def kshv_pos_selection(base_path: str, generations: int = 50) -> float:
"""Simulate KSHV with and without positive selection and plot the results.
Args:
base_path (str): path into which the data should be saved
pos_coefficient (float, optional): the nonzero positive selection coefficient to use. Defaults to 0.10.
generations (int, optional): the number of generations to simulate. Defaults to 50.
Returns:
float: the fraction of cells with zero plasmids per cell after generations
"""
kshv_pop = lpp.LatentPlasmidPopulation()
kshv_pop.set_virus('kshv')
# Set up a large population and burn in
kshv_pop.population(5000, lambda_=0.02)
kshv_pop.simulate(generations)
ppc = kshv_pop.get_plasmids_per_cell()
av_nonzero_ppc = np.average(np.arange(len(ppc) - 1) + 1, weights=ppc[1:])
print(av_nonzero_ppc)
ts = kshv_pop.save(base_path, {})
plasmid_rep.plotting.standard_plots.plasmids_per_cell(
kshv_pop,
os.path.join(base_path, f'{ts}-kshv_complete.pdf'),
xmax=50,
add_text=f'KSHV {generations} gens both selections')
return ppc[0]
def kshv_pos_selection(base_path: str, generations: int = 50) -> float:
"""Simulate KSHV with and without positive selection and plot the results.
Args:
base_path (str): path into which the data should be saved
generations (int, optional): the number of generations to simulate. Defaults to 50.
"""
for sel in [None]:
kshv_pop = lpp.LatentPlasmidPopulation()
kshv_pop.set_virus('kshv')
kshv_pop.update_parameters({
'negative_cluster_selection_coefficient': 0,
's_phase_duplication_prob': 0.92,
'positive_selection_coefficient': sel
})
sel = kshv_pop.positive_selection_coefficient # In case of None, get default
# Set up a large population and burn in
kshv_pop.population(3000, lambda_=0.02)
kshv_pop.simulate(generations)
ppc = kshv_pop.get_plasmids_per_cell()
av_nonzero_ppc = np.average(np.arange(len(ppc) - 1) + 1,
weights=ppc[1:])
print(av_nonzero_ppc)
ts = kshv_pop.save(base_path, {})
plasmid_rep.plotting.standard_plots.plasmids_per_cell(
kshv_pop,
os.path.join(base_path,
f'{ts}-kshv_sel{sel}_plasmids_oer_cell.pdf'),
xmax=50,
add_text=f'KSHV 50 gens positive sel: {sel}')
def ebv_pos_selection(base_path: str, generations: int = 50) -> float:
"""Simulate EBV with and without positive selection and plot the results.
Args:
base_path (str): path into which the data should be saved
generations (int, optional): the number of generations to simulate. Defaults to 50.
Returns:
float: the fraction of cells with zero plasmids per cell after generations
"""
for sel in [0, None]:
ebv_pop = lpp.LatentPlasmidPopulation()
ebv_pop.set_virus('ebv')
ebv_pop.update_parameters({'positive_selection_coefficient': sel})
sel = ebv_pop.positive_selection_coefficient # In case of None, get default
# Set up a large population and burn in
ebv_pop.population(5000, lambda_=2)
ebv_pop.simulate(generations)
ppc = ebv_pop.get_plasmids_per_cell()
av_nonzero_ppc = np.average(np.arange(len(ppc) - 1) + 1, weights=ppc[1:])
print(av_nonzero_ppc)
ts = ebv_pop.save(base_path, {})
plasmid_rep.plotting.standard_plots.plasmids_per_cell(ebv_pop,
os.path.join(base_path, f'{ts}-ebv_sel{sel}_plasmids_oer_cell.pdf'))
return ppc[0]
def fraction_of_ebvpos_cells(pos_coefficient: float = 0.10,
generations: int = 100) -> float:
ebv_pop = lpp.LatentPlasmidPopulation()
ebv_pop.set_virus('ebv')
ebv_pop.positive_selection_coefficient = pos_coefficient
# Set up a large population and burn in
ebv_pop.population(5000, lambda_=2)
ebv_pop.simulate(generations)
ppc = ebv_pop.get_plasmids_per_cell()
av_nonzero_ppc = np.average(np.arange(len(ppc) - 1) + 1, weights=ppc[1:])
print(av_nonzero_ppc)
return ppc[0]
def kshv_pos_selection(base_path: str, generations: int = 50, turn_off_neg: bool = False) -> float:
"""Simulate KSHV with and without positive selection and plot the results.
Args:
base_path (str): path into which the data should be saved
pos_coefficient (float, optional): the nonzero positive selection coefficient to use. Defaults to 0.10.
generations (int, optional): the number of generations to simulate. Defaults to 50.
Returns:
float: the fraction of cells with zero plasmids per cell after generations
"""
# kshv_pop = lpp.LatentPlasmidPopulation()
# kshv_pop.set_virus('kshv')
# if turn_off_neg:
# kshv_pop.update_parameters({'negative_cluster_selection_coefficient': 0,
# 's_phase_duplication_prob': 0.92})
# # Set up a large population and burn in
# kshv_pop.population(2000, mu=1, sd=0)
# kshv_pop.simulate(generations)
# ts = kshv_pop.save(base_path, {})
big_pop = lpp.LatentPlasmidPopulation()
big_pop.set_virus('kshv')
if turn_off_neg:
big_pop.update_parameters({'negative_cluster_selection_coefficient': 0,
's_phase_duplication_prob': 0.92})
# Set up a large population and burn in
big_pop.population(2000, mu=2, sd=0)
big_pop.simulate(generations)
ts2 = big_pop.save(base_path, {})
neg_text = 'noneg' if turn_off_neg else 'neg'
plasmid_rep.plotting.standard_plots.multi_plasmids_per_cell([big_pop],
os.path.join(base_path, f'{ts2}-{neg_text}-kshv_small_big.pdf'),
xmax=50,
add_text=f'KSHV {generations} gens both selections')
# ts = kshv_pop.save(base_path, {})
big_pop = lpp.LatentPlasmidPopulation()
big_pop.set_virus('kshv')
if turn_off_neg:
big_pop.update_parameters({'negative_cluster_selection_coefficient': 0,
's_phase_duplication_prob': 0.92})
# Set up a large population and burn in
big_pop.population(2000, mu=2, sd=0)
big_pop.simulate(generations)
ts2 = big_pop.save(base_path, {})
neg_text = 'noneg' if turn_off_neg else 'neg'
plasmid_rep.plotting.standard_plots.multi_plasmids_per_cell([big_pop],
os.path.join(base_path, f'{ts2}-{neg_text}-kshv_small_big.pdf'),
xmax=50,
add_text=f'KSHV {generations} gens both selections')
if __name__ == '__main__':
import logging
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
pop = lpp.LatentPlasmidPopulation()
base_path = pop.load_config()['base_path']
kshv_pos_selection(base_path)