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        "author": "Charlie Laughton",
        "author_email": "charles.laughton@nottingham.ac.uk",
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        "description": "# mdio\n\nA Python library to read, write, and manipulate molecular dynamics (MD) trajectory files.\n\n`mdio` is designed to provide basic MD file I/O capabilities. It's not supposed to replace great packages like [mdtraj](www.mdtraj.org) and [mdanalysis](www.mdanalysis.org), but is a lighter weight alternative when all you need is basic MD trajectory file I/O and nothing much more.\n\nFor example, the following script would read in a Gromacs `.xtc` format trajectory file, strip it of water molecules, correct molecule coordinates split up by periodic boundary condition artifacts, least-squares fit each snaphot to the first, and then write out the new coordinates in Amber netcdf (`.nc`) format:\n\n\n```python\nimport mdio\n\ntopfile = '../test/examples/test.gro'\ntrajfile = '../test/examples/test.xtc'\noutfile = 'protein.nc'\n\ntraj = mdio.load(trajfile, top=topfile, selection='not water')\ntraj = traj.make_whole()\ntraj = traj.fitted_to(traj[0])\ntraj.save(outfile)\n```\n\n## Installation\n\nEasiest via `pip`. You need `numpy` and `cython` pre-installed:\n```\n% pip install numpy cython\n% pip install mdio\n```\n\n## User Guide\n\n### Loading data\n\n`mdio` can load data from a variety of file formats: Gromacs (`.xtc` and `.gro`), NAMD/CHARMM (`.dcd`), AMBER (`.nc`, `.mdcrd`, `.rst`), and PDB (`.pdb`). `mdio` auto-detects file formats, so although it may be useful to use the standard file extensions, you don't have to - `mdio` will happily load an AMBER netcdf file that has the extension `.traj` (or even `.xtc`!).\n\n\n```python\nt = mdio.load('../test/examples/test.nc')\nprint(t)\n```\n\n    mdio.Trajectory with 10 frames, 892 atoms and box info.\n\n\nAlternative ways of reading files are supported:\n\n\n```python\nf = mdio.open('../test/examples/test.nc', top='../test/examples/test.pdb')\nt2 = f.read()\nf.close()\nprint(t2)\n```\n\n    mdio.Trajectory with 10 frames, 892 atoms and box info.\n\n\nOr using a context manager, and in a frame-by-frame way:\n\n\n```python\nwith mdio.open('../test/examples/test.dcd') as f:\n    frames = []\n    frame = f.read_frame()\n    while frame is not None:\n        frames.append(frame)\n        frame = f.read_frame()\nt3 = mdio.Trajectory(frames)\nprint(t3)\n```\n\n    mdio.Trajectory with 10 frames, 892 atoms and box info.\n\n\nYou can create an `mdio` trajectory object from a suitably-shaped numpy array. `mdio` assumes the numbers in the array are in nanometers.\n\n\n```python\nimport numpy as np\nxyz = np.random.random((120, 55, 3))\nt4 = mdio.Trajectory(xyz)\nprint(t4)\n```\n\n    mdio.Trajectory with 120 frames, and 55 atoms.\n\n\n### Saving data\n\nTrajectory files can also be written in a variety of ways. The required format is inferred from the filename extension, so unlike for file reading, this must be appropriate.\n\n\n```python\n# a) Using the save() method of a trajectory object:\nt.save('test.nc')\n\n# b) Using mopen():\nwith mdio.open('test2.dcd', \"w\") as f:\n    f.write(t)\n\n# c) Frame-by-frame:\nf =  mdio.open('test3.xtc', \"w\")\nfor frame in t.frames():\n    f.write_frame(frame)\nf.close()\n```\n\n### Manipulating trajectories\n\n#### a) Frame-wise:\n\nTrajectories can be sliced and concatenated/appended (if they are compatible):\n\n\n```python\nt5 = t[2:9:3] + t2\nt5 += t3\nprint(t5)\n```\n\n    mdio.Trajectory with 23 frames, 892 atoms and box info.\n\n\n#### b) Atom-wise:\n\nTrajectories with selected subsets of atoms can be created. There are two methods for doing this. One is to specify a list of atom indices:\n\n\n```python\nt6 = t.select([0, 1, 3, 5, 23, 34])\nprint(t6)\n```\n\n    mdio.Trajectory with 10 frames, 6 atoms and box info.\n\n\nThe other is to specify a selection string, which uses a syntax very similar to that used by `mdtraj`, see [here](http://mdtraj.org/latest/atom_selection.html):\n\n\n```python\nt7 = t2.select('name CA')\nprint(t7)\n```\n\n    mdio.Trajectory with 10 frames, 58 atoms and box info.\n\n\n#### c) Coordinate-wise:\n\nIt is not the purpose of `mdio` to provide a rich variety of trajectory analysis facilities, but a few common functions are implemented. Firstly coordinates can be least-squares fitted to reference structures and the fitting can be weighted:\n\n\n```python\n# a) Simple fit\nt8 = t.fitted_to(t[0])\n\n# b) Mass-weighted fit of t2 to the 6th frame in trajectory t3:\nweights = [atom.element.mass for atom in t2.topology.atoms]\nt9 = t2.fitted_to(t3[5], weights=weights)\n\n# c) Use just residue 1 for the fit:\nweights = np.zeros(t2.n_atoms)\nweights[t2.topology.select('residue 1')] = 1.0\nt10 = t2.fitted_to(t3[5], weights=weights)\n```\n\nSecondly some PBC-related transformations are supported. \n\n* `packed_around()` transforms coordinates so they lie within the unit cell whose centre would be the centre of geometry of the selected atoms. \n* `make_whole()` corrects for molecules split by PBC imaging; this uses bond information generated from analysis of the topology file, so this needs to have 'good' geometry.\n\n\n```python\nt11 = t2.packed_around('residue 3')\nt12 = t11.make_whole()\n```\n\nFinally RMSDs can be calculated:\n\n\n```python\nr2 = t2.rmsd_from(t3[6])\nprint(r2)\n```\n\n    [0.09093863981724826, 0.07614511939046421, 0.07949020859896917, 0.07497944478603998, 0.06590847615210413, 0.051843638106744715, 5.960464477539063e-08, 0.07091305468398602, 0.07106069413686344, 0.08153553252567688]\n\n\n### NGLViewer compatibility\n\n`mdio.Trajectory` objects look enough like the ones generated by MDTraj that they can be viewed in Jupyter notebooks with [nglview](https://github.com/arose/nglview) using the `show_mdtraj()` function.\n\n### Author:\n\nCharlie Laughton charles.laughton@nottingham.ac.uk\n\n### License:\n\nBSD 3-clause",
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