def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
base_dir = RetinalUtil._landscape_base()
step = Pipeline.Step.REDUCED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.SANITIZED)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
limit = None
functor = lambda: to_iwt(in_dir)
data = CheckpointUtilities.multi_load(cache_dir=out_dir,
load_func=functor,
force=force,
limit=limit,
name_func=FEC_Util.fec_name_func)
ProcessingUtil.plot_data(base_dir, step, data, xlim=[-50, 150])
plot_subdir = Pipeline._plot_subdir(base_dir, step)
out_name = plot_subdir + "heatmap.png"
ProcessingUtil.heatmap_ensemble_plot(data, out_name=out_name)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
base_input_processing = RetinalUtil._processing_base()
base_dir = RetinalUtil._landscape_base()
step = Pipeline.Step.MANUAL
in_dir = Pipeline._cache_dir(base=base_input_processing,
enum=Pipeline.Step.POLISH)
out_dir = Pipeline._cache_dir(base=base_dir,enum=step)
data_input = CheckpointUtilities.lazy_multi_load(in_dir)
force = True
functor = lambda : ProcessingUtil.\
_filter_by_bl(data_input,base_input_processing)
data = CheckpointUtilities.multi_load(cache_dir=out_dir,load_func=functor,
force=force,
name_func=FEC_Util.fec_name_func)
# plot each individual
ProcessingUtil.plot_data(base_dir,step,data,xlim=[-50,150])
plot_subdir = Pipeline._plot_subdir(base_dir, step)
out_name = plot_subdir + "heatmap.png"
ProcessingUtil.heatmap_ensemble_plot(data, out_name=out_name)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.FILTERED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.READ)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
limit = None
f_filter_Hz = 5e3
# filter to X s
t_filter_s = 1 / f_filter_Hz
# t_filter -> n_filter_points
# after filtering, take every N points, where
# N = f_decimate * n_filter_points
# in other words, we oversample by 1/f_decimate
f_decimate = 0.33
assert f_decimate < 1 and f_decimate > 0
data = filter_data(in_dir, out_dir, force, t_filter_s, f_decimate)
ProcessingUtil.plot_data(base_dir, step, data, markevery=1)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
base_dir = RetinalUtil._landscape_base()
step = Pipeline.Step.POLISH
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.REDUCED)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
limit = None
functor = lambda: generate_landscape(in_dir)
energy_obj = CheckpointUtilities.\
getCheckpoint(filePath=out_dir + "energy.pkl",
orCall=functor,force=force)
data = CheckpointUtilities.lazy_multi_load(in_dir)
# also load all the data
fig = PlotUtilities.figure((3, 6))
PlotUtil.plot_landscapes(data, energy_obj)
PlotUtilities.savefig(fig, out_dir + "out_G.png")
pass
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
base_dir = RetinalUtil._landscape_base()
step = Pipeline.Step.SANITIZED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.MANUAL)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
limit = None
min_sep = RetinalUtil.min_sep_landscape()
max_sep = min_sep + 100e-9
functor = lambda: slice_data(in_dir, min_sep=min_sep, max_sep=max_sep)
data = CheckpointUtilities.multi_load(cache_dir=out_dir,
load_func=functor,
force=force,
limit=limit,
name_func=FEC_Util.fec_name_func)
plot_dir = Pipeline._plot_subdir(base=base_dir, enum=step)
ProcessingUtil.heatmap_ensemble_plot(data,
out_name=plot_dir + "heatmap.png",
xlim=[-20, max_sep * 1e9])
# plot each individual
ProcessingUtil.plot_data(base_dir, step, data)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.ALIGNED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.SANITIZED)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
max_n_pool = multiprocessing.cpu_count() - 1
n_pool = 6
N_fit_pts = 20
min_F_N = 175e-12 if "+Retinal" in base_dir else 90e-12
data = RetinalUtil.align_data(in_dir,
out_dir,
force=force,
n_pool=n_pool,
min_F_N=min_F_N,
N_fit_pts=N_fit_pts)
plot_subdir = Pipeline._plot_subdir(base_dir, step)
xlim_heatmap_nm = [-40, 100]
ProcessingUtil.heatmap_ensemble_plot(data,
xlim=xlim_heatmap_nm,
out_name=plot_subdir + "heatmap.png")
# get the post-blacklist heapmap, too..
data_filtered = ProcessingUtil._filter_by_bl(data, in_dir)
# align the data...
data_aligned = [RetinalUtil._polish_helper(d) for d in data_filtered]
out_name = plot_subdir + "heatmap_bl.png"
ProcessingUtil.heatmap_ensemble_plot(data_aligned,
xlim=xlim_heatmap_nm,
out_name=out_name)
# make individual plots
ProcessingUtil.make_aligned_plot(base_dir,
step,
data,
xlim=[-30, 150],
use_shift=True)
def make_aligned_plot(base_dir, step, data, xlim=None, post="", **kw):
plot_subdir = Pipeline._plot_subdir(base_dir, step)
f_x = lambda x: x.Separation
xlim_tmp, ylim = nm_and_pN_limits(data, f_x)
xlim = [xlim[0], 200] if xlim is None else xlim
name_func = FEC_Util.fec_name_func
for i, d in enumerate(data):
f = PlotUtilities.figure()
_aligned_plot(d, f_x, xlim, ylim, i=i, **kw)
PlotUtilities.savefig(f, plot_subdir + name_func(0, d) + post + ".png")
def read_dir(base_dir,enum):
in_dir = Pipeline._cache_dir(base=base_dir,
enum=enum)
dir_exists = os.path.exists(in_dir)
if (dir_exists and \
len(GenUtilities.getAllFiles(in_dir, ext=".pkl")) > 0):
data = CheckpointUtilities.lazy_multi_load(in_dir)
else:
data = []
return data
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.CORRECTED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.FILTERED)
out_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
n_filter = 10
data = filter_data(in_dir, out_dir, force, n_filter)
ProcessingUtil.plot_data(base_dir, step, data, markevery=1)
def read_energy_lists(subdirs):
"""
:param subdirs: which directories to look in (e.g. BR+Retinal/)
:return: list, elements are all the landscapes in the subdirs
"""
energy_list_arr = []
# get all the energy objects
for base in subdirs:
in_dir = Pipeline._cache_dir(base=base, enum=Pipeline.Step.CORRECTED)
in_file = in_dir + "energy.pkl"
e = CheckpointUtilities.lazy_load(in_file)
energy_list_arr.append(e)
return energy_list_arr
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.READ
cache_dir = Pipeline._cache_dir(base=base_dir, enum=step)
force = True
limit = None
functor = lambda: read_all_data(base_dir)
data = CheckpointUtilities.multi_load(cache_dir=cache_dir,
load_func=functor,
force=force,
limit=limit,
name_func=FEC_Util.fec_name_func)
ProcessingUtil.plot_data(base_dir, step, data, markevery=100)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.SANITIZED
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.CORRECTED)
out_dir = Pipeline._cache_dir(base=base_dir,enum=step)
force = True
max_n_pool = 6
n_pool = max_n_pool
kw_feather = RetinalUtil._def_kw_FEATHER()
data = align_data(in_dir,out_dir,force=force,n_pool=n_pool,
**kw_feather)
# plot all of the FEATHER information
plot_subdir = Pipeline._plot_subdir(base_dir, step)
PlotUtil._feather_plot(data,plot_subdir)
def read_in_energy(base_dir):
"""
:param base_dir: where the landscape lives; should be a series of FECs of
about the same spring constant (e.g. /BR+Retinal/300/170321FEC/)
:return: RetinalUtil.EnergyWithMeta
"""
landscape_base = _landscape_dir(base_dir)
cache_tmp = \
Pipeline._cache_dir(base=landscape_base,
enum=Pipeline.Step.POLISH)
file_load = cache_tmp + "energy.pkl"
energy_obj = CheckpointUtilities.lazy_load(file_load)
obj = EnergyWithMeta(file_load,landscape_base, energy_obj)
# read in the data, determine how many curves there are
data_tmp = read_fecs(obj)
n_data = len(data_tmp)
obj.set_n_fecs(n_data)
return obj
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
base_dir_analysis = RetinalUtil._analysis_base()
out_dir = Pipeline._cache_dir(base=base_dir_analysis,
enum=Pipeline.Step.CORRECTED)
force = True
GenUtilities.ensureDirExists(out_dir)
energy_list = CheckpointUtilities.getCheckpoint(out_dir + \
"energy.pkl",
get_energy_list,force,
base_dir_analysis)
_fec_demo_plot(energy_list, out_dir)
def _processing_base(default_base="../../../Data/BR+Retinal/170321FEC/",**kw):
return Pipeline._base_dir_from_cmd(default=default_base,**kw)
def run():
"""
<Description>
Args:
param1: This is the first param.
Returns:
This is a description of what is returned.
"""
default_base = "../../../Data/170321FEC/"
base_dir = Pipeline._base_dir_from_cmd(default=default_base)
step = Pipeline.Step.POLISH
in_dir = Pipeline._cache_dir(base=base_dir, enum=Pipeline.Step.ALIGNED)
out_dir = Pipeline._cache_dir(base=base_dir,enum=step)
plot_dir = Pipeline._plot_subdir(base=base_dir, enum=step)
force = True
limit = None
functor = lambda : polish_data(in_dir)
data =CheckpointUtilities.multi_load(cache_dir=out_dir,load_func=functor,
force=force,
limit=limit,
name_func=FEC_Util.fec_name_func)
sizes = [d.Force.size for d in data]
min_s = min(sizes)
sliced_data = [d._slice(slice(0,min_s,1)) for d in data]
for d in sliced_data:
d.Offset = d.ZSnsr[0]
d.Extension = d.Separation
d.kT = 4.1e-21
data_wham = UtilWHAM.to_wham_input(objs=sliced_data, n_ext_bins=75)
data_wham.z = np.array([d.ZSnsr for d in sliced_data])
obj_wham = WeightedHistogram.wham(data_wham)
data_unpolished = CheckpointUtilities.lazy_multi_load(in_dir)
f_x_zsnsr = lambda x: x.ZSnsr
xlim = [-40,150]
ProcessingUtil.heatmap_ensemble_plot(data,out_name=plot_dir + "heatmap.png",
xlim=xlim)
ProcessingUtil.heatmap_ensemble_plot(data,f_x=f_x_zsnsr,
out_name=plot_dir + "heatmap_Z.png",
kw_map=dict(x_func=f_x_zsnsr),
xlim=xlim)
# plot each individual
f_x = lambda x_tmp : x_tmp.Separation
plot_subdir = Pipeline._plot_subdir(base_dir, step)
name_func = FEC_Util.fec_name_func
_, ylim = ProcessingUtil.nm_and_pN_limits(data,f_x)
for i,(d_unpolish,d_polish) in enumerate(zip(data_unpolished,data)):
kw_plot = dict(d_unpolish=d_unpolish,
d_polish=d_polish,
xlim=xlim,ylim=ylim,i=i)
fig = PlotUtilities.figure((6,6))
# make the Separation column
ax1,ax2 = plt.subplot(2,2,1), plt.subplot(2,2,3)
polish_plot(ax1, ax2, f_x = lambda x: x.Separation,
plot_components_1=True,**kw_plot)
# make the ZSnsr column
ax3,ax4 = plt.subplot(2,2,2), plt.subplot(2,2,4)
polish_plot(ax3, ax4, f_x = lambda x: x.ZSnsr,plot_components_1=False,
**kw_plot)
PlotUtilities.xlabel("Stage Position (nm)", ax=ax4)
for a in [ax3,ax4]:
PlotUtilities.no_y_label(ax=a)
PlotUtilities.ylabel("",ax=a)
name = plot_subdir + name_func(0, d_polish) + ".png"
PlotUtilities.savefig(fig,name)
def _snapsnot(base_dir, step):
corrected_dir = Pipeline._cache_dir(base=base_dir, enum=step)
data = CheckpointUtilities.lazy_multi_load(corrected_dir)
return SnapshotFEC(step, data)