From 816f202aac5212d8612b2d9575167bfd97cef847 Mon Sep 17 00:00:00 2001
From: github-actions Hydrogen bonds\(r_{DA}\)) must be less than a specified cutoff, typically 3 Å
the donor-hydrogen-acceptor angle (\(\theta_{DHA}\)) must be greater than a specified cutoff, typically 150°
NMP: residues 30-59 (blue)
LID: residues 122-159 (yellow)
[26]:
In stand-alone use, an auxiliary reader allows you to iterate over each step in a set of auxiliary data.
@@ -460,7 +460,7 @@The ‘description’ of any or all the auxiliaries added to a trajectory can be @@ -575,7 +575,7 @@
In [45]: import matplotlib.pyplot as plt
In [46]: plt.plot(temp["Time"], temp["Temperature"])
-Out[46]: [<matplotlib.lines.Line2D at 0x7f23b3a92910>]
+Out[46]: [<matplotlib.lines.Line2D at 0x7fd022280670>]
In [47]: plt.ylabel("Temperature [K]")
Out[47]: Text(0, 0.5, 'Temperature [K]')
diff --git a/2.7.0-dev0/index.html b/2.7.0-dev0/index.html
index 4f43b01e2..b00df45a3 100644
--- a/2.7.0-dev0/index.html
+++ b/2.7.0-dev0/index.html
@@ -190,7 +190,7 @@
Welcome to MDAnalysis User Guide’s documentation!
MDAnalysis version: 2.7.0-dev0
-Last updated: Nov 21, 2023
+Last updated: Nov 24, 2023
MDAnalysis (www.mdanalysis.org) is a Python
toolkit to analyse molecular dynamics files and trajectories in many popular formats. MDAnalysis can write
most of these formats, too, together with atom selections for use in visualisation tools or other analysis programs.
diff --git a/2.7.0-dev0/reading_and_writing.html b/2.7.0-dev0/reading_and_writing.html
index 23f89d779..ae930de7f 100644
--- a/2.7.0-dev0/reading_and_writing.html
+++ b/2.7.0-dev0/reading_and_writing.html
@@ -320,13 +320,13 @@
Building trajectories in memoryIn [16]: universe.atoms.positions
Out[16]:
-array([[0.95849204, 0.7487938 , 0.9704986 ],
- [0.42354256, 0.2889326 , 0.06184179],
- [0.5241738 , 0.642687 , 0.6946113 ],
+array([[0.01341493, 0.8954516 , 0.48900408],
+ [0.0416302 , 0.81844276, 0.6713378 ],
+ [0.32844332, 0.8965664 , 0.8370764 ],
...,
- [0.7527609 , 0.1350824 , 0.80041414],
- [0.1707926 , 0.5413459 , 0.8568687 ],
- [0.0265642 , 0.16363254, 0.5556602 ]], dtype=float32)
+ [0.04501093, 0.29551986, 0.05062775],
+ [0.6661255 , 0.7034433 , 0.08090701],
+ [0.52956533, 0.7413073 , 0.81429756]], dtype=float32)
or they can be directly passed in when creating a Universe.
@@ -334,13 +334,13 @@C
O
N
O
C
CA
RA5
RA3
DC
RA
DG3
U
GUA
RU3
DA5
DA3
RU5
DC
DT
THY
ADE
RC3
DA5
A
GUA
DA3
RU5
RA3
DT5
RU
ADE
DG
RG3
DT3
RC5
URA
RC3
RU3
DC5
C
DT5
DT3
RA
DG5
DA
T
CYT
RG5
RA5
DG5
RG
DC3
RG5
DA
DT
RC5
DG
C
RC
CYT
DG3
RG3
RC
G
O3’
O5’
P
C3’
O5’
O3’
C5’
O6
N1
C8
N6
N4
C6
C5
O2
N3
N6
N2
O4
C2
N9
C6
O4
N7
N4
N1
N9
C8
C4
C5M
C2
N3
C5
N7
C1’
O4’
C3’
C4’
C4’
C2’
C1’
C3’
O4’
To mark an expected failure, use pytest.mark.xfail()
decorator:
To mark an expected failure, use pytest.mark.xfail()
decorator:
@pytest.mark.xfail
def tested_expected_failure():
assert 1 == 2
To manually fail a test, make a call to pytest.fail()
:
To manually fail a test, make a call to pytest.fail()
:
def test_open(self, tmpdir):
outfile = str(tmpdir.join('lammps-writer-test.dcd'))
try:
@@ -452,7 +452,7 @@ Failing tests
Skipping tests
-To skip tests based on a condition, use pytest.mark.skipif(condition)
decorator:
+To skip tests based on a condition, use pytest.mark.skipif(condition)
decorator:
import numpy as np
try:
from numpy import shares_memory
@@ -467,7 +467,7 @@ Skipping testsassert not np.shares_memory(original.ts.positions, copy.ts.positions)
-To skip a test if a module is not available for importing, use pytest.importorskip('module_name')
+To skip a test if a module is not available for importing, use pytest.importorskip('module_name')
def test_write_trajectory_netCDF4(self, universe, outfile):
pytest.importorskip("netCDF4")
return self._test_write_trajectory(universe, outfile)
@@ -477,7 +477,7 @@ Skipping tests
Fixtures
-Use fixtures as much as possible to reuse “resources” between test methods/functions. Pytest fixtures are functions that run before each test function that uses that fixture. A fixture is typically set up with the pytest.fixture()
decorator, over a function that returns the object you need:
+Use fixtures as much as possible to reuse “resources” between test methods/functions. Pytest fixtures are functions that run before each test function that uses that fixture. A fixture is typically set up with the pytest.fixture()
decorator, over a function that returns the object you need:
@pytest.fixture
def universe(self):
return mda.Universe(self.ref_filename)
diff --git a/dev/_downloads/899668c9e2e17d4c2fc64131d19cdebc/auxiliary-1.pdf b/dev/_downloads/899668c9e2e17d4c2fc64131d19cdebc/auxiliary-1.pdf
index d1544b2f65facafeda6fd227240a076221c3e256..f09507d2fca4be01eb9bbdf876112f436ba09fb5 100644
GIT binary patch
delta 19
acmZ4CzrufmpbDFbfw6(9$!2ktiOc{$2?dM*
delta 19
acmZ4CzrufmpbDFzfw6&^!DexliOc{#*9C?E
diff --git a/dev/examples/analysis/hydrogen_bonds/hbonds.html b/dev/examples/analysis/hydrogen_bonds/hbonds.html
index 349e37d30..1cf94294d 100644
--- a/dev/examples/analysis/hydrogen_bonds/hbonds.html
+++ b/dev/examples/analysis/hydrogen_bonds/hbonds.html
@@ -266,7 +266,7 @@ Hydrogen bonds\(r_{DA}\)) must be less than a specified cutoff, typically 3 Å
the donor-hydrogen-acceptor angle (\(\theta_{DHA}\)) must be greater than a specified cutoff, typically 150°
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LYCAAAAAAAAAAAAAAAAAAAgKhBsBQAAAAAAAAAAAAAAAAAAQFQg2AoAAAAAAAAAAAAAAAAAAICoQLAVAAAAAAAAAAAAAAAAAAAAUYFgKwAAAAAAAAAAAAAAAAAAAKICwVYAAAAAAAAAAAAAAAAAAABEBYKtAAAAAAAAAAAAAAAAAAAAiAoEWwEAAAAAAAAAAAAAAAAAABAVCLYCAAAAAAAAAAAAAAAAAAAgKvw/gK1qs83TtooAAAAASUVORK5CYII=)
Find water-water hydrogen bonds
diff --git a/dev/examples/constructing_universe.html b/dev/examples/constructing_universe.html
index 41f20b514..752646443 100644
--- a/dev/examples/constructing_universe.html
+++ b/dev/examples/constructing_universe.html
@@ -715,7 +715,7 @@ Adding a new segmentNMP: residues 30-59 (blue)
LID: residues 122-159 (yellow)
-![f3284674c13f481fb3bdefdeddca6b44](https://github.com/MDAnalysis/MDAnalysisTutorial/blob/master/doc/sphinx/figs/angle_defs.png?raw=true)
+![1cc00375615b413d878cc6f95e7cfcda](https://github.com/MDAnalysis/MDAnalysisTutorial/blob/master/doc/sphinx/figs/angle_defs.png?raw=true)
[26]:
diff --git a/dev/formats/auxiliary.html b/dev/formats/auxiliary.html
index eba683e3e..cc928d8a4 100644
--- a/dev/formats/auxiliary.html
+++ b/dev/formats/auxiliary.html
@@ -261,7 +261,7 @@ Reading data directlyIn [3]: aux = mda.auxiliary.core.auxreader(XVG_BZ2)
In [4]: aux
-Out[4]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7f23e7d58fd0>
+Out[4]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7fd021ce3070>
In stand-alone use, an auxiliary reader allows you to iterate over each step in a set of auxiliary data.
@@ -460,7 +460,7 @@ Recreating auxiliariesIn [30]: del aux
In [31]: mda.auxiliary.core.auxreader(**description)
-Out[31]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7f23b3eedcd0>
+Out[31]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7fd022ba1b50>
The ‘description’ of any or all the auxiliaries added to a trajectory can be
@@ -575,7 +575,7 @@
Standalone UsageIn [45]: import matplotlib.pyplot as plt
In [46]: plt.plot(temp["Time"], temp["Temperature"])
-Out[46]: [<matplotlib.lines.Line2D at 0x7f23b3a92910>]
+Out[46]: [<matplotlib.lines.Line2D at 0x7fd022280670>]
In [47]: plt.ylabel("Temperature [K]")
Out[47]: Text(0, 0.5, 'Temperature [K]')
diff --git a/dev/index.html b/dev/index.html
index 4f43b01e2..b00df45a3 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -190,7 +190,7 @@
Welcome to MDAnalysis User Guide’s documentation!
MDAnalysis version: 2.7.0-dev0
-Last updated: Nov 21, 2023
+Last updated: Nov 24, 2023
MDAnalysis (www.mdanalysis.org) is a Python
toolkit to analyse molecular dynamics files and trajectories in many popular formats. MDAnalysis can write
most of these formats, too, together with atom selections for use in visualisation tools or other analysis programs.
diff --git a/dev/reading_and_writing.html b/dev/reading_and_writing.html
index 23f89d779..ae930de7f 100644
--- a/dev/reading_and_writing.html
+++ b/dev/reading_and_writing.html
@@ -320,13 +320,13 @@
Building trajectories in memoryIn [16]: universe.atoms.positions
Out[16]:
-array([[0.95849204, 0.7487938 , 0.9704986 ],
- [0.42354256, 0.2889326 , 0.06184179],
- [0.5241738 , 0.642687 , 0.6946113 ],
+array([[0.01341493, 0.8954516 , 0.48900408],
+ [0.0416302 , 0.81844276, 0.6713378 ],
+ [0.32844332, 0.8965664 , 0.8370764 ],
...,
- [0.7527609 , 0.1350824 , 0.80041414],
- [0.1707926 , 0.5413459 , 0.8568687 ],
- [0.0265642 , 0.16363254, 0.5556602 ]], dtype=float32)
+ [0.04501093, 0.29551986, 0.05062775],
+ [0.6661255 , 0.7034433 , 0.08090701],
+ [0.52956533, 0.7413073 , 0.81429756]], dtype=float32)
or they can be directly passed in when creating a Universe.
@@ -334,13 +334,13 @@ Building trajectories in memoryIn [18]: universe2.atoms.positions
Out[18]:
-array([[0.95849204, 0.7487938 , 0.9704986 ],
- [0.42354256, 0.2889326 , 0.06184179],
- [0.5241738 , 0.642687 , 0.6946113 ],
+array([[0.01341493, 0.8954516 , 0.48900408],
+ [0.0416302 , 0.81844276, 0.6713378 ],
+ [0.32844332, 0.8965664 , 0.8370764 ],
...,
- [0.7527609 , 0.1350824 , 0.80041414],
- [0.1707926 , 0.5413459 , 0.8568687 ],
- [0.0265642 , 0.16363254, 0.5556602 ]], dtype=float32)
+ [0.04501093, 0.29551986, 0.05062775],
+ [0.6661255 , 0.7034433 , 0.08090701],
+ [0.52956533, 0.7413073 , 0.81429756]], dtype=float32)
diff --git a/dev/searchindex.js b/dev/searchindex.js
index 3fc79fdd6..0c212c24c 100644
--- a/dev/searchindex.js
+++ b/dev/searchindex.js
@@ -1 +1 @@
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"formats/reference/tpr.rst", "formats/reference/trj.rst", "formats/reference/trr.rst", "formats/reference/trz.rst", "formats/reference/txyz.rst", "formats/reference/xml.rst", "formats/reference/xpdb.rst", "formats/reference/xtc.rst", "formats/reference/xyz.rst", "formats/selection_exporters.rst", "formats/topology.rst", "groups_of_atoms.rst", "index.rst", "installation.rst", "module_imports.rst", "preparing_releases_and_hotfixes.rst", "reading_and_writing.rst", "references.rst", "releases.md", "selections.rst", "standard_selections.rst", "testing.rst", "topology_system.rst", "trajectories/slicing_trajectories.rst", "trajectories/trajectories.rst", "trajectories/transformations.rst", "units.rst", "universe.rst"], "titles": ["Advanced topology concepts", "AtomGroup", "Contributing to MDAnalysis", "Contributing to the main codebase", "Contributing to the user guide", "Example data", "Examples", "Analysis", "Alignments and RMS fitting", "Aligning a structure to another", "Aligning a 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7, 26, 32, 50, 102], "some": [0, 2, 3, 9, 10, 14, 15, 22, 23, 24, 26, 27, 30, 34, 45, 46, 50, 52, 53, 55, 60, 66, 100, 102, 103, 105, 108], "analysi": [0, 1, 3, 4, 8, 9, 10, 11, 12, 13, 14, 17, 22, 23, 24, 27, 28, 30, 31, 32, 33, 35, 36, 40, 41, 43, 45, 46, 48, 49, 52, 55, 57, 85, 98, 99, 100, 103, 104, 105, 109, 110, 113, 114], "A": [0, 1, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 56, 57, 60, 61, 63, 67, 71, 72, 75, 81, 82, 83, 84, 87, 88, 95, 98, 101, 102, 103, 104, 105, 106, 107, 108, 109, 112, 113, 114], "group": [0, 13, 22, 30, 31, 34, 40, 42, 43, 49, 50, 83, 84, 96, 103, 105, 106, 108, 109, 112, 114], "consid": [0, 2, 3, 7, 13, 15, 16, 27, 32, 34, 38, 41, 42, 98, 100, 101, 106, 108, 114], "defin": [0, 3, 4, 15, 20, 22, 24, 26, 30, 31, 36, 37, 40, 42, 44, 46, 50, 53, 55, 57, 61, 67, 82, 86, 95, 96, 98, 105, 106, 108, 109], "moleculetyp": [0, 109], "section": [0, 3, 4, 9, 10, 11, 12, 13, 14, 19, 33, 53, 55, 57, 66, 81, 84, 100, 104, 105, 108], "ar": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 61, 65, 66, 67, 68, 69, 70, 72, 73, 75, 76, 77, 78, 79, 81, 82, 83, 84, 87, 88, 89, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114], "file": [0, 2, 3, 4, 5, 50, 53, 56, 58, 59, 60, 61, 63, 66, 67, 68, 70, 77, 78, 79, 80, 82, 85, 86, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 109, 111], "e": [0, 1, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 67, 80, 82, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 112, 113, 114], "tpr": [0, 26, 36, 40, 48, 55, 59, 61, 71, 75, 97, 98, 103, 105, 106, 109, 112, 114], "extens": [0, 3, 4, 18, 30, 53, 57, 61, 79, 81, 88, 96, 99, 102, 103, 105], "unlik": [0, 1, 3, 7, 10, 19, 30, 32, 33, 38, 68, 69, 72, 75, 82, 106], "fragment": [0, 31, 48, 49, 50, 55, 105, 106, 108], "thei": [0, 3, 4, 7, 10, 12, 15, 16, 19, 20, 27, 32, 34, 37, 38, 42, 46, 48, 50, 57, 60, 61, 66, 79, 89, 92, 98, 100, 101, 102, 103, 105, 107, 108, 109, 111], "access": [0, 1, 3, 5, 16, 42, 50, 53, 56, 89, 94, 98, 99, 105, 106, 107, 109, 114], "directli": [0, 1, 4, 16, 18, 19, 20, 23, 30, 37, 38, 44, 48, 52, 53, 99, 102, 103, 106, 109, 111, 112], "traceback": [0, 108], "most": [0, 1, 3, 7, 10, 14, 15, 16, 26, 27, 32, 34, 39, 40, 41, 53, 60, 68, 69, 83, 87, 99, 100, 103, 105, 106, 108, 109, 114], "recent": [0, 3, 67, 87, 102, 108, 109], "call": [0, 1, 3, 4, 7, 12, 15, 16, 19, 28, 30, 32, 34, 37, 50, 53, 68, 98, 99, 105, 106, 108, 109, 112], "stdin": 0, "line": [0, 3, 12, 13, 26, 27, 30, 33, 43, 48, 49, 57, 81, 83, 91, 95, 96, 98, 100, 105, 108, 114], "modul": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 25, 30, 38, 39, 41, 42, 43, 44, 45, 46, 53, 55, 79, 100, 105, 109, 112], "core": [0, 2, 3, 13, 14, 15, 22, 23, 42, 44, 50, 53, 57, 83, 100, 102, 103, 105, 109, 112], "py": [0, 3, 4, 5, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 28, 33, 37, 38, 39, 40, 42, 43, 44, 45, 49, 53, 101, 102, 104, 105, 108], "2278": 0, "__getattr__": 0, "cl": [0, 30], "self": [0, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 28, 33, 38, 39, 42, 43, 44, 45, 49, 108], "__class__": 0, "__name__": 0, "attr": 0, "attributeerror": 0, "ha": [0, 2, 3, 7, 9, 10, 11, 13, 14, 15, 16, 18, 19, 22, 23, 24, 28, 29, 30, 34, 36, 40, 42, 43, 44, 45, 46, 48, 50, 52, 53, 55, 70, 87, 96, 98, 100, 102, 103, 105, 106, 108, 109, 111, 114], "attribut": [0, 1, 3, 14, 15, 26, 30, 39, 40, 53, 56, 60, 65, 66, 77, 81, 83, 87, 99, 103, 105, 106, 108], "howev": [0, 1, 3, 7, 9, 10, 13, 14, 15, 16, 19, 26, 27, 30, 31, 32, 34, 39, 44, 48, 50, 52, 53, 55, 57, 67, 69, 71, 72, 84, 87, 89, 96, 98, 100, 101, 102, 103, 105, 108, 109, 114], "order": [0, 3, 7, 12, 13, 15, 16, 19, 26, 31, 32, 33, 34, 37, 42, 43, 44, 48, 52, 53, 57, 61, 66, 67, 82, 90, 98, 100, 101, 102, 103, 105, 109, 110, 111, 112, 114], "molnum": [0, 61, 87, 97, 108, 109], "name": [0, 3, 4, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 36, 37, 38, 39, 40, 42, 43, 44, 46, 48, 50, 53, 57, 60, 61, 65, 67, 70, 78, 81, 82, 83, 86, 87, 92, 95, 96, 97, 98, 100, 102, 103, 105, 106, 107, 108, 109, 114], "moltyp": [0, 61, 87, 97, 106, 109], "each": [0, 1, 3, 4, 6, 7, 9, 10, 12, 13, 14, 15, 16, 21, 22, 23, 26, 27, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 50, 53, 55, 57, 60, 61, 66, 96, 98, 99, 101, 102, 103, 105, 108, 109, 111, 112, 113, 114], "11086": 0, "11087": 0, "11088": 0, "akeco": 0, "na": [0, 1, 30, 105], "2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114], "7": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114], "dev0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114], "univers": [1, 3, 5, 6, 7, 9, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 56, 60, 61, 62, 66, 68, 71, 77, 80, 81, 82, 88, 89, 93, 94, 95, 96, 98, 99, 103, 105, 106, 108, 110, 111, 112], "contain": [1, 3, 4, 7, 8, 12, 14, 15, 17, 26, 30, 34, 37, 39, 40, 41, 42, 44, 46, 48, 50, 53, 55, 57, 66, 72, 81, 84, 87, 88, 89, 91, 92, 93, 96, 98, 101, 102, 103, 104, 105, 106, 108, 109, 111, 112, 114], "all": [1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 26, 30, 33, 34, 37, 38, 44, 46, 48, 50, 53, 57, 66, 81, 83, 92, 95, 98, 99, 100, 101, 102, 103, 105, 106, 108, 109, 112, 114], "particl": [1, 9, 13, 14, 34, 36, 40, 50, 52, 53, 72, 105], "molecular": [1, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 58, 59, 61, 66, 80, 85, 97, 99, 104, 114], "system": [1, 2, 3, 4, 14, 15, 22, 26, 30, 36, 40, 50, 53, 57, 61, 80, 81, 83, 105, 108, 114], "regardless": [1, 3, 53, 113], "whether": [1, 3, 9, 10, 14, 16, 34, 50, 114], "realli": [1, 13, 15, 108], "g": [1, 3, 4, 5, 7, 14, 15, 19, 20, 26, 30, 32, 33, 34, 37, 53, 57, 67, 80, 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 112, 113, 114], "mai": [1, 3, 4, 7, 9, 12, 14, 15, 16, 18, 19, 26, 27, 28, 30, 32, 33, 34, 36, 38, 42, 43, 44, 48, 50, 53, 57, 93, 100, 101, 102, 103, 105, 111], "unit": [1, 5, 26, 48, 49, 50, 52, 53, 63, 66, 67, 68, 76, 79, 81, 82, 88, 101, 103, 105, 106, 108, 112], "coars": [1, 9, 19, 20, 53, 104], "grain": [1, 9, 19, 20, 53, 104], "bead": [1, 9, 53], "": [1, 2, 3, 4, 9, 10, 13, 16, 19, 26, 27, 30, 33, 34, 37, 39, 40, 42, 44, 45, 48, 50, 52, 53, 55, 56, 57, 60, 67, 81, 87, 96, 98, 101, 102, 103, 104, 105, 109, 111, 113, 114], "master": [1, 3, 109, 114], "The": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 56, 57, 60, 61, 62, 66, 67, 68, 69, 70, 72, 73, 77, 78, 79, 80, 83, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 110, 111, 112, 113, 114], "probabl": [1, 7, 15, 38, 41, 42, 44, 53, 88], "import": [1, 3, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 56, 57, 60, 78, 83, 87, 93, 96, 98, 102, 103, 105, 106, 109, 110, 111, 112, 114], "virtual": [1, 3, 4, 93, 98], "everyth": [1, 3, 48, 53, 98, 101, 102], "can": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 74, 78, 79, 80, 81, 83, 84, 89, 91, 92, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114], "through": [1, 2, 3, 7, 12, 14, 15, 32, 41, 49, 53, 57, 98, 103, 105, 106, 109, 111, 114], "instanc": [1, 36, 50, 53, 57, 78, 106, 114], "typic": [1, 3, 7, 8, 13, 14, 26, 50, 52, 53, 96, 98, 99, 108, 109, 111, 112, 114], "select_atom": [1, 9, 11, 13, 14, 15, 16, 18, 19, 21, 22, 23, 24, 26, 31, 34, 36, 37, 40, 48, 50, 52, 53, 55, 96, 98, 103, 105, 106, 110, 111, 112, 114], "manipul": [1, 53, 80, 99], "test": [1, 2, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 70, 93, 96, 98, 102, 103, 106, 109, 110, 111, 112, 114], "datafil": [1, 5, 9, 10, 11, 12, 13, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 93, 96, 98, 101, 103, 106, 108, 109, 110, 111, 112, 114], "pdb": [1, 9, 10, 13, 14, 22, 23, 24, 34, 37, 46, 50, 52, 53, 57, 58, 59, 61, 77, 78, 83, 96, 97, 98, 103, 105, 108, 109, 114], "3": [1, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 78, 81, 82, 83, 84, 87, 93, 94, 95, 96, 98, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114], "4": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 78, 82, 83, 87, 88, 93, 96, 98, 100, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "resnam": [1, 18, 19, 21, 26, 27, 28, 36, 37, 40, 48, 50, 53, 55, 61, 65, 72, 81, 82, 86, 87, 96, 97, 98, 105, 106, 109, 112], "arg": [1, 15, 18, 19, 21, 27, 28, 53, 83, 98, 106, 107, 109], "out": [1, 3, 4, 9, 10, 11, 12, 14, 19, 30, 34, 39, 48, 50, 52, 55, 57, 61, 70, 78, 93, 98, 102, 103, 105, 106, 109, 110, 111, 113, 114], "312": [1, 98, 104, 108], "see": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 18, 19, 20, 21, 22, 26, 27, 28, 30, 32, 33, 34, 37, 38, 40, 42, 43, 44, 46, 48, 50, 52, 53, 55, 56, 57, 60, 61, 63, 66, 67, 69, 76, 81, 84, 87, 94, 96, 98, 99, 100, 101, 102, 103, 105, 106, 108, 109, 112, 114], "more": [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 26, 27, 30, 33, 34, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 53, 56, 57, 60, 61, 65, 69, 87, 98, 101, 103, 105, 106, 108, 109, 112, 114], "inform": [1, 3, 4, 7, 10, 12, 14, 15, 16, 19, 22, 26, 28, 30, 32, 33, 34, 37, 40, 42, 43, 44, 46, 50, 52, 56, 57, 60, 62, 63, 66, 69, 70, 71, 72, 80, 81, 82, 84, 87, 88, 94, 95, 98, 101, 103, 105, 106, 108, 111, 112, 114], "like": [1, 3, 4, 5, 13, 16, 19, 26, 30, 34, 39, 40, 43, 48, 53, 55, 57, 60, 83, 100, 106, 108], "list": [1, 2, 3, 5, 13, 15, 16, 19, 26, 30, 38, 39, 40, 42, 43, 44, 46, 48, 50, 53, 57, 61, 65, 67, 82, 87, 96, 99, 100, 102, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114], "5": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 67, 71, 78, 82, 87, 93, 98, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "print": [1, 5, 9, 10, 11, 13, 14, 15, 19, 23, 26, 27, 28, 30, 31, 34, 36, 37, 39, 40, 42, 43, 44, 45, 46, 49, 50, 53, 57, 101, 103, 108, 109, 110, 111, 114], "n": [1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 83, 95, 96, 98, 104, 105, 106, 107, 113], "type": [1, 3, 26, 42, 50, 52, 53, 58, 61, 65, 66, 72, 80, 81, 82, 83, 84, 85, 86, 87, 91, 92, 96, 97, 98, 104, 105, 106, 109, 113], "met": [1, 53, 83, 107, 109], "resid": [1, 13, 14, 23, 24, 26, 27, 36, 40, 46, 50, 53, 61, 65, 66, 69, 72, 75, 81, 82, 87, 89, 98, 105, 106, 109], "altloc": [1, 14, 61, 81, 82, 97, 106, 109], "return": [1, 3, 4, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 24, 27, 30, 33, 34, 37, 40, 42, 44, 50, 53, 57, 98, 102, 105, 106, 108, 110, 111, 112], "below": [1, 2, 3, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 61, 98, 103, 106, 107, 108, 109, 112, 113, 114], "code": [1, 2, 4, 5, 6, 7, 11, 13, 14, 15, 16, 30, 36, 48, 50, 57, 67, 78, 81, 82, 84, 93, 98, 99, 102, 103, 104, 105, 106, 108, 109, 112, 114], "everi": [1, 3, 4, 10, 14, 15, 16, 24, 27, 30, 31, 34, 37, 39, 42, 44, 48, 50, 52, 53, 57, 60, 61, 98, 103, 105, 106, 108, 109, 111, 114], "second": [1, 10, 12, 18, 19, 21, 40, 44, 53, 113], "element": [1, 42, 50, 53, 60, 61, 65, 66, 70, 81, 82, 86, 97, 105, 109, 114], "first": [1, 3, 4, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 26, 27, 30, 31, 32, 33, 34, 36, 37, 38, 40, 42, 44, 45, 46, 48, 49, 50, 53, 55, 57, 61, 69, 72, 81, 83, 89, 94, 98, 100, 101, 105, 106, 108, 110, 111, 114], "6th": [1, 106], "correspond": [1, 3, 12, 13, 14, 16, 24, 26, 27, 40, 55, 71, 98, 108, 109], "indic": [1, 3, 12, 13, 14, 19, 24, 26, 27, 30, 33, 34, 38, 40, 43, 50, 53, 81, 82, 84, 87, 91, 96, 101, 105, 106, 108, 109, 110], "6": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 66, 69, 78, 81, 82, 83, 84, 87, 91, 93, 98, 102, 103, 104, 106, 109, 110, 111, 112, 114], "ag": [1, 10, 13, 40, 96, 98, 103], "also": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 26, 27, 28, 30, 32, 34, 37, 39, 40, 41, 42, 44, 48, 53, 55, 57, 61, 62, 81, 82, 87, 89, 98, 100, 101, 102, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "support": [1, 3, 13, 19, 30, 46, 53, 66, 71, 72, 82, 84, 85, 88, 100, 103, 105, 106, 109, 111, 114], "fanci": [1, 53, 110], "pass": [1, 3, 4, 13, 16, 18, 19, 22, 23, 24, 26, 28, 30, 31, 34, 36, 37, 39, 42, 43, 44, 45, 49, 50, 53, 55, 60, 61, 62, 66, 80, 81, 93, 95, 96, 100, 102, 103, 105, 106, 108, 110, 112, 114], "ndarrai": [1, 53, 110], "8": [1, 4, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 22, 23, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 87, 98, 103, 105, 106, 109, 110, 111, 112, 114], "10": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 71, 78, 82, 83, 87, 98, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114], "9": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 84, 87, 98, 102, 103, 105, 106, 108, 109, 110, 111, 112, 114], "47680": 1, "boolean": [1, 53, 108, 110], "allow": [1, 2, 3, 10, 13, 14, 19, 27, 28, 30, 36, 37, 40, 42, 43, 44, 52, 53, 56, 57, 60, 96, 99, 100, 103, 105, 106, 108, 110, 114], "you": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 26, 27, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 61, 62, 64, 67, 78, 79, 80, 89, 96, 98, 99, 100, 101, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "true": [1, 3, 9, 10, 11, 12, 13, 14, 16, 26, 27, 28, 30, 34, 37, 38, 40, 42, 44, 45, 46, 48, 50, 52, 53, 55, 66, 68, 89, 94, 103, 105, 106, 108, 110, 114], "fals": [1, 4, 9, 11, 14, 19, 20, 26, 27, 30, 40, 44, 46, 48, 50, 53, 57, 78, 103, 105, 106, 108, 109, 110, 114], "valu": [1, 3, 7, 8, 11, 12, 13, 14, 15, 19, 24, 26, 27, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 57, 60, 65, 66, 67, 76, 81, 82, 83, 89, 92, 105, 106, 108, 110, 114], "must": [1, 2, 3, 4, 11, 12, 13, 16, 18, 22, 23, 26, 27, 30, 31, 34, 42, 43, 46, 48, 50, 53, 60, 66, 79, 83, 84, 100, 101, 108, 109, 112, 114], "same": [1, 3, 4, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 24, 26, 28, 30, 31, 33, 34, 36, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 53, 55, 60, 67, 93, 95, 96, 98, 102, 103, 105, 106, 109, 110, 114], "length": [1, 3, 6, 7, 12, 13, 22, 24, 27, 29, 39, 40, 42, 44, 45, 46, 48, 50, 52, 53, 63, 66, 67, 68, 71, 79, 88, 99, 104, 105, 110, 111], "origin": [1, 3, 4, 7, 9, 13, 14, 23, 27, 32, 33, 34, 39, 50, 53, 55, 57, 67, 98, 102, 106, 108, 110, 113], "condit": [1, 15, 105, 106, 108, 110, 112], "arr": 1, "11": [1, 3, 9, 10, 13, 14, 15, 16, 19, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 83, 87, 98, 103, 104, 105, 106, 109, 110, 112, 113, 114], "len": [1, 12, 23, 24, 30, 33, 34, 36, 37, 38, 40, 42, 44, 45, 46, 48, 50, 53, 57, 98, 103, 108, 109, 111], "12": [1, 3, 9, 10, 13, 14, 15, 16, 19, 26, 27, 30, 33, 34, 36, 37, 40, 42, 43, 44, 46, 48, 49, 50, 52, 53, 55, 57, 78, 82, 86, 98, 103, 104, 106, 109, 110, 111, 112, 113, 114], "13": [1, 9, 10, 13, 14, 15, 16, 19, 26, 27, 30, 34, 36, 37, 40, 42, 43, 44, 48, 49, 50, 52, 53, 55, 57, 81, 82, 98, 103, 106, 109, 110, 112, 114], "number": [1, 3, 4, 6, 7, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 27, 30, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 48, 50, 52, 53, 57, 60, 65, 69, 71, 72, 75, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 96, 103, 105, 106, 108, 109, 114], "wai": [1, 3, 4, 7, 10, 12, 13, 14, 15, 16, 18, 19, 26, 27, 30, 33, 34, 48, 52, 53, 79, 98, 99, 103, 105, 106, 108, 109, 111, 114], "compar": [1, 6, 7, 9, 10, 11, 12, 14, 15, 17, 19, 22, 32, 34, 40, 41, 42, 44, 45, 108], "one": [1, 2, 3, 4, 10, 11, 12, 13, 15, 19, 27, 28, 30, 31, 39, 46, 50, 53, 56, 57, 83, 87, 88, 91, 92, 96, 98, 99, 100, 103, 104, 105, 106, 108, 109, 114], "concaten": [1, 15, 53, 103, 106, 114], "subtract": [1, 13, 84], "union": [1, 106], "differ": [1, 3, 4, 7, 9, 10, 12, 13, 15, 16, 19, 26, 28, 34, 36, 38, 40, 41, 45, 46, 48, 50, 53, 55, 60, 66, 67, 70, 71, 75, 82, 87, 91, 93, 95, 96, 98, 99, 100, 102, 103, 104, 105, 106, 112, 114], "achiev": [1, 15], "similar": [1, 3, 4, 6, 12, 15, 19, 23, 24, 30, 32, 33, 34, 37, 38, 53, 73, 82, 83, 87, 92, 93, 98, 104], "outcom": [1, 105], "kei": [1, 2, 30, 38, 39, 49, 53, 57, 98, 99, 114], "preserv": [1, 4, 19, 66, 105], "ani": [1, 2, 3, 4, 11, 15, 16, 18, 27, 28, 30, 31, 37, 42, 43, 44, 46, 49, 50, 53, 56, 57, 61, 67, 89, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112], "duplic": [1, 30, 53, 103, 106], "where": [1, 4, 5, 7, 9, 14, 15, 16, 19, 20, 21, 22, 27, 31, 32, 33, 36, 37, 38, 40, 43, 44, 46, 48, 50, 53, 57, 60, 61, 66, 71, 89, 98, 100, 102, 105, 106, 108, 109], "its": [1, 2, 3, 4, 7, 9, 13, 15, 18, 19, 20, 22, 30, 32, 33, 44, 46, 49, 52, 53, 98, 99, 100, 101, 103, 106, 108, 109, 111, 112], "topologi": [1, 5, 14, 15, 26, 28, 52, 53, 59, 63, 65, 66, 69, 70, 71, 72, 73, 76, 77, 78, 80, 81, 82, 83, 84, 91, 92, 93, 95, 98, 99, 103, 105, 106, 108, 111], "14": [1, 9, 10, 13, 15, 16, 19, 26, 27, 30, 34, 36, 40, 42, 43, 44, 48, 49, 50, 52, 53, 55, 57, 82, 98, 100, 103, 104, 105, 106, 109, 110, 112, 114], "ag1": [1, 40], "15": [1, 7, 10, 13, 15, 16, 19, 26, 27, 30, 34, 36, 40, 41, 42, 43, 44, 45, 48, 49, 50, 52, 53, 57, 81, 82, 98, 103, 104, 106, 109, 110, 112, 114], "ag2": [1, 40], "16": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 81, 82, 98, 103, 104, 105, 106, 109, 110, 111, 112], "concat": 1, "17": [1, 12, 15, 16, 19, 20, 21, 26, 27, 30, 33, 34, 36, 39, 42, 44, 48, 49, 50, 52, 53, 57, 69, 81, 82, 87, 98, 103, 104, 106, 109, 110, 111, 112], "18": [1, 9, 15, 16, 19, 26, 27, 30, 42, 44, 46, 48, 49, 50, 52, 53, 57, 78, 81, 82, 86, 98, 103, 105, 106, 109, 110, 112, 113], "19": [1, 15, 16, 19, 22, 23, 24, 26, 27, 28, 30, 37, 40, 42, 44, 48, 49, 50, 52, 53, 57, 82, 98, 103, 104, 105, 106, 109, 112, 113], "avail": [1, 2, 3, 7, 15, 16, 19, 32, 33, 36, 40, 42, 43, 44, 53, 56, 57, 60, 66, 73, 77, 79, 82, 87, 96, 98, 99, 100, 102, 105, 106, 108, 109, 114], "keep": [1, 2, 3, 10, 14, 30, 34, 39, 43, 44, 48, 55, 72, 96, 99, 101, 102, 108], "well": [1, 2, 4, 7, 15, 32, 33, 44, 48, 61, 67, 99, 101, 105, 108], "equival": [1, 27, 48, 87], "result": [1, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 24, 30, 31, 36, 37, 38, 39, 40, 42, 43, 44, 48, 49, 52, 53, 61, 89, 94, 105, 106, 108, 110], "t": [1, 3, 4, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 24, 26, 27, 28, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 52, 53, 55, 56, 57, 66, 82, 92, 96, 98, 100, 103, 104, 105, 106, 107, 108, 110, 111, 112, 114], "sort": [1, 26, 27, 30, 34, 53, 105, 106], "isdisjoint": 1, "do": [1, 3, 4, 6, 10, 11, 12, 13, 15, 16, 19, 22, 26, 27, 28, 30, 34, 37, 42, 44, 45, 46, 48, 50, 52, 53, 95, 99, 101, 102, 103, 105, 108, 114], "share": [1, 3, 105], "issubset": 1, "part": [1, 2, 3, 4, 15, 30, 48, 57, 101, 103, 105, 106, 108, 111], "is_strict_subset": 1, "issuperset": 1, "is_strict_superset": 1, "both": [1, 3, 4, 13, 15, 16, 22, 30, 38, 48, 50, 52, 53, 60, 63, 72, 81, 85, 87, 91, 98, 100, 102, 103, 105, 108, 109, 114], "intersect": [1, 106], "common": [1, 3, 7, 12, 13, 14, 15, 22, 26, 31, 32, 34, 53, 55, 68, 70, 104, 106, 108], "symmetric_differ": 1, "separ": [1, 5, 7, 15, 16, 19, 30, 31, 36, 40, 42, 43, 81, 83, 84, 100, 102, 106, 108, 114], "properti": [1, 16, 30, 39, 53, 60, 98, 105, 106, 109, 111, 112], "level": [1, 3, 4, 15, 48, 85, 98, 101, 108, 114], "connect": [1, 3, 4, 33, 50, 52, 53, 60, 61, 82, 87, 91, 105, 114], "residu": [1, 3, 7, 13, 14, 17, 19, 21, 22, 26, 28, 30, 36, 37, 38, 39, 40, 48, 49, 50, 52, 53, 55, 61, 65, 69, 70, 71, 72, 75, 81, 82, 83, 86, 87, 92, 93, 95, 96, 99, 105, 106, 109, 112, 114], "molecul": [1, 4, 10, 13, 26, 31, 38, 50, 55, 58, 61, 66, 78, 87, 92, 95, 98, 105, 106, 108, 109, 112], "segment": [1, 10, 49, 53, 61, 65, 70, 81, 87, 95, 99, 103, 105, 106, 109, 114], "20": [1, 15, 16, 19, 26, 30, 31, 42, 44, 48, 49, 50, 52, 53, 57, 82, 98, 103, 105, 106, 110, 111, 112, 113], "21": [1, 3, 9, 15, 16, 26, 27, 28, 30, 31, 33, 42, 44, 48, 49, 50, 52, 53, 57, 71, 81, 82, 98, 99, 103, 104, 105, 106, 109, 112, 113, 114], "24": [1, 15, 16, 26, 27, 30, 34, 39, 44, 49, 50, 53, 57, 81, 82, 87, 98, 103, 105, 112], "accord": [1, 13, 14, 87, 91, 106, 109], "produc": [1, 23, 27, 34, 53, 57, 67, 105, 108], "dictionari": [1, 18, 30, 57, 60, 89, 105, 108, 114], "22": [1, 15, 16, 26, 30, 44, 46, 48, 49, 50, 53, 57, 81, 82, 98, 103, 105, 106, 112], "mass": [1, 6, 7, 9, 10, 15, 16, 23, 24, 28, 30, 36, 40, 45, 46, 47, 48, 53, 55, 61, 63, 65, 66, 86, 87, 92, 97, 106, 109, 112, 113, 114], "32": [1, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 49, 50, 52, 53, 55, 57, 98, 104, 105], "06": [1, 38, 104, 113], "008": [1, 53], "23853": 1, "11084": 1, "011": [1, 39, 53], "1040": [1, 98], "007": [1, 53, 82], "289": 1, "999": [1, 53, 65, 93], "11404": 1, "98977": 1, "multipl": [1, 3, 27, 39, 48, 53, 57, 62, 89, 94, 98, 100, 105, 106, 112, 114], "them": [1, 3, 4, 7, 9, 10, 13, 15, 18, 19, 22, 34, 38, 39, 42, 43, 44, 45, 46, 50, 52, 53, 55, 60, 61, 66, 68, 79, 81, 82, 89, 95, 101, 102, 103, 104, 105, 106, 108, 109, 112], "23": [1, 15, 16, 26, 30, 34, 44, 48, 49, 50, 53, 57, 81, 82, 87, 93, 98, 103, 106, 112], "sol": [1, 30, 36, 37, 40, 48, 50, 55, 87, 112], "22168": 1, "iter": [1, 12, 14, 15, 16, 30, 44, 49, 53, 103, 105, 108, 109, 110, 111], "atom1": [1, 95], "25": [1, 15, 26, 27, 30, 34, 46, 49, 50, 53, 57, 81, 82, 83, 98, 103, 105], "atom2": [1, 95], "26": [1, 15, 26, 30, 34, 49, 50, 53, 57, 81, 82, 83, 87, 93, 98, 103], "atom3": 1, "27": [1, 15, 26, 30, 49, 50, 53, 57, 81, 82, 93, 98, 103, 104, 107, 113], "28": [1, 3, 15, 30, 49, 50, 53, 57, 78, 98, 103], "ca": [1, 9, 10, 11, 12, 13, 14, 16, 18, 20, 21, 23, 24, 36, 37, 38, 39, 40, 42, 43, 44, 46, 53, 60, 98, 103, 106, 107], "c": [1, 3, 13, 27, 30, 31, 34, 36, 37, 38, 39, 46, 48, 50, 52, 53, 62, 67, 78, 81, 82, 85, 87, 88, 98, 100, 102, 103, 104, 105, 106, 107, 113], "cb": [1, 36, 53, 98], "h2": [1, 26, 28, 50, 78], "h": [1, 3, 27, 31, 36, 39, 45, 50, 52, 53, 78, 87, 96, 98, 103, 106], "neat": [1, 4], "shortcut": [1, 106], "simpli": [1, 3, 12, 13, 15, 21, 24, 26, 34, 40, 50, 52, 53, 60, 79, 84, 88, 101, 104, 105, 106, 109, 113], "29": [1, 13, 15, 30, 34, 46, 49, 50, 53, 57, 98, 103, 113], "30": [1, 13, 14, 15, 22, 23, 30, 33, 42, 49, 50, 53, 57, 78, 98, 103, 104, 110, 112, 113], "31": [1, 15, 16, 19, 30, 48, 49, 50, 53, 57, 81, 82, 98, 103], "altern": [1, 3, 13, 20, 30, 48, 57, 77, 81, 82, 96, 103, 106, 108, 109, 112], "provid": [1, 3, 4, 7, 12, 13, 15, 16, 17, 25, 26, 30, 31, 34, 37, 38, 43, 44, 46, 52, 53, 55, 57, 60, 61, 62, 65, 71, 81, 82, 83, 88, 94, 96, 99, 100, 103, 105, 106, 108, 109, 114], "belong": [1, 28, 50, 53, 61, 98, 106, 107], "33": [1, 15, 49, 50, 53, 57, 81, 82], "34": [1, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 28, 30, 33, 37, 38, 39, 40, 42, 43, 44, 45, 49, 50, 52, 53, 55, 57, 81, 82, 98, 104], "These": [1, 3, 4, 5, 7, 8, 17, 24, 37, 43, 48, 53, 57, 72, 81, 82, 89, 94, 98, 106, 108, 109], "user": [1, 2, 3, 6, 7, 15, 19, 26, 28, 39, 42, 43, 44, 45, 49, 53, 60, 61, 66, 68, 69, 72, 78, 79, 87, 88, 100, 101, 102, 103, 105, 106, 108, 109, 114], "work": [1, 2, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 55, 56, 57, 62, 67, 79, 96, 99, 100, 102, 104, 105, 106, 108, 111], "35": [1, 19, 20, 30, 49, 50, 53, 57, 98, 103, 104], "null": 1, "36": [1, 49, 50, 53, 57, 83], "abov": [1, 3, 10, 13, 16, 23, 24, 26, 27, 28, 39, 48, 53, 55, 57, 83, 102, 103, 105, 106, 108, 109, 111], "37": [1, 38, 49, 50, 53, 57, 83, 98, 104], "For": [1, 2, 3, 4, 7, 9, 12, 13, 15, 16, 19, 26, 28, 30, 32, 33, 34, 37, 39, 40, 42, 43, 44, 46, 50, 52, 53, 55, 57, 60, 61, 66, 78, 83, 87, 89, 96, 98, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 114], "38": [1, 16, 23, 49, 52, 53, 57, 81, 82], "does_not_exist": 1, "39": [1, 13, 15, 16, 18, 20, 21, 23, 24, 26, 30, 31, 36, 37, 38, 39, 40, 42, 43, 44, 48, 49, 50, 52, 53, 57, 81, 82], "40": [1, 19, 30, 49, 53, 57, 81, 82, 98, 103, 110], "have": [1, 2, 3, 4, 10, 11, 12, 13, 14, 15, 16, 18, 19, 22, 24, 26, 27, 30, 31, 33, 34, 36, 37, 40, 42, 44, 45, 46, 48, 50, 53, 55, 57, 60, 63, 66, 67, 69, 71, 75, 79, 81, 82, 84, 87, 89, 95, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 114], "evalu": [1, 6, 7, 34, 41, 49, 106], "context": [1, 15, 30, 52, 53, 96, 108], "41": [1, 30, 49, 57, 81, 82, 98], "bool": [1, 3, 18, 50, 109, 110], "skip": [1, 3, 16, 46, 57, 100], "over": [1, 6, 7, 8, 12, 15, 16, 17, 18, 20, 21, 22, 26, 27, 30, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 53, 55, 72, 102, 103, 105, 108, 110, 111, 112], "instead": [1, 3, 4, 9, 10, 12, 14, 15, 16, 19, 23, 24, 30, 34, 37, 39, 43, 44, 48, 50, 55, 61, 81, 82, 88, 92, 93, 100, 105, 106, 108, 111, 114], "rais": [1, 3, 33, 49, 53, 65, 79, 81, 82, 83, 99, 100, 101, 105, 108], "error": [1, 3, 4, 9, 49, 53, 72, 79, 89, 94, 100, 101, 105, 108], "which": [1, 3, 4, 10, 12, 13, 14, 15, 16, 19, 20, 21, 26, 27, 28, 30, 31, 33, 34, 37, 38, 39, 40, 42, 43, 44, 46, 48, 50, 53, 55, 57, 60, 61, 67, 68, 83, 87, 89, 91, 94, 96, 98, 99, 100, 102, 103, 105, 106, 108, 109, 112, 114], "help": [1, 2, 3, 4, 13, 16, 22, 96, 99, 100, 106, 108], "occasion": [1, 89, 94], "aris": [1, 4], "logic": 1, "too": [1, 16, 42, 44, 48, 53, 55, 56, 99, 108], "restrict": [1, 13, 14, 87], "geometr": [1, 6, 7, 26, 41, 52], "normal": [1, 3, 15, 30, 38, 39, 43, 44, 84, 91, 96, 102, 104, 105, 108], "static": [1, 10, 15, 53, 106, 109, 111, 114], "within": [1, 6, 7, 16, 17, 18, 19, 20, 22, 26, 28, 30, 36, 38, 40, 42, 43, 44, 45, 50, 53, 57, 60, 67, 77, 105, 106, 111, 112], "chang": [1, 5, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 26, 27, 28, 30, 33, 34, 38, 39, 42, 43, 44, 45, 49, 53, 55, 57, 61, 71, 72, 81, 87, 89, 100, 102, 103, 106, 108, 111], "trajectori": [1, 5, 6, 8, 9, 17, 18, 20, 21, 26, 27, 32, 33, 36, 37, 38, 40, 42, 43, 44, 45, 48, 49, 50, 52, 54, 58, 59, 61, 66, 71, 72, 81, 82, 91, 93, 99, 104, 105, 106, 108, 112, 113, 114], "frame": [1, 7, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 26, 27, 30, 32, 33, 34, 36, 37, 38, 39, 40, 43, 44, 46, 48, 49, 52, 55, 56, 61, 66, 71, 81, 82, 89, 94, 99, 105, 106, 108, 110, 111, 112, 114], "sever": [1, 3, 7, 16, 29, 30, 33, 34, 53, 82, 101, 105, 108, 114], "requir": [1, 3, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 65, 66, 79, 81, 82, 83, 95, 96, 98, 100, 103, 105, 106, 108, 109], "function": [1, 2, 3, 4, 6, 7, 8, 13, 17, 19, 20, 21, 27, 30, 33, 34, 35, 37, 41, 42, 43, 44, 45, 46, 52, 53, 55, 56, 57, 85, 87, 98, 100, 101, 102, 104, 105, 106, 109, 111, 112, 114], "implement": [1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 32, 33, 34, 39, 53, 57, 62, 66, 76, 82, 87, 95, 106, 112], "mani": [1, 2, 3, 4, 13, 15, 16, 21, 26, 30, 34, 39, 40, 42, 43, 44, 53, 99, 100, 108, 109, 111, 114], "interest": [1, 19, 30, 48, 53, 100], "bond": [1, 13, 31, 49, 52, 53, 55, 57, 61, 66, 78, 80, 81, 82, 84, 86, 91, 92, 97, 98, 99, 104, 105, 106, 109, 114], "angl": [1, 6, 7, 22, 26, 27, 28, 35, 39, 50, 53, 57, 61, 66, 67, 84, 86, 92, 97, 99, 104, 105, 106, 109, 113, 114], "dihedr": [1, 6, 7, 35, 53, 61, 66, 84, 86, 97, 105, 106, 109, 114], "improperdihedr": [1, 109], "present": [1, 3, 19, 21, 26, 27, 40, 52, 65, 68, 81, 92, 95, 98, 105], "residuegroup": [1, 53, 98, 105, 109], "free": [2, 3, 101], "open": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 87, 89, 94, 104, 108], "sourc": [2, 3, 4, 16, 30, 53, 57, 84, 96, 100, 102, 105, 108], "project": [2, 3, 4, 7, 32, 37, 39, 43, 44, 48, 99, 105], "It": [2, 3, 4, 7, 9, 12, 13, 14, 15, 16, 21, 22, 26, 27, 28, 30, 32, 33, 34, 36, 37, 40, 45, 48, 49, 50, 53, 57, 60, 61, 67, 76, 78, 82, 85, 87, 89, 99, 104, 106, 108, 110, 112, 114], "evolv": 2, "grow": [2, 5, 108], "demand": 2, "base": [2, 4, 5, 9, 10, 11, 12, 13, 14, 15, 16, 19, 28, 33, 39, 42, 44, 46, 50, 52, 53, 60, 61, 65, 79, 87, 89, 94, 102, 103, 104, 105, 106, 107, 108, 109], "develop": [2, 15, 52, 72, 101, 108], "team": 2, "veri": [2, 3, 12, 13, 15, 19, 23, 24, 27, 44, 45, 48, 79, 93, 103, 108], "much": [2, 3, 10, 11, 12, 13, 14, 16, 19, 30, 34, 42, 43, 48, 50, 60, 105, 108], "welcom": 2, "take": [2, 3, 4, 7, 14, 15, 16, 17, 23, 26, 30, 33, 42, 44, 48, 93, 96, 99, 100, 103, 106, 108, 109, 112, 114], "form": [2, 13, 19, 26, 42, 53, 55, 60], "bug": [2, 3, 16, 88, 102], "report": [2, 52, 99, 108], "enhanc": [2, 3], "request": [2, 4, 101, 102, 108], "issu": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 33, 38, 39, 43, 44, 49, 50, 57, 61, 63, 67, 79, 84, 87, 89, 94, 99, 100, 101, 102, 106, 108], "tracker": [2, 87, 99, 108], "fix": [2, 3, 4, 48, 49, 68, 72, 102], "improv": [2, 27, 99, 108], "speed": [2, 105, 108], "clariti": [2, 26], "modernis": 2, "featur": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 83, 99, 100, 102, 114], "addit": [2, 3, 7, 44, 57, 61, 82, 96, 98, 101, 102, 103, 105, 106, 108, 109], "document": [2, 5, 7, 15, 19, 22, 26, 30, 43, 48, 53, 66, 76, 89, 94, 105, 106, 108, 113], "includ": [2, 3, 4, 7, 9, 10, 13, 14, 15, 16, 19, 34, 36, 37, 39, 43, 48, 50, 52, 53, 55, 57, 84, 85, 87, 95, 96, 98, 99, 100, 102, 104, 105, 106, 108, 109, 111, 112, 114], "typo": [2, 3], "build": [2, 15, 16, 50, 53, 65, 87, 99, 100, 105], "question": [2, 3, 99], "discuss": [2, 3, 15, 27, 60, 63, 79, 87, 99, 101, 108], "mdnalysi": [2, 99, 108], "mail": [2, 99, 100, 101, 102, 108], "commun": [2, 3, 88], "subscrib": [2, 99], "conduct": [2, 99], "member": 2, "agre": [2, 99], "adher": [2, 102], "pleas": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 26, 27, 28, 30, 33, 34, 38, 39, 42, 43, 44, 45, 46, 53, 61, 87, 99, 100, 102, 104, 105, 112], "read": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 30, 33, 38, 39, 43, 44, 49, 50, 52, 60, 62, 63, 64, 74, 80, 90, 91, 92, 99, 105, 106, 109, 110, 111, 113, 114], "mdanalysistest": [2, 3, 9, 10, 11, 12, 13, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 55, 57, 100, 101, 102, 103, 105, 108], "packag": [2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 61, 79, 86, 96, 100, 101, 104, 105, 106, 108], "distribut": [2, 3, 6, 7, 15, 30, 35, 38, 41, 42, 44, 45, 70, 100, 102], "under": [2, 3, 15, 19, 53, 87, 101, 105, 108, 110], "gnu": [2, 108], "gener": [2, 3, 4, 7, 8, 9, 10, 14, 15, 18, 19, 20, 28, 30, 34, 37, 38, 39, 48, 50, 53, 55, 58, 59, 61, 67, 77, 80, 89, 94, 99, 102, 103, 104, 105, 113, 114], "public": [2, 3, 16, 30, 99, 102, 104, 108], "licens": [2, 15, 105, 108], "later": [2, 4, 13, 15, 102, 105, 106], "copyleft": 2, "deriv": [2, 3, 84, 87, 98, 105, 109], "made": [2, 3, 4, 26, 48, 71, 103, 105, 106, 109], "Be": [2, 15, 100], "sure": [2, 3, 4, 18, 100, 102, 106, 108], "comfort": 2, "befor": [2, 3, 4, 13, 16, 30, 36, 45, 48, 60, 89, 100, 101, 102, 105, 106, 108, 111], "push": [2, 4, 15, 102, 108], "page": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 61, 102, 104, 106, 108], "came": [2, 108], "panda": [2, 13, 15, 16, 18, 19, 20, 21, 26, 33, 34, 49, 53, 108], "guid": [2, 3, 6, 7, 11, 12, 13, 15, 16, 18, 20, 21, 28, 33, 37, 38, 40, 42, 43, 44, 45, 49, 69, 100, 108], "idea": [2, 3, 53, 102], "If": [2, 3, 4, 7, 9, 10, 11, 12, 13, 15, 16, 23, 26, 27, 30, 34, 36, 37, 38, 40, 42, 44, 46, 50, 52, 53, 55, 57, 61, 65, 66, 67, 68, 79, 81, 82, 83, 84, 88, 89, 92, 96, 99, 100, 101, 102, 103, 105, 106, 108, 109, 112, 114], "look": [2, 3, 4, 10, 12, 13, 15, 19, 23, 24, 26, 28, 36, 37, 39, 48, 55, 61, 83, 86, 92, 96, 98, 108, 111], "brand": 2, "we": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 68, 82, 88, 96, 98, 99, 100, 101, 102, 105, 108, 109], "recommend": [2, 3, 21, 26, 45, 61, 100, 101, 102, 108], "go": [2, 3, 4, 15, 16, 96, 102, 106], "main": [2, 4, 100], "codebas": [2, 16, 105], "your": [2, 4, 5, 6, 7, 10, 13, 15, 17, 19, 20, 21, 22, 26, 27, 30, 32, 34, 36, 37, 40, 42, 43, 44, 45, 46, 48, 49, 50, 53, 55, 60, 61, 67, 75, 84, 87, 99, 100, 103, 104, 105, 108, 111, 112], "own": [2, 3, 4, 6, 7, 13, 15, 17, 19, 20, 21, 30, 42, 52, 53, 66, 99, 101, 108, 112], "encourag": [2, 108], "an": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18, 19, 20, 21, 22, 23, 24, 26, 31, 33, 34, 37, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 52, 53, 57, 60, 62, 66, 67, 69, 71, 72, 78, 80, 83, 84, 85, 87, 88, 89, 94, 96, 98, 99, 100, 101, 102, 104, 105, 106, 108, 110, 112, 114], "toolkit": [2, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 49, 52, 53, 55, 99, 104], "mdakit": [2, 105], "standalon": [2, 16, 108], "solv": [2, 87], "specif": [2, 3, 4, 7, 14, 16, 26, 27, 29, 30, 36, 53, 56, 60, 66, 73, 98, 100, 101, 103, 104, 105, 108, 110, 114], "scientif": [2, 3, 15, 85, 104, 105], "technic": [2, 3, 4, 88], "problem": [2, 3, 4, 13, 61, 68, 87, 99], "option": [2, 9, 10, 14, 16, 19, 22, 23, 24, 30, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 57, 61, 66, 67, 82, 83, 95, 100, 103, 104, 105, 108, 109, 114], "regist": [2, 44], "registri": 2, "advertis": [2, 105], "broader": 2, "continu": [2, 3, 19, 27, 48, 89, 94, 102, 103, 105, 109, 114], "against": [2, 4, 102, 105], "latest": [2, 3, 15, 100, 108], "make": [2, 3, 4, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 24, 26, 28, 33, 34, 38, 39, 40, 42, 43, 44, 45, 49, 53, 55, 56, 98, 100, 102, 103, 106, 108, 109, 111, 112, 114], "peopl": [2, 3, 102], "togeth": [2, 53, 99, 103, 112], "daunt": 2, "aspect": 2, "stick": [2, 50], "guidelin": [2, 105], "process": [2, 3, 7, 15, 17, 18, 19, 23, 24, 26, 27, 48, 67, 102, 105, 108, 114], "straightforward": [2, 53, 103, 106], "mostli": [2, 15], "troubl": [2, 40], "As": [2, 3, 4, 10, 12, 13, 14, 15, 16, 23, 24, 30, 33, 34, 39, 42, 43, 44, 48, 50, 53, 55, 57, 61, 84, 93, 95, 96, 98, 100, 102, 103, 105, 106, 108, 109, 113], "alwai": [2, 3, 4, 10, 15, 16, 20, 26, 33, 34, 53, 57, 60, 61, 67, 79, 81, 83, 84, 87, 88, 96, 99, 101, 103, 106, 108, 109, 111, 113], "difficulti": [2, 99], "feel": [2, 3], "ask": [2, 3, 16, 99, 100, 102, 108], "host": [2, 3, 5], "need": [2, 3, 7, 8, 9, 14, 15, 16, 19, 24, 30, 33, 34, 42, 43, 44, 46, 48, 50, 52, 53, 55, 67, 98, 99, 100, 101, 102, 103, 105, 108, 111, 112], "sign": [2, 34], "up": [2, 3, 4, 12, 15, 16, 27, 31, 42, 44, 46, 52, 53, 57, 66, 93, 96, 99, 100, 103, 105, 106, 108, 112], "account": [2, 3, 4, 7, 32, 34, 40, 49], "great": 2, "resourc": [2, 3, 15, 42, 44, 55, 99, 108], "learn": [2, 3, 7, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 33, 38, 39, 42, 43, 44, 49, 99], "numpi": [2, 3, 10, 12, 15, 16, 18, 19, 20, 21, 23, 24, 26, 27, 28, 30, 33, 34, 37, 40, 42, 43, 44, 45, 46, 48, 49, 50, 53, 55, 100, 101, 102, 103, 105, 108, 109, 112], "matthew": [2, 52], "brett": 2, "pydagogu": 2, "instruct": [2, 53, 100], "instal": [2, 3, 4, 5, 7, 10, 30, 33, 34, 42, 43, 44, 46, 48, 53, 55, 79, 101, 104, 105, 108], "set": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 19, 23, 26, 27, 30, 31, 34, 37, 46, 48, 50, 52, 53, 57, 61, 66, 67, 68, 81, 83, 87, 88, 89, 92, 96, 101, 102, 103, 104, 105, 106, 108, 109, 111, 114], "ssh": 2, "configur": [2, 7, 19, 23, 32, 41, 67, 108], "step": [2, 3, 10, 13, 14, 15, 16, 26, 34, 37, 39, 44, 48, 52, 53, 55, 57, 68, 72, 88, 105, 112, 114], "complet": [2, 3, 5, 30, 53, 57, 112], "seamlessli": [2, 67], "between": [2, 3, 4, 6, 7, 8, 9, 11, 14, 16, 17, 18, 19, 21, 24, 26, 27, 30, 32, 33, 34, 36, 38, 39, 40, 41, 46, 50, 53, 60, 80, 82, 83, 91, 99, 105, 108, 114], "local": [2, 3, 4, 7, 15, 16, 19, 20, 32, 33, 39, 48, 101, 102, 104, 108], "repositori": [2, 3, 4, 5, 99, 102], "would": [3, 13, 15, 16, 19, 39, 43, 100, 101, 105, 106, 109], "start": [3, 4, 6, 12, 13, 15, 16, 26, 30, 50, 57, 61, 69, 83, 87, 91, 101, 105, 106, 108], "search": [3, 30, 106], "someon": 3, "els": [3, 37, 83, 101], "don": [3, 10, 11, 18, 24, 26, 30, 40, 42, 53, 55, 100, 105, 111], "follow": [3, 4, 13, 16, 18, 23, 26, 27, 30, 34, 38, 46, 48, 53, 57, 60, 61, 63, 65, 67, 79, 81, 83, 87, 88, 91, 95, 100, 101, 102, 103, 105, 106, 108, 109, 114], "minor": [3, 102, 105], "ahead": [3, 102, 108], "major": [3, 53, 102], "That": [3, 19], "other": [3, 4, 6, 7, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 37, 38, 39, 42, 43, 44, 45, 46, 49, 53, 60, 61, 66, 67, 68, 69, 71, 72, 76, 78, 79, 80, 81, 86, 87, 95, 96, 98, 99, 100, 105, 106, 108, 109, 112, 113], "weigh": 3, "should": [3, 4, 5, 12, 13, 14, 15, 22, 26, 27, 30, 31, 34, 36, 42, 44, 53, 57, 68, 72, 77, 78, 83, 87, 92, 100, 101, 102, 105, 108, 109, 114], "link": [3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 102, 104], "descript": [3, 5, 15, 30, 53, 57, 58, 61, 83, 87, 96, 97, 105, 108, 109], "here": [3, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 60, 78, 100, 102, 105, 107, 109], "overview": [3, 4], "workflow": [3, 4, 48, 108], "inlin": [3, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 53], "expand": [3, 4, 44, 84], "throughout": [3, 4, 9, 10, 14, 19, 26, 27, 30, 34, 39, 48, 50, 53, 55, 98, 105], "rest": [3, 4, 14, 19, 26], "isol": [3, 4], "version": [3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 67, 70, 71, 85, 99, 105, 109], "comput": [3, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 52, 53, 55, 56, 99, 100, 104, 108, 109, 113], "off": [3, 4, 12, 16, 46, 50, 89, 106, 108], "run": [3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 40, 42, 44, 46, 48, 49, 52, 53, 55, 59, 61, 97, 100, 101, 102, 105, 110], "hit": [3, 4, 108], "button": [3, 4], "want": [3, 4, 7, 9, 10, 15, 16, 18, 19, 21, 22, 23, 26, 30, 31, 36, 42, 43, 44, 48, 50, 52, 55, 96, 100, 108, 111], "clone": [3, 100], "machin": [3, 4, 15, 48, 100, 105, 108], "git": [3, 4, 100, 102], "http": [3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 55, 100, 104, 105], "com": [3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 55, 100, 104, 105, 108], "cd": [3, 4, 100, 102, 104, 108], "remot": [3, 4], "upstream": [3, 4], "directori": [3, 4, 5, 30, 89, 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37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 72, 99, 100, 105, 106, 108, 110, 114], "without": [3, 4, 13, 15, 28, 30, 34, 37, 50, 53, 61, 87, 99, 103, 104, 105, 106, 108, 109, 114], "touch": [3, 61], "exist": [3, 15, 19, 37, 53, 60, 103, 108, 109], "dev": [3, 102], "activ": [3, 4, 82, 100], "when": [3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 22, 23, 24, 26, 27, 28, 30, 33, 34, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 57, 60, 61, 63, 66, 67, 79, 83, 87, 89, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 111, 113, 114], "info": [3, 27], "current": [3, 4, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 28, 30, 33, 38, 39, 42, 43, 44, 45, 48, 49, 50, 53, 55, 57, 63, 67, 70, 72, 79, 81, 84, 87, 88, 89, 96, 100, 102, 103, 104, 106, 111, 112, 114], "finish": [3, 16], "deactiv": 3, "root": [3, 6, 7, 8, 9, 11, 15, 16, 38, 78, 82], "full": [3, 4, 26, 30, 44, 57, 66, 85, 89, 100, 102, 112], "detail": [3, 15, 30, 45, 53, 61, 80, 81, 98, 105, 108, 112], "purpos": [3, 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[[7, "imports-and-dependencies"]], "Alignments and RMS fitting": [[7, "alignments-and-rms-fitting"], [8, "alignments-and-rms-fitting"]], "Distances and contacts": [[7, "distances-and-contacts"], [17, "distances-and-contacts"]], "Trajectory similarity": [[7, "trajectory-similarity"], [41, "trajectory-similarity"]], "Structure": [[7, "structure"], [35, "structure"]], "Volumetric analyses": [[7, "volumetric-analyses"], [47, "volumetric-analyses"]], "Dimension reduction": [[7, "dimension-reduction"], [32, "dimension-reduction"]], "Polymers and membranes": [[7, "polymers-and-membranes"], [29, "polymers-and-membranes"]], "Hydrogen Bond Analysis": [[7, "hydrogen-bond-analysis"], [25, "hydrogen-bond-analysis"]], "Aligning a structure to another": [[9, "Aligning-a-structure-to-another"]], "Loading files": [[9, "Loading-files"], [10, "Loading-files"], [11, "Loading-files"], [12, "Loading-files"], [13, "Loading-files"], [14, "Loading-files"], [15, "Loading-files"], [16, "Loading-files"], [18, "Loading-files"], [19, "Loading-files"], [20, "Loading-files"], [21, "Loading-files"], [22, "Loading-files"], [23, "Loading-files"], [24, "Loading-files"], [26, "Loading-files"], [27, "Loading-files"], [28, "Loading-files"], [31, "Loading-files"], [33, "Loading-files"], [34, "Loading-files"], [36, "Loading-files"], [37, "Loading-files"], [38, "Loading-files"], [39, "Loading-files"], [40, "Loading-files"], [42, "Loading-files"], [43, "Loading-files"], [44, "Loading-files"], [45, "Loading-files"], [46, "Loading-files"], [48, "Loading-files"], [49, "Loading-files"], [55, "Loading-files"]], "Aligning a structure with align.alignto": [[9, "Aligning-a-structure-with-align.alignto"]], "References": [[9, "References"], [10, "References"], [11, "References"], [12, "References"], [13, "References"], [14, "References"], [15, "References"], [16, "References"], [18, "References"], [19, "References"], [20, "References"], [21, "References"], [22, "References"], [23, "References"], [24, "References"], [26, "References"], [27, "References"], [28, "References"], [30, "References"], [31, "References"], [33, "References"], [34, "References"], [36, "References"], [37, "References"], [38, "References"], [39, "References"], [40, "References"], [42, "References"], [43, "References"], [44, "References"], [45, "References"], [45, "id5"], [46, "References"], [48, "References"], [49, "References"], [50, "References"], [52, "References"], [53, "References"], [55, "References"], [104, "references"]], "Aligning a trajectory to a reference": [[10, "Aligning-a-trajectory-to-a-reference"]], "Aligning a trajectory with AlignTraj": [[10, "Aligning-a-trajectory-with-AlignTraj"]], "Copying coordinates into a new Universe": [[10, "Copying-coordinates-into-a-new-Universe"]], "Writing trajectories to a file": [[10, "Writing-trajectories-to-a-file"]], "Aligning a trajectory to itself": [[11, "Aligning-a-trajectory-to-itself"]], "Aligning a trajectory to the first frame": [[11, "Aligning-a-trajectory-to-the-first-frame"]], "Aligning a trajectory to the third frame": [[11, "Aligning-a-trajectory-to-the-third-frame"]], "Calculating the pairwise RMSD of a trajectory": [[12, "Calculating-the-pairwise-RMSD-of-a-trajectory"]], "Background": [[12, "Background"], [13, "Background"], [14, "Background"], [15, "Background"], [18, "Background"], [19, "Background"], [20, "Background"], [21, "Background"], [30, "Background"]], "Pairwise RMSD of a trajectory to itself": [[12, "Pairwise-RMSD-of-a-trajectory-to-itself"]], "Pairwise RMSD between two trajectories": [[12, "Pairwise-RMSD-between-two-trajectories"]], "Calculating the root mean square deviation of atomic structures": [[13, "Calculating-the-root-mean-square-deviation-of-atomic-structures"]], "RMSD between two sets of coordinates": [[13, "RMSD-between-two-sets-of-coordinates"]], "RMSD of a Universe with multiple selections": [[13, "RMSD-of-a-Universe-with-multiple-selections"]], "Plotting the data": [[13, "Plotting-the-data"]], "RMSD of an AtomGroup with multiple selections": [[13, "RMSD-of-an-AtomGroup-with-multiple-selections"]], "Weighted RMSD of a trajectory": [[13, "Weighted-RMSD-of-a-trajectory"]], "Mass": [[13, "Mass"]], "Custom weights": [[13, "Custom-weights"]], "Calculating the root mean square fluctuation over a trajectory": [[14, "Calculating-the-root-mean-square-fluctuation-over-a-trajectory"]], "Creating an average structure": [[14, "Creating-an-average-structure"]], "Aligning the trajectory to a reference": [[14, "Aligning-the-trajectory-to-a-reference"]], "Calculating RMSF": [[14, "Calculating-RMSF"]], "Plotting RMSF": [[14, "Plotting-RMSF"]], "Visualising RMSF as B-factors": [[14, "Visualising-RMSF-as-B-factors"]], "Parallelizing analysis": [[15, "Parallelizing-analysis"]], "Radius of gyration": [[15, "Radius-of-gyration"], [16, "Radius-of-gyration"]], "Serial Analysis": [[15, "Serial-Analysis"]], "Parallelization in a simple per-frame fashion": [[15, "Parallelization-in-a-simple-per-frame-fashion"]], "Frame-wise form of the function": [[15, "Frame-wise-form-of-the-function"]], "Parallelization with multiprocessing": [[15, "Parallelization-with-multiprocessing"]], "Parallelization with dask": [[15, "Parallelization-with-dask"]], "Parallelization in a split-apply-combine fashion": [[15, "Parallelization-in-a-split-apply-combine-fashion"]], "Block analysis function": [[15, "Block-analysis-function"]], "Split the trajectory": [[15, "Split-the-trajectory"]], "Apply the analysis per block": [[15, "Apply-the-analysis-per-block"]], "Combine the results": [[15, "Combine-the-results"]], "Other possible parallelism approaches for multiple analyses": [[15, "Other-possible-parallelism-approaches-for-multiple-analyses"]], "See Also": [[15, "See-Also"]], "Writing your own trajectory analysis": [[16, "Writing-your-own-trajectory-analysis"]], "Creating an analysis from a function": [[16, "Creating-an-analysis-from-a-function"]], "Transforming a function into a class": [[16, "Transforming-a-function-into-a-class"]], "Creating your own class": [[16, "Creating-your-own-class"]], "1. Define __init__": [[16, "1.-Define-__init__"]], "2. Define your analysis in _single_frame() and other methods": [[16, "2.-Define-your-analysis-in-_single_frame()-and-other-methods"]], "Write your own native contacts analysis method": [[18, "Write-your-own-native-contacts-analysis-method"]], "Defining salt bridges": [[18, "Defining-salt-bridges"]], "Define your own function": [[18, "Define-your-own-function"]], "Plotting": [[18, "Plotting"], [19, "Plotting"], [19, "id6"], [19, "id7"], [20, "Plotting"], [21, "Plotting"], [22, "Plotting"], [22, "id4"], [23, "Plotting"], [24, "Plotting"], [24, "id4"], [30, "Plotting"], [30, "id8"], [37, "Plotting"], [42, "Plotting"], [42, "id5"], [42, "id6"], [44, "Plotting"], [44, "id5"], [44, "id6"], [45, "Plotting"], [46, "Plotting"], [46, "id5"]], "Fraction of native contacts over a trajectory": [[19, "Fraction-of-native-contacts-over-a-trajectory"]], "Defining the groups for contact analysis": [[19, "Defining-the-groups-for-contact-analysis"], [21, "Defining-the-groups-for-contact-analysis"]], "Hard cutoff with a single reference": [[19, "Hard-cutoff-with-a-single-reference"]], "Radius cutoff": [[19, "Radius-cutoff"]], "Soft cutoff and multiple references": [[19, "Soft-cutoff-and-multiple-references"]], "Multiple references": [[19, "Multiple-references"]], "Soft cutoff": [[19, "Soft-cutoff"]], "Q1 vs Q2 contact analysis": [[20, "Q1-vs-Q2-contact-analysis"]], "Calculating Q1 vs Q2": [[20, "Calculating-Q1-vs-Q2"]], "Contact analysis: number of contacts within a cutoff": [[21, "Contact-analysis:-number-of-contacts-within-a-cutoff"]], "Calculating number of contacts within a cutoff": [[21, "Calculating-number-of-contacts-within-a-cutoff"]], "Atom-wise distances between matching AtomGroups": [[22, "Atom-wise-distances-between-matching-AtomGroups"]], "Calculating the distance between CA atoms": [[22, "Calculating-the-distance-between-CA-atoms"]], "Calculating the distance with periodic boundary conditions": [[22, "Calculating-the-distance-with-periodic-boundary-conditions"]], "All distances between two selections": [[23, "All-distances-between-two-selections"]], "Calculating atom-to-atom distances between non-matching coordinate arrays": [[23, "Calculating-atom-to-atom-distances-between-non-matching-coordinate-arrays"]], "Plotting distance as a heatmap": [[23, "Plotting-distance-as-a-heatmap"]], "Calculating residue-to-residue distances": [[23, "Calculating-residue-to-residue-distances"]], "All distances within a selection": [[24, "All-distances-within-a-selection"]], "Calculating atom-wise distances": [[24, "Calculating-atom-wise-distances"]], "Calculating distances for each residue": [[24, "Calculating-distances-for-each-residue"]], "Calculating hydrogen bonds: the basics": [[26, "Calculating-hydrogen-bonds:-the-basics"]], "Hydrogen bonds": [[26, "Hydrogen-bonds"]], "Find water-water hydrogen bonds": [[26, "Find-water-water-hydrogen-bonds"]], "Accessing the results": [[26, "Accessing-the-results"]], "Helper functions": [[26, "Helper-functions"]], "Further analysis": [[26, "Further-analysis"]], "Store data": [[26, "Store-data"]], "Calculating hydrogen bond lifetimes": [[27, "Calculating-hydrogen-bond-lifetimes"]], "Find all hydrogen bonds": [[27, "Find-all-hydrogen-bonds"], [28, "Find-all-hydrogen-bonds"]], "Calculate hydrogen bond lifetimes": [[27, "Calculate-hydrogen-bond-lifetimes"]], "Calculating the time constant": [[27, "Calculating-the-time-constant"]], "Intermittent lifetime": [[27, "Intermittent-lifetime"]], "Hydrogen bond lifetime of individual hydrogen bonds": [[27, "Hydrogen-bond-lifetime-of-individual-hydrogen-bonds"]], "Calculating hydrogen bonds: advanced selections": [[28, "Calculating-hydrogen-bonds:-advanced-selections"]], "Use guess_acceptors and guess_hydrogens to create atom selections": [[28, "Use-guess_acceptors-and-guess_hydrogens-to-create-atom-selections"]], "More advanced selections": [[28, "More-advanced-selections"]], "Hydrogen bonds between specific groups": [[28, "Hydrogen-bonds-between-specific-groups"]], "Analysing pore dimensions with HOLE2": [[30, "Analysing-pore-dimensions-with-HOLE2"]], "Using HOLE with a PDB file": [[30, "Using-HOLE-with-a-PDB-file"]], "Using HOLE with a trajectory": [[30, "Using-HOLE-with-a-trajectory"]], "Working with the data": [[30, "Working-with-the-data"]], "Visualising the VMD surface": [[30, "Visualising-the-VMD-surface"]], "Ordering HOLE profiles with an order parameter": [[30, "Ordering-HOLE-profiles-with-an-order-parameter"]], "Deleting HOLE files": [[30, "Deleting-HOLE-files"]], "Determining the persistence length of a polymer": [[31, "Determining-the-persistence-length-of-a-polymer"]], "Choosing the chains and backbone atoms": [[31, "Choosing-the-chains-and-backbone-atoms"]], "Calculating the persistence length": [[31, "Calculating-the-persistence-length"]], "Non-linear dimension reduction to diffusion maps": [[33, "Non-linear-dimension-reduction-to-diffusion-maps"]], "Diffusion maps": [[33, "Diffusion-maps"]], "Principal component analysis of a trajectory": [[34, "Principal-component-analysis-of-a-trajectory"]], "Principal component analysis": [[34, "Principal-component-analysis"]], "Visualising projections into a reduced dimensional space": [[34, "Visualising-projections-into-a-reduced-dimensional-space"]], "Measuring convergence with cosine content": [[34, "Measuring-convergence-with-cosine-content"]], "Average radial distribution functions": [[36, "Average-radial-distribution-functions"]], "Calculating the average radial distribution function for two groups of atoms": [[36, "Calculating-the-average-radial-distribution-function-for-two-groups-of-atoms"]], "Calculating the average radial distribution function for a group of atoms to itself": [[36, "Calculating-the-average-radial-distribution-function-for-a-group-of-atoms-to-itself"]], "Protein dihedral angle analysis": [[37, "Protein-dihedral-angle-analysis"]], "Selecting dihedral atom groups": [[37, "Selecting-dihedral-atom-groups"]], "Calculating dihedral angles": [[37, "Calculating-dihedral-angles"]], "Ramachandran analysis": [[37, "Ramachandran-analysis"]], "Janin analysis": [[37, "Janin-analysis"]], "Elastic network analysis": [[38, "Elastic-network-analysis"]], "Using a Gaussian network model": [[38, "Using-a-Gaussian-network-model"]], "Using a Gaussian network model with only close contacts": [[38, "Using-a-Gaussian-network-model-with-only-close-contacts"]], "Helix analysis": [[39, "Helix-analysis"], [39, "id6"]], "Running the analysis": [[39, "Running-the-analysis"]], "Calculating the RDF atom-to-atom": [[40, "Calculating-the-RDF-atom-to-atom"]], "Calculating the site-specific radial distribution function": [[40, "Calculating-the-site-specific-radial-distribution-function"]], "The site-specific RDF without densities": [[40, "The-site-specific-RDF-without-densities"]], "Calculating the Clustering Ensemble Similarity between ensembles": [[42, "Calculating-the-Clustering-Ensemble-Similarity-between-ensembles"]], "Calculating clustering similarity with default settings": [[42, "Calculating-clustering-similarity-with-default-settings"]], "Calculating clustering similarity with one method": [[42, "Calculating-clustering-similarity-with-one-method"]], "Calculating clustering similarity with multiple methods": [[42, "Calculating-clustering-similarity-with-multiple-methods"]], "Trying out different clustering parameters": [[42, "Trying-out-different-clustering-parameters"]], "Estimating the error in a clustering ensemble similarity analysis": [[42, "Estimating-the-error-in-a-clustering-ensemble-similarity-analysis"]], "Evaluating convergence": [[43, "Evaluating-convergence"]], "Evaluating convergence with similarity measures": [[43, "Evaluating-convergence-with-similarity-measures"]], "Using default arguments with clustering ensemble similarity": [[43, "Using-default-arguments-with-clustering-ensemble-similarity"]], "Comparing different clustering methods": [[43, "Comparing-different-clustering-methods"]], "Using default arguments with dimension reduction ensemble similarity": [[43, "Using-default-arguments-with-dimension-reduction-ensemble-similarity"]], "Comparing different dimensionality reduction methods": [[43, "Comparing-different-dimensionality-reduction-methods"]], "Calculating the Dimension Reduction Ensemble Similarity between ensembles": [[44, "Calculating-the-Dimension-Reduction-Ensemble-Similarity-between-ensembles"]], "Calculating dimension reduction similarity with default settings": [[44, "Calculating-dimension-reduction-similarity-with-default-settings"]], "Calculating dimension reduction similarity with one method": [[44, "Calculating-dimension-reduction-similarity-with-one-method"]], "Calculating dimension reduction similarity with multiple methods": [[44, "Calculating-dimension-reduction-similarity-with-multiple-methods"]], "Trying out different dimension reduction parameters": [[44, "Trying-out-different-dimension-reduction-parameters"]], "Estimating the error in a dimension reduction ensemble similarity analysis": [[44, "Estimating-the-error-in-a-dimension-reduction-ensemble-similarity-analysis"]], "Calculating the Harmonic Ensemble Similarity between ensembles": [[45, "Calculating-the-Harmonic-Ensemble-Similarity-between-ensembles"]], "Calculating harmonic similarity": [[45, "Calculating-harmonic-similarity"]], "Comparing the geometric similarity of trajectories": [[46, "Comparing-the-geometric-similarity-of-trajectories"]], "Aligning trajectories": [[46, "Aligning-trajectories"]], "Generating paths": [[46, "Generating-paths"]], "Hausdorff method": [[46, "Hausdorff-method"]], "Discrete Fr\u00e9chet distances": [[46, "Discrete-Fr\u00e9chet-distances"]], "Calculating the solvent density around a protein": [[48, "Calculating-the-solvent-density-around-a-protein"]], "Centering, aligning, and making molecules whole with on-the-fly transformations": [[48, "Centering,-aligning,-and-making-molecules-whole-with-on-the-fly-transformations"]], "Analysing the density of water around the protein": [[48, "Analysing-the-density-of-water-around-the-protein"]], "Visualisation": [[48, "Visualisation"]], "matplotlib (3D static plot)": [[48, "matplotlib-(3D-static-plot)"]], "nglview (interactive)": [[48, "nglview-(interactive)"]], "scikit-image (triangulated surface)": [[48, "scikit-image-(triangulated-surface)"]], "pyvista (3D surface)": [[48, "pyvista-(3D-surface)"]], "2D averaging": [[48, "2D-averaging"]], "Computing mass and charge density on each axis": [[49, "Computing-mass-and-charge-density-on-each-axis"]], "Constructing, modifying, and adding to a Universe": [[50, "Constructing,-modifying,-and-adding-to-a-Universe"]], "Creating and populating a Universe with water": [[50, "Creating-and-populating-a-Universe-with-water"]], "Creating a blank Universe": [[50, "Creating-a-blank-Universe"]], "Adding topology attributes": [[50, "Adding-topology-attributes"]], "Adding positions": [[50, "Adding-positions"]], "Adding bonds": [[50, "Adding-bonds"]], "Merging with a protein": [[50, "Merging-with-a-protein"]], "Adding a new segment": [[50, "Adding-a-new-segment"]], "Tiling into a larger Universe": [[50, "Tiling-into-a-larger-Universe"]], "Acknowledgments": [[50, "Acknowledgments"]], "Other": [[51, "other"]], "Using ParmEd with MDAnalysis and OpenMM to simulate a selection of atoms": [[52, "Using-ParmEd-with-MDAnalysis-and-OpenMM-to-simulate-a-selection-of-atoms"]], "Loading files: the difference between ParmEd and MDAnalysis": [[52, "Loading-files:-the-difference-between-ParmEd-and-MDAnalysis"]], "Using MDAnalysis to select atoms": [[52, "Using-MDAnalysis-to-select-atoms"]], "Using ParmEd and OpenMM to create a simulation system": [[52, "Using-ParmEd-and-OpenMM-to-create-a-simulation-system"]], "Quick start guide": [[53, "Quick-start-guide"]], "Overview": [[53, "Overview"]], "Loading a structure or trajectory": [[53, "Loading-a-structure-or-trajectory"]], "Working with groups of atoms": [[53, "Working-with-groups-of-atoms"]], "Selecting atoms": [[53, "Selecting-atoms"]], "Getting atom information from AtomGroups": [[53, "Getting-atom-information-from-AtomGroups"]], "AtomGroup positions and methods": [[53, "AtomGroup-positions-and-methods"]], "Working with trajectories": [[53, "Working-with-trajectories"]], "Dynamic selection": [[53, "Dynamic-selection"]], "Writing out coordinates": [[53, "Writing-out-coordinates"]], "Single frame": [[53, "Single-frame"]], "Trajectories": [[53, "Trajectories"], [56, "trajectories"], [111, "trajectories"]], "RMSD": [[53, "RMSD"]], "Automatic citations with duecredit": [[53, "Automatic-citations-with-duecredit"]], "Transformations": [[54, "transformations"]], "Centering a trajectory in the box": [[55, "Centering-a-trajectory-in-the-box"]], "Before transformation": [[55, "Before-transformation"]], "Unwrapping the protein": [[55, "Unwrapping-the-protein"]], "Centering in the box": [[55, "Centering-in-the-box"]], "Wrapping the solvent back into the box": [[55, "Wrapping-the-solvent-back-into-the-box"]], "Doing all this on-the-fly": [[55, "Doing-all-this-on-the-fly"]], "Frequently asked questions": [[56, "frequently-asked-questions"]], "Why do the atom positions change over trajectories?": [[56, "why-do-the-atom-positions-change-over-trajectories"]], "Auxiliary files": [[57, "auxiliary-files"]], "Supported formats": [[57, "supported-formats"]], "XVG Files": [[57, "xvg-files"]], "Reading data directly": [[57, "reading-data-directly"]], "Loading data into a Universe": [[57, "loading-data-into-a-universe"]], "Passing arguments to auxiliary data": [[57, "passing-arguments-to-auxiliary-data"]], "Iterating over auxiliary data": [[57, "iterating-over-auxiliary-data"]], "Accessing auxiliary attributes": [[57, "accessing-auxiliary-attributes"]], "Recreating auxiliaries": [[57, "recreating-auxiliaries"]], "EDR Files": [[57, "edr-files"]], "Standalone Usage": [[57, "standalone-usage"]], "Unit Handling": [[57, "unit-handling"]], "Use with Trajectories": [[57, "use-with-trajectories"]], "Selecting Trajectory Frames Based on Auxiliary Data": [[57, "selecting-trajectory-frames-based-on-auxiliary-data"]], "Memory Usage": [[57, "memory-usage"]], "Coordinates": [[58, "coordinates"], [61, "coordinates"]], "Table of supported coordinate readers and the information read": [[58, "id2"], [61, "id12"]], "Format reference": [[59, "format-reference"]], "Guessing": [[60, "guessing"]], "Masses": [[60, "masses"]], "Types": [[60, "types"]], "Bonds, Angles, Dihedrals, Impropers": [[60, "bonds-angles-dihedrals-impropers"]], "Format overview": [[61, "format-overview"]], "Table of all supported formats in MDAnalysis": [[61, "id10"]], "Topology": [[61, "topology"], [97, "topology"]], "Table of supported topology parsers and the attributes read": [[61, "id11"], [97, "id1"]], "chemfiles (chemfiles Trajectory or file)": [[62, "chemfiles-chemfiles-trajectory-or-file"]], "CONFIG (DL_Poly Config)": [[63, "config-dl-poly-config"]], "HISTORY (DL_Poly Config)": [[63, "history-dl-poly-config"]], "COOR, NAMBDIN (NAMD binary restart files)": [[64, "coor-nambdin-namd-binary-restart-files"]], "CRD (CHARMM CARD files)": [[65, "crd-charmm-card-files"]], "Reading in": [[65, "reading-in"], [66, "reading-in"], [67, "reading-in"], [70, "reading-in"], [71, "reading-in"], [72, "reading-in"], [76, "reading-in"], [79, "reading-in"], [81, "reading-in"], [82, "reading-in"], [83, "reading-in"], [84, "reading-in"], [85, "reading-in"], [88, "reading-in"], [89, "reading-in"], [94, "reading-in"], [95, "reading-in"]], "Writing out": [[65, "writing-out"], [66, "writing-out"], [67, "writing-out"], [71, "writing-out"], [79, "writing-out"], [81, "writing-out"], [82, "writing-out"], [83, "writing-out"], [89, "writing-out"]], "DATA (LAMMPS)": [[66, "data-lammps"]], "DCD (CHARMM, NAMD, or LAMMPS trajectory)": [[67, "dcd-charmm-namd-or-lammps-trajectory"]], "DCD (Flexible LAMMPS trajectory)": [[68, "dcd-flexible-lammps-trajectory"]], "DMS (Desmond Molecular Structure files)": [[69, "dms-desmond-molecular-structure-files"]], "GMS (Gamess trajectory)": [[70, "gms-gamess-trajectory"]], "GRO (GROMACS structure file)": [[71, "gro-gromacs-structure-file"]], "GSD (HOOMD GSD file)": [[72, "gsd-hoomd-gsd-file"]], "IN, FHIAIMS (FHI-aims input files)": [[73, "in-fhiaims-fhi-aims-input-files"]], "INPCRD, RESTRT (AMBER restart files)": [[74, "inpcrd-restrt-amber-restart-files"]], "ITP (GROMACS portable topology files)": [[75, "itp-gromacs-portable-topology-files"]], "LAMMPSDUMP (LAMMPS ascii dump file)": [[76, "lammpsdump-lammps-ascii-dump-file"]], "MMTF (Macromolecular Transmission Format)": [[77, "mmtf-macromolecular-transmission-format"]], "MOL2 (Tripos structure)": [[78, "mol2-tripos-structure"]], "MOL2 specification": [[78, "mol2-specification"]], "NCDF, NC (AMBER NetCDF trajectory)": [[79, "ncdf-nc-amber-netcdf-trajectory"]], "ParmEd (ParmEd Structure)": [[80, "parmed-parmed-structure"]], "PDB, ENT (Standard PDB file)": [[81, "pdb-ent-standard-pdb-file"]], "PDB specification": [[81, "pdb-specification"]], "CRYST1 fields": [[81, "id1"]], "ATOM/HETATM fields": [[81, "id2"]], "PDBQT (Autodock structure)": [[82, "pdbqt-autodock-structure"]], "PDBQT specification": [[82, "pdbqt-specification"]], "PDB format with AutoDOCK extensions for the PDBQT format.": [[82, "id1"]], "PQR file (PDB2PQR / APBS)": [[83, "pqr-file-pdb2pqr-apbs"]], "PQR specification": [[83, "pqr-specification"]], "PSF (CHARMM, NAMD, or XPLOR protein structure file)": [[84, "psf-charmm-namd-or-xplor-protein-structure-file"]], "PSF specification": [[84, "psf-specification"]], "TNG (Trajectory Next Generation)": [[85, "tng-trajectory-next-generation"]], "TOP, PRMTOP, PARM7 (AMBER topology)": [[86, "top-prmtop-parm7-amber-topology"]], "AMBER specification": [[86, "amber-specification"]], "Attributes parsed from AMBER keywords": [[86, "id1"]], "Developer notes": [[86, "developer-notes"], [87, "developer-notes"], [89, "developer-notes"], [91, "developer-notes"]], "TPR (GROMACS run topology files)": [[87, "tpr-gromacs-run-topology-files"]], "Supported versions": [[87, "supported-versions"]], "TPR format versions and generations read by MDAnalysis.topology.TPRParser.parse().": [[87, "id1"]], "TPR specification": [[87, "tpr-specification"]], "segid and chainID": [[87, "segid-and-chainid"]], "Bonds": [[87, "bonds"]], "GROMACS entries used to create bonds.": [[87, "id2"]], "GROMACS entries used to create angles.": [[87, "id3"]], "GROMACS entries used to create dihedrals.": [[87, "id4"]], "GROMACS entries used to create improper dihedrals.": [[87, "id5"]], "TRJ, MDCRD, CRDBOX (AMBER ASCII trajectory)": [[88, "trj-mdcrd-crdbox-amber-ascii-trajectory"]], "TRR (GROMACS lossless trajectory file)": [[89, "trr-gromacs-lossless-trajectory-file"]], "TRZ (IBIsCO and YASP trajectory)": [[90, "trz-ibisco-and-yasp-trajectory"]], "TXYZ, ARC (Tinker)": [[91, "txyz-arc-tinker"]], "XML (HOOMD)": [[92, "xml-hoomd"]], "XPDB (Extended PDB file)": 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\ No newline at end of file
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70, 71, 72, 74, 78, 79, 80, 81, 83, 84, 89, 91, 92, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114], "through": [1, 2, 3, 7, 12, 14, 15, 32, 41, 49, 53, 57, 98, 103, 105, 106, 109, 111, 114], "instanc": [1, 36, 50, 53, 57, 78, 106, 114], "typic": [1, 3, 7, 8, 13, 14, 26, 50, 52, 53, 96, 98, 99, 108, 109, 111, 112, 114], "select_atom": [1, 9, 11, 13, 14, 15, 16, 18, 19, 21, 22, 23, 24, 26, 31, 34, 36, 37, 40, 48, 50, 52, 53, 55, 96, 98, 103, 105, 106, 110, 111, 112, 114], "manipul": [1, 53, 80, 99], "test": [1, 2, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 70, 93, 96, 98, 102, 103, 106, 109, 110, 111, 112, 114], "datafil": [1, 5, 9, 10, 11, 12, 13, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 93, 96, 98, 101, 103, 106, 108, 109, 110, 111, 112, 114], "pdb": [1, 9, 10, 13, 14, 22, 23, 24, 34, 37, 46, 50, 52, 53, 57, 58, 59, 61, 77, 78, 83, 96, 97, 98, 103, 105, 108, 109, 114], "3": [1, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 78, 81, 82, 83, 84, 87, 93, 94, 95, 96, 98, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114], "4": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 78, 82, 83, 87, 88, 93, 96, 98, 100, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "resnam": [1, 18, 19, 21, 26, 27, 28, 36, 37, 40, 48, 50, 53, 55, 61, 65, 72, 81, 82, 86, 87, 96, 97, 98, 105, 106, 109, 112], "arg": [1, 15, 18, 19, 21, 27, 28, 53, 83, 98, 106, 107, 109], "out": [1, 3, 4, 9, 10, 11, 12, 14, 19, 30, 34, 39, 48, 50, 52, 55, 57, 61, 70, 78, 93, 98, 102, 103, 105, 106, 109, 110, 111, 113, 114], "312": [1, 98, 104, 108], "see": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 18, 19, 20, 21, 22, 26, 27, 28, 30, 32, 33, 34, 37, 38, 40, 42, 43, 44, 46, 48, 50, 52, 53, 55, 56, 57, 60, 61, 63, 66, 67, 69, 76, 81, 84, 87, 94, 96, 98, 99, 100, 101, 102, 103, 105, 106, 108, 109, 112, 114], "more": [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 26, 27, 30, 33, 34, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 53, 56, 57, 60, 61, 65, 69, 87, 98, 101, 103, 105, 106, 108, 109, 112, 114], "inform": [1, 3, 4, 7, 10, 12, 14, 15, 16, 19, 22, 26, 28, 30, 32, 33, 34, 37, 40, 42, 43, 44, 46, 50, 52, 56, 57, 60, 62, 63, 66, 69, 70, 71, 72, 80, 81, 82, 84, 87, 88, 94, 95, 98, 101, 103, 105, 106, 108, 111, 112, 114], "like": [1, 3, 4, 5, 13, 16, 19, 26, 30, 34, 39, 40, 43, 48, 53, 55, 57, 60, 83, 100, 106, 108], "list": [1, 2, 3, 5, 13, 15, 16, 19, 26, 30, 38, 39, 40, 42, 43, 44, 46, 48, 50, 53, 57, 61, 65, 67, 82, 87, 96, 99, 100, 102, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114], "5": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 67, 71, 78, 82, 87, 93, 98, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "print": [1, 5, 9, 10, 11, 13, 14, 15, 19, 23, 26, 27, 28, 30, 31, 34, 36, 37, 39, 40, 42, 43, 44, 45, 46, 49, 50, 53, 57, 101, 103, 108, 109, 110, 111, 114], "n": [1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 83, 95, 96, 98, 104, 105, 106, 107, 113], "type": [1, 3, 26, 42, 50, 52, 53, 58, 61, 65, 66, 72, 80, 81, 82, 83, 84, 85, 86, 87, 91, 92, 96, 97, 98, 104, 105, 106, 109, 113], "met": [1, 53, 83, 107, 109], "resid": [1, 13, 14, 23, 24, 26, 27, 36, 40, 46, 50, 53, 61, 65, 66, 69, 72, 75, 81, 82, 87, 89, 98, 105, 106, 109], "altloc": [1, 14, 61, 81, 82, 97, 106, 109], "return": [1, 3, 4, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 24, 27, 30, 33, 34, 37, 40, 42, 44, 50, 53, 57, 98, 102, 105, 106, 108, 110, 111, 112], "below": [1, 2, 3, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 61, 98, 103, 106, 107, 108, 109, 112, 113, 114], "code": [1, 2, 4, 5, 6, 7, 11, 13, 14, 15, 16, 30, 36, 48, 50, 57, 67, 78, 81, 82, 84, 93, 98, 99, 102, 103, 104, 105, 106, 108, 109, 112, 114], "everi": [1, 3, 4, 10, 14, 15, 16, 24, 27, 30, 31, 34, 37, 39, 42, 44, 48, 50, 52, 53, 57, 60, 61, 98, 103, 105, 106, 108, 109, 111, 114], "second": [1, 10, 12, 18, 19, 21, 40, 44, 53, 113], "element": [1, 42, 50, 53, 60, 61, 65, 66, 70, 81, 82, 86, 97, 105, 109, 114], "first": [1, 3, 4, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 26, 27, 30, 31, 32, 33, 34, 36, 37, 38, 40, 42, 44, 45, 46, 48, 49, 50, 53, 55, 57, 61, 69, 72, 81, 83, 89, 94, 98, 100, 101, 105, 106, 108, 110, 111, 114], "6th": [1, 106], "correspond": [1, 3, 12, 13, 14, 16, 24, 26, 27, 40, 55, 71, 98, 108, 109], "indic": [1, 3, 12, 13, 14, 19, 24, 26, 27, 30, 33, 34, 38, 40, 43, 50, 53, 81, 82, 84, 87, 91, 96, 101, 105, 106, 108, 109, 110], "6": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 66, 69, 78, 81, 82, 83, 84, 87, 91, 93, 98, 102, 103, 104, 106, 109, 110, 111, 112, 114], "ag": [1, 10, 13, 40, 96, 98, 103], "also": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 26, 27, 28, 30, 32, 34, 37, 39, 40, 41, 42, 44, 48, 53, 55, 57, 61, 62, 81, 82, 87, 89, 98, 100, 101, 102, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "support": [1, 3, 13, 19, 30, 46, 53, 66, 71, 72, 82, 84, 85, 88, 100, 103, 105, 106, 109, 111, 114], "fanci": [1, 53, 110], "pass": [1, 3, 4, 13, 16, 18, 19, 22, 23, 24, 26, 28, 30, 31, 34, 36, 37, 39, 42, 43, 44, 45, 49, 50, 53, 55, 60, 61, 62, 66, 80, 81, 93, 95, 96, 100, 102, 103, 105, 106, 108, 110, 112, 114], "ndarrai": [1, 53, 110], "8": [1, 4, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 22, 23, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 87, 98, 103, 105, 106, 109, 110, 111, 112, 114], "10": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 71, 78, 82, 83, 87, 98, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114], "9": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 84, 87, 98, 102, 103, 105, 106, 108, 109, 110, 111, 112, 114], "47680": 1, "boolean": [1, 53, 108, 110], "allow": [1, 2, 3, 10, 13, 14, 19, 27, 28, 30, 36, 37, 40, 42, 43, 44, 52, 53, 56, 57, 60, 96, 99, 100, 103, 105, 106, 108, 110, 114], "you": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 26, 27, 30, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 61, 62, 64, 67, 78, 79, 80, 89, 96, 98, 99, 100, 101, 102, 103, 104, 106, 108, 109, 110, 111, 112, 114], "true": [1, 3, 9, 10, 11, 12, 13, 14, 16, 26, 27, 28, 30, 34, 37, 38, 40, 42, 44, 45, 46, 48, 50, 52, 53, 55, 66, 68, 89, 94, 103, 105, 106, 108, 110, 114], "fals": [1, 4, 9, 11, 14, 19, 20, 26, 27, 30, 40, 44, 46, 48, 50, 53, 57, 78, 103, 105, 106, 108, 109, 110, 114], "valu": [1, 3, 7, 8, 11, 12, 13, 14, 15, 19, 24, 26, 27, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 57, 60, 65, 66, 67, 76, 81, 82, 83, 89, 92, 105, 106, 108, 110, 114], "must": [1, 2, 3, 4, 11, 12, 13, 16, 18, 22, 23, 26, 27, 30, 31, 34, 42, 43, 46, 48, 50, 53, 60, 66, 79, 83, 84, 100, 101, 108, 109, 112, 114], "same": [1, 3, 4, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 24, 26, 28, 30, 31, 33, 34, 36, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 53, 55, 60, 67, 93, 95, 96, 98, 102, 103, 105, 106, 109, 110, 114], "length": [1, 3, 6, 7, 12, 13, 22, 24, 27, 29, 39, 40, 42, 44, 45, 46, 48, 50, 52, 53, 63, 66, 67, 68, 71, 79, 88, 99, 104, 105, 110, 111], "origin": [1, 3, 4, 7, 9, 13, 14, 23, 27, 32, 33, 34, 39, 50, 53, 55, 57, 67, 98, 102, 106, 108, 110, 113], "condit": [1, 15, 105, 106, 108, 110, 112], "arr": 1, "11": [1, 3, 9, 10, 13, 14, 15, 16, 19, 24, 26, 27, 28, 30, 33, 34, 36, 37, 38, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 78, 81, 82, 83, 87, 98, 103, 104, 105, 106, 109, 110, 112, 113, 114], "len": [1, 12, 23, 24, 30, 33, 34, 36, 37, 38, 40, 42, 44, 45, 46, 48, 50, 53, 57, 98, 103, 108, 109, 111], "12": [1, 3, 9, 10, 13, 14, 15, 16, 19, 26, 27, 30, 33, 34, 36, 37, 40, 42, 43, 44, 46, 48, 49, 50, 52, 53, 55, 57, 78, 82, 86, 98, 103, 104, 106, 109, 110, 111, 112, 113, 114], "13": [1, 9, 10, 13, 14, 15, 16, 19, 26, 27, 30, 34, 36, 37, 40, 42, 43, 44, 48, 49, 50, 52, 53, 55, 57, 81, 82, 98, 103, 106, 109, 110, 112, 114], "number": [1, 3, 4, 6, 7, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 27, 30, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 48, 50, 52, 53, 57, 60, 65, 69, 71, 72, 75, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 96, 103, 105, 106, 108, 109, 114], "wai": [1, 3, 4, 7, 10, 12, 13, 14, 15, 16, 18, 19, 26, 27, 30, 33, 34, 48, 52, 53, 79, 98, 99, 103, 105, 106, 108, 109, 111, 114], "compar": [1, 6, 7, 9, 10, 11, 12, 14, 15, 17, 19, 22, 32, 34, 40, 41, 42, 44, 45, 108], "one": [1, 2, 3, 4, 10, 11, 12, 13, 15, 19, 27, 28, 30, 31, 39, 46, 50, 53, 56, 57, 83, 87, 88, 91, 92, 96, 98, 99, 100, 103, 104, 105, 106, 108, 109, 114], "concaten": [1, 15, 53, 103, 106, 114], "subtract": [1, 13, 84], "union": [1, 106], "differ": [1, 3, 4, 7, 9, 10, 12, 13, 15, 16, 19, 26, 28, 34, 36, 38, 40, 41, 45, 46, 48, 50, 53, 55, 60, 66, 67, 70, 71, 75, 82, 87, 91, 93, 95, 96, 98, 99, 100, 102, 103, 104, 105, 106, 112, 114], "achiev": [1, 15], "similar": [1, 3, 4, 6, 12, 15, 19, 23, 24, 30, 32, 33, 34, 37, 38, 53, 73, 82, 83, 87, 92, 93, 98, 104], "outcom": [1, 105], "kei": [1, 2, 30, 38, 39, 49, 53, 57, 98, 99, 114], "preserv": [1, 4, 19, 66, 105], "ani": [1, 2, 3, 4, 11, 15, 16, 18, 27, 28, 30, 31, 37, 42, 43, 44, 46, 49, 50, 53, 56, 57, 61, 67, 89, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112], "duplic": [1, 30, 53, 103, 106], "where": [1, 4, 5, 7, 9, 14, 15, 16, 19, 20, 21, 22, 27, 31, 32, 33, 36, 37, 38, 40, 43, 44, 46, 48, 50, 53, 57, 60, 61, 66, 71, 89, 98, 100, 102, 105, 106, 108, 109], "its": [1, 2, 3, 4, 7, 9, 13, 15, 18, 19, 20, 22, 30, 32, 33, 44, 46, 49, 52, 53, 98, 99, 100, 101, 103, 106, 108, 109, 111, 112], "topologi": [1, 5, 14, 15, 26, 28, 52, 53, 59, 63, 65, 66, 69, 70, 71, 72, 73, 76, 77, 78, 80, 81, 82, 83, 84, 91, 92, 93, 95, 98, 99, 103, 105, 106, 108, 111], "14": [1, 9, 10, 13, 15, 16, 19, 26, 27, 30, 34, 36, 40, 42, 43, 44, 48, 49, 50, 52, 53, 55, 57, 82, 98, 100, 103, 104, 105, 106, 109, 110, 112, 114], "ag1": [1, 40], "15": [1, 7, 10, 13, 15, 16, 19, 26, 27, 30, 34, 36, 40, 41, 42, 43, 44, 45, 48, 49, 50, 52, 53, 57, 81, 82, 98, 103, 104, 106, 109, 110, 112, 114], "ag2": [1, 40], "16": [1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 81, 82, 98, 103, 104, 105, 106, 109, 110, 111, 112], "concat": 1, "17": [1, 12, 15, 16, 19, 20, 21, 26, 27, 30, 33, 34, 36, 39, 42, 44, 48, 49, 50, 52, 53, 57, 69, 81, 82, 87, 98, 103, 104, 106, 109, 110, 111, 112], "18": [1, 9, 15, 16, 19, 26, 27, 30, 42, 44, 46, 48, 49, 50, 52, 53, 57, 78, 81, 82, 86, 98, 103, 105, 106, 109, 110, 112, 113], "19": [1, 15, 16, 19, 22, 23, 24, 26, 27, 28, 30, 37, 40, 42, 44, 48, 49, 50, 52, 53, 57, 82, 98, 103, 104, 105, 106, 109, 112, 113], "avail": [1, 2, 3, 7, 15, 16, 19, 32, 33, 36, 40, 42, 43, 44, 53, 56, 57, 60, 66, 73, 77, 79, 82, 87, 96, 98, 99, 100, 102, 105, 106, 108, 109, 114], "keep": [1, 2, 3, 10, 14, 30, 34, 39, 43, 44, 48, 55, 72, 96, 99, 101, 102, 108], "well": [1, 2, 4, 7, 15, 32, 33, 44, 48, 61, 67, 99, 101, 105, 108], "equival": [1, 27, 48, 87], "result": [1, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 24, 30, 31, 36, 37, 38, 39, 40, 42, 43, 44, 48, 49, 52, 53, 61, 89, 94, 105, 106, 108, 110], "t": [1, 3, 4, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 24, 26, 27, 28, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 52, 53, 55, 56, 57, 66, 82, 92, 96, 98, 100, 103, 104, 105, 106, 107, 108, 110, 111, 112, 114], "sort": [1, 26, 27, 30, 34, 53, 105, 106], "isdisjoint": 1, "do": [1, 3, 4, 6, 10, 11, 12, 13, 15, 16, 19, 22, 26, 27, 28, 30, 34, 37, 42, 44, 45, 46, 48, 50, 52, 53, 95, 99, 101, 102, 103, 105, 108, 114], "share": [1, 3, 105], "issubset": 1, "part": [1, 2, 3, 4, 15, 30, 48, 57, 101, 103, 105, 106, 108, 111], "is_strict_subset": 1, "issuperset": 1, "is_strict_superset": 1, "both": [1, 3, 4, 13, 15, 16, 22, 30, 38, 48, 50, 52, 53, 60, 63, 72, 81, 85, 87, 91, 98, 100, 102, 103, 105, 108, 109, 114], "intersect": [1, 106], "common": [1, 3, 7, 12, 13, 14, 15, 22, 26, 31, 32, 34, 53, 55, 68, 70, 104, 106, 108], "symmetric_differ": 1, "separ": [1, 5, 7, 15, 16, 19, 30, 31, 36, 40, 42, 43, 81, 83, 84, 100, 102, 106, 108, 114], "properti": [1, 16, 30, 39, 53, 60, 98, 105, 106, 109, 111, 112], "level": [1, 3, 4, 15, 48, 85, 98, 101, 108, 114], "connect": [1, 3, 4, 33, 50, 52, 53, 60, 61, 82, 87, 91, 105, 114], "residu": [1, 3, 7, 13, 14, 17, 19, 21, 22, 26, 28, 30, 36, 37, 38, 39, 40, 48, 49, 50, 52, 53, 55, 61, 65, 69, 70, 71, 72, 75, 81, 82, 83, 86, 87, 92, 93, 95, 96, 99, 105, 106, 109, 112, 114], "molecul": [1, 4, 10, 13, 26, 31, 38, 50, 55, 58, 61, 66, 78, 87, 92, 95, 98, 105, 106, 108, 109, 112], "segment": [1, 10, 49, 53, 61, 65, 70, 81, 87, 95, 99, 103, 105, 106, 109, 114], "20": [1, 15, 16, 19, 26, 30, 31, 42, 44, 48, 49, 50, 52, 53, 57, 82, 98, 103, 105, 106, 110, 111, 112, 113], "21": [1, 3, 9, 15, 16, 26, 27, 28, 30, 31, 33, 42, 44, 48, 49, 50, 52, 53, 57, 71, 81, 82, 98, 103, 104, 105, 106, 109, 112, 113, 114], "24": [1, 15, 16, 26, 27, 30, 34, 39, 44, 49, 50, 53, 57, 81, 82, 87, 98, 99, 103, 105, 112], "accord": [1, 13, 14, 87, 91, 106, 109], "produc": [1, 23, 27, 34, 53, 57, 67, 105, 108], "dictionari": [1, 18, 30, 57, 60, 89, 105, 108, 114], "22": [1, 15, 16, 26, 30, 44, 46, 48, 49, 50, 53, 57, 81, 82, 98, 103, 105, 106, 112], "mass": [1, 6, 7, 9, 10, 15, 16, 23, 24, 28, 30, 36, 40, 45, 46, 47, 48, 53, 55, 61, 63, 65, 66, 86, 87, 92, 97, 106, 109, 112, 113, 114], "32": [1, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 49, 50, 52, 53, 55, 57, 98, 104, 105], "06": [1, 38, 104, 113], "008": [1, 53], "23853": 1, "11084": 1, "011": [1, 39, 53], "1040": [1, 98], "007": [1, 53, 82], "289": 1, "999": [1, 53, 65, 93], "11404": 1, "98977": 1, "multipl": [1, 3, 27, 39, 48, 53, 57, 62, 89, 94, 98, 100, 105, 106, 112, 114], "them": [1, 3, 4, 7, 9, 10, 13, 15, 18, 19, 22, 34, 38, 39, 42, 43, 44, 45, 46, 50, 52, 53, 55, 60, 61, 66, 68, 79, 81, 82, 89, 95, 101, 102, 103, 104, 105, 106, 108, 109, 112], "23": [1, 15, 16, 26, 30, 34, 44, 48, 49, 50, 53, 57, 81, 82, 87, 93, 98, 103, 106, 112], "sol": [1, 30, 36, 37, 40, 48, 50, 55, 87, 112], "22168": 1, "iter": [1, 12, 14, 15, 16, 30, 44, 49, 53, 103, 105, 108, 109, 110, 111], "atom1": [1, 95], "25": [1, 15, 26, 27, 30, 34, 46, 49, 50, 53, 57, 81, 82, 83, 98, 103, 105], "atom2": [1, 95], "26": [1, 15, 26, 30, 34, 49, 50, 53, 57, 81, 82, 83, 87, 93, 98, 103], "atom3": 1, "27": [1, 15, 26, 30, 49, 50, 53, 57, 81, 82, 93, 98, 103, 104, 107, 113], "28": [1, 3, 15, 30, 49, 50, 53, 57, 78, 98, 103], "ca": [1, 9, 10, 11, 12, 13, 14, 16, 18, 20, 21, 23, 24, 36, 37, 38, 39, 40, 42, 43, 44, 46, 53, 60, 98, 103, 106, 107], "c": [1, 3, 13, 27, 30, 31, 34, 36, 37, 38, 39, 46, 48, 50, 52, 53, 62, 67, 78, 81, 82, 85, 87, 88, 98, 100, 102, 103, 104, 105, 106, 107, 113], "cb": [1, 36, 53, 98], "h2": [1, 26, 28, 50, 78], "h": [1, 3, 27, 31, 36, 39, 45, 50, 52, 53, 78, 87, 96, 98, 103, 106], "neat": [1, 4], "shortcut": [1, 106], "simpli": [1, 3, 12, 13, 15, 21, 24, 26, 34, 40, 50, 52, 53, 60, 79, 84, 88, 101, 104, 105, 106, 109, 113], "29": [1, 13, 15, 30, 34, 46, 49, 50, 53, 57, 98, 103, 113], "30": [1, 13, 14, 15, 22, 23, 30, 33, 42, 49, 50, 53, 57, 78, 98, 103, 104, 110, 112, 113], "31": [1, 15, 16, 19, 30, 48, 49, 50, 53, 57, 81, 82, 98, 103], "altern": [1, 3, 13, 20, 30, 48, 57, 77, 81, 82, 96, 103, 106, 108, 109, 112], "provid": [1, 3, 4, 7, 12, 13, 15, 16, 17, 25, 26, 30, 31, 34, 37, 38, 43, 44, 46, 52, 53, 55, 57, 60, 61, 62, 65, 71, 81, 82, 83, 88, 94, 96, 99, 100, 103, 105, 106, 108, 109, 114], "belong": [1, 28, 50, 53, 61, 98, 106, 107], "33": [1, 15, 49, 50, 53, 57, 81, 82], "34": [1, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 28, 30, 33, 37, 38, 39, 40, 42, 43, 44, 45, 49, 50, 52, 53, 55, 57, 81, 82, 98, 104], "These": [1, 3, 4, 5, 7, 8, 17, 24, 37, 43, 48, 53, 57, 72, 81, 82, 89, 94, 98, 106, 108, 109], "user": [1, 2, 3, 6, 7, 15, 19, 26, 28, 39, 42, 43, 44, 45, 49, 53, 60, 61, 66, 68, 69, 72, 78, 79, 87, 88, 100, 101, 102, 103, 105, 106, 108, 109, 114], "work": [1, 2, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 55, 56, 57, 62, 67, 79, 96, 99, 100, 102, 104, 105, 106, 108, 111], "35": [1, 19, 20, 30, 49, 50, 53, 57, 98, 103, 104], "null": 1, "36": [1, 49, 50, 53, 57, 83], "abov": [1, 3, 10, 13, 16, 23, 24, 26, 27, 28, 39, 48, 53, 55, 57, 83, 102, 103, 105, 106, 108, 109, 111], "37": [1, 38, 49, 50, 53, 57, 83, 98, 104], "For": [1, 2, 3, 4, 7, 9, 12, 13, 15, 16, 19, 26, 28, 30, 32, 33, 34, 37, 39, 40, 42, 43, 44, 46, 50, 52, 53, 55, 57, 60, 61, 66, 78, 83, 87, 89, 96, 98, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 114], "38": [1, 16, 23, 49, 52, 53, 57, 81, 82], "does_not_exist": 1, "39": [1, 13, 15, 16, 18, 20, 21, 23, 24, 26, 30, 31, 36, 37, 38, 39, 40, 42, 43, 44, 48, 49, 50, 52, 53, 57, 81, 82], "40": [1, 19, 30, 49, 53, 57, 81, 82, 98, 103, 110], "have": [1, 2, 3, 4, 10, 11, 12, 13, 14, 15, 16, 18, 19, 22, 24, 26, 27, 30, 31, 33, 34, 36, 37, 40, 42, 44, 45, 46, 48, 50, 53, 55, 57, 60, 63, 66, 67, 69, 71, 75, 79, 81, 82, 84, 87, 89, 95, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 114], "evalu": [1, 6, 7, 34, 41, 49, 106], "context": [1, 15, 30, 52, 53, 96, 108], "41": [1, 30, 49, 57, 81, 82, 98], "bool": [1, 3, 18, 50, 109, 110], "skip": [1, 3, 16, 46, 57, 100], "over": [1, 6, 7, 8, 12, 15, 16, 17, 18, 20, 21, 22, 26, 27, 30, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 53, 55, 72, 102, 103, 105, 108, 110, 111, 112], "instead": [1, 3, 4, 9, 10, 12, 14, 15, 16, 19, 23, 24, 30, 34, 37, 39, 43, 44, 48, 50, 55, 61, 81, 82, 88, 92, 93, 100, 105, 106, 108, 111, 114], "rais": [1, 3, 33, 49, 53, 65, 79, 81, 82, 83, 99, 100, 101, 105, 108], "error": [1, 3, 4, 9, 49, 53, 72, 79, 89, 94, 100, 101, 105, 108], "which": [1, 3, 4, 10, 12, 13, 14, 15, 16, 19, 20, 21, 26, 27, 28, 30, 31, 33, 34, 37, 38, 39, 40, 42, 43, 44, 46, 48, 50, 53, 55, 57, 60, 61, 67, 68, 83, 87, 89, 91, 94, 96, 98, 99, 100, 102, 103, 105, 106, 108, 109, 112, 114], "help": [1, 2, 3, 4, 13, 16, 22, 96, 99, 100, 106, 108], "occasion": [1, 89, 94], "aris": [1, 4], "logic": 1, "too": [1, 16, 42, 44, 48, 53, 55, 56, 99, 108], "restrict": [1, 13, 14, 87], "geometr": [1, 6, 7, 26, 41, 52], "normal": [1, 3, 15, 30, 38, 39, 43, 44, 84, 91, 96, 102, 104, 105, 108], "static": [1, 10, 15, 53, 106, 109, 111, 114], "within": [1, 6, 7, 16, 17, 18, 19, 20, 22, 26, 28, 30, 36, 38, 40, 42, 43, 44, 45, 50, 53, 57, 60, 67, 77, 105, 106, 111, 112], "chang": [1, 5, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 26, 27, 28, 30, 33, 34, 38, 39, 42, 43, 44, 45, 49, 53, 55, 57, 61, 71, 72, 81, 87, 89, 100, 102, 103, 106, 108, 111], "trajectori": [1, 5, 6, 8, 9, 17, 18, 20, 21, 26, 27, 32, 33, 36, 37, 38, 40, 42, 43, 44, 45, 48, 49, 50, 52, 54, 58, 59, 61, 66, 71, 72, 81, 82, 91, 93, 99, 104, 105, 106, 108, 112, 113, 114], "frame": [1, 7, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 26, 27, 30, 32, 33, 34, 36, 37, 38, 39, 40, 43, 44, 46, 48, 49, 52, 55, 56, 61, 66, 71, 81, 82, 89, 94, 99, 105, 106, 108, 110, 111, 112, 114], "sever": [1, 3, 7, 16, 29, 30, 33, 34, 53, 82, 101, 105, 108, 114], "requir": [1, 3, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 52, 53, 55, 65, 66, 79, 81, 82, 83, 95, 96, 98, 100, 103, 105, 106, 108, 109], "function": [1, 2, 3, 4, 6, 7, 8, 13, 17, 19, 20, 21, 27, 30, 33, 34, 35, 37, 41, 42, 43, 44, 45, 46, 52, 53, 55, 56, 57, 85, 87, 98, 100, 101, 102, 104, 105, 106, 109, 111, 112, 114], "implement": [1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 32, 33, 34, 39, 53, 57, 62, 66, 76, 82, 87, 95, 106, 112], "mani": [1, 2, 3, 4, 13, 15, 16, 21, 26, 30, 34, 39, 40, 42, 43, 44, 53, 99, 100, 108, 109, 111, 114], "interest": [1, 19, 30, 48, 53, 100], "bond": [1, 13, 31, 49, 52, 53, 55, 57, 61, 66, 78, 80, 81, 82, 84, 86, 91, 92, 97, 98, 99, 104, 105, 106, 109, 114], "angl": [1, 6, 7, 22, 26, 27, 28, 35, 39, 50, 53, 57, 61, 66, 67, 84, 86, 92, 97, 99, 104, 105, 106, 109, 113, 114], "dihedr": [1, 6, 7, 35, 53, 61, 66, 84, 86, 97, 105, 106, 109, 114], "improperdihedr": [1, 109], "present": [1, 3, 19, 21, 26, 27, 40, 52, 65, 68, 81, 92, 95, 98, 105], "residuegroup": [1, 53, 98, 105, 109], "free": [2, 3, 101], "open": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 87, 89, 94, 104, 108], "sourc": [2, 3, 4, 16, 30, 53, 57, 84, 96, 100, 102, 105, 108], "project": [2, 3, 4, 7, 32, 37, 39, 43, 44, 48, 99, 105], "It": [2, 3, 4, 7, 9, 12, 13, 14, 15, 16, 21, 22, 26, 27, 28, 30, 32, 33, 34, 36, 37, 40, 45, 48, 49, 50, 53, 57, 60, 61, 67, 76, 78, 82, 85, 87, 89, 99, 104, 106, 108, 110, 112, 114], "evolv": 2, "grow": [2, 5, 108], "demand": 2, "base": [2, 4, 5, 9, 10, 11, 12, 13, 14, 15, 16, 19, 28, 33, 39, 42, 44, 46, 50, 52, 53, 60, 61, 65, 79, 87, 89, 94, 102, 103, 104, 105, 106, 107, 108, 109], "develop": [2, 15, 52, 72, 101, 108], "team": 2, "veri": [2, 3, 12, 13, 15, 19, 23, 24, 27, 44, 45, 48, 79, 93, 103, 108], "much": [2, 3, 10, 11, 12, 13, 14, 16, 19, 30, 34, 42, 43, 48, 50, 60, 105, 108], "welcom": 2, "take": [2, 3, 4, 7, 14, 15, 16, 17, 23, 26, 30, 33, 42, 44, 48, 93, 96, 99, 100, 103, 106, 108, 109, 112, 114], "form": [2, 13, 19, 26, 42, 53, 55, 60], "bug": [2, 3, 16, 88, 102], "report": [2, 52, 99, 108], "enhanc": [2, 3], "request": [2, 4, 101, 102, 108], "issu": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 33, 38, 39, 43, 44, 49, 50, 57, 61, 63, 67, 79, 84, 87, 89, 94, 99, 100, 101, 102, 106, 108], "tracker": [2, 87, 99, 108], "fix": [2, 3, 4, 48, 49, 68, 72, 102], "improv": [2, 27, 99, 108], "speed": [2, 105, 108], "clariti": [2, 26], "modernis": 2, "featur": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 83, 99, 100, 102, 114], "addit": [2, 3, 7, 44, 57, 61, 82, 96, 98, 101, 102, 103, 105, 106, 108, 109], "document": [2, 5, 7, 15, 19, 22, 26, 30, 43, 48, 53, 66, 76, 89, 94, 105, 106, 108, 113], "includ": [2, 3, 4, 7, 9, 10, 13, 14, 15, 16, 19, 34, 36, 37, 39, 43, 48, 50, 52, 53, 55, 57, 84, 85, 87, 95, 96, 98, 99, 100, 102, 104, 105, 106, 108, 109, 111, 112, 114], "typo": [2, 3], "build": [2, 15, 16, 50, 53, 65, 87, 99, 100, 105], "question": [2, 3, 99], "discuss": [2, 3, 15, 27, 60, 63, 79, 87, 99, 101, 108], "mdnalysi": [2, 99, 108], "mail": [2, 99, 100, 101, 102, 108], "commun": [2, 3, 88], "subscrib": [2, 99], "conduct": [2, 99], "member": 2, "agre": [2, 99], "adher": [2, 102], "pleas": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 26, 27, 28, 30, 33, 34, 38, 39, 42, 43, 44, 45, 46, 53, 61, 87, 99, 100, 102, 104, 105, 112], "read": [2, 3, 11, 12, 13, 15, 16, 18, 19, 20, 21, 26, 30, 33, 38, 39, 43, 44, 49, 50, 52, 60, 62, 63, 64, 74, 80, 90, 91, 92, 99, 105, 106, 109, 110, 111, 113, 114], "mdanalysistest": [2, 3, 9, 10, 11, 12, 13, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 55, 57, 100, 101, 102, 103, 105, 108], "packag": [2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 61, 79, 86, 96, 100, 101, 104, 105, 106, 108], "distribut": [2, 3, 6, 7, 15, 30, 35, 38, 41, 42, 44, 45, 70, 100, 102], "under": [2, 3, 15, 19, 53, 87, 101, 105, 108, 110], "gnu": [2, 108], "gener": [2, 3, 4, 7, 8, 9, 10, 14, 15, 18, 19, 20, 28, 30, 34, 37, 38, 39, 48, 50, 53, 55, 58, 59, 61, 67, 77, 80, 89, 94, 99, 102, 103, 104, 105, 113, 114], "public": [2, 3, 16, 30, 99, 102, 104, 108], "licens": [2, 15, 105, 108], "later": [2, 4, 13, 15, 102, 105, 106], "copyleft": 2, "deriv": [2, 3, 84, 87, 98, 105, 109], "made": [2, 3, 4, 26, 48, 71, 103, 105, 106, 109], "Be": [2, 15, 100], "sure": [2, 3, 4, 18, 100, 102, 106, 108], "comfort": 2, "befor": [2, 3, 4, 13, 16, 30, 36, 45, 48, 60, 89, 100, 101, 102, 105, 106, 108, 111], "push": [2, 4, 15, 102, 108], "page": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 53, 55, 61, 102, 104, 106, 108], "came": [2, 108], "panda": [2, 13, 15, 16, 18, 19, 20, 21, 26, 33, 34, 49, 53, 108], "guid": [2, 3, 6, 7, 11, 12, 13, 15, 16, 18, 20, 21, 28, 33, 37, 38, 40, 42, 43, 44, 45, 49, 69, 100, 108], "idea": [2, 3, 53, 102], "If": [2, 3, 4, 7, 9, 10, 11, 12, 13, 15, 16, 23, 26, 27, 30, 34, 36, 37, 38, 40, 42, 44, 46, 50, 52, 53, 55, 57, 61, 65, 66, 67, 68, 79, 81, 82, 83, 84, 88, 89, 92, 96, 99, 100, 101, 102, 103, 105, 106, 108, 109, 112, 114], "look": [2, 3, 4, 10, 12, 13, 15, 19, 23, 24, 26, 28, 36, 37, 39, 48, 55, 61, 83, 86, 92, 96, 98, 108, 111], "brand": 2, "we": [2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 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66, 76, 105, 106], "won": 4, "prevent": [4, 105], "spuriou": 4, "possibl": [4, 28, 30, 46, 57, 60, 82, 99, 103, 104, 106, 108], "extra": [4, 15, 16, 30, 83, 100], "softwar": [4, 15, 42, 43, 44, 45, 100, 104, 106, 108], "program": [4, 7, 14, 30, 34, 39, 50, 53, 93, 96, 99, 100, 104], "therefor": [4, 13, 14, 15, 19, 33, 37, 42, 43, 50, 52, 53, 72, 79, 92, 95, 98, 108], "ignor": [4, 9, 10, 14, 22, 23, 24, 27, 30, 33, 34, 37, 46, 50, 52, 53, 81, 82, 84, 95, 105, 106, 114], "unless": [4, 42, 61, 87, 100, 106, 114], "Of": 4, "cours": [4, 37, 55], "occur": [4, 19], "unix": [4, 30, 106], "impli": 4, "environment": 4, "path_to_hole2": 4, "ex": [4, 30], "usual": [4, 13, 34, 53, 56, 82], "difficult": [4, 7, 19, 32, 40, 55], "emb": [4, 7, 32, 33, 55], "imag": [4, 9, 10, 12, 14, 15, 19, 22, 23, 34, 39, 50, 55, 105, 108], "edit": [4, 34, 102, 104], "conf": 4, "url": [4, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 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41, 46, 52, 56, 58, 59, 61, 96, 97, 103, 104, 106, 108, 111, 114], "calcul": [6, 7, 8, 9, 10, 11, 16, 18, 19, 25, 33, 34, 35, 39, 41, 46, 47, 53, 55, 57, 61, 100, 104, 105, 109], "squar": [6, 7, 8, 9, 11, 12, 15, 16, 24, 38, 104, 105], "deviat": [6, 7, 8, 9, 11, 30, 37, 39, 42, 44, 83, 104], "pairwis": [6, 7, 8, 13, 14, 19, 39, 40], "rmsd": [6, 7, 8, 9, 10, 11, 14, 30, 33, 41, 46, 57, 104, 110], "fluctuat": [6, 7, 8, 13, 19, 27, 34], "contact": [6, 99, 104], "wise": [6, 7, 17, 23, 53], "fraction": [6, 7, 17, 18, 20, 21, 76], "nativ": [6, 7, 15, 17, 20, 21, 63, 100, 104], "q1": [6, 7, 17, 18, 19, 21], "q2": [6, 7, 17, 18, 19, 21], "cutoff": [6, 7, 17, 18, 20, 26, 38, 57, 106], "write": [6, 7, 9, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 30, 34, 46, 48, 50, 52, 61, 62, 64, 68, 78, 90, 99, 105, 111, 113, 114], "harmon": [6, 7, 19, 20, 33, 41, 87, 104], "ensembl": [6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 37, 38, 39, 40, 41, 46, 48, 49, 50, 55, 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11, 12, 13, 14, 15, 33, 53, 79, 99, 103, 104], "qcp": [7, 8, 9, 10, 11, 12, 13, 14, 33], "the05": [7, 8, 9, 10, 11, 12, 13, 14, 33, 104], "lat09": [7, 8, 9, 10, 11, 14, 104], "cite": [7, 8, 9, 10, 11, 12, 13, 14, 16, 26, 27, 28, 30, 33, 34, 38, 39, 42, 43, 44, 45, 46, 53, 104], "rapidli": [7, 17, 37], "retain": [7, 17, 19, 21, 85], "insight": [7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 55, 104], "fold": [7, 17, 19, 20, 104], "movement": [7, 13, 17, 19, 34, 57], "3n": [7, 32, 34, 41], "dimension": [7, 12, 32, 33, 41, 44, 45, 104], "tpb": [7, 41, 42, 43, 44, 45, 104], "estim": [7, 12, 41, 45], "measur": [7, 12, 13, 16, 30, 31, 41, 48, 66, 108], "pair": [7, 26, 28, 30, 36, 40, 41, 44, 46, 105], "transit": [7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 32, 33, 34, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 53, 55, 104], "reduc": [7, 11, 30, 33, 41, 42, 43, 44, 85, 94, 100], "remain": 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36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 104], "kinas": [9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 104], "phosophotransferas": [9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 55], "enzym": [9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55], "bdpw09": [9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 55, 104], "close": [9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 33, 34, 37, 39, 40, 42, 43, 44, 45, 46, 48, 50, 53, 55, 104], "adk_open": [9, 10, 12, 19], "adk_clos": [9, 10, 12, 19], "adk_open_view": [9, 19], "show_mdanalysi": [9, 10, 14, 19, 34, 39, 48, 50, 55], "adk_closed_view": [9, 19], "even": [9, 15, 18, 22, 48, 53, 60, 61, 66, 68, 102, 104, 105, 106, 108, 109, 110, 111], "obviou": [9, 10], "posit": [9, 10, 11, 13, 14, 15, 16, 19, 20, 21, 23, 24, 26, 34, 38, 45, 48, 52, 66, 67, 76, 103, 105, 106, 108, 109, 110, 111, 112], "merged_view": [9, 10], "mobil": [9, 11, 13, 14], "target": [9, 13, 23, 100, 105], "old_rmsd": 9, "new_rmsd": 9, "By": [9, 19, 26, 30, 42, 44, 49, 61, 66, 68, 99, 106], "match_atom": [9, 10], "attempt": [9, 81, 105], "712154435976014": 9, "817293751703919": 9, "aligned_view": 9, "could": [9, 13, 16, 18, 23, 24, 34, 48, 88, 109], "alpha": [9, 11, 13, 14, 19, 20, 22, 23, 24, 39, 40, 48, 50, 53, 55, 60, 67, 81, 82, 103, 106], "carbon": [9, 11, 13, 14, 19, 20, 22, 23, 24, 31, 36, 40, 60, 106], "atomist": [9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 33, 34, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 50, 55, 104], "backbon": [9, 11, 13, 14, 33, 34, 37, 45, 53, 98, 106], "somewhat": [9, 34], "contriv": 9, "214": [9, 13, 24, 27, 37, 46, 50, 53], "991465038265208": 9, "603704620787127": 9, 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100, "flag": 100, "optimis": 100, "addit": 100, "dataset": 100, "rule": 101, "suit": [101, 108], "core": 101, "visual": [101, 108], "prepar": 102, "releas": [102, 105], "polici": 102, "typic": 102, "workflow": [102, 112], "summari": 102, "task": 102, "readi": 102, "packag": 102, "complet": 102, "manual": 102, "upload": 102, "cirru": [102, 108], "ci": [102, 108], "wheel": 102, "temporari": [102, 108], "forg": 102, "blog": 102, "post": 102, "outlin": 102, "increment": 102, "clean": 102, "up": 102, "old": 102, "In": 103, "transfer": 103, "output": 103, "pickl": 103, "6": 105, "bug": 105, "fix": 105, "contributor": 105, "0": 105, "major": 105, "enhanc": 105, "deprec": 105, "5": 105, "enchanc": 105, "4": 105, "3": 105, "czi": 105, "eoss": 105, "perform": 105, "improv": 105, "known": 105, "failur": 105, "issu": 105, "boolean": 106, "connect": [106, 109], "preexist": 106, "nucleic": 107, "acid": 107, "nucleobas": 107, "sugar": 107, "coverag": 108, "continu": 108, "integr": 108, "tool": 108, "action": 108, "azur": 108, "codecov": 108, "convent": 108, "assert": 108, "except": 108, "warn": 108, "fail": 108, "skip": 108, "fixtur": 108, "same": 108, "directori": 108, "canon": 109, "valu": 109, "level": 109, "object": 109, "convers": 113, "speed": 113, "forc": 113, "energi": 113, "scratch": 114, "properti": 114}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinx.ext.viewcode": 1, "nbsphinx": 4, "sphinxcontrib.bibtex": 9, "sphinx": 57}, "alltitles": {"Advanced topology concepts": [[0, "advanced-topology-concepts"]], "Adding a Residue or Segment to a Universe": [[0, "adding-a-residue-or-segment-to-a-universe"]], "Molecules": [[0, "molecules"]], "Adding custom TopologyAttrs": [[0, "adding-custom-topologyattrs"]], "AtomGroup": [[1, "atomgroup"]], "Creating an AtomGroup": [[1, "creating-an-atomgroup"]], "Atom selection language": [[1, "atom-selection-language"], [106, "atom-selection-language"]], "Indexing and slicing": [[1, "indexing-and-slicing"]], "Group operators and set methods": [[1, "group-operators-and-set-methods"]], "Groupby and split": [[1, "groupby-and-split"]], "Constructing from Atoms": [[1, "constructing-from-atoms"]], "Order and uniqueness": [[1, "order-and-uniqueness"]], "Empty AtomGroups": [[1, "empty-atomgroups"]], "Dynamically updating AtomGroups": [[1, "dynamically-updating-atomgroups"]], "Methods": [[1, "methods"]], "Contributing to MDAnalysis": [[2, "contributing-to-mdanalysis"], [16, "Contributing-to-MDAnalysis"]], "Where to start?": [[2, "where-to-start"]], "Version control, Git, and GitHub": [[2, "version-control-git-and-github"]], "Getting started with Git": [[2, "getting-started-with-git"]], "Contributing to the main codebase": [[3, "contributing-to-the-main-codebase"]], "Working with the code": [[3, "working-with-the-code"]], "Forking": [[3, "forking"]], "Creating a development environment": [[3, "creating-a-development-environment"], [4, "creating-a-development-environment"]], "With conda": [[3, "with-conda"], [3, "id1"]], "With pip and virtualenv": [[3, "with-pip-and-virtualenv"], [3, "id2"]], "On a Mac": [[3, "on-a-mac"]], "Building MDAnalysis": [[3, "building-mdanalysis"]], "Branches in MDAnalysis": [[3, "branches-in-mdanalysis"]], "Creating a branch": [[3, "creating-a-branch"]], "Writing new code": [[3, "writing-new-code"]], "Code formatting in Python": [[3, "code-formatting-in-python"]], "Modules and dependencies": [[3, "modules-and-dependencies"]], "Developing in Cython": [[3, "developing-in-cython"]], "Testing your code": [[3, "testing-your-code"]], "Documenting your code": [[3, "documenting-your-code"]], "Ensure PEP8 compliance (mandatory) and format your code with Darker (optional)": [[3, "ensure-pep8-compliance-mandatory-and-format-your-code-with-darker-optional"]], "Adding your code to MDAnalysis": [[3, "adding-your-code-to-mdanalysis"]], "Committing your code": [[3, "committing-your-code"]], "Pushing your code to GitHub": [[3, "pushing-your-code-to-github"]], "Rebasing your code": [[3, "rebasing-your-code"]], "Creating a pull request": [[3, "creating-a-pull-request"]], "Working with the code documentation": [[3, "working-with-the-code-documentation"]], "Building the documentation": [[3, "building-the-documentation"]], "Where to write docstrings": [[3, "where-to-write-docstrings"]], "Guidelines for writing docstrings": [[3, "guidelines-for-writing-docstrings"]], "Documenting changes": [[3, "documenting-changes"]], "Writing docs for abstract base classes": [[3, "writing-docs-for-abstract-base-classes"]], "Adding your documentation to MDAnalysis": [[3, "adding-your-documentation-to-mdanalysis"]], "Viewing the documentation interactively": [[3, "viewing-the-documentation-interactively"]], "Contributing to the user guide": [[4, "contributing-to-the-user-guide"]], "Forking and cloning the User Guide": [[4, "forking-and-cloning-the-user-guide"]], "Building the user guide": [[4, "building-the-user-guide"]], "Saving state in Jupyter notebooks": [[4, "saving-state-in-jupyter-notebooks"]], "Test with pytest and nbval": [[4, "test-with-pytest-and-nbval"]], "Sanitization": [[4, "sanitization"]], "On the hole2 notebook": [[4, "on-the-hole2-notebook"]], "Adding changes to the UserGuide": [[4, "adding-changes-to-the-userguide"]], "Automatically building documentation": [[4, "automatically-building-documentation"]], "Using pre-commit hooks": [[4, "using-pre-commit-hooks"]], "Example data": [[5, "example-data"]], "MDAnalysisTests": [[5, "mdanalysistests"]], "MDAnalysisData": [[5, "mdanalysisdata"]], "Examples": [[6, "examples"]], "General": [[6, null]], "Analysis": [[6, null], [7, "analysis"], [53, "Analysis"]], "Imports and dependencies": [[7, "imports-and-dependencies"]], "Alignments and RMS fitting": [[7, "alignments-and-rms-fitting"], [8, "alignments-and-rms-fitting"]], "Distances and contacts": [[7, "distances-and-contacts"], [17, "distances-and-contacts"]], "Trajectory similarity": [[7, "trajectory-similarity"], [41, "trajectory-similarity"]], "Structure": [[7, "structure"], [35, "structure"]], "Volumetric analyses": [[7, "volumetric-analyses"], [47, "volumetric-analyses"]], "Dimension reduction": [[7, "dimension-reduction"], [32, "dimension-reduction"]], "Polymers and membranes": [[7, "polymers-and-membranes"], [29, "polymers-and-membranes"]], "Hydrogen Bond Analysis": [[7, "hydrogen-bond-analysis"], [25, "hydrogen-bond-analysis"]], "Aligning a structure to another": [[9, "Aligning-a-structure-to-another"]], "Loading files": [[9, "Loading-files"], [10, "Loading-files"], [11, "Loading-files"], [12, "Loading-files"], [13, "Loading-files"], [14, "Loading-files"], [15, "Loading-files"], [16, "Loading-files"], [18, "Loading-files"], [19, "Loading-files"], [20, "Loading-files"], [21, "Loading-files"], [22, "Loading-files"], [23, "Loading-files"], [24, "Loading-files"], [26, "Loading-files"], [27, "Loading-files"], [28, "Loading-files"], [31, "Loading-files"], [33, "Loading-files"], [34, "Loading-files"], [36, "Loading-files"], [37, "Loading-files"], [38, "Loading-files"], [39, "Loading-files"], [40, "Loading-files"], [42, "Loading-files"], [43, "Loading-files"], [44, "Loading-files"], [45, "Loading-files"], [46, "Loading-files"], [48, "Loading-files"], [49, "Loading-files"], [55, "Loading-files"]], "Aligning a structure with align.alignto": [[9, "Aligning-a-structure-with-align.alignto"]], "References": [[9, "References"], [10, "References"], [11, "References"], [12, "References"], [13, "References"], [14, "References"], [15, "References"], [16, "References"], [18, "References"], [19, "References"], [20, "References"], [21, "References"], [22, "References"], [23, "References"], [24, "References"], [26, "References"], [27, "References"], [28, "References"], [30, "References"], [31, "References"], [33, "References"], [34, "References"], [36, "References"], [37, "References"], [38, "References"], [39, "References"], [40, "References"], [42, "References"], [43, "References"], [44, "References"], [45, "References"], [45, "id5"], [46, "References"], [48, "References"], [49, "References"], [50, "References"], [52, "References"], [53, "References"], [55, "References"], [104, "references"]], "Aligning a trajectory to a reference": [[10, "Aligning-a-trajectory-to-a-reference"]], "Aligning a trajectory with AlignTraj": [[10, "Aligning-a-trajectory-with-AlignTraj"]], "Copying coordinates into a new Universe": [[10, "Copying-coordinates-into-a-new-Universe"]], "Writing trajectories to a file": [[10, "Writing-trajectories-to-a-file"]], "Aligning a trajectory to itself": [[11, "Aligning-a-trajectory-to-itself"]], "Aligning a trajectory to the first frame": [[11, "Aligning-a-trajectory-to-the-first-frame"]], "Aligning a trajectory to the third frame": [[11, "Aligning-a-trajectory-to-the-third-frame"]], "Calculating the pairwise RMSD of a trajectory": [[12, "Calculating-the-pairwise-RMSD-of-a-trajectory"]], "Background": [[12, "Background"], [13, "Background"], [14, "Background"], [15, "Background"], [18, "Background"], [19, "Background"], [20, "Background"], [21, "Background"], [30, "Background"]], "Pairwise RMSD of a trajectory to itself": [[12, "Pairwise-RMSD-of-a-trajectory-to-itself"]], "Pairwise RMSD between two trajectories": [[12, "Pairwise-RMSD-between-two-trajectories"]], "Calculating the root mean square deviation of atomic structures": [[13, "Calculating-the-root-mean-square-deviation-of-atomic-structures"]], "RMSD between two sets of coordinates": [[13, "RMSD-between-two-sets-of-coordinates"]], "RMSD of a Universe with multiple selections": [[13, "RMSD-of-a-Universe-with-multiple-selections"]], "Plotting the data": [[13, "Plotting-the-data"]], "RMSD of an AtomGroup with multiple selections": [[13, "RMSD-of-an-AtomGroup-with-multiple-selections"]], "Weighted RMSD of a trajectory": [[13, "Weighted-RMSD-of-a-trajectory"]], "Mass": [[13, "Mass"]], "Custom weights": [[13, "Custom-weights"]], "Calculating the root mean square fluctuation over a trajectory": [[14, "Calculating-the-root-mean-square-fluctuation-over-a-trajectory"]], "Creating an average structure": [[14, "Creating-an-average-structure"]], "Aligning the trajectory to a reference": [[14, "Aligning-the-trajectory-to-a-reference"]], "Calculating RMSF": [[14, "Calculating-RMSF"]], "Plotting RMSF": [[14, "Plotting-RMSF"]], "Visualising RMSF as B-factors": [[14, "Visualising-RMSF-as-B-factors"]], "Parallelizing analysis": [[15, "Parallelizing-analysis"]], "Radius of gyration": [[15, "Radius-of-gyration"], [16, "Radius-of-gyration"]], "Serial Analysis": [[15, "Serial-Analysis"]], "Parallelization in a simple per-frame fashion": [[15, "Parallelization-in-a-simple-per-frame-fashion"]], "Frame-wise form of the function": [[15, "Frame-wise-form-of-the-function"]], "Parallelization with multiprocessing": [[15, "Parallelization-with-multiprocessing"]], "Parallelization with dask": [[15, "Parallelization-with-dask"]], "Parallelization in a split-apply-combine fashion": [[15, "Parallelization-in-a-split-apply-combine-fashion"]], "Block analysis function": [[15, "Block-analysis-function"]], "Split the trajectory": [[15, "Split-the-trajectory"]], "Apply the analysis per block": [[15, "Apply-the-analysis-per-block"]], "Combine the results": [[15, "Combine-the-results"]], "Other possible parallelism approaches for multiple analyses": [[15, "Other-possible-parallelism-approaches-for-multiple-analyses"]], "See Also": [[15, "See-Also"]], "Writing your own trajectory analysis": [[16, "Writing-your-own-trajectory-analysis"]], "Creating an analysis from a function": [[16, "Creating-an-analysis-from-a-function"]], "Transforming a function into a class": [[16, "Transforming-a-function-into-a-class"]], "Creating your own class": [[16, "Creating-your-own-class"]], "1. Define __init__": [[16, "1.-Define-__init__"]], "2. Define your analysis in _single_frame() and other methods": [[16, "2.-Define-your-analysis-in-_single_frame()-and-other-methods"]], "Write your own native contacts analysis method": [[18, "Write-your-own-native-contacts-analysis-method"]], "Defining salt bridges": [[18, "Defining-salt-bridges"]], "Define your own function": [[18, "Define-your-own-function"]], "Plotting": [[18, "Plotting"], [19, "Plotting"], [19, "id6"], [19, "id7"], [20, "Plotting"], [21, "Plotting"], [22, "Plotting"], [22, "id4"], [23, "Plotting"], [24, "Plotting"], [24, "id4"], [30, "Plotting"], [30, "id8"], [37, "Plotting"], [42, "Plotting"], [42, "id5"], [42, "id6"], [44, "Plotting"], [44, "id5"], [44, "id6"], [45, "Plotting"], [46, "Plotting"], [46, "id5"]], "Fraction of native contacts over a trajectory": [[19, "Fraction-of-native-contacts-over-a-trajectory"]], "Defining the groups for contact analysis": [[19, "Defining-the-groups-for-contact-analysis"], [21, "Defining-the-groups-for-contact-analysis"]], "Hard cutoff with a single reference": [[19, "Hard-cutoff-with-a-single-reference"]], "Radius cutoff": [[19, "Radius-cutoff"]], "Soft cutoff and multiple references": [[19, "Soft-cutoff-and-multiple-references"]], "Multiple references": [[19, "Multiple-references"]], "Soft cutoff": [[19, "Soft-cutoff"]], "Q1 vs Q2 contact analysis": [[20, "Q1-vs-Q2-contact-analysis"]], "Calculating Q1 vs Q2": [[20, "Calculating-Q1-vs-Q2"]], "Contact analysis: number of contacts within a cutoff": [[21, "Contact-analysis:-number-of-contacts-within-a-cutoff"]], "Calculating number of contacts within a cutoff": [[21, "Calculating-number-of-contacts-within-a-cutoff"]], "Atom-wise distances between matching AtomGroups": [[22, "Atom-wise-distances-between-matching-AtomGroups"]], "Calculating the distance between CA atoms": [[22, "Calculating-the-distance-between-CA-atoms"]], "Calculating the distance with periodic boundary conditions": [[22, "Calculating-the-distance-with-periodic-boundary-conditions"]], "All distances between two selections": [[23, "All-distances-between-two-selections"]], "Calculating atom-to-atom distances between non-matching coordinate arrays": [[23, "Calculating-atom-to-atom-distances-between-non-matching-coordinate-arrays"]], "Plotting distance as a heatmap": [[23, "Plotting-distance-as-a-heatmap"]], "Calculating residue-to-residue distances": [[23, "Calculating-residue-to-residue-distances"]], "All distances within a selection": [[24, "All-distances-within-a-selection"]], "Calculating atom-wise distances": [[24, "Calculating-atom-wise-distances"]], "Calculating distances for each residue": [[24, "Calculating-distances-for-each-residue"]], "Calculating hydrogen bonds: the basics": [[26, "Calculating-hydrogen-bonds:-the-basics"]], "Hydrogen bonds": [[26, "Hydrogen-bonds"]], "Find water-water hydrogen bonds": [[26, "Find-water-water-hydrogen-bonds"]], "Accessing the results": [[26, "Accessing-the-results"]], "Helper functions": [[26, "Helper-functions"]], "Further analysis": [[26, "Further-analysis"]], "Store data": [[26, "Store-data"]], "Calculating hydrogen bond lifetimes": [[27, "Calculating-hydrogen-bond-lifetimes"]], "Find all hydrogen bonds": [[27, "Find-all-hydrogen-bonds"], [28, "Find-all-hydrogen-bonds"]], "Calculate hydrogen bond lifetimes": [[27, "Calculate-hydrogen-bond-lifetimes"]], "Calculating the time constant": [[27, "Calculating-the-time-constant"]], "Intermittent lifetime": [[27, "Intermittent-lifetime"]], "Hydrogen bond lifetime of individual hydrogen bonds": [[27, "Hydrogen-bond-lifetime-of-individual-hydrogen-bonds"]], "Calculating hydrogen bonds: advanced selections": [[28, "Calculating-hydrogen-bonds:-advanced-selections"]], "Use guess_acceptors and guess_hydrogens to create atom selections": [[28, "Use-guess_acceptors-and-guess_hydrogens-to-create-atom-selections"]], "More advanced selections": [[28, "More-advanced-selections"]], "Hydrogen bonds between specific groups": [[28, "Hydrogen-bonds-between-specific-groups"]], "Analysing pore dimensions with HOLE2": [[30, "Analysing-pore-dimensions-with-HOLE2"]], "Using HOLE with a PDB file": [[30, "Using-HOLE-with-a-PDB-file"]], "Using HOLE with a trajectory": [[30, "Using-HOLE-with-a-trajectory"]], "Working with the data": [[30, "Working-with-the-data"]], "Visualising the VMD surface": [[30, "Visualising-the-VMD-surface"]], "Ordering HOLE profiles with an order parameter": [[30, "Ordering-HOLE-profiles-with-an-order-parameter"]], "Deleting HOLE files": [[30, "Deleting-HOLE-files"]], "Determining the persistence length of a polymer": [[31, "Determining-the-persistence-length-of-a-polymer"]], "Choosing the chains and backbone atoms": [[31, "Choosing-the-chains-and-backbone-atoms"]], "Calculating the persistence length": [[31, "Calculating-the-persistence-length"]], "Non-linear dimension reduction to diffusion maps": [[33, "Non-linear-dimension-reduction-to-diffusion-maps"]], "Diffusion maps": [[33, "Diffusion-maps"]], "Principal component analysis of a trajectory": [[34, "Principal-component-analysis-of-a-trajectory"]], "Principal component analysis": [[34, "Principal-component-analysis"]], "Visualising projections into a reduced dimensional space": [[34, "Visualising-projections-into-a-reduced-dimensional-space"]], "Measuring convergence with cosine content": [[34, "Measuring-convergence-with-cosine-content"]], "Average radial distribution functions": [[36, "Average-radial-distribution-functions"]], "Calculating the average radial distribution function for two groups of atoms": [[36, "Calculating-the-average-radial-distribution-function-for-two-groups-of-atoms"]], "Calculating the average radial distribution function for a group of atoms to itself": [[36, "Calculating-the-average-radial-distribution-function-for-a-group-of-atoms-to-itself"]], "Protein dihedral angle analysis": [[37, "Protein-dihedral-angle-analysis"]], "Selecting dihedral atom groups": [[37, "Selecting-dihedral-atom-groups"]], "Calculating dihedral angles": [[37, "Calculating-dihedral-angles"]], "Ramachandran analysis": [[37, "Ramachandran-analysis"]], "Janin analysis": [[37, "Janin-analysis"]], "Elastic network analysis": [[38, "Elastic-network-analysis"]], "Using a Gaussian network model": [[38, "Using-a-Gaussian-network-model"]], "Using a Gaussian network model with only close contacts": [[38, "Using-a-Gaussian-network-model-with-only-close-contacts"]], "Helix analysis": [[39, "Helix-analysis"], [39, "id6"]], "Running the analysis": [[39, "Running-the-analysis"]], "Calculating the RDF atom-to-atom": [[40, "Calculating-the-RDF-atom-to-atom"]], "Calculating the site-specific radial distribution function": [[40, "Calculating-the-site-specific-radial-distribution-function"]], "The site-specific RDF without densities": [[40, "The-site-specific-RDF-without-densities"]], "Calculating the Clustering Ensemble Similarity between ensembles": [[42, "Calculating-the-Clustering-Ensemble-Similarity-between-ensembles"]], "Calculating clustering similarity with default settings": [[42, "Calculating-clustering-similarity-with-default-settings"]], "Calculating clustering similarity with one method": [[42, "Calculating-clustering-similarity-with-one-method"]], "Calculating clustering similarity with multiple methods": [[42, "Calculating-clustering-similarity-with-multiple-methods"]], "Trying out different clustering parameters": [[42, "Trying-out-different-clustering-parameters"]], "Estimating the error in a clustering ensemble similarity analysis": [[42, "Estimating-the-error-in-a-clustering-ensemble-similarity-analysis"]], "Evaluating convergence": [[43, "Evaluating-convergence"]], "Evaluating convergence with similarity measures": [[43, "Evaluating-convergence-with-similarity-measures"]], "Using default arguments with clustering ensemble similarity": [[43, "Using-default-arguments-with-clustering-ensemble-similarity"]], "Comparing different clustering methods": [[43, "Comparing-different-clustering-methods"]], "Using default arguments with dimension reduction ensemble similarity": [[43, "Using-default-arguments-with-dimension-reduction-ensemble-similarity"]], "Comparing different dimensionality reduction methods": [[43, "Comparing-different-dimensionality-reduction-methods"]], "Calculating the Dimension Reduction Ensemble Similarity between ensembles": [[44, "Calculating-the-Dimension-Reduction-Ensemble-Similarity-between-ensembles"]], "Calculating dimension reduction similarity with default settings": [[44, "Calculating-dimension-reduction-similarity-with-default-settings"]], "Calculating dimension reduction similarity with one method": [[44, "Calculating-dimension-reduction-similarity-with-one-method"]], "Calculating dimension reduction similarity with multiple methods": [[44, "Calculating-dimension-reduction-similarity-with-multiple-methods"]], "Trying out different dimension reduction parameters": [[44, "Trying-out-different-dimension-reduction-parameters"]], "Estimating the error in a dimension reduction ensemble similarity analysis": [[44, "Estimating-the-error-in-a-dimension-reduction-ensemble-similarity-analysis"]], "Calculating the Harmonic Ensemble Similarity between ensembles": [[45, "Calculating-the-Harmonic-Ensemble-Similarity-between-ensembles"]], "Calculating harmonic similarity": [[45, "Calculating-harmonic-similarity"]], "Comparing the geometric similarity of trajectories": [[46, "Comparing-the-geometric-similarity-of-trajectories"]], "Aligning trajectories": [[46, "Aligning-trajectories"]], "Generating paths": [[46, "Generating-paths"]], "Hausdorff method": [[46, "Hausdorff-method"]], "Discrete Fr\u00e9chet distances": [[46, "Discrete-Fr\u00e9chet-distances"]], "Calculating the solvent density around a protein": [[48, "Calculating-the-solvent-density-around-a-protein"]], "Centering, aligning, and making molecules whole with on-the-fly transformations": [[48, "Centering,-aligning,-and-making-molecules-whole-with-on-the-fly-transformations"]], "Analysing the density of water around the protein": [[48, "Analysing-the-density-of-water-around-the-protein"]], "Visualisation": [[48, "Visualisation"]], "matplotlib (3D static plot)": [[48, "matplotlib-(3D-static-plot)"]], "nglview (interactive)": [[48, "nglview-(interactive)"]], "scikit-image (triangulated surface)": [[48, "scikit-image-(triangulated-surface)"]], "pyvista (3D surface)": [[48, "pyvista-(3D-surface)"]], "2D averaging": [[48, "2D-averaging"]], "Computing mass and charge density on each axis": [[49, "Computing-mass-and-charge-density-on-each-axis"]], "Constructing, modifying, and adding to a Universe": [[50, "Constructing,-modifying,-and-adding-to-a-Universe"]], "Creating and populating a Universe with water": [[50, "Creating-and-populating-a-Universe-with-water"]], "Creating a blank Universe": [[50, "Creating-a-blank-Universe"]], "Adding topology attributes": [[50, "Adding-topology-attributes"]], "Adding positions": [[50, "Adding-positions"]], "Adding bonds": [[50, "Adding-bonds"]], "Merging with a protein": [[50, "Merging-with-a-protein"]], "Adding a new segment": [[50, "Adding-a-new-segment"]], "Tiling into a larger Universe": [[50, "Tiling-into-a-larger-Universe"]], "Acknowledgments": [[50, "Acknowledgments"]], "Other": [[51, "other"]], "Using ParmEd with MDAnalysis and OpenMM to simulate a selection of atoms": [[52, "Using-ParmEd-with-MDAnalysis-and-OpenMM-to-simulate-a-selection-of-atoms"]], "Loading files: the difference between ParmEd and MDAnalysis": [[52, "Loading-files:-the-difference-between-ParmEd-and-MDAnalysis"]], "Using MDAnalysis to select atoms": [[52, "Using-MDAnalysis-to-select-atoms"]], "Using ParmEd and OpenMM to create a simulation system": [[52, "Using-ParmEd-and-OpenMM-to-create-a-simulation-system"]], "Quick start guide": [[53, "Quick-start-guide"]], "Overview": [[53, "Overview"]], "Loading a structure or trajectory": [[53, "Loading-a-structure-or-trajectory"]], "Working with groups of atoms": [[53, "Working-with-groups-of-atoms"]], "Selecting atoms": [[53, "Selecting-atoms"]], "Getting atom information from AtomGroups": [[53, "Getting-atom-information-from-AtomGroups"]], "AtomGroup positions and methods": [[53, "AtomGroup-positions-and-methods"]], "Working with trajectories": [[53, "Working-with-trajectories"]], "Dynamic selection": [[53, "Dynamic-selection"]], "Writing out coordinates": [[53, "Writing-out-coordinates"]], "Single frame": [[53, "Single-frame"]], "Trajectories": [[53, "Trajectories"], [56, "trajectories"], [111, "trajectories"]], "RMSD": [[53, "RMSD"]], "Automatic citations with duecredit": [[53, "Automatic-citations-with-duecredit"]], "Transformations": [[54, "transformations"]], "Centering a trajectory in the box": [[55, "Centering-a-trajectory-in-the-box"]], "Before transformation": [[55, "Before-transformation"]], "Unwrapping the protein": [[55, "Unwrapping-the-protein"]], "Centering in the box": [[55, "Centering-in-the-box"]], "Wrapping the solvent back into the box": [[55, "Wrapping-the-solvent-back-into-the-box"]], "Doing all this on-the-fly": [[55, "Doing-all-this-on-the-fly"]], "Frequently asked questions": [[56, "frequently-asked-questions"]], "Why do the atom positions change over trajectories?": [[56, "why-do-the-atom-positions-change-over-trajectories"]], "Auxiliary files": [[57, "auxiliary-files"]], "Supported formats": [[57, "supported-formats"]], "XVG Files": [[57, "xvg-files"]], "Reading data directly": [[57, "reading-data-directly"]], "Loading data into a Universe": [[57, "loading-data-into-a-universe"]], "Passing arguments to auxiliary data": [[57, "passing-arguments-to-auxiliary-data"]], "Iterating over auxiliary data": [[57, "iterating-over-auxiliary-data"]], "Accessing auxiliary attributes": [[57, "accessing-auxiliary-attributes"]], "Recreating auxiliaries": [[57, "recreating-auxiliaries"]], "EDR Files": [[57, "edr-files"]], "Standalone Usage": [[57, "standalone-usage"]], "Unit Handling": [[57, "unit-handling"]], "Use with Trajectories": [[57, "use-with-trajectories"]], "Selecting Trajectory Frames Based on Auxiliary Data": [[57, "selecting-trajectory-frames-based-on-auxiliary-data"]], "Memory Usage": [[57, "memory-usage"]], "Coordinates": [[58, "coordinates"], [61, "coordinates"]], "Table of supported coordinate readers and the information read": [[58, "id2"], [61, "id12"]], "Format reference": [[59, "format-reference"]], "Guessing": [[60, "guessing"]], "Masses": [[60, "masses"]], "Types": [[60, "types"]], "Bonds, Angles, Dihedrals, Impropers": [[60, "bonds-angles-dihedrals-impropers"]], "Format overview": [[61, "format-overview"]], "Table of all supported formats in MDAnalysis": [[61, "id10"]], "Topology": [[61, "topology"], [97, "topology"]], "Table of supported topology parsers and the attributes read": [[61, "id11"], [97, "id1"]], "chemfiles (chemfiles Trajectory or file)": [[62, "chemfiles-chemfiles-trajectory-or-file"]], "CONFIG (DL_Poly Config)": [[63, "config-dl-poly-config"]], "HISTORY (DL_Poly Config)": [[63, "history-dl-poly-config"]], "COOR, NAMBDIN (NAMD binary restart files)": [[64, "coor-nambdin-namd-binary-restart-files"]], "CRD (CHARMM CARD files)": [[65, "crd-charmm-card-files"]], "Reading in": [[65, "reading-in"], [66, "reading-in"], [67, "reading-in"], [70, "reading-in"], [71, "reading-in"], [72, "reading-in"], [76, "reading-in"], [79, "reading-in"], [81, "reading-in"], [82, "reading-in"], [83, "reading-in"], [84, "reading-in"], [85, "reading-in"], [88, "reading-in"], [89, "reading-in"], [94, "reading-in"], [95, "reading-in"]], "Writing out": [[65, "writing-out"], [66, "writing-out"], [67, "writing-out"], [71, "writing-out"], [79, "writing-out"], [81, "writing-out"], [82, "writing-out"], [83, "writing-out"], [89, "writing-out"]], "DATA (LAMMPS)": [[66, "data-lammps"]], "DCD (CHARMM, NAMD, or LAMMPS trajectory)": [[67, "dcd-charmm-namd-or-lammps-trajectory"]], "DCD (Flexible LAMMPS trajectory)": [[68, "dcd-flexible-lammps-trajectory"]], "DMS (Desmond Molecular Structure files)": [[69, "dms-desmond-molecular-structure-files"]], "GMS (Gamess trajectory)": [[70, "gms-gamess-trajectory"]], "GRO (GROMACS structure file)": [[71, "gro-gromacs-structure-file"]], "GSD (HOOMD GSD file)": [[72, "gsd-hoomd-gsd-file"]], "IN, FHIAIMS (FHI-aims input files)": [[73, "in-fhiaims-fhi-aims-input-files"]], "INPCRD, RESTRT (AMBER restart files)": [[74, "inpcrd-restrt-amber-restart-files"]], "ITP (GROMACS portable topology files)": [[75, "itp-gromacs-portable-topology-files"]], "LAMMPSDUMP (LAMMPS ascii dump file)": [[76, "lammpsdump-lammps-ascii-dump-file"]], "MMTF (Macromolecular Transmission Format)": [[77, "mmtf-macromolecular-transmission-format"]], "MOL2 (Tripos structure)": [[78, "mol2-tripos-structure"]], "MOL2 specification": [[78, "mol2-specification"]], "NCDF, NC (AMBER NetCDF trajectory)": [[79, "ncdf-nc-amber-netcdf-trajectory"]], "ParmEd (ParmEd Structure)": [[80, "parmed-parmed-structure"]], "PDB, ENT (Standard PDB file)": [[81, "pdb-ent-standard-pdb-file"]], "PDB specification": [[81, "pdb-specification"]], "CRYST1 fields": [[81, "id1"]], "ATOM/HETATM fields": [[81, "id2"]], "PDBQT (Autodock structure)": [[82, "pdbqt-autodock-structure"]], "PDBQT specification": [[82, "pdbqt-specification"]], "PDB format with AutoDOCK extensions for the PDBQT format.": [[82, "id1"]], "PQR file (PDB2PQR / APBS)": [[83, "pqr-file-pdb2pqr-apbs"]], "PQR specification": [[83, "pqr-specification"]], "PSF (CHARMM, NAMD, or XPLOR protein structure file)": [[84, "psf-charmm-namd-or-xplor-protein-structure-file"]], "PSF specification": [[84, "psf-specification"]], "TNG (Trajectory Next Generation)": [[85, "tng-trajectory-next-generation"]], "TOP, PRMTOP, PARM7 (AMBER topology)": [[86, "top-prmtop-parm7-amber-topology"]], "AMBER specification": [[86, "amber-specification"]], "Attributes parsed from AMBER keywords": [[86, "id1"]], "Developer notes": [[86, "developer-notes"], [87, "developer-notes"], [89, "developer-notes"], [91, "developer-notes"]], "TPR (GROMACS run topology files)": [[87, "tpr-gromacs-run-topology-files"]], "Supported versions": [[87, "supported-versions"]], "TPR format versions and generations read by MDAnalysis.topology.TPRParser.parse().": [[87, "id1"]], "TPR specification": [[87, "tpr-specification"]], "segid and chainID": [[87, "segid-and-chainid"]], "Bonds": [[87, "bonds"]], "GROMACS entries used to create bonds.": [[87, "id2"]], "GROMACS entries used to create angles.": [[87, "id3"]], "GROMACS entries used to create dihedrals.": [[87, "id4"]], "GROMACS entries used to create improper dihedrals.": [[87, "id5"]], "TRJ, MDCRD, CRDBOX (AMBER ASCII trajectory)": [[88, "trj-mdcrd-crdbox-amber-ascii-trajectory"]], "TRR (GROMACS lossless trajectory file)": [[89, "trr-gromacs-lossless-trajectory-file"]], "TRZ (IBIsCO and YASP trajectory)": [[90, "trz-ibisco-and-yasp-trajectory"]], "TXYZ, ARC (Tinker)": [[91, "txyz-arc-tinker"]], "XML (HOOMD)": [[92, "xml-hoomd"]], "XPDB (Extended PDB file)": [[93, "xpdb-extended-pdb-file"]], "XTC (GROMACS compressed trajectory file)": [[94, "xtc-gromacs-compressed-trajectory-file"]], "XYZ trajectory": [[95, "xyz-trajectory"]], "XYZ specification": [[95, "xyz-specification"]], "Note": [[95, "note"]], "Selection exporters": [[96, "selection-exporters"]], "Supported selection exporters": [[96, "id2"]], "Writing selections": [[96, "writing-selections"]], "Single AtomGroup": [[96, "single-atomgroup"]], "Multiple selections": [[96, "multiple-selections"]], "Reading in selections": [[96, "reading-in-selections"]], "Groups of atoms": [[98, "groups-of-atoms"]], "Residues and Segments": [[98, "residues-and-segments"]], "Use case: Sequence of residues by segment": [[98, "use-case-sequence-of-residues-by-segment"]], "Use case: Atoms list grouped by residues": [[98, "use-case-atoms-list-grouped-by-residues"]], "Fragments": [[98, "fragments"]], "Welcome to MDAnalysis User Guide\u2019s documentation!": [[99, "welcome-to-mdanalysis-user-guide-s-documentation"]], "Why MDAnalysis?": [[99, "why-mdanalysis"]], "Participating": [[99, "participating"]], "Communications": [[99, "communications"]], "Installation": [[100, "installation"]], "conda": [[100, "conda"]], "pip": [[100, "pip"]], "Development versions": [[100, "development-versions"]], "Testing": [[100, "testing"]], "Custom compiler flags and optimised installations": [[100, "custom-compiler-flags-and-optimised-installations"]], "Additional datasets": [[100, "additional-datasets"]], "Module imports in MDAnalysis": [[101, "module-imports-in-mdanalysis"]], "General rules for importing": [[101, "general-rules-for-importing"]], "Module imports in MDAnalysis.analysis": [[101, "module-imports-in-mdanalysis-analysis"]], "Module imports in the test suite": [[101, "module-imports-in-the-test-suite"]], "Module dependencies in the code": [[101, "module-dependencies-in-the-code"]], "List of core module dependencies": [[101, "list-of-core-module-dependencies"]], "Modules in the \u201ccore\u201d": [[101, "modules-in-the-core"]], "Optional modules in MDAnalysis.analysis and MDAnalysis.visualization": [[101, "optional-modules-in-mdanalysis-analysis-and-mdanalysis-visualization"]], "Preparing a release": [[102, "preparing-a-release"]], "Release policy and release numbering": [[102, "release-policy-and-release-numbering"]], "Typical workflow for preparing a release": [[102, "typical-workflow-for-preparing-a-release"]], "Summary of tasks": [[102, "summary-of-tasks"]], "Getting the develop branch ready for a release": [[102, "getting-the-develop-branch-ready-for-a-release"]], "Packaging the release": [[102, "packaging-the-release"]], "Completing the release": [[102, "completing-the-release"]], "Manually upload Cirrus CI wheels (temporary)": [[102, "manually-upload-cirrus-ci-wheels-temporary"]], "Update conda-forge packages": [[102, "update-conda-forge-packages"]], "Create a release of the UserGuide": [[102, "create-a-release-of-the-userguide"]], "Create a blog post outlining the release": [[102, "create-a-blog-post-outlining-the-release"]], "Increment develop branch files ready for the next version": [[102, "increment-develop-branch-files-ready-for-the-next-version"]], "Clean up old developer builds of the documentation": [[102, "clean-up-old-developer-builds-of-the-documentation"]], "Reading and writing files": [[103, "reading-and-writing-files"]], "Input": [[103, "input"]], "Reading multiple trajectories": [[103, "reading-multiple-trajectories"]], "Trajectory formats": [[103, "trajectory-formats"]], "In-memory trajectories": [[103, "in-memory-trajectories"]], "Reading trajectories into memory": [[103, "reading-trajectories-into-memory"]], "Transferring trajectories into memory": [[103, "transferring-trajectories-into-memory"]], "Building trajectories in memory": [[103, "building-trajectories-in-memory"]], "In-memory trajectories of an atom selection": [[103, "in-memory-trajectories-of-an-atom-selection"]], "Output": [[103, "output"]], "Frames and trajectories": [[103, "frames-and-trajectories"]], "Pickling": [[103, "pickling"]], "Citations using Duecredit": [[104, "citations-using-duecredit"]], "MDAnalysis Release Notes": [[105, "mdanalysis-release-notes"]], "Release 2.6.1 of MDAnalysis": [[105, "release-2-6-1-of-mdanalysis"]], "Bug fixes and changes": [[105, "bug-fixes-and-changes"]], "New Contributors": [[105, "new-contributors"], [105, "id1"], [105, "id5"], [105, "id13"]], "Release 2.6.0 of MDAnalysis": [[105, "release-2-6-0-of-mdanalysis"]], "Major changes:": [[105, "major-changes"], [105, "id2"], [105, "id8"], [105, "id14"], [105, "id19"], [105, "id24"], [105, "id30"]], "Fixes:": [[105, "fixes"], [105, "id3"], [105, "id9"], [105, "id15"], [105, "id21"], [105, "id26"], [105, "id32"]], "Enhancements:": [[105, "enhancements"], [105, "id16"], [105, "id20"], [105, "id25"], [105, "id31"]], "Changes:": [[105, "changes"], [105, "id4"], [105, "id11"], [105, "id17"], [105, "id22"], [105, "id27"], [105, "id33"]], "Deprecations:": [[105, "deprecations"], [105, "id12"], [105, "id18"], [105, "id23"], [105, "id28"], [105, "id34"]], "Release 2.5.0 of MDAnalysis": [[105, "release-2-5-0-of-mdanalysis"]], "Enchancements:": [[105, "enchancements"], [105, "id10"]], "Release 2.4.3 of MDAnalysis": [[105, "release-2-4-3-of-mdanalysis"]], "Bug fixes": [[105, "bug-fixes"], [105, "id6"], [105, "id7"]], "Release 2.4.2 of MDAnalysis": [[105, "release-2-4-2-of-mdanalysis"]], "Release 2.4.1 of MDAnalysis": [[105, "release-2-4-1-of-mdanalysis"]], "Release 2.4.0 of MDAnalysis": [[105, "release-2-4-0-of-mdanalysis"]], "Release 2.3.0 of MDAnalysis": [[105, "release-2-3-0-of-mdanalysis"]], "CZI EOSS Performance Improvements:": [[105, "czi-eoss-performance-improvements"]], "Release 2.2.0 of MDAnalysis": [[105, "release-2-2-0-of-mdanalysis"]], "Known test failures:": [[105, "known-test-failures"], [105, "id29"]], "Release 2.1.0 of MDAnalysis": [[105, "release-2-1-0-of-mdanalysis"]], "Release 2.0.0 of MDAnalysis": [[105, "release-2-0-0-of-mdanalysis"]], "Notes:": [[105, "notes"]], "Known issues:": [[105, "known-issues"]], "Selection Keywords": [[106, "selection-keywords"]], "Simple selections": [[106, "simple-selections"]], "Boolean": [[106, "boolean"]], "Geometric": [[106, "geometric"]], "Similarity and connectivity": [[106, "similarity-and-connectivity"]], "Index": [[106, "index"]], "Preexisting selections and modifiers": [[106, "preexisting-selections-and-modifiers"]], "Dynamic selections": [[106, "dynamic-selections"]], "Ordered selections": [[106, "ordered-selections"]], "Standard residues in MDAnalysis selections": [[107, "standard-residues-in-mdanalysis-selections"]], "Proteins": [[107, "proteins"]], "Protein backbone": [[107, "protein-backbone"]], "Nucleic acids": [[107, "nucleic-acids"]], "Nucleic backbone": [[107, "nucleic-backbone"]], "Nucleobases": [[107, "nucleobases"]], "Nucleic sugars": [[107, "nucleic-sugars"]], "Tests in MDAnalysis": [[108, "tests-in-mdanalysis"]], "Running the test suite": [[108, "running-the-test-suite"]], "Testing in parallel": [[108, "testing-in-parallel"]], "Test coverage": [[108, "test-coverage"]], "Continuous Integration tools": [[108, "continuous-integration-tools"]], "GitHub Actions": [[108, "github-actions"]], "Azure": [[108, "azure"]], "Cirrus CI": [[108, "cirrus-ci"]], "Codecov": [[108, "codecov"]], "Writing new tests": [[108, "writing-new-tests"]], "General conventions": [[108, "general-conventions"]], "Assertions": [[108, "assertions"]], "Testing exceptions and warnings": [[108, "testing-exceptions-and-warnings"]], "Failing tests": [[108, "failing-tests"]], "Skipping tests": [[108, "skipping-tests"]], "Fixtures": [[108, "fixtures"]], "Testing the same function with different inputs": [[108, "testing-the-same-function-with-different-inputs"]], "Temporary files and directories": [[108, "temporary-files-and-directories"]], "Module imports": [[108, "module-imports"]], "Tests for analysis and visualization modules": [[108, "tests-for-analysis-and-visualization-modules"]], "Using test data files": [[108, "using-test-data-files"]], "The topology system": [[109, "the-topology-system"]], "Topology attributes": [[109, "topology-attributes"]], "Canonical attributes": [[109, "canonical-attributes"]], "Format-specific attributes": [[109, "format-specific-attributes"]], "Connectivity information": [[109, "connectivity-information"]], "Adding TopologyAttrs": [[109, "adding-topologyattrs"]], "Modifying TopologyAttrs": [[109, "modifying-topologyattrs"]], "Default values and attribute levels": [[109, "default-values-and-attribute-levels"]], "Topology objects": [[109, "topology-objects"]], "Adding to a Universe": [[109, "adding-to-a-universe"]], "Creating with an AtomGroup": [[109, "creating-with-an-atomgroup"]], "Deleting from a Universe": [[109, "deleting-from-a-universe"]], "Topology-specific methods": [[109, "topology-specific-methods"]], "Slicing trajectories": [[110, "slicing-trajectories"]], "On-the-fly transformations": [[112, "on-the-fly-transformations"]], "Example workflows": [[112, "example-workflows"]], "Custom transformations": [[112, "custom-transformations"]], "Units and constants": [[113, "units-and-constants"]], "Base units in MDAnalysis": [[113, "id1"]], "Unit conversion": [[113, "unit-conversion"]], "Constants": [[113, "constants"]], "Length": [[113, "length"]], "Density": [[113, "density"]], "Time": [[113, "time"]], "Charge": [[113, "charge"]], "Speed": [[113, "speed"]], "Force": [[113, "force"]], "Energy": [[113, "energy"]], "Universe": [[114, "universe"]], "Creating a Universe": [[114, "creating-a-universe"]], "Loading from files": [[114, "loading-from-files"]], "Constructing from AtomGroups": [[114, "constructing-from-atomgroups"]], "Constructing from scratch": [[114, "constructing-from-scratch"]], "Guessing topology attributes": [[114, "guessing-topology-attributes"]], "Universe properties and methods": [[114, "universe-properties-and-methods"]]}, "indexentries": {"x y z": [[83, "term-X-Y-Z"]], "atomname": [[83, "term-atomName"]], "chainid": [[83, "term-chainID"]], "charge": [[83, "term-charge"]], "radius": [[83, "term-radius"]], "recordname": [[83, "term-recordName"]], "residuename": [[83, "term-residueName"]], "residuenumber": [[83, "term-residueNumber"]], "serial": [[83, "term-serial"]], "duecredit_enable": [[104, "index-0"]], "environment variable": [[104, "index-0"]]}})
\ No newline at end of file
diff --git a/dev/standard_selections.html b/dev/standard_selections.html
index 9fbbd77ab..770c418d8 100644
--- a/dev/standard_selections.html
+++ b/dev/standard_selections.html
@@ -332,9 +332,9 @@ Protein backbone
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@@ -399,10 +399,10 @@ Nucleic backbone
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-C5
-N7
@@ -439,11 +439,11 @@ Nucleic sugars
-C1’
-O4’
-C3’
-C4’
+C4’
C2’
+C1’
+C3’
+O4’
diff --git a/dev/testing.html b/dev/testing.html
index c156cdfde..de96bd59a 100644
--- a/dev/testing.html
+++ b/dev/testing.html
@@ -433,13 +433,13 @@ Testing exceptions and warnings
Failing tests
-To mark an expected failure, use pytest.mark.xfail()
decorator:
+To mark an expected failure, use pytest.mark.xfail()
decorator:
@pytest.mark.xfail
def tested_expected_failure():
assert 1 == 2
-To manually fail a test, make a call to pytest.fail()
:
+To manually fail a test, make a call to pytest.fail()
:
def test_open(self, tmpdir):
outfile = str(tmpdir.join('lammps-writer-test.dcd'))
try:
@@ -452,7 +452,7 @@ Failing tests
Skipping tests
-To skip tests based on a condition, use pytest.mark.skipif(condition)
decorator:
+To skip tests based on a condition, use pytest.mark.skipif(condition)
decorator:
import numpy as np
try:
from numpy import shares_memory
@@ -467,7 +467,7 @@ Skipping testsassert not np.shares_memory(original.ts.positions, copy.ts.positions)
-To skip a test if a module is not available for importing, use pytest.importorskip('module_name')
+To skip a test if a module is not available for importing, use pytest.importorskip('module_name')
def test_write_trajectory_netCDF4(self, universe, outfile):
pytest.importorskip("netCDF4")
return self._test_write_trajectory(universe, outfile)
@@ -477,7 +477,7 @@ Skipping tests
Fixtures
-Use fixtures as much as possible to reuse “resources” between test methods/functions. Pytest fixtures are functions that run before each test function that uses that fixture. A fixture is typically set up with the pytest.fixture()
decorator, over a function that returns the object you need:
+Use fixtures as much as possible to reuse “resources” between test methods/functions. Pytest fixtures are functions that run before each test function that uses that fixture. A fixture is typically set up with the pytest.fixture()
decorator, over a function that returns the object you need:
@pytest.fixture
def universe(self):
return mda.Universe(self.ref_filename)