From 0f0f105cdeea3dc860f9e864a2107394886d282d Mon Sep 17 00:00:00 2001 From: Shivesh Mandalia Date: Thu, 5 Mar 2020 17:21:29 +0000 Subject: Minor formatting in notebooks --- examples/tutorial.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) (limited to 'examples/tutorial.ipynb') diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 529bb6e..669223e 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -274,7 +274,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [`GolemFit` package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task." + "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [GolemFit package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task." ] }, { @@ -422,7 +422,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can define the wrappers to the [`emcee` package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:" + "Now we can define the wrappers to the [emcee package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:" ] }, { @@ -463,8 +463,9 @@ "metadata": {}, "source": [ "Here we have 2 `ParamSet` objects:\n", - "* `asimov_paramset` contains the measured parameters\n", - "* `llh_paramset` contains the model parameter values\n", + "\n", + "- `asimov_paramset` contains the measured parameters\n", + "- `llh_paramset` contains the model parameter values\n", "\n", "In this example, they contain the same parameters since we are doing a simple scan over the measured flavor angles to generate some fake data." ] -- cgit v1.2.3