def test_from_dataframe():
import numpy as np
import pandas
a = np.random.random([100, 3]) * 100
atoms = pandas.DataFrame(a, columns=['z', 'y', 'x'])
s = coarseactin.Scene(atoms)
assert s['x'][20] == atoms['x'][20]
close_abps = list(set([mean_abp_paired.index[random.choice([a, b])] for a, b in pairs_colliding]))
close_abps.sort()
sel = mean_abp_paired.index.difference(close_abps)
mean_abp_paired = mean_abp_paired.loc[sel]
print('Number of pairs:', len(mean_abp_paired))
pairs_colliding = cKDTree(mean_abp_paired).query_pairs(colliding_distance*1.5)
print('Number of collisions:', len(pairs_colliding))
print(pairs_colliding)
full_model = full_model[full_model['chain_index'].isin(mean_abp_paired.index) | full_model['resName'].isin(['ACT', 'ACD'])]
print('Rejoining model')
# Split and rejoin the model so that ABPs are on a different chain
full_model = coarseactin.Scene(full_model.sort_values(['chainID', 'resSeq', 'name']))
full_model_actin = coarseactin.Scene(full_model[full_model['resName'].isin(['ACT', 'ACD'])])
full_model_abps = coarseactin.Scene(full_model[~full_model['resName'].isin(['ACT', 'ACD'])])
# full_model_abps['chainID'] = 'A'
full_model = coarseactin.Scene.concatenate([full_model_actin, full_model_abps])
full_model.write_cif(f'{Sname}.cif', verbose=True)
##############
# Simulation #
##############
import sys
sys.path.insert(0, '.')
import openmm
import openmm.app
def test_from_matrix():
s = coarseactin.Scene([[0, 0, 0], [0, 0, 1]])
assert len(s) == 2
def test_from_numpy():
import numpy as np
a = np.random.random([100, 3]) * 100
s = coarseactin.Scene(a)
assert len(s) == 100