf | logz: -inf +/- 0.350 | dlogz: inf > 0.059] + + 98it [00:00, 151.08it/s, bound: 0 | nc: 11 | ncall: 302 | eff(%): 32.450 | loglstar: -inf < -660222.268 < inf | logz: -660228.814 +/- 0.360 | dlogz: 472141.147 > 0.059] + + 114it [00:00, 101.84it/s, bound: 0 | nc: 4 | ncall: 455 | eff(%): 25.055 | loglstar: -inf < -654344.798 < inf | logz: -654351.661 +/- 0.369 | dlogz: 454035.388 > 0.059] + + 127it [00:01, 71.00it/s, bound: 0 | nc: 6 | ncall: 660 | eff(%): 19.242 | loglstar: -inf < -638613.090 < inf | logz: -638620.210 +/- 0.375 | dlogz: 456334.842 > 0.059] + + 137it [00:01, 57.62it/s, bound: 0 | nc: 17 | ncall: 826 | eff(%): 16.586 | loglstar: -inf < -623466.760 < inf | logz: -623474.078 +/- 0.381 | dlogz: 441499.942 > 0.059] + + 145it [00:01, 40.28it/s, bound: 0 | nc: 12 | ncall: 1073 | eff(%): 13.514 | loglstar: -inf < -606925.822 < inf | logz: -606933.299 +/- 0.385 | dlogz: 537317.491 > 0.059] + + 151it [00:02, 37.03it/s, bound: 0 | nc: 8 | ncall: 1184 | eff(%): 12.753 | loglstar: -inf < -597212.469 < inf | logz: -597220.065 +/- 0.388 | dlogz: 527467.105 > 0.059] + + 156it [00:02, 37.18it/s, bound: 0 | nc: 29 | ncall: 1259 | eff(%): 12.391 | loglstar: -inf < -588879.698 < inf | logz: -588887.392 +/- 0.390 | dlogz: 520980.455 > 0.059] + + 161it [00:02, 37.43it/s, bound: 0 | nc: 13 | ncall: 1329 | eff(%): 12.114 | loglstar: -inf < -577439.448 < inf | logz: -577447.241 +/- 0.393 | dlogz: 509594.745 > 0.059] + + 166it [00:02, 33.34it/s, bound: 0 | nc: 13 | ncall: 1430 | eff(%): 11.608 | loglstar: -inf < -569292.970 < inf | logz: -569300.862 +/- 0.395 | dlogz: 502413.291 > 0.059] + + 170it [00:02, 34.45it/s, bound: 0 | nc: 1 | ncall: 1484 | eff(%): 11.456 | loglstar: -inf < -557089.818 < inf | logz: -557097.789 +/- 0.397 | dlogz: 489121.509 > 0.059] + + 174it [00:03, 25.94it/s, bound: 0 | nc: 88 | ncall: 1665 | eff(%): 10.450 | loglstar: -inf < -532586.243 < inf | logz: -532594.294 +/- 0.399 | dlogz: 462850.670 > 0.059] + + 178it [00:03, 21.05it/s, bound: 1 | nc: 6 | ncall: 1808 | eff(%): 9.845 | loglstar: -inf < -517231.856 < inf | logz: -517239.986 +/- 0.401 | dlogz: 449692.119 > 0.059] + + 206it [00:03, 60.76it/s, bound: 4 | nc: 5 | ncall: 1948 | eff(%): 10.575 | loglstar: -inf < -376633.234 < inf | logz: -376640.271 +/- 0.370 | dlogz: 306741.381 > 0.059] + + 232it [00:03, 96.14it/s, bound: 7 | nc: 5 | ncall: 2078 | eff(%): 11.165 | loglstar: -inf < -259663.684 < inf | logz: -259672.893 +/- 0.419 | dlogz: 192112.354 > 0.059] + + 254it [00:03, 121.09it/s, bound: 10 | nc: 5 | ncall: 2188 | eff(%): 11.609 | loglstar: -inf < -215968.889 < inf | logz: -215977.419 +/- 0.399 | dlogz: 159152.803 > 0.059] + + 277it [00:03, 145.32it/s, bound: 13 | nc: 5 | ncall: 2303 | eff(%): 12.028 | loglstar: -inf < -192876.534 < inf | logz: -192883.958 +/- 0.363 | dlogz: 136057.945 > 0.059] + + 303it [00:03, 171.95it/s, bound: 17 | nc: 5 | ncall: 2433 | eff(%): 12.454 | loglstar: -inf < -150065.150 < inf | logz: -150075.788 +/- 0.438 | dlogz: 127941.780 > 0.059] + + 325it [00:03, 182.98it/s, bound: 20 | nc: 5 | ncall: 2543 | eff(%): 12.780 | loglstar: -inf < -128347.662 < inf | logz: -128358.739 +/- 0.445 | dlogz: 104129.156 > 0.059] + + 349it [00:04, 196.34it/s, bound: 23 | nc: 5 | ncall: 2663 | eff(%): 13.106 | loglstar: -inf < -107212.722 < inf | logz: -107224.279 +/- 0.451 | dlogz: 85647.186 > 0.059] + + 371it [00:04, 191.42it/s, bound: 26 | nc: 5 | ncall: 2773 | eff(%): 13.379 | loglstar: -inf < -91571.469 < inf | logz: -91583.479 +/- 0.456 | dlogz: 70172.079 > 0.059] + + 394it [00:04, 198.91it/s, bound: 29 | nc: 5 | ncall: 2888 | eff(%): 13.643 | loglstar: -inf < -74099.303 < inf | logz: -74110.653 +/- 0.439 | dlogz: 49482.768 > 0.059] + + 417it [00:04, 206.77it/s, bound: 31 | nc: 5 | ncall: 3003 | eff(%): 13.886 | loglstar: -inf < -69895.339 < inf | logz: -69906.263 +/- 0.423 | dlogz: 45320.090 > 0.059] + + 442it [00:04, 217.37it/s, bound: 35 | nc: 5 | ncall: 3128 | eff(%): 14.130 | loglstar: -inf < -61937.750 < inf | logz: -61949.531 +/- 0.439 | dlogz: 37363.034 > 0.059] + + 468it [00:04, 228.80it/s, bound: 38 | nc: 5 | ncall: 3258 | eff(%): 14.365 | loglstar: -inf < -54346.309 < inf | logz: -54360.266 +/- 0.481 | dlogz: 42091.543 > 0.059] + + 492it [00:04, 226.06it/s, bound: 41 | nc: 5 | ncall: 3378 | eff(%): 14.565 | loglstar: -inf < -45738.839 < inf | logz: -45753.276 +/- 0.488 | dlogz: 35838.867 > 0.059] + + 516it [00:04, 228.51it/s, bound: 44 | nc: 5 | ncall: 3498 | eff(%): 14.751 | loglstar: -inf < -33860.503 < inf | logz: -33875.420 +/- 0.495 | dlogz: 23594.654 > 0.059] + + 540it [00:04, 223.25it/s, bound: 47 | nc: 5 | ncall: 3618 | eff(%): 14.925 | loglstar: -inf < -28051.476 < inf | logz: -28065.753 +/- 0.479 | dlogz: 17452.334 > 0.059] + + 564it [00:05, 225.77it/s, bound: 50 | nc: 5 | ncall: 3738 | eff(%): 15.088 | loglstar: -inf < -27846.479 < inf | logz: -27858.385 +/- 0.415 | dlogz: 17243.365 > 0.059] + + 587it [00:05, 222.66it/s, bound: 53 | nc: 5 | ncall: 3853 | eff(%): 15.235 | loglstar: -inf < -24621.141 < inf | logz: -24636.447 +/- 0.485 | dlogz: 14021.998 > 0.059] + + 610it [00:05, 222.12it/s, bound: 56 | nc: 5 | ncall: 3968 | eff(%): 15.373 | loglstar: -inf < -20407.133 < inf | logz: -20424.017 +/- 0.514 | dlogz: 10033.528 > 0.059] + + 633it [00:05, 206.12it/s, bound: 59 | nc: 5 | ncall: 4083 | eff(%): 15.503 | loglstar: -inf < -16686.800 < inf | logz: -16704.144 +/- 0.520 | dlogz: 6117.784 > 0.059] + + 655it [00:05, 209.56it/s, bound: 62 | nc: 5 | ncall: 4193 | eff(%): 15.621 | loglstar: -inf < -15663.456 < inf | logz: -15680.127 +/- 0.503 | dlogz: 5216.347 > 0.059] + + 678it [00:05, 213.57it/s, bound: 65 | nc: 5 | ncall: 4308 | eff(%): 15.738 | loglstar: -inf < -14542.138 < inf | logz: -14560.405 +/- 0.529 | dlogz: 6389.229 > 0.059] + + 700it [00:05, 202.61it/s, bound: 68 | nc: 5 | ncall: 4418 | eff(%): 15.844 | loglstar: -inf < -13489.622 < inf | logz: -13508.326 +/- 0.535 | dlogz: 5266.006 > 0.059] + + 722it [00:05, 205.94it/s, bound: 71 | nc: 5 | ncall: 4528 | eff(%): 15.945 | loglstar: -inf < -12327.516 < inf | logz: -12344.655 +/- 0.501 | dlogz: 4060.165 > 0.059] + + 745it [00:05, 210.54it/s, bound: 74 | nc: 5 | ncall: 4643 | eff(%): 16.046 | loglstar: -inf < -11670.525 < inf | logz: -11690.075 +/- 0.537 | dlogz: 6824.719 > 0.059] + + 767it [00:05, 212.94it/s, bound: 77 | nc: 5 | ncall: 4753 | eff(%): 16.137 | loglstar: -inf < -11254.449 < inf | logz: -11274.509 +/- 0.546 | dlogz: 6457.507 > 0.059] + + 791it [00:06, 217.66it/s, bound: 81 | nc: 5 | ncall: 4873 | eff(%): 16.232 | loglstar: -inf < -10451.714 < inf | logz: -10472.254 +/- 0.553 | dlogz: 5621.037 > 0.059] + + 816it [00:06, 226.38it/s, bound: 84 | nc: 5 | ncall: 4998 | eff(%): 16.327 | loglstar: -inf < -9353.321 < inf | logz: -9372.349 +/- 0.521 | dlogz: 4550.190 > 0.059] + + 841it [00:06, 231.58it/s, bound: 87 | nc: 5 | ncall: 5123 | eff(%): 16.416 | loglstar: -inf < -8533.746 < inf | logz: -8555.286 +/- 0.564 | dlogz: 4089.582 > 0.059] + + 866it [00:06, 235.31it/s, bound: 91 | nc: 5 | ncall: 5248 | eff(%): 16.502 | loglstar: -inf < -8181.712 < inf | logz: -8203.752 +/- 0.570 | dlogz: 3713.754 > 0.059] + + 892it [00:06, 241.91it/s, bound: 94 | nc: 5 | ncall: 5378 | eff(%): 16.586 | loglstar: -inf < -7581.311 < inf | logz: -7603.868 +/- 0.576 | dlogz: 3232.837 > 0.059] + + 919it [00:06, 249.39it/s, bound: 97 | nc: 5 | ncall: 5513 | eff(%): 16.670 | loglstar: -inf < -6621.450 < inf | logz: -6644.549 +/- 0.582 | dlogz: 2179.460 > 0.059] + + 944it [00:06, 244.33it/s, bound: 101 | nc: 5 | ncall: 5638 | eff(%): 16.744 | loglstar: -inf < -6101.019 < inf | logz: -6122.968 +/- 0.559 | dlogz: 1615.372 > 0.059] + + 969it [00:06, 241.74it/s, bound: 104 | nc: 5 | ncall: 5763 | eff(%): 16.814 | loglstar: -inf < -5514.974 < inf | logz: -5539.073 +/- 0.594 | dlogz: 1209.237 > 0.059] + + 994it [00:06, 242.82it/s, bound: 107 | nc: 5 | ncall: 5888 | eff(%): 16.882 | loglstar: -inf < -5118.183 < inf | logz: -5142.788 +/- 0.599 | dlogz: 805.671 > 0.059] + + 1019it [00:07, 235.33it/s, bound: 110 | nc: 5 | ncall: 6013 | eff(%): 16.947 | loglstar: -inf < -4877.390 < inf | logz: -4902.495 +/- 0.605 | dlogz: 964.078 > 0.059] + + 1044it [00:07, 235.85it/s, bound: 113 | nc: 5 | ncall: 6138 | eff(%): 17.009 | loglstar: -inf < -4758.843 < inf | logz: -4783.242 +/- 0.582 | dlogz: 835.420 > 0.059] + + 1068it [00:07, 222.31it/s, bound: 116 | nc: 5 | ncall: 6258 | eff(%): 17.066 | loglstar: -inf < -4645.077 < inf | logz: -4669.528 +/- 0.585 | dlogz: 755.328 > 0.059] + + 1091it [00:07, 220.93it/s, bound: 119 | nc: 5 | ncall: 6373 | eff(%): 17.119 | loglstar: -inf < -4489.133 < inf | logz: -4514.580 +/- 0.599 | dlogz: 600.502 > 0.059] + + 1114it [00:07, 219.34it/s, bound: 123 | nc: 5 | ncall: 6488 | eff(%): 17.170 | loglstar: -inf < -4370.616 < inf | logz: -4397.642 +/- 0.622 | dlogz: 508.564 > 0.059] + + 1139it [00:07, 227.37it/s, bound: 126 | nc: 5 | ncall: 6613 | eff(%): 17.224 | loglstar: -inf < -4262.123 < inf | logz: -4289.649 +/- 0.627 | dlogz: 452.599 > 0.059] + + 1162it [00:07, 227.29it/s, bound: 129 | nc: 5 | ncall: 6728 | eff(%): 17.271 | loglstar: -inf < -4118.627 < inf | logz: -4146.613 +/- 0.632 | dlogz: 409.056 > 0.059] + + 1186it [00:07, 230.25it/s, bound: 132 | nc: 5 | ncall: 6848 | eff(%): 17.319 | loglstar: -inf < -4032.806 < inf | logz: -4060.465 +/- 0.621 | dlogz: 387.738 > 0.059] + + 1211it [00:07, 234.32it/s, bound: 135 | nc: 5 | ncall: 6973 | eff(%): 17.367 | loglstar: -inf < -3987.210 < inf | logz: -4015.823 +/- 0.631 | dlogz: 421.739 > 0.059] + + 1236it [00:07, 237.83it/s, bound: 139 | nc: 5 | ncall: 7098 | eff(%): 17.413 | loglstar: -inf < -3927.754 < inf | logz: -3955.937 +/- 0.624 | dlogz: 360.308 > 0.059] + + 1260it [00:08, 236.24it/s, bound: 142 | nc: 5 | ncall: 7218 | eff(%): 17.456 | loglstar: -inf < -3873.872 < inf | logz: -3903.319 +/- 0.636 | dlogz: 307.789 > 0.059] + + 1284it [00:08, 230.37it/s, bound: 145 | nc: 5 | ncall: 7338 | eff(%): 17.498 | loglstar: -inf < -3816.619 < inf | logz: -3846.655 +/- 0.641 | dlogz: 250.587 > 0.059] + + 1308it [00:08, 224.75it/s, bound: 148 | nc: 5 | ncall: 7458 | eff(%): 17.538 | loglstar: -inf < -3773.926 < inf | logz: -3803.503 +/- 0.639 | dlogz: 432.127 > 0.059] + + 1331it [00:08, 219.66it/s, bound: 151 | nc: 5 | ncall: 7573 | eff(%): 17.576 | loglstar: -inf < -3737.201 < inf | logz: -3767.361 +/- 0.650 | dlogz: 475.830 > 0.059] + + 1355it [00:08, 223.15it/s, bound: 154 | nc: 5 | ncall: 7693 | eff(%): 17.613 | loglstar: -inf < -3655.797 < inf | logz: -3687.641 +/- 0.677 | dlogz: 1171.039 > 0.059] + + 1378it [00:08, 220.96it/s, bound: 157 | nc: 5 | ncall: 7808 | eff(%): 17.649 | loglstar: -inf < -3613.095 < inf | logz: -3643.452 +/- 0.651 | dlogz: 2407.662 > 0.059] + + 1401it [00:08, 222.86it/s, bound: 160 | nc: 5 | ncall: 7923 | eff(%): 17.683 | loglstar: -inf < -3458.276 < inf | logz: -3490.096 +/- 0.662 | dlogz: 2254.245 > 0.059] + + 1424it [00:08, 221.00it/s, bound: 162 | nc: 5 | ncall: 8038 | eff(%): 17.716 | loglstar: -inf < -3320.550 < inf | logz: -3352.486 +/- 0.669 | dlogz: 2149.616 > 0.059] + + 1447it [00:08, 218.12it/s, bound: 165 | nc: 5 | ncall: 8153 | eff(%): 17.748 | loglstar: -inf < -3150.581 < inf | logz: -3184.282 +/- 0.694 | dlogz: 1989.778 > 0.059] + + 1469it [00:09, 199.98it/s, bound: 168 | nc: 5 | ncall: 8263 | eff(%): 17.778 | loglstar: -inf < -2803.335 < inf | logz: -2836.357 +/- 0.683 | dlogz: 1632.866 > 0.059] + + 1490it [00:09, 175.47it/s, bound: 171 | nc: 5 | ncall: 8368 | eff(%): 17.806 | loglstar: -inf < -2490.732 < inf | logz: -2525.275 +/- 0.702 | dlogz: 1634.016 > 0.059] + + 1509it [00:09, 169.28it/s, bound: 173 | nc: 5 | ncall: 8463 | eff(%): 17.831 | loglstar: -inf < -2254.130 < inf | logz: -2289.069 +/- 0.707 | dlogz: 1590.304 > 0.059] + + 1528it [00:09, 173.13it/s, bound: 176 | nc: 5 | ncall: 8558 | eff(%): 17.855 | loglstar: -inf < -1953.218 < inf | logz: -1988.538 +/- 0.710 | dlogz: 1387.445 > 0.059] + + 1549it [00:09, 182.47it/s, bound: 178 | nc: 5 | ncall: 8663 | eff(%): 17.881 | loglstar: -inf < -1782.083 < inf | logz: -1815.551 +/- 0.680 | dlogz: 1086.293 > 0.059] + + 1570it [00:09, 188.66it/s, bound: 181 | nc: 5 | ncall: 8768 | eff(%): 17.906 | loglstar: -inf < -1586.633 < inf | logz: -1621.621 +/- 0.697 | dlogz: 981.686 > 0.059] + + 1592it [00:09, 196.67it/s, bound: 185 | nc: 5 | ncall: 8878 | eff(%): 17.932 | loglstar: -inf < -1477.266 < inf | logz: -1512.940 +/- 0.688 | dlogz: 1021.788 > 0.059] + + 1616it [00:09, 207.57it/s, bound: 188 | nc: 5 | ncall: 8998 | eff(%): 17.960 | loglstar: -inf < -1242.027 < inf | logz: -1278.972 +/- 0.718 | dlogz: 800.642 > 0.059] + + 1639it [00:09, 212.95it/s, bound: 191 | nc: 5 | ncall: 9113 | eff(%): 17.985 | loglstar: -inf < -1106.836 < inf | logz: -1144.392 +/- 0.729 | dlogz: 828.987 > 0.059] + + 1661it [00:10, 213.01it/s, bound: 194 | nc: 5 | ncall: 9223 | eff(%): 18.009 | loglstar: -inf < -960.817 < inf | logz: -996.653 +/- 0.700 | dlogz: 721.204 > 0.059] + + 1683it [00:10, 214.98it/s, bound: 197 | nc: 5 | ncall: 9333 | eff(%): 18.033 | loglstar: -inf < -851.485 < inf | logz: -889.927 +/- 0.737 | dlogz: 892.405 > 0.059] + + 1705it [00:10, 195.78it/s, bound: 200 | nc: 5 | ncall: 9443 | eff(%): 18.056 | loglstar: -inf < -625.408 < inf | logz: -664.289 +/- 0.741 | dlogz: 688.229 > 0.059] + + 1725it [00:10, 184.09it/s, bound: 203 | nc: 5 | ncall: 9543 | eff(%): 18.076 | loglstar: -inf < -510.803 < inf | logz: -550.066 +/- 0.742 | dlogz: 553.677 > 0.059] + + 1744it [00:10, 185.45it/s, bound: 205 | nc: 5 | ncall: 9638 | eff(%): 18.095 | loglstar: -inf < -447.294 < inf | logz: -485.556 +/- 0.723 | dlogz: 485.349 > 0.059] + + 1765it [00:10, 191.25it/s, bound: 208 | nc: 5 | ncall: 9743 | eff(%): 18.116 | loglstar: -inf < -371.639 < inf | logz: -411.216 +/- 0.736 | dlogz: 411.349 > 0.059] + + 1788it [00:10, 201.74it/s, bound: 210 | nc: 5 | ncall: 9858 | eff(%): 18.138 | loglstar: -inf < -298.082 < inf | logz: -337.442 +/- 0.738 | dlogz: 336.684 > 0.059] + + 1811it [00:10, 208.67it/s, bound: 213 | nc: 5 | ncall: 9973 | eff(%): 18.159 | loglstar: -inf < -245.349 < inf | logz: -285.728 +/- 0.747 | dlogz: 310.711 > 0.059] + + 1835it [00:10, 217.49it/s, bound: 216 | nc: 5 | ncall: 10093 | eff(%): 18.181 | loglstar: -inf < -179.112 < inf | logz: -219.787 +/- 0.746 | dlogz: 252.218 > 0.059] + + 1860it [00:11, 225.29it/s, bound: 220 | nc: 5 | ncall: 10218 | eff(%): 18.203 | loglstar: -inf < -125.204 < inf | logz: -166.402 +/- 0.748 | dlogz: 230.138 > 0.059] + + 1886it [00:11, 233.09it/s, bound: 223 | nc: 5 | ncall: 10348 | eff(%): 18.226 | loglstar: -inf < -74.191 < inf | logz: -116.703 +/- 0.775 | dlogz: 185.902 > 0.059] + + 1910it [00:11, 226.86it/s, bound: 226 | nc: 5 | ncall: 10468 | eff(%): 18.246 | loglstar: -inf < -25.731 < inf | logz: -68.310 +/- 0.770 | dlogz: 156.313 > 0.059] + + 1934it [00:11, 229.27it/s, bound: 229 | nc: 5 | ncall: 10588 | eff(%): 18.266 | loglstar: -inf < 16.707 < inf | logz: -26.505 +/- 0.777 | dlogz: 114.410 > 0.059] + + 1957it [00:11, 197.75it/s, bound: 232 | nc: 5 | ncall: 10703 | eff(%): 18.285 | loglstar: -inf < 31.788 < inf | logz: -10.515 +/- 0.757 | dlogz: 109.161 > 0.059] + + 1978it [00:11, 188.98it/s, bound: 235 | nc: 5 | ncall: 10808 | eff(%): 18.301 | loglstar: -inf < 47.065 < inf | logz: 4.591 +/- 0.761 | dlogz: 96.010 > 0.059] + + 1999it [00:11, 192.36it/s, bound: 238 | nc: 5 | ncall: 10913 | eff(%): 18.318 | loglstar: -inf < 68.437 < inf | logz: 24.683 +/- 0.786 | dlogz: 78.098 > 0.059] + + 2022it [00:11, 201.32it/s, bound: 240 | nc: 5 | ncall: 11028 | eff(%): 18.335 | loglstar: -inf < 91.629 < inf | logz: 48.450 +/- 0.778 | dlogz: 53.181 > 0.059] + + 2045it [00:11, 207.94it/s, bound: 243 | nc: 5 | ncall: 11143 | eff(%): 18.352 | loglstar: -inf < 103.193 < inf | logz: 58.786 +/- 0.779 | dlogz: 45.572 > 0.059] + + 2070it [00:12, 217.34it/s, bound: 246 | nc: 5 | ncall: 11268 | eff(%): 18.371 | loglstar: -inf < 112.505 < inf | logz: 68.453 +/- 0.778 | dlogz: 46.180 > 0.059] + + 2095it [00:12, 223.10it/s, bound: 250 | nc: 5 | ncall: 11393 | eff(%): 18.388 | loglstar: -inf < 128.271 < inf | logz: 83.549 +/- 0.788 | dlogz: 37.245 > 0.059] + + 2119it [00:12, 224.24it/s, bound: 253 | nc: 5 | ncall: 11513 | eff(%): 18.405 | loglstar: -inf < 137.649 < inf | logz: 93.471 +/- 0.787 | dlogz: 34.872 > 0.059] + + 2142it [00:12, 209.89it/s, bound: 255 | nc: 5 | ncall: 11628 | eff(%): 18.421 | loglstar: -inf < 141.127 < inf | logz: 95.842 +/- 0.783 | dlogz: 31.972 > 0.059] + + 2164it [00:12, 197.20it/s, bound: 258 | nc: 5 | ncall: 11738 | eff(%): 18.436 | loglstar: -inf < 144.620 < inf | logz: 98.984 +/- 0.790 | dlogz: 28.369 > 0.059] + + 2185it [00:12, 198.47it/s, bound: 261 | nc: 5 | ncall: 11843 | eff(%): 18.450 | loglstar: -inf < 149.245 < inf | logz: 102.912 +/- 0.797 | dlogz: 24.078 > 0.059] + + 2208it [00:12, 206.95it/s, bound: 264 | nc: 5 | ncall: 11958 | eff(%): 18.465 | loglstar: -inf < 153.435 < inf | logz: 107.116 +/- 0.804 | dlogz: 19.357 > 0.059] + + 2229it [00:12, 206.62it/s, bound: 266 | nc: 5 | ncall: 12063 | eff(%): 18.478 | loglstar: -inf < 156.682 < inf | logz: 110.032 +/- 0.803 | dlogz: 22.708 > 0.059] + + 2250it [00:12, 196.48it/s, bound: 269 | nc: 5 | ncall: 12168 | eff(%): 18.491 | loglstar: -inf < 160.087 < inf | logz: 112.715 +/- 0.808 | dlogz: 19.647 > 0.059] + + 2271it [00:13, 196.90it/s, bound: 272 | nc: 5 | ncall: 12273 | eff(%): 18.504 | loglstar: -inf < 162.889 < inf | logz: 115.502 +/- 0.811 | dlogz: 16.374 > 0.059] + + 2291it [00:13, 181.18it/s, bound: 274 | nc: 5 | ncall: 12373 | eff(%): 18.516 | loglstar: -inf < 164.926 < inf | logz: 117.113 +/- 0.812 | dlogz: 14.385 > 0.059] + + 2310it [00:13, 169.17it/s, bound: 276 | nc: 5 | ncall: 12468 | eff(%): 18.527 | loglstar: -inf < 166.655 < inf | logz: 118.847 +/- 0.817 | dlogz: 12.231 > 0.059] + + 2328it [00:13, 153.66it/s, bound: 279 | nc: 5 | ncall: 12558 | eff(%): 18.538 | loglstar: -inf < 167.812 < inf | logz: 119.957 +/- 0.817 | dlogz: 10.743 > 0.059] + + 2344it [00:13, 152.92it/s, bound: 281 | nc: 5 | ncall: 12638 | eff(%): 18.547 | loglstar: -inf < 168.725 < inf | logz: 120.817 +/- 0.819 | dlogz: 9.548 > 0.059] + + 2363it [00:13, 160.89it/s, bound: 283 | nc: 5 | ncall: 12733 | eff(%): 18.558 | loglstar: -inf < 170.149 < inf | logz: 121.671 +/- 0.821 | dlogz: 8.325 > 0.059] + + 2381it [00:13, 165.90it/s, bound: 285 | nc: 5 | ncall: 12823 | eff(%): 18.568 | loglstar: -inf < 170.849 < inf | logz: 122.427 +/- 0.823 | dlogz: 8.122 > 0.059] + + 2398it [00:13, 166.06it/s, bound: 287 | nc: 5 | ncall: 12908 | eff(%): 18.578 | loglstar: -inf < 171.370 < inf | logz: 122.892 +/- 0.824 | dlogz: 7.308 > 0.059] + + 2417it [00:13, 171.32it/s, bound: 290 | nc: 5 | ncall: 13003 | eff(%): 18.588 | loglstar: -inf < 172.423 < inf | logz: 123.402 +/- 0.826 | dlogz: 6.421 > 0.059] + + 2435it [00:14, 167.67it/s, bound: 292 | nc: 5 | ncall: 13093 | eff(%): 18.598 | loglstar: -inf < 173.725 < inf | logz: 124.108 +/- 0.832 | dlogz: 5.370 > 0.059] + + 2455it [00:14, 174.53it/s, bound: 295 | nc: 5 | ncall: 13193 | eff(%): 18.608 | loglstar: -inf < 174.520 < inf | logz: 124.739 +/- 0.836 | dlogz: 4.335 > 0.059] + + 2474it [00:14, 177.98it/s, bound: 297 | nc: 5 | ncall: 13288 | eff(%): 18.618 | loglstar: -inf < 175.433 < inf | logz: 125.269 +/- 0.839 | dlogz: 5.315 > 0.059] + + 2492it [00:14, 169.98it/s, bound: 299 | nc: 5 | ncall: 13378 | eff(%): 18.628 | loglstar: -inf < 176.045 < inf | logz: 125.726 +/- 0.842 | dlogz: 4.500 > 0.059] + + 2510it [00:14, 171.40it/s, bound: 301 | nc: 5 | ncall: 13468 | eff(%): 18.637 | loglstar: -inf < 176.519 < inf | logz: 126.088 +/- 0.844 | dlogz: 3.784 > 0.059] + + 2530it [00:14, 177.38it/s, bound: 304 | nc: 5 | ncall: 13568 | eff(%): 18.647 | loglstar: -inf < 177.117 < inf | logz: 126.460 +/- 0.846 | dlogz: 3.033 > 0.059] + + 2549it [00:14, 179.74it/s, bound: 308 | nc: 5 | ncall: 13663 | eff(%): 18.656 | loglstar: -inf < 177.494 < inf | logz: 126.718 +/- 0.847 | dlogz: 2.433 > 0.059] + + 2568it [00:14, 178.51it/s, bound: 311 | nc: 5 | ncall: 13758 | eff(%): 18.666 | loglstar: -inf < 177.633 < inf | logz: 126.900 +/- 0.848 | dlogz: 2.727 > 0.059] + + 2586it [00:14, 175.70it/s, bound: 313 | nc: 5 | ncall: 13848 | eff(%): 18.674 | loglstar: -inf < 177.997 < inf | logz: 127.027 +/- 0.849 | dlogz: 2.277 > 0.059] + + 2606it [00:15, 180.69it/s, bound: 316 | nc: 5 | ncall: 13948 | eff(%): 18.684 | loglstar: -inf < 178.445 < inf | logz: 127.145 +/- 0.850 | dlogz: 1.827 > 0.059] + + 2627it [00:15, 188.03it/s, bound: 319 | nc: 5 | ncall: 14053 | eff(%): 18.694 | loglstar: -inf < 178.978 < inf | logz: 127.275 +/- 0.851 | dlogz: 1.998 > 0.059] + + 2646it [00:15, 184.73it/s, bound: 322 | nc: 5 | ncall: 14148 | eff(%): 18.702 | loglstar: -inf < 179.628 < inf | logz: 127.401 +/- 0.853 | dlogz: 1.579 > 0.059] + + 2665it [00:15, 185.77it/s, bound: 325 | nc: 5 | ncall: 14243 | eff(%): 18.711 | loglstar: -inf < 179.827 < inf | logz: 127.516 +/- 0.855 | dlogz: 1.650 > 0.059] + + 2685it [00:15, 186.69it/s, bound: 328 | nc: 5 | ncall: 14343 | eff(%): 18.720 | loglstar: -inf < 180.140 < inf | logz: 127.609 +/- 0.856 | dlogz: 1.271 > 0.059] + + 2708it [00:15, 198.32it/s, bound: 330 | nc: 5 | ncall: 14458 | eff(%): 18.730 | loglstar: -inf < 180.570 < inf | logz: 127.706 +/- 0.858 | dlogz: 0.903 > 0.059] + + 2729it [00:15, 200.41it/s, bound: 333 | nc: 5 | ncall: 14563 | eff(%): 18.739 | loglstar: -inf < 180.918 < inf | logz: 127.780 +/- 0.860 | dlogz: 0.850 > 0.059] + + 2750it [00:15, 198.74it/s, bound: 336 | nc: 5 | ncall: 14668 | eff(%): 18.748 | loglstar: -inf < 181.140 < inf | logz: 127.841 +/- 0.861 | dlogz: 0.603 > 0.059] + + 2770it [00:15, 195.81it/s, bound: 338 | nc: 5 | ncall: 14768 | eff(%): 18.757 | loglstar: -inf < 181.499 < inf | logz: 127.888 +/- 0.862 | dlogz: 0.424 > 0.059] + + 2790it [00:15, 187.78it/s, bound: 341 | nc: 5 | ncall: 14868 | eff(%): 18.765 | loglstar: -inf < 181.678 < inf | logz: 127.930 +/- 0.863 | dlogz: 0.292 > 0.059] + + 2811it [00:16, 193.73it/s, bound: 344 | nc: 5 | ncall: 14973 | eff(%): 18.774 | loglstar: -inf < 181.937 < inf | logz: 127.964 +/- 0.864 | dlogz: 0.195 > 0.059] + + 2833it [00:16, 200.30it/s, bound: 347 | nc: 5 | ncall: 15083 | eff(%): 18.783 | loglstar: -inf < 182.106 < inf | logz: 127.992 +/- 0.865 | dlogz: 0.142 > 0.059] + + 2854it [00:16, 198.67it/s, bound: 350 | nc: 5 | ncall: 15188 | eff(%): 18.791 | loglstar: -inf < 182.268 < inf | logz: 128.012 +/- 0.865 | dlogz: 0.094 > 0.059] + + 2874it [00:16, 196.75it/s, bound: 353 | nc: 5 | ncall: 15288 | eff(%): 18.799 | loglstar: -inf < 182.441 < inf | logz: 128.027 +/- 0.866 | dlogz: 0.083 > 0.059] + + 2891it [00:16, 175.08it/s, +50 | bound: 355 | nc: 1 | ncall: 15423 | eff(%): 19.131 | loglstar: -inf < 183.400 < inf | logz: 128.070 +/- 0.872 | dlogz: 0.001 > 0.059] + + + + + 2026-07-11 16:24:27,006 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:24:27,245 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:24:27,341 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +Lets print the result info and plot the fit to the dataset to confirm the reduced model complexity has +provided a better model-fit. + + +```python +print(result.info) + +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + color="k", + ecolor="k", + elinewidth=1, + capsize=2, + linestyle="", +) +plt.plot(range(data.shape[0]), model_data, color="r") +for model_data_1d_individual in model_data_list: + plt.plot(range(data.shape[0]), model_data_1d_individual, "--") +plt.title(f"Fit (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.clf() +plt.close() + +residual_map = data - model_data +normalized_residual_map = residual_map / noise_map +plt.plot(xvalues, normalized_residual_map, color="k") +plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Normalized Residuals ($\sigma$)") +plt.show() +plt.clf() +plt.close() +``` + + Bayesian Evidence 128.06963951 + Maximum Log Likelihood 183.39954200 + + model Collection (N=11) + gaussian_0 - gaussian_4 Gaussian (N=3) + + Maximum Log Likelihood Model: + + gaussian_0 - gaussian_4 + centre 49.999 + ... [42 lines of output truncated] ... + gaussian_0 + normalization 0.00 (0.00, 0.00) + sigma 18.66 (16.22, 21.61) + gaussian_1 + normalization 99.47 (91.47, 107.33) + sigma 12.09 (11.90, 12.30) + gaussian_2 + normalization 129.93 (124.16, 136.62) + sigma 19.42 (19.11, 19.79) + gaussian_3 + normalization 49.48 (47.84, 51.33) + sigma 5.33 (5.26, 5.43) + gaussian_4 + normalization 20.24 (20.11, 20.40) + sigma 1.01 (1.01, 1.02) + + instances + + + + + <>:28: SyntaxWarning: invalid escape sequence '\s' + <>:28: SyntaxWarning: invalid escape sequence '\s' + /tmp/ipykernel_20726/2299532750.py:28: SyntaxWarning: invalid escape sequence '\s' + plt.ylabel("Normalized Residuals ($\sigma$)") + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_43_2.png) + + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_43_3.png) + + + +Let’s consider the advantages and disadvantages of simplifying the model: + +Advantages: + +- By reducing the complexity of the parameter space, we increase the chances of finding the global maximum log +likelihood, and the search requires less time to do so. + +- Unlike with tuned priors, this approach is not specific to a single dataset, allowing us to use it on many datasets. + +Disadvantages: + +- Our model is less realistic, which may negatively impact the accuracy of our fit and the scientific results we +derive from it. + +__Search More Thoroughly__ + +In approaches 1 and 2, we assisted our non-linear search to find the highest log likelihood regions of parameter +space. In approach 3, we're simply going to tell the search to look more thoroughly through parameter space. + +Every non-linear search has settings that control how thoroughly it explores parameter space. For Dynesty, the +primary setting is the number of live points `nlive`. The more thoroughly the search examines the space, the more +likely it is to find the global maximum model. However, this also means the search will take longer! + +Below, we configure a more thorough Dynesty search with `nlive=500`. It is currently unclear what changing +this setting actually does and what the number of live points actually means. These will be covered in chapter 2 +of the **HowToFit** lectures, where we will also expand on how a non-linear search actually works and the different +types of methods that can be used to search parameter space. + + +```python +model = af.Collection( + gaussian_0=Gaussian, + gaussian_1=Gaussian, + gaussian_2=Gaussian, + gaussian_3=Gaussian, + gaussian_4=Gaussian, +) +``` + +The `model.info` confirms the model is the same model fitted initially, composed of 5 `Gaussian` profiles. + + +```python +print(model.info) +``` + + Total Free Parameters = 15 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + gaussian_0 + centre UniformPrior [55], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [56], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [57], lower_limit = 0.0, upper_limit = 25.0 + gaussian_1 + centre UniformPrior [58], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [59], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [60], lower_limit = 0.0, upper_limit = 25.0 + gaussian_2 + centre UniformPrior [61], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [62], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [63], lower_limit = 0.0, upper_limit = 25.0 + gaussian_3 + centre UniformPrior [64], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [65], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [66], lower_limit = 0.0, upper_limit = 25.0 + gaussian_4 + centre UniformPrior [67], lower_limit = 0.0, upper_limit = 100.0 + normalization LogUniformPrior [68], lower_limit = 1e-06, upper_limit = 1000000.0 + sigma UniformPrior [69], lower_limit = 0.0, upper_limit = 25.0 + + +__Search__ + +We again use the nested sampling algorithm Dynesty to fit the model to the data, but now increase the number of live +points to 300 meaning it will search parameter space more thoroughly. + + +```python +search = af.DynestyStatic( + nlive=300, + sample="rwalk", # This makes dynesty run faster, don't worry about what it means for now! +) +``` + +__Model Fit__ + +Perform the fit using our five `Gaussian` model, which has 15 free parameters. + + +```python +analysis = Analysis(data=data, noise_map=noise_map) + +print( + """ + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") + +``` + + + The non-linear search has begun running. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:24:27,728 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:24:27,740 - root - INFO - Output to hard-disk disabled, input a search name to enable. + + + 2026-07-11 16:24:27,741 - root - INFO - Starting new Dynesty non-linear search (no previous samples found). + + + 2026-07-11 16:24:27,748 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:24:27,986 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + ~/venv/PyAuto/lib/python3.12/site-packages/dynesty/dynesty.py:194: UserWarning: Specifying slice option while using rwalk sampler does not make sense + warnings.warn('Specifying slice option while using rwalk sampler' + + + 0it [00:00, ?it/s] + + 42it [00:00, 413.74it/s, bound: 0 | nc: 1 | ncall: 344 | eff(%): 12.209 | loglstar: -inf < -inf < inf | logz: -inf +/- 827.978 | dlogz: inf > 0.309] + + 84it [00:00, 340.34it/s, bound: 0 | nc: 1 | ncall: 393 | eff(%): 21.374 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.309] + + 119it [00:00, 302.21it/s, bound: 0 | nc: 1 | ncall: 442 | eff(%): 26.923 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.309] + + 150it [00:00, 298.15it/s, bound: 0 | nc: 2 | ncall: 485 | eff(%): 30.928 | loglstar: -inf < -inf < inf | logz: -inf +/- 20.140 | dlogz: inf > 0.309] + + 181it [00:00, 274.04it/s, bound: 0 | nc: 3 | ncall: 540 | eff(%): 33.519 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.309] + + 209it [00:00, 251.89it/s, bound: 0 | nc: 1 | ncall: 593 | eff(%): 35.245 | loglstar: -inf < -inf < inf | logz: -inf +/- 0.815 | dlogz: inf > 0.309] + + 235it [00:00, 230.69it/s, bound: 0 | nc: 3 | ncall: 643 | eff(%): 36.547 | loglstar: -inf < -inf < inf | logz: -inf +/- 0.228 | dlogz: inf > 0.309] + + 259it [00:01, 210.97it/s, bound: 0 | nc: 5 | ncall: 709 | eff(%): 36.530 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.309] + + 281it [00:01, 186.35it/s, bound: 0 | nc: 2 | ncall: 765 | eff(%): 36.732 | loglstar: -inf < -inf < inf | logz: -inf +/- nan | dlogz: inf > 0.309] + + 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4782 | eff(%): 17.440 | loglstar: -inf < -623978.617 < inf | logz: -623987.789 +/- 0.175 | dlogz: 551362.494 > 0.309] + + 838it [00:10, 36.38it/s, bound: 0 | nc: 18 | ncall: 4842 | eff(%): 17.307 | loglstar: -inf < -622871.468 < inf | logz: -622880.654 +/- 0.175 | dlogz: 550842.936 > 0.309] + + 847it [00:10, 48.12it/s, bound: 0 | nc: 5 | ncall: 4905 | eff(%): 17.268 | loglstar: -inf < -619652.165 < inf | logz: -619661.381 +/- 0.175 | dlogz: 547176.269 > 0.309] + + 853it [00:11, 42.62it/s, bound: 0 | nc: 35 | ncall: 4995 | eff(%): 17.077 | loglstar: -inf < -614485.393 < inf | logz: -614494.629 +/- 0.175 | dlogz: 541629.342 > 0.309] + + 858it [00:11, 26.13it/s, bound: 0 | nc: 12 | ncall: 5171 | eff(%): 16.593 | loglstar: -inf < -612177.796 < inf | logz: -612187.048 +/- 0.175 | dlogz: 540235.430 > 0.309] + + 862it [00:11, 25.28it/s, bound: 0 | nc: 25 | ncall: 5235 | eff(%): 16.466 | loglstar: -inf < -609695.699 < inf | logz: -609704.965 +/- 0.176 | dlogz: 537743.416 > 0.309] + + 867it [00:11, 26.91it/s, bound: 0 | nc: 21 | ncall: 5288 | eff(%): 16.396 | loglstar: -inf < -606538.602 < inf | logz: -606547.884 +/- 0.176 | dlogz: 533871.617 > 0.309] + + 871it [00:11, 26.35it/s, bound: 0 | nc: 8 | ncall: 5357 | eff(%): 16.259 | loglstar: -inf < -605101.971 < inf | logz: -605111.252 +/- 0.175 | dlogz: 532176.348 > 0.309] + + 876it [00:12, 29.93it/s, bound: 0 | nc: 12 | ncall: 5409 | eff(%): 16.195 | loglstar: -inf < -601668.592 < inf | logz: -601677.904 +/- 0.176 | dlogz: 529266.601 > 0.309] + + 880it [00:12, 26.21it/s, bound: 0 | nc: 16 | ncall: 5505 | eff(%): 15.985 | loglstar: -inf < -599990.486 < inf | logz: -599999.812 +/- 0.176 | dlogz: 527206.543 > 0.309] + + 883it [00:12, 20.28it/s, bound: 0 | nc: 13 | ncall: 5640 | eff(%): 15.656 | loglstar: -inf < -599132.353 < inf | logz: -599141.688 +/- 0.176 | dlogz: 526220.219 > 0.309] + + 886it [00:12, 20.04it/s, bound: 0 | nc: 57 | ncall: 5707 | eff(%): 15.525 | loglstar: -inf < -598441.268 < inf | logz: -598450.613 +/- 0.176 | dlogz: 525667.433 > 0.309] + + 893it [00:12, 29.04it/s, bound: 0 | nc: 1 | ncall: 5743 | eff(%): 15.549 | loglstar: -inf < -596767.728 < inf | logz: -596777.097 +/- 0.177 | dlogz: 523883.356 > 0.309] + + 897it [00:13, 22.90it/s, bound: 0 | nc: 2 | ncall: 5879 | eff(%): 15.258 | loglstar: -inf < -595029.674 < inf | logz: -595039.056 +/- 0.177 | dlogz: 522353.861 > 0.309] + + 900it [00:13, 23.03it/s, bound: 0 | nc: 4 | ncall: 5936 | eff(%): 15.162 | loglstar: -inf < -592728.619 < inf | logz: -592738.011 +/- 0.177 | dlogz: 519900.693 > 0.309] + + 909it [00:13, 34.51it/s, bound: 0 | nc: 16 | ncall: 6006 | eff(%): 15.135 | loglstar: -inf < -589798.230 < inf | logz: -589807.652 +/- 0.177 | dlogz: 516908.898 > 0.309] + + 915it [00:13, 34.30it/s, bound: 0 | nc: 56 | ncall: 6107 | eff(%): 14.983 | loglstar: -inf < -587421.577 < inf | logz: -587431.019 +/- 0.177 | dlogz: 515327.006 > 0.309] + + 919it [00:13, 33.85it/s, bound: 0 | nc: 19 | ncall: 6172 | eff(%): 14.890 | loglstar: -inf < 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ncall: 6703 | eff(%): 14.098 | loglstar: -inf < -571992.236 < inf | logz: -572001.778 +/- 0.178 | dlogz: 499247.366 > 0.309] + + 948it [00:14, 22.68it/s, bound: 0 | nc: 2 | ncall: 6746 | eff(%): 14.053 | loglstar: -inf < -570149.597 < inf | logz: -570159.149 +/- 0.178 | dlogz: 498162.236 > 0.309] + + 951it [00:15, 21.63it/s, bound: 0 | nc: 20 | ncall: 6809 | eff(%): 13.967 | loglstar: -inf < -566909.934 < inf | logz: -566919.496 +/- 0.178 | dlogz: 494061.412 > 0.309] + + 954it [00:15, 14.02it/s, bound: 0 | nc: 125 | ncall: 7006 | eff(%): 13.617 | loglstar: -inf < -565953.467 < inf | logz: -565963.039 +/- 0.178 | dlogz: 493186.876 > 0.309] + + 958it [00:15, 17.77it/s, bound: 0 | nc: 19 | ncall: 7064 | eff(%): 13.562 | loglstar: -inf < -565055.366 < inf | logz: -565064.951 +/- 0.179 | dlogz: 492623.583 > 0.309] + + 963it [00:15, 23.08it/s, bound: 0 | nc: 14 | ncall: 7124 | eff(%): 13.518 | loglstar: -inf < -564369.875 < inf | logz: -564379.477 +/- 0.179 | dlogz: 491681.736 > 0.309] + + 967it [00:16, 19.50it/s, bound: 0 | nc: 30 | ncall: 7257 | eff(%): 13.325 | loglstar: -inf < -562658.863 < inf | logz: -562668.478 +/- 0.179 | dlogz: 489831.177 > 0.309] + + 970it [00:16, 19.05it/s, bound: 0 | nc: 36 | ncall: 7340 | eff(%): 13.215 | loglstar: -inf < -559859.379 < inf | logz: -559869.004 +/- 0.179 | dlogz: 487824.293 > 0.309] + + 973it [00:16, 19.40it/s, bound: 0 | nc: 28 | ncall: 7395 | eff(%): 13.158 | loglstar: -inf < -558571.156 < inf | logz: -558580.791 +/- 0.179 | dlogz: 486036.987 > 0.309] + + 976it [00:16, 15.95it/s, bound: 0 | nc: 107 | ncall: 7535 | eff(%): 12.953 | loglstar: -inf < -556274.846 < inf | logz: -556284.491 +/- 0.179 | dlogz: 484500.833 > 0.309] + + 978it [00:16, 14.33it/s, bound: 0 | nc: 53 | ncall: 7612 | eff(%): 12.848 | loglstar: -inf < -555233.562 < inf | logz: -555243.214 +/- 0.179 | dlogz: 482826.146 > 0.309] + + 980it [00:16, 13.60it/s, bound: 0 | nc: 56 | ncall: 7686 | eff(%): 12.750 | loglstar: -inf < -554215.585 < inf | logz: -554225.244 +/- 0.179 | dlogz: 481941.020 > 0.309] + + 982it [00:17, 14.52it/s, bound: 0 | nc: 34 | ncall: 7743 | eff(%): 12.682 | loglstar: -inf < -552415.609 < inf | logz: -552425.274 +/- 0.179 | dlogz: 480069.253 > 0.309] + + 984it [00:17, 14.81it/s, bound: 0 | nc: 10 | ncall: 7803 | eff(%): 12.611 | loglstar: -inf < -551526.264 < inf | logz: -551535.935 +/- 0.179 | dlogz: 479068.346 > 0.309] + + 987it [00:17, 17.06it/s, bound: 0 | nc: 16 | ncall: 7865 | eff(%): 12.549 | loglstar: -inf < -550563.040 < inf | logz: -550572.721 +/- 0.179 | dlogz: 477965.174 > 0.309] + + 989it [00:17, 14.65it/s, bound: 0 | nc: 49 | ncall: 7948 | eff(%): 12.443 | loglstar: -inf < -549983.951 < inf | logz: -549993.639 +/- 0.180 | dlogz: 493448.142 > 0.309] + + 994it [00:17, 21.34it/s, bound: 0 | nc: 11 | ncall: 8003 | eff(%): 12.420 | loglstar: -inf < -547698.433 < inf | logz: -547708.138 +/- 0.180 | dlogz: 491127.384 > 0.309] + + 997it [00:17, 17.57it/s, bound: 0 | nc: 2 | ncall: 8116 | eff(%): 12.284 | loglstar: -inf < -546073.876 < inf | logz: -546083.591 +/- 0.180 | dlogz: 490500.209 > 0.309] + + 1000it [00:18, 15.57it/s, bound: 0 | nc: 56 | ncall: 8225 | eff(%): 12.158 | loglstar: -inf < -545048.278 < inf | logz: -545058.002 +/- 0.180 | dlogz: 489356.936 > 0.309] + + 1004it [00:18, 14.98it/s, bound: 0 | nc: 79 | ncall: 8351 | eff(%): 12.023 | loglstar: -inf < -542606.773 < inf | logz: -542616.511 +/- 0.180 | dlogz: 486640.008 > 0.309] + + 1009it [00:18, 19.18it/s, bound: 0 | nc: 35 | ncall: 8426 | eff(%): 11.975 | loglstar: -inf < -540253.124 < inf | logz: -540262.879 +/- 0.180 | dlogz: 484073.052 > 0.309] + + 1016it [00:18, 25.60it/s, bound: 0 | nc: 32 | ncall: 8514 | eff(%): 11.933 | loglstar: -inf < -536191.026 < inf | logz: -536200.804 +/- 0.180 | dlogz: 480640.537 > 0.309] + + 1019it [00:19, 18.17it/s, bound: 0 | nc: 90 | ncall: 8677 | eff(%): 11.744 | loglstar: -inf < -534601.683 < inf | logz: -534611.471 +/- 0.180 | dlogz: 478151.939 > 0.309] + + 1022it [00:19, 15.73it/s, bound: 0 | nc: 25 | ncall: 8806 | eff(%): 11.606 | loglstar: -inf < -532764.256 < inf | logz: -532774.054 +/- 0.181 | dlogz: 476809.607 > 0.309] + + 1024it [00:19, 15.59it/s, bound: 0 | nc: 47 | ncall: 8873 | eff(%): 11.541 | loglstar: -inf < -532535.568 < inf | logz: -532545.373 +/- 0.181 | dlogz: 475987.937 > 0.309] + + 1028it [00:19, 19.40it/s, bound: 0 | nc: 14 | ncall: 8935 | eff(%): 11.505 | loglstar: -inf < -529479.561 < inf | logz: -529489.379 +/- 0.181 | dlogz: 473643.148 > 0.309] + + 1034it [00:19, 25.78it/s, bound: 0 | nc: 27 | ncall: 9012 | eff(%): 11.474 | loglstar: -inf < -525531.537 < inf | logz: -525541.375 +/- 0.181 | dlogz: 469078.823 > 0.309] + + 1038it [00:19, 23.32it/s, bound: 0 | nc: 6 | ncall: 9119 | eff(%): 11.383 | loglstar: -inf < -524804.030 < inf | logz: -524813.881 +/- 0.181 | dlogz: 468492.106 > 0.309] + + 1041it [00:20, 17.40it/s, bound: 0 | nc: 31 | ncall: 9284 | eff(%): 11.213 | loglstar: -inf < -523802.087 < inf | logz: -523811.948 +/- 0.181 | dlogz: 467254.635 > 0.309] + + 1044it [00:20, 11.82it/s, bound: 0 | nc: 45 | ncall: 9530 | eff(%): 10.955 | loglstar: -inf < -520706.631 < inf | logz: -520716.502 +/- 0.181 | dlogz: 464251.549 > 0.309] + + 1047it [00:21, 11.18it/s, bound: 0 | nc: 103 | ncall: 9686 | eff(%): 10.809 | loglstar: -inf < -519618.896 < inf | logz: -519628.777 +/- 0.181 | dlogz: 463444.662 > 0.309] + + 1050it [00:21, 12.56it/s, bound: 0 | nc: 61 | ncall: 9760 | eff(%): 10.758 | loglstar: -inf < -518051.945 < inf | logz: -518061.836 +/- 0.181 | dlogz: 461927.810 > 0.309] + + 1052it [00:21, 11.14it/s, bound: 0 | nc: 83 | ncall: 9872 | eff(%): 10.656 | loglstar: -inf < -516493.692 < inf | logz: -516503.590 +/- 0.181 | dlogz: 460561.903 > 0.309] + + 1056it [00:21, 13.69it/s, bound: 0 | nc: 37 | ncall: 9952 | eff(%): 10.611 | loglstar: -inf < -515629.018 < inf | logz: -515638.929 +/- 0.182 | dlogz: 459241.377 > 0.309] + + 1059it [00:21, 14.50it/s, bound: 0 | nc: 36 | ncall: 10024 | eff(%): 10.565 | loglstar: -inf < -511716.615 < inf | logz: -511726.536 +/- 0.182 | dlogz: 455311.606 > 0.309] + + 1061it [00:22, 12.06it/s, bound: 0 | nc: 42 | ncall: 10125 | eff(%): 10.479 | loglstar: -inf < -511221.673 < inf | logz: -511231.601 +/- 0.182 | dlogz: 454985.559 > 0.309] + + 1065it [00:22, 14.76it/s, bound: 0 | nc: 29 | ncall: 10202 | eff(%): 10.439 | loglstar: -inf < -510585.378 < inf | logz: -510595.319 +/- 0.182 | dlogz: 454173.320 > 0.309] + + 1069it [00:22, 16.27it/s, bound: 0 | nc: 65 | ncall: 10305 | eff(%): 10.374 | loglstar: -inf < -509347.275 < inf | logz: -509357.229 +/- 0.182 | dlogz: 453280.831 > 0.309] + + 1073it [00:22, 14.85it/s, bound: 0 | nc: 114 | ncall: 10459 | eff(%): 10.259 | loglstar: -inf < -508336.642 < inf | logz: -508346.609 +/- 0.182 | dlogz: 451897.859 > 0.309] + + 1075it [00:23, 11.48it/s, bound: 0 | nc: 109 | ncall: 10596 | eff(%): 10.145 | loglstar: -inf < -508193.535 < inf | logz: -508203.510 +/- 0.182 | dlogz: 451707.769 > 0.309] + + 1077it [00:23, 10.08it/s, bound: 0 | nc: 128 | ncall: 10729 | eff(%): 10.038 | loglstar: -inf < -507458.829 < inf | logz: -507468.810 +/- 0.182 | dlogz: 451075.400 > 0.309] + + 1079it [00:23, 9.35it/s, bound: 0 | nc: 122 | ncall: 10858 | eff(%): 9.937 | loglstar: -inf < -506895.895 < inf | logz: -506905.883 +/- 0.182 | dlogz: 450786.497 > 0.309] + + 1104it [00:23, 42.00it/s, bound: 1 | nc: 5 | ncall: 10984 | eff(%): 10.051 | loglstar: -inf < -490194.038 < inf | logz: -490204.109 +/- 0.183 | dlogz: 433808.707 > 0.309] + + 1130it [00:23, 77.39it/s, bound: 2 | nc: 5 | ncall: 11114 | eff(%): 10.167 | loglstar: -inf < -476463.843 < inf | logz: -476474.000 +/- 0.184 | dlogz: 420060.792 > 0.309] + + 1152it [00:23, 103.85it/s, bound: 2 | nc: 5 | ncall: 11224 | eff(%): 10.264 | loglstar: -inf < -465039.873 < inf | logz: -465050.103 +/- 0.184 | dlogz: 408667.593 > 0.309] + + 1175it [00:24, 129.81it/s, bound: 3 | nc: 5 | ncall: 11339 | eff(%): 10.362 | loglstar: -inf < -441310.296 < inf | logz: -441320.604 +/- 0.185 | dlogz: 384887.770 > 0.309] + + 1199it [00:24, 154.46it/s, bound: 3 | nc: 5 | ncall: 11459 | eff(%): 10.463 | loglstar: -inf < -426665.017 < inf | logz: -426675.404 +/- 0.186 | dlogz: 370133.904 > 0.309] + + 1222it [00:24, 171.66it/s, bound: 4 | nc: 5 | ncall: 11574 | eff(%): 10.558 | loglstar: -inf < -416350.907 < inf | logz: -416361.371 +/- 0.186 | dlogz: 360583.086 > 0.309] + + 1245it [00:24, 185.27it/s, bound: 4 | nc: 5 | ncall: 11689 | eff(%): 10.651 | loglstar: -inf < -396218.645 < inf | logz: -396228.083 +/- 0.177 | dlogz: 339628.026 > 0.309] + + 1270it [00:24, 202.18it/s, bound: 5 | nc: 5 | ncall: 11814 | eff(%): 10.750 | loglstar: -inf < -382785.230 < inf | logz: -382794.752 +/- 0.177 | dlogz: 326194.612 > 0.309] + + 1301it [00:24, 231.39it/s, bound: 5 | nc: 5 | ncall: 11969 | eff(%): 10.870 | loglstar: -inf < -361613.050 < inf | logz: -361623.776 +/- 0.188 | dlogz: 306281.897 > 0.309] + + 1326it [00:24, 231.07it/s, bound: 6 | nc: 5 | ncall: 12094 | eff(%): 10.964 | loglstar: -inf < -341822.079 < inf | logz: -341832.889 +/- 0.189 | dlogz: 286445.669 > 0.309] + + 1351it [00:24, 235.01it/s, bound: 7 | nc: 5 | ncall: 12219 | eff(%): 11.057 | loglstar: -inf < -327728.234 < inf | logz: -327739.127 +/- 0.189 | dlogz: 271798.293 > 0.309] + + 1376it [00:24, 235.93it/s, bound: 7 | nc: 5 | ncall: 12344 | eff(%): 11.147 | loglstar: -inf < -314350.956 < inf | logz: -314360.316 +/- 0.175 | dlogz: 257759.236 > 0.309] + + 1401it [00:25, 223.46it/s, bound: 8 | nc: 5 | ncall: 12469 | eff(%): 11.236 | loglstar: -inf < -301998.452 < inf | logz: -302009.512 +/- 0.190 | dlogz: 245507.323 > 0.309] + + 1426it [00:25, 230.58it/s, bound: 8 | nc: 5 | ncall: 12594 | eff(%): 11.323 | loglstar: -inf < -286544.646 < inf | logz: -286555.789 +/- 0.191 | dlogz: 231824.987 > 0.309] + + 1450it [00:25, 224.28it/s, bound: 9 | nc: 5 | ncall: 12714 | eff(%): 11.405 | loglstar: -inf < -280456.080 < inf | logz: -280465.687 +/- 0.177 | dlogz: 223864.360 > 0.309] + + 1476it [00:25, 233.51it/s, bound: 9 | nc: 5 | ncall: 12844 | eff(%): 11.492 | loglstar: -inf < -269605.723 < inf | logz: -269617.033 +/- 0.192 | dlogz: 213029.954 > 0.309] + + 1500it [00:25, 231.07it/s, bound: 10 | nc: 5 | ncall: 12964 | eff(%): 11.571 | loglstar: -inf < -257332.871 < inf | logz: -257344.261 +/- 0.192 | dlogz: 201055.334 > 0.309] + + 1526it [00:25, 238.86it/s, bound: 10 | nc: 5 | ncall: 13094 | eff(%): 11.654 | loglstar: -inf < -247084.214 < inf | logz: -247095.691 +/- 0.193 | dlogz: 195128.793 > 0.309] + + 1551it [00:25, 241.38it/s, bound: 11 | nc: 5 | ncall: 13219 | eff(%): 11.733 | loglstar: -inf < -235195.435 < inf | logz: -235205.893 +/- 0.183 | dlogz: 182731.829 > 0.309] + + 1576it [00:25, 236.01it/s, bound: 12 | nc: 5 | ncall: 13344 | eff(%): 11.811 | loglstar: -inf < -226103.833 < inf | logz: -226115.476 +/- 0.194 | dlogz: 187999.840 > 0.309] + + 1602it [00:25, 241.90it/s, bound: 12 | nc: 5 | ncall: 13474 | eff(%): 11.890 | loglstar: -inf < -215088.515 < inf | logz: -215100.245 +/- 0.194 | dlogz: 175895.966 > 0.309] + + 1627it [00:26, 228.75it/s, bound: 13 | nc: 5 | ncall: 13599 | eff(%): 11.964 | loglstar: -inf < -200370.109 < inf | logz: -200381.922 +/- 0.195 | dlogz: 161188.425 > 0.309] + + 1651it [00:26, 228.61it/s, bound: 13 | nc: 5 | ncall: 13719 | eff(%): 12.034 | loglstar: -inf < -188713.814 < inf | logz: -188723.751 +/- 0.178 | dlogz: 149381.054 > 0.309] + + 1675it [00:26, 215.23it/s, bound: 14 | nc: 5 | ncall: 13839 | eff(%): 12.103 | loglstar: -inf < -186944.095 < inf | logz: -186954.114 +/- 0.178 | dlogz: 147611.335 > 0.309] + + 1697it [00:26, 213.63it/s, bound: 14 | nc: 5 | ncall: 13949 | eff(%): 12.166 | loglstar: -inf < -180720.407 < inf | logz: -180732.454 +/- 0.196 | dlogz: 141397.467 > 0.309] + + 1719it [00:26, 210.66it/s, bound: 15 | nc: 5 | ncall: 14059 | eff(%): 12.227 | loglstar: -inf < -177902.761 < inf | logz: -177914.882 +/- 0.196 | dlogz: 138875.359 > 0.309] + + 1741it [00:26, 209.11it/s, bound: 15 | nc: 5 | ncall: 14169 | eff(%): 12.287 | loglstar: -inf < -173993.607 < inf | logz: -174004.700 +/- 0.187 | dlogz: 134662.463 > 0.309] + + 1762it [00:26, 200.77it/s, bound: 16 | nc: 5 | ncall: 14274 | eff(%): 12.344 | loglstar: -inf < -170374.133 < inf | logz: -170386.398 +/- 0.197 | dlogz: 131957.550 > 0.309] + + 1784it [00:26, 205.84it/s, bound: 16 | nc: 5 | ncall: 14384 | eff(%): 12.403 | loglstar: -inf < -166191.897 < inf | logz: -166204.236 +/- 0.197 | dlogz: 127125.806 > 0.309] + + 1805it [00:26, 188.11it/s, bound: 17 | nc: 5 | ncall: 14489 | eff(%): 12.458 | loglstar: -inf < -159996.635 < inf | logz: -160009.043 +/- 0.197 | dlogz: 121338.237 > 0.309] + + 1825it [00:27, 182.39it/s, bound: 17 | nc: 5 | ncall: 14589 | eff(%): 12.509 | loglstar: -inf < -156236.814 < inf | logz: -156247.333 +/- 0.180 | dlogz: 116904.054 > 0.309] + + 1844it [00:27, 179.81it/s, bound: 18 | nc: 5 | ncall: 14684 | eff(%): 12.558 | loglstar: -inf < -152495.375 < inf | logz: -152507.914 +/- 0.198 | dlogz: 113186.167 > 0.309] + + 1865it [00:27, 187.79it/s, bound: 18 | nc: 5 | ncall: 14789 | eff(%): 12.611 | loglstar: -inf < -149156.052 < inf | logz: -149168.661 +/- 0.198 | dlogz: 109943.086 > 0.309] + + 1886it [00:27, 192.76it/s, bound: 18 | nc: 5 | ncall: 14894 | eff(%): 12.663 | loglstar: -inf < -144335.171 < inf | logz: -144347.850 +/- 0.199 | dlogz: 105016.846 > 0.309] + + 1906it [00:27, 193.28it/s, bound: 19 | nc: 5 | ncall: 14994 | eff(%): 12.712 | loglstar: -inf < -139974.277 < inf | logz: -139987.023 +/- 0.199 | dlogz: 100689.890 > 0.309] + + 1930it [00:27, 204.88it/s, bound: 19 | nc: 5 | ncall: 15114 | eff(%): 12.770 | loglstar: -inf < -135386.208 < inf | logz: -135399.034 +/- 0.199 | dlogz: 96148.957 > 0.309] + + 1951it [00:27, 204.15it/s, bound: 20 | nc: 5 | ncall: 15219 | eff(%): 12.820 | loglstar: -inf < -132064.156 < inf | logz: -132077.052 +/- 0.200 | dlogz: 93642.082 > 0.309] + + 1974it [00:27, 211.52it/s, bound: 20 | nc: 5 | ncall: 15334 | eff(%): 12.873 | loglstar: -inf < -128622.354 < inf | logz: -128635.327 +/- 0.200 | dlogz: 89392.491 > 0.309] + + 1997it [00:27, 214.75it/s, bound: 21 | nc: 5 | ncall: 15449 | eff(%): 12.926 | loglstar: -inf < -123339.080 < inf | logz: -123351.027 +/- 0.191 | dlogz: 84007.936 > 0.309] + + 2022it [00:27, 224.92it/s, bound: 21 | nc: 5 | ncall: 15574 | eff(%): 12.983 | loglstar: -inf < -118889.653 < inf | logz: -118901.684 +/- 0.192 | dlogz: 79558.509 > 0.309] + + 2045it [00:28, 220.84it/s, bound: 22 | nc: 5 | ncall: 15689 | eff(%): 13.035 | loglstar: -inf < -117809.570 < inf | logz: -117821.678 +/- 0.192 | dlogz: 78478.426 > 0.309] + + 2068it [00:28, 223.48it/s, bound: 22 | nc: 5 | ncall: 15804 | eff(%): 13.085 | loglstar: -inf < -114297.686 < inf | logz: -114310.973 +/- 0.202 | dlogz: 75125.675 > 0.309] + + 2091it [00:28, 216.87it/s, bound: 23 | nc: 5 | ncall: 15919 | eff(%): 13.135 | loglstar: -inf < -111801.534 < inf | logz: -111812.312 +/- 0.180 | dlogz: 72467.975 > 0.309] + + 2114it [00:28, 211.56it/s, bound: 24 | nc: 5 | ncall: 16034 | eff(%): 13.184 | loglstar: -inf < -109602.287 < inf | logz: -109615.727 +/- 0.203 | dlogz: 70313.146 > 0.309] + + 2137it [00:28, 214.83it/s, bound: 24 | nc: 5 | ncall: 16149 | eff(%): 13.233 | loglstar: -inf < -107372.905 < inf | logz: -107386.423 +/- 0.203 | dlogz: 68208.815 > 0.309] + + 2159it [00:28, 208.51it/s, bound: 25 | nc: 5 | ncall: 16259 | eff(%): 13.279 | loglstar: -inf < -104325.363 < inf | logz: -104337.852 +/- 0.194 | dlogz: 64994.219 > 0.309] + + 2183it [00:28, 215.63it/s, bound: 25 | nc: 5 | ncall: 16379 | eff(%): 13.328 | loglstar: -inf < -101429.424 < inf | logz: -101443.095 +/- 0.204 | dlogz: 62376.481 > 0.309] + + 2205it [00:28, 209.54it/s, bound: 26 | nc: 5 | ncall: 16489 | eff(%): 13.373 | loglstar: -inf < -98147.535 < inf | logz: -98161.279 +/- 0.204 | dlogz: 59075.017 > 0.309] + + 2228it [00:28, 213.45it/s, bound: 26 | nc: 5 | ncall: 16604 | eff(%): 13.418 | loglstar: -inf < -94206.501 < inf | logz: -94220.321 +/- 0.204 | dlogz: 54963.135 > 0.309] + + 2250it [00:29, 205.10it/s, bound: 27 | nc: 5 | ncall: 16714 | eff(%): 13.462 | loglstar: -inf < -92978.862 < inf | logz: -92992.757 +/- 0.205 | dlogz: 53739.588 > 0.309] + + 2272it [00:29, 207.09it/s, bound: 27 | nc: 5 | ncall: 16824 | eff(%): 13.505 | loglstar: -inf < -90214.691 < inf | logz: -90228.659 +/- 0.205 | dlogz: 50893.269 > 0.309] + + 2293it [00:29, 205.34it/s, bound: 27 | nc: 5 | ncall: 16929 | eff(%): 13.545 | loglstar: -inf < -88210.660 < inf | logz: -88224.698 +/- 0.205 | dlogz: 48931.925 > 0.309] + + 2314it [00:29, 197.17it/s, bound: 28 | nc: 5 | ncall: 17034 | eff(%): 13.585 | loglstar: -inf < -85651.400 < inf | logz: -85664.406 +/- 0.197 | dlogz: 46320.256 > 0.309] + + 2336it [00:29, 200.95it/s, bound: 28 | nc: 5 | ncall: 17144 | eff(%): 13.626 | loglstar: -inf < -84983.553 < inf | logz: -84995.487 +/- 0.182 | dlogz: 45650.405 > 0.309] + + 2357it [00:29, 192.98it/s, bound: 29 | nc: 5 | ncall: 17249 | eff(%): 13.665 | loglstar: -inf < -83647.822 < inf | logz: -83660.972 +/- 0.197 | dlogz: 44316.678 > 0.309] + + 2380it [00:29, 201.87it/s, bound: 29 | nc: 5 | ncall: 17364 | eff(%): 13.707 | loglstar: -inf < -81805.885 < inf | logz: -81820.214 +/- 0.207 | dlogz: 42632.071 > 0.309] + + 2401it [00:29, 196.97it/s, bound: 30 | nc: 5 | ncall: 17469 | eff(%): 13.744 | loglstar: -inf < -80481.314 < inf | logz: -80494.610 +/- 0.198 | dlogz: 41150.170 > 0.309] + + 2422it [00:29, 200.26it/s, bound: 30 | nc: 5 | ncall: 17574 | eff(%): 13.782 | loglstar: -inf < -79059.403 < inf | logz: -79073.871 +/- 0.208 | dlogz: 39856.730 > 0.309] + + 2443it [00:30, 198.59it/s, bound: 31 | nc: 5 | ncall: 17679 | eff(%): 13.819 | loglstar: -inf < -77929.057 < inf | logz: -77943.595 +/- 0.208 | dlogz: 39212.976 > 0.309] + + 2465it [00:30, 202.75it/s, bound: 31 | nc: 5 | ncall: 17789 | eff(%): 13.857 | loglstar: -inf < -77012.045 < inf | logz: -77026.657 +/- 0.208 | dlogz: 38293.769 > 0.309] + + 2486it [00:30, 198.74it/s, bound: 32 | nc: 5 | ncall: 17894 | eff(%): 13.893 | loglstar: -inf < -75386.598 < inf | logz: -75401.279 +/- 0.209 | dlogz: 36678.622 > 0.309] + + 2510it [00:30, 209.62it/s, bound: 32 | nc: 5 | ncall: 18014 | eff(%): 13.934 | loglstar: -inf < -74332.393 < inf | logz: -74347.152 +/- 0.209 | dlogz: 35582.528 > 0.309] + + 2532it [00:30, 208.94it/s, bound: 33 | nc: 5 | ncall: 18124 | eff(%): 13.970 | loglstar: -inf < -73341.778 < inf | logz: -73354.997 +/- 0.196 | dlogz: 36357.899 > 0.309] + + 2553it [00:30, 205.35it/s, bound: 34 | nc: 5 | ncall: 18229 | eff(%): 14.005 | loglstar: -inf < -72845.432 < inf | logz: -72860.337 +/- 0.210 | dlogz: 35950.916 > 0.309] + + 2574it [00:30, 203.01it/s, bound: 35 | nc: 5 | ncall: 18334 | eff(%): 14.039 | loglstar: -inf < -71657.831 < inf | logz: -71671.190 +/- 0.197 | dlogz: 34673.951 > 0.309] + + 2597it [00:30, 209.37it/s, bound: 35 | nc: 5 | ncall: 18449 | eff(%): 14.077 | loglstar: -inf < -70942.899 < inf | logz: -70957.951 +/- 0.210 | dlogz: 33978.338 > 0.309] + + 2618it [00:30, 206.52it/s, bound: 36 | nc: 5 | ncall: 18554 | eff(%): 14.110 | loglstar: -inf < -70405.334 < inf | logz: -70419.354 +/- 0.202 | dlogz: 33422.557 > 0.309] + + 2641it [00:30, 212.47it/s, bound: 36 | nc: 5 | ncall: 18669 | eff(%): 14.146 | loglstar: -inf < -68877.708 < inf | logz: -68892.906 +/- 0.211 | dlogz: 31903.036 > 0.309] + + 2663it [00:31, 208.67it/s, bound: 37 | nc: 5 | ncall: 18779 | eff(%): 14.181 | loglstar: -inf < -66838.915 < inf | logz: -66852.571 +/- 0.198 | dlogz: 29855.036 > 0.309] + + 2685it [00:31, 211.85it/s, bound: 37 | nc: 5 | ncall: 18889 | eff(%): 14.215 | loglstar: -inf < -65567.083 < inf | logz: -65580.218 +/- 0.193 | dlogz: 28582.349 > 0.309] + + 2707it [00:31, 200.58it/s, bound: 38 | nc: 5 | ncall: 18999 | eff(%): 14.248 | loglstar: -inf < -64582.072 < inf | logz: -64597.491 +/- 0.212 | dlogz: 27645.284 > 0.309] + + 2730it [00:31, 208.02it/s, bound: 38 | nc: 5 | ncall: 19114 | eff(%): 14.283 | loglstar: -inf < -63486.109 < inf | logz: -63501.605 +/- 0.212 | dlogz: 26776.144 > 0.309] + + 2751it [00:31, 198.59it/s, bound: 39 | nc: 5 | ncall: 19219 | eff(%): 14.314 | loglstar: -inf < -63007.570 < inf | logz: -63021.520 +/- 0.200 | dlogz: 26023.690 > 0.309] + + 2773it [00:31, 204.07it/s, bound: 39 | nc: 5 | ncall: 19329 | eff(%): 14.346 | loglstar: -inf < -62283.642 < inf | logz: -62299.281 +/- 0.213 | dlogz: 25374.843 > 0.309] + + 2794it [00:31, 201.44it/s, bound: 40 | nc: 5 | ncall: 19434 | eff(%): 14.377 | loglstar: -inf < -61367.375 < inf | logz: -61383.085 +/- 0.213 | dlogz: 24567.938 > 0.309] + + 2816it [00:31, 204.76it/s, bound: 40 | nc: 5 | ncall: 19544 | eff(%): 14.409 | loglstar: -inf < -60534.659 < inf | logz: -60547.702 +/- 0.191 | dlogz: 23549.287 > 0.309] + + 2837it [00:31, 200.23it/s, bound: 41 | nc: 5 | ncall: 19649 | eff(%): 14.438 | loglstar: -inf < -59854.057 < inf | logz: -59869.833 +/- 0.211 | dlogz: 22873.979 > 0.309] + + 2858it [00:32, 186.08it/s, bound: 41 | nc: 5 | ncall: 19754 | eff(%): 14.468 | loglstar: -inf < -59028.400 < inf | logz: -59044.323 +/- 0.214 | dlogz: 23909.534 > 0.309] + + 2877it [00:32, 175.12it/s, bound: 41 | nc: 5 | ncall: 19849 | eff(%): 14.494 | loglstar: -inf < -58482.912 < inf | logz: -58498.899 +/- 0.215 | dlogz: 23360.663 > 0.309] + + 2895it [00:32, 174.43it/s, bound: 42 | nc: 5 | ncall: 19939 | eff(%): 14.519 | loglstar: -inf < -57729.420 < inf | logz: -57745.467 +/- 0.215 | dlogz: 22611.660 > 0.309] + + 2916it [00:32, 183.72it/s, bound: 42 | nc: 5 | ncall: 20044 | eff(%): 14.548 | loglstar: -inf < -56488.077 < inf | logz: -56504.194 +/- 0.215 | dlogz: 21375.554 > 0.309] + + 2935it [00:32, 184.46it/s, bound: 43 | nc: 5 | ncall: 20139 | eff(%): 14.574 | loglstar: -inf < -56045.795 < inf | logz: -56061.975 +/- 0.216 | dlogz: 20919.873 > 0.309] + + 2957it [00:32, 192.81it/s, bound: 43 | nc: 5 | ncall: 20249 | eff(%): 14.603 | loglstar: -inf < -55437.466 < inf | logz: -55453.720 +/- 0.216 | dlogz: 20356.613 > 0.309] + + 2977it [00:32, 188.37it/s, bound: 44 | nc: 5 | ncall: 20349 | eff(%): 14.630 | loglstar: -inf < -54862.040 < inf | logz: -54876.149 +/- 0.199 | dlogz: 19716.688 > 0.309] + + 3000it [00:32, 197.63it/s, bound: 44 | nc: 5 | ncall: 20464 | eff(%): 14.660 | loglstar: -inf < -54417.262 < inf | logz: -54433.411 +/- 0.211 | dlogz: 19275.829 > 0.309] + + 3021it [00:32, 200.11it/s, bound: 45 | nc: 5 | ncall: 20569 | eff(%): 14.687 | loglstar: -inf < -53780.945 < inf | logz: -53797.412 +/- 0.217 | dlogz: 18693.820 > 0.309] + + 3043it [00:33, 198.13it/s, bound: 46 | nc: 5 | ncall: 20679 | eff(%): 14.715 | loglstar: -inf < -53053.592 < inf | logz: -53070.132 +/- 0.217 | dlogz: 17993.135 > 0.309] + + 3063it [00:33, 189.42it/s, bound: 46 | nc: 5 | ncall: 20779 | eff(%): 14.741 | loglstar: -inf < -52811.984 < inf | logz: -52825.731 +/- 0.194 | dlogz: 17665.856 > 0.309] + + 3083it [00:33, 180.51it/s, bound: 46 | nc: 5 | ncall: 20879 | eff(%): 14.766 | loglstar: -inf < -52650.940 < inf | logz: -52666.977 +/- 0.207 | dlogz: 17507.949 > 0.309] + + 3102it [00:33, 170.01it/s, bound: 47 | nc: 5 | ncall: 20974 | eff(%): 14.790 | loglstar: -inf < -52138.170 < inf | logz: -52154.908 +/- 0.218 | dlogz: 17063.525 > 0.309] + + 3122it [00:33, 177.73it/s, bound: 47 | nc: 5 | ncall: 21074 | eff(%): 14.814 | loglstar: -inf < -51776.735 < inf | logz: -51792.438 +/- 0.210 | dlogz: 16633.339 > 0.309] + + 3140it [00:33, 177.88it/s, bound: 48 | nc: 5 | ncall: 21164 | eff(%): 14.837 | loglstar: -inf < -51657.112 < inf | logz: -51672.361 +/- 0.206 | dlogz: 16512.614 > 0.309] + + 3162it [00:33, 189.64it/s, bound: 48 | nc: 5 | ncall: 21274 | eff(%): 14.863 | loglstar: -inf < -51040.121 < inf | logz: -51057.060 +/- 0.219 | dlogz: 15918.010 > 0.309] + + 3182it [00:33, 185.92it/s, bound: 49 | nc: 5 | ncall: 21374 | eff(%): 14.887 | loglstar: -inf < -50717.557 < inf | logz: -50733.461 +/- 0.211 | dlogz: 15574.161 > 0.309] + + 3203it [00:33, 191.71it/s, bound: 49 | nc: 5 | ncall: 21479 | eff(%): 14.912 | loglstar: -inf < -50273.702 < inf | logz: -50290.778 +/- 0.220 | dlogz: 15171.435 > 0.309] + + 3223it [00:34, 186.27it/s, bound: 50 | nc: 5 | ncall: 21579 | eff(%): 14.936 | loglstar: -inf < -49998.918 < inf | logz: -50013.200 +/- 0.197 | dlogz: 14852.790 > 0.309] + + 3244it [00:34, 190.61it/s, bound: 50 | nc: 5 | ncall: 21684 | eff(%): 14.960 | loglstar: -inf < -49711.376 < inf | logz: -49726.633 +/- 0.205 | dlogz: 14566.364 > 0.309] + + 3265it [00:34, 195.81it/s, bound: 50 | nc: 5 | ncall: 21789 | eff(%): 14.985 | loglstar: -inf < -49484.689 < inf | logz: -49500.365 +/- 0.207 | dlogz: 14340.191 > 0.309] + + 3285it [00:34, 192.97it/s, bound: 51 | nc: 5 | ncall: 21889 | eff(%): 15.008 | loglstar: -inf < -49144.105 < inf | logz: -49160.353 +/- 0.212 | dlogz: 14000.709 > 0.309] + + 3305it [00:34, 193.64it/s, bound: 51 | nc: 5 | ncall: 21989 | eff(%): 15.030 | loglstar: -inf < -48718.581 < inf | logz: -48735.998 +/- 0.221 | dlogz: 13634.857 > 0.309] + + 3325it [00:34, 187.63it/s, bound: 52 | nc: 5 | ncall: 22089 | eff(%): 15.053 | loglstar: -inf < -48547.208 < inf | logz: -48564.690 +/- 0.221 | dlogz: 13411.347 > 0.309] + + 3345it [00:34, 191.04it/s, bound: 52 | nc: 5 | ncall: 22189 | eff(%): 15.075 | loglstar: -inf < -48249.696 < inf | logz: -48266.144 +/- 0.213 | dlogz: 13106.300 > 0.309] + + 3365it [00:34, 174.74it/s, bound: 53 | nc: 5 | ncall: 22289 | eff(%): 15.097 | loglstar: -inf < -48039.558 < inf | logz: -48057.136 +/- 0.221 | dlogz: 15218.238 > 0.309] + + 3383it [00:34, 154.74it/s, bound: 54 | nc: 5 | ncall: 22379 | eff(%): 15.117 | loglstar: -inf < -47819.302 < inf | logz: -47836.979 +/- 0.222 | dlogz: 15010.507 > 0.309] + + 3400it [00:35, 155.41it/s, bound: 54 | nc: 5 | ncall: 22464 | eff(%): 15.135 | loglstar: -inf < -47468.372 < inf | logz: -47486.106 +/- 0.223 | dlogz: 14684.071 > 0.309] + + 3416it [00:35, 156.36it/s, bound: 54 | nc: 5 | ncall: 22544 | eff(%): 15.153 | loglstar: -inf < -47323.796 < inf | logz: -47341.583 +/- 0.223 | dlogz: 14509.195 > 0.309] + + 3432it [00:35, 150.88it/s, bound: 55 | nc: 5 | ncall: 22624 | eff(%): 15.170 | loglstar: -inf < -47163.999 < inf | logz: -47181.839 +/- 0.223 | dlogz: 15852.514 > 0.309] + + 3450it [00:35, 156.62it/s, bound: 55 | nc: 5 | ncall: 22714 | eff(%): 15.189 | loglstar: -inf < -47019.935 < inf | logz: -47035.879 +/- 0.208 | dlogz: 15674.446 > 0.309] + + 3466it [00:35, 157.20it/s, bound: 56 | nc: 5 | ncall: 22794 | eff(%): 15.206 | loglstar: -inf < -46945.563 < inf | logz: -46960.657 +/- 0.201 | dlogz: 15598.959 > 0.309] + + 3485it [00:35, 165.91it/s, bound: 56 | nc: 5 | ncall: 22889 | eff(%): 15.226 | loglstar: -inf < -46766.664 < inf | logz: -46783.579 +/- 0.215 | dlogz: 15422.791 > 0.309] + + 3502it [00:35, 166.89it/s, bound: 57 | nc: 5 | ncall: 22974 | eff(%): 15.243 | loglstar: -inf < -46528.605 < inf | logz: -46546.679 +/- 0.224 | dlogz: 15211.370 > 0.309] + + 3522it [00:35, 174.95it/s, bound: 57 | nc: 5 | ncall: 23074 | eff(%): 15.264 | loglstar: -inf < -46264.151 < inf | logz: -46282.237 +/- 0.223 | dlogz: 14923.852 > 0.309] + + 3541it [00:35, 170.13it/s, bound: 58 | nc: 5 | ncall: 23169 | eff(%): 15.283 | loglstar: -inf < -46063.260 < inf | logz: -46079.781 +/- 0.211 | dlogz: 14718.177 > 0.309] + + 3559it [00:36, 171.89it/s, bound: 58 | nc: 5 | ncall: 23259 | eff(%): 15.302 | loglstar: -inf < -45801.313 < inf | logz: -45818.475 +/- 0.217 | dlogz: 14457.441 > 0.309] + + 3577it [00:36, 173.60it/s, bound: 58 | nc: 5 | ncall: 23349 | eff(%): 15.320 | loglstar: -inf < -45504.587 < inf | logz: -45522.911 +/- 0.225 | dlogz: 14194.462 > 0.309] + + 3595it [00:36, 169.26it/s, bound: 59 | nc: 5 | ncall: 23439 | eff(%): 15.338 | loglstar: -inf < -45342.953 < inf | logz: -45361.308 +/- 0.224 | dlogz: 14003.291 > 0.309] + + 3612it [00:36, 159.26it/s, bound: 60 | nc: 5 | ncall: 23524 | eff(%): 15.355 | loglstar: -inf < -45179.223 < inf | logz: -45196.561 +/- 0.217 | dlogz: 13835.349 > 0.309] + + 3631it [00:36, 167.34it/s, bound: 60 | nc: 5 | ncall: 23619 | eff(%): 15.373 | loglstar: -inf < -44943.944 < inf | logz: -44962.449 +/- 0.226 | dlogz: 13610.859 > 0.309] + + 3648it [00:36, 156.46it/s, bound: 61 | nc: 5 | ncall: 23704 | eff(%): 15.390 | loglstar: -inf < -44799.952 < inf | logz: -44818.513 +/- 0.226 | dlogz: 13467.718 > 0.309] + + 3664it [00:36, 155.67it/s, bound: 61 | nc: 5 | ncall: 23784 | eff(%): 15.405 | loglstar: -inf < -44580.529 < inf | logz: -44599.143 +/- 0.227 | dlogz: 13247.586 > 0.309] + + 3680it [00:36, 148.53it/s, bound: 62 | nc: 5 | ncall: 23864 | eff(%): 15.421 | loglstar: -inf < -44304.597 < inf | logz: -44321.554 +/- 0.212 | dlogz: 12959.469 > 0.309] + + 3695it [00:36, 148.46it/s, bound: 62 | nc: 5 | ncall: 23939 | eff(%): 15.435 | loglstar: -inf < -44221.377 < inf | logz: -44238.479 +/- 0.215 | dlogz: 14039.777 > 0.309] + + 3714it [00:36, 159.58it/s, bound: 62 | nc: 5 | ncall: 24034 | eff(%): 15.453 | loglstar: -inf < -44007.725 < inf | logz: -44024.296 +/- 0.210 | dlogz: 13825.271 > 0.309] + + 3731it [00:37, 161.90it/s, bound: 63 | nc: 5 | ncall: 24119 | eff(%): 15.469 | loglstar: -inf < -43932.874 < inf | logz: -43951.677 +/- 0.226 | dlogz: 14220.611 > 0.309] + + 3748it [00:37, 153.76it/s, bound: 63 | nc: 5 | ncall: 24204 | eff(%): 15.485 | loglstar: -inf < -43808.150 < inf | logz: -43826.923 +/- 0.224 | dlogz: 14095.207 > 0.309] + + 3764it [00:37, 148.90it/s, bound: 64 | nc: 5 | ncall: 24284 | eff(%): 15.500 | loglstar: -inf < -43577.397 < inf | logz: -43596.346 +/- 0.228 | dlogz: 14835.930 > 0.309] + + 3783it [00:37, 158.59it/s, bound: 64 | nc: 5 | ncall: 24379 | eff(%): 15.517 | loglstar: -inf < -43379.849 < inf | logz: -43396.905 +/- 0.213 | dlogz: 14614.264 > 0.309] + + 3802it [00:37, 164.88it/s, bound: 64 | nc: 5 | ncall: 24474 | eff(%): 15.535 | loglstar: -inf < -43174.463 < inf | logz: -43193.481 +/- 0.226 | dlogz: 14413.418 > 0.309] + + 3819it [00:37, 162.35it/s, bound: 65 | nc: 5 | ncall: 24559 | eff(%): 15.550 | loglstar: -inf < -42961.277 < inf | logz: -42978.793 +/- 0.216 | dlogz: 14196.205 > 0.309] + + 3840it [00:37, 175.31it/s, bound: 65 | nc: 5 | ncall: 24664 | eff(%): 15.569 | loglstar: -inf < -42849.850 < inf | logz: -42867.950 +/- 0.220 | dlogz: 14085.878 > 0.309] + + 3858it [00:37, 168.35it/s, bound: 66 | nc: 5 | ncall: 24754 | eff(%): 15.585 | loglstar: -inf < -42668.277 < inf | logz: -42687.436 +/- 0.226 | dlogz: 13907.215 > 0.309] + + 3878it [00:37, 176.36it/s, bound: 66 | nc: 5 | ncall: 24854 | eff(%): 15.603 | loglstar: -inf < -42366.741 < inf | logz: -42386.070 +/- 0.229 | dlogz: 13628.655 > 0.309] + + 3897it [00:38, 179.26it/s, bound: 66 | nc: 5 | ncall: 24949 | eff(%): 15.620 | loglstar: -inf < -41992.265 < inf | logz: -42011.575 +/- 0.227 | dlogz: 13231.444 > 0.309] + + 3916it [00:38, 164.02it/s, bound: 68 | nc: 5 | ncall: 25044 | eff(%): 15.636 | loglstar: -inf < -41628.637 < inf | logz: -41648.093 +/- 0.230 | dlogz: 12889.439 > 0.309] + + 3933it [00:38, 159.67it/s, bound: 68 | nc: 5 | ncall: 25129 | eff(%): 15.651 | loglstar: -inf < -41292.818 < inf | logz: -41312.331 +/- 0.230 | dlogz: 12642.113 > 0.309] + + 3950it [00:38, 157.81it/s, bound: 69 | nc: 5 | ncall: 25214 | eff(%): 15.666 | loglstar: -inf < -41125.062 < inf | logz: -41143.530 +/- 0.222 | dlogz: 12361.092 > 0.309] + + 3968it [00:38, 163.22it/s, bound: 69 | nc: 5 | ncall: 25304 | eff(%): 15.681 | loglstar: -inf < -40693.632 < inf | logz: -40712.160 +/- 0.222 | dlogz: 11929.662 > 0.309] + + 3986it [00:38, 167.22it/s, bound: 69 | nc: 5 | ncall: 25394 | eff(%): 15.697 | loglstar: -inf < -40385.724 < inf | logz: -40405.414 +/- 0.231 | dlogz: 11661.155 > 0.309] + + 4003it [00:38, 167.57it/s, bound: 70 | nc: 5 | ncall: 25479 | eff(%): 15.711 | loglstar: -inf < -40052.696 < inf | logz: -40072.442 +/- 0.231 | dlogz: 11322.230 > 0.309] + + 4020it [00:38, 159.99it/s, bound: 70 | nc: 5 | ncall: 25564 | eff(%): 15.725 | loglstar: -inf < -39975.209 < inf | logz: -39992.801 +/- 0.215 | dlogz: 11209.283 > 0.309] + + 4037it [00:38, 161.72it/s, bound: 70 | nc: 5 | ncall: 25649 | eff(%): 15.739 | loglstar: -inf < -39651.495 < inf | logz: -39671.355 +/- 0.231 | dlogz: 10946.229 > 0.309] + + 4054it [00:39, 163.39it/s, bound: 71 | nc: 5 | ncall: 25734 | eff(%): 15.753 | loglstar: -inf < -39337.541 < inf | logz: -39356.202 +/- 0.220 | dlogz: 14548.603 > 0.309] + + 4072it [00:39, 167.05it/s, bound: 72 | nc: 5 | ncall: 25824 | eff(%): 15.768 | loglstar: -inf < -39230.185 < inf | logz: -39250.162 +/- 0.232 | dlogz: 14456.977 > 0.309] + + 4090it [00:39, 170.07it/s, bound: 72 | nc: 5 | ncall: 25914 | eff(%): 15.783 | loglstar: -inf < -38914.943 < inf | logz: -38934.979 +/- 0.232 | dlogz: 14133.892 > 0.309] + + 4108it [00:39, 170.79it/s, bound: 73 | nc: 5 | ncall: 26004 | eff(%): 15.798 | loglstar: -inf < -38724.398 < inf | logz: -38743.393 +/- 0.224 | dlogz: 13935.864 > 0.309] + + 4128it [00:39, 178.06it/s, bound: 73 | nc: 5 | ncall: 26104 | eff(%): 15.814 | loglstar: -inf < -38621.972 < inf | logz: -38642.118 +/- 0.232 | dlogz: 13837.750 > 0.309] + + 4149it [00:39, 181.26it/s, bound: 73 | nc: 5 | ncall: 26209 | eff(%): 15.830 | loglstar: -inf < -38392.793 < inf | logz: -38410.613 +/- 0.215 | dlogz: 13602.048 > 0.309] + + 4168it [00:39, 160.48it/s, bound: 74 | nc: 5 | ncall: 26304 | eff(%): 15.845 | loglstar: -inf < -38276.408 < inf | logz: -38296.028 +/- 0.219 | dlogz: 13487.991 > 0.309] + + 4185it [00:39, 153.08it/s, bound: 74 | nc: 5 | ncall: 26389 | eff(%): 15.859 | loglstar: -inf < -37934.863 < inf | logz: -37955.217 +/- 0.233 | dlogz: 13153.579 > 0.309] + + 4201it [00:40, 138.61it/s, bound: 75 | nc: 5 | ncall: 26469 | eff(%): 15.871 | loglstar: -inf < -37689.993 < inf | logz: -37710.401 +/- 0.233 | dlogz: 12937.218 > 0.309] + + 4216it [00:40, 140.66it/s, bound: 75 | nc: 5 | ncall: 26544 | eff(%): 15.883 | loglstar: -inf < -37508.635 < inf | logz: -37527.138 +/- 0.219 | dlogz: 12718.485 > 0.309] + + 4233it [00:40, 144.21it/s, bound: 76 | nc: 5 | ncall: 26629 | eff(%): 15.896 | loglstar: -inf < -37505.334 < inf | logz: -37523.546 +/- 0.213 | dlogz: 12714.723 > 0.309] + + 4251it [00:40, 152.40it/s, bound: 76 | nc: 5 | ncall: 26719 | eff(%): 15.910 | loglstar: -inf < -37379.032 < inf | logz: -37399.609 +/- 0.234 | dlogz: 12613.993 > 0.309] + + 4271it [00:40, 162.18it/s, bound: 76 | nc: 5 | ncall: 26819 | eff(%): 15.925 | loglstar: -inf < -37088.688 < inf | logz: -37106.601 +/- 0.214 | dlogz: 12297.569 > 0.309] + + 4288it [00:40, 161.77it/s, bound: 77 | nc: 5 | ncall: 26904 | eff(%): 15.938 | loglstar: -inf < -36849.966 < inf | logz: -36869.565 +/- 0.226 | dlogz: 12061.432 > 0.309] + + 4307it [00:40, 168.14it/s, bound: 77 | nc: 5 | ncall: 26999 | eff(%): 15.952 | loglstar: -inf < -36667.854 < inf | logz: -36687.596 +/- 0.227 | dlogz: 11879.434 > 0.309] + + 4325it [00:40, 170.23it/s, bound: 78 | nc: 5 | ncall: 27089 | eff(%): 15.966 | loglstar: -inf < -36521.338 < inf | logz: -36539.443 +/- 0.214 | dlogz: 11730.233 > 0.309] + + 4343it [00:40, 166.64it/s, bound: 78 | nc: 5 | ncall: 27179 | eff(%): 15.979 | loglstar: -inf < -36380.478 < inf | logz: -36400.262 +/- 0.227 | dlogz: 11591.944 > 0.309] + + 4361it [00:40, 168.75it/s, bound: 78 | nc: 5 | ncall: 27269 | eff(%): 15.993 | loglstar: -inf < -36262.934 < inf | logz: -36281.149 +/- 0.215 | dlogz: 11471.815 > 0.309] + + 4378it [00:41, 160.72it/s, bound: 79 | nc: 5 | ncall: 27354 | eff(%): 16.005 | loglstar: -inf < -36091.097 < inf | logz: -36110.144 +/- 0.221 | dlogz: 11300.947 > 0.309] + + 4398it [00:41, 169.79it/s, bound: 79 | nc: 5 | ncall: 27454 | eff(%): 16.020 | loglstar: -inf < -35919.243 < inf | logz: -35938.697 +/- 0.224 | dlogz: 11129.607 > 0.309] + + 4416it [00:41, 165.51it/s, bound: 80 | nc: 5 | ncall: 27544 | eff(%): 16.033 | loglstar: -inf < -35825.413 < inf | logz: -35845.442 +/- 0.228 | dlogz: 11470.697 > 0.309] + + 4435it [00:41, 170.04it/s, bound: 80 | nc: 5 | ncall: 27639 | eff(%): 16.046 | loglstar: -inf < -35387.605 < inf | logz: -35408.124 +/- 0.226 | dlogz: 11033.214 > 0.309] + + 4455it [00:41, 176.31it/s, bound: 80 | nc: 5 | ncall: 27739 | eff(%): 16.060 | loglstar: -inf < -35266.893 < inf | logz: -35285.391 +/- 0.215 | dlogz: 10909.556 > 0.309] + + 4473it [00:41, 166.20it/s, bound: 81 | nc: 5 | ncall: 27829 | eff(%): 16.073 | loglstar: -inf < -35145.469 < inf | logz: -35164.814 +/- 0.222 | dlogz: 10789.108 > 0.309] + + 4490it [00:41, 161.86it/s, bound: 81 | nc: 5 | ncall: 27914 | eff(%): 16.085 | loglstar: -inf < -35063.460 < inf | logz: -35081.807 +/- 0.214 | dlogz: 10705.821 > 0.309] + + 4507it [00:41, 153.16it/s, bound: 82 | nc: 5 | ncall: 27999 | eff(%): 16.097 | loglstar: -inf < -34926.642 < inf | logz: -34948.078 +/- 0.237 | dlogz: 10587.576 > 0.309] + + 4525it [00:41, 158.88it/s, bound: 82 | nc: 5 | ncall: 28089 | eff(%): 16.110 | loglstar: -inf < -34846.233 < inf | logz: -34864.315 +/- 0.212 | dlogz: 10488.177 > 0.309] + + 4543it [00:42, 162.82it/s, bound: 82 | nc: 5 | ncall: 28179 | eff(%): 16.122 | loglstar: -inf < -34846.233 < inf | logz: -34863.508 +/- 0.205 | dlogz: 10487.265 > 0.309] + + 4561it [00:42, 165.71it/s, bound: 83 | nc: 5 | ncall: 28269 | eff(%): 16.134 | loglstar: -inf < -34688.865 < inf | logz: -34710.485 +/- 0.237 | dlogz: 10340.030 > 0.309] + + 4580it [00:42, 172.29it/s, bound: 83 | nc: 5 | ncall: 28364 | eff(%): 16.147 | loglstar: -inf < -34476.533 < inf | logz: -34496.009 +/- 0.221 | dlogz: 10119.861 > 0.309] + + 4598it [00:42, 166.89it/s, bound: 84 | nc: 5 | ncall: 28454 | eff(%): 16.159 | loglstar: -inf < -34256.700 < inf | logz: -34278.026 +/- 0.231 | dlogz: 9903.325 > 0.309] + + 4618it [00:42, 175.02it/s, bound: 84 | nc: 5 | ncall: 28554 | eff(%): 16.173 | loglstar: -inf < -33995.632 < inf | logz: -34016.332 +/- 0.230 | dlogz: 9640.880 > 0.309] + + 4636it [00:42, 166.97it/s, bound: 85 | nc: 5 | ncall: 28644 | eff(%): 16.185 | loglstar: -inf < -33681.891 < inf | logz: -33703.765 +/- 0.238 | dlogz: 9358.486 > 0.309] + + 4660it [00:42, 187.06it/s, bound: 85 | nc: 5 | ncall: 28764 | eff(%): 16.201 | loglstar: -inf < -33427.543 < inf | logz: -33449.496 +/- 0.239 | dlogz: 9091.377 > 0.309] + + 4681it [00:42, 192.89it/s, bound: 86 | nc: 5 | ncall: 28869 | eff(%): 16.215 | loglstar: -inf < -33181.439 < inf | logz: -33201.836 +/- 0.227 | dlogz: 8825.604 > 0.309] + + 4706it [00:42, 207.24it/s, bound: 86 | nc: 5 | ncall: 28994 | eff(%): 16.231 | loglstar: -inf < -32968.304 < inf | logz: -32988.201 +/- 0.223 | dlogz: 12510.188 > 0.309] + + 4730it [00:43, 212.32it/s, bound: 88 | nc: 5 | ncall: 29114 | eff(%): 16.246 | loglstar: -inf < -32778.469 < inf | logz: -32799.305 +/- 0.228 | dlogz: 12321.693 > 0.309] + + 4758it [00:43, 231.82it/s, bound: 88 | nc: 5 | ncall: 29254 | eff(%): 16.264 | loglstar: -inf < -32649.672 < inf | logz: -32671.955 +/- 0.240 | dlogz: 12205.952 > 0.309] + + 4782it [00:43, 230.53it/s, bound: 89 | nc: 5 | ncall: 29374 | eff(%): 16.280 | loglstar: -inf < -32358.543 < inf | logz: -32379.796 +/- 0.232 | dlogz: 11902.360 > 0.309] + + 4806it [00:43, 218.94it/s, bound: 89 | nc: 5 | ncall: 29494 | eff(%): 16.295 | loglstar: -inf < -32110.249 < inf | logz: -32132.479 +/- 0.236 | dlogz: 11656.223 > 0.309] + + 4829it [00:43, 200.60it/s, bound: 90 | nc: 5 | ncall: 29609 | eff(%): 16.309 | loglstar: -inf < -31789.186 < inf | logz: -31811.664 +/- 0.240 | dlogz: 11336.865 > 0.309] + + 4851it [00:43, 205.71it/s, bound: 90 | nc: 5 | ncall: 29719 | eff(%): 16.323 | loglstar: -inf < -31422.514 < inf | logz: -31443.527 +/- 0.230 | dlogz: 10965.311 > 0.309] + + 4872it [00:43, 201.33it/s, bound: 91 | nc: 5 | ncall: 29824 | eff(%): 16.336 | loglstar: -inf < -31422.514 < inf | logz: -31441.256 +/- 0.213 | dlogz: 10962.478 > 0.309] + + 4902it [00:43, 227.63it/s, bound: 91 | nc: 5 | ncall: 29974 | eff(%): 16.354 | loglstar: -inf < -31227.970 < inf | logz: -31250.738 +/- 0.242 | dlogz: 10789.291 > 0.309] + + 4928it [00:43, 235.29it/s, bound: 92 | nc: 5 | ncall: 30104 | eff(%): 16.370 | loglstar: -inf < -30914.391 < inf | logz: -30937.140 +/- 0.238 | dlogz: 10460.517 > 0.309] + + 4954it [00:44, 235.16it/s, bound: 93 | nc: 5 | ncall: 30234 | eff(%): 16.386 | loglstar: -inf < -30670.370 < inf | logz: -30693.312 +/- 0.243 | dlogz: 10250.047 > 0.309] + + 4982it [00:44, 246.72it/s, bound: 93 | nc: 5 | ncall: 30374 | eff(%): 16.402 | loglstar: -inf < -30415.915 < inf | logz: -30438.950 +/- 0.243 | dlogz: 15207.683 > 0.309] + + 5007it [00:44, 245.77it/s, bound: 94 | nc: 5 | ncall: 30499 | eff(%): 16.417 | loglstar: -inf < -30211.944 < inf | logz: -30233.960 +/- 0.236 | dlogz: 14955.347 > 0.309] + + 5036it [00:44, 256.65it/s, bound: 94 | nc: 5 | ncall: 30644 | eff(%): 16.434 | loglstar: -inf < -29956.315 < inf | logz: -29978.880 +/- 0.237 | dlogz: 14700.503 > 0.309] + + 5062it [00:44, 253.54it/s, bound: 95 | nc: 5 | ncall: 30774 | eff(%): 16.449 | loglstar: -inf < -29706.937 < inf | logz: -29730.239 +/- 0.244 | dlogz: 14464.374 > 0.309] + + 5089it [00:44, 256.15it/s, bound: 96 | nc: 5 | ncall: 30909 | eff(%): 16.464 | loglstar: -inf < -29496.040 < inf | logz: -29516.093 +/- 0.221 | dlogz: 14236.184 > 0.309] + + 5118it [00:44, 264.37it/s, bound: 96 | nc: 5 | ncall: 31054 | eff(%): 16.481 | loglstar: -inf < -29295.438 < inf | logz: -29317.829 +/- 0.237 | dlogz: 14038.842 > 0.309] + + 5145it [00:44, 254.77it/s, bound: 97 | nc: 5 | ncall: 31189 | eff(%): 16.496 | loglstar: -inf < -28993.219 < inf | logz: -29016.802 +/- 0.245 | dlogz: 13757.706 > 0.309] + + 5171it [00:44, 241.04it/s, bound: 97 | nc: 5 | ncall: 31319 | eff(%): 16.511 | loglstar: -inf < -28845.747 < inf | logz: -28866.157 +/- 0.222 | dlogz: 13585.978 > 0.309] + + 5196it [00:45, 229.19it/s, bound: 98 | nc: 5 | ncall: 31444 | eff(%): 16.525 | loglstar: -inf < -28750.342 < inf | logz: -28772.983 +/- 0.238 | dlogz: 13493.711 > 0.309] + + 5220it [00:45, 230.77it/s, bound: 98 | nc: 5 | ncall: 31564 | eff(%): 16.538 | loglstar: -inf < -28561.753 < inf | logz: -28583.377 +/- 0.231 | dlogz: 13303.201 > 0.309] + + 5244it [00:45, 213.20it/s, bound: 99 | nc: 5 | ncall: 31684 | eff(%): 16.551 | loglstar: -inf < -28234.644 < inf | logz: -28257.165 +/- 0.233 | dlogz: 12977.125 > 0.309] + + 5267it [00:45, 212.33it/s, bound: 100 | nc: 5 | ncall: 31799 | eff(%): 16.563 | loglstar: -inf < -28088.374 < inf | logz: -28111.263 +/- 0.239 | dlogz: 12831.778 > 0.309] + + 5292it [00:45, 220.11it/s, bound: 100 | nc: 5 | ncall: 31924 | eff(%): 16.577 | loglstar: -inf < -27815.835 < inf | logz: -27839.909 +/- 0.247 | dlogz: 12572.346 > 0.309] + + 5315it [00:45, 216.22it/s, bound: 101 | nc: 5 | ncall: 32039 | eff(%): 16.589 | loglstar: -inf < -27556.516 < inf | logz: -27580.630 +/- 0.246 | dlogz: 12303.862 > 0.309] + + 5337it [00:45, 212.88it/s, bound: 101 | nc: 5 | ncall: 32149 | eff(%): 16.601 | loglstar: -inf < -27317.697 < inf | logz: -27341.921 +/- 0.248 | dlogz: 12076.245 > 0.309] + + 5359it [00:45, 209.76it/s, bound: 102 | nc: 5 | ncall: 32259 | eff(%): 16.612 | loglstar: -inf < -27143.062 < inf | logz: -27166.786 +/- 0.239 | dlogz: 11887.009 > 0.309] + + 5383it [00:45, 216.45it/s, bound: 102 | nc: 5 | ncall: 32379 | eff(%): 16.625 | loglstar: -inf < -26913.126 < inf | logz: -26936.401 +/- 0.241 | dlogz: 11656.527 > 0.309] + + 5405it [00:45, 215.65it/s, bound: 103 | nc: 5 | ncall: 32489 | eff(%): 16.636 | loglstar: -inf < -26614.192 < inf | logz: -26637.541 +/- 0.241 | dlogz: 11357.596 > 0.309] + + 5429it [00:46, 222.40it/s, bound: 103 | nc: 5 | ncall: 32609 | eff(%): 16.649 | loglstar: -inf < -26404.221 < inf | logz: -26428.040 +/- 0.240 | dlogz: 11147.905 > 0.309] + + 5452it [00:46, 216.62it/s, bound: 104 | nc: 5 | ncall: 32724 | eff(%): 16.661 | loglstar: -inf < -26182.216 < inf | logz: -26206.823 +/- 0.249 | dlogz: 10968.959 > 0.309] + + 5479it [00:46, 231.62it/s, bound: 104 | nc: 5 | ncall: 32859 | eff(%): 16.674 | loglstar: -inf < -25972.719 < inf | logz: -25997.417 +/- 0.250 | dlogz: 10725.940 > 0.309] + + 5504it [00:46, 236.36it/s, bound: 105 | nc: 5 | ncall: 32984 | eff(%): 16.687 | loglstar: -inf < -25651.211 < inf | logz: -25675.029 +/- 0.243 | dlogz: 10394.828 > 0.309] + + 5533it [00:46, 250.96it/s, bound: 105 | nc: 5 | ncall: 33129 | eff(%): 16.701 | loglstar: -inf < -25405.397 < inf | logz: -25429.172 +/- 0.243 | dlogz: 10148.799 > 0.309] + + 5560it [00:46, 254.06it/s, bound: 106 | nc: 5 | ncall: 33264 | eff(%): 16.715 | loglstar: -inf < -25260.959 < inf | logz: -25283.195 +/- 0.232 | dlogz: 10001.778 > 0.309] + + 5586it [00:46, 246.59it/s, bound: 107 | nc: 5 | ncall: 33394 | eff(%): 16.728 | loglstar: -inf < -25260.959 < inf | logz: -25281.699 +/- 0.221 | dlogz: 10000.076 > 0.309] + + 5611it [00:46, 241.41it/s, bound: 107 | nc: 5 | ncall: 33519 | eff(%): 16.740 | loglstar: -inf < -25160.975 < inf | logz: -25186.122 +/- 0.251 | dlogz: 9920.235 > 0.309] + + 5636it [00:46, 225.08it/s, bound: 108 | nc: 5 | ncall: 33644 | eff(%): 16.752 | loglstar: -inf < -24995.987 < inf | logz: -25020.112 +/- 0.244 | dlogz: 9739.380 > 0.309] + + 5660it [00:47, 228.91it/s, bound: 108 | nc: 5 | ncall: 33764 | eff(%): 16.763 | loglstar: -inf < -24794.931 < inf | logz: -24818.625 +/- 0.241 | dlogz: 9537.233 > 0.309] + + 5684it [00:47, 230.39it/s, bound: 109 | nc: 5 | ncall: 33884 | eff(%): 16.775 | loglstar: -inf < -24624.932 < inf | logz: -24649.220 +/- 0.245 | dlogz: 10204.932 > 0.309] + + 5708it [00:47, 229.32it/s, bound: 109 | nc: 5 | ncall: 34004 | eff(%): 16.786 | loglstar: -inf < -24490.515 < inf | logz: -24515.038 +/- 0.245 | dlogz: 10070.753 > 0.309] + + 5732it [00:47, 218.93it/s, bound: 110 | nc: 5 | ncall: 34124 | eff(%): 16.798 | loglstar: -inf < -24371.538 < inf | logz: -24396.007 +/- 0.243 | dlogz: 9951.411 > 0.309] + + 5755it [00:47, 210.27it/s, bound: 110 | nc: 5 | ncall: 34239 | eff(%): 16.808 | loglstar: -inf < -24296.124 < inf | logz: -24321.751 +/- 0.253 | dlogz: 9888.004 > 0.309] + + 5777it [00:47, 195.69it/s, bound: 111 | nc: 5 | ncall: 34349 | eff(%): 16.819 | loglstar: -inf < -24106.807 < inf | logz: -24131.310 +/- 0.244 | dlogz: 9686.544 > 0.309] + + 5797it [00:47, 193.44it/s, bound: 111 | nc: 5 | ncall: 34449 | eff(%): 16.828 | loglstar: -inf < -23915.875 < inf | logz: -23941.642 +/- 0.254 | dlogz: 9511.553 > 0.309] + + 5817it [00:47, 191.62it/s, bound: 112 | nc: 5 | ncall: 34549 | eff(%): 16.837 | loglstar: -inf < -23719.447 < inf | logz: -23744.441 +/- 0.245 | dlogz: 9299.676 > 0.309] + + 5839it [00:47, 199.14it/s, bound: 112 | nc: 5 | ncall: 34659 | eff(%): 16.847 | loglstar: -inf < -23535.599 < inf | logz: -23561.307 +/- 0.251 | dlogz: 9117.807 > 0.309] + + 5861it [00:48, 204.00it/s, bound: 113 | nc: 5 | ncall: 34769 | eff(%): 16.857 | loglstar: -inf < -23309.182 < inf | logz: -23335.150 +/- 0.254 | dlogz: 8894.198 > 0.309] + + 5887it [00:48, 219.92it/s, bound: 113 | nc: 5 | ncall: 34899 | eff(%): 16.869 | loglstar: -inf < -23048.510 < inf | logz: -23073.468 +/- 0.248 | dlogz: 9985.359 > 0.309] + + 5910it [00:48, 221.36it/s, bound: 114 | nc: 5 | ncall: 35014 | eff(%): 16.879 | loglstar: -inf < -22805.606 < inf | logz: -22831.750 +/- 0.256 | dlogz: 9780.611 > 0.309] + + 5940it [00:48, 242.73it/s, bound: 114 | nc: 5 | ncall: 35164 | eff(%): 16.892 | loglstar: -inf < -22537.818 < inf | logz: -22564.061 +/- 0.256 | dlogz: 10568.485 > 0.309] + + 5966it [00:48, 245.97it/s, bound: 115 | nc: 5 | ncall: 35294 | eff(%): 16.904 | loglstar: -inf < -22404.515 < inf | logz: -22428.636 +/- 0.242 | dlogz: 10425.821 > 0.309] + + 5991it [00:48, 242.27it/s, bound: 116 | nc: 5 | ncall: 35419 | eff(%): 16.915 | loglstar: -inf < -22220.394 < inf | logz: -22246.809 +/- 0.257 | dlogz: 10256.449 > 0.309] + + 6022it [00:48, 258.75it/s, bound: 116 | nc: 5 | ncall: 35574 | eff(%): 16.928 | loglstar: -inf < -21962.033 < inf | logz: -21987.162 +/- 0.247 | dlogz: 9984.601 > 0.309] + + 6050it [00:48, 262.98it/s, bound: 117 | nc: 5 | ncall: 35714 | eff(%): 16.940 | loglstar: -inf < -21702.820 < inf | logz: -21729.431 +/- 0.257 | dlogz: 9734.229 > 0.309] + + 6077it [00:48, 256.65it/s, bound: 118 | nc: 5 | ncall: 35849 | eff(%): 16.952 | loglstar: -inf < -21450.696 < inf | logz: -21477.398 +/- 0.258 | dlogz: 9486.892 > 0.309] + + 6103it [00:48, 250.94it/s, bound: 118 | nc: 5 | ncall: 35979 | eff(%): 16.963 | loglstar: -inf < -21198.273 < inf | logz: -21223.960 +/- 0.251 | dlogz: 9221.534 > 0.309] + + 6129it [00:49, 236.40it/s, bound: 119 | nc: 5 | ncall: 36109 | eff(%): 16.974 | loglstar: -inf < -20939.295 < inf | logz: -20963.755 +/- 0.243 | dlogz: 8960.345 > 0.309] + + 6153it [00:49, 226.87it/s, bound: 119 | nc: 5 | ncall: 36229 | eff(%): 16.984 | loglstar: -inf < -20808.426 < inf | logz: -20835.382 +/- 0.259 | dlogz: 8869.665 > 0.309] + + 6176it [00:49, 213.76it/s, bound: 120 | nc: 5 | ncall: 36344 | eff(%): 16.993 | loglstar: -inf < -20715.954 < inf | logz: -20741.884 +/- 0.252 | dlogz: 8739.213 > 0.309] + + 6199it [00:49, 217.32it/s, bound: 120 | nc: 5 | ncall: 36459 | eff(%): 17.003 | loglstar: -inf < -20639.424 < inf | logz: -20665.431 +/- 0.252 | dlogz: 8662.685 > 0.309] + + 6222it [00:49, 220.37it/s, bound: 121 | nc: 5 | ncall: 36574 | eff(%): 17.012 | loglstar: -inf < -20539.227 < inf | logz: -20563.553 +/- 0.240 | dlogz: 8559.756 > 0.309] + + 6257it [00:49, 248.65it/s, bound: 122 | nc: 5 | ncall: 36749 | eff(%): 17.026 | loglstar: -inf < -20383.299 < inf | logz: -20410.329 +/- 0.255 | dlogz: 8705.874 > 0.309] + + 6289it [00:49, 267.67it/s, bound: 122 | nc: 5 | ncall: 36909 | eff(%): 17.039 | loglstar: -inf < -20099.967 < inf | logz: -20127.031 +/- 0.255 | dlogz: 10631.576 > 0.309] + + 6319it [00:49, 274.85it/s, bound: 123 | nc: 5 | ncall: 37059 | eff(%): 17.051 | loglstar: -inf < -19895.241 < inf | logz: -19922.751 +/- 0.261 | dlogz: 10434.202 > 0.309] + + 6347it [00:49, 261.48it/s, bound: 124 | nc: 5 | ncall: 37199 | eff(%): 17.062 | loglstar: -inf < -19768.823 < inf | logz: -19795.858 +/- 0.250 | dlogz: 10299.248 > 0.309] + + 6379it [00:50, 277.77it/s, bound: 124 | nc: 5 | ncall: 37359 | eff(%): 17.075 | loglstar: -inf < -19588.236 < inf | logz: -19614.580 +/- 0.249 | dlogz: 10117.623 > 0.309] + + 6408it [00:50, 280.58it/s, bound: 125 | nc: 5 | ncall: 37504 | eff(%): 17.086 | loglstar: -inf < -19465.745 < inf | logz: -19493.552 +/- 0.262 | dlogz: 10006.164 > 0.309] + + 6437it [00:50, 276.39it/s, bound: 126 | nc: 5 | ncall: 37649 | eff(%): 17.097 | loglstar: -inf < -19308.281 < inf | logz: -19333.325 +/- 0.243 | dlogz: 9835.583 > 0.309] + + 6468it [00:50, 285.73it/s, bound: 126 | nc: 5 | ncall: 37804 | eff(%): 17.109 | loglstar: -inf < -19108.666 < inf | logz: -19135.555 +/- 0.255 | dlogz: 9638.649 > 0.309] + + 6497it [00:50, 282.60it/s, bound: 127 | nc: 5 | ncall: 37949 | eff(%): 17.120 | loglstar: -inf < -18926.858 < inf | logz: -18953.861 +/- 0.256 | dlogz: 9456.891 > 0.309] + + 6527it [00:50, 279.28it/s, bound: 128 | nc: 5 | ncall: 38099 | eff(%): 17.132 | loglstar: -inf < -18566.079 < inf | logz: -18594.283 +/- 0.264 | dlogz: 9115.876 > 0.309] + + 6560it [00:50, 292.96it/s, bound: 128 | nc: 5 | ncall: 38264 | eff(%): 17.144 | loglstar: -inf < -18337.116 < inf | logz: -18363.814 +/- 0.254 | dlogz: 8866.047 > 0.309] + + 6590it [00:50, 288.94it/s, bound: 129 | nc: 5 | ncall: 38414 | eff(%): 17.155 | loglstar: -inf < -18115.803 < inf | logz: -18144.212 +/- 0.264 | dlogz: 8651.782 > 0.309] + + 6619it [00:50, 275.71it/s, bound: 130 | nc: 5 | ncall: 38559 | eff(%): 17.166 | loglstar: -inf < -17943.477 < inf | logz: -17971.046 +/- 0.253 | dlogz: 8473.240 > 0.309] + + 6650it [00:51, 284.25it/s, bound: 130 | nc: 5 | ncall: 38714 | eff(%): 17.177 | loglstar: -inf < -17710.379 < inf | logz: -17737.377 +/- 0.255 | dlogz: 8239.310 > 0.309] + + 6679it [00:51, 271.93it/s, bound: 131 | nc: 5 | ncall: 38859 | eff(%): 17.188 | loglstar: -inf < -17574.706 < inf | logz: -17602.633 +/- 0.255 | dlogz: 8104.747 > 0.309] + + 6709it [00:51, 278.72it/s, bound: 132 | nc: 5 | ncall: 39009 | eff(%): 17.199 | loglstar: -inf < -17411.116 < inf | logz: -17438.311 +/- 0.256 | dlogz: 7940.046 > 0.309] + + 6739it [00:51, 283.35it/s, bound: 132 | nc: 5 | ncall: 39159 | eff(%): 17.209 | loglstar: -inf < -17140.983 < inf | logz: -17168.793 +/- 0.260 | dlogz: 7671.016 > 0.309] + + 6768it [00:51, 275.43it/s, bound: 133 | nc: 5 | ncall: 39304 | eff(%): 17.220 | loglstar: -inf < -16884.744 < inf | logz: -16911.797 +/- 0.255 | dlogz: 7413.161 > 0.309] + + 6797it [00:51, 273.22it/s, bound: 134 | nc: 5 | ncall: 39449 | eff(%): 17.230 | loglstar: -inf < -16671.074 < inf | logz: -16700.178 +/- 0.268 | dlogz: 7230.028 > 0.309] + + 6830it [00:51, 286.87it/s, bound: 134 | nc: 5 | ncall: 39614 | eff(%): 17.241 | loglstar: -inf < -16394.318 < inf | logz: -16422.429 +/- 0.261 | dlogz: 6924.348 > 0.309] + + 6859it [00:51, 287.19it/s, bound: 135 | nc: 5 | ncall: 39759 | eff(%): 17.251 | loglstar: -inf < -16211.961 < inf | logz: -16240.939 +/- 0.263 | dlogz: 6743.615 > 0.309] + + 6888it [00:51, 280.70it/s, bound: 136 | nc: 5 | ncall: 39904 | eff(%): 17.261 | loglstar: -inf < -16124.614 < inf | logz: -16151.607 +/- 0.253 | dlogz: 6652.435 > 0.309] + + 6919it [00:52, 288.72it/s, bound: 136 | nc: 5 | ncall: 40059 | eff(%): 17.272 | loglstar: -inf < -16057.055 < inf | logz: -16086.570 +/- 0.269 | dlogz: 7238.402 > 0.309] + + 6948it [00:52, 284.91it/s, bound: 137 | nc: 5 | ncall: 40204 | eff(%): 17.282 | loglstar: -inf < -15921.249 < inf | logz: -15950.856 +/- 0.269 | dlogz: 7098.168 > 0.309] + + 6977it [00:52, 268.81it/s, bound: 138 | nc: 5 | ncall: 40349 | eff(%): 17.292 | loglstar: -inf < -15739.161 < inf | logz: -15768.870 +/- 0.269 | dlogz: 7651.277 > 0.309] + + 7005it [00:52, 255.96it/s, bound: 138 | nc: 5 | ncall: 40489 | eff(%): 17.301 | loglstar: -inf < -15571.075 < inf | logz: -15599.775 +/- 0.263 | dlogz: 7466.833 > 0.309] + + 7031it [00:52, 242.57it/s, bound: 139 | nc: 5 | ncall: 40619 | eff(%): 17.310 | loglstar: -inf < -15389.462 < inf | logz: -15419.152 +/- 0.266 | dlogz: 7287.428 > 0.309] + + 7056it [00:52, 238.17it/s, bound: 139 | nc: 5 | ncall: 40744 | eff(%): 17.318 | loglstar: -inf < -15303.829 < inf | logz: -15332.113 +/- 0.253 | dlogz: 7198.200 > 0.309] + + 7080it [00:52, 230.40it/s, bound: 140 | nc: 5 | ncall: 40864 | eff(%): 17.326 | loglstar: -inf < -15124.059 < inf | logz: -15153.893 +/- 0.267 | dlogz: 7021.923 > 0.309] + + 7108it [00:52, 241.97it/s, bound: 140 | nc: 5 | ncall: 41004 | eff(%): 17.335 | loglstar: -inf < -14915.043 < inf | logz: -14944.087 +/- 0.264 | dlogz: 6810.800 > 0.309] + + 7133it [00:52, 237.99it/s, bound: 141 | nc: 5 | ncall: 41129 | eff(%): 17.343 | loglstar: -inf < -14790.417 < inf | logz: -14819.545 +/- 0.265 | dlogz: 6686.175 > 0.309] + + 7157it [00:53, 232.91it/s, bound: 142 | nc: 5 | ncall: 41249 | eff(%): 17.351 | loglstar: -inf < -14646.141 < inf | logz: -14676.447 +/- 0.272 | dlogz: 6548.245 > 0.309] + + 7184it [00:53, 241.49it/s, bound: 142 | nc: 5 | ncall: 41384 | eff(%): 17.359 | loglstar: -inf < -14507.696 < inf | logz: -14538.095 +/- 0.272 | dlogz: 7124.901 > 0.309] + + 7209it [00:53, 233.59it/s, bound: 143 | nc: 5 | ncall: 41509 | eff(%): 17.367 | loglstar: -inf < -14426.397 < inf | logz: -14453.707 +/- 0.252 | dlogz: 7032.686 > 0.309] + + 7243it [00:53, 261.65it/s, bound: 143 | nc: 5 | ncall: 41679 | eff(%): 17.378 | loglstar: -inf < -14124.493 < inf | logz: -14155.090 +/- 0.273 | dlogz: 6747.247 > 0.309] + + 7270it [00:53, 263.31it/s, bound: 144 | nc: 5 | ncall: 41814 | eff(%): 17.387 | loglstar: -inf < -13976.697 < inf | logz: -14007.293 +/- 0.271 | dlogz: 6588.674 > 0.309] + + 7297it [00:53, 237.81it/s, bound: 145 | nc: 5 | ncall: 41949 | eff(%): 17.395 | loglstar: -inf < -13824.394 < inf | logz: -13854.063 +/- 0.267 | dlogz: 6450.776 > 0.309] + + 7322it [00:53, 221.13it/s, bound: 145 | nc: 5 | ncall: 42074 | eff(%): 17.403 | loglstar: -inf < -13719.255 < inf | logz: -13747.851 +/- 0.259 | dlogz: 6343.630 > 0.309] + + 7345it [00:53, 223.07it/s, bound: 146 | nc: 5 | ncall: 42189 | eff(%): 17.410 | loglstar: -inf < -13590.408 < inf | logz: -13621.345 +/- 0.274 | dlogz: 6235.885 > 0.309] + + 7371it [00:53, 233.09it/s, bound: 146 | nc: 5 | ncall: 42319 | eff(%): 17.418 | loglstar: -inf < -13408.882 < inf | logz: -13439.906 +/- 0.275 | dlogz: 6060.931 > 0.309] + + 7399it [00:54, 245.28it/s, bound: 147 | nc: 5 | ncall: 42459 | eff(%): 17.426 | loglstar: -inf < -13257.037 < inf | logz: -13288.151 +/- 0.275 | dlogz: 5889.278 > 0.309] + + 7429it [00:54, 257.97it/s, bound: 148 | nc: 5 | ncall: 42609 | eff(%): 17.435 | loglstar: -inf < -13200.922 < inf | logz: -13232.099 +/- 0.273 | dlogz: 5830.482 > 0.309] + + 7457it [00:54, 261.31it/s, bound: 148 | nc: 5 | ncall: 42749 | eff(%): 17.444 | loglstar: -inf < -13011.995 < inf | logz: -13041.793 +/- 0.264 | dlogz: 5637.468 > 0.309] + + 7484it [00:54, 244.72it/s, bound: 149 | nc: 5 | ncall: 42884 | eff(%): 17.452 | loglstar: -inf < -12895.089 < inf | logz: -12925.386 +/- 0.269 | dlogz: 6020.286 > 0.309] + + 7513it [00:54, 256.37it/s, bound: 149 | nc: 5 | ncall: 43029 | eff(%): 17.460 | loglstar: -inf < -12733.933 < inf | logz: -12765.432 +/- 0.277 | dlogz: 5902.953 > 0.309] + + 7541it [00:54, 260.44it/s, bound: 150 | nc: 5 | ncall: 43169 | eff(%): 17.469 | loglstar: -inf < -12611.997 < inf | logz: -12642.486 +/- 0.270 | dlogz: 6244.447 > 0.309] + + 7568it [00:54, 260.56it/s, bound: 151 | nc: 5 | ncall: 43304 | eff(%): 17.476 | loglstar: -inf < -12431.919 < inf | logz: -12463.600 +/- 0.277 | dlogz: 6071.495 > 0.309] + + 7599it [00:54, 273.11it/s, bound: 151 | nc: 5 | ncall: 43459 | eff(%): 17.485 | loglstar: -inf < -12247.439 < inf | logz: -12279.146 +/- 0.275 | dlogz: 5882.474 > 0.309] + + 7627it [00:54, 269.12it/s, bound: 152 | nc: 5 | ncall: 43599 | eff(%): 17.494 | loglstar: -inf < -12058.392 < inf | logz: -12090.250 +/- 0.277 | dlogz: 5695.362 > 0.309] + + 7655it [00:54, 268.46it/s, bound: 153 | nc: 5 | ncall: 43739 | eff(%): 17.502 | loglstar: -inf < -11812.957 < inf | logz: -11843.907 +/- 0.270 | dlogz: 5445.413 > 0.309] + + 7688it [00:55, 284.15it/s, bound: 153 | nc: 5 | ncall: 43904 | eff(%): 17.511 | loglstar: -inf < -11540.520 < inf | logz: -11572.600 +/- 0.279 | dlogz: 5179.380 > 0.309] + + 7717it [00:55, 282.96it/s, bound: 154 | nc: 5 | ncall: 44049 | eff(%): 17.519 | loglstar: -inf < -11393.291 < inf | logz: -11425.471 +/- 0.279 | dlogz: 5046.744 > 0.309] + + 7746it [00:55, 281.67it/s, bound: 155 | nc: 5 | ncall: 44194 | eff(%): 17.527 | loglstar: -inf < -11240.888 < inf | logz: -11272.570 +/- 0.272 | dlogz: 5753.977 > 0.309] + + 7777it [00:55, 288.53it/s, bound: 155 | nc: 5 | ncall: 44349 | eff(%): 17.536 | loglstar: -inf < -11038.368 < inf | logz: -11070.679 +/- 0.278 | dlogz: 5554.275 > 0.309] + + 7807it [00:55, 289.93it/s, bound: 156 | nc: 5 | ncall: 44499 | eff(%): 17.544 | loglstar: -inf < -10918.255 < inf | logz: -10950.735 +/- 0.281 | dlogz: 5458.041 > 0.309] + + 7837it [00:55, 278.07it/s, bound: 157 | nc: 5 | ncall: 44649 | eff(%): 17.552 | loglstar: -inf < -10731.240 < inf | logz: -10763.820 +/- 0.281 | dlogz: 5254.395 > 0.309] + + 7868it [00:55, 286.60it/s, bound: 157 | nc: 5 | ncall: 44804 | eff(%): 17.561 | loglstar: -inf < -10583.337 < inf | logz: -10616.022 +/- 0.281 | dlogz: 5105.137 > 0.309] + + 7897it [00:55, 283.28it/s, bound: 158 | nc: 5 | ncall: 44949 | eff(%): 17.569 | loglstar: -inf < -10497.945 < inf | logz: -10528.986 +/- 0.270 | dlogz: 5009.234 > 0.309] + + 7926it [00:55, 274.07it/s, bound: 159 | nc: 5 | ncall: 45094 | eff(%): 17.577 | loglstar: -inf < -10372.304 < inf | logz: -10403.815 +/- 0.273 | dlogz: 4884.249 > 0.309] + + 7962it [00:56, 294.49it/s, bound: 159 | nc: 5 | ncall: 45274 | eff(%): 17.586 | loglstar: -inf < -10225.199 < inf | logz: -10257.095 +/- 0.276 | dlogz: 4737.792 > 0.309] + + 7992it [00:56, 287.20it/s, bound: 160 | nc: 5 | ncall: 45424 | eff(%): 17.594 | loglstar: -inf < -9982.690 < inf | logz: -10015.784 +/- 0.283 | dlogz: 4501.155 > 0.309] + + 8021it [00:56, 284.31it/s, bound: 161 | nc: 5 | ncall: 45569 | eff(%): 17.602 | loglstar: -inf < -9848.890 < inf | logz: -9880.524 +/- 0.273 | dlogz: 4360.446 > 0.309] + + 8051it [00:56, 287.52it/s, bound: 161 | nc: 5 | ncall: 45719 | eff(%): 17.610 | loglstar: -inf < -9717.167 < inf | logz: -9749.545 +/- 0.275 | dlogz: 4229.642 > 0.309] + + 8080it [00:56, 280.52it/s, bound: 162 | nc: 5 | ncall: 45864 | eff(%): 17.617 | loglstar: -inf < -9566.459 < inf | logz: -9599.851 +/- 0.284 | dlogz: 4091.897 > 0.309] + + 8109it [00:56, 273.83it/s, bound: 163 | nc: 5 | ncall: 46009 | eff(%): 17.625 | loglstar: -inf < -9462.154 < inf | logz: -9494.025 +/- 0.275 | dlogz: 3973.643 > 0.309] + + 8139it [00:56, 278.91it/s, bound: 163 | nc: 5 | ncall: 46159 | eff(%): 17.633 | loglstar: -inf < -9251.613 < inf | logz: -9285.201 +/- 0.285 | dlogz: 3773.106 > 0.309] + + 8171it [00:56, 287.64it/s, bound: 164 | nc: 5 | ncall: 46319 | eff(%): 17.641 | loglstar: -inf < -9187.178 < inf | logz: -9220.863 +/- 0.285 | dlogz: 3704.947 > 0.309] + + 8200it [00:56, 281.93it/s, bound: 165 | nc: 5 | ncall: 46464 | eff(%): 17.648 | loglstar: -inf < -9050.445 < inf | logz: -9082.621 +/- 0.276 | dlogz: 3561.936 > 0.309] + + 8234it [00:56, 297.98it/s, bound: 165 | nc: 5 | ncall: 46634 | eff(%): 17.657 | loglstar: -inf < -8910.355 < inf | logz: -8943.897 +/- 0.281 | dlogz: 3424.410 > 0.309] + + 8264it [00:57, 288.57it/s, bound: 166 | nc: 5 | ncall: 46784 | eff(%): 17.664 | loglstar: -inf < -8801.045 < inf | logz: -8831.756 +/- 0.266 | dlogz: 3310.426 > 0.309] + + 8293it [00:57, 281.26it/s, bound: 167 | nc: 5 | ncall: 46929 | eff(%): 17.671 | loglstar: -inf < -8685.549 < inf | logz: -8717.451 +/- 0.274 | dlogz: 3196.193 > 0.309] + + 8324it [00:57, 289.24it/s, bound: 167 | nc: 5 | ncall: 47084 | eff(%): 17.679 | loglstar: -inf < -8552.787 < inf | logz: -8586.994 +/- 0.287 | dlogz: 3073.984 > 0.309] + + 8354it [00:57, 273.20it/s, bound: 168 | nc: 5 | ncall: 47234 | eff(%): 17.686 | loglstar: -inf < -8382.422 < inf | logz: -8416.702 +/- 0.287 | dlogz: 3178.379 > 0.309] + + 8382it [00:57, 255.43it/s, bound: 169 | nc: 5 | ncall: 47374 | eff(%): 17.693 | loglstar: -inf < -8283.078 < inf | logz: -8317.452 +/- 0.287 | dlogz: 3639.669 > 0.309] + + 8408it [00:57, 237.66it/s, bound: 169 | nc: 5 | ncall: 47504 | eff(%): 17.700 | loglstar: -inf < -8215.125 < inf | logz: -8249.610 +/- 0.288 | dlogz: 3574.124 > 0.309] + + 8433it [00:57, 236.62it/s, bound: 170 | nc: 5 | ncall: 47629 | eff(%): 17.706 | loglstar: -inf < -8086.859 < inf | logz: -8120.328 +/- 0.282 | dlogz: 3439.290 > 0.309] + + 8462it [00:57, 244.22it/s, bound: 171 | nc: 5 | ncall: 47774 | eff(%): 17.713 | loglstar: -inf < -8049.233 < inf | logz: -8083.737 +/- 0.284 | dlogz: 3403.553 > 0.309] + + 8497it [00:58, 272.01it/s, bound: 171 | nc: 5 | ncall: 47949 | eff(%): 17.721 | loglstar: -inf < -7924.536 < inf | logz: -7957.384 +/- 0.276 | dlogz: 3275.374 > 0.309] + + 8526it [00:58, 275.26it/s, bound: 172 | nc: 5 | ncall: 48094 | eff(%): 17.728 | loglstar: -inf < -7883.830 < inf | logz: -7917.096 +/- 0.280 | dlogz: 3235.156 > 0.309] + + 8554it [00:58, 254.99it/s, bound: 173 | nc: 5 | ncall: 48234 | eff(%): 17.734 | loglstar: -inf < -7816.781 < inf | logz: -7850.178 +/- 0.280 | dlogz: 3168.168 > 0.309] + + 8585it [00:58, 268.44it/s, bound: 173 | nc: 5 | ncall: 48389 | eff(%): 17.742 | loglstar: -inf < -7713.570 < inf | logz: -7748.590 +/- 0.288 | dlogz: 3069.052 > 0.309] + + 8613it [00:58, 263.89it/s, bound: 174 | nc: 5 | ncall: 48529 | eff(%): 17.748 | loglstar: -inf < -7579.300 < inf | logz: -7613.528 +/- 0.279 | dlogz: 2931.411 > 0.309] + + 8641it [00:58, 267.33it/s, bound: 174 | nc: 5 | ncall: 48669 | eff(%): 17.755 | loglstar: -inf < -7495.403 < inf | logz: -7530.572 +/- 0.288 | dlogz: 2850.388 > 0.309] + + 8668it [00:58, 261.29it/s, bound: 175 | nc: 5 | ncall: 48804 | eff(%): 17.761 | loglstar: -inf < -7384.779 < inf | logz: -7419.104 +/- 0.285 | dlogz: 2737.313 > 0.309] + + 8695it [00:58, 259.17it/s, bound: 176 | nc: 5 | ncall: 48939 | eff(%): 17.767 | loglstar: -inf < -7373.856 < inf | logz: -7405.818 +/- 0.271 | dlogz: 2722.870 > 0.309] + + 8724it [00:58, 265.27it/s, bound: 176 | nc: 5 | ncall: 49084 | eff(%): 17.774 | loglstar: -inf < -7287.382 < inf | logz: -7321.761 +/- 0.284 | dlogz: 2639.629 > 0.309] + + 8751it [00:58, 264.51it/s, bound: 177 | nc: 5 | ncall: 49219 | eff(%): 17.780 | loglstar: -inf < -7204.338 < inf | logz: -7239.499 +/- 0.285 | dlogz: 2557.757 > 0.309] + + 8778it [00:59, 264.86it/s, bound: 178 | nc: 5 | ncall: 49354 | eff(%): 17.786 | loglstar: -inf < -7171.221 < inf | logz: -7205.771 +/- 0.284 | dlogz: 2523.443 > 0.309] + + 8807it [00:59, 271.90it/s, bound: 178 | nc: 5 | ncall: 49499 | eff(%): 17.792 | loglstar: -inf < -7106.524 < inf | logz: -7141.216 +/- 0.286 | dlogz: 2458.871 > 0.309] + + 8838it [00:59, 281.45it/s, bound: 179 | nc: 5 | ncall: 49654 | eff(%): 17.799 | loglstar: -inf < -6996.373 < inf | logz: -7032.140 +/- 0.289 | dlogz: 2350.742 > 0.309] + + 8867it [00:59, 273.43it/s, bound: 180 | nc: 5 | ncall: 49799 | eff(%): 17.806 | loglstar: -inf < -6913.103 < inf | logz: -6947.985 +/- 0.286 | dlogz: 2265.421 > 0.309] + + 8900it [00:59, 287.79it/s, bound: 180 | nc: 5 | ncall: 49964 | eff(%): 17.813 | loglstar: -inf < -6881.192 < inf | logz: -6914.442 +/- 0.276 | dlogz: 2230.859 > 0.309] + + 8929it [00:59, 277.93it/s, bound: 181 | nc: 5 | ncall: 50109 | eff(%): 17.819 | loglstar: -inf < -6819.509 < inf | logz: -6855.316 +/- 0.288 | dlogz: 2173.054 > 0.309] + + 8957it [00:59, 277.53it/s, bound: 182 | nc: 5 | ncall: 50249 | eff(%): 17.825 | loglstar: -inf < -6751.262 < inf | logz: -6785.844 +/- 0.284 | dlogz: 2102.388 > 0.309] + + 8990it [00:59, 290.58it/s, bound: 182 | nc: 5 | ncall: 50414 | eff(%): 17.832 | loglstar: -inf < -6671.586 < inf | logz: -6706.893 +/- 0.288 | dlogz: 2689.104 > 0.309] + + 9020it [00:59, 290.79it/s, bound: 183 | nc: 5 | ncall: 50564 | eff(%): 17.839 | loglstar: -inf < -6601.218 < inf | logz: -6636.646 +/- 0.289 | dlogz: 2618.797 > 0.309] + + 9050it [01:00, 291.54it/s, bound: 184 | nc: 5 | ncall: 50714 | eff(%): 17.845 | loglstar: -inf < -6517.709 < inf | logz: -6552.724 +/- 0.287 | dlogz: 2534.192 > 0.309] + + 9082it [01:00, 297.81it/s, bound: 184 | nc: 5 | ncall: 50874 | eff(%): 17.852 | loglstar: -inf < -6438.956 < inf | logz: -6474.422 +/- 0.287 | dlogz: 2456.097 > 0.309] + + 9112it [01:00, 291.38it/s, bound: 185 | nc: 5 | ncall: 51024 | eff(%): 17.858 | loglstar: -inf < -6339.211 < inf | logz: -6374.510 +/- 0.286 | dlogz: 2355.825 > 0.309] + + 9142it [01:00, 284.65it/s, bound: 186 | nc: 5 | ncall: 51174 | eff(%): 17.865 | loglstar: -inf < -6272.243 < inf | logz: -6309.184 +/- 0.297 | dlogz: 2297.760 > 0.309] + + 9174it [01:00, 293.23it/s, bound: 186 | nc: 5 | ncall: 51334 | eff(%): 17.871 | loglstar: -inf < -6197.745 < inf | logz: -6234.421 +/- 0.290 | dlogz: 2216.222 > 0.309] + + 9204it [01:00, 275.80it/s, bound: 187 | nc: 5 | ncall: 51484 | eff(%): 17.877 | loglstar: -inf < -6103.998 < inf | logz: -6141.145 +/- 0.298 | dlogz: 2128.408 > 0.309] + + 9232it [01:00, 270.52it/s, bound: 188 | nc: 5 | ncall: 51624 | eff(%): 17.883 | loglstar: -inf < -6068.274 < inf | logz: -6103.760 +/- 0.287 | dlogz: 2084.541 > 0.309] + + 9260it [01:00, 270.34it/s, bound: 188 | nc: 5 | ncall: 51764 | eff(%): 17.889 | loglstar: -inf < -6022.703 < inf | logz: -6060.037 +/- 0.299 | dlogz: 2047.261 > 0.309] + + 9288it [01:00, 263.89it/s, bound: 189 | nc: 5 | ncall: 51904 | eff(%): 17.895 | loglstar: -inf < -5948.740 < inf | logz: -5986.148 +/- 0.299 | dlogz: 1970.954 > 0.309] + + 9315it [01:01, 256.06it/s, bound: 190 | nc: 5 | ncall: 52039 | eff(%): 17.900 | loglstar: -inf < -5909.451 < inf | logz: -5945.853 +/- 0.291 | dlogz: 1926.890 > 0.309] + + 9342it [01:01, 259.61it/s, bound: 190 | nc: 5 | ncall: 52174 | eff(%): 17.905 | loglstar: -inf < -5864.055 < inf | logz: -5901.622 +/- 0.299 | dlogz: 1885.195 > 0.309] + + 9369it [01:01, 238.17it/s, bound: 191 | nc: 5 | ncall: 52309 | eff(%): 17.911 | loglstar: -inf < -5805.841 < inf | logz: -5842.852 +/- 0.292 | dlogz: 1823.888 > 0.309] + + 9394it [01:01, 240.82it/s, bound: 191 | nc: 5 | ncall: 52434 | eff(%): 17.916 | loglstar: -inf < -5778.446 < inf | logz: -5815.789 +/- 0.294 | dlogz: 1796.816 > 0.309] + + 9419it [01:01, 240.33it/s, bound: 192 | nc: 5 | ncall: 52559 | eff(%): 17.921 | loglstar: -inf < -5737.584 < inf | logz: -5775.377 +/- 0.299 | dlogz: 1758.120 > 0.309] + + 9444it [01:01, 233.14it/s, bound: 192 | nc: 5 | ncall: 52684 | eff(%): 17.926 | loglstar: -inf < -5713.941 < inf | logz: -5749.078 +/- 0.285 | dlogz: 1728.855 > 0.309] + + 9468it [01:01, 229.12it/s, bound: 193 | nc: 5 | ncall: 52804 | eff(%): 17.930 | loglstar: -inf < -5678.253 < inf | logz: -5716.264 +/- 0.301 | dlogz: 1700.131 > 0.309] + + 9492it [01:01, 224.46it/s, bound: 194 | nc: 5 | ncall: 52924 | eff(%): 17.935 | loglstar: -inf < -5642.163 < inf | logz: -5679.364 +/- 0.293 | dlogz: 1659.642 > 0.309] + + 9518it [01:01, 232.80it/s, bound: 194 | nc: 5 | ncall: 53054 | eff(%): 17.940 | loglstar: -inf < -5606.260 < inf | logz: -5643.437 +/- 0.291 | dlogz: 1623.504 > 0.309] + + 9542it [01:01, 229.22it/s, bound: 195 | nc: 5 | ncall: 53174 | eff(%): 17.945 | loglstar: -inf < -5571.126 < inf | logz: -5608.658 +/- 0.293 | dlogz: 1588.911 > 0.309] + + 9569it [01:02, 238.02it/s, bound: 195 | nc: 5 | ncall: 53309 | eff(%): 17.950 | loglstar: -inf < -5520.683 < inf | logz: -5557.803 +/- 0.295 | dlogz: 1537.893 > 0.309] + + 9593it [01:02, 224.58it/s, bound: 196 | nc: 5 | ncall: 53429 | eff(%): 17.955 | loglstar: -inf < -5480.692 < inf | logz: -5517.177 +/- 0.293 | dlogz: 1496.660 > 0.309] + + 9620it [01:02, 235.29it/s, bound: 196 | nc: 5 | ncall: 53564 | eff(%): 17.960 | loglstar: -inf < -5441.721 < inf | logz: -5479.994 +/- 0.300 | dlogz: 1461.160 > 0.309] + + 9645it [01:02, 237.85it/s, bound: 197 | nc: 5 | ncall: 53689 | eff(%): 17.965 | loglstar: -inf < -5406.295 < inf | logz: -5443.537 +/- 0.295 | dlogz: 1423.088 > 0.309] + + 9670it [01:02, 236.69it/s, bound: 198 | nc: 5 | ncall: 53814 | eff(%): 17.969 | loglstar: -inf < -5358.573 < inf | logz: -5395.797 +/- 0.294 | dlogz: 1375.299 > 0.309] + + 9698it [01:02, 248.35it/s, bound: 198 | nc: 5 | ncall: 53954 | eff(%): 17.975 | loglstar: -inf < -5337.231 < inf | logz: -5375.354 +/- 0.299 | dlogz: 1379.481 > 0.309] + + 9723it [01:02, 238.55it/s, bound: 199 | nc: 5 | ncall: 54079 | eff(%): 17.979 | loglstar: -inf < -5305.323 < inf | logz: -5342.436 +/- 0.295 | dlogz: 1345.505 > 0.309] + + 9747it [01:02, 231.56it/s, bound: 199 | nc: 5 | ncall: 54199 | eff(%): 17.984 | loglstar: -inf < -5232.220 < inf | logz: -5269.980 +/- 0.296 | dlogz: 1273.169 > 0.309] + + 9771it [01:02, 228.90it/s, bound: 200 | nc: 5 | ncall: 54319 | eff(%): 17.988 | loglstar: -inf < -5204.554 < inf | logz: -5243.485 +/- 0.303 | dlogz: 1248.325 > 0.309] + + 9802it [01:03, 251.23it/s, bound: 200 | nc: 5 | ncall: 54474 | eff(%): 17.994 | loglstar: -inf < -5160.275 < inf | logz: -5196.929 +/- 0.291 | dlogz: 1199.496 > 0.309] + + 9832it [01:03, 263.50it/s, bound: 201 | nc: 5 | ncall: 54624 | eff(%): 17.999 | loglstar: -inf < -5133.593 < inf | logz: -5170.210 +/- 0.292 | dlogz: 1172.652 > 0.309] + + 9859it [01:03, 254.88it/s, bound: 202 | nc: 5 | ncall: 54759 | eff(%): 18.004 | loglstar: -inf < -5086.953 < inf | logz: -5126.033 +/- 0.303 | dlogz: 1130.407 > 0.309] + + 9887it [01:03, 259.72it/s, bound: 202 | nc: 5 | ncall: 54899 | eff(%): 18.009 | loglstar: -inf < -5065.971 < inf | logz: -5103.148 +/- 0.295 | dlogz: 1424.363 > 0.309] + + 9914it [01:03, 253.83it/s, bound: 203 | nc: 5 | ncall: 55034 | eff(%): 18.014 | loglstar: -inf < -5064.018 < inf | logz: -5100.770 +/- 0.286 | dlogz: 1421.786 > 0.309] + + 9941it [01:03, 257.76it/s, bound: 204 | nc: 5 | ncall: 55169 | eff(%): 18.019 | loglstar: -inf < -5031.193 < inf | logz: -5069.373 +/- 0.297 | dlogz: 1390.616 > 0.309] + + 9971it [01:03, 267.90it/s, bound: 204 | nc: 5 | ncall: 55319 | eff(%): 18.025 | loglstar: -inf < -4985.057 < inf | logz: -5023.142 +/- 0.299 | dlogz: 1344.336 > 0.309] + + 9998it [01:03, 226.20it/s, bound: 205 | nc: 5 | ncall: 55454 | eff(%): 18.029 | loglstar: -inf < -4959.961 < inf | logz: -4998.547 +/- 0.298 | dlogz: 1319.811 > 0.309] + + 10022it [01:03, 228.65it/s, bound: 205 | nc: 5 | ncall: 55574 | eff(%): 18.034 | loglstar: -inf < -4931.926 < inf | logz: -4970.376 +/- 0.297 | dlogz: 1291.439 > 0.309] + + 10046it [01:04, 225.74it/s, bound: 206 | nc: 5 | ncall: 55694 | eff(%): 18.038 | loglstar: -inf < -4895.330 < inf | logz: -4934.861 +/- 0.303 | dlogz: 1256.632 > 0.309] + + 10072it [01:04, 232.84it/s, bound: 206 | nc: 5 | ncall: 55824 | eff(%): 18.042 | loglstar: -inf < -4871.755 < inf | logz: -4909.542 +/- 0.297 | dlogz: 1230.133 > 0.309] + + 10096it [01:04, 234.37it/s, bound: 207 | nc: 5 | ncall: 55944 | eff(%): 18.047 | loglstar: -inf < -4859.099 < inf | logz: -4897.511 +/- 0.296 | dlogz: 1218.230 > 0.309] + + 10120it [01:04, 233.30it/s, bound: 208 | nc: 5 | ncall: 56064 | eff(%): 18.051 | loglstar: -inf < -4834.479 < inf | logz: -4873.318 +/- 0.296 | dlogz: 1193.871 > 0.309] + + 10146it [01:04, 240.41it/s, bound: 208 | nc: 5 | ncall: 56194 | eff(%): 18.055 | loglstar: -inf < -4813.789 < inf | logz: -4852.980 +/- 0.299 | dlogz: 1173.662 > 0.309] + + 10171it [01:04, 221.26it/s, bound: 209 | nc: 5 | ncall: 56319 | eff(%): 18.060 | loglstar: -inf < -4791.474 < inf | logz: -4830.714 +/- 0.303 | dlogz: 1151.484 > 0.309] + + 10199it [01:04, 235.93it/s, bound: 209 | nc: 5 | ncall: 56459 | eff(%): 18.064 | loglstar: -inf < -4770.092 < inf | logz: -4808.528 +/- 0.298 | dlogz: 1128.760 > 0.309] + + 10225it [01:04, 242.11it/s, bound: 210 | nc: 5 | ncall: 56589 | eff(%): 18.069 | loglstar: -inf < -4751.111 < inf | logz: -4790.985 +/- 0.303 | dlogz: 1111.787 > 0.309] + + 10250it [01:04, 242.81it/s, bound: 210 | nc: 5 | ncall: 56714 | eff(%): 18.073 | loglstar: -inf < -4730.953 < inf | logz: -4770.026 +/- 0.298 | dlogz: 1090.089 > 0.309] + + 10278it [01:05, 252.31it/s, bound: 211 | nc: 5 | ncall: 56854 | eff(%): 18.078 | loglstar: -inf < -4693.677 < inf | logz: -4733.151 +/- 0.303 | dlogz: 1053.427 > 0.309] + + 10305it [01:05, 256.53it/s, bound: 212 | nc: 5 | ncall: 56989 | eff(%): 18.082 | loglstar: -inf < -4669.380 < inf | logz: -4709.718 +/- 0.305 | dlogz: 1030.431 > 0.309] + + 10333it [01:05, 261.77it/s, bound: 212 | nc: 5 | ncall: 57129 | eff(%): 18.087 | loglstar: -inf < -4649.205 < inf | logz: -4689.695 +/- 0.303 | dlogz: 1010.297 > 0.309] + + 10360it [01:05, 259.69it/s, bound: 213 | nc: 5 | ncall: 57264 | eff(%): 18.092 | loglstar: -inf < -4627.914 < inf | logz: -4667.978 +/- 0.304 | dlogz: 990.009 > 0.309] + + 10390it [01:05, 265.84it/s, bound: 214 | nc: 5 | ncall: 57414 | eff(%): 18.097 | loglstar: -inf < -4602.201 < inf | logz: -4642.112 +/- 0.305 | dlogz: 963.882 > 0.309] + + 10420it [01:05, 274.50it/s, bound: 214 | nc: 5 | ncall: 57564 | eff(%): 18.102 | loglstar: -inf < -4575.476 < inf | logz: -4614.983 +/- 0.302 | dlogz: 936.426 > 0.309] + + 10448it [01:05, 272.80it/s, bound: 215 | nc: 5 | ncall: 57704 | eff(%): 18.106 | loglstar: -inf < -4559.564 < inf | logz: -4598.562 +/- 0.302 | dlogz: 919.752 > 0.309] + + 10480it [01:05, 277.14it/s, bound: 216 | nc: 5 | ncall: 57864 | eff(%): 18.111 | loglstar: -inf < -4531.496 < inf | logz: -4572.661 +/- 0.310 | dlogz: 895.374 > 0.309] + + 10508it [01:05, 272.96it/s, bound: 216 | nc: 5 | ncall: 58004 | eff(%): 18.116 | loglstar: -inf < -4502.621 < inf | logz: -4543.502 +/- 0.306 | dlogz: 865.246 > 0.309] + + 10536it [01:05, 269.71it/s, bound: 217 | nc: 5 | ncall: 58144 | eff(%): 18.121 | loglstar: -inf < -4484.345 < inf | logz: -4525.569 +/- 0.312 | dlogz: 848.038 > 0.309] + + 10567it [01:06, 280.41it/s, bound: 217 | nc: 5 | ncall: 58299 | eff(%): 18.126 | loglstar: -inf < -4456.275 < inf | logz: -4497.089 +/- 0.310 | dlogz: 818.768 > 0.309] + + 10596it [01:06, 266.63it/s, bound: 218 | nc: 5 | ncall: 58444 | eff(%): 18.130 | loglstar: -inf < -4435.348 < inf | logz: -4475.390 +/- 0.305 | dlogz: 796.270 > 0.309] + + 10623it [01:06, 262.06it/s, bound: 219 | nc: 5 | ncall: 58579 | eff(%): 18.134 | loglstar: -inf < -4407.772 < inf | logz: -4449.131 +/- 0.311 | dlogz: 770.900 > 0.309] + + 10653it [01:06, 271.68it/s, bound: 219 | nc: 5 | ncall: 58729 | eff(%): 18.139 | loglstar: -inf < -4376.958 < inf | logz: -4418.823 +/- 0.316 | dlogz: 741.730 > 0.309] + + 10681it [01:06, 256.99it/s, bound: 220 | nc: 5 | ncall: 58869 | eff(%): 18.144 | loglstar: -inf < -4355.575 < inf | logz: -4396.836 +/- 0.310 | dlogz: 717.998 > 0.309] + + 10707it [01:06, 249.12it/s, bound: 221 | nc: 5 | ncall: 58999 | eff(%): 18.148 | loglstar: -inf < -4339.071 < inf | logz: -4378.821 +/- 0.303 | dlogz: 699.070 > 0.309] + + 10734it [01:06, 251.62it/s, bound: 221 | nc: 5 | ncall: 59134 | eff(%): 18.152 | loglstar: -inf < -4330.568 < inf | logz: -4371.323 +/- 0.307 | dlogz: 691.746 > 0.309] + + 10760it [01:06, 246.42it/s, bound: 222 | nc: 5 | ncall: 59264 | eff(%): 18.156 | loglstar: -inf < -4316.593 < inf | logz: -4357.112 +/- 0.307 | dlogz: 677.425 > 0.309] + + 10788it [01:06, 253.47it/s, bound: 222 | nc: 5 | ncall: 59404 | eff(%): 18.160 | loglstar: -inf < -4303.817 < inf | logz: -4344.440 +/- 0.309 | dlogz: 664.666 > 0.309] + + 10814it [01:07, 247.00it/s, bound: 223 | nc: 5 | ncall: 59534 | eff(%): 18.164 | loglstar: -inf < -4292.801 < inf | logz: -4334.227 +/- 0.309 | dlogz: 654.522 > 0.309] + + 10839it [01:07, 244.72it/s, bound: 223 | nc: 5 | ncall: 59659 | eff(%): 18.168 | loglstar: -inf < -4266.162 < inf | logz: -4307.144 +/- 0.312 | dlogz: 627.304 > 0.309] + + 10864it [01:07, 242.68it/s, bound: 224 | nc: 5 | ncall: 59784 | eff(%): 18.172 | loglstar: -inf < -4248.350 < inf | logz: -4289.629 +/- 0.306 | dlogz: 609.561 > 0.309] + + 10889it [01:07, 237.31it/s, bound: 225 | nc: 5 | ncall: 59909 | eff(%): 18.176 | loglstar: -inf < -4226.331 < inf | logz: -4267.840 +/- 0.308 | dlogz: 587.823 > 0.309] + + 10914it [01:07, 239.19it/s, bound: 225 | nc: 5 | ncall: 60034 | eff(%): 18.180 | loglstar: -inf < -4192.762 < inf | logz: -4233.755 +/- 0.309 | dlogz: 553.534 > 0.309] + + 10940it [01:07, 244.42it/s, bound: 226 | nc: 5 | ncall: 60164 | eff(%): 18.184 | loglstar: -inf < -4176.672 < inf | logz: -4219.100 +/- 0.317 | dlogz: 543.662 > 0.309] + + 10966it [01:07, 248.32it/s, bound: 226 | nc: 5 | ncall: 60294 | eff(%): 18.188 | loglstar: -inf < -4161.663 < inf | logz: -4203.205 +/- 0.313 | dlogz: 526.729 > 0.309] + + 10991it [01:07, 236.32it/s, bound: 227 | nc: 5 | ncall: 60419 | eff(%): 18.191 | loglstar: -inf < -4151.163 < inf | logz: -4192.809 +/- 0.311 | dlogz: 516.266 > 0.309] + + 11015it [01:07, 219.90it/s, bound: 227 | nc: 5 | ncall: 60539 | eff(%): 18.195 | loglstar: -inf < -4146.437 < inf | logz: -4188.530 +/- 0.306 | dlogz: 511.722 > 0.309] + + 11038it [01:08, 216.89it/s, bound: 228 | nc: 5 | ncall: 60654 | eff(%): 18.198 | loglstar: -inf < -4124.281 < inf | logz: -4167.443 +/- 0.320 | dlogz: 492.449 > 0.309] + + 11064it [01:08, 227.27it/s, bound: 228 | nc: 5 | ncall: 60784 | eff(%): 18.202 | loglstar: -inf < -4100.325 < inf | logz: -4142.382 +/- 0.313 | dlogz: 465.557 > 0.309] + + 11087it [01:08, 219.77it/s, bound: 229 | nc: 5 | ncall: 60899 | eff(%): 18.206 | loglstar: -inf < -4089.742 < inf | logz: -4131.317 +/- 0.309 | dlogz: 539.270 > 0.309] + + 11110it [01:08, 217.14it/s, bound: 230 | nc: 5 | ncall: 61014 | eff(%): 18.209 | loglstar: -inf < -4064.558 < inf | logz: -4106.386 +/- 0.312 | dlogz: 514.344 > 0.309] + + 11134it [01:08, 221.25it/s, bound: 230 | nc: 5 | ncall: 61134 | eff(%): 18.212 | loglstar: -inf < -4042.673 < inf | logz: -4085.242 +/- 0.312 | dlogz: 498.789 > 0.309] + + 11157it [01:08, 198.87it/s, bound: 231 | nc: 5 | ncall: 61249 | eff(%): 18.216 | loglstar: -inf < -4028.307 < inf | logz: -4070.175 +/- 0.315 | dlogz: 483.507 > 0.309] + + 11178it [01:08, 199.56it/s, bound: 231 | nc: 5 | ncall: 61354 | eff(%): 18.219 | loglstar: -inf < -4015.011 < inf | logz: -4058.030 +/- 0.317 | dlogz: 471.865 > 0.309] + + 11199it [01:08, 202.10it/s, bound: 231 | nc: 5 | ncall: 61459 | eff(%): 18.222 | loglstar: -inf < -3992.830 < inf | logz: -4035.366 +/- 0.317 | dlogz: 448.974 > 0.309] + + 11220it [01:08, 202.28it/s, bound: 232 | nc: 5 | ncall: 61564 | eff(%): 18.225 | loglstar: -inf < -3988.806 < inf | logz: -4029.072 +/- 0.307 | dlogz: 441.849 > 0.309] + + 11244it [01:09, 211.34it/s, bound: 232 | nc: 5 | ncall: 61684 | eff(%): 18.228 | loglstar: -inf < -3988.806 < inf | logz: -4028.212 +/- 0.302 | dlogz: 446.301 > 0.309] + + 11266it [01:09, 212.43it/s, bound: 233 | nc: 5 | ncall: 61794 | eff(%): 18.232 | loglstar: -inf < -3988.806 < inf | logz: -4027.788 +/- 0.299 | dlogz: 445.780 > 0.309] + + 11288it [01:09, 203.95it/s, bound: 233 | nc: 5 | ncall: 61904 | eff(%): 18.235 | loglstar: -inf < -3971.261 < inf | logz: -4013.174 +/- 0.313 | dlogz: 431.320 > 0.309] + + 11309it [01:09, 202.69it/s, bound: 234 | nc: 5 | ncall: 62009 | eff(%): 18.238 | loglstar: -inf < -3967.464 < inf | logz: -4009.909 +/- 0.311 | dlogz: 428.073 > 0.309] + + 11333it [01:09, 211.89it/s, bound: 234 | nc: 5 | ncall: 62129 | eff(%): 18.241 | loglstar: -inf < -3952.625 < inf | logz: -3995.355 +/- 0.314 | dlogz: 413.586 > 0.309] + + 11355it [01:09, 213.43it/s, bound: 235 | nc: 5 | ncall: 62239 | eff(%): 18.244 | loglstar: -inf < -3944.158 < inf | logz: -3987.209 +/- 0.314 | dlogz: 405.386 > 0.309] + + 11380it [01:09, 216.78it/s, bound: 236 | nc: 5 | ncall: 62364 | eff(%): 18.248 | loglstar: -inf < -3930.729 < inf | logz: -3973.289 +/- 0.313 | dlogz: 391.162 > 0.309] + + 11405it [01:09, 224.86it/s, bound: 236 | nc: 5 | ncall: 62489 | eff(%): 18.251 | loglstar: -inf < -3915.266 < inf | logz: -3959.336 +/- 0.321 | dlogz: 378.124 > 0.309] + + 11428it [01:09, 215.63it/s, bound: 237 | nc: 5 | ncall: 62604 | eff(%): 18.254 | loglstar: -inf < -3898.764 < inf | logz: -3941.940 +/- 0.316 | dlogz: 359.921 > 0.309] + + 11454it [01:10, 225.15it/s, bound: 237 | nc: 5 | ncall: 62734 | eff(%): 18.258 | loglstar: -inf < -3879.732 < inf | logz: -3924.031 +/- 0.322 | dlogz: 342.717 > 0.309] + + 11478it [01:10, 227.71it/s, bound: 238 | nc: 5 | ncall: 62854 | eff(%): 18.261 | loglstar: -inf < -3865.544 < inf | logz: -3909.033 +/- 0.318 | dlogz: 326.910 > 0.309] + + 11502it [01:10, 229.99it/s, bound: 238 | nc: 5 | ncall: 62974 | eff(%): 18.265 | loglstar: -inf < -3852.672 < inf | logz: -3896.904 +/- 0.322 | dlogz: 315.303 > 0.309] + + 11526it [01:10, 221.44it/s, bound: 239 | nc: 5 | ncall: 63094 | eff(%): 18.268 | loglstar: -inf < -3839.851 < inf | logz: -3883.966 +/- 0.318 | dlogz: 369.065 > 0.309] + + 11549it [01:10, 220.32it/s, bound: 239 | nc: 5 | ncall: 63209 | eff(%): 18.271 | loglstar: -inf < -3826.662 < inf | logz: -3869.912 +/- 0.316 | dlogz: 354.467 > 0.309] + + 11572it [01:10, 205.89it/s, bound: 240 | nc: 5 | ncall: 63324 | eff(%): 18.274 | loglstar: -inf < -3814.941 < inf | logz: -3858.380 +/- 0.318 | dlogz: 397.016 > 0.309] + + 11596it [01:10, 212.90it/s, bound: 240 | nc: 5 | ncall: 63444 | eff(%): 18.278 | loglstar: -inf < -3798.547 < inf | logz: -3843.442 +/- 0.326 | dlogz: 383.342 > 0.309] + + 11618it [01:10, 207.99it/s, bound: 241 | nc: 5 | ncall: 63554 | eff(%): 18.281 | loglstar: -inf < -3793.253 < inf | logz: -3837.200 +/- 0.317 | dlogz: 375.585 > 0.309] + + 11640it [01:10, 209.30it/s, bound: 241 | nc: 5 | ncall: 63664 | eff(%): 18.283 | loglstar: -inf < -3785.594 < inf | logz: -3828.517 +/- 0.317 | dlogz: 366.641 > 0.309] + + 11662it [01:10, 208.34it/s, bound: 242 | nc: 5 | ncall: 63774 | eff(%): 18.286 | loglstar: -inf < -3776.899 < inf | logz: -3819.444 +/- 0.315 | dlogz: 357.412 > 0.309] + + 11685it [01:11, 214.43it/s, bound: 242 | nc: 5 | ncall: 63889 | eff(%): 18.290 | loglstar: -inf < -3768.878 < inf | logz: -3812.124 +/- 0.318 | dlogz: 350.141 > 0.309] + + 11707it [01:11, 208.75it/s, bound: 243 | nc: 5 | ncall: 63999 | eff(%): 18.292 | loglstar: -inf < -3759.986 < inf | logz: -3803.476 +/- 0.318 | dlogz: 341.437 > 0.309] + + 11730it [01:11, 213.23it/s, bound: 243 | nc: 5 | ncall: 64114 | eff(%): 18.296 | loglstar: -inf < -3754.324 < inf | logz: -3797.583 +/- 0.317 | dlogz: 335.415 > 0.309] + + 11752it [01:11, 198.29it/s, bound: 244 | nc: 5 | ncall: 64224 | eff(%): 18.298 | loglstar: -inf < -3750.928 < inf | logz: -3793.586 +/- 0.315 | dlogz: 331.237 > 0.309] + + 11773it [01:11, 200.66it/s, bound: 244 | nc: 5 | ncall: 64329 | eff(%): 18.301 | loglstar: -inf < -3742.900 < inf | logz: -3786.384 +/- 0.318 | dlogz: 324.051 > 0.309] + + 11795it [01:11, 205.02it/s, bound: 245 | nc: 5 | ncall: 64439 | eff(%): 18.304 | loglstar: -inf < -3733.221 < inf | logz: -3777.027 +/- 0.322 | dlogz: 314.738 > 0.309] + + 11820it [01:11, 216.97it/s, bound: 245 | nc: 5 | ncall: 64564 | eff(%): 18.307 | loglstar: -inf < -3728.718 < inf | logz: -3771.527 +/- 0.316 | dlogz: 308.941 > 0.309] + + 11842it [01:11, 216.97it/s, bound: 246 | nc: 5 | ncall: 64674 | eff(%): 18.310 | loglstar: -inf < -3723.375 < inf | logz: -3766.775 +/- 0.319 | dlogz: 312.252 > 0.309] + + 11865it [01:11, 219.29it/s, bound: 246 | nc: 5 | ncall: 64789 | eff(%): 18.313 | loglstar: -inf < -3718.958 < inf | logz: -3762.367 +/- 0.320 | dlogz: 449.210 > 0.309] + + 11888it [01:12, 217.14it/s, bound: 247 | nc: 5 | ncall: 64904 | eff(%): 18.316 | loglstar: -inf < -3710.989 < inf | logz: -3755.390 +/- 0.325 | dlogz: 442.438 > 0.309] + + 11912it [01:12, 223.66it/s, bound: 247 | nc: 5 | ncall: 65024 | eff(%): 18.319 | loglstar: -inf < -3700.594 < inf | logz: -3745.206 +/- 0.323 | dlogz: 432.188 > 0.309] + + 11935it [01:12, 216.92it/s, bound: 248 | nc: 5 | ncall: 65139 | eff(%): 18.322 | loglstar: -inf < -3697.927 < inf | logz: -3740.895 +/- 0.317 | dlogz: 427.426 > 0.309] + + 11961it [01:12, 228.34it/s, bound: 248 | nc: 5 | ncall: 65269 | eff(%): 18.326 | loglstar: -inf < -3688.936 < inf | logz: -3733.789 +/- 0.321 | dlogz: 420.524 > 0.309] + + 11984it [01:12, 221.34it/s, bound: 249 | nc: 5 | ncall: 65384 | eff(%): 18.329 | loglstar: -inf < -3681.931 < inf | logz: -3726.200 +/- 0.322 | dlogz: 412.708 > 0.309] + + 12009it [01:12, 228.34it/s, bound: 249 | nc: 5 | ncall: 65509 | eff(%): 18.332 | loglstar: -inf < -3675.724 < inf | logz: -3720.158 +/- 0.323 | dlogz: 406.645 > 0.309] + + 12032it [01:12, 225.38it/s, bound: 250 | nc: 5 | ncall: 65624 | eff(%): 18.335 | loglstar: -inf < -3669.922 < inf | logz: -3714.763 +/- 0.322 | dlogz: 401.278 > 0.309] + + 12055it [01:12, 214.90it/s, bound: 251 | nc: 5 | ncall: 65739 | eff(%): 18.338 | loglstar: -inf < -3663.274 < inf | logz: -3707.174 +/- 0.322 | dlogz: 393.361 > 0.309] + + 12077it [01:12, 206.33it/s, bound: 252 | nc: 5 | ncall: 65849 | eff(%): 18.340 | loglstar: -inf < -3657.535 < inf | logz: -3702.299 +/- 0.323 | dlogz: 388.568 > 0.309] + + 12098it [01:13, 206.28it/s, bound: 252 | nc: 5 | ncall: 65954 | eff(%): 18.343 | loglstar: -inf < -3654.699 < inf | logz: -3698.486 +/- 0.321 | dlogz: 384.498 > 0.309] + + 12119it [01:13, 205.91it/s, bound: 253 | nc: 5 | ncall: 66059 | eff(%): 18.346 | loglstar: -inf < -3648.988 < inf | logz: -3694.377 +/- 0.323 | dlogz: 432.253 > 0.309] + + 12145it [01:13, 220.08it/s, bound: 253 | nc: 5 | ncall: 66189 | eff(%): 18.349 | loglstar: -inf < -3644.986 < inf | logz: -3689.001 +/- 0.321 | dlogz: 426.580 > 0.309] + + 12168it [01:13, 207.63it/s, bound: 254 | nc: 5 | ncall: 66304 | eff(%): 18.352 | loglstar: -inf < -3640.438 < inf | logz: -3684.363 +/- 0.322 | dlogz: 421.850 > 0.309] + + 12191it [01:13, 211.58it/s, bound: 254 | nc: 5 | ncall: 66419 | eff(%): 18.355 | loglstar: -inf < -3632.445 < inf | logz: -3677.273 +/- 0.325 | dlogz: 414.810 > 0.309] + + 12213it [01:13, 209.05it/s, bound: 255 | nc: 5 | ncall: 66529 | eff(%): 18.357 | loglstar: -inf < -3628.457 < inf | logz: -3673.303 +/- 0.323 | dlogz: 410.730 > 0.309] + + 12237it [01:13, 216.49it/s, bound: 255 | nc: 5 | ncall: 66649 | eff(%): 18.360 | loglstar: -inf < -3625.802 < inf | logz: -3670.191 +/- 0.322 | dlogz: 407.471 > 0.309] + + 12259it [01:13, 214.97it/s, bound: 256 | nc: 5 | ncall: 66759 | eff(%): 18.363 | loglstar: -inf < -3618.978 < inf | logz: -3664.224 +/- 0.326 | dlogz: 401.528 > 0.309] + + 12281it [01:13, 208.92it/s, bound: 256 | nc: 5 | ncall: 66869 | eff(%): 18.366 | loglstar: -inf < -3612.355 < inf | logz: -3657.582 +/- 0.325 | dlogz: 394.797 > 0.309] + + 12302it [01:14, 191.39it/s, bound: 257 | nc: 5 | ncall: 66974 | eff(%): 18.368 | loglstar: -inf < -3610.018 < inf | logz: -3654.641 +/- 0.323 | dlogz: 391.696 > 0.309] + + 12322it [01:14, 184.08it/s, bound: 257 | nc: 5 | ncall: 67074 | eff(%): 18.371 | loglstar: -inf < -3602.256 < inf | logz: -3648.430 +/- 0.331 | dlogz: 385.802 > 0.309] + + 12341it [01:14, 184.61it/s, bound: 257 | nc: 5 | ncall: 67169 | eff(%): 18.373 | loglstar: -inf < -3594.420 < inf | logz: -3639.740 +/- 0.328 | dlogz: 376.748 > 0.309] + + 12360it [01:14, 177.19it/s, bound: 258 | nc: 5 | ncall: 67264 | eff(%): 18.375 | loglstar: -inf < -3589.009 < inf | logz: -3634.270 +/- 0.328 | dlogz: 570.111 > 0.309] + + 12380it [01:14, 181.57it/s, bound: 258 | nc: 5 | ncall: 67364 | eff(%): 18.378 | loglstar: -inf < -3586.942 < inf | logz: -3631.599 +/- 0.324 | dlogz: 567.263 > 0.309] + + 12399it [01:14, 177.77it/s, bound: 259 | nc: 5 | ncall: 67459 | eff(%): 18.380 | loglstar: -inf < -3584.850 < inf | logz: -3629.608 +/- 0.324 | dlogz: 565.221 > 0.309] + + 12422it [01:14, 191.39it/s, bound: 259 | nc: 5 | ncall: 67574 | eff(%): 18.383 | loglstar: -inf < -3579.783 < inf | logz: -3625.124 +/- 0.327 | dlogz: 560.737 > 0.309] + + 12443it [01:14, 195.50it/s, bound: 260 | nc: 5 | ncall: 67679 | eff(%): 18.385 | loglstar: -inf < -3576.056 < inf | logz: -3621.018 +/- 0.326 | dlogz: 556.499 > 0.309] + + 12466it [01:14, 205.29it/s, bound: 260 | nc: 5 | ncall: 67794 | eff(%): 18.388 | loglstar: -inf < -3570.300 < inf | logz: -3616.056 +/- 0.330 | dlogz: 551.577 > 0.309] + + 12487it [01:14, 201.02it/s, bound: 261 | nc: 5 | ncall: 67899 | eff(%): 18.391 | loglstar: -inf < -3567.427 < inf | logz: -3613.032 +/- 0.326 | dlogz: 548.384 > 0.309] + + 12512it [01:15, 212.95it/s, bound: 261 | nc: 5 | ncall: 68024 | eff(%): 18.394 | loglstar: -inf < -3561.133 < inf | logz: -3606.798 +/- 0.329 | dlogz: 542.111 > 0.309] + + 12534it [01:15, 212.19it/s, bound: 262 | nc: 5 | ncall: 68134 | eff(%): 18.396 | loglstar: -inf < -3553.446 < inf | logz: -3598.953 +/- 0.330 | dlogz: 534.155 > 0.309] + + 12556it [01:15, 213.88it/s, bound: 262 | nc: 5 | ncall: 68244 | eff(%): 18.399 | loglstar: -inf < -3548.556 < inf | logz: -3594.028 +/- 0.329 | dlogz: 529.145 > 0.309] + + 12578it [01:15, 199.65it/s, bound: 264 | nc: 5 | ncall: 68354 | eff(%): 18.401 | loglstar: -inf < -3545.260 < inf | logz: -3590.564 +/- 0.328 | dlogz: 525.582 > 0.309] + + 12601it [01:15, 205.00it/s, bound: 264 | nc: 5 | ncall: 68469 | eff(%): 18.404 | loglstar: -inf < -3541.338 < inf | logz: -3587.985 +/- 0.330 | dlogz: 523.974 > 0.309] + + 12624it [01:15, 210.51it/s, bound: 265 | nc: 5 | ncall: 68584 | eff(%): 18.407 | loglstar: -inf < -3537.363 < inf | logz: -3583.295 +/- 0.331 | dlogz: 519.010 > 0.309] + + 12647it [01:15, 216.02it/s, bound: 265 | nc: 5 | ncall: 68699 | eff(%): 18.409 | loglstar: -inf < -3532.982 < inf | logz: -3578.887 +/- 0.330 | dlogz: 514.507 > 0.309] + + 12669it [01:15, 212.14it/s, bound: 266 | nc: 5 | ncall: 68809 | eff(%): 18.412 | loglstar: -inf < -3528.378 < inf | logz: -3575.092 +/- 0.331 | dlogz: 510.758 > 0.309] + + 12695it [01:15, 223.90it/s, bound: 266 | nc: 5 | ncall: 68939 | eff(%): 18.415 | loglstar: -inf < -3521.919 < inf | logz: -3568.417 +/- 0.332 | dlogz: 503.942 > 0.309] + + 12718it [01:16, 219.29it/s, bound: 267 | nc: 5 | ncall: 69054 | eff(%): 18.417 | loglstar: -inf < -3515.821 < inf | logz: -3562.783 +/- 0.333 | dlogz: 498.282 > 0.309] + + 12743it [01:16, 225.84it/s, bound: 267 | nc: 5 | ncall: 69179 | eff(%): 18.420 | loglstar: -inf < -3509.233 < inf | logz: -3555.949 +/- 0.333 | dlogz: 491.312 > 0.309] + + 12766it [01:16, 221.34it/s, bound: 268 | nc: 5 | ncall: 69294 | eff(%): 18.423 | loglstar: -inf < -3500.742 < inf | logz: -3547.826 +/- 0.336 | dlogz: 533.865 > 0.309] + + 12789it [01:16, 222.32it/s, bound: 268 | nc: 5 | ncall: 69409 | eff(%): 18.426 | loglstar: -inf < -3498.165 < inf | logz: -3545.081 +/- 0.332 | dlogz: 530.955 > 0.309] + + 12812it [01:16, 217.38it/s, bound: 269 | nc: 5 | ncall: 69524 | eff(%): 18.428 | loglstar: -inf < -3488.081 < inf | logz: -3536.122 +/- 0.339 | dlogz: 522.282 > 0.309] + + 12838it [01:16, 223.50it/s, bound: 269 | nc: 5 | ncall: 69654 | eff(%): 18.431 | loglstar: -inf < -3478.692 < inf | logz: -3525.176 +/- 0.335 | dlogz: 510.780 > 0.309] + + 12861it [01:16, 214.19it/s, bound: 270 | nc: 5 | ncall: 69769 | eff(%): 18.434 | loglstar: -inf < -3477.184 < inf | logz: -3522.852 +/- 0.330 | dlogz: 508.303 > 0.309] + + 12885it [01:16, 220.86it/s, bound: 270 | nc: 5 | ncall: 69889 | eff(%): 18.436 | loglstar: -inf < -3467.167 < inf | logz: -3514.730 +/- 0.337 | dlogz: 566.289 > 0.309] + + 12908it [01:16, 208.69it/s, bound: 271 | nc: 5 | ncall: 70004 | eff(%): 18.439 | loglstar: -inf < -3460.425 < inf | logz: -3507.610 +/- 0.335 | dlogz: 558.945 > 0.309] + + 12931it [01:17, 212.66it/s, bound: 271 | nc: 5 | ncall: 70119 | eff(%): 18.442 | loglstar: -inf < -3448.962 < inf | logz: -3497.328 +/- 0.339 | dlogz: 548.882 > 0.309] + + 12953it [01:17, 211.58it/s, bound: 272 | nc: 5 | ncall: 70229 | eff(%): 18.444 | loglstar: -inf < -3434.176 < inf | logz: -3481.954 +/- 0.339 | dlogz: 842.383 > 0.309] + + 12975it [01:17, 211.55it/s, bound: 272 | nc: 5 | ncall: 70339 | eff(%): 18.446 | loglstar: -inf < -3432.818 < inf | logz: -3478.818 +/- 0.332 | dlogz: 838.875 > 0.309] + + 12997it [01:17, 205.60it/s, bound: 273 | nc: 5 | ncall: 70449 | eff(%): 18.449 | loglstar: -inf < -3432.818 < inf | logz: -3478.093 +/- 0.329 | dlogz: 838.044 > 0.309] + + 13020it [01:17, 211.11it/s, bound: 273 | nc: 5 | ncall: 70564 | eff(%): 18.451 | loglstar: -inf < -3425.660 < inf | logz: -3473.051 +/- 0.337 | dlogz: 833.072 > 0.309] + + 13042it [01:17, 205.77it/s, bound: 274 | nc: 5 | ncall: 70674 | eff(%): 18.454 | loglstar: -inf < -3415.101 < inf | logz: -3462.187 +/- 0.337 | dlogz: 822.079 > 0.309] + + 13065it [01:17, 210.81it/s, bound: 274 | nc: 5 | ncall: 70789 | eff(%): 18.456 | loglstar: -inf < -3411.456 < inf | logz: -3458.916 +/- 0.337 | dlogz: 818.772 > 0.309] + + 13087it [01:17, 206.36it/s, bound: 275 | nc: 5 | ncall: 70899 | eff(%): 18.459 | loglstar: -inf < -3399.501 < inf | logz: -3448.584 +/- 0.342 | dlogz: 868.578 > 0.309] + + 13111it [01:17, 207.97it/s, bound: 276 | nc: 5 | ncall: 71019 | eff(%): 18.461 | loglstar: -inf < -3389.646 < inf | logz: -3437.412 +/- 0.340 | dlogz: 856.918 > 0.309] + + 13133it [01:18, 208.60it/s, bound: 276 | nc: 5 | ncall: 71129 | eff(%): 18.464 | loglstar: -inf < -3381.728 < inf | logz: -3431.381 +/- 0.345 | dlogz: 851.548 > 0.309] + + 13154it [01:18, 197.71it/s, bound: 276 | nc: 5 | ncall: 71234 | eff(%): 18.466 | loglstar: -inf < -3378.603 < inf | logz: -3425.449 +/- 0.336 | dlogz: 844.684 > 0.309] + + 13175it [01:18, 198.84it/s, bound: 277 | nc: 5 | ncall: 71339 | eff(%): 18.468 | loglstar: -inf < -3370.054 < inf | logz: -3418.795 +/- 0.342 | dlogz: 838.310 > 0.309] + + 13196it [01:18, 201.88it/s, bound: 277 | nc: 5 | ncall: 71444 | eff(%): 18.470 | loglstar: -inf < -3365.777 < inf | logz: -3414.098 +/- 0.338 | dlogz: 833.326 > 0.309] + + 13217it [01:18, 199.57it/s, bound: 278 | nc: 5 | ncall: 71549 | eff(%): 18.473 | loglstar: -inf < -3355.166 < inf | logz: -3404.118 +/- 0.343 | dlogz: 823.472 > 0.309] + + 13241it [01:18, 209.27it/s, bound: 278 | nc: 5 | ncall: 71669 | eff(%): 18.475 | loglstar: -inf < -3350.016 < inf | logz: -3398.021 +/- 0.337 | dlogz: 817.000 > 0.309] + + 13262it [01:18, 205.05it/s, bound: 279 | nc: 5 | ncall: 71774 | eff(%): 18.477 | loglstar: -inf < -3339.145 < inf | logz: -3388.518 +/- 0.345 | dlogz: 807.947 > 0.309] + + 13286it [01:18, 213.71it/s, bound: 279 | nc: 5 | ncall: 71894 | eff(%): 18.480 | loglstar: -inf < -3328.322 < inf | logz: -3377.381 +/- 0.344 | dlogz: 796.462 > 0.309] + + 13308it [01:18, 213.29it/s, bound: 280 | nc: 5 | ncall: 72004 | eff(%): 18.482 | loglstar: -inf < -3320.242 < inf | logz: -3368.795 +/- 0.342 | dlogz: 787.653 > 0.309] + + 13330it [01:18, 214.01it/s, bound: 280 | nc: 5 | ncall: 72114 | eff(%): 18.485 | loglstar: -inf < -3311.086 < inf | logz: -3361.294 +/- 0.345 | dlogz: 780.594 > 0.309] + + 13352it [01:19, 208.10it/s, bound: 281 | nc: 5 | ncall: 72224 | eff(%): 18.487 | loglstar: -inf < -3304.129 < inf | logz: -3352.485 +/- 0.342 | dlogz: 861.851 > 0.309] + + 13373it [01:19, 204.09it/s, bound: 281 | nc: 5 | ncall: 72329 | eff(%): 18.489 | loglstar: -inf < -3289.747 < inf | logz: -3339.371 +/- 0.348 | dlogz: 849.057 > 0.309] + + 13394it [01:19, 195.04it/s, bound: 282 | nc: 5 | ncall: 72434 | eff(%): 18.491 | loglstar: -inf < -3276.328 < inf | logz: -3326.391 +/- 0.345 | dlogz: 836.065 > 0.309] + + 13415it [01:19, 196.71it/s, bound: 282 | nc: 5 | ncall: 72539 | eff(%): 18.494 | loglstar: -inf < -3273.644 < inf | logz: -3321.115 +/- 0.339 | dlogz: 830.171 > 0.309] + + 13435it [01:19, 193.95it/s, bound: 283 | nc: 5 | ncall: 72639 | eff(%): 18.496 | loglstar: -inf < -3273.644 < inf | logz: -3320.437 +/- 0.335 | dlogz: 829.395 > 0.309] + + 13456it [01:19, 198.24it/s, bound: 283 | nc: 5 | ncall: 72744 | eff(%): 18.498 | loglstar: -inf < -3273.644 < inf | logz: -3320.021 +/- 0.333 | dlogz: 828.885 > 0.309] + + 13477it [01:19, 199.47it/s, bound: 284 | nc: 5 | ncall: 72849 | eff(%): 18.500 | loglstar: -inf < -3273.642 < inf | logz: -3319.737 +/- 0.331 | dlogz: 828.505 > 0.309] + + 13501it [01:19, 209.93it/s, bound: 284 | nc: 5 | ncall: 72969 | eff(%): 18.502 | loglstar: -inf < -3260.439 < inf | logz: -3311.226 +/- 0.348 | dlogz: 820.654 > 0.309] + + 13523it [01:19, 199.10it/s, bound: 285 | nc: 5 | ncall: 73079 | eff(%): 18.505 | loglstar: -inf < -3248.541 < inf | logz: -3298.468 +/- 0.347 | dlogz: 807.512 > 0.309] + + 13548it [01:20, 212.90it/s, bound: 285 | nc: 5 | ncall: 73204 | eff(%): 18.507 | loglstar: -inf < -3233.053 < inf | logz: -3283.502 +/- 0.350 | dlogz: 945.262 > 0.309] + + 13571it [01:20, 216.06it/s, bound: 286 | nc: 5 | ncall: 73319 | eff(%): 18.510 | loglstar: -inf < -3222.908 < inf | logz: -3272.010 +/- 0.345 | dlogz: 933.134 > 0.309] + + 13593it [01:20, 211.16it/s, bound: 286 | nc: 5 | ncall: 73429 | eff(%): 18.512 | loglstar: -inf < -3212.750 < inf | logz: -3263.294 +/- 0.350 | dlogz: 924.851 > 0.309] + + 13615it [01:20, 205.87it/s, bound: 287 | nc: 5 | ncall: 73539 | eff(%): 18.514 | loglstar: -inf < -3206.505 < inf | logz: -3256.176 +/- 0.346 | dlogz: 917.236 > 0.309] + + 13636it [01:20, 206.92it/s, bound: 287 | nc: 5 | ncall: 73644 | eff(%): 18.516 | loglstar: -inf < -3186.150 < inf | logz: -3236.200 +/- 0.349 | dlogz: 897.287 > 0.309] + + 13657it [01:20, 203.97it/s, bound: 288 | nc: 5 | ncall: 73749 | eff(%): 18.518 | loglstar: -inf < -3168.975 < inf | logz: -3220.775 +/- 0.354 | dlogz: 882.984 > 0.309] + + 13678it [01:20, 195.55it/s, bound: 288 | nc: 5 | ncall: 73854 | eff(%): 18.520 | loglstar: -inf < -3162.673 < inf | logz: -3213.512 +/- 0.349 | dlogz: 874.809 > 0.309] + + 13698it [01:20, 194.04it/s, bound: 289 | nc: 5 | ncall: 73954 | eff(%): 18.522 | loglstar: -inf < -3152.811 < inf | logz: -3202.972 +/- 0.349 | dlogz: 863.818 > 0.309] + + 13721it [01:20, 203.60it/s, bound: 289 | nc: 5 | ncall: 74069 | eff(%): 18.525 | loglstar: -inf < -3136.737 < inf | logz: -3187.763 +/- 0.350 | dlogz: 848.857 > 0.309] + + 13742it [01:21, 198.82it/s, bound: 290 | nc: 5 | ncall: 74174 | eff(%): 18.527 | loglstar: -inf < -3122.349 < inf | logz: -3173.275 +/- 0.351 | dlogz: 834.307 > 0.309] + + 13765it [01:21, 205.55it/s, bound: 290 | nc: 5 | ncall: 74289 | eff(%): 18.529 | loglstar: -inf < -3104.970 < inf | logz: -3156.986 +/- 0.354 | dlogz: 818.529 > 0.309] + + 13786it [01:21, 197.73it/s, bound: 291 | nc: 5 | ncall: 74394 | eff(%): 18.531 | loglstar: -inf < -3092.476 < inf | logz: -3144.148 +/- 0.354 | dlogz: 805.507 > 0.309] + + 13806it [01:21, 193.25it/s, bound: 291 | nc: 5 | ncall: 74494 | eff(%): 18.533 | loglstar: -inf < -3078.094 < inf | logz: -3128.952 +/- 0.352 | dlogz: 789.581 > 0.309] + + 13826it [01:21, 185.69it/s, bound: 292 | nc: 5 | ncall: 74594 | eff(%): 18.535 | loglstar: -inf < -3058.793 < inf | logz: -3111.249 +/- 0.358 | dlogz: 773.298 > 0.309] + + 13845it [01:21, 186.78it/s, bound: 292 | nc: 5 | ncall: 74689 | eff(%): 18.537 | loglstar: -inf < -3038.833 < inf | logz: -3089.085 +/- 0.350 | dlogz: 749.336 > 0.309] + + 13864it [01:21, 184.22it/s, bound: 293 | nc: 5 | ncall: 74784 | eff(%): 18.539 | loglstar: -inf < -3026.288 < inf | logz: -3078.690 +/- 0.356 | dlogz: 740.030 > 0.309] + + 13885it [01:21, 189.87it/s, bound: 293 | nc: 5 | ncall: 74889 | eff(%): 18.541 | loglstar: -inf < -3018.442 < inf | logz: -3069.601 +/- 0.353 | dlogz: 729.987 > 0.309] + + 13906it [01:21, 194.64it/s, bound: 293 | nc: 5 | ncall: 74994 | eff(%): 18.543 | loglstar: -inf < -3015.117 < inf | logz: -3065.752 +/- 0.347 | dlogz: 725.827 > 0.309] + + 13926it [01:21, 194.15it/s, bound: 294 | nc: 5 | ncall: 75094 | eff(%): 18.545 | loglstar: -inf < -3000.702 < inf | logz: -3052.801 +/- 0.355 | dlogz: 713.513 > 0.309] + + 13947it [01:22, 196.35it/s, bound: 294 | nc: 5 | ncall: 75199 | eff(%): 18.547 | loglstar: -inf < -2984.686 < inf | logz: -3037.288 +/- 0.358 | dlogz: 698.479 > 0.309] + + 13967it [01:22, 186.97it/s, bound: 295 | nc: 5 | ncall: 75299 | eff(%): 18.549 | loglstar: -inf < -2963.576 < inf | logz: -3016.036 +/- 0.357 | dlogz: 676.880 > 0.309] + + 13987it [01:22, 188.66it/s, bound: 295 | nc: 5 | ncall: 75399 | eff(%): 18.551 | loglstar: -inf < -2947.964 < inf | logz: -3001.128 +/- 0.363 | dlogz: 664.310 > 0.309] + + 14006it [01:22, 181.43it/s, bound: 296 | nc: 5 | ncall: 75494 | eff(%): 18.552 | loglstar: -inf < -2940.266 < inf | logz: -2991.928 +/- 0.354 | dlogz: 651.967 > 0.309] + + 14026it [01:22, 184.54it/s, bound: 296 | nc: 5 | ncall: 75594 | eff(%): 18.554 | loglstar: -inf < -2922.798 < inf | logz: -2975.575 +/- 0.360 | dlogz: 636.552 > 0.309] + + 14045it [01:22, 178.16it/s, bound: 297 | nc: 5 | ncall: 75689 | eff(%): 18.556 | loglstar: -inf < -2912.979 < inf | logz: -2964.444 +/- 0.351 | dlogz: 624.110 > 0.309] + + 14065it [01:22, 183.20it/s, bound: 297 | nc: 5 | ncall: 75789 | eff(%): 18.558 | loglstar: -inf < -2898.746 < inf | logz: -2951.482 +/- 0.358 | dlogz: 611.920 > 0.309] + + 14085it [01:22, 187.80it/s, bound: 297 | nc: 5 | ncall: 75889 | eff(%): 18.560 | loglstar: -inf < -2881.477 < inf | logz: -2934.816 +/- 0.361 | dlogz: 596.042 > 0.309] + + 14104it [01:22, 176.85it/s, bound: 298 | nc: 5 | ncall: 75984 | eff(%): 18.562 | loglstar: -inf < -2862.236 < inf | logz: -2915.193 +/- 0.360 | dlogz: 575.707 > 0.309] + + 14124it [01:23, 182.86it/s, bound: 299 | nc: 5 | ncall: 76084 | eff(%): 18.564 | loglstar: -inf < -2835.774 < inf | logz: -2889.428 +/- 0.365 | dlogz: 553.501 > 0.309] + + 14143it [01:23, 178.20it/s, bound: 299 | nc: 5 | ncall: 76179 | eff(%): 18.565 | loglstar: -inf < -2830.661 < inf | logz: -2881.557 +/- 0.352 | dlogz: 540.755 > 0.309] + + 14161it [01:23, 173.07it/s, bound: 300 | nc: 5 | ncall: 76269 | eff(%): 18.567 | loglstar: -inf < -2824.402 < inf | logz: -2876.090 +/- 0.356 | dlogz: 535.381 > 0.309] + + 14180it [01:23, 175.81it/s, bound: 300 | nc: 5 | ncall: 76364 | eff(%): 18.569 | loglstar: -inf < -2810.198 < inf | logz: -2864.023 +/- 0.366 | dlogz: 526.961 > 0.309] + + 14200it [01:23, 181.46it/s, bound: 300 | nc: 5 | ncall: 76464 | eff(%): 18.571 | loglstar: -inf < -2800.251 < inf | logz: -2851.235 +/- 0.352 | dlogz: 510.229 > 0.309] + + 14219it [01:23, 178.58it/s, bound: 301 | nc: 5 | ncall: 76559 | eff(%): 18.573 | loglstar: -inf < -2777.924 < inf | logz: -2829.975 +/- 0.357 | dlogz: 489.133 > 0.309] + + 14238it [01:23, 181.54it/s, bound: 301 | nc: 5 | ncall: 76654 | eff(%): 18.574 | loglstar: -inf < -2770.463 < inf | logz: -2822.140 +/- 0.355 | dlogz: 512.772 > 0.309] + + 14258it [01:23, 184.62it/s, bound: 302 | nc: 5 | ncall: 76754 | eff(%): 18.576 | loglstar: -inf < -2769.687 < inf | logz: -2820.207 +/- 0.349 | dlogz: 510.621 > 0.309] + + 14278it [01:23, 186.62it/s, bound: 302 | nc: 5 | ncall: 76854 | eff(%): 18.578 | loglstar: -inf < -2759.035 < inf | logz: -2810.177 +/- 0.353 | dlogz: 550.519 > 0.309] + + 14297it [01:24, 177.25it/s, bound: 303 | nc: 5 | ncall: 76949 | eff(%): 18.580 | loglstar: -inf < -2753.538 < inf | logz: -2806.958 +/- 0.359 | dlogz: 548.132 > 0.309] + + 14315it [01:24, 171.21it/s, bound: 303 | nc: 5 | ncall: 77039 | eff(%): 18.581 | loglstar: -inf < -2749.828 < inf | logz: -2801.746 +/- 0.352 | dlogz: 542.060 > 0.309] + + 14333it [01:24, 153.86it/s, bound: 303 | nc: 5 | ncall: 77129 | eff(%): 18.583 | loglstar: -inf < -2733.345 < inf | logz: -2786.918 +/- 0.362 | dlogz: 528.192 > 0.309] + + 14349it [01:24, 143.40it/s, bound: 304 | nc: 5 | ncall: 77209 | eff(%): 18.585 | loglstar: -inf < -2723.604 < inf | logz: -2776.581 +/- 0.357 | dlogz: 516.955 > 0.309] + + 14364it [01:24, 143.97it/s, bound: 304 | nc: 5 | ncall: 77284 | eff(%): 18.586 | loglstar: -inf < -2710.252 < inf | logz: -2763.386 +/- 0.361 | dlogz: 504.081 > 0.309] + + 14380it [01:24, 147.23it/s, bound: 304 | nc: 5 | ncall: 77364 | eff(%): 18.587 | loglstar: -inf < -2696.449 < inf | logz: -2749.509 +/- 0.361 | dlogz: 490.026 > 0.309] + + 14397it [01:24, 151.66it/s, bound: 305 | nc: 5 | ncall: 77449 | eff(%): 18.589 | loglstar: -inf < -2691.304 < inf | logz: -2744.722 +/- 0.357 | dlogz: 485.063 > 0.309] + + 14416it [01:24, 160.30it/s, bound: 305 | nc: 5 | ncall: 77544 | eff(%): 18.591 | loglstar: -inf < -2674.406 < inf | logz: -2728.115 +/- 0.361 | dlogz: 468.722 > 0.309] + + 14433it [01:24, 162.04it/s, bound: 306 | nc: 5 | ncall: 77629 | eff(%): 18.592 | loglstar: -inf < -2670.191 < inf | logz: -2721.919 +/- 0.354 | dlogz: 572.264 > 0.309] + + 14453it [01:25, 172.36it/s, bound: 306 | nc: 5 | ncall: 77729 | eff(%): 18.594 | loglstar: -inf < -2657.919 < inf | logz: -2710.796 +/- 0.359 | dlogz: 561.261 > 0.309] + + 14474it [01:25, 181.60it/s, bound: 306 | nc: 5 | ncall: 77834 | eff(%): 18.596 | loglstar: -inf < -2634.247 < inf | logz: -2688.590 +/- 0.363 | dlogz: 539.787 > 0.309] + + 14493it [01:25, 176.87it/s, bound: 307 | nc: 5 | ncall: 77929 | eff(%): 18.598 | loglstar: -inf < -2631.263 < inf | logz: -2683.897 +/- 0.355 | dlogz: 534.154 > 0.309] + + 14511it [01:25, 177.43it/s, bound: 307 | nc: 5 | ncall: 78019 | eff(%): 18.599 | loglstar: -inf < -2620.233 < inf | logz: -2674.229 +/- 0.360 | dlogz: 524.827 > 0.309] + + 14529it [01:25, 175.27it/s, bound: 308 | nc: 5 | ncall: 78109 | eff(%): 18.601 | loglstar: -inf < -2604.574 < inf | logz: -2659.449 +/- 0.368 | dlogz: 511.897 > 0.309] + + 14549it [01:25, 181.71it/s, bound: 308 | nc: 5 | ncall: 78209 | eff(%): 18.603 | loglstar: -inf < -2596.616 < inf | logz: -2649.739 +/- 0.358 | dlogz: 499.857 > 0.309] + + 14568it [01:25, 178.23it/s, bound: 309 | nc: 5 | ncall: 78304 | eff(%): 18.604 | loglstar: -inf < -2592.152 < inf | logz: -2645.024 +/- 0.357 | dlogz: 494.989 > 0.309] + + 14588it [01:25, 184.35it/s, bound: 309 | nc: 5 | ncall: 78404 | eff(%): 18.606 | loglstar: -inf < -2580.221 < inf | logz: -2633.925 +/- 0.360 | dlogz: 484.028 > 0.309] + + 14609it [01:25, 190.79it/s, bound: 309 | nc: 5 | ncall: 78509 | eff(%): 18.608 | loglstar: -inf < -2571.431 < inf | logz: -2625.274 +/- 0.361 | dlogz: 475.436 > 0.309] + + 14629it [01:25, 183.96it/s, bound: 311 | nc: 5 | ncall: 78609 | eff(%): 18.610 | loglstar: -inf < -2562.701 < inf | logz: -2616.020 +/- 0.361 | dlogz: 465.896 > 0.309] + + 14651it [01:26, 192.06it/s, bound: 311 | nc: 5 | ncall: 78719 | eff(%): 18.612 | loglstar: -inf < -2547.070 < inf | logz: -2602.072 +/- 0.367 | dlogz: 453.205 > 0.309] + + 14671it [01:26, 182.45it/s, bound: 312 | nc: 5 | ncall: 78819 | eff(%): 18.614 | loglstar: -inf < -2538.380 < inf | logz: -2592.179 +/- 0.360 | dlogz: 442.067 > 0.309] + + 14690it [01:26, 183.11it/s, bound: 312 | nc: 5 | ncall: 78914 | eff(%): 18.615 | loglstar: -inf < -2536.841 < inf | logz: -2589.058 +/- 0.356 | dlogz: 438.509 > 0.309] + + 14710it [01:26, 187.76it/s, bound: 312 | nc: 5 | ncall: 79014 | eff(%): 18.617 | loglstar: -inf < -2536.215 < inf | logz: -2588.152 +/- 0.352 | dlogz: 437.495 > 0.309] + + 14729it [01:26, 188.40it/s, bound: 313 | nc: 5 | ncall: 79109 | eff(%): 18.619 | loglstar: -inf < -2529.834 < inf | logz: -2582.864 +/- 0.359 | dlogz: 432.257 > 0.309] + + 14752it [01:26, 199.53it/s, bound: 313 | nc: 5 | ncall: 79224 | eff(%): 18.621 | loglstar: -inf < -2522.572 < inf | logz: -2576.787 +/- 0.362 | dlogz: 426.499 > 0.309] + + 14773it [01:26, 197.63it/s, bound: 314 | nc: 5 | ncall: 79329 | eff(%): 18.622 | loglstar: -inf < -2509.493 < inf | logz: -2562.611 +/- 0.361 | dlogz: 411.848 > 0.309] + + 14796it [01:26, 204.51it/s, bound: 314 | nc: 5 | ncall: 79444 | eff(%): 18.624 | loglstar: -inf < -2499.209 < inf | logz: -2553.073 +/- 0.362 | dlogz: 454.528 > 0.309] + + 14817it [01:26, 198.43it/s, bound: 315 | nc: 5 | ncall: 79549 | eff(%): 18.626 | loglstar: -inf < -2492.442 < inf | logz: -2547.451 +/- 0.363 | dlogz: 449.254 > 0.309] + + 14839it [01:27, 204.19it/s, bound: 315 | nc: 5 | ncall: 79659 | eff(%): 18.628 | loglstar: -inf < -2481.108 < inf | logz: -2536.278 +/- 0.364 | dlogz: 438.042 > 0.309] + + 14860it [01:27, 198.95it/s, bound: 316 | nc: 5 | ncall: 79764 | eff(%): 18.630 | loglstar: -inf < -2472.857 < inf | logz: -2526.820 +/- 0.362 | dlogz: 428.050 > 0.309] + + 14880it [01:27, 195.87it/s, bound: 316 | nc: 5 | ncall: 79864 | eff(%): 18.632 | loglstar: -inf < -2467.284 < inf | logz: -2521.595 +/- 0.363 | dlogz: 422.852 > 0.309] + + 14900it [01:27, 186.68it/s, bound: 317 | nc: 5 | ncall: 79964 | eff(%): 18.633 | loglstar: -inf < -2456.297 < inf | logz: -2510.657 +/- 0.364 | dlogz: 501.449 > 0.309] + + 14919it [01:27, 180.60it/s, bound: 317 | nc: 5 | ncall: 80059 | eff(%): 18.635 | loglstar: -inf < -2449.662 < inf | logz: -2504.236 +/- 0.364 | dlogz: 495.036 > 0.309] + + 14938it [01:27, 179.42it/s, bound: 318 | nc: 5 | ncall: 80154 | eff(%): 18.637 | loglstar: -inf < -2445.718 < inf | logz: -2499.292 +/- 0.360 | dlogz: 489.708 > 0.309] + + 14960it [01:27, 189.74it/s, bound: 318 | nc: 5 | ncall: 80264 | eff(%): 18.638 | loglstar: -inf < -2439.634 < inf | logz: -2493.919 +/- 0.361 | dlogz: 484.384 > 0.309] + + 14981it [01:27, 188.43it/s, bound: 319 | nc: 5 | ncall: 80369 | eff(%): 18.640 | loglstar: -inf < -2433.593 < inf | logz: -2487.512 +/- 0.363 | dlogz: 477.827 > 0.309] + + 15002it [01:27, 193.66it/s, bound: 319 | nc: 5 | ncall: 80474 | eff(%): 18.642 | loglstar: -inf < -2427.687 < inf | logz: -2482.878 +/- 0.364 | dlogz: 473.346 > 0.309] + + 15023it [01:28, 196.89it/s, bound: 319 | nc: 5 | ncall: 80579 | eff(%): 18.644 | loglstar: -inf < -2419.948 < inf | logz: -2474.090 +/- 0.362 | dlogz: 464.291 > 0.309] + + 15044it [01:28, 198.66it/s, bound: 320 | nc: 5 | ncall: 80684 | eff(%): 18.646 | loglstar: -inf < -2413.663 < inf | logz: -2468.738 +/- 0.364 | dlogz: 459.005 > 0.309] + + 15064it [01:28, 196.63it/s, bound: 320 | nc: 5 | ncall: 80784 | eff(%): 18.647 | loglstar: -inf < -2407.605 < inf | logz: -2461.829 +/- 0.363 | dlogz: 451.884 > 0.309] + + 15084it [01:28, 183.00it/s, bound: 321 | nc: 5 | ncall: 80884 | eff(%): 18.649 | loglstar: -inf < -2399.046 < inf | logz: -2453.942 +/- 0.366 | dlogz: 444.041 > 0.309] + + 15105it [01:28, 188.21it/s, bound: 321 | nc: 5 | ncall: 80989 | eff(%): 18.651 | loglstar: -inf < -2395.313 < inf | logz: -2449.662 +/- 0.363 | dlogz: 439.565 > 0.309] + + 15124it [01:28, 187.77it/s, bound: 322 | nc: 5 | ncall: 81084 | eff(%): 18.652 | loglstar: -inf < -2386.267 < inf | logz: -2442.058 +/- 0.368 | dlogz: 432.317 > 0.309] + + 15146it [01:28, 195.85it/s, bound: 322 | nc: 5 | ncall: 81194 | eff(%): 18.654 | loglstar: -inf < -2379.140 < inf | logz: -2433.261 +/- 0.365 | dlogz: 422.989 > 0.309] + + 15166it [01:28, 196.70it/s, bound: 323 | nc: 5 | ncall: 81294 | eff(%): 18.656 | loglstar: -inf < -2375.848 < inf | logz: -2430.154 +/- 0.364 | dlogz: 419.830 > 0.309] + + 15187it [01:28, 199.85it/s, bound: 323 | nc: 5 | ncall: 81399 | eff(%): 18.657 | loglstar: -inf < -2369.380 < inf | logz: -2424.342 +/- 0.364 | dlogz: 414.057 > 0.309] + + 15208it [01:28, 196.99it/s, bound: 324 | nc: 5 | ncall: 81504 | eff(%): 18.659 | loglstar: -inf < -2362.615 < inf | logz: -2417.941 +/- 0.367 | dlogz: 407.651 > 0.309] + + 15231it [01:29, 205.05it/s, bound: 324 | nc: 5 | ncall: 81619 | eff(%): 18.661 | loglstar: -inf < -2356.205 < inf | logz: -2411.069 +/- 0.366 | dlogz: 400.582 > 0.309] + + 15252it [01:29, 201.11it/s, bound: 325 | nc: 5 | ncall: 81724 | eff(%): 18.663 | loglstar: -inf < -2349.063 < inf | logz: -2404.417 +/- 0.368 | dlogz: 393.962 > 0.309] + + 15274it [01:29, 206.36it/s, bound: 325 | nc: 5 | ncall: 81834 | eff(%): 18.665 | loglstar: -inf < -2346.824 < inf | logz: -2401.109 +/- 0.364 | dlogz: 390.383 > 0.309] + + 15295it [01:29, 206.22it/s, bound: 325 | nc: 5 | ncall: 81939 | eff(%): 18.666 | loglstar: -inf < -2342.376 < inf | logz: -2397.185 +/- 0.366 | dlogz: 386.440 > 0.309] + + 15316it [01:29, 195.13it/s, bound: 326 | nc: 5 | ncall: 82044 | eff(%): 18.668 | loglstar: -inf < -2336.140 < inf | logz: -2391.336 +/- 0.368 | dlogz: 380.575 > 0.309] + + 15336it [01:29, 185.61it/s, bound: 327 | nc: 5 | ncall: 82144 | eff(%): 18.670 | loglstar: -inf < -2330.500 < inf | logz: -2386.621 +/- 0.369 | dlogz: 375.989 > 0.309] + + 15357it [01:29, 190.70it/s, bound: 327 | nc: 5 | ncall: 82249 | eff(%): 18.671 | loglstar: -inf < -2325.266 < inf | logz: -2380.185 +/- 0.367 | dlogz: 369.213 > 0.309] + + 15378it [01:29, 194.06it/s, bound: 328 | nc: 5 | ncall: 82354 | eff(%): 18.673 | loglstar: -inf < -2319.714 < inf | logz: -2375.210 +/- 0.368 | dlogz: 364.239 > 0.309] + + 15400it [01:29, 200.27it/s, bound: 328 | nc: 5 | ncall: 82464 | eff(%): 18.675 | loglstar: -inf < -2316.667 < inf | logz: -2371.888 +/- 0.367 | dlogz: 360.803 > 0.309] + + 15421it [01:30, 197.08it/s, bound: 329 | nc: 5 | ncall: 82569 | eff(%): 18.677 | loglstar: -inf < -2310.105 < inf | logz: -2366.219 +/- 0.369 | dlogz: 355.191 > 0.309] + + 15443it [01:30, 202.66it/s, bound: 330 | nc: 5 | ncall: 82679 | eff(%): 18.678 | loglstar: -inf < -2299.943 < inf | logz: -2356.121 +/- 0.371 | dlogz: 345.057 > 0.309] + + 15468it [01:30, 215.80it/s, bound: 330 | nc: 5 | ncall: 82804 | eff(%): 18.680 | loglstar: -inf < -2291.791 < inf | logz: -2348.678 +/- 0.371 | dlogz: 337.672 > 0.309] + + 15490it [01:30, 208.39it/s, bound: 331 | nc: 5 | ncall: 82914 | eff(%): 18.682 | loglstar: -inf < -2287.502 < inf | logz: -2342.443 +/- 0.368 | dlogz: 381.129 > 0.309] + + 15512it [01:30, 208.93it/s, bound: 331 | nc: 5 | ncall: 83024 | eff(%): 18.684 | loglstar: -inf < -2280.697 < inf | logz: -2337.469 +/- 0.371 | dlogz: 376.323 > 0.309] + + 15533it [01:30, 204.71it/s, bound: 332 | nc: 5 | ncall: 83129 | eff(%): 18.685 | loglstar: -inf < -2273.678 < inf | logz: -2329.565 +/- 0.371 | dlogz: 385.823 > 0.309] + + 15556it [01:30, 210.31it/s, bound: 332 | nc: 5 | ncall: 83244 | eff(%): 18.687 | loglstar: -inf < -2268.877 < inf | logz: -2324.956 +/- 0.370 | dlogz: 381.161 > 0.309] + + 15578it [01:30, 205.72it/s, bound: 333 | nc: 5 | ncall: 83354 | eff(%): 18.689 | loglstar: -inf < -2262.569 < inf | logz: -2319.231 +/- 0.372 | dlogz: 568.014 > 0.309] + + 15599it [01:30, 204.60it/s, bound: 333 | nc: 5 | ncall: 83459 | eff(%): 18.691 | loglstar: -inf < -2258.005 < inf | logz: -2313.953 +/- 0.371 | dlogz: 562.476 > 0.309] + + 15620it [01:31, 199.74it/s, bound: 334 | nc: 5 | ncall: 83564 | eff(%): 18.692 | loglstar: -inf < -2255.794 < inf | logz: -2311.522 +/- 0.368 | dlogz: 559.933 > 0.309] + + 15644it [01:31, 211.16it/s, bound: 334 | nc: 5 | ncall: 83684 | eff(%): 18.694 | loglstar: -inf < -2248.748 < inf | logz: -2304.944 +/- 0.372 | dlogz: 553.333 > 0.309] + + 15666it [01:31, 208.77it/s, bound: 335 | nc: 5 | ncall: 83794 | eff(%): 18.696 | loglstar: -inf < -2241.336 < inf | logz: -2298.448 +/- 0.374 | dlogz: 547.034 > 0.309] + + 15689it [01:31, 214.04it/s, bound: 335 | nc: 5 | ncall: 83909 | eff(%): 18.698 | loglstar: -inf < -2237.167 < inf | logz: -2293.724 +/- 0.371 | dlogz: 541.991 > 0.309] + + 15711it [01:31, 204.87it/s, bound: 336 | nc: 5 | ncall: 84019 | eff(%): 18.699 | loglstar: -inf < -2228.720 < inf | logz: -2286.063 +/- 0.375 | dlogz: 534.455 > 0.309] + + 15732it [01:31, 201.59it/s, bound: 336 | nc: 5 | ncall: 84124 | eff(%): 18.701 | loglstar: -inf < -2222.805 < inf | logz: -2279.805 +/- 0.377 | dlogz: 528.035 > 0.309] + + 15753it [01:31, 203.29it/s, bound: 337 | nc: 5 | ncall: 84229 | eff(%): 18.703 | loglstar: -inf < -2220.196 < inf | logz: -2276.847 +/- 0.371 | dlogz: 524.890 > 0.309] + + 15777it [01:31, 213.26it/s, bound: 337 | nc: 5 | ncall: 84349 | eff(%): 18.704 | loglstar: -inf < -2213.370 < inf | logz: -2270.104 +/- 0.374 | dlogz: 518.069 > 0.309] + + 15799it [01:31, 207.68it/s, bound: 338 | nc: 5 | ncall: 84459 | eff(%): 18.706 | loglstar: -inf < -2206.318 < inf | logz: -2264.416 +/- 0.376 | dlogz: 740.098 > 0.309] + + 15820it [01:31, 195.66it/s, bound: 338 | nc: 5 | ncall: 84564 | eff(%): 18.708 | loglstar: -inf < -2203.251 < inf | logz: -2259.585 +/- 0.373 | dlogz: 734.850 > 0.309] + + 15840it [01:32, 194.52it/s, bound: 339 | nc: 5 | ncall: 84664 | eff(%): 18.709 | loglstar: -inf < -2193.871 < inf | logz: -2252.418 +/- 0.383 | dlogz: 728.682 > 0.309] + + 15860it [01:32, 195.20it/s, bound: 339 | nc: 5 | ncall: 84764 | eff(%): 18.711 | loglstar: -inf < -2185.373 < inf | logz: -2242.777 +/- 0.377 | dlogz: 718.049 > 0.309] + + 15881it [01:32, 197.38it/s, bound: 340 | nc: 5 | ncall: 84869 | eff(%): 18.712 | loglstar: -inf < -2181.349 < inf | logz: -2238.859 +/- 0.376 | dlogz: 714.112 > 0.309] + + 15901it [01:32, 194.83it/s, bound: 340 | nc: 5 | ncall: 84969 | eff(%): 18.714 | loglstar: -inf < -2174.796 < inf | logz: -2232.251 +/- 0.377 | dlogz: 707.406 > 0.309] + + 15922it [01:32, 198.06it/s, bound: 341 | nc: 5 | ncall: 85074 | eff(%): 18.715 | loglstar: -inf < -2168.332 < inf | logz: -2226.243 +/- 0.376 | dlogz: 701.428 > 0.309] + + 15944it [01:32, 204.36it/s, bound: 341 | nc: 5 | ncall: 85184 | eff(%): 18.717 | loglstar: -inf < -2161.325 < inf | logz: -2219.134 +/- 0.378 | dlogz: 694.145 > 0.309] + + 15965it [01:32, 204.49it/s, bound: 342 | nc: 5 | ncall: 85289 | eff(%): 18.719 | loglstar: -inf < -2154.428 < inf | logz: -2211.932 +/- 0.377 | dlogz: 686.830 > 0.309] + + 15988it [01:32, 210.35it/s, bound: 342 | nc: 5 | ncall: 85404 | eff(%): 18.720 | loglstar: -inf < -2149.546 < inf | logz: -2207.044 +/- 0.377 | dlogz: 681.842 > 0.309] + + 16010it [01:32, 207.89it/s, bound: 343 | nc: 5 | ncall: 85514 | eff(%): 18.722 | loglstar: -inf < -2138.550 < inf | logz: -2195.878 +/- 0.378 | dlogz: 670.561 > 0.309] + + 16034it [01:33, 215.81it/s, bound: 343 | nc: 5 | ncall: 85634 | eff(%): 18.724 | loglstar: -inf < -2133.351 < inf | logz: -2190.902 +/- 0.377 | dlogz: 665.529 > 0.309] + + 16056it [01:33, 208.94it/s, bound: 344 | nc: 5 | ncall: 85744 | eff(%): 18.726 | loglstar: -inf < -2126.652 < inf | logz: -2185.361 +/- 0.381 | dlogz: 660.206 > 0.309] + + 16081it [01:33, 218.76it/s, bound: 344 | nc: 5 | ncall: 85869 | eff(%): 18.727 | loglstar: -inf < -2116.609 < inf | logz: -2174.819 +/- 0.378 | dlogz: 649.309 > 0.309] + + 16103it [01:33, 202.08it/s, bound: 345 | nc: 5 | ncall: 85979 | eff(%): 18.729 | loglstar: -inf < -2108.946 < inf | logz: -2167.566 +/- 0.381 | dlogz: 642.241 > 0.309] + + 16124it [01:33, 203.46it/s, bound: 345 | nc: 5 | ncall: 86084 | eff(%): 18.731 | loglstar: -inf < -2103.139 < inf | logz: -2161.501 +/- 0.378 | dlogz: 635.884 > 0.309] + + 16145it [01:33, 199.49it/s, bound: 346 | nc: 5 | ncall: 86189 | eff(%): 18.732 | loglstar: -inf < -2098.912 < inf | logz: -2157.078 +/- 0.379 | dlogz: 631.390 > 0.309] + + 16169it [01:33, 209.23it/s, bound: 346 | nc: 5 | ncall: 86309 | eff(%): 18.734 | loglstar: -inf < -2092.207 < inf | logz: -2150.996 +/- 0.379 | dlogz: 666.204 > 0.309] + + 16191it [01:33, 210.24it/s, bound: 347 | nc: 5 | ncall: 86419 | eff(%): 18.735 | loglstar: -inf < -2082.517 < inf | logz: -2141.400 +/- 0.384 | dlogz: 656.646 > 0.309] + + 16216it [01:33, 219.47it/s, bound: 347 | nc: 5 | ncall: 86544 | eff(%): 18.737 | loglstar: -inf < -2072.335 < inf | logz: -2131.840 +/- 0.383 | dlogz: 647.071 > 0.309] + + 16239it [01:33, 219.85it/s, bound: 348 | nc: 5 | ncall: 86659 | eff(%): 18.739 | loglstar: -inf < -2057.436 < inf | logz: -2117.150 +/- 0.385 | dlogz: 632.549 > 0.309] + + 16262it [01:34, 219.58it/s, bound: 348 | nc: 5 | ncall: 86774 | eff(%): 18.741 | loglstar: -inf < -2049.409 < inf | logz: -2108.322 +/- 0.383 | dlogz: 623.228 > 0.309] + + 16285it [01:34, 219.16it/s, bound: 349 | nc: 5 | ncall: 86889 | eff(%): 18.742 | loglstar: -inf < -2040.972 < inf | logz: -2100.235 +/- 0.381 | dlogz: 615.069 > 0.309] + + 16309it [01:34, 224.78it/s, bound: 349 | nc: 5 | ncall: 87009 | eff(%): 18.744 | loglstar: -inf < -2034.751 < inf | logz: -2093.390 +/- 0.381 | dlogz: 607.977 > 0.309] + + 16332it [01:34, 213.74it/s, bound: 350 | nc: 5 | ncall: 87124 | eff(%): 18.746 | loglstar: -inf < -2029.615 < inf | logz: -2088.471 +/- 0.381 | dlogz: 603.039 > 0.309] + + 16354it [01:34, 193.59it/s, bound: 350 | nc: 5 | ncall: 87234 | eff(%): 18.747 | loglstar: -inf < -2020.185 < inf | logz: -2079.810 +/- 0.383 | dlogz: 594.407 > 0.309] + + 16374it [01:34, 188.07it/s, bound: 351 | nc: 5 | ncall: 87334 | eff(%): 18.749 | loglstar: -inf < -2013.188 < inf | logz: -2073.497 +/- 0.387 | dlogz: 588.454 > 0.309] + + 16396it [01:34, 193.62it/s, bound: 351 | nc: 5 | ncall: 87444 | eff(%): 18.750 | loglstar: -inf < -2007.592 < inf | logz: -2066.689 +/- 0.383 | dlogz: 581.050 > 0.309] + + 16416it [01:34, 181.27it/s, bound: 352 | nc: 5 | ncall: 87544 | eff(%): 18.752 | loglstar: -inf < -1999.344 < inf | logz: -2059.159 +/- 0.385 | dlogz: 573.662 > 0.309] + + 16439it [01:35, 194.16it/s, bound: 352 | nc: 5 | ncall: 87659 | eff(%): 18.753 | loglstar: -inf < -1992.972 < inf | logz: -2052.263 +/- 0.383 | dlogz: 566.499 > 0.309] + + 16461it [01:35, 199.01it/s, bound: 353 | nc: 5 | ncall: 87769 | eff(%): 18.755 | loglstar: -inf < -1983.653 < inf | logz: -2042.996 +/- 0.384 | dlogz: 631.151 > 0.309] + + 16485it [01:35, 210.15it/s, bound: 353 | nc: 5 | ncall: 87889 | eff(%): 18.757 | loglstar: -inf < -1972.613 < inf | logz: -2032.682 +/- 0.387 | dlogz: 621.037 > 0.309] + + 16507it [01:35, 207.78it/s, bound: 354 | nc: 5 | ncall: 87999 | eff(%): 18.758 | loglstar: -inf < -1964.440 < inf | logz: -2024.636 +/- 0.387 | dlogz: 612.811 > 0.309] + + 16530it [01:35, 211.98it/s, bound: 354 | nc: 5 | ncall: 88114 | eff(%): 18.760 | loglstar: -inf < -1958.312 < inf | logz: -2017.927 +/- 0.384 | dlogz: 605.864 > 0.309] + + 16552it [01:35, 211.67it/s, bound: 355 | nc: 5 | ncall: 88224 | eff(%): 18.761 | loglstar: -inf < -1951.720 < inf | logz: -2011.148 +/- 0.383 | dlogz: 598.889 > 0.309] + + 16576it [01:35, 217.97it/s, bound: 355 | nc: 5 | ncall: 88344 | eff(%): 18.763 | loglstar: -inf < -1943.124 < inf | logz: -2002.421 +/- 0.385 | dlogz: 590.090 > 0.309] + + 16598it [01:35, 217.29it/s, bound: 356 | nc: 5 | ncall: 88454 | eff(%): 18.765 | loglstar: -inf < -1938.016 < inf | logz: -1997.255 +/- 0.385 | dlogz: 584.829 > 0.309] + + 16627it [01:35, 237.70it/s, bound: 356 | nc: 5 | ncall: 88599 | eff(%): 18.767 | loglstar: -inf < -1927.363 < inf | logz: -1987.545 +/- 0.388 | dlogz: 575.254 > 0.309] + + 16651it [01:35, 233.40it/s, bound: 357 | nc: 5 | ncall: 88719 | eff(%): 18.768 | loglstar: -inf < -1920.749 < inf | logz: -1980.231 +/- 0.385 | dlogz: 567.611 > 0.309] + + 16676it [01:36, 238.06it/s, bound: 357 | nc: 5 | ncall: 88844 | eff(%): 18.770 | loglstar: -inf < -1910.838 < inf | logz: -1970.934 +/- 0.388 | dlogz: 558.344 > 0.309] + + 16700it [01:36, 231.08it/s, bound: 358 | nc: 5 | ncall: 88964 | eff(%): 18.772 | loglstar: -inf < -1902.084 < inf | logz: -1962.362 +/- 0.388 | dlogz: 549.675 > 0.309] + + 16725it [01:36, 234.15it/s, bound: 358 | nc: 5 | ncall: 89089 | eff(%): 18.773 | loglstar: -inf < -1891.917 < inf | logz: -1952.442 +/- 0.389 | dlogz: 539.823 > 0.309] + + 16749it [01:36, 223.84it/s, bound: 359 | nc: 5 | ncall: 89209 | eff(%): 18.775 | loglstar: -inf < -1881.703 < inf | logz: -1943.081 +/- 0.389 | dlogz: 530.535 > 0.309] + + 16772it [01:36, 223.86it/s, bound: 359 | nc: 5 | ncall: 89324 | eff(%): 18.777 | loglstar: -inf < -1870.557 < inf | logz: -1931.996 +/- 0.390 | dlogz: 519.445 > 0.309] + + 16795it [01:36, 223.27it/s, bound: 360 | nc: 5 | ncall: 89439 | eff(%): 18.778 | loglstar: -inf < -1862.400 < inf | logz: -1923.325 +/- 0.389 | dlogz: 510.381 > 0.309] + + 16819it [01:36, 227.61it/s, bound: 360 | nc: 5 | ncall: 89559 | eff(%): 18.780 | loglstar: -inf < -1851.337 < inf | logz: -1912.100 +/- 0.389 | dlogz: 499.083 > 0.309] + + 16842it [01:36, 213.43it/s, bound: 361 | nc: 5 | ncall: 89674 | eff(%): 18.781 | loglstar: -inf < -1846.202 < inf | logz: -1906.918 +/- 0.388 | dlogz: 493.835 > 0.309] + + 16865it [01:36, 211.89it/s, bound: 362 | nc: 5 | ncall: 89789 | eff(%): 18.783 | loglstar: -inf < -1832.900 < inf | logz: -1894.332 +/- 0.392 | dlogz: 481.337 > 0.309] + + 16890it [01:37, 222.49it/s, bound: 362 | nc: 5 | ncall: 89914 | eff(%): 18.785 | loglstar: -inf < -1815.070 < inf | logz: -1876.219 +/- 0.394 | dlogz: 463.087 > 0.309] + + 16913it [01:37, 219.73it/s, bound: 363 | nc: 5 | ncall: 90029 | eff(%): 18.786 | loglstar: -inf < -1807.835 < inf | logz: -1868.595 +/- 0.390 | dlogz: 455.184 > 0.309] + + 16936it [01:37, 219.42it/s, bound: 363 | nc: 5 | ncall: 90144 | eff(%): 18.788 | loglstar: -inf < -1795.383 < inf | logz: -1856.213 +/- 0.391 | dlogz: 442.755 > 0.309] + + 16959it [01:37, 211.49it/s, bound: 364 | nc: 5 | ncall: 90259 | eff(%): 18.789 | loglstar: -inf < -1785.865 < inf | logz: -1847.373 +/- 0.390 | dlogz: 597.523 > 0.309] + + 16981it [01:37, 213.75it/s, bound: 364 | nc: 5 | ncall: 90369 | eff(%): 18.791 | loglstar: -inf < -1775.763 < inf | logz: -1836.726 +/- 0.391 | dlogz: 586.732 > 0.309] + + 17003it [01:37, 207.89it/s, bound: 365 | nc: 5 | ncall: 90479 | eff(%): 18.792 | loglstar: -inf < -1763.658 < inf | logz: -1825.348 +/- 0.394 | dlogz: 575.469 > 0.309] + + 17024it [01:37, 208.30it/s, bound: 365 | nc: 5 | ncall: 90584 | eff(%): 18.794 | loglstar: -inf < -1753.971 < inf | logz: -1815.283 +/- 0.394 | dlogz: 565.211 > 0.309] + + 17045it [01:37, 208.39it/s, bound: 366 | nc: 5 | ncall: 90689 | eff(%): 18.795 | loglstar: -inf < -1745.647 < inf | logz: -1807.205 +/- 0.394 | dlogz: 557.128 > 0.309] + + 17068it [01:37, 212.57it/s, bound: 366 | nc: 5 | ncall: 90804 | eff(%): 18.797 | loglstar: -inf < -1735.827 < inf | logz: -1797.430 +/- 0.394 | dlogz: 547.267 > 0.309] + + 17090it [01:37, 211.90it/s, bound: 367 | nc: 5 | ncall: 90914 | eff(%): 18.798 | loglstar: -inf < -1726.652 < inf | logz: -1788.368 +/- 0.396 | dlogz: 538.148 > 0.309] + + 17115it [01:38, 221.29it/s, bound: 367 | nc: 5 | ncall: 91039 | eff(%): 18.800 | loglstar: -inf < -1718.124 < inf | logz: -1780.015 +/- 0.395 | dlogz: 529.718 > 0.309] + + 17138it [01:38, 213.42it/s, bound: 368 | nc: 5 | ncall: 91154 | eff(%): 18.801 | loglstar: -inf < -1711.924 < inf | logz: -1773.208 +/- 0.393 | dlogz: 522.642 > 0.309] + + 17161it [01:38, 217.18it/s, bound: 368 | nc: 5 | ncall: 91269 | eff(%): 18.803 | loglstar: -inf < -1708.324 < inf | logz: -1769.725 +/- 0.392 | dlogz: 519.090 > 0.309] + + 17184it [01:38, 219.01it/s, bound: 369 | nc: 5 | ncall: 91384 | eff(%): 18.804 | loglstar: -inf < -1699.431 < inf | logz: -1761.287 +/- 0.395 | dlogz: 510.654 > 0.309] + + 17206it [01:38, 210.52it/s, bound: 369 | nc: 5 | ncall: 91494 | eff(%): 18.806 | loglstar: -inf < -1697.095 < inf | logz: -1758.050 +/- 0.391 | dlogz: 507.172 > 0.309] + + 17228it [01:38, 200.24it/s, bound: 370 | nc: 5 | ncall: 91604 | eff(%): 18.807 | loglstar: -inf < -1688.996 < inf | logz: -1750.719 +/- 0.394 | dlogz: 499.883 > 0.309] + + 17252it [01:38, 210.92it/s, bound: 370 | nc: 5 | ncall: 91724 | eff(%): 18.809 | loglstar: -inf < -1679.373 < inf | logz: -1741.506 +/- 0.394 | dlogz: 490.650 > 0.309] + + 17274it [01:38, 208.63it/s, bound: 371 | nc: 5 | ncall: 91834 | eff(%): 18.810 | loglstar: -inf < -1671.318 < inf | logz: -1734.596 +/- 0.397 | dlogz: 484.039 > 0.309] + + 17299it [01:38, 218.37it/s, bound: 371 | nc: 5 | ncall: 91959 | eff(%): 18.812 | loglstar: -inf < -1662.926 < inf | logz: -1725.088 +/- 0.395 | dlogz: 474.008 > 0.309] + + 17321it [01:39, 217.86it/s, bound: 372 | nc: 5 | ncall: 92069 | eff(%): 18.813 | loglstar: -inf < -1656.639 < inf | logz: -1719.390 +/- 0.398 | dlogz: 468.545 > 0.309] + + 17346it [01:39, 224.63it/s, bound: 372 | nc: 5 | ncall: 92194 | eff(%): 18.815 | loglstar: -inf < -1647.598 < inf | logz: -1710.334 +/- 0.400 | dlogz: 459.345 > 0.309] + + 17369it [01:39, 213.62it/s, bound: 373 | nc: 5 | ncall: 92309 | eff(%): 18.816 | loglstar: -inf < -1645.234 < inf | logz: -1707.332 +/- 0.392 | dlogz: 455.932 > 0.309] + + 17391it [01:39, 209.25it/s, bound: 373 | nc: 5 | ncall: 92419 | eff(%): 18.818 | loglstar: -inf < -1634.790 < inf | logz: -1697.447 +/- 0.396 | dlogz: 446.141 > 0.309] + + 17413it [01:39, 192.16it/s, bound: 374 | nc: 5 | ncall: 92529 | eff(%): 18.819 | loglstar: -inf < -1628.701 < inf | logz: -1691.193 +/- 0.396 | dlogz: 439.705 > 0.309] + + 17436it [01:39, 201.70it/s, bound: 374 | nc: 5 | ncall: 92644 | eff(%): 18.820 | loglstar: -inf < -1619.289 < inf | logz: -1682.486 +/- 0.400 | dlogz: 431.189 > 0.309] + + 17457it [01:39, 202.60it/s, bound: 375 | nc: 5 | ncall: 92749 | eff(%): 18.822 | loglstar: -inf < -1610.596 < inf | logz: -1673.816 +/- 0.398 | dlogz: 422.434 > 0.309] + + 17478it [01:39, 201.73it/s, bound: 375 | nc: 5 | ncall: 92854 | eff(%): 18.823 | loglstar: -inf < -1601.678 < inf | logz: -1664.755 +/- 0.399 | dlogz: 413.269 > 0.309] + + 17499it [01:39, 190.05it/s, bound: 376 | nc: 5 | ncall: 92959 | eff(%): 18.824 | loglstar: -inf < -1594.634 < inf | logz: -1657.744 +/- 0.399 | dlogz: 406.093 > 0.309] + + 17519it [01:40, 191.06it/s, bound: 376 | nc: 5 | ncall: 93059 | eff(%): 18.826 | loglstar: -inf < -1585.615 < inf | logz: -1649.798 +/- 0.401 | dlogz: 398.492 > 0.309] + + 17539it [01:40, 187.45it/s, bound: 376 | nc: 5 | ncall: 93159 | eff(%): 18.827 | loglstar: -inf < -1578.084 < inf | logz: -1640.454 +/- 0.397 | dlogz: 388.497 > 0.309] + + 17558it [01:40, 179.69it/s, bound: 377 | nc: 5 | ncall: 93254 | eff(%): 18.828 | loglstar: -inf < -1562.754 < inf | logz: -1626.642 +/- 0.402 | dlogz: 375.156 > 0.309] + + 17577it [01:40, 181.33it/s, bound: 377 | nc: 5 | ncall: 93349 | eff(%): 18.829 | loglstar: -inf < -1555.504 < inf | logz: -1618.926 +/- 0.401 | dlogz: 367.044 > 0.309] + + 17596it [01:40, 173.92it/s, bound: 378 | nc: 5 | ncall: 93444 | eff(%): 18.831 | loglstar: -inf < -1552.053 < inf | logz: -1615.069 +/- 0.397 | dlogz: 363.014 > 0.309] + + 17614it [01:40, 173.02it/s, bound: 378 | nc: 5 | ncall: 93534 | eff(%): 18.832 | loglstar: -inf < -1546.175 < inf | logz: -1608.402 +/- 0.398 | dlogz: 576.406 > 0.309] + + 17632it [01:40, 169.30it/s, bound: 379 | nc: 5 | ncall: 93624 | eff(%): 18.833 | loglstar: -inf < -1537.480 < inf | logz: -1601.063 +/- 0.400 | dlogz: 569.233 > 0.309] + + 17651it [01:40, 173.65it/s, bound: 379 | nc: 5 | ncall: 93719 | eff(%): 18.834 | loglstar: -inf < -1529.381 < inf | logz: -1592.450 +/- 0.400 | dlogz: 560.442 > 0.309] + + 17669it [01:40, 165.14it/s, bound: 380 | nc: 5 | ncall: 93809 | eff(%): 18.835 | loglstar: -inf < -1521.960 < inf | logz: -1586.353 +/- 0.401 | dlogz: 554.589 > 0.309] + + 17688it [01:41, 170.18it/s, bound: 380 | nc: 5 | ncall: 93904 | eff(%): 18.836 | loglstar: -inf < -1521.532 < inf | logz: -1583.373 +/- 0.396 | dlogz: 551.088 > 0.309] + + 17706it [01:41, 164.34it/s, bound: 381 | nc: 5 | ncall: 93994 | eff(%): 18.837 | loglstar: -inf < -1515.989 < inf | logz: -1579.050 +/- 0.399 | dlogz: 546.820 > 0.309] + + 17724it [01:41, 167.68it/s, bound: 381 | nc: 5 | ncall: 94084 | eff(%): 18.838 | loglstar: -inf < -1508.486 < inf | logz: -1572.977 +/- 0.402 | dlogz: 541.050 > 0.309] + + 17741it [01:41, 164.91it/s, bound: 381 | nc: 5 | ncall: 94169 | eff(%): 18.840 | loglstar: -inf < -1499.755 < inf | logz: -1563.318 +/- 0.402 | dlogz: 531.059 > 0.309] + + 17758it [01:41, 159.78it/s, bound: 382 | nc: 5 | ncall: 94254 | eff(%): 18.841 | loglstar: -inf < -1491.928 < inf | logz: -1556.718 +/- 0.403 | dlogz: 524.736 > 0.309] + + 17777it [01:41, 166.99it/s, bound: 382 | nc: 5 | ncall: 94349 | eff(%): 18.842 | loglstar: -inf < -1486.994 < inf | logz: -1550.950 +/- 0.402 | dlogz: 518.637 > 0.309] + + 17794it [01:41, 159.54it/s, bound: 383 | nc: 5 | ncall: 94434 | eff(%): 18.843 | loglstar: -inf < -1479.288 < inf | logz: -1543.649 +/- 0.402 | dlogz: 511.327 > 0.309] + + 17812it [01:41, 162.84it/s, bound: 383 | nc: 5 | ncall: 94524 | eff(%): 18.844 | loglstar: -inf < -1471.903 < inf | logz: -1534.863 +/- 0.401 | dlogz: 502.217 > 0.309] + + 17829it [01:41, 163.69it/s, bound: 383 | nc: 5 | ncall: 94609 | eff(%): 18.845 | loglstar: -inf < -1471.903 < inf | logz: -1533.871 +/- 0.396 | dlogz: 501.097 > 0.309] + + 17846it [01:42, 156.49it/s, bound: 384 | nc: 5 | ncall: 94694 | eff(%): 18.846 | loglstar: -inf < -1468.039 < inf | logz: -1532.682 +/- 0.399 | dlogz: 500.255 > 0.309] + + 17862it [01:42, 154.47it/s, bound: 384 | nc: 5 | ncall: 94774 | eff(%): 18.847 | loglstar: -inf < -1461.260 < inf | logz: -1525.923 +/- 0.405 | dlogz: 493.584 > 0.309] + + 17880it [01:42, 160.80it/s, bound: 384 | nc: 5 | ncall: 94864 | eff(%): 18.848 | loglstar: -inf < -1455.588 < inf | logz: -1519.257 +/- 0.402 | dlogz: 486.440 > 0.309] + + 17897it [01:42, 145.37it/s, bound: 385 | nc: 5 | ncall: 94949 | eff(%): 18.849 | loglstar: -inf < -1447.617 < inf | logz: -1511.536 +/- 0.403 | dlogz: 478.697 > 0.309] + + 17914it [01:42, 150.58it/s, bound: 385 | nc: 5 | ncall: 95034 | eff(%): 18.850 | loglstar: -inf < -1440.327 < inf | logz: -1505.337 +/- 0.405 | dlogz: 472.724 > 0.309] + + 17930it [01:42, 152.32it/s, bound: 386 | nc: 5 | ncall: 95114 | eff(%): 18.851 | loglstar: -inf < -1433.189 < inf | logz: -1498.096 +/- 0.406 | dlogz: 465.551 > 0.309] + + 17951it [01:42, 167.03it/s, bound: 386 | nc: 5 | ncall: 95219 | eff(%): 18.852 | loglstar: -inf < -1433.189 < inf | logz: -1495.738 +/- 0.398 | dlogz: 462.556 > 0.309] + + 17972it [01:42, 178.97it/s, bound: 386 | nc: 5 | ncall: 95324 | eff(%): 18.854 | loglstar: -inf < -1433.189 < inf | logz: -1495.093 +/- 0.395 | dlogz: 461.812 > 0.309] + + 17991it [01:42, 181.06it/s, bound: 387 | nc: 5 | ncall: 95419 | eff(%): 18.855 | loglstar: -inf < -1431.040 < inf | logz: -1494.133 +/- 0.396 | dlogz: 460.822 > 0.309] + + 18014it [01:43, 193.09it/s, bound: 387 | nc: 5 | ncall: 95534 | eff(%): 18.856 | loglstar: -inf < -1424.383 < inf | logz: -1489.009 +/- 0.404 | dlogz: 483.608 > 0.309] + + 18034it [01:43, 194.27it/s, bound: 388 | nc: 5 | ncall: 95634 | eff(%): 18.857 | loglstar: -inf < -1418.349 < inf | logz: -1483.116 +/- 0.403 | dlogz: 477.611 > 0.309] + + 18056it [01:43, 199.85it/s, bound: 388 | nc: 5 | ncall: 95744 | eff(%): 18.859 | loglstar: -inf < -1414.165 < inf | logz: -1477.849 +/- 0.401 | dlogz: 472.104 > 0.309] + + 18077it [01:43, 196.48it/s, bound: 389 | nc: 5 | ncall: 95849 | eff(%): 18.860 | loglstar: -inf < -1404.644 < inf | logz: -1469.579 +/- 0.406 | dlogz: 464.006 > 0.309] + + 18100it [01:43, 203.23it/s, bound: 389 | nc: 5 | ncall: 95964 | eff(%): 18.861 | loglstar: -inf < -1395.203 < inf | logz: -1460.838 +/- 0.407 | dlogz: 455.372 > 0.309] + + 18121it [01:43, 197.23it/s, bound: 390 | nc: 5 | ncall: 96069 | eff(%): 18.862 | loglstar: -inf < -1386.471 < inf | logz: -1451.326 +/- 0.405 | dlogz: 445.497 > 0.309] + + 18141it [01:43, 195.48it/s, bound: 390 | nc: 5 | ncall: 96169 | eff(%): 18.864 | loglstar: -inf < -1375.939 < inf | logz: -1441.742 +/- 0.407 | dlogz: 436.107 > 0.309] + + 18161it [01:43, 189.63it/s, bound: 391 | nc: 5 | ncall: 96269 | eff(%): 18.865 | loglstar: -inf < -1369.877 < inf | logz: -1434.971 +/- 0.406 | dlogz: 429.073 > 0.309] + + 18182it [01:43, 194.38it/s, bound: 391 | nc: 5 | ncall: 96374 | eff(%): 18.866 | loglstar: -inf < -1360.506 < inf | logz: -1426.492 +/- 0.408 | dlogz: 420.841 > 0.309] + + 18203it [01:43, 197.90it/s, bound: 392 | nc: 5 | ncall: 96479 | eff(%): 18.867 | loglstar: -inf < -1350.774 < inf | logz: -1415.655 +/- 0.406 | dlogz: 409.520 > 0.309] + + 18227it [01:44, 210.02it/s, bound: 392 | nc: 5 | ncall: 96599 | eff(%): 18.869 | loglstar: -inf < -1345.664 < inf | logz: -1411.239 +/- 0.406 | dlogz: 405.224 > 0.309] + + 18249it [01:44, 206.35it/s, bound: 393 | nc: 5 | ncall: 96709 | eff(%): 18.870 | loglstar: -inf < -1337.520 < inf | logz: -1403.289 +/- 0.407 | dlogz: 397.260 > 0.309] + + 18270it [01:44, 205.03it/s, bound: 393 | nc: 5 | ncall: 96814 | eff(%): 18.871 | loglstar: -inf < -1330.966 < inf | logz: -1396.477 +/- 0.406 | dlogz: 390.162 > 0.309] + + 18291it [01:44, 195.30it/s, bound: 394 | nc: 5 | ncall: 96919 | eff(%): 18.872 | loglstar: -inf < -1324.058 < inf | logz: -1390.403 +/- 0.409 | dlogz: 414.843 > 0.309] + + 18314it [01:44, 201.98it/s, bound: 394 | nc: 5 | ncall: 97034 | eff(%): 18.874 | loglstar: -inf < -1308.659 < inf | logz: -1374.376 +/- 0.409 | dlogz: 398.556 > 0.309] + + 18335it [01:44, 194.90it/s, bound: 395 | nc: 5 | ncall: 97139 | eff(%): 18.875 | loglstar: -inf < -1301.183 < inf | logz: -1366.359 +/- 0.408 | dlogz: 396.707 > 0.309] + + 18355it [01:44, 183.64it/s, bound: 395 | nc: 5 | ncall: 97239 | eff(%): 18.876 | loglstar: -inf < -1301.183 < inf | logz: -1364.953 +/- 0.403 | dlogz: 395.102 > 0.309] + + 18374it [01:44, 181.24it/s, bound: 395 | nc: 5 | ncall: 97334 | eff(%): 18.877 | loglstar: -inf < -1301.183 < inf | logz: -1364.412 +/- 0.400 | dlogz: 394.473 > 0.309] + + 18393it [01:44, 183.21it/s, bound: 396 | nc: 5 | ncall: 97429 | eff(%): 18.878 | loglstar: -inf < -1300.895 < inf | logz: -1364.064 +/- 0.398 | dlogz: 394.041 > 0.309] + + 18416it [01:45, 195.49it/s, bound: 396 | nc: 5 | ncall: 97544 | eff(%): 18.880 | loglstar: -inf < -1293.584 < inf | logz: -1359.023 +/- 0.407 | dlogz: 389.069 > 0.309] + + 18436it [01:45, 190.15it/s, bound: 397 | nc: 5 | ncall: 97644 | eff(%): 18.881 | loglstar: -inf < -1283.578 < inf | logz: -1350.702 +/- 0.410 | dlogz: 507.920 > 0.309] + + 18456it [01:45, 182.71it/s, bound: 397 | nc: 5 | ncall: 97744 | eff(%): 18.882 | loglstar: -inf < -1276.262 < inf | logz: -1342.186 +/- 0.408 | dlogz: 498.951 > 0.309] + + 18476it [01:45, 187.16it/s, bound: 398 | nc: 5 | ncall: 97844 | eff(%): 18.883 | loglstar: -inf < -1270.753 < inf | logz: -1336.545 +/- 0.409 | dlogz: 493.218 > 0.309] + + 18496it [01:45, 188.80it/s, bound: 398 | nc: 5 | ncall: 97944 | eff(%): 18.884 | loglstar: -inf < -1262.553 < inf | logz: -1329.718 +/- 0.412 | dlogz: 486.853 > 0.309] + + 18515it [01:45, 181.90it/s, bound: 399 | nc: 5 | ncall: 98039 | eff(%): 18.885 | loglstar: -inf < -1258.674 < inf | logz: -1325.034 +/- 0.409 | dlogz: 481.698 > 0.309] + + 18538it [01:45, 193.25it/s, bound: 399 | nc: 5 | ncall: 98154 | eff(%): 18.887 | loglstar: -inf < -1253.159 < inf | logz: -1319.483 +/- 0.408 | dlogz: 476.031 > 0.309] + + 18561it [01:45, 203.10it/s, bound: 400 | nc: 5 | ncall: 98269 | eff(%): 18.888 | loglstar: -inf < -1241.314 < inf | logz: -1308.403 +/- 0.413 | dlogz: 465.212 > 0.309] + + 18584it [01:45, 209.94it/s, bound: 400 | nc: 5 | ncall: 98384 | eff(%): 18.889 | loglstar: -inf < -1235.155 < inf | logz: -1301.819 +/- 0.410 | dlogz: 458.281 > 0.309] + + 18607it [01:46, 213.11it/s, bound: 401 | nc: 5 | ncall: 98499 | eff(%): 18.891 | loglstar: -inf < -1226.213 < inf | logz: -1292.341 +/- 0.410 | dlogz: 448.554 > 0.309] + + 18634it [01:46, 227.61it/s, bound: 401 | nc: 5 | ncall: 98634 | eff(%): 18.892 | loglstar: -inf < -1216.752 < inf | logz: -1282.950 +/- 0.410 | dlogz: 439.072 > 0.309] + + 18657it [01:46, 226.02it/s, bound: 402 | nc: 5 | ncall: 98749 | eff(%): 18.893 | loglstar: -inf < -1211.744 < inf | logz: -1278.135 +/- 0.411 | dlogz: 434.203 > 0.309] + + 18684it [01:46, 236.14it/s, bound: 402 | nc: 5 | ncall: 98884 | eff(%): 18.895 | loglstar: -inf < -1202.397 < inf | logz: -1268.924 +/- 0.410 | dlogz: 424.875 > 0.309] + + 18708it [01:46, 234.84it/s, bound: 403 | nc: 5 | ncall: 99004 | eff(%): 18.896 | loglstar: -inf < -1193.316 < inf | logz: -1260.177 +/- 0.412 | dlogz: 416.134 > 0.309] + + 18732it [01:46, 225.78it/s, bound: 403 | nc: 5 | ncall: 99124 | eff(%): 18.898 | loglstar: -inf < -1188.621 < inf | logz: -1255.804 +/- 0.411 | dlogz: 411.715 > 0.309] + + 18755it [01:46, 219.97it/s, bound: 404 | nc: 5 | ncall: 99239 | eff(%): 18.899 | loglstar: -inf < -1179.803 < inf | logz: -1248.246 +/- 0.416 | dlogz: 404.697 > 0.309] + + 18779it [01:46, 223.58it/s, bound: 404 | nc: 5 | ncall: 99359 | eff(%): 18.900 | loglstar: -inf < -1172.370 < inf | logz: -1240.249 +/- 0.413 | dlogz: 396.121 > 0.309] + + 18802it [01:46, 218.77it/s, bound: 405 | nc: 5 | ncall: 99474 | eff(%): 18.901 | loglstar: -inf < -1160.376 < inf | logz: -1228.513 +/- 0.415 | dlogz: 384.397 > 0.309] + + 18828it [01:46, 224.75it/s, bound: 406 | nc: 5 | ncall: 99604 | eff(%): 18.903 | loglstar: -inf < -1152.914 < inf | logz: -1220.420 +/- 0.412 | dlogz: 488.997 > 0.309] + + 18854it [01:47, 232.88it/s, bound: 406 | nc: 5 | ncall: 99734 | eff(%): 18.904 | loglstar: -inf < -1142.237 < inf | logz: -1210.242 +/- 0.416 | dlogz: 478.910 > 0.309] + + 18878it [01:47, 221.05it/s, bound: 407 | nc: 5 | ncall: 99854 | eff(%): 18.906 | loglstar: -inf < -1136.965 < inf | logz: -1204.070 +/- 0.412 | dlogz: 472.376 > 0.309] + + 18901it [01:47, 221.17it/s, bound: 407 | nc: 5 | ncall: 99969 | eff(%): 18.907 | loglstar: -inf < -1128.439 < inf | logz: -1196.094 +/- 0.415 | dlogz: 464.483 > 0.309] + + 18924it [01:47, 219.31it/s, bound: 408 | nc: 5 | ncall: 100084 | eff(%): 18.908 | loglstar: -inf < -1124.627 < inf | logz: -1192.178 +/- 0.412 | dlogz: 460.387 > 0.309] + + 18948it [01:47, 223.45it/s, bound: 408 | nc: 5 | ncall: 100204 | eff(%): 18.909 | loglstar: -inf < -1119.629 < inf | logz: -1187.383 +/- 0.413 | dlogz: 455.537 > 0.309] + + 18971it [01:47, 215.68it/s, bound: 409 | nc: 5 | ncall: 100319 | eff(%): 18.911 | loglstar: -inf < -1111.996 < inf | logz: -1179.538 +/- 0.414 | dlogz: 447.575 > 0.309] + + 18995it [01:47, 221.30it/s, bound: 409 | nc: 5 | ncall: 100439 | eff(%): 18.912 | loglstar: -inf < -1105.240 < inf | logz: -1172.862 +/- 0.415 | dlogz: 440.835 > 0.309] + + 19018it [01:47, 215.55it/s, bound: 410 | nc: 5 | ncall: 100554 | eff(%): 18.913 | loglstar: -inf < -1101.997 < inf | logz: -1170.351 +/- 0.413 | dlogz: 438.358 > 0.309] + + 19041it [01:47, 217.00it/s, bound: 410 | nc: 5 | ncall: 100669 | eff(%): 18.914 | loglstar: -inf < -1094.349 < inf | logz: -1162.996 +/- 0.415 | dlogz: 430.970 > 0.309] + + 19064it [01:48, 220.25it/s, bound: 411 | nc: 5 | ncall: 100784 | eff(%): 18.916 | loglstar: -inf < -1087.565 < inf | logz: -1156.124 +/- 0.416 | dlogz: 423.982 > 0.309] + + 19089it [01:48, 227.72it/s, bound: 411 | nc: 5 | ncall: 100909 | eff(%): 18.917 | loglstar: -inf < -1081.515 < inf | logz: -1149.114 +/- 0.416 | dlogz: 416.709 > 0.309] + + 19112it [01:48, 218.06it/s, bound: 412 | nc: 5 | ncall: 101024 | eff(%): 18.918 | loglstar: -inf < -1075.174 < inf | logz: -1143.332 +/- 0.417 | dlogz: 410.937 > 0.309] + + 19137it [01:48, 225.91it/s, bound: 412 | nc: 5 | ncall: 101149 | eff(%): 18.920 | loglstar: -inf < -1068.505 < inf | logz: -1137.137 +/- 0.416 | dlogz: 404.743 > 0.309] + + 19160it [01:48, 220.80it/s, bound: 413 | nc: 5 | ncall: 101264 | eff(%): 18.921 | loglstar: -inf < -1064.191 < inf | logz: -1132.152 +/- 0.417 | dlogz: 399.531 > 0.309] + + 19184it [01:48, 225.64it/s, bound: 413 | nc: 5 | ncall: 101384 | eff(%): 18.922 | loglstar: -inf < -1058.561 < inf | logz: -1127.718 +/- 0.418 | dlogz: 395.266 > 0.309] + + 19207it [01:48, 204.91it/s, bound: 414 | nc: 5 | ncall: 101499 | eff(%): 18.923 | loglstar: -inf < -1050.291 < inf | logz: -1118.128 +/- 0.417 | dlogz: 385.306 > 0.309] + + 19228it [01:48, 202.54it/s, bound: 414 | nc: 5 | ncall: 101604 | eff(%): 18.924 | loglstar: -inf < -1042.773 < inf | logz: -1112.885 +/- 0.422 | dlogz: 380.804 > 0.309] + + 19250it [01:48, 206.70it/s, bound: 415 | nc: 5 | ncall: 101714 | eff(%): 18.926 | loglstar: -inf < -1036.539 < inf | logz: -1105.653 +/- 0.419 | dlogz: 372.929 > 0.309] + + 19276it [01:49, 221.30it/s, bound: 415 | nc: 5 | ncall: 101844 | eff(%): 18.927 | loglstar: -inf < -1027.993 < inf | logz: -1097.634 +/- 0.419 | dlogz: 364.874 > 0.309] + + 19299it [01:49, 214.97it/s, bound: 416 | nc: 5 | ncall: 101959 | eff(%): 18.928 | loglstar: -inf < -1021.435 < inf | logz: -1091.074 +/- 0.420 | dlogz: 358.292 > 0.309] + + 19323it [01:49, 217.72it/s, bound: 417 | nc: 5 | ncall: 102079 | eff(%): 18.929 | loglstar: -inf < -1016.276 < inf | logz: -1084.547 +/- 0.418 | dlogz: 351.346 > 0.309] + + 19346it [01:49, 218.71it/s, bound: 417 | nc: 5 | ncall: 102194 | eff(%): 18.931 | loglstar: -inf < -1007.746 < inf | logz: -1076.709 +/- 0.419 | dlogz: 343.529 > 0.309] + + 19368it [01:49, 218.73it/s, bound: 418 | nc: 5 | ncall: 102304 | eff(%): 18.932 | loglstar: -inf < -997.915 < inf | logz: -1067.772 +/- 0.423 | dlogz: 334.853 > 0.309] + + 19392it [01:49, 223.21it/s, bound: 418 | nc: 5 | ncall: 102424 | eff(%): 18.933 | loglstar: -inf < -993.800 < inf | logz: -1062.333 +/- 0.418 | dlogz: 339.892 > 0.309] + + 19415it [01:49, 210.04it/s, bound: 419 | nc: 5 | ncall: 102539 | eff(%): 18.934 | loglstar: -inf < -985.139 < inf | logz: -1055.446 +/- 0.422 | dlogz: 333.315 > 0.309] + + 19438it [01:49, 214.66it/s, bound: 419 | nc: 5 | ncall: 102654 | eff(%): 18.935 | loglstar: -inf < -975.055 < inf | logz: -1045.522 +/- 0.425 | dlogz: 376.104 > 0.309] + + 19461it [01:49, 217.57it/s, bound: 420 | nc: 5 | ncall: 102769 | eff(%): 18.937 | loglstar: -inf < -968.088 < inf | logz: -1037.471 +/- 0.420 | dlogz: 367.225 > 0.309] + + 19487it [01:50, 228.38it/s, bound: 420 | nc: 5 | ncall: 102899 | eff(%): 18.938 | loglstar: -inf < -961.515 < inf | logz: -1030.552 +/- 0.421 | dlogz: 360.202 > 0.309] + + 19510it [01:50, 225.87it/s, bound: 421 | nc: 5 | ncall: 103014 | eff(%): 18.939 | loglstar: -inf < -952.675 < inf | logz: -1022.038 +/- 0.422 | dlogz: 351.641 > 0.309] + + 19533it [01:50, 221.73it/s, bound: 421 | nc: 5 | ncall: 103129 | eff(%): 18.940 | loglstar: -inf < -947.025 < inf | logz: -1017.028 +/- 0.421 | dlogz: 354.678 > 0.309] + + 19556it [01:50, 207.43it/s, bound: 422 | nc: 5 | ncall: 103244 | eff(%): 18.942 | loglstar: -inf < -941.843 < inf | logz: -1011.958 +/- 0.421 | dlogz: 349.546 > 0.309] + + 19580it [01:50, 215.35it/s, bound: 422 | nc: 5 | ncall: 103364 | eff(%): 18.943 | loglstar: -inf < -933.389 < inf | logz: -1003.390 +/- 0.422 | dlogz: 340.942 > 0.309] + + 19602it [01:50, 206.73it/s, bound: 423 | nc: 5 | ncall: 103474 | eff(%): 18.944 | loglstar: -inf < -928.140 < inf | logz: -997.641 +/- 0.422 | dlogz: 334.982 > 0.309] + + 19623it [01:50, 205.37it/s, bound: 423 | nc: 5 | ncall: 103579 | eff(%): 18.945 | loglstar: -inf < -924.970 < inf | logz: -994.426 +/- 0.420 | dlogz: 361.710 > 0.309] + + 19644it [01:50, 202.28it/s, bound: 424 | nc: 5 | ncall: 103684 | eff(%): 18.946 | loglstar: -inf < -921.579 < inf | logz: -991.108 +/- 0.421 | dlogz: 358.360 > 0.309] + + 19665it [01:50, 203.76it/s, bound: 424 | nc: 5 | ncall: 103789 | eff(%): 18.947 | loglstar: -inf < -921.435 < inf | logz: -989.655 +/- 0.418 | dlogz: 356.714 > 0.309] + + 19688it [01:50, 208.81it/s, bound: 425 | nc: 5 | ncall: 103904 | eff(%): 18.948 | loglstar: -inf < -913.687 < inf | logz: -984.597 +/- 0.424 | dlogz: 351.941 > 0.309] + + 19711it [01:51, 214.91it/s, bound: 425 | nc: 5 | ncall: 104019 | eff(%): 18.949 | loglstar: -inf < -908.254 < inf | logz: -977.434 +/- 0.422 | dlogz: 344.379 > 0.309] + + 19733it [01:51, 215.77it/s, bound: 426 | nc: 5 | ncall: 104129 | eff(%): 18.951 | loglstar: -inf < -899.740 < inf | logz: -970.871 +/- 0.428 | dlogz: 384.801 > 0.309] + + 19757it [01:51, 221.77it/s, bound: 426 | nc: 5 | ncall: 104249 | eff(%): 18.952 | loglstar: -inf < -889.204 < inf | logz: -959.850 +/- 0.427 | dlogz: 373.304 > 0.309] + + 19780it [01:51, 221.23it/s, bound: 427 | nc: 5 | ncall: 104364 | eff(%): 18.953 | loglstar: -inf < -879.878 < inf | logz: -950.050 +/- 0.425 | dlogz: 363.250 > 0.309] + + 19806it [01:51, 226.04it/s, bound: 428 | nc: 5 | ncall: 104494 | eff(%): 18.954 | loglstar: -inf < -872.274 < inf | logz: -943.098 +/- 0.426 | dlogz: 356.345 > 0.309] + + 19831it [01:51, 230.47it/s, bound: 428 | nc: 5 | ncall: 104619 | eff(%): 18.955 | loglstar: -inf < -861.632 < inf | logz: -932.757 +/- 0.427 | dlogz: 346.019 > 0.309] + + 19855it [01:51, 222.95it/s, bound: 429 | nc: 5 | ncall: 104739 | eff(%): 18.957 | loglstar: -inf < -855.460 < inf | logz: -925.941 +/- 0.425 | dlogz: 338.884 > 0.309] + + 19881it [01:51, 232.20it/s, bound: 429 | nc: 5 | ncall: 104869 | eff(%): 18.958 | loglstar: -inf < -850.119 < inf | logz: -919.643 +/- 0.423 | dlogz: 332.388 > 0.309] + + 19905it [01:51, 229.64it/s, bound: 430 | nc: 5 | ncall: 104989 | eff(%): 18.959 | loglstar: -inf < -840.905 < inf | logz: -911.835 +/- 0.427 | dlogz: 324.714 > 0.309] + + 19929it [01:52, 226.49it/s, bound: 431 | nc: 5 | ncall: 105109 | eff(%): 18.960 | loglstar: -inf < -836.315 < inf | logz: -906.681 +/- 0.425 | dlogz: 319.335 > 0.309] + + 19952it [01:52, 224.54it/s, bound: 431 | nc: 5 | ncall: 105224 | eff(%): 18.961 | loglstar: -inf < -830.163 < inf | logz: -901.393 +/- 0.427 | dlogz: 314.104 > 0.309] + + 19975it [01:52, 210.59it/s, bound: 432 | nc: 5 | ncall: 105339 | eff(%): 18.963 | loglstar: -inf < -827.027 < inf | logz: -897.163 +/- 0.424 | dlogz: 388.047 > 0.309] + + 19997it [01:52, 208.27it/s, bound: 432 | nc: 5 | ncall: 105449 | eff(%): 18.964 | loglstar: -inf < -822.675 < inf | logz: -893.805 +/- 0.425 | dlogz: 384.703 > 0.309] + + 20019it [01:52, 206.24it/s, bound: 433 | nc: 5 | ncall: 105559 | eff(%): 18.965 | loglstar: -inf < -815.976 < inf | logz: -887.930 +/- 0.429 | dlogz: 379.090 > 0.309] + + 20041it [01:52, 208.82it/s, bound: 433 | nc: 5 | ncall: 105669 | eff(%): 18.966 | loglstar: -inf < -807.177 < inf | logz: -879.509 +/- 0.431 | dlogz: 370.738 > 0.309] + + 20064it [01:52, 209.15it/s, bound: 434 | nc: 5 | ncall: 105784 | eff(%): 18.967 | loglstar: -inf < -804.410 < inf | logz: -875.139 +/- 0.426 | dlogz: 365.759 > 0.309] + + 20088it [01:52, 217.71it/s, bound: 434 | nc: 5 | ncall: 105904 | eff(%): 18.968 | loglstar: -inf < -799.810 < inf | logz: -871.113 +/- 0.427 | dlogz: 361.730 > 0.309] + + 20110it [01:52, 210.51it/s, bound: 435 | nc: 5 | ncall: 106014 | eff(%): 18.969 | loglstar: -inf < -789.978 < inf | logz: -862.655 +/- 0.432 | dlogz: 353.718 > 0.309] + + 20136it [01:53, 222.40it/s, bound: 435 | nc: 5 | ncall: 106144 | eff(%): 18.970 | loglstar: -inf < -783.230 < inf | logz: -855.052 +/- 0.430 | dlogz: 345.612 > 0.309] + + 20160it [01:53, 225.58it/s, bound: 436 | nc: 5 | ncall: 106264 | eff(%): 18.972 | loglstar: -inf < -778.585 < inf | logz: -850.325 +/- 0.428 | dlogz: 340.770 > 0.309] + + 20184it [01:53, 228.82it/s, bound: 436 | nc: 5 | ncall: 106384 | eff(%): 18.973 | loglstar: -inf < -772.488 < inf | logz: -844.247 +/- 0.429 | dlogz: 334.577 > 0.309] + + 20207it [01:53, 223.24it/s, bound: 437 | nc: 5 | ncall: 106499 | eff(%): 18.974 | loglstar: -inf < -763.920 < inf | logz: -835.334 +/- 0.428 | dlogz: 325.463 > 0.309] + + 20233it [01:53, 231.71it/s, bound: 437 | nc: 5 | ncall: 106629 | eff(%): 18.975 | loglstar: -inf < -758.381 < inf | logz: -831.095 +/- 0.430 | dlogz: 321.437 > 0.309] + + 20257it [01:53, 215.46it/s, bound: 438 | nc: 5 | ncall: 106749 | eff(%): 18.976 | loglstar: -inf < -749.730 < inf | logz: -821.232 +/- 0.429 | dlogz: 311.228 > 0.309] + + 20281it [01:53, 221.52it/s, bound: 438 | nc: 5 | ncall: 106869 | eff(%): 18.977 | loglstar: -inf < -745.017 < inf | logz: -816.700 +/- 0.429 | dlogz: 306.633 > 0.309] + + 20304it [01:53, 199.13it/s, bound: 439 | nc: 5 | ncall: 106984 | eff(%): 18.979 | loglstar: -inf < -737.266 < inf | logz: -810.318 +/- 0.432 | dlogz: 300.537 > 0.309] + + 20326it [01:53, 202.77it/s, bound: 439 | nc: 5 | ncall: 107094 | eff(%): 18.980 | loglstar: -inf < -733.432 < inf | logz: -805.966 +/- 0.430 | dlogz: 295.913 > 0.309] + + 20348it [01:54, 207.22it/s, bound: 440 | nc: 5 | ncall: 107204 | eff(%): 18.981 | loglstar: -inf < -728.744 < inf | logz: -800.517 +/- 0.431 | dlogz: 290.206 > 0.309] + + 20373it [01:54, 217.51it/s, bound: 440 | nc: 5 | ncall: 107329 | eff(%): 18.982 | loglstar: -inf < -722.672 < inf | logz: -795.366 +/- 0.432 | dlogz: 285.107 > 0.309] + + 20396it [01:54, 209.98it/s, bound: 441 | nc: 5 | ncall: 107444 | eff(%): 18.983 | loglstar: -inf < -720.047 < inf | logz: -792.239 +/- 0.430 | dlogz: 281.811 > 0.309] + + 20421it [01:54, 217.38it/s, bound: 441 | nc: 5 | ncall: 107569 | eff(%): 18.984 | loglstar: -inf < -710.648 < inf | logz: -784.033 +/- 0.434 | dlogz: 273.838 > 0.309] + + 20443it [01:54, 202.58it/s, bound: 442 | nc: 5 | ncall: 107679 | eff(%): 18.985 | loglstar: -inf < -704.321 < inf | logz: -776.454 +/- 0.432 | dlogz: 396.664 > 0.309] + + 20464it [01:54, 201.72it/s, bound: 442 | nc: 5 | ncall: 107784 | eff(%): 18.986 | loglstar: -inf < -702.775 < inf | logz: -774.413 +/- 0.429 | dlogz: 394.479 > 0.309] + + 20486it [01:54, 205.30it/s, bound: 443 | nc: 5 | ncall: 107894 | eff(%): 18.987 | loglstar: -inf < -699.554 < inf | logz: -771.678 +/- 0.431 | dlogz: 391.722 > 0.309] + + 20509it [01:54, 211.14it/s, bound: 443 | nc: 5 | ncall: 108009 | eff(%): 18.988 | loglstar: -inf < -694.539 < inf | logz: -767.849 +/- 0.434 | dlogz: 388.084 > 0.309] + + 20531it [01:54, 195.19it/s, bound: 444 | nc: 5 | ncall: 108119 | eff(%): 18.989 | loglstar: -inf < -690.744 < inf | logz: -763.291 +/- 0.431 | dlogz: 383.170 > 0.309] + + 20551it [01:55, 188.23it/s, bound: 444 | nc: 5 | ncall: 108219 | eff(%): 18.990 | loglstar: -inf < -686.460 < inf | logz: -758.447 +/- 0.432 | dlogz: 378.233 > 0.309] + + 20571it [01:55, 186.45it/s, bound: 445 | nc: 5 | ncall: 108319 | eff(%): 18.991 | loglstar: -inf < -681.519 < inf | logz: -754.384 +/- 0.434 | dlogz: 374.225 > 0.309] + + 20593it [01:55, 194.81it/s, bound: 445 | nc: 5 | ncall: 108429 | eff(%): 18.992 | loglstar: -inf < -676.859 < inf | logz: -749.187 +/- 0.433 | dlogz: 368.856 > 0.309] + + 20613it [01:55, 194.03it/s, bound: 446 | nc: 5 | ncall: 108529 | eff(%): 18.993 | loglstar: -inf < -673.529 < inf | logz: -746.184 +/- 0.433 | dlogz: 365.808 > 0.309] + + 20638it [01:55, 209.02it/s, bound: 446 | nc: 5 | ncall: 108654 | eff(%): 18.994 | loglstar: -inf < -669.564 < inf | logz: -743.054 +/- 0.433 | dlogz: 362.713 > 0.309] + + 20660it [01:55, 208.88it/s, bound: 447 | nc: 5 | ncall: 108764 | eff(%): 18.995 | loglstar: -inf < -662.023 < inf | logz: -735.543 +/- 0.437 | dlogz: 355.190 > 0.309] + + 20681it [01:55, 208.14it/s, bound: 447 | nc: 5 | ncall: 108869 | eff(%): 18.996 | loglstar: -inf < -655.638 < inf | logz: -729.040 +/- 0.436 | dlogz: 348.556 > 0.309] + + 20702it [01:55, 207.02it/s, bound: 448 | nc: 5 | ncall: 108974 | eff(%): 18.997 | loglstar: -inf < -650.742 < inf | logz: -724.728 +/- 0.434 | dlogz: 344.213 > 0.309] + + 20727it [01:55, 217.32it/s, bound: 448 | nc: 5 | ncall: 109099 | eff(%): 18.998 | loglstar: -inf < -643.797 < inf | logz: -717.563 +/- 0.437 | dlogz: 336.993 > 0.309] + + 20749it [01:55, 198.49it/s, bound: 449 | nc: 5 | ncall: 109209 | eff(%): 18.999 | loglstar: -inf < -638.836 < inf | logz: -712.340 +/- 0.436 | dlogz: 331.575 > 0.309] + + 20775it [01:56, 215.33it/s, bound: 449 | nc: 5 | ncall: 109339 | eff(%): 19.001 | loglstar: -inf < -629.140 < inf | logz: -702.975 +/- 0.437 | dlogz: 322.213 > 0.309] + + 20797it [01:56, 210.12it/s, bound: 450 | nc: 5 | ncall: 109449 | eff(%): 19.002 | loglstar: -inf < -625.819 < inf | logz: -699.149 +/- 0.435 | dlogz: 318.180 > 0.309] + + 20820it [01:56, 214.80it/s, bound: 450 | nc: 5 | ncall: 109564 | eff(%): 19.003 | loglstar: -inf < -622.112 < inf | logz: -695.510 +/- 0.435 | dlogz: 314.454 > 0.309] + + 20842it [01:56, 212.40it/s, bound: 451 | nc: 5 | ncall: 109674 | eff(%): 19.004 | loglstar: -inf < -618.752 < inf | logz: -692.449 +/- 0.435 | dlogz: 311.329 > 0.309] + + 20866it [01:56, 219.65it/s, bound: 451 | nc: 5 | ncall: 109794 | eff(%): 19.005 | loglstar: -inf < -613.747 < inf | logz: -687.678 +/- 0.437 | dlogz: 306.553 > 0.309] + + 20889it [01:56, 221.32it/s, bound: 452 | nc: 5 | ncall: 109909 | eff(%): 19.006 | loglstar: -inf < -608.731 < inf | logz: -682.961 +/- 0.437 | dlogz: 301.754 > 0.309] + + 20912it [01:56, 213.74it/s, bound: 452 | nc: 5 | ncall: 110024 | eff(%): 19.007 | loglstar: -inf < -607.026 < inf | logz: -680.045 +/- 0.435 | dlogz: 298.614 > 0.309] + + 20935it [01:56, 215.73it/s, bound: 453 | nc: 5 | ncall: 110139 | eff(%): 19.008 | loglstar: -inf < -600.563 < inf | logz: -675.456 +/- 0.442 | dlogz: 294.415 > 0.309] + + 20957it [01:56, 213.22it/s, bound: 453 | nc: 5 | ncall: 110249 | eff(%): 19.009 | loglstar: -inf < -596.370 < inf | logz: -670.612 +/- 0.438 | dlogz: 289.172 > 0.309] + + 20981it [01:57, 219.16it/s, bound: 454 | nc: 5 | ncall: 110369 | eff(%): 19.010 | loglstar: -inf < -588.875 < inf | logz: -663.390 +/- 0.440 | dlogz: 281.938 > 0.309] + + 21006it [01:57, 225.19it/s, bound: 454 | nc: 5 | ncall: 110494 | eff(%): 19.011 | loglstar: -inf < -581.489 < inf | logz: -656.124 +/- 0.440 | dlogz: 274.600 > 0.309] + + 21029it [01:57, 212.22it/s, bound: 455 | nc: 5 | ncall: 110609 | eff(%): 19.012 | loglstar: -inf < -574.093 < inf | logz: -649.056 +/- 0.440 | dlogz: 267.501 > 0.309] + + 21054it [01:57, 222.05it/s, bound: 456 | nc: 5 | ncall: 110734 | eff(%): 19.013 | loglstar: -inf < -569.868 < inf | logz: -643.897 +/- 0.438 | dlogz: 262.029 > 0.309] + + 21079it [01:57, 229.90it/s, bound: 456 | nc: 5 | ncall: 110859 | eff(%): 19.014 | loglstar: -inf < -567.749 < inf | logz: -641.809 +/- 0.436 | dlogz: 259.846 > 0.309] + + 21103it [01:57, 225.21it/s, bound: 457 | nc: 5 | ncall: 110979 | eff(%): 19.015 | loglstar: -inf < -563.288 < inf | logz: -637.415 +/- 0.438 | dlogz: 255.390 > 0.309] + + 21131it [01:57, 240.68it/s, bound: 457 | nc: 5 | ncall: 111119 | eff(%): 19.017 | loglstar: -inf < -555.300 < inf | logz: -630.297 +/- 0.440 | dlogz: 248.315 > 0.309] + + 21156it [01:57, 231.34it/s, bound: 458 | nc: 5 | ncall: 111244 | eff(%): 19.018 | loglstar: -inf < -551.555 < inf | logz: -625.884 +/- 0.439 | dlogz: 243.686 > 0.309] + + 21180it [01:57, 231.03it/s, bound: 458 | nc: 5 | ncall: 111364 | eff(%): 19.019 | loglstar: -inf < -547.163 < inf | logz: -622.001 +/- 0.440 | dlogz: 239.781 > 0.309] + + 21204it [01:58, 225.27it/s, bound: 459 | nc: 5 | ncall: 111484 | eff(%): 19.020 | loglstar: -inf < -540.250 < inf | logz: -616.049 +/- 0.443 | dlogz: 234.042 > 0.309] + + 21232it [01:58, 238.06it/s, bound: 459 | nc: 5 | ncall: 111624 | eff(%): 19.021 | loglstar: -inf < -534.216 < inf | logz: -609.385 +/- 0.442 | dlogz: 226.997 > 0.309] + + 21256it [01:58, 206.58it/s, bound: 460 | nc: 5 | ncall: 111744 | eff(%): 19.022 | loglstar: -inf < -528.431 < inf | logz: -603.140 +/- 0.441 | dlogz: 220.604 > 0.309] + + 21278it [01:58, 177.60it/s, bound: 460 | nc: 5 | ncall: 111854 | eff(%): 19.023 | loglstar: -inf < -525.322 < inf | logz: -599.851 +/- 0.440 | dlogz: 217.223 > 0.309] + + 21297it [01:58, 172.61it/s, bound: 461 | nc: 5 | ncall: 111949 | eff(%): 19.024 | loglstar: -inf < -523.107 < inf | logz: -597.580 +/- 0.440 | dlogz: 214.877 > 0.309] + + 21317it [01:58, 177.07it/s, bound: 461 | nc: 5 | ncall: 112049 | eff(%): 19.025 | loglstar: -inf < -518.717 < inf | logz: -594.111 +/- 0.443 | dlogz: 211.470 > 0.309] + + 21336it [01:58, 168.31it/s, bound: 462 | nc: 5 | ncall: 112144 | eff(%): 19.026 | loglstar: -inf < -515.021 < inf | logz: -591.001 +/- 0.443 | dlogz: 208.473 > 0.309] + + 21354it [01:58, 165.65it/s, bound: 462 | nc: 5 | ncall: 112234 | eff(%): 19.026 | loglstar: -inf < -510.495 < inf | logz: -586.485 +/- 0.445 | dlogz: 203.841 > 0.309] + + 21371it [01:59, 164.72it/s, bound: 463 | nc: 5 | ncall: 112319 | eff(%): 19.027 | loglstar: -inf < -508.432 < inf | logz: -583.289 +/- 0.442 | dlogz: 200.352 > 0.309] + + 21388it [01:59, 164.48it/s, bound: 463 | nc: 5 | ncall: 112404 | eff(%): 19.028 | loglstar: -inf < -503.797 < inf | logz: -579.413 +/- 0.444 | dlogz: 196.497 > 0.309] + + 21407it [01:59, 167.91it/s, bound: 463 | nc: 5 | ncall: 112499 | eff(%): 19.029 | loglstar: -inf < -502.346 < inf | logz: -577.065 +/- 0.441 | dlogz: 193.986 > 0.309] + + 21424it [01:59, 163.87it/s, bound: 464 | nc: 5 | ncall: 112584 | eff(%): 19.029 | loglstar: -inf < -499.934 < inf | logz: -575.361 +/- 0.441 | dlogz: 192.268 > 0.309] + + 21447it [01:59, 181.90it/s, bound: 464 | nc: 5 | ncall: 112699 | eff(%): 19.030 | loglstar: -inf < -498.680 < inf | logz: -573.210 +/- 0.440 | dlogz: 189.975 > 0.309] + + 21466it [01:59, 178.94it/s, bound: 465 | nc: 5 | ncall: 112794 | eff(%): 19.031 | loglstar: -inf < -496.499 < inf | logz: -571.970 +/- 0.441 | dlogz: 188.752 > 0.309] + + 21490it [01:59, 194.26it/s, bound: 465 | nc: 5 | ncall: 112914 | eff(%): 19.032 | loglstar: -inf < -494.498 < inf | logz: -569.824 +/- 0.441 | dlogz: 186.495 > 0.309] + + 21510it [01:59, 195.28it/s, bound: 466 | nc: 5 | ncall: 113014 | eff(%): 19.033 | loglstar: -inf < -490.205 < inf | logz: -566.280 +/- 0.445 | dlogz: 206.717 > 0.309] + + 21537it [01:59, 213.47it/s, bound: 466 | nc: 5 | ncall: 113149 | eff(%): 19.034 | loglstar: -inf < -481.943 < inf | logz: -558.360 +/- 0.447 | dlogz: 198.726 > 0.309] + + 21559it [01:59, 211.78it/s, bound: 467 | nc: 5 | ncall: 113259 | eff(%): 19.035 | loglstar: -inf < -478.871 < inf | logz: -554.698 +/- 0.444 | dlogz: 194.891 > 0.309] + + 21584it [02:00, 221.96it/s, bound: 467 | nc: 5 | ncall: 113384 | eff(%): 19.036 | loglstar: -inf < -475.277 < inf | logz: -551.248 +/- 0.445 | dlogz: 191.367 > 0.309] + + 21607it [02:00, 217.19it/s, bound: 468 | nc: 5 | ncall: 113499 | eff(%): 19.037 | loglstar: -inf < -474.397 < inf | logz: -549.559 +/- 0.442 | dlogz: 189.510 > 0.309] + + 21632it [02:00, 224.16it/s, bound: 468 | nc: 5 | ncall: 113624 | eff(%): 19.038 | loglstar: -inf < -470.310 < inf | logz: -546.918 +/- 0.445 | dlogz: 186.926 > 0.309] + + 21655it [02:00, 221.04it/s, bound: 469 | nc: 5 | ncall: 113739 | eff(%): 19.039 | loglstar: -inf < -464.727 < inf | logz: -541.646 +/- 0.447 | dlogz: 181.658 > 0.309] + + 21678it [02:00, 223.35it/s, bound: 469 | nc: 5 | ncall: 113854 | eff(%): 19.040 | loglstar: -inf < -460.667 < inf | logz: -537.467 +/- 0.447 | dlogz: 177.355 > 0.309] + + 21703it [02:00, 228.38it/s, bound: 470 | nc: 5 | ncall: 113979 | eff(%): 19.041 | loglstar: -inf < -459.587 < inf | logz: -535.050 +/- 0.444 | dlogz: 174.679 > 0.309] + + 21729it [02:00, 229.92it/s, bound: 471 | nc: 5 | ncall: 114109 | eff(%): 19.042 | loglstar: -inf < -458.197 < inf | logz: -533.810 +/- 0.442 | dlogz: 173.354 > 0.309] + + 21753it [02:00, 228.41it/s, bound: 471 | nc: 5 | ncall: 114229 | eff(%): 19.043 | loglstar: -inf < -456.280 < inf | logz: -532.289 +/- 0.443 | dlogz: 171.777 > 0.309] + + 21776it [02:00, 224.62it/s, bound: 472 | nc: 5 | ncall: 114344 | eff(%): 19.044 | loglstar: -inf < -451.878 < inf | logz: -528.607 +/- 0.447 | dlogz: 168.104 > 0.309] + + 21801it [02:01, 231.86it/s, bound: 472 | nc: 5 | ncall: 114469 | eff(%): 19.045 | loglstar: -inf < -449.738 < inf | logz: -526.035 +/- 0.445 | dlogz: 165.364 > 0.309] + + 21825it [02:01, 217.40it/s, bound: 473 | nc: 5 | ncall: 114589 | eff(%): 19.046 | loglstar: -inf < -447.049 < inf | logz: -523.312 +/- 0.446 | dlogz: 162.553 > 0.309] + + 21849it [02:01, 222.03it/s, bound: 473 | nc: 5 | ncall: 114709 | eff(%): 19.047 | loglstar: -inf < -442.674 < inf | logz: -519.961 +/- 0.448 | dlogz: 159.236 > 0.309] + + 21872it [02:01, 220.04it/s, bound: 474 | nc: 5 | ncall: 114824 | eff(%): 19.048 | loglstar: -inf < -441.119 < inf | logz: -517.594 +/- 0.446 | dlogz: 156.679 > 0.309] + + 21898it [02:01, 230.61it/s, bound: 474 | nc: 5 | ncall: 114954 | eff(%): 19.049 | loglstar: -inf < -437.057 < inf | logz: -514.184 +/- 0.448 | dlogz: 153.242 > 0.309] + + 21922it [02:01, 232.36it/s, bound: 475 | nc: 5 | ncall: 115074 | eff(%): 19.050 | loglstar: -inf < -434.403 < inf | logz: -511.238 +/- 0.447 | dlogz: 150.190 > 0.309] + + 21949it [02:01, 241.72it/s, bound: 475 | nc: 5 | ncall: 115209 | eff(%): 19.051 | loglstar: -inf < -432.140 < inf | logz: -508.782 +/- 0.447 | dlogz: 147.609 > 0.309] + + 21974it [02:01, 230.94it/s, bound: 476 | nc: 5 | ncall: 115334 | eff(%): 19.052 | loglstar: -inf < -430.015 < inf | logz: -506.463 +/- 0.447 | dlogz: 157.728 > 0.309] + + 21998it [02:01, 233.50it/s, bound: 476 | nc: 5 | ncall: 115454 | eff(%): 19.053 | loglstar: -inf < -423.393 < inf | logz: -501.200 +/- 0.451 | dlogz: 152.552 > 0.309] + + 22022it [02:01, 232.42it/s, bound: 477 | nc: 5 | ncall: 115574 | eff(%): 19.054 | loglstar: -inf < -419.740 < inf | logz: -496.710 +/- 0.449 | dlogz: 186.938 > 0.309] + + 22047it [02:02, 235.63it/s, bound: 478 | nc: 5 | ncall: 115699 | eff(%): 19.055 | loglstar: -inf < -416.572 < inf | logz: -494.003 +/- 0.450 | dlogz: 184.184 > 0.309] + + 22075it [02:02, 245.69it/s, bound: 478 | nc: 5 | ncall: 115839 | eff(%): 19.057 | loglstar: -inf < -410.959 < inf | logz: -488.464 +/- 0.451 | dlogz: 178.550 > 0.309] + + 22100it [02:02, 237.76it/s, bound: 479 | nc: 5 | ncall: 115964 | eff(%): 19.058 | loglstar: -inf < -407.061 < inf | logz: -484.790 +/- 0.450 | dlogz: 174.802 > 0.309] + + 22128it [02:02, 248.78it/s, bound: 479 | nc: 5 | ncall: 116104 | eff(%): 19.059 | loglstar: -inf < -404.491 < inf | logz: -481.684 +/- 0.449 | dlogz: 171.542 > 0.309] + + 22153it [02:02, 242.83it/s, bound: 480 | nc: 5 | ncall: 116229 | eff(%): 19.060 | loglstar: -inf < -399.270 < inf | logz: -477.440 +/- 0.452 | dlogz: 167.324 > 0.309] + + 22179it [02:02, 242.65it/s, bound: 481 | nc: 5 | ncall: 116359 | eff(%): 19.061 | loglstar: -inf < -396.085 < inf | logz: -473.587 +/- 0.450 | dlogz: 163.291 > 0.309] + + 22206it [02:02, 249.24it/s, bound: 481 | nc: 5 | ncall: 116494 | eff(%): 19.062 | loglstar: -inf < -392.914 < inf | logz: -470.603 +/- 0.451 | dlogz: 199.398 > 0.309] + + 22231it [02:02, 246.44it/s, bound: 482 | nc: 5 | ncall: 116619 | eff(%): 19.063 | loglstar: -inf < -391.403 < inf | logz: -468.730 +/- 0.450 | dlogz: 197.402 > 0.309] + + 22256it [02:02, 241.45it/s, bound: 482 | nc: 5 | ncall: 116744 | eff(%): 19.064 | loglstar: -inf < -389.758 < inf | logz: -467.106 +/- 0.450 | dlogz: 195.692 > 0.309] + + 22281it [02:03, 232.50it/s, bound: 483 | nc: 5 | ncall: 116869 | eff(%): 19.065 | loglstar: -inf < -387.279 < inf | logz: -465.161 +/- 0.451 | dlogz: 193.698 > 0.309] + + 22308it [02:03, 242.82it/s, bound: 483 | nc: 5 | ncall: 117004 | eff(%): 19.066 | loglstar: -inf < -383.197 < inf | logz: -461.617 +/- 0.453 | dlogz: 190.125 > 0.309] + + 22333it [02:03, 241.81it/s, bound: 484 | nc: 5 | ncall: 117129 | eff(%): 19.067 | loglstar: -inf < -380.135 < inf | logz: -458.809 +/- 0.453 | dlogz: 187.240 > 0.309] + + 22359it [02:03, 242.41it/s, bound: 485 | nc: 5 | ncall: 117259 | eff(%): 19.068 | loglstar: -inf < -377.013 < inf | logz: -455.452 +/- 0.453 | dlogz: 183.759 > 0.309] + + 22385it [02:03, 246.85it/s, bound: 485 | nc: 5 | ncall: 117389 | eff(%): 19.069 | loglstar: -inf < -374.216 < inf | logz: -452.743 +/- 0.453 | dlogz: 180.956 > 0.309] + + 22410it [02:03, 246.32it/s, bound: 486 | nc: 5 | ncall: 117514 | eff(%): 19.070 | loglstar: -inf < -371.634 < inf | logz: -449.947 +/- 0.453 | dlogz: 178.051 > 0.309] + + 22437it [02:03, 252.37it/s, bound: 486 | nc: 5 | ncall: 117649 | eff(%): 19.071 | loglstar: -inf < -368.410 < inf | logz: -446.878 +/- 0.454 | dlogz: 174.902 > 0.309] + + 22463it [02:03, 249.47it/s, bound: 487 | nc: 5 | ncall: 117779 | eff(%): 19.072 | loglstar: -inf < -364.847 < inf | logz: -443.401 +/- 0.455 | dlogz: 171.333 > 0.309] + + 22491it [02:03, 255.99it/s, bound: 487 | nc: 5 | ncall: 117919 | eff(%): 19.073 | loglstar: -inf < -361.467 < inf | logz: -439.930 +/- 0.455 | dlogz: 167.757 > 0.309] + + 22517it [02:04, 235.66it/s, bound: 488 | nc: 5 | ncall: 118049 | eff(%): 19.074 | loglstar: -inf < -359.947 < inf | logz: -438.145 +/- 0.453 | dlogz: 165.856 > 0.309] + + 22541it [02:04, 224.76it/s, bound: 489 | nc: 5 | ncall: 118169 | eff(%): 19.075 | loglstar: -inf < -356.876 < inf | logz: -435.810 +/- 0.455 | dlogz: 163.501 > 0.309] + + 22564it [02:04, 217.55it/s, bound: 489 | nc: 5 | ncall: 118284 | eff(%): 19.076 | loglstar: -inf < -356.045 < inf | logz: -434.292 +/- 0.453 | dlogz: 161.840 > 0.309] + + 22586it [02:04, 214.45it/s, bound: 490 | nc: 5 | ncall: 118394 | eff(%): 19.077 | loglstar: -inf < -353.129 < inf | logz: -432.057 +/- 0.455 | dlogz: 159.579 > 0.309] + + 22609it [02:04, 217.92it/s, bound: 490 | nc: 5 | ncall: 118509 | eff(%): 19.078 | loglstar: -inf < -348.960 < inf | logz: -428.414 +/- 0.457 | dlogz: 155.888 > 0.309] + + 22632it [02:04, 220.19it/s, bound: 491 | nc: 5 | ncall: 118624 | eff(%): 19.079 | loglstar: -inf < -345.712 < inf | logz: -425.500 +/- 0.457 | dlogz: 152.967 > 0.309] + + 22660it [02:04, 237.33it/s, bound: 491 | nc: 5 | ncall: 118764 | eff(%): 19.080 | loglstar: -inf < -341.718 < inf | logz: -421.087 +/- 0.457 | dlogz: 148.373 > 0.309] + + 22684it [02:04, 235.82it/s, bound: 492 | nc: 5 | ncall: 118884 | eff(%): 19.081 | loglstar: -inf < -340.160 < inf | logz: -419.033 +/- 0.456 | dlogz: 146.196 > 0.309] + + 22712it [02:04, 246.54it/s, bound: 492 | nc: 5 | ncall: 119024 | eff(%): 19.082 | loglstar: -inf < -337.379 < inf | logz: -416.653 +/- 0.456 | dlogz: 143.749 > 0.309] + + 22737it [02:04, 244.93it/s, bound: 493 | nc: 5 | ncall: 119149 | eff(%): 19.083 | loglstar: -inf < -334.180 < inf | logz: -414.080 +/- 0.458 | dlogz: 141.158 > 0.309] + + 22762it [02:05, 229.93it/s, bound: 493 | nc: 5 | ncall: 119274 | eff(%): 19.084 | loglstar: -inf < -330.066 < inf | logz: -409.979 +/- 0.459 | dlogz: 136.960 > 0.309] + + 22786it [02:05, 203.21it/s, bound: 494 | nc: 5 | ncall: 119394 | eff(%): 19.085 | loglstar: -inf < -327.320 < inf | logz: -407.290 +/- 0.458 | dlogz: 134.190 > 0.309] + + 22809it [02:05, 202.82it/s, bound: 495 | nc: 5 | ncall: 119509 | eff(%): 19.086 | loglstar: -inf < -324.975 < inf | logz: -404.378 +/- 0.458 | dlogz: 131.132 > 0.309] + + 22838it [02:05, 225.56it/s, bound: 495 | nc: 5 | ncall: 119654 | eff(%): 19.087 | loglstar: -inf < -321.891 < inf | logz: -401.622 +/- 0.458 | dlogz: 128.300 > 0.309] + + 22862it [02:05, 221.74it/s, bound: 496 | nc: 5 | ncall: 119774 | eff(%): 19.088 | loglstar: -inf < -319.364 < inf | logz: -399.073 +/- 0.458 | dlogz: 125.658 > 0.309] + + 22890it [02:05, 236.05it/s, bound: 496 | nc: 5 | ncall: 119914 | eff(%): 19.089 | loglstar: -inf < -315.733 < inf | logz: -395.998 +/- 0.459 | dlogz: 122.544 > 0.309] + + 22916it [02:05, 240.55it/s, bound: 497 | nc: 5 | ncall: 120044 | eff(%): 19.090 | loglstar: -inf < -313.800 < inf | logz: -393.663 +/- 0.458 | dlogz: 120.068 > 0.309] + + 22944it [02:05, 248.72it/s, bound: 498 | nc: 5 | ncall: 120184 | eff(%): 19.091 | loglstar: -inf < -310.738 < inf | logz: -391.047 +/- 0.459 | dlogz: 117.386 > 0.309] + + 22970it [02:05, 251.32it/s, bound: 498 | nc: 5 | ncall: 120314 | eff(%): 19.092 | loglstar: -inf < -308.117 < inf | logz: -388.447 +/- 0.460 | dlogz: 114.702 > 0.309] + + 22996it [02:06, 241.38it/s, bound: 499 | nc: 5 | ncall: 120444 | eff(%): 19.093 | loglstar: -inf < -304.987 < inf | logz: -385.474 +/- 0.460 | dlogz: 111.644 > 0.309] + + 23022it [02:06, 244.59it/s, bound: 499 | nc: 5 | ncall: 120574 | eff(%): 19.094 | loglstar: -inf < -303.145 < inf | logz: -383.165 +/- 0.459 | dlogz: 109.201 > 0.309] + + 23047it [02:06, 238.17it/s, bound: 500 | nc: 5 | ncall: 120699 | eff(%): 19.095 | loglstar: -inf < -299.523 < inf | logz: -380.689 +/- 0.461 | dlogz: 123.108 > 0.309] + + 23073it [02:06, 242.85it/s, bound: 500 | nc: 5 | ncall: 120829 | eff(%): 19.096 | loglstar: -inf < -297.502 < inf | logz: -377.744 +/- 0.461 | dlogz: 119.936 > 0.309] + + 23098it [02:06, 235.81it/s, bound: 501 | nc: 5 | ncall: 120954 | eff(%): 19.097 | loglstar: -inf < -296.917 < inf | logz: -376.703 +/- 0.459 | dlogz: 118.771 > 0.309] + + 23124it [02:06, 234.22it/s, bound: 502 | nc: 5 | ncall: 121084 | eff(%): 19.097 | loglstar: -inf < -293.437 < inf | logz: -374.355 +/- 0.461 | dlogz: 116.421 > 0.309] + + 23150it [02:06, 241.06it/s, bound: 502 | nc: 5 | ncall: 121214 | eff(%): 19.098 | loglstar: -inf < -290.526 < inf | logz: -371.227 +/- 0.462 | dlogz: 113.168 > 0.309] + + 23175it [02:06, 228.64it/s, bound: 503 | nc: 5 | ncall: 121339 | eff(%): 19.099 | loglstar: -inf < -288.628 < inf | logz: -369.305 +/- 0.461 | dlogz: 111.154 > 0.309] + + 23199it [02:06, 231.51it/s, bound: 503 | nc: 5 | ncall: 121459 | eff(%): 19.100 | loglstar: -inf < -286.617 < inf | logz: -367.426 +/- 0.461 | dlogz: 109.201 > 0.309] + + 23223it [02:07, 228.08it/s, bound: 504 | nc: 5 | ncall: 121579 | eff(%): 19.101 | loglstar: -inf < -285.181 < inf | logz: -365.867 +/- 0.461 | dlogz: 107.549 > 0.309] + + 23249it [02:07, 234.20it/s, bound: 504 | nc: 5 | ncall: 121709 | eff(%): 19.102 | loglstar: -inf < -283.194 < inf | logz: -364.262 +/- 0.461 | dlogz: 105.880 > 0.309] + + 23273it [02:07, 228.40it/s, bound: 505 | nc: 5 | ncall: 121829 | eff(%): 19.103 | loglstar: -inf < -279.613 < inf | logz: -361.031 +/- 0.464 | dlogz: 102.599 > 0.309] + + 23296it [02:07, 221.86it/s, bound: 505 | nc: 5 | ncall: 121944 | eff(%): 19.104 | loglstar: -inf < -278.935 < inf | logz: -359.433 +/- 0.462 | dlogz: 100.848 > 0.309] + + 23319it [02:07, 221.49it/s, bound: 506 | nc: 5 | ncall: 122059 | eff(%): 19.105 | loglstar: -inf < -276.760 < inf | logz: -357.867 +/- 0.462 | dlogz: 99.234 > 0.309] + + 23346it [02:07, 233.24it/s, bound: 506 | nc: 5 | ncall: 122194 | eff(%): 19.106 | loglstar: -inf < -275.263 < inf | logz: -356.060 +/- 0.462 | dlogz: 97.313 > 0.309] + + 23370it [02:07, 227.79it/s, bound: 507 | nc: 5 | ncall: 122314 | eff(%): 19.107 | loglstar: -inf < -273.154 < inf | logz: -354.419 +/- 0.463 | dlogz: 95.618 > 0.309] + + 23394it [02:07, 230.82it/s, bound: 508 | nc: 5 | ncall: 122434 | eff(%): 19.107 | loglstar: -inf < -270.485 < inf | logz: -352.220 +/- 0.464 | dlogz: 93.366 > 0.309] + + 23422it [02:07, 244.06it/s, bound: 508 | nc: 5 | ncall: 122574 | eff(%): 19.108 | loglstar: -inf < -267.692 < inf | logz: -349.068 +/- 0.465 | dlogz: 90.090 > 0.309] + + 23447it [02:08, 239.99it/s, bound: 509 | nc: 5 | ncall: 122699 | eff(%): 19.109 | loglstar: -inf < -266.064 < inf | logz: -347.595 +/- 0.464 | dlogz: 89.763 > 0.309] + + 23474it [02:08, 247.30it/s, bound: 509 | nc: 5 | ncall: 122834 | eff(%): 19.110 | loglstar: -inf < -263.432 < inf | logz: -345.181 +/- 0.465 | dlogz: 87.269 > 0.309] + + 23499it [02:08, 234.94it/s, bound: 510 | nc: 5 | ncall: 122959 | eff(%): 19.111 | loglstar: -inf < -261.430 < inf | logz: -343.285 +/- 0.465 | dlogz: 85.285 > 0.309] + + 23523it [02:08, 228.82it/s, bound: 511 | nc: 5 | ncall: 123079 | eff(%): 19.112 | loglstar: -inf < -258.654 < inf | logz: -340.551 +/- 0.466 | dlogz: 82.465 > 0.309] + + 23550it [02:08, 239.78it/s, bound: 511 | nc: 5 | ncall: 123214 | eff(%): 19.113 | loglstar: -inf < -255.426 < inf | logz: -337.488 +/- 0.466 | dlogz: 79.326 > 0.309] + + 23575it [02:08, 238.25it/s, bound: 512 | nc: 5 | ncall: 123339 | eff(%): 19.114 | loglstar: -inf < -253.365 < inf | logz: -335.397 +/- 0.466 | dlogz: 77.135 > 0.309] + + 23604it [02:08, 251.79it/s, bound: 512 | nc: 5 | ncall: 123484 | eff(%): 19.115 | loglstar: -inf < -252.076 < inf | logz: -333.536 +/- 0.465 | dlogz: 75.145 > 0.309] + + 23630it [02:08, 249.07it/s, bound: 513 | nc: 5 | ncall: 123614 | eff(%): 19.116 | loglstar: -inf < -250.051 < inf | logz: -332.124 +/- 0.465 | dlogz: 73.675 > 0.309] + + 23658it [02:08, 248.72it/s, bound: 514 | nc: 5 | ncall: 123754 | eff(%): 19.117 | loglstar: -inf < -248.768 < inf | logz: -330.403 +/- 0.466 | dlogz: 71.832 > 0.309] + + 23686it [02:08, 256.58it/s, bound: 514 | nc: 5 | ncall: 123894 | eff(%): 19.118 | loglstar: -inf < -246.410 < inf | logz: -328.723 +/- 0.466 | dlogz: 70.091 > 0.309] + + 23712it [02:09, 234.11it/s, bound: 515 | nc: 5 | ncall: 124024 | eff(%): 19.119 | loglstar: -inf < -244.171 < inf | logz: -326.603 +/- 0.467 | dlogz: 67.881 > 0.309] + + 23737it [02:09, 236.61it/s, bound: 515 | nc: 5 | ncall: 124149 | eff(%): 19.120 | loglstar: -inf < -243.759 < inf | logz: -325.371 +/- 0.466 | dlogz: 102.034 > 0.309] + + 23761it [02:09, 218.64it/s, bound: 516 | nc: 5 | ncall: 124269 | eff(%): 19.121 | loglstar: -inf < -242.590 < inf | logz: -324.626 +/- 0.465 | dlogz: 101.217 > 0.309] + + 23785it [02:09, 223.54it/s, bound: 516 | nc: 5 | ncall: 124389 | eff(%): 19.121 | loglstar: -inf < -241.264 < inf | logz: -323.622 +/- 0.466 | dlogz: 100.144 > 0.309] + + 23809it [02:09, 227.70it/s, bound: 517 | nc: 5 | ncall: 124509 | eff(%): 19.122 | loglstar: -inf < -240.805 < inf | logz: -322.758 +/- 0.466 | dlogz: 99.180 > 0.309] + + 23837it [02:09, 240.72it/s, bound: 517 | nc: 5 | ncall: 124649 | eff(%): 19.123 | loglstar: -inf < -237.027 < inf | logz: -320.707 +/- 0.469 | dlogz: 97.140 > 0.309] + + 23862it [02:09, 238.47it/s, bound: 518 | nc: 5 | ncall: 124774 | eff(%): 19.124 | loglstar: -inf < -235.525 < inf | logz: -318.389 +/- 0.469 | dlogz: 94.669 > 0.309] + + 23888it [02:09, 244.09it/s, bound: 519 | nc: 5 | ncall: 124904 | eff(%): 19.125 | loglstar: -inf < -233.519 < inf | logz: -316.920 +/- 0.469 | dlogz: 93.159 > 0.309] + + 23915it [02:09, 249.96it/s, bound: 519 | nc: 5 | ncall: 125039 | eff(%): 19.126 | loglstar: -inf < -231.464 < inf | logz: -314.766 +/- 0.470 | dlogz: 90.893 > 0.309] + + 23941it [02:10, 238.02it/s, bound: 520 | nc: 5 | ncall: 125169 | eff(%): 19.127 | loglstar: -inf < -229.180 < inf | logz: -312.584 +/- 0.470 | dlogz: 88.621 > 0.309] + + 23968it [02:10, 246.02it/s, bound: 520 | nc: 5 | ncall: 125304 | eff(%): 19.128 | loglstar: -inf < -227.134 < inf | logz: -310.370 +/- 0.471 | dlogz: 86.301 > 0.309] + + 23993it [02:10, 240.91it/s, bound: 521 | nc: 5 | ncall: 125429 | eff(%): 19.129 | loglstar: -inf < -224.768 < inf | logz: -308.499 +/- 0.471 | dlogz: 84.380 > 0.309] + + 24018it [02:10, 238.36it/s, bound: 522 | nc: 5 | ncall: 125554 | eff(%): 19.130 | loglstar: -inf < -222.965 < inf | logz: -306.305 +/- 0.471 | dlogz: 82.066 > 0.309] + + 24046it [02:10, 249.10it/s, bound: 522 | nc: 5 | ncall: 125694 | eff(%): 19.131 | loglstar: -inf < -220.676 < inf | logz: -304.414 +/- 0.471 | dlogz: 80.100 > 0.309] + + 24072it [02:10, 239.47it/s, bound: 523 | nc: 5 | ncall: 125824 | eff(%): 19.131 | loglstar: -inf < -218.818 < inf | logz: -302.420 +/- 0.471 | dlogz: 77.998 > 0.309] + + 24100it [02:10, 249.77it/s, bound: 523 | nc: 5 | ncall: 125964 | eff(%): 19.132 | loglstar: -inf < -216.829 < inf | logz: -300.573 +/- 0.471 | dlogz: 76.066 > 0.309] + + 24126it [02:10, 244.02it/s, bound: 524 | nc: 5 | ncall: 126094 | eff(%): 19.133 | loglstar: -inf < -214.771 < inf | logz: -298.619 +/- 0.472 | dlogz: 74.022 > 0.309] + + 24151it [02:10, 232.30it/s, bound: 525 | nc: 5 | ncall: 126219 | eff(%): 19.134 | loglstar: -inf < -213.129 < inf | logz: -296.784 +/- 0.472 | dlogz: 72.087 > 0.309] + + 24175it [02:11, 217.85it/s, bound: 525 | nc: 5 | ncall: 126339 | eff(%): 19.135 | loglstar: -inf < -212.119 < inf | logz: -295.544 +/- 0.471 | dlogz: 101.074 > 0.309] + + 24198it [02:11, 211.41it/s, bound: 526 | nc: 5 | ncall: 126454 | eff(%): 19.136 | loglstar: -inf < -211.414 < inf | logz: -294.664 +/- 0.471 | dlogz: 100.107 > 0.309] + + 24220it [02:11, 213.14it/s, bound: 526 | nc: 5 | ncall: 126564 | eff(%): 19.137 | loglstar: -inf < -210.010 < inf | logz: -293.622 +/- 0.471 | dlogz: 99.002 > 0.309] + + 24242it [02:11, 207.96it/s, bound: 527 | nc: 5 | ncall: 126674 | eff(%): 19.137 | loglstar: -inf < -207.714 < inf | logz: -291.975 +/- 0.473 | dlogz: 97.316 > 0.309] + + 24264it [02:11, 209.62it/s, bound: 527 | nc: 5 | ncall: 126784 | eff(%): 19.138 | loglstar: -inf < -206.814 < inf | logz: -290.572 +/- 0.473 | dlogz: 95.808 > 0.309] + + 24286it [02:11, 209.48it/s, bound: 528 | nc: 5 | ncall: 126894 | eff(%): 19.139 | loglstar: -inf < -205.187 < inf | logz: -289.430 +/- 0.473 | dlogz: 94.614 > 0.309] + + 24313it [02:11, 224.32it/s, bound: 528 | nc: 5 | ncall: 127029 | eff(%): 19.140 | loglstar: -inf < -203.434 < inf | logz: -287.570 +/- 0.474 | dlogz: 92.652 > 0.309] + + 24336it [02:11, 225.18it/s, bound: 529 | nc: 5 | ncall: 127144 | eff(%): 19.141 | loglstar: -inf < -201.760 < inf | logz: -286.005 +/- 0.474 | dlogz: 91.010 > 0.309] + + 24361it [02:11, 231.36it/s, bound: 529 | nc: 5 | ncall: 127269 | eff(%): 19.141 | loglstar: -inf < -199.972 < inf | logz: -284.437 +/- 0.474 | dlogz: 89.364 > 0.309] + + 24388it [02:12, 240.69it/s, bound: 530 | nc: 5 | ncall: 127404 | eff(%): 19.142 | loglstar: -inf < -197.859 < inf | logz: -282.577 +/- 0.475 | dlogz: 87.426 > 0.309] + + 24413it [02:12, 240.87it/s, bound: 531 | nc: 5 | ncall: 127529 | eff(%): 19.143 | loglstar: -inf < -197.006 < inf | logz: -281.003 +/- 0.475 | dlogz: 85.731 > 0.309] + + 24438it [02:12, 238.98it/s, bound: 531 | nc: 5 | ncall: 127654 | eff(%): 19.144 | loglstar: -inf < -196.364 < inf | logz: -280.303 +/- 0.473 | dlogz: 84.937 > 0.309] + + 24462it [02:12, 234.52it/s, bound: 532 | nc: 5 | ncall: 127774 | eff(%): 19.145 | loglstar: -inf < -194.842 < inf | logz: -279.391 +/- 0.474 | dlogz: 83.969 > 0.309] + + 24489it [02:12, 243.33it/s, bound: 532 | nc: 5 | ncall: 127909 | eff(%): 19.146 | loglstar: -inf < -192.366 < inf | logz: -277.666 +/- 0.476 | dlogz: 82.203 > 0.309] + + 24514it [02:12, 233.87it/s, bound: 533 | nc: 5 | ncall: 128034 | eff(%): 19.146 | loglstar: -inf < -191.287 < inf | logz: -275.925 +/- 0.476 | dlogz: 80.326 > 0.309] + + 24538it [02:12, 235.61it/s, bound: 533 | nc: 5 | ncall: 128154 | eff(%): 19.147 | loglstar: -inf < -191.287 < inf | logz: -275.166 +/- 0.475 | dlogz: 79.455 > 0.309] + + 24562it [02:12, 230.82it/s, bound: 534 | nc: 5 | ncall: 128274 | eff(%): 19.148 | loglstar: -inf < -190.840 < inf | logz: -274.695 +/- 0.474 | dlogz: 78.896 > 0.309] + + 24587it [02:12, 233.09it/s, bound: 535 | nc: 5 | ncall: 128399 | eff(%): 19.149 | loglstar: -inf < -189.328 < inf | logz: -274.056 +/- 0.474 | dlogz: 78.194 > 0.309] + + 24611it [02:12, 233.82it/s, bound: 535 | nc: 5 | ncall: 128519 | eff(%): 19.150 | loglstar: -inf < -187.920 < inf | logz: -272.939 +/- 0.475 | dlogz: 77.006 > 0.309] + + 24635it [02:13, 227.85it/s, bound: 536 | nc: 5 | ncall: 128639 | eff(%): 19.150 | loglstar: -inf < -186.385 < inf | logz: -271.529 +/- 0.477 | dlogz: 75.520 > 0.309] + + 24658it [02:13, 225.46it/s, bound: 536 | nc: 5 | ncall: 128754 | eff(%): 19.151 | loglstar: -inf < -185.258 < inf | logz: -270.445 +/- 0.476 | dlogz: 74.354 > 0.309] + + 24681it [02:13, 218.75it/s, bound: 537 | nc: 5 | ncall: 128869 | eff(%): 19.152 | loglstar: -inf < -184.072 < inf | logz: -269.282 +/- 0.477 | dlogz: 73.116 > 0.309] + + 24703it [02:13, 215.95it/s, bound: 537 | nc: 5 | ncall: 128979 | eff(%): 19.153 | loglstar: -inf < -182.003 < inf | logz: -267.889 +/- 0.478 | dlogz: 71.683 > 0.309] + + 24725it [02:13, 211.77it/s, bound: 538 | nc: 5 | ncall: 129089 | eff(%): 19.153 | loglstar: -inf < -180.755 < inf | logz: -266.436 +/- 0.478 | dlogz: 70.142 > 0.309] + + 24749it [02:13, 218.78it/s, bound: 538 | nc: 5 | ncall: 129209 | eff(%): 19.154 | loglstar: -inf < -179.465 < inf | logz: -264.841 +/- 0.479 | dlogz: 68.447 > 0.309] + + 24771it [02:13, 216.82it/s, bound: 539 | nc: 5 | ncall: 129319 | eff(%): 19.155 | loglstar: -inf < -178.211 < inf | logz: -263.948 +/- 0.478 | dlogz: 67.494 > 0.309] + + 24797it [02:13, 227.54it/s, bound: 539 | nc: 5 | ncall: 129449 | eff(%): 19.156 | loglstar: -inf < -176.529 < inf | logz: -262.384 +/- 0.479 | dlogz: 65.840 > 0.309] + + 24820it [02:13, 225.22it/s, bound: 540 | nc: 5 | ncall: 129564 | eff(%): 19.157 | loglstar: -inf < -174.926 < inf | logz: -260.800 +/- 0.480 | dlogz: 64.181 > 0.309] + + 24846it [02:14, 233.05it/s, bound: 540 | nc: 5 | ncall: 129694 | eff(%): 19.157 | loglstar: -inf < -173.925 < inf | logz: -259.488 +/- 0.479 | dlogz: 62.763 > 0.309] + + 24870it [02:14, 211.84it/s, bound: 541 | nc: 5 | ncall: 129814 | eff(%): 19.158 | loglstar: -inf < -173.490 < inf | logz: -258.794 +/- 0.478 | dlogz: 61.974 > 0.309] + + 24892it [02:14, 199.09it/s, bound: 541 | nc: 5 | ncall: 129924 | eff(%): 19.159 | loglstar: -inf < -173.426 < inf | logz: -258.329 +/- 0.477 | dlogz: 61.424 > 0.309] + + 24913it [02:14, 183.82it/s, bound: 542 | nc: 5 | ncall: 130029 | eff(%): 19.160 | loglstar: -inf < -172.562 < inf | logz: -257.904 +/- 0.477 | dlogz: 60.937 > 0.309] + + 24932it [02:14, 184.56it/s, bound: 542 | nc: 5 | ncall: 130124 | eff(%): 19.160 | loglstar: -inf < -171.523 < inf | logz: -257.259 +/- 0.478 | dlogz: 60.755 > 0.309] + + 24951it [02:14, 184.31it/s, bound: 543 | nc: 5 | ncall: 130219 | eff(%): 19.161 | loglstar: -inf < -170.380 < inf | logz: -256.562 +/- 0.478 | dlogz: 60.010 > 0.309] + + 24975it [02:14, 198.72it/s, bound: 543 | nc: 5 | ncall: 130339 | eff(%): 19.162 | loglstar: -inf < -169.700 < inf | logz: -255.649 +/- 0.479 | dlogz: 59.004 > 0.309] + + 24998it [02:14, 206.15it/s, bound: 544 | nc: 5 | ncall: 130454 | eff(%): 19.162 | loglstar: -inf < -168.959 < inf | logz: -254.886 +/- 0.479 | dlogz: 93.581 > 0.309] + + 25023it [02:14, 217.50it/s, bound: 544 | nc: 5 | ncall: 130579 | eff(%): 19.163 | loglstar: -inf < -167.053 < inf | logz: -253.772 +/- 0.480 | dlogz: 92.419 > 0.309] + + 25045it [02:15, 216.87it/s, bound: 545 | nc: 5 | ncall: 130689 | eff(%): 19.164 | loglstar: -inf < -166.412 < inf | logz: -252.727 +/- 0.481 | dlogz: 91.272 > 0.309] + + 25072it [02:15, 232.17it/s, bound: 545 | nc: 5 | ncall: 130824 | eff(%): 19.165 | loglstar: -inf < -165.702 < inf | logz: -251.834 +/- 0.480 | dlogz: 90.279 > 0.309] + + 25096it [02:15, 203.31it/s, bound: 546 | nc: 5 | ncall: 130944 | eff(%): 19.165 | loglstar: -inf < -164.739 < inf | logz: -251.019 +/- 0.480 | dlogz: 89.388 > 0.309] + + 25118it [02:15, 187.43it/s, bound: 546 | nc: 5 | ncall: 131054 | eff(%): 19.166 | loglstar: -inf < -162.855 < inf | logz: -249.895 +/- 0.482 | dlogz: 88.224 > 0.309] + + 25138it [02:15, 174.35it/s, bound: 547 | nc: 5 | ncall: 131154 | eff(%): 19.167 | loglstar: -inf < -161.789 < inf | logz: -248.734 +/- 0.482 | dlogz: 86.986 > 0.309] + + 25160it [02:15, 184.01it/s, bound: 547 | nc: 5 | ncall: 131264 | eff(%): 19.167 | loglstar: -inf < -161.184 < inf | logz: -247.771 +/- 0.482 | dlogz: 85.930 > 0.309] + + 25179it [02:15, 184.89it/s, bound: 548 | nc: 5 | ncall: 131359 | eff(%): 19.168 | loglstar: -inf < -159.969 < inf | logz: -246.868 +/- 0.482 | dlogz: 84.974 > 0.309] + + 25203it [02:15, 196.75it/s, bound: 548 | nc: 5 | ncall: 131479 | eff(%): 19.169 | loglstar: -inf < -158.575 < inf | logz: -245.525 +/- 0.483 | dlogz: 83.550 > 0.309] + + 25226it [02:16, 204.82it/s, bound: 549 | nc: 5 | ncall: 131594 | eff(%): 19.170 | loglstar: -inf < -157.281 < inf | logz: -244.382 +/- 0.483 | dlogz: 82.330 > 0.309] + + 25251it [02:16, 217.10it/s, bound: 549 | nc: 5 | ncall: 131719 | eff(%): 19.170 | loglstar: -inf < -156.345 < inf | logz: -243.169 +/- 0.483 | dlogz: 81.023 > 0.309] + + 25275it [02:16, 221.48it/s, bound: 550 | nc: 5 | ncall: 131839 | eff(%): 19.171 | loglstar: -inf < -155.304 < inf | logz: -242.265 +/- 0.483 | dlogz: 80.038 > 0.309] + + 25304it [02:16, 239.00it/s, bound: 550 | nc: 5 | ncall: 131984 | eff(%): 19.172 | loglstar: -inf < -154.018 < inf | logz: -241.093 +/- 0.483 | dlogz: 78.772 > 0.309] + + 25329it [02:16, 229.74it/s, bound: 551 | nc: 5 | ncall: 132109 | eff(%): 19.173 | loglstar: -inf < -151.953 < inf | logz: -239.886 +/- 0.484 | dlogz: 77.522 > 0.309] + + 25353it [02:16, 221.27it/s, bound: 552 | nc: 5 | ncall: 132229 | eff(%): 19.174 | loglstar: -inf < -150.815 < inf | logz: -238.405 +/- 0.485 | dlogz: 75.934 > 0.309] + + 25377it [02:16, 224.86it/s, bound: 552 | nc: 5 | ncall: 132349 | eff(%): 19.174 | loglstar: -inf < -150.246 < inf | logz: -237.453 +/- 0.484 | dlogz: 74.883 > 0.309] + + 25400it [02:16, 224.96it/s, bound: 553 | nc: 5 | ncall: 132464 | eff(%): 19.175 | loglstar: -inf < -149.778 < inf | logz: -236.812 +/- 0.484 | dlogz: 74.159 > 0.309] + + 25428it [02:16, 239.25it/s, bound: 553 | nc: 5 | ncall: 132604 | eff(%): 19.176 | loglstar: -inf < -148.278 < inf | logz: -235.847 +/- 0.484 | dlogz: 73.115 > 0.309] + + 25453it [02:16, 239.32it/s, bound: 554 | nc: 5 | ncall: 132729 | eff(%): 19.177 | loglstar: -inf < -147.463 < inf | logz: -234.889 +/- 0.485 | dlogz: 72.065 > 0.309] + + 25479it [02:17, 243.49it/s, bound: 554 | nc: 5 | ncall: 132859 | eff(%): 19.177 | loglstar: -inf < -146.414 < inf | logz: -234.051 +/- 0.485 | dlogz: 71.145 > 0.309] + + 25504it [02:17, 242.53it/s, bound: 555 | nc: 5 | ncall: 132984 | eff(%): 19.178 | loglstar: -inf < -145.119 < inf | logz: -233.083 +/- 0.485 | dlogz: 70.103 > 0.309] + + 25530it [02:17, 245.82it/s, bound: 555 | nc: 5 | ncall: 133114 | eff(%): 19.179 | loglstar: -inf < -144.176 < inf | logz: -231.809 +/- 0.486 | dlogz: 68.728 > 0.309] + + 25555it [02:17, 235.24it/s, bound: 556 | nc: 5 | ncall: 133239 | eff(%): 19.180 | loglstar: -inf < -143.345 < inf | logz: -231.070 +/- 0.486 | dlogz: 67.902 > 0.309] + + 25579it [02:17, 233.48it/s, bound: 557 | nc: 5 | ncall: 133359 | eff(%): 19.181 | loglstar: -inf < -141.829 < inf | logz: -230.084 +/- 0.486 | dlogz: 66.854 > 0.309] + + 25603it [02:17, 233.14it/s, bound: 557 | nc: 5 | ncall: 133479 | eff(%): 19.181 | loglstar: -inf < -140.216 < inf | logz: -228.830 +/- 0.487 | dlogz: 65.537 > 0.309] + + 25627it [02:17, 235.00it/s, bound: 558 | nc: 5 | ncall: 133599 | eff(%): 19.182 | loglstar: -inf < -139.265 < inf | logz: -227.611 +/- 0.488 | dlogz: 64.218 > 0.309] + + 25653it [02:17, 239.22it/s, bound: 558 | nc: 5 | ncall: 133729 | eff(%): 19.183 | loglstar: -inf < -138.207 < inf | logz: -226.429 +/- 0.488 | dlogz: 62.942 > 0.309] + + 25677it [02:17, 232.62it/s, bound: 559 | nc: 5 | ncall: 133849 | eff(%): 19.184 | loglstar: -inf < -137.068 < inf | logz: -225.590 +/- 0.487 | dlogz: 65.532 > 0.309] + + 25703it [02:18, 238.92it/s, bound: 559 | nc: 5 | ncall: 133979 | eff(%): 19.184 | loglstar: -inf < -135.793 < inf | logz: -224.314 +/- 0.488 | dlogz: 64.168 > 0.309] + + 25727it [02:18, 238.90it/s, bound: 560 | nc: 5 | ncall: 134099 | eff(%): 19.185 | loglstar: -inf < -135.221 < inf | logz: -223.414 +/- 0.488 | dlogz: 63.173 > 0.309] + + 25755it [02:18, 249.15it/s, bound: 560 | nc: 5 | ncall: 134239 | eff(%): 19.186 | loglstar: -inf < -134.150 < inf | logz: -222.649 +/- 0.488 | dlogz: 62.320 > 0.309] + + 25780it [02:18, 243.60it/s, bound: 561 | nc: 5 | ncall: 134364 | eff(%): 19.187 | loglstar: -inf < -134.049 < inf | logz: -221.996 +/- 0.487 | dlogz: 61.566 > 0.309] + + 25805it [02:18, 241.13it/s, bound: 562 | nc: 5 | ncall: 134489 | eff(%): 19.187 | loglstar: -inf < -132.880 < inf | logz: -221.429 +/- 0.487 | dlogz: 60.925 > 0.309] + + 25833it [02:18, 251.48it/s, bound: 562 | nc: 5 | ncall: 134629 | eff(%): 19.188 | loglstar: -inf < -132.280 < inf | logz: -220.635 +/- 0.488 | dlogz: 60.031 > 0.309] + + 25859it [02:18, 248.95it/s, bound: 563 | nc: 5 | ncall: 134759 | eff(%): 19.189 | loglstar: -inf < -131.554 < inf | logz: -220.043 +/- 0.488 | dlogz: 59.353 > 0.309] + + 25891it [02:18, 267.36it/s, bound: 563 | nc: 5 | ncall: 134919 | eff(%): 19.190 | loglstar: -inf < -130.260 < inf | logz: -219.060 +/- 0.489 | dlogz: 58.268 > 0.309] + + 25918it [02:18, 263.33it/s, bound: 564 | nc: 5 | ncall: 135054 | eff(%): 19.191 | loglstar: -inf < -129.500 < inf | logz: -218.331 +/- 0.489 | dlogz: 57.447 > 0.309] + + 25945it [02:18, 260.19it/s, bound: 565 | nc: 5 | ncall: 135189 | eff(%): 19.192 | loglstar: -inf < -129.104 < inf | logz: -217.621 +/- 0.489 | dlogz: 56.636 > 0.309] + + 25974it [02:19, 268.13it/s, bound: 565 | nc: 5 | ncall: 135334 | eff(%): 19.193 | loglstar: -inf < -128.277 < inf | logz: -217.066 +/- 0.489 | dlogz: 55.985 > 0.309] + + 26001it [02:19, 260.74it/s, bound: 566 | nc: 5 | ncall: 135469 | eff(%): 19.193 | loglstar: -inf < -127.298 < inf | logz: -216.380 +/- 0.489 | dlogz: 55.214 > 0.309] + + 26028it [02:19, 241.66it/s, bound: 567 | nc: 5 | ncall: 135604 | eff(%): 19.194 | loglstar: -inf < -126.488 < inf | logz: -215.672 +/- 0.490 | dlogz: 54.415 > 0.309] + + 26055it [02:19, 247.18it/s, bound: 567 | nc: 5 | ncall: 135739 | eff(%): 19.195 | loglstar: -inf < -125.417 < inf | logz: -214.730 +/- 0.491 | dlogz: 53.385 > 0.309] + + 26080it [02:19, 243.62it/s, bound: 568 | nc: 5 | ncall: 135864 | eff(%): 19.196 | loglstar: -inf < -124.700 < inf | logz: -214.043 +/- 0.491 | dlogz: 52.612 > 0.309] + + 26108it [02:19, 250.36it/s, bound: 568 | nc: 5 | ncall: 136004 | eff(%): 19.196 | loglstar: -inf < -124.078 < inf | logz: -213.354 +/- 0.491 | dlogz: 51.825 > 0.309] + + 26134it [02:19, 244.28it/s, bound: 569 | nc: 5 | ncall: 136134 | eff(%): 19.197 | loglstar: -inf < -124.078 < inf | logz: -212.883 +/- 0.490 | dlogz: 51.252 > 0.309] + + 26162it [02:19, 248.83it/s, bound: 570 | nc: 5 | ncall: 136274 | eff(%): 19.198 | loglstar: -inf < -123.891 < inf | logz: -212.547 +/- 0.490 | dlogz: 59.560 > 0.309] + + 26188it [02:19, 251.76it/s, bound: 570 | nc: 5 | ncall: 136404 | eff(%): 19.199 | loglstar: -inf < -123.317 < inf | logz: -212.248 +/- 0.490 | dlogz: 59.176 > 0.309] + + 26214it [02:20, 253.72it/s, bound: 571 | nc: 5 | ncall: 136534 | eff(%): 19.200 | loglstar: -inf < -122.340 < inf | logz: -211.870 +/- 0.490 | dlogz: 58.720 > 0.309] + + 26245it [02:20, 269.11it/s, bound: 571 | nc: 5 | ncall: 136689 | eff(%): 19.201 | loglstar: -inf < -121.321 < inf | logz: -211.257 +/- 0.491 | dlogz: 58.010 > 0.309] + + 26272it [02:20, 265.69it/s, bound: 572 | nc: 5 | ncall: 136824 | eff(%): 19.201 | loglstar: -inf < -120.432 < inf | logz: -210.513 +/- 0.492 | dlogz: 57.177 > 0.309] + + 26299it [02:20, 250.85it/s, bound: 573 | nc: 5 | ncall: 136959 | eff(%): 19.202 | loglstar: -inf < -119.785 < inf | logz: -209.776 +/- 0.493 | dlogz: 56.346 > 0.309] + + 26325it [02:20, 229.49it/s, bound: 573 | nc: 5 | ncall: 137089 | eff(%): 19.203 | loglstar: -inf < -118.267 < inf | logz: -208.986 +/- 0.493 | dlogz: 55.484 > 0.309] + + 26349it [02:20, 225.73it/s, bound: 574 | nc: 5 | ncall: 137209 | eff(%): 19.204 | loglstar: -inf < -117.687 < inf | logz: -208.060 +/- 0.494 | dlogz: 54.470 > 0.309] + + 26372it [02:20, 224.84it/s, bound: 574 | nc: 5 | ncall: 137324 | eff(%): 19.204 | loglstar: -inf < -116.543 < inf | logz: -207.216 +/- 0.495 | dlogz: 53.557 > 0.309] + + 26395it [02:20, 210.10it/s, bound: 575 | nc: 5 | ncall: 137439 | eff(%): 19.205 | loglstar: -inf < -116.483 < inf | logz: -206.600 +/- 0.494 | dlogz: 52.844 > 0.309] + + 26417it [02:20, 207.41it/s, bound: 575 | nc: 5 | ncall: 137549 | eff(%): 19.206 | loglstar: -inf < -115.844 < inf | logz: -206.124 +/- 0.494 | dlogz: 52.296 > 0.309] + + 26438it [02:21, 200.73it/s, bound: 576 | nc: 5 | ncall: 137654 | eff(%): 19.206 | loglstar: -inf < -114.974 < inf | logz: -205.579 +/- 0.494 | dlogz: 51.687 > 0.309] + + 26459it [02:21, 198.74it/s, bound: 576 | nc: 5 | ncall: 137759 | eff(%): 19.207 | loglstar: -inf < -114.189 < inf | logz: -205.014 +/- 0.495 | dlogz: 51.054 > 0.309] + + 26479it [02:21, 185.21it/s, bound: 577 | nc: 5 | ncall: 137859 | eff(%): 19.207 | loglstar: -inf < -113.632 < inf | logz: -204.414 +/- 0.495 | dlogz: 50.386 > 0.309] + + 26501it [02:21, 194.03it/s, bound: 577 | nc: 5 | ncall: 137969 | eff(%): 19.208 | loglstar: -inf < -113.632 < inf | logz: -203.922 +/- 0.495 | dlogz: 49.808 > 0.309] + + 26521it [02:21, 194.78it/s, bound: 578 | nc: 5 | ncall: 138069 | eff(%): 19.209 | loglstar: -inf < -113.274 < inf | logz: -203.618 +/- 0.495 | dlogz: 49.430 > 0.309] + + 26543it [02:21, 199.12it/s, bound: 578 | nc: 5 | ncall: 138179 | eff(%): 19.209 | loglstar: -inf < -112.454 < inf | logz: -203.224 +/- 0.495 | dlogz: 48.969 > 0.309] + + 26564it [02:21, 196.43it/s, bound: 578 | nc: 5 | ncall: 138284 | eff(%): 19.210 | loglstar: -inf < -111.808 < inf | logz: -202.724 +/- 0.495 | dlogz: 48.403 > 0.309] + + 26584it [02:21, 189.36it/s, bound: 579 | nc: 5 | ncall: 138384 | eff(%): 19.210 | loglstar: -inf < -110.756 < inf | logz: -202.095 +/- 0.496 | dlogz: 47.718 > 0.309] + + 26604it [02:21, 190.47it/s, bound: 579 | nc: 5 | ncall: 138484 | eff(%): 19.211 | loglstar: -inf < -110.450 < inf | logz: -201.471 +/- 0.496 | dlogz: 47.016 > 0.309] + + 26624it [02:22, 191.23it/s, bound: 580 | nc: 5 | ncall: 138584 | eff(%): 19.211 | loglstar: -inf < -109.486 < inf | logz: -201.017 +/- 0.496 | dlogz: 46.507 > 0.309] + + 26649it [02:22, 207.15it/s, bound: 580 | nc: 5 | ncall: 138709 | eff(%): 19.212 | loglstar: -inf < -108.196 < inf | logz: -200.024 +/- 0.498 | dlogz: 45.441 > 0.309] + + 26670it [02:22, 204.61it/s, bound: 581 | nc: 5 | ncall: 138814 | eff(%): 19.213 | loglstar: -inf < -107.601 < inf | logz: -199.203 +/- 0.498 | dlogz: 54.145 > 0.309] + + 26695it [02:22, 217.10it/s, bound: 581 | nc: 5 | ncall: 138939 | eff(%): 19.213 | loglstar: -inf < -107.266 < inf | logz: -198.476 +/- 0.498 | dlogz: 53.321 > 0.309] + + 26717it [02:22, 217.95it/s, bound: 582 | nc: 5 | ncall: 139049 | eff(%): 19.214 | loglstar: -inf < -106.313 < inf | logz: -197.889 +/- 0.498 | dlogz: 52.668 > 0.309] + + 26739it [02:22, 217.61it/s, bound: 582 | nc: 5 | ncall: 139159 | eff(%): 19.215 | loglstar: -inf < -106.089 < inf | logz: -197.396 +/- 0.498 | dlogz: 52.093 > 0.309] + + 26761it [02:22, 216.95it/s, bound: 583 | nc: 5 | ncall: 139269 | eff(%): 19.215 | loglstar: -inf < -105.792 < inf | logz: -197.002 +/- 0.497 | dlogz: 51.621 > 0.309] + + 26784it [02:22, 220.61it/s, bound: 583 | nc: 5 | ncall: 139384 | eff(%): 19.216 | loglstar: -inf < -104.914 < inf | logz: -196.481 +/- 0.498 | dlogz: 51.028 > 0.309] + + 26807it [02:22, 214.93it/s, bound: 584 | nc: 5 | ncall: 139499 | eff(%): 19.217 | loglstar: -inf < -104.405 < inf | logz: -195.998 +/- 0.498 | dlogz: 50.468 > 0.309] + + 26831it [02:22, 220.09it/s, bound: 584 | nc: 5 | ncall: 139619 | eff(%): 19.217 | loglstar: -inf < -104.008 < inf | logz: -195.553 +/- 0.498 | dlogz: 49.939 > 0.309] + + 26854it [02:23, 219.65it/s, bound: 585 | nc: 5 | ncall: 139734 | eff(%): 19.218 | loglstar: -inf < -103.582 < inf | logz: -195.127 +/- 0.498 | dlogz: 49.434 > 0.309] + + 26880it [02:23, 223.55it/s, bound: 586 | nc: 5 | ncall: 139864 | eff(%): 19.219 | loglstar: -inf < -102.734 < inf | logz: -194.588 +/- 0.498 | dlogz: 48.812 > 0.309] + + 26903it [02:23, 219.02it/s, bound: 586 | nc: 5 | ncall: 139979 | eff(%): 19.219 | loglstar: -inf < -102.304 < inf | logz: -194.131 +/- 0.499 | dlogz: 48.277 > 0.309] + + 26926it [02:23, 220.47it/s, bound: 587 | nc: 5 | ncall: 140094 | eff(%): 19.220 | loglstar: -inf < -101.029 < inf | logz: -193.546 +/- 0.499 | dlogz: 65.591 > 0.309] + + 26951it [02:23, 226.18it/s, bound: 587 | nc: 5 | ncall: 140219 | eff(%): 19.221 | loglstar: -inf < -100.301 < inf | logz: -192.713 +/- 0.500 | dlogz: 64.667 > 0.309] + + 26974it [02:23, 226.16it/s, bound: 588 | nc: 5 | ncall: 140334 | eff(%): 19.221 | loglstar: -inf < -99.402 < inf | logz: -191.910 +/- 0.501 | dlogz: 63.788 > 0.309] + + 26998it [02:23, 229.45it/s, bound: 588 | nc: 5 | ncall: 140454 | eff(%): 19.222 | loglstar: -inf < -98.130 < inf | logz: -190.872 +/- 0.502 | dlogz: 62.677 > 0.309] + + 27021it [02:23, 224.48it/s, bound: 589 | nc: 5 | ncall: 140569 | eff(%): 19.223 | loglstar: -inf < -97.386 < inf | logz: -190.058 +/- 0.502 | dlogz: 61.780 > 0.309] + + 27045it [02:23, 227.45it/s, bound: 589 | nc: 5 | ncall: 140689 | eff(%): 19.223 | loglstar: -inf < -96.689 < inf | logz: -189.238 +/- 0.502 | dlogz: 60.874 > 0.309] + + 27068it [02:24, 222.14it/s, bound: 590 | nc: 5 | ncall: 140804 | eff(%): 19.224 | loglstar: -inf < -96.689 < inf | logz: -188.764 +/- 0.502 | dlogz: 60.306 > 0.309] + + 27094it [02:24, 230.71it/s, bound: 590 | nc: 5 | ncall: 140934 | eff(%): 19.225 | loglstar: -inf < -96.420 < inf | logz: -188.415 +/- 0.501 | dlogz: 59.863 > 0.309] + + 27118it [02:24, 232.38it/s, bound: 591 | nc: 5 | ncall: 141054 | eff(%): 19.225 | loglstar: -inf < -95.542 < inf | logz: -188.039 +/- 0.501 | dlogz: 59.414 > 0.309] + + 27142it [02:24, 233.17it/s, bound: 591 | nc: 5 | ncall: 141174 | eff(%): 19.226 | loglstar: -inf < -94.695 < inf | logz: -187.543 +/- 0.501 | dlogz: 58.844 > 0.309] + + 27166it [02:24, 220.16it/s, bound: 592 | nc: 5 | ncall: 141294 | eff(%): 19.227 | loglstar: -inf < -93.268 < inf | logz: -186.763 +/- 0.503 | dlogz: 58.004 > 0.309] + + 27189it [02:24, 220.47it/s, bound: 592 | nc: 5 | ncall: 141409 | eff(%): 19.227 | loglstar: -inf < -92.496 < inf | logz: -185.835 +/- 0.504 | dlogz: 56.988 > 0.309] + + 27212it [02:24, 222.67it/s, bound: 593 | nc: 5 | ncall: 141524 | eff(%): 19.228 | loglstar: -inf < -91.896 < inf | logz: -185.090 +/- 0.504 | dlogz: 56.160 > 0.309] + + 27238it [02:24, 232.03it/s, bound: 593 | nc: 5 | ncall: 141654 | eff(%): 19.229 | loglstar: -inf < -90.427 < inf | logz: -184.174 +/- 0.504 | dlogz: 55.175 > 0.309] + + 27263it [02:24, 236.70it/s, bound: 594 | nc: 5 | ncall: 141779 | eff(%): 19.229 | loglstar: -inf < -89.335 < inf | logz: -183.098 +/- 0.505 | dlogz: 54.012 > 0.309] + + 27287it [02:24, 233.81it/s, bound: 595 | nc: 5 | ncall: 141899 | eff(%): 19.230 | loglstar: -inf < -88.268 < inf | logz: -182.048 +/- 0.505 | dlogz: 52.880 > 0.309] + + 27315it [02:25, 245.90it/s, bound: 595 | nc: 5 | ncall: 142039 | eff(%): 19.231 | loglstar: -inf < -88.057 < inf | logz: -181.290 +/- 0.505 | dlogz: 52.008 > 0.309] + + 27340it [02:25, 237.82it/s, bound: 596 | nc: 5 | ncall: 142164 | eff(%): 19.231 | loglstar: -inf < -87.452 < inf | logz: -180.804 +/- 0.504 | dlogz: 51.438 > 0.309] + + 27364it [02:25, 237.21it/s, bound: 596 | nc: 5 | ncall: 142284 | eff(%): 19.232 | loglstar: -inf < -86.573 < inf | logz: -180.229 +/- 0.504 | dlogz: 50.787 > 0.309] + + 27388it [02:25, 227.93it/s, bound: 597 | nc: 5 | ncall: 142404 | eff(%): 19.233 | loglstar: -inf < -85.732 < inf | logz: -179.580 +/- 0.505 | dlogz: 50.064 > 0.309] + + 27411it [02:25, 224.64it/s, bound: 597 | nc: 5 | ncall: 142519 | eff(%): 19.233 | loglstar: -inf < -85.501 < inf | logz: -179.044 +/- 0.505 | dlogz: 49.440 > 0.309] + + 27434it [02:25, 204.15it/s, bound: 598 | nc: 5 | ncall: 142634 | eff(%): 19.234 | loglstar: -inf < -84.655 < inf | logz: -178.536 +/- 0.505 | dlogz: 48.861 > 0.309] + + 27458it [02:25, 212.45it/s, bound: 598 | nc: 5 | ncall: 142754 | eff(%): 19.234 | loglstar: -inf < -83.556 < inf | logz: -177.859 +/- 0.506 | dlogz: 48.113 > 0.309] + + 27480it [02:25, 212.88it/s, bound: 599 | nc: 5 | ncall: 142864 | eff(%): 19.235 | loglstar: -inf < -82.785 < inf | logz: -177.023 +/- 0.507 | dlogz: 47.201 > 0.309] + + 27503it [02:25, 216.23it/s, bound: 599 | nc: 5 | ncall: 142979 | eff(%): 19.236 | loglstar: -inf < -82.007 < inf | logz: -176.301 +/- 0.507 | dlogz: 46.401 > 0.309] + + 27529it [02:26, 226.97it/s, bound: 600 | nc: 5 | ncall: 143109 | eff(%): 19.236 | loglstar: -inf < -80.660 < inf | logz: -175.409 +/- 0.507 | dlogz: 45.436 > 0.309] + + 27555it [02:26, 231.72it/s, bound: 601 | nc: 5 | ncall: 143239 | eff(%): 19.237 | loglstar: -inf < -79.358 < inf | logz: -174.166 +/- 0.509 | dlogz: 44.107 > 0.309] + + 27581it [02:26, 239.14it/s, bound: 601 | nc: 5 | ncall: 143369 | eff(%): 19.238 | loglstar: -inf < -78.172 < inf | logz: -173.037 +/- 0.509 | dlogz: 42.889 > 0.309] + + 27606it [02:26, 237.17it/s, bound: 602 | nc: 5 | ncall: 143494 | eff(%): 19.238 | loglstar: -inf < -75.809 < inf | logz: -171.515 +/- 0.510 | dlogz: 41.327 > 0.309] + + 27634it [02:26, 247.83it/s, bound: 602 | nc: 5 | ncall: 143634 | eff(%): 19.239 | loglstar: -inf < -74.324 < inf | logz: -169.347 +/- 0.511 | dlogz: 39.023 > 0.309] + + 27660it [02:26, 249.81it/s, bound: 603 | nc: 5 | ncall: 143764 | eff(%): 19.240 | loglstar: -inf < -74.190 < inf | logz: -168.594 +/- 0.509 | dlogz: 38.157 > 0.309] + + 27687it [02:26, 255.48it/s, bound: 603 | nc: 5 | ncall: 143899 | eff(%): 19.241 | loglstar: -inf < -74.190 < inf | logz: -168.150 +/- 0.508 | dlogz: 37.610 > 0.309] + + 27713it [02:26, 247.64it/s, bound: 604 | nc: 5 | ncall: 144029 | eff(%): 19.241 | loglstar: -inf < -73.554 < inf | logz: -167.809 +/- 0.508 | dlogz: 37.179 > 0.309] + + 27738it [02:26, 235.89it/s, bound: 605 | nc: 5 | ncall: 144154 | eff(%): 19.242 | loglstar: -inf < -73.003 < inf | logz: -167.378 +/- 0.508 | dlogz: 36.664 > 0.309] + + 27762it [02:27, 234.36it/s, bound: 605 | nc: 5 | ncall: 144274 | eff(%): 19.243 | loglstar: -inf < -72.395 < inf | logz: -166.997 +/- 0.508 | dlogz: 36.205 > 0.309] + + 27786it [02:27, 229.37it/s, bound: 606 | nc: 5 | ncall: 144394 | eff(%): 19.243 | loglstar: -inf < -71.484 < inf | logz: -166.401 +/- 0.508 | dlogz: 35.533 > 0.309] + + 27814it [02:27, 241.87it/s, bound: 606 | nc: 5 | ncall: 144534 | eff(%): 19.244 | loglstar: -inf < -70.550 < inf | logz: -165.739 +/- 0.509 | dlogz: 34.780 > 0.309] + + 27841it [02:27, 247.70it/s, bound: 607 | nc: 5 | ncall: 144669 | eff(%): 19.245 | loglstar: -inf < -69.342 < inf | logz: -164.893 +/- 0.510 | dlogz: 36.473 > 0.309] + + 27867it [02:27, 250.12it/s, bound: 607 | nc: 5 | ncall: 144799 | eff(%): 19.245 | loglstar: -inf < -68.747 < inf | logz: -164.098 +/- 0.510 | dlogz: 39.382 > 0.309] + + 27893it [02:27, 242.99it/s, bound: 608 | nc: 5 | ncall: 144929 | eff(%): 19.246 | loglstar: -inf < -67.227 < inf | logz: -163.170 +/- 0.511 | dlogz: 38.384 > 0.309] + + 27918it [02:27, 240.78it/s, bound: 609 | nc: 5 | ncall: 145054 | eff(%): 19.247 | loglstar: -inf < -66.519 < inf | logz: -162.203 +/- 0.512 | dlogz: 37.322 > 0.309] + + 27947it [02:27, 253.37it/s, bound: 609 | nc: 5 | ncall: 145199 | eff(%): 19.247 | loglstar: -inf < -65.436 < inf | logz: -161.331 +/- 0.512 | dlogz: 36.358 > 0.309] + + 27973it [02:27, 252.71it/s, bound: 610 | nc: 5 | ncall: 145329 | eff(%): 19.248 | loglstar: -inf < -64.120 < inf | logz: -160.151 +/- 0.513 | dlogz: 35.092 > 0.309] + + 28001it [02:27, 260.58it/s, bound: 610 | nc: 5 | ncall: 145469 | eff(%): 19.249 | loglstar: -inf < -63.522 < inf | logz: -159.301 +/- 0.512 | dlogz: 34.138 > 0.309] + + 28028it [02:28, 259.19it/s, bound: 611 | nc: 5 | ncall: 145604 | eff(%): 19.249 | loglstar: -inf < -62.622 < inf | logz: -158.572 +/- 0.512 | dlogz: 33.320 > 0.309] + + 28054it [02:28, 257.59it/s, bound: 612 | nc: 5 | ncall: 145734 | eff(%): 19.250 | loglstar: -inf < -61.692 < inf | logz: -157.816 +/- 0.512 | dlogz: 32.481 > 0.309] + + 28082it [02:28, 262.20it/s, bound: 612 | nc: 5 | ncall: 145874 | eff(%): 19.251 | loglstar: -inf < -60.716 < inf | logz: -156.980 +/- 0.513 | dlogz: 33.911 > 0.309] + + 28109it [02:28, 259.27it/s, bound: 613 | nc: 5 | ncall: 146009 | eff(%): 19.252 | loglstar: -inf < -60.048 < inf | logz: -156.233 +/- 0.513 | dlogz: 33.070 > 0.309] + + 28136it [02:28, 261.52it/s, bound: 613 | nc: 5 | ncall: 146144 | eff(%): 19.252 | loglstar: -inf < -59.169 < inf | logz: -155.465 +/- 0.513 | dlogz: 32.212 > 0.309] + + 28163it [02:28, 257.51it/s, bound: 614 | nc: 5 | ncall: 146279 | eff(%): 19.253 | loglstar: -inf < -57.982 < inf | logz: -154.602 +/- 0.514 | dlogz: 31.267 > 0.309] + + 28189it [02:28, 251.70it/s, bound: 615 | nc: 5 | ncall: 146409 | eff(%): 19.254 | loglstar: -inf < -57.513 < inf | logz: -153.898 +/- 0.514 | dlogz: 30.465 > 0.309] + + 28217it [02:28, 259.72it/s, bound: 615 | nc: 5 | ncall: 146549 | eff(%): 19.254 | loglstar: -inf < -56.323 < inf | logz: -153.203 +/- 0.514 | dlogz: 29.684 > 0.309] + + 28244it [02:28, 253.83it/s, bound: 616 | nc: 5 | ncall: 146684 | eff(%): 19.255 | loglstar: -inf < -55.618 < inf | logz: -152.311 +/- 0.515 | dlogz: 28.699 > 0.309] + + 28273it [02:29, 262.71it/s, bound: 616 | nc: 5 | ncall: 146829 | eff(%): 19.256 | loglstar: -inf < -55.092 < inf | logz: -151.614 +/- 0.514 | dlogz: 27.897 > 0.309] + + 28300it [02:29, 245.50it/s, bound: 617 | nc: 5 | ncall: 146964 | eff(%): 19.256 | loglstar: -inf < -54.282 < inf | logz: -150.962 +/- 0.515 | dlogz: 27.157 > 0.309] + + 28325it [02:29, 235.02it/s, bound: 618 | nc: 5 | ncall: 147089 | eff(%): 19.257 | loglstar: -inf < -53.786 < inf | logz: -150.436 +/- 0.515 | dlogz: 26.545 > 0.309] + + 28349it [02:29, 234.47it/s, bound: 618 | nc: 5 | ncall: 147209 | eff(%): 19.258 | loglstar: -inf < -52.783 < inf | logz: -149.806 +/- 0.515 | dlogz: 25.843 > 0.309] + + 28373it [02:29, 212.18it/s, bound: 619 | nc: 5 | ncall: 147329 | eff(%): 19.258 | loglstar: -inf < -52.302 < inf | logz: -149.253 +/- 0.515 | dlogz: 25.203 > 0.309] + + 28395it [02:29, 208.36it/s, bound: 619 | nc: 5 | ncall: 147439 | eff(%): 19.259 | loglstar: -inf < -51.787 < inf | logz: -148.751 +/- 0.515 | dlogz: 24.627 > 0.309] + + 28417it [02:29, 202.56it/s, bound: 620 | nc: 5 | ncall: 147549 | eff(%): 19.259 | loglstar: -inf < -51.403 < inf | logz: -148.301 +/- 0.515 | dlogz: 24.102 > 0.309] + + 28440it [02:29, 208.42it/s, bound: 620 | nc: 5 | ncall: 147664 | eff(%): 19.260 | loglstar: -inf < -50.757 < inf | logz: -147.802 +/- 0.516 | dlogz: 23.528 > 0.309] + + 28462it [02:29, 206.50it/s, bound: 621 | nc: 5 | ncall: 147774 | eff(%): 19.260 | loglstar: -inf < -50.291 < inf | logz: -147.385 +/- 0.516 | dlogz: 23.036 > 0.309] + + 28484it [02:30, 208.06it/s, bound: 621 | nc: 5 | ncall: 147884 | eff(%): 19.261 | loglstar: -inf < -49.888 < inf | logz: -146.956 +/- 0.516 | dlogz: 22.532 > 0.309] + + 28505it [02:30, 203.98it/s, bound: 622 | nc: 5 | ncall: 147989 | eff(%): 19.262 | loglstar: -inf < -49.454 < inf | logz: -146.547 +/- 0.516 | dlogz: 22.052 > 0.309] + + 28526it [02:30, 205.33it/s, bound: 622 | nc: 5 | ncall: 148094 | eff(%): 19.262 | loglstar: -inf < -49.002 < inf | logz: -146.168 +/- 0.516 | dlogz: 21.602 > 0.309] + + 28547it [02:30, 200.74it/s, bound: 623 | nc: 5 | ncall: 148199 | eff(%): 19.263 | loglstar: -inf < -48.441 < inf | logz: -145.770 +/- 0.516 | dlogz: 21.137 > 0.309] + + 28569it [02:30, 203.83it/s, bound: 623 | nc: 5 | ncall: 148309 | eff(%): 19.263 | loglstar: -inf < -47.922 < inf | logz: -145.325 +/- 0.517 | dlogz: 20.618 > 0.309] + + 28590it [02:30, 195.91it/s, bound: 624 | nc: 5 | ncall: 148414 | eff(%): 19.264 | loglstar: -inf < -47.599 < inf | logz: -144.936 +/- 0.517 | dlogz: 20.367 > 0.309] + + 28616it [02:30, 211.74it/s, bound: 624 | nc: 5 | ncall: 148544 | eff(%): 19.264 | loglstar: -inf < -47.321 < inf | logz: -144.539 +/- 0.517 | dlogz: 24.053 > 0.309] + + 28640it [02:30, 217.25it/s, bound: 625 | nc: 5 | ncall: 148664 | eff(%): 19.265 | loglstar: -inf < -46.727 < inf | logz: -144.154 +/- 0.517 | dlogz: 23.590 > 0.309] + + 28667it [02:30, 231.34it/s, bound: 625 | nc: 5 | ncall: 148799 | eff(%): 19.266 | loglstar: -inf < -46.234 < inf | logz: -143.724 +/- 0.517 | dlogz: 23.070 > 0.309] + + 28692it [02:30, 234.76it/s, bound: 626 | nc: 5 | ncall: 148924 | eff(%): 19.266 | loglstar: -inf < -45.675 < inf | logz: -143.328 +/- 0.517 | dlogz: 28.429 > 0.309] + + 28720it [02:31, 247.43it/s, bound: 626 | nc: 5 | ncall: 149064 | eff(%): 19.267 | loglstar: -inf < -45.368 < inf | logz: -142.907 +/- 0.517 | dlogz: 27.911 > 0.309] + + 28745it [02:31, 247.33it/s, bound: 627 | nc: 5 | ncall: 149189 | eff(%): 19.268 | loglstar: -inf < -44.886 < inf | logz: -142.565 +/- 0.517 | dlogz: 27.487 > 0.309] + + 28770it [02:31, 245.86it/s, bound: 628 | nc: 5 | ncall: 149314 | eff(%): 19.268 | loglstar: -inf < -44.365 < inf | logz: -142.215 +/- 0.518 | dlogz: 27.055 > 0.309] + + 28797it [02:31, 251.46it/s, bound: 628 | nc: 5 | ncall: 149449 | eff(%): 19.269 | loglstar: -inf < -44.026 < inf | logz: -141.814 +/- 0.518 | dlogz: 26.561 > 0.309] + + 28823it [02:31, 241.76it/s, bound: 629 | nc: 5 | ncall: 149579 | eff(%): 19.269 | loglstar: -inf < -43.477 < inf | logz: -141.464 +/- 0.518 | dlogz: 26.127 > 0.309] + + 28849it [02:31, 246.26it/s, bound: 629 | nc: 5 | ncall: 149709 | eff(%): 19.270 | loglstar: -inf < -43.141 < inf | logz: -141.111 +/- 0.518 | dlogz: 25.685 > 0.309] + + 28874it [02:31, 243.39it/s, bound: 630 | nc: 5 | ncall: 149834 | eff(%): 19.271 | loglstar: -inf < -42.721 < inf | logz: -140.770 +/- 0.518 | dlogz: 25.261 > 0.309] + + 28902it [02:31, 252.87it/s, bound: 630 | nc: 5 | ncall: 149974 | eff(%): 19.271 | loglstar: -inf < -41.895 < inf | logz: -140.312 +/- 0.519 | dlogz: 24.714 > 0.309] + + 28928it [02:31, 247.41it/s, bound: 631 | nc: 5 | ncall: 150104 | eff(%): 19.272 | loglstar: -inf < -41.242 < inf | logz: -139.810 +/- 0.520 | dlogz: 24.126 > 0.309] + + 28953it [02:32, 244.96it/s, bound: 632 | nc: 5 | ncall: 150229 | eff(%): 19.273 | loglstar: -inf < -40.965 < inf | logz: -139.396 +/- 0.520 | dlogz: 23.625 > 0.309] + + 28978it [02:32, 244.60it/s, bound: 632 | nc: 5 | ncall: 150354 | eff(%): 19.273 | loglstar: -inf < -40.695 < inf | logz: -139.063 +/- 0.520 | dlogz: 23.206 > 0.309] + + 29003it [02:32, 231.63it/s, bound: 633 | nc: 5 | ncall: 150479 | eff(%): 19.274 | loglstar: -inf < -40.198 < inf | logz: -138.713 +/- 0.520 | dlogz: 22.774 > 0.309] + + 29030it [02:32, 242.28it/s, bound: 633 | nc: 5 | ncall: 150614 | eff(%): 19.274 | loglstar: -inf < -39.776 < inf | logz: -138.345 +/- 0.520 | dlogz: 22.316 > 0.309] + + 29055it [02:32, 242.25it/s, bound: 634 | nc: 5 | ncall: 150739 | eff(%): 19.275 | loglstar: -inf < -39.090 < inf | logz: -137.962 +/- 0.521 | dlogz: 21.852 > 0.309] + + 29082it [02:32, 248.55it/s, bound: 634 | nc: 5 | ncall: 150874 | eff(%): 19.276 | loglstar: -inf < -38.751 < inf | logz: -137.518 +/- 0.521 | dlogz: 21.315 > 0.309] + + 29107it [02:32, 244.81it/s, bound: 635 | nc: 5 | ncall: 150999 | eff(%): 19.276 | loglstar: -inf < -38.568 < inf | logz: -137.214 +/- 0.521 | dlogz: 20.924 > 0.309] + + 29132it [02:32, 238.98it/s, bound: 636 | nc: 5 | ncall: 151124 | eff(%): 19.277 | loglstar: -inf < -38.187 < inf | logz: -136.931 +/- 0.521 | dlogz: 20.558 > 0.309] + + 29157it [02:32, 241.82it/s, bound: 636 | nc: 5 | ncall: 151249 | eff(%): 19.277 | loglstar: -inf < -37.862 < inf | logz: -136.640 +/- 0.521 | dlogz: 20.183 > 0.309] + + 29182it [02:32, 242.15it/s, bound: 637 | nc: 5 | ncall: 151374 | eff(%): 19.278 | loglstar: -inf < -37.476 < inf | logz: -136.371 +/- 0.521 | dlogz: 19.831 > 0.309] + + 29208it [02:33, 244.90it/s, bound: 637 | nc: 5 | ncall: 151504 | eff(%): 19.279 | loglstar: -inf < -37.083 < inf | logz: -136.069 +/- 0.522 | dlogz: 19.443 > 0.309] + + 29233it [02:33, 239.84it/s, bound: 638 | nc: 5 | ncall: 151629 | eff(%): 19.279 | loglstar: -inf < -36.815 < inf | logz: -135.789 +/- 0.522 | dlogz: 19.078 > 0.309] + + 29258it [02:33, 233.46it/s, bound: 638 | nc: 5 | ncall: 151754 | eff(%): 19.280 | loglstar: -inf < -36.547 < inf | logz: -135.532 +/- 0.522 | dlogz: 18.737 > 0.309] + + 29282it [02:33, 233.81it/s, bound: 639 | nc: 5 | ncall: 151874 | eff(%): 19.280 | loglstar: -inf < -36.307 < inf | logz: -135.312 +/- 0.522 | dlogz: 18.436 > 0.309] + + 29309it [02:33, 243.85it/s, bound: 639 | nc: 5 | ncall: 152009 | eff(%): 19.281 | loglstar: -inf < -35.886 < inf | logz: -135.048 +/- 0.522 | dlogz: 18.082 > 0.309] + + 29334it [02:33, 225.07it/s, bound: 641 | nc: 5 | ncall: 152134 | eff(%): 19.282 | loglstar: -inf < -35.558 < inf | logz: -134.815 +/- 0.522 | dlogz: 17.766 > 0.309] + + 29357it [02:33, 219.85it/s, bound: 642 | nc: 5 | ncall: 152249 | eff(%): 19.282 | loglstar: -inf < -35.290 < inf | logz: -134.598 +/- 0.522 | dlogz: 17.472 > 0.309] + + 29381it [02:33, 223.15it/s, bound: 642 | nc: 5 | ncall: 152369 | eff(%): 19.283 | loglstar: -inf < -35.034 < inf | logz: -134.380 +/- 0.523 | dlogz: 17.174 > 0.309] + + 29404it [02:33, 217.74it/s, bound: 643 | nc: 5 | ncall: 152484 | eff(%): 19.283 | loglstar: -inf < -34.764 < inf | logz: -134.180 +/- 0.523 | dlogz: 16.897 > 0.309] + + 29428it [02:34, 221.37it/s, bound: 643 | nc: 5 | ncall: 152604 | eff(%): 19.284 | loglstar: -inf < -34.336 < inf | logz: -133.938 +/- 0.523 | dlogz: 16.576 > 0.309] + + 29451it [02:34, 215.91it/s, bound: 644 | nc: 5 | ncall: 152719 | eff(%): 19.284 | loglstar: -inf < -33.870 < inf | logz: -133.683 +/- 0.523 | dlogz: 16.246 > 0.309] + + 29474it [02:34, 219.72it/s, bound: 644 | nc: 5 | ncall: 152834 | eff(%): 19.285 | loglstar: -inf < -33.769 < inf | logz: -133.442 +/- 0.524 | dlogz: 15.926 > 0.309] + + 29497it [02:34, 209.65it/s, bound: 646 | nc: 5 | ncall: 152949 | eff(%): 19.286 | loglstar: -inf < -33.555 < inf | logz: -133.230 +/- 0.524 | dlogz: 15.637 > 0.309] + + 29519it [02:34, 208.20it/s, bound: 646 | nc: 5 | ncall: 153059 | eff(%): 19.286 | loglstar: -inf < -33.329 < inf | logz: -133.042 +/- 0.524 | dlogz: 15.376 > 0.309] + + 29540it [02:34, 198.17it/s, bound: 646 | nc: 5 | ncall: 153164 | eff(%): 19.287 | loglstar: -inf < -33.051 < inf | logz: -132.864 +/- 0.524 | dlogz: 15.128 > 0.309] + + 29560it [02:34, 189.28it/s, bound: 648 | nc: 5 | ncall: 153264 | eff(%): 19.287 | loglstar: -inf < -32.831 < inf | logz: -132.698 +/- 0.524 | dlogz: 15.478 > 0.309] + + 29583it [02:34, 199.76it/s, bound: 648 | nc: 5 | ncall: 153379 | eff(%): 19.288 | loglstar: -inf < -32.730 < inf | logz: -132.515 +/- 0.524 | dlogz: 15.215 > 0.309] + + 29605it [02:34, 202.82it/s, bound: 649 | nc: 5 | ncall: 153489 | eff(%): 19.288 | loglstar: -inf < -32.473 < inf | logz: -132.361 +/- 0.524 | dlogz: 14.988 > 0.309] + + 29630it [02:35, 214.34it/s, bound: 649 | nc: 5 | ncall: 153614 | eff(%): 19.289 | loglstar: -inf < -32.115 < inf | logz: -132.169 +/- 0.524 | dlogz: 14.714 > 0.309] + + 29652it [02:35, 214.36it/s, bound: 650 | nc: 5 | ncall: 153724 | eff(%): 19.289 | loglstar: -inf < -31.743 < inf | logz: -131.986 +/- 0.525 | dlogz: 14.459 > 0.309] + + 29676it [02:35, 219.19it/s, bound: 650 | nc: 5 | ncall: 153844 | eff(%): 19.290 | loglstar: -inf < -31.543 < inf | logz: -131.779 +/- 0.525 | dlogz: 14.170 > 0.309] + + 29698it [02:35, 209.08it/s, bound: 652 | nc: 5 | ncall: 153954 | eff(%): 19.290 | loglstar: -inf < -31.279 < inf | logz: -131.595 +/- 0.525 | dlogz: 22.660 > 0.309] + + 29721it [02:35, 208.26it/s, bound: 653 | nc: 5 | ncall: 154069 | eff(%): 19.291 | loglstar: -inf < -31.031 < inf | logz: -131.395 +/- 0.525 | dlogz: 22.383 > 0.309] + + 29743it [02:35, 210.75it/s, bound: 653 | nc: 5 | ncall: 154179 | eff(%): 19.291 | loglstar: -inf < -30.936 < inf | logz: -131.230 +/- 0.526 | dlogz: 22.143 > 0.309] + + 29765it [02:35, 196.67it/s, bound: 654 | nc: 5 | ncall: 154289 | eff(%): 19.292 | loglstar: -inf < -30.735 < inf | logz: -131.085 +/- 0.526 | dlogz: 21.924 > 0.309] + + 29785it [02:35, 185.51it/s, bound: 654 | nc: 5 | ncall: 154389 | eff(%): 19.292 | loglstar: -inf < -30.543 < inf | logz: -130.944 +/- 0.526 | dlogz: 21.716 > 0.309] + + 29807it [02:35, 193.78it/s, bound: 654 | nc: 5 | ncall: 154499 | eff(%): 19.293 | loglstar: -inf < -30.305 < inf | logz: -130.797 +/- 0.526 | dlogz: 21.496 > 0.309] + + 29827it [02:36, 192.27it/s, bound: 655 | nc: 5 | ncall: 154599 | eff(%): 19.293 | loglstar: -inf < -30.103 < inf | logz: -130.653 +/- 0.526 | dlogz: 22.092 > 0.309] + + 29848it [02:36, 196.99it/s, bound: 655 | nc: 5 | ncall: 154704 | eff(%): 19.294 | loglstar: -inf < -29.972 < inf | logz: -130.513 +/- 0.526 | dlogz: 21.881 > 0.309] + + 29869it [02:36, 198.76it/s, bound: 656 | nc: 5 | ncall: 154809 | eff(%): 19.294 | loglstar: -inf < -29.862 < inf | logz: -130.382 +/- 0.526 | dlogz: 21.680 > 0.309] + + 29892it [02:36, 207.06it/s, bound: 656 | nc: 5 | ncall: 154924 | eff(%): 19.295 | loglstar: -inf < -29.418 < inf | logz: -130.235 +/- 0.526 | dlogz: 21.457 > 0.309] + + 29913it [02:36, 207.47it/s, bound: 657 | nc: 5 | ncall: 155029 | eff(%): 19.295 | loglstar: -inf < -29.094 < inf | logz: -130.082 +/- 0.527 | dlogz: 24.980 > 0.309] + + 29938it [02:36, 217.56it/s, bound: 657 | nc: 5 | ncall: 155154 | eff(%): 19.296 | loglstar: -inf < -28.855 < inf | logz: -129.885 +/- 0.527 | dlogz: 24.700 > 0.309] + + 29960it [02:36, 214.91it/s, bound: 658 | nc: 5 | ncall: 155264 | eff(%): 19.296 | loglstar: -inf < -28.563 < inf | logz: -129.716 +/- 0.527 | dlogz: 24.458 > 0.309] + + 29982it [02:36, 215.60it/s, bound: 658 | nc: 5 | ncall: 155374 | eff(%): 19.297 | loglstar: -inf < -28.383 < inf | logz: -129.547 +/- 0.528 | dlogz: 24.215 > 0.309] + + 30004it [02:36, 212.94it/s, bound: 659 | nc: 5 | ncall: 155484 | eff(%): 19.297 | loglstar: -inf < -28.125 < inf | logz: -129.380 +/- 0.528 | dlogz: 23.974 > 0.309] + + 30028it [02:36, 219.72it/s, bound: 659 | nc: 5 | ncall: 155604 | eff(%): 19.298 | loglstar: -inf < -27.702 < inf | logz: -129.179 +/- 0.528 | dlogz: 23.695 > 0.309] + + 30051it [02:37, 217.43it/s, bound: 660 | nc: 5 | ncall: 155719 | eff(%): 19.298 | loglstar: -inf < -27.539 < inf | logz: -128.986 +/- 0.529 | dlogz: 23.424 > 0.309] + + 30075it [02:37, 221.25it/s, bound: 660 | nc: 5 | ncall: 155839 | eff(%): 19.299 | loglstar: -inf < -27.225 < inf | logz: -128.791 +/- 0.529 | dlogz: 23.150 > 0.309] + + 30098it [02:37, 211.12it/s, bound: 661 | nc: 5 | ncall: 155954 | eff(%): 19.299 | loglstar: -inf < -26.867 < inf | logz: -128.585 +/- 0.529 | dlogz: 22.867 > 0.309] + + 30120it [02:37, 211.08it/s, bound: 661 | nc: 5 | ncall: 156064 | eff(%): 19.300 | loglstar: -inf < -26.644 < inf | logz: -128.399 +/- 0.529 | dlogz: 22.607 > 0.309] + + 30142it [02:37, 210.28it/s, bound: 662 | nc: 5 | ncall: 156174 | eff(%): 19.300 | loglstar: -inf < -26.383 < inf | logz: -128.203 +/- 0.530 | dlogz: 23.069 > 0.309] + + 30166it [02:37, 217.57it/s, bound: 662 | nc: 5 | ncall: 156294 | eff(%): 19.301 | loglstar: -inf < -26.031 < inf | logz: -127.991 +/- 0.530 | dlogz: 22.777 > 0.309] + + 30188it [02:37, 215.34it/s, bound: 663 | nc: 5 | ncall: 156404 | eff(%): 19.301 | loglstar: -inf < -25.719 < inf | logz: -127.777 +/- 0.530 | dlogz: 22.491 > 0.309] + + 30211it [02:37, 219.10it/s, bound: 663 | nc: 5 | ncall: 156519 | eff(%): 19.302 | loglstar: -inf < -25.345 < inf | logz: -127.548 +/- 0.531 | dlogz: 22.185 > 0.309] + + 30233it [02:37, 210.14it/s, bound: 664 | nc: 5 | ncall: 156629 | eff(%): 19.302 | loglstar: -inf < -25.033 < inf | logz: -127.330 +/- 0.531 | dlogz: 21.894 > 0.309] + + 30256it [02:38, 213.07it/s, bound: 664 | nc: 5 | ncall: 156744 | eff(%): 19.303 | loglstar: -inf < -24.724 < inf | logz: -127.101 +/- 0.531 | dlogz: 22.405 > 0.309] + + 30278it [02:38, 210.23it/s, bound: 665 | nc: 5 | ncall: 156854 | eff(%): 19.303 | loglstar: -inf < -24.710 < inf | logz: -126.898 +/- 0.532 | dlogz: 22.125 > 0.309] + + 30302it [02:38, 216.52it/s, bound: 665 | nc: 5 | ncall: 156974 | eff(%): 19.304 | loglstar: -inf < -24.411 < inf | logz: -126.707 +/- 0.532 | dlogz: 21.852 > 0.309] + + 30327it [02:38, 225.51it/s, bound: 666 | nc: 5 | ncall: 157099 | eff(%): 19.304 | loglstar: -inf < -24.169 < inf | logz: -126.520 +/- 0.532 | dlogz: 21.581 > 0.309] + + 30353it [02:38, 234.03it/s, bound: 667 | nc: 5 | ncall: 157229 | eff(%): 19.305 | loglstar: -inf < -23.870 < inf | logz: -126.317 +/- 0.532 | dlogz: 21.291 > 0.309] + + 30380it [02:38, 241.88it/s, bound: 667 | nc: 5 | ncall: 157364 | eff(%): 19.306 | loglstar: -inf < -23.467 < inf | logz: -126.089 +/- 0.532 | dlogz: 20.974 > 0.309] + + 30405it [02:38, 236.04it/s, bound: 668 | nc: 5 | ncall: 157489 | eff(%): 19.306 | loglstar: -inf < -23.190 < inf | logz: -125.874 +/- 0.533 | dlogz: 20.676 > 0.309] + + 30430it [02:38, 239.51it/s, bound: 668 | nc: 5 | ncall: 157614 | eff(%): 19.307 | loglstar: -inf < -22.941 < inf | logz: -125.674 +/- 0.533 | dlogz: 20.392 > 0.309] + + 30455it [02:38, 225.07it/s, bound: 669 | nc: 5 | ncall: 157739 | eff(%): 19.307 | loglstar: -inf < -22.618 < inf | logz: -125.463 +/- 0.533 | dlogz: 20.098 > 0.309] + + 30480it [02:39, 231.83it/s, bound: 669 | nc: 5 | ncall: 157864 | eff(%): 19.308 | loglstar: -inf < -22.229 < inf | logz: -125.241 +/- 0.533 | dlogz: 19.794 > 0.309] + + 30504it [02:39, 226.44it/s, bound: 670 | nc: 5 | ncall: 157984 | eff(%): 19.308 | loglstar: -inf < -21.828 < inf | logz: -125.004 +/- 0.534 | dlogz: 19.477 > 0.309] + + 30527it [02:39, 221.81it/s, bound: 670 | nc: 5 | ncall: 158099 | eff(%): 19.309 | loglstar: -inf < -21.515 < inf | logz: -124.774 +/- 0.534 | dlogz: 19.171 > 0.309] + + 30551it [02:39, 226.10it/s, bound: 671 | nc: 5 | ncall: 158219 | eff(%): 19.309 | loglstar: -inf < -21.154 < inf | logz: -124.537 +/- 0.535 | dlogz: 19.516 > 0.309] + + 30575it [02:39, 224.17it/s, bound: 672 | nc: 5 | ncall: 158339 | eff(%): 19.310 | loglstar: -inf < -20.832 < inf | logz: -124.295 +/- 0.535 | dlogz: 19.193 > 0.309] + + 30600it [02:39, 230.13it/s, bound: 672 | nc: 5 | ncall: 158464 | eff(%): 19.310 | loglstar: -inf < -20.442 < inf | logz: -124.026 +/- 0.535 | dlogz: 18.841 > 0.309] + + 30624it [02:39, 222.46it/s, bound: 673 | nc: 5 | ncall: 158584 | eff(%): 19.311 | loglstar: -inf < -20.134 < inf | logz: -123.742 +/- 0.536 | dlogz: 18.476 > 0.309] + + 30647it [02:39, 220.51it/s, bound: 673 | nc: 5 | ncall: 158699 | eff(%): 19.311 | loglstar: -inf < -19.875 < inf | logz: -123.510 +/- 0.536 | dlogz: 18.165 > 0.309] + + 30670it [02:39, 217.76it/s, bound: 674 | nc: 5 | ncall: 158814 | eff(%): 19.312 | loglstar: -inf < -19.528 < inf | logz: -123.291 +/- 0.536 | dlogz: 17.869 > 0.309] + + 30694it [02:39, 223.68it/s, bound: 674 | nc: 5 | ncall: 158934 | eff(%): 19.312 | loglstar: -inf < -19.114 < inf | logz: -123.041 +/- 0.536 | dlogz: 17.540 > 0.309] + + 30717it [02:40, 217.44it/s, bound: 675 | nc: 5 | ncall: 159049 | eff(%): 19.313 | loglstar: -inf < -18.629 < inf | logz: -122.776 +/- 0.537 | dlogz: 17.200 > 0.309] + + 30741it [02:40, 221.77it/s, bound: 675 | nc: 5 | ncall: 159169 | eff(%): 19.313 | loglstar: -inf < -18.338 < inf | logz: -122.474 +/- 0.537 | dlogz: 16.817 > 0.309] + + 30764it [02:40, 215.61it/s, bound: 676 | nc: 5 | ncall: 159284 | eff(%): 19.314 | loglstar: -inf < -18.231 < inf | logz: -122.234 +/- 0.537 | dlogz: 16.498 > 0.309] + + 30787it [02:40, 218.87it/s, bound: 676 | nc: 5 | ncall: 159399 | eff(%): 19.314 | loglstar: -inf < -17.888 < inf | logz: -122.024 +/- 0.538 | dlogz: 16.212 > 0.309] + + 30809it [02:40, 215.36it/s, bound: 677 | nc: 5 | ncall: 159509 | eff(%): 19.315 | loglstar: -inf < -17.665 < inf | logz: -121.817 +/- 0.538 | dlogz: 15.932 > 0.309] + + 30831it [02:40, 216.14it/s, bound: 677 | nc: 5 | ncall: 159619 | eff(%): 19.315 | loglstar: -inf < -17.122 < inf | logz: -121.587 +/- 0.538 | dlogz: 33.962 > 0.309] + + 30853it [02:40, 212.48it/s, bound: 678 | nc: 5 | ncall: 159729 | eff(%): 19.316 | loglstar: -inf < -16.814 < inf | logz: -121.327 +/- 0.538 | dlogz: 33.628 > 0.309] + + 30877it [02:40, 219.81it/s, bound: 678 | nc: 5 | ncall: 159849 | eff(%): 19.316 | loglstar: -inf < -16.567 < inf | logz: -121.060 +/- 0.539 | dlogz: 33.280 > 0.309] + + 30900it [02:40, 213.90it/s, bound: 679 | nc: 5 | ncall: 159964 | eff(%): 19.317 | loglstar: -inf < -16.121 < inf | logz: -120.795 +/- 0.539 | dlogz: 32.939 > 0.309] + + 30924it [02:41, 219.15it/s, bound: 679 | nc: 5 | ncall: 160084 | eff(%): 19.317 | loglstar: -inf < -15.763 < inf | logz: -120.509 +/- 0.539 | dlogz: 32.573 > 0.309] + + 30946it [02:41, 218.15it/s, bound: 680 | nc: 5 | ncall: 160194 | eff(%): 19.318 | loglstar: -inf < -15.519 < inf | logz: -120.257 +/- 0.539 | dlogz: 32.247 > 0.309] + + 30970it [02:41, 222.88it/s, bound: 680 | nc: 5 | ncall: 160314 | eff(%): 19.318 | loglstar: -inf < -15.281 < inf | logz: -120.006 +/- 0.540 | dlogz: 31.914 > 0.309] + + 30993it [02:41, 216.50it/s, bound: 681 | nc: 5 | ncall: 160429 | eff(%): 19.319 | loglstar: -inf < -15.055 < inf | logz: -119.790 +/- 0.540 | dlogz: 31.621 > 0.309] + + 31016it [02:41, 219.08it/s, bound: 681 | nc: 5 | ncall: 160544 | eff(%): 19.319 | loglstar: -inf < -14.617 < inf | logz: -119.554 +/- 0.540 | dlogz: 31.309 > 0.309] + + 31038it [02:41, 216.17it/s, bound: 682 | nc: 5 | ncall: 160654 | eff(%): 19.320 | loglstar: -inf < -14.249 < inf | logz: -119.321 +/- 0.540 | dlogz: 31.004 > 0.309] + + 31061it [02:41, 217.79it/s, bound: 682 | nc: 5 | ncall: 160769 | eff(%): 19.320 | loglstar: -inf < -13.937 < inf | logz: -119.071 +/- 0.540 | dlogz: 30.677 > 0.309] + + 31083it [02:41, 210.26it/s, bound: 683 | nc: 5 | ncall: 160879 | eff(%): 19.321 | loglstar: -inf < -13.486 < inf | logz: -118.820 +/- 0.541 | dlogz: 30.353 > 0.309] + + 31105it [02:41, 211.97it/s, bound: 683 | nc: 5 | ncall: 160989 | eff(%): 19.321 | loglstar: -inf < -13.310 < inf | logz: -118.572 +/- 0.541 | dlogz: 30.030 > 0.309] + + 31127it [02:42, 210.49it/s, bound: 684 | nc: 5 | ncall: 161099 | eff(%): 19.322 | loglstar: -inf < -12.904 < inf | logz: -118.338 +/- 0.541 | dlogz: 29.724 > 0.309] + + 31150it [02:42, 215.69it/s, bound: 684 | nc: 5 | ncall: 161214 | eff(%): 19.322 | loglstar: -inf < -12.638 < inf | logz: -118.086 +/- 0.541 | dlogz: 29.395 > 0.309] + + 31172it [02:42, 206.55it/s, bound: 685 | nc: 5 | ncall: 161324 | eff(%): 19.323 | loglstar: -inf < -12.305 < inf | logz: -117.839 +/- 0.542 | dlogz: 29.075 > 0.309] + + 31193it [02:42, 204.26it/s, bound: 685 | nc: 5 | ncall: 161429 | eff(%): 19.323 | loglstar: -inf < -12.027 < inf | logz: -117.604 +/- 0.542 | dlogz: 28.770 > 0.309] + + 31214it [02:42, 198.20it/s, bound: 686 | nc: 5 | ncall: 161534 | eff(%): 19.323 | loglstar: -inf < -11.497 < inf | logz: -117.355 +/- 0.542 | dlogz: 28.453 > 0.309] + + 31234it [02:42, 198.55it/s, bound: 686 | nc: 5 | ncall: 161634 | eff(%): 19.324 | loglstar: -inf < -11.055 < inf | logz: -117.093 +/- 0.543 | dlogz: 28.126 > 0.309] + + 31254it [02:42, 198.72it/s, bound: 687 | nc: 5 | ncall: 161734 | eff(%): 19.324 | loglstar: -inf < -10.671 < inf | logz: -116.793 +/- 0.543 | dlogz: 27.759 > 0.309] + + 31277it [02:42, 207.00it/s, bound: 687 | nc: 5 | ncall: 161849 | eff(%): 19.325 | loglstar: -inf < -10.233 < inf | logz: -116.446 +/- 0.543 | dlogz: 27.335 > 0.309] + + 31298it [02:42, 196.88it/s, bound: 688 | nc: 5 | ncall: 161954 | eff(%): 19.325 | loglstar: -inf < -9.991 < inf | logz: -116.155 +/- 0.544 | dlogz: 26.972 > 0.309] + + 31318it [02:42, 188.60it/s, bound: 688 | nc: 5 | ncall: 162054 | eff(%): 19.326 | loglstar: -inf < -9.784 < inf | logz: -115.896 +/- 0.544 | dlogz: 26.646 > 0.309] + + 31337it [02:43, 182.59it/s, bound: 689 | nc: 5 | ncall: 162149 | eff(%): 19.326 | loglstar: -inf < -9.609 < inf | logz: -115.672 +/- 0.544 | dlogz: 26.357 > 0.309] + + 31357it [02:43, 185.61it/s, bound: 689 | nc: 5 | ncall: 162249 | eff(%): 19.326 | loglstar: -inf < -8.928 < inf | logz: -115.411 +/- 0.544 | dlogz: 26.225 > 0.309] + + 31376it [02:43, 173.68it/s, bound: 689 | nc: 5 | ncall: 162344 | eff(%): 19.327 | loglstar: -inf < -8.730 < inf | logz: -115.132 +/- 0.545 | dlogz: 25.882 > 0.309] + + 31394it [02:43, 169.76it/s, bound: 690 | nc: 5 | ncall: 162434 | eff(%): 19.327 | loglstar: -inf < -8.479 < inf | logz: -114.890 +/- 0.545 | dlogz: 25.578 > 0.309] + + 31412it [02:43, 163.73it/s, bound: 691 | nc: 5 | ncall: 162524 | eff(%): 19.328 | loglstar: -inf < -7.927 < inf | logz: -114.629 +/- 0.545 | dlogz: 27.361 > 0.309] + + 31430it [02:43, 167.22it/s, bound: 691 | nc: 5 | ncall: 162614 | eff(%): 19.328 | loglstar: -inf < -7.639 < inf | logz: -114.343 +/- 0.545 | dlogz: 27.014 > 0.309] + + 31447it [02:43, 166.33it/s, bound: 691 | nc: 5 | ncall: 162699 | eff(%): 19.328 | loglstar: -inf < -7.520 < inf | logz: -114.106 +/- 0.545 | dlogz: 26.718 > 0.309] + + 31465it [02:43, 170.03it/s, bound: 692 | nc: 5 | ncall: 162789 | eff(%): 19.329 | loglstar: -inf < -7.275 < inf | logz: -113.884 +/- 0.546 | dlogz: 26.436 > 0.309] + + 31487it [02:43, 183.20it/s, bound: 692 | nc: 5 | ncall: 162899 | eff(%): 19.329 | loglstar: -inf < -6.830 < inf | logz: -113.606 +/- 0.546 | dlogz: 26.085 > 0.309] + + 31506it [02:44, 181.49it/s, bound: 693 | nc: 5 | ncall: 162994 | eff(%): 19.330 | loglstar: -inf < -6.509 < inf | logz: -113.365 +/- 0.546 | dlogz: 25.780 > 0.309] + + 31529it [02:44, 193.93it/s, bound: 693 | nc: 5 | ncall: 163109 | eff(%): 19.330 | loglstar: -inf < -6.286 < inf | logz: -113.064 +/- 0.546 | dlogz: 25.401 > 0.309] + + 31549it [02:44, 184.64it/s, bound: 694 | nc: 5 | ncall: 163209 | eff(%): 19.330 | loglstar: -inf < -5.963 < inf | logz: -112.838 +/- 0.546 | dlogz: 25.108 > 0.309] + + 31572it [02:44, 195.14it/s, bound: 694 | nc: 5 | ncall: 163324 | eff(%): 19.331 | loglstar: -inf < -5.532 < inf | logz: -112.557 +/- 0.546 | dlogz: 24.752 > 0.309] + + 31592it [02:44, 193.54it/s, bound: 695 | nc: 5 | ncall: 163424 | eff(%): 19.331 | loglstar: -inf < -5.121 < inf | logz: -112.285 +/- 0.547 | dlogz: 24.414 > 0.309] + + 31612it [02:44, 193.35it/s, bound: 696 | nc: 5 | ncall: 163524 | eff(%): 19.332 | loglstar: -inf < -4.749 < inf | logz: -112.007 +/- 0.547 | dlogz: 24.070 > 0.309] + + 31634it [02:44, 198.43it/s, bound: 696 | nc: 5 | ncall: 163634 | eff(%): 19.332 | loglstar: -inf < -4.453 < inf | logz: -111.724 +/- 0.547 | dlogz: 23.712 > 0.309] + + 31654it [02:44, 183.28it/s, bound: 696 | nc: 5 | ncall: 163734 | eff(%): 19.333 | loglstar: -inf < -3.936 < inf | logz: -111.434 +/- 0.547 | dlogz: 23.359 > 0.309] + + 31673it [02:44, 182.69it/s, bound: 697 | nc: 5 | ncall: 163829 | eff(%): 19.333 | loglstar: -inf < -3.782 < inf | logz: -111.160 +/- 0.548 | dlogz: 23.018 > 0.309] + + 31693it [02:45, 187.37it/s, bound: 697 | nc: 5 | ncall: 163929 | eff(%): 19.333 | loglstar: -inf < -3.441 < inf | logz: -110.900 +/- 0.548 | dlogz: 22.692 > 0.309] + + 31712it [02:45, 185.32it/s, bound: 698 | nc: 5 | ncall: 164024 | eff(%): 19.334 | loglstar: -inf < -3.127 < inf | logz: -110.646 +/- 0.548 | dlogz: 22.375 > 0.309] + + 31734it [02:45, 193.14it/s, bound: 698 | nc: 5 | ncall: 164134 | eff(%): 19.334 | loglstar: -inf < -2.811 < inf | logz: -110.367 +/- 0.548 | dlogz: 22.022 > 0.309] + + 31754it [02:45, 194.47it/s, bound: 699 | nc: 5 | ncall: 164234 | eff(%): 19.335 | loglstar: -inf < -2.625 < inf | logz: -110.134 +/- 0.548 | dlogz: 21.720 > 0.309] + + 31776it [02:45, 200.81it/s, bound: 699 | nc: 5 | ncall: 164344 | eff(%): 19.335 | loglstar: -inf < -2.314 < inf | logz: -109.882 +/- 0.548 | dlogz: 21.395 > 0.309] + + 31797it [02:45, 199.51it/s, bound: 700 | nc: 5 | ncall: 164449 | eff(%): 19.335 | loglstar: -inf < -2.044 < inf | logz: -109.645 +/- 0.549 | dlogz: 23.922 > 0.309] + + 31820it [02:45, 206.33it/s, bound: 700 | nc: 5 | ncall: 164564 | eff(%): 19.336 | loglstar: -inf < -1.620 < inf | logz: -109.393 +/- 0.549 | dlogz: 23.594 > 0.309] + + 31841it [02:45, 207.02it/s, bound: 701 | nc: 5 | ncall: 164669 | eff(%): 19.336 | loglstar: -inf < -1.384 < inf | logz: -109.151 +/- 0.549 | dlogz: 23.280 > 0.309] + + 31862it [02:45, 207.72it/s, bound: 701 | nc: 5 | ncall: 164774 | eff(%): 19.337 | loglstar: -inf < -0.904 < inf | logz: -108.891 +/- 0.549 | dlogz: 28.558 > 0.309] + + 31883it [02:46, 182.53it/s, bound: 702 | nc: 5 | ncall: 164879 | eff(%): 19.337 | loglstar: -inf < -0.684 < inf | logz: -108.626 +/- 0.549 | dlogz: 28.222 > 0.309] + + 31902it [02:46, 184.02it/s, bound: 702 | nc: 5 | ncall: 164974 | eff(%): 19.338 | loglstar: -inf < -0.581 < inf | logz: -108.428 +/- 0.549 | dlogz: 27.957 > 0.309] + + 31921it [02:46, 184.64it/s, bound: 703 | nc: 5 | ncall: 165069 | eff(%): 19.338 | loglstar: -inf < -0.150 < inf | logz: -108.224 +/- 0.550 | dlogz: 27.692 > 0.309] + + 31944it [02:46, 195.86it/s, bound: 703 | nc: 5 | ncall: 165184 | eff(%): 19.338 | loglstar: -inf < 0.091 < inf | logz: -107.956 +/- 0.550 | dlogz: 27.345 > 0.309] + + 31964it [02:46, 196.30it/s, bound: 704 | nc: 5 | ncall: 165284 | eff(%): 19.339 | loglstar: -inf < 0.506 < inf | logz: -107.728 +/- 0.550 | dlogz: 27.951 > 0.309] + + 31985it [02:46, 197.59it/s, bound: 704 | nc: 5 | ncall: 165389 | eff(%): 19.339 | loglstar: -inf < 0.875 < inf | logz: -107.467 +/- 0.550 | dlogz: 27.620 > 0.309] + + 32005it [02:46, 193.87it/s, bound: 705 | nc: 5 | ncall: 165489 | eff(%): 19.340 | loglstar: -inf < 1.071 < inf | logz: -107.236 +/- 0.550 | dlogz: 27.320 > 0.309] + + 32025it [02:46, 192.62it/s, bound: 705 | nc: 5 | ncall: 165589 | eff(%): 19.340 | loglstar: -inf < 1.263 < inf | logz: -107.024 +/- 0.551 | dlogz: 34.762 > 0.309] + + 32046it [02:46, 193.30it/s, bound: 706 | nc: 5 | ncall: 165694 | eff(%): 19.340 | loglstar: -inf < 1.832 < inf | logz: -106.786 +/- 0.551 | dlogz: 34.456 > 0.309] + + 32068it [02:46, 200.32it/s, bound: 706 | nc: 5 | ncall: 165804 | eff(%): 19.341 | loglstar: -inf < 2.342 < inf | logz: -106.493 +/- 0.551 | dlogz: 34.092 > 0.309] + + 32090it [02:47, 204.12it/s, bound: 706 | nc: 5 | ncall: 165914 | eff(%): 19.341 | loglstar: -inf < 2.768 < inf | logz: -106.113 +/- 0.552 | dlogz: 33.638 > 0.309] + + 32111it [02:47, 201.84it/s, bound: 707 | nc: 5 | ncall: 166019 | eff(%): 19.342 | loglstar: -inf < 3.176 < inf | logz: -105.792 +/- 0.552 | dlogz: 33.246 > 0.309] + + 32133it [02:47, 205.63it/s, bound: 707 | nc: 5 | ncall: 166129 | eff(%): 19.342 | loglstar: -inf < 3.385 < inf | logz: -105.479 +/- 0.552 | dlogz: 32.858 > 0.309] + + 32154it [02:47, 201.21it/s, bound: 708 | nc: 5 | ncall: 166234 | eff(%): 19.343 | loglstar: -inf < 3.385 < inf | logz: -105.239 +/- 0.552 | dlogz: 32.540 > 0.309] + + 32177it [02:47, 207.72it/s, bound: 708 | nc: 5 | ncall: 166349 | eff(%): 19.343 | loglstar: -inf < 3.577 < inf | logz: -105.033 +/- 0.552 | dlogz: 32.252 > 0.309] + + 32198it [02:47, 201.55it/s, bound: 709 | nc: 5 | ncall: 166454 | eff(%): 19.343 | loglstar: -inf < 4.079 < inf | logz: -104.847 +/- 0.552 | dlogz: 31.998 > 0.309] + + 32219it [02:47, 202.05it/s, bound: 709 | nc: 5 | ncall: 166559 | eff(%): 19.344 | loglstar: -inf < 4.496 < inf | logz: -104.594 +/- 0.553 | dlogz: 31.677 > 0.309] + + 32240it [02:47, 192.43it/s, bound: 710 | nc: 5 | ncall: 166664 | eff(%): 19.344 | loglstar: -inf < 5.018 < inf | logz: -104.336 +/- 0.553 | dlogz: 31.351 > 0.309] + + 32260it [02:47, 189.99it/s, bound: 710 | nc: 5 | ncall: 166764 | eff(%): 19.345 | loglstar: -inf < 5.507 < inf | logz: -104.045 +/- 0.553 | dlogz: 30.996 > 0.309] + + 32280it [02:48, 186.74it/s, bound: 711 | nc: 5 | ncall: 166864 | eff(%): 19.345 | loglstar: -inf < 5.866 < inf | logz: -103.728 +/- 0.554 | dlogz: 30.611 > 0.309] + + 32300it [02:48, 190.00it/s, bound: 711 | nc: 5 | ncall: 166964 | eff(%): 19.345 | loglstar: -inf < 6.265 < inf | logz: -103.422 +/- 0.554 | dlogz: 30.239 > 0.309] + + 32320it [02:48, 174.56it/s, bound: 712 | nc: 5 | ncall: 167064 | eff(%): 19.346 | loglstar: -inf < 6.668 < inf | logz: -103.085 +/- 0.554 | dlogz: 29.835 > 0.309] + + 32338it [02:48, 172.40it/s, bound: 712 | nc: 5 | ncall: 167154 | eff(%): 19.346 | loglstar: -inf < 7.094 < inf | logz: -102.786 +/- 0.555 | dlogz: 29.478 > 0.309] + + 32357it [02:48, 176.08it/s, bound: 712 | nc: 5 | ncall: 167249 | eff(%): 19.347 | loglstar: -inf < 7.469 < inf | logz: -102.461 +/- 0.555 | dlogz: 29.089 > 0.309] + + 32375it [02:48, 173.45it/s, bound: 713 | nc: 5 | ncall: 167339 | eff(%): 19.347 | loglstar: -inf < 7.565 < inf | logz: -102.182 +/- 0.555 | dlogz: 28.746 > 0.309] + + 32395it [02:48, 180.60it/s, bound: 713 | nc: 5 | ncall: 167439 | eff(%): 19.347 | loglstar: -inf < 7.827 < inf | logz: -101.918 +/- 0.555 | dlogz: 28.415 > 0.309] + + 32414it [02:48, 179.26it/s, bound: 714 | nc: 5 | ncall: 167534 | eff(%): 19.348 | loglstar: -inf < 8.351 < inf | logz: -101.650 +/- 0.555 | dlogz: 28.086 > 0.309] + + 32437it [02:48, 192.63it/s, bound: 714 | nc: 5 | ncall: 167649 | eff(%): 19.348 | loglstar: -inf < 8.780 < inf | logz: -101.301 +/- 0.556 | dlogz: 27.660 > 0.309] + + 32459it [02:48, 199.49it/s, bound: 715 | nc: 5 | ncall: 167759 | eff(%): 19.349 | loglstar: -inf < 9.039 < inf | logz: -100.995 +/- 0.556 | dlogz: 27.279 > 0.309] + + 32484it [02:49, 213.45it/s, bound: 715 | nc: 5 | ncall: 167884 | eff(%): 19.349 | loglstar: -inf < 9.424 < inf | logz: -100.649 +/- 0.556 | dlogz: 31.311 > 0.309] + + 32506it [02:49, 213.52it/s, bound: 716 | nc: 5 | ncall: 167994 | eff(%): 19.350 | loglstar: -inf < 9.652 < inf | logz: -100.375 +/- 0.556 | dlogz: 30.962 > 0.309] + + 32529it [02:49, 217.54it/s, bound: 716 | nc: 5 | ncall: 168109 | eff(%): 19.350 | loglstar: -inf < 9.969 < inf | logz: -100.117 +/- 0.556 | dlogz: 30.626 > 0.309] + + 32551it [02:49, 217.21it/s, bound: 717 | nc: 5 | ncall: 168219 | eff(%): 19.350 | loglstar: -inf < 10.403 < inf | logz: -99.861 +/- 0.556 | dlogz: 30.299 > 0.309] + + 32573it [02:49, 213.66it/s, bound: 717 | nc: 5 | ncall: 168329 | eff(%): 19.351 | loglstar: -inf < 11.020 < inf | logz: -99.546 +/- 0.557 | dlogz: 29.914 > 0.309] + + 32595it [02:49, 204.09it/s, bound: 718 | nc: 5 | ncall: 168439 | eff(%): 19.351 | loglstar: -inf < 11.471 < inf | logz: -99.181 +/- 0.557 | dlogz: 29.476 > 0.309] + + 32617it [02:49, 207.48it/s, bound: 718 | nc: 5 | ncall: 168549 | eff(%): 19.352 | loglstar: -inf < 11.995 < inf | logz: -98.825 +/- 0.558 | dlogz: 29.048 > 0.309] + + 32638it [02:49, 204.86it/s, bound: 719 | nc: 5 | ncall: 168654 | eff(%): 19.352 | loglstar: -inf < 12.221 < inf | logz: -98.489 +/- 0.558 | dlogz: 28.638 > 0.309] + + 32659it [02:49, 188.96it/s, bound: 720 | nc: 5 | ncall: 168759 | eff(%): 19.352 | loglstar: -inf < 12.721 < inf | logz: -98.166 +/- 0.558 | dlogz: 28.247 > 0.309] + + 32680it [02:50, 192.98it/s, bound: 720 | nc: 5 | ncall: 168864 | eff(%): 19.353 | loglstar: -inf < 13.209 < inf | logz: -97.810 +/- 0.558 | dlogz: 27.823 > 0.309] + + 32700it [02:50, 193.66it/s, bound: 721 | nc: 5 | ncall: 168964 | eff(%): 19.353 | loglstar: -inf < 13.455 < inf | logz: -97.483 +/- 0.559 | dlogz: 27.426 > 0.309] + + 32720it [02:50, 194.46it/s, bound: 721 | nc: 5 | ncall: 169064 | eff(%): 19.354 | loglstar: -inf < 13.863 < inf | logz: -97.184 +/- 0.559 | dlogz: 27.061 > 0.309] + + 32740it [02:50, 185.41it/s, bound: 722 | nc: 5 | ncall: 169164 | eff(%): 19.354 | loglstar: -inf < 14.370 < inf | logz: -96.847 +/- 0.559 | dlogz: 26.659 > 0.309] + + 32763it [02:50, 194.94it/s, bound: 722 | nc: 5 | ncall: 169279 | eff(%): 19.354 | loglstar: -inf < 14.516 < inf | logz: -96.496 +/- 0.559 | dlogz: 30.214 > 0.309] + + 32783it [02:50, 195.46it/s, bound: 723 | nc: 5 | ncall: 169379 | eff(%): 19.355 | loglstar: -inf < 14.895 < inf | logz: -96.222 +/- 0.559 | dlogz: 29.873 > 0.309] + + 32804it [02:50, 199.51it/s, bound: 723 | nc: 5 | ncall: 169484 | eff(%): 19.355 | loglstar: -inf < 15.157 < inf | logz: -95.949 +/- 0.559 | dlogz: 29.530 > 0.309] + + 32825it [02:50, 200.72it/s, bound: 724 | nc: 5 | ncall: 169589 | eff(%): 19.356 | loglstar: -inf < 15.520 < inf | logz: -95.698 +/- 0.559 | dlogz: 29.209 > 0.309] + + 32848it [02:50, 207.73it/s, bound: 724 | nc: 5 | ncall: 169704 | eff(%): 19.356 | loglstar: -inf < 16.032 < inf | logz: -95.379 +/- 0.560 | dlogz: 28.815 > 0.309] + + 32870it [02:51, 210.10it/s, bound: 725 | nc: 5 | ncall: 169814 | eff(%): 19.356 | loglstar: -inf < 16.450 < inf | logz: -95.035 +/- 0.560 | dlogz: 28.398 > 0.309] + + 32892it [02:51, 212.37it/s, bound: 725 | nc: 5 | ncall: 169924 | eff(%): 19.357 | loglstar: -inf < 16.716 < inf | logz: -94.730 +/- 0.560 | dlogz: 28.018 > 0.309] + + 32914it [02:51, 207.00it/s, bound: 726 | nc: 5 | ncall: 170034 | eff(%): 19.357 | loglstar: -inf < 16.943 < inf | logz: -94.455 +/- 0.560 | dlogz: 27.668 > 0.309] + + 32937it [02:51, 211.23it/s, bound: 726 | nc: 5 | ncall: 170149 | eff(%): 19.358 | loglstar: -inf < 17.520 < inf | logz: -94.154 +/- 0.560 | dlogz: 27.293 > 0.309] + + 32959it [02:51, 206.87it/s, bound: 727 | nc: 5 | ncall: 170259 | eff(%): 19.358 | loglstar: -inf < 18.067 < inf | logz: -93.810 +/- 0.561 | dlogz: 26.878 > 0.309] + + 32982it [02:51, 211.67it/s, bound: 727 | nc: 5 | ncall: 170374 | eff(%): 19.359 | loglstar: -inf < 18.805 < inf | logz: -93.400 +/- 0.561 | dlogz: 26.396 > 0.309] + + 33004it [02:51, 204.38it/s, bound: 728 | nc: 5 | ncall: 170484 | eff(%): 19.359 | loglstar: -inf < 19.451 < inf | logz: -92.934 +/- 0.562 | dlogz: 25.858 > 0.309] + + 33027it [02:51, 210.94it/s, bound: 728 | nc: 5 | ncall: 170599 | eff(%): 19.359 | loglstar: -inf < 19.918 < inf | logz: -92.427 +/- 0.562 | dlogz: 25.272 > 0.309] + + 33049it [02:51, 204.80it/s, bound: 729 | nc: 5 | ncall: 170709 | eff(%): 19.360 | loglstar: -inf < 20.500 < inf | logz: -91.973 +/- 0.563 | dlogz: 35.328 > 0.309] + + 33071it [02:51, 208.18it/s, bound: 729 | nc: 5 | ncall: 170819 | eff(%): 19.360 | loglstar: -inf < 21.003 < inf | logz: -91.523 +/- 0.563 | dlogz: 34.803 > 0.309] + + 33093it [02:52, 206.01it/s, bound: 730 | nc: 5 | ncall: 170929 | eff(%): 19.361 | loglstar: -inf < 21.585 < inf | logz: -91.030 +/- 0.563 | dlogz: 34.238 > 0.309] + + 33114it [02:52, 202.39it/s, bound: 730 | nc: 5 | ncall: 171034 | eff(%): 19.361 | loglstar: -inf < 22.094 < inf | logz: -90.586 +/- 0.564 | dlogz: 33.724 > 0.309] + + 33135it [02:52, 192.68it/s, bound: 730 | nc: 5 | ncall: 171139 | eff(%): 19.361 | loglstar: -inf < 22.605 < inf | logz: -90.137 +/- 0.564 | dlogz: 33.204 > 0.309] + + 33155it [02:52, 188.01it/s, bound: 731 | nc: 5 | ncall: 171239 | eff(%): 19.362 | loglstar: -inf < 22.808 < inf | logz: -89.769 +/- 0.564 | dlogz: 32.765 > 0.309] + + 33177it [02:52, 194.72it/s, bound: 731 | nc: 5 | ncall: 171349 | eff(%): 19.362 | loglstar: -inf < 23.198 < inf | logz: -89.386 +/- 0.564 | dlogz: 32.308 > 0.309] + + 33197it [02:52, 192.11it/s, bound: 732 | nc: 5 | ncall: 171449 | eff(%): 19.363 | loglstar: -inf < 23.436 < inf | logz: -89.086 +/- 0.564 | dlogz: 31.939 > 0.309] + + 33217it [02:52, 191.37it/s, bound: 732 | nc: 5 | ncall: 171549 | eff(%): 19.363 | loglstar: -inf < 23.705 < inf | logz: -88.830 +/- 0.564 | dlogz: 31.614 > 0.309] + + 33237it [02:52, 188.21it/s, bound: 733 | nc: 5 | ncall: 171649 | eff(%): 19.363 | loglstar: -inf < 23.971 < inf | logz: -88.572 +/- 0.564 | dlogz: 31.289 > 0.309] + + 33256it [02:52, 188.14it/s, bound: 733 | nc: 5 | ncall: 171744 | eff(%): 19.364 | loglstar: -inf < 24.483 < inf | logz: -88.311 +/- 0.564 | dlogz: 30.968 > 0.309] + + 33275it [02:53, 187.42it/s, bound: 734 | nc: 5 | ncall: 171839 | eff(%): 19.364 | loglstar: -inf < 24.765 < inf | logz: -88.039 +/- 0.564 | dlogz: 30.631 > 0.309] + + 33297it [02:53, 193.85it/s, bound: 734 | nc: 5 | ncall: 171949 | eff(%): 19.364 | loglstar: -inf < 25.240 < inf | logz: -87.714 +/- 0.564 | dlogz: 30.233 > 0.309] + + 33317it [02:53, 187.18it/s, bound: 735 | nc: 5 | ncall: 172049 | eff(%): 19.365 | loglstar: -inf < 25.546 < inf | logz: -87.433 +/- 0.564 | dlogz: 29.886 > 0.309] + + 33339it [02:53, 195.69it/s, bound: 735 | nc: 5 | ncall: 172159 | eff(%): 19.365 | loglstar: -inf < 25.892 < inf | logz: -87.125 +/- 0.565 | dlogz: 29.504 > 0.309] + + 33359it [02:53, 194.77it/s, bound: 736 | nc: 5 | ncall: 172259 | eff(%): 19.366 | loglstar: -inf < 26.475 < inf | logz: -86.795 +/- 0.565 | dlogz: 29.110 > 0.309] + + 33381it [02:53, 201.65it/s, bound: 736 | nc: 5 | ncall: 172369 | eff(%): 19.366 | loglstar: -inf < 27.028 < inf | logz: -86.413 +/- 0.565 | dlogz: 28.657 > 0.309] + + 33402it [02:53, 201.75it/s, bound: 737 | nc: 5 | ncall: 172474 | eff(%): 19.366 | loglstar: -inf < 27.230 < inf | logz: -86.048 +/- 0.566 | dlogz: 28.217 > 0.309] + + 33425it [02:53, 208.73it/s, bound: 737 | nc: 5 | ncall: 172589 | eff(%): 19.367 | loglstar: -inf < 27.641 < inf | logz: -85.703 +/- 0.566 | dlogz: 27.795 > 0.309] + + 33446it [02:53, 204.84it/s, bound: 738 | nc: 5 | ncall: 172694 | eff(%): 19.367 | loglstar: -inf < 28.096 < inf | logz: -85.381 +/- 0.566 | dlogz: 27.403 > 0.309] + + 33467it [02:54, 182.73it/s, bound: 738 | nc: 5 | ncall: 172799 | eff(%): 19.368 | loglstar: -inf < 28.364 < inf | logz: -85.081 +/- 0.566 | dlogz: 27.032 > 0.309] + + 33486it [02:54, 170.60it/s, bound: 739 | nc: 5 | ncall: 172894 | eff(%): 19.368 | loglstar: -inf < 28.557 < inf | logz: -84.827 +/- 0.566 | dlogz: 26.713 > 0.309] + + 33504it [02:54, 170.90it/s, bound: 739 | nc: 5 | ncall: 172984 | eff(%): 19.368 | loglstar: -inf < 28.747 < inf | logz: -84.610 +/- 0.566 | dlogz: 26.435 > 0.309] + + 33522it [02:54, 164.31it/s, bound: 740 | nc: 5 | ncall: 173074 | eff(%): 19.369 | loglstar: -inf < 29.217 < inf | logz: -84.386 +/- 0.566 | dlogz: 26.154 > 0.309] + + 33541it [02:54, 170.32it/s, bound: 740 | nc: 5 | ncall: 173169 | eff(%): 19.369 | loglstar: -inf < 29.652 < inf | logz: -84.112 +/- 0.566 | dlogz: 25.817 > 0.309] + + 33563it [02:54, 183.90it/s, bound: 740 | nc: 5 | ncall: 173279 | eff(%): 19.369 | loglstar: -inf < 30.254 < inf | logz: -83.772 +/- 0.567 | dlogz: 32.500 > 0.309] + + 33583it [02:54, 186.30it/s, bound: 741 | nc: 5 | ncall: 173379 | eff(%): 19.370 | loglstar: -inf < 30.692 < inf | logz: -83.407 +/- 0.567 | dlogz: 32.068 > 0.309] + + 33606it [02:54, 197.82it/s, bound: 741 | nc: 5 | ncall: 173494 | eff(%): 19.370 | loglstar: -inf < 30.989 < inf | logz: -83.016 +/- 0.568 | dlogz: 31.598 > 0.309] + + 33626it [02:54, 197.54it/s, bound: 742 | nc: 5 | ncall: 173594 | eff(%): 19.370 | loglstar: -inf < 31.238 < inf | logz: -82.719 +/- 0.568 | dlogz: 31.233 > 0.309] + + 33646it [02:55, 191.88it/s, bound: 742 | nc: 5 | ncall: 173694 | eff(%): 19.371 | loglstar: -inf < 31.705 < inf | logz: -82.421 +/- 0.568 | dlogz: 30.869 > 0.309] + + 33666it [02:55, 187.90it/s, bound: 743 | nc: 5 | ncall: 173794 | eff(%): 19.371 | loglstar: -inf < 31.930 < inf | logz: -82.139 +/- 0.568 | dlogz: 30.519 > 0.309] + + 33688it [02:55, 196.48it/s, bound: 743 | nc: 5 | ncall: 173904 | eff(%): 19.372 | loglstar: -inf < 32.364 < inf | logz: -81.839 +/- 0.568 | dlogz: 30.147 > 0.309] + + 33708it [02:55, 195.17it/s, bound: 744 | nc: 5 | ncall: 174004 | eff(%): 19.372 | loglstar: -inf < 32.777 < inf | logz: -81.551 +/- 0.568 | dlogz: 29.792 > 0.309] + + 33731it [02:55, 202.51it/s, bound: 744 | nc: 5 | ncall: 174119 | eff(%): 19.372 | loglstar: -inf < 32.955 < inf | logz: -81.238 +/- 0.568 | dlogz: 29.400 > 0.309] + + 33752it [02:55, 200.83it/s, bound: 745 | nc: 5 | ncall: 174224 | eff(%): 19.373 | loglstar: -inf < 33.437 < inf | logz: -80.966 +/- 0.569 | dlogz: 29.059 > 0.309] + + 33775it [02:55, 208.12it/s, bound: 745 | nc: 5 | ncall: 174339 | eff(%): 19.373 | loglstar: -inf < 33.936 < inf | logz: -80.620 +/- 0.569 | dlogz: 28.638 > 0.309] + + 33796it [02:55, 206.59it/s, bound: 746 | nc: 5 | ncall: 174444 | eff(%): 19.374 | loglstar: -inf < 34.462 < inf | logz: -80.281 +/- 0.569 | dlogz: 28.231 > 0.309] + + 33820it [02:55, 213.31it/s, bound: 746 | nc: 5 | ncall: 174564 | eff(%): 19.374 | loglstar: -inf < 34.969 < inf | logz: -79.852 +/- 0.570 | dlogz: 27.723 > 0.309] + + 33842it [02:55, 201.41it/s, bound: 747 | nc: 5 | ncall: 174674 | eff(%): 19.374 | loglstar: -inf < 35.182 < inf | logz: -79.516 +/- 0.570 | dlogz: 27.309 > 0.309] + + 33863it [02:56, 198.38it/s, bound: 747 | nc: 5 | ncall: 174779 | eff(%): 19.375 | loglstar: -inf < 35.649 < inf | logz: -79.202 +/- 0.570 | dlogz: 26.927 > 0.309] + + 33883it [02:56, 177.78it/s, bound: 748 | nc: 5 | ncall: 174879 | eff(%): 19.375 | loglstar: -inf < 36.126 < inf | logz: -78.854 +/- 0.570 | dlogz: 26.514 > 0.309] + + 33903it [02:56, 183.41it/s, bound: 748 | nc: 5 | ncall: 174979 | eff(%): 19.375 | loglstar: -inf < 36.361 < inf | logz: -78.547 +/- 0.570 | dlogz: 26.137 > 0.309] + + 33923it [02:56, 185.99it/s, bound: 749 | nc: 5 | ncall: 175079 | eff(%): 19.376 | loglstar: -inf < 36.529 < inf | logz: -78.286 +/- 0.570 | dlogz: 25.808 > 0.309] + + 33944it [02:56, 192.06it/s, bound: 749 | nc: 5 | ncall: 175184 | eff(%): 19.376 | loglstar: -inf < 36.767 < inf | logz: -78.037 +/- 0.570 | dlogz: 25.488 > 0.309] + + 33964it [02:56, 193.23it/s, bound: 750 | nc: 5 | ncall: 175284 | eff(%): 19.377 | loglstar: -inf < 36.923 < inf | logz: -77.822 +/- 0.570 | dlogz: 25.204 > 0.309] + + 33987it [02:56, 203.37it/s, bound: 750 | nc: 5 | ncall: 175399 | eff(%): 19.377 | loglstar: -inf < 37.158 < inf | logz: -77.610 +/- 0.570 | dlogz: 24.915 > 0.309] + + 34008it [02:56, 204.21it/s, bound: 751 | nc: 5 | ncall: 175504 | eff(%): 19.377 | loglstar: -inf < 37.545 < inf | logz: -77.409 +/- 0.570 | dlogz: 24.645 > 0.309] + + 34032it [02:56, 212.08it/s, bound: 751 | nc: 5 | ncall: 175624 | eff(%): 19.378 | loglstar: -inf < 37.885 < inf | logz: -77.148 +/- 0.571 | dlogz: 24.304 > 0.309] + + 34054it [02:57, 211.95it/s, bound: 752 | nc: 5 | ncall: 175734 | eff(%): 19.378 | loglstar: -inf < 38.304 < inf | logz: -76.907 +/- 0.571 | dlogz: 23.991 > 0.309] + + 34078it [02:57, 218.76it/s, bound: 752 | nc: 5 | ncall: 175854 | eff(%): 19.379 | loglstar: -inf < 38.588 < inf | logz: -76.634 +/- 0.571 | dlogz: 23.637 > 0.309] + + 34100it [02:57, 207.06it/s, bound: 753 | nc: 5 | ncall: 175964 | eff(%): 19.379 | loglstar: -inf < 39.019 < inf | logz: -76.370 +/- 0.571 | dlogz: 23.301 > 0.309] + + 34123it [02:57, 212.09it/s, bound: 753 | nc: 5 | ncall: 176079 | eff(%): 19.379 | loglstar: -inf < 39.122 < inf | logz: -76.124 +/- 0.571 | dlogz: 22.976 > 0.309] + + 34145it [02:57, 211.60it/s, bound: 754 | nc: 5 | ncall: 176189 | eff(%): 19.380 | loglstar: -inf < 39.364 < inf | logz: -75.908 +/- 0.571 | dlogz: 22.686 > 0.309] + + 34168it [02:57, 214.05it/s, bound: 754 | nc: 5 | ncall: 176304 | eff(%): 19.380 | loglstar: -inf < 39.696 < inf | logz: -75.698 +/- 0.572 | dlogz: 22.400 > 0.309] + + 34190it [02:57, 211.70it/s, bound: 755 | nc: 5 | ncall: 176414 | eff(%): 19.381 | loglstar: -inf < 40.068 < inf | logz: -75.481 +/- 0.572 | dlogz: 22.110 > 0.309] + + 34213it [02:57, 216.95it/s, bound: 755 | nc: 5 | ncall: 176529 | eff(%): 19.381 | loglstar: -inf < 40.697 < inf | logz: -75.221 +/- 0.572 | dlogz: 21.777 > 0.309] + + 34235it [02:57, 212.58it/s, bound: 756 | nc: 5 | ncall: 176639 | eff(%): 19.381 | loglstar: -inf < 40.983 < inf | logz: -74.927 +/- 0.572 | dlogz: 21.409 > 0.309] + + 34259it [02:58, 217.16it/s, bound: 756 | nc: 5 | ncall: 176759 | eff(%): 19.382 | loglstar: -inf < 41.471 < inf | logz: -74.621 +/- 0.573 | dlogz: 21.024 > 0.309] + + 34281it [02:58, 207.10it/s, bound: 757 | nc: 5 | ncall: 176869 | eff(%): 19.382 | loglstar: -inf < 41.854 < inf | logz: -74.305 +/- 0.573 | dlogz: 20.633 > 0.309] + + 34302it [02:58, 196.30it/s, bound: 757 | nc: 5 | ncall: 176974 | eff(%): 19.383 | loglstar: -inf < 42.318 < inf | logz: -73.986 +/- 0.574 | dlogz: 20.246 > 0.309] + + 34322it [02:58, 192.39it/s, bound: 758 | nc: 5 | ncall: 177074 | eff(%): 19.383 | loglstar: -inf < 42.652 < inf | logz: -73.691 +/- 0.574 | dlogz: 19.884 > 0.309] + + 34343it [02:58, 195.51it/s, bound: 758 | nc: 5 | ncall: 177179 | eff(%): 19.383 | loglstar: -inf < 43.076 < inf | logz: -73.380 +/- 0.574 | dlogz: 19.504 > 0.309] + + 34365it [02:58, 194.96it/s, bound: 759 | nc: 5 | ncall: 177289 | eff(%): 19.384 | loglstar: -inf < 43.288 < inf | logz: -73.076 +/- 0.574 | dlogz: 19.124 > 0.309] + + 34388it [02:58, 202.53it/s, bound: 759 | nc: 5 | ncall: 177404 | eff(%): 19.384 | loglstar: -inf < 43.641 < inf | logz: -72.784 +/- 0.575 | dlogz: 18.755 > 0.309] + + 34410it [02:58, 201.87it/s, bound: 760 | nc: 5 | ncall: 177514 | eff(%): 19.384 | loglstar: -inf < 44.160 < inf | logz: -72.502 +/- 0.575 | dlogz: 18.402 > 0.309] + + 34431it [02:58, 202.82it/s, bound: 760 | nc: 5 | ncall: 177619 | eff(%): 19.385 | loglstar: -inf < 44.534 < inf | logz: -72.179 +/- 0.575 | dlogz: 18.009 > 0.309] + + 34454it [02:58, 209.53it/s, bound: 760 | nc: 5 | ncall: 177734 | eff(%): 19.385 | loglstar: -inf < 44.817 < inf | logz: -71.860 +/- 0.575 | dlogz: 21.078 > 0.309] + + 34476it [02:59, 210.36it/s, bound: 761 | nc: 5 | ncall: 177844 | eff(%): 19.386 | loglstar: -inf < 45.120 < inf | logz: -71.588 +/- 0.575 | dlogz: 20.732 > 0.309] + + 34500it [02:59, 209.83it/s, bound: 762 | nc: 5 | ncall: 177964 | eff(%): 19.386 | loglstar: -inf < 45.662 < inf | logz: -71.260 +/- 0.576 | dlogz: 20.325 > 0.309] + + 34523it [02:59, 214.20it/s, bound: 762 | nc: 5 | ncall: 178079 | eff(%): 19.386 | loglstar: -inf < 45.792 < inf | logz: -70.962 +/- 0.576 | dlogz: 19.948 > 0.309] + + 34545it [02:59, 211.89it/s, bound: 763 | nc: 5 | ncall: 178189 | eff(%): 19.387 | loglstar: -inf < 46.150 < inf | logz: -70.708 +/- 0.576 | dlogz: 19.620 > 0.309] + + 34567it [02:59, 205.10it/s, bound: 763 | nc: 5 | ncall: 178299 | eff(%): 19.387 | loglstar: -inf < 46.491 < inf | logz: -70.424 +/- 0.576 | dlogz: 19.263 > 0.309] + + 34590it [02:59, 204.48it/s, bound: 764 | nc: 5 | ncall: 178414 | eff(%): 19.387 | loglstar: -inf < 46.770 < inf | logz: -70.154 +/- 0.576 | dlogz: 18.916 > 0.309] + + 34614it [02:59, 213.40it/s, bound: 764 | nc: 5 | ncall: 178534 | eff(%): 19.388 | loglstar: -inf < 46.952 < inf | logz: -69.914 +/- 0.576 | dlogz: 22.644 > 0.309] + + 34636it [02:59, 211.93it/s, bound: 765 | nc: 5 | ncall: 178644 | eff(%): 19.388 | loglstar: -inf < 47.386 < inf | logz: -69.694 +/- 0.576 | dlogz: 22.353 > 0.309] + + 34660it [02:59, 217.65it/s, bound: 765 | nc: 5 | ncall: 178764 | eff(%): 19.389 | loglstar: -inf < 47.741 < inf | logz: -69.425 +/- 0.577 | dlogz: 27.218 > 0.309] + + 34682it [03:00, 198.57it/s, bound: 766 | nc: 5 | ncall: 178874 | eff(%): 19.389 | loglstar: -inf < 47.941 < inf | logz: -69.193 +/- 0.577 | dlogz: 26.911 > 0.309] + + 34703it [03:00, 200.73it/s, bound: 766 | nc: 5 | ncall: 178979 | eff(%): 19.389 | loglstar: -inf < 48.292 < inf | logz: -68.988 +/- 0.577 | dlogz: 26.636 > 0.309] + + 34724it [03:00, 201.53it/s, bound: 766 | nc: 5 | ncall: 179084 | eff(%): 19.390 | loglstar: -inf < 48.607 < inf | logz: -68.758 +/- 0.577 | dlogz: 26.337 > 0.309] + + 34745it [03:00, 194.87it/s, bound: 767 | nc: 5 | ncall: 179189 | eff(%): 19.390 | loglstar: -inf < 48.821 < inf | logz: -68.533 +/- 0.577 | dlogz: 26.042 > 0.309] + + 34765it [03:00, 193.22it/s, bound: 768 | nc: 5 | ncall: 179289 | eff(%): 19.390 | loglstar: -inf < 49.115 < inf | logz: -68.342 +/- 0.577 | dlogz: 25.783 > 0.309] + + 34788it [03:00, 203.17it/s, bound: 768 | nc: 5 | ncall: 179404 | eff(%): 19.391 | loglstar: -inf < 49.257 < inf | logz: -68.123 +/- 0.577 | dlogz: 25.486 > 0.309] + + 34809it [03:00, 203.44it/s, bound: 769 | nc: 5 | ncall: 179509 | eff(%): 19.391 | loglstar: -inf < 49.632 < inf | logz: -67.925 +/- 0.578 | dlogz: 25.219 > 0.309] + + 34833it [03:00, 211.85it/s, bound: 769 | nc: 5 | ncall: 179629 | eff(%): 19.392 | loglstar: -inf < 50.172 < inf | logz: -67.669 +/- 0.578 | dlogz: 24.886 > 0.309] + + 34855it [03:00, 210.96it/s, bound: 770 | nc: 5 | ncall: 179739 | eff(%): 19.392 | loglstar: -inf < 50.556 < inf | logz: -67.395 +/- 0.578 | dlogz: 24.539 > 0.309] + + 34878it [03:01, 214.30it/s, bound: 770 | nc: 5 | ncall: 179854 | eff(%): 19.392 | loglstar: -inf < 50.917 < inf | logz: -67.116 +/- 0.579 | dlogz: 24.183 > 0.309] + + 34900it [03:01, 211.57it/s, bound: 771 | nc: 5 | ncall: 179964 | eff(%): 19.393 | loglstar: -inf < 51.301 < inf | logz: -66.834 +/- 0.579 | dlogz: 23.829 > 0.309] + + 34922it [03:01, 208.46it/s, bound: 771 | nc: 5 | ncall: 180074 | eff(%): 19.393 | loglstar: -inf < 51.792 < inf | logz: -66.525 +/- 0.579 | dlogz: 23.448 > 0.309] + + 34943it [03:01, 204.19it/s, bound: 772 | nc: 5 | ncall: 180179 | eff(%): 19.393 | loglstar: -inf < 52.157 < inf | logz: -66.225 +/- 0.580 | dlogz: 23.078 > 0.309] + + 34967it [03:01, 212.39it/s, bound: 772 | nc: 5 | ncall: 180299 | eff(%): 19.394 | loglstar: -inf < 52.588 < inf | logz: -65.872 +/- 0.580 | dlogz: 22.644 > 0.309] + + 34989it [03:01, 209.58it/s, bound: 773 | nc: 5 | ncall: 180409 | eff(%): 19.394 | loglstar: -inf < 53.009 < inf | logz: -65.563 +/- 0.580 | dlogz: 22.263 > 0.309] + + 35012it [03:01, 214.51it/s, bound: 773 | nc: 5 | ncall: 180524 | eff(%): 19.395 | loglstar: -inf < 53.341 < inf | logz: -65.244 +/- 0.581 | dlogz: 21.866 > 0.309] + + 35034it [03:01, 211.70it/s, bound: 774 | nc: 5 | ncall: 180634 | eff(%): 19.395 | loglstar: -inf < 53.684 < inf | logz: -64.942 +/- 0.581 | dlogz: 21.490 > 0.309] + + 35058it [03:01, 217.73it/s, bound: 774 | nc: 5 | ncall: 180754 | eff(%): 19.395 | loglstar: -inf < 54.115 < inf | logz: -64.619 +/- 0.581 | dlogz: 21.087 > 0.309] + + 35080it [03:01, 215.03it/s, bound: 775 | nc: 5 | ncall: 180864 | eff(%): 19.396 | loglstar: -inf < 54.289 < inf | logz: -64.343 +/- 0.581 | dlogz: 20.735 > 0.309] + + 35104it [03:02, 220.77it/s, bound: 775 | nc: 5 | ncall: 180984 | eff(%): 19.396 | loglstar: -inf < 54.674 < inf | logz: -64.057 +/- 0.581 | dlogz: 20.370 > 0.309] + + 35127it [03:02, 211.84it/s, bound: 776 | nc: 5 | ncall: 181099 | eff(%): 19.397 | loglstar: -inf < 55.158 < inf | logz: -63.765 +/- 0.582 | dlogz: 20.003 > 0.309] + + 35150it [03:02, 216.39it/s, bound: 776 | nc: 5 | ncall: 181214 | eff(%): 19.397 | loglstar: -inf < 55.547 < inf | logz: -63.458 +/- 0.582 | dlogz: 19.619 > 0.309] + + 35172it [03:02, 212.28it/s, bound: 777 | nc: 5 | ncall: 181324 | eff(%): 19.397 | loglstar: -inf < 55.779 < inf | logz: -63.186 +/- 0.582 | dlogz: 19.272 > 0.309] + + 35196it [03:02, 217.68it/s, bound: 777 | nc: 5 | ncall: 181444 | eff(%): 19.398 | loglstar: -inf < 56.174 < inf | logz: -62.896 +/- 0.582 | dlogz: 18.903 > 0.309] + + 35218it [03:02, 212.23it/s, bound: 778 | nc: 5 | ncall: 181554 | eff(%): 19.398 | loglstar: -inf < 56.424 < inf | logz: -62.635 +/- 0.582 | dlogz: 18.729 > 0.309] + + 35240it [03:02, 203.66it/s, bound: 778 | nc: 5 | ncall: 181664 | eff(%): 19.398 | loglstar: -inf < 56.689 < inf | logz: -62.385 +/- 0.582 | dlogz: 20.791 > 0.309] + + 35261it [03:02, 192.19it/s, bound: 779 | nc: 5 | ncall: 181769 | eff(%): 19.399 | loglstar: -inf < 57.134 < inf | logz: -62.142 +/- 0.583 | dlogz: 20.479 > 0.309] + + 35281it [03:02, 193.27it/s, bound: 780 | nc: 5 | ncall: 181869 | eff(%): 19.399 | loglstar: -inf < 57.345 < inf | logz: -61.908 +/- 0.583 | dlogz: 20.177 > 0.309] + + 35302it [03:03, 197.71it/s, bound: 780 | nc: 5 | ncall: 181974 | eff(%): 19.399 | loglstar: -inf < 57.671 < inf | logz: -61.677 +/- 0.583 | dlogz: 19.876 > 0.309] + + 35322it [03:03, 193.75it/s, bound: 781 | nc: 5 | ncall: 182074 | eff(%): 19.400 | loglstar: -inf < 57.876 < inf | logz: -61.462 +/- 0.583 | dlogz: 19.594 > 0.309] + + 35342it [03:03, 194.22it/s, bound: 781 | nc: 5 | ncall: 182174 | eff(%): 19.400 | loglstar: -inf < 58.039 < inf | logz: -61.259 +/- 0.583 | dlogz: 19.323 > 0.309] + + 35362it [03:03, 187.46it/s, bound: 782 | nc: 5 | ncall: 182274 | eff(%): 19.400 | loglstar: -inf < 58.498 < inf | logz: -61.052 +/- 0.583 | dlogz: 19.052 > 0.309] + + 35383it [03:03, 192.89it/s, bound: 782 | nc: 5 | ncall: 182379 | eff(%): 19.401 | loglstar: -inf < 58.716 < inf | logz: -60.820 +/- 0.583 | dlogz: 18.749 > 0.309] + + 35403it [03:03, 185.81it/s, bound: 782 | nc: 5 | ncall: 182479 | eff(%): 19.401 | loglstar: -inf < 58.830 < inf | logz: -60.626 +/- 0.584 | dlogz: 18.486 > 0.309] + + 35423it [03:03, 187.76it/s, bound: 783 | nc: 5 | ncall: 182579 | eff(%): 19.401 | loglstar: -inf < 59.007 < inf | logz: -60.449 +/- 0.584 | dlogz: 18.241 > 0.309] + + 35446it [03:03, 192.23it/s, bound: 784 | nc: 5 | ncall: 182694 | eff(%): 19.402 | loglstar: -inf < 59.376 < inf | logz: -60.252 +/- 0.584 | dlogz: 19.126 > 0.309] + + 35469it [03:03, 202.41it/s, bound: 784 | nc: 5 | ncall: 182809 | eff(%): 19.402 | loglstar: -inf < 59.936 < inf | logz: -59.998 +/- 0.584 | dlogz: 18.797 > 0.309] + + 35490it [03:04, 195.29it/s, bound: 785 | nc: 5 | ncall: 182914 | eff(%): 19.403 | loglstar: -inf < 60.160 < inf | logz: -59.756 +/- 0.584 | dlogz: 18.484 > 0.309] + + 35511it [03:04, 198.29it/s, bound: 785 | nc: 5 | ncall: 183019 | eff(%): 19.403 | loglstar: -inf < 60.273 < inf | logz: -59.550 +/- 0.584 | dlogz: 23.948 > 0.309] + + 35532it [03:04, 201.36it/s, bound: 785 | nc: 5 | ncall: 183124 | eff(%): 19.403 | loglstar: -inf < 60.751 < inf | logz: -59.362 +/- 0.584 | dlogz: 23.691 > 0.309] + + 35553it [03:04, 197.44it/s, bound: 786 | nc: 5 | ncall: 183229 | eff(%): 19.404 | loglstar: -inf < 61.022 < inf | logz: -59.132 +/- 0.585 | dlogz: 23.392 > 0.309] + + 35573it [03:04, 197.92it/s, bound: 787 | nc: 5 | ncall: 183329 | eff(%): 19.404 | loglstar: -inf < 61.153 < inf | logz: -58.931 +/- 0.585 | dlogz: 23.122 > 0.309] + + 35596it [03:04, 206.74it/s, bound: 787 | nc: 5 | ncall: 183444 | eff(%): 19.404 | loglstar: -inf < 61.405 < inf | logz: -58.711 +/- 0.585 | dlogz: 22.825 > 0.309] + + 35617it [03:04, 204.75it/s, bound: 788 | nc: 5 | ncall: 183549 | eff(%): 19.405 | loglstar: -inf < 61.727 < inf | logz: -58.511 +/- 0.585 | dlogz: 22.555 > 0.309] + + 35641it [03:04, 212.36it/s, bound: 788 | nc: 5 | ncall: 183669 | eff(%): 19.405 | loglstar: -inf < 62.086 < inf | logz: -58.268 +/- 0.585 | dlogz: 22.232 > 0.309] + + 35663it [03:04, 210.43it/s, bound: 789 | nc: 5 | ncall: 183779 | eff(%): 19.405 | loglstar: -inf < 62.263 < inf | logz: -58.057 +/- 0.586 | dlogz: 21.947 > 0.309] + + 35687it [03:04, 218.18it/s, bound: 789 | nc: 5 | ncall: 183899 | eff(%): 19.406 | loglstar: -inf < 62.528 < inf | logz: -57.843 +/- 0.586 | dlogz: 21.652 > 0.309] + + 35709it [03:05, 211.87it/s, bound: 790 | nc: 5 | ncall: 184009 | eff(%): 19.406 | loglstar: -inf < 63.015 < inf | logz: -57.625 +/- 0.586 | dlogz: 21.364 > 0.309] + + 35731it [03:05, 195.29it/s, bound: 790 | nc: 5 | ncall: 184119 | eff(%): 19.406 | loglstar: -inf < 63.287 < inf | logz: -57.392 +/- 0.586 | dlogz: 21.057 > 0.309] + + 35751it [03:05, 189.22it/s, bound: 791 | nc: 5 | ncall: 184219 | eff(%): 19.407 | loglstar: -inf < 63.532 < inf | logz: -57.188 +/- 0.587 | dlogz: 20.785 > 0.309] + + 35771it [03:05, 183.72it/s, bound: 791 | nc: 5 | ncall: 184319 | eff(%): 19.407 | loglstar: -inf < 63.822 < inf | logz: -56.984 +/- 0.587 | dlogz: 21.954 > 0.309] + + 35790it [03:05, 165.40it/s, bound: 792 | nc: 5 | ncall: 184414 | eff(%): 19.407 | loglstar: -inf < 64.334 < inf | logz: -56.759 +/- 0.587 | dlogz: 21.669 > 0.309] + + 35807it [03:05, 160.68it/s, bound: 792 | nc: 5 | ncall: 184499 | eff(%): 19.408 | loglstar: -inf < 64.563 < inf | logz: -56.547 +/- 0.587 | dlogz: 21.399 > 0.309] + + 35824it [03:05, 149.81it/s, bound: 793 | nc: 5 | ncall: 184584 | eff(%): 19.408 | loglstar: -inf < 64.870 < inf | logz: -56.343 +/- 0.588 | dlogz: 21.139 > 0.309] + + 35841it [03:05, 153.47it/s, bound: 793 | nc: 5 | ncall: 184669 | eff(%): 19.408 | loglstar: -inf < 65.165 < inf | logz: -56.119 +/- 0.588 | dlogz: 20.859 > 0.309] + + 35860it [03:06, 161.42it/s, bound: 793 | nc: 5 | ncall: 184764 | eff(%): 19.409 | loglstar: -inf < 65.446 < inf | logz: -55.878 +/- 0.588 | dlogz: 20.959 > 0.309] + + 35877it [03:06, 163.12it/s, bound: 794 | nc: 5 | ncall: 184849 | eff(%): 19.409 | loglstar: -inf < 65.601 < inf | logz: -55.671 +/- 0.588 | dlogz: 20.694 > 0.309] + + 35894it [03:06, 164.66it/s, bound: 794 | nc: 5 | ncall: 184934 | eff(%): 19.409 | loglstar: -inf < 65.819 < inf | logz: -55.477 +/- 0.588 | dlogz: 23.969 > 0.309] + + 35911it [03:06, 162.21it/s, bound: 794 | nc: 5 | ncall: 185019 | eff(%): 19.409 | loglstar: -inf < 66.078 < inf | logz: -55.286 +/- 0.589 | dlogz: 23.721 > 0.309] + + 35928it [03:06, 151.64it/s, bound: 795 | nc: 5 | ncall: 185104 | eff(%): 19.410 | loglstar: -inf < 66.308 < inf | logz: -55.099 +/- 0.589 | dlogz: 23.477 > 0.309] + + 35945it [03:06, 155.39it/s, bound: 795 | nc: 5 | ncall: 185189 | eff(%): 19.410 | loglstar: -inf < 66.509 < inf | logz: -54.909 +/- 0.589 | dlogz: 23.231 > 0.309] + + 35961it [03:06, 154.90it/s, bound: 796 | nc: 5 | ncall: 185269 | eff(%): 19.410 | loglstar: -inf < 66.840 < inf | logz: -54.730 +/- 0.589 | dlogz: 22.999 > 0.309] + + 35980it [03:06, 163.13it/s, bound: 796 | nc: 5 | ncall: 185364 | eff(%): 19.410 | loglstar: -inf < 67.095 < inf | logz: -54.518 +/- 0.589 | dlogz: 22.723 > 0.309] + + 35998it [03:06, 167.00it/s, bound: 796 | nc: 5 | ncall: 185454 | eff(%): 19.411 | loglstar: -inf < 67.547 < inf | logz: -54.293 +/- 0.589 | dlogz: 22.440 > 0.309] + + 36015it [03:07, 165.55it/s, bound: 797 | nc: 5 | ncall: 185539 | eff(%): 19.411 | loglstar: -inf < 67.987 < inf | logz: -54.050 +/- 0.590 | dlogz: 22.142 > 0.309] + + 36036it [03:07, 176.18it/s, bound: 797 | nc: 5 | ncall: 185644 | eff(%): 19.411 | loglstar: -inf < 68.157 < inf | logz: -53.755 +/- 0.590 | dlogz: 22.029 > 0.309] + + 36054it [03:07, 176.93it/s, bound: 798 | nc: 5 | ncall: 185734 | eff(%): 19.412 | loglstar: -inf < 68.542 < inf | logz: -53.495 +/- 0.590 | dlogz: 21.711 > 0.309] + + 36075it [03:07, 183.98it/s, bound: 798 | nc: 5 | ncall: 185839 | eff(%): 19.412 | loglstar: -inf < 68.735 < inf | logz: -53.234 +/- 0.590 | dlogz: 21.377 > 0.309] + + 36094it [03:07, 176.82it/s, bound: 799 | nc: 5 | ncall: 185934 | eff(%): 19.412 | loglstar: -inf < 68.867 < inf | logz: -53.024 +/- 0.591 | dlogz: 21.102 > 0.309] + + 36115it [03:07, 184.00it/s, bound: 799 | nc: 5 | ncall: 186039 | eff(%): 19.413 | loglstar: -inf < 69.445 < inf | logz: -52.780 +/- 0.591 | dlogz: 20.791 > 0.309] + + 36135it [03:07, 187.03it/s, bound: 799 | nc: 5 | ncall: 186139 | eff(%): 19.413 | loglstar: -inf < 69.805 < inf | logz: -52.506 +/- 0.591 | dlogz: 20.451 > 0.309] + + 36154it [03:07, 184.53it/s, bound: 800 | nc: 5 | ncall: 186234 | eff(%): 19.413 | loglstar: -inf < 70.115 < inf | logz: -52.240 +/- 0.591 | dlogz: 23.754 > 0.309] + + 36174it [03:07, 188.88it/s, bound: 800 | nc: 5 | ncall: 186334 | eff(%): 19.414 | loglstar: -inf < 70.717 < inf | logz: -51.922 +/- 0.592 | dlogz: 23.373 > 0.309] + + 36193it [03:07, 185.40it/s, bound: 801 | nc: 5 | ncall: 186429 | eff(%): 19.414 | loglstar: -inf < 71.150 < inf | logz: -51.588 +/- 0.592 | dlogz: 22.976 > 0.309] + + 36214it [03:08, 191.37it/s, bound: 801 | nc: 5 | ncall: 186534 | eff(%): 19.414 | loglstar: -inf < 71.456 < inf | logz: -51.252 +/- 0.592 | dlogz: 22.568 > 0.309] + + 36234it [03:08, 188.70it/s, bound: 802 | nc: 5 | ncall: 186634 | eff(%): 19.414 | loglstar: -inf < 71.775 < inf | logz: -50.942 +/- 0.593 | dlogz: 22.191 > 0.309] + + 36253it [03:08, 188.83it/s, bound: 802 | nc: 5 | ncall: 186729 | eff(%): 19.415 | loglstar: -inf < 71.951 < inf | logz: -50.688 +/- 0.593 | dlogz: 21.871 > 0.309] + + 36273it [03:08, 183.91it/s, bound: 803 | nc: 5 | ncall: 186829 | eff(%): 19.415 | loglstar: -inf < 72.358 < inf | logz: -50.436 +/- 0.593 | dlogz: 21.554 > 0.309] + + 36292it [03:08, 181.89it/s, bound: 803 | nc: 5 | ncall: 186924 | eff(%): 19.415 | loglstar: -inf < 72.696 < inf | logz: -50.172 +/- 0.593 | dlogz: 21.226 > 0.309] + + 36311it [03:08, 176.40it/s, bound: 803 | nc: 5 | ncall: 187019 | eff(%): 19.416 | loglstar: -inf < 72.821 < inf | logz: -49.921 +/- 0.593 | dlogz: 20.909 > 0.309] + + 36329it [03:08, 170.15it/s, bound: 804 | nc: 5 | ncall: 187109 | eff(%): 19.416 | loglstar: -inf < 73.158 < inf | logz: -49.700 +/- 0.593 | dlogz: 22.230 > 0.309] + + 36349it [03:08, 177.10it/s, bound: 804 | nc: 5 | ncall: 187209 | eff(%): 19.416 | loglstar: -inf < 73.495 < inf | logz: -49.448 +/- 0.593 | dlogz: 21.911 > 0.309] + + 36367it [03:08, 176.19it/s, bound: 805 | nc: 5 | ncall: 187299 | eff(%): 19.417 | loglstar: -inf < 73.718 < inf | logz: -49.235 +/- 0.593 | dlogz: 21.638 > 0.309] + + 36386it [03:09, 179.38it/s, bound: 805 | nc: 5 | ncall: 187394 | eff(%): 19.417 | loglstar: -inf < 74.009 < inf | logz: -49.015 +/- 0.593 | dlogz: 21.353 > 0.309] + + 36407it [03:09, 185.92it/s, bound: 805 | nc: 5 | ncall: 187499 | eff(%): 19.417 | loglstar: -inf < 74.419 < inf | logz: -48.750 +/- 0.594 | dlogz: 21.020 > 0.309] + + 36426it [03:09, 185.41it/s, bound: 806 | nc: 5 | ncall: 187594 | eff(%): 19.417 | loglstar: -inf < 74.634 < inf | logz: -48.517 +/- 0.594 | dlogz: 20.723 > 0.309] + + 36447it [03:09, 189.86it/s, bound: 806 | nc: 5 | ncall: 187699 | eff(%): 19.418 | loglstar: -inf < 74.956 < inf | logz: -48.260 +/- 0.594 | dlogz: 20.395 > 0.309] + + 36467it [03:09, 188.36it/s, bound: 807 | nc: 5 | ncall: 187799 | eff(%): 19.418 | loglstar: -inf < 75.141 < inf | logz: -48.030 +/- 0.594 | dlogz: 20.097 > 0.309] + + 36487it [03:09, 189.76it/s, bound: 807 | nc: 5 | ncall: 187899 | eff(%): 19.418 | loglstar: -inf < 75.751 < inf | logz: -47.779 +/- 0.594 | dlogz: 19.783 > 0.309] + + 36506it [03:09, 184.23it/s, bound: 808 | nc: 5 | ncall: 187994 | eff(%): 19.419 | loglstar: -inf < 75.853 < inf | logz: -47.536 +/- 0.594 | dlogz: 19.474 > 0.309] + + 36527it [03:09, 190.69it/s, bound: 808 | nc: 5 | ncall: 188099 | eff(%): 19.419 | loglstar: -inf < 76.089 < inf | logz: -47.300 +/- 0.595 | dlogz: 19.168 > 0.309] + + 36547it [03:09, 187.79it/s, bound: 809 | nc: 5 | ncall: 188199 | eff(%): 19.419 | loglstar: -inf < 76.303 < inf | logz: -47.094 +/- 0.595 | dlogz: 18.954 > 0.309] + + 36568it [03:10, 192.09it/s, bound: 809 | nc: 5 | ncall: 188304 | eff(%): 19.420 | loglstar: -inf < 76.564 < inf | logz: -46.890 +/- 0.595 | dlogz: 18.679 > 0.309] + + 36588it [03:10, 186.28it/s, bound: 810 | nc: 5 | ncall: 188404 | eff(%): 19.420 | loglstar: -inf < 76.875 < inf | logz: -46.687 +/- 0.595 | dlogz: 18.410 > 0.309] + + 36609it [03:10, 192.42it/s, bound: 810 | nc: 5 | ncall: 188509 | eff(%): 19.420 | loglstar: -inf < 77.311 < inf | logz: -46.449 +/- 0.595 | dlogz: 18.103 > 0.309] + + 36629it [03:10, 185.87it/s, bound: 810 | nc: 5 | ncall: 188609 | eff(%): 19.421 | loglstar: -inf < 77.482 < inf | logz: -46.217 +/- 0.595 | dlogz: 17.804 > 0.309] + + 36648it [03:10, 184.88it/s, bound: 811 | nc: 5 | ncall: 188704 | eff(%): 19.421 | loglstar: -inf < 77.889 < inf | logz: -45.997 +/- 0.595 | dlogz: 17.520 > 0.309] + + 36670it [03:10, 192.73it/s, bound: 811 | nc: 5 | ncall: 188814 | eff(%): 19.421 | loglstar: -inf < 78.380 < inf | logz: -45.711 +/- 0.596 | dlogz: 23.600 > 0.309] + + 36690it [03:10, 193.29it/s, bound: 812 | nc: 5 | ncall: 188914 | eff(%): 19.422 | loglstar: -inf < 78.741 < inf | logz: -45.446 +/- 0.596 | dlogz: 23.268 > 0.309] + + 36712it [03:10, 199.25it/s, bound: 812 | nc: 5 | ncall: 189024 | eff(%): 19.422 | loglstar: -inf < 78.896 < inf | logz: -45.168 +/- 0.596 | dlogz: 22.915 > 0.309] + + 36733it [03:10, 200.70it/s, bound: 813 | nc: 5 | ncall: 189129 | eff(%): 19.422 | loglstar: -inf < 79.212 < inf | logz: -44.929 +/- 0.596 | dlogz: 22.606 > 0.309] + + 36755it [03:10, 205.17it/s, bound: 813 | nc: 5 | ncall: 189239 | eff(%): 19.423 | loglstar: -inf < 79.420 < inf | logz: -44.672 +/- 0.597 | dlogz: 22.274 > 0.309] + + 36776it [03:11, 201.02it/s, bound: 814 | nc: 5 | ncall: 189344 | eff(%): 19.423 | loglstar: -inf < 79.756 < inf | logz: -44.451 +/- 0.597 | dlogz: 21.982 > 0.309] + + 36799it [03:11, 208.77it/s, bound: 814 | nc: 5 | ncall: 189459 | eff(%): 19.423 | loglstar: -inf < 80.024 < inf | logz: -44.195 +/- 0.597 | dlogz: 21.648 > 0.309] + + 36820it [03:11, 207.39it/s, bound: 815 | nc: 5 | ncall: 189564 | eff(%): 19.424 | loglstar: -inf < 80.364 < inf | logz: -43.983 +/- 0.597 | dlogz: 21.367 > 0.309] + + 36842it [03:11, 208.49it/s, bound: 815 | nc: 5 | ncall: 189674 | eff(%): 19.424 | loglstar: -inf < 80.741 < inf | logz: -43.739 +/- 0.597 | dlogz: 21.051 > 0.309] + + 36863it [03:11, 202.56it/s, bound: 816 | nc: 5 | ncall: 189779 | eff(%): 19.424 | loglstar: -inf < 81.068 < inf | logz: -43.507 +/- 0.597 | dlogz: 20.749 > 0.309] + + 36884it [03:11, 200.14it/s, bound: 816 | nc: 5 | ncall: 189884 | eff(%): 19.424 | loglstar: -inf < 81.392 < inf | logz: -43.272 +/- 0.598 | dlogz: 20.444 > 0.309] + + 36905it [03:11, 200.17it/s, bound: 817 | nc: 5 | ncall: 189989 | eff(%): 19.425 | loglstar: -inf < 81.593 < inf | logz: -43.028 +/- 0.598 | dlogz: 20.677 > 0.309] + + 36927it [03:11, 205.84it/s, bound: 817 | nc: 5 | ncall: 190099 | eff(%): 19.425 | loglstar: -inf < 81.848 < inf | logz: -42.804 +/- 0.598 | dlogz: 20.378 > 0.309] + + 36948it [03:11, 200.81it/s, bound: 818 | nc: 5 | ncall: 190204 | eff(%): 19.425 | loglstar: -inf < 82.159 < inf | logz: -42.584 +/- 0.598 | dlogz: 20.089 > 0.309] + + 36969it [03:12, 196.15it/s, bound: 818 | nc: 5 | ncall: 190309 | eff(%): 19.426 | loglstar: -inf < 82.360 < inf | logz: -42.378 +/- 0.598 | dlogz: 20.616 > 0.309] + + 36991it [03:12, 201.32it/s, bound: 818 | nc: 5 | ncall: 190419 | eff(%): 19.426 | loglstar: -inf < 82.643 < inf | logz: -42.166 +/- 0.598 | dlogz: 20.331 > 0.309] + + 37012it [03:12, 193.98it/s, bound: 819 | nc: 5 | ncall: 190524 | eff(%): 19.426 | loglstar: -inf < 82.923 < inf | logz: -41.973 +/- 0.598 | dlogz: 20.067 > 0.309] + + 37033it [03:12, 196.98it/s, bound: 819 | nc: 5 | ncall: 190629 | eff(%): 19.427 | loglstar: -inf < 83.263 < inf | logz: -41.752 +/- 0.599 | dlogz: 21.181 > 0.309] + + 37053it [03:12, 187.86it/s, bound: 820 | nc: 5 | ncall: 190729 | eff(%): 19.427 | loglstar: -inf < 83.575 < inf | logz: -41.536 +/- 0.599 | dlogz: 20.900 > 0.309] + + 37075it [03:12, 194.59it/s, bound: 820 | nc: 5 | ncall: 190839 | eff(%): 19.427 | loglstar: -inf < 83.918 < inf | logz: -41.302 +/- 0.599 | dlogz: 20.592 > 0.309] + + 37095it [03:12, 193.09it/s, bound: 821 | nc: 5 | ncall: 190939 | eff(%): 19.428 | loglstar: -inf < 84.116 < inf | logz: -41.093 +/- 0.599 | dlogz: 20.316 > 0.309] + + 37116it [03:12, 196.78it/s, bound: 821 | nc: 5 | ncall: 191044 | eff(%): 19.428 | loglstar: -inf < 84.484 < inf | logz: -40.878 +/- 0.599 | dlogz: 20.792 > 0.309] + + 37136it [03:12, 193.00it/s, bound: 822 | nc: 5 | ncall: 191144 | eff(%): 19.428 | loglstar: -inf < 84.621 < inf | logz: -40.669 +/- 0.600 | dlogz: 20.516 > 0.309] + + 37159it [03:12, 201.22it/s, bound: 822 | nc: 5 | ncall: 191259 | eff(%): 19.429 | loglstar: -inf < 84.868 < inf | logz: -40.458 +/- 0.600 | dlogz: 20.227 > 0.309] + + 37180it [03:13, 196.80it/s, bound: 823 | nc: 5 | ncall: 191364 | eff(%): 19.429 | loglstar: -inf < 85.114 < inf | logz: -40.264 +/- 0.600 | dlogz: 19.963 > 0.309] + + 37201it [03:13, 200.16it/s, bound: 823 | nc: 5 | ncall: 191469 | eff(%): 19.429 | loglstar: -inf < 85.386 < inf | logz: -40.080 +/- 0.600 | dlogz: 19.709 > 0.309] + + 37222it [03:13, 196.11it/s, bound: 824 | nc: 5 | ncall: 191574 | eff(%): 19.430 | loglstar: -inf < 85.683 < inf | logz: -39.892 +/- 0.600 | dlogz: 19.452 > 0.309] + + 37242it [03:13, 193.75it/s, bound: 824 | nc: 5 | ncall: 191674 | eff(%): 19.430 | loglstar: -inf < 85.949 < inf | logz: -39.707 +/- 0.600 | dlogz: 19.200 > 0.309] + + 37263it [03:13, 192.76it/s, bound: 825 | nc: 5 | ncall: 191779 | eff(%): 19.430 | loglstar: -inf < 86.136 < inf | logz: -39.512 +/- 0.601 | dlogz: 18.935 > 0.309] + + 37284it [03:13, 197.30it/s, bound: 825 | nc: 5 | ncall: 191884 | eff(%): 19.430 | loglstar: -inf < 86.329 < inf | logz: -39.330 +/- 0.601 | dlogz: 18.682 > 0.309] + + 37304it [03:13, 192.68it/s, bound: 825 | nc: 5 | ncall: 191984 | eff(%): 19.431 | loglstar: -inf < 86.638 < inf | logz: -39.158 +/- 0.601 | dlogz: 18.444 > 0.309] + + 37324it [03:13, 184.61it/s, bound: 826 | nc: 5 | ncall: 192084 | eff(%): 19.431 | loglstar: -inf < 87.042 < inf | logz: -38.965 +/- 0.601 | dlogz: 18.185 > 0.309] + + 37344it [03:13, 187.68it/s, bound: 826 | nc: 5 | ncall: 192184 | eff(%): 19.431 | loglstar: -inf < 87.277 < inf | logz: -38.758 +/- 0.601 | dlogz: 17.912 > 0.309] + + 37363it [03:14, 182.65it/s, bound: 827 | nc: 5 | ncall: 192279 | eff(%): 19.432 | loglstar: -inf < 87.471 < inf | logz: -38.571 +/- 0.601 | dlogz: 17.661 > 0.309] + + 37383it [03:14, 185.77it/s, bound: 827 | nc: 5 | ncall: 192379 | eff(%): 19.432 | loglstar: -inf < 87.723 < inf | logz: -38.381 +/- 0.602 | dlogz: 17.404 > 0.309] + + 37402it [03:14, 173.88it/s, bound: 828 | nc: 5 | ncall: 192474 | eff(%): 19.432 | loglstar: -inf < 87.858 < inf | logz: -38.210 +/- 0.602 | dlogz: 17.169 > 0.309] + + 37420it [03:14, 173.91it/s, bound: 828 | nc: 5 | ncall: 192564 | eff(%): 19.433 | loglstar: -inf < 88.159 < inf | logz: -38.051 +/- 0.602 | dlogz: 16.951 > 0.309] + + 37438it [03:14, 170.16it/s, bound: 828 | nc: 5 | ncall: 192654 | eff(%): 19.433 | loglstar: -inf < 88.266 < inf | logz: -37.893 +/- 0.602 | dlogz: 16.732 > 0.309] + + 37456it [03:14, 163.39it/s, bound: 829 | nc: 5 | ncall: 192744 | eff(%): 19.433 | loglstar: -inf < 88.455 < inf | logz: -37.742 +/- 0.602 | dlogz: 19.035 > 0.309] + + 37474it [03:14, 167.55it/s, bound: 829 | nc: 5 | ncall: 192834 | eff(%): 19.433 | loglstar: -inf < 88.645 < inf | logz: -37.594 +/- 0.602 | dlogz: 18.827 > 0.309] + + 37491it [03:14, 166.75it/s, bound: 830 | nc: 5 | ncall: 192919 | eff(%): 19.434 | loglstar: -inf < 88.813 < inf | logz: -37.461 +/- 0.602 | dlogz: 18.637 > 0.309] + + 37511it [03:14, 174.87it/s, bound: 830 | nc: 5 | ncall: 193019 | eff(%): 19.434 | loglstar: -inf < 89.079 < inf | logz: -37.301 +/- 0.602 | dlogz: 18.411 > 0.309] + + 37532it [03:15, 182.88it/s, bound: 830 | nc: 5 | ncall: 193124 | eff(%): 19.434 | loglstar: -inf < 89.451 < inf | logz: -37.126 +/- 0.603 | dlogz: 18.167 > 0.309] + + 37551it [03:15, 183.14it/s, bound: 831 | nc: 5 | ncall: 193219 | eff(%): 19.434 | loglstar: -inf < 89.648 < inf | logz: -36.952 +/- 0.603 | dlogz: 17.929 > 0.309] + + 37570it [03:15, 183.37it/s, bound: 831 | nc: 5 | ncall: 193314 | eff(%): 19.435 | loglstar: -inf < 89.883 < inf | logz: -36.786 +/- 0.603 | dlogz: 17.700 > 0.309] + + 37589it [03:15, 174.95it/s, bound: 832 | nc: 5 | ncall: 193409 | eff(%): 19.435 | loglstar: -inf < 90.019 < inf | logz: -36.623 +/- 0.603 | dlogz: 17.472 > 0.309] + + 37607it [03:15, 171.98it/s, bound: 832 | nc: 5 | ncall: 193499 | eff(%): 19.435 | loglstar: -inf < 90.112 < inf | logz: -36.484 +/- 0.603 | dlogz: 17.272 > 0.309] + + 37625it [03:15, 166.25it/s, bound: 833 | nc: 5 | ncall: 193589 | eff(%): 19.436 | loglstar: -inf < 90.292 < inf | logz: -36.354 +/- 0.603 | dlogz: 17.082 > 0.309] + + 37645it [03:15, 174.36it/s, bound: 833 | nc: 5 | ncall: 193689 | eff(%): 19.436 | loglstar: -inf < 90.467 < inf | logz: -36.213 +/- 0.603 | dlogz: 16.874 > 0.309] + + 37666it [03:15, 183.49it/s, bound: 833 | nc: 5 | ncall: 193794 | eff(%): 19.436 | loglstar: -inf < 90.672 < inf | logz: -36.068 +/- 0.604 | dlogz: 16.658 > 0.309] + + 37685it [03:15, 177.14it/s, bound: 834 | nc: 5 | ncall: 193889 | eff(%): 19.436 | loglstar: -inf < 90.904 < inf | logz: -35.937 +/- 0.604 | dlogz: 16.464 > 0.309] + + 37706it [03:16, 184.25it/s, bound: 834 | nc: 5 | ncall: 193994 | eff(%): 19.437 | loglstar: -inf < 91.312 < inf | logz: -35.770 +/- 0.604 | dlogz: 16.228 > 0.309] + + 37725it [03:16, 182.83it/s, bound: 835 | nc: 5 | ncall: 194089 | eff(%): 19.437 | loglstar: -inf < 91.592 < inf | logz: -35.604 +/- 0.604 | dlogz: 16.000 > 0.309] + + 37745it [03:16, 187.11it/s, bound: 835 | nc: 5 | ncall: 194189 | eff(%): 19.437 | loglstar: -inf < 91.880 < inf | logz: -35.425 +/- 0.604 | dlogz: 15.755 > 0.309] + + 37765it [03:16, 189.21it/s, bound: 836 | nc: 5 | ncall: 194289 | eff(%): 19.438 | loglstar: -inf < 92.141 < inf | logz: -35.236 +/- 0.605 | dlogz: 15.499 > 0.309] + + 37786it [03:16, 194.28it/s, bound: 836 | nc: 5 | ncall: 194394 | eff(%): 19.438 | loglstar: -inf < 92.332 < inf | logz: -35.048 +/- 0.605 | dlogz: 15.240 > 0.309] + + 37806it [03:16, 181.01it/s, bound: 837 | nc: 5 | ncall: 194494 | eff(%): 19.438 | loglstar: -inf < 92.612 < inf | logz: -34.870 +/- 0.605 | dlogz: 14.996 > 0.309] + + 37825it [03:16, 172.68it/s, bound: 837 | nc: 5 | ncall: 194589 | eff(%): 19.438 | loglstar: -inf < 92.788 < inf | logz: -34.709 +/- 0.605 | dlogz: 14.770 > 0.309] + + 37846it [03:16, 180.42it/s, bound: 837 | nc: 5 | ncall: 194694 | eff(%): 19.439 | loglstar: -inf < 93.049 < inf | logz: -34.523 +/- 0.606 | dlogz: 16.057 > 0.309] + + 37865it [03:16, 181.64it/s, bound: 838 | nc: 5 | ncall: 194789 | eff(%): 19.439 | loglstar: -inf < 93.218 < inf | logz: -34.363 +/- 0.606 | dlogz: 15.833 > 0.309] + + 37887it [03:16, 191.44it/s, bound: 838 | nc: 5 | ncall: 194899 | eff(%): 19.439 | loglstar: -inf < 93.396 < inf | logz: -34.184 +/- 0.606 | dlogz: 15.580 > 0.309] + + 37907it [03:17, 191.90it/s, bound: 839 | nc: 5 | ncall: 194999 | eff(%): 19.440 | loglstar: -inf < 93.614 < inf | logz: -34.035 +/- 0.606 | dlogz: 15.364 > 0.309] + + 37927it [03:17, 191.93it/s, bound: 839 | nc: 5 | ncall: 195099 | eff(%): 19.440 | loglstar: -inf < 93.757 < inf | logz: -33.883 +/- 0.606 | dlogz: 15.145 > 0.309] + + 37948it [03:17, 194.81it/s, bound: 840 | nc: 5 | ncall: 195204 | eff(%): 19.440 | loglstar: -inf < 94.019 < inf | logz: -33.725 +/- 0.606 | dlogz: 14.918 > 0.309] + + 37968it [03:17, 194.11it/s, bound: 840 | nc: 5 | ncall: 195304 | eff(%): 19.440 | loglstar: -inf < 94.248 < inf | logz: -33.581 +/- 0.606 | dlogz: 14.707 > 0.309] + + 37988it [03:17, 192.00it/s, bound: 841 | nc: 5 | ncall: 195404 | eff(%): 19.441 | loglstar: -inf < 94.489 < inf | logz: -33.431 +/- 0.606 | dlogz: 14.491 > 0.309] + + 38010it [03:17, 199.39it/s, bound: 841 | nc: 5 | ncall: 195514 | eff(%): 19.441 | loglstar: -inf < 94.672 < inf | logz: -33.267 +/- 0.607 | dlogz: 14.253 > 0.309] + + 38030it [03:17, 198.46it/s, bound: 842 | nc: 5 | ncall: 195614 | eff(%): 19.441 | loglstar: -inf < 94.884 < inf | logz: -33.123 +/- 0.607 | dlogz: 15.676 > 0.309] + + 38053it [03:17, 207.53it/s, bound: 842 | nc: 5 | ncall: 195729 | eff(%): 19.442 | loglstar: -inf < 95.048 < inf | logz: -32.958 +/- 0.607 | dlogz: 15.505 > 0.309] + + 38074it [03:17, 201.95it/s, bound: 843 | nc: 5 | ncall: 195834 | eff(%): 19.442 | loglstar: -inf < 95.422 < inf | logz: -32.807 +/- 0.607 | dlogz: 15.284 > 0.309] + + 38098it [03:18, 210.20it/s, bound: 843 | nc: 5 | ncall: 195954 | eff(%): 19.442 | loglstar: -inf < 95.785 < inf | logz: -32.616 +/- 0.607 | dlogz: 15.014 > 0.309] + + 38120it [03:18, 206.51it/s, bound: 844 | nc: 5 | ncall: 196064 | eff(%): 19.443 | loglstar: -inf < 95.970 < inf | logz: -32.422 +/- 0.608 | dlogz: 14.746 > 0.309] + + 38143it [03:18, 212.00it/s, bound: 844 | nc: 5 | ncall: 196179 | eff(%): 19.443 | loglstar: -inf < 96.243 < inf | logz: -32.235 +/- 0.608 | dlogz: 14.483 > 0.309] + + 38165it [03:18, 209.62it/s, bound: 845 | nc: 5 | ncall: 196289 | eff(%): 19.443 | loglstar: -inf < 96.387 < inf | logz: -32.064 +/- 0.608 | dlogz: 14.237 > 0.309] + + 38187it [03:18, 210.27it/s, bound: 845 | nc: 5 | ncall: 196399 | eff(%): 19.444 | loglstar: -inf < 96.609 < inf | logz: -31.906 +/- 0.608 | dlogz: 14.006 > 0.309] + + 38209it [03:18, 203.24it/s, bound: 846 | nc: 5 | ncall: 196509 | eff(%): 19.444 | loglstar: -inf < 96.854 < inf | logz: -31.746 +/- 0.608 | dlogz: 13.773 > 0.309] + + 38231it [03:18, 206.15it/s, bound: 846 | nc: 5 | ncall: 196619 | eff(%): 19.444 | loglstar: -inf < 96.973 < inf | logz: -31.594 +/- 0.609 | dlogz: 13.546 > 0.309] + + 38252it [03:18, 201.64it/s, bound: 847 | nc: 5 | ncall: 196724 | eff(%): 19.445 | loglstar: -inf < 97.223 < inf | logz: -31.456 +/- 0.609 | dlogz: 16.095 > 0.309] + + 38274it [03:18, 203.85it/s, bound: 847 | nc: 5 | ncall: 196834 | eff(%): 19.445 | loglstar: -inf < 97.602 < inf | logz: -31.299 +/- 0.609 | dlogz: 15.866 > 0.309] + + 38295it [03:19, 193.62it/s, bound: 848 | nc: 5 | ncall: 196939 | eff(%): 19.445 | loglstar: -inf < 97.966 < inf | logz: -31.119 +/- 0.609 | dlogz: 15.617 > 0.309] + + 38315it [03:19, 194.03it/s, bound: 848 | nc: 5 | ncall: 197039 | eff(%): 19.445 | loglstar: -inf < 98.261 < inf | logz: -30.931 +/- 0.609 | dlogz: 15.363 > 0.309] + + 38335it [03:19, 190.02it/s, bound: 849 | nc: 5 | ncall: 197139 | eff(%): 19.446 | loglstar: -inf < 98.534 < inf | logz: -30.748 +/- 0.610 | dlogz: 15.113 > 0.309] + + 38356it [03:19, 195.33it/s, bound: 849 | nc: 5 | ncall: 197244 | eff(%): 19.446 | loglstar: -inf < 98.729 < inf | logz: -30.559 +/- 0.610 | dlogz: 14.853 > 0.309] + + 38377it [03:19, 192.86it/s, bound: 850 | nc: 5 | ncall: 197349 | eff(%): 19.446 | loglstar: -inf < 98.988 < inf | logz: -30.378 +/- 0.610 | dlogz: 14.602 > 0.309] + + 38397it [03:19, 194.39it/s, bound: 850 | nc: 5 | ncall: 197449 | eff(%): 19.447 | loglstar: -inf < 99.064 < inf | logz: -30.214 +/- 0.610 | dlogz: 14.370 > 0.309] + + 38417it [03:19, 195.19it/s, bound: 850 | nc: 5 | ncall: 197549 | eff(%): 19.447 | loglstar: -inf < 99.397 < inf | logz: -30.045 +/- 0.611 | dlogz: 14.135 > 0.309] + + 38437it [03:19, 191.18it/s, bound: 851 | nc: 5 | ncall: 197649 | eff(%): 19.447 | loglstar: -inf < 99.532 < inf | logz: -29.887 +/- 0.611 | dlogz: 13.909 > 0.309] + + 38457it [03:19, 192.15it/s, bound: 851 | nc: 5 | ncall: 197749 | eff(%): 19.447 | loglstar: -inf < 99.770 < inf | logz: -29.733 +/- 0.611 | dlogz: 13.689 > 0.309] + + 38477it [03:19, 187.96it/s, bound: 852 | nc: 5 | ncall: 197849 | eff(%): 19.448 | loglstar: -inf < 100.100 < inf | logz: -29.568 +/- 0.611 | dlogz: 13.458 > 0.309] + + 38498it [03:20, 192.85it/s, bound: 852 | nc: 5 | ncall: 197954 | eff(%): 19.448 | loglstar: -inf < 100.307 < inf | logz: -29.396 +/- 0.611 | dlogz: 13.217 > 0.309] + + 38518it [03:20, 191.22it/s, bound: 853 | nc: 5 | ncall: 198054 | eff(%): 19.448 | loglstar: -inf < 100.557 < inf | logz: -29.224 +/- 0.611 | dlogz: 16.768 > 0.309] + + 38540it [03:20, 198.32it/s, bound: 853 | nc: 5 | ncall: 198164 | eff(%): 19.449 | loglstar: -inf < 100.722 < inf | logz: -29.047 +/- 0.612 | dlogz: 16.517 > 0.309] + + 38561it [03:20, 200.03it/s, bound: 854 | nc: 5 | ncall: 198269 | eff(%): 19.449 | loglstar: -inf < 101.093 < inf | logz: -28.875 +/- 0.612 | dlogz: 16.276 > 0.309] + + 38582it [03:20, 192.56it/s, bound: 854 | nc: 5 | ncall: 198374 | eff(%): 19.449 | loglstar: -inf < 101.346 < inf | logz: -28.688 +/- 0.612 | dlogz: 16.018 > 0.309] + + 38602it [03:20, 189.61it/s, bound: 855 | nc: 5 | ncall: 198474 | eff(%): 19.449 | loglstar: -inf < 101.644 < inf | logz: -28.507 +/- 0.612 | dlogz: 15.772 > 0.309] + + 38624it [03:20, 197.30it/s, bound: 855 | nc: 5 | ncall: 198584 | eff(%): 19.450 | loglstar: -inf < 102.062 < inf | logz: -28.288 +/- 0.613 | dlogz: 15.480 > 0.309] + + 38646it [03:20, 203.43it/s, bound: 855 | nc: 5 | ncall: 198694 | eff(%): 19.450 | loglstar: -inf < 102.355 < inf | logz: -28.050 +/- 0.613 | dlogz: 15.169 > 0.309] + + 38667it [03:20, 203.92it/s, bound: 856 | nc: 5 | ncall: 198799 | eff(%): 19.450 | loglstar: -inf < 102.687 < inf | logz: -27.823 +/- 0.613 | dlogz: 14.873 > 0.309] + + 38691it [03:21, 212.33it/s, bound: 856 | nc: 5 | ncall: 198919 | eff(%): 19.451 | loglstar: -inf < 102.993 < inf | logz: -27.570 +/- 0.614 | dlogz: 14.539 > 0.309] + + 38713it [03:21, 210.62it/s, bound: 857 | nc: 5 | ncall: 199029 | eff(%): 19.451 | loglstar: -inf < 103.200 < inf | logz: -27.349 +/- 0.614 | dlogz: 14.244 > 0.309] + + 38736it [03:21, 216.00it/s, bound: 857 | nc: 5 | ncall: 199144 | eff(%): 19.451 | loglstar: -inf < 103.327 < inf | logz: -27.143 +/- 0.614 | dlogz: 15.352 > 0.309] + + 38758it [03:21, 214.81it/s, bound: 858 | nc: 5 | ncall: 199254 | eff(%): 19.452 | loglstar: -inf < 103.552 < inf | logz: -26.966 +/- 0.614 | dlogz: 15.101 > 0.309] + + 38780it [03:21, 214.78it/s, bound: 858 | nc: 5 | ncall: 199364 | eff(%): 19.452 | loglstar: -inf < 103.629 < inf | logz: -26.804 +/- 0.614 | dlogz: 14.864 > 0.309] + + 38802it [03:21, 203.27it/s, bound: 859 | nc: 5 | ncall: 199474 | eff(%): 19.452 | loglstar: -inf < 103.824 < inf | logz: -26.656 +/- 0.614 | dlogz: 14.643 > 0.309] + + 38826it [03:21, 212.98it/s, bound: 859 | nc: 5 | ncall: 199594 | eff(%): 19.452 | loglstar: -inf < 104.032 < inf | logz: -26.503 +/- 0.614 | dlogz: 17.618 > 0.309] + + 38848it [03:21, 211.68it/s, bound: 860 | nc: 5 | ncall: 199704 | eff(%): 19.453 | loglstar: -inf < 104.243 < inf | logz: -26.363 +/- 0.614 | dlogz: 17.405 > 0.309] + + 38871it [03:21, 216.86it/s, bound: 860 | nc: 5 | ncall: 199819 | eff(%): 19.453 | loglstar: -inf < 104.379 < inf | logz: -26.217 +/- 0.614 | dlogz: 17.182 > 0.309] + + 38893it [03:21, 207.59it/s, bound: 861 | nc: 5 | ncall: 199929 | eff(%): 19.453 | loglstar: -inf < 104.537 < inf | logz: -26.090 +/- 0.615 | dlogz: 16.981 > 0.309] + + 38916it [03:22, 211.22it/s, bound: 861 | nc: 5 | ncall: 200044 | eff(%): 19.454 | loglstar: -inf < 104.913 < inf | logz: -25.944 +/- 0.615 | dlogz: 16.759 > 0.309] + + 38938it [03:22, 200.45it/s, bound: 862 | nc: 5 | ncall: 200154 | eff(%): 19.454 | loglstar: -inf < 105.268 < inf | logz: -25.794 +/- 0.615 | dlogz: 16.536 > 0.309] + + 38960it [03:22, 205.34it/s, bound: 862 | nc: 5 | ncall: 200264 | eff(%): 19.454 | loglstar: -inf < 105.618 < inf | logz: -25.615 +/- 0.615 | dlogz: 16.286 > 0.309] + + 38981it [03:22, 199.00it/s, bound: 863 | nc: 5 | ncall: 200369 | eff(%): 19.455 | loglstar: -inf < 105.892 < inf | logz: -25.434 +/- 0.615 | dlogz: 16.034 > 0.309] + + 39003it [03:22, 202.28it/s, bound: 863 | nc: 5 | ncall: 200479 | eff(%): 19.455 | loglstar: -inf < 105.892 < inf | logz: -25.263 +/- 0.616 | dlogz: 15.786 > 0.309] + + 39024it [03:22, 195.36it/s, bound: 864 | nc: 5 | ncall: 200584 | eff(%): 19.455 | loglstar: -inf < 105.953 < inf | logz: -25.127 +/- 0.616 | dlogz: 15.573 > 0.309] + + 39048it [03:22, 205.84it/s, bound: 864 | nc: 5 | ncall: 200704 | eff(%): 19.456 | loglstar: -inf < 106.142 < inf | logz: -24.992 +/- 0.616 | dlogz: 15.358 > 0.309] + + 39069it [03:22, 201.90it/s, bound: 865 | nc: 5 | ncall: 200809 | eff(%): 19.456 | loglstar: -inf < 106.230 < inf | logz: -24.882 +/- 0.616 | dlogz: 15.177 > 0.309] + + 39092it [03:22, 209.09it/s, bound: 865 | nc: 5 | ncall: 200924 | eff(%): 19.456 | loglstar: -inf < 106.611 < inf | logz: -24.754 +/- 0.616 | dlogz: 14.973 > 0.309] + + 39114it [03:23, 210.33it/s, bound: 866 | nc: 5 | ncall: 201034 | eff(%): 19.456 | loglstar: -inf < 106.875 < inf | logz: -24.618 +/- 0.616 | dlogz: 14.764 > 0.309] + + 39138it [03:23, 216.42it/s, bound: 866 | nc: 5 | ncall: 201154 | eff(%): 19.457 | loglstar: -inf < 107.123 < inf | logz: -24.465 +/- 0.616 | dlogz: 14.531 > 0.309] + + 39160it [03:23, 217.12it/s, bound: 867 | nc: 5 | ncall: 201264 | eff(%): 19.457 | loglstar: -inf < 107.360 < inf | logz: -24.316 +/- 0.616 | dlogz: 14.309 > 0.309] + + 39183it [03:23, 220.19it/s, bound: 867 | nc: 5 | ncall: 201379 | eff(%): 19.457 | loglstar: -inf < 107.604 < inf | logz: -24.168 +/- 0.617 | dlogz: 14.085 > 0.309] + + 39206it [03:23, 213.47it/s, bound: 868 | nc: 5 | ncall: 201494 | eff(%): 19.458 | loglstar: -inf < 107.766 < inf | logz: -24.019 +/- 0.617 | dlogz: 13.858 > 0.309] + + 39229it [03:23, 217.26it/s, bound: 868 | nc: 5 | ncall: 201609 | eff(%): 19.458 | loglstar: -inf < 107.947 < inf | logz: -23.879 +/- 0.617 | dlogz: 13.642 > 0.309] + + 39251it [03:23, 214.32it/s, bound: 869 | nc: 5 | ncall: 201719 | eff(%): 19.458 | loglstar: -inf < 108.106 < inf | logz: -23.749 +/- 0.617 | dlogz: 13.437 > 0.309] + + 39275it [03:23, 220.36it/s, bound: 869 | nc: 5 | ncall: 201839 | eff(%): 19.459 | loglstar: -inf < 108.291 < inf | logz: -23.613 +/- 0.617 | dlogz: 19.330 > 0.309] + + 39298it [03:23, 216.29it/s, bound: 870 | nc: 5 | ncall: 201954 | eff(%): 19.459 | loglstar: -inf < 108.422 < inf | logz: -23.489 +/- 0.617 | dlogz: 19.129 > 0.309] + + 39322it [03:24, 214.05it/s, bound: 871 | nc: 5 | ncall: 202074 | eff(%): 19.459 | loglstar: -inf < 108.581 < inf | logz: -23.367 +/- 0.618 | dlogz: 18.927 > 0.309] + + 39344it [03:24, 214.87it/s, bound: 871 | nc: 5 | ncall: 202184 | eff(%): 19.460 | loglstar: -inf < 108.770 < inf | logz: -23.260 +/- 0.618 | dlogz: 18.746 > 0.309] + + 39366it [03:24, 207.94it/s, bound: 871 | nc: 5 | ncall: 202294 | eff(%): 19.460 | loglstar: -inf < 108.886 < inf | logz: -23.150 +/- 0.618 | dlogz: 18.562 > 0.309] + + 39387it [03:24, 202.48it/s, bound: 872 | nc: 5 | ncall: 202399 | eff(%): 19.460 | loglstar: -inf < 108.998 < inf | logz: -23.054 +/- 0.618 | dlogz: 18.395 > 0.309] + + 39409it [03:24, 206.74it/s, bound: 872 | nc: 5 | ncall: 202509 | eff(%): 19.460 | loglstar: -inf < 109.192 < inf | logz: -22.954 +/- 0.618 | dlogz: 18.222 > 0.309] + + 39430it [03:24, 204.98it/s, bound: 873 | nc: 5 | ncall: 202614 | eff(%): 19.461 | loglstar: -inf < 109.371 < inf | logz: -22.858 +/- 0.618 | dlogz: 18.056 > 0.309] + + 39453it [03:24, 211.52it/s, bound: 873 | nc: 5 | ncall: 202729 | eff(%): 19.461 | loglstar: -inf < 109.466 < inf | logz: -22.756 +/- 0.618 | dlogz: 17.878 > 0.309] + + 39475it [03:24, 211.38it/s, bound: 874 | nc: 5 | ncall: 202839 | eff(%): 19.461 | loglstar: -inf < 109.665 < inf | logz: -22.665 +/- 0.618 | dlogz: 17.713 > 0.309] + + 39498it [03:24, 215.04it/s, bound: 874 | nc: 5 | ncall: 202954 | eff(%): 19.462 | loglstar: -inf < 109.929 < inf | logz: -22.560 +/- 0.618 | dlogz: 17.531 > 0.309] + + 39520it [03:24, 211.55it/s, bound: 875 | nc: 5 | ncall: 203064 | eff(%): 19.462 | loglstar: -inf < 110.054 < inf | logz: -22.456 +/- 0.619 | dlogz: 17.354 > 0.309] + + 39543it [03:25, 216.73it/s, bound: 875 | nc: 5 | ncall: 203179 | eff(%): 19.462 | loglstar: -inf < 110.260 < inf | logz: -22.350 +/- 0.619 | dlogz: 17.172 > 0.309] + + 39565it [03:25, 215.74it/s, bound: 876 | nc: 5 | ncall: 203289 | eff(%): 19.462 | loglstar: -inf < 110.497 < inf | logz: -22.244 +/- 0.619 | dlogz: 16.993 > 0.309] + + 39587it [03:25, 215.21it/s, bound: 876 | nc: 5 | ncall: 203399 | eff(%): 19.463 | loglstar: -inf < 110.617 < inf | logz: -22.141 +/- 0.619 | dlogz: 16.815 > 0.309] + + 39609it [03:25, 204.04it/s, bound: 877 | nc: 5 | ncall: 203509 | eff(%): 19.463 | loglstar: -inf < 110.966 < inf | logz: -22.030 +/- 0.619 | dlogz: 16.632 > 0.309] + + 39630it [03:25, 201.22it/s, bound: 877 | nc: 5 | ncall: 203614 | eff(%): 19.463 | loglstar: -inf < 111.134 < inf | logz: -21.916 +/- 0.620 | dlogz: 16.448 > 0.309] + + 39651it [03:25, 188.49it/s, bound: 878 | nc: 5 | ncall: 203719 | eff(%): 19.464 | loglstar: -inf < 111.417 < inf | logz: -21.796 +/- 0.620 | dlogz: 16.259 > 0.309] + + 39673it [03:25, 196.46it/s, bound: 878 | nc: 5 | ncall: 203829 | eff(%): 19.464 | loglstar: -inf < 111.513 < inf | logz: -21.671 +/- 0.620 | dlogz: 16.059 > 0.309] + + 39693it [03:25, 197.29it/s, bound: 879 | nc: 5 | ncall: 203929 | eff(%): 19.464 | loglstar: -inf < 111.665 < inf | logz: -21.564 +/- 0.620 | dlogz: 15.886 > 0.309] + + 39717it [03:25, 208.21it/s, bound: 879 | nc: 5 | ncall: 204049 | eff(%): 19.464 | loglstar: -inf < 111.867 < inf | logz: -21.437 +/- 0.620 | dlogz: 15.678 > 0.309] + + 39739it [03:26, 209.94it/s, bound: 880 | nc: 5 | ncall: 204159 | eff(%): 19.465 | loglstar: -inf < 112.060 < inf | logz: -21.326 +/- 0.621 | dlogz: 15.494 > 0.309] + + 39761it [03:26, 210.66it/s, bound: 880 | nc: 5 | ncall: 204269 | eff(%): 19.465 | loglstar: -inf < 112.319 < inf | logz: -21.207 +/- 0.621 | dlogz: 15.302 > 0.309] + + 39783it [03:26, 208.53it/s, bound: 881 | nc: 5 | ncall: 204379 | eff(%): 19.465 | loglstar: -inf < 112.582 < inf | logz: -21.079 +/- 0.621 | dlogz: 15.101 > 0.309] + + 39807it [03:26, 217.25it/s, bound: 881 | nc: 5 | ncall: 204499 | eff(%): 19.466 | loglstar: -inf < 112.683 < inf | logz: -20.945 +/- 0.621 | dlogz: 14.887 > 0.309] + + 39829it [03:26, 216.74it/s, bound: 882 | nc: 5 | ncall: 204609 | eff(%): 19.466 | loglstar: -inf < 112.819 < inf | logz: -20.831 +/- 0.622 | dlogz: 14.699 > 0.309] + + 39853it [03:26, 221.17it/s, bound: 882 | nc: 5 | ncall: 204729 | eff(%): 19.466 | loglstar: -inf < 112.963 < inf | logz: -20.712 +/- 0.622 | dlogz: 14.500 > 0.309] + + 39876it [03:26, 215.78it/s, bound: 883 | nc: 5 | ncall: 204844 | eff(%): 19.467 | loglstar: -inf < 113.200 < inf | logz: -20.601 +/- 0.622 | dlogz: 14.312 > 0.309] + + 39898it [03:26, 199.54it/s, bound: 883 | nc: 5 | ncall: 204954 | eff(%): 19.467 | loglstar: -inf < 113.615 < inf | logz: -20.479 +/- 0.622 | dlogz: 14.118 > 0.309] + + 39919it [03:26, 188.56it/s, bound: 884 | nc: 5 | ncall: 205059 | eff(%): 19.467 | loglstar: -inf < 113.748 < inf | logz: -20.353 +/- 0.622 | dlogz: 13.921 > 0.309] + + 39941it [03:27, 196.06it/s, bound: 884 | nc: 5 | ncall: 205169 | eff(%): 19.467 | loglstar: -inf < 113.921 < inf | logz: -20.226 +/- 0.623 | dlogz: 13.721 > 0.309] + + 39961it [03:27, 196.03it/s, bound: 885 | nc: 5 | ncall: 205269 | eff(%): 19.468 | loglstar: -inf < 114.140 < inf | logz: -20.112 +/- 0.623 | dlogz: 13.541 > 0.309] + + 39983it [03:27, 202.12it/s, bound: 885 | nc: 5 | ncall: 205379 | eff(%): 19.468 | loglstar: -inf < 114.217 < inf | logz: -19.989 +/- 0.623 | dlogz: 13.343 > 0.309] + + 40004it [03:27, 202.56it/s, bound: 886 | nc: 5 | ncall: 205484 | eff(%): 19.468 | loglstar: -inf < 114.366 < inf | logz: -19.880 +/- 0.623 | dlogz: 13.164 > 0.309] + + 40025it [03:27, 204.43it/s, bound: 886 | nc: 5 | ncall: 205589 | eff(%): 19.468 | loglstar: -inf < 114.510 < inf | logz: -19.774 +/- 0.623 | dlogz: 12.988 > 0.309] + + 40047it [03:27, 206.55it/s, bound: 887 | nc: 5 | ncall: 205699 | eff(%): 19.469 | loglstar: -inf < 114.661 < inf | logz: -19.665 +/- 0.624 | dlogz: 12.806 > 0.309] + + 40072it [03:27, 219.13it/s, bound: 887 | nc: 5 | ncall: 205824 | eff(%): 19.469 | loglstar: -inf < 114.749 < inf | logz: -19.551 +/- 0.624 | dlogz: 12.607 > 0.309] + + 40094it [03:27, 215.66it/s, bound: 888 | nc: 5 | ncall: 205934 | eff(%): 19.469 | loglstar: -inf < 114.985 < inf | logz: -19.455 +/- 0.624 | dlogz: 12.438 > 0.309] + + 40119it [03:27, 223.69it/s, bound: 888 | nc: 5 | ncall: 206059 | eff(%): 19.470 | loglstar: -inf < 115.152 < inf | logz: -19.342 +/- 0.624 | dlogz: 12.242 > 0.309] + + 40142it [03:27, 222.51it/s, bound: 889 | nc: 5 | ncall: 206174 | eff(%): 19.470 | loglstar: -inf < 115.247 < inf | logz: -19.244 +/- 0.624 | dlogz: 12.066 > 0.309] + + 40166it [03:28, 226.19it/s, bound: 889 | nc: 5 | ncall: 206294 | eff(%): 19.470 | loglstar: -inf < 115.386 < inf | logz: -19.147 +/- 0.624 | dlogz: 11.889 > 0.309] + + 40189it [03:28, 223.24it/s, bound: 890 | nc: 5 | ncall: 206409 | eff(%): 19.471 | loglstar: -inf < 115.572 < inf | logz: -19.056 +/- 0.624 | dlogz: 11.721 > 0.309] + + 40215it [03:28, 231.34it/s, bound: 890 | nc: 5 | ncall: 206539 | eff(%): 19.471 | loglstar: -inf < 115.909 < inf | logz: -18.945 +/- 0.625 | dlogz: 11.525 > 0.309] + + 40239it [03:28, 229.30it/s, bound: 891 | nc: 5 | ncall: 206659 | eff(%): 19.471 | loglstar: -inf < 116.117 < inf | logz: -18.833 +/- 0.625 | dlogz: 11.332 > 0.309] + + 40264it [03:28, 233.24it/s, bound: 891 | nc: 5 | ncall: 206784 | eff(%): 19.472 | loglstar: -inf < 116.365 < inf | logz: -18.709 +/- 0.625 | dlogz: 11.442 > 0.309] + + 40288it [03:28, 226.87it/s, bound: 892 | nc: 5 | ncall: 206904 | eff(%): 19.472 | loglstar: -inf < 116.574 < inf | logz: -18.593 +/- 0.625 | dlogz: 11.246 > 0.309] + + 40312it [03:28, 223.11it/s, bound: 893 | nc: 5 | ncall: 207024 | eff(%): 19.472 | loglstar: -inf < 116.782 < inf | logz: -18.473 +/- 0.625 | dlogz: 11.369 > 0.309] + + 40337it [03:28, 229.50it/s, bound: 893 | nc: 5 | ncall: 207149 | eff(%): 19.472 | loglstar: -inf < 116.927 < inf | logz: -18.349 +/- 0.626 | dlogz: 11.162 > 0.309] + + 40361it [03:28, 225.92it/s, bound: 894 | nc: 5 | ncall: 207269 | eff(%): 19.473 | loglstar: -inf < 117.141 < inf | logz: -18.236 +/- 0.626 | dlogz: 10.968 > 0.309] + + 40385it [03:29, 228.92it/s, bound: 894 | nc: 5 | ncall: 207389 | eff(%): 19.473 | loglstar: -inf < 117.270 < inf | logz: -18.123 +/- 0.626 | dlogz: 10.775 > 0.309] + + 40408it [03:29, 223.15it/s, bound: 895 | nc: 5 | ncall: 207504 | eff(%): 19.473 | loglstar: -inf < 117.468 < inf | logz: -18.019 +/- 0.626 | dlogz: 10.594 > 0.309] + + 40431it [03:29, 223.29it/s, bound: 895 | nc: 5 | ncall: 207619 | eff(%): 19.474 | loglstar: -inf < 117.581 < inf | logz: -17.917 +/- 0.626 | dlogz: 10.415 > 0.309] + + 40454it [03:29, 217.10it/s, bound: 896 | nc: 5 | ncall: 207734 | eff(%): 19.474 | loglstar: -inf < 117.776 < inf | logz: -17.817 +/- 0.627 | dlogz: 10.239 > 0.309] + + 40476it [03:29, 216.83it/s, bound: 896 | nc: 5 | ncall: 207844 | eff(%): 19.474 | loglstar: -inf < 118.063 < inf | logz: -17.714 +/- 0.627 | dlogz: 10.063 > 0.309] + + 40498it [03:29, 212.89it/s, bound: 897 | nc: 5 | ncall: 207954 | eff(%): 19.474 | loglstar: -inf < 118.408 < inf | logz: -17.601 +/- 0.627 | dlogz: 9.877 > 0.309] + + 40520it [03:29, 203.56it/s, bound: 897 | nc: 5 | ncall: 208064 | eff(%): 19.475 | loglstar: -inf < 118.513 < inf | logz: -17.477 +/- 0.627 | dlogz: 9.679 > 0.309] + + 40541it [03:29, 196.43it/s, bound: 898 | nc: 5 | ncall: 208169 | eff(%): 19.475 | loglstar: -inf < 118.648 < inf | logz: -17.370 +/- 0.628 | dlogz: 9.502 > 0.309] + + 40564it [03:29, 205.56it/s, bound: 898 | nc: 5 | ncall: 208284 | eff(%): 19.475 | loglstar: -inf < 118.793 < inf | logz: -17.260 +/- 0.628 | dlogz: 12.112 > 0.309] + + 40588it [03:29, 214.06it/s, bound: 899 | nc: 5 | ncall: 208404 | eff(%): 19.476 | loglstar: -inf < 119.091 < inf | logz: -17.134 +/- 0.628 | dlogz: 11.908 > 0.309] + + 40612it [03:30, 215.73it/s, bound: 900 | nc: 5 | ncall: 208524 | eff(%): 19.476 | loglstar: -inf < 119.337 < inf | logz: -17.000 +/- 0.628 | dlogz: 11.694 > 0.309] + + 40636it [03:30, 222.32it/s, bound: 900 | nc: 5 | ncall: 208644 | eff(%): 19.476 | loglstar: -inf < 119.449 < inf | logz: -16.875 +/- 0.628 | dlogz: 11.488 > 0.309] + + 40659it [03:30, 214.92it/s, bound: 901 | nc: 5 | ncall: 208759 | eff(%): 19.477 | loglstar: -inf < 119.569 < inf | logz: -16.765 +/- 0.629 | dlogz: 11.300 > 0.309] + + 40683it [03:30, 219.89it/s, bound: 901 | nc: 5 | ncall: 208879 | eff(%): 19.477 | loglstar: -inf < 119.703 < inf | logz: -16.656 +/- 0.629 | dlogz: 11.110 > 0.309] + + 40706it [03:30, 219.18it/s, bound: 902 | nc: 5 | ncall: 208994 | eff(%): 19.477 | loglstar: -inf < 119.877 < inf | logz: -16.555 +/- 0.629 | dlogz: 10.933 > 0.309] + + 40730it [03:30, 223.66it/s, bound: 902 | nc: 5 | ncall: 209114 | eff(%): 19.477 | loglstar: -inf < 120.126 < inf | logz: -16.448 +/- 0.629 | dlogz: 10.746 > 0.309] + + 40753it [03:30, 198.98it/s, bound: 903 | nc: 5 | ncall: 209229 | eff(%): 19.478 | loglstar: -inf < 120.252 < inf | logz: -16.342 +/- 0.629 | dlogz: 10.563 > 0.309] + + 40774it [03:30, 201.68it/s, bound: 903 | nc: 5 | ncall: 209334 | eff(%): 19.478 | loglstar: -inf < 120.480 < inf | logz: -16.246 +/- 0.630 | dlogz: 10.398 > 0.309] + + 40795it [03:30, 203.60it/s, bound: 904 | nc: 5 | ncall: 209439 | eff(%): 19.478 | loglstar: -inf < 120.641 < inf | logz: -16.150 +/- 0.630 | dlogz: 10.232 > 0.309] + + 40817it [03:31, 207.37it/s, bound: 904 | nc: 5 | ncall: 209549 | eff(%): 19.478 | loglstar: -inf < 120.805 < inf | logz: -16.050 +/- 0.630 | dlogz: 10.058 > 0.309] + + 40839it [03:31, 208.82it/s, bound: 905 | nc: 5 | ncall: 209659 | eff(%): 19.479 | loglstar: -inf < 120.930 < inf | logz: -15.951 +/- 0.630 | dlogz: 9.885 > 0.309] + + 40862it [03:31, 212.47it/s, bound: 905 | nc: 5 | ncall: 209774 | eff(%): 19.479 | loglstar: -inf < 121.173 < inf | logz: -15.844 +/- 0.630 | dlogz: 10.528 > 0.309] + + 40884it [03:31, 214.57it/s, bound: 906 | nc: 5 | ncall: 209884 | eff(%): 19.479 | loglstar: -inf < 121.384 < inf | logz: -15.738 +/- 0.630 | dlogz: 10.348 > 0.309] + + 40909it [03:31, 222.76it/s, bound: 906 | nc: 5 | ncall: 210009 | eff(%): 19.480 | loglstar: -inf < 121.535 < inf | logz: -15.620 +/- 0.631 | dlogz: 10.148 > 0.309] + + 40932it [03:31, 217.00it/s, bound: 907 | nc: 5 | ncall: 210124 | eff(%): 19.480 | loglstar: -inf < 121.768 < inf | logz: -15.512 +/- 0.631 | dlogz: 9.963 > 0.309] + + 40954it [03:31, 213.07it/s, bound: 907 | nc: 5 | ncall: 210234 | eff(%): 19.480 | loglstar: -inf < 122.020 < inf | logz: -15.402 +/- 0.631 | dlogz: 9.779 > 0.309] + + 40976it [03:31, 214.71it/s, bound: 908 | nc: 5 | ncall: 210344 | eff(%): 19.480 | loglstar: -inf < 122.316 < inf | logz: -15.281 +/- 0.631 | dlogz: 9.586 > 0.309] + + 40998it [03:31, 213.70it/s, bound: 908 | nc: 5 | ncall: 210454 | eff(%): 19.481 | loglstar: -inf < 122.441 < inf | logz: -15.161 +/- 0.632 | dlogz: 9.392 > 0.309] + + 41020it [03:32, 215.14it/s, bound: 909 | nc: 5 | ncall: 210564 | eff(%): 19.481 | loglstar: -inf < 122.667 < inf | logz: -15.038 +/- 0.632 | dlogz: 9.196 > 0.309] + + 41044it [03:32, 220.26it/s, bound: 909 | nc: 5 | ncall: 210684 | eff(%): 19.481 | loglstar: -inf < 122.774 < inf | logz: -14.910 +/- 0.632 | dlogz: 8.987 > 0.309] + + 41067it [03:32, 218.12it/s, bound: 910 | nc: 5 | ncall: 210799 | eff(%): 19.482 | loglstar: -inf < 122.987 < inf | logz: -14.791 +/- 0.632 | dlogz: 8.792 > 0.309] + + 41091it [03:32, 222.24it/s, bound: 910 | nc: 5 | ncall: 210919 | eff(%): 19.482 | loglstar: -inf < 123.199 < inf | logz: -14.668 +/- 0.633 | dlogz: 8.589 > 0.309] + + 41114it [03:32, 215.04it/s, bound: 911 | nc: 5 | ncall: 211034 | eff(%): 19.482 | loglstar: -inf < 123.389 < inf | logz: -14.553 +/- 0.633 | dlogz: 10.703 > 0.309] + + 41139it [03:32, 223.60it/s, bound: 911 | nc: 5 | ncall: 211159 | eff(%): 19.482 | loglstar: -inf < 123.522 < inf | logz: -14.429 +/- 0.633 | dlogz: 10.496 > 0.309] + + 41162it [03:32, 212.25it/s, bound: 912 | nc: 5 | ncall: 211274 | eff(%): 19.483 | loglstar: -inf < 123.631 < inf | logz: -14.322 +/- 0.633 | dlogz: 10.312 > 0.309] + + 41187it [03:32, 220.96it/s, bound: 912 | nc: 5 | ncall: 211399 | eff(%): 19.483 | loglstar: -inf < 123.839 < inf | logz: -14.213 +/- 0.633 | dlogz: 10.689 > 0.309] + + 41210it [03:32, 217.73it/s, bound: 913 | nc: 5 | ncall: 211514 | eff(%): 19.483 | loglstar: -inf < 124.011 < inf | logz: -14.114 +/- 0.633 | dlogz: 10.512 > 0.309] + + 41237it [03:32, 229.37it/s, bound: 913 | nc: 5 | ncall: 211649 | eff(%): 19.484 | loglstar: -inf < 124.265 < inf | logz: -13.990 +/- 0.634 | dlogz: 10.299 > 0.309] + + 41261it [03:33, 224.65it/s, bound: 914 | nc: 5 | ncall: 211769 | eff(%): 19.484 | loglstar: -inf < 124.454 < inf | logz: -13.878 +/- 0.634 | dlogz: 10.107 > 0.309] + + 41287it [03:33, 223.41it/s, bound: 915 | nc: 5 | ncall: 211899 | eff(%): 19.484 | loglstar: -inf < 124.722 < inf | logz: -13.756 +/- 0.634 | dlogz: 9.899 > 0.309] + + 41310it [03:33, 223.53it/s, bound: 915 | nc: 5 | ncall: 212014 | eff(%): 19.485 | loglstar: -inf < 124.794 < inf | logz: -13.648 +/- 0.634 | dlogz: 9.713 > 0.309] + + 41333it [03:33, 221.66it/s, bound: 916 | nc: 5 | ncall: 212129 | eff(%): 19.485 | loglstar: -inf < 124.853 < inf | logz: -13.550 +/- 0.634 | dlogz: 9.537 > 0.309] + + 41359it [03:33, 231.82it/s, bound: 916 | nc: 5 | ncall: 212259 | eff(%): 19.485 | loglstar: -inf < 125.052 < inf | logz: -13.449 +/- 0.635 | dlogz: 9.349 > 0.309] + + 41383it [03:33, 226.62it/s, bound: 917 | nc: 5 | ncall: 212379 | eff(%): 19.485 | loglstar: -inf < 125.212 < inf | logz: -13.355 +/- 0.635 | dlogz: 9.174 > 0.309] + + 41406it [03:33, 222.43it/s, bound: 918 | nc: 5 | ncall: 212494 | eff(%): 19.486 | loglstar: -inf < 125.379 < inf | logz: -13.268 +/- 0.635 | dlogz: 9.011 > 0.309] + + 41430it [03:33, 224.97it/s, bound: 918 | nc: 5 | ncall: 212614 | eff(%): 19.486 | loglstar: -inf < 125.600 < inf | logz: -13.174 +/- 0.635 | dlogz: 8.837 > 0.309] + + 41453it [03:33, 215.54it/s, bound: 919 | nc: 5 | ncall: 212729 | eff(%): 19.486 | loglstar: -inf < 125.747 < inf | logz: -13.082 +/- 0.635 | dlogz: 8.669 > 0.309] + + 41476it [03:34, 218.26it/s, bound: 919 | nc: 5 | ncall: 212844 | eff(%): 19.487 | loglstar: -inf < 125.947 < inf | logz: -12.990 +/- 0.635 | dlogz: 8.500 > 0.309] + + 41498it [03:34, 209.76it/s, bound: 920 | nc: 5 | ncall: 212954 | eff(%): 19.487 | loglstar: -inf < 126.116 < inf | logz: -12.896 +/- 0.635 | dlogz: 8.333 > 0.309] + + 41520it [03:34, 210.28it/s, bound: 920 | nc: 5 | ncall: 213064 | eff(%): 19.487 | loglstar: -inf < 126.243 < inf | logz: -12.806 +/- 0.636 | dlogz: 8.169 > 0.309] + + 41542it [03:34, 194.54it/s, bound: 921 | nc: 5 | ncall: 213174 | eff(%): 19.487 | loglstar: -inf < 126.508 < inf | logz: -12.716 +/- 0.636 | dlogz: 8.006 > 0.309] + + 41562it [03:34, 188.32it/s, bound: 921 | nc: 5 | ncall: 213274 | eff(%): 19.488 | loglstar: -inf < 126.715 < inf | logz: -12.626 +/- 0.636 | dlogz: 7.850 > 0.309] + + 41582it [03:34, 167.17it/s, bound: 922 | nc: 5 | ncall: 213374 | eff(%): 19.488 | loglstar: -inf < 126.832 < inf | logz: -12.537 +/- 0.636 | dlogz: 7.694 > 0.309] + + 41601it [03:34, 171.98it/s, bound: 922 | nc: 5 | ncall: 213469 | eff(%): 19.488 | loglstar: -inf < 126.986 < inf | logz: -12.454 +/- 0.636 | dlogz: 7.777 > 0.309] + + 41619it [03:34, 168.85it/s, bound: 923 | nc: 5 | ncall: 213559 | eff(%): 19.488 | loglstar: -inf < 127.093 < inf | logz: -12.374 +/- 0.637 | dlogz: 7.637 > 0.309] + + 41642it [03:34, 185.03it/s, bound: 923 | nc: 5 | ncall: 213674 | eff(%): 19.489 | loglstar: -inf < 127.238 < inf | logz: -12.274 +/- 0.637 | dlogz: 9.011 > 0.309] + + 41665it [03:35, 195.73it/s, bound: 924 | nc: 5 | ncall: 213789 | eff(%): 19.489 | loglstar: -inf < 127.410 < inf | logz: -12.177 +/- 0.637 | dlogz: 8.837 > 0.309] + + 41690it [03:35, 209.53it/s, bound: 924 | nc: 5 | ncall: 213914 | eff(%): 19.489 | loglstar: -inf < 127.659 < inf | logz: -12.066 +/- 0.637 | dlogz: 10.606 > 0.309] + + 41713it [03:35, 213.16it/s, bound: 925 | nc: 5 | ncall: 214029 | eff(%): 19.489 | loglstar: -inf < 127.842 < inf | logz: -11.965 +/- 0.637 | dlogz: 10.428 > 0.309] + + 41737it [03:35, 220.49it/s, bound: 925 | nc: 5 | ncall: 214149 | eff(%): 19.490 | loglstar: -inf < 128.122 < inf | logz: -11.857 +/- 0.638 | dlogz: 10.240 > 0.309] + + 41760it [03:35, 222.97it/s, bound: 926 | nc: 5 | ncall: 214264 | eff(%): 19.490 | loglstar: -inf < 128.263 < inf | logz: -11.744 +/- 0.638 | dlogz: 10.051 > 0.309] + + 41786it [03:35, 232.66it/s, bound: 926 | nc: 5 | ncall: 214394 | eff(%): 19.490 | loglstar: -inf < 128.434 < inf | logz: -11.625 +/- 0.638 | dlogz: 9.844 > 0.309] + + 41810it [03:35, 221.03it/s, bound: 927 | nc: 5 | ncall: 214514 | eff(%): 19.491 | loglstar: -inf < 128.642 < inf | logz: -11.516 +/- 0.638 | dlogz: 9.655 > 0.309] + + 41833it [03:35, 213.86it/s, bound: 927 | nc: 5 | ncall: 214629 | eff(%): 19.491 | loglstar: -inf < 128.795 < inf | logz: -11.414 +/- 0.638 | dlogz: 9.477 > 0.309] + + 41855it [03:35, 210.73it/s, bound: 928 | nc: 5 | ncall: 214739 | eff(%): 19.491 | loglstar: -inf < 128.927 < inf | logz: -11.319 +/- 0.639 | dlogz: 9.308 > 0.309] + + 41877it [03:36, 211.32it/s, bound: 928 | nc: 5 | ncall: 214849 | eff(%): 19.491 | loglstar: -inf < 129.157 < inf | logz: -11.222 +/- 0.639 | dlogz: 9.138 > 0.309] + + 41899it [03:36, 209.04it/s, bound: 929 | nc: 5 | ncall: 214959 | eff(%): 19.492 | loglstar: -inf < 129.224 < inf | logz: -11.127 +/- 0.639 | dlogz: 8.969 > 0.309] + + 41923it [03:36, 215.08it/s, bound: 929 | nc: 5 | ncall: 215079 | eff(%): 19.492 | loglstar: -inf < 129.410 < inf | logz: -11.029 +/- 0.639 | dlogz: 8.791 > 0.309] + + 41945it [03:36, 212.36it/s, bound: 930 | nc: 5 | ncall: 215189 | eff(%): 19.492 | loglstar: -inf < 129.562 < inf | logz: -10.938 +/- 0.639 | dlogz: 8.626 > 0.309] + + 41970it [03:36, 222.85it/s, bound: 930 | nc: 5 | ncall: 215314 | eff(%): 19.492 | loglstar: -inf < 129.697 < inf | logz: -10.840 +/- 0.640 | dlogz: 8.591 > 0.309] + + 41993it [03:36, 219.46it/s, bound: 931 | nc: 5 | ncall: 215429 | eff(%): 19.493 | loglstar: -inf < 129.913 < inf | logz: -10.746 +/- 0.640 | dlogz: 8.420 > 0.309] + + 42017it [03:36, 224.80it/s, bound: 931 | nc: 5 | ncall: 215549 | eff(%): 19.493 | loglstar: -inf < 130.091 < inf | logz: -10.649 +/- 0.640 | dlogz: 8.244 > 0.309] + + 42040it [03:36, 218.73it/s, bound: 932 | nc: 5 | ncall: 215664 | eff(%): 19.493 | loglstar: -inf < 130.231 < inf | logz: -10.555 +/- 0.640 | dlogz: 8.072 > 0.309] + + 42062it [03:36, 210.92it/s, bound: 932 | nc: 5 | ncall: 215774 | eff(%): 19.494 | loglstar: -inf < 130.358 < inf | logz: -10.468 +/- 0.640 | dlogz: 7.912 > 0.309] + + 42084it [03:37, 186.86it/s, bound: 933 | nc: 5 | ncall: 215884 | eff(%): 19.494 | loglstar: -inf < 130.474 < inf | logz: -10.385 +/- 0.640 | dlogz: 7.756 > 0.309] + + 42106it [03:37, 194.44it/s, bound: 933 | nc: 5 | ncall: 215994 | eff(%): 19.494 | loglstar: -inf < 130.613 < inf | logz: -10.305 +/- 0.641 | dlogz: 7.602 > 0.309] + + 42126it [03:37, 193.15it/s, bound: 934 | nc: 5 | ncall: 216094 | eff(%): 19.494 | loglstar: -inf < 130.763 < inf | logz: -10.230 +/- 0.641 | dlogz: 7.461 > 0.309] + + 42149it [03:37, 203.04it/s, bound: 934 | nc: 5 | ncall: 216209 | eff(%): 19.495 | loglstar: -inf < 130.962 < inf | logz: -10.146 +/- 0.641 | dlogz: 7.300 > 0.309] + + 42170it [03:37, 198.73it/s, bound: 935 | nc: 5 | ncall: 216314 | eff(%): 19.495 | loglstar: -inf < 131.028 < inf | logz: -10.069 +/- 0.641 | dlogz: 7.153 > 0.309] + + 42191it [03:37, 201.34it/s, bound: 935 | nc: 5 | ncall: 216419 | eff(%): 19.495 | loglstar: -inf < 131.161 < inf | logz: -9.996 +/- 0.641 | dlogz: 7.010 > 0.309] + + 42212it [03:37, 200.19it/s, bound: 936 | nc: 5 | ncall: 216524 | eff(%): 19.495 | loglstar: -inf < 131.306 < inf | logz: -9.922 +/- 0.641 | dlogz: 6.867 > 0.309] + + 42235it [03:37, 208.00it/s, bound: 936 | nc: 5 | ncall: 216639 | eff(%): 19.496 | loglstar: -inf < 131.443 < inf | logz: -9.842 +/- 0.641 | dlogz: 6.710 > 0.309] + + 42256it [03:37, 207.33it/s, bound: 937 | nc: 5 | ncall: 216744 | eff(%): 19.496 | loglstar: -inf < 131.500 < inf | logz: -9.772 +/- 0.642 | dlogz: 7.380 > 0.309] + + 42279it [03:37, 212.78it/s, bound: 937 | nc: 5 | ncall: 216859 | eff(%): 19.496 | loglstar: -inf < 131.661 < inf | logz: -9.699 +/- 0.642 | dlogz: 7.230 > 0.309] + + 42301it [03:38, 209.10it/s, bound: 938 | nc: 5 | ncall: 216969 | eff(%): 19.496 | loglstar: -inf < 131.863 < inf | logz: -9.627 +/- 0.642 | dlogz: 7.085 > 0.309] + + 42325it [03:38, 217.32it/s, bound: 938 | nc: 5 | ncall: 217089 | eff(%): 19.497 | loglstar: -inf < 131.898 < inf | logz: -9.551 +/- 0.642 | dlogz: 8.031 > 0.309] + + 42347it [03:38, 214.33it/s, bound: 939 | nc: 5 | ncall: 217199 | eff(%): 19.497 | loglstar: -inf < 132.087 < inf | logz: -9.485 +/- 0.642 | dlogz: 7.893 > 0.309] + + 42371it [03:38, 219.38it/s, bound: 939 | nc: 5 | ncall: 217319 | eff(%): 19.497 | loglstar: -inf < 132.357 < inf | logz: -9.405 +/- 0.642 | dlogz: 7.733 > 0.309] + + 42393it [03:38, 216.58it/s, bound: 940 | nc: 5 | ncall: 217429 | eff(%): 19.497 | loglstar: -inf < 132.427 < inf | logz: -9.330 +/- 0.642 | dlogz: 7.585 > 0.309] + + 42416it [03:38, 218.92it/s, bound: 940 | nc: 5 | ncall: 217544 | eff(%): 19.498 | loglstar: -inf < 132.541 < inf | logz: -9.258 +/- 0.643 | dlogz: 7.436 > 0.309] + + 42438it [03:38, 216.26it/s, bound: 941 | nc: 5 | ncall: 217654 | eff(%): 19.498 | loglstar: -inf < 132.759 < inf | logz: -9.189 +/- 0.643 | dlogz: 7.293 > 0.309] + + 42463it [03:38, 224.22it/s, bound: 941 | nc: 5 | ncall: 217779 | eff(%): 19.498 | loglstar: -inf < 132.921 < inf | logz: -9.106 +/- 0.643 | dlogz: 7.127 > 0.309] + + 42486it [03:38, 224.57it/s, bound: 942 | nc: 5 | ncall: 217894 | eff(%): 19.498 | loglstar: -inf < 133.018 < inf | logz: -9.030 +/- 0.643 | dlogz: 6.974 > 0.309] + + 42510it [03:39, 227.56it/s, bound: 942 | nc: 5 | ncall: 218014 | eff(%): 19.499 | loglstar: -inf < 133.149 < inf | logz: -8.955 +/- 0.643 | dlogz: 6.819 > 0.309] + + 42533it [03:39, 219.73it/s, bound: 943 | nc: 5 | ncall: 218129 | eff(%): 19.499 | loglstar: -inf < 133.259 < inf | logz: -8.884 +/- 0.643 | dlogz: 6.672 > 0.309] + + 42557it [03:39, 225.28it/s, bound: 943 | nc: 5 | ncall: 218249 | eff(%): 19.499 | loglstar: -inf < 133.379 < inf | logz: -8.813 +/- 0.644 | dlogz: 6.521 > 0.309] + + 42580it [03:39, 220.59it/s, bound: 944 | nc: 5 | ncall: 218364 | eff(%): 19.500 | loglstar: -inf < 133.454 < inf | logz: -8.750 +/- 0.644 | dlogz: 6.381 > 0.309] + + 42604it [03:39, 222.16it/s, bound: 944 | nc: 5 | ncall: 218484 | eff(%): 19.500 | loglstar: -inf < 133.569 < inf | logz: -8.685 +/- 0.644 | dlogz: 6.237 > 0.309] + + 42627it [03:39, 219.27it/s, bound: 945 | nc: 5 | ncall: 218599 | eff(%): 19.500 | loglstar: -inf < 133.709 < inf | logz: -8.626 +/- 0.644 | dlogz: 6.101 > 0.309] + + 42649it [03:39, 218.01it/s, bound: 945 | nc: 5 | ncall: 218709 | eff(%): 19.500 | loglstar: -inf < 133.786 < inf | logz: -8.569 +/- 0.644 | dlogz: 5.971 > 0.309] + + 42671it [03:39, 204.07it/s, bound: 946 | nc: 5 | ncall: 218819 | eff(%): 19.501 | loglstar: -inf < 133.977 < inf | logz: -8.515 +/- 0.644 | dlogz: 7.018 > 0.309] + + 42694it [03:39, 210.17it/s, bound: 946 | nc: 5 | ncall: 218934 | eff(%): 19.501 | loglstar: -inf < 134.052 < inf | logz: -8.455 +/- 0.644 | dlogz: 6.881 > 0.309] + + 42716it [03:40, 191.64it/s, bound: 947 | nc: 5 | ncall: 219044 | eff(%): 19.501 | loglstar: -inf < 134.169 < inf | logz: -8.400 +/- 0.644 | dlogz: 6.753 > 0.309] + + 42738it [03:40, 197.20it/s, bound: 947 | nc: 5 | ncall: 219154 | eff(%): 19.501 | loglstar: -inf < 134.264 < inf | logz: -8.345 +/- 0.644 | dlogz: 6.625 > 0.309] + + 42759it [03:40, 191.83it/s, bound: 948 | nc: 5 | ncall: 219259 | eff(%): 19.502 | loglstar: -inf < 134.404 < inf | logz: -8.294 +/- 0.645 | dlogz: 6.504 > 0.309] + + 42783it [03:40, 203.15it/s, bound: 948 | nc: 5 | ncall: 219379 | eff(%): 19.502 | loglstar: -inf < 134.511 < inf | logz: -8.236 +/- 0.645 | dlogz: 6.366 > 0.309] + + 42804it [03:40, 203.16it/s, bound: 949 | nc: 5 | ncall: 219484 | eff(%): 19.502 | loglstar: -inf < 134.666 < inf | logz: -8.186 +/- 0.645 | dlogz: 6.247 > 0.309] + + 42826it [03:40, 206.23it/s, bound: 949 | nc: 5 | ncall: 219594 | eff(%): 19.502 | loglstar: -inf < 134.768 < inf | logz: -8.134 +/- 0.645 | dlogz: 6.121 > 0.309] + + 42847it [03:40, 203.32it/s, bound: 950 | nc: 5 | ncall: 219699 | eff(%): 19.503 | loglstar: -inf < 134.974 < inf | logz: -8.083 +/- 0.645 | dlogz: 7.064 > 0.309] + + 42869it [03:40, 207.30it/s, bound: 950 | nc: 5 | ncall: 219809 | eff(%): 19.503 | loglstar: -inf < 135.151 < inf | logz: -8.024 +/- 0.645 | dlogz: 6.932 > 0.309] + + 42890it [03:40, 192.18it/s, bound: 951 | nc: 5 | ncall: 219914 | eff(%): 19.503 | loglstar: -inf < 135.257 < inf | logz: -7.966 +/- 0.645 | dlogz: 6.804 > 0.309] + + 42912it [03:41, 198.69it/s, bound: 951 | nc: 5 | ncall: 220024 | eff(%): 19.503 | loglstar: -inf < 135.453 < inf | logz: -7.907 +/- 0.646 | dlogz: 7.107 > 0.309] + + 42933it [03:41, 196.84it/s, bound: 952 | nc: 5 | ncall: 220129 | eff(%): 19.504 | loglstar: -inf < 135.513 < inf | logz: -7.850 +/- 0.646 | dlogz: 6.980 > 0.309] + + 42958it [03:41, 210.15it/s, bound: 952 | nc: 5 | ncall: 220254 | eff(%): 19.504 | loglstar: -inf < 135.589 < inf | logz: -7.785 +/- 0.646 | dlogz: 6.832 > 0.309] + + 42980it [03:41, 208.76it/s, bound: 953 | nc: 5 | ncall: 220364 | eff(%): 19.504 | loglstar: -inf < 135.770 < inf | logz: -7.730 +/- 0.646 | dlogz: 6.703 > 0.309] + + 43003it [03:41, 212.62it/s, bound: 953 | nc: 5 | ncall: 220479 | eff(%): 19.504 | loglstar: -inf < 135.890 < inf | logz: -7.670 +/- 0.646 | dlogz: 6.567 > 0.309] + + 43025it [03:41, 208.70it/s, bound: 954 | nc: 5 | ncall: 220589 | eff(%): 19.505 | loglstar: -inf < 135.992 < inf | logz: -7.614 +/- 0.646 | dlogz: 7.825 > 0.309] + + 43046it [03:41, 206.36it/s, bound: 954 | nc: 5 | ncall: 220694 | eff(%): 19.505 | loglstar: -inf < 136.049 < inf | logz: -7.563 +/- 0.647 | dlogz: 7.705 > 0.309] + + 43067it [03:41, 194.70it/s, bound: 955 | nc: 5 | ncall: 220799 | eff(%): 19.505 | loglstar: -inf < 136.049 < inf | logz: -7.515 +/- 0.647 | dlogz: 7.584 > 0.309] + + 43088it [03:41, 197.79it/s, bound: 955 | nc: 5 | ncall: 220904 | eff(%): 19.505 | loglstar: -inf < 136.162 < inf | logz: -7.471 +/- 0.647 | dlogz: 7.470 > 0.309] + + 43108it [03:41, 196.36it/s, bound: 956 | nc: 5 | ncall: 221004 | eff(%): 19.506 | loglstar: -inf < 136.222 < inf | logz: -7.431 +/- 0.647 | dlogz: 7.363 > 0.309] + + 43131it [03:42, 203.68it/s, bound: 956 | nc: 5 | ncall: 221119 | eff(%): 19.506 | loglstar: -inf < 136.365 < inf | logz: -7.386 +/- 0.647 | dlogz: 7.241 > 0.309] + + 43152it [03:42, 201.33it/s, bound: 957 | nc: 5 | ncall: 221224 | eff(%): 19.506 | loglstar: -inf < 136.446 < inf | logz: -7.344 +/- 0.647 | dlogz: 7.129 > 0.309] + + 43176it [03:42, 210.06it/s, bound: 957 | nc: 5 | ncall: 221344 | eff(%): 19.506 | loglstar: -inf < 136.660 < inf | logz: -7.295 +/- 0.647 | dlogz: 7.001 > 0.309] + + 43198it [03:42, 209.91it/s, bound: 958 | nc: 5 | ncall: 221454 | eff(%): 19.507 | loglstar: -inf < 136.744 < inf | logz: -7.249 +/- 0.647 | dlogz: 6.881 > 0.309] + + 43223it [03:42, 220.35it/s, bound: 958 | nc: 5 | ncall: 221579 | eff(%): 19.507 | loglstar: -inf < 136.826 < inf | logz: -7.199 +/- 0.648 | dlogz: 6.748 > 0.309] + + 43246it [03:42, 212.29it/s, bound: 959 | nc: 5 | ncall: 221694 | eff(%): 19.507 | loglstar: -inf < 136.928 < inf | logz: -7.155 +/- 0.648 | dlogz: 6.627 > 0.309] + + 43269it [03:42, 215.28it/s, bound: 959 | nc: 5 | ncall: 221809 | eff(%): 19.507 | loglstar: -inf < 137.086 < inf | logz: -7.110 +/- 0.648 | dlogz: 6.505 > 0.309] + + 43291it [03:42, 186.31it/s, bound: 960 | nc: 5 | ncall: 221919 | eff(%): 19.508 | loglstar: -inf < 137.264 < inf | logz: -7.065 +/- 0.648 | dlogz: 7.320 > 0.309] + + 43311it [03:43, 165.60it/s, bound: 960 | nc: 5 | ncall: 222019 | eff(%): 19.508 | loglstar: -inf < 137.359 < inf | logz: -7.022 +/- 0.648 | dlogz: 7.211 > 0.309] + + 43329it [03:43, 162.55it/s, bound: 961 | nc: 5 | ncall: 222109 | eff(%): 19.508 | loglstar: -inf < 137.376 < inf | logz: -6.986 +/- 0.648 | dlogz: 7.113 > 0.309] + + 43349it [03:43, 171.43it/s, bound: 961 | nc: 5 | ncall: 222209 | eff(%): 19.508 | loglstar: -inf < 137.481 < inf | logz: -6.947 +/- 0.648 | dlogz: 7.008 > 0.309] + + 43372it [03:43, 185.88it/s, bound: 961 | nc: 5 | ncall: 222324 | eff(%): 19.508 | loglstar: -inf < 137.624 < inf | logz: -6.902 +/- 0.648 | dlogz: 6.886 > 0.309] + + 43392it [03:43, 185.58it/s, bound: 962 | nc: 5 | ncall: 222424 | eff(%): 19.509 | loglstar: -inf < 137.710 < inf | logz: -6.863 +/- 0.649 | dlogz: 6.781 > 0.309] + + 43414it [03:43, 192.95it/s, bound: 962 | nc: 5 | ncall: 222534 | eff(%): 19.509 | loglstar: -inf < 137.840 < inf | logz: -6.821 +/- 0.649 | dlogz: 6.665 > 0.309] + + 43434it [03:43, 193.11it/s, bound: 963 | nc: 5 | ncall: 222634 | eff(%): 19.509 | loglstar: -inf < 137.954 < inf | logz: -6.782 +/- 0.649 | dlogz: 6.560 > 0.309] + + 43457it [03:43, 202.60it/s, bound: 963 | nc: 5 | ncall: 222749 | eff(%): 19.509 | loglstar: -inf < 137.976 < inf | logz: -6.739 +/- 0.649 | dlogz: 6.440 > 0.309] + + 43478it [03:43, 202.45it/s, bound: 964 | nc: 5 | ncall: 222854 | eff(%): 19.510 | loglstar: -inf < 138.091 < inf | logz: -6.702 +/- 0.649 | dlogz: 6.333 > 0.309] + + 43502it [03:43, 211.05it/s, bound: 964 | nc: 5 | ncall: 222974 | eff(%): 19.510 | loglstar: -inf < 138.193 < inf | logz: -6.660 +/- 0.649 | dlogz: 6.210 > 0.309] + + 43524it [03:44, 205.31it/s, bound: 965 | nc: 5 | ncall: 223084 | eff(%): 19.510 | loglstar: -inf < 138.271 < inf | logz: -6.622 +/- 0.649 | dlogz: 6.099 > 0.309] + + 43547it [03:44, 210.48it/s, bound: 965 | nc: 5 | ncall: 223199 | eff(%): 19.510 | loglstar: -inf < 138.431 < inf | logz: -6.582 +/- 0.649 | dlogz: 5.983 > 0.309] + + 43569it [03:44, 207.33it/s, bound: 966 | nc: 5 | ncall: 223309 | eff(%): 19.511 | loglstar: -inf < 138.625 < inf | logz: -6.542 +/- 0.650 | dlogz: 5.870 > 0.309] + + 43590it [03:44, 204.03it/s, bound: 967 | nc: 5 | ncall: 223414 | eff(%): 19.511 | loglstar: -inf < 138.714 < inf | logz: -6.502 +/- 0.650 | dlogz: 5.760 > 0.309] + + 43612it [03:44, 208.09it/s, bound: 967 | nc: 5 | ncall: 223524 | eff(%): 19.511 | loglstar: -inf < 138.893 < inf | logz: -6.460 +/- 0.650 | dlogz: 5.646 > 0.309] + + 43633it [03:44, 202.01it/s, bound: 968 | nc: 5 | ncall: 223629 | eff(%): 19.511 | loglstar: -inf < 138.982 < inf | logz: -6.419 +/- 0.650 | dlogz: 5.535 > 0.309] + + 43656it [03:44, 208.58it/s, bound: 968 | nc: 5 | ncall: 223744 | eff(%): 19.512 | loglstar: -inf < 139.126 < inf | logz: -6.374 +/- 0.650 | dlogz: 5.414 > 0.309] + + 43677it [03:44, 207.42it/s, bound: 969 | nc: 5 | ncall: 223849 | eff(%): 19.512 | loglstar: -inf < 139.242 < inf | logz: -6.331 +/- 0.650 | dlogz: 6.386 > 0.309] + + 43700it [03:44, 212.57it/s, bound: 969 | nc: 5 | ncall: 223964 | eff(%): 19.512 | loglstar: -inf < 139.335 < inf | logz: -6.285 +/- 0.651 | dlogz: 6.263 > 0.309] + + 43722it [03:45, 206.42it/s, bound: 970 | nc: 5 | ncall: 224074 | eff(%): 19.512 | loglstar: -inf < 139.402 < inf | logz: -6.243 +/- 0.651 | dlogz: 7.361 > 0.309] + + 43743it [03:45, 206.35it/s, bound: 970 | nc: 5 | ncall: 224179 | eff(%): 19.513 | loglstar: -inf < 139.466 < inf | logz: -6.205 +/- 0.651 | dlogz: 7.253 > 0.309] + + 43764it [03:45, 201.40it/s, bound: 971 | nc: 5 | ncall: 224284 | eff(%): 19.513 | loglstar: -inf < 139.638 < inf | logz: -6.167 +/- 0.651 | dlogz: 7.144 > 0.309] + + 43787it [03:45, 207.69it/s, bound: 971 | nc: 5 | ncall: 224399 | eff(%): 19.513 | loglstar: -inf < 139.718 < inf | logz: -6.124 +/- 0.651 | dlogz: 7.428 > 0.309] + + 43808it [03:45, 203.57it/s, bound: 972 | nc: 5 | ncall: 224504 | eff(%): 19.513 | loglstar: -inf < 139.849 < inf | logz: -6.085 +/- 0.651 | dlogz: 7.320 > 0.309] + + 43831it [03:45, 210.69it/s, bound: 972 | nc: 5 | ncall: 224619 | eff(%): 19.513 | loglstar: -inf < 139.984 < inf | logz: -6.042 +/- 0.652 | dlogz: 7.200 > 0.309] + + 43853it [03:45, 209.60it/s, bound: 973 | nc: 5 | ncall: 224729 | eff(%): 19.514 | loglstar: -inf < 140.102 < inf | logz: -6.001 +/- 0.652 | dlogz: 7.085 > 0.309] + + 43875it [03:45, 206.74it/s, bound: 973 | nc: 5 | ncall: 224839 | eff(%): 19.514 | loglstar: -inf < 140.148 < inf | logz: -5.960 +/- 0.652 | dlogz: 6.971 > 0.309] + + 43896it [03:45, 202.94it/s, bound: 974 | nc: 5 | ncall: 224944 | eff(%): 19.514 | loglstar: -inf < 140.255 < inf | logz: -5.924 +/- 0.652 | dlogz: 6.865 > 0.309] + + 43917it [03:46, 191.32it/s, bound: 974 | nc: 5 | ncall: 225049 | eff(%): 19.514 | loglstar: -inf < 140.335 < inf | logz: -5.887 +/- 0.652 | dlogz: 6.758 > 0.309] + + 43937it [03:46, 189.25it/s, bound: 975 | nc: 5 | ncall: 225149 | eff(%): 19.515 | loglstar: -inf < 140.409 < inf | logz: -5.853 +/- 0.652 | dlogz: 6.657 > 0.309] + + 43960it [03:46, 197.91it/s, bound: 975 | nc: 5 | ncall: 225264 | eff(%): 19.515 | loglstar: -inf < 140.589 < inf | logz: -5.813 +/- 0.652 | dlogz: 6.541 > 0.309] + + 43980it [03:46, 197.90it/s, bound: 976 | nc: 5 | ncall: 225364 | eff(%): 19.515 | loglstar: -inf < 140.670 < inf | logz: -5.778 +/- 0.653 | dlogz: 6.439 > 0.309] + + 44004it [03:46, 207.68it/s, bound: 976 | nc: 5 | ncall: 225484 | eff(%): 19.515 | loglstar: -inf < 140.849 < inf | logz: -5.736 +/- 0.653 | dlogz: 6.317 > 0.309] + + 44025it [03:46, 207.49it/s, bound: 977 | nc: 5 | ncall: 225589 | eff(%): 19.516 | loglstar: -inf < 140.961 < inf | logz: -5.697 +/- 0.653 | dlogz: 6.209 > 0.309] + + 44047it [03:46, 208.24it/s, bound: 977 | nc: 5 | ncall: 225699 | eff(%): 19.516 | loglstar: -inf < 141.088 < inf | logz: -5.656 +/- 0.653 | dlogz: 6.094 > 0.309] + + 44068it [03:46, 192.75it/s, bound: 978 | nc: 5 | ncall: 225804 | eff(%): 19.516 | loglstar: -inf < 141.203 < inf | logz: -5.616 +/- 0.653 | dlogz: 5.985 > 0.309] + + 44089it [03:46, 196.80it/s, bound: 978 | nc: 5 | ncall: 225909 | eff(%): 19.516 | loglstar: -inf < 141.305 < inf | logz: -5.576 +/- 0.653 | dlogz: 5.876 > 0.309] + + 44110it [03:46, 199.90it/s, bound: 978 | nc: 5 | ncall: 226014 | eff(%): 19.516 | loglstar: -inf < 141.365 < inf | logz: -5.538 +/- 0.654 | dlogz: 6.504 > 0.309] + + 44131it [03:47, 196.61it/s, bound: 979 | nc: 5 | ncall: 226119 | eff(%): 19.517 | loglstar: -inf < 141.453 < inf | logz: -5.501 +/- 0.654 | dlogz: 6.542 > 0.309] + + 44151it [03:47, 177.22it/s, bound: 980 | nc: 5 | ncall: 226219 | eff(%): 19.517 | loglstar: -inf < 141.552 < inf | logz: -5.466 +/- 0.654 | dlogz: 6.440 > 0.309] + + 44170it [03:47, 176.09it/s, bound: 980 | nc: 5 | ncall: 226314 | eff(%): 19.517 | loglstar: -inf < 141.663 < inf | logz: -5.433 +/- 0.654 | dlogz: 6.344 > 0.309] + + 44191it [03:47, 182.92it/s, bound: 980 | nc: 5 | ncall: 226419 | eff(%): 19.517 | loglstar: -inf < 141.784 < inf | logz: -5.396 +/- 0.654 | dlogz: 6.237 > 0.309] + + 44210it [03:47, 183.12it/s, bound: 981 | nc: 5 | ncall: 226514 | eff(%): 19.518 | loglstar: -inf < 141.904 < inf | logz: -5.362 +/- 0.654 | dlogz: 6.140 > 0.309] + + 44230it [03:47, 182.62it/s, bound: 981 | nc: 5 | ncall: 226614 | eff(%): 19.518 | loglstar: -inf < 142.014 < inf | logz: -5.325 +/- 0.654 | dlogz: 6.137 > 0.309] + + 44249it [03:47, 157.44it/s, bound: 982 | nc: 5 | ncall: 226709 | eff(%): 19.518 | loglstar: -inf < 142.087 < inf | logz: -5.290 +/- 0.655 | dlogz: 6.039 > 0.309] + + 44266it [03:47, 158.77it/s, bound: 982 | nc: 5 | ncall: 226794 | eff(%): 19.518 | loglstar: -inf < 142.136 < inf | logz: -5.260 +/- 0.655 | dlogz: 8.557 > 0.309] + + 44283it [03:48, 159.70it/s, bound: 983 | nc: 5 | ncall: 226879 | eff(%): 19.518 | loglstar: -inf < 142.216 < inf | logz: -5.231 +/- 0.655 | dlogz: 8.470 > 0.309] + + 44303it [03:48, 168.97it/s, bound: 983 | nc: 5 | ncall: 226979 | eff(%): 19.519 | loglstar: -inf < 142.311 < inf | logz: -5.196 +/- 0.655 | dlogz: 8.369 > 0.309] + + 44324it [03:48, 179.33it/s, bound: 983 | nc: 5 | ncall: 227084 | eff(%): 19.519 | loglstar: -inf < 142.382 < inf | logz: -5.161 +/- 0.655 | dlogz: 8.263 > 0.309] + + 44343it [03:48, 179.33it/s, bound: 984 | nc: 5 | ncall: 227179 | eff(%): 19.519 | loglstar: -inf < 142.489 < inf | logz: -5.129 +/- 0.655 | dlogz: 8.168 > 0.309] + + 44364it [03:48, 180.53it/s, bound: 985 | nc: 5 | ncall: 227284 | eff(%): 19.519 | loglstar: -inf < 142.605 < inf | logz: -5.093 +/- 0.655 | dlogz: 8.062 > 0.309] + + 44384it [03:48, 185.64it/s, bound: 985 | nc: 5 | ncall: 227384 | eff(%): 19.519 | loglstar: -inf < 142.697 < inf | logz: -5.059 +/- 0.656 | dlogz: 7.962 > 0.309] + + 44406it [03:48, 193.90it/s, bound: 985 | nc: 5 | ncall: 227494 | eff(%): 19.520 | loglstar: -inf < 142.772 < inf | logz: -5.022 +/- 0.656 | dlogz: 7.851 > 0.309] + + 44426it [03:48, 191.71it/s, bound: 986 | nc: 5 | ncall: 227594 | eff(%): 19.520 | loglstar: -inf < 142.824 < inf | logz: -4.990 +/- 0.656 | dlogz: 7.753 > 0.309] + + 44447it [03:48, 194.38it/s, bound: 986 | nc: 5 | ncall: 227699 | eff(%): 19.520 | loglstar: -inf < 143.010 < inf | logz: -4.956 +/- 0.656 | dlogz: 7.649 > 0.309] + + 44467it [03:48, 193.23it/s, bound: 987 | nc: 5 | ncall: 227799 | eff(%): 19.520 | loglstar: -inf < 143.077 < inf | logz: -4.924 +/- 0.656 | dlogz: 7.550 > 0.309] + + 44489it [03:49, 198.97it/s, bound: 987 | nc: 5 | ncall: 227909 | eff(%): 19.521 | loglstar: -inf < 143.211 < inf | logz: -4.887 +/- 0.656 | dlogz: 7.440 > 0.309] + + 44509it [03:49, 195.17it/s, bound: 988 | nc: 5 | ncall: 228009 | eff(%): 19.521 | loglstar: -inf < 143.277 < inf | logz: -4.854 +/- 0.656 | dlogz: 7.340 > 0.309] + + 44531it [03:49, 201.06it/s, bound: 988 | nc: 5 | ncall: 228119 | eff(%): 19.521 | loglstar: -inf < 143.399 < inf | logz: -4.818 +/- 0.657 | dlogz: 7.231 > 0.309] + + 44552it [03:49, 197.49it/s, bound: 989 | nc: 5 | ncall: 228224 | eff(%): 19.521 | loglstar: -inf < 143.439 < inf | logz: -4.784 +/- 0.657 | dlogz: 7.126 > 0.309] + + 44574it [03:49, 202.09it/s, bound: 989 | nc: 5 | ncall: 228334 | eff(%): 19.521 | loglstar: -inf < 143.578 < inf | logz: -4.750 +/- 0.657 | dlogz: 7.019 > 0.309] + + 44595it [03:49, 199.46it/s, bound: 990 | nc: 5 | ncall: 228439 | eff(%): 19.522 | loglstar: -inf < 143.709 < inf | logz: -4.717 +/- 0.657 | dlogz: 6.915 > 0.309] + + 44616it [03:49, 202.32it/s, bound: 990 | nc: 5 | ncall: 228544 | eff(%): 19.522 | loglstar: -inf < 143.775 < inf | logz: -4.683 +/- 0.657 | dlogz: 6.812 > 0.309] + + 44637it [03:49, 200.43it/s, bound: 991 | nc: 5 | ncall: 228649 | eff(%): 19.522 | loglstar: -inf < 143.926 < inf | logz: -4.649 +/- 0.657 | dlogz: 6.708 > 0.309] + + 44660it [03:49, 207.95it/s, bound: 991 | nc: 5 | ncall: 228764 | eff(%): 19.522 | loglstar: -inf < 144.011 < inf | logz: -4.612 +/- 0.658 | dlogz: 6.594 > 0.309] + + 44681it [03:50, 206.85it/s, bound: 992 | nc: 5 | ncall: 228869 | eff(%): 19.523 | loglstar: -inf < 144.109 < inf | logz: -4.579 +/- 0.658 | dlogz: 6.491 > 0.309] + + 44704it [03:50, 212.41it/s, bound: 992 | nc: 5 | ncall: 228984 | eff(%): 19.523 | loglstar: -inf < 144.334 < inf | logz: -4.541 +/- 0.658 | dlogz: 6.377 > 0.309] + + 44726it [03:50, 204.32it/s, bound: 993 | nc: 5 | ncall: 229094 | eff(%): 19.523 | loglstar: -inf < 144.484 < inf | logz: -4.501 +/- 0.658 | dlogz: 6.264 > 0.309] + + 44747it [03:50, 205.61it/s, bound: 993 | nc: 5 | ncall: 229199 | eff(%): 19.523 | loglstar: -inf < 144.578 < inf | logz: -4.462 +/- 0.658 | dlogz: 6.987 > 0.309] + + 44768it [03:50, 203.03it/s, bound: 994 | nc: 5 | ncall: 229304 | eff(%): 19.523 | loglstar: -inf < 144.720 < inf | logz: -4.423 +/- 0.658 | dlogz: 6.879 > 0.309] + + 44790it [03:50, 207.48it/s, bound: 994 | nc: 5 | ncall: 229414 | eff(%): 19.524 | loglstar: -inf < 144.829 < inf | logz: -4.383 +/- 0.659 | dlogz: 6.765 > 0.309] + + 44813it [03:50, 206.36it/s, bound: 995 | nc: 5 | ncall: 229529 | eff(%): 19.524 | loglstar: -inf < 144.950 < inf | logz: -4.341 +/- 0.659 | dlogz: 6.646 > 0.309] + + 44837it [03:50, 214.13it/s, bound: 995 | nc: 5 | ncall: 229649 | eff(%): 19.524 | loglstar: -inf < 145.027 < inf | logz: -4.297 +/- 0.659 | dlogz: 6.522 > 0.309] + + 44859it [03:50, 212.65it/s, bound: 996 | nc: 5 | ncall: 229759 | eff(%): 19.524 | loglstar: -inf < 145.091 < inf | logz: -4.259 +/- 0.659 | dlogz: 6.411 > 0.309] + + 44881it [03:50, 214.48it/s, bound: 996 | nc: 5 | ncall: 229869 | eff(%): 19.525 | loglstar: -inf < 145.231 < inf | logz: -4.221 +/- 0.659 | dlogz: 6.299 > 0.309] + + 44903it [03:51, 197.05it/s, bound: 997 | nc: 5 | ncall: 229979 | eff(%): 19.525 | loglstar: -inf < 145.304 < inf | logz: -4.183 +/- 0.660 | dlogz: 7.845 > 0.309] + + 44924it [03:51, 199.67it/s, bound: 997 | nc: 5 | ncall: 230084 | eff(%): 19.525 | loglstar: -inf < 145.364 < inf | logz: -4.148 +/- 0.660 | dlogz: 7.740 > 0.309] + + 44947it [03:51, 206.07it/s, bound: 997 | nc: 5 | ncall: 230199 | eff(%): 19.525 | loglstar: -inf < 145.416 < inf | logz: -4.112 +/- 0.660 | dlogz: 7.626 > 0.309] + + 44968it [03:51, 202.39it/s, bound: 998 | nc: 5 | ncall: 230304 | eff(%): 19.525 | loglstar: -inf < 145.603 < inf | logz: -4.078 +/- 0.660 | dlogz: 7.523 > 0.309] + + 44991it [03:51, 210.16it/s, bound: 998 | nc: 5 | ncall: 230419 | eff(%): 19.526 | loglstar: -inf < 145.745 < inf | logz: -4.039 +/- 0.660 | dlogz: 7.407 > 0.309] + + 45013it [03:51, 207.57it/s, bound: 999 | nc: 5 | ncall: 230529 | eff(%): 19.526 | loglstar: -inf < 145.926 < inf | logz: -4.001 +/- 0.660 | dlogz: 7.296 > 0.309] + + 45034it [03:51, 207.73it/s, bound: 999 | nc: 5 | ncall: 230634 | eff(%): 19.526 | loglstar: -inf < 146.068 < inf | logz: -3.963 +/- 0.661 | dlogz: 7.189 > 0.309] + + 45055it [03:51, 205.49it/s, bound: 1000 | nc: 5 | ncall: 230739 | eff(%): 19.526 | loglstar: -inf < 146.145 < inf | logz: -3.925 +/- 0.661 | dlogz: 7.080 > 0.309] + + 45077it [03:51, 209.53it/s, bound: 1000 | nc: 5 | ncall: 230849 | eff(%): 19.527 | loglstar: -inf < 146.386 < inf | logz: -3.883 +/- 0.661 | dlogz: 7.314 > 0.309] + + 45098it [03:52, 208.37it/s, bound: 1001 | nc: 5 | ncall: 230954 | eff(%): 19.527 | loglstar: -inf < 146.508 < inf | logz: -3.839 +/- 0.661 | dlogz: 7.200 > 0.309] + + 45122it [03:52, 215.19it/s, bound: 1001 | nc: 5 | ncall: 231074 | eff(%): 19.527 | loglstar: -inf < 146.650 < inf | logz: -3.790 +/- 0.661 | dlogz: 7.071 > 0.309] + + 45144it [03:52, 206.34it/s, bound: 1002 | nc: 5 | ncall: 231184 | eff(%): 19.527 | loglstar: -inf < 146.741 < inf | logz: -3.744 +/- 0.662 | dlogz: 6.952 > 0.309] + + 45165it [03:52, 206.22it/s, bound: 1002 | nc: 5 | ncall: 231289 | eff(%): 19.528 | loglstar: -inf < 146.927 < inf | logz: -3.700 +/- 0.662 | dlogz: 6.838 > 0.309] + + 45186it [03:52, 200.42it/s, bound: 1003 | nc: 5 | ncall: 231394 | eff(%): 19.528 | loglstar: -inf < 146.996 < inf | logz: -3.655 +/- 0.662 | dlogz: 6.723 > 0.309] + + 45208it [03:52, 205.96it/s, bound: 1003 | nc: 5 | ncall: 231504 | eff(%): 19.528 | loglstar: -inf < 147.077 < inf | logz: -3.610 +/- 0.662 | dlogz: 6.605 > 0.309] + + 45229it [03:52, 204.60it/s, bound: 1004 | nc: 5 | ncall: 231609 | eff(%): 19.528 | loglstar: -inf < 147.107 < inf | logz: -3.569 +/- 0.663 | dlogz: 6.494 > 0.309] + + 45252it [03:52, 210.49it/s, bound: 1004 | nc: 5 | ncall: 231724 | eff(%): 19.528 | loglstar: -inf < 147.240 < inf | logz: -3.526 +/- 0.663 | dlogz: 6.752 > 0.309] + + 45274it [03:52, 201.94it/s, bound: 1005 | nc: 5 | ncall: 231834 | eff(%): 19.529 | loglstar: -inf < 147.344 < inf | logz: -3.484 +/- 0.663 | dlogz: 6.638 > 0.309] + + 45297it [03:52, 207.45it/s, bound: 1005 | nc: 5 | ncall: 231949 | eff(%): 19.529 | loglstar: -inf < 147.541 < inf | logz: -3.440 +/- 0.663 | dlogz: 6.517 > 0.309] + + 45318it [03:53, 206.82it/s, bound: 1006 | nc: 5 | ncall: 232054 | eff(%): 19.529 | loglstar: -inf < 147.634 < inf | logz: -3.397 +/- 0.663 | dlogz: 6.938 > 0.309] + + 45342it [03:53, 215.44it/s, bound: 1006 | nc: 5 | ncall: 232174 | eff(%): 19.529 | loglstar: -inf < 147.741 < inf | logz: -3.350 +/- 0.664 | dlogz: 6.810 > 0.309] + + 45364it [03:53, 211.22it/s, bound: 1007 | nc: 5 | ncall: 232284 | eff(%): 19.530 | loglstar: -inf < 147.837 < inf | logz: -3.308 +/- 0.664 | dlogz: 6.695 > 0.309] + + 45387it [03:53, 215.69it/s, bound: 1007 | nc: 5 | ncall: 232399 | eff(%): 19.530 | loglstar: -inf < 147.992 < inf | logz: -3.263 +/- 0.664 | dlogz: 6.574 > 0.309] + + 45409it [03:53, 211.45it/s, bound: 1008 | nc: 5 | ncall: 232509 | eff(%): 19.530 | loglstar: -inf < 148.173 < inf | logz: -3.218 +/- 0.664 | dlogz: 6.793 > 0.309] + + 45431it [03:53, 212.75it/s, bound: 1008 | nc: 5 | ncall: 232619 | eff(%): 19.530 | loglstar: -inf < 148.264 < inf | logz: -3.173 +/- 0.664 | dlogz: 6.675 > 0.309] + + 45453it [03:53, 209.73it/s, bound: 1009 | nc: 5 | ncall: 232729 | eff(%): 19.530 | loglstar: -inf < 148.402 < inf | logz: -3.128 +/- 0.665 | dlogz: 6.557 > 0.309] + + 45475it [03:53, 211.31it/s, bound: 1009 | nc: 5 | ncall: 232839 | eff(%): 19.531 | loglstar: -inf < 148.504 < inf | logz: -3.083 +/- 0.665 | dlogz: 6.439 > 0.309] + + 45497it [03:53, 206.46it/s, bound: 1010 | nc: 5 | ncall: 232949 | eff(%): 19.531 | loglstar: -inf < 148.619 < inf | logz: -3.039 +/- 0.665 | dlogz: 7.183 > 0.309] + + 45520it [03:54, 211.31it/s, bound: 1010 | nc: 5 | ncall: 233064 | eff(%): 19.531 | loglstar: -inf < 148.726 < inf | logz: -2.993 +/- 0.665 | dlogz: 7.061 > 0.309] + + 45542it [03:54, 211.62it/s, bound: 1011 | nc: 5 | ncall: 233174 | eff(%): 19.531 | loglstar: -inf < 148.809 < inf | logz: -2.950 +/- 0.665 | dlogz: 6.944 > 0.309] + + 45565it [03:54, 216.31it/s, bound: 1011 | nc: 5 | ncall: 233289 | eff(%): 19.532 | loglstar: -inf < 148.898 < inf | logz: -2.905 +/- 0.666 | dlogz: 6.822 > 0.309] + + 45587it [03:54, 211.57it/s, bound: 1012 | nc: 5 | ncall: 233399 | eff(%): 19.532 | loglstar: -inf < 149.020 < inf | logz: -2.863 +/- 0.666 | dlogz: 7.318 > 0.309] + + 45609it [03:54, 213.78it/s, bound: 1012 | nc: 5 | ncall: 233509 | eff(%): 19.532 | loglstar: -inf < 149.176 < inf | logz: -2.821 +/- 0.666 | dlogz: 7.203 > 0.309] + + 45631it [03:54, 210.29it/s, bound: 1013 | nc: 5 | ncall: 233619 | eff(%): 19.532 | loglstar: -inf < 149.326 < inf | logz: -2.776 +/- 0.666 | dlogz: 7.085 > 0.309] + + 45656it [03:54, 220.08it/s, bound: 1013 | nc: 5 | ncall: 233744 | eff(%): 19.532 | loglstar: -inf < 149.440 < inf | logz: -2.726 +/- 0.666 | dlogz: 6.951 > 0.309] + + 45679it [03:54, 217.07it/s, bound: 1014 | nc: 5 | ncall: 233859 | eff(%): 19.533 | loglstar: -inf < 149.532 < inf | logz: -2.682 +/- 0.667 | dlogz: 6.830 > 0.309] + + 45702it [03:54, 219.27it/s, bound: 1014 | nc: 5 | ncall: 233974 | eff(%): 19.533 | loglstar: -inf < 149.654 < inf | logz: -2.637 +/- 0.667 | dlogz: 6.709 > 0.309] + + 45724it [03:54, 216.62it/s, bound: 1015 | nc: 5 | ncall: 234084 | eff(%): 19.533 | loglstar: -inf < 149.717 < inf | logz: -2.596 +/- 0.667 | dlogz: 6.595 > 0.309] + + 45748it [03:55, 222.07it/s, bound: 1015 | nc: 5 | ncall: 234204 | eff(%): 19.533 | loglstar: -inf < 149.876 < inf | logz: -2.551 +/- 0.667 | dlogz: 6.470 > 0.309] + + 45771it [03:55, 214.61it/s, bound: 1016 | nc: 5 | ncall: 234319 | eff(%): 19.534 | loglstar: -inf < 149.987 < inf | logz: -2.507 +/- 0.667 | dlogz: 6.349 > 0.309] + + 45794it [03:55, 217.52it/s, bound: 1016 | nc: 5 | ncall: 234434 | eff(%): 19.534 | loglstar: -inf < 150.134 < inf | logz: -2.463 +/- 0.668 | dlogz: 6.229 > 0.309] + + 45816it [03:55, 206.22it/s, bound: 1017 | nc: 5 | ncall: 234544 | eff(%): 19.534 | loglstar: -inf < 150.180 < inf | logz: -2.422 +/- 0.668 | dlogz: 6.115 > 0.309] + + 45838it [03:55, 209.04it/s, bound: 1017 | nc: 5 | ncall: 234654 | eff(%): 19.534 | loglstar: -inf < 150.205 < inf | logz: -2.384 +/- 0.668 | dlogz: 6.003 > 0.309] + + 45860it [03:55, 206.42it/s, bound: 1018 | nc: 5 | ncall: 234764 | eff(%): 19.535 | loglstar: -inf < 150.299 < inf | logz: -2.347 +/- 0.668 | dlogz: 5.893 > 0.309] + + 45881it [03:55, 197.63it/s, bound: 1020 | nc: 5 | ncall: 234869 | eff(%): 19.535 | loglstar: -inf < 150.404 < inf | logz: -2.313 +/- 0.668 | dlogz: 5.789 > 0.309] + + 45905it [03:55, 208.42it/s, bound: 1020 | nc: 5 | ncall: 234989 | eff(%): 19.535 | loglstar: -inf < 150.492 < inf | logz: -2.275 +/- 0.668 | dlogz: 5.671 > 0.309] + + 45926it [03:55, 201.04it/s, bound: 1021 | nc: 5 | ncall: 235094 | eff(%): 19.535 | loglstar: -inf < 150.556 < inf | logz: -2.242 +/- 0.668 | dlogz: 5.569 > 0.309] + + 45948it [03:56, 202.84it/s, bound: 1021 | nc: 5 | ncall: 235204 | eff(%): 19.535 | loglstar: -inf < 150.703 < inf | logz: -2.208 +/- 0.669 | dlogz: 5.462 > 0.309] + + 45969it [03:56, 184.38it/s, bound: 1022 | nc: 5 | ncall: 235309 | eff(%): 19.536 | loglstar: -inf < 150.763 < inf | logz: -2.175 +/- 0.669 | dlogz: 5.360 > 0.309] + + 45989it [03:56, 187.35it/s, bound: 1022 | nc: 5 | ncall: 235409 | eff(%): 19.536 | loglstar: -inf < 150.856 < inf | logz: -2.145 +/- 0.669 | dlogz: 5.263 > 0.309] + + 46009it [03:56, 182.20it/s, bound: 1023 | nc: 5 | ncall: 235509 | eff(%): 19.536 | loglstar: -inf < 150.985 < inf | logz: -2.115 +/- 0.669 | dlogz: 5.167 > 0.309] + + 46030it [03:56, 188.79it/s, bound: 1023 | nc: 5 | ncall: 235614 | eff(%): 19.536 | loglstar: -inf < 151.072 < inf | logz: -2.082 +/- 0.669 | dlogz: 5.065 > 0.309] + + 46050it [03:56, 189.73it/s, bound: 1024 | nc: 5 | ncall: 235714 | eff(%): 19.536 | loglstar: -inf < 151.165 < inf | logz: -2.052 +/- 0.669 | dlogz: 4.968 > 0.309] + + 46072it [03:56, 195.70it/s, bound: 1024 | nc: 5 | ncall: 235824 | eff(%): 19.537 | loglstar: -inf < 151.318 < inf | logz: -2.017 +/- 0.669 | dlogz: 4.861 > 0.309] + + 46092it [03:56, 196.59it/s, bound: 1024 | nc: 5 | ncall: 235924 | eff(%): 19.537 | loglstar: -inf < 151.493 < inf | logz: -1.985 +/- 0.670 | dlogz: 4.763 > 0.309] + + 46112it [03:56, 192.36it/s, bound: 1025 | nc: 5 | ncall: 236024 | eff(%): 19.537 | loglstar: -inf < 151.569 < inf | logz: -1.951 +/- 0.670 | dlogz: 4.664 > 0.309] + + 46132it [03:57, 194.16it/s, bound: 1025 | nc: 5 | ncall: 236124 | eff(%): 19.537 | loglstar: -inf < 151.659 < inf | logz: -1.918 +/- 0.670 | dlogz: 4.565 > 0.309] + + 46152it [03:57, 187.50it/s, bound: 1026 | nc: 5 | ncall: 236224 | eff(%): 19.537 | loglstar: -inf < 151.726 < inf | logz: -1.885 +/- 0.670 | dlogz: 4.466 > 0.309] + + 46172it [03:57, 190.51it/s, bound: 1026 | nc: 5 | ncall: 236324 | eff(%): 19.538 | loglstar: -inf < 151.821 < inf | logz: -1.853 +/- 0.670 | dlogz: 6.319 > 0.309] + + 46192it [03:57, 166.45it/s, bound: 1027 | nc: 5 | ncall: 236424 | eff(%): 19.538 | loglstar: -inf < 152.001 < inf | logz: -1.820 +/- 0.670 | dlogz: 6.219 > 0.309] + + 46210it [03:57, 160.08it/s, bound: 1027 | nc: 5 | ncall: 236514 | eff(%): 19.538 | loglstar: -inf < 152.063 < inf | logz: -1.788 +/- 0.670 | dlogz: 6.128 > 0.309] + + 46229it [03:57, 161.11it/s, bound: 1028 | nc: 5 | ncall: 236609 | eff(%): 19.538 | loglstar: -inf < 152.155 < inf | logz: -1.756 +/- 0.671 | dlogz: 6.032 > 0.309] + + 46249it [03:57, 171.01it/s, bound: 1028 | nc: 5 | ncall: 236709 | eff(%): 19.538 | loglstar: -inf < 152.223 < inf | logz: -1.723 +/- 0.671 | dlogz: 5.933 > 0.309] + + 46268it [03:57, 175.06it/s, bound: 1028 | nc: 5 | ncall: 236804 | eff(%): 19.539 | loglstar: -inf < 152.315 < inf | logz: -1.692 +/- 0.671 | dlogz: 5.839 > 0.309] + + 46286it [03:57, 173.51it/s, bound: 1029 | nc: 5 | ncall: 236894 | eff(%): 19.539 | loglstar: -inf < 152.409 < inf | logz: -1.662 +/- 0.671 | dlogz: 5.749 > 0.309] + + 46306it [03:58, 179.50it/s, bound: 1029 | nc: 5 | ncall: 236994 | eff(%): 19.539 | loglstar: -inf < 152.529 < inf | logz: -1.630 +/- 0.671 | dlogz: 5.650 > 0.309] + + 46325it [03:58, 180.82it/s, bound: 1030 | nc: 5 | ncall: 237089 | eff(%): 19.539 | loglstar: -inf < 152.592 < inf | logz: -1.598 +/- 0.671 | dlogz: 6.245 > 0.309] + + 46345it [03:58, 185.71it/s, bound: 1030 | nc: 5 | ncall: 237189 | eff(%): 19.539 | loglstar: -inf < 152.683 < inf | logz: -1.566 +/- 0.671 | dlogz: 6.145 > 0.309] + + 46364it [03:58, 185.79it/s, bound: 1031 | nc: 5 | ncall: 237284 | eff(%): 19.539 | loglstar: -inf < 152.747 < inf | logz: -1.535 +/- 0.672 | dlogz: 6.051 > 0.309] + + 46385it [03:58, 191.45it/s, bound: 1031 | nc: 5 | ncall: 237389 | eff(%): 19.540 | loglstar: -inf < 152.798 < inf | logz: -1.503 +/- 0.672 | dlogz: 5.949 > 0.309] + + 46407it [03:58, 198.68it/s, bound: 1031 | nc: 5 | ncall: 237499 | eff(%): 19.540 | loglstar: -inf < 152.863 < inf | logz: -1.472 +/- 0.672 | dlogz: 5.844 > 0.309] + + 46427it [03:58, 193.62it/s, bound: 1032 | nc: 5 | ncall: 237599 | eff(%): 19.540 | loglstar: -inf < 152.942 < inf | logz: -1.444 +/- 0.672 | dlogz: 5.750 > 0.309] + + 46450it [03:58, 202.67it/s, bound: 1032 | nc: 5 | ncall: 237714 | eff(%): 19.540 | loglstar: -inf < 153.018 < inf | logz: -1.412 +/- 0.672 | dlogz: 5.641 > 0.309] + + 46471it [03:58, 200.50it/s, bound: 1033 | nc: 5 | ncall: 237819 | eff(%): 19.540 | loglstar: -inf < 153.100 < inf | logz: -1.384 +/- 0.672 | dlogz: 5.544 > 0.309] + + 46494it [03:59, 207.74it/s, bound: 1033 | nc: 5 | ncall: 237934 | eff(%): 19.541 | loglstar: -inf < 153.215 < inf | logz: -1.354 +/- 0.672 | dlogz: 5.437 > 0.309] + + 46515it [03:59, 206.57it/s, bound: 1034 | nc: 5 | ncall: 238039 | eff(%): 19.541 | loglstar: -inf < 153.334 < inf | logz: -1.325 +/- 0.673 | dlogz: 5.339 > 0.309] + + 46539it [03:59, 213.96it/s, bound: 1034 | nc: 5 | ncall: 238159 | eff(%): 19.541 | loglstar: -inf < 153.413 < inf | logz: -1.293 +/- 0.673 | dlogz: 5.228 > 0.309] + + 46561it [03:59, 211.39it/s, bound: 1035 | nc: 5 | ncall: 238269 | eff(%): 19.541 | loglstar: -inf < 153.526 < inf | logz: -1.264 +/- 0.673 | dlogz: 5.125 > 0.309] + + 46585it [03:59, 218.12it/s, bound: 1035 | nc: 5 | ncall: 238389 | eff(%): 19.542 | loglstar: -inf < 153.613 < inf | logz: -1.232 +/- 0.673 | dlogz: 5.014 > 0.309] + + 46607it [03:59, 216.49it/s, bound: 1036 | nc: 5 | ncall: 238499 | eff(%): 19.542 | loglstar: -inf < 153.724 < inf | logz: -1.203 +/- 0.673 | dlogz: 4.912 > 0.309] + + 46630it [03:59, 218.98it/s, bound: 1036 | nc: 5 | ncall: 238614 | eff(%): 19.542 | loglstar: -inf < 153.814 < inf | logz: -1.173 +/- 0.673 | dlogz: 4.806 > 0.309] + + 46652it [03:59, 210.30it/s, bound: 1037 | nc: 5 | ncall: 238724 | eff(%): 19.542 | loglstar: -inf < 153.922 < inf | logz: -1.144 +/- 0.673 | dlogz: 4.705 > 0.309] + + 46674it [03:59, 207.52it/s, bound: 1037 | nc: 5 | ncall: 238834 | eff(%): 19.542 | loglstar: -inf < 153.960 < inf | logz: -1.116 +/- 0.674 | dlogz: 4.699 > 0.309] + + 46695it [03:59, 203.60it/s, bound: 1038 | nc: 5 | ncall: 238939 | eff(%): 19.543 | loglstar: -inf < 154.051 < inf | logz: -1.091 +/- 0.674 | dlogz: 4.604 > 0.309] + + 46717it [04:00, 207.68it/s, bound: 1038 | nc: 5 | ncall: 239049 | eff(%): 19.543 | loglstar: -inf < 154.210 < inf | logz: -1.063 +/- 0.674 | dlogz: 4.504 > 0.309] + + 46738it [04:00, 204.41it/s, bound: 1039 | nc: 5 | ncall: 239154 | eff(%): 19.543 | loglstar: -inf < 154.355 < inf | logz: -1.035 +/- 0.674 | dlogz: 4.408 > 0.309] + + 46759it [04:00, 205.17it/s, bound: 1039 | nc: 5 | ncall: 239259 | eff(%): 19.543 | loglstar: -inf < 154.421 < inf | logz: -1.007 +/- 0.674 | dlogz: 4.311 > 0.309] + + 46780it [04:00, 202.15it/s, bound: 1040 | nc: 5 | ncall: 239364 | eff(%): 19.543 | loglstar: -inf < 154.490 < inf | logz: -0.980 +/- 0.674 | dlogz: 4.215 > 0.309] + + 46802it [04:00, 207.02it/s, bound: 1040 | nc: 5 | ncall: 239474 | eff(%): 19.544 | loglstar: -inf < 154.572 < inf | logz: -0.953 +/- 0.674 | dlogz: 4.116 > 0.309] + + 46823it [04:00, 195.88it/s, bound: 1041 | nc: 5 | ncall: 239579 | eff(%): 19.544 | loglstar: -inf < 154.621 < inf | logz: -0.927 +/- 0.674 | dlogz: 4.022 > 0.309] + + 46843it [04:00, 185.12it/s, bound: 1041 | nc: 5 | ncall: 239679 | eff(%): 19.544 | loglstar: -inf < 154.676 < inf | logz: -0.904 +/- 0.675 | dlogz: 3.933 > 0.309] + + 46862it [04:00, 183.03it/s, bound: 1042 | nc: 5 | ncall: 239774 | eff(%): 19.544 | loglstar: -inf < 154.739 < inf | logz: -0.882 +/- 0.675 | dlogz: 3.850 > 0.309] + + 46882it [04:00, 187.55it/s, bound: 1042 | nc: 5 | ncall: 239874 | eff(%): 19.544 | loglstar: -inf < 154.786 < inf | logz: -0.860 +/- 0.675 | dlogz: 3.763 > 0.309] + + 46904it [04:01, 187.65it/s, bound: 1043 | nc: 5 | ncall: 239984 | eff(%): 19.545 | loglstar: -inf < 154.850 < inf | logz: -0.837 +/- 0.675 | dlogz: 3.668 > 0.309] + + 46926it [04:01, 196.53it/s, bound: 1043 | nc: 5 | ncall: 240094 | eff(%): 19.545 | loglstar: -inf < 154.915 < inf | logz: -0.814 +/- 0.675 | dlogz: 3.575 > 0.309] + + 46946it [04:01, 184.11it/s, bound: 1043 | nc: 5 | ncall: 240194 | eff(%): 19.545 | loglstar: -inf < 154.967 < inf | logz: -0.794 +/- 0.675 | dlogz: 3.491 > 0.309] + + 46965it [04:01, 181.75it/s, bound: 1044 | nc: 5 | ncall: 240289 | eff(%): 19.545 | loglstar: -inf < 155.026 < inf | logz: -0.776 +/- 0.675 | dlogz: 3.412 > 0.309] + + 46987it [04:01, 192.07it/s, bound: 1044 | nc: 5 | ncall: 240399 | eff(%): 19.545 | loglstar: -inf < 155.114 < inf | logz: -0.755 +/- 0.675 | dlogz: 3.320 > 0.309] + + 47007it [04:01, 193.27it/s, bound: 1045 | nc: 5 | ncall: 240499 | eff(%): 19.546 | loglstar: -inf < 155.195 < inf | logz: -0.736 +/- 0.675 | dlogz: 3.238 > 0.309] + + 47028it [04:01, 195.95it/s, bound: 1045 | nc: 5 | ncall: 240604 | eff(%): 19.546 | loglstar: -inf < 155.329 < inf | logz: -0.716 +/- 0.675 | dlogz: 3.152 > 0.309] + + 47048it [04:01, 190.93it/s, bound: 1046 | nc: 5 | ncall: 240704 | eff(%): 19.546 | loglstar: -inf < 155.393 < inf | logz: -0.697 +/- 0.676 | dlogz: 3.070 > 0.309] + + 47072it [04:01, 203.20it/s, bound: 1046 | nc: 5 | ncall: 240824 | eff(%): 19.546 | loglstar: -inf < 155.449 < inf | logz: -0.674 +/- 0.676 | dlogz: 2.971 > 0.309] + + 47093it [04:02, 201.04it/s, bound: 1047 | nc: 5 | ncall: 240929 | eff(%): 19.546 | loglstar: -inf < 155.519 < inf | logz: -0.654 +/- 0.676 | dlogz: 2.886 > 0.309] + + 47114it [04:02, 200.83it/s, bound: 1047 | nc: 5 | ncall: 241034 | eff(%): 19.547 | loglstar: -inf < 155.587 < inf | logz: -0.636 +/- 0.676 | dlogz: 2.803 > 0.309] + + 47135it [04:02, 200.94it/s, bound: 1048 | nc: 5 | ncall: 241139 | eff(%): 19.547 | loglstar: -inf < 155.651 < inf | logz: -0.617 +/- 0.676 | dlogz: 2.719 > 0.309] + + 47156it [04:02, 197.95it/s, bound: 1048 | nc: 5 | ncall: 241244 | eff(%): 19.547 | loglstar: -inf < 155.694 < inf | logz: -0.599 +/- 0.676 | dlogz: 2.681 > 0.309] + + 47176it [04:02, 185.23it/s, bound: 1049 | nc: 5 | ncall: 241344 | eff(%): 19.547 | loglstar: -inf < 155.783 < inf | logz: -0.582 +/- 0.676 | dlogz: 2.603 > 0.309] + + 47197it [04:02, 190.03it/s, bound: 1049 | nc: 5 | ncall: 241449 | eff(%): 19.547 | loglstar: -inf < 155.879 < inf | logz: -0.565 +/- 0.676 | dlogz: 2.522 > 0.309] + + 47218it [04:02, 193.71it/s, bound: 1049 | nc: 5 | ncall: 241554 | eff(%): 19.548 | loglstar: -inf < 155.917 < inf | logz: -0.548 +/- 0.676 | dlogz: 2.442 > 0.309] + + 47238it [04:02, 193.53it/s, bound: 1050 | nc: 5 | ncall: 241654 | eff(%): 19.548 | loglstar: -inf < 155.987 < inf | logz: -0.532 +/- 0.676 | dlogz: 2.367 > 0.309] + + 47262it [04:02, 204.19it/s, bound: 1050 | nc: 5 | ncall: 241774 | eff(%): 19.548 | loglstar: -inf < 156.083 < inf | logz: -0.513 +/- 0.676 | dlogz: 3.550 > 0.309] + + 47283it [04:02, 204.77it/s, bound: 1051 | nc: 5 | ncall: 241879 | eff(%): 19.548 | loglstar: -inf < 156.169 < inf | logz: -0.497 +/- 0.677 | dlogz: 4.206 > 0.309] + + 47306it [04:03, 210.06it/s, bound: 1051 | nc: 5 | ncall: 241994 | eff(%): 19.548 | loglstar: -inf < 156.252 < inf | logz: -0.479 +/- 0.677 | dlogz: 4.113 > 0.309] + + 47328it [04:03, 209.00it/s, bound: 1052 | nc: 5 | ncall: 242104 | eff(%): 19.549 | loglstar: -inf < 156.318 < inf | logz: -0.462 +/- 0.677 | dlogz: 4.024 > 0.309] + + 47350it [04:03, 211.06it/s, bound: 1052 | nc: 5 | ncall: 242214 | eff(%): 19.549 | loglstar: -inf < 156.428 < inf | logz: -0.445 +/- 0.677 | dlogz: 3.936 > 0.309] + + 47372it [04:03, 202.68it/s, bound: 1053 | nc: 5 | ncall: 242324 | eff(%): 19.549 | loglstar: -inf < 156.473 < inf | logz: -0.429 +/- 0.677 | dlogz: 3.848 > 0.309] + + 47393it [04:03, 201.91it/s, bound: 1053 | nc: 5 | ncall: 242429 | eff(%): 19.549 | loglstar: -inf < 156.605 < inf | logz: -0.413 +/- 0.677 | dlogz: 3.764 > 0.309] + + 47414it [04:03, 196.52it/s, bound: 1054 | nc: 5 | ncall: 242534 | eff(%): 19.549 | loglstar: -inf < 156.708 < inf | logz: -0.396 +/- 0.677 | dlogz: 3.679 > 0.309] + + 47436it [04:03, 201.94it/s, bound: 1054 | nc: 5 | ncall: 242644 | eff(%): 19.550 | loglstar: -inf < 156.760 < inf | logz: -0.379 +/- 0.677 | dlogz: 3.591 > 0.309] + + 47457it [04:03, 194.40it/s, bound: 1055 | nc: 5 | ncall: 242749 | eff(%): 19.550 | loglstar: -inf < 156.814 < inf | logz: -0.364 +/- 0.677 | dlogz: 3.508 > 0.309] + + 47477it [04:03, 191.69it/s, bound: 1055 | nc: 5 | ncall: 242849 | eff(%): 19.550 | loglstar: -inf < 156.902 < inf | logz: -0.349 +/- 0.677 | dlogz: 3.429 > 0.309] + + 47497it [04:04, 190.78it/s, bound: 1056 | nc: 5 | ncall: 242949 | eff(%): 19.550 | loglstar: -inf < 156.951 < inf | logz: -0.335 +/- 0.677 | dlogz: 3.350 > 0.309] + + 47519it [04:04, 198.77it/s, bound: 1056 | nc: 5 | ncall: 243059 | eff(%): 19.550 | loglstar: -inf < 157.028 < inf | logz: -0.319 +/- 0.678 | dlogz: 3.265 > 0.309] + + 47539it [04:04, 187.27it/s, bound: 1057 | nc: 5 | ncall: 243159 | eff(%): 19.551 | loglstar: -inf < 157.067 < inf | logz: -0.305 +/- 0.678 | dlogz: 3.187 > 0.309] + + 47560it [04:04, 191.93it/s, bound: 1057 | nc: 5 | ncall: 243264 | eff(%): 19.551 | loglstar: -inf < 157.089 < inf | logz: -0.291 +/- 0.678 | dlogz: 3.106 > 0.309] + + 47580it [04:04, 179.90it/s, bound: 1058 | nc: 5 | ncall: 243364 | eff(%): 19.551 | loglstar: -inf < 157.160 < inf | logz: -0.279 +/- 0.678 | dlogz: 3.031 > 0.309] + + 47599it [04:04, 173.38it/s, bound: 1058 | nc: 5 | ncall: 243459 | eff(%): 19.551 | loglstar: -inf < 157.205 < inf | logz: -0.267 +/- 0.678 | dlogz: 2.959 > 0.309] + + 47618it [04:04, 175.95it/s, bound: 1058 | nc: 5 | ncall: 243554 | eff(%): 19.551 | loglstar: -inf < 157.252 < inf | logz: -0.256 +/- 0.678 | dlogz: 2.888 > 0.309] + + 47636it [04:04, 172.56it/s, bound: 1059 | nc: 5 | ncall: 243644 | eff(%): 19.551 | loglstar: -inf < 157.283 < inf | logz: -0.245 +/- 0.678 | dlogz: 2.822 > 0.309] + + 47654it [04:04, 173.37it/s, bound: 1059 | nc: 5 | ncall: 243734 | eff(%): 19.552 | loglstar: -inf < 157.325 < inf | logz: -0.235 +/- 0.678 | dlogz: 2.756 > 0.309] + + 47672it [04:05, 171.56it/s, bound: 1060 | nc: 5 | ncall: 243824 | eff(%): 19.552 | loglstar: -inf < 157.415 < inf | logz: -0.225 +/- 0.678 | dlogz: 2.690 > 0.309] + + 47693it [04:05, 180.46it/s, bound: 1060 | nc: 5 | ncall: 243929 | eff(%): 19.552 | loglstar: -inf < 157.472 < inf | logz: -0.213 +/- 0.678 | dlogz: 2.614 > 0.309] + + 47714it [04:05, 181.88it/s, bound: 1061 | nc: 5 | ncall: 244034 | eff(%): 19.552 | loglstar: -inf < 157.524 < inf | logz: -0.202 +/- 0.678 | dlogz: 2.539 > 0.309] + + 47735it [04:05, 189.74it/s, bound: 1061 | nc: 5 | ncall: 244139 | eff(%): 19.552 | loglstar: -inf < 157.568 < inf | logz: -0.191 +/- 0.678 | dlogz: 3.447 > 0.309] + + 47756it [04:05, 193.90it/s, bound: 1061 | nc: 5 | ncall: 244244 | eff(%): 19.553 | loglstar: -inf < 157.626 < inf | logz: -0.180 +/- 0.678 | dlogz: 3.369 > 0.309] + + 47776it [04:05, 189.54it/s, bound: 1062 | nc: 5 | ncall: 244344 | eff(%): 19.553 | loglstar: -inf < 157.695 < inf | logz: -0.170 +/- 0.679 | dlogz: 3.295 > 0.309] + + 47797it [04:05, 193.02it/s, bound: 1062 | nc: 5 | ncall: 244449 | eff(%): 19.553 | loglstar: -inf < 157.752 < inf | logz: -0.159 +/- 0.679 | dlogz: 3.218 > 0.309] + + 47817it [04:05, 185.72it/s, bound: 1063 | nc: 5 | ncall: 244549 | eff(%): 19.553 | loglstar: -inf < 157.844 < inf | logz: -0.149 +/- 0.679 | dlogz: 3.144 > 0.309] + + 47837it [04:05, 186.12it/s, bound: 1063 | nc: 5 | ncall: 244649 | eff(%): 19.553 | loglstar: -inf < 157.897 < inf | logz: -0.140 +/- 0.679 | dlogz: 3.071 > 0.309] + + 47856it [04:06, 182.59it/s, bound: 1064 | nc: 5 | ncall: 244744 | eff(%): 19.553 | loglstar: -inf < 157.952 < inf | logz: -0.131 +/- 0.679 | dlogz: 3.002 > 0.309] + + 47876it [04:06, 186.78it/s, bound: 1064 | nc: 5 | ncall: 244844 | eff(%): 19.554 | loglstar: -inf < 157.992 < inf | logz: -0.121 +/- 0.679 | dlogz: 2.929 > 0.309] + + 47895it [04:06, 182.21it/s, bound: 1065 | nc: 5 | ncall: 244939 | eff(%): 19.554 | loglstar: -inf < 158.054 < inf | logz: -0.112 +/- 0.679 | dlogz: 2.861 > 0.309] + + 47914it [04:06, 181.54it/s, bound: 1065 | nc: 5 | ncall: 245034 | eff(%): 19.554 | loglstar: -inf < 158.094 < inf | logz: -0.104 +/- 0.679 | dlogz: 2.793 > 0.309] + + 47933it [04:06, 175.20it/s, bound: 1065 | nc: 5 | ncall: 245129 | eff(%): 19.554 | loglstar: -inf < 158.134 < inf | logz: -0.095 +/- 0.679 | dlogz: 2.726 > 0.309] + + 47951it [04:06, 171.33it/s, bound: 1066 | nc: 5 | ncall: 245219 | eff(%): 19.554 | loglstar: -inf < 158.208 < inf | logz: -0.088 +/- 0.679 | dlogz: 2.662 > 0.309] + + 47969it [04:06, 173.30it/s, bound: 1066 | nc: 5 | ncall: 245309 | eff(%): 19.555 | loglstar: -inf < 158.292 < inf | logz: -0.080 +/- 0.679 | dlogz: 2.600 > 0.309] + + 47987it [04:06, 172.37it/s, bound: 1067 | nc: 5 | ncall: 245399 | eff(%): 19.555 | loglstar: -inf < 158.341 < inf | logz: -0.072 +/- 0.679 | dlogz: 2.537 > 0.309] + + 48008it [04:06, 182.11it/s, bound: 1067 | nc: 5 | ncall: 245504 | eff(%): 19.555 | loglstar: -inf < 158.381 < inf | logz: -0.063 +/- 0.679 | dlogz: 2.465 > 0.309] + + 48029it [04:06, 182.73it/s, bound: 1068 | nc: 5 | ncall: 245609 | eff(%): 19.555 | loglstar: -inf < 158.425 < inf | logz: -0.055 +/- 0.679 | dlogz: 2.393 > 0.309] + + 48048it [04:07, 183.04it/s, bound: 1068 | nc: 5 | ncall: 245704 | eff(%): 19.555 | loglstar: -inf < 158.479 < inf | logz: -0.047 +/- 0.679 | dlogz: 2.329 > 0.309] + + 48067it [04:07, 181.91it/s, bound: 1068 | nc: 5 | ncall: 245799 | eff(%): 19.555 | loglstar: -inf < 158.583 < inf | logz: -0.040 +/- 0.679 | dlogz: 2.827 > 0.309] + + 48086it [04:07, 178.17it/s, bound: 1069 | nc: 5 | ncall: 245894 | eff(%): 19.556 | loglstar: -inf < 158.608 < inf | logz: -0.032 +/- 0.679 | dlogz: 2.760 > 0.309] + + 48105it [04:07, 181.01it/s, bound: 1069 | nc: 5 | ncall: 245989 | eff(%): 19.556 | loglstar: -inf < 158.651 < inf | logz: -0.025 +/- 0.680 | dlogz: 2.694 > 0.309] + + 48124it [04:07, 176.57it/s, bound: 1070 | nc: 5 | ncall: 246084 | eff(%): 19.556 | loglstar: -inf < 158.769 < inf | logz: -0.017 +/- 0.680 | dlogz: 2.628 > 0.309] + + 48142it [04:07, 166.88it/s, bound: 1070 | nc: 5 | ncall: 246174 | eff(%): 19.556 | loglstar: -inf < 158.843 < inf | logz: -0.010 +/- 0.680 | dlogz: 2.566 > 0.309] + + 48159it [04:07, 159.57it/s, bound: 1070 | nc: 5 | ncall: 246259 | eff(%): 19.556 | loglstar: -inf < 158.898 < inf | logz: -0.004 +/- 0.680 | dlogz: 2.508 > 0.309] + + 48176it [04:07, 155.07it/s, bound: 1071 | nc: 5 | ncall: 246344 | eff(%): 19.556 | loglstar: -inf < 158.979 < inf | logz: 0.003 +/- 0.680 | dlogz: 2.450 > 0.309] + + 48192it [04:07, 152.43it/s, bound: 1071 | nc: 5 | ncall: 246424 | eff(%): 19.557 | loglstar: -inf < 159.055 < inf | logz: 0.010 +/- 0.680 | dlogz: 2.395 > 0.309] + + 48209it [04:08, 149.22it/s, bound: 1072 | nc: 5 | ncall: 246509 | eff(%): 19.557 | loglstar: -inf < 159.119 < inf | logz: 0.017 +/- 0.680 | dlogz: 4.641 > 0.309] + + 48228it [04:08, 158.09it/s, bound: 1072 | nc: 5 | ncall: 246604 | eff(%): 19.557 | loglstar: -inf < 159.172 < inf | logz: 0.024 +/- 0.680 | dlogz: 4.793 > 0.309] + + 48247it [04:08, 166.43it/s, bound: 1072 | nc: 5 | ncall: 246699 | eff(%): 19.557 | loglstar: -inf < 159.286 < inf | logz: 0.032 +/- 0.680 | dlogz: 4.723 > 0.309] + + 48264it [04:08, 166.48it/s, bound: 1073 | nc: 5 | ncall: 246784 | eff(%): 19.557 | loglstar: -inf < 159.394 < inf | logz: 0.039 +/- 0.680 | dlogz: 5.395 > 0.309] + + 48285it [04:08, 176.75it/s, bound: 1073 | nc: 5 | ncall: 246889 | eff(%): 19.557 | loglstar: -inf < 159.413 < inf | logz: 0.048 +/- 0.680 | dlogz: 5.315 > 0.309] + + 48303it [04:08, 174.78it/s, bound: 1074 | nc: 5 | ncall: 246979 | eff(%): 19.558 | loglstar: -inf < 159.510 < inf | logz: 0.056 +/- 0.680 | dlogz: 5.248 > 0.309] + + 48324it [04:08, 184.74it/s, bound: 1074 | nc: 5 | ncall: 247084 | eff(%): 19.558 | loglstar: -inf < 159.558 < inf | logz: 0.064 +/- 0.680 | dlogz: 5.169 > 0.309] + + 48344it [04:08, 183.83it/s, bound: 1075 | nc: 5 | ncall: 247184 | eff(%): 19.558 | loglstar: -inf < 159.625 < inf | logz: 0.073 +/- 0.680 | dlogz: 5.095 > 0.309] + + 48366it [04:08, 191.71it/s, bound: 1075 | nc: 5 | ncall: 247294 | eff(%): 19.558 | loglstar: -inf < 159.696 < inf | logz: 0.081 +/- 0.680 | dlogz: 5.013 > 0.309] + + 48387it [04:09, 196.72it/s, bound: 1075 | nc: 5 | ncall: 247399 | eff(%): 19.558 | loglstar: -inf < 159.747 < inf | logz: 0.090 +/- 0.680 | dlogz: 4.935 > 0.309] + + 48407it [04:09, 193.68it/s, bound: 1076 | nc: 5 | ncall: 247499 | eff(%): 19.558 | loglstar: -inf < 159.835 < inf | logz: 0.098 +/- 0.681 | dlogz: 4.861 > 0.309] + + 48429it [04:09, 201.24it/s, bound: 1076 | nc: 5 | ncall: 247609 | eff(%): 19.559 | loglstar: -inf < 159.955 < inf | logz: 0.107 +/- 0.681 | dlogz: 4.780 > 0.309] + + 48450it [04:09, 197.58it/s, bound: 1077 | nc: 5 | ncall: 247714 | eff(%): 19.559 | loglstar: -inf < 160.048 < inf | logz: 0.115 +/- 0.681 | dlogz: 4.702 > 0.309] + + 48472it [04:09, 202.32it/s, bound: 1077 | nc: 5 | ncall: 247824 | eff(%): 19.559 | loglstar: -inf < 160.153 < inf | logz: 0.124 +/- 0.681 | dlogz: 4.620 > 0.309] + + 48493it [04:09, 196.67it/s, bound: 1078 | nc: 5 | ncall: 247929 | eff(%): 19.559 | loglstar: -inf < 160.195 < inf | logz: 0.133 +/- 0.681 | dlogz: 4.542 > 0.309] + + 48513it [04:09, 195.82it/s, bound: 1078 | nc: 5 | ncall: 248029 | eff(%): 19.559 | loglstar: -inf < 160.252 < inf | logz: 0.141 +/- 0.681 | dlogz: 4.468 > 0.309] + + 48533it [04:09, 194.80it/s, bound: 1079 | nc: 5 | ncall: 248129 | eff(%): 19.560 | loglstar: -inf < 160.336 < inf | logz: 0.149 +/- 0.681 | dlogz: 4.394 > 0.309] + + 48557it [04:09, 204.84it/s, bound: 1079 | nc: 5 | ncall: 248249 | eff(%): 19.560 | loglstar: -inf < 160.463 < inf | logz: 0.159 +/- 0.681 | dlogz: 4.305 > 0.309] + + 48578it [04:10, 199.66it/s, bound: 1080 | nc: 5 | ncall: 248354 | eff(%): 19.560 | loglstar: -inf < 160.602 < inf | logz: 0.169 +/- 0.681 | dlogz: 4.227 > 0.309] + + 48602it [04:10, 208.35it/s, bound: 1080 | nc: 5 | ncall: 248474 | eff(%): 19.560 | loglstar: -inf < 160.692 < inf | logz: 0.179 +/- 0.681 | dlogz: 4.137 > 0.309] + + 48623it [04:10, 208.76it/s, bound: 1081 | nc: 5 | ncall: 248579 | eff(%): 19.560 | loglstar: -inf < 160.777 < inf | logz: 0.189 +/- 0.681 | dlogz: 4.059 > 0.309] + + 48647it [04:10, 216.48it/s, bound: 1081 | nc: 5 | ncall: 248699 | eff(%): 19.561 | loglstar: -inf < 160.870 < inf | logz: 0.200 +/- 0.682 | dlogz: 3.970 > 0.309] + + 48669it [04:10, 212.24it/s, bound: 1082 | nc: 5 | ncall: 248809 | eff(%): 19.561 | loglstar: -inf < 160.983 < inf | logz: 0.210 +/- 0.682 | dlogz: 3.888 > 0.309] + + 48693it [04:10, 218.35it/s, bound: 1082 | nc: 5 | ncall: 248929 | eff(%): 19.561 | loglstar: -inf < 161.075 < inf | logz: 0.221 +/- 0.682 | dlogz: 3.798 > 0.309] + + 48715it [04:10, 213.80it/s, bound: 1083 | nc: 5 | ncall: 249039 | eff(%): 19.561 | loglstar: -inf < 161.149 < inf | logz: 0.231 +/- 0.682 | dlogz: 3.716 > 0.309] + + 48739it [04:10, 220.62it/s, bound: 1083 | nc: 5 | ncall: 249159 | eff(%): 19.561 | loglstar: -inf < 161.201 < inf | logz: 0.242 +/- 0.682 | dlogz: 3.625 > 0.309] + + 48762it [04:10, 214.67it/s, bound: 1084 | nc: 5 | ncall: 249274 | eff(%): 19.562 | loglstar: -inf < 161.260 < inf | logz: 0.252 +/- 0.682 | dlogz: 3.541 > 0.309] + + 48784it [04:10, 213.14it/s, bound: 1084 | nc: 5 | ncall: 249384 | eff(%): 19.562 | loglstar: -inf < 161.295 < inf | logz: 0.262 +/- 0.682 | dlogz: 3.460 > 0.309] + + 48806it [04:11, 204.18it/s, bound: 1085 | nc: 5 | ncall: 249494 | eff(%): 19.562 | loglstar: -inf < 161.379 < inf | logz: 0.271 +/- 0.682 | dlogz: 3.381 > 0.309] + + 48827it [04:11, 204.87it/s, bound: 1085 | nc: 5 | ncall: 249599 | eff(%): 19.562 | loglstar: -inf < 161.463 < inf | logz: 0.279 +/- 0.682 | dlogz: 3.305 > 0.309] + + 48848it [04:11, 201.18it/s, bound: 1086 | nc: 5 | ncall: 249704 | eff(%): 19.562 | loglstar: -inf < 161.566 < inf | logz: 0.288 +/- 0.682 | dlogz: 3.229 > 0.309] + + 48870it [04:11, 204.63it/s, bound: 1086 | nc: 5 | ncall: 249814 | eff(%): 19.563 | loglstar: -inf < 161.625 < inf | logz: 0.297 +/- 0.683 | dlogz: 3.150 > 0.309] + + 48891it [04:11, 193.42it/s, bound: 1087 | nc: 5 | ncall: 249919 | eff(%): 19.563 | loglstar: -inf < 161.727 < inf | logz: 0.307 +/- 0.683 | dlogz: 3.074 > 0.309] + + 48911it [04:11, 193.66it/s, bound: 1087 | nc: 5 | ncall: 250019 | eff(%): 19.563 | loglstar: -inf < 161.822 < inf | logz: 0.315 +/- 0.683 | dlogz: 3.002 > 0.309] + + 48931it [04:11, 192.78it/s, bound: 1088 | nc: 5 | ncall: 250119 | eff(%): 19.563 | loglstar: -inf < 161.938 < inf | logz: 0.324 +/- 0.683 | dlogz: 2.931 > 0.309] + + 48953it [04:11, 199.72it/s, bound: 1088 | nc: 5 | ncall: 250229 | eff(%): 19.563 | loglstar: -inf < 162.052 < inf | logz: 0.334 +/- 0.683 | dlogz: 2.852 > 0.309] + + 48974it [04:11, 190.49it/s, bound: 1089 | nc: 5 | ncall: 250334 | eff(%): 19.563 | loglstar: -inf < 162.128 < inf | logz: 0.343 +/- 0.683 | dlogz: 2.777 > 0.309] + + 48997it [04:12, 200.63it/s, bound: 1089 | nc: 5 | ncall: 250449 | eff(%): 19.564 | loglstar: -inf < 162.209 < inf | logz: 0.354 +/- 0.683 | dlogz: 2.696 > 0.309] + + 49018it [04:12, 198.92it/s, bound: 1090 | nc: 5 | ncall: 250554 | eff(%): 19.564 | loglstar: -inf < 162.293 < inf | logz: 0.364 +/- 0.683 | dlogz: 5.753 > 0.309] + + 49039it [04:12, 199.73it/s, bound: 1090 | nc: 5 | ncall: 250659 | eff(%): 19.564 | loglstar: -inf < 162.351 < inf | logz: 0.373 +/- 0.683 | dlogz: 5.673 > 0.309] + + 49061it [04:12, 195.77it/s, bound: 1091 | nc: 5 | ncall: 250769 | eff(%): 19.564 | loglstar: -inf < 162.433 < inf | logz: 0.383 +/- 0.684 | dlogz: 5.590 > 0.309] + + 49082it [04:12, 199.62it/s, bound: 1091 | nc: 5 | ncall: 250874 | eff(%): 19.564 | loglstar: -inf < 162.519 < inf | logz: 0.393 +/- 0.684 | dlogz: 5.511 > 0.309] + + 49104it [04:12, 202.96it/s, bound: 1091 | nc: 5 | ncall: 250984 | eff(%): 19.565 | loglstar: -inf < 162.631 < inf | logz: 0.403 +/- 0.684 | dlogz: 5.428 > 0.309] + + 49125it [04:12, 198.43it/s, bound: 1092 | nc: 5 | ncall: 251089 | eff(%): 19.565 | loglstar: -inf < 162.670 < inf | logz: 0.412 +/- 0.684 | dlogz: 5.348 > 0.309] + + 49148it [04:12, 206.66it/s, bound: 1092 | nc: 5 | ncall: 251204 | eff(%): 19.565 | loglstar: -inf < 162.724 < inf | logz: 0.422 +/- 0.684 | dlogz: 5.262 > 0.309] + + 49169it [04:12, 202.48it/s, bound: 1093 | nc: 5 | ncall: 251309 | eff(%): 19.565 | loglstar: -inf < 162.836 < inf | logz: 0.432 +/- 0.684 | dlogz: 5.183 > 0.309] + + 49190it [04:12, 204.29it/s, bound: 1093 | nc: 5 | ncall: 251414 | eff(%): 19.565 | loglstar: -inf < 162.905 < inf | logz: 0.441 +/- 0.684 | dlogz: 5.104 > 0.309] + + 49211it [04:13, 204.08it/s, bound: 1094 | nc: 5 | ncall: 251519 | eff(%): 19.566 | loglstar: -inf < 162.961 < inf | logz: 0.450 +/- 0.684 | dlogz: 5.075 > 0.309] + + 49232it [04:13, 205.34it/s, bound: 1094 | nc: 5 | ncall: 251624 | eff(%): 19.566 | loglstar: -inf < 163.038 < inf | logz: 0.459 +/- 0.684 | dlogz: 4.996 > 0.309] + + 49253it [04:13, 205.61it/s, bound: 1095 | nc: 5 | ncall: 251729 | eff(%): 19.566 | loglstar: -inf < 163.160 < inf | logz: 0.469 +/- 0.685 | dlogz: 4.918 > 0.309] + + 49278it [04:13, 217.69it/s, bound: 1095 | nc: 5 | ncall: 251854 | eff(%): 19.566 | loglstar: -inf < 163.236 < inf | logz: 0.480 +/- 0.685 | dlogz: 4.824 > 0.309] + + 49300it [04:13, 216.67it/s, bound: 1096 | nc: 5 | ncall: 251964 | eff(%): 19.566 | loglstar: -inf < 163.355 < inf | logz: 0.490 +/- 0.685 | dlogz: 4.741 > 0.309] + + 49324it [04:13, 221.42it/s, bound: 1096 | nc: 5 | ncall: 252084 | eff(%): 19.566 | loglstar: -inf < 163.453 < inf | logz: 0.501 +/- 0.685 | dlogz: 4.651 > 0.309] + + 49347it [04:13, 214.32it/s, bound: 1097 | nc: 5 | ncall: 252199 | eff(%): 19.567 | loglstar: -inf < 163.541 < inf | logz: 0.511 +/- 0.685 | dlogz: 4.565 > 0.309] + + 49369it [04:13, 203.71it/s, bound: 1097 | nc: 5 | ncall: 252309 | eff(%): 19.567 | loglstar: -inf < 163.627 < inf | logz: 0.521 +/- 0.685 | dlogz: 4.482 > 0.309] + + 49390it [04:13, 194.92it/s, bound: 1098 | nc: 5 | ncall: 252414 | eff(%): 19.567 | loglstar: -inf < 163.740 < inf | logz: 0.531 +/- 0.685 | dlogz: 4.403 > 0.309] + + 49410it [04:14, 194.93it/s, bound: 1098 | nc: 5 | ncall: 252514 | eff(%): 19.567 | loglstar: -inf < 163.805 < inf | logz: 0.541 +/- 0.685 | dlogz: 4.328 > 0.309] + + 49430it [04:14, 187.56it/s, bound: 1099 | nc: 5 | ncall: 252614 | eff(%): 19.567 | loglstar: -inf < 163.846 < inf | logz: 0.550 +/- 0.686 | dlogz: 4.253 > 0.309] + + 49449it [04:14, 178.89it/s, bound: 1100 | nc: 5 | ncall: 252709 | eff(%): 19.568 | loglstar: -inf < 163.918 < inf | logz: 0.559 +/- 0.686 | dlogz: 4.182 > 0.309] + + 49467it [04:14, 175.70it/s, bound: 1100 | nc: 5 | ncall: 252799 | eff(%): 19.568 | loglstar: -inf < 163.998 < inf | logz: 0.567 +/- 0.686 | dlogz: 4.114 > 0.309] + + 49485it [04:14, 168.38it/s, bound: 1101 | nc: 5 | ncall: 252889 | eff(%): 19.568 | loglstar: -inf < 164.075 < inf | logz: 0.576 +/- 0.686 | dlogz: 4.047 > 0.309] + + 49505it [04:14, 174.94it/s, bound: 1101 | nc: 5 | ncall: 252989 | eff(%): 19.568 | loglstar: -inf < 164.177 < inf | logz: 0.585 +/- 0.686 | dlogz: 3.972 > 0.309] + + 49524it [04:14, 175.25it/s, bound: 1102 | nc: 5 | ncall: 253084 | eff(%): 19.568 | loglstar: -inf < 164.234 < inf | logz: 0.594 +/- 0.686 | dlogz: 3.901 > 0.309] + + 49542it [04:14, 176.45it/s, bound: 1102 | nc: 5 | ncall: 253174 | eff(%): 19.568 | loglstar: -inf < 164.308 < inf | logz: 0.603 +/- 0.686 | dlogz: 3.834 > 0.309] + + 49564it [04:14, 186.95it/s, bound: 1102 | nc: 5 | ncall: 253284 | eff(%): 19.569 | loglstar: -inf < 164.439 < inf | logz: 0.613 +/- 0.686 | dlogz: 3.752 > 0.309] + + 49583it [04:15, 185.02it/s, bound: 1103 | nc: 5 | ncall: 253379 | eff(%): 19.569 | loglstar: -inf < 164.517 < inf | logz: 0.623 +/- 0.686 | dlogz: 3.681 > 0.309] + + 49606it [04:15, 195.47it/s, bound: 1103 | nc: 5 | ncall: 253494 | eff(%): 19.569 | loglstar: -inf < 164.619 < inf | logz: 0.634 +/- 0.687 | dlogz: 3.595 > 0.309] + + 49626it [04:15, 196.25it/s, bound: 1104 | nc: 5 | ncall: 253594 | eff(%): 19.569 | loglstar: -inf < 164.714 < inf | logz: 0.645 +/- 0.687 | dlogz: 3.521 > 0.309] + + 49646it [04:15, 196.33it/s, bound: 1104 | nc: 5 | ncall: 253694 | eff(%): 19.569 | loglstar: -inf < 164.768 < inf | logz: 0.655 +/- 0.687 | dlogz: 3.446 > 0.309] + + 49666it [04:15, 194.02it/s, bound: 1105 | nc: 5 | ncall: 253794 | eff(%): 19.569 | loglstar: -inf < 164.839 < inf | logz: 0.665 +/- 0.687 | dlogz: 3.372 > 0.309] + + 49689it [04:15, 204.28it/s, bound: 1105 | nc: 5 | ncall: 253909 | eff(%): 19.570 | loglstar: -inf < 164.898 < inf | logz: 0.676 +/- 0.687 | dlogz: 3.287 > 0.309] + + 49710it [04:15, 193.46it/s, bound: 1106 | nc: 5 | ncall: 254014 | eff(%): 19.570 | loglstar: -inf < 165.025 < inf | logz: 0.687 +/- 0.687 | dlogz: 3.965 > 0.309] + + 49730it [04:15, 189.28it/s, bound: 1106 | nc: 5 | ncall: 254114 | eff(%): 19.570 | loglstar: -inf < 165.133 < inf | logz: 0.697 +/- 0.687 | dlogz: 3.889 > 0.309] + + 49750it [04:15, 175.63it/s, bound: 1107 | nc: 5 | ncall: 254214 | eff(%): 19.570 | loglstar: -inf < 165.227 < inf | logz: 0.707 +/- 0.688 | dlogz: 3.814 > 0.309] + + 49769it [04:16, 177.44it/s, bound: 1107 | nc: 5 | ncall: 254309 | eff(%): 19.570 | loglstar: -inf < 165.293 < inf | logz: 0.717 +/- 0.688 | dlogz: 3.742 > 0.309] + + 49787it [04:16, 175.58it/s, bound: 1107 | nc: 5 | ncall: 254399 | eff(%): 19.570 | loglstar: -inf < 165.383 < inf | logz: 0.727 +/- 0.688 | dlogz: 3.674 > 0.309] + + 49805it [04:16, 173.50it/s, bound: 1108 | nc: 5 | ncall: 254489 | eff(%): 19.571 | loglstar: -inf < 165.420 < inf | logz: 0.737 +/- 0.688 | dlogz: 3.606 > 0.309] + + 49826it [04:16, 182.23it/s, bound: 1108 | nc: 5 | ncall: 254594 | eff(%): 19.571 | loglstar: -inf < 165.499 < inf | logz: 0.748 +/- 0.688 | dlogz: 3.528 > 0.309] + + 49845it [04:16, 181.78it/s, bound: 1109 | nc: 5 | ncall: 254689 | eff(%): 19.571 | loglstar: -inf < 165.615 < inf | logz: 0.758 +/- 0.688 | dlogz: 3.456 > 0.309] + + 49864it [04:16, 177.46it/s, bound: 1109 | nc: 5 | ncall: 254784 | eff(%): 19.571 | loglstar: -inf < 165.669 < inf | logz: 0.768 +/- 0.688 | dlogz: 3.385 > 0.309] + + 49883it [04:16, 174.91it/s, bound: 1110 | nc: 5 | ncall: 254879 | eff(%): 19.571 | loglstar: -inf < 165.763 < inf | logz: 0.779 +/- 0.689 | dlogz: 3.314 > 0.309] + + 49904it [04:16, 184.49it/s, bound: 1110 | nc: 5 | ncall: 254984 | eff(%): 19.571 | loglstar: -inf < 165.854 < inf | logz: 0.790 +/- 0.689 | dlogz: 3.235 > 0.309] + + 49927it [04:16, 196.83it/s, bound: 1110 | nc: 5 | ncall: 255099 | eff(%): 19.572 | loglstar: -inf < 165.966 < inf | logz: 0.803 +/- 0.689 | dlogz: 3.150 > 0.309] + + 49947it [04:16, 189.69it/s, bound: 1111 | nc: 5 | ncall: 255199 | eff(%): 19.572 | loglstar: -inf < 165.996 < inf | logz: 0.814 +/- 0.689 | dlogz: 3.075 > 0.309] + + 49969it [04:17, 194.37it/s, bound: 1111 | nc: 5 | ncall: 255309 | eff(%): 19.572 | loglstar: -inf < 166.087 < inf | logz: 0.826 +/- 0.689 | dlogz: 3.638 > 0.309] + + 49989it [04:17, 189.05it/s, bound: 1112 | nc: 5 | ncall: 255409 | eff(%): 19.572 | loglstar: -inf < 166.161 < inf | logz: 0.837 +/- 0.689 | dlogz: 3.563 > 0.309] + + 50011it [04:17, 197.72it/s, bound: 1112 | nc: 5 | ncall: 255519 | eff(%): 19.572 | loglstar: -inf < 166.243 < inf | logz: 0.849 +/- 0.690 | dlogz: 3.480 > 0.309] + + 50032it [04:17, 199.79it/s, bound: 1113 | nc: 5 | ncall: 255624 | eff(%): 19.572 | loglstar: -inf < 166.275 < inf | logz: 0.860 +/- 0.690 | dlogz: 4.375 > 0.309] + + 50055it [04:17, 206.60it/s, bound: 1113 | nc: 5 | ncall: 255739 | eff(%): 19.573 | loglstar: -inf < 166.310 < inf | logz: 0.871 +/- 0.690 | dlogz: 4.288 > 0.309] + + 50076it [04:17, 197.31it/s, bound: 1114 | nc: 5 | ncall: 255844 | eff(%): 19.573 | loglstar: -inf < 166.375 < inf | logz: 0.881 +/- 0.690 | dlogz: 4.209 > 0.309] + + 50096it [04:17, 193.00it/s, bound: 1114 | nc: 5 | ncall: 255944 | eff(%): 19.573 | loglstar: -inf < 166.473 < inf | logz: 0.891 +/- 0.690 | dlogz: 4.134 > 0.309] + + 50116it [04:17, 170.18it/s, bound: 1115 | nc: 5 | ncall: 256044 | eff(%): 19.573 | loglstar: -inf < 166.572 < inf | logz: 0.901 +/- 0.690 | dlogz: 4.059 > 0.309] + + 50135it [04:18, 173.50it/s, bound: 1115 | nc: 5 | ncall: 256139 | eff(%): 19.573 | loglstar: -inf < 166.620 < inf | logz: 0.910 +/- 0.690 | dlogz: 4.493 > 0.309] + + 50154it [04:18, 176.24it/s, bound: 1116 | nc: 5 | ncall: 256234 | eff(%): 19.574 | loglstar: -inf < 166.691 < inf | logz: 0.919 +/- 0.690 | dlogz: 4.421 > 0.309] + + 50176it [04:18, 188.27it/s, bound: 1116 | nc: 5 | ncall: 256344 | eff(%): 19.574 | loglstar: -inf < 166.789 < inf | logz: 0.930 +/- 0.691 | dlogz: 4.338 > 0.309] + + 50197it [04:18, 194.20it/s, bound: 1116 | nc: 5 | ncall: 256449 | eff(%): 19.574 | loglstar: -inf < 166.881 < inf | logz: 0.941 +/- 0.691 | dlogz: 4.259 > 0.309] + + 50217it [04:18, 187.97it/s, bound: 1117 | nc: 5 | ncall: 256549 | eff(%): 19.574 | loglstar: -inf < 166.980 < inf | logz: 0.951 +/- 0.691 | dlogz: 4.183 > 0.309] + + 50241it [04:18, 200.83it/s, bound: 1117 | nc: 5 | ncall: 256669 | eff(%): 19.574 | loglstar: -inf < 167.090 < inf | logz: 0.963 +/- 0.691 | dlogz: 4.092 > 0.309] + + 50262it [04:18, 199.13it/s, bound: 1118 | nc: 5 | ncall: 256774 | eff(%): 19.574 | loglstar: -inf < 167.197 < inf | logz: 0.974 +/- 0.691 | dlogz: 4.013 > 0.309] + + 50286it [04:18, 208.24it/s, bound: 1118 | nc: 5 | ncall: 256894 | eff(%): 19.575 | loglstar: -inf < 167.290 < inf | logz: 0.987 +/- 0.691 | dlogz: 3.922 > 0.309] + + 50308it [04:18, 210.14it/s, bound: 1119 | nc: 5 | ncall: 257004 | eff(%): 19.575 | loglstar: -inf < 167.429 < inf | logz: 0.999 +/- 0.692 | dlogz: 3.838 > 0.309] + + 50332it [04:18, 216.09it/s, bound: 1119 | nc: 5 | ncall: 257124 | eff(%): 19.575 | loglstar: -inf < 167.546 < inf | logz: 1.012 +/- 0.692 | dlogz: 3.747 > 0.309] + + 50354it [04:19, 212.48it/s, bound: 1120 | nc: 5 | ncall: 257234 | eff(%): 19.575 | loglstar: -inf < 167.636 < inf | logz: 1.025 +/- 0.692 | dlogz: 3.663 > 0.309] + + 50378it [04:19, 211.63it/s, bound: 1121 | nc: 5 | ncall: 257354 | eff(%): 19.575 | loglstar: -inf < 167.752 < inf | logz: 1.039 +/- 0.692 | dlogz: 3.572 > 0.309] + + 50402it [04:19, 217.78it/s, bound: 1121 | nc: 5 | ncall: 257474 | eff(%): 19.576 | loglstar: -inf < 167.869 < inf | logz: 1.053 +/- 0.692 | dlogz: 3.480 > 0.309] + + 50424it [04:19, 213.65it/s, bound: 1122 | nc: 5 | ncall: 257584 | eff(%): 19.576 | loglstar: -inf < 167.921 < inf | logz: 1.066 +/- 0.693 | dlogz: 3.396 > 0.309] + + 50446it [04:19, 213.43it/s, bound: 1122 | nc: 5 | ncall: 257694 | eff(%): 19.576 | loglstar: -inf < 168.015 < inf | logz: 1.079 +/- 0.693 | dlogz: 3.313 > 0.309] + + 50468it [04:19, 214.07it/s, bound: 1123 | nc: 5 | ncall: 257804 | eff(%): 19.576 | loglstar: -inf < 168.069 < inf | logz: 1.092 +/- 0.693 | dlogz: 3.230 > 0.309] + + 50490it [04:19, 205.09it/s, bound: 1123 | nc: 5 | ncall: 257914 | eff(%): 19.576 | loglstar: -inf < 168.182 < inf | logz: 1.104 +/- 0.693 | dlogz: 3.147 > 0.309] + + 50513it [04:19, 207.17it/s, bound: 1124 | nc: 5 | ncall: 258029 | eff(%): 19.576 | loglstar: -inf < 168.246 < inf | logz: 1.118 +/- 0.693 | dlogz: 3.061 > 0.309] + + 50536it [04:19, 209.98it/s, bound: 1124 | nc: 5 | ncall: 258144 | eff(%): 19.577 | loglstar: -inf < 168.357 < inf | logz: 1.131 +/- 0.693 | dlogz: 2.976 > 0.309] + + 50558it [04:20, 197.32it/s, bound: 1125 | nc: 5 | ncall: 258254 | eff(%): 19.577 | loglstar: -inf < 168.438 < inf | logz: 1.144 +/- 0.694 | dlogz: 2.894 > 0.309] + + 50578it [04:20, 188.98it/s, bound: 1125 | nc: 5 | ncall: 258354 | eff(%): 19.577 | loglstar: -inf < 168.515 < inf | logz: 1.155 +/- 0.694 | dlogz: 2.820 > 0.309] + + 50598it [04:20, 190.12it/s, bound: 1125 | nc: 5 | ncall: 258454 | eff(%): 19.577 | loglstar: -inf < 168.622 < inf | logz: 1.167 +/- 0.694 | dlogz: 4.582 > 0.309] + + 50618it [04:20, 189.87it/s, bound: 1126 | nc: 5 | ncall: 258554 | eff(%): 19.577 | loglstar: -inf < 168.729 < inf | logz: 1.179 +/- 0.694 | dlogz: 4.504 > 0.309] + + 50639it [04:20, 195.00it/s, bound: 1126 | nc: 5 | ncall: 258659 | eff(%): 19.578 | loglstar: -inf < 168.782 < inf | logz: 1.192 +/- 0.694 | dlogz: 4.422 > 0.309] + + 50660it [04:20, 198.29it/s, bound: 1127 | nc: 5 | ncall: 258764 | eff(%): 19.578 | loglstar: -inf < 168.874 < inf | logz: 1.205 +/- 0.694 | dlogz: 4.341 > 0.309] + + 50685it [04:20, 211.96it/s, bound: 1127 | nc: 5 | ncall: 258889 | eff(%): 19.578 | loglstar: -inf < 168.966 < inf | logz: 1.219 +/- 0.695 | dlogz: 4.244 > 0.309] + + 50707it [04:20, 212.07it/s, bound: 1128 | nc: 5 | ncall: 258999 | eff(%): 19.578 | loglstar: -inf < 169.008 < inf | logz: 1.232 +/- 0.695 | dlogz: 4.159 > 0.309] + + 50730it [04:20, 216.68it/s, bound: 1128 | nc: 5 | ncall: 259114 | eff(%): 19.578 | loglstar: -inf < 169.102 < inf | logz: 1.245 +/- 0.695 | dlogz: 4.070 > 0.309] + + 50752it [04:20, 214.23it/s, bound: 1129 | nc: 5 | ncall: 259224 | eff(%): 19.578 | loglstar: -inf < 169.156 < inf | logz: 1.258 +/- 0.695 | dlogz: 3.986 > 0.309] + + 50775it [04:21, 218.35it/s, bound: 1129 | nc: 5 | ncall: 259339 | eff(%): 19.579 | loglstar: -inf < 169.228 < inf | logz: 1.270 +/- 0.695 | dlogz: 3.898 > 0.309] + + 50797it [04:21, 205.81it/s, bound: 1130 | nc: 5 | ncall: 259449 | eff(%): 19.579 | loglstar: -inf < 169.296 < inf | logz: 1.282 +/- 0.696 | dlogz: 3.815 > 0.309] + + 50820it [04:21, 211.36it/s, bound: 1130 | nc: 5 | ncall: 259564 | eff(%): 19.579 | loglstar: -inf < 169.414 < inf | logz: 1.294 +/- 0.696 | dlogz: 3.728 > 0.309] + + 50842it [04:21, 205.47it/s, bound: 1131 | nc: 5 | ncall: 259674 | eff(%): 19.579 | loglstar: -inf < 169.484 < inf | logz: 1.307 +/- 0.696 | dlogz: 3.645 > 0.309] + + 50866it [04:21, 212.74it/s, bound: 1131 | nc: 5 | ncall: 259794 | eff(%): 19.579 | loglstar: -inf < 169.551 < inf | logz: 1.320 +/- 0.696 | dlogz: 3.554 > 0.309] + + 50888it [04:21, 195.99it/s, bound: 1132 | nc: 5 | ncall: 259904 | eff(%): 19.580 | loglstar: -inf < 169.621 < inf | logz: 1.331 +/- 0.696 | dlogz: 3.471 > 0.309] + + 50910it [04:21, 201.68it/s, bound: 1132 | nc: 5 | ncall: 260014 | eff(%): 19.580 | loglstar: -inf < 169.693 < inf | logz: 1.342 +/- 0.696 | dlogz: 3.389 > 0.309] + + 50931it [04:21, 195.49it/s, bound: 1133 | nc: 5 | ncall: 260119 | eff(%): 19.580 | loglstar: -inf < 169.779 < inf | logz: 1.353 +/- 0.697 | dlogz: 3.311 > 0.309] + + 50955it [04:21, 205.34it/s, bound: 1133 | nc: 5 | ncall: 260239 | eff(%): 19.580 | loglstar: -inf < 169.871 < inf | logz: 1.366 +/- 0.697 | dlogz: 3.223 > 0.309] + + 50977it [04:22, 209.27it/s, bound: 1134 | nc: 5 | ncall: 260349 | eff(%): 19.580 | loglstar: -inf < 169.959 < inf | logz: 1.377 +/- 0.697 | dlogz: 3.142 > 0.309] + + 51000it [04:22, 214.73it/s, bound: 1134 | nc: 5 | ncall: 260464 | eff(%): 19.580 | loglstar: -inf < 170.027 < inf | logz: 1.389 +/- 0.697 | dlogz: 3.057 > 0.309] + + 51023it [04:22, 217.47it/s, bound: 1135 | nc: 5 | ncall: 260579 | eff(%): 19.581 | loglstar: -inf < 170.081 < inf | logz: 1.400 +/- 0.697 | dlogz: 2.973 > 0.309] + + 51047it [04:22, 223.25it/s, bound: 1135 | nc: 5 | ncall: 260699 | eff(%): 19.581 | loglstar: -inf < 170.188 < inf | logz: 1.412 +/- 0.697 | dlogz: 2.886 > 0.309] + + 51070it [04:22, 216.74it/s, bound: 1136 | nc: 5 | ncall: 260814 | eff(%): 19.581 | loglstar: -inf < 170.252 < inf | logz: 1.423 +/- 0.697 | dlogz: 2.803 > 0.309] + + 51093it [04:22, 218.68it/s, bound: 1136 | nc: 5 | ncall: 260929 | eff(%): 19.581 | loglstar: -inf < 170.289 < inf | logz: 1.434 +/- 0.698 | dlogz: 2.721 > 0.309] + + 51115it [04:22, 217.38it/s, bound: 1137 | nc: 5 | ncall: 261039 | eff(%): 19.581 | loglstar: -inf < 170.397 < inf | logz: 1.445 +/- 0.698 | dlogz: 3.700 > 0.309] + + 51141it [04:22, 227.49it/s, bound: 1137 | nc: 5 | ncall: 261169 | eff(%): 19.582 | loglstar: -inf < 170.500 < inf | logz: 1.457 +/- 0.698 | dlogz: 3.604 > 0.309] + + 51164it [04:22, 217.02it/s, bound: 1138 | nc: 5 | ncall: 261284 | eff(%): 19.582 | loglstar: -inf < 170.551 < inf | logz: 1.468 +/- 0.698 | dlogz: 3.519 > 0.309] + + 51188it [04:23, 219.56it/s, bound: 1139 | nc: 5 | ncall: 261404 | eff(%): 19.582 | loglstar: -inf < 170.588 < inf | logz: 1.479 +/- 0.698 | dlogz: 3.430 > 0.309] + + 51212it [04:23, 225.13it/s, bound: 1139 | nc: 5 | ncall: 261524 | eff(%): 19.582 | loglstar: -inf < 170.682 < inf | logz: 1.489 +/- 0.698 | dlogz: 3.343 > 0.309] + + 51235it [04:23, 219.73it/s, bound: 1140 | nc: 5 | ncall: 261639 | eff(%): 19.582 | loglstar: -inf < 170.755 < inf | logz: 1.499 +/- 0.699 | dlogz: 3.259 > 0.309] + + 51258it [04:23, 222.09it/s, bound: 1140 | nc: 5 | ncall: 261754 | eff(%): 19.583 | loglstar: -inf < 170.827 < inf | logz: 1.509 +/- 0.699 | dlogz: 3.176 > 0.309] + + 51281it [04:23, 221.36it/s, bound: 1141 | nc: 5 | ncall: 261869 | eff(%): 19.583 | loglstar: -inf < 170.870 < inf | logz: 1.519 +/- 0.699 | dlogz: 3.093 > 0.309] + + 51306it [04:23, 227.55it/s, bound: 1141 | nc: 5 | ncall: 261994 | eff(%): 19.583 | loglstar: -inf < 171.012 < inf | logz: 1.529 +/- 0.699 | dlogz: 3.963 > 0.309] + + 51329it [04:23, 219.38it/s, bound: 1142 | nc: 5 | ncall: 262109 | eff(%): 19.583 | loglstar: -inf < 171.124 < inf | logz: 1.539 +/- 0.699 | dlogz: 3.878 > 0.309] + + 51354it [04:23, 227.44it/s, bound: 1142 | nc: 5 | ncall: 262234 | eff(%): 19.583 | loglstar: -inf < 171.259 < inf | logz: 1.550 +/- 0.699 | dlogz: 3.786 > 0.309] + + 51377it [04:23, 216.54it/s, bound: 1143 | nc: 5 | ncall: 262349 | eff(%): 19.583 | loglstar: -inf < 171.372 < inf | logz: 1.561 +/- 0.699 | dlogz: 3.701 > 0.309] + + 51400it [04:23, 219.11it/s, bound: 1143 | nc: 5 | ncall: 262464 | eff(%): 19.584 | loglstar: -inf < 171.454 < inf | logz: 1.572 +/- 0.700 | dlogz: 3.615 > 0.309] + + 51423it [04:24, 212.82it/s, bound: 1144 | nc: 5 | ncall: 262579 | eff(%): 19.584 | loglstar: -inf < 171.514 < inf | logz: 1.582 +/- 0.700 | dlogz: 3.530 > 0.309] + + 51446it [04:24, 215.14it/s, bound: 1144 | nc: 5 | ncall: 262694 | eff(%): 19.584 | loglstar: -inf < 171.529 < inf | logz: 1.593 +/- 0.700 | dlogz: 3.445 > 0.309] + + 51468it [04:24, 212.66it/s, bound: 1145 | nc: 5 | ncall: 262804 | eff(%): 19.584 | loglstar: -inf < 171.591 < inf | logz: 1.602 +/- 0.700 | dlogz: 3.365 > 0.309] + + 51490it [04:24, 206.08it/s, bound: 1145 | nc: 5 | ncall: 262914 | eff(%): 19.584 | loglstar: -inf < 171.716 < inf | logz: 1.611 +/- 0.700 | dlogz: 3.285 > 0.309] + + 51511it [04:24, 199.16it/s, bound: 1146 | nc: 5 | ncall: 263019 | eff(%): 19.585 | loglstar: -inf < 171.757 < inf | logz: 1.620 +/- 0.700 | dlogz: 3.209 > 0.309] + + 51535it [04:24, 208.06it/s, bound: 1146 | nc: 5 | ncall: 263139 | eff(%): 19.585 | loglstar: -inf < 171.817 < inf | logz: 1.630 +/- 0.700 | dlogz: 3.123 > 0.309] + + 51556it [04:24, 201.94it/s, bound: 1147 | nc: 5 | ncall: 263244 | eff(%): 19.585 | loglstar: -inf < 171.878 < inf | logz: 1.638 +/- 0.700 | dlogz: 3.048 > 0.309] + + 51580it [04:24, 210.17it/s, bound: 1147 | nc: 5 | ncall: 263364 | eff(%): 19.585 | loglstar: -inf < 171.958 < inf | logz: 1.647 +/- 0.701 | dlogz: 2.963 > 0.309] + + 51602it [04:24, 207.99it/s, bound: 1148 | nc: 5 | ncall: 263474 | eff(%): 19.585 | loglstar: -inf < 172.031 < inf | logz: 1.656 +/- 0.701 | dlogz: 2.886 > 0.309] + + 51623it [04:25, 206.36it/s, bound: 1148 | nc: 5 | ncall: 263579 | eff(%): 19.585 | loglstar: -inf < 172.080 < inf | logz: 1.664 +/- 0.701 | dlogz: 2.812 > 0.309] + + 51644it [04:25, 200.72it/s, bound: 1149 | nc: 5 | ncall: 263684 | eff(%): 19.586 | loglstar: -inf < 172.150 < inf | logz: 1.672 +/- 0.701 | dlogz: 2.739 > 0.309] + + 51666it [04:25, 204.74it/s, bound: 1149 | nc: 5 | ncall: 263794 | eff(%): 19.586 | loglstar: -inf < 172.260 < inf | logz: 1.680 +/- 0.701 | dlogz: 2.663 > 0.309] + + 51687it [04:25, 199.67it/s, bound: 1150 | nc: 5 | ncall: 263899 | eff(%): 19.586 | loglstar: -inf < 172.297 < inf | logz: 1.688 +/- 0.701 | dlogz: 2.591 > 0.309] + + 51710it [04:25, 207.09it/s, bound: 1150 | nc: 5 | ncall: 264014 | eff(%): 19.586 | loglstar: -inf < 172.398 < inf | logz: 1.696 +/- 0.701 | dlogz: 2.512 > 0.309] + + 51731it [04:25, 204.01it/s, bound: 1151 | nc: 5 | ncall: 264119 | eff(%): 19.586 | loglstar: -inf < 172.486 < inf | logz: 1.704 +/- 0.701 | dlogz: 2.441 > 0.309] + + 51752it [04:25, 203.87it/s, bound: 1151 | nc: 5 | ncall: 264224 | eff(%): 19.586 | loglstar: -inf < 172.558 < inf | logz: 1.712 +/- 0.702 | dlogz: 2.370 > 0.309] + + 51773it [04:25, 201.92it/s, bound: 1152 | nc: 5 | ncall: 264329 | eff(%): 19.587 | loglstar: -inf < 172.611 < inf | logz: 1.720 +/- 0.702 | dlogz: 2.299 > 0.309] + + 51797it [04:25, 211.66it/s, bound: 1152 | nc: 5 | ncall: 264449 | eff(%): 19.587 | loglstar: -inf < 172.703 < inf | logz: 1.729 +/- 0.702 | dlogz: 2.220 > 0.309] + + 51819it [04:26, 208.57it/s, bound: 1153 | nc: 5 | ncall: 264559 | eff(%): 19.587 | loglstar: -inf < 172.808 < inf | logz: 1.737 +/- 0.702 | dlogz: 2.148 > 0.309] + + 51843it [04:26, 215.21it/s, bound: 1153 | nc: 5 | ncall: 264679 | eff(%): 19.587 | loglstar: -inf < 172.878 < inf | logz: 1.746 +/- 0.702 | dlogz: 2.069 > 0.309] + + 51865it [04:26, 214.71it/s, bound: 1154 | nc: 5 | ncall: 264789 | eff(%): 19.587 | loglstar: -inf < 172.935 < inf | logz: 1.754 +/- 0.702 | dlogz: 1.999 > 0.309] + + 51889it [04:26, 221.97it/s, bound: 1154 | nc: 5 | ncall: 264909 | eff(%): 19.587 | loglstar: -inf < 173.021 < inf | logz: 1.762 +/- 0.702 | dlogz: 2.116 > 0.309] + + 51912it [04:26, 211.44it/s, bound: 1155 | nc: 5 | ncall: 265024 | eff(%): 19.588 | loglstar: -inf < 173.109 < inf | logz: 1.771 +/- 0.702 | dlogz: 2.628 > 0.309] + + 51934it [04:26, 210.25it/s, bound: 1155 | nc: 5 | ncall: 265134 | eff(%): 19.588 | loglstar: -inf < 173.188 < inf | logz: 1.778 +/- 0.703 | dlogz: 2.553 > 0.309] + + 51956it [04:26, 200.04it/s, bound: 1156 | nc: 5 | ncall: 265244 | eff(%): 19.588 | loglstar: -inf < 173.266 < inf | logz: 1.786 +/- 0.703 | dlogz: 2.478 > 0.309] + + 51977it [04:26, 195.55it/s, bound: 1156 | nc: 5 | ncall: 265349 | eff(%): 19.588 | loglstar: -inf < 173.348 < inf | logz: 1.794 +/- 0.703 | dlogz: 2.407 > 0.309] + + 51998it [04:26, 197.22it/s, bound: 1157 | nc: 5 | ncall: 265454 | eff(%): 19.588 | loglstar: -inf < 173.408 < inf | logz: 1.801 +/- 0.703 | dlogz: 2.337 > 0.309] + + 52020it [04:27, 202.42it/s, bound: 1157 | nc: 5 | ncall: 265564 | eff(%): 19.588 | loglstar: -inf < 173.501 < inf | logz: 1.809 +/- 0.703 | dlogz: 2.264 > 0.309] + + 52041it [04:27, 195.89it/s, bound: 1157 | nc: 5 | ncall: 265669 | eff(%): 19.589 | loglstar: -inf < 173.520 < inf | logz: 1.817 +/- 0.703 | dlogz: 2.194 > 0.309] + + 52061it [04:27, 191.58it/s, bound: 1158 | nc: 5 | ncall: 265769 | eff(%): 19.589 | loglstar: -inf < 173.568 < inf | logz: 1.823 +/- 0.703 | dlogz: 2.129 > 0.309] + + 52083it [04:27, 197.99it/s, bound: 1158 | nc: 5 | ncall: 265879 | eff(%): 19.589 | loglstar: -inf < 173.691 < inf | logz: 1.831 +/- 0.703 | dlogz: 2.059 > 0.309] + + 52103it [04:27, 196.96it/s, bound: 1159 | nc: 5 | ncall: 265979 | eff(%): 19.589 | loglstar: -inf < 173.770 < inf | logz: 1.838 +/- 0.703 | dlogz: 2.399 > 0.309] + + 52123it [04:27, 194.46it/s, bound: 1160 | nc: 5 | ncall: 266079 | eff(%): 19.589 | loglstar: -inf < 173.803 < inf | logz: 1.844 +/- 0.703 | dlogz: 2.332 > 0.309] + + 52146it [04:27, 203.86it/s, bound: 1160 | nc: 5 | ncall: 266194 | eff(%): 19.589 | loglstar: -inf < 173.887 < inf | logz: 1.852 +/- 0.704 | dlogz: 2.256 > 0.309] + + 52167it [04:27, 205.42it/s, bound: 1161 | nc: 5 | ncall: 266299 | eff(%): 19.590 | loglstar: -inf < 173.949 < inf | logz: 1.859 +/- 0.704 | dlogz: 2.188 > 0.309] + + 52192it [04:27, 216.65it/s, bound: 1161 | nc: 5 | ncall: 266424 | eff(%): 19.590 | loglstar: -inf < 174.038 < inf | logz: 1.867 +/- 0.704 | dlogz: 2.107 > 0.309] + + 52214it [04:27, 200.61it/s, bound: 1162 | nc: 5 | ncall: 266534 | eff(%): 19.590 | loglstar: -inf < 174.099 < inf | logz: 1.874 +/- 0.704 | dlogz: 2.122 > 0.309] + + 52235it [04:28, 189.39it/s, bound: 1162 | nc: 5 | ncall: 266639 | eff(%): 19.590 | loglstar: -inf < 174.196 < inf | logz: 1.881 +/- 0.704 | dlogz: 2.054 > 0.309] + + 52255it [04:28, 178.95it/s, bound: 1163 | nc: 5 | ncall: 266739 | eff(%): 19.590 | loglstar: -inf < 174.262 < inf | logz: 1.887 +/- 0.704 | dlogz: 1.991 > 0.309] + + 52278it [04:28, 190.82it/s, bound: 1163 | nc: 5 | ncall: 266854 | eff(%): 19.590 | loglstar: -inf < 174.346 < inf | logz: 1.895 +/- 0.704 | dlogz: 1.919 > 0.309] + + 52300it [04:28, 192.28it/s, bound: 1164 | nc: 5 | ncall: 266964 | eff(%): 19.591 | loglstar: -inf < 174.431 < inf | logz: 1.902 +/- 0.704 | dlogz: 1.850 > 0.309] + + 52324it [04:28, 204.35it/s, bound: 1164 | nc: 5 | ncall: 267084 | eff(%): 19.591 | loglstar: -inf < 174.485 < inf | logz: 1.910 +/- 0.704 | dlogz: 1.776 > 0.309] + + 52345it [04:28, 205.57it/s, bound: 1165 | nc: 5 | ncall: 267189 | eff(%): 19.591 | loglstar: -inf < 174.675 < inf | logz: 1.916 +/- 0.705 | dlogz: 3.081 > 0.309] + + 52369it [04:28, 214.88it/s, bound: 1165 | nc: 5 | ncall: 267309 | eff(%): 19.591 | loglstar: -inf < 174.762 < inf | logz: 1.925 +/- 0.705 | dlogz: 2.996 > 0.309] + + 52391it [04:28, 214.19it/s, bound: 1166 | nc: 5 | ncall: 267419 | eff(%): 19.591 | loglstar: -inf < 174.872 < inf | logz: 1.933 +/- 0.705 | dlogz: 2.919 > 0.309] + + 52416it [04:28, 221.94it/s, bound: 1166 | nc: 5 | ncall: 267544 | eff(%): 19.592 | loglstar: -inf < 174.941 < inf | logz: 1.942 +/- 0.705 | dlogz: 2.832 > 0.309] + + 52439it [04:29, 221.65it/s, bound: 1167 | nc: 5 | ncall: 267659 | eff(%): 19.592 | loglstar: -inf < 174.989 < inf | logz: 1.950 +/- 0.705 | dlogz: 2.752 > 0.309] + + 52464it [04:29, 227.59it/s, bound: 1167 | nc: 5 | ncall: 267784 | eff(%): 19.592 | loglstar: -inf < 175.069 < inf | logz: 1.958 +/- 0.705 | dlogz: 2.667 > 0.309] + + 52487it [04:29, 221.90it/s, bound: 1168 | nc: 5 | ncall: 267899 | eff(%): 19.592 | loglstar: -inf < 175.133 < inf | logz: 1.966 +/- 0.705 | dlogz: 2.588 > 0.309] + + 52510it [04:29, 223.12it/s, bound: 1168 | nc: 5 | ncall: 268014 | eff(%): 19.592 | loglstar: -inf < 175.153 < inf | logz: 1.973 +/- 0.706 | dlogz: 2.510 > 0.309] + + 52533it [04:29, 216.06it/s, bound: 1169 | nc: 5 | ncall: 268129 | eff(%): 19.592 | loglstar: -inf < 175.239 < inf | logz: 1.980 +/- 0.706 | dlogz: 2.433 > 0.309] + + 52556it [04:29, 219.05it/s, bound: 1169 | nc: 5 | ncall: 268244 | eff(%): 19.593 | loglstar: -inf < 175.294 < inf | logz: 1.987 +/- 0.706 | dlogz: 2.357 > 0.309] + + 52578it [04:29, 210.78it/s, bound: 1170 | nc: 5 | ncall: 268354 | eff(%): 19.593 | loglstar: -inf < 175.334 < inf | logz: 1.994 +/- 0.706 | dlogz: 2.285 > 0.309] + + 52600it [04:29, 209.59it/s, bound: 1170 | nc: 5 | ncall: 268464 | eff(%): 19.593 | loglstar: -inf < 175.418 < inf | logz: 2.000 +/- 0.706 | dlogz: 2.213 > 0.309] + + 52622it [04:29, 205.24it/s, bound: 1171 | nc: 5 | ncall: 268574 | eff(%): 19.593 | loglstar: -inf < 175.468 < inf | logz: 2.006 +/- 0.706 | dlogz: 2.143 > 0.309] + + 52645it [04:30, 210.60it/s, bound: 1171 | nc: 5 | ncall: 268689 | eff(%): 19.593 | loglstar: -inf < 175.568 < inf | logz: 2.013 +/- 0.706 | dlogz: 2.070 > 0.309] + + 52667it [04:30, 210.00it/s, bound: 1172 | nc: 5 | ncall: 268799 | eff(%): 19.593 | loglstar: -inf < 175.654 < inf | logz: 2.019 +/- 0.706 | dlogz: 2.001 > 0.309] + + 52690it [04:30, 214.84it/s, bound: 1172 | nc: 5 | ncall: 268914 | eff(%): 19.594 | loglstar: -inf < 175.717 < inf | logz: 2.026 +/- 0.706 | dlogz: 1.929 > 0.309] + + 52712it [04:30, 211.65it/s, bound: 1173 | nc: 5 | ncall: 269024 | eff(%): 19.594 | loglstar: -inf < 175.785 < inf | logz: 2.032 +/- 0.706 | dlogz: 1.861 > 0.309] + + 52737it [04:30, 221.67it/s, bound: 1173 | nc: 5 | ncall: 269149 | eff(%): 19.594 | loglstar: -inf < 175.867 < inf | logz: 2.039 +/- 0.707 | dlogz: 1.786 > 0.309] + + 52760it [04:30, 218.39it/s, bound: 1174 | nc: 5 | ncall: 269264 | eff(%): 19.594 | loglstar: -inf < 175.920 < inf | logz: 2.045 +/- 0.707 | dlogz: 1.717 > 0.309] + + 52783it [04:30, 218.00it/s, bound: 1174 | nc: 5 | ncall: 269379 | eff(%): 19.594 | loglstar: -inf < 176.013 < inf | logz: 2.051 +/- 0.707 | dlogz: 1.664 > 0.309] + + 52805it [04:30, 193.57it/s, bound: 1175 | nc: 5 | ncall: 269489 | eff(%): 19.594 | loglstar: -inf < 176.053 < inf | logz: 2.057 +/- 0.707 | dlogz: 1.600 > 0.309] + + 52825it [04:30, 180.47it/s, bound: 1175 | nc: 5 | ncall: 269589 | eff(%): 19.595 | loglstar: -inf < 176.119 < inf | logz: 2.062 +/- 0.707 | dlogz: 1.543 > 0.309] + + 52844it [04:31, 174.86it/s, bound: 1176 | nc: 5 | ncall: 269684 | eff(%): 19.595 | loglstar: -inf < 176.160 < inf | logz: 2.067 +/- 0.707 | dlogz: 1.490 > 0.309] + + 52862it [04:31, 172.87it/s, bound: 1176 | nc: 5 | ncall: 269774 | eff(%): 19.595 | loglstar: -inf < 176.234 < inf | logz: 2.072 +/- 0.707 | dlogz: 1.440 > 0.309] + + 52880it [04:31, 167.90it/s, bound: 1177 | nc: 5 | ncall: 269864 | eff(%): 19.595 | loglstar: -inf < 176.299 < inf | logz: 2.076 +/- 0.707 | dlogz: 1.391 > 0.309] + + 52901it [04:31, 177.16it/s, bound: 1177 | nc: 5 | ncall: 269969 | eff(%): 19.595 | loglstar: -inf < 176.375 < inf | logz: 2.082 +/- 0.707 | dlogz: 1.335 > 0.309] + + 52922it [04:31, 178.86it/s, bound: 1178 | nc: 5 | ncall: 270074 | eff(%): 19.595 | loglstar: -inf < 176.429 < inf | logz: 2.087 +/- 0.707 | dlogz: 1.280 > 0.309] + + 52940it [04:31, 179.00it/s, bound: 1178 | nc: 5 | ncall: 270164 | eff(%): 19.596 | loglstar: -inf < 176.471 < inf | logz: 2.091 +/- 0.707 | dlogz: 1.234 > 0.309] + + 52960it [04:31, 184.28it/s, bound: 1178 | nc: 5 | ncall: 270264 | eff(%): 19.596 | loglstar: -inf < 176.532 < inf | logz: 2.096 +/- 0.708 | dlogz: 1.184 > 0.309] + + 52979it [04:31, 167.49it/s, bound: 1179 | nc: 5 | ncall: 270359 | eff(%): 19.596 | loglstar: -inf < 176.589 < inf | logz: 2.101 +/- 0.708 | dlogz: 1.137 > 0.309] + + 52997it [04:31, 169.13it/s, bound: 1179 | nc: 5 | ncall: 270449 | eff(%): 19.596 | loglstar: -inf < 176.624 < inf | logz: 2.105 +/- 0.708 | dlogz: 1.094 > 0.309] + + 53015it [04:32, 165.95it/s, bound: 1180 | nc: 5 | ncall: 270539 | eff(%): 19.596 | loglstar: -inf < 176.672 < inf | logz: 2.109 +/- 0.708 | dlogz: 1.052 > 0.309] + + 53034it [04:32, 169.30it/s, bound: 1180 | nc: 5 | ncall: 270634 | eff(%): 19.596 | loglstar: -inf < 176.727 < inf | logz: 2.113 +/- 0.708 | dlogz: 1.008 > 0.309] + + 53055it [04:32, 179.11it/s, bound: 1180 | nc: 5 | ncall: 270739 | eff(%): 19.596 | loglstar: -inf < 176.799 < inf | logz: 2.118 +/- 0.708 | dlogz: 0.962 > 0.309] + + 53075it [04:32, 182.86it/s, bound: 1181 | nc: 5 | ncall: 270839 | eff(%): 19.597 | loglstar: -inf < 176.863 < inf | logz: 2.123 +/- 0.708 | dlogz: 0.918 > 0.309] + + 53096it [04:32, 188.90it/s, bound: 1181 | nc: 5 | ncall: 270944 | eff(%): 19.597 | loglstar: -inf < 176.922 < inf | logz: 2.127 +/- 0.708 | dlogz: 0.874 > 0.309] + + 53115it [04:32, 186.86it/s, bound: 1182 | nc: 5 | ncall: 271039 | eff(%): 19.597 | loglstar: -inf < 176.997 < inf | logz: 2.131 +/- 0.708 | dlogz: 0.835 > 0.309] + + 53135it [04:32, 189.78it/s, bound: 1182 | nc: 5 | ncall: 271139 | eff(%): 19.597 | loglstar: -inf < 177.038 < inf | logz: 2.136 +/- 0.708 | dlogz: 0.796 > 0.309] + + 53155it [04:32, 184.95it/s, bound: 1183 | nc: 5 | ncall: 271239 | eff(%): 19.597 | loglstar: -inf < 177.091 < inf | logz: 2.140 +/- 0.708 | dlogz: 0.757 > 0.309] + + 53174it [04:32, 186.11it/s, bound: 1183 | nc: 5 | ncall: 271334 | eff(%): 19.597 | loglstar: -inf < 177.149 < inf | logz: 2.144 +/- 0.708 | dlogz: 0.887 > 0.309] + + 53193it [04:33, 180.99it/s, bound: 1184 | nc: 5 | ncall: 271429 | eff(%): 19.597 | loglstar: -inf < 177.250 < inf | logz: 2.148 +/- 0.708 | dlogz: 1.094 > 0.309] + + 53214it [04:33, 188.86it/s, bound: 1184 | nc: 5 | ncall: 271534 | eff(%): 19.598 | loglstar: -inf < 177.306 < inf | logz: 2.153 +/- 0.708 | dlogz: 1.045 > 0.309] + + 53235it [04:33, 193.42it/s, bound: 1184 | nc: 5 | ncall: 271639 | eff(%): 19.598 | loglstar: -inf < 177.345 < inf | logz: 2.157 +/- 0.709 | dlogz: 0.997 > 0.309] + + 53255it [04:33, 180.83it/s, bound: 1185 | nc: 5 | ncall: 271739 | eff(%): 19.598 | loglstar: -inf < 177.408 < inf | logz: 2.161 +/- 0.709 | dlogz: 0.953 > 0.309] + + 53279it [04:33, 196.38it/s, bound: 1185 | nc: 5 | ncall: 271859 | eff(%): 19.598 | loglstar: -inf < 177.460 < inf | logz: 2.166 +/- 0.709 | dlogz: 0.902 > 0.309] + + 53300it [04:33, 197.93it/s, bound: 1186 | nc: 5 | ncall: 271964 | eff(%): 19.598 | loglstar: -inf < 177.558 < inf | logz: 2.170 +/- 0.709 | dlogz: 0.858 > 0.309] + + 53322it [04:33, 200.15it/s, bound: 1187 | nc: 5 | ncall: 272074 | eff(%): 19.598 | loglstar: -inf < 177.616 < inf | logz: 2.174 +/- 0.709 | dlogz: 0.814 > 0.309] + + 53343it [04:33, 202.61it/s, bound: 1187 | nc: 5 | ncall: 272179 | eff(%): 19.598 | loglstar: -inf < 177.680 < inf | logz: 2.179 +/- 0.709 | dlogz: 0.774 > 0.309] + + 53364it [04:33, 204.23it/s, bound: 1187 | nc: 5 | ncall: 272284 | eff(%): 19.599 | loglstar: -inf < 177.705 < inf | logz: 2.183 +/- 0.709 | dlogz: 0.734 > 0.309] + + 53385it [04:33, 189.93it/s, bound: 1188 | nc: 5 | ncall: 272389 | eff(%): 19.599 | loglstar: -inf < 177.735 < inf | logz: 2.186 +/- 0.709 | dlogz: 0.696 > 0.309] + + 53405it [04:34, 191.22it/s, bound: 1188 | nc: 5 | ncall: 272489 | eff(%): 19.599 | loglstar: -inf < 177.790 < inf | logz: 2.190 +/- 0.709 | dlogz: 0.662 > 0.309] + + 53425it [04:34, 176.95it/s, bound: 1189 | nc: 5 | ncall: 272589 | eff(%): 19.599 | loglstar: -inf < 177.821 < inf | logz: 2.193 +/- 0.709 | dlogz: 0.628 > 0.309] + + 53445it [04:34, 181.55it/s, bound: 1189 | nc: 5 | ncall: 272689 | eff(%): 19.599 | loglstar: -inf < 177.911 < inf | logz: 2.197 +/- 0.709 | dlogz: 0.596 > 0.309] + + 53464it [04:34, 180.53it/s, bound: 1190 | nc: 5 | ncall: 272784 | eff(%): 19.599 | loglstar: -inf < 177.946 < inf | logz: 2.200 +/- 0.709 | dlogz: 0.567 > 0.309] + + 53485it [04:34, 186.26it/s, bound: 1190 | nc: 5 | ncall: 272889 | eff(%): 19.600 | loglstar: -inf < 177.976 < inf | logz: 2.203 +/- 0.709 | dlogz: 0.536 > 0.309] + + 53504it [04:34, 184.66it/s, bound: 1191 | nc: 5 | ncall: 272984 | eff(%): 19.600 | loglstar: -inf < 178.004 < inf | logz: 2.206 +/- 0.709 | dlogz: 0.509 > 0.309] + + 53525it [04:34, 189.53it/s, bound: 1191 | nc: 5 | ncall: 273089 | eff(%): 19.600 | loglstar: -inf < 178.034 < inf | logz: 2.210 +/- 0.709 | dlogz: 0.480 > 0.309] + + 53545it [04:34, 191.33it/s, bound: 1191 | nc: 5 | ncall: 273189 | eff(%): 19.600 | loglstar: -inf < 178.086 < inf | logz: 2.213 +/- 0.710 | dlogz: 0.454 > 0.309] + + 53565it [04:34, 188.10it/s, bound: 1192 | nc: 5 | ncall: 273289 | eff(%): 19.600 | loglstar: -inf < 178.154 < inf | logz: 2.215 +/- 0.710 | dlogz: 0.430 > 0.309] + + 53586it [04:35, 193.06it/s, bound: 1192 | nc: 5 | ncall: 273394 | eff(%): 19.600 | loglstar: -inf < 178.175 < inf | logz: 2.218 +/- 0.710 | dlogz: 0.405 > 0.309] + + 53606it [04:35, 188.04it/s, bound: 1193 | nc: 5 | ncall: 273494 | eff(%): 19.600 | loglstar: -inf < 178.228 < inf | logz: 2.221 +/- 0.710 | dlogz: 0.382 > 0.309] + + 53628it [04:35, 196.98it/s, bound: 1193 | nc: 5 | ncall: 273604 | eff(%): 19.601 | loglstar: -inf < 178.294 < inf | logz: 2.224 +/- 0.710 | dlogz: 0.358 > 0.309] + + 53648it [04:35, 192.57it/s, bound: 1194 | nc: 5 | ncall: 273704 | eff(%): 19.601 | loglstar: -inf < 178.339 < inf | logz: 2.227 +/- 0.710 | dlogz: 0.338 > 0.309] + + 53670it [04:35, 198.32it/s, bound: 1194 | nc: 5 | ncall: 273814 | eff(%): 19.601 | loglstar: -inf < 178.391 < inf | logz: 2.229 +/- 0.710 | dlogz: 0.317 > 0.309] + + 53690it [04:35, 187.47it/s, bound: 1195 | nc: 5 | ncall: 273914 | eff(%): 19.601 | loglstar: -inf < 178.426 < inf | logz: 2.232 +/- 0.710 | dlogz: 0.414 > 0.309] + + 53712it [04:35, 196.24it/s, bound: 1195 | nc: 5 | ncall: 274024 | eff(%): 19.601 | loglstar: -inf < 178.500 < inf | logz: 2.235 +/- 0.710 | dlogz: 0.389 > 0.309] + + 53732it [04:35, 194.19it/s, bound: 1196 | nc: 5 | ncall: 274124 | eff(%): 19.601 | loglstar: -inf < 178.524 < inf | logz: 2.237 +/- 0.710 | dlogz: 0.367 > 0.309] + + 53752it [04:35, 192.50it/s, bound: 1196 | nc: 5 | ncall: 274224 | eff(%): 19.601 | loglstar: -inf < 178.610 < inf | logz: 2.239 +/- 0.710 | dlogz: 0.347 > 0.309] + + 53772it [04:36, 188.50it/s, bound: 1197 | nc: 5 | ncall: 274324 | eff(%): 19.602 | loglstar: -inf < 178.678 < inf | logz: 2.242 +/- 0.710 | dlogz: 0.327 > 0.309] + + 53790it [04:36, 194.73it/s, +300 | bound: 1197 | nc: 1 | ncall: 274714 | eff(%): 19.711 | loglstar: -inf < 181.059 < inf | logz: 2.310 +/- 0.729 | dlogz: 0.001 > 0.309] + + + + + 2026-07-11 16:29:15,166 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF. + + + 2026-07-11 16:29:21,761 - root - INFO - Removing search internal folder. + + + 2026-07-11 16:29:21,853 - root - INFO - Search complete, returning result + + + The search has finished run - you may now continue the notebook. + + +Lets print the result info and plot the fit to the dataset to confirm the more thorough search has provided a better +model-fit. + + +```python +print(result.info) + +plt.errorbar( + x=xvalues, + y=data, + yerr=noise_map, + color="k", + ecolor="k", + elinewidth=1, + capsize=2, + linestyle="", +) +plt.plot(range(data.shape[0]), model_data, color="r") +for model_data_1d_individual in model_data_list: + plt.plot(range(data.shape[0]), model_data_1d_individual, "--") +plt.title(f"Fit (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.clf() +plt.close() + +residual_map = data - model_data +normalized_residual_map = residual_map / noise_map +plt.plot(xvalues, normalized_residual_map, color="k") +plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})") +plt.xlabel("x values of profile") +plt.ylabel("Normalized Residuals ($\sigma$)") +plt.show() +plt.clf() +plt.close() +``` + + <>:28: SyntaxWarning: invalid escape sequence '\s' + <>:28: SyntaxWarning: invalid escape sequence '\s' + /tmp/ipykernel_20726/2299532750.py:28: SyntaxWarning: invalid escape sequence '\s' + plt.ylabel("Normalized Residuals ($\sigma$)") + + + Bayesian Evidence 2.31047815 + Maximum Log Likelihood 181.05850686 + + model Collection (N=15) + gaussian_0 - gaussian_4 Gaussian (N=3) + + Maximum Log Likelihood Model: + + gaussian_0 + centre 49.241 + ... [51 lines of output truncated] ... + centre 50.00 (49.99, 50.00) + normalization 20.40 (20.23, 20.62) + sigma 1.01 (1.01, 1.02) + gaussian_2 + centre 78.99 (72.92, 86.73) + normalization 5.85 (4.39, 7.93) + sigma 20.31 (16.88, 23.43) + gaussian_3 + centre 49.89 (49.85, 49.93) + normalization 53.26 (49.45, 57.62) + sigma 5.49 (5.35, 5.68) + gaussian_4 + centre 51.10 (50.62, 52.22) + normalization 71.07 (65.35, 76.17) + sigma 11.90 (11.45, 12.53) + + instances + + + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_53_2.png) + + + + + +![png](tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_53_3.png) + + + +If you repeat the fit multiple times, you will find that the model-fit is more likely to produce a good fit than +previously. + +However, the run-time of the search is noticeably longer, taking a few minutes to complete, owining +to the increased number of live points and fact it is searching parameter space more thoroughly. + +Let's list the advantages and disadvantages of simply adjusting the non-linear search: + +**Advantages:** + +- It’s easy to set up; just change the settings of the non-linear search. + +- It generalizes to any dataset. + +- We can retain a more complex model. + +**Disadvantage:** + +- It can be very expensive in terms of run time, producing run-times that are five, tens or even longer than the + original run-time. + +__Summary__ + +We have covered three strategies for ensuring the non-linear search estimates the correct solution: + +1) Prior Tuning: By setting our priors more accurately, we can help the search find the global solution instead of + getting stuck at a local maxima. + +2) Reducing Complexity: By making certain assumptions, we can reduce the number of model parameters, thereby lowering + the dimensionality of the parameter space and improving the search's performance. + +3) Search More Thoroughly: By increasing the number of live points, we can make the search explore parameter space + more thoroughly, increasing the likelihood of finding the global maximum model. + +Each of these strategies has its advantages and disadvantages, and your ability to fit models successfully will +likely rely on a combination of these strategies. Which one works best depends on the specific model, dataset and +scientific question you are trying to answer. Therefore, when you begin your own model-fitting, it is a good idea to +try each of these strategies and assess which works best for your model-fit. + +__Run Times__ + +One challenging aspect of model-fitting which was not properly covered in this tutorial is the run-time of a model-fit. +This example fits simple 1D datasets, which are computationally inexpensive to fit. That is, the `log_likelihood_function` +is evaluated in a fraction of a second, meaning the non-linear search fitted the model in mere minutes. + +Many model-fitting tasks are not as fast. For example, when fitting a model to a 2D image, the `log_likelihood_function` +may take of order seconds, or longer, because it comprises a number of expensive calculations (e.g. a Fourier transform, +2D convolution, etc.). Depending on the model complexity, this means that the non-linear search may take hours, days +or even weeks to fit the model. + +Run times are also dictated by the complexity of the model and the nature of the log likelihood function. For models +with many more dimensions than the simple 1D model used in this tutorial (e.g. hundreds or thousands of free parameters), +non-linear search may take tens or hundreds of more iterations to converge on a solution. This is because the parameter +space is significantly more complex and difficult to sample accurately. More iterations mean longer run times, +which in combination with a slow likelihood function can make model-fitting infeasible. + +Whether or not run times will pose a challenge to your model-fitting task depends on the complexity of the model and +nature of the log likelihood function. If your problem is computationally expensive, **PyAutoFit** provides many +tools to help, which will be the topic of tutorials in chapter 2 of the **HowToFit** lectures. + +__Model Mismatch__ + +In this example, interpreting how well the model fitted the data, and whether it found the global maxima, was +relatively straightforward. This is because the same model was used to simulate the data and fit it, meaning the +global maxima fit corresponded to one where the normalized residuals were minimized and consistent with the noise +(e.g. they went to sigma values below 3.0 or so). + +In many scientific studies, the data that one is fitting may have come from an instrument or simulation where the +exact physical processes that generate the data are not perfectly known. This then means that the model is +not a perfect representation of the data, and it may not ever be possible to fit the data perfectly. In this case, +we might infer a fit with significant residuals, but it may still correspond to the global maxima solution, +at least for that particular model. + +This makes it even more difficult to be certain if the non-linear search is sampling parameter space correctly, +and therefore requires even more care and attention to the strategies we have discussed above. + +Whether or not this is the case for your model-fitting task is something you will have to determine yourself. +**PyAutoFit** provides many tools to help assess the quality of a model-fit, which will be the topic of tutorials +in chapter 2 of the **HowToFit** lectures. + +__Astronomy Example__ + +At the end of chapter 1, we will fit a complex model to a real astronomical dataset in order to quantify +the distribution of stars in 2D images of galaxies. + +This example will illustrate many of the challenges discussed in this tutorial, including: + +- Fits using more complex models consisting of 15-20 parameters often infer local maxima, unless we assist the + non-linear search with tuned priors, reduced complexity or a more thorough search. + +- Fitting 2D imaging data requires a 2D convolution, which is somewhat computationally expensive and means run times + become something we must balance with model complexity. + +- The model is not a perfect representation of the data. For example, the model assumes the galaxy is elliptically + symmetric, whereas the real galaxy may not be. In certain examples, this means that the global maxima solution + actually leaves significant residuals, above 3.0 $\sigma$, in the data. + +__Wrap Up__ + +Now is a good time to assess how straightforward or difficult you think your model-fitting task will be. + +Are the models you will be fitting made up of tens of parameters? or thousands? Are there ways you can simplify +the model parameter or tune priors to make the model-fitting task more feasible? Will run times be an issue, or is +your likelihood function computationally cheap? And how confident are you that the model you are fitting is a good +representation of the data? + +These are all questions you should be asking yourself before beginning your model-fitting task, but they will +become easier to answer as you gain experience with model-fitting and **PyAutoFit**. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_23_0.png b/markdown/chapter_1_introduction/tutorial_4_why_modeling_is_hard_files/tutorial_4_why_modeling_is_hard_23_0.png new file mode 100644 index 0000000000000000000000000000000000000000..00a89b2559473df10ebb3dc4975ae59706cd8e73 GIT binary patch literal 46283 zcmb@ubyQdH_BHweX;2UlkQ7irIs^nMkrqTkM7mSD8wDh!LqcgO6%nM75J8X*0qO1z zK}zo0c;0j0`y21Qf88_28DI7DVYB!1tY@vc=A3JXK71g15swlNg+g7Fmy=ROq0l8z zD6|$FEclnlEsO2&A7N){O=mSbGiNtLM^luNp|kx{JLjjCM%P?T9i1%gZ235OxH*N` zuUR-d+dBz!aoPO$8=Q8I=3GoN{ZHU3xb|{SoKPr2L*)Nx`Qmw&C{(GSywqKF_cv?f 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script](../../scripts/chapter_1_introduction/tutorial_5_results_and_samples.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb). + +Tutorial 5: Results And Samples +=============================== + +In this tutorial, we'll cover all of the output that comes from a non-linear search's `Result` object. + +We used this object at various points in the chapter. The bulk of material covered here is described in the example +script `autofit_workspace/overview/simple/result.py`. Nevertheless, it is a good idea to refresh ourselves about how +results in **PyAutoFit** work before covering more advanced material. + +__Contents__ + +This tutorial is split into the following sections: + +- **Data**: Load the dataset from the HowToFit/dataset folder. +- **Reused Functions**: Reuse the `plot_profile_1d` and `Analysis` classes from the previous tutorial. +- **Model Fit**: Run a non-linear search to generate a `Result` object. +- **Result**: Examine the `Result` object and its info attribute. +- **Samples**: Introduce the `Samples` object containing the non-linear search samples. +- **Parameters**: Access parameter values from the samples. +- **Figures of Merit**: Examine log likelihood, log prior, and log posterior values. +- **Instances**: Return results as model instances from samples. +- **Vectors**: Return results as 1D parameter vectors. +- **Labels**: Access the paths, names, and labels for model parameters. +- **Posterior / PDF**: Access median PDF estimates for the model parameters. +- **Plot**: Visualize model fit results using instances. +- **Errors**: Compute parameter error estimates at specified sigma confidence limits. +- **PDF**: Plot Probability Density Functions using corner.py. +- **Other Results**: Access maximum log posterior and other sample statistics. +- **Sample Instance**: Create instances from individual samples in the sample list. +- **Bayesian Evidence**: Access the log evidence for nested sampling searches. +- **Derived Errors (PDF from samples)**: Compute errors on derived quantities from sample PDFs. +- **Samples Filtering**: Filter samples by parameter paths for specific parameter analysis. +- **Latex**: Generate LaTeX table code for modeling results. + + +```python + +from autoconf import setup_notebook; setup_notebook() + +import autofit as af +import autofit.plot as aplt +import os +from os import path +import numpy as np +import matplotlib.pyplot as plt +``` + + Working Directory has been set to `HowToFit` + + +__Data__ + +Load the dataset from the `HowToFit/dataset` folder. + + +```python +dataset_path = path.join("dataset", "example_1d", "gaussian_x1__exponential_x1") +``` + +__Dataset Auto-Simulation__ + +If the dataset does not already exist on your system, it will be created by running the corresponding +simulator script. This ensures that all example scripts can be run without manually simulating data first. + + +```python +if not path.exists(dataset_path): + import subprocess + import sys + + subprocess.run( + [sys.executable, "scripts/simulators/simulators.py"], + check=True, + ) + +data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json")) +noise_map = af.util.numpy_array_from_json( + file_path=path.join(dataset_path, "noise_map.json") +) +``` + +__Reused Functions__ + +We'll reuse the `plot_profile_1d` and `Analysis` classes of the previous tutorial. + + +```python + + +def plot_profile_1d( + xvalues, + profile_1d, + title=None, + ylabel=None, + errors=None, + color="k", + output_path=None, + output_filename=None, +): + plt.errorbar( + x=xvalues, + y=profile_1d, + yerr=errors, + linestyle="", + color=color, + ecolor="k", + elinewidth=1, + capsize=2, + ) + plt.title(title) + plt.xlabel("x value of profile") + plt.ylabel(ylabel) + if not path.exists(output_path): + os.makedirs(output_path) + plt.savefig(path.join(output_path, f"{output_filename}.png")) + plt.clf() + + +class Analysis(af.Analysis): + def __init__(self, data, noise_map): + super().__init__() + + self.data = data + self.noise_map = noise_map + + def log_likelihood_function(self, instance): + model_data = self.model_data_from_instance(instance=instance) + + residual_map = self.data - model_data + chi_squared_map = (residual_map / self.noise_map) ** 2.0 + chi_squared = sum(chi_squared_map) + noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0)) + log_likelihood = -0.5 * (chi_squared + noise_normalization) + + return log_likelihood + + def model_data_from_instance(self, instance): + """ + To create the summed profile of all individual profiles in an instance, we can use a dictionary comprehension + to iterate over all profiles in the instance. + """ + xvalues = np.arange(self.data.shape[0]) + + return sum([profile.model_data_from(xvalues=xvalues) for profile in instance]) + + def visualize(self, paths, instance, during_analysis): + """ + This method is identical to the previous tutorial, except it now uses the `model_data_from_instance` method + to create the profile. + """ + xvalues = np.arange(self.data.shape[0]) + + model_data = self.model_data_from_instance(instance=instance) + + residual_map = self.data - model_data + chi_squared_map = (residual_map / self.noise_map) ** 2.0 + + """The visualizer now outputs images of the best-fit results to hard-disk (checkout `visualizer.py`).""" + plot_profile_1d( + xvalues=xvalues, + profile_1d=self.data, + title="Data", + ylabel="Data Values", + color="k", + output_path=paths.image_path, + output_filename="data", + ) + + plot_profile_1d( + xvalues=xvalues, + profile_1d=model_data, + title="Model Data", + ylabel="Model Data Values", + color="k", + output_path=paths.image_path, + output_filename="model_data", + ) + + plot_profile_1d( + xvalues=xvalues, + profile_1d=residual_map, + title="Residual Map", + ylabel="Residuals", + color="k", + output_path=paths.image_path, + output_filename="residual_map", + ) + + plot_profile_1d( + xvalues=xvalues, + profile_1d=chi_squared_map, + title="Chi-Squared Map", + ylabel="Chi-Squareds", + color="k", + output_path=paths.image_path, + output_filename="chi_squared_map", + ) + +``` + +__Model Fit__ + +Now lets run the non-linear search to get ourselves a `Result`. + + +```python + + +class Gaussian: + def __init__( + self, + centre=30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization=1.0, # <- are the Gaussian`s model parameters. + sigma=5.0, + ): + """ + Represents a 1D Gaussian profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to fit example datasets + via a non-linear search. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + sigma + The sigma value controlling the size of the Gaussian. + """ + self.centre = centre + self.normalization = normalization + self.sigma = sigma + + def model_data_from(self, xvalues: np.ndarray): + """ + + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return np.multiply( + np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)), + np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))), + ) + + +class Exponential: + def __init__( + self, + centre=30.0, # <- **PyAutoFit** recognises these constructor arguments + normalization=1.0, # <- are the Exponential`s model parameters. + rate=0.01, + ): + """ + Represents a 1D Exponential profile. + + This is a model-component of example models in the **HowToFit** lectures and is used to fit example datasets + via a non-linear search. + + Parameters + ---------- + centre + The x coordinate of the profile centre. + normalization + Overall normalization of the profile. + ratw + The decay rate controlling has fast the Exponential declines. + """ + self.centre = centre + self.normalization = normalization + self.rate = rate + + def model_data_from(self, xvalues: np.ndarray): + """ + Returns a 1D Gaussian on an input list of Cartesian x coordinates. + + The input xvalues are translated to a coordinate system centred on the Gaussian, via its `centre`. + + The output is referred to as the `model_data` to signify that it is a representation of the data from the + model. + + Parameters + ---------- + xvalues + The x coordinates in the original reference frame of the data. + """ + transformed_xvalues = np.subtract(xvalues, self.centre) + return self.normalization * np.multiply( + self.rate, np.exp(-1.0 * self.rate * abs(transformed_xvalues)) + ) + + +model = af.Collection(gaussian=af.Model(Gaussian), exponential=af.Model(Exponential)) + +analysis = Analysis(data=data, noise_map=noise_map) + +search = af.Emcee( + name="tutorial_5_results_and_samples", + path_prefix="chapter_1_introduction", +) + +print( + """ + The non-linear search has begun running. + Checkout the HowToFit/output/chapter_1_introduction/tutorial_5_results_and_samples + folder for live output of the results. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + """ +) + +result = search.fit(model=model, analysis=analysis) + +print("The search has finished run - you may now continue the notebook.") +``` + + + The non-linear search has begun running. + Checkout the HowToFit/output/chapter_1_introduction/tutorial_5_results_and_samples + folder for live output of the results. + This Jupyter notebook cell with progress once the search has completed - this could take a few minutes! + + 2026-07-11 16:29:32,999 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores. + + + 2026-07-11 16:29:33,011 - tutorial_5_results_and_samples - INFO - The output path of this fit is HowToFit/output/chapter_1_introduction/tutorial_5_results_and_samples/f7b96c133609a46d15a374506304464c + + + 2026-07-11 16:29:33,012 - tutorial_5_results_and_samples - INFO - Outputting pre-fit files (e.g. model.info, visualization). + + + 2026-07-11 16:29:33,575 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores + + + 2026-07-11 16:29:33,597 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search + + + 2026-07-11 16:29:33,598 - tutorial_5_results_and_samples - INFO - Visualizing Starting Point Model in image_start folder. + + + 2026-07-11 16:29:34,071 - tutorial_5_results_and_samples - INFO - Starting new Emcee non-linear search (no previous samples found). + + + 0%| | 0/2000 [00:00 + + +__Result__ + +Here, we'll look in detail at what information is contained in the `Result`. + +It contains an `info` attribute which prints the result in readable format. + + +```python +print(result.info) +``` + + Maximum Log Likelihood 157.53479909 + + model Collection (N=6) + gaussian Gaussian (N=3) + exponential Exponential (N=3) + + Maximum Log Likelihood Model: + + gaussian + centre -294101561365780736.000 + ... [15 lines of output truncated] ... + centre 50.10 (49.60, 157.08) + normalization 51.74 (0.37, 54.07) + rate 0.06 (0.05, 1.15) + + + Summary (1.0 sigma limits): + + gaussian + centre 50.14 (-10295526.34, 192063.19) + normalization 8.73 (2.66, 78.09) + sigma 10.80 (-936478.33, 26484.73) + exponential + centre 50.10 (50.05, 50.15) + normalization 51.74 (51.54, 51.92) + rate 0.06 (0.06, 0.06) + + instances + + + + +__Samples__ + +The result contains a `Samples` object, which contains all of the non-linear search samples. + +Each sample corresponds to a set of model parameters that were evaluated and accepted by our non linear search, +in this example emcee. + +This also includes their log likelihoods, which are used for computing additional information about the model-fit, +for example the error on every parameter. + +Our model-fit used the MCMC algorithm Emcee, so the `Samples` object returned is a `SamplesMCMC` object. + + +```python +samples = result.samples + +print("MCMC Samples: \n") +print(samples) +``` + + MCMC Samples: + + SamplesMCMC(500) + + +__Parameters__ + +The parameters are stored as a list of lists, where: + + - The outer list is the size of the total number of samples. + - The inner list is the size of the number of free parameters in the fit. + +Below, we print the first sample — its second parameter (Gaussian -> normalization) and its third +parameter (Gaussian -> sigma). Any other sample index would work the same way; index `0` is used +here so the example remains valid regardless of how many samples the search produced. + + +```python +samples = result.samples +print("Sample 0's second parameter value (Gaussian -> normalization):") +print(samples.parameter_lists[0][1]) +print("Sample 0's third parameter value (Gaussian -> sigma)") +print(samples.parameter_lists[0][2], "\n") +``` + + Sample 0's second parameter value (Gaussian -> normalization): + 0.11335924010781939 + Sample 0's third parameter value (Gaussian -> sigma) + 3099.028904610647 + + + +__Figures of Merit__ + +The Samples class also contains the log likelihood, log prior, log posterior and weight_list of every accepted sample, +where: + +- The log likelihood is the value evaluated from the likelihood function (e.g. -0.5 * chi_squared + the noise +normalized). + +- The log prior encodes information on how the priors on the parameters maps the log likelihood value to the log +posterior value. + +- The log posterior is log_likelihood + log_prior. + +- The weight gives information on how samples should be combined to estimate the posterior. The weight values depend on +the sampler used, for MCMC samples they are all 1 (e.g. all weighted equally). + +Below, we inspect the first sample. Any sample index would work the same way; index `0` is used here +so the example is valid regardless of how many samples the search produced. + + +```python +print("log(likelihood), log(prior), log(posterior) and weight of the first sample.") +print(samples.log_likelihood_list[0]) +print(samples.log_prior_list[0]) +print(samples.log_posterior_list[0]) +print(samples.weight_list[0]) +``` + + log(likelihood), log(prior), log(posterior) and weight of the first sample. + 68.35981822308696 + -1.7760622584685666 + 66.58375596461839 + 1.0 + + +__Instances__ + +The `Samples` contains many results which are returned as an instance of the model, using the Python class structure +of the model composition. + +For example, we can return the model parameters corresponding to the maximum log likelihood sample. + + +```python +max_lh_instance = samples.max_log_likelihood() + +print("Max Log Likelihood `Gaussian` Instance:") +print("Centre = ", max_lh_instance.gaussian.centre) +print("Normalization = ", max_lh_instance.gaussian.normalization) +print("Sigma = ", max_lh_instance.gaussian.sigma, "\n") + +print("Max Log Likelihood Exponential Instance:") +print("Centre = ", max_lh_instance.exponential.centre) +print("Normalization = ", max_lh_instance.exponential.normalization) +print("Sigma = ", max_lh_instance.exponential.rate, "\n") +``` + + Max Log Likelihood `Gaussian` Instance: + Centre = -2.9410156136578074e+17 + Normalization = 523915696.11279744 + Sigma = -3.023887584844618e+16 + + Max Log Likelihood Exponential Instance: + Centre = 50.2399798087453 + Normalization = 51.65027804241896 + Sigma = 0.0623539817262462 + + + +__Vectors__ + +All results can alternatively be returned as a 1D vector of values, by passing `as_instance=False`: + + +```python +max_lh_vector = samples.max_log_likelihood(as_instance=False) +print("Max Log Likelihood Model Parameters: \n") +print(max_lh_vector, "\n\n") +``` + + Max Log Likelihood Model Parameters: + + [-2.9410156136578074e+17, 523915696.11279744, -3.023887584844618e+16, 50.2399798087453, 51.65027804241896, 0.0623539817262462] + + + + +__Labels__ + +Vectors return a lists of all model parameters, but do not tell us which values correspond to which parameters. + +The following quantities are available in the `Model`, where the order of their entries correspond to the parameters +in the `ml_vector` above: + + - `paths`: a list of tuples which give the path of every parameter in the `Model`. + - `parameter_names`: a list of shorthand parameter names derived from the `paths`. + - `parameter_labels`: a list of parameter labels used when visualizing non-linear search results (see below). + + +```python +model = samples.model + +print(model.paths) +print(model.parameter_names) +print(model.parameter_labels) +print(model.model_component_and_parameter_names) +print("\n") +``` + + [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')] + ['centre', 'normalization', 'sigma', 'centre', 'normalization', 'rate'] + ['x', 'norm', '\\sigma', 'x', 'norm', '\\lambda'] + ['gaussian.centre', 'gaussian.normalization', 'gaussian.sigma', 'exponential.centre', 'exponential.normalization', 'exponential.rate'] + + + + +From here on, we will returned all results information as instances, but every method below can be returned as a +vector via the `as_instance=False` input. + +__Posterior / PDF__ + +The ``Result`` object contains the full posterior information of our non-linear search, which can be used for +parameter estimation. + +The median pdf vector is available from the `Samples` object, which estimates the every parameter via 1D +marginalization of their PDFs. + + +```python +median_pdf_instance = samples.median_pdf() + +print("Max Log Likelihood `Gaussian` Instance:") +print("Centre = ", median_pdf_instance.gaussian.centre) +print("Normalization = ", median_pdf_instance.gaussian.normalization) +print("Sigma = ", median_pdf_instance.gaussian.sigma, "\n") + +print("Max Log Likelihood Exponential Instance:") +print("Centre = ", median_pdf_instance.exponential.centre) +print("Normalization = ", median_pdf_instance.exponential.normalization) +print("Sigma = ", median_pdf_instance.exponential.rate, "\n") +``` + + Max Log Likelihood `Gaussian` Instance: + Centre = 50.13507900894478 + Normalization = 8.730203550030652 + Sigma = 10.797678770589716 + + Max Log Likelihood Exponential Instance: + Centre = 50.1016222399324 + Normalization = 51.74042118842236 + Sigma = 0.06213782821625456 + + + +__Plot__ + +Because results are returned as instances, it is straight forward to use them and their associated functionality +to make plots of the results: + + +```python +model_gaussian = max_lh_instance.gaussian.model_data_from( + xvalues=np.arange(data.shape[0]) +) +model_exponential = max_lh_instance.exponential.model_data_from( + xvalues=np.arange(data.shape[0]) +) +model_data = model_gaussian + model_exponential + +plt.plot(range(data.shape[0]), data) +plt.plot(range(data.shape[0]), model_data) +plt.plot(range(data.shape[0]), model_gaussian, "--") +plt.plot(range(data.shape[0]), model_exponential, "--") +plt.title("Illustrative model fit to 1D `Gaussian` + Exponential profile data.") +plt.xlabel("x values of profile") +plt.ylabel("Profile normalization") +plt.show() +plt.close() +``` + + + +![png](tutorial_5_results_and_samples_files/tutorial_5_results_and_samples_27_0.png) + + + +__Errors__ + +The samples include methods for computing the error estimates of all parameters, via 1D marginalization at an +input sigma confidence limit. + + +```python +errors_at_upper_sigma_instance = samples.errors_at_upper_sigma(sigma=3.0) +errors_at_lower_sigma_instance = samples.errors_at_lower_sigma(sigma=3.0) + +print("Upper Error values of Gaussian (at 3.0 sigma confidence):") +print("Centre = ", errors_at_upper_sigma_instance.gaussian.centre) +print("Normalization = ", errors_at_upper_sigma_instance.gaussian.normalization) +print("Sigma = ", errors_at_upper_sigma_instance.gaussian.sigma, "\n") + +print("lower Error values of Gaussian (at 3.0 sigma confidence):") +print("Centre = ", errors_at_lower_sigma_instance.gaussian.centre) +print("Normalization = ", errors_at_lower_sigma_instance.gaussian.normalization) +print("Sigma = ", errors_at_lower_sigma_instance.gaussian.sigma, "\n") +``` + + Upper Error values of Gaussian (at 3.0 sigma confidence): + Centre = 1.9035565111885748e+16 + Normalization = 876330725.5982871 + Sigma = 1954631025976492.2 + + lower Error values of Gaussian (at 3.0 sigma confidence): + Centre = 6.468665924711259e+17 + Normalization = 8.730203390689214 + Sigma = 6.650671449666327e+16 + + + +They can also be returned at the values of the parameters at their error values: + + +```python +values_at_upper_sigma_instance = samples.values_at_upper_sigma(sigma=3.0) +values_at_lower_sigma_instance = samples.values_at_lower_sigma(sigma=3.0) + +print("Upper Parameter values w/ error of Gaussian (at 3.0 sigma confidence):") +print("Centre = ", values_at_upper_sigma_instance.gaussian.centre) +print("Normalization = ", values_at_upper_sigma_instance.gaussian.normalization) +print("Sigma = ", values_at_upper_sigma_instance.gaussian.sigma, "\n") + +print("lower Parameter values w/ errors of Gaussian (at 3.0 sigma confidence):") +print("Centre = ", values_at_lower_sigma_instance.gaussian.centre) +print("Normalization = ", values_at_lower_sigma_instance.gaussian.normalization) +print("Sigma = ", values_at_lower_sigma_instance.gaussian.sigma, "\n") +``` + + Upper Parameter values w/ error of Gaussian (at 3.0 sigma confidence): + Centre = 1.90355651118858e+16 + Normalization = 876330734.3284906 + Sigma = 1954631025976503.0 + + lower Parameter values w/ errors of Gaussian (at 3.0 sigma confidence): + Centre = -6.468665924711259e+17 + Normalization = 1.5934143739195753e-07 + Sigma = -6.650671449666326e+16 + + + +__PDF__ + +The Probability Density Functions (PDF's) of the results can be plotted using the Emcee's visualization +tool `corner.py`, which is wrapped via the `aplt.corner_cornerpy` function. + + +```python +aplt.corner_cornerpy(samples=result.samples) +``` + + 2026-07-11 16:30:08,517 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,545 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,566 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,593 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,613 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,633 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,663 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,682 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,700 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,717 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,743 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,763 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,781 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,802 - root - WARNING - Too few points to create valid contours + + + 2026-07-11 16:30:08,820 - root - WARNING - Too few points to create valid contours + + + + +![png](tutorial_5_results_and_samples_files/tutorial_5_results_and_samples_33_15.png) + + + +__Other Results__ + +The samples contain many useful vectors, including the samples with the highest posterior values. + + +```python +max_log_posterior_instance = samples.max_log_posterior() + +print("Maximum Log Posterior Vector:") +print("Centre = ", max_log_posterior_instance.gaussian.centre) +print("Normalization = ", max_log_posterior_instance.gaussian.normalization) +print("Sigma = ", max_log_posterior_instance.gaussian.sigma, "\n") + +``` + + Maximum Log Posterior Vector: + Centre = -24011277044876.77 + Normalization = 3125270.2307336275 + Sigma = -2387925142098.1606 + + + +All methods above are available as a vector: + + +```python +median_pdf_instance = samples.median_pdf(as_instance=False) +values_at_upper_sigma = samples.values_at_upper_sigma(sigma=3.0, as_instance=False) +values_at_lower_sigma = samples.values_at_lower_sigma(sigma=3.0, as_instance=False) +errors_at_upper_sigma = samples.errors_at_upper_sigma(sigma=3.0, as_instance=False) +errors_at_lower_sigma = samples.errors_at_lower_sigma(sigma=3.0, as_instance=False) +``` + +__Sample Instance__ + +A non-linear search retains every model that is accepted during the model-fit. + +We can create an instance of any lens model -- below we create an instance of the last accepted model. + + +```python +instance = samples.from_sample_index(sample_index=-1) + +print("Gaussian Instance of last sample") +print("Centre = ", instance.gaussian.centre) +print("Normalization = ", instance.gaussian.normalization) +print("Sigma = ", instance.gaussian.sigma, "\n") +``` + + Gaussian Instance of last sample + Centre = -2.5614444939533196e+16 + Normalization = 798.0401817263084 + Sigma = -2627054060301375.5 + + + +__Bayesian Evidence__ + +If a nested sampling `NonLinearSearch` is used, the evidence of the model is also available which enables Bayesian +model comparison to be performed (given we are using Emcee, which is not a nested sampling algorithm, the log evidence +is None).: + + +```python +log_evidence = samples.log_evidence +``` + +__Derived Errors (PDF from samples)__ + +Computing the errors of a quantity like the `sigma` of the Gaussian is simple, because it is sampled by the non-linear +search. Thus, to get their errors above we used the `Samples` object to simply marginalize over all over parameters +via the 1D Probability Density Function (PDF). + +Computing errors on derived quantities is more tricky, because they are not sampled directly by the non-linear search. +For example, what if we want the error on the full width half maximum (FWHM) of the Gaussian? In order to do this +we need to create the PDF of that derived quantity, which we can then marginalize over using the same function we +use to marginalize model parameters. + +Below, we compute the FWHM of every accepted model sampled by the non-linear search and use this determine the PDF +of the FWHM. When combining the FWHM's we weight each value by its `weight`. For Emcee, an MCMC algorithm, the +weight of every sample is 1, but weights may take different values for other non-linear searches. + +In order to pass these samples to the function `marginalize`, which marginalizes over the PDF of the FWHM to compute +its error, we also pass the weight list of the samples. + +(Computing the error on the FWHM could be done in much simpler ways than creating its PDF from the list of every +sample. We chose this example for simplicity, in order to show this functionality, which can easily be extended to more +complicated derived quantities.) + + +```python +fwhm_list = [] + +for sample in samples.sample_list: + instance = sample.instance_for_model(model=samples.model) + + sigma = instance.gaussian.sigma + + fwhm = 2 * np.sqrt(2 * np.log(2)) * sigma + + fwhm_list.append(fwhm) + +median_fwhm, lower_fwhm, upper_fwhm = af.marginalize( + parameter_list=fwhm_list, sigma=3.0, weight_list=samples.weight_list +) + +print(f"FWHM = {median_fwhm} ({upper_fwhm} {lower_fwhm}") +``` + + FWHM = 25.4265904087898 (6898791711474904.0 -6.302666628080957e+17 + + +__Samples Filtering__ + +Our samples object has the results for all three parameters in our model. However, we might only be interested in the +results of a specific parameter. + +The basic form of filtering specifies parameters via their path, which was printed above via the model and is printed +again below. + + +```python +samples = result.samples + +print("Parameter paths in the model which are used for filtering:") +print(samples.model.paths) + +print("All parameters of the very first sample") +print(samples.parameter_lists[0]) + +samples = samples.with_paths([("gaussian", "centre")]) + +print("All parameters of the very first sample (containing only the Gaussian centre.") +print(samples.parameter_lists[0]) + +print("Maximum Log Likelihood Model Instances (containing only the Gaussian centre):\n") +print(samples.max_log_likelihood(as_instance=False)) +``` + + Parameter paths in the model which are used for filtering: + [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')] + All parameters of the very first sample + [31513.97299772367, 0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713] + All parameters of the very first sample (containing only the Gaussian centre. + [31513.97299772367] + Maximum Log Likelihood Model Instances (containing only the Gaussian centre): + + [-2.9410156136578074e+17] + + +Above, we specified each path as a list of tuples of strings. + +This is how the source code internally stores the path to different components of the model, but it is not +in-profile_1d with the PyAutoFIT API used to compose a model. + +We can alternatively use the following API: + + +```python +samples = result.samples + +samples = samples.with_paths(["gaussian.centre"]) + +print("All parameters of the very first sample (containing only the Gaussian centre).") +print(samples.parameter_lists[0]) +``` + + All parameters of the very first sample (containing only the Gaussian centre). + [31513.97299772367] + + +Above, we filtered the `Samples` but asking for all parameters which included the path ("gaussian", "centre"). + +We can alternatively filter the `Samples` object by removing all parameters with a certain path. Below, we remove +the Gaussian's `centre` to be left with 2 parameters; the `normalization` and `sigma`. + + +```python +samples = result.samples + +print("Parameter paths in the model which are used for filtering:") +print(samples.model.paths) + +print("All parameters of the very first sample") +print(samples.parameter_lists[0]) + +samples = samples.without_paths(["gaussian.centre"]) + +print( + "All parameters of the very first sample (containing only the Gaussian normalization and sigma)." +) +print(samples.parameter_lists[0]) +``` + + Parameter paths in the model which are used for filtering: + [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')] + All parameters of the very first sample + [31513.97299772367, 0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713] + All parameters of the very first sample (containing only the Gaussian normalization and sigma). + [0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713] + + +__Latex__ + +If you are writing modeling results up in a paper, you can use inbuilt latex tools to create latex table +code which you can copy to your .tex document. + +By combining this with the filtering tools below, specific parameters can be included or removed from the latex. + +Remember that the superscripts of a parameter are loaded from the config file `notation/label.yaml`, providing high +levels of customization for how the parameter names appear in the latex table. This is especially useful if your model +uses the same model components with the same parameter, which therefore need to be distinguished via superscripts. + + +```python +latex = af.text.Samples.latex( + samples=result.samples, + median_pdf_model=True, + sigma=3.0, + name_to_label=True, + include_name=True, + include_quickmath=True, + prefix="Example Prefix ", + suffix=" \\[-2pt]", +) + +print(latex) +``` + + Example Prefix $x^{\rm{g}} = 50.14^{+19035565111885748.00}_{-646866592471125888.00}$ & $norm^{\rm{g}} = 8.73^{+876330725.60}_{-8.73}$ & $\sigma^{\rm{g}} = 10.80^{+1954631025976492.25}_{-66506714496663272.00}$ & $x^{\rm{e}} = 50.10^{+106.98}_{-0.50}$ & $norm^{\rm{e}} = 51.74^{+2.33}_{-51.37}$ & $\lambda^{\rm{e}} = 0.06^{+1.09}_{-0.01}$ \[-2pt] + + +Finish. + + +```python + +``` diff --git a/markdown/chapter_1_introduction/tutorial_5_results_and_samples_files/tutorial_5_results_and_samples_27_0.png 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