def read(self, simufname="results.zip", verbose=False):
"""
Creates a simul_spindle.Metaphase from a results.zip file.
:param simufname: The .zip file where results from the existing
simulation are (zip which contains xml and npy)
:type simufname: string, optional
:param verbose: Set Metaphase verbose
:type verbose: bool
:return: a Metaphase instance
"""
# Unzip results.xml and data.npy and put it in
# tempfile
try:
zipf = zipfile.ZipFile(simufname, "r")
except:
logger.info("%s does not appear to be a Zipfile" % simufname)
return False
# Test if simu results file are in the archive
if "results.xml" not in zipf.namelist() or "data.npy" not in zipf.namelist():
logger.info("%s does not contain results.xml and data.npy" % simufname)
return False
xmltemp = StringIO.StringIO(zipf.read("results.xml"))
datatemp = StringIO.StringIO(zipf.read("data.npy"))
restree = ResultTree(xmltemp, datatemp)
param_root = restree.root.find('parameters')
paramtree = ParamTree(root=param_root)
params = paramtree.relative_dic
measure_root = restree.root.find('measures')
measuretree = ParamTree(root=measure_root,
adimentionalized=False)
metaphase = Metaphase(paramtree=paramtree,
measuretree=measuretree,
verbose=verbose)
traj_matrix = restree.get_all_trajs()
correct_matrix = restree.get_all_correct()
erroneous_matrix = restree.get_all_erroneous()
state_hist_matrix = restree.get_all_plug_state()
KD = KinetoDynamics(params)
KD.spbR.traj = traj_matrix[:, 0]
KD.spbL.traj = traj_matrix[:, 1]
col_num = 2
state_num = 0
for n, ch in enumerate(KD.chromosomes):
ch.cen_A.traj = traj_matrix[:, col_num]
ch.cen_A.pos = ch.cen_A.pos
col_num += 1
ch.cen_B.traj = traj_matrix[:, col_num]
ch.cen_B.pos = ch.cen_B.pos
col_num += 1
ch.erroneous_history = (erroneous_matrix[:, n * 2: n * 2 + 2])
ch.correct_history = (correct_matrix[:, n * 2: n * 2 + 2])
for plugsite in ch.cen_A.plugsites:
plugsite.traj = traj_matrix[:, col_num]
col_num += 1
plugsite.state_hist = state_hist_matrix[:, state_num]
plugsite.plug_state = plugsite.state_hist[-1]
state_num += 1
for plugsite in ch.cen_B.plugsites:
plugsite.traj = traj_matrix[:, col_num]
col_num += 1
plugsite.state_hist = state_hist_matrix[:, state_num]
plugsite.plug_state = plugsite.state_hist[-1]
state_num += 1
metaphase.KD = KD
metaphase.KD.simulation_done = True
return metaphase
def read(self, simufname, paramtree=None, measuretree=None, verbose=False):
"""
Creates a simul_spindle.Metaphase from a results.zip file.
Parameters
----------
simufname : string, optional
The .zip file where results from the existing simulation are
(zip which contains xml and npy)
verbose : bool
Set Metaphase verbose
Returns
-------
:class:`~kt_simul.core.simul_spindle.Metaphase`
"""
store = pd.HDFStore(simufname)
if not paramtree:
param_root = build_tree(store['params'])
paramtree = ParamTree(root=param_root)
params = paramtree.relative_dic
if not measuretree:
measure_root = build_tree(store['measures'])
measuretree = ParamTree(root=measure_root,
adimentionalized=False)
meta = Metaphase(paramtree=paramtree, measuretree=measuretree, verbose=False)
KD = KinetoDynamics(params)
spbs = store['spbs']
KD.spbL.traj = spbs.xs('A', level='side').values.T[0]
KD.spbR.traj = spbs.xs('B', level='side').values.T[0]
kts = store['kts']
plug_sites = store['plug_sites']
store.close()
for ch, (ch_id, ch_df) in zip(KD.chromosomes, kts.groupby(level="id")):
ch.cen_A.traj = ch_df.xs('A', level='side')['x'].values.T
ch.cen_B.traj = ch_df.xs('B', level='side')['x'].values.T
ch.cen_A.tag = 'A'
ch.cen_B.tag = 'B'
for i, plugsite in enumerate(ch.cen_A.plugsites):
ps = plug_sites.xs((ch_id, 'A', i), level=['id', 'side', 'plug_id'])
plugsite.traj = ps['x'].values
plugsite.state_hist = ps['state_hist'].values
plugsite.plug_state = plugsite.state_hist[-1]
plugsite.tag = 'A'
for i, plugsite in enumerate(ch.cen_B.plugsites):
ps = plug_sites.xs((ch_id, 'B', i), level=['id', 'side', 'plug_id'])
plugsite.traj = ps['x'].values
plugsite.state_hist = ps['state_hist'].values
plugsite.plug_state = plugsite.state_hist[-1]
plugsite.tag = 'B'
ch.cen_A.calc_plug_vector()
ch.cen_B.calc_plug_vector()
ch.calc_erroneous_history()
ch.calc_correct_history()
meta.KD = KD
meta.KD.simulation_done = True
return meta
