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    "info": {
        "author": "Josh Borrow",
        "author_email": "joshua.borrow@durham.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "SWIFTsimIO\n==========\n\n[![Build Status](https://travis-ci.com/SWIFTSIM/swiftsimio.svg?branch=master)](https://travis-ci.com/SWIFTSIM/swiftsimio)\n\nThe SWIFT astrophysical simulation code (http://swift.dur.ac.uk) is used\nwidely. There exists many ways of reading the data from SWIFT, which outputs\nHDF5 files. These range from reading directly using `h5py` to using a complex\nsystem such as `yt`; however these either are unsatisfactory (e.g. a lack of\nunit information in reading HDF5), or too complex for most use-cases. This\n(thin) wrapper provides an object-oriented API to read (dynamically) data\nfrom SWIFT.\n\n\nRequirements\n------------\n\nThis requires `python3.6.0` or higher. No effort will be made to support\npython versions below this. Please update your systems.\n\n### Python packages\n\n+ `h5py`\n+ `unyt`\n\n\nUsage\n-----\n\nExample usage is shown below, which plots a density-temperature phase\ndiagram, with density and temperature given in CGS units:\n\n```python\nimport swiftsimio as sw\n\n# This loads all metadata but explicitly does _not_ read any particle data yet\ndata = sw.load(\"/path/to/swift/output\")\n\nimport matplotlib.pyplot as plt\n\ndata.gas.density.convert_to_cgs()\ndata.gas.temperature.convert_to_cgs()\n\nplt.loglog()\n\nplt.scatter(\n    data.gas.density,\n    data.gas.temperature,\n    s=1\n)\n\nplt.xlabel(fr\"Gas density $\\left[{data.gas.density.units.latex_repr}\\right]$\")\nplt.ylabel(fr\"Gas temperature $\\left[{data.gas.temperature.units.latex_repr}\\right]$\")\n\nplt.tight_layout()\n\nplt.savefig(\"test_plot.png\", dpi=300)\n```\n\nIn the above it's important to note the following:\n\n+ All metadata is read in when the `load` function is called.\n+ Only the density and temperature (corresponding to the `PartType0/Density` and\n  `PartType0/Temperature`) datasets are read in.\n+ That data is only read in once the `convert_to_cgs` method is called.\n+ `convert_to_cgs` converts data in-place; i.e. it returns `None`.\n+ The data is cached and not re-read in when `plt.scatter` is called.\n\n\nWriting datasets\n----------------\n\nWriting datasets that are valid for consumption for cosmological codes can be\ndifficult, especially when considering how to best use units. SWIFT uses a\ndifferent set of internal units (specified in your parameter file) that does\nnot necessarily need to be the same set of units that initial conditions are\nspecified in. Nevertheless, it is important to ensure that units in the\ninitial conditions are all _consistent_ with each other. To facilitate this,\nwe use `unyt` arrays. The below example generates randomly placed gas\nparticles with uniform densities.\n\n```python\nfrom swiftsimio import Writer\nfrom swiftsimio.units import cosmo_units\n\nimport unyt\nimport numpy as np\n\n# Box is 100 Mpc\nboxsize = 100 * unyt.Mpc\n\n# Generate object. cosmo_units corresponds to default Gadget-oid units\n# of 10^10 Msun, Mpc, and km/s\nx = Writer(cosmo_units, boxsize)\n\n# 32^3 particles.\nn_p = 32**3\n\n# Randomly spaced coordinates from 0, 100 Mpc in each direction\nx.gas.coordinates = np.random.rand(n_p, 3) * (100 * unyt.Mpc)\n\n# Random velocities from 0 to 1 km/s\nx.gas.velocities = np.random.rand(n_p, 3) * (unyt.km / unyt.s)\n\n# Generate uniform masses as 10^6 solar masses for each particle\nx.gas.masses = np.ones(n_p, dtype=float) * (1e6 * unyt.msun)\n\n# Generate internal energy corresponding to 10^4 K\nx.gas.internal_energy = np.ones(n_p, dtype=float) * (1e4 * unyt.kb * unyt.K) / (1e6 * unyt.msun)\n\n# Generate initial guess for smoothing lengths based on MIPS\nx.gas.generate_smoothing_lengths(boxsize=boxsize, dimension=3)\n\n# If IDs are not present, this automatically generates    \nx.write(\"test.hdf5\")\n```\n\nThen, running `h5glance` on the resulting `test.hdf5` produces:\n```\ntest.hdf5\n\u251cHeader\n\u2502 \u25145 attributes:\n\u2502   \u251cBoxSize: 100.0\n\u2502   \u251cDimension: array [int64: 1]\n\u2502   \u251cFlag_Entropy_ICs: 0\n\u2502   \u251cNumPart_Total: array [int64: 6]\n\u2502   \u2514NumPart_Total_HighWord: array [int64: 6]\n\u251cPartType0\n\u2502 \u251cCoordinates  [float64: 32768 \u00d7 3]\n\u2502 \u251cInternalEnergy       [float64: 32768]\n\u2502 \u251cMasses       [float64: 32768]\n\u2502 \u251cParticleIDs  [float64: 32768]\n\u2502 \u251cSmoothingLength      [float64: 32768]\n\u2502 \u2514Velocities   [float64: 32768 \u00d7 3]\n\u2514Units\n  \u25145 attributes:\n    \u251cUnit current in cgs (U_I): array [float64: 1]\n    \u251cUnit length in cgs (U_L): array [float64: 1]\n    \u251cUnit mass in cgs (U_M): array [float64: 1]\n    \u251cUnit temperature in cgs (U_T): array [float64: 1]\n    \u2514Unit time in cgs (U_t): array [float64: 1]\n```\n\n**Note** you do need to be careful that your choice of unit system does\n_not_ allow values over 2^31, i.e. you need to ensure that your\nprovided values (with units) when _written_ to the file are safe to \nbe interpreted as (single-precision) floats. The only exception to\nthis is coordinates which are stored in double precision.\n\n\nAhead-of-time Masking\n---------------------\n\nSWIFT snapshots contain cell metadata that allow us to spatially mask the\ndata ahead of time. `swiftsimio` provides a number of objects that help with\nthis. See the example below.\n\n```python\nimport swiftsimio as sw\n\n# This creates and sets up the masking object.\nmask = sw.mask(\"/path/to/swift/snapshot\")\n\n# This ahead-of-time creates a spatial mask based on the cell metadata.\nmask.constrain_spatial([[0.2 * mask.metadata.boxsize[0], 0.7 * mask.metadata.boxsize[0]], None, None])\n\n# Now, just for fun, we also constrain the density between 0.4 g/cm^3 and 0.8. This reads in\n# the relevant data in the region, and tests it element-by-element.\ndensity_units = mask.units.mass / mask.units.length**3\nmask.constrain_mask(\"gas\", \"density\", 0.4 * density_units, 0.8 * density_units)\n\n# Now we can grab the actual data object. This includes the mask as a parameter.\ndata = sw.load(\"/Users/josh/Documents/swiftsim-add-anarchy/examples/SodShock_3D/sodShock_0001.hdf5\", mask=mask)\n```\n\nWhen the attributes of this data object are accessed, transparently _only_ the ones that \nbelong to the masked region (in both density and spatial) are read. I.e. if I ask for the\ntemperature of particles, it will recieve an array containing temperatures of particles\nthat lie in the region [0.2, 0.7] and have a density between 0.4 and 0.8 g/cm^3.\n\n\nUser-defined particle types\n---------------------------\n\nIt is now possible to add user-defined particle types that are not already\npresent in the `swiftsimio` metadata. All you need to do is specify the three\nnames (see below) and then the particle datasets that you have provided in\nSWIFT will be automatically read.\n\n```python\nimport swiftsimio as sw\nimport swiftsimio.metadata.particle as swp\nfrom swiftsimio.objects import cosmo_factor, a\n\nswp.particle_name_underscores[6] = \"extratype\"\nswp.particle_name_class[6] = \"Extratype\"\nswp.particle_name_text[6] = \"Extratype\"\n\ndata = sw.load(\n    \"extra_test.hdf5\",\n)\n```\nPreviously, there was a generator function API that performed this task; this has now been\nremoved (last version was v0.8.0).\n\n\nImage Creation\n--------------\n\n`swiftsimio` provides some very basic visualisation software that is designed\nto create projections of the entire box along the z-axis, with x and y\nrepresenting the final square. Note that it only currently supports square\nboxes. These are accelerated with `numba`, and we do not suggest using these\nroutines unless you have it installed. Finally, you can use parallel versions\nof these functions (by default they run in serial) by passing the argument\n`parallel=True`.\n\nYou can do the following:\n\n```python\nfrom swiftsimio import load\nfrom swiftsimio.visualisation import project_gas\n\ndata = load(\"my_snapshot_0000.hdf5\")\n# This creates a grid that has units K / Mpc^2, and can be transformed like\n# any other unyt quantity\ntemperature_map = project_gas(data, resolution=1024, project=\"temperature\")\n\nfrom matplotlib.pyplot import imsave\nfrom matplotlib.colors import LogNorm\n\n# There is huge dynamic range usually in temperature, so log normalize it before\n# saving.\nimsave(\"temperature_map.png\", LogNorm()(temperature_map.value), cmap=\"twilight\")\n```\n\nIt's also possible to create videos in a fairly straightforward way:\n```python\nfrom swiftsimio import load\nfrom swiftsimio.visualisation import project_gas_pixel_grid\n\n# project_gas_pixel_grid does not perform the unit calculation.\n\nfrom p_tqdm import p_map\n\ndef load_and_make_image(number):\n    filename = f\"snapshot_{number:04d}.hdf5\"\n    data = load(filename)\n    image = project_gas_pixel_grid(data, 1024, \"temperature\")\n\n    return image\n\n# Make frames in parallel (reading also parallel!)\nframes = p_map(load_and_make_image, range(0, 120))\n\n#... You can do your funcAnimation stuff here now you have the frames.\n```\n\n\nIt is also possible to create _slice_ plots, rather than projections.\n```python\nfrom swiftsimio import load\nfrom swiftsimio.visualisation import slice_gas\n\ndata = load(\"my_snapshot_0000.hdf5\")\n# This creates a grid that has units K / Mpc^3, and can be transformed like\n# any other unyt quantity. The slice variable gives where we want to slice through\n# as a function of the box-size, so here we slice through the centre of the box.\ntemperature_map = slice_gas(data, slice=0.5, resolution=1024, project=\"temperature\")\n\nfrom matplotlib.pyplot import imsave\nfrom matplotlib.colors import LogNorm\n\n# There is huge dynamic range usually in temperature, so log normalize it before\n# saving.\nimsave(\"temperature_map.png\", LogNorm()(temperature_map.value), cmap=\"twilight\")\n```\n\n\nFinally, we provide a wrapper of the ever-popular\n[`py-sphviewer`](https://github.com/alejandrobll/py-sphviewer) for easy use\nwith `swiftsimio` datasets. Particle datasets that do not contain smoothing\nlengths will have them generated through the use of the scipy `cKDTree`. You\ncan get access to the objects through a sub-module as follows:\n```python\nfrom swiftsimio import load\nfrom swiftsimio.visualisation.sphviewer import SPHViewerWrapper\n\ndata = load(\"my_snapshot_0000.hdf5\")\n\nresolution = 2048\n\ngas = SPHViewer(data.gas, smooth_over=\"masses\")\ngas_temp = SPHViewer(\n    data.gas,\n    smooth_over=data.gas.masses * data.gas.temperatures\n)\ndark_matter = SPHViewer(data.dark_matter, smooth_over=\"masses\")\n\ngas.quick_view(xsize=resolution, ysize=resolution, r=\"infinity\")\ngas_temp.quick_view(xsize=resolution, ysize=resolution, r=\"infinity\")\ndark_matter.quick_view(xsize=resolution, ysize=resolution, r=\"infinity\")\n\nplt.imsave(\"gas_image.png\", gas.image)\nplt.imsave(\"gas_temp.png\", gas_temp.image / gas.image)\nplt.imsave(\"dm_image.png\", dark_matter.image)\n```\n\n\n",
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