def export_to_cluster_file(cells,
cluster_file,
neighbours=None,
neighbours_kwargs={},
new_cell=Translocations,
distributed_kwargs={},
**kwargs):
not_2D = ValueError('data are not 2D')
if isinstance(cells, Distributed):
if cells.dim != 2:
raise not_2D
zones = cells
else:
if cells.tessellation._cell_centers.shape[1] != 2:
raise not_2D
zones = distributed(cells, new_cell=new_cell, **distributed_kwargs)
if neighbours is None:
neighbours = neighbours_per_axis
with open(cluster_file, 'w') as f:
for i in zones:
zone = zones[i]
f.write('ZONE: {}\n'.format(i + 1))
f.write('NUMBER_OF_TRANSLOCATIONS: {}\n'.format(len(zone)))
f.write('X-CENTRE: {}\n'.format(zone.center[0]))
f.write('Y-CENTRE: {}\n'.format(zone.center[1]))
#area = np.prod(np.max(zone.span, axis=0) - np.min(zone.span, axis=0))
area = zone.volume
f.write('AREA: {}\n'.format(area))
f.write('AREA_CONVHULL: {}\n'.format(area))
below, above = neighbours(i, zones, **neighbours_kwargs)
_neighbours = zones.adjacency[i].indices
_neighbours = OrderedDict(
left=_neighbours[below[0]],
right=_neighbours[above[0]],
top=_neighbours[above[1]],
bottom=_neighbours[below[1]],
)
for side in _neighbours:
f.write('NUMBER_OF_{}_NEIGHBOURS: {}\n'.format(
side.upper(), _neighbours[side].size))
if _neighbours[side].size:
f.write('{}_NEIGHBOURS: '.format(side.upper()))
for j in _neighbours[side]:
f.write('{}\t'.format(j + 1))
f.write('\n')
f.write('DX\tDY\tDT\n')
for dx, dy, dt in np.hstack((zone.dr, zone.dt[:, np.newaxis])):
f.write('{}\t{}\t{}\n'.format(dx, dy, dt))
f.write('\n\n')
def truth(cells,
t=None,
diffusivity=None,
force=None,
potential=None,
**kwargs):
"""
Generate maps for the true diffusivity/force distribution.
If `cells` is a :class:`~tramway.tessellation.base.CellStats` object,
:func:`~tramway.inference.base.distributed` is called on `cells` and with the trailing
keyword arguments to build a :class:`~tramway.inference.base.Distributed` object.
Arguments:
cells (CellStats or Distributed): distributed data ready for inference
t (float): time as understood by `diffusivity` and `force`
diffusivity (callable): admits the coordinates of a single location (array-like)
and time (float, if `t` is defined) and returns the local diffusivity (float)
force (callable): admits the coordinates of a single location (array-like)
and time (float, if `t` is defined) and returns the local force (array-like)
potential (callable): admits the coordinates of a single location (array-like)
and time (float, if `t` is defined) and returns the local potential energy (float)
Returns:
pandas.DataFrame: diffusivity/force maps
"""
try:
cells.cells
except AttributeError:
import tramway.inference as ti
cells = ti.distributed(cells, **kwargs)
I, DVF = [], []
for i in cells.cells:
cell = cells.cells[i]
if not I:
dim = cell.center.size
I.append(i)
if diffusivity is None:
D = []
elif t is None:
D = [diffusivity(cell.center)]
else:
D = [diffusivity(cell.center, t)]
if force is None:
F = []
elif t is None:
F = force(cell.center)
else:
F = force(cell.center, t)
if potential is None:
V = []
elif t is None:
V = [potential(cell.center)]
else:
V = [potential(cell.center, t)]
DVF.append(np.concatenate((D, V, F)))
DVF = np.vstack(DVF)
if diffusivity is None:
columns = []
else:
columns = ['diffusivity']
if potential is not None:
columns.append('potential')
if force is not None:
columns += ['force x' + str(col + 1) for col in range(dim)]
return pd.DataFrame(index=I, data=DVF, columns=columns)
def export_to_vmesh_file(cells,
maps,
vmesh_file,
cluster_file=None,
auto=False,
new_cell=Translocations,
distributed_kwargs={},
**kwargs):
not_2D = ValueError('data are not 2D')
if isinstance(cells, Distributed):
if cells.dim != 2:
raise not_2D
zones = cells
else:
if cells.tessellation._cell_centers.shape[1] != 2:
raise not_2D
zones = distributed(cells, new_cell=new_cell, **distributed_kwargs)
with open(vmesh_file, 'w') as f:
# Input File
if cluster_file is None and auto:
dirname, basename = os.path.split(vmesh_file)
cluster_files = [
fn for fn in os.listdir(dirname if dirname else '.')
if fn.endswith('.cluster') and basename.startswith(fn[:8])
]
if cluster_files:
if cluster_files[1:]:
print('multiple cluster files match:')
for fn in cluster_files:
print('\t' + fn)
cluster_file = cluster_files[0]
else:
cluster_file = ''
if cluster_file is not None:
f.write('Input File: {}\n'.format(cluster_file))
# Inference Mode
mode = maps.mode.upper()
if mode is None and auto:
if 'potential' in maps.features:
mode = 'DV'
elif 'force' in maps.features:
mode = 'DF'
elif 'drift' in maps.features:
mode = 'DD'
else:
mode = 'D'
if mode is not None:
f.write('Inference Mode: {}\n'.format(mode))
# Localization Error
err = maps.localization_error
if err is None and auto:
err = .03
if err is not None:
f.write('Localization Error: {:f} [nm]\n'.format(err * 1e3))
# Number of Zones
f.write('Number of Zones: {}\n'.format(len(zones)))
# Diffusion Prior
dp = maps.diffusivity_prior
if dp is None and auto:
dp = 0.
if dp is not None:
f.write('Diffusion Prior: {:f}\n'.format(dp))
# Potential Prior
vp = maps.potential_prior
if vp is None and auto:
vp = 0.
if vp is not None:
f.write('Potential Prior: {:f}\n'.format(vp))
# Jeffreys' Prior
jp = maps.jeffreys_prior
if jp is None and auto:
jp = 0
if jp is not None:
f.write("Jeffreys' Prior: {:d}\n".format(jp))
# table header
f.write('\nZone ID\tTranslocations\tx-Centre\ty-Centre')
na = np.isnan(maps.maps.values) | np.isinf(maps.maps.values)
ok = ~np.all(na, axis=1)
data0 = [[i, len(zones[i]), zones[i].center]
for i in maps.maps.index[ok]]
data1 = []
nvars = 0
if 'diffusivity' in maps.features:
f.write('\tDiffusion')
data1.append(maps['diffusivity'].values[ok])
nvars += 1
if 'force' in maps.features:
f.write('\tx-Force\ty-Force')
data1.append(maps['force'].values[ok])
nvars += 2
if 'potential' in maps.features:
f.write('\tPotential')
data1.append(maps['potential'].values[ok])
nvars += 1
# table data
data1 = np.hstack(data1)
fmt0 = '\n{:d}\t{:d}\t{:f}\t{:f}'
for r, s in zip(data0, data1):
i, n, r = r
s = ['NA' if np.isnan(x) or np.isinf(x) else x for x in s]
fmt1 = '\t{' + '}\t{'.join('' if x is 'NA' else ':f'
for x in s) + '}'
f.write((fmt0 + fmt1).format(i, n, *(r.tolist() + s)))
def export_to_vmesh_file(cells,
maps,
vmesh_file,
cluster_file=None,
auto=False,
new_cell=Translocations,
distributed_kwargs={},
**kwargs):
not_2D = ValueError("data are not 2D")
if isinstance(cells, Distributed):
if cells.dim != 2:
raise not_2D
zones = cells
else:
if cells.tessellation._cell_centers.shape[1] != 2:
raise not_2D
zones = distributed(cells, new_cell=new_cell, **distributed_kwargs)
with open(vmesh_file, "w") as f:
# Input File
if cluster_file is None and auto:
dirname, basename = os.path.split(vmesh_file)
cluster_files = [
fn for fn in os.listdir(dirname if dirname else ".")
if fn.endswith(".cluster") and basename.startswith(fn[:8])
]
if cluster_files:
if cluster_files[1:]:
print("multiple cluster files match:")
for fn in cluster_files:
print("\t" + fn)
cluster_file = cluster_files[0]
else:
cluster_file = ""
if cluster_file is not None:
f.write("Input File: {}\n".format(cluster_file))
# Inference Mode
mode = maps.mode.upper()
if mode is None and auto:
if "potential" in maps.features:
mode = "DV"
elif "force" in maps.features:
mode = "DF"
elif "drift" in maps.features:
mode = "DD"
else:
mode = "D"
if mode is not None:
f.write("Inference Mode: {}\n".format(mode))
# Localization Error
err = maps.localization_error
if err is None and auto:
err = 0.03
if err is not None:
f.write("Localization Error: {:f} [nm]\n".format(err * 1e3))
# Number of Zones
f.write("Number of Zones: {}\n".format(len(zones)))
# Diffusion Prior
dp = maps.diffusivity_prior
if dp is None and auto:
dp = 0.0
if dp is not None:
f.write("Diffusion Prior: {:f}\n".format(dp))
# Potential Prior
vp = maps.potential_prior
if vp is None and auto:
vp = 0.0
if vp is not None:
f.write("Potential Prior: {:f}\n".format(vp))
# Jeffreys' Prior
jp = maps.jeffreys_prior
if jp is None and auto:
jp = 0
if jp is not None:
f.write("Jeffreys' Prior: {:d}\n".format(jp))
# table header
f.write("\nZone ID\tTranslocations\tx-Centre\ty-Centre")
na = np.isnan(maps.maps.values) | np.isinf(maps.maps.values)
ok = ~np.all(na, axis=1)
data0 = [[i, len(zones[i]), zones[i].center]
for i in maps.maps.index[ok]]
data1 = []
nvars = 0
if "diffusivity" in maps.features:
f.write("\tDiffusion")
data1.append(maps["diffusivity"].values[ok])
nvars += 1
if "force" in maps.features:
f.write("\tx-Force\ty-Force")
data1.append(maps["force"].values[ok])
nvars += 2
if "potential" in maps.features:
f.write("\tPotential")
data1.append(maps["potential"].values[ok])
nvars += 1
# table data
data1 = np.hstack(data1)
fmt0 = "\n{:d}\t{:d}\t{:f}\t{:f}"
for r, s in zip(data0, data1):
i, n, r = r
s = ["NA" if np.isnan(x) or np.isinf(x) else x for x in s]
fmt1 = "\t{" + "}\t{".join("" if x == "NA" else ":f"
for x in s) + "}"
f.write((fmt0 + fmt1).format(i, n, *(r.tolist() + s)))