{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Broad Overview of `hmf`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial/demo, we provide a broad overview of the way `hmf` works, and its features. \n", "For those really just needing to make a quick plot of a mass function, the best place to start is in the [Quickstart](your_first_plot.html) tutorial. This tutorial will go into a little more depth (without exploring more advanced niche features)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Package Layout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`hmf` is quite modular, and contains a number of sub-packages concerning each of the physical components that go into defining the halo mass function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:48:50.517186Z", "start_time": "2020-05-20T04:48:49.906027Z" } }, "outputs": [], "source": [ "# Defines power spectra and transfer functions, as well as window functions/filters on those\n", "from hmf import density_field" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While each of these modules has tools that can be useful for more advanced usage, the primary point of contact with `hmf` is the `MassFunction` object, which essentially contains all the working of the full package. This lives in the `mass_function` submodule, but can be imported from the top level:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:48:50.593598Z", "start_time": "2020-05-20T04:48:50.590921Z" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Frameworks -- Caching and Updating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each main entrypoint class in `hmf` (this includes `Cosmology`, `Transfer` and `MassFunction`) is what we call a `Framework`. This name is not particularly descriptive, but it means that each of these objects offers a number of similar points of functionality. Here, we'll demonstrate a few of these bits of functionality on the `Transfer` class, but it should be remembered that they are the same for all of these." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:48:53.551677Z", "start_time": "2020-05-20T04:48:53.547743Z" } }, "outputs": [], "source": [ "from hmf import Transfer" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:48:54.384231Z", "start_time": "2020-05-20T04:48:54.378074Z" } }, "outputs": [], "source": [ "tr = Transfer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first common point is that each of the frameworks has defaults for all of its parameters, and a reasonable object can be created by passing no parameters, as we just did.\n", "\n", "We can, like any object in Python, get some help with what parameters are available by using `help`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:47:53.755042Z", "start_time": "2020-05-20T03:47:53.741694Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class Transfer in module hmf.density_field.transfer:\n", "\n", "class Transfer(hmf.cosmology.cosmo.Cosmology)\n", " | Transfer(*args, **kwargs)\n", " | \n", " | A transfer function framework.\n", " | \n", " | The purpose of this :class:`hmf._frameworks.Framework` is to calculate\n", " | transfer functions, power spectra and several tightly associated\n", " | quantities given a basic model for the transfer function.\n", " | \n", " | As in all frameworks, to update parameters optimally, use the\n", " | :meth:`update` method. All output quantities are calculated only when needed\n", " | (but stored after first calculation for quick access).\n", " | \n", " | In addition to the parameters directly passed to this class, others are available\n", " | which are passed on to its superclass. To read a standard documented list of (all)\n", " | parameters, use ``Transfer.parameter_info()``. If you want to just see the plain\n", " | list of available parameters, use ``Transfer.get_all_parameters()``.To see the\n", " | actual defaults for each parameter, use ``Transfer.get_all_parameter_defaults()``.\n", " | \n", " | By default, the `growth_model` is :class:`~growth_factor.GrowthFactor`. However, if\n", " | using a wCDM cosmology and camb is installed, it will default to\n", " | :class:`~growth_factor.CambGrowth`.\n", " | \n", " | Method resolution order:\n", " | Transfer\n", " | hmf.cosmology.cosmo.Cosmology\n", " | hmf._internals._framework.Framework\n", " | builtins.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, sigma_8=0.8159, n=0.9667, z=0.0, lnk_min=-18.420680743952367, lnk_max=9.903487552536127, dlnk=0.05, transfer_model=, transfer_params=None, takahashi=True, growth_model=None, growth_params=None, use_splined_growth=False, **kwargs)\n", " | Initialize self. See help(type(self)) for accurate signature.\n", " | \n", " | validate(self)\n", " | Perform validation of the input parameters as they relate to each other.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties defined here:\n", " | \n", " | delta_k\n", " | Dimensionless power spectrum, :math:`\\Delta_k = \\frac{k^3 P(k)}{2\\pi^2}`.\n", " | \n", " | growth\n", " | The instantiated growth model.\n", " | \n", " | growth_factor\n", " | The growth factor.\n", " | \n", " | k\n", " | Wavenumbers, [h/Mpc]\n", " | \n", " | nonlinear_delta_k\n", " | Dimensionless nonlinear power spectrum.\n", " | \n", " | .. math:: \\Delta_k = \\frac{k^3 P_{\\rm nl}(k)}{2\\pi^2}\n", " | \n", " | nonlinear_power\n", " | Non-linear log power [units :math:`Mpc^3/h^3`].\n", " | \n", " | Non-linear corrections come from HALOFIT.\n", " | \n", " | power\n", " | Normalised log power spectrum [units :math:`Mpc^3/h^3`].\n", " | \n", " | transfer\n", " | The instantiated transfer model\n", " | \n", " | transfer_function\n", " | Normalised CDM log transfer function.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | dlnk\n", " | **Parameter**: Step-size of log wave-numbers\n", " | \n", " | :type: float\n", " | \n", " | growth_model\n", " | **Parameter**: The model to use to calculate the growth function/growth rate.\n", " | \n", " | :type: `hmf.growth_factor._GrowthFactor` subclass\n", " | \n", " | growth_params\n", " | **Parameter**: Relevant parameters of the :attr:`growth_model`.\n", " | \n", " | :type: dict\n", " | \n", " | lnk_max\n", " | **Parameter**: Maximum (natural) log wave-number, :attr:`k` [h/Mpc].\n", " | \n", " | :type: float\n", " | \n", " | lnk_min\n", " | **Parameter**: Minimum (natural) log wave-number, :attr:`k` [h/Mpc].\n", " | \n", " | :type: float\n", " | \n", " | n\n", " | **Parameter**: Spectral index of fluctuations\n", " | \n", " | Must be greater than -3 and less than 4.\n", " | \n", " | :type: float\n", " | \n", " | sigma_8\n", " | **Parameter**: RMS linear density fluctuations in spheres of radius 8 Mpc/h\n", " | \n", " | :type: float\n", " | \n", " | takahashi\n", " | **Parameter**: Whether to use updated HALOFIT coefficients from Takahashi+12.\n", " | \n", " | If False, use the original coefficients from Smith+2003.\n", " | \n", " | :type: bool\n", " | \n", " | transfer_model\n", " | **Parameter**: Defines which transfer function model to use.\n", " | \n", " | Built-in available models are found in the :mod:`hmf.transfer_models` module.\n", " | Default is CAMB if installed, otherwise EH.\n", " | \n", " | :type: str or :class:`hmf.transfer_models.TransferComponent` subclass, optional\n", " | \n", " | transfer_params\n", " | **Parameter**: Relevant parameters of the `transfer_model`.\n", " | \n", " | :type: dict\n", " | \n", " | z\n", " | **Parameter**: Redshift.\n", " | \n", " | Must be greater than 0.\n", " | \n", " | :type: float\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties inherited from hmf.cosmology.cosmo.Cosmology:\n", " | \n", " | cosmo\n", " | Cosmographic object (:class:`astropy.cosmology.FLRW` object), with custom\n", " | cosmology from :attr:`~.cosmo_params` applied.\n", " | \n", " | mean_density0\n", " | Mean density of universe at z=0, [Msun h^2 / Mpc**3]\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from hmf.cosmology.cosmo.Cosmology:\n", " | \n", " | cosmo_model\n", " | **Parameter**: The basis for the cosmology -- see astropy documentation. Can be a custom\n", " | subclass. Defaults to Planck18.\n", " | \n", " | :type: instance of `astropy.cosmology.FLRW` subclass\n", " | \n", " | cosmo_params\n", " | **Parameter**: Parameters for the cosmology that deviate from the base cosmology passed.\n", " | This is useful for repeated updates of a single parameter (leaving others\n", " | the same). Default is the empty dict. The parameters passed must match\n", " | the allowed parameters of `cosmo_model`. For the basic class this is\n", " | \n", " | :Tcmb0: Temperature of the CMB at z=0\n", " | :Neff: Number of massless neutrino species\n", " | :m_nu: Mass of neutrino species (list)\n", " | :H0: The hubble constant at z=0\n", " | :Om0: The normalised matter density at z=0\n", " | \n", " | :type: dict\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from hmf._internals._framework.Framework:\n", " | \n", " | clone(self, **kwargs)\n", " | Create and return an updated clone of the current object.\n", " | \n", " | get_dependencies(self, *q)\n", " | Determine all parameter dependencies of the quantities in q.\n", " | \n", " | Parameters\n", " | ----------\n", " | q : str\n", " | String(s) labelling a quantity\n", " | \n", " | Returns\n", " | -------\n", " | deps : set\n", " | A set containing all parameters on which quantities in q are dependent.\n", " | \n", " | update(self, **kwargs)\n", " | Update parameters of the framework with kwargs.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Class methods inherited from hmf._internals._framework.Framework:\n", " | \n", " | get_all_parameter_defaults(recursive=True) from hmf._internals._framework._Validator\n", " | Dictionary of all parameters and defaults.\n", " | \n", " | get_all_parameter_names() from hmf._internals._framework._Validator\n", " | Yield all parameter names in the class.\n", " | \n", " | parameter_info(names=None) from hmf._internals._framework._Validator\n", " | Prints information about each parameter in the class.\n", " | \n", " | Optionally, restrict printed parameters to those found in the list of names\n", " | provided.\n", " | \n", " | quantities_available() from hmf._internals._framework._Validator\n", " | Obtain a list of all available output quantities.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties inherited from hmf._internals._framework.Framework:\n", " | \n", " | parameter_values\n", " | Dictionary of all parameters and their current values\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from hmf._internals._framework.Framework:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", "\n" ] } ], "source": [ "help(Transfer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's a lot of help! You can also consult the [API documentation](../api.html). However, since many of the parameters to `Transfer` merely get passed through to `Cosmology`, they get lost in this documentation. You can get a list of all possible parameters for a framework like this:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:40:58.937485Z", "start_time": "2020-05-20T03:40:58.922927Z" } }, "outputs": [ { "data": { "text/plain": [ "{'cosmo_model': FlatLambdaCDM(name=\"Planck18\", H0=67.66 km / (Mpc s), Om0=0.30966, Tcmb0=2.7255 K, Neff=3.046, m_nu=[0. 0. 0.06] eV, Ob0=0.04897),\n", " 'cosmo_params': {},\n", " 'n': 0.9667,\n", " 'sigma_8': 0.8159,\n", " 'growth_params': {'dlna': 0.01, 'amin': 1e-08},\n", " 'lnk_min': -18.420680743952367,\n", " 'lnk_max': 9.903487552536127,\n", " 'dlnk': 0.05,\n", " 'z': 0.0,\n", " 'transfer_model': hmf.density_field.transfer_models.CAMB,\n", " 'transfer_params': {'camb_params': None,\n", " 'dark_energy_params': {},\n", " 'extrapolate_with_eh': False,\n", " 'kmax': None},\n", " 'takahashi': True,\n", " 'growth_model': hmf.cosmology.growth_factor.GrowthFactor}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Transfer.get_all_parameter_defaults()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, you can consult the API docs for information on each one, bu you can also use this special function:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:43:35.792099Z", "start_time": "2020-05-20T03:43:35.782674Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cosmo_model : instance of `astropy.cosmology.FLRW` subclass\n", " The basis for the cosmology -- see astropy documentation. Can be a custom\n", " subclass. Defaults to Planck18.\n", "\n", "cosmo_params : dict\n", " Parameters for the cosmology that deviate from the base cosmology passed.\n", " This is useful for repeated updates of a single parameter (leaving others\n", " the same). Default is the empty dict. The parameters passed must match\n", " the allowed parameters of `cosmo_model`. For the basic class this is\n", " :Tcmb0: Temperature of the CMB at z=0\n", " :Neff: Number of massless neutrino species\n", " :m_nu: Mass of neutrino species (list)\n", " :H0: The hubble constant at z=0\n", " :Om0: The normalised matter density at z=0\n", "\n", "n : float\n", " Spectral index of fluctuations\n", " Must be greater than -3 and less than 4.\n", "\n", "sigma_8 : float\n", " RMS linear density fluctuations in spheres of radius 8 Mpc/h\n", "\n", "growth_params : dict\n", " Relevant parameters of the :attr:`growth_model`.\n", "\n", "lnk_min : float\n", " Minimum (natural) log wave-number, :attr:`k` [h/Mpc].\n", "\n", "lnk_max : float\n", " Maximum (natural) log wave-number, :attr:`k` [h/Mpc].\n", "\n", "dlnk : float\n", " Step-size of log wave-numbers\n", "\n", "z : float\n", " Redshift.\n", " Must be greater than 0.\n", "\n", "transfer_model : str or :class:`hmf.transfer_models.TransferComponent` subclass, optional\n", " Defines which transfer function model to use.\n", " Built-in available models are found in the :mod:`hmf.transfer_models` module.\n", " Default is CAMB if installed, otherwise EH.\n", "\n", "transfer_params : dict\n", " Relevant parameters of the `transfer_model`.\n", "\n", "takahashi : bool\n", " Whether to use updated HALOFIT coefficients from Takahashi+12.\n", " If False, use the original coefficients from Smith+2003.\n", "\n", "growth_model : `hmf.growth_factor._GrowthFactor` subclass\n", " The model to use to calculate the growth function/growth rate.\n", "\n" ] } ], "source": [ "Transfer.parameter_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Almost all of the things that a framework can calculate -- whether they be transfer functions, growth factors or mass functions -- will appear to be attributes of the object. That is, you don't \"call\" them like functions, but instead just access them like data. In fact, they are lazily calculated as needed, and then stored in memory once calculated. So, for example, let's calculate the matter power spectrum:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:51:41.167260Z", "start_time": "2020-05-20T03:51:38.306435Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.08 s, sys: 18.5 ms, total: 3.1 s\n", "Wall time: 1 s\n" ] }, { "data": { "text/plain": [ "25208.59372934649" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time tr.power.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This took almost 3 seconds on this system, as it called `CAMB` in the background to calculate the power spectrum.\n", "However, it is now cached, and if we call it again:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:52:32.633400Z", "start_time": "2020-05-20T03:52:32.618554Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 68 µs, sys: 8 µs, total: 76 µs\n", "Wall time: 96.1 µs\n" ] }, { "data": { "text/plain": [ "25208.59372934649" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time tr.power.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It takes less than 1/1000 of a second, as its just accessing memory. More than that, each (non-trivial) quantity that the power spectrum depends on is also cached, so to access the transfer function:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T03:54:38.006836Z", "start_time": "2020-05-20T03:54:37.995756Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 224 µs, sys: 0 ns, total: 224 µs\n", "Wall time: 236 µs\n" ] }, { "data": { "text/plain": [ "1795.5138644898466" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time tr.transfer_function.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also returns instantly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Updating Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often you'll want to compute a certain quantity over a large number of values of a given parameter (or multiple parameters). Of course, you could just create a new framework each time (eg. a `Transfer` object), but that is often going to be much slower than necessary, because often the parameter does not affect many of the underlying quantities. For instance, updating the redshift doesn't change the underlying transfer function, and the power spectrum just changes by an overall factor. \n", "\n", "Internally, each framework keeps precise track of which parameters affect each quantity, which enables robust cache invalidation -- in other words, we can keep a computed quantity cached when a parameter is updated that doesn't affect it, and when other quantities that depend on that quantity are required, they can just access it again directly. Let's see this with an example -- 20 calculations of the power spectrum at different redshifts:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:49:16.925067Z", "start_time": "2020-05-20T04:49:16.764042Z" } }, "outputs": [], "source": [ "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:02:27.817305Z", "start_time": "2020-05-20T04:02:27.809316Z" } }, "outputs": [], "source": [ "rng = np.random.default_rng(seed=42)\n", "redshifts = rng.uniform(0, 3, size=20)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:10:48.179515Z", "start_time": "2020-05-20T04:09:43.395542Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min, sys: 335 ms, total: 1min 1s\n", "Wall time: 19.8 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "\n", "for z in redshifts:\n", " tr_ = Transfer(z=z)\n", " plt.plot(tr_.k, tr_.power)\n", "\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If instead we use our original transfer object and merely update the redshift in-place:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:10:53.070856Z", "start_time": "2020-05-20T04:10:52.613604Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 62 ms, sys: 2.97 ms, total: 65 ms\n", "Wall time: 69.9 ms\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "\n", "for z in redshifts:\n", " tr.z = z\n", " plt.plot(tr.k, tr.power)\n", "\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See that the output plots are precisely the same -- the power spectrum is being updated for each redshift, but by using the caching mechanism we improve performance by three orders of magnitude." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use the in-built `.update` method to update parameters:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:12:20.578501Z", "start_time": "2020-05-20T04:12:20.573100Z" } }, "outputs": [], "source": [ "tr.update(z=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Components" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inside frameworks are a whole bunch of parameters. Some of these are simple numerical parameters, but many of them are themselves complex components tasked with computing specialized quantities. Furthermore, many of these components have various possible models that you might want to switch between. To make it easy to do so, we make each of these a formal `Component` object. Once instance of these is are the transfer function models themselves. We have been using CAMB to compute the transfer function, but a popular approximation is the Eisenstein-Hu model:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:16:40.536376Z", "start_time": "2020-05-20T04:16:40.532871Z" } }, "outputs": [], "source": [ "tr.transfer_model = \"EH\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For components, you can always pass a string referring to the name of the class, or the actual class itself:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:17:53.501490Z", "start_time": "2020-05-20T04:17:53.497332Z" } }, "outputs": [], "source": [ "tr.transfer_model = density_field.transfer_models.EH" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The latter is useful because it gives you a lot of flexibility -- you could write your own class and pass it in! For more on that, see [Plugins and Extensions](plugins_and_extending.html).\n", "\n", "All formal Components within `hmf` are passed to a framework via the `componentname_model` parameter. So Filters are passed as `filter_model`. The actual model instance (the thing that will do the calculations) is then available within the framework as as the component name:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:20:21.250767Z", "start_time": "2020-05-20T04:20:21.241153Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr.transfer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this, we can compute a bunch of stuff, like the (log) transfer function:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:20:57.884074Z", "start_time": "2020-05-20T04:20:57.874971Z" } }, "outputs": [ { "data": { "text/plain": [ "array([-5.36444121, -5.54587766, -5.72851634, -5.91231484, -6.09721705,\n", " -6.28319132, -6.47019445, -6.65818265, -6.84712149, -7.03697125])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr.transfer.lnt(np.linspace(0, 1, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is of course what is used to generate the transfer function accessible in the `Transfer` framework. But sometimes there are other goodies hidden away in the components that can be useful!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To update parameters of the components requires passing a dictionary of parameters to the parameter `componentname_params`. For example, the growth function is a component:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:25:05.763829Z", "start_time": "2020-05-20T04:25:05.755394Z" } }, "outputs": [ { "data": { "text/plain": [ "hmf.cosmology.growth_factor.GrowthFactor" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr.growth_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can update its params like so:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:27:42.480823Z", "start_time": "2020-05-20T04:27:42.474988Z" } }, "outputs": [], "source": [ "tr.growth_params = {\"dlna\": 1} # By default it is 0.01" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute the growth factor:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:27:43.483881Z", "start_time": "2020-05-20T04:27:43.473967Z" } }, "outputs": [ { "data": { "text/plain": [ "0.11447082321729078" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr.growth.growth_factor(z=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now update back to the original" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:27:47.562230Z", "start_time": "2020-05-20T04:27:47.554877Z" } }, "outputs": [], "source": [ "tr.growth_params = {\"dlna\": 0.01}" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:27:48.080916Z", "start_time": "2020-05-20T04:27:48.073360Z" } }, "outputs": [ { "data": { "text/plain": [ "0.11510206460830207" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr.growth.growth_factor(z=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example of a Component is the cosmology, and you can see the [Dealing with Cosmology](deal_with_cosmology.html) tutorial for more details there -- but it follows the same pattern." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A full list of the available components in `hmf` is as follows:\n", "\n", "* `Cosmology`\n", "* `GrowthFactor`\n", "* `TransferModel`\n", "* `Filter`\n", "* `MassDefinition`\n", "* `FittingFunction`\n", "\n", "And each of these has several models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `hmf` Efficiently" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have already discussed caching and how it speeds up many calculations very significantly. However, it is only useful if used correctly. Let's say you want to calculate the transfer function for multiple values of both $\\Omega_m$ and the redshift $z$. Then to get the speedup, you must use the faster updater as the inner loop, otherwise the object still needs to compute the slower update many times. \n", "\n", "In this case, the redshift should be the inner loop, since $\\Omega_m$ affects the basic transfer function, which is typically the slowest calculation of all. Much of the time, the relevant order of parameters should be clear, but you can determine them explicitly using a helper function:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:36:38.147314Z", "start_time": "2020-05-20T04:36:38.141738Z" } }, "outputs": [], "source": [ "from hmf import get_best_param_order" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:37:21.330867Z", "start_time": "2020-05-20T04:37:18.399922Z" } }, "outputs": [ { "data": { "text/plain": [ "['growth_model',\n", " 'takahashi',\n", " 'transfer_params',\n", " 'transfer_model',\n", " 'z',\n", " 'dlnk',\n", " 'lnk_max',\n", " 'lnk_min',\n", " 'growth_params',\n", " 'sigma_8',\n", " 'n',\n", " 'cosmo_params',\n", " 'cosmo_model']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_best_param_order(Transfer, q=\"power\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This call should be interpreted as determining the best order for calculating the `power` in the `Transfer` framework, and the output is in order of fastest to slowest. We see, for example, that `cosmo_params` (where $\\Omega_m$ lives) is far down the list compared to redshift. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can go even further than that and use another helper function to \"just get\" the output quantities over the loop for you:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:48:59.670583Z", "start_time": "2020-05-20T04:48:59.667144Z" } }, "outputs": [], "source": [ "from hmf import get_hmf" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2020-05-20T04:49:30.025294Z", "start_time": "2020-05-20T04:49:19.915703Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Om0': 0.3} 0.0\n", "{'Om0': 0.3} 1.0\n", "{'Om0': 0.3} 2.0\n", "{'Om0': 0.3} 3.0\n", "{'Om0': 0.3} 4.0\n", "{'Om0': 0.3} 5.0\n", "{'Om0': 0.2} 0.0\n", "{'Om0': 0.2} 1.0\n", "{'Om0': 0.2} 2.0\n", "{'Om0': 0.2} 3.0\n", "{'Om0': 0.2} 4.0\n", "{'Om0': 0.2} 5.0\n", "{'Om0': 0.4} 0.0\n", "{'Om0': 0.4} 1.0\n", "{'Om0': 0.4} 2.0\n", "{'Om0': 0.4} 3.0\n", "{'Om0': 0.4} 4.0\n", "{'Om0': 0.4} 5.0\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for _power, tr, _label in get_hmf(\n", " req_qauntities=[\"power\"],\n", " framework=Transfer,\n", " fast_kwargs={\"transfer_model\": \"EH\"},\n", " z=[0, 1, 2, 3, 4, 5],\n", " cosmo_params=[{\"Om0\": 0.3}, {\"Om0\": 0.2}, {\"Om0\": 0.4}],\n", "):\n", " print(tr.cosmo_params, tr.z)\n", " plt.plot(tr.k, tr.power)\n", "\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Technically, the `get_hmf` function is an iterator, yielding the quantities you ask for (and the full Framework object updated with parameters) on each iteration, but doing it in the optimal order." ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }