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AnalysisRunner #1661

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merged 7 commits into from
Jun 18, 2024
Merged

AnalysisRunner #1661

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AnalysisRunner
hainm Jun 18, 2024
be12796
more
hainm Jun 18, 2024
fbd9853
fix
hainm Jun 18, 2024
fb128ca
fix more
hainm Jun 18, 2024
a620671
fix
hainm Jun 18, 2024
a4253dc
small refactoring
hainm Jun 18, 2024
2d60660
restore rotdif
hainm Jun 18, 2024
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146 changes: 78 additions & 68 deletions pytraj/all_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,38 @@ class DatasetType(StrEnum):
MODES = 'modes'
XYMESH = 'xymesh'
MATRIX3x3 = 'matrix3x3'

MATRIX_DBL = 'matrix_dbl'

class AnalysisRunner:
def __init__(self, analysis_class):
self.datasets = CpptrajDatasetList()
self.analysis = analysis_class()

def add_dataset(self, dataset_type, dataset_name, data, aspect=None):
if dataset_type == DatasetType.COORDS:
crdname = '_DEFAULTCRD_'
self.datasets.add(dataset_type.value, name=crdname)
self.datasets[0].top = data.top
for frame in data:
self.datasets[0].append(frame)
else:
dataset = self.datasets.add(dataset_type, dataset_name)
if dataset_type == DatasetType.XYMESH:
dataset._append_from_array(data)
elif dataset_type == DatasetType.MATRIX_DBL:
dataset.data = np.asarray(data).astype('f8')
elif dataset_type == DatasetType.MODES:
# For MODES, we don't set the data immediately
pass
elif dataset_type == DatasetType.MATRIX3x3:
dataset.aspect = aspect
dataset._append_from_array(data)
else:
dataset.data = np.asarray(data).astype('f8')

def run_analysis(self, command):
self.analysis(command, dslist=self.datasets)
return self.datasets


def _assert_mutable(trajiter):
Expand Down Expand Up @@ -1935,18 +1966,14 @@ def timecorr(vec0, vec1, order=2, tstep=1., tcorr=10000., norm=False, dtype='nda
norm : bool, default False
dtype : str, default 'ndarray'
"""
time_correlation_action = c_analysis.Analysis_Timecorr()
action_datasets = CpptrajDatasetList()

action_datasets.add(DatasetType.VECTOR, "_vec0")
action_datasets.add(DatasetType.VECTOR, "_vec1")
action_datasets[0].data = np.asarray(vec0).astype('f8')
action_datasets[1].data = np.asarray(vec1).astype('f8')
runner = AnalysisRunner(c_analysis.Analysis_Timecorr)
runner.add_dataset(DatasetType.VECTOR, "_vec0", vec0)
runner.add_dataset(DatasetType.VECTOR, "_vec1", vec1)

command = f"vec1 _vec0 vec2 _vec1 order {order} tstep {tstep} tcorr {tcorr} {'norm' if norm else ''}"
time_correlation_action(command, dslist=action_datasets)
runner.run_analysis(command)

return get_data_from_dtype(action_datasets[2:], dtype=dtype)
return get_data_from_dtype(runner.datasets[2:], dtype=dtype)


@super_dispatch()
Expand Down Expand Up @@ -2079,17 +2106,13 @@ def crank(data0, data1, mode='distance', dtype='ndarray'):
-----
Same as `crank` in cpptraj
"""
action_datasets = CpptrajDatasetList()
action_datasets.add(DatasetType.DOUBLE, "d0")
action_datasets.add(DatasetType.DOUBLE, "d1")

action_datasets[0].data = np.asarray(data0)
action_datasets[1].data = np.asarray(data1)
runner = AnalysisRunner(c_analysis.Analysis_CrankShaft)
runner.add_dataset(DatasetType.DOUBLE, "d0", data0)
runner.add_dataset(DatasetType.DOUBLE, "d1", data1)

act = c_analysis.Analysis_CrankShaft()
command = ' '.join((mode, 'd0', 'd1'))
with capture_stdout() as (out, err):
act(command, dslist=action_datasets)
runner.run_analysis(command)
return out.read()


Expand Down Expand Up @@ -2768,14 +2791,12 @@ def lowestcurve(data, points=10, step=0.2):

data = np.asarray(data).T

action_datasets = CpptrajDatasetList()
action_datasets.add(DatasetType.XYMESH, 'mydata')
action_datasets[0]._append_from_array(data)
runner = AnalysisRunner(c_analysis.Analysis_LowestCurve)
runner.add_dataset(DatasetType.XYMESH, 'mydata', data)

analysis_lowest_curve = c_analysis.Analysis_LowestCurve()
analysis_lowest_curve(command, dslist=action_datasets)
runner.run_analysis(command)

return np.array([action_datasets[-1]._xcrd(), np.array(action_datasets[-1].values)])
return np.array([runner.datasets[-1]._xcrd(), np.array(runner.datasets[-1].values)])


def acorr(data, dtype='ndarray', option=''):
Expand All @@ -2793,15 +2814,13 @@ def acorr(data, dtype='ndarray', option=''):
-----
Same as `autocorr` in cpptraj
"""
action_datasets = CpptrajDatasetList()
action_datasets.add(DatasetType.DOUBLE, "d0")

action_datasets[0].data = np.asarray(data)
runner = AnalysisRunner(c_analysis.Analysis_AutoCorr)
runner.add_dataset(DatasetType.DOUBLE, "d0", np.asarray(data))

act = c_analysis.Analysis_AutoCorr()
command = "d0 out _tmp.out"
act(command, dslist=action_datasets)
return get_data_from_dtype(action_datasets[1:], dtype=dtype)
runner.run_analysis(command)

return get_data_from_dtype(runner.datasets[1:], dtype=dtype)


auto_correlation_function = acorr
Expand All @@ -2820,17 +2839,14 @@ def xcorr(data0, data1, dtype='ndarray'):
-----
Same as `corr` in cpptraj
"""
runner = AnalysisRunner(c_analysis.Analysis_Corr)
runner.add_dataset(DatasetType.DOUBLE, "d0", np.asarray(data0))
runner.add_dataset(DatasetType.DOUBLE, "d1", np.asarray(data1))

action_datasets = CpptrajDatasetList()
action_datasets.add(DatasetType.DOUBLE, "d0")
action_datasets.add(DatasetType.DOUBLE, "d1")

action_datasets[0].data = np.asarray(data0)
action_datasets[1].data = np.asarray(data1)
command = "d0 d1 out _tmp.out"
runner.run_analysis(command)

act = c_analysis.Analysis_Corr()
act("d0 d1 out _tmp.out", dslist=action_datasets)
return get_data_from_dtype(action_datasets[2:3], dtype=dtype)
return get_data_from_dtype(runner.datasets[2:3], dtype=dtype)


cross_correlation_function = xcorr
Expand Down Expand Up @@ -2898,6 +2914,7 @@ def strip(obj, mask):
raise ValueError('object must be either Trajectory or Topology')


# FIXME: use AnalysisRunner
def rotdif(matrices, command):
"""

Expand All @@ -2920,11 +2937,13 @@ def rotdif(matrices, command):
# TODO: update this method if cpptraj dumps data to CpptrajDatasetList
matrices = np.asarray(matrices)


action_datasets = CpptrajDatasetList()
action_datasets.add(DatasetType.MATRIX3x3, name='myR0')
action_datasets[-1].aspect = "RM"
action_datasets[-1]._append_from_array(matrices)


command = 'rmatrix myR0[RM] ' + command
act = c_analysis.Analysis_Rotdif()
with capture_stdout() as (out, _):
Expand Down Expand Up @@ -2964,19 +2983,11 @@ def wavelet(traj, command):
>>> command = ' '.join((c0, c1))
>>> wavelet_dict = pt.wavelet(traj, command)
"""

action_datasets = CpptrajDatasetList()
crdname = '_DEFAULTCRD_'
action_datasets.add(DatasetType.COORDS.value, name=crdname)
action_datasets[0].top = traj.top

for frame in traj:
action_datasets[0].append(frame)

act = c_analysis.Analysis_Wavelet()
act(command, dslist=action_datasets)
action_datasets.remove_set(action_datasets[crdname])
return get_data_from_dtype(action_datasets, dtype='dict')
runner = AnalysisRunner(c_analysis.Analysis_Wavelet)
runner.add_dataset(DatasetType.COORDS, "_DEFAULTCRD_", traj)
runner.run_analysis(command)
runner.datasets.remove_set(runner.datasets["_DEFAULTCRD_"])
return get_data_from_dtype(runner.datasets, dtype='dict')


def atom_map(traj, ref, rmsfit=False):
Expand Down Expand Up @@ -3123,19 +3134,21 @@ def analyze_modes(mode_type,
scalar_type='mwcovar',
options='',
dtype='dict'):
analysis_modes = c_analysis.Analysis_Modes()
action_datasets = CpptrajDatasetList()
runner = AnalysisRunner(c_analysis.Analysis_Modes)
my_modes = 'my_modes'
modes = action_datasets.add('modes', name=my_modes)
runner.add_dataset(DatasetType.MODES, my_modes, None)

modes = runner.datasets[-1]
modes.scalar_type = scalar_type
# cpptraj will use natoms = modes.NavgCrd()
modes._allocate_avgcoords(eigenvectors.shape[1])
modes._set_modes(False, eigenvectors.shape[0], eigenvectors.shape[1],
eigenvalues, eigenvectors.flatten())

command = ' '.join((mode_type, 'name {}'.format(my_modes), options))
analysis_modes(command, dslist=action_datasets)
action_datasets._pop(0)
return get_data_from_dtype(action_datasets, dtype=dtype)
runner.run_analysis(command)

runner.datasets._pop(0)
return get_data_from_dtype(runner.datasets, dtype=dtype)


def ti(fn, options=''):
Expand Down Expand Up @@ -3168,7 +3181,6 @@ def ti(fn, options=''):

def hausdorff(matrix, options='', dtype='ndarray'):
"""

Parameters
----------
matrix : 2D array
Expand All @@ -3183,17 +3195,15 @@ def hausdorff(matrix, options='', dtype='ndarray'):
-----
- cpptraj help: pytraj.info('hausdorff')
"""
action_datasets = CpptrajDatasetList()
matrix_dataset = action_datasets.add('matrix_dbl', name='my_matrix')
matrix_dataset.data = np.asarray(matrix, dtype='f8')
runner = AnalysisRunner(c_analysis.Analysis_Hausdorff)
runner.add_dataset(DatasetType.MATRIX_DBL, "my_matrix", matrix)

act = c_analysis.Analysis_Hausdorff()
command = f"my_matrix {options}"
act(command, dslist=action_datasets)
runner.run_analysis(command)

action_datasets._pop(0)
runner.datasets._pop(0)

data = get_data_from_dtype(action_datasets, dtype)
data = get_data_from_dtype(runner.datasets, dtype)
return data


Expand Down
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