13726590978

Committed 07 Mar 2025 06:07PM UTC coverage: 1.726%. First build

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Merge pull request #45 from CCPBioSim/39-implement-ci-pipeline-pre-commit-hooks

Implement CI pipeline pre-commit hooks

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27 of 1135 new or added lines in 23 files covered. (2.38%)

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8.0

/CodeEntropy/ConformationFunctions.py

import numpy as np

# from MDAnalysis.analysis.dihedrals import Dihedral


def assign_conformation(
    data_container, dihedral, number_frames, bin_width, start, end, step
):
    """
    Create a state vector, showing the state in which the input dihedral is
    as a function of time. The function creates a histogram from the timeseries of the
    dihedral angle values and identifies points of dominant occupancy
    (called CONVEX TURNING POINTS).
    Based on the identified TPs, states are assigned to each configuration of the
    dihedral.

    Input
    -----
    dihedral_atom_group : the group of 4 atoms defining the dihedral
    number_frames : number of frames in the trajectory
    bin_width : the width of the histogram bit, default 30 degrees
    start : int, starting frame, will default to 0
    end : int, ending frame, will default to -1 (last frame in trajectory)
    step : int, spacing between frames, will default to 1

    Return
    ------
    A timeseries with integer labels describing the state at each point in time.

    """
    conformations = np.zeros(number_frames)
    phi = np.zeros(number_frames)

    # get the values of the angle for the dihedral
    # dihedral angle values have a range from -180 to 180
    for timestep in data_container.trajectory[start:end:step]:
        value = dihedral.value()
        # we want postive values in range 0 to 360 to make the peak assignment work
        # using the fact that dihedrals have circular symetry
        # (i.e. -15 degrees = +345 degrees)
        if value < 0:
            value += 360
        phi[timestep.frame] = value

    # create a histogram using numpy
    number_bins = int(360 / bin_width)
    popul, bin_edges = np.histogram(a=phi, bins=number_bins, range=(0, 360))
    bin_value = [0.5 * (bin_edges[i] + bin_edges[i + 1]) for i in range(0, len(popul))]

    # identify "convex turning-points" and populate a list of peaks
    # peak : a bin whose neighboring bins have smaller population
    # NOTE might have problems if the peak is wide with a flat or sawtooth top
    peak_values = []

    for bin_index in range(number_bins):
        # if there is no dihedrals in a bin then it cannot be a peak
        if popul[bin_index] == 0:
            pass
        # being careful of the last bin
        # (dihedrals have circular symmetry, the histogram does not)
        elif (
            bin_index == number_bins - 1
        ):  # the -1 is because the index starts with 0 not 1
            if (
                popul[bin_index] >= popul[bin_index - 1]
                and popul[bin_index] >= popul[0]
            ):
                peak_values.append(bin_value[bin_index])
        else:
            if (
                popul[bin_index] >= popul[bin_index - 1]
                and popul[bin_index] >= popul[bin_index + 1]
            ):
                peak_values.append(bin_value[bin_index])

    # go through each frame again and assign conformation state
    for frame in range(number_frames):
        # find the TP that the snapshot is least distant from
        distances = [abs(phi[frame] - peak) for peak in peak_values]
        conformations[frame] = np.argmin(distances)

    return conformations


# END assign_conformation

1	import numpy as np	3✔
2
3	# from MDAnalysis.analysis.dihedrals import Dihedral
4
5
6	def assign_conformation(	3✔
7	data_container, dihedral, number_frames, bin_width, start, end, step
8	):
9	"""
10	Create a state vector, showing the state in which the input dihedral is
11	as a function of time. The function creates a histogram from the timeseries of the
12	dihedral angle values and identifies points of dominant occupancy
13	(called CONVEX TURNING POINTS).
14	Based on the identified TPs, states are assigned to each configuration of the
15	dihedral.
16
17	Input
18	-----
19	dihedral_atom_group : the group of 4 atoms defining the dihedral
20	number_frames : number of frames in the trajectory
21	bin_width : the width of the histogram bit, default 30 degrees
22	start : int, starting frame, will default to 0
23	end : int, ending frame, will default to -1 (last frame in trajectory)
24	step : int, spacing between frames, will default to 1
25
26	Return
27	------
28	A timeseries with integer labels describing the state at each point in time.
29
30	"""
NEW 31	conformations = np.zeros(number_frames)	×
NEW 32	phi = np.zeros(number_frames)	×
33
34	# get the values of the angle for the dihedral
35	# dihedral angle values have a range from -180 to 180
36	for timestep in data_container.trajectory[start:end:step]:	×
37	value = dihedral.value()	×
38	# we want postive values in range 0 to 360 to make the peak assignment work
39	# using the fact that dihedrals have circular symetry
40	# (i.e. -15 degrees = +345 degrees)
41	if value < 0:	×
42	value += 360	×
43	phi[timestep.frame] = value	×
44
45	# create a histogram using numpy
NEW 46	number_bins = int(360 / bin_width)	×
NEW 47	popul, bin_edges = np.histogram(a=phi, bins=number_bins, range=(0, 360))	×
NEW 48	bin_value = [0.5 * (bin_edges[i] + bin_edges[i + 1]) for i in range(0, len(popul))]	×
49
50	# identify "convex turning-points" and populate a list of peaks
51	# peak : a bin whose neighboring bins have smaller population
52	# NOTE might have problems if the peak is wide with a flat or sawtooth top
53	peak_values = []	×
54
55	for bin_index in range(number_bins):	×
56	# if there is no dihedrals in a bin then it cannot be a peak
57	if popul[bin_index] == 0:	×
58	pass	×
59	# being careful of the last bin
60	# (dihedrals have circular symmetry, the histogram does not)
NEW 61	elif (	×
62	bin_index == number_bins - 1
63	): # the -1 is because the index starts with 0 not 1
NEW 64	if (	×
65	popul[bin_index] >= popul[bin_index - 1]
66	and popul[bin_index] >= popul[0]
67	):
68	peak_values.append(bin_value[bin_index])	×
69	else:
NEW 70	if (	×
71	popul[bin_index] >= popul[bin_index - 1]
72	and popul[bin_index] >= popul[bin_index + 1]
73	):
74	peak_values.append(bin_value[bin_index])	×
75
76	# go through each frame again and assign conformation state
77	for frame in range(number_frames):	×
78	# find the TP that the snapshot is least distant from
79	distances = [abs(phi[frame] - peak) for peak in peak_values]	×
NEW 80	conformations[frame] = np.argmin(distances)	×
81
82	return conformations	×
83
84
85	# END assign_conformation

CCPBioSim / CodeEntropy / 13726590978

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