metadata
dict | text
stringlengths 0
40.6M
| id
stringlengths 14
255
|
|---|---|---|
{
"filename": "_unselected.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/choropleth/_unselected.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "choropleth"
_path_str = "choropleth.unselected"
_valid_props = {"marker"}
# marker
# ------
@property
def marker(self):
"""
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.choropleth.unselected.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.choropleth.unselected.Marker
"""
return self["marker"]
@marker.setter
def marker(self, val):
self["marker"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
marker
:class:`plotly.graph_objects.choropleth.unselected.Mark
er` instance or dict with compatible properties
"""
def __init__(self, arg=None, marker=None, **kwargs):
"""
Construct a new Unselected object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.choropleth.Unselected`
marker
:class:`plotly.graph_objects.choropleth.unselected.Mark
er` instance or dict with compatible properties
Returns
-------
Unselected
"""
super(Unselected, self).__init__("unselected")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.choropleth.Unselected
constructor must be a dict or
an instance of :class:`plotly.graph_objs.choropleth.Unselected`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("marker", None)
_v = marker if marker is not None else _v
if _v is not None:
self["marker"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@choropleth@_unselected.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "icecube/skyllh",
"repo_path": "skyllh_extracted/skyllh-master/skyllh/datasets/__init__.py",
"type": "Python"
}
|
icecubeREPO_NAMEskyllhPATH_START.@skyllh_extracted@skyllh-master@skyllh@datasets@__init__.py@.PATH_END.py
|
|
{
"filename": "_side.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/treemap/pathbar/_side.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SideValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="side", parent_name="treemap.pathbar", **kwargs):
super(SideValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
values=kwargs.pop("values", ["top", "bottom"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@treemap@pathbar@_side.py@.PATH_END.py
|
{
"filename": "Guide-checkpoint.ipynb",
"repo_name": "kevin-flaherty/disk_model3",
"repo_path": "disk_model3_extracted/disk_model3-master/.ipynb_checkpoints/Guide-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
This is a quick guide to running the Wesleyan python disk modeling code. It is based heavily on Katherine Rosenfeld's original idl code (Rosenfeld et al. 2013, among others), which is based on the structure in Dartois et al. 2003. More detail on the underlying disk structure used in this code can be found in Flaherty et al. 2015.
The general purpose of this code is to general a model image from a parametric model of a protoplanetary/debris disk. It has been used extensively in constraining turbulence from gas line observations (Flaherty et al. 2015, 2017a, 2018) and gas structure within debris disks (Flaherty et al. 2016). Additional functionality has been included for modeling dust emission, in particular dust emission around debris disks (Daly et al. 2018).
# Calculating Disk Structure:
The code is currently split into three main programs: one calculates the disk structure (ie. density and temperature as a function of location within the disk), one performs the radiative transfer calculation, and one handles log-likelihood comparisons to data.
The disk structure calculation is handled in a similar manner by a number of different pieces of code. Each of these files defines a 'Disk' class of objects, and calculates the gas and/or dust density and temperature as a function of position within the disk. The density and temperature structure is then interpolated onto a radiative transfer grid, that is aligned with the line of sight and is used later for the radiative transer calculation.
There are files for protoplanetary disks (disk.py), disks with simple power law surface density profiles (disk_pow.py), eccentric disks (disk_ecc.py), and debris disks (debris_disk.py). While the assumed underlying functional forms of the density and temperature structure vary between the different models, the outputs of the model (density and temperature as a function of position) are similar. This allows for a disk object generated from any one of these codes to be inserted into the radiative transfer code without any adjustements by the user.
The example below is for an object created using disk.py, although similar objects, and functionality, are available in disk_pow.py, disk_ecc.py, and debris_disk.py. The required inputs will vary between the different models, as described in more detail below.
The simplest way to create a disk object is:
```python
from disk import *
x=Disk()
```
This creates an instance of the Disk class, called 'x', with all of the generic parameters. The other disk models can be imported in a similar manner (e.g. `from debris_disk import *`), and the disk object is created using the same code (`x=Disk()`).
Each model uses a list of parameters (`params`) to set the underlying structure. Given that each model assumes a different functional form for the underlying density and temperature structure, they require a different set of input parameters.
### disk.py
*Protoplanetary disk structure*
This model uses parametric forms of the temperature and density structure that are mean to represent protoplanetary disks, with a vertical temperature gradient, and a self-similar surface density distribution.
$$T_{atm} = T_{atm0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{mid} = T_{mid0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{gas} = \begin{cases} T_{atm}+(T_{mid}-T_{atm})\left(cos\frac{\pi z}{2 Z_q}\right)^{2} \; \; \; \; &z<Z_q\\
T_{atm} \; \; \; \; &z>Z_q \end{cases}$$
$$Z_q = Z_{q0} \left(\frac{r}{150 au}\right)^{1.3}$$
$$\Sigma_{gas}(r) = \frac{M_{gas}(2-\gamma)}{2 \pi R_c^2} \left(\frac{r}{R_c}\right)^{-\gamma}\exp\left[-\left(\frac{r}{R_c}\right)^{2-\gamma}\right]$$
The volume density is calculated using hydrostatic equilibrium, based on the specified surface density and temperature structure.
$$-\frac{\partial\ln\rho}{\partial z} = \frac{\partial\ln T}{\partial z} = \frac{1}{c_s^2}\left[\frac{GM_*z}{(r^2+z^2)^{3/2}}\right]$$
$$c_s^2 = \frac{k_B T}{\mu m_h}$$
where $\mu$ is the mean molecular weight (=2.37). The velocity profile is Keplerian motion, with corrections for the height above the midplane and the pressure gradient.
$$\frac{v_{\phi}^2}{r} = \frac{GM_*r}{(r^2+z^2)^{3/2}}+\frac{1}{\rho_{gas}}\frac{\partial P_{gas}}{\partial r}$$
**params**
- `q`
*Temperature radial power law index (T(r)$\sim$r$^{q}$)*
- `Mdisk`
*Disk gas mass, in solar masses*
- `p`
*Surface density radial power law index ($\Sigma\sim$r$^{-p}$ for r<R$_c$)*
- `Rin`
*Inner boundary of disk density/temperature calculation, in au*
- `Rout`
*Outer boundary of disk density/temperature calculation, in au*
- `Rc`
*Disk critical radius, in au*
- `incl`
*Inclination, in degrees*
- `Mstar`
*Stellar mass, in solar masses*
- `Xco`
*CO gas fraction relative to H$_2$*
- `vturb`
*Turbulent velocity*
- `Zq`
*normalization of Zq parameter for vertical temperature structure, defined at r=150 au*
- `Tmid`
*Midplane temperature normalization, in K, defined at r=150 au*
- `Tatm`
*Atmosphere temperature normalization, in K, defined at r=150 au*
- `[Sig1,Sig2]`
*Min and max column density boundaries (corresponding to upper and lower boundaries) of molecular zone, in units of 1.59$\times$10$^{21}$ particles cm$^{-2}$*
- `[Rain,Raout]`
*Inner and outer radial boundaries for the distribution of the molecule of interest*
- `-1`
*handeness of rotation*
### disk_pow.py
*Power law disk structure*
This model is similar to disk.py, but assumes that the surface density follows a simple power law, rather than a self-similar solution. There is still a vertical gradient in the temperature, and volume density is calculated from hydrostatic equilibrium.
$$T_{atm} = T_{atm0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{mid} = T_{mid0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{gas} = \begin{cases} T_{atm}+(T_{mid}-T_{atm})\left(cos\frac{\pi z}{2 Z_q}\right)^{2} \; \; \; \; &z<Z_q\\
T_{atm} \; \; \; \; &z>Z_q \end{cases}$$
$$Z_q = Z_{q0} \left(\frac{r}{150 au}\right)^{1.3}$$
$$\Sigma_{gas} = \frac{M_{gas}(2-\gamma)}{2\pi (R_{out}^{2-\gamma}-R_{in}^{2-\gamma})}r^{-\gamma}$$
**params**
- `q`
*Temperature radial power law index (T(r)$\sim$r$^{q}$)*
- `Mdisk`
*Disk gas mass, in solar masses*
- `p`
*Surface density radial power law index ($\Sigma\sim$r$^{-p}$ for all r)*
- `Rin`
*Inner boundary of disk density/temperature calculation, in au*
- `Rout`
*Outer boundary of disk density/temperature calculation, in au*
- `Rc`
*Disk outer edge, in au*
- `incl`
*Inclination, in degrees*
- `Mstar`
*Stellar mass, in solar masses*
- `Xco`
*CO gas fraction relative to H$_2$*
- `vturb`
*Turbulent velocity*
- `Zq`
*normalization of Zq parameter for vertical temperature structure, defined at r=150 au*
- `Tmid`
*Midplane temperature normalization, in K, defined at r=150 au*
- `Tatm`
*Atmosphere temperature normalization, in K, defined at r=150 au*
- `-1`
*handeness of rotation*
### disk_ecc.py
*Eccentric protoplanetary disk*
This model is similar to disk.py, but it includes a prescription for eccentricity within the disk. The temperature structure is identical to disk.py and disk_pow.py
$$T_{atm} = T_{atm0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{mid} = T_{mid0} \left(\frac{r}{150 au}\right)^{q}$$
$$T_{gas} = \begin{cases} T_{atm}+(T_{mid}-T_{atm})\left(cos\frac{\pi z}{2 Z_q}\right)^{2} \; \; \; \; &z<Z_q\\
T_{atm} \; \; \; \; &z>Z_q \end{cases}$$
$$Z_q = Z_{q0} \left(\frac{r}{150 au}\right)^{1.3}$$
One of the main modifications is in the surface density profile. The disk is treated as a series of concentric elliptical rings of semi-major axis *a*. The surface density is the same tapered power law as in disk.py, with the semi-major axis in place of the radial distance from the central star.
$$\Sigma_{gas}(a) = \frac{M_{gas}(2-\gamma)}{R_c^2} \left(\frac{a}{R_c}\right)^{-\gamma}\exp\left[-\left(\frac{a}{R_c}\right)^{2-\gamma}\right]$$
The surface density also needs a dependence on the angle from periapsis, $\phi$, since there will be a higher density near apastron, where the material spends most of its orbit, than at periastron, where the material spends less of its orbit. The density per unit length around a given orbit, $\lambda$, is given by
$$\lambda = \frac{m\sqrt{1-e^2}}{2\pi a(1+e\cos\phi)}$$
where $m$ is the mass contained within the ring. The linear density of the $i$th ring is related to the surface density through:
$$\Sigma(\phi) = \frac{\lambda_i(\phi)}{(r_{i+1}(\phi)-r_{i-1}(\phi))/2}$$
where $r$ is the distance from the central star (at one of the foci of the ellipse) as a function of semi-major axis and angle from periapsis.
$$r_i(\phi) = \frac{a_i(1-e^2)}{1+e\cos\phi}$$
The result is a surface density that depends on both semi-major axis and angle from periapsis
$$\Sigma_{gas} = \frac{\Sigma_{gas}(a)(1-e^2)^{3/2}}{2\pi (1+e\cos\phi)^2}$$
In addition, the velocity profile is modified to account for the variations with $\phi$ (see derivation [here](https://pdfs.semanticscholar.org/75d1/c8533025d0a7c42d64a7fef87b0d96aba47e.pdf)).
$$v = \sqrt{\frac{GM_*}{a(1-e^2)}}\cos(\omega+\phi)+e\cos\omega$$
where $\omega$ in the angle of periastron (= the angle between periastron and the major axis). When multiplied by $\sin i$ this becomes the projected velocity along the line of sight. This does not include the correction for the height above the midplane, or for the pressure gradient.
**params**
- `q`
*Temperature radial power law index (T(r)$\sim$r$^{q}$)*
- `Mdisk`
*Disk gas mass, in solar masses*
- `pp`
*Surface density radial power law index ($\Sigma\sim$r$^{-pp}$ for r<R$_c$)*
- `Rin`
*Inner boundary of disk density/temperature calculation, in au*
- `Rout`
*Outer boundary of disk density/temperature calculation, in au*
- `Rc`
*Disk critical radius, in au*
- `incl`
*Inclination, in degrees*
- `Mstar`
*Stellar mass, in solar masses*
- `Xco`
*CO gas fraction relative to H$_2$*
- `vturb`
*Turbulent velocity*
- `Zq`
*normalization of Zq parameter for vertical temperature structure, defined at r=150 au*
- `Tmid`
*Midplane temperature normalization, in K, defined at r=150 au*
- `Tatm`
*Atmosphere temperature normalization, in K, defined at r=150 au*
- `-1`
*handeness of rotation*
- `ecc`
*eccentricity of the disk*
- `aop`
*angle of periastron of the disk*
- `[Sig1,Sig2]`
*Min and max column density boundaries (corresponding to upper and lower boundaries) of molecular zone, in units of 1.59$\times$10$^{21}$ particles cm$^{-2}$*
- `[Rain,Raout]`
*Inner and outer radial boundaries for the distribution of the molecule of interest*
### debris_disk.py
*Dusty debris disk*
This model is intended to predict the dust emission from a debris disk. This model calculates the dust temperature assuming blackbody dust grains, based on the stellar luminosity. It assumes that the vertical density distribution is has a Gaussian shape, with the scale height a free parameter ($h = H/R$, or $h=H$, where $h$ is one of the model inputs). The surface density is a power law with radius (*note that the sign on $p$ is different than in the previous models*). This model only includes dust, and does not model the gas.
$$T_d = \left( \frac{L_*}{16 \pi d^2 \sigma_B} \right)^{1/4}$$
$$\Sigma = \begin{cases} \Sigma_c r^p \; \; \; \; &r_i < r < r_o \\
0 \; \; \; \; &\rm{otherwise} \end{cases}$$
$$\Sigma_c = \frac{M_{dust}(2+p)}{2\pi(R_{out}^{2+p}-R_{in}^{2+p})} $$
$$\rho_{dust} = \frac{\Sigma}{H\sqrt{\pi}}\exp\left(-\left(\frac{z}{H}\right)^2\right)$$
**params**
- `q`
*Not Used*
- `Mdisk`
*Disk gas mass, in solar masses*
- `p`
*Surface density radial power law index ($\Sigma\sim$r$^{-pp}$)*
- `Rin`
*Inner boundary of disk density/temperature calculation, in au*
- `Rout`
*Outer boundary of disk density/temperature calculation, in au*
- `Rc`
*Not Used*
- `incl`
*Inclination, in degrees*
- `Mstar`
*Not Used*
- `Xco`
*Not Used*
- `vturb`
*Not Used*
- `Zq`
*Not Used*
- `[Sig1,Sig2]`
*Not Used*
- `[Rain,Raout]`
*Not Used*
- `-1`
*Not Used*
- `r_gridsize`
*number of grid elements in the radial direction, when calculating density/temperature*
- `z_gridsize`
*number of grid elements in the vertical direction, when calculating density/temperature*
- `L*`
*Stellar luminosity, in solar luminosities*
- `h`
*Scale height, defined as h = H/R, or h=H, depending on the `sh_relation` parameter, as described below*
In addition to the `params` list there is also the `obs` list. This list specifies the binning within the radiative transfer grid, as well as the maximum height that is modeled within the disk.
**obs**
- `nr`
*number of radial bins in radiative transfer grid*
- `nphi`
*number of azimuthal bins in the radiative transfer grid*
- `nz`
*number of height bins in the radiative transfer grid*
- `zmax`
*maximum height of the disk in the radiative transfer grid, in au*
An example of creating a disk with custom parameters:
```python
y = Disk(params=[-0.529,0.119,1.,10.,1000.,135.6,51.5,2.3,1e-4,0.081,70.,17.5,94.,[.79,1000],[50,500],-1])
```
Given that the input parameters are specified in a single list, it is not possible to specify a subset of parameters, while keeping the rest as the default values. The entire list must be specified if a single parameter is changed from the default values.
### Optional Parameters:
Beyond the `params` and `obs` list, the models have a number of optional parameters that can be specified during the creation of the `Disk` object.
`rtg` (default = `True`):
Setting this parameter to True causes the code to map the density structure from the original grid onto the radiative transfer grid (which is necessary to do the radiative transfer with the next piece of code). The function `disk.set_rt_grid()` will do the interpolation onto the radiative transfer grid if this calculation was not performed during the creation of the disk object.
`vcs` (default = `True`):
By default the turbulence is parameterized as a fraction of the local thermal broadening of CO (which is ~3.438x smaller than the local sound speed). Set this parameter to False to instead specify the turbulent velocity as a fixed speed (e.g. vturb = 0.1km/sec)
`exp_temp` (default = `False`):
Set this parameter to True to use an exponential profile for the shape of the vertical temperature profile instead of the Dartois Type II structure. Tmid still specifies the temperature at the midplane, while Tatm still specifies the temperature at height Zq. [*Not available in disk_ecc.py or debris_disk.py*]
`line` (default = `'co'`):
Specify the molecule being used. This sets the molecule mass used in the velocity broadening term. Currently only knows `'co'`, `'13co'`, and `'c18o'`, `'dco+'`, `'hco+'`, `'hcn'`, and `'cs'`. If the line is not one of these molecules then an error will occur.
`ring` (default = `None`):
Include a change in the power law slope of the temperature structure within the inner disk. Set `ring` to a list of [R$_{ring}$, W$_{ring}$, q$_{ring}$] where R$_{ring}$ is the central location of the ring in au, W$_{ring}$ is the width of the ring in au and q$_{ring}$ is the radial power law temperature slope within the ring.
`include_selfgrav` (default = `False`):
Include a modification to the velocity profile that accounts for the self-gravity of the disk. This follows the prescription in Lodato 2007, Bertin & Lodato 1999 (used by Verosini et al. 2021 to model Elias 2-27). In the presence of self-gravity the orbital frequency becomes:
$$\frac{v_{\phi}^2}{r} = \frac{GM_*r}{(r^2+z^2)^{3/2}}+\frac{1}{\rho_{gas}}\frac{\partial P_{gas}}{\partial r}+\frac{\partial \phi_{gas}}{\partial r}$$
where $\phi_{gas}$ is the potential due to the self-gravity of the disk. This code does *not* include the modification to the vertical hydrostatic equilibrium due to self-gravity (e.g. eqn 18 of Rosenfeld et al. 2013). It only includes the modification to the velocity field. The gradient in the potential is calculated using:
$$\frac{\partial \phi_{gas}}{\partial r}(r,z) = \frac{G}{r}\int^{\inf}_{0}\left[K(k)-\frac{1}{4}\left(\frac{k^2}{1-k^2}\right)\left(\frac{R}{r}-\frac{r}{R}+\frac{z}{rR}\right)E(k)\right]\sqrt{\frac{R}{r}}k\Sigma (R)dR$$
where $k^2=4Rr/[(r+R)^2+z^2]$ and $E$ and $K$ are complete elliptic integrals of the first kind. When using self-gravity, you are encouraged to increase Rout to improve the accuracy of the integral above, especially if you are interested in the velocities near the outer edge of the disk. [*Only available in disk.py and disk_pow.py*]
`sh_relation` (default = `'linear'`):
Specify the functional form of the relationship between scale height, as specified in `params` and radius. If `'linear'` then $H=hr$, if `'constant'` then $H=h$, independent of radius [*Only available in debris_disk.py*]
### Functions:
Once the disk object is created, there are a number of functions that can be called from the disk object.
**plot_structure**(*sound_speed*=False,*beta*=None,*dust*=False): Generate of plot of density and temperature for the disk. Colored contours are density while black contours are temperature. Density is number density of H_2. The dashed lines mark $\Sigma$=0.1 and 0.01 g cm$^{-2}$ [*Not available in debris_disk.py*]
Setting *sound_speed* to True causes the map the show contours of constant sound speed instead of temperature
Setting *beta* to a value plots contours of constant magnetic field strength
Setting *dust* to True plots density contours of the dust (assuming dust has been included)
*Note:* This code uses the radiative transfer grid when plotting the density and temperature structure. This means can it will crash if this grid has not been generated yet. Also, it will produce weird looking results for disks at high inclination. To avoid this, simply rerun the disk object with a more face-on inclination before generating the plot of the density and temperature structure.
```python
%matplotlib inline
x.plot_structure()
```
**calcH**(*verbose*=True,*return_pow*=False): Calculate the pressure scale height as a function of radius. Outputs the parameters of a power law fit to H(r). H is calculated as 2$^{-0.5}$ times the height where the density drops by 1/e, making H consistent with a density that falls off as exp(-z$^2$/2H$^2$) [*Not available in debris_disk.py*]
Set *verbose* to False to no longer output the parameters from the power law fit.
Set *return_pow* to True to return the power law parameters. Otherwise the code returns H at each value of `disk.rf`.
```python
h=x.calcH()
plt.plot(x.rf/x.AU,h/x.AU,color='k',ls='--',lw=3)
```
H100 (AU): 4.939
power law: 1.263
[<matplotlib.lines.Line2D at 0x1c1a912ac8>]
**add_dust_ring**(*Rin,Rout,dtg,ppD,initialize*=False): Add a ring of dust with specified inner radius (R$_{in}$, in au), outer radius (R$_{out}$, in au), dust-to-gas ratio enhancement (dtg) and slope to a radial power law for the dust-to-gas ratio (ppD). The dust-to-gas ratio is taken as additive relative to any previous rings of dust.
With `disk_pow.py` and `disk_ecc.py`, if this is the first dust ring that has been added, then specify *initialize*=True to inizialize the create the intrinsic dust-to-gas ratio parameter. [*Not available in debris_disk.py*]
```python
x.add_dust_ring(10,500,.01,0.,initialize=True)
x.add_dust_ring(100,200,.1,.5)
x.plot_structure(dust=True)
```
/Users/kevinflahertyastro/disk_model3/disk.py:503: RuntimeWarning: divide by zero encountered in log10
cs3 = plt.contour(self.r[0,:,:]/Disk.AU,self.Z[0,:,:]/Disk.AU,np.log10(self.rhoD[0,:,:]),100,colors='k',linestyles='--')
**set_line**(*line*='co',*vcs*=True): Specifiy the molecule that is being used so that the proper mass is used in the velocity broadening calculation. This is currently split between 'co' and everything else (where everything else is assumed to be DCO+). The vcs keyword specifies where the turbulent broadening is treated as proportional to the local sound speed (vcs=True) or if it is a fixed velocity, in units of km/sec (vcs=False). [*Available, but meaningless, in debris_disk.py*]
**set_rt_grid**(*vcs*=True): Construct the radiative transfer grid.
**add_dust_gap**(*Rin*,*Rout*): Add a gap within the dust, while maintaining the same total dust mass [*Only available in debris_disk.py*]
**add dust_mass**(*ring_mas*,*Rin*,*Rout*): Add an additional ring of a given mass (in Earth masses). [*Only availble in debris_disk.py*]
**add_mol_ring**(*Rin*,*Rout*,*Sig0*,*Sig1*,*abund*,*initialize*=False): Add a ring of fixed molecular abundance. The inner and outer radial boundaries are given by `Rin` and `Rout` (in units of au), while ther vertical boundaries are given by *Sig0*, *Sig1* based on the vertically intergrating surface density (similar to in the `params` list. The abundance is additive; it does not replace any previous molecular abundance within the disk, but adds on top of it. [*Available, but meaningless, in debris_disk.py*]
[*Most of the rest of these functions don't need to be called by the user, but are listed for completeness*]
**set_params**(*params*): Set the object variables associated with the elements in the params list
**set_obs**(*obs*): Set the observational object variables associated with the elements in the obs list
**set_structure**(*exp_temp*=False): Generate the temperature and density structure of the disk. This function is called when initializing the disk
**calc_hydrostatic**(*tempg*,*siggas*,*grid*): Perform the vertical hydrostatic equilibrium calculation using the know temperature and surface density structure. [*Not available in debris_disk.py*]
**density**: Returns the mass density.
**temperature**: Returns the temperature structure.
**grid**: Returns the grid parameters.
**get_params**: Returns the params list.
**get_obs**: Returns the obs list.
**set_scale_height**(*sh_relation*,*rcf*): Set the scale height of the disk (called within set_structure within `debris_disk.py`)
# Radiative Transfer:
Once the disk has been created, the radiative transfer code can be called. There are a series of functions defined within the file raytrace.py that handle the radiative transfer (plus some ancillary things). To load these functions into your python session, use the command `run raytrace.py`
The main function is called `total_model`. This takes in a Disk object, along with a number of other parameters, and outputs a model fits image. The simplest call would be:
```python
%run raytrace
total_model(x)
```
<Figure size 432x288 with 0 Axes>
You should now find a file called *testpy_almap.fits* which is the model fits image.
#### Optional Parameters:
In order to accurately reproduce your data, you will need to specify the ancillary parameters.
`imres` (default = `0.05`): Model image resolution in arcsec. This should match the pixel size in the data image.
`distance` (default = `122`): Distance to the target, in parsecs.
`chanmin` (default = `-2.24`): Minimum channel velocity in km/sec. This value is relative to the line center (ie. the minimum velocity assuming the line is centered at a velocity of 0). [*Not needed for dust-only models*]
`nchans` (default = `15`): Number of velocity channels to model. Note that `nchans` can be greater than 1 even when modeling dust emission; in this case the model image is repeated over all of the channels.
`chanstep` (default = `0.32`): Velocity resolution of each channel, in km/sec. [*Not needed for dust-only models*]
`flipme` (default = `True`): To save time the code can calculate the radiative transfer for half of the line, and then mirror the results to fill in the rest of the line. (Make sure that nchan, chanstep and chanmin are set such that the central channel has zero velocity, otherwise flipping the line will return spurious results). [*Not needed for dust-only models*]
`Jnum` (default = `2`): The lower J quantum number of the transition of interest, e.g. for the CO J=3-2 transition, set Jnum=2. [*Not needed for dust-only models*]
`freq0` (default = `345.79599`): The rest frequency of the transition, in GHz.
`xnpix` (default = `512`): Number of pixels along each spatial dimension in the model image
`vsys` (default = `5.79`): Systemic velocity of the star, in km/sec [*Not needed for dust-only models*]
`PA` (default = `312.8`): Position angle of the disk (measured east of north) in degrees.
`offs` (default = `[0.150,0.05]`): A list containing the RA and Dec offset, in arcseconds, of the disk center relative to the phase center.
`modfile` (default=`'testpy_alma'`): Base of model file name. Resulting model image will be contained in modfile+'.fits'
`abund` (default = `1.`): Abundance relative to the abundance set in the Disk object. This is useful for e.g. modeling C18O using a Disk object that was generated with a CO abundance. [*Not needed for dust-only models*]
`obsv` (default = `None`): A list of velocities for the channels in the data. If set, the model is interpolated from the grid defined by nchans, chanstep and chanmin onto this grid. Necessary if the spectral channels of the observations are not centered on the stellar velocity. [*Not needed for dust-only models*]
`wind` (default = `False`): Include a rudimentary wind. This wind is simply a vertical motion whose speed is proportional to the local sound speed. Currently that speed is set to 0. [*Not needed for dust-only models*]
`isgas` (default = `True`): Set to `True` if line emission is being computed.
`includeDust` (default = `False`): Set to `True` to include dust emission in addition to gas emission. This is *only* necessary if you are computing the emission from gas; when computing a dust-only model you only need to specify `isgas=False`.
`bin` (default=1): This keyword will generate the spectrum based on the binning of a higher resolution model. e.g. If bin=2, then the model is generated at twice the specified resolution, and then binned down to the specified resolution. This mimics any binning that may have been done in the creation of the data, to ensure that any process that is applied to the data is also applied to the model. [*Not needed for dust-only models*]
`hanning` (default = `False`): Set to `True` to apply Hanning smoothing to a spectrum. This applies a running average that replaces the flux in channel `i` with 25% of the flux from channel `i-1`, 50% from channel `i`, and 25% from channel `i+1`. [*Not needed for dust-only models*]
A note about chanmin/chanstep/nchans and vsys: The radiative transfer portion of the code works best if the central channel corresponds to the center of the line. Using flipme without setting the central channel at line center will cause problems (e.g. it will look like the images repeat themselves halfway through the channels). The systematic velocity (vsys) is applied after the full spectrum has been generated. This means that chanmin should be the minimum velocity, assuming the line is centered at zero velocity. It should not be the minimum velocity relative to the systemic velocity. The best way to specify chanmin is simply to use -(nchans/2)*chanstep.
The code uses the value of `Jnum` and `freq0` to know which transition is being used, and hence which molecule data file to read in. Currently the code only looks for $^{13}$CO(2-1), C$^{18}$O(2-1), DCO$^+$(3-2), HCO$^+$(4-3), HCN(4-3), CO(3-2), CS(7-6), CO(1-0), CO(2-1), CO(6-5), DCO$^+$(5-4), DCO$^+$(4-3), C$^{18}$O(3-2), and C$^{18}$O(1-0). If `Jnum` and `freq0` do not match the values for any of these lines, either because of a typo or because the desired line is not on this list, then an error will occur.
# Likelihood Calculation:
Having made it this far there is a pretty good chance that you are interested in comparing your model image to some data. There is code that provides a framework for this, although by necessicity there is a great deal of customization based on your exact data. Below is outlined the basic functionality, and some of the areas where the code needs to be customized are pointed out.
The basic functionality is contained in the file `single_model.py`. The main functions are `make_model_vis`, `compare_vis`, `compare_vis_galario`, and `lnlike`
`make_model_vis`: Generate model visibilities, based on the baselines in a data file. **CAUTION** This function relies on the script *sample_alma.csh*, which performs the calculations using the *MIRIAD* codebase. If you have not installed *MIRIAD* then this script will fail.
`compare_vis`: Calculate the chi-squared by comparing the model and data visibilities
`compare_vis_galario`: Generate model visibilities, and use them to calculate the chi-squared, using Galario [link]. This uses the CPU version of Galario, and saves writing the model into, and out of, the `MIRAD` data format, which saves time in the model calculation.
`lnlike`: Calculate the log-liklihood based on a set of input model parameters. This function is used when e.g. feeding into the Markov-Chain Monte-Carlo code `EMCEE`. It essentially automates the model calculation, where the main input is a list of parameters that you wish to vary from one model to the next (e.g. turbulent velocity, midplane temperature, etc.). This function will need to be heavily modified to match your particular input data set. Once this is done, it will save a great deal of time in future model generation.
**make_model_vis**:
`datfile`: The base name of the data files. The code assumes that there is a visibility dataset named datfile+'.vis' and an image file named datfile+'.cm', both in the *MIRIAD* format.
`modfile`: The base name of the model file. This is the same as the input to *total_model*, which would have generated a file name modfile+'.fits'. The model visibilities are named modfile+'.model.vis.fits'
`isgas` (default=True): Is this a gas emission model? If so then set this flag to True (the exact script that is called differs depending on whether or not the model has multiple channels or is a single channel.)
`freq0` (default=345.79599): Rest frequency, in units of GHz. This number is put in the header of the model file.
**compare_vis**:
`datfile`: The base name of the data file. The code assumes that there is a visibility fits file named datfile+'.vis.fits'
`modfile`: The base name of the model file. The code assumes that there is a visibility fits file named modfile+'.model.vis.fits'
`new_weight` (default=`None`): An array containing new weights to be used in the chi-square calculation. This should have the same dimensions as the real and imaginary part of the visibilities (ie. N_base x N_chan for line data)
`systematic` (default=`False`): The systematic correction to be applied. The value sent with this keyword is used to scale down the absolute flux level of the model (the model visibilities are *divided* by this value). It is meant to mimic a true flux for the data which is larger or smaller by the supplied value (e.g. specifying systematic=1.2 is equivalent to saying that the true flux is 20% higher than what has been observed. The model visibilities are divided by 1.2 to mimic this effect). The scaling is applied to the model to ensure that the data are not adjusted in any way.
`isgas` (default=`True`): If you are dealing with data with multiple channels, set this parameter to True to ensure the data is read in correctly.
`plot_resid` (default=`False`): Plot the distribution of residuals. If the errors are truly Gaussian, then this distribution should be Gaussian in shape. If the errors are properly scaled, then this Gaussian will have a sigma of 1. If the data perfectly matches the data then the center of this Gaussian is identically zero. This is useful for checking that the uncertainties are Gaussian (making the chi-squared a good measure of log-liklihood) and that the errors are properly scaled (to ensure a proper comparison between different models).
**compare_vis_galario**:
`datfile`: The base name of the data file. The code assumes that there is a visibility fits file named datfile+'.vis.fits'
`modfile`: The base name of the model file. The code assumes that there is a visibility fits file named modfile+'.model.vis.fits' [*Not used in this function*]
`new_weight` (default=`None`): An array containing new weights to be used in the chi-square calculation. This should have the same dimensions as the real and imaginary part of the visibilities (ie. N_base x N_chan for line data)
`systematic` (default=`False`): The systematic correction to be applied. The value sent with this keyword is used to scale down the absolute flux level of the model (the model visibilities are *divided* by this value). It is meant to mimic a true flux for the data which is larger or smaller by the supplied value (e.g. specifying systematic=1.2 is equivalent to saying that the true flux is 20% higher than what has been observed. The model visibilities are divided by 1.2 to mimic this effect). The scaling is applied to the model to ensure that the data are not adjusted in any way.
`isgas` (default=`True`): If you are dealing with data with multiple channels (either an emission line, or continuum data with multiple channels), set this parameter to True to ensure the data is read in correctly.
`plot_resid` (default=`False`): Plot the distribution of residuals. If the errors are truly Gaussian, then this distribution should be Gaussian in shape. If the errors are properly scaled, then this Gaussian will have a sigma of 1. If the data perfectly matches the data then the center of this Gaussian is identically zero. This is useful for checking that the uncertainties are Gaussian (making the chi-squared a good measure of log-liklihood) and that the errors are properly scaled (to ensure a proper comparison between different models).
```python
```
|
kevin-flahertyREPO_NAMEdisk_model3PATH_START.@disk_model3_extracted@disk_model3-master@.ipynb_checkpoints@Guide-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "conftest.py",
"repo_name": "ultralytics/ultralytics",
"repo_path": "ultralytics_extracted/ultralytics-main/tests/conftest.py",
"type": "Python"
}
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
import shutil
from pathlib import Path
from tests import TMP
def pytest_addoption(parser):
"""
Add custom command-line options to pytest.
Args:
parser (pytest.config.Parser): The pytest parser object for adding custom command-line options.
Returns:
(None)
"""
parser.addoption("--slow", action="store_true", default=False, help="Run slow tests")
def pytest_collection_modifyitems(config, items):
"""
Modify the list of test items to exclude tests marked as slow if the --slow option is not specified.
Args:
config (pytest.config.Config): The pytest configuration object that provides access to command-line options.
items (list): The list of collected pytest item objects to be modified based on the presence of --slow option.
Returns:
(None) The function modifies the 'items' list in place, and does not return a value.
"""
if not config.getoption("--slow"):
# Remove the item entirely from the list of test items if it's marked as 'slow'
items[:] = [item for item in items if "slow" not in item.keywords]
def pytest_sessionstart(session):
"""
Initialize session configurations for pytest.
This function is automatically called by pytest after the 'Session' object has been created but before performing
test collection. It sets the initial seeds and prepares the temporary directory for the test session.
Args:
session (pytest.Session): The pytest session object.
Returns:
(None)
"""
from ultralytics.utils.torch_utils import init_seeds
init_seeds()
shutil.rmtree(TMP, ignore_errors=True) # delete any existing tests/tmp directory
TMP.mkdir(parents=True, exist_ok=True) # create a new empty directory
def pytest_terminal_summary(terminalreporter, exitstatus, config):
"""
Cleanup operations after pytest session.
This function is automatically called by pytest at the end of the entire test session. It removes certain files
and directories used during testing.
Args:
terminalreporter (pytest.terminal.TerminalReporter): The terminal reporter object used for terminal output.
exitstatus (int): The exit status of the test run.
config (pytest.config.Config): The pytest config object.
Returns:
(None)
"""
from ultralytics.utils import WEIGHTS_DIR
# Remove files
models = [path for x in ["*.onnx", "*.torchscript"] for path in WEIGHTS_DIR.rglob(x)]
for file in ["bus.jpg", "yolo11n.onnx", "yolo11n.torchscript"] + models:
Path(file).unlink(missing_ok=True)
# Remove directories
models = [path for x in ["*.mlpackage", "*_openvino_model"] for path in WEIGHTS_DIR.rglob(x)]
for directory in [TMP.parents[1] / ".pytest_cache", TMP] + models:
shutil.rmtree(directory, ignore_errors=True)
|
ultralyticsREPO_NAMEultralyticsPATH_START.@ultralytics_extracted@ultralytics-main@tests@conftest.py@.PATH_END.py
|
{
"filename": "common.py",
"repo_name": "itseez/opencv",
"repo_path": "opencv_extracted/opencv-master/samples/python/common.py",
"type": "Python"
}
|
#!/usr/bin/env python
'''
This module contains some common routines used by other samples.
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
from functools import reduce
import numpy as np
import cv2 as cv
# built-in modules
import os
import itertools as it
from contextlib import contextmanager
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
class Bunch(object):
def __init__(self, **kw):
self.__dict__.update(kw)
def __str__(self):
return str(self.__dict__)
def splitfn(fn):
path, fn = os.path.split(fn)
name, ext = os.path.splitext(fn)
return path, name, ext
def anorm2(a):
return (a*a).sum(-1)
def anorm(a):
return np.sqrt( anorm2(a) )
def homotrans(H, x, y):
xs = H[0, 0]*x + H[0, 1]*y + H[0, 2]
ys = H[1, 0]*x + H[1, 1]*y + H[1, 2]
s = H[2, 0]*x + H[2, 1]*y + H[2, 2]
return xs/s, ys/s
def to_rect(a):
a = np.ravel(a)
if len(a) == 2:
a = (0, 0, a[0], a[1])
return np.array(a, np.float64).reshape(2, 2)
def rect2rect_mtx(src, dst):
src, dst = to_rect(src), to_rect(dst)
cx, cy = (dst[1] - dst[0]) / (src[1] - src[0])
tx, ty = dst[0] - src[0] * (cx, cy)
M = np.float64([[ cx, 0, tx],
[ 0, cy, ty],
[ 0, 0, 1]])
return M
def lookat(eye, target, up = (0, 0, 1)):
fwd = np.asarray(target, np.float64) - eye
fwd /= anorm(fwd)
right = np.cross(fwd, up)
right /= anorm(right)
down = np.cross(fwd, right)
R = np.float64([right, down, fwd])
tvec = -np.dot(R, eye)
return R, tvec
def mtx2rvec(R):
w, u, vt = cv.SVDecomp(R - np.eye(3))
p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0])
c = np.dot(vt[0], p)
s = np.dot(vt[1], p)
axis = np.cross(vt[0], vt[1])
return axis * np.arctan2(s, c)
def draw_str(dst, target, s):
x, y = target
cv.putText(dst, s, (x+1, y+1), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv.LINE_AA)
cv.putText(dst, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv.LINE_AA)
class Sketcher:
def __init__(self, windowname, dests, colors_func):
self.prev_pt = None
self.windowname = windowname
self.dests = dests
self.colors_func = colors_func
self.dirty = False
self.show()
cv.setMouseCallback(self.windowname, self.on_mouse)
def show(self):
cv.imshow(self.windowname, self.dests[0])
def on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv.EVENT_LBUTTONDOWN:
self.prev_pt = pt
elif event == cv.EVENT_LBUTTONUP:
self.prev_pt = None
if self.prev_pt and flags & cv.EVENT_FLAG_LBUTTON:
for dst, color in zip(self.dests, self.colors_func()):
cv.line(dst, self.prev_pt, pt, color, 5)
self.dirty = True
self.prev_pt = pt
self.show()
# palette data from matplotlib/_cm.py
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
cmap_data = { 'jet' : _jet_data }
def make_cmap(name, n=256):
data = cmap_data[name]
xs = np.linspace(0.0, 1.0, n)
channels = []
eps = 1e-6
for ch_name in ['blue', 'green', 'red']:
ch_data = data[ch_name]
xp, yp = [], []
for x, y1, y2 in ch_data:
xp += [x, x+eps]
yp += [y1, y2]
ch = np.interp(xs, xp, yp)
channels.append(ch)
return np.uint8(np.array(channels).T*255)
def nothing(*arg, **kw):
pass
def clock():
return cv.getTickCount() / cv.getTickFrequency()
@contextmanager
def Timer(msg):
print(msg, '...',)
start = clock()
try:
yield
finally:
print("%.2f ms" % ((clock()-start)*1000))
class StatValue:
def __init__(self, smooth_coef = 0.5):
self.value = None
self.smooth_coef = smooth_coef
def update(self, v):
if self.value is None:
self.value = v
else:
c = self.smooth_coef
self.value = c * self.value + (1.0-c) * v
class RectSelector:
def __init__(self, win, callback):
self.win = win
self.callback = callback
cv.setMouseCallback(win, self.onmouse)
self.drag_start = None
self.drag_rect = None
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
return
if self.drag_start:
if flags & cv.EVENT_FLAG_LBUTTON:
xo, yo = self.drag_start
x0, y0 = np.minimum([xo, yo], [x, y])
x1, y1 = np.maximum([xo, yo], [x, y])
self.drag_rect = None
if x1-x0 > 0 and y1-y0 > 0:
self.drag_rect = (x0, y0, x1, y1)
else:
rect = self.drag_rect
self.drag_start = None
self.drag_rect = None
if rect:
self.callback(rect)
def draw(self, vis):
if not self.drag_rect:
return False
x0, y0, x1, y1 = self.drag_rect
cv.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
return True
@property
def dragging(self):
return self.drag_rect is not None
def grouper(n, iterable, fillvalue=None):
'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx'''
args = [iter(iterable)] * n
if PY3:
output = it.zip_longest(fillvalue=fillvalue, *args)
else:
output = it.izip_longest(fillvalue=fillvalue, *args)
return output
def mosaic(w, imgs):
'''Make a grid from images.
w -- number of grid columns
imgs -- images (must have same size and format)
'''
imgs = iter(imgs)
if PY3:
img0 = next(imgs)
else:
img0 = imgs.next()
pad = np.zeros_like(img0)
imgs = it.chain([img0], imgs)
rows = grouper(w, imgs, pad)
return np.vstack(list(map(np.hstack, rows)))
def getsize(img):
h, w = img.shape[:2]
return w, h
def mdot(*args):
return reduce(np.dot, args)
def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
for kp in keypoints:
x, y = kp.pt
cv.circle(vis, (int(x), int(y)), 2, color)
|
itseezREPO_NAMEopencvPATH_START.@opencv_extracted@opencv-master@samples@python@common.py@.PATH_END.py
|
{
"filename": "javelin_ai_gateway.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/chat_models/javelin_ai_gateway.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_models.javelin_ai_gateway import (
ChatJavelinAIGateway,
ChatParams,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"ChatJavelinAIGateway": "langchain_community.chat_models.javelin_ai_gateway",
"ChatParams": "langchain_community.chat_models.javelin_ai_gateway",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"ChatJavelinAIGateway",
"ChatParams",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@chat_models@javelin_ai_gateway.py@.PATH_END.py
|
{
"filename": "vtk_xml_file_reader.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/mayavi/sources/vtk_xml_file_reader.py",
"type": "Python"
}
|
"""A VTK XML file reader object.
"""
# Author: Prabhu Ramachandran <prabhu_r@users.sf.net>
# Copyright (c) 2005, Enthought, Inc.
# License: BSD Style.
# Standard library imports.
from os.path import basename
# Enthought library imports.
from traits.api import Instance, List, Str, Bool, Button
from traitsui.api import View, Group, Item, Include
from tvtk.api import tvtk
# Local imports.
from mayavi.core.common import error
from mayavi.core.file_data_source import FileDataSource
from mayavi.core.trait_defs import DEnum
from mayavi.core.pipeline_info import (PipelineInfo,
get_tvtk_dataset_name)
######################################################################
# Utility functions.
######################################################################
def find_file_data_type(file_name):
"Parses the named file to see what type of data there is."
r = tvtk.XMLFileReadTester(file_name=file_name)
if r.test_read_file():
return r.file_data_type
else:
error("File %s is not a valid VTK XML file!"%(file_name))
def get_array_type(arr):
"""Returns if the array is a scalar ('scalars'), vector
('vectors') or tensor ('tensors'). It looks at the number of
components to decide. If it has a wierd number of components it
returns the empty string.
"""
n = arr.number_of_components
ret = {1: 'scalars', 3: 'vectors', 4: 'scalars', 9:'tensors'}
return ret.get(n) or ''
def get_attribute_list(data):
""" Gets scalar, vector and tensor information from the given data
(either cell or point data).
"""
attr = {'scalars':[], 'vectors':[], 'tensors':[]}
if data is not None:
n = data.number_of_arrays
for i in range(n):
name = data.get_array_name(i)
arr = data.get_array(i)
if arr is not None:
# Some VTK datasets claim they have n arrays, but
# actually some of these are None (eg the output of a
# tvtk.GraphToPolyData())
t = get_array_type(arr)
if len(t) > 0 and name is not None:
attr[t].extend([name])
def _mk_first(lst, value):
"""Makes the specified `value` the first item in `lst`."""
lst.remove(value)
lst.insert(0, value)
attr1 = attr.copy()
for a in attr:
v = getattr(data, a)
if v is not None:
name = v.name
if name is not None:
try:
_mk_first(attr[a], v.name)
except ValueError:
# Sometimes we have a multi-component scalar.
attr1[a].insert(0, name)
return attr1
def get_all_attributes(obj):
"""Gets the scalar, vector and tensor attributes that are
available in the given VTK data object.
"""
point_attr = get_attribute_list(obj.point_data)
cell_attr = get_attribute_list(obj.cell_data)
return point_attr, cell_attr
######################################################################
# `VTKXMLFileReader` class
######################################################################
class VTKXMLFileReader(FileDataSource):
"""A VTK XML file reader. The reader supports all the different
types of data sets. This reader also supports a time series.
Currently, this reader assumes that there is only one output that
has configurable attributes.
"""
# The version of this class. Used for persistence.
__version__ = 0
########################################
# Dynamic traits: These traits are dynamic and are automatically
# updated depending on the contents of the file.
# The active point scalar name. An empty string indicates that
# the attribute is "deactivated". This is useful when you have
# both point and cell attributes and want to use cell data by
# default.
point_scalars_name = DEnum(values_name='_point_scalars_list',
desc='scalar point data attribute to use')
# The active point vector name.
point_vectors_name = DEnum(values_name='_point_vectors_list',
desc='vectors point data attribute to use')
# The active point tensor name.
point_tensors_name = DEnum(values_name='_point_tensors_list',
desc='tensor point data attribute to use')
# The active cell scalar name.
cell_scalars_name = DEnum(values_name='_cell_scalars_list',
desc='scalar cell data attribute to use')
# The active cell vector name.
cell_vectors_name = DEnum(values_name='_cell_vectors_list',
desc='vectors cell data attribute to use')
# The active cell tensor name.
cell_tensors_name = DEnum(values_name='_cell_tensors_list',
desc='tensor cell data attribute to use')
########################################
# The VTK data file reader.
reader = Instance(tvtk.XMLReader)
refresh = Button('Update reader')
# Information about what this object can produce.
output_info = PipelineInfo(datasets=['any'],
attribute_types=['any'],
attributes=['any'])
# Our view.
view = View(Group(Include('time_step_group'),
Item(name='point_scalars_name'),
Item(name='point_vectors_name'),
Item(name='point_tensors_name'),
Item(name='cell_scalars_name'),
Item(name='cell_vectors_name'),
Item(name='cell_tensors_name'),
Item(name='reader'),
Item(name='refresh', show_label=False)
))
########################################
# Private traits.
# These private traits store the list of available data
# attributes. The non-private traits use these lists internally.
_point_scalars_list = List(Str)
_point_vectors_list = List(Str)
_point_tensors_list = List(Str)
_cell_scalars_list = List(Str)
_cell_vectors_list = List(Str)
_cell_tensors_list = List(Str)
# This filter allows us to change the attributes of the data
# object and will ensure that the pipeline is properly taken care
# of. Directly setting the array in the VTK object will not do
# this.
_assign_attribute = Instance(tvtk.AssignAttribute, args=(),
allow_none=False)
# Toggles if this is the first time this object has been used.
_first = Bool(True)
######################################################################
# `object` interface
######################################################################
def __get_pure_state__(self):
d = super(VTKXMLFileReader, self).__get_pure_state__()
for name in ('_assign_attribute', '_first'):
d.pop(name, None)
# Pickle the 'point_scalars_name' etc. since these are
# properties and not in __dict__.
attr = {}
for name in ('point_scalars', 'point_vectors',
'point_tensors', 'cell_scalars',
'cell_vectors', 'cell_tensors'):
d.pop('_' + name + '_list', None)
d.pop('_' + name + '_name', None)
x = name + '_name'
attr[x] = getattr(self, x)
d.update(attr)
return d
def __set_pure_state__(self, state):
# The reader has its own file_name which needs to be fixed.
state.reader.file_name = state.file_path.abs_pth
# Now call the parent class to setup everything.
super(VTKXMLFileReader, self).__set_pure_state__(state)
######################################################################
# `Base` interface
######################################################################
def start(self):
"""This is invoked when this object is added to the mayavi
pipeline.
"""
# Do nothing if we are already running.
if self.running:
return
# Call the parent method to do its thing. This will typically
# start all our children.
super(VTKXMLFileReader, self).start()
def stop(self):
"""Invoked when this object is removed from the mayavi
pipeline.
"""
if not self.running:
return
# Call the parent method to do its thing.
super(VTKXMLFileReader, self).stop()
######################################################################
# `FileDataSource` interface
######################################################################
def update(self):
if len(self.file_path.get()) == 0:
return
reader = self.reader
reader.update()
self.render()
def update_data(self):
if len(self.file_path.get()) == 0:
return
self.reader.update()
pnt_attr, cell_attr = get_all_attributes(self.reader.output)
def _setup_data_traits(obj, attributes, d_type):
"""Given the object, the dict of the attributes from the
`get_all_attributes` function and the data type
(point/cell) data this will setup the object and the data.
"""
attrs = ['scalars', 'vectors', 'tensors']
aa = obj._assign_attribute
data = getattr(obj.reader.output, '%s_data'%d_type)
for attr in attrs:
values = attributes[attr]
values.append('')
setattr(obj, '_%s_%s_list'%(d_type, attr), values)
if len(values) > 1:
default = getattr(obj, '%s_%s_name'%(d_type, attr))
if obj._first and len(default) == 0:
default = values[0]
getattr(data, 'set_active_%s'%attr)(default)
aa.assign(default, attr.upper(),
d_type.upper() +'_DATA')
aa.update()
kw = {'%s_%s_name'%(d_type, attr): default,
'trait_change_notify': False}
obj.trait_set(**kw)
_setup_data_traits(self, cell_attr, 'cell')
_setup_data_traits(self, pnt_attr, 'point')
if self._first:
self._first = False
# Propagate the data changed event.
self.data_changed = True
def has_output_port(self):
""" Return True as the reader has output port."""
return True
def get_output_object(self):
""" Return the reader output port."""
return self.reader.output_port
######################################################################
# Non-public interface
######################################################################
def _file_path_changed(self, fpath):
value = fpath.get()
if len(value) == 0:
return
else:
if self.reader is None:
d_type = find_file_data_type(fpath.get())
self.reader = eval('tvtk.XML%sReader()'%d_type)
reader = self.reader
reader.file_name = value
reader.update()
aa = self._assign_attribute
self.configure_input(aa, self.reader)
self.update_data()
aa.update()
outputs = [aa]
self.outputs = outputs
self.output_info.datasets = [get_tvtk_dataset_name(outputs[0])]
# Change our name on the tree view
self.name = self._get_name()
def _set_data_name(self, data_type, attr_type, value):
if value is None:
return
reader_output = self.reader.output
if len(value) == 0:
# If the value is empty then we deactivate that attribute.
d = getattr(reader_output, attr_type + '_data')
method = getattr(d, 'set_active_%s'%data_type)
method(None)
self.data_changed = True
return
aa = self._assign_attribute
data = None
if attr_type == 'point':
data = reader_output.point_data
elif attr_type == 'cell':
data = reader_output.cell_data
method = getattr(data, 'set_active_%s'%data_type)
method(value)
aa.assign(value, data_type.upper(), attr_type.upper() +'_DATA')
aa.update()
# Fire an event, so the changes propagate.
self.data_changed = True
def _point_scalars_name_changed(self, value):
self._set_data_name('scalars', 'point', value)
def _point_vectors_name_changed(self, value):
self._set_data_name('vectors', 'point', value)
def _point_tensors_name_changed(self, value):
self._set_data_name('tensors', 'point', value)
def _cell_scalars_name_changed(self, value):
self._set_data_name('scalars', 'cell', value)
def _cell_vectors_name_changed(self, value):
self._set_data_name('vectors', 'cell', value)
def _cell_tensors_name_changed(self, value):
self._set_data_name('tensors', 'cell', value)
def _get_name(self):
""" Gets the name to display on the tree view.
"""
fname = basename(self.file_path.get())
ret = "VTK XML file (%s)"%fname
if len(self.file_list) > 1:
ret += " (timeseries)"
if '[Hidden]' in self.name:
ret += ' [Hidden]'
return ret
def _refresh_fired(self):
self.reader.modified()
self.update_data()
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@mayavi@sources@vtk_xml_file_reader.py@.PATH_END.py
|
{
"filename": "_zerolinecolor.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/xaxis/_zerolinecolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ZerolinecolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="zerolinecolor", parent_name="layout.xaxis", **kwargs
):
super(ZerolinecolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "ticks"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@xaxis@_zerolinecolor.py@.PATH_END.py
|
{
"filename": "fisher.py",
"repo_name": "wcoulton/CompressedFisher",
"repo_path": "CompressedFisher_extracted/CompressedFisher-main/CompressedFisher/fisher.py",
"type": "Python"
}
|
import numpy as np
import warnings
import scipy.linalg
central_difference_weights={
1: np.array([1.]),
2:np.array([-1/2.,+1/2.]),
4:np.array([1/12.,-2./3,+2./3.,-1./12.]),
6:np.array([-1./60,3./20,-3./4,0,3./4,-3./20,1./60])
}
def geometricMean(A,B):
AB = np.linalg.solve(A,B)
sqrt_AB = scipy.linalg.sqrtm(AB)
return A.dot(sqrt_AB)
class baseFisher(object):
""" The base class for Fisher forecasting
This class contains common routines for all the Fisher forecasts including:
compressed and combined Fisher forecasts, convergence tests and more.
"""
def __init__(self):
pass
def _get_deriv_split_indices(self,f_split):
"""
A function to compute get the ids of the two splits of the derivative simulations
Args:
f_split ([float]): Fraction of sims (between 0 and 1)to assign to split 0 and split 1
Returns:
[list,list]: Two lists containing the simulations assigned to split 0 and split 1
"""
indices = np.arange(self.n_sims_derivs)
np.random.shuffle(indices)
ids_a = indices[:int(self.n_sims_derivs*f_split)]
ids_b = indices[int(self.n_sims_derivs*f_split):]
return ids_a,ids_b
def _get_covmat_split_indices(self,f_split):
"""
A function to compute get the ids of the two splits of the covariance matrix simulations
Args:
f_split ([float]): Fraction of sims (between 0 and 1)to assign to split 0 and split 1
Returns:
[list,list]: Two lists containing the simulations assigned to split 0 and split 1
"""
indices = np.arange(self.n_sims_covmat)
np.random.shuffle(indices)
ids_a = indices[:int(self.n_sims_covmat*f_split)]
ids_b = indices[int(self.n_sims_covmat*f_split):]
return ids_a,ids_b
@property
def deriv_finite_dif_weights(self):
return self._deriv_finite_dif_weights
def initailize_spline_weights(self,dict_param_spline_weights=None):
"""
Set the weights for the splines for the numerical derivatives.
Args:
dict_param_spline_weights ([dictionary]): The per parameter weights for the derivatives. (default: `None`)
If not argument is supplied the default weights, given on line 5 are used.
"""
if dict_param_spline_weights is None:
self._deriv_finite_dif_weights ={}
for param_name in self.param_names:
self._deriv_finite_dif_weights[param_name] = central_difference_weights[self._deriv_finite_dif_accuracy]
else:
self._deriv_finite_dif_weights=dict_param_spline_weights
def generate_deriv_sim_splits(self,compress_fraction=None,compress_number=None,ids_comp=None,ids_fish=None):
"""
Perform the split of the sims into the set for computing the compression and the set for computing the derivatives.
There are three different ways.
1) To specify some fraction of the total number of simulations to be used for compression (compress_fraction)
2) To specfiy a specific number of simulations to be used for the compression (compress_number)
3) To specify specificly which sims should be used for which part (ids_comp and ids_fish)
Args:
compress_fraction ([float]): For mode 1) The fraction of simulations to be used for the compression(default: `None`)
compress_number ([int]): For mode 2) The number of simulations to be used for the compression (default: `None`)
ids_comp ([list]): For mode 3) IDs of simulations to be used for computing the compression (default: `None`)
ids_fish ([list]): For mode 3) IDs of simulations to be used for computing the fisher information (default: `None`)
"""
if compress_fraction is None and compress_number is None:
raise AssertionError(' Need to specify either the fraction of sims to use in the compression or the total number')
if compress_fraction is None:
compress_fraction = compress_number*1./self.n_sims_derivs
ids_comp,ids_fish = self._get_deriv_split_indices(compress_fraction)
self._apply_deriv_split(ids_comp, ids_fish)
def generate_covmat_sim_splits(self,compress_fraction=None,compress_number=None):
"""
A function to split the covariance matrix simulations between the compression and the fisher evaluation.
Typically this is not needed as the 'noise' on the covariance matrix is typically subdominant.
Two modes
1) To specify some fraction of the total number of simulations to be used for compression (compress_fraction)
2) To specfiy a specific number of simulations to be used for the compression (compress_number)
Args:
compress_fraction ([float]): For mode 1) The fraction of simulations to be used for the compression(default: `None`)
compress_number ([int]): For mode 2) The number of simulations to be used for the compression (default: `None`)
"""
if compress_fraction is None and compress_number is None:
raise AssertionError(' Need to specify either the fraction of sims to use in the compression or the total number')
if compress_fraction is None:
compress_fraction = compress_number*1./self.n_sims_derivs
if not (self._store_covmat_sims):
raise AssertionError('Cannot split covmat sims as these are not stored. Reinitalize covmat with store_covmat_sims set to True')
ids_comp,ids_fish = self._get_covmat_split_indices(compress_fraction)
self. _apply_covmat_split(ids_fish,ids_comp)
def _compressed_fisher_matrix(self,compressed_derivs,compressed_covmat):
"""
Compute the compressed fisher matrix using eq. 10 in Coulton and Wandelt
Args:
compressed_derivs ([matrix n_params, dimension): The compressed derivatives for each parametered
compressed_covmat ([matrix dimension, dimension]): The comprsesed covariance matrix
Returns:
[matrix n_params, n_params]: The compressed fisher information.
"""
n_params = compressed_derivs.shape[0]
fisher = np.zeros([n_params,n_params])
for i in range(n_params):
for j in range(i+1):
fisher[i,j] = fisher[j,i] = np.dot(compressed_derivs[i], np.linalg.solve(compressed_covmat,compressed_derivs[j]))
return fisher
def compute_fisher_forecast(self,params_names):
"""
Compute the standard fisher forecast parameter covariances
Eq. 5 in Coulton and Wandelt
Args:
params_names ([list]): A list of which parameters to include in the forecast.
Returns:
[matrix]: The forecast parameter covariances
"""
return np.linalg.inv(self._compute_fisher_matrix(params_names))
def est_fisher_forecast_bias(self,params_names):
"""
Estimate the bias to the standard fisher forecast parameter variances.
The ratio of this to the parameter *variances* gives a measure of whether there are enough
simulations for the forecast to be converged
Computed Eq. 6 and Eq. 42. in Coulton and Wandelt
Args:
params_names ([list]): Parameters to include in the fisher forecast
Returns:
[matrix]: The bias terms to each element in the fisher forecast.
"""
fish = self._compute_fisher_matrix(params_names)
fish_err = self._compute_fisher_matrix_error(params_names)
return np.linalg.solve(fish,np.linalg.solve(fish,fish_err).T)
def compute_compressed_fisher_forecast(self,params_names):
"""
Compute the compressed fisher forecast parameter covariances
The inverse of Eq. 10 in Coulton and Wandelt
Args:
params_names ([list]): A list of which parameters to include in the forecast.
Returns:
[matrix]: The compressed forecast parameter covariances
"""
return np.linalg.inv(self._compute_compressed_fisher_matrix(params_names))
def est_compressed_fisher_forecast_bias(self,params_names):
"""
Estimate the bias to the compressed fisher forecast parameter variances.
The ratio of this to the compressed fisher parameter *variances* gives a measure of whether there
are enough simulations for the forecast to be converged.
Computed with Eq. 11 and Eq. 42 in Coulton and Wandelt
Args:
params_names ([list]): Parameters to include in the fisher forecast
Returns:
[matrix]: The bias terms to each element in the fisher forecast.
"""
fish = self._compute_compressed_fisher_matrix(params_names)
fish_err = self._compute_compressed_fisher_matrix_error(params_names)
return np.linalg.solve(fish,np.linalg.solve(fish,fish_err).T)
def _compute_combined_fisher_matrix(self,params_names):
""" Computes the combined fisher information
Eq. 18 in Coulton and Wandelt
Args:
params_names ([list]): list of the parameters that you want to included in the Fisher forecast.
Returns:
[np.array([n_params,n_params])]: An matrix or size ([n_params,n_params]) with the Fisher information
"""
fisher1 = self._compute_fisher_matrix(params_names)
fisher2 = self._compute_compressed_fisher_matrix(params_names)
return geometricMean(fisher1,fisher2)
def compute_combined_fisher_forecast(self,params_names):
""" Computes the combined fisher parameter constraints
The inverse of Eq. 18 in Coulton and Wandelt
Args:
params_names ([list]): list of the parameters that you want to included in the Fisher forecast.
Returns:
[np.array([n_params,n_params])]: An matrix or size ([n_params,n_params]) with the forecast Fisher covariance matrix.
"""
return np.linalg.inv(self._compute_combined_fisher_matrix(params_names))
def run_fisher_deriv_stablity_test(self,params_names,sample_fractions=None,verbose=False,max_repeats=None):
"""
This function provides a second means to test the convergence of the standard Fisher forecasts.
Fisher forecasts are performed using only a subset of the total available set of derivative simulations (as set by sample_fractions).
If the size of the forecasts increases rapidly with the number of simulations, it is a sign that the Fisher forecast is not converged.
For small subsets the Fisher forecast is repeated using as different divisions, by default all the possibe divissions. Eg. 2 for a sample_fraction of 1/2.
Args:
params_names ([list of names]): A list of the parameters to consider in the Fisher forecast
sample_fractions (array of floats): An array of fractions (between 0 and 1) specifing the subsets of the total number of simulations to consider.
If no arguement is given, use a default set of (1/10,1/5,1/3,2/5,1/2,1) (default: `None`)
verbose (bool): Print the Fisher forecast values for each sample fraction as they are evaluated (default: `False`)
max_repeats (int): Limit the number of subdivisions to consider. E.g. for a sample fraction of 0.1 there are 10 divisions of the data.
Leaving this as None will use all 10, however setting it to say 3 would mean only 3 of these are considered. (default: `None`)
Returns:
3 arrays : The number of simulations at each sample_fraction, the mean fisher information at each sample_fraction and the error on the mean fisher information (computed using the subsets)
"""
if sample_fractions is None:
sample_fractions = np.array([.1,.2,1./3.,.4,.5,1.])
sample_fractions = np.atleast_1d(sample_fractions)
n_params = len(params_names)
nSims = np.zeros(sample_fractions.shape[0])
mns = np.zeros([sample_fractions.shape[0],n_params,n_params ])
stds = np.zeros([sample_fractions.shape[0],n_params,n_params ])
for i,s_frac in enumerate(sample_fractions):
tmp = []
nRepeats = int(1/s_frac)
n_split = s_frac*self.n_sims_derivs
if max_repeats is not None: nRepeats = min(nRepeats, max_repeats)
nSims[i] = n_split
ids_all = np.arange(0,self.n_sims_derivs)
np.random.shuffle(ids_all)
for I in range(nRepeats):
ids_fish = ids_all[int(I*n_split):int((I+1)*n_split)]#np.arange(I*n_split,(I+1)*n_split).astype('int')
self._apply_deriv_split(None,ids_fish)
tmp.append(self.compute_fisher_forecast(params_names))
#print(tmp)
mns[i] = np.median(tmp,axis=0)
if nRepeats!=1:
stds[i] = np.std(tmp,axis=0)*np.sqrt(1/(len(tmp)-1))
if verbose: print(f"{nSims[i]} \n {np.diag(mns[i])**.5} \n {np.diag(stds[i])**.5}")
return nSims,mns,stds
def run_compressed_fisher_deriv_stablity_test(self,params_names,compress_fraction,sample_fractions=None,verbose=False,max_repeats=None):
"""
This function provides a second means to test the convergence of the compressed Fisher forecasts.
The compressed fisher forecasts are performed using only a subset of the total available set of derivative simulations (as set by sample_fractions).
If the size of the forecasts increases rapidly with the number of simulations, it is a sign that the Fisher forecast is not converged.
If the size of the compressed decreases wit the number of simulations, it is actually a sing the compressed forecast is reliable
For small subsets the Fisher forecast is repeated using as different divisions, by default all the possibe divissions. Eg. 2 for a sample_fraction of 1/2.
Args:
params_names ([list of names]): A list of the parameters to consider in the Fisher forecast
compress_fraction (float) : The fraction of the total sims used in the compression.
sample_fractions (array of floats): An array of fractions (between 0 and 1) specifing the subsets of the total number of simulations to consider.
If no arguement is given, use a default set of (1/10,1/5,1/3,2/5,1/2,1) (default: `None`)
verbose (bool): Print the Fisher forecast values for each sample fraction as they are evaluated (default: `False`)
max_repeats (int): Limit the number of subdivisions to consider. E.g. for a sample fraction of 0.1 there are 10 divisions of the data.
Leaving this as None will use all 10, however setting it to say 3 would mean only 3 of these are considered. (default: `None`)
Returns:
3 arrays : The number of simulations at each sample_fraction, the mean fisher information at each sample_fraction and the error on the mean fisher information (computed using the subsets)
"""
if sample_fractions is None:
sample_fractions = np.array([.1,.2,1./3.,.4,.5,1.])
n_params = len(params_names)
nSims = np.zeros(sample_fractions.shape[0])
mns = np.zeros([sample_fractions.shape[0],n_params,n_params ])
stds = np.zeros([sample_fractions.shape[0],n_params,n_params ])
for i,s_frac in enumerate(sample_fractions):
repeats_sfracs = []
nRepeats = int(1/s_frac)
if max_repeats is not None: nRepeats = min(nRepeats, max_repeats)
n_split = s_frac*self.n_sims_derivs
nSims[i] = n_split
ids_all = np.arange(0,self.n_sims_derivs)
np.random.shuffle(ids_all)
for I in range(nRepeats):
ids_rpt = ids_all[int(I*n_split):int((I+1)*n_split)]#np.arange(I*n_split,(I+1)*n_split).astype('int')
#indices = np.arange(self.n_sims_covmat)
np.random.shuffle(ids_rpt)
ids_comp = ids_rpt[:int(n_split*compress_fraction)]
ids_fish = ids_rpt[int(n_split*compress_fraction):]
self._apply_deriv_split(ids_comp,ids_fish)
repeats_sfracs.append(self.compute_compressed_fisher_forecast(params_names))
#print(tmp)
mns[i] = np.median(repeats_sfracs,axis=0)
if nRepeats!=1:
stds[i] = np.std(repeats_sfracs,axis=0)*np.sqrt(1/(len(repeats_sfracs)-1))
if verbose: print(f"{nSims[i]} \n {np.diag(mns[i])**.5} \n {np.diag(stds[i])**.5}")
return nSims,mns,stds
def run_combined_fisher_deriv_stablity_test(self,params_names,compress_fraction,sample_fractions=None,verbose=False,max_repeats=None):
"""
This function provides a second means to test the convergence of the compbined Fisher forecasts.
The combined fisher forecasts are performed using only a subset of the total available set of derivative simulations (as set by sample_fractions).
The combined forecast should be roughly constant to changes of the number of simulations, when it is converged.
For small subsets the Fisher forecast is repeated using as different divisions, by default all the possibe divissions. Eg. 2 for a sample_fraction of 1/2.
Args:
params_names ([list of names]): A list of the parameters to consider in the Fisher forecast
compress_fraction (float) : The fraction of the total sims used in the compression.
sample_fractions (array of floats): An array of fractions (between 0 and 1) specifing the subsets of the total number of simulations to consider.
If no arguement is given, use a default set of (1/10,1/5,1/3,2/5,1/2,1) (default: `None`)
verbose (bool): Print the Fisher forecast values for each sample fraction as they are evaluated (default: `False`)
max_repeats (int): Limit the number of subdivisions to consider. E.g. for a sample fraction of 0.1 there are 10 divisions of the data.
Leaving this as None will use all 10, however setting it to say 3 would mean only 3 of these are considered. (default: `None`)
Returns:
3 arrays : The number of simulations at each sample_fraction, the mean fisher information at each sample_fraction and the error on the mean fisher information (computed using the subsets)
"""
if sample_fractions is None:
sample_fractions = np.array([.1,.2,1./3.,.4,.5,1.])
n_params = len(params_names)
nSims = np.zeros(sample_fractions.shape[0])
mns = np.zeros([sample_fractions.shape[0],n_params,n_params ])
stds = np.zeros([sample_fractions.shape[0],n_params,n_params ])
for i,s_frac in enumerate(sample_fractions):
repeats_sfracs = []
nRepeats = int(1/s_frac)
n_split = s_frac*self.n_sims_derivs
nSims[i] = n_split
if max_repeats is not None: nRepeats = min(nRepeats, max_repeats)
ids_all = np.arange(0,self.n_sims_derivs)
np.random.shuffle(ids_all)
for I in range(nRepeats):
ids_rpt = ids_all[int(I*n_split):int((I+1)*n_split)]#np.arange(I*n_split,(I+1)*n_split).astype('int')
#indices = np.arange(self.n_sims_covmat)
np.random.shuffle(ids_rpt)
ids_comp = ids_rpt[:int(n_split*compress_fraction)]
ids_fish = ids_rpt[int(n_split*compress_fraction):]
self._apply_deriv_split(ids_comp,ids_fish)
repeats_sfracs.append(self.compute_combined_fisher_forecast(params_names))
#print(tmp)
mns[i] = np.median(repeats_sfracs,axis=0)
if nRepeats!=1:
stds[i] = np.std(repeats_sfracs,axis=0)*np.sqrt(1/(len(repeats_sfracs)-1))
if verbose: print(f"{nSims[i]} \n {np.diag(mns[i])**.5} \n {np.diag(stds[i])**.5}")
return nSims,mns,stds
def compute_compressed_fisher_forecast_wShuffle(self,params_names,compress_fraction,nShuffles=10,verbose=False):
"""
Computes the compressed fisher parameter constraints and this version uses repeated estimates (with the number given by nShuffles)
where for each repeat the sims are shuffled and redivded between the compression and fisher estimation.
This reduces the variance of the estimator, but requires more simulations for it to be valid.
Args:
params_names ([list]): list of the parameters that you want to included in the Fisher forecast.
compress_fraction (float): the fraction (between 0 and 1) of the simulations to use for the compression
nShuffles (int): the number of times to iterate. Each iteration redivides the simulations and reestimates the fisher constraints
Returns:
[np.array([n_params,n_params])]: An matrix or size ([n_params,n_params]) with the forecast Fisher covariance matrix.
"""
n_params = len(params_names)
results = np.zeros([nShuffles,n_params,n_params ])
for i in range(nShuffles):
ids_all = np.arange(self.n_sims_derivs)
np.random.shuffle(ids_all)
ids_comp = ids_all[:int(self.n_sims_derivs*compress_fraction)]
ids_fish = ids_all[int(self.n_sims_derivs*compress_fraction):]
self._apply_deriv_split(ids_comp,ids_fish)
results[i] = self.compute_compressed_fisher_forecast(params_names)
if verbose==True:
print(np.diag(np.mean(results,axis=0))**.5,np.diag(np.std(results,axis=0))**.5)
return np.median(results,axis=0)
def compute_combined_fisher_forecast_wShuffle(self,params_names,compress_fraction,nShuffles=10,verbose=False):
"""
Computes the combined fisher parameter constraints and this version uses repeated estimates (with the number given by nShuffles)
where for each repeat the sims are shuffled and redivded between the compression and fisher estimation.
This reduces the variance of the estimator, but requires more simulations for it to be unbiased.
Args:
params_names ([list]): list of the parameters that you want to included in the Fisher forecast.
compress_fraction (float): the fraction (between 0 and 1) of the simulations to use for the compression
nShuffles (int): the number of times to iterate. Each iteration redivides the simulations and reestimates the fisher constraints
Returns:
[np.array([n_params,n_params])]: An matrix or size ([n_params,n_params]) with the forecast Fisher covariance matrix.
"""
n_params = len(params_names)
results = np.zeros([nShuffles,n_params,n_params ])
for i in range(nShuffles):
ids_all = np.arange(self.n_sims_derivs)
np.random.shuffle(ids_all)
ids_comp = ids_all[:int(self.n_sims_derivs*compress_fraction)]
ids_fish = ids_all[int(self.n_sims_derivs*compress_fraction):]
self._apply_deriv_split(ids_comp,ids_fish)
fisher1 = self._compute_fisher_matrix(params_names)
fisher2 = self._compute_compressed_fisher_matrix(params_names)
results[i] = geometricMean(fisher1,fisher2)
if verbose==True:
print(np.diag(np.mean(results,axis=0))**.5,np.diag(np.std(results,axis=0))**.5)
return np.linalg.inv(np.mean(results,axis=0))
def run_combined_wShuffles_fisher_deriv_stablity_test(self,params_names,compress_fraction,nShuffles=10,sample_fractions=None,verbose=True,max_repeats=None):
"""
This function provides a second means to test the convergence of the compbined Fisher forecasts using the reshuffling.
There is an iterative step, where for each of nShuffles repeats the sims are shuffled and redivded between the compression and fisher estimation.
The combined fisher forecasts are performed using only a subset of the total available set of derivative simulations (as set by sample_fractions).
The combined forecast should be roughly constant to changes of the number of simulations, when it is converged.
For small subsets the Fisher forecast is repeated using as different divisions, by default all the possibe divissions. Eg. 2 for a sample_fraction of 1/2.
Args:
params_names ([list of names]): A list of the parameters to consider in the Fisher forecast
compress_fraction (float) : The fraction of the total sims used in the compression.
nShuffles (int): The number of redivisions of the simulations between compression and Fisher estimation to perform.
sample_fractions (array of floats): An array of fractions (between 0 and 1) specifing the subsets of the total number of simulations to consider.
If no arguement is given, use a default set of (1/10,1/5,1/3,2/5,1/2,1) (default: `None`)
verbose (bool): Print the Fisher forecast values for each sample fraction as they are evaluated (default: `False`)
max_repeats (int): Limit the number of subdivisions to consider. E.g. for a sample fraction of 0.1 there are 10 divisions of the data.
Leaving this as None will use all 10, however setting it to say 3 would mean only 3 of these are considered. (default: `None`)
Returns:
3 arrays : The number of simulations at each sample_fraction, the mean fisher information at each sample_fraction and the error on the mean fisher information (computed using the subsets)
"""
if sample_fractions is None:
sample_fractions = np.array([.1,.2,1./3.,.4,.5,1.])
n_params = len(params_names)
nSims = np.zeros(sample_fractions.shape[0])
mns = np.zeros([sample_fractions.shape[0],n_params,n_params ])
stds = np.zeros([sample_fractions.shape[0],n_params,n_params ])
for i,s_frac in enumerate(sample_fractions):
repeats_sfracs = []
nRepeats = int(1/s_frac)
n_split = s_frac*self.n_sims_derivs
nSims[i] = n_split
ids_all = np.arange(0,self.n_sims_derivs)
np.random.shuffle(ids_all)
if max_repeats is not None: nRepeats = min(nRepeats, max_repeats)
for I in range(nRepeats):
ids_rpt = ids_all[int(I*n_split):int((I+1)*n_split)]#np.arange(I*n_split,(I+1)*n_split).astype('int')
#indices = np.arange(self.n_sims_covmat)
mean_accummilate_Folds=0
for J in range(nShuffles):
np.random.shuffle(ids_rpt)
ids_comp = ids_rpt[:int(n_split*compress_fraction)]
ids_fish = ids_rpt[int(n_split*compress_fraction):]
self._apply_deriv_split(ids_comp,ids_fish)
fisher1 = self._compute_fisher_matrix(params_names)
fisher2 = self._compute_compressed_fisher_matrix(params_names)
mean_accummilate_Folds +=1/nShuffles* geometricMean(fisher1,fisher2)#self.compute_combined_fisher_forecast(params_names)
repeats_sfracs.append(np.linalg.inv(mean_accummilate_Folds))
#print(tmp)
mns[i] = np.median(repeats_sfracs,axis=0)
if nRepeats!=1:
stds[i] = np.std(repeats_sfracs,axis=0)*np.sqrt(1/(len(repeats_sfracs)-1))
if verbose: print(f"{nSims[i]} \n {np.diag(mns[i])**.5} \n {np.diag(stds[i])**.5}")
return nSims,mns,stds
|
wcoultonREPO_NAMECompressedFisherPATH_START.@CompressedFisher_extracted@CompressedFisher-main@CompressedFisher@fisher.py@.PATH_END.py
|
{
"filename": "_error_x.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/bar/_error_x.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class Error_XValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="error_x", parent_name="bar", **kwargs):
super(Error_XValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "ErrorX"),
data_docs=kwargs.pop(
"data_docs",
"""
array
Sets the data corresponding the length of each
error bar. Values are plotted relative to the
underlying data.
arrayminus
Sets the data corresponding the length of each
error bar in the bottom (left) direction for
vertical (horizontal) bars Values are plotted
relative to the underlying data.
arrayminussrc
Sets the source reference on Chart Studio Cloud
for `arrayminus`.
arraysrc
Sets the source reference on Chart Studio Cloud
for `array`.
color
Sets the stoke color of the error bars.
copy_ystyle
symmetric
Determines whether or not the error bars have
the same length in both direction (top/bottom
for vertical bars, left/right for horizontal
bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error
bars. If *constant`, the bar lengths are of a
constant value. Set this constant in `value`.
If "percent", the bar lengths correspond to a
percentage of underlying data. Set this
percentage in `value`. If "sqrt", the bar
lengths correspond to the square of the
underlying data. If "data", the bar lengths are
set with data set `array`.
value
Sets the value of either the percentage (if
`type` is set to "percent") or the constant (if
`type` is set to "constant") corresponding to
the lengths of the error bars.
valueminus
Sets the value of either the percentage (if
`type` is set to "percent") or the constant (if
`type` is set to "constant") corresponding to
the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
visible
Determines whether or not this set of error
bars is visible.
width
Sets the width (in px) of the cross-bar at both
ends of the error bars.
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@bar@_error_x.py@.PATH_END.py
|
{
"filename": "_shadow.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/indicator/number/font/_shadow.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShadowValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="shadow", parent_name="indicator.number.font", **kwargs
):
super(ShadowValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@indicator@number@font@_shadow.py@.PATH_END.py
|
{
"filename": "_tickmode.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/contour/colorbar/_tickmode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickmodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="tickmode", parent_name="contour.colorbar", **kwargs
):
super(TickmodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
implied_edits=kwargs.pop("implied_edits", {}),
values=kwargs.pop("values", ["auto", "linear", "array"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@contour@colorbar@_tickmode.py@.PATH_END.py
|
{
"filename": "snap.py",
"repo_name": "AFD-Illinois/ebhlight",
"repo_path": "ebhlight_extracted/ebhlight-master/script/analysis/snap.py",
"type": "Python"
}
|
#!/usr/bin/env python
import matplotlib
import matplotlib.pyplot as plt
import plot as bplt
import sys, os
import hdf5_to_dict as io
import numpy as np
import sys
font = {'size' : 16}
matplotlib.rc('font', **font)
from argparse import ArgumentParser
parser = ArgumentParser(
description='Plot 2d slices of your simulation at a given time.')
parser.add_argument('dumpfile',type=str,
help='File name to plot')
parser.add_argument('variable',type=str,
help='Variable to plot')
parser.add_argument('--coords',type=str,
choices=['cart','mks'],default='cart',
help='Coordinate system. Cartesian or Modified Kerr-Schild')
parser.add_argument('-s','--size',
type=float,default=40,
help='Size of domain to plot')
parser.add_argument('--lin',
dest='log',
default=True,
action='store_false',
help='Sets scale to linear. Default is log.')
parser.add_argument('--vmin',
type=float,default=-4,
help='Colormap lower bound')
parser.add_argument('--vmax',
type=float,default=0,
help='Colormap upper bound')
parser.add_argument('-c','--cmap',
type=str,default='jet',
help='Colormap used')
parser.add_argument('--save',
type=str,default=None,
help='Figure filename if you want to save the figure')
parser.add_argument('--label',
type=str,default=None,
help='Label for colormap')
def make_snap(dfnam,vnam,coords,size,cmap,logplot,
savefig,label,
vmin,vmax,
geom=None):
if not os.path.exists(dfnam):
print('ERROR File ' + dfnam + ' does not exist!')
sys.exit()
hdr = io.load_hdr(dfnam)
if geom is None:
geom = io.load_geom(hdr)
dump = io.load_dump(dfnam,geom=geom)
if not vnam in dump.keys():
print('ERROR Variable ' + vnam + ' is not contained in dump file!')
print('Available variables are:')
for key in dump.keys():
if type(dump[key]) is np.ndarray:
if (len(dump[key].shape) == 3 and dump[key].shape[0] == hdr['N1'] and
dump[key].shape[1] == hdr['N2'] and dump[key].shape[2] == hdr['N3']):
print(key, end=' ')
print('')
sys.exit()
IS_3D = hdr['N3'] > 1
if label is None:
label = vnam
var = dump[vnam]
if logplot:
var = np.log10(var)
if IS_3D:
if coords == 'mks':
fig, (a0, a1) = plt.subplots(1,2,gridspec_kw={'width_ratios':[1,1]}, figsize=(12,6))
elif coords == 'cart':
fig, (a0, a1) = plt.subplots(1,2,gridspec_kw={'width_ratios':[1,1]}, figsize=(13,7))
ax = a0
if coords == 'mks':
bplt.plot_X1X2(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=False, label=label, ticks=None, shading='gouraud')
elif coords == 'cart':
bplt.plot_xz(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=False, label=label, ticks=None, shading='gouraud')
ax.set_xlim([-size,size]); ax.set_ylim([-size,size])
ax = a1
if coords == 'mks':
bplt.plot_X1X3(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=True, label=label, ticks=None, shading='gouraud')
elif coords == 'cart':
bplt.plot_xy(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=True, label=label, ticks=None, shading='gouraud')
ax.set_xlim([-size,size]); ax.set_ylim([-size,size])
else:
if coords == 'mks':
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
bplt.plot_X1X2(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=True, label=label, ticks=None, shading='gouraud')
elif coords == 'cart':
fig, ax = plt.subplots(1, 1, figsize=(7, 10))
bplt.plot_xz(ax, geom, var, dump, cmap=cmap, vmin=vmin, vmax=vmax,
cbar=True, label=label, ticks=None, shading='gouraud')
ax.set_xlim([0,size]); ax.set_ylim([-size,size])
if savefig == False:
plt.show()
else:
plt.savefig(savefig,bbox_inches='tight')
plt.cla()
plt.clf()
plt.close()
if __name__ == "__main__":
args = parser.parse_args()
dfnam = args.dumpfile
vnam = args.variable
coords = args.coords
size = args.size
cmap = args.cmap
logplot = args.log
savefig = args.save if args.save is not None else False
label = args.label
vmin,vmax = args.vmin,args.vmax
make_snap(dfnam,vnam,coords,size,cmap,logplot,savefig,label,vmin,vmax)
|
AFD-IllinoisREPO_NAMEebhlightPATH_START.@ebhlight_extracted@ebhlight-master@script@analysis@snap.py@.PATH_END.py
|
{
"filename": "_showticksuffix.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/splom/marker/colorbar/_showticksuffix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowticksuffixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="showticksuffix",
parent_name="splom.marker.colorbar",
**kwargs,
):
super(ShowticksuffixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@splom@marker@colorbar@_showticksuffix.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "jonathansick/MoAstro",
"repo_path": "MoAstro_extracted/MoAstro-master/setup.py",
"type": "Python"
}
|
#!/usr/bin/env python
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import glob
import os
import sys
from setuptools import find_packages, setup
import moastro
# Set affiliated package-specific settings
PACKAGENAME = 'moastro'
DESCRIPTION = 'MongoDB framework for observational astronomers'
LONG_DESCRIPTION = moastro.__doc__
AUTHOR = 'Jonathan Sick'
AUTHOR_EMAIL = 'jonathansick@mac.com'
LICENSE = 'BSD'
URL = 'http://jonathansick.ca'
# VERSION should be PEP386 compatible (http://www.python.org/dev/peps/pep-0386)
VERSION = '0.3.dev'
# Indicates if this version is a release version
RELEASE = 'dev' not in VERSION
# Treat everything in scripts except README.rst as a script to be installed
scripts = [fname for fname in glob.glob(os.path.join('scripts', '*'))
if os.path.basename(fname) != 'README.rst']
setup(name=PACKAGENAME,
version=VERSION,
description=DESCRIPTION,
scripts=scripts,
requires=['astropy'],
install_requires=['astropy'],
provides=[PACKAGENAME],
author=AUTHOR,
author_email=AUTHOR_EMAIL,
license=LICENSE,
url=URL,
long_description=LONG_DESCRIPTION,
zip_safe=False,
)
|
jonathansickREPO_NAMEMoAstroPATH_START.@MoAstro_extracted@MoAstro-master@setup.py@.PATH_END.py
|
{
"filename": "meta.py",
"repo_name": "icrar/daliuge",
"repo_path": "daliuge_extracted/daliuge-master/daliuge-engine/dlg/meta.py",
"type": "Python"
}
|
#
# ICRAR - International Centre for Radio Astronomy Research
# (c) UWA - The University of Western Australia, 2016
# Copyright by UWA (in the framework of the ICRAR)
# All rights reserved
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
#
import collections
dlg_bool_param = collections.namedtuple("dlg_bool_param", "description default_value")
dlg_int_param = collections.namedtuple("dlg_int_param", "description default_value")
dlg_float_param = collections.namedtuple("dlg_float_param", "description default_value")
dlg_string_param = collections.namedtuple(
"dlg_string_param", "description default_value"
)
dlg_enum_param = collections.namedtuple("dlg_enum_param", "cls description default_value")
dlg_list_param = collections.namedtuple("dlg_list_param", "description default_value")
dlg_dict_param = collections.namedtuple("dlg_dict_param", "description default_value")
class dlg_batch_input(object):
def __init__(self, mime_type, input_drop_class):
self.input_drop_class = input_drop_class
self.mime_type = mime_type
class dlg_batch_output(object):
def __init__(self, mime_type, output_drop_class):
self.output_drop_class = output_drop_class
self.mime_type = mime_type
class dlg_streaming_input(object):
def __init__(self, mime_type):
self.mime_type = mime_type
class dlg_component(object):
def __init__(
self, name, description, batch_inputs, batch_outputs, streaming_inputs
):
self.name = name
self.description = description
self.batch_inputs = batch_inputs
self.batch_outputs = batch_outputs
self.streaming_inputs = streaming_inputs
|
icrarREPO_NAMEdaliugePATH_START.@daliuge_extracted@daliuge-master@daliuge-engine@dlg@meta.py@.PATH_END.py
|
{
"filename": "_sunburst.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/_sunburst.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Sunburst(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "sunburst"
_valid_props = {
"branchvalues",
"count",
"customdata",
"customdatasrc",
"domain",
"hoverinfo",
"hoverinfosrc",
"hoverlabel",
"hovertemplate",
"hovertemplatesrc",
"hovertext",
"hovertextsrc",
"ids",
"idssrc",
"insidetextfont",
"insidetextorientation",
"labels",
"labelssrc",
"leaf",
"legend",
"legendgrouptitle",
"legendrank",
"legendwidth",
"level",
"marker",
"maxdepth",
"meta",
"metasrc",
"name",
"opacity",
"outsidetextfont",
"parents",
"parentssrc",
"root",
"rotation",
"sort",
"stream",
"text",
"textfont",
"textinfo",
"textsrc",
"texttemplate",
"texttemplatesrc",
"type",
"uid",
"uirevision",
"values",
"valuessrc",
"visible",
}
# branchvalues
# ------------
@property
def branchvalues(self):
"""
Determines how the items in `values` are summed. When set to
"total", items in `values` are taken to be value of all its
descendants. When set to "remainder", items in `values`
corresponding to the root and the branches sectors are taken to
be the extra part not part of the sum of the values at their
leaves.
The 'branchvalues' property is an enumeration that may be specified as:
- One of the following enumeration values:
['remainder', 'total']
Returns
-------
Any
"""
return self["branchvalues"]
@branchvalues.setter
def branchvalues(self, val):
self["branchvalues"] = val
# count
# -----
@property
def count(self):
"""
Determines default for `values` when it is not provided, by
inferring a 1 for each of the "leaves" and/or "branches",
otherwise 0.
The 'count' property is a flaglist and may be specified
as a string containing:
- Any combination of ['branches', 'leaves'] joined with '+' characters
(e.g. 'branches+leaves')
Returns
-------
Any
"""
return self["count"]
@count.setter
def count(self, val):
self["count"] = val
# customdata
# ----------
@property
def customdata(self):
"""
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note that,
"scatter" traces also appends customdata items in the markers
DOM elements
The 'customdata' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["customdata"]
@customdata.setter
def customdata(self, val):
self["customdata"] = val
# customdatasrc
# -------------
@property
def customdatasrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`customdata`.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["customdatasrc"]
@customdatasrc.setter
def customdatasrc(self, val):
self["customdatasrc"] = val
# domain
# ------
@property
def domain(self):
"""
The 'domain' property is an instance of Domain
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Domain`
- A dict of string/value properties that will be passed
to the Domain constructor
Supported dict properties:
column
If there is a layout grid, use the domain for
this column in the grid for this sunburst trace
.
row
If there is a layout grid, use the domain for
this row in the grid for this sunburst trace .
x
Sets the horizontal domain of this sunburst
trace (in plot fraction).
y
Sets the vertical domain of this sunburst trace
(in plot fraction).
Returns
-------
plotly.graph_objs.sunburst.Domain
"""
return self["domain"]
@domain.setter
def domain(self, val):
self["domain"] = val
# hoverinfo
# ---------
@property
def hoverinfo(self):
"""
Determines which trace information appear on hover. If `none`
or `skip` are set, no information is displayed upon hovering.
But, if `none` is set, click and hover events are still fired.
The 'hoverinfo' property is a flaglist and may be specified
as a string containing:
- Any combination of ['label', 'text', 'value', 'name', 'current path', 'percent root', 'percent entry', 'percent parent'] joined with '+' characters
(e.g. 'label+text')
OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip')
- A list or array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["hoverinfo"]
@hoverinfo.setter
def hoverinfo(self, val):
self["hoverinfo"] = val
# hoverinfosrc
# ------------
@property
def hoverinfosrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
The 'hoverinfosrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hoverinfosrc"]
@hoverinfosrc.setter
def hoverinfosrc(self, val):
self["hoverinfosrc"] = val
# hoverlabel
# ----------
@property
def hoverlabel(self):
"""
The 'hoverlabel' property is an instance of Hoverlabel
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Hoverlabel`
- A dict of string/value properties that will be passed
to the Hoverlabel constructor
Supported dict properties:
align
Sets the horizontal alignment of the text
content within hover label box. Has an effect
only if the hover label text spans more two or
more lines
alignsrc
Sets the source reference on Chart Studio Cloud
for `align`.
bgcolor
Sets the background color of the hover labels
for this trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud
for `bgcolor`.
bordercolor
Sets the border color of the hover labels for
this trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud
for `bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of
characters) of the trace name in the hover
labels for all traces. -1 shows the whole name
regardless of length. 0-3 shows the first 0-3
characters, and an integer >3 will show the
whole name if it is less than that many
characters, but if it is longer, will truncate
to `namelength - 3` characters and add an
ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud
for `namelength`.
Returns
-------
plotly.graph_objs.sunburst.Hoverlabel
"""
return self["hoverlabel"]
@hoverlabel.setter
def hoverlabel(self, val):
self["hoverlabel"] = val
# hovertemplate
# -------------
@property
def hovertemplate(self):
"""
Template string used for rendering the information that appear
on hover box. Note that this will override `hoverinfo`.
Variables are inserted using %{variable}, for example "y: %{y}"
as well as %{xother}, {%_xother}, {%_xother_}, {%xother_}. When
showing info for several points, "xother" will be added to
those with different x positions from the first point. An
underscore before or after "(x|y)other" will add a space on
that side, only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format for
details on the formatting syntax. Dates are formatted using
d3-time-format's syntax %{variable|d3-time-format}, for example
"Day: %{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the date
formatting syntax. The variables available in `hovertemplate`
are the ones emitted as event data described at this link
https://plotly.com/javascript/plotlyjs-events/#event-data.
Additionally, every attributes that can be specified per-point
(the ones that are `arrayOk: true`) are available. Finally, the
template string has access to variables `currentPath`, `root`,
`entry`, `percentRoot`, `percentEntry` and `percentParent`.
Anything contained in tag `<extra>` is displayed in the
secondary box, for example "<extra>{fullData.name}</extra>". To
hide the secondary box completely, use an empty tag
`<extra></extra>`.
The 'hovertemplate' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["hovertemplate"]
@hovertemplate.setter
def hovertemplate(self, val):
self["hovertemplate"] = val
# hovertemplatesrc
# ----------------
@property
def hovertemplatesrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
The 'hovertemplatesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hovertemplatesrc"]
@hovertemplatesrc.setter
def hovertemplatesrc(self, val):
self["hovertemplatesrc"] = val
# hovertext
# ---------
@property
def hovertext(self):
"""
Sets hover text elements associated with each sector. If a
single string, the same string appears for all data points. If
an array of string, the items are mapped in order of this
trace's sectors. To be seen, trace `hoverinfo` must contain a
"text" flag.
The 'hovertext' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["hovertext"]
@hovertext.setter
def hovertext(self, val):
self["hovertext"] = val
# hovertextsrc
# ------------
@property
def hovertextsrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hovertext`.
The 'hovertextsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hovertextsrc"]
@hovertextsrc.setter
def hovertextsrc(self, val):
self["hovertextsrc"] = val
# ids
# ---
@property
def ids(self):
"""
Assigns id labels to each datum. These ids for object constancy
of data points during animation. Should be an array of strings,
not numbers or any other type.
The 'ids' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["ids"]
@ids.setter
def ids(self, val):
self["ids"] = val
# idssrc
# ------
@property
def idssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `ids`.
The 'idssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["idssrc"]
@idssrc.setter
def idssrc(self, val):
self["idssrc"] = val
# insidetextfont
# --------------
@property
def insidetextfont(self):
"""
Sets the font used for `textinfo` lying inside the sector.
The 'insidetextfont' property is an instance of Insidetextfont
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Insidetextfont`
- A dict of string/value properties that will be passed
to the Insidetextfont constructor
Supported dict properties:
color
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for `family`.
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud
for `lineposition`.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
shadowsrc
Sets the source reference on Chart Studio Cloud
for `shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
style
Sets whether a font should be styled with a
normal or italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud
for `style`.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud
for `textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud
for `variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud
for `weight`.
Returns
-------
plotly.graph_objs.sunburst.Insidetextfont
"""
return self["insidetextfont"]
@insidetextfont.setter
def insidetextfont(self, val):
self["insidetextfont"] = val
# insidetextorientation
# ---------------------
@property
def insidetextorientation(self):
"""
Controls the orientation of the text inside chart sectors. When
set to "auto", text may be oriented in any direction in order
to be as big as possible in the middle of a sector. The
"horizontal" option orients text to be parallel with the bottom
of the chart, and may make text smaller in order to achieve
that goal. The "radial" option orients text along the radius of
the sector. The "tangential" option orients text perpendicular
to the radius of the sector.
The 'insidetextorientation' property is an enumeration that may be specified as:
- One of the following enumeration values:
['horizontal', 'radial', 'tangential', 'auto']
Returns
-------
Any
"""
return self["insidetextorientation"]
@insidetextorientation.setter
def insidetextorientation(self, val):
self["insidetextorientation"] = val
# labels
# ------
@property
def labels(self):
"""
Sets the labels of each of the sectors.
The 'labels' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["labels"]
@labels.setter
def labels(self, val):
self["labels"] = val
# labelssrc
# ---------
@property
def labelssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `labels`.
The 'labelssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["labelssrc"]
@labelssrc.setter
def labelssrc(self, val):
self["labelssrc"] = val
# leaf
# ----
@property
def leaf(self):
"""
The 'leaf' property is an instance of Leaf
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Leaf`
- A dict of string/value properties that will be passed
to the Leaf constructor
Supported dict properties:
opacity
Sets the opacity of the leaves. With colorscale
it is defaulted to 1; otherwise it is defaulted
to 0.7
Returns
-------
plotly.graph_objs.sunburst.Leaf
"""
return self["leaf"]
@leaf.setter
def leaf(self, val):
self["leaf"] = val
# legend
# ------
@property
def legend(self):
"""
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2", "legend3",
etc. Settings for these legends are set in the layout, under
`layout.legend`, `layout.legend2`, etc.
The 'legend' property is an identifier of a particular
subplot, of type 'legend', that may be specified as the string 'legend'
optionally followed by an integer >= 1
(e.g. 'legend', 'legend1', 'legend2', 'legend3', etc.)
Returns
-------
str
"""
return self["legend"]
@legend.setter
def legend(self, val):
self["legend"] = val
# legendgrouptitle
# ----------------
@property
def legendgrouptitle(self):
"""
The 'legendgrouptitle' property is an instance of Legendgrouptitle
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Legendgrouptitle`
- A dict of string/value properties that will be passed
to the Legendgrouptitle constructor
Supported dict properties:
font
Sets this legend group's title font.
text
Sets the title of the legend group.
Returns
-------
plotly.graph_objs.sunburst.Legendgrouptitle
"""
return self["legendgrouptitle"]
@legendgrouptitle.setter
def legendgrouptitle(self, val):
self["legendgrouptitle"] = val
# legendrank
# ----------
@property
def legendrank(self):
"""
Sets the legend rank for this trace. Items and groups with
smaller ranks are presented on top/left side while with
"reversed" `legend.traceorder` they are on bottom/right side.
The default legendrank is 1000, so that you can use ranks less
than 1000 to place certain items before all unranked items, and
ranks greater than 1000 to go after all unranked items. When
having unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and layout.
The 'legendrank' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["legendrank"]
@legendrank.setter
def legendrank(self, val):
self["legendrank"] = val
# legendwidth
# -----------
@property
def legendwidth(self):
"""
Sets the width (in px or fraction) of the legend for this
trace.
The 'legendwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["legendwidth"]
@legendwidth.setter
def legendwidth(self, val):
self["legendwidth"] = val
# level
# -----
@property
def level(self):
"""
Sets the level from which this trace hierarchy is rendered. Set
`level` to `''` to start from the root node in the hierarchy.
Must be an "id" if `ids` is filled in, otherwise plotly
attempts to find a matching item in `labels`.
The 'level' property accepts values of any type
Returns
-------
Any
"""
return self["level"]
@level.setter
def level(self, val):
self["level"] = val
# marker
# ------
@property
def marker(self):
"""
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.colorscale`. Has an
effect only if colors is set to a numerical
array. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette
will be chosen according to whether numbers in
the `color` array are all positive, all
negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
colors) or the bounds set in `marker.cmin` and
`marker.cmax` Has an effect only if colors is
set to a numerical array. Defaults to `false`
when `marker.cmin` and `marker.cmax` are set by
the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if colors is set to a numerical
array. Value should have the same units as
colors and if set, `marker.cmin` must be set as
well.
cmid
Sets the mid-point of the color domain by
scaling `marker.cmin` and/or `marker.cmax` to
be equidistant to this point. Has an effect
only if colors is set to a numerical array.
Value should have the same units as colors. Has
no effect when `marker.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if colors is set to a numerical
array. Value should have the same units as
colors and if set, `marker.cmax` must be set as
well.
coloraxis
Sets a reference to a shared color axis.
References to these shared color axes are
"coloraxis", "coloraxis2", "coloraxis3", etc.
Settings for these shared color axes are set in
the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple
color scales can be linked to the same color
axis.
colorbar
:class:`plotly.graph_objects.sunburst.marker.Co
lorBar` instance or dict with compatible
properties
colors
Sets the color of each sector of this trace. If
not specified, the default trace color set is
used to pick the sector colors.
colorscale
Sets the colorscale. Has an effect only if
colors is set to a numerical array. The
colorscale must be an array containing arrays
mapping a normalized value to an rgb, rgba,
hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and
highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in
color space, use `marker.cmin` and
`marker.cmax`. Alternatively, `colorscale` may
be a palette name string of the following list:
Blackbody,Bluered,Blues,Cividis,Earth,Electric,
Greens,Greys,Hot,Jet,Picnic,Portland,Rainbow,Rd
Bu,Reds,Viridis,YlGnBu,YlOrRd.
colorssrc
Sets the source reference on Chart Studio Cloud
for `colors`.
line
:class:`plotly.graph_objects.sunburst.marker.Li
ne` instance or dict with compatible properties
pattern
Sets the pattern within the marker.
reversescale
Reverses the color mapping if true. Has an
effect only if colors is set to a numerical
array. If true, `marker.cmin` will correspond
to the last color in the array and
`marker.cmax` will correspond to the first
color.
showscale
Determines whether or not a colorbar is
displayed for this trace. Has an effect only if
colors is set to a numerical array.
Returns
-------
plotly.graph_objs.sunburst.Marker
"""
return self["marker"]
@marker.setter
def marker(self, val):
self["marker"] = val
# maxdepth
# --------
@property
def maxdepth(self):
"""
Sets the number of rendered sectors from any given `level`. Set
`maxdepth` to "-1" to render all the levels in the hierarchy.
The 'maxdepth' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
Returns
-------
int
"""
return self["maxdepth"]
@maxdepth.setter
def maxdepth(self, val):
self["maxdepth"] = val
# meta
# ----
@property
def meta(self):
"""
Assigns extra meta information associated with this trace that
can be used in various text attributes. Attributes such as
trace `name`, graph, axis and colorbar `title.text`, annotation
`text` `rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta` values in
an attribute in the same trace, simply use `%{meta[i]}` where
`i` is the index or key of the `meta` item in question. To
access trace `meta` in layout attributes, use
`%{data[n[.meta[i]}` where `i` is the index or key of the
`meta` and `n` is the trace index.
The 'meta' property accepts values of any type
Returns
-------
Any|numpy.ndarray
"""
return self["meta"]
@meta.setter
def meta(self, val):
self["meta"] = val
# metasrc
# -------
@property
def metasrc(self):
"""
Sets the source reference on Chart Studio Cloud for `meta`.
The 'metasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["metasrc"]
@metasrc.setter
def metasrc(self, val):
self["metasrc"] = val
# name
# ----
@property
def name(self):
"""
Sets the trace name. The trace name appears as the legend item
and on hover.
The 'name' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["name"]
@name.setter
def name(self, val):
self["name"] = val
# opacity
# -------
@property
def opacity(self):
"""
Sets the opacity of the trace.
The 'opacity' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float
"""
return self["opacity"]
@opacity.setter
def opacity(self, val):
self["opacity"] = val
# outsidetextfont
# ---------------
@property
def outsidetextfont(self):
"""
Sets the font used for `textinfo` lying outside the sector.
This option refers to the root of the hierarchy presented at
the center of a sunburst graph. Please note that if a hierarchy
has multiple root nodes, this option won't have any effect and
`insidetextfont` would be used.
The 'outsidetextfont' property is an instance of Outsidetextfont
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Outsidetextfont`
- A dict of string/value properties that will be passed
to the Outsidetextfont constructor
Supported dict properties:
color
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for `family`.
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud
for `lineposition`.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
shadowsrc
Sets the source reference on Chart Studio Cloud
for `shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
style
Sets whether a font should be styled with a
normal or italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud
for `style`.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud
for `textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud
for `variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud
for `weight`.
Returns
-------
plotly.graph_objs.sunburst.Outsidetextfont
"""
return self["outsidetextfont"]
@outsidetextfont.setter
def outsidetextfont(self, val):
self["outsidetextfont"] = val
# parents
# -------
@property
def parents(self):
"""
Sets the parent sectors for each of the sectors. Empty string
items '' are understood to reference the root node in the
hierarchy. If `ids` is filled, `parents` items are understood
to be "ids" themselves. When `ids` is not set, plotly attempts
to find matching items in `labels`, but beware they must be
unique.
The 'parents' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["parents"]
@parents.setter
def parents(self, val):
self["parents"] = val
# parentssrc
# ----------
@property
def parentssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `parents`.
The 'parentssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["parentssrc"]
@parentssrc.setter
def parentssrc(self, val):
self["parentssrc"] = val
# root
# ----
@property
def root(self):
"""
The 'root' property is an instance of Root
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Root`
- A dict of string/value properties that will be passed
to the Root constructor
Supported dict properties:
color
sets the color of the root node for a
sunburst/treemap/icicle trace. this has no
effect when a colorscale is used to set the
markers.
Returns
-------
plotly.graph_objs.sunburst.Root
"""
return self["root"]
@root.setter
def root(self, val):
self["root"] = val
# rotation
# --------
@property
def rotation(self):
"""
Rotates the whole diagram counterclockwise by some angle. By
default the first slice starts at 3 o'clock.
The 'rotation' property is a angle (in degrees) that may be
specified as a number between -180 and 180.
Numeric values outside this range are converted to the equivalent value
(e.g. 270 is converted to -90).
Returns
-------
int|float
"""
return self["rotation"]
@rotation.setter
def rotation(self, val):
self["rotation"] = val
# sort
# ----
@property
def sort(self):
"""
Determines whether or not the sectors are reordered from
largest to smallest.
The 'sort' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["sort"]
@sort.setter
def sort(self, val):
self["sort"] = val
# stream
# ------
@property
def stream(self):
"""
The 'stream' property is an instance of Stream
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Stream`
- A dict of string/value properties that will be passed
to the Stream constructor
Supported dict properties:
maxpoints
Sets the maximum number of points to keep on
the plots from an incoming stream. If
`maxpoints` is set to 50, only the newest 50
points will be displayed on the plot.
token
The stream id number links a data trace on a
plot with a stream. See https://chart-
studio.plotly.com/settings for more details.
Returns
-------
plotly.graph_objs.sunburst.Stream
"""
return self["stream"]
@stream.setter
def stream(self, val):
self["stream"] = val
# text
# ----
@property
def text(self):
"""
Sets text elements associated with each sector. If trace
`textinfo` contains a "text" flag, these elements will be seen
on the chart. If trace `hoverinfo` contains a "text" flag and
"hovertext" is not set, these elements will be seen in the
hover labels.
The 'text' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["text"]
@text.setter
def text(self, val):
self["text"] = val
# textfont
# --------
@property
def textfont(self):
"""
Sets the font used for `textinfo`.
The 'textfont' property is an instance of Textfont
that may be specified as:
- An instance of :class:`plotly.graph_objs.sunburst.Textfont`
- A dict of string/value properties that will be passed
to the Textfont constructor
Supported dict properties:
color
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for `family`.
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud
for `lineposition`.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
shadowsrc
Sets the source reference on Chart Studio Cloud
for `shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
style
Sets whether a font should be styled with a
normal or italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud
for `style`.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud
for `textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud
for `variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud
for `weight`.
Returns
-------
plotly.graph_objs.sunburst.Textfont
"""
return self["textfont"]
@textfont.setter
def textfont(self, val):
self["textfont"] = val
# textinfo
# --------
@property
def textinfo(self):
"""
Determines which trace information appear on the graph.
The 'textinfo' property is a flaglist and may be specified
as a string containing:
- Any combination of ['label', 'text', 'value', 'current path', 'percent root', 'percent entry', 'percent parent'] joined with '+' characters
(e.g. 'label+text')
OR exactly one of ['none'] (e.g. 'none')
Returns
-------
Any
"""
return self["textinfo"]
@textinfo.setter
def textinfo(self, val):
self["textinfo"] = val
# textsrc
# -------
@property
def textsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `text`.
The 'textsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["textsrc"]
@textsrc.setter
def textsrc(self, val):
self["textsrc"] = val
# texttemplate
# ------------
@property
def texttemplate(self):
"""
Template string used for rendering the information text that
appear on points. Note that this will override `textinfo`.
Variables are inserted using %{variable}, for example "y:
%{y}". Numbers are formatted using d3-format's syntax
%{variable:d3-format}, for example "Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format for
details on the formatting syntax. Dates are formatted using
d3-time-format's syntax %{variable|d3-time-format}, for example
"Day: %{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the date
formatting syntax. Every attributes that can be specified per-
point (the ones that are `arrayOk: true`) are available.
Finally, the template string has access to variables
`currentPath`, `root`, `entry`, `percentRoot`, `percentEntry`,
`percentParent`, `label` and `value`.
The 'texttemplate' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["texttemplate"]
@texttemplate.setter
def texttemplate(self, val):
self["texttemplate"] = val
# texttemplatesrc
# ---------------
@property
def texttemplatesrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`texttemplate`.
The 'texttemplatesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["texttemplatesrc"]
@texttemplatesrc.setter
def texttemplatesrc(self, val):
self["texttemplatesrc"] = val
# uid
# ---
@property
def uid(self):
"""
Assign an id to this trace, Use this to provide object
constancy between traces during animations and transitions.
The 'uid' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["uid"]
@uid.setter
def uid(self, val):
self["uid"] = val
# uirevision
# ----------
@property
def uirevision(self):
"""
Controls persistence of some user-driven changes to the trace:
`constraintrange` in `parcoords` traces, as well as some
`editable: true` modifications such as `name` and
`colorbar.title`. Defaults to `layout.uirevision`. Note that
other user-driven trace attribute changes are controlled by
`layout` attributes: `trace.visible` is controlled by
`layout.legend.uirevision`, `selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)` (accessible
with `config: {editable: true}`) is controlled by
`layout.editrevision`. Trace changes are tracked by `uid`,
which only falls back on trace index if no `uid` is provided.
So if your app can add/remove traces before the end of the
`data` array, such that the same trace has a different index,
you can still preserve user-driven changes if you give each
trace a `uid` that stays with it as it moves.
The 'uirevision' property accepts values of any type
Returns
-------
Any
"""
return self["uirevision"]
@uirevision.setter
def uirevision(self, val):
self["uirevision"] = val
# values
# ------
@property
def values(self):
"""
Sets the values associated with each of the sectors. Use with
`branchvalues` to determine how the values are summed.
The 'values' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["values"]
@values.setter
def values(self, val):
self["values"] = val
# valuessrc
# ---------
@property
def valuessrc(self):
"""
Sets the source reference on Chart Studio Cloud for `values`.
The 'valuessrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["valuessrc"]
@valuessrc.setter
def valuessrc(self, val):
self["valuessrc"] = val
# visible
# -------
@property
def visible(self):
"""
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as a
legend item (provided that the legend itself is visible).
The 'visible' property is an enumeration that may be specified as:
- One of the following enumeration values:
[True, False, 'legendonly']
Returns
-------
Any
"""
return self["visible"]
@visible.setter
def visible(self, val):
self["visible"] = val
# type
# ----
@property
def type(self):
return self._props["type"]
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
branchvalues
Determines how the items in `values` are summed. When
set to "total", items in `values` are taken to be value
of all its descendants. When set to "remainder", items
in `values` corresponding to the root and the branches
sectors are taken to be the extra part not part of the
sum of the values at their leaves.
count
Determines default for `values` when it is not
provided, by inferring a 1 for each of the "leaves"
and/or "branches", otherwise 0.
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
`customdata`.
domain
:class:`plotly.graph_objects.sunburst.Domain` instance
or dict with compatible properties
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
hoverlabel
:class:`plotly.graph_objects.sunburst.Hoverlabel`
instance or dict with compatible properties
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}" as well as %{xother}, {%_xother},
{%_xother_}, {%xother_}. When showing info for several
points, "xother" will be added to those with different
x positions from the first point. An underscore before
or after "(x|y)other" will add a space on that side,
only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for
example "Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. The variables available in
`hovertemplate` are the ones emitted as event data
described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Finally, the template string has access
to variables `currentPath`, `root`, `entry`,
`percentRoot`, `percentEntry` and `percentParent`.
Anything contained in tag `<extra>` is displayed in the
secondary box, for example
"<extra>{fullData.name}</extra>". To hide the secondary
box completely, use an empty tag `<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
hovertext
Sets hover text elements associated with each sector.
If a single string, the same string appears for all
data points. If an array of string, the items are
mapped in order of this trace's sectors. To be seen,
trace `hoverinfo` must contain a "text" flag.
hovertextsrc
Sets the source reference on Chart Studio Cloud for
`hovertext`.
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
`ids`.
insidetextfont
Sets the font used for `textinfo` lying inside the
sector.
insidetextorientation
Controls the orientation of the text inside chart
sectors. When set to "auto", text may be oriented in
any direction in order to be as big as possible in the
middle of a sector. The "horizontal" option orients
text to be parallel with the bottom of the chart, and
may make text smaller in order to achieve that goal.
The "radial" option orients text along the radius of
the sector. The "tangential" option orients text
perpendicular to the radius of the sector.
labels
Sets the labels of each of the sectors.
labelssrc
Sets the source reference on Chart Studio Cloud for
`labels`.
leaf
:class:`plotly.graph_objects.sunburst.Leaf` instance or
dict with compatible properties
legend
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2",
"legend3", etc. Settings for these legends are set in
the layout, under `layout.legend`, `layout.legend2`,
etc.
legendgrouptitle
:class:`plotly.graph_objects.sunburst.Legendgrouptitle`
instance or dict with compatible properties
legendrank
Sets the legend rank for this trace. Items and groups
with smaller ranks are presented on top/left side while
with "reversed" `legend.traceorder` they are on
bottom/right side. The default legendrank is 1000, so
that you can use ranks less than 1000 to place certain
items before all unranked items, and ranks greater than
1000 to go after all unranked items. When having
unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and
layout.
legendwidth
Sets the width (in px or fraction) of the legend for
this trace.
level
Sets the level from which this trace hierarchy is
rendered. Set `level` to `''` to start from the root
node in the hierarchy. Must be an "id" if `ids` is
filled in, otherwise plotly attempts to find a matching
item in `labels`.
marker
:class:`plotly.graph_objects.sunburst.Marker` instance
or dict with compatible properties
maxdepth
Sets the number of rendered sectors from any given
`level`. Set `maxdepth` to "-1" to render all the
levels in the hierarchy.
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
`meta`.
name
Sets the trace name. The trace name appears as the
legend item and on hover.
opacity
Sets the opacity of the trace.
outsidetextfont
Sets the font used for `textinfo` lying outside the
sector. This option refers to the root of the hierarchy
presented at the center of a sunburst graph. Please
note that if a hierarchy has multiple root nodes, this
option won't have any effect and `insidetextfont` would
be used.
parents
Sets the parent sectors for each of the sectors. Empty
string items '' are understood to reference the root
node in the hierarchy. If `ids` is filled, `parents`
items are understood to be "ids" themselves. When `ids`
is not set, plotly attempts to find matching items in
`labels`, but beware they must be unique.
parentssrc
Sets the source reference on Chart Studio Cloud for
`parents`.
root
:class:`plotly.graph_objects.sunburst.Root` instance or
dict with compatible properties
rotation
Rotates the whole diagram counterclockwise by some
angle. By default the first slice starts at 3 o'clock.
sort
Determines whether or not the sectors are reordered
from largest to smallest.
stream
:class:`plotly.graph_objects.sunburst.Stream` instance
or dict with compatible properties
text
Sets text elements associated with each sector. If
trace `textinfo` contains a "text" flag, these elements
will be seen on the chart. If trace `hoverinfo`
contains a "text" flag and "hovertext" is not set,
these elements will be seen in the hover labels.
textfont
Sets the font used for `textinfo`.
textinfo
Determines which trace information appear on the graph.
textsrc
Sets the source reference on Chart Studio Cloud for
`text`.
texttemplate
Template string used for rendering the information text
that appear on points. Note that this will override
`textinfo`. Variables are inserted using %{variable},
for example "y: %{y}". Numbers are formatted using
d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. Every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Finally, the template string has access
to variables `currentPath`, `root`, `entry`,
`percentRoot`, `percentEntry`, `percentParent`, `label`
and `value`.
texttemplatesrc
Sets the source reference on Chart Studio Cloud for
`texttemplate`.
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
values
Sets the values associated with each of the sectors.
Use with `branchvalues` to determine how the values are
summed.
valuessrc
Sets the source reference on Chart Studio Cloud for
`values`.
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
"""
def __init__(
self,
arg=None,
branchvalues=None,
count=None,
customdata=None,
customdatasrc=None,
domain=None,
hoverinfo=None,
hoverinfosrc=None,
hoverlabel=None,
hovertemplate=None,
hovertemplatesrc=None,
hovertext=None,
hovertextsrc=None,
ids=None,
idssrc=None,
insidetextfont=None,
insidetextorientation=None,
labels=None,
labelssrc=None,
leaf=None,
legend=None,
legendgrouptitle=None,
legendrank=None,
legendwidth=None,
level=None,
marker=None,
maxdepth=None,
meta=None,
metasrc=None,
name=None,
opacity=None,
outsidetextfont=None,
parents=None,
parentssrc=None,
root=None,
rotation=None,
sort=None,
stream=None,
text=None,
textfont=None,
textinfo=None,
textsrc=None,
texttemplate=None,
texttemplatesrc=None,
uid=None,
uirevision=None,
values=None,
valuessrc=None,
visible=None,
**kwargs,
):
"""
Construct a new Sunburst object
Visualize hierarchal data spanning outward radially from root
to leaves. The sunburst sectors are determined by the entries
in "labels" or "ids" and in "parents".
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.Sunburst`
branchvalues
Determines how the items in `values` are summed. When
set to "total", items in `values` are taken to be value
of all its descendants. When set to "remainder", items
in `values` corresponding to the root and the branches
sectors are taken to be the extra part not part of the
sum of the values at their leaves.
count
Determines default for `values` when it is not
provided, by inferring a 1 for each of the "leaves"
and/or "branches", otherwise 0.
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
`customdata`.
domain
:class:`plotly.graph_objects.sunburst.Domain` instance
or dict with compatible properties
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
hoverlabel
:class:`plotly.graph_objects.sunburst.Hoverlabel`
instance or dict with compatible properties
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}" as well as %{xother}, {%_xother},
{%_xother_}, {%xother_}. When showing info for several
points, "xother" will be added to those with different
x positions from the first point. An underscore before
or after "(x|y)other" will add a space on that side,
only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for
example "Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. The variables available in
`hovertemplate` are the ones emitted as event data
described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Finally, the template string has access
to variables `currentPath`, `root`, `entry`,
`percentRoot`, `percentEntry` and `percentParent`.
Anything contained in tag `<extra>` is displayed in the
secondary box, for example
"<extra>{fullData.name}</extra>". To hide the secondary
box completely, use an empty tag `<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
hovertext
Sets hover text elements associated with each sector.
If a single string, the same string appears for all
data points. If an array of string, the items are
mapped in order of this trace's sectors. To be seen,
trace `hoverinfo` must contain a "text" flag.
hovertextsrc
Sets the source reference on Chart Studio Cloud for
`hovertext`.
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
`ids`.
insidetextfont
Sets the font used for `textinfo` lying inside the
sector.
insidetextorientation
Controls the orientation of the text inside chart
sectors. When set to "auto", text may be oriented in
any direction in order to be as big as possible in the
middle of a sector. The "horizontal" option orients
text to be parallel with the bottom of the chart, and
may make text smaller in order to achieve that goal.
The "radial" option orients text along the radius of
the sector. The "tangential" option orients text
perpendicular to the radius of the sector.
labels
Sets the labels of each of the sectors.
labelssrc
Sets the source reference on Chart Studio Cloud for
`labels`.
leaf
:class:`plotly.graph_objects.sunburst.Leaf` instance or
dict with compatible properties
legend
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2",
"legend3", etc. Settings for these legends are set in
the layout, under `layout.legend`, `layout.legend2`,
etc.
legendgrouptitle
:class:`plotly.graph_objects.sunburst.Legendgrouptitle`
instance or dict with compatible properties
legendrank
Sets the legend rank for this trace. Items and groups
with smaller ranks are presented on top/left side while
with "reversed" `legend.traceorder` they are on
bottom/right side. The default legendrank is 1000, so
that you can use ranks less than 1000 to place certain
items before all unranked items, and ranks greater than
1000 to go after all unranked items. When having
unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and
layout.
legendwidth
Sets the width (in px or fraction) of the legend for
this trace.
level
Sets the level from which this trace hierarchy is
rendered. Set `level` to `''` to start from the root
node in the hierarchy. Must be an "id" if `ids` is
filled in, otherwise plotly attempts to find a matching
item in `labels`.
marker
:class:`plotly.graph_objects.sunburst.Marker` instance
or dict with compatible properties
maxdepth
Sets the number of rendered sectors from any given
`level`. Set `maxdepth` to "-1" to render all the
levels in the hierarchy.
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
`meta`.
name
Sets the trace name. The trace name appears as the
legend item and on hover.
opacity
Sets the opacity of the trace.
outsidetextfont
Sets the font used for `textinfo` lying outside the
sector. This option refers to the root of the hierarchy
presented at the center of a sunburst graph. Please
note that if a hierarchy has multiple root nodes, this
option won't have any effect and `insidetextfont` would
be used.
parents
Sets the parent sectors for each of the sectors. Empty
string items '' are understood to reference the root
node in the hierarchy. If `ids` is filled, `parents`
items are understood to be "ids" themselves. When `ids`
is not set, plotly attempts to find matching items in
`labels`, but beware they must be unique.
parentssrc
Sets the source reference on Chart Studio Cloud for
`parents`.
root
:class:`plotly.graph_objects.sunburst.Root` instance or
dict with compatible properties
rotation
Rotates the whole diagram counterclockwise by some
angle. By default the first slice starts at 3 o'clock.
sort
Determines whether or not the sectors are reordered
from largest to smallest.
stream
:class:`plotly.graph_objects.sunburst.Stream` instance
or dict with compatible properties
text
Sets text elements associated with each sector. If
trace `textinfo` contains a "text" flag, these elements
will be seen on the chart. If trace `hoverinfo`
contains a "text" flag and "hovertext" is not set,
these elements will be seen in the hover labels.
textfont
Sets the font used for `textinfo`.
textinfo
Determines which trace information appear on the graph.
textsrc
Sets the source reference on Chart Studio Cloud for
`text`.
texttemplate
Template string used for rendering the information text
that appear on points. Note that this will override
`textinfo`. Variables are inserted using %{variable},
for example "y: %{y}". Numbers are formatted using
d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. Every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Finally, the template string has access
to variables `currentPath`, `root`, `entry`,
`percentRoot`, `percentEntry`, `percentParent`, `label`
and `value`.
texttemplatesrc
Sets the source reference on Chart Studio Cloud for
`texttemplate`.
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
values
Sets the values associated with each of the sectors.
Use with `branchvalues` to determine how the values are
summed.
valuessrc
Sets the source reference on Chart Studio Cloud for
`values`.
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
Returns
-------
Sunburst
"""
super(Sunburst, self).__init__("sunburst")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.Sunburst
constructor must be a dict or
an instance of :class:`plotly.graph_objs.Sunburst`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("branchvalues", None)
_v = branchvalues if branchvalues is not None else _v
if _v is not None:
self["branchvalues"] = _v
_v = arg.pop("count", None)
_v = count if count is not None else _v
if _v is not None:
self["count"] = _v
_v = arg.pop("customdata", None)
_v = customdata if customdata is not None else _v
if _v is not None:
self["customdata"] = _v
_v = arg.pop("customdatasrc", None)
_v = customdatasrc if customdatasrc is not None else _v
if _v is not None:
self["customdatasrc"] = _v
_v = arg.pop("domain", None)
_v = domain if domain is not None else _v
if _v is not None:
self["domain"] = _v
_v = arg.pop("hoverinfo", None)
_v = hoverinfo if hoverinfo is not None else _v
if _v is not None:
self["hoverinfo"] = _v
_v = arg.pop("hoverinfosrc", None)
_v = hoverinfosrc if hoverinfosrc is not None else _v
if _v is not None:
self["hoverinfosrc"] = _v
_v = arg.pop("hoverlabel", None)
_v = hoverlabel if hoverlabel is not None else _v
if _v is not None:
self["hoverlabel"] = _v
_v = arg.pop("hovertemplate", None)
_v = hovertemplate if hovertemplate is not None else _v
if _v is not None:
self["hovertemplate"] = _v
_v = arg.pop("hovertemplatesrc", None)
_v = hovertemplatesrc if hovertemplatesrc is not None else _v
if _v is not None:
self["hovertemplatesrc"] = _v
_v = arg.pop("hovertext", None)
_v = hovertext if hovertext is not None else _v
if _v is not None:
self["hovertext"] = _v
_v = arg.pop("hovertextsrc", None)
_v = hovertextsrc if hovertextsrc is not None else _v
if _v is not None:
self["hovertextsrc"] = _v
_v = arg.pop("ids", None)
_v = ids if ids is not None else _v
if _v is not None:
self["ids"] = _v
_v = arg.pop("idssrc", None)
_v = idssrc if idssrc is not None else _v
if _v is not None:
self["idssrc"] = _v
_v = arg.pop("insidetextfont", None)
_v = insidetextfont if insidetextfont is not None else _v
if _v is not None:
self["insidetextfont"] = _v
_v = arg.pop("insidetextorientation", None)
_v = insidetextorientation if insidetextorientation is not None else _v
if _v is not None:
self["insidetextorientation"] = _v
_v = arg.pop("labels", None)
_v = labels if labels is not None else _v
if _v is not None:
self["labels"] = _v
_v = arg.pop("labelssrc", None)
_v = labelssrc if labelssrc is not None else _v
if _v is not None:
self["labelssrc"] = _v
_v = arg.pop("leaf", None)
_v = leaf if leaf is not None else _v
if _v is not None:
self["leaf"] = _v
_v = arg.pop("legend", None)
_v = legend if legend is not None else _v
if _v is not None:
self["legend"] = _v
_v = arg.pop("legendgrouptitle", None)
_v = legendgrouptitle if legendgrouptitle is not None else _v
if _v is not None:
self["legendgrouptitle"] = _v
_v = arg.pop("legendrank", None)
_v = legendrank if legendrank is not None else _v
if _v is not None:
self["legendrank"] = _v
_v = arg.pop("legendwidth", None)
_v = legendwidth if legendwidth is not None else _v
if _v is not None:
self["legendwidth"] = _v
_v = arg.pop("level", None)
_v = level if level is not None else _v
if _v is not None:
self["level"] = _v
_v = arg.pop("marker", None)
_v = marker if marker is not None else _v
if _v is not None:
self["marker"] = _v
_v = arg.pop("maxdepth", None)
_v = maxdepth if maxdepth is not None else _v
if _v is not None:
self["maxdepth"] = _v
_v = arg.pop("meta", None)
_v = meta if meta is not None else _v
if _v is not None:
self["meta"] = _v
_v = arg.pop("metasrc", None)
_v = metasrc if metasrc is not None else _v
if _v is not None:
self["metasrc"] = _v
_v = arg.pop("name", None)
_v = name if name is not None else _v
if _v is not None:
self["name"] = _v
_v = arg.pop("opacity", None)
_v = opacity if opacity is not None else _v
if _v is not None:
self["opacity"] = _v
_v = arg.pop("outsidetextfont", None)
_v = outsidetextfont if outsidetextfont is not None else _v
if _v is not None:
self["outsidetextfont"] = _v
_v = arg.pop("parents", None)
_v = parents if parents is not None else _v
if _v is not None:
self["parents"] = _v
_v = arg.pop("parentssrc", None)
_v = parentssrc if parentssrc is not None else _v
if _v is not None:
self["parentssrc"] = _v
_v = arg.pop("root", None)
_v = root if root is not None else _v
if _v is not None:
self["root"] = _v
_v = arg.pop("rotation", None)
_v = rotation if rotation is not None else _v
if _v is not None:
self["rotation"] = _v
_v = arg.pop("sort", None)
_v = sort if sort is not None else _v
if _v is not None:
self["sort"] = _v
_v = arg.pop("stream", None)
_v = stream if stream is not None else _v
if _v is not None:
self["stream"] = _v
_v = arg.pop("text", None)
_v = text if text is not None else _v
if _v is not None:
self["text"] = _v
_v = arg.pop("textfont", None)
_v = textfont if textfont is not None else _v
if _v is not None:
self["textfont"] = _v
_v = arg.pop("textinfo", None)
_v = textinfo if textinfo is not None else _v
if _v is not None:
self["textinfo"] = _v
_v = arg.pop("textsrc", None)
_v = textsrc if textsrc is not None else _v
if _v is not None:
self["textsrc"] = _v
_v = arg.pop("texttemplate", None)
_v = texttemplate if texttemplate is not None else _v
if _v is not None:
self["texttemplate"] = _v
_v = arg.pop("texttemplatesrc", None)
_v = texttemplatesrc if texttemplatesrc is not None else _v
if _v is not None:
self["texttemplatesrc"] = _v
_v = arg.pop("uid", None)
_v = uid if uid is not None else _v
if _v is not None:
self["uid"] = _v
_v = arg.pop("uirevision", None)
_v = uirevision if uirevision is not None else _v
if _v is not None:
self["uirevision"] = _v
_v = arg.pop("values", None)
_v = values if values is not None else _v
if _v is not None:
self["values"] = _v
_v = arg.pop("valuessrc", None)
_v = valuessrc if valuessrc is not None else _v
if _v is not None:
self["valuessrc"] = _v
_v = arg.pop("visible", None)
_v = visible if visible is not None else _v
if _v is not None:
self["visible"] = _v
# Read-only literals
# ------------------
self._props["type"] = "sunburst"
arg.pop("type", None)
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@_sunburst.py@.PATH_END.py
|
{
"filename": "pipe_commands.py",
"repo_name": "LSSTDESC/rail_pipelines",
"repo_path": "rail_pipelines_extracted/rail_pipelines-main/src/rail/cli/rail_pipe/pipe_commands.py",
"type": "Python"
}
|
from typing import Any
import click
from rail.core import __version__
from ...utils.project import RailProject
from . import pipe_options, pipe_scripts
from .reduce_roman_rubin_data import reduce_roman_rubin_data
@click.group()
@click.version_option(__version__)
def pipe_cli() -> None:
"""RAIL pipeline scripts"""
@pipe_cli.command(name="inspect")
@pipe_options.config_file()
def inspect_command(config_file: str) -> int:
"""Inspect a rail pipeline project config"""
return pipe_scripts.inspect(config_file)
@pipe_cli.command(name="build")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.force()
def build_command(config_file: str, **kwargs: Any) -> int:
"""Build the ceci pipeline configuration files"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.build_pipelines(project, **kw, **kwargs)
return ok
@pipe_cli.command(name="subsample")
@pipe_options.config_file()
@pipe_options.selection()
@pipe_options.flavor()
@pipe_options.label()
@pipe_options.run_mode()
def subsample_command(config_file: str, **kwargs: Any) -> int:
"""Make a training or test data set by randomly selecting objects"""
"""Make a training data set by randomly selecting objects"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.subsample_data(project, **kw, **kwargs)
return ok
@pipe_cli.group(name="reduce")
def reduce_group() -> None:
"""Reduce input data for PZ analysis"""
@reduce_group.command(name="roman_rubin")
@pipe_options.config_file()
@pipe_options.input_tag()
@pipe_options.input_selection()
@pipe_options.selection()
@pipe_options.run_mode()
def reduce_roman_rubin(config_file: str, **kwargs: Any) -> int:
"""Reduce the roman rubin simulations for PZ analysis"""
project = RailProject.load_config(config_file)
selections = project.get_selection_args(kwargs.pop('selection'))
input_selections = kwargs.pop('input_selection')
iter_kwargs = project.generate_kwargs_iterable(selection=selections, input_selection=input_selections)
input_tag = kwargs.pop('input_tag', 'truth')
ok = 0
for kw in iter_kwargs:
ok |= reduce_roman_rubin_data(project, input_tag, **kw, **kwargs)
return ok
@pipe_cli.group(name="run")
def run_group() -> None:
"""Run a pipeline"""
@run_group.command(name="phot-errors")
@pipe_options.config_file()
@pipe_options.selection()
@pipe_options.flavor()
@pipe_options.run_mode()
def photmetric_errors_pipeline(config_file: str, **kwargs: Any) -> int:
"""Run the photometric errors analysis pipeline"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
pipeline_name = "photometric_errors"
pipeline_info = project.get_pipeline(pipeline_name)
input_catalog_name = pipeline_info['InputCatalogTag']
pipeline_catalog_config = pipe_scripts.PhotmetricErrorsPipelineCatalogConfiguration(
project, source_catalog_tag=input_catalog_name, sink_catalog_tag='degraded',
)
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_catalog(
project, pipeline_name,
pipeline_catalog_config,
**kw, **kwargs,
)
return ok
@run_group.command(name="truth-to-observed")
@pipe_options.config_file()
@pipe_options.selection()
@pipe_options.flavor()
@pipe_options.run_mode()
def truth_to_observed_pipeline(config_file: str, **kwargs: Any) -> int:
"""Run the truth-to-observed data pipeline"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
pipeline_name = "truth_to_observed"
pipeline_info = project.get_pipeline(pipeline_name)
input_catalog_name = pipeline_info['InputCatalogTag']
pipeline_catalog_config = pipe_scripts.SpectroscopicPipelineCatalogConfiguration(
project,
source_catalog_tag=input_catalog_name,
sink_catalog_tag='degraded',
source_catalog_basename="output_dereddener_errors.pq",
)
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_catalog(
project, pipeline_name,
pipeline_catalog_config,
**kw, **kwargs,
)
return ok
@run_group.command(name="blending")
@pipe_options.config_file()
@pipe_options.selection()
@pipe_options.flavor()
@pipe_options.run_mode()
def blending_pipeline(config_file: str, **kwargs: Any) -> int:
"""Run the blending analysis pipeline"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
pipeline_name = "blending"
pipeline_info = project.get_pipeline(pipeline_name)
input_catalog_name = pipeline_info['InputCatalogTag']
pipeline_catalog_config = pipe_scripts.BlendingPipelineCatalogConfiguration(
project,
source_catalog_tag=input_catalog_name,
sink_catalog_tag='degraded',
)
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_catalog(
project, pipeline_name,
pipeline_catalog_config,
**kw, **kwargs,
)
return ok
@run_group.command(name="spec-selection")
@pipe_options.config_file()
@pipe_options.selection()
@pipe_options.flavor()
@pipe_options.run_mode()
def spectroscopic_selection_pipeline(config_file: str, **kwargs: Any) -> int:
"""Run the spectroscopic selection data pipeline"""
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
pipeline_name = "spec_selection"
pipeline_info = project.get_pipeline(pipeline_name)
input_catalog_name = pipeline_info['InputCatalogTag']
pipeline_catalog_config = pipe_scripts.SpectroscopicPipelineCatalogConfiguration(
project,
source_catalog_tag=input_catalog_name,
sink_catalog_tag='degraded',
source_catalog_basename="output_dereddener_errors.pq",
)
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_catalog(
project, pipeline_name,
pipeline_catalog_config,
**kw, **kwargs,
)
return ok
@run_group.command(name="inform")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def inform_single(config_file: str, **kwargs: Any) -> int:
"""Run the inform pipeline"""
pipeline_name = "inform"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.inform_input_callback,
**kw, **kwargs,
)
return ok
@run_group.command(name="estimate")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def estimate_single(config_file: str, **kwargs: Any) -> int:
"""Run the estimation pipeline"""
pipeline_name = "estimate"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.estimate_input_callback,
**kw, **kwargs,
)
return ok
@run_group.command(name="evaluate")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def evaluate_single(config_file: str, **kwargs: Any) -> int:
"""Run the evaluation pipeline"""
pipeline_name = "evaluate"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.evaluate_input_callback,
**kw, **kwargs,
)
return ok
@run_group.command(name="pz")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def pz_single(config_file: str, **kwargs: Any) -> int:
"""Run the pz pipeline"""
pipeline_name = "pz"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.pz_input_callback,
**kw, **kwargs,
)
return ok
@run_group.command(name="tomography")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def tomography_single(config_file : str, **kwargs: Any) -> int:
"""Run the tomography pipeline"""
pipeline_name = "tomography"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.tomography_input_callback,
**kw, **kwargs,
)
return ok
@run_group.command(name="sompz")
@pipe_options.config_file()
@pipe_options.flavor()
@pipe_options.selection()
@pipe_options.run_mode()
def sompz_single(config_file: str, **kwargs: Any) -> int:
"""Run the sompz pipeline"""
pipeline_name = "sompz"
project = RailProject.load_config(config_file)
flavors = project.get_flavor_args(kwargs.pop('flavor'))
selections = project.get_selection_args(kwargs.pop('selection'))
iter_kwargs = project.generate_kwargs_iterable(flavor=flavors, selection=selections)
ok = 0
for kw in iter_kwargs:
ok |= pipe_scripts.run_pipeline_on_single_input(
project, pipeline_name,
pipe_scripts.sompz_input_callback,
**kw, **kwargs,
)
return ok
|
LSSTDESCREPO_NAMErail_pipelinesPATH_START.@rail_pipelines_extracted@rail_pipelines-main@src@rail@cli@rail_pipe@pipe_commands.py@.PATH_END.py
|
{
"filename": "all-objectives-and-metrics.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/docs/en/_includes/work_src/reusage/all-objectives-and-metrics.md",
"type": "Markdown"
}
|
- {{ error-function--RMSE }}
- {{ error-function--Logit }}
- {{ error-function--MAE }}
- {{ error-function--CrossEntropy }}
- {{ error-function--Quantile }}
- {{ error-function--LogLinQuantile }}
- {{ error-function__lq }}
- {{ error-function__MultiRMSE }}
- {{ error-function--MultiClass }}
- {{ error-function--MultiClassOneVsAll }}
- {{ error-function__MultiLogloss }}
- {{ error-function__MultiCrossEntropy }}
- {{ error-function--MAPE }}
- {{ error-function--Poisson }}
- {{ error-function__PairLogit }}
- {{ error-function__PairLogitPairwise }}
- {{ error-function__QueryRMSE }}
- {{ error-function__QuerySoftMax }}
- {{ error-function__GroupQuantile }}
- {{ error-function__Tweedie }}
- {{ error-function__SMAPE }}
- {{ error-function--Recall }}
- {{ error-function--Precision }}
- {{ error-function--F }}
- {{ error-function--F1 }}
- {{ error-function--totalF1 }}
- {{ error-function--Accuracy }}
- {{ error-function__BalancedAccuracy }}
- {{ error-function__BalancedErrorRate }}
- {{ error-function__Kappa }}
- {{ error-function__WKappa }}
- {{ error-function__LLP }}
- {{ error-function--AUC }}
- {{ error-function--QueryAUC }}
- {{ error-function--R2 }}
- {{ error-function__FairLoss }}
- {{ error-function__NumErrors }}
- {{ error-function--MCC }}
- {{ error-function__Brierscore }}
- {{ error-function__HingeLoss }}
- {{ error-function__HammingLoss }}
- {{ error-function__ZeroOneLoss }}
- {{ error-function__MSLE }}
- {{ error-function__MedianAbsoluteError }}
- {{ error-function__Cox }}
- {{ error-function__Huber }}
- {{ error-function__Expectile }}
- {{ error-function__MultiRMSE }}
- {{ error-function__PairAccuracy }}
- {{ error-function__QueryAverage }}
- {{ error-function__PFound }}
- {{ error-function__ndcg }}
- {{ error-function__dcg }}
- {{ error-function__FilteredDCG }}
- {{ error-function--NormalizedGini }}
- {{ error-function__PrecisionAtK }}
- {{ error-function__RecallAtK }}
- {{ error-function__mapk }}
- {{ error-function__CtrFactor }}
- {{ error-function__YetiRank }}
- {{ error-function__YetiRankPairwise }}
- {{ error-function__StochasticFilter }}
- {{ error-function__StochasticRank }}
- {{ error-function__LambdaMart }}
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@docs@en@_includes@work_src@reusage@all-objectives-and-metrics.md@.PATH_END.py
|
{
"filename": "_namelength.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/violin/hoverlabel/_namelength.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class NamelengthValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self, plotly_name="namelength", parent_name="violin.hoverlabel", **kwargs
):
super(NamelengthValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
min=kwargs.pop("min", -1),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@violin@hoverlabel@_namelength.py@.PATH_END.py
|
{
"filename": "eleanor.py",
"repo_name": "KeplerGO/lightkurve",
"repo_path": "lightkurve_extracted/lightkurve-main/src/lightkurve/io/eleanor.py",
"type": "Python"
}
|
"""Reader for GSFC-ELEANOR-LITE light curve files.
Details can be found at https://archive.stsci.edu/hlsp/eleanor and https://archive.stsci.edu/hlsp/gsfc-eleanor-lite
"""
from ..lightcurve import TessLightCurve
from ..utils import TessQualityFlags
from astropy import units as u
from .generic import read_generic_lightcurve
import numpy as np
def read_eleanor_lightcurve(filename,
flux_column="CORR_FLUX",
quality_bitmask="default"
):
"""Returns a `~lightkurve.lightcurve.LightCurve` object given a light curve file from
eleanor package or GSFC-ELEANOR-LITE Pipeline.
By default, eleanor's `CORR_FLUX` column is used to populate the `flux` values. Note that the "FLUX_ERR"
column in the Eleanor FITS file is referred to the uncertainty of "RAW_FLUX", not "CORR_FLUX". Thus the
uncertainty reported in the 'flux_err' column here is calculated as follows:
corr_flux_err = corr_flux*raw_flux_err/raw_flux.
For completeness, the original raw_flux's error is added as a "raw_flux_err" column.
In terms of quality flags, eleanor uses the TESS SPOC quality flags by identifying short-cadence targets that
fall on each camera-CCD pairing for a given sector. However, eleanor, also adds two new quality flags -- bit 17
(decimal value 131072)) and bit 18 (decimal value 262144).
More information on eleanor: https://github.com/afeinstein20/eleanor
More information on GSFC-ELEANOR-LITE Pipeline: https://archive.stsci.edu/hlsp/gsfc-eleanor-lite
Parameters
----------
filename : str
Local path or remote url of a GSFC-ELEANOR-LITE light curve FITS file.
flux_column : 'RAW_FLUX', 'CORR_FLUX', 'PCA_FLUX', or 'FLUX_BKG'
Which column in the FITS file contains the preferred flux data?
By default the "Corrected Flux" flux (CORR_FLUX) is used.
quality_bitmask : str or int
Bitmask (integer) which identifies the quality flag bitmask that should
be used to mask out bad cadences. If a string is passed, it has the
following meaning:
* "none": no cadences will be ignored (`quality_bitmask=0`).
* "default": cadences with flags indicating AttitudeTweak, SafeMode, CoarsePoint, EarthPoint, Desat, or
ManualExclude will be ignored.
* "hard": cadences with default flags, ApertureCosmic, CollateralCosmic, Straylight, or Straylight2 will be
ignored.
* "hardest": cadences with all the above flags will be ignored, in addition to cadences with GSFC-ELEANOR-LITE
bit flags of 17 (decimal value 131072) and 18 (decimal value 262144).
"""
lc = read_generic_lightcurve(
filename,
time_column="TIME".lower(),
flux_column=flux_column.lower(),
flux_err_column = "FLUX_ERR".lower(),
time_format="btjd",
quality_column= "QUALITY".lower(),
centroid_col_column = "X_CENTROID".lower(),
centroid_row_column = "Y_CENTROID".lower(),
cadenceno_column = "FFIINDEX".lower()
)
if quality_bitmask == "hardest":
# Eleanor has 2 additional bits on top of the 16 TESS SPOC bits
# they are excluded when hardest is specified.
quality_bitmask = TessQualityFlags.HARDEST_BITMASK | 2** 17 | 2**18
quality_mask = TessQualityFlags.create_quality_mask(
quality_array=lc["quality"], bitmask=quality_bitmask
)
lc = lc[quality_mask]
# Eleanor FITS file do not have units specified. re-add them.
for colname in ["flux", "flux_err", "raw_flux", "corr_flux", "pca_flux", "psf_flux"]:
if colname in lc.colnames:
if lc[colname].unit is not None:
# for case flux, flux_err, lightkurve has forced it to be u.dimensionless_unscaled
# can't reset a unit, so we create a new column
lc[colname] = u.Quantity(lc[colname].value, "electron/s")
else:
lc[colname].unit = "electron/s"
for colname in ["flux_bkg"]:
if colname in lc.colnames:
lc[colname].unit = u.percent
for colname in ["centroid_col", "centroid_row", "x_centroid", "y_centroid", "x_com", "y_com"]:
if colname in lc.colnames:
lc[colname].unit = u.pix
for colname in ["barycorr"]:
if colname in lc.colnames:
lc[colname].unit = u.day
# In Eleanor fits file, raw_flux's error is in flux_err, which breaks Lightkurve convention.
# To account for this, the corr_flux error is calculated from corr_flux_err = corr_flux*raw_flux_err/raw_flux. For completeness,
# the original raw_flux's error is added as a "raw_flux_err" column
lc["raw_flux_err"] = lc["flux_err"]
if flux_column.lower() != 'raw_flux':
lc["flux_err"] = lc[flux_column.lower()]*lc["raw_flux_err"]/lc["raw_flux"]
# vanilla eleanor has cadence saved as float,
# convert to int to ensure we stick with the convention
for colname in ["ffiindex", "cadenceno"]:
if colname in lc.colnames:
if not np.issubdtype(lc[colname].dtype, np.int_):
lc[colname] = np.asarray(lc[colname].value, dtype=int)
if (
lc.meta.get("TVERSION") is not None
and lc.meta.get("GITHUB") == "https://github.com/afeinstein20/eleanor"
):
# the above headers are GSFC-ELEANOR-LITE-specific, and are not present in vanilla eleanor
# cf. https://github.com/afeinstein20/eleanor/blob/main/eleanor/targetdata.py
lc.meta["AUTHOR"] = "GSFC-ELEANOR-LITE"
else:
lc.meta["AUTHOR"] = "ELEANOR"
# Eleanor light curves are not normalized by default
lc.meta["NORMALIZED"] = False
tic = lc.meta.get("TIC_ID")
if tic is not None:
# compatibility with SPOC, QLP, etc.
lc.meta["TARGETID"] = tic
lc.meta["TICID"] = tic
lc.meta["OBJECT"] = f"TIC {tic}"
# for Lightkurve's plotting methods
lc.meta["LABEL"] = f"TIC {tic}"
return TessLightCurve(data=lc)
|
KeplerGOREPO_NAMElightkurvePATH_START.@lightkurve_extracted@lightkurve-main@src@lightkurve@io@eleanor.py@.PATH_END.py
|
{
"filename": "_valign.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/legend/_valign.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ValignValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="valign", parent_name="layout.legend", **kwargs):
super(ValignValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "legend"),
values=kwargs.pop("values", ["top", "middle", "bottom"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@legend@_valign.py@.PATH_END.py
|
{
"filename": "radial_integrals.py",
"repo_name": "temken/DarkARC",
"repo_path": "DarkARC_extracted/DarkARC-master/src/radial_integrals.py",
"type": "Python"
}
|
import sys
import time
import scipy.integrate as integrate
import scipy.special as special
import hankel
import mpmath as mp
import math
from hankel import HankelTransform
from wave_functions import *
def main():
start_tot = time.time()
# Test individual integrals with different methods
element = Ar
n = 3
l = 1
integral = 2
lPrime = 2
L = 1
k = 5*keV
q = 10*keV
for method in ["Hankel","analytic","tanh-sinh-stepwise","numpy-stepwise","quadosc"]:
print(method)
start = time.time()
int1 = radial_integral(integral,element,n,l,k,lPrime,L,q,method)
end = time.time()
print(int1,"\t(", end-start,"s)\n")
####################################################################################
end_tot = time.time()
print("\nProcessing time:\t", end_tot - start_tot, "s\n")
# Radial integral wrapper function
def radial_integral(integral, element, n, l, kPrime, lPrime, L, q,method):
if method == "quadosc":
return radial_integral_quadosc(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "Hankel":
return radial_integral_hankel(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "analytic":
return radial_integral_analytic(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "tanh-sinh":
return radial_integral_tanhsinh(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "tanh-sinh-stepwise":
return radial_integral_tanhsinh_stepwise(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "numpy":
return radial_integral_numpy(integral,element, n, l, kPrime, lPrime, L, q)
elif method == "numpy-stepwise":
return radial_integral_numpy_stepwise(integral,element, n, l, kPrime, lPrime, L, q)
else:
sys.exit("Error in radial_integral: Method not recognized.")
# Various integration methods
def radial_integral_quadosc(integral, element, n, l, kPrime, lPrime, L, q):
if integral == 1:
integrand = lambda r : r*r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 2:
integrand = lambda r : r*r*element.dRdr(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 3:
integrand = lambda r : r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
else:
sys.exit("Error in radial_integral_quadosc(): Invalid integral.")
frequency = max(kPrime/2/mp.pi , q/2/mp.pi , keV/2/mp.pi)
return mp.quadosc(integrand,[0,mp.inf],omega = frequency)
def radial_integral_analytic(integral,element, n, l, kPrime, lPrime, L, q):
S=0
SMAX = 200
result = 0
tol = 1e-20
eps_1=1;
eps_2=1;
if integral == 1 or integral == 3:
a = lPrime + 1 + 1j* element.Z_effective(n,l) / (kPrime*a0);
b = 2 * lPrime + 2;
while( (eps_1 > tol or eps_2 > tol) and S <= SMAX ):
eps_2 = eps_1
As = 0
for j in range(len(element.C_nlj[n-1][l])):
alpha = lPrime + element.n_lj[l][j] + S;
if integral == 1:
alpha += 1
beta = element.Z_lj[l][j]/a0 + 1j * kPrime
As += 4 * mp.pi * mp.power(2*kPrime,lPrime) * element.C_nlj[n-1][l][j] * mp.power(2*element.Z_lj[l][j],element.n_lj[l][j]+0.5) / mp.power(a0,element.n_lj[l][j]+0.5) * (mp.sqrt(mp.pi)/mp.power(2,L+1) * mp.power(q,L) * mp.hyp2f1(0.5*(L+alpha+1),0.5*(L+alpha+2),L+1.5,-mp.power(q/beta,2))) * mp.exp(S*mp.log(2j*kPrime) - (alpha+L+1)*mp.log(beta)+ mp.loggamma(lPrime+1-1j*element.Z_effective(n,l)/kPrime/a0).real + mp.loggamma(S+a)+mp.loggamma(b)-mp.loggamma(2*lPrime+2)-mp.loggamma(S+b)-mp.loggamma(a)-mp.loggamma(S+1)+mp.pi*element.Z_effective(n,l)/2/kPrime/a0-0.5*mp.loggamma(2*element.n_lj[l][j]+1)+mp.loggamma(L+alpha+1)-mp.loggamma(L+1.5))
result += As
eps_1 = abs(As) / abs(result)
S += 1
elif integral == 2:
result = 0
else:
sys.exit("Error in radial_integral_analytic(): Invalid integral.")
if S > SMAX:
return False
else:
return result.real
def radial_integral_hankel(integral,element, n, l, kPrime, lPrime, L, q):
ht = HankelTransform(
nu= L+1/2 , # The order of the bessel function
N = 500, # Number of steps in the integration
h = 0.001 # Proxy for "size" of steps in integration
)
if integral == 1:
f = lambda r: np.sqrt(np.pi*r/2/q) * element.R_alternative(n,l,r) * R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l))
elif integral ==2:
f = lambda r: np.sqrt(np.pi*r/2/q) * element.dRdr_alternative(n,l,r) * R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l))
elif integral == 3:
f = lambda r: np.sqrt(np.pi/2/q/r) * element.R_alternative(n,l,r) * R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l))
else:
sys.exit("Error in radial_integral_hankel(): Invalid integral.")
return ht.transform(f,q,ret_err=False).real
def radial_integral_tanhsinh(integral,element, n, l, kPrime, lPrime, L, q):
if integral == 1:
integrand = lambda r : r*r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 2:
integrand = lambda r : r*r*element.dRdr(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 3:
integrand = lambda r : r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
else:
sys.exit("Error in radial_integral_tanhsinh(): Invalid integral.")
return mp.quad(integrand, [0, 100*a0],method='tanh-sinh')
def radial_integral_tanhsinh_stepwise(integral,element, n, l, kPrime, lPrime, L, q):
if integral == 1:
integrand = lambda r : r*r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 2:
integrand = lambda r : r*r*element.dRdr(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
elif integral == 3:
integrand = lambda r : r*element.R(n,l,r)*R_final_kl(r,kPrime,lPrime,element.Z_effective(n,l)) * mp.sqrt(mp.pi / 2 / q / r) * mp.besselj(L+1/2,q*r)
else:
sys.exit("Error in radial_integral_tanhsinh_stepwise(): Invalid integral.")
da0 = 1
integral = 0
eps_1 = 1
eps_2 = 1
tol = 1e-6
i=0
while eps_1 > tol or eps_2 > tol:
eps_2 = eps_1
dintegral = mp.quad(integrand, [i*a0,(i+da0)*a0],method='tanh-sinh')
integral += dintegral
eps_1 = abs(dintegral) / abs(integral)
i+=da0
return integral
def radial_integral_numpy(integral,element, n, l, kPrime, lPrime, L, q):
if integral == 1:
integrand = lambda r : r*r*element.R_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
elif integral == 2:
integrand = lambda r : r*r*element.dRdr_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
elif integral == 3:
integrand = lambda r : r*element.R_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
else:
sys.exit("Error in radial_integral_numpy(): Invalid integral.")
return integrate.quad(integrand,0,100*a0)[0]
def radial_integral_numpy_stepwise(integral,element, n, l, kPrime, lPrime, L, q):
if integral == 1:
integrand = lambda r : r*r*element.R_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
elif integral == 2:
integrand = lambda r : r*r*element.dRdr_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
elif integral == 3:
integrand = lambda r : r*element.R_alternative(n,l,r)*R_final_kl_alternative(r,kPrime,lPrime,element.Z_effective(n,l)) * special.spherical_jn(L,q*r)
else:
sys.exit("Error in radial_integral_numpy_stepwise(): Invalid integral.")
da0 = 1
integral = 0
eps_1 = 1
eps_2 = 1
tol = 1e-6
i=0
while eps_1 > tol or eps_2 > tol:
eps_2 = eps_1
dintegral = integrate.quad(integrand, i*a0,(i+da0)*a0,epsrel = 1e-3)[0]
integral += dintegral
eps_1 = abs(dintegral) / abs(integral)
i+=da0
return integral
if __name__ == "__main__":
main()
|
temkenREPO_NAMEDarkARCPATH_START.@DarkARC_extracted@DarkARC-master@src@radial_integrals.py@.PATH_END.py
|
{
"filename": "sliderdemo.py",
"repo_name": "mhammond/pywin32",
"repo_path": "pywin32_extracted/pywin32-main/Pythonwin/pywin/Demos/sliderdemo.py",
"type": "Python"
}
|
# sliderdemo.py
# Demo of the slider control courtesy of Mike Fletcher.
import win32con
import win32ui
from pywin.mfc import dialog
class MyDialog(dialog.Dialog):
"""
Example using simple controls
"""
_dialogstyle = (
win32con.WS_MINIMIZEBOX
| win32con.WS_DLGFRAME
| win32con.DS_MODALFRAME
| win32con.WS_POPUP
| win32con.WS_VISIBLE
| win32con.WS_CAPTION
| win32con.WS_SYSMENU
| win32con.DS_SETFONT
)
_buttonstyle = (
win32con.BS_PUSHBUTTON
| win32con.WS_TABSTOP
| win32con.WS_CHILD
| win32con.WS_VISIBLE
)
### The static template, contains all "normal" dialog items
DIALOGTEMPLATE = [
# the dialog itself is the first element in the template
["Example slider", (0, 0, 50, 43), _dialogstyle, None, (8, "MS SansSerif")],
# rest of elements are the controls within the dialog
# standard "Close" button
[128, "Close", win32con.IDCANCEL, (0, 30, 50, 13), _buttonstyle],
]
### ID of the control to be created during dialog initialisation
IDC_SLIDER = 9500
def __init__(self):
dialog.Dialog.__init__(self, self.DIALOGTEMPLATE)
def OnInitDialog(self):
rc = dialog.Dialog.OnInitDialog(self)
# now initialise your controls that you want to create
# programmatically, including those which are OLE controls
# those created directly by win32ui.Create*
# and your "custom controls" which are subclasses/whatever
win32ui.EnableControlContainer()
self.slider = win32ui.CreateSliderCtrl()
self.slider.CreateWindow(
win32con.WS_TABSTOP | win32con.WS_VISIBLE,
(0, 0, 100, 30),
self._obj_,
self.IDC_SLIDER,
)
self.HookMessage(self.OnSliderMove, win32con.WM_HSCROLL)
return rc
def OnSliderMove(self, params):
print("Slider moved")
def OnCancel(self):
print("The slider control is at position", self.slider.GetPos())
self._obj_.OnCancel()
###
def demo():
dia = MyDialog()
dia.DoModal()
if __name__ == "__main__":
demo()
|
mhammondREPO_NAMEpywin32PATH_START.@pywin32_extracted@pywin32-main@Pythonwin@pywin@Demos@sliderdemo.py@.PATH_END.py
|
{
"filename": "tfsa-2021-177.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/security/advisory/tfsa-2021-177.md",
"type": "Markdown"
}
|
## TFSA-2021-177: Use after free / memory leak in `CollectiveReduceV2`
### CVE Number
CVE-2021-41220
### Impact
The [async implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/collective_ops.cc#L604-L615) of `CollectiveReduceV2` suffers from a memory leak and a use after free:
```python
import tensorflow as tf
tf.raw_ops.CollectiveReduceV2(
input=[],
group_size=[-10, -10, -10],
group_key=[-10, -10],
instance_key=[-10],
ordering_token=[],
merge_op='Mul',
final_op='Div')
```
This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed:
```cc
auto done_with_cleanup = [col_params, done = std::move(done)]() {
done();
col_params->Unref();
};
OP_REQUIRES_OK_ASYNC(c,
FillCollectiveParams(col_params, REDUCTION_COLLECTIVE,
/*group_size*/ c->input(1),
/*group_key*/ c->input(2),
/*instance_key*/ c->input(3)),
done);
```
Here, `done` is already moved from by the time `OP_REQUIRES_OK_ASYNC` macro needs to invoke it in case of errors. In this case, we get an undefined behavior, which can manifest via crashes, `std::bad_alloc` throws or just memory leaks.
### Patches
We have patched the issue in GitHub commit [ca38dab9d3ee66c5de06f11af9a4b1200da5ef75](https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@security@advisory@tfsa-2021-177.md@.PATH_END.py
|
{
"filename": "_weight.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/choroplethmap/colorbar/tickfont/_weight.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WeightValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self,
plotly_name="weight",
parent_name="choroplethmap.colorbar.tickfont",
**kwargs,
):
super(WeightValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
extras=kwargs.pop("extras", ["normal", "bold"]),
max=kwargs.pop("max", 1000),
min=kwargs.pop("min", 1),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@choroplethmap@colorbar@tickfont@_weight.py@.PATH_END.py
|
{
"filename": "test_chemistry_struct_synched.py",
"repo_name": "grackle-project/grackle",
"repo_path": "grackle_extracted/grackle-main/src/python/tests/test_chemistry_struct_synched.py",
"type": "Python"
}
|
########################################################################
#
# Explicitly test that the API for dynamically accessing fields of
# chemistry_data is synchronized with the members of chemistry_data
# (this is meant to identify the scenario where a new member gets added to
# chemistry_data but the dynamic API is not synchronized)
#
#
# Copyright (c) 2013, Enzo/Grackle Development Team.
#
# Distributed under the terms of the Enzo Public Licence.
#
# The full license is in the file LICENSE, distributed with this
# software.
########################################################################
import os
import os.path
import shutil
import subprocess
import tempfile
import warnings
import xml.etree.ElementTree # may not be the optimal xml parser
import pytest
from pygrackle.grackle_wrapper import _wrapped_c_chemistry_data
_CASTXML_INSTALLED = shutil.which('castxml') is not None
def _find_element_by_ids(id_str, root, expect_single = False):
# id_str provides a list of one or more space separated ids
id_set = set(id_str.split(' '))
assert len(id_set) or (len(id_set) != 0 and not expect_single)
# implicit assumption: every element has a unique id
out = [e for e in root if e.attrib['id'] in id_set]
return out
def _field_type_props(elem, root):
assert elem.tag == 'Field'
ptr_level = 0
while True:
elem = _find_element_by_ids(elem.attrib['type'], root, True)[0]
if elem.tag == 'PointerType':
ptr_level += 1
elif elem.tag == 'CvQualifiedType':
pass # just ignore this
else:
return elem.attrib['name'] + (ptr_level * "*")
def _unwrap_struct_from_typedef(elem, root):
# this is called when elem corresponds to a type declared with typedef
# -> this function returns the xml-object representing the underlying struct
# that is aliased by the typedef
_tmp = _find_element_by_ids(elem.attrib['type'], root,
expect_single = True)[0]
if _tmp.tag == 'ElaboratedType':
struct_elem = _find_element_by_ids(_tmp.attrib['type'], root,
expect_single = True)[0]
else:
struct_elem = _tmp
assert struct_elem.tag == 'Struct' # <-- sanity check!
return struct_elem
def query_struct_fields(struct_name, path):
"""
Query the members of a struct using the castxml commandline tool.
castxml is used to parse a file c++ (specified by path) and spit out an xml
file that summarizes all of the contained information. Since it's
technically designed for C++ there will be some extra info that we don't
care about
Note
----
pygccxml is a python module that can be used to simplify this function.
However, it doesn't look like that module has been updated recently. To try
to mitigate future potential module compatibility issues, we currently
choose not to use it.
"""
assert _CASTXML_INSTALLED
# Step 1: get temp file name (it's probably ok to we use old insecure API)
xml_fname = tempfile.mktemp(suffix='.xml')
# Step 2: build up the command
command = [
'castxml',
# tell castxml's compiler to preprocess/compile but don't link
'-c',
# tell castxml's compiler that the language is c++
'-x', 'c++',
# the next required option configure castxml's internal Clang compiler.
# The second part needs to specify an installed compiler (we may need
# to support configuration of that path)
'--castxml-cc-gnu', 'g++',
# specify the output xml format version
'--castxml-output=1',
# specify the output location
'-o', xml_fname,
# specify the src file to process
path
]
# Step 3: run the command
subprocess.run(command, check=True)
if not os.path.isfile(xml_fname):
raise RuntimeError(
f"something went wrong while executing {' '.join(command)}"
)
# Step 4: parse the output and then delete the temporary file
tree = xml.etree.ElementTree.parse(xml_fname)
os.remove(xml_fname)
# in version 1.1.0 of gccxml format, there is a root node whose tag is
# CastXML & lists the version attribute
root = tree.getroot()
assert root.tag == 'CastXML'
if root.attrib['format'] not in ['1.1.0', '1.1.5']:
warnings.warn(
"Code was only tested against CastXML format versions 1.1.0 & "
"1.1.5. The file produced by CastXML uses version "
f"{root.attrib['format']}"
)
# under the root node, the children are listed in a flat structure can be
# - typedef declarations, struct/class declarations, namespace declarations,
# Field declarations, class member declarations, global variable
# declarations
# - it also has entries for describing other types
# Step 5: now extract the necessary information
# Step 5a: find the element with name attribute that matches struct_name
matches = (
tree.getroot().findall(".//Struct[@name='" + struct_name + "']") +
tree.getroot().findall(".//Typedef[@name='" + struct_name + "']")
)
if len(matches) == 0:
raise RuntimeError(f"no struct name '{struct_name}' was found")
elif len(matches) > 2:
raise RuntimeError(f"found more than 2 matches for '{struct_name}'")
elif (len(matches) == 1) and (matches[0].tag == 'Struct'):
# in this case, we defined a struct named {struct_name} and didn't
# use typedef to declare any type aliases of the struct
# -> in other words, we can only refer to the type in our C code as
# `struct {struct_name}`
struct_elem = matches[0]
elif (len(matches) == 1) and (matches[0].tag == 'Typedef'):
# in this case, we used typedef to define a type called {struct_name}
# that aliases an "anonymous struct"
# -> in other words, we can only refer to the type in our C code as
# `{struct_name}`
struct_elem = _unwrap_struct_from_typedef(matches[0], root)
elif ((len(matches) == 2) and (matches[0].tag == 'Struct') and
(matches[1].tag == 'Typedef')):
# in this case we probably defined a struct called {struct_name} and
# used typedef to declare an alias type called {struct_name}
# -> in other words, the types `struct {struct_name}` and
# `{struct_name}` refer to the same type
struct_elem = matches[0]
# perform a sanity check to confirm that typedef indeed aliases the
# corresponding struct
# -> note: I'm not entirely sure this is always guaranteed to work
assert struct_elem == _unwrap_struct_from_typedef(matches[1], root)
else:
raise RuntimeError("SOMETHING WENT WRONG")
# Step 5b, find the child elements of the struct
out = []
for member in _find_element_by_ids(struct_elem.attrib['members'], root,
expect_single = False):
if member.tag == 'Field': # ignore auto-generated class methods
out.append(
(_field_type_props(member, root), member.attrib['name'])
)
return out
@pytest.mark.skipif(not _CASTXML_INSTALLED,
reason = 'requires the castxml program')
def test_grackle_chemistry_field_synched():
# use the castxml to construct a list of the members of the chemistry_data
# struct (by directly parsing the header file)
member_list = query_struct_fields(
struct_name = "chemistry_data",
path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../../include/grackle_chemistry_data.h")
)
# now, categorize the fields by their datatype
field_sets = {'char*' : set(), 'int' : set(), 'double' : set()}
for dtype, name in member_list:
field_sets[dtype].add(name)
# finally lets compare
for param_type, ref_set in [('int', field_sets['int']),
('double', field_sets['double']),
('string', field_sets['char*'])]:
if param_type == 'int':
parameters = _wrapped_c_chemistry_data.int_keys()
elif param_type == 'double':
parameters = _wrapped_c_chemistry_data.double_keys()
elif param_type == 'string':
parameters = _wrapped_c_chemistry_data.string_keys()
else:
raise RuntimeError(f"unrecognized parameter type: {param_type}")
diff = ref_set.symmetric_difference(parameters)
for parameter in diff:
if parameter in ref_set:
raise RuntimeError(
f"{parameter}, a {param_type} field of the chemistry_data "
"struct is not accessible from the dynamic api"
)
else:
raise RuntimeError(
f"the dynamic api provides access to '{parameter}', a "
f"{param_type} parameter. But it's not a member of the "
"chemistry_data struct."
)
|
grackle-projectREPO_NAMEgracklePATH_START.@grackle_extracted@grackle-main@src@python@tests@test_chemistry_struct_synched.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/choroplethmapbox/colorbar/tickfont/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="textcase",
parent_name="choroplethmapbox.colorbar.tickfont",
**kwargs,
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@choroplethmapbox@colorbar@tickfont@_textcase.py@.PATH_END.py
|
{
"filename": "_style.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/polar/radialaxis/title/font/_style.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class StyleValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="style",
parent_name="layout.polar.radialaxis.title.font",
**kwargs,
):
super(StyleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "ticks"),
values=kwargs.pop("values", ["normal", "italic"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@polar@radialaxis@title@font@_style.py@.PATH_END.py
|
{
"filename": "simple_contour_in_slice.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/doc/source/cookbook/simple_contour_in_slice.py",
"type": "Python"
}
|
import yt
# Load the data file.
ds = yt.load("Sedov_3d/sedov_hdf5_chk_0002")
# Make a traditional slice plot.
sp = yt.SlicePlot(ds, "x", ("gas", "density"))
# Overlay the slice plot with thick red contours of density.
sp.annotate_contour(
("gas", "density"),
levels=3,
clim=(1e-2, 1e-1),
label=True,
plot_args={"colors": "red", "linewidths": 2},
)
# What about some nice temperature contours in blue?
sp.annotate_contour(
("gas", "temperature"),
levels=3,
clim=(1e-8, 1e-6),
label=True,
plot_args={"colors": "blue", "linewidths": 2},
)
# This is the plot object.
po = sp.plots[("gas", "density")]
# Turn off the colormap image, leaving just the contours.
po.axes.images[0].set_visible(False)
# Remove the colorbar and its label.
po.figure.delaxes(po.figure.axes[1])
# Save it and ask for a close fit to get rid of the space used by the colorbar.
sp.save(mpl_kwargs={"bbox_inches": "tight"})
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@doc@source@cookbook@simple_contour_in_slice.py@.PATH_END.py
|
{
"filename": "triangulation.md",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/doc/unconverted/python/triangulation.md",
"type": "Markdown"
}
|
---
jupyter:
jupytext:
notebook_metadata_filter: all
text_representation:
extension: .md
format_name: markdown
format_version: '1.1'
jupytext_version: 1.1.1
kernelspec:
display_name: Python 2
language: python
name: python2
plotly:
permalink: python/triangulation/
redirect_to: ../surface-triangulation/
sitemap: false
---
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
<br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
#### Defining and plotting triangulated surfaces
#### with Plotly `Mesh3d`
A triangulation of a compact surface is a finite collection of triangles that cover the surface in such a way that every point on the surface is in a triangle, and the intersection of any two triangles is either void, a common edge or a common vertex. A triangulated surface is called tri-surface.
The triangulation of a surface defined as the graph of a continuous function, $z=f(x,y), (x,y)\in D\subset\mathbb{R}^2$ or in a parametric form:
$$x=x(u,v), y=y(u,v), z=z(u,v), (u,v)\in U\subset\mathbb{R}^2,$$
is the image through $f$,respectively through the parameterization, of the Delaunay triangulation or an user defined triangulation of the planar domain $D$, respectively $U$.
The Delaunay triangulation of a planar region is defined and illustrated in a Python Plotly tutorial posted [here](https://plot.ly/python/alpha-shapes/).
If the planar region $D$ ($U$) is rectangular, then one defines a meshgrid on it, and the points
of the grid are the input points for the `scipy.spatial.Delaunay` function that defines the planar triangulation of $D$, respectively $U$.
### Triangulation of the Moebius band ###
The Moebius band is parameterized by:
$$\begin{align*}
x(u,v)&=(1+0.5 v\cos(u/2))\cos(u)\\
y(u,v)&=(1+0.5 v\cos(u/2))\sin(u)\quad\quad u\in[0,2\pi],\: v\in[-1,1]\\
z(u,v)&=0.5 v\sin(u/2)
\end{align*}
$$
Define a meshgrid on the rectangle $U=[0,2\pi]\times[-1,1]$:
```python
import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
import matplotlib.cm as cm
from scipy.spatial import Delaunay
u=np.linspace(0,2*np.pi, 24)
v=np.linspace(-1,1, 8)
u,v=np.meshgrid(u,v)
u=u.flatten()
v=v.flatten()
#evaluate the parameterization at the flattened u and v
tp=1+0.5*v*np.cos(u/2.)
x=tp*np.cos(u)
y=tp*np.sin(u)
z=0.5*v*np.sin(u/2.)
#define 2D points, as input data for the Delaunay triangulation of U
points2D=np.vstack([u,v]).T
tri = Delaunay(points2D)#triangulate the rectangle U
```
`tri.simplices` is a `np.array` of integers, of shape (`ntri`,3), where `ntri` is the number of triangles generated by `scipy.spatial.Delaunay`.
Each row in this array contains three indices, i, j, k, such that points2D[i,:], points2D[j,:], points2D[k,:] are vertices of a triangle in the Delaunay triangularization of the rectangle $U$.
```python
print tri.simplices.shape, '\n', tri.simplices[0]
```
The images of the `points2D` through the surface parameterization are 3D points. The same simplices define the triangles on the surface.
Setting a combination of keys in `Mesh3d` leads to generating and plotting of a tri-surface, in the same way as `plot_trisurf` in matplotlib or `trisurf` in Matlab does.
We note that `Mesh3d` with different combination of keys can generate [alpha-shapes](https://plot.ly/python/alpha-shapes/).
In order to plot a tri-surface, we choose a colormap, and associate to each triangle on the surface, the color in colormap, corresponding to the normalized mean value of z-coordinates of the triangle vertices.
Define a function that maps a mean z-value to a matplotlib color, converted to a Plotly color:
```python
def map_z2color(zval, colormap, vmin, vmax):
#map the normalized value zval to a corresponding color in the colormap
if vmin>vmax:
raise ValueError('incorrect relation between vmin and vmax')
t=(zval-vmin)/float((vmax-vmin))#normalize val
R, G, B, alpha=colormap(t)
return 'rgb('+'{:d}'.format(int(R*255+0.5))+','+'{:d}'.format(int(G*255+0.5))+\
','+'{:d}'.format(int(B*255+0.5))+')'
```
To plot the triangles on a surface, we set in Plotly `Mesh3d` the lists of x, y, respectively z- coordinates of the vertices, and the lists of indices, i, j, k, for x, y, z coordinates of all vertices:
```python
def tri_indices(simplices):
#simplices is a numpy array defining the simplices of the triangularization
#returns the lists of indices i, j, k
return ([triplet[c] for triplet in simplices] for c in range(3))
def plotly_trisurf(x, y, z, simplices, colormap=cm.RdBu, plot_edges=None):
#x, y, z are lists of coordinates of the triangle vertices
#simplices are the simplices that define the triangularization;
#simplices is a numpy array of shape (no_triangles, 3)
#insert here the type check for input data
points3D=np.vstack((x,y,z)).T
tri_vertices=map(lambda index: points3D[index], simplices)# vertices of the surface triangles
zmean=[np.mean(tri[:,2]) for tri in tri_vertices ]# mean values of z-coordinates of
#triangle vertices
min_zmean=np.min(zmean)
max_zmean=np.max(zmean)
facecolor=[map_z2color(zz, colormap, min_zmean, max_zmean) for zz in zmean]
I,J,K=tri_indices(simplices)
triangles=go.Mesh3d(x=x,
y=y,
z=z,
facecolor=facecolor,
i=I,
j=J,
k=K,
name=''
)
if plot_edges is None:# the triangle sides are not plotted
return [triangles]
else:
#define the lists Xe, Ye, Ze, of x, y, resp z coordinates of edge end points for each triangle
#None separates data corresponding to two consecutive triangles
lists_coord=[[[T[k%3][c] for k in range(4)]+[ None] for T in tri_vertices] for c in range(3)]
Xe, Ye, Ze=[reduce(lambda x,y: x+y, lists_coord[k]) for k in range(3)]
#define the lines to be plotted
lines=go.Scatter3d(x=Xe,
y=Ye,
z=Ze,
mode='lines',
line=dict(color= 'rgb(50,50,50)', width=1.5)
)
return [triangles, lines]
```
Call this function for data associated to Moebius band:
```python
data1=plotly_trisurf(x,y,z, tri.simplices, colormap=cm.RdBu, plot_edges=True)
```
Set the layout of the plot:
```python
axis = dict(
showbackground=True,
backgroundcolor="rgb(230, 230,230)",
gridcolor="rgb(255, 255, 255)",
zerolinecolor="rgb(255, 255, 255)",
)
layout = go.Layout(
title='Moebius band triangulation',
width=800,
height=800,
scene=dict(
xaxis=dict(axis),
yaxis=dict(axis),
zaxis=dict(axis),
aspectratio=dict(
x=1,
y=1,
z=0.5
),
)
)
fig1 = go.Figure(data=data1, layout=layout)
py.iplot(fig1, filename='Moebius-band-trisurf')
```
### Triangularization of the surface $z=\sin(-xy)$, defined over a disk ###
We consider polar coordinates on the disk, $D(0, 1)$, centered at origin and of radius 1, and define
a meshgrid on the set of points $(r, \theta)$, with $r\in[0,1]$ and $\theta\in[0,2\pi]$:
```python
n=12 # number of radii
h=1.0/(n-1)
r = np.linspace(h, 1.0, n)
theta= np.linspace(0, 2*np.pi, 36)
r,theta=np.meshgrid(r,theta)
r=r.flatten()
theta=theta.flatten()
#Convert polar coordinates to cartesian coordinates (x,y)
x=r*np.cos(theta)
y=r*np.sin(theta)
x=np.append(x, 0)# a trick to include the center of the disk in the set of points. It was avoided
# initially when we defined r=np.linspace(h, 1.0, n)
y=np.append(y,0)
z = np.sin(-x*y)
points2D=np.vstack([x,y]).T
tri=Delaunay(points2D)
```
Plot the surface with a modified layout:
```python
data2=plotly_trisurf(x,y,z, tri.simplices, colormap=cm.cubehelix, plot_edges=None)
fig2 = go.Figure(data=data2, layout=layout)
fig2['layout'].update(dict(title='Triangulated surface',
scene=dict(camera=dict(eye=dict(x=1.75,
y=-0.7,
z= 0.75)
)
)))
py.iplot(fig2, filename='trisurf-cubehx')
```
This example is also given as a demo for matplotlib [`plot_trisurf`](http://matplotlib.org/examples/mplot3d/trisurf3d_demo.html).
### Plotting tri-surfaces from data stored in ply-files ###
A PLY (Polygon File Format or Stanford Triangle Format) format is a format for storing graphical objects
that are represented by a triangulation of an object, resulted usually from scanning that object. A Ply file contains the coordinates of vertices, the codes for faces (triangles) and other elements, as well as the color for faces or the normal direction to faces.
In the following we show how we can read a ply file via the Python package, `plyfile`. This package can be installed with `pip`.
We choose a ply file from a list provided [here](http://people.sc.fsu.edu/~jburkardt/data/ply/ply.html).
```python
!pip install plyfile
from plyfile import PlyData, PlyElement
import urllib2
req = urllib2.Request('http://people.sc.fsu.edu/~jburkardt/data/ply/chopper.ply')
opener = urllib2.build_opener()
f = opener.open(req)
plydata = PlyData.read(f)
```
Read the file header:
```python
for element in plydata.elements:
print element
nr_points=plydata.elements[0].count
nr_faces=plydata.elements[1].count
```
Read the vertex coordinates:
```python
points=np.array([plydata['vertex'][k] for k in range(nr_points)])
points[0]
x,y,z=zip(*points)
faces=[plydata['face'][k][0] for k in range(nr_faces)]
faces[0]
```
Now we can get data for a Plotly plot of the graphical object read from the ply file:
```python
data3=plotly_trisurf(x,y,z, faces, colormap=cm.RdBu, plot_edges=None)
title="Trisurf from a PLY file<br>"+\
"Data Source:<a href='http://people.sc.fsu.edu/~jburkardt/data/ply/airplane.ply'> [1]</a>"
noaxis=dict(showbackground=False,
showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
title=''
)
fig3 = go.Figure(data=data3, layout=layout)
fig3['layout'].update(dict(title=title,
width=1000,
height=1000,
scene=dict(xaxis=noaxis,
yaxis=noaxis,
zaxis=noaxis,
aspectratio=dict(x=1, y=1, z=0.4),
camera=dict(eye=dict(x=1.25, y=1.25, z= 1.25)
)
)
))
py.iplot(fig3, filename='Chopper-Ply-cls')
```
This a version of the same object plotted along with triangle edges:
```python
from IPython.display import HTML
HTML('<iframe src=https://plot.ly/~empet/13734/trisurf-from-a-ply-file-data-source-1/ \
width=800 height=800></iframe>')
```
#### Reference
See https://plot.ly/python/reference/ for more information and chart attribute options!
```python
from IPython.display import display, HTML
display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))
! pip install git+https://github.com/plotly/publisher.git --upgrade
import publisher
publisher.publish(
'triangulation.ipynb', 'python/surface-triangulation/', 'Surface Triangulation',
'How to make Tri-Surf plots in Python with Plotly.',
title = 'Python Surface Triangulation | plotly',
name = 'Surface Triangulation',
has_thumbnail='true', thumbnail='thumbnail/trisurf.jpg',
language='python',
display_as='3d_charts', order=11,
ipynb= '~notebook_demo/71')
```
```python
```
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@doc@unconverted@python@triangulation.md@.PATH_END.py
|
{
"filename": "utils.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/smith/evaluation/utils.py",
"type": "Python"
}
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@smith@evaluation@utils.py@.PATH_END.py
|
|
{
"filename": "_style.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/contourcarpet/colorbar/tickfont/_style.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class StyleValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="style",
parent_name="contourcarpet.colorbar.tickfont",
**kwargs,
):
super(StyleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop("values", ["normal", "italic"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@contourcarpet@colorbar@tickfont@_style.py@.PATH_END.py
|
{
"filename": "plots_waCDM.ipynb",
"repo_name": "igomezv/simplemc_tests",
"repo_path": "simplemc_tests_extracted/simplemc_tests-main/plots_waCDM.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import numdifftools as nd
import scipy.linalg as la
import matplotlib.pyplot as plt
from simplemc.DriverMC import DriverMC
from simplemc.plots import Simple_Plots
from simplemc.plots.Plot_elipses import plot_elipses
import corner
import arviz
from getdist import plots, MCSamples
import getdist
```
Hello, World! I am process 0 of 1 on isidro-pc
Initalizing nu density look up table... Done
# With MCMC
```python
analysis1 = DriverMC(analyzername='mcmc', model='waCDM', datasets='HD+SN+BBAO+Planck')
```
Loading simplemc/data/HDiagramCompilacion-data_31.txt
Loading simplemc/data/HDiagramCompilacion-cov_31.txt
Eigenvalues of cov matrix: [16.0, 25.0, 64.0] ... 3844.0
Adding marginalising constant
Loading simplemc/data/jla_binned_distances_31nodes_v1.txt
Loading simplemc/data/cov_jla_binned_distances_31nodes_v1.txt
Eigenvalues of cov matrix: [0.0005070806003107887, 0.0005683516014088356, 0.000739250907386215] ... 0.03645982276128593
Adding marginalising constant
DR11LOWZ measurement in Cuesta : 8.468173048634462 +- 0.16748759985432085
Loading simplemc/data/sdss_DR11CMASS_consensus.dat
Aperp min,max,step,N: 0.961142836 1.12057144 0.0005714290000000677 280
Aparl min,max,step,N: 0.85171431 1.09085718 0.0008571430000000602 280
Loading done
rd = 149.26478152260088 Mpc
Fiducials at z= 0.57 : 14.3015431061137 21.466392296218515
Loading simplemc/data/chi2_surface_dr11_baseline_fit.txt
Aperp min,max,step,N: 0.5 1.5 0.016666999999999987 61
Aparl min,max,step,N: 0.8 1.2 0.0066669999999999785 61
Loading done
rd = 149.73834899440558 Mpc
Fiducials at z= 2.34 : 38.71393250862439 8.704556344366967
Loading simplemc/data/lyabaocross.scan
Aperp min,max,step,N: 0.7 1.3 0.020000000000000018 31
Aparl min,max,step,N: 0.8 1.2 0.010000000000000009 41
Loading done
rd = 149.56742181392625 Mpc
Fiducials at z= 2.36 : 38.931124520156345 8.641577445046805
SixdFGS measurement in Cuesta : 3.041334103142206 +- 0.18007899294920957
Loading simplemc/data/chidavexi8stavePk5staverec.dat
MGS measurement in Cuesta : DV= 664.1865351303804 +- 24.86880740958958
with rd= 148.6516589371155 DV_fid= 638.9490468680368 alphamin= 1.0394984363558428
Initializing CMB likelihood: SPlanck
Free parameters:
Om = 0.3038 +/- 0.05
Obh2 = 0.02234 +/- 0.001
h = 0.6821 +/- 0.05
w = -1.0 +/- 0.1
wa = 0.0 +/- 0.1
/home/isidro/Documents/github/simplemc_tests/simplemc/likelihoods/TabulatedBAODVLikelihood.py:37: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.
If increasing the limit yields no improvement it is advised to analyze
the integrand in order to determine the difficulties. If the position of a
local difficulty can be determined (singularity, discontinuity) one will
probably gain from splitting up the interval and calling the integrator
on the subranges. Perhaps a special-purpose integrator should be used.
rms /= quad(lambda x: sp.exp(-self.chi2i(alphamin+x)/2), -0.1, 0.1)[0]
```python
res1 = analysis1.executer(GRstop=0.01)
```
INFO:simplemc:
nsamp: 50000
skip: 300
temp: 2
evidence: False
Bounds: [ 0.1 0.02 0.4 -2. -2. ] [0.5 0.025 0.9 0. 2. ]
Starting chain...
Accepted: 299 | loglike: -33.6192 | Gelman-Rubin: None
Re-initializing covariance matrix after burn-in
[[ 1.86492621e-04 -4.43668301e-06 -1.55201051e-04 1.89742717e-04
-6.47983573e-04]
[-4.43668301e-06 1.98226857e-07 3.67624438e-06 -3.80133978e-06
2.66703336e-05]
[-1.55201051e-04 3.67624438e-06 1.49202870e-04 -3.20822379e-04
5.62191388e-04]
[ 1.89742717e-04 -3.80133978e-06 -3.20822379e-04 1.88724148e-03
-1.73850186e-03]
[-6.47983573e-04 2.66703336e-05 5.62191388e-04 -1.73850186e-03
6.99005797e-03]]
Accepted: 8500 | loglike: -31.7994 | Gelman-Rubin: [0.01239041 0.00228534 0.00030445 0.07798489 0.12633509]
INFO:simplemc:
Elapsed time: 28.045 minutes = 1682.726 seconds
---- Gelman-Rubin achived ----
Om: 0.3058 +/- 0.0103
Obh2: 0.0224 +/- 0.0003
h: 0.6769 +/- 0.0111
w: -0.9592 +/- 0.0966
wa: -0.0765 +/- 0.3715
```python
pars_labels = ['${}$'.format(p.Ltxname) for p in analysis1.pars_info]
pars_labels
```
['$\\Omega_m$', '$\\Omega_{b}h^2$', '$h$', '$w_0$', '$w_a$']
```python
res1
```
{'analyzer': 'mcmc',
'result': {'samples': array([[ 0.289206 , 0.023096 , 0.689856 , -1.00932 , 0.206902 ],
[ 0.286545 , 0.0230767, 0.696779 , -1.04691 , 0.187049 ],
[ 0.304195 , 0.0226816, 0.67383 , -0.959844 , 0.132741 ],
...,
[ 0.287324 , 0.0226117, 0.698898 , -1.00581 , -0.086426 ],
[ 0.299985 , 0.0227068, 0.688277 , -0.990752 , -0.12875 ],
[ 0.307521 , 0.0224024, 0.677308 , -0.952362 , -0.184401 ]]),
'weights': array([2.78130e-08, 1.61653e-07, 7.62228e-08, ..., 3.38892e-08,
1.39613e-07, 2.93634e-07]),
'maxlike': -30.72159856940455,
'gr_diagnostic': array([0.00013052, 0.00013622, 0.00010847, 0.00441101, 0.00947772])},
'time': 1682.7263741493225}
```python
samples1 = res1['result']['samples']
weights1 = res1['result']['weights']
```
```python
om1 = res1['result']['samples'][:,0]
obh21 = res1['result']['samples'][:,1]
h1 = res1['result']['samples'][:,2]
```
```python
plt.figure(figsize=(6,6), dpi=200)
fig1 = corner.hist2d(om1, h1, bins=100, weights=weights1, smooth=0.8, quantiles=(0.16, 0.84),
levels=(1-np.exp(-0.5),1-np.exp(-2)), color='cadetblue')
plt.xlabel('$\Omega_m$')
plt.ylabel('$h$')
plt.show()
```
```python
len(weights1)
```
8200
$ x-\sigma = 1 - exp(-\frac{(x/s)^2}{2})$
```python
plt.figure(figsize=(10,10), dpi=300)
plt.clf()
figcorner1 = corner.corner(samples1, weights=weights1, smooth=0.9, smooth1d=0.5,
# quantiles=(0.16, 0.84),
# levels=(1-np.exp(-0.5),1-np.exp(-2)), labels=pars_labels, color='cadetblue',
levels=(1-np.exp(-0.5),), labels=pars_labels, label_kwargs ={'fontsize':15},
color='gold',
plot_density=True, plot_datapoints=False, divergences=True)
plt.savefig('wacdm_mcmc.png')
plt.savefig('wacdm_mcmc.svg')
plt.show()
```
<Figure size 3000x3000 with 0 Axes>
## The same with only SN+HD
```python
analysis2 = DriverMC(analyzername='mcmc', model='waCDM', datasets='HD+SN')
```
Loading simplemc/data/HDiagramCompilacion-data_31.txt
Loading simplemc/data/HDiagramCompilacion-cov_31.txt
Eigenvalues of cov matrix: [16.0, 25.0, 64.0] ... 3844.0
Adding marginalising constant
Loading simplemc/data/jla_binned_distances_31nodes_v1.txt
Loading simplemc/data/cov_jla_binned_distances_31nodes_v1.txt
Eigenvalues of cov matrix: [0.0005070806003107887, 0.0005683516014088356, 0.000739250907386215] ... 0.03645982276128593
Adding marginalising constant
Free parameters:
Om = 0.3038 +/- 0.05
Obh2 = 0.02234 +/- 0.001
h = 0.6821 +/- 0.05
w = -1.0 +/- 0.1
wa = 0.0 +/- 0.1
```python
res2 = analysis2.executer()
```
INFO:simplemc:
nsamp: 50000
skip: 300
temp: 2
evidence: False
Bounds: [ 0.1 0.02 0.4 -2. -2. ] [0.5 0.025 0.9 0. 2. ]
Starting chain...
Accepted: 299 | loglike: -24.5077 | Gelman-Rubin: None
Re-initializing covariance matrix after burn-in
[[ 3.44414307e-04 1.18209292e-06 -9.05806718e-05 -5.32853343e-05
-8.83275989e-04]
[ 1.18209292e-06 1.62471513e-07 -1.61641539e-06 2.54113870e-06
-1.67423203e-05]
[-9.05806718e-05 -1.61641539e-06 2.88176464e-04 -1.38662835e-04
2.00926770e-04]
[-5.32853343e-05 2.54113870e-06 -1.38662835e-04 8.84630333e-04
-3.00283371e-04]
[-8.83275989e-04 -1.67423203e-05 2.00926770e-04 -3.00283371e-04
9.48539425e-03]]
Accepted: 5501 | loglike: -24.3675 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5501 | loglike: -24.3675 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5501 | loglike: -24.3675 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5502 | loglike: -24.3087 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5502 | loglike: -24.3087 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5502 | loglike: -24.3087 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5503 | loglike: -26.1948 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5504 | loglike: -27.5514 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5504 | loglike: -27.5514 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5505 | loglike: -26.6108 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5506 | loglike: -29.3021 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5507 | loglike: -26.8250 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5508 | loglike: -26.7359 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5509 | loglike: -29.7724 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5510 | loglike: -28.4470 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5511 | loglike: -27.7888 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5511 | loglike: -27.7888 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5511 | loglike: -27.7888 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5512 | loglike: -26.6534 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5513 | loglike: -26.0215 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5514 | loglike: -25.2499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5514 | loglike: -25.2499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5514 | loglike: -25.2499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5514 | loglike: -25.2499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5514 | loglike: -25.2499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5515 | loglike: -25.0573 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5516 | loglike: -26.0519 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5517 | loglike: -26.4238 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5517 | loglike: -26.4238 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5518 | loglike: -25.9117 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5518 | loglike: -25.9117 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5519 | loglike: -25.4056 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5520 | loglike: -25.4930 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5521 | loglike: -26.6294 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5522 | loglike: -27.6737 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5522 | loglike: -27.6737 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5523 | loglike: -27.4342 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5523 | loglike: -27.4342 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5523 | loglike: -27.4342 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5523 | loglike: -27.4342 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5524 | loglike: -27.1379 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5525 | loglike: -26.7615 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5526 | loglike: -25.2898 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5527 | loglike: -24.3311 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5528 | loglike: -24.7523 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5529 | loglike: -25.9008 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5530 | loglike: -26.5722 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5531 | loglike: -26.2977 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5532 | loglike: -27.5872 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5532 | loglike: -27.5872 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5533 | loglike: -26.6426 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5534 | loglike: -26.7264 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5535 | loglike: -25.4469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5535 | loglike: -25.4469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5536 | loglike: -25.0812 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5536 | loglike: -25.0812 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5537 | loglike: -24.1156 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5538 | loglike: -24.8024 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5538 | loglike: -24.8024 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5539 | loglike: -25.6619 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5540 | loglike: -25.7387 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5541 | loglike: -25.2518 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5542 | loglike: -25.0246 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5543 | loglike: -24.5018 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5544 | loglike: -26.1589 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5545 | loglike: -26.1893 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5546 | loglike: -26.2453 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5546 | loglike: -26.2453 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5547 | loglike: -26.2419 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5548 | loglike: -24.7342 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5549 | loglike: -26.1680 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5550 | loglike: -26.0763 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5551 | loglike: -27.8368 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5551 | loglike: -27.8368 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5551 | loglike: -27.8368 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5552 | loglike: -26.0200 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5552 | loglike: -26.0200 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5552 | loglike: -26.0200 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5553 | loglike: -25.7869 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5553 | loglike: -25.7869 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5554 | loglike: -24.4533 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5554 | loglike: -24.4533 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5555 | loglike: -25.3770 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5556 | loglike: -24.7717 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5557 | loglike: -24.6489 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5558 | loglike: -24.8571 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5559 | loglike: -27.0136 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5560 | loglike: -26.5609 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5561 | loglike: -27.8657 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5562 | loglike: -27.7538 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5562 | loglike: -27.7538 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5563 | loglike: -26.3002 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5564 | loglike: -26.2128 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5565 | loglike: -26.2202 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5566 | loglike: -26.7191 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5567 | loglike: -27.8821 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5568 | loglike: -27.6893 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5569 | loglike: -26.4903 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5570 | loglike: -26.0782 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5571 | loglike: -24.4739 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5572 | loglike: -25.1330 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5573 | loglike: -26.4410 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5574 | loglike: -27.2733 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5574 | loglike: -27.2733 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5574 | loglike: -27.2733 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5575 | loglike: -25.8133 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5576 | loglike: -26.0764 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5577 | loglike: -25.8237 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5578 | loglike: -25.6418 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5579 | loglike: -25.6173 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5579 | loglike: -25.6173 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5580 | loglike: -25.0102 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5580 | loglike: -25.0102 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5580 | loglike: -25.0102 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5580 | loglike: -25.0102 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5581 | loglike: -24.5889 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5582 | loglike: -24.9949 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5583 | loglike: -24.8821 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5584 | loglike: -25.4322 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5585 | loglike: -25.8929 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5586 | loglike: -25.7855 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5586 | loglike: -25.7855 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5586 | loglike: -25.7855 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5587 | loglike: -26.2860 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5588 | loglike: -26.5439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5589 | loglike: -26.0724 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5589 | loglike: -26.0724 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5590 | loglike: -25.4510 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5591 | loglike: -25.1702 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5592 | loglike: -25.4000 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5593 | loglike: -25.6410 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5594 | loglike: -25.4925 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5595 | loglike: -25.1812 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5595 | loglike: -25.1812 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5596 | loglike: -24.5856 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5597 | loglike: -24.6951 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5597 | loglike: -24.6951 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5598 | loglike: -25.0513 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5598 | loglike: -25.0513 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5599 | loglike: -24.9469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5599 | loglike: -24.9469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5599 | loglike: -24.9469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5599 | loglike: -24.9469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5600 | loglike: -26.2918 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5600 | loglike: -26.2918 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5600 | loglike: -26.2918 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5600 | loglike: -26.2918 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5601 | loglike: -26.3159 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5601 | loglike: -26.3159 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5602 | loglike: -26.7026 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5603 | loglike: -27.0791 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5603 | loglike: -27.0791 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5604 | loglike: -25.4079 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5605 | loglike: -24.6590 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5606 | loglike: -25.0714 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5607 | loglike: -24.9104 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5607 | loglike: -24.9104 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5608 | loglike: -25.8720 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5609 | loglike: -26.4884 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5610 | loglike: -27.4070 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5611 | loglike: -29.7599 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5612 | loglike: -31.1794 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5612 | loglike: -31.1794 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5613 | loglike: -29.1799 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5614 | loglike: -29.9204 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5615 | loglike: -28.7920 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5615 | loglike: -28.7920 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5615 | loglike: -28.7920 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5616 | loglike: -26.8496 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5616 | loglike: -26.8496 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5617 | loglike: -27.5096 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5618 | loglike: -25.5460 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5619 | loglike: -25.2530 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5620 | loglike: -24.6374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5621 | loglike: -24.7599 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5622 | loglike: -26.0065 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5622 | loglike: -26.0065 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5623 | loglike: -27.5178 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5624 | loglike: -25.9884 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5625 | loglike: -26.2435 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5626 | loglike: -28.6181 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5626 | loglike: -28.6181 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5627 | loglike: -28.1623 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5627 | loglike: -28.1623 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5628 | loglike: -30.3329 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5629 | loglike: -27.8704 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5629 | loglike: -27.8704 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5629 | loglike: -27.8704 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5629 | loglike: -27.8704 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5630 | loglike: -29.1569 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5631 | loglike: -27.5489 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5632 | loglike: -26.9806 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5633 | loglike: -26.5200 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5634 | loglike: -28.4339 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5635 | loglike: -28.9999 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5636 | loglike: -28.9269 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5637 | loglike: -28.3492 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5638 | loglike: -26.4144 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5639 | loglike: -25.3784 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5640 | loglike: -25.1094 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5641 | loglike: -25.3719 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5642 | loglike: -25.6253 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5643 | loglike: -24.5541 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5644 | loglike: -24.3025 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5644 | loglike: -24.3025 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5645 | loglike: -25.6248 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5645 | loglike: -25.6248 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5646 | loglike: -28.8727 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5647 | loglike: -27.7693 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5647 | loglike: -27.7693 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5648 | loglike: -27.8283 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5649 | loglike: -26.6366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5650 | loglike: -26.5544 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5651 | loglike: -26.5856 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5652 | loglike: -25.4641 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5652 | loglike: -25.4641 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5652 | loglike: -25.4641 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5652 | loglike: -25.4641 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5653 | loglike: -27.8029 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5654 | loglike: -29.2934 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5655 | loglike: -26.3855 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5655 | loglike: -26.3855 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5656 | loglike: -25.5041 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5657 | loglike: -25.8795 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5658 | loglike: -25.6185 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5658 | loglike: -25.6185 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5659 | loglike: -25.6921 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5660 | loglike: -25.9240 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5661 | loglike: -26.5341 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5662 | loglike: -24.6472 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5662 | loglike: -24.6472 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5663 | loglike: -24.2256 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5664 | loglike: -24.5294 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5665 | loglike: -25.3607 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5666 | loglike: -26.7382 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5667 | loglike: -25.8733 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5668 | loglike: -26.1749 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5668 | loglike: -26.1749 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5669 | loglike: -25.7631 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5670 | loglike: -26.6934 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5671 | loglike: -25.6088 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5672 | loglike: -25.0094 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5673 | loglike: -25.0648 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5674 | loglike: -25.0374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5674 | loglike: -25.0374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5674 | loglike: -25.0374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5674 | loglike: -25.0374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5675 | loglike: -25.3230 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5676 | loglike: -24.9537 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5677 | loglike: -25.2861 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5678 | loglike: -25.9508 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5679 | loglike: -26.1079 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5679 | loglike: -26.1079 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5679 | loglike: -26.1079 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5679 | loglike: -26.1079 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5680 | loglike: -28.6721 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5680 | loglike: -28.6721 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5681 | loglike: -28.1661 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5681 | loglike: -28.1661 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5682 | loglike: -28.4964 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5682 | loglike: -28.4964 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5683 | loglike: -27.4998 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5684 | loglike: -27.0678 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5685 | loglike: -27.2721 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5686 | loglike: -27.0347 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5686 | loglike: -27.0347 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5687 | loglike: -27.0148 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5688 | loglike: -25.9760 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5689 | loglike: -26.3555 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5690 | loglike: -25.9830 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5691 | loglike: -26.2851 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5692 | loglike: -28.1602 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5692 | loglike: -28.1602 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5693 | loglike: -28.3143 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5693 | loglike: -28.3143 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5693 | loglike: -28.3143 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5694 | loglike: -26.4247 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5695 | loglike: -28.4469 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5696 | loglike: -27.5374 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5697 | loglike: -27.3163 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5698 | loglike: -26.4750 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5699 | loglike: -27.1663 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5700 | loglike: -26.7663 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5700 | loglike: -26.7663 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5701 | loglike: -28.1182 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5702 | loglike: -28.7311 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5702 | loglike: -28.7311 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5703 | loglike: -31.5581 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5703 | loglike: -31.5581 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5704 | loglike: -30.6482 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5705 | loglike: -31.2707 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5705 | loglike: -31.2707 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5705 | loglike: -31.2707 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5706 | loglike: -31.2029 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5707 | loglike: -30.4931 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5708 | loglike: -29.7410 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5709 | loglike: -30.1927 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5709 | loglike: -30.1927 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5710 | loglike: -30.5065 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5711 | loglike: -31.3434 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5712 | loglike: -31.8115 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5713 | loglike: -31.5067 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5714 | loglike: -31.0467 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5715 | loglike: -29.8863 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5716 | loglike: -29.7735 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5717 | loglike: -29.5552 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5718 | loglike: -28.0275 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5718 | loglike: -28.0275 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5718 | loglike: -28.0275 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5718 | loglike: -28.0275 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5719 | loglike: -28.4514 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5720 | loglike: -30.4510 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5720 | loglike: -30.4510 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5721 | loglike: -31.1120 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5721 | loglike: -31.1120 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5721 | loglike: -31.1120 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5722 | loglike: -30.5335 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5723 | loglike: -31.2766 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5723 | loglike: -31.2766 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5724 | loglike: -30.4582 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5725 | loglike: -29.6838 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5725 | loglike: -29.6838 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5726 | loglike: -29.5580 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5727 | loglike: -28.5674 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5727 | loglike: -28.5674 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5728 | loglike: -28.1704 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5729 | loglike: -26.8549 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5730 | loglike: -29.6049 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5731 | loglike: -25.8333 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5732 | loglike: -25.4117 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5733 | loglike: -25.7413 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5734 | loglike: -25.6830 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5735 | loglike: -27.0712 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5735 | loglike: -27.0712 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5736 | loglike: -27.4479 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5737 | loglike: -27.3676 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5738 | loglike: -26.2808 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5738 | loglike: -26.2808 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5739 | loglike: -26.9366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5740 | loglike: -27.4221 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5741 | loglike: -26.2534 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5742 | loglike: -26.4439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5742 | loglike: -26.4439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5742 | loglike: -26.4439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5743 | loglike: -27.5416 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5744 | loglike: -27.2059 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5745 | loglike: -28.4439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5745 | loglike: -28.4439 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5746 | loglike: -28.7080 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5747 | loglike: -28.4186 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5748 | loglike: -28.5479 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5749 | loglike: -28.2504 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5750 | loglike: -28.0326 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5750 | loglike: -28.0326 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5751 | loglike: -28.1394 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5751 | loglike: -28.1394 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5752 | loglike: -28.5141 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5753 | loglike: -27.6307 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5754 | loglike: -27.3533 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5755 | loglike: -28.3341 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5756 | loglike: -27.9781 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5757 | loglike: -26.0994 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5758 | loglike: -27.2021 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5759 | loglike: -28.1185 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5760 | loglike: -27.6671 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5760 | loglike: -27.6671 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5761 | loglike: -28.5805 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5761 | loglike: -28.5805 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5762 | loglike: -26.4601 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5762 | loglike: -26.4601 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5763 | loglike: -25.6178 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5763 | loglike: -25.6178 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5764 | loglike: -26.5032 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5764 | loglike: -26.5032 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5765 | loglike: -27.5199 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5766 | loglike: -29.5372 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5766 | loglike: -29.5372 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5767 | loglike: -27.0424 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5768 | loglike: -26.1491 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5769 | loglike: -26.7100 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5770 | loglike: -25.0440 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5771 | loglike: -25.1914 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5771 | loglike: -25.1914 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5772 | loglike: -26.1073 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5773 | loglike: -26.0997 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5774 | loglike: -28.9982 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5774 | loglike: -28.9982 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5774 | loglike: -28.9982 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5774 | loglike: -28.9982 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5775 | loglike: -27.8499 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5776 | loglike: -29.1727 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5777 | loglike: -27.1968 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5778 | loglike: -26.9365 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5779 | loglike: -26.6780 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5780 | loglike: -28.1434 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5780 | loglike: -28.1434 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5781 | loglike: -29.7639 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5782 | loglike: -30.4572 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5783 | loglike: -31.1366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5783 | loglike: -31.1366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5783 | loglike: -31.1366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5784 | loglike: -30.9147 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5785 | loglike: -30.6159 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5786 | loglike: -31.1328 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5787 | loglike: -31.9959 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5787 | loglike: -31.9959 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5788 | loglike: -30.8387 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5789 | loglike: -30.3680 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5790 | loglike: -32.5895 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5790 | loglike: -32.5895 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5790 | loglike: -32.5895 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5791 | loglike: -29.8650 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5791 | loglike: -29.8650 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5791 | loglike: -29.8650 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5792 | loglike: -29.9717 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5793 | loglike: -28.9684 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5794 | loglike: -29.7873 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5795 | loglike: -29.6083 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5796 | loglike: -26.7876 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5797 | loglike: -25.9986 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5797 | loglike: -25.9986 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5798 | loglike: -26.4659 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5799 | loglike: -27.0423 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5799 | loglike: -27.0423 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5799 | loglike: -27.0423 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5800 | loglike: -26.8349 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5801 | loglike: -26.3722 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5802 | loglike: -26.3986 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5803 | loglike: -26.5183 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5804 | loglike: -26.3783 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5804 | loglike: -26.3783 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5805 | loglike: -25.7171 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5805 | loglike: -25.7171 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5806 | loglike: -25.7724 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5807 | loglike: -25.5728 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5808 | loglike: -24.9190 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5809 | loglike: -24.8317 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5809 | loglike: -24.8317 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5809 | loglike: -24.8317 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5810 | loglike: -25.3698 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5811 | loglike: -26.6002 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5812 | loglike: -28.1565 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5812 | loglike: -28.1565 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5812 | loglike: -28.1565 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5812 | loglike: -28.1565 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5813 | loglike: -28.1777 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5814 | loglike: -27.1411 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5814 | loglike: -27.1411 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5815 | loglike: -25.1247 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5816 | loglike: -25.1290 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5817 | loglike: -25.0619 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5818 | loglike: -25.4725 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5819 | loglike: -25.0036 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5820 | loglike: -24.5533 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5821 | loglike: -24.4281 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5822 | loglike: -25.0863 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5823 | loglike: -26.6015 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5824 | loglike: -28.2793 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5824 | loglike: -28.2793 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5824 | loglike: -28.2793 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5824 | loglike: -28.2793 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5825 | loglike: -27.4431 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5825 | loglike: -27.4431 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5826 | loglike: -27.7421 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5827 | loglike: -27.1786 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5827 | loglike: -27.1786 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5827 | loglike: -27.1786 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5828 | loglike: -25.9955 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5829 | loglike: -25.9349 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5830 | loglike: -27.9038 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5831 | loglike: -27.3880 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5831 | loglike: -27.3880 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5831 | loglike: -27.3880 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5832 | loglike: -26.6689 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5833 | loglike: -25.8204 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5833 | loglike: -25.8204 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5834 | loglike: -26.0575 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5835 | loglike: -25.3861 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5836 | loglike: -26.0129 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5836 | loglike: -26.0129 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5837 | loglike: -26.0202 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5838 | loglike: -28.9356 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5838 | loglike: -28.9356 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5839 | loglike: -28.6772 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5840 | loglike: -26.7233 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5841 | loglike: -26.5305 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5841 | loglike: -26.5305 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5842 | loglike: -28.9429 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5843 | loglike: -31.8286 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5843 | loglike: -31.8286 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5844 | loglike: -29.1564 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5845 | loglike: -27.4710 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5845 | loglike: -27.4710 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5845 | loglike: -27.4710 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5845 | loglike: -27.4710 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5846 | loglike: -26.2840 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5847 | loglike: -26.3700 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5848 | loglike: -25.9988 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5848 | loglike: -25.9988 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5849 | loglike: -27.7406 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5850 | loglike: -28.3967 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5851 | loglike: -26.7711 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5852 | loglike: -27.9785 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5853 | loglike: -27.6118 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5853 | loglike: -27.6118 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5854 | loglike: -27.9088 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5855 | loglike: -28.8404 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5855 | loglike: -28.8404 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5855 | loglike: -28.8404 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5856 | loglike: -27.5909 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5857 | loglike: -27.4120 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5858 | loglike: -27.9875 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5858 | loglike: -27.9875 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5859 | loglike: -26.2364 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5860 | loglike: -26.6371 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5861 | loglike: -25.7653 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5862 | loglike: -25.9097 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5863 | loglike: -25.7897 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5864 | loglike: -26.0776 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5865 | loglike: -26.4865 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5866 | loglike: -27.7400 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5867 | loglike: -27.7805 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5867 | loglike: -27.7805 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5868 | loglike: -29.3352 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5868 | loglike: -29.3352 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5869 | loglike: -28.7647 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5870 | loglike: -28.7781 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5870 | loglike: -28.7781 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5870 | loglike: -28.7781 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5871 | loglike: -29.0041 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5872 | loglike: -27.5515 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5873 | loglike: -28.2228 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5873 | loglike: -28.2228 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5874 | loglike: -27.5720 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5875 | loglike: -26.5164 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5876 | loglike: -27.3148 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5877 | loglike: -26.6369 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5878 | loglike: -27.3419 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5879 | loglike: -26.4651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5879 | loglike: -26.4651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5879 | loglike: -26.4651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5879 | loglike: -26.4651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5880 | loglike: -25.1216 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5880 | loglike: -25.1216 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5881 | loglike: -25.5941 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5882 | loglike: -25.2689 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5883 | loglike: -25.3722 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5884 | loglike: -25.3786 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5885 | loglike: -24.8270 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5885 | loglike: -24.8270 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5886 | loglike: -25.4879 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5886 | loglike: -25.4879 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5887 | loglike: -24.9575 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5888 | loglike: -25.2729 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5889 | loglike: -25.7157 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5890 | loglike: -24.7541 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5891 | loglike: -24.0651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5892 | loglike: -24.4133 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5892 | loglike: -24.4133 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5892 | loglike: -24.4133 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5893 | loglike: -25.3808 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5894 | loglike: -25.1842 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5894 | loglike: -25.1842 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5895 | loglike: -24.5850 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5896 | loglike: -25.3184 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5897 | loglike: -26.6259 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5898 | loglike: -26.2250 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5899 | loglike: -25.8363 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5900 | loglike: -25.5066 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5901 | loglike: -27.1921 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5901 | loglike: -27.1921 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5902 | loglike: -27.0961 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5903 | loglike: -28.0653 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5904 | loglike: -27.7508 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5905 | loglike: -25.8504 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5905 | loglike: -25.8504 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5905 | loglike: -25.8504 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5905 | loglike: -25.8504 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5906 | loglike: -25.9080 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5907 | loglike: -26.5335 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5907 | loglike: -26.5335 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5907 | loglike: -26.5335 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5908 | loglike: -27.6158 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5909 | loglike: -26.8341 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5909 | loglike: -26.8341 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5910 | loglike: -26.4412 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5911 | loglike: -28.6880 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5912 | loglike: -28.1772 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5913 | loglike: -25.5317 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5914 | loglike: -25.5789 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5915 | loglike: -26.5258 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5916 | loglike: -27.5004 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5917 | loglike: -26.2310 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5918 | loglike: -25.9895 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5919 | loglike: -27.8243 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5920 | loglike: -26.0223 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5921 | loglike: -27.5585 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5922 | loglike: -27.6861 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5922 | loglike: -27.6861 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5923 | loglike: -26.4742 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5923 | loglike: -26.4742 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5924 | loglike: -27.0032 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5925 | loglike: -28.5247 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5926 | loglike: -25.7598 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5927 | loglike: -25.8489 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5927 | loglike: -25.8489 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5928 | loglike: -24.6905 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5928 | loglike: -24.6905 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5929 | loglike: -25.6299 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5929 | loglike: -25.6299 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5930 | loglike: -25.4759 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5931 | loglike: -25.5591 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5932 | loglike: -25.0789 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5932 | loglike: -25.0789 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5933 | loglike: -24.6836 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5934 | loglike: -25.1430 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5935 | loglike: -25.0982 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5936 | loglike: -25.4306 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5937 | loglike: -24.5662 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5938 | loglike: -24.4424 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5939 | loglike: -24.8662 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5939 | loglike: -24.8662 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5939 | loglike: -24.8662 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5940 | loglike: -24.7422 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5940 | loglike: -24.7422 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5941 | loglike: -25.4813 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5942 | loglike: -25.4769 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5943 | loglike: -24.5160 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5944 | loglike: -26.3385 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5945 | loglike: -25.4761 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5946 | loglike: -26.5000 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5947 | loglike: -27.1642 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5948 | loglike: -26.1711 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5949 | loglike: -30.0381 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5950 | loglike: -32.1525 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5951 | loglike: -30.8016 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5952 | loglike: -32.4547 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5952 | loglike: -32.4547 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5953 | loglike: -30.8010 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5953 | loglike: -30.8010 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5954 | loglike: -28.0262 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5955 | loglike: -28.8972 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5956 | loglike: -29.0753 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5957 | loglike: -28.3911 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5957 | loglike: -28.3911 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5957 | loglike: -28.3911 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5957 | loglike: -28.3911 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5958 | loglike: -28.3253 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5959 | loglike: -29.0685 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5959 | loglike: -29.0685 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5960 | loglike: -27.7838 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5961 | loglike: -29.1945 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5962 | loglike: -29.4834 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5962 | loglike: -29.4834 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5963 | loglike: -26.7974 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5964 | loglike: -27.6672 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5965 | loglike: -26.6332 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5966 | loglike: -25.6041 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5967 | loglike: -25.4784 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5968 | loglike: -25.5942 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5969 | loglike: -28.2131 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5969 | loglike: -28.2131 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5970 | loglike: -29.2622 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5971 | loglike: -30.8832 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5972 | loglike: -31.9560 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5972 | loglike: -31.9560 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5973 | loglike: -27.9651 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5974 | loglike: -27.5991 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5975 | loglike: -25.9738 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5976 | loglike: -26.8245 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5977 | loglike: -26.1427 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5978 | loglike: -24.9026 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5978 | loglike: -24.9026 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5979 | loglike: -24.2870 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5979 | loglike: -24.2870 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5980 | loglike: -24.9605 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5981 | loglike: -25.5314 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5982 | loglike: -26.5710 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5983 | loglike: -26.4955 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5983 | loglike: -26.4955 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5983 | loglike: -26.4955 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5984 | loglike: -27.3491 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5985 | loglike: -26.8403 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5985 | loglike: -26.8403 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5986 | loglike: -28.2413 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5986 | loglike: -28.2413 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5987 | loglike: -28.5437 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5988 | loglike: -26.8791 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5989 | loglike: -28.5641 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5990 | loglike: -29.3955 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5991 | loglike: -28.7366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5991 | loglike: -28.7366 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5992 | loglike: -27.1165 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5992 | loglike: -27.1165 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5993 | loglike: -27.4089 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5993 | loglike: -27.4089 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5994 | loglike: -25.0461 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5995 | loglike: -25.2076 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5996 | loglike: -25.4883 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5997 | loglike: -24.6733 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5998 | loglike: -24.7143 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5999 | loglike: -25.3645 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 5999 | loglike: -25.3645 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 6000 | loglike: -25.8980 | Gelman-Rubin: [1.10495624e-03 9.50847525e-05 2.57368783e-03 2.26608286e-02
Accepted: 6501 | loglike: -28.3448 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6502 | loglike: -29.5973 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6503 | loglike: -30.3512 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6504 | loglike: -30.7011 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6505 | loglike: -31.3001 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6505 | loglike: -31.3001 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6506 | loglike: -33.0670 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6507 | loglike: -31.4156 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6508 | loglike: -30.3997 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6509 | loglike: -26.5441 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6509 | loglike: -26.5441 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6509 | loglike: -26.5441 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6510 | loglike: -25.0226 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6510 | loglike: -25.0226 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6511 | loglike: -25.1043 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6511 | loglike: -25.1043 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6512 | loglike: -27.1891 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6513 | loglike: -27.6448 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6514 | loglike: -26.7414 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6515 | loglike: -27.1813 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6516 | loglike: -24.5699 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6516 | loglike: -24.5699 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6517 | loglike: -24.3109 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6518 | loglike: -25.7693 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6518 | loglike: -25.7693 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6518 | loglike: -25.7693 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6519 | loglike: -25.8980 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6520 | loglike: -26.1524 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6521 | loglike: -27.2751 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6522 | loglike: -27.9311 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6523 | loglike: -27.6399 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6524 | loglike: -27.5100 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6524 | loglike: -27.5100 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6525 | loglike: -28.0028 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6526 | loglike: -27.5436 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6526 | loglike: -27.5436 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6526 | loglike: -27.5436 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6527 | loglike: -27.5827 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6528 | loglike: -25.7125 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6529 | loglike: -26.4046 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6529 | loglike: -26.4046 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6530 | loglike: -26.3018 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6531 | loglike: -27.0307 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6532 | loglike: -28.2863 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6532 | loglike: -28.2863 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6533 | loglike: -27.1042 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6533 | loglike: -27.1042 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6534 | loglike: -27.5709 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6535 | loglike: -28.6040 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6536 | loglike: -27.8949 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6537 | loglike: -27.7891 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6538 | loglike: -29.5641 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6538 | loglike: -29.5641 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6539 | loglike: -30.7253 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6539 | loglike: -30.7253 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6540 | loglike: -29.7199 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6540 | loglike: -29.7199 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6541 | loglike: -29.9372 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6541 | loglike: -29.9372 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6541 | loglike: -29.9372 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6541 | loglike: -29.9372 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6542 | loglike: -29.9642 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6543 | loglike: -27.3123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6543 | loglike: -27.3123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6543 | loglike: -27.3123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6544 | loglike: -26.5329 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6544 | loglike: -26.5329 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6545 | loglike: -28.3207 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6545 | loglike: -28.3207 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6546 | loglike: -30.0745 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6546 | loglike: -30.0745 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6547 | loglike: -30.7127 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6548 | loglike: -30.5650 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6548 | loglike: -30.5650 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6549 | loglike: -30.2411 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6549 | loglike: -30.2411 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6550 | loglike: -27.6595 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6550 | loglike: -27.6595 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6551 | loglike: -27.1221 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6552 | loglike: -25.9732 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6552 | loglike: -25.9732 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6553 | loglike: -25.5427 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6554 | loglike: -26.4848 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6554 | loglike: -26.4848 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6555 | loglike: -26.2979 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6556 | loglike: -26.7825 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6557 | loglike: -26.9107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6558 | loglike: -26.4598 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6559 | loglike: -27.1653 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6560 | loglike: -26.1631 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6561 | loglike: -24.8943 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6562 | loglike: -25.4006 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6563 | loglike: -24.8204 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6564 | loglike: -24.3629 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6565 | loglike: -24.3366 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6566 | loglike: -24.3688 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6567 | loglike: -25.0571 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6567 | loglike: -25.0571 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6568 | loglike: -27.0988 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6569 | loglike: -28.1332 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6570 | loglike: -28.5784 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6571 | loglike: -28.4819 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6571 | loglike: -28.4819 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6571 | loglike: -28.4819 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6572 | loglike: -28.4113 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6572 | loglike: -28.4113 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6573 | loglike: -29.4452 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6573 | loglike: -29.4452 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6574 | loglike: -28.2020 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6575 | loglike: -27.7254 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6576 | loglike: -26.9157 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6576 | loglike: -26.9157 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6577 | loglike: -26.0123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6578 | loglike: -26.6985 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6579 | loglike: -26.4620 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6580 | loglike: -25.5386 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6581 | loglike: -25.0954 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6582 | loglike: -26.2869 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6583 | loglike: -26.0473 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6583 | loglike: -26.0473 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6584 | loglike: -27.4826 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6584 | loglike: -27.4826 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6585 | loglike: -27.4969 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6586 | loglike: -29.1726 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6587 | loglike: -27.3403 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6588 | loglike: -26.4985 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6589 | loglike: -26.5970 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6589 | loglike: -26.5970 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6589 | loglike: -26.5970 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6590 | loglike: -26.6436 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6591 | loglike: -27.6608 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6591 | loglike: -27.6608 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6591 | loglike: -27.6608 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6592 | loglike: -28.8639 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6592 | loglike: -28.8639 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6592 | loglike: -28.8639 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6592 | loglike: -28.8639 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6593 | loglike: -29.1498 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6594 | loglike: -29.3480 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6595 | loglike: -32.2448 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6596 | loglike: -36.3338 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6597 | loglike: -36.0733 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6598 | loglike: -32.4002 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6599 | loglike: -31.7166 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6600 | loglike: -27.2107 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6601 | loglike: -27.1918 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6601 | loglike: -27.1918 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6602 | loglike: -28.0455 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6603 | loglike: -26.6362 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6604 | loglike: -30.7849 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6605 | loglike: -31.3455 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6606 | loglike: -27.8284 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6607 | loglike: -25.0963 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6607 | loglike: -25.0963 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6607 | loglike: -25.0963 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6608 | loglike: -26.1617 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6609 | loglike: -26.6383 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6610 | loglike: -24.2776 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6611 | loglike: -24.1016 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6612 | loglike: -24.6364 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6613 | loglike: -25.1988 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6614 | loglike: -26.1139 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6615 | loglike: -28.0412 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6616 | loglike: -27.6076 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6616 | loglike: -27.6076 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6616 | loglike: -27.6076 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6617 | loglike: -25.4923 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6617 | loglike: -25.4923 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6617 | loglike: -25.4923 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6618 | loglike: -25.9950 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6619 | loglike: -25.6419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6619 | loglike: -25.6419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6619 | loglike: -25.6419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6620 | loglike: -26.6279 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6621 | loglike: -25.8476 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6622 | loglike: -29.2675 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6623 | loglike: -27.1186 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6623 | loglike: -27.1186 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6624 | loglike: -27.6197 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6625 | loglike: -27.8124 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6625 | loglike: -27.8124 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6626 | loglike: -28.0024 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6626 | loglike: -28.0024 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6626 | loglike: -28.0024 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6627 | loglike: -28.7106 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6628 | loglike: -26.0997 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6628 | loglike: -26.0997 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6629 | loglike: -25.7667 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6630 | loglike: -26.5927 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6631 | loglike: -26.8952 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6632 | loglike: -26.7583 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6633 | loglike: -27.8657 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6634 | loglike: -26.0291 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6634 | loglike: -26.0291 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6635 | loglike: -25.7937 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6636 | loglike: -25.2893 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6637 | loglike: -24.4400 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6637 | loglike: -24.4400 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6638 | loglike: -24.1126 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6639 | loglike: -24.6642 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6639 | loglike: -24.6642 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6639 | loglike: -24.6642 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6640 | loglike: -24.5051 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6641 | loglike: -24.7805 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6642 | loglike: -24.7886 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6643 | loglike: -24.9396 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6644 | loglike: -24.7418 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6645 | loglike: -25.4009 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6646 | loglike: -27.7170 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6647 | loglike: -26.5286 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6648 | loglike: -27.5884 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6649 | loglike: -26.4814 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6650 | loglike: -26.1060 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6651 | loglike: -25.9359 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6652 | loglike: -26.7361 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6653 | loglike: -25.7335 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6654 | loglike: -26.2378 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6655 | loglike: -30.0201 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6655 | loglike: -30.0201 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6656 | loglike: -30.3249 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6656 | loglike: -30.3249 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6656 | loglike: -30.3249 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6657 | loglike: -28.3268 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6658 | loglike: -28.9946 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6659 | loglike: -28.1181 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6660 | loglike: -29.6727 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6661 | loglike: -33.5523 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6662 | loglike: -32.6233 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6663 | loglike: -37.8054 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6664 | loglike: -35.6764 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6664 | loglike: -35.6764 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6665 | loglike: -34.6214 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6666 | loglike: -29.4116 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6667 | loglike: -30.9663 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6667 | loglike: -30.9663 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6668 | loglike: -29.6305 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6669 | loglike: -27.5623 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6670 | loglike: -27.7739 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6671 | loglike: -26.8142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6672 | loglike: -26.1384 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6673 | loglike: -26.0352 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6674 | loglike: -25.6174 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6675 | loglike: -24.5208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6676 | loglike: -24.8603 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6676 | loglike: -24.8603 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6677 | loglike: -25.0392 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6677 | loglike: -25.0392 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6678 | loglike: -25.1346 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6678 | loglike: -25.1346 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6679 | loglike: -25.5248 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6679 | loglike: -25.5248 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6679 | loglike: -25.5248 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6680 | loglike: -25.1849 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6680 | loglike: -25.1849 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6681 | loglike: -24.6312 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6682 | loglike: -25.5677 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6682 | loglike: -25.5677 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6683 | loglike: -26.0385 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6684 | loglike: -25.8723 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6685 | loglike: -25.9704 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6686 | loglike: -25.2008 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6687 | loglike: -25.3516 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6688 | loglike: -25.5877 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6688 | loglike: -25.5877 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6689 | loglike: -25.1108 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6690 | loglike: -24.7938 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6691 | loglike: -25.9573 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6692 | loglike: -27.1792 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6692 | loglike: -27.1792 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6693 | loglike: -25.7342 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6694 | loglike: -26.5825 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6694 | loglike: -26.5825 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6695 | loglike: -28.1034 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6695 | loglike: -28.1034 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6696 | loglike: -29.2203 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6697 | loglike: -27.0065 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6697 | loglike: -27.0065 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6697 | loglike: -27.0065 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6698 | loglike: -27.6868 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6699 | loglike: -29.2660 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6700 | loglike: -29.5775 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6700 | loglike: -29.5775 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6701 | loglike: -26.6323 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6702 | loglike: -25.9742 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6702 | loglike: -25.9742 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6702 | loglike: -25.9742 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6703 | loglike: -25.1804 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6704 | loglike: -24.4866 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6704 | loglike: -24.4866 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6705 | loglike: -25.9874 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6706 | loglike: -24.4511 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6707 | loglike: -23.9829 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6708 | loglike: -24.2174 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6709 | loglike: -24.1933 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6709 | loglike: -24.1933 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6710 | loglike: -24.9869 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6711 | loglike: -25.4824 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6712 | loglike: -26.1166 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6712 | loglike: -26.1166 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6713 | loglike: -28.4793 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6714 | loglike: -30.7427 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6714 | loglike: -30.7427 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6714 | loglike: -30.7427 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6715 | loglike: -32.7466 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6716 | loglike: -33.6675 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6717 | loglike: -35.6667 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6718 | loglike: -35.9544 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6719 | loglike: -36.5047 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6719 | loglike: -36.5047 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6719 | loglike: -36.5047 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6720 | loglike: -32.9883 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6721 | loglike: -32.7735 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6722 | loglike: -33.4905 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6723 | loglike: -35.3837 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6723 | loglike: -35.3837 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6723 | loglike: -35.3837 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6724 | loglike: -31.0355 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6725 | loglike: -29.4276 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6725 | loglike: -29.4276 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6726 | loglike: -29.4047 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6727 | loglike: -32.0037 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6728 | loglike: -31.9376 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6729 | loglike: -31.5928 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6730 | loglike: -27.1992 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6731 | loglike: -27.1294 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6732 | loglike: -26.7170 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6733 | loglike: -26.8914 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6734 | loglike: -27.9208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6734 | loglike: -27.9208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6734 | loglike: -27.9208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6735 | loglike: -28.7655 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6736 | loglike: -28.9735 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6737 | loglike: -28.8989 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6738 | loglike: -29.0526 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6738 | loglike: -29.0526 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6739 | loglike: -26.8408 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6740 | loglike: -27.1934 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6740 | loglike: -27.1934 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6741 | loglike: -25.9083 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6741 | loglike: -25.9083 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6742 | loglike: -27.5208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6742 | loglike: -27.5208 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6743 | loglike: -26.5211 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6743 | loglike: -26.5211 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6744 | loglike: -25.5040 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6744 | loglike: -25.5040 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6744 | loglike: -25.5040 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6744 | loglike: -25.5040 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6745 | loglike: -26.4503 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6746 | loglike: -28.6870 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6747 | loglike: -27.4278 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6747 | loglike: -27.4278 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6748 | loglike: -27.6101 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6749 | loglike: -26.1458 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6750 | loglike: -25.7170 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6751 | loglike: -25.4572 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6752 | loglike: -25.7083 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6752 | loglike: -25.7083 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6752 | loglike: -25.7083 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6753 | loglike: -26.5190 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6753 | loglike: -26.5190 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6753 | loglike: -26.5190 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6754 | loglike: -27.0161 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6754 | loglike: -27.0161 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6755 | loglike: -27.0706 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6756 | loglike: -26.2797 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6757 | loglike: -25.8234 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6758 | loglike: -26.0844 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6758 | loglike: -26.0844 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6758 | loglike: -26.0844 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6759 | loglike: -25.9266 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6760 | loglike: -26.1079 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6761 | loglike: -26.6357 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6762 | loglike: -25.9590 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6763 | loglike: -26.3680 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6764 | loglike: -26.1215 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6765 | loglike: -25.9399 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6765 | loglike: -25.9399 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6766 | loglike: -26.1556 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6767 | loglike: -25.6793 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6768 | loglike: -25.7457 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6769 | loglike: -26.4860 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6769 | loglike: -26.4860 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6769 | loglike: -26.4860 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6770 | loglike: -26.5528 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6771 | loglike: -27.6700 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6771 | loglike: -27.6700 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6772 | loglike: -28.8552 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6773 | loglike: -28.8502 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6773 | loglike: -28.8502 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6774 | loglike: -32.7068 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6775 | loglike: -30.8268 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6775 | loglike: -30.8268 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6776 | loglike: -31.5148 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6776 | loglike: -31.5148 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6776 | loglike: -31.5148 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6776 | loglike: -31.5148 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6777 | loglike: -31.6532 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6778 | loglike: -32.5516 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6779 | loglike: -29.5586 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6780 | loglike: -29.4415 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6781 | loglike: -30.8705 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6782 | loglike: -31.5691 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6783 | loglike: -31.9916 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6784 | loglike: -29.2661 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6785 | loglike: -29.3052 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6786 | loglike: -28.4955 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6786 | loglike: -28.4955 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6787 | loglike: -28.5789 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6787 | loglike: -28.5789 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6788 | loglike: -28.4993 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6789 | loglike: -29.2973 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6790 | loglike: -27.8480 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6790 | loglike: -27.8480 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6791 | loglike: -29.0488 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6792 | loglike: -29.5431 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6793 | loglike: -27.0012 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6794 | loglike: -26.2718 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6795 | loglike: -27.1719 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6796 | loglike: -26.8595 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6796 | loglike: -26.8595 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6797 | loglike: -28.3302 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6798 | loglike: -28.2737 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6798 | loglike: -28.2737 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6799 | loglike: -28.2884 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6799 | loglike: -28.2884 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6800 | loglike: -30.8269 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6800 | loglike: -30.8269 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6801 | loglike: -31.7558 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6802 | loglike: -29.8123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6803 | loglike: -28.7449 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6804 | loglike: -29.4815 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6805 | loglike: -30.2417 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6806 | loglike: -27.9399 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6806 | loglike: -27.9399 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6807 | loglike: -28.2445 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6808 | loglike: -31.2628 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6809 | loglike: -30.9990 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6809 | loglike: -30.9990 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6809 | loglike: -30.9990 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6810 | loglike: -31.6319 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6811 | loglike: -30.8654 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6812 | loglike: -28.8006 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6813 | loglike: -27.2783 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6813 | loglike: -27.2783 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6814 | loglike: -30.5176 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6815 | loglike: -27.3550 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6816 | loglike: -27.9229 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6817 | loglike: -26.5593 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6818 | loglike: -25.9550 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6819 | loglike: -26.0275 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6820 | loglike: -27.9360 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6820 | loglike: -27.9360 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6820 | loglike: -27.9360 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6820 | loglike: -27.9360 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6821 | loglike: -27.8679 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6821 | loglike: -27.8679 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6822 | loglike: -27.7189 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6822 | loglike: -27.7189 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6822 | loglike: -27.7189 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6823 | loglike: -29.2475 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6824 | loglike: -29.6932 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6825 | loglike: -28.1972 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6826 | loglike: -28.4105 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6827 | loglike: -27.1674 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6828 | loglike: -27.4457 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6829 | loglike: -27.4182 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6830 | loglike: -29.2061 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6831 | loglike: -29.9214 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6832 | loglike: -29.3960 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6833 | loglike: -30.5726 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6834 | loglike: -29.9590 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6834 | loglike: -29.9590 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6834 | loglike: -29.9590 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6835 | loglike: -30.6320 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6836 | loglike: -30.8462 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6836 | loglike: -30.8462 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6836 | loglike: -30.8462 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6837 | loglike: -33.7636 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6838 | loglike: -31.7955 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6838 | loglike: -31.7955 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6839 | loglike: -29.5722 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6840 | loglike: -29.3576 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6841 | loglike: -28.1814 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6841 | loglike: -28.1814 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6842 | loglike: -32.3118 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6843 | loglike: -36.0419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6843 | loglike: -36.0419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6843 | loglike: -36.0419 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6844 | loglike: -35.6571 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6845 | loglike: -33.9113 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6846 | loglike: -33.4266 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6847 | loglike: -37.3238 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6847 | loglike: -37.3238 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6848 | loglike: -38.9946 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6849 | loglike: -38.2433 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6849 | loglike: -38.2433 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6850 | loglike: -38.1842 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6850 | loglike: -38.1842 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6851 | loglike: -35.5800 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6851 | loglike: -35.5800 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6852 | loglike: -34.9548 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6852 | loglike: -34.9548 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6853 | loglike: -31.9260 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6854 | loglike: -29.6600 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6855 | loglike: -28.6168 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6856 | loglike: -28.2527 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6856 | loglike: -28.2527 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6857 | loglike: -27.9031 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6858 | loglike: -28.1215 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6858 | loglike: -28.1215 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6858 | loglike: -28.1215 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6858 | loglike: -28.1215 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6859 | loglike: -27.9933 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6859 | loglike: -27.9933 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6860 | loglike: -27.8945 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6861 | loglike: -27.2844 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6862 | loglike: -27.9185 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6863 | loglike: -27.5568 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6863 | loglike: -27.5568 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6864 | loglike: -27.5253 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6864 | loglike: -27.5253 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6865 | loglike: -27.1046 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6865 | loglike: -27.1046 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6866 | loglike: -27.7988 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6867 | loglike: -27.9113 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6867 | loglike: -27.9113 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6868 | loglike: -27.3200 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6869 | loglike: -28.9464 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6870 | loglike: -28.9003 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6870 | loglike: -28.9003 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6871 | loglike: -29.7446 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6872 | loglike: -30.4821 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6873 | loglike: -30.6711 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6873 | loglike: -30.6711 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6874 | loglike: -29.7746 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6875 | loglike: -29.6681 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6876 | loglike: -29.9177 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6876 | loglike: -29.9177 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6877 | loglike: -33.3849 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6878 | loglike: -32.3087 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6879 | loglike: -33.2732 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6880 | loglike: -33.4565 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6881 | loglike: -36.1389 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6882 | loglike: -35.0704 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6883 | loglike: -34.8937 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6884 | loglike: -32.5796 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6885 | loglike: -34.2615 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6886 | loglike: -35.9404 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6887 | loglike: -35.6140 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6887 | loglike: -35.6140 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6887 | loglike: -35.6140 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6888 | loglike: -35.6582 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6889 | loglike: -36.3922 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6890 | loglike: -38.7765 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6891 | loglike: -37.2959 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6892 | loglike: -35.5401 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6892 | loglike: -35.5401 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6892 | loglike: -35.5401 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6893 | loglike: -34.4346 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6893 | loglike: -34.4346 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6894 | loglike: -33.2738 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6895 | loglike: -35.1532 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6895 | loglike: -35.1532 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6896 | loglike: -32.9371 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6897 | loglike: -34.1199 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6898 | loglike: -31.2817 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6899 | loglike: -32.0748 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6899 | loglike: -32.0748 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6900 | loglike: -30.7634 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6900 | loglike: -30.7634 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6901 | loglike: -30.9890 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6902 | loglike: -30.6968 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6903 | loglike: -29.7134 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6904 | loglike: -29.8512 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6905 | loglike: -29.0960 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6906 | loglike: -29.8983 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6907 | loglike: -30.2267 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6908 | loglike: -32.2832 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6909 | loglike: -29.1767 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6910 | loglike: -27.5689 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6911 | loglike: -26.2095 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6911 | loglike: -26.2095 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6912 | loglike: -25.5901 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6913 | loglike: -25.4643 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6914 | loglike: -25.4678 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6915 | loglike: -25.6134 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6916 | loglike: -26.1492 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6917 | loglike: -26.4330 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6918 | loglike: -27.9829 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6919 | loglike: -26.8442 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6920 | loglike: -26.1913 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6921 | loglike: -26.9924 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6922 | loglike: -26.3283 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6923 | loglike: -25.9048 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6924 | loglike: -26.3886 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6925 | loglike: -28.6375 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6926 | loglike: -28.3653 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6927 | loglike: -27.2080 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6928 | loglike: -27.6579 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6929 | loglike: -28.1220 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6929 | loglike: -28.1220 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6929 | loglike: -28.1220 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6929 | loglike: -28.1220 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6930 | loglike: -26.6936 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6931 | loglike: -27.0445 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6932 | loglike: -28.1835 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6932 | loglike: -28.1835 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6933 | loglike: -27.6761 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6933 | loglike: -27.6761 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6934 | loglike: -26.4470 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6934 | loglike: -26.4470 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6935 | loglike: -27.7563 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6935 | loglike: -27.7563 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6935 | loglike: -27.7563 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6936 | loglike: -28.4130 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6937 | loglike: -26.8745 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6938 | loglike: -26.6565 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6939 | loglike: -26.8389 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6940 | loglike: -31.5659 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6940 | loglike: -31.5659 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6941 | loglike: -31.1123 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6942 | loglike: -30.8879 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6943 | loglike: -29.9355 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6944 | loglike: -30.3204 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6944 | loglike: -30.3204 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6944 | loglike: -30.3204 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6944 | loglike: -30.3204 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6945 | loglike: -31.4038 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6946 | loglike: -33.9576 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6946 | loglike: -33.9576 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6947 | loglike: -30.1273 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6948 | loglike: -28.5799 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6949 | loglike: -28.4557 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6949 | loglike: -28.4557 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6949 | loglike: -28.4557 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6949 | loglike: -28.4557 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6950 | loglike: -27.2249 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6950 | loglike: -27.2249 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6951 | loglike: -27.5059 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6951 | loglike: -27.5059 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6952 | loglike: -28.7661 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6952 | loglike: -28.7661 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6953 | loglike: -28.6048 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6954 | loglike: -27.9293 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6955 | loglike: -27.7212 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6956 | loglike: -26.8729 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6957 | loglike: -28.1067 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6958 | loglike: -28.5186 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6959 | loglike: -29.3658 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6960 | loglike: -31.0911 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6960 | loglike: -31.0911 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6961 | loglike: -30.9142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6961 | loglike: -30.9142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6961 | loglike: -30.9142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6961 | loglike: -30.9142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6961 | loglike: -30.9142 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6962 | loglike: -32.5875 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6962 | loglike: -32.5875 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6963 | loglike: -30.3306 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6963 | loglike: -30.3306 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6963 | loglike: -30.3306 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6964 | loglike: -28.4250 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6965 | loglike: -26.9275 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6966 | loglike: -28.2364 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6967 | loglike: -25.7455 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6968 | loglike: -29.9838 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6969 | loglike: -29.9509 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6970 | loglike: -29.9611 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6971 | loglike: -29.0945 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6972 | loglike: -28.0565 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6972 | loglike: -28.0565 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6973 | loglike: -26.5059 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6973 | loglike: -26.5059 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6974 | loglike: -27.7381 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6975 | loglike: -27.4434 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6976 | loglike: -25.9366 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6977 | loglike: -25.5869 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6978 | loglike: -27.2212 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6979 | loglike: -25.7975 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6979 | loglike: -25.7975 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6979 | loglike: -25.7975 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6980 | loglike: -25.6290 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6981 | loglike: -25.8984 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6981 | loglike: -25.8984 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6982 | loglike: -27.1430 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6982 | loglike: -27.1430 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6983 | loglike: -26.6438 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6984 | loglike: -26.2644 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6985 | loglike: -26.5112 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6986 | loglike: -25.0866 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6986 | loglike: -25.0866 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6987 | loglike: -25.0444 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6988 | loglike: -25.1644 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6988 | loglike: -25.1644 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6988 | loglike: -25.1644 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6989 | loglike: -25.4499 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6990 | loglike: -26.4862 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6991 | loglike: -27.7820 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6991 | loglike: -27.7820 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6992 | loglike: -27.5823 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6993 | loglike: -29.0662 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6994 | loglike: -29.2273 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6995 | loglike: -28.6376 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6996 | loglike: -26.4678 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6996 | loglike: -26.4678 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6997 | loglike: -26.5487 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6998 | loglike: -27.4824 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6999 | loglike: -28.1090 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 6999 | loglike: -28.1090 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 7000 | loglike: -28.0641 | Gelman-Rubin: [3.08757103e-01 1.46573380e-04 3.03250948e-02 2.37146498e-01
Accepted: 17500 | loglike: -26.5819 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17500 | loglike: -26.5819 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17501 | loglike: -27.0786 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17501 | loglike: -27.0786 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17502 | loglike: -29.3546 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17503 | loglike: -28.8117 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17503 | loglike: -28.8117 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17504 | loglike: -27.1248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17505 | loglike: -26.2986 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17506 | loglike: -26.0234 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17506 | loglike: -26.0234 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17507 | loglike: -24.7119 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17508 | loglike: -24.5553 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17509 | loglike: -25.0547 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17510 | loglike: -24.4578 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17511 | loglike: -25.0016 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17512 | loglike: -25.9136 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17513 | loglike: -28.0748 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17513 | loglike: -28.0748 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17514 | loglike: -26.3472 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17515 | loglike: -26.9976 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17515 | loglike: -26.9976 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17516 | loglike: -26.9401 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17516 | loglike: -26.9401 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17516 | loglike: -26.9401 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17516 | loglike: -26.9401 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17517 | loglike: -28.5765 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17518 | loglike: -27.8264 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17519 | loglike: -28.3935 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17519 | loglike: -28.3935 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17520 | loglike: -26.9286 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17521 | loglike: -26.0529 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17522 | loglike: -25.4992 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17522 | loglike: -25.4992 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17523 | loglike: -24.7162 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17523 | loglike: -24.7162 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17524 | loglike: -24.7078 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17525 | loglike: -24.6674 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17526 | loglike: -24.4875 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17527 | loglike: -24.4935 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17528 | loglike: -24.1478 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17529 | loglike: -24.2506 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17530 | loglike: -24.0872 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17531 | loglike: -25.1107 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17532 | loglike: -24.4477 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17533 | loglike: -27.3986 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17533 | loglike: -27.3986 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17534 | loglike: -27.7729 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17535 | loglike: -26.0301 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17536 | loglike: -24.5576 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17537 | loglike: -25.0162 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17538 | loglike: -25.1666 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17539 | loglike: -25.3505 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17540 | loglike: -25.4488 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17541 | loglike: -26.6132 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17542 | loglike: -28.1512 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17543 | loglike: -29.1633 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17543 | loglike: -29.1633 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17544 | loglike: -25.4857 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17544 | loglike: -25.4857 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17544 | loglike: -25.4857 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17545 | loglike: -25.5065 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17546 | loglike: -25.4178 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17546 | loglike: -25.4178 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17547 | loglike: -26.6648 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17548 | loglike: -26.7091 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17549 | loglike: -26.2970 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17549 | loglike: -26.2970 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17550 | loglike: -25.9858 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17550 | loglike: -25.9858 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17551 | loglike: -26.8552 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17552 | loglike: -26.7058 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17553 | loglike: -26.8844 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17554 | loglike: -27.3974 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17555 | loglike: -32.7576 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17556 | loglike: -29.4811 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17557 | loglike: -29.0852 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17558 | loglike: -29.8191 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17559 | loglike: -27.8952 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17560 | loglike: -27.4105 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17561 | loglike: -27.0897 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17561 | loglike: -27.0897 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17562 | loglike: -26.6768 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17562 | loglike: -26.6768 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17562 | loglike: -26.6768 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17563 | loglike: -27.3583 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17564 | loglike: -28.1475 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17565 | loglike: -28.9702 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17566 | loglike: -27.9052 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17566 | loglike: -27.9052 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17566 | loglike: -27.9052 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17567 | loglike: -29.2181 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17568 | loglike: -29.2630 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17569 | loglike: -31.1220 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17570 | loglike: -31.6577 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17571 | loglike: -27.7257 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17572 | loglike: -28.1473 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17573 | loglike: -28.1595 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17574 | loglike: -25.9646 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17575 | loglike: -24.9171 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17575 | loglike: -24.9171 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17576 | loglike: -24.5998 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17576 | loglike: -24.5998 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17576 | loglike: -24.5998 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17577 | loglike: -25.2183 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17578 | loglike: -25.1434 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17579 | loglike: -24.3758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17579 | loglike: -24.3758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17579 | loglike: -24.3758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17579 | loglike: -24.3758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17580 | loglike: -24.9866 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17581 | loglike: -25.3255 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17582 | loglike: -25.1625 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17583 | loglike: -24.1902 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17583 | loglike: -24.1902 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17584 | loglike: -24.8353 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17584 | loglike: -24.8353 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17585 | loglike: -24.7799 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17586 | loglike: -25.2948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17586 | loglike: -25.2948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17587 | loglike: -24.8364 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17588 | loglike: -25.1761 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17589 | loglike: -26.2643 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17590 | loglike: -26.7846 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17591 | loglike: -26.6509 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17592 | loglike: -25.4383 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17592 | loglike: -25.4383 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17593 | loglike: -26.2571 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17593 | loglike: -26.2571 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17593 | loglike: -26.2571 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17594 | loglike: -25.5629 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17595 | loglike: -25.9383 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17596 | loglike: -25.9161 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17597 | loglike: -26.3059 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17598 | loglike: -26.0203 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17599 | loglike: -27.2401 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17600 | loglike: -25.8152 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17600 | loglike: -25.8152 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17601 | loglike: -25.8463 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17602 | loglike: -24.8475 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17603 | loglike: -25.2745 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17604 | loglike: -25.3665 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17605 | loglike: -24.7452 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17606 | loglike: -24.3901 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17607 | loglike: -25.3227 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17608 | loglike: -26.2713 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17609 | loglike: -25.4110 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17610 | loglike: -26.6601 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17611 | loglike: -27.1048 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17612 | loglike: -29.9893 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17613 | loglike: -30.1925 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17614 | loglike: -32.2400 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17615 | loglike: -32.4496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17615 | loglike: -32.4496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17615 | loglike: -32.4496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17616 | loglike: -31.9238 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17617 | loglike: -25.9600 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17618 | loglike: -25.9505 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17619 | loglike: -25.9248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17620 | loglike: -25.3363 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17621 | loglike: -24.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17621 | loglike: -24.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17621 | loglike: -24.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17621 | loglike: -24.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17622 | loglike: -24.8411 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17623 | loglike: -25.2466 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17624 | loglike: -25.2895 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17625 | loglike: -26.7704 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17625 | loglike: -26.7704 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17626 | loglike: -27.8559 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17626 | loglike: -27.8559 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17626 | loglike: -27.8559 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17626 | loglike: -27.8559 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17627 | loglike: -29.3335 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17627 | loglike: -29.3335 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17627 | loglike: -29.3335 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17628 | loglike: -34.2643 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17629 | loglike: -29.7126 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17630 | loglike: -29.4216 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17631 | loglike: -26.9040 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17632 | loglike: -26.3156 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17633 | loglike: -25.6689 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17634 | loglike: -27.0777 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17635 | loglike: -27.9765 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17635 | loglike: -27.9765 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17636 | loglike: -27.3196 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17637 | loglike: -28.3700 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17637 | loglike: -28.3700 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17637 | loglike: -28.3700 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17638 | loglike: -28.3287 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17639 | loglike: -26.9231 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17640 | loglike: -25.5756 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17640 | loglike: -25.5756 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17640 | loglike: -25.5756 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17641 | loglike: -25.6465 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17641 | loglike: -25.6465 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17641 | loglike: -25.6465 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17642 | loglike: -25.9430 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17643 | loglike: -26.9072 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17643 | loglike: -26.9072 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17644 | loglike: -27.8751 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17645 | loglike: -27.5496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17646 | loglike: -27.5839 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17646 | loglike: -27.5839 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17647 | loglike: -26.4775 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17648 | loglike: -26.3379 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17649 | loglike: -25.2834 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17650 | loglike: -27.1231 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17650 | loglike: -27.1231 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17651 | loglike: -27.6729 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17652 | loglike: -27.9155 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17653 | loglike: -26.7638 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17654 | loglike: -29.6213 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17655 | loglike: -27.9986 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17656 | loglike: -27.2979 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17657 | loglike: -26.3564 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17657 | loglike: -26.3564 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17658 | loglike: -27.1915 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17658 | loglike: -27.1915 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17659 | loglike: -26.6719 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17660 | loglike: -26.9241 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17661 | loglike: -26.3252 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17662 | loglike: -29.0368 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17662 | loglike: -29.0368 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17663 | loglike: -26.9160 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17663 | loglike: -26.9160 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17664 | loglike: -26.5812 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17664 | loglike: -26.5812 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17664 | loglike: -26.5812 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17665 | loglike: -26.7422 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17666 | loglike: -27.0243 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17666 | loglike: -27.0243 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17667 | loglike: -25.6158 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17668 | loglike: -25.7072 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17668 | loglike: -25.7072 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17668 | loglike: -25.7072 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17669 | loglike: -25.1648 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17670 | loglike: -25.1483 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17671 | loglike: -25.2109 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17672 | loglike: -25.3709 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17673 | loglike: -26.7411 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17674 | loglike: -26.6713 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17674 | loglike: -26.6713 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17675 | loglike: -26.2556 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17676 | loglike: -27.4104 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17677 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17677 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17677 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17677 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17677 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17678 | loglike: -25.4063 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17678 | loglike: -25.4063 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17679 | loglike: -27.0064 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17680 | loglike: -27.3708 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17681 | loglike: -28.2123 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17681 | loglike: -28.2123 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17682 | loglike: -26.6227 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17683 | loglike: -26.0218 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17684 | loglike: -26.5743 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17685 | loglike: -26.5005 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17686 | loglike: -26.6555 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17687 | loglike: -25.9733 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17688 | loglike: -25.7523 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17689 | loglike: -26.2551 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17690 | loglike: -26.2528 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17691 | loglike: -26.5411 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17691 | loglike: -26.5411 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17692 | loglike: -25.7657 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17692 | loglike: -25.7657 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17693 | loglike: -27.1242 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17694 | loglike: -26.8451 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17694 | loglike: -26.8451 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17694 | loglike: -26.8451 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17695 | loglike: -26.4038 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17696 | loglike: -27.0229 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17697 | loglike: -27.9511 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17698 | loglike: -29.0221 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17699 | loglike: -27.8837 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17700 | loglike: -26.9760 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17701 | loglike: -29.8918 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17701 | loglike: -29.8918 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17702 | loglike: -26.5523 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17703 | loglike: -27.1862 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17703 | loglike: -27.1862 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17704 | loglike: -26.6592 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17705 | loglike: -25.9863 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17706 | loglike: -25.7066 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17707 | loglike: -25.4361 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17708 | loglike: -26.4698 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17709 | loglike: -27.2750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17710 | loglike: -26.8645 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17711 | loglike: -28.2611 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17712 | loglike: -28.6677 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17713 | loglike: -27.3582 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17714 | loglike: -28.5037 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17715 | loglike: -26.1489 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17716 | loglike: -28.3787 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17717 | loglike: -26.7953 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17718 | loglike: -27.7454 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17718 | loglike: -27.7454 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17718 | loglike: -27.7454 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17718 | loglike: -27.7454 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17719 | loglike: -30.4559 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17720 | loglike: -29.3804 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17721 | loglike: -29.4564 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17722 | loglike: -29.4542 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17723 | loglike: -29.9977 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17724 | loglike: -30.3001 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17725 | loglike: -30.8254 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17725 | loglike: -30.8254 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17725 | loglike: -30.8254 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17726 | loglike: -32.1975 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17727 | loglike: -31.2985 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17727 | loglike: -31.2985 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17728 | loglike: -29.2694 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17728 | loglike: -29.2694 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17728 | loglike: -29.2694 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17729 | loglike: -28.6315 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17730 | loglike: -25.9677 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17731 | loglike: -26.3543 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17732 | loglike: -26.4237 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17733 | loglike: -26.2595 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17734 | loglike: -26.4201 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17734 | loglike: -26.4201 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17734 | loglike: -26.4201 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17734 | loglike: -26.4201 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17735 | loglike: -27.3647 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17736 | loglike: -26.7683 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17737 | loglike: -27.0042 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17737 | loglike: -27.0042 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17738 | loglike: -25.8745 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17739 | loglike: -25.6164 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17740 | loglike: -29.3893 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17741 | loglike: -26.3247 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17742 | loglike: -26.8279 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17743 | loglike: -28.1118 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17744 | loglike: -27.1505 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17745 | loglike: -25.9356 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17745 | loglike: -25.9356 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17746 | loglike: -26.8040 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17746 | loglike: -26.8040 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17747 | loglike: -27.2939 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17748 | loglike: -26.5865 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17749 | loglike: -25.8934 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17750 | loglike: -25.7676 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17751 | loglike: -25.8098 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17751 | loglike: -25.8098 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17752 | loglike: -25.2837 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17753 | loglike: -25.1759 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17754 | loglike: -24.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17755 | loglike: -24.9120 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17756 | loglike: -26.8234 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17757 | loglike: -25.2106 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17758 | loglike: -25.4008 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17759 | loglike: -25.9357 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17760 | loglike: -25.8022 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17761 | loglike: -25.5948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17761 | loglike: -25.5948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17761 | loglike: -25.5948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17761 | loglike: -25.5948 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17762 | loglike: -27.1366 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17763 | loglike: -27.5889 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17763 | loglike: -27.5889 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17764 | loglike: -27.8710 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17765 | loglike: -30.5594 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17766 | loglike: -31.7792 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17766 | loglike: -31.7792 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17767 | loglike: -29.7886 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17768 | loglike: -29.9134 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17768 | loglike: -29.9134 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17769 | loglike: -29.1506 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17770 | loglike: -28.6762 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17771 | loglike: -27.0992 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17772 | loglike: -26.9328 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17773 | loglike: -26.1798 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17774 | loglike: -25.9307 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17775 | loglike: -26.9197 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17776 | loglike: -28.6534 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17777 | loglike: -28.5297 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17777 | loglike: -28.5297 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17778 | loglike: -30.7659 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17779 | loglike: -28.1713 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17780 | loglike: -26.5558 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17781 | loglike: -26.9093 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17781 | loglike: -26.9093 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17781 | loglike: -26.9093 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17782 | loglike: -26.8358 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17783 | loglike: -27.8923 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17784 | loglike: -26.9280 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17785 | loglike: -26.4001 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17785 | loglike: -26.4001 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17785 | loglike: -26.4001 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17786 | loglike: -25.8849 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17787 | loglike: -26.3377 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17788 | loglike: -26.1254 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17788 | loglike: -26.1254 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17789 | loglike: -25.3984 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17790 | loglike: -25.8903 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17791 | loglike: -27.3661 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17792 | loglike: -27.0767 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17793 | loglike: -27.2777 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17794 | loglike: -27.4438 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17795 | loglike: -27.3763 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17796 | loglike: -27.0348 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17797 | loglike: -27.2809 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17798 | loglike: -27.3892 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17799 | loglike: -30.1647 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17800 | loglike: -30.0555 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17801 | loglike: -29.9732 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17802 | loglike: -28.8611 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17803 | loglike: -28.2460 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17804 | loglike: -26.6699 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17804 | loglike: -26.6699 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17805 | loglike: -28.8580 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17805 | loglike: -28.8580 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17805 | loglike: -28.8580 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17806 | loglike: -30.6280 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17807 | loglike: -29.6965 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17808 | loglike: -28.7016 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17809 | loglike: -29.4187 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17810 | loglike: -32.3056 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17811 | loglike: -29.6758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17811 | loglike: -29.6758 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17812 | loglike: -26.8755 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17813 | loglike: -26.7865 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17814 | loglike: -26.2743 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17815 | loglike: -27.1340 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17816 | loglike: -26.2718 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17817 | loglike: -26.5041 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17817 | loglike: -26.5041 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17818 | loglike: -26.2764 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17818 | loglike: -26.2764 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17819 | loglike: -25.8812 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17819 | loglike: -25.8812 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17820 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17820 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17820 | loglike: -25.5248 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17821 | loglike: -25.5727 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17822 | loglike: -25.0276 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17822 | loglike: -25.0276 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17822 | loglike: -25.0276 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17822 | loglike: -25.0276 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17823 | loglike: -25.0194 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17824 | loglike: -25.2114 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17825 | loglike: -25.2727 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17826 | loglike: -25.0638 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17827 | loglike: -26.1651 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17827 | loglike: -26.1651 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17828 | loglike: -26.2152 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17829 | loglike: -25.0776 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17830 | loglike: -25.3048 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17831 | loglike: -26.7135 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17831 | loglike: -26.7135 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17832 | loglike: -27.0595 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17833 | loglike: -25.9866 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17834 | loglike: -26.9597 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17835 | loglike: -29.8005 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17835 | loglike: -29.8005 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17836 | loglike: -28.2043 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17837 | loglike: -27.2807 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17837 | loglike: -27.2807 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17838 | loglike: -27.1239 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17839 | loglike: -27.8515 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17840 | loglike: -27.5789 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17841 | loglike: -29.8162 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17842 | loglike: -29.2219 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17843 | loglike: -34.0380 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17844 | loglike: -31.1128 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17845 | loglike: -29.8086 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17845 | loglike: -29.8086 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17846 | loglike: -30.8445 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17847 | loglike: -29.5961 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17848 | loglike: -30.3794 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17849 | loglike: -31.5426 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17850 | loglike: -32.0202 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17851 | loglike: -31.8873 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17852 | loglike: -29.8898 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17852 | loglike: -29.8898 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17853 | loglike: -29.6602 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17854 | loglike: -28.7545 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17854 | loglike: -28.7545 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17855 | loglike: -31.2289 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17856 | loglike: -30.9384 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17856 | loglike: -30.9384 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17857 | loglike: -29.9811 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17858 | loglike: -28.6106 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17859 | loglike: -29.9904 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17860 | loglike: -29.4345 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17861 | loglike: -28.9007 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17862 | loglike: -32.0023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17862 | loglike: -32.0023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17862 | loglike: -32.0023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17862 | loglike: -32.0023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17862 | loglike: -32.0023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17863 | loglike: -32.1301 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17864 | loglike: -29.8014 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17864 | loglike: -29.8014 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17865 | loglike: -28.9347 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17866 | loglike: -28.9056 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17866 | loglike: -28.9056 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17867 | loglike: -30.3068 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17867 | loglike: -30.3068 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17868 | loglike: -32.6165 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17868 | loglike: -32.6165 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17869 | loglike: -31.1163 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17869 | loglike: -31.1163 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17869 | loglike: -31.1163 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17870 | loglike: -33.2001 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17871 | loglike: -31.8793 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17872 | loglike: -32.6399 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17873 | loglike: -31.9473 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17874 | loglike: -32.0485 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17875 | loglike: -31.2061 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17876 | loglike: -29.3820 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17876 | loglike: -29.3820 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17877 | loglike: -28.8098 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17878 | loglike: -29.1303 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17879 | loglike: -29.0297 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17879 | loglike: -29.0297 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17880 | loglike: -28.9593 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17881 | loglike: -27.7996 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17881 | loglike: -27.7996 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17882 | loglike: -26.7645 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17883 | loglike: -26.1472 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17884 | loglike: -26.3116 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17884 | loglike: -26.3116 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17884 | loglike: -26.3116 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17884 | loglike: -26.3116 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17885 | loglike: -26.6607 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17886 | loglike: -26.9832 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17887 | loglike: -26.1963 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17887 | loglike: -26.1963 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17888 | loglike: -27.6277 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17888 | loglike: -27.6277 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17889 | loglike: -26.3400 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17889 | loglike: -26.3400 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17890 | loglike: -26.4128 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17891 | loglike: -27.0158 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17892 | loglike: -26.5630 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17893 | loglike: -26.7292 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17894 | loglike: -28.6487 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17894 | loglike: -28.6487 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17894 | loglike: -28.6487 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17895 | loglike: -28.5789 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17895 | loglike: -28.5789 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17896 | loglike: -26.4277 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17897 | loglike: -27.5602 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17897 | loglike: -27.5602 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17898 | loglike: -26.7363 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17899 | loglike: -26.8962 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17899 | loglike: -26.8962 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17899 | loglike: -26.8962 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17900 | loglike: -28.6105 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17900 | loglike: -28.6105 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17901 | loglike: -27.4043 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17902 | loglike: -28.8127 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17903 | loglike: -26.9496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17903 | loglike: -26.9496 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17904 | loglike: -25.5970 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17904 | loglike: -25.5970 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17905 | loglike: -25.5539 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17906 | loglike: -25.6787 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17907 | loglike: -26.1957 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17908 | loglike: -25.3889 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17909 | loglike: -25.9924 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17910 | loglike: -25.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17910 | loglike: -25.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17910 | loglike: -25.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17910 | loglike: -25.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17910 | loglike: -25.6023 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17911 | loglike: -25.6309 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17911 | loglike: -25.6309 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17912 | loglike: -25.8519 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17913 | loglike: -25.6898 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17914 | loglike: -25.7538 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17915 | loglike: -26.2457 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17916 | loglike: -27.3168 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17916 | loglike: -27.3168 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17917 | loglike: -26.0650 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17918 | loglike: -25.6497 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17919 | loglike: -25.4404 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17920 | loglike: -25.4240 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17921 | loglike: -24.8610 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17922 | loglike: -25.2163 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17923 | loglike: -25.5110 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17923 | loglike: -25.5110 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17924 | loglike: -24.6784 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17924 | loglike: -24.6784 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17925 | loglike: -24.9160 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17925 | loglike: -24.9160 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17926 | loglike: -24.8700 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17927 | loglike: -25.0336 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17928 | loglike: -26.4962 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17928 | loglike: -26.4962 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17929 | loglike: -27.8852 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17929 | loglike: -27.8852 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17929 | loglike: -27.8852 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17930 | loglike: -27.5707 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17931 | loglike: -28.4299 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17931 | loglike: -28.4299 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17932 | loglike: -29.2200 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17933 | loglike: -26.8206 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17934 | loglike: -26.8017 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17935 | loglike: -26.1969 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17936 | loglike: -26.7047 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17937 | loglike: -26.9051 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17937 | loglike: -26.9051 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17937 | loglike: -26.9051 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17938 | loglike: -29.9239 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17938 | loglike: -29.9239 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17938 | loglike: -29.9239 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17939 | loglike: -32.4340 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17939 | loglike: -32.4340 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17939 | loglike: -32.4340 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17940 | loglike: -31.8458 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17941 | loglike: -33.1616 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17942 | loglike: -30.6464 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17943 | loglike: -31.4414 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17944 | loglike: -32.0795 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17945 | loglike: -33.7587 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17946 | loglike: -33.2006 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17947 | loglike: -30.8173 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17947 | loglike: -30.8173 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17947 | loglike: -30.8173 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17948 | loglike: -33.3243 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17949 | loglike: -31.1584 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17950 | loglike: -31.2665 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17951 | loglike: -31.9373 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17952 | loglike: -34.6519 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17952 | loglike: -34.6519 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17952 | loglike: -34.6519 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17953 | loglike: -34.4612 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17953 | loglike: -34.4612 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17954 | loglike: -36.9480 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17954 | loglike: -36.9480 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17955 | loglike: -37.5663 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17955 | loglike: -37.5663 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17956 | loglike: -30.0716 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17957 | loglike: -29.2319 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17958 | loglike: -28.0807 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17959 | loglike: -27.6195 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17959 | loglike: -27.6195 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17960 | loglike: -30.2147 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17960 | loglike: -30.2147 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17961 | loglike: -33.6143 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17962 | loglike: -33.8502 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17963 | loglike: -33.6465 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17963 | loglike: -33.6465 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17964 | loglike: -32.3751 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17965 | loglike: -32.9161 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17965 | loglike: -32.9161 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17965 | loglike: -32.9161 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17966 | loglike: -29.5386 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17967 | loglike: -27.4352 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17968 | loglike: -25.8930 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17969 | loglike: -26.1375 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17970 | loglike: -26.4159 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17971 | loglike: -26.1982 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17971 | loglike: -26.1982 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17972 | loglike: -27.0790 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17972 | loglike: -27.0790 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17972 | loglike: -27.0790 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17972 | loglike: -27.0790 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17973 | loglike: -27.3849 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17974 | loglike: -28.8231 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17975 | loglike: -30.3080 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17976 | loglike: -29.1053 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17977 | loglike: -30.0899 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17978 | loglike: -28.1703 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17979 | loglike: -28.0451 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17980 | loglike: -27.5817 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17980 | loglike: -27.5817 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17981 | loglike: -27.9966 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17982 | loglike: -27.3750 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17983 | loglike: -27.8274 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17984 | loglike: -28.4552 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17984 | loglike: -28.4552 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17985 | loglike: -29.1466 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17986 | loglike: -28.1674 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17987 | loglike: -28.5057 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17988 | loglike: -29.9704 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17989 | loglike: -30.8307 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17990 | loglike: -31.2393 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17991 | loglike: -29.9711 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17992 | loglike: -30.6970 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17993 | loglike: -29.5163 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17994 | loglike: -29.5175 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17995 | loglike: -28.9105 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17996 | loglike: -28.6009 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17996 | loglike: -28.6009 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17997 | loglike: -28.4840 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17998 | loglike: -28.3963 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 17999 | loglike: -29.5190 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 18000 | loglike: -28.0746 | Gelman-Rubin: [9.20552870e-02 3.65039393e-03 1.28530927e-04 3.50715313e-05
Accepted: 20501 | loglike: -32.8430 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20502 | loglike: -33.0090 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20502 | loglike: -33.0090 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20503 | loglike: -31.9867 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20504 | loglike: -31.8581 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20505 | loglike: -31.7209 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20506 | loglike: -31.9713 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20506 | loglike: -31.9713 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20507 | loglike: -31.8868 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20508 | loglike: -34.2991 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20509 | loglike: -33.4632 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20510 | loglike: -32.6707 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20511 | loglike: -32.4902 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20512 | loglike: -31.9792 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20513 | loglike: -31.8407 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20514 | loglike: -31.3005 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20515 | loglike: -33.5548 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20516 | loglike: -33.0659 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20517 | loglike: -33.3019 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20517 | loglike: -33.3019 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20517 | loglike: -33.3019 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20518 | loglike: -35.5509 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20519 | loglike: -37.0796 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20520 | loglike: -36.5074 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20521 | loglike: -33.3927 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20522 | loglike: -33.5224 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20523 | loglike: -33.7316 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20524 | loglike: -33.7626 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20525 | loglike: -34.8002 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20525 | loglike: -34.8002 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20526 | loglike: -33.9786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20526 | loglike: -33.9786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20527 | loglike: -32.8775 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20528 | loglike: -31.4280 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20529 | loglike: -31.0706 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20529 | loglike: -31.0706 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20530 | loglike: -30.4524 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20531 | loglike: -30.9836 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20532 | loglike: -31.4255 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20533 | loglike: -31.9411 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20534 | loglike: -33.4203 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20535 | loglike: -32.9327 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20536 | loglike: -33.1963 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20537 | loglike: -30.5000 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20538 | loglike: -31.2731 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20539 | loglike: -31.0520 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20540 | loglike: -31.1270 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20541 | loglike: -31.0005 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20542 | loglike: -30.1713 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20542 | loglike: -30.1713 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20543 | loglike: -30.5238 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20544 | loglike: -30.6222 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20544 | loglike: -30.6222 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20545 | loglike: -32.7248 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20545 | loglike: -32.7248 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20545 | loglike: -32.7248 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20546 | loglike: -30.4657 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20547 | loglike: -30.0046 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20547 | loglike: -30.0046 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20547 | loglike: -30.0046 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20548 | loglike: -31.1256 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20549 | loglike: -31.3311 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20549 | loglike: -31.3311 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20550 | loglike: -32.9145 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20550 | loglike: -32.9145 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20551 | loglike: -32.5276 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20551 | loglike: -32.5276 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20552 | loglike: -31.0138 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20553 | loglike: -30.7728 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20553 | loglike: -30.7728 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20554 | loglike: -31.6425 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20555 | loglike: -29.3732 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20556 | loglike: -32.5861 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20557 | loglike: -32.8733 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20558 | loglike: -30.3688 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20558 | loglike: -30.3688 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20558 | loglike: -30.3688 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20559 | loglike: -28.1868 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20560 | loglike: -27.9998 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20561 | loglike: -28.2848 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20562 | loglike: -29.0405 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20563 | loglike: -29.6724 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20564 | loglike: -28.7283 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20565 | loglike: -28.9656 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20566 | loglike: -32.7791 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20566 | loglike: -32.7791 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20566 | loglike: -32.7791 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20566 | loglike: -32.7791 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20566 | loglike: -32.7791 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20567 | loglike: -34.3397 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20568 | loglike: -30.8435 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20569 | loglike: -29.8997 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20570 | loglike: -28.8542 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20571 | loglike: -29.6650 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20572 | loglike: -29.4403 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20573 | loglike: -29.1685 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20573 | loglike: -29.1685 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20574 | loglike: -28.4484 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20575 | loglike: -28.4693 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20576 | loglike: -28.3539 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20577 | loglike: -29.7725 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20578 | loglike: -27.6981 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20579 | loglike: -27.6060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20579 | loglike: -27.6060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20580 | loglike: -27.5583 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20580 | loglike: -27.5583 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20581 | loglike: -28.1545 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20581 | loglike: -28.1545 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20582 | loglike: -27.0687 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20583 | loglike: -27.3447 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20584 | loglike: -27.3103 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20584 | loglike: -27.3103 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20585 | loglike: -28.4691 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20586 | loglike: -28.8214 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20587 | loglike: -30.5515 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20587 | loglike: -30.5515 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20587 | loglike: -30.5515 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20588 | loglike: -29.5637 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20588 | loglike: -29.5637 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20589 | loglike: -28.6776 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20590 | loglike: -29.1949 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20591 | loglike: -27.9470 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20592 | loglike: -30.3915 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20593 | loglike: -30.3046 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20593 | loglike: -30.3046 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20594 | loglike: -28.6733 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20595 | loglike: -27.6661 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20595 | loglike: -27.6661 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20596 | loglike: -27.4500 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20597 | loglike: -26.7532 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20598 | loglike: -27.0028 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20598 | loglike: -27.0028 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20598 | loglike: -27.0028 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20599 | loglike: -27.7259 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20599 | loglike: -27.7259 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20600 | loglike: -27.6042 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20600 | loglike: -27.6042 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20601 | loglike: -28.9699 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20602 | loglike: -27.0963 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20603 | loglike: -27.1306 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20604 | loglike: -27.0054 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20605 | loglike: -27.1365 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20606 | loglike: -29.0342 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20607 | loglike: -30.6439 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20608 | loglike: -29.8985 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20609 | loglike: -28.9587 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20610 | loglike: -31.3495 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20611 | loglike: -30.7594 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20612 | loglike: -32.5520 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20612 | loglike: -32.5520 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20613 | loglike: -33.5985 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20614 | loglike: -31.7048 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20615 | loglike: -34.0443 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20616 | loglike: -34.8625 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20617 | loglike: -35.9937 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20618 | loglike: -36.3165 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20619 | loglike: -34.2893 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20620 | loglike: -34.5792 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20621 | loglike: -33.3459 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20622 | loglike: -32.0107 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20623 | loglike: -31.4397 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20624 | loglike: -32.3790 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20625 | loglike: -32.9630 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20626 | loglike: -33.0383 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20627 | loglike: -31.9287 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20627 | loglike: -31.9287 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20628 | loglike: -32.1165 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20629 | loglike: -31.7102 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20629 | loglike: -31.7102 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20630 | loglike: -31.6490 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20630 | loglike: -31.6490 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20630 | loglike: -31.6490 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20630 | loglike: -31.6490 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20631 | loglike: -32.6032 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20631 | loglike: -32.6032 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20631 | loglike: -32.6032 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20632 | loglike: -32.0153 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20633 | loglike: -32.0654 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20634 | loglike: -32.6015 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20635 | loglike: -32.3781 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20636 | loglike: -36.6309 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20637 | loglike: -36.0123 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20638 | loglike: -35.9743 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20639 | loglike: -34.2374 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20640 | loglike: -33.2736 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20641 | loglike: -37.3899 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20642 | loglike: -35.9842 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20643 | loglike: -37.2902 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20644 | loglike: -35.0954 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20645 | loglike: -35.6613 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20645 | loglike: -35.6613 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20646 | loglike: -34.7033 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20646 | loglike: -34.7033 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20646 | loglike: -34.7033 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20647 | loglike: -33.1361 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20648 | loglike: -32.6091 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20648 | loglike: -32.6091 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20649 | loglike: -31.3210 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20650 | loglike: -30.9408 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20651 | loglike: -28.3684 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20652 | loglike: -27.3878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20652 | loglike: -27.3878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20652 | loglike: -27.3878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20653 | loglike: -27.3465 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20653 | loglike: -27.3465 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20653 | loglike: -27.3465 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20653 | loglike: -27.3465 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20654 | loglike: -26.8831 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20655 | loglike: -28.8108 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20656 | loglike: -28.8621 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20657 | loglike: -28.5797 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20658 | loglike: -29.6062 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20659 | loglike: -30.4540 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20660 | loglike: -31.3976 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20661 | loglike: -28.0180 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20662 | loglike: -28.0213 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20662 | loglike: -28.0213 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20663 | loglike: -28.4098 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20664 | loglike: -28.1281 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20664 | loglike: -28.1281 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20665 | loglike: -26.1200 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20666 | loglike: -26.8273 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20667 | loglike: -27.5386 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20668 | loglike: -28.0617 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20669 | loglike: -27.0995 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20670 | loglike: -26.4208 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20671 | loglike: -26.6667 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20671 | loglike: -26.6667 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20672 | loglike: -26.6564 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20673 | loglike: -26.2059 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20674 | loglike: -24.9818 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20675 | loglike: -26.0048 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20676 | loglike: -26.1097 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20676 | loglike: -26.1097 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20677 | loglike: -25.3872 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20678 | loglike: -25.2400 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20679 | loglike: -25.1684 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20680 | loglike: -25.1986 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20681 | loglike: -26.1830 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20681 | loglike: -26.1830 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20682 | loglike: -26.5879 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20682 | loglike: -26.5879 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20683 | loglike: -25.8786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20683 | loglike: -25.8786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20684 | loglike: -27.2173 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20685 | loglike: -26.8708 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20685 | loglike: -26.8708 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20685 | loglike: -26.8708 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20686 | loglike: -29.0786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20686 | loglike: -29.0786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20686 | loglike: -29.0786 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20687 | loglike: -28.8243 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20688 | loglike: -26.6006 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20689 | loglike: -25.7023 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20690 | loglike: -25.6982 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20691 | loglike: -27.1468 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20692 | loglike: -26.7876 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20692 | loglike: -26.7876 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20693 | loglike: -27.9503 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20693 | loglike: -27.9503 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20693 | loglike: -27.9503 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20694 | loglike: -29.4174 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20694 | loglike: -29.4174 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20695 | loglike: -28.9182 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20695 | loglike: -28.9182 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20695 | loglike: -28.9182 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20695 | loglike: -28.9182 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20696 | loglike: -28.7580 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20696 | loglike: -28.7580 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20697 | loglike: -30.0443 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20697 | loglike: -30.0443 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20698 | loglike: -30.5856 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20698 | loglike: -30.5856 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20699 | loglike: -31.9030 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20699 | loglike: -31.9030 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20699 | loglike: -31.9030 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20699 | loglike: -31.9030 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20700 | loglike: -32.6653 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20701 | loglike: -30.5563 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20701 | loglike: -30.5563 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20701 | loglike: -30.5563 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20702 | loglike: -31.4573 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20703 | loglike: -31.0795 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20704 | loglike: -30.5144 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20704 | loglike: -30.5144 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20705 | loglike: -28.6989 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20706 | loglike: -29.1363 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20707 | loglike: -30.5831 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20708 | loglike: -30.6432 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20708 | loglike: -30.6432 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20709 | loglike: -28.4107 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20710 | loglike: -29.4763 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20711 | loglike: -29.8060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20712 | loglike: -28.8788 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20713 | loglike: -28.9245 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20714 | loglike: -30.2006 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20715 | loglike: -31.6807 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20716 | loglike: -30.0618 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20717 | loglike: -30.5477 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20718 | loglike: -29.7237 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20719 | loglike: -29.7166 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20720 | loglike: -29.2564 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20720 | loglike: -29.2564 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20721 | loglike: -28.6176 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20722 | loglike: -28.0881 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20723 | loglike: -27.0239 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20724 | loglike: -25.3106 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20725 | loglike: -24.9450 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20726 | loglike: -24.7381 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20727 | loglike: -25.8483 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20728 | loglike: -25.8469 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20729 | loglike: -26.5302 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20730 | loglike: -27.7928 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20730 | loglike: -27.7928 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20731 | loglike: -27.2804 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20732 | loglike: -26.8627 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20733 | loglike: -26.8051 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20734 | loglike: -25.9173 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20735 | loglike: -26.2159 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20735 | loglike: -26.2159 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20735 | loglike: -26.2159 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20736 | loglike: -28.2620 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20736 | loglike: -28.2620 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20737 | loglike: -28.5881 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20738 | loglike: -30.9439 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20739 | loglike: -30.3140 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20740 | loglike: -31.5552 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20741 | loglike: -29.9188 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20742 | loglike: -31.0176 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20743 | loglike: -33.0418 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20744 | loglike: -29.1885 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20745 | loglike: -30.7692 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20746 | loglike: -29.5096 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20747 | loglike: -29.0124 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20747 | loglike: -29.0124 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20747 | loglike: -29.0124 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20748 | loglike: -29.0260 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20749 | loglike: -29.8479 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20750 | loglike: -30.1669 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20751 | loglike: -29.5229 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20752 | loglike: -29.0768 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20753 | loglike: -25.7802 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20754 | loglike: -24.5362 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20755 | loglike: -25.0456 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20756 | loglike: -25.2401 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20757 | loglike: -26.7048 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20758 | loglike: -25.9320 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20759 | loglike: -24.6740 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20760 | loglike: -25.0837 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20761 | loglike: -25.5474 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20762 | loglike: -26.4662 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20763 | loglike: -25.7773 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20764 | loglike: -28.0593 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20765 | loglike: -28.7439 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20766 | loglike: -29.2315 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20767 | loglike: -31.4843 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20767 | loglike: -31.4843 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20768 | loglike: -29.8198 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20768 | loglike: -29.8198 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20768 | loglike: -29.8198 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20768 | loglike: -29.8198 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20769 | loglike: -27.3432 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20770 | loglike: -27.4570 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20771 | loglike: -26.0799 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20772 | loglike: -27.1630 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20772 | loglike: -27.1630 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20772 | loglike: -27.1630 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20773 | loglike: -27.3654 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20773 | loglike: -27.3654 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20774 | loglike: -25.4022 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20775 | loglike: -25.6005 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20776 | loglike: -26.2811 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20777 | loglike: -25.5117 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20778 | loglike: -26.0186 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20779 | loglike: -26.2106 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20780 | loglike: -25.2373 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20781 | loglike: -25.3148 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20782 | loglike: -26.5398 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20783 | loglike: -27.1605 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20783 | loglike: -27.1605 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20784 | loglike: -28.8015 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20785 | loglike: -28.5819 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20786 | loglike: -26.3501 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20786 | loglike: -26.3501 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20787 | loglike: -25.5644 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20787 | loglike: -25.5644 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20788 | loglike: -26.8878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20789 | loglike: -27.4371 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20790 | loglike: -27.3596 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20791 | loglike: -28.5748 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20792 | loglike: -29.0760 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20793 | loglike: -29.7678 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20794 | loglike: -27.0190 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20794 | loglike: -27.0190 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20795 | loglike: -25.7110 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20795 | loglike: -25.7110 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20795 | loglike: -25.7110 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20795 | loglike: -25.7110 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20796 | loglike: -24.6271 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20797 | loglike: -25.2667 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20798 | loglike: -28.2888 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20799 | loglike: -25.7431 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20800 | loglike: -25.2745 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20801 | loglike: -24.7962 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20802 | loglike: -24.4867 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20803 | loglike: -24.3396 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20804 | loglike: -24.2598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20804 | loglike: -24.2598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20804 | loglike: -24.2598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20805 | loglike: -24.4540 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20806 | loglike: -24.3152 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20807 | loglike: -24.2247 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20808 | loglike: -24.3657 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20809 | loglike: -24.5598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20810 | loglike: -24.9034 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20810 | loglike: -24.9034 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20811 | loglike: -24.6365 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20811 | loglike: -24.6365 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20812 | loglike: -24.9816 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20813 | loglike: -24.5420 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20813 | loglike: -24.5420 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20814 | loglike: -24.6958 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20815 | loglike: -24.6175 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20816 | loglike: -24.4741 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20816 | loglike: -24.4741 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20817 | loglike: -25.2083 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20818 | loglike: -24.7088 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20818 | loglike: -24.7088 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20819 | loglike: -24.3955 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20820 | loglike: -24.8064 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20821 | loglike: -28.3269 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20821 | loglike: -28.3269 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20822 | loglike: -27.4795 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20823 | loglike: -27.7317 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20823 | loglike: -27.7317 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20824 | loglike: -27.6715 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20824 | loglike: -27.6715 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20825 | loglike: -29.0271 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20826 | loglike: -28.8062 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20827 | loglike: -26.9520 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20828 | loglike: -27.6396 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20829 | loglike: -27.9859 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20830 | loglike: -26.0676 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20831 | loglike: -26.6306 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20832 | loglike: -25.9599 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20832 | loglike: -25.9599 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20833 | loglike: -26.3606 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20834 | loglike: -25.3432 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20835 | loglike: -28.2644 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20836 | loglike: -27.1598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20836 | loglike: -27.1598 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20837 | loglike: -26.8257 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20837 | loglike: -26.8257 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20838 | loglike: -27.6590 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20839 | loglike: -27.1116 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20840 | loglike: -26.1695 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20840 | loglike: -26.1695 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20841 | loglike: -26.3583 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20842 | loglike: -26.9745 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20843 | loglike: -27.1945 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20844 | loglike: -27.3253 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20845 | loglike: -28.4197 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20846 | loglike: -27.9077 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20847 | loglike: -26.8038 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20847 | loglike: -26.8038 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20848 | loglike: -28.4350 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20849 | loglike: -27.6393 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20850 | loglike: -28.1603 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20850 | loglike: -28.1603 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20851 | loglike: -28.1193 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20852 | loglike: -28.0999 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20853 | loglike: -27.1679 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20854 | loglike: -26.7122 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20855 | loglike: -27.2063 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20856 | loglike: -26.6889 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20857 | loglike: -26.8194 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20858 | loglike: -28.6393 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20858 | loglike: -28.6393 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20859 | loglike: -28.1840 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20860 | loglike: -27.5401 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20861 | loglike: -26.5408 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20861 | loglike: -26.5408 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20861 | loglike: -26.5408 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20861 | loglike: -26.5408 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20862 | loglike: -26.3632 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20863 | loglike: -27.5132 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20863 | loglike: -27.5132 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20863 | loglike: -27.5132 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20864 | loglike: -25.7802 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20865 | loglike: -26.3158 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20866 | loglike: -28.2890 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20867 | loglike: -30.0023 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20867 | loglike: -30.0023 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20867 | loglike: -30.0023 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20867 | loglike: -30.0023 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20868 | loglike: -28.4701 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20869 | loglike: -30.4047 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20870 | loglike: -30.7635 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20870 | loglike: -30.7635 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20871 | loglike: -28.7060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20871 | loglike: -28.7060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20871 | loglike: -28.7060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20872 | loglike: -29.3529 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20873 | loglike: -28.0773 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20874 | loglike: -27.9306 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20874 | loglike: -27.9306 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20875 | loglike: -28.3695 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20876 | loglike: -28.4210 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20877 | loglike: -31.1299 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20878 | loglike: -31.4077 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20879 | loglike: -32.7156 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20880 | loglike: -32.0467 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20881 | loglike: -33.4529 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20882 | loglike: -32.6170 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20882 | loglike: -32.6170 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20883 | loglike: -30.9740 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20884 | loglike: -30.4201 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20885 | loglike: -30.9905 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20886 | loglike: -28.7070 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20887 | loglike: -28.8170 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20887 | loglike: -28.8170 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20888 | loglike: -30.0641 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20888 | loglike: -30.0641 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20889 | loglike: -29.2288 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20889 | loglike: -29.2288 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20890 | loglike: -29.4040 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20891 | loglike: -32.3617 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20892 | loglike: -30.7297 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20893 | loglike: -30.9489 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20894 | loglike: -31.3615 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20894 | loglike: -31.3615 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20894 | loglike: -31.3615 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20895 | loglike: -30.5889 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20896 | loglike: -31.3063 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20897 | loglike: -31.8970 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20898 | loglike: -29.8474 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20899 | loglike: -31.3384 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20900 | loglike: -27.9266 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20900 | loglike: -27.9266 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20901 | loglike: -26.2655 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20902 | loglike: -24.9910 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20903 | loglike: -24.9430 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20904 | loglike: -26.1146 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20905 | loglike: -26.5977 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20906 | loglike: -28.2484 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20906 | loglike: -28.2484 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20907 | loglike: -27.0039 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20908 | loglike: -24.3900 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20909 | loglike: -24.5861 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20910 | loglike: -25.2809 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20911 | loglike: -24.8352 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20912 | loglike: -24.8593 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20913 | loglike: -24.2741 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20913 | loglike: -24.2741 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20913 | loglike: -24.2741 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20914 | loglike: -25.0343 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20914 | loglike: -25.0343 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20914 | loglike: -25.0343 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20915 | loglike: -24.8737 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20915 | loglike: -24.8737 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20915 | loglike: -24.8737 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20915 | loglike: -24.8737 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20916 | loglike: -25.3458 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20916 | loglike: -25.3458 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20917 | loglike: -27.7765 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20918 | loglike: -25.9708 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20918 | loglike: -25.9708 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20919 | loglike: -26.2102 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20919 | loglike: -26.2102 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20920 | loglike: -25.5060 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20921 | loglike: -26.0971 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20922 | loglike: -26.6782 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20922 | loglike: -26.6782 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20923 | loglike: -28.2011 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20923 | loglike: -28.2011 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20924 | loglike: -27.9267 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20925 | loglike: -26.7955 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20925 | loglike: -26.7955 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20925 | loglike: -26.7955 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20926 | loglike: -25.3559 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20927 | loglike: -25.2779 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20928 | loglike: -25.1635 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20929 | loglike: -25.0379 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20929 | loglike: -25.0379 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20929 | loglike: -25.0379 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20930 | loglike: -25.9537 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20930 | loglike: -25.9537 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20931 | loglike: -27.3277 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20932 | loglike: -25.7562 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20933 | loglike: -25.9836 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20933 | loglike: -25.9836 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20933 | loglike: -25.9836 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20934 | loglike: -25.6066 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20935 | loglike: -29.1157 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20935 | loglike: -29.1157 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20935 | loglike: -29.1157 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20936 | loglike: -30.1501 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20937 | loglike: -28.5128 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20938 | loglike: -29.8688 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20939 | loglike: -30.6032 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20940 | loglike: -29.6350 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20941 | loglike: -32.3004 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20942 | loglike: -33.9965 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20943 | loglike: -32.6980 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20944 | loglike: -33.0548 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20945 | loglike: -32.6127 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20946 | loglike: -31.9642 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20947 | loglike: -35.4854 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20948 | loglike: -32.3965 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20949 | loglike: -32.3207 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20949 | loglike: -32.3207 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20950 | loglike: -32.7378 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20950 | loglike: -32.7378 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20950 | loglike: -32.7378 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20951 | loglike: -29.9643 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20952 | loglike: -31.8334 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20952 | loglike: -31.8334 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20953 | loglike: -31.6629 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20954 | loglike: -30.6047 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20955 | loglike: -30.5383 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20956 | loglike: -30.4457 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20956 | loglike: -30.4457 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20957 | loglike: -29.9753 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20957 | loglike: -29.9753 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20957 | loglike: -29.9753 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20957 | loglike: -29.9753 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20957 | loglike: -29.9753 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20958 | loglike: -29.3795 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20959 | loglike: -26.9220 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20960 | loglike: -28.3366 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20961 | loglike: -28.0570 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20962 | loglike: -31.6052 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20963 | loglike: -30.3133 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20964 | loglike: -26.7108 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20965 | loglike: -27.0109 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20965 | loglike: -27.0109 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20965 | loglike: -27.0109 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20966 | loglike: -28.2587 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20967 | loglike: -26.2714 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20968 | loglike: -27.1940 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20969 | loglike: -27.5878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20969 | loglike: -27.5878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20969 | loglike: -27.5878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20969 | loglike: -27.5878 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20970 | loglike: -27.5983 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20971 | loglike: -26.5591 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20972 | loglike: -26.0805 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20973 | loglike: -27.4856 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20973 | loglike: -27.4856 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20974 | loglike: -26.7153 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20975 | loglike: -28.8882 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20976 | loglike: -31.5037 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20977 | loglike: -31.8603 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20978 | loglike: -30.9713 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20979 | loglike: -28.1976 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20979 | loglike: -28.1976 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20980 | loglike: -31.7951 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20981 | loglike: -32.9160 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20982 | loglike: -34.8150 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20983 | loglike: -32.6436 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20984 | loglike: -30.2591 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20985 | loglike: -29.1718 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20986 | loglike: -31.7009 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20987 | loglike: -30.7266 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20988 | loglike: -28.2743 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20989 | loglike: -26.3986 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20989 | loglike: -26.3986 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20989 | loglike: -26.3986 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20989 | loglike: -26.3986 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20990 | loglike: -26.3879 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20991 | loglike: -27.3064 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20992 | loglike: -30.4610 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20992 | loglike: -30.4610 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20993 | loglike: -30.2961 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20993 | loglike: -30.2961 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20993 | loglike: -30.2961 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20994 | loglike: -30.9731 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20995 | loglike: -30.6801 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20996 | loglike: -29.4362 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20996 | loglike: -29.4362 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20996 | loglike: -29.4362 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20996 | loglike: -29.4362 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20997 | loglike: -29.0835 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20998 | loglike: -28.0835 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20998 | loglike: -28.0835 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 20999 | loglike: -26.5418 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
Accepted: 21000 | loglike: -27.6746 | Gelman-Rubin: [2.56944727e-02 7.54060232e-05 1.14937029e-02 7.29796263e-03
1.75268802e-02]
INFO:simplemc:
Elapsed time: 20.566 minutes = 1233.962 seconds
---- Gelman-Rubin achived ----
Om: 0.3050 +/- 0.0775
Obh2: 0.0220 +/- 0.0005
h: 0.6850 +/- 0.0324
w: -1.0202 +/- 0.1624
wa: -0.1983 +/- 0.9194
```python
samples2 = res2['result']['samples']
weights2 = res2['result']['weights']
om2 = res2['result']['samples'][:,0]
obh22 = res2['result']['samples'][:,1]
h2 = res2['result']['samples'][:,2]
```
```python
plt.figure(figsize=(10,10), dpi=300)
plt.clf()
cornerfig2 = corner.corner(samples2[1000:,:], weights=weights2[1000:,], smooth=0.5, smooth1d=0.5,
# quantiles=(0.16, 0.84),
levels=(1-np.exp(-0.5), ), labels=pars_labels, color='darkred', plot_density=True,
plot_datapoints=False, fig=figcorner1)
# cornerfig2.text(0.8, 0.9,'SN+HD+BBAO+Planck', fontsize=20, color='cadetblue')
# cornerfig2.text(0.8, 0.85,'SN+HD', fontsize=20, color='darkred')
# cornerfig2.savefig('wacdm_datasets.png')
```
<Figure size 3000x3000 with 0 Axes>
```python
cornerfig2
```
# HD+SN+BBAO
```python
analysis3 = DriverMC(analyzername='mcmc', model='waCDM', datasets='HD+SN+BBAO')
```
Loading simplemc/data/HDiagramCompilacion-data_31.txt
Loading simplemc/data/HDiagramCompilacion-cov_31.txt
Eigenvalues of cov matrix: [16.0, 25.0, 64.0] ... 3844.0
Adding marginalising constant
Loading simplemc/data/jla_binned_distances_31nodes_v1.txt
Loading simplemc/data/cov_jla_binned_distances_31nodes_v1.txt
Eigenvalues of cov matrix: [0.0005070806003107887, 0.0005683516014088356, 0.000739250907386215] ... 0.03645982276128593
Adding marginalising constant
DR11LOWZ measurement in Cuesta : 8.468173048634462 +- 0.16748759985432085
Loading simplemc/data/sdss_DR11CMASS_consensus.dat
Aperp min,max,step,N: 0.961142836 1.12057144 0.0005714290000000677 280
Aparl min,max,step,N: 0.85171431 1.09085718 0.0008571430000000602 280
Loading done
rd = 149.26478152260088 Mpc
Fiducials at z= 0.57 : 14.3015431061137 21.466392296218515
Loading simplemc/data/chi2_surface_dr11_baseline_fit.txt
Aperp min,max,step,N: 0.5 1.5 0.016666999999999987 61
Aparl min,max,step,N: 0.8 1.2 0.0066669999999999785 61
Loading done
rd = 149.73834899440558 Mpc
Fiducials at z= 2.34 : 38.71393250862439 8.704556344366967
Loading simplemc/data/lyabaocross.scan
Aperp min,max,step,N: 0.7 1.3 0.020000000000000018 31
Aparl min,max,step,N: 0.8 1.2 0.010000000000000009 41
Loading done
rd = 149.56742181392625 Mpc
Fiducials at z= 2.36 : 38.931124520156345 8.641577445046805
SixdFGS measurement in Cuesta : 3.041334103142206 +- 0.18007899294920957
Loading simplemc/data/chidavexi8stavePk5staverec.dat
MGS measurement in Cuesta : DV= 664.1865351303804 +- 24.86880740958958
with rd= 148.6516589371155 DV_fid= 638.9490468680368 alphamin= 1.0394984363558428
Free parameters:
Om = 0.3038 +/- 0.05
Obh2 = 0.02234 +/- 0.001
h = 0.6821 +/- 0.05
w = -1.0 +/- 0.1
wa = 0.0 +/- 0.1
/home/isidro/Documents/github/simplemc_tests/simplemc/likelihoods/TabulatedBAODVLikelihood.py:37: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.
If increasing the limit yields no improvement it is advised to analyze
the integrand in order to determine the difficulties. If the position of a
local difficulty can be determined (singularity, discontinuity) one will
probably gain from splitting up the interval and calling the integrator
on the subranges. Perhaps a special-purpose integrator should be used.
rms /= quad(lambda x: sp.exp(-self.chi2i(alphamin+x)/2), -0.1, 0.1)[0]
```python
res3 = analysis3.executer()
```
INFO:simplemc:
nsamp: 50000
skip: 300
temp: 2
evidence: False
Bounds: [ 0.1 0.02 0.4 -2. -2. ] [0.5 0.025 0.9 0. 2. ]
Starting chain...
Accepted: 299 | loglike: -43.6723 | Gelman-Rubin: None
Re-initializing covariance matrix after burn-in
[[ 4.71886285e-04 -4.90637085e-06 -1.35131048e-04 7.60185422e-04
-5.20410366e-04]
[-4.90637085e-06 4.45863712e-07 8.38676546e-06 -2.31703132e-05
2.05077518e-06]
[-1.35131048e-04 8.38676546e-06 2.67775876e-04 -5.80078804e-04
1.43256055e-04]
[ 7.60185422e-04 -2.31703132e-05 -5.80078804e-04 2.85759879e-03
-7.24822600e-04]
[-5.20410366e-04 2.05077518e-06 1.43256055e-04 -7.24822600e-04
3.28240595e-03]]
Accepted: 1500 | loglike: -36.7371 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1500 | loglike: -36.7371 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1500 | loglike: -36.7371 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1500 | loglike: -36.7371 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1501 | loglike: -38.0856 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1502 | loglike: -38.0424 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1502 | loglike: -38.0424 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1502 | loglike: -38.0424 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1503 | loglike: -37.4365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1503 | loglike: -37.4365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1503 | loglike: -37.4365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1504 | loglike: -35.0253 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1504 | loglike: -35.0253 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1505 | loglike: -34.3489 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1505 | loglike: -34.3489 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1506 | loglike: -33.3958 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1507 | loglike: -32.8632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1507 | loglike: -32.8632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1507 | loglike: -32.8632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1507 | loglike: -32.8632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1507 | loglike: -32.8632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1508 | loglike: -33.2412 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1508 | loglike: -33.2412 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1508 | loglike: -33.2412 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1509 | loglike: -34.8952 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1509 | loglike: -34.8952 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1509 | loglike: -34.8952 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1509 | loglike: -34.8952 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1509 | loglike: -34.8952 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1510 | loglike: -35.4738 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1510 | loglike: -35.4738 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1511 | loglike: -35.4545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1512 | loglike: -37.8977 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1513 | loglike: -38.4584 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1513 | loglike: -38.4584 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1514 | loglike: -37.1204 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1514 | loglike: -37.1204 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1515 | loglike: -38.3583 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1516 | loglike: -37.9755 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1516 | loglike: -37.9755 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1516 | loglike: -37.9755 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1517 | loglike: -41.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1517 | loglike: -41.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1518 | loglike: -41.1035 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1518 | loglike: -41.1035 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1519 | loglike: -41.2742 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1520 | loglike: -35.9590 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1520 | loglike: -35.9590 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1520 | loglike: -35.9590 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1521 | loglike: -38.8630 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1522 | loglike: -37.1420 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1522 | loglike: -37.1420 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1523 | loglike: -40.1635 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1523 | loglike: -40.1635 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1523 | loglike: -40.1635 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1524 | loglike: -35.1693 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1525 | loglike: -36.5301 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1526 | loglike: -33.5265 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1526 | loglike: -33.5265 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1527 | loglike: -36.0747 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1527 | loglike: -36.0747 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1527 | loglike: -36.0747 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1527 | loglike: -36.0747 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1528 | loglike: -32.7289 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1529 | loglike: -33.0708 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1529 | loglike: -33.0708 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1530 | loglike: -37.7865 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1531 | loglike: -36.6342 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1532 | loglike: -32.3325 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1533 | loglike: -32.5478 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1534 | loglike: -34.5068 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1534 | loglike: -34.5068 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1534 | loglike: -34.5068 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1535 | loglike: -34.4152 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1535 | loglike: -34.4152 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1535 | loglike: -34.4152 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1535 | loglike: -34.4152 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1536 | loglike: -37.6880 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1537 | loglike: -36.5680 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1538 | loglike: -35.6568 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1539 | loglike: -33.6344 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1540 | loglike: -32.5197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1541 | loglike: -35.1097 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1541 | loglike: -35.1097 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1542 | loglike: -36.0881 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1542 | loglike: -36.0881 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1542 | loglike: -36.0881 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1542 | loglike: -36.0881 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1543 | loglike: -38.3224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1544 | loglike: -37.9779 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1544 | loglike: -37.9779 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1545 | loglike: -38.1962 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1546 | loglike: -33.8830 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1546 | loglike: -33.8830 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1546 | loglike: -33.8830 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1546 | loglike: -33.8830 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1547 | loglike: -39.0591 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1547 | loglike: -39.0591 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1548 | loglike: -40.8165 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1549 | loglike: -41.1921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1549 | loglike: -41.1921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1549 | loglike: -41.1921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1550 | loglike: -39.8670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1551 | loglike: -38.5052 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1552 | loglike: -41.4068 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1552 | loglike: -41.4068 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1553 | loglike: -40.2849 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1554 | loglike: -40.3925 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1555 | loglike: -41.3005 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1555 | loglike: -41.3005 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1556 | loglike: -41.2278 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1556 | loglike: -41.2278 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1556 | loglike: -41.2278 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1557 | loglike: -44.6188 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1557 | loglike: -44.6188 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1558 | loglike: -37.4913 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1558 | loglike: -37.4913 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1559 | loglike: -37.6522 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1559 | loglike: -37.6522 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1560 | loglike: -36.2893 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1561 | loglike: -37.2083 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1562 | loglike: -35.5963 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1562 | loglike: -35.5963 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1563 | loglike: -34.2084 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1563 | loglike: -34.2084 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1563 | loglike: -34.2084 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1563 | loglike: -34.2084 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1563 | loglike: -34.2084 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1564 | loglike: -35.9656 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1565 | loglike: -34.8626 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1566 | loglike: -34.3181 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1566 | loglike: -34.3181 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1567 | loglike: -36.2526 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1568 | loglike: -34.8628 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1568 | loglike: -34.8628 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1569 | loglike: -36.5327 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1569 | loglike: -36.5327 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1569 | loglike: -36.5327 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1569 | loglike: -36.5327 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1569 | loglike: -36.5327 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1570 | loglike: -36.0106 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1570 | loglike: -36.0106 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1571 | loglike: -36.5437 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1571 | loglike: -36.5437 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1571 | loglike: -36.5437 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1572 | loglike: -39.0566 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1572 | loglike: -39.0566 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1572 | loglike: -39.0566 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1572 | loglike: -39.0566 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1572 | loglike: -39.0566 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1573 | loglike: -37.0414 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1573 | loglike: -37.0414 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1574 | loglike: -38.8623 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1575 | loglike: -38.8205 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1575 | loglike: -38.8205 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1576 | loglike: -39.5913 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1576 | loglike: -39.5913 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1577 | loglike: -39.2399 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1578 | loglike: -40.2719 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1579 | loglike: -44.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1579 | loglike: -44.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1579 | loglike: -44.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1579 | loglike: -44.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1579 | loglike: -44.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1580 | loglike: -42.0834 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1580 | loglike: -42.0834 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1581 | loglike: -43.5256 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1581 | loglike: -43.5256 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1582 | loglike: -44.7690 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1583 | loglike: -44.3087 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1584 | loglike: -45.5543 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1584 | loglike: -45.5543 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1585 | loglike: -38.0481 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1586 | loglike: -37.2374 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1587 | loglike: -36.7958 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1588 | loglike: -39.9413 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1588 | loglike: -39.9413 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1588 | loglike: -39.9413 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1588 | loglike: -39.9413 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1589 | loglike: -37.8958 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1589 | loglike: -37.8958 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1590 | loglike: -35.0335 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1590 | loglike: -35.0335 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1591 | loglike: -35.2639 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1592 | loglike: -33.8004 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1593 | loglike: -34.4009 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1594 | loglike: -34.3104 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1594 | loglike: -34.3104 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1595 | loglike: -36.9836 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1595 | loglike: -36.9836 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1595 | loglike: -36.9836 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1596 | loglike: -39.5511 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1596 | loglike: -39.5511 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1596 | loglike: -39.5511 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1597 | loglike: -38.0332 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1598 | loglike: -37.9521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1598 | loglike: -37.9521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1598 | loglike: -37.9521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1598 | loglike: -37.9521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1598 | loglike: -37.9521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1599 | loglike: -35.0727 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1600 | loglike: -34.4497 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1601 | loglike: -34.8746 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1601 | loglike: -34.8746 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1601 | loglike: -34.8746 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1602 | loglike: -36.1506 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1602 | loglike: -36.1506 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1602 | loglike: -36.1506 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1603 | loglike: -35.3590 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1603 | loglike: -35.3590 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1604 | loglike: -35.2928 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1604 | loglike: -35.2928 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1604 | loglike: -35.2928 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1605 | loglike: -35.9954 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1605 | loglike: -35.9954 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1605 | loglike: -35.9954 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1605 | loglike: -35.9954 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1606 | loglike: -37.6199 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1606 | loglike: -37.6199 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1606 | loglike: -37.6199 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1606 | loglike: -37.6199 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1607 | loglike: -37.1385 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1607 | loglike: -37.1385 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1607 | loglike: -37.1385 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1608 | loglike: -35.7266 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1609 | loglike: -34.9433 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1609 | loglike: -34.9433 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1610 | loglike: -36.8835 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1610 | loglike: -36.8835 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1611 | loglike: -39.2197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1611 | loglike: -39.2197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1611 | loglike: -39.2197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1611 | loglike: -39.2197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1612 | loglike: -36.5318 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1612 | loglike: -36.5318 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1613 | loglike: -38.7791 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1613 | loglike: -38.7791 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1614 | loglike: -38.1559 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1614 | loglike: -38.1559 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1614 | loglike: -38.1559 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1614 | loglike: -38.1559 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1614 | loglike: -38.1559 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1615 | loglike: -36.3118 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1615 | loglike: -36.3118 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1615 | loglike: -36.3118 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1616 | loglike: -38.4742 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1617 | loglike: -35.9947 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1618 | loglike: -36.0680 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1619 | loglike: -33.9419 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1620 | loglike: -34.6270 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1620 | loglike: -34.6270 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1620 | loglike: -34.6270 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1621 | loglike: -34.6989 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1621 | loglike: -34.6989 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1621 | loglike: -34.6989 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1621 | loglike: -34.6989 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1621 | loglike: -34.6989 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1622 | loglike: -39.9041 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1623 | loglike: -40.6832 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1623 | loglike: -40.6832 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1624 | loglike: -42.5702 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1625 | loglike: -43.8967 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1625 | loglike: -43.8967 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1625 | loglike: -43.8967 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1626 | loglike: -45.0801 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1626 | loglike: -45.0801 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1627 | loglike: -41.7623 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1628 | loglike: -37.3027 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1629 | loglike: -37.5876 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1629 | loglike: -37.5876 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1629 | loglike: -37.5876 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1630 | loglike: -37.8744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1631 | loglike: -39.8224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1631 | loglike: -39.8224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1631 | loglike: -39.8224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1631 | loglike: -39.8224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1631 | loglike: -39.8224 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1632 | loglike: -37.4123 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1633 | loglike: -38.8883 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1634 | loglike: -42.3447 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1634 | loglike: -42.3447 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1635 | loglike: -41.6227 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1636 | loglike: -41.7545 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1637 | loglike: -40.1436 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1638 | loglike: -37.5588 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1639 | loglike: -37.7723 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1640 | loglike: -37.9490 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1640 | loglike: -37.9490 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1641 | loglike: -37.4025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1641 | loglike: -37.4025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1641 | loglike: -37.4025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1642 | loglike: -37.9912 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1642 | loglike: -37.9912 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1643 | loglike: -39.1865 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1644 | loglike: -38.4934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1644 | loglike: -38.4934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1644 | loglike: -38.4934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1645 | loglike: -42.3860 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1646 | loglike: -40.2790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1646 | loglike: -40.2790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1646 | loglike: -40.2790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1646 | loglike: -40.2790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1646 | loglike: -40.2790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1647 | loglike: -37.9915 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1647 | loglike: -37.9915 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1647 | loglike: -37.9915 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1647 | loglike: -37.9915 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1648 | loglike: -37.4677 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1648 | loglike: -37.4677 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1649 | loglike: -35.6597 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1650 | loglike: -35.1621 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1651 | loglike: -37.2961 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1652 | loglike: -37.0308 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1653 | loglike: -37.1790 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1654 | loglike: -37.2299 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1654 | loglike: -37.2299 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1655 | loglike: -38.0654 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1656 | loglike: -37.3339 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1657 | loglike: -36.8370 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1657 | loglike: -36.8370 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1657 | loglike: -36.8370 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1658 | loglike: -35.5198 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1658 | loglike: -35.5198 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1659 | loglike: -34.7406 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1659 | loglike: -34.7406 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1660 | loglike: -36.1719 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1661 | loglike: -36.3155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1662 | loglike: -35.9472 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1663 | loglike: -35.8020 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1664 | loglike: -35.7397 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1664 | loglike: -35.7397 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1665 | loglike: -34.5611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1665 | loglike: -34.5611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1665 | loglike: -34.5611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1666 | loglike: -35.8612 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1666 | loglike: -35.8612 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1667 | loglike: -36.3270 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1668 | loglike: -36.4445 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1668 | loglike: -36.4445 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1669 | loglike: -34.6833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1669 | loglike: -34.6833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1669 | loglike: -34.6833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1669 | loglike: -34.6833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1670 | loglike: -37.0376 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1671 | loglike: -36.1670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1672 | loglike: -36.1208 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1672 | loglike: -36.1208 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1672 | loglike: -36.1208 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1672 | loglike: -36.1208 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1672 | loglike: -36.1208 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1673 | loglike: -36.4691 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1673 | loglike: -36.4691 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1674 | loglike: -36.1390 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1675 | loglike: -38.9509 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1675 | loglike: -38.9509 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1675 | loglike: -38.9509 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1676 | loglike: -36.8934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1677 | loglike: -38.3795 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1678 | loglike: -36.9073 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1678 | loglike: -36.9073 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1679 | loglike: -36.5142 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1680 | loglike: -33.4115 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1681 | loglike: -34.3734 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1682 | loglike: -37.3290 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1683 | loglike: -35.4072 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1684 | loglike: -36.2241 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1684 | loglike: -36.2241 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1685 | loglike: -35.6488 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1685 | loglike: -35.6488 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1686 | loglike: -33.7664 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1687 | loglike: -35.4964 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1688 | loglike: -33.6644 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1689 | loglike: -33.9010 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1689 | loglike: -33.9010 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1689 | loglike: -33.9010 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1690 | loglike: -35.8328 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1690 | loglike: -35.8328 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1691 | loglike: -33.1330 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1692 | loglike: -35.9157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1693 | loglike: -35.8332 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1694 | loglike: -36.3813 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1695 | loglike: -31.4826 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1695 | loglike: -31.4826 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1695 | loglike: -31.4826 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1695 | loglike: -31.4826 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1696 | loglike: -32.1176 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1696 | loglike: -32.1176 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1697 | loglike: -33.4292 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1698 | loglike: -33.8705 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1698 | loglike: -33.8705 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1698 | loglike: -33.8705 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1699 | loglike: -33.6295 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1699 | loglike: -33.6295 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1700 | loglike: -34.3180 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1700 | loglike: -34.3180 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1701 | loglike: -37.5953 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1702 | loglike: -34.6938 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1703 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1704 | loglike: -32.2825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1704 | loglike: -32.2825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1704 | loglike: -32.2825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1704 | loglike: -32.2825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1705 | loglike: -33.5729 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1705 | loglike: -33.5729 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1705 | loglike: -33.5729 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1705 | loglike: -33.5729 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1706 | loglike: -36.0930 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1707 | loglike: -36.6542 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1708 | loglike: -36.8619 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1708 | loglike: -36.8619 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1709 | loglike: -32.9021 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1710 | loglike: -32.8612 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1711 | loglike: -34.4546 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1712 | loglike: -34.8037 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1713 | loglike: -35.5489 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1714 | loglike: -39.5916 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1715 | loglike: -36.7930 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1715 | loglike: -36.7930 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1715 | loglike: -36.7930 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1716 | loglike: -36.9901 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1716 | loglike: -36.9901 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1717 | loglike: -33.7440 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1717 | loglike: -33.7440 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1718 | loglike: -34.8356 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1718 | loglike: -34.8356 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1719 | loglike: -32.5846 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1719 | loglike: -32.5846 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1719 | loglike: -32.5846 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1719 | loglike: -32.5846 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1720 | loglike: -31.3365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1720 | loglike: -31.3365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1721 | loglike: -33.9209 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1722 | loglike: -32.0684 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1723 | loglike: -31.8252 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1724 | loglike: -33.3721 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1725 | loglike: -35.5865 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1726 | loglike: -37.7754 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1727 | loglike: -38.5810 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1727 | loglike: -38.5810 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1727 | loglike: -38.5810 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1727 | loglike: -38.5810 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1728 | loglike: -32.9665 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1729 | loglike: -32.1158 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1729 | loglike: -32.1158 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1729 | loglike: -32.1158 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1730 | loglike: -35.7579 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1731 | loglike: -33.6086 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1732 | loglike: -33.4140 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1732 | loglike: -33.4140 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1733 | loglike: -33.1806 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1733 | loglike: -33.1806 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1733 | loglike: -33.1806 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1734 | loglike: -32.0395 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1734 | loglike: -32.0395 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1734 | loglike: -32.0395 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1735 | loglike: -33.8579 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1736 | loglike: -32.1636 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1737 | loglike: -32.4036 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1738 | loglike: -32.7478 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1738 | loglike: -32.7478 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1738 | loglike: -32.7478 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1738 | loglike: -32.7478 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1739 | loglike: -33.6891 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1739 | loglike: -33.6891 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1740 | loglike: -33.4749 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1741 | loglike: -33.3041 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1741 | loglike: -33.3041 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1741 | loglike: -33.3041 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1742 | loglike: -33.2626 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1742 | loglike: -33.2626 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1742 | loglike: -33.2626 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1743 | loglike: -33.2058 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1744 | loglike: -32.8703 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1745 | loglike: -32.0869 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1746 | loglike: -35.5975 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1746 | loglike: -35.5975 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1747 | loglike: -37.4524 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1748 | loglike: -35.2934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1748 | loglike: -35.2934 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1749 | loglike: -38.1339 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1749 | loglike: -38.1339 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1749 | loglike: -38.1339 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1750 | loglike: -36.4538 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1751 | loglike: -36.1283 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1752 | loglike: -38.8631 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1753 | loglike: -38.1126 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1754 | loglike: -35.7155 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1755 | loglike: -34.5023 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1756 | loglike: -35.9850 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1757 | loglike: -35.6744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1757 | loglike: -35.6744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1757 | loglike: -35.6744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1757 | loglike: -35.6744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1757 | loglike: -35.6744 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1758 | loglike: -32.0418 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1759 | loglike: -34.2654 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1760 | loglike: -32.9353 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1761 | loglike: -31.8100 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1761 | loglike: -31.8100 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1762 | loglike: -32.1819 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1763 | loglike: -31.8863 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1764 | loglike: -33.8004 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1764 | loglike: -33.8004 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1765 | loglike: -31.6477 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1765 | loglike: -31.6477 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1765 | loglike: -31.6477 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1766 | loglike: -31.8880 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1766 | loglike: -31.8880 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1767 | loglike: -31.8636 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1768 | loglike: -31.7632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1768 | loglike: -31.7632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1768 | loglike: -31.7632 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1769 | loglike: -35.0399 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1770 | loglike: -31.5193 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1771 | loglike: -33.6873 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1771 | loglike: -33.6873 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1772 | loglike: -34.4139 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1773 | loglike: -33.3311 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1774 | loglike: -33.6978 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1775 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1776 | loglike: -32.2770 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1776 | loglike: -32.2770 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1776 | loglike: -32.2770 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1777 | loglike: -33.8266 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1778 | loglike: -34.0247 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1778 | loglike: -34.0247 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1778 | loglike: -34.0247 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1778 | loglike: -34.0247 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1778 | loglike: -34.0247 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1779 | loglike: -36.0256 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1780 | loglike: -35.3661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1780 | loglike: -35.3661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1781 | loglike: -33.3016 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1782 | loglike: -33.1912 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1783 | loglike: -31.9855 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1784 | loglike: -31.8061 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1784 | loglike: -31.8061 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1784 | loglike: -31.8061 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1784 | loglike: -31.8061 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1785 | loglike: -33.3123 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1785 | loglike: -33.3123 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1786 | loglike: -32.5671 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1786 | loglike: -32.5671 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1787 | loglike: -33.2795 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1787 | loglike: -33.2795 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1788 | loglike: -32.8637 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1788 | loglike: -32.8637 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1788 | loglike: -32.8637 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1789 | loglike: -33.5110 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1790 | loglike: -33.0776 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1791 | loglike: -32.0504 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1791 | loglike: -32.0504 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1792 | loglike: -31.9397 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1792 | loglike: -31.9397 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1792 | loglike: -31.9397 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1793 | loglike: -36.4611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1793 | loglike: -36.4611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1794 | loglike: -32.5683 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1795 | loglike: -32.9082 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1796 | loglike: -31.2998 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1796 | loglike: -31.2998 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1797 | loglike: -32.0359 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1798 | loglike: -41.6567 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1799 | loglike: -41.4316 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1799 | loglike: -41.4316 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1799 | loglike: -41.4316 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1799 | loglike: -41.4316 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1800 | loglike: -41.4457 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1800 | loglike: -41.4457 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1801 | loglike: -41.8316 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1802 | loglike: -34.2924 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1802 | loglike: -34.2924 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1802 | loglike: -34.2924 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1802 | loglike: -34.2924 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1802 | loglike: -34.2924 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1803 | loglike: -32.5670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1803 | loglike: -32.5670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1804 | loglike: -36.1786 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1804 | loglike: -36.1786 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1804 | loglike: -36.1786 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1804 | loglike: -36.1786 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1805 | loglike: -35.8066 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1805 | loglike: -35.8066 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1806 | loglike: -32.5422 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1807 | loglike: -32.3127 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1807 | loglike: -32.3127 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1808 | loglike: -31.9053 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1809 | loglike: -31.7105 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1810 | loglike: -32.9393 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1810 | loglike: -32.9393 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1811 | loglike: -36.2284 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1812 | loglike: -36.3833 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1813 | loglike: -32.5280 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1814 | loglike: -31.7488 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1814 | loglike: -31.7488 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1814 | loglike: -31.7488 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1815 | loglike: -35.2722 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1816 | loglike: -35.1625 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1817 | loglike: -34.9681 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1818 | loglike: -35.0133 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1818 | loglike: -35.0133 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1819 | loglike: -37.7848 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1819 | loglike: -37.7848 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1819 | loglike: -37.7848 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1820 | loglike: -37.1351 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1821 | loglike: -37.5237 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1821 | loglike: -37.5237 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1822 | loglike: -38.1019 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1822 | loglike: -38.1019 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1823 | loglike: -39.9851 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1824 | loglike: -39.8106 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1824 | loglike: -39.8106 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1825 | loglike: -37.7504 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1826 | loglike: -38.8370 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1827 | loglike: -36.6245 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1828 | loglike: -33.4129 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1828 | loglike: -33.4129 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1828 | loglike: -33.4129 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1828 | loglike: -33.4129 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1829 | loglike: -36.5336 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1829 | loglike: -36.5336 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1830 | loglike: -36.4991 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1830 | loglike: -36.4991 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1830 | loglike: -36.4991 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1830 | loglike: -36.4991 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1830 | loglike: -36.4991 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1831 | loglike: -35.1381 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1832 | loglike: -39.2946 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1833 | loglike: -40.4387 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1833 | loglike: -40.4387 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1833 | loglike: -40.4387 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1834 | loglike: -36.2573 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1835 | loglike: -35.6245 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1836 | loglike: -33.7121 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1837 | loglike: -32.1054 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1837 | loglike: -32.1054 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1838 | loglike: -33.8871 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1839 | loglike: -33.7233 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1839 | loglike: -33.7233 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1840 | loglike: -31.6195 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1840 | loglike: -31.6195 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1840 | loglike: -31.6195 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1840 | loglike: -31.6195 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1841 | loglike: -31.7583 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1842 | loglike: -31.7629 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1843 | loglike: -36.6747 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1844 | loglike: -35.2720 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1844 | loglike: -35.2720 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1844 | loglike: -35.2720 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1845 | loglike: -34.3036 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1846 | loglike: -32.2728 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1846 | loglike: -32.2728 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1846 | loglike: -32.2728 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1847 | loglike: -32.6025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1848 | loglike: -37.8358 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1849 | loglike: -38.2338 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1850 | loglike: -38.0919 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1851 | loglike: -36.1613 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1851 | loglike: -36.1613 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1851 | loglike: -36.1613 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1852 | loglike: -36.3700 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1853 | loglike: -36.3383 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1854 | loglike: -34.2279 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1855 | loglike: -33.5458 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1855 | loglike: -33.5458 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1856 | loglike: -33.0297 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1856 | loglike: -33.0297 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1857 | loglike: -33.1938 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1858 | loglike: -39.2476 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1858 | loglike: -39.2476 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1859 | loglike: -39.6942 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1860 | loglike: -38.1696 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1860 | loglike: -38.1696 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1861 | loglike: -32.6951 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1862 | loglike: -32.7635 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1863 | loglike: -34.6190 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1864 | loglike: -34.2713 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1865 | loglike: -33.9554 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1865 | loglike: -33.9554 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1865 | loglike: -33.9554 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1865 | loglike: -33.9554 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1866 | loglike: -33.8137 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1867 | loglike: -35.8272 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1868 | loglike: -37.1773 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1869 | loglike: -38.1241 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1869 | loglike: -38.1241 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1870 | loglike: -40.1168 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1870 | loglike: -40.1168 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1871 | loglike: -38.6203 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1871 | loglike: -38.6203 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1872 | loglike: -38.1423 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1872 | loglike: -38.1423 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1872 | loglike: -38.1423 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1873 | loglike: -37.3071 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1874 | loglike: -38.0322 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1874 | loglike: -38.0322 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1875 | loglike: -37.5025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1875 | loglike: -37.5025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1875 | loglike: -37.5025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1875 | loglike: -37.5025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1875 | loglike: -37.5025 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1876 | loglike: -38.4659 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1876 | loglike: -38.4659 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1876 | loglike: -38.4659 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1876 | loglike: -38.4659 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1876 | loglike: -38.4659 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1877 | loglike: -36.9082 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1878 | loglike: -34.3699 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1878 | loglike: -34.3699 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1879 | loglike: -33.6967 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1880 | loglike: -33.6877 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1881 | loglike: -38.2115 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1881 | loglike: -38.2115 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1881 | loglike: -38.2115 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1882 | loglike: -36.8980 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1883 | loglike: -34.7096 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1884 | loglike: -31.7162 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1885 | loglike: -32.6699 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1886 | loglike: -33.1157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1886 | loglike: -33.1157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1886 | loglike: -33.1157 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1887 | loglike: -31.1406 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1888 | loglike: -32.6480 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1888 | loglike: -32.6480 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1889 | loglike: -31.1919 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1890 | loglike: -33.0620 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1890 | loglike: -33.0620 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1890 | loglike: -33.0620 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1891 | loglike: -32.5605 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1892 | loglike: -31.3890 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1893 | loglike: -35.5634 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1893 | loglike: -35.5634 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1894 | loglike: -33.5426 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1894 | loglike: -33.5426 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1894 | loglike: -33.5426 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1894 | loglike: -33.5426 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1895 | loglike: -33.8289 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1895 | loglike: -33.8289 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1895 | loglike: -33.8289 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1896 | loglike: -34.9187 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1896 | loglike: -34.9187 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1897 | loglike: -34.8872 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1898 | loglike: -33.3899 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1899 | loglike: -33.4684 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1900 | loglike: -34.0825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1900 | loglike: -34.0825 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1901 | loglike: -35.1869 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1902 | loglike: -33.4063 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1902 | loglike: -33.4063 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1902 | loglike: -33.4063 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1903 | loglike: -31.4341 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1903 | loglike: -31.4341 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1904 | loglike: -31.4359 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1905 | loglike: -34.4663 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1905 | loglike: -34.4663 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1906 | loglike: -33.4857 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1906 | loglike: -33.4857 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1906 | loglike: -33.4857 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1907 | loglike: -32.2185 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1908 | loglike: -32.1062 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1909 | loglike: -32.3431 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1909 | loglike: -32.3431 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1910 | loglike: -32.7908 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1910 | loglike: -32.7908 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1911 | loglike: -33.5617 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1911 | loglike: -33.5617 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1911 | loglike: -33.5617 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1912 | loglike: -33.7931 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1912 | loglike: -33.7931 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1913 | loglike: -35.6929 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1913 | loglike: -35.6929 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1914 | loglike: -33.6499 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1915 | loglike: -34.6948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1915 | loglike: -34.6948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1915 | loglike: -34.6948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1916 | loglike: -34.7544 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1917 | loglike: -33.1872 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1917 | loglike: -33.1872 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1918 | loglike: -33.7117 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1918 | loglike: -33.7117 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1918 | loglike: -33.7117 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1918 | loglike: -33.7117 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1918 | loglike: -33.7117 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1919 | loglike: -34.5214 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1919 | loglike: -34.5214 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1920 | loglike: -33.5402 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1920 | loglike: -33.5402 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1920 | loglike: -33.5402 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1920 | loglike: -33.5402 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1921 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1921 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1921 | loglike: -32.3028 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1922 | loglike: -31.8369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1922 | loglike: -31.8369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1922 | loglike: -31.8369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1922 | loglike: -31.8369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1922 | loglike: -31.8369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1923 | loglike: -32.1094 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1923 | loglike: -32.1094 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1924 | loglike: -33.8831 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1925 | loglike: -33.2250 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1926 | loglike: -34.6968 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1927 | loglike: -32.3342 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1928 | loglike: -31.2812 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1928 | loglike: -31.2812 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1929 | loglike: -32.9751 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1929 | loglike: -32.9751 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1930 | loglike: -33.1376 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1930 | loglike: -33.1376 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1930 | loglike: -33.1376 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1931 | loglike: -31.7069 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1931 | loglike: -31.7069 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1931 | loglike: -31.7069 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1932 | loglike: -31.2948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1932 | loglike: -31.2948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1932 | loglike: -31.2948 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1933 | loglike: -33.0921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1934 | loglike: -34.0661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1934 | loglike: -34.0661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1934 | loglike: -34.0661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1934 | loglike: -34.0661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1935 | loglike: -33.2027 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1936 | loglike: -32.7814 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1937 | loglike: -32.9098 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1937 | loglike: -32.9098 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1938 | loglike: -34.5008 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1939 | loglike: -36.4565 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1940 | loglike: -35.9114 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1941 | loglike: -35.2369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1941 | loglike: -35.2369 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1942 | loglike: -33.4334 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1943 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1943 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1943 | loglike: -32.6726 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1944 | loglike: -34.3177 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1945 | loglike: -35.0628 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1946 | loglike: -34.5661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1946 | loglike: -34.5661 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1947 | loglike: -38.1519 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1947 | loglike: -38.1519 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1947 | loglike: -38.1519 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1948 | loglike: -36.5610 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1948 | loglike: -36.5610 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1948 | loglike: -36.5610 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1949 | loglike: -38.5047 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1950 | loglike: -39.4204 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1950 | loglike: -39.4204 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1951 | loglike: -39.8149 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1952 | loglike: -39.4135 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1952 | loglike: -39.4135 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1953 | loglike: -37.2235 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1954 | loglike: -34.9179 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1954 | loglike: -34.9179 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1954 | loglike: -34.9179 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1954 | loglike: -34.9179 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1955 | loglike: -34.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1955 | loglike: -34.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1955 | loglike: -34.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1955 | loglike: -34.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1955 | loglike: -34.1315 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1956 | loglike: -33.1844 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1956 | loglike: -33.1844 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1957 | loglike: -34.2796 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1957 | loglike: -34.2796 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1957 | loglike: -34.2796 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1958 | loglike: -32.5569 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1959 | loglike: -33.1669 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1960 | loglike: -33.4562 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1961 | loglike: -31.9391 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1962 | loglike: -33.9896 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1963 | loglike: -33.9522 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1963 | loglike: -33.9522 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1963 | loglike: -33.9522 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1964 | loglike: -35.1088 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1964 | loglike: -35.1088 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1964 | loglike: -35.1088 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1964 | loglike: -35.1088 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1965 | loglike: -32.8183 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1965 | loglike: -32.8183 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1965 | loglike: -32.8183 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1966 | loglike: -32.7197 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1967 | loglike: -31.5251 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1968 | loglike: -31.5067 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1969 | loglike: -32.8650 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1969 | loglike: -32.8650 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1969 | loglike: -32.8650 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1969 | loglike: -32.8650 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1970 | loglike: -31.5700 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1970 | loglike: -31.5700 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1971 | loglike: -31.5993 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1972 | loglike: -31.6053 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1973 | loglike: -33.5290 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1974 | loglike: -36.2365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1974 | loglike: -36.2365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1974 | loglike: -36.2365 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1975 | loglike: -36.3628 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1976 | loglike: -33.6781 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1976 | loglike: -33.6781 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1976 | loglike: -33.6781 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1976 | loglike: -33.6781 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1977 | loglike: -34.7711 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1977 | loglike: -34.7711 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1978 | loglike: -33.1416 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1979 | loglike: -35.7159 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1980 | loglike: -32.5670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1980 | loglike: -32.5670 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1981 | loglike: -32.4358 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1981 | loglike: -32.4358 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1982 | loglike: -32.4834 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1982 | loglike: -32.4834 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1983 | loglike: -32.8611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1983 | loglike: -32.8611 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1984 | loglike: -33.8147 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1985 | loglike: -34.9299 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1986 | loglike: -36.1521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1986 | loglike: -36.1521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1986 | loglike: -36.1521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1986 | loglike: -36.1521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1986 | loglike: -36.1521 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1987 | loglike: -33.4941 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1987 | loglike: -33.4941 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1987 | loglike: -33.4941 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1988 | loglike: -32.0305 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1989 | loglike: -32.1135 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1990 | loglike: -31.3935 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1990 | loglike: -31.3935 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1991 | loglike: -32.6793 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1992 | loglike: -32.9262 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1992 | loglike: -32.9262 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1993 | loglike: -33.0230 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1994 | loglike: -32.8300 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1995 | loglike: -32.1922 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1996 | loglike: -33.8103 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1997 | loglike: -33.3306 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1998 | loglike: -34.9969 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 1999 | loglike: -36.2921 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 2000 | loglike: -35.9379 | Gelman-Rubin: [1.44182138e-03 4.81124092e-05 1.84005670e-04 1.48168901e-03
Accepted: 3501 | loglike: -44.6032 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3502 | loglike: -43.2856 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3503 | loglike: -38.7972 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3504 | loglike: -38.8417 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3505 | loglike: -38.2436 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3505 | loglike: -38.2436 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3506 | loglike: -38.9622 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3507 | loglike: -37.2265 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3508 | loglike: -37.1141 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3509 | loglike: -38.4273 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3509 | loglike: -38.4273 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3509 | loglike: -38.4273 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3509 | loglike: -38.4273 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3510 | loglike: -34.8505 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3510 | loglike: -34.8505 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3511 | loglike: -33.9841 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3511 | loglike: -33.9841 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3512 | loglike: -36.2135 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3513 | loglike: -37.7843 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3514 | loglike: -34.2921 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3515 | loglike: -35.4686 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3516 | loglike: -35.0726 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3517 | loglike: -38.0788 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3517 | loglike: -38.0788 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3518 | loglike: -35.2927 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3518 | loglike: -35.2927 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3519 | loglike: -35.2187 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3519 | loglike: -35.2187 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3519 | loglike: -35.2187 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3520 | loglike: -36.6511 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3521 | loglike: -35.2747 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3522 | loglike: -38.4152 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3522 | loglike: -38.4152 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3522 | loglike: -38.4152 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3522 | loglike: -38.4152 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3522 | loglike: -38.4152 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3523 | loglike: -39.6360 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3524 | loglike: -38.5391 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3525 | loglike: -37.7471 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3525 | loglike: -37.7471 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3526 | loglike: -38.4203 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3526 | loglike: -38.4203 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3527 | loglike: -38.1726 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3527 | loglike: -38.1726 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3527 | loglike: -38.1726 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3528 | loglike: -36.2367 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3528 | loglike: -36.2367 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3528 | loglike: -36.2367 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3528 | loglike: -36.2367 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3529 | loglike: -37.0209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3530 | loglike: -34.0254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3530 | loglike: -34.0254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3530 | loglike: -34.0254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3530 | loglike: -34.0254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3530 | loglike: -34.0254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3531 | loglike: -34.7596 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3531 | loglike: -34.7596 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3532 | loglike: -36.5186 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3533 | loglike: -33.7651 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3533 | loglike: -33.7651 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3533 | loglike: -33.7651 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3533 | loglike: -33.7651 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3533 | loglike: -33.7651 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3534 | loglike: -38.0866 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3534 | loglike: -38.0866 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3534 | loglike: -38.0866 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3534 | loglike: -38.0866 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3535 | loglike: -35.7410 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3535 | loglike: -35.7410 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3536 | loglike: -36.5932 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3536 | loglike: -36.5932 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3536 | loglike: -36.5932 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3537 | loglike: -38.8369 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3538 | loglike: -33.7067 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3539 | loglike: -35.8686 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3540 | loglike: -34.3275 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3541 | loglike: -34.4612 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3541 | loglike: -34.4612 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3542 | loglike: -34.5553 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3543 | loglike: -33.0150 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3544 | loglike: -34.8563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3545 | loglike: -34.6246 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3546 | loglike: -34.6980 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3546 | loglike: -34.6980 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3547 | loglike: -36.3262 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3547 | loglike: -36.3262 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3548 | loglike: -34.0862 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3549 | loglike: -35.5017 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3550 | loglike: -35.6171 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3551 | loglike: -33.5586 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3551 | loglike: -33.5586 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3551 | loglike: -33.5586 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3552 | loglike: -35.0823 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3553 | loglike: -33.3354 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3554 | loglike: -34.4391 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3554 | loglike: -34.4391 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3555 | loglike: -34.7832 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3556 | loglike: -33.8534 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3557 | loglike: -34.3658 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3557 | loglike: -34.3658 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3557 | loglike: -34.3658 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3558 | loglike: -36.5059 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3558 | loglike: -36.5059 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3559 | loglike: -32.8056 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3560 | loglike: -33.8107 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3561 | loglike: -34.3579 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3561 | loglike: -34.3579 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3562 | loglike: -37.0646 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3563 | loglike: -34.6379 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3564 | loglike: -35.0670 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3565 | loglike: -37.7462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3565 | loglike: -37.7462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3566 | loglike: -36.9161 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3566 | loglike: -36.9161 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3567 | loglike: -34.7569 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3567 | loglike: -34.7569 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3568 | loglike: -33.0357 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3569 | loglike: -33.9576 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3569 | loglike: -33.9576 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3570 | loglike: -34.7765 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3571 | loglike: -36.7243 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3571 | loglike: -36.7243 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3572 | loglike: -36.3679 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3572 | loglike: -36.3679 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3573 | loglike: -36.3677 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3574 | loglike: -36.0230 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3575 | loglike: -35.8319 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3576 | loglike: -36.2219 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3577 | loglike: -39.0046 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3578 | loglike: -41.6787 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3579 | loglike: -40.4198 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3580 | loglike: -42.0158 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3581 | loglike: -42.7927 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3581 | loglike: -42.7927 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3582 | loglike: -37.5540 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3583 | loglike: -39.8705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3584 | loglike: -40.0096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3585 | loglike: -46.1169 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3586 | loglike: -49.3637 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3587 | loglike: -45.6918 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3588 | loglike: -40.3147 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3588 | loglike: -40.3147 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3589 | loglike: -35.0142 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3589 | loglike: -35.0142 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3589 | loglike: -35.0142 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3589 | loglike: -35.0142 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3589 | loglike: -35.0142 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3590 | loglike: -32.3560 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3590 | loglike: -32.3560 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3590 | loglike: -32.3560 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3591 | loglike: -33.8546 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3592 | loglike: -33.8166 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3592 | loglike: -33.8166 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3593 | loglike: -31.7557 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3594 | loglike: -32.6707 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3595 | loglike: -32.2272 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3596 | loglike: -32.7019 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3596 | loglike: -32.7019 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3597 | loglike: -35.6934 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3598 | loglike: -33.8518 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3598 | loglike: -33.8518 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3599 | loglike: -34.2447 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3599 | loglike: -34.2447 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3600 | loglike: -35.4599 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3601 | loglike: -33.1581 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3602 | loglike: -31.8570 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3602 | loglike: -31.8570 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3602 | loglike: -31.8570 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3603 | loglike: -32.6702 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3603 | loglike: -32.6702 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3604 | loglike: -33.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3605 | loglike: -32.8439 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3606 | loglike: -32.0161 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3607 | loglike: -31.9539 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3607 | loglike: -31.9539 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3607 | loglike: -31.9539 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3608 | loglike: -33.0519 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3609 | loglike: -33.6269 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3610 | loglike: -31.4334 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3611 | loglike: -34.9148 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3611 | loglike: -34.9148 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3611 | loglike: -34.9148 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3612 | loglike: -34.0378 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3613 | loglike: -33.3606 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3613 | loglike: -33.3606 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3613 | loglike: -33.3606 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3614 | loglike: -33.8193 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3614 | loglike: -33.8193 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3615 | loglike: -33.8472 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3615 | loglike: -33.8472 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3616 | loglike: -33.7052 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3616 | loglike: -33.7052 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3617 | loglike: -32.4051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3617 | loglike: -32.4051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3617 | loglike: -32.4051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3617 | loglike: -32.4051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3618 | loglike: -34.2983 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3618 | loglike: -34.2983 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3618 | loglike: -34.2983 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3618 | loglike: -34.2983 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3618 | loglike: -34.2983 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3619 | loglike: -35.5317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3620 | loglike: -37.4543 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3621 | loglike: -33.3819 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3622 | loglike: -35.6677 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3622 | loglike: -35.6677 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3623 | loglike: -35.2181 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3623 | loglike: -35.2181 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3623 | loglike: -35.2181 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3623 | loglike: -35.2181 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3623 | loglike: -35.2181 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3624 | loglike: -38.8366 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3624 | loglike: -38.8366 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3625 | loglike: -47.9204 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3625 | loglike: -47.9204 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3626 | loglike: -37.0328 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3626 | loglike: -37.0328 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3627 | loglike: -32.5037 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3627 | loglike: -32.5037 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3627 | loglike: -32.5037 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3628 | loglike: -33.9064 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3629 | loglike: -35.6761 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3630 | loglike: -36.7374 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3630 | loglike: -36.7374 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3630 | loglike: -36.7374 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3630 | loglike: -36.7374 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3631 | loglike: -34.3025 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3631 | loglike: -34.3025 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3631 | loglike: -34.3025 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3631 | loglike: -34.3025 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3632 | loglike: -32.9875 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3633 | loglike: -32.6167 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3634 | loglike: -32.3061 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3634 | loglike: -32.3061 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3634 | loglike: -32.3061 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3635 | loglike: -32.1361 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3635 | loglike: -32.1361 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3635 | loglike: -32.1361 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3636 | loglike: -40.5162 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3636 | loglike: -40.5162 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3636 | loglike: -40.5162 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3637 | loglike: -41.1536 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3638 | loglike: -39.8232 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3639 | loglike: -38.9254 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3640 | loglike: -38.7442 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3641 | loglike: -36.6879 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3642 | loglike: -40.4595 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3643 | loglike: -36.3319 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3644 | loglike: -35.8503 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3645 | loglike: -35.0546 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3646 | loglike: -34.5310 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3647 | loglike: -36.1464 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3647 | loglike: -36.1464 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3647 | loglike: -36.1464 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3648 | loglike: -37.4463 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3649 | loglike: -34.8912 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3650 | loglike: -34.9758 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3651 | loglike: -35.7226 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3652 | loglike: -37.5520 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3653 | loglike: -37.8171 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3653 | loglike: -37.8171 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3654 | loglike: -39.4312 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3655 | loglike: -41.3830 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3655 | loglike: -41.3830 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3655 | loglike: -41.3830 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3656 | loglike: -37.1850 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3657 | loglike: -39.4549 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3657 | loglike: -39.4549 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3658 | loglike: -39.5526 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3658 | loglike: -39.5526 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3659 | loglike: -40.2483 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3659 | loglike: -40.2483 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3659 | loglike: -40.2483 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3660 | loglike: -34.0348 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3660 | loglike: -34.0348 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3661 | loglike: -38.2767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3662 | loglike: -33.9727 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3662 | loglike: -33.9727 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3662 | loglike: -33.9727 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3663 | loglike: -35.2022 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3663 | loglike: -35.2022 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3664 | loglike: -34.6949 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3665 | loglike: -35.4372 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3665 | loglike: -35.4372 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3666 | loglike: -37.8678 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3666 | loglike: -37.8678 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3666 | loglike: -37.8678 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3667 | loglike: -35.1362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3667 | loglike: -35.1362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3668 | loglike: -33.3051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3669 | loglike: -33.9207 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3669 | loglike: -33.9207 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3669 | loglike: -33.9207 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3670 | loglike: -33.2563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3671 | loglike: -33.8943 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3672 | loglike: -36.9989 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3672 | loglike: -36.9989 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3673 | loglike: -36.0617 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3674 | loglike: -38.2508 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3675 | loglike: -36.8408 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3676 | loglike: -34.5338 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3677 | loglike: -33.1705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3678 | loglike: -31.2439 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3679 | loglike: -35.6297 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3680 | loglike: -36.5110 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3681 | loglike: -35.4105 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3681 | loglike: -35.4105 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3681 | loglike: -35.4105 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3682 | loglike: -35.9703 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3682 | loglike: -35.9703 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3683 | loglike: -38.4572 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3683 | loglike: -38.4572 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3684 | loglike: -38.4306 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3685 | loglike: -38.3739 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3686 | loglike: -38.6425 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3687 | loglike: -36.5598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3687 | loglike: -36.5598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3687 | loglike: -36.5598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3688 | loglike: -31.0892 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3689 | loglike: -33.4814 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3689 | loglike: -33.4814 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3690 | loglike: -37.7183 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3691 | loglike: -37.0248 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3692 | loglike: -35.9053 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3692 | loglike: -35.9053 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3692 | loglike: -35.9053 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3693 | loglike: -33.8925 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3694 | loglike: -33.7030 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3695 | loglike: -35.9407 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3695 | loglike: -35.9407 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3695 | loglike: -35.9407 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3695 | loglike: -35.9407 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3696 | loglike: -33.0588 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3696 | loglike: -33.0588 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3697 | loglike: -32.9100 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3698 | loglike: -36.0676 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3699 | loglike: -35.4195 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3700 | loglike: -36.0905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3701 | loglike: -34.7418 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3702 | loglike: -36.5931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3702 | loglike: -36.5931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3703 | loglike: -34.6936 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3703 | loglike: -34.6936 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3704 | loglike: -34.7223 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3705 | loglike: -33.8649 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3706 | loglike: -36.6280 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3707 | loglike: -34.5754 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3708 | loglike: -38.1411 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3709 | loglike: -36.0493 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3709 | loglike: -36.0493 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3710 | loglike: -32.8838 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3711 | loglike: -34.0981 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3712 | loglike: -33.7670 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3712 | loglike: -33.7670 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3713 | loglike: -33.5205 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3713 | loglike: -33.5205 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3714 | loglike: -32.2656 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3715 | loglike: -32.8584 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3716 | loglike: -33.9220 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3717 | loglike: -33.3153 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3718 | loglike: -35.5562 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3719 | loglike: -32.6018 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3720 | loglike: -32.3226 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3721 | loglike: -36.0386 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3721 | loglike: -36.0386 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3721 | loglike: -36.0386 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3722 | loglike: -33.3889 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3722 | loglike: -33.3889 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3722 | loglike: -33.3889 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3723 | loglike: -31.6068 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3723 | loglike: -31.6068 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3723 | loglike: -31.6068 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3724 | loglike: -35.1330 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3724 | loglike: -35.1330 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3724 | loglike: -35.1330 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3725 | loglike: -32.3008 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3725 | loglike: -32.3008 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3725 | loglike: -32.3008 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3726 | loglike: -32.2056 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3727 | loglike: -31.3189 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3728 | loglike: -31.6837 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3729 | loglike: -33.0767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3729 | loglike: -33.0767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3729 | loglike: -33.0767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3729 | loglike: -33.0767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3729 | loglike: -33.0767 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3730 | loglike: -35.8881 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3731 | loglike: -36.3922 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3731 | loglike: -36.3922 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3731 | loglike: -36.3922 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3732 | loglike: -34.4288 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3733 | loglike: -35.5530 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3734 | loglike: -34.9412 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3734 | loglike: -34.9412 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3734 | loglike: -34.9412 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3735 | loglike: -35.6234 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3736 | loglike: -34.3388 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3736 | loglike: -34.3388 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3736 | loglike: -34.3388 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3736 | loglike: -34.3388 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3737 | loglike: -35.3645 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3738 | loglike: -34.0475 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3738 | loglike: -34.0475 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3739 | loglike: -35.8312 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3740 | loglike: -34.3223 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3741 | loglike: -32.1258 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3742 | loglike: -31.8362 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3743 | loglike: -32.6625 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3744 | loglike: -32.0021 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3745 | loglike: -31.3359 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3746 | loglike: -30.9307 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3746 | loglike: -30.9307 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3746 | loglike: -30.9307 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3747 | loglike: -31.8249 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3748 | loglike: -31.6016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3749 | loglike: -31.1509 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3749 | loglike: -31.1509 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3749 | loglike: -31.1509 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3750 | loglike: -31.7972 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3751 | loglike: -32.4130 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3752 | loglike: -31.5591 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3752 | loglike: -31.5591 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3753 | loglike: -31.0133 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3753 | loglike: -31.0133 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3754 | loglike: -32.7450 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3754 | loglike: -32.7450 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3755 | loglike: -33.9537 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3755 | loglike: -33.9537 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3756 | loglike: -34.1805 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3756 | loglike: -34.1805 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3757 | loglike: -36.4509 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3758 | loglike: -34.3209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3758 | loglike: -34.3209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3758 | loglike: -34.3209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3758 | loglike: -34.3209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3758 | loglike: -34.3209 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3759 | loglike: -34.0233 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3760 | loglike: -31.7507 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3761 | loglike: -31.4725 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3762 | loglike: -32.4307 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3763 | loglike: -37.4055 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3764 | loglike: -33.9211 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3765 | loglike: -33.4424 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3765 | loglike: -33.4424 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3766 | loglike: -31.3991 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3766 | loglike: -31.3991 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3766 | loglike: -31.3991 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3766 | loglike: -31.3991 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3767 | loglike: -33.1858 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3767 | loglike: -33.1858 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3768 | loglike: -33.7351 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3769 | loglike: -33.2837 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3770 | loglike: -32.8530 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3770 | loglike: -32.8530 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3770 | loglike: -32.8530 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3771 | loglike: -34.4124 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3771 | loglike: -34.4124 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3771 | loglike: -34.4124 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3771 | loglike: -34.4124 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3771 | loglike: -34.4124 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3772 | loglike: -37.1626 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3772 | loglike: -37.1626 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3772 | loglike: -37.1626 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3772 | loglike: -37.1626 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3773 | loglike: -36.2258 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3773 | loglike: -36.2258 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3774 | loglike: -33.3931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3774 | loglike: -33.3931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3774 | loglike: -33.3931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3774 | loglike: -33.3931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3774 | loglike: -33.3931 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3775 | loglike: -32.8154 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3775 | loglike: -32.8154 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3775 | loglike: -32.8154 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3776 | loglike: -32.7817 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3777 | loglike: -34.4035 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3778 | loglike: -35.3645 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3779 | loglike: -34.4111 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3780 | loglike: -36.8286 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3781 | loglike: -35.7487 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3782 | loglike: -36.0184 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3782 | loglike: -36.0184 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3782 | loglike: -36.0184 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3782 | loglike: -36.0184 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3783 | loglike: -36.3619 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3784 | loglike: -41.6307 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3785 | loglike: -38.1523 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3785 | loglike: -38.1523 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3786 | loglike: -35.7118 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3787 | loglike: -35.8860 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3787 | loglike: -35.8860 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3787 | loglike: -35.8860 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3788 | loglike: -36.6559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3788 | loglike: -36.6559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3788 | loglike: -36.6559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3788 | loglike: -36.6559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3789 | loglike: -43.4833 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3790 | loglike: -44.6837 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3790 | loglike: -44.6837 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3790 | loglike: -44.6837 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3791 | loglike: -46.7993 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3792 | loglike: -44.3246 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3793 | loglike: -35.5354 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3793 | loglike: -35.5354 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3794 | loglike: -34.2824 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3795 | loglike: -34.7574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3796 | loglike: -35.9741 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3797 | loglike: -33.0438 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3797 | loglike: -33.0438 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3797 | loglike: -33.0438 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3798 | loglike: -33.2308 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3798 | loglike: -33.2308 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3798 | loglike: -33.2308 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3798 | loglike: -33.2308 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3798 | loglike: -33.2308 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3799 | loglike: -35.3849 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3799 | loglike: -35.3849 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3799 | loglike: -35.3849 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3800 | loglike: -33.0140 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3800 | loglike: -33.0140 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3800 | loglike: -33.0140 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3801 | loglike: -33.9822 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3801 | loglike: -33.9822 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3802 | loglike: -34.8962 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3802 | loglike: -34.8962 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3802 | loglike: -34.8962 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3803 | loglike: -35.4573 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3803 | loglike: -35.4573 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3803 | loglike: -35.4573 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3804 | loglike: -35.7914 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3804 | loglike: -35.7914 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3805 | loglike: -35.3338 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3805 | loglike: -35.3338 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3806 | loglike: -36.6264 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3806 | loglike: -36.6264 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3806 | loglike: -36.6264 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3807 | loglike: -37.3553 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3808 | loglike: -37.9921 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3809 | loglike: -37.1165 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3809 | loglike: -37.1165 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3810 | loglike: -35.5069 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3811 | loglike: -34.5462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3811 | loglike: -34.5462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3811 | loglike: -34.5462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3812 | loglike: -32.5578 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3812 | loglike: -32.5578 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3812 | loglike: -32.5578 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3812 | loglike: -32.5578 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3812 | loglike: -32.5578 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3813 | loglike: -33.6916 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3813 | loglike: -33.6916 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3813 | loglike: -33.6916 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3814 | loglike: -37.4819 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3815 | loglike: -39.6401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3815 | loglike: -39.6401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3816 | loglike: -41.0508 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3817 | loglike: -45.0413 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3818 | loglike: -45.2896 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3819 | loglike: -34.1939 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3820 | loglike: -33.7920 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3821 | loglike: -35.7417 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3822 | loglike: -34.4315 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3822 | loglike: -34.4315 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3822 | loglike: -34.4315 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3822 | loglike: -34.4315 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3823 | loglike: -31.8021 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3824 | loglike: -31.9758 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3824 | loglike: -31.9758 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3825 | loglike: -31.2421 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3825 | loglike: -31.2421 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3826 | loglike: -37.9317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3826 | loglike: -37.9317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3826 | loglike: -37.9317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3826 | loglike: -37.9317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3826 | loglike: -37.9317 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3827 | loglike: -36.0140 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3828 | loglike: -34.4475 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3828 | loglike: -34.4475 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3828 | loglike: -34.4475 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3829 | loglike: -34.2705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3829 | loglike: -34.2705 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3830 | loglike: -31.3101 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3831 | loglike: -32.6905 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3832 | loglike: -35.5756 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3833 | loglike: -35.6881 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3834 | loglike: -38.6632 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3835 | loglike: -40.4725 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3835 | loglike: -40.4725 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3836 | loglike: -35.2866 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3837 | loglike: -35.1082 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3838 | loglike: -34.9607 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3839 | loglike: -36.7432 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3839 | loglike: -36.7432 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3839 | loglike: -36.7432 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3840 | loglike: -35.1790 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3841 | loglike: -32.8143 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3842 | loglike: -31.1223 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3843 | loglike: -30.6418 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3843 | loglike: -30.6418 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3843 | loglike: -30.6418 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3844 | loglike: -32.2947 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3845 | loglike: -32.8889 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3846 | loglike: -34.5802 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3847 | loglike: -33.4549 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3847 | loglike: -33.4549 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3848 | loglike: -33.4716 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3849 | loglike: -31.7596 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3849 | loglike: -31.7596 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3850 | loglike: -32.4751 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3850 | loglike: -32.4751 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3851 | loglike: -39.1939 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3851 | loglike: -39.1939 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3851 | loglike: -39.1939 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3852 | loglike: -34.5299 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3853 | loglike: -32.5681 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3854 | loglike: -32.4484 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3854 | loglike: -32.4484 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3855 | loglike: -30.8742 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3856 | loglike: -30.6267 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3856 | loglike: -30.6267 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3857 | loglike: -31.9644 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3858 | loglike: -32.8615 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3859 | loglike: -33.1702 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3859 | loglike: -33.1702 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3859 | loglike: -33.1702 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3860 | loglike: -32.6228 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3860 | loglike: -32.6228 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3861 | loglike: -33.0451 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3862 | loglike: -32.8849 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3863 | loglike: -32.1813 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3863 | loglike: -32.1813 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3863 | loglike: -32.1813 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3863 | loglike: -32.1813 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3863 | loglike: -32.1813 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3864 | loglike: -34.5160 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3864 | loglike: -34.5160 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3864 | loglike: -34.5160 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3864 | loglike: -34.5160 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3865 | loglike: -36.9338 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3865 | loglike: -36.9338 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3866 | loglike: -36.7865 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3867 | loglike: -36.9978 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3868 | loglike: -34.9059 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3869 | loglike: -34.6669 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3869 | loglike: -34.6669 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3870 | loglike: -38.3922 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3870 | loglike: -38.3922 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3871 | loglike: -36.0675 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3871 | loglike: -36.0675 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3871 | loglike: -36.0675 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3871 | loglike: -36.0675 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3871 | loglike: -36.0675 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3872 | loglike: -36.4710 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3872 | loglike: -36.4710 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3872 | loglike: -36.4710 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3873 | loglike: -35.7980 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3873 | loglike: -35.7980 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3874 | loglike: -33.5997 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3875 | loglike: -39.6417 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3875 | loglike: -39.6417 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3876 | loglike: -35.1672 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3876 | loglike: -35.1672 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3877 | loglike: -31.2036 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3878 | loglike: -37.0752 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3879 | loglike: -41.4910 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3879 | loglike: -41.4910 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3879 | loglike: -41.4910 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3880 | loglike: -37.8462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3880 | loglike: -37.8462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3881 | loglike: -35.4950 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3882 | loglike: -34.1460 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3883 | loglike: -33.9574 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3884 | loglike: -33.8642 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3885 | loglike: -31.3741 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3885 | loglike: -31.3741 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3885 | loglike: -31.3741 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3886 | loglike: -31.7131 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3887 | loglike: -32.9927 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3888 | loglike: -34.6867 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3888 | loglike: -34.6867 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3889 | loglike: -34.8889 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3890 | loglike: -34.6062 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3890 | loglike: -34.6062 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3890 | loglike: -34.6062 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3890 | loglike: -34.6062 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3891 | loglike: -33.7354 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3891 | loglike: -33.7354 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3892 | loglike: -33.4094 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3892 | loglike: -33.4094 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3893 | loglike: -33.4736 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3893 | loglike: -33.4736 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3893 | loglike: -33.4736 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3893 | loglike: -33.4736 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3893 | loglike: -33.4736 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3894 | loglike: -38.8812 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3895 | loglike: -41.3862 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3895 | loglike: -41.3862 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3896 | loglike: -38.1001 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3897 | loglike: -34.9174 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3898 | loglike: -31.2136 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3899 | loglike: -34.1203 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3900 | loglike: -34.7693 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3901 | loglike: -34.5573 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3901 | loglike: -34.5573 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3902 | loglike: -34.5584 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3903 | loglike: -36.1164 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3903 | loglike: -36.1164 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3904 | loglike: -35.7766 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3905 | loglike: -32.7227 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3906 | loglike: -33.5376 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3907 | loglike: -32.5602 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3907 | loglike: -32.5602 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3907 | loglike: -32.5602 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3908 | loglike: -32.2051 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3909 | loglike: -33.3421 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3910 | loglike: -38.9950 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3911 | loglike: -33.6381 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3911 | loglike: -33.6381 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3912 | loglike: -32.5389 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3913 | loglike: -33.0395 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3914 | loglike: -34.7007 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3915 | loglike: -36.4037 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3915 | loglike: -36.4037 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3916 | loglike: -35.3428 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3917 | loglike: -34.9257 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3917 | loglike: -34.9257 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3917 | loglike: -34.9257 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3918 | loglike: -35.5174 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3918 | loglike: -35.5174 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3918 | loglike: -35.5174 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3918 | loglike: -35.5174 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3919 | loglike: -33.2738 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3919 | loglike: -33.2738 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3919 | loglike: -33.2738 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3920 | loglike: -31.4990 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3921 | loglike: -33.0087 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3922 | loglike: -33.7162 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3923 | loglike: -33.3797 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3924 | loglike: -32.4970 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3924 | loglike: -32.4970 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3924 | loglike: -32.4970 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3925 | loglike: -33.0839 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3926 | loglike: -32.6048 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3926 | loglike: -32.6048 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3927 | loglike: -34.0075 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3928 | loglike: -33.5682 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3928 | loglike: -33.5682 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3929 | loglike: -34.2395 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3930 | loglike: -33.8611 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3930 | loglike: -33.8611 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3931 | loglike: -38.2629 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3932 | loglike: -36.3498 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3932 | loglike: -36.3498 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3933 | loglike: -35.3551 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3934 | loglike: -34.3850 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3934 | loglike: -34.3850 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3934 | loglike: -34.3850 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3934 | loglike: -34.3850 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3935 | loglike: -32.7835 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3935 | loglike: -32.7835 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3935 | loglike: -32.7835 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3935 | loglike: -32.7835 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3935 | loglike: -32.7835 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3936 | loglike: -32.4428 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3937 | loglike: -32.3650 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3938 | loglike: -31.2195 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3939 | loglike: -31.6200 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3939 | loglike: -31.6200 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3940 | loglike: -32.0401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3940 | loglike: -32.0401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3940 | loglike: -32.0401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3940 | loglike: -32.0401 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3941 | loglike: -32.6904 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3941 | loglike: -32.6904 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3942 | loglike: -32.9489 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3943 | loglike: -32.5556 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3943 | loglike: -32.5556 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3944 | loglike: -31.7048 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3945 | loglike: -32.2016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3945 | loglike: -32.2016 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3946 | loglike: -32.4207 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3947 | loglike: -31.8732 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3947 | loglike: -31.8732 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3947 | loglike: -31.8732 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3948 | loglike: -33.0096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3949 | loglike: -32.4018 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3949 | loglike: -32.4018 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3949 | loglike: -32.4018 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3950 | loglike: -31.8085 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3950 | loglike: -31.8085 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3950 | loglike: -31.8085 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3951 | loglike: -31.7715 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3951 | loglike: -31.7715 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3952 | loglike: -34.3330 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3952 | loglike: -34.3330 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3953 | loglike: -35.6228 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3953 | loglike: -35.6228 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3954 | loglike: -36.3455 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3955 | loglike: -35.5743 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3956 | loglike: -43.8245 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3957 | loglike: -39.1379 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3958 | loglike: -39.4671 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3958 | loglike: -39.4671 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3959 | loglike: -36.2591 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3960 | loglike: -34.5414 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3961 | loglike: -34.4351 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3962 | loglike: -33.2167 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3963 | loglike: -32.2756 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3964 | loglike: -31.5259 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3964 | loglike: -31.5259 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3965 | loglike: -32.0240 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3966 | loglike: -34.9122 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3966 | loglike: -34.9122 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3967 | loglike: -35.7028 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3967 | loglike: -35.7028 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3968 | loglike: -33.3822 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3969 | loglike: -36.0159 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3969 | loglike: -36.0159 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3970 | loglike: -37.4386 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3971 | loglike: -33.5250 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3971 | loglike: -33.5250 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3971 | loglike: -33.5250 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3972 | loglike: -37.5425 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3972 | loglike: -37.5425 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3973 | loglike: -37.8462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3973 | loglike: -37.8462 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3974 | loglike: -37.3088 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3974 | loglike: -37.3088 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3975 | loglike: -39.5563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3975 | loglike: -39.5563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3976 | loglike: -40.6958 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3977 | loglike: -40.1598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3977 | loglike: -40.1598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3977 | loglike: -40.1598 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3978 | loglike: -36.5752 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3978 | loglike: -36.5752 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3979 | loglike: -35.4081 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3980 | loglike: -34.7058 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3981 | loglike: -32.5391 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3982 | loglike: -32.9490 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3983 | loglike: -32.3951 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3983 | loglike: -32.3951 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3984 | loglike: -31.8096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3984 | loglike: -31.8096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3984 | loglike: -31.8096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3984 | loglike: -31.8096 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3985 | loglike: -31.8559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3985 | loglike: -31.8559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3985 | loglike: -31.8559 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3986 | loglike: -37.2730 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3986 | loglike: -37.2730 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3986 | loglike: -37.2730 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3987 | loglike: -34.9368 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3987 | loglike: -34.9368 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3988 | loglike: -35.5821 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3989 | loglike: -36.6563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3989 | loglike: -36.6563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3989 | loglike: -36.6563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3989 | loglike: -36.6563 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3990 | loglike: -33.6831 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3991 | loglike: -33.5224 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3991 | loglike: -33.5224 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3991 | loglike: -33.5224 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3992 | loglike: -33.2027 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3993 | loglike: -33.5726 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3994 | loglike: -34.3225 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3995 | loglike: -32.8818 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3996 | loglike: -33.8929 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3997 | loglike: -34.3852 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3997 | loglike: -34.3852 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3998 | loglike: -35.7040 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3999 | loglike: -34.8127 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 3999 | loglike: -34.8127 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 4000 | loglike: -33.1873 | Gelman-Rubin: [1.01421821e-01 6.42889362e-04 1.19764468e-02 4.71014245e-01
Accepted: 6000 | loglike: -31.7931 | Gelman-Rubin: [0.00713425 0.00034078 0.00042263 0.03788728 0.06153067]
INFO:simplemc:
Elapsed time: 10.304 minutes = 618.261 seconds
---- Gelman-Rubin achived ----
Om: 0.2993 +/- 0.0180
Obh2: 0.0221 +/- 0.0005
h: 0.6651 +/- 0.0206
w: -0.9420 +/- 0.0978
wa: -0.1199 +/- 0.4291
```python
samples3 = res3['result']['samples']
weights3 = res3['result']['weights']
```
```python
plt.figure(figsize=(10,10), dpi=300)
plt.clf()
cornerfig3 = corner.corner(samples2, weights=weights2, smooth=0.5, smooth1d=0.5,
# quantiles=(0.16, 0.84),
levels=(1-np.exp(-0.5), ), labels=pars_labels, color='indigo', plot_density=True,
plot_datapoints=False, fig=cornerfig2)
# cornerfig3.text(0.8, 0.95,'SN+HD', fontsize=20, color='darkred')
# cornerfig3.text(0.8, 0.9,'SN+HD+BBAO', fontsize=20, color='indigo')
# cornerfig3.text(0.8, 0.85,'SN+HD+BBAO+Planck', fontsize=20, color='cadetblue')
# cornerfig3.savefig('wacdm_datasets_3.png')
```
<Figure size 3000x3000 with 0 Axes>
```python
cornerfig3
```
# SN+BBAO
```python
analysis5 = DriverMC(analyzername='mcmc', model='waCDM', datasets='SN+BBAO')
```
Loading simplemc/data/jla_binned_distances_31nodes_v1.txt
Loading simplemc/data/cov_jla_binned_distances_31nodes_v1.txt
Eigenvalues of cov matrix: [0.0005070806003107887, 0.0005683516014088356, 0.000739250907386215] ... 0.03645982276128593
Adding marginalising constant
DR11LOWZ measurement in Cuesta : 8.468173048634462 +- 0.16748759985432085
Loading simplemc/data/sdss_DR11CMASS_consensus.dat
Aperp min,max,step,N: 0.961142836 1.12057144 0.0005714290000000677 280
Aparl min,max,step,N: 0.85171431 1.09085718 0.0008571430000000602 280
Loading done
rd = 149.26478152260088 Mpc
Fiducials at z= 0.57 : 14.3015431061137 21.466392296218515
Loading simplemc/data/chi2_surface_dr11_baseline_fit.txt
Aperp min,max,step,N: 0.5 1.5 0.016666999999999987 61
Aparl min,max,step,N: 0.8 1.2 0.0066669999999999785 61
Loading done
rd = 149.73834899440558 Mpc
Fiducials at z= 2.34 : 38.71393250862439 8.704556344366967
Loading simplemc/data/lyabaocross.scan
Aperp min,max,step,N: 0.7 1.3 0.020000000000000018 31
Aparl min,max,step,N: 0.8 1.2 0.010000000000000009 41
Loading done
rd = 149.56742181392625 Mpc
Fiducials at z= 2.36 : 38.931124520156345 8.641577445046805
SixdFGS measurement in Cuesta : 3.041334103142206 +- 0.18007899294920957
Loading simplemc/data/chidavexi8stavePk5staverec.dat
MGS measurement in Cuesta : DV= 664.1865351303804 +- 24.86880740958958
with rd= 148.6516589371155 DV_fid= 638.9490468680368 alphamin= 1.0394984363558428
Free parameters:
Om = 0.3038 +/- 0.05
Obh2 = 0.02234 +/- 0.001
h = 0.6821 +/- 0.05
w = -1.0 +/- 0.1
wa = 0.0 +/- 0.1
/home/isidro/Documents/github/simplemc_tests/simplemc/likelihoods/TabulatedBAODVLikelihood.py:37: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.
If increasing the limit yields no improvement it is advised to analyze
the integrand in order to determine the difficulties. If the position of a
local difficulty can be determined (singularity, discontinuity) one will
probably gain from splitting up the interval and calling the integrator
on the subranges. Perhaps a special-purpose integrator should be used.
rms /= quad(lambda x: sp.exp(-self.chi2i(alphamin+x)/2), -0.1, 0.1)[0]
```python
res5 = analysis5.executer()
```
INFO:simplemc:
nsamp: 50000
skip: 300
temp: 2
evidence: False
Bounds: [ 0.1 0.02 0.4 -2. -2. ] [0.5 0.025 0.9 0. 2. ]
Starting chain...
Accepted: 299 | loglike: -24.7207 | Gelman-Rubin: None
Re-initializing covariance matrix after burn-in
[[ 2.20470347e-04 1.65797158e-06 6.57199877e-05 1.46158775e-04
2.60715675e-06]
[ 1.65797158e-06 1.69888490e-07 2.37655622e-06 5.00618374e-06
-7.64466080e-06]
[ 6.57199877e-05 2.37655622e-06 1.97700776e-04 -7.93932118e-05
-3.01909099e-04]
[ 1.46158775e-04 5.00618374e-06 -7.93932118e-05 9.40050525e-04
-3.69154186e-06]
[ 2.60715675e-06 -7.64466080e-06 -3.01909099e-04 -3.69154186e-06
2.20805095e-03]]
Accepted: 4001 | loglike: -22.7705 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4002 | loglike: -22.4398 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4003 | loglike: -25.0351 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4003 | loglike: -25.0351 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4004 | loglike: -23.8588 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4005 | loglike: -23.7433 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4006 | loglike: -23.8599 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4006 | loglike: -23.8599 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4007 | loglike: -23.1484 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4007 | loglike: -23.1484 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4008 | loglike: -26.2065 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4008 | loglike: -26.2065 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4009 | loglike: -21.6238 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4010 | loglike: -29.5050 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4010 | loglike: -29.5050 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4010 | loglike: -29.5050 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4011 | loglike: -29.7352 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4011 | loglike: -29.7352 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4012 | loglike: -29.7448 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4012 | loglike: -29.7448 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4013 | loglike: -29.8305 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4013 | loglike: -29.8305 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4013 | loglike: -29.8305 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4013 | loglike: -29.8305 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4014 | loglike: -23.9514 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4014 | loglike: -23.9514 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4015 | loglike: -24.5912 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4015 | loglike: -24.5912 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4015 | loglike: -24.5912 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4015 | loglike: -24.5912 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4016 | loglike: -24.6374 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4016 | loglike: -24.6374 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4016 | loglike: -24.6374 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4016 | loglike: -24.6374 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4017 | loglike: -22.7003 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4018 | loglike: -24.6226 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4019 | loglike: -25.3055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4020 | loglike: -24.9508 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4020 | loglike: -24.9508 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4021 | loglike: -26.6892 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4022 | loglike: -29.7218 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4023 | loglike: -29.6809 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4023 | loglike: -29.6809 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4023 | loglike: -29.6809 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4024 | loglike: -32.8323 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4024 | loglike: -32.8323 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4025 | loglike: -26.1888 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4025 | loglike: -26.1888 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4025 | loglike: -26.1888 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4025 | loglike: -26.1888 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4026 | loglike: -27.1142 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4027 | loglike: -29.3956 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4028 | loglike: -25.5621 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4029 | loglike: -25.9175 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4030 | loglike: -25.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4030 | loglike: -25.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4030 | loglike: -25.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4030 | loglike: -25.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4030 | loglike: -25.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4031 | loglike: -24.9526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4031 | loglike: -24.9526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4031 | loglike: -24.9526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4031 | loglike: -24.9526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4031 | loglike: -24.9526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4032 | loglike: -25.7227 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4033 | loglike: -24.4751 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4034 | loglike: -24.6637 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4034 | loglike: -24.6637 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4035 | loglike: -24.2490 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4035 | loglike: -24.2490 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4036 | loglike: -25.6631 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4036 | loglike: -25.6631 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4037 | loglike: -26.2589 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4037 | loglike: -26.2589 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4038 | loglike: -24.5049 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4039 | loglike: -24.1177 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4039 | loglike: -24.1177 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4039 | loglike: -24.1177 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4039 | loglike: -24.1177 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4040 | loglike: -24.3541 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4040 | loglike: -24.3541 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4040 | loglike: -24.3541 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4041 | loglike: -26.1293 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4041 | loglike: -26.1293 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4041 | loglike: -26.1293 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4042 | loglike: -27.7515 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4042 | loglike: -27.7515 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4042 | loglike: -27.7515 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4043 | loglike: -29.2210 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4043 | loglike: -29.2210 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4044 | loglike: -30.0678 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4044 | loglike: -30.0678 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4045 | loglike: -29.9647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4045 | loglike: -29.9647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4045 | loglike: -29.9647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4046 | loglike: -27.3448 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4047 | loglike: -28.9082 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4048 | loglike: -23.7021 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4048 | loglike: -23.7021 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4048 | loglike: -23.7021 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4049 | loglike: -24.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4049 | loglike: -24.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4049 | loglike: -24.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4049 | loglike: -24.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4049 | loglike: -24.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4050 | loglike: -22.5853 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4051 | loglike: -21.9397 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4051 | loglike: -21.9397 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4052 | loglike: -21.9319 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4053 | loglike: -25.4183 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4054 | loglike: -23.5306 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4055 | loglike: -24.2791 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4055 | loglike: -24.2791 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4055 | loglike: -24.2791 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4056 | loglike: -24.0481 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4057 | loglike: -24.6890 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4058 | loglike: -27.0353 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4058 | loglike: -27.0353 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4058 | loglike: -27.0353 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4058 | loglike: -27.0353 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4059 | loglike: -23.3775 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4060 | loglike: -22.4917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4060 | loglike: -22.4917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4060 | loglike: -22.4917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4060 | loglike: -22.4917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4061 | loglike: -23.6524 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4061 | loglike: -23.6524 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4062 | loglike: -36.4448 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4063 | loglike: -34.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4063 | loglike: -34.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4063 | loglike: -34.2572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4064 | loglike: -32.6276 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4065 | loglike: -28.1824 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4066 | loglike: -23.0394 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4066 | loglike: -23.0394 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4066 | loglike: -23.0394 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4066 | loglike: -23.0394 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4067 | loglike: -23.6955 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4068 | loglike: -22.2416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4069 | loglike: -24.2778 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4069 | loglike: -24.2778 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4069 | loglike: -24.2778 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4070 | loglike: -23.9054 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4071 | loglike: -23.8224 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4072 | loglike: -22.0591 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4073 | loglike: -23.5055 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4074 | loglike: -23.0861 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4074 | loglike: -23.0861 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4074 | loglike: -23.0861 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4075 | loglike: -22.4105 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4075 | loglike: -22.4105 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4076 | loglike: -25.6476 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4076 | loglike: -25.6476 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4076 | loglike: -25.6476 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4076 | loglike: -25.6476 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4076 | loglike: -25.6476 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4077 | loglike: -22.7265 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4077 | loglike: -22.7265 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4077 | loglike: -22.7265 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4078 | loglike: -23.9429 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4079 | loglike: -25.9239 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4079 | loglike: -25.9239 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4079 | loglike: -25.9239 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4080 | loglike: -25.0311 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4081 | loglike: -23.6200 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4081 | loglike: -23.6200 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4082 | loglike: -23.6942 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4082 | loglike: -23.6942 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4083 | loglike: -25.9591 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4084 | loglike: -27.2699 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4085 | loglike: -22.9723 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4085 | loglike: -22.9723 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4085 | loglike: -22.9723 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4086 | loglike: -23.6530 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4087 | loglike: -23.8907 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4087 | loglike: -23.8907 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4088 | loglike: -25.8236 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4089 | loglike: -25.6547 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4089 | loglike: -25.6547 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4089 | loglike: -25.6547 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4089 | loglike: -25.6547 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4090 | loglike: -24.0521 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4091 | loglike: -24.0615 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4092 | loglike: -23.2088 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4093 | loglike: -22.9457 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4093 | loglike: -22.9457 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4093 | loglike: -22.9457 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4094 | loglike: -26.3620 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4095 | loglike: -24.9169 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4095 | loglike: -24.9169 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4095 | loglike: -24.9169 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4095 | loglike: -24.9169 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4096 | loglike: -27.2100 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4097 | loglike: -27.0618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4097 | loglike: -27.0618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4097 | loglike: -27.0618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4097 | loglike: -27.0618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4098 | loglike: -26.8813 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4099 | loglike: -28.6785 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4099 | loglike: -28.6785 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4100 | loglike: -27.0570 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4101 | loglike: -25.5396 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4102 | loglike: -24.4142 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4103 | loglike: -25.5589 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4103 | loglike: -25.5589 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4104 | loglike: -26.1867 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4105 | loglike: -29.1241 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4106 | loglike: -29.1078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4106 | loglike: -29.1078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4106 | loglike: -29.1078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4107 | loglike: -28.4364 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4108 | loglike: -32.0121 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4109 | loglike: -29.3522 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4110 | loglike: -31.0890 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4111 | loglike: -32.3647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4111 | loglike: -32.3647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4111 | loglike: -32.3647 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4112 | loglike: -32.6254 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4113 | loglike: -26.1248 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4113 | loglike: -26.1248 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4113 | loglike: -26.1248 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4113 | loglike: -26.1248 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4114 | loglike: -22.0380 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4114 | loglike: -22.0380 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4114 | loglike: -22.0380 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4115 | loglike: -23.3212 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4116 | loglike: -23.0564 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4117 | loglike: -22.7926 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4118 | loglike: -23.0645 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4119 | loglike: -24.2179 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4119 | loglike: -24.2179 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4120 | loglike: -25.4512 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4121 | loglike: -23.2490 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4122 | loglike: -23.1571 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4123 | loglike: -24.0746 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4123 | loglike: -24.0746 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4123 | loglike: -24.0746 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4124 | loglike: -23.1374 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4125 | loglike: -23.2798 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4126 | loglike: -24.0412 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4127 | loglike: -25.5835 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4127 | loglike: -25.5835 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4128 | loglike: -24.9869 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4129 | loglike: -25.2223 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4129 | loglike: -25.2223 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4130 | loglike: -25.5626 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4131 | loglike: -24.1245 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4132 | loglike: -24.0346 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4133 | loglike: -23.5028 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4134 | loglike: -24.7941 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4135 | loglike: -26.7568 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4135 | loglike: -26.7568 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4135 | loglike: -26.7568 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4136 | loglike: -26.1931 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4136 | loglike: -26.1931 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4137 | loglike: -27.4336 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4138 | loglike: -28.2084 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4139 | loglike: -27.9769 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4140 | loglike: -23.6916 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4141 | loglike: -24.6291 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4141 | loglike: -24.6291 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4141 | loglike: -24.6291 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4142 | loglike: -24.3477 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4143 | loglike: -26.3328 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4144 | loglike: -25.9694 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4144 | loglike: -25.9694 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4144 | loglike: -25.9694 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4145 | loglike: -24.6909 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4145 | loglike: -24.6909 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4146 | loglike: -29.3367 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4147 | loglike: -29.3771 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4148 | loglike: -24.8459 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4148 | loglike: -24.8459 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4148 | loglike: -24.8459 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4149 | loglike: -24.3486 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4150 | loglike: -25.0875 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4150 | loglike: -25.0875 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4151 | loglike: -23.1675 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4152 | loglike: -23.2894 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4153 | loglike: -22.4209 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4154 | loglike: -22.8756 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4155 | loglike: -22.8421 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4156 | loglike: -23.1441 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4156 | loglike: -23.1441 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4157 | loglike: -25.2052 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4157 | loglike: -25.2052 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4158 | loglike: -23.2326 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4159 | loglike: -23.8837 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4159 | loglike: -23.8837 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4159 | loglike: -23.8837 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4159 | loglike: -23.8837 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4160 | loglike: -24.8927 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4161 | loglike: -26.2607 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4162 | loglike: -28.0654 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4163 | loglike: -32.6078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4163 | loglike: -32.6078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4164 | loglike: -31.2668 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4164 | loglike: -31.2668 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4165 | loglike: -35.8282 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4165 | loglike: -35.8282 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4165 | loglike: -35.8282 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4166 | loglike: -34.9409 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4167 | loglike: -34.8271 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4168 | loglike: -33.6535 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4169 | loglike: -28.5674 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4169 | loglike: -28.5674 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4170 | loglike: -29.4060 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4171 | loglike: -27.6992 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4172 | loglike: -24.9203 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4173 | loglike: -23.4884 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4173 | loglike: -23.4884 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4173 | loglike: -23.4884 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4173 | loglike: -23.4884 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4174 | loglike: -23.7362 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4174 | loglike: -23.7362 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4175 | loglike: -23.9720 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4175 | loglike: -23.9720 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4176 | loglike: -23.9858 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4177 | loglike: -24.5495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4177 | loglike: -24.5495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4177 | loglike: -24.5495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4177 | loglike: -24.5495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4177 | loglike: -24.5495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4178 | loglike: -25.9863 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4179 | loglike: -25.1928 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4180 | loglike: -23.8858 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4181 | loglike: -23.3522 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4182 | loglike: -23.0610 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4183 | loglike: -24.6569 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4184 | loglike: -27.3482 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4185 | loglike: -26.6170 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4186 | loglike: -27.7273 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4187 | loglike: -25.2803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4188 | loglike: -23.4735 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4188 | loglike: -23.4735 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4188 | loglike: -23.4735 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4188 | loglike: -23.4735 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4189 | loglike: -24.1817 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4189 | loglike: -24.1817 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4189 | loglike: -24.1817 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4190 | loglike: -26.1496 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4190 | loglike: -26.1496 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4191 | loglike: -24.6710 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4192 | loglike: -24.6492 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4193 | loglike: -25.3079 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4194 | loglike: -25.1251 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4195 | loglike: -23.1883 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4196 | loglike: -23.3546 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4197 | loglike: -22.9671 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4198 | loglike: -23.7987 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4198 | loglike: -23.7987 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4199 | loglike: -23.9929 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4200 | loglike: -25.5438 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4201 | loglike: -24.7001 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4201 | loglike: -24.7001 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4202 | loglike: -25.9324 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4203 | loglike: -24.3620 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4204 | loglike: -23.9456 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4205 | loglike: -24.0574 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4206 | loglike: -24.3681 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4207 | loglike: -26.3047 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4208 | loglike: -22.5098 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4209 | loglike: -22.0598 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4210 | loglike: -22.3563 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4211 | loglike: -22.4609 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4211 | loglike: -22.4609 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4211 | loglike: -22.4609 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4212 | loglike: -23.4909 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4213 | loglike: -22.9723 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4214 | loglike: -25.7804 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4214 | loglike: -25.7804 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4215 | loglike: -25.7339 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4215 | loglike: -25.7339 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4216 | loglike: -25.1191 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4217 | loglike: -26.4893 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4218 | loglike: -27.1963 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4219 | loglike: -28.2602 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4219 | loglike: -28.2602 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4219 | loglike: -28.2602 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4220 | loglike: -27.9447 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4221 | loglike: -26.3384 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4222 | loglike: -28.1565 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4222 | loglike: -28.1565 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4223 | loglike: -24.9004 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4223 | loglike: -24.9004 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4223 | loglike: -24.9004 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4223 | loglike: -24.9004 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4224 | loglike: -22.8977 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4224 | loglike: -22.8977 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4225 | loglike: -25.6422 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4226 | loglike: -28.4511 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4227 | loglike: -29.9783 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4227 | loglike: -29.9783 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4227 | loglike: -29.9783 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4228 | loglike: -27.9048 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4229 | loglike: -27.0693 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4230 | loglike: -26.8527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4230 | loglike: -26.8527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4230 | loglike: -26.8527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4231 | loglike: -23.9133 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4232 | loglike: -25.3790 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4232 | loglike: -25.3790 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4232 | loglike: -25.3790 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4232 | loglike: -25.3790 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4233 | loglike: -25.8763 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4234 | loglike: -27.0085 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4235 | loglike: -26.5753 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4236 | loglike: -25.0948 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4237 | loglike: -25.0108 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4238 | loglike: -25.4686 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4239 | loglike: -25.2961 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4239 | loglike: -25.2961 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4239 | loglike: -25.2961 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4240 | loglike: -25.4012 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4240 | loglike: -25.4012 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4240 | loglike: -25.4012 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4241 | loglike: -26.3762 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4242 | loglike: -26.6223 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4242 | loglike: -26.6223 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4243 | loglike: -26.4831 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4244 | loglike: -25.2924 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4245 | loglike: -26.7920 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4245 | loglike: -26.7920 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4245 | loglike: -26.7920 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4246 | loglike: -26.5112 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4246 | loglike: -26.5112 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4247 | loglike: -26.7593 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4248 | loglike: -26.4192 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4248 | loglike: -26.4192 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4248 | loglike: -26.4192 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4249 | loglike: -27.8596 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4249 | loglike: -27.8596 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4249 | loglike: -27.8596 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4250 | loglike: -26.3782 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4250 | loglike: -26.3782 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4251 | loglike: -24.9270 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4251 | loglike: -24.9270 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4252 | loglike: -27.4889 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4253 | loglike: -24.7653 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4254 | loglike: -23.6493 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4254 | loglike: -23.6493 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4255 | loglike: -23.3011 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4256 | loglike: -23.1290 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4256 | loglike: -23.1290 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4256 | loglike: -23.1290 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4257 | loglike: -23.8456 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4258 | loglike: -23.4568 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4258 | loglike: -23.4568 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4259 | loglike: -22.7390 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4260 | loglike: -23.4467 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4260 | loglike: -23.4467 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4261 | loglike: -23.4989 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4262 | loglike: -23.0276 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4263 | loglike: -23.1427 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4263 | loglike: -23.1427 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4264 | loglike: -23.3179 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4264 | loglike: -23.3179 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4265 | loglike: -22.8232 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4266 | loglike: -22.1663 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4266 | loglike: -22.1663 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4266 | loglike: -22.1663 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4267 | loglike: -22.8554 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4268 | loglike: -22.4696 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4269 | loglike: -22.4694 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4270 | loglike: -22.3709 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4271 | loglike: -23.4034 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4271 | loglike: -23.4034 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4272 | loglike: -24.5530 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4273 | loglike: -23.5053 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4274 | loglike: -27.4717 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4275 | loglike: -25.3041 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4275 | loglike: -25.3041 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4275 | loglike: -25.3041 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4276 | loglike: -23.4227 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4277 | loglike: -23.1648 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4277 | loglike: -23.1648 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4278 | loglike: -22.8073 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4278 | loglike: -22.8073 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4279 | loglike: -23.5240 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4280 | loglike: -24.9498 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4280 | loglike: -24.9498 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4281 | loglike: -23.4112 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4282 | loglike: -23.1615 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4283 | loglike: -22.9762 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4283 | loglike: -22.9762 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4284 | loglike: -22.3740 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4284 | loglike: -22.3740 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4284 | loglike: -22.3740 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4285 | loglike: -23.8607 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4286 | loglike: -23.9953 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4287 | loglike: -22.3804 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4288 | loglike: -23.7750 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4288 | loglike: -23.7750 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4289 | loglike: -22.3526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4289 | loglike: -22.3526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4290 | loglike: -23.0526 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4291 | loglike: -24.0913 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4292 | loglike: -25.7174 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4293 | loglike: -27.7530 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4294 | loglike: -26.9034 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4294 | loglike: -26.9034 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4294 | loglike: -26.9034 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4295 | loglike: -26.6313 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4296 | loglike: -25.5274 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4296 | loglike: -25.5274 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4296 | loglike: -25.5274 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4296 | loglike: -25.5274 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4296 | loglike: -25.5274 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4297 | loglike: -23.6904 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4297 | loglike: -23.6904 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4297 | loglike: -23.6904 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4298 | loglike: -23.5267 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4299 | loglike: -23.8401 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4300 | loglike: -23.3685 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4301 | loglike: -24.0791 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4302 | loglike: -24.6725 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4303 | loglike: -23.9464 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4304 | loglike: -23.2902 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4304 | loglike: -23.2902 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4304 | loglike: -23.2902 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4305 | loglike: -23.4873 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4306 | loglike: -23.9210 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4306 | loglike: -23.9210 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4306 | loglike: -23.9210 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4307 | loglike: -25.1572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4307 | loglike: -25.1572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4308 | loglike: -24.9310 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4309 | loglike: -24.3728 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4309 | loglike: -24.3728 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4310 | loglike: -24.5110 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4310 | loglike: -24.5110 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4311 | loglike: -22.6000 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4312 | loglike: -22.5926 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4313 | loglike: -23.2120 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4313 | loglike: -23.2120 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4314 | loglike: -23.3134 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4314 | loglike: -23.3134 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4314 | loglike: -23.3134 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4315 | loglike: -23.5322 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4316 | loglike: -24.0524 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4317 | loglike: -23.2250 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4318 | loglike: -23.1700 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4318 | loglike: -23.1700 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4318 | loglike: -23.1700 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4318 | loglike: -23.1700 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4319 | loglike: -23.0183 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4320 | loglike: -24.7202 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4321 | loglike: -23.4777 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4322 | loglike: -22.9033 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4322 | loglike: -22.9033 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4322 | loglike: -22.9033 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4322 | loglike: -22.9033 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4323 | loglike: -23.0212 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4323 | loglike: -23.0212 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4323 | loglike: -23.0212 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4323 | loglike: -23.0212 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4324 | loglike: -22.1993 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4325 | loglike: -23.7157 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4325 | loglike: -23.7157 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4325 | loglike: -23.7157 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4326 | loglike: -22.3307 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4326 | loglike: -22.3307 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4326 | loglike: -22.3307 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4326 | loglike: -22.3307 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4327 | loglike: -25.0090 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4327 | loglike: -25.0090 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4328 | loglike: -24.8961 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4329 | loglike: -22.5507 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4329 | loglike: -22.5507 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4330 | loglike: -25.6314 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4331 | loglike: -24.4462 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4331 | loglike: -24.4462 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4331 | loglike: -24.4462 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4332 | loglike: -23.6332 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4332 | loglike: -23.6332 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4332 | loglike: -23.6332 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4333 | loglike: -22.2386 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4334 | loglike: -21.5856 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4335 | loglike: -22.7563 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4336 | loglike: -24.8203 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4336 | loglike: -24.8203 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4336 | loglike: -24.8203 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4337 | loglike: -22.6064 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4338 | loglike: -22.8452 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4338 | loglike: -22.8452 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4338 | loglike: -22.8452 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4339 | loglike: -22.0344 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4339 | loglike: -22.0344 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4339 | loglike: -22.0344 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4340 | loglike: -24.6077 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4340 | loglike: -24.6077 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4341 | loglike: -25.1150 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4342 | loglike: -24.7551 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4342 | loglike: -24.7551 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4343 | loglike: -24.4123 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4343 | loglike: -24.4123 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4344 | loglike: -23.1025 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4345 | loglike: -23.5498 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4345 | loglike: -23.5498 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4346 | loglike: -24.2197 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4347 | loglike: -25.1702 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4347 | loglike: -25.1702 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4347 | loglike: -25.1702 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4348 | loglike: -24.6522 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4349 | loglike: -24.6053 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4350 | loglike: -22.8304 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4350 | loglike: -22.8304 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4350 | loglike: -22.8304 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4351 | loglike: -23.4104 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4351 | loglike: -23.4104 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4351 | loglike: -23.4104 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4351 | loglike: -23.4104 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4352 | loglike: -23.4281 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4352 | loglike: -23.4281 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4352 | loglike: -23.4281 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4353 | loglike: -23.0737 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4354 | loglike: -22.9170 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4354 | loglike: -22.9170 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4354 | loglike: -22.9170 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4355 | loglike: -22.5910 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4356 | loglike: -22.3893 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4357 | loglike: -24.9575 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4357 | loglike: -24.9575 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4357 | loglike: -24.9575 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4358 | loglike: -26.6092 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4359 | loglike: -26.3211 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4360 | loglike: -27.9617 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4361 | loglike: -27.3444 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4362 | loglike: -27.8925 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4363 | loglike: -30.1011 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4364 | loglike: -36.0585 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4365 | loglike: -26.1200 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4366 | loglike: -27.4005 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4366 | loglike: -27.4005 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4366 | loglike: -27.4005 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4367 | loglike: -27.8416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4367 | loglike: -27.8416 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4368 | loglike: -26.7420 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4369 | loglike: -24.5156 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4370 | loglike: -23.6684 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4371 | loglike: -24.0818 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4371 | loglike: -24.0818 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4372 | loglike: -23.4342 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4373 | loglike: -21.9813 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4374 | loglike: -22.2890 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4374 | loglike: -22.2890 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4374 | loglike: -22.2890 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4375 | loglike: -22.4390 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4376 | loglike: -23.1245 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4376 | loglike: -23.1245 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4377 | loglike: -23.7169 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4378 | loglike: -23.3312 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4379 | loglike: -24.0249 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4380 | loglike: -24.8304 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4381 | loglike: -23.4983 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4381 | loglike: -23.4983 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4382 | loglike: -24.0396 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4383 | loglike: -24.8482 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4384 | loglike: -24.2426 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4384 | loglike: -24.2426 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4384 | loglike: -24.2426 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4385 | loglike: -25.4351 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4386 | loglike: -25.6471 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4386 | loglike: -25.6471 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4387 | loglike: -22.4813 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4388 | loglike: -23.6986 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4389 | loglike: -25.4450 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4390 | loglike: -23.4771 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4391 | loglike: -22.3669 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4391 | loglike: -22.3669 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4392 | loglike: -22.2551 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4392 | loglike: -22.2551 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4393 | loglike: -22.3056 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4394 | loglike: -23.2655 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4394 | loglike: -23.2655 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4394 | loglike: -23.2655 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4394 | loglike: -23.2655 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4395 | loglike: -25.7766 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4396 | loglike: -24.8249 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4397 | loglike: -22.7226 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4397 | loglike: -22.7226 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4398 | loglike: -22.2157 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4399 | loglike: -23.5535 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4400 | loglike: -26.5527 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4401 | loglike: -23.5698 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4401 | loglike: -23.5698 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4402 | loglike: -27.7819 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4403 | loglike: -28.9092 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4404 | loglike: -32.1593 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4404 | loglike: -32.1593 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4405 | loglike: -26.3208 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4405 | loglike: -26.3208 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4406 | loglike: -25.5923 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4407 | loglike: -23.9531 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4408 | loglike: -25.3917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4408 | loglike: -25.3917 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4409 | loglike: -26.5293 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4410 | loglike: -25.7776 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4410 | loglike: -25.7776 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4411 | loglike: -28.8951 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4411 | loglike: -28.8951 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4411 | loglike: -28.8951 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4412 | loglike: -25.2838 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4413 | loglike: -23.0939 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4414 | loglike: -26.7514 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4414 | loglike: -26.7514 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4414 | loglike: -26.7514 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4415 | loglike: -25.3524 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4416 | loglike: -24.2188 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4416 | loglike: -24.2188 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4417 | loglike: -23.7752 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4418 | loglike: -23.3122 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4418 | loglike: -23.3122 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4419 | loglike: -23.5539 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4419 | loglike: -23.5539 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4419 | loglike: -23.5539 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4419 | loglike: -23.5539 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4420 | loglike: -22.3620 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4421 | loglike: -24.0204 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4421 | loglike: -24.0204 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4422 | loglike: -24.7372 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4423 | loglike: -23.9974 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4424 | loglike: -25.7458 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4425 | loglike: -26.6922 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4425 | loglike: -26.6922 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4426 | loglike: -24.3455 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4426 | loglike: -24.3455 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4426 | loglike: -24.3455 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4426 | loglike: -24.3455 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4427 | loglike: -23.7380 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4428 | loglike: -22.7873 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4428 | loglike: -22.7873 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4428 | loglike: -22.7873 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4428 | loglike: -22.7873 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4429 | loglike: -24.2962 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4430 | loglike: -25.1726 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4430 | loglike: -25.1726 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4431 | loglike: -27.4193 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4432 | loglike: -27.2718 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4432 | loglike: -27.2718 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4433 | loglike: -26.3078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4433 | loglike: -26.3078 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4434 | loglike: -23.7390 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4435 | loglike: -23.2929 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4436 | loglike: -23.0764 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4436 | loglike: -23.0764 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4437 | loglike: -25.9195 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4438 | loglike: -24.8659 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4439 | loglike: -25.1003 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4439 | loglike: -25.1003 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4439 | loglike: -25.1003 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4439 | loglike: -25.1003 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4440 | loglike: -24.9811 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4441 | loglike: -23.6190 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4442 | loglike: -24.9060 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4442 | loglike: -24.9060 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4443 | loglike: -23.8782 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4443 | loglike: -23.8782 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4444 | loglike: -26.3146 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4444 | loglike: -26.3146 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4445 | loglike: -28.4636 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4446 | loglike: -35.0945 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4447 | loglike: -31.2618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4447 | loglike: -31.2618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4447 | loglike: -31.2618 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4448 | loglike: -29.7342 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4449 | loglike: -29.2292 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4449 | loglike: -29.2292 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4449 | loglike: -29.2292 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4449 | loglike: -29.2292 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4450 | loglike: -27.6914 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4451 | loglike: -27.8480 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4452 | loglike: -29.1171 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4453 | loglike: -23.8364 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4454 | loglike: -24.1220 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4455 | loglike: -24.1557 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4456 | loglike: -23.8114 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4457 | loglike: -23.3588 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4458 | loglike: -23.3395 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4458 | loglike: -23.3395 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4459 | loglike: -24.7868 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4460 | loglike: -26.4970 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4460 | loglike: -26.4970 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4460 | loglike: -26.4970 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4460 | loglike: -26.4970 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4461 | loglike: -27.0324 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4462 | loglike: -26.0658 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4463 | loglike: -25.8447 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4463 | loglike: -25.8447 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4464 | loglike: -22.6803 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4465 | loglike: -25.4373 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4465 | loglike: -25.4373 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4465 | loglike: -25.4373 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4465 | loglike: -25.4373 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4466 | loglike: -23.9156 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4467 | loglike: -25.3855 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4467 | loglike: -25.3855 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4467 | loglike: -25.3855 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4468 | loglike: -26.0402 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4469 | loglike: -24.3011 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4469 | loglike: -24.3011 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4470 | loglike: -23.9348 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4471 | loglike: -22.5309 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4472 | loglike: -25.1431 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4472 | loglike: -25.1431 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4473 | loglike: -25.8194 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4474 | loglike: -23.6815 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4474 | loglike: -23.6815 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4474 | loglike: -23.6815 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4475 | loglike: -23.5940 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4475 | loglike: -23.5940 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4475 | loglike: -23.5940 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4475 | loglike: -23.5940 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4476 | loglike: -22.7495 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4477 | loglike: -23.1989 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4478 | loglike: -23.1444 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4478 | loglike: -23.1444 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4479 | loglike: -23.9265 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4480 | loglike: -23.6656 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4480 | loglike: -23.6656 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4480 | loglike: -23.6656 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4481 | loglike: -24.5354 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4482 | loglike: -28.3445 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4483 | loglike: -27.9018 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4484 | loglike: -31.1498 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4485 | loglike: -36.2229 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4486 | loglike: -36.8716 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4486 | loglike: -36.8716 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4486 | loglike: -36.8716 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4487 | loglike: -38.3081 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4487 | loglike: -38.3081 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4488 | loglike: -37.6602 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4489 | loglike: -38.7456 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4490 | loglike: -38.7318 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4491 | loglike: -45.5572 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4492 | loglike: -40.8398 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4493 | loglike: -41.2232 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4493 | loglike: -41.2232 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4493 | loglike: -41.2232 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4494 | loglike: -40.7082 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4494 | loglike: -40.7082 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4494 | loglike: -40.7082 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4495 | loglike: -36.7678 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4496 | loglike: -39.3343 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4496 | loglike: -39.3343 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4497 | loglike: -29.2358 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4497 | loglike: -29.2358 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4498 | loglike: -25.3217 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4499 | loglike: -27.0450 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 4500 | loglike: -26.4302 | Gelman-Rubin: [5.53384442e-02 5.97213974e-04 3.21992300e-02 6.30801135e-05
Accepted: 5500 | loglike: -23.5360 | Gelman-Rubin: [0.05150024 0.0093761 0.04409006 0.01793166 0.0145263 ]
INFO:simplemc:
Elapsed time: 7.845 minutes = 470.692 seconds
---- Gelman-Rubin achived ----
Om: 0.2399 +/- 0.0472
Obh2: 0.0220 +/- 0.0004
h: 0.5865 +/- 0.0673
w: -0.8932 +/- 0.0934
wa: 0.4076 +/- 0.3272
```python
samples5 = res5['result']['samples']
weights5 = res5['result']['weights']
```
```python
plt.figure(figsize=(10,10), dpi=300)
plt.clf()
cornerfig5 = corner.corner(samples5, weights=weights5, smooth=0.5, smooth1d=0.5,
# quantiles=(0.16, 0.84),
levels=(1-np.exp(-0.5), ), labels=pars_labels, color='forestgreen', plot_density=True,
plot_datapoints=False, fig=cornerfig3)
cornerfig5.text(0.8, 0.95,'SN+BBAO', fontsize=20, color='forestgreen')
cornerfig5.text(0.8, 0.90,'SN+HD', fontsize=20, color='darkred')
cornerfig5.text(0.8, 0.85,'SN+HD+BBAO', fontsize=20, color='indigo')
cornerfig5.text(0.8, 0.8,'SN+HD+BBAO+Planck', fontsize=20, color='gold')
cornerfig5.savefig('wacdm_datasets_5.png')
```
<Figure size 3000x3000 with 0 Axes>
```python
cornerfig5
```
# Plots with getdist
```python
# names = ["SN+BBAO", "SN+HD", "SN+HD+BBAO", "SN+HD+BBAO+Planck"]
labels = ['{}'.format(p.Ltxname) for p in analysis1.pars_info]
labels, np.shape(samples1)
```
(['\\Omega_m', '\\Omega_{b}h^2', 'h', 'w_0', 'w_a'], (8200, 5))
```python
samp1 = MCSamples(samples=samples1, names=labels, labels = labels, label='SN+HD+BBAO+Planck')
samp2 = MCSamples(samples=samples2, names = labels, labels = labels, label='SN+HD')
samp3 = MCSamples(samples=samples3, names = labels, labels = labels, label='SN+HD+BBAO')
samp4 = MCSamples(samples=samples5, names = labels, labels = labels, label='SN+BBAO')
```
Removed no burn in
Removed no burn in
Removed no burn in
Removed no burn in
```python
# Triangle plot
g = plots.get_subplot_plotter()
g.triangle_plot([samp2, samp4, samp3, samp1], filled=False, line_args={'lw':2},
settings={ 'smooth_scale_2D':0.3, 'smooth_scale_1D':0.6},)
# alphas=[0.5,0.5,0.5,0.5])
plt.savefig('wacdm_getdist.png', dpi=300)
```
```python
# Customized 2D filled comparison plot
# plt.Figure(figsize=(8,8))
g = plots.get_single_plotter(width_inch=6, ratio= 2. / 3.)
g.settings.legend_fontsize = 12
g.plot_2d([samp2, samp4, samp3, samp1], labels[3], labels[4], filled=False, line_args={'lw':3},
colors=['green', 'red', 'indigo', 'gold'], lims=[-2.1, 0.2, -4, 7], alphas=[0.4,0.4,0.6,0.8], )
# filled=False)
g.add_legend(['SN+HD', 'SN+BBAO', 'SN+HD+BBAO', 'SN+HD+BBAO+Planck'], legend_loc='upper right');
plt.savefig('wowa_wacdm_datasets_nofilled.png', dpi=300)
```
<IPython.core.display.Javascript object>
<img 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" width="599.5333333333333">
```python
%matplotlib notebook
```
```python
# with projection onto axes, colorbar, custom limits, colors, etc.
g = plots.get_single_plotter()
g.plot_4d([samp1, samp2, samp3, samp4], [labels[3], labels[4], labels[0], labels[2]], cmap='jet',
alpha=0.6, shadow_alpha=0.1, shadow_color=False,
max_scatter_points=1000,
# lims={'x2': (-3, 3), 'x3': (-3, 3)},
colorbar_args={'shrink': 0.6})
```
<IPython.core.display.Javascript object>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA/wAAAL8CAYAAABd1r5PAAAL5ElEQVR4nO3BAQ0AAADCoPdPbQ8HFAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAwJ8Blf0AAfXS9dEAAAAASUVORK5CYII=" width="782">
```python
```
|
igomezvREPO_NAMEsimplemc_testsPATH_START.@simplemc_tests_extracted@simplemc_tests-main@plots_waCDM.ipynb@.PATH_END.py
|
{
"filename": "OpResolverType.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/lite/g3doc/api_docs/python/tf/lite/experimental/OpResolverType.md",
"type": "Markdown"
}
|
page_type: reference
description: Different types of op resolvers for Tensorflow Lite.
<link rel="stylesheet" href="/site-assets/css/style.css">
<!-- DO NOT EDIT! Automatically generated file. -->
<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="tf.lite.experimental.OpResolverType" />
<meta itemprop="path" content="Stable" />
<meta itemprop="property" content="AUTO"/>
<meta itemprop="property" content="BUILTIN"/>
<meta itemprop="property" content="BUILTIN_REF"/>
<meta itemprop="property" content="BUILTIN_WITHOUT_DEFAULT_DELEGATES"/>
</div>
# tf.lite.experimental.OpResolverType
<!-- Insert buttons and diff -->
<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
<a target="_blank" href="https://github.com/tensorflow/tensorflow/blob/v2.11.0/tensorflow/lite/python/interpreter.py#L309-L337">
<img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
View source on GitHub
</a>
</td>
</table>
Different types of op resolvers for Tensorflow Lite.
<!-- Placeholder for "Used in" -->
* `AUTO`: Indicates the op resolver that is chosen by default in TfLite
Python, which is the "BUILTIN" as described below.
* `BUILTIN`: Indicates the op resolver for built-in ops with optimized kernel
implementation.
* `BUILTIN_REF`: Indicates the op resolver for built-in ops with reference
kernel implementation. It's generally used for testing and debugging.
* `BUILTIN_WITHOUT_DEFAULT_DELEGATES`: Indicates the op resolver for
built-in ops with optimized kernel implementation, but it will disable
the application of default TfLite delegates (like the XNNPACK delegate) to
the model graph. Generally this should not be used unless there are issues
with the default configuration.
<!-- Tabular view -->
<table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Class Variables</h2></th></tr>
<tr>
<td>
AUTO<a id="AUTO"></a>
</td>
<td>
`<OpResolverType.AUTO: 0>`
</td>
</tr><tr>
<td>
BUILTIN<a id="BUILTIN"></a>
</td>
<td>
`<OpResolverType.BUILTIN: 1>`
</td>
</tr><tr>
<td>
BUILTIN_REF<a id="BUILTIN_REF"></a>
</td>
<td>
`<OpResolverType.BUILTIN_REF: 2>`
</td>
</tr><tr>
<td>
BUILTIN_WITHOUT_DEFAULT_DELEGATES<a id="BUILTIN_WITHOUT_DEFAULT_DELEGATES"></a>
</td>
<td>
`<OpResolverType.BUILTIN_WITHOUT_DEFAULT_DELEGATES: 3>`
</td>
</tr>
</table>
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@lite@g3doc@api_docs@python@tf@lite@experimental@OpResolverType.md@.PATH_END.py
|
{
"filename": "_lineposition.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/histogram2d/colorbar/tickfont/_lineposition.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LinepositionValidator(_plotly_utils.basevalidators.FlaglistValidator):
def __init__(
self,
plotly_name="lineposition",
parent_name="histogram2d.colorbar.tickfont",
**kwargs,
):
super(LinepositionValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
extras=kwargs.pop("extras", ["none"]),
flags=kwargs.pop("flags", ["under", "over", "through"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@histogram2d@colorbar@tickfont@_lineposition.py@.PATH_END.py
|
{
"filename": "pyuvdata_inspect.py",
"repo_name": "RadioAstronomySoftwareGroup/pyuvdata",
"repo_path": "pyuvdata_extracted/pyuvdata-main/scripts/pyuvdata_inspect.py",
"type": "Python"
}
|
#!/usr/bin/env python
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Inspect attributes of pyuvdata objects."""
import argparse
import os
from pyuvdata import UVBeam, UVCal, UVData
# setup argparse
a = argparse.ArgumentParser(
description=(
"Inspect attributes of pyuvdata objects.\n"
"Example: pyuvdata_inspect.py -a=ant_array.shape,"
"Ntimes zen.xx.HH.omni.calfits zen.yy.HH.uvc"
),
formatter_class=argparse.RawDescriptionHelpFormatter,
)
a.add_argument(
"-a",
"--attrs",
dest="attrs",
type=str,
default="",
help="attribute(s) of object to print. Ex: ant_array.shape,Ntimes",
)
a.add_argument(
"-v",
"--verbose",
action="store_true",
default=False,
help="Send feedback to stdout.",
)
a.add_argument(
"-i",
"--interactive",
action="store_true",
default=False,
help="Exit into a python interpretor with objects in memory as 'uv'.",
)
a.add_argument(
"files",
metavar="files",
type=str,
nargs="*",
default=[],
help="pyuvdata object files to run on",
)
# parse arguments
args = a.parse_args()
# check for empty attributes
if len(args.attrs) == 0 and args.interactive is False:
raise Exception("no attributes fed...")
if len(args.files) == 0:
raise Exception("no files fed...")
# pack data objects, their names, and read functions
objs = [UVData, UVCal, UVBeam]
ob_names = ["UVData", "UVCal", "UVBeam"]
ob_reads = [
["read", "read_miriad", "read_fhd", "read_ms", "read_uvfits", "read_uvh5"],
["read_calfits", "read_fhd_cal"],
["read_beamfits", "read_cst_beam"],
]
# iterate through files
Nfiles = len(args.files)
uv = []
exit_clean = True
for i, f in enumerate(args.files):
# check file exists
if os.path.exists(f) is False:
print(f"{f} doesn't exist")
if i == (Nfiles - 1):
exit(1)
else:
continue
opened = False
filetype = None
# try to open object
for j, ob in enumerate(objs):
for r in ob_reads[j]:
try:
# instantiate data class and try to read file
UV = ob()
getattr(UV, r)(f)
opened = True
uv.append(UV)
filetype = r.split("_")[-1]
if args.verbose is True:
print(
f"opened {f} as a {filetype} file with the {ob_names[j]} "
"pyuvdata object"
)
except (OSError, KeyError, ValueError, RuntimeError):
continue
# exit loop if opened
if opened is True:
break
if opened is True:
break
# if object isn't opened continue
if opened is False:
print(f"couldn't open {f} with any of the pyuvdata objects {ob_names}")
continue
# print out desired attribute(s) of data object
attrs = [x.split(".") for x in args.attrs.split(",")]
for attr in attrs:
# try to get attribute
try:
Nnest = len(attr)
this_attr = getattr(UV, attr[0])
for k in range(Nnest - 1):
this_attr = getattr(this_attr, attr[k + 1])
# print to stdout
print("{} of {} is: {}".format(".".join(attr), f, this_attr))
exit_clean = True
except AttributeError:
print("Couldn't access '{}' from {}".format(".".join(attr), f))
exit_clean = False
if args.interactive:
if len(uv) == 1:
uv = uv[0]
try:
from IPython import embed
embed()
except ImportError:
import code
code.interact(local=dict(globals(), **locals()))
else:
if exit_clean is True:
exit(0)
else:
exit(1)
|
RadioAstronomySoftwareGroupREPO_NAMEpyuvdataPATH_START.@pyuvdata_extracted@pyuvdata-main@scripts@pyuvdata_inspect.py@.PATH_END.py
|
{
"filename": "foregrounds.py",
"repo_name": "HERA-Team/hera_sim",
"repo_path": "hera_sim_extracted/hera_sim-main/hera_sim/foregrounds.py",
"type": "Python"
}
|
"""Visibility-space foreground models.
This module defines several cheap foreground models evaluated in visibility space.
"""
import numpy as np
from astropy import units
from . import utils
from .components import component
@component
class Foreground:
"""Base class for foreground models."""
pass
class DiffuseForeground(Foreground):
"""
Produce a rough simulation of diffuse foreground-like structure.
Parameters
----------
Tsky_mdl : interpolation object
Sky temperature model, in units of Kelvin. Must be callable
with signature Tsky_mdl(lsts, freqs), formatted so that lsts
are in radians and freqs are in GHz.
omega_p : interpolation object or array-like of float
Beam size model, in units of steradian. If passing an array,
then it must be the same shape as the frequency array passed
to the ``freqs`` parameter.
delay_filter_kwargs : dict, optional
Keyword arguments and associated values to be passed to
:func:`~hera_sim.utils.rough_delay_filter`. Default is to use the
following settings: ``standoff : 0.0``, ``delay_filter_type : tophat``.
fringe_filter_kwargs : dict, optional
Keyword arguments and associated values to be passed to
:func:`~hera_sim.utils.rough_fringe_filter`. Default is to use the
following settings: ``fringe_filter_type : tophat``.
rng: np.random.Generator, optional
Random number generator.
Notes
-----
This algorithm provides a rough simulation of visibilities from
diffuse foregrounds by using a sky temperature model. The sky
temperature models provided in this package are appropriate for
the HERA H1C observing season, and are only valid for frequencies
between 100 MHz and 200 MHz; anything beyond this range is just a
copy of the value at the nearest edge. Simulated autocorrelations
(i.e. zero magnitude ``bl_vec``) are returned as complex arrays,
but have zero imaginary component everywhere. For cross-correlations,
the sky model is convolved with white noise (in delay/fringe-rate
space), and rough delay and fringe filters are applied to the
visibility. As a standalone component model, this is does not
produce consistent simulated visibilities for baselines within a
redundant group (except for autocorrelations); however, the
:class:`~hera_sim.simulate.Simulator` class provides the functionality to ensure
that redundant baselines see the same sky. Additionally, visibilities
simulated with this model are not invariant under complex conjugation
and baseline conjugation, since the delay filter applied is symmetric;
however, the :class:`~.simulate.Simulator` class is aware of this and ensures
invariance under complex conjugation and baseline conjugation.
"""
_alias = ("diffuse_foreground",)
is_smooth_in_freq = True
is_randomized = True
return_type = "per_baseline"
attrs_to_pull = dict(bl_vec=None)
def __init__(
self,
Tsky_mdl=None,
omega_p=None,
delay_filter_kwargs=None,
fringe_filter_kwargs=None,
rng=None,
):
if delay_filter_kwargs is None:
delay_filter_kwargs = {
"standoff": 0.0,
"delay_filter_type": "tophat",
"normalize": None,
}
if fringe_filter_kwargs is None:
fringe_filter_kwargs = {"fringe_filter_type": "tophat"}
super().__init__(
Tsky_mdl=Tsky_mdl,
omega_p=omega_p,
delay_filter_kwargs=delay_filter_kwargs,
fringe_filter_kwargs=fringe_filter_kwargs,
rng=rng,
)
def __call__(self, lsts, freqs, bl_vec, **kwargs):
"""Compute the foregrounds.
Parameters
----------
lsts : array-like of float
Array of LST values in units of radians.
freqs : array-like of float
Array of frequency values in units of GHz.
bl_vec : array-like of float
Length-3 array specifying the baseline vector in units of ns.
Returns
-------
vis : ndarray of complex
Array of visibilities at each LST and frequency appropriate
for the given sky temperature model, beam size model, and
baseline vector. Returned in units of Jy with shape
(lsts.size, freqs.size).
"""
# validate the kwargs
self._check_kwargs(**kwargs)
# unpack the kwargs
(Tsky_mdl, omega_p, delay_filter_kwargs, fringe_filter_kwargs, rng) = (
self._extract_kwarg_values(**kwargs)
)
if Tsky_mdl is None:
raise ValueError(
"A sky temperature model must be specified in "
"order to use this function."
)
if omega_p is None:
raise ValueError(
"A beam area array or interpolation object is "
"required to use this function."
)
# support passing beam as an interpolator
if callable(omega_p):
omega_p = omega_p(freqs)
# resample the sky temperature model
Tsky = Tsky_mdl(lsts=lsts, freqs=freqs) # K
vis = np.asarray(Tsky / utils.jansky_to_kelvin(freqs, omega_p), complex)
if np.isclose(np.linalg.norm(bl_vec), 0):
return vis
vis *= utils.gen_white_noise(size=vis.shape, rng=rng)
vis = utils.rough_fringe_filter(
vis, lsts, freqs, bl_vec[0], **fringe_filter_kwargs
)
vis = utils.rough_delay_filter(
vis, freqs, np.linalg.norm(bl_vec), **delay_filter_kwargs
)
return vis
class PointSourceForeground(Foreground):
"""
Produce a uniformly-random point-source sky observed with a truncated Gaussian beam.
Parameters
----------
nsrcs : int, optional
Number of sources to place on the sky. Point sources are
simulated to have a flux-density drawn from a power-law
distribution specified by the ``Smin``, ``Smax``, and
``beta`` parameters. Additionally, each source has a chromatic
flux-density given by a power law; the spectral index is drawn
from a normal distribution with mean ``spectral_index_mean`` and
standard deviation ``spectral_index_std``.
Smin : float, optional
Lower bound of the power-law distribution to draw flux-densities
from, in units of Jy.
Smax : float, optional
Upper bound of the power-law distribution to draw flux-densities
from, in units of Jy.
beta : float, optional
Power law index for the source counts versus flux-density.
spectral_index_mean : float, optional
The mean of the normal distribution to draw source spectral indices
from.
spectral_index_std : float, optional
The standard deviation of the normal distribution to draw source
spectral indices from.
reference_freq : float, optional
Reference frequency used to make the point source flux densities
chromatic, in units of GHz.
rng: np.random.Generator, optional
Random number generator.
"""
_alias = ("pntsrc_foreground",)
is_randomized = True
return_type = "per_baseline"
attrs_to_pull = dict(bl_vec=None)
def __init__(
self,
nsrcs=1000,
Smin=0.3,
Smax=300,
beta=-1.5,
spectral_index_mean=-1,
spectral_index_std=0.5,
reference_freq=0.15,
rng=None,
):
super().__init__(
nsrcs=nsrcs,
Smin=Smin,
Smax=Smax,
beta=beta,
spectral_index_mean=spectral_index_mean,
spectral_index_std=spectral_index_std,
reference_freq=reference_freq,
rng=rng,
)
def __call__(self, lsts, freqs, bl_vec, **kwargs):
"""Compute the point source foregrounds.
Parameters
----------
lsts : array-like of float
Local Sidereal Times for the simulated observation, in units
of radians.
freqs : array-like of float
Frequency array for the simulated observation, in units of GHz.
bl_vec : array-like of float
Baseline vector for the simulated observation, given in
East-North-Up coordinates in units of nanoseconds. Must have
length 3.
Returns
-------
vis : np.ndarray of complex
Simulated observed visibilities for the specified LSTs, frequencies,
and baseline. Complex-valued with shape (lsts.size, freqs.size).
Notes
-----
The beam used here is a Gaussian with width hard-coded to HERA's width,
and truncated at the horizon.
This is a *very* rough simulator, use at your own risk.
"""
# validate the kwargs
self._check_kwargs(**kwargs)
# unpack the kwargs
(nsrcs, Smin, Smax, beta, spectral_index_mean, spectral_index_std, f0, rng) = (
self._extract_kwarg_values(**kwargs)
)
# get baseline length (it should already be in ns)
bl_len_ns = np.linalg.norm(bl_vec)
# Randomly generate source positions and spectral indices.
rng = rng or np.random.default_rng()
ras = rng.uniform(0, 2 * np.pi, nsrcs)
spec_indices = rng.normal(
loc=spectral_index_mean, scale=spectral_index_std, size=nsrcs
)
# calculate beam width, hardcoded for HERA
beam_width = (40 * 60) * (f0 / freqs) / units.sday.to("s") * 2 * np.pi
# draw flux densities from a power law
alpha = beta + 1
flux_densities = (
Smax**alpha + Smin**alpha * (1 - rng.uniform(size=nsrcs))
) ** (1 / alpha)
# initialize the visibility array
vis = np.zeros((lsts.size, freqs.size), dtype=complex)
# Compute the visibility source-by-source.
for ra, flux, index in zip(ras, flux_densities, spec_indices):
# Figure out when the source crosses the meridian.
lst_ind = np.argmin(np.abs(utils.compute_ha(lsts, ra)))
# This is effectively baking in that up to 10% of the baseline
# length is oriented along the North-South direction, since this
# is the delay measured when the source transits the meridian.
# (Still need to think more carefully about this, but this seems
# like the right explanation for this, as well as the factor of
# 0.9 in the calculation of w further down.)
dtau = rng.uniform(-1, 1) * 0.1 * bl_len_ns
# Add the contribution from the source as it transits the meridian.
vis[lst_ind, :] += flux * (freqs / f0) ** index
vis[lst_ind, :] *= np.exp(2j * np.pi * freqs * dtau)
# Figure out the hour angles to use for computing the beam kernel.
has = utils.compute_ha(lsts, 0)
# convolve vis with beam at each frequency
for j, freq in enumerate(freqs):
# Treat the beam as if it's a Gaussian with a sharp horizon.
beam = np.exp(-(has**2) / (2 * beam_width[j] ** 2))
beam = np.where(np.abs(has) > np.pi / 2, 0, beam)
# Compute the phase evolution as the source transits the sky.
w = 0.9 * bl_len_ns * np.sin(has) * freq
phase = np.exp(2j * np.pi * w)
# Now actually apply the mock source transit.
kernel = beam * phase
vis[:, j] = np.fft.ifft(np.fft.fft(kernel) * np.fft.fft(vis[:, j]))
return vis
diffuse_foreground = DiffuseForeground()
pntsrc_foreground = PointSourceForeground()
|
HERA-TeamREPO_NAMEhera_simPATH_START.@hera_sim_extracted@hera_sim-main@hera_sim@foregrounds.py@.PATH_END.py
|
{
"filename": "plot.py",
"repo_name": "moble/GWFrames",
"repo_path": "GWFrames_extracted/GWFrames-master/Code/GWFrames/plot.py",
"type": "Python"
}
|
"""
This submodule specializes the usual pyplot.plot function to be used
as methods with Waveform objects.
"""
def plotWaveform(this, WaveformPart='Abs', Modes=(), t_fid=None, *pyplot_args, **pyplot_kwargs) :
"""
This function should be called as a method of the Waveform class,
e.g., as
>>> W.plot('Abs', Modes=[[2,2], [2,-2]], c='r')
where W is a Waveform object. The first parameter should be a
string --- one of ['Abs', 'LogAbs', 'LogLogAbs', 'Arg',
'ArgUnwrapped', 'Re', Im']. The second (optional) parameter is a
list (using square brackets), where each element is some [l,m].
Only modes included in this list will be plotted, unless the list
is empty (the default), in which case all modes are plotted.
All following arguments are passed to the usual pyplot.plot (or
semilogx, semilogy, or loglog) function; in the example above, the
"c='r'" argument is passed, making the line color red. The only
exception to this rule is the "label" keyword, which is handled
internally when there is just one line. Otherwise, the legend
labels are set automatically to contain the [l,m] mode (though the
legend is not shown by default).
"""
from matplotlib.pyplot import plot, semilogx, semilogy, loglog, xlabel, ylabel, setp, isinteractive, ioff, ion, draw, show, gcf
try : # If this matplotlib is too old, just ignore this
from matplotlib.pyplot import tight_layout
except :
pass
import matplotlib
# # We probably shouldn't be overwriting people's color choices
# try:
# matplotlib.rcParams['axes.color_cycle'] = ['#000000', '#cc79a7', '#d55e00', '#0072b2', '#f0e442', '#56b4e9', '#e69f00', '#2b9f78']
# except KeyError :
# matplotlib.axes.set_default_color_cycle(['#000000', '#cc79a7', '#d55e00', '#0072b2', '#f0e442', '#56b4e9', '#e69f00', '#2b9f78'])
from warnings import warn
from numpy import array, empty, transpose, sin, cos, modf, pi
def NotAbs(Anything) :
return Anything
def _ArgUnwrapped(Mode=None) :
if (t_fid) :
i_fid = int(array(range(this.NTimes()))[this.T()>t_fid][0])
if (Mode is None) :
Arg = this.ArgUnwrapped()
x = numpy.modf(Arg[:,i_fid]/(2*pi))
x[1][x[0]<0.0] -= 1.0
return (Arg.transpose() - x[1]*2*pi).transpose()
else :
Arg = this.ArgUnwrapped(Mode)
w,x = numpy.modf(Arg[i_fid]/(2*pi))
if (w<0.0) : x -= 1.0
return Arg - x*2*pi
else :
if (Mode is None) :
return this.ArgUnwrapped()
else :
return this.ArgUnwrapped(Mode)
XLabel = r'$(t-r_\ast)/M$'
YLabel = ''
Labels = []
Lines = []
WasInteractive = isinteractive()
ioff()
## This decides which quantity to plot, what type of plot, and what the labels should be
if (WaveformPart.lower()=='abs') :
YLabel =r'$\mathrm{abs} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = plot
quantity = this.Abs
AbsOrNot = NotAbs
elif (WaveformPart.lower()=='logabs') :
YLabel =r'$\mathrm{abs} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = semilogy
quantity = this.Abs
AbsOrNot = abs
elif (WaveformPart.lower()=='loglogabs') :
YLabel =r'$\mathrm{abs} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = loglog
quantity = this.Abs
AbsOrNot = abs
elif (WaveformPart.lower()=='arg') :
YLabel =r'$\mathrm{arg} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = plot
quantity = this.Arg
AbsOrNot = NotAbs
elif (WaveformPart.lower()=='argunwrapped' or WaveformPart.lower()=='uarg' or WaveformPart.lower()=='argu') :
YLabel =r'$\mathrm{uarg} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = plot
quantity = _ArgUnwrapped
AbsOrNot = NotAbs
elif (WaveformPart.lower()=='real' or WaveformPart.lower()=='re') :
YLabel =r'$\mathrm{Re} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = plot
quantity = this.Re
AbsOrNot = NotAbs
elif (WaveformPart.lower()=='imaginary' or WaveformPart.lower()=='imag' or WaveformPart.lower()=='im') :
YLabel =r'$\mathrm{Im} \left( '+this.GetLaTeXDataDescription()+r' \right) $'
styledplot = plot
quantity = this.Im
AbsOrNot = NotAbs
else :
print("Unsupported plot type '{0}'".format(WaveformPart))
return []
## This does the actual work of plotting, depending on what Modes are needed
if (type(Modes)==int) : # The requested mode is plotted
Lines = styledplot(this.T(), AbsOrNot(quantity(Modes)), *pyplot_args, **pyplot_kwargs)
Labels = [str(tuple(this.LM(Modes)))]
elif (len(Modes)==0) : # All modes are plotted
Lines = styledplot(this.T(), AbsOrNot(quantity()).transpose(), *pyplot_args, **pyplot_kwargs)
if ((this.NModes()==1) and ('label' in pyplot_kwargs)) :
Labels = [pyplot_kwargs['label']]
else :
Labels = [str(tuple(this.LM(mode))) for mode in range(this.NModes())]
elif ( (len(Modes)==2) and (type(Modes[0])==int and type(Modes[1])==int) ) :
ModeIndex = this.FindModeIndex(Modes[0], Modes[1])
Lines = styledplot(this.T(), transpose(AbsOrNot(quantity(ModeIndex))), *pyplot_args, **pyplot_kwargs)
if ('label' in pyplot_kwargs) :
Labels = [pyplot_kwargs['label']]
else :
Labels = [str(tuple(this.LM(ModeIndex)))]
elif ( (len(Modes)==1) and (type(Modes[0])==list) and (type(Modes[0][0])==int and type(Modes[0][1])==int) ) :
ModeIndex = this.FindModeIndex(Modes[0][0], Modes[0][1])
Lines = styledplot(this.T(), transpose(AbsOrNot(quantity(ModeIndex))), *pyplot_args, **pyplot_kwargs)
if ('label' in pyplot_kwargs) :
Labels = [pyplot_kwargs['label']]
else :
Labels = [str(tuple(this.LM(ModeIndex)))]
else :
Modes = array(Modes, dtype=int)
for i in range(Modes.shape[0]) :
ModeIndex = this.FindModeIndex(int(Modes[i][0]), int(Modes[i][1]))
Labels.append(str(tuple(Modes[i])))
Lines.append(styledplot(this.T(), AbsOrNot(quantity(ModeIndex)).transpose(), *pyplot_args, **pyplot_kwargs))
xlabel(XLabel)
ylabel(YLabel)
# draw()
if(WasInteractive) :
ion()
# gcf().show()
# try :
# tight_layout(pad=0.1)
# except :
# pass
for i in range(len(Lines)) :
setp(Lines[i], label=Labels[i])
return Lines
### The following allows us to write things like
### W = GWFrames.Waveform()
### W.plot('Abs', ((2,2), (2,-2)))
import GWFrames
GWFrames.Waveform.plot = plotWaveform
GWFrames.PNWaveform.plot = plotWaveform
|
mobleREPO_NAMEGWFramesPATH_START.@GWFrames_extracted@GWFrames-master@Code@GWFrames@plot.py@.PATH_END.py
|
{
"filename": "_fresnel.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/volume/lighting/_fresnel.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FresnelValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="fresnel", parent_name="volume.lighting", **kwargs):
super(FresnelValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
max=kwargs.pop("max", 5),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@volume@lighting@_fresnel.py@.PATH_END.py
|
{
"filename": "spark_sql.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/utilities/spark_sql.py",
"type": "Python"
}
|
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
if TYPE_CHECKING:
from pyspark.sql import DataFrame, Row, SparkSession
class SparkSQL:
"""SparkSQL is a utility class for interacting with Spark SQL."""
def __init__(
self,
spark_session: Optional[SparkSession] = None,
catalog: Optional[str] = None,
schema: Optional[str] = None,
ignore_tables: Optional[List[str]] = None,
include_tables: Optional[List[str]] = None,
sample_rows_in_table_info: int = 3,
):
"""Initialize a SparkSQL object.
Args:
spark_session: A SparkSession object.
If not provided, one will be created.
catalog: The catalog to use.
If not provided, the default catalog will be used.
schema: The schema to use.
If not provided, the default schema will be used.
ignore_tables: A list of tables to ignore.
If not provided, all tables will be used.
include_tables: A list of tables to include.
If not provided, all tables will be used.
sample_rows_in_table_info: The number of rows to include in the table info.
Defaults to 3.
"""
try:
from pyspark.sql import SparkSession
except ImportError:
raise ImportError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
self._spark = (
spark_session if spark_session else SparkSession.builder.getOrCreate()
)
if catalog is not None:
self._spark.catalog.setCurrentCatalog(catalog)
if schema is not None:
self._spark.catalog.setCurrentDatabase(schema)
self._all_tables = set(self._get_all_table_names())
self._include_tables = set(include_tables) if include_tables else set()
if self._include_tables:
missing_tables = self._include_tables - self._all_tables
if missing_tables:
raise ValueError(
f"include_tables {missing_tables} not found in database"
)
self._ignore_tables = set(ignore_tables) if ignore_tables else set()
if self._ignore_tables:
missing_tables = self._ignore_tables - self._all_tables
if missing_tables:
raise ValueError(
f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_table_info must be an integer")
self._sample_rows_in_table_info = sample_rows_in_table_info
@classmethod
def from_uri(
cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
) -> SparkSQL:
"""Creating a remote Spark Session via Spark connect.
For example: SparkSQL.from_uri("sc://localhost:15002")
"""
try:
from pyspark.sql import SparkSession
except ImportError:
raise ImportError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
spark = SparkSession.builder.remote(database_uri).getOrCreate()
return cls(spark, **kwargs)
def get_usable_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
if self._include_tables:
return self._include_tables
# sorting the result can help LLM understanding it.
return sorted(self._all_tables - self._ignore_tables)
def _get_all_table_names(self) -> Iterable[str]:
rows = self._spark.sql("SHOW TABLES").select("tableName").collect()
return list(map(lambda row: row.tableName, rows))
def _get_create_table_stmt(self, table: str) -> str:
statement = (
self._spark.sql(f"SHOW CREATE TABLE {table}").collect()[0].createtab_stmt
)
# Ignore the data source provider and options to reduce the number of tokens.
using_clause_index = statement.find("USING")
return statement[:using_clause_index] + ";"
def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
all_table_names = self.get_usable_table_names()
if table_names is not None:
missing_tables = set(table_names).difference(all_table_names)
if missing_tables:
raise ValueError(f"table_names {missing_tables} not found in database")
all_table_names = table_names
tables = []
for table_name in all_table_names:
table_info = self._get_create_table_stmt(table_name)
if self._sample_rows_in_table_info:
table_info += "\n\n/*"
table_info += f"\n{self._get_sample_spark_rows(table_name)}\n"
table_info += "*/"
tables.append(table_info)
final_str = "\n\n".join(tables)
return final_str
def _get_sample_spark_rows(self, table: str) -> str:
query = f"SELECT * FROM {table} LIMIT {self._sample_rows_in_table_info}"
df = self._spark.sql(query)
columns_str = "\t".join(list(map(lambda f: f.name, df.schema.fields)))
try:
sample_rows = self._get_dataframe_results(df)
# save the sample rows in string format
sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])
except Exception:
sample_rows_str = ""
return (
f"{self._sample_rows_in_table_info} rows from {table} table:\n"
f"{columns_str}\n"
f"{sample_rows_str}"
)
def _convert_row_as_tuple(self, row: Row) -> tuple:
return tuple(map(str, row.asDict().values()))
def _get_dataframe_results(self, df: DataFrame) -> list:
return list(map(self._convert_row_as_tuple, df.collect()))
def run(self, command: str, fetch: str = "all") -> str:
df = self._spark.sql(command)
if fetch == "one":
df = df.limit(1)
return str(self._get_dataframe_results(df))
def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
"""Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
"""
try:
return self.get_table_info(table_names)
except ValueError as e:
"""Format the error message"""
return f"Error: {e}"
def run_no_throw(self, command: str, fetch: str = "all") -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
"""
try:
return self.run(command, fetch)
except Exception as e:
"""Format the error message"""
return f"Error: {e}"
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@utilities@spark_sql.py@.PATH_END.py
|
{
"filename": "sunau.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/tools/python3/Lib/sunau.py",
"type": "Python"
}
|
"""Stuff to parse Sun and NeXT audio files.
An audio file consists of a header followed by the data. The structure
of the header is as follows.
+---------------+
| magic word |
+---------------+
| header size |
+---------------+
| data size |
+---------------+
| encoding |
+---------------+
| sample rate |
+---------------+
| # of channels |
+---------------+
| info |
| |
+---------------+
The magic word consists of the 4 characters '.snd'. Apart from the
info field, all header fields are 4 bytes in size. They are all
32-bit unsigned integers encoded in big-endian byte order.
The header size really gives the start of the data.
The data size is the physical size of the data. From the other
parameters the number of frames can be calculated.
The encoding gives the way in which audio samples are encoded.
Possible values are listed below.
The info field currently consists of an ASCII string giving a
human-readable description of the audio file. The info field is
padded with NUL bytes to the header size.
Usage.
Reading audio files:
f = sunau.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
When the setpos() and rewind() methods are not used, the seek()
method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' or 'ULAW')
getcompname() -- returns human-readable version of
compression type ('not compressed' matches 'NONE')
getparams() -- returns a namedtuple consisting of all of the
above in the above order
getmarkers() -- returns None (for compatibility with the
aifc module)
getmark(id) -- raises an error since the mark does not
exist (for compatibility with the aifc module)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell() and the position given to setpos()
are compatible and have nothing to do with the actual position in the
file.
The close() method is called automatically when the class instance
is destroyed.
Writing audio files:
f = sunau.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)-- set all parameters at once
tell() -- return current position in output file
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes(b'') or
close() to patch up the sizes in the header.
The close() method is called automatically when the class instance
is destroyed.
"""
from collections import namedtuple
import warnings
warnings._deprecated(__name__, remove=(3, 13))
_sunau_params = namedtuple('_sunau_params',
'nchannels sampwidth framerate nframes comptype compname')
# from <multimedia/audio_filehdr.h>
AUDIO_FILE_MAGIC = 0x2e736e64
AUDIO_FILE_ENCODING_MULAW_8 = 1
AUDIO_FILE_ENCODING_LINEAR_8 = 2
AUDIO_FILE_ENCODING_LINEAR_16 = 3
AUDIO_FILE_ENCODING_LINEAR_24 = 4
AUDIO_FILE_ENCODING_LINEAR_32 = 5
AUDIO_FILE_ENCODING_FLOAT = 6
AUDIO_FILE_ENCODING_DOUBLE = 7
AUDIO_FILE_ENCODING_ADPCM_G721 = 23
AUDIO_FILE_ENCODING_ADPCM_G722 = 24
AUDIO_FILE_ENCODING_ADPCM_G723_3 = 25
AUDIO_FILE_ENCODING_ADPCM_G723_5 = 26
AUDIO_FILE_ENCODING_ALAW_8 = 27
# from <multimedia/audio_hdr.h>
AUDIO_UNKNOWN_SIZE = 0xFFFFFFFF # ((unsigned)(~0))
_simple_encodings = [AUDIO_FILE_ENCODING_MULAW_8,
AUDIO_FILE_ENCODING_LINEAR_8,
AUDIO_FILE_ENCODING_LINEAR_16,
AUDIO_FILE_ENCODING_LINEAR_24,
AUDIO_FILE_ENCODING_LINEAR_32,
AUDIO_FILE_ENCODING_ALAW_8]
class Error(Exception):
pass
def _read_u32(file):
x = 0
for i in range(4):
byte = file.read(1)
if not byte:
raise EOFError
x = x*256 + ord(byte)
return x
def _write_u32(file, x):
data = []
for i in range(4):
d, m = divmod(x, 256)
data.insert(0, int(m))
x = d
file.write(bytes(data))
class Au_read:
def __init__(self, f):
if isinstance(f, str):
import builtins
f = builtins.open(f, 'rb')
self._opened = True
else:
self._opened = False
self.initfp(f)
def __del__(self):
if self._file:
self.close()
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
def initfp(self, file):
self._file = file
self._soundpos = 0
magic = int(_read_u32(file))
if magic != AUDIO_FILE_MAGIC:
raise Error('bad magic number')
self._hdr_size = int(_read_u32(file))
if self._hdr_size < 24:
raise Error('header size too small')
if self._hdr_size > 100:
raise Error('header size ridiculously large')
self._data_size = _read_u32(file)
if self._data_size != AUDIO_UNKNOWN_SIZE:
self._data_size = int(self._data_size)
self._encoding = int(_read_u32(file))
if self._encoding not in _simple_encodings:
raise Error('encoding not (yet) supported')
if self._encoding in (AUDIO_FILE_ENCODING_MULAW_8,
AUDIO_FILE_ENCODING_ALAW_8):
self._sampwidth = 2
self._framesize = 1
elif self._encoding == AUDIO_FILE_ENCODING_LINEAR_8:
self._framesize = self._sampwidth = 1
elif self._encoding == AUDIO_FILE_ENCODING_LINEAR_16:
self._framesize = self._sampwidth = 2
elif self._encoding == AUDIO_FILE_ENCODING_LINEAR_24:
self._framesize = self._sampwidth = 3
elif self._encoding == AUDIO_FILE_ENCODING_LINEAR_32:
self._framesize = self._sampwidth = 4
else:
raise Error('unknown encoding')
self._framerate = int(_read_u32(file))
self._nchannels = int(_read_u32(file))
if not self._nchannels:
raise Error('bad # of channels')
self._framesize = self._framesize * self._nchannels
if self._hdr_size > 24:
self._info = file.read(self._hdr_size - 24)
self._info, _, _ = self._info.partition(b'\0')
else:
self._info = b''
try:
self._data_pos = file.tell()
except (AttributeError, OSError):
self._data_pos = None
def getfp(self):
return self._file
def getnchannels(self):
return self._nchannels
def getsampwidth(self):
return self._sampwidth
def getframerate(self):
return self._framerate
def getnframes(self):
if self._data_size == AUDIO_UNKNOWN_SIZE:
return AUDIO_UNKNOWN_SIZE
if self._encoding in _simple_encodings:
return self._data_size // self._framesize
return 0 # XXX--must do some arithmetic here
def getcomptype(self):
if self._encoding == AUDIO_FILE_ENCODING_MULAW_8:
return 'ULAW'
elif self._encoding == AUDIO_FILE_ENCODING_ALAW_8:
return 'ALAW'
else:
return 'NONE'
def getcompname(self):
if self._encoding == AUDIO_FILE_ENCODING_MULAW_8:
return 'CCITT G.711 u-law'
elif self._encoding == AUDIO_FILE_ENCODING_ALAW_8:
return 'CCITT G.711 A-law'
else:
return 'not compressed'
def getparams(self):
return _sunau_params(self.getnchannels(), self.getsampwidth(),
self.getframerate(), self.getnframes(),
self.getcomptype(), self.getcompname())
def getmarkers(self):
return None
def getmark(self, id):
raise Error('no marks')
def readframes(self, nframes):
if self._encoding in _simple_encodings:
if nframes == AUDIO_UNKNOWN_SIZE:
data = self._file.read()
else:
data = self._file.read(nframes * self._framesize)
self._soundpos += len(data) // self._framesize
if self._encoding == AUDIO_FILE_ENCODING_MULAW_8:
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=DeprecationWarning)
import audioop
data = audioop.ulaw2lin(data, self._sampwidth)
return data
return None # XXX--not implemented yet
def rewind(self):
if self._data_pos is None:
raise OSError('cannot seek')
self._file.seek(self._data_pos)
self._soundpos = 0
def tell(self):
return self._soundpos
def setpos(self, pos):
if pos < 0 or pos > self.getnframes():
raise Error('position not in range')
if self._data_pos is None:
raise OSError('cannot seek')
self._file.seek(self._data_pos + pos * self._framesize)
self._soundpos = pos
def close(self):
file = self._file
if file:
self._file = None
if self._opened:
file.close()
class Au_write:
def __init__(self, f):
if isinstance(f, str):
import builtins
f = builtins.open(f, 'wb')
self._opened = True
else:
self._opened = False
self.initfp(f)
def __del__(self):
if self._file:
self.close()
self._file = None
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
def initfp(self, file):
self._file = file
self._framerate = 0
self._nchannels = 0
self._sampwidth = 0
self._framesize = 0
self._nframes = AUDIO_UNKNOWN_SIZE
self._nframeswritten = 0
self._datawritten = 0
self._datalength = 0
self._info = b''
self._comptype = 'ULAW' # default is U-law
def setnchannels(self, nchannels):
if self._nframeswritten:
raise Error('cannot change parameters after starting to write')
if nchannels not in (1, 2, 4):
raise Error('only 1, 2, or 4 channels supported')
self._nchannels = nchannels
def getnchannels(self):
if not self._nchannels:
raise Error('number of channels not set')
return self._nchannels
def setsampwidth(self, sampwidth):
if self._nframeswritten:
raise Error('cannot change parameters after starting to write')
if sampwidth not in (1, 2, 3, 4):
raise Error('bad sample width')
self._sampwidth = sampwidth
def getsampwidth(self):
if not self._framerate:
raise Error('sample width not specified')
return self._sampwidth
def setframerate(self, framerate):
if self._nframeswritten:
raise Error('cannot change parameters after starting to write')
self._framerate = framerate
def getframerate(self):
if not self._framerate:
raise Error('frame rate not set')
return self._framerate
def setnframes(self, nframes):
if self._nframeswritten:
raise Error('cannot change parameters after starting to write')
if nframes < 0:
raise Error('# of frames cannot be negative')
self._nframes = nframes
def getnframes(self):
return self._nframeswritten
def setcomptype(self, type, name):
if type in ('NONE', 'ULAW'):
self._comptype = type
else:
raise Error('unknown compression type')
def getcomptype(self):
return self._comptype
def getcompname(self):
if self._comptype == 'ULAW':
return 'CCITT G.711 u-law'
elif self._comptype == 'ALAW':
return 'CCITT G.711 A-law'
else:
return 'not compressed'
def setparams(self, params):
nchannels, sampwidth, framerate, nframes, comptype, compname = params
self.setnchannels(nchannels)
self.setsampwidth(sampwidth)
self.setframerate(framerate)
self.setnframes(nframes)
self.setcomptype(comptype, compname)
def getparams(self):
return _sunau_params(self.getnchannels(), self.getsampwidth(),
self.getframerate(), self.getnframes(),
self.getcomptype(), self.getcompname())
def tell(self):
return self._nframeswritten
def writeframesraw(self, data):
if not isinstance(data, (bytes, bytearray)):
data = memoryview(data).cast('B')
self._ensure_header_written()
if self._comptype == 'ULAW':
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=DeprecationWarning)
import audioop
data = audioop.lin2ulaw(data, self._sampwidth)
nframes = len(data) // self._framesize
self._file.write(data)
self._nframeswritten = self._nframeswritten + nframes
self._datawritten = self._datawritten + len(data)
def writeframes(self, data):
self.writeframesraw(data)
if self._nframeswritten != self._nframes or \
self._datalength != self._datawritten:
self._patchheader()
def close(self):
if self._file:
try:
self._ensure_header_written()
if self._nframeswritten != self._nframes or \
self._datalength != self._datawritten:
self._patchheader()
self._file.flush()
finally:
file = self._file
self._file = None
if self._opened:
file.close()
#
# private methods
#
def _ensure_header_written(self):
if not self._nframeswritten:
if not self._nchannels:
raise Error('# of channels not specified')
if not self._sampwidth:
raise Error('sample width not specified')
if not self._framerate:
raise Error('frame rate not specified')
self._write_header()
def _write_header(self):
if self._comptype == 'NONE':
if self._sampwidth == 1:
encoding = AUDIO_FILE_ENCODING_LINEAR_8
self._framesize = 1
elif self._sampwidth == 2:
encoding = AUDIO_FILE_ENCODING_LINEAR_16
self._framesize = 2
elif self._sampwidth == 3:
encoding = AUDIO_FILE_ENCODING_LINEAR_24
self._framesize = 3
elif self._sampwidth == 4:
encoding = AUDIO_FILE_ENCODING_LINEAR_32
self._framesize = 4
else:
raise Error('internal error')
elif self._comptype == 'ULAW':
encoding = AUDIO_FILE_ENCODING_MULAW_8
self._framesize = 1
else:
raise Error('internal error')
self._framesize = self._framesize * self._nchannels
_write_u32(self._file, AUDIO_FILE_MAGIC)
header_size = 25 + len(self._info)
header_size = (header_size + 7) & ~7
_write_u32(self._file, header_size)
if self._nframes == AUDIO_UNKNOWN_SIZE:
length = AUDIO_UNKNOWN_SIZE
else:
length = self._nframes * self._framesize
try:
self._form_length_pos = self._file.tell()
except (AttributeError, OSError):
self._form_length_pos = None
_write_u32(self._file, length)
self._datalength = length
_write_u32(self._file, encoding)
_write_u32(self._file, self._framerate)
_write_u32(self._file, self._nchannels)
self._file.write(self._info)
self._file.write(b'\0'*(header_size - len(self._info) - 24))
def _patchheader(self):
if self._form_length_pos is None:
raise OSError('cannot seek')
self._file.seek(self._form_length_pos)
_write_u32(self._file, self._datawritten)
self._datalength = self._datawritten
self._file.seek(0, 2)
def open(f, mode=None):
if mode is None:
if hasattr(f, 'mode'):
mode = f.mode
else:
mode = 'rb'
if mode in ('r', 'rb'):
return Au_read(f)
elif mode in ('w', 'wb'):
return Au_write(f)
else:
raise Error("mode must be 'r', 'rb', 'w', or 'wb'")
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@tools@python3@Lib@sunau.py@.PATH_END.py
|
{
"filename": "_dtick.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/scene/yaxis/_dtick.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class DtickValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(self, plotly_name="dtick", parent_name="layout.scene.yaxis", **kwargs):
super(DtickValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
implied_edits=kwargs.pop("implied_edits", {"tickmode": "linear"}),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@scene@yaxis@_dtick.py@.PATH_END.py
|
{
"filename": "_colorscale.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolargl/marker/line/_colorscale.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorscaleValidator(_plotly_utils.basevalidators.ColorscaleValidator):
def __init__(
self,
plotly_name="colorscale",
parent_name="scatterpolargl.marker.line",
**kwargs,
):
super(ColorscaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"autocolorscale": False}),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolargl@marker@line@_colorscale.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "spacetelescope/hstaxe",
"repo_path": "hstaxe_extracted/hstaxe-main/cookbooks/ACS/SBC_basic_extraction/README.md",
"type": "Markdown"
}
|
In this Jupyter Notebook tutorial, we present the workflow for performing a basic spectral extraction using ACS/SBC Prism full frame data with the HSTaXe software.
This directory, once cloned/downloaded, should contain this README.md, the tutorial Jupyter Notebook acs_sbc_hstaxe.ipynb, and a .HTML copy of the notebook. In the .HTML version of the notebook each cell has been run and the corresponding output is shown.
|
spacetelescopeREPO_NAMEhstaxePATH_START.@hstaxe_extracted@hstaxe-main@cookbooks@ACS@SBC_basic_extraction@README.md@.PATH_END.py
|
{
"filename": "renderer.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/matplotlylib/renderer.py",
"type": "Python"
}
|
"""
Renderer Module
This module defines the PlotlyRenderer class and a single function,
fig_to_plotly, which is intended to be the main way that user's will interact
with the matplotlylib package.
"""
from __future__ import absolute_import
import six
import warnings
import plotly.graph_objs as go
from plotly.matplotlylib.mplexporter import Renderer
from plotly.matplotlylib import mpltools
# Warning format
def warning_on_one_line(msg, category, filename, lineno, file=None, line=None):
return "%s:%s: %s:\n\n%s\n\n" % (filename, lineno, category.__name__, msg)
warnings.formatwarning = warning_on_one_line
class PlotlyRenderer(Renderer):
"""A renderer class inheriting from base for rendering mpl plots in plotly.
A renderer class to be used with an exporter for rendering matplotlib
plots in Plotly. This module defines the PlotlyRenderer class which handles
the creation of the JSON structures that get sent to plotly.
All class attributes available are defined in __init__().
Basic Usage:
# (mpl code) #
fig = gcf()
renderer = PlotlyRenderer(fig)
exporter = Exporter(renderer)
exporter.run(fig) # ... et voila
"""
def __init__(self):
"""Initialize PlotlyRenderer obj.
PlotlyRenderer obj is called on by an Exporter object to draw
matplotlib objects like figures, axes, text, etc.
All class attributes are listed here in the __init__ method.
"""
self.plotly_fig = go.Figure()
self.mpl_fig = None
self.current_mpl_ax = None
self.bar_containers = None
self.current_bars = []
self.axis_ct = 0
self.x_is_mpl_date = False
self.mpl_x_bounds = (0, 1)
self.mpl_y_bounds = (0, 1)
self.msg = "Initialized PlotlyRenderer\n"
def open_figure(self, fig, props):
"""Creates a new figure by beginning to fill out layout dict.
The 'autosize' key is set to false so that the figure will mirror
sizes set by mpl. The 'hovermode' key controls what shows up when you
mouse around a figure in plotly, it's set to show the 'closest' point.
Positional agurments:
fig -- a matplotlib.figure.Figure object.
props.keys(): [
'figwidth',
'figheight',
'dpi'
]
"""
self.msg += "Opening figure\n"
self.mpl_fig = fig
self.plotly_fig["layout"] = go.Layout(
width=int(props["figwidth"] * props["dpi"]),
height=int(props["figheight"] * props["dpi"]),
autosize=False,
hovermode="closest",
)
self.mpl_x_bounds, self.mpl_y_bounds = mpltools.get_axes_bounds(fig)
margin = go.layout.Margin(
l=int(self.mpl_x_bounds[0] * self.plotly_fig["layout"]["width"]),
r=int((1 - self.mpl_x_bounds[1]) * self.plotly_fig["layout"]["width"]),
t=int((1 - self.mpl_y_bounds[1]) * self.plotly_fig["layout"]["height"]),
b=int(self.mpl_y_bounds[0] * self.plotly_fig["layout"]["height"]),
pad=0,
)
self.plotly_fig["layout"]["margin"] = margin
def close_figure(self, fig):
"""Closes figure by cleaning up data and layout dictionaries.
The PlotlyRenderer's job is to create an appropriate set of data and
layout dictionaries. When the figure is closed, some cleanup and
repair is necessary. This method removes inappropriate dictionary
entries, freeing up Plotly to use defaults and best judgements to
complete the entries. This method is called by an Exporter object.
Positional arguments:
fig -- a matplotlib.figure.Figure object.
"""
self.plotly_fig["layout"]["showlegend"] = False
self.msg += "Closing figure\n"
def open_axes(self, ax, props):
"""Setup a new axes object (subplot in plotly).
Plotly stores information about subplots in different 'xaxis' and
'yaxis' objects which are numbered. These are just dictionaries
included in the layout dictionary. This function takes information
from the Exporter, fills in appropriate dictionary entries,
and updates the layout dictionary. PlotlyRenderer keeps track of the
number of plots by incrementing the axis_ct attribute.
Setting the proper plot domain in plotly is a bit tricky. Refer to
the documentation for mpltools.convert_x_domain and
mpltools.convert_y_domain.
Positional arguments:
ax -- an mpl axes object. This will become a subplot in plotly.
props.keys() -- [
'axesbg', (background color for axes obj)
'axesbgalpha', (alpha, or opacity for background)
'bounds', ((x0, y0, width, height) for axes)
'dynamic', (zoom/pan-able?)
'axes', (list: [xaxis, yaxis])
'xscale', (log, linear, or date)
'yscale',
'xlim', (range limits for x)
'ylim',
'xdomain' (xdomain=xlim, unless it's a date)
'ydomain'
]
"""
self.msg += " Opening axes\n"
self.current_mpl_ax = ax
self.bar_containers = [
c
for c in ax.containers # empty is OK
if c.__class__.__name__ == "BarContainer"
]
self.current_bars = []
self.axis_ct += 1
# set defaults in axes
xaxis = go.layout.XAxis(
anchor="y{0}".format(self.axis_ct), zeroline=False, ticks="inside"
)
yaxis = go.layout.YAxis(
anchor="x{0}".format(self.axis_ct), zeroline=False, ticks="inside"
)
# update defaults with things set in mpl
mpl_xaxis, mpl_yaxis = mpltools.prep_xy_axis(
ax=ax, props=props, x_bounds=self.mpl_x_bounds, y_bounds=self.mpl_y_bounds
)
xaxis.update(mpl_xaxis)
yaxis.update(mpl_yaxis)
bottom_spine = mpltools.get_spine_visible(ax, "bottom")
top_spine = mpltools.get_spine_visible(ax, "top")
left_spine = mpltools.get_spine_visible(ax, "left")
right_spine = mpltools.get_spine_visible(ax, "right")
xaxis["mirror"] = mpltools.get_axis_mirror(bottom_spine, top_spine)
yaxis["mirror"] = mpltools.get_axis_mirror(left_spine, right_spine)
xaxis["showline"] = bottom_spine
yaxis["showline"] = top_spine
# put axes in our figure
self.plotly_fig["layout"]["xaxis{0}".format(self.axis_ct)] = xaxis
self.plotly_fig["layout"]["yaxis{0}".format(self.axis_ct)] = yaxis
# let all subsequent dates be handled properly if required
if "type" in dir(xaxis) and xaxis["type"] == "date":
self.x_is_mpl_date = True
def close_axes(self, ax):
"""Close the axes object and clean up.
Bars from bar charts are given to PlotlyRenderer one-by-one,
thus they need to be taken care of at the close of each axes object.
The self.current_bars variable should be empty unless a bar
chart has been created.
Positional arguments:
ax -- an mpl axes object, not required at this time.
"""
self.draw_bars(self.current_bars)
self.msg += " Closing axes\n"
self.x_is_mpl_date = False
def draw_bars(self, bars):
# sort bars according to bar containers
mpl_traces = []
for container in self.bar_containers:
mpl_traces.append(
[
bar_props
for bar_props in self.current_bars
if bar_props["mplobj"] in container
]
)
for trace in mpl_traces:
self.draw_bar(trace)
def draw_bar(self, coll):
"""Draw a collection of similar patches as a bar chart.
After bars are sorted, an appropriate data dictionary must be created
to tell plotly about this data. Just like draw_line or draw_markers,
draw_bar translates patch/path information into something plotly
understands.
Positional arguments:
patch_coll -- a collection of patches to be drawn as a bar chart.
"""
tol = 1e-10
trace = [mpltools.make_bar(**bar_props) for bar_props in coll]
widths = [bar_props["x1"] - bar_props["x0"] for bar_props in trace]
heights = [bar_props["y1"] - bar_props["y0"] for bar_props in trace]
vertical = abs(sum(widths[0] - widths[iii] for iii in range(len(widths)))) < tol
horizontal = (
abs(sum(heights[0] - heights[iii] for iii in range(len(heights)))) < tol
)
if vertical and horizontal:
# Check for monotonic x. Can't both be true!
x_zeros = [bar_props["x0"] for bar_props in trace]
if all(
(x_zeros[iii + 1] > x_zeros[iii] for iii in range(len(x_zeros[:-1])))
):
orientation = "v"
else:
orientation = "h"
elif vertical:
orientation = "v"
else:
orientation = "h"
if orientation == "v":
self.msg += " Attempting to draw a vertical bar chart\n"
old_heights = [bar_props["y1"] for bar_props in trace]
for bar in trace:
bar["y0"], bar["y1"] = 0, bar["y1"] - bar["y0"]
new_heights = [bar_props["y1"] for bar_props in trace]
# check if we're stacked or not...
for old, new in zip(old_heights, new_heights):
if abs(old - new) > tol:
self.plotly_fig["layout"]["barmode"] = "stack"
self.plotly_fig["layout"]["hovermode"] = "x"
x = [bar["x0"] + (bar["x1"] - bar["x0"]) / 2 for bar in trace]
y = [bar["y1"] for bar in trace]
bar_gap = mpltools.get_bar_gap(
[bar["x0"] for bar in trace], [bar["x1"] for bar in trace]
)
if self.x_is_mpl_date:
x = [bar["x0"] for bar in trace]
formatter = (
self.current_mpl_ax.get_xaxis()
.get_major_formatter()
.__class__.__name__
)
x = mpltools.mpl_dates_to_datestrings(x, formatter)
else:
self.msg += " Attempting to draw a horizontal bar chart\n"
old_rights = [bar_props["x1"] for bar_props in trace]
for bar in trace:
bar["x0"], bar["x1"] = 0, bar["x1"] - bar["x0"]
new_rights = [bar_props["x1"] for bar_props in trace]
# check if we're stacked or not...
for old, new in zip(old_rights, new_rights):
if abs(old - new) > tol:
self.plotly_fig["layout"]["barmode"] = "stack"
self.plotly_fig["layout"]["hovermode"] = "y"
x = [bar["x1"] for bar in trace]
y = [bar["y0"] + (bar["y1"] - bar["y0"]) / 2 for bar in trace]
bar_gap = mpltools.get_bar_gap(
[bar["y0"] for bar in trace], [bar["y1"] for bar in trace]
)
bar = go.Bar(
orientation=orientation,
x=x,
y=y,
xaxis="x{0}".format(self.axis_ct),
yaxis="y{0}".format(self.axis_ct),
opacity=trace[0]["alpha"], # TODO: get all alphas if array?
marker=go.bar.Marker(
color=trace[0]["facecolor"], # TODO: get all
line=dict(width=trace[0]["edgewidth"]),
),
) # TODO ditto
if len(bar["x"]) > 1:
self.msg += " Heck yeah, I drew that bar chart\n"
self.plotly_fig.add_trace(bar),
if bar_gap is not None:
self.plotly_fig["layout"]["bargap"] = bar_gap
else:
self.msg += " Bar chart not drawn\n"
warnings.warn(
"found box chart data with length <= 1, "
"assuming data redundancy, not plotting."
)
def draw_marked_line(self, **props):
"""Create a data dict for a line obj.
This will draw 'lines', 'markers', or 'lines+markers'.
props.keys() -- [
'coordinates', ('data', 'axes', 'figure', or 'display')
'data', (a list of xy pairs)
'mplobj', (the matplotlib.lines.Line2D obj being rendered)
'label', (the name of the Line2D obj being rendered)
'linestyle', (linestyle dict, can be None, see below)
'markerstyle', (markerstyle dict, can be None, see below)
]
props['linestyle'].keys() -- [
'alpha', (opacity of Line2D obj)
'color', (color of the line if it exists, not the marker)
'linewidth',
'dasharray', (code for linestyle, see DASH_MAP in mpltools.py)
'zorder', (viewing precedence when stacked with other objects)
]
props['markerstyle'].keys() -- [
'alpha', (opacity of Line2D obj)
'marker', (the mpl marker symbol, see SYMBOL_MAP in mpltools.py)
'facecolor', (color of the marker face)
'edgecolor', (color of the marker edge)
'edgewidth', (width of marker edge)
'markerpath', (an SVG path for drawing the specified marker)
'zorder', (viewing precedence when stacked with other objects)
]
"""
self.msg += " Attempting to draw a line "
line, marker = {}, {}
if props["linestyle"] and props["markerstyle"]:
self.msg += "... with both lines+markers\n"
mode = "lines+markers"
elif props["linestyle"]:
self.msg += "... with just lines\n"
mode = "lines"
elif props["markerstyle"]:
self.msg += "... with just markers\n"
mode = "markers"
if props["linestyle"]:
color = mpltools.merge_color_and_opacity(
props["linestyle"]["color"], props["linestyle"]["alpha"]
)
# print(mpltools.convert_dash(props['linestyle']['dasharray']))
line = go.scatter.Line(
color=color,
width=props["linestyle"]["linewidth"],
dash=mpltools.convert_dash(props["linestyle"]["dasharray"]),
)
if props["markerstyle"]:
marker = go.scatter.Marker(
opacity=props["markerstyle"]["alpha"],
color=props["markerstyle"]["facecolor"],
symbol=mpltools.convert_symbol(props["markerstyle"]["marker"]),
size=props["markerstyle"]["markersize"],
line=dict(
color=props["markerstyle"]["edgecolor"],
width=props["markerstyle"]["edgewidth"],
),
)
if props["coordinates"] == "data":
marked_line = go.Scatter(
mode=mode,
name=(
str(props["label"])
if isinstance(props["label"], six.string_types)
else props["label"]
),
x=[xy_pair[0] for xy_pair in props["data"]],
y=[xy_pair[1] for xy_pair in props["data"]],
xaxis="x{0}".format(self.axis_ct),
yaxis="y{0}".format(self.axis_ct),
line=line,
marker=marker,
)
if self.x_is_mpl_date:
formatter = (
self.current_mpl_ax.get_xaxis()
.get_major_formatter()
.__class__.__name__
)
marked_line["x"] = mpltools.mpl_dates_to_datestrings(
marked_line["x"], formatter
)
self.plotly_fig.add_trace(marked_line),
self.msg += " Heck yeah, I drew that line\n"
else:
self.msg += " Line didn't have 'data' coordinates, " "not drawing\n"
warnings.warn(
"Bummer! Plotly can currently only draw Line2D "
"objects from matplotlib that are in 'data' "
"coordinates!"
)
def draw_image(self, **props):
"""Draw image.
Not implemented yet!
"""
self.msg += " Attempting to draw image\n"
self.msg += " Not drawing image\n"
warnings.warn(
"Aw. Snap! You're gonna have to hold off on "
"the selfies for now. Plotly can't import "
"images from matplotlib yet!"
)
def draw_path_collection(self, **props):
"""Add a path collection to data list as a scatter plot.
Current implementation defaults such collections as scatter plots.
Matplotlib supports collections that have many of the same parameters
in common like color, size, path, etc. However, they needn't all be
the same. Plotly does not currently support such functionality and
therefore, the style for the first object is taken and used to define
the remaining paths in the collection.
props.keys() -- [
'paths', (structure: [vertices, path_code])
'path_coordinates', ('data', 'axes', 'figure', or 'display')
'path_transforms', (mpl transform, including Affine2D matrix)
'offsets', (offset from axes, helpful if in 'data')
'offset_coordinates', ('data', 'axes', 'figure', or 'display')
'offset_order',
'styles', (style dict, see below)
'mplobj' (the collection obj being drawn)
]
props['styles'].keys() -- [
'linewidth', (one or more linewidths)
'facecolor', (one or more facecolors for path)
'edgecolor', (one or more edgecolors for path)
'alpha', (one or more opacites for path)
'zorder', (precedence when stacked)
]
"""
self.msg += " Attempting to draw a path collection\n"
if props["offset_coordinates"] == "data":
markerstyle = mpltools.get_markerstyle_from_collection(props)
scatter_props = {
"coordinates": "data",
"data": props["offsets"],
"label": None,
"markerstyle": markerstyle,
"linestyle": None,
}
self.msg += " Drawing path collection as markers\n"
self.draw_marked_line(**scatter_props)
else:
self.msg += " Path collection not linked to 'data', " "not drawing\n"
warnings.warn(
"Dang! That path collection is out of this "
"world. I totally don't know what to do with "
"it yet! Plotly can only import path "
"collections linked to 'data' coordinates"
)
def draw_path(self, **props):
"""Draw path, currently only attempts to draw bar charts.
This function attempts to sort a given path into a collection of
horizontal or vertical bar charts. Most of the actual code takes
place in functions from mpltools.py.
props.keys() -- [
'data', (a list of verticies for the path)
'coordinates', ('data', 'axes', 'figure', or 'display')
'pathcodes', (code for the path, structure: ['M', 'L', 'Z', etc.])
'style', (style dict, see below)
'mplobj' (the mpl path object)
]
props['style'].keys() -- [
'alpha', (opacity of path obj)
'edgecolor',
'facecolor',
'edgewidth',
'dasharray', (style for path's enclosing line)
'zorder' (precedence of obj when stacked)
]
"""
self.msg += " Attempting to draw a path\n"
is_bar = mpltools.is_bar(self.current_mpl_ax.containers, **props)
if is_bar:
self.current_bars += [props]
else:
self.msg += " This path isn't a bar, not drawing\n"
warnings.warn(
"I found a path object that I don't think is part "
"of a bar chart. Ignoring."
)
def draw_text(self, **props):
"""Create an annotation dict for a text obj.
Currently, plotly uses either 'page' or 'data' to reference
annotation locations. These refer to 'display' and 'data',
respectively for the 'coordinates' key used in the Exporter.
Appropriate measures are taken to transform text locations to
reference one of these two options.
props.keys() -- [
'text', (actual content string, not the text obj)
'position', (an x, y pair, not an mpl Bbox)
'coordinates', ('data', 'axes', 'figure', 'display')
'text_type', ('title', 'xlabel', or 'ylabel')
'style', (style dict, see below)
'mplobj' (actual mpl text object)
]
props['style'].keys() -- [
'alpha', (opacity of text)
'fontsize', (size in points of text)
'color', (hex color)
'halign', (horizontal alignment, 'left', 'center', or 'right')
'valign', (vertical alignment, 'baseline', 'center', or 'top')
'rotation',
'zorder', (precedence of text when stacked with other objs)
]
"""
self.msg += " Attempting to draw an mpl text object\n"
if not mpltools.check_corners(props["mplobj"], self.mpl_fig):
warnings.warn(
"Looks like the annotation(s) you are trying \n"
"to draw lies/lay outside the given figure size.\n\n"
"Therefore, the resulting Plotly figure may not be \n"
"large enough to view the full text. To adjust \n"
"the size of the figure, use the 'width' and \n"
"'height' keys in the Layout object. Alternatively,\n"
"use the Margin object to adjust the figure's margins."
)
align = props["mplobj"]._multialignment
if not align:
align = props["style"]["halign"] # mpl default
if "annotations" not in self.plotly_fig["layout"]:
self.plotly_fig["layout"]["annotations"] = []
if props["text_type"] == "xlabel":
self.msg += " Text object is an xlabel\n"
self.draw_xlabel(**props)
elif props["text_type"] == "ylabel":
self.msg += " Text object is a ylabel\n"
self.draw_ylabel(**props)
elif props["text_type"] == "title":
self.msg += " Text object is a title\n"
self.draw_title(**props)
else: # just a regular text annotation...
self.msg += " Text object is a normal annotation\n"
if props["coordinates"] != "data":
self.msg += (
" Text object isn't linked to 'data' " "coordinates\n"
)
x_px, y_px = (
props["mplobj"].get_transform().transform(props["position"])
)
x, y = mpltools.display_to_paper(x_px, y_px, self.plotly_fig["layout"])
xref = "paper"
yref = "paper"
xanchor = props["style"]["halign"] # no difference here!
yanchor = mpltools.convert_va(props["style"]["valign"])
else:
self.msg += " Text object is linked to 'data' " "coordinates\n"
x, y = props["position"]
axis_ct = self.axis_ct
xaxis = self.plotly_fig["layout"]["xaxis{0}".format(axis_ct)]
yaxis = self.plotly_fig["layout"]["yaxis{0}".format(axis_ct)]
if (
xaxis["range"][0] < x < xaxis["range"][1]
and yaxis["range"][0] < y < yaxis["range"][1]
):
xref = "x{0}".format(self.axis_ct)
yref = "y{0}".format(self.axis_ct)
else:
self.msg += (
" Text object is outside "
"plotting area, making 'paper' reference.\n"
)
x_px, y_px = (
props["mplobj"].get_transform().transform(props["position"])
)
x, y = mpltools.display_to_paper(
x_px, y_px, self.plotly_fig["layout"]
)
xref = "paper"
yref = "paper"
xanchor = props["style"]["halign"] # no difference here!
yanchor = mpltools.convert_va(props["style"]["valign"])
annotation = go.layout.Annotation(
text=(
str(props["text"])
if isinstance(props["text"], six.string_types)
else props["text"]
),
opacity=props["style"]["alpha"],
x=x,
y=y,
xref=xref,
yref=yref,
align=align,
xanchor=xanchor,
yanchor=yanchor,
showarrow=False, # change this later?
font=go.layout.annotation.Font(
color=props["style"]["color"], size=props["style"]["fontsize"]
),
)
self.plotly_fig["layout"]["annotations"] += (annotation,)
self.msg += " Heck, yeah I drew that annotation\n"
def draw_title(self, **props):
"""Add a title to the current subplot in layout dictionary.
If there exists more than a single plot in the figure, titles revert
to 'page'-referenced annotations.
props.keys() -- [
'text', (actual content string, not the text obj)
'position', (an x, y pair, not an mpl Bbox)
'coordinates', ('data', 'axes', 'figure', 'display')
'text_type', ('title', 'xlabel', or 'ylabel')
'style', (style dict, see below)
'mplobj' (actual mpl text object)
]
props['style'].keys() -- [
'alpha', (opacity of text)
'fontsize', (size in points of text)
'color', (hex color)
'halign', (horizontal alignment, 'left', 'center', or 'right')
'valign', (vertical alignment, 'baseline', 'center', or 'top')
'rotation',
'zorder', (precedence of text when stacked with other objs)
]
"""
self.msg += " Attempting to draw a title\n"
if len(self.mpl_fig.axes) > 1:
self.msg += (
" More than one subplot, adding title as " "annotation\n"
)
x_px, y_px = props["mplobj"].get_transform().transform(props["position"])
x, y = mpltools.display_to_paper(x_px, y_px, self.plotly_fig["layout"])
annotation = go.layout.Annotation(
text=props["text"],
font=go.layout.annotation.Font(
color=props["style"]["color"], size=props["style"]["fontsize"]
),
xref="paper",
yref="paper",
x=x,
y=y,
xanchor="center",
yanchor="bottom",
showarrow=False, # no arrow for a title!
)
self.plotly_fig["layout"]["annotations"] += (annotation,)
else:
self.msg += (
" Only one subplot found, adding as a " "plotly title\n"
)
self.plotly_fig["layout"]["title"] = props["text"]
titlefont = dict(
size=props["style"]["fontsize"], color=props["style"]["color"]
)
self.plotly_fig["layout"]["titlefont"] = titlefont
def draw_xlabel(self, **props):
"""Add an xaxis label to the current subplot in layout dictionary.
props.keys() -- [
'text', (actual content string, not the text obj)
'position', (an x, y pair, not an mpl Bbox)
'coordinates', ('data', 'axes', 'figure', 'display')
'text_type', ('title', 'xlabel', or 'ylabel')
'style', (style dict, see below)
'mplobj' (actual mpl text object)
]
props['style'].keys() -- [
'alpha', (opacity of text)
'fontsize', (size in points of text)
'color', (hex color)
'halign', (horizontal alignment, 'left', 'center', or 'right')
'valign', (vertical alignment, 'baseline', 'center', or 'top')
'rotation',
'zorder', (precedence of text when stacked with other objs)
]
"""
self.msg += " Adding xlabel\n"
axis_key = "xaxis{0}".format(self.axis_ct)
self.plotly_fig["layout"][axis_key]["title"] = str(props["text"])
titlefont = dict(size=props["style"]["fontsize"], color=props["style"]["color"])
self.plotly_fig["layout"][axis_key]["titlefont"] = titlefont
def draw_ylabel(self, **props):
"""Add a yaxis label to the current subplot in layout dictionary.
props.keys() -- [
'text', (actual content string, not the text obj)
'position', (an x, y pair, not an mpl Bbox)
'coordinates', ('data', 'axes', 'figure', 'display')
'text_type', ('title', 'xlabel', or 'ylabel')
'style', (style dict, see below)
'mplobj' (actual mpl text object)
]
props['style'].keys() -- [
'alpha', (opacity of text)
'fontsize', (size in points of text)
'color', (hex color)
'halign', (horizontal alignment, 'left', 'center', or 'right')
'valign', (vertical alignment, 'baseline', 'center', or 'top')
'rotation',
'zorder', (precedence of text when stacked with other objs)
]
"""
self.msg += " Adding ylabel\n"
axis_key = "yaxis{0}".format(self.axis_ct)
self.plotly_fig["layout"][axis_key]["title"] = props["text"]
titlefont = dict(size=props["style"]["fontsize"], color=props["style"]["color"])
self.plotly_fig["layout"][axis_key]["titlefont"] = titlefont
def resize(self):
"""Revert figure layout to allow plotly to resize.
By default, PlotlyRenderer tries its hardest to precisely mimic an
mpl figure. However, plotly is pretty good with aesthetics. By
running PlotlyRenderer.resize(), layout parameters are deleted. This
lets plotly choose them instead of mpl.
"""
self.msg += "Resizing figure, deleting keys from layout\n"
for key in ["width", "height", "autosize", "margin"]:
try:
del self.plotly_fig["layout"][key]
except (KeyError, AttributeError):
pass
def strip_style(self):
self.msg += "Stripping mpl style is no longer supported\n"
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@matplotlylib@renderer.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/streamtube/legendgrouptitle/font/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="textcase",
parent_name="streamtube.legendgrouptitle.font",
**kwargs,
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@streamtube@legendgrouptitle@font@_textcase.py@.PATH_END.py
|
{
"filename": "example_split.py",
"repo_name": "icrar/daliuge",
"repo_path": "daliuge_extracted/daliuge-master/daliuge-engine/test/integrate/example_split.py",
"type": "Python"
}
|
"""
This is an example of splitting a measurementset using the CASA MSTRANSFORM task
We need to develop a template based on it so it can get multiple spws based on
frequency ranges and widths
To run it standalone, change the directories, which are now hardcoded
"""
# pylint: skip-file
import datetime
import os
import time
if __name__ == "__main__":
dt = datetime.datetime.now()
timestr = dt.strftime("%Y-%m-%dT%H-%M-%S")
ms_dir = "/scratch/partner1024/chiles/final_products/20131122_941_6_FINAL_PRODUCTS/13B-266.sb27261805.eb28549602.56618.334173599535_calibrated_deepfield.ms"
# ms_dir = '/scratch/jason/13B-266.sb28624226.eb28625769.56669.43262586805_calibrated_deepfield_adios.ms'
log_dir = "/home/cwu/chiles/processing"
# log_dir = '/scratch/jason/tmp'
output_vis = "/scratch/partner1024/chiles/split_vis/{0}".format(timestr)
# output_vis = '/scratch/jason/tmp/split_vis/{0}'.format(timestr)
log_file = "{0}/{1}_split_time.log".format(log_dir, timestr)
os.mkdir(output_vis)
gap = 4
st = time.time()
for i in range(1):
mstransform(
vis=ms_dir,
outputvis="{0}/vis_{1}~{2}".format(
output_vis, 1020 + i * gap, 1024 + i * gap
),
start="{0}MHz".format(1020 + i * gap),
width="15.625kHz",
spw="2~2",
nchan=256,
regridms=T,
restfreq="1420.405752MHz",
mode="frequency",
outframe="lsrk",
interpolation="linear",
veltype="radio",
combinespws=True,
nspw=1,
createmms=False,
datacolumn="data",
)
tt = time.time() - st
f = open(log_file, "w")
f.write("total split time = {0} seconds".format(tt))
f.close()
"""
mstransform(vis=/scratch/partner1024/chiles/final_products//20131025_951_4_FINAL_PRODUCTS/13B-266.sb27248272.eb28094627.56590.41308579861_calibrated_deepfield.ms,
outputvis=/scratch/partner1024/chiles/split_vis/12345/20131025_951_4/vis_1024~1028,
start=1024MHz,width=15.625,spw=2~2,nchan=256)
mstransform(vis=/scratch/partner1024/chiles/final_products//20131025_951_4_FINAL_PRODUCTS/13B-266.sb27248272.eb28094627.56590.41308579861_calibrated_deepfield.ms,
outputvis=/scratch/partner1024/chiles/split_vis/12345/20131025_951_4/vis_1028~1032,
start=1028MHz,width=15.625,spw=2~2,nchan=256)
mstransform(vis=/scratch/partner1024/chiles/final_products//20131025_951_4_FINAL_PRODUCTS/13B-266.sb27248272.eb28094627.56590.41308579861_calibrated_deepfield.ms,
outputvis=/scratch/partner1024/chiles/split_vis/12345/20131025_951_4/vis_1032~1036,
start=1032MHz,width=15.625,spw=2~2,nchan=256)
"""
|
icrarREPO_NAMEdaliugePATH_START.@daliuge_extracted@daliuge-master@daliuge-engine@test@integrate@example_split.py@.PATH_END.py
|
{
"filename": "par_hierarchical_inference.py",
"repo_name": "sterinaldi/figaro",
"repo_path": "figaro_extracted/figaro-main/figaro/_pipelines/par_hierarchical_inference.py",
"type": "Python"
}
|
import numpy as np
import optparse
import importlib
import warnings
from pathlib import Path
from tqdm import tqdm
from figaro.mixture import DPGMM, HDPGMM
from figaro.utils import save_options, load_options, get_priors
from figaro.plot import plot_median_cr, plot_multidim
from figaro.load import load_data, load_single_event, load_selection_function, save_density, load_density, supported_pars
from figaro.rate import sample_rate, normalise_alpha_factor, plot_integrated_rate, plot_differential_rate
from figaro.cosmology import _decorator_dVdz, dVdz_approx_planck18, dVdz_approx_planck15
from figaro.marginal import marginalise
import ray
from ray.util import ActorPool
@ray.remote
class worker:
def __init__(self, bounds,
out_folder_plots,
out_folder_draws,
ext = 'json',
se_sigma = None,
hier_sigma = None,
scale = None,
events = None,
label = None,
unit = None,
save_se = True,
MC_draws = None,
probit = True,
selfunc = None,
inj_pdf = None,
n_total_inj = None,
):
self.dim = bounds.shape[0]
self.bounds = bounds
self.mixture = DPGMM(self.bounds, probit = probit)
self.hierarchical_mixture = HDPGMM(self.bounds,
MC_draws = MC_draws,
probit = probit,
selection_function = selfunc,
injection_pdf = inj_pdf,
total_injections = n_total_inj,
prior_pars = get_priors(self.bounds,
samples = events,
std = hier_sigma,
scale = scale,
probit = probit,
hierarchical = True,
),
)
self.out_folder_plots = out_folder_plots
self.out_folder_draws = out_folder_draws
self.se_sigma = se_sigma
self.scale = scale
self.save_se = save_se
self.label = label
self.unit = unit
self.probit = probit
self.ext = ext
def run_event(self, pars):
# Unpack data
samples, name, n_draws = pars
# Copying (issues with shuffling)
ev = np.copy(samples)
ev.setflags(write = True)
# Actual inference
prior_pars = get_priors(self.bounds, samples = ev, probit = self.probit, std = self.se_sigma, scale = self.scale, hierarchical = False)
self.mixture.initialise(prior_pars = prior_pars)
draws = [self.mixture.density_from_samples(ev, make_comp = False) for _ in range(n_draws)]
# Plots
plt_bounds = np.atleast_2d([ev.min(axis = 0), ev.max(axis = 0)]).T
if self.save_se:
if self.dim == 1:
plot_median_cr(draws,
samples = ev,
bounds = plt_bounds[0],
out_folder = self.out_folder_plots,
name = name,
label = self.label,
unit = self.unit,
subfolder = True,
)
else:
plot_multidim(draws,
samples = ev,
bounds = plt_bounds,
out_folder = self.out_folder_plots,
name = name,
labels = self.label,
units = self.unit,
subfolder = True,
)
# Saving
save_density(draws, folder = self.out_folder_draws, name = 'draws_'+name, ext = self.ext)
return draws
def draw_hierarchical(self):
return self.hierarchical_mixture.density_from_samples(self.posteriors, make_comp = False)
def load_posteriors(self, posteriors):
self.posteriors = np.copy(posteriors)
self.posteriors.setflags(write = True)
for i in range(len(self.posteriors)):
self.posteriors[i].setflags(write = True)
def main():
parser = optparse.OptionParser(prog = 'figaro-par-hierarchical', description = 'Parallelised hierarchical probability density reconstruction')
# Input/output
parser.add_option("-i", "--input", type = "string", dest = "input", help = "Folder with single-event samples files", default = None)
parser.add_option("-b", "--bounds", type = "string", dest = "bounds", help = "Density bounds. Must be a string formatted as '[[xmin, xmax], [ymin, ymax],...]'. For 1D distributions use '[xmin, xmax]'. Quotation marks are required and scientific notation is accepted", default = None)
parser.add_option("-o", "--output", type = "string", dest = "output", help = "Output folder. Default: same directory as samples folder", default = None)
parser.add_option("--ext", dest = "ext", type = "choice", choices = ['pkl', 'json'], help = "Format of mixture output file", default = 'json')
parser.add_option("--inj_density", type = "string", dest = "inj_density_file", help = "Python module with injected density - please name the method 'density'", default = None)
parser.add_option("--selfunc", type = "string", dest = "selfunc_file", help = "Python module with selection function - please name the method 'selection_function'", default = None)
parser.add_option("--parameter", type = "string", dest = "par", help = "GW parameter(s) to be read from file", default = None)
parser.add_option("--waveform", type = "choice", dest = "wf", help = "Waveform to load from samples file. To be used in combination with --parameter.", choices = ['combined', 'seob', 'imr'], default = 'combined')
# Plot
parser.add_option("--name", type = "string", dest = "hier_name", help = "Name to be given to hierarchical inference files. Default: same name as samples folder parent directory", default = None)
parser.add_option("-p", "--postprocess", dest = "postprocess", action = 'store_true', help = "Postprocessing", default = False)
parser.add_option("-s", "--save_se", dest = "save_single_event", action = 'store_true', help = "Save single event plots", default = False)
parser.add_option("--symbol", type = "string", dest = "symbol", help = "LaTeX-style quantity symbol, for plotting purposes", default = None)
parser.add_option("--unit", type = "string", dest = "unit", help = "LaTeX-style quantity unit, for plotting purposes", default = None)
parser.add_option("--hier_samples", type = "string", dest = "hier_samples", help = "Samples from hierarchical distribution (true single-event values, for simulations only)", default = None)
# Settings
parser.add_option("--draws", type = "int", dest = "draws", help = "Number of draws for hierarchical distribution", default = 100)
parser.add_option("--se_draws", type = "int", dest = "se_draws", help = "Number of draws for single-event distribution. Default: same as hierarchical distribution", default = None)
parser.add_option("--n_samples_dsp", type = "int", dest = "n_samples_dsp", help = "Number of samples to analyse (downsampling). Default: all", default = -1)
parser.add_option("--exclude_points", dest = "exclude_points", action = 'store_true', help = "Exclude points outside bounds from analysis", default = False)
parser.add_option("--cosmology", type = "choice", dest = "cosmology", help = "Set of cosmological parameters. Default values from Planck (2021)", choices = ['Planck18', 'Planck15'], default = 'Planck18')
parser.add_option("-e", "--events", dest = "run_events", action = 'store_false', help = "Skip single-event analysis", default = True)
parser.add_option("--se_sigma_prior", dest = "se_sigma_prior", type = "string", help = "Expected standard deviation (prior) for single-event inference - single value or n-dim values. If None, it is estimated from samples", default = None)
parser.add_option("--sigma_prior", dest = "sigma_prior", type = "string", help = "Expected standard deviation (prior) for hierarchical inference - single value or n-dim values. If None, it is estimated from samples", default = None)
parser.add_option("--fraction", dest = "fraction", type = "float", help = "Fraction of samples standard deviation for sigma prior. Overrided by sigma_prior.", default = None)
parser.add_option("--n_parallel", dest = "n_parallel", type = "int", help = "Number of parallel threads", default = 2)
parser.add_option("--mc_draws", dest = "mc_draws", type = "int", help = "Number of draws for assignment MC integral", default = None)
parser.add_option("--far_threshold", dest = "far_threshold", type = "float", help = "FAR threshold for LVK sensitivity estimate injections", default = 1.)
parser.add_option("--snr_threshold", dest = "snr_threshold", type = "float", help = "SNR threshold for LVK sensitivity estimate injections", default = 10.)
parser.add_option("--no_probit", dest = "probit", action = 'store_false', help = "Disable probit transformation", default = True)
parser.add_option("--config", dest = "config", type = "string", help = "Config file. Warning: command line options override config options", default = None)
parser.add_option("--rate", dest = "rate", action = 'store_true', help = "Compute rate", default = False)
parser.add_option("--include_dvdz", dest = "include_dvdz", action = 'store_true', help = "Include dV/dz*(1+z)^{-1} term in selection effects.", default = False)
parser.add_option("-l", "--likelihood", dest = "likelihood", action = 'store_true', help = "Resample posteriors to get likelihood samples (only for GW data)", default = False)
(options, args) = parser.parse_args()
if options.config is not None:
options = load_options(options, parser)
# Paths
if options.input is not None:
options.input = Path(options.input).resolve()
elif options.config is not None:
options.input = Path('.').resolve()
else:
raise Exception("Please provide path to samples.")
if options.output is not None:
options.output = Path(options.output).resolve()
if not options.output.exists():
options.output.mkdir(parents=True)
else:
options.output = options.input.parent
if options.config is not None:
options.config = Path(options.config).resolve()
output_plots = Path(options.output, 'plots')
if not output_plots.exists():
output_plots.mkdir()
output_draws = Path(options.output, 'draws')
if not output_draws.exists():
output_draws.mkdir()
if options.rate:
output_rate = Path(options.output, 'rate')
if not output_rate.exists():
output_rate.mkdir()
# Read hierarchical name
if options.hier_name is None:
options.hier_name = options.output.parts[-1]
if options.selfunc_file is None:
hier_name = 'observed_'+options.hier_name
else:
hier_name = 'intrinsic_'+options.hier_name
if options.config is None:
save_options(options, options.output, name = options.hier_name)
# Read bounds
if options.bounds is not None:
options.bounds = np.array(np.atleast_2d(eval(options.bounds)), dtype = np.float64)
elif options.bounds is None and not options.postprocess:
raise Exception("Please provide bounds for the inference (use -b '[[xmin,xmax],[ymin,ymax],...]')")
# Read parameter(s)
if options.par is not None:
options.par = options.par.split(',')
if not np.all([par in supported_pars for par in options.par]):
raise Exception("Please provide parameters from this list: "+', '.join(supported_pars[:-2]))
# Read number of single-event draws
if options.se_draws is None:
options.se_draws = options.draws
if options.se_sigma_prior is not None:
options.se_sigma_prior = np.array([float(s) for s in options.se_sigma_prior.split(',')])
if options.sigma_prior is not None:
options.sigma_prior = np.array([float(s) for s in options.sigma_prior.split(',')])
# Cosmology
if options.cosmology == 'Planck18':
approx_dVdz = dVdz_approx_planck18
else:
approx_dVdz = dVdz_approx_planck15
# If provided, load injected density
inj_density = None
if options.inj_density_file is not None:
inj_file_name = Path(options.inj_density_file).parts[-1].split('.')[0]
spec = importlib.util.spec_from_file_location(inj_file_name, options.inj_density_file)
inj_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(inj_module)
inj_density = inj_module.density
# If provided, load selecton function
selfunc = None
inj_pdf = None
n_total_inj = None
duration = 1.
if options.selfunc_file is not None:
selfunc, inj_pdf, n_total_inj, duration = load_selection_function(options.selfunc_file,
par = options.par,
far_threshold = options.far_threshold,
snr_threshold = options.snr_threshold,
)
if not callable(selfunc):
# Keeping only the samples within bounds
inj_pdf = inj_pdf[np.where((np.prod(options.bounds[:,0] < selfunc, axis = 1) & np.prod(selfunc < options.bounds[:,1], axis = 1)))]
selfunc = selfunc[np.where((np.prod(options.bounds[:,0] < selfunc, axis = 1) & np.prod(selfunc < options.bounds[:,1], axis = 1)))]
if options.include_dvdz and not callable(selfunc):
raise Exception("The inclusion of dV/dz*(1+z)^{-1} is available only with a selection function approximant.")
if options.include_dvdz:
if options.par is None:
print("Redshift is assumed to be the last parameter")
z_index = -1
elif 'z' in options.par:
z_index = np.where(np.array(options.par)=='z')[0][0]
else:
raise Exception("Redshift must be included in the rate analysis")
dec_selfunc = _decorator_dVdz(selfunc, approx_dVdz, z_index, options.bounds[z_index][1])
else:
dec_selfunc = selfunc
# If provided, load true values
hier_samples = None
if options.hier_samples is not None:
options.hier_samples = Path(options.hier_samples).resolve()
hier_samples, true_name = load_single_event(options.hier_samples,
par = options.par,
cosmology = options.cosmology,
waveform = options.wf,
)
if np.shape(hier_samples)[-1] == 1:
hier_samples = hier_samples.flatten()
# Load samples
events, names = load_data(options.input,
par = options.par,
n_samples = options.n_samples_dsp,
cosmology = options.cosmology,
waveform = options.wf,
likelihood = options.likelihood,
)
try:
dim = np.shape(events[0][0])[-1]
except IndexError:
dim = 1
if options.exclude_points:
print("Ignoring points outside bounds.")
for i, ev in enumerate(events):
events[i] = ev[np.where((np.prod(options.bounds[:,0] < ev, axis = 1) & np.prod(ev < options.bounds[:,1], axis = 1)))]
else:
# Check if all samples are within bounds
all_samples = np.atleast_2d(np.concatenate(events))
if options.probit:
if not np.all([(all_samples[:,i] > options.bounds[i,0]).all() and (all_samples[:,i] < options.bounds[i,1]).all() for i in range(dim)]):
raise ValueError("One or more samples are outside the given bounds.")
# Plot labels
if dim > 1:
if options.symbol is not None:
symbols = options.symbol.split(',')
else:
symbols = options.symbol
if options.unit is not None:
units = options.unit.split(',')
else:
units = options.unit
else:
symbols = options.symbol
units = options.unit
# Reconstruction
if not options.postprocess:
ray.init(num_cpus = options.n_parallel)
pool = ActorPool([worker.remote(bounds = options.bounds,
out_folder_plots = output_plots,
out_folder_draws = output_draws,
ext = options.ext,
se_sigma = options.se_sigma_prior,
hier_sigma = options.sigma_prior,
scale = options.fraction,
events = events,
label = symbols,
unit = units,
save_se = options.save_single_event,
MC_draws = options.mc_draws,
probit = options.probit,
selfunc = dec_selfunc,
inj_pdf = inj_pdf,
n_total_inj = n_total_inj,
)
for _ in range(options.n_parallel)])
if options.run_events:
# Run each single-event analysis
posteriors = []
for s in tqdm(pool.map_unordered(lambda a, v: a.run_event.remote(v), [[ev, name, options.se_draws] for ev, name in zip(events, names)]), total = len(events), desc = 'Events'):
posteriors.append(s)
# Save all single-event draws together
posteriors = np.array(posteriors)
save_density(posteriors, folder = output_draws, name = 'posteriors_single_event', ext = options.ext)
else:
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
posteriors = load_density(Path(output_draws, 'posteriors_single_event.'+options.ext), make_comp = False)
# Load posteriors
for s in pool.map(lambda a, v: a.load_posteriors.remote(v), [posteriors for _ in range(options.n_parallel)]):
pass
# Run hierarchical analysis
draws = []
for s in tqdm(pool.map_unordered(lambda a, v: a.draw_hierarchical.remote(), [_ for _ in range(options.draws)]), total = options.draws, desc = 'Sampling'):
draws.append(s)
draws = np.array(draws)
if options.include_dvdz:
normalise_alpha_factor(draws, dvdz = approx_dVdz, z_index = z_index, z_max = options.bounds[z_index][1])
# Save draws
save_density(draws, folder = output_draws, name = 'draws_'+hier_name, ext = options.ext)
else:
draws = load_density(Path(output_draws, 'draws_'+hier_name+'.'+options.ext))
# Plot
if dim == 1:
plot_median_cr(draws,
injected = inj_density,
samples = hier_samples,
out_folder = output_plots,
name = options.hier_name,
label = options.symbol,
unit = options.unit,
hierarchical = True,
)
else:
plot_multidim(draws,
samples = hier_samples,
out_folder = output_plots,
name = hier_name,
labels = symbols,
units = units,
hierarchical = True,
)
if options.rate:
R_samples = sample_rate(draws,
n_obs = len(events),
selfunc = selfunc,
T = duration,
size = 1e4,
dvdz = approx_dVdz,
z_index = z_index,
)
plot_integrated_rate(R_samples,
out_folder = output_rate,
name = options.hier_name,
)
np.savetxt(Path(output_rate, 'samples_integrated_rate_{}.txt'.format(options.hier_name)), R_samples)
# Best estimate for rate
rates = sample_rate(draws,
n_obs = len(events),
selfunc = selfunc,
T = duration,
size = 1e4,
dvdz = approx_dVdz,
z_index = z_index,
each = True,
)
if options.par is not None:
names = options.par
else:
names = np.arange(dim)
# Marginal rates
if dim == 1:
plot_differential_rate(draws,
rate = rates,
out_folder = output_rate,
name = options.hier_name,
label = options.symbol,
unit = options.unit,
hierarchical = True,
)
else:
for i in range(dim):
dims = list(np.arange(dim))
dims.remove(i)
dd = marginalise(draws, dims)
plot_differential_rate(dd,
rate = rates,
out_folder = output_rate,
name = names[i],
label = symbols[i],
unit = units[i],
hierarchical = True,
)
if __name__ == '__main__':
main()
|
sterinaldiREPO_NAMEfigaroPATH_START.@figaro_extracted@figaro-main@figaro@_pipelines@par_hierarchical_inference.py@.PATH_END.py
|
{
"filename": "compute_second_order_aperture_mass_correlations_HOWLS.py",
"repo_name": "sheydenreich/threepoint",
"repo_path": "threepoint_extracted/threepoint-main/python_scripts/old/compute_second_order_aperture_mass_correlations_HOWLS.py",
"type": "Python"
}
|
from utility import extract_second_order_aperture_masses
import numpy as np
import sys
from tqdm import tqdm
import multiprocessing.managers
from multiprocessing import Pool
from astropy.io import fits
import os
class MyManager(multiprocessing.managers.BaseManager):
pass
MyManager.register('np_zeros', np.zeros, multiprocessing.managers.ArrayProxy)
startpath = '/vol/euclid2/euclid2_raid2/sven/HOWLS/'
def compute_aperture_masses_of_field(filepath,theta_ap_array,save_map=None,use_polynomial_filter=False):
slics = ('SLICS' in filepath)
if(slics):
fieldsize = 600.
npix = 4096
else:
fieldsize = 5*60.
npix = 2048
field = fits.open(filepath)
data = field[1].data
# if(slics):
# X_pos = data['x_arcmin']
# Y_pos = data['y_arcmin']
# shear = data['shear1']+1.0j*data['shear2']
# noise = data['e1_intr']+1.0j*data['e2_intr']
# shear_noise = (shear+noise)/(1+shear*np.conj(noise))
# else:
X_pos = data['ra_gal']*60.
Y_pos = data['dec_gal']*60.
shear_noise = -data['gamma1_noise']+1.0j*data['gamma2_noise']
# print("Flipping e2!")
# shear_noise = -data['gamma1_noise']-1.0j*data['gamma2_noise']
result = extract_second_order_aperture_masses(X_pos,Y_pos,shear_noise,npix,theta_ap_array,fieldsize,compute_mcross=False,save_map=save_map,
use_polynomial_filter=use_polynomial_filter)
return result
def compute_all_aperture_masses(openpath,filenames,savepath,aperture_masses = [1.17,2.34,4.69,9.37],n_processes = 64,use_polynomial_filter=False):
n_files = len(filenames)
with Pool(processes=n_processes) as p:
# print('test')
result = [p.apply_async(compute_aperture_masses_of_field, args=(openpath+filenames[i],aperture_masses,None,use_polynomial_filter,)) for i in range(n_files)]
data = [p.get() for p in result]
datavec = np.array([data[i] for i in range(len(data))])
np.savetxt(savepath+'map_squared',datavec)
def cond_for_analysis(x):
cond1 = '.fits' in x
cond2 = 'cone1041' in x
cond3 = 'cone1042' in x
cond4 = 'cone1046' in x
cond5 = 'cone1069' in x
cond = cond1 and (cond2 or cond3 or cond4 or cond5)
return cond
if(__name__=='__main__'):
# print("Computing test aperture mass maps:")
# path_kappa_dustgrain = "/vol/euclid7/euclid7_2/llinke/HOWLS/convergence_maps/DUSTGRAIN_COSMO_128/kappa_noise_0_LCDM_Om02_ks_nomask_shear.fits"
for (dirpath,_,_filenames) in os.walk(startpath+"shear_catalogues/"):
if(len(_filenames)>2 and 'SLICS' in dirpath):
filenames = np.sort([filename for filename in _filenames if ".fits" in filename])
# if not 'SLICS' in dirpath:
# dir_end_path = dirpath.split('/')[-1]
savepath = dirpath.split('shear_catalogues')[0]+'map_squared_4096_pix'+dirpath.split('shear_catalogues')[1]
print('Reading shear catalogues from ',dirpath)
print('Writing summary statistics to ',savepath)
if not os.path.exists(savepath):
os.makedirs(savepath)
compute_all_aperture_masses(dirpath+'/',filenames,savepath+'/',n_processes=64)#,aperture_masses = [0.5,1,2,4,8,16,32])
# for (dirpath,_,_filenames) in os.walk(startpath+"shear_catalogues/"):
# if(len(_filenames)>2 and 'SLICS' in dirpath):
# filenames = np.sort([filename for filename in _filenames if '.fits' in filename])
# # if not 'SLICS' in dirpath:
# # dir_end_path = dirpath.split('/')[-1]
# savepath = dirpath.split('shear_catalogues')[0]+'map_cubed_same_cutoff'+dirpath.split('shear_catalogues')[1]
# print('Reading shear catalogues from ',dirpath)
# print('Writing summary statistics to ',savepath)
# if not os.path.exists(savepath):
# os.makedirs(savepath)
# compute_all_aperture_masses(dirpath+'/',filenames,savepath+'/',n_processes=64)#,aperture_masses = [0.5,1,2,4,8,16,32])
# for (dirpath,_,_filenames) in os.walk(startpath+"shear_catalogues/"):
# if(len(_filenames)>2):
# filenames = np.sort([filename for filename in _filenames if '.fits' in filename])
# # dir_end_path = dirpath.split('/')[-1]
# savepath = dirpath.split('shear_catalogues')[0]+'map_cubed_our_thetas'+dirpath.split('shear_catalogues')[1]
# print('Reading shear catalogues from ',dirpath)
# print('Writing summary statistics to ',savepath)
# if not os.path.exists(savepath):
# os.makedirs(savepath)
# compute_all_aperture_masses(dirpath+'/',filenames,savepath+'/',aperture_masses = [0.5,1,2,4,8,16,32],n_processes=32)
# for (dirpath,_,_filenames) in os.walk(startpath+"shear_catalogues/"):
# if(len(_filenames)>2):
# filenames = np.sort([filename for filename in _filenames if '.fits' in filename])
# # dir_end_path = dirpath.split('/')[-1]
# savepath = dirpath.split('shear_catalogues')[0]+'map_cubed_1_to_8_arcmin'+dirpath.split('shear_catalogues')[1]
# print('Reading shear catalogues from ',dirpath)
# print('Writing summary statistics to ',savepath)
# if not os.path.exists(savepath):
# os.makedirs(savepath)
# compute_all_aperture_masses(dirpath+'/',filenames,savepath+'/',aperture_masses = [1,2,4,8],n_processes=16)
# for (dirpath,_,_filenames) in os.walk(startpath+"shear_catalogues/"):
# if(len(_filenames)>2):
# filenames = [filename for filename in _filenames if '.fits' in filename]
# # dir_end_path = dirpath.split('/')[-1]
# savepath = dirpath.split('shear_catalogues')[0]+'map_cubed_lower_resolution_intermediate_thetas'+dirpath.split('shear_catalogues')[1]
# print('Reading shear catalogues from ',dirpath)
# print('Writing summary statistics to ',savepath)
# if not os.path.exists(savepath):
# os.makedirs(savepath)
# compute_all_aperture_masses(dirpath+'/',filenames,savepath+'/',aperture_masses = [1.085,1.085*2,1.085*4,1.085*8],n_processes=32)
|
sheydenreichREPO_NAMEthreepointPATH_START.@threepoint_extracted@threepoint-main@python_scripts@old@compute_second_order_aperture_mass_correlations_HOWLS.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "toros-astro/corral",
"repo_path": "corral_extracted/corral-master/corral/template/template/__init__.py",
"type": "Python"
}
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Created at ${timestamp} by corral ${version}
# =============================================================================
# DOCS
# =============================================================================
"""${project_name} a corral pipeline
"""
|
toros-astroREPO_NAMEcorralPATH_START.@corral_extracted@corral-master@corral@template@template@__init__.py@.PATH_END.py
|
{
"filename": "rfi_flagging.py",
"repo_name": "TianlaiProject/tlpipe",
"repo_path": "tlpipe_extracted/tlpipe-master/tlpipe/timestream/rfi_flagging.py",
"type": "Python"
}
|
"""RFI flagging.
Inheritance diagram
-------------------
.. inheritance-diagram:: Flag
:parts: 2
"""
import numpy as np
import timestream_task
from tlpipe.container.raw_timestream import RawTimestream
from tlpipe.container.timestream import Timestream
from tlpipe.rfi import interpolate
from tlpipe.rfi import gaussian_filter
from tlpipe.rfi import sum_threshold
class Flag(timestream_task.TimestreamTask):
"""RFI flagging.
RFI flagging by using the SumThreshold method.
"""
params_init = {
'first_threshold': 6.0,
'exp_factor': 1.5,
'distribution': 'Rayleigh',
'max_threshold_len': 1024,
'sensitivity': 1.0,
'min_connected': 1,
'flag_direction': ('time', 'freq'),
'tk_size': 1.0, # 128.0 for dish
'fk_size': 3.0, # 2.0 for dish
'threshold_num': 2, # number of threshold
}
prefix = 'rf_'
def process(self, ts):
ts.redistribute('baseline')
if isinstance(ts, RawTimestream):
func = ts.bl_data_operate
elif isinstance(ts, Timestream):
func = ts.pol_and_bl_data_operate
show_progress = self.params['show_progress']
progress_step = self.params['progress_step']
func(self.flag, full_data=True, show_progress=show_progress, progress_step=progress_step, keep_dist_axis=False)
return super(Flag, self).process(ts)
def flag(self, vis, vis_mask, li, gi, tf, ts, **kwargs):
"""Function that does the actual flag."""
# if all have been masked, no need to flag again
if vis_mask.all():
return
first_threshold = self.params['first_threshold']
exp_factor = self.params['exp_factor']
distribution = self.params['distribution']
max_threshold_len = self.params['max_threshold_len']
sensitivity = self.params['sensitivity']
min_connected = self.params['min_connected']
flag_direction = self.params['flag_direction']
tk_size = self.params['tk_size']
fk_size = self.params['fk_size']
threshold_num = max(0, int(self.params['threshold_num']))
vis_abs = np.abs(vis) # operate only on the amplitude
# first round
# first complete masked vals due to ns by interpolate
itp = interpolate.Interpolate(vis_abs, vis_mask)
background = itp.fit()
# Gaussian fileter
gf = gaussian_filter.GaussianFilter(background, time_kernal_size=tk_size, freq_kernal_size=fk_size, filter_direction=flag_direction)
background = gf.fit()
# sum-threshold
vis_diff = vis_abs - background
# an initial run of N = 1 only to remove extremely high amplitude RFI
st = sum_threshold.SumThreshold(vis_diff, vis_mask, first_threshold, exp_factor, distribution, 1, min_connected)
st.execute(sensitivity, flag_direction)
# if all have been masked, no need to flag again
if st.vis_mask.all():
vis_mask[:] = st.vis_mask
return
# next rounds
for i in xrange(threshold_num):
# Gaussian fileter
gf = gaussian_filter.GaussianFilter(vis_diff, st.vis_mask, time_kernal_size=tk_size, freq_kernal_size=fk_size, filter_direction=flag_direction)
background = gf.fit()
# sum-threshold
vis_diff = vis_diff - background
st = sum_threshold.SumThreshold(vis_diff, st.vis_mask, first_threshold, exp_factor, distribution, max_threshold_len, min_connected)
st.execute(sensitivity, flag_direction)
# if all have been masked, no need to flag again
if st.vis_mask.all():
break
# replace vis_mask with the flagged mask
vis_mask[:] = st.vis_mask
|
TianlaiProjectREPO_NAMEtlpipePATH_START.@tlpipe_extracted@tlpipe-master@tlpipe@timestream@rfi_flagging.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "ARCLab-MIT/kspdg",
"repo_path": "kspdg_extracted/kspdg-main/src/kspdg/agent_api/README.md",
"type": "Markdown"
}
|
# Agent API
This subpackage provides an API, or base classes, for defining differential game agents and running those agents within specific KSPDG environments
|
ARCLab-MITREPO_NAMEkspdgPATH_START.@kspdg_extracted@kspdg-main@src@kspdg@agent_api@README.md@.PATH_END.py
|
{
"filename": "test_normalize.py",
"repo_name": "grahambell/pymoc",
"repo_path": "pymoc_extracted/pymoc-main/test/test_normalize.py",
"type": "Python"
}
|
# Copyright (C) 2014 Science and Technology Facilities Council.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from unittest import TestCase
from pymoc import MOC
class NormalizeTestCase(TestCase):
def test_aggregate(self):
m = MOC(10, set([0, 1, 2, 3, 4]))
self.assertEqual(m.order, 10)
self.assertEqual(m[10], frozenset([0, 1, 2, 3, 4]))
self.assertEqual(m[9], frozenset())
m.normalize()
self.assertEqual(m.order, 10)
self.assertEqual(m[10], frozenset([4]))
self.assertEqual(m[9], frozenset([0]))
def test_included(self):
m = MOC(10, set([0, 1, 2, 3, 4]))
m.add(8, set([0]))
self.assertEqual(m.order, 10)
self.assertEqual(m[8], frozenset([0]))
self.assertEqual(m[9], frozenset())
self.assertEqual(m[10], frozenset([0, 1, 2, 3, 4]))
m.normalize()
self.assertEqual(m.order, 8)
self.assertEqual(m[8], frozenset([0]))
self.assertEqual(m[9], frozenset())
self.assertEqual(m[10], frozenset())
|
grahambellREPO_NAMEpymocPATH_START.@pymoc_extracted@pymoc-main@test@test_normalize.py@.PATH_END.py
|
{
"filename": "_dtickrange.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/treemap/marker/colorbar/tickformatstop/_dtickrange.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class DtickrangeValidator(_plotly_utils.basevalidators.InfoArrayValidator):
def __init__(
self,
plotly_name="dtickrange",
parent_name="treemap.marker.colorbar.tickformatstop",
**kwargs,
):
super(DtickrangeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
items=kwargs.pop(
"items",
[
{"editType": "colorbars", "valType": "any"},
{"editType": "colorbars", "valType": "any"},
],
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@treemap@marker@colorbar@tickformatstop@_dtickrange.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "Zafiirah13/FRBID",
"repo_path": "FRBID_extracted/FRBID-master/FRBID_code/setup.py",
"type": "Python"
}
|
from setuptools import setup, find_packages
setup(name = 'FRBID',
version = '1.0.0',
description = 'Fast Radio Burst Intelligent Distinguisher using Deep Learning',
author = 'Zafiirah Hosenie',
author_email = 'zafiirah.hosenie@gmail.com',
license = 'MIT',
url = 'https://github.com/Zafiirah13/FRBID',
packages = find_packages(),
install_requires=['pandas==0.24.1',
'numpy==1.14.6',
'tensorflow-gpu==1.9.0',
'imbalanced_learn==0.4.3',
'matplotlib==3.0.3',
'scipy==1.2.1',
'Keras==2.0.9',
'imblearn==0.0',
'Pillow==7.1.2',
'scikit_learn==0.23.1'
],
classifiers=[
'Programming Language :: Python :: 3.6',
],
keywords=['Convolutional Neural Network', 'Deep Learning', 'FRB and RFI', 'MeerKAT'],
include_package_data = True)
|
Zafiirah13REPO_NAMEFRBIDPATH_START.@FRBID_extracted@FRBID-master@FRBID_code@setup.py@.PATH_END.py
|
{
"filename": "map_funcs.py",
"repo_name": "ebaxter/CMBClusterLens",
"repo_path": "CMBClusterLens_extracted/CMBClusterLens-main/map_funcs.py",
"type": "Python"
}
|
import numpy as np
def get_theta_maps(map_settings):
"""Get theta maps in radians."""
N_pix = map_settings['N_pix']
pix_size_arcmin = map_settings['pix_size_arcmin']
x_row = (np.arange(N_pix) - 0.5*(N_pix-1.))*pix_size_arcmin + map_settings['map_center_x_arcmin']
y_row = (np.arange(N_pix) - 0.5*(N_pix-1.))*pix_size_arcmin + map_settings['map_center_y_arcmin']
x_map = np.outer(np.ones(N_pix), x_row)
y_map = np.outer(y_row, np.ones(N_pix))
#Convert to radians
return x_map*np.pi/180./60., y_map*np.pi/180./60.
#WARNING: this is extremely slow and memory intensive for large maps
def get_angsep_mat(theta_x_mat, theta_y_mat, dx_mat, dy_mat):
N_pix = len(theta_x_mat)
#Get angular separation between all pairs of pixels as in the absence of lensing deflection
theta_x_mat_undolens = theta_x_mat + dx_mat
theta_y_mat_undolens = theta_y_mat + dy_mat
x1 = np.tile(theta_x_mat_undolens.flatten(),(N_pix**2,1))
y1 = np.tile(theta_y_mat_undolens.flatten(),(N_pix**2,1))
angsep_mat = np.sqrt((x1 - x1.transpose())**2. + (y1-y1.transpose())**2.)
return angsep_mat
def get_lxly(map_settings):
N_pix = map_settings['N_pix']
dx = map_settings['pix_size_arcmin']/60.0*np.pi/180.0
lx, ly = np.meshgrid( np.fft.fftfreq( N_pix, dx )*2.*np.pi,
np.fft.fftfreq( N_pix, dx )*2.*np.pi)
return lx, ly
if __name__ == '__main__':
print("runing map funcs")
|
ebaxterREPO_NAMECMBClusterLensPATH_START.@CMBClusterLens_extracted@CMBClusterLens-main@map_funcs.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/layout/yaxis/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._autorangeoptions import Autorangeoptions
from ._minor import Minor
from ._rangebreak import Rangebreak
from ._tickfont import Tickfont
from ._tickformatstop import Tickformatstop
from ._title import Title
from . import title
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[".title"],
[
"._autorangeoptions.Autorangeoptions",
"._minor.Minor",
"._rangebreak.Rangebreak",
"._tickfont.Tickfont",
"._tickformatstop.Tickformatstop",
"._title.Title",
],
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@layout@yaxis@__init__.py@.PATH_END.py
|
{
"filename": "index.md",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/docs/docs/integrations/providers/portkey/index.md",
"type": "Markdown"
}
|
# Portkey
[Portkey](https://portkey.ai) is the Control Panel for AI apps. With it's popular AI Gateway and Observability Suite, hundreds of teams ship **reliable**, **cost-efficient**, and **fast** apps.
## LLMOps for Langchain
Portkey brings production readiness to Langchain. With Portkey, you can
- [x] Connect to 150+ models through a unified API,
- [x] View 42+ **metrics & logs** for all requests,
- [x] Enable **semantic cache** to reduce latency & costs,
- [x] Implement automatic **retries & fallbacks** for failed requests,
- [x] Add **custom tags** to requests for better tracking and analysis and [more](https://portkey.ai/docs).
## Quickstart - Portkey & Langchain
Since Portkey is fully compatible with the OpenAI signature, you can connect to the Portkey AI Gateway through the `ChatOpenAI` interface.
- Set the `base_url` as `PORTKEY_GATEWAY_URL`
- Add `default_headers` to consume the headers needed by Portkey using the `createHeaders` helper method.
To start, get your Portkey API key by [signing up here](https://app.portkey.ai/signup). (Click the profile icon on the bottom left, then click on "Copy API Key") or deploy the open source AI gateway in [your own environment](https://github.com/Portkey-AI/gateway/blob/main/docs/installation-deployments.md).
Next, install the Portkey SDK
```python
pip install -U portkey_ai
```
We can now connect to the Portkey AI Gateway by updating the `ChatOpenAI` model in Langchain
```python
from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
PORTKEY_API_KEY = "..." # Not needed when hosting your own gateway
PROVIDER_API_KEY = "..." # Add the API key of the AI provider being used
portkey_headers = createHeaders(api_key=PORTKEY_API_KEY,provider="openai")
llm = ChatOpenAI(api_key=PROVIDER_API_KEY, base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers)
llm.invoke("What is the meaning of life, universe and everything?")
```
The request is routed through your Portkey AI Gateway to the specified `provider`. Portkey will also start logging all the requests in your account that makes debugging extremely simple.
## Using 150+ models through the AI Gateway
The power of the AI gateway comes when you're able to use the above code snippet to connect with 150+ models across 20+ providers supported through the AI gateway.
Let's modify the code above to make a call to Anthropic's `claude-3-opus-20240229` model.
Portkey supports **[Virtual Keys](https://docs.portkey.ai/docs/product/ai-gateway-streamline-llm-integrations/virtual-keys)** which are an easy way to store and manage API keys in a secure vault. Lets try using a Virtual Key to make LLM calls. You can navigate to the Virtual Keys tab in Portkey and create a new key for Anthropic.
The `virtual_key` parameter sets the authentication and provider for the AI provider being used. In our case we're using the Anthropic Virtual key.
> Notice that the `api_key` can be left blank as that authentication won't be used.
```python
from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
PORTKEY_API_KEY = "..."
VIRTUAL_KEY = "..." # Anthropic's virtual key we copied above
portkey_headers = createHeaders(api_key=PORTKEY_API_KEY,virtual_key=VIRTUAL_KEY)
llm = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers, model="claude-3-opus-20240229")
llm.invoke("What is the meaning of life, universe and everything?")
```
The Portkey AI gateway will authenticate the API request to Anthropic and get the response back in the OpenAI format for you to consume.
The AI gateway extends Langchain's `ChatOpenAI` class making it a single interface to call any provider and any model.
## Advanced Routing - Load Balancing, Fallbacks, Retries
The Portkey AI Gateway brings capabilities like load-balancing, fallbacks, experimentation and canary testing to Langchain through a configuration-first approach.
Let's take an **example** where we might want to split traffic between `gpt-4` and `claude-opus` 50:50 to test the two large models. The gateway configuration for this would look like the following:
```python
config = {
"strategy": {
"mode": "loadbalance"
},
"targets": [{
"virtual_key": "openai-25654", # OpenAI's virtual key
"override_params": {"model": "gpt4"},
"weight": 0.5
}, {
"virtual_key": "anthropic-25654", # Anthropic's virtual key
"override_params": {"model": "claude-3-opus-20240229"},
"weight": 0.5
}]
}
```
We can then use this config in our requests being made from langchain.
```python
portkey_headers = createHeaders(
api_key=PORTKEY_API_KEY,
config=config
)
llm = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers)
llm.invoke("What is the meaning of life, universe and everything?")
```
When the LLM is invoked, Portkey will distribute the requests to `gpt-4` and `claude-3-opus-20240229` in the ratio of the defined weights.
You can find more config examples [here](https://docs.portkey.ai/docs/api-reference/config-object#examples).
## **Tracing Chains & Agents**
Portkey's Langchain integration gives you full visibility into the running of an agent. Let's take an example of a [popular agentic workflow](https://python.langchain.com/docs/use_cases/tool_use/quickstart/#agents).
We only need to modify the `ChatOpenAI` class to use the AI Gateway as above.
```python
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders
prompt = hub.pull("hwchase17/openai-tools-agent")
portkey_headers = createHeaders(
api_key=PORTKEY_API_KEY,
virtual_key=OPENAI_VIRTUAL_KEY,
trace_id="uuid-uuid-uuid-uuid"
)
@tool
def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
return first_int * second_int
@tool
def exponentiate(base: int, exponent: int) -> int:
"Exponentiate the base to the exponent power."
return base**exponent
tools = [multiply, exponentiate]
model = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers, temperature=0)
# Construct the OpenAI Tools agent
agent = create_openai_tools_agent(model, tools, prompt)
# Create an agent executor by passing in the agent and tools
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({
"input": "Take 3 to the fifth power and multiply that by thirty six, then square the result"
})
```
**You can see the requests' logs along with the trace id on Portkey dashboard:**
Additional Docs are available here:
- Observability - https://portkey.ai/docs/product/observability-modern-monitoring-for-llms
- AI Gateway - https://portkey.ai/docs/product/ai-gateway-streamline-llm-integrations
- Prompt Library - https://portkey.ai/docs/product/prompt-library
You can check out our popular Open Source AI Gateway here - https://github.com/portkey-ai/gateway
For detailed information on each feature and how to use it, [please refer to the Portkey docs](https://portkey.ai/docs). If you have any questions or need further assistance, [reach out to us on Twitter.](https://twitter.com/portkeyai) or our [support email](mailto:hello@portkey.ai).
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@docs@docs@integrations@providers@portkey@index.md@.PATH_END.py
|
{
"filename": "setup_package.py",
"repo_name": "astropy/astroquery",
"repo_path": "astroquery_extracted/astroquery-main/astroquery/exoplanet_orbit_database/setup_package.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
def get_package_data():
paths_core = [os.path.join('data', '*.json'),
os.path.join('tests', 'data', '*.csv')]
return {'astroquery.exoplanet_orbit_database': paths_core}
|
astropyREPO_NAMEastroqueryPATH_START.@astroquery_extracted@astroquery-main@astroquery@exoplanet_orbit_database@setup_package.py@.PATH_END.py
|
{
"filename": "MFieldCosmology.py",
"repo_name": "ja-vazquez/SimpleMC",
"repo_path": "SimpleMC_extracted/SimpleMC-master/simplemc/models/MFieldCosmology.py",
"type": "Python"
}
|
## This is Multi field cosmology
#Using the dynamical system approach
import numpy as np
from simplemc.models.LCDMCosmology import LCDMCosmology
from scipy.interpolate import interp1d
from scipy.integrate import odeint
from scipy.optimize import newton
import sys
class MFieldCosmology(LCDMCosmology):
def __init__(self):
"""Let's test with one field"""
self.lna = np.linspace(-6, 0, 500)
self.z = np.exp(-self.lna) - 1.
self.zvals = np.linspace(0, 5, 200)
self.phiini = 2.5
self.psiini = 1.5
LCDMCosmology.__init__(self, mnu=0)
self.updateParams([])
## my free parameters. We add Ok on top of LCDM ones (we inherit LCDM)
def freeParameters(self):
l=LCDMCosmology.freeParameters(self)
return l
def updateParams(self,pars):
ok=LCDMCosmology.updateParams(self, pars)
if not ok:
return False
self.set_ini()
return True
def dphilnV(self, phi, psi):
return 3./phi
def dpsilnV(self, phi, psi):
return 2./psi
def RHS(self, x_vec , lna):
xphi, xpsi, phi, psi, y = x_vec
fac = np.sqrt(1.5)
Pi = -1.5*(1 + xphi**2 - xpsi**2 - y**2 )
xphi_prime = -xphi*(3 + Pi) - fac*y**2*self.dphilnV(phi, psi)
xpsi_prime = -xpsi*(3 + Pi) + fac*y**2*self.dpsilnV(phi, psi)
phi_prime = np.sqrt(6)*xphi
psi_prime = np.sqrt(6)*xpsi
y_prime = -Pi*y + fac*xphi*y*self.dphilnV(phi, psi) + fac*xpsi*y*self.dpsilnV(phi, psi)
return [xphi_prime, xpsi_prime, phi_prime, psi_prime, y_prime]
def solver(self, ini_y):
ini_xphi = 0.01
ini_xpsi = 0.5
ini_phi = self.phiini
ini_psi = self.psiini
y0 = [ini_xphi, ini_xpsi, ini_phi, ini_psi, 10**(-ini_y)]
y_result = odeint(self.RHS, y0, self.lna, h0=1E-5)
return y_result
def logatoz(self, func):
#change functions from lna to z
tmp = interp1d(self.lna, func)
functmp = tmp(self.lna)
return np.interp(self.zvals, self.z[::-1], functmp[::-1])
#shooting to find the initial condition
def rfunc(self, ini_y):
sol = self.solver(ini_y).T
Ophi = (sol[0][-1]**2 - sol[1][-1]**2 + sol[4][-1]**2)
return (1.0-self.Om) - Ophi
def set_ini(self):
try:
y0 = newton(self.rfunc, 4) #7.5
x_vec = self.solver(y0).T
self.do = 1
self.hub_SF = interp1d(self.lna, x_vec[3])
#useful only for plotting purposes
#self.hub_SF_z = self.logatoz(x_vec[3])
pe = x_vec[0]**2 - x_vec[1]**2 - x_vec[4]**2
rho = x_vec[0]**2 - x_vec[1]**2 + x_vec[4]**2
self.w_eos = interp1d(self.lna, pe/rho)
self.Ophi = interp1d(self.lna, rho)
#self.phi = interp1d(self.lna, x_vec[2])
except RuntimeError:
print('--'*10)
print('not working')
print('--'*10)
self.do = 2
self.w_eos = interp1d(self.lna, -1+np.zeros(len(self.lna)))
#self.phi = interp1d(self.lna, np.zeros(len(self.lna)))
def hubble(self, a):
#NuContrib = self.NuDensity.rho(a)/self.h**2
return (self.Ocb/a**3 + self.Ok/a**2 + self.Omrad/a**4 + (1.0-self.Om-self.Ok))
## this is relative hsquared as a function of a
## i.e. H(z)^2/H(z=0)^2
def RHSquared_a(self,a):
lna = np.log(a)
if self.do==2:
return self.Ocb/a**3
else:
if (lna > self.lna[0]):
hubble = (self.hub_SF(lna)/100./self.h)**2.
else:
hubble = self.hubble(a)
return hubble
def w_de(self, a):
lna = np.log(a)
return self.w_eos(lna)
def phi_de(self, a):
lna = np.log(a)
return self.phi(lna)
def Omegaphi(self, a):
lna = np.log(a)
return self.Ophi(lna)
def Omegak(self, lna):
return self.Oka(lna)
if __name__=='__main__':
import matplotlib.pyplot as plt
import numpy as np
zl = np.arange(0, 6.0, 0.05)
lna = np.linspace(-6, 0, 500)
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'hspace': 0, 'height_ratios': [1, 1]}, figsize=(7,7))
#plt.suptitle('$V=V_0\phi^2$, $A=%d$, $\phi_{ini}=%.1f$'%(a, c) , y=0.95, fontsize=15)
#for i in np.linspace(2.0 ,2.2, 5):
M = MFieldCosmology()
y1=[M.w_de(1./(1+z)) for z in zl]
y2=[M.Omegaphi(1./(1+z)) for z in zl]
ax1.plot(zl, y1)
ax2.plot(zl, y2)
#ax1.set_ylabel('$w(z)$', fontsize=20, labelpad = -5)
ax1.grid()
#ax1.legend(loc='best')
#ax1.axhline(-1, color='k')
#for i in np.linspace(2.0 ,2.2, 5):
# M = MFieldCosmology(a=a, b=i, c=c)
# y2=[M.phi_de(1./(1+z)) for z in zl]
# ax2.plot(zl, y2)
#ax2.set_ylabel('$\phi(z)$', fontsize=20, labelpad = -5)
#ax2.set_xlabel("$z$", fontsize=20)
ax2.grid()
plt.tight_layout()
#plt.savefig('tanh_field_2.pdf')
plt.show()
|
ja-vazquezREPO_NAMESimpleMCPATH_START.@SimpleMC_extracted@SimpleMC-master@simplemc@models@MFieldCosmology.py@.PATH_END.py
|
{
"filename": "_style.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/shape/legendgrouptitle/font/_style.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class StyleValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="style",
parent_name="layout.shape.legendgrouptitle.font",
**kwargs,
):
super(StyleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc+arraydraw"),
values=kwargs.pop("values", ["normal", "italic"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@shape@legendgrouptitle@font@_style.py@.PATH_END.py
|
{
"filename": "demo.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/ipython/py2/IPython/lib/demo.py",
"type": "Python"
}
|
"""Module for interactive demos using IPython.
This module implements a few classes for running Python scripts interactively
in IPython for demonstrations. With very simple markup (a few tags in
comments), you can control points where the script stops executing and returns
control to IPython.
Provided classes
----------------
The classes are (see their docstrings for further details):
- Demo: pure python demos
- IPythonDemo: demos with input to be processed by IPython as if it had been
typed interactively (so magics work, as well as any other special syntax you
may have added via input prefilters).
- LineDemo: single-line version of the Demo class. These demos are executed
one line at a time, and require no markup.
- IPythonLineDemo: IPython version of the LineDemo class (the demo is
executed a line at a time, but processed via IPython).
- ClearMixin: mixin to make Demo classes with less visual clutter. It
declares an empty marquee and a pre_cmd that clears the screen before each
block (see Subclassing below).
- ClearDemo, ClearIPDemo: mixin-enabled versions of the Demo and IPythonDemo
classes.
Inheritance diagram:
.. inheritance-diagram:: IPython.lib.demo
:parts: 3
Subclassing
-----------
The classes here all include a few methods meant to make customization by
subclassing more convenient. Their docstrings below have some more details:
- marquee(): generates a marquee to provide visible on-screen markers at each
block start and end.
- pre_cmd(): run right before the execution of each block.
- post_cmd(): run right after the execution of each block. If the block
raises an exception, this is NOT called.
Operation
---------
The file is run in its own empty namespace (though you can pass it a string of
arguments as if in a command line environment, and it will see those as
sys.argv). But at each stop, the global IPython namespace is updated with the
current internal demo namespace, so you can work interactively with the data
accumulated so far.
By default, each block of code is printed (with syntax highlighting) before
executing it and you have to confirm execution. This is intended to show the
code to an audience first so you can discuss it, and only proceed with
execution once you agree. There are a few tags which allow you to modify this
behavior.
The supported tags are:
# <demo> stop
Defines block boundaries, the points where IPython stops execution of the
file and returns to the interactive prompt.
You can optionally mark the stop tag with extra dashes before and after the
word 'stop', to help visually distinguish the blocks in a text editor:
# <demo> --- stop ---
# <demo> silent
Make a block execute silently (and hence automatically). Typically used in
cases where you have some boilerplate or initialization code which you need
executed but do not want to be seen in the demo.
# <demo> auto
Make a block execute automatically, but still being printed. Useful for
simple code which does not warrant discussion, since it avoids the extra
manual confirmation.
# <demo> auto_all
This tag can _only_ be in the first block, and if given it overrides the
individual auto tags to make the whole demo fully automatic (no block asks
for confirmation). It can also be given at creation time (or the attribute
set later) to override what's in the file.
While _any_ python file can be run as a Demo instance, if there are no stop
tags the whole file will run in a single block (no different that calling
first %pycat and then %run). The minimal markup to make this useful is to
place a set of stop tags; the other tags are only there to let you fine-tune
the execution.
This is probably best explained with the simple example file below. You can
copy this into a file named ex_demo.py, and try running it via::
from IPython.lib.demo import Demo
d = Demo('ex_demo.py')
d()
Each time you call the demo object, it runs the next block. The demo object
has a few useful methods for navigation, like again(), edit(), jump(), seek()
and back(). It can be reset for a new run via reset() or reloaded from disk
(in case you've edited the source) via reload(). See their docstrings below.
Note: To make this simpler to explore, a file called "demo-exercizer.py" has
been added to the "docs/examples/core" directory. Just cd to this directory in
an IPython session, and type::
%run demo-exercizer.py
and then follow the directions.
Example
-------
The following is a very simple example of a valid demo file.
::
#################### EXAMPLE DEMO <ex_demo.py> ###############################
'''A simple interactive demo to illustrate the use of IPython's Demo class.'''
print 'Hello, welcome to an interactive IPython demo.'
# The mark below defines a block boundary, which is a point where IPython will
# stop execution and return to the interactive prompt. The dashes are actually
# optional and used only as a visual aid to clearly separate blocks while
# editing the demo code.
# <demo> stop
x = 1
y = 2
# <demo> stop
# the mark below makes this block as silent
# <demo> silent
print 'This is a silent block, which gets executed but not printed.'
# <demo> stop
# <demo> auto
print 'This is an automatic block.'
print 'It is executed without asking for confirmation, but printed.'
z = x+y
print 'z=',x
# <demo> stop
# This is just another normal block.
print 'z is now:', z
print 'bye!'
################### END EXAMPLE DEMO <ex_demo.py> ############################
"""
from __future__ import unicode_literals
#*****************************************************************************
# Copyright (C) 2005-2006 Fernando Perez. <Fernando.Perez@colorado.edu>
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#
#*****************************************************************************
from __future__ import print_function
import os
import re
import shlex
import sys
from IPython.utils import io
from IPython.utils.text import marquee
from IPython.utils import openpy
from IPython.utils import py3compat
__all__ = ['Demo','IPythonDemo','LineDemo','IPythonLineDemo','DemoError']
class DemoError(Exception): pass
def re_mark(mark):
return re.compile(r'^\s*#\s+<demo>\s+%s\s*$' % mark,re.MULTILINE)
class Demo(object):
re_stop = re_mark('-*\s?stop\s?-*')
re_silent = re_mark('silent')
re_auto = re_mark('auto')
re_auto_all = re_mark('auto_all')
def __init__(self,src,title='',arg_str='',auto_all=None):
"""Make a new demo object. To run the demo, simply call the object.
See the module docstring for full details and an example (you can use
IPython.Demo? in IPython to see it).
Inputs:
- src is either a file, or file-like object, or a
string that can be resolved to a filename.
Optional inputs:
- title: a string to use as the demo name. Of most use when the demo
you are making comes from an object that has no filename, or if you
want an alternate denotation distinct from the filename.
- arg_str(''): a string of arguments, internally converted to a list
just like sys.argv, so the demo script can see a similar
environment.
- auto_all(None): global flag to run all blocks automatically without
confirmation. This attribute overrides the block-level tags and
applies to the whole demo. It is an attribute of the object, and
can be changed at runtime simply by reassigning it to a boolean
value.
"""
if hasattr(src, "read"):
# It seems to be a file or a file-like object
self.fname = "from a file-like object"
if title == '':
self.title = "from a file-like object"
else:
self.title = title
else:
# Assume it's a string or something that can be converted to one
self.fname = src
if title == '':
(filepath, filename) = os.path.split(src)
self.title = filename
else:
self.title = title
self.sys_argv = [src] + shlex.split(arg_str)
self.auto_all = auto_all
self.src = src
# get a few things from ipython. While it's a bit ugly design-wise,
# it ensures that things like color scheme and the like are always in
# sync with the ipython mode being used. This class is only meant to
# be used inside ipython anyways, so it's OK.
ip = get_ipython() # this is in builtins whenever IPython is running
self.ip_ns = ip.user_ns
self.ip_colorize = ip.pycolorize
self.ip_showtb = ip.showtraceback
self.ip_run_cell = ip.run_cell
self.shell = ip
# load user data and initialize data structures
self.reload()
def fload(self):
"""Load file object."""
# read data and parse into blocks
if hasattr(self, 'fobj') and self.fobj is not None:
self.fobj.close()
if hasattr(self.src, "read"):
# It seems to be a file or a file-like object
self.fobj = self.src
else:
# Assume it's a string or something that can be converted to one
self.fobj = openpy.open(self.fname)
def reload(self):
"""Reload source from disk and initialize state."""
self.fload()
self.src = "".join(openpy.strip_encoding_cookie(self.fobj))
src_b = [b.strip() for b in self.re_stop.split(self.src) if b]
self._silent = [bool(self.re_silent.findall(b)) for b in src_b]
self._auto = [bool(self.re_auto.findall(b)) for b in src_b]
# if auto_all is not given (def. None), we read it from the file
if self.auto_all is None:
self.auto_all = bool(self.re_auto_all.findall(src_b[0]))
else:
self.auto_all = bool(self.auto_all)
# Clean the sources from all markup so it doesn't get displayed when
# running the demo
src_blocks = []
auto_strip = lambda s: self.re_auto.sub('',s)
for i,b in enumerate(src_b):
if self._auto[i]:
src_blocks.append(auto_strip(b))
else:
src_blocks.append(b)
# remove the auto_all marker
src_blocks[0] = self.re_auto_all.sub('',src_blocks[0])
self.nblocks = len(src_blocks)
self.src_blocks = src_blocks
# also build syntax-highlighted source
self.src_blocks_colored = list(map(self.ip_colorize,self.src_blocks))
# ensure clean namespace and seek offset
self.reset()
def reset(self):
"""Reset the namespace and seek pointer to restart the demo"""
self.user_ns = {}
self.finished = False
self.block_index = 0
def _validate_index(self,index):
if index<0 or index>=self.nblocks:
raise ValueError('invalid block index %s' % index)
def _get_index(self,index):
"""Get the current block index, validating and checking status.
Returns None if the demo is finished"""
if index is None:
if self.finished:
print('Demo finished. Use <demo_name>.reset() if you want to rerun it.')
return None
index = self.block_index
else:
self._validate_index(index)
return index
def seek(self,index):
"""Move the current seek pointer to the given block.
You can use negative indices to seek from the end, with identical
semantics to those of Python lists."""
if index<0:
index = self.nblocks + index
self._validate_index(index)
self.block_index = index
self.finished = False
def back(self,num=1):
"""Move the seek pointer back num blocks (default is 1)."""
self.seek(self.block_index-num)
def jump(self,num=1):
"""Jump a given number of blocks relative to the current one.
The offset can be positive or negative, defaults to 1."""
self.seek(self.block_index+num)
def again(self):
"""Move the seek pointer back one block and re-execute."""
self.back(1)
self()
def edit(self,index=None):
"""Edit a block.
If no number is given, use the last block executed.
This edits the in-memory copy of the demo, it does NOT modify the
original source file. If you want to do that, simply open the file in
an editor and use reload() when you make changes to the file. This
method is meant to let you change a block during a demonstration for
explanatory purposes, without damaging your original script."""
index = self._get_index(index)
if index is None:
return
# decrease the index by one (unless we're at the very beginning), so
# that the default demo.edit() call opens up the sblock we've last run
if index>0:
index -= 1
filename = self.shell.mktempfile(self.src_blocks[index])
self.shell.hooks.editor(filename,1)
with open(filename, 'r') as f:
new_block = f.read()
# update the source and colored block
self.src_blocks[index] = new_block
self.src_blocks_colored[index] = self.ip_colorize(new_block)
self.block_index = index
# call to run with the newly edited index
self()
def show(self,index=None):
"""Show a single block on screen"""
index = self._get_index(index)
if index is None:
return
print(self.marquee('<%s> block # %s (%s remaining)' %
(self.title,index,self.nblocks-index-1)))
print(self.src_blocks_colored[index])
sys.stdout.flush()
def show_all(self):
"""Show entire demo on screen, block by block"""
fname = self.title
title = self.title
nblocks = self.nblocks
silent = self._silent
marquee = self.marquee
for index,block in enumerate(self.src_blocks_colored):
if silent[index]:
print(marquee('<%s> SILENT block # %s (%s remaining)' %
(title,index,nblocks-index-1)))
else:
print(marquee('<%s> block # %s (%s remaining)' %
(title,index,nblocks-index-1)))
print(block, end=' ')
sys.stdout.flush()
def run_cell(self,source):
"""Execute a string with one or more lines of code"""
exec(source, self.user_ns)
def __call__(self,index=None):
"""run a block of the demo.
If index is given, it should be an integer >=1 and <= nblocks. This
means that the calling convention is one off from typical Python
lists. The reason for the inconsistency is that the demo always
prints 'Block n/N, and N is the total, so it would be very odd to use
zero-indexing here."""
index = self._get_index(index)
if index is None:
return
try:
marquee = self.marquee
next_block = self.src_blocks[index]
self.block_index += 1
if self._silent[index]:
print(marquee('Executing silent block # %s (%s remaining)' %
(index,self.nblocks-index-1)))
else:
self.pre_cmd()
self.show(index)
if self.auto_all or self._auto[index]:
print(marquee('output:'))
else:
print(marquee('Press <q> to quit, <Enter> to execute...'), end=' ')
ans = py3compat.input().strip()
if ans:
print(marquee('Block NOT executed'))
return
try:
save_argv = sys.argv
sys.argv = self.sys_argv
self.run_cell(next_block)
self.post_cmd()
finally:
sys.argv = save_argv
except:
self.ip_showtb(filename=self.fname)
else:
self.ip_ns.update(self.user_ns)
if self.block_index == self.nblocks:
mq1 = self.marquee('END OF DEMO')
if mq1:
# avoid spurious print if empty marquees are used
print()
print(mq1)
print(self.marquee('Use <demo_name>.reset() if you want to rerun it.'))
self.finished = True
# These methods are meant to be overridden by subclasses who may wish to
# customize the behavior of of their demos.
def marquee(self,txt='',width=78,mark='*'):
"""Return the input string centered in a 'marquee'."""
return marquee(txt,width,mark)
def pre_cmd(self):
"""Method called before executing each block."""
pass
def post_cmd(self):
"""Method called after executing each block."""
pass
class IPythonDemo(Demo):
"""Class for interactive demos with IPython's input processing applied.
This subclasses Demo, but instead of executing each block by the Python
interpreter (via exec), it actually calls IPython on it, so that any input
filters which may be in place are applied to the input block.
If you have an interactive environment which exposes special input
processing, you can use this class instead to write demo scripts which
operate exactly as if you had typed them interactively. The default Demo
class requires the input to be valid, pure Python code.
"""
def run_cell(self,source):
"""Execute a string with one or more lines of code"""
self.shell.run_cell(source)
class LineDemo(Demo):
"""Demo where each line is executed as a separate block.
The input script should be valid Python code.
This class doesn't require any markup at all, and it's meant for simple
scripts (with no nesting or any kind of indentation) which consist of
multiple lines of input to be executed, one at a time, as if they had been
typed in the interactive prompt.
Note: the input can not have *any* indentation, which means that only
single-lines of input are accepted, not even function definitions are
valid."""
def reload(self):
"""Reload source from disk and initialize state."""
# read data and parse into blocks
self.fload()
lines = self.fobj.readlines()
src_b = [l for l in lines if l.strip()]
nblocks = len(src_b)
self.src = ''.join(lines)
self._silent = [False]*nblocks
self._auto = [True]*nblocks
self.auto_all = True
self.nblocks = nblocks
self.src_blocks = src_b
# also build syntax-highlighted source
self.src_blocks_colored = map(self.ip_colorize,self.src_blocks)
# ensure clean namespace and seek offset
self.reset()
class IPythonLineDemo(IPythonDemo,LineDemo):
"""Variant of the LineDemo class whose input is processed by IPython."""
pass
class ClearMixin(object):
"""Use this mixin to make Demo classes with less visual clutter.
Demos using this mixin will clear the screen before every block and use
blank marquees.
Note that in order for the methods defined here to actually override those
of the classes it's mixed with, it must go /first/ in the inheritance
tree. For example:
class ClearIPDemo(ClearMixin,IPythonDemo): pass
will provide an IPythonDemo class with the mixin's features.
"""
def marquee(self,txt='',width=78,mark='*'):
"""Blank marquee that returns '' no matter what the input."""
return ''
def pre_cmd(self):
"""Method called before executing each block.
This one simply clears the screen."""
from IPython.utils.terminal import term_clear
term_clear()
class ClearDemo(ClearMixin,Demo):
pass
class ClearIPDemo(ClearMixin,IPythonDemo):
pass
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@ipython@py2@IPython@lib@demo.py@.PATH_END.py
|
{
"filename": "_marker.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/funnelarea/_marker.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Marker(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "funnelarea"
_path_str = "funnelarea.marker"
_valid_props = {"colors", "colorssrc", "line", "pattern"}
# colors
# ------
@property
def colors(self):
"""
Sets the color of each sector. If not specified, the default
trace color set is used to pick the sector colors.
The 'colors' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["colors"]
@colors.setter
def colors(self, val):
self["colors"] = val
# colorssrc
# ---------
@property
def colorssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `colors`.
The 'colorssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["colorssrc"]
@colorssrc.setter
def colorssrc(self, val):
self["colorssrc"] = val
# line
# ----
@property
def line(self):
"""
The 'line' property is an instance of Line
that may be specified as:
- An instance of :class:`plotly.graph_objs.funnelarea.marker.Line`
- A dict of string/value properties that will be passed
to the Line constructor
Supported dict properties:
color
Sets the color of the line enclosing each
sector. Defaults to the `paper_bgcolor` value.
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
width
Sets the width (in px) of the line enclosing
each sector.
widthsrc
Sets the source reference on Chart Studio Cloud
for `width`.
Returns
-------
plotly.graph_objs.funnelarea.marker.Line
"""
return self["line"]
@line.setter
def line(self, val):
self["line"] = val
# pattern
# -------
@property
def pattern(self):
"""
Sets the pattern within the marker.
The 'pattern' property is an instance of Pattern
that may be specified as:
- An instance of :class:`plotly.graph_objs.funnelarea.marker.Pattern`
- A dict of string/value properties that will be passed
to the Pattern constructor
Supported dict properties:
bgcolor
When there is no colorscale sets the color of
background pattern fill. Defaults to a
`marker.color` background when `fillmode` is
"overlay". Otherwise, defaults to a transparent
background.
bgcolorsrc
Sets the source reference on Chart Studio Cloud
for `bgcolor`.
fgcolor
When there is no colorscale sets the color of
foreground pattern fill. Defaults to a
`marker.color` background when `fillmode` is
"replace". Otherwise, defaults to dark grey or
white to increase contrast with the `bgcolor`.
fgcolorsrc
Sets the source reference on Chart Studio Cloud
for `fgcolor`.
fgopacity
Sets the opacity of the foreground pattern
fill. Defaults to a 0.5 when `fillmode` is
"overlay". Otherwise, defaults to 1.
fillmode
Determines whether `marker.color` should be
used as a default to `bgcolor` or a `fgcolor`.
shape
Sets the shape of the pattern fill. By default,
no pattern is used for filling the area.
shapesrc
Sets the source reference on Chart Studio Cloud
for `shape`.
size
Sets the size of unit squares of the pattern
fill in pixels, which corresponds to the
interval of repetition of the pattern.
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
solidity
Sets the solidity of the pattern fill. Solidity
is roughly the fraction of the area filled by
the pattern. Solidity of 0 shows only the
background color without pattern and solidty of
1 shows only the foreground color without
pattern.
soliditysrc
Sets the source reference on Chart Studio Cloud
for `solidity`.
Returns
-------
plotly.graph_objs.funnelarea.marker.Pattern
"""
return self["pattern"]
@pattern.setter
def pattern(self, val):
self["pattern"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
colors
Sets the color of each sector. If not specified, the
default trace color set is used to pick the sector
colors.
colorssrc
Sets the source reference on Chart Studio Cloud for
`colors`.
line
:class:`plotly.graph_objects.funnelarea.marker.Line`
instance or dict with compatible properties
pattern
Sets the pattern within the marker.
"""
def __init__(
self, arg=None, colors=None, colorssrc=None, line=None, pattern=None, **kwargs
):
"""
Construct a new Marker object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.funnelarea.Marker`
colors
Sets the color of each sector. If not specified, the
default trace color set is used to pick the sector
colors.
colorssrc
Sets the source reference on Chart Studio Cloud for
`colors`.
line
:class:`plotly.graph_objects.funnelarea.marker.Line`
instance or dict with compatible properties
pattern
Sets the pattern within the marker.
Returns
-------
Marker
"""
super(Marker, self).__init__("marker")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.funnelarea.Marker
constructor must be a dict or
an instance of :class:`plotly.graph_objs.funnelarea.Marker`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("colors", None)
_v = colors if colors is not None else _v
if _v is not None:
self["colors"] = _v
_v = arg.pop("colorssrc", None)
_v = colorssrc if colorssrc is not None else _v
if _v is not None:
self["colorssrc"] = _v
_v = arg.pop("line", None)
_v = line if line is not None else _v
if _v is not None:
self["line"] = _v
_v = arg.pop("pattern", None)
_v = pattern if pattern is not None else _v
if _v is not None:
self["pattern"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@funnelarea@_marker.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/document_compressors/__init__.py",
"type": "Python"
}
|
import importlib
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from langchain_community.document_compressors.dashscope_rerank import (
DashScopeRerank,
)
from langchain_community.document_compressors.flashrank_rerank import (
FlashrankRerank,
)
from langchain_community.document_compressors.infinity_rerank import (
InfinityRerank,
)
from langchain_community.document_compressors.jina_rerank import (
JinaRerank,
)
from langchain_community.document_compressors.llmlingua_filter import (
LLMLinguaCompressor,
)
from langchain_community.document_compressors.openvino_rerank import (
OpenVINOReranker,
)
from langchain_community.document_compressors.rankllm_rerank import (
RankLLMRerank,
)
from langchain_community.document_compressors.volcengine_rerank import (
VolcengineRerank,
)
_module_lookup = {
"LLMLinguaCompressor": "langchain_community.document_compressors.llmlingua_filter",
"OpenVINOReranker": "langchain_community.document_compressors.openvino_rerank",
"JinaRerank": "langchain_community.document_compressors.jina_rerank",
"RankLLMRerank": "langchain_community.document_compressors.rankllm_rerank",
"FlashrankRerank": "langchain_community.document_compressors.flashrank_rerank",
"DashScopeRerank": "langchain_community.document_compressors.dashscope_rerank",
"VolcengineRerank": "langchain_community.document_compressors.volcengine_rerank",
"InfinityRerank": "langchain_community.document_compressors.infinity_rerank",
}
def __getattr__(name: str) -> Any:
if name in _module_lookup:
module = importlib.import_module(_module_lookup[name])
return getattr(module, name)
raise AttributeError(f"module {__name__} has no attribute {name}")
__all__ = [
"LLMLinguaCompressor",
"OpenVINOReranker",
"FlashrankRerank",
"JinaRerank",
"RankLLMRerank",
"DashScopeRerank",
"VolcengineRerank",
"InfinityRerank",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@document_compressors@__init__.py@.PATH_END.py
|
{
"filename": "_side.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/smith/realaxis/_side.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SideValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="side", parent_name="layout.smith.realaxis", **kwargs
):
super(SideValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
values=kwargs.pop("values", ["top", "bottom"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@smith@realaxis@_side.py@.PATH_END.py
|
{
"filename": "omniglot.py",
"repo_name": "pytorch/vision",
"repo_path": "vision_extracted/vision-main/torchvision/datasets/omniglot.py",
"type": "Python"
}
|
from os.path import join
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset
class Omniglot(VisionDataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory
``omniglot-py`` exists.
background (bool, optional): If True, creates dataset from the "background" set, otherwise
creates from the "evaluation" set. This terminology is defined by the authors.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset zip files from the internet and
puts it in root directory. If the zip files are already downloaded, they are not
downloaded again.
"""
folder = "omniglot-py"
download_url_prefix = "https://raw.githubusercontent.com/brendenlake/omniglot/master/python"
zips_md5 = {
"images_background": "68d2efa1b9178cc56df9314c21c6e718",
"images_evaluation": "6b91aef0f799c5bb55b94e3f2daec811",
}
def __init__(
self,
root: Union[str, Path],
background: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super().__init__(join(root, self.folder), transform=transform, target_transform=target_transform)
self.background = background
if download:
self.download()
if not self._check_integrity():
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
self.target_folder = join(self.root, self._get_target_folder())
self._alphabets = list_dir(self.target_folder)
self._characters: List[str] = sum(
([join(a, c) for c in list_dir(join(self.target_folder, a))] for a in self._alphabets), []
)
self._character_images = [
[(image, idx) for image in list_files(join(self.target_folder, character), ".png")]
for idx, character in enumerate(self._characters)
]
self._flat_character_images: List[Tuple[str, int]] = sum(self._character_images, [])
def __len__(self) -> int:
return len(self._flat_character_images)
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target character class.
"""
image_name, character_class = self._flat_character_images[index]
image_path = join(self.target_folder, self._characters[character_class], image_name)
image = Image.open(image_path, mode="r").convert("L")
if self.transform:
image = self.transform(image)
if self.target_transform:
character_class = self.target_transform(character_class)
return image, character_class
def _check_integrity(self) -> bool:
zip_filename = self._get_target_folder()
if not check_integrity(join(self.root, zip_filename + ".zip"), self.zips_md5[zip_filename]):
return False
return True
def download(self) -> None:
if self._check_integrity():
return
filename = self._get_target_folder()
zip_filename = filename + ".zip"
url = self.download_url_prefix + "/" + zip_filename
download_and_extract_archive(url, self.root, filename=zip_filename, md5=self.zips_md5[filename])
def _get_target_folder(self) -> str:
return "images_background" if self.background else "images_evaluation"
|
pytorchREPO_NAMEvisionPATH_START.@vision_extracted@vision-main@torchvision@datasets@omniglot.py@.PATH_END.py
|
{
"filename": "_cmax.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattercarpet/marker/_cmax.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CmaxValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="cmax", parent_name="scattercarpet.marker", **kwargs
):
super(CmaxValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
implied_edits=kwargs.pop("implied_edits", {"cauto": False}),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scattercarpet@marker@_cmax.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "dtamayo/reboundx",
"repo_path": "reboundx_extracted/reboundx-main/reboundx/test/__init__.py",
"type": "Python"
}
|
dtamayoREPO_NAMEreboundxPATH_START.@reboundx_extracted@reboundx-main@reboundx@test@__init__.py@.PATH_END.py
|
|
{
"filename": "_pi_k_k.py",
"repo_name": "LoganAMorrison/Hazma",
"repo_path": "Hazma_extracted/Hazma-master/hazma/form_factors/vector/_pi_k_k.py",
"type": "Python"
}
|
"""Module implementing the pi-k-k form factors."""
import abc
from dataclasses import InitVar, dataclass, field
from typing import overload
import numpy as np
from hazma.phase_space import PhaseSpaceDistribution1D
from hazma.utils import ComplexArray, ComplexOrComplexArray, RealArray, RealOrRealArray
from . import _utils
from ._three_body import Couplings, VectorFormFactorPPP
MK = _utils.MK_GEV * 1e3
MK0 = _utils.MK0_GEV * 1e3
MPI = _utils.MPI_GEV * 1e3
MPI0 = _utils.MPI0_GEV * 1e3
KS_MASS_GEV = 0.8956 # KStar mass
KS_WIDTH_GEV = 0.047 # KStar width
ISO_SCALAR_MASSES = np.array([1019.461e-3, 1633.4e-3, 1957e-3])
ISO_SCALAR_WIDTHS = np.array([4.249e-3, 218e-3, 267e-3])
ISO_SCALAR_AMPS = np.array([0.0, 0.233, 0.0405])
ISO_SCALAR_PHASES = np.array([0, 1.1e-07, 5.19]) # * np.pi / 180.0
ISO_VECTOR_MASSES = np.array([775.26e-3, 1470e-3, 1720e-3])
ISO_VECTOR_WIDTHS = np.array([149.1e-3, 400e-3, 250e-3])
ISO_VECTOR_AMPS = np.array([-2.34, 0.594, -0.0179])
ISO_VECTOR_PHASES = np.array([0, 0.317, 2.57]) # * np.pi / 180.0
@dataclass
class VectorFormFactorPiKKFitData: # pylint: disable=too-many-instance-attributes
r"""Storage class for the fit-data needed to compute pion-kaon-kaon
form-factors.
Attributes
----------
iso_scalar_masses: RealArray
VMD iso-scalar resonance masses.
iso_scalar_widths: RealArray
VMD iso-scalar resonance widths.
iso_scalar_amps: RealArray
VMD iso-scalar resonance amplitudes.
iso_scalar_phases: RealArray
VMD iso-scalar resonance phases.
iso_vector_masses: RealArray
VMD iso-vector resonance masses.
iso_vector_widths: RealArray
VMD iso-vector resonance widths.
iso_vector_amps: RealArray
VMD iso-vector resonance amplitudes.
iso_vector_phases: RealArray
VMD iso-vector resonance phases.
g_ks_k_pi: float
Coupling of K^*, K and pion.
"""
iso_scalar_masses: RealArray = field(repr=False)
iso_scalar_widths: RealArray = field(repr=False)
iso_scalar_amps: RealArray = field(repr=False)
iso_scalar_phases: RealArray = field(repr=False)
iso_vector_masses: RealArray = field(repr=False)
iso_vector_widths: RealArray = field(repr=False)
iso_vector_amps: RealArray = field(repr=False)
iso_vector_phases: RealArray = field(repr=False)
g_ks_k_pi: float = field(repr=False)
def iso_scalar_amplitudes(self):
"""Compute the iso-scalar amplitudes with phases included."""
return self.iso_scalar_amps * np.exp(1j * self.iso_scalar_phases)
def iso_vector_amplitudes(self):
"""Compute the iso-vector amplitudes with phases included."""
return self.iso_vector_amps * np.exp(1j * self.iso_vector_phases)
@dataclass
class _VectorFormFactorPiKKBase(VectorFormFactorPPP):
r"""Abstract base class for the pion-kaon-kaon vector form-factors.
Implements the common functions between the three different final states.
"""
fsp_masses: tuple[float, float, float] = field(init=False)
_fsp_masses: tuple[float, float, float] = field(init=False)
fit_data: VectorFormFactorPiKKFitData = field(init=False)
iso_scalar_masses: InitVar[RealArray] = field(default=ISO_SCALAR_MASSES)
iso_scalar_widths: InitVar[RealArray] = field(default=ISO_SCALAR_WIDTHS)
iso_scalar_amps: InitVar[RealArray] = field(default=ISO_SCALAR_AMPS)
iso_scalar_phases: InitVar[RealArray] = field(default=ISO_SCALAR_PHASES)
iso_vector_masses: InitVar[RealArray] = field(default=ISO_VECTOR_MASSES)
iso_vector_widths: InitVar[RealArray] = field(default=ISO_VECTOR_WIDTHS)
iso_vector_amps: InitVar[RealArray] = field(default=ISO_VECTOR_AMPS)
iso_vector_phases: InitVar[RealArray] = field(default=ISO_VECTOR_PHASES)
g_ks_k_pi: InitVar[float] = field(default=5.37392360229)
def __post_init__( # pylint: disable=too-many-arguments
self,
iso_scalar_masses: RealArray,
iso_scalar_widths: RealArray,
iso_scalar_amps: RealArray,
iso_scalar_phases: RealArray,
iso_vector_masses: RealArray,
iso_vector_widths: RealArray,
iso_vector_amps: RealArray,
iso_vector_phases: RealArray,
g_ks_k_pi: float,
):
self._fsp_masses = tuple(m * 1e-3 for m in self.fsp_masses)
self.fit_data = VectorFormFactorPiKKFitData(
iso_scalar_masses=iso_scalar_masses,
iso_scalar_widths=iso_scalar_widths,
iso_scalar_amps=iso_scalar_amps,
iso_scalar_phases=iso_scalar_phases,
iso_vector_masses=iso_vector_masses,
iso_vector_widths=iso_vector_widths,
iso_vector_amps=iso_vector_amps,
iso_vector_phases=iso_vector_phases,
g_ks_k_pi=g_ks_k_pi,
)
@abc.abstractmethod
def _form_factor(self, q, s, t, couplings: Couplings) -> float:
raise NotImplementedError()
def _iso_spin_amplitudes(
self, m: RealOrRealArray, couplings: Couplings
) -> tuple[ComplexOrComplexArray, ComplexOrComplexArray]:
r"""Compute the amplitude coefficients grouped in terms of iso-spin."""
ci1 = couplings[0] - couplings[1]
cs = -3 * couplings[2]
s = m**2
a0 = np.sum(
cs
* self.fit_data.iso_scalar_amplitudes()
* _utils.breit_wigner_fw(
s, self.fit_data.iso_scalar_masses, self.fit_data.iso_scalar_widths
)
)
a1 = np.sum(
ci1
* self.fit_data.iso_vector_amplitudes()
* _utils.breit_wigner_fw(
s, self.fit_data.iso_vector_masses, self.fit_data.iso_vector_widths
)
)
return (a0, a1) # type: ignore
@staticmethod
def _kstar_propagator(s, m1, m2):
r"""Returns the K^* energy-dependent propagator for a K^* transitioning
into two other particles.
Parameters
----------
s: float or array-like
Squared momentum of K^*.
m1, m2: float
Masses of the particles the K^* transitions into.
"""
return (
_utils.breit_wigner_pwave(s, KS_MASS_GEV, KS_WIDTH_GEV, m1, m2)
/ KS_MASS_GEV**2
)
@overload
def form_factor( # pylint: disable=arguments-differ
self, q: float, s: float, t: float, couplings: Couplings
) -> complex: ...
@overload
def form_factor( # pylint: disable=arguments-differ
self, q: float, s: RealArray, t: RealArray, couplings: Couplings
) -> ComplexArray: ...
def form_factor( # pylint: disable=arguments-differ
self, q, s: RealOrRealArray, t: RealOrRealArray, couplings: Couplings
) -> ComplexOrComplexArray:
r"""Compute the vector form-factor for a pion and two kaons.
Parameters
----------
q: float or array-like
Center-of-mass energy in MeV.
s: float or array-like
Squared invariant mass of the kaons s = (p2+p3)^2.
t: float or array-like
Squared invariant mass of the pion and last kaon t=(p1+p3)^2.
gvuu, gvdd, gvss: float
Couplings of vector to up-, down- and strange-quarks.
Returns
-------
ff: complex or array-like
The form-factor.
"""
qq = 1e-3 * q
ss = 1e-6 * s
tt = 1e-6 * t
ff = self._form_factor(qq, ss, tt, couplings)
return ff * 1e-9
@overload
def integrated_form_factor( # pylint: disable=arguments-differ,too-many-arguments
self,
q: float,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> float: ...
@overload
def integrated_form_factor( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealArray,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> RealArray: ...
def integrated_form_factor( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealOrRealArray,
couplings: Couplings,
*,
method: str = "quad",
npts: int = 10000,
epsrel: float = 1e-3,
epsabs: float = 0.0,
) -> RealOrRealArray:
"""Compute the pion-kaon-kaon form factor integrated over phase-space.
Parameters
----------
q: float or array-like
Center of mass energy.
gvuu: float
Coupling of vector to up-quarks.
gvdd: float
Coupling of vector to down-quarks.
gvss: float
Coupling of vector to strange-quarks.
Returns
-------
iff: float or array-like
Integrated form-factor.
Other Parameters
----------------
method: str, optional
Method used to integrate. Default is 'quad'. Options are 'quad' or
'rambo'.
npts: int, optional
Number of phase-space points to use in integration. Ignored is
method isn't 'rambo'. Default is 10_000.
epsrel: float, optional
Relative error tolerance. Default is 1e-3.
epsabs: float, optional
Absolute error tolerance. Default is 0.0.
"""
return self._integrated_form_factor(
q=q,
method=method,
npts=npts,
couplings=couplings,
epsrel=epsrel,
epsabs=epsabs,
)
@overload
def width( # pylint: disable=arguments-differ,too-many-arguments
self,
mv: float,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> float: ...
@overload
def width( # pylint: disable=arguments-differ,too-many-arguments
self,
mv: RealArray,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> RealArray: ...
def width( # pylint: disable=arguments-differ,too-many-arguments
self,
mv: RealOrRealArray,
couplings: Couplings,
*,
method: str = "quad",
npts: int = 1 << 14,
epsrel: float = 1e-3,
epsabs: float = 0.0,
) -> RealOrRealArray:
r"""Compute the partial decay width of a massive vector into a pion and
two kaons.
Parameters
----------
mv: float or array-like
Mass of the vector.
gvuu, gvdd, gvss: float
Coupling of vector to up-, donw- and strange-quarks.
nbins: float
Number of bins used to generate distribution.
Returns
-------
width: float or array-like
Decay width of vector into a pion and two kaons.
Other Parameters
----------------
method: str, optional
Method used to integrate over phase-space.
See `hazma.phase_space.energy_distributions_three_body`
for availible methods.
npts: int, optional
Number of phase-space points to use in integration. Only used if
`method='rambo'`. Default is 2^14.
epsrel: float, optional
Relative error tolerance. Default is 1e-3.
epsabs: float, optional
Absolute error tolerance. Default is 0.0.
"""
return self._width(
mv=mv,
couplings=couplings,
method=method,
npts=npts,
epsrel=epsrel,
epsabs=epsabs,
)
@overload
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: float,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> float: ...
@overload
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealArray,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
*,
method: str = ...,
npts: int = ...,
epsrel: float = ...,
epsabs: float = ...,
) -> RealArray: ...
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealOrRealArray,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
*,
method: str = "quad",
npts: int = 1 << 14,
epsrel: float = 1e-3,
epsabs: float = 0.0,
) -> RealOrRealArray:
r"""Compute the cross section for dark matter annihilating into a pion
and two kaons.
Parameters
----------
q: float or array-like
Center-of-mass energy.
mx: float
Mass of the dark matter in MeV.
mv: float
Mass of the vector mediator in MeV.
gvxx: float
Coupling of vector to dark matter.
wv: float
Width of the vector in MeV.
gvuu, gvdd, gvss: float
Coupling of vector to up-, donw- and strange-quarks.
Returns
-------
cs: float or array-like
Annihilation cross section into a pion and two kaons.
Other Parameters
----------------
method: str, optional
Method used to integrate over phase-space.
See `hazma.phase_space.energy_distributions_three_body`
for availible methods.
npts: int, optional
Number of phase-space points to use in integration. Only used if
`method='rambo'`. Default is 2^14.
epsrel: float, optional
Relative error tolerance. Default is 1e-3.
epsabs: float, optional
Absolute error tolerance. Default is 0.0.
"""
return self._cross_section(
q=q,
mx=mx,
mv=mv,
gvxx=gvxx,
wv=wv,
couplings=couplings,
method=method,
npts=npts,
epsrel=epsrel,
epsabs=epsabs,
)
def energy_distributions( # pylint: disable=arguments-differ,too-many-arguments
self,
q: float,
couplings: Couplings,
nbins: int,
method: str = "quad",
npts: int = 1 << 14,
epsrel: float = 1e-3,
epsabs: float = 0.0,
) -> list[PhaseSpaceDistribution1D]:
r"""Compute the energy distributions of the final state pion, and kaons.
Parameters
----------
q: float
Center-of-mass energy.
gvuu, gvdd, gvss: float
Coupling of vector to up-, donw- and strange-quarks.
nbins: float
Number of bins used to generate distribution.
Returns
-------
dists: List[PhaseSpaceDistribution1D]
List of the energy distributions.
Other Parameters
----------------
method: str, optional
Method used to integrate over phase-space.
See `hazma.phase_space.energy_distributions_three_body`
for availible methods.
npts: int, optional
Number of phase-space points to use in integration. Only used if
`method='rambo'`.
epsrel: float, optional
Relative error tolerance. Default is 1e-3.
epsabs: float, optional
Absolute error tolerance. Default is 0.0.
"""
return self._energy_distributions(
q=q,
nbins=nbins,
couplings=couplings,
method=method,
npts=npts,
epsrel=epsrel,
epsabs=epsabs,
)
# pylint: disable=arguments-differ,too-many-arguments
def invariant_mass_distributions(
self,
q: float,
couplings: Couplings,
nbins: int,
method: str = "quad",
npts: int = 1 << 14,
epsrel: float = 1e-3,
epsabs: float = 0.0,
) -> dict[tuple[int, int], PhaseSpaceDistribution1D]:
r"""Compute the invariant-mass distributions of the all pairs of the
final-state particles.
Parameters
----------
q: float
Center-of-mass energy.
gvuu, gvdd, gvss: float
Coupling of vector to up-, donw- and strange-quarks.
nbins: float
Number of bins used to generate distribution.
Returns
-------
dists: Dict[(int,int), PhaseSpaceDistribution1D]
Dictionary of the invariant-mass distributions. Keys specify the
pair of particles the distribution represents.
Other Parameters
----------------
method: str, optional
Method used to integrate over phase-space.
See `hazma.phase_space.energy_distributions_three_body`
for availible methods.
npts: int, optional
Number of phase-space points to use in integration. Only used if
`method='rambo'`.
epsrel: float, optional
Relative error tolerance. Default is 1e-3.
epsabs: float, optional
Absolute error tolerance. Default is 0.0.
"""
return self._invariant_mass_distributions(
q=q,
nbins=nbins,
couplings=couplings,
method=method,
npts=npts,
epsrel=epsrel,
epsabs=epsabs,
)
@dataclass
class VectorFormFactorPi0K0K0(_VectorFormFactorPiKKBase):
"""
Class for computing the vector form factor for a neutral pion and two
neutral kaons.
Attributes
----------
fsp_masses: (float, float, float)
Masses of the final-state particles.
fit_data: VectorFormFactorPiKKFitData
Stored data used to compute form-factor.
Methods
-------
form_factor
Compute the un-integrated form-factor into a neutral pion and two
neutral kaons.
integrated_form_factor
Compute the form-factor into a neutral pion and two neutral kaons
integrated over phase-space.
width
Compute the decay width of a vector into a neutral pion and two neutral
kaons.
cross_section
Compute the dark matter annihilation cross section into a neutral pion
and two neutral kaons.
"""
fsp_masses: tuple[float, float, float] = field(init=False, default=(MK0, MK0, MPI0))
def _form_factor(self, q, s, t, couplings: Couplings) -> float:
a0, a1 = self._iso_spin_amplitudes(q, couplings)
m1, m2, m3 = self._fsp_masses
coeff = (a0 + a1) / np.sqrt(6.0) * 2 * self.fit_data.g_ks_k_pi
return coeff * (
self._kstar_propagator(s, m2, m3) + self._kstar_propagator(t, m1, m3)
)
@dataclass
class VectorFormFactorPi0KpKm(_VectorFormFactorPiKKBase):
"""
Class for computing the vector form factor for a neutral pion and two
charged kaons.
Attributes
----------
fsp_masses: (float, float, float)
Masses of the final-state particles.
fit_data: VectorFormFactorPiKKFitData
Stored data used to compute form-factor.
Methods
-------
form_factor
Compute the un-integrated form-factor into a neutral pion and two
charged kaons.
integrated_form_factor
Compute the form-factor into a neutral pion and two charged kaons
integrated over phase-space.
width
Compute the decay width of a vector into a neutral pion and two charged
kaons.
cross_section
Compute the dark matter annihilation cross section into a neutral pion
and two charged kaons.
"""
fsp_masses: tuple[float, float, float] = field(init=False, default=(MK, MK, MPI0))
def _form_factor(self, q, s, t, couplings) -> float:
a0, a1 = self._iso_spin_amplitudes(q, couplings)
m1, m2, m3 = self._fsp_masses
coeff = (a0 - a1) / np.sqrt(6.0) * 2 * self.fit_data.g_ks_k_pi
return coeff * (
self._kstar_propagator(s, m2, m3) + self._kstar_propagator(t, m1, m3)
)
@dataclass
class VectorFormFactorPiKK0(_VectorFormFactorPiKKBase):
"""
Class for computing the vector form factor for a charged pion, a neutral
kaon, and a charged kaon.
Attributes
----------
fsp_masses: (float, float, float)
Masses of the final-state particles.
fit_data: VectorFormFactorPiKKFitData
Stored data used to compute form-factor.
Methods
-------
form_factor
Compute the un-integrated form-factor into a charged pion, a charged
kaon and a neutral kaon.
integrated_form_factor
Compute the form-factor into a charged pion, a charged kaon, and a
neutral kaon, integrated over phase-space.
width
Compute the decay width of a vector into a charged pion, a charged kaon,
and a neutral kaon.
cross_section
Compute the dark matter annihilation cross section into a charged pion,
a charged kaon, and a neutral kaon.
"""
fsp_masses: tuple[float, float, float] = field(init=False, default=(MK0, MK, MPI))
def _form_factor(self, q, s, t, couplings: Couplings) -> float:
a0, a1 = self._iso_spin_amplitudes(q, couplings)
m1, m2, m3 = self._fsp_masses
cs = (a0 + a1) / np.sqrt(6.0) * 2 * self.fit_data.g_ks_k_pi
ct = (a0 - a1) / np.sqrt(6.0) * 2 * self.fit_data.g_ks_k_pi
return ct * self._kstar_propagator(s, m2, m3) + cs * self._kstar_propagator(
t, m1, m3
)
|
LoganAMorrisonREPO_NAMEHazmaPATH_START.@Hazma_extracted@Hazma-master@hazma@form_factors@vector@_pi_k_k.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/partners/ollama/README.md",
"type": "Markdown"
}
|
# langchain-ollama
This package contains the LangChain integration with Ollama
## Installation
```bash
pip install -U langchain-ollama
```
You will also need to run the Ollama server locally.
You can download it [here](https://ollama.com/download).
## Chat Models
`ChatOllama` class exposes chat models from Ollama.
```python
from langchain_ollama import ChatOllama
llm = ChatOllama(model="llama3-groq-tool-use")
llm.invoke("Sing a ballad of LangChain.")
```
## Embeddings
`OllamaEmbeddings` class exposes embeddings from Ollama.
```python
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(model="llama3")
embeddings.embed_query("What is the meaning of life?")
```
## LLMs
`OllamaLLM` class exposes LLMs from Ollama.
```python
from langchain_ollama import OllamaLLM
llm = OllamaLLM(model="llama3")
llm.invoke("The meaning of life is")
```
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@partners@ollama@README.md@.PATH_END.py
|
{
"filename": "json_to_phot.py",
"repo_name": "astrocatalogs/supernovae",
"repo_path": "supernovae_extracted/supernovae-master/scripts/json_to_phot.py",
"type": "Python"
}
|
import json
import numpy as np
import matplotlib.pyplot as plt
file = raw_input('File to read: ')
sn = file.split('.')[0]
f = open(file,'r')
data=json.load(f)
f.close()
lc = []
for i in data[sn]['photometry']:
try:
time = i['time']
mag = i['magnitude']
try: err = i['e_magnitude']
except: err = 'nan'
band = i['band']
lc.append(np.array([time,mag,err,band]))
except: pass
lc = np.array(lc)
filters = np.unique(lc[:,3])
filters = filters.tolist()
filtNames = ''
for i in filters:
filtNames += '\t'+i+'\terr'
dates = np.unique(lc[:,0])
tab = np.empty((len(dates),2*len(filters)+1))
tab.fill(np.nan)
tab[:,0] = dates
for i in tab:
for j in range(len(lc[:,0])):
if float(lc[j,0])==i[0]:
i[2*(filters.index(lc[j,3]))+1] = lc[j,1]
i[2*(filters.index(lc[j,3]))+2] = lc[j,2]
lc_file = open(sn+'_lc.txt','w')
lc_file.write('#Time'+filtNames+'\n')
np.savetxt(lc_file,tab,fmt='%.2f',delimiter='\t')
lc_file.close()
plt.figure(1)
plt.clf()
for i in range(len(filters)):
plt.errorbar(tab[:,0],tab[:,2*i+1],fmt='o',label=filters[i])
plt.legend()
plt.gca().invert_yaxis()
plt.show()
|
astrocatalogsREPO_NAMEsupernovaePATH_START.@supernovae_extracted@supernovae-master@scripts@json_to_phot.py@.PATH_END.py
|
{
"filename": "_column.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/sankey/domain/_column.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColumnValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(self, plotly_name="column", parent_name="sankey.domain", **kwargs):
super(ColumnValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@sankey@domain@_column.py@.PATH_END.py
|
{
"filename": "function_utils_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/util/function_utils_test.py",
"type": "Python"
}
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for function args retrieval utils."""
import functools
from tensorflow.python.platform import test
from tensorflow.python.util import function_utils
def silly_example_function():
pass
class SillyCallableClass(object):
def __call__(self):
pass
class FnArgsTest(test.TestCase):
def test_simple_function(self):
def fn(a, b):
return a + b
self.assertEqual(('a', 'b'), function_utils.fn_args(fn))
def test_callable(self):
class Foo(object):
def __call__(self, a, b):
return a + b
self.assertEqual(('a', 'b'), function_utils.fn_args(Foo()))
def test_bound_method(self):
class Foo(object):
def bar(self, a, b):
return a + b
self.assertEqual(('a', 'b'), function_utils.fn_args(Foo().bar))
def test_bound_method_no_self(self):
class Foo(object):
def bar(*args): # pylint:disable=no-method-argument
return args[1] + args[2]
self.assertEqual((), function_utils.fn_args(Foo().bar))
def test_partial_function(self):
expected_test_arg = 123
def fn(a, test_arg):
if test_arg != expected_test_arg:
return ValueError('partial fn does not work correctly')
return a
wrapped_fn = functools.partial(fn, test_arg=123)
self.assertEqual(('a',), function_utils.fn_args(wrapped_fn))
def test_partial_function_with_positional_args(self):
expected_test_arg = 123
def fn(test_arg, a):
if test_arg != expected_test_arg:
return ValueError('partial fn does not work correctly')
return a
wrapped_fn = functools.partial(fn, 123)
self.assertEqual(('a',), function_utils.fn_args(wrapped_fn))
self.assertEqual(3, wrapped_fn(3))
self.assertEqual(3, wrapped_fn(a=3))
def test_double_partial(self):
expected_test_arg1 = 123
expected_test_arg2 = 456
def fn(a, test_arg1, test_arg2):
if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
return ValueError('partial does not work correctly')
return a
wrapped_fn = functools.partial(fn, test_arg2=456)
double_wrapped_fn = functools.partial(wrapped_fn, test_arg1=123)
self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
def test_double_partial_with_positional_args_in_outer_layer(self):
expected_test_arg1 = 123
expected_test_arg2 = 456
def fn(test_arg1, a, test_arg2):
if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
return ValueError('partial fn does not work correctly')
return a
wrapped_fn = functools.partial(fn, test_arg2=456)
double_wrapped_fn = functools.partial(wrapped_fn, 123)
self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
self.assertEqual(3, double_wrapped_fn(3)) # pylint: disable=no-value-for-parameter
self.assertEqual(3, double_wrapped_fn(a=3)) # pylint: disable=no-value-for-parameter
def test_double_partial_with_positional_args_in_both_layers(self):
expected_test_arg1 = 123
expected_test_arg2 = 456
def fn(test_arg1, test_arg2, a):
if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
return ValueError('partial fn does not work correctly')
return a
wrapped_fn = functools.partial(fn, 123) # binds to test_arg1
double_wrapped_fn = functools.partial(wrapped_fn, 456) # binds to test_arg2
self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
self.assertEqual(3, double_wrapped_fn(3)) # pylint: disable=no-value-for-parameter
self.assertEqual(3, double_wrapped_fn(a=3)) # pylint: disable=no-value-for-parameter
class HasKwargsTest(test.TestCase):
def test_simple_function(self):
fn_has_kwargs = lambda **x: x
self.assertTrue(function_utils.has_kwargs(fn_has_kwargs))
fn_has_no_kwargs = lambda x: x
self.assertFalse(function_utils.has_kwargs(fn_has_no_kwargs))
def test_callable(self):
class FooHasKwargs(object):
def __call__(self, **x):
del x
self.assertTrue(function_utils.has_kwargs(FooHasKwargs()))
class FooHasNoKwargs(object):
def __call__(self, x):
del x
self.assertFalse(function_utils.has_kwargs(FooHasNoKwargs()))
def test_bound_method(self):
class FooHasKwargs(object):
def fn(self, **x):
del x
self.assertTrue(function_utils.has_kwargs(FooHasKwargs().fn))
class FooHasNoKwargs(object):
def fn(self, x):
del x
self.assertFalse(function_utils.has_kwargs(FooHasNoKwargs().fn))
def test_partial_function(self):
expected_test_arg = 123
def fn_has_kwargs(test_arg, **x):
if test_arg != expected_test_arg:
return ValueError('partial fn does not work correctly')
return x
wrapped_fn = functools.partial(fn_has_kwargs, test_arg=123)
self.assertTrue(function_utils.has_kwargs(wrapped_fn))
some_kwargs = dict(x=1, y=2, z=3)
self.assertEqual(wrapped_fn(**some_kwargs), some_kwargs)
def fn_has_no_kwargs(x, test_arg):
if test_arg != expected_test_arg:
return ValueError('partial fn does not work correctly')
return x
wrapped_fn = functools.partial(fn_has_no_kwargs, test_arg=123)
self.assertFalse(function_utils.has_kwargs(wrapped_fn))
some_arg = 1
self.assertEqual(wrapped_fn(some_arg), some_arg)
def test_double_partial(self):
expected_test_arg1 = 123
expected_test_arg2 = 456
def fn_has_kwargs(test_arg1, test_arg2, **x):
if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
return ValueError('partial does not work correctly')
return x
wrapped_fn = functools.partial(fn_has_kwargs, test_arg2=456)
double_wrapped_fn = functools.partial(wrapped_fn, test_arg1=123)
self.assertTrue(function_utils.has_kwargs(double_wrapped_fn))
some_kwargs = dict(x=1, y=2, z=3)
self.assertEqual(double_wrapped_fn(**some_kwargs), some_kwargs)
def fn_has_no_kwargs(x, test_arg1, test_arg2):
if test_arg1 != expected_test_arg1 or test_arg2 != expected_test_arg2:
return ValueError('partial does not work correctly')
return x
wrapped_fn = functools.partial(fn_has_no_kwargs, test_arg2=456)
double_wrapped_fn = functools.partial(wrapped_fn, test_arg1=123)
self.assertFalse(function_utils.has_kwargs(double_wrapped_fn))
some_arg = 1
self.assertEqual(double_wrapped_fn(some_arg), some_arg) # pylint: disable=no-value-for-parameter
def test_raises_type_error(self):
with self.assertRaisesRegex(TypeError,
'should be a callable'):
function_utils.has_kwargs('not a function')
class GetFuncNameTest(test.TestCase):
def testWithSimpleFunction(self):
self.assertEqual(
'silly_example_function',
function_utils.get_func_name(silly_example_function))
def testWithClassMethod(self):
self.assertEqual(
'GetFuncNameTest.testWithClassMethod',
function_utils.get_func_name(self.testWithClassMethod))
def testWithCallableClass(self):
callable_instance = SillyCallableClass()
self.assertRegex(
function_utils.get_func_name(callable_instance),
'<.*SillyCallableClass.*>')
def testWithFunctoolsPartial(self):
partial = functools.partial(silly_example_function)
self.assertRegex(
function_utils.get_func_name(partial), '<.*functools.partial.*>')
def testWithLambda(self):
anon_fn = lambda x: x
self.assertEqual('<lambda>', function_utils.get_func_name(anon_fn))
def testRaisesWithNonCallableObject(self):
with self.assertRaises(ValueError):
function_utils.get_func_name(None)
class GetFuncCodeTest(test.TestCase):
def testWithSimpleFunction(self):
code = function_utils.get_func_code(silly_example_function)
self.assertIsNotNone(code)
self.assertRegex(code.co_filename, 'function_utils_test.py')
def testWithClassMethod(self):
code = function_utils.get_func_code(self.testWithClassMethod)
self.assertIsNotNone(code)
self.assertRegex(code.co_filename, 'function_utils_test.py')
def testWithCallableClass(self):
callable_instance = SillyCallableClass()
code = function_utils.get_func_code(callable_instance)
self.assertIsNotNone(code)
self.assertRegex(code.co_filename, 'function_utils_test.py')
def testWithLambda(self):
anon_fn = lambda x: x
code = function_utils.get_func_code(anon_fn)
self.assertIsNotNone(code)
self.assertRegex(code.co_filename, 'function_utils_test.py')
def testWithFunctoolsPartial(self):
partial = functools.partial(silly_example_function)
code = function_utils.get_func_code(partial)
self.assertIsNone(code)
def testRaisesWithNonCallableObject(self):
with self.assertRaises(ValueError):
function_utils.get_func_code(None)
if __name__ == '__main__':
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@util@function_utils_test.py@.PATH_END.py
|
{
"filename": "random.py",
"repo_name": "keras-team/keras",
"repo_path": "keras_extracted/keras-master/keras/src/backend/jax/random.py",
"type": "Python"
}
|
import jax
from keras.src.backend.config import floatx
from keras.src.random.seed_generator import SeedGenerator
from keras.src.random.seed_generator import draw_seed
from keras.src.random.seed_generator import make_default_seed
def jax_draw_seed(seed):
if isinstance(seed, jax.Array):
return seed
else:
return draw_seed(seed)
def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
dtype = dtype or floatx()
seed = jax_draw_seed(seed)
sample = jax.random.normal(seed, shape=shape, dtype=dtype)
return sample * stddev + mean
def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
dtype = dtype or floatx()
seed = jax_draw_seed(seed)
return jax.random.uniform(
seed, shape=shape, dtype=dtype, minval=minval, maxval=maxval
)
def categorical(logits, num_samples, dtype="int32", seed=None):
seed = jax_draw_seed(seed)
output_shape = list(logits.shape)
output_shape[1] = num_samples
output_shape = tuple(output_shape)
output = jax.random.categorical(
seed, logits[..., None], shape=output_shape, axis=1
)
return output.astype(dtype)
def randint(shape, minval, maxval, dtype="int32", seed=None):
seed = jax_draw_seed(seed)
return jax.random.randint(
seed, shape=shape, dtype=dtype, minval=minval, maxval=maxval
)
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
dtype = dtype or floatx()
seed = jax_draw_seed(seed)
sample = jax.random.truncated_normal(
seed, shape=shape, lower=-2.0, upper=2.0, dtype=dtype
)
return sample * stddev + mean
def _get_concrete_noise_shape(inputs, noise_shape):
if noise_shape is None:
return inputs.shape
concrete_inputs_shape = inputs.shape
concrete_noise_shape = []
for i, value in enumerate(noise_shape):
concrete_noise_shape.append(
concrete_inputs_shape[i] if value is None else value
)
return concrete_noise_shape
def dropout(inputs, rate, noise_shape=None, seed=None):
seed = jax_draw_seed(seed)
keep_prob = 1.0 - rate
# The `noise_shape` may contain `None` so we need to convert it
# into a concrete shape before passing it on to jax.
noise_shape = _get_concrete_noise_shape(inputs, noise_shape)
mask = jax.random.bernoulli(seed, p=keep_prob, shape=noise_shape)
mask = jax.numpy.broadcast_to(mask, inputs.shape)
return jax.lax.select(
mask, inputs / keep_prob, jax.numpy.zeros_like(inputs)
)
def shuffle(x, axis=0, seed=None):
seed = jax_draw_seed(seed)
return jax.random.permutation(seed, x, axis, independent=True)
def gamma(shape, alpha, dtype=None, seed=None):
seed = jax_draw_seed(seed)
dtype = dtype or floatx()
return jax.random.gamma(seed, alpha, shape=shape, dtype=dtype)
def binomial(shape, counts, probabilities, dtype=None, seed=None):
dtype = dtype or floatx()
seed = jax_draw_seed(seed)
# jax doesn't accept python lists as arguments
counts = jax.numpy.array(counts)
probabilities = jax.numpy.array(probabilities)
sample = jax.random.binomial(
key=seed, n=counts, p=probabilities, shape=shape, dtype=dtype
)
return sample
def beta(shape, alpha, beta, dtype=None, seed=None):
dtype = dtype or floatx()
seed = jax_draw_seed(seed)
# jax doesn't accept python lists as arguments
alpha = jax.numpy.array(alpha)
beta = jax.numpy.array(beta)
sample = jax.random.beta(
key=seed, a=alpha, b=beta, shape=shape, dtype=dtype
)
return sample
|
keras-teamREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@src@backend@jax@random.py@.PATH_END.py
|
{
"filename": "pysr_demo.ipynb",
"repo_name": "MilesCranmer/PySR",
"repo_path": "PySR_extracted/PySR-master/examples/pysr_demo.ipynb",
"type": "Jupyter Notebook"
}
|
# Setup
## Instructions
1. Work on a copy of this notebook: _File_ > _Save a copy in Drive_ (you will need a Google account).
2. (Optional) If you would like to do the deep learning component of this tutorial, turn on the GPU with Edit->Notebook settings->Hardware accelerator->GPU
```
!pip install -U pysr
```
Julia and Julia dependencies are installed at first import:
```
import pysr
```
Now, let's import everything else as well as the PySRRegressor:
```
import sympy
import numpy as np
from matplotlib import pyplot as plt
from pysr import PySRRegressor
from sklearn.model_selection import train_test_split
```
# Simple PySR example:
First, let's learn a simple function
$$2.5382 \cos(x3) + x0^2 - 2$$
```
# Dataset
np.random.seed(0)
X = 2 * np.random.randn(100, 5)
y = 2.5382 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 2
```
By default, we will set up 30 populations of expressions (which evolve independently except for migrations), use 4 threads, and use `"best"` for our model selection strategy:
```
default_pysr_params = dict(
populations=30,
model_selection="best",
)
```
PySR can run for arbitrarily long, and continue to find more and more accurate expressions. You can set the total number of cycles of evolution with `niterations`, although there are also a [few more ways](https://github.com/MilesCranmer/PySR/pull/134) to stop execution.
**This first execution will take a bit longer to startup, as the library is JIT-compiled. The next execution will be much faster.**
```
# Learn equations
model = PySRRegressor(
niterations=30,
binary_operators=["+", "*"],
unary_operators=["cos", "exp", "sin"],
**default_pysr_params,
)
model.fit(X, y)
```
We can print the model, which will print out all the discovered expressions:
```
model
```
We can also view the SymPy format of the best expression:
```
model.sympy()
```
We can also view the SymPy of any other expression in the list, using the index of it in `model.equations_`.
```
model.sympy(2)
```
## Output
`model.equations_` is a Pandas DataFrame. We can export the results in various ways:
```
model.latex()
```
These is also `model.sympy(), model.jax(), model.pytorch()`. All of these can take an index as input, to get the result for an arbitrary equation in the list.
We can also use `model.predict` for arbitrary equations, with the default equation being the one chosen by `model_selection`:
```
ypredict = model.predict(X)
ypredict_simpler = model.predict(X, 2)
print("Default selection MSE:", np.power(ypredict - y, 2).mean())
print("Manual selection MSE for index 2:", np.power(ypredict_simpler - y, 2).mean())
```
# Custom operators
A full list of operators is given here: https://ai.damtp.cam.ac.uk/pysr/operators,
but we can also use any binary or unary operator in `julia`, or define our own as arbitrary functions.
Say that we want a command to do quartic powers:
$$ y = x_0^4 - 2 $$
```
y = X[:, 0] ** 4 - 2
```
We can do this by passing a string in Julia syntax.
We also define the operator in sympy, with `extra_sympy_mappings`, to enable its use in `predict`, and other export functions.
```
model = PySRRegressor(
niterations=5,
populations=40,
binary_operators=["+", "*"],
unary_operators=["cos", "exp", "sin", "quart(x) = x^4"],
extra_sympy_mappings={"quart": lambda x: x**4},
)
model.fit(X, y)
```
```
model.sympy()
```
Since `quart` is arguably more complex than the other operators, you can also give it a different complexity, using, e.g., `complexity_of_operators={"quart": 2}` to give it a complexity of 2 (instead of the default 1). You can also define custom complexities for variables and constants (`complexity_of_variables` and `complexity_of_constants`, respectively - both take a single number).
One can also add a binary operator, with, e.g., `"myoperator(x, y) = x^2 * y"`. All Julia operators that work on scalar 32-bit floating point values are available.
Make sure that any operator you add is valid over the real line. So, e.g., you will need to define `"mysqrt(x) = sqrt(abs(x))"` to enable it for negative numbers,
or, simply have it return a very large number for bad inputs (to prevent negative input in a soft way):
`"mysqrt(x::T) where {T} = (x >= 0) ? x : T(-1e9)"` (Julia syntax for a template function of input type `T`), which will make `mysqrt(x)` return -10^9 for negative x–hurting the loss of the equation.
## Scoring
Using `model_selection="best"`selects the equation with the max score and prints it. But in practice it is best to look through all the equations manually, select an equation above some MSE threshold, and then use the score to select among that loss threshold.
Here, "score" is defined by:
$$ \text{score} = - \log(\text{loss}_i/\text{loss}_{i-1})/
(\text{complexity}_i - \text{complexity}_{i-1})$$
This scoring is motivated by the common strategy of looking for drops in the loss-complexity curve.
From Schmidt & Lipson (2009) -
# Noise example
Here is an example with noise. Known Gaussian noise with $\sigma$ between 0.1 and 5.0. We record samples of $y$:
$$ \sigma \sim U(0.1, 5.0) $$
$$ \epsilon \sim \mathcal{N}(0, \sigma^2)$$
$$ y = 5\;\cos(3.5 x_0) - 1.3 + \epsilon.$$
We have 5 features, say. The weights change the loss function to be:
$$MSE = \sum [(y - f(x))^2*w],$$
so in this example, we can set:
$$w = 1/\sigma^2.$$
```
np.random.seed(0)
N = 3000
upper_sigma = 5
X = 2 * np.random.rand(N, 5)
sigma = np.random.rand(N) * (5 - 0.1) + 0.1
eps = sigma * np.random.randn(N)
y = 5 * np.cos(3.5 * X[:, 0]) - 1.3 + eps
```
Let's look at this dataset:
```
plt.scatter(X[:, 0], y, alpha=0.2)
plt.xlabel("$x_0$")
plt.ylabel("$y$")
```
Define some weights to use:
```
weights = 1 / sigma**2
```
```
weights[:5]
```
Let's run PySR again:
```
model = PySRRegressor(
elementwise_loss="myloss(x, y, w) = w * abs(x - y)", # Custom loss function with weights.
niterations=20,
populations=20, # Use more populations
binary_operators=["+", "*"],
unary_operators=["cos"],
)
model.fit(X, y, weights=weights)
```
Let's see if we get similar results to the true equation
```
model
```
We can also filter all equations up to 2x the most accurate equation, then select the best score from that list:
```
best_idx = model.equations_.query(
f"loss < {2 * model.equations_.loss.min()}"
).score.idxmax()
model.sympy(best_idx)
```
We can also use `denoise=True`, which will run the input through a Gaussian process to denoise the dataset, before fitting on it.
Let's look at the fit:
```
plt.scatter(X[:, 0], y, alpha=0.1)
y_prediction = model.predict(X, index=best_idx)
plt.scatter(X[:, 0], y_prediction)
```
# Multiple outputs
For multiple outputs, multiple equations are returned:
```
X = 2 * np.random.randn(100, 5)
y = 1 / X[:, [0, 1, 2]]
```
```
model = PySRRegressor(
binary_operators=["+", "*"],
unary_operators=["inv(x) = 1/x"],
extra_sympy_mappings={"inv": lambda x: 1 / x},
)
model.fit(X, y)
```
```
model
```
# Julia packages and types
PySR uses [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl)
as its search backend. This is a pure Julia package, and so can interface easily with any other
Julia package.
For some tasks, it may be necessary to load such a package.
For example, let's say we wish to discovery the following relationship:
$$ y = p_{3x + 1} - 5, $$
where $p_i$ is the $i$th prime number, and $x$ is the input feature.
Let's see if we can discover this using
the [Primes.jl](https://github.com/JuliaMath/Primes.jl) package.
`jl` stores the Julia runtime.
Now, let's run some Julia code to add the Primes.jl
package to the PySR environment:
```
from pysr import jl
jl.seval(
"""
import Pkg
Pkg.add("Primes")
"""
)
```
This imports the Julia package manager, and uses it to install
`Primes.jl`. Now let's import `Primes.jl`:
```
jl.seval("using Primes: prime")
```
Note that PySR should automatically load the `juliacall.ipython` extension for you,
which means that you can also execute Julia code in the notebook using the `%%julia` magic:
```
%julia using Primes: prime
```
Now, we define a custom operator:
```
jl.seval(
"""
function p(i::T) where T
if 0.5 < i < 1000
return T(prime(round(Int, i)))
else
return T(NaN)
end
end
"""
)
```
Or, equivalently:
```julia
%%julia
function p(i::T) where T
if 0.5 < i < 1000
return T(prime(round(Int, i)))
else
return T(NaN)
end
end
```
We have created a function `p`, which takes a number `i` of type `T` (e.g., `T=Float64`).
`p` first checks whether the input is between 0.5 and 1000.
If out-of-bounds, it returns `NaN`.
If in-bounds, it rounds it to the nearest integer, computes the corresponding prime number, and then
converts it to the same type as input.
The equivalent function in Python would be:
```python
import sympy
def p(i):
if 0.5 < i < 1000:
return float(sympy.prime(int(round(i))))
else:
return float("nan")
```
(However, note that this version assumes 64-bit float input, rather than any input type `T`)
Next, let's generate a list of primes for our test dataset.
Since we are using juliacall, we can just call `p` directly to do this:
```
primes = {i: jl.p(i * 1.0) for i in range(1, 999)}
```
Next, let's use this list of primes to create a dataset of $x, y$ pairs:
```
import numpy as np
X = np.random.randint(0, 100, 100)[:, None]
y = [primes[3 * X[i, 0] + 1] - 5 + np.random.randn() * 0.001 for i in range(100)]
```
Note that we have also added a tiny bit of noise to the dataset.
Finally, let's create a PySR model, and pass the custom operator. We also need to define the sympy equivalent, which we can leave as a placeholder for now:
```
from pysr import PySRRegressor
import sympy
class sympy_p(sympy.Function):
pass
model = PySRRegressor(
binary_operators=["+", "-", "*", "/"],
unary_operators=["p"],
niterations=20,
extra_sympy_mappings={"p": sympy_p},
)
```
We are all set to go! Let's see if we can find the true relation:
```
model.fit(X, y)
```
if all works out, you should be able to see the true relation (note that the constant offset might not be exactly 1, since it is allowed to round to the nearest integer).
You can get the sympy version of the best equation with:
```
model.sympy()
```
# High-dimensional input: Neural Nets + Symbolic Regression
In this example, let's learn a high-dimensional problem. **This will use the method proposed in our NeurIPS paper: https://arxiv.org/abs/2006.11287.**
Let's consider a time series problem:
$$ z = y^2,\quad y = \frac{1}{10} \sum(y_i),\quad y_i = x_{i0}^2 + 6 \cos(2*x_{i2})$$
Imagine our time series is 10 timesteps. That is very hard for symbolic regression, even if we impose the inductive bias of $$z=f(\sum g(x_i))$$ - it is the square of the number of possible equations!
But, as in our paper, **we can break this problem down into parts with a neural network. Then approximate the neural network with the symbolic regression!**
Then, instead of, say, $(10^9)^2=10^{18}$ equations, we only have to consider $2\times 10^9$ equations.
```
import numpy as np
rstate = np.random.RandomState(0)
N = 100000
Nt = 10
X = 6 * rstate.rand(N, Nt, 5) - 3
y_i = X[..., 0] ** 2 + 6 * np.cos(2 * X[..., 2])
y = np.sum(y_i, axis=1) / y_i.shape[1]
z = y**2
X.shape, y.shape
```
## Neural Network definition
So, as described above, let's first use a neural network with the sum inductive bias to solve this problem.
Essentially, we will learn two neural networks:
- `f`
- `g`
each defined as a multi-layer perceptron. We will sum over `g` the same way as in our equation, but we won't define the summed part beforehand.
Then, we will fit `g` and `f` **separately** using symbolic regression.
> **Warning**
>
> We import torch *after* already starting PyJulia. This is required due to interference between their C bindings. If you use torch, and then run PyJulia, you will likely hit a segfault. So keep this in mind for mixed deep learning + PyJulia/PySR workflows.
```
!pip install pytorch_lightning
```
```
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, TensorDataset
import pytorch_lightning as pl
hidden = 128
total_steps = 50_000
def mlp(size_in, size_out, act=nn.ReLU):
return nn.Sequential(
nn.Linear(size_in, hidden),
act(),
nn.Linear(hidden, hidden),
act(),
nn.Linear(hidden, hidden),
act(),
nn.Linear(hidden, size_out),
)
class SumNet(pl.LightningModule):
def __init__(self):
super().__init__()
########################################################
# The same inductive bias as above!
self.g = mlp(5, 1)
self.f = mlp(1, 1)
def forward(self, x):
y_i = self.g(x)[:, :, 0]
y = torch.sum(y_i, dim=1, keepdim=True) / y_i.shape[1]
z = self.f(y)
return z[:, 0]
########################################################
# PyTorch Lightning bookkeeping:
def training_step(self, batch, batch_idx):
x, z = batch
predicted_z = self(x)
loss = F.mse_loss(predicted_z, z)
return loss
def validation_step(self, batch, batch_idx):
return self.training_step(batch, batch_idx)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.max_lr)
scheduler = {
"scheduler": torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=self.max_lr,
total_steps=self.trainer.estimated_stepping_batches,
final_div_factor=1e4,
),
"interval": "step",
}
return [optimizer], [scheduler]
```
## Data bookkeeping
Put everything into PyTorch and do a train/test split:
```
from multiprocessing import cpu_count
Xt = torch.tensor(X).float()
zt = torch.tensor(z).float()
X_train, X_test, z_train, z_test = train_test_split(Xt, zt, random_state=0)
train_set = TensorDataset(X_train, z_train)
train = DataLoader(
train_set, batch_size=128, num_workers=cpu_count(), shuffle=True, pin_memory=True
)
test_set = TensorDataset(X_test, z_test)
test = DataLoader(test_set, batch_size=256, num_workers=cpu_count(), pin_memory=True)
```
## Train the model with PyTorch Lightning on GPUs:
Start the model:
```
pl.seed_everything(0)
model = SumNet()
model.total_steps = total_steps
model.max_lr = 1e-2
```
PyTorch Lightning trainer object:
```
trainer = pl.Trainer(max_steps=total_steps, accelerator="gpu", devices=1)
```
Here, we fit the neural network:
```
trainer.fit(model, train_dataloaders=train, val_dataloaders=test)
```
## Latent vectors of network
Let's get the input and output of the learned `g` function from the network over some random data:
```
np.random.seed(0)
idx = np.random.randint(0, 10000, size=1000)
X_for_pysr = Xt[idx]
y_i_for_pysr = model.g(X_for_pysr)[:, :, 0]
y_for_pysr = torch.sum(y_i_for_pysr, dim=1) / y_i_for_pysr.shape[1]
z_for_pysr = zt[idx] # Use true values.
X_for_pysr.shape, y_i_for_pysr.shape
```
## Learning over the network:
Now, let's fit `g` using PySR.
> **Warning**
>
> First, let's save the data, because sometimes PyTorch and juliacall's C bindings interfere and cause the colab kernel to crash. If we need to restart, we can just load the data without having to retrain the network:
```
nnet_recordings = {
"g_input": X_for_pysr.detach().cpu().numpy().reshape(-1, 5),
"g_output": y_i_for_pysr.detach().cpu().numpy().reshape(-1),
"f_input": y_for_pysr.detach().cpu().numpy().reshape(-1, 1),
"f_output": z_for_pysr.detach().cpu().numpy().reshape(-1),
}
# Save the data for later use:
import pickle as pkl
with open("nnet_recordings.pkl", "wb") as f:
pkl.dump(nnet_recordings, f)
```
We can now load the data, including after a crash (be sure to re-run the import cells at the top of this notebook, including the one that starts juliacall).
```
import pickle as pkl
nnet_recordings = pkl.load(open("nnet_recordings.pkl", "rb"))
f_input = nnet_recordings["f_input"]
f_output = nnet_recordings["f_output"]
g_input = nnet_recordings["g_input"]
g_output = nnet_recordings["g_output"]
```
And now fit using a subsample of the data (symbolic regression only needs a small sample to find the best equation):
```
rstate = np.random.RandomState(0)
f_sample_idx = rstate.choice(f_input.shape[0], size=500, replace=False)
model = PySRRegressor(
niterations=50,
binary_operators=["+", "-", "*"],
unary_operators=["cos", "square"],
)
model.fit(g_input[f_sample_idx], g_output[f_sample_idx])
```
If this segfaults, restart the notebook, and run the initial imports and juliacall part, but skip the PyTorch training. This is because PyTorch's C binding tends to interefere with juliacall. You can then re-run the `pkl.load` cell to import the data.
## Validation
Recall we are searching for $f$ and $g$ such that:
$$z=f(\sum g(x_i))$$
which approximates the true relation:
$$ z = y^2,\quad y = \frac{1}{10} \sum(y_i),\quad y_i = x_{i0}^2 + 6 \cos(2 x_{i2})$$
Let's see how well we did in recovering $g$:
```
model.equations_[["complexity", "loss", "equation"]]
```
A neural network can easily undo a linear transform (which commutes with the summation), so any affine transform in $g$ is to be expected. The network for $f$ has learned to undo the linear transform.
This likely won't find the exact result, but it should find something similar. You may wish to try again but with many more `total_steps` for the neural network (10,000 is quite small!).
Then, we can learn another analytic equation for $f$.
**Now, we can compose these together to get the time series model!**
Think about what we just did: we found an analytical equation for $z$ in terms of $500$ datapoints, under the assumption that $z$ is a function of a sum of another function over an axis:
$$ z = f(\sum_i g(x_i)) $$
And we pulled out analytical copies for $g$ using symbolic regression.
# Other PySR Options
The full list of PySR parameters can be found here: https://ai.damtp.cam.ac.uk/pysr/api
|
MilesCranmerREPO_NAMEPySRPATH_START.@PySR_extracted@PySR-master@examples@pysr_demo.ipynb@.PATH_END.py
|
{
"filename": "_widthsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattercarpet/marker/line/_widthsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WidthsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="widthsrc", parent_name="scattercarpet.marker.line", **kwargs
):
super(WidthsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scattercarpet@marker@line@_widthsrc.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "marblestation/posidonius",
"repo_path": "posidonius_extracted/posidonius-master/posidonius/effects/__init__.py",
"type": "Python"
}
|
import posidonius.effects.disk
import posidonius.effects.general_relativity
import posidonius.effects.rotational_flattening
import posidonius.effects.tides
import posidonius.effects.wind
import posidonius.effects.evolution
|
marblestationREPO_NAMEposidoniusPATH_START.@posidonius_extracted@posidonius-master@posidonius@effects@__init__.py@.PATH_END.py
|
{
"filename": "_line.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/ohlc/_line.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Line(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "ohlc"
_path_str = "ohlc.line"
_valid_props = {"dash", "width"}
# dash
# ----
@property
def dash(self):
"""
Sets the dash style of lines. Set to a dash type string
("solid", "dot", "dash", "longdash", "dashdot", or
"longdashdot") or a dash length list in px (eg
"5px,10px,2px,2px"). Note that this style setting can also be
set per direction via `increasing.line.dash` and
`decreasing.line.dash`.
The 'dash' property is an enumeration that may be specified as:
- One of the following dash styles:
['solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot']
- A string containing a dash length list in pixels or percentages
(e.g. '5px 10px 2px 2px', '5, 10, 2, 2', '10% 20% 40%', etc.)
Returns
-------
str
"""
return self["dash"]
@dash.setter
def dash(self, val):
self["dash"] = val
# width
# -----
@property
def width(self):
"""
[object Object] Note that this style setting can also be set
per direction via `increasing.line.width` and
`decreasing.line.width`.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
dash
Sets the dash style of lines. Set to a dash type string
("solid", "dot", "dash", "longdash", "dashdot", or
"longdashdot") or a dash length list in px (eg
"5px,10px,2px,2px"). Note that this style setting can
also be set per direction via `increasing.line.dash`
and `decreasing.line.dash`.
width
[object Object] Note that this style setting can also
be set per direction via `increasing.line.width` and
`decreasing.line.width`.
"""
def __init__(self, arg=None, dash=None, width=None, **kwargs):
"""
Construct a new Line object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.ohlc.Line`
dash
Sets the dash style of lines. Set to a dash type string
("solid", "dot", "dash", "longdash", "dashdot", or
"longdashdot") or a dash length list in px (eg
"5px,10px,2px,2px"). Note that this style setting can
also be set per direction via `increasing.line.dash`
and `decreasing.line.dash`.
width
[object Object] Note that this style setting can also
be set per direction via `increasing.line.width` and
`decreasing.line.width`.
Returns
-------
Line
"""
super(Line, self).__init__("line")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.ohlc.Line
constructor must be a dict or
an instance of :class:`plotly.graph_objs.ohlc.Line`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("dash", None)
_v = dash if dash is not None else _v
if _v is not None:
self["dash"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@ohlc@_line.py@.PATH_END.py
|
{
"filename": "_warnings.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/pluggy/py3/pluggy/_warnings.py",
"type": "Python"
}
|
from typing import final
class PluggyWarning(UserWarning):
"""Base class for all warnings emitted by pluggy."""
__module__ = "pluggy"
@final
class PluggyTeardownRaisedWarning(PluggyWarning):
"""A plugin raised an exception during an :ref:`old-style hookwrapper
<old_style_hookwrappers>` teardown.
Such exceptions are not handled by pluggy, and may cause subsequent
teardowns to be executed at unexpected times, or be skipped entirely.
This is an issue in the plugin implementation.
If the exception is unintended, fix the underlying cause.
If the exception is intended, switch to :ref:`new-style hook wrappers
<hookwrappers>`, or use :func:`result.force_exception()
<pluggy.Result.force_exception>` to set the exception instead of raising.
"""
__module__ = "pluggy"
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@pluggy@py3@pluggy@_warnings.py@.PATH_END.py
|
{
"filename": "test_hm12.py",
"repo_name": "galtay/rabacus",
"repo_path": "rabacus_extracted/rabacus-master/rabacus/unittest/test_hm12.py",
"type": "Python"
}
|
""" Test the HM12 UV background model.
http://adsabs.harvard.edu/abs/2012ApJ...746..125H
"""
import unittest
import numpy as np
import rabacus as ra
H1i_z0p00 = 0.228e-13 / ra.U.s
H1h_z0p00 = 0.889e-13 * ra.U.eV / ra.U.s
He2i_z15p10 = 0.192e-22 / ra.U.s
He2h_z15p10 = 0.154e-19 * ra.U.eV / ra.U.s
class TestHM12(unittest.TestCase):
def test_photorates_table(self):
""" verify I/O of photorates table """
TOL = 1.0e-6
prt = ra.uv_bgnd.HM12_Photorates_Table()
z = 0.00
err = np.abs( ( prt.H1i(z) - H1i_z0p00 ) / H1i_z0p00 )
self.assertTrue( err < TOL )
err = np.abs( ( prt.H1h(z) - H1h_z0p00 ) / H1h_z0p00 )
self.assertTrue( err < TOL )
z = 15.10
err = np.abs( ( prt.He2i(z) - He2i_z15p10 ) / He2i_z15p10 )
self.assertTrue( err < TOL )
err = np.abs( ( prt.He2h(z) - He2h_z15p10 ) / He2h_z15p10 )
self.assertTrue( err < TOL )
def test_spectrum_integrals(self):
""" compare integrated values to tabulated values """
# instantiate objects
#---------------------------------------------------
prt = ra.uv_bgnd.HM12_Photorates_Table()
uvt = ra.uv_bgnd.HM12_UVB_Table()
px = ra.atomic.PhotoXsections()
# loop over all redshifts in the photorates table
# and check that integrating produces the value in
# the table
#---------------------------------------------------
E_min = 13.6
four_pi_sr = 4 * np.pi * ra.U.sr
for z in prt.z:
if z < 8.75:
E_max = E_min * 400
Npts = 2048
log_E = np.linspace( np.log10(E_min), np.log10(E_max), Npts )
E = 10**log_E * ra.U.eV
TOL = 5.0e-2
elif z >= 8.75:
E_max = E_min * 1000
Npts = 8192
log_E = np.linspace( np.log10(E_min), np.log10(E_max), Npts )
E = 10**log_E * ra.U.eV
TOL = 1.5e-1
# create spectrum
#---------------------------------------------------
Inu = uvt.return_spectrum_E( z, E )
# integrate and compare to table
#---------------------------------------------------
# energy flux per unit frequency
dFu_dnu = four_pi_sr * Inu
# photon flux per unit energy
dFn_dE = dFu_dnu / ( ra.PC.h * E )
# test photo-ionization rates
#---------------------------------------
dH1i_dE = dFn_dE * px.sigma_H1(E)
H1i = ra.utils.trap( E, dH1i_dE )
err = np.abs( ( H1i - prt.H1i(z) ) / prt.H1i(z) ).simplified
self.assertTrue( err < TOL )
dHe1i_dE = dFn_dE * px.sigma_He1(E)
He1i = ra.utils.trap( E, dHe1i_dE )
err = np.abs( ( He1i - prt.He1i(z) ) / prt.He1i(z) ).simplified
self.assertTrue( err < TOL )
dHe2i_dE = dFn_dE * px.sigma_He2(E)
He2i = ra.utils.trap( E, dHe2i_dE )
err = np.abs( ( He2i - prt.He2i(z) ) / prt.He2i(z) ).simplified
self.assertTrue( err < TOL )
# test photo-heating rates
#---------------------------------------
dH1h_dE = dH1i_dE * ( E - px.Eth_H1 )
H1h = ra.utils.trap( E, dH1h_dE )
err = np.abs( ( H1h - prt.H1h(z) ) / prt.H1h(z) ).simplified
self.assertTrue( err < TOL )
dHe1h_dE = dHe1i_dE * ( E - px.Eth_He1 )
He1h = ra.utils.trap( E, dHe1h_dE )
err = np.abs( ( He1h - prt.He1h(z) ) / prt.He1h(z) ).simplified
self.assertTrue( err < TOL )
dHe2h_dE = dHe2i_dE * ( E - px.Eth_He2 )
He2h = ra.utils.trap( E, dHe2h_dE )
err = np.abs( ( He2h - prt.He2h(z) ) / prt.He2h(z) ).simplified
self.assertTrue( err < TOL )
suite = unittest.TestLoader().loadTestsFromTestCase(TestHM12)
|
galtayREPO_NAMErabacusPATH_START.@rabacus_extracted@rabacus-master@rabacus@unittest@test_hm12.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/barpolar/unselected/textfont/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._color import ColorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._color.ColorValidator"]
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@barpolar@unselected@textfont@__init__.py@.PATH_END.py
|
{
"filename": "_arrowhead.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/annotation/_arrowhead.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ArrowheadValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self, plotly_name="arrowhead", parent_name="layout.annotation", **kwargs
):
super(ArrowheadValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "arraydraw"),
max=kwargs.pop("max", 8),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@annotation@_arrowhead.py@.PATH_END.py
|
{
"filename": "_bordercolor.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/choropleth/hoverlabel/_bordercolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BordercolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="bordercolor", parent_name="choropleth.hoverlabel", **kwargs
):
super(BordercolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@choropleth@hoverlabel@_bordercolor.py@.PATH_END.py
|
{
"filename": "knownissues.md",
"repo_name": "HETDEX/hetdex_api",
"repo_path": "hetdex_api_extracted/hetdex_api-master/known_issues/hdr1/knownissues.md",
"type": "Markdown"
}
|
# List of known issues
## Bad shots
A list of badshots that should be removed according to science goals are contained in:
badshots.list
Any detections contained in the shot are removed upon initialization of Detections() API. Users not using this call should remove the detections in their code.
These engineering shots were accidentally labelled as science shots. Should be ignored for main science.
* 20180511v015 189.25340 62.20436 324.60000
* 20180308v002 87.00297 56.10039 303.60000
* 20180309v001 78.00181 56.09970 303.48000
* 20180309v002 83.00311 56.09934 303.48000
* 20180309v003 89.00241 56.09940 303.54000
* 20180309v004 97.00384 56.09990 303.50000
* 20181205v012 41.85607 19.36482 117.50000
These shots have bad astrometry:
* 20190106008 - was flagged as borderline accepted by RAs due to clouds in 3rd dither, but fails due to astrometry (could be related to the clouds in 3rd dither. Its automatically moved in detections with the TP = 0.0813 so not adding to the bad list for now.
These shots were rejected due to Human Error (ie. any dither has an 'H' ra_flag)
[20170124013, 20170306018, 20170306020, 20170306021, 20170306022,
20170421016, 20170427006, 20170615009, 20171025009, 20180210009,
20180421009, 20180519007, 20190208035]
These shots were rejected due to Equipment Error ie. RA_flag = E
[20170126003, 20170127007, 20170922025, 20170922028, 20171014007,
20171016114, 20171029022, 20171222013, 20180213015, 20180310002,
20180310007, 20180313015, 20180313016, 20180313017, 20180316015,
20180316016, 20180317012, 20180320009, 20180517015, 20180714007,
20190104017]
These shots are rejected due to duplicate dither frames:
* 20170126v003
* 20170127v007
* 20171012v014
* 20171012v015
* 20171012v016
* 20171012v017
* 20171222v013
* 20180308v002
* 20180310v002
* 20180310v007
* 20180320v009
These shots are removed for an extremely high number of detections on most amps.. possible reasons given in brackets if known
* 20180211v019 (['B'] ra_flags )
* 20180321v012 (response_4540 = 0.0923)
These shots weren't reduced well.. probably used a bad twilight.
* 20180509007
* 20180509009
* 20180509010
## Some Weird Shots
These shots have lines everywhere.. should look into. They have NOT been removed from Detections.refine()
* 20180113012
* 20180212011
* 20180221010
# Weird detections
These detectids have ra or decs with 0.0 values. Still unclear why.
sel = detects.ra == 0.0
print(detects.detectid[sel])
[1000034866 1000034922 1000034925 1000034926 1000034927 1000035648
1000035658 1000038799 1000038835 1000040473 1000040474 1000044930
1000044931 1000044932 1000044933 1000047027 1000047052 1000047055
1000047058 1000047080 1000047118 1000047248 1000061232 1000082583
1000115641 1000115657 1000193138 1000253379 1000253386 1000253415
1000253478 1000253481 1000268209 1000396108 1000437529 1000437530
1000437531 1000437534 1000437535 1000437538 1000437541 1000437542
1000437544 1000437545 1000437546 1000437547 1000437549 1000437550
1000437552 1000437555 1000437557 1000437558 1000437559 1000437560
1000437561 1000437564 1000437565 1000437566 1000437568 1000437570
1000437574 1000437576 1000437577 1000437579 1000437583 1000437584
1000437585 1000437590 1000437592 1000437593 1000437595 1000437601
1000437603 1000437604 1000437605 1000437606 1000437608 1000437609
1000437611 1000437612 1000437613 1000437614]
|
HETDEXREPO_NAMEhetdex_apiPATH_START.@hetdex_api_extracted@hetdex_api-master@known_issues@hdr1@knownissues.md@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "ucberkeleyseti/blimpy",
"repo_path": "blimpy_extracted/blimpy-master/blimpy/__init__.py",
"type": "Python"
}
|
from __future__ import absolute_import
try:
from . import waterfall
from .waterfall import Waterfall
from .guppi import GuppiRaw
from . import utils
from . import fil2h5
from . import h52fil
from . import h5diag
from . import bl_scrunch
from . import calcload
from . import rawhdr
from . import stax
from . import stix
from . import dsamp
from blimpy.io import file_wrapper
except:
print("Warning: At least one utility could not be imported!")
from pkg_resources import get_distribution, DistributionNotFound
try:
__version__ = get_distribution('blimpy').version
except DistributionNotFound:
__version__ = '0.0.0 - please install via pip/setup.py'
|
ucberkeleysetiREPO_NAMEblimpyPATH_START.@blimpy_extracted@blimpy-master@blimpy@__init__.py@.PATH_END.py
|
{
"filename": "_showticksuffix.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/indicator/gauge/axis/_showticksuffix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowticksuffixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="showticksuffix", parent_name="indicator.gauge.axis", **kwargs
):
super(ShowticksuffixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@indicator@gauge@axis@_showticksuffix.py@.PATH_END.py
|
{
"filename": "main.py",
"repo_name": "gmbrandt/HTOF",
"repo_path": "HTOF_extracted/HTOF-main/htof/main.py",
"type": "Python"
}
|
"""
Driver script for htof.
The Astrometry class is what a user should use to both parse and fit the intermediate astrometric data.
Author: G. Mirek Brandt
"""
from astropy.time import Time
from astropy.coordinates import Angle
import warnings
from htof.fit import AstrometricFitter
from htof.special_parse import to_ra_dec_basis, Hipparcos2Recalibrated, Hipparcos2ParserFactory
from htof.parse import GaiaeDR3, GaiaDR2, GaiaData
from htof.parse import HipparcosOriginalData, HipparcosRereductionJavaTool, HipparcosRereductionDVDBook
from htof.sky_path import parallactic_motion, earth_ephemeris, earth_sun_l2_ephemeris
class Astrometry(object):
"""
General wrapper for the different fitting and parsing classes.
:param use_catalog_parallax_factors: True if you want to load and use the hipparcos catalog parallax factors given
with the IAD, or the gaia parallax factors shipped with the GOST scanning law.
Set to False if you want to recompute the parallax factors. Default is False.
data_choice hip2or21 will attempt to see if the input data file is Hip2 (DVD) or Hip21 (java tool), then proceed accordingly.
"""
parsers = {'gaiaedr3': GaiaeDR3, 'gaiadr2': GaiaDR2, 'gaia': GaiaData, 'hip21': HipparcosRereductionJavaTool,
'hip1': HipparcosOriginalData, 'hip2': HipparcosRereductionDVDBook, 'hip2or21': Hipparcos2ParserFactory,
'hip2recalibrated': Hipparcos2Recalibrated}
ephemeri = {'gaiadr2': earth_sun_l2_ephemeris, 'gaia': earth_sun_l2_ephemeris, 'gaiaedr3': earth_sun_l2_ephemeris,
'hip1': earth_ephemeris, 'hip2': earth_ephemeris, 'hip21': earth_ephemeris, 'hip2or21': earth_ephemeris,
'hip2recalibrated': earth_ephemeris}
def __init__(self, data_choice, star_id, intermediate_data_directory, fitter=None, data=None,
central_epoch_ra=0, central_epoch_dec=0, format='jd', fit_degree=1,
use_parallax=False, central_ra=None, central_dec=None,
use_catalog_parallax_factors=False, along_scan_error_scaling=1.0, **kwargs):
self.along_scan_error_scaling = along_scan_error_scaling
if data_choice.lower() == 'hip2recalibrated':
warnings.warn(f'You have selected {data_choice}, the recalibrated Hipparcos 2 data. Note that for this,'
f' you should be feeding in the filepaths to the Hip21 (Hip2 java tool data), because'
f' htof applies the recalibration on-the-fly for each file. As well, be sure to read'
f' Brandt et al. 2022 to understand the limitations of using the recalibrated data. ') # pragma: no cover
if data is None:
ThisDataParser = self.parsers[data_choice.lower()]
data = ThisDataParser.parse_and_instantiate(star_id=star_id,
intermediate_data_directory=intermediate_data_directory)
data.scale_along_scan_errs(self.along_scan_error_scaling)
data.calculate_inverse_covariance_matrices()
parallactic_pertubations = None
if use_parallax:
if not use_catalog_parallax_factors:
# recompute the parallax factors at the new central_ra and central_ra epoch.
if not (isinstance(central_ra, Angle) and isinstance(central_dec, Angle)):
raise ValueError('Cannot compute parallax factors. central_ra and central_dec must be instances'
' of astropy.coordinates.Angle.') # pragma: no cover
if central_epoch_dec != central_epoch_ra:
warnings.warn('central_epoch_dec != central_epoch_ra. '
'Using central_epoch_ra as the central_epoch to compute the parallax motion.',
UserWarning) # pragma: no cover
ra_motion, dec_motion = parallactic_motion(Time(data.julian_day_epoch(), format='jd').jyear,
central_ra.mas, central_dec.mas, 'mas',
Time(central_epoch_ra, format=format).jyear,
ephemeris=self.ephemeri[data_choice.lower()])
else:
ra_motion, dec_motion = to_ra_dec_basis(data.parallax_factors.values, data.scan_angle.values)
parallactic_pertubations = {'ra_plx': ra_motion, 'dec_plx': dec_motion}
if fitter is None and data is not None:
fitter = AstrometricFitter(inverse_covariance_matrices=data.inverse_covariance_matrix,
epoch_times=Time(Time(data.julian_day_epoch(), format='jd'), format=format).value,
central_epoch_dec=Time(central_epoch_dec, format=format).value,
central_epoch_ra=Time(central_epoch_ra, format=format).value,
fit_degree=fit_degree,
use_parallax=use_parallax,
parallactic_pertubations=parallactic_pertubations)
self.data = data
self.fitter = fitter
def fit(self, ra_vs_epoch, dec_vs_epoch, return_all=False):
return self.fitter.fit_line(ra_vs_epoch=ra_vs_epoch, dec_vs_epoch=dec_vs_epoch, return_all=return_all)
def optimal_central_epochs(self):
return {'ra': self.fitter.find_optimal_central_epoch('ra'), 'dec': self.fitter.find_optimal_central_epoch('dec')}
|
gmbrandtREPO_NAMEHTOFPATH_START.@HTOF_extracted@HTOF-main@htof@main.py@.PATH_END.py
|
{
"filename": "test_ssodnet.py",
"repo_name": "maxmahlke/rocks",
"repo_path": "rocks_extracted/rocks-master/tests/test_ssodnet.py",
"type": "Python"
}
|
import warnings
import pytest
import rocks
@pytest.mark.parametrize("id_", ["Ceres", "doesnotexist"])
def test_get_ssoCard(id_):
warnings.filterwarnings("ignore", "UserWarning")
card = rocks.ssodnet.get_ssocard(id_)
if id_ == "Ceres":
assert isinstance(card, dict)
else:
assert card is None
|
maxmahlkeREPO_NAMErocksPATH_START.@rocks_extracted@rocks-master@tests@test_ssodnet.py@.PATH_END.py
|
{
"filename": "conf.py",
"repo_name": "ArgonneCPAC/dsps",
"repo_path": "dsps_extracted/dsps-main/docs/source/conf.py",
"type": "Python"
}
|
"""
"""
from pkg_resources import DistributionNotFound, get_distribution
try:
__version__ = get_distribution("emcee").version
except DistributionNotFound:
__version__ = "unknown version"
project = "dsps"
copyright = "2023, Andrew Hearin"
author = "Andrew Hearin"
version = __version__
release = __version__
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.mathjax",
"sphinx.ext.ifconfig",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
"sphinx_copybutton",
"nbsphinx",
]
templates_path = ["_templates"]
exclude_patterns = [".ipynb_checkpoints/*"]
nbsphinx_kernel_name = "python3"
add_module_names = False
# -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
html_theme = "alabaster"
html_static_path = ["_static"]
|
ArgonneCPACREPO_NAMEdspsPATH_START.@dsps_extracted@dsps-main@docs@source@conf.py@.PATH_END.py
|
{
"filename": "_bordercolorsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/violin/hoverlabel/_bordercolorsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BordercolorsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="bordercolorsrc", parent_name="violin.hoverlabel", **kwargs
):
super(BordercolorsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@violin@hoverlabel@_bordercolorsrc.py@.PATH_END.py
|
{
"filename": "inset_locator_demo.py",
"repo_name": "matplotlib/matplotlib",
"repo_path": "matplotlib_extracted/matplotlib-main/galleries/examples/axes_grid1/inset_locator_demo.py",
"type": "Python"
}
|
"""
==================
Inset locator demo
==================
"""
# %%
# The `.inset_locator`'s `~.inset_locator.inset_axes` allows
# easily placing insets in the corners of the Axes by specifying a width and
# height and optionally a location (loc) that accepts locations as codes,
# similar to `~matplotlib.axes.Axes.legend`.
# By default, the inset is offset by some points from the axes,
# controlled via the *borderpad* parameter.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
fig, (ax, ax2) = plt.subplots(1, 2, figsize=[5.5, 2.8])
# Create inset of width 1.3 inches and height 0.9 inches
# at the default upper right location.
axins = inset_axes(ax, width=1.3, height=0.9)
# Create inset of width 30% and height 40% of the parent Axes' bounding box
# at the lower left corner.
axins2 = inset_axes(ax, width="30%", height="40%", loc="lower left")
# Create inset of mixed specifications in the second subplot;
# width is 30% of parent Axes' bounding box and
# height is 1 inch at the upper left corner.
axins3 = inset_axes(ax2, width="30%", height=1., loc="upper left")
# Create an inset in the lower right corner with borderpad=1, i.e.
# 10 points padding (as 10pt is the default fontsize) to the parent Axes.
axins4 = inset_axes(ax2, width="20%", height="20%", loc="lower right", borderpad=1)
# Turn ticklabels of insets off
for axi in [axins, axins2, axins3, axins4]:
axi.tick_params(labelleft=False, labelbottom=False)
plt.show()
# %%
# The parameters *bbox_to_anchor* and *bbox_transform* can be used for a more
# fine-grained control over the inset position and size or even to position
# the inset at completely arbitrary positions.
# The *bbox_to_anchor* sets the bounding box in coordinates according to the
# *bbox_transform*.
#
fig = plt.figure(figsize=[5.5, 2.8])
ax = fig.add_subplot(121)
# We use the Axes transform as bbox_transform. Therefore, the bounding box
# needs to be specified in axes coordinates ((0, 0) is the lower left corner
# of the Axes, (1, 1) is the upper right corner).
# The bounding box (.2, .4, .6, .5) starts at (.2, .4) and ranges to (.8, .9)
# in those coordinates.
# Inside this bounding box an inset of half the bounding box' width and
# three quarters of the bounding box' height is created. The lower left corner
# of the inset is aligned to the lower left corner of the bounding box.
# The inset is then offset by the default 0.5 in units of the font size.
axins = inset_axes(ax, width="50%", height="75%",
bbox_to_anchor=(.2, .4, .6, .5),
bbox_transform=ax.transAxes, loc="lower left")
# For visualization purposes we mark the bounding box by a rectangle
ax.add_patch(plt.Rectangle((.2, .4), .6, .5, ls="--", ec="c", fc="none",
transform=ax.transAxes))
# We set the axis limits to something other than the default, in order to not
# distract from the fact that axes coordinates are used here.
ax.set(xlim=(0, 10), ylim=(0, 10))
# Note how the two following insets are created at the same positions, one by
# use of the default parent Axes' bbox and the other via a bbox in Axes
# coordinates and the respective transform.
ax2 = fig.add_subplot(222)
axins2 = inset_axes(ax2, width="30%", height="50%")
ax3 = fig.add_subplot(224)
axins3 = inset_axes(ax3, width="100%", height="100%",
bbox_to_anchor=(.7, .5, .3, .5),
bbox_transform=ax3.transAxes)
# For visualization purposes we mark the bounding box by a rectangle
ax2.add_patch(plt.Rectangle((0, 0), 1, 1, ls="--", lw=2, ec="c", fc="none"))
ax3.add_patch(plt.Rectangle((.7, .5), .3, .5, ls="--", lw=2,
ec="c", fc="none"))
# Turn ticklabels off
for axi in [axins2, axins3, ax2, ax3]:
axi.tick_params(labelleft=False, labelbottom=False)
plt.show()
# %%
# In the above the Axes transform together with 4-tuple bounding boxes has been
# used as it mostly is useful to specify an inset relative to the Axes it is
# an inset to. However, other use cases are equally possible. The following
# example examines some of those.
#
fig = plt.figure(figsize=[5.5, 2.8])
ax = fig.add_subplot(131)
# Create an inset outside the Axes
axins = inset_axes(ax, width="100%", height="100%",
bbox_to_anchor=(1.05, .6, .5, .4),
bbox_transform=ax.transAxes, loc="upper left", borderpad=0)
axins.tick_params(left=False, right=True, labelleft=False, labelright=True)
# Create an inset with a 2-tuple bounding box. Note that this creates a
# bbox without extent. This hence only makes sense when specifying
# width and height in absolute units (inches).
axins2 = inset_axes(ax, width=0.5, height=0.4,
bbox_to_anchor=(0.33, 0.25),
bbox_transform=ax.transAxes, loc="lower left", borderpad=0)
ax2 = fig.add_subplot(133)
ax2.set_xscale("log")
ax2.set(xlim=(1e-6, 1e6), ylim=(-2, 6))
# Create inset in data coordinates using ax.transData as transform
axins3 = inset_axes(ax2, width="100%", height="100%",
bbox_to_anchor=(1e-2, 2, 1e3, 3),
bbox_transform=ax2.transData, loc="upper left", borderpad=0)
# Create an inset horizontally centered in figure coordinates and vertically
# bound to line up with the Axes.
from matplotlib.transforms import blended_transform_factory # noqa
transform = blended_transform_factory(fig.transFigure, ax2.transAxes)
axins4 = inset_axes(ax2, width="16%", height="34%",
bbox_to_anchor=(0, 0, 1, 1),
bbox_transform=transform, loc="lower center", borderpad=0)
plt.show()
|
matplotlibREPO_NAMEmatplotlibPATH_START.@matplotlib_extracted@matplotlib-main@galleries@examples@axes_grid1@inset_locator_demo.py@.PATH_END.py
|
{
"filename": "additional-simulation-initialization-examples.ipynb",
"repo_name": "small-body-dynamics/SBDynT",
"repo_path": "SBDynT_extracted/SBDynT-main/example-notebooks/Advanced-Features-and-Tools/additional-simulation-initialization-examples.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import rebound
import sys
#change the next line to reflect where you have downloaded the source code
sys.path.insert(0, '/Users/kvolk/Documents/GitHub/SBDynT/src')
import sbdynt as sbd
```
**Example of setting up an integration based on an orbit fit from find_orb:**
```python
obj_name='example'
#Find_orb orbital elements:
epoch = 2457217.5
a = 39.4060972335126 #(semimajor axis in au)
ec = 0.19121388131996062 #(eccentricity)
inc=0.1279988198070029 #radians! (inclination relative to the ecliptic)
node = -2.3766713138417384 #radians! (longitude of ascending node)
argperi = 3.033977870435759 #radians! (argument of pericenter)
ma = 0.15683745626851575 #radians! (mean anomaly)
flag, sim = sbd.initialize_from_heliocentric_Find_Orb_orbit(des=obj_name,
a=a,e=ec,inc=inc,node=node,
aperi=argperi,ma=ma,
planets = ['all'],
epoch=epoch)
helio_orbit = sim.particles[obj_name].orbit(sim.particles[0])
print("Added heliocentric orbit: ")
print("a=",helio_orbit.a)
print("e=",helio_orbit.e)
print("i=",helio_orbit.inc)
print("node=",helio_orbit.Omega)
print("aperi=",helio_orbit.omega)
print("mean anomaly=",helio_orbit.M)
```
Added heliocentric orbit:
a= 39.40609718926083
e= 0.1912138804260118
i= 0.1279988198070029
node= -2.3766713138417384
aperi= 3.0339778695017916
mean anomaly= 0.15683745721351627
**Note** that there are tiny difference between the provided find_orb elements and those in the final simulation. This is because there is actually a slight difference between the masses for the Sun and planets in JPL's database and what's assumed in the find_orb code. We choose to account for this by converting the find_orb elements to heliocentric cartesian positions and velocities using find_orb's GM for the Sun and then adding the particles to the rebound simulation (which uses JPL's masses for everything) in cartesian space. Both sets of orbital elements represent the same cartesian positions and velocities.
Additional small differences between input and output elements will occur if only a subset of the planets are included because we account for the missing planet masses.
**NOTE**: you can initialize clones generaged by Find_Orb by passing orbital element arrays (e.g., a = [39.4,39.41,...]) rather
than single element values to sbd.**initialize_from_heliocentric_Find_Orb_orbit**
```python
```
|
small-body-dynamicsREPO_NAMESBDynTPATH_START.@SBDynT_extracted@SBDynT-main@example-notebooks@Advanced-Features-and-Tools@additional-simulation-initialization-examples.ipynb@.PATH_END.py
|
{
"filename": "test_literal_validator.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/_plotly_utils/tests/validators/test_literal_validator.py",
"type": "Python"
}
|
import pytest
from _plotly_utils.basevalidators import LiteralValidator
import numpy as np
# Fixtures
# --------
@pytest.fixture()
def validator():
return LiteralValidator("prop", "parent", "scatter")
# Tests
# -----
# ### Acceptance ###
@pytest.mark.parametrize("val", ["scatter"])
def test_acceptance(val, validator):
assert validator.validate_coerce(val) is val
# ### Test rejection ###
@pytest.mark.parametrize("val", ["hello", (), [], [1, 2, 3], set(), "34"])
def test_rejection(val, validator):
with pytest.raises(ValueError) as validation_failure:
validator.validate_coerce(val)
assert "read-only" in str(validation_failure.value)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@_plotly_utils@tests@validators@test_literal_validator.py@.PATH_END.py
|
{
"filename": "remstriping.py",
"repo_name": "spacetelescope/jwebbinar_prep",
"repo_path": "jwebbinar_prep_extracted/jwebbinar_prep-main/ceers/nircam/remstriping.py",
"type": "Python"
}
|
import os
import shutil
from glob import glob
import argparse
import logging
from datetime import datetime
import numpy as np
from astropy.io import fits
from astropy.stats import sigma_clipped_stats
from scipy.optimize import curve_fit
import yaml
# jwst-related imports
from jwst.datamodels import ImageModel, FlatModel, dqflags
from jwst.flatfield.flat_field import do_correction
from stdatamodels import util
import crds
# logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
"""
Measure striping by collapsing image over rows and columns, using the
sigma-clipped median value to avoid source flux.
The measurement/subtraction needs to be done along one axis at a time,
since the measurement along x will depend on what has been subtracted
from y.
"""
def gaussian(x, a, mu, sig):
return a * np.exp(-(x-mu)**2/(2*sig**2))
def fit_sky(data):
"""Fit distribution of sky fluxes with a Gaussian"""
bins = np.arange(-0.5, 0.5, 0.001)
h,b = np.histogram(data, bins=bins)
bc = 0.5 * (b[1:] + b[:-1])
binsize = b[1] - b[0]
p0 = [10, bc[np.argmax(h)], 0.01]
popt,pcov = curve_fit(gaussian, bc, h, p0=p0)
return popt[1]
def collapse_image(im, mask, dimension='y', sig=2.):
"""collapse an image along one dimension to check for striping.
By default, collapse columns to show horizontal striping (collapsing
along columns). Switch to vertical striping (collapsing along rows)
with dimension='x'
Striping is measured as a sigma-clipped median of all unmasked pixels
in the row or column.
Args:
im (float array): image data array
mask (bool array): image mask array, True where pixels should be
masked from the fit (where DQ>0, source flux has been masked, etc.)
dimension (Optional [str]): specifies which dimension along which
to collapse the image. If 'y', collapses along columns to
measure horizontal striping. If 'x', collapses along rows to
measure vertical striping. Default is 'y'
sig (Optional [float]): sigma to use in sigma clipping
"""
# axis=1 results in array along y
# axis=0 results in array along x
if dimension == 'y':
# collapsed = np.median(im, axis=1)
res = sigma_clipped_stats(im, mask=mask, sigma=sig, cenfunc='median',
stdfunc='std', axis=1)
elif dimension == 'x':
# collapsed = np.median(im, axis=0)
res = sigma_clipped_stats(im, mask=mask, sigma=sig, cenfunc='median',
stdfunc='std', axis=0)
return res[1]
def measure_striping(image, apply_flat=True, mask_sources=True, seedim_directory='./', threshold=0.01):
"""Removes striping in rate.fits files before flat fielding.
Measures and subtracts the horizontal & vertical striping present in
countrate images. The striping is most likely due to 1/f noise, and
the RefPixStep with odd_even_columns=True and use_side_ref_pixels=True
does not fully remove the pattern, no matter what value is chosen for
side_smoothing_length. There is also residual vertical striping in NIRCam
images simulated with Mirage.
Note:
The original rate image file is copied to *_rate_orig.fits, and
the rate image with the striping patterns removed is saved to
*_rate.fits, overwriting the input filename
Args:
image (str): image filename, including full relative path
apply_flat (Optional [bool]): if True, identifies and applies the
corresponding flat field before measuring striping pattern.
Applying the flat first allows for a cleaner measure of the
striping, especially for the long wavelength detectors.
Default is True.
mask_sources (Optional [bool]): If True, masks out sources in image
before measuring the striping pattern so that source flux is
not included in the calculation of the sigma-clipped median.
Sources are identified using the Mirage seed images.
Default is True.
seedim_directory (Optional [bool]): Directory containing
Mirage seed images, used if mask_sources is True.
Default is working directory.
threshold (Optional [float]): threshold (in ADU/s) to use in the
seed images when identifying pixels to mask. This will depend on
the seed image and brightness of input sources. Default is 0.01
"""
try:
crds_context = os.environ['CRDS_CONTEXT']
except KeyError:
crds_context = crds.get_default_context()
model = ImageModel(image)
log.info('Measuring image striping')
log.info('Working on %s'%image)
# check that striping hasn't already been removed
for entry in model.history:
for k,v in entry.items():
if 'Removed horizontal,vertical striping; remstriping.py' in v:
print('%s already cleaned. Skipping!'%os.path.basename(image))
return
# apply the flat to get a cleaner meausurement of the striping
if apply_flat:
log.info('Applying flat for cleaner measurement of striping patterns')
# pull flat from CRDS using the current context
crds_dict = {'INSTRUME':'NIRCAM',
'DETECTOR':model.meta.instrument.detector,
'FILTER':model.meta.instrument.filter,
'PUPIL':model.meta.instrument.pupil,
'DATE-OBS':model.meta.observation.date,
'TIME-OBS':model.meta.observation.time}
flats = crds.getreferences(crds_dict, reftypes=['flat'],
context=crds_context)
# if the CRDS loopup fails, should return a CrdsLookupError, but
# just in case:
try:
flatfile = flats['flat']
except KeyError:
log.error('Flat was not found in CRDS with the parameters: {}'.format(crds_dict))
exit()
log.info('Using flat: %s'%(os.path.basename(flatfile)))
with FlatModel(flatfile) as flat:
# use the JWST Calibration Pipeline flat fielding Step
model,applied_flat = do_correction(model, flat)
# construct mask for median calculation
mask = np.zeros(model.data.shape, dtype=bool)
mask[model.dq > 0] = True
# mask out sources
if mask_sources:
log.info('Using the input seed image to mask out source flux')
base = os.path.basename(image).split('_rate.fits')[0]
seedimage = glob(os.path.join(seedim_directory,
'%s_*_galaxy_seed_image.fits'%base))
log.info('Using seedim: %s'%(os.path.basename(seedimage[0])))
seedim = fits.getdata(seedimage[0])
log.info('Masking flux above threshold %f'%threshold)
wobj = np.where(seedim > threshold)
mask[wobj] = True
# measure the pedestal in the unmasked parts of the image
log.info('Measuring the pedestal in the image')
pedestal_data = model.data[~mask]
pedestal_data = pedestal_data.flatten()
median_image = np.median(pedestal_data)
log.info('Image median (unmasked and DQ==0): %f'%(median_image))
try:
pedestal = fit_sky(pedestal_data)
except RuntimeError as e:
log.error("Can't fit sky, using median value instead")
pedestal = median_image
else:
log.info('Fit pedestal: %f'%pedestal)
# subtract off pedestal to make it easier to fit
model.data -= pedestal
# fit horizontal striping, collapsing along columns
horizontal_striping = collapse_image(model.data, mask, dimension='y')
# remove horizontal striping, requires taking transpose of image
temp_image = model.data.T - horizontal_striping
# transpose back
temp_image2 = temp_image.T
# fit vertical striping, collapsing along rows
vertical_striping = collapse_image(temp_image2, mask, dimension='x')
model.close()
# copy image
log.info('Copying input to %s'%image.replace('rate.fits', 'rate_orig.fits'))
shutil.copy2(image, image.replace('rate.fits', 'rate_orig.fits'))
# remove striping from science image
with ImageModel(image) as immodel:
sci = immodel.data
temp_sci = sci.T - horizontal_striping
# transpose back
temp_sci2 = temp_sci.T
outsci = temp_sci2 - vertical_striping
# replace NaNs with zeros and update DQ array
# the image has NaNs where an entire row/column has been masked out
# so no median could be calculated.
# All of the NaNs on LW detectors and most of them on SW detectors
# are the reference pixels around the image edges. But there is one
# additional row on some SW detectors
# refpixflag = dqflags.pixel['REFERENCE_PIXEL']
# wref = np.bitwise_and(immodel.dq, refpixflag)
# outsci[np.where(wref)] = 0
wnan = np.isnan(outsci)
bpflag = dqflags.pixel['DO_NOT_USE']
outsci[wnan] = 0
immodel.dq[wnan] = np.bitwise_or(immodel.dq[wnan], bpflag)
# write output
immodel.data = outsci
# add history entry
time = datetime.now()
stepdescription = 'Removed horizontal,vertical striping; remstriping.py %s'%time.strftime('%Y-%m-%d %H:%M:%S')
# writing to file doesn't save the time stamp or software dictionary
# with the History object, but left here for completeness
software_dict = {'name':'remstriping.py',
'author':'Micaela Bagley',
'version':'1.0',
'homepage':'ceers.github.io'}
substr = util.create_history_entry(stepdescription,
software=software_dict)
immodel.history.append(substr)
log.info('Saving cleaned image to %s'%image)
immodel.save(image)
def main():
parser = argparse.ArgumentParser(description=
'Measure and remove horizontal/vertical striping from rate images')
parser.add_argument('--output_dir', type=str, default='calibrated',
help='Output directory for cleaned images. Default is calibrated.')
parser.add_argument('--runone', type=str,
help='Filename of single file to clean. If set, overrides the runall argument')
parser.add_argument('--runall', action='store_true',
help='Set to run all *rate.fits images in the output_dir directory.')
parser.add_argument('--apply_flat', action='store_true',
help='Set to apply the flat field before measuring striping pattern.')
parser.add_argument('--mask_sources', action='store_true',
help='Set to mask out sources in image before measuring striping pattern.')
parser.add_argument('--seedim_directory', type=str, default='./',
help='Directory containing Mirage seed images, used if mask_sources is True. Default is working directory.')
parser.add_argument('--threshold', type=float, default=0.01,
help='Threshold (in ADU/s) to use in the seed images when identifying pixels to mask. Default is 0.01')
args = parser.parse_args()
if args.runone:
image = os.path.join(args.output_dir, args.runone)
measure_striping(image, apply_flat=args.apply_flat,
mask_sources=args.mask_sources,
seedim_directory=args.seedim_directory,
threshold=args.threshold)
elif args.runall:
rates = glob(os.path.join(args.output_dir, '*rate.fits'))
rates.sort()
for rate in rates:
measure_striping(rate, apply_flat=args.apply_flat,
mask_sources=args.mask_sources,
seedim_directory=args.seedim_directory,
threshold=args.threshold)
else:
print('Specify either --runone with a single input filename or --runall to process all *_rate.fits files')
if __name__ == '__main__':
main()
|
spacetelescopeREPO_NAMEjwebbinar_prepPATH_START.@jwebbinar_prep_extracted@jwebbinar_prep-main@ceers@nircam@remstriping.py@.PATH_END.py
|
{
"filename": "train.py",
"repo_name": "deepskies/DeepSLIDE",
"repo_path": "DeepSLIDE_extracted/DeepSLIDE-main/src/scripts/train.py",
"type": "Python"
}
|
"""
Simple stubs to use for re-train of the final model
Can leave a default data source, or specify that 'load data' loads the dataset used in the final version
"""
import argparse
def architecture():
"""
:return: compiled architecture of the model you want to have trained
"""
return 0
def load_data(data_source):
"""
:return: data loader or full training data, split in val and train
"""
return 0, 0
def train_model(data_source, n_epochs):
"""
:param data_source:
:param n_epochs:
:return: trained model, or simply None, but saved trained model
"""
data = load_data(data_source)
model = architecture()
return 0
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--data_source", type=str, help="Data used to train the model")
parser.add_argument("--n_epochs", type=int, help='Integer number of epochs to train the model')
args = parser.parse_args()
train_model(data_source=args.data_source, n_epochs=args.n_epochs)
|
deepskiesREPO_NAMEDeepSLIDEPATH_START.@DeepSLIDE_extracted@DeepSLIDE-main@src@scripts@train.py@.PATH_END.py
|
{
"filename": "sampler.py",
"repo_name": "brinckmann/montepython_public",
"repo_path": "montepython_public_extracted/montepython_public-master/montepython/sampler.py",
"type": "Python"
}
|
"""
.. module:: sampler
:synopsis: Generic sampler
.. moduleauthor:: Benjamin Audren <benjamin.audren@epfl.ch>
.. moduleauthor:: Surhudm More <>
This module defines one key function, :func:`run`, that distributes the work to
the desired actual sampler (Metropolis Hastings, MultiNest, or PolyChord so far).
It also defines a serie of helper functions, that aim to be generically used by
all different sampler methods:
* :func:`get_covariance_matrix`
* :func:`read_args_from_chain`
* :func:`read_args_from_bestfit`
* :func:`accept_step`
* :func:`compute_lkl`
"""
from __future__ import print_function
import numpy as np
import sys
import warnings
import io_mp
from io_mp import dictitems,dictvalues,dictkeys
import os
import scipy.linalg as la
import scipy.optimize as op
def run(cosmo, data, command_line):
"""
Depending on the choice of sampler, dispatch the appropriate information
The :mod:`mcmc` module is used as previously, except the call to
:func:`mcmc.chain`, or :func:`MultiNest.run` is now within
this function, instead of from within :mod:`MontePython`.
In the long term, this function should contain any potential hybrid scheme.
"""
if command_line.method == 'MH':
import mcmc
mcmc.chain(cosmo, data, command_line)
data.out.close()
elif command_line.method == 'NS':
import MultiNest as mn
mn.run(cosmo, data, command_line)
elif command_line.method == 'PC':
import PolyChord as pc
pc.run(cosmo, data, command_line)
elif command_line.method == 'CH':
import cosmo_hammer as hammer
hammer.run(cosmo, data, command_line)
elif command_line.method == 'IS':
import importance_sampling as ims
ims.run(cosmo, data, command_line)
elif command_line.method == 'Der':
import add_derived as der
der.run(cosmo, data, command_line)
elif command_line.method == 'Fisher':
command_line.fisher = True
import mcmc
mcmc.chain(cosmo, data, command_line)
try:
data.out.close()
except AttributeError:
pass
else:
raise io_mp.ConfigurationError(
"Sampling method %s not understood" % command_line.method)
def read_args_from_chain(data, chain):
"""
Pick up the last accepted values from an input chain as a starting point
Function used only when the restart flag is set. It will simply read the
last line of an input chain, using the tail command from the extended
:class:`io_mp.File` class.
.. warning::
That method was not tested since the adding of derived parameters. The
method :func:`read_args_from_bestfit` is the prefered one.
.. warning::
This method works because of the particular presentation of the chain,
and the use of tabbings (not spaces). Please keep this in mind if you
are having difficulties
Parameters
----------
chain : str
Name of the input chain provided with the command line.
"""
chain_file = io_mp.File(chain, 'r')
parameter_names = data.get_mcmc_parameters(['varying'])
commented_line = 0
success = 0
# Test if last chain entry contains a step or a commented line
while not success:
if chain_file.tail(1)[0].split('\t')[0] == '#':
commented_line += 1
else:
success += 1
if commented_line == 1000:
raise ConfigurationError('Error loading chains files. '
'Last 1000 entries of a chain are commented')
i = 1
for elem in parameter_names:
#data.mcmc_parameters[elem]['last_accepted'] = float(
# chain_file.tail(1)[0].split('\t')[i])
data.mcmc_parameters[elem]['last_accepted'] = float(
chain_file.tail(commented_line+1)[commented_line].split('\t')[i])
i += 1
def read_args_from_bestfit(data, bestfit):
"""
Deduce the starting point either from the input file, or from a best fit
file.
Parameters
----------
bestfit : str
Name of the bestfit file from the command line.
"""
parameter_names = data.get_mcmc_parameters(['varying'])
bestfit_file = open(bestfit, 'r')
for line in bestfit_file:
if line.find('#') != -1:
bestfit_names = line.strip('#').replace(' ', '').\
replace('\n', '').split(',')
bestfit_values = np.zeros(len(bestfit_names), 'float64')
else:
line = line.split()
for i in range(len(line)):
bestfit_values[i] = line[i]
print
print('\nStarting point for rescaled parameters:')
for elem in parameter_names:
if elem in bestfit_names:
data.mcmc_parameters[elem]['last_accepted'] = \
bestfit_values[bestfit_names.index(elem)] / \
data.mcmc_parameters[elem]['scale']
print('from best-fit file : {} = '.format(elem))
print(bestfit_values[bestfit_names.index(elem)])
else:
data.mcmc_parameters[elem]['last_accepted'] = \
data.mcmc_parameters[elem]['initial'][0]
print('from input file : {} = '.format(elem))
print(data.mcmc_parameters[elem]['initial'][0])
def get_covariance_matrix(cosmo, data, command_line):
"""
Compute the covariance matrix, from an input file or from an existing
matrix.
Reordering of the names and scaling take place here, in a serie of
potentially hard to read methods. For the sake of clarity, and to avoid
confusions, the code will, by default, print out a succession of 4
covariance matrices at the beginning of the run, if starting from an
existing one. This way, you can control that the paramters are set
properly.
.. note::
The set of parameters from the run need not to be the exact same
set of parameters from the existing covariance matrix (not even the
ordering). Missing parameter from the existing covariance matrix will
use the sigma given as an input.
"""
# Setting numpy options in terms of precision (useful when writing to files
# or displaying a result, but does not affect the precision of the
# computation).
np.set_printoptions(precision=2, linewidth=150)
parameter_names = data.get_mcmc_parameters(['varying'])
# Define quiet setting if not previously defined
try:
command_line.quiet
except:
command_line.quiet = False
# if the user provides a .covmat file or if user asks to compute a fisher matrix
if command_line.cov is not None:
cov = open('{0}'.format(command_line.cov), 'r')
i = 0
for line in cov:
if line.find('#') != -1:
# Extract the names from the first line
covnames = line.strip('#').replace(' ', '').\
replace('\n', '').split(',')
# Initialize the matrices
matrix = np.zeros((len(covnames), len(covnames)), 'float64')
rot = np.zeros((len(covnames), len(covnames)))
else:
line = line.split()
for j in range(len(line)):
matrix[i][j] = np.array(line[j], 'float64')
i += 1
# First print out
if not command_line.silent and not command_line.quiet:
print('\nInput covariance matrix:')
print(covnames)
print(matrix)
# Deal with the all problematic cases.
# First, adjust the scales between stored parameters and the ones used
# in mcmc
scales = []
for elem in covnames:
if elem in parameter_names:
scales.append(data.mcmc_parameters[elem]['scale'])
else:
scales.append(1)
scales = np.diag(scales)
# Compute the inverse matrix, and assert that the computation was
# precise enough, by comparing the product to the identity matrix.
invscales = np.linalg.inv(scales)
np.testing.assert_array_almost_equal(
np.dot(scales, invscales), np.eye(np.shape(scales)[0]),
decimal=5)
# Apply the newly computed scales to the input matrix
matrix = np.dot(invscales.T, np.dot(matrix, invscales))
# Second print out, after having applied the scale factors
if not command_line.silent and not command_line.quiet:
print('\nFirst treatment (scaling)')
print(covnames)
print(matrix)
# Rotate matrix for the parameters to be well ordered, even if some
# names are missing or some are in extra.
# First, store the parameter names in temp_names that also appear in
# the covariance matrix, in the right ordering for the code (might be
# different from the input matri)
temp_names = [elem for elem in parameter_names if elem in covnames]
# If parameter_names contains less things than covnames, we will do a
# small trick. Create a second temporary array, temp_names_2, that will
# have the same dimension as covnames, and containing:
# - the elements of temp_names, in the order of parameter_names (h
# index)
# - an empty string '' for the remaining unused parameters
temp_names_2 = []
h = 0
not_in = [elem for elem in covnames if elem not in temp_names]
for k in range(len(covnames)):
if covnames[k] not in not_in:
temp_names_2.append(temp_names[h])
h += 1
else:
temp_names_2.append('')
# Create the rotation matrix, that will put the covariance matrix in
# the right order, and also assign zeros to the unused parameters from
# the input. These empty columns will be removed in the next step.
for k in range(len(covnames)):
for h in range(len(covnames)):
try:
if covnames[k] == temp_names_2[h]:
rot[h][k] = 1.
else:
rot[h][k] = 0.
except IndexError:
# The IndexError exception means that we are dealing with
# an unused parameter. By enforcing the corresponding
# rotation matrix element to 0, the resulting matrix will
# still have the same size as the original, but with zeros
# on the unused lines.
rot[h][k] = 0.
matrix = np.dot(rot, np.dot(matrix, np.transpose(rot)))
# Third print out
if not command_line.silent and not command_line.quiet:
print('\nSecond treatment (partial reordering and cleaning)')
print(temp_names_2)
print(matrix)
# Final step, creating a temporary matrix, filled with 1, that will
# eventually contain the result.
matrix_temp = np.ones((len(parameter_names),
len(parameter_names)), 'float64')
indices_final = np.zeros(len(parameter_names))
indices_initial = np.zeros(len(covnames))
# Remove names that are in parameter names but not in covnames, and
# set to zero the corresponding columns of the final result.
for k in range(len(parameter_names)):
if parameter_names[k] in covnames:
indices_final[k] = 1
for zeros in np.where(indices_final == 0)[0]:
matrix_temp[zeros, :] = 0
matrix_temp[:, zeros] = 0
# Remove names that are in covnames but not in param_names
for h in range(len(covnames)):
if covnames[h] in parameter_names:
indices_initial[h] = 1
# There, put a place holder number (we are using a pure imaginary
# number: i, to avoid any problem) in the initial matrix, so that the
# next step only copy the interesting part of the input to the final
# matrix.
max_value = np.finfo(np.float64).max
for zeros in np.where(indices_initial == 0)[0]:
matrix[zeros, :] = [max_value for _ in range(
len(matrix[zeros, :]))]
matrix[:, zeros] = [max_value for _ in range(
len(matrix[:, zeros]))]
# Now put in the temporary matrix, where the 1 were, the interesting
# quantities from the input (the one that are not equal to i).
matrix_temp[matrix_temp == 1] = matrix[matrix != max_value]
matrix = np.copy(matrix_temp)
# on all other lines, that contain 0, just use sigma^2
for zeros in np.where(indices_final == 0)[0]:
matrix[zeros, zeros] = np.array(
data.mcmc_parameters[parameter_names[zeros]]['initial'][3],
'float64')**2
# else, take sigmas^2.
else:
matrix = np.identity(len(parameter_names), 'float64')
for index, elem in enumerate(parameter_names):
matrix[index][index] = np.array(
data.mcmc_parameters[elem]['initial'][3], 'float64')**2
# Final print out, the actually used covariance matrix
if not command_line.silent and not command_line.quiet:
print('\nDeduced starting covariance matrix:\n')
print(parameter_names)
print(matrix)
#inverse, and diagonalization
eigv, eigV = np.linalg.eig(np.linalg.inv(matrix))
#if command_line.start_from_fisher:
# command_line.fisher = True
#if command_line.fisher:
# eigv, eigV, matrix = get_fisher_matrix(cosmo, data, command_line, matrix)
return eigv, eigV, matrix
def get_minimum(cosmo, data, command_line, covmat):
if not command_line.silent:
warnings.warn("Minimization implementation is being tested")
# Create the center dictionary, which will hold the center point
# information
center = {}
parameter_names = data.get_mcmc_parameters(['varying'])
if not command_line.bf:
for elem in parameter_names:
center[elem] = data.mcmc_parameters[elem]['initial'][0]
else:
read_args_from_bestfit(data, command_line.bf)
for elem in parameter_names:
center[elem] = data.mcmc_parameters[elem]['last_accepted']
stepsizes = np.zeros(len(parameter_names), 'float64')
parameters = np.zeros(len(parameter_names), 'float64')
bounds = np.zeros([len(parameter_names),2], 'float64')
cons = ()
for index, elem in enumerate(parameter_names):
parameters[index] = center[elem]
stepsizes[index] = 0.1*covmat[index,index]**0.5
if data.mcmc_parameters[elem]['initial'][1] == None:
bounds[index,0] = center[elem] - 1.*covmat[index,index]**0.5
else:
bounds[index,0] = data.mcmc_parameters[elem]['initial'][1]
if data.mcmc_parameters[elem]['initial'][2] == None:
bounds[index,1] = center[elem] + 1.*covmat[index,index]**0.5
else:
bounds[index,1] = data.mcmc_parameters[elem]['initial'][2]
cons += ({'type': 'ineq', 'fun': lambda x: x[index] - bounds[index,0]},
{'type': 'ineq', 'fun': lambda x: bounds[index,1] - x[index]},)
print('bounds on ',elem,' : ',bounds[index,0],bounds[index,1])
#FK: use list-comprehension so that the parameter values are distinguishable from step to step
print('parameters: ',[param for param in parameters])
print('stepsizes: ',stepsizes[0])
print('bounds: ',bounds)
#minimum, chi2 = op.fmin_cg(chi2_eff,
# Use unconstrained Polak & Ribiere conjugate gradient algorithm
# CosmoMC uses a constrained (Fletcher & Reeves) version of this
#xopt, fopt, func_calls, grad_calls, warnflags, allvecs = op.fmin_cg(chi2_eff,
# parameters,
# #fprime = gradient_chi2_eff,
# epsilon = stepsizes,
# args = (cosmo,data),#bounds),
# full_output = True,
# disp = True,
# retall = True)
# Use constrained Newton conjugate gradient algorithm
#x, nfeval, rc = op.fmin_tnc(chi2_eff,
# parameters,
# #fprime = gradient_chi2_eff,
# args = (cosmo,data),
# approx_grad = True,
# bounds = bounds,
# epsilon = stepsizes,
# disp = 5)
#result = op.minimize(chi2_eff,
# parameters,
# args = (cosmo,data),
# method='COBYLA',
# #method='SLSQP',
# constraints=cons,
# #bounds=bounds,
# tol=0.000001,
# options = {'disp': True,
# 'rhobeg': stepsizes})
# #'eps': stepsizes})
# For HST with 1 param the best is TNC with 'eps':stepsizes, bounds, tol, although bounds make it smlower (but avoids htting unphysical region)
# For forecasts or Planck lite SLSQP with tol=0.00001 works well, but does not work for full Planck TTTEEE highl
result = op.minimize(chi2_eff,
parameters,
args = (cosmo,data),
#method='trust-region-exact',
#method='BFGS',
#method='TNC',
#method='L-BFGS-B',
method='SLSQP',
#options={'eps':stepsizes},
#constraints=cons,
bounds=bounds,
tol=command_line.minimize_tol)
#options = {'disp': True})
#'initial_tr_radius': stepsizes,
#'max_tr_radius': stepsizes})
#result = op.differential_evolution(chi2_eff,
# bounds,
# args = (cosmo,data))
print('Final output of minimize')
for index,elem in enumerate(parameter_names):
print(elem, 'new:', result.x[index], ', old:', parameters[index])
#FK: return also min chi^2:
return result.x, result.fun
def chi2_eff(params, cosmo, data, bounds=False):
parameter_names = data.get_mcmc_parameters(['varying'])
for index, elem in enumerate(parameter_names):
#print(elem,params[index])
data.mcmc_parameters[elem]['current'] = params[index]
if not type(bounds) == type(False):
if (params[index] < bounds[index,0]) or (params[index] > bounds[index,1]):
chi2 = 1e30
print(elem+' exceeds bounds with value %f and bounds %f < x < %f' %(params[index],bounds[index,0],bounds[index,1]))
return chi2
# Update current parameters to the new parameters, only taking steps as requested
data.update_cosmo_arguments()
# Compute loglike value for the new parameters
chi2 = -2.*compute_lkl(cosmo, data)
#FK: use list-comprehension so that the parameter values are distinguishable from step to step
print('In minimization: ',chi2,' at ',[param for param in params])
return chi2
def gradient_chi2_eff(params, cosmo, data, bounds=False):
parameter_names = data.get_mcmc_parameters(['varying'])
for index, elem in enumerate(parameter_names):
data.mcmc_parameters[elem]['current'] = params[index]
# Update current parameters to the new parameters, only taking steps as requested
data.update_cosmo_arguments()
# Compute loglike value for the new parameters
chi2 = -2.*compute_lkl(cosmo, data)
# Initialise the gradient field
gradient = np.zeros(len(parameter_names), 'float64')
for index, elem in enumerate(parameter_names):
dx = 0.01*params[index]
#
data.mcmc_parameters[elem]['current'] += dx
data.update_cosmo_arguments()
chi2_plus = -2.*compute_lkl(cosmo, data)
#
data.mcmc_parameters[elem]['current'] -= 2.*dx
data.update_cosmo_arguments()
chi2_minus = -2.*compute_lkl(cosmo, data)
#
gradient[index] = (chi2_plus - chi2_minus)/2./dx
#
data.mcmc_parameters[elem]['current'] += dx
return gradient
def get_fisher_matrix(cosmo, data, command_line, inv_fisher_matrix, minimum=0):
# Fisher matrix method by T. Brinckmann
# Contributions from T. Tram, J. Lesgourgues
# We will work out the fisher matrix for all the parameters and
# write it and its inverse to a file
# Pass input to data structure
data.fisher_step_it = int(command_line.fisher_step_it)
data.fisher_delta = command_line.fisher_delta
data.fisher_tol = command_line.fisher_tol
data.fisher_sym_lkl = command_line.fisher_sym_lkl
# Whether to use symmetric step. Symmetric step is recommended, as it makes the
# computation faster and in most cases is sufficient.
data.use_symmetric_step = True
if command_line.fisher_asymmetric:
data.use_symmetric_step = False
if not command_line.silent:
warnings.warn("Fisher implementation is being tested")
# Create the center dictionary, which will hold the center point information
center = {}
parameter_names = data.get_mcmc_parameters(['varying'])
if not type(minimum) == int:
for index, elem in enumerate(parameter_names):
center[elem] = minimum[index]
elif not command_line.bf:
for elem in parameter_names:
center[elem] = data.mcmc_parameters[elem]['initial'][0]
else:
read_args_from_bestfit(data, command_line.bf)
for elem in parameter_names:
center[elem] = data.mcmc_parameters[elem]['last_accepted']
scales = np.zeros((len(parameter_names)))
invscales = np.zeros((len(parameter_names)))
for index, elem in enumerate(parameter_names):
data.mcmc_parameters[elem]['current'] = center[elem]
scales[index] = data.mcmc_parameters[elem]['scale']
invscales[index] = 1./data.mcmc_parameters[elem]['scale']
# Load stepsize from input covmat or covmat generated from param file
# JL TODO: check this, and try another scheme to be sure that index and elem refer to the same params in the same order
# here the stepsizes are for the scaled parameter (e.g. 100*omega_b)
stepsize = np.zeros([len(parameter_names),3])
for index in range(len(parameter_names)):
stepsize[index,0] = -(inv_fisher_matrix[index][index])**0.5
stepsize[index,1] = (inv_fisher_matrix[index][index])**0.5
# Adjust stepsize in case step exceeds boundary
stepsize = adjust_fisher_bounds(data,command_line,center,stepsize)
fisher_iteration = 0
fisher_status = 0
while fisher_iteration < data.fisher_step_it and not fisher_status:
fisher_iteration += 1
# Compute the Fisher matrix and the gradient array at the center point.
print("Compute Fisher [iteration %d/%d] with following stepsizes for scaled parameters:" % (fisher_iteration,data.fisher_step_it))
for index in range(len(parameter_names)):
print("%s : diagonal element = %e" % (parameter_names[index],inv_fisher_matrix[index,index]))
# For rotating the step array, not implemented
step_matrix = np.identity(len(parameter_names), dtype='float64')
# Compute fisher matrix
fisher_matrix, gradient = compute_fisher(data, command_line, cosmo, center, stepsize, step_matrix)
# If we want to rotate back to the cosmological parameter basis
if not command_line.silent:
print("Fisher matrix computed [iteration %d/%d]" % (fisher_iteration,data.fisher_step_it))
# Compute inverse of the fisher matrix, catch LinAlgError exception
try:
inv_fisher_matrix = np.linalg.inv(fisher_matrix)
except np.linalg.LinAlgError:
raise io_mp.ConfigurationError(
"Could not find Fisher matrix inverse. Try with different "
"fisher_tol, fisher_delta, input sigmas (or covmat), bestfit, or remove the "
"option --fisher and run with Metropolis-Hastings or another sampling method.")
# Check if the inverse fisher matrix is positive definite
try:
la.cholesky(inv_fisher_matrix).T
fisher_status = 1
print("Inverse Fisher matrix computation successful! Saving Fisher and inverse Fisher matrices.")
except:
if not command_line.silent:
print('Fisher matrix computation failed - inverse not positive definite')
if data.fisher_step_it < 2:
raise io_mp.ConfigurationError(
"Could not find Fisher matrix inverse and fisher step iteration is not enabled. Use flag "
"--fisher-step-it or change the input sigmas (or covariance matrix) or the bestfit.")
if fisher_iteration == data.fisher_step_it:
raise io_mp.ConfigurationError(
"Could not find Fisher matrix inverse after %d attempts. Try with different "
"fisher_tol, fisher_delta, input sigmas (or covmat), bestfit, or remove the "
"option --fisher and run with Metropolis-Hastings or another sampling method." %(data.fisher_step_it))
# If the inverse Fisher matrix is not positive definite we want to
# iterate on the target fisher delta (--fisher-delta), increasing
# it incrementally by an amount equal to the original fisher delta.
if not command_line.silent:
print('Increasing fisher_iteration by 1, to %d, and adjusting fisher_delta from %f to %f' %(fisher_iteration+1, data.fisher_delta, data.fisher_delta + command_line.fisher_delta))
data.fisher_delta += command_line.fisher_delta
fisher_status = 0
# When inverse Fisher matrix was successfully computed, take
# scalings into account and write the matrices in files. The scale
# factors are removed in order to store true parameter covariance
if fisher_status:
fisher_matrix = invscales[:,np.newaxis]*fisher_matrix*invscales[np.newaxis,:]
io_mp.write_covariance_matrix(
fisher_matrix, parameter_names,
os.path.join(command_line.folder, 'fisher.mat'))
inv_fisher_matrix = scales[:,np.newaxis]*inv_fisher_matrix*scales[np.newaxis,:]
io_mp.write_covariance_matrix(
inv_fisher_matrix, parameter_names,
os.path.join(command_line.folder, 'inv_fisher.mat'))
# FK: also write-out gradient:
fname = os.path.join(command_line.folder, 'fisher_gradient.vec')
header = ''
for param in parameter_names:
header += '{:}, '.format(param)
header = header[:-2]
np.savetxt(fname, gradient, header=header)
return inv_fisher_matrix
def accept_step(data):
"""
Transfer the 'current' point in the varying parameters to the last accepted
one.
"""
for elem in data.get_mcmc_parameters(['varying']):
data.mcmc_parameters[elem]['last_accepted'] = \
data.mcmc_parameters[elem]['current']
for elem in data.get_mcmc_parameters(['derived']):
data.mcmc_parameters[elem]['last_accepted'] = \
data.mcmc_parameters[elem]['current']
for elem in data.get_mcmc_parameters(['derived_lkl']):
data.mcmc_parameters[elem]['last_accepted'] = \
data.mcmc_parameters[elem]['current']
def check_flat_bound_priors(parameters, names):
"""
Ensure that all varying parameters are bound and flat
It is a necessary condition to use the code with MultiNest, PolyChord or the
Cosmo Hammer.
"""
is_flat = all(parameters[name]['prior'].prior_type == 'flat'
for name in names)
is_bound = all(parameters[name]['prior'].is_bound()
for name in names)
return is_flat, is_bound
def compute_lkl(cosmo, data):
"""
Compute the likelihood, given the current point in parameter space.
This function now performs a test before calling the cosmological model
(**new in version 1.2**). If any cosmological parameter changed, the flag
:code:`data.need_cosmo_update` will be set to :code:`True`, from the
routine :func:`check_for_slow_step <data.Data.check_for_slow_step>`.
Returns
-------
loglike : float
The log of the likelihood (:math:`\\frac{-\chi^2}2`) computed from the
sum of the likelihoods of the experiments specified in the input
parameter file.
This function returns :attr:`data.boundary_loglike
<data.data.boundary_loglike>`, defined in the module :mod:`data` if
*i)* the current point in the parameter space has hit a prior edge, or
*ii)* the cosmological module failed to compute the model. This value
is chosen to be extremly small (large negative value), so that the step
will always be rejected.
"""
from classy import CosmoSevereError, CosmoComputationError
# If the cosmological module has already been called once, and if the
# cosmological parameters have changed, then clean up, and compute.
if cosmo.state and data.need_cosmo_update is True:
cosmo.struct_cleanup()
# If the data needs to change, then do a normal call to the cosmological
# compute function. Note that, even if need_cosmo update is True, this
# function must be called if the jumping factor is set to zero. Indeed,
# this means the code is called for only one point, to set the fiducial
# model.
if ((data.need_cosmo_update) or
(not cosmo.state) or
(data.jumping_factor == 0)):
# Prepare the cosmological module with the new set of parameters
cosmo.set(data.cosmo_arguments)
# Compute the model, keeping track of the errors
# In classy.pyx, we made use of two type of python errors, to handle
# two different situations.
# - CosmoSevereError is returned if a parameter was not properly set
# during the initialisation (for instance, you entered Ommega_cdm
# instead of Omega_cdm). Then, the code exits, to prevent running with
# imaginary parameters. This behaviour is also used in case you want to
# kill the process.
# - CosmoComputationError is returned if Class fails to compute the
# output given the parameter values. This will be considered as a valid
# point, but with minimum likelihood, so will be rejected, resulting in
# the choice of a new point.
try:
data.cosmo_arguments['output']
except:
data.cosmo_arguments.update({'output': ''})
if 'SZ' in data.cosmo_arguments['output']:
try:
if 'SZ_counts':
cosmo.compute(["szcount"])
else:
cosmo.compute(["szpowerspectrum"])
except CosmoComputationError as failure_message:
sys.stderr.write(str(failure_message)+'\n')
sys.stderr.flush()
return data.boundary_loglike
except CosmoSevereError as critical_message:
raise io_mp.CosmologicalModuleError(
"Something went wrong when calling CLASS" +
str(critical_message))
except KeyboardInterrupt:
raise io_mp.CosmologicalModuleError(
"You interrupted execution")
else:
try:
cosmo.compute()
except CosmoComputationError as failure_message:
# could be useful to uncomment for debugging:
#np.set_printoptions(precision=30, linewidth=150)
#print('cosmo params')
#print(data.cosmo_arguments)
#print(data.cosmo_arguments['tau_reio'])
sys.stderr.write(str(failure_message)+'\n')
sys.stderr.flush()
return data.boundary_loglike
except CosmoSevereError as critical_message:
raise io_mp.CosmologicalModuleError(
"Something went wrong when calling CLASS" +
str(critical_message))
except KeyboardInterrupt:
raise io_mp.CosmologicalModuleError(
"You interrupted execution")
# For each desired likelihood, compute its value against the theoretical
# model
loglike = 0
# This flag holds the information whether a fiducial model was written. In
# this case, the log likelihood returned will be '1j', meaning the
# imaginary number i.
flag_wrote_fiducial = 0
for likelihood in dictvalues(data.lkl):
if likelihood.need_update is True:
value = likelihood.loglkl(cosmo, data)
# Storing the result
likelihood.backup_value = value
# Otherwise, take the existing value
else:
value = likelihood.backup_value
if data.command_line.display_each_chi2:
print("-> for ",likelihood.name,": loglkl=",value,", chi2eff=",-2.*value)
loglike += value
# In case the fiducial file was written, store this information
if value == 1j:
flag_wrote_fiducial += 1
if data.command_line.display_each_chi2:
print("-> Total: loglkl=",loglike,", chi2eff=",-2.*loglike)
# Compute the derived parameters if relevant
if data.get_mcmc_parameters(['derived']) != []:
try:
derived = cosmo.get_current_derived_parameters(
data.get_mcmc_parameters(['derived']))
for name, value in dictitems(derived):
data.mcmc_parameters[name]['current'] = value
except AttributeError:
# This happens if the classy wrapper is still using the old
# convention, expecting data as the input parameter
cosmo.get_current_derived_parameters(data)
except CosmoSevereError:
raise io_mp.CosmologicalModuleError(
"Could not write the current derived parameters")
# DCH adding a check to make sure the derived_lkl are passed properly
if data.get_mcmc_parameters(['derived_lkl']) != [] or hasattr(data,'derived_lkl'):
try:
for (name, value) in data.derived_lkl.items():
data.mcmc_parameters[name]['current'] = value
except Exception as missing:
raise io_mp.CosmologicalModuleError(
"You requested derived_lkl parameters, but you are missing the following ones in your param file:" + str(missing))
for elem in data.get_mcmc_parameters(['derived']):
data.mcmc_parameters[elem]['current'] /= \
data.mcmc_parameters[elem]['scale']
# If fiducial files were created, inform the user, and exit
if flag_wrote_fiducial > 0:
if flag_wrote_fiducial == len(data.lkl):
raise io_mp.FiducialModelWritten(
"This is not an error but a normal abort, because " +
"fiducial file(s) was(were) created. " +
"You may now start a new run. ")
else:
raise io_mp.FiducialModelWritten(
"This is not an error but a normal abort, because " +
"fiducial file(s) was(were) created. " +
"However, be careful !!! Some previously non-existing " +
"fiducial files were created, but potentially not all of them. " +
"Some older fiducial files will keep being used. If you have doubts, " +
"you are advised to check manually in the headers of the " +
"corresponding files that all fiducial parameters are consistent "+
"with each other. If everything looks fine, "
"you may now start a new run.")
return loglike/data.command_line.temperature
def compute_fisher(data, command_line, cosmo, center, step_size, step_matrix):
# Function used by Fisher matrix method by T. Brinckmann
# Initialise
parameter_names = data.get_mcmc_parameters(['varying'])
fisher_matrix = np.zeros(
(len(parameter_names), len(parameter_names)), 'float64')
# Initialise the gradient field
gradient = np.zeros(len(parameter_names), 'float64')
# Re-center all parameters
for elem in center:
data.mcmc_parameters[elem]['current'] = center[elem]
# Compute loglike at the point supposed to be a good estimate of the best-fit
data.update_cosmo_arguments()
loglike_min = compute_lkl(cosmo, data)
# Loop through diagonal elements first, followed by off-diagonal elements
for elem in ['diag','off-diag']:
for k, elem_k in enumerate(parameter_names):
kdiff = step_size[k]
# loop over step direction
for step_index in [0,1]:
if elem == 'diag' and step_index == 1:
continue
# loop over second parameter
for h, elem_h in enumerate(parameter_names):
hdiff = step_size[h]
# Since the matrix is symmetric, we only compute the
# elements of one half of it plus the diagonal.
if k > h:
continue
if k == h and elem == 'diag':
if not command_line.silent:
print('---> Computing fisher element (%d,%d)' % (k,h))
temp1, temp2, diff_1 = compute_fisher_element(
data, command_line, cosmo, center, step_matrix, loglike_min, step_index,
(elem_k, kdiff))
fisher_matrix[k][k] += temp1
gradient[k] += temp2
step_size[k] = diff_1
elif k < h and elem == 'off-diag':
if not command_line.silent:
print('---> Computing fisher element (%d,%d), part %d/2' % (k,h,step_index+1))
fisher_matrix[k][h] += compute_fisher_element(
data, command_line, cosmo, center, step_matrix, loglike_min, step_index,
(elem_k, kdiff),
(elem_h, hdiff))
fisher_matrix[h][k] = fisher_matrix[k][h]
return fisher_matrix, gradient
def compute_fisher_element(data, command_line, cosmo, center, step_matrix, loglike_min, step_index_1, one, two=None):
# Function used by Fisher matrix method by T. Brinckmann
# Unwrap
name_1, diff_1 = one
if two:
name_2, diff_2 = two
if step_index_1 == 1:
# Calculate step (++)
step_index_2 = 1
loglike_1, rotated_step_xx = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# Calculate step (+-)
step_index_2 = 0
loglike_2, rotated_step_xy = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
else:
loglike_1 = 0
loglike_2 = 0
if step_index_1 == 0:
# Calculate step (-+). Assuming symmetry rotated_step_yx = rotated_step_xy
step_index_2 = 1
loglike_3, rotated_step_xy = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# Calculate step (--). Assuming symmetry rotated_step_yy = rotated_step_xx
step_index_2 = 0
loglike_4, rotated_step_xx = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
else:
loglike_3 = 0
loglike_4 = 0
# If the left and right step sizes are equal these terms will cancel
if abs(diff_2[0]) == abs(diff_2[1]) or diff_2[2] or data.use_symmetric_step:
loglike_5 = 0.
loglike_6 = 0.
else:
if step_index_1 == 1:
# Calculate step (+/)
step_index_2 = None
loglike_5, rotated_step_x = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
else:
loglike_5 = 0
if step_index_1 == 0:
# Calculate step (-/)
step_index_2 = None
loglike_6, rotated_step_x = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
else:
loglike_6 = 0
# If the left and right step sizes are equal these terms will cancel
if abs(diff_1[0]) == abs(diff_1[1]) or diff_1[2] or data.use_symmetric_step:
loglike_7 = 0.
loglike_8 = 0.
else:
step_index_1 = None
# Calculate step (/+)
step_index_2 = 1
loglike_7, rotated_step_y1 = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# Calculate step (/-)
step_index_2 = 0
loglike_8, rotated_step_y2 = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# Count the bestfit term at most once
if step_index_1:
loglike_min = 0
# In the following we want only the step magnitude, not sign
diff_1_backup = diff_1[:]
diff_2_backup = diff_2[:]
diff_1 = abs(diff_1)
diff_2 = abs(diff_2)
# Remember that in some cases only used diff_1[2] or diff_1[0]
if diff_1[2]:
diff_1[0] = diff_1[2]
diff_1[1] = diff_1[2]
elif data.use_symmetric_step:
diff_1[1] = diff_1[0]
# And only used diff_2[2] or diff_2[0]
if diff_2[2]:
diff_2[0] = diff_2[2]
diff_2[1] = diff_2[2]
elif data.use_symmetric_step:
diff_2[1] = diff_2[0]
# Extract parameter indices
parameter_names = data.get_mcmc_parameters(['varying'])
index_1 = parameter_names.index(name_1)
index_2 = parameter_names.index(name_2)
# In the case of symmetric steps reduces to -(loglike_1-loglike_2-loglike_3+loglike_4)/(4.*diff_1*diff_2)
fisher_off_diagonal = -((1./(diff_2[0]**2./diff_2[1]+diff_2[0]))* # sym. \Delta p_j: reduces to 1/(2 \Delta p_j)
(1./(diff_1[0]**2./diff_1[1]+diff_1[0]))* # sym. \Delta p_i: reduces to 1/(2 \Delta p_i)
((diff_2[0]/diff_2[1])**2. * ((diff_1[0]/diff_1[1])**2.*loglike_1 - loglike_3) # sym. \Delta p_i and \Delta p_j: reduces to loglike_1 - loglike_3
-(diff_1[0]/diff_1[1])**2.*loglike_2 + loglike_4 # sym \Delta p_i: reduces to loglike_4 - loglike_2
+((diff_2[0]/diff_2[1])**2.-1.) * (loglike_6 - (diff_1[0]/diff_1[1])**2.*loglike_5 + ((diff_1[0]/diff_1[1])**2.-1.)*loglike_min) # cancels if sym. \Delta p_j
+((diff_1[0]/diff_1[1])**2.-1.) * (loglike_8 - (diff_2[0]/diff_2[1])**2.*loglike_7))) # cancels if sym. \Delta p_i
# For multiple parameters using the symmetric likelihood
# assumption we will likely encounter problems!
if diff_1[2] and diff_2[2]:
warnings.warn('Symmetric likelihood assumed for multiple parameters, with fisher_off_diagonal = %f, setting off-diagonal to zero' % fisher_off_diagonal)
fisher_off_diagonal = 0.
# Restore step sign
diff_1 = diff_1_backup
diff_2 = diff_2_backup
return fisher_off_diagonal
# It is otherwise a diagonal component
else:
# Calculate left step
step_index_1 = 0
step_index_2 = None
loglike_left, diff_1, rotated_step_left = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# Update 'one' with the new step size to pass to the next step
one = (one[0],diff_1)
# Calculate right step
step_index_1 = 1
loglike_right, diff_1, rotated_step_right = compute_fisher_step(data,command_line,cosmo,center,step_matrix,loglike_min,one,two,step_index_1,step_index_2)
# In the following we want only the step magnitude, not sign
diff_1_backup = diff_1[:]
diff_1 = abs(diff_1)
# Remember that in some cases only used diff_1[2] or diff_1[0]
if diff_1[2]:
diff_1[0] = diff_1[2]
diff_1[1] = diff_1[2]
elif data.use_symmetric_step:
diff_1[1] = diff_1[0]
parameter_names = data.get_mcmc_parameters(['varying'])
index = parameter_names.index(name_1)
# In case of symmetric steps reduces to -(loglike_right-2.*loglike_min+loglike_left)/(diff_1**2)
fisher_diagonal = -2.*((diff_1[0]/diff_1[1])*loglike_right-(diff_1[0]/diff_1[1]+1.)
*loglike_min+loglike_left)/(diff_1[0]*diff_1[1]+diff_1[0]**2.)
# In case of symmetric steps reduces to -(loglike_right-loglike_left)/(2.*diff_1)
gradient = -((diff_1[0]/diff_1[1])**2.*loglike_right-loglike_left-((diff_1[0]/diff_1[1])**2.-1.)
*loglike_min)/((diff_1[0]**2./diff_1[1])+diff_1[0])
# Restore step sign
diff_1 = diff_1_backup
return fisher_diagonal, gradient, diff_1
def compute_fisher_step(data, command_line, cosmo, center, step_matrix, loglike_min, one, two, step_index_1, step_index_2):
# Function used by Fisher matrix method by T. Brinckmann
# Unwrap
name_1, diff_1 = one
if two:
name_2, diff_2 = two
# Save input parameters
deltaloglkl_req = data.fisher_delta
deltaloglkl_tol = data.fisher_tol
# Create an array of the center value
parameter_names = data.get_mcmc_parameters(['varying'])
center_array = np.zeros(len(center), 'float64')
for elem in parameter_names:
index = parameter_names.index(elem)
center_array[index] = center[elem]
backup_step = [0.]
repeat = 1
while repeat:
# Create array with new steps in the basis of the parameters
step_array = np.zeros(len(parameter_names), 'float64')
if not step_index_1 == None:
index = parameter_names.index(name_1)
# Check if the parameter step had exceeded a boundary.
# Assume symmetric likelihood and use opposite step if so.
# I.e. both steps (+/-) will be the same and will return the same -loglkl.
if diff_1[2]:
step_array[index] = diff_1[2]
# If symmetric step is required use diff_1[0] to determine size of step.
# Only step_index_1=0 goes through the iteration cycle in this case.
# If diff_1[2] is defined, will instead use that value.
elif data.use_symmetric_step:
step_array[index] = np.sign(diff_1[step_index_1])*abs(diff_1[0])
else:
step_array[index] = diff_1[step_index_1]
# If this is a diagonal element computation and the other parameter
# uses the symmetric assumption (i.e. diff_2[2] is non-zero) and we
# are considering the step direction that would have exceeded the
# boundary (so it would have had a sign opposite the sign of diff_2[2]),
# then we want to flip the sign for *this* (not the other) parameter.
if two:
if diff_1[2] and diff_2[2]:
warnings.warn('BOTH PARAMETERS SYMMETRIC! Need one-sided derivative (not-implemented)')
elif (step_index_2 == 0 and diff_2[2] > 0) or (step_index_2 == 1 and diff_2[2] < 0):
step_array[index] = -step_array[index]
if not command_line.silent:
print('changed step array from (%f,%f) to (%f,%f)' %(-step_array[index],diff_2[2],step_array[index],diff_2[2]))
if two and not step_index_2 == None:
index = parameter_names.index(name_2)
# Check if the parameter step had exceeded a boundary.
# Assume symmetric likelihood and use opposite step if so.
# I.e. both steps (+/-) will be the same and will return the same -loglkl.
if diff_2[2]:
step_array[index] = diff_2[2]
# If symmetric step is required use diff_2[0] to determine size of step.
# Only step_index_2=0 goes through the iteration cycle in this case.
# If diff_2[2] is defined, will instead use that value.
elif data.use_symmetric_step:
step_array[index] = np.sign(diff_2[step_index_2])*abs(diff_2[0])
else:
step_array[index] = diff_2[step_index_2]
# If this is a diagonal element computation and the other parameter
# uses the symmetric assumption (i.e. diff_2[2] is non-zero) and we
# are considering the step direction that would have exceeded the
# boundary (so it would have had a sign opposite the sign of diff_2[2]),
# then we want to flip the sign for *this* (not the other) parameter.
if diff_1[2] and diff_2[2]:
warnings.warn('BOTH PARAMETERS SYMMETRIC! Need one-sided derivative (not implemented)')
elif (step_index_1 == 0 and diff_1[2] > 0) or (step_index_1 == 1 and diff_1[2] < 0):
step_array[index] = -step_array[index]
if not command_line.silent:
print('changed step array from (%f,%f) to (%f,%f)' %(diff_1[2],-step_array[index],diff_1[2],step_array[index]))
# Rotate the step vector to the basis of the covariance matrix
# Not implemented, step_matrix is a unit matrix
rotated_array = np.dot(step_matrix, step_array)
# Construct step vector for comparison with previous step
step_array = center_array + rotated_array
# Check for slow/fast parameters, comparing to the last step in the Fisher matrix calculation.
# This means calling CLASS can be skipped if only nuisance parameters changed.
data.check_for_slow_step(step_array)
# In order to take the correct new step we need to re-center
# the parameters and add the rotated step vector.
for elem in center:
index = parameter_names.index(elem)
data.mcmc_parameters[elem]['current'] = center[elem] + rotated_array[index]
# Update current parameters to the new parameters, only taking steps as requested
data.update_cosmo_arguments()
# Compute loglike value for the new parameters
loglike = compute_lkl(cosmo, data)
# Iterative stepsize. If -Delta ln(L) > 1, change step size and repeat steps above
# if data.use_symmetric_step=True only runs for step_index_1=0
if not two and not (data.use_symmetric_step and step_index_1 == 1) and data.fisher_step_it:
# Save previous step
# For symmetric step
if diff_1[2]:
backup_step.append(diff_1[2])
# For normal step
else:
backup_step.append(diff_1[step_index_1])
# Calculate Delta ln(L)
deltaloglike = loglike - loglike_min
if not command_line.silent:
print(">>>> For %s[%d],Delta ln(L)=%e using min(ln(L))=%e"%(name_1,int(np.sign(diff_1[step_index_1])),deltaloglike,loglike_min))
if loglike-loglike_min > 0.:
warnings.warn('Loglike(step) < Loglike(center): Fisher matrix calculation not centered on the minimum, exercise caution')
# If -Delta ln(L) is larger than desired reduce stepsize
if -deltaloglike > (deltaloglkl_req + deltaloglkl_tol):
if diff_1[2]:
diff_1[2] -= np.sign(diff_1[2]) * 0.5 * abs(backup_step[abs(repeat)-1] - diff_1[2])
if not command_line.silent:
print('Updated stepsize. Before, after =',backup_step[abs(repeat)],diff_1[2])
else:
diff_1[step_index_1] -= np.sign(diff_1[step_index_1]) * 0.5 * abs(backup_step[abs(repeat)-1] - diff_1[step_index_1])
if not command_line.silent:
print('Updated stepsize. Before, after =',backup_step[abs(repeat)],diff_1[step_index_1])
repeat = len(backup_step)
# If stepsize is not converging to the required tolerance within 10 steps
# relax tolerance recursively tolerance_i+1 = tolerance_i * (1 + N_step)/10,
# with the first tolerance_i given by the input delta loglkl tolerance.
if repeat > 10:
deltaloglkl_tol *= (1. + repeat)/10.
# If -Delta ln(L) is smaller than desired increase stepsize
# ISSUE: what about boundaries when increasing stepsize?
elif -deltaloglike < (deltaloglkl_req - deltaloglkl_tol):
if repeat > 1:
if diff_1[2]:
diff_1[2] += np.sign(diff_1[2]) * 0.5 * abs(backup_step[abs(repeat)-1] - diff_1[2])
if not command_line.silent:
print('Updated stepsize. Before, after =',backup_step[abs(repeat)],diff_1[2])
else:
diff_1[step_index_1] += np.sign(diff_1[step_index_1]) * 0.5 * abs(backup_step[abs(repeat)-1] - diff_1[step_index_1])
if not command_line.silent:
print('Updated stepsize. Before, after =',backup_step[abs(repeat)],diff_1[step_index_1])
repeat = len(backup_step)
# If stepsize is not converging to the required tolerance within 10 steps
# relax tolerance recursively tolerance_i+1 = tolerance_i * (1 + N_step)/10,
# with the first tolerance_i given by the input delta loglkl tolerance.
if repeat > 10:
deltaloglkl_tol *= (1. + repeat)/10.
else:
if diff_1[2]:
diff_1[2] *= 2.
else:
diff_1[step_index_1] *= 2.
if not command_line.silent:
print('Updated stepsize. Before, after =',backup_step[abs(repeat)],diff_1[step_index_1])
repeat = -len(backup_step)
# If stepsize is not converging to the required tolerance within 10 steps
# relax tolerance recursively tolerance_i+1 = tolerance_i * (1 + N_step)/10,
# with the first tolerance_i given by the input delta loglkl tolerance.
if repeat > 10:
deltaloglkl_tol *= (1. + repeat)/10.
else:
if deltaloglkl_tol > data.fisher_tol:
warnings.warn('Could not converge to the desired delta within the expected tolerance. '
'Adjusted tolerance from %f to %f. Final Delta ln(L) value was %f.'
% (data.fisher_tol,deltaloglkl_tol,deltaloglike))
return loglike, diff_1, rotated_array
elif not two:
return loglike, diff_1, rotated_array
else:
return loglike, rotated_array
def adjust_fisher_bounds(data, command_line, center, step_size):
# Function used by Fisher matrix method by T. Brinckmann
# For the Fisher approach we may need to adjust the step size
# if the step exceed the bounds on the parameter given in the
# param file. We Loop through all parameters, adjusting the
# step size of any parameter where that step exceeded the bounds.
# If the distance between the boundary and the center value
# is larger than the threshold value (default: 0.1*sigma)
# the initial step size is given by the distance between
# the center and the boundary.
# However, if the distance is less than the threshold,
# the code will switch to using the symmetric likelihood
# assumption and only do likelihood evaluations in one
# direction of parameter space (e.g. positive) and mirror
# the likelihood value for the other direction. This is
# useful for parameters where the best fit of the likelihood
# is close to a boundary.
# This behavior is controlled via --fisher-sym-lkl, which
# defines the threshold for when to switch to the symmetric
# likelihood assumption.
# WARNING: causes problems if multiple parameters use the
# symmetric likelihood assumption. In this case we need to
# switch to a one-sided derivative computation (instead of
# two-sided with mirroring), which has not been implemented.
for index, elem in enumerate(data.get_mcmc_parameters(['varying'])):
param = data.mcmc_parameters[elem]['initial']
if not command_line.silent:
print(elem,'with center =',center[elem],', lower bound =',param[1],' and upper bound =',param[2])
# Non-Gaussian parameters are intrinsically problematic and
# may need to be handled separately by the advanced user.
# In this case, the resulting Fisher matrix will never be
# very accurate, but may still serve as a useful input
# covariance matrix for an MCMC run. Some trial and error
# will be necessary in order to successfully compute a
# positive definite inverse Fisher matrix.
# Alternately, consider fixing problematic parameters and
# only computing the Fisher matrix for Gaussian parameters.
# The inverse Fisher matrix can then be used for MCMC runs
# along with best guess sigma values from the param file
#if elem == 'xi_sz_cib':
# step_size[index,2] = param[2] - center[elem]#step_size[index,1]
# print('Encountered Planck nuisance parameter %s, assuming symmetry and setting stepsize to +%f' %(elem,step_size[index,2]))
# continue
#if center[elem] + step_size[index,2] < param[2]:
# continue
#else:
# step_size[index,2] = param[2] - center[elem]
# print('New step for %s with center %f exceeded boundary %f, instead setting stepsize to +%f' %(elem,center[elem],param[2],step_size[index,2]))
# pass
#elif elem == 'A_sz':
# step_size[index,2] = param[1] - center[elem]#step_size[index,0]
# print('Encountered Planck nuisance parameter %s, assuming symmetry and setting stepsize to %f' %(elem,step_size[index,2]))
# continue
#if center[elem] + step_size[index,2] > param[1]:
# continue
#else:
# step_size[index,2] = param[1] - center[elem]
# print('New step for %s with center %f exceeded boundary %f, instead setting stepsize to %f' %(elem,center[elem],param[1],step_size[index,2]))
# continue
#elif elem == 'ksz_norm':
# step_size[index,2] = param[1] - center[elem]#step_size[index,0]
# print('Encountered Planck nuisance parameter %s, assuming symmetry and setting stepsize to %f' %(elem,step_size[index,2]))
# continue
#elif elem == 'M_tot':
# step_size[index,2] = step_size[index,1]
# print('Encountered Planck nuisance parameter %s, assuming symmetry and setting stepsize to %f' %(elem,step_size[index,2]))
# continue
boundary_flag = 0
if param[1] != None:
if param[1] > center[elem]:
raise io_mp.ConfigurationError("Error in parameter ranges: left edge %e bigger than central value %e for %s.\n"
%(param[1],center[elem],elem))
# When encountering a boundary, set stepsize to boundary limit if reasonable
if center[elem] + data.fisher_sym_lkl*step_size[index,0] > param[1] > center[elem] + step_size[index,0]:
if not command_line.silent:
print('For %s encountered lower boundary %f with center value %f, changing stepsize from %f and +%f' %(elem,param[1],center[elem],step_size[index,0],step_size[index,1]))
step_size[index,0] = -(center[elem] - param[1])
if data.use_symmetric_step:
step_size[index,1] = center[elem] - param[1]
if not command_line.silent:
print('to %f and +%f' %(step_size[index,0],step_size[index,1]))
boundary_flag = 1
# Otherwise assumme symmetric likelihood and use positive step
elif param[1] >= center[elem] + data.fisher_sym_lkl*step_size[index,0]:
if not command_line.silent:
print('For %s encountered lower boundary %f with center value %f and step_size %f, this is closer to the central value than %f*step_size' %(elem,param[1],center[elem],step_size[index,0],data.fisher_sym_lkl))
boundary_flag = 2
if param[2] == None or param[2] > center[elem] + step_size[index,1]:
if not command_line.silent:
print('Upper boundary not a problem, so assuming symmetry and only computing the positive step')
step_size[index,2] = step_size[index,1]
warnings.warn('Negative step exceeded boundary for '+elem+' - using symmetry assumption with stepsize = %f' % step_size[index,2])
else:
if not command_line.silent:
print('Upper boundary also a problem, will adjust step later')
else:
pass
if param[2] != None:
if param[2] < center[elem]:
raise io_mp.ConfigurationError("Error in parameter ranges: right edge %e smaller than central value %e for %s.\n"
%(param[2],center[elem],elem))
# When encountering a boundary, set stepsize to boundary limit if reasonable.
if center[elem] + data.fisher_sym_lkl*step_size[index,1] < param[2] < center[elem] + step_size[index,1] and boundary_flag < 2:
if not command_line.silent:
print('For %s encountered upper boundary %f with center value %f, changing stepsize from %f and +%f' %(elem,param[2],center[elem],step_size[index,0],step_size[index,1]))
# If both boundaries are smaller than the stepsize, set stepsize to the
# smaller of the two.
if not boundary_flag or (boundary_flag == 1 and abs(step_size[index,0]) > abs((param[2] - center[elem]))):
if data.use_symmetric_step:
step_size[index,0] = -(param[2] - center[elem])
step_size[index,1] = param[2] - center[elem]
if not command_line.silent:
print('to %f and +%f' %(step_size[index,0],step_size[index,1]))
else:
if not command_line.silent:
print('Lower boundary nearer center than the upper boundary, will use previously adjusted step_size %f and %f' %(step_size[index,0],step_size[index,1]))
else:
pass
# Otherwise assumme symmetric likelihood and use negative step
elif param[2] <= center[elem] + step_size[index,1]:
if not command_line.silent:
print('For %s encountered upper boundary %f with center value %f and step_size %f, this is closer to the central value than %f*step_size' %(elem,param[2],center[elem],step_size[index,1],data.fisher_sym_lkl))
# Check if the lower bound poses a problem for symmetric step
if boundary_flag < 2:
step_size[index,2] = step_size[index,0]
warnings.warn('Positive step exceeded boundary for '+elem+' - using symmetry assumption with stepsize = %f' % step_size[index,2])
# If it does, select the largest difference between center and boundary instead
else:
if param[2] - center[elem] > center[elem] - param[1]:
step_size[index,2] = param[2] - center[elem]
warnings.warn('Boundary exceeded for both step directions for '+elem+' - using symmetry assumption with the larger of the two with stepsize = %f' % step_size[index,2])
else:
step_size[index,2] = center[elem] - param[1]
warnings.warn('Boundary exceeded for both step directions for '+elem+' - using symmetry assumption with the larger of the two with stepsize = %f' % step_size[index,2])
return step_size
def vectorize_dictionary(data, center, one, two, step_index_1, step_index_2):
# Function used by Fisher matrix method by T. Brinckmann
name_1, diff_1 = one
if two:
name_2, diff_2 = two
# In order to compare the last step to the current step via
# data.check_for_slow_step we need the parameters ordered
# correctly as an array.
parameter_names = data.get_mcmc_parameters(['varying'])
step_vector = np.zeros(len(center))
for elem in parameter_names:
index = parameter_names.index(elem)
step_vector[index] = center[elem]
if not step_index_1 == None:
if elem == name_1:
step_vector[index] += diff_1[step_index_1]
if not step_index_2 == None:
if elem == name_2:
step_vector[index] += diff_2[step_index_2]
return step_vector
|
brinckmannREPO_NAMEmontepython_publicPATH_START.@montepython_public_extracted@montepython_public-master@montepython@sampler.py@.PATH_END.py
|
{
"filename": "noise_model.py",
"repo_name": "simonsobs/sotodlib",
"repo_path": "sotodlib_extracted/sotodlib-master/sotodlib/mapmaking/noise_model.py",
"type": "Python"
}
|
################################
####### Noise model stuff ######
################################
import numpy as np
import so3g
from pixell import fft, utils, bunch
from .utilities import *
class Nmat:
def __init__(self):
"""Initialize the noise model. In subclasses this will typically set up parameters, but not
build the details that depend on the actual time-ordered data"""
self.ivar = np.ones(1, dtype=np.float32)
self.ready = True
def build(self, tod, **kwargs):
"""Measure the noise properties of the given time-ordered data tod[ndet,nsamp], and
return a noise model object tailored for that specific tod. The returned object
needs to provide the .apply(tod) method, which multiplies the tod by the inverse noise
covariance matrix. Usually the returned object will be of the same class as the one
we call .build(tod) on, just with more of the internal state initialized."""
return self
def apply(self, tod):
"""Multiply the time-ordered data tod[ndet,nsamp] by the inverse noise covariance matrix.
This is done in-pace, but the result is also returned."""
return tod*self.ivar
def white(self, tod):
"""Like apply, but without detector or time correlations"""
return tod*self.ivar
def write(self, fname):
bunch.write(fname, bunch.Bunch(type="Nmat"))
@staticmethod
def from_bunch(data): return Nmat()
class NmatUncorr(Nmat):
def __init__(self, spacing="exp", nbin=100, nmin=10, window=2, bins=None, ips_binned=None, ivar=None, nwin=None):
self.spacing = spacing
self.nbin = nbin
self.nmin = nmin
self.bins = bins
self.ips_binned = ips_binned
self.ivar = ivar
self.window = window
self.nwin = nwin
self.ready = bins is not None and ips_binned is not None and ivar is not None
def build(self, tod, srate, **kwargs):
# Apply window while taking fft
nwin = utils.nint(self.window*srate)
apply_window(tod, nwin)
ft = fft.rfft(tod)
# Unapply window again
apply_window(tod, nwin, -1)
ps = np.abs(ft)**2
del ft
if self.spacing == "exp": bins = utils.expbin(ps.shape[-1], nbin=self.nbin, nmin=self.nmin)
elif self.spacing == "lin": bins = utils.expbin(ps.shape[-1], nbin=self.nbin, nmin=self.nmin)
else: raise ValueError("Unrecognized spacing '%s'" % str(self.spacing))
ps_binned = utils.bin_data(bins, ps) / tod.shape[1]
ips_binned = 1/ps_binned
# Compute the representative inverse variance per sample
ivar = np.zeros(len(tod))
for bi, b in enumerate(bins):
ivar += ips_binned[:,bi]*(b[1]-b[0])
ivar /= bins[-1,1]-bins[0,0]
return NmatUncorr(spacing=self.spacing, nbin=len(bins), nmin=self.nmin, bins=bins, ips_binned=ips_binned, ivar=ivar, window=self.window, nwin=nwin)
def apply(self, tod, inplace=False):
if inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
ftod = fft.rfft(tod)
# Candidate for speedup in C
norm = tod.shape[1]
for bi, b in enumerate(self.bins):
ftod[:,b[0]:b[1]] *= self.ips_binned[:,None,bi]/norm
# I divided by the normalization above instead of passing normalize=True
# here to reduce the number of operations needed
fft.irfft(ftod, tod)
apply_window(tod, self.nwin)
return tod
def white(self, tod, inplace=True):
if not inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
tod *= self.ivar[:,None]
apply_window(tod, self.nwin)
return tod
def write(self, fname):
data = bunch.Bunch(type="NmatUncorr")
for field in ["spacing", "nbin", "nmin", "bins", "ips_binned", "ivar", "window", "nwin"]:
data[field] = getattr(self, field)
bunch.write(fname, data)
@staticmethod
def from_bunch(data):
return NmatUncorr(spacing=data.spacing, nbin=data.nbin, nmin=data.nmin, bins=data.bins, ips_binned=data.ips_binned, ivar=data.ivar, window=window, nwin=nwin)
class NmatDetvecs(Nmat):
def __init__(self, bin_edges=None, eig_lim=16, single_lim=0.55, mode_bins=[0.25,4.0,20],
downweight=[], window=2, nwin=None, verbose=False, bins=None, D=None, V=None, iD=None, iV=None, s=None, ivar=None):
# This is all taken from act, not tuned to so yet
if bin_edges is None: bin_edges = np.array([
0.16, 0.25, 0.35, 0.45, 0.55, 0.70, 0.85, 1.00,
1.20, 1.40, 1.70, 2.00, 2.40, 2.80, 3.40, 3.80,
4.60, 5.00, 5.50, 6.00, 6.50, 7.00, 8.00, 9.00, 10.0, 11.0,
12.0, 13.0, 14.0, 16.0, 18.0, 20.0, 22.0,
24.0, 26.0, 28.0, 30.0, 32.0, 36.5, 41.0,
45.0, 50.0, 55.0, 65.0, 70.0, 80.0, 90.0,
100., 110., 120., 130., 140., 150., 160., 170.,
180., 190.
])
self.bin_edges = bin_edges
self.mode_bins = mode_bins
self.eig_lim = np.zeros(len(mode_bins))+eig_lim
self.single_lim= np.zeros(len(mode_bins))+single_lim
self.verbose = verbose
self.downweight= downweight
self.bins = bins
self.window = window
self.nwin = nwin
self.D, self.V, self.iD, self.iV, self.s, self.ivar = D, V, iD, iV, s, ivar
self.ready = all([a is not None for a in [D, V, iD, iV, s, ivar]])
def build(self, tod, srate, **kwargs):
# Apply window before measuring noise model
nwin = utils.nint(self.window*srate)
apply_window(tod, nwin)
ft = fft.rfft(tod)
# Unapply window again
apply_window(tod, nwin, -1)
ndet, nfreq = ft.shape
nsamp = tod.shape[1]
# First build our set of eigenvectors in two bins. The first goes from
# 0.25 to 4 Hz the second from 4Hz and up
mode_bins = makebins(self.mode_bins, srate, nfreq, 1000, rfun=np.round)[1:]
if np.any(np.diff(mode_bins) < 0):
raise RuntimeError(f"At least one of the frequency bins has a negative range: \n{mode_bins}")
# Then use these to get our set of basis vectors
vecs = find_modes_jon(ft, mode_bins, eig_lim=self.eig_lim, single_lim=self.single_lim, verbose=self.verbose)
nmode= vecs.shape[1]
if vecs.size == 0: raise errors.ModelError("Could not find any noise modes")
# Cut bins that extend beyond our max frequency
bin_edges = self.bin_edges[self.bin_edges < srate/2 * 0.99]
bins = makebins(bin_edges, srate, nfreq, nmin=2*nmode, rfun=np.round)
nbin = len(bins)
# Now measure the power of each basis vector in each bin. The residual
# noise will be modeled as uncorrelated
E = np.zeros([nbin,nmode])
D = np.zeros([nbin,ndet])
Nd = np.zeros([nbin,ndet])
for bi, b in enumerate(bins):
# Skip the DC mode, since it's it's unmeasurable and filtered away
b = np.maximum(1,b)
E[bi], D[bi], Nd[bi] = measure_detvecs(ft[:,b[0]:b[1]], vecs)
# Optionally downweight the lowest frequency bins
if self.downweight != None and len(self.downweight) > 0:
D[:len(self.downweight)] /= np.array(self.downweight)[:,None]
# Instead of VEV' we can have just VV' if we bake sqrt(E) into V
V = vecs[None]*E[:,None]**0.5
# At this point we have a model for the total noise covariance as
# N = D + VV'. But since we're doing inverse covariance weighting
# we need a similar representation for the inverse iN. The function
# woodbury_invert computes iD, iV, s such that iN = iD + s iV iV'
# where s usually is -1, but will become +1 if one inverts again
iD, iV, s = woodbury_invert(D, V)
# Also compute a representative white noise level
bsize = bins[:,1]-bins[:,0]
ivar = np.sum(iD*bsize[:,None],0)/np.sum(bsize)
# What about units? I haven't applied any fourier unit factors so far,
# so we're in plain power units. From the uncorrelated model I found
# that factor of tod.shape[1] is needed
iD *= nsamp
iV *= nsamp**0.5
ivar *= nsamp
# Fix dtype
bins = np.ascontiguousarray(bins.astype(np.int32))
D = np.ascontiguousarray(iD.astype(tod.dtype))
V = np.ascontiguousarray(iV.astype(tod.dtype))
iD = np.ascontiguousarray(D.astype(tod.dtype))
iV = np.ascontiguousarray(V.astype(tod.dtype))
return NmatDetvecs(bin_edges=self.bin_edges, eig_lim=self.eig_lim, single_lim=self.single_lim,
window=self.window, nwin=nwin, downweight=self.downweight, verbose=self.verbose,
bins=bins, D=D, V=V, iD=iD, iV=iV, s=s, ivar=ivar)
def apply(self, tod, inplace=True, slow=False):
if not inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
ftod = fft.rfft(tod)
norm = tod.shape[1]
if slow:
for bi, b in enumerate(self.bins):
# Want to multiply by iD + siViV'
ft = ftod[:,b[0]:b[1]]
iD = self.iD[bi]/norm
iV = self.iV[bi]/norm**0.5
ft[:] = iD[:,None]*ft + self.s*iV.dot(iV.T.dot(ft))
else:
so3g.nmat_detvecs_apply(ftod.view(tod.dtype), self.bins, self.iD, self.iV, float(self.s), float(norm))
# I divided by the normalization above instead of passing normalize=True
# here to reduce the number of operations needed
fft.irfft(ftod, tod)
apply_window(tod, self.nwin)
return tod
def white(self, tod, inplace=True):
if not inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
tod *= self.ivar[:,None]
apply_window(tod, self.nwin)
return tod
def write(self, fname):
data = bunch.Bunch(type="NmatDetvecs")
for field in ["bin_edges", "eig_lim", "single_lim", "window", "nwin", "downweight",
"bins", "D", "V", "iD", "iV", "s", "ivar"]:
data[field] = getattr(self, field)
try:
bunch.write(fname, data)
except Exception as e:
msg = f"Failed to write {fname}: {e}"
raise RuntimeError(msg)
@staticmethod
def from_bunch(data):
return NmatDetvecs(bin_edges=data.bin_edges, eig_lim=data.eig_lim, single_lim=data.single_lim,
window=data.window, nwin=data.nwin, downweight=data.downweight,
bins=data.bins, D=data.D, V=data.V, iD=data.iD, iV=data.iV, s=data.s, ivar=data.ivar)
class NmatWhite(Nmat):
def __init__(self, window=2, ivar=None, nwin=None):
"""
This is a white noise model for the mapmaker.
The white noise model is characterized by
(1) no correlations between detectors
(2) a white (flat) spectrum per detector
(3) the weights, if not passed in through ivar, are computed simply
from the inverse variance of the timestream.
Parameters
----------
window : float, optional
Size of the window in seconds to apply before taking the FFT and applying the model
in harmonic space
ivar : numpy.ndarray or None, optional
Overwrite the inverse variance per detector
nwin: int or None, optional
Overwrite the window size in number of samples
Returns
-------
noise_model : An Nmat object with the noise model
"""
self.ivar = ivar
self.window = window
self.nwin = nwin
self.ready = ivar is not None
def build(self, tod, srate, **kwargs):
#ndet, nsamps = tod.shape
nwin = utils.nint(self.window*srate)
ivar = 1.0/np.var(tod, 1)
return NmatWhite(ivar=ivar, window=self.window, nwin=nwin)
def apply(self, tod, inplace=True):
if not inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
tod *= self.ivar[:,None]
apply_window(tod, self.nwin)
return tod
def white(self, tod, inplace=True):
if not inplace: tod = np.array(tod)
apply_window(tod, self.nwin)
tod *= self.ivar[:,None]
apply_window(tod, self.nwin)
return tod
def write(self, fname):
bunch.write(fname, bunch.Bunch(type="NmatWhite"))
@staticmethod
def from_bunch(data):
return NmatWhite(ivar=data.ivar, window=window, nwin=nwin)
class NmatUnit(Nmat):
"""
This is a noise model that does nothing, equivalent to multiply by a
unit noise matrix
"""
def __init__(self, ivar=None):
self.ivar = ivar
self.ready = ivar is not None
def build(self, tod, **kwargs):
ndet, nsamps = tod.shape
ivar = np.ones(ndet)
return NmatUnit(ivar=ivar)
def apply(self, tod):
# the tod is returned intact
return tod
def white(self, tod):
# the tod is returned intact
return tod
def write(self, fname):
bunch.write(fname, bunch.Bunch(type="NmatUnit"))
@staticmethod
def from_bunch(data):
return NmatUnit(ivar=data.ivar)
def write_nmat(fname, nmat):
nmat.write(fname)
def read_nmat(fname):
data = bunch.read(fname)
typ = data.type.decode()
if typ == "NmatDetvecs": return NmatDetvecs.from_bunch(data)
elif typ == "NmatUncorr": return NmatUncorr .from_bunch(data)
elif typ == "Nmat": return Nmat .from_bunch(data)
else: raise IOError("Unrecognized noise matrix type '%s' in '%s'" % (str(typ), fname))
|
simonsobsREPO_NAMEsotodlibPATH_START.@sotodlib_extracted@sotodlib-master@sotodlib@mapmaking@noise_model.py@.PATH_END.py
|
{
"filename": "HypervelocityWhiteDwarfCandidatesInGaiaDR3.ipynb",
"repo_name": "astro-datalab/notebooks-latest",
"repo_path": "notebooks-latest_extracted/notebooks-latest-master/03_ScienceExamples/WhiteDwarfs/HypervelocityWhiteDwarfCandidatesInGaiaDR3.ipynb",
"type": "Jupyter Notebook"
}
|
```python
__nbid__ = '0032'
__author__ = 'Alice Jacques <alice.jacques@noirlab.edu>'
__version__ = '20240606' # yyyymmdd
__datasets__ = ['gaia_dr3']
__keywords__ = ['science example', 'stars', 'parallax', 'vospace']
```
# Using Gaia DR3 to identify hyper-runaway and hypervelocity white dwarf candidates
### Table of contents
* [Goals & summary](#goals)
* [Disclaimer & attribution](#attribution)
* [Imports & setup](#imports)
* [Authentication](#auth)
* [Query Gaia DR3 part I: for HVS WD candidates](#query)
* [Query Gaia DR3 part II: for objects within 100 pc](#query100pc)
* [HR diagram for WDs and sdBs candidates](#hr)
* [Galactocentric distances and heights above/below the Galactic plane for HVS WD candidates](#galacto)
* [Heights above/below the Galactic plane of the observed HVS WD candidates](#heights)
* [Resources and references](#ref)
<a class="anchor" id="goals"></a>
# Goals
To reproduce Figures 1, 3, and 4 from [Igoshev et al. 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.6223I/abstract) "Hyper-runaway and hypervelocity white dwarf candidates in Gaia Data Release 3: possible remnants from Ia/Iax supernova explosions or dynamical encounters". These figures show the locations of 46 hyper-runaway/hypervelocity (HVS) white dwarf (WD) candidates on the Hertzsprung–Russell diagram as well as their transversal velocities and heights above/below the Galactic plane.
# Summary
In their paper, Igoshev et al. use data from Gaia DR3 (Gaia Collaboration et al. [2016b](https://gea.esac.esa.int/archive/documentation/GDR3/bib.html#bib375) and [2022k](https://gea.esac.esa.int/archive/documentation/GDR3/bib.html#bib179)) to search for and analyze white dwarfs and other peculiar objects possibly ejected from the Galactic disk at very high velocities (> 400 km/s) because of thermonuclear type Ia/Iax peculiar supernovae (SNe) or dynamical encounters.
<a class="anchor" id="attribution"></a>
# Disclaimer & attribution
Disclaimers
-----------
Note that using the Astro Data Lab constitutes your agreement with our minimal [Disclaimers](https://datalab.noirlab.edu/disclaimers.php).
Acknowledgments
---------------
If you use **Astro Data Lab** in your published research, please include the text in your paper's Acknowledgments section:
_This research uses services or data provided by the Astro Data Lab, which is part of the Community Science and Data Center (CSDC) Program of NSF NOIRLab. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under a cooperative agreement with the U.S. National Science Foundation._
If you use **SPARCL jointly with the Astro Data Lab platform** (via JupyterLab, command-line, or web interface) in your published research, please include this text below in your paper's Acknowledgments section:
_This research uses services or data provided by the SPectra Analysis and Retrievable Catalog Lab (SPARCL) and the Astro Data Lab, which are both part of the Community Science and Data Center (CSDC) Program of NSF NOIRLab. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under a cooperative agreement with the U.S. National Science Foundation._
In either case **please cite the following papers**:
* Data Lab concept paper: Fitzpatrick et al., "The NOAO Data Laboratory: a conceptual overview", SPIE, 9149, 2014, https://doi.org/10.1117/12.2057445
* Astro Data Lab overview: Nikutta et al., "Data Lab - A Community Science Platform", Astronomy and Computing, 33, 2020, https://doi.org/10.1016/j.ascom.2020.100411
If you are referring to the Data Lab JupyterLab / Jupyter Notebooks, cite:
* Juneau et al., "Jupyter-Enabled Astrophysical Analysis Using Data-Proximate Computing Platforms", CiSE, 23, 15, 2021, https://doi.org/10.1109/MCSE.2021.3057097
If publishing in a AAS journal, also add the keyword: `\facility{Astro Data Lab}`
And if you are using SPARCL, please also add `\software{SPARCL}` and cite:
* Juneau et al., "SPARCL: SPectra Analysis and Retrievable Catalog Lab", Conference Proceedings for ADASS XXXIII, 2024
https://doi.org/10.48550/arXiv.2401.05576
The NOIRLab Library maintains [lists of proper acknowledgments](https://noirlab.edu/science/about/scientific-acknowledgments) to use when publishing papers using the Lab's facilities, data, or services.
<a class="anchor" id="imports"></a>
# Imports and setup
```python
# Standard lib
from getpass import getpass
# 3rd party imports
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
# Data Lab
from dl import authClient as ac, queryClient as qc
# plots default setup
plt.rcParams['font.size'] = 14
```
<a class="anchor" id="auth"></a>
# Authentication
Much of the functionality of Data Lab can be accessed without explicitly logging in (the service then uses an anonymous login). But some capacities, for instance saving the results of your queries to your virtual storage space, require a login (i.e. you will need a registered user account).
If you need to log in to Data Lab, un-comment the cell below and execute it:
```python
#token = ac.login(input("Enter user name: (+ENTER) "),getpass("Enter password: (+ENTER) "))
#ac.whoAmI()
```
<a class="anchor" id="query"></a>
# Query Gaia DR3 part I: for HVS WD candidates
The following query can be executed in order to extract potential hyper-runaway/hypervelocity (HVS) white dwarf (WD) candidates from the `gaia_dr3.gaia_source` table at Astro Data Lab. Since the query runtime takes about 40 minutes, we have commented out the cell that would actually execute the query and instead import the resulting table (from executing the query prior to now).
The following constraints are applied in the query in order to exclude objects with large uncertainties:
(1) parallax / parallax uncertainty > 4
--> `parallax_over_error > 4`
(2) parallax > 0.25 mas (i.e. objects with nominal distances that are smaller than 4 kpc)
--> `parallax > 0.25`
(3) renormalized unit weight error (RUWE) < 1.4
--> `ruwe < 1.4`
(4) Percent of successful-IPD windows with more than one peak <= 2
--> `ipd_frac_multi_peak <= 2`
(5) Amplitude of the IPD GoF versus position angle of scan < 0.1
--> `ipd_gof_harmonic_amplitude < 0.1`
(6) Matched FOV transits used in the AGIS solution < 1.5
--> `astrometric_sigma5d_max < 1.5`
(7) G_abs > 6 + 5*(G_BP - G_RP) (color-magnitude cut suggested by [Gentile Fusillo et al. 2021](https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.3877G/abstract))
--> `phot_g_mean_mag - 5 * log10(1000.0 / parallax) + 5 > 6 + 5 * (bp_rp)`
```python
q = """
SELECT TOP 10000
source_id, phot_g_mean_mag, bp_rp, parallax, parallax_over_error,
pm, b, l, ecl_lat, ecl_lon, ruwe, 4.74*(ABS(pm)/parallax) AS v
FROM
gaia_dr3.gaia_source
WHERE
parallax_over_error > 4
AND parallax > 0.25
AND ruwe < 1.4
AND ipd_frac_multi_peak <= 2
AND ipd_gof_harmonic_amplitude < 0.1
AND astrometric_sigma5d_max < 1.5
AND phot_g_mean_mag - 5 * log10(1000.0 / parallax) + 5 > 6 + 5 * (bp_rp)
ORDER BY v DESC
"""
print(q)
```
SELECT TOP 10000
source_id, phot_g_mean_mag, bp_rp, parallax, parallax_over_error,
pm, b, l, ecl_lat, ecl_lon, ruwe, 4.74*(ABS(pm)/parallax) AS v
FROM
gaia_dr3.gaia_source
WHERE
parallax_over_error > 4
AND parallax > 0.25
AND ruwe < 1.4
AND ipd_frac_multi_peak <= 2
AND ipd_gof_harmonic_amplitude < 0.1
AND astrometric_sigma5d_max < 1.5
AND phot_g_mean_mag - 5 * log10(1000.0 / parallax) + 5 > 6 + 5 * (bp_rp)
ORDER BY v DESC
```python
# Uncomment the following line to run the 40-minute query and save the output to VOSpace
#re = qc.query(adql=q,async_=True,wait=True,poll=180,verbose=1,timeout=8000,fmt='csv',out='vos://gaia_hvs.csv')
```
<a class="anchor" id="query100pc"></a>
# Query Gaia DR3 part II: for objects within 100 pc
The following query can be executed in order to extract objects within 100 pc of the sun from the `gaia_dr3.gaia_source` table at Astro Data Lab. Since the query runtime takes about 20 minutes, we have commented out the cell that would actually execute the query and instead import the resulting table (from executing the query prior to now).
```python
q = """
SELECT TOP 250000
phot_g_mean_mag, bp_rp, parallax
FROM
gaia_dr3.gaia_source
WHERE
parallax > 10
AND parallax_over_error > 10
AND phot_bp_mean_flux_over_error > 10
AND phot_rp_mean_flux_over_error > 10
AND bp_rp != 'NaN'
AND parallax != 'NaN'
AND ruwe < 1.4
AND ipd_frac_multi_peak < 2
AND ipd_gof_harmonic_amplitude < 0.1
AND astrometric_sigma5d_max < 1.5
"""
print(q)
```
SELECT TOP 250000
phot_g_mean_mag, bp_rp, parallax
FROM
gaia_dr3.gaia_source
WHERE
parallax > 10
AND parallax_over_error > 10
AND phot_bp_mean_flux_over_error > 10
AND phot_rp_mean_flux_over_error > 10
AND bp_rp != 'NaN'
AND parallax != 'NaN'
AND ruwe < 1.4
AND ipd_frac_multi_peak < 2
AND ipd_gof_harmonic_amplitude < 0.1
AND astrometric_sigma5d_max < 1.5
```python
# Uncomment the following line to execute the 21min query and save output to VOSpace
#re = qc.query(adql=q,async_=True,wait=True,poll=180,verbose=1,timeout=8000,fmt='csv',out='vos://gaia100pc.csv')
```
### Import the CSV files as Pandas Dataframes
We didn't run the actual queries, so we now read in the results from CSV files, using Pandas.
```python
df_hvs = pd.read_csv('gaia_hvs.csv')
df_100pc = pd.read_csv('gaia100pc.csv')
```
From our resulting HVS WD candidate list, keep only those that have a nominal two-dimensional velocity (`v`) greater than 400 km/s.
```python
df_hvs = df_hvs[df_hvs['v'] > 400]
print(f"Number of HVS WD candidates: {len(df_hvs)}")
df_hvs
```
Number of HVS WD candidates: 46
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>source_id</th>
<th>phot_g_mean_mag</th>
<th>bp_rp</th>
<th>parallax</th>
<th>parallax_over_error</th>
<th>pm</th>
<th>b</th>
<th>l</th>
<th>ecl_lat</th>
<th>ecl_lon</th>
<th>ruwe</th>
<th>v</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>5703888058542880896</td>
<td>19.597963</td>
<td>0.322973</td>
<td>1.361697</td>
<td>4.278472</td>
<td>207.876310</td>
<td>13.599432</td>
<td>245.993343</td>
<td>-37.630457</td>
<td>141.643368</td>
<td>0.982530</td>
<td>723.606972</td>
</tr>
<tr>
<th>1</th>
<td>3688712561723372672</td>
<td>18.766459</td>
<td>-0.016319</td>
<td>1.419275</td>
<td>7.231967</td>
<td>211.641890</td>
<td>61.822172</td>
<td>306.715100</td>
<td>4.852452</td>
<td>193.869764</td>
<td>1.008971</td>
<td>706.827620</td>
</tr>
<tr>
<th>2</th>
<td>6368583523760274176</td>
<td>16.869844</td>
<td>-0.374060</td>
<td>0.307886</td>
<td>4.725116</td>
<td>36.638474</td>
<td>-33.492952</td>
<td>316.983358</td>
<td>-55.462151</td>
<td>286.838664</td>
<td>1.049720</td>
<td>564.059875</td>
</tr>
<tr>
<th>3</th>
<td>6416314659255288704</td>
<td>20.001963</td>
<td>0.335079</td>
<td>1.893672</td>
<td>4.914148</td>
<td>221.716230</td>
<td>-28.731589</td>
<td>322.764442</td>
<td>-49.805712</td>
<td>280.409670</td>
<td>1.025437</td>
<td>554.971949</td>
</tr>
<tr>
<th>4</th>
<td>6841322701358236416</td>
<td>19.097380</td>
<td>0.016804</td>
<td>2.787141</td>
<td>6.454955</td>
<td>321.476530</td>
<td>-42.125951</td>
<td>35.729228</td>
<td>-1.036853</td>
<td>319.736853</td>
<td>1.058983</td>
<td>546.724665</td>
</tr>
<tr>
<th>5</th>
<td>5808675437975384320</td>
<td>19.800177</td>
<td>-0.026350</td>
<td>2.055741</td>
<td>5.592335</td>
<td>232.483370</td>
<td>-15.014658</td>
<td>320.480755</td>
<td>-46.940755</td>
<td>259.895155</td>
<td>1.009515</td>
<td>536.045778</td>
</tr>
<tr>
<th>6</th>
<td>4376935406816933120</td>
<td>17.927696</td>
<td>-0.150414</td>
<td>2.056039</td>
<td>19.390610</td>
<td>231.208560</td>
<td>19.294956</td>
<td>26.834778</td>
<td>26.769946</td>
<td>262.120461</td>
<td>0.989464</td>
<td>533.029034</td>
</tr>
<tr>
<th>7</th>
<td>4739233769591077376</td>
<td>19.407776</td>
<td>-0.340424</td>
<td>1.473667</td>
<td>6.882322</td>
<td>165.588090</td>
<td>-55.996983</td>
<td>282.717935</td>
<td>-63.432466</td>
<td>352.094213</td>
<td>1.033190</td>
<td>532.608429</td>
</tr>
<tr>
<th>8</th>
<td>6640949596389193856</td>
<td>17.880573</td>
<td>0.288610</td>
<td>1.741432</td>
<td>11.009797</td>
<td>193.174640</td>
<td>-28.516559</td>
<td>341.862553</td>
<td>-33.625771</td>
<td>286.268124</td>
<td>1.046736</td>
<td>525.801773</td>
</tr>
<tr>
<th>9</th>
<td>4753345692095875328</td>
<td>19.539387</td>
<td>0.849884</td>
<td>6.489097</td>
<td>24.482530</td>
<td>714.484100</td>
<td>-60.610271</td>
<td>261.984526</td>
<td>-57.735627</td>
<td>13.238637</td>
<td>1.107528</td>
<td>521.899165</td>
</tr>
<tr>
<th>10</th>
<td>6777159394645734400</td>
<td>19.289864</td>
<td>-0.213778</td>
<td>1.640846</td>
<td>5.097662</td>
<td>169.819630</td>
<td>-41.029410</td>
<td>7.995048</td>
<td>-17.935625</td>
<td>307.594832</td>
<td>1.032221</td>
<td>490.566984</td>
</tr>
<tr>
<th>11</th>
<td>855361055035055104</td>
<td>17.639692</td>
<td>0.287199</td>
<td>17.332254</td>
<td>187.286560</td>
<td>1782.656700</td>
<td>51.414977</td>
<td>148.333837</td>
<td>46.231876</td>
<td>134.789486</td>
<td>1.062821</td>
<td>487.518422</td>
</tr>
<tr>
<th>12</th>
<td>6778670265357654656</td>
<td>16.160376</td>
<td>-0.420788</td>
<td>0.383550</td>
<td>5.716695</td>
<td>37.951294</td>
<td>-40.408617</td>
<td>7.806270</td>
<td>-17.810472</td>
<td>306.938503</td>
<td>1.023417</td>
<td>469.010921</td>
</tr>
<tr>
<th>13</th>
<td>4943575978388814976</td>
<td>20.428818</td>
<td>0.481579</td>
<td>3.849991</td>
<td>6.171465</td>
<td>380.566440</td>
<td>-65.471226</td>
<td>265.965463</td>
<td>-53.760616</td>
<td>7.319232</td>
<td>1.045171</td>
<td>468.542603</td>
</tr>
<tr>
<th>14</th>
<td>2463291012727113216</td>
<td>19.807120</td>
<td>0.349174</td>
<td>3.542406</td>
<td>9.101999</td>
<td>348.217530</td>
<td>-65.130943</td>
<td>166.572706</td>
<td>-19.109624</td>
<td>24.777674</td>
<td>0.973043</td>
<td>465.940662</td>
</tr>
<tr>
<th>15</th>
<td>1241636356209099264</td>
<td>19.657074</td>
<td>0.320280</td>
<td>3.870557</td>
<td>11.631180</td>
<td>379.020260</td>
<td>64.394291</td>
<td>27.609415</td>
<td>35.738081</td>
<td>209.779864</td>
<td>0.962409</td>
<td>464.159574</td>
</tr>
<tr>
<th>16</th>
<td>5995439960564759296</td>
<td>19.905184</td>
<td>0.141214</td>
<td>2.842153</td>
<td>5.533226</td>
<td>274.697540</td>
<td>8.647302</td>
<td>334.391463</td>
<td>-22.316620</td>
<td>245.030768</td>
<td>0.983604</td>
<td>458.126704</td>
</tr>
<tr>
<th>17</th>
<td>2737084320170352256</td>
<td>18.906157</td>
<td>-0.190376</td>
<td>1.754255</td>
<td>6.268135</td>
<td>167.723390</td>
<td>-33.932151</td>
<td>78.946764</td>
<td>24.161275</td>
<td>344.210151</td>
<td>1.008052</td>
<td>453.188944</td>
</tr>
<tr>
<th>18</th>
<td>2119975000945142272</td>
<td>19.975832</td>
<td>0.026985</td>
<td>1.550769</td>
<td>5.380763</td>
<td>148.248800</td>
<td>20.985254</td>
<td>78.135062</td>
<td>71.336363</td>
<td>294.009808</td>
<td>0.995098</td>
<td>453.129537</td>
</tr>
<tr>
<th>19</th>
<td>1544331701176666624</td>
<td>18.863573</td>
<td>-0.152073</td>
<td>1.042345</td>
<td>6.211896</td>
<td>99.500000</td>
<td>67.747171</td>
<td>128.960230</td>
<td>47.477098</td>
<td>162.465039</td>
<td>1.037403</td>
<td>452.470139</td>
</tr>
<tr>
<th>20</th>
<td>1470682632777169664</td>
<td>19.366830</td>
<td>-0.108576</td>
<td>1.560937</td>
<td>5.655800</td>
<td>148.647340</td>
<td>76.336098</td>
<td>68.983942</td>
<td>41.686037</td>
<td>188.393494</td>
<td>0.967321</td>
<td>451.388011</td>
</tr>
<tr>
<th>21</th>
<td>3528713077053554432</td>
<td>17.200209</td>
<td>-0.416523</td>
<td>0.370357</td>
<td>4.123400</td>
<td>34.636738</td>
<td>49.130895</td>
<td>301.552748</td>
<td>-7.926042</td>
<td>196.340910</td>
<td>0.976791</td>
<td>443.297459</td>
</tr>
<tr>
<th>22</th>
<td>729192473703851264</td>
<td>19.817461</td>
<td>-0.065245</td>
<td>2.058632</td>
<td>4.768104</td>
<td>191.878400</td>
<td>59.385915</td>
<td>202.242769</td>
<td>17.884971</td>
<td>149.042830</td>
<td>1.032098</td>
<td>441.799910</td>
</tr>
<tr>
<th>23</th>
<td>6438915331219654400</td>
<td>14.608497</td>
<td>-0.443252</td>
<td>0.868092</td>
<td>24.284718</td>
<td>80.888405</td>
<td>-26.272828</td>
<td>333.465343</td>
<td>-40.006949</td>
<td>280.692098</td>
<td>0.938566</td>
<td>441.670847</td>
</tr>
<tr>
<th>24</th>
<td>3537042874067950336</td>
<td>17.492294</td>
<td>-0.375303</td>
<td>1.334430</td>
<td>13.162349</td>
<td>123.765080</td>
<td>32.473451</td>
<td>273.591733</td>
<td>-27.658871</td>
<td>177.363761</td>
<td>1.038275</td>
<td>439.623289</td>
</tr>
<tr>
<th>25</th>
<td>1415765359864865408</td>
<td>20.210850</td>
<td>0.791828</td>
<td>5.145613</td>
<td>12.678640</td>
<td>475.628720</td>
<td>35.939551</td>
<td>79.296648</td>
<td>74.424224</td>
<td>242.394686</td>
<td>0.997524</td>
<td>438.136323</td>
</tr>
<tr>
<th>26</th>
<td>3737057611255721472</td>
<td>16.019308</td>
<td>-0.422135</td>
<td>0.394936</td>
<td>6.729687</td>
<td>35.903190</td>
<td>74.482936</td>
<td>316.106382</td>
<td>17.487009</td>
<td>190.349379</td>
<td>1.062222</td>
<td>430.907800</td>
</tr>
<tr>
<th>27</th>
<td>3195038476578336256</td>
<td>16.828493</td>
<td>-0.342909</td>
<td>0.325199</td>
<td>4.167587</td>
<td>29.504170</td>
<td>-43.075677</td>
<td>198.558290</td>
<td>-28.241633</td>
<td>54.168810</td>
<td>1.001899</td>
<td>430.043138</td>
</tr>
<tr>
<th>28</th>
<td>4615529846653846016</td>
<td>20.086353</td>
<td>0.631851</td>
<td>3.480455</td>
<td>9.098217</td>
<td>311.918200</td>
<td>-31.701720</td>
<td>297.517789</td>
<td>-71.769342</td>
<td>281.348211</td>
<td>1.003829</td>
<td>424.798562</td>
</tr>
<tr>
<th>29</th>
<td>6670029411202563584</td>
<td>16.823410</td>
<td>-0.433559</td>
<td>0.322370</td>
<td>4.575238</td>
<td>28.596610</td>
<td>-30.701662</td>
<td>349.078675</td>
<td>-28.408228</td>
<td>291.067456</td>
<td>1.039328</td>
<td>420.473467</td>
</tr>
<tr>
<th>30</th>
<td>4925179671389315968</td>
<td>19.805325</td>
<td>-0.027245</td>
<td>1.668109</td>
<td>5.252374</td>
<td>147.563640</td>
<td>-64.641937</td>
<td>308.425738</td>
<td>-49.839129</td>
<td>339.394776</td>
<td>1.012964</td>
<td>419.308138</td>
</tr>
<tr>
<th>31</th>
<td>2914272062095015552</td>
<td>19.896057</td>
<td>0.081667</td>
<td>1.980328</td>
<td>5.899406</td>
<td>174.780440</td>
<td>-24.569904</td>
<td>230.413729</td>
<td>-49.039967</td>
<td>85.952792</td>
<td>1.035363</td>
<td>418.344485</td>
</tr>
<tr>
<th>32</th>
<td>1682129610835350400</td>
<td>18.403294</td>
<td>-0.286423</td>
<td>2.360036</td>
<td>19.830904</td>
<td>208.248150</td>
<td>49.918404</td>
<td>124.546404</td>
<td>60.798961</td>
<td>141.495755</td>
<td>1.043308</td>
<td>418.254770</td>
</tr>
<tr>
<th>33</th>
<td>3948319763985443200</td>
<td>17.487194</td>
<td>-0.144045</td>
<td>0.452369</td>
<td>4.585209</td>
<td>39.747307</td>
<td>82.028699</td>
<td>279.503247</td>
<td>21.851148</td>
<td>180.489066</td>
<td>0.996125</td>
<td>416.478664</td>
</tr>
<tr>
<th>34</th>
<td>2335322500798589184</td>
<td>16.516691</td>
<td>-0.479513</td>
<td>0.418569</td>
<td>5.123904</td>
<td>36.656930</td>
<td>-81.860881</td>
<td>40.620675</td>
<td>-25.025862</td>
<td>352.601502</td>
<td>1.032337</td>
<td>415.113995</td>
</tr>
<tr>
<th>35</th>
<td>2654214506741818880</td>
<td>20.266537</td>
<td>0.856575</td>
<td>3.397146</td>
<td>5.382121</td>
<td>297.382900</td>
<td>-47.669084</td>
<td>68.265887</td>
<td>8.500126</td>
<td>341.306037</td>
<td>0.984546</td>
<td>414.935090</td>
</tr>
<tr>
<th>36</th>
<td>3611573712136684928</td>
<td>19.663612</td>
<td>-0.114841</td>
<td>2.067008</td>
<td>4.701821</td>
<td>180.324920</td>
<td>50.884132</td>
<td>319.288765</td>
<td>-0.873349</td>
<td>205.462175</td>
<td>1.019510</td>
<td>413.515564</td>
</tr>
<tr>
<th>37</th>
<td>4026695083122023552</td>
<td>20.360512</td>
<td>0.710457</td>
<td>4.122370</td>
<td>5.053454</td>
<td>358.180360</td>
<td>77.915450</td>
<td>185.828244</td>
<td>29.202139</td>
<td>166.146965</td>
<td>1.063057</td>
<td>411.844343</td>
</tr>
<tr>
<th>38</th>
<td>6414789778364569216</td>
<td>19.278860</td>
<td>-0.068787</td>
<td>2.069065</td>
<td>9.141049</td>
<td>178.332920</td>
<td>-24.594756</td>
<td>320.131294</td>
<td>-50.972480</td>
<td>273.169992</td>
<td>1.045757</td>
<td>408.541144</td>
</tr>
<tr>
<th>39</th>
<td>1378348017099023360</td>
<td>16.789663</td>
<td>-0.480228</td>
<td>0.268312</td>
<td>5.517413</td>
<td>23.111185</td>
<td>47.209753</td>
<td>59.485485</td>
<td>56.739805</td>
<td>228.068414</td>
<td>0.986462</td>
<td>408.282663</td>
</tr>
<tr>
<th>40</th>
<td>2497775064628920832</td>
<td>19.165173</td>
<td>0.203854</td>
<td>3.719366</td>
<td>12.304443</td>
<td>318.334170</td>
<td>-51.287104</td>
<td>174.412596</td>
<td>-15.983456</td>
<td>39.548579</td>
<td>0.968408</td>
<td>405.688457</td>
</tr>
<tr>
<th>41</th>
<td>5142197118950177280</td>
<td>17.536055</td>
<td>0.481670</td>
<td>13.035936</td>
<td>134.009060</td>
<td>1111.310200</td>
<td>-73.567721</td>
<td>178.734310</td>
<td>-26.370141</td>
<td>18.258917</td>
<td>1.077071</td>
<td>404.083792</td>
</tr>
<tr>
<th>42</th>
<td>1217609832414369536</td>
<td>20.586790</td>
<td>1.139254</td>
<td>7.335976</td>
<td>9.668915</td>
<td>624.631040</td>
<td>52.274193</td>
<td>35.457456</td>
<td>40.632394</td>
<td>224.826985</td>
<td>0.962873</td>
<td>403.593328</td>
</tr>
<tr>
<th>43</th>
<td>5863122429179888000</td>
<td>19.580624</td>
<td>1.322128</td>
<td>13.236653</td>
<td>40.579160</td>
<td>1124.303200</td>
<td>0.829789</td>
<td>303.551738</td>
<td>-49.876658</td>
<td>225.132089</td>
<td>1.033894</td>
<td>402.609125</td>
</tr>
<tr>
<th>44</th>
<td>3905910019954089856</td>
<td>19.468430</td>
<td>0.071825</td>
<td>3.291318</td>
<td>9.916897</td>
<td>279.459300</td>
<td>69.348107</td>
<td>270.615708</td>
<td>9.249490</td>
<td>177.965065</td>
<td>1.049781</td>
<td>402.463999</td>
</tr>
<tr>
<th>45</th>
<td>1212348119518459392</td>
<td>19.748020</td>
<td>1.345055</td>
<td>11.216547</td>
<td>31.732930</td>
<td>951.561600</td>
<td>56.070370</td>
<td>27.195411</td>
<td>35.772383</td>
<td>220.044733</td>
<td>1.034582</td>
<td>402.120365</td>
</tr>
</tbody>
</table>
</div>
<a class="anchor" id="hr"></a>
## HR diagram for WDs and sdBs candidates
Below is the Hertzsprung–Russell diagram for WD and sdB candidates that have the largest nominal two-dimensional velocities from Gaia DR3.
```python
def abs_mag(app_mag, parallax):
"Calculate the absolute magnitude of an object."
abs_mag = app_mag - 5 * np.log10(1000.0 / parallax) + 5
return abs_mag
```
```python
gmags_hvs = abs_mag(df_hvs['phot_g_mean_mag'], df_hvs['parallax'])
gmags_100pc = abs_mag(df_100pc['phot_g_mean_mag'], df_100pc['parallax'])
fig = plt.figure(figsize=(10,7))
plt.xlabel('$G_{BP} - G_{RP}$ (mag)')
plt.ylabel('$M_{G}$ (mag)')
plt.ylim(0,16)
plt.xlim(-0.7,2.0)
plt.gca().invert_yaxis()
plt.scatter(df_100pc['bp_rp'], gmags_100pc, marker='.', color='0.7', alpha=0.05)
plt.plot(df_hvs['bp_rp'], gmags_hvs, 'bo', markersize=4)
plt.show()
```
Compare the above plot with Figure 1 in [Igoshev et al. 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.6223I/abstract). Gaia DR3 objects within 100 pc of the sun are plotted in greyscale for reference, where part of the main sequence is seen between $M_{G}\approx 0$ and $M_{G}\approx 9$ and part of the white dwarf sequence is seen between $M_{G}\approx 10$ and $M_{G}\approx 16$. The HVS WD candidates plotted in blue can be split into three different groups:
1. WD-like objects located on or just above the WD sequence
2. Hot subdwarf B-like objects gathered around $M_{G}\approx 4$ and $G_{BP}-G_{RP}\approx -0.4$
3. Objects located between the WD sequence and main sequence
Some hot subdwarf O and B stars are identified as hyper-velocity sources (see [Heber 2009](https://ui.adsabs.harvard.edu/abs/2009ARA%26A..47..211H/abstract), for a review and references). Furthermore, up to 20% of sdB stars reside in the Milky Way halo ([Napiwotzki 2008](https://myasp.astrosociety.org/product/CS392/vol-392-hot-subdwarf-stars-and-related-objects)), so their velocities are expected to be hundreds of km/s with respect to the local standard of rest (LSR) of the thin disc. The authors classify candidates with $4 \le M_{G} \le 6$ and $-0.5 \le G_{BP}-G_{RP} \le -0.25$ as HVsdBC, that is, high-velocity sdB candidates.
<a class="anchor" id="galacto"></a>
## Galactocentric distances and heights above/below the Galactic plane for HVS WD candidates
We can use the parallax and galactic coordinates from Gaia DR3 to compute the Galactocentric position of each HVS WD candidate. The Galactocentric position in cylindrical coordinates has the following three components: the Galactocentric distance $R$, the Galactocentric azimuth $\phi$ (not used in this notebook), and the vertical distance $Z$. The conversion formulas are the following ([López-Corredoira et al. 2000](https://ui.adsabs.harvard.edu/abs/2000MNRAS.313..392L/abstract)):
$r=\frac{1AU}{parallax}$
$X(r,l,b) = R_{\odot} - r\cos l \cos b$
$Y(r,l,b) = r \sin l \cos b$
$Z(r,b) = r \sin b$
$R(r,l,b) = \sqrt{X^{2}+Y^2}$
We assume the radius of the sun to be $R_{\odot}$ = 8.5 kpc
$X$ and $Y$ are Cartesian coordinates, measured in parsecs. $R$ and $Z$ are cylindrical coordinates in kpc. $l$ and $b$ stand for the galactic longitude and galactic latitude in degrees, respectively. $r$ is the distance to the star in parsecs given the parallax in arcseconds.
```python
def galactocentric(parallax, l, b):
"Calculate the Galactocentric distance and vertical distance."
l = np.deg2rad(l)
b = np.deg2rad(b)
R_sun = 8.5
r = 1/parallax
X = R_sun - r*np.cos(l)*np.cos(b)
Y = r*np.sin(l)*np.cos(b)
Z = r*np.sin(b)
R = np.sqrt(X**2 + Y**2)
return R, Z
```
```python
R, Z = galactocentric(df_hvs['parallax'], df_hvs['l'], df_hvs['b'])
```
```python
fig = plt.figure(figsize=(9,8))
plt.xlabel('$G_{BP} - G_{RP}$ (mag)')
plt.ylabel('$M_{G}$ (mag)')
plt.xlim(-1.0,2.0)
plt.ylim(0,17)
plt.gca().invert_yaxis()
plt.scatter(df_100pc['bp_rp'], gmags_100pc, marker='.', color='0.7', alpha=0.05)
plt.scatter(df_hvs['bp_rp'], gmags_hvs, c=[abs(Zs) for Zs in Z], linewidths=0.5)
cbar = plt.colorbar(label='|Z| (kpc)')
```
Compare the above plot with Figure 3 in [Igoshev et al. 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.6223I/abstract). This plot shows the location of the HVS WD candidates on the HR diagram, colored by the absolute vertical distance above/below the Galactic disc. Gaia DR3 objects within 100 pc of the sun are plotted in greyscale for reference, where part of the main sequence is seen between $M_{G}\approx 0$ and $M_{G}\approx 9$ and part of the white dwarf sequence is seen between $M_{G}\approx 10$ and $M_{G}\approx 16$. Note that in their Figure 3, the authors used a custom approach to deredden their HVS WD candidates. We decided not to follow that same approach since the difference in values are diminutive.
<a class="anchor" id="heights"></a>
## Heights above the Galactic plane of the observed HVS WD candidates
In order to potentially provide an alternative age estimate for the candidates whose WD cooling ages and total ages couldn't be estimated because their masses were lower than 0.5 solar masses and could not form through normal stellar evolution of a single stellar progenitor over a Hubble time, the authors plot the heights of the observed HVS WDs above/below the Galactic disc. In [Casagrande et al. 2016](https://ui.adsabs.harvard.edu/abs/2016MNRAS.455..987C/abstract), it is shown most stars that reside below 1 kpc from the Galactic plane are younger than the 10 Gyr age of halo stars. We can see from the plot below that most of our HVS WD candidates are located below 1 kpc (red dashed line) from the plane, which is consistent with a disc origin for the majority of the sample and would require a non-trivial velocity kick. However, further study of the radial velocities are needed to better constrain/confirm their origin.
```python
fig = plt.figure(figsize=(10,7))
plt.xlabel('$v_{t}$ (km/s)')
plt.ylabel('$Z$ (kpc)')
plt.plot(df_hvs['v'], Z, 'ko', markersize=4)
plt.axhline(y = 1, color = 'r', linestyle = '--', alpha=0.6)
plt.show()
```
Compare the above plot with Figure 4 in [Igoshev et al. 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.6223I/abstract). This plot shows the spread of transversal velocities and vertical distances above the Galactic plane for HVS WD candidates. Stars below the dashed red line are considered to be younger than the 10 Gyr age of halo stars ([Casagrande et al. 2016](https://ui.adsabs.harvard.edu/abs/2016MNRAS.455..987C/abstract)) and may indicate a disc origin.
<a class="anchor" id="ref"></a>
## Resources and references
Casagrande et al. 2016, "Measuring the vertical age structure of the Galactic disc using asteroseismology and SAGA", MNRAS, 455, 987. https://doi.org/10.1093/mnras/stv2320
Gentile Fusillo, N. P. et al. 2021, "A catalogue of white dwarfs in Gaia EDR3", MNRAS, 508, 3877. https://doi.org/10.1093/mnras/stab2672
Heber 2009, "Hot Subdwarf Stars", ARA&A, 47, 211. https://www.annualreviews.org/doi/10.1146/annurev-astro-082708-101836
Igoshev et al. 2023, "Hyper-runaway and hypervelocity white dwarf candidates in Gaia Data Release 3: Possible remnants from Ia/Iax supernova explosions or dynamical encounters", MNRAS, 518, 6223. https://doi.org/10.1093/mnras/stac3488
López-Corredoira, M. et al. 2000, "Inversion of stellar statistics equation for the Galactic bulge", MNRAS, 313, 392. https://doi.org/10.1046/j.1365-8711.2000.03220.x
Napiwotzki R., 2008, in Heber U., Jeffery C. S., Napiwotzki R., eds, Astronomical Society of the Pacific Conference Series Vol. 392, Hot Subdwarf Stars and Related Objects. p. 139
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astro-datalabREPO_NAMEnotebooks-latestPATH_START.@notebooks-latest_extracted@notebooks-latest-master@03_ScienceExamples@WhiteDwarfs@HypervelocityWhiteDwarfCandidatesInGaiaDR3.ipynb@.PATH_END.py
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